{"text": "\nmodel.full.to.short = c(\n 'lstm'='LSTM',\n 'nac'='$\\\\mathrm{NAC}_{+}$',\n 'nac-nac-n'='$\\\\mathrm{NAC}_{\\\\bullet}$',\n 'posnac-nac-n'='$\\\\mathrm{NAC}_{\\\\bullet,\\\\sigma}$',\n 'reregualizedlinearposnac-nac-n'='$\\\\mathrm{NAC}_{\\\\bullet,\\\\mathrm{NMU}}$',\n 'nalu'='NALU',\n 'reregualizedlinearnac'='NAU',\n 'reregualizedlinearnac-nac-m'='NMU'\n)\n\nmodel.latex.to.exp = c(\n '$\\\\mathrm{NAC}_{+}$'=expression(paste(\"\", \"\", plain(paste(\"NAC\")), \n phantom()[{\n paste(\"\", \"+\")\n }], \"\")),\n '$\\\\mathrm{NAC}_{+,R_z}$'=expression(paste(\"\", \"\", plain(paste(\"NAC\")), \n phantom()[{\n paste(\"\", \"+\", \",\", \"R\", phantom()[{\n paste(\"z\")\n }])\n }], \"\")),\n '$\\\\mathrm{NAC}_{\\\\bullet}$'=expression(paste(\"\", \"\", plain(paste(\"NAC\")), \n phantom()[{\n paste(\"\", symbol(\"\\xb7\"))\n }], \"\")),\n '$\\\\mathrm{NAC}_{\\\\bullet,\\\\sigma}$'=expression(paste(\"\", \"\", plain(paste(\"NAC\")), \n phantom()[{\n paste(\"\", symbol(\"\\xb7\"), \",\", sigma)\n }], \"\")),\n '$\\\\mathrm{NAC}_{\\\\bullet,\\\\mathrm{NMU}}$'=expression(paste(\"\", \"\", plain(paste(\"NAC\")), \n phantom()[{\n paste(\"\", symbol(\"\\xb7\"), \",\", plain(paste(\"NMU\")))\n }], \"\")),\n 'LSTM'='LSTM',\n 'NALU'='NALU',\n 'NAU'='NAU',\n 'NMU'='NMU'\n)\n\nmodel.to.exp = function(v) {\n return(unname(revalue(v, model.latex.to.exp)))\n}\n\noperation.full.to.short = c(\n 'op-cumsum'='cumsum',\n 'op-cumprod'='cumprod'\n)\n\nextract.by.split = function (name, index, default=NA) {\n split = strsplit(as.character(name), '_')[[1]]\n if (length(split) >= index) {\n return(split[index])\n } else {\n return(default)\n }\n}\n\nrange.full.to.short = function (range) {\n range = substring(range, 3)\n \n if (substring(range, 0, 1) == '[') {\n return(paste0('U', gsub('\\\\]-\\\\[', '] \u222a U[', gsub(' ', '', range))))\n } else {\n return(paste0('U[', gsub('^,', '-', gsub('-', ',', range)), ']'))\n }\n}\n\nregualizer.get.part = function (regualizer, index) {\n split = strsplit(regualizer, '-')[[1]]\n return(as.double(split[index + 1]))\n}\n\nregualizer.get.type = function (regualizer, index) {\n split = strsplit(regualizer, '-')[[1]]\n return(split[index + 1])\n}\n\nregualizer.scaling.get = function (regualizer, index) {\n split = strsplit(regualizer, '-')[[1]]\n return(as.numeric(split[index + 1]))\n}\n\ndataset.get.part = function (dataset, index, simple.value) {\n split = strsplit(dataset, '-')[[1]]\n if (split[2] == 'simple') {\n return(simple.value)\n } else {\n return(as.numeric(split[index + 1]))\n }\n}\n\nmodel.get.mnist.setup = function (model) {\n split = strsplit(model, '-')[[1]]\n if (split[2] == 'l') {\n return('linear')\n } else if (split[2] == 's') {\n return('softmax')\n } else {\n return(NA)\n }\n}\n\nmodel.get.simplification.setup = function (model) {\n split = strsplit(model, '-')[[1]]\n if (split[3] == 'n') {\n return('none')\n } else if (split[3] == 's') {\n return('solved-accumulator')\n } else if (split[3] == 'p') {\n return('pass-though')\n } else {\n return(NA)\n }\n}\n\nextrapolation.loss.name.to.integer = function (loss.name) {\n split = strsplit(loss.name, '\\\\.')[[1]]\n return(as.integer(split[4]))\n}\n\nexpand.name = function (df) {\n names = data.frame(name=unique(df$name))\n \n df.expand.name = names %>%\n rowwise() %>%\n mutate(\n model=revalue(extract.by.split(name, 1), model.full.to.short, warn_missing=FALSE),\n digits=substring(extract.by.split(name, 2), 3),\n hidden.size=as.integer(substring(extract.by.split(name, 3), 3)),\n operation=revalue(extract.by.split(name, 4), operation.full.to.short, warn_missing=FALSE),\n \n oob.control = ifelse(substring(extract.by.split(name, 5), 5) == \"r\", \"regualized\", \"clip\"),\n regualizer.scaling = regualizer.get.type(extract.by.split(name, 6), 1), # rs[1]\n regualizer.shape = regualizer.get.type(extract.by.split(name, 6), 2), # rs[2]\n epsilon.zero = as.numeric(substring(extract.by.split(name, 7), 5)),\n \n regualizer.scaling.start=regualizer.scaling.get(extract.by.split(name, 8), 1),\n regualizer.scaling.end=regualizer.scaling.get(extract.by.split(name, 8), 2),\n \n regualizer=regualizer.get.part(extract.by.split(name, 9), 1),\n regualizer.z=regualizer.get.part(extract.by.split(name, 9), 2),\n regualizer.oob=regualizer.get.part(extract.by.split(name, 9), 3),\n \n model.mnist = model.get.mnist.setup(extract.by.split(name, 10)),\n model.final = model.get.simplification.setup(extract.by.split(name, 10)),\n \n interpolation.length=as.integer(substring(extract.by.split(name, 11), 3)),\n extrapolation.length=substring(extract.by.split(name, 12), 3),\n \n batch.size=as.integer(substring(extract.by.split(name, 13), 2)),\n seed=as.integer(substring(extract.by.split(name, 14), 2))\n )\n \n df.expand.name$name = as.factor(df.expand.name$name)\n df.expand.name$operation = factor(df.expand.name$operation, c('cumsum', 'cumprod'))\n df.expand.name$model = as.factor(df.expand.name$model)\n\n return(merge(df, df.expand.name))\n}\n", "meta": {"hexsha": "247c7bfc5effc144494a0a0b654378b3a0a0741b", "size": 5728, "ext": "r", "lang": "R", "max_stars_repo_path": "export/_sequential_mnist_expand_name.r", "max_stars_repo_name": "bmistry4/nalm-benchmark", "max_stars_repo_head_hexsha": "273c95cc75241f56e48bcd0b18b043969ef82004", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 147, "max_stars_repo_stars_event_min_datetime": "2019-10-07T11:01:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-16T02:51:18.000Z", "max_issues_repo_path": "export/_sequential_mnist_expand_name.r", "max_issues_repo_name": "bmistry4/nalm-benchmark", "max_issues_repo_head_hexsha": "273c95cc75241f56e48bcd0b18b043969ef82004", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-12-03T12:40:21.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-03T12:40:21.000Z", "max_forks_repo_path": "export/_sequential_mnist_expand_name.r", "max_forks_repo_name": "bmistry4/nalm-benchmark", "max_forks_repo_head_hexsha": "273c95cc75241f56e48bcd0b18b043969ef82004", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2019-12-21T15:58:44.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-03T08:32:38.000Z", "avg_line_length": 35.3580246914, "max_line_length": 115, "alphanum_fraction": 0.5141410615, "num_tokens": 1553, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.34993201789070827}} {"text": "# Script to create histogram of eta values\n# ShinyMixR will add a vector with the selected models, e.g.:\n# models <- c(\"run1\",\"run2\")\nlibrary(ggplot2)\nlapply(models,function(x){\n dat <- readRDS(paste0(\"./shinyMixR/\",x,\".res.rds\"))\n etav <- nlme::ranef(dat)[, -1];\n if(length(names(etav))!=0){\n pll <- lapply(names(etav),function(pl){\n ggplot(etav,aes_string(pl)) + geom_histogram(fill=\"grey\",color=\"black\") + labs(title=pl)\n })\n dir.create(paste0(\"./analysis/\",x),showWarnings=FALSE)\n R3port::html_plot(pll,out=paste0(\"./analysis/\",x,\"/hist.eta.html\"),show=FALSE,title=\"ETA distribution\")\n }\n})\n", "meta": {"hexsha": "16eee0732c10d246f0de81dc85906116b1e229ae", "size": 617, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/Other/eta.plot.r", "max_stars_repo_name": "RichardHooijmaijers/shinyMixR", "max_stars_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-02-21T12:58:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-19T14:22:46.000Z", "max_issues_repo_path": "inst/Other/eta.plot.r", "max_issues_repo_name": "RichardHooijmaijers/shinyMixR", "max_issues_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2018-05-29T13:01:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-17T14:25:31.000Z", "max_forks_repo_path": "inst/Other/eta.plot.r", "max_forks_repo_name": "RichardHooijmaijers/shinyMixR", "max_forks_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-06-30T08:07:46.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-17T20:59:33.000Z", "avg_line_length": 38.5625, "max_line_length": 107, "alphanum_fraction": 0.659643436, "num_tokens": 190, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3499320166333547}} {"text": "context(\"mr system\")\nlibrary(simulateGP)\n\ntest_that(\"create system\", {\n\n\tdat <- create_system(nidx=1000, nidy=1000, nidu=0, nu=0, na=0, nb=0, var_x.y=0.1, nsnp_x=100, var_gx.x=0.1, var_gx.y=0, mu_gx.y=0, prop_gx.y=1, nsnp_y=0, var_gy.y=0, var_gy.x=0, mu_gy.x=0, prop_gy.x=1)\n\texpect_true(is.list(dat))\n})\n\ntest_that(\"init_parameters\", {\n\n\tdat <- init_parameters(var_x.y=0.1, nsnp_x=100, var_gx.x=0.1, var_gx.y=0, mu_gx.y=0, prop_gx.y=1, nsnp_y=0, var_gy.y=0, var_gy.x=0, mu_gy.x=0, prop_gy.x=1)\n\texpect_true(is.list(dat))\n\n\tdat <- add_u(dat, nsnp_u=100, var_u.x=0.1, var_u.y=0.1, var_gu.u=0.1)\n\texpect_true(is.list(dat))\n\n\tdat2 <- sample_system_effects(dat)\n\texpect_true(is.list(dat2))\n\n\tdat3 <- simulate_population(dat2, 1000)\n\texpect_true(is.list(dat3))\n\n\tdat4 <- estimate_system_effects(dat3)\n\texpect_true(is.list(dat4))\n})\n\n\ntest_that(\"test_system\", {\n\n\tskip(\"need random forest\")\n\tss <- create_system(nidx=10000, nidy=10000, nidu=0, nu=0, na=0, nb=0, var_x.y=0.1, nsnp_x=10, var_gx.x=0.1, var_gx.y=0, mu_gx.y=0, prop_gx.y=1, nsnp_y=10, var_gy.y=0.1, var_gy.x=0, mu_gy.x=0, prop_gy.x=1)\n\tres <- test_system(ss)\n\texpect_true(is.list(res))\n})\n", "meta": {"hexsha": "f07876186b1bb2477753f90c95d802db193a8e5a", "size": 1145, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_mr_system.r", "max_stars_repo_name": "explodecomputer/simulateGP", "max_stars_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-05T16:58:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-05T16:58:46.000Z", "max_issues_repo_path": "tests/testthat/test_mr_system.r", "max_issues_repo_name": "aeaswar81/simulateGP", "max_issues_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-20T16:14:15.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-20T16:14:15.000Z", "max_forks_repo_path": "tests/testthat/test_mr_system.r", "max_forks_repo_name": "aeaswar81/simulateGP", "max_forks_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-10T19:27:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-05T16:59:02.000Z", "avg_line_length": 31.8055555556, "max_line_length": 205, "alphanum_fraction": 0.6899563319, "num_tokens": 470, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3499320087587727}} {"text": "#Spectrum costs\nlibrary(tidyverse)\nrequire(\"ggrepel\")\nlibrary(ggpubr)\n\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, 'data_inputs', 'spectrum_costs_old.csv'))\n\ndata$income = factor(data$income, levels=c(\"High\", \"Upper Middle\", \"Lower Middle\", \"Unknown\"))\n\ncoverage <- data[data$type == \"coverage\",]\n\ncoverage$group = factor(coverage$group, levels=c(\"High\", \"Median\", \"Low\"))\n\ncoverage <- ggplot(coverage, aes(x=year, y=dollars.mhz.pop, colour=income, shape=group)) +\n geom_point(size=3) +\n geom_label_repel(aes(label = iso3), size = 3) +\n scale_x_continuous(expand = c(0, 0.1), limits = c(2008,2017),\n breaks= c(2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017)) +\n scale_y_continuous(expand = c(0, 0.1)) +\n annotate(\"text\", x = 2009.2, y = 2.86, label = \"Extreme\", vjust=-1) +\n geom_hline(yintercept=2.86, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 1.88, label = \"Outliers\", vjust=-1) +\n geom_hline(yintercept=1.88, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 0.91, label = \"High\", vjust=-1) +\n geom_hline(yintercept=0.91, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 0.58, label = \"Median\", vjust=-1) +\n geom_hline(yintercept=0.58, linetype=\"dashed\", color = \"grey\", size=.5) +\n theme(axis.text.x = element_text(angle = 30)) +\n guides(colour=guide_legend(title=\"Income\"), shape=guide_legend(title=\"Price\")) +\n labs(title = \"Coverage spectrum prices comprising 700 MHz, 800 MHz, 850 MHz and 900 MHz\", \n x=NULL, \n y='$ / MHz / Per Capita (USD)',\n subtitle = \"Prices adjusted for PPP exchange rates, inflation and license duration, and include annual fees.\"\n )\n\ncapacity <- data[data$type == \"capacity\",]\n\ncapacity$group = factor(capacity$group, levels=c(\"Extreme outlier\", \"Outlier\", \"High\", \"Median\", \"Low\"))\n\ncapacity <- ggplot(capacity, aes(x=year, y=dollars.mhz.pop, colour=income, shape=group)) +\n geom_point(size=3) +\n geom_label_repel(aes(label = iso3), size = 3) +\n scale_x_continuous(expand = c(0, 0.1), limits = c(2008,2017),\n breaks= c(2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017)) +\n scale_y_continuous(expand = c(0, 0.035)) +\n annotate(\"text\", x = 2009.2, y = 1.26, size=3.5,label = \"Extreme\", vjust=-1) +\n geom_hline(yintercept=1.26, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 0.80, size=3.5,label = \"Outliers\", vjust=-1) +\n geom_hline(yintercept=0.80, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 0.35, size=3.5,label = \"High\", vjust=-1) +\n geom_hline(yintercept=0.35, linetype=\"dashed\", color = \"grey\", size=.5) +\n annotate(\"text\", x = 2009.2, y = 0.13, size=3.5,label = \"Median\", vjust=-1) +\n geom_hline(yintercept=0.13, linetype=\"dashed\", color = \"grey\", size=.5) +\n theme(axis.text.x = element_text(angle = 30)) +\n guides(colour=guide_legend(title=\"Income\"), shape=guide_legend(title=\"Price\")) +\n labs(title = \"Capacity spectrum prices comprising AWS, PCS, 1800 MHz, 2100 MHz and 2600 MHz\", \n x=NULL, \n y='$ / MHz / Per Capita (USD)',\n subtitle = \"Prices adjusted for PPP exchange rates, inflation and license duration, and include annual fees.\"\n )\n\n\npanel <- ggarrange(coverage, capacity, ncol = 1, nrow = 2, align = c(\"hv\"), common.legend = TRUE, legend=\"bottom\")\n\npath = file.path(folder, 'figures', 'panel.png')\nggsave(path, units=\"in\", width=8, height=12)\nprint(panel)\ndev.off()\n\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, 'data_inputs', 'spectrum_by_cluster_country.csv'))\n\ndata$income[data$Country == 'Uganda'] <- 'Lower'\ndata$income[data$Country == 'Malawi'] <- 'Lower'\ndata$income[data$Country == 'Kenya'] <- 'Lower Middle'\ndata$income[data$Country == 'Senegal'] <- 'Lower Middle'\ndata$income[data$Country == 'Pakistan'] <- 'Lower Middle'\ndata$income[data$Country == 'Albania'] <- 'Upper Middle'\ndata$income[data$Country == 'Peru'] <- 'Upper Middle'\ndata$income[data$Country == 'Mexico'] <- 'Upper Middle'\n\ndata$income = factor(data$income, levels=c(\"Upper Middle\", \"Lower Middle\", \"Lower\"))\n\ncluster_prices <- ggplot(data, aes(x=Year, y=usd_per_mhz_per_pop, colour=income)) + \n geom_point(size=3) +\n geom_label_repel(aes(label = Country), size = 3) +\n scale_x_continuous(limits = c(2010,2018),\n breaks= c(2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018)) +\n scale_y_continuous(limits=c(0, 0.2)) +\n theme(axis.text.x = element_text(angle = 30)) +\n guides(colour=guide_legend(title=\"Income\")) +\n labs(title = \"Spectrum Prices\", x=NULL, y='$ / MHz / Per Capita (USD)')\n\n#export to folder\npath = file.path(folder, 'figures', 'cluster_prices.png')\nggsave(path, units=\"in\", width=8, height=8)\nprint(cluster_prices)\ndev.off()\n\nsubset <- select(data, Country, usd_per_mhz_per_pop)\n\nmean_spectrum_prices <- aggregate(x = subset$usd_per_mhz_per_pop, \n by = list(subset$Country), \n FUN = mean) \n ", "meta": {"hexsha": "3b9096d9aad6c60492de74af15a4bc3d8029c3f8", "size": 5229, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/spectrum/spectrum.r", "max_stars_repo_name": "edwardoughton/pytal", "max_stars_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-01-16T12:12:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-04T11:46:00.000Z", "max_issues_repo_path": "vis/spectrum/spectrum.r", "max_issues_repo_name": "edwardoughton/pytal", "max_issues_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/spectrum/spectrum.r", "max_forks_repo_name": "edwardoughton/pytal", "max_forks_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-01-15T14:46:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-27T02:42:15.000Z", "avg_line_length": 47.5363636364, "max_line_length": 116, "alphanum_fraction": 0.6488812392, "num_tokens": 1668, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3499320087587727}} {"text": "library(dplyr)\n#' Load dataset with publications and GRIDs\ngrids <- readr::read_csv(\"grids.csv\")\ngrids\n#' Remove NA and `not_in_grid`\ngrids <-\n filter(grids,\n !is.na(grid_id_consolidated) &\n !grid_id_consolidated == \"not_in_grid\")\n#' Get bipartite network, and calculate unipartite representation\ngrids_mat <- table(grids$grid_id_consolidated, grids$pub_id)\nmat_t <- grids_mat %*% t(grids_mat)\n#' convert to edge list using igraph library, we want a undirected network without loops (self-links)\nmy_net <- igraph::graph_from_adjacency_matrix(mat_t, mode = c(\"undirected\"), diag = FALSE)\nmy_graph <- igraph::get.edgelist(my_net)\n#' let's get geocodes using the Solr store. See also `solr_grid.r`\n#' Call Solr API within R\nlibrary(solrium)\n#' Connect\nsolrium::solr_connect(\"localhost:8983/solr/grid/select\",\n errors = \"complete\",\n verbose = FALSE)\n#' Call\nmy_graph_in <- plyr::ldply(my_graph[,1], function(x) solrium::solr_search(q = paste0(\"id:\", x))) \nmy_graph_out <- plyr::ldply(my_graph[,2], function(x) solrium::solr_search(q = paste0(\"id:\", x)))\n#' Remove missing geocodes\np1 <- my_graph_in %>% select(lng, lat) %>% filter(!is.na(lng))\np2 <- my_graph_out %>% select(lng, lat) %>% filter(!is.na(lng))\n#' convert to numeric values\np1 <- sapply(p1, as.numeric) %>% dplyr::as_data_frame()\np2 <- sapply(p2, as.numeric) %>% dplyr::as_data_frame()\n#' Get intermediate points (way points) between the two locations with longitude/latitude coordinates\narch <- geosphere::gcIntermediate(p1,\n p2,\n n=50,\n breakAtDateLine=FALSE, \n addStartEnd=TRUE, \n sp=TRUE)\n\n#' http://docs.ggplot2.org/0.9.3.1/fortify.map.html\narch_fortified <- plyr::ldply(arch@lines, ggplot2::fortify)\n#' get world map\nlibrary(ggplot2)\nlibrary(ggmap)\nlibrary(sp)\nlibrary(grid)\nlibrary(geosphere)\nworld <- map_data(\"world\")\nworld <- world[world$region != \"Antarctica\",] \n\n#' ggplot2 code\nmy_plot <- ggplot() +\n geom_map(data=world, map=world,\n aes(x=long, y=lat, map_id=region),\n color=\"#191919\", fill=\"#7f7f7f\", size=0.05, alpha=1/4) +\n geom_line(aes(long,lat,group=group), data=arch_fortified, alpha= 1/100 ,size=0.5, colour=\"skyblue1\") +\n ggthemes::theme_map() +\n theme(strip.background = element_blank()) +\n theme(panel.background = element_rect(fill = \"#01001C\", colour=NA)) +\n theme(legend.position = \"none\") +\n geom_point(data = p1, aes(lng, lat), alpha = 1/100, size = 0.3, colour = \"#B0E2FF\") +\n geom_point(data = p2, aes(lng, lat), alpha = 1/100, size = 0.3, colour = \"#B0E2FF\") \n#' export\n#+ fig.width=12, fig.height=6\nmy_plot\nggsave(\"network.pdf\", width = 12, height = 6)\n\n#' Europe and North America\n#+ fig.width=12, fig.height=6\n\nmy_plot + coord_cartesian(xlim=c(-160,35),ylim=c(15,70))\nggsave(\"network_na_europe.pdf\", width = 12, height = 6)\n#' Links\n#' - https://github.com/ricardo-bion/medium_visualization", "meta": {"hexsha": "4b7c18b34ebe369291d95a12f9f5f20dfd7e485a", "size": 2984, "ext": "r", "lang": "R", "max_stars_repo_path": "solr_grid/plot_map.r", "max_stars_repo_name": "subugoe/r-recipes", "max_stars_repo_head_hexsha": "f1f33247598e97cb1fa0b65d790f73b4fd3a9700", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2017-03-21T21:32:53.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-18T11:15:35.000Z", "max_issues_repo_path": "solr_grid/plot_map.r", "max_issues_repo_name": "subugoe/r-recipes", "max_issues_repo_head_hexsha": "f1f33247598e97cb1fa0b65d790f73b4fd3a9700", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-04-05T14:07:08.000Z", "max_issues_repo_issues_event_max_datetime": "2018-04-05T15:08:46.000Z", "max_forks_repo_path": "solr_grid/plot_map.r", "max_forks_repo_name": "subugoe/r-recipes", "max_forks_repo_head_hexsha": "f1f33247598e97cb1fa0b65d790f73b4fd3a9700", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-05T14:28:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-05T14:28:22.000Z", "avg_line_length": 40.3243243243, "max_line_length": 104, "alphanum_fraction": 0.6632037534, "num_tokens": 907, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593171945417, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3499320008841906}} {"text": "library(dplyr)\nlibrary(tidyr)\nlibrary(shiny)\nlibrary(ggplot2)\nload(\"app_data.rda\")\n\ninput <- list(House_Can = \"Young\", Gov_Can = \"Walker\", Senate_Can = \"Sullivan\",SenateQuantile = 0.5, HouseQuantile = .75, GovQuantile = 0.6, Prop1Quantile = .3, Prop2Quantile = .5, Prop3Quantile = .5, Prop4Quantile = .2)\nshinyServer(function(input, output, session) {\n \nreactive_plot <- reactive({\nif(input$House_Can == \"Dunbar\") {house_percentile <- quantile(app_data$Dunbar, input$HouseQuantile) }\nif(input$House_Can == \"McDermott\") {house_percentile <- quantile(app_data$McDermott, input$HouseQuantile)}\nif(input$House_Can == \"Young\") {house_percentile <- quantile(app_data$Young, input$HouseQuantile)}\n\nif(input$Gov_Can == \"Clift\") {gov_percentile <- quantile(app_data$Clift, input$GovQuantile) }\nif(input$Gov_Can == \"Myers\") {gov_percentile <- quantile(app_data$Myers, input$GovQuantile)}\nif(input$Gov_Can == \"Parnell\") {gov_percentile <- quantile(app_data$Parnell, input$GovQuantile)}\nif(input$Gov_Can == \"Walker\") {gov_percentile <- quantile(app_data$Walker, input$HouseQuantile)}\n\nif(input$Gov_Can == \"Clift\") {gov_percentile <- quantile(app_data$Clift, input$GovQuantile) }\nif(input$Gov_Can == \"Myers\") {gov_percentile <- quantile(app_data$Myers, input$GovQuantile)}\nif(input$Gov_Can == \"Parnell\") {gov_percentile <- quantile(app_data$Parnell, input$GovQuantile)}\nif(input$Gov_Can == \"Walker\") {gov_percentile <- quantile(app_data$Walker, input$HouseQuantile)}\n\nif(input$Senate_Can == \"Begich\") {sen_percentile <- quantile(app_data$Begich, input$SenateQuantile) }\nif(input$Senate_Can == \"Fish\") {sen_percentile <- quantile(app_data$Fish, input$SenateQuantile)}\nif(input$Senate_Can == \"Gianoutsos\") {sen_percentile <- quantile(app_data$Gianoutsos, input$SenateQuantile)}\nif(input$Senate_Can == \"Sullivan\") {sen_percentile <- quantile(app_data$Sullivan, input$SenateQuantile)}\n\n\none_percentile <- quantile(app_data$Prop1, input$Prop1Quantile)\ntwo_percentile <- quantile(app_data$Prop2, input$Prop2Quantile)\nthree_percentile <- quantile(app_data$Prop3, input$Prop3Quantile)\nfour_percentile <- quantile(app_data$Prop4, input$Prop3Quantile)\n\napp_data <- app_data[, c(\"District\",\"Total_Votes\", input$House_Can, input$Senate_Can, input$Gov_Can, \"Prop1\", \"Prop2\", \"Prop3\", \"Prop4\")] \n\ngov_diff <- gov_percentile - app_data[,input$Gov_Can]\nhouse_diff <- house_percentile - app_data[,input$House_Can]\nsen_diff<- sen_percentile - app_data[,input$Senate_Can] \none_diff <- one_percentile - app_data$Prop1\ntwo_diff <- two_percentile- app_data$Prop2\nthree_diff <- three_percentile - app_data$Prop3\nfour_diff <- four_percentile - app_data$Prop4\n\nvote_distance <- sqrt(\n gov_diff^2 +\n house_diff^2 +\n sen_diff^2 +\n one_diff^2 + \n two_diff^2 +\n three_diff^2 + \n four_diff^2\n )\n\ndf <- data.frame(District = app_data$District, \n gov_diff, \n house_diff, \n one_diff, \n two_diff, \n three_diff, \n four_diff,\n vote_distance,\n Total_Votes = app_data$Total_Votes)\n\nif(input$leverage != TRUE) { df$vote_distance <- (df$vote_distance/6)/df$Total_Votes}\ndf$District <- factor(df$District, levels = as.character(df$District[order(-df$vote_distance)]))\n\n\n\nplot_list <- list()\nplot_list$hist <- ggplot(data = df, aes(y = vote_distance/6, x = District)) + geom_bar(stat = \"identity\", position = \"dodge\") +\n coord_flip()\n\n\nplot_list\n})\n\noutput$testplot <- renderPlot({\n print(reactive_plot()$hist) })\n})\n\n", "meta": {"hexsha": "1caf6e606a0fae4247d1b63bd2c2f48897cbe41b", "size": 3644, "ext": "r", "lang": "R", "max_stars_repo_path": "precinctvoting/server.r", "max_stars_repo_name": "codeforanchorage/shiny-server", "max_stars_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "precinctvoting/server.r", "max_issues_repo_name": "codeforanchorage/shiny-server", "max_issues_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-10-20T20:41:10.000Z", "max_issues_repo_issues_event_max_datetime": "2015-10-20T20:44:49.000Z", "max_forks_repo_path": "precinctvoting/server.r", "max_forks_repo_name": "codeforanchorage/shiny-server", "max_forks_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-03-25T21:05:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-25T21:05:40.000Z", "avg_line_length": 43.9036144578, "max_line_length": 220, "alphanum_fraction": 0.691273326, "num_tokens": 1080, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7401743505760728, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3498681853355692}} {"text": "require(procPharm)\r\nrequire(reticulate)\r\npyPharm <- import('python_pharmer')\r\n\r\ntmpRD <- get(load(\"./extras/RD.200309.30.m.m3.p1.Rdata\"))\r\n\r\n# Find where the AITC is located and go out 120 points\r\n\r\nfancyBin<- function(dat){\r\n pulsesWithNN <- c('^[bB]ob.*', \"^AITC.*\", \"^[cC]aps.*\", \"^[mM]enth.*\", \"[kK][.]40.*\")\r\n nnNames <- c('blob','aitc', 'menthol', 'capsaicin', 'k40')\r\n\r\n for( i in 1:length(pulsesWithNN)){\r\n print(i)\r\n \r\n # Make sure the pulse exists\r\n pulse <- grep(pulsesWithNN[i], dat$w.dat$wr1)\r\n \r\n if(length(pulse) > 0){\r\n minWin <- min( pulse )\r\n maxWin <- minWin + 119\r\n\r\n # Snag the pulse for all cells\r\n pulseToScore <- as.data.frame(t(dat$blc[minWin:maxWin,-1]))\r\n\r\n # Now use the python score all the responses of interest\r\n featureFrame <- pyPharm$featureMaker(pulseToScore, 10)\r\n featureScores <- pyPharm$modelRunner(featureFrame, nnNames[i])\r\n\r\n # Transfer these scoring to the binary dataframe\r\n binName <- grep(pulsesWithNN[i], names(dat$bin), value=T)\r\n dat$bin[binName] <- featureScores\r\n }\r\n\r\n }\r\n return(dat)\r\n}\r\n\r\ntmpRD <- fancyBin(tmpRD)\r\n", "meta": {"hexsha": "ec299ce14f2ae7adaa5c0452cef56122b738683f", "size": 1243, "ext": "r", "lang": "R", "max_stars_repo_path": "extras/deployPeakDeepDetect.r", "max_stars_repo_name": "leeleavitt/procPharm", "max_stars_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "extras/deployPeakDeepDetect.r", "max_issues_repo_name": "leeleavitt/procPharm", "max_issues_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-01-08T18:50:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T01:23:47.000Z", "max_forks_repo_path": "extras/deployPeakDeepDetect.r", "max_forks_repo_name": "leeleavitt/procPharm", "max_forks_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T20:45:06.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T20:45:06.000Z", "avg_line_length": 31.075, "max_line_length": 90, "alphanum_fraction": 0.5623491553, "num_tokens": 343, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3497575550127163}} {"text": "writePlotToFile <- function(x, y, xLabel, yLabel, color, pathOutputFile){\n png(filename= pathOutputFile, width = 700, height = 500)\n plot(x, y, xlab=xLabel, ylab=yLabel, col=color); # ylim=c(0,400000)\n # Draw vertical lines\n abline(v=10, col=\"red\")\n abline(v=15, col=\"red\")\n}\n\naddPlot <- function(x, y, color){\n\tpoints(x,y, col=color)\n}\n\nconvertSecToMin <- function(sec){\n\treturn(sec/60)\n}\n\nconvertMsToMin <- function(ms){\n return(ms/60000)\n}\n\nargs <- commandArgs(trailingOnly = TRUE)\nargLength <- length(args)\nif(argLength != 6){\n \tcat(\"Usage: script \\n\")\n\tquit()\n}\n\nthroughputFile = args[1]\ninsertFile = args[2];\nupdateFile = args[3];\nreadFile = args[4]\nscanFile = args[5];\npathOutputFile = args[6];\n\nthroughputData <- read.csv(file=throughputFile,head=TRUE,sep=\",\")\ninsertData <- read.csv(file=insertFile,head=TRUE,sep=\",\")\nupdateData <- read.csv(file=updateFile,head=TRUE,sep=\",\")\nreadData <- read.csv(file=readFile,head=TRUE,sep=\",\")\nscanData <- read.csv(file=scanFile,head=TRUE,sep=\",\")\n\nwritePlotToFile(convertSecToMin(throughputData$sec), throughputData$throughput, \"Minuten\", \"throughput (ops/sec)\", 1, paste(pathOutputFile, '_throughput_plot.png', sep=''))\nwritePlotToFile(convertMsToMin(insertData$ms), insertData$latency, \"Minuten\", \"Latency (\u00b5s)\", 1, paste(pathOutputFile, '_latency_plot.png', sep=''))\naddPlot(convertMsToMin(updateData$ms), updateData$latency, 2)\naddPlot(convertMsToMin(readData$ms), readData$latency, 3)\naddPlot(convertMsToMin(scanData$ms), scanData$latency, 4)\n\nlegend(\"topright\", inset=.05, title=\"Legend\", c('Insert', 'Update', 'Read', 'Scan'), col=1:4, pch=1)\n", "meta": {"hexsha": "08f42aca45becf05511554635ef0c542778a2492", "size": 1675, "ext": "r", "lang": "R", "max_stars_repo_path": "front_end/plot/plot_availability_mult.r", "max_stars_repo_name": "arnaudsjs/YCSB-1", "max_stars_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "front_end/plot/plot_availability_mult.r", "max_issues_repo_name": "arnaudsjs/YCSB-1", "max_issues_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "front_end/plot/plot_availability_mult.r", "max_forks_repo_name": "arnaudsjs/YCSB-1", "max_forks_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8958333333, "max_line_length": 172, "alphanum_fraction": 0.7164179104, "num_tokens": 525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34975754789168645}} {"text": "## @knitr code1\ncos(pi)\n## @knitr end_code1", "meta": {"hexsha": "a6e11dc5b886a65238182bbdaa61b085f0082207", "size": 43, "ext": "r", "lang": "R", "max_stars_repo_path": "rmd_with_kevin/code.r", "max_stars_repo_name": "CNuge/RUsersGroup", "max_stars_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-26T16:52:42.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-19T21:32:38.000Z", "max_issues_repo_path": "rmd_with_kevin/code.r", "max_issues_repo_name": "CNuge/RUsersGroup", "max_issues_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rmd_with_kevin/code.r", "max_forks_repo_name": "CNuge/RUsersGroup", "max_forks_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-09-21T13:02:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-07T16:43:22.000Z", "avg_line_length": 14.3333333333, "max_line_length": 19, "alphanum_fraction": 0.6744186047, "num_tokens": 17, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.34975754077065635}} {"text": "#!/usr/bin/env Rscript\nlibrary(data.table)\nlibrary(\"optparse\")\nlibrary(\"qqman\")\nt_col <- function(color, percent = 50, name = NULL) {\n # color = color name\n # percent = % transparency\n # name = an optional name for the color\n\n## Get RGB values for named color\nrgb.val <- col2rgb(color)\n\n## Make new color using input color as base and alpha set by transparency\nt.col <- rgb(rgb.val[1], rgb.val[2], rgb.val[3],\n max = 255,\n alpha = (100 - percent) * 255 / 100,\n names = name)\n\n## Save the color\ninvisible(t.col)\n}\n\n\n\ncomputedher<-function(beta, se, af,N){\n#https://journals.plos.org/plosone/article/file?type=supplementary&id=info:doi/10.1371/journal.pone.0120758.s001\nmaf<-af\nmaf[!is.na(af) & af>0.5]<- 1 - maf[!is.na(af) & af>0.5]\nba<-!is.na(beta) & !is.na(se) & !is.na(maf) & !is.na(N)\na<-rep(NA, length(beta))\nb<-rep(NA, length(beta))\na<-2*(beta[ba]**2)*(maf[ba]*(1-maf[ba]))\nb<-2*(se[ba]**2)*N[ba]*maf[ba]*(1-maf[ba])\nres<-rep(NA, length(beta))\nres[ba]<-a[ba]/(a[ba]+b[ba])\nres\n}\n\n\n\noption_list = list(\n make_option(c( \"--gwascat\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c( \"--gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c( \"--ld_file\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c( \"--pheno\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--chr_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--bp_gwascat\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--ps_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--a1_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--a2_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--beta_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--se_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--af_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--N_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--ps_gwascat\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--chr_gwascat\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--p_gwas\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--min_pvalue\"), type=\"numeric\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--min_r2\"), type=\"numeric\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--info_gwascat\"), type=\"character\", default=NULL,\n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--out\"), type=\"character\", default=\"out.txt\",\n help=\"output file name [default= %default]\", metavar=\"character\")\n);\n\ncheckhead<-function(head,Data, type){\nif(length(which(head %in% names(Data)))==0){\nprint(names(Data))\nprint(paste('not found ', head,'for',type ,'data'))\nq(2)\n}\n}\nTest=F\n#--chr_gwas ${params.head_chr} --ps_gwas ${params.head_bp} --a1_gwas ${params.head_A1} --a2_gwas ${params.head_A2\nopt_parser = OptionParser(option_list=option_list);\nopt = parse_args(opt_parser);\nif(Test)opt=list(gwascat='Diastolic_AwigenLD_all.csv',gwas='Diastolic_AwigenLD_range.init',chr_gwas='CHR',ps_gwas='BP',a1_gwas='ALLELE1',a2_gwas='ALLELE0',beta_gwas='BETA',se_gwas='SE',af_gwas='A1FREQ',chr_gwascat='chrom',bp_gwascat='chromEnd',p_gwas='P_BOLT_LMM',ps_gwascat='chromEnd',chr_gwascat='chrom',out='Diastolic_AwigenLD_ld',ld_file='Diastolic_AwigenLD_ld.ld',min_pvalue='0.001',min_r2=0.2,info_gwascat=\"pubMedID;author;trait;initSample\")\n\n\nheadse=opt[['se_gwas']];headbp=opt[['ps_gwas']];headchr=opt[['chr_gwas']];headbeta=opt[['beta_gwas']];heada1=opt[['a1_gwas']];heada2=opt[['a2_gwas']];headpval=opt[['p_gwas']];headaf<-opt[['af_gwas']];headbeta=opt[['beta_gwas']]\nheadchrcat=opt[['chr_gwascat']];headbpcat=opt[['ps_gwascat']];heada1catrs<-\"riskAllele\";headzcat=\"z.cat\";headafcat<-'risk.allele.af';heada1cat<-'risk.allele.cat'\nouthead=opt[['out']]\n\n\ndatagwascat=read.csv(opt[['gwascat']])\n#datagwascat[,heada1cat]<-sapply(strsplit(as.character(datagwascat[,heada1catrs]),split='-'),function(x)x[2])\ndatagwas<-read.table(opt[['gwas']], header=T)\ncheckhead(headpval, datagwas,'pval');checkhead(headse, datagwas,'se');checkhead(headbp, datagwas,'bp');checkhead(headchr, datagwas, 'chr');checkhead(headbeta, datagwas, 'beta')\n\ncheckhead(headbpcat,datagwascat,'bp cat');checkhead(headchrcat,datagwascat,'chro cat');\n\n# CHR_A BP_A SNP_A CHR_B BP_B SNP_B R2 \ndatald<-fread(opt[['ld_file']])\ntmpa<-unique(datald[,c('CHR_A','BP_A','SNP_A')]);names(tmpa)<-c('CHR','BP','SNP');\ntmpb<-unique(datald[,c('CHR_B','BP_B','SNP_B')]);names(tmpb)<-c('CHR','BP','SNP');\ntmp<-rbind(tmpa,tmpb)\ntmpall<-unique(rbind(tmpa,tmpb))\ntmpall<-tmpall[,c('CHR','BP','SNP','CHR','BP','SNP')]\ntmpall$R2<-1\nnames(tmpall)<-names(datald)\ndatald<-rbind(datald,tmpall)\n\n# CHR_A BP_A SNP_A CHR_B BP_B SNP_B R2\n#1: 18 48132646 rs1437649 18 48133241 rs61148001 0.896039\n\n\n\n\n\nheadN<-opt[['N_value']]\nif(is.null(opt[['N_gwas']])){\nif(is.null(opt[['N_value']]))Nval<-10000 else Nval=opt[['N_value']]\ndatagwas[,'N_gwas']<-Nval\nheadN<-'N_gwas'\n}\n\nbaliseh2=F\nif(!is.null(headaf)){\ncheckhead(headaf, datagwas,'af');\ndatagwas$h2.gwas<-computedher(datagwas[,headbeta], datagwas[,headse], datagwas[,headaf],datagwas[,headN])\ndatagwas$z.gwas<-datagwas[,headbeta]/datagwas[,headse]\n}else{\ncat('no frequencie\\n')\ndatagwas$h2.gwas<-NA\ndatagwas$z.gwas<-NA\nbaliseh2=T\n\n}\n\n\n\ndatalda1<-merge(datagwascat, datald, by.x=c(headchrcat,headbpcat), by.y=c(\"CHR_A\", \"BP_A\"));names(datalda1)[names(datalda1)==\"CHR_B\"]<-headchr;names(datalda1)[names(datalda1)==\"BP_B\"]<-headbp;names(datalda1)[names(datalda1)==\"SNP_B\"]<-'rs_gwas';names(datalda1)[names(datalda1)==\"SNP_A\"]<-'rs_cat'\ndatalda2<-merge(datagwascat, datald, by.x=c(headchrcat,headbpcat), by.y=c(\"CHR_B\", \"BP_B\"));names(datalda2)[names(datalda2)==\"CHR_A\"]<-headchr;names(datalda2)[names(datalda2)==\"BP_A\"]<-headbp;names(datalda2)[names(datalda2)==\"SNP_A\"]<-'rs_gwas';names(datalda2)[names(datalda2)==\"SNP_B\"]<-'rs_cat'\n\ndataldallcat<-rbind(datalda1,datalda2)\n\nif(Test){\nrsclump<-\"12:2523697\";rscat=\"rs55935819\";chrocat<-12;bpcat<-2521579\ndatagwas[datagwas[,headchr]==12 & datagwas[,headbp]==2523697,]\ndatagwascat[datagwascat[,headchrcat]==chrocat & datagwascat[,headbpcat]==bpcat,]\ndatagwascat[datagwascat[,'name']==rscat ,]\n\ndataldallcat[dataldallcat$rs_gwas==rsclump,]\ndataldallcat[dataldallcat$rs_cat==rsclump,]\ndataldallcat[dataldallcat$rs_cat==rscat,]\ndatald[datald$SNP_B==rsclump | datald$SNP_A==rsclump,]\ndatagwas[datagwas$CHR=='18' & datagwas$BP==48133241,]\ndatald[datald$SNP_B==rscat | datald$SNP_A==rscat,]\n#datagwascat[datagwascat$name=='rs745821',]\ndataldallcat[dataldallcat$rs_cat==rscat,]\ndataldallcat[dataldallcat$rs_gwas==rsclump,]\n}\n\n\ndatalda1<-merge(dataldallcat, datagwas, by.x=c(headchr,headbp), by.y=c(headchr,headbp))\nif(Test)datalda1[datalda1$name==rscat,]\n#datald[datald$CHR_A==18 & datald$BP_A==48144571 ,]\n#datalda1[datalda1$rs_cat=='rs745821',]\n#datalda1[datalda1$rs_gwas=='18:48144571',]\n\n\nwrite.table(datalda1, file=paste(opt[['out']],'_all.txt',sep=''), row.names=F, col.names=T,quote=F)\n\n\n\n\ninfocat=strsplit(opt[['info_gwascat']],split=';')[[1]]\n\ndatalda1$info_gwas<-paste(datalda1[,headchr],':',datalda1[,headbp],'-beta:',datalda1[,headbeta], ',se:',datalda1[,headse],',pval:',datalda1[,headpval],',R2:', datalda1[,'R2'],sep='')\ndatalda1$info_gwascat<-\"\"\nfor(cat in infocat)datalda1$info_gwascat<-paste(datalda1$info_gwascat,cat,':',datalda1[,cat],',',sep='')\ndatagwassumm<-aggregate(as.formula(paste('info_gwas~',headbpcat, '+',headchrcat)), data=datalda1,function(x)paste(unique(x), collapse=';'))\ndatagwascatsumm<-aggregate(as.formula(paste('info_gwascat~',headbpcat, '+',headchrcat)), data=datalda1, function(x)paste(unique(x), collapse=';'))\ndatagwasminpval<-aggregate(as.formula(paste(headpval,'~',headbpcat, '+',headchrcat)), data=datalda1,min)\ndatagwasminpval<-merge(datagwasminpval,datalda1, by=c(headchrcat,headbpcat,headpval),all=F)\n\n\nallresume<-merge(merge(datagwassumm,datagwascatsumm,all=T, by=c(headchrcat,headbpcat)),datagwasminpval, all=T, by=c(headchrcat,headbpcat))\nnames(allresume)[c(1,2)]<-c('chr_gwas', 'bp_gwas_cat')\nwrite.csv(allresume, file=paste(opt[['out']],'_resume.csv',sep=''),row.names=F)\n\n###\n#head(datalda1)\n#print(range(datalda1$R2))\n#cat(opt[['min_pvalue']])\ndatalda1sig<-datalda1[datalda1[,headpval]0){\n#datalda1sig$info_gwas<-paste(datalda1sig[,headchr],':',datalda1sig[,headbp],'-beta:',datalda1sig[,headbeta], ',se:',datalda1sig[,headse],',pval:',datalda1sig[,headpval],',R2:', datalda1sig[,'R2'],sep='')\n#}else{\n#}\n#datalda1sig$info_gwascat<-\"\"\n#for(cat in infocat)datalda1sig$info_gwascat<-paste(datalda1sig$info_gwascat,cat,':',datalda1sig[,cat],',',sep='')\nif(nrow(datalda1sig)>0){\ndatagwasminpval<-aggregate(as.formula(paste(headpval,'~',headbpcat, '+',headchrcat)), data=datalda1sig,min)\ndatagwassumm<-aggregate(as.formula(paste('info_gwas~',headbpcat, '+',headchrcat)), data=datalda1sig,function(x)paste(unique(x), collapse=';'))\ndatagwascatsumm<-aggregate(as.formula(paste('info_gwascat~',headbpcat, '+',headchrcat)), data=datalda1sig, function(x)paste(unique(x), collapse=';'))\n}else{\ndatagwasminpval<-datalda1sig[F,c(headbpcat,headchrcat,headpval)]\ndatagwassumm<-datalda1sig[F,c('info_gwas',headchrcat,headbpcat)]\ndatagwascatsumm<-datalda1sig[F,c('info_gwascat',headchrcat,headbpcat)]\n}\n\n\nallresume<-merge(merge(datagwassumm,datagwascatsumm,all=T, by=c(headchrcat,headbpcat)), datagwasminpval,all=T, by=c(headchrcat,headbpcat))\nallresume<-allresume[allresume[,headpval] 1)]\n\t\t\n\t\ttest.ol <- rownames(x.e.test)[which(x.e.test$overlap < 0.1)]\n\n\t\ttest.eff <- rownames(x.e.test)[which(abs(x.e.test$effect) > 1)]\n\t\t\n\n\t\ttest.diff.eff <- intersect(test.diff, test.eff) \n\t\ttest.diff.ol <- intersect(test.diff, test.ol) \n\t\ttest.ol.eff.diff <- intersect(test.eff, intersect(test.ol, test.diff)) \n\n\t\tsummary.stats[i,1] <- length(intersect(ty.eff, test.eff))\n\t\tsummary.stats[i,2] <- length(setdiff(test.eff, ty.eff))\n\n\t\tsummary.stats[i,3] <- length(intersect(ty.diff, test.diff))\n\t\tsummary.stats[i,4] <- length(setdiff(test.diff, ty.diff))\n\n\t\tsummary.stats[i,5] <- length(intersect(ty.diff.eff, test.diff.eff))\n\t\tsummary.stats[i,6] <- length(setdiff(test.diff.eff, ty.diff.eff))\n\n\t\tsummary.stats[i,7] <- length(intersect(ty.ol, test.ol))\n\t\tsummary.stats[i,8] <- length(setdiff(test.ol, ty.ol))\n\n\t\tsummary.stats[i,9] <- length(intersect(ty.diff.ol, test.diff.ol))\n\t\tsummary.stats[i,10] <- length(setdiff(test.diff.ol, ty.diff.ol))\n\t\t\n\t\tsummary.stats[i,11] <- length(intersect(ty.diff.eff, test.ol.eff.diff))\n\t\tsummary.stats[i,12] <- length(setdiff(test.ol.eff.diff, ty.diff.eff))\n\t}\n \n output.summary2[[j]] <- summary.stats\n}\nsave(output.summary.ty, file=paste(my.path,'data/output.summary.ty.Rda', sep=''))\n} # end run.this if statement\n\n\n\n\n\n", "meta": {"hexsha": "1619d78023724d02433020e98ab8caed01161bf1", "size": 2960, "ext": "r", "lang": "R", "max_stars_repo_path": "peerj/code/TP-FP-summary-parallel-ty.r", "max_stars_repo_name": "ggloor/effect", "max_stars_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "peerj/code/TP-FP-summary-parallel-ty.r", "max_issues_repo_name": "ggloor/effect", "max_issues_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "peerj/code/TP-FP-summary-parallel-ty.r", "max_forks_repo_name": "ggloor/effect", "max_forks_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-06-17T01:55:39.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-17T01:55:39.000Z", "avg_line_length": 32.8888888889, "max_line_length": 182, "alphanum_fraction": 0.6766891892, "num_tokens": 895, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3497261564093474}} {"text": "##########################################################################################################################################################\u00e0\n# Qt V PC M Qin Qout\n\n############################################################################################################################\n\nsetwd(\"\")\nchem <- read.csv(\"data/chem_data_prob.csv\", stringsAsFactors = FALSE) # physicochemical parameters constant are in 1/minutes\nTK <- read.csv(\"data/tk_data/TK_prob.csv\",stringsAsFactors=FALSE) # toxicokinetic parameters\nfBW <- read.csv(\"data/physio/fBW_animals_prob.csv\",stringsAsFactors = FALSE) # organ fractions, incl var-distribution parameters\nfCO <- read.csv(\"data/physio/fCO_animals_prob.csv\",stringsAsFactors = FALSE) # blood flow fractions, incl var-distribution parameters\nrates <- read.csv(\"data/physio/rates_animals_prob.csv\",stringsAsFactors = FALSE) # physiological rates (not chemical-dependent)\nfPC <- read.csv(\"data/physio/PC_tissue_prob.csv\",stringsAsFactors = FALSE) # tissue composition, fixed during var-analysis\n\n############################################################################################################################\n\nBW <- BW # bodyweight (kg)\nCO <- CO/20 # cardiac output (fro minutes to L/3sec)\n\n# fBW (peso frazione) fCO (blood flow fractions) PC V (volume) Q\n\nQ_abomasum=Q_adipose=Q_brain=Q_carcass=Q_crop=Q_gizzard=Q_heart=Q_intestine=Q_kidney=0;\nQ_liver=Q_lun=Q_mamgland=Q_muscle=Q_omasum=Q_reprod=Q_reticulum=Q_rumen=Q_stomach=Q_lumen=0;\nQ_ven=Q_art=0;\nQ_milk=Q_egg=Q_urine=Q_air=Q_feces=Q_metab=0\n\n\n\n\ndQ_met <- ifelse(!is.na(phys$Vmax)&!is.na(phys$Km),(phys$Vmax*(phys$C/phys$PC))/(phys$Km+phys$C/phys$PC),0)\ndQ_met_liver=phys$C[phys$comp==\"liver\"]*phys$Cl[phys$comp==\"liver\"]\n\n\nphys$dM_in <- phys$dM_in + phys$dM_met[phys$comp==\"liver\"]*phys$fbile*phys$fbact\nphys$dM_met[phys$comp==\"liver\"]*(1-phys$fbile[phys$comp==\"lumen\"]*phys$fbact[phys$comp==\"lumen\"]) \n\n#ELIMINATION\n\nphys$dM_exh <- ifelse(phys$comp==\"ven\", CO*phys$C[phys$comp==\"ven\"],0) #all mass in venous blood is transported\n\nphys$dM_out <- phys$dM_out + phys$dM_exh\n\nphys$dM_in[phys$comp==\"art\"] <- phys$dM_in[phys$comp==\"art\"] + CO*phys$C[phys$comp==\"ven\"] * (CO/(CO+Qexhale*phys$PC[phys$comp==\"air\"]))\nphys$dM_in[phys$comp==\"air\"] <- phys$dM_in[phys$comp==\"air\"] + CO*phys$C[phys$comp==\"ven\"] * ((Qexhale*phys$PC[phys$comp==\"air\"])/(CO+Qexhale*phys$PC[phys$comp==\"air\"]))\n\n\ndQ_urine <- phys$C[phys$comp==\"kidney\"]*phys$Cl[phys$comp==\"kidney\"]\ndQ_milk <- phys$C[phys$comp==\"mamgland\"]*phys$Qmilk[phys$comp==\"mamgland\"]\ndQ_reprod <- phys$C[phys$comp==\"reprod\"]*phys$Qegg[phys$comp==\"reprod\"]\ndQ_feces <- phys$M*phys$kgastric\n\ndQ_exh=dQ_urine+dQ_milk+dQ_reprod+dQ_feces\n", "meta": {"hexsha": "12c8e6da358a9ffcb93e8fd829ed86d158f074d8", "size": 2798, "ext": "r", "lang": "R", "max_stars_repo_path": "pbkm_modeling/R_code/building_model.r", "max_stars_repo_name": "alfcrisci/michyf", "max_stars_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:35.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:35.000Z", "max_issues_repo_path": "pbkm_modeling/R_code/building_model.r", "max_issues_repo_name": "alfcrisci/Mychif", "max_issues_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pbkm_modeling/R_code/building_model.r", "max_forks_repo_name": "alfcrisci/Mychif", "max_forks_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.8076923077, "max_line_length": 169, "alphanum_fraction": 0.6004288778, "num_tokens": 873, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.8128673359709796, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.3496527293199544}} {"text": "##############################################################\n# TODO:\n# 1. get the env rasters at start and end,\n#calculate scaled 3d distance between each pixel before and after change,\n# then run spatial regression of phenotype change on env change\n\n# 2. go back and look at draft yosemite fig and work on other parts of it\n\n# 3. generate gif of rotating 3d landscape with phenotype and phenotype change on it\n# (figure out hillshading/shadow for that??)\n\n# 4. do same thing for pop density! (still need to KDE it; see py script)\n\n# 5. get drafts of all figs combined and plopped into paper to get Ian's feedback\n\nlibrary(raster)\nlibrary(rgdal)\nlibrary(rasterVis)\nlibrary(rayshader)\nlibrary(pals)\nlibrary(wesanderson)\nlibrary(MASS)\nlibrary(nlme)\nlibrary(ggplot2)\nlibrary(ggthemes)\nlibrary(cowplot)\n\n# output controls\nsave_vid = F\nsave_figs = F\n\n\n###################\n###################\n###### MAPS #######\n###################\n###################\n\n# read in the phenotype rasters\nrasterize_table = function(filename, dem){\n rast = raster(as.matrix(read.table(filename)))\n rast@extent = dem@extent\n rast@crs = dem@crs\n return(rast)\n}\n\n# function to normalize a matrix to values between low and high,\n# optionally flooring them integers\nnormalize_low_to_high = function(mat, low, high, floor_it=F){\n minval = min(mat)\n maxval = max(mat)\n out = (mat-minval)/(maxval-minval)\n out = ((high-low) * out)+low\n if (floor_it){\n out = floor(out)\n }\n return(out)\n}\n\n\n# function to get an ixjx3 array of RGB values\n# corresponding to the given palette's hex values\n# for an ixj array of data\nget_rgb_vals = function(arr, pal){\n rgbs = lapply(seq(1, length(pal)), \n function(i){return(col2rgb(pal[i]))})\n out_arr = array(0, dim=c(nrow(arr), ncol(arr),3))\n for (i in seq(dim(arr)[1])){\n for (j in seq(dim(arr)[2])){\n out_arr[i, j, ] = rgbs[[arr[i, j]]][,1]\n }\n }\n return(out_arr)\n}\n\n\n# function to make a RGB-contrast ixjx3 array\n# from the ixj raster of data, using the given palette\nmake_rgb_contrast_array = function(rast, pal){\n # convert raster to a matrix\n mat = rayshader::raster_to_matrix(rast)\n # normalize to ints between 1 and palette length\n norm = normalize_low_to_high(mat, 1, length(pal), floor_it=T)\n # convert to a 3-layer array of the RGB values\n rgb_arr = get_rgb_vals(norm, pal)\n # permute to line up correctly for the plot_3D function (I believe?)\n rgb_arr = aperm(rgb_arr, c(2,1,3))\n # create a contrast array\n rgb_contrast = scales::rescale(rgb_arr, to=c(0,1))\n return(rgb_contrast)\n}\n\n\n# function to make a hillshade plot of the given raster and given DEM,\n# colored by the given palette, using the given title\nmake_hillshade_plot = function(rast, dem, pal, title){\n slope = terrain(dem, opt='slope')\n aspect = terrain(dem, opt='aspect')\n hill = hillShade(slope, aspect, angle=65, direction=180)\n # plot the overlay for phenotype change\n plot(hill, main = title, col = grey(1:100/100), legend = FALSE)\n plot(rast, add = TRUE, alpha = .5, col=pal)\n}\n\n\n# function to make a 3d plot of the given raster and given DEM,\n# colored by the given palette, using the given title\nmake_3d_plot = function(rast, dem, pal, title, var_name='no_var', zscale=10,\n phi=30, theta_start=-45, fov=0,\n background_color='#000000', title_bar_color='#ffffff',\n title_color='#000000', snapshot=F, video=F){\n # get the dem as a matrix\n demmat = raster_to_matrix(dem)\n #use some of rayshader's built-in textures on the dem\n # NOTE: FIGURE OUT HOW TO ADD THIS TO GET HILLSHADING? CAN I EVEN DO IT?\n if (F){\n demmat_texture = demmat %>%\n sphere_shade(texture = \"desert\") %>%\n add_water(detect_water(demmat), color='desert') %>%\n add_shadow(ray_shade(demmat), 0.5) %>%\n add_shadow(ambient_shade(demmat), 0) \n }\n # get the raster as an RGB-contrast 3-layer array\n rgb_contrast = make_rgb_contrast_array(rast, pal)\n # make the plot \n plot_3d(rgb_contrast, demmat, windowsize = c(1100,900), zscale = zscale,\n shadowdepth = -50, zoom=0.5, phi=phi, theta=theta_start, fov=fov,\n background = background_color, shadowcolor = \"#523E2B\")\n if (snapshot){\n render_snapshot(title_text = title, title_bar_color = title_bar_color,\n title_color = title_color, title_bar_alpha = 1)\n }\n if (video){\n # save 3d rotating video\n angles= seq(0,360,length.out = 1441)[-1]\n for(i in 1:(length(angles)-1)) {\n render_camera(theta=theta_start+angles[i], phi=phi)\n render_snapshot(filename = sprintf(\"./video_files/yosemite_vid_%s_%i.png\", var_name, i),\n title_text = title, title_bar_color = title_bar_color,\n title_color = title_color, title_bar_alpha = 1) }\n rgl::rgl.close()\n system(paste0(sprintf(\"ffmpeg -framerate 60 -i ./video_files/yosemite_vid_%s\", var_name),\n \"_%d.png -pix_fmt yuv420p \",\n sprintf(\"./video_files/yosemite_%s.mp4\", var_name)))\n }\n} \n\n# function to normalize the values in the before and after rasters\n# (so that differences calculated as 3d Euclidean distances weight\n# each var equally), but to do so using the full range of var values\n# across both the b4 and after layers (so that calculated differences\n# still reflect the fact that changes in temp are largely increases,\n# changes in sdm are largely decreases, ppt is mixed)\nget_normed_stacks = function(b4, af){\n for (i in seq(nlayers(b4))){\n vals = c(b4[[i]][,], af[[i]][,])\n minval = min(vals)\n maxval = max(vals)\n b4[[i]] = (b4[[i]] - minval)/(maxval-minval)\n af[[i]] = (af[[i]] - minval)/(maxval-minval)\n }\n return(list(b4, af))\n}\n\n\n# function to make a hillshaded ggplot with the given titile\n# and from the given input raster using the layer data\n# (which will be labeled in the legend\n# using the given display_name, and the colors for which will be 'cols' \n# will be scaled between minval and maxval, with the mean of those two\n# vals being used as the contour-line value)\nmake_gg_hillshade_plot = function(rast, hillrast, display_name,\n minval, maxval, cols, title,\n include_title=FALSE){\n if (!include_title){\n title=''\n }\n # make both rasters into data.frames\n df = as.data.frame(rast, xy=T)\n hill_df = as.data.frame(hillrast, xy=T)\n colnames(df) = c('x', 'y', 'layer')\n # create the ggplot object\n out = ggplot() +\n # plot the main raster, filling by its value column\n geom_raster(data = df, \n aes(x = x, y = y, fill=layer)) + \n # add the hillshade raster, filling by its value column\n geom_raster(data = hill_df,\n aes(x = x, y = y, alpha=layer)) +\n # add the contour line at the midway value\n # between the min and max of this variable across time\n geom_contour(data = df, colour='#edede8',\n breaks=c(minval, mean(c(minval, maxval)), 1.01*maxval),\n aes(x=x, y=y, z=layer)) +\n # scale the fill using the given colors and display_name\n scale_fill_gradientn(colours=cols, name=display_name,\n limits=c(minval, maxval*1.01)) +\n # scale the alpha vals of the hillshade layer\n scale_alpha(range = c(0.15, 0.5), guide = \"none\") + \n # use the ggthemes map theme\n ggthemes::theme_map() + \n # tweak theme aspects\n theme(legend.position = 'right',\n legend.background = element_rect(fill = \"darkgray\"), \n legend.key = element_rect(fill = \"lightblue\", color = NA),\n #legend.key.size = unit(2, 'cm'),\n #legend.key.height = unit(1.2, \"cm\"), #legend.key.width = unit(0.4,\"cm\"),\n legend.text = element_text(size=14),\n legend.title = element_text(size=16),\n plot.title = element_text(size = 18),\n axis.ticks.length = unit(0.2, 'cm'),\n #axis.title = element_text(),\n plot.margin = unit(c(0,0,0,0), 'cm'),\n axis.text = element_text(size=10), #, angle=45),\n axis.ticks = element_line(),\n axis.title.x = element_blank(),\n axis.title.y = element_blank()) +\n #xlab(\"lon\") +\n #ylab(\"lat\") + \n # add the title\n ggtitle(title) +\n # set to map coordinates, \n # and fix the x- and y-axis limits to remove the annoying margin\n coord_quickmap(xlim = c(min(df$x), max(df$x)),\n ylim = c(min(df$y), max(df$y)),\n expand=F)\n return(out)\n}\n\n\n# function to create mirrored sets of individuals and pad them around the\n# real individuals, to take care of edge effects\nmirror_pad_inds = function(inds, extent){\n # get original and mirrored x and y coords\n xs = inds$x\n ys = inds$y\n mirr_xs = -xs\n mirr_ys = -ys\n # create a set of inds mirrored to the left\n lpad = inds \n lpad$x = mirr_xs\n # create a set of inds mirrored to the right\n rpad = inds\n rpad$x = mirr_xs+(2*extent[[1]])\n # create a set of inds mirrored below \n bpad = inds \n bpad$y = mirr_ys\n # create a set of inds mirrored above \n upad = inds\n upad$y = mirr_ys+(2*extent[[2]])\n # concatenate all into new data.frame\n out = rbind(inds, lpad, rpad, bpad, upad) \n return(out)\n}\n\n\n####################\n# Get and prep data:\n\n# TODO: DELETE LO-RES DEM??\n# read in the yosemite DEM\ndem = raster('yosemite_DEM_90x90.tif')\n\n# read in the before and after kriged phenotypes\nb4_z = rasterize_table('b4_cc_krig.txt', dem)\naf_z = rasterize_table('af_cc_krig.txt', dem)\n# and get the phenotype-change raster\ndel_z = af_z - b4_z\n\n# downscale everything (for better viz)\nfactor = 20\n# TODO: DELETE LO-RES DEM??\ndem = disaggregate(dem, factor, method='bilinear')\nb4_z = disaggregate(b4_z, factor, method='bilinear')\naf_z = disaggregate(af_z, factor, method='bilinear')\ndel_z = disaggregate(del_z, factor, method='bilinear')\n\n# read in the hi-res DEM, mosaic, and clip (NOTE: doesn't need reprojection)\nhrdem1 = raster('./hi_res_DEM/n37_w120_1arc_v3.tif')\nhrdem2 = raster('./hi_res_DEM/n37_w121_1arc_v3.tif')\nhrdem3 = raster('./hi_res_DEM/n38_w120_1arc_v3.tif')\nhrdem4 = raster('./hi_res_DEM/n38_w121_1arc_v3.tif')\nhrdem = mosaic(hrdem1, hrdem2, hrdem3, hrdem4, fun='mean')\nhrdem = crop(hrdem, dem)\n\n# get the 3 variables before climate change\nb4 = stack(c('./yosemite_env_layers/ppt_1980-2010_90x90.tif', \n './yosemite_env_layers/tmp_1980-2010_90x90.tif',\n './yosemite_env_layers/sdm_1980-2010_90x90.tif'))\n\n# get the 3 vars after\naf = stack(c('./yosemite_env_layers/594_ppt_2100_90x90.tif', \n './yosemite_env_layers/594_tmp_2100_90x90.tif',\n './yosemite_env_layers/594_sdm_2100_90x90.tif'))\n\n# norm them\nnormed = get_normed_stacks(b4, af)\n\n# get the Euclidean-distance raster\n# (Euclidean distance each pixel traveled in normed env space)\neuc = sqrt(sum((normed[[2]] - normed[[1]])^2))\n\n\n# read in the CSVs of individs' points\nb4_inds = read.csv('./b4_cc_individs.csv')\naf_inds = read.csv('./af_cc_individs.csv')\n\n\n# produce 2D KDE plots of population density, as rasters\n# NOTE: transpose the KDE matrices because, per the docs,\n# x vals are on the rows (not sure why?)\n# NOTE: add a margin to the limits, then subset the core of the KDE\n# matrix, to attempt to ameliorate the edge effect\n# NOTE: normalizing the raster by its own sum,\n# then multiplying by total pop size, as I believe this\n# gives me cells that can be interpreted as actual pop densities\nh=8\nreal_width = nrow(b4)\nmargin_width=10\nlims=c(-margin_width, real_width+margin_width,\n -margin_width, real_width+margin_width)\n\nmirr_b4_inds = mirror_pad_inds(b4_inds, c(90,90)) \nmirr_af_inds = mirror_pad_inds(af_inds, c(90,90)) \n\nb4_kde = kde2d(mirr_b4_inds$x, mirr_b4_inds$y,\n n=real_width + (2*margin_width),\n h=c(h,h), lims=lims)\nb4_kde = b4_kde[[3]][(margin_width+1):(margin_width+real_width),\n (margin_width+1):(margin_width+real_width)]\nb4_kde = raster(t(b4_kde))\nb4_kde = nrow(b4_inds) * b4_kde/cellStats(b4_kde, 'sum')\nb4_kde@extent = b4@extent\nb4_kde@crs = b4@crs\naf_kde = kde2d(mirr_af_inds$x, mirr_af_inds$y,\n n=real_width + (2*margin_width),\n h=c(h,h), lims=lims)\naf_kde = af_kde[[3]][(margin_width+1):(margin_width+real_width),\n (margin_width+1):(margin_width+real_width)]\naf_kde = raster(t(af_kde))\naf_kde = nrow(af_inds) * af_kde/cellStats(af_kde, 'sum')\naf_kde@extent = af@extent\naf_kde@crs = af@crs\n \n\n\n\n#################\n# Make the plots:\n\n# get hillshade raster\nslope = terrain(hrdem, opt='slope')\naspect = terrain(hrdem, opt='aspect')\nhill = hillShade(slope, aspect, angle=15, direction=10); \n\n\n# plot temperature and phenotype hillshade plots,\n# before and after climate change\nn_breaks = 100\n#cols = coolwarm(n_breaks)\ncols = brewer.rdbu(n_breaks*1.5)[125:26]\n\nb4_tmp_hillplot = make_gg_hillshade_plot(b4[[2]], hill, '\u00b0C ',\n b4[[2]]@data@min, af[[2]]@data@max,\n cols, 'temperature before climate change')\naf_tmp_hillplot = make_gg_hillshade_plot(af[[2]], hill, '\u00b0C ',\n b4[[2]]@data@min, af[[2]]@data@max,\n cols, 'temperature after climate change')\nb4_z_hillplot = make_gg_hillshade_plot(b4_z, hill, 'pheno\\nval',\n b4_z@data@min, af_z@data@max,\n cols, 'phenotypes before climate change')\naf_z_hillplot = make_gg_hillshade_plot(af_z, hill, 'pheno\\nval',\n b4_z@data@min, af_z@data@max,\n cols, 'phenotypes after climate change')\n\npg_tmp_z = plot_grid(b4_tmp_hillplot, af_tmp_hillplot,\n b4_z_hillplot, af_z_hillplot,\n ncol=2)\n\nif (save_figs){\n ggsave(pg_tmp_z, file='b4_af_tmp_z_maps.pdf',\n width=30, height=20, units = \"cm\", dpi=1200)\n}\n\n\n# plot hab suitability and pop density hillshade plots,\n# before and after climate change\nn_cols = 100\nzissou = wes_palette(\"Zissou1\", n_cols, type = \"continuous\")\n\nmin_sdm_val = min(b4[[3]]@data@min, af[[3]]@data@min)\nmax_sdm_val = max(b4[[3]]@data@max, af[[3]]@data@max)\nmin_kde_val = min(b4_kde@data@min, af_kde@data@min)\nmax_kde_val = max(b4_kde@data@max, af_kde@data@max)\n\nb4_sdm_hillplot = make_gg_hillshade_plot(b4[[3]], hill, 'hab\\nsuit',\n min_sdm_val, max_sdm_val,\n zissou, 'habitat suitability before climate change')\naf_sdm_hillplot = make_gg_hillshade_plot(af[[3]], hill, 'hab\\nsuit',\n min_sdm_val, max_sdm_val,\n zissou, 'habitat suitability after climate change')\nb4_nt_hillplot = make_gg_hillshade_plot(b4_kde, hill, 'inds/\\ncell',\n min_kde_val, max_kde_val,\n zissou, 'population density before climate change')\naf_nt_hillplot = make_gg_hillshade_plot(af_kde, hill, 'inds/\\ncell',\n min_kde_val, max_kde_val,\n zissou, 'population density after climate change')\n\npg_sdm_nt = plot_grid(b4_sdm_hillplot, af_sdm_hillplot,\n b4_nt_hillplot, af_nt_hillplot,\n ncol=2)\n\nif (save_figs){\n ggsave(pg_sdm_nt, file='b4_af_sdm_nt_maps.pdf',\n width=30, height=20, units = \"cm\", dpi=1200)\n}\n\n\n\n\n# 3d plots\nzscale=5\nif (save_vid){\n make_3d_plot(b4_z, dem, coolwarm, 'Kriged phenotype before climate change',\n var_name='pheno', zscale=zscale, background_color='#a6a490',\n title_bar_color='#ebebeb', snapshot=F, video=T, phi=22, fov=70)\n}\n\nmake_3d_plot(del_z, dem, zissou,\n 'Difference in kriged phenotypes before and after climate change',\n zscale=zscale, background_color='#000000', title_bar_color='#99cde0')\n\n\n\n###############################################################\n\n\n\n###############################################################\n\n# get a normalized difference raster, then plot it\ndiff_inds = (af_kde-b4_kde)/b4_kde\ncol = colorRampPalette(c(\"red\", \"white\", \"blue\"))(255)\nplot(diff_inds, col=col)\n\n# investigate relationship between change in pop density and env change\ndiff_inds_vals = diff_inds[,]\neuc_vals = euc[,]\ncoords = lapply(seq(length(euc_vals)), function(i){return(xyFromCell(diff_inds, i))})\ncoords = data.frame(matrix(unlist(coords), ncol=2, byrow=T))\ncolnames(coords) = c('lon', 'lat')\ndf = data.frame(inds = diff_inds_vals, euc = euc_vals,\n lon = coords$lon, lat = coords$lat, dummy=1)\nmod = lme(fixed=inds ~ euc, data=df, random= ~1|dummy,\n correlation=corGaus(1, form = ~ lon + lat))\n# plot scatterplot along with model trendline\nplot(euc[,], diff_inds[,])\n\n\n\n###################\n###################\n###### ETC. #######\n###################\n###################\n\n# make the pop-growth plot\nnt = read.csv('./Nt_EXTENDED.csv')\n# set total burn-in time, then truncate dataset\nburn_t = 98\nnt$t = nt$t-98\nnt = nt[nt$t>=0, ] \n# set ylims\nylims = c(min(nt$Nt)*0.9, max(nt$Nt) * 1.1)\n\nnt_plot = ggplot() +\n geom_line(aes(t, Nt), col='#47b3ab', size=2, data=nt) +\n geom_vline(xintercept= 509, colour = '#fc033d') + \n geom_vline(xintercept= 594, colour = '#fc033d') + \n scale_x_continuous(name = '') +\n #scale_x_continuous(name = 't (time steps)') +\n scale_y_continuous(name = '', limits=ylims) + \n #scale_y_continuous(name = 'Nt (individuals/cell)', limits=ylims) + \n ggthemes::theme_gdocs() +\n #ggtitle(\"Population dynamics\") +\n theme(axis.title = element_text(size=30),\n axis.text = element_text(size=28, color='black'))\nnt_plot\n\nggsave(nt_plot, file='Nt_plot.pdf',\n width=40, height=20, units='cm', dpi=500)\n\n# note that mean popsize af/mean popsize b4 = sum(sdm_af)/sum(sdm_b4)\nb4sum = sum(b4[[3]][,])\nafsum = sum(af[[3]][,])\nsdm_ratio = afsum/b4sum\nprint('sdm ratio')\nprint(sdm_ratio)\nb4meanNt = mean(nt[200:400,'Nt']) \nafmeanNt = mean(nt[600:650,'Nt']) \nNt_ratio = afmeanNt/b4meanNt\nprint(\"Nt ratio\")\nprint(Nt_ratio)\n\n\n", "meta": {"hexsha": "9f28ef2e9004647fb06897e31e3f30dca36f2153", "size": 18377, "ext": "r", "lang": "R", "max_stars_repo_path": "make_yosemite_fig/make_figs.r", "max_stars_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_stars_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make_yosemite_fig/make_figs.r", "max_issues_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_issues_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "make_yosemite_fig/make_figs.r", "max_forks_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_forks_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.3181818182, "max_line_length": 98, "alphanum_fraction": 0.6271426239, "num_tokens": 5239, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6992544210587586, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3496272105293793}} {"text": "\n# Analyze Stan fit objects generated on the cluster.\n\nrm(list = ls())\ngc()\n\nsetwd(\"~/Desktop/Code/laplace_approximation/Script/\")\n.libPaths(\"~/Rlib\")\nscriptDir <- getwd()\n\nlibrary(rstan)\nlibrary(posterior)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(boot)\nlibrary(latex2exp)\nsource(\"tools/cmdStanTools.r\")\nsource(\"tools/stanTools.r\")\nsource(\"tools/analysisTools.r\")\n\npars <- c(\"lambda\", \"tau\", \"caux\", \"f\", \"lp__\")\n\nnChains <- 6 # 4 + 2 chains over two runs.\nnIter <- 2000 # 2000 # number of samples (so not including warmup)\nnIter_total <- nChains * nIter\n\n##########################################################################\n## Read in results from Bernoulli logit model with adapt_delta = 0.99\nmodelName <- \"bernoulli_logit_glm_rhs\"\n\ndelivDir <- file.path(\"deliv\", modelName, \"cluster\")\n# stanfit <- readRDS(file = file.path(delivDir, paste0(modelName,\n# \"Fit_long_warmup.Rsave\")))\nstanfit <- readRDS(file = file.path(delivDir, paste0(modelName,\n \"_1_to_4\", \"Fit.Rsave\")))\nstanfit2 <- readRDS(file = file.path(delivDir, paste0(modelName,\n \"_5_to_6\", \"Fit.Rsave\")))\ncheck_div(stanfit)\ncheck_div(stanfit2)\n\n# closer examination\nsampler_params <- get_sampler_params(stanfit, inc_warmup = FALSE)\n# sampler_params_chain1 <- sampler_params[[1]]\nif (FALSE) {\ndivergence_by_chain <- \n sapply(sampler_params, function(x) sum(x[, \"divergent__\"]))\ndivergence_by_chain\nstep_size_by_chain <- \n sapply(sampler_params, function(x) mean(x[, \"stepsize__\"]))\nstep_size_by_chain\naccept_by_chain <- \n sapply(sampler_params, function(x) mean(x[, \"accept_stat__\"]))\naccept_by_chain\n\nmass_matrix <- get_mass_matrix(stanfit, nChains = 4)\nstr(mass_matrix)\n}\n\n# adapt_info <- get_adaptation_info(stanfit2)\n# string <- \"# Step size = 0.00654987\\n# Diagonal elements of inverse mass matrix:\\n#\"\n# start <- nchar(string) + 1\n# end <- nchar(adapt_info[1]) - nchar(\"\\n# \")\n# mass_matrix_chain1 <- substring(adapt_info[1], start, end)\n# mass_matrix_chain1 <- \n# as.numeric(strsplit(mass_matrix_chain1, split =\", \")[[1]])\n\nsamples <- rstan::extract(stanfit, pars = pars)\nsamples2 <- rstan::extract(stanfit2, pars = pars)\nlog_lambda <- log(rbind(samples$lambda, samples2$lambda))\n\nindex <- select_lambda(log_lambda, quant = 0.9, n_select = 6)\nlog_lambda_select <- log_lambda[, index]\nnames <- c(paste0(\"log_lambda[\", index, \"]\"), \"iteration\", \"chain\")\n\nposterior.sample <- construct_plot_data(log_lambda_select, nIter, nChains,\n names)\n# trace_plot(posterior.sample)\ndensity_hist(posterior.sample)\nquant_select_plot(log_lambda, quant = 0.9, threshold = 2.5)\n\n#extract other parameters\ntau <- c(samples$tau, samples2$tau)\ncaux <- c(samples$caux, samples2$caux)\nsummary_table(log_lambda_select, tau, caux, index)\n\nsamples_standard <- data.frame(log_lambda_1816 = log_lambda[, 1816],\n log_lambda_2586 = log_lambda[, 2586],\n tau = tau,\n caux = caux)\n\n\n# inspect the predicted f\nf <- rbind(samples$f, samples2$f)\np <- inv.logit(f)\np_expected <- colMeans(p)\n\n##########################################################################\n## Read in results from Bernoulli logit using ela\nmodelName2 <- \"bernoulli_logit_glm_ela\"\ndelivDir2 <- file.path(\"deliv\", modelName2, \"cluster\")\nstanfit_ela <- readRDS(file = file.path(delivDir2, paste0(modelName2, \"Fit.Rsave\")))\ncheck_div(stanfit_ela)\n\npars <- c(\"lambda\", \"tau\", \"caux\", \"f\", \"lp__\")\nsamples_ela <- rstan::extract(stanfit_ela, pars = pars)\n\n# plot estimated probability\n# inspect the predicted f\nf_ela <- samples_ela$f\np_ela <- inv.logit(f_ela)\np_ela_expected <- colMeans(p_ela)\n\n\nlog_lambda_ela <- log(samples_ela$lambda)\n\n# log_lambda <- log_lambda_ela\nindex <- select_lambda(log_lambda_ela, quant = 0.9, n_select = 6)\ntau <- samples_ela$tau\ncaux <- samples_ela$caux\nparm_select <- log_lambda_ela[, index]\nnames <- c(paste0(\"log_lambda[\", index, \"]\"), \"iteration\", \"chain\")\n\n# check samples for selected log_lambdas.\nposterior.sample <- construct_plot_data(parm_select, nIter, nChains, names)\ndensity_hist(posterior.sample)\n\n# check samples for log tau.\nposterior.sample.tau <- construct_plot_data(log(tau), nIter, nChains,\n c(\"log tau\", \"iteration\", \"chain\"))\ndensity_hist(posterior.sample.tau, bins = 30)\nquant_select_plot(log_lambda, quant = 0.9, threshold = 2.5)\n\ncaux <- samples_ela$caux\nsummary_table(log_lambda_select, tau, caux, index)\n\n#####################################################################\n## Plots to save\n\npdf(file = file.path(\"deliv\", \"cluster_analysis\",\n paste(modelName,\"Plots%03d.pdf\", sep = \"\")),\n width = 6, height = 6, onefile = F)\n\n# do quantile plot using results from both models\nquant_select_plot2(log_lambda, log_lambda_ela, quant = 0.9, threshold = 2.3)\n\nsamples_ela <- data.frame(log_lambda_1816 = log_lambda_ela[, 1816],\n log_lambda_2586 = log_lambda_ela[, 2586],\n tau = tau,\n caux = caux) #,\n # f = f)\n\nsamples_all <- rbind(samples_standard, samples_ela)\nsamples_all$log_tau <- log(samples_all$tau)\nsamples_all$log_caux <- log(samples_all$caux)\nsamples_all <- samples_all[, c(1, 2, 5, 6)]\n\nsamples_all <- gather(samples_all)\nalgorithm <- rep(rep(c(\"(full) HMC\", \"HMC + Laplace\"), each = 12000), 4)\nsamples_all$method <- algorithm\nkey_labels <- c(TeX(\"$\\\\log \\ c_{aux}$\"), TeX(\"$\\\\log \\\\lambda_{1816}$\"),\n TeX(\"$\\\\log \\\\lambda_{2586}$\"), TeX(\"$\\\\log \\\\tau$\"))\nsamples_all$key <- factor(samples_all$key, label = key_labels)\n\ncomp_plot <- ggplot(data = samples_all) +\n geom_histogram(aes(x = value, fill = method), alpha = 0.5, color = \"black\",\n bins = 30, position = \"identity\") + theme_bw() +\n facet_wrap(~key, scale = \"free\", ncol = 1, labeller = \"label_parsed\") +\n theme(\n legend.position = c(.95, 0.17),\n legend.justification = c(\"right\", \"top\"),\n legend.box.just = \"right\",\n legend.margin = margin(6, 6, 6, 6),\n text = element_text(size = 15)\n )\ncomp_plot\n\n# next, examine ESS / s\npars = c(\"tau\", \"caux\", \"lambda[1816]\", \"lambda[2586]\")\ntable_ela <- summary(stanfit_ela, pars = pars)[1]\nness_ela <- table_ela$summary[, 9]\ntime <- sum(get_elapsed_time(stanfit_ela)) / 6\neff_ela <- ness_ela / time\n\n# table_standard1 <- summary(stanfit, pars = pars)[1]\nness_1 <- summary(stanfit, pars = pars)[1]$summary[, 9]\ntime1 <- sum(get_elapsed_time(stanfit))\n\ntable_standard2 <- summary(stanfit2, pars = pars)[1]\nness_2 <- table_standard2$summary[, 9]\ntime2 <- sum(get_elapsed_time(stanfit))\n\nness_standard <- ness_1 + ness_2\ntime <- (time1 + time2) / 6\neff_standard <- ness_standard / time \n\ndata_eff <- data.frame(parameter = rep(pars, 2), eff = c(eff_standard, eff_ela),\n method = rep(c(\"(full) HMC\", \"HMC + Laplace\"),\n each = 4))\n\nplot_eff <- ggplot(data = data_eff,\n aes(x = parameter, y = eff, fill = method)) +\n geom_bar(stat = \"identity\", width = 0.3, alpha = 0.8, position = \"dodge\") + \n # facet_wrap(~ parameter, scale = \"free\", nrow = 1) +\n theme_bw() + theme(text = element_text(size = 10)) + coord_flip() +\n ylab(\"ESS / s\") + xlab(\" \") +\n theme(\n legend.position = c(.95, 0.98),\n legend.justification = c(\"right\", \"top\"),\n legend.box.just = \"right\",\n legend.margin = margin(6, 6, 6, 6)\n ) + theme(text = element_text(size = 18)) +\n scale_x_discrete(labels = c(\"tau\" = parse(text = TeX(\"$\\\\tau$\")),\n \"caux\" = parse(text = TeX(\"$\\\\c_{aux}$\")),\n \"lambda[1816]\" = parse(text = TeX(\"$\\\\lambda_{1816}$\")),\n \"lambda[2586]\" = parse(text = TeX(\"$\\\\lambda_{2586}$\"))))\nplot_eff\n\nplot_prob <- ggplot(data = data.frame(prob = p_expected, \n prob_ela = p_ela_expected),\n aes(x = prob, y = prob_ela)) +\n geom_point() + theme_bw() + geom_abline(intercept = 0, slope = 1, \n color = \"red\", \n linetype = \"dashed\", size = 1.0) +\n xlim(0, 1) + ylim(0, 1) + xlab(\"Probability (full HMC)\") +\n ylab(\"Probability (HMC + Laplace)\") +\n theme(text = element_text(size = 15))\nplot_prob\n\ndev.off()\n\n###############################################################################\n", "meta": {"hexsha": "f04db79828504545f018b9447581184e93416afc", "size": 8470, "ext": "r", "lang": "R", "max_stars_repo_path": "cluster_analysis.r", "max_stars_repo_name": "SteveBronder/laplace_manuscript", "max_stars_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-13T14:10:09.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-16T15:20:20.000Z", "max_issues_repo_path": "cluster_analysis.r", "max_issues_repo_name": "SteveBronder/laplace_manuscript", "max_issues_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cluster_analysis.r", "max_forks_repo_name": "SteveBronder/laplace_manuscript", "max_forks_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-06-27T15:17:43.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T21:54:30.000Z", "avg_line_length": 36.5086206897, "max_line_length": 87, "alphanum_fraction": 0.6113341204, "num_tokens": 2356, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.34958202995258786}} {"text": "\nnox<-2; noy<-3;\npaper<-T # graphics on paper=file (TRUE) or on screen (FALSE)\nrun.ID<-'SumPerc' # file id used for paper output\ncleanup()\n\nfirst.year.on.plot<-1990\nlast.year.on.plot<-2040\nsingle.species<-F # single species mode or multispecies mode\nPortrait<-F\n\npercentile<-c(0.50,0.25,0.75,0.05,0.95) #first value must be 50 and last value the highest\npercentile<-c(0.50,0.05,0.95) #first value must be 50 and last value the highest\n\ninclude.assess.forcast.line<-T # verical line at last assessment year\ninclude.F.reference.points<-F\ninclude.SSB.reference.points<-T\nincl.sp<-seq(15,23) # species number to be included. Numbers or \"all\"\nincl.sp<-\"all\"\n\nfirst.pch<-0 # first pch symbol\nfirst.color<-1 # first color\n\npalette(\"default\") # good for clolorfull plots\n#palette(gray(seq(0,.9,len=10))) # gray scale for papers, use len =500 to get black only\n\ndirs<-c(\"T1-T2_stoc_rec_assess_run01\", \"T1-T2_stoc_rec_assess_run02\",\"T1-T2_stoc_rec_assess_run03\")\nlabels<-c(\"Run 1 Single sp Fmsy\",\"Run 2 Multi sp Fmsy\",\"Run 3, with TAC constraints\")\n\n ##################### \nfor (dir in dirs) {\n if ( file.access(file.path(data.path,dir,\"sms.dat\"), mode = 0)!=0) stop(paste('Directory',dir,'does not exist'))\n} \n\nInit.function() # get SMS.contol object including sp.names\n\nfor (dir in dirs) {\n if (!single.species) {\n file<-file.path(data.path,dir,'mcout_eaten_M2.out')\n if ( file.access(file, mode = 0) ==0) { \n eaten<-read.table(file,header=TRUE)\n eaten<-data.frame(scen=dir,vari='Eaten',eaten)\n eaten<-subset(eaten,select=c(-Repetion,-Iteration))\n names(eaten)<-c(\"scenario\",\"Variable\",\"Species.n\",\"Year\",\"Value\")\n }\n }\n \n file<-file.path(data.path,dir,'mcout_recruit.out')\n rec<-read.table(file,header=TRUE)\n rec<-data.frame(scen=dir,vari='Rec',rec)\n rec<-subset(rec,select=c(-Repetion,-Iteration))\n names(rec)<-c(\"scenario\",\"Variable\",\"Species.n\",\"Year\",\"Value\")\n\n file<-file.path(data.path,dir,'mcout_mean_F.out')\n FF<-read.table(file,header=TRUE)\n FF<-data.frame(scen=dir,vari='F',FF)\n FF<-subset(FF,select=c(-Repetion,-Iteration))\n names(FF)<-c(\"scenario\",\"Variable\",\"Species.n\",\"Year\",\"Value\")\n\n file<-file.path(data.path,dir,'mcout_SSB.out')\n ssb<-read.table(file,header=TRUE)\n ssb<-data.frame(scen=dir,vari='SSB',ssb)\n ssb<-subset(ssb,select=c(-Repetion,-Iteration))\n names(ssb)<-c(\"scenario\",\"Variable\",\"Species.n\",\"Year\",\"Value\")\n\n file<-file.path(data.path,dir,'mcout_yield.out')\n yield<-read.table(file,header=TRUE)\n yield<-data.frame(scen=dir,vari='Yield',yield)\n yield<-subset(yield,select=c(-Repetion,-Iteration))\n names(yield)<-c(\"scenario\",\"Variable\",\"Species.n\",\"Year\",\"Value\")\n\n if (!single.species) { if (dir==dirs[1]) all<-rbind(rec,FF,ssb,yield,eaten) else all<-rbind(all,rec,FF,ssb,yield,eaten)}\n else {if (dir==dirs[1]) {all<-rbind(rec,FF,ssb,yield)} else all<-rbind(all,rec,FF,ssb,yield)}\n}\n\nall<-subset(all,(Year>=first.year.on.plot & Year<=last.year.on.plot) ,drop=T)\nlen.per<-length(percentile)\n\nif (include.F.reference.points | include.SSB.reference.points) ref.points<-Read.reference.points()\n\nif (paper) {dev<-\"png\"; w8=10} else {dev<-\"screen\"; w8=8}\nif (incl.sp==\"all\") sp.plot<-unique(yield$Species.n) else sp.plot<-incl.sp\nlen.dir<-length(dirs)\n\n plotvar<-function(sp=sp,vari='SSB',tit=vari,ylab='(1000t)',div=1) {\n v<-droplevels(subset(values,Variable==vari,select=c(-Variable,-Species.n)))\n\n if ((gi %% (nox*noy))==0 | gi==0) {\n newplot(dev,nox,noy,filename=paste(\"com_\",run.ID,'_',sp.names[sp],sep=''),Portrait=Portrait,w8=w8);\n\n # make legends\n if (paper) lwds<-2\n else lwds<-2\n par(mar=c(0,0,0,0))\n plot(10,10,axes=FALSE,xlab=' ',ylab=' ',xlim=c(0,1),ylim=c(0,1))\n legend(\"center\",legend=labels,col=first.color:(first.color+len.dir-1),\n pch=first.pch:(first.pch-1+len.dir),cex=2,title=sp.names[sp])\n gi<<-gi+1 \n par(mar=c(2.5,5,3,1)) # c(bottom, left, top, right)\n }\n\n gi<<-gi+1\n\n b<-tapply(v$Value/div,list(v$scenario,v$Year), function(x) quantile(x,probs = percentile))\n y<-as.numeric(dimnames(b)[[2]])\n maxval<-max(unlist(b))\n v<-matrix(unlist(b[1,]),nrow=len.per)\n\n if (paper) {lwds<-1}\n else { lwds<-2;}\n\n plot(y,v[1,],main=tit,xlab=\"\",ylab=ylab,type='b',lwd=lwds,ylim=c(0,maxval),col=first.color,pch=first.pch)\n for (j in (2:len.per)) lines(y,v[j,],col=first.color,type='l',lwd=lwds,lty=2)\n \n for (i in (2:len.dir)) {\n v<-matrix(unlist(b[i,]),nrow=len.per)\n lines(y,v[1,],col=first.color+i-1,pch=first.pch+i-1,type='b',lwd=lwds)\n for (j in (2:len.per)) lines(y,v[j,],col=first.color+i-1,type='l',lwd=lwds,lty=2)\n }\n\n if (include.assess.forcast.line) abline(v=SMS.control@last.year.model)\n \n if (include.SSB.reference.points & vari==\"SSB\") {\n if (ref.points[sp,\"Blim\"] >0) abline(h=ref.points[sp,\"Blim\"]/div,lty=2)\n if (ref.points[sp,\"Bpa\"] >0) abline(h=ref.points[sp,\"Bpa\"]/div,lty=3)\n }\n if (include.F.reference.points & vari==\"F\") {\n if (ref.points[sp,\"Flim\"] >0) abline(h=ref.points[sp,\"Flim\"],lty=2)\n if (ref.points[sp,\"Fpa\"] >0) abline(h=ref.points[sp,\"Fpa\"],lty=3)\n }\n }\n \n for (sp in (sp.plot)) {\n gi<-0\n values<-subset(all,Species.n==sp)\n \n plotvar(sp=sp,vari=\"Rec\",tit=\"Recruits\",ylab=\"(billions)\",div=1000000)\n plotvar(sp=sp,vari=\"F\",ylab=' ',div=1)\n plotvar(sp=sp,vari=\"SSB\",div=1000)\n plotvar(sp=sp,vari=\"Yield\",tit=\"Yield\",div=1000)\n #plotvar(sp=sp,vari=\"Yield.hat\",tit=\"Expected Yield\")\n if (!single.species) plotvar(sp=sp,vari=\"Eaten\",tit=\"Eaten biomass\",div=1000)\n if (paper) cleanup()\n}\n\n\n\n", "meta": {"hexsha": "479711c04f1d1ddf5f1373e8b51c13f35ea35c56", "size": 5663, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_compare_runs_percentiles.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_compare_runs_percentiles.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_compare_runs_percentiles.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.2635135135, "max_line_length": 121, "alphanum_fraction": 0.6410030019, "num_tokens": 1921, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.3495820221622447}} {"text": "# Visualize data from approval_averages and averages_by_grade\r\n\r\n# Packages: ggplot2, plyr, dplyr, tidyr\r\n\r\n\r\n#SETUP -----------------------------------------------------------------------------------------\r\n\r\n# load required packages\r\nlibrary(ggplot2)\r\nlibrary(plyr)\r\nlibrary(dplyr)\r\nlibrary(tidyr)\r\n\r\n# set wd\r\nsetwd('c:/rstuff/projects/dt_approval/')\r\n\r\n# read in csv files\r\naverages = read.csv('approval_averages.csv')\r\naverages_by_grade = read.csv('averages_by_grade.csv')\r\n\r\n# summary of both (SHOULD BE IDENTICAL)\r\nsummary(averages)\r\nsummary(averages_by_grade)\r\n\r\n# rename column X\r\naverages = rename(averages, c(\"Pollster\"=\"X\"))\r\naverages_by_grade = rename(averages_by_grade, c(\"Pollster\"=\"X\"))\r\n\r\n\r\n#DIVERGING LOLLIPOP (APPROVE) -----------------------------------------------------------------------------------------------\r\n\r\n# create copy of averages\r\navg_copy = data.frame(averages)\r\n\r\n# set rownames\r\navg_copy$Pollster = rownames\r\n\r\n# compute normalized adjusted_approve_avg\r\navg_copy$Adjusted_Approve_Z = round((avg_copy$Adjusted_Approve_AVG - mean(avg_copy$Adjusted_Approve_AVG))\r\n /sd(avg_copy$Adjusted_Approve_AVG), 2)\r\n\r\n# above/below flag (for potential diverging bar)\r\navg_copy$Average_Rank = ifelse(avg_copy$Adjusted_Approve_Z < 0, \"Below\", \"Above\")\r\n\r\n# order by Adjusted_Approve_Z\r\navg_copy = avg_copy[order(avg_copy$Adjusted_Approve_Z),]\r\n\r\n# make pollster factor\r\navg_copy$Pollster = factor(avg_copy$Pollster, levels = avg_copy$Pollster)\r\n\r\n# create plot\r\nggplot(avg_copy, aes(x=Pollster, y=Adjusted_Approve_Z, label=Adjusted_Approve_Z))+\r\n geom_point(stat='identity', fill='black', size=7)+\r\n geom_segment(aes(y=0, x=Pollster, yend=Adjusted_Approve_Z, xend=Pollster), color='black')+\r\n geom_text(color='white', size=2)+\r\n labs(title='Adjusted Approval Comparison', subtitle='Normalized Mean Adjusted Approval from approval_averages.csv')+\r\n ylim(-3, 3)+\r\n coord_flip()\r\n\r\n\r\n\r\n#DIVERGING LOLLIPOP (DISAPPROVE) ----------------------------------------------------------------------------------------\r\n\r\n# create copy of averages\r\navg_copy2 = data.frame(averages)\r\n\r\n# set rownames\r\navg_copy2$Pollster = rownames\r\n\r\n# compute normalized adjusted_disapprove_avg\r\navg_copy2$Adjusted_Disapprove_Z = round((avg_copy2$Adjusted_Disapprove_AVG - mean(avg_copy2$Adjusted_Disapprove_AVG))\r\n /sd(avg_copy2$Adjusted_Disapprove_AVG), 2)\r\n\r\n# above/below flag (for potential diverging bar)\r\navg_copy2$Average_Rank = ifelse(avg_copy2$Adjusted_Disapprove_Z < 0, \"Below\", \"Above\")\r\n\r\n# order by Adjusted_Disapprove_Z\r\navg_copy2 = avg_copy2[order(avg_copy2$Adjusted_Disapprove_Z),]\r\n\r\n# make pollster factor\r\navg_copy2$Pollster = factor(avg_copy2$Pollster, levels = avg_copy2$Pollster)\r\n\r\n# create plot\r\nggplot(avg_copy2, aes(x=Pollster, y=Adjusted_Disapprove_Z, label=Adjusted_Disapprove_Z))+\r\n geom_point(stat='identity', fill='black', size=7)+\r\n geom_segment(aes(y=0, x=Pollster, yend=Adjusted_Disapprove_Z, xend=Pollster), color='black')+\r\n geom_text(color='white', size=2)+\r\n labs(title='Adjusted Disapproval Comparison', subtitle='Normalized Mean Adjusted Disapproval from approval_averages.csv')+\r\n ylim(-3, 5)+\r\n coord_flip()\r\n\r\n\r\n#ORDERED BAR (APPROVE BY GRADE) ---------------------------------------------------------------------------\r\n\r\n# create copy of averages_by_grade\r\ngrade_avg = data.frame(averages_by_grade)\r\n\r\n# group mean adjusted approve by grade\r\nGraded_Adjusted_Approve = aggregate(grade_avg$Adjusted_Approve_AVG, by=list(grade_avg$Grade),\r\n FUN=mean)\r\n\r\n# rename columns\r\ncolnames(Graded_Adjusted_Approve) = c(\"Grade\", \"Adjusted_Approval_Average\")\r\n\r\n# sort by Adjusted_Approval_Average\r\nGraded_Adjusted_Approve = Graded_Adjusted_Approve[order(Graded_Adjusted_Approve$Adjusted_Approval_Average),]\r\n\r\n# make grades a factor to retain order\r\nGraded_Adjusted_Approve$Grade = factor(Graded_Adjusted_Approve$Grade, levels=Graded_Adjusted_Approve$Grade)\r\n\r\n# create plot\r\nggplot(Graded_Adjusted_Approve, aes(x=Grade, y=Adjusted_Approval_Average))+\r\n geom_bar(stat='identity', width=0.5, fill='springgreen4')+\r\n scale_y_continuous(breaks=seq(0,100,10))+\r\n labs(title=\"Average Adjusted Approval by Grade\",\r\n subtitle=\"Ordered from lowest to highest\")\r\n\r\n\r\n#ORDERED BAR (DISAPPROVE BY GRADE) -------------------------------------------------------------------------\r\n\r\n# group mean adjusted disapprove by grade\r\nGraded_Adjusted_Disapprove = aggregate(grade_avg$Adjusted_Disapprove_AVG, by=list(grade_avg$Grade),\r\n FUN=mean)\r\n\r\n# rename columns\r\ncolnames(Graded_Adjusted_Disapprove) = c(\"Grade\", \"Adjusted_Disapproval_Average\")\r\n\r\n# sort by Adjusted_Disapproval_Average\r\nGraded_Adjusted_Disapprove = Graded_Adjusted_Disapprove[order(Graded_Adjusted_Disapprove$Adjusted_Disapproval_Average),]\r\n\r\n# make grades a factor to retain order\r\nGraded_Adjusted_Disapprove$Grade = factor(Graded_Adjusted_Disapprove$Grade, levels=Graded_Adjusted_Disapprove$Grade)\r\n\r\n# create plot\r\nggplot(Graded_Adjusted_Disapprove, aes(x=Grade, y=Adjusted_Disapproval_Average))+\r\n geom_bar(stat='identity', width=0.5, fill='red3')+\r\n scale_y_continuous(breaks=seq(0,100,10))+\r\n labs(title=\"Average Adjusted Disapproval by Grade\",\r\n subtitle=\"Ordered from lowest to highest\")\r\n\r\n\r\n#MULTIPLE VARIABLE BAR (BY GRADE) ------------------------------------------------------------------------------------\r\n\r\n# merge Graded_Adjusted_Approve and Graded_Adjusted_Disapprove\r\nGraded_Totals = merge(Graded_Adjusted_Approve, Graded_Adjusted_Disapprove, by=1, all=TRUE)\r\n\r\n# make grade a factor\r\nGraded_Totals$Grade = factor(Graded_Totals$Grade, levels=c('A+', 'A', 'A-', 'A/B', 'B+', 'B',\r\n 'B-', 'B/C', 'C+', 'C', 'C-', 'C/D',\r\n 'D+', 'D', 'D-'))\r\n# order by grade\r\nGraded_Totals = Graded_Totals[order(Graded_Totals$Grade),]\r\n\r\n# 'gather' the data into a new variable\r\nGathered_Totals = Graded_Totals %>%\r\n gather(\"Key\", \"Adjusted_Average\", -Grade)\r\n \r\n# create plot\r\nggplot(Gathered_Totals, aes(x=Grade, y=Adjusted_Average, fill=Key))+\r\n scale_fill_manual(values=c(\"springgreen4\", \"red3\"))+\r\n scale_y_continuous(breaks=seq(0,50,10))+\r\n geom_col(position=\"dodge\", width=0.5)+\r\n labs(title=\"Adjusted Averages Comparison\",\r\n subtitle=\"Ordered by Grade (descending)\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "e1a85333c160adc90a141c7fc8d458a8a43c5088", "size": 6471, "ext": "r", "lang": "R", "max_stars_repo_path": "visual.r", "max_stars_repo_name": "coy0tecode/Trump-Approval-Ratings", "max_stars_repo_head_hexsha": "620d02c0af846aa23c64dd6613985665edb72093", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "visual.r", "max_issues_repo_name": "coy0tecode/Trump-Approval-Ratings", "max_issues_repo_head_hexsha": "620d02c0af846aa23c64dd6613985665edb72093", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "visual.r", "max_forks_repo_name": "coy0tecode/Trump-Approval-Ratings", "max_forks_repo_head_hexsha": "620d02c0af846aa23c64dd6613985665edb72093", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.6220930233, "max_line_length": 126, "alphanum_fraction": 0.664503168, "num_tokens": 1640, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369905, "lm_q2_score": 0.6261241702517976, "lm_q1q2_score": 0.349582016854403}} {"text": "#' comput mc hierarchucal clustering using the normalized confusion matrix\n#'\n#' @param mc_id metacell id\n#' @param graph_id cgraph to define edges and impose them on the metacells\n#' @param confu a confusion matrix derived from e.g. mcell_mc_confusion_matm or mcell_mc_coclust_confusion_mat\n#'\n#' @export\n\nmcell_mc_hclust_confu = function(mc_id, graph_id, confu=NULL)\n{\n\tmc = scdb_mc(mc_id)\n\tif(is.null(mc)) {\n\t\tstop(\"undefined meta cell object \" , mc_id)\n\t}\n\tif(is.null(graph_id)) {\n\t\tif(is.null(confu)) {\n\t\t\tstop(\"MC-ERR, missing graph_id AND confu object when trying to hclust mc by confusion matrix\")\n\t\t}\n\t\tif(nrow(confu) != ncol(mc@mc_fp) | ncol(confu) != nrow(confu)) {\n\t\t\tstop(\"MC-ERR, bad ocnfusion matrix dimension when trying to hcluster mc by confusion matrix\")\n\t\t}\n\t} else {\n\t\tcgraph = scdb_cgraph(graph_id)\n\t\tif(is.null(cgraph)) {\n\t\t\tstop(\"undefined cgraph object when trying to plot confusion, id \" , graph_id)\n\t\t}\n\t\n\t\tmax_deg = nrow(cgraph@edges)\n\t\tconfu = mcell_mc_confusion_mat(mc_id, graph_id, max_deg, ignore_mismatch=T)\n\t}\n\tr_confu = rowSums(confu)\n\tc_confu = colSums(confu)\n\tnorm = r_confu %*% t(c_confu)\n\tconfu_n = confu/norm\n\tconfu_n[is.na(confu_n)] = 0\n\tconfu_n[is.nan(confu_n)] = 0\n\n\tconfu_nodiag = confu_n\n\tdiag(confu_nodiag) = 0\n\tconfu_n = pmin(confu_n, max(confu_nodiag))\n\tconfu_n = pmin(confu_n, quantile(confu_n, 1-3/nrow(confu_n)))\n\tepsilon = quantile(confu_n[confu_n!=0],0.02)\n\thc = hclust(as.dist(-log10(epsilon+confu_n)),\"average\")\n\treturn(hc)\n}\n\n#' identify super structure in an mc cover, based on hcluster of the confusion matrix\n#'\n#' @param mc_id id of metacell object ina scdb\n#' @param mc_hc hclust object onthe metacells (e.g. derive from mcell_mc_hclust_confu)\n#' @param T_gap the minimal branch length for defining a supper metacell structure\n#'\n#' @export\n\nmcell_mc_hierarchy = function(mc_id, mc_hc, T_gap)\n{\n\tmc = scdb_mc(mc_id)\n\tif(is.null(mc)) {\n\t\tstop(\"undefined meta cell object \" , mc_id)\n\t}\n\t\n\tmc_ord = rep(0, length(mc_hc$order))\n\tmc_ord[mc_hc$order] = 1:length(mc_hc$order)\n\n\tn_mc = ncol(mc@mc_fp)\n\n\tlfp = log2(mc@mc_fp)\n\n\tparent = rep(-1, n_mc-1)\n\tcells = rep(0, times=n_mc-1)\n\tmcs = list()\n\tgaps = rep(0, times=n_mc-1)\n\tfor(i in 1:nrow(mc_hc$merge)) {\n\t\tleft = mc_hc$merge[i,1]\n\t\tright = mc_hc$merge[i,2]\n\t\tif(left < 0) {\n\t\t\tcs = names(which(mc@mc == -left))\n\t\t\tcell_left = length(cs)\n\t\t\tmc_left = -left\n\t\t} else {\n\t\t\tcell_left = cells[left]\n\t\t\tmc_left = mcs[left]\n\t\t\tparent[left] = i\n\t\t\tgaps[left] = mc_hc$height[i] - mc_hc$height[left]\n\t\t}\n\t\tif(right < 0) {\n\t\t\tcs = names(which(mc@mc == -right))\n\t\t\tcell_right= length(cs)\n\t\t\tmc_right = -right\n\t\t} else {\n\t\t\tcell_right= cells[right]\n\t\t\tmc_right = mcs[right]\n\t\t\tparent[right] = i\n\t\t\tgaps[right] = mc_hc$height[i] - mc_hc$height[right]\n\t\t}\n#\t\tmessage(\"in node \", i, \" l \", left, \" r \", right)\n\t\tmcs[[i]] = c(unlist(mc_right), unlist(mc_left))\n\t\tcells[i] = cell_right + cell_left\n\t}\n\tn_min_outcells = 300\n\thits = list()\n#\tcovered = rep(0, times = length(gaps))\n\thit_i = 1\n\tsup_x = c()\n\tfor(i in which(gaps > T_gap)) {\n\t\tj = parent[i]\n\t\tmincells = cells[i] + n_min_outcells\n\t\twhile(j != -1 & cells[j] < mincells) {\n\t\t\tj = parent[j]\t\n\t\t}\n\t\tif(j != -1) { \n#\t\t\tmessage(\"node \", i, \" sup \", j)\n#& covered[j] == 0\n\t\t\tN = cells[j]\n\t\t\tn = cells[i]\n\t\t\tmcs_in = mcs[[i]]\n\t\t\tmcs_out = setdiff(mcs[[j]], mcs[[i]])\n\t\t\tlfp_avg_in = apply(lfp[,mcs_in], 1, mean)\n\t\t\tlfp_min_in = apply(lfp[,mcs_in], 1, min)\n\t\t\tlfp_max_in = apply(lfp[,mcs_in], 1, max)\n\t\t\tif(length(mcs_out) > 1) {\n\t\t\t\tlfp_max_out= apply(lfp[,mcs_out], 1, max)\n\t\t\t\tlfp_min_out= apply(lfp[,mcs_out], 1, min)\n\t\t\t\tlfp_avg_out= apply(lfp[,mcs_out], 1, mean)\n\t\t\t} else {\n\t\t\t\tif(length(mcs_out) == 0) {\n\t\t\t\t\tmessage(\"zero length mcs out??\")\n\t\t\t\t\tstop(\"boom\")\n\t\t\t\t}\n\t\t\t\tlfp_max_out= lfp[,mcs_out]\n\t\t\t\tlfp_min_out= lfp[,mcs_out]\n\t\t\t\tlfp_avg_out= lfp[,mcs_out]\n\t\t\t}\n\n\t\t\te_marks = tail(sort(lfp_avg_in),20)\n\t\t\tsep_marks = tail(sort(lfp_min_in),20)\n\t\t\tmarks_gap = tail(sort(lfp_avg_in - lfp_avg_out),20)\n\t\t\tmarks_gap_anti = head(sort(lfp_avg_in - lfp_avg_out),20)\n\t\t\t\n\t\t\tx_ord = mean(mc_ord[mcs[[i]]])\n\t\t\tsup_x = c(sup_x, x_ord)\n\t\t\thits[[hit_i]] = list(marks = e_marks, \n\t\t\t\t\t\t\t\tmin_marks = sep_marks,\n\t\t\t\t\t\t\t\tmarks_gap = marks_gap, \n\t\t\t\t\t\t\t\tmarks_gap_anti=marks_gap_anti, \n\t\t\t\t\t\t\t\tmcs = mcs[[i]], \n\t\t\t\t\t\t\t\tx_ord = x_ord, sup_mcs = mcs[[j]])\n\t\t\thit_i = hit_i + 1\n\t\t}\n\t}\n\n\thits = hits[order(sup_x)]\n\treturn(hits)\n}\n\n#' plot super strucutre: super clust mc footprint, and selected genes\n#'\n#' @param mc_id id of metacell object\n#' @param graph_id id of graph for (Re-) constructing confusion matrix\n#' @param mc_order the mc ordering (e.g., hc$order using the output of mcell_mc_hclust_confu)\n#' @param sup_mcs the list you get from mcell_mc_hierarchy (for now)\n#' @param width width of figure in pixels\n#' @param height height of figure in pixels\n#' @param fig_fn figure name (NULL will create a figure named [mc_id]_supmc_confu in the figure directory)\n#' @param min_nmc minimal number of mc in supmc set, smaller gruops will not be plotted\n#' @param shades heatmap color palette \n#' @param plot_grid plot vertical grid in the heatmap\n#' @param show_mc_ids plot mc ids below the heatmap\n#'\n#' @export\n\nmcell_mc_plot_hierarchy = function(mc_id, graph_id, mc_order, sup_mcs, width, height, fig_fn=NULL, min_nmc=2, shades = colorRampPalette(c(\"white\", \"pink\", \"red\", \"black\", \"brown\", \"orange\")), plot_grid=T, show_mc_ids=F)\n{\n\tmc = scdb_mc(mc_id)\n\tif(is.null(mc)) {\n\t\tstop(\"undefined meta cell object \" , mc_id)\n\t}\n\tn_mc = ncol(mc@mc_fp)\n\tcolors = mc@colors\n\n\tconfu = mcell_mc_confusion_mat(mc_id, graph_id, 1000, ignore_mismatch=T)\n\tr_confu = rowSums(confu)\n\tc_confu = colSums(confu)\n\tnorm = r_confu %*% t(c_confu)\n\tconfu_n = confu/norm\n\n\tconfu_n = confu_n[mc_order, mc_order]\n\tcolors = colors[mc_order]\n\tcolnames(confu_n) = (1:ncol(confu_n))[mc_order]\n\trownames(confu_n) = (1:ncol(confu_n))[mc_order]\n\tcolors[is.na(colors)] = \"gray\"\n\n\tfps = as.matrix(do.call(cbind, lapply(sup_mcs, \n\t\t\t\t\tfunction(x) { fp = rep(0, n_mc); \n\t\t\t\t\t\t\t\t\t\tfp[unlist(x$sup_mcs)]=1; \n\t\t\t\t\t\t\t\t\t\tfp[unlist(x$mcs)]=2; \n\t\t\t\t\t\t\t\t\t\tfp })))\n\n\n\tif(is.null(fig_fn)) {\n\t\tfig_fn = scfigs_fn(mc_id, \"supmc_confu\")\n\t}\n\tmarks = unlist(lapply(sup_mcs, function(x) {\n\t\t\t\t\t\tm = x$marks[x$marks > 0.4]\n\t\t\t\t\t\treturn(paste(names(tail(m,5)),collapse=\",\"))\n\t\t\t\t\t}\n\t\t\t\t))\n\n\tgap_marks = unlist(lapply(sup_mcs, function(x) {\n\t\t\t\t\t\tm = x$marks_gap[x$marks_gap > 0.4];\n\t\t\t\t\t\ts = rev(names(tail(m,5)));\n\t\t\t\t\t\treturn(substr(paste(s, collapse=\",\"),1,30))\n\t\t\t\t\t}\n\t\t\t\t\t))\n\tgap_amarks = unlist(lapply(sup_mcs, function(x) {\n\t\t\t\t\t\tm = x$marks_gap_anti[x$marks_gap_anti < -0.4];\n\t\t\t\t\t\ts = rev(names(head(m,5)));\n\t\t\t\t\t\treturn(substr(paste(s, collapse=\",\"),1,30))\n\t\t\t\t\t}\n\t\t\t\t\t))\n\tgap_marks = paste(gap_marks,gap_amarks,sep=\" | \")\n\tmarks = paste(marks, 1:length(marks), sep=\" :\")\n\tvert = F\n\n\tf_sup = colSums(fps>1)>min_nmc\n\n\tgrids = -1+apply(fps[mc_order,f_sup],2,function(x) min(which(x>1)))\n\tgrids = c(grids, apply(fps[mc_order,f_sup],2,function(x) max(which(x>1))))\n\n\tmarks = marks[f_sup]\n\tgap_marks = gap_marks[f_sup]\n\n\t.plot_start(fig_fn, w=width, h=height)\n\n\tlayout(matrix(c(1,2,3), ncol=1), heights=c(10,10,0.5))\n\n\tpar(mar=c(0,30,2,40))\n\timage(fps[mc_order, f_sup], col=c(\"white\", \"lightgray\", \"blue\"), xaxt='n', yaxt='n')\n\tmtext(marks, side = 2, at=seq(0,1,length.out=length(marks)), las=2, cex=1)\n\tmtext(gap_marks, side = 4, at=seq(0,1,len=length(marks)), las=2, cex=1)\n\tif (plot_grid) {\n\t\tabline(v=(-0.5+grids)/(n_mc-1), lwd=0.5)\n\t}\n\n\tpar(mar=c(0,30,0,40))\n\tconfu_nodiag = confu_n\n\tdiag(confu_nodiag) = 0\n\tconfu_n = pmin(confu_n, max(confu_nodiag))\n\tconfu_n = pmin(confu_n, quantile(confu_n, 1-3/nrow(confu_n)))\n\timage(confu_n,col=shades(1000),xaxt='n', yaxt='n')\n\tif (plot_grid) {\n\t\tabline(v=(-0.5+grids)/(n_mc-1), lwd=0.5)\n\t}\n\tpar(mar=c(5,30,0,40))\n\timage(as.matrix(1:n_mc,nrow=1), col=colors, yaxt='n', xaxt='n')\n\n\tif (show_mc_ids) {\n\t\tmtext(colnames(confu_n), side=1, at=seq(0, 1, len=ncol(confu_n)), las=2, line=1, cex=0.7)\n\t}\n\tdev.off()\n}\n", "meta": {"hexsha": "219bcc0529835cc7243c2a59910052fd60e1acdd", "size": 7935, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mc_hierarchy.r", "max_stars_repo_name": "echomsky/metacell", "max_stars_repo_head_hexsha": "39b91cf2cda6192994035ffe62c90b83142647aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-14T14:08:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-14T14:08:10.000Z", "max_issues_repo_path": "R/mc_hierarchy.r", "max_issues_repo_name": "breme86/metacell", "max_issues_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mc_hierarchy.r", "max_forks_repo_name": "breme86/metacell", "max_forks_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.286259542, "max_line_length": 219, "alphanum_fraction": 0.6559546314, "num_tokens": 2824, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316860482763, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.34929064632608}} {"text": "# This is a test/example rd file\n# Two spheres, metallic shader, three lights\n# Left interpolated; right, not\n\nDisplay \"Metal Shader\" \"Screen\" \"rgbdouble\"\nFormat 640 480\nCameraFOV 5\nCameraEye 0 0 50\n\nWorldBegin\n\nFarLight 0 0 -1 1.0 1.0 1.0 1.0\n\nFarLight 1 1 -0.5 1.0 0.0 0.0 0.5 #Red Light -- Lower Left\nFarLight 0 -1 -0.5 0.0 1.0 0.0 0.5 #Green Light -- Up\nFarLight -1 1 -0.5 0.0 0.0 1.0 0.5 #Blue Light -- Lower Right\n\nKa 0.5\nKd 1.0\nKs 0.5\n\nColor 0.9 0.8 0.2\n\nSpecular 1.0 1.0 1.0 10\n\nSurface \"metal\"\n#Surface \"plastic\"\n\nTranslate -1.25 0 0\n\nSphere 1.0 -1.0 1.0 360.0\n\nTranslate 2.5 0 0 \n\nOptionBool \"Interpolate\" off\n\nSphere 1.0 -1.0 1.0 360.0\n\nWorldEnd\n", "meta": {"hexsha": "fee98846838d0a81cf4fa560042d721c3f7e7dd2", "size": 681, "ext": "rd", "lang": "R", "max_stars_repo_path": "scenes/metal_shader_db.rd", "max_stars_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_stars_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scenes/metal_shader_db.rd", "max_issues_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_issues_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scenes/metal_shader_db.rd", "max_forks_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_forks_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.025, "max_line_length": 67, "alphanum_fraction": 0.6519823789, "num_tokens": 335, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.34928432044220414}} {"text": "## Written by Eilis\n## takes .info files (1 per chromosome) and creates plots to summarise imputation\n\n\nargs<-commandArgs(trailingOnly = TRUE)\n\nlibrary(data.table)\ndirName<-args[1]\nrefFile<-args[2]\ncolName<-args[3]\n\nrefPanel<-fread(refFile, data.table = FALSE)\nif(!\"id\" %in% colnames(refPanel)){\n\trefPanel$id<-paste(refPanel$\"#CHROM\", refPanel$POS, refPanel$REF, refPanel$ALT, sep = \":\")\n}\n\n\ncompRefPanel<-matrix(data = NA, ncol = 2, nrow = 22)\ncolnames(compRefPanel)<-c(\"cor\", \"MAD\")\nnVar<-matrix(data = 0, nrow = 10, ncol = 10)\nfor(chr in 1:22){\n\tpng(paste(dirName,\"/ImputationQualityPlots_chr\", chr, \".png\", sep = \"\"), width = 12, height = 8, units = \"in\", res = 200)\n\tpar(mfrow = c(2,2))\n\t\n\timputScores<-read.table(gzfile(paste(dirName,\"/chr\", chr, \".info.gz\", sep = \"\")), header = TRUE, na.strings = \"-\")\n\tindex<-match(imputScores$SNP,refPanel$id)\n\n\tplot(density(imputScores$Rsq, na.rm = TRUE), main = paste(\"chr\", chr, sep = \"\"), xlab = \"Rsq\")\n\n\t## compare freq against refPanel\n\tplot(refPanel[index,colName],imputScores$ALT_Frq,xlab = \"RefPanel MAF\", ylab = \"Imputed sample MAF\", pch = 16)\n\t## calculate median absolute deviation\n\tmtext(side = 3, line = 0.5, adj = 1, paste(\"MAD =\", signif(median(abs(refPanel[index,colName] -imputScores$ALT_Frq), na.rm = TRUE), 3), sep = \"\"))\n\t\n\tcompRefPanel[chr,1]<-cor(refPanel[index,colName],imputScores$ALT_Frq, use = \"p\")\n\tcompRefPanel[chr,2]<-median(abs(refPanel[index,colName] -imputScores$ALT_Frq), na.rm = TRUE)\t\n\n\t## summarise INFO score distribution by MAF\t\n\ttabRsqMAF<-table(cut(imputScores$Rsq, seq(0,1,0.1)), cut(imputScores$MAF, seq(0,0.5,0.05)))\n\tbarplot(tabRsqMAF,ylab = \"N variants\", legend = rownames(tabRsqMAF), xlab = \"MAF\", col = colorRampPalette(c(\"white\", \"navy\"))(10), main = paste(\"chr\", chr, sep = \"\"))\n\ttabRsqMAFPer<-t(t(tabRsqMAF)/colSums(tabRsqMAF)*100)\n\tbarplot(tabRsqMAFPer,ylab = \"%\", xlab = \"MAF\", col = colorRampPalette(c(\"white\", \"navy\"))(10))\n\n\tnVar<-nVar+tabRsqMAF\n\t\n\n\tdev.off()\n}\n\nwrite.csv(nVar, paste(dirName,\"/nVariantsSummary.csv\", sep = \"\"))\nwrite.csv(compRefPanel, paste(dirName,\"/CompRefPanelSummary.csv\", sep = \"\"))\n\n", "meta": {"hexsha": "01f3df31ded198099dfe7e11426de81298fe6b9a", "size": 2106, "ext": "r", "lang": "R", "max_stars_repo_path": "SNPdata/summarizeImputation.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SNPdata/summarizeImputation.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "SNPdata/summarizeImputation.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7358490566, "max_line_length": 167, "alphanum_fraction": 0.6809116809, "num_tokens": 736, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.34928432044220414}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of uniform prior wonky world model predictions\"\n#' author: \"Judith Degen\"\n#' date: \"January 26, 2014\"\n#' ---\n\nlibrary(ggplot2)\nlibrary(wesanderson)\ntheme_set(theme_bw(18))\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/writing/_2015/_journal_cognition/\")\nsource(\"rscripts/helpers.r\")\n\n## load priors for generating plots \npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorprobs) = priorprobs$Item\nnrow(priorprobs)\n\n# prior all-state probability for each item\nprior_allprobs = priorprobs[,c(\"Item\",\"X15\")]\nrow.names(prior_allprobs) = prior_allprobs$Item\n\n# prior expectation for each item\ngathered_probs <- priorprobs %>%\n gather(State,Probability,X0:X15)\ngathered_probs$State = as.numeric(as.character(gsub(\"X\",\"\",gathered_probs$State)))\nprior_exps <- gathered_probs %>%\n group_by(Item) %>%\n summarise(exp.val=sum(State*Probability))\nprior_exps = as.data.frame(prior_exps)\nrow.names(prior_exps) = prior_exps$Item\nsummary(prior_exps)\n\n# histogram of expectations\nexps = ggplot(prior_exps, aes(x=exp.val)) +\n geom_histogram() +\n scale_x_continuous(name=\"Expected value of prior distribution\",breaks=seq(1,15, by=2)) +\n scale_y_continuous(name=\"Number of cases\",breaks=seq(0,8, by=2))\nggsave(\"pics/priorexpectations-histogram.pdf\",width=5,height=3.7)\n\n# histogram of allstate-probs\nallprobs = ggplot(prior_allprobs, aes(x=X15)) +\n geom_histogram() +\n scale_x_continuous(name=\"Prior all-state probability\") +\n scale_y_continuous(name=\"Number of cases\")\nggsave(\"priorallprobs-histogram.pdf\")\n\npdf(\"pics/priordistributions.pdf\",width=10,height=3.5)\ngrid.arrange(exps, allprobs, nrow=1,widths=unit.c(unit(.5, \"npc\"), unit(.5, \"npc\")))\ndev.off()\n\n# load inferred priors (with guessing parameter)\nexps_inferred_phi = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/bayesian_model_comparison/priors/results/summary_priorBDA-binomials-phi_incrMH100000burn50000.csv\",sep=\",\", header=T, quote=\"\")\nexps_inferred_phi$X.Item. = gsub(\"\\\"\",\"\",exps_inferred_phi$X.Item.)\nrow.names(exps_inferred_phi) = exps_inferred_phi$X.Item.\nnrow(exps_inferred_phi)\nhead(exps_inferred_phi)\n\n# histogram of expectations\nexps = ggplot(exps_inferred_phi, aes(x=X.expectation.*15)) +\n geom_histogram() +\n scale_x_continuous(name=\"Expected value of prior distribution\",breaks=seq(1,15, by=2)) +\n scale_y_continuous(name=\"Number of cases\")\nggsave(\"pics/priorexpectations-inferred-phi-histogram.pdf\",width=5,height=3.7)\n\n# load inferred priors (without guessing parameter)\nexps_inferred = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/bayesian_model_comparison/priors/results/summary_priorBDA-binomials_incrMH100000burn50000.csv\",sep=\",\", header=T, quote=\"\")\nexps_inferred$X.Item. = gsub(\"\\\"\",\"\",exps_inferred$X.Item.)\nrow.names(exps_inferred) = exps_inferred$X.Item.\nnrow(exps_inferred)\nhead(exps_inferred)\nrow.names(exps_inferred) = exps_inferred$X.Item.\n\n# histogram of expectations\nexps = ggplot(exps_inferred, aes(x=X.expectation.*15)) +\n geom_histogram() +\n scale_x_continuous(name=\"Expected value of prior distribution\",breaks=seq(1,15, by=2)) +\n scale_y_continuous(name=\"Number of cases\")\nggsave(\"pics/priorexpectations-inferred-histogram.pdf\",width=5,height=3.7)\n\n\n### PLOT COMPREHENSION RESULTS\n# expectations\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\n\nr$PriorExpectationProportion = prior_exps[as.character(r$Item),]$exp.val\nr$PriorExpectationProportionInferred = exps_inferred[as.character(r$Item),]$X.expectation.\nr$PriorExpectationProportionInferredPhi = exps_inferred_phi[as.character(r$Item),]$X.expectation.\n\ncor(r$PriorExpectationProportionInferred*15,r$PriorExpectationProportion)\ncor(r$PriorExpectationProportionInferredPhi*15,r$PriorExpectationProportion)\n# exclude people who weren't literal above some threshold\n# lit = droplevels(r[r$quantifier %in% c(\"All\",\"None\"),]) %>% \n# group_by(workerid,quantifier) %>%\n# summarise(Mean=mean(response))\n# lit = as.data.frame(lit)\n# lit = lit %>% spread(quantifier,Mean)\n# lit$Literal = as.factor(as.character(ifelse(lit$All > .80*15 & lit$None < .20*15,\"literal\",\"non-literal\")))\n# table(lit$Literal)\n# row.names(lit) = as.character(lit$workerid)\n# r$Literal = lit[as.character(r$workerid),]$Literal\n\nagr = aggregate(ProportionResponse ~ PriorExpectationProportion + quantifier + Item,data=r,FUN=mean)\n\nmin(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nmax(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nagr$Quantifier = factor(x=agr$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eexps = ggplot(agr, aes(x=PriorExpectationProportion, y=ProportionResponse*15, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(breaks=seq(1,15,by=2),name=\"Posterior mean number of objects\") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") \np_eexps\nggsave(file=\"empirical_exps.pdf\")#,width=5,height=3.7)\n\n# plot with inferred prior expectation on x axis\nagr = aggregate(ProportionResponse ~ PriorExpectationProportionInferred + quantifier + Item,data=r,FUN=mean)\n\nmin(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nmax(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nagr$Quantifier = factor(x=agr$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eexps = ggplot(agr, aes(x=PriorExpectationProportionInferred*15, y=ProportionResponse*15, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(breaks=seq(1,15,by=2),name=\"Posterior mean number of objects\") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") \np_eexps\nggsave(file=\"pics/empirical_exps_inferredprior.pdf\",width=7)#,width=5,height=3.7)\n\n# plot with inferred prior (phi) expectation on x axis\nagr = aggregate(ProportionResponse ~ PriorExpectationProportionInferredPhi + quantifier + Item,data=r,FUN=mean)\n\nmin(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nmax(agr[agr$quantifier == \"Some\",]$ProportionResponse)*15\nagr$Quantifier = factor(x=agr$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eexps = ggplot(agr, aes(x=PriorExpectationProportionInferredPhi*15, y=ProportionResponse*15, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(breaks=seq(1,15,by=2),name=\"Posterior mean number of objects\") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") \np_eexps\nggsave(file=\"pics/empirical_exps_inferredprior_phi.pdf\",width=7)#,width=5,height=3.7)\n\n\n\n# allstate-probs\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/16_sinking-marbles-sliders-certain/results/data/r.RData\")\n\n# exclude people who are doing some sort of bullshit and not responding reasonably to all/none (see subject-variability.pdf for behavior on zero-slider)\n#tmp = subset(r,!workerid %in% c(0,22,43,98,100,103,117,118))\nr$AllPriorProbability = prior_allprobs[as.character(r$Item),]$X15\ntmp=r\n#tmpub = droplevels(subset(tmp, Proportion == \"100\"))\n#tmpub$Quantifier = factor(x=tmpub$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\nagrr = aggregate(normresponse ~ AllPriorProbability + Proportion + quantifier + Item,data=r,FUN=mean)\nub = subset(agrr, Proportion == \"100\")\nub = droplevels(ub)\nub$Quantifier = factor(x=ub$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eprobs = ggplot(ub, aes(x=AllPriorProbability, y = normresponse, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(limits=c(0,1),breaks=seq(0,1,by=.2),name=\"Posterior probability of all-state \") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Prior probability of all-state\") \np_eprobs\nggsave(file=\"empirical-allprobs.pdf\")#,width=5,height=3.7)\n\nlibrary(gridExtra)\n# share a legend between multiple plots\ng <- ggplotGrob(p_eexps + theme(legend.position=\"right\"))$grobs\nlegend <- g[[which(sapply(g, function(x) x$name) == \"guide-box\")]]\np_eexps_nolegend = p_eexps + theme(legend.position=\"none\")\np_eprobs_nolegend = p_eprobs + theme(legend.position=\"none\")\n\npdf(\"pics/empirical-results.pdf\",width=13,height=5)\ngrid.arrange(p_eexps_nolegend, p_eprobs_nolegend, legend,nrow=1,widths=unit.c(unit(.45, \"npc\"), unit(.45, \"npc\"), unit(.1, \"npc\")))\ndev.off()\n\n### PLOT WONKY RESULTS\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/11_sinking-marbles-normal/results/data/r.RData\")\n\nr$PriorExpectation = prior_exps[as.character(r$Item),]$exp.val\nans = r %>%\n filter(quantifier %in% c(\"All\",\"None\",\"Some\")) %>%\n group_by(quantifier, Item, PriorExpectation) %>%\n summarise(mean.prop=mean(numWonky))\n\np_wempirical =ggplot(ans,aes(x=PriorExpectation,y=mean.prop,color=quantifier)) +\n geom_point() +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3])) +\n scale_y_continuous(limits=c(-0.1,1.2),breaks=seq(0,1,by=.2),name=\"Proportion of `wonky' judgments\") +\n scale_x_continuous(limits=c(0,15),breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") \n\n# get wonky model predictions\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/data/wr-uniform.RData\")\n\n# plot expectations for best basic model: \ntoplot = droplevels(subset(wr, WonkyWorldPrior == .5 & Wonky == \"true\"))\nnrow(toplot)\n\ntoplot = droplevels(subset(toplot, SpeakerOptimality == 2))\nnrow(toplot)\nhead(toplot)\n#toplot$quantifier = capitalize(as.character(toplot$Quantifier))\ntoplot$Quantifier = factor(toplot$Quantifier, levels=c(\"all\",\"none\",\"some\"))\n\np_wmodel = ggplot(toplot, aes(x=PriorExpectation, y=PosteriorProbability,color=Quantifier,shape=Quantifier)) +\n geom_point() + \n geom_smooth() + \n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) + \n #scale_color_manual(values=c(\"#F8766D\",\"#00BF7D\",\"#00B0F6\")) +\n scale_x_continuous(limits=c(0,15),breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") +\n scale_y_continuous(limits=c(-0.1,1.2),breaks=seq(0,1,by=.25),name=\"Predicted posterior wonkiness probability\") +\n theme(plot.margin=unit(c(0,0,0,0),units=\"cm\")) \np_wmodel\n\n\npdf(\"pics/wonkiness-results.pdf\",width=10,height=4)\n#grid.arrange(p_wmodel, p_wempirical, legend,nrow=1,widths=unit.c(unit(.45, \"npc\"), unit(.45, \"npc\"), unit(.1, \"npc\")))\ngrid.arrange(p_wmodel, p_wempirical, nrow=1,widths=unit.c(unit(.5, \"npc\"), unit(.5, \"npc\"), unit(.1, \"npc\")))\ndev.off()\n\n\n## PLOT SPEAKER RELIABILITY RESULTS\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/19_speaker_reliability_withins/results/data/r.RData\")\nr = r[!is.na(r$normresponse),]\nr$AllPriorProbability = prior_allprobs[as.character(r$Item),]$X15\n\nsome100 = droplevels(subset(r, Proportion == \"100\" & quantifier == \"Some\"))\nagr = aggregate(normresponse ~ AllPriorProbability + trial_type + Item,data=some100,FUN=mean)\nagr$CILow = aggregate(normresponse ~ AllPriorProbability + trial_type + Item,data=some100, FUN=ci.low)$normresponse\nagr$CIHigh = aggregate(normresponse ~ AllPriorProbability + trial_type + Item,data=some100,FUN=ci.high)$normresponse\nagr$YMin = agr$normresponse - agr$CILow\nagr$YMax = agr$normresponse + agr$CIHigh\n\np=ggplot(agr, aes(x=AllPriorProbability,y=normresponse, color=trial_type)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n# geom_smooth(method=\"lm\") +\n scale_y_continuous(name=\"Posterior probability of all-state\") +\n scale_x_continuous(name=\"Prior probability of all-state\") +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3]),name=\"Trial type\",breaks=levels(agr$trial_type),labels=c(\"uninformative (court)\", \"unreliable (drunk)\", \"cooperative (sober)\"))\np\nggsave(\"pics/speakerreliabilityresults.pdf\",width=7,height=4)\n\n\n\nsome100$Reliability = as.factor(ifelse(some100$trial_type == \"sober\",\"reliable\",\"unreliable\"))\ncentered = cbind(some100, myCenter(some100[,c(\"AllPriorProbability\",\"Reliability\",\"Trial\")]))\nm = lmer(normresponse ~ cAllPriorProbability * cReliability + (cAllPriorProbability * cReliability | Item) + (cAllPriorProbability * cReliability | workerid), data=centered)\nsummary(m)\nsave(m, file=\"data/model.RData\")\n\nm = lmer(normresponse ~ cAllPriorProbability * trial_type + (1 | Item) + (1 | workerid), data=centered)\nsummary(m)\n\nm.trial = lmer(normresponse ~ cAllPriorProbability * cReliability * cTrial + (cAllPriorProbability * cReliability| Item) + (cAllPriorProbability * cReliability| workerid), data=centered)\nsummary(m.trial)\n\nm.trial.nopr = lmer(normresponse ~ cAllPriorProbability + cReliability + cTrial + cReliability:cTrial + cAllPriorProbability:cTrial + cAllPriorProbability:cReliability:cTrial + (cAllPriorProbability * cReliability| Item) + (cAllPriorProbability * cReliability| workerid), data=centered)\nsummary(m.trial.nopr)\n\nanova(m.trial.nopr,m.trial) # \n\nm.trial.nopt = lmer(normresponse ~ cAllPriorProbability + cReliability + cTrial + cAllPriorProbability:cReliability + cReliability:cTrial + cAllPriorProbability:cReliability:cTrial + (cAllPriorProbability * cReliability| Item) + (cAllPriorProbability * cReliability| workerid), data=centered)\nsummary(m.trial.nopr)\n\nanova(m.trial.nopt,m.trial)\n\n# spell out all the interactions\n#m.trial = lmer(normresponse ~ cAllPriorProbability + cReliability + cTrial + cAllPriorProbability:cReliability + cReliability:cTrial + cAllPriorProbability:cTrial + cAllPriorProbability:cReliability:cTrial + (cAllPriorProbability * cReliability| Item) + (cAllPriorProbability * cReliability| workerid), data=centered)\n\n\n# PLOT SPEAKER RELIABILITY RESULTS BY BLOCK\nagr = aggregate(normresponse ~ AllPriorProbability + trial_type + Item + block,data=some100,FUN=mean)\nagr$CILow = aggregate(normresponse ~ AllPriorProbability + trial_type + Item + block,data=some100, FUN=ci.low)$normresponse\nagr$CIHigh = aggregate(normresponse ~ AllPriorProbability + trial_type + Item + block,data=some100,FUN=ci.high)$normresponse\nagr$YMin = agr$normresponse - agr$CILow\nagr$YMax = agr$normresponse + agr$CIHigh\n\np=ggplot(agr, aes(x=AllPriorProbability,y=normresponse, color=trial_type)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_smooth(method=\"lm\") +\n scale_y_continuous(name=\"Posterior probability of all-state\") +\n scale_x_continuous(name=\"Prior probability of all-state\") +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3]),name=\"Trial type\",breaks=levels(agr$trial_type),labels=c(\"uninformative (court)\", \"unreliable (drunk)\", \"cooperative (sober)\")) +\n facet_wrap(~block)\np\nggsave(\"pics/speakerreliabilityresults-byblock.pdf\",width=12,height=4)\n\n\n\n#####################################\n# plot model predictions: expectations\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/data/mp-uniform.RData\")\n\n\nlibrary(dplyr)\nlibrary(plyr)\n# plot expectations for best basic model: \ntoplot = droplevels(subset(mp, Quantifier == \"some\" & WonkyWorldPrior == .5))\nnrow(toplot)\npexpectations = ddply(toplot, .(Item), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability)/15)\nhead(pexpectations)\nsome = pexpectations#droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ntoplot = some\nnrow(toplot)\nhead(toplot)\ntoplot$Mode = priormodes[as.character(toplot$Item),]$Mode\n\nggplot(toplot, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted)) +\n geom_point(color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth(color=\"#00B0F6\") +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,1), name=\"Model predicted posterior expectation\")\n\n\n\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/data/mp-uniform.RData\")\n\n# plot expectations for best basic model: \ntoplot = droplevels(subset(mp, Quantifier == \"some\" & WonkyWorldPrior == .5 & State == 15))\nnrow(toplot)\n\n# adjust speaker optimality at will\ntoplot = droplevels(subset(toplot, SpeakerOptimality == 2))\nnrow(toplot)\nhead(toplot)\n\ntoplot_w = toplot\n# get rRSA predictions for qud=how-many, alts=0_basic, spopt=2\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/complex_prior/smoothed_unbinned15/results/data/mp.RData\")\nsummary(mp)\ntoplot = droplevels(subset(mp, QUD == \"how-many\" & Alternatives == \"0_basic\" & State == 15))\nnrow(toplot)\n\n# adjust speaker optimality at will\ntoplot = droplevels(subset(toplot, SpeakerOptimality == 2))\nnrow(toplot)\nhead(toplot)\n\ntoplot_r = toplot\ntoplot_r$Model = \"RSA\"\ntoplot_w$Model = \"wRSA\"\n\n# plot both rRSA and uniform wRSA expectation predictions in same plot\ntoplot = merge(toplot_r,toplot_w, all=T)\nhead(toplot)\nnrow(toplot)\np_probs = ggplot(toplot, aes(x=PriorProbability, y=PosteriorProbability, color=Model)) +\n geom_point() + #color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth() + #color=\"#00B0F6\") +\n scale_color_manual(values=c(\"darkred\",wes_palette(\"Darjeeling\")[1])) +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior mean all-state probability\") +\n scale_y_continuous(limits=c(0,1), name=\"Predicted posterior all-state probability\") \np_probs\nggsave(\"pred_probs.pdf\")\n\np_probs = ggplot(toplot_r, aes(x=PriorProbability, y=PosteriorProbability, color=Model)) +\n geom_point() + #color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth() + #color=\"#00B0F6\") +\n scale_color_manual(values=c(\"darkred\")) +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior all-state probability\") +\n scale_y_continuous(limits=c(0,1), name=\"Posterior all-state probability\") \np_probs\nggsave(\"pred_prob_rsa.pdf\")\n\n\n# plot model predictions: expectations\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/data/mp-uniform.RData\")\n\n\n# plot expectations for best basic model: \ntoplot = droplevels(subset(mp, Quantifier == \"some\" & WonkyWorldPrior == .5))\nnrow(toplot)\n\npexpectations = ddply(toplot, .(Item, SpeakerOptimality,PriorExpectation), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability)/15)\nhead(pexpectations)\nsome = pexpectations#droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ntoplot = droplevels(subset(some, SpeakerOptimality == 2))\nnrow(toplot)\nhead(toplot)\ntoplot_w = toplot\n\n# get rRSA predictions for qud=how-many, alts=0_basic, spopt=2\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/complex_prior/smoothed_unbinned15/results/data/toplot-expectations.RData\")\ntoplot_r = toplot\nhead(toplot_w)\nsummary(toplot_r)\ntoplot_r$Model = \"RSA\"\ntoplot_r$PriorExpectation = toplot_r$PriorExpectation_smoothed*15\ntoplot_w$Model = \"wRSA\"\n\n# plot both rRSA and uniform wRSA expectation predictions in same plot\ntoplot = merge(toplot_r,toplot_w, all=T)\nhead(toplot)\nnrow(toplot)\n\np_exps = ggplot(toplot, aes(x=PriorExpectation, y=PosteriorExpectation_predicted*15, color=Model)) +\n geom_point() + #color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth() + #color=\"#00B0F6\") +\n scale_color_manual(values=c(\"darkred\",wes_palette(\"Darjeeling\")[1])) +#values=c(\"#007fb1\", \"#4ecdff\")) +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,15), breaks=seq(1,15,by=2), name=\"Prior mean number of objects\") +\n scale_y_continuous(limits=c(0,15), breaks=seq(1,15,by=2), name=\"Predicted posterior number of objects\") \np_exps\nggsave(\"pred_exps.pdf\")\n\np_exps = ggplot(toplot_r, aes(x=PriorExpectation, y=PosteriorExpectation_predicted*15, color=Model)) +\n geom_point() + #color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth() + #color=\"#00B0F6\") +\n scale_color_manual(values=c(\"darkred\")) +#values=c(\"#007fb1\", \"#4ecdff\")) +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,15), breaks=seq(1,15,by=2), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,15), breaks=seq(1,15,by=2), name=\"Posterior expectation\") \np_exps\nggsave(\"pred_exps_rsa.pdf\")\n\nlibrary(gridExtra)\n# share a legend between multiple plots\ng <- ggplotGrob(p_exps + theme(legend.position=\"right\"))$grobs\nlegend <- g[[which(sapply(g, function(x) x$name) == \"guide-box\")]]\np_exps_nolegend = p_exps + theme(legend.position=\"none\")\np_probs_nolegend = p_probs + theme(legend.position=\"none\")\n\npdf(\"rsa-predictions.pdf\",width=12,height=4.9)\ngrid.arrange(p_exps_nolegend, p_probs_nolegend, legend,nrow=1,widths=unit.c(unit(.45, \"npc\"), unit(.45, \"npc\"), unit(.1, \"npc\")))\ndev.off()\n\n\n# expectations\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\n\nagr = aggregate(ProportionResponse ~ PriorExpectationProportion + quantifier + Item,data=r,FUN=mean)\n\nmin(agr[agr$quantifier == \"Some\",]$ProportionResponse)\nmax(agr[agr$quantifier == \"Some\",]$ProportionResponse)\nagr$Quantifier = factor(x=agr$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eexps = ggplot(agr, aes(x=PriorExpectationProportion*15, y=ProportionResponse*15, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_smooth(method=\"lm\") +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(breaks=seq(1,15,by=2),name=\"Posterior mean number of objects\") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") \np_eexps\nggsave(file=\"empirical_exps.pdf\")#,width=5,height=3.7)\n\n\n# allstate-probs\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/16_sinking-marbles-sliders-certain/results/data/r.RData\")\n\n# exclude people who are doing some sort of bullshit and not responding reasonably to all/none (see subject-variability.pdf for behavior on zero-slider)\ntmp = subset(r,!workerid %in% c(0,22,43,98,100,103,117,118))\nagrr = aggregate(normresponse ~ AllPriorProbability + Proportion + quantifier + Item,data=tmp,FUN=mean)\nagrr = aggregate(normresponse ~ AllPriorProbability + Proportion + quantifier + Item,data=r,FUN=mean)\nub = subset(agrr, Proportion == \"100\")\nub = droplevels(ub)\nub$Quantifier = factor(x=ub$quantifier, levels=c(\"Some\",\"All\",\"None\",\"long_filler\",\"short_filler\"))\n\np_eprobs = ggplot(ub, aes(x=AllPriorProbability, y = normresponse, color=Quantifier, shape=Quantifier)) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) +#values=c(\"#F8766D\", \"black\", \"#00BF7D\", \"gray30\", \"#00B0F6\"),breaks=levels(agr$quantifier),labels=c(\"all\",\"long filler\",\"none\",\"short filler\",\"some\")) +\n scale_y_continuous(limits=c(0,1),name=\"Posterior probability of all-state \") +\n # geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(limits=c(0,1),name=\"Prior probability of all-state\") \np_eprobs\nggsave(file=\"empirical-allprobs.pdf\")#,width=5,height=3.7)\n\nlibrary(gridExtra)\n# share a legend between multiple plots\ng <- ggplotGrob(p_eexps + theme(legend.position=\"right\"))$grobs\nlegend <- g[[which(sapply(g, function(x) x$name) == \"guide-box\")]]\np_eexps_nolegend = p_eexps + theme(legend.position=\"none\")\np_eprobs_nolegend = p_eprobs + theme(legend.position=\"none\")\n\npdf(\"empirical-results.pdf\",width=12,height=4.9)\ngrid.arrange(p_eexps_nolegend, p_eprobs_nolegend, legend,nrow=1,widths=unit.c(unit(.45, \"npc\"), unit(.45, \"npc\"), unit(.1, \"npc\")))\ndev.off()\n\n\nload(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/data/wr-uniform.RData\")\n\n# plot expectations for best basic model: \ntoplot = droplevels(subset(wr, WonkyWorldPrior == .5 & Wonky == \"true\"))\nnrow(toplot)\ntoplot$Mode = priormodes[as.character(toplot$Item),]$Mode\n\ntoplot = droplevels(subset(toplot, SpeakerOptimality == 2))\nnrow(toplot)\nhead(toplot)\n#toplot$quantifier = capitalize(as.character(toplot$Quantifier))\ntoplot$Quantifier = factor(toplot$Quantifier, levels=c(\"some\",\"all\",\"none\"))\n\np_wmodel = ggplot(toplot, aes(x=PriorExpectation, y=PosteriorProbability,color=Quantifier,shape=Quantifier)) +\n geom_point() + \n geom_smooth() + \n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) + \n #scale_color_manual(values=c(\"#F8766D\",\"#00BF7D\",\"#00B0F6\")) +\n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") +\n scale_y_continuous(breaks=seq(0,1,by=.25),name=\"Predicted posterior wonkiness probability\") +\n theme(plot.margin=unit(c(0,0,0,0),units=\"cm\")) \np_wmodel\nggsave(\"model-wonkiness-uniform.pdf\",width=6.8,height=5)#,width=30,height=10)\n\n############\n# empirical wonkiness posteriors\n############\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/17_sinking-marbles-normal-sliders/results/data/r.RData\")\nhead(r)\nnrow(r)\n\ntoplot = aggregate(response ~ quantifier + Item + PriorExpectation, FUN=\"mean\", data=r)\ntoplot = droplevels(subset(toplot, quantifier %in% c(\"All\",\"Some\",\"None\")))\ntoplot$Quantifier = factor(tolower(toplot$quantifier), levels=c(\"all\", \"none\",\"some\"))\ntoplot$Mode = priormodes[as.character(toplot$Item),]$Mode\ntoplot$quantifier = capitalize(as.character(toplot$Quantifier))\ntoplot$Quantifier = factor(toplot$quantifier, levels=c(\"Some\",\"All\",\"None\"))\n\np_wempirical = ggplot(toplot, aes(x=PriorExpectation, y=response, color=Quantifier,shape=Quantifier)) +\n geom_point() +\n geom_smooth() +\n scale_color_manual(values=c(wes_palette(\"Darjeeling\")[1:3],\"black\",\"gray40\")) + \n scale_x_continuous(breaks=seq(1,15,by=2),name=\"Prior mean number of objects\") +\n scale_y_continuous(breaks=seq(0,1,by=.25),name=\"Mean empirical wonkiness probability\") +\n theme(plot.margin=unit(c(0,0,0,0),units=\"cm\")) \np_wempirical\nggsave(file=\"empirical-wonkiness.pdf\",width=6.8,height=5)\n\n\nlibrary(gridExtra)\n# share a legend between multiple plots\ng <- ggplotGrob(p_wmodel + theme(legend.position=\"right\"))$grobs\nlegend <- g[[which(sapply(g, function(x) x$name) == \"guide-box\")]]\np_wmodel_nolegend = p_wmodel + theme(legend.position=\"none\")\np_wempirical_nolegend = p_wempirical + theme(legend.position=\"none\")\n\n#pdf(\"rsa-predictions-uniform.pdf\",width=10,height=4)\npdf(\"wonkiness-fullplot.pdf\",width=12,height=5)\ngrid.arrange(p_wmodel_nolegend, p_wempirical_nolegend, legend,nrow=1,widths=unit.c(unit(.45, \"npc\"), unit(.45, \"npc\"), unit(.1, \"npc\")))\ndev.off()\n\n\n## load four-step priors for generating appendix plots \npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/24_sinking-marbles-prior-fourstep/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorprobs) = priorprobs$Item\nnrow(priorprobs)\n\n# prior all-state probability for each item\nprior_allprobs = priorprobs[,c(\"Item\",\"X15\")]\nrow.names(prior_allprobs) = prior_allprobs$Item\n\n# prior expectation for each item\ngathered_probs <- priorprobs %>%\n gather(State,Probability,X0:X15)\ngathered_probs$State = as.numeric(as.character(gsub(\"X\",\"\",gathered_probs$State)))\nprior_exps <- gathered_probs %>%\n group_by(Item) %>%\n summarise(exp.val=sum(State*Probability))\nprior_exps = as.data.frame(prior_exps)\nrow.names(prior_exps) = prior_exps$Item\nsummary(prior_exps)\n\n# histogram of expectations\nexps = ggplot(prior_exps, aes(x=exp.val)) +\n geom_histogram() +\n scale_x_continuous(name=\"Expected value of prior distribution\",breaks=seq(1,15, by=2)) +\n scale_y_continuous(name=\"Number of cases\",breaks=seq(0,8, by=2))\nggsave(\"pics/priorexpectations-histogram-fourstep.pdf\",width=5,height=3.7)\n\n# histogram of allstate-probs\nallprobs = ggplot(prior_allprobs, aes(x=X15)) +\n geom_histogram() +\n scale_x_continuous(name=\"Prior all-state probability\") +\n scale_y_continuous(name=\"Number of cases\")\nggsave(\"priorallprobs-histogram-fourstep.pdf\")\n\npdf(\"pics/priordistributions-fourstep.pdf\",width=10,height=3.5)\ngrid.arrange(exps, allprobs, nrow=1,widths=unit.c(unit(.5, \"npc\"), unit(.5, \"npc\")))\ndev.off()\n\n", "meta": {"hexsha": "50a0164e57bd0946efedf43134e5548f5769814d", "size": 30024, "ext": "r", "lang": "R", "max_stars_repo_path": "writing/_2015/_journal_cognition/rscripts/plots_new.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "writing/_2015/_journal_cognition/rscripts/plots_new.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "writing/_2015/_journal_cognition/rscripts/plots_new.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.626446281, "max_line_length": 318, "alphanum_fraction": 0.7501998401, "num_tokens": 9110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.629774621301746, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.34919142709345613}} {"text": "compare <- function(a, b)\n{\n cat(paste(a, \"is of type\", class(a), \"and\", b, \"is of type\", class(b), \"\\n\"))\n\n if (a < b) cat(paste(a, \"is strictly less than\", b, \"\\n\"))\n if (a <= b) cat(paste(a, \"is less than or equal to\", b, \"\\n\"))\n if (a > b) cat(paste(a, \"is strictly greater than\", b, \"\\n\"))\n if (a >= b) cat(paste(a, \"is greater than or equal to\", b, \"\\n\"))\n if (a == b) cat(paste(a, \"is equal to\", b, \"\\n\"))\n if (a != b) cat(paste(a, \"is not equal to\", b, \"\\n\"))\n\n invisible()\n}\n\ncompare('YUP', 'YUP')\ncompare('BALL', 'BELL')\ncompare('24', '123')\ncompare(24, 123)\ncompare(5.0, 5)\n", "meta": {"hexsha": "ade613e8213bb7c068f222d3a69c0a9bf5780aef", "size": 593, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/String-comparison/R/string-comparison-1.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/String-comparison/R/string-comparison-1.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/String-comparison/R/string-comparison-1.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 29.65, "max_line_length": 79, "alphanum_fraction": 0.5413153457, "num_tokens": 223, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704796847396, "lm_q2_score": 0.6297746004557471, "lm_q1q2_score": 0.34919142480796334}} {"text": "# Dual Luciferase Analysis Pipeline\n# Elias Brandorff, SILS Amsterdam Version 1\n# import excel data from GloMax luminescence machine\n# level 1 calculations: normalize expression to internal control \n# plot normalized expression values\n# produce a summary table\n# level 2 calculations: normalize expression of experimental to control condition\n# plot relative expression \n\n\n#set working path\nsetwd(\"C:/DualReporterPipeline/\")\n\n# load the relevant libraries\nlibrary(readxl)\nlibrary(tidyr)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(gridExtra)\nlibrary(magrittr)\n\n# import complete excel output GloMax with readxl into a dataframe.\n# either point to number or name of the correct sheet \nall_data_from_excel <- read_excel(\"Dual Luciferase Reporter Assay System ELIAS 2020.03.10 05_34_44.xlsx\", sheet = \"Results\")\n\n#Subset firefly data\nfirefly <- all_data_from_excel[19:26,6:17] %>% unlist()\n\n#Subset renilla data\nrenilla <- all_data_from_excel[40:47,6:17] %>% unlist()\n\n#Read conditions from a csv file - empty cells are interpreted as NA\ndf_conditions <- read.csv(\"conditions_exp1.csv\", stringsAsFactors = FALSE, na.strings = c(\"\",\"NA\",\"na\"))\n\n#Subset the dataframe, to select only conditions (get rid of row names)\ndf_conditions <- df_conditions[1:8,2:13] \n\n#Convert the dataframe with conditions to a vector\ncondition <- df_conditions %>% unlist(use.names = FALSE)\n\n#Combine all vectors in a (tidy) dataframe, remove data that has no condition associated with it\n#Note: By using filtering for NA, the 'condition' vector defines the data you want to analyze/display\ndf <- data.frame(condition,firefly,renilla) %>% filter(!is.na(condition))\n\n#add a column that in which data is normalized to internal control\ndf <- df %>% mutate(FR=firefly/renilla) %>% na.omit() #Remove NA from the table (i.e. cells without data)\n\n#Ensure that the order of the conditions is kept (otherwise ordering is alphabetical)\ndf$condition <- factor(df$condition, levels=unique(df$condition))\n\n#Use this line of code when you want to sort the data acoording to median of FR\n# df$condition <- reorder(df$condition, df$FR, median, na.rm = TRUE)\n\nFR_tidy <- df\n\n#plotting FR ratios for data overview\np1 <- ggplot(FR_tidy, aes(x = condition, y = FR)) +\n geom_jitter(position=position_jitter(0.1), cex=2, color=\"grey40\") +\n stat_summary(fun = median, fun.min = median, fun.max = median,\n geom = \"crossbar\", width = 0.2)\n\np1\n\n#Save p1 in png format\npng(\"FR_summary_exp1.png\", width = 300, height = 300)\nplot(p1)\ndev.off()\n\n#Attemp to spread the dataframe. \nFR_tidy5 <- df %>%\n select(condition, FR) %>%\n group_by(condition) %>%\n mutate(grouped_id = row_number()) %>%\n pivot_wider(id_cols = NULL, names_from = condition, values_from = FR) %>%\n summarize_all(funs(median)) %>%\n select(-grouped_id)\nFR_tidy5\n\n#The table is in the correct orientation now. But we want to ad the mean and median as the 2 bottom rows to a the FR-values\n\n#produce a table in PDF for publication containing the mean FR ratio per condition\npdf(\"FRtable_exp1.pdf\", height=11, width=10, pivot = TRUE)\ngrid.table(FR_tidy5)\ndev.off()\n\n#FR summary\nFR2_tidy2 <- FR_tidy %>%\n group_by(condition) %>%\n summarise(median = median(FR, na.rm = TRUE), mean = mean(FR, na.rm = TRUE))\nFR2_tidy2\n\n#doing the relative calculation i.e. normalize to expression of the empty vector \"Ev\"\n#Calculate the average of the EV control\nEV_mean <- FR_tidy %>% filter(condition==\"control\") %>% summarise(mean_EV_FR=mean(FR)) %>% unlist(use.names = FALSE)\n\n#Divide FR by EV control value\nFR_tidy <- FR_tidy %>% mutate(FC = FR/EV_mean)\n\n#### Here a next step in the normalization can be made. I tested some data generated by my student. In this case we have an empty-vector control\n#### and two reporter constructs that should be compared to each other, after normalization to the empty reporter vector. \n#### eventually, the \"control\" conditions can be omitted from the second plot.\n\np2 <- ggplot(FR_tidy, aes(condition, FC)) + \n geom_point() +\n stat_summary(fun = median, fun.min = median, fun.max = median,\n geom = \"crossbar\", width = 0.2) +\n ylab(\"FC luciferase expression / EV normalized to renilla\") +\n ylim(0,1)\n\np2\n\n#Save p2 in png format \npng(\"FC_lucexpression1.png\", width = 300, height = 300)\nplot(p2)\ndev.off()\n\n\ntbl_median <- tableGrob(t(FR2_tidy2), theme = ttheme_default(8))\ngrid.arrange(p1, p2, tbl_median, ncol=2, nrow=2, as.table=TRUE, heights=c(3,1))\n", "meta": {"hexsha": "21b4a3af69866b8f1254d594e80a0e75fdfb6071", "size": 4418, "ext": "r", "lang": "R", "max_stars_repo_path": "dualluc_scriptV1.r", "max_stars_repo_name": "ScienceParkStudyGroup/dual-luciferase", "max_stars_repo_head_hexsha": "2d705f5d0b21e10e0951ec3b967460756b4167a4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dualluc_scriptV1.r", "max_issues_repo_name": "ScienceParkStudyGroup/dual-luciferase", "max_issues_repo_head_hexsha": "2d705f5d0b21e10e0951ec3b967460756b4167a4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-03-05T13:10:12.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-25T16:37:08.000Z", "max_forks_repo_path": "dualluc_scriptV1.r", "max_forks_repo_name": "ScienceParkStudyGroup/dual-luciferase", "max_forks_repo_head_hexsha": "2d705f5d0b21e10e0951ec3b967460756b4167a4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-12T16:35:29.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-12T16:35:29.000Z", "avg_line_length": 36.8166666667, "max_line_length": 144, "alphanum_fraction": 0.7320054323, "num_tokens": 1201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.34919141938779563}} {"text": "\n#' Traceplot of the occupancy index rate\n#' \n#' @description Traceplot of the index occupancy rate, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_year Index of the year to plot. Indexes go from 1 to the number of years.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_OccupancyIndexRate(sampleResults, 1)\n#' \ntracePlot_OccupancyIndexRate <- function(modelResults, index_year) {\n \n psi_mean_output <- modelResults$modelOutput$psi_mean_output\n \n nchain <- nrow(psi_mean_output)\n niter <- ncol(psi_mean_output)\n \n psi_mean_output <-\n matrix(psi_mean_output[, , index_year], nrow = nchain, ncol = niter)\n \n psi_mean_output_long <- reshape2::melt(psi_mean_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = psi_mean_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n \n plotTitle <- createPlotTitle(psi_mean_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n diagnosticsPlot\n}\n\n#' Traceplot of the year-specific random effects\n#' \n#' @description Traceplot of the year-specific random effects, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_year Index of the year of the random effect to plot. Indexes go from 1 to the number of years.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_OccupancyYearEffect(sampleResults, 1)\n#' \ntracePlot_OccupancyYearEffect <- function(modelResults, index_year) {\n beta_psi_output <- modelResults$modelOutput$beta_psi_output\n \n nchain <- nrow(beta_psi_output)\n niter <- ncol(beta_psi_output)\n \n beta_psi_output <-\n matrix(beta_psi_output[, , index_year], nrow = nchain, ncol = niter)\n \n beta_psi_output_long <- reshape2::melt(beta_psi_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = beta_psi_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n \n plotTitle <- createPlotTitle(beta_psi_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n diagnosticsPlot\n}\n\n#' Traceplot of the site-specific autocorrelated random effects\n#' \n#' @description Traceplot of the site-specific random effects, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_site Index of the random effect to plot. Indexes go from 1 to the number of sites in the approximation.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_OccupancySiteEffect(sampleResults, 1)\n#' \ntracePlot_OccupancySiteEffect <- function(modelResults, index_site) {\n usingSpatial <- modelResults$dataCharacteristics$usingSpatial\n \n if (!usingSpatial) {\n print(\"No spatial effect\")\n } else {\n years <- modelResults$dataCharacteristics$Years\n Y <- length(years)\n \n beta_psi_output <- modelResults$modelOutput$beta_psi_output\n \n nchain <- nrow(beta_psi_output)\n niter <- ncol(beta_psi_output)\n \n beta_psi_output <-\n matrix(beta_psi_output[, , Y + index_site], nrow = nchain, ncol = niter)\n \n beta_psi_output_long <- reshape2::melt(beta_psi_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = beta_psi_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n \n plotTitle <- createPlotTitle(beta_psi_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n return(diagnosticsPlot)\n }\n \n}\n\n#' Traceplot of the covariates of the occupancy probability\n#' \n#' @description Traceplot of the covariates coefficient, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_cov Index of the covariate to plot. Indexes go from 1 to the number of covariate.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_OccupancyCovariate(sampleResults, 1)\n#' \ntracePlot_OccupancyCovariate <- function(modelResults, index_cov) {\n beta_psi_output <- modelResults$modelOutput$beta_psi_output\n \n usingSpatial <- modelResults$dataCharacteristics$usingSpatial\n \n Y <- length(modelResults$dataCharacteristics$Years)\n \n if (usingSpatial) {\n X_centers <- nrow(modelResults$dataCharacteristics$X_tilde)\n } else {\n X_centers <- 0\n }\n \n numTimeSpaceCov <-\n modelResults$dataCharacteristics$numTimeSpaceCov\n \n if ((Y + X_centers + numTimeSpaceCov + index_cov) <= dim(beta_psi_output)[3]) {\n nchain <- nrow(beta_psi_output)\n niter <- ncol(beta_psi_output)\n \n beta_psi_output <-\n matrix(beta_psi_output[, , Y + X_centers + numTimeSpaceCov + index_cov], nrow = nchain, ncol = niter)\n \n beta_psi_output_long <- reshape2::melt(beta_psi_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = beta_psi_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n plotTitle <- createPlotTitle(beta_psi_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n return(diagnosticsPlot)\n \n } else {\n print(\"Index above the number of covariates\")\n \n }\n \n}\n\n#' Traceplot of the intercept of the detection probability\n#' \n#' @description Traceplot of the intercept, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_year Index of the year to plot, in case \\code{usingYearDetProb} is \n#' set to \\code{TRUE}. Indexes go from 1 to the number of years.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_DetectionIntercept(sampleResults, 1)\n#' \ntracePlot_DetectionIntercept <- function(modelResults, index_year) {\n \n usingYearDetProb <- modelResults$dataCharacteristics$usingYearDetProb\n \n years <- modelResults$dataCharacteristics$Years\n Y <- length(years)\n \n if(!usingYearDetProb){\n index_year <- 1\n } else if(index_year > Y){\n \n print(\"Index above the number of years\")\n return(NULL) \n \n }\n \n beta_p_output <- modelResults$modelOutput$beta_p_output\n \n nchain <- nrow(beta_p_output)\n niter <- ncol(beta_p_output)\n \n beta_p_output <-\n matrix(beta_p_output[, , index_year], nrow = nchain, ncol = niter)\n \n beta_psi_output_long <- reshape2::melt(beta_p_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = beta_psi_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n plotTitle <- createPlotTitle(beta_p_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n diagnosticsPlot\n}\n\n#' Traceplot of the covariates of the detection probability\n#' \n#' @description Traceplot of the covariates coefficient, with \n#' effective sample size.\n#' \n#' @param modelResults Output of the function \\code{runModel}\n#' @param index_cov Index of the covariate to plot. Indexes go from 1 to the number of covariate.\n#' \n#' @importFrom magrittr %>%\n#' \n#' @export\n#' \n#' @return The traceplot with the estimate of the effective sample size.\n#' \n#' @examples\n#' \n#' tracePlot_DetectionCovariate(sampleResults, 1)\n#' \ntracePlot_DetectionCovariate <- function(modelResults, index_cov) {\n beta_p_output <- modelResults$modelOutput$beta_p_output\n \n usingYearDetProb <- modelResults$dataCharacteristics$usingYearDetProb\n p_intercepts <- ifelse(usingYearDetProb, Y, 1)\n \n if ((p_intercepts + index_cov) <= dim(beta_p_output)[3]) {\n nchain <- nrow(beta_p_output)\n niter <- ncol(beta_p_output)\n \n beta_p_output <-\n matrix(beta_p_output[, , p_intercepts + index_cov], nrow = nchain, ncol = niter)\n \n beta_psi_output_long <- reshape2::melt(beta_p_output)\n \n diagnosticsPlot <-\n ggplot2::ggplot(data = beta_psi_output_long, ggplot2::aes(\n x = Var2,\n y = value,\n group = Var1,\n color = factor(Var1)\n )) + ggplot2::geom_line() +\n ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n ggplot2::theme(\n plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n axis.text.x = ggplot2::element_text(\n size = 11,\n face = \"bold\",\n hjust = 1\n ),\n axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n legend.position = \"none\"\n )\n \n plotTitle <- createPlotTitle(beta_p_output, nchain)\n \n diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n \n return(diagnosticsPlot)\n \n } else {\n print(\"Index above the number of covariates\")\n \n }\n \n}\n\n# tracePlot_l_T <- function(modelResults) {\n# l_T_output <- modelResults$modelOutput$l_T_output\n# \n# nchain <- nrow(l_T_output)\n# \n# l_T_psi_output_long <- reshape2::melt(l_T_output)\n# \n# diagnosticsPlot <-\n# ggplot(data = l_T_psi_output_long, aes(\n# x = Var2,\n# y = value,\n# group = Var1,\n# color = factor(Var1)\n# )) + geom_line() +\n# xlab(\"Iterations\") + ylab(\"Value\") +\n# theme(\n# plot.title = element_text(hjust = 0.5, size = 17),\n# axis.title = element_text(size = 16, face = \"bold\"),\n# axis.text.y = element_text(size = 11, face = \"bold\"),\n# axis.text.x = element_text(\n# size = 11,\n# face = \"bold\",\n# hjust = 1\n# ),\n# axis.line = element_line(colour = \"black\", size = 0.15),\n# # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n# panel.grid.major = element_line(colour = \"grey\", size = 0.15),\n# panel.background = element_rect(fill = \"white\", color = \"black\"),\n# legend.position = \"none\"\n# )\n# \n# plotTitle <- createPlotTitle(l_T_output, nchain)\n# \n# diagnosticsPlot <- diagnosticsPlot + ggtitle(plotTitle)\n# \n# diagnosticsPlot\n# \n# }\n# \n# tracePlot_sigma_T <- function(modelResults) {\n# sigma_T_output <- modelResults$modelOutput$sigma_T_output\n# \n# nchain <- nrow(sigma_T_output)\n# \n# sigma_T_output_long <- reshape2::melt(sigma_T_output)\n# \n# diagnosticsPlot <-\n# ggplot(data = sigma_T_output_long, aes(\n# x = Var2,\n# y = value,\n# group = Var1,\n# color = factor(Var1)\n# )) + geom_line() +\n# xlab(\"Iterations\") + ylab(\"Value\") +\n# theme(\n# plot.title = element_text(hjust = 0.5, size = 17),\n# axis.title = element_text(size = 16, face = \"bold\"),\n# axis.text.y = element_text(size = 11, face = \"bold\"),\n# axis.text.x = element_text(\n# size = 11,\n# face = \"bold\",\n# hjust = 1\n# ),\n# axis.line = element_line(colour = \"black\", size = 0.15),\n# # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n# panel.grid.major = element_line(colour = \"grey\", size = 0.15),\n# panel.background = element_rect(fill = \"white\", color = \"black\"),\n# legend.position = \"none\"\n# )\n# \n# plotTitle <- createPlotTitle(sigma_T_output, nchain)\n# \n# diagnosticsPlot <- diagnosticsPlot + ggtitle(plotTitle)\n# \n# diagnosticsPlot\n# \n# }\n# \n# tracePlot_l_S <- function(modelResults) {\n# usingSpatial <- modelResults$dataCharacteristics$usingSpatial\n# \n# if (usingSpatial) {\n# l_s_output <- modelResults$modelOutput$l_s_output\n# \n# nchain <- nrow(l_s_output)\n# \n# l_s_psi_output_long <- reshape2::melt(l_s_output)\n# \n# diagnosticsPlot <-\n# ggplot(data = l_s_psi_output_long, aes(\n# x = Var2,\n# y = value,\n# group = Var1,\n# color = factor(Var1)\n# )) + geom_line() +\n# xlab(\"Iterations\") + ylab(\"Value\") +\n# theme(\n# plot.title = element_text(hjust = 0.5, size = 17),\n# axis.title = element_text(size = 16, face = \"bold\"),\n# axis.text.y = element_text(size = 11, face = \"bold\"),\n# axis.text.x = element_text(\n# size = 11,\n# face = \"bold\",\n# hjust = 1\n# ),\n# axis.line = element_line(colour = \"black\", size = 0.15),\n# # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n# panel.grid.major = element_line(colour = \"grey\", size = 0.15),\n# panel.background = element_rect(fill = \"white\", color = \"black\"),\n# legend.position = \"none\"\n# )\n# \n# plotTitle <- createPlotTitle(l_s_output, nchain)\n# \n# diagnosticsPlot <- diagnosticsPlot + ggtitle(plotTitle)\n# \n# return(diagnosticsPlot)\n# } else {\n# print(\"No spatial effect\")\n# }\n# \n# \n# }\n# \n# tracePlot_sigma_S <- function(modelResults) {\n# sigma_s_output <- modelResults$modelOutput$sigma_s_output\n# \n# nchain <- nrow(sigma_s_output)\n# \n# sigma_s_output_long <- reshape2::melt(sigma_s_output)\n# \n# diagnosticsPlot <-\n# ggplot2::ggplot(data = sigma_s_output_long, aes(\n# x = Var2,\n# y = value,\n# group = Var1,\n# color = factor(Var1)\n# )) + ggplot2::geom_line() +\n# ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n# ggplot2::theme(\n# plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n# axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n# axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n# axis.text.x = ggplot2::element_text(\n# size = 11,\n# face = \"bold\",\n# hjust = 1\n# ),\n# axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n# # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n# panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n# panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n# legend.position = \"none\"\n# )\n# \n# plotTitle <- createPlotTitle(sigma_s_output, nchain)\n# \n# diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n# \n# diagnosticsPlot\n# \n# }\n# \n# tracePlot_sigma_eps <- function(modelResults) {\n# sigma_as_output <- modelResults$modelOutput$sigma_as_output\n# \n# nchain <- nrow(sigma_as_output)\n# \n# sigma_as_output_long <- reshape2::melt(sigma_as_output)\n# \n# ggplot2::diagnosticsPlot <-\n# ggplot2::ggplot(data = sigma_as_output_long, aes(\n# x = Var2,\n# y = value,\n# group = Var1,\n# color = factor(Var1)\n# )) + ggplot2::geom_line() +\n# ggplot2::xlab(\"Iterations\") + ggplot2::ylab(\"Value\") +\n# ggplot2::theme(\n# plot.title = ggplot2::element_text(hjust = 0.5, size = 17),\n# axis.title = ggplot2::element_text(size = 16, face = \"bold\"),\n# axis.text.y = ggplot2::element_text(size = 11, face = \"bold\"),\n# axis.text.x = ggplot2::element_text(\n# size = 11,\n# face = \"bold\",\n# hjust = 1\n# ),\n# axis.line = ggplot2::element_line(colour = \"black\", size = 0.15),\n# # panel.grid.minor = element_line(colour=\"grey\", size=0.15),\n# panel.grid.major = ggplot2::element_line(colour = \"grey\", size = 0.15),\n# panel.background = ggplot2::element_rect(fill = \"white\", color = \"black\"),\n# legend.position = \"none\"\n# )\n# \n# plotTitle <- createPlotTitle(sigma_as_output, nchain)\n# \n# diagnosticsPlot <- diagnosticsPlot + ggplot2::ggtitle(plotTitle)\n# \n# diagnosticsPlot\n# \n# }\n\ncreatePlotTitle <- function(mcmc_output, nchain) {\n eff_samplesize <- ess(mcmc_output)\n \n plotTitle <-\n paste0(\"Effective sample size = \", round(eff_samplesize, 1))\n \n if (nchain > 1) {\n Rhat <- compute_rhat(mcmc_output)\n \n plotTitle <-\n paste0(plotTitle, paste0(\" / R.hat = \", round(Rhat, 3)))\n }\n \n plotTitle\n}\n\ncompute_rhat <- function(mcmc_output) {\n mcmc_output_list <- lapply(1:nrow(mcmc_output), function(i) {\n coda::mcmc(mcmc_output[i, ])\n })\n mcmc_output_list_2 <- coda::as.mcmc.list(mcmc_output_list)\n \n Rhat <- coda::gelman.diag(mcmc_output_list_2)\n \n Rhat$psrf[2]\n}\n\ness <- function(mcmc_output) {\n mcmc_output_list <- lapply(1:nrow(mcmc_output), function(i) {\n coda::mcmc(mcmc_output[i, ])\n })\n mcmc_output_list_2 <- coda::as.mcmc.list(mcmc_output_list)\n \n coda::effectiveSize(mcmc_output_list_2)\n \n}", "meta": {"hexsha": "ff7853c51311fb35cc5015bd465a8076da374a2a", "size": 20807, "ext": "r", "lang": "R", "max_stars_repo_path": "R/diagnostics.r", "max_stars_repo_name": "alexdiana1992/FastOccupancy", "max_stars_repo_head_hexsha": "49855f99dcd8b256ee3167374d3753c12e0d21a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/diagnostics.r", "max_issues_repo_name": "alexdiana1992/FastOccupancy", "max_issues_repo_head_hexsha": "49855f99dcd8b256ee3167374d3753c12e0d21a2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/diagnostics.r", "max_forks_repo_name": "alexdiana1992/FastOccupancy", "max_forks_repo_head_hexsha": "49855f99dcd8b256ee3167374d3753c12e0d21a2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.5735963581, "max_line_length": 119, "alphanum_fraction": 0.6298361128, "num_tokens": 5942, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.6297746074044134, "lm_q1q2_score": 0.34919141938779563}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\nlibrary(rJava)\n\nsetwd(\"/Users/mengmengjiang/all datas/print\")\n\nlibrary(xlsx)\n\n# reading ux and sd\n\nk1<-read.xlsx(\"dotx.xlsx\",sheetName=\"600\",header=TRUE)\nk2<-read.xlsx(\"dotx.xlsx\",sheetName=\"1khz\",header=TRUE)\nk3<-read.xlsx(\"dotx.xlsx\",sheetName=\"2khz\",header=TRUE)\nk4<-read.xlsx(\"dotx.xlsx\",sheetName=\"2.5khz\",header=TRUE)\n\n# errorbar\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\nif(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\nstop(\"vectors must be same length\")\narrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n# color setting\n\nyan<-c(\"red\",\"blue\",\"black\",\"green3\")\npcc<-c(0,1,2,5)\n\n\n# plot\npar(fig=c(0,1,0,1),new=T)\n\nplot(k1$ux,k1$sdeva, col=0,xlab = expression(italic(U[\"x\"]) (mm/s)),\n ylab = expression(italic(S[\"d\"]) (um)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(-50,300),ylim=c(0,400))\n\nmtext(\"Sd+d and fitting\",3,line=0.2,font=2,cex=1.2)\n\npoints(k1$ux,(k1$sdeva+k1$deva),col=yan[1],pch=pcc[1])\npoints(k2$ux,(k2$sdeva+k2$deva),col=yan[2],pch=pcc[2])\npoints(k3$ux,(k3$sdeva+k3$deva),col=yan[3],pch=pcc[3])\npoints(k4$ux,(k4$sdeva+k4$deva),col=yan[4],pch=pcc[4])\n\nz1<-lm((k1$sdeva+k1$deva)~k1$ux)\nz2<-lm((k2$sdeva+k2$deva)~k2$ux)\nz3<-lm((k3$sdeva+k3$deva)~k3$ux)\nz4<-lm((k4$deva+k4$sdeva)~k4$ux)\n\n\nabline(z1,col=yan[1],lwd=1.5,lty=2)\nabline(z2,col=yan[2],lwd=1.5,lty=2)\nabline(z3,col=yan[3],lwd=1.5,lty=2)\nabline(z4,col=yan[4],lwd=1.5,lty=2)\n\nerror.bar(k1$ux,(k1$deva+k1$sdeva),(k1$stdd+k1$sdstd)/2,col=yan[1])\nerror.bar(k2$ux,(k2$deva+k2$sdeva),(k2$stdd+k2$sdstd)/2,col=yan[2])\nerror.bar(k3$ux,(k3$deva+k3$sdeva),(k3$stdd+k3$sdstd)/2,col=yan[3])\nerror.bar(k4$ux,(k4$deva+k4$sdeva),(k4$stdd+k4$sdstd)/2,col=yan[4])\n\nleg<-c(\"600Hz\",\"1KHz\",\"2KHz\",\"2.5KHz\")\n\nlegend(\"topright\",legend=leg,col=yan,pch=pcc,lwd=1.5,lty=2,inset=.02,\nbty=\"n\")\n\n# \u659c\u7387\u83b7\u53d6\nb11<-z1$coefficients[2] #b11 = 2.113 ,b1=2.13\nb2<-z2$coefficients[2]\nb3<-z3$coefficients[2]\nb4<-z4$coefficients[2]\n\nx<-b1/b11\n\na1<-z1$coefficients[1]\na2<-z2$coefficients[1]\na3<-z3$coefficients[1]\na4<-z4$coefficients[1]\n\nb<-c(b1,b2,b3,b4)\n\n\n# \u8fd1\u4f3c\u7684\u659c\u7387\u83b7\u53d6\nb1<-2.13\n\n\nkv2<-b1*0.6\n\nkv3<-b1*0.3\n\nkv4<-b1*0.24\n\nkv<-c(b1,kv2,kv3,kv4) # kv \u662f\u57fa\u4e8esd\u4e0b\u7684\u659c\u7387\u62df\u5408\n\nkv22<-kv2*x\nkv33<-kv3*x\nkv44<-kv4*x\n\nkv1<-c(b1,kv22,kv33,kv44) # kv1\u662f\u57fa\u4e8esd+d\u4e0b\u7684\u659c\u7387\u62df\u5408\n\n\n# \u753b\u659c\u7ebf\npar(fig=c(0.08,0.6,0.4,0.98),new=T)\n\nux<-c(0.6,1,2,2.5)\n\nplot(ux,b, col=0,bty=\"n\",xlab = expression(italic(f[\"p\"]) (KHz)),\n ylab = expression(italic(Slope)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(0.5,2.5),ylim=c(0,2.2),cex=0.8)\n\n lines(ux,b,col=\"red\",lwd=1.5,lty=2,pch=pcc[1],type=\"b\")\n lines(ux,kv,col=\"blue\",lwd=1.5,lty=2,pch=pcc[2],type=\"b\")\n lines(ux,kv1,col=\"black\",lwd=1.5,lty=2,pch=pcc[3],type=\"b\")\n\nleg1<-c(\"fit\",\"fit at Sd\",\"fit at Sd+d\")\n\nlegend(\"topright\",legend=leg1,col=c(\"red\",\"blue\",\"black\"),pch=c(0,1,2),lwd=1.5,lty=2,inset=.02,bty=\"n\")\n", "meta": {"hexsha": "78f7eae25f900c297665693ce712a01f134c6875", "size": 3041, "ext": "r", "lang": "R", "max_stars_repo_path": "print-chap7/Y/fig11_fit_kv.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "print-chap7/Y/fig11_fit_kv.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "print-chap7/Y/fig11_fit_kv.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.6754385965, "max_line_length": 104, "alphanum_fraction": 0.6333442946, "num_tokens": 1433, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6477982315512488, "lm_q1q2_score": 0.34915237719727826}} {"text": "# There are some config parameters that depends on the data it-self. In this file we are going to create the functions\n# that allow us to compute the best config parameter for Data Processing in the cellSizeDistribution step.\n\n\n# CalculateBarcodeInflections calculates an adaptive inflection point (\"knee\")\n# of the barcode distribution for each sample group. This is\n# useful for determining a threshold for removing low-quality\n# samples.\ngenerate_default_values_cellSizeDistribution <- function(seurat_obj, config) {\n # Q: should we check for precalculated values? e.g.:\n # is.null(Tool(seurat_obj, slot = \"CalculateBarcodeInflections\"))\n # Returns Seurat object with a new list in the `tools` slot,\n # `CalculateBarcodeInflections` including inflection point calculatio\n\n seurat_obj_tmp <- CalculateBarcodeInflections(\n object = seurat_obj,\n barcode.column = \"nCount_RNA\",\n group.column = \"orig.ident\",\n # [HARDCODED]\n threshold.low = 1e2,\n threshold.high = NULL\n )\n # extracting the inflection point value which can serve as minCellSize threshold\n # all other side effects to the scdate object will be discarded\n # [TODO unittest]: this calculation assumes a single sample, i.e. only one group in orig.ident!\n # otherwise it will return multiple values for each group!\n # returned is both the rank(s) as well as inflection point\n # orig.ident nCount_RNA rank\n # SeuratProject 1106 10722\n tmp <- Tool(seurat_obj_tmp, slot = \"CalculateBarcodeInflections\")$inflection_points\n # extracting only inflection point(s)\n return(tmp$nCount_RNA)\n}\n\ncellSizeDistribution_config <- function(seurat_obj, config) {\n \n minCellSize <- generate_default_values_cellSizeDistribution(seurat_obj, config)\n # update config\n config$filterSettings$minCellSize <- minCellSize\n\n return(config)\n}\n", "meta": {"hexsha": "65171bb9692d6880f045850a82fba9f8d3d761b5", "size": 2066, "ext": "r", "lang": "R", "max_stars_repo_path": "src/QC_helpers/cellSizeDistribution_config.r", "max_stars_repo_name": "biomage-ltd/data-ingest", "max_stars_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-23T17:41:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-10T20:50:49.000Z", "max_issues_repo_path": "src/QC_helpers/cellSizeDistribution_config.r", "max_issues_repo_name": "biomage-ltd/data-ingest", "max_issues_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2021-01-07T11:34:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-22T15:46:46.000Z", "max_forks_repo_path": "src/QC_helpers/cellSizeDistribution_config.r", "max_forks_repo_name": "biomage-ltd/data-ingest", "max_forks_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-10T23:17:30.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-10T23:17:30.000Z", "avg_line_length": 48.0465116279, "max_line_length": 118, "alphanum_fraction": 0.6737657309, "num_tokens": 434, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3491523698675708}} {"text": "############\r\n##Import Step\r\n###########\r\nlibrary(vegan)\r\nlibrary(MASS)\r\nlibrary(ggplot2)\r\nlibrary(plyr)\r\nlibrary(dplyr)\r\nlibrary(magrittr)\r\nlibrary(scales)\r\nlibrary(grid)\r\nlibrary(reshape2)\r\nlibrary(phyloseq)\r\nlibrary(randomForest)\r\nlibrary(knitr)\r\nlibrary(ggpubr)\r\nlibrary(multcompView)\r\nlibrary(lmerTest)\r\nlibrary(lme4)\r\nlibrary(tiff)\r\nlibrary(glmmTMB)\r\nlibrary(bbmle)\r\nlibrary(GGally)\r\nlibrary(nlme)\r\nlibrary(DHARMa)\r\nlibrary(patchwork)\r\n\r\nset.seed(45682)\r\n#DRIFT FILES all taxa\r\notufull=read.table(\"DataClean\\\\SturgeonDriftInvertAbundanceMatrix8.15.19.txt\",header=TRUE)\r\n#head(otufull)\r\nmetadata=read.csv(\"DataClean\\\\SturgeonDriftMetadata10.28.2020.csv\",header=TRUE)\r\nmetadata<-subset(metadata,Ninverts!=0) #Remove sample dates where inverts were not collected\r\nmetadata$PercentRiverDischargeSampled<-metadata$DischargeSampledByNight/metadata$Q*100\r\nmetadata$InvertsByDischargeSampled<-metadata$Ninverts100/metadata$DischargeSampledByNight\r\nmetadata$InvertsByRiverDischarge<-metadata$Ninverts100/metadata$Q\r\nmetadata$BiomassByRiverDischarge<- metadata$InvertBiomass100/metadata$Q\r\nmetadata$BiomassByDischargeSampled<-metadata$InvertBiomass100/metadata$DischargeSampledByNight\r\nmetadata$DriftInvertConc<-((metadata$Ninverts100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nmetadata$DriftBiomassConc<-((metadata$InvertBiomass100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate biomass/ 100 m3 water\r\nmetadata$SturgeonConc<-((metadata$Nsturgeon*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate sturgeon larvae/ 100 m3 water\r\nmetadata$SuckerConc<-((metadata$Nsuckers100*100)/(60*4*60*metadata$AreaSampled.m2.*metadata$AverageNetFlowByNight)) #Calculate sturgeon larvae/ 100 m3 water\r\n\r\n\r\n# \r\n# HistDischarge<-ggplot(metadata,aes(x=DischargeSampledByNight))+geom_histogram(fill=\"grey\",color=\"black\")+xlab(expression(Discharge~(m^3/sec)~Sampled))+ylab(\"Frequency\")#+facet_wrap(~Year)\r\n# HistDischarge\r\n# theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n# mean(metadata$DischargeSampledByNight,na.rm=T)\r\n# sd(metadata$DischargeSampledByNight,na.rm=T)\r\n# \r\n# dev.off()\r\n# tiff(\"Figures/Discharge_Sampled.tiff\", width = 3.3, height = 3.3, units = 'in', res = 800)\r\n# HistDischarge\r\n# dev.off()\r\n# \r\n# \r\n# HistQ<-ggplot(metadata,aes(x=Q))+geom_histogram(fill=\"grey\",color=\"black\")+xlab(expression(River~Discharge~(m^3/sec)))+ylab(\"Frequency\")#+facet_wrap(~Year)\r\n# HistQ\r\n# theme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n# \r\n# dev.off()\r\n# tiff(\"Figures/Discharge_Q.tiff\", width = 3.3, height = 3.3, units = 'in', res = 800)\r\n# HistQ\r\n# dev.off()\r\n\r\n\r\n#discharge sampled sd = 0.407179 mean = 0.9361\r\n#metadataOutliers\r\n\r\ntaxmatrixfull=as.matrix(read.table(\"DataClean\\\\SturgeonDriftInvertTaxNames8.15.19.txt\"))\r\n#head(taxmatrixfull)\r\n\r\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#000000\",\"#CC79A7\")\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n#sets the plotting theme\r\nOTU=otu_table(otufull, taxa_are_rows=TRUE)\r\n#OTU\r\nTAX=tax_table(taxmatrixfull)\r\ncolnames(TAX)=(\"Family\")\r\n\r\nsampdat=sample_data(metadata)\r\nsample_names(sampdat)=sampdat$SampleID\r\n#head(sampdat)\r\ntaxa_names(TAX)=row.names(OTU)\r\nphyseq=phyloseq(OTU,TAX,sampdat)#joins together OTU,TAX, and metadata \r\nphyseq\r\nlevels(sample_data(physeq)$MoonPhase)\r\nsample_data(physeq)$MoonPhase = factor(sample_data(physeq)$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n#levels(sample_data(physeq)$CODE)=c(\"NM\",\"WXC\",\"FQ\",\"WXG\",\"FM\",\"WAG\",\"LQ\",\"WNC\")\r\n\r\nRichness=plot_richness(physeq, x=\"DPFS\", measures=c(\"Shannon\"))#+geom_boxplot(aes(x=DPFS, y=value, color=DPFS), alpha=0.05)\r\n#Richness$data\r\n\r\nwrite.csv(Richness$data, \"SturgeonMetadataWDiversity.csv\")\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n#####################\r\n#General Result info\r\n####################\r\nAllData<-metadata\r\nAllData$Date2<-as.Date(AllData$Date,format= \"%d-%B\")\r\nhead(AllData)\r\nAllData$Year\r\nSamplesByYear <- ddply(AllData, c(\"Year\"), summarise,\r\n N = length(SampleID),\r\n Sturgeon = sum(Nsturgeon),\r\n Inverts5 = sum(Ninverts),\r\n Inverts100 = sum(Ninverts100),\r\n Catostomidae = sum(Nsuckers100)\r\n)\r\n\r\n\r\n\r\nSamplesByYear\r\nmean(SamplesByYear$N) #Average number of days per year= 30\r\nsum(SamplesByYear$N) #Total number of sampling days= 240\r\nsum(AllData$Ninverts) #Number of invertebrates IDed = 21356\r\nsum(AllData$Ninverts100) #100% numbers for inverts = 427,120\r\nsum(AllData$Nsturgeon) #Total number of sturgeon larvae collected = 108,674\r\nsum(AllData$Nsuckers100)#number of catostomidae larvae = 1,619,320\r\n\r\n\r\nmean(AllData$percillum) #48.96%\r\nmin(AllData$percillum) #0\r\nmax(AllData$percillum) #100\r\n\r\n\r\nmean(AllData$DPFS) #36.425\r\nmedian(AllData$DPFS) #36\r\nmin(AllData$DPFS) #min 15\r\nmax(AllData$DPFS) #max 68\r\n\r\n\r\nmean(AllData$Ninverts100) #1779.66\r\nmin(AllData$Ninverts100) #40\r\nmax(AllData$Ninverts100) #12340\r\nsd = sd(AllData$Ninverts100) #\r\nsd\r\nsd(AllData$Ninverts100) #1574.3\r\nsd / sqrt(length(AllData$Ninverts100))\r\nse #101.62\r\n\r\n\r\nmean(AllData$Nsturgeon) #452.8\r\nmin(AllData$Nsturgeon) #0\r\nmax(AllData$Nsturgeon) #16,897\r\nsd = sd(AllData$Nsturgeon) #\r\nsd(AllData$Nsturgeon)\r\n sd / sqrt(length(AllData$Nsturgeon)) #101,6\r\n\r\nmean(AllData$Nsuckers100) #6747.16\r\nmin(AllData$Nsuckers100) #0\r\nmax(AllData$Nsuckers100)# 185,380\r\n\r\nsd = sd(AllData$Nsuckers100) #\r\nsd / sqrt(length(AllData$Nsuckers100)) #1261.7\r\n\r\nhead(AllData)\r\n\r\nmean(AllData$InvertBiomass100) #96.4\r\nmin(AllData$InvertBiomass100) #0.59\r\nmax(AllData$InvertBiomass100)#85.52\r\n\r\n\r\n#RiverQ\r\nAllDataQ<-subset(AllData,Q!= \"NA\") #Remove sample dates where no flow data was collected (to calc se correctly using length)\r\nsd(metadata$PercentRiverDischargeSampled,na.rm=T)\r\nmean(AllDataQ$Q,na.rm=T) #8.165 m3/sec\r\nmin(AllDataQ$Q,na.rm=T) #4.035 m3/sec\r\nmax(AllDataQ$Q,na.rm=T) #16.83 m3/sec\r\nsd = sd(AllDataQ$Q,na.rm=T)\r\nse = sd / sqrt(length(AllDataQ$Q))\r\nsd #2.22\r\nse # 0.152\r\nhist(AllDataQ$Q,xlab = \"River Discharge (m3/sec)\")\r\nRiverDischargePlot<-ggplot(AllDataQ,aes(x=Q))+geom_histogram(binwidth = 1,fill=\"grey\",color=\"black\")+xlab(expression(River~Discharge~(m^3/~sec)))+ylab(\"Frequency\")\r\ndev.off()\r\ntiff(\"Figures/RiverDischarge.tiff\", width = 74, height = 74, units = 'mm', res = 1000)\r\nRiverDischargePlot\r\ndev.off()\r\n\r\nAllDataTemp<-subset(AllData,Temp!= \"NA\") #Remove sample dates where no temp data was collected (to calc se correctly using length)\r\n\r\nmean(AllDataTemp$Temp,na.rm=T) #19.03\r\nmin(AllDataTemp$Temp,na.rm=T) #12.22\r\nmax(AllDataTemp$Temp,na.rm=T) #23.34\r\nsd = sd(AllDataTemp$Temp,na.rm=T)\r\nse = sd / sqrt(length(AllDataTemp$Temp))\r\nsd #2.53\r\nse #0.165\r\n\r\n#Day of year sampled\r\nmin(AllData$DayOfYear) #130 April 17\r\nmax(AllData$DayOfYear) #186 May 9th \r\n\r\n#Percent River discharge sampled\r\nAllDataPercent<-subset(AllData,PercentRiverDischargeSampled!= \"NA\") #Remove sample dates where no flow data was collected (to calc se correctly using length)\r\n\r\nmean(AllDataPercent$PercentRiverDischargeSampled,na.rm=T) #12.44\r\nmin(AllDataPercent$PercentRiverDischargeSampled,na.rm=T) #4.89\r\nmax(AllDataPercent$PercentRiverDischargeSampled,na.rm=T) #45.31\r\nsd= sd(AllDataPercent$PercentRiverDischargeSampled, na.rm=T)\r\nse = sd / sqrt(length(AllDataPercent$PercentRiverDischargeSampled))\r\nsd #6.16\r\nse #0.433\r\n\r\nhead(AllData)\r\nggplot(AllData,aes(x=Date,y=InvertBiomass100))+geom_point()+geom_smooth()+ylab(\"Inverts Biomass (g)\")+xlab(\"Percent Illumination\")\r\nggplot(AllData,aes(x=Q,y=Ninverts100))+geom_point()+facet_wrap(~Year)+xlab(\"River Discharge (m3/sec)\")\r\nggplot(AllData,aes(x=Q,y=Ninverts100))+geom_point()+facet_wrap(~Year)+geom_smooth()+xlab(\"River Discharge (m3/sec)\")\r\nggplot(AllData,aes(x=Q,y=Ninverts100))+geom_point()+xlab(\"River Discharge (m3/sec)\")\r\n\r\nggplot(AllData,aes(x=DischargeSampledByNight,y=Ninverts100))+geom_point()+xlab(\"Discharge Sampled (m3/sec)\")+facet_wrap(~Year)\r\nggplot(AllData,aes(x=DischargeSampledByNight,y=InvertBiomass100))+geom_point()+xlab(\"Discharge Sampled (m3/sec)\")+geom_smooth()\r\n\r\nggplot(AllData,aes(x=DPFS,y=DischargeSampledByNight))+geom_point()+facet_wrap(~Year)\r\n\r\ntheme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\nAllDataGaps=read.csv(\"DataClean\\\\SturgeonDriftMetadata5.6.2020WGAPS.csv\",header=TRUE)#USE THIS FILE INSTEAD OF METADATA TO ADD BREAKS TO DISCHARGE LINE FOR GAPS IN DRIFT STURGEON COLLECTIONS\r\n\r\nSturgeonCTU<-ggplot(AllDataGaps,aes(x=CTUSturgeon))+geom_point(size=0.75,aes(y=Nsturgeon),shape=19)+geom_line(aes(y=Q/0.002),color=\"blue\")+scale_y_continuous(sec.axis = sec_axis(~.*0.002,name=expression(River~Discharge~(m^3/sec))))+xlab(\"Cumulative Thermal Units from First Spawn\")+facet_wrap(~Year,scale=\"free_y\")+ylab(\"Lake Sturgeon Larvae\")\r\nSturgeonCTU\r\ndev.off()\r\ntiff(\"Figures/SturgeonCTU.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nSturgeonCTU\r\ndev.off()\r\n\r\n\r\nggplot(AllData,aes(x=CTUSturgeon,y=Nsuckers100))+geom_point()+xlab(\"Cumulative Thermal Units\")+facet_wrap(~Year)+ylab(\"Sucker Larvae\")\r\n\r\nmin(AllData$Nsturgeon,na.rm=T)\r\nmax(AllData$Nsturgeon,na.rm=T)\r\n\r\nmin(AllData$Q,na.rm=T)\r\nmax(AllData$Q,na.rm=T)\r\n\r\ncor(AllData$DayOfYear,AllData$percillum,method=\"pearson\")\r\ncor(AllData$DayOfYear,AllData$CTUSturgeon,method=\"pearson\")\r\n\r\nggplot(AllData,aes(x=Date2,y=Ninverts100))+geom_bar(stat=\"identity\")+xlab(\"Date\")\r\n\r\nggplot(AllData,aes(x=DayOfYear,y=log(Ninverts100)))+geom_point()+xlab(\"Day of Year\")#+facet_wrap(~Year)\r\n\r\nggplot(AllData,aes(x=Date2,y=DayOfYear))+geom_point()+ facet_wrap(~Year)#geom_jitter()\r\n\r\nggplot(AllData,aes(x=DayOfYear,y=LunarDay))+geom_point()+ facet_wrap(~Year)#geom_jitter()\r\nAllData$MoonPhase = factor(AllData$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n\r\nggplot(AllData,aes(x=MoonPhase,y=percillum))+geom_point()+ facet_wrap(~Year)#geom_jitter()\r\nLightDate<-ggplot(AllData,aes(x=Date2,y=percillum))+geom_point()+ facet_wrap(~Year)+xlab(\"Date\")+ylab(\"Lunar Illumination (%)\")#geom_jitter()\r\nLightDate\r\nLightCTU<-ggplot(AllData,aes(x=CTUSturgeon,y=percillum))+geom_point()+ facet_wrap(~Year)+xlab(\"Cumulative Temperature Units (CTU)\")+ylab(\"Lunar Illumination (%)\")#geom_jitter()\r\nLightCTU\r\nAllData$Ctu\r\nggplot(AllData,aes(x=Date2,y=CTUSturgeon))+geom_point()+facet_wrap(~Year)\r\n\r\ndev.off()\r\ntiff(\"Figures/IllumByDate.tiff\", width = 174, height = 174, units = 'mm', res = 1200)\r\nLightDate\r\ndev.off()\r\n################\r\n#Total Abu graphs\r\n#################\r\n\r\nSturgeonDataFrame<-data.frame(\"Sturgeon\",ShannonRichness$DayOfYear, ShannonRichness$CTUSturgeon,ShannonRichness$Year,ShannonRichness$Nsturgeon,ShannonRichness$InvertsByDischargeSampled)\r\ncolnames(SturgeonDataFrame)<-c(\"Taxa\",\"DayOfYear\",\"CTUSturgeon\",\"Year\",\"Abundance\",\"Conc\")\r\n\r\nSuckerDataFrame<-data.frame(\"Catostomidae\",ShannonRichness$DayOfYear, ShannonRichness$CTUSturgeon,ShannonRichness$Year,ShannonRichness$Nsuckers100,ShannonRichness$SuckerConc)\r\ncolnames(SuckerDataFrame)<-c(\"Taxa\",\"DayOfYear\",\"CTUSturgeon\",\"Year\",\"Abundance\",\"Conc\")\r\n\r\nInvertDataFrame<-data.frame(\"Invertebrate\",ShannonRichness$DayOfYear,ShannonRichness$CTUSturgeon,ShannonRichness$Year,ShannonRichness$Ninverts100, ShannonRichness$SturgeonConc)\r\ncolnames(InvertDataFrame)<-c(\"Taxa\",\"DayOfYear\",\"CTUSturgeon\",\"Year\",\"Abundance\",\"Conc\")\r\n\r\nPlottingDataframe<-rbind(SturgeonDataFrame,SuckerDataFrame,InvertDataFrame)\r\nPlottingDataframe[is.na(PlottingDataframe)] <- 0 #Change NA values to 0 as no taxa were observed on those dates but sampling occured\r\n\r\nhead(PlottingDataframe)\r\nPlottingDataframe$Taxa = factor(PlottingDataframe$Taxa, levels = c(\"Invertebrate\",\"Sturgeon\",\"Catostomidae\"))\r\n\r\ntheme_set(theme_bw(base_size = 8)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\nTotalAbuAllPlot<-ggplot(PlottingDataframe, aes(x=DayOfYear, y= Abundance))+facet_grid(Taxa~.,scales = \"free_y\")+geom_point(size=1)+\r\n theme(legend.justification=c(1,0), legend.position=c(1,-0.01))+ylab(expression(Abundance~Per~100~m^3~Drift))+xlab(\"Calendar Date\")+\r\n scale_fill_manual(values=cbPalette)+scale_color_manual(values=cbPalette)+theme(legend.text = element_text(size = 7),legend.title = element_blank())+ \r\n theme(legend.background=element_blank())+ guides(shape = guide_legend(override.aes = list(size=2)))#+geom_line()\r\nTotalAbuAllPlot\r\n\r\n#######\r\n########Data by quantile ranks\r\n#######\r\n\r\nAllDataDPFS= mutate(AllData, quantile_rank = ntile(AllData$percillum,4))\r\nhead(AllDataDPFS)\r\nTrtdata <- ddply(AllDataDPFS, c(\"quantile_rank\"), summarise,\r\n N = length(Ninverts100),\r\n meanInverts = mean(Ninverts100),\r\n sd = sd(Ninverts100),\r\n se = sd / sqrt(N)\r\n)\r\nggplot(Trtdata,aes(x=quantile_rank,y=meanInverts))+geom_point()+xlab(\"Percent Illumination Quartiles\")+\r\n geom_errorbar(aes(ymin=meanInverts-se,ymax=meanInverts+se))+ylab(\"Invertebrates Per Night (+/- SE)\")\r\n\r\n\r\n\r\n\r\nShannonRichnessDischargeSampled = mutate(AllData, quantile_rank = ntile(AllData$DischargeSampledByNight,4))\r\nhead(ShannonRichnessDischargeSampled)\r\nTrtdata <- ddply(ShannonRichnessDischargeSampled, c(\"quantile_rank\"), summarise,\r\n N = length(Ninverts100),\r\n meanInverts = mean(Ninverts100),\r\n sd = sd(Ninverts100),\r\n se = sd / sqrt(N)\r\n)\r\nggplot(Trtdata,aes(x=quantile_rank,y=meanInverts))+geom_point()+xlab(\"Discharge Sampled Quartiles\")+\r\n geom_errorbar(aes(ymin=meanInverts-sd,ymax=meanInverts+sd))+ylab(\"Invertebrates Per Night (+/- 1 SD)\")\r\n\r\nTrtdata <- ddply(ShannonRichnessDischargeSampled, c(\"quantile_rank\"), summarise,\r\n N = length(InvertBiomass100),\r\n meanInverts = mean(InvertBiomass100),\r\n sd = sd(InvertBiomass100),\r\n se = sd / sqrt(N)\r\n)\r\nggplot(Trtdata,aes(x=quantile_rank,y=meanInverts))+geom_point()+xlab(\"Discharge Sampled Quartiles\")+\r\n geom_errorbar(aes(ymin=meanInverts-sd,ymax=meanInverts+sd))+ylab(\"Invertebrate Biomass Per Night (+/- 1 SD)\")\r\n############\r\n#Combined Summary plots\r\n###########\r\nAllData<-metadata\r\nSamplesByYear <- ddply(AllData, c(\"Year\"), summarise,\r\n N = length(SampleID),\r\n Sturgeon = sum(Nsturgeon),\r\n SturgeonByNight = mean(Nsturgeon),\r\n sdSturgeonByNight = sd(Nsturgeon),\r\n seSturgeonByNight = sdSturgeonByNight/sqrt(N),\r\n Inverts5 = sum(Ninverts),\r\n Inverts100 = sum(Ninverts100),\r\n InvertsByNight = mean(Ninverts100),\r\n sdInvertsByNight = sd(Ninverts100),\r\n seInvertsByNight = sdInvertsByNight/sqrt(N),\r\n CatostomidaeByNight = mean(Nsuckers100),\r\n sdSuckers = sd(Nsuckers100),\r\n seSuckers = sdSuckers/sqrt(N)\r\n)\r\n\r\nSamplesByYear\r\nSturgeonByYearByNight<- ggplot(SamplesByYear, aes(x=Year,y=SturgeonByNight))+geom_bar(stat=\"identity\",color=\"black\",fill = \"grey\")+ylab(\"Sturgeon Per Night (SE)\")+\r\n geom_errorbar(aes(ymin=SturgeonByNight-seSturgeonByNight,ymax=SturgeonByNight+seSturgeonByNight))\r\nInvertsByYearByNight<-ggplot(SamplesByYear, aes(x=Year,y=InvertsByNight))+geom_bar(stat=\"identity\",color=\"black\",fill = \"grey\")+ylab(\"Invertebrates Per Night (SE)\")+\r\n geom_errorbar(aes(ymin=InvertsByNight-seInvertsByNight,ymax=InvertsByNight+seInvertsByNight))\r\nSuckersByYearByNight<-ggplot(SamplesByYear, aes(x=Year,y=CatostomidaeByNight))+geom_bar(stat=\"identity\",color=\"black\",fill = \"grey\")+ylab(\"Catostomidae larvae Per Night (SE)\")+\r\n geom_errorbar(aes(ymin=CatostomidaeByNight-seSuckers,ymax=CatostomidaeByNight+seSuckers))\r\nSturgeonByYearByNight\r\nInvertsByYearByNight\r\nSuckersByYearByNight\r\n\r\nSturgeonByYear<- ggplot(SamplesByYear, aes(x=Year,y=Sturgeon))+geom_bar(stat=\"identity\",color=\"black\",fill = \"grey\")+ylab(\"Sturgeon larvae\")\r\nSturgeonByYear\r\nInvertsByYear<-ggplot(SamplesByYear, aes(x=Year,y=Inverts100))+geom_bar(stat=\"identity\",color=\"black\",fill = \"grey\")+ylab(\"Invertebrates collected\")\r\n\r\n\r\n\r\n\r\ntheme_set(theme_bw(base_size = 14)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/TotalsByYearCombined.tiff\", width = 6.85, height = 6.85, units = 'in', res = 300)\r\nggarrange(SturgeonByYear,InvertsByYear,SturgeonByYearByNight,InvertsByYearByNight,\r\n labels = c(\"a\", \"b\",\"c\",\"d\"),\r\n ncol = 2, nrow = 2)\r\ndev.off()\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\n#Sucker abu by day and year\r\nTrtdata <- ddply(AllData, c(\"DPFS\",\"Year\"), summarise,\r\n N = length(Nsuckers100),\r\n meanSuckers = mean(Nsuckers100)\r\n)\r\n#Trtdata\r\n\r\n\r\n\r\n\r\n\r\nSuckersByDPFS<-ggplot(Trtdata, aes(x=DPFS,y=meanSuckers))+geom_bar(colour=\"black\", stat=\"identity\")+xlab(\"Days Post First Spawning\")+ylab(\"Catostomidae Abundance\")+\r\n theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)\r\nSuckersByDPFS\r\n\r\ntheme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/SuckerAbundanceSupplementalFig.tiff\", width = 6.85, height = 4, units = 'in', res = 800)\r\nggarrange(SuckersByYearByNight,SuckersByDPFS,\r\n labels = c(\"a\", \"b\"),\r\n ncol = 2, nrow = 1)\r\ndev.off()\r\n#Sturgeon abu by day and year\r\nTrtdata <- ddply(AllData, c(\"DPFS\",\"Year\"), summarise,\r\n N = length(Nsuckers100),\r\n meanSturgeon = mean(Nsturgeon)\r\n)\r\n#Trtdata\r\nSturgeonByDPFS<-ggplot(Trtdata, aes(x=DPFS,y=meanSturgeon))+geom_bar(colour=\"black\", stat=\"identity\")+xlab(\"Days Post First Spawning\")+ylab(\"Larval Sturgeon Abundance\")+\r\n theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)\r\nSturgeonByDPFS\r\n\r\n#Invertebrate abu by day and year\r\nTrtdata <- ddply(AllData, c(\"DPFS\",\"Year\"), summarise,\r\n N = length(Ninverts100),\r\n meanInvert = mean(Ninverts100)\r\n)\r\n#Trtdata\r\nInvertByDPFS<-ggplot(Trtdata, aes(x=DPFS,y=meanInvert))+geom_bar(colour=\"black\", stat=\"identity\")+xlab(\"Days Post First Spawning\")+ylab(\"Invertebrate Abundance\")+\r\n theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)\r\nInvertByDPFS\r\n\r\ntheme_set(theme_bw(base_size = 9)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/SturgeonInvertByDPFSByYear.tiff\", width = 174, height = 174, units = 'mm', res = 1000)\r\nggarrange(SturgeonByYearByNight,InvertsByYearByNight,SturgeonByDPFS, InvertByDPFS,\r\n labels = c(\"a\", \"b\",\"c\",\"d\"),\r\n ncol = 2, nrow = 2)\r\ndev.off()\r\n#Sucker supplemental figure\r\ndev.off()\r\ntiff(\"Figures/SuckersCombinedByYear.tiff\", width = 174, height = 84, units = 'mm', res = 1000)\r\nggarrange(SuckersByYearByNight,SuckersByDPFS,\r\n labels = c(\"a\", \"b\"),\r\n ncol = 2, nrow = 1)\r\ndev.off()\r\n\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\n\r\n\r\n\r\n#####################\r\n#Alpha diversity\r\n####################\r\n#Shannon Richness\r\nhead(sample_data(physeq))\r\nRichness=plot_richness(physeq, x=\"DPFS\", measures=c(\"Shannon\"))#+geom_boxplot(aes(x=DPFS, y=value, color=DPFS), alpha=0.05)\r\nRichness+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+xlab(\"DPFS\")\r\n#Richness$data\r\n\r\nwrite.csv(Richness$data, \"SturgeonMetadataWDiversity.csv\")\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\nhead(ShannonRichness)\r\nlevels(ShannonRichness$MoonPhase)\r\nlevels(metadata$MoonPhase)\r\n#Days post first spawn\r\nTrtdata <- ddply(ShannonRichness, c(\"DPFS\"), summarise,\r\n N = length(value),\r\n meanShannon = mean(value),\r\n sd = sd(value),\r\n se = sd / sqrt(N)\r\n)\r\n#Trtdata\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\nggplot(Trtdata, aes(x=DPFS,y=meanShannon))+geom_bar(aes(),colour=\"black\", stat=\"identity\")+xlab(\"Days post first spawn\")+ylab(\"Shannon (SEM)\")+\r\n geom_errorbar(aes(ymin=meanShannon-se,ymax=meanShannon+se))\r\n\r\nhist(ShannonRichness$percillum)\r\nhist(log(ShannonRichness$Ninverts100))\r\nhist((ShannonRichness$Ninverts100))\r\n\r\n\r\n#ShannonSubset$percillum\r\nhist(ShannonRichness$Ninverts100)\r\nm1 = glmer(Ninverts100~1+(1|Year),data=ShannonRichness,family = poisson)\r\nm2 = glmer(Ninverts100~percillum+(1|Year),data=ShannonRichness,family = poisson)\r\n#m3 = glmer.nb(Ninverts100~DPFS+percillum+(1|Year),data=ShannonSubset)\r\n\r\nm1=glm(Ninverts100~percillum,data=ShannonRichness,family=poisson)\r\nsummary(m1)\r\n\r\n\r\nhead(ShannonRichness)\r\nAIC(m1,m2)\r\nsummary(m2)\r\nplot(Ninverts100~percillum,data=ShannonRichness)\r\nlines(predict(m2) ~ ShannonRichness$percillum)\r\n\r\nShannonRichness$MoonPhase = factor(ShannonRichness$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\nlevels(ShannonRichness$MoonPhase)\r\nAbuBoxplot<-ggplot(ShannonRichness, aes(x=MoonPhase,y=Ninverts100))+geom_boxplot()+scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))+\r\n theme(axis.text.x = element_text(angle = 45, hjust = 1))+ylab(\"Total Invertebrates Per Night\")+xlab(\"Moon Phase\")\r\n\r\ndev.off()\r\ntiff(\"Figures/AbuBoxplotByMoonPhase.tiff\", width = 3.3, height = 3.3, units = 'in', res = 800)\r\nAbuBoxplot\r\ndev.off()\r\n\r\nAbuDotplotDPFS<-ggplot(ShannonRichness, aes(x=DPFS,y=Ninverts100))+geom_point()+\r\n theme(axis.text.x = element_text(angle = 0, hjust = 0.5))+ylab(\"Total Invertebrates Per Night\")+xlab(\"Days Post First Spawning\")\r\nAbuDotplotDPFS\r\ndev.off()\r\ntiff(\"Figures/AbuDotplotByMoonPhase.tiff\", width = 3.3, height = 3.3, units = 'in', res = 800)\r\nAbuDotplotDPFS\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#By calendar date\r\nTrtdata <- ddply(ShannonRichness, c(\"DayOfYear\"), summarise,\r\n N = length(value),\r\n meanShannon = mean(value),\r\n sd = sd(value),\r\n se = sd / sqrt(N)\r\n)\r\n\r\nggplot(Trtdata, aes(x=DayOfYear,y=meanShannon))+geom_bar(colour=\"black\", stat=\"identity\")+xlab(\"Calendar date\")+ylab(\"Shannon (SEM)\")+\r\n geom_errorbar(aes(ymin=meanShannon-se,ymax=meanShannon+se))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))\r\n\r\n\r\n#Moon phase\r\nTrtdata <- ddply(ShannonRichness, c(\"MoonPhase\"), summarise,\r\n N = length(value),\r\n meanShannon = mean(value),\r\n sd = sd(value),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdata\r\nggplot(Trtdata, aes(x=MoonPhase,y=meanShannon))+geom_bar(aes(fill = MoonPhase),colour=\"black\", stat=\"identity\")\r\nggplot(Trtdata, aes(x=MoonPhase,y=meanShannon))+geom_bar(aes(fill = MoonPhase),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+ylab(\"Shannon Diversity (SEM)\")+\r\n geom_errorbar(aes(ymin=meanShannon-se,ymax=meanShannon+se))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+scale_fill_manual(values=cbPalette)+ theme(legend.position = \"none\")+\r\n geom_text(x=6.5,y=2,label=\"KW, Chi2=10.5, p= 0.158\")\r\n\r\nkruskal.test(value~MoonPhase,data=ShannonRichness)\r\n\r\nhist(ShannonRichness$value)\r\n\r\n\r\nggplot(ShannonRichness,aes(x=LunarDay,y=value))+geom_point()\r\n\r\n#Temperature \r\nggplot(ShannonRichness, aes(x=Temp,y=value))+geom_point()+xlab(\"Calendar date\")+ylab(\"Shannon (SEM)\")+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+\r\n geom_smooth()\r\nTemps<-subset(ShannonRichness, Temp!= \"NA\")\r\nTemps\r\nhead(Temps)\r\nmean(Temps$Temp)\r\nTemps$temp_centered = Temps$Temp - mean(Temps$Temp)\r\nplot(Temps$value~Temps$temp_centered,xlab=\"Change from mean temp (19.027)\", ylab=\"Shannon diversity\")+abline(1.504,0.02275)\r\n\r\nm = glm(value~ temp_centered, data=Temps)\r\nsummary(m)\r\nm2 = lmer(value~temp_centered+(1|Year),data=Temps)\r\nm1 = lmer(value~1+(1|Year),data=Temps)\r\n\r\nanova(m1,m2,test='Chisq')\r\nconfint(m2)\r\nsummary(m2)\r\n\r\nm4 = lmer(value~temp_centered*MoonPhase+(1|Year),data=Temps)\r\nm5 = lmer(value~temp_centered+MoonPhase+(1|Year),data=Temps)\r\nsummary(m2)\r\n\r\n\r\n\r\nAIC(m1,m3,m4,m5)\r\n\r\nm1=glm(value~temp_centered+Year,data=Temps)\r\n\r\nplot(residuals(m1)~Temp,data=Temps)\r\nplot(resid(m1) ~ predict(m1))\r\n\r\n\r\nm2 = lmer(value~DPFS+(1|Year),data=ShannonRichness,family)\r\nm1 = lmer(value~1+(1|Year),data=ShannonRichness)\r\nm1<-glm(value~temp_centered+percillum,data=Temps)\r\nanova(m1,m2,test='Chisq')\r\nconfint(m2)\r\nsummary(m2)\r\nhead(Temps)\r\n\r\nPercillum<-subset(ShannonRichness, percillum!= \"#VALUE!\")\r\n\r\nPercillum$ni\r\nplot(Ninverts100~percillum,data=Percillum)\r\nPercillum$percillum<-as.numeric(Percillum$percillum)\r\nm2<-(glm(value~percillum+Q,data=Percillum))\r\nm2\r\nplot(residuals(m2)~percillum,data=Percillum)\r\n\r\nconfint(m2)\r\nhist(Percillum$percillum)\r\nPercillum$percillum\r\n#Total abundance percent illum test\r\nm4 = glmer(Ninverts100~percillum+(1|Year),data=Percillum,family=\"poisson\")\r\nm5 = glmer(Ninverts100~1+(1|Year),data=Percillum,family=\"poisson\")\r\nanova(m4,m5,test='chisq')\r\nconfint(m4)\r\nexp(.01)\r\nm5 = glmer(value~temp_centered+MoonPhase+(1|Year),data=Temps,family=\"poisson\")\r\nsummary(m4)\r\nconfint(m4)\r\nsummary(glm(Ninverts100~percillum,data=Percillum))\r\n \r\n\r\n\r\n\r\n#By discharge\r\n\r\nShannonRichnessQ = mutate(ShannonRichness, quantile_rank = ntile(ShannonRichness$Q,4))\r\nhead(ShannonRichnessQ)\r\nTrtdata <- ddply(ShannonRichnessQ, c(\"quantile_rank\"), summarise,\r\n N = length(value),\r\n meanShannon = mean(value),\r\n sd = sd(value),\r\n se = sd / sqrt(N)\r\n)\r\n#Trtdata\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\nggplot(Trtdata, aes(x=quantile_rank,y=meanShannon))+geom_bar(aes(),colour=\"black\", stat=\"identity\")+xlab(\"Shannon Quartile\")+ylab(\"Shannon (SEM)\")+\r\n geom_errorbar(aes(ymin=meanShannon-se,ymax=meanShannon+se))\r\n\r\nhist(ShannonRichnessQ$value)\r\n\r\n####################\r\n##Total Abundance larval fish\r\n####################\r\n#Sturgeon\r\nggplot(ShannonRichness,aes(x=CTUSturgeon,y=Nsturgeon))+geom_point()#+geom_bar(aes(fill=DPFS),stat = \"identity\")\r\n\r\nSubsetCTU<-subset(ShannonRichness, CTUSturgeon < 500&CTUSturgeon>250)\r\nsum(SubsetCTU$Nsturgeon)/sum(ShannonRichness$Nsturgeon)*100\r\nmax(ShannonRichness$CTUSturgeon)\r\nSubsetDrift<-subset(ShannonRichness,Nsturgeon>0)\r\nSubsetDrift$Nsturgeon\r\nmin(SubsetDrift$CTUSturgeon)\r\nTrtdata <- ddply(ShannonRichness, c(\"DPFS\",\"Year\"), summarise,\r\n N = length(Nsturgeon),\r\n meanSturgeon = mean(Nsturgeon),\r\n sd = sd(Nsturgeon),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdata\r\n#Subset<-subset(Trtdata,Year==\"2018\"|Year==\"2017\"|Year==\"2016\")\r\n\r\nSturgeonByDPFS<-ggplot(Trtdata, aes(x=DPFS,y=meanSturgeon))+geom_bar(colour=\"black\", stat=\"identity\")+xlab(\"Days Post First Spawning\")+ylab(\"Larval Sturgeon Abundance\")+\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+facet_grid(Year~.)#+scale_fill_manual(values=cbPalette)\r\n\r\ndev.off()\r\ntiff(\"Figures/SturgeonByDPFSYear.tiff\", width = 3.3, height = 3.3, units = 'in', res = 300)\r\nSturgeonByDPFS\r\ndev.off()\r\n\r\n\r\n\r\n#SturgeonByMoonPhase\r\nSturgeonLarvaeSubset<-subset(ShannonRichness,Nsturgeon>0)\r\nTrtdata <- ddply(SturgeonLarvaeSubset, c(\"DayOfYear\",\"Year\",\"MoonPhase\"), summarise,\r\n N = length(Nsturgeon),\r\n meanSturgeon = mean(Nsturgeon),\r\n sd = sd(Nsturgeon),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdata\r\n\r\nggplot(Trtdata, aes(x=MoonPhase,y=meanSturgeon,color=MoonPhase))+geom_point()+xlab(\"DoY\")+ylab(\"Larval Sturgeon Abundance (SEM)\")+\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))#+facet_grid(~Year)#+scale_fill_manual(values=cbPalette)\r\n\r\nggplot(ShannonRichness, aes(x=CTUSturgeon,y=SturgeonConc))+geom_point()+xlab(\"DoY\")+ylab(\"Larval Sturgeon Abundance (SEM)\")#+\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_boxplot()#+facet_grid(~Year)#+scale_fill_manual(values=cbPalette)\r\n\r\n\r\n \r\nggplot(ShannonRichness, aes(x=CTUSturgeon,y=Nsuckers100))+geom_point()\r\nggplot(ShannonRichness, aes(x=Temp,y=Nsturgeon))+geom_point()\r\nggplot(ShannonRichness, aes(x=Temp,y=Ninverts100))+geom_point()\r\n\r\nhead(ShannonRichness)\r\ngam_y <- gam(log(Nsuckers100+10e-5) ~ s(CTUSturgeon)+s(percillum)+s(Q)+s(Year,bs=\"re\"), method = \"REML\",data=ShannonRichness)\r\ngam_x <- gam(log(Nsuckers100+10e-5) ~ s(CTUSturgeon)+s(Year,bs=\"re\")+s(percillum)+s(DischargeSampledByNight), method = \"REML\",data=ShannonRichness)\r\n#gam.check(gam_y)\r\nsummary(gam_y)\r\ngam.check(gam_y)\r\n\r\nacf(resid(gam_y), lag.max = 36, main = \"ACF\")\r\n\r\n\r\n\r\nlibrary(mgcViz)\r\nplot(gam_y)\r\n\r\n\r\ndat <- gamSim(6,n=200,scale=.2,dist=\"poisson\")\r\nb2 <- gamm(y~s(x0)+s(x1)+s(x2),family=poisson,\r\n data=dat,random=list(fac=~1))\r\n\r\n\r\nfac <- dat$fac\r\nShannonCatGamSubset<-subset(ShannonRichness, CTUSturgeon!=\"NA\")\r\nhead(ShannonCatGamSubset)\r\nfac<-ShannonRichness$Year\r\n\r\n\r\nShannonRichness$Nsuckers100\r\n\r\nSuckerGAM<-ggplot(ShannonRichness, aes(CTUSturgeon, (log(Nsuckers100)) )) + geom_point(color=\"grey\") +stat_smooth(method = \"gam\", formula = y ~ s(x),se=FALSE,color=\"black\")+\r\n stat_smooth(method = \"gam\", formula = (y) ~ s(x),geom=\"ribbon\",linetype=\"dashed\",fill=NA,color=\"black\",level = 0.95)+ylab(\"log(Larval Catostomidae Abundance)\")+xlab(\"Cumulative Temperature Units\")\r\nSuckerGAM$layers\r\n\r\n\r\n\r\nShannonRichness$CTUSturgeon\r\nSuckerAbuGamma <- gamm(((Nsuckers100+10e-5))~ s(CTUSturgeon), data = ShannonRichness, method = \"REML\",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = \"log\"))\r\nsummary(SuckerAbuGamma$gam)\r\ngam.check(SuckerAbuGamma$gam)\r\n\r\nfac<-ShannonRichness$Year\r\nhist(log(ShannonRichness$Nsuckers100))\r\nShannonRichness$Year<-as.factor(ShannonRichness$Year)\r\nSuckerAbuLog <- gam(log(Nsuckers100+10e-5)~ s(CTUSturgeon)+s(Year,bs=\"re\"), data = ShannonRichness, method = \"REML\",correlation=corAR1(form = ~DayOfYear|Year))\r\nsummary(SuckerAbuLog)\r\ngam.check(SuckerAbuLog)#default K doing ok no k significance, model residuals mostly ok around zero but a couple outliers around -10 to -15 (<5)\r\nappraise(SuckerAbuLog)\r\nresponse1 <- predict(SuckerAbuLog, type=\"response\", se.fit=T)\r\nhead(response1)\r\n\r\nplot(0,type=\"n\",xlim=c(150,800),ylim=c(-10,500))\r\nlines((smooth.spline(SuckerAbuLog$model$CTUSturgeon,response1$fit)),col=\"red\")\r\n\r\ndata<-smooth.spline(SuckerAbuLog$model$CTUSturgeon,response1$fit)\r\nhead(data)\r\nGAMConfints<-confint(SuckerAbuLog,parm=\"CTUSturgeon\",type=\"confidence\",nsim=1000)\r\nhead(GAMConfints)\r\nPredictedDataFrame<-data.frame(exp(GAMConfints$est),GAMMConfints$CTUSturgeon,exp(GAMMConfints$upper),exp(GAMMConfints$lower))\r\ncolnames(PredictedDataFrame)<-c(\"Nsuckers100\",\"CTUSturgeon\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\n\r\n\r\n\r\n\r\nplot(SuckerAbuLog)\r\n\r\n\r\n\r\n\r\n\r\n\r\nobserved_fitted_plot(\r\n SuckerAbuLog,\r\n ylab = NULL,\r\n xlab = NULL,\r\n title = NULL,\r\n subtitle = NULL,\r\n caption = NULL\r\n)\r\n\r\nlibrary(mgcViz)\r\n\r\nRandom<-ranef(SuckerAbuLog$lme)\r\nRandom$fac\r\n\r\nnewCTUDataata<-seq(min(ShannonRichness$CTUSturgeon, max(ShannonRichness$CTUSturgeon,1000)))\r\npredict(SuckerAbuLog,newCTUDataata)\r\nplot(SuckerAbuLog$gam)\r\n\r\n\r\nAICctab(SuckerAbuLog$lme,SuckerAbuGamma$lme)\r\nsummary(SuckerAbuLog$gam)\r\n\r\nnew.CTU = seq(min(ShannonRichness$CTUSturgeon),max(ShannonRichness$CTUSturgeon),length= 10000)\r\nSuckerAbuLog <- gam(log(Nsuckers100+10e-5)~ s(CTUSturgeon), data = ShannonRichness, method = \"REML\")\r\n\r\npredict(SuckerAbuLog,newdata=new.CTU,type=\"response\",se.fit=T)\r\n\r\n\r\nnewCTUData<-seq(min(ShannonRichness$CTUSturgeon, max(ShannonRichness$CTUSturgeon,length=1000)))\r\nmin(ShannonRichness$CTUSturgeon)\r\nhead(newCTUData)\r\nfac<-ShannonRichness$Year\r\nlibrary(mgcViz)\r\n\r\n\r\nlibrary(gratia)\r\nSuckerAbuLogViz <- gammV(log(Nsuckers100+10e-5)~ s(CTUSturgeon),random=list(fac=~1), data = ShannonRichness, method = \"REML\")\r\nsummary(SuckerAbuLogViz)\r\n\r\nSuckerAbuLog <- gamm((log(Nsuckers100+10e-5))~ s(CTUSturgeon), data = ShannonRichness, method = \"REML\")\r\n\r\nGAMMConfints<-confint(SuckerAbuGamma,parm=\"CTUSturgeon\",type=\"confidence\",nsim=1000)\r\nhead(GAMMConfints)\r\n\r\nPredictedDataFrame<-data.frame(exp(GAMMConfints$est-10e-5),GAMMConfints$CTUSturgeon,exp(GAMMConfints$upper),exp(GAMMConfints$lower))\r\ncolnames(PredictedDataFrame)<-c(\"Nsuckers100\",\"CTUSturgeon\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\n\r\nGAMMSucker<-ggplot(ShannonRichness,aes(CTUSturgeon,Nsuckers100))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)#+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(expression(Discharge~(m^3/sec)~Sampled))+ylab(\"Shannon Diversity\")#+ theme(axis.title.y = element_text(size = 9))\r\nGAMMSucker\r\n\r\nShannonSuckerSubset<-subset(ShannonRichness, Nsuckers100< 150000)\r\nSuckerAbuGamma <- gam((Nsuckers100+10e-5)~ s(CTUSturgeon)+s(Year,bs=\"re\"), data = ShannonRichness, method = \"REML\",correlation=corAR1(form = ~DayOfYear|Year),family= Gamma(link = \"log\"))\r\n\r\nappraise(SuckerAbuGamma)\r\n\r\n\r\n\r\n\r\nSuckerAbuGamma$residuals\r\nSuckerAbuGamma$aic\r\nhist(ShannonRichness$Nsuckers100)\r\n###########\r\n#Total InvertN By Moon Phase Normalized \r\n############\r\n\r\nModel<-aov(log(DriftInvertConc)~MoonPhase,data=ShannonRichness)\r\nsummary(Model)\r\nhist(resid(Model))\r\n\r\nkruskal.test(DriftInvertConc~MoonPhase, data=ShannonRichness)\r\nTukey<-TukeyHSD(Model,\"MoonPhase\")\r\n\r\nTukey\r\ndifference<-Tukey$MoonPhase[,\"p adj\"]\r\nLetters<-multcompLetters(difference)\r\n\r\nLetters\r\n \r\n# \r\n# \r\n# compare_means(DriftInvertConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n# \r\n# Means=compare_means(DriftInvertConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n# \r\n# Hyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\n# difference<-Means$p.adj\r\n# names(difference)<-Hyphenated\r\n# Letters<-multcompLetters(difference)\r\n# Letters\r\n# Letters$Letters\r\n# #manually renamed due to some weirdness with plotting where new moon donsn't start with a\r\n\r\n LettersRearranged<-c(\"bc\",\"abc\",\"abc\",\"a\",\"ab\",\"ab\",\"ab\",\"c\")\r\nDriftPlotNAsRemoved<-subset(ShannonRichness, DriftInvertConc!=\"NA\")\r\nDriftPlotNAsRemoved\r\nTrtdata <- ddply(DriftPlotNAsRemoved, c(\"MoonPhase\"), summarise,\r\n N = length(DriftInvertConc),\r\n meanSturgeon = mean(DriftInvertConc),\r\n sd = sd(DriftInvertConc),\r\n se = sd / sqrt(N),na.rm =T\r\n \r\n)\r\nTrtdata\r\n\r\nTrtdata$MoonPhase = factor(Trtdata$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n\r\nTotalInvertAbuMoonPhase<-ggplot(Trtdata, aes(x=MoonPhase,y=meanSturgeon))+geom_bar(aes(fill=MoonPhase),stat=\"identity\")+xlab(\"Moon Phase\")+ylab(expression(Invertebrates~Per~100~m^3~Drift~(SE)))+\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 0, hjust = 0.5),axis.title.y = element_text(size = 10))+scale_fill_manual(values=cbPalette)+\r\n geom_text(aes(x=MoonPhase, y=meanSturgeon+se+1,label=LettersRearranged))+theme(legend.position = \"none\")+geom_text(aes(x=5,y=25,label= \"ANOVA, F = 5.99, P < 0.001\"),size=4)+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))\r\nTotalInvertAbuMoonPhase\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/TotalInvertebrateAbuByPhaseNormalized.tiff\", width = 74, height = 74, units = 'mm', res = 1200)\r\nTotalInvertAbuMoonPhase\r\ndev.off()\r\n\r\n####################\r\n#Total invert biomass By Moon Phase\r\n#####################\r\nhead(ShannonRichness)\r\nTotalInvertBiomass<-subset(ShannonRichness, DriftBiomassConc!=\"NA\")\r\nhead(TotalInvertBiomass)\r\nTrtdata <- ddply(TotalInvertBiomass, c(\"MoonPhase\"), summarise,\r\n N = length(DriftBiomassConc),\r\n meanSturgeon = mean(DriftBiomassConc),\r\n sd = sd(DriftBiomassConc),\r\n se = sd / sqrt(N)\r\n)\r\nhead(Trtdata)\r\nTrtdata\r\nTrtdata$MoonPhase = factor(Trtdata$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n\r\n\r\nkruskal.test(DriftBiomassConc~MoonPhase, data=ShannonRichness)\r\n\r\ncompare_means(DriftBiomassConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\nMeans=compare_means(DriftBiomassConc ~ MoonPhase, data = ShannonRichness, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLetters<-multcompLetters(difference)\r\nLetters\r\nTrtdata\r\nvector<-c(\"bc\",\"bc\",\"abc\",\"a\",\"ab\",\"b\",\"ab\",\"c\")\r\nDriftTotalInvertBiomass<-ggplot(Trtdata, aes(x=MoonPhase,y=meanSturgeon))+geom_bar(aes(fill=MoonPhase),stat=\"identity\")+xlab(\"Moon Phase\")+ylab(expression(Invertebrate~Biomass~(g)~Per~100~m^3~Drift~(SE)))+#Invertebrate Biomass (g) per 100 m3 drift (SEM)\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ theme(axis.text.x = element_text(angle = 0, hjust = 0.5),axis.title.y = element_text(size = 8))+scale_fill_manual(values=cbPalette)+theme(legend.position = \"none\")+\r\n geom_text(aes(x=MoonPhase, y=meanSturgeon+se+.08,label=vector))+geom_text(aes(x=5,y=1.5,label= \"KW, chi-squared = 49.3, P < 0.001\"),size=3)+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))\r\nDriftTotalInvertBiomass\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/BiomassByPhase100m3.tiff\", width = 3.3, height = 3.3, units = 'in', res = 800)\r\nDriftTotalInvertBiomass\r\ndev.off()\r\n\r\n\r\n\r\n\r\nShannonRichness\r\n\r\n\r\n###############\r\n#Relative abundance invert families by moon phase\r\n######\r\nRelTaxa3Per = transform_sample_counts(physeq, function(x) x / sum(x) ) #transform samples based on relative abundance\r\nRelTaxa3Per = filter_taxa(RelTaxa3Per, function(x) mean(x) > 3e-2, TRUE) #filter out any taxa lower tha 0.1%\r\n\r\ndf <- psmelt(RelTaxa3Per)\r\ndf$Abundance<-df$Abundance*100\r\nhead(df)\r\nTrtdata <- ddply(df, c(\"MoonPhase\",\"Family\"), summarise,\r\n N = length(Abundance),\r\n mean = mean(Abundance),\r\n sd = sd(Abundance),\r\n se = sd / sqrt(N)\r\n)\r\n\r\nTrtdata\r\nSubsetTotalAbuFamilies<-subset(Trtdata,Family==\"Chironomidae\"|Family==\"Crayfish\"|Family==\"Ephemerillidae\"|Family==\"Heptageniidae\"|Family==\"Hydropsychidae\"|Family==\"Isonychiidae\"|Family==\"Leptoceridae\")\r\nSubsetTotalAbuFamilies\r\nwrite.csv(SubsetTotalAbuFamilies,file=\"FamilyLevelTotalAbundanceMoonPhase.csv\") #Write in other category sum to 100\r\nTrtdata<-read.csv(\"FamilyLevelTotalAbundanceMoonPhase.csv\", header=T)\r\nhead(Trtdata)\r\nTrtdata$MoonPhase = factor(Trtdata$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n#Trtdata\r\n\r\ncompare_means(Abundance ~ MoonPhase, data = df, group.by = \"Family\", p.adjust.method = \"fdr\",method=\"kruskal.test\")\r\nMeans<-compare_means(Abundance ~ MoonPhase, data = df, group.by = \"Family\", p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\nSigList<-length(unique(Trtdata$Family))\r\nfor (i in levels(Means$Family)){\r\n Tax<-i\r\n TaxAbundance<-subset(Means,Family==i )\r\n Hyphenated<-as.character(paste0(TaxAbundance$group1,\"-\",TaxAbundance$group2))\r\n difference<-TaxAbundance$p.adj\r\n names(difference)<-Hyphenated\r\n Letters<-multcompLetters(difference)\r\n #print(Letters)\r\n SigList[i]<-Letters\r\n \r\n}\r\nvec<-unlist(SigList)\r\nvec<-vec[-1]\r\n\r\n(vec)\r\n#For unadjusted p values\r\n# vector<-c(\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n# \"a\",\"ab\",\"ab\",\"b\",\"b\",\"ab\",\"a\",\"a\",\r\n# \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n# \"abc\",\"ab\",\"abc\",\"a\",\"abc\",\"c\",\"bc\",\"bc\",\r\n# \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n# \"a\",\"ab\",\"cd\",\"c\",\"bcd\",\"d\",\"abd\",\"a\",\r\n# \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\")\r\n# length(vec)\r\n# length(vector)\r\n\r\nTrtdataSorted<-Trtdata[order(Trtdata$Family),]\r\nhead(TrtdataSorted)\r\n\r\nKruskalLabel<- c(\"Kruskal-Wallis,\\n P-adj = 0.45\",\"KW, P-adj = 0.04\",\"KW, P-adj = 1\", \"KW, P-adj = 0.016\",\"KW, P-adj = 0.5\",\" P-adj <0.001\",\"KW, P-adj = 0.62\")\r\nFamilyRelativeAbu=ggplot(TrtdataSorted, aes(x=MoonPhase,y=mean))+geom_bar(aes(fill = Family),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+\r\n ylab(\"Relative Invertebrate Abundance (%, SEM)\") + theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se))+\r\n facet_wrap(Family~.)+scale_fill_manual(values=cbPalette)+theme(legend.position = \"none\")+ annotate(\"text\", label = KruskalLabel, size = 1.75, x = 4, y = 50)+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))\r\nFamilyRelativeAbu\r\ntheme_set(theme_bw(base_size = 8)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/FamiyLevelRelAbu.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nFamilyRelativeAbu\r\ndev.off()\r\n\r\n\r\n\r\n############\r\n#Total Abundance Family by Moon phase\r\n############\r\nDriftDataCombined<-read.csv(\"DataClean\\\\AllDriftDataCombined2011-2018FamilyRichness.csv\",header=T)\r\nhead(DriftDataCombined)\r\n\r\n\r\n#Isonychiidae\r\nkruskal.test(Isonychiidae~MoonPhase,data=DriftDataCombined)\r\nMeans<-compare_means(Isonychiidae ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersIso<-multcompLetters(difference)\r\nLettersIso\r\n#Rearrange letters so New moon letter = a instead of d\r\n#d=a, c=b,a=c, b=d\r\n\r\n\r\n#Heptageniidae\r\nkruskal.test(Heptageniidae~MoonPhase,data=DriftDataCombined)\r\nMeans<-compare_means(Heptageniidae ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersHep<-multcompLetters(difference)\r\nLettersHep\r\n\r\n\r\n#Crayfish\r\nkruskal.test(Crayfish~MoonPhase,data=DriftDataCombined)\r\nMeans<-compare_means(Crayfish ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersCray<-multcompLetters(difference)\r\nLettersCray\r\n\r\n#Hydropsychiidae\r\nkruskal.test(Hydropsychiidae~MoonPhase,data=DriftDataCombined)\r\nMeans<-compare_means(Hydropsychiidae ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersHyd<-multcompLetters(difference)\r\nLettersHyd\r\n\r\n#Graph\r\nphyseqSubset<-subset_taxa(physeq,Family==\"Chironomidae\"|Family==\"Crayfish\"|Family==\"Ephemerillidae\"|Family==\"Heptageniidae\"|Family==\"Hydropsychidae\"|Family==\"Isonychiidae\"|Family==\"Leptoceridae\")\r\nphyseqSubset\r\ndf <- psmelt(physeqSubset)\r\ndf$Abundance<-df$Abundance*20 #To get total number based on 5% subsample ID\r\nhead(df)\r\ndf$AbuPer100<-((df$Abundance*100)/(60*4*60*df$AreaSampled.m2.*df$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\ndf<-subset(df,AbuPer100!=\"NA\")\r\n\r\nhead(df)\r\nlevels(df$Family)\r\nFamiliesPer100m3<-compare_means(AbuPer100 ~ MoonPhase, data = df, group.by = \"Family\", p.adjust.method = \"fdr\",method=\"kruskal.test\")\r\nFamiliesPer100m3\r\n\r\nTrtdata <- ddply(df, c(\"MoonPhase\",\"Family\"), summarise,\r\n N = length(AbuPer100),\r\n mean = mean(AbuPer100),\r\n sd = sd(AbuPer100),\r\n se = sd / sqrt(N)\r\n)\r\n\r\nTrtdata\r\n\r\nTrtdata$MoonPhase = factor(Trtdata$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n\r\nLettersCray\r\nLettersHep\r\nLettersIso\r\n#Rearrange iso letters so New moon letter = a instead of d\r\n#d=a, c=b,a=c, b=d\r\nTrtdataSorted<-Trtdata[order(Trtdata$Family),]\r\nhead(TrtdataSorted)\r\nvector<-c(\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n\"a\",\"a\",\"ab\",\"b\",\"ab\",\"a\",\"a\",\"a\",\r\n\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\"a\",\r\n\"a\",\"ab\",\"cd\",\"d\",\"bcd\",\"bc\",\"abc\",\"abc\",\r\n\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\")\r\n\r\n\r\nlength(vector)\r\nlength(TrtdataSorted$N)\r\nKruskalLabel<- c(\"Kruskal-Wallis,\\n P-adj = 0.19\",\"KW, P-adj = 0.053\",\"KW, P-adj = 0.89\", \"KW, P-adj < 0.001\",\"KW, P-adj = 0.01\",\" P-adj < 0.001\",\"KW, P-adj = 0.17\")\r\n\r\ndat_text <- data.frame(\r\n label = c(\"Kruskal-Wallis,\\n P-adj = 0.19\",\"KW, P-adj = 0.053\",\"KW, P-adj = 0.89\", \"KW, P-adj < 0.001\",\"KW, P-adj = 0.01\",\" P-adj < 0.001\",\"KW, P-adj = 0.17\"),\r\n Family = c(\"Chironomidae\",\"Crayfish\",\"Ephemerillidae\",\"Heptageniidae\",\"Hydropsychidae\",\"Isonychiidae\",\"Leptoceridae\")\r\n)\r\nTrtdataSorted\r\n\r\nTopFamilyAbuPer100=ggplot(TrtdataSorted, aes(x=MoonPhase,y=mean))+geom_bar(aes(fill = Family),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+\r\n ylab(expression(Invertebrates~Per~100~m^3~Drift~(SE))) + theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se))+\r\n facet_wrap(Family~.)+scale_fill_manual(values=cbPalette)+theme(legend.position = \"none\")+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))+geom_text(aes(x=MoonPhase,y= mean+se+1.5),label=vector,size=1.6)\r\nTopFamilyAbuPer100\r\nTopFamilyAbuPer100<-TopFamilyAbuPer100+ geom_text(data=dat_text,size=2,mapping = aes(x = 4, y = 15, label = label))\r\nTopFamilyAbuPer100\r\n\r\ntheme_set(theme_bw(base_size = 9)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/FamilyLevelAbuPer100m3.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nTopFamilyAbuPer100\r\ndev.off()\r\n\r\n##############\r\n#Biomass By Family\r\n##############\r\n\r\nBiomassAverages<-read.table(\"DataClean\\\\BiomassFamilyAveragesR.txt\",header=T)\r\nhead(BiomassAverages)\r\nlength(BiomassAverages)\r\nDriftDataCombined<-read.csv(\"DataClean\\\\AllDriftDataCombined2011-2018FamilyRichness.csv\",header=T)\r\nhead(DriftDataCombined)\r\nhead(metadata)\r\notubiomass<-otufull\r\n\r\nrow.names(otubiomass)<-taxmatrixfull\r\n#Top famlies Rel Abu above 3% <-c(\"Chironomidae\",\"Crayfish\",\"Ephemerillidae\",\"Heptageniidae\",\"Hydropsychidae\",\"Isonychiidae\",\"Leptoceridae\")\r\n\r\notubiomasst<-t(otubiomass)\r\notubiomasst<-as.data.frame(otubiomasst)\r\notubiomasstAbove20 <- otubiomasst[,colSums(otubiomasst) > 20] #Remove families with Total N collected < 20\r\nhead(otubiomasstAbove20)\r\ncolnames(otubiomasstAbove20)[1]\r\n\r\n\r\nfor (i in colnames(otubiomasstAbove20)){\r\n Name<-factor(i)\r\n otubiomasstAbove20[i]<-otubiomasstAbove20[i]*sum(BiomassAverages[i])\r\n\r\n}\r\notubiomasstAbove20[i]<-otubiomasstAbove20[i]*sum(BiomassAverages[i])\r\n\r\nhead(otubiomasstAbove20)\r\n\r\n\r\nOTUBiomassObject<-as.matrix(t(otubiomasstAbove20))\r\nOTUBiomassObject\r\n\r\nTaxMatrixAbove20N<-as.matrix(colnames(otubiomasstAbove20))\r\nTaxMatrixAbove20N\r\n\r\n\r\nOTUBiomass=otu_table(OTUBiomassObject, taxa_are_rows=TRUE)\r\n#OTU\r\nTAXBiomass=tax_table(TaxMatrixAbove20N)\r\ncolnames(TAXBiomass)=(\"Family\")\r\n\r\nsampdat=sample_data(metadata)\r\nsample_names(sampdat)=sampdat$SampleID\r\n#head(sampdat)\r\ntaxa_names(TAXBiomass)=TaxMatrixAbove20N\r\nphyseqBiomass=phyloseq(OTUBiomass,TAXBiomass,sampdat)#joins together OTU,TAX, and metadata \r\nphyseqBiomass\r\nsample_data(physeqBiomass)$MoonPhase = factor(sample_data(physeqBiomass)$MoonPhase, levels = c(\"New Moon\",\"Waxing Crescent\",\"First Quarter\",\"Waxing Gibbous\",\"Full Moon\",\"Waning Gibbous\",\"Last Quarter\",\"Waning Crescent\"))\r\n\r\n\r\ndf <- psmelt(physeqBiomass)\r\ndf$Abundance<-df$Abundance*20# To account for 5% subsample used for ID\r\ndf$BiomassPer100<-((df$Abundance*100)/(60*4*60*df$AreaSampled.m2.*df$AverageNetFlowByNight)) #Calculate biomass/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nhead(df)\r\ndf$PercentageTotalBiomass<-df$BiomassPer100/df$DriftBiomassConc*100 #Percentage of total biomass for each family\r\n\r\ndfSubset<-subset(df,BiomassPer100!=\"NA\")\r\n\r\nBiomassPer100m3<-compare_means(BiomassPer100 ~ MoonPhase, data = df, group.by = \"Family\", p.adjust.method = \"fdr\",method=\"kruskal.test\")\r\nBiomassPer100m3\r\n\r\nsubset(BiomassPer100m3, p.adj < 0.05) #Isonychiidae, Heptageniidae, Hydropsychidae\r\n\r\nTrtdata <- ddply(dfSubset, c(\"Family\"), summarise,\r\n N = length(BiomassPer100),\r\n mean = mean(BiomassPer100))\r\nTrtdata \r\nTrtdataSorted<-Trtdata[order(-Trtdata$mean),]\r\nhead(TrtdataSorted) \r\n\r\n\r\nTrtdata <- ddply(dfSubset, c(\"MoonPhase\",\"Family\"), summarise,\r\n N = length(BiomassPer100),\r\n mean = mean(BiomassPer100),\r\n sd = sd(BiomassPer100),\r\n se = sd / sqrt(N),\r\n meanPercent = mean(PercentageTotalBiomass),\r\n sdPercent = sd(PercentageTotalBiomass),\r\n sePercent = sdPercent / sqrt(N)\r\n)\r\nTrtdata\r\noptions(scipen = 999)\r\nTrtdata\r\n\r\n\r\n\r\nAllFamilyBiomass=ggplot(Trtdata, aes(x=MoonPhase,y=mean))+geom_bar(aes(fill = Family),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+\r\n ylab(expression(Biomass~(g)~Per~100~m^3~Drift~(SE))) + theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se))+\r\n facet_wrap(Family~.)+theme(legend.position = \"none\")+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))#+ annotate(\"text\", label = KruskalLabel, size = 2, x = 4, y = .7)+scale_fill_manual(values=cbPalette)\r\nAllFamilyBiomass\r\ntheme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/AllFamilyBiomass.tiff\", width = 174, height = 174, units = 'mm', res = 1200)\r\nAllFamilyBiomass\r\ndev.off()\r\n\r\n\r\n#Percentage biomass\r\nAllFamilyBiomassPercent=ggplot(Trtdata, aes(x=MoonPhase,y=meanPercent))+geom_bar(aes(fill = Family),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+\r\n ylab(\"Relative Biomass (%, SE)\") + theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=meanPercent-sePercent,ymax=meanPercent+sePercent))+\r\n facet_wrap(Family~.)+theme(legend.position = \"none\")+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))#+ annotate(\"text\", label = KruskalLabel, size = 2, x = 4, y = .7)+scale_fill_manual(values=cbPalette)\r\nAllFamilyBiomassPercent\r\ntheme_set(theme_bw(base_size = 10)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/AllFamilyBiomassPercent.tiff\", width = 174, height = 174, units = 'mm', res = 1200)\r\nAllFamilyBiomassPercent\r\ndev.off()\r\n\r\n\r\n########\r\n#Biomass top families only\r\n###########\r\n#See section above for data/table formatting\r\n\r\n#\r\nTrtdata <- ddply(dfSubset, c(\"Family\"), summarise,\r\n N = length(BiomassPer100),\r\n mean = mean(BiomassPer100),\r\n sd = sd(BiomassPer100),\r\n se = sd / sqrt(N),\r\n meanPercent = mean(PercentageTotalBiomass),\r\n sdPercent = sd(PercentageTotalBiomass),\r\n sePercent = sdPercent / sqrt(N)\r\n)\r\n\r\nTrtdataSorted<-Trtdata[order(-Trtdata$meanPercent),]\r\nTrtdataSorted\r\n\r\nTop6BiomassFam<-(TrtdataSorted)[1:6,]\r\nsum(Top6BiomassFam$meanPercent) #81% of total biomass comes from top six families\r\nTop6BiomassFam\r\nBiomassPer100m3<-compare_means(BiomassPer100 ~ MoonPhase, data = df, group.by = \"Family\", p.adjust.method = \"fdr\",method=\"kruskal.test\")\r\n#BiomassPer100m3\r\nsubset(BiomassPer100m3,Family %in% Top6BiomassFam$Family)\r\nsubset(BiomassPer100m3, p.adj < 0.05) #Isonychiidae, Heptageniidae, Hydropsychidae\r\n\r\n\r\nTrtdata <- ddply(dfSubset, c(\"Family\",\"MoonPhase\"), summarise,\r\n N = length(BiomassPer100),\r\n mean = mean(BiomassPer100),\r\n sd = sd(BiomassPer100),\r\n se = sd / sqrt(N))\r\nTrtdataSubset<-subset(Trtdata,Family %in% Top6BiomassFam$Family)\r\nTrtdataSubset\r\n\r\n\r\n#Heptageniidae\r\n#kruskal.test(Heptageniidae~MoonPhase,data=DriftDataCombined) Use adj-p values\r\nMeans<-compare_means(Heptageniidae ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersHep<-multcompLetters(difference)\r\nLettersHep\r\n\r\n#Isonychiidae\r\n#kruskal.test(Isonychiidae~MoonPhase,data=DriftDataCombined) Use adj-p values\r\nMeans<-compare_means(Isonychiidae ~ MoonPhase, data = DriftDataCombined, p.adjust.method = \"fdr\")\r\nMeans\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersIso<-multcompLetters(difference)\r\nLettersIso\r\n\r\nTrtdataSubset<-TrtdataSubset[order(Trtdata$Family),]\r\nTrtdataSubset<-subset(TrtdataSubset,Family!=\"NA\")\r\n\r\n\r\nLettersHep\r\nLettersIso\r\nvector<-c(\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\",\r\n \"a\",\"a\",\"ab\",\"b\",\"ab\",\"a\",\"a\",\"a\",\r\n \"a\",\"ab\",\"cd\",\"d\",\"bcd\",\"bc\",\"abc\",\"abc\",\r\n \"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\")\r\n\r\n\r\ndat_text <- data.frame(\r\n label = c(\"Kruskal-Wallis,\\n P-adj = 0.2\",\"KW, P-adj = 0.89\",\"KW, P-adj = 0.55\", \"KW, P-adj < 0.001\", \" P-adj < 0.001\",\"KW, P-adj = 0.89\"),\r\n Family = c(\"Crayfish\",\"Ephemerillidae\",\"Gomphidae\",\"Heptageniidae\",\"Isonychiidae\",\"Lepidostomatidae\")\r\n)\r\nTopFamilyBiomass=ggplot(TrtdataSubset, aes(x=MoonPhase,y=mean))+geom_bar(aes(fill = Family),colour=\"black\", stat=\"identity\")+xlab(\"Moon Phase\")+\r\n ylab(expression(Biomass~(g)~Per~100~m^3~Drift~(SE))) + theme(axis.text.x = element_text(angle = 45, hjust = 1))+geom_errorbar(aes(ymin=mean-se,ymax=mean+se))+\r\n facet_wrap(Family~.)+theme(legend.position = \"none\")+\r\n scale_x_discrete(labels=c(\"New\",\"WXC\",\"FQ\",\"WXG\",\"Full\",\"WAG\",\"LQ\",\"WNC\"))+ geom_text(data=dat_text,size=2,mapping = aes(x = 4, y = 0.9, label = label))+scale_fill_manual(values=cbPalette)+\r\n geom_text(aes(x=MoonPhase,y= mean+se+.05),label=vector,size=1.6)\r\nTopFamilyBiomass\r\ntheme_set(theme_bw(base_size = 9)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/TopFamiliesBiomass.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nTopFamilyBiomass\r\ndev.off()\r\n\r\ncor(ShannonSubset$DPFS,ShannonSubset$percillum)\r\n\r\n\r\n\r\n\r\n\r\n\r\n###########\r\n#Beta diversity/Envfit\r\n##############\r\n\r\nphyseq\r\nord=ordinate(physeq,\"PCoA\", \"jaccard\")\r\nordplot=plot_ordination(physeq, ord,\"samples\", color=\"MoonPhase\")+scale_colour_manual(values=cbPalette)+scale_fill_manual(values=cbPalette)\r\nordplotTax<-ordplot+ stat_ellipse(type= \"norm\",geom = \"polygon\", alpha = 0, aes(fill = MoonPhase))+\r\n theme(legend.text = element_text(size = 8))+ theme(legend.background=element_blank())+geom_point(size=2.5)\r\nordplotTax\r\ndev.off()\r\ntiff(\"Figures/PCoATax.tiff\", width = 3.3, height = 3.3, units = 'in', res = 300)\r\nordplotTax\r\ndev.off()\r\n\r\n\r\n#Envfit\r\n#\r\n\r\nplot(value~DischargeSampledByNight,data=ShannonSubset)\r\nphyseqSubset<- subset_samples(physeq, percillum!=\"NA\")\r\nphyseqSubset\r\nphyseqSubset<- subset_samples(physeqSubset, DischargeSampledByNight !=\"NA\") #Remove samples without discharge info\r\n\r\nphyseqSubset<- subset_samples(physeqSubset, DischargeSampledByNight < 2) #Remove discharge outliers\r\n\r\nphyseqSubset\r\nGPdist=phyloseq::distance(physeqSubset, \"bray\")\r\n\r\nvare.mds= ordinate(physeqSubset, \"NMDS\",GPdist)\r\n#vare.mds <- metaMDS(VeganDist, trace = FALSE)\r\n#vare.mds\r\nmetadataEnvfitSubset<-subset(metadata,DischargeSampledByNight !=\"NA\"&DischargeSampledByNight < 2)\r\nEnvFitMeta=data.frame(metadataEnvfitSubset$percillum,metadataEnvfitSubset$DayOfYear,metadataEnvfitSubset$DischargeSampledByNight,metadataEnvfitSubset$Q)\r\n\r\nhead(EnvFitMeta)\r\ncolnames(EnvFitMeta)<-c(\"Percent \\n Illumination\",\"Day of Year\",\"Discharge\\n Sampled\",\"Q\")\r\n#EnvFitMeta <- EnvFitMeta[!is.na(EnvFitMeta)]\r\n#EnvFitMeta\r\nef =envfit(vare.mds, EnvFitMeta, na.rm=TRUE, permu=999)\r\n\r\n#cor(metadataEnvfitSubset$DischargeSampledByNight,metadataEnvfitSubset$Q)\r\nef\r\nplot(vare.mds,display=\"sites\")\r\nenvplot=plot(ef, p.max = 0.05)\r\n#ordisurf(ord ~ A1, data = dune.env, add = TRUE, knots = 1)\r\nenvplot\r\n\r\ndev.off()\r\ntiff(\"Figures/EnvfitPlot.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\npar(mar = c(4, 4, 0.5, 0.5))\r\nplot(vare.mds,display=\"sites\",cex=0.5)\r\nplot(ef, p.max = 0.05,cex=0.7)\r\n\r\n#mtext(\"NMDS2\", side = 2, line = 1, cex = 1)\r\n\r\nenvplot=plot(ef, p.max = 0.05,cex=0.7)\r\ndev.off()\r\n\r\n#Save the plot as a single object \r\ncolnames(EnvFitMeta)<-c(\"\",\"\",\"\",\"\")\r\nef =envfit(vare.mds, EnvFitMeta, na.rm=TRUE, permu=999)\r\n\r\ndev.off()\r\npdf(NULL)\r\ndev.control(displaylist = \"enable\")\r\npar(mar = c(4, 4, 0.5, 0.5))\r\nplot(vare.mds,display=\"sites\",cex=0.5)\r\nplot(ef, p.max = 0.05,cex=0.7)\r\np1<-recordPlot()\r\ndev.off()\r\np1\r\n\r\n#mtext(\"NMDS2\", side = 2, line = 1, cex = 1)\r\n\r\nenvplot=plot(ef, p.max = 0.05,cex=0.7)\r\n\r\n\r\n\r\nGPdist=phyloseq::distance(physeq, \"jaccard\")\r\nadonis(GPdist ~ Year, as(sample_data(physeq), \"data.frame\"))\r\n\r\n\r\nord=ordinate(physeq,\"PCoA\", \"jaccard\")\r\nordplot=plot_ordination(physeq, ord,\"samples\", color=\"MoonPhase\")+geom_point(size=4)+scale_colour_manual(values=cbPalette)+scale_fill_manual(values=cbPalette)\r\nordplot+ stat_ellipse(type= \"norm\",geom = \"polygon\", alpha = 1/4, aes(fill = MoonPhase))+ #theme(legend.justification=c(1,0), legend.position=c(1,0))+\r\n theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+facet_wrap(~Year)\r\nordplot\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n##########\r\n#LMM Invertebrate Abundance Model\r\n###########\r\n\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n\r\nShannonSubset<-ShannonRichness\r\nShannonSubset<-subset(ShannonSubset, Temp!= \"NA\")\r\n\r\nShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= \"NA\")\r\n\r\nShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers \r\nShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)\r\nShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)\r\nShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)\r\nShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)\r\nShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)\r\nShannonSubset$DoYCentered = ShannonSubset$DayOfYear - mean(ShannonSubset$DayOfYear)\r\n\r\nmin(ShannonSubset$DischargeSampledByNight,na.rm=T)\r\nmax(ShannonSubset$DischargeSampledByNight,na.rm=T)\r\nmean(ShannonSubset$DischargeSampledByNight,na.rm=T)\r\n\r\nmin(ShannonSubset$Q,na.rm=T)\r\nmax(ShannonSubset$Q,na.rm=T)\r\nmean(ShannonSubset$Q,na.rm=T)\r\n\r\n(ShannonSubset$Year)\r\n\r\nInvertsByDischargeSampledDL0 = lme(log(DriftInvertConc)~1,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nInvertsByDischargeSampledDL1 = lme(log(DriftInvertConc)~percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nInvertsByDischargeSampledDL2 = lme(log(DriftInvertConc)~temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nInvertsByDischargeSampledDL3 = lme(log(DriftInvertConc)~DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\n\r\nInvertsByDischargeSampledDL4 = lme(log(DriftInvertConc)~percillum+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nInvertsByDischargeSampledDL5 = lme(log(DriftInvertConc)~percillum+DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nInvertsByDischargeSampledDL6 = lme(log(DriftInvertConc)~temp_centered+DoYCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\n\r\nInvertsByDischargeSampledDL7 = lme(log(DriftInvertConc)~percillum+DoYCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DayOfYear|Year))\r\nplot(intervals(InvertsByDischargeSampledDL3))\r\nAICctab(InvertsByDischargeSampledDL0,InvertsByDischargeSampledDL1,InvertsByDischargeSampledDL2,InvertsByDischargeSampledDL3,InvertsByDischargeSampledDL4,InvertsByDischargeSampledDL5,InvertsByDischargeSampledDL6,InvertsByDischargeSampledDL7, weights=TRUE)\r\nsummary(InvertsByDischargeSampledDL3) #AIC weight = 0.68\r\n\r\n\r\n\r\n\r\nintervals(InvertsByDischargeSampledDL3)\r\n\r\n\r\n\r\nsummary(InvertsByDischargeSampledDL2) #AIC weight = 0.188\r\nsummary(InvertsByDischargeSampledDL6) #phi1=0.597\r\nsummary(InvertsByDischargeSampledDL0) #phi1=0.727\r\nsummary(InvertsByDischargeSampledDL5) #phi1=0.592\r\nsummary(InvertsByDischargeSampledDL4) #phi1=0.658\r\nsummary(InvertsByDischargeSampledDL1) #phi1=0.723\r\nsummary(InvertsByDischargeSampledDL7) #phi1=0.594\r\n\r\n\r\n\r\n###############\r\n#Invert abundance Effects of discharge\r\n################\r\n\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n\r\nShannonSubset<-ShannonRichness\r\nShannonSubset<-subset(ShannonSubset, Temp!= \"NA\")\r\n\r\nShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= \"NA\")\r\n\r\nShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers \r\nDischargeSubset<-subset(ShannonSubset, Q!=\"NA\")\r\nDischargeSubset$QCentered = DischargeSubset$Q - mean(DischargeSubset$Q)\r\nhead(DischargeSubset)\r\n\r\nDischargeModel = lme(log(Ninverts100)~QCentered,random=~1|Year,data=DischargeSubset,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(DischargeModel)\r\n\r\nintervals(DischargeModel,which=c(\"fixed\"))\r\n\r\n\r\n\r\n\r\n\r\nDischargeModel = lme(log(Ninverts100)~QCentered+DayOfYear,random=~1|Year,data=DischargeSubset,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(DischargeModel)\r\n\r\nDischargeSampledSubset<-subset(ShannonSubset, DischargeSampledByNight!=\"NA\")\r\nDischargeSampledSubset$DSPerNightCentered = DischargeSampledSubset$DischargeSampledByNight - mean(DischargeSampledSubset$DischargeSampledByNight)\r\n\r\n\r\nDischargeModel2 = lme(log(Ninverts100)~DSPerNightCentered,random=~1|Year,data=DischargeSampledSubset,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(DischargeModel2)\r\n\r\n\r\n\r\nintervals(DischargeModel2,which=c(\"fixed\"))\r\n\r\n\r\n\r\n\r\n###############\r\n#Best LMM Invertebrate Abundance Model\r\n#########\r\n\r\nplot(resid(InvertsByDischargeSampledDL3) ~ temp_centered, data=ShannonSubset)\r\nlines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$temp_centered), col=2)\r\n\r\nplot(resid(InvertsByDischargeSampledDL3) ~ percillum, data=ShannonSubset)\r\nlines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$percillum), col=2)\r\n\r\nplot(resid(InvertsByDischargeSampledDL3) ~ DayOfYear, data=ShannonSubset)\r\nlines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$DayOfYear), col=2)\r\n\r\n\r\nhist(resid(InvertsByDischargeSampledDL3))\r\nplot(resid(InvertsByDischargeSampledDL3) ~ DischargeSampledByNight, data=ShannonSubset)\r\nlines(lowess(resid(InvertsByDischargeSampledDL3) ~ ShannonSubset$DischargeSampledByNight), col=2)\r\n\r\nplot(resid(InvertsByDischargeSampledDL3) ~ predict(InvertsByDischargeSampledDL12))\r\nlines(lowess(resid(InvertsByDischargeSampledDL3) ~ predict(InvertsByDischargeSampledDL12)), col=2)\r\n\r\nsummary(InvertsByDischargeSampledDL3)\r\nconfint(InvertsByDischargeSampledDL3)\r\nfixef(InvertsByDischargeSampledDL3)\r\nranef(InvertsByDischargeSampledDL3)\r\ncoef(InvertsByDischargeSampledDL3)\r\n\r\n#Days Post First Spawning\r\nsummary(InvertsByDischargeSampledDL3)\r\n#FixedEffect DPFSCentered = -0.0325416\r\n#Intercept 2.2887841\r\n\r\nInvertsByDischargeSampledDL3 = lme(log(DriftInvertConc)~DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nintervals(InvertsByDischargeSampledDL3,which=c(\"fixed\"))\r\n\r\n\r\n#Lower est DPFS -0.04604928\r\n#Upper est DPFS -0.01903387\r\n\r\n\r\nInvertsByDischargeSampledDL3NoCor = lmer(log(DriftInvertConc)~DayOfYear+(1|Year),data=ShannonSubset)\r\nconfint(InvertsByDischargeSampledDL3NoCor)\r\n\r\nsamps<-sim(InvertsByDischargeSampledDL3NoCor,n.sim=1000)\r\n\r\n(quantile(samps@fixef[,'DayOfYear'],c(0.025,0.975)))\r\n\r\nhead(samps@fixef)\r\n\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"DayOfYear\"]*X\r\nnew.DPFS = seq(min(ShannonSubset$DayOfYear),max(ShannonSubset$DayOfYear),length= 10000)\r\nm.fixef.out<-sapply(new.DPFS,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\nhead(m.pred)\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.DPFS,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"DriftInvertConc\",\"DayOfYear\",\"UpperCI\",\"LowerCI\")\r\n#PredictedDataFrame$DPFS<-PredictedDataFrame$DayOfYear+mean(ShannonSubset$DayOfYear) #Remove centering on DPFS for plotting\r\nhead(PredictedDataFrame)\r\n\r\n#ggplot\r\nDPFSBestInvertModel<-ggplot(ShannonSubset,aes(DayOfYear,DriftInvertConc))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Calendar Date\")+ylab(expression(Invertebrates~Per~100~m^3~Drift))\r\nDPFSBestInvertModel\r\n\r\ndev.off()\r\ntiff(\"Figures/LMM_DPFS_BestNInvertModel.tiff\", width = 84, height = 84, units = 'mm', res = 1000)\r\nDPFSBestInvertModel\r\ndev.off()\r\n###############\r\n#Parameter estimates DPFS model invert abundance\r\n###################\r\n#Top model\r\nsummary(InvertsByDischargeSampledDL3)\r\nintervals(InvertsByDischargeSampledDL3,which=c(\"fixed\"))\r\n\r\n(exp(-0.03254157)-1)*100 #-3.20178% estimate DPFS\r\n(exp(-0.04604928)-1)*100 #-4.501% CI\r\n(exp(-0.01903387)-1)*100 #-1.885% CI\r\n\r\n#Second best model\r\nsummary(InvertsByDischargeSampledDL2)\r\nintervals(InvertsByDischargeSampledDL2,which=c(\"fixed\"))\r\n\r\n(exp(-0.1212155)-1)*100 #-11.415% estimate temp centered\r\n(exp(-0.1926145)-1)*100 #-17.52% CI\r\n(exp(-0.04981647)-1)*100 #-4.8595% CI\r\n\r\n\r\n\r\n######\r\n#InvertsByDischargePlotWith CI bootstrapping\r\n######\r\n\r\nInvertsByDischargeSampledDL3 = lme(log(DriftInvertConc)~DoYCentered,random=~1|Year,data=ShannonSubset,)#correlation=corAR1(form = ~DayOfYear|Year)\r\n\r\nxvals <- with(ShannonSubset,seq(min(DoYCentered),max(DoYCentered),length.out=100))\r\nnresamp<-1000\r\n## pick new parameter values by sampling from multivariate normal distribution based on fit\r\npars.picked <- mvrnorm(nresamp, mu = fixef(InvertsByDischargeSampledDL3), Sigma = vcov(InvertsByDischargeSampledDL3))\r\nhead(pars.picked)\r\n#pars.picked[,1]\r\n## predicted values: useful below\r\npframe <- with(ShannonSubset,data.frame(DoYCentered=xvals))\r\n#pframe\r\npframe$DriftInvertConc <- predict(InvertsByDischargeSampledDL3,newdata=pframe,level=0)\r\n\r\n## utility function\r\nget_CI <- function(y,pref=\"\") {\r\n r1 <- t(apply(y,1,quantile,c(0.025,0.975)))\r\n setNames(as.data.frame(r1),paste0(pref,c(\"lwr\",\"upr\")))\r\n}\r\nhead(pars.picked)\r\nhead(xvals)\r\nset.seed(101)\r\n\r\n\r\n\r\n\r\nhead(yvals)\r\nyvals <- apply(pars.picked,1,\r\n function(x) { SSasymp(xvals,x[1], x[2], x[3]) }\r\n)\r\n\r\nc1 <- get_CI(yvals)\r\n\r\n################3\r\n#Invert Abu Lmm temp plot\r\n################\r\n\r\nInvertsByDischargeSampledDL3NoCor = lmer(log(DriftInvertConc)~temp_centered+(1|Year),data=ShannonSubset)\r\nconfint(InvertsByDischargeSampledDL3NoCor)\r\n\r\nsamps<-sim(InvertsByDischargeSampledDL3NoCor,n.sim=1000)\r\n\r\n(quantile(samps@fixef[,'temp_centered'],c(0.025,0.975)))\r\n\r\nhead(samps@fixef)\r\n\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"temp_centered\"]*X\r\nnew.Temp = seq(min(ShannonSubset$temp_centered),max(ShannonSubset$temp_centered),length= 10000)\r\nm.fixef.out<-sapply(new.Temp,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\nhead(m.pred)\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.Temp,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"DriftInvertConc\",\"Temp\",\"UpperCI\",\"LowerCI\")\r\nPredictedDataFrame$Temp<-PredictedDataFrame$Temp+mean(ShannonSubset$Temp) #Remove centering on Temp for plotting\r\nhead(PredictedDataFrame)\r\n\r\n#ggplot\r\nTempBestInvertModel<-ggplot(ShannonSubset,aes(Temp,DriftInvertConc))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Temperature (\u00b0C)\")+ylab(expression(Macroinvertebrates~Per~100~m^3~Drift))\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\nTempBestInvertModel\r\n\r\ndev.off()\r\ntiff(\"Figures/LMM_Temp_BestNInvertModel.tiff\", width = 84, height = 84, units = 'mm', res = 1000)\r\nTempBestInvertModel\r\ndev.off()\r\n\r\n##########\r\n#Join together Figure 2\r\n##########\r\n\r\ndev.off()\r\ntiff(\"Figures/Figure2InvertAbundance.tiff\", width = 174, height = 174, units = 'mm', res = 1200)\r\nggarrange(DPFSBestInvertModel,TempBestInvertModel,TotalInvertAbuMoonPhase,HourlyInvertAbu,\r\n labels = c(\"a\", \"b\",\"c\",\"d\"),\r\n ncol = 2, nrow = 2)\r\ndev.off()\r\n\r\n\r\n\r\n\r\n#########\r\n#Models Invert Biomass standardized by Discharge Sampled\r\n#########\r\n\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n\r\nShannonSubset<-ShannonRichness\r\nShannonSubset<-subset(ShannonSubset, Temp!= \"NA\")\r\nShannonSubset<-subset(ShannonSubset, AverageNetFlowByNight!= \"NA\")\r\nShannonSubset<-subset(ShannonSubset,ShannonSubset$DischargeSampledByNight < 2) #Remove discharge sampled outliers \r\nShannonSubset$temp_centered = ShannonSubset$Temp - mean(ShannonSubset$Temp)\r\nShannonSubset$Qcentered = ShannonSubset$Q - mean(ShannonSubset$Q)\r\nShannonSubset$AverageFlowCentered = ShannonSubset$AverageNetFlowByNight - mean(ShannonSubset$AverageNetFlowByNight)\r\nShannonSubset$DischargeCentered = ShannonSubset$DischargeSampledByNight - mean(ShannonSubset$DischargeSampledByNight)\r\nShannonSubset$DPFSCentered = ShannonSubset$DPFS - mean(ShannonSubset$DPFS)\r\n\r\n\r\n\r\nlength(ShannonSubset$SampleID)\r\n\r\nhead(ShannonSubset)\r\nhist(ShannonSubset$DriftBiomassConc)\r\nhist(log(ShannonSubset$DriftBiomassConc))\r\n\r\nShannonSubset$percillumScale<-scale(ShannonSubset$percillum)\r\nShannonSubset$TempScale<-scale(ShannonSubset$Temp)\r\nShannonSubset$DischargeByNightScale<-scale(ShannonSubset$DischargeSampledByNight)\r\nShannonSubset$DPFSScale<-scale(ShannonSubset$DPFS)\r\n\r\n\r\n# \r\n# Biomass0 = lmer(DriftBiomassConc~1+(1|Year),data=ShannonSubset,REML=F)\r\n# Biomass1 = lmer(DriftBiomassConc~percillum+(1|Year),data=ShannonSubset,REML=F)\r\n# Biomass2 = lmer(DriftBiomassConc~temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\n# Biomass4 = lmer(DriftBiomassConc~DPFS+(1|Year),data=ShannonSubset,REML=F)\r\n# \r\n# Biomass5 = lmer(DriftBiomassConc~percillum+temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\n# Biomass6 = lmer(DriftBiomassConc~percillum+DPFS+(1|Year),data=ShannonSubset,REML=F)\r\n# Biomass10 = lmer(DriftBiomassConc~temp_centered+DPFS+(1|Year),data=ShannonSubset,REML=F)\r\n# \r\n# Biomass11 = lmer(DriftBiomassConc~temp_centered+DPFS+percillum+(1|Year),data=ShannonSubset,REML=F)\r\n# AICctab(Biomass0,Biomass1,Biomass2,Biomass4,Biomass5,Biomass6,Biomass10,Biomass11, weights=TRUE)\r\n# Log transforming doing a better job for biomass compared to linear model\r\n\r\n\r\nBiomassLog0 = lme(log(DriftBiomassConc)~1,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nBiomassLog1 = lme(log(DriftBiomassConc)~percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nBiomassLog2 = lme(log(DriftBiomassConc)~DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nBiomassLog3 = lme(log(DriftBiomassConc)~temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nBiomassLog4 = lme(log(DriftBiomassConc)~percillum+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nBiomassLog5 = lme(log(DriftBiomassConc)~percillum+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nBiomassLog6 = lme(log(DriftBiomassConc)~temp_centered+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nBiomassLog7 = lme(log(DriftBiomassConc)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\n\r\nAICctab(BiomassLog0,BiomassLog1,BiomassLog2,BiomassLog3,BiomassLog4,BiomassLog5,BiomassLog6,BiomassLog7, weights=TRUE) \r\nsummary(BiomassLog2)\r\nsummary(BiomassLog3)\r\n\r\n\r\n\r\nintervals(BiomassLog2,which=c(\"fixed\"))\r\n(exp(-0.0454973)-1)*100 #-4.45% estimate DPFS\r\n(exp(-0.06147474)-1)*100 #-5.96 CI\r\n(exp(-0.02951985)-1)*100 #-2.91% CI\r\n\r\n\r\n\r\nsummary(BiomassLog6)\r\n\r\nintervals(BiomassLog6,which=c(\"fixed\"))\r\n(exp(-0.03705362)-1)*100 #-3.63755 estimate DPFS\r\n(exp(-0.05652343)-1)*100 #-5.4955% CI\r\n(exp(-0.01758381)-1)*100 #-1.7739% CI\r\n\r\nintervals(BiomassLog6,which=c(\"fixed\"))\r\n(exp(-0.06976298)-1)*100 #-6.7385 estimate temp\r\n(exp(-0.16277468)-1)*100 #-15.02% CI\r\n(exp(0.02324872)-1)*100 #2.352% CI\r\n\r\nsummary(BiomassLog3) #t=-3.40\r\nintervals(BiomassLog3,which=c(\"fixed\"))\r\n(exp(-0.1497092)-1)*100 #-13.904% estimate temp only\r\n(exp(-0.2365605)-1)*100 #-21.07 CI\r\n(exp(-0.06285792)-1)*100 #-6.09% CI\r\n\r\n\r\ncor(ShannonSubset$DriftInvertConc,ShannonSubset$DriftBiomassConc) #Drift biomass and total inverts are highly correlated 0.9647\r\ncor(ShannonSubset$temp_centered,ShannonSubset$DPFSCentered) #Temp and discharge are relatively highlty correlated (0.4933)\r\n######################\r\n#Biomass LMM plot\r\n#####################\r\nBiomassLog2NoCor = lmer(log(DriftBiomassConc)~DPFSCentered+(1|Year),data=ShannonSubset)\r\n\r\nsamps <-sim(BiomassLog2NoCor,n.sims=10000)\r\n(quantile(samps@fixef[,'DPFSCentered'],c(0.025,0.975)))\r\n\r\nhead(samps@fixef)\r\n\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"DPFSCentered\"]*X\r\nnew.DPFS = seq(min(ShannonSubset$DPFSCentered),max(ShannonSubset$DPFSCentered),length= 10000)\r\nm.fixef.out<-sapply(new.DPFS,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\n\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.DPFS,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"DriftBiomassConc\",\"DPFS\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\n\r\nPredictedDataFrame$DPFS<- PredictedDataFrame$DPFS+mean(ShannonSubset$DPFS)#Remove centering of DPFS\r\nhead(PredictedDataFrame)\r\n\r\n#ggplot\r\nDPFSBestBiomassModel<-ggplot(ShannonSubset,aes(DPFS,DriftBiomassConc))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+\r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Days Post First Spawning\")+ylab(expression(Invertebrate~Biomass~(g)~Per~100~m^3~Drift))+ theme(axis.title.y = element_text(size = 9))\r\nDPFSBestBiomassModel\r\ndev.off()\r\ntiff(\"Figures/LMM_DPFS_BestBiomassModel.tiff\", width = 84, height = 84, units = 'mm', res = 1000)\r\nDPFSBestBiomassModel\r\ndev.off()\r\n\r\n###############\r\n#Discharge effects on invert biomass\r\n###############\r\nDischargeSubset<-subset(ShannonSubset, Q!=\"NA\")\r\nDischargeSubset$QCentered = DischargeSubset$Q - mean(DischargeSubset$Q)\r\n\r\nDischargeModel = lme(log(InvertBiomass100)~QCentered,random=~1|Year,data=DischargeSubset,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(DischargeModel)\r\n\r\n\r\n\r\nintervals(DischargeModel,which=c(\"fixed\"))\r\n(exp(0.04696164)-1)*100 #4.81 estimate River discharge\r\n(exp(-0.02652691)-1)*100 #-2.617% CI\r\n(exp(0.1204502)-1)*100 #12.80% CI\r\n\r\n\r\nDischargeModel = lme(log(InvertBiomass100)~DischargeCentered,random=~1|Year,data=DischargeSubset,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(DischargeModel)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n############\r\n#Best LMM model biomass diagnostic plots\r\n###########\r\n\r\n\r\n\r\nplot(resid(BiomassLog2) ~ DPFS, data=ShannonSubset)\r\nlines(lowess(resid(BiomassLog2) ~ ShannonSubset$DPFS), col=2)\r\n\r\n\r\nplot(resid(BiomassLog2) ~ temp_centered, data=ShannonSubset)\r\nlines(lowess(resid(BiomassLog2) ~ ShannonSubset$temp_centered), col=2)\r\n\r\n\r\nhist(resid(BiomassLog2))\r\nplot(resid(BiomassLog2) ~ DischargeSampledByNight, data=ShannonSubset)\r\nlines(lowess(resid(BiomassLog2) ~ ShannonSubset$DischargeSampledByNight), col=2)\r\n\r\nplot(resid(BiomassLog2) ~ predict(BiomassLog2))\r\nlines(lowess(resid(BiomassLog2) ~ predict(BiomassLog2)), col=2)\r\n\r\nsummary(BiomassLog2)\r\nconfint(BiomassLog2)\r\nfixef(BiomassLog2)\r\nranef(BiomassLog2)\r\n\r\n\r\n############\r\n#Join Together Figure 4\r\n############\r\n\r\nDPFSBestBiomassModel\r\nDriftTotalInvertBiomass\r\nDPFSBestRichnessModel\r\nlibrary(gridGraphics)\r\n\r\ndev.off()\r\ntiff(\"Figures/Figure4Biomass.tiff\", width = 6.85, height = 6.85, units = 'in', res = 1000)\r\nggarrange(DPFSBestBiomassModel,DriftTotalInvertBiomass,DPFSBestRichnessModel,p1,\r\n labels = c(\"a\", \"b\",\"c\",\"d\"),\r\n ncol = 2, nrow = 2)\r\ndev.off()\r\n\r\n\r\n\r\n#################\r\n#LMM models top families\r\n############\r\nFamilyLMMData<-psmelt(physeq)\r\nhead(FamilyLMMData)\r\n\r\nFamilyLMMData<-subset(FamilyLMMData, Temp!= \"NA\")\r\nFamilyLMMData<-subset(FamilyLMMData, AverageNetFlowByNight!= \"NA\")\r\nFamilyLMMData<-subset(FamilyLMMData,FamilyLMMData$DischargeSampledByNight < 2) #Remove discharge sampled outliers \r\nFamilyLMMData$temp_centered = FamilyLMMData$Temp - mean(FamilyLMMData$Temp)\r\nFamilyLMMData$Qcentered = FamilyLMMData$Q - mean(FamilyLMMData$Q)\r\nFamilyLMMData$AverageFlowCentered = FamilyLMMData$AverageNetFlowByNight - mean(FamilyLMMData$AverageNetFlowByNight)\r\nFamilyLMMData$DischargeCentered = FamilyLMMData$DischargeSampledByNight - mean(FamilyLMMData$DischargeSampledByNight)\r\nFamilyLMMData$DPFSCentered = FamilyLMMData$DPFS - mean(FamilyLMMData$DPFS)\r\nhead(FamilyLMMData)\r\n\r\n\r\n#########\r\n#Crayfish model\r\n#########\r\nCrayfishAbu<-subset(FamilyLMMData,Family==\"Crayfish\")\r\nhead(CrayfishAbu)\r\nCrayfishAbu$Abundance<-CrayfishAbu$Abundance*20 #Account for 5% sampling\r\nCrayfishAbu$CrayfishPer100<-((CrayfishAbu$Abundance*100)/(60*4*60*CrayfishAbu$AreaSampled.m2.*CrayfishAbu$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nCrayfishAbu$SturgeonPer100<-((CrayfishAbu$Nsturgeon*100)/(60*4*60*CrayfishAbu$AreaSampled.m2.*CrayfishAbu$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nCrayfishAbu$SuckerPer100<-((CrayfishAbu$Nsuckers100*100)/(60*4*60*CrayfishAbu$AreaSampled.m2.*CrayfishAbu$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\n\r\n\r\nhist(CrayfishAbu$CrayfishPer100)\r\nhist(log(CrayfishAbu$CrayfishPer100))\r\nCrayfishAbu$percillumScale<-scale(CrayfishAbu$percillum)\r\nCrayfishAbu$TempScale<-scale(CrayfishAbu$Temp)\r\nCrayfishAbu$DischargeByNightScale<-scale(CrayfishAbu$DischargeSampledByNight)\r\nCrayfishAbu$DPFSScale<-scale(CrayfishAbu$DPFS)\r\n\r\n\r\nhead(CrayfishAbu)\r\nCrayfishLog0 = lme(log(CrayfishPer100+10e-5)~1,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nCrayfishLog1 = lme(log(CrayfishPer100+10e-5)~percillum,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nCrayfishLog2 = lme(log(CrayfishPer100+10e-5)~DPFSCentered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n# CrayfishLog2Poly = lme((CrayfishPer100)~poly(DPFS,2),random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n# summary(CrayfishLog2Poly)\r\n\r\n\r\n\r\nCrayfishLog3 = lme(log(CrayfishPer100+10e-5)~temp_centered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nCrayfishLog4 = lme(log(CrayfishPer100+10e-5)~percillum+temp_centered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\nCrayfishLog5 = lme(log(CrayfishPer100+10e-5)~percillum+DPFSCentered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\nCrayfishLog6 = lme(log(CrayfishPer100+10e-5)~temp_centered+DPFSCentered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nCrayfishLog7 = lme(log(CrayfishPer100+10e-5)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\nAICctab(CrayfishLog0,CrayfishLog1,CrayfishLog2,CrayfishLog3,CrayfishLog4,CrayfishLog5,CrayfishLog6,CrayfishLog7, weights=TRUE)\r\n\r\nplot(resid(CrayfishLog2) ~ DPFS, data=CrayfishAbu)\r\nlines(lowess(resid(CrayfishLog2) ~ CrayfishAbu$DPFS), col=2)\r\n\r\nplot(resid(CrayfishLog2) ~ predict(CrayfishLog2Poly))\r\nlines(lowess(resid(CrayfishLog2) ~ predict(CrayfishLog2)), col=2)\r\n\r\n\r\nCrayfishGAM<-ggplot(CrayfishAbu, aes(DPFS, CrayfishPer100) ) + geom_point(color=\"grey\") +stat_smooth(method = \"gam\", formula = y ~ s(x),se=FALSE,color=\"black\")+\r\n stat_smooth(method = \"gam\", formula = y ~ s(x),geom=\"ribbon\",linetype=\"dashed\",fill=NA,color=\"black\",level = 0.95)+ylab(expression(Crayfish~Per~100~m^3~Drift))\r\n\r\nCrayfishGAM\r\n\r\ndev.off()\r\ntiff(\"Figures/LMM_DPFSCrayfishGAM.tiff\", width = 84, height = 84, units = 'mm', res = 600)\r\nCrayfishGAM\r\ndev.off()\r\n\r\nhead(CrayfishAbu)\r\n\r\nlibrary(tidymv)\r\nlibrary(mgcv)\r\ngam_y <- gam(log(CrayfishPer100+10e-5) ~ s(DPFS)+s(Year,bs=\"re\")+s(DischargeCentered)+s(percillum), method = \"REML\",data=CrayfishAbu)\r\n#gam.check(gam_y)\r\nsummary(gam_y)\r\n\r\nlibrary(mgcViz)\r\nplot(gam_y)\r\n\r\ngam_yViz <- getViz(gam_y)\r\no <- plot( sm(gam_yViz, 1) )\r\no+ l_fitLine(colour = \"red\") + l_rug(mapping = aes(x=x, y=y), alpha = 0.8) +\r\n l_ciLine(mul = 5, colour = \"blue\", linetype = 2) + \r\n l_points(shape = 19, size = 1, alpha = 0.1) + theme_classic()\r\n\r\nqq(gam_yViz, method = \"simul1\", a.qqpoi = list(\"shape\" = 1), a.ablin = list(\"linetype\" = 2))\r\n\r\nm2 = glmer(Abundance~DPFSCentered+(1|Year),data=CrayfishAbu,family = poisson)\r\n\r\nmodel_p <- predict_gam(gam_y)\r\nmodel_p\r\n\r\nmodel_p %>%\r\n ggplot(aes(DPFS, fit))\r\n\r\ncheck(gam_yViz,\r\n a.qq = list(method = \"tnorm\", \r\n a.cipoly = list(fill = \"light blue\")), \r\n a.respoi = list(size = 0.5), \r\n a.hist = list(bins = 10))\r\n\r\n\r\n\r\n\r\nCrayfishAbu$PresenceSuckers <- 0\r\nCrayfishAbu$PresenceSturgeon <- 0\r\n\r\n\r\nCrayfishAbu$PresenceSuckers[CrayfishAbu$Nsuckers100 > 0] <- 1\r\nCrayfishAbu$PresenceSturgeon[CrayfishAbu$NSturgeon > 0] <- 1\r\nCrayfishSucker = lme(log(CrayfishPer100+10e-5)~SuckerPer100,random=~1|Year,data=CrayfishAbu,correlation=corAR1(form = ~DPFS|Year))\r\nsummary(CrayfishSucker)\r\n#\r\n######\r\n#Isonychiidae abundance model\r\n########\r\nFamilyLMMData<-psmelt(physeq)\r\nhead(FamilyLMMData)\r\n\r\nFamilyLMMData<-subset(FamilyLMMData, Temp!= \"NA\")\r\nFamilyLMMData<-subset(FamilyLMMData, AverageNetFlowByNight!= \"NA\")\r\nFamilyLMMData<-subset(FamilyLMMData,FamilyLMMData$DischargeSampledByNight < 2) #Remove discharge sampled outliers \r\nFamilyLMMData$temp_centered = FamilyLMMData$Temp - mean(FamilyLMMData$Temp)\r\nFamilyLMMData$Qcentered = FamilyLMMData$Q - mean(FamilyLMMData$Q)\r\nFamilyLMMData$AverageFlowCentered = FamilyLMMData$AverageNetFlowByNight - mean(FamilyLMMData$AverageNetFlowByNight)\r\nFamilyLMMData$DischargeCentered = FamilyLMMData$DischargeSampledByNight - mean(FamilyLMMData$DischargeSampledByNight)\r\nFamilyLMMData$DPFSCentered = FamilyLMMData$DPFS - mean(FamilyLMMData$DPFS)\r\nhead(FamilyLMMData)\r\nIsoAbu<-subset(FamilyLMMData,Family==\"Isonychiidae\")\r\n\r\nIsoAbu$Abundance<-IsoAbu$Abundance*20 #Account for 5% subsample\r\n\r\nhist(IsoAbu$Abundance)\r\nhist(log(IsoAbu$Abundance))\r\nIsoAbu$IsoPer100<-((IsoAbu$Abundance*100)/(60*4*60*IsoAbu$AreaSampled.m2.*IsoAbu$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nhist(IsoAbu$IsoPer100)\r\nhist(log(IsoAbu$IsoPer100))\r\n\r\n\r\nIsoLog0 = lme(log(IsoPer100+10e-5)~1,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nIsoLog1 = lme(log(IsoPer100+10e-5)~percillum,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\nIsoLog2 = lme(log(IsoPer100+10e-5)~DPFSCentered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\nIsoLog3 = lme(log(IsoPer100+10e-5)~temp_centered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nIsoLog4 = lme(log(IsoPer100+10e-5)~percillum+temp_centered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\nIsoLog5 = lme(log(IsoPer100+10e-5)~percillum+DPFSCentered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\nIsoLog6 = lme(log(IsoPer100+10e-5)~temp_centered+DPFSCentered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nIsoLog7 = lme(log(IsoPer100+10e-5)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=IsoAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\n\r\nAICctab(IsoLog0,IsoLog1,IsoLog2,IsoLog3,IsoLog4,IsoLog5,IsoLog6,IsoLog7, weights=TRUE)\r\n\r\n\r\nsummary(IsoLog2)\r\nsummary(IsoLog1)\r\n\r\n\r\n\r\n# summary(L6)\r\n# summary(n6)\r\n\r\nplot(resid(IsoLog2) ~ DPFS, data=IsoAbu)\r\nlines(lowess(resid(IsoLog2) ~ IsoAbu$DPFS), col=2)\r\n\r\n\r\n\r\nplot(resid(IsoLog2) ~ temp_centered, data=IsoAbu)\r\nlines(lowess(resid(IsoLog2) ~ IsoAbu$temp_centered), col=2)\r\n\r\nplot(resid(IsoLog2) ~ percillum, data=IsoAbu)\r\nlines(lowess(resid(IsoLog2) ~ IsoAbu$percillum), col=2)\r\n\r\nintervals(IsoLog2,which=c(\"fixed\"))\r\n(exp(-0.08181913)-1)*100 #-7.85614% estimate DPFS\r\n\r\n(exp(-0.1138543)-1)*100 #-10.7612% CI\r\n(exp(-0.04978396)-1)*100 #-4.856505% CI \r\n#DPFS\r\n\r\nIsoLog2NoCor = lmer(log(IsoPer100+10e-5)~DPFSCentered+(1|Year),data=IsoAbu)\r\n\r\nsamps <-sim(IsoLog2NoCor,n.sims=10000)\r\n\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"DPFSCentered\"]*X\r\nnew.DPFS = seq(min(IsoAbu$DPFSCentered),max(IsoAbu$DPFSCentered),length= 1000)\r\nm.fixef.out<-sapply(new.DPFS,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\nhead(m.pred)\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.DPFS,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"IsoPer100\",\"DPFS\",\"UpperCI\",\"LowerCI\")\r\n\r\nPredictedDataFrame$DPFS<-PredictedDataFrame$DPFS+mean(IsoAbu$DPFS) #Remove centering for plotting\r\n#Remove the +1 from the effect for plotting\r\n# PredictedDataFrame$IsoPer100<-PredictedDataFrame$IsoPer100-1\r\n# PredictedDataFrame$UpperCI<-PredictedDataFrame$UpperCI-1\r\n# PredictedDataFrame$LowerCI<-PredictedDataFrame$LowerCI-1\r\nhead(PredictedDataFrame)\r\nhead(IsoAbu)\r\n#ggplot\r\nDPFSBestIsoModel<-ggplot(IsoAbu,aes(DPFS,IsoPer100))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Days post first spawning\")+ylab(expression(Isonychiidae~Per~100~m^3~Drift))\r\nDPFSBestIsoModel\r\ndev.off()\r\ntiff(\"Figures/LMM_DPFSIsoModel.tiff\", width = 84, height = 84, units = 'mm', res = 1000)\r\nDPFSBestIsoModel\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n##############\r\n#Heptageniidae model\r\n############\r\nHepAbu<-subset(FamilyLMMData,Family==\"Heptageniidae\")\r\nhead(HepAbu)\r\nhist(HepAbu$Abundance)\r\nHepAbu$HepPer100<-((HepAbu$Abundance*100)/(60*4*60*HepAbu$AreaSampled.m2.*HepAbu$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nhist(log(HepAbu$Abundance))\r\n\r\n\r\nHepLog0 = lme(log(HepPer100+10e-5)~1,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nHepLog1 = lme(log(HepPer100+10e-5)~percillum,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\nHepLog2 = lme(log(HepPer100+10e-5)~DPFSCentered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\nHepLog3 = lme(log(HepPer100+10e-5)~temp_centered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nHepLog4 = lme(log(HepPer100+10e-5)~percillum+temp_centered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\nHepLog5 = lme(log(HepPer100+10e-5)~percillum+DPFSCentered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\nHepLog6 = lme(log(HepPer100+10e-5)~temp_centered+DPFSCentered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nHepLog7 = lme(log(HepPer100+10e-5)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=HepAbu,correlation=corAR1(form = ~DPFS|Year))\r\n\r\n\r\n\r\nAICctab(HepLog0,HepLog1,HepLog2,HepLog3,HepLog4,HepLog5,HepLog6,HepLog7, weights=TRUE)\r\n\r\n\r\nsummary(HepLog2)\r\n\r\n\r\nplot(resid(HepLog2) ~ DPFS, data=HepAbu)\r\nlines(lowess(resid(HepLog2) ~ HepAbu$DPFS), col=2)\r\n\r\n\r\nplot(resid(HepLog2) ~ temp_centered, data=HepAbu)\r\nlines(lowess(resid(HepLog2) ~ HepAbu$temp_centered), col=2)\r\n\r\n\r\nplot(resid(HepLog2) ~ percillum, data=HepAbu)\r\nlines(lowess(resid(HepLog2) ~ HepAbu$percillum), col=2)\r\n\r\n\r\nhist(resid(HepLog2))\r\nplot(resid(HepLog2) ~ DischargeSampledByNight, data=ShannonSubset)\r\nlines(lowess(resid(HepLog2) ~ ShannonSubset$DischargeSampledByNight), col=2)\r\n\r\nplot(resid(HepLog2) ~ predict(HepLog2))\r\nlines(lowess(resid(HepLog2) ~ predict(HepLog2)), col=2)\r\n\r\nintervals(HepLog2,which=c(\"fixed\"))\r\n\r\nsummary(HepLog2)\r\n(exp(-0.05397137)-1)*100 #-5.254% estimate DPFS\r\n\r\n(exp(-0.07527147)-1)*100 #-7.2508% CI\r\n(exp(-0.03267128)-1)*100 #-3.2143% CI \r\n\r\n\r\n\r\n############\r\n#LMM models Shannon\r\n#############\r\nhead(ShannonSubset)\r\n\r\nShannonLog0 = lmer(log(value)~1+(1|Year),data=ShannonSubset,REML=F)\r\nShannonLog1 = lmer(log(value)~percillum+(1|Year),data=ShannonSubset,REML=F)\r\nShannonLog2 = lmer(log(value)~temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\nShannonLog3 = lmer(log(value)~DPFSCentered+(1|Year),data=ShannonSubset,REML=F)\r\n\r\nShannonLog4 = lmer(log(value)~percillum+temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\nShannonLog5 = lmer(log(value)~percillum+DPFSCentered+(1|Year),data=ShannonSubset,REML=F)\r\nShannonLog6 = lmer(log(value)~temp_centered+DPFSCentered+(1|Year),data=ShannonSubset,REML=F)\r\n\r\nShannonLog7 = lmer(log(value)~percillum+DPFSCentered+temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\n\r\nAICctab(ShannonLog0,ShannonLog1,ShannonLog2,ShannonLog3,ShannonLog4,ShannonLog5,ShannonLog6,ShannonLog7, weights=TRUE)\r\nsummary(ShannonLog2) #Top shannon model not including autocorrelation AIC =71.9\r\n\r\n\r\nShannonLog0A = lme(log(value)~1,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nShannonLog1A = lme(log(value)~percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog2A = lme(log(value)~DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog3A = lme(log(value)~temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nShannonLog4A = lme(log(value)~percillum+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog5A = lme(log(value)~percillum+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog6A = lme(log(value)~temp_centered+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nShannonLog7A = lme(log(value)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog8A = lme(log(value)~DischargeCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog9A = lme(log(value)~DischargeCentered+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog10A = lme(log(value)~DischargeCentered+percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog11A = lme(log(value)~DischargeCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nShannonLog12A = lme(log(value)~DischargeCentered+temp_centered+percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\n\r\nAICctab(ShannonLog0A,ShannonLog1A,ShannonLog2A,ShannonLog3A,ShannonLog4A,ShannonLog5A,ShannonLog6A,ShannonLog7A,ShannonLog8A,ShannonLog9A,ShannonLog10A,ShannonLog11A,ShannonLog12A, weights=TRUE)\r\n\r\n\r\nsummary(ShannonLog8A)\r\nsummary(ShannonLog0A)\r\n\r\n\r\nintervals(ShannonLog8A,which=c(\"fixed\"))\r\n(exp(0.4876861)-1)*100 #62.85% estimate \r\n(exp(0.15201898)-1)*100 #16.418% CI \r\n(exp(0.8233531)-1)*100 #127.81% CI\r\n\r\n#############\r\n#Diagnostic plots Shannon LMM\r\n############\r\n\r\n\r\n\r\n\r\nplot(resid(ShannonLog8A) ~ temp_centered, data=ShannonSubset)\r\nlines(lowess(resid(ShannonLog8A) ~ ShannonSubset$temp_centered), col=2)\r\n\r\n\r\nhist(resid(ShannonLog8A))\r\nplot(resid(ShannonLog8A) ~ DischargeCentered, data=ShannonSubset)\r\nlines(lowess(resid(ShannonLog8A) ~ ShannonSubset$DischargeCentered), col=2)\r\n\r\nplot(resid(ShannonLog8A) ~ predict(ShannonLog8A))\r\nlines(lowess(resid(ShannonLog8A) ~ predict(ShannonLog8A)), col=2)\r\n\r\nsummary(ShannonLog8A)\r\nconfint(ShannonLog8A)\r\nfixef(ShannonLog8A)\r\nranef(ShannonLog8A)\r\nconfint(ShannonLog8A)\r\ncoef(ShannonLog8A)\r\n\r\n\r\n#############\r\n#Shannon LMM Plot\r\n#############\r\nShannonLog8NoCorr = lmer(log(value)~DischargeCentered+ (1|Year),data=ShannonSubset)\r\n\r\nsummary(ShannonLog8NoCorr)\r\nsamps <-sim(ShannonLog8NoCorr,n.sims=10000)\r\n#Temperature graph\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"DischargeCentered\"]*X\r\nnew.discharge = seq(min(ShannonSubset$DischargeCentered),max(ShannonSubset$DischargeCentered),length= 10000)\r\nm.fixef.out<-sapply(new.discharge,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.discharge,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"value\",\"DischargeSampledByNight\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\nPredictedDataFrame$DischargeSampledByNight<-PredictedDataFrame$DischargeSampledByNight+mean(ShannonSubset$DischargeSampledByNight)\r\n\r\n\r\n\r\n#ggplot\r\nDischargeShannonLMM<-ggplot(ShannonSubset,aes(DischargeSampledByNight,value))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(expression(Discharge~(m^3/sec)~Sampled))+ylab(\"Shannon Diversity\")#+ theme(axis.title.y = element_text(size = 9))\r\nDischargeShannonLMM\r\n\r\ndev.off()\r\ntiff(\"Figures/LMM_ShannonDischarge.tiff\", width = 84, height = 84, units = 'mm', res = 1000)\r\nDischargeShannonLMM\r\ndev.off()\r\n\r\n####################\r\n#Invertebrate family richness\r\n####################\r\nRichnessLog0A = lme(log(Nfamilies)~1,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nRichnessLog1A = lme(log(Nfamilies)~percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog2A = lme(log(Nfamilies)~DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog3A = lme(log(Nfamilies)~temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nRichnessLog4A = lme(log(Nfamilies)~percillum+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog5A = lme(log(Nfamilies)~percillum+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog6A = lme(log(Nfamilies)~temp_centered+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nRichnessLog7A = lme(log(Nfamilies)~percillum+DPFSCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog8A = lme(log(Nfamilies)~DischargeCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog9A = lme(log(Nfamilies)~DischargeCentered+DPFSCentered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog10A = lme(log(Nfamilies)~DischargeCentered+percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog11A = lme(log(Nfamilies)~DischargeCentered+temp_centered,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\nRichnessLog12A = lme(log(Nfamilies)~DischargeCentered+temp_centered+percillum,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\n\r\nAICctab(RichnessLog0A,RichnessLog1A,RichnessLog2A,RichnessLog3A,RichnessLog4A,RichnessLog5A,RichnessLog6A,RichnessLog7A,RichnessLog8A,RichnessLog9A,RichnessLog10A,RichnessLog11A,RichnessLog12A, weights=TRUE)\r\n\r\n\r\nsummary(RichnessLog2A)\r\n\r\nintervals(RichnessLog2A,which=c(\"fixed\"))\r\n\r\n#Percentages\r\n(exp(-0.01022522)-1)*100 #-1.0277% estimate \r\n(exp(-0.01509176)-1)*100 #-1.5206% estimate \r\n(exp(-0.005358687)-1)*100 #-0.5373% estimate \r\n\r\n\r\n\r\n#Day 15\r\nmin(ShannonSubset$DPFS)\r\nmean(ShannonSubset$DPFS) #-21.98624 to account for centering \r\nexp(2.16784519+(-21.98624*-0.01022522)) #N families Estimate at 15 days 10.94\r\nexp(2.16784519+(-21.98624*-0.01509176)) #Upper CI at 15 days 12.18\r\nexp(2.16784519+(-21.98624*-0.005358687)) #Lower CI at 15 days 9.83\r\n\r\n\r\n#Day 68\r\nmax(ShannonSubset$DPFS) #68\r\n60-36.98624\r\nexp(2.16784519+(23.01376*-0.01022522)) #N families Estimate at 60 days 6.906\r\nexp(2.16784519+(23.01376*-0.01509176)) #Upper CI at 60 days 6.175\r\nexp(2.16784519+(23.01376*-0.005358687)) #Lower CI at 60 days 7.725\r\n\r\n###############\r\n#LMM Richness plot DPFS\r\n#################\r\n\r\nRichnessLog2NoCorr = lmer(log(Nfamilies)~DPFSCentered+(1|Year),data=ShannonSubset)\r\n\r\nsamps <-sim(RichnessLog2NoCorr,n.sims=10000)\r\n\r\nm.fixef <-function(X) samps@fixef[,'(Intercept)']+samps@fixef[,\"DPFSCentered\"]*X\r\nnew.DPFS = seq(min(ShannonSubset$DPFSCentered),max(ShannonSubset$DPFSCentered),length= 10000)\r\nm.fixef.out<-sapply(new.DPFS,m.fixef)\r\n\r\nm.pred<-colMeans(m.fixef.out)\r\nm.975<- apply(m.fixef.out,2,quantile, 0.975)\r\nm.025 <-apply(m.fixef.out, 2 ,quantile, 0.025)\r\n\r\n\r\nPredictedDataFrame<-data.frame(exp(m.pred),new.DPFS,exp(m.975),exp(m.025))\r\ncolnames(PredictedDataFrame)<-c(\"Nfamilies\",\"DPFS\",\"UpperCI\",\"LowerCI\")\r\nPredictedDataFrame$DPFS<-PredictedDataFrame$DPFS+mean(ShannonSubset$DPFS) #Remove centering for plotting\r\n\r\n\r\nhead(PredictedDataFrame)\r\nhead(ShannonSubset)\r\n#ggplot\r\nDPFSBestRichnessModel<-ggplot(ShannonSubset,aes(DPFS,Nfamilies))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Days Post First Spawning\")+ylab(\"Family Richness\")\r\nDPFSBestRichnessModel\r\n\r\ndev.off()\r\ntiff(\"Figures/LMM_NfamiliesDPFS.tiff\", width = 84, height = 84, units = 'mm', res = 800)\r\nDPFSBestRichnessModel\r\ndev.off()\r\n\r\n\r\n\r\n##########\r\n#Join together figure 5\r\n##########\r\n\r\ndev.off()\r\ntiff(\"Figures/Figure5.tiff\", width = 84, height = 174, units = 'mm', res = 1200)\r\nggarrange(DischargeShannonLMM,TemperatureShannonLMM,\r\n labels = c(\"a\", \"b\"),\r\n ncol = 1, nrow = 2)\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n#############\r\n#Autocorrelation\r\n############\r\nData2011<-subset(ShannonSubset,Year==2011)\r\nhead(Data2011)\r\nmodel<-lmer(log(DriftInvertConc)~DPFSCentered+(1|Year),data=Data2011)\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2011$DPFSCentered)\r\n\r\nData2012<-subset(ShannonSubset,Year==2012)\r\nhead(Data2012)\r\nmodel<-glm(DriftInvertConc~DPFSCentered,data=Data2012,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2012$DPFSCentered)\r\n\r\nData2013<-subset(ShannonSubset,Year==2013)\r\nhead(Data2013)\r\nmodel<-glm(DriftInvertConc~DPFSCentered,data=Data2013,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2013$DPFSCentered)\r\n\r\nData2014<-subset(ShannonSubset,Year==2014)\r\nhead(Data2014)\r\nmodel<-glm(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale,data=Data2014,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2014$DPFS)\r\n\r\nData2015<-subset(ShannonSubset,Year==2015)\r\nhead(Data2015)\r\nmodel<-glm(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale,data=Data2015,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2015$DPFS)\r\n\r\nData2016<-subset(ShannonSubset,Year==2016)\r\nhead(Data2016)\r\nmodel<-glm(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale,data=Data2016,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2016$DPFS)\r\n\r\n\r\nData2017<-subset(ShannonSubset,Year==2017)\r\nhead(Data2017)\r\nmodel<-glm(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale,data=Data2017,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2017$DPFS)\r\n\r\n\r\n\r\nData2018<-subset(ShannonSubset,Year==2018)\r\nhead(Data2018)\r\nmodel<-glm(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale,data=Data2018,family = Gamma(link=log))\r\n\r\nres<-simulateResiduals(model)\r\ntestTemporalAutocorrelation(res, time = Data2018$DPFS)\r\n\r\n\r\nacf(resid(BiomassLog13), main=\"\")\r\nacf(resid(n13), plot=FALSE)$acf[2]\r\n\r\n\r\n\r\n\r\nlibrary(nlme)\r\n\r\nBiomassLog13 = lmer(log(DriftInvertConc)~percillum+DPFSCentered+temp_centered+(1|Year),data=ShannonSubset,REML=F)\r\n\r\nBiomassLog3Auto = lme(log(DriftInvertConc)~DPFS,random=~1|Year,data=ShannonSubset,correlation=corAR1(form = ~DPFS|Year))\r\n\r\nsummary(BiomassLog3Auto)#\r\nAICtab(BiomassLog13,BiomassLog3Auto)\r\nhist(resid(BiomassLog13Auto))\r\n\r\n\r\n\r\nacf(resid(BiomassLog13))\r\npacf(resid(BiomassLog13Auto))\r\n\r\nmodel<-glmmTMB(DriftInvertConc~DischargeByNightScale+percillumScale+DPFSScale+(1|Year),data=simdat,family = Gamma(link=log))\r\nacf(resid(BiomassLog13), main=\"acf(resid(m1))\")\r\n\r\n\r\n\r\n################\r\n#Hourly results\r\n###############\r\nHourly<-read.csv(\"DataClean\\\\SturgeonDriftDataHourly12.21.19.csv\",header=T)\r\nhead(Hourly)\r\nHourly$Time = factor(Hourly$Time, levels = c(\"Ten\",\"Eleven\",\"Twelve\",\"One\",\"Two\"))\r\nHourly$DriftInvertConc<-((Hourly$InvertSample100*100)/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nHourly$DriftSuckerConc<-(Hourly$Suckers100*100/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\nHourly$DriftSturgeonConc<-(Hourly$LiveSturgeon*100/(60*60*Hourly$AreaSampled*Hourly$AverageNetFlowByNight)) #Calculate inverts/ 100 m3 water (N*100(final vol )/time in sec*flow*area sampled)\r\n\r\nhead(Hourly$Suckers100)\r\n\r\n\r\n\r\n\r\n\r\nStatSubset<-subset(Hourly, InvertSample100 > 0&DriftInvertConc!= \"NA\") #Remove sample dates where Inverts were not collected (no dates where drift collections occured which had no drifting invertebrates)\r\n\r\nlength(StatSubset$\u00ef..SampleID)\r\nlength(Hourly$\u00ef..SampleID)\r\n\r\nhist(log(Hourly$InvertSample100))\r\nplot(DriftInvertConc~Time,data= StatSubset)\r\nModel<-aov(log(DriftInvertConc)~Time,data=StatSubset)\r\n\r\nsummary(Model)\r\nplot(Model)\r\nTukey<-TukeyHSD(Model,\"Time\")\r\nplot(Tukey)\r\nTukey\r\n(exp(1.510104612)-1)*100 #352.7%Difference between 10 pm collection and midnight\r\n(exp(1.1965172)-1)*100 #230.8574% lower CI\r\n(exp(1.8236920)-1)*100 #519.4687% upper CI\r\n\r\ndifference<-Tukey$Time[,\"p adj\"]\r\nLetters<-multcompLetters(difference)\r\nLetters \r\n#rearrange letters so a is at ten pm, c=a,a=b,c=c\r\nLetterRearranged<-c(\"a\",\"b\",\"c\",\"c\",\"c\")\r\n\r\n\r\nhist(resid(Model))\r\n\r\n\r\nTrtdata <- ddply(StatSubset, c(\"Time\"), summarise,\r\n N = length(DriftInvertConc),\r\n meanInverts = mean(DriftInvertConc),\r\n sd = sd(DriftInvertConc),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdata\r\n\r\nHourlyInvertAbu<-ggplot(Trtdata,aes(x=Time,y=meanInverts))+geom_point()+xlab(\"Collection Time\")+\r\n geom_errorbar(aes(ymin=meanInverts-se,ymax=meanInverts+se))+ylab(expression(Invertebrates~Per~100~m^3~Drift~(SE)))+\r\n scale_x_discrete(labels=c(\"22:00\",\"23:00\",\"0:00\",\"1:00\",\"2:00\"))+geom_text(aes(x=Time, y=meanInverts+se+0.5,label=LetterRearranged))+\r\n annotate(\"text\", label = \"ANOVA, F = 63.7,\\n P < 0.001\", size = 3.5, x = 1.5, y = 15)+ theme(axis.title.y = element_text(size = 10))\r\nHourlyInvertAbu\r\n\r\n\r\ndev.off()\r\ntiff(\"Figures/Hourly_InvertAbundance.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nHourlyInvertAbu\r\ndev.off()\r\n\r\n\r\n#Sucker by collection\r\nTrtdataSucker <- ddply(StatSubset, c(\"Time\"), summarise,\r\n N = length(DriftSuckerConc),\r\n meanSucker = mean(DriftSuckerConc),\r\n sd = sd(DriftSuckerConc),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdataSucker\r\n\r\n\r\n\r\nplot(DriftSuckerConc~Time,data= StatSubset)\r\nkruskal.test((DriftSuckerConc)~Time,data=StatSubset)\r\n\r\ncompare_means(DriftSuckerConc ~ Time, data = StatSubset, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\n\r\nMeans=compare_means(DriftSuckerConc ~ Time, data = StatSubset, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\n Hyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\n difference<-Means$p.adj\r\n names(difference)<-Hyphenated\r\n LettersSucker<-multcompLetters(difference)\r\n LettersSucker\r\n\r\nHourlySuckerAbu<-ggplot(TrtdataSucker,aes(x=Time,y=meanSucker))+geom_point()+xlab(\"Collection Time\")+\r\n geom_errorbar(aes(ymin=meanSucker-se,ymax=meanSucker+se))+ylab(expression(Catastomidae~Per~100~m^3~Drift~(SE)))+\r\n scale_x_discrete(labels=c(\"22:00\",\"23:00\",\"0:00\",\"1:00\",\"2:00\"))+geom_text(aes(x=Time, y=meanSucker+se+5,label=LettersSucker$Letters))+\r\n annotate(\"text\", label = \"Kruskal-Wallis,\\n chi-sq = 126.1,\\n P < 0.001\", size = 3.5, x = 4.5, y = 90)+ theme(axis.title.y = element_text(size = 10))\r\nHourlySuckerAbu\r\n\r\n\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\n\r\ndev.off()\r\ntiff(\"Figures/Hourly_SuckerAbundance.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nHourlySuckerAbu\r\ndev.off()\r\n\r\n\r\n#Sturgeon\r\nTrtdataSturgeon <- ddply(StatSubset, c(\"Time\"), summarise,\r\n N = length(DriftSturgeonConc),\r\n meanSturgeon = mean(DriftSturgeonConc),\r\n sd = sd(DriftSturgeonConc),\r\n se = sd / sqrt(N)\r\n)\r\nTrtdataSturgeon\r\n\r\n\r\n\r\nplot(DriftSturgeonConc~Time,data= StatSubset)\r\nkruskal.test((DriftSturgeonConc)~Time,data=StatSubset)\r\n\r\ncompare_means(DriftSturgeonConc ~ Time, data = StatSubset, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\nplot(Model)\r\n\r\n\r\nMeans=compare_means(DriftSturgeonConc ~ Time, data = StatSubset, p.adjust.method = \"fdr\",method=\"wilcox.test\")\r\n\r\nHyphenated<-as.character(paste0(Means$group1,\"-\",Means$group2))\r\ndifference<-Means$p.adj\r\nnames(difference)<-Hyphenated\r\nLettersSturgeon<-multcompLetters(difference)\r\nLettersSturgeon\r\nTrtdataSturgeon\r\nLettersSturgeon<-c(\"a\",\"b\",\"c\",\"bc\",\"bc\")\r\nHourlySturgeonAbu<-ggplot(TrtdataSturgeon,aes(x=Time,y=meanSturgeon))+geom_point()+xlab(\"Collection Time\")+\r\n geom_errorbar(aes(ymin=meanSturgeon-se,ymax=meanSturgeon+se))+ylab(expression(Sturgeon~larvae~Per~100~m^3~Drift~(SE)))+\r\n scale_x_discrete(labels=c(\"22:00\",\"23:00\",\"0:00\",\"1:00\",\"2:00\"))+geom_text(aes(x=Time, y=meanSturgeon+se+0.5,label=LettersSturgeon))+\r\n annotate(\"text\", label = \"Kruskal-Wallis,\\n chi-sq = 107.7,\\n P < 0.001\", size = 3.5, x = 2, y = 7.5)+ theme(axis.title.y = element_text(size = 10))\r\nHourlySturgeonAbu\r\n\r\n\r\ntheme_set(theme_bw(base_size = 12)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\n\r\ndev.off()\r\ntiff(\"Figures/Hourly_SturgeonAbundance.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nHourlySturgeonAbu\r\ndev.off()\r\n\r\n\r\n###########\r\n#Join together hourly figure\r\n###########\r\ndev.off()\r\ntiff(\"Figures/Hourly_Abundance.tiff\", width = 174, height = 174, units = 'mm', res = 1200)\r\n\r\nggarrange(HourlyInvertAbu,HourlySuckerAbu,HourlySturgeonAbu,\r\n labels = c(\"a\", \"b\",\"c\"),\r\n ncol = 2, nrow = 2)\r\n\r\ndev.off()\r\n\r\n##################\r\n#Sturgeon/Sucker Biomass\r\n###################\r\n\r\n#See UBRDriftFiguresRCode for an updated version of this section!!!!\r\n\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n\r\nhead(ShannonRichness)\r\n#Sturgeon average dry weight = 0.005 g\r\n#Sucker average dry weight = 0.00204\r\nShannonRichness$SturgeonBiomass<-ShannonRichness$SturgeonConc*0.005\r\n\r\nShannonRichness$SuckerBiomass<-ShannonRichness$SuckerConc*0.00204\r\n\r\nShannonRichness$CombinedBiomassConc<-ShannonRichness$SturgeonBiomass+ShannonRichness$SuckerBiomass+ShannonRichness$DriftBiomassConc\r\n\r\n#Sturgeon Biomass\r\nmean(ShannonRichness$SturgeonBiomass,na.rm=T) #0.0167 g per 100 m3 drift\r\nmin(ShannonRichness$SturgeonBiomass,na.rm=T) #0\r\nmax(ShannonRichness$SturgeonBiomass,na.rm=T) #0.453\r\n\r\n\r\nShannonSubsetSampled<-subset(ShannonRichness,SturgeonBiomass!=\"NA\")\r\nlength(ShannonSubsetSampled$SampleID)\r\nsd(ShannonSubsetSampled$SturgeonBiomass)/sqrt(229) #0.003\r\n\r\n\r\n#Sucker Biomass\r\nmean(ShannonRichness$SuckerBiomass,na.rm=T) #0.111 g\r\nmin(ShannonRichness$SuckerBiomass,na.rm=T) #0\r\nmax(ShannonRichness$SuckerBiomass,na.rm=T) #3.09 g\r\n\r\n\r\nShannonSubsetSampled<-subset(ShannonRichness,SuckerBiomass!=\"NA\")\r\nlength(ShannonSubsetSampled$SampleID)\r\nsd(ShannonSubsetSampled$SuckerBiomass)/sqrt(229) #0.022\r\n\r\n\r\n\r\n#InvertBiomass\r\nmean(ShannonRichness$DriftBiomassConc,na.rm=T) #0.746 g\r\nmin(ShannonRichness$DriftBiomassConc,na.rm=T) #0.005297\r\nmax(ShannonRichness$DriftBiomassConc,na.rm=T) #6.781\r\n\r\n\r\nShannonSubsetSampled<-subset(ShannonRichness,DriftBiomassConc!=\"NA\")\r\nlength(ShannonSubsetSampled$SampleID)\r\nsd(ShannonSubsetSampled$DriftBiomassConc)/sqrt(229) #0.056\r\n\r\n\r\n\r\n\r\n#Total Biomass Conc\r\nmean(ShannonRichness$CombinedBiomassConc,na.rm=T) #0.874 g per 100 m3 drift\r\nmin(ShannonRichness$CombinedBiomassConc,na.rm=T) #0.0105\r\nmax(ShannonRichness$CombinedBiomassConc,na.rm=T) #7.688\r\n\r\n\r\n#\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#Extrapolating out to total river discharge\r\n\r\nShannonRichness$SuckerBiomassPerNight<-ShannonRichness$Nsuckers100*0.00204\r\nShannonRichness$SturgeonBiomassPerNight<-ShannonRichness$Nsturgeon*0.005\r\n\r\nShannonRichness$TotalSampledBiomassNight<-ShannonRichness$SuckerBiomassPerNight+ShannonRichness$SturgeonBiomassPerNight+ShannonRichness$InvertBiomass100\r\nShannonRichness$TotalRiverBiomassNightly<-ShannonRichness$TotalSampledBiomassNight/(ShannonRichness$PercentRiverDischargeSampled/100)\r\n#Sampled Nightly Biomass\r\nmean(ShannonRichness$TotalSampledBiomassNight,na.rm=T) #112.43 g per night sampled\r\nmin(ShannonRichness$TotalSampledBiomassNight,na.rm=T) #1.246\r\nmax(ShannonRichness$TotalSampledBiomassNight,na.rm=T) #940.43\r\n\r\n\r\n\r\nShannonSubsetSampled<-subset(ShannonRichness,TotalSampledBiomassNight!=\"NA\")\r\nlength(ShannonSubsetSampled$SampleID)\r\nsd(ShannonSubsetSampled$TotalSampledBiomassNight)/sqrt(240) #7.78\r\n\r\n\r\n\r\n\r\n#River Biomass Nightly\r\nmean(ShannonRichness$TotalRiverBiomassNightly,na.rm=T) #919.93 g per night sampled\r\nmin(ShannonRichness$TotalRiverBiomassNightly,na.rm=T) #13.31\r\nmax(ShannonRichness$TotalRiverBiomassNightly,na.rm=T) #7885.5\r\n\r\n#Percentage Nightly Biomass\r\nShannonRichness<-read.csv(\"SturgeonMetadataWDiversity.csv\",header=T)\r\n\r\nShannonRichness$SturgeonBiomassNightly<-ShannonRichness$Nsturgeon*0.005\r\nShannonRichness$SuckerBiomassNightly<-ShannonRichness$Nsuckers100*0.00204\r\nShannonRichness$CombinedNightlyBiomass<-ShannonRichness$SturgeonBiomassNightly+ShannonRichness$SuckerBiomassNightly+ShannonRichness$InvertBiomass100\r\n\r\n\r\nShannonRichness$PercentNightlyBiomassSturgeon<- ShannonRichness$SturgeonBiomassNightly/ShannonRichness$CombinedNightlyBiomass*100\r\nShannonRichness$PercentNightlyBiomassSucker<- ShannonRichness$SuckerBiomassNightly/ShannonRichness$CombinedNightlyBiomass*100\r\nShannonRichness$PercentNightlyBiomassInvert<- ShannonRichness$InvertBiomass100/ShannonRichness$CombinedNightlyBiomass*100\r\n\r\n\r\nSturgeonDataFrame<-data.frame(\"Sturgeon\",ShannonRichness$DPFS, ShannonRichness$DayOfYear,ShannonRichness$Year,ShannonRichness$PercentNightlyBiomassSturgeon)\r\ncolnames(SturgeonDataFrame)<-c(\"Taxa\",\"DPFS\",\"DayOfYear\",\"Year\",\"PercentNightly\")\r\n\r\nSuckerDataFrame<-data.frame(\"Catostomidae\",ShannonRichness$DPFS, ShannonRichness$DayOfYear,ShannonRichness$Year,ShannonRichness$PercentNightlyBiomassSucker)\r\ncolnames(SuckerDataFrame)<-c(\"Taxa\",\"DPFS\",\"DayOfYear\",\"Year\",\"PercentNightly\")\r\n\r\nInvertDataFrame<-data.frame(\"Invertebrate\",ShannonRichness$DPFS,ShannonRichness$DayOfYear,ShannonRichness$Year,ShannonRichness$PercentNightlyBiomassInvert)\r\ncolnames(InvertDataFrame)<-c(\"Taxa\",\"DPFS\",\"DayOfYear\",\"Year\",\"PercentNightly\")\r\n\r\nPlottingDataframe<-rbind(SturgeonDataFrame,SuckerDataFrame,InvertDataFrame)\r\nPlottingDataframe[is.na(PlottingDataframe)] <- 0 #Change NA values to 0 as no taxa were observed on those dates but sampling occured\r\n\r\nhead(PlottingDataframe)\r\nPlottingDataframe$Taxa = factor(PlottingDataframe$Taxa, levels = c(\"Invertebrate\",\"Sturgeon\",\"Catostomidae\"))\r\n\r\ncbPalette2 <- c(\"#E69F00\", \"#000000\", \"#0072B2\")\r\ncbrewer<-c(\"#66c2a5\",\"#fc8d62\",\"#8da0cb\")\r\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#000000\",\"#CC79A7\")\r\n\r\nPercentBiomassPlot<-ggplot(PlottingDataframe, aes(x=DayOfYear, y= PercentNightly,fill=Taxa))+facet_wrap(~Year)+geom_bar(stat=\"identity\",lwd=0.1)+\r\n theme(legend.justification=c(1,0), legend.position=c(1,-0.04))+ylab(\"Nightly Biomass (%)\")+xlab(\"Day of Year\")+\r\n scale_fill_manual(values=cbPalette2)+scale_color_manual(values=cbPalette2)+theme(legend.text = element_text(size = 5),legend.title = element_blank())+ \r\n theme(legend.background=element_blank(), axis.text.x = element_text(angle = 45, hjust = 1))#+ guides(shape = guide_legend(override.aes = list(size=2)))#+geom_line()\r\nPercentBiomassPlot\r\n\r\ntheme_set(theme_bw(base_size = 9)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\ndev.off()\r\ntiff(\"Figures/PercentBiomassPlot.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nPercentBiomassPlot\r\ndev.off()\r\n###\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nSturgeonDataFrame<-data.frame(\"Sturgeon\",ShannonRichness$DayOfYear, ShannonRichness$CTUMay2,ShannonRichness$Year,ShannonRichness$SturgeonBiomass)\r\ncolnames(SturgeonDataFrame)<-c(\"Taxa\",\"DPFS\",\"CTUMay2\",\"Year\",\"BiomassConc\")\r\n\r\nSuckerDataFrame<-data.frame(\"Catostomidae\",ShannonRichness$DPFS, ShannonRichness$CTUMay2,ShannonRichness$Year,ShannonRichness$SuckerBiomass)\r\ncolnames(SuckerDataFrame)<-c(\"Taxa\",\"DPFS\",\"CTUMay2\",\"Year\",\"BiomassConc\")\r\n\r\nInvertDataFrame<-data.frame(\"Invertebrate\",ShannonRichness$DPFS,ShannonRichness$CTUMay2,ShannonRichness$Year,ShannonRichness$DriftBiomassConc)\r\ncolnames(InvertDataFrame)<-c(\"Taxa\",\"DPFS\",\"CTUMay2\",\"Year\",\"BiomassConc\")\r\n\r\nPlottingDataframe<-rbind(SturgeonDataFrame,SuckerDataFrame,InvertDataFrame)\r\nPlottingDataframe[is.na(PlottingDataframe)] <- 0 #Change NA values to 0 as no taxa were observed on those dates but sampling occured\r\n\r\nhead(PlottingDataframe)\r\nPlottingDataframe$Taxa = factor(PlottingDataframe$Taxa, levels = c(\"Invertebrate\",\"Sturgeon\",\"Catostomidae\"))\r\n\r\ntheme_set(theme_bw(base_size = 8)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()))\r\n\r\nBiomassAllPlot<-ggplot(PlottingDataframe, aes(x=DPFS, y= BiomassConc,color=Taxa,shape=Taxa,group=Taxa))+facet_wrap(~Year)+geom_point(size=1)+\r\n theme(legend.justification=c(1,0), legend.position=c(1,-0.01))+ylab(expression(Biomass~(g)~Per~100~m^3~Drift))+xlab(\"Days Post First Spawning\")+\r\n scale_fill_manual(values=cbPalette)+scale_color_manual(values=cbPalette)+theme(legend.text = element_text(size = 7),legend.title = element_blank())+ \r\n theme(legend.background=element_blank())+ guides(shape = guide_legend(override.aes = list(size=2)))#+geom_line()\r\nBiomassAllPlot\r\n\r\nBiomassAllPlot2<-ggplot(PlottingDataframe, aes(x=CTUMay2, y= BiomassConc,color=Taxa,shape=Taxa,group=Taxa))+facet_wrap(~Year)+geom_point(size=1)+\r\n theme(legend.justification=c(1,0), legend.position=c(1,-0.01),axis.text.x = element_text(angle = 45, hjust = 1))+ylab(expression(Biomass~(g)~Per~100~m^3~Drift))+xlab(\"Cumulative Thermal Units\")+\r\n scale_fill_manual(values=cbPalette)+scale_color_manual(values=cbPalette)+theme(legend.text = element_text(size = 7),legend.title = element_blank())+ \r\n theme(legend.background=element_blank())+ guides(shape = guide_legend(override.aes = list(size=2)))+ scale_x_continuous( limits=c(100, 1200))#+geom_line()\r\nBiomassAllPlot2\r\n\r\n\r\n\r\n\r\ndev.off()\r\ntiff(\"Figures/BiomassAllPlot.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nBiomassAllPlot\r\ndev.off()\r\n\r\ndev.off()\r\ntiff(\"Figures/BiomassAllPlot2.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nBiomassAllPlot2\r\ndev.off()\r\n\r\n\r\n############\r\n#Combine fig 6\r\n################\r\nTopFamilyAbuPer100 #A\r\nCrayfishGAM #B\r\nTopFamilyBiomass #C\r\nBiomassAllPlot #D\r\n\r\ndev.off()\r\ntiff(\"Figures/Figure6.tiff\", width = 174, height = 174, units = 'mm', res = 1000)\r\nggarrange(TopFamilyAbuPer100,CrayfishGAM,TopFamilyBiomass,BiomassAllPlot,\r\n labels = c(\"a\", \"b\",\"c\",\"d\"),\r\n ncol = 2, nrow = 2)\r\ndev.off()\r\n\r\n\r\n#############\r\n#Beta diversity exploration\r\n##############\r\n\r\n\r\n#Homogeneity of multivariate dispersions\r\n\r\nGPdist=phyloseq::distance(physeq, \"jaccard\")\r\nbeta=betadisper(GPdist, sample_data(physeq)$Year)\r\npermutest(beta)\r\nboxplot(beta)\r\n\r\n\r\nadonis(GPdist ~ Year, as(sample_data(physeq), \"data.frame\"))\r\n\r\nord=ordinate(physeq,\"PCoA\", \"jaccard\")\r\nordplot=plot_ordination(physeq, ord,\"samples\", color=\"MoonPhase\")+geom_point(size=4)+\r\n stat_ellipse(type= \"norm\",geom = \"polygon\", alpha = 1/4, aes(fill = MoonPhase))+facet_wrap(~Year)\r\nordplot\r\n\r\n\r\n###############\r\n#Nightly Total River Biomass\r\n###############\r\nhead(metadata)\r\n\r\n#############\r\n#Sturgeon Abu Gam\r\n################\r\n#Model residuals not great (maybe zero inflated model would work?), not included\r\nSturgeonAbuLog <- gam(log(Nsturgeon+10e-5)~ s(CTUSturgeon)+s(Year,bs=\"re\")+s(DischargeSampled), data = ShannonRichness, method = \"REML\",correlation=corAR1(form = ~DayOfYear|Year))\r\nappraise(SturgeonAbuLog)\r\n\r\nsummary(SturgeonAbuLog)\r\nhist(resid(SturgeonAbuLog))\r\n\r\nplot<-plot_smooth(SturgeonAbuLog, view=\"CTUSturgeon\",rm.ranef=F,sim.ci = T)\r\n#plot_sm\r\n#plot$fv\r\nFittedValues<-exp(plot$fv$fit)\r\nhead(FittedValues)\r\n\r\nexp(7.5)\r\nPredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$CTUSturgeon,exp(plot$fv$ul),exp(plot$fv$ll))\r\n\r\ncolnames(PredictedDataFrame)<-c(\"Nsturgeon\",\"CTUSturgeon\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\n\r\n\r\nggplot(PredictedDataFrame, aes(x=CTUSturgeon,y=Nsturgeon))+geom_point()+geom_line(aes(CTUSturgeon, UpperCI),color=\"red\")+geom_line(aes(CTUSturgeon,LowerCI),color=\"red\")\r\n\r\n\r\nggplot(ShannonRichness,aes(CTUSturgeon,Nsturgeon))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Cumulative Temperature Units\")+ylab(expression(\"Drifting Larval Sturgeon Abundance\"))\r\n\r\n\r\n\r\n\r\n\r\n\r\n##############\r\n#Scratch\r\n##############\r\nSuckerAbuLog <- gam(log(SuckerConc+10e-5)~ s(percillum)+s(CTUSturgeon)+s(Year,bs=\"re\"), data = ShannonSubset, method = \"REML\",correlation=corAR1(form = ~DayOfYear|Year))\r\n\r\nappraise(SuckerAbuLog)\r\nplot<-plot_smooth(SuckerAbuLog, view=\"percillum\",rm.ranef=F,sim.ci = T)\r\n#plot_sm\r\n#plot$fv\r\nFittedValues<-exp(plot$fv$fit)\r\nhead(FittedValues)\r\n\r\nexp(7.5)\r\nPredictedDataFrame<-data.frame(exp(plot$fv$fit),plot$fv$percillum,exp(plot$fv$ul),exp(plot$fv$ll))\r\n\r\ncolnames(PredictedDataFrame)<-c(\"SuckerConc\",\"percillum\",\"UpperCI\",\"LowerCI\")\r\nhead(PredictedDataFrame)\r\n\r\n\r\nggplot(PredictedDataFrame, aes(x=percillum,y=SuckerConc))+geom_point()+geom_line(aes(percillum, UpperCI),color=\"red\")+geom_line(aes(percillum,LowerCI),color=\"red\")\r\n\r\n\r\nSuckerGAMPlot<-ggplot(ShannonSubset,aes(percillum,SuckerConc))+geom_point(color=\"darkgrey\")+geom_line(data=PredictedDataFrame,size=1.5)+ \r\n geom_line(data = PredictedDataFrame, aes(y = LowerCI), size = .75,linetype=\"dashed\")+geom_line(data = PredictedDataFrame,aes(y=UpperCI),size=0.75,linetype=\"dashed\")+\r\n xlab(\"Cumulative Temperature Units\")+ylab(expression(Larval~Catostomidae~Per~100~m^3~Drift))+ylim(NA, 500)\r\nSuckerGAMPlot\r\n\r\ndev.off()\r\ntiff(\"Figures/SuckerGAM.tiff\", width = 84, height = 84, units = 'mm', res = 1200)\r\nSuckerGAMPlot\r\ndev.off()\r\n\r\n################\r\n#Random Forest\r\n###############\r\n\r\n\r\nRFData<-read.csv(\"RandomForestData.csv\",header=T)\r\nhead(RFData)\r\nrownames(RFData)<-RFData$\u00ef..Sample\r\n\r\nRFDataCut<-RFData[,-1]\r\nhead(RFDataCut)\r\nRFDataCut<-subset(RFDataCut,Q!=\"NA\"&DischargeSampledByNight!=\"NA\")\r\n\r\nFrame<-RFDataCut[,71:ncol(RFDataCut)]\r\nhead(Frame)\r\nFrameCut<-Frame[,-10]\r\nhead(FrameCut)\r\nFrameNoZero<-subset(Frame,Nsturgeon!=0)\r\nRFNoYear<-RFDataCut[,1:79]\r\nhead(RFNoYear)\r\nRFNoYear$Nsturgeon<-RFDataCut[,81]\r\n\r\nrf <- randomForest(formula=Ninverts100 ~ .,\r\n data=RFDataCut,importance=T,keep.inbag=T,ntree=1000)\r\nrf\r\nplot(rf)\r\n\r\nvarImpPlot(rf,type=1)\r\nsqrt(1982496)\r\n\r\nggplot(Frame, aes(x=Ninverts100,y=Nsuckers100))+geom_point()\r\nFrame2<-Frame\r\nFrame2$Crayfish<-RFDataCut$Crayfish\r\nFrame2$Neophemeridae<-RFDataCut$Neophemeridae\r\n#Frame2$Baetidae<-RFDataCut$Baetidae\r\nFrame2$Heptageniidae<-RFDataCut$Heptageniidae\r\n\r\nrf$forest\r\nm2 <- tuneRF(\r\n x = RFDataCut[1:ncol(RFDataCut)-1],\r\n y = RFDataCut$Nsturgeon,\r\n ntreeTry = 1500,\r\n mtryStart = 5,\r\n stepFactor = 1.5,\r\n improve = 0.01,\r\n trace = FALSE\r\n)\r\n\r\n\r\n\r\n\r\nCHCMatrix<-subset(CHCMatrix,Dessication!=\"NA\")\r\n\r\nsample = sample.split(Frame, SplitRatio = .75)\r\ntrain = subset(Frame, sample == TRUE)\r\ntest = subset(Frame, sample == FALSE)\r\ndim(train)\r\ndim(test)\r\n\r\n\r\nrf_oob_comp <- randomForest(\r\n formula = Nsturgeon ~ .,\r\n data = train,\r\n xtest = train,\r\n ytest = test\r\n)\r\n\r\n\r\nPredict<-predict(rf,newdata=test)\r\n\r\nPredict\r\n\r\nObserved<-test$Nsturgeon\r\nplot(Observed~Predict)\r\n\r\n\r\n\r\nrf <- randomForest(formula=Dessication ~ .,\r\n data=CHCMatrix,mtry= 15\r\n)\r\nrf\r\nplot(rf)\r\nvarImpPlot(rf,type=1)\r\n\r\n\r\nrf <- randomForest(formula=Dessication ~ .,\r\n data=CHCMatrix,mtry= 22,keep.inbag=T,importance=T)\r\n\r\n\r\n \r\nTreeFile<-data.frame(seq(1,500,by=1),rf$mse)\r\ncolnames(TreeFile)<-c(\"Tree\",\"MSE\")\r\nhead(TreeFile)\r\nErrorByTree<-ggplot(TreeFile,aes(x=Tree,y=MSE))+geom_line()+xlab(\"Tree\")+ylab(\"Mean Squared Error\")\r\nErrorByTree\r\nhead(TreeFile)\r\nvarImpPlot(rf,type=1)\r\n\r\nX <- test\r\nvar_hat <- randomForestInfJack(rf, RFDataCut, calibrate = TRUE)\r\n\r\nhead(var_hat)\r\nplot(var_hat)\r\n\r\ndf <- data.frame(y = RFDataCut, var_hat)\r\ndf <- mutate(df, se = sqrt(var.hat))\r\nhead(df)\r\n\r\np1 <- ggplot(df, aes(x = y.Ninverts100, y = y.hat))\r\np1<-p1 + geom_errorbar(aes(ymin=y.hat-se, ymax=y.hat+se),color=\"grey\", width=.1) +\r\n geom_point() +\r\n geom_abline(intercept=0, slope=1, linetype=2)+\r\n xlab(\"Observed Macroinvert larvae\") +\r\n ylab(\"Predicted Macroinvert larvae\")#+xlim(0,5000)+ylim(0,5000)#+annotate(\"text\", label = \"% Var explained = 90.8\\n MSE = 14.67\", size = 4, x = 20, y = 55)\r\np1\r\nrf$y\r\nDiff<-rf$y-rf$predicted\r\nhist(Diff)\r\n", "meta": 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"max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.172883555, "max_line_length": 344, "alphanum_fraction": 0.7255294583, "num_tokens": 40525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3491523698675708}} {"text": "setwd(\".\")\n\n# Always generate the same data\nset.seed(111)\n\n# Number of samples\nsample_size = 1500\ntraining_size = 0.8\nvalidation_size = 0.1\ntest_size = 0.1\n\n###################################################################\n# To generate the waveform data we use mlbench\n# mlbench uses Breiman's original waveform source modified for R\nlibrary(mlbench)\nwaves = mlbench.waveform(sample_size)\ndata = data.frame(cbind(waves$x, waves$class))\n###################################################################\n\n###################################################################\n# Divide the vectors according to the classes\nw1 = data[data$X22 == 1, ]\n\n# Create training, test and validation sets for w1\nw1_validation_size = floor(nrow(w1) * validation_size)\nw1_test_size = floor(nrow(w1) * test_size)\nw1_training_size = nrow(w1) - w1_test_size - w1_validation_size\n\nw1_training = w1[1:w1_training_size, ]\nw1_validation = w1[(w1_training_size + 1):(w1_training_size + w1_validation_size), ]\nw1_test = w1[(w1_training_size + w1_validation_size + 1):(w1_training_size + w1_validation_size + w1_test_size), ]\n\n###################################################################\n# Divide the vectors according to the classes\nw2 = data[data$X22 == 2, ]\n\n# Create training, test and validation sets for w2\nw2_validation_size = floor(nrow(w2) * validation_size)\nw2_test_size = floor(nrow(w2) * test_size)\nw2_training_size = nrow(w2) - w2_test_size - w2_validation_size\n\nw2_training = w2[1:w2_training_size, ]\nw2_validation = w2[(w2_training_size + 1):(w2_training_size + w2_validation_size), ]\nw2_test = w2[(w2_training_size + w2_validation_size + 1):(w2_training_size + w2_validation_size + w2_test_size), ]\n\n###################################################################\n# Divide the vectors according to the classes\nw3 = data[data$X22 == 3, ]\n\n# Create training, test and validation sets for w3\nw3_validation_size = floor(nrow(w3) * validation_size)\nw3_test_size = floor(nrow(w3) * test_size)\nw3_training_size = nrow(w3) - w3_test_size - w3_validation_size\n\nw3_training = w3[1:w3_training_size, ]\nw3_validation = w3[(w3_training_size + 1):(w3_training_size + w3_validation_size), ]\nw3_test = w3[(w3_training_size + w3_validation_size + 1):(w3_training_size + w3_validation_size + w3_test_size), ]\n###################################################################\n", "meta": {"hexsha": "7b7ace5438623e58b401cd8c0f3fb1480737bcac", "size": 2363, "ext": "r", "lang": "R", "max_stars_repo_path": "classwise_data.r", "max_stars_repo_name": "srijanshetty/linear-discriminants", "max_stars_repo_head_hexsha": "1fc58949ddcb89601bc93c44ebed5ef2de61c853", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "classwise_data.r", "max_issues_repo_name": "srijanshetty/linear-discriminants", "max_issues_repo_head_hexsha": "1fc58949ddcb89601bc93c44ebed5ef2de61c853", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "classwise_data.r", "max_forks_repo_name": "srijanshetty/linear-discriminants", "max_forks_repo_head_hexsha": "1fc58949ddcb89601bc93c44ebed5ef2de61c853", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.0508474576, "max_line_length": 114, "alphanum_fraction": 0.633516716, "num_tokens": 599, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6477982043529715, "lm_q1q2_score": 0.34915236253786314}} {"text": "fn test\n x = 3 \n y = 5 * 1 + 2\n\n z = \"hello world\"\n aasdf = \"test \"\n\n print(x + y - 3)\n print(\"testing hello world\")\n print(aasdf + z)\nendfn\n\nfn main\n x = 3\n y = 4\n\n if x > y\n test\n else\n if x < y\n test\n else\n print(\"NOPE2\")\n endif\n\n print(\"NOPE\")\n endif\nendfn ", "meta": {"hexsha": "840e529b4553033dd6bc644c5e41a6dcbf0d07df", "size": 354, "ext": "rd", "lang": "R", "max_stars_repo_path": "goal/example.rd", "max_stars_repo_name": "yashpatel5400/ruddy", "max_stars_repo_head_hexsha": "1766f87ba147f75b7d9947572fca10febf60097f", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "goal/example.rd", "max_issues_repo_name": "yashpatel5400/ruddy", "max_issues_repo_head_hexsha": "1766f87ba147f75b7d9947572fca10febf60097f", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "goal/example.rd", "max_forks_repo_name": "yashpatel5400/ruddy", "max_forks_repo_head_hexsha": "1766f87ba147f75b7d9947572fca10febf60097f", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.6428571429, "max_line_length": 32, "alphanum_fraction": 0.4124293785, "num_tokens": 122, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3490428696386238}} {"text": "#!/usr/bin/env Rscript\n#\tMaking figures for AGU Fall Meeting presentation 9 Dec 2019\n#\tEric Barefoot\n#\tDec 2019\n\n# load packages\n\nlibrary(tidyverse)\nlibrary(here)\nlibrary(broom)\nlibrary(lme4)\nlibrary(devtools) # for the next thing\nlibrary(colorspace)\nlibrary(paleohydror)\nlibrary(purrr)\nlibrary(rsample)\nlibrary(segmented)\n\nif (interactive()) {\n require(colorout)\n}\n\n# establish directories\n\nfigdir = here('figures', 'outputs', 'agu_2019_talk')\n\n# load data\n\nchamb_data = readRDS(file = here('data','derived_data', 'chamberlin_model_data.rds'))\nchamb_model = readRDS(file = here('data','derived_data', 'chamberlin_stat_model.rds'))\nmodel = readRDS(file = here('data','derived_data','piceance_3d_model_data.rds'))\nfield = readRDS(file = here('data','derived_data','piceance_field_data.rds'))\ncombined = readRDS(file = here('data', 'derived_data', 'piceance_field_model_data.rds'))\nbarpres = readRDS(file = here('data', 'derived_data', 'piceance_bar_preservation.rds'))\nstrat_model_data = readRDS(here('data', 'derived_data', 'strat_model_results_20191202.rds'))\n\n# Define a color palette for the stratigraphy.\n\ncpal = hcl(h = c(80, 120, 240), c = rep(100, 3), l = c(85, 65, 20))\n\n# Get data from field organized into tables for plotting\n\nfield_depths = field %>% filter(structure %in% c('bar','channel') & meas_type %in% c('thickness', 'dimensions')) %>% mutate(meas_a = case_when(meas_type == 'thickness' ~ meas_a, meas_type == 'dimensions' ~ meas_b)) \n\nmodel_depths = model %>% filter(interpretations %in% c('full'))\n\nall_depths = bind_rows(field_depths, model_depths, .id = 'data_source') %>% mutate(data_source = recode(.$data_source, `1` = 'field', `2` = 'model'))\n\nall_depths = all_depths %>% mutate(formID = case_when(formation == 'ohio_creek' ~ 3, formation == 'atwell_gulch' ~ 2, formation == 'molina' ~ 1, formation == 'shire' ~ 0))\n\nlegend_ord = levels(with(all_depths, reorder(formation, formID)))\n\nformation_labeller = as_labeller(c(`0` = 'Shire', `1` = 'Molina', `2` = 'Atwell Gulch'))\nsource_labeller = as_labeller(c('field' = 'Field Data', 'model' = '3D Model Data'))\n\nbedloadSamples = field %>% \nfilter(meas_type == 'sample' & !(structure %in% c('outcrop', 'formation', 'bar_drape')) & meas_a < 2000) %>% \nmutate(D50 = meas_a * 1e-6) %>% \nmutate(formID = case_when(\n formation == 'ohio_creek' ~ 3, \n formation == 'atwell_gulch' ~ 2, \n formation == 'molina' ~ 1, \n formation == 'shire' ~ 0))\n\nlegend_ord = levels(with(bedloadSamples, reorder(formation, formID)))\n\nsamples_for_slope = bedloadSamples %>% select(set_id, D50, textures, composition, samp_ind, samp_code, samp_description)\ndepths_for_slope = all_depths %>% filter(data_source == 'field') %>% select(-c(textures, composition, samp_ind, samp_code, samp_description))\n\npaleohydroset = inner_join(samples_for_slope, depths_for_slope, by = 'set_id') %>% \nmutate(S = trampush_slp(D50, meas_a))\n\n# modify and model data from suite of model runs.\n\nstrat_model_dataFilt = strat_model_data %>% \nfilter(between(IR, 0.3, 0.55)) %>% \nfilter(most_common_avulsion == 'compensational') %>% \nmutate(datatype = 'Model Run')\n\nstrat_model_dataFilt2 = strat_model_dataFilt %>% mutate(presInv = 1 / (pres_percents-1.01), n2gInv = 1/n2g)\n\nn2gMod = lm(n2gInv ~ presInv + latmob + IR, data = strat_model_dataFilt2, weight = 1-pres_percents)\n\nn2gModSimple = lm(n2gInv ~ presInv, data = strat_model_dataFilt2, weight = 1-pres_percents)\n\nreworkingMod = lm(reworking ~ pres_percents + latmob + IR, data = strat_model_dataFilt2)\nreworkingModSeg = segmented(reworkingMod, seg.Z = ~pres_percents)\n\nreworkingModSimple = lm(reworking ~ pres_percents, data = strat_model_dataFilt2)\nreworkingModSegSimple = segmented(reworkingModSimple, seg.Z = ~pres_percents)\n\nstrat_model_dataFilt3 = strat_model_dataFilt2 %>% mutate(n2gPred = 1/predict(n2gModSimple)) %>% \nmutate(reworkingPred = predict(reworkingModSegSimple))\n\navul = rep('compensational', nrow(barpres))\nIR = rnorm(nrow(barpres), 0.4, 0.05)\nlatmob = rnorm(nrow(barpres), 8, 2)\n\nbarpres2 = bind_cols(barpres, tibble(avul, latmob, IR))\n\nbarpres3 = barpres2 %>% rename(pres_percents = 'perc_full') %>%\nmutate(formation = recode(formation, shire = 'atwell_gulch', atwell_gulch = 'shire'), presInv = 1 / (pres_percents-1.01)) %>% ungroup()\n\nbarpres_pred = barpres3 %>% mutate(n2g = 1/predict(n2gMod, newdata = .), reworking = predict(reworkingModSeg, newdata = .)) %>%\nmutate(formID = case_when(\n formation == 'ohio_creek' ~ 3, \n formation == 'atwell_gulch' ~ 2, \n formation == 'molina' ~ 1, \n formation == 'shire' ~ 0))\n\nbarpres_pred_means = barpres_pred %>% group_by(formation) %>% summarize(mean_reworking = mean(reworking), mean_elems = mean(pres_percents)) %>%\nmutate(formID = case_when(\n formation == 'ohio_creek' ~ 3, \n formation == 'atwell_gulch' ~ 2, \n formation == 'molina' ~ 1, \n formation == 'shire' ~ 0))\n\nd = barpres_pred_means\n\nann_data = tibble(x = c(rep(0,3), d$mean_elems), y = c(d$mean_reworking, rep(0.2,3)), xend = rep(d$mean_elems,2), yend = rep(d$mean_reworking,2), formation = rep(d$formation,2), formID = rep(d$formID,2))\n\n#######################################################################\n\nstart_theme = theme_get()\n\npres_theme = theme_update(#line = element_line(color = '#bdbdbd'),\npanel.background = element_rect(fill = '#bdbdbd', color = '#bdbdbd'), \nlegend.key = element_rect(fill = '#bdbdbd', color = '#bdbdbd'), \npanel.grid = element_line(color = '#a79c99'), \nstrip.background = element_blank(), \nstrip.text.x = element_blank(), \nplot.background = element_rect(fill = \"transparent\", color = 'transparent'), \nlegend.background = element_rect(fill = \"transparent\"))\n\ndepth_freq_field = ggplot() +\nstat_bin(aes(x = meas_a, y = ..density.., fill = reorder(formation, formID)), geom = 'bar', position = \"identity\", bins = 20, data = filter(all_depths, data_source == 'field'), color = 'black', size = 0.5) +\nscale_fill_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) + \nlabs(x = 'Flow Depth (m)', y = 'Probability Density') + \nfacet_wrap(vars(formID), ncol = 1, labeller = formation_labeller)\n\nslope_freq = paleohydroset %>% ggplot() + stat_bin(aes(x = S, y = ..density.., fill = reorder(formation, formID)), geom = 'bar', position = 'identity', bins = 8, color = 'black', size = 0.5) +\nscale_x_log10(breaks = c(1e-04, 2e-04, 5e-04, 0.001, 0.003), labels = c(expression(10^-4), expression(2%*%10^-4), expression(5%*%10^-4), expression(10^-3), expression(3%*%10^-3))) +\nscale_fill_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) + \nlabs(x = 'Slope (-)', y = 'Probability Density') + \nfacet_wrap(vars(formID), ncol = 1)\n\nplot_width = 5\n\nggsave(plot = slope_freq, filename = \"petm_slope_piceance_frequency.png\", path = figdir, width = plot_width, height = plot_width, units = \"in\", bg = 'transparent')\n\nggsave(plot = depth_freq_field, filename = \"petm_depth_piceance_frequency.png\", path = figdir, width = plot_width, height = plot_width, units = \"in\", bg = 'transparent')\n\nstrat_model_runs = ggplot(aes(x = pres_percents, y = reworking), data = strat_model_dataFilt3) + \ngeom_point() + \nxlim(0,1) + ylim(0.4,1) +\nscale_color_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nscale_fill_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nlabs(x = '% fully preserved bars', y = '% undisturbed stratigraphy') \n\nggsave(plot = strat_model_runs, filename = \"strat_model_runs.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nstrat_model_runs_trend = strat_model_runs + \ngeom_line(aes(x = pres_percents, y = reworkingPred), data = strat_model_dataFilt3, color = 'red3', size = 1.5)\n\nggsave(plot = strat_model_runs_trend, filename = \"strat_model_runs_trend.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nstrat_model_runs_overlay = strat_model_runs +\ngeom_point(aes(x = pres_percents, y = reworking, color = formation), data = barpres_pred) + \ntheme(legend.position=\"none\")\n\nggsave(plot = strat_model_runs_overlay, filename = \"strat_model_runs_overlay.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nstrat_model_runs_overlay_legend = strat_model_runs + \ngeom_point(aes(x = pres_percents, y = reworking, color = formation), data = barpres_pred) \n\nggsave(plot = strat_model_runs_overlay_legend, filename = \"strat_model_runs_overlay_legend.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nbootstrap_plot = ggplot(aes(y = ..density.., fill = reorder(formation, formID)), data = barpres_pred) +\nscale_fill_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nscale_color_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nfacet_wrap(vars(formID), ncol = 1) \n\npetm_bar_preservation = bootstrap_plot + \nstat_bin(aes(x = pres_percents, color = reorder(formation, formID)), bins = 20, geom = 'bar', size = 0.5, alpha = 0.4) + \ngeom_vline(aes(xintercept = mean_elems, color = reorder(formation, formID)), data = d, size = 2) +\nlabs(x = '% fully preserved bars', y = 'Probability Density', color = 'Member')\n\nggsave(plot = petm_bar_preservation, filename = \"petm_bar_preservation.png\", path = figdir, width = plot_width*1.3, height = plot_width, units = \"in\", bg = 'transparent')\n\npetm_sediment_retention = bootstrap_plot + \nstat_bin(aes(x = reworking, color = reorder(formation, formID)), data = barpres_pred, bins = 20, geom = 'bar', size = 0.5, alpha = 0.4) + \ngeom_vline(aes(xintercept = mean_reworking, color = reorder(formation, formID)), data = d, size = 2) +\nlabs(x = '% undisturbed stratigraphy', y = 'Probability Density', color = 'Member')\n\nggsave(plot = last_plot(), filename = \"petm_sediment_retention.png\", path = figdir, width = plot_width*1.3, height = plot_width, units = \"in\", bg = 'transparent')\n\nstrat_model_runs_n2g = ggplot(aes(x = pres_percents, y = n2g), data = strat_model_dataFilt3) + \ngeom_point() + \nxlim(0,1) + ylim(0,1) +\nscale_color_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nscale_fill_manual(values = cpal, name = 'Member', labels = c('Shire','Molina','Atwell Gulch'), breaks = legend_ord) +\nlabs(x = '% fully preserved bars', y = '% Sand') \n\nggsave(plot = strat_model_runs_n2g, filename = \"strat_model_runs_n2g.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nstrat_model_runs_n2g_trend = strat_model_runs_n2g + \ngeom_line(aes(x = pres_percents, y = n2gPred), data = strat_model_dataFilt3, color = 'red3', size = 1.5)\n\nggsave(plot = strat_model_runs_n2g_trend, filename = \"strat_model_runs_n2g_trend.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\n############### BONUS SLIDES ###############\n\n\nstrat_model_runs_n2g_overlay = strat_model_runs_n2g + \ngeom_point(aes(x = pres_percents, y = n2g, color = formation), data = barpres_pred) + \ntheme(legend.position=\"none\")\n\nggsave(plot = strat_model_runs_n2g_overlay, filename = \"strat_model_runs_n2g_overlay.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n\nstratFilt = strat_model_data %>% \nfilter(between(IR, 0.3, 0.55))\n\nreworking_by_avulsion_type = ggplot(aes(x = pres_percents, y = reworking, color = most_common_avulsion), data = stratFilt) + \ngeom_point() + \ngeom_line(aes(x = pres_percents, y = reworkingPred), data = strat_model_dataFilt3, color = '#009e2f', size = 1.5) + \nxlim(0,1) + ylim(0.4,1) +\ngeom_abline() + \nlabs(x = '% fully preserved bars', y = '% undisturbed stratigraphy', color = 'Avulsion Rule') \n\nggsave(plot = reworking_by_avulsion_type, filename = \"reworking_by_avulsion_type.png\", path = figdir, width = 7, height = 5, units = \"in\", bg = 'transparent')\n", "meta": {"hexsha": "0ab3a47f58c00464744f73dd76f925b1804b1304", "size": 11891, "ext": "r", "lang": "R", "max_stars_repo_path": "figures/src/agu_2019_talk_figs.r", "max_stars_repo_name": "ericbarefoot/barefoot_fluvial_reworking_manuscript_2020", "max_stars_repo_head_hexsha": "379d70bafcdc624064b052b8b7e86dd05ffff076", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "figures/src/agu_2019_talk_figs.r", "max_issues_repo_name": "ericbarefoot/barefoot_fluvial_reworking_manuscript_2020", "max_issues_repo_head_hexsha": "379d70bafcdc624064b052b8b7e86dd05ffff076", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "figures/src/agu_2019_talk_figs.r", "max_forks_repo_name": "ericbarefoot/barefoot_fluvial_reworking_manuscript_2020", "max_forks_repo_head_hexsha": "379d70bafcdc624064b052b8b7e86dd05ffff076", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.4761904762, "max_line_length": 215, "alphanum_fraction": 0.7012866874, "num_tokens": 3679, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3490428696386238}} {"text": "construct.knots <- function(argvals, knots, knots.option, p) {\n if (length(knots) == 1) {\n allknots <- select.knots(argvals, knots, p = p, option = knots.option)\n }\n\n if (length(knots) > 1) {\n K <- length(knots) - 1\n knots_left <- 2 * knots[1] - knots[p:1 + 1]\n knots_right <- 2 * knots[K] - knots[K - (1:p)]\n if (p > 0) allknots <- c(knots_left, knots, knots_right)\n if (p == 0) allknots <- knots\n }\n\n return(allknots)\n} # wrapper for select.knots", "meta": {"hexsha": "1c0058fe4964aa8a85c5fdf33d69f9e7bf9d565a", "size": 471, "ext": "r", "lang": "R", "max_stars_repo_path": "R/construct.knots.r", "max_stars_repo_name": "ZhuolinSong/Ftesting", "max_stars_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/construct.knots.r", "max_issues_repo_name": "ZhuolinSong/Ftesting", "max_issues_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/construct.knots.r", "max_forks_repo_name": "ZhuolinSong/Ftesting", "max_forks_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4, "max_line_length": 74, "alphanum_fraction": 0.5923566879, "num_tokens": 175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6619228691808012, "lm_q1q2_score": 0.34904286612184815}} {"text": "#Comparing tedersoo vs. neonCV fits to core and plot scale data.\nrm(list=ls())\nsource('paths.r')\n\n#load forecasts, reformat for analyses.----\nneo.core.cast <- readRDS(core.CV_NEON_fcast.path)\nneo.plot.cast <- readRDS(plot.CV_NEON_fcast.path)\nted.cast <- readRDS(NEON_dmulti.ddirch_fcast_fg.path)\n\n#break apart ted.cast to match NEON organization.\nted.core.cast <- list()\nted.plot.cast <- list()\nfor(i in 1:length(ted.cast)){\n ted.core.cast[[i]] <- list(ted.cast[[i]]$core.fit, ted.cast[[i]]$core.preds, ted.cast[[i]]$core.sd)\n ted.plot.cast[[i]] <- list(ted.cast[[i]]$plot.fit, ted.cast[[i]]$plot.preds, ted.cast[[i]]$plot.sd)\n names(ted.core.cast[[i]]) <- c('core.fit','core.preds','core.sd')\n names(ted.plot.cast[[i]]) <- c('plot.fit','plot.preds','plot.sd')\n}\nnames(ted.core.cast) <- names(ted.cast)\nnames(ted.plot.cast) <- names(ted.cast)\n\n#Load observed core and plot scale data, reformat for analysis.----\ncore.dat <- readRDS(core.CV_NEON_cal.val_data.path)\nplot.dat <- readRDS(plot.CV_NEON_cal.val_data.path)\n\n#Massage into nested by phylo/function level format.\nneo.core.dat <- list()\nneo.plot.dat <- list()\nted.core.dat <- list()\nted.plot.dat <- list()\nfor(i in 1:length(core.dat$cal$y.cal)){\n cal.core <- core.dat$cal$y.cal[[i]]$rel.abundances\n val.core <- core.dat$val$y.val[[i]]$rel.abundances\n cal.plot <- plot.dat$cal$y.cal[[i]]$mean\n val.plot <- plot.dat$val$y.val[[i]]$mean\n rownames(cal.core) <- gsub('-GEN','',rownames(cal.core))\n rownames(val.core) <- gsub('-GEN','',rownames(val.core))\n ted.core.dat[[i]] <- rbind(cal.core, val.core)\n ted.plot.dat[[i]] <- rbind(cal.plot, val.plot)\n neo.core.dat[[i]] <- val.core\n neo.plot.dat[[i]] <- val.plot\n}\nnames(ted.core.dat) <- names(ted.cast)\nnames(ted.plot.dat) <- names(ted.cast)\nnames(neo.core.dat) <- names(ted.cast)\nnames(neo.plot.dat) <- names(ted.cast)\n\n#Make model and data lists.\ncast <- list(neo.core.cast, neo.plot.cast, ted.core.cast, ted.plot.cast)\ndata <- list(neo.core.dat , neo.plot.dat , ted.core.dat , ted.plot.dat )\nnames(cast) <- c('neo.core.cast','neo.plot.cast','ted.core.cast','ted.plot.cast')\n\n#Get observed ~ predicted R2-best and R2-1:1 for each set of models.----\nall.rsq <- list()\nfor(i in 1:length(cast)){\n this.cast <- cast[[i]]\n this.data <- data[[i]]\n this.rsq <- list()\n for(j in 1:length(this.cast)){\n y <- this.data[[j]]\n x <- this.cast[[j]][[1]]$mean\n #make sure rownames are in same order\n x <- x[rownames(x) %in% rownames(y),]\n x <- x[order(match(rownames(x), rownames(y))),]\n #calculate R2 values\n rsq <- list()\n for(k in 1:ncol(x)){\n mod <- lm(y[,k] ~ x[,k])\n #rsq of best fit line.\n rsq.b <- summary(mod)$r.squared\n #resq of 1:1 line.\n rss <- sum((x[,k] - y[,k]) ^ 2) ## residual sum of squares\n tss <- sum((y[,k] - mean(y[,k])) ^ 2) ## total sum of squares\n rsq.1 <- 1 - rss/tss\n rsq.1 <- ifelse(rsq.1 < 0, 0, rsq.1)\n return <- c(rsq.b, rsq.1)\n rsq[[k]] <- return\n }\n rsq <- do.call(rbind, rsq)\n rownames(rsq) <- colnames(x)\n colnames(rsq) <- c('rsq.b','rsq.1')\n this.rsq[[j]] <- rsq\n }\n names(this.rsq) <- names(this.cast)\n all.rsq[[i]] <- this.rsq\n}\nnames(all.rsq) <- names(cast)\n\n#Visualize differences in R2 by function/phylo group, scale (core vs. plot) and forecast (tedersoo vs. NEON.cv).-----\nto_plot.rsq <- list()\nfor(i in 1:length(all.rsq)){\n this.rsq <- all.rsq[[i]]\n rsq.sum <- list()\n for(k in 1:length(this.rsq)){\n rsq.sum[[k]] <- mean(this.rsq[[k]][,1], na.rm = T)\n }\n rsq.sum <- unlist(rsq.sum)\n names(rsq.sum) <- names(this.rsq)\n to_plot.rsq[[i]] <- rsq.sum\n}\nnames(to_plot.rsq) <- names(all.rsq)\n\n\n", "meta": {"hexsha": "7f8a5ce5f86cb468c5e79083d0ba6533e1ae97cb", "size": 3644, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/spatial_forecast_analysis/multinomial_ddirch_cast/comparing_ted_NEON.CV_fits.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "ITS/analysis/spatial_forecast_analysis/multinomial_ddirch_cast/comparing_ted_NEON.CV_fits.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ITS/analysis/spatial_forecast_analysis/multinomial_ddirch_cast/comparing_ted_NEON.CV_fits.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 35.0384615385, "max_line_length": 117, "alphanum_fraction": 0.6215697036, "num_tokens": 1198, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228625116081, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.34904286260507245}} {"text": "#=============================================================\n# c:/1work/Modelling/QFC_workshop/examples/extract_data/slh_whitefish.r\n# Created: 11 Nov 2013 13:56:36\n\n#\n# DESCRIPTION:\n#\n#\n#\n# A. Cottrill\n#=============================================================\n\n# LIBRARIES:\n\nlibrary(RODBC)\nlibrary(reshape2)\n\n# OTHER SCRIPTS:\n#source(\"\")\n\n#=============================================================\n\ndbase <- c(\"E:/Data Warehouse/Merged Datasets/Species_Databases/Lake_Whitefish/091_United.mdb\")\n\nqmas <- \"'6-1'\"\n#qmas <- \"'5-8','5-9'\"\nyr <- 2002\nbasin <- switch(qmas,\n \"'6-1'\"='nc',\n \"'4-5'\"='smb',\n 'gb') \n\n\nsql <- \n\"SELECT AGE, FLEN FROM All_BioData\nINNER JOIN [LOCATION with LATLONG] ON All_BioData.GRID = [LOCATION with LATLONG].GRID\nWHERE ((([LOCATION with LATLONG].QUOTA_ZONE) IN (qmas)) AND ((All_BioData.YEAR)='yr') AND ((All_BioData.AGE) Is Not Null) AND ((All_BioData.FLEN) Is Not Null))\nORDER BY All_BioData.AGE, All_BioData.FLEN;\"\n\n\nsql <- gsub('yr', yr, sql)\nsql <- gsub('qmas', qmas, sql)\n\n\nDBConnection <- odbcConnectAccess(dbase,uid = \"\", pwd = \"\")\ndata <- sqlQuery(DBConnection, sql)\n head(data)\n str(data)\n nrow(data)\nodbcClose(DBConnection) \n\nN <- 10\nages <- unique(data$AGE)\nselected <- subset(data, data$AGE<0)\n\nfor (i in seq(along=ages)){\n sub <- subset(data, data$AGE==ages[i])\n if (nrow(sub) <= N){\n selected <- rbind(selected, sub)\n } else {\n selected <- rbind(selected, sub[sample(nrow(sub),N,replace=F),])\n }\n}\n\noutdir <- 'c:/1work/Modelling/QFC_workshop/examples/extract_data/'\nfname <-paste0(outdir,'whitefish-',basin,'-', yr, '.csv')\n\n \njj <- selected[with(selected, order('AGE', 'FLEN')),]\nwrite.csv(selected, fname,row.names=FALSE)\n\nf <- function()\n{\n ## Purpose:\n ## ----------------------------------------------------------------------\n ## Arguments:\n ## ----------------------------------------------------------------------\n ## Author: , Date: 5 Dec 2013, 14:54\n}\n\n\nlibrary(reshape2)\n\n\n\n\n#fit the model and predicte size at age\nstart = list('Linf'=max(data$FLEN), 't0'=0.0, 'k'=0.25)\nvonB <- nls(FLEN ~ Linf * (1 - exp(-k * (AGE - t0))),\n data=data, start=start)\n\nsummary(vonB)\n\npredicted <- data.frame('AGE' = seq((min(ages) -1),\n max(ages) + 1, by=0.1), 'FLEN'=0)\n\npredicted$FLEN <- predict(vonB,newdata=predicted)\n\n\n#make a plot\nplot(data$AGE, data$FLEN, xlab=\"Age\",\n ylab=\"Fork Length (mm)\")\nlines(predicted$AGE, predicted$FLEN, col='blue')\n\n\n#calculate mean size at age\nmu <- cast(data, AGE~., value='FLEN', fun=mean)\nnames(mu) <- c('Age','Observed')\nmu$Predicted <- predict(vonB, newdata=data.frame('AGE'=ages))\n\n\n", "meta": {"hexsha": "23a26b9cb14d62fc91240152cfb6faf01b935105", "size": 2717, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/extract_data/slh_whitefish.r", "max_stars_repo_name": "AdamCottrill/QFC_Workshop", "max_stars_repo_head_hexsha": "d488ce3672b0c0fe1e33d05aae29b803cd16a8ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-08-07T03:58:03.000Z", "max_stars_repo_stars_event_max_datetime": "2015-08-07T03:58:03.000Z", "max_issues_repo_path": "examples/extract_data/slh_whitefish.r", "max_issues_repo_name": "AdamCottrill/QFC_Workshop", "max_issues_repo_head_hexsha": "d488ce3672b0c0fe1e33d05aae29b803cd16a8ef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/extract_data/slh_whitefish.r", "max_forks_repo_name": "AdamCottrill/QFC_Workshop", "max_forks_repo_head_hexsha": "d488ce3672b0c0fe1e33d05aae29b803cd16a8ef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.2589285714, "max_line_length": 159, "alphanum_fraction": 0.5421420685, "num_tokens": 785, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6926419958239132, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.3490265756632475}} {"text": "\n# nocov start\nfgcodes <- c(paste0('\\x1b[38;5;', 0:255, 'm'), '\\x1b[39m')\nbgcodes <- c(paste0('\\x1b[48;5;', 0:255, 'm'), '\\x1b[49m')\n\nrgb_index <- 17:232\ngray_index <- 233:256\nreset_index <- 257\n#nocov end\n\nansi256 <- function(rgb, bg = FALSE, grey = FALSE) {\n codes <- if (bg) bgcodes else fgcodes\n if (grey) {\n ## Gray\n list(\n open = codes[gray_index][scale(rgb[1], to = c(0, 23)) + 1],\n close = codes[reset_index]\n )\n \n } else {\n ## Not gray\n list(\n open = codes[ansi256_rgb_index(rgb[1L], rgb[2L], rgb[3L])],\n close = codes[reset_index]\n )\n }\n}\n\n## This is based off the algorithm in the ruby \"paint\" gem, as\n## implemented in rainbowrite.\nansi256_rgb_index <- function(red, green, blue) {\n gray_possible <- TRUE\n sep <- 42.5\n while (gray_possible) {\n if (red < sep || green < sep || blue < sep) {\n gray <- red < sep && green < sep && blue < sep\n gray_possible <- FALSE\n }\n sep <- sep + 42.5\n }\n\n ## NOTE: The +1 here translates from base0 to base1 for the index\n ## that does the same. Not ideal, but that does get the escape\n ## characters in nicely.\n if (gray) {\n 232 + round((red + green + blue) / 33) + 1\n } else {\n 16 + sum(floor(6 * c(red, green, blue) / 256) * c(36, 6, 1)) + 1\n }\n}\n", "meta": {"hexsha": "b20fd6e574a3a9784543f77f91b8ca5dc9df41d4", "size": 1276, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ansi-256.r", "max_stars_repo_name": "tiegz/crayon", "max_stars_repo_head_hexsha": "a355cab152eca592685c7c08e41d5798fb1cbbf8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 227, "max_stars_repo_stars_event_min_datetime": "2017-06-21T13:25:16.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T11:08:56.000Z", "max_issues_repo_path": "R/ansi-256.r", "max_issues_repo_name": "tiegz/crayon", "max_issues_repo_head_hexsha": "a355cab152eca592685c7c08e41d5798fb1cbbf8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 90, "max_issues_repo_issues_event_min_datetime": "2017-06-27T15:05:28.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T21:59:46.000Z", "max_forks_repo_path": "R/ansi-256.r", "max_forks_repo_name": "r-pkgs/crayon", "max_forks_repo_head_hexsha": "0b5a8a808341ff18ae404eed5b02108e8d06a8ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 32, "max_forks_repo_forks_event_min_datetime": "2017-07-28T23:07:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-14T04:15:37.000Z", "avg_line_length": 25.0196078431, "max_line_length": 68, "alphanum_fraction": 0.5744514107, "num_tokens": 445, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3490265692690928}} {"text": "rm(list=ls())\nlibrary(plyr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\nlibrary(readxl)\n\n\n\n\n\nsetwd(\"/Users/ChatNoir/Projects/Squam/Graphs/\")\n\n# Read in data\nmid <- read_excel(\"/Users/chatnoir/Projects/Squam/Graphs/Tables/AllSchmoosh.xlsx\")\nload(\"/Users/chatnoir/Projects/Squam/scripts_ch1/Graphing/DataFiles/corsubsample\")\ntotalLoci <- max(mid$Loci)\n# Remove non subsampled values \nmid <- mid %>% filter(Sample != \"all\")\nresults <- results %>% filter(Sample != \"all\")\n\n# Edit dataframes \nmid <- mid %>% filter(Sample == \"sig80m\" | Sample == \"sig60m\") %>% mutate(Sample = sub('sig', '', Sample))\nmid$Subsample <- paste(mid$Hypothesis,mid$Sample,sep = \" - \")\n# factor \nmid$Subsample <- factor(mid$Subsample, levels = c(\"AIvSA - 80m\", \"AIvSI - 80m\", \"SAvSI - 80m\", \"AIvSA - 60m\", \"AIvSI - 60m\", \"SAvSI - 60m\"))\n# add proportion of loci in subsample \nmid$propLoci <- mid$Loci/totalLoci\n\n\n# Select null distributions for 2k-4k \nnull2k4k <- results %>% filter(Loci >= 2000) \nnull4k <- results %>% filter(Loci >= 4000) \nnull2k <- results %>% filter(Loci >= 2000) %>% filter(Loci <= 4000) \n\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# Pvals ---------------------------------------------- Pvals --------------------------------------------------------------------------------------------------------\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\nh <- 'TvS'\nn <- null2k %>% filter(Hypothesis == h) \ns <- '80m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\ns <- '60m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\nh <- 'AIvSA'\nn <- null2k %>% filter(Hypothesis == h) \ns <- '80m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\ns <- '60m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\nh <- 'AIvSA'\nn <- null2k %>% filter(Hypothesis == h) \ns <- '80m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\ns <- '60m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\nh <- 'AIvSI'\nn <- null2k %>% filter(Hypothesis == h) \ns <- '80m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\ns <- '60m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\nh <- 'SAvSI'\nn <- null2k %>% filter(Hypothesis == h) \ns <- '80m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\ns <- '60m'\nm <- mid %>% filter(Hypothesis == h) %>% filter(Sample == s)\ne <- m$corr.eff\np.s <- sum(abs(n$corr.eff) >= abs(e)) / length(n$corr.eff)\np.s\n\n# all p = 1, ie all sim cor eff are higher than emp vals. all of them. \n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# Histogram ---------------------------------------------- Histogram --------------------------------------------------------------------------------------------------------\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\nH <- function(df,xval,cc,hbin,xlab){\n ggplot(data=df, aes(x=xval)) +\n geom_histogram(aes(y=..count../sum(..count..)),bins=hbin, alpha=1, position=\"dodge\", fill=cc, color=\"grey\",size=0.1) +\n #geom_histogram(breaks=brx, alpha=1, position=\"dodge\", fill=cc, color=\"grey\",size=0.1)+ \n #geom_histogram(binwidth = max(abs(xval))*0.01, alpha=1, position=\"dodge\",color=\"grey\", fill=cc, size=0.1)+ \n theme_classic() + \n theme(plot.title = element_text(hjust = 0.5, size=10),\n axis.text = element_text(size=6, color=\"black\"),\n text = element_text(size=6),\n legend.title = element_text(size = 6),\n legend.text = element_text(size = 6), \n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid = element_blank()) +\n labs(y=\"Proportion of Loci\",x=xlab) + \n coord_cartesian(xlim = c(0,1))\n }\n\n\nquartz()\n\n\n# Set up numbers - TOX\ndf <- results %>% filter(Loci >= 4000) %>% filter(Hypothesis != \"TvS\") \n#d <- null2k4k %>% filter(Hypothesis != \"TvS\") \nm <- mid %>% filter(Hypothesis != \"TvS\") \n\nx.val <- df$corr.eff \n# 95% pval \nh05 <- unlist(df %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.975,0.0275))))\nh01 <- unlist(df %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.995,0.005))))\n\n\nlines <- m$corr.eff \n\n# Graph \nh.bin <- 20\ncc <- 'grey'\nmax.x <- round_any(max(abs(x.val)),10,f=ceiling)\n\ngl <- H(df,x.val,cc,h.bin,'Correlation coefficient') + geom_vline(xintercept=h01,color=c(\"black\"), linetype=\"dashed\", size=0.2)\n\ntitle <- \"4000 loci\"\n\ng <- gl + geom_point(m, mapping = aes(x=corr.eff, y=p, color=Subsample), position = position_dodge(width = 0.01)) + \n ggtitle(title) \ng\n\n\n# Set up numbers - TVS\ndft <- results %>% filter(Loci == 2000) %>% filter(Hypothesis == \"TvS\") \nmt <- mid %>% filter(Hypothesis == \"TvS\") \nmt$Subsample <- paste(mt$Hypothesis,mt$Sample,sep = \" - \")\n# factor \nmt$Subsample <- factor(mt$Subsample, levels = c(\"TvS - 80m\", \"TvS - 60m\"))\nx.val <- dft$corr.eff \n# 95% pval \nh05 <- unlist(dft %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.975,0.0275))))\nh01 <- unlist(dft %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.995,0.005))))\n\n\nlines <- mt$corr.eff \n\n# Graph \nh.bin <-40\ncc <- 'grey'\nmax.x <- round_any(max(abs(x.val)),10,f=ceiling)\n\ngl2 <- H(dft,x.val,cc,h.bin,'Correlation coefficient') + geom_vline(xintercept=h01,color=c(\"black\"), linetype=\"dashed\", size=0.2)\n\ntitle <- \"2000 loci\"\n\ng2 <- gl2 + geom_point(mt, mapping = aes(x=corr.eff, y=p, color=Subsample), position = position_dodge(width = 0.01)) + \n ggtitle(title) \n\ngg <- ggarrange(g,g2, ncol=2, nrow=1,align=\"hv\", common.legend = F, legend = \"right\")\n\n\nggsave(\"CorrcoeffNull_all.pdf\", plot=gg,width = 7, height = 2, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# two types subsample -----------------------------------------------------------------------------------------------------------------------------------------------------\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n\n\n\nsetwd(\"/Users/ChatNoir/Projects/Squam/Graphs/\")\n\n# Read in data\nmid <- read_excel(\"/Users/chatnoir/Projects/Squam/Graphs/Tables/AllSchmoosh_mb.xlsx\")\nload(\"/Users/chatnoir/Projects/Squam/scripts_ch1/Graphing/DataFiles/corsubsample\")\ntotalLoci <- max(mid$Loci)\n# Remove non subsampled values \nmid <- mid %>% filter(Sample != \"all\")\nresults <- results %>% filter(Sample != \"all\")\n\n# Edit dataframes \nmid <- mid %>% mutate(Sample = sub('quant', '', Sample))\nmid$Subsample <- paste(mid$Hypothesis,mid$Sample,sep = \" - \")\n# factor \nmid$Subsample <- factor(mid$Subsample, levels = c(\"AIvSA - 80m\", \"AIvSI - 80m\", \"SAvSI - 80m\", \n \"AIvSA - 80mb\", \"AIvSI - 80mb\", \"SAvSI - 80mb\", \n \"AIvSA - 60m\", \"AIvSI - 60m\", \"SAvSI - 60m\",\n \"AIvSA - 60mb\", \"AIvSI - 60mb\", \"SAvSI - 60mb\"))\n# add proportion of loci in subsample \nmid$propLoci <- mid$Loci/totalLoci\n\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# Histogram ---------------------------------------------- Histogram --------------------------------------------------------------------------------------------------------\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\nquartz()\n\n\n# Set up numbers - TOX\ndf <- results %>% filter(Loci >= 4000) %>% filter(Hypothesis != \"TvS\") \n#d <- null2k4k %>% filter(Hypothesis != \"TvS\") \nm <- mid %>% filter(Hypothesis != \"TvS\") \n\nx.val <- df$corr.eff \n# 95% pval \nh05 <- unlist(df %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.975,0.0275))))\nh01 <- unlist(df %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.995,0.005))))\n\n\nlines <- m$corr.eff \n\n# Graph \nh.bin <- 20\ncc <- 'grey'\nmax.x <- round_any(max(abs(x.val)),10,f=ceiling)\n\ngl <- H(df,x.val,cc,h.bin,'Correlation coefficient') + geom_vline(xintercept=h01,color=c(\"black\"), linetype=\"dashed\", size=0.2)\n\ntitle <- \"Null correlation coefficient distribution for random subsample of 4k loci\"\n\ng <- gl + geom_point(m, mapping = aes(x=corr.eff, y=p, color=Subsample), position = position_dodge(width = 0.01)) + \n ggtitle(title) \ng\n#ggsave(\"CorrcoeffNull_tox_all4k_mb.pdf\", plot=g,width = 6, height = 4, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n# Set up numbers - TVS\n\ndft <- results %>% filter(Loci == 1000) %>% filter(Hypothesis == \"TvS\") \nmt <- mid %>% filter(Hypothesis == \"TvS\") \nmt$Subsample <- paste(mt$Hypothesis,mt$Sample,sep = \" - \")\n# factor \nmt$Subsample <- factor(mt$Subsample, levels = c(\"TvS - 80m\",\"TvS - 80mb\", \"TvS - 60m\", \"TvS - 60mb\"))\nx.val <- dft$corr.eff \n# 95% pval \nh05 <- unlist(dft %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.975,0.0275))))\nh01 <- unlist(dft %>% summarise(Bin_q.95 = quantile(corr.eff, c(0.995,0.005))))\n\n\nlines <- mt$corr.eff \n\n# Graph \nh.bin <-40\ncc <- 'grey'\nmax.x <- round_any(max(abs(x.val)),10,f=ceiling)\n\ngl <- H(dft,x.val,cc,h.bin,'Correlation coefficient') + geom_vline(xintercept=h01,color=c(\"black\"), linetype=\"dashed\", size=0.2)\n\ntitle <- \"Null correlation coefficient distribution for random subsample of 1k loci\"\n\ng <- gl + geom_point(mt, mapping = aes(x=corr.eff, y=p, color=Subsample), position = position_dodge(width = 0.01)) + \n ggtitle(title) \ng\n\n\n#ggsave(\"CorrcoeffNull_tvs_all1k_mb.pdf\", plot=g,width = 6, height = 4, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n\n", "meta": {"hexsha": "2d52eb5cb247ec6352cd5c275482b8fcee5294f0", "size": 10802, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/aManuScripts/All_Graphs_Histogram_corsub.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/aManuScripts/All_Graphs_Histogram_corsub.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/aManuScripts/All_Graphs_Histogram_corsub.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4932432432, "max_line_length": 173, "alphanum_fraction": 0.5157378263, "num_tokens": 3195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6926419704455588, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.3490265628749381}} {"text": "#' Parse data for deep learning model training\n#'\n#' @importFrom recipes all_predictors\n#' @importFrom recipes all_outcomes\n#'\n#' @param input_data A dataframe containing the input data.\n#' @param partitioning_type A character string indicating the desired spatial data partitioning method. Can be \"default\", \"block\", \"checkerboard1\", or \"checkerboard2\".\n#'\n#' @return A dataframe containing the prepared data.\n#' @examples\n#' \\dontrun{\n#' # download benchmarking data\n#' benchmarking_data <- get_benchmarking_data(\"Lynx lynx\",\n#' limit = 1500)\n#'\n#' # transform benchmarking data into a format suitable for deep learning\n#' # if you have previously used a partitioning method you should specify it here\n#' benchmarking_data_dl <- prepare_dl_data(input_data = benchmarking_data$df_data,\n#' partitioning_type = \"default\")\n#'\n#' # perform sanity check on the transformed dataset\n#' # for the training set\n#' head(benchmarking_data_dl$train_tbl)\n#' table(benchmarking_data_dl$y_train_vec)\n#'\n#' # for the test set\n#' head(benchmarking_data_dl$test_tbl)\n#' table(benchmarking_data_dl$y_test_vec)\n#'}\n#'@export\nprepare_dl_data <- function(input_data, partitioning_type) {\n if (partitioning_type %in% c(\"checkerboard1\", \"checkerboard2\")) {\n input_data$grp_checkerboard <- NULL\n input_data$label <- as.integer(input_data$label)\n\n # fix coercion error (for plotting)\n input_data$label <- ifelse(input_data$label == 2, 1, 0)\n }\n\n input_data$grp <- NULL\n\n input_data <- input_data %>%\n tidyr::drop_na() %>%\n dplyr::select(label, dplyr::everything())\n\n train_test_split <- rsample::initial_split(input_data, prop = 0.8)\n train_tbl <- rsample::training(train_test_split)\n test_tbl <- rsample::testing(train_test_split)\n\n # create a recipe for centering and scaling\n rec_obj <- recipes::recipe(label ~ ., data = train_tbl) %>%\n recipes::step_center(all_predictors(),\n -all_outcomes()) %>%\n recipes::step_scale(all_predictors(), -all_outcomes()) %>%\n recipes::prep(data = train_tbl)\n\n # use recipe\n x_train_tbl <- recipes::bake(rec_obj, new_data = train_tbl) %>%\n dplyr::select(-label)\n x_test_tbl <- recipes::bake(rec_obj, new_data = test_tbl) %>%\n dplyr::select(-label)\n\n y_train_vec <- train_tbl$label\n y_test_vec <- test_tbl$label\n\n result_list <- list(train_tbl = x_train_tbl,\n test_tbl = x_test_tbl,\n y_train_vec = y_train_vec,\n y_test_vec = y_test_vec,\n rec_obj = rec_obj)\n\n return(result_list)\n}\n", "meta": {"hexsha": "eed5cc89643ec38e7e4acc023476d96aadf5c796", "size": 2696, "ext": "r", "lang": "R", "max_stars_repo_path": "R/prepare_dl_data.r", "max_stars_repo_name": "boyanangelov/sdmbench", "max_stars_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2018-06-25T19:55:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-06T08:36:48.000Z", "max_issues_repo_path": "R/prepare_dl_data.r", "max_issues_repo_name": "boyanangelov/sdmbench", "max_issues_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2018-08-01T01:31:09.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-12T16:02:07.000Z", "max_forks_repo_path": "R/prepare_dl_data.r", "max_forks_repo_name": "boyanangelov/sdmbench", "max_forks_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-10-12T06:07:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-02T17:52:53.000Z", "avg_line_length": 36.4324324324, "max_line_length": 167, "alphanum_fraction": 0.6572700297, "num_tokens": 653, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34901492735579864}} {"text": "times <- function (x, y) {\n return (x*y)\n}\n", "meta": {"hexsha": "16989b243f47fb6dda37dde4b553ee52e05bdade", "size": 44, "ext": "r", "lang": "R", "max_stars_repo_path": "test/test-functions.r", "max_stars_repo_name": "dareid/gr", "max_stars_repo_head_hexsha": "43e330bee47f85486610908751a4e188d083d12e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 284, "max_stars_repo_stars_event_min_datetime": "2015-08-19T13:02:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T17:21:06.000Z", "max_issues_repo_path": "test/test-functions.r", "max_issues_repo_name": "dareid/gr", "max_issues_repo_head_hexsha": "43e330bee47f85486610908751a4e188d083d12e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 40, "max_issues_repo_issues_event_min_datetime": "2015-08-20T21:43:57.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-06T23:28:15.000Z", "max_forks_repo_path": "test/test-functions.r", "max_forks_repo_name": "dareid/gr", "max_forks_repo_head_hexsha": "43e330bee47f85486610908751a4e188d083d12e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 38, "max_forks_repo_forks_event_min_datetime": "2015-08-19T18:32:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-24T08:51:44.000Z", "avg_line_length": 11.0, "max_line_length": 26, "alphanum_fraction": 0.5227272727, "num_tokens": 16, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540699, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34901491888207864}} {"text": "#Replicating UNIX assignment in R\n\n### Data Inspection\ngeno<-read.table(\"fang_et_al_genotypes.txt\", header=T,stringsAsFactors = F)\nsnp<-read.delim(\"snp_position.txt\",header=T,stringsAsFactors = F) #ooohhhh it's tab deliminated and not a table/csv......\n\n#\"Fang_et_al_genotypes.txt\"Inspection\nclass(geno)\nattributes(geno)\ncolnames(geno[1:10])\nrow.names(geno)\ndim(t(snp))\n\n#snp_position Inspection\nclass(snp)\ncolnames(snp)\nrow.names(snp) #Rows = SNP_ID\nattributes(snp)\ndim(snp)\n\n### Extracting and Processing Data\nmaize_geno <- geno[(geno$Group == \"ZMMIL\")|(geno$Group == \"ZMMLR\")|(geno$Group ==\"ZMMMR\"),]\nteo_geno<- geno[(geno$Group == \"ZMPBA\")|(geno$Group == \"ZMPIL\")|(geno$Group == \"ZMPJA\"),]\n\n#Transpose each of the genotype files\ntmaize_geno<- t(maize_geno)\ntteo_geno<- t(teo_geno)\n\n#Merge genotype files with snp data\ntmaize_joined<- merge(snp, tmaize_geno, by.x = \"SNP_ID\",by.y = \"row.names\")\ntteo_joined<- merge(snp, tteo_geno, by.x = \"SNP_ID\",by.y = \"row.names\")\n\n#Find dimensions of file to extract columns needed\ndim(tmaize_joined)\ncut_maize<-tmaize_joined[,c(1,3,4,16:1588)]\n\ndim(tteo_joined)\ncut_teo<-tteo_joined[,c(1,3,4,16:990)]\n\n#Sort the joined files by snp position.\ncut_maize[order(as.numeric(as.character(cut_maize$Position))),]->maize_increasing_snps\ncut_maize[order(as.numeric(as.character(cut_maize$Position)),decreasing = T),]->maize_decreasing_snps\n\n\ncut_teo[order(as.numeric(as.character(cut_teo$Position))),]->teo_increasing_snps\ncut_teo[order(as.numeric(as.character(cut_teo$Position)),decreasing=T),]->teo_decreasing_snps\n\n#Changing out '?' for '-' for decreasing position files\n\nmaize_dashes <- maize_decreasing_snps\nmaize_dashes[maize_dashes == \"?/?\"] <- \"-/-\"\n\nteo_dashes <- cut_teo\nteo_dashes[teo_dashes == \"?/?\"] <- \"-/-\"\n\n#Making Folders for the four chromosome sets\ndir.create(\"maize_chr_increasing\")\ndir.create(\"maize_chr_decreasing\")\ndir.create(\"teosinte_chr_increasing\")\ndir.create(\"teosinte_chr_decreasing\")\n\n#Extracting data for each chromosome and writing files\nfor (i in 1:10) {\n maize_chr_loop <- maize_increasing_snps[maize_increasing_snps$Chromosome == i,]\n write.csv(maize_chr_loop, sprintf(\"maize_chr_increasing/maize_chromosome_%d_increasing_snps\", i), row.names = F)\n}\n\nfor (i in 1:10) {\n maize_chr_loop <- maize_dashes[maize_dashes$Chromosome == i,]\n write.csv(maize_chr_loop, sprintf(\"maize_chr_decreasing/maize_chromosome_%d_decreasing_snps\", i), row.names = F)\n}\n\nfor (i in 1:10) {\n teo_chr_loop <- teo_increasing_snps[teo_increasing_snps$Chromosome == i,]\n write.csv(teo_chr_loop, sprintf(\"teosinte_chr_increasing/teosinte_chromosome_%d_increasing_snps\", i), row.names = F)\n}\n\nfor (i in 1:10) {\n teo_chr_loop <- teo_dashes[teo_dashes$Chromosome == i,]\n write.csv(teo_chr_loop, sprintf(\"teosinte_chr_decreasing/teosinte_chromosome_%d_decreasing_snps\", i), row.names = F)\n}\n\n", "meta": {"hexsha": "0ba9e4799da5f344bac9f79babea2f39f7fa552a", "size": 2842, "ext": "r", "lang": "R", "max_stars_repo_path": "R_Assignment/class R files/r_assignment.r", "max_stars_repo_name": "erossow/homeworks", "max_stars_repo_head_hexsha": "a595e9fd0b712820b8306e090cc3c064a07b050e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R_Assignment/class R files/r_assignment.r", "max_issues_repo_name": "erossow/homeworks", "max_issues_repo_head_hexsha": "a595e9fd0b712820b8306e090cc3c064a07b050e", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R_Assignment/class R files/r_assignment.r", "max_forks_repo_name": "erossow/homeworks", "max_forks_repo_head_hexsha": "a595e9fd0b712820b8306e090cc3c064a07b050e", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.2409638554, "max_line_length": 129, "alphanum_fraction": 0.7512315271, "num_tokens": 925, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032313, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.348973722284497}} {"text": "#' Experimental package to learn how to create packages in R.\n#'\n#' myFirstRPackage package has two functions:\n#' euclidean() and dijkstra().\n#' \n#' @section euclidean():\n#' Computes the greatest common divisor (GCD) of two integers (numbers).\n#'\n#' @section dijkstra():\n#' Finds the shortest paths between nodes in a graph\n#' \n#' @docType package\n#' @name myFirstRPackage\nNULL\n#> NULL", "meta": {"hexsha": "e24aadeb8339f4e3b8538794631c62e0edd13c4c", "size": 385, "ext": "r", "lang": "R", "max_stars_repo_path": "R/myFirstRPackage.r", "max_stars_repo_name": "ahmedNwayyir/myFirstRPackage", "max_stars_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/myFirstRPackage.r", "max_issues_repo_name": "ahmedNwayyir/myFirstRPackage", "max_issues_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/myFirstRPackage.r", "max_forks_repo_name": "ahmedNwayyir/myFirstRPackage", "max_forks_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6666666667, "max_line_length": 72, "alphanum_fraction": 0.7090909091, "num_tokens": 98, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.34897372228449697}} {"text": "plot(1:10, main=\"My Graph\", xlab=\"The x-axis\", ylab=\"The y-axis\",\n col=\"red\",\n type=\"l\",\n lwd = 2,\n cex = 1,\n pch = 20)\n", "meta": {"hexsha": "e27308cff470ad524a0875f3a3c1f3f4852beb87", "size": 140, "ext": "r", "lang": "R", "max_stars_repo_path": "Graph.r", "max_stars_repo_name": "teddythinh/R-language-Practice", "max_stars_repo_head_hexsha": "32da21de35ec1b93947149b80e09a6c1c4c39979", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graph.r", "max_issues_repo_name": "teddythinh/R-language-Practice", "max_issues_repo_head_hexsha": "32da21de35ec1b93947149b80e09a6c1c4c39979", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graph.r", "max_forks_repo_name": "teddythinh/R-language-Practice", "max_forks_repo_head_hexsha": "32da21de35ec1b93947149b80e09a6c1c4c39979", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.0, "max_line_length": 65, "alphanum_fraction": 0.4714285714, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.34897372228449697}} {"text": "suppressPackageStartupMessages(library(kazaam))\n\n\nif (comm.rank() == 0){\n m = 10\n x = matrix(1:20, m)\n y = matrix(1:30, m)\n z = matrix(1:10, m)\n c = cbind(x, y, z)\n} else {\n x = NULL\n y = NULL\n z = NULL\n}\n\ndx = expand(x)\ndy = expand(y)\ndz = expand(z)\n\ndc = cbind(dx, dy, dz)\nc_test = collapse(dc)\n\ncomm.print(all.equal(c, c_test))\n\n\nfinalize()\n", "meta": {"hexsha": "d8f07cdf4bc9ec5ec021f2d3aba6d75784625b6a", "size": 352, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/batchtests/cbind.r", "max_stars_repo_name": "cran/kazaam", "max_stars_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2017-07-16T19:21:57.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-24T13:07:08.000Z", "max_issues_repo_path": "inst/batchtests/cbind.r", "max_issues_repo_name": "cran/kazaam", "max_issues_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2017-06-24T21:33:17.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-07T03:33:48.000Z", "max_forks_repo_path": "inst/batchtests/cbind.r", "max_forks_repo_name": "cran/kazaam", "max_forks_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-06-24T21:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2017-06-24T21:22:10.000Z", "avg_line_length": 13.037037037, "max_line_length": 47, "alphanum_fraction": 0.5852272727, "num_tokens": 129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.34897372228449697}} {"text": "#' Correlation Animated Bubble plot.\n#'\n#' anibubble function will draw Animated Bubble plot for correlation analysis.\n#' @param data input data.frame\n#' @param x x variable\n#' @param y y variable\n#' @param col.var color variable\n#' @param size.var size variable\n#' @param frame.var frame variable\n#' @param fps frame change per second\n#' @param width width plot size\n#' @param height height plot size\n#' @param title main title\n#' @param subtitle subtitle\n#' @param xtitle x axis title\n#' @param ytitle y axis title\n#' @param caption caption\n#' @return An object of class \\code{ggplot}\n#' @examples\n#' plot<- anibubble(data=gapminder,x=\"gdpPercap\",y=\"lifeExp\",\n#' col.var=\"continent\",size.var = \"pop\", frame.var = \"year\")\n#' plot\n#'\n#' @import ggplot2\n#' @import scales\n#' @import reshape2\n#' @import ggthemes\n#' @import gganimate\n#' @import gapminder\n#' @import ggalt\n#' @import ggExtra\n#' @import ggcorrplot\n#' @import dplyr\n#' @import treemapify\n#' @import ggfortify\n#' @import zoo\n#' @import ggdendro\n#' @export\nanibubble<-function(data,x,y,col.var,size.var,frame.var,\n fps=5,width= 1900, height=1068.75,\n title=NULL,subtitle=NULL,xtitle=NULL,ytitle=NULL,caption=NULL){\n df<- data\n x<- x\n y<- y\n color<- col.var\n size<- size.var\n frame<-frame.var\n\n #animate par\n fps<- fps\n width<- width\n height<- height\n\n p <- ggplot(df, aes_string(x, y, col=color, size=size, frame=frame)) +\n geom_point() +\n transition_states(year) +\n theme_fivethirtyeight() +\n theme(axis.title = element_text(),\n legend.title = element_text(face = 4,size = 10),\n legend.direction = \"horizontal\", legend.box = \"horizontal\") +\n scale_color_tableau() +\n labs(subtitle=paste(subtitle,\"{closest_state}\"),\n y=ytitle,\n x=xtitle,\n title=title,\n caption = caption)\n\n return(p)\n\n}\n", "meta": {"hexsha": "ae0287001f9b4c12537fbb2c346f163bb6fa4c16", "size": 1886, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bubble_ani.r", "max_stars_repo_name": "HeeseokMoon/ggedachart", "max_stars_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/bubble_ani.r", "max_issues_repo_name": "HeeseokMoon/ggedachart", "max_issues_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/bubble_ani.r", "max_forks_repo_name": "HeeseokMoon/ggedachart", "max_forks_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5633802817, "max_line_length": 84, "alphanum_fraction": 0.6495227996, "num_tokens": 519, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.34897372228449697}} {"text": "context(\"compute_density\")\n\ntest_that(\"compute_density respects arguments\", {\n # Uses n\n res <- mtcars %>% compute_density(~mpg, n = 10)\n expect_equal(10, nrow(res))\n\n # When trim = FALSE (default), result goes past bounds of original data\n expect_true(min(res$pred_) < min(mtcars$mpg) && max(res$pred_) > max(mtcars$mpg))\n\n # When trim = TRUE, bounds of result match bounds of original data\n res <- mtcars %>% compute_density(~mpg, n = 10, trim = TRUE)\n expect_true(all(range(res$pred_) == range(mtcars$mpg)))\n})\n\n\ntest_that(\"Zero-row inputs\", {\n res <- mtcars[0,] %>% compute_density(~mpg)\n expect_equal(nrow(res), 0)\n expect_true(setequal(names(res), c(\"pred_\", \"resp_\")))\n\n # Grouped\n res <- mtcars %>% group_by(cyl) %>% dplyr::filter(FALSE) %>% compute_density(~mpg)\n expect_equal(nrow(res), 0)\n expect_true(setequal(names(res), c(\"cyl\", \"pred_\", \"resp_\")))\n})\n", "meta": {"hexsha": "b53c9819a103df300a7e32cb6a436b49ec91dd03", "size": 881, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-compute-density.r", "max_stars_repo_name": "romainfrancois/ggvis", "max_stars_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 469, "max_stars_repo_stars_event_min_datetime": "2015-01-07T19:21:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-18T00:46:01.000Z", "max_issues_repo_path": "tests/testthat/test-compute-density.r", "max_issues_repo_name": "romainfrancois/ggvis", "max_issues_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 178, "max_issues_repo_issues_event_min_datetime": "2015-01-02T16:37:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T23:17:53.000Z", "max_forks_repo_path": "tests/testthat/test-compute-density.r", "max_forks_repo_name": "romainfrancois/ggvis", "max_forks_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 134, "max_forks_repo_forks_event_min_datetime": "2015-01-07T20:47:08.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-16T23:39:06.000Z", "avg_line_length": 32.6296296296, "max_line_length": 84, "alphanum_fraction": 0.6696935301, "num_tokens": 274, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3489737222844969}} {"text": "library(rlist)\nlibrary(nloptr)\nlibrary(tictoc)\nlibrary(future.apply)\nlibrary(doParallel)\nlibrary(rjson)\n\n#setwd(\"scripts/funciones\")\nsource(\"f_get_expected.r\", encoding = \"UTF-8\")\nsource(\"f_fit.r\", encoding = \"UTF-8\")\nsource(\"f_Rcaso.r\", encoding = \"UTF-8\")\nsource(\"f_bootstrap_IC.r\", encoding = \"UTF-8\")\n\n## Load Data ##\n#currently working on dummy data.\nresult <- fromJSON(file = \"../case_study/fitted_models.json\")\najuste_procesoAnalitico = list(\"beta\"=result$poisson_exp$beta_mode,\n \"N0\"= 81.11421,\n \"tau1\"=1.632911,\n \"tau2\"=0.123283)\n\n#incidences\nColombiaB = list(\"newCases\"=result$I)\n#### Ajustes Ygorro y tiempos ---- \nYgorro <- get_expected(beta = ajuste_procesoAnalitico$beta,\n N0 = ajuste_procesoAnalitico$N0,\n tau1 = ajuste_procesoAnalitico$tau1, \n tau2 = ajuste_procesoAnalitico$tau2)$NN\ntiempos <- length(ajuste_procesoAnalitico$beta)\n\n#### Corroboraci\u00f3n por suma de cuadrados ----\n\nprueba <- data.frame(Ygorro, newCases = ColombiaB$newCases)\n\nscm <- sum( ( prueba$Ygorro - mean(prueba$Ygorro) )^2 )\nsct <- sum( ( prueba$newCases - mean(prueba$newCases) )^2 ) \n\nprint(\"sanity check R^2:\", 1 - scm/sct)\n### N\u00famero de iteracacciones ---- \n\nnum_iteracciones <- 1000\n#This has to be adjusted according to JF experiment\nejecucionCastigo <- 2^12\n\nbootstrap_distribucion_NumCasos <- bootstrap_samples( expected_I = Ygorro,\n observed_I = ColombiaB$newCases[1:tiempos],\n number_samples = num_iteracciones , # Aumentar a minimo 1000\n window_size=4 , use_wa = FALSE, \n beta = 0.9)\n\nbootstrap_distribucion_NumCasos[ bootstrap_distribucion_NumCasos < 0 ] <- NA\n\nfor( j in 1:ncol(bootstrap_distribucion_NumCasos) ){\n temp_j <- bootstrap_distribucion_NumCasos[ , j ]\n temp_j[is.na(temp_j)] <- median(temp_j, na.rm = T )\n bootstrap_distribucion_NumCasos[ , j ] <- temp_j \n}\n\n# #### Do Parallel ------\n#beta_ultimo <- rep(ajuste_procesoAnalitico$beta[length( ajuste_procesoAnalitico$beta)], length( ajuste_procesoAnalitico$beta)) \nbeta_ultimo <- ajuste_procesoAnalitico$beta\nX <- 1:nrow(bootstrap_distribucion_NumCasos)\ntmp <- vector( length = nrow(bootstrap_distribucion_NumCasos) , mode = \"list\" )\n\nF_INC_SHAPE = 3.16\nF_INC_RATE = 5.16\nF_INF_SCALE = 24.2\ntau1 = 1 / (F_INC_SHAPE / F_INC_RATE)\ntau2 = 1/8.111421 # 1 / MEAN_INFECTIOUS_TIME\nA0 = ColombiaB$newCases[1] / pexp(1, rate=tau1)\nN0_max = A0*pexp(1, rate=tau1)/(1 * tau2) # A0 = A0*(1 - pexp(1, rate=tau1)) + N0*beta0\nN0_min = A0*pexp(1, rate=tau1)/(2 * tau2)\n\ncl <- parallel::makeCluster( detectCores()-1 )\ndoParallel::registerDoParallel(cl)\n\ntic()\nlista_OptimizacionBetas <- foreach( i = X ) %dopar% {\n library(nloptr)\n # setwd(ruta_proyecto)\n # source( \"scripts/funciones/f_fit.R\", encoding = \"UTF-8\" )\n \n cat(i, \" \")\n tmp2 <- fit(observed_I = as.numeric(as.matrix(bootstrap_distribucion_NumCasos[i,1:(tiempos)])),\n beta0 = beta_ultimo,\n beta_min = 0.6 * tau2, beta_max = 2.75 * tau2,\n tau10 = tau1, tau1_min = tau1,\n tau1_max = tau1,\n tau20 = tau2, tau2_min = tau2,\n tau2_max = tau2,\n N00 = N0_min, N0_min = N0_min, N0_max = N0_max,\n Castigo = ejecucionCastigo, ignore_beta_diff = NULL,\n max_eval = 1000)\n}\ntoc()\n\nparallel::stopCluster(cl)\n\n\n#### \nm_beta <- matrix(NA, nrow = nrow(bootstrap_distribucion_NumCasos),\n ncol = ncol(bootstrap_distribucion_NumCasos))\n\nm_Rt <- matrix(NA, nrow = nrow(bootstrap_distribucion_NumCasos),\n ncol = ncol(bootstrap_distribucion_NumCasos))\n\nm_Rcaso <- matrix(NA, nrow = nrow(bootstrap_distribucion_NumCasos),\n ncol = ncol(bootstrap_distribucion_NumCasos))\n\ntic()\nlags1 <- 7\npar1_inf=1\ndistribucion_inf=\"gamma\"\nfor(i in 1:nrow(bootstrap_distribucion_NumCasos)){\n par2_inf <- lista_OptimizacionBetas[[i]]$tau2\n m_beta[i,] <- lista_OptimizacionBetas[[i]]$beta\n m_Rt[i,] <- m_beta[i,] * (1 / lista_OptimizacionBetas[[i]]$tau2) \n omegas=weightConstructionIncubacionInfeccion(lags1, par1_inf, par2_inf, distribucion_inf ) \n m_Rcaso[i,] <- R_caso(lista_OptimizacionBetas[[i]]$beta, omegas)\n rm(omegas)\n }\n\n# Rt = beta \u00a8(1/ tau2) \n\ntoc(); \n\ncolnames(m_beta) <- colnames(bootstrap_distribucion_NumCasos)\ncolnames(m_Rt) <- colnames(bootstrap_distribucion_NumCasos)\ncolnames(m_Rcaso) <- colnames(bootstrap_distribucion_NumCasos)\n\n\n\n######################## Intervalos de confianza ##############\nconfianza <- 0.95\nalpha <- 1 - confianza\n\n# Beta\nLimite_inferior_Beta <- apply(m_beta, 2, FUN = quantile, probs = alpha/2)\nBeta_puntual <- colMeans(m_beta)\nLimite_superior_Beta <- apply(m_beta, 2, FUN = quantile, probs = 1-alpha/2)\n\ndf_estimaIC_Beta <- data.frame(LI_beta = Limite_inferior_Beta, \n Beta_estima = Beta_puntual,\n LS_beta = Limite_superior_Beta)\n\n# Rt\nLimite_inferior_Rt <- apply(m_Rt, 2, FUN = quantile, probs = alpha/2)\nRt_puntual <- colMeans(m_Rt)\nLimite_superior_Rt <- apply(m_Rt, 2, FUN = quantile, probs = 1-alpha/2)\n\ndf_estimaIC_Rt <- data.frame(LI_Rt = Limite_inferior_Rt, \n Rt_estima = Rt_puntual,\n LS_Rt = Limite_superior_Rt)\n \nprint(df_estimaIC_Rt)\n\n# # Rt Caso\nLimite_inferior_RtCaso <- apply(m_Rcaso, 2, FUN = quantile, probs = alpha/2)\nRtCaso_puntual <- colMeans(m_Rcaso)\nLimite_superior_RtCaso <- apply(m_Rcaso, 2, FUN = quantile, probs = 1-alpha/2)\n\ndf_estimaIC_RtCaso <- data.frame(LI_RtCaso = Limite_inferior_RtCaso, \n RtCaso_estima = RtCaso_puntual,\n LS_RtCaso = Limite_superior_RtCaso)\n\nprint(df_estimaIC_RtCaso)\n\nsaveRDS(df_estimaIC_Rt, \"df_estimaIC_Rt_procesoAnalitico_.rds\")\nsaveRDS(df_estimaIC_RtCaso, \"df_estimaIC_RtCaso_procesoAnalitico_.rds\")", "meta": {"hexsha": "1a8ed58290b94aa7d9b5c98ce7bb0770f26b20fe", "size": 6002, "ext": "r", "lang": "R", "max_stars_repo_path": "fit_confidence_intervals/run_ci_analytic.r", "max_stars_repo_name": "secg95/INS_COVID", "max_stars_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fit_confidence_intervals/run_ci_analytic.r", "max_issues_repo_name": "secg95/INS_COVID", "max_issues_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fit_confidence_intervals/run_ci_analytic.r", "max_forks_repo_name": "secg95/INS_COVID", "max_forks_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.5147928994, "max_line_length": 128, "alphanum_fraction": 0.656781073, "num_tokens": 1914, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3489737222844969}} {"text": "#' Bin and summarise in 2d (rectangle & hexagons)\n#'\n#' `stat_summary_2d()` is a 2d variation of [stat_summary()].\n#' `stat_summary_hex()` is a hexagonal variation of\n#' [stat_summary_2d()]. The data are divided into bins defined\n#' by `x` and `y`, and then the values of `z` in each cell is\n#' are summarised with `fun`.\n#'\n#' @section Aesthetics:\n#' - `x`: horizontal position\n#' - `y`: vertical position\n#' - `z`: value passed to the summary function\n#' @section Computed variables:\n#' \\describe{\n#' \\item{x,y}{Location}\n#' \\item{value}{Value of summary statistic.}\n#' }\n#' @seealso [stat_summary_hex()] for hexagonal summarization.\n#' [stat_bin2d()] for the binning options.\n#' @inheritParams layer\n#' @inheritParams geom_point\n#' @inheritParams stat_bin_2d\n#' @param drop drop if the output of `fun` is `NA`.\n#' @param fun function for summary.\n#' @param fun.args A list of extra arguments to pass to `fun`\n#' @export\n#' @examples\n#' d <- ggplot(diamonds, aes(carat, depth, z = price))\n#' d + stat_summary_2d()\n#'\n#' # Specifying function\n#' d + stat_summary_2d(fun = function(x) sum(x^2))\n#' d + stat_summary_2d(fun = ~ sum(.x^2))\n#' d + stat_summary_2d(fun = var)\n#' d + stat_summary_2d(fun = \"quantile\", fun.args = list(probs = 0.1))\n#'\n#' if (requireNamespace(\"hexbin\")) {\n#' d + stat_summary_hex()\n#' d + stat_summary_hex(fun = ~ sum(.x^2))\n#' }\nstat_summary_2d <- function(mapping = NULL, data = NULL,\n geom = \"tile\", position = \"identity\",\n ...,\n bins = 30,\n binwidth = NULL,\n drop = TRUE,\n fun = \"mean\",\n fun.args = list(),\n na.rm = FALSE,\n show.legend = NA,\n inherit.aes = TRUE) {\n layer(\n data = data,\n mapping = mapping,\n stat = StatSummary2d,\n geom = geom,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n bins = bins,\n binwidth = binwidth,\n drop = drop,\n fun = fun,\n fun.args = fun.args,\n na.rm = na.rm,\n ...\n )\n )\n}\n\n#' @export\n#' @rdname stat_summary_2d\n#' @usage NULL\nstat_summary2d <- function(...) {\n message(\"Please use stat_summary_2d() instead\")\n stat_summary_2d(...)\n}\n\n#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\nStatSummary2d <- ggproto(\"StatSummary2d\", Stat,\n default_aes = aes(fill = after_stat(value)),\n\n required_aes = c(\"x\", \"y\", \"z\"),\n\n compute_group = function(data, scales, binwidth = NULL, bins = 30,\n breaks = NULL, origin = NULL, drop = TRUE,\n fun = \"mean\", fun.args = list()) {\n origin <- dual_param(origin, list(NULL, NULL))\n binwidth <- dual_param(binwidth, list(NULL, NULL))\n breaks <- dual_param(breaks, list(NULL, NULL))\n bins <- dual_param(bins, list(x = 30, y = 30))\n\n xbreaks <- bin2d_breaks(scales$x, breaks$x, origin$x, binwidth$x, bins$x)\n ybreaks <- bin2d_breaks(scales$y, breaks$y, origin$y, binwidth$y, bins$y)\n\n xbin <- cut(data$x, xbreaks, include.lowest = TRUE, labels = FALSE)\n ybin <- cut(data$y, ybreaks, include.lowest = TRUE, labels = FALSE)\n\n fun <- as_function(fun)\n f <- function(x) {\n do.call(fun, c(list(quote(x)), fun.args))\n }\n out <- tapply_df(data$z, list(xbin = xbin, ybin = ybin), f, drop = drop)\n\n xdim <- bin_loc(xbreaks, out$xbin)\n out$x <- xdim$mid\n out$width <- xdim$length\n\n ydim <- bin_loc(ybreaks, out$ybin)\n out$y <- ydim$mid\n out$height <- ydim$length\n\n out\n }\n)\n\n# Adaptation of tapply that returns a data frame instead of a matrix\ntapply_df <- function(x, index, fun, ..., drop = TRUE) {\n labels <- lapply(index, ulevels)\n out <- expand.grid(labels, KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE)\n\n grps <- split(x, index)\n names(grps) <- NULL\n out$value <- unlist(lapply(grps, fun, ...))\n\n if (drop) {\n n <- vapply(grps, length, integer(1))\n out <- out[n > 0, , drop = FALSE]\n }\n\n out\n}\n", "meta": {"hexsha": "1c7526425db4cb1c85cdb0fb0785beb625d53ef4", "size": 4125, "ext": "r", "lang": "R", "max_stars_repo_path": "R/stat-summary-2d.r", "max_stars_repo_name": "netique/ggplot2", "max_stars_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3746, "max_stars_repo_stars_event_min_datetime": "2016-10-31T17:39:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T02:50:11.000Z", "max_issues_repo_path": "R/stat-summary-2d.r", "max_issues_repo_name": "netique/ggplot2", "max_issues_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3277, "max_issues_repo_issues_event_min_datetime": "2016-11-01T19:23:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:44:20.000Z", "max_forks_repo_path": "R/stat-summary-2d.r", "max_forks_repo_name": "netique/ggplot2", "max_forks_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1693, "max_forks_repo_forks_event_min_datetime": "2016-11-02T07:26:55.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T01:51:19.000Z", "avg_line_length": 30.1094890511, "max_line_length": 78, "alphanum_fraction": 0.5789090909, "num_tokens": 1179, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3489737222844969}} {"text": "library(\"factoextra\")\nlibrary(\"reshape2\")\n\npodatki <- dcast(cetrtatabela, DRZAVA + LETO ~ NAMENPORABEGDP)\npodatki <- subset(podatki, DRZAVA==\"Slovenija\"|DRZAVA==\"Velika Britanija\"|DRZAVA==\"Luksemburg\"|\n DRZAVA==\"\u0160vedska\"|DRZAVA==\"Estonija\"|DRZAVA==\"Tur\u010dija\"|DRZAVA==\"Litva\"|\n DRZAVA==\"Bolgarija\"|DRZAVA==\"Hrva\u0161ka\"|DRZAVA==\"Nem\u010dija\"|DRZAVA==\"\u0160panija\"|\n DRZAVA==\"Francija\"|DRZAVA==\"Romunija\"|DRZAVA==\"Nizozemska\"|DRZAVA==\"Italija\")\nrownames(podatki) <- paste(podatki$DRZAVA, podatki$LETO, sep =\", \")\npodatki <- na.omit(podatki)[-c(1,2)]\npodatki$`DELE\u017d BDP, NAMENJEN VAROVANJU OKOLJA` <- podatki$`DELEZ BDP, NAMENJEN VAROVANJU OKOLJA V OKVIRU JAVNEGA SEKTORJA` + podatki$`DELEZ BDP, NAMENJEN VAROVANJU OKOLJA V OKVIRU INDUSTRIJE`\npodatki$`DELE\u017d BDP, NAMENJEN INVESTICIJAM V VAROVANJE OKOLJA` <- podatki$`DELEZ BDP, NAMENJEN INVESTICIJAM ZA VAROVANJE OKOLJA, PORABLJENIM V JAVNEM SEKTORJU` + podatki$`DELEZ BDP, NAMENJEN INVESTICIJAM ZA VAROVANJE OKOLJA, PORABLJENIM V INDUSTRIJI`\npodatki <- podatki[-c(1,2,3,4)]\n\nfviz_nbclust(podatki, kmeans, method = \"gap_stat\")\nneki <- kmeans(podatki, 3)\ngraf4 <- fviz_cluster(neki, data = podatki, main = \"DELE\u017d GDP, NAMENJEN VAROVANJU OKOLJA IN INVESTICIJAM VANJ\",\n ellipse.type = \"convex\",\n ggtheme = theme_minimal())\n\ntabela <- na.omit(tabela1)\ngraf5 <- ggplot(tabela) + aes(x=LETO, y=`EMISIJE TOPLOGREDNIH PLINOV V TONAH`) + geom_point() + geom_smooth(method = \"lm\")", "meta": {"hexsha": "8890981141f94278f637d68b9684112b2b0d3900", "size": 1488, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "RenkoT97/APPR-2018-19", "max_stars_repo_head_hexsha": "0c568785290d840a1b4e8a8b0b2296fd02764307", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "RenkoT97/APPR-2018-19", "max_issues_repo_head_hexsha": "0c568785290d840a1b4e8a8b0b2296fd02764307", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2019-01-05T20:00:41.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-24T14:59:02.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "RenkoT97/APPR-2018-19", "max_forks_repo_head_hexsha": "0c568785290d840a1b4e8a8b0b2296fd02764307", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 67.6363636364, "max_line_length": 249, "alphanum_fraction": 0.7009408602, "num_tokens": 621, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3489737222844969}} {"text": "#\n# This (currently incomplete) R script is intended to extract equations\n# from the BIOPAK equation library, parse them into executable R\n# functions, and apply them appropriately to the individual tree records\n# in the \"FIA_indivtrees.csv\" file.\n#\n# The code is mostly working (in prototype fashion), but some issues\n# are:\n# - Matching up of species codes in the FIA data and in the equation\n# library is imperfect. Several issues still to work out here.\n# - In cases where there are more than one equation for the same quantity\n# for a particular tree species, the code currently just takes the\n# mean of the different outputs\n# - Parts of the code might break if you change things like column\n# headers, etc. This could be handled easily enough with function\n# arguments, but for the most part isn't yet.\n# - Speed problems: The cast/melt statement that converts the final list\n# into a table is slooooooowwwww on the full 230k+ record FIA dataset.\n# Note that everything before that happens in <10 minutes, which is\n# not great but probably fast enough. Almost certainly can do some\n# things to make the code faster if necessary. Also note that\n# performance would probably get worse if you wanted more advanced\n# logic about which equation to use in particular cases.\n#\n# Note: the \"reshape\" package is currently required, although this may\n# change\n#\n# Author: Jim Regetz, with David LeBauer (Functional Ecology DGS)\n# Created on 28-Mar-2008\n# NCEAS\n\nlibrary(reshape)\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Function definitions\n\n# Formula generator. Takes a character string of a BIOPAK equation, and\n# converts the right hand side of the equation into an executable R\n# expression. This expression is returned as a function. Currently only\n# works if the RHS of the equation only has numeric constants, standard\n# math operators, and the variable \"DBH\". If the equation give the LHS\n# as ln(varname), the result is exponentiated.\ngetEquation <- function(equation) {\n\n equation <- as.character(equation)\n ln <- log\n\n eq.split <- strsplit(equation, \" = \")[[1]]\n\n LHS <- eq.split[1]\n responseName <- gsub(\"^ln\\\\(([[:alpha:]]*)\\\\)\", \"\\\\1\", LHS)\n\n RHS <- eq.split[2]\n\n function(DBH) {\n ans <- eval(parse(text=RHS))\n if (substr(LHS, 1, 2)==\"ln\") ans <- exp(ans)\n names(ans) <- responseName\n return(ans)\n }\n\n}\n\n# Function to apply the equations to each tree (row) of an input\n# dataframe that contains (at least) a \"spp\" column (4-letter species\n# codes used in the BIOPAK equation library) and a \"dbh\" column\n# (numeric dbh values)\nrunEquations <- function(dat, sp.col, dbh.col) {\n mapply(function(id, spp, dbh) sapply(equation[[spp]],\n function(calc) calc(dbh)), id=seq_len(nrow(dat)),\n spp=as.character(dat[,sp.col]), dbh=dat[,dbh.col], SIMPLIFY=FALSE)\n}\n\n# Function to take the mean of calculated variables in cases where more\n# than one equation is used for the same outcome variable\ngetMeans <- function(listOfVals) {\n lapply(listOfVals, function(x) tapply(x, names(x), mean))\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# Read in some input data\neqlib <- read.csv(\"biolib10.csv\")\nfiadb <- read.csv(\"FIA_individtrees.csv\", row.names=1)\nspcodes <- read.csv(\"spcodes.csv\", fill=TRUE)\nspcodes$autocode <- toupper(paste(substr(spcodes$Genus, 1, 2),\n substr(spcodes$Species, 1, 2), sep=\"\"))\n\n# --- TEMPORARY --- #\n# Add a new column for 4-letter species code by automatically combining\n# the first 2 letters of the genus name with the first two letters with\n# the species name (both as given in Table 4 of [whatever it is].\nfiadb <- merge(fiadb, spcodes[c(\"FIA.ID\", \"autocode\")], by.x=\"FIA.SPCD\",\n by.y=\"FIA.ID\", sort=FALSE, all.x=TRUE)\n# ----------------- #\n\n# Reduce equation library to the relevant subset of equations\nresponses <- c(\"BAT\", \"BBD\", \"BBL\", \"BBT\", \"BFN\", \"BFT\", \"BRT\", \"BSB\",\n \"BST\", \"BSW\", \"PFT\")\neqlib.sub <- subset(eqlib, LIFEFORM==\"T\" & PA1_CODE==\"DBH\" & is.na(PA2_CODE) &\n BIO_COMP %in% responses)\neqlib.sub$SPP_CODE <- as.character(eqlib.sub$SPP_CODE)\neqlib.sub$BPK_EQFM <- as.character(eqlib.sub$BPK_EQFM)\n\n# Parse the equations for each species into executable R functions\n# stored in a list\nequation <- sapply(unique(eqlib.sub$SPP_CODE), function(sp)\n lapply(eqlib.sub$BPK_EQFM[eqlib.sub$SPP_CODE==sp], getEquation))\n\n# Generate fake data\n#numInd <- 1000\n#testdata <- data.frame(\n# id=seq_len(numInd),\n# spp=sample(eqlib.sub$SPP_CODE, numInd, replace=TRUE),\n# DBH=runif(numInd, 1, 100)\n#)\n\n# Run the equations on each individual. Then in cases where there are\n# multiple estimated values for a given variable (b/c multiple equations\n# in the equation library for this species), take the mean\nallVals <- runEquations(fiadb, sp.col=\"autocode\", dbh.col=\"FIA.DIA\")\n\n# TEMPORARY HACK? #\n# This deals with null elements that arise in the allVals when running\n# the code on the FIA data. My UNCONFIRMED suspicion is that these nulls\n# arise when species codes in the data are not matched by any species\n# codes in the equations list. Obviously this should be handled in the\n# \"runEquations\" functions, but isn't yet.\nallVals <- allVals[sapply(allVals, function(x) length(x)>0)]\n#-----------------#\n\nmeanVals <- getMeans(allVals) # Turn the ragged list above into a dataframe, and clean up\nmeanValsDF <- cast(melt(meanVals), L1 ~ indicies)\n\n#---THIS LAST STEP TAKES A LONG TIME WITH THE FULL FIA DATA---#:w\n# Merge calculated values with the original FIA table\n# next line gets error \"Error in as.data.frame(y) : object \"meanValsDF\" not found\" :\n\n# result <- merge(fiadb, meanValsDF, by.x=0, by.y=\"L1\", sort=FALSE)\n\n", "meta": {"hexsha": "33d3612980703ad5bda4f8c53d7fc89e366cd84c", "size": 5641, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/data.land/inst/FIA_allometry/biomass.r", "max_stars_repo_name": "ankurdesai/pecan", "max_stars_repo_head_hexsha": "feaf4a6ebe8452560b4d25658218a356399bdba4", "max_stars_repo_licenses": ["NCSA", "Unlicense"], "max_stars_count": 151, "max_stars_repo_stars_event_min_datetime": "2015-01-26T13:43:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T19:06:56.000Z", "max_issues_repo_path": "modules/data.land/inst/FIA_allometry/biomass.r", "max_issues_repo_name": "ankurdesai/pecan", "max_issues_repo_head_hexsha": "feaf4a6ebe8452560b4d25658218a356399bdba4", "max_issues_repo_licenses": ["NCSA", "Unlicense"], "max_issues_count": 1771, "max_issues_repo_issues_event_min_datetime": "2015-01-02T03:33:13.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T13:16:40.000Z", "max_forks_repo_path": "modules/data.land/inst/FIA_allometry/biomass.r", "max_forks_repo_name": "ankurdesai/pecan", "max_forks_repo_head_hexsha": "feaf4a6ebe8452560b4d25658218a356399bdba4", "max_forks_repo_licenses": ["NCSA", "Unlicense"], "max_forks_count": 171, "max_forks_repo_forks_event_min_datetime": "2015-01-08T18:44:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T07:18:53.000Z", "avg_line_length": 40.2928571429, "max_line_length": 89, "alphanum_fraction": 0.7016486439, "num_tokens": 1534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.4921881357207955, "lm_q1q2_score": 0.3489708056480583}} {"text": "## # Gene expression analysis\n##\n## Perform a (fairly meaningless) gene expression analysis on some samples. The\n## data comes from [Rudolph, Schmitt *& al.* (2016)][@Rudolph2016]. The full\n## data can be found [on\n## figshare](https://dx.doi.org/10.6084/m9.figshare.2056227.v1).\n\n#+ packages, echo=FALSE, message=FALSE\nbox::use(\n dplyr[...],\n ggplot2[...],\n knitr,\n readr,\n tibble\n)\n\noptions(readr.num_columns = 0)\n\n#+ load-data\nexpression_data = readr$read_tsv(box::file('data/rnaseq-vst.tsv'))\nlibrary_design = readr$read_tsv(box::file('data/rnaseq-design.tsv'), col_names = c('ID', 'Condition'))\n\nlinkify = function (gene_id) {\n sprintf('%s', gene_id, gene_id)\n}\n\ntop_genes = readr$read_tsv(box::file('data/rnaseq-top-10-genes.tsv')) %>%\n mutate(Gene = linkify(Gene))\n\n## To verify that biological replicates cluster together, we can perform a PCA:\n\n#+ prcomp\nexpr_matrix = expression_data %>%\n as.data.frame() %>%\n tibble$column_to_rownames('Gene') %>%\n as.matrix()\npca = prcomp(t(expr_matrix))\npcx = pca$x %>%\n as.data.frame() %>%\n tibble$rownames_to_column('Library') %>%\n inner_join(library_design, by = c('Library' = 'ID'))\n\nvar_expl = function (pca, axis) {\n vars = pca$sdev ^ 2\n (vars / sum(vars))[axis] * 100\n}\n\n#+ pca-scatter-plot, fig.width=4, fig.height=4\nggplot(pcx) +\n aes(x = PC1, y = PC2, color = Condition) +\n geom_point() +\n labs(\n x = sprintf('PC1 (%0.0f%% variance explained)', var_expl(pca, 1)),\n y = sprintf('PC2 (%0.0f%% variance explained)', var_expl(pca, 2))\n ) +\n theme_minimal()\n\n## Here\u2019s a table of the top `r nrow(top_genes)` genes:\n\n#+ top-genes, echo=FALSE, results='asis'\nknitr$kable(top_genes)\n\n## [@Rudolph2016]: http://dx.doi.org/10.1371/journal.pgen.1006024 \"Rudolph, Schmitt & al., Codon-Driven Translational Efficiency Is Stable across Diverse Mammalian Cell States. PLoS Genet; 2016\"\n", "meta": {"hexsha": "9ca82851f103cabdfd5e31948967224427e1558d", "size": 1940, "ext": "r", "lang": "R", "max_stars_repo_path": "report.r", "max_stars_repo_name": "klmr/example-r-analysis", "max_stars_repo_head_hexsha": "64f15f13bfecd4e497c25c3402d15cff07bc7389", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2016-09-11T21:16:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-16T20:49:10.000Z", "max_issues_repo_path": "report.r", "max_issues_repo_name": "klmr/example-r-analysis", "max_issues_repo_head_hexsha": "64f15f13bfecd4e497c25c3402d15cff07bc7389", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "report.r", "max_forks_repo_name": "klmr/example-r-analysis", "max_forks_repo_head_hexsha": "64f15f13bfecd4e497c25c3402d15cff07bc7389", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-02-20T15:12:54.000Z", "max_forks_repo_forks_event_max_datetime": "2017-02-20T15:12:54.000Z", "avg_line_length": 30.3125, "max_line_length": 194, "alphanum_fraction": 0.6567010309, "num_tokens": 615, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.34889134820669665}} {"text": "require('hash')\n\n#define hash to lookup column numbers using strings\noutcome_hash <- hash()\noutcome_hash[\"heart attack\"] <- 11\noutcome_hash[\"heart failure\"] <- 17\noutcome_hash[\"pneumonia\"] <- 23\n\nrankall <- function(outcome, num = \"best\") {\n\tdata = read.csv(\"./rprog-data-ProgAssignment3-data/outcome-of-care-measures.csv\", colClasses = \"character\")\n\n\t#validations\n\n\t#check if the outcome is correct\n\tif (!has.key(outcome,outcome_hash)){\n\t\tstop(\"invalid outcome\")\n\t}\n\n\t#extract the rows for the state we need, and keep the columns for hospital, state and userdefined mortality\n\tdf = data[ , c(2,7, outcome_hash[[outcome]]) ]\n\t\n\t#assign friendly names to columns\n\tcolnames(df) <- c('hospital','state','mortality')\n\t\n\t#convert the mortality column into numeric type\n\tdf$mortality = as.numeric(as.character(df$mortality))\n\t\n\t#removed rows where mortality is NA\n\tdf = na.omit(df)\n\n\t#order the dataframe by mortality and then by hospital\n\tdf = df[order(df$state, df$mortality, df$hospital), ]\n\t\n\t#Split the dataframe based on state\n\tdf_list <- split( df , df$state )\n\t\n\t#return the first row as result\n\t\n\tif(num == \"best\") num <- 1\n\t\n\tif(num == \"worst\"){\n\t\tas.data.frame(do.call(rbind, lapply(df_list, function(x){ x[ nrow(x) ,]} ) ))\t\n\t}\n\telse{\n\t\tas.data.frame(do.call(rbind, lapply(df_list, function(x){ x[num,]} ) ))\n\t}\n}\t\n\nhead(rankall(\"heart attack\", 20), 10)\n#tail(rankall(\"pneumonia\", \"worst\"), 3)\n#tail(rankall(\"heart failure\"), 10)\n", "meta": {"hexsha": "78cab1f2efe2d6156d5706bd5b8f58883dcedb59", "size": 1441, "ext": "r", "lang": "R", "max_stars_repo_path": "assignment3/rankbyallstates.r", "max_stars_repo_name": "shaunakv1/r-experiments", "max_stars_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-01-18T19:46:27.000Z", "max_stars_repo_stars_event_max_datetime": "2016-01-18T19:46:27.000Z", "max_issues_repo_path": "assignment3/rankbyallstates.r", "max_issues_repo_name": "shaunakv1/r-experiments", "max_issues_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assignment3/rankbyallstates.r", "max_forks_repo_name": "shaunakv1/r-experiments", "max_forks_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7115384615, "max_line_length": 108, "alphanum_fraction": 0.6891047883, "num_tokens": 411, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.34889134820669665}} {"text": "################################################################################\r\n# hapDistMatrix_withinBetweenComplete function - (matrix)\r\n#\r\n# Author: Heidi Lischer\r\n# Date: 10.2008\r\n################################################################################\r\n\r\n\r\nhapDistMatrix_withinBetweenComplete <- function(hapDistMatrixLabels,\r\n hapDistMatrix, absHapMatrix, timeAttr, outfile){\r\n \r\n #get pop names, hap names and absHapFrequeny matrix\r\n popNames <- absHapMatrix[1, 2:ncol(absHapMatrix)]\r\n hapNames <- absHapMatrix[2:nrow(absHapMatrix), 1]\r\n hapNames <- gsub(\" \", \"\", hapNames) # trim white space\r\n absHapFreq <- absHapMatrix[2:nrow(absHapMatrix), 2:ncol(absHapMatrix)]\r\n \r\n \r\n #get list of all haplotypes in all populations --------------------------------\r\n popSizeList <- list()\r\n hapList <- c(NULL)\r\n s <- 1\r\n for ( i in 1:ncol(absHapFreq)){\r\n popSize <- 0\r\n for(n in 1:length(absHapFreq[,i])){\r\n absFreq <- as.numeric(absHapFreq[n, i])\r\n if(absFreq != 0){\r\n for(t in 1: absFreq){\r\n hapList[s] <- hapNames[n]\r\n s <- s + 1\r\n popSize <- popSize + 1\r\n }\r\n }\r\n }\r\n popSizeList[[i]] <- popSize \r\n }\r\n\r\n\r\n # get whole DistanceMatrix (upper and under) ---------------------------------\r\n dimnames(hapDistMatrix) <- list(hapDistMatrixLabels, hapDistMatrixLabels)\r\n \r\n wholeDistanceMatrix <- hapDistMatrix\r\n for(x in 1:ncol(wholeDistanceMatrix)){\r\n twholeDistanceMatrix <- t(wholeDistanceMatrix)\r\n wholeDistanceMatrix[x,(x:ncol(wholeDistanceMatrix))]<-twholeDistanceMatrix[x,\r\n (x:ncol(wholeDistanceMatrix))]\r\n }\r\n \r\n \r\n #get complete distance matrix (all populations included) ---------------------\r\n Length <- length(hapList)\r\n \r\n #get all rows\r\n distanceMatrix <- wholeDistanceMatrix[hapList[1],] \r\n for(i in 2:Length){\r\n distanceMatrix <- rbind(distanceMatrix, wholeDistanceMatrix[hapList[i],])\r\n }\r\n \r\n #get all columns and include only half matrix\r\n distanceMatrixComplete <- distanceMatrix[, hapList[1]]\r\n for(i in 2:Length){\r\n nextCol <- as.matrix(distanceMatrix[i:Length,hapList[i]])\r\n distanceMatrixComplete <- cbind(distanceMatrixComplete, rbind( matrix(NA,\r\n ncol=1, nrow=(i-1)), nextCol))\r\n }\r\n \r\n\r\n \r\n #draw graphic ----------------------------------------------------------------\r\n # Mirror matrix (left-right)----\r\n mirror.matrix <- function(x) {\r\n xx <- as.data.frame(x);\r\n xx <- rev(xx);\r\n xx <- as.matrix(xx);\r\n xx;\r\n }\r\n\r\n # Rotate matrix 270 clockworks----\r\n rotate270.matrix <- function(x) {\r\n mirror.matrix(t(x))\r\n }\r\n\r\n distanceMatrixComplete <- rotate270.matrix(distanceMatrixComplete)\r\n\r\n\r\n # draw matrix -------------------------------\r\n ColorRamp <- colorRampPalette(c(\"white\", \"steelblue1\", \"blue3\"))\r\n\r\n nCol <- ncol(distanceMatrixComplete)\r\n nRow <- nrow(distanceMatrixComplete)\r\n\r\n\r\n outfileGraphic <- paste(outfile, \"hapDistMatrix_withinBetweenComplete \", \r\n timeAttr, \".png\", sep=\"\")\r\n\r\n #save graphic\r\n png(outfileGraphic, width=1300, height=1300, res=144) \r\n \r\n smallplot <- c(0.874, 0.9, 0.18, 0.83)\r\n bigplot <- c(0.13, 0.85, 0.14, 0.87)\r\n \r\n old.par <- par(no.readonly = TRUE)\r\n \r\n \r\n #draw legend -----------------------------------\r\n par(plt = smallplot)\r\n \r\n # get legend values\r\n Min <- min(distanceMatrixComplete, na.rm=TRUE)\r\n Max <- max(distanceMatrixComplete, na.rm=TRUE)\r\n binwidth <- (Max - Min) / 64\r\n y <- seq(Min + binwidth/2, Max - binwidth/2, by = binwidth)\r\n z <- matrix(y, nrow = 1, ncol = length(y))\r\n \r\n image(1, y, z, col = ColorRamp(64),xlab=\"\", ylab=\"\", axes=FALSE)\r\n \r\n #adjust axis if only one value exists\r\n if(Min == Max){\r\n axis(side=4, las = 2, cex.axis=0.8, at=Min, labels=round(Min, 2))\r\n } else {\r\n axis(side=4, las = 2, cex.axis=0.8)\r\n }\r\n \r\n box()\r\n mtext(text=\"Number of pairwise differences\", side=4,line=2.2,font=2)\r\n \r\n \r\n \r\n #draw main graphic -----------------------------\r\n par(new = TRUE, plt = bigplot)\r\n \r\n image(c(1:nCol), c(1:nRow), distanceMatrixComplete, col=ColorRamp(64),\r\n main=\"Molecular distances within and between populations\",\r\n ylab=\"\", xlab=\"\", axes=FALSE)\r\n \r\n box()\r\n \r\n # add lines which seperate the populations\r\n nRowShort <- 0\r\n for(i in 1: (length(popSizeList)-1)){\r\n nRowShort <- nRowShort + popSizeList[[i]]\r\n nRowLong <- nrow(distanceMatrixComplete) - nRowShort\r\n \r\n lines(c(0, nRowShort + 0.5),c(nRowLong + 0.5, nRowLong + 0.5), lty=2)\r\n lines(c(nRowShort + 0.5, nRowShort + 0.5),c(0, nRowLong + 0.5), lty=2)\r\n }\r\n \r\n lines(c(0, nrow(distanceMatrixComplete)),\r\n c(nrow(distanceMatrixComplete), 0), lty=2)\r\n \r\n # add population indexes\r\n nRow <- 0\r\n Legend <- c(NULL)\r\n for(i in 1: length(popNames)){\r\n nRow2 <- popSizeList[[i]]\r\n rowText <- nRow + (nRow2 /2)\r\n nRow <- nRow + nRow2\r\n \r\n mtext(side = 1, at = rowText + 0.5, line = 0.5, text =popNames[i],\r\n cex=0.8, las = 2)\r\n mtext(side = 2, at = nrow(distanceMatrixComplete) - rowText + 0.5,\r\n line = 0.7, text = popNames[i], cex=0.8, las=2, , las = 2)\r\n \r\n }\r\n\r\n par(old.par) #reset graphic parameters\r\n\r\n dev.off()\r\n\r\n}", "meta": {"hexsha": "16ed22dae24b94626a3e58a4d4cbe991b3ed9027", "size": 5882, "ext": "r", "lang": "R", "max_stars_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/hapDistMatrix_withinBetweenComplete.r", "max_stars_repo_name": "kibet-gilbert/co1_metaanalysis", "max_stars_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-01T05:57:08.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-01T05:57:08.000Z", "max_issues_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/hapDistMatrix_withinBetweenComplete.r", "max_issues_repo_name": "kibet-gilbert/co1_metaanalysis", "max_issues_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/hapDistMatrix_withinBetweenComplete.r", "max_forks_repo_name": "kibet-gilbert/co1_metaanalysis", "max_forks_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-01T06:15:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-01T06:15:56.000Z", "avg_line_length": 34.6, "max_line_length": 85, "alphanum_fraction": 0.4998299898, "num_tokens": 1505, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832805, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.34876784309844666}} {"text": "\n\n##' @title\n##' Classification Optimal Cutoff\n##'\n##' @description Optimal cutoff\n##'\n##' @inheritParams classification_params\n##' @param objective \\code{[character]}\n##' @return A scalar numeric output\n##' @author An Chu\nmtr_optimal_cutoff <- function(actual, predicted,\n objective = c(\"positive_class\", \"negative_class\",\n \"both\", \"missclassified_error\")) {\n\n check_equal_length(actual, predicted)\n check_binary(actual)\n\n objective <- match.arg(objective)\n thresholds <- seq(min(predicted), max(actual), 0.01)\n\n pos_idx <- which()\n neg_idx <- which()\n both_idx <- which()\n mce_idx <- which()\n\n optimal_cutoff <- switch(\n objective,\n positive_class = {thresholds[pos_idx]},\n negative_class = {thresholds[neg_idx]},\n both = {thresholds[both_idx]},\n missclassified_error = {thresholds[mce_idx]}\n )\n\n optimal_cutoff\n}\n", "meta": {"hexsha": "ec1017c44de771dfb2efea41b0114e6a85dea128", "size": 958, "ext": "r", "lang": "R", "max_stars_repo_path": "R/optimal-cutoff.r", "max_stars_repo_name": "maiing/metrics", "max_stars_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/optimal-cutoff.r", "max_issues_repo_name": "maiing/metrics", "max_issues_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/optimal-cutoff.r", "max_forks_repo_name": "maiing/metrics", "max_forks_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8918918919, "max_line_length": 80, "alphanum_fraction": 0.6096033403, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3487678348649093}} {"text": "# Run the code below to set up the data\n# Change the plot to use a facet_wrap by name\n\n\nlibrary(tidyverse)\n# Load and reshape data\nunisex_data <- read_csv(\"data/unisex.csv\")\nunisex_data <- unisex_data %>%\n select(-prop) %>%\n spread(sex, n, fill=0)\nunisex_data\n\n\n# Change the plot to use a facet_wrap by name\n\nggplot(unisex_data, aes(x=Male, y=Female)) + \n geom_point() +\n theme_minimal() +\n facet_wrap(~name)\n\n\n# Bonus: Reverse the order of the names in the facets\n\nggplot(unisex_data, aes(x=Male, y=Female)) + \n geom_point() +\n theme_minimal() +\n facet_wrap(~factor(name, levels=rev(unique(name))))\n", "meta": {"hexsha": "0bffe5b02641c3cf6df1b42a1d033939442c817d", "size": 608, "ext": "r", "lang": "R", "max_stars_repo_path": "exercises/answer1g.r", "max_stars_repo_name": "nuitrcs/r_ggplot_june2018", "max_stars_repo_head_hexsha": "181a60e1d4481f1a986bc46b372c515972a35c55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercises/answer1g.r", "max_issues_repo_name": "nuitrcs/r_ggplot_june2018", "max_issues_repo_head_hexsha": "181a60e1d4481f1a986bc46b372c515972a35c55", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exercises/answer1g.r", "max_forks_repo_name": "nuitrcs/r_ggplot_june2018", "max_forks_repo_head_hexsha": "181a60e1d4481f1a986bc46b372c515972a35c55", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.7142857143, "max_line_length": 53, "alphanum_fraction": 0.7023026316, "num_tokens": 177, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.6334102775181399, "lm_q1q2_score": 0.3487603642072085}} {"text": "cube <- function(x) {\n x * x * x\n}\n", "meta": {"hexsha": "a6feb8915ff10eaf5709a0553d3ab1683148d441", "size": 35, "ext": "r", "lang": "R", "max_stars_repo_path": "cube.r", "max_stars_repo_name": "YuYT98/copy-r-cube", "max_stars_repo_head_hexsha": "4b185002d7184a42f30b1cf68b06d4f5caf5b9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cube.r", "max_issues_repo_name": "YuYT98/copy-r-cube", "max_issues_repo_head_hexsha": "4b185002d7184a42f30b1cf68b06d4f5caf5b9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cube.r", "max_forks_repo_name": "YuYT98/copy-r-cube", "max_forks_repo_head_hexsha": "4b185002d7184a42f30b1cf68b06d4f5caf5b9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.75, "max_line_length": 21, "alphanum_fraction": 0.4571428571, "num_tokens": 13, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3486032313851564}} {"text": "#' Title\n#'\n#' @param HPOpatient\n#'\n#' @return\n#' @export\n#'\n#' @examples\nproteinScore<-function(HPOpatient)\n{\n library(igraph)\n library(Matrix)\n\n HPO2genes<-HPO2genes\n treureHPO<-treureHPO\n HPOqueryGene<-HPOqueryGene\n total_unique<-total_unique\n\n g1<-as.undirected(g1)\n HPOpatient<-as.matrix(unique(HPOpatient))\n\n treureHPO <- paste(treureHPO,collapse=\"|\")\n posTreure<-grep(treureHPO, HPO2genes[,2])\n if(length(posTreure)>0) HPO2genes<-HPO2genes[-posTreure,]\n\n # Delete some irrelevant HPOs\n posTreure<-grep(treureHPO, HPOpatient)\n if(length(posTreure)>0) HPOpatient<-HPOpatient[-posTreure]\n\n #create a subgraph with the expanded (1 order) patient HPOs\n HPOorig_expanded<-unique(unlist(sapply(HPOpatient, function (x) rownames(as.matrix(igraph::ego(g1, order = 1, x)[[1]])))))\n g.sub <- induced.subgraph(graph = g1, HPOorig_expanded)\n res<-cluster_edge_betweenness(g.sub)\n HPOorig_expanded<-cbind(res$names,res$membership)\n HPOorigGroups <<- HPOorig_expanded[match(HPOpatient,HPOorig_expanded[,1]),]\n\n genes <<- unique(HPO2genes[, 1]) #genes with HPO\n posGenes<-match(genes,rownames(HPOadj))\n acumulat<-Matrix(matrix(0,ncol = 1,nrow =length(rownames(HPOadj)[posGenes])))\n acumulatFreq<-acumulat\n\n for(z in 1:length(HPOpatient))\n {\n pos<-match(HPOpatient[z],colnames(HPOadj))\n HPOPatientItem = HPOdistance[pos,]\n column<-HPOadj[posGenes,] %*% HPOPatientItem\n acumulat<-cbind(acumulat,column)\n }\n acumulat<-acumulat[,-1]\n\n if(length(HPOorigGroups[,2])!=length(unique(HPOorigGroups[,2])))\n {\n memo = \"\"\n dupli = unique(HPOorigGroups[duplicated(HPOorigGroups[,2]),2])\n for(i in 1:length(dupli))\n {\n pos<-which(HPOorigGroups[,2] == dupli[i])\n new<-Matrix::rowSums(acumulat[,pos])\n acumulat[,pos[1]]<-new\n memo<-c(memo,pos[-1])\n }\n memo <- memo[-1]\n acumulat<-acumulat[,-as.numeric(memo)]\n\n }\n\n\n\n acumulat = acumulat / acumulat\n acumulat[is.na(acumulat)]=0\n acumulat = Matrix(acumulat)\n HPOmatch_quant<-Matrix::rowSums(acumulat)\n\n\n q=HPOmatch_quant-1\n m=length(unique(HPOorigGroups[,2]))\n n=length(V(g1)$name)-length(unique(HPOorigGroups[,2]))\n k=as.matrix(HPOqueryGene)*10\n stats<-phyper(q,m,n,k,lower.tail = FALSE, log.p = FALSE)\n gc()\n stats[stats == -Inf] = 1\n stats[stats == 0] = 10^(log10(min(stats[stats != 0]))-1)\n testResult<-stats\n\n D = abs(log10(abs(testResult)))\n Dred <- as.numeric(D)\n pos <- match(genes, total_unique[,2])\n D <- matrix(0, nrow = length(total_unique[,2]))\n D[pos] <- Dred\n DNormed <- (D - min(D, na.rm = TRUE))/(max(D, na.rm = TRUE) - min(D, na.rm = TRUE))\n Y = 1/(1 + exp((DNormed * (-12)) + log(9999)))\n Y = as.numeric(Y)\n\n\n\n return(Y)\n}\n", "meta": {"hexsha": "00d8fb4720d971fdd4e8e98649def6879b9601d7", "size": 2811, "ext": "r", "lang": "R", "max_stars_repo_path": "R/proteinScore.r", "max_stars_repo_name": "aschluter/ClinPrior", "max_stars_repo_head_hexsha": "7ffb8a66299d240ea2141e1892e8aa20009b4880", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-03-08T12:42:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-17T09:54:50.000Z", "max_issues_repo_path": "R/proteinScore.r", "max_issues_repo_name": "aschluter/ClinPrior", "max_issues_repo_head_hexsha": "7ffb8a66299d240ea2141e1892e8aa20009b4880", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/proteinScore.r", "max_forks_repo_name": "aschluter/ClinPrior", "max_forks_repo_head_hexsha": "7ffb8a66299d240ea2141e1892e8aa20009b4880", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6836734694, "max_line_length": 126, "alphanum_fraction": 0.6374955532, "num_tokens": 943, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7690802264851919, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.34859472472876984}} {"text": "residplot <- function(residuals, xpos, ypos, maxsize = 0.25, poscol = 2, linecol = 1, lwd = 1, \n n = 50, maxn, negcol, txt = FALSE, csi = 0.1, xlab = \"\", ylab = \"\", main=\"\", axes = T,\n arg = T, argcol = 20, arglty = 2, cn = c(\"x\", \"y\", \"z\"), append = F,refdot=F,start.year=0,end.year=0)\n{ \n par(err = -1)\n if(is.data.frame(residuals)) {\n x <- residuals[, cn[1]]\n y <- residuals[, cn[2]]\n residuals <- residuals[, cn[3]]\n }\n else {\n residuals <- t(residuals)\n max.r <- max(abs(residuals), na.rm = T)\n \n if(missing(maxn))\n maxn <- max(abs(residuals), na.rm = T)\n if(missing(xpos))\n xpos <- 1:nrow(residuals)\n if(missing(ypos))\n ypos <- 1:ncol(residuals)\n x <- matrix(xpos, length(xpos), length(ypos))\n y <- matrix(ypos, length(xpos), length(ypos), byrow = T)\n }\n if(refdot) add.year<-6 else add.year<-2\n xx<-x; \n xx[1,1]<-min(x)-2\n if (start.year >0) xx[1,1]<-start.year\n xx[1,2]<-max(x)+add.year\n if (end.year>0)xx[1,2]<-end.year\n \n plot(xx, y, type = \"n\", xlab = xlab, ylab = ylab, axes = FALSE, main=main, las=1,ylim=c(min(y)-0.5,max(y)+0.5))\n axis(1)\n axis(2,min(ypos):max(ypos), tick=F,las=2)\n\n box()\n x.bck <- x\n y.bck <- y\n if(arg) {\n r <- x.bck - y.bck\n tmp <- unique(r)\n for(i in 1:length(tmp)) {\n j <- r == tmp[i]\n lines(x.bck[j], y.bck[j], col = argcol, lty = arglty)\n }\n }\n plt <- par()$pin\n xscale <- (par()$usr[2] - par()$usr[1])/plt[1] * maxsize\n yscale <- (par()$usr[4] - par()$usr[3])/plt[2] * maxsize\n rx <- c(unlist(sqrt(abs(residuals)/maxn) * xscale))\n ry <- c(unlist(sqrt(abs(residuals)/maxn) * yscale))\n theta <- seq(0, 2 * pi, length = n)\n n1 <- length(rx)\n theta <- matrix(theta, n1, n, byrow = T)\n x <- matrix(x, n1, n)\n y <- matrix(y, n1, n)\n rx <- matrix(rx, n1, n)\n ry <- matrix(ry, n1, n)\n x <- x + rx * cos(theta)\n y <- y + ry * sin(theta)\n x <- cbind(x, rep(NA, nrow(x)))\n y <- cbind(y, rep(NA, nrow(y)))\n x<-x\n i <- residuals > 0\n \n if(any(i)) {\n polygon(c(t(x[i, ])), c(t(y[i, ])), col = poscol)\n lines(c(t(x[i, ])), c(t(y[i, ])), col = linecol, lwd = lwd)\n }\n i <- residuals < 0\n if(any(i)) {\n if(!missing(negcol))\n polygon(c(t(x[i, ])), c(t(y[i, ])), col = negcol)\n lines(c(t(x[i, ])), c(t(y[i, ])), col = linecol, lwd = lwd)\n }\n if(txt)text(x.bck, y.bck, as.character(round(residuals)), csi = csi)\n\n if(refdot){\n # if (end.year==0) x <- max(xpos)+4 +xscale*cos(theta)\n # if (end.year>0) x <- end.year-1 +xscale*cos(theta)\n if (end.year==0) x <- max(xpos)+4 +sqrt(max.r/maxn) * xscale*cos(theta)\n if (end.year>0) x <- end.year-1 +sqrt(max.r/maxn) * xscale*cos(theta)\n\n #y <- max(max(ypos)%/%3+min(ypos),1) + yscale*sin(theta) \n y <- max(max(ypos)%/%3+min(ypos),1) +sqrt(max.r/maxn)* yscale*sin(theta) \n polygon(x, y, col = 7)\n if (end.year==0) text(max(xpos)+4, max(max(ypos)%/%3+min(ypos),1), as.character(round(max.r,digit=2)))\n if (end.year>0) text(end.year-1, max(max(ypos)%/%3+min(ypos),1), as.character(round(max.r,digit=2)))\n }\n return(invisible())\n}\n", "meta": {"hexsha": "a492cffd6975507876f27810d93114a3d5217054", "size": 3324, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/function/bubbleplots.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/function/bubbleplots.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/function/bubbleplots.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5274725275, "max_line_length": 120, "alphanum_fraction": 0.4984957882, "num_tokens": 1206, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.34853896456110495}} {"text": "dest <- fread('c:/perforce/daniel/ex/orig_data/destinations.csv')\r\n\r\ntrain_13[hotel_continent==2 & hotel_market==701 & srch_destination_id==8260][,.N,by=hotel_cluster][order(-N)][1:10]\r\n\r\ntrain_13[hotel_continent==2 & hotel_market==628][,.N,by=srch_destination_id][order(+N)][1:10]\r\n\r\nclusters <- kmeans(dest[,2:150,with=F],50000,iter.max=100)\r\ndest$cluster <- clusters$cluster\r\ndestClusters <- dest[,c('srch_destination_id','cluster'),with=F]\r\nwrite.csv(destClusters,'c:/perforce/daniel/ex/statistics/clusterByDest_50k.csv',row.names=F,quote=F)", "meta": {"hexsha": "7293fd34a44ce862a54c76749bb17c66afabe6ca", "size": 545, "ext": "r", "lang": "R", "max_stars_repo_path": "src/test/resources/destinations_analysis.r", "max_stars_repo_name": "danielkorzekwa/expedia-hotel-predictor", "max_stars_repo_head_hexsha": "c98b4c87b34c3eaa323d0a211862be8680ed904d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/test/resources/destinations_analysis.r", "max_issues_repo_name": "danielkorzekwa/expedia-hotel-predictor", "max_issues_repo_head_hexsha": "c98b4c87b34c3eaa323d0a211862be8680ed904d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/test/resources/destinations_analysis.r", "max_forks_repo_name": "danielkorzekwa/expedia-hotel-predictor", "max_forks_repo_head_hexsha": "c98b4c87b34c3eaa323d0a211862be8680ed904d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.5, "max_line_length": 116, "alphanum_fraction": 0.7431192661, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.34853896456110484}} {"text": "load(\"temporal_edgelist.Rdata\")\n\nlibrary(igraph)\nlibrary(plyr)\nlibrary(RColorBrewer)\n\nn_slices <- 60\n\ndseq <- seq(from=min(temporal_edgelist$time), to=max(temporal_edgelist$time), length.out = n_slices)\nxseq <- seq(from=min(temporal_edgelist$time), to=max(temporal_edgelist$time), length.out = n_slices + n_slices - 1)\nxseq <- xseq[!xseq %in% dseq]\n\ntemporal <- list()\naggregated <- list()\nfor(i in c(2:length(dseq)))\n{\n tweets <- subset(temporal_edgelist, time >= dseq[i-1] & time <= dseq[i])\n a_tweets <- subset(temporal_edgelist, time <= dseq[i])\n temporal[[i-1]] <- graph.data.frame(tweets)\n aggregated[[i-1]] <- graph.data.frame(a_tweets)\n}\n\ncomplete <- graph.data.frame(temporal_edgelist)\ncomplete_layout <- layout.fruchterman.reingold(complete, area = 50*vcount(complete)^2)\ncomms <- walktrap.community(complete, steps=10)\n\ncomm_color <- colorRampPalette(brewer.pal(10, 'Spectral'))(length(unique(comms$membership)))[comms$membership]\n\nplot(complete, layout=complete_layout, vertex.label=NA, vertex.frame.color=NA, vertex.color=comm_color, edge.arrow.size=0, vertex.size=5)\n\n# v_index <- c(1:vcount(complete))\n# names(v_index) <- V(complete)$name\n# png(file = \"temp_%04d.png\", width = 950, height = 950)\n# for(i in c(1:length(temporal)))\n# {\n# temp <- temporal[[i]]\n# plot(complete, layout=complete_layout,\n# rescale = FALSE,\n# xlim=range(complete_layout[,1]), ylim=range(complete_layout[,2]),\n# vertex.label=NA, vertex.size=2,\n# vertex.color='lightgrey',\n# vertex.frame.color='darkgrey',\n# edge.color='lightgrey',\n# edge.arrow.size=0)\n# title(xseq[i])\n# t_vertices <- v_index[V(temp)$name]\n# plot(temp, layout=complete_layout[t_vertices,],\n# add=TRUE, rescale=FALSE,\n# vertex.color='purple',\n# vertex.frame.color='purple',\n# vertex.size=5,\n# edge.arrow.size=0,\n# vertex.label=NA,\n# xlim=range(complete_layout[,1]), ylim=range(complete_layout[,2]),\n# edge.color='black')\n# }\n# dev.off()\n# \n# # The we use ImageMagick to convert all images to a gif file\n# system(\"convert -delay 50 temp_*.png animation_esa.gif\")\n# # And remove the images\n# file.remove(list.files(pattern = \"temp_\"))", "meta": {"hexsha": "a69a2a7821683b5cb654bec23812ce6e4040d9ed", "size": 2208, "ext": "r", "lang": "R", "max_stars_repo_path": "code/network_analysis/split_networks_by_date.r", "max_stars_repo_name": "noamross/esa2014twitter", "max_stars_repo_head_hexsha": "c095a6fada575d56f856407f90ae2023805b195f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-03-31T18:11:04.000Z", "max_stars_repo_stars_event_max_datetime": "2015-03-31T18:11:04.000Z", "max_issues_repo_path": "code/network_analysis/split_networks_by_date.r", "max_issues_repo_name": "noamross/esa2014twitter", "max_issues_repo_head_hexsha": "c095a6fada575d56f856407f90ae2023805b195f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/network_analysis/split_networks_by_date.r", "max_forks_repo_name": "noamross/esa2014twitter", "max_forks_repo_head_hexsha": "c095a6fada575d56f856407f90ae2023805b195f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6129032258, "max_line_length": 137, "alphanum_fraction": 0.6766304348, "num_tokens": 636, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.34853896456110484}} {"text": "\nlibrary(shiny)\nlibrary(dygraphs)\n\n# Define UI for application that draws a histogram\nshinyUI(fluidPage(\n\n navbarPage(\n theme = \"cerulean\", # <--- To use a theme, uncomment this\n \"Aplicacion para Series de Tiempo\",\n tabPanel(\"Cargar Serie\",\n sidebarLayout(\n sidebarPanel(\n fileInput('file1', 'Elija Archivo',\n accept=c('text/csv', \n 'text/comma-separated-values,text/plain', \n '.csv')),\n checkboxInput('header', 'Cabecera', FALSE),\n selectInput('sep', 'Separador', \n c('Coma'=',',\n 'Punto y coma' =';',\n 'Tabulador' ='\\t'),\n '\\t'),\n selectInput('deci', 'Separador decimal',\n c('Coma'=\",\",\n 'Punto'=\".\"),\n \",\"),\n hr(),\n \n selectInput('frec', 'Periodicidad:',\n c('Diaria'=\"days\",\n 'Mensual'=\"month\",\n 'Trimestral'=\"quarter\",\n 'Anual'=\"years\"),\n \"Diario\"),\n dateInput(\"fecha\", \"Fecha Inicio:\", value = \"2017-09-29\"),\n div(strong(\"Desde: \"), textOutput(\"from\", inline = TRUE)),\n div(strong(\"Hasta: \"), textOutput(\"to\", inline = TRUE)),\n hr(),\n selectInput('xcol', 'X Variable', \"\")\n \n ),\n mainPanel(\n dygraphOutput(\"dygraph\")\n )\n )\n \n ),\n tabPanel(\"Analisis Inicial\", \n tabsetPanel(type = \"tabs\",\n tabPanel(\"LM\", sidebarPanel(\n radioButtons(\"tipo\", \"Tipo:\",\n c(\"Aditivo\" = \"Aditivos\",\n \"Multiplicativo\" = \"Multiplicativos\"), \"Aditivos\"),\n radioButtons(\"metodo\", \"Grado:\",\n c(\"Lineal\" = \"Lineal\",\n \"Cuadratico\" = \"Cuadratico\",\n \"Cubico\" = \"Cubico\",\n \"Orden 4\" = \"Orden 4\"), \"Lineal\"),\n checkboxInput(\"est\", \"Estacionaria\", FALSE),\n \n br(),\n \n checkboxInput(\"show.resid\", \"Show residuals\", FALSE),\n \n br(),\n \n helpText(\"Texto de ayuda.\"),\n br()),\n mainPanel(\n plotOutput(\"scatter\"),\n br(),\n br(),\n plotOutput(\"residuals\")\n \n )),\n tabPanel(\"Holt-Winter\", sidebarPanel(\n radioButtons(\"tipohw\", \"Tipo:\",\n c(\"Aditivo\" = \"Aditivos\",\n \"Multiplicativo\" = \"Multiplicativos\"), \"Aditivos\"),\n \n br(),\n \n checkboxInput(\"show.resid\", \"Show residuals\", FALSE),\n \n br(),\n \n helpText(\"Texto de ayuda.\"),\n br()),\n mainPanel(\n plotOutput(\"scatter2\"),\n br(),\n br(),\n plotOutput(\"residuals2\")\n \n )),\n tabPanel(\"Descomposicion y LOESS\", sidebarPanel(\n radioButtons(\"tipolo\", \"LOESS:\",\n c(\"Lineal\" = \"Lineal\",\n \"Cuadratico\" = \"Cuadratico\"), \"Lineal\"),\n radioButtons(\"tipob\", \"Descomposicion:\",\n c(\"Aditivo\" = \"Aditivos\",\n \"Multiplicativo\" = \"Multiplicativos\"), \"Aditivos\"),\n \n br(),\n \n checkboxInput(\"show.resid\", \"Show residuals\", FALSE),\n \n br(),\n \n helpText(\"Texto de ayuda.\"),\n br()),\n mainPanel(\n plotOutput(\"scatter3\"),\n br(),\n br(),\n plotOutput(\"residuals3\")\n \n \n ))\n )\n ),\n tabPanel(\"Resultados\", \"Este panel muestra los resultados\")\n )\n))\n", "meta": {"hexsha": "d5a0b81851ecd39f85f70fa4ed2ca631e133deb2", "size": 5351, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ui.r", "max_stars_repo_name": "jcfiallos/miprueba", "max_stars_repo_head_hexsha": "0500e9fafe69f003290ead9b6b286e983169a236", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/ui.r", "max_issues_repo_name": "jcfiallos/miprueba", "max_issues_repo_head_hexsha": "0500e9fafe69f003290ead9b6b286e983169a236", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ui.r", "max_forks_repo_name": "jcfiallos/miprueba", "max_forks_repo_head_hexsha": "0500e9fafe69f003290ead9b6b286e983169a236", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.4682539683, "max_line_length": 93, "alphanum_fraction": 0.2982620071, "num_tokens": 852, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.34853896456110484}} {"text": "context(\"cast\")\n\ns2 <- array(seq.int(3 * 4), c(3,4))\ns2m <- melt(s2)\ncolnames(s2m) <- c(\"X1\", \"X2\", \"value\")\n\ns3 <- array(seq.int(3 * 4 * 5), c(3,4,5))\ns3m <- melt(s3)\ncolnames(s3m) <- c(\"X1\", \"X2\", \"X3\", \"value\")\n\ntest_that(\"reshaping matches t and aperm\", {\n # 2d\n expect_equivalent(s2, acast(s2m, X1 ~ X2))\n expect_equivalent(t(s2), acast(s2m, X2 ~ X1))\n expect_equivalent(as.vector(s2), as.vector(acast(s2m, X2 + X1 ~ .)))\n\n # 3d\n expect_equivalent(s3, acast(s3m, X1 ~ X2 ~ X3))\n expect_equivalent(as.vector(s3), as.vector(acast(s3m, X3 + X2 + X1 ~ .)))\n expect_equivalent(aperm(s3, c(1,3,2)), acast(s3m, X1 ~ X3 ~ X2))\n expect_equivalent(aperm(s3, c(2,1,3)), acast(s3m, X2 ~ X1 ~ X3))\n expect_equivalent(aperm(s3, c(2,3,1)), acast(s3m, X2 ~ X3 ~ X1))\n expect_equivalent(aperm(s3, c(3,1,2)), acast(s3m, X3 ~ X1 ~ X2))\n expect_equivalent(aperm(s3, c(3,2,1)), acast(s3m, X3 ~ X2 ~ X1))\n})\n\ntest_that(\"aggregation matches apply\", {\n\n # 2d -> 1d\n expect_equivalent(colMeans(s2), as.vector(acast(s2m, X2 ~ ., mean)))\n expect_equivalent(rowMeans(s2), as.vector(acast(s2m, X1 ~ ., mean)))\n\n # 3d -> 1d\n expect_equivalent(apply(s3, 1, mean), as.vector(acast(s3m, X1 ~ ., mean)))\n expect_equivalent(apply(s3, 1, mean), as.vector(acast(s3m, . ~ X1, mean)))\n expect_equivalent(apply(s3, 2, mean), as.vector(acast(s3m, X2 ~ ., mean)))\n expect_equivalent(apply(s3, 3, mean), as.vector(acast(s3m, X3 ~ ., mean)))\n\n # 3d -> 2d\n expect_equivalent(apply(s3, c(1,2), mean), acast(s3m, X1 ~ X2, mean))\n expect_equivalent(apply(s3, c(1,3), mean), acast(s3m, X1 ~ X3, mean))\n expect_equivalent(apply(s3, c(2,3), mean), acast(s3m, X2 ~ X3, mean))\n})\n\nnames(ChickWeight) <- tolower(names(ChickWeight))\nchick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE)\n\ntest_that(\"aggregation matches table\", {\n tab <- unclass(with(chick_m, table(chick, time)))\n cst <- acast(chick_m, chick ~ time, length)\n\n expect_that(tab, is_equivalent_to(cst))\n})\n\ntest_that(\"grand margins are computed correctly\", {\n col <- acast(s2m, X1 ~ X2, mean, margins = \"X1\")[4, ]\n row <- acast(s2m, X1 ~ X2, mean, margins = \"X2\")[, 5]\n grand <- acast(s2m, X1 ~ X2, mean, margins = TRUE)[4, 5]\n\n expect_equivalent(col, colMeans(s2))\n expect_equivalent(row, rowMeans(s2))\n expect_equivalent(grand, mean(s2))\n})\n#\ntest_that(\"internal margins are computed correctly\", {\n cast <- dcast(chick_m, diet + chick ~ time, length, margins=\"diet\")\n\n marg <- subset(cast, diet == \"(all)\")[-(1:2)]\n expect_that(as.vector(as.matrix(marg)),\n equals(as.vector(acast(chick_m, time ~ ., length))))\n\n joint <- subset(cast, diet != \"(all)\")\n expect_that(joint,\n is_equivalent_to(dcast(chick_m, diet + chick ~ time, length)))\n})\n\ntest_that(\"missing combinations filled correctly\", {\n s2am <- subset(s2m, !(X1 == 1 & X2 == 1))\n\n expect_equal(acast(s2am, X1 ~ X2)[1, 1], NA_integer_)\n expect_equal(acast(s2am, X1 ~ X2, length)[1, 1], 0)\n expect_equal(acast(s2am, X1 ~ X2, length, fill = 1)[1, 1], 1)\n\n})\n\ntest_that(\"drop = FALSE generates all combinations\", {\n df <- data.frame(x = c(\"a\", \"b\"), y = c(\"a\", \"b\"), value = 1:2)\n\n expect_that(as.vector(acast(df, x + y ~ ., drop = FALSE)),\n is_equivalent_to(as.vector(acast(df, x ~ y))))\n\n})\n\ntest_that(\"aggregated values computed correctly\", {\n ffm <- melt(french_fries, id = 1:4)\n\n count_c <- function(vars) as.table(acast(ffm, as.list(vars), length))\n count_t <- function(vars) table(ffm[vars], useNA = \"ifany\")\n\n combs <- matrix(names(ffm)[1:5][t(combn(5, 2))], ncol = 2)\n a_ply(combs, 1, function(vars) {\n expect_that(count_c(vars), is_equivalent_to(count_t(vars)),\n label = paste(vars, collapse = \", \"))\n })\n\n})\n\ntest_that(\"value.var overrides value col\", {\n df <- data.frame(\n id1 = rep(letters[1:2],2),\n id2 = rep(LETTERS[1:2],each=2), var1=1:4)\n\n df.m <- melt(df)\n df.m$value2 <- df.m$value * 2\n expect_that(acast(df.m, id2 + id1 ~ ., value.var=\"value\")[, 1],\n equals(1:4, check.attributes = FALSE))\n expect_that(acast(df.m, id2 + id1 ~ ., value.var=\"value2\")[, 1],\n equals(2 * 1:4, check.attributes = FALSE))\n})\n\ntest_that(\"labels are correct when missing combinations dropped/kept\", {\n df <- data.frame(fac1 = letters[1:4], fac2 = LETTERS[1:4], x = 1:4)\n mx <- melt(df, id = c(\"fac1\", \"fac2\"), measure.var = \"x\")\n\n c1 <- dcast(mx[1:2, ], fac1 + fac2 ~ variable, length, drop = F)\n expect_that(nrow(c1), equals(16))\n\n c2 <- dcast(droplevels(mx[1:2, ]), fac1 + fac2 ~ variable, length, drop = F)\n expect_that(nrow(c2), equals(4))\n\n c3 <- dcast(mx[1:2, ], fac1 + fac2 ~ variable, length, drop = T)\n expect_that(nrow(c3), equals(2))\n\n\n})\n\ntest_that(\"factor value columns are handled\", {\n df <- data.frame(fac1 = letters[1:4], fac2 = LETTERS[1:4], x = factor(1:4))\n mx <- melt(df, id = c(\"fac1\", \"fac2\"), measure.var = \"x\")\n\n c1 <- dcast(mx, fac1 + fac2 ~ variable)\n expect_that(nrow(c1), equals(4))\n expect_that(ncol(c1), equals(3))\n expect_is(c1$x, \"character\")\n\n c2 <- dcast(mx, fac1 ~ fac2 + variable)\n expect_that(nrow(c2), equals(4))\n expect_that(ncol(c2), equals(5))\n expect_is(c2$A_x, \"character\")\n expect_is(c2$B_x, \"character\")\n expect_is(c2$C_x, \"character\")\n expect_is(c2$D_x, \"character\")\n\n c3 <- acast(mx, fac1 + fac2 ~ variable)\n expect_that(nrow(c3), equals(4))\n expect_that(ncol(c3), equals(1))\n expect_true(is.character(c3))\n\n c4 <- acast(mx, fac1 ~ fac2 + variable)\n expect_that(nrow(c4), equals(4))\n expect_that(ncol(c4), equals(4))\n expect_true(is.character(c4))\n\n})\n\ntest_that(\"dcast evaluated in correct argument\", {\n g <- c(\"a\", \"b\")\n expr <- quote({\n df <- data.frame(x = letters[1:2], y = letters[1:3], z = rnorm(6))\n g <- c('b', 'a')\n dcast(df, y ~ ordered(x, levels = g))\n })\n\n res <- eval(expr, envir = new.env())\n expect_equal(names(res), c(\"y\", \"b\", \"a\"))\n\n})\n\ntest_that(\". ~ . returns single value\", {\n one <- acast(s2m, . ~ ., sum)\n expect_equal(as.vector(one), 78)\n expect_equal(dimnames(one), list(\".\", \".\"))\n})\n\ntest_that(\"drop = TRUE retains NA values\", {\n df <- data.frame(x = 1:5, y = c(letters[1:4], NA), value = 5:1)\n out <- dcast(df, x + y ~ .)\n\n expect_equal(dim(out), c(5, 3))\n expect_equal(out$., 5:1)\n})\n\ntest_that(\"useful error message if you use value_var\", {\n expect_error(dcast(mtcars, vs ~ am, value_var = \"cyl\"),\n \"Please use value.var\", fixed = TRUE)\n expect_equal(dim(dcast(mtcars, vs ~ am, value.var = \"cyl\")), c(2, 3))\n\n})\n\ntest_that(\"useful error message if value.var doesn't exist\", {\n expect_error(dcast(airquality, month ~ day, value.var = \"test\"),\n \"value.var (test) not found in input\", fixed = TRUE)\n})\n", "meta": {"hexsha": "fc09271a5c4cbbeccecfc73b7d041dd99228445c", "size": 6658, "ext": "r", "lang": "R", "max_stars_repo_path": "source/gdaexperience6/reshape2/tests/testthat/test-cast.r", "max_stars_repo_name": "pxgamer/ms-developer-immersion-data", "max_stars_repo_head_hexsha": "b48d291ad5a03d56c0228d00e0b290b638d50194", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 82, "max_stars_repo_stars_event_min_datetime": "2017-05-24T22:55:14.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-31T00:56:05.000Z", "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/reshape2/tests/testthat/test-cast.r", "max_issues_repo_name": "lordbitin/ESWA-2017", "max_issues_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2017-05-20T16:10:54.000Z", "max_issues_repo_issues_event_max_datetime": "2018-09-30T18:04:46.000Z", "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/reshape2/tests/testthat/test-cast.r", "max_forks_repo_name": "lordbitin/ESWA-2017", "max_forks_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 55, "max_forks_repo_forks_event_min_datetime": "2017-05-20T12:42:19.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-26T16:38:16.000Z", "avg_line_length": 32.637254902, "max_line_length": 78, "alphanum_fraction": 0.618954641, "num_tokens": 2440, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6513548646660542, "lm_q1q2_score": 0.3485389645611048}} {"text": "library(tidyr)\n\nstocks <- data.frame(\n time = as.Date('2009-01-01') + 0:1,\n X = rnorm(2, 0, 1),\n Y = rnorm(2, 0, 2),\n Z = rnorm(2, 0, 4)\n)\n\n# wide format data -> long format data\nstocks_long <- gather(stocks, key, value, -time)\nhead(stocks_long)\n\n\n# long format data -> wide format data\nstocks_wide <- spread(stocks_long, key, value)\nhead(stocks_wide)\n\n", "meta": {"hexsha": "ca200f7ede98c519ef3bd1dbe21c621104559bf1", "size": 357, "ext": "r", "lang": "R", "max_stars_repo_path": "r_training/using_package_sample/tidyr/sample.r", "max_stars_repo_name": "yukimura1227/trainings", "max_stars_repo_head_hexsha": "d3df8017b905353062768244bd4281fa7b495f5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r_training/using_package_sample/tidyr/sample.r", "max_issues_repo_name": "yukimura1227/trainings", "max_issues_repo_head_hexsha": "d3df8017b905353062768244bd4281fa7b495f5c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 34, "max_issues_repo_issues_event_min_datetime": "2020-09-05T20:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T20:50:22.000Z", "max_forks_repo_path": "r_training/using_package_sample/tidyr/sample.r", "max_forks_repo_name": "yukimura1227/trainings", "max_forks_repo_head_hexsha": "d3df8017b905353062768244bd4281fa7b495f5c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-25T13:12:07.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T13:12:07.000Z", "avg_line_length": 18.7894736842, "max_line_length": 48, "alphanum_fraction": 0.6470588235, "num_tokens": 127, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.651354857898194, "lm_q1q2_score": 0.34853896093963344}} {"text": "cat(sapply(as.numeric(readLines(tail(commandArgs(), n=1))), function(s) {\n a <- as.integer(s)\n r <- paste(toString(a), \".\", sep=\"\")\n s <- (s - a) * 60\n a <- as.integer(s)\n if (a < 10) { f <- \"0\" } else { f <- \"\" }\n r <- paste(r, f, toString(a), \"'\", sep=\"\")\n s <- (s - a) * 60\n a <- as.integer(s)\n if (a < 10) { f <- \"0\" } else { f <- \"\" }\n r <- paste(r, f, toString(a), \"\\\"\", sep=\"\")\n r\n}), sep=\"\\n\")\n", "meta": {"hexsha": "8237d95db2aed7036cd0bf75c6d1a7f9e9568528", "size": 413, "ext": "r", "lang": "R", "max_stars_repo_path": "easy/nice_angles.r", "max_stars_repo_name": "IlkhamGaysin/ce-challenges", "max_stars_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-06-24T17:09:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-03T11:44:54.000Z", "max_issues_repo_path": "easy/nice_angles.r", "max_issues_repo_name": "IlkhamGaysin/ce-challenges", "max_issues_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "easy/nice_angles.r", "max_forks_repo_name": "IlkhamGaysin/ce-challenges", "max_forks_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5, "max_line_length": 73, "alphanum_fraction": 0.4430992736, "num_tokens": 156, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.6513548714339145, "lm_q1q2_score": 0.34853895852445926}} {"text": "##For non-split BiSSE\n##Load dependencies\nlibrary(diversitree)\nlibrary(ape)\n#For this analysis, we use a data file that's been made a little smaller than the 8000-taxon data set. Load it.\ndata <- read.csv('/home/april/projectfiles/squamates/squam/PyronParityData.csv', row.names=1)\n# Initialize vectors. These will be used for formatting output.\noutput_vector <- vector()\novip_root_vector <- vector()\nvivp_root_vector <- vector()\n\n#File currently set up to accept input from Make, or if trees in working directory\na<- list.files(path='./trees/', pattern=\"*.tre\", full.names=TRUE)\n\n#Main function to fit model to each tree in BS sample\nmodel_fit <- function(tree_list){\nfor (x in tree_list){\n\tprint(x)\n#For each tree in our vector of trees, read it in and distill down the tree to the shared tips between it and the data set\n\tphy <- read.tree(x)\n\tpruned.tree<-drop.tip(phy, setdiff(phy$tip.label, row.names(data)))\n\tsorteddata <- data[phy$tip.label, ]\n\tno_na <- na.omit(sorteddata)\n\tnames(no_na) <- pruned.tree$tip.label\n \tpruned.tree <- multi2di(pruned.tree, random = TRUE)\n\t#Make the BiSSE function\n\tfunc <- make.bisse(pruned.tree, no_na)\n\tsp<-starting.point.bisse(pruned.tree)\n\t# Find MLE and use it to do ancestral state reconstruction \n\tfit_bisse <- find.mle(func, sp)\n#\tprint(fit_bisse)\n\tst <- asr.marginal(func, coef(fit_bisse))\n\t#Add root states to a vector of states \n\tovip_root_vector <- append(ovip_root_vector, c(st[,2][1], 'Oviparity'))\n\tvivip_root_vector <- append(ovip_root_vector, c(st[,2][2], 'Viviparity'))\n\toutput_vector <- append(output_vector, c(fit_bisse$par[1:6], fit_bisse$lnLik))\n\t#Plot tree\n\tplot(pruned.tree, show.tip.label=FALSE)\n\tfit_plot <- nodelabels(pie=t(st), piecol=1:2, cex=.5)\n\t#Output various parameters: the model and ancestral states \n}\n\n\tlapply(output_vector, write, \"model_params.csv\", append=TRUE, ncolumns=7)\n\tlapply(ovip_root_vector, write, \"ovip_support.csv\", append=TRUE,ncolumns=2)\n\tlapply(vivip_root_vector, write, \"vivip_support.csv\", append=TRUE, ncolumns=2)\n}\n\n#Call the function if you want\nmodel_fit(a)\n\n", "meta": {"hexsha": "f11b64fcbbb4b53c2e05573db448705754bcec91", "size": 2058, "ext": "r", "lang": "R", "max_stars_repo_path": "bisse.r", "max_stars_repo_name": "wrightaprilm/squamates", "max_stars_repo_head_hexsha": "18998772d703c0235b58fb477e07a5efabc14d01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bisse.r", "max_issues_repo_name": "wrightaprilm/squamates", "max_issues_repo_head_hexsha": "18998772d703c0235b58fb477e07a5efabc14d01", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bisse.r", "max_forks_repo_name": "wrightaprilm/squamates", "max_forks_repo_head_hexsha": "18998772d703c0235b58fb477e07a5efabc14d01", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3529411765, "max_line_length": 122, "alphanum_fraction": 0.7414965986, "num_tokens": 593, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300573952054, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3485208264916123}} {"text": "#THIS SCRIPT GENERATES THE PLOT FOR THE DIAGONAL K=1 IN THE CONFIGURATION TOTAL_TRIPLE_IN_WINDOW/EW PLOTTING THE FOUR BASELINES IN THE SAME FIGURE IN 4 DIFFERENT PLOTS\n#IT USES LOG SCALING\n\nlog_plot_dash <- function(x,y,sdx,sdy, col, pch){\n \n lx <- log(x)\n ly <- log(y)\n \n par(new=TRUE)\n points(x = lx, y = ly, col=col, type = \"p\", pch=pch)\n #arrows(lx, log(y-sdy),lx,log(y+sdy),, length=0.05, angle=90, code=3, col=\"grey55\")\n #arrows(log(sdx), ly,log(x+2*sdx),ly, length=0.05, angle=90, code=3, col=\"gray55\")\n \n}\n\nlog_dashboard<- function(a,b,c,d, xlim,ylim,ss = c(FALSE, FALSE, FALSE, FALSE, FALSE)){\n \n exp <- paste(lat_report_rhodf$STREAM[a], substr(lat_report_rhodf$REASONING[a], 0, 6))\n \n myxlim <- c(0.01, log(xlim[2]))\n myylim <- c(log(ylim[1])-1, log(ylim[2])+10)\n\n \n plot(seq(min(xlim[1], ylim[1]),max(xlim[2], ylim[2]),0.001),seq(min(xlim[1], ylim[1]),max(xlim[2], ylim[2]),0.001), log=\"xy\", type = \"n\", ylab = \"Memory\", xlab = \"Latency\", xlim=myxlim, ylim=myylim)\n \n abline( h = seq(0.001, 10, 0.1 ), lty = 3, col = colors()[ 440 ] )\n abline( v = seq(0.001, 10, 0.1 ), lty = 3, col = colors()[ 440 ] )\n \n \n z<-a\n if(ss[1]){\n pch <- 0\n }else{\n pch <- 15\n }\n pch_legend <- pch\n log_plot_dash(lat_report_rhodf$Mean.SS[z], mem_report_rhodf$A..Mean.SS[z], lat_report_rhodf$Dev.StdSS[z],mem_report_rhodf$A.Dev.Std.SS[z], \"red\", pch)\n \n z<-b\n if(ss[2]){\n pch <- 1\n }else{\n pch <- 16\n }\n pch_legend <- c(pch_legend,pch)\n log_plot_dash(lat_report_rhodf$Mean.SS[z], mem_report_rhodf$A..Mean.SS[z], lat_report_rhodf$Dev.StdSS[z], mem_report_rhodf$A.Dev.Std.SS[z], \"blue\", pch)\n \n \n z<-c\n if(ss[3]){\n pch <- 2\n }else{\n pch <- 17\n }\n pch_legend <- c(pch_legend,pch)\n log_plot_dash(lat_report_rhodf$Mean.SS[z], mem_report_rhodf$A..Mean.SS[z], lat_report_rhodf$Dev.StdSS[z], mem_report_rhodf$A.Dev.Std.SS[z], \"black\", pch)\n \n z <- d\n if(ss[4]){\n pch <- 5\n }else{\n pch <- 18\n }\n pch_legend <- c(pch_legend,pch)\n log_plot_dash(lat_report_rhodf$Mean.SS[z], mem_report_rhodf$A..Mean.SS[z], lat_report_rhodf$Dev.StdSS[z], mem_report_rhodf$A.Dev.Std.SS[z], \"chartreuse4\", pch)\n \n\n legend(x=0.02,y=15,pch=pch_legend,bg = \"white\",bty=\"o\", legend=c(\n paste( \"K\", lat_report_rhodf$K[a], \"EW\", lat_report_rhodf$EW[a]) ,\n paste( \"K\", lat_report_rhodf$K[b], \"EW\", lat_report_rhodf$EW[b]) ,\n paste( \"K\", lat_report_rhodf$K[c], \"EW\", lat_report_rhodf$EW[c]), \n paste( \"K\", lat_report_rhodf$K[d], \"EW\", lat_report_rhodf$EW[d])), cex=1 ,y.intersp=1, col=c(\"red\",\"blue\",\"black\",\"chartreuse4\", \"purple\"))\n title(exp, paste(\"ENs\", lat_report_rhodf$EN[a], lat_report_rhodf$EN[b], lat_report_rhodf$EN[c], lat_report_rhodf$EN[d]))\n \n}\n\n\npng(\"DASHBOARD K=X EW=1 LOG DIAGONAL.png\", 1000, 1000)\n\nmem_report_rhodf <- mem_report_rhodf[with(mem_report_rhodf, order(EW)),]\nlat_report_rhodf <- lat_report_rhodf[with(lat_report_rhodf, order(EW)),]\n\na <- 1\nb <- a+1\nc <- b+1\nd <- c+1\nsplit.screen(c(2,1))\nsplit.screen(c(1,2), screen=1)\n\nxlim <- c(min(lat_report_rhodf$Mean.SS), max(lat_report_rhodf$Mean.SS))\nylim <- c(sort(mem_report_rhodf$A..Mean.SS)[1], sort(mem_report_rhodf$A..Mean.SS, decreasing = TRUE)[1])\n \n \n#INC GRAPH\nscreen(3)\nlog_dashboard(a,a+4,a+8,a+12,xlim,ylim, c(FALSE, TRUE, FALSE, FALSE, TRUE))\n#INC STMT\nscreen(4) \nlog_dashboard(b,b+4,b+8,b+12,xlim,ylim, c(FALSE, TRUE, FALSE, FALSE, TRUE))\nsplit.screen(c(1,2), screen=2)\n\n\n#NAIVE GRAPH\nscreen(5)\nlog_dashboard(c,c+4,c+8,c+12,xlim,ylim, c(TRUE, TRUE, FALSE, FALSE, TRUE))\nscreen(6)\n#NAIVE STMT\nlog_dashboard(d,d+4,d+8,d+12,xlim,ylim, c(FALSE, TRUE, FALSE, FALSE, TRUE))\n\ndev.off()\n\n\n\n", "meta": {"hexsha": "d77b429fb0c5676694b126a1b2a0465b66f1f875", "size": 3612, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/dashboard/dashboard_log.ew.r", "max_stars_repo_name": "streamreasoning/HeavenTeststand", "max_stars_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/dashboard/dashboard_log.ew.r", "max_issues_repo_name": "streamreasoning/HeavenTeststand", "max_issues_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/dashboard/dashboard_log.ew.r", "max_forks_repo_name": "streamreasoning/HeavenTeststand", "max_forks_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.6842105263, "max_line_length": 201, "alphanum_fraction": 0.650055371, "num_tokens": 1452, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300449389326, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3485208203121322}} {"text": "get.operation <- function (x, primes = FALSE)\n{\n query <- unique(unlist(attr(x, \"query\")))\n prime.counter <- setNames(rep(1, length(query)), query)\n target.sym <- \"^*(\"\n single.source <- FALSE\n if (!is.null(attr(x, \"algorithm\"))) {\n if (attr(x, \"algorithm\") == \"zid\")\n target.sym <- \"(\"\n }\n if (!is.null(attr(x, \"sources\"))) {\n if (attr(x, \"sources\") == 1)\n single.source <- TRUE\n }\n return(get.operation.internal(x, primes, prime.counter,\n FALSE, target.sym, single.source))\n}\n\nget.expression.mystyle <- function (\n x\n, primes\n, prime.counter\n, start.sum\n, target.sym\n, single.source\n)\n{\n P <- \"\"\n s.print <- length(x$sumset) > 0\n super <- character(0)\n sum.string <- character(0)\n var.string <- character(0)\n cond.string <- character(0)\n if (s.print) {\n if (primes) {\n update <- set.primes(x$sumset, TRUE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n sum.string <- paste0(\n x$sumset\n , super[x$sumset]\n , collapse = \",\"\n )\n }\n else {\n sum.string <- paste0(\n x$sumset\n , collapse = \",\"\n )\n }\n if (start.sum)\n P <- paste0(\n P\n , \"\\\\\\\\left(\\\\\\\\sum_{\"\n , sum.string\n , \"}\"\n , collapse = \"\"\n )\n else {\n P <- paste0(\n P\n , \"\\\\\\\\sum_{\"\n , sum.string\n , \"}\"\n , collapse = \"\"\n )\n }\n }\n if (x$fraction) {\n P <- paste0(\n P\n , \"\\\\\\\\frac{\"\n , get.operation.internal(\n x$num\n , primes\n , prime.counter\n , FALSE\n , target.sym\n , single.source\n )\n , \"}{\"\n , get.operation.internal(\n x$den\n , primes\n , prime.counter\n , FALSE\n , target.sym\n , single.source\n )\n , \"}\"\n , collapse = \"\"\n )\n }\n if (x$sum) {\n P <- paste(\n P\n , \"\\\\\\\\left(\"\n , sep = \"\"\n , collapse = \"\"\n )\n add.strings <- c()\n for (i in 1:length(x$children)) {\n new.sum <- FALSE\n if (x$children[[i]]$product || x$children[[i]]$sum)\n new.sum <- TRUE\n add.strings[i] <- paste0(\n c(\n \"w_{\"\n , i\n , \"}^{(\"\n , x$weight\n , \")}\"\n , get.operation.internal(x$children[[i]]\n , primes\n , prime.counter\n , new.sum\n , target.sym\n , single.source\n )\n )\n , collapse = \"\"\n )\n }\n add.strings <- paste(add.strings, sep = \"\", collapse = \" + \")\n P <- paste0(P, add.strings, \"\\\\\\\\right)\", collapse = \"\")\n }\n if (x$product) {\n for (i in 1:length(x$children)) {\n new.sum <- FALSE\n if (x$children[[i]]$product || x$children[[i]]$sum)\n new.sum <- TRUE\n P <- paste0(\n P\n , get.operation.internal(\n x$children[[i]]\n , primes\n , prime.counter\n , new.sum\n , target.sym\n , single.source\n )\n , collapse = \"\"\n )\n }\n }\n if (!(x$sum || x$product || x$fraction)) {\n P <- paste0(P, \"P\", collapse = \"\")\n if (length(x$do) > 0) {\n do.string <- paste0(x$do, collapse = \",\")\n P <- paste0(\n P\n , \"_{\"\n , do.string\n , \"}\"\n , collapse = \"\"\n )\n }\n if (primes) {\n update <- set.primes(x$var, FALSE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n var.string <- paste0(\n x$var\n , super[x$var]\n , collapse = \",\"\n )\n }\n else {\n var.string <- paste0(x$var, collapse = \",\")\n }\n if (x$domain > 0) {\n if (x$domain == 1)\n P <- paste0(P, target.sym, var.string, collapse = \"\")\n else {\n if (single.source)\n P <- paste0(\n P\n , \"(\"\n , var.string\n , collapse = \"\"\n )\n else P <- paste0(\n P\n , \"^{(\"\n , x$domain - 1\n , \")}(\"\n , var.string\n , collapse = \"\"\n )\n }\n }\n else {\n P <- paste0(\n P\n , \"(\"\n , var.string\n , collapse = \"\"\n )\n }\n if (length(x$cond) > 0) {\n if (primes) {\n update <- set.primes(x$cond, FALSE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n cond.string <- paste0(\n x$cond\n , super[x$cond]\n , collapse = \",\"\n )\n }\n else {\n cond.string <- paste0(\n x$cond\n , collapse = \",\"\n )\n }\n cond.string <- paste0(\"|\", cond.string, \")\", collapse = \"\")\n }\n else cond.string <- \")\"\n P <- paste0(P, cond.string)\n }\n if (s.print & start.sum)\n P <- paste0(P, \"\\\\\\\\right)\", collapse = \",\")\n return(P)\n}\n\nget.expression.mystyle <- function (\n x\n, primes\n, prime.counter\n, start.sum\n, target.sym\n, single.source\n)\n{\n P <- \"\"\n s.print <- length(x$sumset) > 0\n super <- character(0)\n sum.string <- character(0)\n var.string <- character(0)\n cond.string <- character(0)\n if (s.print) {\n if (primes) {\n update <- set.primes(x$sumset, TRUE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n sum.string <- paste0(\n x$sumset\n , super[x$sumset]\n , collapse = \",\"\n )\n }\n else {\n sum.string <- paste0(\n x$sumset\n , collapse = \",\"\n )\n }\n if (start.sum)\n P <- paste0(\n P\n , \"\\\\\\\\left(\\\\\\\\sum_{\"\n , sum.string\n , \"}\"\n , collapse = \"\"\n )\n else {\n P <- paste0(\n P\n , \"\\\\\\\\sum_{\"\n , sum.string\n , \"}\"\n , collapse = \"\"\n )\n }\n }\n if (x$fraction) {\n P <- paste0(\n P\n , \"\\\\\\\\frac{\"\n , get.expression.internal(\n x$num\n , primes\n , prime.counter\n , FALSE\n , target.sym\n , single.source\n )\n , \"}{\"\n , get.expression.internal(\n x$den\n , primes\n , prime.counter\n , FALSE\n , target.sym\n , single.source\n )\n , \"}\"\n , collapse = \"\"\n )\n }\n if (x$sum) {\n P <- paste(\n P\n , \"\\\\\\\\left(\"\n , sep = \"\"\n , collapse = \"\"\n )\n add.strings <- c()\n for (i in 1:length(x$children)) {\n new.sum <- FALSE\n if (x$children[[i]]$product || x$children[[i]]$sum)\n new.sum <- TRUE\n add.strings[i] <- paste0(\n c(\n \"w_{\"\n , i\n , \"}^{(\"\n , x$weight\n , \")}\"\n , get.expression.internal(x$children[[i]]\n , primes\n , prime.counter\n , new.sum\n , target.sym\n , single.source\n )\n )\n , collapse = \"\"\n )\n }\n add.strings <- paste(add.strings, sep = \"\", collapse = \" + \")\n P <- paste0(P, add.strings, \"\\\\\\\\right)\", collapse = \"\")\n }\n if (x$product) {\n for (i in 1:length(x$children)) {\n new.sum <- FALSE\n if (x$children[[i]]$product || x$children[[i]]$sum)\n new.sum <- TRUE\n P <- paste0(\n P\n , get.expression.internal(\n x$children[[i]]\n , primes\n , prime.counter\n , new.sum\n , target.sym\n , single.source\n )\n , collapse = \"\"\n )\n }\n }\n if (!(x$sum || x$product || x$fraction)) {\n P <- paste0(P, \"P\", collapse = \"\")\n if (length(x$do) > 0) {\n do.string <- paste0(x$do, collapse = \",\")\n P <- paste0(\n P\n , \"_{\"\n , do.string\n , \"}\"\n , collapse = \"\"\n )\n }\n if (primes) {\n update <- set.primes(x$var, FALSE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n var.string <- paste0(\n x$var\n , super[x$var]\n , collapse = \",\"\n )\n }\n else {\n var.string <- paste0(x$var, collapse = \",\")\n }\n if (x$domain > 0) {\n if (x$domain == 1)\n P <- paste0(P, target.sym, var.string, collapse = \"\")\n else {\n if (single.source)\n P <- paste0(\n P\n , \"(\"\n , var.string\n , collapse = \"\"\n )\n else P <- paste0(\n P\n , \"^{(\"\n , x$domain - 1\n , \")}(\"\n , var.string\n , collapse = \"\"\n )\n }\n }\n else {\n P <- paste0(\n P\n , \"(\"\n , var.string\n , collapse = \"\"\n )\n }\n if (length(x$cond) > 0) {\n if (primes) {\n update <- set.primes(x$cond, FALSE, prime.counter)\n super <- update$super\n prime.counter <- update$counter\n cond.string <- paste0(\n x$cond\n , super[x$cond]\n , collapse = \",\"\n )\n }\n else {\n cond.string <- paste0(\n x$cond\n , collapse = \",\"\n )\n }\n cond.string <- paste0(\"|\", cond.string, \")\", collapse = \"\")\n }\n else cond.string <- \")\"\n P <- paste0(P, cond.string)\n }\n if (s.print & start.sum)\n P <- paste0(P, \"\\\\\\\\right)\", collapse = \",\")\n return(P)\n}\n", "meta": {"hexsha": "f4a45085b1bb4221dd405fe067d923f85dc82ade", "size": 8922, "ext": "r", "lang": "R", "max_stars_repo_path": "get_expression.r", "max_stars_repo_name": "johnchower/tensoreffect", "max_stars_repo_head_hexsha": "6df3417447d9019634e85dec30c070b1dd74d4fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "get_expression.r", "max_issues_repo_name": "johnchower/tensoreffect", "max_issues_repo_head_hexsha": "6df3417447d9019634e85dec30c070b1dd74d4fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "get_expression.r", "max_forks_repo_name": "johnchower/tensoreffect", "max_forks_repo_head_hexsha": "6df3417447d9019634e85dec30c070b1dd74d4fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.5576036866, "max_line_length": 65, "alphanum_fraction": 0.4262497198, "num_tokens": 2491, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6757646140788307, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3484376933599228}} {"text": "# Compare current habitat temperatures\n# to upper thermal limits in marine and terrestrial species\n# Use operative temperature on land, use SST in ocean\n# Use temp at each location, rather than lat averages\n\nrequire(mgcv)\nrequire(data.table)\nrequire(lme4)\n\n##################\n# Functions\n##################\n\n# round a single value (x) to nearest value in a vector (y)\nroundto <- function(x, y){\n\ta <- abs(x - y)\n\ti <- which(a==min(a)) # can return 1 or 2 matches\n\treturn(y[i])\n}\n\n\n##################\n# Parameters\n##################\n# for adjusting max air temperature to account for elevation\nlapse <- 0.0055 # degC/m elevation. See Sunday et al. 2014 PNAS supplement\n\n#set Acclimation Response Ratio for terrestrial and marine organisms\n# from Gunderson and Stillman model-averaged results (Gunderson pers. comm.)\nARRland <- 0.11\nARRoce <- 0.24\n\n#################\n# Read in data\n#################\n\n# Measurements of upper thermal tolerance and NicheMapR (terrestrial microclimates)\ndat <- fread(file=\"output/dataset_1_hotwater_Nichemapped_GHCNDnonequil.csv\") # nonequilibrium Tb calculations. Built from dataset_1_hotwater_Nichemapped_GHCND.csv\n\n\t# fix column names\n\tsetnames(dat, c('tmax', 'REF_max', 'lat_max', 'long_max'), c('Tmax', 'citation', 'lat', 'lon'))\n\n\t# read in traits\n\ttraits <- fread('data/tmax_data/dataset_1_traits.csv')\n\n\t\t# fix benthopelagic and pelagic\n\t\ttraits[demers_pelag_fb == 'benthopelagic', demers_pelag := 'demersal']\n\t\ttraits[demers_pelag_fb == 'pelagic-neritic', demers_pelag := 'pelagic-neritic']\n\n\t# read in species-specific ARR\n\tarr <- fread('data/tmax_data/dataset_1_ARRs.tsv')\n\n\t\t# merge traits and arr\n\t\ttraits2 <- merge(traits, arr[, .(Genus, Species, ctmax_ARR_GS)], by=c('Genus', 'Species'), all.x=TRUE)\n\n\t# merge\ttraits and data\n\tdat <- merge(dat[,.(Genus, Species, Tmax, tmax_metric, tmax_acc, lat, lon, altitude, citation, Phylum, Class, Order, Family, NMGHCND_2m_shade_Tb95, NMGHCND_exposed_Tb95, NMGHCND_exposed_Tb_wet95, NMGHCND_shade_Tb_wet95)], traits2[, .(Genus, Species, thermy, Realm, Realm_detail, demers_pelag, mobility, weight, length, ctmax_ARR_GS)], all.x=TRUE, by=c('Genus', 'Species'))\n\t\tdim(dat)\n\n\n\t# trim out freshwater and intertidal\n\tdat <- dat[Realm %in% c('Marine', 'Terrestrial') & Genus != 'Gillichthys' & !(Realm_detail %in% c('catadromous', 'anadromous', 'amphidromous', 'freshwater')),]\n\t\tdim(dat)\n\n\t# re-order dat alphabetically\n\tdat <- dat[order(dat$Genus, dat$Species, dat$citation),]\n\n\t# how much trait data?\n\tdat[,sort(unique(Realm))]\n\tdat[Realm=='Marine', summary(length)]\n\tdat[Realm=='Marine', sort(unique(demers_pelag))]\n\tdat[Realm=='Marine', sort(unique(mobility))]\n\n# Elevation\n\tload('temp/elev.rdata') # elev data.frame 0.5x0.5\n\t\t\n# Current temperatures\n\t# read in climatologies for annual mean\n\tload('temp/lstclimatology.rdata') # lstclim (0.25x0.25)\n\tload('temp/sstclimatology.rdata') # sstclim\n#\t\tlstclimn <- lstclim\n#\t\tsstclimn <- sstclim\n#\n#\t# read in climatologies for summer mean\n\tload('temp/lstclimatology_warm3.rdata') # lstclimwarm3 (0.25x0.25 grid)\n\tload('temp/sstclimatology_warm3.rdata') # sstclimwarm3\n\t\tlstclimwarm3n <- lstclimwarm3\n\t\tsstclimwarm3n <- sstclimwarm3\n#\n#\t# read in climatology for the warmest month\n\tload('temp/sstclimatology_warmestmonth.rdata') # sstclimwarmestmo (0.25x0.25 grid)\n\tload('temp/lstclimatology_warmestmonth.rdata')\n\t\tlstclimwarmestmon <- lstclimwarmestmo\n\t\tsstclimwarmestmon <- sstclimwarmestmo\n\t\t\n\t# read in data for the 95% warmest daily maximum\n\tload('temp/tos95max.rdata') # tos95max (0.25x0.25 grid): ocean (95% warmest day + 50% DTR)\n\n\t# read in data for the warmest daily maximum\n\tload('temp/tosmax.rdata') # tosmax (0.25x0.25 grid): ocean (warmest day + 50% DTR)\n#\tload('temp/tasmax.rdata') # tasmax: air 0.25x0.25 (warmest daily max value averaged across any month). Use NicheMapR instead\n\n\n\n\n############################################################################################################\n# match habitat temperature to lat/lon of thermal tolerance (mostly for ocean since NicheMapR used on land\n############################################################################################################\n\n# Initialize columns for current habitat temperatures (those not from NicheMapR) and elevation\ndat$elev.grid <- dat$thab95hr <- dat$thabmaxmo <- dat$thabsum <- dat$thabann <- NA\n\n# Get lists of latitude and longitude values in the temperature data\nllons <- as.numeric(colnames(lstclimwarmestmon))\nllats <- as.numeric(rownames(lstclimwarmestmon))\nslons <- as.numeric(colnames(tosmax))\nslats <- as.numeric(rownames(tosmax))\n\nllonstep <- abs(diff(llons)[1])\nslonstep <- abs(diff(slons)[1])\nllatstep <- abs(diff(llats)[1])\nslatstep <- abs(diff(slats)[1])\n\n# Match each data point to temperature and elevation data based on lat/lon and habitat\nfor(i in 1:nrow(dat)){\n\tif(!is.na(dat$lat[i]) & !is.na(dat$lon[i])){\n\n\t\t# land\n\t\tif(dat$Realm[i]=='Terrestrial'){\n\t\t\tif(dat$lon[i] < 0){ # may need to correct lon to 0-360\n\t\t\t\tlons <- roundto(dat$lon[i] + 360, llons) # find the closest longitude (or lons)\n\t\t\t} else {\n\t\t\t\tlons <- roundto(dat$lon[i], llons) \n\t\t\t}\n\t\t\tlats <- roundto(dat$lat[i], llats) # find the closest latitudes (or latitudes)\n\t\t\t\n\t\t\t# look for grid cell with data\n\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # test\n\n\t\t\tif(is.na(ltemp)){ # if didn't get a value, try searching in a slightly wider area (may have moved off land in rounding)\n\t\t\t\tcat(paste('Going to wider search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\toriglats <- lats\n\t\t\t\toriglons <- lons\n\t\t\t\t\n\t\t\t\tif(all(lats > dat$lat[i])){ # move down one step if all rounded lats are greater\n\t\t\t\t\tlats <- c(lats, lats - llatstep)\n\t\t\t\t}\n\t\t\t\tif(all(lats < dat$lat[i])){\n\t\t\t\t\tlats <- c(lats, lats + llatstep)\n\t\t\t\t}\n\t\t\t\tif(all(lons > dat$lon[i])){\n\t\t\t\t\tlons <- c(lons, lons - llonstep)\n\t\t\t\t}\n\t\t\t\tif(all(lons < dat$lon[i])){\n\t\t\t\t\tlons <- c(lons, lons + llonstep)\n\t\t\t\t}\n\n\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t\n\t\t\t\t# take only the first element so that addition and subtraction work in following steps\n\t\t\t\toriglats <- lats[1]\n\t\t\t\toriglons <- lons[1]\n\n\t\t\t\tif(is.na(ltemp)){ # if still don't get a value, try searching in 9-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 9-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep, origlats, origlats+llatstep)\n\t\t\t\t\tlons <- c(origlons-llonstep, origlons, origlons+llonstep)\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){ # if still don't get a value, try searching in 25-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 25-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(2:1), origlats, origlats+llatstep*(1:2))\n\t\t\t\t\tlons <- c(origlons-llonstep*(2:1), origlons, origlons+llonstep*(1:2))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){ # if still don't get a value, try searching in 49-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 49-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(3:1), origlats, origlats+llatstep*(1:3))\n\t\t\t\t\tlons <- c(origlons-llonstep*(3:1), origlons, origlons+llonstep*(1:3))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){ # if still don't get a value, try searching in 81-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 81-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(4:1), origlats, origlats+llatstep*(1:4))\n\t\t\t\t\tlons <- c(origlons-llonstep*(4:1), origlons, origlons+llonstep*(1:4))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){\n\t\t\t\t\tcat(paste('\\tGoing to 121-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(5:1), origlats, origlats+llatstep*(1:5))\n\t\t\t\t\tlons <- c(origlons-llonstep*(5:1), origlons, origlons+llonstep*(1:5))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){\n\t\t\t\t\tcat(paste('\\tGoing to 169-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(6:1), origlats, origlats+llatstep*(1:6))\n\t\t\t\t\tlons <- c(origlons-llonstep*(6:1), origlons, origlons+llonstep*(1:6))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){\n\t\t\t\t\tcat(paste('\\tGoing to 225-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(7:1), origlats, origlats+llatstep*(1:7))\n\t\t\t\t\tlons <- c(origlons-llonstep*(7:1), origlons, origlons+llonstep*(1:7))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(ltemp)){\n\t\t\t\t\tcat(paste('\\tGoing to 289-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'on land\\n'))\n\t\t\t\t\tlats <- c(origlats-llatstep*(9:1), origlats, origlats+llatstep*(1:9))\n\t\t\t\t\tlons <- c(origlons-llonstep*(9:1), origlons, origlons+llonstep*(1:9))\n\t\t\t\t\tltemp <- mean(lstclimwarmestmon[as.character(lats), as.character(lons)], na.rm=TRUE) # land temp\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tcat(paste('\\tltemp now', ltemp, '\\n'))\n\n\t\t\t}\n\n\t\t\t# for annual mean\n\t\t\tdat$thabann[i] <- mean(lstclim[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for summer mean\n\t\t\tdat$thabsum[i] <- mean(lstclimwarm3n[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for max month\n\t\t\tdat$thabmaxmo[i] <- mean(lstclimwarmestmo[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for elevation\n\t\t\tdat$elev.grid[i] <- mean(elev[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t}\n\n\t\t# ocean\n\t\tif(dat$Realm[i]=='Marine'){\n\t\t\tif(dat$lon[i] < 0){\n\t\t\t\tlons <- roundto(dat$lon[i] + 360, slons)\n\t\t\t} else {\n\t\t\t\tlons <- roundto(dat$lon[i], slons)\n\t\t\t}\n\t\t\tlats <- roundto(dat$lat[i], slats)\n\n\n\t\t\t# look for grid cell with data\n\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE) # ocean temp\n\n\t\t\tif(is.na(stemp)){ # if didn't get a value, try searching in a slightly wider area (may have moved off ocean in rounding)\n\t\t\t\tcat(paste('Going to wider search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\toriglats <- lats\n\t\t\t\toriglons <- lons\n\n\t\t\t\tif(all(lats > dat$lat[i])){ # move down one step if all rounded lats are greater\n\t\t\t\t\tlats <- c(lats, lats - slatstep)\n\t\t\t\t}\n\t\t\t\tif(all(lats < dat$lat[i])){\n\t\t\t\t\tlats <- c(lats, lats + slatstep)\n\t\t\t\t}\n\t\t\t\tif(all(lons > dat$lon[i])){\n\t\t\t\t\tlons <- c(lons, lons - slonstep)\n\t\t\t\t}\n\t\t\t\tif(all(lons < dat$lon[i])){\n\t\t\t\t\tlons <- c(lons, lons + slonstep)\n\t\t\t\t}\n\n\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE) # ocean temp\n\n\t\t\t\t# take only the first element so that addition and subtraction work in following steps\n\t\t\t\toriglats <- lats[1]\n\t\t\t\toriglons <- lons[1]\n\n\t\t\t\tif(is.na(stemp)){ # if still don't get a value, try searching in 9-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 9-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep, origlats, origlats+slatstep)\n\t\t\t\t\tlons <- c(origlons-slonstep, origlons, origlons+slonstep)\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE) # ocean temp\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ # if still don't get a value, try searching in 25-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 25-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(2:1), origlats, origlats+slatstep*(1:2))\n\t\t\t\t\tlons <- c(origlons-slonstep*(2:1), origlons, origlons+slonstep*(1:2))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ # if still don't get a value, try searching in 49-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 49-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(3:1), origlats, origlats+slatstep*(1:3))\n\t\t\t\t\tlons <- c(origlons-slonstep*(3:1), origlons, origlons+slonstep*(1:3))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ # if still don't get a value, try searching in 81-grid area\n\t\t\t\t\tcat(paste('\\tGoing to 81-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(4:1), origlats, origlats+slatstep*(1:4))\n\t\t\t\t\tlons <- c(origlons-slonstep*(4:1), origlons, origlons+slonstep*(1:4))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ \n\t\t\t\t\tcat(paste('\\tGoing to 121-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(5:1), origlats, origlats+slatstep*(1:5))\n\t\t\t\t\tlons <- c(origlons-slonstep*(5:1), origlons, origlons+slonstep*(1:5))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ \n\t\t\t\t\tcat(paste('\\tGoing to 169-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(6:1), origlats, origlats+slatstep*(1:6))\n\t\t\t\t\tlons <- c(origlons-slonstep*(6:1), origlons, origlons+slonstep*(1:6))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tif(is.na(stemp)){ \n\t\t\t\t\tcat(paste('\\tGoing to 225-grid search area for i=', i, dat$Genus[i], dat$Species[i], 'at sea\\n'))\n\t\t\t\t\tlats <- c(origlats-slatstep*(7:1), origlats, origlats+slatstep*(1:7))\n\t\t\t\t\tlons <- c(origlons-slonstep*(7:1), origlons, origlons+slonstep*(1:7))\n\t\t\t\t\tstemp <- mean(tosmax[as.character(lats), as.character(lons)], na.rm=TRUE)\n\t\t\t\t}\n\n\t\t\t\tcat(paste('\\tstemp now', stemp, '\\n'))\n\t\t\t}\n\n\t\t\t# for annual mean\n\t\t\tdat$thabann[i] <- mean(sstclim[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for summer mean\n\t\t\tdat$thabsum[i] <- mean(sstclimwarm3n[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for max month\n\t\t\tdat$thabmaxmo[i] <- mean(sstclimwarmestmo[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t\t# for 95% max hour\n\t\t\tdat$thab95hr[i] <- mean(tos95max[as.character(lats), as.character(lons)], na.rm=TRUE)\n\n\t\t}\n\n\t}\n}\n\n\n\t# check\n\tdat[,summary(thabann)]\n\tdat[,summary(thabsum)]\n\tdat[,summary(thabmaxmo)]\n\tdat[Realm=='Marine',summary(thabann)]\n\tdat[Realm=='Marine',summary(thab95hr)]\n\n\tdat[!is.na(NMGHCND_2m_shade_Tb95),summary(thabann)] # have NM data, but not thabann\n\n\tdat[Realm=='Marine' & is.na(thabann),]\n\n\n##############################################################\n# adjust terrestrial habitat temperatures based on elevation differences \n##############################################################\ndat[,thabann.adj := thabann]\ndat[,thabsum.adj := thabsum]\ndat[,thabmaxmo.adj := thabmaxmo]\n\ni <- dat[,!is.na(altitude) & !is.na(elev.grid) & Realm=='Terrestrial']\ndat[i,thabann.adj := thabann + lapse*(elev.grid - altitude)] # adjust for altitude offset from grid cell\ndat[i,thabsum.adj := thabsum + lapse*(elev.grid - altitude)] # adjust for altitude offset from grid cell\ndat[i,thabmaxmo.adj := thabmaxmo + lapse*(elev.grid - altitude)] # adjust for altitude offset from grid cell\n\n\n\n##############################################################################\n# adjust marine habitat temperatures based on behavioral thermoregulation\n##############################################################################\n# let marine species access cooler microhabitats through behavioral thermoregulation, down to -2degC (for warmest hours)\n\n# initialize columns\ndat[,thab95hr.marineBT := as.numeric(NA)]\ndat[Realm=='Marine',thab95hr.marineBT := thab95hr]\ndat[,thab95hr.marineBTmore := as.numeric(NA)]\ndat[Realm=='Marine',thab95hr.marineBTmore := thab95hr] # 50% more\ndat[,thab95hr.marineBTless := as.numeric(NA)]\ndat[Realm=='Marine',thab95hr.marineBTless := thab95hr] # 50% less\n\n# marine behavioral thermoregulation table\nmarBT <- data.table(mobility=c('swim', 'swim', 'swim', 'swim', 'swim', 'swim', 'crawl', 'crawl', 'sessile', 'sessile'), demers_pelag=c('pelagic-neritic', 'pelagic-neritic', 'demersal', 'pelagic', 'demersal', 'pelagic', 'demersal', 'demersal', 'demersal', 'demersal'), big=c(TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE), MBTrefuge=c(10, 3, 3, 10, 1, 10, 1, 0.5, 0, 0))\n\tmarBT\n\n# merge in the MBT table\ndat[,big := length>50] # mark the big species (>50cm)\ndat <- merge(dat, marBT, all.x=TRUE, by=c('demers_pelag', 'mobility', 'big'))\n\tnrow(dat)\n\n# calculate refugium temperatures\ndat[, thab95hr.marineBT := pmax(-2, thab95hr - MBTrefuge)] # for 95% warmest hour\ndat[, thab95hr.marineBTmore := pmax(-2, thab95hr - MBTrefuge*1.5)] # 50% more\ndat[, thab95hr.marineBTless := pmax(-2, thab95hr - MBTrefuge*0.5)] # 50% less\n\n\n############################################\n# adjust Tmax for acclimation temperature\n############################################\ndat[,tmax.accsum := as.numeric(NA)] # use summer temp for acclimation\ndat[,tmax.accsum.elev := as.numeric(NA)]\ndat[,tmax.accsumbyspp := as.numeric(NA)] # with species-specific ARRs\ndat[,tmax.accsumbyspp.elev := as.numeric(NA)]\n\n# if acclimation value is \"F\" (field?), set to missing\ndat[tmax_acc=='F', tmax_acc := as.character(NA)]\nif(class(dat$tmax_acc)!='numeric') dat[,tmax_acc := as.numeric(tmax_acc)]\n\n# adjust Tmax based on acclimation (average ARR)\nlnd <- dat[,Realm=='Terrestrial' & !is.na(tmax_acc)]\ndat[lnd, tmax.accsum := Tmax + ARRland*(thabsum-tmax_acc)] # adjust from acclimation temp to the summer temperature\ndat[lnd, tmax.accsum.elev := Tmax + ARRland*(thabsum.adj-tmax_acc)] # adjust from acclimation temp to the summer temperature. with elevation correction\n\noce <- dat[,Realm=='Marine' & !is.na(tmax_acc)]\ndat[oce, tmax.accsum := Tmax + ARRoce*(thabsum-tmax_acc)]\ndat[oce, tmax.accsum.elev := Tmax + ARRoce*(thabsum.adj-tmax_acc)]\n\n\t# no adjustment to Tmax where acclimation temp unknown\n\tdat[Realm=='Terrestrial' & is.na(tmax.accsum), tmax.accsum := Tmax]\n\tdat[Realm=='Terrestrial' & is.na(tmax.accsum.elev), tmax.accsum.elev := Tmax]\n\n\tdat[Realm=='Marine' & is.na(tmax.accsum), tmax.accsum := Tmax]\n\tdat[Realm=='Marine' & is.na(tmax.accsum.elev), tmax.accsum.elev := Tmax]\n\n\n# adjust Tmax based on species-specific ARRs\nlnd <- dat[,Realm=='Terrestrial' & !is.na(tmax_acc) & !is.na(ctmax_ARR_GS)]\ndat[lnd, tmax.accsumbyspp := Tmax + ctmax_ARR_GS*(thabsum-tmax_acc)]\ndat[lnd, tmax.accsumbyspp.elev := Tmax + ctmax_ARR_GS*(thabsum.adj-tmax_acc)]\n\noce <- dat[,Realm=='Marine' & !is.na(tmax_acc) & !is.na(ctmax_ARR_GS)]\ndat[oce, tmax.accsumbyspp := Tmax + ctmax_ARR_GS*(thabsum-tmax_acc)]\ndat[oce, tmax.accsumbyspp.elev := Tmax + ctmax_ARR_GS*(thabsum.adj-tmax_acc)]\n\n\n# Examine\ndat[,summary(Tmax)]\ndat[,summary(tmax.accsum)]\ndat[,summary(tmax.accsum.elev)]\ndat[,summary(tmax.accsumbyspp.elev)]\n\n\t# examine missing values\ndat[is.na(tmax.accsumbyspp),]\n\n\t# difference from uncorrected tmax\ndat[!is.na(tmax.accsum),.(mean=mean(Tmax - tmax.accsum), se=sd(Tmax - tmax.accsum)/sqrt(.N)), by=Realm]\n\n\t# difference from average ARR\ndat[!is.na(tmax.accsumbyspp),.(mean=mean(tmax.accsum - tmax.accsumbyspp), se=sd(tmax.accsum - tmax.accsumbyspp)/sqrt(.N))]\ndat[!is.na(tmax.accsumbyspp.elev),.(mean=mean(tmax.accsum.elev - tmax.accsumbyspp.elev), se=sd(tmax.accsum.elev - tmax.accsumbyspp.elev)/sqrt(.N))]\ndat[!is.na(tmax.accsumbyspp),.(N=.N)]\ndat[!is.na(tmax.accsumbyspp.elev),.(N=.N)]\n\n\n#\tdat[,plot(Tmax, tmax.accsum)]; abline(0,1)\n#\tdat[,plot(Tmax, tmax.accsum.elev)]; abline(0,1)\n#\tdat[,plot(tmax.accsum.elev, tmax.accsumbyspp.elev)]; abline(0,1)\n\t\tdat[,cor.test(tmax.accsum.elev, tmax.accsumbyspp.elev)]\n\n#####################################################################################\n# calculate thermal safety margin for each measurement of upper thermal tolerance\n#####################################################################################\n\n# thermal safety margin annual (elevation adjusted air temp/not, no tmax acclimation corrections)\n\t# body temp from air/water\ndat[, tsm_ann := Tmax - thabann] # air temperature no corrections\ndat[, tsm_ann_elev := Tmax - thabann.adj] # air temp elevation corrected, no acclimation\n\n# thermal safety margin summer (elevation adjusted air temp/not, no tmax acclimation corrections)\n\t# body temp from air/water climatologies\ndat[, tsm_sum := Tmax - thabsum] # air temperature no corrections\ndat[, tsm_sum_elev := Tmax - thabsum.adj] # air temp elevation corrected, no acclimation\n\n# thermal safety margin warmest month (elevation adjusted air temp/not, no tmax acclimation corrections)\n\t# body temp from air/water climatologies\ndat[, tsm_maxmo := Tmax - thabmaxmo] # air temperature no corrections\ndat[, tsm_maxmo_elev := Tmax - thabmaxmo.adj] # air temp elevation corrected, no acclimation\n\n\n# thermal safety margin 95% max hour (elevation adjusted air temp/not/NM, tmax acclimation corrected to summer/max month/none)\n\t# body temp from water climatologies (thab95hr not available on land)\ndat[, tsm_95hr_accsum := tmax.accsum - thab95hr] # water temp, acclimation to summer\n\n\t# body temp with marine behavioral thermoregulation (no sense to include elevation corrections since all ocean)\ndat[, tsm_95hr_marineBT := Tmax - thab95hr.marineBT]\ndat[, tsm_95hr_marineBT_accsum := tmax.accsum - thab95hr.marineBT]\ndat[, tsm_95hr_marineBT_accsumbyspp := tmax.accsumbyspp - thab95hr.marineBT]\n\ndat[, tsm_95hr_marineBTmore_accsum := tmax.accsum - thab95hr.marineBTmore] # more marine BT\ndat[, tsm_95hr_marineBTless_accsum := tmax.accsum - thab95hr.marineBTless] # less marine BT\n\n\n# thermal safety margin 95% GHCND max hour Tb calculation (only on land)\n\t# body temp from NM 2m shade (on land)\ndat[, tsm_NMGHCND_2m_shade_Tb95 := Tmax - NMGHCND_2m_shade_Tb95]\ndat[, tsm_NMGHCND_2m_shade_Tb95_accsum := tmax.accsum - NMGHCND_2m_shade_Tb95]\ndat[ ,tsm_NMGHCND_2m_shade_Tb95_accsum.elev := tmax.accsum.elev - NMGHCND_2m_shade_Tb95]\ndat[ ,tsm_NMGHCND_2m_shade_Tb95_accsumbyspp.elev := tmax.accsumbyspp.elev - NMGHCND_2m_shade_Tb95]\n\n\t# body temp from NM exposed (on land)\ndat[, tsm_NMGHCND_exposed_Tb95 := Tmax - NMGHCND_exposed_Tb95]\ndat[, tsm_NMGHCND_exposed_Tb95_accsum := tmax.accsum - NMGHCND_exposed_Tb95]\ndat[ ,tsm_NMGHCND_exposed_Tb95_accsum.elev := tmax.accsum.elev - NMGHCND_exposed_Tb95]\n\n\t# body temp from 100% wet skin sun in shade (on land)\ndat[, tsm_NMGHCND_shade_Tb_wet95 := Tmax - NMGHCND_shade_Tb_wet95]\ndat[, tsm_NMGHCND_shade_Tb_wet95_accsum := tmax.accsum - NMGHCND_shade_Tb_wet95]\ndat[, tsm_NMGHCND_shade_Tb_wet95_accsum.elev := tmax.accsum.elev - NMGHCND_shade_Tb_wet95]\ndat[, tsm_NMGHCND_shade_Tb_wet95_accsumbyspp.elev := tmax.accsumbyspp.elev - NMGHCND_shade_Tb_wet95]\n\n\n\n###########################\n# Add species types\n###########################\n\n# add species types\ndat[, animal.type := as.character(rep(NA, .N))]\ndat[Class=='Amphibia' & Realm=='Terrestrial', animal.type := 'amphibian']\ndat[Phylum=='Arthropoda' & Class %in% c('Insecta', 'Entognatha') & Realm=='Terrestrial', animal.type := 'insect']\ndat[Class %in% c('Malacostraca', 'Collembola', 'Gastropoda') & Realm=='Terrestrial', animal.type := 'other terrestrial invert']\ndat[Phylum=='Chordata' & Class %in% c('Reptilia', 'Archelosauria', 'Lepidosauria'), animal.type := 'reptile']\ndat[Phylum=='Arthropoda' & Class == 'Arachnida' & Realm=='Terrestrial', animal.type := 'spider']\n\ndat[Phylum=='Arthropoda' & Realm=='Marine', animal.type := 'crustacean']\ndat[Phylum=='Chordata' & Realm=='Marine', animal.type := 'fish']\ndat[Phylum=='Mollusca' & Realm=='Marine', animal.type := 'mollusc']\ndat[Phylum %in% c('Bryozoa', 'Echinodermata', 'Brachiopoda') & Realm=='Marine', animal.type := 'other marine invert']\n\n\t# any missing?\n\tdat[is.na(animal.type), sort(unique(paste(Realm, Phylum, Class)))]\n\tdat[is.na(animal.type), sort(unique(paste(Realm, Phylum, Class, Order)))]\n\n########################################\n# Make aggregate vectors of Te and TSM \n########################################\n\n## Tb\n\t# make a single vector of body temperatures (Tb) on land and at sea in favorable microclimates \n\t# (shade or wet skin on land, with behavioral thermoregulation at sea for all animals)\n\t# 95% max hour from GHCND\n\tdat[, tb_favorableGHCND95 := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tb_favorableGHCND95 := NMGHCND_2m_shade_Tb95]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tb_favorableGHCND95 := NMGHCND_shade_Tb_wet95]\n\tdat[Realm=='Marine', tb_favorableGHCND95 := thab95hr.marineBT]\n\n\t# make a single vector of body temperatures (Tb) on land and at sea in full sun\n\t# 95% max hour GHCND\n\tdat[, tb_sunGHCND95 := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tb_sunGHCND95 := NMGHCND_exposed_Tb95]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tb_sunGHCND95 := NMGHCND_exposed_Tb_wet95]\n\tdat[Realm=='Marine', tb_sunGHCND95 := thab95hr]\n\n## TSM\n\t# make a single vector of TSMs on land and at sea in full sun/at surface\n\t# use 95% GHCND Tb\n\t# includes acclimation to summer temperatures\n\tdat[, tsm_exposedGHCND95 := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial', tsm_exposedGHCND95 := tsm_NMGHCND_exposed_Tb95_accsum.elev]\n\tdat[Realm=='Marine', tsm_exposedGHCND95 := tsm_95hr_accsum]\n\n\t# use 95% warmest hour GHCND Tb for TSMs. favorable microclimates (shade or wet skin + behavioral thermoregulation)\n\tdat[, tsm_favorableGHCND95 := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95 := tsm_NMGHCND_2m_shade_Tb95_accsum.elev]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95 := tsm_NMGHCND_shade_Tb_wet95_accsum.elev]\n\tdat[Realm=='Marine', tsm_favorableGHCND95 := tsm_95hr_marineBT_accsum]\n\n\tdat[, tsm_favorableGHCND95_marBTmore := as.numeric(rep(NA, .N))] # more marine BT\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95_marBTmore := tsm_NMGHCND_2m_shade_Tb95_accsum.elev]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95_marBTmore := tsm_NMGHCND_shade_Tb_wet95_accsum.elev]\n\tdat[Realm=='Marine', tsm_favorableGHCND95_marBTmore := tsm_95hr_marineBTmore_accsum]\n\n\tdat[, tsm_favorableGHCND95_marBTless := as.numeric(rep(NA, .N))] # less marine BT\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95_marBTless := tsm_NMGHCND_2m_shade_Tb95_accsum.elev]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95_marBTless := tsm_NMGHCND_shade_Tb_wet95_accsum.elev]\n\tdat[Realm=='Marine', tsm_favorableGHCND95_marBTless := tsm_95hr_marineBTless_accsum]\n\n\t# sensitivity analysis: use species-specific ARRS\n\t# use 95% warmest hour GHCND for TSMs. favorable microclimates (shade or wet skin + behavioral thermoregulation)\n\tdat[, tsm_favorableGHCND95byspp := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95byspp := tsm_NMGHCND_2m_shade_Tb95_accsumbyspp.elev]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95byspp := tsm_NMGHCND_shade_Tb_wet95_accsumbyspp.elev]\n\tdat[Realm=='Marine', tsm_favorableGHCND95byspp := tsm_95hr_marineBT_accsumbyspp]\n\n\t# sensitivity analysis: make a single vector of TSMs on land and at sea in favorable microclimates\n\t# (shade or wet skin on land, WITHOUT behavioral thermoregulation at sea)\n\t# 95% warmest hour from GHCND Tb\n\t# includes acclimation to summer temperatures\n\tdat[, tsm_favorableGHCND95_nomarineBT := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95_nomarineBT := tsm_NMGHCND_2m_shade_Tb95_accsum.elev]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95_nomarineBT := tsm_NMGHCND_shade_Tb_wet95_accsum.elev]\n\tdat[Realm=='Marine', tsm_favorableGHCND95_nomarineBT := tsm_95hr_accsum]\n\n\t# make a single vector of TSMs on land and at sea in favorable microclimates\n\t# (shade or wet skin on land, with behavioral thermoregulation at sea)\n\t# 95% warmest hour from GHCND Tb\n\t# no acclimation: for comparison against maxmo, sum, and ann TSMs\n\tdat[, tsm_favorableGHCND95_noacc := as.numeric(rep(NA, .N))]\n\tdat[Realm=='Terrestrial' & animal.type!='amphibian', tsm_favorableGHCND95_noacc := tsm_NMGHCND_2m_shade_Tb95]\n\tdat[Realm=='Terrestrial' & animal.type=='amphibian', tsm_favorableGHCND95_noacc := tsm_NMGHCND_shade_Tb_wet95]\n\tdat[Realm=='Marine', tsm_favorableGHCND95_noacc := tsm_95hr_marineBT]\n\n\n#######################\n# write out\n#######################\nwrite.csv(dat, 'temp/warmingtolerance_byspecies.csv', row.names=FALSE)\n\n\n\n", "meta": {"hexsha": "eb644e7fd95c315e4871ae1b0ad4bbe0ff827c85", "size": 28733, "ext": "r", "lang": "R", "max_stars_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/thermal_safety_vs_lat_bylatlon_calculations.r", "max_stars_repo_name": "HuckleyLab/phyto-mhw", "max_stars_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-13T02:37:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-27T04:41:09.000Z", "max_issues_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/thermal_safety_vs_lat_bylatlon_calculations.r", "max_issues_repo_name": "HuckleyLab/phyto-mhw", "max_issues_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2020-07-19T10:54:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-17T19:53:09.000Z", "max_forks_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/thermal_safety_vs_lat_bylatlon_calculations.r", "max_forks_repo_name": "HuckleyLab/phyto-mhw", "max_forks_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.8727569331, "max_line_length": 387, "alphanum_fraction": 0.680993979, "num_tokens": 9588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6757646010190475, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.34843768662603847}} {"text": "#' Use tree metrics to compare multiple inferred ancestries (ARGs) over a genomic region\n#'\n#' Runs genome.trees.dist to compare multiple ancestry estimates against a known original\n#' See ?genome.trees.dist for more information.\n#' @param treeseq.base The base multiPhylo object, or a path to a .nex file\n#' @param treeseq.multi A list of n multiPhylo objects (or list of n paths to .nex files).\n#' @param weights If provided, these are treated as weights, and instead of returning a matrix\n#' with columns for each measure, and n rows, a single row is returned giving the distance\n#' measures averaged over all the different tree sequences. Set to 1 for a \"standard\" unweighted average\n#' @param acceptable.length.diff.pct How much difference in sequence length is allowed between the 2 trees? (Default: 0.1 percent)\n#' @param variant.positions A list of positions of each variant (not implemented)\n#' @param randomly.resolve.multi Should we randomly break polytomies in the list of multiPhylo objects passed in to treeseq.multi?\n#' @export\n#' @examples\n#' genome.trees.dist.multi()\n\ngenome.trees.dist.multi <- function(treeseq.base, treeseq.multi, weights=NULL, acceptable.length.diff.pct = 0.1, variant.positions=NULL, randomly.resolve.multi = FALSE) { \n if (class(treeseq.multi) == \"multiPhylo\") {\n stop(\"treeseq.multi should contain a *list* of multiPhylo objects, not simply a single multiPhylo object.\")\n }\n metrics <- do.call(rbind,lapply(treeseq.multi, \n genome.trees.dist, \n treeseq.b = treeseq.base,\n acceptable.length.diff.pct = acceptable.length.diff.pct,\n randomly.resolve.a = randomly.resolve.multi,\n variant.positions = variant.positions))\n if (is.numeric(weights)) {\n return(apply(metrics, 2, weighted.mean, rep_len(weights, nrow(metrics))))\n } else {\n return(metrics)\n }\n}\n", "meta": {"hexsha": "4b929314d45da450fcd53224c84cc7494061a817", "size": 2007, "ext": "r", "lang": "R", "max_stars_repo_path": "ARGmetrics/R/genome_trees_dist_multi.r", "max_stars_repo_name": "HDRUK/treeseq-inference", "max_stars_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2018-11-01T21:07:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-19T15:47:20.000Z", "max_issues_repo_path": "ARGmetrics/R/genome_trees_dist_multi.r", "max_issues_repo_name": "HDRUK/treeseq-inference", "max_issues_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2018-09-26T13:27:01.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-02T10:58:21.000Z", "max_forks_repo_path": "ARGmetrics/R/genome_trees_dist_multi.r", "max_forks_repo_name": "HDRUK/treeseq-inference", "max_forks_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2018-09-26T13:21:04.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-15T18:24:09.000Z", "avg_line_length": 60.8181818182, "max_line_length": 171, "alphanum_fraction": 0.6761335326, "num_tokens": 452, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7310585669110202, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.348407636725565}} {"text": "begin.ref.year <- 1970\nend.ref.year <- 2000\ntimescale <- 3 ## Valid values are 3, 6 and 12 months\nseasons <- c(\"ann\", \"djf\", \"mam\", \"jja\", \"son\")\nspi_colorbar_max <- 0.75\nmy.colors <- colorRampPalette(c(\"brown\",\"orange\",\"white\",\"lightblue\",\"blue\"))\n\n## Specific settings for PNG output\npng_width <- 1600\npng_height <- 960\npng_units <- \"px\"\npng_pointsize <- 12\npng_bg <- \"white\"\n", "meta": {"hexsha": "756d38d6c0e2cd0fdd2a9e9deb54fd41d6d59894", "size": 379, "ext": "r", "lang": "R", "max_stars_repo_path": "nml/test_suites/smhi/cfg_diag_scripts/cfg_SPI/cfg_SPI.r", "max_stars_repo_name": "c3s-magic/ESMValTool", "max_stars_repo_head_hexsha": "799150e4784f334262755a39022c72b2d39585c9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "nml/test_suites/smhi/cfg_diag_scripts/cfg_SPI/cfg_SPI.r", "max_issues_repo_name": "c3s-magic/ESMValTool", "max_issues_repo_head_hexsha": "799150e4784f334262755a39022c72b2d39585c9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "nml/test_suites/smhi/cfg_diag_scripts/cfg_SPI/cfg_SPI.r", "max_forks_repo_name": "c3s-magic/ESMValTool", "max_forks_repo_head_hexsha": "799150e4784f334262755a39022c72b2d39585c9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-03-24T04:18:09.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-19T06:04:05.000Z", "avg_line_length": 27.0714285714, "max_line_length": 77, "alphanum_fraction": 0.6701846966, "num_tokens": 127, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3483976385084915}} {"text": "source('positiveNumberValidator.r')\n\npurchaseCosts = setRefClass(\"purchaseCosts\",\n contains = \"positiveNumberValidator\",\n fields = list(\n purchaseTax = 'numeric',\n inspection = 'numeric',\n notary = 'numeric',\n other = 'numeric',\n total = 'numeric'\n ),\n methods = list(\n initialize = function(pT, i, n, o){\n validateInputs(pT, i, n, o)\n \n purchaseTax <<- pT\n inspection <<- i\n notary <<- n\n other <<- o\n total <<- purchaseTax + inspection + notary + other\n },\n validateInputs = function(pT, i, n, o){\n validateNumber(pT)\n validateNumber(i)\n validateNumber(n)\n validateNumber(o)\n }\n )\n)\n\nrenderPurchaseCostsInput = function(title, ptId, iId, nId, oId){\n fluidRow(\n column(12,\n h4(title, style = \"margin-top: 0px; border-bottom: 1px solid #e3e3e3;\"),\n numericInput(ptId, \"Purchase Tax ($)\", 3000, min = 0),\n numericInput(iId, \"Inspection ($)\", 0, min = 0),\n numericInput(nId, \"Notary ($)\", 0, min = 0),\n numericInput(oId, \"Other ($)\", 0, min = 0),\n )\n )\n}", "meta": {"hexsha": "d0ac96da143ddbf6c7069661af390a0119179285", "size": 1072, "ext": "r", "lang": "R", "max_stars_repo_path": "mortage-calculator/purchaseCosts.r", "max_stars_repo_name": "danVatnik/mortgage-calculator", "max_stars_repo_head_hexsha": "70385ee31767ccb8ed2195e4f4763eac56866e6a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "mortage-calculator/purchaseCosts.r", "max_issues_repo_name": "danVatnik/mortgage-calculator", "max_issues_repo_head_hexsha": "70385ee31767ccb8ed2195e4f4763eac56866e6a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mortage-calculator/purchaseCosts.r", "max_forks_repo_name": "danVatnik/mortgage-calculator", "max_forks_repo_head_hexsha": "70385ee31767ccb8ed2195e4f4763eac56866e6a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.1463414634, "max_line_length": 78, "alphanum_fraction": 0.5848880597, "num_tokens": 317, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.348397629576001}} {"text": "options(bitmapType='cairo')\r\n\r\nlibrary(ggplot2)\r\nlibrary(cowplot)\r\nlibrary(dplyr)\r\n\r\nargs = commandArgs(trailingOnly=TRUE)\r\ninputfile = args[1]\r\noutputfile = args[2]\r\nxlim=as.numeric(args[3])\r\n\r\ncount = read.table(inputfile, sep=\"\\t\", header=T)\r\npercCount = data.frame(count %>% group_by(File) %>% mutate(Percentage=round(100 * ReadCount / sum(ReadCount), 2)) %>% ungroup)\r\n\r\nnumberOfSamples=length(unique(count$File))\r\nsqrtOfSample = ceiling(sqrt(numberOfSamples))\r\n\r\nif(length(args)>=5){\r\n height=as.numeric(args[4])\r\n width=as.numeric(args[5])\r\n}else{\r\n height=600 * sqrtOfSample\r\n width=600 * sqrtOfSample\r\n}\r\n\r\npng(file=outputfile, height=height, width=width, res=300)\r\ng=ggplot(data=percCount, aes(x=NumberOfMismatch, y=Percentage)) + \r\n geom_bar(stat=\"identity\") + \r\n facet_wrap(~File) + \r\n xlab(\"Insert Size\")\r\n\t\t\r\nif(xlim > 0){\r\n g = g + scale_x_continuous(limits=c(0,xlim), breaks = c(0:xlim))\r\n}\r\n\r\nprint(g)\r\ndev.off()\r\n", "meta": {"hexsha": "086b604af9cf06e360943fd66ca660c54294fae3", "size": 940, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/QC/bamInsertSize.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/QC/bamInsertSize.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/QC/bamInsertSize.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 24.7368421053, "max_line_length": 128, "alphanum_fraction": 0.6808510638, "num_tokens": 274, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.685949467848392, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3483332780694201}} {"text": "#Objetivo1: mostrar como se divide en training y testing\n#Objetivo2: mostrar como se calcula la ganancia y el AUC\n#Objetivo3: que la clase vea la gran varianza que hay de las ganancias al utilizar distintas semillas\n\n#Modelo con libreria rpart\n#Entreno con training y mido la ganancia en testing (una verdadera porqueria que genera mucha varianza)\n\n\n\n#source(\"M:\\\\R\\\\elementary\\\\MiPrimerModelo_01.r\")\n\n#limpio la memoria\nrm(list=ls())\ngc()\n\n\nlibrary(\"rpart\")\nlibrary(\"data.table\")\nlibrary(\"dplyr\")\nlibrary(\"rpart.plot\")\nlibrary(\"gtools\")\nlibrary(\"bitops\")\nlibrary(\"caTools\")\nlibrary(\"gplots\")\nlibrary(\"ROCR\")\n\n\nsetwd(\"E:/UBA/2019-II/DM en Finanzas/Dropbox Prof/datasets\")\n\n\n#Parametros entrada\nkarchivo_entrada <- \"201902.txt\"\nkcampos_separador <- \"\\t\"\nkcampo_id <- \"numero_de_cliente\"\nkclase_nomcampo <- \"clase_ternaria\"\nkclase_valor_positivo <- \"BAJA+2\"\nkcampos_a_borrar <- c(kcampo_id)\n\n\nktraining_prob <- 0.70\nksemilla_azar <- 9123437 # poner aqui SU propia semilla aleatoria\n#91237-> 4596667 \n\n#Parametros salida\nkarchivo_imagen <- \"..\\\\work\\\\modelo_01.jpg\"\n\n\n#constantes de la funcion ganancia del problema\nkprob_corte <- 0.025\nkganancia_acierto <- 19500 \nkganancia_noacierto <- -500\n\n#------------------------------------------------------\n#Esta funcion calcula la ganancia de una prediccion\n#Quedarse solo con las predicciones con probabilidad mayor a kprob_corte (0.025)\n#Si es un acierto sumar kganancia_acierto (+19500) \n#Si NO es acierto sumar kganancia_noacierto ( -500)\n\nfmetrica_ganancia_rpart = function(probs, clases)\n{\n \n return( sum( (probs > kprob_corte ) * \n ifelse(clases== kclase_valor_positivo, kganancia_acierto, kganancia_noacierto) \n )\n )\n \n}\n#------------------------------------------------------\n#Esta funcion calcula AUC Area Under Curve de la Curva ROC\n\nfmetrica_auc_rpart = function(probs, clases)\n{\n testing_binaria <- as.numeric(clases == kclase_valor_positivo )\n pred <- ROCR::prediction( probs, testing_binaria, label.ordering=c(0, 1))\n auc_testing <- ROCR::performance(pred,\"auc\"); \n \n return(unlist(auc_testing@y.values))\n \n}\n#------------------------------------------------------\n\n\n#cargo los datos\ndataset <- fread(karchivo_entrada, header=TRUE, sep=kcampos_separador)\n\n#borro las variables que no me interesan\ndataset[ , (kcampos_a_borrar) := NULL ] \n\n#Divido el dataset en training 70% y testing 30% , usando la libreria dplyr\ndataset <- as.data.table(mutate(dataset, idtempo = row_number()))\n\nset.seed(ksemilla_azar )\ndataset_training <- as.data.table(dataset %>%\n group_by(!!as.name(kclase_nomcampo)) %>%\n sample_frac(ktraining_prob) %>%\n ungroup)\ndataset_testing <- as.data.table(anti_join(dataset, dataset_training, by = \"idtempo\"))\n\ndataset_training[ , idtempo := NULL ] \ndataset_testing[ , idtempo := NULL ] \n\n\n\nnrow( dataset[ clase_ternaria=='BAJA+1' , ] ) / nrow(dataset)\nnrow( dataset_training[ clase_ternaria=='BAJA+1' , ] ) / nrow(dataset_training)\nnrow( dataset_testing[ clase_ternaria=='BAJA+1' , ] ) / nrow(dataset_testing)\n\n\n# generacion del modelo\nformula <- formula(paste(kclase_nomcampo, \"~ .\"))\n\nt0 <- Sys.time()\nmodelo <- rpart(formula, data = dataset_training, cp=0.005, xval=0)\nt1 <- Sys.time()\n\ntcorrida <- as.numeric( t1 - t0, units = \"secs\")\nprint( tcorrida)\n\n\n#impresion un poco mas elaborada del arbol\njpeg(file = karchivo_imagen, width = 12, height = 4, units = 'in', res = 300)\nprp(modelo, extra=101, digits=5, branch=1, type=4, varlen=0, faclen=0)\ndev.off()\n\n\n\n\n#aplico el modelo a datos nuevos\ntesting_prediccion <- predict( modelo, dataset_testing , type = \"prob\")\n\n#calculo la ganancia en testing\nganancia_testing <- fmetrica_ganancia_rpart(testing_prediccion[ ,kclase_valor_positivo], dataset_testing[ , get(kclase_nomcampo)])\n\n#normalizo la ganancia de testing\nganancia_testing_normalizada <- ganancia_testing / (1 - ktraining_prob)\ncat(\"Ganancia Testing Normalizada : \", ganancia_testing_normalizada, \"\\n\") \n\n#comparar en clase las ganancias que obtienen los alumnos ! \n\n\n\nauc_testing <- fmetrica_auc_rpart(testing_prediccion[ ,kclase_valor_positivo], dataset_testing[ , get(kclase_nomcampo)])\n\ncat(\"AUC Testing : \", auc_testing , \"\\n\") \n", "meta": {"hexsha": "761ceb76a6ca67b7f3734a18a564e114f9a5d959", "size": 4433, "ext": "r", "lang": "R", "max_stars_repo_path": "elementary/MiPrimerModelo_01.r", "max_stars_repo_name": "ktavo/dm-finanzas-2019", "max_stars_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "elementary/MiPrimerModelo_01.r", "max_issues_repo_name": "ktavo/dm-finanzas-2019", "max_issues_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "elementary/MiPrimerModelo_01.r", "max_forks_repo_name": "ktavo/dm-finanzas-2019", "max_forks_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.5724137931, "max_line_length": 132, "alphanum_fraction": 0.6652379878, "num_tokens": 1303, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6859494421679929, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.34833326502860884}} {"text": "pdf(\"output.clipping_profile.pdf\")\nread_pos=c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50)\nclip_count=c(16.0,12.0,11.0,8.0,7.0,6.0,1.0,1.0,1.0,1.0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.0,1.0,1.0,2.0,3.0,4.0,4.0)\nnonclip_count= 40 - clip_count\nplot(read_pos, nonclip_count*100/(clip_count+nonclip_count),col=\"blue\",main=\"clipping profile\",xlab=\"Position of read\",ylab=\"Non-clipped %\",type=\"b\")\ndev.off()\n", "meta": {"hexsha": "b79ff3bb5d746d0c6acb598bd5d73a363117e598", "size": 534, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/rseqc/test-data/output.clipping_profile.r", "max_stars_repo_name": "ic4f/tools-iuc", "max_stars_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-07-19T05:26:21.000Z", "max_stars_repo_stars_event_max_datetime": "2016-07-19T05:26:21.000Z", "max_issues_repo_path": "tools/rseqc/test-data/output.clipping_profile.r", "max_issues_repo_name": "ic4f/tools-iuc", "max_issues_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-05-27T20:54:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-04T09:33:30.000Z", "max_forks_repo_path": "tools/rseqc/test-data/output.clipping_profile.r", "max_forks_repo_name": "willemdek11/tools-iuc", "max_forks_repo_head_hexsha": "dc0a0cf275168c2a88ee3dc47652dd7ca1137871", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-09-12T14:56:37.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-16T00:30:14.000Z", "avg_line_length": 76.2857142857, "max_line_length": 154, "alphanum_fraction": 0.6666666667, "num_tokens": 313, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6654105720171531, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3482894337911373}} {"text": "# Load necessary packages\nlibrary('swat')\n\n#options(cas.print.messages = FALSE)\n\nconn <- CAS('hostname.com', \n port=8777, protocol = \"http\",\n caslib = 'casuser', \n authinfo = './.authinfo')\n\n## Carregando Actionsets no CAS\nactionsets <- c('sampling', 'decisionTree', 'autotune', 'percentile')\n\nfor(i in actionsets){\n loadActionSet(conn, i)\n}\n\n# Carregando dados para CAS\ncastbl <- cas.read.csv(conn, './data/hmeq.csv')\n\n# Particionamento de dados\ncas.sampling.srs(conn,\n table = 'hmeq',\n samppct = 30,\n partind = TRUE,\n output = list(casOut = list(name = 'hmeq', replace = T), \n copyVars = 'ALL')\n)\n\nindata <- 'hmeq'\n\n# Pega infromacao das variaveis\ncolinfo <- head(cas.table.columnInfo(conn, table = indata)$ColumnInfo, -1)\n\n# Variavel target\ntarget <- colinfo$Column[1]\n\n\n# Separacao para modelos que lidam com missing\ninputs <- colinfo$Column[-1]\nnominals <- c(target, subset(colinfo, Type == 'varchar')$Column)\n\nresult <- cas.autotune.tuneGradientBoostTree(conn,\n trainOptions = list(\n table = list(\"name\"= \"hmeq\", where = '_PartInd_ = 0'),\n inputs = inputs,\n target = target,\n nominal = nominals,\n casout = list(name =\"tune_boost_model\", replace = TRUE)\n ),\n tunerOptions=list(seed = 12345)\n )\n\nprint(result$TunerInfo)\n\n print(result$TunerResults)\n\n print(result$IterationHistory)\n\n print(result$IterationHistory)\n\n print(result$EvaluationHistory)\n\n print(result$BestConfiguration)\n\n print(result$TunerSummary)\n\n print(result$TunerTiming)\n\n print(result$TunerCasOutputTables)\n\n print(result$HyperparameterImportance)\n\n\n### Prevendo um unico modelo\ncas.decisionTree.gbtreeScore(conn,\n table = list(name = 'hmeq'),\n modelTable = list(name = 'tune_boost_model'),\n copyVars = list(target, '_PartInd_'),\n assessonerow = TRUE,\n casOut = list(name = 'gb_tune_scored', replace = T)\n)\n\ndt_scores <- defCasTable(conn, 'gb_tune_scored')\n\nhead(dt_scores)\n\nasses_info <- cas.percentile.assess(conn,\n table = list(name = paste0('gb_tune_scored'), \n where = '_PartInd_ = 1'),\n inputs = paste0('_GBT_P_ 1'),\n response = target,\n event = '1')\n\nroc <- asses_info$ROCInfo\n\n# Manipulacao do DF\ncompare <- subset(roc, round(roc$CutOff, 2) == 0.49)\nrownames(compare) <- NULL\ncompare[,c('TP','FP','FN','TN')]\n\nlibrary('ggplot2')\nlibrary(\"plotly\")\n\n# Cria curva ROC\noptions(repr.plot.width=14, repr.plot.height=6)\n\nplt <- ggplot(data = roc[c('FPR', 'Sensitivity')],\n aes(x = FPR, y = Sensitivity)) +\n geom_line(size =1.2) +\n labs(x = 'False Positive Rate', y = 'True Positive Rate') +\n theme_bw()\nplt\n\nggplotly(plt)\n\nplt2 <- ggplot(data = roc[,c('ACC', 'CutOff')],\n aes(y = ACC, x = CutOff, color = ACC)) +\n geom_segment(aes(x=CutOff, xend=dplyr::lead(CutOff), y=ACC, yend=dplyr::lead(ACC))) +\n scale_colour_gradient2(low=\"red\", mid = 'red', high=\"green\")+\n labs(x = 'CutOff', y = 'Accuracy') +\n theme_bw()\nplt2\n\nggplotly(plt2)\n#embed_notebook(ggplotly(plt2))\n\ncas.session.endSession(conn)\n", "meta": {"hexsha": "634577531880f74145a62a6d61caab17e5d1bbf6", "size": 3170, "ext": "r", "lang": "R", "max_stars_repo_path": "PT/R_Swat/Extras/AutoTune.r", "max_stars_repo_name": "pinduzera/R-SAS-Webinar", "max_stars_repo_head_hexsha": "bc5b59c3f1c43eee5fa760d1dc32445ef9fe13b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-08-13T17:36:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-01T18:10:41.000Z", "max_issues_repo_path": "PT/R_Swat/Extras/AutoTune.r", "max_issues_repo_name": "pinduzera/R-SAS-Webinar", "max_issues_repo_head_hexsha": "bc5b59c3f1c43eee5fa760d1dc32445ef9fe13b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "PT/R_Swat/Extras/AutoTune.r", "max_forks_repo_name": "pinduzera/R-SAS-Webinar", "max_forks_repo_head_hexsha": "bc5b59c3f1c43eee5fa760d1dc32445ef9fe13b9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.5736434109, "max_line_length": 89, "alphanum_fraction": 0.6299684543, "num_tokens": 906, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3482894268451717}} {"text": "Sys.setenv(TAR = \"/bin/tar\") # nolint\nlibrary(s2dverification)\nlibrary(multiApply) # nolint\nlibrary(ggplot2)\nlibrary(yaml)\nlibrary(ncdf4)\nlibrary(ClimProjDiags) #nolint\nlibrary(abind)\nlibrary(climdex.pcic)\n\n#Parsing input file paths and creating output dirs\nargs <- commandArgs(trailingOnly = TRUE)\nparams <- read_yaml(args[1])\n\nplot_dir <- params$plot_dir\nrun_dir <- params$run_dir\nwork_dir <- params$work_dir\n\n## Create working dirs if they do not exist\ndir.create(plot_dir, recursive = TRUE)\ndir.create(run_dir, recursive = TRUE)\ndir.create(work_dir, recursive = TRUE)\n\n\n# setup provenance file and list\nprovenance_file <- paste0(run_dir, \"/\", \"diagnostic_provenance.yml\")\nprovenance <- list()\n\ninput_files_per_var <- yaml::read_yaml(params$input_files[1])\nvar_names <- names(input_files_per_var)\nmodel_names <- lapply(input_files_per_var, function(x) x$dataset)\nmodel_names <- unique(unlist(unname(model_names)))\n\nvar0 <- lapply(input_files_per_var, function(x) x$short_name)\nfullpath_filenames <- names(var0)\nvar0 <- unname(var0)[1]\nvar0 <- unlist(var0)\n\nstart_year <- lapply(input_files_per_var, function(x) x$start_year)\nstarting <- c(unlist(unname(start_year)))[1]\nend_year <- lapply(input_files_per_var, function(x) x$end_year)\nending <- c(unlist(unname(end_year)))[1]\nstart_year <- as.POSIXct(as.Date(paste0(starting, \"-01-01\"), \"%Y-%m-%d\"))\nend_year <- as.POSIXct(as.Date(paste0(ending, \"-12-31\"), \"%Y-%m-%d\"))\n\n#Parameters for Season() function\nmonini <- 1\nmoninf <- params$moninf\nmonsup <- params$monsup\nmonths <- \"\"\nregion <- params$region\nrunning_mean <- params$running_mean\ntimestamp <- \"\"\nstandardized <- params$standardized\n\nif (region == \"Nino3\") {\n lon_min <- 360 - 150\n lon_max <- 360 - 90\n lat_min <- -5\n lat_max <- 5\n} else if (region == \"Nino3.4\") {\n lon_min <- 360 - 170\n lon_max <- 360 - 120\n lat_min <- -5\n lat_max <- 5\n} else if (region == \"Nino4\") {\n lon_min <- 360 - 160\n lon_max <- 360 - 150\n lat_min <- -5\n lat_max <- 5\n} else if (region == \"NAO\") {\n lon_min <- 360 + c(-90, -90)\n lon_max <- c(40, 40)\n lat_min <- c(25, 60)\n lat_max <- c(45, 80)\n} else if (region == \"SOI\") {\n lon_min <- c(90, 360 - 130)\n lon_max <- c(140, 360 - 80)\n lat_min <- c(-5, -5)\n lat_max <- c(5, 5)\n}\n### Load data\ndata_nc <- nc_open(fullpath_filenames)\nlat <- as.vector(ncvar_get(data_nc, \"lat\"))\nlon <- as.vector(ncvar_get(data_nc, \"lon\"))\nunits <- ncatt_get(data_nc, var0, \"units\")$value\nlong_names <- ncatt_get(data_nc, var0, \"long_name\")$value\n\ndata <- InsertDim(ncvar_get(data_nc, var0), 1, 1) # nolint\nnames(dim(data)) <- c(\"model\", \"lon\", \"lat\", \"time\")\ntime <- seq(start_year, end_year, \"month\")\nnc_close(data_nc)\n\nif (standardized) {\n data <- Apply(list(data), target_dims = c(\"time\"),\n fun = function(x) {(x - mean(x)) / sqrt(var(x))}) #nolint\n data <- aperm(data$output1, c(2, 3, 4, 1))\n names(dim(data)) <- c(\"model\", \"lon\", \"lat\", \"time\")\n}\n\nif (!is.null(running_mean)) {\n data <- Smoothing(data, runmeanlen = running_mean, numdimt = 4) #nolint\n timestamp <- paste0(running_mean, \"-month-running-mean-\")\n}\n\nif (!is.null(moninf)) {\n data <- Season(data, posdim = 4, monini = monini, #nolint\n moninf = moninf, monsup = monsup)\n months <- paste0(month.abb[moninf], \"-\", month.abb[monsup])\n}\n\nif (length(lon_min) == 1) {\n data <- WeightedMean(data, lon = lon, lat = lat, #nolint\n region = c(lon_min, lon_max, lat_min, lat_max),\n londim = 2, latdim = 3, mask = NULL)\n\n data <- drop(data)\n} else {\n data1 <- WeightedMean(data, lon = lon, lat = lat, #nolint\n region = c(lon_min[1], lon_max[1], lat_min[1], lat_max[1]),\n londim = 2, latdim = 3, mask = NULL)\n data2 <- WeightedMean(data, lon = lon, lat = lat, #nolint\n region = c(lon_min[2], lon_max[2], lat_min[2], lat_max[2]),\n londim = 2, latdim = 3, mask = NULL)\n data1 <- drop(data1)\n data2 <- drop(data2)\n data <- CombineIndices(list(data1, data2), weights = c(1, -1), #nolint\n operation = \"add\")\n}\n\nif (moninf > monsup) {\n period <- (starting : ending)[-1]\n} else {\n period <- starting : ending\n}\n\ndimtime <- ncdim_def(name = \"Time\", units = \"years\",\n vals = period, longname = \"Time\")\ndefdata <- ncvar_def(name = \"data\", units = units, dim = list(time = dimtime),\n longname = paste(\"Index for region\", region, \"Variable\", var0))\nfilencdf <- paste0(work_dir, \"/\", var0, \"_\", timestamp, \"_\", months, \"_\",\n starting, ending, \"_\", \".nc\")\nfile <- nc_create(filencdf, list(defdata))\nncvar_put(file, defdata, data)\nnc_close(file)\n\n\npng(paste0(plot_dir, \"/\", \"Index_\", region, \".png\"), width = 7, height = 4,\n units = \"in\", res = 150)\nplot(period, data, type = \"l\", col = \"purple\", lwd = 2, bty = \"n\",\n xlab = \"Time (years)\", ylab = \"Index\",\n main = paste(\"Region\", region, \"and Variable\", var0))\nabline(h = 0, col = \"grey\", lty = 4)\ndev.off()\n\n\n# Set provenance for output files\nxprov <- list(ancestors = list(fullpath_filenames),\n authors = list(\"hunter_alasdair\", \"manubens_nicolau\"),\n projects = list(\"c3s-magic\"),\n caption = \"Combined selection\",\n statistics = list(\"other\"),\n realms = list(\"atmos\"),\n themes = list(\"phys\"))\nprovenance[[filencdf]] <- xprov\n\n# Write provenance to file\nwrite_yaml(provenance, provenance_file)\n", "meta": {"hexsha": "024c6e8d61f3cc0dbc833cd03914445f51452340", "size": 5474, "ext": "r", "lang": "R", "max_stars_repo_path": "esmvaltool/diag_scripts/magic_bsc/combined_indices.r", "max_stars_repo_name": "jeromaerts/ESMValTool", "max_stars_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "esmvaltool/diag_scripts/magic_bsc/combined_indices.r", "max_issues_repo_name": "jeromaerts/ESMValTool", "max_issues_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "esmvaltool/diag_scripts/magic_bsc/combined_indices.r", "max_forks_repo_name": "jeromaerts/ESMValTool", "max_forks_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3905325444, "max_line_length": 79, "alphanum_fraction": 0.6225794666, "num_tokens": 1689, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3482894268451717}} {"text": "#Tree-reweighted belief propagation for\n#continuous-valued density estimation\n\nrm(list=ls())\n\nif(Sys.info()[\"user\"]==\"eric\") setwd(\"~/Dropbox/trw\")\nif(Sys.info()[\"user\"]==\"ebjan\")setwd(\"~/trw\")\n\nlibs=c(\"Matrix\",\"orthopolynom\",\"igraph\",\"Rcpp\",\"inline\",\"RcppArmadillo\",\"parallel\",\"SnowballC\",\"ape\",\"mclust\",\"glasso\")\nif (sum(!(libs %in% .packages(all.available = TRUE))) > 0) {\n install.packages(libs[!(libs %in% .packages(all.available = TRUE))],\n \trepos=\"http://cran.stat.ucla.edu\")\n}\nfor (i in 1:length(libs)) {\n library(libs[i],character.only = TRUE,quietly=TRUE)\n}\nlibrary(\"RBGL\")\n\n\n\n################\n#GLOBAL VARS AND FNS\nsource(\"http://dl.dropbox.com/s/xk0gq4ae3faec52/trw_c.r\")\nsource(\"http://dl.dropbox.com/s/b04iktw8bgosxki/trw_global.r\")\nsource(\"http://dl.dropbox.com/s/mgreojd9wl2gmfr/trw_fns.r\")\n\n\n#####\n\n#d=4 #num dimensions\n\n# dat<-gauss.mix.sim(100)\n# \n# #create graph object\n# #graph.obj<-graph.full(d,directed=F)\n# graph.obj<-graph.tree(d,mode=\"undirected\")\n# #graph.obj<-graph.lattice(c(5,3))\n# graph.obj<-graph.create(graph.obj,dat=dat)\n# graph.obj<-ISTA(graph.obj,alpha.start=1,lambda=.0,damp=1)\n# \n# \n# \t\n#graph.obj<-graph.full(d,directed=F)\nset.seed(121455)\t\nn.set=floor(seq(from=10,to=1000,length.out=10))\nd.set=c(3,4,5,6)\n\nl=1\noutfinal<-mclapply(mc.cores=3,X=d.set,FUN=function(d){\n\tgraph.obj<-graph.full(d,directed=F)\n\toutp<-mclapply(mc.cores=8,X=1:25,FUN=function(x){\n\tgraph.obj<-graph.full(d,directed=F)\n\n\t\tss=sample(1:1000,1)\n\t\tk=1\n\t\too=vector()\n\t\t\n\t\tfor(nn in n.set){\n\t\t\ttest<-gauss.sim(1000,d=d,mu=.5,Sigma=diag(d)*.05)$dat\n\t\t\tprint(paste(\"n: \",nn,\"d: \",d))\n\t\t\tgt<-gauss.sim(nn,d=d,mu=.5,Sigma=diag(d)*.05)\n\t\t\ttrain<-gt$dat\n\t\t\tnll<-gt$nll\n\t\t\tgraph.obj<-graph.create(graph.obj=graph.obj,dat=train,weight=2/d)\n\t\t\tgraph.obj<-try(ISTA(graph.obj,alpha.start=1,lambda=0,damp=1))\n\t\t\tgraph.obj<-graph.create(graph.obj=graph.obj,dat=test,weight=2/d)\n\t\t\too[k]<-neg.ll(graph.obj)$nll-nll\n\t\t\tk=k+1\n\t\t}\n\t\too\t\n\t})\n\toutd<-rowMeans(\n\t\t\tmatrix(unlist(outp),nrow=length(n.set))\n\t,na.rm=T)\n\treturn(outd)\t\n})\n\n\n\nsave.image(\"simrun.rda\")\n# \n# ISTA(graph.obj,lambda=0,alpha.start=5)\n\n\n\n# \n# \n# \n# \t\n# #starting values for ML\n# edge.Co1<-lapply(1:nedge.unique,function(x) matrix(0,m.set,m.set))\n# names(edge.Co1)<-edges[which(less.edge(edges))]\n# node.Co1<-lapply(1:d,function(x) rep(0,m.set))\n# names(node.Co1)<-1:d\n# co.start=c(unlist(node.Co1),unlist(edge.Co1))\n# #ll<-lik.Pass(co.start,adj.Sim,mp.sim,edge.Weight=ew)\n# #co.start<-c( rep( c(3/4,-1/4,rep(0,m.set-2)) , d ) , unlist(edge.Co1) )\n# co.start<-c(unlist(node.Co1),unlist(edge.Co1))\n# \n# \n# assign(\"MES.START\",lapply(1:6,function(x) rep(1,res)),envir=lik.env)\n# \n# ew1<-edge.Start(adj.Sim,burn.in=25)\n# system.time(\n# \tfista<-FISTA(co.start,x.Node.Mean,x.Edge.Mean,edge.Weight=ew1,lambda=lmax*2)\n# )\n# \n# \n# lik.Graph(fista[[1]],mean.Edge=x.Edge.Mean,mean.Node=x.Node.Mean,adj.Mat=adj.Sim,\n# \t\t\t\tdamp=1,edge.Weight=ew1,tree.optim=F)\n# \n# q<-get(\"qua\",lik.env)\n# tt=tree.mix.lik(x.train,ew1,q$den)\n# -mean(tt);fista[[2]]\n# \n# \n# \n# system.time(\n# \tista<-ISTA2(co.start,x.Node.Mean,x.Edge.Mean,edge.Weight=ew1,lambda=2*0)\n# )\n# pgo<-list()\n# i=0\n# lam.seq<-exp(seq(from=log(lmax*.001),to=log(lmax*2),length.out=10))\n# for(l in rev(lam.seq)){\n# \ti=i+1\n# \tprint(paste(\"Step number\",i))\n# pgo[[i]]<-ISTA2(co.start,x.Node.Mean,x.Edge.Mean,edge.Weight=ew1,lambda=l)\n# co.start<-pgo[[i]][[1]]\n# }\n# \n# \n# \n# \n# \n# \n# ist<-ISTA2(co.start,x.Node.Mean,x.Edge.Mean,edge.Weight=ew1,lambda=lmax+.05)\n# \n# \n# \n# \n# ll=lik.Graph(co.start,mean.Edge=x.Edge.Mean,mean.Node=x.Node.Mean,adj.Mat=adj.Sim,\n# \t\t\t\tdamp=1,edge.Weight=ew1,tree.optim=F)\n# q<-get(\"qua\",lik.env)\n# #pdf(\"testist.pdf\")\n# contour(q[[3]][[2]][[5]],nlevels=50)\n# points(x.train[,c(3,2)],pch=16,cex=.5)\n# dev.off()\n# \n# \n# gr<-attr(ll,\"grad\")\n# \n# co2<-co.start - 1*gr\n# ll2=lik.Graph(co2,mean.Edge=x.Edge.Mean,mean.Node=x.Node.Mean,adj.Mat=adj.Sim,\n# \t\t\t\tdamp=1,edge.Weight=ew1,tree.optim=F)\n# \t\t\t\t\n# \t\t\t\tll[1];ll2[1]\n# \n# \tpdf(\"ist.pdf\")\n# \tplot(ist[[2]])\n# \tdev.off()\n# \tpdf(\"ist2.pdf\")\n# \tplot(attr(ll,\"grad\"))\n# \tdev.off()\n# \n# \n# \n# \n# \n# \n# \n# \n# pgo<-list()\n# i=0\n# for(l in rev(seq(from=0,to=lmax+.05,length.out=5))){\n# \ti=i+1\n# \tprint(paste(\"Step number\",i))\n# pgo[[i]]<-ISTA2(co.start,x.Node.Mean,x.Edge.Mean,edge.Weight=ew1,lambda=l)\n# co.start<-pgo[[i]][[1]]\n# }\n# \n# max.lik<-optim(fn=lik.Pass,gr=lik.Grad,par=co.start,damp=1,mp=mp.sim,\n# \tedge.Weight=ew1,tree.optim=F,\n# \tmethod=\"CG\",control=list(trace=6))\n# \n# save.image(file=\"save.RData\")\n# \n# #global variable to save edge weights\t\n# assign(\"EDGE.START\",edge.Start(adj.Sim,burn.in=25),envir=lik.env)\n# \n# max.lik.Edge<-optim(fn=lik.Pass,gr=lik.Grad,par=max.lik$par,damp=1,mp=mp.sim,\n# \tedge.Weight=NULL,tree.optim=F,\n# \tmethod=\"CG\",control=list(trace=4,factr=1e12))\n# save.image(file=\"save.RData\")\n# \n# \n# node.Q<-lapply(1:d,function(x) max.lik$par[((x-1)*m.set+1):(x*m.set)])\n# \tnames(node.Q)<-1:d\n# edge.Q<-lapply(1:nedge.unique,function(x){\n# \t\tmatrix(max.lik$par[(d*m.set+(x-1)*m.set*m.set+1):(d*m.set+x*m.set*m.set)],m.set,m.set)\n# \t})\n# \tnames(edge.Q)<-edges[which(less.edge(edges))]\n# two<- tree.weight.optim(edge.Q,node.Q,ew.start=edge.Start(adj.Sim))\n# two$tree.list<-two$tree.list[order(two$weight.list,decreasing=T)]\n# two$weight.list<-sort(two$weight.list,decreasing=T)\n# par(mfcol=c(3,3))\n# for(i in 1:9){\n# \tg<-graph.adjacency(two$tree.list[[i]],mode=\"undirected\")\n# \t#g$x<-rep(1:3,3);g$y=sort(rep(1:3,3))\n# \t#attr(g,\"x\")<-rep(1:3,3);attr(g,\"y\")<-sort(rep(1:3,3))\n# \tg$layout<-layout.circle\n# \tplot(g,margin=0,main=paste(two$weight.list[i]))\n# }\n# \n# a<- lik.Graph(max.lik.Edge$par,mean.Edge=test.Edge.Mean,test.Node.Mean,adj.Mat=adj.Sim,damp=1,\n# \tedge.Weight=NULL,tree.optim=T)\n# \t\n# lik.Graph(max.lik$par,mean.Edge=test.Edge.Mean,test.Node.Mean,adj.Mat=adj.Sim,damp=1,\n# \tedge.Weight=NULL,tree.optim=T)\n\t \n# lik.Graph(max.lik.Edge$par,mean.Edge=test.Edge.Mean,test.Node.Mean,adj.Mat=adj.Sim,damp=1,\n# \tedge.Weight=NULL,tree.optim=T)\n# \t\n# lik.Graph(max.lik$par,mean.Edge=test.Edge.Mean,test.Node.Mean,adj.Mat=adj.Sim,damp=1,\n# \tedge.Weight=NULL,tree.optim=T)\n# \t\n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# ###################\n# #Preset coefs\n# \n# \n# dec.co=.8\n# dec<-dec.co^(1:m.set)%*%t(dec.co^(1:m.set))\n# eco<-dec*matrix(rnorm(m.set^2,0,.1),m.set,m.set)\n# edge.Co<-lapply(1:nedge.unique,function(x) dec*matrix(rnorm(m.set^2,0,.5),m.set,m.set))\n# names(edge.Co)<-edges[which(less.edge(edges))]\n# \n# #dxm matrix of node potential coefficients\n# #node.Co<-lapply(1:d,function(x)rnorm(m.set,0,1)*dec.co^(1:m.set))\n# node.Co<-lapply(1:d,function(x) c(1,rep(0,m.set-1)))\n# \n# names(node.Co)<-1:d\n# co1=c(unlist(node.Co),unlist(edge.Co))\n# \n# \n# ew<-edge.Start(adj.Sim,burn.in=1)\n# two=tree.weight.optim(edge.Co,node.Co,ew.start=ew)\n# two$tree.list<-two$tree.list[order(two$weight.list,decreasing=T)]\n# two$weight.list<-sort(two$weight.list,decreasing=T)\n# par(mfcol=c(3,4))\n# for(i in 1:9){\n# \tg<-graph.adjacency(two$tree.list[[i]],mode=\"undirected\")\n# \t#g$x<-rep(1:3,3);g$y=sort(rep(1:3,3))\n# \t#attr(g,\"x\")<-rep(1:3,3);attr(g,\"y\")<-sort(rep(1:3,3))\n# \tg$layout<-layout.circle\n# \tplot(g,margin=0,main=paste(two$weight.list[i]))\n# }\n# dev.off()\n# ################\n# assign(\"MES.START\",lapply(edges,function(x) rep(1,res)),envir=lik.env)\n# assign(\"EDGE.START\",edge.Start(adj.Sim),envir=lik.env)\n# \n# Rprof()\n# \n# mp<-get.Quasi(m.set=m.set,edge.Co=edge.Co,node.Co=node.Co,damp=1,edge.Weight=edge.Start(adj.Sim)$ew)\n# mut.info(mp[[3]])\n# #\ta<-series.bp(adj.Sim,edge.Co=edge.Co,\n# #\tnode.Co=node.Co,bp.tol=1e-12,m=m.set,damp=.95)\n# Rprof(NULL) \n# summaryRprof()\n# \n# \n# xxx<-cbind(unlist(mut.info(mp[[3]])$mut.info),summary(two$ew)$x)\n# cor(xxx)\n# \n# \n# \tlik=0\n# \tedge.Mat<-get(\"EDGE.START\",lik.env)\n# \t\n# \t#put likelihood of maximum tree and mixture of trees\n# \t\n# \t\n# node.Q<-lapply(1:d,function(x) max.lik$par[((x-1)*m.set+1):(x*m.set)])\n# \tnames(node.Q)<-1:d\n# edge.Q<-lapply(1:nedge.unique,function(x){\n# \t\tmatrix(max.lik$par[(d*m.set+(x-1)*m.set*m.set+1):(d*m.set+x*m.set*m.set)],m.set,m.set)\n# \t})\n# \tnames(edge.Q)<-edges[which(less.edge(edges))]\n# tree.weight.optim(edge.Q,node.Q,ew.start=get(\"EDGE.START\",lik.env))\n# \n# \n# qm<-get.Quasi(m.set=m.set,edge.Co=edge.Q,\n# \tnode.Co=node.Q,damp=1,edge.Weight=edge.Mat)\n# \t\n# # a2<-series.bp(adj.Sim,edge.Co=edge.Q,\n# # \tnode.Co=node.Q,bp.tol=1e-12,m=m.set,damp=.95,edge.Weight=ew)\n# \n# \n# \n# save.image(file=\"trw_out.RData\")\n# pdf(\"trw_plot2.pdf\",width=14,height=8)\n# par(mfcol=c(2,length(edges)/4),oma=c(0,0,3,0))\n# i=1\n# for(e in edges[less.edge(edges)]){\n# f<-strsplit(e,\".\",fixed=T)[[1]]\n# t<-f[2];s<-f[1]\n# contour(a2[[2]][[i]],nlevels=15,xlab=paste(s),ylab=paste(t))\n# points(x[,as.numeric(s)],x[,as.numeric(t)],pch=16,cex=.5)\n# i=i+1\n# if(i>length(edges)/2)break\n# }\n# mtext(paste(\"TRW approx neg. log lik.: \",round(max.lik$value,3),\"\\n Exact mixture of Gaussian neg. log lik.: \",round(mix.lik(x),3)),outer=T,cex=1)\n# \n# dev.off()\n", "meta": {"hexsha": "d55b6c947ffa0c0038661f982cbc3b2d920719a4", "size": 8939, "ext": "r", "lang": "R", "max_stars_repo_path": "trw_run.r", "max_stars_repo_name": "geb5101h/trw", "max_stars_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "trw_run.r", "max_issues_repo_name": "geb5101h/trw", "max_issues_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-04-28T00:31:07.000Z", "max_issues_repo_issues_event_max_datetime": "2016-04-28T00:31:24.000Z", "max_forks_repo_path": "trw_run.r", "max_forks_repo_name": "geb5101h/trw", "max_forks_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0060422961, "max_line_length": 149, "alphanum_fraction": 0.6297124958, "num_tokens": 3443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3482894268451717}} {"text": "#!/usr/bin/R\r\nlibrary(RMySQL)\r\nlibrary(tidyverse)\r\nlibrary(survival)\r\nlibrary(survminer)\r\nlibrary(broom)\r\nlibrary(stringr)\r\nlibrary(jsonlite)\r\n\r\n\r\nargs <- commandArgs(TRUE)\r\n# all\r\n# args <- 'VTg7RU5TRzAwMDAwMjAwNDYzO1NOT1JEMTE4I1RDR0EtQUNDI2FsbA=='\r\n# 0\r\n# args <- 'VTg7RU5TRzAwMDAwMjAwNDYzO1NOT1JEMTE4I1RDR0EtQ0hPTCMw'\r\n# Stage I\r\n# args <- 'VTg7RU5TRzAwMDAwMjAwNDYzO1NOT1JEMTE4I1RDR0EtQUNDI1N0YWdlIEk='\r\narg_encode <- args[1]\r\narg_decode <- str_split(rawToChar(base64_dec(arg_encode)),\"#\", simplify=T)\r\n\r\n# output dir\r\nroot <- \"/home/liucj/web/snorna_data_portal\"\r\nresource_jsons = file.path(root, 'snorna/resource/jsons')\r\n#resource_jsons = \"./\"\r\nresource_pngs = file.path(root, 'snorna/resource/pngs')\r\n#resource_pngs = \"./\"\r\n\r\nq_name <- arg_decode[1,1]\r\ndataset_id <- arg_decode[1,2]\r\nsubtype_id <- arg_decode[1,3]\r\n\r\ndatabase <- paste(\"snorna_snorna_expression\", str_to_lower(str_replace(dataset_id, \"TCGA-\", \"\")), sep = \"_\")\r\n\r\n# connect mysql\r\ncon <- dbConnect(RMySQL::MySQL(), username = \"username\", password=\"password\", dbname=\"db\", host=\"localhost\")\r\nres <- dbSendQuery(con, statement = paste(\r\n \"SELECT * FROM\", database,\r\n \"WHERE snorna = \", paste('\"',q_name,'\"', sep=''),\r\n \"AND dataset_id = \",\r\n paste('\"',dataset_id,'\"', sep = \"\"), sep=\" \"))\r\nexpr <- dbFetch(res, n = -1) %>% as_tibble()\r\ndbClearResult(res)\r\n\r\nif(! subtype_id %in% c(\"all\", \"0\")) {\r\n res_clinical <- dbSendQuery(con, statement = str_c(\r\n \"SELECT * FROM snorna_clinical WHERE stage = \",\r\n str_c('\"', subtype_id, '\"', sep = ''),\r\n \"AND dataset_id = \",\r\n str_c('\"', dataset_id, '\"', sep = ''),\r\n sep = \" \"))\r\n clinical <- dbFetch(res_clinical, n = -1) %>% as_tibble()\r\n dbClearResult(res_clinical)\r\n}else{\r\n res_clinical <- dbSendQuery(con, statement = str_c(\r\n \"SELECT * FROM snorna_clinical WHERE \",\r\n \"dataset_id = \",\r\n str_c('\"', dataset_id, '\"', sep = ''),\r\n sep = \" \"))\r\n clinical <- dbFetch(res_clinical, n = -1) %>% as_tibble()\r\n dbClearResult(res_clinical)\r\n}\r\n\r\n\r\n# disconnect\r\ndbDisconnect(con)\r\n\r\n# calculate coxp\r\nexpr %>%\r\n dplyr::select(snorna, sample_id, expression = snorna_expression) %>%\r\n left_join(clinical, by=\"sample_id\") %>%\r\n filter(!is.na(time), time > 0, !is.na(status)) %>%\r\n group_by(snorna) %>%\r\n mutate(expression = log2(expression + 1)) %>%\r\n mutate(group = as.factor(ifelse(expression <= median(expression),\"Low\", \"High\"))) %>%\r\n mutate(months = ifelse(time / 30 > 60, 60, time / 30)) %>%\r\n mutate(status = ifelse(months >= 60 & status == 1, 0, status)) %>%\r\n ungroup() ->\r\n for_survival\r\n\r\n\r\nif(nrow(for_survival) < 20 || nlevels(for_survival$group) != 2 || mean(for_survival$expression) < 1){\r\n write_json(NULL, path=file.path(resource_jsons, paste(arg_encode,'survival','json',sep='.')))\r\n print(\"Not enough samples OR can't cut group OR mean expression less than 1!\")\r\n quit(\"no\", status = 0)\r\n}\r\n\r\n\r\n\r\nsurvival_coxph_model <-\r\n for_survival %>%\r\n group_by(snorna) %>%\r\n do(tidy(coxph(Surv(months, status) ~ expression, data = ., na.action = na.exclude))) %>%\r\n ungroup() %>%\r\n mutate(dataset_id = dataset_id,\r\n encode = arg_encode)\r\nwrite_json(survival_coxph_model, path=file.path(resource_jsons, paste(arg_encode,'survival','json',sep='.')))\r\n\r\n\r\nfor_survival_distinct <- unique(for_survival$snorna)\r\nplot_survival <- function(x){\r\n fit_x <- survfit(Surv(months,status) ~ group, data = for_survival %>%\r\n filter(snorna == x), na.action = na.exclude)\r\n plot_x <- ggsurvplot(fit_x,\r\n data= for_survival %>% filter(snorna == x),\r\n pval=T,\r\n pval.method = T,\r\n title = paste(\"5-year Survival, Coxph =\",\r\n ifelse(survival_coxph_model$p.value < 1e-04, 0.0001,\r\n signif(survival_coxph_model$p.value, 3))),\r\n xlab = \"5-year survival (months)\",\r\n ylab = 'Probability of survival')\r\n ggsave(filename = file.path(resource_pngs, paste(arg_encode,\"survival\",\"png\",sep = \".\")), device = \"png\")\r\n}\r\nfor_survival_distinct %>% sapply(plot_survival)\r\n", "meta": {"hexsha": "806498dae7c61af4533836971971cb7af828fd23", "size": 4350, "ext": "r", "lang": "R", "max_stars_repo_path": "04.SNORic/snoric_plot/basic_survival.r", "max_stars_repo_name": "chunjie-sam-liu/R_Leng_2", "max_stars_repo_head_hexsha": "2b33fab577e62380f0dd2446c8d169619d0bfdb2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "04.SNORic/snoric_plot/basic_survival.r", "max_issues_repo_name": "chunjie-sam-liu/R_Leng_2", "max_issues_repo_head_hexsha": "2b33fab577e62380f0dd2446c8d169619d0bfdb2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "04.SNORic/snoric_plot/basic_survival.r", "max_forks_repo_name": "chunjie-sam-liu/R_Leng_2", "max_forks_repo_head_hexsha": "2b33fab577e62380f0dd2446c8d169619d0bfdb2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8260869565, "max_line_length": 110, "alphanum_fraction": 0.591954023, "num_tokens": 1246, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6654105454764746, "lm_q2_score": 0.5234203489363239, "lm_q1q2_score": 0.348289419899206}} {"text": "##############################################################################\r\n# Plot K-mer heatmaps\r\n##############################################################################\r\nrrheat2 <- function(dat, f, levels, facetvar, nbp){\r\n\tp <- ggplot()+\r\n\t# log(v4*10000+1,2)\r\n\t# limits=c(min(dat$v4), max(dat$v4))\r\n\tgeom_tile(data=dat, aes(x=v3, y=v2a, fill=v4))+\r\n\t# geom_text(data=dat, aes(x=v2a, y=v3, label=v4a, family=\"Courier\", size=0.1))+\r\n\tgeom_rect(data=f, size=0.6, colour=\"grey70\",\r\n\t\taes(xmin=xlo, xmax=xhi, ymin=ylo, ymax=yhi), fill=NA)+\r\n\tscale_fill_gradientn(\"Relative Rate\",\r\n\t\t# colours=myPalette((nbp-1)^4),\r\n colours=myPaletteO(11),\r\n\t\ttrans=\"log10\",\r\n\t\tbreaks=10^(seq(-3.65,-.84,0.281)),\r\n\t\tlabels=signif(10^(seq(-3.65,-.84,0.281)), 2),\r\n\t\tlimits=c(0.0002, 0.2))+\r\n\txlab(\"3' flank\")+\r\n\tylab(\"5' flank\")+\r\n theme_classic()+\r\n\ttheme(\r\n\t\t# legend.position=\"none\",\r\n\t\tlegend.title = element_text(size=18),\r\n\t\tlegend.text = element_text(size=16),\r\n\t\tlegend.key.height = unit(1.5, \"cm\"),\r\n\t\taxis.ticks.x = element_blank(),\r\n\t\taxis.ticks.y = element_blank(),\r\n\t\taxis.text.y = element_blank(),\r\n\t\taxis.text.x = element_blank(),\r\n axis.title.y = element_blank(),\r\n\t\taxis.title.x = element_blank())\r\n\r\n\treturn(p)\r\n}\r\n\r\nfor(i in 1:adj){\r\n adjtmp <- i\r\n nbp <- adjtmp*2+1\r\n rates <- read.table(paste0(parentdir, \"/output/\", nbp, \"bp_1000k_rates.txt\"),\r\n\t\theader=T, stringsAsFactors=F)\r\n rates$v2 <- substr(rates$Sequence,1,adjtmp)\r\n rates$v2a <- as.character(lapply(as.vector(rates$v2), reverse_chars))\r\n rates$v2a <- factor(rates$v2a)\r\n rates$v3 <- substr(rates$Sequence, adjtmp+2, adjtmp*2+1)\r\n rates$v4 <- rates$rel_prop\r\n rates$Category <- gsub(\"cpg_\", \"\", rates$Category2)\r\n rates$v5 <- gsub(\"_\", \">\", rates$Category)\r\n rates$v5 <- factor(rates$v5)\r\n\r\n nbox <- length(unique(rates$v2a))\r\n nint <- nbox/(4^(adjtmp-1))\r\n xhi <- rep(1:(4^(adjtmp-1)),4^(adjtmp-1))*nint+0.5\r\n xlo <- xhi-nint\r\n yhi <- rep(1:(4^(adjtmp-1)),each=4^(adjtmp-1))*nint+0.5\r\n ylo <- yhi-nint\r\n f <- data.frame(xlo,xhi,ylo,yhi)\r\n\r\n levs_a <- as.character(lapply(as.vector(levels(rates$v2a)), reverse_chars))\r\n # p1<-rrheat(rates, f, levs_a, \"v5\", nbp)\r\n\tfor(j in 1:6){\r\n\t\tcateg <- orderedcats[j]\r\n\t\tp1 <- rrheat2(rates[rates$Category==categ,], f, levs_a, \"v5\", nbp)\r\n\t\tp1a <- p1+theme(legend.position=\"none\")\r\n\t\t png(paste0(parentdir, \"/images/\", categ, \"_\", nbp, \"bp_heatmap.png\"),\r\n\t\t \theight=5, width=5, units=\"in\", res=300)\r\n\t\t pushViewport(viewport(width=unit(5, \"in\"), height=unit(5, \"in\")))\r\n\t\t grid.draw(ggplotGrob(p1a))\r\n\t\t dev.off()\r\n\t}\r\n}\r\n\r\n# trim whitespace on panels with imagemagick mogrify\r\ntrimcmd <- paste0(\"mogrify -trim \", parentdir, \"/images/*_*bp_heatmap.png\")\r\nsystem(trimcmd)\r\n\r\n# extract legend\r\nlegend <- get_legend(p1)\r\npng(paste0(parentdir, \"/images/heatmap_legend.png\"),\r\n\theight=8, width=3, units=\"in\", res=300)\r\ngrid.draw(legend)\r\ndev.off()\r\n\r\n##############################################################################\r\n# 1-mer heatmap\r\n##############################################################################\r\nrrheat1 <- function(dat, facetvar){\r\n p <- ggplot()+\r\n geom_tile(data=dat, aes(x=v5, y=1, fill=v4))+\r\n scale_fill_gradientn(\"Relative Rate\\n\",\r\n \t\tcolours=myPaletteO(11),\r\n \t\ttrans=\"log\",\r\n \t\tbreaks=c(0.0002, 0.008, 0.15),\r\n \t\tlabels=c(0.0002, 0.008, 0.15),\r\n \t\tlimits=c(0.0002, 0.15))+\r\n ylab(\" \")+\r\n theme_classic()+\r\n theme(\r\n\t legend.title = element_text(size=18),\r\n\t legend.key.size = unit(0.2, \"in\"),\r\n legend.text = element_text(size=16),\r\n strip.text.x = element_text(size=20),\r\n axis.title.x = element_blank(),\r\n axis.title.y = element_text(size=20),\r\n axis.text.y = element_blank(),\r\n axis.text.x = element_blank(),\r\n axis.ticks.x= element_blank(),\r\n axis.ticks.y= element_blank())+\r\n facet_wrap(as.formula(paste(\"~\", facetvar)), ncol=6, scales=\"free_x\")\r\n\r\n return(p)\r\n}\r\n\r\nrates3 <- read.table(paste0(parentdir, \"/output/3bp_1000k_rates.txt\"), header=T, stringsAsFactors=F)\r\nrates3$v5 <- factor(gsub(\"cpg_\", \"\", rates3$Category2))\r\nrates3$v5 <- gsub(\"_\", \">\", rates3$v5)\r\nrates1 <- rates3 %>%\r\n group_by(v5) %>%\r\n summarise(num=sum(num), COUNT=sum(COUNT), v4=num/COUNT)\r\nrrheat1(rates1, \"v5\")\r\nggsave(paste0(parentdir, \"/images/1bp_heatmap.png\"), height=2, width=24)\r\n\r\nrates7out <- read.table(paste0(parentdir, \"/output/7bp_1000k_rates.txt\"),\r\n\theader=T, stringsAsFactors=F)\r\nrates7out <- mutate(rates7out, Category=gsub(\"cpg_\", \"\", Category2))\r\n\r\nrates7out <- merge(rates7out, r5m, by=c(\"Category\", \"Sequence\"), all.x=T)\r\nrates7out <- rates7out %>% dplyr::select(Type=Category, Motif=Sequence,\r\n\tnERVs=num, nMotifs=COUNT, ERV_rel_rate=rel_prop.x, nERVs_DS=num.x,\r\n\tERV_DS_rel_rate=rel_prop.y, nMAC10=num.y, MAC10_rel_rate=common_rel_prop)\r\n\r\nrates5 <- read.table(paste0(parentdir, \"/output/5bp_1000k_rates.txt\"),\r\n\theader=T, stringsAsFactors=F)\r\nrates5out <- rates5 %>%\r\n\tmutate(Category=gsub(\"cpg_\", \"\", Category2)) %>%\r\n\tdplyr::select(Type=Category, Motif=Sequence,\r\n\tnERVs=num, nMotifs=COUNT, ERV_rel_rate=rel_prop)\r\n\r\nrates3 <- read.table(paste0(parentdir, \"/output/3bp_1000k_rates.txt\"),\r\n\theader=T, stringsAsFactors=F)\r\nrates3out <- rates3 %>%\r\n\tmutate(Category=gsub(\"cpg_\", \"\", Category2)) %>%\r\n\tdplyr::select(Type=Category, Motif=Sequence,\r\n\tnERVs=num, nMotifs=COUNT, ERV_rel_rate=rel_prop)\r\n\r\nrates1out <- rates3out %>%\r\n\tgroup_by(Type) %>%\r\n\tsummarise(nERVs=sum(nERVs), nMotifs=sum(nMotifs), ERV_rel_rate=nERVs/nMotifs)\r\n\r\nwrite.table(rates7out, paste0(parentdir, \"/output/7bp_final_rates.txt\"),\r\n\tcol.names=T, row.names=F, quote=F, sep=\"\\t\")\r\n\r\nwrite.table(rates5out, paste0(parentdir, \"/output/5bp_final_rates.txt\"),\r\n\tcol.names=T, row.names=F, quote=F, sep=\"\\t\")\r\n\r\nwrite.table(rates3out, paste0(parentdir, \"/output/3bp_final_rates.txt\"),\r\n\tcol.names=T, row.names=F, quote=F, sep=\"\\t\")\r\n\r\nwrite.table(rates1out, paste0(parentdir, \"/output/1bp_final_rates.txt\"),\r\n\tcol.names=T, row.names=F, quote=F, sep=\"\\t\")\r\n", "meta": {"hexsha": "0798e7398e5f574a4f6e0abf02ec500482c05995", "size": 5976, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/R_deprecated/kmer_heatmaps.r", "max_stars_repo_name": "theandyb/smaug-genetics", "max_stars_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-09-18T20:54:24.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-16T05:30:06.000Z", "max_issues_repo_path": "sandbox/R_deprecated/kmer_heatmaps.r", "max_issues_repo_name": "theandyb/smaug-genetics", "max_issues_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-07-24T12:43:50.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-24T12:43:50.000Z", "max_forks_repo_path": "sandbox/R_deprecated/kmer_heatmaps.r", "max_forks_repo_name": "theandyb/smaug-genetics", "max_forks_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-07-16T20:50:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T10:41:40.000Z", "avg_line_length": 37.1180124224, "max_line_length": 101, "alphanum_fraction": 0.6174698795, "num_tokens": 1908, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3482890393314951}} {"text": "# **********************************************************************************************************************\r\n# ** Pennekamp & Schtickzelle (2013) **\r\n# ** Implementing image analysis in laboratory-based experimental systems for ecology and evolution: a hands-on guide **\r\n# ** R and EBImage script for digital image analysis ** \r\n# **********************************************************************************************************************\r\n\r\n#load EBImage package to perform image analysis\r\nlibrary(\"EBImage\")\r\n\r\n# **************************************************************************** \r\n# ** Start of USER SECTION **\r\n# ****************************************************************************\r\n\r\n#specify input directory\r\ndir_input = \"C:\\\\MEE\\\\Images\\\\1 - Photos to analyze\\\\\"\r\n\r\n#specify directory with photos for comparison\r\ndir_compare = \"C:\\\\MEE\\\\Images\\\\2 - Photos for comparison\\\\\"\r\n\r\n#specify output directory\r\ndir_output = \"C:\\\\MEE\\\\Results\\\\R\\\\\"\r\n\r\n#specify segmentation approach (i.e. 'threshold', 'difference image' or 'edge detection')\r\nseg = 'threshold'\r\n\r\n#specify whether you want to split objects by watershed after segmentation (if yes, put 'ws', else '_')\r\nsplit = '_ws_'\r\n\r\n#specify size boundaries\r\nmin_size = 200\r\nmax_size = 1500\r\n\r\n# **************************************************************************** \r\n# ** End of USER SECTION **\r\n# ****************************************************************************\r\n\r\n# 1. LOOPING OVER THE IMAGE DIRECTORY AND READING OF IMAGE DATA\r\n#--------------------------------------------------------------\r\n# initialize counter\r\ni = 1\r\n\r\n# loop over the input directory\r\nwhile (i <= length(list.files(dir_input))){\r\n \r\n # check that one segmentation approach is selected, otherwise stop the program\r\n if (seg == 'threshold' | seg == 'difference image' | seg == 'edge detection') {\r\n \r\n # read reference image to be analyzed (the image origin (0,0) is the lower left corner)\r\n image <- readImage(paste(dir_input,i,\".jpg\",sep=\"\"))\r\n # convert image to grayscale\r\n image <- channel(image,'gray')\r\n # flip image to have same position of the image origin at the lower left corner facilitating comparison with ImageJ and Python\r\n image <- flip(image)\r\n\r\n# 2a. SEGMENTING IMAGE DATA BY THRESHOLDING, DIFFERENCE IMAGE OR EDGE DETECTION\r\n#-----------------------------------------------------------------------------\r\n\r\n if (seg == 'threshold'){\r\n # select global threshold to segment image (NB: intensity values (0-255) are normalized between 0 and 1)\r\n image_segmented <- image > 0.546875\r\n\t}\r\n \r\n if (seg == 'difference image'){\r\n # produce difference image to detect moving particles\r\n # load sequential image that is used for the comparison\r\n image2 <- readImage(paste(dir_compare,i,\".jpg\",sep=\"\"))\r\n image2 <- channel(image2,'gray')\r\n image2 <- flip(image2)\r\n # subtract first from second image, to get difference in intensity values\r\n difference_image <- image - image2\r\n # threshold difference image to get mask for labelling (images normalized after conversion to float, therefore intensity between 0 and 1)\r\n image_segmented <- difference_image > 0.25\r\n\t}\r\n \r\n if (seg == 'edge detection'){\r\n # perform edge detection.\r\n f = array(1, dim=c(3, 3))\r\n f[2, 2] = -4\r\n edges = filter2(image, f)\r\n # threshold edges to select only strong contours (intensities are always normalized)\r\n image_segmented <- edges > 0.99\r\n }\r\n\r\n # fill potential holes in segmented objects\r\n image_segmented_filled <- fillHull(image_segmented)\r\n \r\n # watershed segmentation to split touching objects, if activated in the user's section\t\r\n if (split == '_ws_'){\r\n # distance map is created where the distance of each foreground pixel to the closest background pixel is calculated and used for watershed splitting\r\n map <- distmap(image_segmented_filled)/10\r\n # return labelled image (array of unique integers per object)\r\n image_label <- watershed(map, tolerance=0.15, ext=1)\r\n\t}\r\n\r\n# 2b. WATERSHED SPLIT\r\n#--------------------\t\r\n\r\n if (split != '_ws_'){\r\n # return labelled image (array of unique integers per object)\r\n image_label <- bwlabel(image_segmented_filled)\r\n\t}\r\n\t\r\n# 3. MEASURE OBJECT PROPERTIES AND CONDUCT SIZE-BASED EXCLUSION\r\n#--------------------------------------------------------------\r\n\t\t\r\n # measure object size on labelled image\r\n size <- computeFeatures.shape(image_label, properties=FALSE)\r\n intensity <- computeFeatures.basic(image_label, image, properties=FALSE)\r\n spatial <- computeFeatures.moment(image_label, properties=FALSE)\r\n intensity[,1] <- intensity[,1]*256\r\n intense <- intensity[,0:1]\r\n space <- spatial[,1:3]\r\n morph <- size[,1:2]\r\n results <- cbind(intense,space,morph)\r\n results_df <- as.data.frame(results)\r\n \r\n # exclude objects outside of size boundaries\r\n # re-label image after exclusion of too small and too big objects\r\n image_label_clean <- rmObjects(image_label,which(results[,5] < min_size | results[,5] > max_size, ))\r\n \r\n # check whether there are objects that meet the size range, otherwise skip output production\r\n if (!max(image_label_clean)==0){\r\n results_clean <- subset(results_df, results[,5] > min_size & results[,5] < max_size, select=1:6)\r\n results_clean$label <- seq(1:length(results_clean[,1]))\r\n results_clean <- results_clean[c(\"label\",\"intense\", \"m.cx\",\"m.cy\",\"s.area\",\"s.perimeter\",\"m.majoraxis\")]\r\n \r\n# 4. EXPORT RESULTS AS OVERLAY OF IDENTIFIED OBJECTS AND TABLE OF OBJECT MEASUREMENTS\r\n#-----------------------------------------------------------------------------------\r\n\t\r\n # plot overlay between original picture and identified objects\r\n image_RGB <- channel(image, 'rgb')\r\n # find outline of objects for plotting\r\n overlayObjects <- paintObjects(image_label_clean, image_RGB, col=c('red'))\r\n # reverse initial flip of image before plotting\r\n overlayObjects <- flip(overlayObjects) \r\n\r\n # export results and create overlay for error checking\r\n width <- length(image[,1])\r\n height <- length(image[1,])\r\n write.table(results_clean, paste(dir_output,seg,split,i,\"_results.txt\",sep=\"\"), sep=\"\\t\",row.names = FALSE, col.names=TRUE)\r\n jpeg(filename = paste(dir_output,\"overlay_\",seg,split,i,\".jpg\",sep=\"\"), width = width, height = height, pointsize = 12, quality = 400)\r\n par(mar=c(0,0,0,0))\r\n plot(x = NULL, y = NULL, xlim = c(0,as.numeric(width)), ylim = c(0,as.numeric(height)), pch = '', xaxt = 'n', yaxt = 'n', xlab = '', ylab = '', xaxs = 'i', yaxs = 'i', bty = 'n') # plot empty figure\r\n rasterImage(overlayObjects, xleft = 0, ybottom = 0, xright = width, ytop = height) \r\n text(results_clean$m.cx,results_clean$m.cy,labels=results_clean$label, col='red')\r\n dev.off()\r\n # increase counter\r\n i <- i+1 }\r\n else {\r\n # increase counter\r\n i <- i+1 }\r\n }\r\n \r\n else{\r\n stop(\"Select one of four segmentation approaches\")\r\n }\r\n\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "6f3bf4050913132d030fc88bb53fe1399948d04a", "size": 7363, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mee312036-sup-0002-r-script.r", "max_stars_repo_name": "JoeyBernhardt/chlamytippee", "max_stars_repo_head_hexsha": "2d7c7b67fe309b8a9180f74490eaf227425dd708", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/mee312036-sup-0002-r-script.r", "max_issues_repo_name": "JoeyBernhardt/chlamytippee", "max_issues_repo_head_hexsha": "2d7c7b67fe309b8a9180f74490eaf227425dd708", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-08-23T12:18:22.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-31T16:54:19.000Z", "max_forks_repo_path": "R/mee312036-sup-0002-r-script.r", "max_forks_repo_name": "JoeyBernhardt/chlamytippee", "max_forks_repo_head_hexsha": "2d7c7b67fe309b8a9180f74490eaf227425dd708", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6242424242, "max_line_length": 203, "alphanum_fraction": 0.564579655, "num_tokens": 1591, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.348289039331495}} {"text": "library(ASCAT)\n\nargs <- commandArgs(TRUE)\ntvaf <- args[1]\nnvaf <- args[2]\ntlogR <- args[3]\nnlogR <- args[4]\nof <- args[5] # outfile\n\nascat.bc = ascat.loadData(tlogR,tvaf,nlogR,nvaf)\n#ascat.plotRawData(ascat.bc)\nascat.bc = ascat.aspcf(ascat.bc)\n#ascat.plotSegmentedData(ascat.bc)\nascat.output = ascat.runAscat(ascat.bc)\n\npurity <- ascat.output$aberrantcellfraction\nnames(purity) <- NULL\n\nploidy <- ascat.output$ploidy\nnames(ploidy) <- NULL\n\npurity_new <- NULL\nploidy_new <- NULL\n\nif (is.null(purity) | is.null(ploidy)) {\n\t# no solution for purity & ploidy, return NA\n\t# set purity as 100% and ploidy as 2 when we can not get solution for purity & ploidy\n\tpurity_new <- 1\n\tploidy_new <- 2\n\tprint(\"WARNING:This sample can not get optimal purity & ploidy by ASCAT software\")\n}else{\n\tpurity_new <- purity\n\tploidy_new <- ploidy\n}\n\nval <- c(purity_new,ploidy_new)\nnames(val) <- c(\"purity\",\"ploidy\")\n\nprint(paste(\"purity is:\",purity_new,sep=\"\"))\nprint(paste(\"ploidy is:\",ploidy_new,sep=\"\"))\n\nwrite.table(val,file=of,quote=FALSE,row.names=TRUE,col.names=FALSE)\n\n\n", "meta": {"hexsha": "7dbe56b03e98031e8704797f5f248e6d76713c12", "size": 1054, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/ascat.r", "max_stars_repo_name": "Xiaohuaniu0032/HLALOH", "max_stars_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tools/ascat.r", "max_issues_repo_name": "Xiaohuaniu0032/HLALOH", "max_issues_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-10-26T01:39:33.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-04T02:41:11.000Z", "max_forks_repo_path": "tools/ascat.r", "max_forks_repo_name": "Xiaohuaniu0032/HLALOH", "max_forks_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.4222222222, "max_line_length": 86, "alphanum_fraction": 0.7144212524, "num_tokens": 353, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7248702880639791, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.34828471776518627}} {"text": "# Returns the cost of database projecting X months forwards (postgres or Redshift)\r\ndatabaseCostByMonth <- function(databaseType, eventsPerMonth, numberOfMonths){\r\n\tif (databaseType == 'postgres') \r\n\t\t{postgresCostByMonth(eventsPerMonth, numberOfMonths)} else\r\n\t\t{redshiftCostByMonth(eventsPerMonth, numberOfMonths)}\r\n}\r\n\r\n\r\n# Returns the cost of Redshift projecting X months forwards\r\nredshiftCostByMonth <- function(eventsPerMonth, numberOfMonths) {\r\n\teventsStoredByMonth <- seq(eventsPerMonth, by=eventsPerMonth, length=numberOfMonths)\r\n\tnodesRequiredByMonth <- redshiftNodesRequired(eventsStoredByMonth)\r\n\tredshiftEffectiveCostPerMonth(nodesRequiredByMonth)\r\n}\r\n\r\n\r\nredshiftNodesRequired <- function(eventsStored){\r\n\t# We believe that 4 billion events can be stored on each Redshift XL node\r\n\tmaxEventsPerNode <- 4000000000 \r\n\tceiling(eventsStored/maxEventsPerNode)\r\n}\r\n\r\n\r\n# Amazon Redshift cost\r\n# Assumes year reserved instance pricing and XL nodes (not 8XL nodes)\r\nredshiftEffectiveCostPerMonth <- function(numberOfNodes){\r\n\tupfrontCostPerNode <- 3000\r\n\thourlyCostPerNode <- 0.114\r\n\r\n\t# Cost per month = cost for entire 3 years / 36\r\n\tthreeYearCost <- (upfrontCostPerNode + hourlyCostPerNode * 24 * 365.25 * 3) * numberOfNodes\r\n\tthreeYearCost / 36\r\n}\r\n\r\n\r\n# Returns the cost of PostgreSQL projecting X months forward\r\npostgresCostByMonth <- function(eventsPerMonth, numberOfMonths) {\r\n\teventsStoredByMonth <- seq(eventsPerMonth, by=eventsPerMonth, length=numberOfMonths)\r\n\tinstancesRequiredByMonth <- postgresInstancesRequired(eventsStoredByMonth)\r\n\tpostgresEffectiveCostPerMonth(instancesRequiredByMonth)\r\n}\r\n\r\n# Assume PostgreSQL is run on m1.xlarge instances, and that each instance can handle 100M lines of data\r\npostgresInstancesRequired <- function(eventsStored){\r\n\tmaxEventsPerInstance <- 100000000\r\n\tceiling(eventsStored/maxEventsPerInstance)\r\n}\r\n\r\n\r\n# Assume m1.xlarge instances reserved for 3 years\r\npostgresEffectiveCostPerMonth <- function(numberOfInstances){\r\n\tupfrontCostPerInstance <- 1028\r\n\thourlyCostPerInstance <- 0.046\r\n\r\n\t# Cost per month = cost for entire 3 years / 36\r\n\tthreeYearCost <- (upfrontCostPerInstance + hourlyCostPerInstance * 24 * 365.25 * 3) * numberOfInstances\r\n\tthreeYearCost / 36\r\n}\r\n\r\n", "meta": {"hexsha": "30e9605ec473ef57b16f6312ed7368a9274f7259", "size": 2232, "ext": "r", "lang": "R", "max_stars_repo_path": "R/database-costs.r", "max_stars_repo_name": "snowplow/snowplow-tco-model", "max_stars_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2015-03-26T15:32:58.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-08T13:50:12.000Z", "max_issues_repo_path": "R/database-costs.r", "max_issues_repo_name": "snowplow-archive/snowplow-tco-model", "max_issues_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-07T00:44:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-24T15:13:20.000Z", "max_forks_repo_path": "R/database-costs.r", "max_forks_repo_name": "snowplow-archive/snowplow-tco-model", "max_forks_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2016-01-16T23:33:14.000Z", "max_forks_repo_forks_event_max_datetime": "2017-05-02T04:55:21.000Z", "avg_line_length": 37.2, "max_line_length": 105, "alphanum_fraction": 0.7840501792, "num_tokens": 542, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7490872131147275, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.34825182247274916}} {"text": "library(ggplot2)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(rstan)\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(gdata)\nlibrary(EnvStats)\nlibrary(matrixStats)\nlibrary(scales)\nlibrary(gridExtra)\nlibrary(bayesplot)\nlibrary(cowplot)\n\n\n#---------------------------------------------------------------------------\nformat_data <- function(i, dates, states, estimated_cases_raw, estimated_deaths_raw, \n reported_cases, reported_deaths, out, forecast=0, deaths_predicted=estimated_deaths_raw){\n \n N <- length(dates[[i]])\n if(forecast > 0) {\n dates[[i]] = c(dates[[i]], max(dates[[i]]) + 1:forecast)\n N = N + forecast\n reported_cases[[i]] = c(reported_cases[[i]],rep(NA,forecast))\n reported_deaths[[i]] = c(reported_deaths[[i]],rep(NA,forecast))\n }\n \n state <- states[[i]]\n \n estimated_cases <- colMeans(estimated_cases_raw[,1:N,i])\n estimated_cases_li <- colQuantiles(estimated_cases_raw[,1:N,i],prob=.025)\n estimated_cases_ui <- colQuantiles(estimated_cases_raw[,1:N,i],prob=.975)\n estimated_cases_li2 <- colQuantiles(estimated_cases_raw[,1:N,i],prob=.25)\n estimated_cases_ui2 <- colQuantiles(estimated_cases_raw[,1:N,i],prob=.75)\n \n estimated_deaths <- colMeans(estimated_deaths_raw[,1:N,i])\n estimated_deaths_li <- colQuantiles(estimated_deaths_raw[,1:N,i],prob=.025)\n estimated_deaths_ui <- colQuantiles(estimated_deaths_raw[,1:N,i],prob=.975)\n estimated_deaths_li2 <- colQuantiles(estimated_deaths_raw[,1:N,i],prob=.25)\n estimated_deaths_ui2 <- colQuantiles(estimated_deaths_raw[,1:N,i],prob=.75)\n \n deaths_predicted_li <- colQuantiles(deaths_predicted[,1:N,i],prob=.025)\n deaths_predicted_ui <- colQuantiles(deaths_predicted[,1:N,i],prob=.975)\n deaths_predicted_li2 <- colQuantiles(deaths_predicted[,1:N,i],prob=.25)\n deaths_predicted_ui2 <- colQuantiles(deaths_predicted[,1:N,i],prob=.75)\n rt <- colMeans(out$Rt_adj[,1:N,i])\n rt_li <- colQuantiles(out$Rt_adj[,1:N,i],prob=.025)\n rt_ui <- colQuantiles(out$Rt_adj[,1:N,i],prob=.975)\n rt_li2 <- colQuantiles(out$Rt_adj[,1:N,i],prob=.25)\n rt_ui2 <- colQuantiles(out$Rt_adj[,1:N,i],prob=.75)\n\n data_state_plotting <- data.frame(\"date\" = dates[[i]],\n \"state\" = rep(state, length(dates[[i]])),\n \"reported_cases\" = reported_cases[[i]], \n \"predicted_cases\" = estimated_cases,\n \"cases_min\" = estimated_cases_li,\n \"cases_max\" = estimated_cases_ui,\n \"cases_min2\" = estimated_cases_li2,\n \"cases_max2\" = estimated_cases_ui2,\n \"reported_deaths\" = reported_deaths[[i]],\n \"estimated_deaths\" = estimated_deaths,\n \"deaths_min\" = estimated_deaths_li,\n \"deaths_max\"= estimated_deaths_ui,\n \"deaths_min2\" = estimated_deaths_li2,\n \"deaths_max2\"= estimated_deaths_ui2,\n \"deaths_predicted_li\"=deaths_predicted_li,\n \"deaths_predicted_ui\"=deaths_predicted_ui,\n \"deaths_predicted_li2\"=deaths_predicted_li2,\n \"deaths_predicted_ui2\"=deaths_predicted_ui2,\n \"rt\" = rt,\n \"rt_min\" = rt_li,\n \"rt_max\" = rt_ui,\n \"rt_min2\" = rt_li2,\n \"rt_max2\" = rt_ui2)\n \n return(data_state_plotting)\n \n}\n", "meta": {"hexsha": "896647f0647cc2ac07a021d02f7246ec93127880", "size": 3735, "ext": "r", "lang": "R", "max_stars_repo_path": "usa/code/plotting/format-data-plotting.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "usa/code/plotting/format-data-plotting.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "usa/code/plotting/format-data-plotting.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 47.2784810127, "max_line_length": 113, "alphanum_fraction": 0.5665327979, "num_tokens": 891, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.34815113498359035}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\nlibrary(rJava)\n\nsetwd(\"/Users/mengmengjiang/all datas/print\")\n\nlibrary(xlsx)\n\n# reading ux and sd\n\nk1<-read.xlsx(\"dotx.xlsx\",sheetName=\"600\",header=TRUE)\nk2<-read.xlsx(\"dotx.xlsx\",sheetName=\"1khz\",header=TRUE)\nk3<-read.xlsx(\"dotx.xlsx\",sheetName=\"2khz\",header=TRUE)\nk4<-read.xlsx(\"dotx.xlsx\",sheetName=\"2.5khz\",header=TRUE)\n\n# errorbar\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\nif(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\nstop(\"vectors must be same length\")\narrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n# color setting\n\nyan<-c(\"red\",\"blue\",\"black\",\"green3\")\npcc<-c(0,1,2,5)\n\nz1<-(k1$sdeva+k1$deva)*0.6;\nz2<-(k2$sdeva+k2$deva)*1;\nz3<-(k3$sdeva+k3$deva)*2;\nz4<-(k4$sdeva+k4$deva)*2.5;\n\n#z1<-(k1$sdeva)*0.6;\n#z2<-(k2$sdeva)*1;\n#z3<-(k3$sdeva)*2;\n#z4<-(k4$sdeva)*2.5;\n\ne1<-(k1$sdstd+k1$stdd)*0.6;\ne2<-(k2$sdstd+k2$stdd)*1;\ne3<-(k3$sdstd+k3$stdd)*2;\ne4<-(k4$sdstd+k4$stdd)*2.5;\n\nplot(k1$ux,z1,col=0,xlab = expression(italic(U[\"x\"]) (mm/s)),\n ylab = expression(italic((S[\"d\"]+D)/fv)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(0,300),ylim=c(0,300))\n\n mtext(\"Sd_ratio\",3,line=0.2,font=2,cex=1.2)\n\nlines(k1$ux,z1,lwd=1.5,lty=2,col=yan[1],pch=pcc[1],type=\"b\")\nlines(k2$ux,z2,lwd=1.5,lty=2,col=yan[2],pch=pcc[2],type=\"b\")\nlines(k3$ux,z3,lwd=1.5,lty=2,col=yan[3],pch=pcc[3],type=\"b\")\nlines(k4$ux,z4,lwd=1.5,lty=2,col=yan[4],pch=pcc[4],type=\"b\")\n\nerror.bar(k1$ux,z1,e1/2,col=yan[1])\nerror.bar(k2$ux,z2,e2/2,col=yan[2])\nerror.bar(k3$ux,z3,e3/2,col=yan[3])\nerror.bar(k4$ux,z4,e4/2,col=yan[4])\n\nleg<-c(\"600Hz\",\"1KHz\",\"2KHz\",\"2.5KHz\")\n\nlegend(\"topright\",legend=leg,col=yan,pch=pcc,lwd=1.5,lty=2,inset=.02,\nbty=\"n\")\n", "meta": {"hexsha": "e6c5db93485c2a4ee6fa6f9ca0ae5511ed82762c", "size": 1855, "ext": "r", "lang": "R", "max_stars_repo_path": "print-chap7/X/fig8_sd-fv.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "print-chap7/X/fig8_sd-fv.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "print-chap7/X/fig8_sd-fv.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9193548387, "max_line_length": 104, "alphanum_fraction": 0.6388140162, "num_tokens": 831, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3481511269961152}} {"text": "# 3. faza: Vizualizacija podatkov\n\n# Uvozimo zemljevid.\nzemljevid <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/OB.zip\",\n \"OB/OB\", encoding = \"Windows-1250\")\nlevels(zemljevid$OB_UIME) <- levels(zemljevid$OB_UIME) %>%\n { gsub(\"Slovenskih\", \"Slov.\", .) } %>% { gsub(\"-\", \" - \", .) }\nzemljevid$OB_UIME <- factor(zemljevid$OB_UIME, levels = levels(obcine$obcina))\nzemljevid <- pretvori.zemljevid(zemljevid)\n\n# Izra\u010dunamo povpre\u010dno velikost dru\u017eine\npovprecja <- druzine %>% group_by(obcina) %>%\n summarise(povprecje = sum(velikost.druzine * stevilo.druzin) / sum(stevilo.druzin))\n", "meta": {"hexsha": "2d22500f7c9a403ec56947eaad62cdc9ba43c9d7", "size": 605, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "jaanos/APPR-2017-18", "max_stars_repo_head_hexsha": "4426195649ff251034566285d2a985e356290799", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-10-14T10:47:41.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-14T10:47:41.000Z", "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "jaanos/APPR-2017-18", "max_issues_repo_head_hexsha": "4426195649ff251034566285d2a985e356290799", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-11-09T09:01:13.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-27T06:14:12.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "jaanos/APPR-2017-18", "max_forks_repo_head_hexsha": "4426195649ff251034566285d2a985e356290799", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 43, "max_forks_repo_forks_event_min_datetime": "2017-10-30T09:17:13.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-30T16:39:46.000Z", "avg_line_length": 43.2142857143, "max_line_length": 85, "alphanum_fraction": 0.6661157025, "num_tokens": 243, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3481511269961152}} {"text": "#' Normalize Infinium HumanMethylation450 BeadChip\n#'\n#' Normalize sample methylation data using normalized quantiles.\n#'\n#' @param norm.object An element of \\code{\\link{meffil.normalize.quantiles}()}.\n#' @param remove.poor.signal Set methylation values for poorly detected probes\n#' to missing (Default: \\code{FALSE}). Poor signal was \n#' identified during QC by \\code{\\link{meffil.qc}()}\n#' as signal that failed to pass the \n#' detection p-value threshold (\\code{detection.threshold})\n#' or bead threshold (\\code{bead.threshold}).\n#' @param verbose If \\code{TRUE}, then status messages are printed during execution (Default: \\code{FALSE}).\n#' @return List containing normalized methylated and unmethylated signals.\n#'\n#' @export\nmeffil.normalize.sample <- function(norm.object, remove.poor.signal=F, verbose=F) {\n stopifnot(is.normalized.object(norm.object))\n\n ## begin backwards compatibility\n if (any(c(\"chrX\",\"chrY\") %in% names(norm.object$quantiles))) {\n idx <- which(names(norm.object$quantiles) %in% c(\"chrX\",\"chrY\"))\n names(norm.object$quantiles)[idx] <- tolower(names(norm.object$quantiles)[idx])\n }\n if (is.null(norm.object$featureset)) {\n norm.object$chip <- norm.object$featureset <- \"450k\"\n }\n ## end backwards compatibility\n \n probes <- meffil.probe.info(norm.object$chip, norm.object$featureset)\n \n rg <- read.rg(norm.object$basename, verbose=verbose)\n rg <- background.correct(rg, probes, verbose=verbose)\n rg <- dye.bias.correct(rg, probes, norm.object$reference.intensity, verbose=verbose)\n mu <- rg.to.mu(rg, probes)\n\n sites <- meffil.get.sites(norm.object$featureset)\n mu$M <- mu$M[sites]\n mu$U <- mu$U[sites]\n\n msg(\"Normalizing methylated and unmethylated signals.\", verbose=verbose)\n probe.subsets <- get.quantile.site.subsets(norm.object$featureset)\n for (name in names(norm.object$norm)) {\n for (target in names(norm.object$norm[[name]])) {\n probe.idx <- which(names(mu[[target]]) %in% probe.subsets[[name]])\n if (length(probe.idx) > 0) {\n orig.signal <- mu[[target]][probe.idx]\n norm.target <- compute.quantiles.target(norm.object$norm[[name]][[target]])\n norm.signal <- preprocessCore::normalize.quantiles.use.target(matrix(orig.signal),\n norm.target)\n mu[[target]][probe.idx] <- norm.signal\n }\n }\n }\n\n if (remove.poor.signal) {\n bad.probes <- union(\n names(norm.object$bad.probes.beadnum),\n names(norm.object$bad.probes.detectionp))\n for (target in setdiff(names(mu), c(\"class\",\"version\"))) {\n probe.idx <- which(names(mu[[target]]) %in% bad.probes)\n mu[[target]][probe.idx] <- NA\n }\n }\n \n mu\n}\n\ncompute.quantiles.target <- function(quantiles) {\n n <- length(quantiles)\n unlist(lapply(1:(n-1), function(j) {\n start <- quantiles[j]\n end <- quantiles[j+1]\n seq(start,end,(end-start)/n)[-n]\n }))\n}\n", "meta": {"hexsha": "67269a4b5f5fc165d850d8090cb7db40369bc3e7", "size": 3096, "ext": "r", "lang": "R", "max_stars_repo_path": "R/normalize-sample.r", "max_stars_repo_name": "perishky/meffil", "max_stars_repo_head_hexsha": "aed59f4545c4c00935bcae0ad47ac6280323c11b", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": 33, "max_stars_repo_stars_event_min_datetime": "2015-04-21T18:35:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-21T10:48:31.000Z", "max_issues_repo_path": "R/normalize-sample.r", "max_issues_repo_name": "perishky/meffil", "max_issues_repo_head_hexsha": "aed59f4545c4c00935bcae0ad47ac6280323c11b", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2015-02-17T11:13:33.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-28T21:48:56.000Z", "max_forks_repo_path": "R/normalize-sample.r", "max_forks_repo_name": "perishky/meffil", "max_forks_repo_head_hexsha": "aed59f4545c4c00935bcae0ad47ac6280323c11b", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-11-17T22:40:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T16:10:42.000Z", "avg_line_length": 40.7368421053, "max_line_length": 108, "alphanum_fraction": 0.6243540052, "num_tokens": 760, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3479791625167263}} {"text": "library('stringr')\nlibrary('Cairo')\nlibrary('MASS')\nlibrary('mixtools')\nlibrary('Hmisc')\n\nfiles = list.files('.','*.tsv')\ngames = read.table('indie.tsv',quote=\"\",sep='\\t',header=T,stringsAsFactors=F,fill=T)\ngames[,'publisher'] = 'Indie'\ngames[,'group'] = 'Indie'\n\nfor(file in files){\n publisher = str_extract(file,\"^[^\\\\.]+\")\n if(file != 'indie.tsv'){\n new_data = read.table(file,quote=\"\",sep='\\t',header=T,stringsAsFactors=F,fill=T)\n games = games[!(games$Idx %in% new_data$Idx), ]\n }\n}\n\n# CLEAN UP #\n# Delete rows with inconsistent date format (mostly \"MMM D[D], YYYY\")\ngames=games[grep(\"^\\\\w\\\\w\\\\w \\\\d+, \\\\d\\\\d\\\\d\\\\d$\",games$Released),]\n\n# Bin into month and year, restrict to 2013-2014\nmonths = 1:12\nnames(months) = c(\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\")\n\ngames[,'release.month'] = months[tolower(str_extract(games$Released,\"^\\\\w\\\\w\\\\w\"))]\ngames[,'release.year'] = as.numeric(str_extract(games$Released,\"\\\\d\\\\d\\\\d\\\\d$\"))\ngames = games[games$release.year %in% c(2013,2014,2015),]\n\n# get number of owners\ngames[,'owners.numeric'] = as.numeric(str_replace_all(str_extract(games$Owners,\"^[\\\\d,]+\"),',',''))\n\n# remove F2P and games under 2.99\ngames = games[games$Price != 'Free',]\ngames[,'price.numeric'] = round(as.numeric(str_replace(games$Price,\"\\\\$\",\"\")))\ngames = games[games$price.numeric>=3,]\n\n# pull out the userscore\ngames[,'rating'] = as.numeric(str_replace(games$Userscore,\"% \\\\([\\\\dNA/%]+\\\\)\",\"\"))\ngames$rating[is.na(games$rating)] = 0\n\n# THE INDIEPOCOLYPSE! AHHHHH!\ngames[,'yearmonth'] = games$release.month\ngames$yearmonth[games$release.year==2014] = games$yearmonth[games$release.year==2014] + 12\ngames$yearmonth[games$release.year==2015] = games$yearmonth[games$release.year==2015] + 24\ngames = games[games$yearmonth<32,]\n\nreleased = 1:31\nare_good = 1:31\nfor(i in 1:31){\n includes = games$yearmonth==i\n released[i] = sum(includes)\n are_good[i] = sum(includes & games$rating>69)\n}\n\n# Rate of release\nCairoPDF('steam_release_rate',width=9,height=3)\npar(mfrow=c(1,2))\nprefixes = c('','Good')\nminima = c(-1, 69)\nfor(i in 1:2){\n h = hist(games$yearmonth[games$rating>minima[i]],seq((1:31)-.5),plot=F)\n plot(h$mids,h$counts,main=paste(prefixes[i],\"Indie games released by month\"),xlab=\"Release Date\",xaxt='n',\n ylab=\"Games released\",type='l',pch=16,col='dodgerblue',lwd=3)\n axis(1,at=c(1,13,25,31),label=c('2013','2014','2015','July'))\n}\ndev.off()\n\nCairoPDF('steam_success_rate',width=6,height=9)\nhists = list('2013'=0,'2014'=0,'2015'=0)\ncolors = c('red','dodgerblue','black')\nyears = 2013:2015\npar(mfrow=c(3,2))\nfor(i in 1:3){\n y=years[i]\n col = colors[i]\n owners = games$owners.numeric[games$owners.numeric>0 & games$release.year==y]\n hist(owners, 100, main=paste(\"Indie success (\",y,')',sep=''),xlab=\"Owners per Game\",ylab=\"Number of Games\" )\n \n owners = log10(games$owners.numeric[games$owners.numeric>0 & games$release.year==y])\n h=hist(owners, 20,plot=F )\n plot(h$mids,h$density, main=paste(\"Indie success (transformed, \",y,')',sep=''),xlab=\"log(Owners per Game)\",ylab=\"Fraction of Games\")\n normfit = fitdistr(owners,'normal')\n normaldat = dnorm(h$mids,mean=normfit$estimate[1],sd=normfit$estimate[2])\n lines(h$mids,normaldat,col=col,lwd=2)\n legend('topright',c(paste('mean =',round(normfit$estimate[1],2)),\n paste('sd =',round(normfit$estimate[2],2))),\n text.col=col,bty='n')\n hists[[y]]=h\n}\ndev.off()\n\nCairoPDF('steam_swamp',width=4,height=3)\nplot(hists[[2015]]$mids,hists[[2015]]$counts,type='l',lwd=3,yaxt='n',\n main=\"Indie success decreases by year\",xlab=\"log(Owners per Game)\",col=colors[3],\n ylab=\"Number of Games\" )\nlines(hists[[2013]]$mids,hists[[2013]]$counts,type='l',lwd=3,col=colors[1])\nlines(hists[[2014]]$mids,hists[[2014]]$counts,type='l',lwd=3,col=colors[2])\naxis(2,at=c(0,50,100))\nlegend('topright',c('2013','2014','2015'),text.col=colors,bty='n')\ndev.off()\n\nCairoPDF('steam_success_quality',width=4,height=4)\nplot(NA,NA,ylim=c(0,35),xlim=c(2.5,6.5),ylab=\"Number of Games\",\n xlab=\"log(Owned)\",main=paste(\"Success of good indie games\",sep=\"\"))\nfor(i in 1:3){\n y = years[i]\n # Need distributions of 'success' for good and bad games for both years\n good = log10(games$owners.numeric[games$owners.numeric>0 & games$release.year==y & games$rating>69])\n bad = log10(games$owners.numeric[games$owners.numeric>0 & games$release.year==y & games$rating<70])\n h.good = hist(good,seq(from=2.5,to=7,by=.25),plot=F)\n h.bad = hist(bad ,seq(from=2.5,to=7,by=.25),plot=F)\n ymax = max(c(h.good$counts,h.bad$counts))\n xlims = range(c(good,bad))\n lines(h.good$mids,h.good$counts,ylim=c(0,ymax),xlim=c(2.5,6.5),lwd=2,col=colors[i])\n #lines(h.bad$mids,h.bad$counts,lwd=2,lty=3,col=colors[i])\n}\n#legend('topright',c('good','others'),bty='n',text.col='gray',col='gray',lty=c(1,3))\nlegend('topleft',c('2013','2014','2015'),text.col=colors,bty='n')\ndev.off()\n\nCairoPDF(\"Indiepocolypse\",width=12,height=5)\npar(mfrow=c(1,2))\npostfixes = c('','(good games)')\nfor(i in 1:2){\n x = games$yearmonth[games$rating>minima[i]]\n y = log10(games$owners.numeric[games$rating>minima[i]])\n plot(x,y,pch=16,cex=.5,col=rgb(0,0,0,.3),ylim=c(2,7),\n main=paste(\"Success vs. release month\",postfixes[i]),xlab=\"Release Date\",ylab=\"log(Owners)\", xaxt='n')\n axis(1,at=c(1,13,25,31),label=c('2013','2014','2015','July'))\n medians = 1:31\n upperq = 1:31\n lowerq = 1:31\n total = 1:31\n for(j in 1:31){\n medians[j] = median(games$owners.numeric[games$yearmonth==j & games$rating>minima[i]])\n upperq[j] = quantile(games$owners.numeric[games$yearmonth==j & games$rating>minima[i]],.75)\n lowerq[j] = quantile(games$owners.numeric[games$yearmonth==j & games$rating>minima[i]],.25)\n total[j] = sum(games$owners.numeric[games$yearmonth==j & games$rating>minima[i]])\n }\n points(1:31,log10(medians),pch=16,cex=1,col='red')\n points(1:31,log10(upperq),pch=3,cex=1,col='red')\n points(1:31,log10(lowerq),pch=3,cex=1,col='red')\n points(1:31,log10(total),pch=15,cex=1,col='blue')\n fit = lm(y~x)\n slope = fit$coefficients[2]\n intercept = fit$coefficients[1]\n rsq = summary(fit)$r.squared\n #abline(b=slope,a=intercept)\n correlation = rcorr(x,y,'pearson')$r[1,2]\n pvalue = rcorr(x,y,'pearson')$P[1,2]\n legend('bottomleft',c(paste('pc =',round(correlation,3)),paste('-log(P) =',round(-log10(pvalue),2))),bty='n')\n}\n\ndev.off()\n", "meta": {"hexsha": "41ad487a8002b7d6d6d043406b7185a414eee920", "size": 6368, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis.r", "max_stars_repo_name": "adam-coster/indiepocalypse", "max_stars_repo_head_hexsha": "6fe8fefa7c74974f9b6157839cc2bda8ffe81e9d", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis.r", "max_issues_repo_name": "adam-coster/indiepocalypse", "max_issues_repo_head_hexsha": "6fe8fefa7c74974f9b6157839cc2bda8ffe81e9d", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis.r", "max_forks_repo_name": "adam-coster/indiepocalypse", "max_forks_repo_head_hexsha": "6fe8fefa7c74974f9b6157839cc2bda8ffe81e9d", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3037974684, "max_line_length": 134, "alphanum_fraction": 0.6604899497, "num_tokens": 2310, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3479791625167263}} {"text": "#####################################\n# Collection of functions to be used\n#####################################\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Logit and Inverse logit (for back-tranforming logit fits)\nlogit<-function(x){log(x/(1-x))}\nlogit_tr<-function(x){exp(x)/(1+exp(x))}\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Transparent fills\nalpha.col<-function(col,alpha){\n col<-as.vector(col2rgb(col)/255)\n rgb(col[1],col[2],col[3], alpha )\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Consistent letter positions for figure labels\nfig.let <- function(label, location=\"topleft\", x=NULL, y=NULL, \n offset=c(0, 0), ...) {\n if(length(label) > 1) {\n warning(\"length(label) > 1, using label[1]\")\n }\n if(is.null(x) | is.null(y)) {\n coords <- switch(location,\n topleft = c(0.015,0.98),\n topcenter = c(0.5525,0.98),\n topright = c(0.985, 0.98),\n bottomleft = c(0.015, 0.02), \n bottomcenter = c(0.5525, 0.02), \n bottomright = c(0.985, 0.02),\n c(0.015, 0.98) )\n } else {\n coords <- c(x,y)\n }\n this.x <- grconvertX(coords[1] + offset[1], from=\"nfc\", to=\"user\")\n this.y <- grconvertY(coords[2] + offset[2], from=\"nfc\", to=\"user\")\n text(labels=label[1], x=this.x, y=this.y, xpd=T, ...)\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Which elements are not in the vector\n'%!in%' <- function(x,y)!('%in%'(x,y))\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Are all elements non-NA\nNoNA<-function(x){!any(is.na(x))}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Are all elements NA\nAllNA<-function(x){all(is.na(x))}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ISOdate and ISOdatetime only work for specific combinations, so create own\ncoll<-function(x,sel,sep='-'){if(length(sel)>0){apply(x,1,paste0,collapse=sep)}}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Geometric mean\ngmean<-function(x){exp(mean(log(x[!is.infinite(x)]),na.rm=TRUE))}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Capitalize first letter of taxon names\nfirstup <- function(x) {substr(x, 1, 1) <- toupper(substr(x, 1, 1));x}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Fix taxon names\nFixNames<-function(dat){\n dat$taxon<-gsub(' ','_',dat$taxon)\n dat$taxon<-firstup(dat$taxon)\n dat<-subset(dat,taxon!='')\n dat$taxon<-word(dat$taxon,1,2,sep='_') # Remove subspecies names\n return(dat)\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Posterior sampling to get confidence interval for min to max range of a smooth term\nCIonDiff_postSamp <- function(fit, focal.term, subset=NULL, probs=c(0.025, 0.5, 0.975), samps=10000, plot.samps=FALSE){\n require(MASS)\n newdata <- fit$model[,-1]\n if(!is.null(subset)){\n newdata <- newdata[subset,]\n }\n if(nrow(newdata)<1000){\n newdata <- newdata[rep_len(seq_len(nrow(newdata)), 1000), ] # ensure there are at least 1000 points\n }\n # min and max of focal term\n foc.rng <- range(newdata[,focal.term])\n # 1,000 equally spaced points along focal term range.\n # The other covariates can be fixed to anything since BY ASSUMPTION, \n # they don't interact with the focal term\n newdata[,focal.term] <- seq(foc.rng[1],foc.rng[2],length=nrow(newdata))\n\n Xp <- predict(fit, newdata=newdata, type=\"lpmatrix\") # extract basis function etc. values at locations defined above\n Xp = Xp[,grep(focal.term, colnames(Xp))] # subset to only consider focal term\n \n beta <- coef(fit); beta = beta[grep(focal.term,names(beta))] # posterior mean vector for s(x1) parameters\n Vb <- vcov(fit); Vb = Vb[grep(focal.term,rownames(Vb)), grep(focal.term,colnames(Vb))] # posterior covar. matrix for s(x1) params.\n \n n <- samps # number of posterior samples to draw\n br <- mvrnorm(n,beta,Vb) ## simulate n rep coef vectors from posterior\n MinMaxRange <- rep(NA,n)\n for (i in 1:n) { ## loop to get trough to peak diff for each sim\n pred <- Xp %*% br[i,] ## curve for this replicate\n if(plot.samps){\n if(i == 1) plot(pred ~ newdata[,focal.term])\n if(i > 1 & i < 100) lines(pred ~ newdata[,focal.term])\n }\n MinMaxRange[i] <- max(pred)-min(pred) ## range for this curve\n }\n quantile(MinMaxRange, probs)\n}\n\n# CI_postSamp(fit, 'x1',plot.samps=T)\n# exp(CI_postSamp(fit, 'x1'))\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Gradient-filled polygon\nshaded.polygon <- function(x, y, se, col=c('blue','red'), n=500, breaks=NULL,horiz=TRUE) {\n y_high <- y + se\n y_low <- y - se\n\n if(horiz){\n if(!is.null(breaks)){\n x_ats <- breaks \n height <- unique(diff(breaks))\n }else{\n height <- diff(c(min(x),max(x)))/(n)\n x_ats <- seq(min(x), max(x), height)\n }\n pal <- if(!is.function(col)) colorRampPalette(col)(n) else col(n)[x_ats]\n # plot rectangles to simulate colour gradient\n sapply(seq_len(n),\n function(i) {\n rect(max(min(x), x_ats[i] - height), min(y_low), \n min(max(x), x_ats[i] + height), max(y_high), \n col=pal[i], border=NA)\n })\n }\n if(!horiz){\n if(!is.null(breaks)){\n y_ats <- breaks \n height <- unique(diff(breaks))\n }else{\n height <- diff(c(min(y),max(y)))/(n)\n y_ats <- seq(min(y), max(y), height)\n }\n pal <- if(!is.function(col)) colorRampPalette(col)(n) else col(n)[y_ats]\n # plot rectangles to simulate colour gradient\n sapply(seq_len(n-1),\n function(i) {\n rect(min(x), max(min(y_low), y_ats[i] - height), \n max(x), min(max(y_high), y_ats[i] + height), \n col=pal[i], border=NA)\n })\n }\n # plot white polygons representing the inverse of the area of interest\n e <- par(\"usr\")\n polygon(c(x[1], x, x[length(x)]),\n c(max(y_high), y_high, max(y_high)),\n col='white', border='white') \n polygon(c(x[1], x, x[length(x)]),\n c(min(y_low), y_low, min(y_low)),\n col='white', border='white') \n polygon(c(e[1], min(x), min(x), e[1]),\n c(e[3], e[3], e[4], e[4]),\n col='white', border='white') \n polygon(c(e[2], max(x), max(x), e[2]),\n c(e[3], e[3], e[4], e[4]),\n col='white', border='white') \n}\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# For violin plot\ndata_summary <- function(x) {\n n <- length(x)\n m <- mean(x,na.rm=T)\n if(n==1){ ymin<-ymax<-m} else{\n ymin <- m-sd(x,na.rm=T)\n ymax <- min(m+sd(x,na.rm=T),100,na.rm=T)\n }\n return(c(y=m,ymin=ymin,ymax=ymax))\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Multiple plot function\n#\n# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n# - cols: Number of columns in layout\n# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n#\n# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n# then plot 1 will go in the upper left, 2 will go in the upper right, and\n# 3 will go all the way across the bottom.\n#\nmultiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n library(grid)\n\n # Make a list from the ... arguments and plotlist\n plots <- c(list(...), plotlist)\n\n numPlots = length(plots)\n\n # If layout is NULL, then use 'cols' to determine layout\n if (is.null(layout)) {\n # Make the panel\n # ncol: Number of columns of plots\n # nrow: Number of rows needed, calculated from # of cols\n layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n ncol = cols, nrow = ceiling(numPlots/cols))\n }\n\n if (numPlots==1) {\n print(plots[[1]])\n\n } else {\n # Set up the page\n grid.newpage()\n pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n\n # Make each plot, in the correct location\n for (i in 1:numPlots) {\n # Get the i,j matrix positions of the regions that contain this subplot\n matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n\n print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n layout.pos.col = matchidx$col))\n }\n }\n}\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Following from\n# https://github.com/lukejharmon/traitathon/blob/master/EOLtraithack/traitbankSource.R\n\n## --------------------------------- ##\n## ##\n## TraitBank Source Code ##\n## Very Primitive API Interface ##\n## written by B.Banbury ##\n## 18 Sept 14 ##\n## ##\n## --------------------------------- ##\n\n# \n# require(Reol)\n# require(rjson)\n# require(RCurl)\n# require(ape)\n# \n# DownloadEOLtraits <- function (pages, to.file = TRUE, MyKey = NULL, verbose = TRUE, cache=2419200, ...) {\n# #this will download EOL trait data json either to file or to R console (user)\n# if (Sys.getlocale(\"LC_ALL\") == \"C\") \n# warning(\"Sys.getlocale is set to C. In order to read UTF characters, you need to set the locale aspect to UTF-8 using Sys.setlocale\")\n# EOLpages <- vector(\"list\", length = length(pages))\n# for (i in sequence(length(pages))) {\n# pageNum <- pages[i]\n# #http://eol.org/api/traits/328598?cache_ttl=2419200 #cache_ttl is the amount of seconds you are willing to wait\n# web <- paste(\"http://eol.org/api/traits/\", pageNum, \"?cache_ttl=\", cache, sep = \"\")\n# if (!is.null(MyKey)) \n# web <- paste(web, \"&key=\", MyKey, sep = \"\")\n# if (to.file) {\n# write(getURL(web, ...), file = paste(\"eol\", pages[i], \".xml\", sep = \"\"))\n# if (verbose) \n# print(paste(\"Downloaded \", \"eol\", pages[i], \".json\", sep = \"\"))\n# }\n# else {\n# EOLpages[[i]] <- getURL(web, ...)\n# names(EOLpages)[[i]] <- paste(\"eol\", pages[i], sep = \"\")\n# if (verbose) \n# print(paste(\"eol\", pages[i], \" saved as R object\", sep = \"\"))\n# }\n# Sys.sleep(1)\n# }\n# if (to.file) \n# return(paste(\"eol\", pages, \".xml\", sep = \"\"))\n# else return(EOLpages)\n# }\n# #whaledata <- DownloadEOLtraits(328574, to.file=FALSE) \n# \n# #whaleJSON <- rjson::fromJSON(file=\"~/eol328547.xml\") #files work\n# #whaleJSON <- rjson::fromJSON(json_str=unlist(whaledata)) #works\n# \n# \n# ReadJSON <- function(rawJSON){\n# #this will parse and unlist a json file from EOL (internal)\n# if(regexpr(\"^eol[0-9]+.xml$\", rawJSON) > 0) #it is a file\n# return(unlist(rjson::fromJSON(file=unlist(rawJSON))$`@graph`))\n# return(unlist(rjson::fromJSON(json_str=unlist(rawJSON))$`@graph`))\n# }\n# #ReadJSON(whaledata)\n# \n# \n# WhichTraits <- function(JSON){\n# #find which trait values are available for this JSON (user)\n# whichTraits <- NULL\n# for(i in sequence(length(JSON))){\n# j <- ReadJSON(JSON[[i]])\n# tt <- paste(j[which(names(j) == \"dwc:measurementType.rdfs:label.en\")])\n# whichTraits <- c(whichTraits, tt)\n# }\n# return(unique(whichTraits))\n# }\n# #WhichTraits(whaledata)\n# #WhichTraits(allwhales)\n# \n# \n# GetLocationInJSON <- function(traitOfInterest, JSON){\n# #this will find where in the json your trait is (internal)\n# if(any(traitOfInterest == WhichTraits(JSON))){\n# j <- ReadJSON(JSON)\n# placesOfInterest <- grep(traitOfInterest, j)\n# return(placesOfInterest)\n# }\n# return(\"NO matches\")\n# }\n# #GetLocationInJSON(\"body mass\", whaledata)\n# \n# \n# GetTaxonName <- function(JSON){\n# #this function will retrieve a taxon name from the JSON (internal)\n# j <- ReadJSON(JSON)\n# return((j[[grep(\"scientificName\", names(j))]]))\n# }\n# #GetTaxonName(whaledata)\n# \n# \n# GetDataforOne <- function(traitOfInterest, JSON){\n# #this will return a data frame with hacky trait info (internal)\n# #it will only work if the data is in the same order...we can/should fix this later \n# if(length(GetLocationInJSON(traitOfInterest, JSON)) > 0){\n# locs <- GetLocationInJSON(traitOfInterest, JSON)\n# if(locs[1] != \"NO matches\"){\n# datamat <- matrix(nrow=length(locs), ncol=5) #ncols hard coded...no likey\n# tax <- GetTaxonName(JSON)\n# j <- ReadJSON(JSON)\n# for(i in sequence(length(locs))){\n# datamat[i,] <- c(tax, j[locs[i]], j[locs[i]+2], j[locs[i]+3], j[locs[i]+5])\n# }\n# }\n# else\n# return(NULL)\n# }\n# return(as.data.frame(datamat, stringsAsFactors=FALSE))\n# }\n# #GetDataforOne(\"body mass\", whaledata)\n# #GetDataforOne(\"total life span\", whaledata)\n# \n# GetData <- function(traitOfInterest, JSON, chatty=FALSE){\n# #this function rbinds all the species pages together (user)\n# tot <- NULL\n# for(i in sequence(length(JSON))){\n# if(chatty)\n# print(paste(\"file\", i))\n# inddata <- GetDataforOne(traitOfInterest, JSON[[i]])\n# tot <- rbind(tot, inddata)\n# }\n# return(tot)\n# }\n# #GetData(\"body mass\", whaledata)\n# \n# # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "meta": {"hexsha": "31c4d5cf026e53a03465e3855fd51b7c89ec72af", "size": 13197, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/FracFeed-Functions.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/FracFeed-Functions.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/FracFeed-Functions.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8137535817, "max_line_length": 139, "alphanum_fraction": 0.5391376828, "num_tokens": 3713, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.3478862186402444}} {"text": "# Formatting the 2011 phenotype data: NAM and F1\n# Jinliang Yang\n# Jan 19th, 2012\n# log: change Genotype [. to \"\"]\n\nsetwd(\"~/Documents/Heterosis_GWAS/pheno2011\")\n\n#############################################################\n# Check the data completeness and correct the notes\n#Notes: Two replications in Curtiss and Johnson\n#Curtiss: 4651-6100\n#Johnson J1251-J2350\n\nfile1 <- read.csv(\"data/rawdata/LL_cob.test.csv\")\ndim(file1)\n#[1] 8773 10 | [1] 8286 11\n\nnam <- subset(file1, Note.y!=\"Diallel\" & Note.y!=\"NAM Filler\");\ndim(nam)\n#[1] 5806 10 | [1] 5467 11 | [1] 5828 8\nsummary(nam)\n\n###########################################################\n### Delete duplicated input and missing data\nbarcode <- nam[duplicated(nam$Barcode),]$Barcode\nnam[nam$Barcode%in%barcode,]\nlength(barcode)\n# Get ride of 2 duplicated records\nnam <- nam[!duplicated(nam$Barcode),]\n\n##########################################################\n### checking for outlyers\nnam$CL<- as.numeric(as.character(nam$CL));\nnam$CD <- as.numeric(as.character(nam$CD));\nnam$CW <- as.numeric(as.character(nam$CW));\n\nhist(nam$CL)\nhist(nam$CD)\nhist(nam$CW)\n\nhead(nam[order(nam$CL),])\ntail(nam[order(nam$CL),])\n\nidx <- which.max(nam$CW)\nnam <- nam[-idx,]\n\n#extreme long ear: removed\nnam <- nam[nam$Barcode!=\"11-5635-22 OP\",]\nnam <- nam[nam$Barcode!=\"11-5622-22 OP\",]\n\nnambackup <- nam\ndim(nam)\n#[1] 5801 10\n#######################################################\n\nidx0 <- grep(\" x B73\", nam$Genotype)\nsub1 <- nam[idx0,]\nsub1$Genotype <- gsub(\" x B73\", \"\", sub1$Genotype)\nsub1$Genotype <- paste(\"B73 x \", sub1$Genotype, sep=\"\")\n\nnam2011cob <- rbind(nam[-idx0,], sub1)\n\n#######################################################\n### BLUE\n\nnamcl <- BLUE(data=nam2011cob, model=CL~Genotype, random=~1|Farm, trait=\"CL\", intercept=\"B73 x Z001E0002\")\nnamcl$Genotype <- gsub(\"@\", \"\", namcl$Genotype)\n\nnamcd <- BLUE(data=nam2011cob, model=CD~Genotype, random=~1|Farm, trait=\"CD\", intercept=\"B73 x Z001E0002\")\nnamcd$Genotype <- gsub(\"@\", \"\", namcd$Genotype)\n\nnamcw <- BLUE(data=nam2011cob, model=CW~Genotype, random=~1|Farm, trait=\"CW\", intercept=\"B73 x Z001E0002\")\nnamcw$Genotype <- gsub(\"@\", \"\", namcw$Genotype)\n", "meta": {"hexsha": "0b281adbeecb9fd8fe0f12d7eb088bcf32c0ea0b", "size": 2168, "ext": "r", "lang": "R", "max_stars_repo_path": "profiling/pheno2011/03-B.data_NAMF1_cob.r", "max_stars_repo_name": "yangjl/Heterosis-GWAS", "max_stars_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-04-16T08:27:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-31T13:00:43.000Z", "max_issues_repo_path": "profiling/pheno2011/03-B.data_NAMF1_cob.r", "max_issues_repo_name": "yangjl/Heterosis-GWAS", "max_issues_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_issues_repo_licenses": ["RSA-MD"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "profiling/pheno2011/03-B.data_NAMF1_cob.r", "max_forks_repo_name": "yangjl/Heterosis-GWAS", "max_forks_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_forks_repo_licenses": ["RSA-MD"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-01-03T14:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-03T01:34:08.000Z", "avg_line_length": 29.2972972973, "max_line_length": 106, "alphanum_fraction": 0.5871771218, "num_tokens": 686, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3478862114842677}} {"text": "#!/usr/bin/env Rscript\n#autor: Joao Sollari Lopes\n#local: University of Reading, Reading, UK\n#Rversion: 3.2.3\n#criado: 01.03.2010\n#modificado: 20.11.2017\n\n# @arg data_file - file with summary of 'real' data (.trg)\n# @arg rej_file - file with rejection step results (.rej)\ncheck_fit <- function(data_file , rej_file){\n\n\t#import the .rej file\n\tabc.rej <- data.matrix(read.table(rej_file))\n\n\t#import the .trg files\n\ttarget <- data.matrix(read.table(data_file))\n\n\t#check target on summstats\n fname <- \"../results/check_fit.eps\"\n postscript(file=fname,width=7,height=7,colormodel=\"rgb\",horizontal=FALSE,onefile=FALSE,paper=\"special\")\n\toldpar <- par(mfrow=c(1,3),xaxt=\"n\",yaxt=\"n\",mar=c(2,2,2,2))\n\tnparams <- 6\n\tisstats <- 1\n\tfsstats <- 3\n\tfor(i in isstats:fsstats){\n\t\thist(abc.rej[,nparams+i],main=i)\n\t\tabline(v=target[i],lwd=2,lty=1,col=\"blue\")\n\t}\n\tpar(oldpar)\n\tdev.off()\n\tprint(\"done target check\")\n\n}\n\nargs = commandArgs(trailingOnly=TRUE)\ncheck_fit(args[1],args[2])\n", "meta": {"hexsha": "5a2edd4f1930af3de722422d0eda7740d742b716", "size": 988, "ext": "r", "lang": "R", "max_stars_repo_path": "practicals/practical4/bin/practical4_part2.r", "max_stars_repo_name": "jsollari/IABC2017", "max_stars_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "practicals/practical4/bin/practical4_part2.r", "max_issues_repo_name": "jsollari/IABC2017", "max_issues_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "practicals/practical4/bin/practical4_part2.r", "max_forks_repo_name": "jsollari/IABC2017", "max_forks_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7027027027, "max_line_length": 107, "alphanum_fraction": 0.6831983806, "num_tokens": 333, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.34786381905871405}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# set dataset \n\ndataset1 <- \"Singhal\"\ndataset2 <- \"SinghalOG\"\ndataset <- \"SinghalOG\"\n\nh <- \"TvS\"\n\n\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data and rename data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset1,\"/Calcs_\",dataset1,\".RData\", sep=''))\nmLBF1 <- mLBF\nmLGL1 <- mLGL\nrm(mLBF,mLGL)\n\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset2,\"/Calcs_\",dataset2,\".RData\", sep=''))\nmLBF2 <- mLBF\nmLGL2 <- mLGL\nrm(mLBF,mLGL)\n\n\n## Number of overlapping\nlength(intersect(mLBF1$Locus,mLBF2$Locus))\n\n# Remove any other from the larger dataset\nmLBF1 <- mLBF1[mLBF1$Locus %in% intersect(mLBF1$Locus,mLBF2$Locus),]\nmLBF2 <- mLBF2[mLBF2$Locus %in% intersect(mLBF2$Locus,mLBF1$Locus),]\n\nmLGL1 <- mLGL1[mLGL1$Locus %in% intersect(mLGL1$Locus,mLGL2$Locus),]\nmLGL2 <- mLGL2[mLGL2$Locus %in% intersect(mLGL2$Locus,mLGL1$Locus),]\n\n\n\n\nGL <- function(df.g,xval.g,xlablab.g,tic.g,lines.g,limit.g,cc.g,title.g){\n GL_hist <- ggplot(data=df.g, aes(x=xval.g)) + \n geom_histogram(binwidth = limit.g*0.01, alpha=1, position=\"dodge\", color=cc.g, fill=cc.g)+ \n theme_classic() + \n theme(plot.title = element_text(hjust = 0.5, size=16),\n axis.text = element_text(size=10, color=\"black\"),\n text = element_text(size=14),\n legend.title = element_text(size = 12),\n legend.text = element_text(size = 10)) +\n labs(y=\"Number of Loci\",x=xlablab.g) + \n ggtitle(title.g) +\n coord_cartesian(ylim=ymax, xlim = tic.g) +\n scale_x_continuous(breaks = c(0,tic.g)) +\n scale_y_continuous(breaks = ymax) + \n # geom_vline(xintercept=lines.g,color=c(\"black\"), linetype=\"dashed\", size=0.5) +\n geom_vline(xintercept=c(0),color=c(\"black\"), linetype=\"dashed\", size=0.2)\n return(GL_hist)\n}\n\n# Colors \ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"#C2DF23FF\"\n\n\n# you'll have to graph first, then reset this \nmaxloci <- 600\nymax <- seq(0,maxloci,100)\n\n# Set title and input data \ntitle.1 <- paste(dataset1,\" - \",h,sep=\"\") \ntitle.2 <- paste(dataset2,\" - \",h,sep=\"\") \ncc.1 <- color_TP\n# GL\n#df.1 <- mLGL1\n#df.2 <- mLGL2\n#xval.1 <- mLGL1$TvS\n#xval.2 <- mLGL2$TvS\n\n# GL\ndf.1 <- mLBF1\ndf.2 <- mLBF2\nxval.1 <- mLBF1$TvS\nxval.2 <- mLBF2$TvS\n\n\nxlablab.1 <- 'dGLS values'\n\nxlablab.1 <- '2ln(BF) values'\n\n# Get max min for graph to set x axis values\nlimit.1 <- 5 + round(max(abs(min(xval.1)),abs(max(xval.1))),-1)\nlimit.2 <- 5 + round(max(abs(min(xval.2)),abs(max(xval.2))),-1)\nmax(c(limit.1,limit.2))\nlimit.1 <- 210\ntic.1 <- seq(-limit.1,limit.1,20)\nlines.1 <- c(0.5,-0.5)\n\n#quartz()\nGL.1 <- GL(df.1,xval.1,xlablab.1,tic.1,lines.1,limit.1,cc.1,title.1)\nGL.2 <- GL(df.2,xval.2,xlablab.1,tic.1,lines.1,limit.1,cc.1,title.2)\n\nG.all <- ggarrange(GL.1,GL.2, ncol=1, nrow=2, align=\"v\")\n#quartz()\nG.all\n\n# For tox add hypoth you mentioned at the beginning \nggsave(paste(dataset,\"_histo_\",h,\"bf.pdf\",sep=\"\"), plot=G.all,width = 9, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n", "meta": {"hexsha": "eecebf7085a4d77d29a396410f1442c9e56b68e6", "size": 3064, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/PrelimScripts/Graphs_Histogram_multiData.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/PrelimScripts/Graphs_Histogram_multiData.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/PrelimScripts/Graphs_Histogram_multiData.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.6434782609, "max_line_length": 133, "alphanum_fraction": 0.6638381201, "num_tokens": 1163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3477776760270927}} {"text": "# CALAND.r\n\n# Copyright (c) 2016-2019, Alan Di Vittorio and Maegen Simmonds\n\n# This software and its associated input data are licensed under the 3-Clause BSD open source license\n# Please see license.txt for details\n\n# This is the carbon accounting model for California\n\n# CAlifornia natural and working LANDs carbon and greenhouse gas model \n\n############################################# Overview of `CALAND()`############################################# \n\n# The `CALAND()` function is the carbon and greenhouse gas accounting model. It uses the input files generated \n# by `write_caland_inputs()`. A single scenario file is simulated each time `CALAND()` is run, producing a \n# single main output .xls file that summarizes outputs for 214 variables including annual and cumulative metrics, \n# each in an individual worksheet. There is a suite of settings (arguments) with various options that you choose \n# when running `CALAND()`, such as which carbon values to use from the carbon inputs file (i.e., mean, mean+sd, \n# mean-sd, min, or max) and which level of forest non-regeneration to assume following high severity wildfire.\n \n# Model structure & order of operations:\n# This model follows basic density (stock) and flow guidelines similar to IPCC Tier 3 protocols by integrating \n# observed historical carbon flows (fluxes) and initial carbon densities with wildfire, climate effects, and \n# land cover change derived from external models.\n# Resolution is at the land category level (region-landtype-ownership combination), which only has a spatial \n# boundary for the intial simulation year. Beyond that, processes within each land category are implemented \n# on non-spatially explicit _areas_ within static region-ownership boundaries. In other words, there are \n# no grid-cell level computations. This highlights how `CALAND()` is not a stand-level model. \n# Carbon calculations occur in `start_year` up to `end_year - 1`\n# `end_year` denotes the final area after the changes in `end_year - 1 `\n# Initial carbon density input values are the univariate statistics of the total pixel population within each \n# land category (i.e., land type-region-ownership combination)\n# Carbon accumulation (flux) input values are univariate statistics of literature values for a given land type \n# (coarse resolution) or land category (fine resolution), depending on the availability of data\n# The initial land category area data are used for carbon operations in the first year \n# Annual ecosystem carbon fluxes are calculated for each land category using inputs for historic carbon fluxes \n# and mortality rates, scaled up to the annual input area data with adjustments made for (in order of \n# operation) climate, management, and wildfire. \n# Land conversion carbon fluxes for each land category are calculated after ecosystem carbon fluxes within the \n# same year \n# Each subsequent step uses the updated carbon values from the previous step \n# The new carbon density and area for each land category are assigned to the beginning of the next year \n# All wood products are lumped together and labeled as \"wood\" \n# Wood product emissions are based on landfill decay of discarded products\n# Bioenergy emissions are based on combustion of biomass feedstock by current California bioenergy plants\n \n############################################# Input files to `CALAND()`###########################################\n \n# The input .xls files for `CALAND()` are in caland/inputs/ (unless a sub-directory `indir` is specified \n# differently from the default of no subdirectory (`indir = \"\"`) (see Arguments section below). The following \n# Excel input files have matching number of header rows (rows preceding the first row of data): \n \n# Carbon input file: The initial carbon state (carbon densities) of the seven carbon pools, and all the carbon \n# flow parameters (fluxes and scalars) are in a .xls file in the inputs/ directory. This file is created by \n# `write_caland_inputs`, and it does not change unless the raw land carbon input file lc_params.xls is \n# modified. \n# `carbon_input_nwl.xls` is default filename, containing the initial carbon densities (mean, min, max, SD) \n# in seven carbon pools and the historical soil and vegetation carbon fluxes (mean, min, max, SD) for \n# each land category, the carbon adjustment parameters for wildfire, conversion of any land type to \n# Cultivated or Developed lands, and Forest, Developed, and Rangeland (Grassland, Savanna, Woodland) \n# management, and the soil carbon fluxes (mean, min, max, SD) in Cultivated lands under the \n# 'soil conservation' practice. \n \n# Scenario input file: The scenario that will be simulated.\n# `.xls` contains the initial areas of each land type per region-ownership combination \n# (i.e., land category), annual net area changes per land category; annual wildfire area per region-ownership \n# combination; annual mortality per land category; annual managed area per land category; and climate change \n# scalars for vegetation and soil carbon fluxes per land category. \n\n# Example scenario input files: There are five scenario input xls files in the caland/inputs/ directory \n# that were created using `write_caland_inputs()` for the Draft California 2030 Natural and Working Lands \n# Climate Change Implementation Plan (2019). These scenarios incorporate RCP8.5 climate effects on carbon \n# exchange and wildfire area. 'Default' in the filename means that the scenarios include doubled forest \n# mortality from 2015 to 2024 to emulate recent and ongoing die-off due to insects and drought. \n# - Historical Baseline Scenario: The NWL_Historical_v6_default_RCP85.xls file is the reference scenario \n# used to compare the changes in management that are prescribed in the following alternative scenarios. \n# It does not include any California State-funded management or increases in the forest fraction of urban \n# lands. Note that this scenario was intended to run in `CALAND()` with the setting for maximum \n# non-regeneration (i.e., forest conversion to shrubland) in forest areas burned by high-severity \n# wildfire. \n# - Alternative A Scenario: The NWL_Alt_A_v6_default_RCP85.xls file adds desired, *low levels* of \n# California State-funded management to the Historical Baseline Scenario. Note that agricultural \n# management is limited in scope, and only represents California Natural Resources Agency-funded areas \n# for soil conservation in cultivated lands. Note that this scenario was intended to run in `CALAND()` \n# with the setting for full regeneration post-wildfire to represent maximum reforestation of forest \n# areas that would not otherwise recover fully following high-severity wildfire. \n# - Alternative B Scenario: The NWL_Alt_B_v6_default_RCP85.xls file adds desired, *high levels* \n# of California State-funded management to the Historical Baseline Scenario. Note that agricultural \n# management is limited in scope, and only represents California Natural Resources Agency-funded \n# areas for soil conservation in cultivated lands. Note that this scenario was intended to run in \n# `CALAND()` with the setting for full regeneration post-wildfire to represent maximum reforestation \n# of forest areas that would not otherwise recover fully following high-severity wildfire. \n# - Alternative A Scenario without Avoided Conversion: The NWL_Alt_A_v6_NoAC_default_RCP85.xls file \n# mimics the *low levels* of state-funded management in Alternative A, but *without avoided conversion*. \n# - Alternative B Scenario without Avoided Conversion: The NWL_Alt_B_v6_NoAC_default_RCP85.xls file \n# mimics the *high levels* of state-funded management in Alternative B, but *without avoided conversion*.\n \n############################################# Arguments in `CALAND()`################################################\n\n# `CALAND()` has 15 arguments that control which input files and values are used, and how the model will operate for \n# each run. A single scenario is simulated at a time. At a minimum, you must specify the scenario filename each \n# time you run `CALAND()` and the other arguments will automatically be assigned default values explained here:\n \n# 1. `scen_file_arg`: Assigns the scenario .xls file; assumed to be in caland/inptus/`indir`. This is the only \n# argument that does not have a default value. Thus, it is required that you assign it. All other arguments will \n# be assigned default values unless you change them.\n# 2. `c_file_arg`: Assigns the carbon parameter input file; assumed to be in caland/inputs/`outdir`. If nothing is \n# specified, default is: `c_file_arg = \"carbon_input_nwl.xls\"`. \n# 3. `indir`: Assigns the directory in caland/inputs/ that contains `scen_file` and `c_file`; do not include \"/\" \n# character at the end. If nothing is specified, the default is: `indir = \"\"` meaning that it is located in \n# caland/inputs/.\n# 4. `outdir`: Assigns the directory in caland/outputs/ to save `CALAND()` output files; do not include \"/\" \n# character at the end. If nothing is specified, default is blank: `outdir = \"\"` meaning that output files \n# will be saved in caland/outputs/.\n# 5. `start_year`: Simulation begins at the beginning of this year. If nothing is specified, default is: \n# `start_year = 2010`. \n# 6. `end_year`: Simulation ends at the beginning of this year (so the simulation goes through the end of \n# end_year - 1). If nothing is specified, default is: `end_year = 2101`. \n# 7. `value_col_dens`: Selects which carbon density values to use; 5 = min, 6 = max, 7 = mean, 8 = std dev. \n# If nothing is specified, default is the mean: `value_col_dens = 7`. \n# 8. `value_col_accum`: Integer code identifying which soil and vegetation carbon flux values to use (i.e., \n# 5 = min, 6 = max, 7 = mean, 8 = std dev). If nothing is specified, default is the mean: \n# `value_col_accum = 7`. \n# 9. `value_col_soilcon`: Integer code identifying which soil carbon flux values to use for the cultivated \n# soil conservation practice (i.e., 6 = min, 7 = max, 8 = mean, 9 = std dev). If nothing is specified, \n# default is the mean: `value_col_soilcon = 8`. \n# 10. `ADD_dens`: For use with `value_col_dens = 8`; assign `ADD_dens = TRUE` to add the std dev to the \n# mean carbon density values, or assign `ADD_dens = FALSE` to subtract the std dev from the mean carbon \n# density values. If nothing is specified, default is addition: `ADD_dens = TRUE`. \n# 11. `ADD_accum`: For use with `value_col_accum = 8`; assign `ADD_accum = TRUE` to add the std dev to the \n# mean carbon flux values, or assign `ADD_accum = FALSE` to subtract the std dev from the mean carbon \n# flux values. If nothing is specified, default is addition: `ADD_accum = TRUE`. \n# 12. `ADD_soilcon`: For use with `value_col_soilcon = 9`; assign `ADD_soilcon = TRUE` to add the std dev \n# to the mean carbon flux value of the cultivated soil conservation practice, or assign \n# `ADD_soilcon = FALSE` to subtract the std dev from the mean carbon flux of the cultivated soil \n# conservation practice. If nothing is specified, default is addition: `ADD_soilcon = TRUE`. \n# 13. `NR_Dist`: For adjusting the amount of non-regenerating forest after high severity wildfire, use \n# -1 for full regeneration (i.e., no non-rengeration) or 120 for maximum non-regeneration, which is \n# the threshold distance (m) to the edge of a burn patch, beyond which the forest will not regenerate \n# and will convert to shrubland. The default is maximum non-regeneration: `NR_Dist = 120`. A shorter \n# distance increases non-regenerated area, and a longer distance decreases non-regenerated area.\n# 14. `WRITE_OUT_FILE`: Chooses whether to save the output file; `WRITE_OUT_FILE = TRUE` saves the output \n# file, and `WRITE_OUT_FILE = FALSE` does not save the output file. If nothing is specified, default \n# is to save: `WRITE_OUT_FILE = TRUE`. \n# 15. `blackC`: Chooses how the global warming potential (GWP) of black carbon is computed; `blackC = TRUE` \n# assigns a GWP of 900, and `blackC = FALSE` assigns a GWP of 1 (equivalent to CO2). If \n# nothing is specified, default is to treat black C the same as CO2: `blackC = FALSE`, which is \n# recommended as our current understanding is that black C does not behave like the main greenhouse \n# gases.\n \n############################################# Outputs from `CALAND()`###########################################\n\n# Two output files are written to caland/outputs/ (unless a sub-directory is specified differently from the default \n# `outdir = \"\"`). There are two outputs files: \n \n# (1) Main output .xls file: _output_.xls \n# - Sign (+/-) of output carbon values: carbon emissions versus land carbon uptake depends on the sign and \n# the varibale name: \n# - Carbon variables in which a negative value indicates carbon emissions and positive value indicates land \n# carbon uptake:\n# - variable names containing \"den\": a positive value indicates land carbon uptake.\n# - variable names containing \"Gain_C_stock\" but not \"Atmos\": a positive value indicates land carbon uptake. \t\n# - all other stock variables except those containing \"Loss_C_stock\" or \"Atmos\" \n# - Carbon variables in which a positive value indicates carbon emissions and negative value indicates land \n# carbon uptake: \n# - variable names containing \"CumCO2\", \"CumCH4eq\", CumBCeq\", \"AnnCO2\", \"AnnCH4eq\", AnnBCeq\"\n# - variable names containing \"Loss_C_stock\" \n# - variable names containing \"Atmos\"\n# - Precision: to the integer for ha, Mg C, and Mg C/ha \n# - Filename description: \"_output_\" is appended to the input scenario name, followed by a series of tags \n# that denote (i) which type of input value was used (mean, mean+/-sd, min, or max) for carbon density, \n# historical carbon fluxes, and the 'soil conservation' soil carbon flux on Cultivated lands; (ii) how black \n# carbon was accounted for; and (iii) the level of forest non-regeneration following high-intensity wildfire. \n# - Key for labeling of filename: \n# - D = carbon density \n# - A = historical carbon accumulation (flux) \n# - S = soil carbon accumulation under 'soil conservation' management \n# - No variable specified means that the same value type was used for the three variables \n# - sd = standard deviation \n# - mean, max, min are self explanatory \n# - `+` = add (applied to sd only) \n# - `-` = subtract (applied to sd only) \n# - BC1 = black carbon treated like CO2 \n# - BC900 = black carbon segregated as a greenhouse gas with a global warming potential of 900 \n# - NR = non-regeneration threshold distance [m] to edge of a high-severity burn patch, above which \n# forest will not regenerate (i.e., converts to shrubland) \n# (2) Log file output of soil carbon depletion: \n# _output__land_cats_depleted_of_soil_c_&_sum_neg_cleared-.csv \n# - Filename description: Same as above plus an aggregate sum of soil carbon depletion across all land categories at \n# the end of the filename.\n \n################################################# Start script ############################################################\n\n# this enables java to use up to 4GB of memory for reading and writing excel files\noptions(java.parameters = \"-Xmx4g\" )\n\n# Load all the required packages\n libs <- c( \"XLConnect\" )\n for( i in libs ) {\n if( !require( i, character.only=T ) ) {\n cat( \"Couldn't load\", i, \"\\n\" )\n stop( \"Use install.packages() to download this library\\nOr use the GUI Package Installer\\nInclude dependencies, and install it \n for local user if you do not have root access\\n\" )\n }\n library( i, character.only=T )\n}\n\n# Assign CALC.GWP() (1) finds out whether each table in df.list is CO2, CH4, or BCC \n# by reading the name of the table (i.e. element name in list)\n# and (2) converts gas columns to GWP accordingly\n\nCALC.GWP <- function(df, gwp_CO2, gwp_CH4, gwp_BC) { \n # get name of list of data frames \n name <- names(df)\n new.df <- df\n for (l in 1:length(name)) { #loop over each data frame\n #loop over each data frame and get the unique number of columns (different for annual and cumulative)\n # loop over the C emissions columns\n for (i in 5:ncol(df[[l]])) { \n # if not BCC and not CH4C\n if ((substr(name[[l]], nchar(name[[l]])-2, nchar(name[[l]]))) != \"BCC\" & \n (substr(name[[l]], nchar(name[[l]])-2, nchar(name[[l]]))) != \"H4C\") {\n # then it's CO2C, so covert C to CO2 according to molar mass ratio \n new.df[[l]][,i] <- df[[l]][,i] * (44.01/12.0107) * gwp_CO2 \n # otherwise if it's CH4C\n } else { if (substr(name[[l]], nchar(name[[l]])-2, nchar(name[[l]])) == \"H4C\") {\n # convert CH4-C to [Mg CO2-eq/ha/y]\n new.df[[l]][,i] <- df[[l]][,i] * (16.04/12.0107) * gwp_CH4\n # otherwise it's BCC, so convert to [Mg CO2-eq/ha/y]\n # multiplying by 1/0.6 based on assumption that 60% black C is C.\n } else { new.df[[l]][,i] <- df[[l]][,i] * (1/0.6) * gwp_BC } \n }\n }\n }\n return(new.df)\n}\n\n# asign function GET.NAMES() that produces a list of new names that drops the last 'C' in CO2C, CH4C, and BCC \n# and adds 'eq' for CH4 and BC \nGET.NAMES <- function(df, new.name) {\n name <- names(df)\n for (i in 1:length(name)) {\n if ((substr(name[[i]], nchar(name[[i]])-2, nchar(name[[i]]))) == \"BCC\" | (substr(name[[i]], nchar(name[[i]])-2, nchar(name[[i]]))) == \"H4C\") {\n new.name[[i]] <- substr(name[[i]], 1, nchar(name[[i]]) - 1)\n new.name[[i]] <- paste0(new.name[[i]], \"eq\")\n } else { new.name[[i]] <- substr(name[[i]], 1, nchar(name[[i]]) - 1) }\n }\n return(new.name)\n}\n\n# assign CALAND()\n\nCALAND <- function(scen_file_arg, c_file_arg = \"carbon_input_nwl.xls\", indir = \"\", outdir = \"\", start_year = 2010, end_year = 2101, value_col_dens = 7, ADD_dens = TRUE, value_col_accum = 7, ADD_accum = TRUE, value_col_soilcon=8, ADD_soilcon = TRUE, NR_Dist = 120, WRITE_OUT_FILE = TRUE, blackC = FALSE) {\n cat(\"Start CALAND at\", date(), \"\\n\")\n \n # output label for: value_col and ADD select which carbon density and accumulation values to use; see notes above\n ftag = c(\"\", \"\", \"\", \"\", \"min\", \"max\", \"mean\", \"sd\")\n # for soil cons flux values\n ftag_soilcon = c(\"\", \"\", \"\", \"\", \"\", \"min\", \"max\", \"mean\", \"sd\")\n \n inputdir = paste0(\"inputs/\", indir)\n if(substr(inputdir,nchar(inputdir), nchar(inputdir)) != \"/\") { inputdir = paste0(inputdir, \"/\") }\n outputdir = paste0(\"outputs/\", outdir)\n if(substr(outputdir,nchar(outputdir), nchar(outputdir)) != \"/\") { outputdir = paste0(outputdir, \"/\") }\n dir.create(outputdir, recursive=TRUE)\n \n # get scenario name as file name without extension\n scen_name = substr(scen_file_arg, 1, nchar(scen_file_arg) - 4)\n # add the directory to the scen_file_arg name and c_file_arg name\n scen_file = paste0(inputdir, scen_file_arg)\n c_file = paste0(inputdir, c_file_arg)\n \n # the start row of the tables is the same for all sheets\n start_row = 12\n \n # Several assumptions are contained between this line down to the output table lines\n # They shouldn't need to be changed for differenct scenarios, but they would be useful for testing sensitivity of the model\n # below the output tables and before the library load lines are names specific to columns in the input xls file\n \n # total area change threshold for round off error\n # also used for checking transitions\n tot_area_change_thresh = 1.0/1000000.0\n \n # this is used only for forest understory mortality\n # default mortality is 1%\n default_mort_frac = 0.01\n \n # this is the default fration of root carbon to above ground veg carbon\n default_below2above_frac = 0.20\n \n # default fraction of understory to above main c\n # this is for types not listed here: forest, savanna, woodland\n default_under_frac = 0.10\n \n # default fractions of dead material to assign to dead pools\n # this is for types not listed here: forest, savanna, woodland\n default_standdead_frac = 0.11\n default_downdead_frac = 0.23\n default_litter_frac = 0.66\n \n # forest component biomass fractions from jenkins et al. 2003\n leaffrac = 0.05\n barkfrac = 0.12\n branchfrac = 0.17\n stemfrac = 0.66\n \n # average half-life for all CA wood products (years) (stewart and nakamura 2012)\n wp_half_life = 52\n # weighted average CH4 correction factor for anerobic decomposition in landfills (IPCC waste model)\n MCF = 0.71\n # default CH4 gas fraction in landfills (ARB 2016 GHG inventoty technical support)\n landfill_gas_frac = 0.5\n # default CH4 collection efficiency in landfills (ARB 2016 GHG inventoty technical support)\n CE = 0.75\n # default CH4 destruction efficiency via C filter in landfills (ARB 2016 GHG inventoty technical support)\n DE_filter = 0.01\n # default CH4 collection efficiency in landfills via combustion/oxidation in landfills (ARB 2016 GHG inventoty technical support)\n DE_combust = 0.99\n # default CH4 oxidation factor in landfill cover (ARB 2016 GHG inventoty technical support)\n OX = 0.1\n \n #### set the number of years an ecosystem exchange benefit occurs due to management\n \n # rangeland depends on the repeat period for the defined compost amendments\n range_lowfreq_period = 30\n range_medfreq_period = 10\n \n # forest\n # vegetation studies indicate that carbon benefits of managment can continue for at least 35 years\n # use 20 years here, as suggested by the TAC; also note that the current input data are for a 10-year estimate\n # soil measurements indicate that benefits are not significant after 10 years, and are gone by 30 years\n # thus the input data currenlty show no soil c benefits\n # this is implicit for the fire severity reduction due to forest management because it is based on the benefited cumulative manage area\n forest_benefit_period = 20\n\n # urban forest\n # use the same value as forest\n urban_forest_benefit_period = 20\n\n # fire decay\n # temporal decay of newly dead material due to fire\n # bole and branch are set to recommended branch decay rate value from harmon et al 1987 to ensure more realistic rapid loss\n # for decay rate = 0.09, in 10 years 59% of the material has decayed\n # also, stand dead may include varied fallen material in this case; using these two pools because the transfer pathways already exist\n # allow decay for 50 years to vent 99% of material\n fire_bole_decay_rate = 0.09\n fire_branch_decay_rate = 0.09\n fire_decay_years = c(1:50)\n standdead_decay_frac = c(1,exp(-fire_bole_decay_rate * fire_decay_years)[fire_decay_years[-length(fire_decay_years)]]) - exp(-fire_bole_decay_rate * fire_decay_years)\n downdead_decay_frac = c(1,exp(-fire_branch_decay_rate * fire_decay_years)[fire_decay_years[-length(fire_decay_years)]]) - exp(-fire_branch_decay_rate * fire_decay_years)\n \n ######### Determine output file names based on arguments in CALAND() that specify which input statistics are used for c density ######### \n ############################ and c accumulation (baseline and cultivated soil conservation) ############################################\n \n # first check to make sure that the val cols are valid (min, max, mean, std dev)\n #\t\tso the numbers have to be 5 - 8 for dens or 6 - 9 for soil conservation\n if ( value_col_dens < 5 | value_col_dens > 8 | value_col_accum < 5 | value_col_accum > 8 | value_col_soilcon < 6 | value_col_soilcon > 9) {\n \tcat( \"Invalid value column for value_col_dens, value_col_accum, or value_col_soilcon\\n\" )\n stop( \"Please make sure that all of these arguemnts are one of the following:\\nmin (5), max (6), mean (7), std_dev (8)\\n\" )\n }\n \n # if c density and c accumulation use _same_ input statisitic: they're both either min (5), max (6), mean (7), std_dev (8)\n if (value_col_dens == value_col_accum) {\n # same but not std_dev\n if (value_col_dens != 8) {\n # out_file is labeled by the statisitic without specifying density or accumulation\n out_file = paste0(outputdir, scen_name, \"_output_\", ftag[value_col_dens], \".xls\")\n # otherwise they are both std_dev so attach \"add\" or \"sub\"\n } else { \n # same sign\n if(ADD_dens == ADD_accum) {\n # both std_dev add\n if (ADD_dens) { \n out_file = paste0(outputdir, scen_name, \"_output_D+\" , ftag[value_col_dens], \"_A+\", ftag[value_col_dens], \".xls\")\n } else { \n # both std_dev subtract\n out_file = paste0(outputdir, scen_name, \"_output_D-\" , ftag[value_col_dens], \"_A-\", ftag[value_col_dens], \".xls\") }\n # otherwise both std_dev but different sign\n } else { \n # dens add and accum subtract\n if (ADD_dens) {\n out_file = paste0(outputdir, scen_name, \"_output_D+\" , ftag[value_col_dens], \"_A-\", ftag[value_col_accum], \".xls\")\n } else { \n # dens sub and accum add\n out_file = paste0(outputdir, scen_name, \"_output_D-\" , ftag[value_col_dens], \"_A+\", ftag[value_col_accum], \".xls\") \n } # end else sub\n } # end else not same sign\n } # end else value_col_dens == 8 (std_dev)\n # otherwise they are _not_ same statisitic\n } else { \n # first case: neither is std_dev\n if (value_col_dens != 8 & value_col_accum != 8) {\n out_file = paste0(outputdir, scen_name, \"_output_D=\" , ftag[value_col_dens], \"_A=\", ftag[value_col_accum], \".xls\")\n # second case, one is std_dev and accum is something else\n } else {\n # std_dev dens and accum something else\n if (value_col_dens == 8) {\n # std_dev add dens\n if (ADD_dens) { \n out_file = paste0(outputdir, scen_name, \"_output_D+\", ftag[value_col_dens], \"_A=\", ftag[value_col_accum], \".xls\")\n # std_dev sub dens\n } else { \n out_file = paste0(outputdir, scen_name, \"_output_D-\", ftag[value_col_dens], \"_A=\", ftag[value_col_accum], \".xls\")\n }\n # third case, std_dev accum and dens something else \n } else { \n if (value_col_accum == 8) { \n # label them each \n # std_dev add accum\n if (ADD_accum) { \n out_file = paste0(outputdir, scen_name, \"_output_D=\", ftag[value_col_dens], \"_A+\", ftag[value_col_accum], \".xls\")\n # std_dev sub accum\n } else { \n out_file = paste0(outputdir, scen_name, \"_output_D=\", ftag[value_col_dens], \"_A-\", ftag[value_col_accum], \".xls\")\n } # end else sub accum\n } # end if accum is std dev\n } # end else accum is std dev\n } # end else one val col is 8 (std_dev)\n } # end else dens and accum have different stat\n \n # paste extended name to output file to indicate which statistic is used for soil conservation flux\n if (ftag[value_col_dens] == \"mean\" & ftag[value_col_accum] == \"mean\" & ftag_soilcon[value_col_soilcon] == \"mean\") {\n # do nothing, filename stays the same: outputs/[scen_name]_output_mean.xls\n } else {\n # otherwise subtract \".xls\" and replace with [mean, add, or sub]_soilcon.xls\n delete.xls <- \".xls\"\n if (value_col_soilcon == 8) {\n # mean soil conservation\n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_S=mean.xls\")\n } else if(value_col_soilcon == 6) {\n \t# min soil conservation\n \tout_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_S=min.xls\")\n } else if(value_col_soilcon == 7) {\n \t# max soil conservation\n \tout_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_S=max.xls\")\n\t} else {\n if (ADD_soilcon) {\n # plus sd soil conservation\n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_S+sd.xls\")\n } else {\n # sub sd soil conservation\n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_S-sd.xls\")\n }\n }\n }\n \n # paste extended name to output file to indicate if BC GWP is 1 or 900\n if (blackC == TRUE) {\n # subtract \".xls\" and replace with GWP \n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_BC900.xls\")\n } else {\n # subtract \".xls\" and replace with GWP\n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_BC1.xls\")\n }\n \n # paste extended name to output file to indicate non-regeneration\n if (NR_Dist >0) {\n # subtract \".xls\" and replace with non-regen tag and distance to non-regen in meters\n out_file = paste0(substr(out_file,1,nchar(out_file)-4), \"_NR\", NR_Dist, \".xls\")\n }\n \n #########################################################################################################################################\n \n # assign 100 yr global warming potential of CO2, CH4, and black C (BC)\n gwp_CO2 <- 1\n gwp_CH4 <- 25\n # GWP of black C can be turned on/off as argument to caland\n if (blackC == TRUE) {\n gwp_BC <- 900\n } else {\n # otherwise BC is treated like CO2 with 12.0107 g of C per 44.01 g of CO2, with a GWP of 1. Multiplying by 0.6 to cancel out the ratio of C to BC (1/0.6) \n # further down in script\n gwp_BC <- 0.6 * (44.01/12.0107)\n }\n \n # assign fractions of soil c accumulation that is CO2-C and CH4-C in fresh marsh \n marsh_CO2_C_frac <- -1.14\n marsh_CH4_C_frac <- 0.14\n \n # assign emissions fractions of total C emissions for all fires (Jenkins et al 1996)\n CO2C_fire_frac <- 0.9952\n CH4C_fire_frac <- 0.0021\n BCC_fire_frac <- 0.0027\n \n # assign emissions fractions of total C emissions for all bioenergy (Dabdub et al 2015)\n CO2C_energy_frac <- 0.9994\n CH4C_energy_frac <- 0.0001\n BCC_energy_frac <- 0.0005\n \n ####### assign burned fraction of 2Atmos fluxes due to management activities #######\n \n # do lit search regarding slash burning in logging and thinning practices to get fractions below:\n # forest clearcut and above-main removed to atmos\n # clrcut_mainremoved_burn <- 0.25\n # forest partial_cut and above-main removed to atmos\n # parcut_mainremoved_burn <- 0.25\n # forest Thinning and above-main removed to atmos\n # thin_mainremoved_burn <- 0.25\n # forest Prescribed_burn and above-main removed to atmos (currently above2atmos is 0; only understory, litter and down dead go to atmos)\n # prescrburn_mainremoved_burn <- 1\n # forest Weed_treatment and above-main removed to atmos\n # weedtrt_mainremoved_burn <- 0.25\n # forest clearcut and understory to atmos\n # clrcut_under_burn <- 0.25\n # forest partial_cut and understory to atmos\n # parcut_under_burn <- 0.25\n # forest Thinning and understory to atmos\n # thin_under_burn <- 0.25\n # forest Prescribed_burn and understory to atmos\n # prescrburn_under_burn <- 1\n # forest weed_treatment and understory removed to atmos\n # weedtrt_under_burn <- 0.25\n # forest clearcut and down dead to atmos\n # clrcut_down_burn <- 0.25\n # forest partial_cut and down dead to atmos\n # parcut_down_burn <- 0.25\n # forest Thinning and down dead to atmos\n # thin_down_burn <- 0.25\n # forest Prescribed_burn and down dead to atmos\n # prescrburn_down_burn <- 1\n # forest Weed_treatment and down dead removed to atmos\n # weedtrt_down_burn <- 0.25\n # forest clearcut and litter to atmos\n # clrcut_litter_burn <- 0.25\n # forest partial_cut and litter to atmos\n # parcut_litter_burn <- 0.25\n # forest Thinning and litter to atmos\n # thin_litter_burn <- 0.25\n # forest Prescribed_burn and litter to atmos\n # prescrburn_litter_burn <- 1\n # forest weed_treatment and litter removed to atmos\n # weedtrt_litter_burn <- 0.25 \n \n # output tables\n out_area_sheets = c(\"Area\", \"Managed_area\", \"Wildfire_area\")\n num_out_area_sheets = length(out_area_sheets)\n out_density_sheets = c(\"All_orgC_den\", \"All_biomass_C_den\", \"Above_main_C_den\", \"Below_main_C_den\", \"Understory_C_den\", \"StandDead_C_den\", \n \"DownDead_C_den\", \"Litter_C_den\", \"Soil_orgC_den\")\n num_out_density_sheets = length(out_density_sheets)\n out_stock_sheets = c(\"All_orgC_stock\", \"All_biomass_C_stock\", \"Above_main_C_stock\", \"Below_main_C_stock\", \"Understory_C_stock\", \n \"StandDead_C_stock\", \"DownDead_C_stock\", \"Litter_C_stock\", \"Soil_orgC_stock\")\n num_out_stock_sheets = length(out_stock_sheets)\n out_atmos_sheets = c(\"Eco_CumGain_C_stock\", \"Total_Atmos_CumGain_C_stock\", \"Manage_Atmos_CumGain_C_stock\", \"Fire_Atmos_CumGain_C_stock\", \n \"LCC_Atmos_CumGain_C_stock\", \"Wood_Atmos_CumGain_C_stock\", \"Total_Energy2Atmos_C_stock\", \"Eco_AnnGain_C_stock\", \n \"Total_Atmos_AnnGain_C_stock\", \"Manage_Atmos_AnnGain_C_stock\", \"Fire_Atmos_AnnGain_C_stock\", \n \"LCC_Atmos_AnnGain_C_stock\", \"Wood_Atmos_AnnGain_C_stock\", \"Total_AnnEnergy2Atmos_C_stock\", \n \"Manage_Atmos_CumGain_FireC\", \"Manage_Atmos_CumGain_TotEnergyC\", \"Man_Atmos_CumGain_Harv2EnergyC\",\n \"Man_Atmos_CumGain_Slash2EnergyC\", \"Manage_Atmos_CumGain_NonBurnedC\",\"Fire_Atmos_CumGain_BurnedC\", \n \"Fire_Atmos_CumGain_NonBurnedC\", \"LCC_Atmos_CumGain_FireC\", \"LCC_Atmos_CumGain_TotEnergyC\", \n \"LCC_Atmos_CumGain_Harv2EnergyC\", \"LCC_Atmos_CumGain_Slash2EnergyC\", \"LCC_Atmos_CumGain_NonBurnedC\", \n \"Manage_Atmos_AnnGain_FireC\", \"Manage_Atmos_AnnGain_TotEnergyC\", \"Man_Atmos_AnnGain_Harv2EnergyC\",\n \"Man_Atmos_AnnGain_Slash2EnergyC\", \"Manage_Atmos_AnnGain_NonBurnedC\", \"Fire_Atmos_AnnGain_BurnedC\", \n \"Fire_Atmos_AnnGain_NonBurnedC\", \"LCC_Atmos_AnnGain_FireC\", \"LCC_Atmos_AnnGain_TotEnergyC\", \n \"LCC_Atmos_AnnGain_Harv2EnergyC\", \"LCC_Atmos_AnnGain_Slash2EnergyC\", \"LCC_Atmos_AnnGain_NonBurnedC\",\n \"Man_Atmos_AnnGain_SawmillDecayC\", \"Man_Atmos_AnnGain_InFrstDecayC\", \"Man_Atmos_CumGain_SawmillDecayC\", \n \"Man_Atmos_CumGain_InFrstDecayC\", \"LCC_Atmos_AnnGain_SawmillDecayC\", \"LCC_Atmos_AnnGain_OnSiteDecayC\", \n \"LCC_Atmos_CumGain_SawmillDecayC\", \"LCC_Atmos_CumGain_OnSiteDecayC\" )\n num_out_atmos_sheets = length(out_atmos_sheets)\n out_wood_sheets = c(\"Total_Wood_C_stock\", \"Total_Wood_CumGain_C_stock\", \"Total_Wood_CumLoss_C_stock\", \"Total_Wood_AnnGain_C_stock\", \n \"Total_Wood_AnnLoss_C_stock\", \"Manage_Wood_C_stock\", \"Manage_Wood_CumGain_C_stock\", \"Man_Harv2Wood_CumGain_C_stock\",\n \"Man_Slash2Wood_CumGain_C_stock\", \"Manage_Wood_CumLoss_C_stock\", \"Manage_Wood_AnnGain_C_stock\", \n \"Man_Harv2Wood_AnnGain_C_stock\", \"Man_Slash2Wood_AnnGain_C_stock\", \"Manage_Wood_AnnLoss_C_stock\", \n \"LCC_Wood_C_stock\", \"LCC_Wood_CumGain_C_stock\", \"LCC_Harv2Wood_CumGain_C_stock\", \"LCC_Slash2Wood_CumGain_C_stock\",\n \"LCC_Wood_CumLoss_C_stock\", \"LCC_Wood_AnnGain_C_stock\", \"LCC_Harv2Wood_AnnGain_C_stock\", \"LCC_Slash2Wood_AnnGain_C_stock\",\n \"LCC_Wood_AnnLoss_C_stock\")\n num_out_wood_sheets = length(out_wood_sheets)\n \n # column names from the management table to calculate non-accum manage carbon adjustments\n man_frac_names = c(\"Above_harvested_frac\", \"StandDead_harvested_frac\", \"Harvested2Wood_frac\", \"Harvested2Energy_frac\", \"Harvested2SawmillDecay_frac\", \n \"Harvested2Slash_frac\", \"Under2Slash_frac\", \"DownDead2Slash_frac\", \"Litter2Slash_frac\", \"Slash2Energy_frac\", \"Slash2Wood_frac\",\n \"Slash2Burn_frac\", \"Slash2Decay_frac\", \"Under2DownDead_frac\", \"Soil2Atmos_frac\", \"Above2StandDead_frac\", \"Below2Atmos_frac\", \"Below2Soil_frac\")\n #man_frac_names = c(\"Above_removed_frac\", \"StandDead_removed_frac\", \"Removed2Wood_frac\", \"Removed2Energy_frac\", \"Removed2Atmos_frac\", \n # \"Understory2Atmos_frac\", \"DownDead2Atmos_frac\", \"Litter2Atmos_frac\", \"Soil2Atmos_frac\", \"Understory2DownDead_frac\", \n # \"Above2StandDead_frac\", \"Below2Atmos_frac\", \"Below2Soil_frac\")\n\n num_manfrac_cols = length(man_frac_names)\n # new c trans column names matching the non-accum manage frac names\n c_trans_names = c(\"Above_harvested_c\", \"StandDead_harvested_c\", \"Harvested2Wood_c\", \"Harvested2Energy_c\", \"Harvested2SawmillDecay_c\", \"Harvested2Slash_c\",\n \"Under2Slash_c\", \"DownDead2Slash_c\", \"Litter2Slash_c\", \"Slash2Energy_c\", \"Slash2Wood_c\", \"Slash2Burn_c\", \"Slash2Decay_c\", \"Under2DownDead_c\", \n \"Soil2Atmos_c\", \"Above2StandDead_c\", \"Below2Atmos_c\", \"Below2Soil_c\")\n #c_trans_names = c(\"Above_removed_c\", \"StandDead_removed_c\", \"Removed2Wood_c\", \"Removed2Energy_c\", \"Removed2Atmos_c\", \"Understory2Atmos_c\", \n # \"DownDead2Atmos_c\", \"Litter2Atmos_c\", \"Soil2Atmos_c\", \"Understory2DownDead_c\", \"Above2StandDead_c\", \"Below2Atmos_c\", \n # \"Below2Soil_c\")\n # indices of the appropriate density source df for the non-accum manage frac to c calcs; corresponds with out_density_sheets above\n # value == -1 indicates that the source is the harvested c; take the sum of the first two c trans columns\n # value == -2 indicates that the source is all slash-contributing pools; take the sum of c trans columns 6, 7 and 8 (\"Under2Slash_c\", \"DownDead2Slash_c\", \"Litter2Slash_c\")\n manage_density_inds = c(3, 6, -1, -1, -1, -1, 5, 7, 8, -2, -2, -2, -2, 5, 9, 3, 4, 4)\n # manage_density_inds = c(3, 6, -1, -1, -1, 5, 7, 8, 9, 5, 3, 4, 4)\n #manage_density_inds = c(3 (above), 6 (standdead), -1, -1, -1, 5 (under), 7 (down), 8 (litter), -2 (slash2energy), -2 (slash2wood), -2 (slash2burn), -2 (slash2decay), 5 (under2down),\n # 9 (soil), 3 (above2stand), 4 (below), 4 (below))\n # out_density_sheets = c(\"All_orgC_den\" (1), \"All_biomass_C_den\" (2), \"Above_main_C_den\" (3), \"Below_main_C_den\" (4), \"Understory_C_den\" (5), \"StandDead_C_den\" (6), \n # \"DownDead_C_den\" (7), \"Litter_C_den\" (8), \"Soil_orgC_den\" (9))\n \n # column names from the fire table to calculate fire carbon adjustments\n fire_frac_names = c(\"Above2Atmos_frac\", \"StandDead2Atmos_frac\", \"Understory2Atmos_frac\", \"DownDead2Atmos_frac\", \"Litter2Atmos_frac\", \n \"Above2StandDead_frac\", \"Understory2DownDead_frac\", \"Below2Atmos_frac\", \"Soil2Atmos_frac\")\n num_firefrac_cols = length(fire_frac_names)\n # new c trans column names matching the fire frac names\n firec_trans_names = c(\"Above2Atmos_c\", \"StandDead2Atmos_c\", \"Understory2Atmos_c\", \"DownDead2Atmos_c\", \"Litter2Atmos_c\", \"Above2StandDead_c\", \n \"Understory2DownDead_c\", \"Below2Atmos_c\", \"Soil2Atmos_c\")\n # indices of the appropriate density source df for the fire frac to c calcs; corresponds with out_density_sheets above\n fire_density_inds = c(3, 6, 5, 7, 8, 3, 5, 4, 9)\n \n # column names from the conversion to ag/urban table to calculate conversion to ag/urban carbon adjustments\n conv_frac_names = c(\"Above_harvested_conv_frac\", \"StandDead_harvested_conv_frac\", \"Harvested2Wood_conv_frac\", \"Harvested2Energy_conv_frac\", \n \"Harvested2SawmillDecay_conv_frac\", \"Harvested2Slash_conv_frac\", \"Under2Slash_conv_frac\", \"DownDead2Slash_conv_frac\", \n \"Litter2Slash_conv_frac\", \"Slash2Energy_conv_frac\", \"Slash2Wood_conv_frac\", \"Slash2Burn_conv_frac\", \"Slash2Decay_conv_frac\", \n \"Under2DownDead_conv_frac\", \"Soil2Atmos_conv_frac\", \"Below2Atmos_conv_frac\", \"Below2Soil_conv_frac\")\n num_convfrac_cols = length(conv_frac_names)\n # new c trans column names matching the conversion frac names\n convc_trans_names = c(\"Above_harvested_conv_c\", \"StandDead_harvested_conv_c\", \"Harvested2Wood_conv_c\", \"Harvested2Energy_conv_c\", \n \"Harvested2SawmillDecay_conv_c\", \"Harvested2Slash_conv_c\", \"Under2Slash_conv_c\", \"DownDead2Slash_conv_c\", \n \"Litter2Slash_conv_c\", \"Slash2Energy_conv_c\", \"Slash2Wood_conv_c\", \"Slash2Burn_conv_c\", \"Slash2Decay_conv_c\", \n \"Under2DownDead_conv_c\", \"Soil2Atmos_conv_c\", \"Below2Atmos_conv_c\", \"Below2Soil_conv_c\")\n # indices of the appropriate density source df for the conversion frac to c calcs; corresponds with out_density_sheets above\n # value == -1 indicates that the source is the removed c; take the sum of the first two c trans columns\n conv_density_inds = c(3, 6, -1, -1, -1, -1, 5, 7, 8, -2, -2, -2, -2, 5, 9, 4, 4)\n \n # Load the input files\n c_wrkbk = loadWorkbook(c_file)\n scen_wrkbk = loadWorkbook(scen_file)\n \n # worksheet/table names\n c_sheets = getSheets(c_wrkbk)\n num_c_sheets = length(c_sheets)\n scen_sheets = getSheets(scen_wrkbk)\n num_scen_sheets = length(scen_sheets)\n \n # NA values need to be converted to numeric\n # the warnings thrown by readWorksheet below are ok because they just state that the NA string can't be converted a number so it is \n # converted to NA value\n c_col_types1 = c(\"numeric\", \"character\", \"character\", \"character\", rep(\"numeric\",100))\n c_col_types2 = c(\"numeric\", \"character\", \"character\", \"character\", \"character\", rep(\"numeric\",100))\n c_col_types3 = c(\"character\", rep(\"numeric\",100))\n \n # Load the worksheets into a list of data frames\n c_df_list <- list()\n scen_df_list <- list()\n for (i in 1:12) { # through conversion2ag_urban\n c_df_list[[i]] <- readWorksheet(c_wrkbk, i, startRow = start_row, colTypes = c_col_types1, forceConversion = TRUE)\n }\n for (i in 13:16) { # forest_manage to ag_manage\n c_df_list[[i]] <- readWorksheet(c_wrkbk, i, startRow = start_row, colTypes = c_col_types2, forceConversion = TRUE)\n }\n for (i in 17:17) { # wildfire\n c_df_list[[i]] <- readWorksheet(c_wrkbk, i, startRow = start_row, colTypes = c_col_types3, forceConversion = TRUE)\n }\n for (i in 1:2) { # annual change and initial area\n scen_df_list[[i]] <- readWorksheet(scen_wrkbk, i, startRow = start_row, colTypes = c_col_types1, forceConversion = TRUE)\n }\n for (i in 3:4) { # annual managed area and annual wildfire area\n scen_df_list[[i]] <- readWorksheet(scen_wrkbk, i, startRow = start_row, colTypes = c_col_types2, forceConversion = TRUE)\n }\n \n # Check that all management areas in scen_df_list[[3]] have corresponding initial 2010 areas scen_df_list[[1]] \n # get all category ID's for the managed areas and initial areas\n cat_ID_man <- scen_df_list[[3]][,1]\n cat_ID_exist <- scen_df_list[[1]][,1]\n if (any(!((cat_ID_man) %in% cat_ID_exist))) {\n stop(\"Error: each land category in the management area table must exist in the initial area table\")\n }\n \n # Check that all slash2..._frac add to 1\n columns_1_check <- c_df_list[[12]][,c(\"Slash2Energy_conv_frac\", \"Slash2Burn_conv_frac\", \"Slash2Wood_conv_frac\", \"Slash2Decay_conv_frac\")]\n sums <- rowSums(columns_1_check)\n if (any(((sums)!= 1))) {\n stop(\"Error: sum of slash pathways (frac) for each land category in the conversion2ag_urban table must equal 1\")\n }\n columns_2_check <- c_df_list[[13]][,c(\"Slash2Energy_frac\", \"Slash2Burn_frac\", \"Slash2Wood_frac\", \"Slash2Decay_frac\")]\n sums <- rowSums(columns_2_check)\n if (any((sums!= 1) & (sums!= 0))) {\n stop(\"Error: sum of slash pathways (frac) for each land category in the forest_manage table must equal 1 or 0\")\n }\n columns_3_check <- c_df_list[[14]][,c(\"Slash2Energy_frac\", \"Slash2Burn_frac\", \"Slash2Wood_frac\", \"Slash2Decay_frac\")]\n sums <- rowSums(columns_3_check)\n if (any(((sums)!= 1) & ((sums)!= 0))) {\n stop(\"Error: sum of slash pathways (frac) for each land category in the dev_manage table must equal 1\")\n }\n \n for (i in 5:5) { # annual mortality fraction\n scen_df_list[[i]] <- readWorksheet(scen_wrkbk, i, startRow = start_row, colTypes = c_col_types1, forceConversion = TRUE)\n }\n for (i in 6:7) { # annual climate effects on veg and soil C fluxes, respectively\n scen_df_list[[i]] <- readWorksheet(scen_wrkbk, i, startRow = start_row, colTypes = c_col_types1, forceConversion = TRUE)\n }\n names(c_df_list) <- c_sheets\n names(scen_df_list) <- scen_sheets\n \n # remove the Xs added to the front of the year columns, and get the years as numbers\n # first check if there are any prescribed management practices \n if (nrow(scen_df_list[[3]])>0) {\n man_targetyear_labels = names(scen_df_list[[3]])[c(6:ncol(scen_df_list[[3]]))]\n man_targetyear_labels = substr(man_targetyear_labels,2,nchar(man_targetyear_labels[1]))\n names(scen_df_list[[3]])[c(6:ncol(scen_df_list[[3]]))] = man_targetyear_labels\n man_targetyears = as.integer(substr(man_targetyear_labels,1,4))\n }\n \n fire_targetyear_labels = names(scen_df_list[[4]])[c(6:ncol(scen_df_list[[4]]))]\n fire_targetyear_labels = substr(fire_targetyear_labels,2,nchar(fire_targetyear_labels[1]))\n names(scen_df_list[[4]])[c(6:ncol(scen_df_list[[4]]))] = fire_targetyear_labels\n fire_targetyears = as.integer(substr(fire_targetyear_labels,1,4))\n \n mortality_targetyear_labels = names(scen_df_list[[5]])[c(5:ncol(scen_df_list[[5]]))]\n mortality_targetyear_labels = substr(mortality_targetyear_labels,2,nchar(mortality_targetyear_labels[1]))\n names(scen_df_list[[5]])[c(5:ncol(scen_df_list[[5]]))] = mortality_targetyear_labels\n mortality_targetyears = as.integer(substr(mortality_targetyear_labels,1,4))\n \n veg_clim_targetyear_labels = names(scen_df_list[[6]])[c(5:ncol(scen_df_list[[6]]))]\n # subtract 'X'\n veg_clim_targetyear_labels = substr(veg_clim_targetyear_labels,2,nchar(veg_clim_targetyear_labels[1]))\n # subtract \"ha\" at end if it's there\n if (all(nchar(veg_clim_targetyear_labels)>4)) {\n veg_clim_targetyear_labels = substr(veg_clim_targetyear_labels,1,nchar(veg_clim_targetyear_labels[1])-3)\n }\n names(scen_df_list[[6]])[c(5:ncol(scen_df_list[[6]]))] = veg_clim_targetyear_labels\n veg_clim_targetyears = as.integer(substr(veg_clim_targetyear_labels,1,4))\n \n soil_clim_targetyear_labels = names(scen_df_list[[7]])[c(5:ncol(scen_df_list[[7]]))]\n soil_clim_targetyear_labels = substr(soil_clim_targetyear_labels,2,nchar(soil_clim_targetyear_labels[1]))\n # subtract \"ha\" at end if it's there\n if (all(nchar(soil_clim_targetyear_labels)>4)) {\n soil_clim_targetyear_labels = substr(soil_clim_targetyear_labels,1,nchar(soil_clim_targetyear_labels[1])-3)\n }\n names(scen_df_list[[7]])[c(5:ncol(scen_df_list[[7]]))] = soil_clim_targetyear_labels\n soil_clim_targetyears = as.integer(substr(soil_clim_targetyear_labels,1,4))\n \n # get some tables\n \n # these include all target years\n man_target_df <- scen_df_list[[3]]\n fire_target_df <- scen_df_list[[4]]\n mortality_target_df <- scen_df_list[[5]]\n climate_veg_target_df <- scen_df_list[[6]]\n climate_soil_target_df <- scen_df_list[[7]]\n \n # these are useful\n # assign the conversion area sheet from sceario file to conv_area_df\n conv_area_df = scen_df_list[[2]]\n names(conv_area_df)[ncol(conv_area_df)] = \"base_area_change\"\n vegc_uptake_df = c_df_list[[10]]\n vegc_uptake_df$init_vegc_uptake_val = vegc_uptake_df[,value_col_accum]\n deadc_frac_df = c_df_list[[3]][,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\")]\n soilc_accum_df = c_df_list[[11]]\n soilc_accum_df$init_soilc_accum_val = soilc_accum_df[,value_col_accum]\n conv_df = c_df_list[[12]]\n man_forest_df = c_df_list[[13]]\n forest_soilcaccumfrac_colind = which(names(man_forest_df) == \"SoilCaccum_frac\")\n man_dev_df = c_df_list[[14]]\n dev_soilcaccumfrac_colind = which(names(man_dev_df) == \"SoilCaccum_frac\")\n man_grass_df = c_df_list[[15]]\n man_ag_df = c_df_list[[16]]\n # assign the correct soil conservation column (mean=8, SD=9) to man_ag_df$soilc_accum_val_soilcon \n man_ag_df$soilc_accum_val_soilcon = man_ag_df[,value_col_soilcon]\n fire_df = c_df_list[[17]]\n \n # merge deadc_frac_df and mortality_target_df because zero rows do not exist and allrows are needed \n mortality_target_df = merge(deadc_frac_df, mortality_target_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n if (ncol(mortality_target_df) == 5) {\n \tmortality_target_df[which(is.na(mortality_target_df[,5])),5] = 0.00\n } else {\n \tmortality_target_df[,c(5:ncol(mortality_target_df))] <- apply(mortality_target_df[,c(5:ncol(mortality_target_df))], 2, function (x) {replace(x, is.na(x), 0.00)})\n }\t\n \n # get the correct values for the baseline c accum tables if value is std dev\n if(value_col_accum == 8) { # std dev as value\n if(ADD_accum) {\n vegc_uptake_df$init_vegc_uptake_val = vegc_uptake_df$init_vegc_uptake_val + vegc_uptake_df$Mean_Mg_ha_yr\n soilc_accum_df$init_soilc_accum_val = soilc_accum_df$init_soilc_accum_val + soilc_accum_df$Mean_Mg_ha_yr\n } else {\n vegc_uptake_df$init_vegc_uptake_val = vegc_uptake_df$Mean_Mg_ha_yr - vegc_uptake_df$init_vegc_uptake_val\n soilc_accum_df$init_soilc_accum_val = soilc_accum_df$Mean_Mg_ha_yr - soilc_accum_df$init_soilc_accum_val\n }\n }\n \n # get the correct values for the cutivated soil conservation soil c accum if value is std dev\n if(value_col_soilcon == 9) { # std dev as value\n if(ADD_soilcon) {\n man_ag_df$soilc_accum_val_soilcon = man_ag_df$soilc_accum_val_soilcon + man_ag_df$Mean_Mg_ha_yr\n } else {\n man_ag_df$soilc_accum_val_soilcon = man_ag_df$Mean_Mg_ha_yr - man_ag_df$soilc_accum_val_soilcon\n }\n }\n \n ### Create a dummy variable for the soil_c_flux_frac for cultivated lands so that:\n ### man_soilc_flux = man_frac * baseline_soilc_flux\n ### man_soilc_flux = (man_soilc_flux/baseline_soilc_flux) * baseline_soilc_flux\n ### Thus, for cultivated lands, man_frac = man_soilc_flux/baseline_soilc_flux\n \n # subset baseline c accum values and landcat ID's to be merged into man_ag_df\n df <- soilc_accum_df[,c(\"Land_Cat_ID\",\"init_soilc_accum_val\")]\n man_ag_df <- merge(man_ag_df, df, by=\"Land_Cat_ID\")\n # calc the dummy cultivated soil c flux frac\n man_ag_df$SoilCaccum_frac <- man_ag_df$soilc_accum_val_soilcon/man_ag_df$init_soilc_accum_val\n \n # create lists of the output tables\n # change the NA value to zero for calculations\n out_area_df_list <- list()\n out_density_df_list <- list()\n out_atmos_df_list <- list()\n out_stock_df_list <- list()\n out_wood_df_list <- list()\n start_area_label = paste0(start_year, \"_ha\")\n end_area_label = paste0(end_year, \"_ha\")\n start_density_label = paste0(start_year, \"_Mg_ha\")\n end_density_label = paste0(end_year, \"_Mg_ha\")\n start_atmos_label = paste0(start_year, \"_Mg\")\n end_atmos_label = paste0(end_year, \"_Mg\")\n start_stock_label = paste0(start_year, \"_Mg\")\n end_stock_label = paste0(end_year, \"_Mg\")\n start_wood_label = paste0(start_year, \"_Mg\")\n end_wood_label = paste0(end_year, \"_Mg\")\n \n # area\n # Assign the initial 2010 area table to \"Area\" in out_area_df_list[[1]]\n out_area_df_list[[1]] <- scen_df_list[[1]]\n names(out_area_df_list[[1]])[ncol(out_area_df_list[[1]])] <- as.character(start_area_label)\n # Assign the target management areas table to \"Managed_area\" in out_area_df_list[[2]]\n # First check if there are any prescribed management practices\n if (nrow(scen_df_list[[3]])>0) {\n out_area_df_list[[2]] <- scen_df_list[[3]][,c(1:6)]\n names(out_area_df_list[[2]])[ncol(out_area_df_list[[2]])] <- as.character(start_area_label)\n } else {\n out_area_df_list[[2]] <- scen_df_list[[3]][,c(1:5)]\n }\n #the wildfire out area df is added at the end because it has the breakdown across land type ids\n for ( i in 1:(num_out_area_sheets-1)) {\n out_area_df_list[[i]][is.na(out_area_df_list[[i]])] <- 0.0\n }\n \n # c density\n # assign input c density values to out_density c_df_list(9 density sheets): \n # totalC, totalbiomass, mainC, root, under, deadstand, deaddown, litter, SOC\n # note: missing values for totalC and totalbiomass is due to missing c pools for some of the land categories \n for (i in 1:num_out_density_sheets) {\n # populate out_density_df_list with the first 4 columns from each of the 9 C density sheets and either mean or +/-stdev\n out_density_df_list[[i]] <- c_df_list[[i]][,c(1,2,3,4,value_col_dens)]\n # out_density_df_list now has 5 columns for each sheet/C pool (Land_Cat_ID, Region, Land_Type, Ownership, Mean or Stdev)\n names(out_density_df_list[[i]])[ncol(out_density_df_list[[i]])] <- as.character(start_density_label)\n if(value_col_dens == 8) { # if std dev as value\n # this will not be the same as the sum of the components, so update it later\n if(ADD_dens) {\n # if we want +stddev, then C_density = std_dev + mean\n out_density_df_list[[i]][,5] = out_density_df_list[[i]][,5] + c_df_list[[i]][,\"Mean_Mg_ha\"]\n } else {\n # if we want -stddev, then C_density = mean - stdev\n out_density_df_list[[i]][,5] = c_df_list[[i]][,\"Mean_Mg_ha\"] - out_density_df_list[[i]][,5]\n }\n }\n out_density_df_list[[i]][is.na(out_density_df_list[[i]])] <- 0.0\n out_density_df_list[[i]][,5] <- replace(out_density_df_list[[i]][,5], out_density_df_list[[i]][,5] < 0, 0.00)\n }\n names(out_density_df_list) <- out_density_sheets\n \n # sum the total c pool density\n out_density_df_list[[1]][, start_density_label] = 0 \n # sum the following for each corresponding row: \n ## totalC = Above_main_C_den + Below_main_C_den + Understory_C_den + StandDead_C_den + DownDead_C_den + Litter_C_den + Soil_orgC_den\n for (i in 3:num_out_density_sheets) {\n out_density_df_list[[1]][, start_density_label] = out_density_df_list[[1]][, start_density_label] + out_density_df_list[[i]][, start_density_label] \n }\n \n # sum the biomass c pool density (all non-decomposed veg material; i.e. all non-soil c)\n out_density_df_list[[2]][, start_density_label] = 0 \n # All_biomass_C = Above_main_C_den + Below_main_C_den + Understory_C_den + StandDead_C_den + DownDead_C_den + Litter_C_den \n for (i in 3:(num_out_density_sheets-1)) {\n out_density_df_list[[2]][, start_density_label] = out_density_df_list[[2]][, start_density_label] + out_density_df_list[[i]][, start_density_label]\n }\n \n # c stock\n for (i in 1:num_out_stock_sheets) {\n out_stock_df_list[[i]] <- out_density_df_list[[1]]\n names(out_stock_df_list[[i]])[ncol(out_stock_df_list[[i]])] <- as.character(start_stock_label)\n out_stock_df_list[[i]][, start_stock_label] = out_density_df_list[[i]][, start_density_label] * out_area_df_list[[1]][,start_area_label]\n }\n names(out_stock_df_list) <- out_stock_sheets\n for ( i in 1:num_out_stock_sheets) {\n out_stock_df_list[[i]][is.na(out_stock_df_list[[i]])] <- 0.0\n }\n \n # c to atmosphere (and c from atmosphere to ecosystems)\n for (i in 1:num_out_atmos_sheets) {\n # fill all the (empty) dataframes in the out_atmos_df_list with the All_orgC_den dataframe. This arbitrary, as it's only needed \n # to fill in the first 3 columns with Land_Cat_ID, Land_Type and Ownership.\n out_atmos_df_list[[i]] <- out_density_df_list[[1]]\n # assign the name of 'start_atmos_label' (i.e. \"2010_Mg\"),\n names(out_atmos_df_list[[i]])[ncol(out_atmos_df_list[[i]])] <- as.character(start_atmos_label)\n # and clear all values and replace with 0's \n out_atmos_df_list[[i]][,ncol(out_atmos_df_list[[i]])] = 0.0\n }\n names(out_atmos_df_list) <- out_atmos_sheets\n for ( i in 1:num_out_atmos_sheets) {\n out_atmos_df_list[[i]][is.na(out_atmos_df_list[[i]])] <- 0.0\n }\n \n # wood c stock\n for (i in 1:num_out_wood_sheets) {\n out_wood_df_list[[i]] <- c_df_list[[1]][,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\")]\n out_wood_df_list[[i]][,start_wood_label] = 0.0\n }\n names(out_wood_df_list) <- out_wood_sheets\n \n # useful variables\n \n man_area_sum = out_area_df_list[[2]]\n # check if there are any prescribed management practices\n if (nrow(out_area_df_list[[2]])>0) {\n names(man_area_sum)[ncol(man_area_sum)] <- \"man_area\"\n man_area_sum$man_area_sum = 0.0\n } \n \n # set sum neginds eco to 0\n out_cum_neginds_eco_tot <- 0\n \n ##########################################################################\n # loop over the years\n for (year in start_year:(end_year-1)) {\n\n cat(\"\\nStarting year \", year, \"...\\n\")\n \n cur_density_label = paste0(year, \"_Mg_ha\")\n next_density_label = paste0(year+1, \"_Mg_ha\")\n cur_wood_label = paste0(year, \"_Mg\")\n next_wood_label = paste0(year+1, \"_Mg\")\n cur_area_label = paste0(year, \"_ha\")\n next_area_label = paste0(year+1, \"_ha\")\n cur_stock_label = paste0(year, \"_Mg\")\n next_stock_label = paste0(year+1, \"_Mg\")\n cur_atmos_label = paste0(year, \"_Mg\")\n next_atmos_label = paste0(year+1, \"_Mg\")\n \n # Determine the area weighted average eco fluxes based on the amount of managed land\n # use the running sum of the managed area for forest and range amendment, up to the total available area\n # this is because the management changes the flux for an extended period of time, especially if the management is repeated later\n # rangeland amendment is repeated on 10, 30, or 100 year periods\n # but ag is annual, and developed has its own system of independent areas\n # restoration and Afforestation and reforestation practices are not dependent on existing area, and are applied in land conversion\n # Afforestation and reforestation area and prescribed burn area should not be included in forest aggregate managed area and aggregate managed area sum\n # store the difference between the unmanaged and averaged with management eco fluxes, per ag, soil, and forest\n \n # this is the current year total area by category id\n # assign the first 4 columns (Land_Cat_ID, Region, Land_Type, Ownership) and the last column (2010_ha) of the out_area_df_list dataframe to \n # 'tot_area_df'.\n tot_area_df = out_area_df_list[[1]][,c(1:4,ncol(out_area_df_list[[1]]))]\n names(tot_area_df)[names(tot_area_df) == cur_area_label] <- \"tot_area\"\n \n # determine the climate scalars for this year from the prescribed years \n # do linear interpolation between years \n # if the year is past the final target year then use the final prescribed year\n \n ### veg scalars ###\n # indices of prior or current target years\n linds = which(veg_clim_targetyears <= year)\n # indices of upcoming or current target years\n # integer(0) if past last target year\n hinds = which(veg_clim_targetyears >= year)\n # assign most recent (or current) target year\n prev_targetyear = max(veg_clim_targetyears[linds])\n # set next (or current) target year\n # warning message when there are no more target years\n next_targetyear = min(veg_clim_targetyears[hinds])\n # index of previous target year\n pind = which(veg_clim_targetyears == prev_targetyear)\n # index of next target year\n # integer(0) if past last target year\n nind = which(veg_clim_targetyears == next_targetyear)\n # column header of previous target year\n pcol = veg_clim_targetyear_labels[pind]\n # column header of next target year\n # returns character(0) if past last target year\n ncol = veg_clim_targetyear_labels[nind]\n \n # assign the veg climate scalar identifier columns to climate_veg_df\n climate_veg_df = climate_veg_target_df[,c(1:4)]\n # if current year is a target year or past all target years, \n if (prev_targetyear == next_targetyear | length(hinds) == 0) {\n # create column for previous (or current) target scalar and name it current year\n climate_veg_df[,as.character(year)] <- climate_veg_target_df[,pcol]\n # else add a column with previous year target area\n } else {\n #climate_veg_df = climate_veg_target_df[,c(1:4,pcol)]\n # update the column with the linear interpolation of the scalars between target years\n climate_veg_df[,as.character(year)] = climate_veg_target_df[,pcol] + (year - prev_targetyear) * \n (climate_veg_target_df[,ncol] - climate_veg_target_df[,pcol]) / \n (next_targetyear - prev_targetyear)\n }\n climate_veg_df[which(is.na(climate_veg_df))] = 0.0\n \n \n ### soil scalars ###\n # indices of prior or current target years\n linds = which(soil_clim_targetyears <= year)\n # indices of upcoming or current target years\n hinds = which(soil_clim_targetyears >= year)\n # assign most recent (or current) target year\n prev_targetyear = max(soil_clim_targetyears[linds])\n # set next (or current) target year\n next_targetyear = min(soil_clim_targetyears[hinds])\n # index of previous target year\n pind = which(soil_clim_targetyears == prev_targetyear)\n # index of next target year\n nind = which(soil_clim_targetyears == next_targetyear)\n # column header of previous target year\n pcol = soil_clim_targetyear_labels[pind]\n # column header of next target year\n ncol = soil_clim_targetyear_labels[nind]\n \n # assign the soil climate scalar identifier columns to climate_soil_df\n climate_soil_df = climate_soil_target_df[,c(1:4)]\n # if current year is a target year or past all target years, \n if (prev_targetyear == next_targetyear | length(hinds) == 0) {\n # create column for previous (or current) target scalar and name it current year\n climate_soil_df[,as.character(year)] <- climate_soil_target_df[,pcol]\n # else add a column with previous year target area\n } else {\n climate_soil_df = climate_soil_target_df[,c(1:4,pcol)]\n # update the column with the linear interpolation of the scalars between target years\n climate_soil_df[,as.character(year)] = climate_soil_target_df[,pcol] + (year - prev_targetyear) * \n (climate_soil_target_df[,ncol] - climate_soil_target_df[,pcol]) / \n (next_targetyear - prev_targetyear)\n }\n climate_soil_df[which(is.na(climate_soil_df))] = 0.0\n \n \n ##### check if there any prescibed management practices #####\n if (nrow(man_area_sum)>0) {\n # reset the man_area_sum df\n man_area_sum = man_area_sum[,1:7]\n \n # determine the managed areas for this year from target years\n # linear interpolation between target years\n # if the year is past the final target year then use the final target year\n \n # man_targetyears (baseline man_targetyears: 2010, 2020, 2021, 2050), otherwise: 2010, 2016, 2017, 2020, 2021, 2050\n # assign the index of the management years (1:4) that are on or before the current year loop to 'linds'\n linds = which(man_targetyears <= year)\n # assign the index of the management years that are on or later to current year in loop to 'hinds'.\n hinds = which(man_targetyears >= year)\n # assign the most recent target year to prev_targetyear\n prev_targetyear = max(man_targetyears[linds])\n # assign the next closest target year to next_targetyear\n next_targetyear = min(man_targetyears[hinds])\n # assign the index of the most previous target year to pind\n pind = which(man_targetyears == prev_targetyear)\n # assign the index of the next target year to nind\n nind = which(man_targetyears == next_targetyear)\n # assign the previous label (\"2010_ha\" \"2020_ha\" \"2021_ha\" \"2050_ha\")\n pcol = man_targetyear_labels[pind]\n # assign the next label (\"2010_ha\" \"2020_ha\" \"2021_ha\" \"2050_ha\")\n ncol = man_targetyear_labels[nind]\n \n # if the current year is on the last management target year or there are no more target years ahead,\n if (prev_targetyear == next_targetyear | length(hinds) == 0) {\n # assign the the previous/current label \"2050_ha\" to man_area column in man_area_sum df\n man_area_sum$man_area = man_target_df[,pcol]\n } else {\n # otherwise assign man_area = (previous/current target area) + (# years since last target) * \n # (difference in areas between target years)/(# years between target years)\n man_area_sum$man_area = man_target_df[,pcol] + (year - prev_targetyear) * (man_target_df[,ncol] - \n man_target_df[,pcol]) / (next_targetyear - prev_targetyear)\n }\n \n # check if any man_area <0 that are != Growth ### use this later after checking all scenario files ###\n # man_area_sum$man_area[man_area_sum$man_area < 0 & man_area_sum$Management != Growth] <- 0\n \n # Assign 0 to any negative man_area with dead_removal or urban_forest\n man_area_sum$man_area[man_area_sum$man_area < 0 & (man_area_sum$Management == \"Dead_removal\" | man_area_sum$Management == \"Urban_forest\")] <- 0\n \n ######## New: check for excess man_area before man_area_sum ######## \n # Check that the sum of man_area is not > tot_area\n man_area_agg_pre = aggregate(man_area ~ Land_Cat_ID, man_area_sum[man_area_sum$Management != \"Afforestation\" & man_area_sum$Management != \"Reforestation\" &\n man_area_sum$Management != \"Restoration\",], FUN=sum)\n # update aggregated column name (man_area) to man_area_agg_pre\n names(man_area_agg_pre)[ncol(man_area_agg_pre)] <- \"man_area_agg_pre\"\n # merge df's man_area_sum & tot_area_df, and assign to man_area_sum dataframe (essentially, add additional \n # tot_area column to man_area_sum)\n man_area_sum = merge(man_area_sum, tot_area_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\",\"Ownership\"), all.x = TRUE)\n # merge man_area_sum & man_area_agg_pre dataframes by Land_Cat_ID\n man_area_sum = merge(man_area_sum, man_area_agg_pre, by = \"Land_Cat_ID\", all.x = TRUE)\n # replace NA's in the new man_area_agg_pre column with 0's \n man_area_sum$man_area_agg_pre = replace(man_area_sum$man_area_agg_pre, is.na(man_area_sum$man_area_agg_pre), 0)\n # sort the man_area_sum df by land cat ID and management \n man_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n \n # (7) don't use (untrimmed) aggregated areas for Developed_all: replace man_area_agg_pre column with individual man_area \n # (note: dead removal in Dev_all = tot_area; urban_forest = fraction of tot_area that's urban forest)\n man_area_sum$man_area_agg_pre[man_area_sum$Land_Type == \"Developed_all\"] = \n man_area_sum$man_area[man_area_sum$Land_Type == \"Developed_all\"]\n # (8) assign 0's to aggregate (untrimmed) areas for afforestation and reforestation and restoration \n man_area_sum$man_area_agg_pre[man_area_sum$Management == \"Afforestation\" | man_area_sum$Management == \"Reforestation\" | man_area_sum$Management == \"Restoration\"] = 0\n \n # add new column \"excess_area\", which is equal to the aggregated areas minus total land type areas \n man_area_sum$excess_area_pre = man_area_sum$man_area_agg_pre - man_area_sum$tot_area\n # if excess_area > 0, assign the index to excess_area_inds\n excess_area_pre_inds = which(man_area_sum$excess_area_pre > 0)\n # trim the manaagement areas: subtract out a proportional amount of any excess from each man_area\n man_area_sum$man_area[excess_area_pre_inds] = man_area_sum$man_area[excess_area_pre_inds] - \n man_area_sum$excess_area[excess_area_pre_inds] * man_area_sum$man_area[excess_area_pre_inds] / \n man_area_sum$man_area_agg_pre[excess_area_pre_inds]\n # replace NaN (not a number) values in man_area with 0's \n man_area_sum$man_area = replace(man_area_sum$man_area, is.nan(man_area_sum$man_area), 0)\n # replace Inf values in man_area_sum with 0's \n man_area_sum$man_area = replace(man_area_sum$man_area, man_area_sum$man_area == Inf, 0)\n man_area_sum$man_area = replace(man_area_sum$man_area, man_area_sum$man_area == -Inf, 0)\n # replace neg values in man_area_sum with 0's \n man_area_sum$man_area[man_area_sum$Management != \"Growth\"] = replace(man_area_sum$man_area[man_area_sum$Management != \"Growth\"], man_area_sum$man_area[man_area_sum$Management != \"Growth\"] < 0, 0) \n # (9) create a _trimmed_ aggregated (current year) areas dataframe (man_area_agg2): aggregate and sum man_area vector by Land_Cat_ID for all management activities \n # _except_ afforestation and reforestation and restoration areas\n man_area_agg2 = aggregate(man_area ~ Land_Cat_ID, man_area_sum[man_area_sum$Management != \"Afforestation\" & man_area_sum$Management != \"Reforestation\" & man_area_sum$Management != \"Restoration\",], FUN=sum)\n # rename header of _trimmed_ aggregate (current year) areas to \"man_area_agg\" in man_area_agg2 dataframe\n names(man_area_agg2)[ncol(man_area_agg2)] <- \"man_area_agg\"\n # (10) add column of _trimmed_ aggregated current areas called \"man_area_agg\" (Developed_all = _untrimmed_ agg areas, & afforestation and reforestation and restoration excluded)\n # merge man_area_sum & man_area_agg2 dataframes by Land_Cat_ID\n man_area_sum = merge(man_area_sum, man_area_agg2, by = \"Land_Cat_ID\", all.x =TRUE)\n # replace NA's in the man_area_agg column with 0's\n man_area_sum$man_area_agg = replace(man_area_sum$man_area_agg, is.na(man_area_sum$man_area_agg), 0)\n # sort man_area_sum by land cat ID, then management \n man_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n # (11) don't use trimmed aggregated current year areas for Developed_all: replace man_area_agg column with individual man_area \n man_area_sum$man_area_agg[man_area_sum$Land_Type == \"Developed_all\"] = man_area_sum$man_area[man_area_sum$Land_Type == \"Developed_all\"]\n # (12) assign 0's to aggregate (untrimmed) areas for afforestation and reforestation and restoration \n man_area_sum$man_area_agg[man_area_sum$Management == \"Afforestation\" | man_area_sum$Management == \"Reforestation\" | man_area_sum$Management == \"Restoration\"] = 0\n \n ##### adjust afforestation and reforestation and restoration man_area values to available area if necessary\n # these same prioritizations are used in the land conversion section\n \n # prioritize reforestation over afforestation and meadow because these come from more types (reforestation is only from shrubland)\n # first get shrubland areas for each region-ownership, then take the min of man_area and avail area\n avail_ro_area = aggregate(tot_area ~ Region + Ownership, tot_area_df[tot_area_df$Land_Type == \"Shrubland\",], FUN=sum)\n names(avail_ro_area)[ncol(avail_ro_area)] <- \"tot_ro_area\"\n avail_ro_area$avail_ro_area = avail_ro_area$tot_ro_area\n check_avail_df = merge(man_area_sum[man_area_sum$Management == \"Reforestation\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n man_area_sum$man_area[man_area_sum$Management == \"Reforestation\"] = apply(check_avail_df[,c(\"man_area\", \"avail_ro_area\")], 1, FUN=min, na.rm=TRUE)\n # also calc the amount of shrub needed\n rfrst_ro_areas = merge(tot_area_df[tot_area_df$Land_Type == \"Shrubland\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n rfrst_ro_areas = merge(rfrst_ro_areas, check_avail_df, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n rfrst_ro_areas$lt_area_need = rfrst_ro_areas$man_area * rfrst_ro_areas$tot_area.x / rfrst_ro_areas$tot_ro_area.x\n rfrst_ro_areas$lt_area_need[is.na(rfrst_ro_areas$lt_area_need)] = 0.00\n rfrst_ro_areas$lt_area_need[is.nan(rfrst_ro_areas$lt_area_need)] = 0.00\n rfrst_ro_areas$lt_area_need[rfrst_ro_areas$lt_area_need == Inf] = 0.00\n rfrst_ro_areas$lt_area_need[rfrst_ro_areas$lt_area_need == -Inf] = 0.00\n rfrst_ro_areas = rfrst_ro_areas[,c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\", \"lt_area_need\")]\n # need all shrubs for afforestation and meadow\n\trfrst_ro_areas_agg = aggregate(lt_area_need ~ Region + Ownership, rfrst_ro_areas, FUN=sum, na.rm = TRUE)\n\tnames(rfrst_ro_areas_agg)[ncol(rfrst_ro_areas_agg)] <- \"ro_area_need\"\n \n # prioritize afforestation over meadow because meadow comes from more types\n # first get shrubland + grassland areas for each region-ownership, then take the min of man_area and avail area\n avail_ro_area = aggregate(tot_area ~ Region + Ownership, tot_area_df[tot_area_df$Land_Type == \"Shrubland\" | tot_area_df$Land_Type == \"Grassland\",], FUN=sum)\n names(avail_ro_area)[ncol(avail_ro_area)] <- \"tot_ro_area\"\n avail_ro_area$avail_ro_area = avail_ro_area$tot_ro_area\n avail_ro_area = merge(avail_ro_area, rfrst_ro_areas_agg, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n avail_ro_area$avail_ro_area = avail_ro_area$avail_ro_area - avail_ro_area$ro_area_need\n check_avail_df = merge(man_area_sum[man_area_sum$Management == \"Afforestation\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n man_area_sum$man_area[man_area_sum$Management == \"Afforestation\"] = apply(check_avail_df[,c(\"man_area\", \"avail_ro_area\")], 1, FUN=min, na.rm=TRUE)\n # also calc the amount of shrub and grass needed\n frst_ro_areas = merge(tot_area_df[tot_area_df$Land_Type == \"Shrubland\" | tot_area_df$Land_Type == \"Grassland\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n frst_ro_areas = merge(frst_ro_areas, check_avail_df, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n frst_ro_areas = merge(frst_ro_areas, rfrst_ro_areas, by = c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\"), all.x = TRUE)\n # need to clean up the land types that don't exist in the previous ro_areas df\n frst_ro_areas$lt_area_need[is.na(frst_ro_areas$lt_area_need)] = 0.00\n frst_ro_areas$lt_area_need[is.nan(frst_ro_areas$lt_area_need)] = 0.00\n # now replace lt_area_need\n frst_ro_areas$lt_area_need = frst_ro_areas$man_area * (frst_ro_areas$tot_area.x - frst_ro_areas$lt_area_need) / frst_ro_areas$avail_ro_area.x\n frst_ro_areas$lt_area_need[is.na(frst_ro_areas$lt_area_need)] = 0.00\n frst_ro_areas$lt_area_need[is.nan(frst_ro_areas$lt_area_need)] = 0.00\n frst_ro_areas$lt_area_need[frst_ro_areas$lt_area_need == Inf] = 0.00\n frst_ro_areas$lt_area_need[frst_ro_areas$lt_area_need == -Inf] = 0.00\n frst_ro_areas = frst_ro_areas[,c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\", \"lt_area_need\")]\n # need all shrubs and grass for meadow\n frst_ro_areas_agg = aggregate(lt_area_need ~ Region + Ownership, frst_ro_areas, FUN=sum, na.rm = TRUE)\n names(frst_ro_areas_agg)[ncol(frst_ro_areas_agg)] <- \"ro_area_need\"\n \n # meadow comes out of shrub, grass, savanna, woodland\n avail_ro_area = aggregate(tot_area ~ Region + Ownership, tot_area_df[tot_area_df$Land_Type == \"Shrubland\" | tot_area_df$Land_Type == \"Grassland\" | tot_area_df$Land_Type == \"Savanna\" | tot_area_df$Land_Type == \"Woodland\",], FUN=sum)\n names(avail_ro_area)[ncol(avail_ro_area)] <- \"tot_ro_area\"\n avail_ro_area$avail_ro_area = avail_ro_area$tot_ro_area\n prev_ro_areas_agg = merge(rfrst_ro_areas_agg, frst_ro_areas_agg, by = c(\"Region\", \"Ownership\"), all = TRUE)\n avail_ro_area = merge(avail_ro_area, prev_ro_areas_agg, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n avail_ro_area$avail_ro_area = avail_ro_area$avail_ro_area - avail_ro_area$ro_area_need.x - avail_ro_area$ro_area_need.y\n check_avail_df = merge(man_area_sum[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Meadow\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n man_area_sum$man_area[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Meadow\"] = apply(check_avail_df[,c(\"man_area\", \"avail_ro_area\")], 1, FUN=min, na.rm=TRUE)\n # calc the meadow land_type needs\n mdw_ro_areas = merge(tot_area_df[tot_area_df$Land_Type == \"Shrubland\" | tot_area_df$Land_Type == \"Grassland\" | tot_area_df$Land_Type == \"Savanna\" | tot_area_df$Land_Type == \"Woodland\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n mdw_ro_areas = merge(mdw_ro_areas, check_avail_df, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n prev_ro_areas = merge(rfrst_ro_areas, frst_ro_areas, by = c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\"), all = TRUE)\n # clean up the NAs from merging\n prev_ro_areas[,c(\"lt_area_need.x\", \"lt_area_need.y\")] <- apply(prev_ro_areas[,c(\"lt_area_need.x\", \"lt_area_need.y\")], 2, function (x) {replace(x, is.na(x), 0.00)})\n prev_ro_areas[,c(\"lt_area_need.x\", \"lt_area_need.y\")] <- apply(prev_ro_areas[,c(\"lt_area_need.x\", \"lt_area_need.y\")], 2, function (x) {replace(x, is.nan(x), 0.00)})\n prev_ro_areas$lt_area_need = prev_ro_areas$lt_area_need.x + prev_ro_areas$lt_area_need.y\n mdw_ro_areas = merge(mdw_ro_areas, prev_ro_areas, by = c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\"), all.x = TRUE)\n # need to clean up the land types that don't exist in the previous ro_areas df\n mdw_ro_areas$lt_area_need[is.na(mdw_ro_areas$lt_area_need)] = 0.00\n mdw_ro_areas$lt_area_need[is.nan(mdw_ro_areas$lt_area_need)] = 0.00\n # now replace lt_area_need\n mdw_ro_areas$lt_area_need = mdw_ro_areas$man_area * (mdw_ro_areas$tot_area.x - mdw_ro_areas$lt_area_need) / mdw_ro_areas$avail_ro_area.x\n mdw_ro_areas$lt_area_need[is.na(mdw_ro_areas$lt_area_need)] = 0.00\n mdw_ro_areas$lt_area_need[is.nan(mdw_ro_areas$lt_area_need)] = 0.00\n mdw_ro_areas$lt_area_need[mdw_ro_areas$lt_area_need == Inf] = 0.00\n mdw_ro_areas$lt_area_need[mdw_ro_areas$lt_area_need == -Inf] = 0.00\n mdw_ro_areas = mdw_ro_areas[,c(\"Region\", \"Ownership\", \"Land_Cat_ID.x\", \"Land_Type.x\", \"lt_area_need\")]\n # aggregate the meadow needs, but don't need this right now\n mdw_ro_areas_agg = aggregate(lt_area_need ~ Region + Ownership, mdw_ro_areas, FUN=sum, na.rm = TRUE)\n names(mdw_ro_areas_agg)[ncol(mdw_ro_areas_agg)] <- \"ro_area_need\"\n \n # fresh marsh and coastal marsh restoration both come from cultivated\n # if there is prescribed restoration, sum the restoration then scale the man_area and take the min of man_area and scaled man_area\n if (nrow(man_area_sum[man_area_sum$Management == \"Restoration\" & (man_area_sum$Land_Type == \"Fresh_marsh\" | man_area_sum$Land_Type == \"Coastal_marsh\"),])>0) {\n \ttot_rest_area = aggregate(man_area ~ Region + Ownership, man_area_sum[man_area_sum$Management == \"Restoration\" & (man_area_sum$Land_Type == \"Fresh_marsh\" | \n \t\t\t\t\tman_area_sum$Land_Type == \"Coastal_marsh\"),], FUN=sum)\n \tnames(tot_rest_area)[ncol(tot_rest_area)] <- \"tot_rest_area\"\n \tcheck_avail_df = merge(man_area_sum[man_area_sum$Management == \"Restoration\" & (man_area_sum$Land_Type == \"Fresh_marsh\" | man_area_sum$Land_Type == \"Coastal_marsh\"),], \n \t\t\t\t\ttot_rest_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n \tcheck_avail_df = merge(check_avail_df, tot_area_df[tot_area_df$Land_Type == \"Cultivated\",], by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n \tcheck_avail_df$avail_rest_area = check_avail_df$tot_area.y / check_avail_df$tot_rest_area * check_avail_df$man_area\n \tcheck_avail_df$avail_rest_area[is.na(check_avail_df$avail_rest_area)] = 0.00\n \tcheck_avail_df$avail_rest_area[is.nan(check_avail_df$avail_rest_area)] = 0.00\n \tcheck_avail_df$avail_rest_area[check_avail_df$avail_rest_area == Inf] = 0.00\n \tcheck_avail_df$avail_rest_area[check_avail_df$avail_rest_area == -Inf] = 0.00\n \t# fresh marsh - do these separately due to ordering issues\n \tman_area_sum$man_area[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Fresh_marsh\"] = \n \t\tapply(check_avail_df[check_avail_df$Land_Type.x == \"Fresh_marsh\", c(\"man_area\", \"avail_rest_area\")], 1, FUN=min, na.rm=TRUE)\n \t# coastal marsh - do these separately due to ordering issues\n \tman_area_sum$man_area[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Coastal_marsh\"] = \n \t\tapply(check_avail_df[check_avail_df$Land_Type.x == \"Coastal_marsh\", c(\"man_area\", \"avail_rest_area\")], 1, FUN=min, na.rm=TRUE)\n \t# calc the wetland cultivated need\n \twet_ro_areas = aggregate(man_area ~ Region + Ownership, man_area_sum[man_area_sum$Management == \"Restoration\" & (man_area_sum$Land_Type == \"Fresh_marsh\" | \n \t\t\t\tman_area_sum$Land_Type == \"Coastal_marsh\"),], FUN = sum)\n \tnames(wet_ro_areas)[ncol(wet_ro_areas)] <- \"wet_area_need\"\n \twet_ro_areas$wet_area_need[is.na(wet_ro_areas$wet_area_need)] = 0.00\n \twet_ro_areas$wet_area_need[is.nan(wet_ro_areas$wet_area_need)] = 0.00\n \twet_ro_areas$wet_area_need[wet_ro_areas$wet_area_need == Inf] = 0.00\n \t\twet_ro_areas$wet_area_need[wet_ro_areas$wet_area_need == -Inf] = 0.00\n } # end if there is prescribed restoration\n \n # woodland gets the leftovers of grassland and cultivated\n avail_ro_area = aggregate(tot_area ~ Region + Ownership, tot_area_df[tot_area_df$Land_Type == \"Grassland\" | tot_area_df$Land_Type == \"Cultivated\",], FUN=sum)\n names(avail_ro_area)[ncol(avail_ro_area)] <- \"tot_ro_area\"\n avail_ro_area$avail_ro_area = avail_ro_area$tot_ro_area\n avail_ro_area = merge(avail_ro_area, frst_ro_areas[frst_ro_areas$Land_Type.x == \"Grassland\", c(\"Region\", \"Ownership\", \"lt_area_need\")], by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n avail_ro_area = merge(avail_ro_area, mdw_ro_areas[mdw_ro_areas$Land_Type.x == \"Grassland\", c(\"Region\", \"Ownership\", \"lt_area_need\")], by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n avail_ro_area$lt_area_need.x[is.na(avail_ro_area$lt_area_need.x)] = 0.00\n avail_ro_area$lt_area_need.x[is.nan(avail_ro_area$lt_area_need.x)] = 0.00\n avail_ro_area$lt_area_need.y[is.na(avail_ro_area$lt_area_need.y)] = 0.00\n avail_ro_area$lt_area_need.y[is.nan(avail_ro_area$lt_area_need.y)] = 0.00\n avail_ro_area$avail_ro_area = avail_ro_area$avail_ro_area - avail_ro_area$lt_area_need.x - avail_ro_area$lt_area_need.y\n if (exists(\"wet_ro_areas\")) {\n\t\tavail_ro_area = merge(avail_ro_area, wet_ro_areas, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n\t\tavail_ro_area$wet_area_need[is.na(avail_ro_area$wet_area_need)] = 0.00\n \tavail_ro_area$wet_area_need[is.nan(avail_ro_area$wet_area_need)] = 0.00\n\t\tavail_ro_area$avail_ro_area = avail_ro_area$avail_ro_area - avail_ro_area$wet_area_need\n\t}\n check_avail_df = merge(man_area_sum[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Woodland\",], avail_ro_area, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n man_area_sum$man_area[man_area_sum$Management == \"Restoration\" & man_area_sum$Land_Type == \"Woodland\"] = apply(check_avail_df[,c(\"man_area\", \"avail_ro_area\")], 1, FUN=min, na.rm=TRUE)\n \n \n ############# end pre man_area check #############\n \n # the developed practices are independent of each other and so calc cum sum independently, even though they are not currently used\n # dead_removal values are assigned to the \"aggregate\" dev_all values for man_area and man_area_sum; these are not used either\n # current ag management does not use sum area because they are annual practices to maintain the benefits\n # prescribed burn is not included in man_area sum for long-term benefits\n #\tit is assumed that prescribed burn occurs on land that has been managed during the prior benefit period\n # Afforestation and reforestation and restoration are not dependent on existing area and are not included in aggregate managed area\n # these use the man area sum to ensure protection of restored area\n #\t\tbut man area sum cannot be used to calculate weighted fluxes for afforestaion and restoration because it isn't counted as actual managed area\n # forest and rangeland compost use the cumulative area for adjusting c accumulation\n # calculate these cumulative sums based on the current and previous years up to the benefit period limit\n # range_lowfreq_period = 30\n \t# range_medfreq_period = 10\n \t# forest_benefit_period = 20\n \t# urban_forest_benefit_period = 20\n \t\n \t# first add the current year management as the general calculation of man_area_sum\n \t# keep track of prescribed burn man_area_sum for fire severity adjustment, but omit it from c accum adjustment (similarly to restoration/etc.)\n\tman_area_sum$man_area_sum = man_area_sum$man_area_sum + man_area_sum$man_area\n\tman_area_sum$man_area_sum[man_area_sum$man_area_sum > man_area_sum$tot_area] = man_area_sum$tot_area[man_area_sum$man_area_sum > man_area_sum$tot_area]\n \t\n # now make some calculations for specific practices\n # the out area table only has the previous years (except the first year target area, but use the calculated man area for the current year)\n \n\ttemp_df = out_area_df_list[[2]]\n\ttemp_df$sum = 0\n\tsum_end_col = which(names(temp_df) == paste0(year-1, \"_ha\"))\n\t\n\t# the first year will not have previous year values, so the value will be the first year value as calculated above\n\tif(length(sum_end_col) > 0) {\n\t\t\n \t\t# rangeland low frequency compost\n\t\tsum_start = year - range_lowfreq_period + 1\n\t\tif (sum_start < start_year) { sum_start = start_year }\n\t\tsum_start_col = which(names(temp_df) == paste0(sum_start, \"_ha\"))\n\t\tif (sum_start_col == sum_end_col) {\n\t\t\t# only one year in sum, so rowSums() won't work\n\t\t\ttemp_df$sum[temp_df$Management == \"Low_frequency\"] = temp_df[temp_df$Management == \"Low_frequency\",sum_start_col]\n\t\t} else { temp_df$sum[temp_df$Management == \"Low_frequency\"] = rowSums(temp_df[temp_df$Management == \"Low_frequency\", c(sum_start_col:sum_end_col)]) }\n \t# rangeland med frequency compost\n\t\tsum_start = year - range_medfreq_period + 1\n\t\tif (sum_start < start_year) { sum_start = start_year }\n\t\tsum_start_col = which(names(temp_df) == paste0(sum_start, \"_ha\"))\n\t\tif (sum_start_col == sum_end_col) {\n\t\t\t# only one year in sum, so rowSums() won't work\n\t\t\ttemp_df$sum[temp_df$Management == \"Med_frequency\"] = temp_df[temp_df$Management == \"Med_frequency\",sum_start_col]\n\t\t} else { temp_df$sum[temp_df$Management == \"Med_frequency\"] = rowSums(temp_df[temp_df$Management == \"Med_frequency\", c(sum_start_col:sum_end_col)]) }\n \t\n \t# forest non-afforestation and non-reforestation and non-prescribed burn\n \t# the assumption is that all prescribed burn is applied to land that has been managed within the past benefit period\n \tsum_start = year - forest_benefit_period + 1\n\t\tif (sum_start < start_year) { sum_start = start_year }\n\t\tsum_start_col = which(names(temp_df) == paste0(sum_start, \"_ha\"))\n\t\tif (sum_start_col == sum_end_col) {\n\t\t\t# only one year in sum, so rowSums() won't work\n\t\t\ttemp_df$sum[temp_df$Land_Type == \"Forest\" & temp_df$Management != \"Afforestation\" & temp_df$Management != \"Reforestation\" & \n\t\t\t\t\ttemp_df$Management != \"Prescribed_burn\" & temp_df$Management != \"Prescribed_burn_med_slash_util\" & temp_df$Management != \"Prescribed_burn_hi_slash_util\"] =\n\t\t\t\ttemp_df[temp_df$Land_Type == \"Forest\" & temp_df$Management != \"Afforestation\" & temp_df$Management != \"Reforestation\" & \n\t\t\t\t\ttemp_df$Management != \"Prescribed_burn\" & temp_df$Management != \"Prescribed_burn_med_slash_util\" & temp_df$Management != \"Prescribed_burn_hi_slash_util\",sum_start_col]\n\t\t} else { \n\t\t\ttemp_df$sum[temp_df$Land_Type == \"Forest\" & temp_df$Management != \"Afforestation\" & temp_df$Management != \"Reforestation\" & \n\t\t\t\t\ttemp_df$Management != \"Prescribed_burn\" & temp_df$Management != \"Prescribed_burn_med_slash_util\" & temp_df$Management != \"Prescribed_burn_hi_slash_util\"] =\n\t\t\t\trowSums(temp_df[temp_df$Land_Type == \"Forest\" & temp_df$Management != \"Afforestation\" & temp_df$Management != \"Reforestation\" & \n\t\t\t\t\ttemp_df$Management != \"Prescribed_burn\" & temp_df$Management != \"Prescribed_burn_med_slash_util\" & temp_df$Management != \"Prescribed_burn_hi_slash_util\", \n\t\t\t\t\tc(sum_start_col:sum_end_col)])\n\t\t}\n\t\t\n\t\t# developed_all dead_removal\n\t\tsum_start = year - urban_forest_benefit_period + 1\n\t\tif (sum_start < start_year) { sum_start = start_year }\n\t\tsum_start_col = which(names(temp_df) == paste0(sum_start, \"_ha\"))\n\t\tif (sum_start_col == sum_end_col) {\n\t\t\t# only one year in sum, so rowSums() won't work\n\t\t\ttemp_df$sum[temp_df$Land_Type == \"Developed_all\" & temp_df$Management == \"Dead_removal\"] =\n\t\t\t\ttemp_df[temp_df$Land_Type == \"Developed_all\" & temp_df$Management == \"Dead_removal\",sum_start_col]\n\t\t} else { \n\t\t\ttemp_df$sum[temp_df$Land_Type == \"Developed_all\" & temp_df$Management == \"Dead_removal\"] =\n\t\t\t\trowSums(temp_df[temp_df$Land_Type == \"Developed_all\" & temp_df$Management == \"Dead_removal\", c(sum_start_col:sum_end_col)])\n\t\t}\n\t\t\n\t\t# update man_area_sum by adding the previous years sum and the current year man area - but only for the appropriate practices\n \tman_area_sum = merge(man_area_sum, temp_df[,c(1:5,ncol(temp_df))], by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"), all.x = TRUE)\n \tman_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n \tman_area_sum$man_area_sum[man_area_sum$Management == \"Low_frequency\" | man_area_sum$Management == \"Med_frequency\" | \n \t\t\t(man_area_sum$Land_Type == \"Forest\" & man_area_sum$Management != \"Afforestation\" & man_area_sum$Management != \"Reforestation\" & \n \t\t\tman_area_sum$Management != \"Prescribed_burn\" & man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & man_area_sum$Management != \"Prescribed_burn_hi_slash_util\") | \n \t\t\t(man_area_sum$Land_Type == \"Developed_all\" & man_area_sum$Management == \"Dead_removal\")] = \n \t\tman_area_sum$sum[man_area_sum$Management == \"Low_frequency\" | man_area_sum$Management == \"Med_frequency\" | \n \t\t\t(man_area_sum$Land_Type == \"Forest\" & man_area_sum$Management != \"Afforestation\" & man_area_sum$Management != \"Reforestation\" & \n \t\t\tman_area_sum$Management != \"Prescribed_burn\" & man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & man_area_sum$Management != \"Prescribed_burn_hi_slash_util\") |\n \t\t\t(man_area_sum$Land_Type == \"Developed_all\" & man_area_sum$Management == \"Dead_removal\")] +\n \t\tman_area_sum$man_area[man_area_sum$Management == \"Low_frequency\" | man_area_sum$Management == \"Med_frequency\" | \n \t\t\t(man_area_sum$Land_Type == \"Forest\" & man_area_sum$Management != \"Afforestation\" & man_area_sum$Management != \"Reforestation\" & \n \t\t\tman_area_sum$Management != \"Prescribed_burn\" & man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & man_area_sum$Management != \"Prescribed_burn_hi_slash_util\") | \n \t\t\t(man_area_sum$Land_Type == \"Developed_all\" & man_area_sum$Management == \"Dead_removal\")]\n \tman_area_sum$sum = NULL\n\t} #end if second year or more\n\t\n ### make sure the cumulative sum is not greater than the existing area\n # this is only an issue for forest and rangeland and cultivated, where the practices are assumed to be mutually exclusive in area,\n # and the cum sum matters\n # developed_all cum man area is ultimately limited to dev_all total area, and dead_removal man area is the only one that matters\n # afforestation and reforestation and restoration are already limited by man area above, so man_area_sum is already limited\n \n ### merge df's man_area_sum & tot_area_df, and assign to man_area_sum dataframe (essentially, add additional \n ### tot_area column to man_area_sum), excludes any land types from tot_area that are not in man_area_sum\n ### man_area_sum = merge(man_area_sum, tot_area_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n ### sort man_area_sum dataframe by Land_Type_ID, then by Manage_ID \n ###man_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n # (1) [if there are any other man areas] aggregate cumulative areas by landcat for all management except afforestation & reforestation & restoration & developd_all & prescribed burn\n # create df of aggregated cumulative areas (man_area_sum_agg): aggregate by summing man_area_sum with the same Land_Cat_ID _except_ \n # \tfor areas with Afforestation and reforestation and Restoration and prescribed burn management\n\t if (nrow(man_area_sum[man_area_sum$Management != \"Afforestation\" & \n\t \t\t\t\t\t\tman_area_sum$Management != \"Reforestation\" &\n\t man_area_sum$Management != \"Restoration\" &\n\t man_area_sum$Management != \"Prescribed_burn\" & \n\t man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & man_area_sum$Management != \"Prescribed_burn_hi_slash_util\" & \n\t man_area_sum$Land_Type != \"Developed_all\",])>0) { \n man_area_sum_agg = aggregate(man_area_sum ~ Land_Cat_ID, man_area_sum[man_area_sum$Management != \"Afforestation\" & \n \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tman_area_sum$Management != \"Reforestation\" &\n man_area_sum$Management != \"Restoration\" &\n man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & \n man_area_sum$Management != \"Prescribed_burn_hi_slash_util\" &\n man_area_sum$Management != \"Prescribed_burn\" & \n man_area_sum$Land_Type != \"Developed_all\",], FUN=sum)\n # update aggregated sums column name (man_area_sum) to man_area_sum_agg_extra\n names(man_area_sum_agg)[ncol(man_area_sum_agg)] <- \"man_area_sum_agg_extra\"\n # end if other type of prescribed management\t\n\t } else {\n\t # create empty man_area_sum_agg\n\t man_area_sum_agg <- data.frame(Land_Cat_ID=numeric(0), man_area_sum_agg_extra=numeric(0))\n\t}\n # merge man_area_sum & man_area_sum_agg dataframes by Land_Type_ID\n man_area_sum = merge(man_area_sum, man_area_sum_agg, by = \"Land_Cat_ID\", all.x = TRUE)\n # replace 'NA' values in man_area_sum$man_area_sum_agg_extra with 0's (these are land types that didn't have any management)\n man_area_sum$man_area_sum_agg_extra = replace(man_area_sum$man_area_sum_agg_extra, is.na(man_area_sum$man_area_sum_agg_extra), 0)\n # sort man_area_sum dataframe by man_area_sum$Land_Type_ID and man_area_sum$Manage_ID\n man_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n # (2) update man_area_sum for developed_all with individual cumulative area\n # For developed_all land type only, replace values in (extra) aggregated areas column with values in man_area_sum \n # (i.e. don't aggregate for developed_all, use individual cumulative mangagement areas)\n man_area_sum$man_area_sum_agg_extra[man_area_sum$Land_Type == \"Developed_all\"] = man_area_sum$man_area_sum[man_area_sum$Land_Type == \"Developed_all\"]\n # (3) assign 0's to aggregate cumulative areas for afforestation and reforestation and restoration and prescribed burn\n # For afforestation or restoration management activities only, replace values in the (extra) aggregated area column with 0 \n # (i.e. afforestation and restoration not included in aggregate sum)\n man_area_sum$man_area_sum_agg_extra[man_area_sum$Management == \"Afforestation\" | man_area_sum$Management == \"Reforestation\" | man_area_sum$Management == \"Restoration\" | \n \tman_area_sum$Management == \"Prescribed_burn\" | man_area_sum$Management == \"Prescribed_burn_med_slash_util\" | man_area_sum$Management == \"Prescribed_burn_hi_slash_util\"] = 0\n # add new column \"excess_sum_area\", which is equal to the (extra) aggregated areas minus total land type areas \n man_area_sum$excess_sum_area = man_area_sum$man_area_sum_agg_extra - man_area_sum$tot_area\n # if excess_sum_area > 0, assign the index to excess_sum_area_inds\n excess_sum_area_inds = which(man_area_sum$excess_sum_area > 0)\n # trim the cumulative manaagement areas: subtract out a proportional amount of any excess from each man_area_sum \n man_area_sum$man_area_sum[excess_sum_area_inds] = man_area_sum$man_area_sum[excess_sum_area_inds] - \n man_area_sum$excess_sum_area[excess_sum_area_inds] * man_area_sum$man_area_sum[excess_sum_area_inds] / \n man_area_sum$man_area_sum_agg_extra[excess_sum_area_inds]\n # replace NaN (not a number) values in man_area_sum with 0's \n man_area_sum$man_area_sum = replace(man_area_sum$man_area_sum, is.nan(man_area_sum$man_area_sum), 0)\n # replace neg values in man_area_sum with 0's \n man_area_sum$man_area_sum = replace(man_area_sum$man_area_sum, man_area_sum$man_area_sum < 0, 0) \n # replace Infinite values in man_area_sum with 0's \n man_area_sum$man_area_sum = replace(man_area_sum$man_area_sum, man_area_sum$man_area_sum == Inf, 0)\n man_area_sum$man_area_sum = replace(man_area_sum$man_area_sum, man_area_sum$man_area_sum == -Inf, 0)\n \n # if there are any other man areas create a _trimmed_ aggregated man_area_sum df (man_area_sum_agg2)\n if (nrow(man_area_sum[man_area_sum$Management != \"Afforestation\" &\n \t\t\t\t\t man_area_sum$Management != \"Reforestation\" & \n man_area_sum$Management != \"Restoration\" &\n man_area_sum$Management != \"Prescribed_burn\" &\n man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & man_area_sum$Management != \"Prescribed_burn_hi_slash_util\" &\n man_area_sum$Land_Type != \"Developed_all\",])>0) {\n # aggregate sum man_area_sum vector by Land_Cat_ID for all management activities _except_ afforestation and restoration and prescribed burn areas \n man_area_sum_agg2 = aggregate(man_area_sum ~ Land_Cat_ID, man_area_sum[man_area_sum$Management != \"Afforestation\" & \n \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tman_area_sum$Management != \"Reforestation\" &\n man_area_sum$Management != \"Restoration\" &\n man_area_sum$Management != \"Prescribed_burn\" &\n man_area_sum$Management != \"Prescribed_burn_med_slash_util\" & \n man_area_sum$Management != \"Prescribed_burn_hi_slash_util\" &\n man_area_sum$Land_Type != \"Developed_all\",], FUN=sum)\n # rename _trimmed_ aggregate cumulative areas to \"man_area_sum_agg\" in man_area_sum_agg2 df\n names(man_area_sum_agg2)[ncol(man_area_sum_agg2)] <- \"man_area_sum_agg\"\n # end if there are other prescribed management practices\n } else {\n # create empty man_area_sum_agg2\n man_area_sum_agg2 <- data.frame(Land_Cat_ID=numeric(0), man_area_sum_agg=numeric(0))\n }\n # (4) add column \"man_area_sum_agg\" (Developed_all = _untrimmed_ individual cummulative areas, & afforestation and reforestation and restoration and prescribed burn excluded)\n # by merging man_area_sum & man_area_sum_agg2 dataframes by Land_Type_ID\n man_area_sum = merge(man_area_sum, man_area_sum_agg2, by = \"Land_Cat_ID\", all.x =TRUE)\n # replace NA's in the man_area_sum_agg column with 0's\n man_area_sum$man_area_sum_agg = replace(man_area_sum$man_area_sum_agg, is.na(man_area_sum$man_area_sum_agg), 0)\n # sort man_area_sum by land type, then management ID \n man_area_sum = man_area_sum[order(man_area_sum$Land_Cat_ID, man_area_sum$Management),]\n # (5) don't use man_area_sum_agg for Developed_all: replace (trimmed & aggregated) man_area_sum_agg column with individual (trimmed) man_area_sum \n man_area_sum$man_area_sum_agg[man_area_sum$Land_Type == \"Developed_all\"] = man_area_sum$man_area_sum[man_area_sum$Land_Type == \"Developed_all\"]\n # (6) assign 0's to aggregate cumulative areas for afforestation and reforestation and restoration and prescribed burn\n # For afforestation or restoration or prescribed burn management activities only, replace values in the aggregated area column with 0 \n # (i.e. afforestation and restoration and prescribed burn not included in aggregate sum)\n man_area_sum$man_area_sum_agg[man_area_sum$Management == \"Afforestation\" | man_area_sum$Management == \"Reforestation\" | man_area_sum$Management == \"Restoration\" | \n \tman_area_sum$Management == \"Prescribed_burn\" | man_area_sum$Management == \"Prescribed_burn_med_slash_util\" | man_area_sum$Management == \"Prescribed_burn_hi_slash_util\"] = 0\n \n } # end if there are any prescribed management practices\n \n # build some useful data frames\n all_c_flux = tot_area_df\n \n # check if there any prescibed management practices\n # if there are merge man_are_agg (2 columns: Land_Cat_ID, man_area_agg) with all_c_flux (5 columns: Land_Cat_ID, Region, Land_Type, Ownership, tot_area)\n if (nrow(man_area_sum)>0){\n all_c_flux = merge(all_c_flux, man_area_agg2, by = \"Land_Cat_ID\", all.x = TRUE)\n } \n \n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n \n if (nrow(man_area_sum)>0){\n all_c_flux$man_area_agg[all_c_flux$Land_Type == \"Developed_all\"] = man_area_sum$man_area[man_area_sum$Management == \"Dead_removal\"]\n na_inds = which(is.na(all_c_flux[,\"man_area_agg\"]))\n all_c_flux[na_inds,\"man_area_agg\"] = 0\n all_c_flux$unman_area = all_c_flux[,\"tot_area\"] - all_c_flux[,\"man_area_agg\"]\n all_c_flux = merge(all_c_flux, man_area_sum_agg2, by = \"Land_Cat_ID\", all.x = TRUE)\n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n all_c_flux$man_area_sum_agg[all_c_flux$Land_Type == \"Developed_all\"] = \n man_area_sum$man_area_sum[man_area_sum$Management == \"Dead_removal\"]\n na_inds = which(is.na(all_c_flux[,\"man_area_sum_agg\"]))\n all_c_flux[na_inds,\"man_area_sum_agg\"] = 0\n all_c_flux$unman_area_sum = all_c_flux[,\"tot_area\"] - all_c_flux[,\"man_area_sum_agg\"]\n # all_c_flux now has 9 columns: Land_Cat_ID, Region, Land_Type, Ownership, tot_area, man_area_agg, unman_area, man_area_sum_agg, unman_area_sum\n } else { # end if there any prescribed management practices\n # if no prescribed practices, assign 0's to managed area variables\n all_c_flux$man_area_agg <- 0\n all_c_flux$unman_area = all_c_flux[,\"tot_area\"] - all_c_flux[,\"man_area_agg\"]\n # all_c_flux = merge(all_c_flux, man_area_sum_agg2, by = \"Land_Cat_ID\", all.x = TRUE)\n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n all_c_flux$man_area_sum_agg <- 0\n all_c_flux$unman_area_sum = all_c_flux[,\"tot_area\"] - all_c_flux[,\"man_area_sum_agg\"]\n }\n \n # merge rangeland management (soil) effect and cultivated land df's. Then merge with developed and forest management \n # assign the common column names between man_grass_df and man_ag_df to common_cols since man_ag_df has extra columns now\n common_cols <- intersect(colnames(man_ag_df), colnames(man_grass_df))\n # rbind the commmon columns into man_adjust_df\n man_adjust_df = rbind(subset(man_grass_df, select = common_cols),subset(man_ag_df, select = common_cols))\n man_adjust_df = rbind(man_adjust_df, man_forest_df[,c(1:5,forest_soilcaccumfrac_colind)])\n man_adjust_df = rbind(man_adjust_df, man_dev_df[,c(1:5,dev_soilcaccumfrac_colind)])\n man_adjust_df = merge(man_adjust_df, rbind(man_forest_df, man_dev_df), by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \n \"Management\", \"SoilCaccum_frac\"), all.x = TRUE)\n \n # if there are no prescribed mgmt practices, add some empty columns to man_area_sum so that when it's merged with man_adjust_df it has same structure as\n # other cases. This will ensure that later calcs won't be affected by missing columns.\n if (nrow(man_area_sum)==0 & year == start_year) {\n columnnames <- data.frame(man_area=numeric(0), man_area_sum=numeric(0), tot_area=numeric(0), man_area_agg_pre=numeric(0), excess_area_pre=numeric(0), \n man_area_agg=numeric(0), man_area_sum_agg_extra=numeric(0), excess_sum_area=numeric(0), man_area_sum_agg=numeric(0))\n man_area_sum<-cbind(man_area_sum,columnnames) \n }\n # merge compiled management effects df with area calcs\n # this adds man_area, man_area_sum, tot_area, man_area_agg_pre, excess_area_pre, man_area_agg, man_area_sum_agg_extra, excess_sum_area, man_area_sum_agg\n # merge creates man_adjust_df with 38 variables\n man_adjust_df = merge(man_area_sum, man_adjust_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"), \n all.x = TRUE)\n \n man_adjust_df = man_adjust_df[order(man_adjust_df$Land_Cat_ID, man_adjust_df$Management),]\n # if there are any prescribed management practice, replace the NA values with more appropriate ones \n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,c(\"SoilCaccum_frac\", \"VegCuptake_frac\", \"DeadCaccum_frac\")] <- \n apply(man_adjust_df[,c(\"SoilCaccum_frac\", \"VegCuptake_frac\", \"DeadCaccum_frac\")], 2, function (x) {replace(x, is.na(x), 1.00)})\n man_adjust_df[,c(6:ncol(man_adjust_df))] <- apply(man_adjust_df[,c(6:ncol(man_adjust_df))], 2, function (x) {replace(x, is.na(x), 0.00)})\n }\n \n # set the working c accumulation values to the initial values for adjustments below\n vegc_uptake_df$vegc_uptake_val = vegc_uptake_df$init_vegc_uptake_val\n soilc_accum_df$soilc_accum_val = soilc_accum_df$init_soilc_accum_val\n \n # the proportional increase in urban forest area is represented as a proportional increase in veg c uptake\n # apply this to developed all using the iniital value\n # and this needs to be applied dirtectly to the value, not the frac, in case not all developed land has dead removal\n # if there were a soil c accum value it would have to be adjusted also\n man_adjust_df$start_urban_forest_fraction = 1\n man_adjust_df$current_urban_forest_fraction = 1\n if (year == start_year) {\n start_urban_forest_fraction = man_adjust_df[man_adjust_df$Management == \"Urban_forest\", \"man_area\"] / \n man_adjust_df[man_adjust_df$Management == \"Urban_forest\", \"tot_area\"]\n }\n man_adjust_df$start_urban_forest_fraction[man_adjust_df$Management == \"Urban_forest\"] = start_urban_forest_fraction\n man_adjust_df$current_urban_forest_fraction[man_adjust_df$Management == \"Urban_forest\"] = man_adjust_df[man_adjust_df$Management == \"Urban_forest\", \"man_area\"] / \n man_adjust_df[man_adjust_df$Management == \"Urban_forest\", \"tot_area\"]\n # veg\n vegc_uptake_df = merge(vegc_uptake_df, man_adjust_df[man_adjust_df$Management == \"Urban_forest\", c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"start_urban_forest_fraction\", \"current_urban_forest_fraction\")], by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n vegc_uptake_df$vegc_uptake_val[vegc_uptake_df$Land_Type == \"Developed_all\"] <- vegc_uptake_df$vegc_uptake_val[vegc_uptake_df$Land_Type == \"Developed_all\"] * \n \tvegc_uptake_df$current_urban_forest_fraction[vegc_uptake_df$Land_Type == \"Developed_all\"] / \n \tvegc_uptake_df$start_urban_forest_fraction[vegc_uptake_df$Land_Type == \"Developed_all\"]\n # soil\n soilc_accum_df = merge(soilc_accum_df, man_adjust_df[man_adjust_df$Management == \"Urban_forest\", c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"start_urban_forest_fraction\", \"current_urban_forest_fraction\")], by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n soilc_accum_df$soilc_accum_val[soilc_accum_df$Land_Type == \"Developed_all\"] <- soilc_accum_df$soilc_accum_val[soilc_accum_df$Land_Type == \"Developed_all\"] * \n \tsoilc_accum_df$current_urban_forest_fraction[soilc_accum_df$Land_Type == \"Developed_all\"] / \n \tsoilc_accum_df$start_urban_forest_fraction[soilc_accum_df$Land_Type == \"Developed_all\"]\n\t# these are not needed any more, and they need to be replaced each year\n\tvegc_uptake_df$start_urban_forest_fraction = NULL\n\tvegc_uptake_df$current_urban_forest_fraction = NULL\n\tsoilc_accum_df$start_urban_forest_fraction = NULL\n\tsoilc_accum_df$current_urban_forest_fraction = NULL\n \n # soil\n # apply climate effect to baseline soil c flux. use current year loop to determine which column to use in climate_soil_df (first clim factor col ind is 5)\n # note that the soil conservation flux will be modified too: man_soilc_flux = (man_soilc_flux/baseline_soilc_flux) * (baseline_soilc_flux * soil climate scalar)\n soilc_accum_df$soilc_accum_val <- soilc_accum_df$soilc_accum_val * climate_soil_df[,as.character(year)]\n # Cultivated uses the current year managed area\n man_soil_df = merge(man_adjust_df, soilc_accum_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all = TRUE)\n man_soil_df = man_soil_df[order(man_soil_df$Land_Cat_ID, man_soil_df$Management),]\n # omit growth and urban forest because only dead removal determines managed area\n man_soil_df = man_soil_df[(man_soil_df $Management != \"Urban_forest\" & man_soil_df$Management != \"Growth\") | is.na(man_soil_df$Management),]\n # if there are prescribed management practice(s),\n if (nrow(man_soil_df)>0) {\n # soil C flux * area = cumulative managed area * soil C mgmt frac * baseline soil c flux\n # for rows with NA management or NA soilc_accum_val, this equals NA\n man_soil_df$soilcfluxXarea[man_soil_df$Land_Type != \"Cultivated\"] = man_soil_df$man_area_sum[man_soil_df$Land_Type != \"Cultivated\"] * \n \tman_soil_df$SoilCaccum_frac[man_soil_df$Land_Type != \"Cultivated\"] * \n \tman_soil_df$soilc_accum_val[man_soil_df$Land_Type != \"Cultivated\"]\n # for cultivated lands: soilcfluxXarea = current year managed area * modified-SoilCaccum_frac * climate-affected baseline soil C flux\n man_soil_df$soilcfluxXarea[man_soil_df$Land_Type == \"Cultivated\"] = man_soil_df$man_area[man_soil_df$Land_Type == \"Cultivated\"] * \n \tman_soil_df$SoilCaccum_frac[man_soil_df$Land_Type == \"Cultivated\"] * \n \tman_soil_df$soilc_accum_val[man_soil_df$Land_Type == \"Cultivated\"]\n # this needs to be zero for afforestation and reforestation and restoration and prescribed burn because these areas are not included in actual managed area for flux adjustment\n # \tthe man_area_sum values for afforestation and reforestation and restoration are used later for protection area, so they can't be zeroed out above\n man_soil_df$soilcfluxXarea[man_soil_df$Management == \"Afforestation\" | man_soil_df$Management == \"Reforestation\" | man_soil_df$Management == \"Restoration\" | \n \tman_soil_df$Management == \"Prescribed_burn\" | man_soil_df$Management == \"Prescribed_burn_med_slash_util\" | man_soil_df$Management == \"Prescribed_burn_hi_slash_util\"] = 0.00\n } else {\n # if there are no prescribed management practices assign 0 to \"soil C flux * managed area\"\n man_soil_df$soilcfluxXarea <- 0.00 \n }\n \n # replace all NA soilcfluxXarea values with 0, otherwise they will not aggregate properly below\n na_inds <- which(is.na(man_soil_df$soilcfluxXarea))\n man_soil_df$soilcfluxXarea[na_inds] <- 0.00\n \n # aggregate soilcfluxXarea by landcat\n man_soilflux_agg = aggregate(soilcfluxXarea ~ Land_Cat_ID + Region + Land_Type + Ownership, man_soil_df, FUN=sum)\n # merge aggregated soil flux df with all_c_flux, which has man_area_agg, unman_area, man_area_sum_agg & unman_area_sum\n man_soilflux_agg = merge(all_c_flux, man_soilflux_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all = TRUE)\n # merge the soilc_accum_val from man_soil_df with man_soilflux_agg\n man_soilflux_agg = merge(man_soilflux_agg, man_soil_df[,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"soilc_accum_val\")], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x=TRUE)\n man_soilflux_agg = man_soilflux_agg[order(man_soilflux_agg$Land_Cat_ID),]\n # only save the individual landcats (omit extra rows)\n man_soilflux_agg = unique(man_soilflux_agg)\n # replace the landcats without a soil C flux value with 0\n na_inds = which(is.na(man_soilflux_agg$soilc_accum_val))\n man_soilflux_agg[na_inds, \"soilc_accum_val\"] = 0.00\n \n # Calculate the area-weighted soil C flux value \n # final non-cultivated soil c flux = [(managed soil c flux * cumulative mgmt area) + (baseline soil c flux * cumulative unmanaged area)]/ total area \n # if no mgmt, unman_area_sum==tot_area, and fin_soilc_accum = baseline soil C flux\n man_soilflux_agg$fin_soilc_accum[man_soilflux_agg$Land_Type != \"Cultivated\"] = \n (man_soilflux_agg$soilcfluxXarea[man_soilflux_agg$Land_Type != \"Cultivated\"] + \n man_soilflux_agg$unman_area_sum[man_soilflux_agg$Land_Type != \"Cultivated\"] * \n man_soilflux_agg$soilc_accum_val[man_soilflux_agg$Land_Type != \"Cultivated\"]) / \n tot_area_df$tot_area[tot_area_df$Land_Type != \"Cultivated\"]\n # final cultivated soil c flux = [(managed soil c flux * annual mgmt area) + (baseline soil c flux * annual unmanaged area)]/ total area \n man_soilflux_agg$fin_soilc_accum[man_soilflux_agg$Land_Type == \"Cultivated\"] = \n (man_soilflux_agg$soilcfluxXarea[man_soilflux_agg$Land_Type == \"Cultivated\"] + \n man_soilflux_agg$unman_area[man_soilflux_agg$Land_Type == \"Cultivated\"] * \n man_soilflux_agg$soilc_accum_val[man_soilflux_agg$Land_Type == \"Cultivated\"]) / \n tot_area_df$tot_area[tot_area_df$Land_Type == \"Cultivated\"]\n nan_inds = which(is.nan(man_soilflux_agg$fin_soilc_accum) | man_soilflux_agg$fin_soilc_accum == Inf | man_soilflux_agg$fin_soilc_accum == -Inf)\n man_soilflux_agg$fin_soilc_accum[nan_inds] = man_soilflux_agg[nan_inds, \"soilc_accum_val\"]\n man_soilflux_agg$man_change_soilc_accum = man_soilflux_agg$fin_soilc_accum - man_soilflux_agg$soilc_accum_val\n \n ############################################################################################################\n ######################################### All land types ##################################################\n ########### calc managed/unmanaged area-weighted VEG C FLUXES [MgC/ha/y] (fin_vegc_uptake) #################\n ############################################################################################################\n \n # all developed area veg c uptake is adjusted because urban forest increased\n # (recall: current year's \"VegCuptake_frac\" for Urban_forest management = proportional change in managed urban forest area to the initial \n # urban forest area)\n # so remove the other developed managements from this table and multiply by total area and use unman area = 0\n \n # apply this year's veg climate effect to baseline veg c flux (first year clim factor col ind is 5)\n vegc_uptake_df$vegc_uptake_val <- vegc_uptake_df$vegc_uptake_val * climate_veg_df[,as.character(year)]\n # merge man_adjust_df and vegc_uptake_df and assign to man_veg_df \n man_veg_df = merge(man_adjust_df, vegc_uptake_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all = TRUE)\n man_veg_df = man_veg_df[order(man_veg_df$Land_Cat_ID, man_veg_df$Management),] \n # omit all records for Urban forest & Growth and keep all others because dead removal determines developed managed area\n # maybe need to keep dead removal and not urban forest\n man_veg_df = man_veg_df[(man_veg_df$Management != \"Urban_forest\" & man_veg_df$Management != \"Growth\") | is.na(man_veg_df$Management),]\n \n ################ First, calc MANAGED AREA VEG C UPTAKE [MgC/y] (vegcfluxXarea) #############################\n \n # calc managed area's total veg C uptake for all landtypes using cumulative areas: \n \n # first check if there are no prescribed management practices and assign 0 to man_area_sum\n if (nrow(man_veg_df)==0) {\n man_veg_df$man_area_sum <- 0\n man_veg_df$vegcfluxXarea <- 0\n } else {\n # vegcfluxXarea = cumulative_management_area x VegCuptake_frac x vegc_uptake_val \n # recall that only dead removal is here for developed and man_area_sum is correct\n man_veg_df$vegcfluxXarea = man_veg_df$man_area_sum * man_veg_df$VegCuptake_frac * man_veg_df$vegc_uptake_val\n # this needs to be zero for afforestation and reforestation and restoration and prescribed burn because these areas are not included in actual managed area for flux adjustment\n # \tthe man_area_sum values for lcc are used later for protection area, so they can't be zeroed out above\n man_veg_df$vegcfluxXarea[man_veg_df$Management == \"Afforestation\" | man_veg_df$Management == \"Reforestation\" | man_veg_df$Management == \"Restoration\" | \n \tman_veg_df$Management == \"Prescribed_burn\" | man_veg_df$Management == \"Prescribed_burn_med_slash_util\" | man_veg_df$Management == \"Prescribed_burn_hi_slash_util\"] = 0.00\n }\n # aggregate sum veg C uptake across Land_Cat_ID + Region + Land_Type + Ownership \n man_vegflux_agg = aggregate(vegcfluxXarea ~ Land_Cat_ID + Region + Land_Type + Ownership, man_veg_df, FUN=sum)\n # merge aggregate sums with all_c_flux (management areas and total areas) \n man_vegflux_agg = merge(all_c_flux, man_vegflux_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n \n # clean up vegcfluxXarea values\n na_inds = which(is.na(man_vegflux_agg$vegcfluxXarea))\n man_vegflux_agg$vegcfluxXarea[na_inds] = 0\n \n # merge \"vegc_uptake_val\" (baseline veg c flux) column to man_vegflux_agg dataframe \n man_vegflux_agg = merge(man_vegflux_agg, man_veg_df[,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"vegc_uptake_val\")], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n man_vegflux_agg = man_vegflux_agg[order(man_vegflux_agg$Land_Cat_ID),]\n man_vegflux_agg = unique(man_vegflux_agg) \n # not all land types have veg C accumulation value - these cases have NA and represent 0 C accum\n na_inds = which(is.na(man_vegflux_agg$vegc_uptake_val)) \n man_vegflux_agg[na_inds, \"vegc_uptake_val\"] = 0\n \n ################ Last, calc area-weighted VEG C FLUXES [MgC/ha/y] (fin_vegc_uptake) ##########################\n # calc veg C flux for all landtypes using cumulative areas:\n # add column \"fin_vegc_uptake\" = ((veg C uptake due to mangement) + (cumulative unmanaged area)(vegc_uptake_val)) / (total area)\n man_vegflux_agg$fin_vegc_uptake = (man_vegflux_agg$vegcfluxXarea + man_vegflux_agg$unman_area_sum * \n man_vegflux_agg$vegc_uptake_val) / tot_area_df$tot_area\n #man_vegflux_agg$fin_vegc_uptake[man_vegflux_agg$Land_Type == \"Developed_all\"] = \n # man_vegflux_agg$vegcfluxXarea[man_vegflux_agg$Land_Type == \"Developed_all\"] / \n # tot_area_df$tot_area[man_vegflux_agg$Land_Type == \"Developed_all\"]\n nan_inds = which(is.nan(man_vegflux_agg$fin_vegc_uptake) | man_vegflux_agg$fin_vegc_uptake == Inf | man_vegflux_agg$fin_vegc_uptake == -Inf)\n man_vegflux_agg$fin_vegc_uptake[nan_inds] = man_vegflux_agg[nan_inds, \"vegc_uptake_val\"]\n # for cases without any prescribed management, fin_vegc_uptake should equal vegc_uptake_val, but due to rounding error the difference is not exactly 0\n # however, all(abs(man_vegflux_agg$fin_vegc_uptake-man_vegflux_agg$vegc_uptake_val)<0.000000000000001) == TRUE\n # thus, assign man_vegflux_agg$fin_vegc_uptake <- man_vegflux_agg$vegc_uptake_val for cases without prescribed management\n if (nrow(man_adjust_df)==0) {\n man_vegflux_agg$fin_vegc_uptake <- man_vegflux_agg$vegc_uptake_val\n }\n man_vegflux_agg$man_change_vegc_uptake = man_vegflux_agg$fin_vegc_uptake - man_vegflux_agg$vegc_uptake_val\n \n # dead\n \n # determine the fractional mortality c rate of above ground main for this year from target years (from the current year mortality fraction)\n # this is then applied to the above ground main c pools and the below ground main c pools\n # the understory mortality is set to a default value at the beginning of this script\n # linear interpolation between target years\n # if the year is past the final target year than use the final target year\n # the developed_all mortality is transferred to Above_harvest_frac in man_adjust_df, and zeroed in the deadfrac df\n \n linds = which(mortality_targetyears <= year)\n hinds = which(mortality_targetyears >= year)\n prev_targetyear = max(mortality_targetyears[linds])\n next_targetyear = min(mortality_targetyears[hinds])\n pind = which(mortality_targetyears == prev_targetyear)\n nind = which(mortality_targetyears == next_targetyear)\n pcol = mortality_targetyear_labels[pind]\n ncol = mortality_targetyear_labels[nind]\n \n if (prev_targetyear == next_targetyear | length(hinds) == 0) {\n deadc_frac_df$deadc_frac_in = mortality_target_df[,pcol]\n } else {\n deadc_frac_df$deadc_frac_in = mortality_target_df[,pcol] + \n (year - prev_targetyear) * (mortality_target_df[,ncol] - mortality_target_df[,pcol]) / (next_targetyear - prev_targetyear)\n }\n \n # merge the initial deadc_frac from dead_c_frac_df with man_adjust_df (tot areas, all the man areas, and effect params)\n man_dead_df = merge(man_adjust_df, deadc_frac_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all = TRUE)\n # remove all but Dead_removal (i.e. Growth and Urban_forest) for Developed_all, which is the total Developed_all landcat area. \n # this will ensure the aggregated management areas are correct (not larger than total landcat area)\n man_dead_df <- man_dead_df[(man_dead_df$Management!=\"Growth\" & man_dead_df$Management!=\"Urban_forest\") | is.na(man_dead_df$Management),]\n man_dead_df = man_dead_df[order(man_dead_df$Land_Cat_ID, man_dead_df$Management),]\n \n # deadCaccum_frac is from the forest_manage tab in c_input, which is the effect of forest management on mortality \n # and deadc_frac_in is determined by linear interpolation above\n # man_dead_area = agg_man_area * man_mort_factor * interp_mort_frac \n # first check if no prescribed management practices\n if (nrow(man_adjust_df)==0) {\n # assign 0 to man_dead_df$deadcfracXarea if no prescribed mgmt practices\n man_dead_df$deadcfracXarea <- 0\n } else {\n \tman_dead_df$adjDeadCfrac = man_dead_df$DeadCaccum_frac * man_dead_df$deadc_frac_in\n \tman_dead_df$adjDeadCfrac[man_dead_df$adjDeadCfrac >= 1] = 1\n \tman_dead_df$deadcfracXarea = man_dead_df$man_area_sum * man_dead_df$adjDeadCfrac\n \t# set these to zero becasue they are not included in managed sums\n \tman_dead_df$deadcfracXarea[man_dead_df$Management == \"Afforestation\" | man_dead_df$Management == \"Reforestation\" | man_dead_df$Management == \"Restoration\" | \n \t\tman_dead_df$Management == \"Prescribed_burn\" | man_dead_df$Management == \"Prescribed_burn_med_slash_util\" | man_dead_df$Management == \"Prescribed_burn_hi_slash_util\"] = 0.00\n }\n # aggregate all the man_dead_area\n man_deadfrac_agg = aggregate(deadcfracXarea ~ Land_Cat_ID + Region + Land_Type + Ownership, man_dead_df, FUN=sum)\n \n man_deadfrac_agg = merge(all_c_flux, man_deadfrac_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all = TRUE)\n na_inds = which(is.na(man_deadfrac_agg$deadcfracXarea))\n man_deadfrac_agg$deadcfracXarea[na_inds] = 0\n man_deadfrac_agg = merge(man_deadfrac_agg, deadc_frac_df, \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"))\n man_deadfrac_agg = man_deadfrac_agg[order(man_deadfrac_agg$Land_Cat_ID),]\n na_inds = which(is.na(man_deadfrac_agg$deadc_frac_in))\n man_deadfrac_agg[na_inds, \"deadc_frac_in\"] = 0\n \n # check if no prescribed management practices\n if (nrow(man_adjust_df)==0) {\n # if no prescribed mgmt practices assign deadc_frac_in to fin_deadc_frac \n man_deadfrac_agg$fin_deadc_frac <- man_deadfrac_agg$deadc_frac_in\n } else {\n # fin_dead_c_frac = (agg_man_dead_area + agg_unman_area * interp_mort_frac) / tot_area\n # which is the area-weighted mortality_fraction to later apply to total above- and below-ground C in Forest, Savanna/Woodland, and shrubland\n man_deadfrac_agg$fin_deadc_frac = (man_deadfrac_agg$deadcfracXarea + man_deadfrac_agg$unman_area_sum * \n man_deadfrac_agg$deadc_frac_in) / tot_area_df$tot_area\n nan_inds = which(is.nan(man_deadfrac_agg$fin_deadc_frac) | is.na(man_deadfrac_agg$fin_deadc_frac) | man_deadfrac_agg$fin_deadc_frac == Inf | man_deadfrac_agg$fin_deadc_frac == -Inf)\n # if NA or Inf, assign the interp_mort_frac to fin_deadc_frac\n man_deadfrac_agg$fin_deadc_frac[nan_inds] = man_deadfrac_agg[nan_inds, \"deadc_frac_in\"]\n }\n \n # man_change_deadc_accum = diff between area-weighted man & unman mort_frac and interp_mort_frac \n man_deadfrac_agg$man_change_deadc_accum = man_deadfrac_agg$fin_deadc_frac - man_deadfrac_agg$deadc_frac_in\n \n ## now transfer the developed_all mortality to \"Above_harvested_frac\" in man_adjust_df\n # 1. assign the developed_all records in man_deadfrac_agg to dev_deadfrac\n dev_deadfrac = man_deadfrac_agg[man_deadfrac_agg$Land_Type == \"Developed_all\",]\n # 2. assign 0's to fin_deadc_frac & man_change_deadc_accum for all developed_all records in man_deadfrac_agg\n man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Developed_all\"] = 0\n man_deadfrac_agg$man_change_deadc_accum[man_deadfrac_agg$Land_Type == \"Developed_all\"] = 0\n \n # merge the dev_deadcfrac with the man_adjust_df\n man_adjust_df = merge(man_adjust_df, dev_deadfrac[,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"fin_deadc_frac\")],\n \t\t\t\t\t\tby = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n # assign the non-zeroed fin_deadc_frac for Dead-removal in Developed_all to Above_harvested_frac\n man_adjust_df$Above_harvested_frac[man_adjust_df$Land_Type == \"Developed_all\" & man_adjust_df$Management==\"Dead_removal\"] = \n man_adjust_df$fin_deadc_frac[man_adjust_df$Land_Type == \"Developed_all\" & man_adjust_df$Management==\"Dead_removal\"]\n # now assign NULL to all fin_deadc_frac in man_adjust_df\n man_adjust_df$fin_deadc_frac = NULL\n man_adjust_df = man_adjust_df[order(man_adjust_df$Land_Cat_ID, man_adjust_df$Management),]\n \n cat(\"Starting eco c transfers\\n\")\n \n # apply the eco fluxes to the carbon pools (current year area and carbon)\n # (final flux * tot area + density * tot area) / tot area\n # above main, below main, understory, stand dead, down dead, litter, soil\n \n # general procedure\n # assume veg uptake is net live standing biomass accum (sans mortality)\n # calculate net below ground accum based on above to below ratio\n # assume dead accum is net mortality, subtract from above veg uptake - this goes to standing dead, downed dead, litter\n # calculate net below ground mortality from dead accum to above accum ratio - this is only subtracted from below pool because soil c values \n # are assumed to be net density changes\n # calculate net understory uptake and mortality from above values - this goes to downed dead and litter pools\n # estimate litter accum values from mortality and litter fraction of dead pools\n \n # notes\n # developed and ag have only above ground and soil c pools\n # developed mortality is transferred to above harvest to be able to control what happens to this biomass\n # no mortality for ag because woody crops are not tracked\n # treat na values as zeros\n # mortality fractions are zero in the input table if no veg c accum is listed in the carbon inputs (this is also checked below)\n # these flux transfers are normalized to current tot_area, and gains are positive\n \n # forest above main accum needs net foliage and branches/bark accums added to it based on estimated component fractions\n # forest downed dead and litter accum are estimated from the added above c based on mort:vegc flux ratio - this goes from above to downed \n # dead and litter - and this value is also a net value\n # forest dead standing is subtracted from above main\n # forest below main accum and understory accum need to be calculated based on ratio of these existing densities to the above densities\n # forest understory mortality uses a 1% default value (so it is not directly affected by prescribed tree mortality) - this is added to \n # downed dead and litter - as the additional veg c uptake is a net value, this accumulation is also a net value\n # forest below mortality is estimated based upon standing dead accum to vegc uptake ratio - this is only subtracted from below as soil c is \n # a net value\n \n # savanna/woodland veg uptake is net above and below (sans mortality) - so split it based on existing ratios\n # savanna/woodland has net eco exchange flux based on the tree uptake and the soil accum so don't add any other flux unless it cancels out\n # savanna/woodland to include mortality: transfer mortality to standing and downed dead and litter proportionally\n # savanna/woodland to include mortality: transfer mortality of below density to soil c\n # savanna/woodland understory will stay the same over time\n \n # out density sheet names: c(\"All_orgC_den\", \"All_biomass_C_den\", \"Above_main_C_den\", \"Below_main_C_den\", \"Understory_C_den\", \n # \"StandDead_C_den\", \"DownDead_C_den\", \"Litter_C_den\", \"Soil_orgC_den\")\n # put the current year density values into the next year column to start with\n # then add the carbon transfers to the next year column\n # eco accum names\n egnames = NULL\n for (i in 1:num_out_density_sheets) {\n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, cur_density_label]\n if(i >= 3) {\n egnames = c(egnames, paste0(out_density_sheets[i], \"_gain_eco\"))\n all_c_flux[,egnames[i-2]] = 0\n }\n }\n \n ######################### forest ######################### \n \n # above main\n # above main c density\n above_vals = out_density_df_list[[3]][out_density_df_list[[3]]$Land_Type == \"Forest\", cur_density_label]\n # above main stem c flux = area-weighted VEG C FLUXES\n vegc_flux_vals = man_vegflux_agg$fin_vegc_uptake[man_vegflux_agg$Land_Type == \"Forest\"]\n # above main leaf + bark + branch c flux \n added_vegc_flux_vals = vegc_flux_vals * (leaffrac + barkfrac + branchfrac) / stemfrac \n # dead C flux from stem = area-weighted mortality_fraction * above main c density * stemfrac = area-weighted mortality_fraction * stem c density\n deadc_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Forest\"] * above_vals * stemfrac\n # dead C flux from leaf + bark + branch = area-weighted mortality_fraction * above main c density * (1-stemfrac) = area-weighted mortality_fraction * remaining main c density\n above2dldead_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Forest\"] * above_vals * (1.0 - stemfrac)\n \n #deadc2vegc_ratios = deadc_flux_vals / vegc_flux_vals\n #above2dldead_flux_vals = deadc2vegc_ratios * added_vegc_flux_vals\n \n # Above_main_C_den_gain_eco = (stem + leaf + bark + branch c flux) - (mortality) \n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[1]] = vegc_flux_vals + added_vegc_flux_vals - deadc_flux_vals - above2dldead_flux_vals\n \n # standing dead C flux = c loss from stem\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[4]] = deadc_flux_vals\n \n # understory\n # understory c density\n under_vals = out_density_df_list[[5]][out_density_df_list[[5]]$Land_Type == \"Forest\", cur_density_label]\n # understory frac = understory/above main; constrain this to a max of the same growth rate for above and under\n underfrac = under_vals / above_vals\n biginds = which(underfrac > 1)\n if (length(biginds) > 0) { underfrac[biginds] = 1 }\n # understory c flux = (understory c dens/above main c dens) * (above main stem c flux) * (above main c dens/stem c dens)\n underc_flux_vals = underfrac * vegc_flux_vals / stemfrac\n # dead understory C = 0.01 * understory c dens\n under2dldead_flux_vals = default_mort_frac * out_density_df_list[[5]][out_density_df_list[[5]]$Land_Type == \"Forest\", cur_density_label]\n #under2dldead_flux_vals = deadc2vegc_ratios * underc_flux_vals\n # Understory_C_den_gain_eco = understory c flux - dead understory C\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[3]] = underc_flux_vals - under2dldead_flux_vals\n \n # downed dead and litter\n # down dead frac = DownDead_C_den / (DownDead_C_den + Litter_C_den)\n downfrac = out_density_df_list[[7]][out_density_df_list[[7]]$Land_Type == \"Forest\", cur_density_label] / \n (out_density_df_list[[7]][out_density_df_list[[7]]$Land_Type == \"Forest\", cur_density_label] + \n out_density_df_list[[8]][out_density_df_list[[8]]$Land_Type == \"Forest\", cur_density_label])\n # DownDead_C_den_gain_eco = (DownDead_C_den / (DownDead_C_den + Litter_C_den)) * (dead understory + leaf + bark + branch C)\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[5]] = downfrac * (above2dldead_flux_vals + under2dldead_flux_vals)\n # Litter_C_den_gain_eco = (Litter_C_den / (DownDead_C_den + Litter_C_den)) * (dead understory + leaf + bark + branch C)\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[6]] = (1.0 - downfrac) * (above2dldead_flux_vals + under2dldead_flux_vals)\n \n # below ground\n # recall that the input historical soil c fluxes are net, so the default historical mortality here implicitly goes to the soil\n # but any change from the default mortality needs to be added to the soil\n # so store the initial below ground mortality flux\n # assume that the other soil fluxes do not change (litter input rates and emissions) because I don't have enough info to change these\n # basically, the litter input would change based on its density, and the emissions may increase with additional soil c\n \n # Root C density = Below_main_C_den\n below_vals = out_density_df_list[[4]][out_density_df_list[[4]]$Land_Type == \"Forest\", cur_density_label]\n # Dead root C flux = area-weighted mortality_fraction * root C density\n below2dead_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Forest\"] * below_vals\n # save the initial area-weighted mortality_fraction\n if (year == start_year) { initial_deadc_frac_forest = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Forest\"] }\n \t# { below2dead_flux_initial_forest = below2dead_flux_vals }\n # first calculate the net root biomass increase\n # rootfrac = root c density/above main C density\n rootfrac = below_vals / above_vals\n # Below_main_C_den_gain_eco = (root c density/above main C density) * above main stem c flux/stem frac\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[2]] = rootfrac * (vegc_flux_vals / stemfrac) - below2dead_flux_vals\n \n # soil\n # need to add the difference due to chnages from default/initial mortality\n # Soil_orgC_den_gain_eco = area-weighted soil C flux + (Dead root C flux - initial area-weighted mortality_fraction * root C density)\n all_c_flux[all_c_flux$Land_Type == \"Forest\",egnames[7]] = man_soilflux_agg$fin_soilc_accum[man_soilflux_agg$Land_Type == \"Forest\"] + \n (below2dead_flux_vals - initial_deadc_frac_forest * below_vals)\n #(below2dead_flux_vals - below2dead_flux_initial_forest)\n \n ######################### savanna/woodland ######################### \n \n # above and below main\n # root loss has to go to soil c because the veg gain is tree nee, and the soil flux is ground nee, together they are the net flux\n # so here changing mortality is already accounted for with respect to additions to soil carbon\n # but these additions to the dead pools and soil c may be overestimated because the flux measurements do not include mortality\n # transfer above loss proportionally to standing, down, and litter pools\n # leave understory c static because the available data are for a grass understory, which has no long-term veg accumulation\n \n # Above main C dens = Above_main_C_den \n above_vals = out_density_df_list[[3]][out_density_df_list[[3]]$Land_Type == \"Savanna\" | out_density_df_list[[3]]$Land_Type == \"Woodland\", \n cur_density_label]\n # Above main C flux = area-weighted VEG C FLUX\n vegc_flux_vals = man_vegflux_agg$fin_vegc_uptake[man_vegflux_agg$Land_Type == \"Savanna\" | man_vegflux_agg$Land_Type == \"Woodland\"]\n # Root C dens = Below_main_C_den \n below_vals = out_density_df_list[[4]][out_density_df_list[[4]]$Land_Type == \"Savanna\" | out_density_df_list[[4]]$Land_Type == \"Woodland\", \n cur_density_label]\n # Above main C flux = area-weighted VEG C FLUX * Above C dens/(Above + Root C dens)\n above_flux_vals = vegc_flux_vals * above_vals / (above_vals + below_vals)\n # Root C flux = area-weighted VEG C FLUX * Root C dens/(Above + Root C dens)\n below_flux_vals = vegc_flux_vals * below_vals / (above_vals + below_vals)\n # Dead Above main C flux = area-weighted mortality_fraction * Above main C dens\n above2dead_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Savanna\" | man_deadfrac_agg$Land_Type == \"Woodland\"] * \n above_vals\n #zinds = which(above2dead_flux_vals == 0 & above_flux_vals > 0)\n #above2dead_flux_vals[zinds] = default_mort_frac * above_vals[zinds]\n #deadc2vegc_ratios = above2dead_flux_vals / above_flux_vals\n # Dead root C flux = area-weighted mortality_fraction * root C dens\n below2dead_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type == \"Savanna\" | man_deadfrac_agg$Land_Type == \"Woodland\"] * \n below_vals\n #naninds = which(is.nan(below2dead_flux_vals) & below_flux_vals > 0)\n #below2dead_flux_vals[naninds] = default_mort_frac * below_vals[naninds]\n #naninds = which(is.nan(below2dead_flux_vals))\n #below2dead_flux_vals[naninds] = 0\n \n # above_main_C_den_gain_eco = Above main C flux - Dead Above C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[1]] = above_flux_vals - above2dead_flux_vals\n # Below_main_C_den_gain_eco = Root C flux - Dead Root C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[2]] = below_flux_vals - below2dead_flux_vals\n \n # standing, down, and litter\n # Standing dead C dens = StandDead_C_den\n standdead_vals = out_density_df_list[[6]][out_density_df_list[[6]]$Land_Type == \"Savanna\" | out_density_df_list[[6]]$Land_Type == \"Woodland\", \n cur_density_label]\n # Down dead C dens = DownDead_C_den\n downdead_vals = out_density_df_list[[7]][out_density_df_list[[7]]$Land_Type == \"Savanna\" | out_density_df_list[[7]]$Land_Type == \"Woodland\", \n cur_density_label]\n # Litter C dens = Litter_C_den\n litter_vals = out_density_df_list[[8]][out_density_df_list[[8]]$Land_Type == \"Savanna\" | out_density_df_list[[8]]$Land_Type == \"Woodland\", \n cur_density_label]\n # Standing dead dens fraction = Stand Dead C dens/ Stand Dead + Down Dead + Litter)\n standdead_frac_vals = standdead_vals / (standdead_vals + downdead_vals + litter_vals)\n # Down dead dens fraction = Down Dead C dens/ Stand Dead + Down Dead + Litter)\n downdead_frac_vals = downdead_vals / (standdead_vals + downdead_vals + litter_vals)\n # Litter dens fraction = Litter C dens/ Stand Dead + Down Dead + Litter)\n litter_frac_vals = litter_vals / (standdead_vals + downdead_vals + litter_vals)\n # StandDead_C_den_gain_eco = Stand dead frac * Dead above main C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[4]] = standdead_frac_vals * above2dead_flux_vals\n # DownDead_C_den_gain_eco = Down dead frac * Dead above main C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[5]] = downdead_frac_vals * above2dead_flux_vals\n # Litter_C_den_gain_eco = Litter frac * Dead above main C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[6]] = litter_frac_vals * above2dead_flux_vals\n \n # soil - recall that this is nee flux measurement, not density change, so the root mortality has to go to soil c\n # Soil C flux = area-weighted soil C flux\n soilc_flux_vals = man_soilflux_agg$fin_soilc_accum[man_soilflux_agg$Land_Type == \"Savanna\" | man_soilflux_agg$Land_Type == \"Woodland\"]\n # Soil_orgC_den_gain_eco = area-weighted soil C flux + Dead root C flux\n all_c_flux[all_c_flux$Land_Type == \"Savanna\" | all_c_flux $Land_Type == \"Woodland\",egnames[7]] = soilc_flux_vals + below2dead_flux_vals\n \n ######################### the rest ######################### \n # assume vegc flux is all standing net density change, sans mortality\n # assume above and understory deadc flux is all mort density change - take from above and distribute among stand, down, and litter\n # use mortality only if there is veg c accum due to growth\n # assume soil c flux is net density change - so the below is simply a net root density change,\n #\tand the calculated or implicit mortality implicitly goes to soil\n # the developed all flux input includes above and below, so separate it accordingly, and use the initial ratio for all years rather than the current to maintain input ratio of density\n \n # Above main C dens\n above_vals = out_density_df_list[[3]][out_density_df_list[[3]]$Land_Type != \"Savanna\" & out_density_df_list[[3]]$Land_Type != \"Woodland\" & \n out_density_df_list[[3]]$Land_Type != \"Forest\", cur_density_label]\n # Root main C dens\n below_vals = out_density_df_list[[4]][out_density_df_list[[4]]$Land_Type != \"Savanna\" & out_density_df_list[[4]]$Land_Type != \"Woodland\" & \n out_density_df_list[[4]]$Land_Type != \"Forest\", cur_density_label]\n # Understory C dens\n under_vals = out_density_df_list[[5]][out_density_df_list[[5]]$Land_Type != \"Savanna\" & out_density_df_list[[5]]$Land_Type != \"Woodland\" & \n out_density_df_list[[5]]$Land_Type != \"Forest\", cur_density_label]\n # Standing dead C dens\n standdead_vals = out_density_df_list[[6]][out_density_df_list[[6]]$Land_Type != \"Savanna\" & out_density_df_list[[6]]$Land_Type != \"Woodland\" & \n out_density_df_list[[6]]$Land_Type != \"Forest\", cur_density_label]\n # Down dead C dens\n downdead_vals = out_density_df_list[[7]][out_density_df_list[[7]]$Land_Type != \"Savanna\" & out_density_df_list[[7]]$Land_Type != \"Woodland\" & \n out_density_df_list[[7]]$Land_Type != \"Forest\", cur_density_label]\n # Litter C dens\n litter_vals = out_density_df_list[[8]][out_density_df_list[[8]]$Land_Type != \"Savanna\" & out_density_df_list[[8]]$Land_Type != \"Woodland\" & \n out_density_df_list[[8]]$Land_Type != \"Forest\", cur_density_label]\n # Soil C dens\n soil_vals = out_density_df_list[[9]][out_density_df_list[[9]]$Land_Type != \"Savanna\" & out_density_df_list[[9]]$Land_Type != \"Woodland\" & \n out_density_df_list[[9]]$Land_Type != \"Forest\", cur_density_label]\n # above and below C fluxes\n # Above main c flux = area-weighted above-main C flux\n above_flux_vals = man_vegflux_agg$fin_vegc_uptake[man_vegflux_agg$Land_Type != \"Savanna\" & man_vegflux_agg$Land_Type != \"Woodland\" & \n man_vegflux_agg$Land_Type != \"Forest\"]\n # developed \n dev_inds = which(man_vegflux_agg$Land_Type[man_vegflux_agg$Land_Type != \"Savanna\" & man_vegflux_agg$Land_Type != \"Woodland\" & \n man_vegflux_agg$Land_Type != \"Forest\"] == \"Developed_all\")\n if(year == start_year) { \n \tinitial_dev_a2t_ratio = above_vals[dev_inds] / (above_vals[dev_inds] + below_vals[dev_inds])\n \tinitial_dev_b2a_ratio = below_vals[dev_inds] / above_vals[dev_inds]\n }\n above_flux_vals[dev_inds] = above_flux_vals[dev_inds] * initial_dev_a2t_ratio\n \n # Root main C flux = area-weighted above-main C flux * root C dens/above main C dens\n below_flux_vals = above_flux_vals * below_vals / above_vals\n below_flux_vals[dev_inds] = above_flux_vals[dev_inds] * initial_dev_b2a_ratio\n # index NA values\n naninds = which(is.nan(below_flux_vals))\n # For NA root C flux, assume default root to above frac (0.2) \n # Root main C flux = area-weighted above-main C flux * 0.2\n below_flux_vals[naninds] = above_flux_vals[naninds] * default_below2above_frac\n # Soil C flux = area-weighted soil C flux\n soilc_flux_vals = man_soilflux_agg$fin_soilc_accum[man_soilflux_agg$Land_Type != \"Savanna\" & man_soilflux_agg$Land_Type != \"Woodland\" & \n man_soilflux_agg$Land_Type != \"Forest\"]\n # recall that the input historical soil c fluxes are net, so the default historical mortality here implicitly goes to the soil\n # but any change from the default mortality needs to be added to the soil\n # so store the initial below ground mortality flux\n # assume that the other soil fluxes do not change (litter input rates and emissions) because I don't have enough info to change these\n # basically, the litter input would change based on its density, and the emissions may increase with additional soil c\n # also, if there is no veg c accum, then mortality is zero, even for understory, because only net soil c changes \n \n # dead c frac = area-weighted mortaility frac \n deadc_flux_vals = man_deadfrac_agg$fin_deadc_frac[man_deadfrac_agg$Land_Type != \"Savanna\" & man_deadfrac_agg$Land_Type != \"Woodland\" &\n \t\tman_deadfrac_agg$Land_Type != \"Forest\"]\n # for records with NA or negative above main C flux, assign 0 to the dead c flux\n deadc_flux_vals[is.na(above_flux_vals) | above_flux_vals <= 0] = 0.0\n # Dead above main C flux = area-weighted mortaility frac * above main C dens \n above2dead_flux_vals = deadc_flux_vals * above_vals\n # Dead root C flux = area-weighted mortaility frac * root C dens\n below2dead_flux_vals = deadc_flux_vals * below_vals\n \n # save intital area-weighted mortaility frac\n if (year == start_year) { initial_deadc_frac_rest = deadc_flux_vals }\n \t#{ below2dead_flux_initial_rest = below2dead_flux_vals }\n \n # above\n # Above_main_C_den_gain_eco = above main C flux - dead above main C flux\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[1]] = \n above_flux_vals - above2dead_flux_vals\n \n # root\n # Below_main_C_den_gain_eco = root C flux - dead root C flux\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[2]] = \n below_flux_vals - below2dead_flux_vals\n \n # understory\n # understory frac = understory/above main; constrain this to a max of the same growth rate for above and under\n underfrac = under_vals / above_vals\n biginds = which(underfrac > 1)\n if (length(biginds) > 0) { underfrac[biginds] = 1 }\n # understory C flux = understory/above main * area-weighted above main C flux\n underc_flux_vals = underfrac * above_flux_vals\n # index NA understory C flux\n naninds = which(is.nan(underc_flux_vals))\n # for records with NA understory C flux, assign to it default understory frac (0.1) * area-weighted above main C flux\n underc_flux_vals[naninds] = default_under_frac * above_flux_vals[naninds]\n # assign the dead area-weighted mortaility frac to understory mortality frac\n under_mort_frac = deadc_flux_vals\n # assign the default mortality fraction (0.01) to records with positive area-weighted above main C flux\n under_mort_frac[!is.na(above_flux_vals) & above_flux_vals > 0] = default_mort_frac\n # dead understory c flux = understory dead frac * Understory_C_den\n under2dead_flux_vals = under_mort_frac * out_density_df_list[[5]][out_density_df_list[[5]]$Land_Type != \"Savanna\" & \n out_density_df_list[[5]]$Land_Type != \"Woodland\" & \n out_density_df_list[[5]]$Land_Type != \"Forest\", cur_density_label]\n # Understory_C_den_gain_eco = understory C flux - dead understory c flux\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[3]] = \n underc_flux_vals - under2dead_flux_vals\n \n # stand, down, litter\n \n # Standing dead C frac = Standing dead C dens / (Standing dead + down dead + litter C dens)\n standdead_frac_vals = standdead_vals / (standdead_vals + downdead_vals + litter_vals)\n # index NA Standing dead C frac\n naninds = which(is.nan(standdead_frac_vals))\n # assign the default standing dead frac (0.11) to records with NA standing dead fracs\n standdead_frac_vals[naninds] = default_standdead_frac\n # down dead C frac = down dead C dens / (Standing dead + down dead + litter C dens)\n downdead_frac_vals = downdead_vals / (standdead_vals + downdead_vals + litter_vals)\n # index NA down dead C frac\n naninds = which(is.nan(downdead_frac_vals))\n # assign the default down dead frac (0.23) to records with NA down dead fracs\n downdead_frac_vals[naninds] = default_downdead_frac\n # litter C frac = litter C dens / (Standing dead + down dead + litter C dens)\n litter_frac_vals = litter_vals / (standdead_vals + downdead_vals + litter_vals)\n # index NA litter C frac\n naninds = which(is.nan(litter_frac_vals))\n # assign the default litter frac (0.66) to records with NA litter fracs\n litter_frac_vals[naninds] = default_litter_frac\n # StandDead_C_den_gain_eco = standing dead C frac * (area-weighted dead above main C flux + dead understory c flux)\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[4]] = \n standdead_frac_vals * (above2dead_flux_vals + under2dead_flux_vals)\n # DownDead_C_den_gain_eco = down dead C frac * (area-weighted dead above main C flux + dead understory c flux)\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[5]] = \n downdead_frac_vals * (above2dead_flux_vals + under2dead_flux_vals)\n # Litter_C_den_gain_eco = down dead C frac * (area-weighted dead above main C flux + dead understory c flux)\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[6]] = \n litter_frac_vals * (above2dead_flux_vals + under2dead_flux_vals)\n \n # soil\n # add any c due to changes from default/initial mortality\n \n # Soil_orgC_den_gain_eco = area-weighted soil C flux + (area-weighted mortaility frac - intital area-weighted mortaility frac)\n all_c_flux[all_c_flux$Land_Type != \"Savanna\" & all_c_flux$Land_Type != \"Woodland\" & all_c_flux$Land_Type != \"Forest\",egnames[7]] = \n soilc_flux_vals + (below2dead_flux_vals - initial_deadc_frac_rest * below_vals)\n #(below2dead_flux_vals - below2dead_flux_initial_rest)\n \n # clean up numerical errors\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.na(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.nan(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == Inf, 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # loop over the out density tables to update the carbon pools based on the eco fluxes\n # carbon cannot go below zero\n sum_change = 0\n sum_neg_eco = 0\n for (i in 3:num_out_density_sheets) {\n sum_change = sum_change + sum(all_c_flux[, egnames[i-2]] * all_c_flux$tot_area)\n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, next_density_label] + all_c_flux[, egnames[i-2]]\n # first calc the carbon not subtracted because it sends density negative\n neginds = which(out_density_df_list[[i]][, next_density_label] < 0)\n # print out the indices that have negative c\n cat(\"neginds for out_density_df_list eco\" , i, \"are\", neginds, \"\\n\")\n # print out the areas for land categries that have negative c (if greater than 0 then land category us running out of c, which may \n # be due to a particular case of land conversions, i.e. land category x has negative c accumulation and is gaining area from land category y \n # with lower c density, which can dilute the c density lower than the annual c loss sending the c density negative. \n # Otherwise, if the area == 0 and there is negative c density, it can solely be due to the land cateogry running out of area and having a \n # negative c accumulation rate.)\n cat(\"total areas for neginds out_density_df_list eco\" , i, \"are\", out_area_df_list[[1]][neginds, cur_area_label], \"\\n\")\n # check if any of the negative c density land categories have area > 0 (note: this only works if soil c density (i=9) is the only pool with \n # negative values, as this is re-saved for each i loop)\n if (any((out_area_df_list[[1]][neginds, cur_area_label])>0)) {\n # if so, subset the rows from the out_area_df_list associated with all neginds and assign to area_neginds_df\n area_neginds_df <- out_area_df_list[[1]][neginds,]\n # add column to area_neginds_df that says which c pool ran out of c\n area_neginds_df$neg_c_density_pool <- rep(out_density_sheets[i],nrow(area_neginds_df)) \n # add column that says which year it is\n area_neginds_df$Year <- rep(year,nrow(area_neginds_df)) \n # print the land category ID's that have neginds _and_ area >0\n cat(\"Land_Cat_ID with neginds for\", out_density_sheets[i], \"& non-zero area are\", \n unlist(area_neginds_df[out_area_df_list[[1]][neginds,cur_area_label]>0, c(\"Land_Cat_ID\",\"Region\",\"Ownership\",\"Land_Type\")]), \"\\n\")\n # subset rows from from area_neginds_df with area >0, and assign to out_neginds_eco_df_pre\n out_neginds_eco_df_pre <- area_neginds_df[out_area_df_list[[1]][neginds,cur_area_label]>0, \n c(\"Land_Cat_ID\",\"Region\",\"Ownership\",\"Land_Type\",\"Year\",cur_area_label,\"neg_c_density_pool\")]\n # rename the area column to generic name so rbind() for out_neginds_eco_df_pre & out_neginds_eco_df will work each year \n colnames(out_neginds_eco_df_pre)[6] <- \"Area_ha\"\n # get the c density from next_density_label column in out_density_df_list[[i]] that are <0 (neginds) and have tot_area >0, and add column to area_neginds_df\n out_neginds_eco_df_pre$neg_density <- out_density_df_list[[i]][out_density_df_list[[i]]$Land_Cat_ID %in% out_neginds_eco_df_pre$Land_Cat_ID, next_density_label]\n # turn on check that this exists\n area_neginds_exist <- TRUE\n } else {area_neginds_exist <- FALSE}\n # sum of all negative c cleared = tot_area * c density\n sum_neg_eco = sum_neg_eco + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] <= 0, 0.00)\n } # end loop over out densities for updating due to eco fluxes\n cat(\"eco carbon change is\", sum_change, \"\\n\")\n cat(\"eco negative carbon cleared is\", sum_neg_eco, \"\\n\")\n # add this year's neg sum to cumulative so we get a total negative c cleared at end of annual loop (out_neginds_eco is set to 0 before annual loop is started)\n out_cum_neginds_eco_tot <- sum_neg_eco + out_cum_neginds_eco_tot \n \n # join the out_neginds_eco_df_pre below the last row of the previous year in out_neginds_eco_df\n if (area_neginds_exist & exists(\"out_neginds_eco_df\")) {\n out_neginds_eco_df <- rbind(out_neginds_eco_df, out_neginds_eco_df_pre)\n } else { if (area_neginds_exist) {\n out_neginds_eco_df <- out_neginds_eco_df_pre\n }\n }\n ######\n ######\n # apply the transfer (non-eco, non-accum) flux management to the carbon pools (current year area and updated carbon)\n cat(\"Starting manage c transfers\\n\")\n \n # loop over the non-accum manage frac columns to calculate the transfer carbon density for each frac column\n # the transfer carbon density is based on tot_area so that it can be aggregated and subtracted directly from the current density\n \n ############################################################################################################\n #################### Do management C transfers [MgC/y] for forest & developed areas ########################\n ############################################################################################################\n\n # man_frac_names = c(\"Above_harvested_frac\", \"StandDead_harvested_frac\", \"Harvested2Wood_frac\", \"Harvested2Energy_frac\", \"Harvested2SawmillDecay_frac\", \n # \"Harvested2Slash_frac\", \"Under2Slash_frac\", \"DownDead2Slash_frac\", \"Litter2Slash_frac\", \"Slash2Energy_frac\", \"Slash2Burn_frac\", \"Slash2Decay_frac\", \n # \"Under2DownDead_frac\", \"Soil2Atmos_frac\", \"Above2StandDead_frac\", \"Below2Atmos_frac\", \"Below2Soil_frac\")\n \n # c_trans_names = c(\"Above_harvested_c\", \"StandDead_harvested_c\", \"Harvested2Wood_c\", \"Harvested2Energy_c\", \"Harvested2SawmillDecay_c\", \"Harvested2Slash_c\", \"Under2Slash_c\", \n # \"DownDead2Slash_c\", \"Litter2Slash_c\", \"Slash2Energy_c\", \"Slash2Burn_c\", \"Slash2Decay_c\", \"Under2DownDead_c\", \"Soil2Atmos_c\", \n # \"Above2StandDead_c\", \"Below2Atmos_c\", \"Below2Soil_c\")\n\n # indices of the appropriate density source df for the non-accum manage frac to c calcs; corresponds with out_density_sheets above\n # value == -1 indicates that the source is the harvested c; take the sum of the first two c trans columns\n # value == -2 indicates that the source is all slash-contributing pools; take the sum of c trans columns 6, 7 and 8 (\"Under2Slash_c\", \"DownDead2Slash_c\", \"Litter2Slash_c\")\n # manage_density_inds = c(3 [i=1], 6 [i=2], -1 [i=3], -1 [i=4], -1 [i=5], -1 [i=6], 5 [i=7], 7 [i=8], 8 [i=9], -2 [i=10], -2 [i=11], -2 [i=12], 5 [i=13], 9 [i=14], 3 [i=15], 4 [i=16], 4 [i=17])\n # manage_density_inds = c(3 (above), 6 (standdead), -1, -1, -1, -1 (Harvested2slash), 5 (under2slash), 7 (down2slash), 8 (litter2slash), -2 (slash2energy), -2 (slash2burn), -2 (slash2decay), 5 (under2down), 9 (soil), 3 (above2stand), 4 (below), 4 (below))\n # out_density_sheets = c(\"All_orgC_den\" (1), \"All_biomass_C_den\" (2), \"Above_main_C_den\" (3), \"Below_main_C_den\" (4), \"Understory_C_den\" (5), \"StandDead_C_den\" (6), \n # \"DownDead_C_den\" (7), \"Litter_C_den\" (8), \"Soil_orgC_den\" (9))\n # req'd order of operations: (1) Calc Harvested C (from above and stand dead), (2) Calc harvested 2slash, 2energy, 2decay (3) Calc other transfers 2slash (under, downdead, litter) (4) all others\n # for i in 1:17\n for (i in 1:num_manfrac_cols){\n # the removed values are calculated first, so this will work\n # if manage_density_inds[i] == -1, then the source is the removed pool; use the sum of the first two c trans columns \n if (manage_density_inds[i] == -1 | manage_density_inds[i] == -2) {\n # when i = 3, 4, 5, or 6\n if (manage_density_inds[i] == -1) {\n # (Harvested2Wood_c, Harvested2Energy_c, Harvested2Decay_c, Harvested2Slash_c) [Mg/ha] = (Above_harvested_c + StandDead_harvested_c) * \n # (Harvested2Wood_frac, Harvested2Energy_frac, Harvested2Decay_frac, Harvested2Slash_frac)\n man_adjust_df[,c_trans_names[i]] = (man_adjust_df[,c_trans_names[1]] + man_adjust_df[,c_trans_names[2]]) * man_adjust_df[,man_frac_names[i]]\n } else {\n # else manage_density_inds[i] == -2 (when i = 10, 11, or 12)\n # if manage_density_inds[i] == -2 the source is the slash pool; take the sum of c trans columns 6, 7, 8 & 9 (Harvest2Slash_c + Under2Slash_c + DownDead2Slash_c + Litter2Slash_c)\n\n # (slash2energy_c, slash2wood_c, slash2burn_c, slash2decay_c) [Mg/ha] = (Harvested2Slash_c + Under2Slash_c + DownDead2Slash_c + Litter2Slash_c) * \n # (Harvested2Slsash_frac, Under2Slash_frac, DownDead2Slash_frac, Litter2Slash_frac)\n man_adjust_df[,c_trans_names[i]] = (man_adjust_df[,c_trans_names[6]] + man_adjust_df[,c_trans_names[7]] + man_adjust_df[,c_trans_names[8]] +\n man_adjust_df[,c_trans_names[9]]) * man_adjust_df[,man_frac_names[i]]\n }\n # when i = 1(above2harvest), 2 (downdead2harvest), 7 (under2slash), 8 (downdead2slash), 9 (litter2slash), 13, 14, 15, 16, or 17, so use the C densities\n } else {\n # if any of the C density columns are not already one of the headers in man_adjust_df\n if (!out_density_sheets[manage_density_inds[i]] %in% colnames(man_adjust_df)) {\n # subset next year's C density from corresponding c pool's density dataframe and merge with the man_adjust_df\n man_adjust_df = merge(man_adjust_df, \n out_density_df_list[[manage_density_inds[i]]][,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", next_density_label)], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n # subset next year's corresponding C density header from the man_adjust_df, and assign to it the corresponding name of the c pool's density \n names(man_adjust_df)[names(man_adjust_df) == next_density_label] = out_density_sheets[manage_density_inds[i]]\n }\n # calc C transfer = C density [mgC/ha] * (management transfer fraction) * current year managed area [ha] / total area [ha]\n man_adjust_df[,c_trans_names[i]] = man_adjust_df[,out_density_sheets[manage_density_inds[i]]] * man_adjust_df[,man_frac_names[i]] * \n man_adjust_df$man_area / man_adjust_df$tot_area\n }\n } # end for i loop over the managed transfer fractions for calcuting the transfer carbon\n \n # check if there are any prescribed management practices (if not, don't do the following as it will result in error due to no rows)\n if (nrow(man_adjust_df)>0) { \n man_adjust_df = man_adjust_df[order(man_adjust_df$Land_Cat_ID, man_adjust_df$Management),]\n man_adjust_df[,c(6:ncol(man_adjust_df))] <- apply(man_adjust_df[,c(6:ncol(man_adjust_df))], 2, function (x) {replace(x, is.na(x), 0.00)})\n man_adjust_df[,c(6:ncol(man_adjust_df))] <- apply(man_adjust_df[,c(6:ncol(man_adjust_df))], 2, function (x) {replace(x, is.nan(x), 0.00)})\n man_adjust_df[,c(6:ncol(man_adjust_df))] <- apply(man_adjust_df[,c(6:ncol(man_adjust_df))], 2, function (x) {replace(x, x == Inf, 0.00)})\n man_adjust_df[,c(6:ncol(man_adjust_df))] <- apply(man_adjust_df[,c(6:ncol(man_adjust_df))], 2, function (x) {replace(x, x == -Inf, 0.00)})\n }\n \n # now consolidate the c density transfers to the pools\n # convert these to gains for consistency: all terrestrial gains are positive, losses are negative\n # store the names for aggregation below\n # out_density_sheets = c(\"All_orgC_den\" (1), \"All_biomass_C_den\" (2), \"Above_main_C_den\" (3), \"Below_main_C_den\" (4), \"Understory_C_den\" (5), \"StandDead_C_den\" (6), \n # \"DownDead_C_den\" (7), \"Litter_C_den\" (8), \"Soil_orgC_den\" (9))\n agg_names = NULL\n # above\n # add column called \"Above_main_C_den_gain_man\": above main C density = -(above harvested C) -(above to standing dead C)\n agg_names = c(agg_names, paste0(out_density_sheets[3], \"_gain_man\"))\n man_adjust_df[,agg_names[1]] = -man_adjust_df$Above_harvested_c - man_adjust_df$Above2StandDead_c\n # below\n # add column called \"Below_main_C_den_gain_man\": root C density = -(root to soil C) -(root to atmos C)\n agg_names = c(agg_names, paste0(out_density_sheets[4], \"_gain_man\"))\n man_adjust_df[,agg_names[2]] = -man_adjust_df$Below2Soil_c - man_adjust_df$Below2Atmos_c\n # understory\n # add column called \"Understory_C_den_gain_man\": understory C density = -(understory to slash C) -(understory to down dead C)\n agg_names = c(agg_names, paste0(out_density_sheets[5], \"_gain_man\"))\n man_adjust_df[,agg_names[3]] = -man_adjust_df$Under2Slash_c - man_adjust_df$Under2DownDead_c\n # standing dead\n # add column called \"StandDead_C_den_gain_man\": standing dead C density = -(harvested standing dead C) + (above main to standing dead C)\n agg_names = c(agg_names, paste0(out_density_sheets[6], \"_gain_man\"))\n man_adjust_df[,agg_names[4]] = -man_adjust_df$StandDead_harvested_c + man_adjust_df$Above2StandDead_c\n # down dead\n # add column called \"DownDead_C_den_gain_man\": down dead C density = -(down dead to slash C) + (understory to down dead C)\n agg_names = c(agg_names, paste0(out_density_sheets[7], \"_gain_man\"))\n man_adjust_df[,agg_names[5]] = -man_adjust_df$DownDead2Slash_c + man_adjust_df$Under2DownDead_c\n # litter\n # add column called \"Litter_C_den_gain_man\": litter C density = -(litter to slash C)\n agg_names = c(agg_names, paste0(out_density_sheets[8], \"_gain_man\"))\n man_adjust_df[,agg_names[6]] = -man_adjust_df$Litter2Slash_c\n # soil\n # add column called \"Soil_orgC_C_den_gain_man\": soil C density = -(soil to atmos C) + (root to soil C)\n agg_names = c(agg_names, paste0(out_density_sheets[9], \"_gain_man\"))\n man_adjust_df[,agg_names[7]] = -man_adjust_df$Soil2Atmos_c + man_adjust_df$Below2Soil_c\n \n # to get the carbon must multiply these by the tot_area\n #### C to atmos via 4 pathways (wood product decay(\"Wood\"), all other organic matter decay or root respiration (\"Decay\"), burn, energy) which determine \n # proportial fates of gaseous C emissions (CO2-C, CH4-C, BC-C) ####\n # \"Land2Atmos_DecayC_stock_man\" = -(total area [ha]) * (soil emissons [MgC/ha] + litter emissons [Mg/ha] + down dead emissons [Mg/ha] + \n # understory emissons [Mg/ha] + removed above-ground emissons [Mg/ha] + root emissions [Mg/ha])\n agg_names = c(agg_names, paste0(\"Land2Atmos_DecayC_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[8]] = -man_adjust_df$tot_area * (man_adjust_df$Soil2Atmos_c + man_adjust_df$Harvested2SawmillDecay_c + \n man_adjust_df$Slash2Decay_c + man_adjust_df$Below2Atmos_c)\n } else { \n man_adjust_df[,agg_names[8]] <- numeric(0) \n }\n \n # \"Land2Atmos_BurnC_stock_man\" = -(total area [ha]) * (slash burn emissions [MgC/ha]) \n agg_names = c(agg_names, paste0(\"Land2Atmos_BurnC_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[9]] = -man_adjust_df$tot_area * (man_adjust_df$Slash2Burn_c)\n } else {\n man_adjust_df[,agg_names[9]] <- numeric(0) \n }\n \n # \"Land2Atmos_TotEnergyC_stock_man\") = -(total area [ha]) * (Harvested C removed for energy [MgC/ha] + slash removal for energy [MgC/ha])\n agg_names = c(agg_names, paste0(\"Land2Atmos_TotEnergyC_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[10]] = -man_adjust_df$tot_area * (man_adjust_df$Harvested2Energy_c + man_adjust_df$Slash2Energy_c)\n } else {\n man_adjust_df[,agg_names[10]] <- numeric(0)\n }\n # Get amount of total energy that is from harvest versus slash utilization\n # Harv2Energy\n agg_names = c(agg_names, paste0(\"Land2Atmos_Harv2EnerC_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[11]] = -man_adjust_df$tot_area * man_adjust_df$Harvested2Energy_c\n } else {\n man_adjust_df[,agg_names[11]] <- numeric(0)\n }\n # Slash2Energy\n agg_names = c(agg_names, paste0(\"Land2Atmos_Slash2EnerC_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[12]] = -man_adjust_df$tot_area * man_adjust_df$Slash2Energy_c\n # replace NaN with 0\n man_adjust_df[,agg_names[11]] <- replace( man_adjust_df[,agg_names[11]], is.nan( man_adjust_df[,agg_names[11]]), 0.0)\n man_adjust_df[,agg_names[12]] <- replace( man_adjust_df[,agg_names[12]], is.nan( man_adjust_df[,agg_names[12]]), 0.0)\n } else {\n man_adjust_df[,agg_names[12]] <- numeric(0)\n }\n #### C to wood ####\n # wood - this decays with a half-life\n agg_names = c(agg_names, paste0(\"Land2Wood_c_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[13]] = -man_adjust_df$tot_area * (man_adjust_df$Harvested2Wood_c + man_adjust_df$Slash2Wood_c)\n } else {\n man_adjust_df[,agg_names[13]] <- numeric(0)\n }\n # Get amount of total wood that is from harvest versus slash utilization\n # Harv2Wood\n agg_names = c(agg_names, paste0(\"Harv2Wood_c_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[14]] = -man_adjust_df$tot_area * man_adjust_df$Harvested2Wood_c\n } else {\n man_adjust_df[,agg_names[14]] <- numeric(0)\n }\n # Slash2Wood\n agg_names = c(agg_names, paste0(\"Slash2Wood_c_stock_man\"))\n if (nrow(man_adjust_df)>0) {\n man_adjust_df[,agg_names[15]] = -man_adjust_df$tot_area * man_adjust_df$Slash2Wood_c\n # replace NaN with 0\n man_adjust_df[,agg_names[14]] <- replace( man_adjust_df[,agg_names[14]], is.nan( man_adjust_df[,agg_names[14]]), 0.0)\n man_adjust_df[,agg_names[15]] <- replace( man_adjust_df[,agg_names[15]], is.nan( man_adjust_df[,agg_names[15]]), 0.0)\n } else {\n man_adjust_df[,agg_names[15]] <- numeric(0)\n }\n \n ##### separate out (1) sawmill decay and (2) in-forest decay (slash + soil&root decay) from the total non-burned mangement C emissions\n agg_names = c(agg_names, paste0(\"Land2Atmos_SawmillDecayC_stock_man\"))\n agg_names = c(agg_names, paste0(\"Land2Atmos_InForestDecayC_stock_man\"))\n \n if (nrow(man_adjust_df)>0) {\n # non-burn Sawmill C emissions\n man_adjust_df[agg_names[16]] <- -man_adjust_df$tot_area * man_adjust_df$Harvested2SawmillDecay_c\n # non-burn In-forest C emissions (slash + soil + root)\n man_adjust_df[agg_names[17]] <- -man_adjust_df$tot_area * (man_adjust_df$Soil2Atmos_c + man_adjust_df$Below2Atmos_c +\n man_adjust_df$Slash2Decay_c)\n } else {\n man_adjust_df[agg_names[16]] <- numeric(0)\n man_adjust_df[agg_names[17]] <- numeric(0)\n }\n \n # now aggregate to land type by summing the management options\n # these c density values are the direct changes to the overall c density\n # the c stock values are the total carbon form each land type going to atmos, energy (atmos), and wood\n \n # first, create table that has a row for each land cat ID, and a column for each of the management-caused C density changes [MgC/ha], \n # and corresponding net cumulative C transfers [Mg C] to atmosphere (via decomp, burning, or energy (also burning)) or to wood \n agg_cols = array(dim=c(length(man_adjust_df$Land_Cat_ID),length(agg_names)))\n # second, populate the table by applying loop to each row's land cat ID \n for (i in 1:length(agg_names)) { # 1 to 15 (extra columns with with new slash pathway)\n # fill columns with corresponding management-caused C transfers from the man_adjust_df\n # agg_cols has 15 columns\n agg_cols[,i] = man_adjust_df[,agg_names[i]]\n }\n # if there are prescribed management practices \n if (nrow(man_adjust_df)>0) {\n # third, aggregate the C transfers by summing within each land type and ownership combination and assign to man_adjust_agg df\n # creates man_adjust_agg with 19 columns: Land_Cat_ID, Region, Land_Type, Ownership, V1:V15\n man_adjust_agg = aggregate(agg_cols ~ Land_Cat_ID + Region + Land_Type + Ownership, data=man_adjust_df, FUN=sum)\n } else {\n man_adjust_agg <- cbind(man_adjust_df[,1:4],(data.frame(V1=numeric(0), V2=numeric(0), V3=numeric(0), V4=numeric(0), V5=numeric(0), \n V6=numeric(0), V7=numeric(0), V8=numeric(0), V9=numeric(0), V10=numeric(0), V11=numeric(0),\n V12=numeric(0),V13=numeric(0),V14=numeric(0),V15=numeric(0)))) \n } \n # fourth, label the columns of the aggregated table \n # 15 names: \"Above_main_C_den_gain_man_agg\",\"Below_main_C_den_gain_man_agg\",\"Understory_C_den_gain_man_agg\",\"StandDead_C_den_gain_man_agg\",\"DownDead_C_den_gain_man_agg\", \n # \"Litter_C_den_gain_man_agg\",\"Soil_orgC_den_gain_man_agg\", \"Land2Atmos_DecayC_stock_man_agg\", \"Land2Atmos_BurnC_stock_man_agg\", \"Land2Atmos_TotEnergyC_stock_man_agg\", \n # \"Land2Atmos_Harv2EnerC_stock_man_agg\",\"Land2Atmos_Slash2EnerC_stock_man_agg\", \"Land2Wood_c_stock_man_agg\", \"Harv2Wood_c_stock_man_agg\", \"Slash2Wood_c_stock_man_agg\" \n agg_names2 = paste0(agg_names,\"_agg\")\n # replaces the last 15 columns \"V1\":\"V15\" with agg_names2\n names(man_adjust_agg)[c(5:ncol(man_adjust_agg))] = agg_names2\n \n # merge these values to the unman area table to apply the adjustments to each land type\n all_c_flux = merge(all_c_flux, man_adjust_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.na(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.nan(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == Inf, 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # loop over the out density tables to update the carbon pools based on the management fluxes\n # carbon cannot go below zero\n sum_change = 0\n sum_neg_man = 0\n # for above-main C density through soil organic C density dataframes, do:\n for (i in 3:num_out_density_sheets) {\n # subset each of the columns representing aggregated management-caused C density gains and the C stock transfers (i.e. C emissions and wood), \n # multiply by total area, and\n # sum them all \n # this gives a single value for state-wide cumulative management C changes [Mg C/y] -- used to make sure sure nothing is negative\n sum_change = sum_change + sum(all_c_flux[, agg_names2[i-2]] * all_c_flux$tot_area)\n \n #################################################### UPDATE NEXT YEAR'S C DENSITIES ####################################################\n # add corresponding management C density change to the value that is there (previous year's C density)\n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, next_density_label] + all_c_flux[, agg_names2[i-2]]\n # calc the total state-wide cumulative C not subtracted because it sends density negative -- used as check to make sure this is minimal\n neginds = which(out_density_df_list[[i]][, next_density_label] < 0)\n cat(\"neginds for out_density_df_list manage\" , i, \"are\", neginds, \"\\n\")\n sum_neg_man = sum_neg_man + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n sum_neg_eco = sum_neg_eco + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n \n # replace any negative updated C densities with 0\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] <= 0, 0.00)\n } # end loop over out densities for updating due to veg management\n cat(\"manage carbon change is \", sum_change, \"\\n\")\n cat(\"manage carbon to wood is \", sum(man_adjust_agg$Land2Wood_c_stock_man), \"\\n\")\n cat(\"manage carbon to atmos is \", sum(man_adjust_agg$Land2Atmos_c_stock_man), \"\\n\")\n cat(\"manage carbon to energy is \", sum(man_adjust_agg$Land2Energy_c_stock_man), \"\\n\")\n cat(\"manage burned carbon to atmos is \", sum(man_adjust_agg$Land2Atmos_burnedC_stock_man), \"\\n\")\n cat(\"manage non-burned carbon to atmos is \", sum(man_adjust_agg$Land2Atmos_nonburnedC_stock_man), \"\\n\")\n cat(\"manage negative carbon cleared is \", sum_neg_man, \"\\n\")\n \n # update the managed wood tables\n # recall that the transfers from land are negative values\n # use the IPCC half life equation for first order decay of wood products, and the CA average half life for all products\n # this includes the current year loss on the current year production\n # running stock and cumulative change values are at the beginning of the labeled year - so the next year value is the stock or sum after \n # current year production and loss\n # annual change values are in the year they occurred\n \n k = log(2) / wp_half_life\n # Next year's \"Manage_Wood_C_stock\" = Current year's \"Manage_Wood_C_stock\" * exp(-log(2)/half-life) + \n # wood_accumulated * ((1 - exp(-log(2)/half-life) / log(2)/half-life) \n out_wood_df_list[[6]][,next_wood_label] = out_wood_df_list[[6]][,cur_wood_label] * exp(-k) + ((1 - exp(-k)) / k) * \n -all_c_flux$Land2Wood_c_stock_man_agg\n # Next year's \"Manage_Wood_CumGain_C_stock\" = Current year's \"Manage_Wood_CumGain_C_stock\" + total wood_accumulated\n out_wood_df_list[[7]][,next_wood_label] = out_wood_df_list[[7]][,cur_wood_label] - all_c_flux$Land2Wood_c_stock_man_agg\n # Next year's \"Man_Harv2Wood_CumGain_C_stock\" = Current year's \"Man_Harv2Wood_CumGain_C_stock\" + wood_accumulated from harvest\n out_wood_df_list[[8]][,next_wood_label] = out_wood_df_list[[8]][,cur_wood_label] - all_c_flux$Harv2Wood_c_stock_man_agg\n # Next year's \"Man_Slash2Wood_CumGain_C_stock\" = Current year's \"Man_Slash2Wood_CumGain_C_stock\" + wood_accumulated from slash\n out_wood_df_list[[9]][,next_wood_label] = out_wood_df_list[[9]][,cur_wood_label] - all_c_flux$Slash2Wood_c_stock_man_agg\n # Current year's \"Manage_Wood_AnnGain_C_stock\" = wood_accumulated\n out_wood_df_list[[11]][,cur_wood_label] = -all_c_flux$Land2Wood_c_stock_man_agg\n out_wood_df_list[[12]][,cur_wood_label] = -all_c_flux$Harv2Wood_c_stock_man_agg\n out_wood_df_list[[13]][,cur_wood_label] = -all_c_flux$Slash2Wood_c_stock_man_agg\n # Current year's \"Manage_Wood_AnnLoss_C_stock\" = Current year's \"Manage_Wood_C_stock\" + wood_accumulated - Next year's \"Manage_Wood_C_stock\" \n out_wood_df_list[[14]][,cur_wood_label] = out_wood_df_list[[6]][,cur_wood_label] - all_c_flux$Land2Wood_c_stock_man_agg - \n out_wood_df_list[[6]][,next_wood_label]\n # Next year's \"Manage_Wood_CumLoss_C_stock\" = Current year's \"Manage_Wood_CumLoss_C_stock\" + Current year's \"Manage_Wood_AnnLoss_C_stock\"\n out_wood_df_list[[10]][,next_wood_label] = out_wood_df_list[[10]][,cur_wood_label] + out_wood_df_list[[14]][,cur_wood_label]\n \n ############################################################################################################\n ############################################################################################################\n ######################################### Apply FIRE to C pools ##########################################\n ############################################################################################################\n ############################################################################################################\n \n # apply fire to the carbon pools (current year area and updated carbon)\n # distribute fire to forest, woodland, savanna, shrubland, and grassland, proportionally within the ownerships each year\n # assume that burn area onversion is not reflected in the baseline land type change numbers\n # (which isn't necessarily the case if using the remote sensing landfire data for lulcc)\n cat(\"Starting fire c transfers\\n\")\n \n ############################################################################################################\n ########################## first, calculate this year's FIRE AREA based on the targets #####################\n ############################################################################################################\n \n # if the year is past the final target year than use the final target year\n \n # indices of prior or current target years\n linds = which(fire_targetyears <= year)\n # indices of upcoming or current target years\n hinds = which(fire_targetyears >= year)\n # set latest (or current) target year\n prev_targetyear = max(fire_targetyears[linds])\n # set next (or current) target year\n next_targetyear = min(fire_targetyears[hinds])\n # index of previous target year\n pind = which(fire_targetyears == prev_targetyear)\n # index of next target year\n nind = which(fire_targetyears == next_targetyear)\n # column header of previous target year\n pcol = fire_targetyear_labels[pind]\n # column header of next target year\n ncol = fire_targetyear_labels[nind]\n \n # assign the fire target areas to fire_area_df\n fire_area_df = fire_target_df[,c(1:5)]\n # if current year is a target year or past all target years, \n if (prev_targetyear == next_targetyear | length(hinds) == 0) {\n # then create column for previous year target area and set to previous (or current) year's target area \n fire_area_df[,pcol] = fire_target_df[,pcol]\n # else add a column with previous year target area\n } else {\n # update the column with the linear interpolation of the areas between target years\n # note that this doesn't label the header with the correct year, but that's ok because it's replaced with \"fire_own_area\" below\n fire_area_df[,pcol] = fire_target_df[,pcol] + (year - prev_targetyear) * (fire_target_df[,ncol] - fire_target_df[,pcol]) / \n (next_targetyear - prev_targetyear)\n }\n fire_area_df[which(is.na(fire_area_df))] = 0.0\n \n ############################################################################################################\n ################## second, proportionally distribute ownership fire areas to each landtype #################\n ############################################################################################################\n \n # assign assigned FIRE TARGET AREA BY REGION/OWNERSHIP [ha] to \"fire_own_area\" \n names(fire_area_df)[names(fire_area_df) == pcol] = \"fire_own_area\"\n # merge the fire effects dataframe with the fire target areas and assign to fire_adjust_df\n fire_adjust_df = merge(fire_area_df, fire_df, by = c(\"Severity\"), all.x = TRUE)\n fire_adjust_df$Land_Cat_ID = NULL\n fire_adjust_df$Land_Type = NULL\n # merge with the tot_area_df region and by ownership class\n fire_adjust_df = merge(tot_area_df, fire_adjust_df, by = c(\"Region\", \"Ownership\"), all.x = TRUE)\n # trim dataframe to only include forest, woodland, savanna, grassland, shrubland\n fire_adjust_df = fire_adjust_df[fire_adjust_df$Land_Type == \"Forest\" | fire_adjust_df$Land_Type == \"Woodland\" | \n fire_adjust_df$Land_Type == \"Savanna\" | fire_adjust_df$Land_Type == \"Grassland\" | \n fire_adjust_df$Land_Type == \"Shrubland\",]\n # create new dataframe for REGION/OWNERSHIP AREA [ha]: AGGREGATE total AREAS by REGION/OWNERSHIP\n avail_own_area = aggregate(tot_area ~ Region + Ownership, data = fire_adjust_df[!duplicated(fire_adjust_df$Land_Cat_ID),c(1:5)], sum)\n # rename OWNERSHIP AREA [ha]: \"avail_own_area\"\n names(avail_own_area)[3] = \"avail_own_area\"\n # AGGREGATE severities to get total burn AREAS by REGION/OWNERSHIP\n fire_own_area_agg = aggregate(fire_own_area ~ Land_Cat_ID, data = fire_adjust_df[,c(1:4,6:7)], sum)\n # rename area column to \"fire_own_area_agg\"\n names(fire_own_area_agg)[2] = \"fire_own_area_agg\"\n # merge FIRE C TRANSFER EFFECTS (fractions) dataframe with the aggregated fire area\n fire_adjust_df = merge(fire_own_area_agg, fire_adjust_df, by = c(\"Land_Cat_ID\"), all.y = TRUE)\n # merge FIRE C TRANSFER EFFECTS (fractions) dataframe with the ownership areas dataframe\n fire_adjust_df = merge(avail_own_area, fire_adjust_df, by = c(\"Region\", \"Ownership\"), all.y = TRUE)\n # if assigned FIRE TARGET AREA BY OWNERSHIP [ha] > TOTAL AREA OF OWNERSHIP [ha],\n #\tset target aggregated burn area equal to the total ownership area by scaling the severity areas\n fire_adjust_df$fire_own_area <- replace(fire_adjust_df$fire_own_area, fire_adjust_df$fire_own_area_agg > fire_adjust_df$avail_own_area, \n fire_adjust_df$fire_own_area[fire_adjust_df$fire_own_area_agg > fire_adjust_df$avail_own_area] * \n fire_adjust_df$avail_own_area[fire_adjust_df$fire_own_area_agg > fire_adjust_df$avail_own_area] / \n fire_adjust_df$fire_own_area_agg[fire_adjust_df$fire_own_area_agg > fire_adjust_df$avail_own_area])\n # create column for BURNED AREA [ha] for each landtype-ownership combination and proportinally distribute burned areas \n # BURNED AREA [ha] = (FIRE TARGET AREA BY OWNERSHIP [ha]) * (landtype area / ownership area)\n fire_adjust_df$fire_burn_area = fire_adjust_df$fire_own_area * fire_adjust_df$tot_area / fire_adjust_df$avail_own_area\n # clean up zero area effects\n fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")] <- apply(fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")], 2, function (x) {replace(x, is.na(x), 0.00)})\n fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")] <- apply(fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")], 2, function (x) {replace(x, is.nan(x), 0.00)})\n fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")] <- apply(fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")], 2, function (x) {replace(x, x == Inf, 0.00)})\n fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")] <- apply(fire_adjust_df[,c(\"fire_own_area\", \"fire_burn_area\")], 2, function (x) {replace(x, x == -Inf, 0.00)})\n # Remove delayed fire decay from grassland\n fire_adjust_df$Above2Atmos_frac[fire_adjust_df$Land_Type == \"Grassland\"] = \n \tfire_adjust_df$Above2Atmos_frac[fire_adjust_df$Land_Type == \"Grassland\"] +\n \tfire_adjust_df$Above2StandDead_frac[fire_adjust_df$Land_Type == \"Grassland\"]\n fire_adjust_df$Above2StandDead_frac[fire_adjust_df$Land_Type == \"Grassland\"] = 0.00\n\tfire_adjust_df$Understory2Atmos_frac[fire_adjust_df$Land_Type == \"Grassland\"] = \n \tfire_adjust_df$Understory2Atmos_frac[fire_adjust_df$Land_Type == \"Grassland\"] +\n \tfire_adjust_df$Understory2DownDead_frac[fire_adjust_df$Land_Type == \"Grassland\"]\n fire_adjust_df$Understory2DownDead_frac[fire_adjust_df$Land_Type == \"Grassland\"] = 0.00\n\n \n ############################################################################################################\n ################## third, adjust fire severity for managed forest #################\n ############################################################################################################\n \n # merge the managed area with the fire df\n # man_adjust_df: 78 variables, and fire_adjust_df: 19 variables (including fire_burn_area)\n fire_sevadj_df = merge(fire_adjust_df, man_adjust_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"))\n \n # keep only valid forest management practices\n # remove afforestation and reforestation because it is not an actual managed area\n # prescribed burn cumulative area will replace the other forest fuel reduciton cumulative area for this adjustment\n # the assumption is that prescribed burn is applied to area that has undergone fuel reduction practices within the past 20 years\n #\tso rx burn doesn't add new managed area (see c accum adjustments above), but it adjusts the fire severity on previously managed areas\n # if there is more prescribed burn cum area than thin+understory, then assume that the extra rx burn area represents either:\n #\ta) previous, untracked managed area, as could happen early in the simulation\n #\tb) rx burns not on previously managed area\n #\tc) frequent repeat rx burns that inflate the cum sum faster than other management sums\n #\td) there was never any prior management, which is unlikely, but should be counted (although the c accum increase would be underestimated in this case)\n #\tin all three cases retain the extra rx burn area for fire severity reduction as they all would give valid extra benefits (although (c)'s benefits might be less)\n # even with any extra rx burn area, this is likely more realistic than the case where all rx burn added to the other practices for severity adjustment\n fire_sevadj_df = fire_sevadj_df[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Management != \"Afforestation\" &\n \t fire_sevadj_df$Management != \"Reforestation\",]\n \n # burned area for adjusted severity = burned_area/forest_area * man_area_sum/forest_area * forest_area\n # this is per management practice, per severity, based on managed area and total forest area in the land cat\n # need to calculate decreases/increases based on input fractions, then scale increases so that total man burn area doesn't change\n # check that there are prescribed mmgmt practices relevant to wildfire severity and that there is prescribed wildfire\n if ( nrow(fire_sevadj_df)>0 ) {\n \n # if there are prescribed management practices, create man_burn_area for variable for fire_sevadj_df (79 variables total)\n fire_sevadj_df$man_burn_area = 0.0\n # man_burn_area = fire_burn_area * (man_area_sum/tot_area)\n # subtract rx burn man_area_sum from thinning and understory man_area_sums proportionally\n # first get the fuel reduction sum and add it to the df, if there is fuel reduction\n if (nrow(fire_sevadj_df[fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\",]) > 0) {\n \tthin_undr_agg = aggregate(man_area_sum ~ Land_Cat_ID + Region + Land_Type + Ownership + Severity, \n fire_sevadj_df[fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\",], FUN=sum, na.rm = TRUE)\n \tnames(thin_undr_agg)[names(thin_undr_agg) == \"man_area_sum\"] = \"thin_undr_man_area_sum\" \t\n \tfire_sevadj_df = merge(fire_sevadj_df, thin_undr_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Severity\"), all.x = TRUE)\n \t# clean up bad values due to aggregation and merging\n \tfire_sevadj_df$thin_undr_man_area_sum[fire_sevadj_df$thin_undr_man_area_sum < 0] = 0\n \tfire_sevadj_df$thin_undr_man_area_sum[is.na(fire_sevadj_df$thin_undr_man_area_sum)] = 0\n \tfire_sevadj_df$thin_undr_man_area_sum[is.nan(fire_sevadj_df$thin_undr_man_area_sum)] = 0\n \tfire_sevadj_df$thin_undr_man_area_sum[fire_sevadj_df$thin_undr_man_area_sum == Inf] = 0\n \tfire_sevadj_df$thin_undr_man_area_sum[fire_sevadj_df$thin_undr_man_area_sum == -Inf] = 0\n } else {\n \tfire_sevadj_df$thin_undr_man_area_sum = 0\n }\n # then get the rx burn sum and add it to the df, if it exists\n if (nrow(fire_sevadj_df[fire_sevadj_df$Management == \"Prescribed_burn\" | fire_sevadj_df$Management == \"Prescribed_burn_med_slash_util\" | \n fire_sevadj_df$Management == \"Prescribed_burn_hi_slash_util\",]) > 0) {\n \trxb_agg = aggregate(man_area_sum ~ Land_Cat_ID + Region + Land_Type + Ownership + Severity, \n fire_sevadj_df[fire_sevadj_df$Management == \"Prescribed_burn\" | fire_sevadj_df$Management == \"Prescribed_burn_med_slash_util\" | \n fire_sevadj_df$Management == \"Prescribed_burn_hi_slash_util\",], FUN=sum, na.rm = TRUE)\n \tnames(rxb_agg)[names(rxb_agg) == \"man_area_sum\"] = \"rxb_man_area_sum\"\n \tfire_sevadj_df = merge(fire_sevadj_df, rxb_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Severity\"), all.x = TRUE)\n \t# clean up bad values due to aggregation and merging\n \tfire_sevadj_df$rxb_man_area_sum[fire_sevadj_df$rxb_man_area_sum < 0] = 0\n \tfire_sevadj_df$rxb_man_area_sum[is.na(fire_sevadj_df$rxb_man_area_sum)] = 0\n \tfire_sevadj_df$rxb_man_area_sum[is.nan(fire_sevadj_df$rxb_man_area_sum)] = 0\n \tfire_sevadj_df$rxb_man_area_sum[fire_sevadj_df$rxb_man_area_sum == Inf] = 0\n \tfire_sevadj_df$rxb_man_area_sum[fire_sevadj_df$rxb_man_area_sum == -Inf] = 0\n } else {\n \tfire_sevadj_df$rxb_man_area_sum = 0\n }\n # calc the man_area_sum adjustment\n fire_sevadj_df$thin_undr_mas_adj = 0.00\n fire_sevadj_df$thin_undr_mas_adj[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] =\n \tfire_sevadj_df$rxb_man_area_sum[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] *\n \tfire_sevadj_df$man_area_sum[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] /\n \tfire_sevadj_df$thin_undr_man_area_sum[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")]\n # first calculate all to get the non-thinning and non-understory values \t\n fire_sevadj_df$man_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management)] = \n \tfire_sevadj_df$fire_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management)] *\n \tfire_sevadj_df$man_area_sum[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management)] /\n \tfire_sevadj_df$tot_area.x[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management)]\n # second calculate the adjusted thinning and understory values (which replaces the previous calc) \t\n fire_sevadj_df$man_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] = \n \tfire_sevadj_df$fire_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] *\n \t(fire_sevadj_df$man_area_sum[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")] -\n \tfire_sevadj_df$thin_undr_mas_adj[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")]) /\n \tfire_sevadj_df$tot_area.x[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) &\n \t\t(fire_sevadj_df$Management == \"Thinning\" | fire_sevadj_df$Management == \"Understory_treatment\" |\n \t\tfire_sevadj_df$Management == \"Thinning_med_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_med_slash_util\" |\n \t\tfire_sevadj_df$Management == \"Thinning_hi_slash_util\" | fire_sevadj_df$Management == \"Understory_treatment_hi_slash_util\")]\n # clean up divide by zero and negative values (where rx burn cum area exceeds thin+under cum area)\n fire_sevadj_df$man_burn_area[fire_sevadj_df$man_burn_area < 0] = 0\n fire_sevadj_df$man_burn_area[is.na(fire_sevadj_df$man_burn_area)] = 0\n fire_sevadj_df$man_burn_area[is.nan(fire_sevadj_df$man_burn_area)] = 0\n fire_sevadj_df$man_burn_area[fire_sevadj_df$man_burn_area == Inf] = 0\n fire_sevadj_df$man_burn_area[fire_sevadj_df$man_burn_area == -Inf] = 0\n \t\n # sum the managed burn area across severities for normalization later\n # man_burn_agg (6 variables: Land_Cat_ID, Region, Land_Type, Ownership, Management, man_burn_area)\n man_burn_agg = aggregate(man_burn_area ~ Land_Cat_ID + Region + Land_Type + Ownership + Management, fire_sevadj_df, FUN=sum, na.rm = TRUE)\n # change man_burn_area to man_burn_area_agg\n names(man_burn_agg)[names(man_burn_agg) == \"man_burn_area\"] = \"man_burn_area_agg\"\n # merge the aggregated burn areas with the fire_sevadj_df\n # fire_sevadj_df has 95 variables \n fire_sevadj_df = merge(fire_sevadj_df, man_burn_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"), all.x = TRUE)\t\n # clean up divide by zero and negative values (where rx burn cum area exceeds thin+under cum area)\n fire_sevadj_df$man_burn_area_agg[fire_sevadj_df$man_burn_area_agg < 0] = 0\n fire_sevadj_df$man_burn_area_agg[is.na(fire_sevadj_df$man_burn_area_agg)] = 0\n fire_sevadj_df$man_burn_area_agg[is.nan(fire_sevadj_df$man_burn_area_agg)] = 0\n fire_sevadj_df$man_burn_area_agg[fire_sevadj_df$man_burn_area_agg == Inf] = 0\n fire_sevadj_df$man_burn_area_agg[fire_sevadj_df$man_burn_area_agg == -Inf] = 0\n \n # new adjusted man burn area = managed burn area * adjusted fraction for severity\n # add new column for the new adjusted man burn area (fire_sevadj_df: 96 variables)\n fire_sevadj_df$man_burn_area_new = 0.0\n # calculate the new adjusted man burn area for high severity\n fire_sevadj_df$man_burn_area_new[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"High\"] =\n \tfire_sevadj_df$man_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"High\"] *\n \tfire_sevadj_df$high_sev_frac[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"High\"]\n # calculate the new adjusted man burn area for med severity\n fire_sevadj_df$man_burn_area_new[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Medium\"] =\n \tfire_sevadj_df$man_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Medium\"] *\n \tfire_sevadj_df$med_sev_frac[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Medium\"]\n # calculate the new adjusted man burn area for low severity\n fire_sevadj_df$man_burn_area_new[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Low\"] =\n \tfire_sevadj_df$man_burn_area[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Low\"] *\n \tfire_sevadj_df$low_sev_frac[fire_sevadj_df$Land_Type == \"Forest\" & !is.na(fire_sevadj_df$Management) & fire_sevadj_df$Severity == \"Low\"]\n \n # 1. REDUCED & UNCHANGED SEVERITY AREAS\n # sum the new decreased (and unchanged) burn area severities to normalize the increases so man burn area doesn't change\n # store in mban_dec_agg (Land_Cat_ID, Region, Land_Type, Ownership, Management, man_burn_area_new)\n # \n if (nrow(fire_sevadj_df[fire_sevadj_df$man_burn_area_new <= fire_sevadj_df$man_burn_area,])>0) {\n \t\tmban_dec_agg = aggregate(man_burn_area_new ~ Land_Cat_ID + Region + Land_Type + Ownership + Management, \n fire_sevadj_df[fire_sevadj_df$man_burn_area_new <= fire_sevadj_df$man_burn_area,], FUN=sum, na.rm = TRUE)\n \t# rename the new aggreagated man_burn_area_new in mban_dec_agg: \"man_burn_area_new_dec_agg\"\n \tnames(mban_dec_agg)[names(mban_dec_agg) == \"man_burn_area_new\"] = \"man_burn_area_new_dec_agg\"\n \t# merge mban_dec_agg with fire_sevadj_df (fire_sevadj_df: 97 variables)\n \tfire_sevadj_df = merge(fire_sevadj_df, mban_dec_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"), all.x = TRUE)\n \t# clean up bad values due to aggregation and merging\n \tfire_sevadj_df$man_burn_area_new_dec_agg[fire_sevadj_df$man_burn_area_new_dec_agg < 0] = 0\n \tfire_sevadj_df$man_burn_area_new_dec_agg[is.na(fire_sevadj_df$man_burn_area_new_dec_agg)] = 0\n \tfire_sevadj_df$man_burn_area_new_dec_agg[is.nan(fire_sevadj_df$man_burn_area_new_dec_agg)] = 0\n \tfire_sevadj_df$man_burn_area_new_dec_agg[fire_sevadj_df$man_burn_area_new_dec_agg == Inf] = 0\n \tfire_sevadj_df$man_burn_area_new_dec_agg[fire_sevadj_df$man_burn_area_new_dec_agg == -Inf] = 0\n } else {\n # assign 0 to decreased severity area if there isn't any\n fire_sevadj_df$man_burn_area_new_dec_agg <- 0\n }\n \n # 2a. INCREASED SEVERITY AREA\n # sum the new increased severities to normalize the increases so man burn area doesn't change\n # before aggregating, check that fire_sevadj_df has rows which have new burn areas with higher severity than originally\n if (nrow(fire_sevadj_df[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area,])>0) {\n \tmban_inc_agg = aggregate(man_burn_area_new ~ Land_Cat_ID + Region + Land_Type + Ownership + Management, \n fire_sevadj_df[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area,], FUN=sum, na.rm = TRUE)\n \t# call it \"man_burn_area_new_inc_agg\"\n \tnames(mban_inc_agg)[names(mban_inc_agg) == \"man_burn_area_new\"] = \"man_burn_area_new_inc_agg\"\n \t# add it to fire_sevadj_df (fire_sevadj_df: 98 variables)\n \tfire_sevadj_df = merge(fire_sevadj_df, mban_inc_agg, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"), all.x = TRUE)\n \t# clean up bad values due to aggregation and merging\n \tfire_sevadj_df$man_burn_area_new_inc_agg[fire_sevadj_df$man_burn_area_new_inc_agg < 0] = 0\n \tfire_sevadj_df$man_burn_area_new_inc_agg[is.na(fire_sevadj_df$man_burn_area_new_inc_agg)] = 0\n \tfire_sevadj_df$man_burn_area_new_inc_agg[is.nan(fire_sevadj_df$man_burn_area_new_inc_agg)] = 0\n \tfire_sevadj_df$man_burn_area_new_inc_agg[fire_sevadj_df$man_burn_area_new_inc_agg == Inf] = 0\n \tfire_sevadj_df$man_burn_area_new_inc_agg[fire_sevadj_df$man_burn_area_new_inc_agg == -Inf] = 0\n } else {\n # assign 0 to increased severity area if there isn't any\n fire_sevadj_df$man_burn_area_new_inc_agg <- 0\n }\n \n # 2b. ADJUST INCREASED SEVERITY AREAS\n # scale the increased severities so that total man burn area doesn't change\n # man burn area new = man_burn area new * (total man burn area - total man burn area new decreased) / total man burn area new increased\n # due to the condition, the denominator should always be > 0 if it is calculated, so it shouldn't create any bad values\n fire_sevadj_df$man_burn_area_new[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area] =\n \tfire_sevadj_df$man_burn_area_new[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area] *\n \t(fire_sevadj_df$man_burn_area_agg[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area] -\n \tfire_sevadj_df$man_burn_area_new_dec_agg[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area]) /\n \tfire_sevadj_df$man_burn_area_new_inc_agg[fire_sevadj_df$man_burn_area_new > fire_sevadj_df$man_burn_area]\n \n # 3. now aggregate original managed and new managed burn area across management and get adjustments to fire_burn_area\n man_burn_area_agg = aggregate(man_burn_area ~ Land_Cat_ID + Region + Land_Type + Ownership + Severity, fire_sevadj_df, FUN=sum, na.rm = TRUE)\n # man_burn_area_new_agg includes Land_Cat_ID, Region, Land_Type, Ownership, Severity, man_burn_area_new\n man_burn_area_new_agg = aggregate(man_burn_area_new ~ Land_Cat_ID + Region + Land_Type + Ownership + Severity, fire_sevadj_df, FUN=sum, na.rm = TRUE)\t\n # fire_burn_area_adj = difference between the new aggregated burn severity areas and the original burn severity areas. \n man_burn_area_new_agg$fire_burn_area_adj = man_burn_area_new_agg$man_burn_area_new - man_burn_area_agg$man_burn_area\n \t\n # put the severity adjustments (\"fire_burn_area_adj\") from man_burn_area_new_agg into fire_adjust_df (19 to 20 variables) \n fire_adjust_df = merge(fire_adjust_df, man_burn_area_new_agg[,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Severity\", \"fire_burn_area_adj\")], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Severity\"), all.x = TRUE)\n # clean up divide by zero and negative values (where rx burn cum area exceeds thin+under cum area)\n fire_adjust_df$fire_burn_area_adj[is.na(fire_adjust_df$fire_burn_area_adj)] = 0\n fire_adjust_df$fire_burn_area_adj[is.nan(fire_adjust_df$fire_burn_area_adj)] = 0\n fire_adjust_df$fire_burn_area_adj[fire_adjust_df$fire_burn_area_adj == Inf] = 0\n fire_adjust_df$fire_burn_area_adj[fire_adjust_df$fire_burn_area_adj == -Inf] = 0\n \n # update fire_burn_area\n fire_adjust_df$fire_burn_area[!is.na(fire_adjust_df$fire_burn_area_adj)] =\n \tfire_adjust_df$fire_burn_area[!is.na(fire_adjust_df$fire_burn_area_adj)] +\n \tfire_adjust_df$fire_burn_area_adj[!is.na(fire_adjust_df$fire_burn_area_adj)]\n } else {\n # if there are not prescribed mmgmt practices and prescribed wildfire\n # assign 0's to fire_burn_area_adj and update fire_burn_area (may not even be necessary)\n # and fire_burn_area stays the same\n fire_adjust_df$fire_burn_area_adj <- 0\n # reorder columns to match fire_adjust_df for cases with prescribed management\n fire_adjust_df <- fire_adjust_df[,c(\"Land_Cat_ID\",\"Region\",\"Land_Type\",\"Ownership\",\"Severity\",\n \"avail_own_area\", \"fire_own_area_agg\", \"tot_area\", \"fire_own_area\", \n \"Above2Atmos_frac\", \"StandDead2Atmos_frac\", \"Understory2Atmos_frac\", \n \"DownDead2Atmos_frac\", \"Litter2Atmos_frac\", \"Above2StandDead_frac\", \n \"Understory2DownDead_frac\", \"Below2Atmos_frac\", \"Soil2Atmos_frac\", \n \"fire_burn_area\", \"fire_burn_area_adj\")]\n \n } # end if there are valid forest practices else not\n \n ############################################################################################################\n ################## fourth, estimate non-regenerated area #################\n ############################################################################################################\n \n # aggregate the burn area in each land cat (i.e., sum the severities)\n lc_burn_area = aggregate(fire_burn_area ~ Land_Cat_ID + Region + Land_Type + Ownership, fire_adjust_df, FUN=sum, na.rm = TRUE)\n names(lc_burn_area)[names(lc_burn_area) == \"fire_burn_area\"] = \"lc_burn_area\"\n \n # forest\n # first calculate stand decay coefficient (SDC) from ln(SDC) = -3.34*high sev fraction of burn area - 4\n fire_nonreg_df = merge(lc_burn_area, fire_adjust_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.y = TRUE)\n fire_nonreg_df$sdc = 0.0\n fire_nonreg_df$sdc[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] = \n \texp(-3.34 * fire_nonreg_df$fire_burn_area[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] / \n \tfire_nonreg_df$lc_burn_area[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] - 4.0)\n # now estimate the high severity area that does not regenerate: hs non-reg = hs area / 10^(SDC*Dist)\n #\tDist is the distance from the forest/burn edge beyond which no regeneration occurs, in meters\n # do this only if NR_Dist >= 0\n fire_nonreg_df$non_regen_area = 0.0\n if (NR_Dist >= 0) {\n \tfire_nonreg_df$non_regen_area[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] =\n \t\tfire_nonreg_df$fire_burn_area[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] /\n \t\t10^(fire_nonreg_df$sdc[fire_nonreg_df$Land_Type == \"Forest\" & fire_nonreg_df$Severity == \"High\"] * NR_Dist)\n }\n fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")] <- apply(fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")], 2, function (x) {replace(x, is.na(x), 0.00)})\n fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")] <- apply(fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")], 2, function (x) {replace(x, is.nan(x), 0.00)})\n fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")] <- apply(fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")], 2, function (x) {replace(x, x == Inf, 0.00)})\n fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")] <- apply(fire_nonreg_df[,c(\"sdc\", \"non_regen_area\")], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n ############################################################################################################\n ################# fifth, calc changes in C densities for each of the fire effects within the ###############\n ######################### fire areas withn each landtypes-ownership combination ############################\n ############################################################################################################ \n # loop over the fire frac columns to calculate the transfer carbon density for each frac column\n # the transfer carbon density is based on tot_area so that it can be aggregated and subtracted directly from the current density\n for (i in 1:num_firefrac_cols){\n # if the column names of C densities MgC/ha are not in the fire_adjust df (basically saying stop when done)\n if (!out_density_sheets[fire_density_inds[i]] %in% colnames(fire_adjust_df)) {\n # then merge the C density pool corresponding to the source of all the fire C transfer fractions \n fire_adjust_df = merge(fire_adjust_df, \n out_density_df_list[[fire_density_inds[i]]][,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", next_density_label)], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n # and assign the name of the C density pool to the column\n names(fire_adjust_df)[names(fire_adjust_df) == next_density_label] = out_density_sheets[fire_density_inds[i]]\n }\n # lastly, fill in each of the fire C transfer = C density [Mg/ha] * fraction of effected C pool * landtype-ownership fire area/ total area\n fire_adjust_df[,firec_trans_names[i]] = fire_adjust_df[,out_density_sheets[fire_density_inds[i]]] * fire_adjust_df[,fire_frac_names[i]] * \n fire_adjust_df$fire_burn_area / fire_adjust_df$tot_area\n } # end for loop over the fire transfer fractions for calcuting the transfer carbon\n # clean up fire output\n fire_adjust_df = fire_adjust_df[order(fire_adjust_df$Land_Cat_ID, fire_adjust_df$Severity),]\n fire_adjust_df[,is.na(c(8:ncol(fire_adjust_df)))] <- 0\n fire_adjust_df[,is.nan(c(8:ncol(fire_adjust_df)))] <- 0\n fire_adjust_df[,(c(8:ncol(fire_adjust_df))) == Inf] <- 0\n fire_adjust_df[,(c(8:ncol(fire_adjust_df))) == -Inf] <- 0\n \n ############################################################################################################\n ################## sixth, apply some decay over time for newly added dead material #################\n ############################################################################################################\n # above to stand dead and understory to down dead are these delayed decay pools\n # most of this will go to atmosphere over the decay period\n # can use existing stand dead and understory to atmos transfer variables\n # so the annual losses to atmosphere from these two pools are calculated here\n # and in the next step these losses are applied to these two pools by adding them to the \"to atmosphere\" transfers\n \n # calculate the decay for this year's new dead material and add it to the ongoing decay\n \n # for start_year only, create the 2 df's that track the decay for standdead and down dead\n if (year == start_year) {\n \tfire_decay_standdead = fire_adjust_df\n \tfire_decay_downdead = fire_adjust_df\n \tfor (i in fire_decay_years) {\n \t\tfcname = paste0(\"decay_year_\",i)\n \t\tfire_decay_standdead[,fcname] = 0.0\n \t\tfire_decay_downdead[,fcname] = 0.0 \n \t}\n } # done creating decay df if start_year\n \n # add 2 new columns to fire_adjust_df to distinguish _non-burned_ C emissions of StandDead & DownDead due to decay from burned C emissions\n fire_adjust_df$StandDead2AtmosNonburn_c <- 0\n fire_adjust_df$DownDead2AtmosNonburn_c <- 0\n \n for (i in fire_decay_years) {\n \tfcname = paste0(\"decay_year_\",i)\n \tif (i == 1) {\n \t\t# add the loss of new current year's dead material to the first decay year\n \t # decay_year_1 = prior cumulative decay_year_1 + current year's decay\n \t\tfire_decay_standdead[, fcname] = fire_decay_standdead[, fcname] + standdead_decay_frac[i] * fire_adjust_df$Above2StandDead_c\n \t\t # decay_year_1 = prior cumulative decay_year_1 + current year's decay\n \t\tfire_decay_downdead[, fcname] = fire_decay_downdead[, fcname] + downdead_decay_frac[i] * fire_adjust_df$Understory2DownDead_c\n \t\t# apply this year's loss to the transfer variables\n \t\t # Current year's Non-burned C emissions = current year's decayed C (includes fraction from current year's total C transfer + cumulative fractions of prior year(s))\n \t\tfire_adjust_df$StandDead2AtmosNonburn_c = fire_decay_standdead[,\"decay_year_1\"]\n \t\tfire_adjust_df$DownDead2AtmosNonburn_c = fire_decay_downdead[,\"decay_year_1\"]\n \t} else {\n \t\t# update the decay array\n \t\tfcname_prev = paste0(\"decay_year_\",i-1)\n \t\t# add the loss of new dead material\n \t\tfire_decay_standdead[, fcname_prev] = fire_decay_standdead[, fcname] + standdead_decay_frac[i] * fire_adjust_df$Above2StandDead_c\n \t\tfire_decay_downdead[, fcname_prev] = fire_decay_downdead[, fcname] + downdead_decay_frac[i] * fire_adjust_df$Understory2DownDead_c\n \t\tif (i == length(fire_decay_years)) {\n \t\t\t# also zero out the last value\n \t\t\tfire_decay_standdead[, fcname] = 0.0\n \t\t\tfire_decay_downdead[, fcname] = 0.0\n \t\t}\n \t}\n } # end for i loop over the fire decay years\n \n ############################################################################################################\n ################ seventh, consolidate the changes in C densities within each C density pool #################\n ############################################################################################################\n # now consolidate the c density transfers to the pools\n # convert these to gains for consistency: all terrestrial gains are positive, losses are negative\n # store the names for aggregation below\n fire_agg_names = NULL\n # above\n # add a column called \"Above_main_C_den_gain\": above-main C density = -(above to atmos C) -(above to standing dead C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[3], \"_gain\"))\n fire_adjust_df[,fire_agg_names[1]] = -fire_adjust_df$Above2Atmos_c - fire_adjust_df$Above2StandDead_c\n # below\n # add a column called \"Below_main_C_den_gain\": root C density = -(root to atmos C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[4], \"_gain\"))\n fire_adjust_df[,fire_agg_names[2]] = -fire_adjust_df$Below2Atmos_c\n # understory\n # add a column called \"Understory_C_den_gain\": understory C density = -(understory to atmos C) -(understory to down dead C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[5], \"_gain\"))\n fire_adjust_df[,fire_agg_names[3]] = -fire_adjust_df$Understory2Atmos_c - fire_adjust_df$Understory2DownDead_c\n # standing dead\n # add a column called \"StandDead_C_den_gain\": standing dead C density = -(standing dead to atmos C from burning) + -(standing dead to atmos C from decay) + (above-main to standing dead C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[6], \"_gain\"))\n fire_adjust_df[,fire_agg_names[4]] = -fire_adjust_df$StandDead2Atmos_c + -fire_adjust_df$StandDead2AtmosNonburn_c + fire_adjust_df$Above2StandDead_c\n # down dead\n # add a column called \"DownDead_C_den_gain\": down dead C density = -(down dead to atmos C) + -(down dead to atmos C from decay) + (down dead to down dead C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[7], \"_gain\"))\n fire_adjust_df[,fire_agg_names[5]] = -fire_adjust_df$DownDead2Atmos_c + -fire_adjust_df$DownDead2AtmosNonburn_c + fire_adjust_df$Understory2DownDead_c\n # litter\n # add a column called \"Litter_C_den_gain\": litter C density = -(litter to atmos C)\n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[8], \"_gain\"))\n fire_adjust_df[,fire_agg_names[6]] = -fire_adjust_df$Litter2Atmos_c\n # soil\n # add a column called \"Soil_orgC_den_gain\": soil C density = -(soil to atmos C) \n fire_agg_names = c(fire_agg_names, paste0(out_density_sheets[9], \"_gain\"))\n fire_adjust_df[,fire_agg_names[7]] = -fire_adjust_df$Soil2Atmos_c\n \n ############################################################################################################\n ################################## eighth, calc total C loss to atmosphere ################################# \n ############################################################################################################\n \n # to get the carbon must multiply these by the tot_area\n # atmos\n # calc fire C loss to atmosphere [Mg C] (\"Land2Atmos_c_stock_fire\") = -(total area [ha]) * (soil emissons [MgC/ha] + \n # litter emissons [Mg/ha] + down dead emissons [Mg/ha] + standing dead emissions [Mg/ha] + understory emissons [Mg/ha] + \n # root emissions [Mg/ha] + above-main emissions [Mg/ha] + standing dead non-burn emissions [Mg/ha] + down dead non-burned emissions [Mg/ha])\n fire_agg_names = c(fire_agg_names, paste0(\"Land2Atmos_c_stock\"))\n fire_adjust_df[,fire_agg_names[8]] = -fire_adjust_df$tot_area * (fire_adjust_df$Soil2Atmos_c + fire_adjust_df$Litter2Atmos_c + \n fire_adjust_df$DownDead2Atmos_c + fire_adjust_df$StandDead2Atmos_c + \n fire_adjust_df$Understory2Atmos_c + fire_adjust_df$Below2Atmos_c + \n fire_adjust_df$Above2Atmos_c + fire_adjust_df$StandDead2AtmosNonburn_c +\n fire_adjust_df$DownDead2AtmosNonburn_c)\n # Partition the Land2Atmos_c_stock into total burned (CO2-C+CH4-C+BC-C) and total non-burned C emissions (CO2-C). Currently soil c and root c \n # are assumed not to burn, and decay is captured in ongoing soil accum rates. (i.e. input values for wildfire effects fractions on \n # root and soil c to atmosphere are currently 0. Update if contrary evidence is found)\n # first, calculate burned c emissions from wildfire\n fire_agg_names = c(fire_agg_names, paste0(\"Land2Atmos_BurnedC_stock\"))\n # Land2Atmost_BurnedC_stock [Mg C] = tot_area [ha] * all burned (litter, down dead, stand dead, understory, above-main) C emission densities [MgC/ha]\n fire_adjust_df[,fire_agg_names[9]] = -fire_adjust_df$tot_area * (fire_adjust_df$Litter2Atmos_c + fire_adjust_df$DownDead2Atmos_c + \n fire_adjust_df$StandDead2Atmos_c + \n fire_adjust_df$Understory2Atmos_c + fire_adjust_df$Above2Atmos_c)\n # second, calculate non-burned c emissions from wildfire\n fire_agg_names = c(fire_agg_names, paste0(\"Land2Atmos_NonBurnedC_stock\"))\n # Land2Atmos_NonBurnedC_stock [Mg C] = tot_area [ha] * all non-burned (down dead, stand dead, root, soil) C emission densities [MgC/ha]\n fire_adjust_df[,fire_agg_names[10]] = -fire_adjust_df$tot_area * (fire_adjust_df$DownDead2AtmosNonburn_c + fire_adjust_df$StandDead2AtmosNonburn_c +\n fire_adjust_df$Soil2Atmos_c + fire_adjust_df$Below2Atmos_c)\n \n # check that fire Land2Atmos c flux is equal to the sum of burned and non-burned c stock in the fire_adjust_df\n # \"Land2Atmos_c_stock\" - \"Land2Atmos_BurnedC_stock\" = \"Land2Atmos_NonBurnedC_stock\"\n # not identical due to rouding error\n identical(fire_adjust_df[,fire_agg_names[8]] - fire_adjust_df[,fire_agg_names[9]], fire_adjust_df[,fire_agg_names[10]])\n # this is true in 2010 for BAU wildfire\n all(abs(fire_adjust_df[,fire_agg_names[8]] - fire_adjust_df[,fire_agg_names[9]] - fire_adjust_df[,fire_agg_names[10]]) <= 0.000000001)\n \n ############################################################################################################\n ######### ninth, aggregate changes in each C density pool within each landtype-ownership class ############# \n ############################################################################################################\n # now aggregate to land type by summing the fire intensities\n # these c density values are the direct changes to the overall c density\n # the c stock values are the total carbon form each land type going to atmos\n\n # first, create table that has a row for each land type ID, and a column for each of the fire-caused C density change [MgC/ha], \n # and corresponding C transfer to atmosphere [Mg C] \n fire_agg_cols = array(dim=c(length(fire_adjust_df$Land_Cat_ID),length(fire_agg_names)))\n # second, populate the table by applying loop to each row's land type ID \n for (i in 1:length(fire_agg_names)) {\n #fill columns with corresponding fire-caused C DENSITY CHANGES from the fire_adjust_df\n fire_agg_cols[,i] = fire_adjust_df[,fire_agg_names[i]]\n }\n \n # third, aggregate the C DENSITY CHANGES by summing within each land type-ownership combination and assign to fire_adjust_agg \n fire_adjust_agg = aggregate(fire_agg_cols ~ Land_Cat_ID + Region + Land_Type + Ownership, data=fire_adjust_df, FUN=sum)\n #fourth, label the columns of the aggregated table \n \n ####\n # change names and add to all_c_flux\n last_col = ncol(fire_adjust_agg)\n first_col = last_col - length(fire_agg_names) + 1\n fire_agg_names2 = paste0(fire_agg_names,\"_fire_agg\")\n names(fire_adjust_agg)[first_col:last_col] = fire_agg_names2\n # merge these values to the unman area table to apply the adjustments to each land type\n all_c_flux = merge(all_c_flux, fire_adjust_agg[c(1:4,first_col:last_col)], by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.na(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, is.nan(x), 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == Inf, 0.00)})\n all_c_flux[,c(8:ncol(all_c_flux))] <- apply(all_c_flux[,c(8:ncol(all_c_flux))], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # check that the fire Land2Atmos c flux is equal to the sum of burned and non-burned land2Atmos c flux in the all_c_flux dataframe\n # checks flase due to rounding error\n identical(all_c_flux[,\"Land2Atmos_BurnedC_stock_fire_agg\"] + all_c_flux[,\"Land2Atmos_NonBurnedC_stock_fire_agg\"], all_c_flux[,\"Land2Atmos_c_stock_fire_agg\"])\n # this is true \n all(abs(all_c_flux[,\"Land2Atmos_BurnedC_stock_fire_agg\"] + all_c_flux[,\"Land2Atmos_NonBurnedC_stock_fire_agg\"] - all_c_flux[,\"Land2Atmos_c_stock_fire_agg\"]) <=0.000001)\n \n # loop over the relevant out density tables to update the carbon pools based on the fire fluxes\n # carbon cannot go below zero\n sum_change = 0\n sum_neg_fire = 0\n # for above-main C density through soil organic C density dataframes, do:\n for (i in 3:num_out_density_sheets) {\n # subset each of the columns representing aggregated fire-caused C density changes and the C emissions \n # multiply by total area\n # sum them all \n # this gives a single value for state-wide cumulative fire C changes [Mg C/y] -- used to make sure sure nothing is negative\n sum_change = sum_change + sum(all_c_flux[, fire_agg_names2[i-2]] * all_c_flux$tot_area)\n \n ############################################################################################################\n ################################### Lastly, UPDATE NEXT YEAR'S C DENSITIES #################################\n ############################################################################################################\n \n # add the corresponding fire-caused C density change to next year's C density\n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, next_density_label] + all_c_flux[, fire_agg_names2[i-2]]\n # calc the total state-wide cumulative C not subtracted because it sends density negative (used as check to make sure it's minimal)\n neginds = which(out_density_df_list[[i]][, next_density_label] < 0)\n cat(\"neginds for out_density_df_list fire\" , i, \"are\", neginds, \"\\n\")\n sum_neg_fire = sum_neg_fire + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n sum_neg_eco = sum_neg_eco + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n # replace any negative updated C densities with 0\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] <= 0, 0.00)\n } # end loop over out densities for updating due to fire\n cat(\"fire carbon to atmosphere is \", sum_change, \"\\n\")\n cat(\"fire negative carbon cleared is \", sum_neg_fire, \"\\n\")\n \n ############################################################################################################\n ############################################################################################################\n ################################### Apply LAND CONVERSIONS to C pools ####################################\n ############################################################################################################\n ############################################################################################################\n \n # apply land conversion to the carbon pools (current year area and updated carbon)\n # as the changes are net, the land type area gains will be distributed proportionally among the land type area losses\n # the \"to\" land type columns are in land type id order and do not include seagrass because it is just an expansion\n # do ocean/seagrass separately\n # operate within ownership categories and merge them back together at the end\n cat(\"Starting conversion c transfers\\n\")\n \n # need to adjust historical baseline by the management targets\n # managed adjustments are assumed to be independent of each other so the net adjustments are calculated\n # these initial annual rates are assumed to be included in the baseline annual change numbers:\n # Growth\n # so the adjustment is based on a difference between the target annual change and the baseline annual change\n # the baseline annual change is the year 2010 for these management types\n # urban forest is only tracked internally to determine the carbon accumulation rate,\n # it is not used here in the context of land type converison\n # Restoration & Afforestation & reforestation is an annual addition of a land type\n # assume that these entries do not need aggregation with other similar management activities within land type id\n # in other words, there is only one area-changing management per land type id and each is dealt with uniquely\n \n # create dataframe for conversion adjustments: merge the annual net coversion area changes with the total area dataframe (All 10 Regions)\n conv_adjust_df = merge(tot_area_df, conv_area_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"))\n # sort\n conv_adjust_df = conv_adjust_df[order(conv_adjust_df$Land_Cat_ID),]\n # duplicate column for annual net area conversions (\"base_area_change\") and call it \"area_change\"\n conv_adjust_df$area_change = conv_adjust_df$base_area_change\n # put the total area in the new area column for now\n # so that it can be adjusted as necessary by ownership below\n conv_adjust_df$new_area = conv_adjust_df$tot_area\n \n ############################################################################################################\n ######### FIRST, ADJUST BASELINE AREA CHANGE FOR RESTORATION, Afforestation, Reforestation, LIMITED GROWTH, & non-regen forest ######\n ############################################################################################################\n \n # These area change activities have a priority for available land type area that is set above when calculating man_area\n # Order:\n #\tReforestation (from shrubland)\n #\tAfforestation (from shrubland, grassland)\n #\tMeadow (from shrubland, grassland, woodland, savanna)\n #\tFresh_marsh and Coastal_marsh jointly (from cultivated)\n #\tWoodland (from grassland and cultivated)\n # The resulting area needs are used to adjust the transition matrix according to these activities\n \n # merge the appropriate management data\n man_conv_df = man_adjust_df[man_adjust_df$Management == \"Restoration\" | man_adjust_df$Management == \"Afforestation\" | \n man_adjust_df$Management == \"Reforestation\" | man_adjust_df$Management == \"Growth\",1:7]\n # check if there any prescribed management practices\n if (nrow(man_conv_df)>0) {\n man_conv_df = merge(man_conv_df, man_target_df[,1:6], by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Management\"))\n names(man_conv_df)[names(man_conv_df) == start_area_label] = \"initial_man_area\"\n } # don't merge if there aren't any as there are only 5 columns in man_target_df (no column for start_area_label)\n \n conv_adjust_df = merge(conv_adjust_df, man_conv_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x=TRUE)\n conv_adjust_df = conv_adjust_df[order(conv_adjust_df$Land_Cat_ID),]\n \n # merge the fire non-regen area\n conv_adjust_df = merge(conv_adjust_df, \n \tfire_nonreg_df[fire_nonreg_df$Severity == \"High\", c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"non_regen_area\")], \n \tby = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x=TRUE)\n conv_adjust_df = conv_adjust_df[order(conv_adjust_df$Land_Cat_ID),]\n conv_adjust_df[,c(10:ncol(conv_adjust_df))] <- apply(conv_adjust_df[,c(10:ncol(conv_adjust_df))], 2, function (x) {replace(x, is.na(x), 0.00)})\n \n # initialize the managed adjustments to base area change to zero\n conv_adjust_df$base_change_adjust = 0\n \n # merge the conversion fractions before splitting upon ownership\n conv_adjust_df = merge(conv_adjust_df, conv_df, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n conv_adjust_df = conv_adjust_df[order(conv_adjust_df$Land_Cat_ID),]\n # conv_col_names = unique(conv_adjust_df$Land_Type[conv_adjust_df$Land_Type != \"Seagrass\"])\n conv_col_names = unique(conv_adjust_df$Land_Type)\n num_conv_col_names = length(conv_col_names)\n own_names = unique(conv_adjust_df$Ownership)\n \n # initialize list for the following loop\n \n own_conv_df_list <- list()\n \n ############ START BIG LOOP that ultimately calc C TRANSFER for land conversions ############ \n # outer loop over regions\n for (r in 1:length(unique(conv_adjust_df$Region))) {\n # get region-specific ownerships to send to inner ownership loop\n region.names <- unique(conv_adjust_df$Region) \n # subset rows in conv_adjust_df that have the region ID equal to region.sames[[r]]\n # first get row indices \n current_region_ID <- region.names[[r]]\n # second subset these specific region rows from conv_adjust_df\n region.specific.own <- conv_adjust_df[conv_adjust_df$Region == current_region_ID,]\n # third subset region-specific ownerships from own_names \n own_names <- unique(region.specific.own$Ownership)\n own_conv_df_list_pre <- list()\n \n # loop over ownerships\n for (i in 1:length(own_names)) {\n # subset one ownership class at a time from the conversion adjustment table\n conv_own = region.specific.own[region.specific.own$Ownership == own_names[i],] \n # get region-ownership-specific landtype names and number\n conv_col_names <- unique(conv_own$Land_Type)\n num_conv_col_names <- length(conv_col_names)\n # first need to adjust the baseline change rates and calculate the new area\n # the seagrass adjustment is separate\n if (current_region_ID == \"Ocean\") {\n conv_own$base_change_adjust[conv_own$Land_Type == \"Seagrass\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n conv_own$man_area[conv_own$Land_Type == \"Seagrass\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n } else {\n \n # calc growth adjustment before specific activities\n # change will be distributed to other land types proportionally within land type id, except for fresh marsh, water, and ice\n # because fresh marsh is only a restored type that is protected and water and ice do not change\n # note that developed land doesn't quite play out as prescribed when using the original landfire rs lullc data\n \n # calc adjustment to the baseline growth rate for limited growth management \n # (temp_adjust) [ha] = current year limited growth area - initial limited growth area\n temp_adjust = conv_own$man_area[conv_own$Management == \"Growth\" & !is.na(conv_own$Management)] - \n conv_own$initial_man_area[conv_own$Management == \"Growth\" & !is.na(conv_own$Management)]\n # paste value(s) in the column for base_change_adjust \n conv_own$base_change_adjust[conv_own$Management == \"Growth\" & !is.na(conv_own$Management)] = temp_adjust\n # proportionally distribute the reductions in urban growth area to other land types\n # for each landtype except developed and fresh marsh (not affected by these manipulations since all of it is protected): \n # base_change_adjust [ha] = base_change_adjust - (sum of adjustments to baseline urban growth rate) *\n # (total area of landtype)/(total area of all the other landtypes)\n ta_lt = sum(conv_own$tot_area[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"])\n if ( ta_lt > 0) {\n \tconv_own$base_change_adjust[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"] = \n \t\tconv_own$base_change_adjust[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"] - \n \t\tsum(temp_adjust) * conv_own$tot_area[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"] / \n \t\tta_lt\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"])\n \tif (nlt > 0) {\n \t\t# can add area evenly\n \t\tconv_own$base_change_adjust[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"] = \n \t\t\tconv_own$base_change_adjust[conv_own$Land_Type != \"Developed_all\" & conv_own$Land_Type != \"Fresh_marsh\" & conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"] - \n \t\t\tsum(temp_adjust) / nlt\n \t} else {\n \t\t# developed can't change\n \t\tconv_own$base_change_adjust[conv_own$Management == \"Growth\" & !is.na(conv_own$Management)] = 0.00\n \t}\n } # end if ta_lt > 0\n \n################################## REFORESTATION #####################################################\n # Reforestation activities will come proportionally out of _shrub_ only\n # calc area adjustment for Reforestation (temp_adjust) [ha] = current year Reforestation area \n temp_adjust = conv_own$man_area[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] \n # subset the base_change_adjust areas for shrub, and subtract, proportionally, the sum of all the area adjustments\n\t\t#\tthere should be only one row for shrubland, but the code was borrowed from multiple land types below, and the format is retained\n # for Reforestation (temp_adjust) in shrubland\n # store the needed land type area\n # need to scale the reforestation if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n ta_lt = sum(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\"])\n conv_own$rfrst_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\"] = scale_fact * sum(temp_adjust) * \n \t\tconv_own$tot_area[conv_own$Land_Type == \"Shrubland\"] / ta_lt\n \t# add the prescribed/scaled Reforestation area to the column for base_change_adjust \n \tconv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] = \n \t\tconv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] + scale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\"])\n \t# if no available land forest can't expand due to reforestation so don't alter base_change_adjust or rfrst_need\n \t# but it can contract if negative reforestation is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Forest\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Forest\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\"] = temp_adjust_neg * \n \t\t\tconv_own$tot_area[conv_own$Land_Type == \"Shrubland\"] / nlt\n \t\tconv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] + temp_adjust_neg\n \t}\n } # end else no available lt area\n conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\"] = \n conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\"] - \n conv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\"]\n \n################################## AFFORESTATION #####################################################\n # Afforestation activities will come proportionally out of _shrub_ and _grassland_ only\n # calc area adjustment for Afforestation (temp_adjust) [ha] = current year Afforestation area \n temp_adjust = conv_own$man_area[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] \n # subset the base_change_adjust areas for shrub and grass, and subtract, proportionally, the sum of all the area adjustments \n # for Afforestation (temp_adjust) in shrubland and grassland\n # store the needed land type area - subtract the reforestation needs\n # need to scale the afforestation if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n ta_lt = sum(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] -\n \tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"])\n conv_own$frst_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] = scale_fact * sum(temp_adjust) * \n \t\t(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] -\n \t\tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"]) / ta_lt\n \t# add the prescribed Afforestation area to the column for base_change_adjust \n \tconv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] = \n \t\tconv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] + scale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"])\n \t# if no available land forest can't expand due to afforestation so don't alter base_change_adjust or frst_need\n \t# but it can contract if negative afforestation is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Forest\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Forest\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] = temp_adjust_neg * \n \t\t\t(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] -\n \t\t\tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"]) / nlt\n \t\tconv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] + temp_adjust_neg\n \t}\n } # end else no available lt area\n conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] = \n conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"] - \n conv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\"]\n \n################################# RESTORATION ########################################################\n \n######### coastal marsh restoration will come out of _agriculture_ land only\n\t\t# need to scale the restoration if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n # get area adjustment for COASTAL MARSH RESTORATION (temp_adjust) [ha] = current year management area \n temp_adjust = conv_own$man_area[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n # subtract this area propotionally from Cultivated\n # store the needed land type area\n ta_lt = sum(conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"])\n conv_own$cm_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$cm_need[conv_own$Land_Type == \"Cultivated\"] =\n \t\tscale_fact * sum(temp_adjust) * conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"] / ta_lt\n \t# add the COASTAL MARSH RESTORATION area to the column for base_change_adjust \n \tconv_own$base_change_adjust[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\tscale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"])\n \t# if no available land for restoration don't alter base_change_adjust or land type need\n \t# but it can contract if negative restoration is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Coastal_marsh\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Coastal_marsh\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$cm_need[conv_own$Land_Type == \"Cultivated\"] =\n \t\t\ttemp_adjust_neg * conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"] / nlt\n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\ttemp_adjust_neg\n \t}\n } # end else no available lt area\n conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"] = conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"] - \n conv_own$cm_need[conv_own$Land_Type == \"Cultivated\"]\n \n######### fresh marsh restoration will come out of _agriculture_ land only\n\t\t# need to scale the restoration if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n # get area adjustment for FRESH MARSH RESTORATION (temp_adjust) [ha] = current year management area \n temp_adjust = conv_own$man_area[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n # subtract this area propotionally from Cultivated\n # subtract the available area needed for coastal marsh\n # store the needed land type area\n ta_lt = sum(conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"] - conv_own$cm_need[conv_own$Land_Type == \"Cultivated\"])\n conv_own$fm_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$fm_need[conv_own$Land_Type == \"Cultivated\"] =\n \t\tscale_fact * sum(temp_adjust) * (conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"] - conv_own$cm_need[conv_own$Land_Type == \"Cultivated\"])/ ta_lt\n \t# add the FRESH MARSH RESTORATION area to the column for base_change_adjust\n \tconv_own$base_change_adjust[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\tscale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"])\n \t# if no available land for restoration don't alter base_change_adjust or land type need\n \t# but it can contract if negative restoration is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Fresh_marsh\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Fresh_marsh\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$fm_need[conv_own$Land_Type == \"Cultivated\"] =\n \t\t\ttemp_adjust_neg * (conv_own$tot_area[conv_own$Land_Type == \"Cultivated\"] - conv_own$cm_need[conv_own$Land_Type == \"Cultivated\"])/ nlt\n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\ttemp_adjust_neg\n \t}\n } # end else no available lt area\n conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"] = conv_own$base_change_adjust[conv_own$Land_Type == \"Cultivated\"] - \n conv_own$fm_need[conv_own$Land_Type == \"Cultivated\"]\n \n######### meadow restoration will come proportionally out of _shrubland_, _grassland_, _savanna_, _woodland_ only\n\t\t# need to scale the restoration if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n # get area adjustment for MEADOW RESTORATION (temp_adjust) [ha] = current year management area\n temp_adjust = conv_own$man_area[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n # subtract this area propotionally from SHRUBLAND, GRASSLAND, & SAVANNA and woodland\n # subtract the available area needed for afforestation and reforestation\n # store the needed land type area\n ta_lt = sum( (conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] - \n \tconv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] -\n \tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"]) )\n conv_own$mdw_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$mdw_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | \n \t\tconv_own$Land_Type == \"Woodland\"] = scale_fact * sum(temp_adjust) * \n \t \t\t(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] - \n \t \t\tconv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] -\n \t \t\tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"]) / ta_lt\n \t \t# add the MEADOW RESTORATION area to the column for base_change_adjust \n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\tscale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | \n \t\t\t\t\tconv_own$Land_Type == \"Woodland\"])\n \t# if no available land for restoration don't alter base_change_adjust or land type need\n \t# but it can contract if negative restoration is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Meadow\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Meadow\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$mdw_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | \n \t\t\tconv_own$Land_Type == \"Woodland\"] = temp_adjust_neg * \n \t \t\t\t(conv_own$tot_area[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] - \n \t \t\t\tconv_own$frst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"] -\n \t \t\t\tconv_own$rfrst_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | conv_own$Land_Type == \"Woodland\"]) / nlt\n \t \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\t\ttemp_adjust_neg\n \t}\n } # end else no available lt area\n conv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | \n \tconv_own$Land_Type == \"Woodland\"] = \n \tconv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" |\n \t\tconv_own$Land_Type == \"Woodland\"] - \n conv_own$mdw_need[conv_own$Land_Type == \"Shrubland\" | conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Savanna\" | \n \t\tconv_own$Land_Type == \"Woodland\"]\n\n######### woodland restoration will come proportionally out of _grassland_ and cultivated only\n\t\t# need to scale the restoration if there is not enough available area\n # note that sum(###) just coverts ### from a df to a scalar value\n # get area adjustment for WOODLAND RESTORATION (temp_adjust) [ha] = current year management area\n temp_adjust = conv_own$man_area[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n # subtract this area propotionally from GRASSLAND, & cultivated\n # subtract the available area needed for afforestation and meadow and wetland\n # store the needed land type area\n ta_lt = sum( (conv_own$tot_area[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] - \n \t\t\tconv_own$frst_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t\t\tconv_own$mdw_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t\t\tconv_own$cm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t\t\tconv_own$fm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"]) )\n conv_own$wd_need = 0.00\n if ( ta_lt > 0) {\n \tif (ta_lt >= sum(temp_adjust)) {scale_fact = 1.0\n \t} else {scale_fact = ta_lt / sum(temp_adjust)}\n \tconv_own$wd_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] = scale_fact * sum(temp_adjust) * \n \t \t(conv_own$tot_area[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] - \n \t \t\tconv_own$frst_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\tconv_own$mdw_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\tconv_own$cm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\tconv_own$fm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"]) / ta_lt\n \t \t# add the WOODLAND RESTORATION area to the column for base_change_adjust \n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\tscale_fact * temp_adjust\n } else {\n \tnlt = length(conv_own$base_change_adjust[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"])\n \t# if no available land for restoration don't alter base_change_adjust or land type need\n \t# but it can contract if negative restoration is prescribed, which shouldn't happen\n \tif (nlt > 0 & sum(temp_adjust) < 0) {\n \t\tif (-sum(temp_adjust) > conv_own$tot_area[conv_own$Land_Type == \"Woodland\"]) { temp_adjust_neg = -sum(conv_own$tot_area[conv_own$Land_Type == \"Woodland\"])\n \t\t} else {temp_adjust_neg = sum(temp_adjust)}\n \t\tconv_own$wd_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] = temp_adjust_neg * \n \t \t\t(conv_own$tot_area[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] - \n \t \t\t\tconv_own$frst_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\t\tconv_own$mdw_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\t\tconv_own$cm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] -\n \t \t\t\tconv_own$fm_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"]) / nlt\n \t \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] = \n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] + \n \t\t\t\ttemp_adjust_neg\n \t}\n } # end else no available lt area \t\n conv_own$base_change_adjust[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] = \n \tconv_own$base_change_adjust[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"] - \n conv_own$wd_need[conv_own$Land_Type == \"Grassland\" | conv_own$Land_Type == \"Cultivated\"]\n \t\t\n################################## Non-regeneration of forest after fire #####################################################\n # non-regenerated forest will become shrubland, or will regenerate if shrubland does not exist in this region-ownership\n # Currently there are shrubland land cats where there are forest land cats\n # a test with a flavor of version 3 that shifted to grassland as needed did not have any shifts to grasslands\n # make the adjustment negative (non_regen_area is positive)\n temp_adjust = - conv_own$non_regen_area[conv_own$Land_Type == \"Forest\"] \n # add the negative value for non-regen area to the column for base_change_adjust \n conv_own$base_change_adjust[conv_own$Land_Type == \"Forest\"] = \n conv_own$base_change_adjust[conv_own$Land_Type == \"Forest\"] + temp_adjust\n # add the lost forest area to shrubland (or grassland if necessary)\n # store the added area\n conv_own$nonreg_add = 0.00\n if (nrow(conv_own[conv_own$Land_Type == \"Shrubland\",]) > 0) {\n \tconv_own$nonreg_add[conv_own$Land_Type == \"Shrubland\"] = sum(temp_adjust)\n \tconv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\"] = \n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Shrubland\"] - conv_own$nonreg_add[conv_own$Land_Type == \"Shrubland\"] \n# \t} else if (nrow(conv_own[conv_own$Land_Type == \"Grassland\",]) > 0) {\n# \t\tconv_own$nonreg_add[conv_own$Land_Type == \"Grassland\"] = sum(temp_adjust)\n# \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Grassland\"] = \n# \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Grassland\"] - conv_own$nonreg_add[conv_own$Land_Type == \"Grassland\"]\n# \t\tcat(\"Warning: forest to grassland due to lack of potential converted land types\\n\")\n# \t} else if (nrow(conv_own[conv_own$Land_Type == \"Savanna\",]) > 0) {\n# \t\tconv_own$nonreg_add[conv_own$Land_Type == \"Savanna\"] = sum(temp_adjust)\n# \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Savanna\"] = \n# \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Savanna\"] - conv_own$nonreg_add[conv_own$Land_Type == \"Savanna\"]\n# \t\tcat(\"Warning: forest to savanna due to lack of potential converted land types\\n\")\n \t} else {\n \t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Forest\"] = \n \t\tconv_own$base_change_adjust[cconv_own$Land_Type == \"Forest\"] - temp_adjust\n \t\tcat(\"Warning: regenerating all forest after fire due to lack of potential converted land types; reg =\", r, region.names[r], \", own =\", i, own_names[i], \"\\n\")\n \t} \n } # end else calc land adjusments to baseline area change\n \n # clean up division numerical errors\n conv_own$base_change_adjust[is.na(conv_own$base_change_adjust)] = 0\n conv_own$base_change_adjust[is.nan(conv_own$base_change_adjust)] = 0\n conv_own$base_change_adjust[conv_own$base_change_adjust == Inf] = 0\n conv_own$base_change_adjust[conv_own$base_change_adjust == -Inf] = 0\n \n # calc the area change and the new area\n # recall AREA CHANGE [ha] starts as annual net area conversions (\"base_area_change\")\n # so add to it the newly calculated base_change_adjust:\n conv_own$area_change = conv_own$base_area_change + conv_own$base_change_adjust\n # and recalc NEW AREA [ha] = LANDTYPE AREA + (adjusted) AREA CHANGE\n conv_own$new_area = conv_own$tot_area + conv_own$area_change\n \n ######### ENSURE PROTECTION OF AFFORESTED AREA & RESTORED MARSH & MEADOW & WOODLAND AREA VIA AREA CHANGE ADJUSTMENT ######################## \n # first adjust the new area and area change to account for the protection of afforested and reforested area & restored fresh marsh, meadow and coastal \n # marsh\n # this also accounts for new area going negative\n # new area should always be greater than the cumulative afforested & reforested & restored management area so there is no loss of protected area over time.\n # thus, for forest afforestation, reforestation, fresh or coastal marsh, or meadow, if new area < cumulative management area, do the following two steps: \n # (1) CORRECT \"AREA CHANGE\" for PROTECTED AREA by adding the deficit area to the area change\n # get indices for land categories that don't have at least new area equal to the cumulative protected area\n \n # assume that reforested (and afforested) non-regen areas that re-burn are replanted to meet these overall restoration targets that are \"protected\"\n #\t\tthe fire c losses are still incurred this year, but the cumulative restored area is replanted as necessary\n #\t\teffectively the non-regen area will be a different patch each year, but when enough non-regenerates it could start to encroach upon the \"protected\" area\n \n protected_deficit_inds <- which((conv_own$new_area < conv_own$man_area_sum) & !is.na(conv_own$Management) &\n (conv_own$Management == \"Afforestation\" | conv_own$Management == \"Reforestation\" | conv_own$Management == \"Restoration\")) \n # if these indices exist, then\n if (length(protected_deficit_inds)>0) { \n \t# add the deficit to the area change (effectively will set new_area = man_area_sum) \n conv_own$area_change[protected_deficit_inds] <- conv_own$area_change[protected_deficit_inds] + (conv_own$man_area_sum[protected_deficit_inds] - \n conv_own$new_area[protected_deficit_inds])\n \t# (2) SUM PROTECTED AREA DEFICIT \n \t# sum_restored_neg = -1 * ( (aggregate sum all the above cumulative management areas > new areas) - new area )\n \tsum_restored_neg = -sum(conv_own$man_area_sum[protected_deficit_inds] - conv_own$new_area[protected_deficit_inds])\n \t# (3) CORRECT \"NEW AREA\" for PROTECTED AREA \n \t# NEW AREA = CUMULATIVE MANAGEMENT AREA \n \tconv_own$new_area[protected_deficit_inds] = conv_own$man_area_sum[protected_deficit_inds]\n } else {sum_restored_neg <- 0}\n \n # (4) CORRECT \"AREA CHANGE\" FOR NEGATIVE NEW AREAS\n # if new area is negative, add the magnitude of the negative area to the area_change and subtract the difference proportionally from the \n # positive area changes (except from protected forest, fresh marsh, coastal marsh, and meadow - make sure not negated by changes), then calc \n # new area again\n # AREA CHANGE = AREA CHANGE - NEW AREA (this later sets new area to 0)\n conv_own$area_change[conv_own$new_area < 0] = conv_own$area_change[conv_own$new_area < 0] - conv_own$new_area[conv_own$new_area < 0]\n \n # (5) SUM NEGATIVE NEW AREAS + PROTECTED AREA DEFICIT (i.e. total restored area)\n # sum_neg_new = SUM NEGATIVE NEW AREAS + PROTECTED AREA DEFICIT\n sum_neg_new = sum(conv_own$new_area[conv_own$new_area < 0]) + sum_restored_neg\n \n # (6) From expanding forest (with afforestation/reforestation), meadow and marsh, where new_area >= man_area_sum & man_area_sum >= tot_area, temporarily set aside whatever \n # area is required for: (new_area - area_change >= man_area_sum), so that these cases can have the unprotected portion of area_change adjusted, and what is \n # excluded from adjustments and changes\n area_change_protect_inds <- which((conv_own$new_area >= conv_own$man_area_sum) & (conv_own$man_area_sum >= conv_own$tot_area) & (conv_own$area_change > 0) &\n \t\t\t\t\t\t\t\t\t\t!is.na(conv_own$Management) &\n \t\t\t\t\t\t\t\t\t\t(conv_own$Management == \"Afforestation\" | conv_own$Management == \"Reforestation\" | conv_own$Management == \"Restoration\"))\n\n \t\tconv_own$area_change[area_change_protect_inds] <- conv_own$area_change[area_change_protect_inds] - \n \t\t(conv_own$man_area_sum[area_change_protect_inds] - conv_own$tot_area[area_change_protect_inds]) \n \n # (7) SUM EXPANDING AREAS, INCLUDING UNPROTECTED AREA in FOREST, FRESH & COASTAL MARSH, and MEADOW (excludes protected land categories with no unprotected area)\n # sum_pos_change = SUM EXPANDING AREAS\n sum_pos_change = sum(conv_own$area_change[conv_own$area_change > 0 & !(conv_own$area_change %in% conv_own$area_change[protected_deficit_inds])])\n # (8) CORRECT \"AREA CHANGE\" of all EXPANDING UNPROTECTED areas (includes unprotected area in forest, marshes and meadow) by\n # subtracting the proportional sum of negative new areas from the positive area changes: \n # AREA CHANGE = AREA CHANGE + (sum_neg_new * area change / sum_pos_change)\n conv_own$area_change[conv_own$area_change > 0 & !(conv_own$area_change %in% conv_own$area_change[protected_deficit_inds])] <- \n conv_own$area_change[conv_own$area_change > 0 & !(conv_own$area_change %in% conv_own$area_change[protected_deficit_inds])] + \n sum_neg_new * conv_own$area_change[conv_own$area_change > 0 & !(conv_own$area_change %in% conv_own$area_change[protected_deficit_inds])] / \n sum_pos_change\n # (9) Add back the protected area that was temporarily subtracted from area_change to the adjusted area change \n conv_own$area_change[area_change_protect_inds] <- conv_own$area_change[area_change_protect_inds] + \n (conv_own$man_area_sum[area_change_protect_inds] - conv_own$tot_area[area_change_protect_inds]) \n \n # (10) update new area by adding adjusted area changes to the total areas for each landtype-ownership-region combination: \n # NEW AREA [ha] = TOTAL AREA + AREA CHANGE\n conv_own$new_area = conv_own$tot_area + conv_own$area_change\n \n # check if any of the area changes resulted in negative new area and correct them\n while (any(conv_own$new_area < 0)) {\n # subset any land categories that have negative new areas and add them up to get the area needed to be offset to avoid negative new area\n sum_neg_new_area <- sum(conv_own$new_area[conv_own$new_area < 0])\n # create temporary column for new_area: new_area_temp_df$new_area\n new_area_temp_df <- conv_own\n # get row indices for land categories with protected area and additional unprotected area\n unprotected_inds <- which((new_area_temp_df$new_area > new_area_temp_df$man_area_sum) & !is.na(conv_own$Management) &\n (conv_own$Management == \"Afforestation\" | conv_own$Management == \"Reforestation\" | conv_own$Management == \"Restoration\"))\n # get row indices to exclude from area correction because they are fully protected\n exclude_inds <- which((conv_own$new_area == conv_own$man_area_sum) & !is.na(conv_own$Management) &\n \t\t\t\t\t\t(conv_own$Management == \"Afforestation\" | conv_own$Management == \"Reforestation\" | conv_own$Management == \"Restoration\"))\n # temporarily replace new_area with the excess unprotected area (man_area_sum - new_area) for reforestation/afforestation & fresh_marsh, meadow and coastal_marsh\n new_area_temp_df$new_area[unprotected_inds] <- new_area_temp_df$new_area[unprotected_inds] - new_area_temp_df$man_area_sum[unprotected_inds]\n # sum all the positive new areas, excluding protected area\n sum_pos_new_area <- sum(new_area_temp_df$new_area[new_area_temp_df$new_area > 0 & \n !(new_area_temp_df$new_area %in% new_area_temp_df$new_area[exclude_inds])]) \n # ADJUST \"AREA CHANGE\" for all postive new_area land categories (for the negative new areas) by adding the negative area offset (sum_neg_new_area) \n # proportionally (w.r.t. new_area) from the area_change (lose more from contracting lands, and gain less for expanding lands)\n new_area_temp_df$area_change[new_area_temp_df$new_area > 0 & !(new_area_temp_df$new_area %in% new_area_temp_df$new_area[exclude_inds])] <- \n new_area_temp_df$area_change[new_area_temp_df$new_area > 0 & !(new_area_temp_df$new_area %in% new_area_temp_df$new_area[exclude_inds])] +\n sum_neg_new_area * new_area_temp_df$new_area[new_area_temp_df$new_area > 0 & \n !(new_area_temp_df$new_area %in% new_area_temp_df$new_area[exclude_inds])] / sum_pos_new_area\n # CORRECT \"AREA CHANGE\" for all negative new_area land categories by subtracting their negative new_area from it (effectively 0's it)\n new_area_temp_df$area_change[new_area_temp_df$new_area < 0] <- new_area_temp_df$area_change[new_area_temp_df$new_area < 0] - \n new_area_temp_df$new_area[new_area_temp_df$new_area < 0] \n # CORRECT \"NEW AREA\" for negative new area land categories by assigning 0 to them to avoid roundoff errors\n new_area_temp_df$new_area[new_area_temp_df$new_area < 0] <- 0\n # CORRECT \"NEW AREA\" for postive new area land categories by adding the adjusted area_change to the tot_area\n new_area_temp_df$new_area[new_area_temp_df$new_area > 0] = new_area_temp_df$tot_area[new_area_temp_df$new_area > 0] + \n new_area_temp_df$area_change[new_area_temp_df$new_area > 0]\n # add back the protected portion of new areas to the rows it was subtracted from (unprotected_inds)\n new_area_temp_df$new_area[unprotected_inds] <- new_area_temp_df$new_area[unprotected_inds] + new_area_temp_df$man_area_sum[unprotected_inds]\n # replace conv_own with new_area_temp_df which has the corrected new_area and area_change\n conv_own <- new_area_temp_df\n } # end while loop\n \n # check if sum(new_area) > sum(tot_area) \n if (all((sum(conv_own$new_area) > sum(conv_own$tot_area)) & conv_own$Region != \"Ocean\")) {\n # get sum of area deficit\n new_area_deficit <- sum(conv_own$new_area) - sum(conv_own$tot_area)\n # get sum of postive area changes\n sum_pos_change <- sum(conv_own$area_change[conv_own$area_change > 0]) \n # subtract this proprtionally from the posisitve area_changes with respect to area_change\n conv_own$area_change[conv_own$area_change > 0] <- conv_own$area_change[conv_own$area_change > 0] - \n (new_area_deficit * ((conv_own$area_change[conv_own$area_change > 0])/sum_pos_change))\n # recalc the new_area for all\n conv_own$new_area <- conv_own$area_change + conv_own$tot_area\n }\n \n ##### to do: check for a better way to do this\n # this case could arise when changes should still occur (e.g non-restoration increases in coastal marsh with increases in dveloped all)\n # check if there is any land left to do land conversions in the first place\n if (sum(conv_own$tot_area[conv_own$Land_Type == \"Coastal_marsh\" | conv_own$Land_Type == \"Fresh_marsh\" | conv_own$Land_Type == \"Meadow\"]) == \n sum(conv_own$tot_area)) { \n # if all the area is in potential restored areas, then set all area_change == 0 to avoid round-off error\n conv_own$area_change <- 0.00\n }\n \n ######################################## NOW AREA CHANGES & NEW AREAS ARE CORRECT ######################################## \n \n ################################ CALC SPECIFIC FROM/TO and TO/FROM AREA CONVERSIONS ######################################\n # need the specific amount of area conversions (from/to and to/from) to calculate the C transfers and density changes\n # calculate the conversion area matrices by ownership\n # these store the area change from the Land_Type column to the individual land type, by ownership\n # a from(row)-to(col) value is positive, a to(row)-from(col) value is negative\n # carbon needs to be subracted for the area losses because the density change values are tracked as normalized carbon\n \n # do only land here because ocean/seagrass is different\n if(current_region_ID != \"Ocean\") {\n \n ## distribute generic transitions before specific adjustments\n # this is so that the carbon transitions are correct\n # this also means that the specific transitions do not need to be redistributed\n # \tbecause they are not included yet, and by adding them later the adjusted area_change should be met\n #\tthe area ajustment for limitations also ensures that the required restoration areas to meet protected area are included\n #\t\teffectively maintaining the non-growth conversion rates determined above\n # nonregen area is already limited by available, burned forest area, and has burned, so it shouldn't be adjusted anyway\n \n # remove the management (reforestation/afforestation/restoration) and nonregen adustments from area_change (the non-growth conversions)\n # \tuse these new values for generic transitions\n conv_own$gen_area_change = 0.0\n conv_own$gen_area_change = conv_own$area_change + conv_own$rfrst_need + conv_own$frst_need + conv_own$cm_need +\n \tconv_own$fm_need + conv_own$mdw_need + conv_own$wd_need + conv_own$nonreg_add\n conv_own$gen_area_change[conv_own$Land_Type == \"Forest\"] = conv_own$area_change[conv_own$Land_Type == \"Forest\"] -\n \tsum(conv_own$rfrst_need) - sum(conv_own$frst_need) - sum(conv_own$nonreg_add)\n conv_own$gen_area_change[conv_own$Land_Type == \"Coastal_marsh\"] = conv_own$gen_area_change[conv_own$Land_Type == \"Coastal_marsh\"] - sum(conv_own$cm_need)\n conv_own$gen_area_change[conv_own$Land_Type == \"Fresh_marsh\"] = conv_own$gen_area_change[conv_own$Land_Type == \"Fresh_marsh\"] - sum(conv_own$fm_need)\n conv_own$gen_area_change[conv_own$Land_Type == \"Meadow\"] = conv_own$gen_area_change[conv_own$Land_Type == \"Meadow\"] - sum(conv_own$mdw_need)\n conv_own$gen_area_change[conv_own$Land_Type == \"Woodland\"] = conv_own$gen_area_change[conv_own$Land_Type == \"Woodland\"] - sum(conv_own$wd_need)\n \t\n # add up all positive area changes in new column \"own_gain_sum\" \n conv_own$own_gain_sum = sum(conv_own$gen_area_change[conv_own$gen_area_change > 0])\n # duplicate dataframe and call it conv_own2 \n conv_own2 = conv_own\n # loop over the land types to get the positive from-to area values (from-to: expanding landtypes. if constant then value is 0)\n for (l in 1:length(conv_own$Land_Type)) {\n # l is landtype index. add new columns that are the gains in area for each land type\n conv_own[,conv_own$Land_Type[l]] = 0.0\n # for each from-to land type column, go to rows of decreasing landtypes: \n # from-to value = absolute(neg area change) * (area change of the gaining land type)/(sum all gaining areas) \n conv_own[,conv_own$Land_Type[l]][conv_own$gen_area_change < 0] = - conv_own$gen_area_change[conv_own$gen_area_change < 0] * \n conv_own$gen_area_change[l] / conv_own$own_gain_sum[l]\n } # end for l loop over land type\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, x < 0, 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, is.nan(x), 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, x == Inf, 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # do it again to get the negative to-from values\n for (l in 1:length(conv_own$Land_Type)) {\n conv_own2[,conv_own2$Land_Type[l]] = 0.0\n conv_own2[,conv_own2$Land_Type[l]][conv_own2$gen_area_change > 0] = - conv_own2$gen_area_change[conv_own2$gen_area_change > 0] * \n conv_own2$gen_area_change[l] / conv_own2$own_gain_sum[l]\n } # end for l loop over land type\n conv_own2[,conv_own2$Land_Type] <- apply(conv_own2[,conv_own2$Land_Type], 2, function (x) {replace(x, x < 0, 0.00)})\n conv_own2[,conv_own2$Land_Type] <- apply(conv_own2[,conv_own2$Land_Type], 2, function (x) {replace(x, is.nan(x), 0.00)})\n conv_own2[,conv_own2$Land_Type] <- apply(conv_own2[,conv_own2$Land_Type], 2, function (x) {replace(x, x == Inf, 0.00)})\n conv_own2[,conv_own2$Land_Type] <- apply(conv_own2[,conv_own2$Land_Type], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # put the negative to-from values into conv_own\n # first find which columns are empty\n zinds = which(apply(conv_own[,conv_col_names],2,sum) == 0)\n conv_own[,conv_col_names][,zinds] = -conv_own2[,conv_col_names][,zinds]\n\n # now adjust these conversions based on the specific non-growth conversions above (afforestation/reforestaion and restoration and non-regen)\n # growth and any limitation adjustments have already been distributed proportionally to the appriate land types\n # don't need to scale, as the generic transitions ensure that enough land is available for the non-growth conversions\n # don't operate on the diagnonal, which should always be 0\n # all transitions except forest-shrubland result form a single process\n # base_change_adjust is not the same as man_area: base_change_adjust includes adjustments due to growth rate change and limitations due to available land\n # forest-shrubland transitions are unique because three different processes contribute\n #\treforestation, afforestation, and nonregen\n #\tonly overlap between one listed practice and an unlisted practice is accounted for - basically non-regen + either afforestation or reforestation\n # but only one of afforestation and reforestation can be prescribed at a time! Otherwise the code will break\n \n for (l in 1:length(conv_own$Land_Type)) {\n \t### reforestation\n \tif (conv_col_names[l] != \"Forest\" & length(conv_own$man_area[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$rfrst_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Forest\"] = -conv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t\n \t\t\tif(FALSE){\n \t\t\tif (conv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\tman_area_adj = (conv_own$area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]) /\n \t\t\t\t\tconv_own$base_change_adjust[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]\n \t\t\t} else if ((conv_own$area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]) == 0) {\n \t\t\t\tman_area_adj = 1\n \t\t\t} else {\n \t\t\t\t# this shouldn't happen, but set up to adjust management based on difference, and the assumption that only either afforestation or reforestation is being used\n \t\t\t\tcat(\"Warning: net forest change is non-zero due to protection adjustment; r, i, l = \", r, i, l, \"\\n\")\n \t\t\t\tman_area_adj = ( conv_own$man_area[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] - \n \t\t\t\t\t(conv_own$area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]) ) /\n \t\t\t\t\tconv_own$man_area[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)]\n \t\t\t}\n \t\t\t# scale the needed transitions by the adjusted managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$rfrst_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Forest\"] = -conv_own[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] \n \t\t\t}\n \t\t\t\n \t\t} # end if non-zero man area\n \t} # end reforestation\n \t\n \t### afforestation\n \tif (conv_col_names[l] != \"Forest\" & length(conv_own$man_area[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$frst_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Forest\"] = -conv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] \n \t\t\t\n \t\t\tif(FALSE) {\n \t\t\tif (conv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\tman_area_adj = (conv_own$area_change[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]) /\n \t\t\t\t\tconv_own$base_change_adjust[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]\n \t\t\t} else if ((conv_own$area_change[conv_own$Management == \"Reforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]) == 0) {\n \t\t\t\tman_area_adj = 1\n \t\t\t} else {\n \t\t\t\t# this shouldn't happen, but set up to adjust management based on difference, and the assumption that only either afforestation or reforestation is being used\n \t\t\t\tcat(\"Warning: net forest change is non-zero due to protection adjustment; r, i, l = \", r, i, l, \"\\n\")\n \t\t\t\tman_area_adj = ( conv_own$man_area[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] - \n \t\t\t\t\t(conv_own$area_change[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]) ) /\n \t\t\t\t\tconv_own$man_area[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management)]\n \t\t\t}\n \t\t\t# scale the needed transitions by the adjusted managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$frst_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Forest\"] = -conv_own[conv_own$Management == \"Afforestation\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] \n \t\t\t}\n \t\t\t\n \t\t} # end if non-zero man area\n \t} # end afforestation\n \t\n \t### coastal marsh\n \tif (conv_col_names[l] != \"Coastal_marsh\" & length(conv_own$man_area[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$cm_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Coastal_marsh\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t\n \t\t\tif(FALSE) {\n \t\t\tman_area_adj = \n \t\t\t\t(conv_own$area_change[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] -\n \t\t\t\tconv_own$base_area_change[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) /\n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n \t\t\t# scale the needed transitions by the adjusted managed area and subtract transition\n \t\t\tconv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$cm_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Coastal_marsh\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Coastal_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t}\t\t\n \t\t\t\t\n \t\t} # end if non-zero man area\n \t} # end coastal marsh\n \t\t\n \t### fresh marsh\n \tif (conv_col_names[l] != \"Fresh_marsh\" & length(conv_own$man_area[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$fm_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Fresh_marsh\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t\t\n \t\t\tif(FALSE) {\n \t\t\tman_area_adj = \n \t\t\t\t(conv_own$area_change[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] -\n \t\t\t\tconv_own$base_area_change[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) /\n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n\t\t \t# scale the needed transitions by the adjusted managed area and subtract transition\n \t\t\tconv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$fm_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Fresh_marsh\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Fresh_marsh\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t}\n \t\t\t\t\n \t\t} # end if non-zero man area\n \t} # end fresh marsh\n \t\t\n \t### meadow\n \tif (conv_col_names[l] != \"Meadow\" & length(conv_own$man_area[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$mdw_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Meadow\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t\n \t\t\tif(FALSE) {\n \t\t\tman_area_adj = \n \t\t\t\t(conv_own$area_change[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] -\n \t\t\t\tconv_own$base_area_change[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) /\n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n \t\t\t# scale the needed transitions by the adjusted managed area and subtract transition\n \t\t\tconv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$mdw_need[conv_own$Land_Type == conv_col_names[l]])\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Meadow\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Meadow\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t}\n \t\t\t\t\n \t\t} # end if non-zero man area\n \t} # end meadow\n \t\t\n \t### woodland\n \tif (conv_col_names[l] != \"Woodland\" & length(conv_own$man_area[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) > 0) {\n \t\tif (conv_own$man_area[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] != 0) {\n \t\t\t\n \t\t\t# apply the needed transition based the needed managed area and subtract transition from the row value\n \t\t\tconv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tconv_own$wd_need[conv_own$Land_Type == conv_col_names[l]])\t\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Woodland\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t\t\n \t\t\tif(FALSE) {\n \t\t\tman_area_adj = \n \t\t\t\t(conv_own$area_change[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)] -\n \t\t\t\tconv_own$base_area_change[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]) /\n \t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management)]\n \t\t\t# scale the needed transitions by the adjusted managed area and subtract transition\n \t\t\tconv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] = \n \t\t\t\t(conv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]] -\n \t\t\t\tman_area_adj * conv_own$wd_need[conv_own$Land_Type == conv_col_names[l]])\t\n \t\t\t# fill in the column for this land type with the negative of the row value\n \t\t\tconv_own[l,\"Woodland\"] = \n \t\t\t\t-conv_own[conv_own$Land_Type == \"Woodland\" & conv_own$Management == \"Restoration\" & !is.na(conv_own$Management),conv_own$Land_Type[l]]\n \t\t\t}\n \t\t\t\t\n \t\t} # end if non-zero man area\n \t} # end woodland\n \t\t\n \t### nonregen\n \tif (conv_col_names[l] != \"Forest\" & length(conv_own$Land_Type[conv_own$Land_Type == \"Forest\"]) > 0) {\n \t\t\n \t\t# apply transition - recall that this nonreg_add value is negative\n \t\tconv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]] = \n \t\t\t(conv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]] -\n \t\t\tconv_own$nonreg_add[conv_own$Land_Type == conv_col_names[l]])\n \t\t# fill in the column for this land type with the negative of the row value\n \t\tconv_own[l,\"Forest\"] =\n \t\t\t-conv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]]\n \t\t\n \t\tif(FALSE) {\n \t\t# need to scale this also to be consistent with the overlapping processes because the overall net value might have been adjusted\n \t\t# unless the special case exists where the adjustment ratio is unknown\n \t\tif (conv_own$base_change_adjust[conv_own$Land_Type == \"Forest\"] != 0) {\n \t\t\t\ttemp_area_adj = (conv_own$area_change[conv_own$Land_Type == \"Forest\"] -\n \t\t\t\t\tconv_own$base_area_change[conv_own$Land_Type == \"Forest\"]) /\n \t\t\t\t\tconv_own$base_change_adjust[conv_own$Land_Type == \"Forest\"]\n \t\t} else if ((conv_own$area_change[conv_own$Land_Type == \"Forest\"] - conv_own$base_area_change[conv_own$Land_Type == \"Forest\"]) == 0) {\n \t\t\t\ttemp_area_adj = 1\n \t\t} else {\n \t\t\t# this shouldn't happen, but don't need to scale here because the management above takes it all\n \t\t\tcat(\"Warning: net forest change is non-zero due to protection adjustment; r, i, l = \", r, i, l, \"\\n\")\n \t\t\ttemp_area_adj = 1\n \t\t}\n\n \t\t# add scaled transition - recall that this nonreg_add value is negative\n \t\tconv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]] = \n \t\t\t(conv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]] -\n \t\t\ttemp_area_adj * conv_own$nonreg_add[conv_own$Land_Type == conv_col_names[l]])\n \t\t# fill in the column for this land type with the negative of the row value\n \t\tconv_own[l,\"Forest\"] =\n \t\t\t-conv_own[conv_own$Land_Type == \"Forest\",conv_own$Land_Type[l]]\n \t\t}\n \t\t\t\n \t} # end non regen\n \t\t\n } # end for l loop over land types to incorporate specific transitions\n \n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, is.na(x), 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, is.nan(x), 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, x == Inf, 0.00)})\n conv_own[,conv_own$Land_Type] <- apply(conv_own[,conv_own$Land_Type], 2, function (x) {replace(x, x == -Inf, 0.00)})\n \n # check the row and column sums against area_change\n # note that the row values are the negative of area_change\n conv_own$row_sums = apply(conv_own[,conv_col_names],1,FUN= function(x) {sum(x, na.rm=TRUE)})\n conv_own$row_sums_diff = -conv_own$row_sums - conv_own$area_change\n\t\tcol_sums = apply(conv_own[,conv_col_names],2,FUN= function(x) {sum(x, na.rm=TRUE)})\n\t\tfor (c in 1:length(conv_col_names)) {\n\t\t\tconv_own$col_sums_diff[c] = col_sums[c] - conv_own$area_change[c]\n\t\t}\n\t\trow_bad_inds = which(abs(conv_own$row_sums_diff) > tot_area_change_thresh)\n\t\tcol_bad_inds = which(abs(conv_own$col_sums_diff) > tot_area_change_thresh)\n\t\tif(length(row_bad_inds > 0) | length(col_bad_inds > 0)) {\n\t\t\tcat(\"Error: lulcc transition matrix does not match net area changes; reg =\", r, region.names[r], \", own =\", i, own_names[i], \"\\n\")\n\t\t\tstop()\n\t\t}\n\t\t\n\t\t\n #conv_own$row_gain_sum = apply(conv_own[,conv_col_names],1,FUN= function(x) {sum(x[which(x > 0)], na.rm=TRUE)})\n #conv_own$row_loss_sum = apply(conv_own[,conv_col_names],1,FUN= function(x) {sum(x[which(x < 0)], na.rm=TRUE)})\n #conv_own$row_change_sum = conv_own$row_gain_sum - conv_own$row_loss_sum\n #num_avail_land_types = length(conv_own$Land_Type[conv_own$Land_Type != \"Water\" & conv_own$Land_Type != \"Ice\"]) - 1\n #if (num_avail_land_types < 0 ){num_avail_land_types = 0}\n \n \n ############################# calc 'FROM' land type losses due to conversion to ag and developed ############################# \n # if ag or developed is shrinking, then there is no conversion flux \n # (ag and urban losses don't use conversion fractions, but gains are dictated by the input conversion fractions)\n # calc from land type losses due to conversion to ag and developed\n # if there is an ag or urban loss, then there is no conversion flux\n # assume that these losses are immediate\n # a comprehensive study shows that most soil c loss in conversion to ag happens within the first 3-5 years\n # ag and developed only have above main c, so only need to adjust this as new area with zero carbon\n # loop over the ag/dev conversion frac columns to calculate the transfer carbon density for each frac column\n # this applies to the ag and developed columns only, and add these two areas to get one adjustment\n # the carbon density change is based on land type id tot_area so that it can be aggregated and subtracted directly from the current \n # density values\n # probably should deal with the remaining c transfer here, rather than below, so that all transfers are included\n \n # calculate all the c losses from landtypes that convert to ag or urban\n for (f in 1:num_convfrac_cols) {\n # the removed values are calculated first, so this will work\n # if conv_density_inds[f] == -1 or -2, then the source is the harvested pool or slash pool, so use the sum of its components respectively\n if (conv_density_inds[f] == -1 | conv_density_inds[f] == -2) {\n # FROM HARVESTED POOL\n if (conv_density_inds[f] == -1) { \n # Harvested2Wood_conv_c, Harvested2Energy_conv_c, Harvested2Decay_conv_c, Harvested2Slash_conv_c) [Mg/ha] = (Above_harvested_conv_c + \n # StandDead_harvested_conv_c) * (Harvested2Wood_conv_frac, Harvested2Energy_conv_frac, Harvested2Decay_conv_frac, Harvested2Slash_conv_frac)\n conv_own[,convc_trans_names[f]] = (conv_own[,convc_trans_names[1]] + conv_own[,convc_trans_names[2]]) * conv_own[,conv_frac_names[f]]\n } else {\n # FROM SLASH POOL\n # else conv_density_inds[f] == -2 (when f = 10, 11, 12, 13)\n # sum c trans columns 6, 7, 8 & 9 (Harvest2Slash_conv_c + Under2Slash_conv_c + DownDead2Slash_conv_c + Litter2Slash_conv_c)\n # (slash2energy_conv_c, slash2wood_conv_c, Burn_conv_c, slash2decay_conv_c) [Mg/ha] = (Harvested2Slash_conv_c + Under2Slash_conv_c + \n # DownDead2Slash_conv_c + Litter2Slash_conv_c) * (Harvested2Slash_conv_frac, Under2Slash_conv_frac, DownDead2Slash_conv_frac, Litter2Slash_conv_frac)\n conv_own[,convc_trans_names[f]] = (conv_own[,convc_trans_names[6]] + conv_own[,convc_trans_names[7]] + conv_own[,convc_trans_names[8]] +\n conv_own[,convc_trans_names[9]]) * conv_own[,conv_frac_names[f]]\n }\n } else {\n # FROM C DENSITY POOL\n # get all the C pool densities for each C fraction \n if (!out_density_sheets[conv_density_inds[f]] %in% names(conv_own)) {\n conv_own = merge(conv_own, out_density_df_list[[conv_density_inds[f]]][,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \n next_density_label)], \n by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"), all.x = TRUE)\n names(conv_own)[names(conv_own) == next_density_label] = out_density_sheets[conv_density_inds[f]]\n }\n # if ag or developed land is expanding then calc the C removed or transfered from each of the land types:\n # subset the land type rows that are losing area to ag (i.e. Cultivated is >0) and calc the amount of C transferred from shrinking land type to ag\n # C transfer [MgC/ha] = (C density of _shrinking_ land type) * (conversion frac) * (ag area gain from _shrinking_ land type)/(total ag area)\n \n conv_own[conv_own$Cultivated > 0,convc_trans_names[f]] = \n conv_own[conv_own$Cultivated > 0, out_density_sheets[conv_density_inds[f]]] * \n conv_own[conv_own$Cultivated > 0,conv_frac_names[f]] * conv_own$Cultivated[conv_own$Cultivated > 0] / \n conv_own$tot_area[conv_own$Cultivated > 0]\n conv_own[is.na(conv_own[,convc_trans_names[f]]),convc_trans_names[f]] <- 0.00\n conv_own[is.nan(conv_own[,convc_trans_names[f]]),convc_trans_names[f]] <- 0.00\n conv_own[conv_own[,convc_trans_names[f]] == Inf,convc_trans_names[f]] <- 0.00\n conv_own[conv_own[,convc_trans_names[f]] == -Inf,convc_trans_names[f]] <- 0.00 \n # repeat for developed\n temp_vals = conv_own[,convc_trans_names[f]]\n temp_vals[] = 0.00\n temp_vals[conv_own$Developed_all > 0] =\n conv_own[conv_own$Developed_all > 0,out_density_sheets[conv_density_inds[f]]] * \n conv_own[conv_own$Developed_all > 0,conv_frac_names[f]] * conv_own$Developed_all[conv_own$Developed_all > 0] /\n conv_own$tot_area[conv_own$Developed_all > 0]\n \ttemp_vals[is.na(temp_vals)] <- 0.00\n temp_vals[is.nan(temp_vals)] <- 0.00\n temp_vals[temp_vals == Inf] <- 0.00\n temp_vals[temp_vals == -Inf] <- 0.00 \n conv_own[conv_own$Developed_all > 0,convc_trans_names[f]] = \n conv_own[conv_own$Developed_all > 0,convc_trans_names[f]] + temp_vals[conv_own$Developed_all > 0]\n \n } # end if removed source else density source\n } # end for f loop over the conversion transfer fractions for calculating the transfer carbon\n conv_own[,10:ncol(conv_own)] <- apply(conv_own[,10:ncol(conv_own)], 2, function (x) {replace(x, is.nan(x), 0.00)})\n conv_own[,10:ncol(conv_own)] <- apply(conv_own[,10:ncol(conv_own)], 2, function (x) {replace(x, is.na(x), 0.00)})\n conv_own[,10:ncol(conv_own)] <- apply(conv_own[,10:ncol(conv_own)], 2, function (x) {replace(x, x == Inf, 0.00)})\n conv_own[,10:ncol(conv_own)] <- apply(conv_own[,10:ncol(conv_own)], 2, function (x) {replace(x, x == -Inf, 0.00)})\n conv_own = conv_own[order(conv_own$Land_Cat_ID),]\n # need a copy for below\n conv_own_static = conv_own\n \n # calculate the density changes due to contraction and expansion of land types\n # these are actually normlized carbon values, so the change in density values need to be calculated for area gains and losses\n # the density changes are based on change area, from carbon, and the land type id total area\n # the from-to areas are positive, the to-from areas are negative\n #\n # from-to (positive columns)\n # ag and developed only have above main c (zero new carbon added) and soil c (frac of from carbon added) densities\n # the others are zero, so they are taken care of automatically for contraction\n # they need a special case for above and below addition\n # and the from conversion loss from total clearing has been calculated above\n # assume that loss happens faster than gain\n # above ground carbon expansion:\n # if new land type has less carbon, send the difference to the atmosphere, and calc the to carbon change based on area change and to \n # land type carbon\n # if new land type has more carbon, just calc the to carbon change, based on area change and from land type carbon\n # below ground and soil carbon expansion:\n # regardless of difference, calc the to density change based on area change and from land type carbon\n # assume that these transitions do not alter underground c immediately, and that the new c dynamics will eventually dominate\n #\n # to-from (negative columns)\n # this is the carbon transferred from one land type to another\n # for to ag and urban some of the carbon has been removed already\n # in fact, all above ground pools and a fraction of the underground pools as well\n # just need to remove some carbon from the remaining from land type area\n # from ag and developed only have above main c and soil c densities for now\n # the others are zero, so they are taken care of automatically\n # assume that loss happens faster than gain\n # for all cases:\n # calc density change based on change area and from land type carbon, normalized to from total area\n # but make sure not to remove carbon already removed when the from-to c den diff is positive!\n #\n # loop over the specific land type columns to generate land type specific conversion effect dfs\n # the final change values in these dfs will be aggregated put in columns in conv_own\n # these density changes are with respect to the current land type id total area for consistency with the other changes\n # at the end of the current year multiply the final densities by tot_area/new_area\n \n conv_df_list <- list()\n for (l in 1:num_conv_col_names) {\n # create a dataframe of conversion C transfers for each of the 16 land type\n lt_conv = conv_own_static\n # loop over the c pools\n for (c in 3:num_out_density_sheets) {\n # indices for matching density columns in land-type conversion C transfer dataframe and the output C density dataframe\n cind = which(names(lt_conv) == out_density_sheets[c])\n # paste \"_change\" to the end of the current C density pool in loop (e.g. \"Soil_orgC_den_change\") \n chname = paste0(out_density_sheets[c],\"_change\")\n # create a column with this name in the current land type's conversion C transfer dataframe (fill with 0's)\n lt_conv[, chname] = 0\n # paste \"_diff\" to the end of the current C density pool in loop (e.g. \"Soil_orgC_den_diff\") \n diffname = paste0(out_density_sheets[c],\"_diff\")\n # this is the land type row minus the l column difference\n # i.e., difference in C density of pool 'c' between all landtypes & landtype 'l' = \n # (C densities of pool 'c' [MgC/ha] in each landtype) - (C density of pool 'c' in landtype 'l') \n lt_conv[,diffname] = lt_conv[,cind] - lt_conv[l,cind]\n # do from-to first; don't need to do anything for a zero column\n # if the area change in landtype 'l' is positive (from-to), then do...\n #if(sum(lt_conv[,conv_col_names[l]]) > 0) {\n \tif (TRUE) { # this should work fine because there are checks throughought to make sure operating rows are > 0\n # only operate where the \"to\" area is > 0\n # and on above-ground C density pools \n if (c != 4 & c != 9) { # above\n # and if the current land type dataframe in loop (lt_conv) is for Cultivated or Developed areas\n if(conv_col_names[l] == \"Cultivated\" | conv_col_names[l] == \"Developed_all\") {\n # then, for these growing landtypes only, set the C density changes to 0\n # initialize the changes in C density (don't keep any above-ground C when converting to ag or developed)\n # density change = change in \"from-to\" area * zero carbon / \"to\" total area \n lt_conv[lt_conv[,conv_col_names[l]] > 0, chname] = 0\n } else {\n # the diff matters, but the c to atmos is tallied below in the to-from section\n # positive diff here means that some c is lost to atmosphere\n # calc density for diff positive\n # density change = change in \"from-to\" area * \"to\" carbon / \"to\" total area (\"to\" land type is new land type)\n lt_conv[(lt_conv[,diffname] > 0 & lt_conv[,conv_col_names[l]] > 0), chname] = \n lt_conv[(lt_conv[,diffname] > 0 & lt_conv[,conv_col_names[l]] > 0), conv_col_names[l]] * lt_conv[l, cind] / \n lt_conv$tot_area[l]\n # calc density for diff negative\n # density change = change in \"from-to\" area * \"from\" carbon / \"to\" total area\n lt_conv[(lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] > 0), chname] = \n lt_conv[(lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] > 0), conv_col_names[l]] * \n lt_conv[(lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] > 0), cind] / lt_conv$tot_area[l]\n } # end not to ag or developed\n } else {\t\t# underground\n # if ag or developed and root c, then...\n if(conv_col_names[l] == \"Cultivated\" | conv_col_names[l] == \"Developed_all\") {\n if (c==4) {\n # this should be zero, and the frac should send it all to the atmos above\n # but add it just in case the frac is changed\n # density change = change in \"from-to\" area * \"from\" carbon * rembelowcfrac / \"to\" total area\n # go to all the rows in which a landtype is converting area to the land type l column, and multiply this area by\n # the correpsonding change in C density and 1 minus the fraction that goes to atmosphere (currently 100%)/tot area, so this is 0.\n lt_conv[lt_conv[,conv_col_names[l]] > 0, chname] = lt_conv[lt_conv[,conv_col_names[l]] > 0, conv_col_names[l]] * \n lt_conv[lt_conv[,conv_col_names[l]] > 0, cind] * (1-lt_conv[lt_conv[,conv_col_names[l]] > 0, \"Below2Atmos_conv_frac\"]) / \n lt_conv$tot_area[l]\n } else {\n # a fraction of the from soil c has been removed to atmosphere\n # density change = change in \"from-to\" area * \"from\" carbon * remsoilcfrac / \"to\" total area\n lt_conv[lt_conv[,conv_col_names[l]] > 0, chname] = lt_conv[lt_conv[,conv_col_names[l]] > 0, conv_col_names[l]] * \n lt_conv[lt_conv[,conv_col_names[l]] > 0, cind] * (1-lt_conv[lt_conv[,conv_col_names[l]] > 0, \"Soil2Atmos_conv_frac\"]) / \n lt_conv$tot_area[l]\n } # end else soil c for to ag and dev\n } else {\t# end else underground ag and dev\n # soil org C & root for all other land types:\n # density change = change in \"from-to\" area * \"from\" carbon / \"to\" total area\n lt_conv[lt_conv[,conv_col_names[l]] > 0, chname] = lt_conv[lt_conv[,conv_col_names[l]] > 0, conv_col_names[l]] * \n lt_conv[lt_conv[,conv_col_names[l]] > 0, cind] / lt_conv$tot_area[l]\n }\n } # end else underground\n # else for land types losing area or for a static scenatio where nothing changes....\n #} else if (sum(lt_conv[,conv_col_names[l]]) <= 0) {\n \t} # end if(TRUE) for from-to\n \tif (TRUE) { # this should work fine because ther are checks throughought to make sure operating rows are < 0\n # to-from\n # only operate where the \"from\" area is < 0\n # to ag and dev already have removed carbon based on clearing above\n # all above ground has been removed\n # but some soil carbon still needs to be tallied as transferred\n # and some below ground is the frac is changed in input file (change in root c frac is uncertain, \n # can change fraction from 1 to something else in input file for sensitivity check)\n # so only operate on the non-ag, non-dev rows for all except underground\n # carbon has been sent to atmos when the to-from difference is negative\n \n # if root c or soil org c...\n if (c==4 | c==9) {\n \t# no 2 atmos transfer here, so don't worry about nonregen area\n \t\n # include ag and dev for the underground\n # it doesn't matter what the c den diff is\n # density change = change in \"to-from\" area * \"from\" carbon / \"from\" total area\n # do the \"to\" non-ag non-dev\n # this value should be negative\n lt_conv[lt_conv[,conv_col_names[l]] < 0 & lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", chname] = \n lt_conv[lt_conv[,conv_col_names[l]] < 0 & lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", \n conv_col_names[l]] * \n lt_conv[l, cind] / lt_conv$tot_area[l]\n # \"to\" ag and dev needs the remaining fraction of c for each c pool\n # this value should be negative\n # calc c taken out of 'from' landtype when it IS going to ag or developed (need to know C remaining after loss to atmosphere)\n # if it's root c, then remaining c fraction = 1 - root frac lost to atmosphere\n if(c==4) {remfrac = (1-lt_conv[l,\"Below2Atmos_conv_frac\"])} else\n # else it's soil org c, and remaining c fraction = 1 - soil C frac lost to atmosphere\n {remfrac = (1-lt_conv[l,\"Soil2Atmos_conv_frac\"])}\n # area lost * \"from\" carbon * remaining frac after conversion to ag or dev/ \"from\" total area \n lt_conv[lt_conv[,conv_col_names[l]] < 0 & (lt_conv$Land_Type == \"Cultivated\" | lt_conv$Land_Type == \"Developed_all\"), chname] = \n lt_conv[lt_conv[,conv_col_names[l]] < 0 & (lt_conv$Land_Type == \"Cultivated\" | lt_conv$Land_Type == \"Developed_all\"), \n conv_col_names[l]] * remfrac * lt_conv[l, cind] / lt_conv$tot_area[l]\t\n } else {\t# end if underground for to-from\n # above ground\n # the diff matters here - positive diff values mean all from carbon is transferred\n # density change = change in \"to-from\" area * \"from\" carbon / \"from\" total area\n # this value should be negative\n # calc c dens change (loss) for losing areas with higher C dens than 'to' area (not to ag or dev), by \n # subsetting rows in c dens change column with positive C dens difference & not ag or dev = \n # lost area * diff in C dens\n lt_conv[lt_conv[,diffname] > 0 & lt_conv[,conv_col_names[l]] < 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", chname] = \n lt_conv[lt_conv[,diffname] > 0 & lt_conv[,conv_col_names[l]] < 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", conv_col_names[l]] * \n lt_conv[l, cind] / lt_conv$tot_area[l]\n \n # the diff matters here - negative diff values mean some carbon is sent to atmosphere \n # adjust area change to account for nonregen area, if the from lt is Forest\n # this is to avoid double counting emissions due to fire then conversion\n # basically, do not emit to atmosphere for the non-regen area\n # distribute to appropriate land type transitions\n # carbon transferred to new type (below) is based on the conv_own area and current new type carbon\n # the carbon added to the new type (above in from-to) is consistent with this\n # this means that if the non-regen area has less carbon than the target (after burn), then a little extra carbon is transferred,\n # and if non-regen area has more carbon than the target (after burn), then a litte less carbon is transferred\n # recall that this area is negative\n # need to use the non_regen_area value to ensure the correct land type is adjusted for not regenerating\n lt_conv$area_adj = lt_conv[, conv_col_names[l]]\n if (length(lt_conv$area_adj[lt_conv[,conv_col_names[l]] < 0]) > 0 & conv_col_names[l] == \"Forest\") {\n \tlt_conv$area_adj[lt_conv[,conv_col_names[l]] < 0] = lt_conv$area_adj[lt_conv[,conv_col_names[l]] < 0] -\n \t\tlt_conv$nonreg_add[lt_conv[,conv_col_names[l]] < 0]\n \tlt_conv$area_adj[is.na(lt_conv$area_adj)] = 0.00\n \tlt_conv$area_adj[is.nan(lt_conv$area_adj)] = 0.00\n \tlt_conv$area_adj[lt_conv$area_adj == Inf] = 0.00\n \tlt_conv$area_adj[lt_conv$area_adj == -Inf] = 0.00\n \t# this happens when there is reforestation or afforestation that is less than the non-regen area\n \t# so need to ensure that no emissions are released below\n \t# if the restoration is greater than the non-regen area, area_adj is greater than zero and doesn't need adjustment because the calculations below don't operate on it\n \t#if (TRUE %in% (lt_conv$area_adj[lt_conv[,conv_col_names[l]] < 0] > 0)) {\n \t#\tcat(\"Warning: nonregen error in land conversion at r, i, l, c\", r, i, l, c, \"\\n\")\n \t#}\n \tlt_conv$area_adj[lt_conv[,conv_col_names[l]] < 0 & lt_conv$area_adj > 0] = 0.00\n }\n # the diff matters here - negative diff values mean some carbon is sent to atmosphere\n # send above ground lost carbon to the atmosphere if necessary\n # operate only where to-from diff is negative, and use area adjusted for non-regen area\n # including the case where the values are 0\n # 2atmos = row(to) minus col(from) c diff * \"to-from\" area / \"from\" total area\n # this value ends up positive, consistent with the removed transfers above\n # sent to atmos\n atmosname = paste0(out_density_sheets[c],\"2Atmos\")\n lt_conv[,atmosname] = 0\n lt_conv[(lt_conv[,diffname] <= 0 & lt_conv[,conv_col_names[l]] <= 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\"), atmosname] = \n lt_conv[(lt_conv[,diffname] <= 0 & lt_conv[,conv_col_names[l]] <= 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\"),diffname] * \n lt_conv[(lt_conv[,diffname] <= 0 & lt_conv[,conv_col_names[l]] <= 0 & lt_conv$Land_Type != \"Cultivated\" & \n lt_conv$Land_Type != \"Developed_all\"), \"area_adj\"] / lt_conv$tot_area[l]\n \n # the diff matters here - negative diff values mean some carbon is sent to atmosphere\n # density change = change in \"to-from\" area * \"to\" carbon / \"from\" total area\n # this value should be negative\n lt_conv[lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] < 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", chname] = \n lt_conv[lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] < 0 & lt_conv$Land_Type != \"Cultivated\" & \n lt_conv$Land_Type != \"Developed_all\", conv_col_names[l]] * \n lt_conv[lt_conv[,diffname] < 0 & lt_conv[,conv_col_names[l]] < 0 & \n lt_conv$Land_Type != \"Cultivated\" & lt_conv$Land_Type != \"Developed_all\", cind] / lt_conv$tot_area[l]\n \n # sum all C come out of 'from' land type going to atmosphere\n conv_own[conv_own$Land_Cat_ID == lt_conv$Land_Cat_ID[l],atmosname] = sum(lt_conv[,atmosname])\n # these deal with numerical errors due to roundoff, divide by zero, and any added NA values\n conv_own[,atmosname] = replace(conv_own[,atmosname], is.na(conv_own[,atmosname]), 0.0)\n conv_own[,atmosname] = replace(conv_own[,atmosname], is.nan(conv_own[,atmosname]), 0.0)\n conv_own[,atmosname] = replace(conv_own[,atmosname], conv_own[,atmosname] == Inf, 0.0)\n conv_own[,atmosname] = replace(conv_own[,atmosname], conv_own[,atmosname] == -Inf, 0.0)\n } # end else above ground for to-from\n } # end else to-from\n \n # sum amount of C either lost or gained for each land type\n conv_own[conv_own$Land_Cat_ID == lt_conv$Land_Cat_ID[l],chname] = sum(lt_conv[,chname])\n # these deal with numerical errors due to roundoff, divide by zero, and any added NA values\n conv_own[,chname] = replace(conv_own[,chname], is.na(conv_own[,chname]), 0.0)\n conv_own[,chname] = replace(conv_own[,chname], is.nan(conv_own[,chname]), 0.0)\n conv_own[,chname] = replace(conv_own[,chname], conv_own[,chname] == Inf, 0.0)\n conv_own[,chname] = replace(conv_own[,chname], conv_own[,chname] == -Inf, 0.0)\n } # end for c loop over the c pools\n conv_df_list[[l]] = lt_conv\n } # end for l loop over the \"to\" conversion column names\n \n } else {\n # ocean/seagrass\n # add the columns and update them accordingly\n # there is only expansion and contraction\n # on expansion, do not add carbon because the initial state is unknown\n # so calc carbon density transfers to maintain correct average c density\n # these are also normalized to current tot_area\n # no losses to atmosphere - it is assumed that it stays in the ocean\n skip = length(names(conv_own))\n # 'add' gets a vector of all the needed column names from completed conv_own table\n add = names(own_conv_df_list[[1]])[(skip+1):ncol(own_conv_df_list[[1]])]\n conv_own[,add] = 0\n # fill in all the conv_own columns and fill with 0\n conv_own$own_gain_sum = sum(conv_own$area_change[conv_own$area_change > 0])\n conv_own[conv_own$Land_Type == \"Seagrass\", \"Above_main_C_den\"] = \n out_density_df_list[[3]][out_density_df_list[[3]]$Land_Type == \"Seagrass\",next_density_label]\n conv_own[conv_own$Land_Type == \"Seagrass\", \"Soil_orgC_den\"] = \n out_density_df_list[[9]][out_density_df_list[[9]]$Land_Type == \"Seagrass\",next_density_label]\n # contraction\n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"Above_main_C_den_change\"] = \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"area_change\"] * \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"Above_main_C\"] / \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"tot_area\"]\n conv_own[,\"Above_main_C_den_change\"] = replace(conv_own[,\"Above_main_C_den_change\"], is.nan(conv_own[,\"Above_main_C_den_change\"]), 0.0)\n conv_own[,\"Above_main_C_den_change\"] = replace(conv_own[,\"Above_main_C_den_change\"], conv_own[,\"Above_main_C_den_change\"] == Inf, 0.0)\n conv_own[,\"Above_main_C_den_change\"] = replace(conv_own[,\"Above_main_C_den_change\"], conv_own[,\"Above_main_C_den_change\"] == -Inf, 0.0)\n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"Soil_orgC_den_change\"] = \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"area_change\"] * \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"Soil_orgC_den\"] / \n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change < 0), \"tot_area\"]\n conv_own[,\"Soil_orgC_den_change\"] = replace(conv_own[,\"Soil_orgC_den_change\"], is.nan(conv_own[,\"Soil_orgC_den_change\"]), 0.0)\n conv_own[,\"Soil_orgC_den_change\"] = replace(conv_own[,\"Soil_orgC_den_change\"], conv_own[,\"Soil_orgC_den_change\"] == Inf, 0.0)\n conv_own[,\"Soil_orgC_den_change\"] = replace(conv_own[,\"Soil_orgC_den_change\"], conv_own[,\"Soil_orgC_den_change\"] == -Inf, 0.0)\n # expansion\n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change > 0), \"Above_main_C_den_change\"] = 0\n conv_own[(conv_own$Land_Type == \"Seagrass\" & conv_own$area_change > 0), \"Soil_orgC_den_change\"] = 0\n } # end if land own else ocean/seagrass\n # do not include the extra columns for calculating the transitions\n #conv_own$row_gain_sum = NULL\n #conv_own$row_loss_sum = NULL\n #conv_own$row_change_sum = NULL\n own_conv_df_list_pre[[i]] = conv_own\n # get column number of last column before all the land types - this attempts to make the following routine more generic to allow changes to \n # the c_input file structure without updating the for loop below.\n last_column <- which(names(own_conv_df_list_pre[[i]])==\"own_gain_sum\")\n } # end i loop over ownership for calculating land conversion c adjustments\n # after all the ownership loops within a given region rbind all the ownership tables into one \n # first, get the complete list of land type names \n conv_col_names_all = unique(conv_adjust_df$Land_Type)\n num_conv_col_names = length(conv_col_names_all)\n # Second, check each ownership df within the current region's list (own_conv_df_list_pre) to see if it's \n # missing a land type column name, and replace if it is.\n for (g in 1:length(own_conv_df_list_pre)) { \n if (any(!((conv_col_names_all) %in% (names(own_conv_df_list_pre[[g]]))))) { \n # get indices of full name list that are missing\n missing_inds <- which(!((conv_col_names_all) %in% (names(own_conv_df_list_pre[[g]]))))\n # get names of missing land type columns\n missing_names <- conv_col_names_all[missing_inds]\n # add the missing land type columns and fill with 0's\n own_conv_df_list_pre[[g]][missing_names] <- 0\n # get length of new complete df (=73)\n full_length <- length(own_conv_df_list_pre[[g]])\n # get columns preceding the land type columns\n begin_set_cols <- own_conv_df_list_pre[[g]][,c(1:last_column)]\n # get full set of landtype columns in order\n all_16landtype_cols <- own_conv_df_list_pre[[g]][,c(conv_col_names_all)]\n # get number of _not_ missing column names \n numb_not_missing <- length(conv_col_names_all) - length(missing_names)\n # get column index to start on for end set of columns\n end_start_ind <- last_column + numb_not_missing + 1\n # get column index to end on for end set of columns\n end_end_ind <- full_length - length(missing_names)\n # subset the columns following the original landtype columns and before the ones that were added to the end\n # order the sequence of columns in the last chunk\n end_set_cols <- own_conv_df_list_pre[[g]][,c(\"Above_main_C_den\", \"Above_harvested_conv_c\", \"StandDead_C_den\", \"StandDead_harvested_conv_c\", \n \"Harvested2Wood_conv_c\", \"Harvested2Energy_conv_c\", \"Harvested2SawmillDecay_conv_c\", \"Harvested2Slash_conv_c\",\n \"Understory_C_den\", \"Under2Slash_conv_c\", \"DownDead_C_den\", \"DownDead2Slash_conv_c\", \"Litter_C_den\", \n \"Litter2Slash_conv_c\", \"Slash2Energy_conv_c\", \"Slash2Wood_conv_c\", \"Slash2Burn_conv_c\", \n \"Slash2Decay_conv_c\", \"Under2DownDead_conv_c\", \"Soil_orgC_den\", \"Soil2Atmos_conv_c\", \"Below_main_C_den\", \n \"Below2Atmos_conv_c\", \"Below2Soil_conv_c\", \"Above_main_C_den_change\", \"Below_main_C_den_change\", \n \"Understory_C_den_change\", \"StandDead_C_den_change\", \"DownDead_C_den_change\", \"Litter_C_den_change\", \n \"Soil_orgC_den_change\", \"Above_main_C_den2Atmos\", \"Understory_C_den2Atmos\", \"StandDead_C_den2Atmos\", \n \"DownDead_C_den2Atmos\", \"Litter_C_den2Atmos\")]\n # merge the subsets of columns back together. They're in proper order now to rbind below\n own_conv_df_list_pre[[g]] <- cbind(begin_set_cols, all_16landtype_cols, end_set_cols) \n } # end if\n } # end for g loop\n # combine dataframes for each ownership type within a region\n if (length(own_conv_df_list_pre) > 1) {\n conv_df_pre = rbind(own_conv_df_list_pre[[1]], own_conv_df_list_pre[[2]])\n if (length(own_conv_df_list_pre) > 2) {\n for (z in 3:length(own_names)) {\n conv_df_pre = rbind(conv_df_pre, own_conv_df_list_pre[[z]])\n }\n } \n own_conv_df_list[[r]] = conv_df_pre\n } else own_conv_df_list[[r]] = own_conv_df_list_pre[[1]]\n } # end r loop over Region\n \n # now rebuild the conv_adjust_df by Region\n # reset own_names \n region_names <- unique(conv_adjust_df$Region)\n # start with adding the 1st and 2nd ownership data frames to conv_adjust\n # conv_adjust is currently a single df with x obs. of 73 variables, and own_conv_df_list[[1]]\n # is a list with 1 df, 114 obs., 73 variables.\n ## Reset conv_adjust_df \n conv_adjust_df = rbind(own_conv_df_list[[1]], own_conv_df_list[[2]]) \n # then just add the remaining ownership data frames to conv_adjust\n for (i in 3:length(region_names)) {\n conv_adjust_df = rbind(conv_adjust_df, own_conv_df_list[[i]])\n }\n # sort so it looks like input tables\n conv_adjust_df = conv_adjust_df[order(conv_adjust_df$Land_Cat_ID),]\n \n # aggregate the transfer densities to the density pools\n # recall that the transfer densities are normalized to tot_area\n # so after the sums, multiply by tot_area/new_area, because these are the final adjustments\n # convert these to gains where necessary for consistency: all terrestrial gains are positive, losses are negative\n # store the transfers in all_c_flux\n all_c_flux = merge(conv_adjust_df[,c(1:4,8)], all_c_flux, by = c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\"))\n all_c_flux = all_c_flux[order(all_c_flux$Land_Cat_ID),]\n \n cgnames = NULL\n # above\n # changes in c dens\n cgnames = c(cgnames, paste0(out_density_sheets[3],\"_gain_conv\"))\n # c lost from any landtype going to ag/urban - c loss (emissions) if changing land area from hi to lo c density + \n # c density gain if going to low to hi c density \n all_c_flux[,cgnames[1]] = - conv_adjust_df$Above_harvested_conv_c - conv_adjust_df$Above_main_C_den2Atmos + \n conv_adjust_df$Above_main_C_den_change\n # below\n cgnames = c(cgnames, paste0(out_density_sheets[4],\"_gain_conv\"))\n all_c_flux[,cgnames[2]] = - conv_adjust_df$Below2Atmos_conv_c + conv_adjust_df$Below_main_C_den_change\n # understory\n cgnames = c(cgnames, paste0(out_density_sheets[5],\"_gain_conv\"))\n all_c_flux[,cgnames[3]] = - conv_adjust_df$Under2Slash_conv_c - conv_adjust_df$Under2DownDead_conv_c - \n conv_adjust_df$Understory_C_den2Atmos + conv_adjust_df$Understory_C_den_change\n # standing dead\n cgnames = c(cgnames, paste0(out_density_sheets[6],\"_gain_conv\"))\n all_c_flux[,cgnames[4]] = - conv_adjust_df$StandDead_harvested_conv_c - conv_adjust_df$StandDead_C_den2Atmos + \n conv_adjust_df$StandDead_C_den_change\n # down dead\n cgnames = c(cgnames, paste0(out_density_sheets[7],\"_gain_conv\"))\n all_c_flux[,cgnames[5]] = - conv_adjust_df$DownDead2Slash_conv_c + conv_adjust_df$Under2DownDead_conv_c - \n conv_adjust_df$DownDead_C_den2Atmos + conv_adjust_df$DownDead_C_den_change\n # litter\n cgnames = c(cgnames, paste0(out_density_sheets[8],\"_gain_conv\"))\n all_c_flux[,cgnames[6]] = - conv_adjust_df$Litter2Slash_conv_c - conv_adjust_df$Litter_C_den2Atmos + conv_adjust_df$Litter_C_den_change\n # soil\n cgnames = c(cgnames, paste0(out_density_sheets[9],\"_gain_conv\"))\n all_c_flux[,cgnames[7]] = - conv_adjust_df$Soil2Atmos_conv_c + conv_adjust_df$Soil_orgC_den_change\n \n # loop over the relevant out density tables to update the C pools based on the conversion fluxes\n # carbon cannot go below zero\n sum_change = 0\n sum_change2 = 0\n sum_neg_conv = 0\n # starts on 3 because 1 and 2 are total C and total living and dead biomass\n for (i in 3:num_out_density_sheets) {\n # diagnostic for c stock change\n sum_change = sum_change + sum(all_c_flux[,cgnames[i-2]] * all_c_flux$tot_area)\n # diagnostic for c stock change\n sum_change2 = sum_change2 + sum(conv_adjust_df[,paste0(out_density_sheets[i],\"_change\")] * all_c_flux$tot_area)\n # adds next year total c density columns: \"Above_main_C_den\" \"Below_main_C_den\" \"Understory_C_den\" \"StandDead_C_den\" \n # \"DownDead_C_den\" \"Litter_C_den\" \"Soil_orgC_den\" \n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, next_density_label] + all_c_flux[,cgnames[i-2]]\n # first calc the carbon not subtracted because it sends density negative\n # correction and diagnostic\n neginds = which(out_density_df_list[[i]][, next_density_label] < 0)\n cat(\"neginds for out_density_df_list lcc\" , i, \"are\", neginds, \"\\n\")\n cat(\"total areas for neginds out_density_df_list lcc\" , i, \"are\", all_c_flux[neginds, \"new_area\"], \"\\n\")\n sum_neg_eco = sum_neg_eco + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n # sum all negative\n sum_neg_conv = sum_neg_conv + sum(all_c_flux$tot_area[out_density_df_list[[i]][,next_density_label] < 0] * \n out_density_df_list[[i]][out_density_df_list[[i]][,next_density_label] < 0, next_density_label])\n # set neg desities to 0\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] <= 0, 0.00)\n # normalize it to the new area and check for zero new area (sets correct c densities)\n out_density_df_list[[i]][, next_density_label] = out_density_df_list[[i]][, next_density_label] * all_c_flux$tot_area / all_c_flux$new_area\n # set NAN or INF desities to 0\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n is.nan(out_density_df_list[[i]][, next_density_label]), 0.00)\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] == Inf, 0.00)\n out_density_df_list[[i]][, next_density_label] <- replace(out_density_df_list[[i]][, next_density_label], \n out_density_df_list[[i]][, next_density_label] == -Inf, 0.00)\n } # end loop over out densities for updating due to conversion\n \n # Get total C lost to atmosphere from LCC via 3 potential flux pathways by the tot_area\n # (1) DECAY\n all_c_flux[,\"Land2Atmos_DecayC_stock_conv\"] = -conv_adjust_df$tot_area * \n (conv_adjust_df$Soil2Atmos_conv_c + conv_adjust_df$Slash2Decay_conv_c + conv_adjust_df$Harvested2SawmillDecay_conv_c + \n conv_adjust_df$Below2Atmos_conv_c + \n conv_adjust_df$Above_main_C_den2Atmos + conv_adjust_df$Understory_C_den2Atmos + conv_adjust_df$StandDead_C_den2Atmos + \n conv_adjust_df$DownDead_C_den2Atmos + conv_adjust_df$Litter_C_den2Atmos)\n # split non-burned DECAY into at sawmill and on-site\n all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"] = -conv_adjust_df$tot_area * conv_adjust_df$Harvested2SawmillDecay_conv_c \n all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"] = -conv_adjust_df$tot_area * \n (conv_adjust_df$Soil2Atmos_conv_c + conv_adjust_df$Slash2Decay_conv_c + conv_adjust_df$Below2Atmos_conv_c + \n conv_adjust_df$Above_main_C_den2Atmos + conv_adjust_df$Understory_C_den2Atmos + conv_adjust_df$StandDead_C_den2Atmos + \n conv_adjust_df$DownDead_C_den2Atmos + conv_adjust_df$Litter_C_den2Atmos)\n # replace NA with 0\n all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"] <- replace(all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"], \n is.nan( all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"]), 0.0)\n all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"] <- replace(all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"], \n is.nan( all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"]), 0.0)\n \n \n # (2) MANAGED BURNS - currently assumed this is 0\n all_c_flux[,\"Land2Atmos_BurnC_stock_conv\"] = -conv_adjust_df$tot_area * (conv_adjust_df$Slash2Burn_conv_c)\n # (3) TOTAL ENERGY - this is assumed to go to the atmosphere immediately\n all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"] = -conv_adjust_df$tot_area * (conv_adjust_df$Harvested2Energy_conv_c + \n conv_adjust_df$Slash2Energy_conv_c)\n \n # Get amount of total LCC energy that is from harvest versus slash utilization\n # Harv2Energy\n all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"] = -conv_adjust_df$tot_area * conv_adjust_df$Harvested2Energy_conv_c\n # Slash2Energy\n all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"] = -conv_adjust_df$tot_area * conv_adjust_df$Slash2Energy_conv_c\n # replace NaN with 0\n all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"] <- replace(all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"], \n is.nan( all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"]), 0.0)\n all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"] <- replace(all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"], \n is.nan(all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"]), 0.0)\n # Get C lost to wood \n # WOOD - this decays with a half-life\n all_c_flux[,\"Land2Wood_c_stock_conv\"] = -conv_adjust_df$tot_area * (conv_adjust_df$Harvested2Wood_conv_c + \n conv_adjust_df$Slash2Wood_conv_c)\n # Get amount of total LCC Wood that is from harvest versus slash utilization\n # Harv2Wood\n all_c_flux[,\"Harv2Wood_c_stock_conv\"] = -conv_adjust_df$tot_area * conv_adjust_df$Harvested2Wood_conv_c\n # Slash2Wood\n all_c_flux[,\"Slash2Wood_c_stock_conv\"] = -conv_adjust_df$tot_area * conv_adjust_df$Slash2Wood_conv_c\n # replace NaN with 0\n all_c_flux[,\"Harv2Wood_c_stock_conv\"] <- replace(all_c_flux[,\"Harv2Wood_c_stock_conv\"], \n is.nan(all_c_flux[,\"Harv2Wood_c_stock_conv\"]), 0.0)\n all_c_flux[,\"Slash2Wood_c_stock_conv\"] <- replace(all_c_flux[,\"Slash2Wood_c_stock_conv\"], \n is.nan(all_c_flux[,\"Slash2Wood_c_stock_conv\"]), 0.0)\n \n cat(\"lcc carbon change is \", sum_change, \"\\n\")\n cat(\"lcc net carbon transfer to other land types is \", sum_change2, \"\\n\")\n cat(\"lcc carbon to wood is \", sum(all_c_flux$Land2Wood_c_stock_conv), \"\\n\")\n cat(\"lcc carbon to atmos decayed is \", sum(all_c_flux$Land2Atmos_DecayC_stock_conv), \"\\n\")\n cat(\"lcc carbon to atmos burned is \", sum(all_c_flux$Land2Atmos_BurnC_stock_conv), \"\\n\")\n cat(\"lcc carbon to energy is \", sum(all_c_flux$Land2Atmos_TotEnergyC_stock_conv), \"\\n\")\n cat(\"lcc negative carbon cleared is \", sum_neg_conv, \"\\n\")\n \n # update the conversion wood tables\n # recall that the transfers from land are negative values\n # use the IPCC half life equation for first order decay of wood products, and the CA average half life for all products\n # this includes the current year loss on the current year production\n # running stock and cumulative change values are at the beginning of the labeled year - so the next year value is the stock or sum after \n # current year production and loss\n # annual change values are in the year they occurred\n \n k = log(2) / wp_half_life\n # next year's \"LCC_Wood_C_stock\" = current year's \"LCC_Wood_C_stock\" * decay_term1 + decay_term2 * more_wood\n out_wood_df_list[[15]][,next_wood_label] = out_wood_df_list[[15]][,cur_wood_label] * exp(-k) + ((1 - exp(-k)) / k) * \n (-all_c_flux$Land2Wood_c_stock_conv)\n # next year's \"LCC_Wood_CumGain_C_stock\" = current year's \"LCC_Wood_CumGain_C_stock\" + harvested_wood_conv\n out_wood_df_list[[16]][,next_wood_label] = out_wood_df_list[[16]][,cur_wood_label] - all_c_flux$Land2Wood_c_stock_conv\n # Next year's \"LCC_Harv2Wood_CumGain_C_stock\" = Current year's \"LCC_Harv2Wood_CumGain_C_stock\" + wood_accumulated from harvest\n out_wood_df_list[[17]][,next_wood_label] = out_wood_df_list[[17]][,cur_wood_label] - all_c_flux$Harv2Wood_c_stock_conv\n # Next year's \"LCC_Slash2Wood_CumGain_C_stock\" = Current year's \"LCC_Slash2Wood_CumGain_C_stock\" + wood_accumulated from slash\n out_wood_df_list[[18]][,next_wood_label] = out_wood_df_list[[18]][,cur_wood_label] - all_c_flux$Slash2Wood_c_stock_conv\n # current year's \"LCC_Wood_AnnGain_C_stock\" = total wood accumulated\n out_wood_df_list[[20]][,cur_wood_label] = -all_c_flux$Land2Wood_c_stock_conv\n # current year's \"LCC_Harv2Wood_AnnGain_C_stock\" = total wood accumulated from harvest\n out_wood_df_list[[21]][,cur_wood_label] = -all_c_flux$Harv2Wood_c_stock_conv\n # current year's \"LCC_Slash2Wood_AnnGain_C_stock\" = total wood accumulated from slash utilization\n out_wood_df_list[[22]][,cur_wood_label] = -all_c_flux$Slash2Wood_c_stock_conv\n # current year's \"LCC_Wood_AnnLoss_C_stock\" = current year's \"LCC_Wood_C_stock\" + total wood accumulated - next year's \"LCC_Wood_C_stock\"\n out_wood_df_list[[23]][,cur_wood_label] = out_wood_df_list[[15]][,cur_wood_label] - all_c_flux$Land2Wood_c_stock_conv - \n out_wood_df_list[[15]][,next_wood_label]\n # next year's \"LCC_Wood_CumLoss_C_stock\" = current year's \"LCC_Wood_CumLoss_C_stock\" + current year's \"LCC_Wood_AnnLoss_C_stock\" \n out_wood_df_list[[19]][,next_wood_label] = out_wood_df_list[[19]][,cur_wood_label] + out_wood_df_list[[23]][,cur_wood_label]\n \n # update the total wood tables\n # total wood C stock = total management wood stock + total lcc wood stock\n out_wood_df_list[[1]][,next_wood_label] = out_wood_df_list[[6]][,next_wood_label] + out_wood_df_list[[15]][,next_wood_label]\n # total cumulative wood C gain = total cumulative management wood gain + total cumulative lcc wood gain\n out_wood_df_list[[2]][,next_wood_label] = out_wood_df_list[[7]][,next_wood_label] + out_wood_df_list[[16]][,next_wood_label]\n # total cumulative wood C loss = total cumulative management wood loss + total cumulative lcc wood loss\n out_wood_df_list[[3]][,next_wood_label] = out_wood_df_list[[10]][,next_wood_label] + out_wood_df_list[[19]][,next_wood_label]\n # total annual wood C gain = total annual management wood gain + total annual lcc wood gain\n out_wood_df_list[[4]][,cur_wood_label] = out_wood_df_list[[11]][,cur_wood_label] + out_wood_df_list[[20]][,cur_wood_label]\n # total annual wood C loss = total annual management wood loss + total annual lcc wood loss\n out_wood_df_list[[5]][,cur_wood_label] = out_wood_df_list[[14]][,cur_wood_label] + out_wood_df_list[[23]][,cur_wood_label]\n \n # set the new tot area\n out_area_df_list[[1]][,next_area_label] = all_c_flux$new_area\n \n # set this years actual managed area (the area change activities are still just targets)\n # can have the case where there isn't enough area to do the full input scenario target mangagement area (correction was done earlier)\n out_area_df_list[[2]][,cur_area_label] = man_adjust_df$man_area\n \n # set this years actual fire area - output by the lt breakdown\n if(year == start_year){\n # add 3rd data frame with these introductory columns\n out_area_df_list[[3]] = fire_adjust_df[,c(\"Land_Cat_ID\", \"Region\", \"Land_Type\", \"Ownership\", \"Severity\")]\n }\n # add column for current (initial) burn area determined earlier for how it's distributed\n out_area_df_list[[3]][,cur_area_label] = fire_adjust_df$fire_burn_area\n \n # add up the total org c pool density\n out_density_df_list[[1]][, next_density_label] = 0\n # loop through all c dens pools and add to new column\n for (i in 3:num_out_density_sheets) {\n out_density_df_list[[1]][, next_density_label] = out_density_df_list[[1]][, next_density_label] + \n out_density_df_list[[i]][, next_density_label]\n }\n \n # add up the biomass c pool density (all non-decomposed veg material)\n out_density_df_list[[2]][, next_density_label] = 0\n for (i in 3:(num_out_density_sheets-1)) {\n out_density_df_list[[2]][, next_density_label] = out_density_df_list[[2]][, next_density_label] + \n out_density_df_list[[i]][, next_density_label]\n }\n \n # fill the carbon stock out tables and the atmos tables\n #out_stock_sheets = c(\"All_orgC_stock\", \"All_biomass_C_stock\", \"Above_main_C_stock\", \"Below_main_C_stock\", \"Understory_C_stock\", \n #\"StandDead_C_stock\", \"DownDead_C_stock\", \"Litter_C_stock\", \"Soil_orgC_stock\")\n #out_atmos_sheets = c(\"Eco_CumGain_C_stock\", \"Total_Atmos_CumGain_C_stock\", \"Manage_Atmos_CumGain_C_stock\", \"Fire_Atmos_CumGain_C_stock\", \n #\"LCC_Atmos_CumGain_C_stock\", \"Wood_Atmos_CumGain_C_stock\", \"Total_Energy2Atmos_C_stock\", \"Eco_AnnGain_C_stock\", \"Total_Atmos_AnnGain_C_stock\", \n #\"Manage_Atmos_AnnGain_C_stock\", \"Fire_Atmos_AnnGain_C_stock\", \"LCC_Atmos_AnnGain_C_stock\", \"Wood_Atmos_AnnGain_C_stock\", \n # \"Total_AnnEnergy2Atmos_C_stock\") out_wood_sheets = c(\"Total_Wood_C_stock\", \"Total_Wood_CumGain_C_stock\", \"Total_Wood_CumLoss_C_stock\", \n # \"Total_Wood_AnnGain_C_stock\", \"Total_Wood_AnnLoss_C_stock\", \"Manage_Wood_C_stock\", \"Manage_Wood_CumGain_C_stock\", \n # \"Manage_Wood_CumLoss_C_stock\", \"Manage_Wood_AnnGain_C_stock\", \"Manage_Wood_AnnLoss_C_stock\", \"LCC_Wood_C_stock\", \"LCC_Wood_CumGain_C_stock\", \n # \"LCC_Wood_CumLoss_C_stock\", \"LCC_Wood_AnnGain_C_stock\", \"LCC_Wood_AnnLoss_C_stock\")\n \n # carbon stock\n for (i in 1:num_out_stock_sheets) {\n out_stock_df_list[[i]][, next_stock_label] = out_density_df_list[[i]][, next_density_label] * out_area_df_list[[1]][,next_area_label]\n }\n \n # cumulative c to atmosphere (and net cumulative c from atmos to ecosystems)\n # also store the annual values\n # gains are positive (both land and atmosphere)\n # so need to subtract the releases to atmosphere becuase they are negative in all_c_flux\n # as these are cumulative, they represent the values at the beginning of the labelled year\n \n # net atmos to ecosystems; this includes c accumulation adjustments based on management\n # \"Above_main_C_den_gain_eco\" to \"Soil_orgC_den_gain_eco\"\n sum_change = 0\n for(i in 1:7){\n # checks the eco accum value below\n sum_change = sum_change + sum(all_c_flux[, egnames[i]] * all_c_flux$tot_area)\n }\n \n ##### CUMULATIVE FLUXES ##### \n\n ### Net Ecosystem Flux (i.e. baseline) ###\n # \"Eco_CumGain_C_stock\" = current year \"Eco_CumGain_C_stock\" + total area * (sum of all changes in c density pools)\n out_atmos_df_list[[1]][, next_atmos_label] = out_atmos_df_list[[1]][, cur_atmos_label] + all_c_flux[,\"tot_area\"] * \n (all_c_flux[,11] + all_c_flux[,12] + all_c_flux[,13] + all_c_flux[,14] + all_c_flux[,15] + all_c_flux[,16] + all_c_flux[,17])\n \n ### Management C Emissions ###\n # \"Manage_Atmos_CumGain_C_stock\" based on biomass removal, managed burns and energy (note: actually adding terms because they are negative)\n # \"Manage_Atmos_CumGain_C_stock\" = (current year \"Manage_Atmos_CumGain_C_stock\") - \"Land2Decay_c_stock_man_agg\" -\n # \"Land2Burn_c_stock_man_agg\" - \"Land2Energy_c_stock_man_agg\" \n out_atmos_df_list[[3]][, next_atmos_label] = out_atmos_df_list[[3]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_DecayC_stock_man_agg\"] - \n all_c_flux[,\"Land2Atmos_BurnC_stock_man_agg\"] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"]\n \n ### Wildfire C Emissions ###\n # \"Fire_Atmos_CumGain_C_stock\"\n out_atmos_df_list[[4]][, next_atmos_label] = out_atmos_df_list[[4]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_c_stock_fire_agg\"]\n \n ### Land Cover Change C Emissions ###\n # \"LCC_Atmos_CumGain_C_stock\" based on land cover change with associated biomass removal and energy\n out_atmos_df_list[[5]][, next_atmos_label] = out_atmos_df_list[[5]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_DecayC_stock_conv\"] - \n all_c_flux[,\"Land2Atmos_BurnC_stock_conv\"] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"]\n \n ### Wood C Emissions ###\n # \"Wood_Atmos_CumGain_C_stock\" from the wood tables: \"Total_Wood_CumLoss_C_stock\"\n out_atmos_df_list[[6]][, next_atmos_label] = out_wood_df_list[[3]][,next_wood_label]\n \n ### Total Energy C Emissions ### \n # \"Total_Energy2Atmos_C_stock\" just to compare it with the total cum atmos c\n out_atmos_df_list[[7]][, next_atmos_label] = out_atmos_df_list[[7]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"] - \n all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"]\n \n ### Total C Emissions (all sources and pathways except Net Eco Flux) ###\n # \"Total_Atmos_CumGain_C_stock\" the total release of land and wood product and energy c to the atmosphere\n # the energy release is inluded in the manage and lcc releases\n # Total cum C emissions (less Eco) = Management + Wildfire + LCC + Wood\n out_atmos_df_list[[2]][, next_atmos_label] = out_atmos_df_list[[3]][,next_atmos_label] + out_atmos_df_list[[4]][,next_atmos_label] + \n out_atmos_df_list[[5]][,next_atmos_label] + out_atmos_df_list[[6]][,next_atmos_label]\n \n ##### ANNUAL FLUXES\t#####\n \n ### Net Ecosystem Flux ###\n # \"Eco_AnnGain_C_stock\" = total area * (Above_main_C_den_gain_eco + Below_main_C_den_gain_eco + Understory_C_den_gain_ec +\n # StandDead_C_den_gain_eco + DownDead_C_den_gain_eco + Litter_C_den_gain_eco +\n # Soil_orgC_den_gain_eco)\n out_atmos_df_list[[8]][, cur_atmos_label] = all_c_flux[,\"tot_area\"] * \n (all_c_flux[,11] + all_c_flux[,12] + all_c_flux[,13] + all_c_flux[,14] + all_c_flux[,15] + all_c_flux[,16] + all_c_flux[,17])\n \n ### Management C Emissions ###\n # \"Manage_Atmos_AnnGain_C_stock\" = decay + burn + energy\n out_atmos_df_list[[10]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_DecayC_stock_man_agg\"] - all_c_flux[,\"Land2Atmos_BurnC_stock_man_agg\"] -\n all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"]\n\n ### Wildfire C Emissions ###\n # \"Fire_Atmos_AnnGain_C_stock\" based on fire\n out_atmos_df_list[[11]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_c_stock_fire_agg\"]\n \n ### Land Cover Change C Emissions ###\n # \"LCC_Atmos_AnnGain_C_stock\" based on land cover change with associated biomass removal, includes energy from biomass\n out_atmos_df_list[[12]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_DecayC_stock_conv\"] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"] -\n all_c_flux[,\"Land2Atmos_BurnC_stock_conv\"]\n\n ### Wood C Emissions ###\n # \"Wood_Atmos_AnnGain_C_stock\" from the wood tables: \"Total_Wood_CumLoss_C_stock\"\n out_atmos_df_list[[13]][, cur_atmos_label] = out_wood_df_list[[5]][,cur_wood_label]\n \n ### Total Energy C Emissions ###\n # \"Total_AnnEnergy2Atmos_C_stock\" just to compare it with the total cum atmos c\n out_atmos_df_list[[14]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"]\n \n ### Total C Emissions (all sources and pathways except Net Eco Flux) ###\n # \"Total_Atmos_AnnGain_C_stock\" the total release of land and wood product and energy c to the atmosphere\n # the energy release is inluded in the manage and lcc releases\n out_atmos_df_list[[9]][, cur_atmos_label] = out_atmos_df_list[[10]][,cur_atmos_label] + out_atmos_df_list[[11]][,cur_atmos_label] + \n out_atmos_df_list[[12]][,cur_atmos_label] + out_atmos_df_list[[13]][,cur_atmos_label]\n \n ### cumulative (again) ### \n \n # Get C emissions from individual pathways: CONTROLLED FIRE, ENERGY, and NON-BURNED C fluxes to atmosphere\n \n # Manage BURN: \"Manage_Atmos_CumGain_FireC\" = (current year \"Manage_Atmos_CumGain_FireC\") - \"Land2Atmos_BurnC_stock_man_agg\" \n out_atmos_df_list[[15]][, next_atmos_label] = out_atmos_df_list[[15]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_BurnC_stock_man_agg\"] \n # Manage TOTAL ENERGY: \"Manage_Atmos_CumGain_TotEnergyC\" = (current year \"Manage_Atmos_CumGain_TotEnergyC\") - \"Land2Atmos_TotEnergyC_stock_man_agg\" \n out_atmos_df_list[[16]][, next_atmos_label] = out_atmos_df_list[[16]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"]\n # Manage ENERGY FROM HARVEST: \n out_atmos_df_list[[17]][, next_atmos_label] = out_atmos_df_list[[17]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_man_agg\"]\n # Manage ENERGY FROM SLASH: \n out_atmos_df_list[[18]][, next_atmos_label] = out_atmos_df_list[[18]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_man_agg\"]\n # Manage DECAY: \"Manage_Atmos_CumGain_NonBurnedC\" = (current year \"Manage_Atmos_CumGain_NonBurnedC\") - \"Land2Atmos_DecayC_stock_man_agg\"\n out_atmos_df_list[[19]][, next_atmos_label] = out_atmos_df_list[[19]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_DecayC_stock_man_agg\"] \n \n # check that true: total management land to atmosphere C flux equal to energy + controlled burns + unburned (decay) \n all(out_atmos_df_list[[\"Manage_Atmos_CumGain_C_stock\"]][, next_atmos_label] == out_atmos_df_list[[\"Manage_Atmos_CumGain_FireC\"]][, next_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_CumGain_TotEnergyC\"]][, next_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_CumGain_NonBurnedC\"]][, next_atmos_label])\n # Due to rounding error this checks true that the difference is <0.5 and >-0.5 \n all(out_atmos_df_list[[\"Manage_Atmos_CumGain_C_stock\"]][, next_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_CumGain_FireC\"]][, next_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_CumGain_TotEnergyC\"]][, next_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_CumGain_NonBurnedC\"]][, next_atmos_label]) < 0.5 & \n (out_atmos_df_list[[\"Manage_Atmos_CumGain_C_stock\"]][, next_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_CumGain_FireC\"]][, next_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_CumGain_TotEnergyC\"]][, next_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_CumGain_NonBurnedC\"]][, next_atmos_label])) > -0.5)\n \n # Partition the \"Fire_Atmos_CumGain_C_stock\" into burned and non-burned C sources\n \n # FIRE burned: \"Fire_Atmos_CumGain_BurnedC\" = (current year \"Fire_Atmos_CumGain_BurnedC\") - \"Land2Atmos_BurnedC_stock_fire_agg\" \n out_atmos_df_list[[20]][, next_atmos_label] = out_atmos_df_list[[20]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_BurnedC_stock_fire_agg\"]\n # FIRE non-burned: \"Fire_Atmos_CumGain_NonBurnedC\" = (current year \"Fire_Atmos_CumGain_NonBurnedC\") - \"Land2Atmos_NonBurnedC_stock_fire_agg\" \n out_atmos_df_list[[21]][, next_atmos_label] = out_atmos_df_list[[21]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_NonBurnedC_stock_fire_agg\"]\n \n # Partition the \"LCC_Atmos_CumGain_C_stock\" into burned (energy only) and non-burned C sources \n # With the exception of removed C to energy, we are currently assuming that all lost above- and below-ground c (except removed2wood) \n # is released as CO2 (decomposition) and not burned\n \n # LCC BURN: \"LCC_Atmos_CumGain_FireC\" = (current year \"LCC_Atmos_CumGain_FireC\") - \"Land2Atmos_BurnC_stock_conv\"\n out_atmos_df_list[[22]][, next_atmos_label] = out_atmos_df_list[[22]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_BurnC_stock_conv\"]\n # LCC TOTAL ENERGY: \"LCC_Atmos_CumGain_EnergyC\" = (current year \"LCC_Atmos_CumGain_EnergyC\") - \"Land2Atmos_TotEnergyC_stock_conv\"\n out_atmos_df_list[[23]][, next_atmos_label] = out_atmos_df_list[[23]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"]\n # LCC ENERGY FROM HARVEST: \n out_atmos_df_list[[24]][, next_atmos_label] = out_atmos_df_list[[24]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"]\n # LCC ENERGY FROM SLASH: \n out_atmos_df_list[[25]][, next_atmos_label] = out_atmos_df_list[[25]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"]\n # LCC DECAY\n out_atmos_df_list[[26]][, next_atmos_label] = out_atmos_df_list[[26]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_DecayC_stock_conv\"]\n \n ### annual (again) ###\n \n # Partition the \"Manage_Atmos_AnnGain_C_stock\" into FIRE, ENERGY, and NON-BURNED C fluxes to atmosphere\n \n # Manage BURN: \"Manage_Atmos_AnnGain_FireC\" = - \"Land2Atmos_BurnC_stock_man_agg\" \n out_atmos_df_list[[27]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_BurnC_stock_man_agg\"] \n # Manage TOTAL ENERGY: \"Manage_Atmos_AnnGain_TotEnergyC\" = - \"Land2Atmos_TotEnergyC_stock_man_agg\"\n out_atmos_df_list[[28]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_man_agg\"]\n # Manage ENERGY FROM HARVEST: \n out_atmos_df_list[[29]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_man_agg\"] \n # Manage ENERGY FROM SLASH: \n out_atmos_df_list[[30]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_man_agg\"]\n # Manage DECAY: \"Manage_Atmos_AnnGain_NonBurnedC\" = - \"Land2Atmos_DecayC_stock_man_agg\"\n out_atmos_df_list[[31]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_DecayC_stock_man_agg\"] \n \n # check that true: total management land to atmosphere C flux equal to energy + controlled burns + unburned (decay) \n all(out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] == out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_TotEnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label])\n # Due to rounding error this checks true that the difference is <0.5 and >-0.5 \n all(out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_TotEnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label]) < 0.5 & \n (out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_TotEnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label])) > -0.5) \n \n # check that true: total management land to atmosphere C flux equal to energy + controlled burns + unburned (decay) \n all(out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] == out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_EnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label])\n # Due to rounding error this checks true that the difference is <0.5 and >-0.5 \n all(out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_EnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label]) < 0.5 & \n (out_atmos_df_list[[\"Manage_Atmos_AnnGain_C_stock\"]][, cur_atmos_label] - (out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]][, cur_atmos_label] + \n out_atmos_df_list[[\"Manage_Atmos_AnnGain_EnergyC\"]][, cur_atmos_label] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][, cur_atmos_label])) > -0.5) \n \n \n # Partition the \"Fire_Atmos_AnnGain_C_stock\" into burned and non-burned C sources\n # FIRE burned: \"Fire_Atmos_AnnGain_BurnedC\" = - \"Land2Atmos_BurnedC_stock_fire_agg\" \n out_atmos_df_list[[32]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_BurnedC_stock_fire_agg\"]\n # FIRE non-burned: \"Fire_Atmos_AnnGain_NonBurnedC\" = - \"Land2Atmos_NonBurnedC_stock_fire_agg\" \n out_atmos_df_list[[33]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_NonBurnedC_stock_fire_agg\"]\n \n # Partition the \"LCC_Atmos_AnnGain_C_stock\" into burned (fire + energy) and non-burned (decay) C sources \n # LCC FIRE: \"LCC_Atmos_AnnGain_FireC\" = - \"Land2Atmos_BurnC_stock_conv\"\n out_atmos_df_list[[34]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_BurnC_stock_conv\"]\n # LCC TOTAL ENERGY: \"LCC_Atmos_AnnGain_EnergyC\" = - \"Land2Atmos_TotEnergyC_stock_conv\"\n out_atmos_df_list[[35]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_TotEnergyC_stock_conv\"]\n # LCC ENERGY FROM HARVEST: \n out_atmos_df_list[[36]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_Harv2EnerC_stock_conv\"]\n # LCC ENERGY FROM SLASH: \n out_atmos_df_list[[37]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_Slash2EnerC_stock_conv\"]\n # LCC non-burned: \"LCC_Atmos_AnnGain_NonBurnedC\" = - \"Land2Atmos_DecayC_stock_conv\"\n out_atmos_df_list[[38]][, cur_atmos_label] = - all_c_flux[,\"Land2Atmos_DecayC_stock_conv\"]\n \n ######### Additional breakdown of non-burned ouputs #############\n \n ### MANAGED ###\n ##### separate out (1) sawmill decay and (2) In-Forest decay (slash + soil&root decay) from the total non-burned mangement C emissions\n # Annual #\n out_atmos_df_list[[39]][,cur_atmos_label] = -all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_man_agg\"]\n out_atmos_df_list[[40]][,cur_atmos_label] = -all_c_flux[,\"Land2Atmos_InForestDecayC_stock_man_agg\"]\n # Cumulative #\n out_atmos_df_list[[41]][, next_atmos_label] = out_atmos_df_list[[41]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_man_agg\"] \n out_atmos_df_list[[42]][, next_atmos_label] = out_atmos_df_list[[42]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_InForestDecayC_stock_man_agg\"] \n \n ### LCC ###\n # Annual #\n out_atmos_df_list[[43]][,cur_atmos_label] = -all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"]\n out_atmos_df_list[[44]][,cur_atmos_label] = -all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"]\n # Cumulative #\n out_atmos_df_list[[45]][, next_atmos_label] = out_atmos_df_list[[45]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_SawmillDecayC_stock_conv\"] \n out_atmos_df_list[[46]][, next_atmos_label] = out_atmos_df_list[[46]][, cur_atmos_label] - all_c_flux[,\"Land2Atmos_OnSiteDecayC_stock_conv\"] \n \n } # end loop over calculation years\n ###################################\n \n if (exists(\"out_neginds_eco_df\")) { \n # print sum of negative eco c cleared\n cat(\"sum of eco negative carbon cleared is\", out_cum_neginds_eco_tot, \"\\n\")\n # round to whole number for saving in file\n out_cum_neginds_eco_tot <- round(out_cum_neginds_eco_tot, digits=0)\n # assign file name for .csv file that tracks the land categories that ran out of soil C\n out_file_csv <- substr(out_file,1,nchar(out_file)-4)\n # write .csv file showing all land categories that ran out of soil C but still had area (i.e. neginds eco 9 and tot_area > 0)\n write.csv(out_neginds_eco_df, file = paste0(out_file_csv, \"_land_cats_depleted_of_soil_c_&_sum_neg_cleared\", out_cum_neginds_eco_tot, \".csv\"))\n }\n \n # Calculate CO2-C & CH4-C emissions from fresh marshland based on output table (Eco_CumGain_C_stock & Eco_AnnGain_C_stock). Note that \n # the CO2 portion of Eco C is actually C uptake (negative value), and it's CO2-eq will later be added to CO2-eq of CH4 to determine net GWP. \n # Additionally, here we will account for any negative Eco C values (currently only in grassland) as these are net C fluxes to atmosphere and will \n # be counted as CO2-C. \n \n # get dataframes for the C values for fresh marsh to calculate CO2 & CH4 emissions, and any negative Eco C fluxes to calc CO2 emissions\n # for other land types (i.e grassland)\n Eco_CumGain_C_stock <- out_atmos_df_list[[1]]\n Eco_AnnGain_C_stock <- out_atmos_df_list[[8]]\n \n ## Cummulative ## \n # go through each year column \n Fresh_marsh_Cum_Eco_C <- out_atmos_df_list[[1]][out_atmos_df_list[[1]]$Land_Type == \"Fresh_marsh\", ]\n # get the other land types \n Other_Cum_Eco_C <- out_atmos_df_list[[1]][out_atmos_df_list[[1]]$Land_Type != \"Fresh_marsh\", ]\n for (i in 5:ncol(Eco_CumGain_C_stock)) { # outer i loop by column \n # calc fresh march CO2-C (negative frac because it's net C sequestration based on flux tower measurement by Knox et al (2015)\n Fresh_marsh_Cum_Eco_C[,i] <- Fresh_marsh_Cum_Eco_C[[i]] * marsh_CO2_C_frac \n # for other land type Eco CO2-C,\n for (r in 1:nrow(Other_Cum_Eco_C)) { # inner row loop \n if (Other_Cum_Eco_C[,i][r] < 0) {\n # change sign of negative Eco C values to positive CO2-C emissions \n Other_Cum_Eco_C[,i][r] <- abs(Other_Cum_Eco_C[,i][r])\n # change sign of positive Eco C values to negative CO2-C emissions (i.e sequestration)\n } else Other_Cum_Eco_C[,i][r] <- -1 * Other_Cum_Eco_C[,i][r]\n }\n }\n Eco_CumCO2C <- list(Other_Cum_Eco_C, Fresh_marsh_Cum_Eco_C)\n Eco_CumCO2C <- do.call(rbind, Eco_CumCO2C)\n Eco_CumCO2C <- transform(Eco_CumCO2C, Land_Cat_ID = as.numeric(Land_Cat_ID))\n Eco_CumCO2C = Eco_CumCO2C[order(Eco_CumCO2C$Land_Cat_ID),]\n \n # repeat for CH4-C\n Fresh_marsh_Cum_Eco_C <- out_atmos_df_list[[1]][out_atmos_df_list[[1]]$Land_Type == \"Fresh_marsh\", ]\n Other_Cum_Eco_C <- out_atmos_df_list[[1]][out_atmos_df_list[[1]]$Land_Type != \"Fresh_marsh\", ]\n for (i in 5:ncol(Eco_CumGain_C_stock)) {\n # calc fresh march CH4-C\n Fresh_marsh_Cum_Eco_C[,i] <- Fresh_marsh_Cum_Eco_C[[i]] * marsh_CH4_C_frac \n # set CH4-C to 0 (Assuming no CH4 flux from other land types)\n Other_Cum_Eco_C[,i] <- 0\n }\n Eco_CumCH4C <- list(Other_Cum_Eco_C, Fresh_marsh_Cum_Eco_C)\n Eco_CumCH4C <- do.call(rbind, Eco_CumCH4C)\n Eco_CumCH4C <- transform(Eco_CumCH4C, Land_Cat_ID = as.numeric(Land_Cat_ID))\n Eco_CumCH4C = Eco_CumCH4C[order(Eco_CumCH4C$Land_Cat_ID),]\n \n ## Annual ## \n # go through each year column \n Fresh_marsh_Ann_Eco_C <- out_atmos_df_list[[8]][out_atmos_df_list[[8]]$Land_Type == \"Fresh_marsh\", ]\n # get the other land types \n Other_Ann_Eco_C <- out_atmos_df_list[[8]][out_atmos_df_list[[8]]$Land_Type != \"Fresh_marsh\", ]\n for (i in 5:ncol(Eco_AnnGain_C_stock)) {\n # calc fresh march CO2-C\n Fresh_marsh_Ann_Eco_C[,i] <- Fresh_marsh_Ann_Eco_C[[i]] * marsh_CO2_C_frac \n for (r in 1:nrow(Other_Ann_Eco_C)) {\n # for other land type Eco CO2-C,\n if (Other_Ann_Eco_C[,i][r] < 0) {\n # change sign of negative Eco C values to positive CO2-C emissions \n Other_Ann_Eco_C[,i][r] <- abs(Other_Ann_Eco_C[,i][r])\n # change sign of positive Eco C values to negative CO2-C emissions (i.e sequestration)\n } else Other_Ann_Eco_C[,i][r] <- -1 * Other_Ann_Eco_C[,i][r]\n }\n }\n Eco_AnnCO2C <- list(Other_Ann_Eco_C, Fresh_marsh_Ann_Eco_C)\n Eco_AnnCO2C <- do.call(rbind, Eco_AnnCO2C)\n Eco_AnnCO2C <- transform(Eco_AnnCO2C, Land_Cat_ID = as.numeric(Land_Cat_ID))\n Eco_AnnCO2C = Eco_AnnCO2C[order(Eco_AnnCO2C$Land_Cat_ID),]\n \n # repeat for CH4-C\n Fresh_marsh_Ann_Eco_C <- out_atmos_df_list[[8]][out_atmos_df_list[[8]]$Land_Type == \"Fresh_marsh\", ]\n Other_Ann_Eco_C <- out_atmos_df_list[[8]][out_atmos_df_list[[8]]$Land_Type != \"Fresh_marsh\", ]\n for (i in 5:ncol(Eco_AnnGain_C_stock)) {\n # calc fresh march CH4-C\n Fresh_marsh_Ann_Eco_C[,i] <- Fresh_marsh_Ann_Eco_C[[i]] * marsh_CH4_C_frac \n # set CH4-C to 0 (No soil CH4 emissions from other land types)\n Other_Ann_Eco_C[,i] <- 0\n }\n Eco_AnnCH4C <- list(Other_Ann_Eco_C, Fresh_marsh_Ann_Eco_C)\n Eco_AnnCH4C <- do.call(rbind, Eco_AnnCH4C)\n Eco_AnnCH4C <- transform(Eco_AnnCH4C, Land_Cat_ID = as.numeric(Land_Cat_ID))\n Eco_AnnCH4C = Eco_AnnCH4C[order(Eco_AnnCH4C$Land_Cat_ID),]\n \n # Partition all the C emitted from controlled burn & bioenergy in out_atmos_df_list into CO2C, CH4C and BC-C.\n \n ########## Cumulative ########## \n \n # MANAGE FIRE\n Manage_CumFireC <- out_atmos_df_list[[\"Manage_Atmos_CumGain_FireC\"]]\n ManFire_CumCO2C <- Manage_CumFireC\n for (i in 5:ncol(Manage_CumFireC)) {\n ManFire_CumCO2C[,i] <- CO2C_fire_frac * Manage_CumFireC[,i]\n }\n ManFire_CumCH4C <- Manage_CumFireC\n for (i in 5:ncol(Manage_CumFireC)) {\n ManFire_CumCH4C[,i] <- CH4C_fire_frac * Manage_CumFireC[,i]\n }\n ManFire_CumBCC <- Manage_CumFireC\n for (i in 5:ncol(Manage_CumFireC)) {\n ManFire_CumBCC[,i] <- BCC_fire_frac * Manage_CumFireC[,i]\n }\n \n # MANAGE TOTAL ENERGY\n Manage_CumEnergyC <- out_atmos_df_list[[\"Manage_Atmos_CumGain_TotEnergyC\"]] \n ManTotEnergy_CumCO2C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManTotEnergy_CumCO2C[,i] <- CO2C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManTotEnergy_CumCH4C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManTotEnergy_CumCH4C[,i] <- CH4C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManTotEnergy_CumBCC <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManTotEnergy_CumBCC[,i] <- BCC_energy_frac * Manage_CumEnergyC[,i]\n }\n \n # MANAGE HARVEST 2 ENERGY\n Manage_CumEnergyC <- out_atmos_df_list[[\"Man_Atmos_CumGain_Harv2EnergyC\"]] \n ManHarv2Energy_CumCO2C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManHarv2Energy_CumCO2C[,i] <- CO2C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManHarv2Energy_CumCH4C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManHarv2Energy_CumCH4C[,i] <- CH4C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManHarv2Energy_CumBCC <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManHarv2Energy_CumBCC[,i] <- BCC_energy_frac * Manage_CumEnergyC[,i]\n }\n \n # MANAGE SLASH 2 ENERGY\n Manage_CumEnergyC <- out_atmos_df_list[[\"Man_Atmos_CumGain_Slash2EnergyC\"]] \n ManSlash2Energy_CumCO2C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManSlash2Energy_CumCO2C[,i] <- CO2C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManSlash2Energy_CumCH4C <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManSlash2Energy_CumCH4C[,i] <- CH4C_energy_frac * Manage_CumEnergyC[,i]\n }\n ManSlash2Energy_CumBCC <- Manage_CumEnergyC\n for (i in 5:ncol(Manage_CumEnergyC)) {\n ManSlash2Energy_CumBCC[,i] <- BCC_energy_frac * Manage_CumEnergyC[,i]\n }\n \n # LCC FIRE\n LCC_CumFireC <- out_atmos_df_list[[\"LCC_Atmos_CumGain_FireC\"]]\n LCCFire_CumCO2C <- LCC_CumFireC\n for (i in 5:ncol(LCC_CumFireC)) {\n LCCFire_CumCO2C[,i] <- CO2C_fire_frac * LCC_CumFireC[,i]\n }\n LCCFire_CumCH4C <- LCC_CumFireC\n for (i in 5:ncol(LCC_CumFireC)) {\n LCCFire_CumCH4C[,i] <- CH4C_fire_frac * LCC_CumFireC[,i]\n }\n LCCFire_CumBCC <- LCC_CumFireC\n for (i in 5:ncol(LCC_CumFireC)) {\n LCCFire_CumBCC[,i] <- BCC_fire_frac * LCC_CumFireC[,i]\n }\n \n # LCC TOTAL ENERGY\n LCC_CumEnergyC <- out_atmos_df_list[[\"LCC_Atmos_CumGain_TotEnergyC\"]]\n LCCTotEnergy_CumCO2C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCTotEnergy_CumCO2C[,i] <- CO2C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCTotEnergy_CumCH4C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCTotEnergy_CumCH4C[,i] <- CH4C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCTotEnergy_CumBCC <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCTotEnergy_CumBCC[,i] <- BCC_energy_frac * LCC_CumEnergyC[,i]\n }\n \n # LCC HARVEST 2 ENERGY\n LCC_CumEnergyC <- out_atmos_df_list[[\"LCC_Atmos_CumGain_Harv2EnergyC\"]] \n LCCHarv2Energy_CumCO2C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCHarv2Energy_CumCO2C[,i] <- CO2C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCHarv2Energy_CumCH4C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCHarv2Energy_CumCH4C[,i] <- CH4C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCHarv2Energy_CumBCC <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCHarv2Energy_CumBCC[,i] <- BCC_energy_frac * LCC_CumEnergyC[,i]\n }\n \n # LCC SLASH 2 ENERGY\n LCC_CumEnergyC <- out_atmos_df_list[[\"LCC_Atmos_CumGain_Slash2EnergyC\"]] \n LCCSlash2Energy_CumCO2C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCSlash2Energy_CumCO2C[,i] <- CO2C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCSlash2Energy_CumCH4C <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCSlash2Energy_CumCH4C[,i] <- CH4C_energy_frac * LCC_CumEnergyC[,i]\n }\n LCCSlash2Energy_CumBCC <- LCC_CumEnergyC\n for (i in 5:ncol(LCC_CumEnergyC)) {\n LCCSlash2Energy_CumBCC[,i] <- BCC_energy_frac * LCC_CumEnergyC[,i]\n }\n \n # WILDFIRE\n Wildfire_CumBurnedC <- out_atmos_df_list[[\"Fire_Atmos_CumGain_BurnedC\"]]\n Wildfire_CumCO2C <- Wildfire_CumBurnedC\n for (i in 5:ncol(Wildfire_CumBurnedC)) {\n Wildfire_CumCO2C[,i] <- CO2C_fire_frac * Wildfire_CumBurnedC[,i]\n }\n Wildfire_CumCH4C <- Wildfire_CumBurnedC\n for (i in 5:ncol(Wildfire_CumBurnedC)) {\n Wildfire_CumCH4C[,i] <- CH4C_fire_frac * Wildfire_CumBurnedC[,i]\n }\n Wildfire_CumBCC <- Wildfire_CumBurnedC\n for (i in 5:ncol(Wildfire_CumBurnedC)) {\n Wildfire_CumBCC[,i] <- BCC_fire_frac * Wildfire_CumBurnedC[,i]\n }\n \n # TOTAL CUM ENERGY CO2-C, CH4-C, and BC-C \n TotalEnergy_CumCO2C <- LCCTotEnergy_CumCO2C\n TotalEnergy_CumCO2C[,5:ncol(LCCTotEnergy_CumCO2C)] <- LCCTotEnergy_CumCO2C[,5:ncol(LCCTotEnergy_CumCO2C)] + ManTotEnergy_CumCO2C[,5:ncol(LCCTotEnergy_CumCO2C)]\n TotalEnergy_CumCH4C <- LCCTotEnergy_CumCH4C\n TotalEnergy_CumCH4C[,5:ncol(LCCTotEnergy_CumCH4C)] <- LCCTotEnergy_CumCH4C[,5:ncol(LCCTotEnergy_CumCH4C)] + ManTotEnergy_CumCH4C[,5:ncol(LCCTotEnergy_CumCH4C)]\n TotalEnergy_CumBCC <- LCCTotEnergy_CumBCC\n TotalEnergy_CumBCC[,5:ncol(LCCTotEnergy_CumBCC)] <- LCCTotEnergy_CumBCC[,5:ncol(LCCTotEnergy_CumBCC)] + ManTotEnergy_CumBCC[,5:ncol(LCCTotEnergy_CumBCC)]\n \n # TOTAL CUM CONTROLLED BURN CO2-C, CH4-C, and BC-C \n TotalCntlFire_CumCO2C <- LCCFire_CumCO2C\n TotalCntlFire_CumCO2C[,5:ncol(LCCFire_CumCO2C)] <- LCCFire_CumCO2C[,5:ncol(LCCFire_CumCO2C)] + ManFire_CumCO2C[,5:ncol(LCCFire_CumCO2C)]\n TotalCntlFire_CumCH4C <- LCCFire_CumCH4C\n TotalCntlFire_CumCH4C[,5:ncol(LCCFire_CumCH4C)] <- LCCFire_CumCH4C[,5:ncol(LCCFire_CumCH4C)] + ManFire_CumCH4C[,5:ncol(LCCFire_CumCH4C)]\n TotalCntlFire_CumBCC <- LCCFire_CumBCC\n TotalCntlFire_CumBCC[,5:ncol(LCCFire_CumBCC)] <- LCCFire_CumBCC[,5:ncol(LCCFire_CumBCC)] + ManFire_CumBCC[,5:ncol(LCCFire_CumBCC)]\n \n ########## Annual ########## \n \n # MANAGE FIRE\n Manage_AnnFireC <- out_atmos_df_list[[\"Manage_Atmos_AnnGain_FireC\"]]\n ManFire_AnnCO2C <- Manage_AnnFireC\n for (i in 5:ncol(Manage_AnnFireC)) {\n ManFire_AnnCO2C[,i] <- CO2C_fire_frac * Manage_AnnFireC[,i]\n }\n ManFire_AnnCH4C <- Manage_AnnFireC\n for (i in 5:ncol(Manage_AnnFireC)) {\n ManFire_AnnCH4C[,i] <- CH4C_fire_frac * Manage_AnnFireC[,i]\n }\n ManFire_AnnBCC <- Manage_AnnFireC\n for (i in 5:ncol(Manage_AnnFireC)) {\n ManFire_AnnBCC[,i] <- BCC_fire_frac * Manage_AnnFireC[,i]\n }\n \n # MANAGE TOTAL ENERGY\n Manage_AnnEnergyC <- out_atmos_df_list[[\"Manage_Atmos_AnnGain_TotEnergyC\"]] \n ManTotEnergy_AnnCO2C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManTotEnergy_AnnCO2C[,i] <- CO2C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManTotEnergy_AnnCH4C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManTotEnergy_AnnCH4C[,i] <- CH4C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManTotEnergy_AnnBCC <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManTotEnergy_AnnBCC[,i] <- BCC_energy_frac * Manage_AnnEnergyC[,i]\n }\n \n # MANAGE HARVEST 2 ENERGY\n Manage_AnnEnergyC <- out_atmos_df_list[[\"Man_Atmos_AnnGain_Harv2EnergyC\"]] \n ManHarv2Energy_AnnCO2C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManHarv2Energy_AnnCO2C[,i] <- CO2C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManHarv2Energy_AnnCH4C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManHarv2Energy_AnnCH4C[,i] <- CH4C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManHarv2Energy_AnnBCC <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManHarv2Energy_AnnBCC[,i] <- BCC_energy_frac * Manage_AnnEnergyC[,i]\n }\n \n # MANAGE SLASH 2 ENERGY\n Manage_AnnEnergyC <- out_atmos_df_list[[\"Man_Atmos_AnnGain_Slash2EnergyC\"]] \n ManSlash2Energy_AnnCO2C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManSlash2Energy_AnnCO2C[,i] <- CO2C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManSlash2Energy_AnnCH4C <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManSlash2Energy_AnnCH4C[,i] <- CH4C_energy_frac * Manage_AnnEnergyC[,i]\n }\n ManSlash2Energy_AnnBCC <- Manage_AnnEnergyC\n for (i in 5:ncol(Manage_AnnEnergyC)) {\n ManSlash2Energy_AnnBCC[,i] <- BCC_energy_frac * Manage_AnnEnergyC[,i]\n }\n \n # LCC FIRE\n LCC_AnnFireC <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_FireC\"]]\n LCCFire_AnnCO2C <- LCC_AnnFireC\n for (i in 5:ncol(LCC_AnnFireC)) {\n LCCFire_AnnCO2C[,i] <- CO2C_fire_frac * LCC_AnnFireC[,i]\n }\n LCCFire_AnnCH4C <- LCC_AnnFireC\n for (i in 5:ncol(LCC_AnnFireC)) {\n LCCFire_AnnCH4C[,i] <- CH4C_fire_frac * LCC_AnnFireC[,i]\n }\n LCCFire_AnnBCC <- LCC_AnnFireC\n for (i in 5:ncol(LCC_AnnFireC)) {\n LCCFire_AnnBCC[,i] <- BCC_fire_frac * LCC_AnnFireC[,i]\n }\n \n # LCC TOTAL ENERGY\n LCC_AnnEnergyC <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_TotEnergyC\"]]\n LCCTotEnergy_AnnCO2C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCTotEnergy_AnnCO2C[,i] <- CO2C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCTotEnergy_AnnCH4C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCTotEnergy_AnnCH4C[,i] <- CH4C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCTotEnergy_AnnBCC <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCTotEnergy_AnnBCC[,i] <- BCC_energy_frac * LCC_AnnEnergyC[,i]\n }\n \n # LCC HARVEST 2 ENERGY\n LCC_AnnEnergyC <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_Harv2EnergyC\"]] \n LCCHarv2Energy_AnnCO2C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCHarv2Energy_AnnCO2C[,i] <- CO2C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCHarv2Energy_AnnCH4C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCHarv2Energy_AnnCH4C[,i] <- CH4C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCHarv2Energy_AnnBCC <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCHarv2Energy_AnnBCC[,i] <- BCC_energy_frac * LCC_AnnEnergyC[,i]\n }\n \n # LCC SLASH 2 ENERGY\n LCC_AnnEnergyC <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_Slash2EnergyC\"]] \n LCCSlash2Energy_AnnCO2C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCSlash2Energy_AnnCO2C[,i] <- CO2C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCSlash2Energy_AnnCH4C <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCSlash2Energy_AnnCH4C[,i] <- CH4C_energy_frac * LCC_AnnEnergyC[,i]\n }\n LCCSlash2Energy_AnnBCC <- LCC_AnnEnergyC\n for (i in 5:ncol(LCC_AnnEnergyC)) {\n LCCSlash2Energy_AnnBCC[,i] <- BCC_energy_frac * LCC_AnnEnergyC[,i]\n }\n \n # WILDFIRE\n Wildfire_AnnBurnedC <- out_atmos_df_list[[\"Fire_Atmos_AnnGain_BurnedC\"]]\n Wildfire_AnnCO2C <- Wildfire_AnnBurnedC\n for (i in 5:ncol(Wildfire_AnnBurnedC)) {\n Wildfire_AnnCO2C[,i] <- CO2C_fire_frac * Wildfire_AnnBurnedC[,i]\n }\n Wildfire_AnnCH4C <- Wildfire_AnnBurnedC\n for (i in 5:ncol(Wildfire_AnnBurnedC)) {\n Wildfire_AnnCH4C[,i] <- CH4C_fire_frac * Wildfire_AnnBurnedC[,i]\n }\n Wildfire_AnnBCC <- Wildfire_AnnBurnedC\n for (i in 5:ncol(Wildfire_AnnBurnedC)) {\n Wildfire_AnnBCC[,i] <- BCC_fire_frac * Wildfire_AnnBurnedC[,i]\n }\n \n # TOTAL Ann Controlled Fires CO2-C, CH4-C, and BC-C \n TotalCntlFire_AnnCO2C <- LCCFire_AnnCO2C\n TotalCntlFire_AnnCO2C[,5:ncol(LCCFire_AnnCO2C)] <- LCCFire_AnnCO2C[,5:ncol(LCCFire_AnnCO2C)] + ManFire_AnnCO2C[,5:ncol(LCCFire_AnnCO2C)]\n TotalCntlFire_AnnCH4C <- LCCFire_AnnCH4C\n TotalCntlFire_AnnCH4C[,5:ncol(LCCFire_AnnCH4C)] <- LCCFire_AnnCH4C[,5:ncol(LCCFire_AnnCH4C)] + ManFire_AnnCH4C[,5:ncol(LCCFire_AnnCH4C)]\n TotalCntlFire_AnnBCC <- LCCFire_AnnBCC\n TotalCntlFire_AnnBCC[,5:ncol(LCCFire_AnnBCC)] <- LCCFire_AnnBCC[,5:ncol(LCCFire_AnnBCC)] + ManFire_AnnBCC[,5:ncol(LCCFire_AnnBCC)]\n \n # TOTAL Ann ENERGY CO2-C, CH4-C, and BC-C \n TotalEnergy_AnnCO2C <- LCCTotEnergy_AnnCO2C\n TotalEnergy_AnnCO2C[,5:ncol(LCCTotEnergy_AnnCO2C)] <- LCCTotEnergy_AnnCO2C[,5:ncol(LCCTotEnergy_AnnCO2C)] + ManTotEnergy_AnnCO2C[,5:ncol(LCCTotEnergy_AnnCO2C)]\n TotalEnergy_AnnCH4C <- LCCTotEnergy_AnnCH4C\n TotalEnergy_AnnCH4C[,5:ncol(LCCTotEnergy_AnnCH4C)] <- LCCTotEnergy_AnnCH4C[,5:ncol(LCCTotEnergy_AnnCH4C)] + ManTotEnergy_AnnCH4C[,5:ncol(LCCTotEnergy_AnnCH4C)]\n TotalEnergy_AnnBCC <- LCCTotEnergy_AnnBCC\n TotalEnergy_AnnBCC[,5:ncol(LCCTotEnergy_AnnBCC)] <- LCCTotEnergy_AnnBCC[,5:ncol(LCCTotEnergy_AnnBCC)] + ManTotEnergy_AnnBCC[,5:ncol(LCCTotEnergy_AnnBCC)]\n \n ############## Partition Cumulative C emissions from total wood product decay (management & lcc) into CO2-C and CH4-C ############## \n # subset the total cumulative C emissions from wood decay from the out_atmos_df_list\n wood2atmos_CumC <- out_atmos_df_list[[\"Wood_Atmos_CumGain_C_stock\"]]\n # copy dataframe to fill in the following loop \n CumANDOC <- wood2atmos_CumC\n # calc anaerobically decomposed wood C based on ARB's weighted mean CH4 correction factor (MCF) of 0.71\n for (i in 5:ncol(wood2atmos_CumC)) {\n CumANDOC[,i] <- MCF * wood2atmos_CumC[,i]\n }\n # copy dataframe to fill in the following loop \n wood_CumCH4C_prod <- wood2atmos_CumC\n # calc CH4-C production based on ARB and IPCC default value of 0.5 for fraction of CH4, by volume, in generated landfill gas \n for (i in 5:ncol(wood2atmos_CumC)) {\n wood_CumCH4C_prod[,i] <- CumANDOC[,i] * landfill_gas_frac\n }\n # copy the following dataframes to fill in the following loops for calc CH4-C emissions\n # wood_CumCH4C_combust <- wood2atmos_CumC\n wood_CumCH4C_filter <- wood2atmos_CumC\n wood_CumCH4C <- wood2atmos_CumC\n # calc CH4-C emissions based on ARB equation 89 using landfill gas collection efficiency (CE) = 0.75, landfill gas destruction efficiency \n # via combustion (DE_combust) = 0.99, and oxidation of uncollected CH4 in landfill cover (OX) = 0.1\n # for (i in 5:ncol(wood2atmos_CumC)) {\n # wood_CumCH4C_combust[,i] <- wood_CumCH4C_prod[,i] * CE * (1 - DE_combust) + wood_CumCH4C_prod[,i] * (1 - CE) * (1 - OX)\n # }\n # Same equation except using landfill gas destruction efficiency via C filtration (DE_filter) = 0.01\n for (i in 5:ncol(wood2atmos_CumC)) {\n wood_CumCH4C_filter[,i] <- wood_CumCH4C_prod[,i] * CE * (1 - DE_filter) + wood_CumCH4C_prod[,i] * (1 - CE) * (1 - OX)\n }\n # Average CH4C emissions using the 2 methods of CH4 removal\n for (i in 5:ncol(wood2atmos_CumC)) {\n if (exists(\"wood_CumCH4C_combust\")) {\n wood_CumCH4C[,i] <- (wood_CumCH4C_filter[,i] + wood_CumCH4C_combust[,i]) / 2\n } else wood_CumCH4C[,i] <- wood_CumCH4C_filter[,i] \n }\n # Calc CO2-C emissions from wood\n wood_CumCO2C <- wood2atmos_CumC\n for (i in 5:ncol(wood2atmos_CumC)) {\n wood_CumCO2C[,i] <- wood2atmos_CumC[,i] - wood_CumCH4C[,i]\n }\n \n ############## Partition Annual C emissions from total wood decay (management & lcc) into CO2-C and CH4-C ############## \n # subset the total annual C emissions from wood decay from the out_atmos_df_list\n wood2atmos_AnnC <- out_atmos_df_list[[\"Wood_Atmos_AnnGain_C_stock\"]]\n # copy dataframe to fill in the following loop \n AnnANDOC <- wood2atmos_AnnC\n # calc anaerobically decomposed wood C based on ARB's weighted mean CH4 correction factor (MCF) of 0.71\n for (i in 5:ncol(wood2atmos_AnnC)) {\n AnnANDOC[,i] <- MCF * wood2atmos_AnnC[,i]\n }\n # copy dataframe to fill in the following loop \n wood_AnnCH4C_prod <- wood2atmos_AnnC\n # calc CH4-C production based on ARB and IPCC default value of 0.5 for fraction of CH4, by volume, in generated landfill gas (F)\n for (i in 5:ncol(wood2atmos_AnnC)) {\n wood_AnnCH4C_prod[,i] <- AnnANDOC[,i] * landfill_gas_frac\n }\n # copy the following dataframes to fill in the following loops for calc CH4-C emissions\n # wood_AnnCH4C_combust <- wood2atmos_AnnC\n wood_AnnCH4C_filter <- wood2atmos_AnnC\n wood_AnnCH4C <- wood2atmos_AnnC\n # calc CH4-C emissions based on ARB equation 89 using landfill gas collection efficiency (CE) = 0.75, landfill gas destruction efficiency \n # via combustion (DE_combust) = 0.99, and oxidation of uncollected CH4 in landfill cover (OX) = 0.1\n # for (i in 5:ncol(wood2atmos_AnnC)) {\n # wood_AnnCH4C_combust[,i] <- wood_AnnCH4C_prod[,i] * CE * (1 - DE_combust) + wood_AnnCH4C_prod[,i] * (1 - CE) * (1 - OX)\n # }\n # Same equation except using landfill gas destruction efficiency via C filtration (DE_filter) = 0.01\n for (i in 5:ncol(wood2atmos_AnnC)) {\n wood_AnnCH4C_filter[,i] <- wood_AnnCH4C_prod[,i] * CE * (1 - DE_filter) + wood_AnnCH4C_prod[,i] * (1 - CE) * (1 - OX)\n }\n # Average CH4C emissions using the 2 methods of CH4 removal (currently using only C filtration)\n for (i in 5:ncol(wood2atmos_AnnC)) {\n if (exists(\"wood_AnnCH4C_combust\")) {\n wood_AnnCH4C[,i] <- (wood_AnnCH4C_filter[,i] + wood_AnnCH4C_combust[,i]) / 2\n } else wood_AnnCH4C[,i] <- wood_AnnCH4C_filter[,i] \n }\n # Calc CO2-C emissions from wood\n wood_AnnCO2C <- wood2atmos_AnnC\n for (i in 5:ncol(wood2atmos_AnnC)) {\n wood_AnnCO2C[,i] <- wood2atmos_AnnC[,i] - wood_AnnCH4C[,i]\n }\n \n # SUM total CO2-C, CH4-C, and BC-C emissions from burned and non-burned sources. Total equals net C land-atmos exchange (source vs sink). \n \n ####### Cumulative ####### \n # first, do cumulative CO2-C. Choice of ncol(ManFire_CumCO2C) is arbitrary - just need the total number of columns.\n Total_CumCO2C <- ManFire_CumCO2C\n for (i in 5:ncol(Total_CumCO2C)) {\n Total_CumCO2C[,i] <- 0\n }\n for (i in 5:ncol(Total_CumCO2C)) { \n Total_CumCO2C[,i] <- Eco_CumCO2C[,i] + wood_CumCO2C[,i] + out_atmos_df_list[[\"Manage_Atmos_CumGain_NonBurnedC\"]][,i] + \n out_atmos_df_list[[\"Fire_Atmos_CumGain_NonBurnedC\"]][,i] + out_atmos_df_list[[\"LCC_Atmos_CumGain_NonBurnedC\"]][,i] + \n TotalCntlFire_CumCO2C[,i] + TotalEnergy_CumCO2C[,i] + Wildfire_CumCO2C[,i] \n }\n # Second, do cumulative CH4-C. Choice of ncol(ManFire_CumCH4C) is arbitrary - just need the total number of columns.\n Total_CumCH4C <- ManFire_CumCH4C\n for (i in 5:ncol(Total_CumCH4C)) {\n Total_CumCH4C[,i] <- 0\n }\n for (i in 5:ncol(Total_CumCH4C)) { \n Total_CumCH4C[,i] <- Eco_CumCH4C[,i] + wood_CumCH4C[,i] + TotalCntlFire_CumCH4C[,i] + TotalEnergy_CumCH4C[,i] + \n Wildfire_CumCH4C[,i] \n }\n # Third, do cumulative BC-C. Choice of ncol(ManFire_CumBCC) is arbitrary - just need the total number of columns.\n Total_CumBCC <- ManFire_CumBCC\n for (i in 5:ncol(Total_CumBCC)) {\n Total_CumBCC[,i] <- 0\n }\n for (i in 5:ncol(Total_CumBCC)) {\n Total_CumBCC[,i] <- TotalCntlFire_CumBCC[,i] + TotalEnergy_CumBCC[,i] + Wildfire_CumBCC[,i] \n }\n \n ####### Annual ####### \n # first, do annual CO2-C. Choice of ncol(ManFire_AnnCO2C) is arbitrary - just need the total number of columns.\n Total_AnnCO2C <- ManFire_AnnCO2C\n for (i in 5:ncol(Total_AnnCO2C)) {\n Total_AnnCO2C[,i] <- 0\n }\n for (i in 5:ncol(Total_AnnCO2C)) { \n Total_AnnCO2C[,i] <- Eco_AnnCO2C[,i] + wood_AnnCO2C[,i] + out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]][,i] + \n out_atmos_df_list[[\"Fire_Atmos_AnnGain_NonBurnedC\"]][,i] + out_atmos_df_list[[\"LCC_Atmos_AnnGain_NonBurnedC\"]][,i] + \n TotalCntlFire_AnnCO2C[,i] + TotalEnergy_AnnCO2C[,i] + Wildfire_AnnCO2C[,i] \n }\n # Second, do annual CH4-C. Choice of ncol(ManFire_AnnCH4C) is arbitrary - just need the total number of columns.\n Total_AnnCH4C <- ManFire_AnnCH4C\n for (i in 5:ncol(Total_AnnCH4C)) {\n Total_AnnCH4C[,i] <- 0\n }\n for (i in 5:ncol(Total_AnnCH4C)) { \n Total_AnnCH4C[,i] <- Eco_AnnCH4C[,i] + wood_AnnCH4C[,i] + TotalCntlFire_AnnCH4C[,i] + TotalEnergy_AnnCH4C[,i] + \n Wildfire_AnnCH4C[,i]\n }\n # Third, do annual BC-C. Choice of ncol(ManFire_AnnBCC) is arbitrary - just need the total number of columns.\n Total_AnnBCC <- ManFire_AnnBCC\n for (i in 5:ncol(Total_AnnBCC)) {\n Total_AnnBCC[,i] <- 0\n }\n for (i in 5:ncol(ManFire_AnnBCC)) {\n Total_AnnBCC[,i] <- TotalCntlFire_AnnBCC[,i] + TotalEnergy_AnnBCC[,i] + Wildfire_AnnBCC[,i]\n }\n \n # the following check is used to show that the differences between total annual CO2-C, CH4-C and BC-C, and\n # total atmosphere C gain, less Eco C to atmosphere fluxes (i.e. grassland and coastal marsh) are < 0.5 and > -0.5. Due to \n # rounding error, 0.5 is used instead of 0.\n all((Total_AnnCO2C[,5:ncol(Total_AnnCO2C)] + Total_AnnCH4C[,5:ncol(Total_AnnCO2C)] + Total_AnnBCC[,5:ncol(Total_AnnCO2C)]) - \n (out_atmos_df_list[[\"Total_Atmos_AnnGain_C_stock\"]][1:nrow(Total_AnnCO2C),5:ncol(Total_AnnCO2C)] + \n Eco_AnnCO2C[,5:ncol(Total_AnnCO2C)] + Eco_AnnCH4C[,5:ncol(Total_AnnCO2C)]) < 0.5 & \n (Total_AnnCO2C[,5:ncol(Total_AnnCO2C)] + Total_AnnCH4C[,5:ncol(Total_AnnCO2C)] + Total_AnnBCC[,5:ncol(Total_AnnCO2C)]) - \n (out_atmos_df_list[[\"Total_Atmos_AnnGain_C_stock\"]][1:nrow(Total_AnnCO2C),5:ncol(Total_AnnCO2C)] + \n Eco_AnnCO2C[,5:ncol(Total_AnnCO2C)] + Eco_AnnCH4C[,5:ncol(Total_AnnCO2C)]) > -0.5)\n \n # individually convert total CO2-C, CH4-C and BC-C to CO2-eq. That way we can analyze proportions contributing to total CO2-eq\n # if desired\n ### cumulative ###\n # first, convert total cumulative CO2-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n Total_CumCO2 <- Total_CumCO2C\n for (i in 5:ncol(Total_CumCO2)) {\n Total_CumCO2[,i] <- Total_CumCO2C[,i] * (44.01/12.0107) * gwp_CO2\n }\n # second, convert total cumulative CH4-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n Total_CumCH4eq <- Total_CumCH4C\n for (i in 5:ncol(Total_CumCH4eq)) {\n Total_CumCH4eq[,i] <- Total_CumCH4C[,i] * (16.04/12.0107) * gwp_CH4\n }\n # third, convert total cumulative BC-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n # multiplying by 1/0.6 based on assumption that 60% black C is C.\n Total_CumBCeq <- Total_CumBCC\n for (i in 5:ncol(Total_CumBCC)) {\n Total_CumBCeq[,i] <- Total_CumBCC[,i] * (1/0.6) * gwp_BC\n }\n \n ### annual ###\n # first, convert total annual CO2-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n Total_AnnCO2 <- Total_AnnCO2C\n for (i in 5:ncol(Total_AnnCO2)) {\n Total_AnnCO2[,i] <- Total_AnnCO2C[,i] * (44.01/12.0107) * gwp_CO2\n }\n # second, convert total annual CH4-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n Total_AnnCH4eq <- Total_AnnCH4C\n for (i in 5:ncol(Total_AnnCH4eq)) {\n Total_AnnCH4eq[,i] <- Total_AnnCH4C[,i] * (16.04/12.0107) * gwp_CH4\n }\n # third, convert total annual BC-C [Mg C/ha/y] to [Mg CO2-eq/ha/y]\n Total_AnnBCeq <- Total_AnnBCC\n for (i in 5:ncol(Total_AnnBCC)) {\n Total_AnnBCeq[,i] <- Total_AnnBCC[,i] * (1/0.6) * gwp_BC\n }\n \n # sum all CO2-eq to get total GWP [Mg CO2-eq/ha/y]\n ### cumulative ###\n Total_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(Total_CumCO2)) {\n Total_CumCO2eq_all[,i] <- Total_CumCO2[,i] + Total_CumCH4eq[,i] + Total_CumBCeq[,i]\n }\n ### annual ###\n Total_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(Total_AnnCO2)) {\n Total_AnnCO2eq_all[,i] <- Total_AnnCO2[,i] + Total_AnnCH4eq[,i] + Total_AnnBCeq[,i]\n }\n \n ######### Calculate additonal breakdowns of total CO2, CH4, & BC (CO2-eq) from various sources ######### \n \n # subset outputs from out_atmos to include in df.list below\n ManNonBurn_CumCO2C <- out_atmos_df_list[[\"Manage_Atmos_CumGain_NonBurnedC\"]]\n ManNonBurn_AnnCO2C <- out_atmos_df_list[[\"Manage_Atmos_AnnGain_NonBurnedC\"]]\n LCCNonBurn_CumCO2C <- out_atmos_df_list[[\"LCC_Atmos_CumGain_NonBurnedC\"]]\n LCCNonBurn_AnnCO2C <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_NonBurnedC\"]]\n \n ManSawmillDecay_AnnCO2C <- out_atmos_df_list[[\"Man_Atmos_AnnGain_SawmillDecayC\"]]\n ManForestDecay_AnnCO2C <- out_atmos_df_list[[\"Man_Atmos_AnnGain_InFrstDecayC\"]]\n ManSawmillDecay_CumCO2C <- out_atmos_df_list[[\"Man_Atmos_CumGain_SawmillDecayC\"]]\n ManForestDecay_CumCO2C <- out_atmos_df_list[[\"Man_Atmos_CumGain_InFrstDecayC\"]]\n LCCSawmillDecay_AnnCO2C <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_SawmillDecayC\"]]\n LCCOnSiteDecay_AnnCO2C <- out_atmos_df_list[[\"LCC_Atmos_AnnGain_OnSiteDecayC\"]]\n LCCSawmillDecay_CumCO2C <- out_atmos_df_list[[\"LCC_Atmos_CumGain_SawmillDecayC\"]]\n LCCOnSiteDecay_CumCO2C <- out_atmos_df_list[[\"LCC_Atmos_CumGain_OnSiteDecayC\"]]\n Wildfire_Decay_AnnCO2C <- out_atmos_df_list[[\"Fire_Atmos_AnnGain_NonBurnedC\"]]\n Wildfire_Decay_CumCO2C <- out_atmos_df_list[[\"Fire_Atmos_CumGain_NonBurnedC\"]]\n \n # create list of all the additonal tables from which we want to calculate GWP \n df.list <- list(Eco_CumCO2C = Eco_CumCO2C,\n Eco_CumCH4C = Eco_CumCH4C,\n \n ManTotEnergy_CumCO2C = ManTotEnergy_CumCO2C,\n ManTotEnergy_CumCH4C = ManTotEnergy_CumCH4C,\n ManTotEnergy_CumBCC = ManTotEnergy_CumBCC,\n ManHarv2Energy_CumCO2C = ManHarv2Energy_CumCO2C,\n ManHarv2Energy_CumCH4C = ManHarv2Energy_CumCH4C,\n ManHarv2Energy_CumBCC = ManHarv2Energy_CumBCC,\n ManSlash2Energy_CumCO2C = ManSlash2Energy_CumCO2C,\n ManSlash2Energy_CumCH4C = ManSlash2Energy_CumCH4C,\n ManSlash2Energy_CumBCC = ManSlash2Energy_CumBCC,\n ManFire_CumCO2C = ManFire_CumCO2C,\n ManFire_CumCH4C = ManFire_CumCH4C,\n ManFire_CumBCC = ManFire_CumBCC,\n ManNonBurn_CumCO2C = ManNonBurn_CumCO2C,\n ManSawmillDecay_AnnCO2C = ManSawmillDecay_AnnCO2C, \n ManForestDecay_AnnCO2C = ManForestDecay_AnnCO2C, \n ManSawmillDecay_CumCO2C = ManSawmillDecay_CumCO2C, \n ManForestDecay_CumCO2C = ManForestDecay_CumCO2C, \n \n LCCTotEnergy_CumCO2C = LCCTotEnergy_CumCO2C,\n LCCTotEnergy_CumCH4C = LCCTotEnergy_CumCH4C,\n LCCTotEnergy_CumBCC = LCCTotEnergy_CumBCC,\n LCCHarv2Energy_CumCO2C = LCCHarv2Energy_CumCO2C,\n LCCHarv2Energy_CumCH4C = LCCHarv2Energy_CumCH4C,\n LCCHarv2Energy_CumBCC = LCCHarv2Energy_CumBCC,\n LCCSlash2Energy_CumCO2C = LCCSlash2Energy_CumCO2C,\n LCCSlash2Energy_CumCH4C = LCCSlash2Energy_CumCH4C,\n LCCSlash2Energy_CumBCC = LCCSlash2Energy_CumBCC,\n LCCFire_CumCO2C = LCCFire_CumCO2C,\n LCCFire_CumCH4C = LCCFire_CumCH4C,\n LCCFire_CumBCC = LCCFire_CumBCC,\n LCCNonBurn_CumCO2C = LCCNonBurn_CumCO2C,\n LCCSawmillDecay_AnnCO2C = LCCSawmillDecay_AnnCO2C,\n LCCOnSiteDecay_AnnCO2C = LCCOnSiteDecay_AnnCO2C,\n LCCSawmillDecay_CumCO2C = LCCSawmillDecay_CumCO2C,\n LCCOnSiteDecay_CumCO2C = LCCOnSiteDecay_CumCO2C,\n \n Wildfire_Decay_AnnCO2C = Wildfire_Decay_AnnCO2C,\n Wildfire_Decay_CumCO2C = Wildfire_Decay_CumCO2C,\n \n TotalEnergy_CumCO2C = TotalEnergy_CumCO2C,\n TotalEnergy_CumCH4C = TotalEnergy_CumCH4C,\n TotalEnergy_CumBCC = TotalEnergy_CumBCC,\n \n TotalCntlFire_CumCO2C = TotalCntlFire_CumCO2C,\n TotalCntlFire_CumCH4C = TotalCntlFire_CumCH4C,\n TotalCntlFire_CumBCC = TotalCntlFire_CumBCC,\n \n Wildfire_CumCO2C = Wildfire_CumCO2C,\n Wildfire_CumCH4C = Wildfire_CumCH4C,\n Wildfire_CumBCC = Wildfire_CumBCC,\n \n Wood_CumCO2C = wood_CumCO2C,\n Wood_CumCH4C = wood_CumCH4C,\n \n Eco_AnnCO2C = Eco_AnnCO2C,\n Eco_AnnCH4C = Eco_AnnCH4C,\n \n ManTotEnergy_AnnCO2C = ManTotEnergy_AnnCO2C,\n ManTotEnergy_AnnCH4C = ManTotEnergy_AnnCH4C,\n ManTotEnergy_AnnBCC = ManTotEnergy_AnnBCC,\n ManHarv2Energy_AnnCO2C = ManHarv2Energy_AnnCO2C,\n ManHarv2Energy_AnnCH4C = ManHarv2Energy_AnnCH4C,\n ManHarv2Energy_AnnBCC = ManHarv2Energy_AnnBCC,\n ManSlash2Energy_AnnCO2C = ManSlash2Energy_AnnCO2C,\n ManSlash2Energy_AnnCH4C = ManSlash2Energy_AnnCH4C,\n ManSlash2Energy_AnnBCC = ManSlash2Energy_AnnBCC,\n ManFire_AnnCO2C = ManFire_AnnCO2C, \n ManFire_AnnCH4C = ManFire_AnnCH4C,\n ManFire_AnnBCC = ManFire_AnnBCC,\n ManNonBurn_AnnCO2C = ManNonBurn_AnnCO2C,\n \n LCCTotEnergy_AnnCO2C = LCCTotEnergy_AnnCO2C,\n LCCTotEnergy_AnnCH4C = LCCTotEnergy_AnnCH4C,\n LCCTotEnergy_AnnBCC = LCCTotEnergy_AnnBCC,\n LCCHarv2Energy_AnnCO2C = LCCHarv2Energy_AnnCO2C,\n LCCHarv2Energy_AnnCH4C = LCCHarv2Energy_AnnCH4C,\n LCCHarv2Energy_AnnBCC = LCCHarv2Energy_AnnBCC,\n LCCSlash2Energy_AnnCO2C = LCCSlash2Energy_AnnCO2C,\n LCCSlash2Energy_AnnCH4C = LCCSlash2Energy_AnnCH4C,\n LCCSlash2Energy_AnnBCC = LCCSlash2Energy_AnnBCC,\n LCCFire_AnnCO2C = LCCFire_AnnCO2C,\n LCCFire_AnnCH4C = LCCFire_AnnCH4C,\n LCCFire_AnnBCC = LCCFire_AnnBCC,\n LCCNonBurn_AnnCO2C = LCCNonBurn_AnnCO2C,\n \n TotalEnergy_AnnCO2C = TotalEnergy_AnnCO2C,\n TotalEnergy_AnnCH4C = TotalEnergy_AnnCH4C,\n TotalEnergy_AnnBCC = TotalEnergy_AnnBCC,\n \n TotalCntlFire_AnnCO2C = TotalCntlFire_AnnCO2C,\n TotalCntlFire_AnnCH4C = TotalCntlFire_AnnCH4C,\n TotalCntlFire_AnnBCC = TotalCntlFire_AnnBCC,\n \n Wildfire_AnnCO2C = Wildfire_AnnCO2C,\n Wildfire_AnnCH4C = Wildfire_AnnCH4C,\n Wildfire_AnnBCC = Wildfire_AnnBCC,\n \n Wood_AnnCO2C = wood_AnnCO2C,\n Wood_AnnCH4C = wood_AnnCH4C\n )\n \n # calc GWP [CO2-eq] for each table in df.list\n new.df <- c()\n for (i in length(df.list)) { \n new.df <- c(new.df, CALC.GWP(df.list, gwp_CO2=gwp_CO2, gwp_CH4=gwp_CH4, gwp_BC=gwp_BC))\n }\n \n # get list of new names for df.list that drops the last 'C' in CO2C, CH4C, and BCC \n # and adds 'eq' for CH4 and BC \n new.name <- c()\n for (i in length(df.list)) { \n new.name <- c(new.name, GET.NAMES(df.list, new.name=new.name))\n }\n \n # replace names of the elements in new.df with the CO2-eq names\n names(new.df) <- paste0(new.name)\n \n # sum total wood CO2eq \n ### cumulative ###\n TotalWood_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(TotalWood_CumCO2eq_all)) {\n TotalWood_CumCO2eq_all[,i] <- new.df[[\"Wood_CumCO2\"]][,i] + new.df[[\"Wood_CumCH4eq\"]][,i]\n }\n ### annual ###\n TotalWood_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(TotalWood_AnnCO2eq_all)) {\n TotalWood_AnnCO2eq_all[,i] <- new.df[[\"Wood_AnnCO2\"]][,i] + new.df[[\"Wood_AnnCH4eq\"]][,i]\n }\n \n # sum total burned CO2eq (manage energy + manage fire + lcc fire + lcc energy + wildfire)\n ### cumulative ###\n TotalBurn_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(TotalBurn_CumCO2eq_all)) {\n TotalBurn_CumCO2eq_all[,i] <- new.df[[\"ManTotEnergy_CumCO2\"]][,i] + new.df[[\"ManTotEnergy_CumCH4eq\"]][,i] + new.df[[\"ManTotEnergy_CumBCeq\"]][,i] +\n new.df[[\"TotalCntlFire_CumCO2\"]][,i] + new.df[[\"TotalCntlFire_CumCH4eq\"]][,i] + new.df[[\"TotalCntlFire_CumBCeq\"]][,i] + new.df[[\"LCCTotEnergy_CumCO2\"]][,i] +\n new.df[[\"LCCTotEnergy_CumCH4eq\"]][,i] + new.df[[\"LCCTotEnergy_CumBCeq\"]][,i] + new.df[[\"Wildfire_CumCO2\"]][,i] + new.df[[\"Wildfire_CumCH4eq\"]][,i] +\n new.df[[\"Wildfire_CumBCeq\"]][,i] \n }\n # sum total non-burned CO2eq (eco + manage + lcc) *Wood decay not included\n TotalNonBurn_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(TotalNonBurn_CumCO2eq_all)) {\n TotalNonBurn_CumCO2eq_all[,i] <- new.df[[\"Eco_CumCO2\"]][,i] + new.df[[\"Eco_CumCH4eq\"]][,i] + new.df[[\"ManNonBurn_CumCO2\"]][,i] +\n new.df[[\"LCCNonBurn_CumCO2\"]][,i] \n }\n ### annual ###\n # sum total burned CO2eq (manage energy + manage fire + lcc fire + lcc energy + wildfire)\n TotalBurn_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(TotalBurn_AnnCO2eq_all)) {\n TotalBurn_AnnCO2eq_all[,i] <- new.df[[\"ManTotEnergy_AnnCO2\"]][,i] + new.df[[\"ManTotEnergy_AnnCH4eq\"]][,i] + new.df[[\"ManTotEnergy_AnnBCeq\"]][,i] +\n new.df[[\"TotalCntlFire_AnnCO2\"]][,i] + new.df[[\"TotalCntlFire_AnnCH4eq\"]][,i] + new.df[[\"TotalCntlFire_AnnBCeq\"]][,i] + new.df[[\"LCCTotEnergy_AnnCO2\"]][,i] +\n new.df[[\"LCCTotEnergy_AnnCH4eq\"]][,i] + new.df[[\"LCCTotEnergy_AnnBCeq\"]][,i] + new.df[[\"Wildfire_AnnCO2\"]][,i] + new.df[[\"Wildfire_AnnCH4eq\"]][,i] +\n new.df[[\"Wildfire_AnnBCeq\"]][,i]\n }\n # sum total non-burned CO2eq (eco + manage + lcc) *Wood decay not included\n TotalNonBurn_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(TotalNonBurn_AnnCO2eq_all)) {\n TotalNonBurn_AnnCO2eq_all[,i] <- new.df[[\"Eco_AnnCO2\"]][,i] + new.df[[\"Eco_AnnCH4eq\"]][,i] + new.df[[\"ManNonBurn_AnnCO2\"]][,i] +\n new.df[[\"LCCNonBurn_AnnCO2\"]][,i] \n }\n \n # sum total energy CO2eq (manage energy + lcc energy) and total fire CO2eq (manage fire + lcc fire + wildfire) \n ### cumulative energy ###\n TotalEnergy_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(TotalEnergy_CumCO2eq_all)) {\n TotalEnergy_CumCO2eq_all[,i] <- new.df[[\"ManTotEnergy_CumCO2\"]][,i] + new.df[[\"ManTotEnergy_CumCH4eq\"]][,i] + new.df[[\"ManTotEnergy_CumBCeq\"]][,i] +\n new.df[[\"LCCTotEnergy_CumCO2\"]][,i] + new.df[[\"LCCTotEnergy_CumCH4eq\"]][,i] + new.df[[\"LCCTotEnergy_CumBCeq\"]][,i] \n }\n ### cumulative fire ###\n TotalFire_CumCO2eq_all <- Total_CumCO2\n for (i in 5:ncol(TotalFire_CumCO2eq_all)) { \n TotalFire_CumCO2eq_all[,i] <- new.df[[\"TotalCntlFire_CumCO2\"]][,i] + new.df[[\"TotalCntlFire_CumCH4eq\"]][,i] + new.df[[\"TotalCntlFire_CumBCeq\"]][,i] + \n new.df[[\"Wildfire_CumCO2\"]][,i] + new.df[[\"Wildfire_CumCH4eq\"]][,i] + new.df[[\"Wildfire_CumBCeq\"]][,i]\n }\n ### annual energy ###\n TotalEnergy_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(TotalEnergy_AnnCO2eq_all)) {\n TotalEnergy_AnnCO2eq_all[,i] <- new.df[[\"ManTotEnergy_AnnCO2\"]][,i] + new.df[[\"ManTotEnergy_AnnCH4eq\"]][,i] + new.df[[\"ManTotEnergy_AnnBCeq\"]][,i] +\n new.df[[\"LCCTotEnergy_AnnCO2\"]][,i] + new.df[[\"LCCTotEnergy_AnnCH4eq\"]][,i] + new.df[[\"LCCTotEnergy_AnnBCeq\"]][,i] \n }\n ### annual fire ###\n TotalFire_AnnCO2eq_all <- Total_AnnCO2\n for (i in 5:ncol(TotalFire_AnnCO2eq_all)) {\n TotalFire_AnnCO2eq_all[,i] <- new.df[[\"TotalCntlFire_AnnCO2\"]][,i] + new.df[[\"TotalCntlFire_AnnCH4eq\"]][,i] + new.df[[\"TotalCntlFire_AnnBCeq\"]][,i] + \n new.df[[\"Wildfire_AnnCO2\"]][,i] + new.df[[\"Wildfire_AnnCH4eq\"]][,i] + new.df[[\"Wildfire_AnnBCeq\"]][,i]\n }\n \n # combine new.df with the additional CO2eq breakdowns with the out_atmos_df_list\n out_atmos_df_list <- c(out_atmos_df_list, new.df)\n \n # add GHG dataframes to out_atmos_df_list\n out_atmos_df_list[[\"Total_CumCO2\"]] <- Total_CumCO2\n out_atmos_df_list[[\"Total_CumCH4eq\"]] <- Total_CumCH4eq\n out_atmos_df_list[[\"Total_CumBCeq\"]] <- Total_CumBCeq\n out_atmos_df_list[[\"Total_AnnCO2\"]] <- Total_AnnCO2\n out_atmos_df_list[[\"Total_AnnCH4eq\"]] <- Total_AnnCH4eq\n out_atmos_df_list[[\"Total_AnnBCeq\"]] <- Total_AnnBCeq\n out_atmos_df_list[[\"TotalWood_CumCO2eq_all\"]] <- TotalWood_CumCO2eq_all\n out_atmos_df_list[[\"TotalWood_AnnCO2eq_all\"]] <- TotalWood_AnnCO2eq_all\n out_atmos_df_list[[\"TotalNonBurn_CumCO2eq_all\"]] <- TotalNonBurn_CumCO2eq_all\n out_atmos_df_list[[\"TotalFire_CumCO2eq_all\"]] <- TotalFire_CumCO2eq_all\n out_atmos_df_list[[\"TotalEnergy_CumCO2eq_all\"]] <- TotalEnergy_CumCO2eq_all\n out_atmos_df_list[[\"TotalNonBurn_AnnCO2eq_all\"]] <- TotalNonBurn_AnnCO2eq_all\n out_atmos_df_list[[\"TotalFire_AnnCO2eq_all\"]] <- TotalFire_AnnCO2eq_all\n out_atmos_df_list[[\"TotalEnergy_AnnCO2eq_all\"]] <- TotalEnergy_AnnCO2eq_all\n out_atmos_df_list[[\"TotalBurn_CumCO2eq_all\"]] <- TotalBurn_CumCO2eq_all\n out_atmos_df_list[[\"TotalBurn_AnnCO2eq_all\"]] <- TotalBurn_AnnCO2eq_all\n out_atmos_df_list[[\"Total_CumCO2eq_all\"]] <- Total_CumCO2eq_all\n out_atmos_df_list[[\"Total_AnnCO2eq_all\"]] <- Total_AnnCO2eq_all\n ### add BC-C outputs\n # cumulative\n out_atmos_df_list[[\"ManFire_CumBCC\"]] <- ManFire_CumBCC\n out_atmos_df_list[[\"ManTotEnergy_CumBCC\"]] <- ManTotEnergy_CumBCC\n out_atmos_df_list[[\"ManHarv2Energy_CumBCC\"]] <- ManHarv2Energy_CumBCC\n out_atmos_df_list[[\"ManSlash2Energy_CumBCC\"]] <- ManSlash2Energy_CumBCC\n out_atmos_df_list[[\"LCCFire_CumBCC\"]] <- LCCFire_CumBCC\n out_atmos_df_list[[\"LCCTotEnergy_CumBCC\"]] <- LCCTotEnergy_CumBCC\n out_atmos_df_list[[\"LCCHarv2Energy_CumBCC\"]] <- LCCHarv2Energy_CumBCC\n out_atmos_df_list[[\"LCCSlash2Energy_CumBCC\"]] <- LCCSlash2Energy_CumBCC\n out_atmos_df_list[[\"Wildfire_CumBCC\"]] <- Wildfire_CumBCC\n # annual\n out_atmos_df_list[[\"ManFire_AnnBCC\"]] <- ManFire_AnnBCC\n out_atmos_df_list[[\"ManTotEnergy_AnnBCC\"]] <- ManTotEnergy_AnnBCC\n out_atmos_df_list[[\"ManHarv2Energy_AnnBCC\"]] <- ManHarv2Energy_AnnBCC\n out_atmos_df_list[[\"ManSlash2Energy_AnnBCC\"]] <- ManSlash2Energy_AnnBCC\n out_atmos_df_list[[\"LCCFire_AnnBCC\"]] <- LCCFire_AnnBCC\n out_atmos_df_list[[\"LCCTotEnergy_AnnBCC\"]] <- LCCTotEnergy_AnnBCC\n out_atmos_df_list[[\"LCCHarv2Energy_AnnBCC\"]] <- LCCHarv2Energy_AnnBCC\n out_atmos_df_list[[\"LCCSlash2Energy_AnnBCC\"]] <- LCCSlash2Energy_AnnBCC\n out_atmos_df_list[[\"Wildfire_AnnBCC\"]] <- Wildfire_AnnBCC\n \n # replaces any -0 with 0\n for (i in 1:length(out_atmos_df_list)) {\n for (c in 5:ncol(out_atmos_df_list[[i]])) {\n out_atmos_df_list[[i]][,c] <- replace(out_atmos_df_list[[i]][,c], out_atmos_df_list[[i]][,c] == 0, 0.00000000) \n }\n \n }\n # check that total atmos C gain is equal to the sum of the partitions, less the Eco C emissions (not included in the Total_Atmos gain C)\n zero_test <- Total_AnnCO2\n for (i in 5:ncol(zero_test)) {\n zero_test[,i] <- 0\n }\n for (i in 5:ncol(Total_AnnCO2)) {\n zero_test[,i] <- out_atmos_df_list[[\"Total_Atmos_AnnGain_C_stock\"]][,i] - \n (Total_AnnCO2[,i] * (12.0107/44.01) + Total_AnnCH4eq[,i] * (12.0107/(gwp_CH4*16.04)) + Total_AnnBCeq[,i] * (0.6/gwp_BC) - \n Eco_AnnCO2C[,i] - Eco_AnnCH4C[,i])\n } \n \n # rounding error so this test is false\n all(zero_test[5:ncol(zero_test)] == 0) \n # but this checks to be true\n all(zero_test[5:ncol(zero_test)] < 1 & zero_test[5:ncol(zero_test)] > -1) \n \n ############################### Calculate some changes and totals ############################### \n # also round everything to 2 decimal places: ha, MgC and MgC/ha\n # the realistic precision is integer\n # but going to integer here cuts out some area that has some carbon\n cat(\"Starting change/total calcs...\\n\")\n # create columns in each section below for changes between initial and final year (final - initial) for all rows\n \n ######### (1) TOTAL AREA ######### \n # add column of total land/ocean area change for each land-type - ownership combination###### area ######\n out_area_df_list[[1]][, \"Change_ha\"] = out_area_df_list[[1]][,end_area_label] - out_area_df_list[[1]][,start_area_label]\n \n # (1a) Do each landtype\n # add rows for aggregate sums by landtype:\n # get names of landtypes\n landtype_names <- unique(out_area_df_list[[1]][,\"Land_Type\"])\n # create df to store all the landtype sums in the following loop\n all_landtype_sums <- out_area_df_list[[1]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) {\n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_sums[l,c(1:4)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\")\n # subset landtype-specific df\n landtype_df_temp <- out_area_df_list[[1]][out_area_df_list[[1]][,\"Land_Type\"] == landtype_name,] \n # aggregate sum areas of landtype-specific areas for each column year\n all_landtype_sums[l,c(5:ncol(all_landtype_sums))] <- apply(landtype_df_temp[,c(5:ncol(landtype_df_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_area_df_list[[1]]\n out_area_df_list[[1]] <- rbind(out_area_df_list[[1]], all_landtype_sums)\n \n # (1b) Do each region (Excludes Ocean)\n # add rows for aggregate sums by region:\n # get names of regions\n region_names <- unique(out_area_df_list[[1]][,\"Region\"])\n # remove \"All_region\" from region_names\n region_names <- region_names[!region_names %in% c(\"All_region\",\"Ocean\")]\n # create df to store all the region sums in the following loop\n all_region_sums <- out_area_df_list[[1]][1:length(region_names),]\n for (r in 1:length(region_names)) {\n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_sums[r,c(1:4)] <- c(-1, region_names[r], \"All_land\", \"All_own\")\n # subset region-specific df\n region_df_temp <- out_area_df_list[[1]][out_area_df_list[[1]][,\"Region\"] == region_name,] \n # aggregate sum areas of region-specific areas for each column year\n all_region_sums[r,c(5:ncol(all_region_sums))] <- apply(region_df_temp[,c(5:ncol(region_df_temp))], 2, sum)\n }\n # add the aggregated region rows to out_area_df_list[[1]]\n out_area_df_list[[1]] = rbind(out_area_df_list[[1]], all_region_sums)\n \n # (1c) Do all California land (Excludes Ocean)\n # create single row for it\n sum_row = out_area_df_list[[1]][1,]\n sum_row[,c(1:4)] = c(-1, \"All_region\", \"All_land\", \"All_own\")\n sum_row[,c(5:ncol(sum_row))] = \n apply(out_area_df_list[[1]][out_area_df_list[[1]][, \"Land_Type\"] == \"All_land\", c(5:ncol(out_area_df_list[[1]]))], 2 , sum)\n out_area_df_list[[1]] = rbind(out_area_df_list[[1]], sum_row)\n # do a another subtraction for change column\n out_area_df_list[[1]][,ncol(out_area_df_list[[1]])]<-out_area_df_list[[1]][,end_area_label] - out_area_df_list[[1]][,start_area_label]\n # round\n out_area_df_list[[1]][,c(5:ncol(out_area_df_list[[1]]))] = round(out_area_df_list[[1]][,c(5:ncol(out_area_df_list[[1]]))], 2)\n \n ######### (2) MANAGED & WILFIRE AREA ######### \n for (i in 2:num_out_area_sheets) {\n end_label = ncol(out_area_df_list[[i]])\n out_area_df_list[[i]][, \"Change_ha\"] = out_area_df_list[[i]][,end_label] - out_area_df_list[[i]][,start_area_label]\n # if there are no prescribed management practices, skip this section for the Managed_area output (out_area_df_list[[2]])\n if (nrow(man_adjust_df)>0 | i == 3) {\n # (2a) do each landtype within the current df in out_area_df_list\n landtype_names <- unique(out_area_df_list[[i]][,\"Land_Type\"])\n # create df to store all the landtype sums in the following loop for current df in out_area_df_list\n all_landtype_sums <- out_area_df_list[[i]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) {\n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_sums[l,c(1:5)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\", \"All\")\n # subset landtype-specific df\n # if on management areas, exclude developed_all urban forest and growth to ensure accurate summed management areas for developed_all using only dead_removal\n if (i == 2) {\n landtype_df_temp <- out_area_df_list[[i]][out_area_df_list[[i]][,\"Land_Type\"] == landtype_name & out_area_df_list[[i]][,\"Management\"] != \"Growth\" & \n out_area_df_list[[i]][,\"Management\"] != \"Urban_forest\",]\n } else {\n landtype_df_temp <- out_area_df_list[[i]][out_area_df_list[[i]][,\"Land_Type\"] == landtype_name,]\n }\n \n # aggregate sum areas of landtype-specific areas for each column year\n all_landtype_sums[l,c(6:ncol(all_landtype_sums))] <- apply(landtype_df_temp[,c(6:ncol(landtype_df_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_area_df_list[[i]]\n out_area_df_list[[i]] <- rbind(out_area_df_list[[i]], all_landtype_sums)\n \n # (2b) do each region within the current df in out_area_df_list (Excludes Ocean)\n region_names <- unique(out_area_df_list[[i]][,\"Region\"])\n # remove \"All_region\" from region_names\n region_names <- region_names[!region_names %in% c(\"All_region\",\"Ocean\")]\n # create df to store all the region sums in the following loop for current df in out_area_df_list\n all_region_sums <- out_area_df_list[[i]][1:length(region_names),]\n for (r in 1:length(region_names)) {\n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_sums[r,c(1:5)] <- c(-1, region_names[r], \"All_land\", \"All_own\", \"All\")\n # subset region-specific df\n # if on management areas, exclude developed_all urban forest and growth to ensure accurate summed management areas for developed_all using only dead_removal\n if (i == 2) {\n region_df_temp <- out_area_df_list[[i]][out_area_df_list[[i]][,\"Region\"] == region_name & out_area_df_list[[i]][,\"Management\"] != \"Growth\" & \n out_area_df_list[[i]][,\"Management\"] != \"Urban_forest\",]\n } else {\n region_df_temp <- out_area_df_list[[i]][out_area_df_list[[i]][,\"Region\"] == region_name,] \n }\n # aggregate sum areas of region-specific areas for each column year\n all_region_sums[r,c(6:ncol(all_region_sums))] <- apply(region_df_temp[,c(6:ncol(region_df_temp))], 2, sum)\n }\n # add the aggregated region rows to out_area_df_list[[i]]\n out_area_df_list[[i]] = rbind(out_area_df_list[[i]], all_region_sums)\n \n # (2c) do all California land for the current df in out_area_df_list (Excludes Ocean)\n sum_row = out_area_df_list[[i]][1,]\n sum_row[,c(1:5)] = c(-1, \"All_region\", \"All_land\", \"All_own\", \"All\")\n sum_row[,c(6:ncol(sum_row))] = apply(out_area_df_list[[i]][out_area_df_list[[i]][, \"Land_Type\"] == \"All_land\", \n c(6:ncol(out_area_df_list[[i]]))], 2 , sum)\n out_area_df_list[[i]] = rbind(out_area_df_list[[i]], sum_row)\n # do a another subtraction for change column\n out_area_df_list[[i]][,ncol(out_area_df_list[[i]])]<-out_area_df_list[[i]][,end_label] - out_area_df_list[[i]][,start_area_label]\n # round\n out_area_df_list[[i]][,c(6:ncol(out_area_df_list[[i]]))] = round(out_area_df_list[[i]][,c(6:ncol(out_area_df_list[[i]]))], 2)\n } # end if there are prescribed management practices\n }\n \n ######### (3) DENSITY ######### \n for (i in 1:num_out_density_sheets) {\n out_density_df_list[[i]][, \"Change_Mg_ha\"] <- out_density_df_list[[i]][,end_density_label] - out_density_df_list[[i]][,start_density_label]\n \n # (3a) do each landtype within the current df in out_density_df_list\n landtype_names <- unique(out_density_df_list[[i]][,\"Land_Type\"]) \n # create df to store all the landtype averages in the following loop for current df in out_density_df_list\n all_landtype_avg <- out_density_df_list[[i]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) { \n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_avg[l,c(1:4)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\")\n # subset landtype-specific density df\n landtype_df_dens_temp <- out_density_df_list[[i]][out_density_df_list[[i]][,\"Land_Type\"] == landtype_name,] \n # subset landtype-specific area df (landtype_names already excludes Seagrass)\n landtype_df_area_temp <- out_area_df_list[[1]][out_area_df_list[[1]][,\"Land_Type\"] == landtype_name,] \n # delete last row to exclude the All_region row\n landtype_df_area_temp <- landtype_df_area_temp[landtype_df_area_temp$Region != \"All_region\",]\n # get total C for current landtype: aggregate landtype-specific densities*areas by each column year (excluding Seagrass)\n all_landtype_avg[l,c(5:ncol(all_landtype_avg))] <- \n apply(landtype_df_dens_temp[,c(5:ncol(landtype_df_dens_temp))] * landtype_df_area_temp[,c(5:ncol(landtype_df_area_temp))], \n 2, sum)\n # get total area for current landtype: aggregate landtype-specific\n landtype_tot_area <- out_area_df_list[[1]][(out_area_df_list[[1]][, \"Land_Type\"] == landtype_name) &\n (out_area_df_list[[1]][, \"Region\"] == \"All_region\"),]\n # get avg C density for current landtype (excluding last column for Change), divide total Mg C by total area (get original density?)\n all_landtype_avg[l,c(5:ncol(all_landtype_avg))] <- all_landtype_avg[l, c(5:ncol(all_landtype_avg))] / \n landtype_tot_area[, c(5:ncol(landtype_tot_area))]\n }\n # add the aggregated landtype rows to out_density_df_list[[i]]\n out_density_df_list[[i]] = rbind(out_density_df_list[[i]], all_landtype_avg)\n \n # (3b) do each region within the current df in out_density_df_list (Excluded Ocean)\n region_names <- unique(out_density_df_list[[i]][,\"Region\"])\n # remove Ocean & All_region \n region_names <- region_names[!region_names %in% c(\"Ocean\",\"All_region\")]\n # create df to store all the region averages in the following loop for current df in out_density_df_list\n all_region_avg <- out_density_df_list[[i]][1:length(region_names),]\n for (r in 1:length(region_names)) { \n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_avg[r,c(1:4)] <- c(-1, region_names[r], \"All_land\", \"All_own\")\n # subset region-specific density df\n region_df_dens_temp <- out_density_df_list[[i]][out_density_df_list[[i]][,\"Region\"] == region_name,] \n # delete last row to exclude the All_landtype row\n region_df_dens_temp <- region_df_dens_temp[region_df_dens_temp$Land_Type != \"All_land\",]\n # subset region-specific area df \n region_df_area_temp <- out_area_df_list[[1]][out_area_df_list[[1]][,\"Region\"] == region_name,] \n # delete last row to exclude the All_landtype row\n region_df_area_temp <- region_df_area_temp[region_df_area_temp$Land_Type != \"All_land\",]\n # get total C for current region: aggregate region-specific densities*areas by each column year (excluding Seagrass)\n all_region_avg[r,c(5:ncol(all_region_avg))] <- \n apply(region_df_dens_temp[,c(5:ncol(region_df_dens_temp))] * region_df_area_temp[,c(5:ncol(region_df_area_temp))], \n 2, sum)\n # get total area for current region: aggregate region-specific\n region_tot_area <- out_area_df_list[[1]][(out_area_df_list[[1]][, \"Region\"] == region_name) &\n (out_area_df_list[[1]][, \"Land_Type\"] == \"All_land\"),]\n # get avg C density for current region (excluding last column for Change), divide total Mg C by total area (get original density?)\n all_region_avg[r,c(5:ncol(all_region_avg))] <- all_region_avg[r, c(5:ncol(all_region_avg))] / \n region_tot_area[, c(5:ncol(region_tot_area))]\n }\n # add the aggregated region rows to out_density_df_list[[i]]\n out_density_df_list[[i]] = rbind(out_density_df_list[[i]], all_region_avg)\n \n # (3c) Do all California land for the current df in out_density_df_list (Excludes Ocean)\n avg_row = out_density_df_list[[i]][1,]\n avg_row[,c(1:4)] = c(-1, \"All_region\", \"All_land\", \"All_own\")\n # calc all California land total C for each year\n avg_row[,c(5:ncol(avg_row))] = \n apply(out_density_df_list[[i]][out_density_df_list[[i]][, \"Land_Type\"] == \"All_land\", c(5:ncol(out_density_df_list[[i]]))] * \n out_area_df_list[[1]][out_area_df_list[[1]][, \"Land_Type\"] == \"All_land\" & out_area_df_list[[1]][, \"Region\"] != \"All_region\", \n c(5:ncol(out_area_df_list[[1]]))], 2, sum)\n # calc all California land area-weighted avg C density for each year\n avg_row[1,c(5:(ncol(avg_row)-1))] = avg_row[1,c(5:(ncol(avg_row)-1))] / \n out_area_df_list[[1]][out_area_df_list[[1]][, \"Region\"] == \"All_region\" & out_area_df_list[[1]][, \"Land_Type\"] == \"All_land\", \n c(5:(ncol(out_area_df_list[[1]])-1))]\n out_density_df_list[[i]] = rbind(out_density_df_list[[i]], avg_row)\n # final - initial \n out_density_df_list[[i]][,ncol(out_density_df_list[[i]])] <- out_density_df_list[[i]][,end_density_label] - out_density_df_list[[i]][,start_density_label]\n # round\n out_density_df_list[[i]][,c(5:ncol(out_density_df_list[[i]]))] = round(out_density_df_list[[i]][,c(5:ncol(out_density_df_list[[i]]))], 2)\n }\n \n ######### (4) C STOCK ######### \n for (i in 1:num_out_stock_sheets) {\n out_stock_df_list[[i]][, \"Change_Mg\"] = out_stock_df_list[[i]][,end_stock_label] - out_stock_df_list[[i]][,start_stock_label]\n \n # (4a) do each landtype within the current df in out_stock_df_list\n landtype_names <- unique(out_stock_df_list[[i]][,\"Land_Type\"]) \n # create df to store all the landtype averages in the following loop for current df in out_stock_df_list\n all_landtype_stock <- out_stock_df_list[[i]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) { \n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_stock[l,c(1:4)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\")\n # subset landtype-specific stock df\n landtype_df_stock_temp <- out_stock_df_list[[i]][out_stock_df_list[[i]][,\"Land_Type\"] == landtype_name,] \n # aggregate sum stock of landtype-specific areas for each column year\n all_landtype_stock[l,c(5:ncol(all_landtype_stock))] <- apply(landtype_df_stock_temp[,c(5:ncol(landtype_df_stock_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_stock_df_list[[i]]\n out_stock_df_list[[i]] = rbind(out_stock_df_list[[i]], all_landtype_stock)\n \n # (4b) do each region (Excludes Ocean)\n region_names <- unique(out_stock_df_list[[i]][,\"Region\"])\n # remove All_region \n region_names <- region_names[!region_names %in% \"All_region\"]\n # remove \"All_region\" from region_names\n region_names <- region_names[!region_names %in% c(\"All_region\",\"Ocean\")]\n # create df to store all the region averages in the following loop for current df in out_stock_df_list\n all_region_stock <- out_stock_df_list[[i]][1:length(region_names),]\n for (r in 1:length(region_names)) { \n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_stock[r,c(1:4)] <- c(-1, region_names[r], \"All_land\", \"All_own\")\n # subset region-specific stock df\n region_df_stock_temp <- out_stock_df_list[[i]][out_stock_df_list[[i]][,\"Region\"] == region_name,] \n # delete the All_land row\n region_df_stock_temp <- region_df_stock_temp[region_df_stock_temp$Land_Type != \"All_land\",]\n # aggregate sum stock of region-specific areas for each column year\n all_region_stock[r,c(5:ncol(all_region_stock))] <- apply(region_df_stock_temp[,c(5:ncol(region_df_stock_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_stock_df_list[[i]]\n out_stock_df_list[[i]] = rbind(out_stock_df_list[[i]], all_region_stock)\n \n # (4c) do all Calif land (Excludes Ocean)\n sum_row = out_stock_df_list[[i]][1,]\n sum_row[,c(1:4)] = c(-1, \"All_region\", \"All_land\", \"All_own\")\n sum_row[,c(5:ncol(sum_row))] = apply(out_stock_df_list[[i]][out_stock_df_list[[i]][, \"Land_Type\"] == \"All_land\", \n c(5:ncol(out_stock_df_list[[i]]))], 2, sum)\n out_stock_df_list[[i]] = rbind(out_stock_df_list[[i]], sum_row)\n # subtract final-intial again now that all the sections are done\n out_stock_df_list[[i]][, \"Change_Mg\"] = out_stock_df_list[[i]][,end_stock_label] - out_stock_df_list[[i]][,start_stock_label]\n # round \n out_stock_df_list[[i]][,c(5:ncol(out_stock_df_list[[i]]))] = round(out_stock_df_list[[i]][,c(5:ncol(out_stock_df_list[[i]]))], 2)\n }\n \n ######### (5) WOOD ######### \n for (i in 1:num_out_wood_sheets) {\n end_label = ncol(out_wood_df_list[[i]])\n out_wood_df_list[[i]][, \"Change_Mg\"] = out_wood_df_list[[i]][,end_label] - out_wood_df_list[[i]][,start_wood_label]\n # (5a) do all land types \n landtype_names <- unique(out_wood_df_list[[i]][,\"Land_Type\"]) \n # create df to store all the landtype averages in the following loop for current df in out_wood_df_list\n all_landtype_wood <- out_wood_df_list[[i]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) { \n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_wood[l,c(1:4)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\")\n # subset landtype-specific wood df\n landtype_df_wood_temp <- out_wood_df_list[[i]][out_wood_df_list[[i]][,\"Land_Type\"] == landtype_name,] \n # aggregate sum wood of landtype-specific areas for each column year\n all_landtype_wood[l,c(5:ncol(all_landtype_wood))] <- apply(landtype_df_wood_temp[,c(5:ncol(landtype_df_wood_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_wood_df_list[[i]]\n out_wood_df_list[[i]] = rbind(out_wood_df_list[[i]], all_landtype_wood)\n \n # (5b) do all Regions (Excludes Ocean)\n region_names <- unique(out_wood_df_list[[i]][,\"Region\"])\n # remove All_region \n region_names <- region_names[!region_names %in% \"All_region\"]\n # remove \"All_region\" from region_names\n region_names <- region_names[!region_names %in% c(\"All_region\",\"Ocean\")]\n # create df to store all the region averages in the following loop for current df in out_wood_df_list\n all_region_wood <- out_wood_df_list[[i]][1:length(region_names),]\n for (r in 1:length(region_names)) { \n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_wood[r,c(1:4)] <- c(-1, region_names[r], \"All_land\", \"All_own\")\n # subset region-specific wood df\n region_df_wood_temp <- out_wood_df_list[[i]][out_wood_df_list[[i]][,\"Region\"] == region_name,] \n # delete the All_land row\n region_df_wood_temp <- region_df_wood_temp[region_df_wood_temp$Land_Type != \"All_land\",]\n # aggregate sum wood of region-specific areas for each column year\n all_region_wood[r,c(5:ncol(all_region_wood))] <- apply(region_df_wood_temp[,c(5:ncol(region_df_wood_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_wood_df_list[[i]]\n out_wood_df_list[[i]] = rbind(out_wood_df_list[[i]], all_region_wood)\n \n # (5c) do all California land (excludes Ocean)\n sum_row = out_wood_df_list[[i]][1,]\n sum_row[,c(1:4)] = c(-1, \"All_region\", \"All_land\", \"All_own\")\n sum_row[,c(5:ncol(sum_row))] <- apply(out_wood_df_list[[i]][out_wood_df_list[[i]][, \"Land_Type\"] == \"All_land\", \n c(5:ncol(out_wood_df_list[[i]]))], 2, sum)\n out_wood_df_list[[i]] = rbind(out_wood_df_list[[i]], sum_row)\n # subtract final-intial again now that all the sections are done\n out_wood_df_list[[i]][, \"Change_Mg\"] = out_wood_df_list[[i]][,end_label] - out_wood_df_list[[i]][,start_wood_label]\n # round \n out_wood_df_list[[i]][,c(5:ncol(out_wood_df_list[[i]]))] = round(out_wood_df_list[[i]][,c(5:ncol(out_wood_df_list[[i]]))], 2)\n }\n \n # remove the Xs added to the front of the year columns so that the following atmosphere section can work without error\n colnames_Ann = names(out_atmos_df_list[[8]])\n colnames_Cum = names(out_atmos_df_list[[1]])\n names(out_atmos_df_list[[\"Eco_AnnCO2\"]]) = colnames_Ann\n names(out_atmos_df_list[[\"Eco_AnnCH4eq\"]]) = colnames_Ann\n names(out_atmos_df_list[[\"Eco_CumCO2\"]]) = colnames_Cum\n names(out_atmos_df_list[[\"Eco_CumCH4eq\"]]) = colnames_Cum\n \n \n ######### (6) ATMOSHPHERE #########\n for (i in 1:length(out_atmos_df_list)) {\n end_label = ncol(out_atmos_df_list[[i]])\n out_atmos_df_list[[i]][, \"Change_Mg\"] = out_atmos_df_list[[i]][,end_label] - out_atmos_df_list[[i]][,start_atmos_label]\n # (6a) do each landtype\n landtype_names <- unique(out_atmos_df_list[[i]][,\"Land_Type\"]) \n # create df to store all the landtype averages in the following loop for current df in out_atmos_df_list\n all_landtype_atmos <- out_atmos_df_list[[i]][1:length(landtype_names),]\n for (l in 1:length(landtype_names)) { \n # get current landtype name\n landtype_name <- landtype_names[l]\n # name the landtype cell accordingly\n all_landtype_atmos[l,c(1:4)] <- c(-1, \"All_region\", landtype_names[l], \"All_own\")\n # subset landtype-specific atmos df\n landtype_df_atmos_temp <- out_atmos_df_list[[i]][out_atmos_df_list[[i]][,\"Land_Type\"] == landtype_name,] \n # aggregate sum atmos of landtype-specific areas for each column year\n all_landtype_atmos[l,c(5:ncol(all_landtype_atmos))] <- apply(landtype_df_atmos_temp[,c(5:ncol(landtype_df_atmos_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_atmos_df_list[[i]]\n out_atmos_df_list[[i]] = rbind(out_atmos_df_list[[i]], all_landtype_atmos)\n \n # (6b) do each region (excludes Ocean)\n region_names <- unique(out_atmos_df_list[[i]][,\"Region\"])\n # remove All_region \n region_names <- region_names[!region_names %in% \"All_region\"]\n # remove \"All_region\" from region_names\n region_names <- region_names[!region_names %in% c(\"All_region\",\"Ocean\")]\n # create df to store all the region averages in the following loop for current df in out_atmos_df_list\n all_region_atmos <- out_atmos_df_list[[i]][1:length(region_names),]\n for (r in 1:length(region_names)) { \n # get current region name\n region_name <- region_names[r]\n # name the region cell accordingly\n all_region_atmos[r,c(1:4)] <- c(-1, region_names[r], \"All_land\", \"All_own\")\n # subset region-specific atmos df\n region_df_atmos_temp <- out_atmos_df_list[[i]][out_atmos_df_list[[i]][,\"Region\"] == region_name,] \n # delete the All_land row\n region_df_atmos_temp <- region_df_atmos_temp[region_df_atmos_temp$Land_Type != \"All_land\",]\n # aggregate sum atmos of region-specific areas for each column year\n all_region_atmos[r,c(5:ncol(all_region_atmos))] <- apply(region_df_atmos_temp[,c(5:ncol(region_df_atmos_temp))], 2, sum)\n }\n # add the aggregated landtype rows to out_atmos_df_list[[i]]\n out_atmos_df_list[[i]] = rbind(out_atmos_df_list[[i]], all_region_atmos)\n \n # (6c) do all California (excludes Ocean)\n sum_row = out_atmos_df_list[[i]][1,]\n sum_row[,c(1:4)] = c(-1, \"All_region\", \"All_land\", \"All_own\")\n sum_row[,c(5:ncol(sum_row))] <- apply(out_atmos_df_list[[i]][out_atmos_df_list[[i]][, \"Land_Type\"] == \"All_land\", \n c(5:ncol(out_atmos_df_list[[i]]))], 2, sum)\n out_atmos_df_list[[i]] = rbind(out_atmos_df_list[[i]], sum_row)\n # subtract final-intial again now that all the sections are done\n out_atmos_df_list[[i]][, \"Change_Mg\"] = out_atmos_df_list[[i]][,end_label] - out_atmos_df_list[[i]][,start_atmos_label]\n # round\n out_atmos_df_list[[i]][,c(5:ncol(out_atmos_df_list[[i]]))] = round(out_atmos_df_list[[i]][,c(5:ncol(out_atmos_df_list[[i]]))], 2)\n }\n \n # write to excel file\n if(WRITE_OUT_FILE) {\n \n cat(\"Starting writing output at\", date(), \"\\n\")\n \n # put the output tables in a workbook\n out_wrkbk = loadWorkbook(out_file, create = TRUE)\n \n # area\n createSheet(out_wrkbk, name = out_area_sheets)\n clearSheet(out_wrkbk, sheet = out_area_sheets)\n writeWorksheet(out_wrkbk, data = out_area_df_list, sheet = out_area_sheets, header = TRUE)\n \n # c density\n createSheet(out_wrkbk, name = out_density_sheets)\n clearSheet(out_wrkbk, sheet = out_density_sheets)\n writeWorksheet(out_wrkbk, data = out_density_df_list, sheet = out_density_sheets, header = TRUE)\n \n # c stock\n createSheet(out_wrkbk, name = out_stock_sheets)\n clearSheet(out_wrkbk, sheet = out_stock_sheets)\n writeWorksheet(out_wrkbk, data = out_stock_df_list, sheet = out_stock_sheets, header = TRUE)\n \n # wood\n createSheet(out_wrkbk, name = out_wood_sheets)\n clearSheet(out_wrkbk, sheet = out_wood_sheets)\n writeWorksheet(out_wrkbk, data = out_wood_df_list, sheet = out_wood_sheets, header = TRUE)\n \n # atmosphere\n createSheet(out_wrkbk, name = names(out_atmos_df_list))\n clearSheet(out_wrkbk, sheet = names(out_atmos_df_list))\n writeWorksheet(out_wrkbk, data = out_atmos_df_list, sheet = names(out_atmos_df_list), header = TRUE)\n \n # write the workbook\n saveWorkbook(out_wrkbk)\n \n cat(\"Finished writing output at\", date(), \"\\n\")\n }\n \n cat(\"Finished CALAND at\", date(), \"\\n\")\n \n} # end function CALAND()\n", "meta": {"hexsha": "667d013b497f8cb6d1e87bae5c0db226292d56a9", "size": 442197, "ext": "r", "lang": "R", "max_stars_repo_path": "CALAND.r", "max_stars_repo_name": "khemesphere/caland", "max_stars_repo_head_hexsha": "e0b5eadfb1b43ccedad389926aa2e9a0f4deeaaf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CALAND.r", "max_issues_repo_name": "khemesphere/caland", "max_issues_repo_head_hexsha": "e0b5eadfb1b43ccedad389926aa2e9a0f4deeaaf", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CALAND.r", "max_forks_repo_name": "khemesphere/caland", "max_forks_repo_head_hexsha": "e0b5eadfb1b43ccedad389926aa2e9a0f4deeaaf", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 71.8435418359, "max_line_length": 304, "alphanum_fraction": 0.6904727983, "num_tokens": 125566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7606506526772884, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.34772134201758664}} {"text": "# install packages\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\n# if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n# install.packages(\"BiocManager\")\n# BiocManager::install(\"limma\")\nlibrary(limma)\nlibrary(plotly)\nlibrary(htmlwidgets)\nsource(\"GSEAenricher.R\")\n\ntop_tables <- list()\npea_tables <- list()\nbubble_plots <- list()\n\nrun_dea_pea <- function(matrix_file, metadata_file, doe_file, index, save_path_dea, save_path_pea) {\n matrixx <- readRDS(matrix_file) \n metadata <- readRDS(metadata_file)\n doe <- read_tsv(doe_file)\n \n Gender <- factor(make.names(doe$Gender))\n Age <- doe$AGE\n Braak <- factor(make.names(doe$Braak.stage))\n Batch <- factor(make.names(doe$Gel.Batch))\n \n design <- model.matrix(~0+Gender+Age+Braak+Batch)\n contrast <- makeContrasts(GenderComparison = GenderFemale - GenderMale, levels = design)\n \n fit <- lmFit(matrixx, design) \n # fit <- eBayes(fit)\n fit2 <- contrasts.fit(fit, contrast) \n fit2 <- eBayes (fit2)\n \n # top <- topTable(fit, coef = \"GenderComparison\", adjust.method = \"BH\",n=Inf, sort.by = \"P\")\n top2 <- topTable(fit2, coef = \"GenderComparison\", adjust.method = \"BH\",n=Inf, sort.by = \"P\")\n # top <- merge(top, metadata, by=0, all=TRUE)\n top2 <- merge(top2, metadata, by=0, all=TRUE)\n top2$Shown.ID <- top2$hgnc_symbol\n top2$Shown.ID[top2$Shown.ID==\"\"] <- top2$Protein.IDs[top2$Shown.ID==\"\"]\n top2$setting <- index\n \n top_tables[[index]] <<- top2\n \n top2_sig <- top2 %>%\n filter(P.Value <= 0.05)\n \n print (summary(decideTests(fit,adjust.method = \"BH\", p.value = 0.05)))\n print (summary(decideTests(fit,adjust.method = \"none\", p.value = 0.05)))\n print (summary(decideTests(fit2,adjust.method = \"BH\", p.value = 0.05)))\n print (summary(decideTests(fit2,adjust.method = \"none\", p.value = 0.05)))\n \n # write.table(top2_sig, file='sig.tsv', quote=FALSE, sep='\\t', col.names = NA)\n \n load(\"gmt-go.Rdata\")\n \n g1 <- do_GSEA(top2, 1, gmt.go, group_name = \"F_vs_M\", Protein.ID.Column =\"Protein.IDs\")\n \n g1$df$setting <- index\n \n g1_filtered <- g1$df %>%\n filter(pvalue < 0.05)\n \n pea_tables[[index]] <<- g1\n \n hcal_dea <- max ((length (unique (top2$Protein.IDs)) * 16.5 + 25 + 10 + 100), 500)\n hcal_pea <- max ((length (unique (g1$df$ID)) * 16.5 + 25 + 10 + 100), 500)\n #gp.pt <- ggplotly(p, tooltip = \"text\", height = hcal, source=\"DEAPlotSource\") %>% \n # layout (yaxis=(list(automargin = F)), margin=list (l=200))\n\n # ggplot(top2_sig, aes(y=Protein.IDs, x=logFC)) + \n # geom_point(aes(size=P.Value, color=AveExpr)) + \n # labs(y=\"Protein IDs\", x=\"logFC\", color=\"Average expression\", size=\"P-value\") +\n # theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n \n p1 <- ggplot (data=top2_sig, aes (x=logFC, y=Protein.IDs)) +\n geom_point(aes (size=AveExpr, fill=logFC, color=-log10(P.Value), stroke=0.5, text=paste('Log fold change:', logFC, '
',\n 'Average expression:', AveExpr,'
',\n 't-statistic:', t,'
',\n 'P-value:',P.Value,'
',\n 'Negative log10-P-value:',-log10(P.Value),'
',\n 'B-statistic:',B,'
'))) +\n scale_fill_gradient2(limits=c(-max(abs(top2_sig$logFC)), max(abs(top2_sig$logFC))), low = \"#0199CC\", mid = \"white\", high = \"#FF3705\", midpoint = 0) +\n scale_color_gradient2(limits=c(0, max(abs(log10(top2_sig$P.Value)))), low = \"white\", mid = \"white\", high = \"black\", midpoint = 1) +\n theme_minimal () + xlab(\"Comparison\") + ylab(\"\") + \n theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n \n gp.pt.1 <- ggplotly(p1, tooltip = \"text\", height = hcal_dea) %>% \n layout (yaxis=(list(automargin = F)), margin=list (l=200))\n \n saveWidget(gp.pt.1, save_path_dea)\n \n # ggsave(save_path_dea)\n \n # ggplot(g1_filtered, aes(y=ID, x=rank)) + \n # geom_point(aes(size=pvalue, color=enrichmentScore)) + \n # labs(y=\"Protein IDs\", x=\"logFC\", color=\"Enrichment score\", size=\"P-value\") +\n # theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n \n p2 <- ggplot (data=g1_filtered, aes (x=enrichmentScore, y=ID)) +\n geom_point(aes (size=setSize, fill=NES, color=-log10(pvalue), stroke=0.5, text=paste('Core_enrichment:', core_enrichment, '
',\n 'Comparison:', Group,'
',\n 'Setsize:', setSize,'
',\n 'NES:',NES,'
',\n 'pvalue:',pvalue,'
',\n 'rank:',rank,'
',\n 'leading_edge:',leading_edge,'
'))) +\n scale_fill_gradient2(limits=c(-max(abs(g1_filtered$NES)), max(abs(g1_filtered$NES))), low = \"#0199CC\", mid = \"white\", high = \"#FF3705\", midpoint = 0) +\n scale_color_gradient2(limits=c(0, max(abs(log10(g1_filtered$pvalue)))), low = \"white\", mid = \"white\", high = \"black\", midpoint = 1) +\n theme_minimal () + xlab(\"Comparison\") + ylab(\"\") + \n theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n \n gp.pt.2 <- ggplotly(p2, tooltip = \"text\", height = hcal_pea) %>% \n layout (yaxis=(list(automargin = F)), margin=list (l=200))\n \n saveWidget(gp.pt.2, save_path_pea)\n \n # ggsave(save_path_pea)\n}\n\nrun_dea_pea(\"pgdata_matrix_1.rds\", \"pgdata_metadata_1.rds\", \"manifest2.tsv\", \"filter_mindet\", \"dea_1.html\", \"pea_1.html\")\nrun_dea_pea(\"pgdata_matrix_2.rds\", \"pgdata_metadata_2.rds\", \"manifest2.tsv\", \"filter_bpca\", \"dea_2.html\", \"pea_2.html\")\nrun_dea_pea(\"pgdata_matrix_3.rds\", \"pgdata_metadata_3.rds\", \"manifest2.tsv\", \"nofilter_mindet\", \"dea_3.html\", \"pea_3.html\")\nrun_dea_pea(\"pgdata_matrix_4.rds\", \"pgdata_metadata_4.rds\", \"manifest2.tsv\", \"nofilter_bpca\", \"dea_4.html\", \"pea_4.html\")\n\n# -------------------------\n\n# process top tables\n\ntop2_all <- rbind(top_tables[[\"filter_mindet\"]], top_tables[[\"filter_bpca\"]], top_tables[[\"nofilter_mindet\"]], top_tables[[\"nofilter_bpca\"]])\n\n# process ggplot\n\ngp_all <- rbind(pea_tables[[\"filter_mindet\"]]$df, pea_tables[[\"filter_bpca\"]]$df, pea_tables[[\"nofilter_mindet\"]]$df, pea_tables[[\"nofilter_bpca\"]]$df)\n\nhcal_dea <- max ((length (unique (top2_all$Protein.IDs)) * 16.5 + 25 + 10 + 100), 500)\nhcal_pea <- max ((length (unique (gp_all$ID)) * 16.5 + 25 + 10 + 100), 500)\n#gp.pt <- ggplotly(p, tooltip = \"text\", height = hcal, source=\"DEAPlotSource\") %>% \n# layout (yaxis=(list(automargin = F)), margin=list (l=200))\n\n# ggplot(top2_sig, aes(y=Protein.IDs, x=logFC)) + \n# geom_point(aes(size=P.Value, color=AveExpr)) + \n# labs(y=\"Protein IDs\", x=\"logFC\", color=\"Average expression\", size=\"P-value\") +\n# theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n\np1 <- ggplot (data=top2_all, aes (x=setting, y=Protein.IDs)) +\n geom_point(aes (size=AveExpr, fill=logFC, color=-log10(P.Value), stroke=0.5, text=paste('Log fold change:', logFC, '
',\n 'Average expression:', AveExpr,'
',\n 't-statistic:', t,'
',\n 'P-value:',P.Value,'
',\n 'Negative log10-P-value:',-log10(P.Value),'
',\n 'B-statistic:',B,'
'))) +\n scale_fill_gradient2(limits=c(-max(abs(top2_sig$logFC)), max(abs(top2_sig$logFC))), low = \"#0199CC\", mid = \"white\", high = \"#FF3705\", midpoint = 0) +\n scale_color_gradient2(limits=c(0, max(abs(log10(top2_sig$P.Value)))), low = \"white\", mid = \"white\", high = \"black\", midpoint = 1) +\n theme_minimal () + xlab(\"Comparison\") + ylab(\"\") + \n theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n\ngp.pt.1 <- ggplotly(p1, tooltip = \"text\", height = hcal_dea) %>% \n layout (yaxis=(list(automargin = F)), margin=list (l=200))\n\nsaveWidget(gp.pt.1, \"dea_all.html\")\n\n# ggsave(save_path_dea)\n\n# ggplot(g1_filtered, aes(y=ID, x=rank)) + \n# geom_point(aes(size=pvalue, color=enrichmentScore)) + \n# labs(y=\"Protein IDs\", x=\"logFC\", color=\"Enrichment score\", size=\"P-value\") +\n# theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n\np2 <- ggplot (data=gp_all, aes (x=setting, y=ID, customdata=paste0 (gp_all$Pathway,\"%sep%\", gp_all$core_enrichment))) +\n geom_point(aes (size=setSize, fill=NES, color=-log10(pvalue), stroke=0.5, text=paste('Core_enrichment:', core_enrichment, '
',\n 'Comparison:', Group,'
',\n 'Setsize:', setSize,'
',\n 'NES:',NES,'
',\n 'pvalue:',pvalue,'
',\n 'rank:',rank,'
',\n 'leading_edge:',leading_edge,'
'))) +\n scale_fill_gradient2(limits=c(-max(abs(gp_all$NES)), max(abs(gp_all$NES))), low = \"#0199CC\", mid = \"white\", high = \"#FF3705\", midpoint = 0) +\n scale_color_gradient2(limits=c(0, max(abs(log10(gp_all$pvalue)))), low = \"white\", mid = \"white\", high = \"black\", midpoint = 1) +\n theme_minimal () + xlab(\"Comparison\") + ylab(\"\") + \n theme(axis.text.x=element_text(angle=45, hjust=1), axis.text.y=element_text(size = 8, angle = 0, hjust = 1, face = \"plain\"))\n\ngp.pt.2 <- ggplotly(p2, tooltip = \"text\", height = hcal_pea) %>% \n layout (yaxis=(list(automargin = F)), margin=list (l=200))\n\nsaveWidget(gp.pt.2, \"pea_all.html\")\n\n", "meta": {"hexsha": "dfd105be2f2fdd2b6c12112194b41b42b210383a", "size": 11133, "ext": "r", "lang": "R", "max_stars_repo_path": "main.r", "max_stars_repo_name": "nonname001/AD_vs_N_DEA", "max_stars_repo_head_hexsha": "fd741dde8518d84391c595921a84a15629a1d661", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "main.r", "max_issues_repo_name": "nonname001/AD_vs_N_DEA", "max_issues_repo_head_hexsha": "fd741dde8518d84391c595921a84a15629a1d661", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "main.r", "max_forks_repo_name": "nonname001/AD_vs_N_DEA", "max_forks_repo_head_hexsha": "fd741dde8518d84391c595921a84a15629a1d661", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-24T18:54:34.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-24T18:54:34.000Z", "avg_line_length": 59.5347593583, "max_line_length": 155, "alphanum_fraction": 0.532291386, "num_tokens": 3203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6791787121629466, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3475470245753706}} {"text": "#Comparing R2 density between Tedersoo out-of-sample (OOS) forecast to NEON and NEON cross-validation (CV).\nrm(list=ls())\nsource('paths_fall2019.r')\nsource('paths.r')\nsource('NEFI_functions/zero_truncated_density.r')\nsource('NEFI_functions/rsq_1.1.r')\nlibrary(RColorBrewer)\n\n#set figure out put path.----\noutput.path <- 'figures/Supp._Fig._1._cross.validation_r2.jpg'\n\n#Load ITS cross-validation data..----\n#Cross validation forecasts\ncv.val.core.ITS <- readRDS(core.CV_NEON_fcast.path)\ncv.val.plot.ITS <- readRDS(plot.CV_NEON_fcast.path)\n\n#Cross validation data (calibration and validation)\ncv.dat.core.ITS <- readRDS(core.CV_NEON_cal.val_data.path)\ncv.dat.plot.ITS <- readRDS(plot.CV_NEON_cal.val_data.path)\ncv.dat.core.ITS <- cv.dat.core.ITS$val$y.val\ncv.dat.plot.ITS <- cv.dat.plot.ITS$val$y.val\n\n\n#Load 16S cross-validation data.-----\n#Cross validation forecasts\ncv.val.core.16S <- readRDS(core.CV_NEON_fcast_16S.path)\ncv.val.plot.16S <- readRDS(plot.CV_NEON_fcast_16S.path)\n\n#Cross validation data (calibration and validation)\ncv.dat.core.16S <- readRDS(core.CV_NEON_cal.val_data_16S.path)\ncv.dat.plot.16S <- readRDS(plot.CV_NEON_cal.val_data_16S.path)\ncv.dat.core.16S <- cv.dat.core.16S$val$y.val\ncv.dat.plot.16S <- cv.dat.plot.16S$val$y.val\n\n# Create table to link deprecatedVialID and geneticSampleID\nmap <- readRDS(core_obs.path)[,c(\"geneticSampleID\",\"deprecatedVialID\")]\nmap$geneticSampleID <- gsub('-GEN','',map$geneticSampleID)\n#drop observations with no deprecated Vial ID.\nmap <- map[!(map$deprecatedVialID == ''),]\n\n#16S NEON Cross-validated stats at core and plot level.-----\n#core level.\ncore.list.16S <- list()\nfor(i in 1:length(cv.val.core.16S)){\n x <- cv.val.core.16S[[i]]$core.fit$mean\n y <- cv.dat.core.16S[[i]]$rel.abundances\n #updated y rownames.\n ref <- map[map$deprecatedVialID %in% rownames(y),]\n y <- y[rownames(y) %in% ref$deprecatedVialID,]\n ref <- ref[order(match(ref$deprecatedVialID, rownames(y))),]\n rownames(y) <- ref$geneticSampleID\n \n #match row and column names.\n y <- y[,order(match(colnames(y), colnames(x)))]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[rownames(y) %in% rownames(x),]\n y <- y[order(match(rownames(y), rownames(x))),]\n #Get R2 for each column.\n lev.stats <- list()\n for(j in 1:ncol(y)){\n #r square best fit.\n fit <- lm(y[,j] ~ x[,j])\n rsq <- summary(fit)$r.squared\n #r square 1:1.\n rss <- sum((x[,j] - y[,j]) ^ 2) ## residual sum of squares\n tss <- sum((y[,j] - mean(y[,j])) ^ 2) ## total sum of squares\n rsq1 <- 1 - rss/tss\n rsq1 <- rsq_1.1(y[,j], x[,j]) ## new rsq_1.1 function.\n if(rsq1 < 0){rsq1 <- 0}\n #RMSE.\n rmse <- sqrt(mean(fit$residuals^2))\n return <- c(rsq,rsq1,rmse)\n lev.stats[[j]] <- return\n }\n lev.stats <- do.call(rbind, lev.stats)\n colnames(lev.stats) <- c('rsq','rsq1','rmse')\n rownames(lev.stats) <- colnames(y)\n lev.stats <- lev.stats[-(rownames(lev.stats) %in% 'other'),]\n core.list.16S[[i]] <- lev.stats\n}\nnames(core.list.16S) <- names(cv.val.core.16S)\n\n#plot level.\nplot.list.16S <- list()\nfor(i in 1:length(cv.val.plot.16S)){\n x <- cv.val.plot.16S[[i]]$plot.fit$mean\n y <- cv.dat.plot.16S[[i]]$mean\n #match row and column names.\n y <- y[,order(match(colnames(y), colnames(x)))]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[rownames(y) %in% rownames(x),]\n y <- y[order(match(rownames(y), rownames(x))),]\n #Get R2 for each column.\n lev.stats <- list()\n for(j in 1:ncol(y)){\n #r square best fit.\n fit <- lm(y[,j] ~ x[,j])\n rsq <- summary(fit)$r.squared\n #r square 1:1.\n rss <- sum((x[,j] - y[,j]) ^ 2) ## residual sum of squares\n tss <- sum((y[,j] - mean(y[,j])) ^ 2) ## total sum of squares\n rsq1 <- 1 - rss/tss\n rsq1 <- rsq_1.1(y[,j], x[,j]) ## new rsq_1.1 function.\n if(rsq1 < 0){rsq1 <- 0}\n #RMSE.\n rmse <- sqrt(mean(fit$residuals^2))\n return <- c(rsq,rsq1,rmse)\n lev.stats[[j]] <- return\n }\n lev.stats <- do.call(rbind, lev.stats)\n colnames(lev.stats) <- c('rsq','rsq1','rmse')\n rownames(lev.stats) <- colnames(y)\n lev.stats <- lev.stats[-(rownames(lev.stats) %in% 'other'),]\n plot.list.16S[[i]] <- lev.stats\n}\nnames(plot.list.16S) <- names(cv.val.plot.16S)\n\n#16S collapse functional groups.----\nphylo <- c('phylum','class','order','family','genus')\nref <- core.list.16S[!(names(core.list.16S) %in% phylo)]\ncore.list.16S <- core.list.16S[names(core.list.16S) %in% phylo]\ncore.list.16S$functional <- do.call(rbind, ref)\ncore.all.16S <- data.frame(do.call(rbind, core.list.16S))\nref <- plot.list.16S[!(names(plot.list.16S) %in% phylo)]\nplot.list.16S <- plot.list.16S[names(plot.list.16S) %in% phylo]\nplot.list.16S$functional <- do.call(rbind, ref)\nplot.all.16S <- data.frame(do.call(rbind, plot.list.16S))\n\n#16S means by taxaonomic level.----\n#core scale.\ncore.lev.mu.16S <- list()\nfor(i in 1:length(core.list.16S)){\n z <- data.frame(core.list.16S[[i]])\n core.lev.mu.16S[[i]] <- mean(z$rsq1, na.rm = T)\n}\ncore.lev.mu.16S <- unlist(core.lev.mu.16S)\nnames(core.lev.mu.16S) <- names(core.list.16S)\nref.order <- c(length(core.lev.mu.16S), 1:(length(core.lev.mu.16S) - 1))\ncore.lev.mu.16S <- core.lev.mu.16S[ref.order]\n\n#plot scale.\nplot.lev.mu.16S <- list()\nfor(i in 1:length(plot.list.16S)){\n z <- data.frame(plot.list.16S[[i]])\n #make sure its in the core-level validation as well.\n z <- z[rownames(z) %in% rownames(core.all.16S),]\n plot.lev.mu.16S[[i]] <- mean(z$rsq1, na.rm = T)\n}\nplot.lev.mu.16S <- unlist(plot.lev.mu.16S)\nnames(plot.lev.mu.16S) <- names(plot.list.16S)\nref.order <- c(length(plot.lev.mu.16S), 1:(length(plot.lev.mu.16S) - 1))\nplot.lev.mu.16S <- plot.lev.mu.16S[ref.order]\n\n#put together.\nto.plot <- cbind(core.lev.mu, plot.lev.mu)\nto.plot.16S <- cbind(core.lev.mu.16S, plot.lev.mu.16S)\n\n#ITS NEON Cross-validated stats at core and plot level.-----\n#core level.\ncore.list.ITS <- list()\nfor(i in 1:length(cv.val.core.ITS)){\n x <- cv.val.core.ITS[[i]]$core.fit$mean\n y <- cv.dat.core.ITS[[i]]$rel.abundances\n #deal with trailing -GEN\n rownames(y) <- gsub('-GEN','', rownames(y))\n rownames(x) <- gsub('-GEN','', rownames(x)) \n #match row and column names.\n y <- y[,order(match(colnames(y), colnames(x)))]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[rownames(y) %in% rownames(x),]\n y <- y[order(match(rownames(y), rownames(x))),]\n #Get R2 for each column.\n lev.stats <- list()\n for(j in 1:ncol(y)){\n #r square best fit.\n fit <- lm(y[,j] ~ x[,j])\n rsq <- summary(fit)$r.squared\n #r square 1:1.\n rss <- sum((x[,j] - y[,j]) ^ 2) ## residual sum of squares\n tss <- sum((y[,j] - mean(y[,j])) ^ 2) ## total sum of squares\n rsq1 <- 1 - rss/tss\n rsq1 <- rsq_1.1(y[,j], x[,j]) ## new rsq_1.1 function.\n if(rsq1 < 0){rsq1 <- 0}\n #RMSE.\n rmse <- sqrt(mean(fit$residuals^2))\n return <- c(rsq,rsq1,rmse)\n lev.stats[[j]] <- return\n }\n lev.stats <- do.call(rbind, lev.stats)\n colnames(lev.stats) <- c('rsq','rsq1','rmse')\n rownames(lev.stats) <- colnames(y)\n lev.stats <- lev.stats[-(rownames(lev.stats) %in% 'other'),]\n core.list.ITS[[i]] <- lev.stats\n}\nnames(core.list.ITS) <- names(cv.val.core.ITS)\n\n#plot level.\nplot.list.ITS <- list()\nfor(i in 1:length(cv.val.plot.ITS)){\n x <- cv.val.plot.ITS[[i]]$plot.fit$mean\n y <- cv.dat.plot.ITS[[i]]$mean\n #deal with trailing -GEN\n rownames(y) <- gsub('-GEN','', rownames(y))\n rownames(x) <- gsub('-GEN','', rownames(x)) \n #match row and column names.\n y <- y[,order(match(colnames(y), colnames(x)))]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[rownames(y) %in% rownames(x),]\n y <- y[order(match(rownames(y), rownames(x))),]\n #Get R2 for each column.\n lev.stats <- list()\n for(j in 1:ncol(y)){\n #r square best fit.\n fit <- lm(y[,j] ~ x[,j])\n rsq <- summary(fit)$r.squared\n #r square 1:1.\n rss <- sum((x[,j] - y[,j]) ^ 2) ## residual sum of squares\n tss <- sum((y[,j] - mean(y[,j])) ^ 2) ## total sum of squares\n rsq1 <- 1 - rss/tss\n rsq1 <- rsq_1.1(y[,j], x[,j]) ## new rsq_1.1 function.\n if(rsq1 < 0){rsq1 <- 0}\n #RMSE.\n rmse <- sqrt(mean(fit$residuals^2))\n return <- c(rsq,rsq1,rmse)\n lev.stats[[j]] <- return\n }\n lev.stats <- do.call(rbind, lev.stats)\n colnames(lev.stats) <- c('rsq','rsq1','rmse')\n rownames(lev.stats) <- colnames(y)\n lev.stats <- lev.stats[-(rownames(lev.stats) %in% 'other'),]\n plot.list.ITS[[i]] <- lev.stats\n}\nnames(plot.list.ITS) <- names(cv.val.plot.ITS)\n\n\n#ITS means by taxaonomic level.----\n#core scale.\ncore.lev.mu.ITS <- list()\nfor(i in 1:length(core.list.ITS)){\n z <- data.frame(core.list.ITS[[i]])\n core.lev.mu.ITS[[i]] <- mean(z$rsq1, na.rm = T)\n}\ncore.lev.mu.ITS <- unlist(core.lev.mu.ITS)\nnames(core.lev.mu.ITS) <- names(core.list.ITS)\nnames(core.lev.mu.ITS)[length(core.lev.mu.ITS)] <- 'functional'\nref.order <- c(length(core.lev.mu.ITS), 1:(length(core.lev.mu.ITS) - 1))\ncore.lev.mu.ITS <- core.lev.mu.ITS[ref.order]\ncore.all.ITS <- data.frame(do.call(rbind, core.list.ITS))\n\n#plot scale.\nplot.lev.mu.ITS <- list()\nfor(i in 1:length(plot.list.ITS)){\n z <- data.frame(plot.list.ITS[[i]])\n #make sure its in the core-level validation as well.\n z <- z[rownames(z) %in% rownames(core.all.ITS),]\n plot.lev.mu.ITS[[i]] <- mean(z$rsq1, na.rm = T)\n}\nplot.lev.mu.ITS <- unlist(plot.lev.mu.ITS)\nnames(plot.lev.mu.ITS) <- names(plot.list.ITS)\nnames(plot.lev.mu.ITS)[length(plot.lev.mu.ITS)] <- 'functional'\nref.order <- c(length(plot.lev.mu.ITS), 1:(length(plot.lev.mu.ITS) - 1))\nplot.lev.mu.ITS <- plot.lev.mu.ITS[ref.order]\nplot.all.ITS <- data.frame(do.call(rbind, plot.list.ITS))\n\n#put together.\nto.plot.ITS <- cbind(core.lev.mu.ITS, plot.lev.mu.ITS)\n\n#jpeg save line.-----\njpeg(filename=output.path,width=8,height=5,units='in',res=300)\n\n#Begin plot: Global plot settings.-----\npar(mfrow = c(1,2),\n mar = c(4,4,2,2))\nlimx <- c(0,1)\ntrans <- 0.2 #shading transparency.\no.cex <- 1.3 #outer label size.\ny.cex <- 1.3\ncols <- brewer.pal(nrow(to.plot.16S) - 1,'Spectral')\ncols <- c(cols,'green')\n\n#16S by spatial and taxonomic scale.----\ny.check <- max(cbind(to.plot.16S, to.plot.ITS))\nlimy <- c(0, max(y.check)*1.1)\nx <- c(1:2)\nlimx <- c(min(x)*0.95, max(x)*1.05)\nplot(to.plot.16S[1,] ~ x, xlim = limx, ylim = limy, bty = 'l', col = 'black', bg = cols[1], \n pch = 21, cex = 2.5, xlab= NA, ylab = NA, xaxt = 'n')\nlines(x, to.plot.16S[1,], lty = 1, col = cols[1], lwd = 1.5)\nfor(i in 2:nrow(to.plot.16S)){\n points(to.plot.16S[i,] ~ x, pch = 21, cex = 2.5, col = 'black', bg = cols[i])\n lines(x, to.plot.16S[i,], lty = 1, lwd = 1.5, col = cols[i])\n}\naxis(1, at = c(1,2,3), labels = F)\nlab <- c('core','plot')\ntext(x=x+0.05, y = limy[1] - limy[2]*0.1, labels= lab, srt=45, adj=1, xpd=TRUE, cex = o.cex)\nrsq.1.lab <- bquote({'Cross-Validated R'^2} [1:1])\nmtext(rsq.1.lab, side = 2, line = 2.5, cex = y.cex)\n#mtext(expression(paste(\"Cross-Validated R\"^\"2\")), side = 2, line = 2.5, cex = y.cex)\nleg.lab <- rownames(to.plot.16S)\nlegend('bottomright',leg.lab, pch = 21, col = 'black', pt.bg = cols, ncol = 2, bty= 'n')\nmtext('(a)', side = 3, adj = 0.98, line = -1)\nmtext('Bacteria', side = 3, adj = 0.03, line = -1, cex = o.cex)\n\n#ITS by spatial and taxonomic scale.----\n#limy <- c(0, max(to.plot.ITS)*1.1)\nx <- c(1:2)\nlimx <- c(min(x)*0.95, max(x)*1.05)\nplot(to.plot.ITS[1,] ~ x, xlim = limx, ylim = limy, bty = 'l', col = 'black', bg = cols[1], \n pch = 21, cex = 2.5, xlab= NA, ylab = NA, xaxt = 'n')\nlines(x, to.plot.ITS[1,], lty = 1, col = cols[1], lwd = 1.5)\nfor(i in 2:nrow(to.plot.ITS)){\n points(to.plot.ITS[i,] ~ x, pch = 21, cex = 2.5, col = 'black', bg = cols[i])\n lines(x, to.plot.ITS[i,], lty = 1, lwd = 1.5, col = cols[i])\n}\naxis(1, at = c(1,2,3), labels = F)\nlab <- c('core','plot')\ntext(x=x+0.05, y = limy[1] - limy[2]*0.1, labels= lab, srt=45, adj=1, xpd=TRUE, cex = o.cex)\nrsq.1.lab <- bquote({'Cross-Validated R'^2} [1:1])\nmtext(rsq.1.lab, side = 2, line = 2.5, cex = y.cex)\n#mtext(expression(paste(\"Cross-Validated R\"^\"2\")), side = 2, line = 2.5, cex = y.cex)\nleg.lab <- rownames(to.plot.ITS)\n#legend('topleft',leg.lab, pch = 21, col = 'black', pt.bg = cols, ncol = 1, bty= 'n')\nmtext('(b)', side = 3, adj = 0.98, line = -1)\nmtext('Fungi', side = 3, adj = 0.03, line = -1, cex = o.cex)\n\n#end plot.-----\ndev.off()\n", "meta": {"hexsha": "3c4985bd93533c5ff2ec6732350b29301e16793c", "size": 12225, "ext": "r", "lang": "R", "max_stars_repo_path": "figure_scripts/Supp._Fig._1._cross.val_space_taxonomic.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "figure_scripts/Supp._Fig._1._cross.val_space_taxonomic.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "figure_scripts/Supp._Fig._1._cross.val_space_taxonomic.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 37.2713414634, "max_line_length": 107, "alphanum_fraction": 0.6148875256, "num_tokens": 4567, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6791787121629465, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.34754702457537057}} {"text": "./tests.verify: test performed on 2004.03.31 \n\n\n#### Test: ./tests.verify running ./make_F77_app.sh \nwe make a test run:\n call gridloop1 was run 50 times!\n in f2 0.\n in f2 0.5\n in f2 1.\n in f2 1.\n in f2 1.5\n in f2 2.\n in f2 2.\n in f2 2.5\n in f2 3.\nvalue at ( 0.000, 0.000) = a( 0, 0) = 0.00000E+00\nvalue at ( 0.500, 0.000) = a( 1, 0) = 0.50000E+00\nvalue at ( 1.000, 0.000) = a( 2, 0) = 0.10000E+01\nvalue at ( 0.000, 0.500) = a( 0, 1) = 0.10000E+01\nvalue at ( 0.500, 0.500) = a( 1, 1) = 0.15000E+01\nvalue at ( 1.000, 0.500) = a( 2, 1) = 0.20000E+01\nvalue at ( 0.000, 1.000) = a( 0, 2) = 0.20000E+01\nvalue at ( 0.500, 1.000) = a( 1, 2) = 0.25000E+01\nvalue at ( 1.000, 1.000) = a( 2, 2) = 0.30000E+01\ncmd: ./tmp.app; elapsed=6.910000.2 cpu=6.860000.2\nCPU time of ./make_F77_app.sh: 7.2 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running ./tmp.app \n call gridloop1 was run 50 times!\n in f2 0.\n in f2 0.5\n in f2 1.\n in f2 1.\n in f2 1.5\n in f2 2.\n in f2 2.\n in f2 2.5\n in f2 3.\nvalue at ( 0.000, 0.000) = a( 0, 0) = 0.00000E+00\nvalue at ( 0.500, 0.000) = a( 1, 0) = 0.50000E+00\nvalue at ( 1.000, 0.000) = a( 2, 0) = 0.10000E+01\nvalue at ( 0.000, 0.500) = a( 0, 1) = 0.10000E+01\nvalue at ( 0.500, 0.500) = a( 1, 1) = 0.15000E+01\nvalue at ( 1.000, 0.500) = a( 2, 1) = 0.20000E+01\nvalue at ( 0.000, 1.000) = a( 0, 2) = 0.20000E+01\nvalue at ( 0.500, 1.000) = a( 1, 2) = 0.25000E+01\nvalue at ( 1.000, 1.000) = a( 2, 2) = 0.30000E+01\nCPU time of ./tmp.app: 6.9 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running ./make_module_1.sh \nrunning build\nrunning run_f2py\ncreating tmp1\nReading fortran codes...\n\tReading file 'gridloop.f'\nLine #6 in gridloop.f:\" external f2, myfunc\"\n\tanalyzeline: ignoring program arguments\nLine #6 in gridloop.f:\" external f2, myfunc\"\n\tanalyzeline: ignoring program arguments\nPost-processing...\n\tBlock: ext_gridloop\n\t\t\tBlock: test\nIn: :ext_gridloop:gridloop.f:test\nanalyzevars:replacing parameter 'nmax' in 'nmax*nmax' (dimension of 'f') with '1100'\n\t\t\tBlock: gridloop1\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop2\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop3\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop4\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop_vec\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop_vec2\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop2_str\n\t\t\tBlock: gridloop1_v1\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop1_v2\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop1_v3\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop1_v4\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: gridloop1_v5\n\t\t\t\t\tBlock: func1\n\t\t\tBlock: transpose2dim\n\t\t\tBlock: dump\n\t\t\tBlock: change\n\t\t\tBlock: gridloop2_fixedfunc1\n\t\t\tBlock: myfunc\n\t\t\tBlock: f2\nSaving signatures to file \"./tmp1/ext_gridloop.pyf\"\nReading fortran codes...\n\tReading file 'tmp1/ext_gridloop.pyf'\nPost-processing...\n\tBlock: gridloop1__user__routines\n\t\tBlock: gridloop1_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop2__user__routines\n\t\tBlock: gridloop2_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop3__user__routines\n\t\tBlock: gridloop3_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop4__user__routines\n\t\tBlock: gridloop4_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop_vec__user__routines\n\t\tBlock: gridloop_vec_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop_vec2__user__routines\n\t\tBlock: gridloop_vec2_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop1_v1__user__routines\n\t\tBlock: gridloop1_v1_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop1_v2__user__routines\n\t\tBlock: gridloop1_v2_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop1_v3__user__routines\n\t\tBlock: gridloop1_v3_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop1_v4__user__routines\n\t\tBlock: gridloop1_v4_user_interface\n\t\t\tBlock: func1\n\tBlock: gridloop1_v5__user__routines\n\t\tBlock: gridloop1_v5_user_interface\n\t\t\tBlock: func1\n\tBlock: ext_gridloop\n\t\t\tBlock: gridloop1\n\t\t\tBlock: gridloop2\n\t\t\tBlock: gridloop3\n\t\t\tBlock: gridloop4\n\t\t\tBlock: gridloop_vec\n\t\t\tBlock: gridloop_vec2\n\t\t\tBlock: gridloop2_str\n\t\t\tBlock: gridloop1_v1\n\t\t\tBlock: gridloop1_v2\n\t\t\tBlock: gridloop1_v3\n\t\t\tBlock: gridloop1_v4\n\t\t\tBlock: gridloop1_v5\n\t\t\tBlock: transpose2dim\n\t\t\tBlock: dump\n\t\t\tBlock: change\n\t\t\tBlock: gridloop2_fixedfunc1\n\t\t\tBlock: myfunc\n\t\t\tBlock: f2\nBuilding modules...\n\tConstructing call-back function \"cb_func1_in_gridloop1__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop2__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop3__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop4__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop_vec__user__routines\"\n\t def func1(a,xcoor,ycoor,[nx,ny]): return a\n\tConstructing call-back function \"cb_func1_in_gridloop_vec2__user__routines\"\n\t def func1(a,[nx,ny]): return a\n\tConstructing call-back function \"cb_func1_in_gridloop1_v1__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop1_v2__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop1_v3__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop1_v4__user__routines\"\n\t def func1(x,y): return func1\n\tConstructing call-back function \"cb_func1_in_gridloop1_v5__user__routines\"\n\t def func1(x,y): return func1\n\tBuilding module \"ext_gridloop\"...\n\t\tConstructing wrapper function \"gridloop1\"...\n\t\t gridloop1(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop2\"...\n\t\t a = gridloop2(xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop3\"...\n\t\t a = gridloop3(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop4\"...\n\t\t a = gridloop4(a,xcoor,ycoor,func1,[nx,ny,overwrite_a,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop_vec\"...\n\t\t a = gridloop_vec(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop_vec2\"...\n\t\t a = gridloop_vec2(a,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop2_str\"...\n\t\t a = gridloop2_str(xcoor,ycoor,func_str,[nx,ny])\n\t\tConstructing wrapper function \"gridloop1_v1\"...\n\t\t gridloop1_v1(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop1_v2\"...\n\t\t a = gridloop1_v2(xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop1_v3\"...\n\t\t gridloop1_v3(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop1_v4\"...\n\t\t gridloop1_v4(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"gridloop1_v5\"...\n\t\t gridloop1_v5(a,xcoor,ycoor,func1,[nx,ny,func1_extra_args])\n\t\tConstructing wrapper function \"transpose2dim\"...\n\t\t transpose2dim(a,at,[nx,ny])\n\t\tConstructing wrapper function \"dump\"...\n\t\t dump(a,xcoor,ycoor,[nx,ny])\n\t\tConstructing wrapper function \"change\"...\n\t\t change(a,xcoor,ycoor,[nx,ny])\n\t\tConstructing wrapper function \"gridloop2_fixedfunc1\"...\n\t\t a = gridloop2_fixedfunc1(xcoor,ycoor,[nx,ny])\n\t\tCreating wrapper for Fortran function \"myfunc\"(\"myfunc\")...\n\t\tConstructing wrapper function \"myfunc\"...\n\t\t myfunc = myfunc(x,y)\n\t\tCreating wrapper for Fortran function \"f2\"(\"f2\")...\n\t\tConstructing wrapper function \"f2\"...\n\t\t f2 = f2(x,y)\n\tWrote C/API module \"ext_gridloop\" to file \"tmp1/ext_gridloopmodule.c\"\n\tFortran 77 wrappers are saved to \"tmp1/ext_gridloop-f2pywrappers.f\"\nrunning build_flib\nrunning build_ext\nbuilding 'ext_gridloop' extension\ncreating tmp1/home\ncreating tmp1/home/hpl\ncreating tmp1/home/hpl/install\ncreating tmp1/home/hpl/install/lib\ncreating tmp1/home/hpl/install/lib/python\ncreating tmp1/home/hpl/install/lib/python/f2py2e\ncreating tmp1/home/hpl/install/lib/python/f2py2e/src\ncreating tmp1/tmp1\ngcc -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC -DF2PY_REPORT_ON_ARRAY_COPY=1 -I/home/hpl/install/lib/python/f2py2e/src -I/usr/include/python2.3 -c /home/hpl/install/lib/python/f2py2e/src/fortranobject.c -o tmp1/home/hpl/install/lib/python/f2py2e/src/fortranobject.o\ngcc -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC -DF2PY_REPORT_ON_ARRAY_COPY=1 -I/home/hpl/install/lib/python/f2py2e/src -I/usr/include/python2.3 -c tmp1/ext_gridloopmodule.c -o tmp1/tmp1/ext_gridloopmodule.o\ng77 -shared tmp1/home/hpl/install/lib/python/f2py2e/src/fortranobject.o tmp1/tmp1/ext_gridloopmodule.o -Ltmp1 -lext_gridloop_f2py -lg2c-pic -o ./ext_gridloop.so\n['__doc__', '__file__', '__name__', '__version__', 'as_column_major_storage', 'change', 'dump', 'f2', 'gridloop1', 'gridloop1_v1', 'gridloop1_v2', 'gridloop1_v3', 'gridloop1_v4', 'gridloop1_v5', 'gridloop2', 'gridloop2_fixedfunc1', 'gridloop2_str', 'gridloop3', 'gridloop4', 'gridloop_vec', 'gridloop_vec2', 'has_column_major_storage', 'myfunc', 'transpose2dim']\nThis module 'ext_gridloop' is auto-generated with f2py (version:2.39.235_1649).\nFunctions:\n gridloop1(a,xcoor,ycoor,func1,nx=len(xcoor),ny=len(ycoor),func1_extra_args=())\n a = gridloop2(xcoor,ycoor,func1,nx=len(xcoor),ny=len(ycoor),func1_extra_args=())\n a = gridloop3(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n a = gridloop4(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),overwrite_a=1,func1_extra_args=())\n a = gridloop_vec(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n a = gridloop_vec2(a,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n a = gridloop2_str(xcoor,ycoor,func_str,nx=len(xcoor),ny=len(ycoor))\n gridloop1_v1(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n a = gridloop1_v2(xcoor,ycoor,func1,nx=len(xcoor),ny=len(ycoor),func1_extra_args=())\n gridloop1_v3(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n gridloop1_v4(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n gridloop1_v5(a,xcoor,ycoor,func1,nx=shape(a,0),ny=shape(a,1),func1_extra_args=())\n transpose2dim(a,at,nx=shape(a,0),ny=shape(a,1))\n dump(a,xcoor,ycoor,nx=shape(a,0),ny=shape(a,1))\n change(a,xcoor,ycoor,nx=shape(a,0),ny=shape(a,1))\n a = gridloop2_fixedfunc1(xcoor,ycoor,nx=len(xcoor),ny=len(ycoor))\n myfunc = myfunc(x,y)\n f2 = f2(x,y)\n.\nCPU time of ./make_module_1.sh: 9.1 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running ../Grid2Deff.py verify1\nx+2*y = [[ 0. 2. ]\n [ 0.5 2.5]\n [ 1. 3. ]]\nx+2*y = 0.0\nx+2*y = 0.5\nx+2*y = 1.0\nx+2*y = 2.0\nx+2*y = 2.5\nx+2*y = 3.0\nf computed by external gridloop1 function:\n[[ 0. 2. ]\n [ 0.5 2.5]\n [ 1. 3. ]]\nf is correct\nx+2*y = 0.0\nx+2*y = 0.5\nx+2*y = 1.0\nx+2*y = 2.0\nx+2*y = 2.5\nx+2*y = 3.0\nf computed by external gridloop2 function:\n[[ 0. 2. ]\n [ 0.5 2.5]\n [ 1. 3. ]]\nf is correct\narray seen from Python:\nvalue at (0,0) \t = a[0,0] = 0\nvalue at (0,1) \t = a[0,1] = 2\nvalue at (0.5,0) \t = a[1,0] = 0.5\nvalue at (0.5,1) \t = a[1,1] = 2.5\nvalue at (1,0) \t = a[2,0] = 1\nvalue at (1,1) \t = a[2,1] = 3\narray seen from Fortran (transposed, but right values):\nvalue at ( 0.000, 0.000) = a( 0, 0) = 0.00000E+00\nvalue at ( 0.500, 0.000) = a( 1, 0) = 0.50000E+00\nvalue at ( 1.000, 0.000) = a( 2, 0) = 0.10000E+01\nvalue at ( 0.000, 1.000) = a( 0, 1) = 0.20000E+01\nvalue at ( 0.500, 1.000) = a( 1, 1) = 0.25000E+01\nvalue at ( 1.000, 1.000) = a( 2, 1) = 0.30000E+01\nCPU time of ../Grid2Deff.py: 0.3 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running ../Grid2Deff.py verify2\nrunning build\nrunning run_f2py\ncreating tmp2\nReading fortran codes...\n\tReading file '_cb.f'\nPost-processing...\n\tBlock: callback\n\t\t\tBlock: fcb\n\t\t\tBlock: gridloop2_fcb\nSaving signatures to file \"./tmp2/callback.pyf\"\nReading fortran codes...\n\tReading file 'tmp2/callback.pyf'\nPost-processing...\n\tBlock: callback\n\t\t\tBlock: fcb\n\t\t\tBlock: gridloop2_fcb\nBuilding modules...\n\tBuilding module \"callback\"...\n\t\tCreating wrapper for Fortran function \"fcb\"(\"fcb\")...\n\t\tConstructing wrapper function \"fcb\"...\n\t\t fcb = fcb(x,y)\n\t\tConstructing wrapper function \"gridloop2_fcb\"...\n\t\t a = gridloop2_fcb(xcoor,ycoor,[nx,ny])\n\tWrote C/API module \"callback\" to file \"tmp2/callbackmodule.c\"\n\tFortran 77 wrappers are saved to \"tmp2/callback-f2pywrappers.f\"\nrunning build_flib\nrunning build_ext\nbuilding 'callback' extension\ncreating tmp2/home\ncreating tmp2/home/hpl\ncreating tmp2/home/hpl/install\ncreating tmp2/home/hpl/install/lib\ncreating tmp2/home/hpl/install/lib/python\ncreating tmp2/home/hpl/install/lib/python/f2py2e\ncreating tmp2/home/hpl/install/lib/python/f2py2e/src\ncreating tmp2/tmp2\ngcc -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC -DF2PY_REPORT_ON_ARRAY_COPY=1 -I/home/hpl/install/lib/python/f2py2e/src -I/usr/include/python2.3 -c tmp2/callbackmodule.c -o tmp2/tmp2/callbackmodule.o\ngcc -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC -DF2PY_REPORT_ON_ARRAY_COPY=1 -I/home/hpl/install/lib/python/f2py2e/src -I/usr/include/python2.3 -c /home/hpl/install/lib/python/f2py2e/src/fortranobject.c -o tmp2/home/hpl/install/lib/python/f2py2e/src/fortranobject.o\ng77 -shared tmp2/home/hpl/install/lib/python/f2py2e/src/fortranobject.o tmp2/tmp2/callbackmodule.o ./ext_gridloop.so -Ltmp2 -lcallback_f2py -lg2c-pic -o ./callback.so\nrunning build\nrunning run_f2py\nReading fortran codes...\n\tReading file '_cb.f'\nPost-processing...\n\tBlock: ext_gridloop2\n\t\t\tBlock: gridloop2\nSaving signatures to file \"./tmp1/ext_gridloop2.pyf\"\nReading fortran codes...\n\tReading file 'tmp1/ext_gridloop2.pyf'\nPost-processing...\n\tBlock: ext_gridloop2\n\t\t\tBlock: gridloop2\nBuilding modules...\n\tBuilding module \"ext_gridloop2\"...\n\t\tConstructing wrapper function \"gridloop2\"...\n\t\t a = gridloop2(xcoor,ycoor,[nx,ny])\n\tWrote C/API module \"ext_gridloop2\" to file \"tmp1/ext_gridloop2module.c\"\nrunning build_flib\nrunning build_ext\nbuilding 'ext_gridloop2' extension\ngcc -pthread -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -fPIC -DF2PY_REPORT_ON_ARRAY_COPY=1 -I/home/hpl/install/lib/python/f2py2e/src -I/usr/include/python2.3 -c tmp1/ext_gridloop2module.c -o tmp1/tmp1/ext_gridloop2module.o\ng77 -shared tmp1/home/hpl/install/lib/python/f2py2e/src/fortranobject.o tmp1/tmp1/ext_gridloop2module.o -Ltmp1 -lext_gridloop2_f2py -lg2c-pic -o ./ext_gridloop2.so\nmyfuncf77, a= [[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\ng.ext_gridloop_vec(myfuncf77): a=\n[[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\ng.ext_gridloop_vec2(myfuncf772): a=\n[[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\n in f2 0.\n in f2 0.333333333\n in f2 0.666666667\n in f2 1.\n in f2 0.666666667\n in f2 1.\n in f2 1.33333333\n in f2 1.66666667\n in f2 1.33333333\n in f2 1.66666667\n in f2 2.\n in f2 2.33333333\n in f2 2.\n in f2 2.33333333\n in f2 2.66666667\n in f2 3.\ng.ext_gridloop2_str('f2'): a=\n[[ 0. 0.66666667 1.33333333 2. ]\n [ 0.33333333 1. 1.66666667 2.33333333]\n [ 0.66666667 1.33333333 2. 2.66666667]\n [ 1. 1.66666667 2.33333333 3. ]]\ng.ext_gridloop_str('myfunc'): a=\n[[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\ng.gridloop2_fcb: a=\n[[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\ncontents of callback module: ['__doc__', '__file__', '__name__', '__version__', 'as_column_major_storage', 'fcb', 'gridloop2_fcb', 'has_column_major_storage']\ng.gridloop2_v2: a=\n[[ 0. 0. 0. 0. ]\n [ 2.66666667 2.7775493 2.88706441 2.99386136]\n [ 5.33333333 5.55373108 5.7632897 5.95170314]\n [ 8. 8.3271947 8.6183698 8.84147098]]\nCPU time of ../Grid2Deff.py: 4.1 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running ../Grid2Deff.py exceptions1\next_gridloop.error failed in converting 1st argument `a' of ext_gridloop.gridloop1 to C/Fortran array\nexceptions.TypeError ext_gridloop.gridloop2() argument 3 must be function, not str\nexceptions.TypeError ext_gridloop.gridloop2() argument 3 must be function, not str\nexceptions.TypeError ext_gridloop.gridloop2() argument 3 must be function, not str\nCPU time of ../Grid2Deff.py: 0.3 seconds on hplx30 i686, Linux\n\n\n\n----- appending file tmp1/ext_gridloop.pyf ------\n! -*- f90 -*-\npython module gridloop1__user__routines \n interface gridloop1_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_user_interface\nend python module gridloop1__user__routines\npython module gridloop2__user__routines \n interface gridloop2_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop2:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop2_user_interface\nend python module gridloop2__user__routines\npython module gridloop3__user__routines \n interface gridloop3_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop3:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop3_user_interface\nend python module gridloop3__user__routines\npython module gridloop4__user__routines \n interface gridloop4_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop4:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop4_user_interface\nend python module gridloop4__user__routines\npython module gridloop_vec__user__routines \n interface gridloop_vec_user_interface \n subroutine func1(a,xcoor,ycoor,nx,ny) ! in :ext_gridloop:gridloop.f:gridloop_vec:unknown_interface\n real*8 dimension(nx,ny),intent(in,out) :: a\n real*8 dimension(nx),intent(in),depend(nx) :: xcoor\n real*8 dimension(ny),intent(in),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n end subroutine func1\n end interface gridloop_vec_user_interface\nend python module gridloop_vec__user__routines\npython module gridloop_vec2__user__routines \n interface gridloop_vec2_user_interface \n subroutine func1(a,nx,ny) ! in :ext_gridloop:gridloop.f:gridloop_vec2:unknown_interface\n real*8 dimension(nx,ny),intent(in,out) :: a\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n end subroutine func1\n end interface gridloop_vec2_user_interface\nend python module gridloop_vec2__user__routines\npython module gridloop1_v1__user__routines \n interface gridloop1_v1_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1_v1:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_v1_user_interface\nend python module gridloop1_v1__user__routines\npython module gridloop1_v2__user__routines \n interface gridloop1_v2_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1_v2:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_v2_user_interface\nend python module gridloop1_v2__user__routines\npython module gridloop1_v3__user__routines \n interface gridloop1_v3_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1_v3:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_v3_user_interface\nend python module gridloop1_v3__user__routines\npython module gridloop1_v4__user__routines \n interface gridloop1_v4_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1_v4:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_v4_user_interface\nend python module gridloop1_v4__user__routines\npython module gridloop1_v5__user__routines \n interface gridloop1_v5_user_interface \n function func1(x,y) ! in :ext_gridloop:gridloop.f:gridloop1_v5:unknown_interface\n real*8 :: x\n real*8 :: y\n real*8 :: func1\n end function func1\n end interface gridloop1_v5_user_interface\nend python module gridloop1_v5__user__routines\npython module ext_gridloop ! in \n interface ! in :ext_gridloop\n subroutine gridloop1(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop1__user__routines\n real*8 dimension(nx,ny),intent(inout),depend(nx,ny) :: a\n real*8 dimension(nx),intent(in) :: xcoor\n real*8 dimension(ny),intent(in) :: ycoor\n integer optional,check(len(xcoor)>=nx),depend(xcoor) :: nx=len(xcoor)\n integer optional,check(len(ycoor)>=ny),depend(ycoor) :: ny=len(ycoor)\n external func1\n end subroutine gridloop1\n subroutine gridloop2(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop2__user__routines\n real*8 dimension(nx,ny),intent(out),depend(nx,ny) :: a\n real*8 dimension(nx),intent(in) :: xcoor\n real*8 dimension(ny),intent(in) :: ycoor\n integer optional,check(len(xcoor)>=nx),depend(xcoor) :: nx=len(xcoor)\n integer optional,check(len(ycoor)>=ny),depend(ycoor) :: ny=len(ycoor)\n external func1\n end subroutine gridloop2\n subroutine gridloop3(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop3__user__routines\n real*8 dimension(nx,ny),intent(in,out) :: a\n real*8 dimension(nx),intent(in),depend(nx) :: xcoor\n real*8 dimension(ny),intent(in),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop3\n subroutine gridloop4(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop4__user__routines\n real*8 dimension(nx,ny),intent(in,out,overwrite) :: a\n real*8 dimension(nx),intent(in),depend(nx) :: xcoor\n real*8 dimension(ny),intent(in),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop4\n subroutine gridloop_vec(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop_vec__user__routines\n real*8 dimension(nx,ny),intent(in,out) :: a\n real*8 dimension(nx),intent(in),depend(nx) :: xcoor\n real*8 dimension(ny),intent(in),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop_vec\n subroutine gridloop_vec2(a,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop_vec2__user__routines\n real*8 dimension(nx,ny),intent(in,out) :: a\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop_vec2\n subroutine gridloop2_str(a,xcoor,ycoor,nx,ny,func_str) ! in :ext_gridloop:gridloop.f\n real*8 dimension(nx,ny),intent(out),depend(nx,ny) :: a\n real*8 dimension(nx),intent(in) :: xcoor\n real*8 dimension(ny),intent(in) :: ycoor\n integer optional,check(len(xcoor)>=nx),depend(xcoor) :: nx=len(xcoor)\n integer optional,check(len(ycoor)>=ny),depend(ycoor) :: ny=len(ycoor)\n character*(*) :: func_str\n end subroutine gridloop2_str\n subroutine gridloop1_v1(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop1_v1__user__routines\n real*8 dimension(nx,ny) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop1_v1\n subroutine gridloop1_v2(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop1_v2__user__routines\n real*8 dimension(nx,ny),intent(out),depend(nx,ny) :: a\n real*8 dimension(nx),intent(in) :: xcoor\n real*8 dimension(ny),intent(in) :: ycoor\n integer optional,check(len(xcoor)>=nx),depend(xcoor) :: nx=len(xcoor)\n integer optional,check(len(ycoor)>=ny),depend(ycoor) :: ny=len(ycoor)\n external func1\n end subroutine gridloop1_v2\n subroutine gridloop1_v3(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop1_v3__user__routines\n real*8 dimension(nx,ny),intent(inout) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop1_v3\n subroutine gridloop1_v4(a,xcoor,ycoor,nx,ny,func1) ! in :ext_gridloop:gridloop.f\n use gridloop1_v4__user__routines\n real*8 dimension(nx,ny),intent(inout,c) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n end subroutine gridloop1_v4\n subroutine gridloop1_v5(a,xcoor,ycoor,nx,ny,func1,at) ! in :ext_gridloop:gridloop.f\n use gridloop1_v5__user__routines\n real*8 dimension(nx,ny),intent(inout,c) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n external func1\n real*8 dimension(nx,ny),intent(in,hide),depend(nx,ny) :: at\n end subroutine gridloop1_v5\n subroutine transpose2dim(a,at,nx,ny) ! in :ext_gridloop:gridloop.f\n real*8 dimension(nx,ny) :: a\n real*8 dimension(ny,nx),depend(ny,nx) :: at\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n end subroutine transpose2dim\n subroutine dump(a,xcoor,ycoor,nx,ny) ! in :ext_gridloop:gridloop.f\n real*8 dimension(nx,ny) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n end subroutine dump\n subroutine change(a,xcoor,ycoor,nx,ny) ! in :ext_gridloop:gridloop.f\n real*8 dimension(nx,ny) :: a\n real*8 dimension(nx),depend(nx) :: xcoor\n real*8 dimension(ny),depend(ny) :: ycoor\n integer optional,check(shape(a,0)==nx),depend(a) :: nx=shape(a,0)\n integer optional,check(shape(a,1)==ny),depend(a) :: ny=shape(a,1)\n end subroutine change\n subroutine gridloop2_fixedfunc1(a,xcoor,ycoor,nx,ny) ! in :ext_gridloop:gridloop.f\n real*8 dimension(nx,ny),intent(out),depend(nx,ny) :: a\n real*8 dimension(nx),intent(in) :: xcoor\n real*8 dimension(ny),intent(in) :: ycoor\n integer optional,check(len(xcoor)>=nx),depend(xcoor) :: nx=len(xcoor)\n integer optional,check(len(ycoor)>=ny),depend(ycoor) :: ny=len(ycoor)\n end subroutine gridloop2_fixedfunc1\n function myfunc(x,y) ! in :ext_gridloop:gridloop.f\n real*8 :: x\n real*8 :: y\n real*8 :: myfunc\n end function myfunc\n function f2(x,y) ! in :ext_gridloop:gridloop.f\n real*8 :: x\n real*8 :: y\n real*8 :: f2\n end function f2\n end interface \nend python module ext_gridloop\n\n! This file was auto-generated with f2py (version:2.39.235_1649).\n! See http://cens.ioc.ee/projects/f2py2e/\n", "meta": {"hexsha": "d02db64c5bd260603b9c36371aeb43ea395bd655", "size": 29782, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/mixed/Grid2D/F77/tests.r", "max_stars_repo_name": "sniemi/SamPy", "max_stars_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2016-05-28T14:12:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-22T10:23:12.000Z", "max_issues_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/mixed/Grid2D/F77/tests.r", "max_issues_repo_name": "sniemi/SamPy", "max_issues_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/mixed/Grid2D/F77/tests.r", "max_forks_repo_name": "sniemi/SamPy", "max_forks_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-07-13T10:04:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-22T10:23:23.000Z", "avg_line_length": 43.9911373708, "max_line_length": 362, "alphanum_fraction": 0.6764488617, "num_tokens": 10178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6791787121629465, "lm_q1q2_score": 0.34754702457537057}} {"text": "library(stringr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\n\ntran_care <- read.csv(\"Trans-Care-by-state-aggragate.csv\",stringsAsFactors = FALSE)\n#2020,12,31\n#2019,12,31\n#2018,12,31\n#2017,12,31\n#2016-12-31\n#2015-12-31\ntwenty_15_data <- tran_care %>%\n filter(Date == \"2015-12-30\")\n\ntwenty_16_data <- tran_care %>%\n filter(Date == \"2016-12-30\")\n\ntwenty_17_data <- tran_care %>%\n filter(Date == \"2017-12-30\")\n\ntwenty_18_data <- tran_care %>%\n filter(Date == \"2018-12-30\")\n\ntwenty_19_data <- tran_care %>%\n filter(Date == \"2019-12-30\")\n\ntwenty_20_data <- tran_care %>%\n filter(Date == \"2020-12-30\")\n#example 2020 data frame to plot\nlong_data_set <- pivot_longer(twenty_15_data,cols = -Date)\nlong_data_set <- long_data_set_20 %>%\n select(name,value)\n\nggplot(long_data_set,aes(x = name, y = value)) +\n geom_segment(aes(x = name, xend = name,y = 0, yend = value, color = name)) +\n geom_point() +\n labs (\n color = \"States\"\n )\n \n\n", "meta": {"hexsha": "b0b00b51010a0aa6ae94235a34ceccb618ad80e1", "size": 981, "ext": "r", "lang": "R", "max_stars_repo_path": "source/transcarebystate.r", "max_stars_repo_name": "info-201a-wi22/final-project-starter-minsuh1004", "max_stars_repo_head_hexsha": "c6783d08b92206327c7881f6d1d92dcc7ef7a02e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "source/transcarebystate.r", "max_issues_repo_name": "info-201a-wi22/final-project-starter-minsuh1004", "max_issues_repo_head_hexsha": "c6783d08b92206327c7881f6d1d92dcc7ef7a02e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/transcarebystate.r", "max_forks_repo_name": "info-201a-wi22/final-project-starter-minsuh1004", "max_forks_repo_head_hexsha": "c6783d08b92206327c7881f6d1d92dcc7ef7a02e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.8139534884, "max_line_length": 83, "alphanum_fraction": 0.6462793068, "num_tokens": 328, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.34753584748593885}} {"text": "#!/usr/bin/env Rscript\n\n# Command line argument processing\nargs <- commandArgs(trailingOnly=TRUE)\n\n# R location\nrlocation <- NULL\nif(substr(args[1], 0, 10) == 'rlocation='){\n rlocation <- substr(args[1], 11, nchar(args[1]))\n args <- args[-1]\n}\n\nif (length(args) < 3) {\n stop(\"Usage: edgeR_heatmap_MDS.r (more bam files optional)\", call.=FALSE)\n}\n\n# Load / install required packages\nif (!is.null(rlocation)) {\n .libPaths( c( rlocation, .libPaths() ) )\n}\nif (!require(\"limma\")){\n source(\"http://bioconductor.org/biocLite.R\")\n biocLite(\"limma\", suppressUpdates=TRUE)\n library(\"limma\")\n}\nif (!require(\"edgeR\")){\n source(\"http://bioconductor.org/biocLite.R\")\n biocLite(\"edgeR\", suppressUpdates=TRUE)\n library(\"edgeR\")\n}\nif (!require(\"data.table\")){\n install.packages(\"data.table\", dependencies=TRUE, repos='http://cloud.r-project.org/')\n library(\"data.table\")\n}\nif (!require(\"gplots\")) {\n install.packages(\"gplots\", dependencies=TRUE, repos='http://cloud.r-project.org/')\n library(\"gplots\")\n}\n\n# Load count column from all files into a list of data frames\n# Use data.tables fread as much much faster than read.table\n# Row names are GeneIDs\ntemp <- lapply(args, fread, skip=\"Geneid\", header=TRUE, colClasses=c(NA, rep(\"NULL\", 5), NA))\n\n# Merge into a single data frame\nmerge.all <- function(x, y) {\n merge(x, y, all=TRUE, by=\"Geneid\")\n}\ndata <- data.frame(Reduce(merge.all, temp))\n\n# Clean sample name headers\ncolnames(data) <- gsub(\"Aligned.sortedByCoord.out.bam\", \"\", colnames(data))\n\n# Set GeneID as row name\nrownames(data) <- data[,1]\ndata[,1] <- NULL\n\n# Convert data frame to edgeR DGE object\ndataDGE <- DGEList( counts=data.matrix(data) )\n\n# Normalise counts\ndataNorm <- calcNormFactors(dataDGE)\n\n# Make MDS plot\npdf('edgeR_MDS_plot.pdf')\nMDSdata <- plotMDS(dataNorm)\ndev.off()\n\n# Print distance matrix to file\nwrite.csv(MDSdata$distance.matrix, 'edgeR_MDS_distance_matrix.csv', quote=FALSE,append=TRUE)\n\n# Print plot x,y co-ordinates to file\nMDSxy = MDSdata$cmdscale.out\ncolnames(MDSxy) = c(paste(MDSdata$axislabel, '1'), paste(MDSdata$axislabel, '2'))\nwrite.csv(MDSxy, 'edgeR_MDS_Aplot_coordinates_mqc.csv', quote=FALSE, append=TRUE)\n\n# Get the log counts per million values\nlogcpm <- cpm(dataNorm, prior.count=2, log=TRUE)\n\n# Calculate the euclidean distances between samples\ndists = dist(t(logcpm))\n\n# Plot a heatmap of correlations\npdf('log2CPM_sample_distances_heatmap.pdf')\nhmap <- heatmap.2(as.matrix(dists),\n main=\"Sample Correlations\", key.title=\"Distance\", trace=\"none\",\n dendrogram=\"row\", margin=c(9, 9)\n)\ndev.off()\n\n# Plot the heatmap dendrogram\npdf('log2CPM_sample_distances_dendrogram.pdf')\nplot(hmap$rowDendrogram, main=\"Sample Dendrogram\")\ndev.off()\n\n# Write clustered distance values to file\nwrite.csv(hmap$carpet, 'log2CPM_sample_distances_mqc.csv', quote=FALSE, append=TRUE)\n\nfile.create(\"corr.done\")\n\n# Printing sessioninfo to standard out\nprint(\"Sample correlation info:\")\nsessionInfo()\n", "meta": {"hexsha": "6746577169e8ce3197c5494e384752fef8aa1dcf", "size": 2997, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/edgeR_heatmap_MDS.r", "max_stars_repo_name": "agenusbio/NGI-RNAseq", "max_stars_repo_head_hexsha": "632aef19ee28fbb2f03f00cb2d54055c4a51b276", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 58, "max_stars_repo_stars_event_min_datetime": "2016-06-08T21:18:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T08:43:06.000Z", "max_issues_repo_path": "bin/edgeR_heatmap_MDS.r", "max_issues_repo_name": "agenusbio/NGI-RNAseq", "max_issues_repo_head_hexsha": "632aef19ee28fbb2f03f00cb2d54055c4a51b276", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 183, "max_issues_repo_issues_event_min_datetime": "2016-04-18T12:15:02.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-20T20:29:39.000Z", "max_forks_repo_path": "bin/edgeR_heatmap_MDS.r", "max_forks_repo_name": "agenusbio/NGI-RNAseq", "max_forks_repo_head_hexsha": "632aef19ee28fbb2f03f00cb2d54055c4a51b276", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 59, "max_forks_repo_forks_event_min_datetime": "2016-05-13T07:34:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-05T11:27:15.000Z", "avg_line_length": 28.8173076923, "max_line_length": 120, "alphanum_fraction": 0.7140473807, "num_tokens": 860, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.3475358474859388}} {"text": "#' Converts a list of character vectors to a logical matrix\n#'\n#' @param x A list of character vectors\n#' @param along Which axis to spread mask on\n#' @return A logical occurrence matrix\n#' @export\nmask = function(x, along=2) {\n if (is.factor(x))\n x = as.character(x)\n\n list2logic = function(xi) stats::setNames(base::rep(TRUE, length(xi)), xi)\n\n vectorList = lapply(x, list2logic)\n stacked = stack(vectorList, fill=FALSE)\n\n if (along == 1)\n stacked\n else\n t(stacked)\n}\n", "meta": {"hexsha": "b65353fe4264be8d1628738a7c627b7a122a9a69", "size": 521, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mask.r", "max_stars_repo_name": "cran/narray", "max_stars_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2016-12-07T16:03:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-20T09:10:42.000Z", "max_issues_repo_path": "R/mask.r", "max_issues_repo_name": "cran/narray", "max_issues_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2016-11-21T09:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-11T16:08:02.000Z", "max_forks_repo_path": "R/mask.r", "max_forks_repo_name": "cran/narray", "max_forks_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-06-21T03:17:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-21T03:17:21.000Z", "avg_line_length": 24.8095238095, "max_line_length": 78, "alphanum_fraction": 0.6276391555, "num_tokens": 139, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5195213368305399, "lm_q2_score": 0.6688802669716107, "lm_q1q2_score": 0.34749757047665963}} {"text": "#!/usr/bin/Rscript\n#Rscript script common-path jaspar-dir id output\n#example:\n#Rscript pwmmatch.r /cluster/thashim/basepiq/common.r /cluster/thashim/basepiq/pwms/jaspar.txt 141 /cluster/thashim/basepiq/tmp/pwmout.RData\n\noptions(echo=TRUE)\nargs <- commandArgs(trailingOnly = TRUE)\nprint(args)\n\ncommonfile = args[1]\njaspardir = args[2]\npwmid = as.double(args[3])\noutdir = args[4]\n\noutdir=paste0(outdir,'/')\nsource(commonfile)\nif(!overwrite & file.exists(paste0(outdir,pwmid,'.pwmout.RData'))){\n stop(\"pwm file already exists\")\n}\n\n\n\n####\n# load PWMs\n####\n\n#pwmin = 'pwms/'\n\n\nimportJaspar <- function(file=myloc) {\n vec <- readLines(file)\n vec <- gsub(\"\\t\",\" \",vec)\n vec <- gsub(\"\\\\[|\\\\]\", \"\", vec)\n start <- grep(\">\", vec); end <- grep(\">\", vec) - 1\n pos <- data.frame(start=start, end=c(end[-1], length(vec)))\n pwm <- sapply(seq(along=pos[,1]), function(x) vec[pos[x,1]:pos[x,2]])\n pwm <- sapply(seq(along=pwm), function(x) strsplit(pwm[[x]], \" {1,}\"))\n pwm <- lapply(seq(along=start), function(x) matrix(as.numeric(t(as.data.frame(pwm[(pos[x,1]+1):pos[x,2]]))[,-1]), nrow=4, dimnames=list(c(\"A\", \"C\", \"G\", \"T\"), NULL)))\n names(pwm) <- gsub(\">\", \"\", vec[start])\n return(pwm)\n}\npwmtable <- importJaspar(jaspardir)\n\npwmnum = pwmid\npwmin = pwmtable[[pwmnum]] + 1e-20\npwmname = names(pwmtable)[pwmnum]\n\n####\n# end input script\n# assert: existence of pwmin and pwmname\n####\n\n\n####\n# motif match\n\npwmnorm=t(t(pwmin)/colSums(pwmin))\n#informbase=colSums((log(pwmnorm+0.01)-log(1/4))*pwmnorm) #\n#pwmnorm = pwmnorm[,(informbase > basecut)]\nipr=log(pwmnorm)-log(1/4)\n\n#chr names\nchrstr = seqnames(genome)\n\nif(exists('blacklist') & !is.null(blacklist)){\n blacktable=read.table(blacklist)\n}\n\nif(exists('whitelist') & !is.null(whitelist)){\n whitetable=read.table(whitelist)\n}\n\n#####\n# fw motif match\n\npwuse = ipr\n\ncoords.list = lapply(chrstr,function(i){\n print(i)\n gi=genome[[i]]\n if(remove.repeatmask & !is.null(masks(gi))){\n active(masks(gi)) <- rep(T,length(masks(gi)))\n }\n if(exists('blacklist') & !is.null(blacklist)){\n blacksel= blacktable[,1]==i\n if(sum(blacksel)>0){\n flsize = wsize*flank.blacklist\n ir=intersect(IRanges(1,length(gi)),reduce(IRanges(blacktable[blacksel,2]-flsize,blacktable[blacksel,3]+flsize)))\n mask=Mask(length(gi),start(ir),end(ir))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }\n }\n if(exists('whitetable')){\n whitesel=whitetable[,1]==i\n if(sum(whitesel)>0){\n wchr=whitetable[whitesel,,drop=F]\n ir=IRanges(wchr[,2],wchr[,3])\n air=IRanges(1,length(gi))\n nir=setdiff(air,ir)\n rir=reduce(IRanges(start(nir)-wsize,end(nir)+wsize))\n maskr=intersect(rir,air)\n mask = Mask(length(gi),start(maskr),end(maskr))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }else{\n mask = Mask(length(gi),1,length(gi))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }\n }\n mpwm=matchPWM(pwuse,gi,min.score=motifcut)\n pscore=PWMscoreStartingAt(pwuse,as(gi,\"DNAString\"),start(mpwm))\n list(mpwm,pscore)\n})\n\nif(sum(sapply(coords.list,function(i){length(i[[2]])}))>0){\n\nallpwm=do.call(c,lapply(coords.list,function(i){i[[2]]}))\npwmcut2=sort(allpwm,decreasing=T)[min(length(allpwm),maxcand)]\nrm(allpwm)\nprint(pwmcut2)\n\ncoords=lapply(1:length(coords.list),function(i){\n as(coords.list[[i]][[1]],'IRanges')[coords.list[[i]][[2]] >= pwmcut2]\n})\n\ncoords.pwm=lapply(coords.list,function(i){i[[2]][i[[2]] >= pwmcut2]})\n\n#coords=lapply(coords.list,unlist)\n\nclengths=sapply(coords,length)\nprint(sum(clengths))\ncoords.short=coords[clengths>0]\nnames(coords.short)=chrstr[clengths>0]\nncoords=chrstr[clengths>0]#names(coords)\ncoords2=sapply(coords.short,flank,width=wsize,both=T)\n\nsave(coords,coords.pwm,ipr,pwmin,pwmname,chrstr,clengths,coords.short,ncoords,coords2,file=paste0(outdir,pwmid,'.pwmout.RData'))\n\n}else{\nclengths=0\nsave(clengths,file=paste0(outdir,pwmid,'.pwmout.RData'))\n}\n\n#\n#####\n\n#####\n# RC motif match\n\npwuse = reverseComplement(ipr)\n\ncoords.list = lapply(chrstr,function(i){\n print(i)\n gi=genome[[i]]\n if(remove.repeatmask & !is.null(masks(gi))){\n active(masks(gi)) <- rep(T,length(masks(gi)))\n }\n if(exists('blacklist') & !is.null(blacklist)){\n blacksel= blacktable[,1]==i\n if(sum(blacksel)>0){\n flsize = wsize*flank.blacklist\n ir=intersect(IRanges(1,length(gi)),reduce(IRanges(blacktable[blacksel,2]-flsize,blacktable[blacksel,3]+flsize)))\n mask=Mask(length(gi),start(ir),end(ir))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }\n }\n if(exists('whitetable')){\n whitesel=whitetable[,1]==i\n if(sum(whitesel)>0){\n wchr=whitetable[whitesel,,drop=F]\n ir=IRanges(wchr[,2],wchr[,3])\n air=IRanges(1,length(gi))\n nir=setdiff(air,ir)\n rir=reduce(IRanges(start(nir)-wsize,end(nir)+wsize))\n maskr=intersect(rir,air)\n mask = Mask(length(gi),start(maskr),end(maskr))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }else{\n mask = Mask(length(gi),1,length(gi))\n if(is.null(masks(gi)))\n masks(gi) = mask\n else\n masks(gi) = append(masks(gi),mask)\n }\n }\n mpwm=matchPWM(pwuse,gi,min.score=motifcut)\n pscore=PWMscoreStartingAt(pwuse,as(gi,\"DNAString\"),start(mpwm))\n list(mpwm,pscore)\n})\n\nif(sum(sapply(coords.list,function(i){length(i[[2]])}))>0){\n\nallpwm=do.call(c,lapply(coords.list,function(i){i[[2]]}))\npwmcut2=sort(allpwm,decreasing=T)[min(length(allpwm),maxcand)]\nrm(allpwm)\nprint(pwmcut2)\n\ncoords=lapply(1:length(coords.list),function(i){\n as(coords.list[[i]][[1]],'IRanges')[coords.list[[i]][[2]] >= pwmcut2]\n})\n\ncoords.pwm=lapply(coords.list,function(i){i[[2]][i[[2]] >= pwmcut2]})\n\n#coords=lapply(coords.list,unlist)\n\nclengths=sapply(coords,length)\nprint(sum(clengths))\ncoords.short=coords[clengths>0]\nnames(coords.short)=chrstr[clengths>0]\nncoords=chrstr[clengths>0]#names(coords)\ncoords2=sapply(coords.short,flank,width=wsize,both=T)\n\nsave(coords,coords.pwm,ipr,pwmin,pwmname,chrstr,clengths,coords.short,ncoords,coords2,file=paste0(outdir,pwmid,'.pwmout.rc.RData'))\n\n}else{\nclengths=0\nsave(clengths,file=paste0(outdir,pwmid,'.pwmout.rc.RData'))\n}\n", "meta": {"hexsha": "bfeea85304975d4d7c9747d0dd87c2946ad080fe", "size": 6779, "ext": "r", "lang": "R", "max_stars_repo_path": "pwmmatch.exact.r", "max_stars_repo_name": "wakilsarfaraz/PIQ", "max_stars_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-21T04:19:43.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-21T04:19:43.000Z", "max_issues_repo_path": "pwmmatch.exact.r", "max_issues_repo_name": "wakilsarfaraz/PIQ", "max_issues_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pwmmatch.exact.r", "max_forks_repo_name": "wakilsarfaraz/PIQ", "max_forks_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0944206009, "max_line_length": 168, "alphanum_fraction": 0.6146924325, "num_tokens": 2141, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3474975639539136}} {"text": "# Formatting the 2011 Diallel phenotype data\n# Jinliang Yang\n# Jan 17th, 2012\n# KC, TKW and AKW\n\n#setwd(\"/Users/yangjl/Documents/workingSpace/pheno2011/codes\")\n\nsetwd(\"/Users/yangjl/Documents/Heterosis_GWAS/pheno2011\")\n\n#############################################################\n# Check the data completeness and correct the notes\n#Notes: \n#Dairy 8651-9000 v\n#Zumwalt 9251-9600 v\n#Johnson J2601-J2950 v\n#Ki11: kill@ => Ki11 v\n#Ms71: MS71 => Ms71 v\n\nfile1 <- read.csv(\"LL_KN_AKW_test.csv\")\ndim(file1)\n#[1] 8286 11 ||||| #8678 8\n\ndiallel <- subset(file1, Note.y==\"Diallel\" | Note.y==\"NAM Filler\"| Note.y==\"B73\");\ndim(diallel)\n#[1] 3204 11 | [1] 2850 8\nsummary(diallel)\n\n###########################################################\n### Delete duplicated input and missing data\n# Get ride of three duplicated records\nbarcode <- diallel[duplicated(diallel$Barcode),]$Barcode\ndiallel[diallel$Barcode %in% barcode,]\ndiallel <- diallel[!duplicated(diallel$Barcode),]\ndim(diallel)\n#[1] 3200 11\n# Get ride of recodes with note.x nad KC<5\ndiallel <- subset(diallel, Note.x==\"\" & KC >= 50)\n#diallel[is.na(diallel$TKW),]\n#diallel <- diallel[!is.na(diallel$KRN),]\n##########################################################\n### checking for outlyers\ndiallel$KC <- as.numeric(as.character(diallel$KC));\nhist(diallel$AKW)\nhist(diallel$KC, xlim=c(0, 1000))\nhist(diallel$TKW)\n\n#######################################################\n### get the LSmean\nlibrary(nlme)\n\n#fit the model (it takes a while to run in R)\nlmeout1 <- lme(KC~Genotype, data=diallel, random=~1|Farm);\nlmeout2 <- lme(TKW~Genotype, data=diallel, random=~1|Farm);\nlmeout3 <- lme(AKW~Genotype, data=diallel, random=~1|Farm);\n\n#extract the estimates of coefficents, which are the lsmeans \nped.hat1 <- lmeout1$coef$fixed\nped.hat2 <- lmeout2$coef$fixed\nped.hat3 <- lmeout3$coef$fixed\n\n#reparameterize the estimates to be more interpretable \nped.hat1[-1] <- ped.hat1[-1]+ped.hat1[1];\nped.hat2[-1] <- ped.hat2[-1]+ped.hat2[1];\nped.hat3[-1] <- ped.hat3[-1]+ped.hat3[1];\n\n#rename the first estimate\nhead(ped.hat1)\nhead(diallel[order(diallel$Genotype),], 30);\n\nnames(ped.hat1)[1]=\"B73 x B97\";\nnames(ped.hat2)[1]=\"B73 x B97\";\nnames(ped.hat3)[1]=\"B73 x B97\";\n\n#get rid of the prefix \"Genotype\" in the levels names so that they are more readable\nnames(ped.hat1) <- gsub(\"Genotype\",\"\",names(ped.hat1));\nnames(ped.hat2) <- gsub(\"Genotype\",\"\",names(ped.hat2));\nnames(ped.hat3) <- gsub(\"Genotype\",\"\",names(ped.hat3));\n#transpose the results\nkc <- data.frame(Genotype=names(ped.hat1), KC=ped.hat1);\ntkw <- data.frame(Genotype=names(ped.hat2), TKW=ped.hat2);\nakw <- data.frame(Genotype=names(ped.hat3), AKW=ped.hat3);\n\n############# H2 ###############################\nfit1 <- aov(KC ~ Farm + Genotype, data=diallel)\n#71424477/(71424477+33294773+9504759)=62.5 pval=2.2e-16 ***\nfit2 <- aov(TKW ~ Farm + Genotype, data=diallel)\n#10406226/(3627354+10406226+1473890)=67.1 pval=2.2e-16 ***\nfit3 <- aov(AKW ~ Farm + Genotype, data=diallel)\n#8.3488/(8.3488+3.8944+0.8990)=63.5 pval=2.2e-16 ***\n###########################################################\n### merge the trait of reciprocal F1\nlibrary(ggplot2)\n########-------KC\nkc$Genotype <- as.character(kc$Genotype);\nkc$p1 <- NA;\nkc$p2 <- NA;\nfor (i in 1:nrow(kc)){\n tem <- sort(unlist(strsplit(kc$Genotype[i], split=\" x \")));\n kc$p1[i] <- tem[1];\n kc$p2[i] <- tem[2];\n}\n\nkc <- kc[order(kc$p1, kc$p2),]\nkc$ped <- paste(kc$p1, kc$p2, sep=\"|\")\nlsmean_kc <- ddply(kc, .(ped), summarise,\n KC=mean(KC))\ndim(lsmean_kc)\n#[1] 249 2\n\n########-------TKW\ntkw$Genotype <- as.character(tkw$Genotype);\ntkw$p1 <- NA;\ntkw$p2 <- NA;\nfor (i in 1:nrow(tkw)){\n tem <- sort(unlist(strsplit(tkw$Genotype[i], split=\" x \")));\n tkw$p1[i] <- tem[1];\n tkw$p2[i] <- tem[2];\n}\n\ntkw <- tkw[order(tkw$p1, tkw$p2),]\ntkw$ped <- paste(tkw$p1, tkw$p2, sep=\"|\")\nlsmean_tkw <- ddply(tkw, .(ped), summarise,\n TKW=mean(TKW))\ndim(lsmean_tkw)\n#[1] 249 2\n\n########-------AKW\nakw$Genotype <- as.character(akw$Genotype);\nakw$p1 <- NA;\nakw$p2 <- NA;\nfor (i in 1:nrow(akw)){\n tem <- sort(unlist(strsplit(akw$Genotype[i], split=\" x \")));\n akw$p1[i] <- tem[1];\n akw$p2[i] <- tem[2];\n}\n\nakw <- akw[order(akw$p1, akw$p2),]\nakw$ped <- paste(akw$p1, akw$p2, sep=\"|\")\nlsmean_akw <- ddply(akw, .(ped), summarise,\n AKW=mean(AKW))\ndim(lsmean_akw)\n#[1] 249 2\n######################################################################\nnathan <- read.csv(\"Nathan_p1_subset.csv\")\nnam <- read.table(\"nam_parents\", header=TRUE)\np <- c(\"B73\", as.character(nam$parent))\np <- toupper(p);\nnathan$INBRED <- toupper(nathan$INBRED)\n\npcob <- subset(nathan, INBRED %in% p)\npcob[pcob==\".\"] <- NA;\npcob$X10KW_Inbred <- as.numeric(as.character(pcob$X10KW_Inbred))\npcob$TKW_Inbred <- as.numeric(as.character(pcob$TKW_Inbred))\npcob$KC_Inbred <- as.numeric(as.character(pcob$KC_Inbred))\npcobmean <- ddply(pcob, .(INBRED), summarise,\n AKW=mean(X10KW_Inbred, na.rm=T)/10,\n TKW=mean(TKW_Inbred, na.rm=T),\n KC=mean(KC_Inbred, na.rm=T)\n )\nb73 <- data.frame(INBRED=\"B73\", AKW=0.2583333 , TKW=62.85, KC=243.2903)\npcobmean <- rbind(b73, pcobmean)\n########################################################\n#------------------ AKW F1 and parents lsmean:\nidx1 <- grep(\"@\", lsmean_akw$ped)\nf1akw <- lsmean_akw[-idx1,]\n\npakw <- lsmean_akw[idx1,]\npakw$ped <- gsub('@\\\\|NA',\"\", pakw$ped)\n\npakw$ped <- toupper(pakw$ped);\npakw2 <- merge(pakw, pcobmean[, c(\"INBRED\", \"AKW\")], by.x=\"ped\", by.y=\"INBRED\", all=T)\n\ncor(subset(pakw2, !is.na(AKW.x) & !is.na(AKW.y))[,2:3])\n#0.50\n\nmymean <- function(x) mean(x, na.rm=T);\npakw2$AKW <- apply(pakw2[,2:3], 1, mymean)\npakw2 <- pakw2[, c(1,4)]\nnames(pakw2)[1] <- \"ped\"\n\nmaster_akw <- rbind(f1akw, pakw2)\ndim(master_akw)\n#[1] 254 2\n#------------------ TKW F1 and parents lsmean:\nidx2 <- grep(\"@\", lsmean_tkw$ped)\nf1tkw <- lsmean_tkw[-idx2,]\n\nptkw <- lsmean_tkw[idx2,]\nptkw$ped <- gsub('@\\\\|NA',\"\", ptkw$ped)\n\nptkw$ped <- toupper(ptkw$ped);\nptkw2 <- merge(ptkw, pcobmean[, c(\"INBRED\", \"TKW\")], by.x=\"ped\", by.y=\"INBRED\", all=T)\n\ncor(subset(ptkw2, !is.na(TKW.x) & !is.na(TKW.y))[,2:3])\n#0.45\n\n#mymean <- function(x) mean(x, na.rm=T);\n#pcw2$CW <- apply(pcw2[,2:3], 1, mymean)\nptkw2 <- ptkw2[, c(1,3)]\nnames(ptkw2)[2] <- \"TKW\"\n\nmaster_tkw <- rbind(f1tkw, ptkw2)\n\n#------------------ KC F1 and parents lsmean:\nidx3 <- grep(\"@\", lsmean_kc$ped)\nf1kc <- lsmean_kc[-idx3,]\n\npkc <- lsmean_kc[idx3,]\npkc$ped <- gsub('@\\\\|NA',\"\", pkc$ped)\n\npkc$ped <- toupper(pkc$ped);\npkc2 <- merge(pkc, pcobmean[, c(\"INBRED\", \"KC\")], by.x=\"ped\", by.y=\"INBRED\", all=T)\n\ncor(subset(pkc2, !is.na(KC.x) & !is.na(KC.y))[,2:3])\n#0.61\n\n#mymean <- function(x) mean(x, na.rm=T);\n#pcd2$CD <- apply(pcd2[,2:3], 1, mymean)\npkc2 <- pkc2[, c(1,3)]\nnames(pkc2)[2] <- \"KC\"\n\nmaster_kc <- rbind(f1kc, pkc2)\n###########################################\n#### Merge the data for output\nmaster_kernel <- merge(master_akw, master_tkw, by=\"ped\", all=T)\nmaster_kernel <- merge(master_kernel, master_kc, by=\"ped\", all=T)\n\nwrite.table(master_kernel, \"diallel_lsmean_kernel_020112.csv\", sep=\",\", row.names=FALSE, quote=FALSE)\n## plot and output lsmean\nidx2 <- grep(\"\\\\|\", master_kc$ped);\nf1 <- master_kc[idx2,]\np <- master_kc[-idx2, ]\nplot(density(f1$KC), main=\"KC of Diallel and parents\", xlab=\"KRN\", lwd=3, col=\"blue\")\nlines(density(p$KC), lwd=3, lty=2, col=\"red\")\n\n", "meta": {"hexsha": "526693345fc3d3815ee30c21a0350f205bd038a7", "size": 7373, "ext": "r", "lang": "R", "max_stars_repo_path": "profiling/pheno2011/archieved_codes/2.data_diallele_kernel.r", "max_stars_repo_name": "yangjl/Heterosis-GWAS", "max_stars_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-04-16T08:27:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-31T13:00:43.000Z", "max_issues_repo_path": "profiling/pheno2011/archieved_codes/2.data_diallele_kernel.r", "max_issues_repo_name": "yangjl/Heterosis-GWAS", "max_issues_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_issues_repo_licenses": ["RSA-MD"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "profiling/pheno2011/archieved_codes/2.data_diallele_kernel.r", "max_forks_repo_name": "yangjl/Heterosis-GWAS", "max_forks_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_forks_repo_licenses": ["RSA-MD"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-01-03T14:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-03T01:34:08.000Z", "avg_line_length": 31.2415254237, "max_line_length": 101, "alphanum_fraction": 0.5933812559, "num_tokens": 2755, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3474975639539135}} {"text": "#############################################################\n# Modelling dose - responses\n\nlibrary(drc)\nlibrary(mixtox)\nlibrary(bmdModeling)\nlibrary(synergyfinder)\nlibrary(multcomp)\nlibrary(bmd)\n\n# library(BIGL)\n\n#############################################################\n# Read and graphics\n\nlibrary(readxl)\nlibrary(ggplot2)\n\n#############################################################\n# Setup working directory\n\nsetwd(\"\")\n\n#############################################################\n# Setup working directory\n\nnames_end=excel_sheets(\"data/TOX_VIVO_CHICKEN.xls\")\n\nendlen=length(names_end)\n\ncases_ls=list()\n\nfor ( i in 1:endlen) {\n \n cases_ls[[i]]=as.data.frame(read_excel(\"data/TOX_VIVO_CHICKEN.xls\",names_end[i]))\n\n }\n\nsaveRDS(cases_ls,\"cases_ls.rds\")\n\n\n###############################################################################################\n# \n\ncases_ls_liv=list()\ncases_ls_mar=list()\n\nfor( i in 1:endlen) {\n cases_ls_liv[[i]]=cases_ls[[i]][which(cases_ls[[i]]$Specie==\"White Leghorn chickens\"),]\n cases_ls_mar[[i]]=cases_ls[[i]][which(cases_ls[[i]]$Specie==\"Marek chickens\"),]\n}\n\nsaveRDS(cases_ls_liv,\"cases_ls_liv.rds\")\nsaveRDS(cases_ls_mar,\"cases_ls_mar.rds\")\n\n############################################################\n# 'LL.3' and 'LL2.3' provide the three-parameter \n# log-logistic function where the lower limit is equal to 0. '\n\n############################################################\nold.par=par()\npar(old.par)\nset.seed(2)\nrange01 <- function(x){(x-min(x,na.rm=T))/(max(x,na.rm=T)-min(x,na.rm=T))}\n\n############################################################\n# BW gain\n# Marek\n\ndatamyc=cases_ls_mar[[1]]\ndataexpand=data.frame(Bodyweight_gain=rep(datamyc$Bodyweight_gain,100)+runif(length(datamyc$Bodyweight_gain)*100,-1.5,1.5)*rep(datamyc$Bodyweight_gain.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\n\n\n############################################################\n# BW gain\n# Marek\n\ndatamyc=cases_ls_mar[[1]]\ndataexpand=data.frame(Bodyweight_gain=rep(datamyc$Bodyweight_gain,100)+runif(length(datamyc$Bodyweight_gain)*100,-1.5,1.5)*rep(datamyc$Bodyweight_gain.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\ndataexpand$response=range01(dataexpand$Bodyweight_gain)\n\nmulti.m3=drm(Bodyweight_gain~Dose_T,Mycotoxin,data=dataexpand,fct = AR.3())\n#multi.s=drm(response~Dose_T,Mycotoxin,data=dataexpand,fct = LL.2())\n\nED(multi.m3,c(10,50), interval = \"delta\")\n\nfile.remove(\"summary_BW_gain_Marek.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_BW_gain_Marek.txt\",\n sep=\"\\n\",append = T)\n\n\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_BW_gain_Marek.txt\",\n sep=\"\\n\",append = T)\n\npng(\"images/BW_gain_Marek.png\")\n\nplot(multi.m3, \n col = TRUE, \n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"BW_gain\", \n main=\"Bodyweight_gain AF & OTA Marek chickens\\n Bootstrap=Yes Model=L.3\")\n\ndev.off()\n\n\n\n\n\n############################################################\n\ndatamyc=cases_ls_liv[[1]]\n\nmulti.m3 <- drm(Terminal_bodyweight~Dose_T,Mycotoxin,data=datamyc,fct = MM.3())\nprint(summary(multi.m3))\n\nfile.remove(\"summary_TW_gain_Liv.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_TW_gain_Liv.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_TW_gain_Liv.txt\",\n sep=\"\\n\")\n\npng(\"images/TW_gain_Liv.png\")\n\nplot(multi.m3, \n col = TRUE, \n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Terminal BW\", \n main=\"Terminal BW AF & OTA White Leghorn chickens\\n Bootstrap=Not Model=MM.3\")\ndev.off()\n\n\n\n########################################################################################\u00e0\n# Feed conversion #Marek\n\ndatamyc=cases_ls_mar[[2]]\ndataexpand=data.frame(Feed_conversion=rep(datamyc$Feed_conversion,100)+runif(length(datamyc$Feed_conversion)*100,-1.5,1.5)*rep(datamyc$Feed_conversion.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\nmulti.m3 <- drm(Feed_conversion~Dose_T,Mycotoxin,data=dataexpand,fct = AR.3())\n\nprint(summary(multi.m3))\n\nfile.remove(\"summary_Feed_conversion_mar.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_Feed_conversion_mar.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_Feed_conversion_mar.txt\",\n sep=\"\\n\")\n\npng(\"images/Feed_conversion_mar.png\")\n\nplot(multi.m3, \n col = TRUE, \n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Feed_conversion\", \n main=\"Feed_conversion AF & OTA Marek chickens\\n Bootstrap=Yes Model=AR.3\",\n legendPos = c(0.4, 2.8))\n\ndev.off()\n\n#########################################################################################\n# Feed intake #Marek\n\ndatamyc=cases_ls_mar[[3]]\nmulti.m3 <- drm(Feed_intake~Dose_T,Mycotoxin,data=datamyc,fct = AR.3())\nprint(summary(multi.m3))\n\n\npng(\"images/Feed_intake_mar_bootno.png\")\n\nplot(multi.m3, \n col = TRUE, \n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Feed_intake\",\n main=\"Feed_intake AF & OTA Marek chickens\\n Bootstrap=No Model=AR.3\")\n\ndev.off()\n\ndataexpand=data.frame(Feed_intake=rep(datamyc$Feed_intake,100)+runif(length(datamyc$Feed_intake)*100,-1.5,1.5)*rep(datamyc$Feed_intake.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\n\nmulti.m3 <- drm(Feed_intake~Dose_T,Mycotoxin,data=dataexpand,fct = AR.3())\nprint(summary(multi.m3))\n\nfile.remove(\"summary_Feed_intake_mar.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_Feed_intake_mar.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_Feed_intake_mar.txt\",\n sep=\"\\n\",append = T)\n\npng(\"images/Feed_intake_mar.png\")\n\nplot(multi.m3, \n col = TRUE, \n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Feed_intake\",\n main=\" Feed_intake AF & OTA Marek chickens\\n Bootstrap=Yes Model=AR.3\")\ndev.off()\n\n##########################################################################################\n\ndatamyc=cases_ls_liv[[3]]\nmulti.m3 <- drm(Feed_intake~Dose_T,Mycotoxin,data=datamyc,fct = L.3())\nprint(summary(multi.m3))\n\nfile.remove(\"summary_Feed_intake_liv.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_Feed_intake_liv.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_Feed_intake_mar.txt\",\n sep=\"\\n\",append = T)\n\n\npng(\"images/Feed_intake_liv_bootno.png\")\n\nplot(multi.m3,\n col = TRUE,\n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Feed_intake\",\n xlim=c(0,10),\n main=\"Feed_intake AF & OTA White Leghorn chickens\\n Bootstrap=No Model=L.3\",\n legendPos = c(9, 5400))\n\ndev.off()\n\n\n\n##########################################################################################\n# Liver_relative_weight #Marek\n\n\ndatamyc=cases_ls_mar[[4]]\n\ndataexpand=data.frame(Liver_relative_weight=rep(datamyc$Liver_relative_weight,100)+runif(length(datamyc$Liver_relative_weight)*100,-1.2,1.2)*rep(datamyc$Liver_relative_weight.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\nmulti.m3 <- drm(Liver_relative_weight~Dose_T,Mycotoxin,data=dataexpand,fct = AR.3())\nprint(summary(multi.m3))\nfile.remove(\"summary_Liver_RW_mar.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_Liver_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_Liver_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\n\npng(\"images/Liver_RW_mar.png\")\n\nplot(multi.m3, col = TRUE,\n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Liver RW\", \n main=\"Liver RW AF & OTA Marek chickens\\n Bootstrap=Yes Model=AR.3\",\n legendPos = c(0.5, 4.8))\n\ndev.off()\n\n\n\n\n##########################################################################################\n# Kidney_relative_weight#Marek\n\ndatamyc=cases_ls_mar[[5]]\n\ndataexpand=data.frame(Kidney_relative_weight=rep(datamyc$Kidney_relative_weight,100)+runif(length(datamyc$Kidney_relative_weight)*100,-1.5,1.5)*rep(datamyc$Kidney_relative_weight.sd,100),\n Mycotoxin=rep(datamyc$Mycotoxin,100),\n Dose_T=rep(datamyc$Dose_T,100))\n\nmulti.m3 <- drm(Kidney_relative_weight~Dose_T,Mycotoxin,data=dataexpand,fct = AR.3())\nprint(summary(multi.m3))\n\nfile.remove(\"summary_Kidney_RW_mar.txt\")\n\ncat(capture.output(ED(multi.m3,c(10,50), interval = \"delta\")),\n file = \"summary_Kidney_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m3)),\n file = \"summary_Kidney_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\n\npng(\"images/Kidney_RW_mar.png\")\n\nplot(multi.m3, col = TRUE,\n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Kidney RW\", \n main=\"Kidney RW AF & OTA Marek chickens\\n Bootstrap=Yes Model=AR.3\",\n legendPos = c(0.5, 0.9))\n\ndev.off()\n\n##########################################################################################\n# Spleen_relative weight\n\ndatamyc=cases_ls_mar[[6]]\nmulti.m4 <- drm(Spleen_relative_weight~Dose_T,Mycotoxin,data=datamyc[,],fct=L.3())\nprint(summary(multi.m4))\n\nfile.remove(\"summary_Spleen_RW_mar.txt\")\n\ncat(capture.output(ED(multi.m4,c(10,50), interval = \"delta\")),\n file = \"summary_Spleen_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\ncat(capture.output(summary(multi.m4)),\n file = \"summary_Spleen_RW_mar.txt\",\n sep=\"\\n\",append = T)\n\n\npng(\"images/Spleen_RW_mar.png\")\n\nplot(multi.m4, col = TRUE,\n xlab = \"Log Dose(mg/kg feed)\",\n ylab = \"Spleen RW\", \n main=\"Spleen RW AF & OTA Marek chickens\\n Bootstrap=No Model=L.3\",\n legendPos = c(0.4, 0.329))\n\ndev.off()\n", "meta": {"hexsha": "28fe081f57258d0a9840bdb7e4957622cf509008", "size": 9832, "ext": "r", "lang": "R", "max_stars_repo_path": "modeling_dose_addition/mixdrm.r", "max_stars_repo_name": "alfcrisci/michyf", "max_stars_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:35.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:35.000Z", "max_issues_repo_path": "modeling_dose_addition/mixdrm.r", "max_issues_repo_name": "alfcrisci/Mychif", "max_issues_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modeling_dose_addition/mixdrm.r", "max_forks_repo_name": "alfcrisci/Mychif", "max_forks_repo_head_hexsha": "9a6a8905f272f9bc7ed9751eeaa75ad5e2418544", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9318181818, "max_line_length": 187, "alphanum_fraction": 0.5980471928, "num_tokens": 2794, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.34749755709560654}} {"text": "library(ggplot2)\r\nlibrary(dplyr)\r\n\r\n# disable scientific notation\r\noptions(scipen=999)\r\n\r\ndf=read.table(\"time.txt\", header=TRUE)\r\n\r\n# compute the median\r\ndfMedian=ddply(df, .(tool), summarise, med = median(time))\r\n\r\nplot1=ggplot(df, aes(x=tool, y=time)) + scale_y_log10() + ylab(\"Time (ms)\") + xlab(element_blank()) +\r\n geom_violin() +\r\n stat_summary(fun.y=\"median\", geom=\"point\") +\r\n geom_text(data=dfMedian, aes(x=tool, y=med, label=med), size=3, vjust=-1.5)\r\n\r\nggsave(\"plot1.pdf\", plot=plot1, width=4, height=4, units=\"in\", dpi=300)\r\n\r\nsum(filter(df, tool == \"RefDiff\")$time)\r\nmedian(filter(df, tool == \"RefDiff\")$time)\r\nmean(filter(df, tool == \"RefDiff\")$time)\r\nmax(filter(df, tool == \"RefDiff\")$time)\r\nsum(filter(df, tool == \"RMiner\")$time)\r\nmedian(filter(df, tool == \"RMiner\")$time)\r\nmean(filter(df, tool == \"RMiner\")$time)\r\nmax(filter(df, tool == \"RMiner\")$time)\r\n\r\n\r\n#> sum(filter(df, tool == \"RefDiff\")$time)\r\n#[1] 394632\r\n#> median(filter(df, tool == \"RefDiff\")$time)\r\n#[1] 157\r\n#> mean(filter(df, tool == \"RefDiff\")$time)\r\n#[1] 368.815\r\n#> max(filter(df, tool == \"RefDiff\")$time)\r\n#[1] 10500\r\n#> sum(filter(df, tool == \"RMiner\")$time)\r\n#[1] 2188286\r\n#> median(filter(df, tool == \"RMiner\")$time)\r\n#[1] 109\r\n#> mean(filter(df, tool == \"RMiner\")$time)\r\n#[1] 2045.127\r\n#> max(filter(df, tool == \"RMiner\")$time)\r\n#[1] 85576\r\n\r\n#dfRefdiff=read.table(\"refdiff.txt\", header=TRUE)\r\n#dfRminer=read.table(\"rminer.txt\", header=TRUE)\r\n\r\n#ggplot(dfRminer, aes(x=files, y=time)) + geom_point(shape=23) + geom_point(data=dfRefdiff, aes(x=files, y=time), shape=1, color=\"blue\")\r\n", "meta": {"hexsha": "95208b4f6e7090bb2c28ca529ad4b83481519cd8", "size": 1576, "ext": "r", "lang": "R", "max_stars_repo_path": "refdiff-evaluation/data/performance/plot.r", "max_stars_repo_name": "rodrigo-brito/RefDiff", "max_stars_repo_head_hexsha": "a6b3b09718a7c7b2edc64e2cfdb581d5f8fd72d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 131, "max_stars_repo_stars_event_min_datetime": "2017-04-05T22:43:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T16:41:18.000Z", "max_issues_repo_path": "refdiff-evaluation/data/performance/plot.r", "max_issues_repo_name": "rodrigo-brito/RefDiff", "max_issues_repo_head_hexsha": "a6b3b09718a7c7b2edc64e2cfdb581d5f8fd72d4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2017-04-27T08:32:29.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-07T07:16:19.000Z", "max_forks_repo_path": "refdiff-evaluation/data/performance/plot.r", "max_forks_repo_name": "rodrigo-brito/RefDiff", "max_forks_repo_head_hexsha": "a6b3b09718a7c7b2edc64e2cfdb581d5f8fd72d4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 38, "max_forks_repo_forks_event_min_datetime": "2017-04-02T01:23:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-09T12:48:00.000Z", "avg_line_length": 31.52, "max_line_length": 137, "alphanum_fraction": 0.6389593909, "num_tokens": 540, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.34749755709560654}} {"text": "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nrm(list=ls()) # clears workspace\n# setwd('/Volumes/NovakLab/Projects/SaturationMetaAnalysis/')\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(devtools)\ndevtools::install_github(\"ropensci/rgpdd\")\nlibrary(rgpdd) # for Global Popn Dynamics Db\nsource('SatMeta-Functions.r')\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Run to update data files\n# source('SatMeta-GoogleImportDataPrep.r')\nload('../Data/Saturation_Data.Rdata')\nload('../Data/Saturation_GAMfit.Rdata')\n\n#####################################################################################\n# Compare estimated taxon effects to percent cycling (Kendall et al. 1998)\n#####################################################################################\n\n# Using means of raw data\nfr.feed <- ddply(dat,.(Taxon.group),summarise,fr.feed=mean(Percent.feeding)/100)\n\n\nfr.feed.eff<-coef(fit)[2:(length(TaxonGroupLevels)-1)]\nfr.feed.eff<-data.frame(Taxon.group=substring(names(fr.feed.eff),11,nchar(names(fr.feed.eff))),fr.feed.eff=fr.feed.eff)\n \nfr.period <- read.csv('../Data/Kendall_et_al_1998-FracPeriodic.csv', stringsAsFactors=F)\ncolnames(fr.period)[1]<-'Taxon.group'\n\n# Aggregate Kendall groups to correspond to our grouping\nfr.period<-rbind(fr.period,fr.period[1,])\nfr.period[nrow(fr.period),1]<-'Mollusca'\nfr.period[nrow(fr.period),c(2,3,5)]<-fr.period[6,c(2,3,5)]+fr.period[7,c(2,3,5)]\nfr.period[nrow(fr.period),c(4,6,7)]<-ddply(rbind(fr.period[6,],fr.period[7,]),.(),summarise, Frac.Popns.Periodic=weighted.mean(Frac.Popns.Periodic,Number.Popns), Frac.Spp.Periodic.Raw=weighted.mean(Frac.Spp.Periodic.Raw,Number.Spp), Frac.Spp.Periodic.Bonf=weighted.mean(Frac.Spp.Periodic.Bonf,Number.Spp))[,-1]\n\nfr.period<-rbind(fr.period,fr.period[1,])\nfr.period[nrow(fr.period),1]<-'Arthropoda'\nfr.period[nrow(fr.period),c(2,3,5)]<-fr.period[4,c(2,3,5)]+fr.period[5,c(2,3,5)]\nfr.period[nrow(fr.period),c(4,6,7)]<-ddply(rbind(fr.period[4,],fr.period[5,]),.(),summarise, Frac.Popns.Periodic=weighted.mean(Frac.Popns.Periodic,Number.Popns), Frac.Spp.Periodic.Raw=weighted.mean(Frac.Spp.Periodic.Raw,Number.Spp), Frac.Spp.Periodic.Bonf=weighted.mean(Frac.Spp.Periodic.Bonf,Number.Spp))[,-1]\n\n\n############\n# Merge data\n############\nfr.comb<-merge(merge(fr.feed,fr.feed.eff,all.y=TRUE),fr.period,all.x=TRUE)\n\n\n#################\npdf('../Output/SatMeta-PerFeed.v.PerCyclic.pdf',width=6,height=5)\npar(pty='s',mfrow=c(2,3),las=2,cex=0.5,mgp=c(1.7,0.3,0),tcl=-0.2,cex.lab=1.1)\n xlims<-ylims<-c(0,1)\n plot(fr.comb$fr.feed,fr.comb$Frac.Popns.Periodic,pch=19,xlim=xlims,ylim=ylims,xlab='Percent feeding',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed,fr.comb$Frac.Popns.Periodic,fr.comb$Taxon.group,pos=3)\n plot(fr.comb$fr.feed,fr.comb$Frac.Spp.Periodic.Raw,pch=19,xlim=xlims,ylim=ylims,xlab='Percent feeding',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed,fr.comb$Frac.Spp.Periodic.Raw,fr.comb$Taxon.group,pos=3)\n plot(fr.comb$fr.feed,fr.comb$Frac.Spp.Periodic.Bonf,pch=19,xlim=xlims,ylim=ylims,xlab='Percent feeding',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed,fr.comb$Frac.Spp.Periodic.Bonf,fr.comb$Taxon.group,pos=3)\n \n xlims<-c(0,2.5)\n plot(fr.comb$fr.feed.eff,fr.comb$Frac.Popns.Periodic,pch=19,xlim=xlims,ylim=ylims,xlab='Taxon effect',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed.eff,fr.comb$Frac.Popns.Periodic,fr.comb$Taxon.group,pos=3)\n plot(fr.comb$fr.feed.eff,fr.comb$Frac.Spp.Periodic.Raw,pch=19,xlim=xlims,ylim=ylims,xlab='Taxon effect',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed.eff,fr.comb$Frac.Spp.Periodic.Raw,fr.comb$Taxon.group,pos=3)\n plot(fr.comb$fr.feed.eff,fr.comb$Frac.Spp.Periodic.Bonf,pch=19,xlim=xlims,ylim=ylims,xlab='Taxon effect',ylab='Percent cycling (Kendall et al.)')\n text(fr.comb$fr.feed.eff,fr.comb$Frac.Spp.Periodic.Bonf,fr.comb$Taxon.group,pos=3)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "meta": {"hexsha": "f75aeebe3c4559727e5f83d3fcd1f7fc72347018", "size": 4348, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/zOld/SatMeta-Analyses-CyclingKendall.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/zOld/SatMeta-Analyses-CyclingKendall.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/zOld/SatMeta-Analyses-CyclingKendall.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 58.7567567568, "max_line_length": 310, "alphanum_fraction": 0.606025759, "num_tokens": 1376, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802603710085, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3474975570956065}} {"text": "\n applyMean = function( f, method=\"fast\", newnames=NULL, ... ) {\n cnames= c(\"id\", \"x\", \"w\" )\n\n if (is.null( newnames )) newnames=names(f)\n\n nv = ncol(f)\n names(f) = cnames[1:nv]\n\n\n if (method==\"slow\") {\n if (nv==2) {\n r = by( f, f$id, with, mean( x, na.rm=TRUE, ... ))\n out = data.frame( id=names(r), x=as.numeric(as.vector(r)) , stringsAsFactors=FALSE )\n }\n if (nv==3 ) {\n r = by( f, f$id, with, weighted.mean( x, w, na.rm=TRUE, ... ))\n out = data.frame( id=names(r), x=as.numeric(as.vector(r)) , stringsAsFactors=FALSE )\n }\n }\n\n\n if (method==\"compact_but_slow\") {\n rowindex = 1:nrow(f)\n if (nv==2) {\n out = aggregate(rowindex ~ id, f, function(i) mean(f$x[i], na.rm=TRUE ), na.action=na.omit ) #arithmetic mean\n }\n if (nv==3 ) {\n out = aggregate(rowindex ~ id, f, function(i) weighted.mean(f$x[i], f$w[i], na.rm=TRUE ), na.action=na.omit ) # weighted mean\n }\n }\n\n\n if (method==\"fast\") {\n if (nv==2 ) {\n # no weighting factor .. simple means\n o = which( is.finite( f$x) )\n if ( length(o) == 0 ) return( \"No data?\")\n f = f[o,]\n\n f$id = as.factor( f$id )\n f$x = as.numeric( f$x )\n l0 = 1/(median(f$x))\n out0 = as.data.frame( xtabs( f$x * l0 ~ f$id, na.action=na.omit) / l0 , stringsAsFactors=FALSE )\n names( out0) = c(\"id\", \"sumx\" )\n\n out1 = as.data.frame( xtabs( ~ f$id, na.action=na.omit) , stringsAsFactors=FALSE )\n names( out1) = c(\"id\", \"n\" )\n\n out = merge( out0, out1, by=\"id\", all=TRUE, sort=FALSE )\n out$res = out$sumx / out$n\n out = out[ ,c( \"id\", \"res\" )]\n }\n\n if( nv==3 ) {\n # has a weighting factor .. weighted average\n\n o = which( is.finite( f$x + f$w) )\n if ( length(o) == 0 ) return( \"No data?\")\n f = f[o,]\n\n f$id = as.factor( f$id )\n f$x = as.numeric( f$x )\n f$xw = as.numeric( f$x * f$w )\n\n l0 = 1 / median( f$xw, na.rm=TRUE ) # a scaling factor to help avoid overflow errors\n l1 = 1 / median( f$w, na.rm=TRUE )\n\n out0 = as.data.frame( xtabs( f$xw * l0 ~ f$id, na.action=na.omit) / l0 , stringsAsFactors=FALSE )\n names( out0) = c(\"id\", \"sumxw\" )\n\n out1 = as.data.frame( xtabs( f$w *l1 ~ f$id, na.action=na.omit) / l1 , stringsAsFactors=FALSE )\n names( out1) = c(\"id\", \"sumw\" )\n\n out = merge( out0, out1, by=\"id\", all=TRUE, sort=FALSE )\n out$res = out$sumxw / out$sumw\n out = out[ ,c( \"id\", \"res\" )]\n\n }\n }\n\n names(out) = newnames[1:2]\n return( out )\n }\n", "meta": {"hexsha": "a9871a1282ff3b3427a8cd66cfde7b73ae79df5e", "size": 2625, "ext": "r", "lang": "R", "max_stars_repo_path": "R/applyMean.r", "max_stars_repo_name": "PEDsnowcrab/aegis", "max_stars_repo_head_hexsha": "92d4b045c17773c184df4ff3ed47c226ba4eddbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/applyMean.r", "max_issues_repo_name": "PEDsnowcrab/aegis", "max_issues_repo_head_hexsha": "92d4b045c17773c184df4ff3ed47c226ba4eddbf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/applyMean.r", "max_forks_repo_name": "PEDsnowcrab/aegis", "max_forks_repo_head_hexsha": "92d4b045c17773c184df4ff3ed47c226ba4eddbf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:58:58.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:58:58.000Z", "avg_line_length": 30.8823529412, "max_line_length": 133, "alphanum_fraction": 0.499047619, "num_tokens": 891, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.668880247169804, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3474975502372993}} {"text": "##CTE534_ClasificacionSupervisada=group\n##Imagen=raster\n##Vector=vector\n##Codigo=field Vector\n##Usos=field Vector\n##TablaUsos=output table\n#CodigoUsos=output table\n##EntrenamientoRaster=output raster\n##Covariables=output raster\n##Trainingbrick=output raster\n##ValueTable=output table\n##Matriz_de_Confusion=output table\n##showplots\n\n#Extraer valores de imagen seg\u00fan vector\nSalida<-raster::extract(Imagen, Vector, df=TRUE)\nVector$ID<-row.names(Vector)\nTablaUsos<-merge(Salida, Vector, x.by=\"ID\", by.y=\"ID\")\n\n#Rasterizar\nEntrenamientoRaster <- rasterize(Vector, Imagen[[1]], field=Codigo)\n\n#CoVariables\nCovariables <- mask(Imagen, EntrenamientoRaster)\n\n#Training Brick\nTrainingbrick <- addLayer(Covariables, EntrenamientoRaster)\n\n#Extraer todos los valores en una matriz\nvaluetable<- getValues(Trainingbrick)\n#Convertir a data frame\nvaluetable <- as.data.frame(valuetable)\n#Eliminar los na\nValueTable <- na.omit(valuetable)\n\n\nLevels <- length(unique(Vector[[Codigo]]))\n\n#RANDOM FOREST\nlibrary(randomForest)\n\n#valuetable <- read.csv(\"CSV/ValueTable.csv\")\nmodelRF <- randomForest(x = ValueTable[ ,c(1:(dim(ValueTable)[2] - 1))], y=factor(ValueTable$layer),\n importance = TRUE, type=\"classification\")\n\n\n#Inspeccionar la matriz de confusi\u00f3n del el analis\u00eds del error OOB\nmodelRF$confusion\nclasificacion <- sort(unique(Vector[[Usos]]))\n\n#Matriz de confusion\nMatriz_de_Confusion <- data.frame(modelRF$confusion)\nnames(Matriz_de_Confusion)[1:Levels] <- clasificacion\n#row.names(Matriz_de_Confusion) <- clasificacion\nMatriz_de_Confusion\n\n#Importancia variables\nvarImpPlot(modelRF)\n", "meta": {"hexsha": "55b68b1e293a80585a85371b3e186bb156d7c312", "size": 1594, "ext": "rsx", "lang": "R", "max_stars_repo_path": "Rscripts/02_Modelo.rsx", "max_stars_repo_name": "klauswiese/QGIS-R", "max_stars_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/02_Modelo.rsx", "max_issues_repo_name": "klauswiese/QGIS-R", "max_issues_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/02_Modelo.rsx", "max_forks_repo_name": "klauswiese/QGIS-R", "max_forks_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0169491525, "max_line_length": 100, "alphanum_fraction": 0.7722710163, "num_tokens": 440, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.3473790170707484}} {"text": "assert.type <- function(x, type, nm=deparse(substitute(x)))\n{\n Rstuff <- c(\"character\", \"numeric\", \"integer\", \"double\", \"logical\", \"matrix\", \"data.frame\", \"vector\")\n type <- match.arg(type, Rstuff)\n \n fun <- eval(parse(text=paste(\"is.\", type, sep=\"\")))\n \n if (!fun(x))\n pbdMPI::comm.stop(paste0(\"argument '\", nm, \"' must be of type \", type), call.=FALSE)\n \n return(invisible(TRUE))\n}\n\n\n\nassert.nonneg <- function(x, nm=deparse(substitute(x)))\n{\n if (x < 0)\n pbdMPI::comm.stop(paste0(\"argument '\", nm, \"' must be >= 0; have \", nm, \"=\", x), call.=FALSE)\n \n return(invisible(TRUE))\n}\n\n\n\nassert.positive <- function(x, nm=deparse(substitute(x)))\n{\n if (x <= 0)\n pbdMPI::comm.stop(paste0(\"argument '\", nm, \"' must be > 0; have \", nm, \"=\", x), call.=FALSE)\n \n return(invisible(TRUE))\n}\n\n\n\nisint <- function(x)\n{\n epsilon <- 1e-8\n \n return(abs(x - round(x)) < epsilon)\n}\n\n\n\nassert.wholenum <- function(x, nm=deparse(substitute(x)))\n{ \n if (!isint(x))\n pbdMPI::comm.stop(paste0(\"argument '\", nm, \"' must be an integer; have \", nm, \"=\", x), call.=FALSE)\n \n return(invisible(TRUE))\n}\n\n\n\nassert.natnum <- function(x)\n{\n nm <- deparse(substitute(x))\n assert.wholenum(x, nm=nm)\n assert.nonneg(x, nm=nm)\n \n return(invisible(TRUE))\n}\n\n\n\nassert.posint <- function(x)\n{\n nm <- deparse(substitute(x))\n assert.wholenum(x, nm=nm)\n assert.positive(x, nm=nm)\n \n return(invisible(TRUE))\n}\n", "meta": {"hexsha": "aa348af45ecd5ffaba015a2d023278589ce1b45b", "size": 1408, "ext": "r", "lang": "R", "max_stars_repo_path": "R/assert.r", "max_stars_repo_name": "wrathematics/pbdML", "max_stars_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/assert.r", "max_issues_repo_name": "wrathematics/pbdML", "max_issues_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-09-22T22:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2015-09-22T22:45:14.000Z", "max_forks_repo_path": "R/assert.r", "max_forks_repo_name": "wrathematics/pbdML", "max_forks_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.027027027, "max_line_length": 103, "alphanum_fraction": 0.6015625, "num_tokens": 434, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6893056231680122, "lm_q1q2_score": 0.3473453568947545}} {"text": "# Install my packages\nlist.of.packages <- c(\"doParallel\",\"doMC\",\"kableExtra\", \"foreach\", \"MASS\", \"copula\", \"foreach\", \"devtools\", \"KernSmooth\")\nnew.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,\"Package\"])]\nif(length(new.packages)) install.packages(new.packages)\nlapply(list.of.packages, require, character.only = TRUE)\n\ncl <- makeCluster(2)\nregisterDoParallel(cl)\n\nsetwd(\"Enrichment-Inference-Supplemental-Materials/simulation_files/difference_bands\")\nsource(\"power_type1_cb.r\")\nsource(\"hypothesis_test_new.r\")\n\nr.vec <- c(2^seq(1, 13), 3^seq(1, 8), 10, 105, 300, 1500, 15000)\nr.vec <- r.vec[order(r.vec)]/150000\nsimreps = 10\n\nmethod.v <- c(\"sup-t\", \"bonf\", \"EmProc\")\n\ndist <- \"binorm\"\nparams.ls <- list(list(c(sqrt(2) * .8, 0), c(sqrt(2) * .6, 0)), list(c(sqrt(2) * .8, 0), c(sqrt(2) * .8, 0)),\n list(c(sqrt(2) * .6, 0), c(sqrt(2) * .6, 0)))\nrho <- c(.9, .9, .9)\n\nk.ls <- rep(list(list()), length(params.ls)) \nk.c.ls <- rep(list(list()), length(params.ls)) \ni <- 1; j <- 1\nfor(i in seq_along(params.ls)) {\n for(j in seq_along(method.v)) {\n print(paste(params.ls[i], method.v[j]))\n k.ls[[i]][[j]] <- power.test(params= params.ls[[i]], dist = dist, pi.0.true = 1/500,\n myseed=569, method = method.v[j], plus = F,\n simreps = simreps, m = 150000,\n r.vec = r.vec, metric = \"k\", rho = rho[i])\n k.c.ls[[i]][[j]] <- power.test(params= params.ls[[i]], dist = dist, pi.0.true = 1/500,\n myseed=569, method = method.v[j], plus = T,\n simreps = simreps, m = 150000,\n r.vec = r.vec, metric = \"k\", rho = rho[i])\n save.image(\"power_band_binorm_corr.rdata\")\n }\n}\n\n\n", "meta": {"hexsha": "1dd37ea64c8abbcc14b62caf14f9dd837f9e3f53", "size": 1807, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation_files/difference_bands/cp_diff_bands_binorm_corr.r", "max_stars_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_stars_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation_files/difference_bands/cp_diff_bands_binorm_corr.r", "max_issues_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_issues_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation_files/difference_bands/cp_diff_bands_binorm_corr.r", "max_forks_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_forks_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.0681818182, "max_line_length": 121, "alphanum_fraction": 0.5511898174, "num_tokens": 571, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6893056167854461, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.34734535367854}} {"text": "\n#**********\n# libs\n#**********\nrm(list = ls())\nlibrary(readr)\nsource('Evaluation.r')\n\n#****************\n# read data\n#****************\nfile_names <- list.files('./data/', pattern = '*.csv')\nj = 1\nfor(file_name in file_names){\n x = read_csv(paste('./data/', file_name, sep = ''))\n \n if(j == 1){\n rslt = x\n }else{\n rslt = rbind(rslt, x)\n }\n \n j = j +1 \n}\n\nsave(file = 'rslt.RData', rslt, compression_level = 9)\n\n#*******************\n# analyze performance\n#******************\n\ncutoffs = c(seq(0.5, 0.9, 0.1), seq(0.91, 0.99, 0.01))\nj = 1\nfor(i in cutoffs){\n perf <- eval(post = rslt$pred_prob, ground.truth = rslt$ground_truth, thres = i)\n \n if(j == 1)\n perfs = perf\n else{\n perfs = rbind(perfs, perf)\n }\n \n print(j)\n j = j + 1\n}\n\nwrite.csv(file = 'perfs.csv', perfs, row.names = F, quote = T)\n\n\n#conduct analysis on cutoff \nload('rslt.RData')\nproportion = c(seq(0.1, 0.02, -0.01),\n seq(0.01, 0.001, -0.001))\nN = nrow(rslt)\nrslt = sort(rslt[,2], decreasing = T, method = 'quick')\nns = c()\nfor (p in proportion){\n ns = c(ns, ceiling(N*p))\n}\ncutoff_prob = rslt[ns]\ncutoff_prop_prob = data.frame(prop = proportion, prob = cutoff_prob)\nwrite.csv(file='Celegans.csv', cutoff_prop_prob, row.names = F)\n\n\n# prop prob\n# 1 0.100 0.5359123\n# 2 0.090 0.5876898\n# 3 0.080 0.6408077\n# 4 0.070 0.6935574\n# 5 0.060 0.7449306\n# 6 0.050 0.7938480\n# 7 0.040 0.8399874\n# 8 0.030 0.8832679\n# 9 0.020 0.9236561\n# 10 0.010 0.9614733\n# 11 0.009 0.9651591\n# 12 0.008 0.9688153\n# 13 0.007 0.9724771\n# 14 0.006 0.9761189\n# 15 0.005 0.9797641\n# 16 0.004 0.9834235\n# 17 0.003 0.9871500\n# 18 0.002 0.9909271\n# 19 0.001 0.9949517\n\n\n", "meta": {"hexsha": "b5a7a6d1b2444f29ef45ba2da6b15c3f2f1b027a", "size": 1659, "ext": "r", "lang": "R", "max_stars_repo_path": "Whole_Genome/wg_analysis.r", "max_stars_repo_name": "tanfei2007/DeepM6A", "max_stars_repo_head_hexsha": "ac8b5543db292516ce10cf42b7506004140d4d41", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2020-08-09T02:21:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-17T03:01:08.000Z", "max_issues_repo_path": "Whole_Genome/wg_analysis.r", "max_issues_repo_name": "Cmanco/DeepM6A", "max_issues_repo_head_hexsha": "ac8b5543db292516ce10cf42b7506004140d4d41", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Whole_Genome/wg_analysis.r", "max_forks_repo_name": "Cmanco/DeepM6A", "max_forks_repo_head_hexsha": "ac8b5543db292516ce10cf42b7506004140d4d41", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-11-30T01:53:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-21T04:08:47.000Z", "avg_line_length": 19.0689655172, "max_line_length": 82, "alphanum_fraction": 0.5696202532, "num_tokens": 702, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.34734535367853997}} {"text": "# install necessary packages ----\nlibrary(ggplot2)\nlibrary(ggradar)\nsuppressPackageStartupMessages(library(dplyr))\nlibrary(scales)\n\n#load necessary data\n#field_radar = read_csv('write_data/field_radar.csv')\n\n# data processing ----\nfield_radar <- field_radar %>%\n #as_tibble(rownames = 'classification') %>%\n mutate_at(vars(-'classification'),function(x) x/100)%>%\n head(5)\n\n# visualization radar plot ----\nggradar(field_radar) +\n ggsave(\"fifa_radar_plot.png\")\n\n", "meta": {"hexsha": "cbd8be344140e55342d47a5a13fede17818ce74a", "size": 465, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/field.r", "max_stars_repo_name": "dirkstrong1/soccer_player_predictor", "max_stars_repo_head_hexsha": "914990b0fa25d2194f14cb1c420334d16bdfc29a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sandbox/field.r", "max_issues_repo_name": "dirkstrong1/soccer_player_predictor", "max_issues_repo_head_hexsha": "914990b0fa25d2194f14cb1c420334d16bdfc29a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sandbox/field.r", "max_forks_repo_name": "dirkstrong1/soccer_player_predictor", "max_forks_repo_head_hexsha": "914990b0fa25d2194f14cb1c420334d16bdfc29a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.25, "max_line_length": 57, "alphanum_fraction": 0.7376344086, "num_tokens": 127, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.34722652274188975}} {"text": "### Single site sensitivity analysis\noptions(warn = -1)\n\nrequire(tidyverse)\noutput <- readRDS(\".data/SingleSim.rds\")\noutput.succes <- do.call(\"bind_rows\", output) %>% \n\tfilter(conv != FALSE) %>% mutate(sd = exp(as.numeric(SIGMA)))\n\n\nnowintering <- output.succes %>% filter(propWint == \"0.0\" & propA == \"1.0\") %>% mutate(MU = MU - mean(baseruns$MU))\n\nbaseruns <- nowintering %>% filter(LoS == 5 & ArrivalDate ==4)\nnowintering <- nowintering%>% mutate(MU = MU - mean(baseruns$MU))\nlm.dat <- mutate(nowintering, LoS = as.numeric(LoS), Pop = as.numeric(Pop), ArrivalDate = as.numeric(ArrivalDate))\nlm.res <- lm(MU~LoS*ArrivalDate, lm.dat)\n\naug_lm <- broom::augment(lm.res, lm.dat)\n\n\n#### Final plot # 1\nsimplt1 <- \nggplot(aug_lm, aes(LoS,.fitted, shape = as.factor(ArrivalDate))) + #geom_line() + \n stat_summary(aes(y=MU), fun.data = \"mean_cl_boot\" , #position = position_dodge(width = 0.15),\n fun.args = c(conf.int = 0.95, B = 2000), geom = \"pointrange\") +\n # geom_hline(yintercept = 0)+ \n # geom_pointrange(position = position_jitter(width = 0.2),aes(y=MU, ymin=MU - sd, ymax = MU + sd )) +\n scale_shape_manual(values=c(2,1,19,15)) + geom_hline(yintercept = 0, linetype=2)+\n scale_y_continuous( breaks = seq(-6, 6, by = 2), minor_breaks = seq(-5, 5, by = 2)) +\n cowplot::background_grid(size.major = 1, major = \"y\", minor = \"y\", size.minor = .8)+\n labs(x = \"\\nMean length of stay\", y= \"Deviation from \\nbaseline peak passage date\", shape = \"Mean\\ndate of\\narrival\")\n\n\n\n\n# Wintering birds ---------------------------------------------------------\n\noutputWinter <- output.succes %>% filter(propA == \"1.0\") %>% mutate(MU = MU - mean(baseruns$MU))\nbaseWint <- outputWinter %>% filter(LoS == 5 & ArrivalDate ==4)\n\n# Winter final plot 1\nwintplt1 <- \n ggplot(baseWint, aes(as.numeric(propWint), MU)) + \n # geom_smooth(method=\"lm\", formula = \"y~poly(x,2)\", se = F, colour = 'grey') +\n # geom_point()+\n stat_summary(fun.data= 'mean_cl_boot', geom=\"pointrange\") +\n labs(x = \"Proportion of Winter\\nResidents in Population\", y=\"Deviation from \\nbaseline peak passage date\") +#+ ggthemes::theme_few()\n geom_hline(yintercept = 0, linetype=2)+\n scale_y_continuous( breaks = seq(-6, 6, by = 1), minor_breaks = seq(-5, 5, by = 2)) +\n cowplot::background_grid(size.major = 1, major = \"y\", minor = \"y\", size.minor = .8)\n\n\nwintplt2 <- \nggplot(outputWinter, aes(as.numeric(propWint), MU, shape = ArrivalDate)) + scale_x_continuous() +\n # geom_smooth(colour = 'grey', aes(linetype = ArrivalDate), method=\"lm\", formula = \"y~poly(x,2)\", se = F) +\n stat_summary(position=position_dodge(width = 0.02), fun.data= 'mean_cl_boot') +\n scale_shape_manual(values=c(2,1,19,15)) +\n #scale_color_grey(start = 0.9, end = 0.2)+\n geom_hline(yintercept = 0, linetype=2)+\n scale_y_continuous( breaks = seq(-60, 20, by = 20), minor_breaks = seq(-60, 20, by = 10)) +\n cowplot::background_grid(size.major = 1, major = \"y\", minor = \"y\", size.minor = .8)+\n labs(x = \"Proportion of Winter Residents in Population\", \n y=\"Deviation from \\nbaseline peak passage date\",\n colour = \"Mean\\nArrival\\nDate\")\n\n\n\n# Two groups --------------------------------------------------------------\n\ntwo.shoes <- output.succes %>% filter(propWint == \"0.0\" & ArrivalDate >1) %>% mutate(MU = MU - mean(baseruns$MU, na.rm=T))\n\nbase2gr <-two.shoes %>% filter(LoS == 5 & ArrivalDate ==4)\n\n\ntwopops.plt <- \nggplot(base2gr, aes(as.numeric(propA), MU)) + \n # geom_smooth(method=\"lm\", formula = \"y~poly(x,2)\", se = F, colour = \"grey\") +\n stat_summary(fun.data= 'mean_cl_boot', geom=\"pointrange\") + \n labs(x = \"Proportion of Baseline\\nStrategy in Population\", y=\"Deviation from baseline passage date\")+\n geom_hline(yintercept = 0, linetype=2)+\n scale_y_continuous( breaks = seq(-6, 6, by = 0.5), minor_breaks = seq(-5, 5, by = 2)) +\n cowplot::background_grid(size.major = 1, major = \"y\", minor = \"y\", size.minor = .8)\n\n\n\n\n\n\n# Smallpop ----------------------------------------------------------------\n\nsmallpop <- readRDS(\".data/SingleSim_smallpop.rds\") %>% \n do.call(\"rbind\", .) %>% filter(conv == TRUE) %>% bind_rows(baseruns) %>% \nmutate(Pop = as.numeric(as.character(Pop)), MU = MU - mean(baseruns$MU)) \n \npop.plt <- \nggplot(smallpop, aes(Pop, MU)) + \n # geom_hline(yintercept = 0, colour = 'grey')+\n stat_summary( fun.data= 'mean_cl_boot') + \n scale_x_log10(breaks = c(1,10,50,100,500,1000, 10000))+ \n # ggthemes::theme_few() +\n labs(y = \"Deviation from baseline passage date\", x = \"Total number of birds stopping at site\") +\n geom_hline(yintercept = 0, linetype=2)+ theme(axis.text.x = element_text(size = 10,angle=30, hjust = 0.9,vjust =0.9))+\n scale_y_continuous( breaks = seq(-6, 6, by = 1), minor_breaks = seq(-5, 5, by = 2)) +\n cowplot::background_grid(size.major = 1, major = \"y\", minor = \"y\", size.minor = .8)\n\n\n\n\n\n\nrequire(cowplot)\nlegend1 <- cowplot::get_legend(wintplt2 + labs(shape = \"Arrival\\nDate\"))\nfinal.p <-plot_grid(simplt1 + theme(legend.position=\"none\", text=element_text(size=10))+ ylab(\"\") + \n background_grid(major = 'y', minor = \"none\") + ylim(-6.5,5),\n twopops.plt + ylab(\"\")+ theme(legend.position=\"none\", text=element_text(size=10))+\n background_grid(major = 'y', minor = \"none\") + \n ylim(-6.5,5),\n wintplt1 + ylab(\"\")+ theme(legend.position=\"none\", text=element_text(size=10))+\n background_grid(major = 'y', minor = \"none\") + \n ylim(-6.5,5),\n pop.plt+theme(legend.position=\"none\", text=element_text(size=10))+ ylab(\"\")+ \n background_grid(major = 'y', minor = \"none\") + \n ylim(-6.5,5),\n nrow=2,ncol=2, rel_heights = c(.45,.45,.1), labels = c(\"(A)\", \"(B)\", \"(C)\", \"(D)\"),\n hjust=-0.1)#,\n # left = \"Deviation from baseline\\npassage date (days)\")\n\nfinal.pLab <- gridExtra::grid.arrange(final.p, legend1, ncol =2, widths = c(0.9, 0.1), padding=unit(0.8, \"line\"),\n left= \"Deviation from baseline passage date (days)\")\n\n# save_plot(\"../6th Submission/Figure5.tiff\", final.pLab, base_width = 7, base_height = 7)\n\n\n## Example of how to read and calculate peak passage date from output of model\n# ---------------------------------------------------------------------------------\n# Test def of peak passage date -------------------------------------------\n\n# require(tidyverse)\n\n# exampleRun <- read_tsv(\".data/singleSimulation_[573]_[5]_[4]_[10000]_[0.0][1.0].csv\") %>% \n# mutate()\n# exPPest <- exampleRun %>% \n# filter(site == 0) %>% \n# group_by(Bird) %>% \n# summarize(meanD = floor(mean(time))) %>% ungroup %>% \n# group_by(meanD) %>% \n# summarize(nPass = n(),\n# peakPassage = mean(meanD),\n# medPassage = median(meanD))\n\n# source(\"muEstimation.R\")\n\n# dataforCalc <- exampleRun %>% group_by(time) %>% \n# filter(site == 0) %>% \n# summarize(WESA = n()) %>% \n# ungroup %>% mutate(Year = 2016,\n# SiteID = \"Simulation\",\n# Day.of.Year = time) %>% filter(Day.of.Year >= quantile(exampleRun$time, 0.025)[1] & Day.of.Year <= quantile(exampleRun$time, 0.975)[1])\n# results_1 <- calc.peak.prog(site = \"Simulation\", year = 2016, data = dataforCalc, species = \"WESA\", estimateCI = T, errorMethod = 'jackknife')\n# results_1$results\n# exPPest\n# weighted.mean(dataforCalc$Day.of.Year, dataforCalc$WESA)\n# options(warn = 0)\n", "meta": {"hexsha": "edd46822de13cc7fdfa4c599f9f4adbc82bd36fb", "size": 7574, "ext": "r", "lang": "R", "max_stars_repo_path": "Simulation2.r", "max_stars_repo_name": "dhope/Hope-etal-Condor-Progression", "max_stars_repo_head_hexsha": "d356037cba16e82d6640969e9660c4637ae2a452", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Simulation2.r", "max_issues_repo_name": "dhope/Hope-etal-Condor-Progression", "max_issues_repo_head_hexsha": "d356037cba16e82d6640969e9660c4637ae2a452", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Simulation2.r", "max_forks_repo_name": "dhope/Hope-etal-Condor-Progression", "max_forks_repo_head_hexsha": "d356037cba16e82d6640969e9660c4637ae2a452", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.4662576687, "max_line_length": 158, "alphanum_fraction": 0.5864800634, "num_tokens": 2315, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.34722652274188975}} {"text": "#####################################################################\n# Fitting generalized additive models to fraction feeding data\n# See '...GAM_robustness.r' for model justifications, including\n# error family and link function and gamma wiggliness penalty\n#####################################################################\n#####################################################################\nrm(list = ls())\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nlibrary(plyr)\nlibrary(mgcv)\nlibrary(xtable) # for LaTeX export\nlibrary(ggplot2)\nsource('R/FracFeed-Functions.r') # Convenience functions\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Load saved 'fdat' w/ tWS\nload('Data/FracFeed_DataPhylo.Rdata') # load full post-ToL FracFeed database\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nnrow(fdat) # total number of surveys in database\nlength(unique(fdat$Consumer.identity)) # total number of species in database\nrange(fdat$Latitude, na.rm = TRUE)\nrange(fdat$Year, na.rm = TRUE)\n\n#####################################################################\n# Summary stats by Taxonomic Group\n##################################\nTaxonGroupLevels <-\n c(\n 'Ctenophores',\n 'Cnidarians',\n 'Annelids',\n 'Chaetognaths',\n 'Molluscs',\n 'Echinoderms',\n 'Arthropods',\n 'Fish',\n 'Reptiles',\n 'Amphibians',\n 'Birds',\n 'Mammals'\n )\nfdat$Taxon.group <- factor(fdat$Taxon.group, levels = TaxonGroupLevels)\nsfdat <-\n ddply(\n fdat,\n .(Taxon.group),\n summarise,\n 'Mean' = mean(fF),\n 'Std.dev.' = sd(fF),\n Min = min(fF),\n Max = max(fF),\n Surveys = length(fF),\n Species = length(unique(Consumer.identity)),\n minObs = min(Total.stomachs.count, na.rm = T),\n maxObs = max(Total.stomachs.count, na.rm = T)\n )\nsfdat[, 2:5] <- round(sfdat[, 2:5], 2)\n\n\nsetwd('Figures/Supp/')\nprint(\n xtable(sfdat, type = \"latex\", caption = 'Summary statistics for all taxa in the database, including those that could not be used in the analyses of the main text for lack of co-variate information. minObs and maxObs refer to the minimum and maximum number of individuals assessed in a single survey across all surveys of the group.'),\n file = \"FracFeed-Tab-SummStats.tex\",\n include.rownames = FALSE,\n table.placement = 'H'\n)\nsetwd('../../')\n\n\npdf(\n 'Figures/Supp/FracFeed-FigS-TaxonViolins.pdf',\n height = 3,\n width = 5\n)\nviol <-\n ggplot(fdat, aes(Taxon.group, Percent.feeding)) + \n geom_violin(scale = \"width\", bg = 'grey') + \n labs(x = '', y = 'Percent feeding') + \n theme_classic() + theme(axis.text.x = element_text(angle = 60, hjust = 1)) + \n stat_summary(fun.data = data_summary, color = \"black\") + \n annotate(\"text\", x = 1:nrow(sfdat), y = 106, \n label = sfdat$Species, size = 1.5) + \n annotate(\"text\", x = 1:nrow(sfdat), y = 103, \n label = paste0(\"(\", sfdat$Surveys, \")\"), size = 1.5)\nprint(viol)\ndev.off()\n\n################################################\n# General model specifications \n# (justified in GAM_robustness)\n################################################\nfam <- quasibinomial(link = 'logit')\n\n# To get actual used sample size (after dropped rows)\n# (needed for determining BIC-like gamma penalty)\nfit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TAlog + SAlog + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method='REML'\n)\ngamma <- log(nrow(fit$model)) / 2\n\n#######################\n# All primary variables\n#######################\nfit1.cont <- fit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TAlog + SAlog + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\n# Compare data lost vs. included\nnrow(fit$model)\nexcl <- fdat[fit$na.action, ]\nnrow(excl)\n\nincl <- fdat[-fit$na.action, ]\nnrow(incl)\nlength(unique(incl$Consumer.identity))\nrange(incl$Year, na.rm = TRUE)\nrange(incl$Lat, na.rm = TRUE)\n\nnrow(fdat)\nnrow(excl) + nrow(incl)\nlength(unique(fdat$Consumer.identity))\n\n# odds ratio\nexp(cbind(\"Odds ratio\" = coef(fit)[c('TAlog', 'SAlog')],\n confint.default(fit, c('TAlog', 'SAlog'), level = 0.95)))\n1 / exp(cbind(\"Odds ratio\" = coef(fit)[c('SAlog')],\n confint.default(fit, c('SAlog'), level = 0.95)))\n\n\nsave(fit, file = 'Output/GAM/GAM_fit_wTaxInf.Rdata')\n\npdf('Output/GAM/GAM_fit_wTaxInf.pdf',\n height = 8,\n width = 10)\nplot(\n fit,\n residuals = F,\n seWithMean = TRUE,\n shade = TRUE,\n rug = TRUE,\n all.terms = TRUE,\n pch = '.',\n pages = 1\n)\npar(mfrow = c(2, 2))\ngam.check(fit)\ndev.off()\n\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Same but with TA and SA as factors\nfit1.fact <- fit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TA + SA + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nsummary(fit)\nlogit_tr(summary(fit)$p.coeff)\n\nsave(fit, file = 'Output/GAM/GAM_fit_wTaxInf_TASAfact.Rdata')\n\npdf('Output/GAM/GAM_fit_wTaxInf_TASAfact.pdf',\n height = 8,\n width = 10)\nplot(\n fit,\n residuals = F,\n seWithMean = TRUE,\n shade = TRUE,\n rug = TRUE,\n all.terms = TRUE,\n pch = '.',\n pages = 1\n)\npar(mfrow = c(2, 2))\ngam.check(fit)\ndev.off()\n\n# Export summary tables\np.out <- summary(fit1.fact)$p.table\np.out[, 1:3] <- signif(p.out[, 1:3], 2)\np.out[, 4] <- signif(p.out[, 4], 3)\np.out[p.out[, 4] < 10 ^ -3, 4] <- '< 0.001'\np.out[p.out == 'NaN'] <- ''\n\nrownames(p.out) <- sub('TA', '', rownames(p.out))\nrownames(p.out) <- sub('SA', '', rownames(p.out))\nrownames(p.out) <- sub('Eco', '', rownames(p.out))\nrownames(p.out) <- sub('EE', '', rownames(p.out))\nrownames(p.out) <- sub('TG', '', rownames(p.out))\n\ns.out <- summary(fit1.fact)$s.table\ns.out[, 1:3] <- round(s.out[, 1:3], 2)\ns.out[, 4] <- round(s.out[, 4], 3)\ns.out[s.out[, 4] < 10 ^ -3, 4] <- '< 0.001'\n\nsetwd('Figures/Supp/')\nprint(\n xtable(\n p.out,\n type = \"latex\",\n caption = 'Estimated effects (log-odds) of the parametric terms in the primary statistical model describing variation in the observed fraction of feeding individuals. The intercept to which all effects are relative corresponds to an ectothermic marine mollusc surveyed at a spatial scale of up to 1 meter for under an hour. Birds correspond to the endotherm baseline.',\n label = 'tab:gam_main_p'\n ),\n file = \"FracFeed-Tab-GAM_main_ptable.tex\",\n table.placement = 'H'\n)\n\nprint(\n xtable(\n s.out,\n type = \"latex\",\n caption = 'Estimated effects of the smooth terms in the primary statistical model describing variation in the observed fraction of feeding individuals. $tWS$ refers to the days since the last winter solsctice, $Lat$ refers to latitude, $DRlog$ refers to $log_{10}$ of minimum diet richness, and $Year$ refers to the year of the survey.',\n label = 'tab:gam_main_s'\n ),\n file = \"FracFeed-Tab-GAM_main_stable.tex\",\n table.placement = 'H'\n)\nsetwd('../../')\n\n##############################################################\n# Same (with TA and SA as factors) but relative to terrestrial\n# (to ease language of main text)\n##############################################################\norig <- levels(fdat$Eco)\nfdat$Eco <- factor(fdat$Eco, levels = rev(orig))\nfit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TA + SA + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nsummary(fit)\ntrms <- summary(fit)$p.coeff\nsel <- grep('Eco', names(trms))\n# Log-odds\ntrms[sel]\n# Odds-ratios\nexp(trms[sel])\nexp(cbind(\"Odds ratio\" = coef(fit)[sel],\n confint.default(fit, sel, level = 0.95)))\n\n# now to compare lakes and streams\nfdat$Eco <- factor(fdat$Eco, levels = orig[c(3, 4, 5, 1, 2)])\nfit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TA + SA + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nsummary(fit)\ntrms <- summary(fit)$p.coeff\nsel <- grep('Eco', names(trms))\n# Log-odds\ntrms[sel]\n# Odds-ratios\nexp(trms[sel])\nexp(cbind(\"Odds ratio\" = coef(fit)[sel],\n confint.default(fit, sel, level = 0.95)))\n\n# Reorder to original\nfdat$Eco <- factor(fdat$Eco, levels = orig)\n\n################################################################\n# Same (with TA and SA as factors) but with DRlog as parametric linear term\n# (to enable language of main text)\n################################################################\nfit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + DRlog + s(Year) +\n TA + SA + Eco + EE + TG,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nsummary(fit)\ntrms <- summary(fit)$p.coeff\nsel <- grep('DRlog', names(trms))\n# Log-odds\ntrms[sel]\n# Odds-ratios\nexp(cbind(\"Odds ratio\" = coef(fit)[sel],\n confint.default(fit, sel, level = 0.95)))\n\n####################################################################\n# Without taxonomic information for phylogenetic tests\n# (i.e. remove Endo/Ecto, Taxon Group, Diet richness and Ecosystem)\n####################################################################\nfit.noTaxInf <- fit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(Year) + \n TAlog + SAlog,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nanova(fit)\nsummary(fit)\n\n# How many data points included (because the others didn't have info for all variables)?\nnrow(fit$model)\nexcl <- fdat[fit$na.action, ]\nnrow(excl)\n\nsave(fit, file = 'Output/GAM/GAM_fit_noTaxInf.Rdata')\n\npdf(paste0('Output/GAM/GAM_fit_noTaxInf.pdf'),\n height = 8,\n width = 10)\nplot(\n fit,\n residuals = F,\n seWithMean = TRUE,\n shade = TRUE,\n rug = TRUE,\n all.terms = TRUE,\n pch = '.',\n pages = 1\n)\npar(mfrow = c(2, 2))\ngam.check(fit)\ndev.off()\n\n########################################################################\n####################################\n# Final model plus feeding data type\n# (dismissed during robustness checks \n# but requested by reviewers)\n####################################\nfit1.fact <- fit <- gam(\n (FSc / TSc) ~ \n s(tWS, bs = 'cc') + s(Lat) + s(DRlog) + s(Year) +\n TA + SA + Eco + EE + TG + FD,\n family = fam,\n weights = TSc,\n knots = list(tWS = c(0, 365)),\n data = fdat,\n na.action = 'na.omit',\n method = 'REML',\n gamma = gamma\n)\n\nsummary(fit)\nlogit_tr(summary(fit)$p.coeff)\n\npdf(\n 'Output/GAM/GAM_fit_wTaxInf_TASAfact_wFD.pdf',\n height = 8,\n width = 10\n)\nplot(\n fit,\n residuals = F,\n seWithMean = TRUE,\n shade = TRUE,\n rug = TRUE,\n all.terms = TRUE,\n pch = '.',\n pages = 1\n)\npar(mfrow = c(2, 2))\ngam.check(fit)\ndev.off()\n\n# Export summary tables\np.out <- summary(fit1.fact)$p.table\np.out[, 1:3] <- signif(p.out[, 1:3], 2)\np.out[, 4] <- signif(p.out[, 4], 3)\np.out[p.out[, 4] < 10 ^ -3, 4] <- '< 0.001'\np.out[p.out == 'NaN'] <- ''\n\nrownames(p.out) <- sub('TA', '', rownames(p.out))\nrownames(p.out) <- sub('SA', '', rownames(p.out))\nrownames(p.out) <- sub('Eco', '', rownames(p.out))\nrownames(p.out) <- sub('EE', '', rownames(p.out))\nrownames(p.out) <- sub('TG', '', rownames(p.out))\nrownames(p.out) <- sub('FD', '', rownames(p.out))\n\ns.out <- summary(fit1.fact)$s.table\ns.out[, 1:3] <- round(s.out[, 1:3], 2)\ns.out[, 4] <- round(s.out[, 4], 3)\ns.out[s.out[, 4] < 10 ^ -3, 4] <- '< 0.001'\n\nsetwd('Figures/Supp/')\nprint(\n xtable(\n p.out,\n type = \"latex\",\n caption = 'Estimated effects (log-odds) of the parametric terms in the primary statistical model but with data type (stomach contents versus direct observation) also included.',\n label = 'tab:gam_main_p'\n ),\n file = \"FracFeed-Tab-GAM_main_wFD_ptable.tex\",\n table.placement = 'H'\n)\n\nprint(\n xtable(\n s.out,\n type = \"latex\",\n caption = 'Estimated effects of the smooth terms in the primary statistical model but with data type (stomach contents versus direct observation) also included.',\n label = 'tab:gam_main_wFD_s'\n ),\n file = \"FracFeed-Tab-GAM_main_wFD_stable.tex\",\n table.placement = 'H'\n)\nsetwd('../../')\n\n\n####################################################################\n####################################################################\n####################################################################", "meta": {"hexsha": "f56efbb4a2cee5e6b8fcfdf22e43b1b9be6d1d8e", "size": 12701, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/FracFeed-Analyses-GAM.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/FracFeed-Analyses-GAM.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/FracFeed-Analyses-GAM.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.731441048, "max_line_length": 375, "alphanum_fraction": 0.5494055586, "num_tokens": 3858, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442250928250376, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.347226515378661}} {"text": "df = expand.table(Titanic)\n", "meta": {"hexsha": "182f2828f277e5dbbda951451fdf2bc3675d1efa", "size": 27, "ext": "r", "lang": "R", "max_stars_repo_path": "Application/DDS2/script/Convert_table2data_frame.r", "max_stars_repo_name": "Sanaxen/Data_analysis_tools", "max_stars_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2022-01-20T13:39:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-20T13:39:39.000Z", "max_issues_repo_path": "Application/DDS2/script/Convert_table2data_frame.r", "max_issues_repo_name": "Sanaxen/Data_analysis_tools", "max_issues_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-11-09T13:16:29.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-09T13:16:52.000Z", "max_forks_repo_path": "Application/DDS2/script/Convert_table2data_frame.r", "max_forks_repo_name": "Sanaxen/Data_analysis_tools", "max_forks_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-08T09:37:23.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-08T09:37:23.000Z", "avg_line_length": 13.5, "max_line_length": 26, "alphanum_fraction": 0.7407407407, "num_tokens": 8, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6584175139669998, "lm_q1q2_score": 0.3471944343972959}} {"text": "# takes g_inputdir,g_filetable and g_outputdir\r\n##and g_duration_slider, g_bin_size\r\n# return activity plots\r\n\r\n\r\nmessage(\"starting activity.r\")\r\n### compute the activities\r\n\t\r\nact_table = data.frame()\r\n\r\n#compute activities for each individum\r\nfor (i in c(1:nrow(id_table))) {\r\n\tact = c.activity(traj[id(traj)==id_table$id[i]],g_duration_slider/10)\r\n\tact_id = rep(id_table$id[i],nrow(act))\r\n\tact_group = rep(id_table$group[i],nrow(act))\r\n\tact_table = rbind(act_table,data.frame(act,id=act_id,group=act_group))\r\n\r\n}\r\n\r\n# filter activities which are smaller than the act slider\r\npause_table = act_table[act_table$act<0,]\r\npause_table$pause = - pause_table$act\r\nact_table = act_table[act_table$act>0,]\r\n\r\n#calculate total activity time for each individual\r\nsum_act = c()\r\nmedian_act = c()\r\nmedian_pause = c()\r\nnumber_pause = c()\r\nfor (i in c(1:nrow(id_table))) {\r\n\tact = act_table[act_table$id==id_table$id[i],]$act\r\n\tpause = pause_table[act_table$id==id_table$id[i],]$act\r\n\tsum_act = c(sum_act,sum(act))\r\n\tmedian_act = c(median_act,median(act))\r\n\tmedian_pause = c(median_pause, median(abs(pause)))\r\n\tnumber_pause = c(number_pause,length(pause))\r\n\t\r\n}\r\nact_table_2 = data.frame(sum_act,median_act,median_pause,number_pause,id=id_table$id,group=id_table$group)\r\n\r\nmessage(\"starting writing activities.txt\")\r\n\r\n\r\n\r\nsetwd(rgghome)\r\n\r\n# create bins\r\nif (g_bin_size==0)\r\n\tg_bin_size = 0.5\t\t\r\nv = act_table$act\r\nbins = seq(min(v[!is.na(v)])-g_bin_size,max(v[!is.na(v)])+g_bin_size,g_bin_size)\r\n\r\n#create mean and sd table\r\nmean_table = create.mean.table(act_table_2,group_ids,data_cols=1:4)\r\n\r\n\r\nmessage(\"starting plots activity\")\r\n### create plots\r\nmybarplot(mean_table$means$sum_act,mean_table$ses$sum_act,rownames(mean_table$means),\r\n\tmain=\"Total activity time\",ylab=\"Total activity time [s]\")\r\nmybarplot(mean_table$means$median_act,mean_table$ses$median_act,rownames(mean_table$means),\r\n\tmain=\"Mean activity time\",ylab=\"Median activity time [s]\")\r\n\r\nmybarplot(mean_table$means$number_pause,mean_table$ses$number_pause,rownames(mean_table$means),\r\n\tmain=\"Number of pauses\",ylab=\"Number of pauses\")\r\n\r\nmybarplot(mean_table$means$median_pause,mean_table$ses$median_pause,rownames(mean_table$means),\r\n\tmain=\"Mean pause time\",ylab=\"Median pause time [s]\")\r\n\r\nmessage(\"starting activity frequence plot\")\r\nhplot2 = histogram(~ act | group ,data=act_table,breaks=bins-0.01, type=\"percent\",col=0)\r\nhplot2 = update(hplot2,main=\"Activity Histogram (Groups)\", ylab=\"frequency\", xlab=\"activity time [s]\",layout=c(2,2))\r\nprint(hplot2)\r\n\r\n\r\n\r\n\r\n\r\nhplot2 = hist(pause_table$pause,breaks= c(seq(0,200, 0.05), seq(220,1000,20)), plot=FALSE)\r\nprint(plot(hplot2, xlim=c(0,100)))\r\n\r\n#hplot2$counts = (hplot2$counts/ length (pause_table$pause))\r\nprint(plot(hplot2, xlim=c(0,100)))\r\n\r\n\r\nhplot2$counts[hplot2$counts==0] <- -1\r\n\r\n\r\nhplot2$counts = log(hplot2$counts)\r\n#hplot2$counts[is.na(hplot2$counts)] <- -1\r\n\r\n\r\nprint(plot(hplot2$counts, xlim=c(0,1000), pch=20, xlab = \"pause length\", ylab= \"ln(counts)\"))\r\n\r\n", "meta": {"hexsha": "0e7c154299907474942429ab6a40615f730d1941", "size": 2985, "ext": "r", "lang": "R", "max_stars_repo_path": "CeTrAn/scripts/unused/Activity_tests.r", "max_stars_repo_name": "brembslab/CeTrAn", "max_stars_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2015-02-26T12:51:15.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-21T08:36:32.000Z", "max_issues_repo_path": "CeTrAn/scripts/unused/Activity_tests.r", "max_issues_repo_name": "brembslab/CeTrAn", "max_issues_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2015-01-09T13:08:07.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-18T13:31:53.000Z", "max_forks_repo_path": "CeTrAn/scripts/unused/Activity_tests.r", "max_forks_repo_name": "brembslab/CeTrAn", "max_forks_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-01-09T13:33:40.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-21T12:55:17.000Z", "avg_line_length": 31.4210526316, "max_line_length": 117, "alphanum_fraction": 0.7239530988, "num_tokens": 873, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3471944273284509}} {"text": "#' Extract wet and dry N deposition data from NADP and CASTNET (2000-2015 average)\n#' There are currently warnings. ignore them.\n#'\n#' @param longitude a vector of site longitude\n#' @param latitude a vector of site latitude\n#' @param folder path to directory that contains sub directories 'wet_dep' and 'dry_dep'. \n#' These then contain all ndep rasters.\n#' folder path currently defaults to the directory in colin's folder on pecan2.\n#'\n#' @return returns a dataframe of wet, dry and total ndeposition values for all sites.\n#' @export\n#'\n#' @examples\n\nextract_ndep <- function(longitude,latitude,folder='/fs/data3/caverill/CASTNET_Ndep/'){\n #load dry deposition rasters\n dry.list <- list()\n for(i in 0:15){\n dry.list[[i+1]] <- raster::raster(file.path(folder,paste0('dry_dep/n_dw-',2000 + i,'.e00')))\n }\n #load wet deposition rasters\n wet.list <- list()\n for(i in 0:15){\n wet.list[[i+1]] <- raster::raster(file.path(folder,paste0('wet_dep/n_ww-',2000 + i,'.e00')))\n }\n \n #Fix the crs of each raster.\n for(i in 1:length(dry.list)){\n raster::crs(dry.list[[i]]) <- \"+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23.0 +lon_0=-96.0 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\"\n }\n for(i in 1:length(wet.list)){\n raster::crs(wet.list[[i]]) <- \"+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23.0 +lon_0=-96.0 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\"\n }\n \n #grab long/lat. (important longitude before latitude)\n points <- cbind(longitude,latitude)\n #reproject points\n points <- sp::SpatialPoints(points, proj4string = sp::CRS(\"+init=epsg:4326\"))\n \n #extract dry deposition, convert to mean annual\n dry.out <- list()\n for(i in 1:length(dry.list)){\n dry.out[[i]] <- raster::extract(dry.list[[i]], points)\n }\n dry.dep <- Reduce('+',dry.out) / length(dry.out)\n #extract wet deposition\n wet.out <- list()\n for(i in 1:length(wet.list)){\n wet.out[[i]] <- raster::extract(wet.list[[i]], points)\n }\n wet.dep <- Reduce('+',wet.out) / length(wet.out)\n \n #total ndep and output.\n n.dep <- dry.dep + wet.dep\n output <- data.frame(n.dep, dry.dep, wet.dep)\n \n return(output)\n}", "meta": {"hexsha": "496879c17c6f90d76bb8f702f622fd2e0400462e", "size": 2151, "ext": "r", "lang": "R", "max_stars_repo_path": "NEFI_functions/extract_ndep.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "NEFI_functions/extract_ndep.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "NEFI_functions/extract_ndep.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 37.0862068966, "max_line_length": 162, "alphanum_fraction": 0.6592282659, "num_tokens": 725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.5273165233795672, "lm_q1q2_score": 0.34719442025960584}} {"text": "# Set up hash table\nkeys <- c(\"John Smith\", \"Lisa Smith\", \"Sam Doe\", \"Sandra Dee\", \"Ted Baker\")\nvalues <- c(152, 1, 254, 152, 153)\nnames(values) <- keys\n# Get value corresponding to a key\nvalues[\"Sam Doe\"] # vals[\"Sam Doe\"]\n# Get all keys corresponding to a value\nnames(values)[values==152] # \"John Smith\" \"Sandra Dee\"\n", "meta": {"hexsha": "e38b5e183144aca17331a533af38d1d925375214", "size": 360, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Hash-from-two-arrays/R/hash-from-two-arrays.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Hash-from-two-arrays/R/hash-from-two-arrays.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Hash-from-two-arrays/R/hash-from-two-arrays.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 40.0, "max_line_length": 75, "alphanum_fraction": 0.5916666667, "num_tokens": 101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165085228825, "lm_q2_score": 0.658417500561683, "lm_q1q2_score": 0.3471944175465497}} {"text": "\nRMSE_row<-function(data0)\n{\n return(apply(data0^2,1,mean))\n}\n\nnormalize_data2<-function(data0)\n{\n data1<-data0\n for(i in 1:nrow(data1))\n {\n data1[i,]<-(data1[i,]-mean(data1[i,]))/sd(data1[i,])\n }\n return(data1)\n}\n\n\nCompute_base_ES_score<-function(ES_c,tg_selected_c)\n{\n\tccc<-c()\n\tfor(i in 1:length(tg_selected_c))\n\t{\t\n\t\tccc<-c(ccc,mean(ES_c[tg_selected_c[[i]]]))\n\t}\n\tnames(ccc)<-names(tg_selected_c)\n\treturn(ccc)\n}\n\ncompute_CompRowspace_NN_selflist<-function(tg_data,tg_data_ng,tg_list,ROUNDS=3)\n{\n\tRbase_selected_R4_og<-Compute_Rbase_SVD(tg_data_ng,tg_list)\n\tRbase_selected_R4<-Compute_Rbase_SVD(tg_data,tg_list)\n\taaa<-matrix(0,length(tg_list),ROUNDS)\n\tbbb<-matrix(0,length(tg_list),ROUNDS)\n\tddd<-matrix(0,length(tg_list),ROUNDS)\n for(j in 1:length(tg_list))\n {\n\t\ttg_data_c<-tg_data[tg_list[[j]],]\n \tBase_c<-Rbase_selected_R4[-j,]\n\t\t#print(tg_data_c[,1:5])\n\t\t#print(j)\n\t\t#print(Base_c[,1:5])\n\t\t#print(j)\n\t\ttg_id_kk<-setdiff(1:length(tg_list),j)\n\t\tccc<-c()\n\t\tccc<-compute_CompRowspace_NN_uni(tg_data_c,module_RB=Base_c,ROUNDS=3)\n\t\t#print(ccc)\n\t\tif(length(ccc)>0)\n\t\t{\n\t\t\ttg_id1<-tg_id_kk[ccc[1,]]\n\t\t\taaa[j,1:length(tg_id1)]<-tg_id1\n\t\t\tbbb[j,1:length(tg_id1)]<-ccc[2,]\n\t\t\tddd[j,1:length(tg_id1)]<-ccc[3,]\n\t\t}\n \t}\n\ttg_ids<-which(apply(bbb,1,min)<0.2)\n\tc_cell_depend<-list()\n\tN<-0\n\tnn<-c()\n\tfor(j in 1:length(tg_ids))\n\t{\n\t\ttg_c<-aaa[tg_ids[j],which(ddd[tg_ids[j],]>0.1)]\n\t\tif(length(tg_c)>0)\n\t\t{\n\t\t\tdata_ccc<-t(Rbase_selected_R4_og[c(tg_ids[j],tg_c),])\n\t\t\tcolnames(data_ccc)<-c(\"Y\",tg_c)\n\t\t\trownames(data_ccc)<-1:nrow(data_ccc)\n\t\t\tdata_ccc<-as.data.frame(data_ccc)\n\t\t\tlm_cc<-lm(Y~.+0,data=data_ccc)\n\t\t\tccc<-summary(lm_cc)$coefficients\n\t\t\tcc1<-which((ccc[,4]<0.01)&ccc[,1]>0)\n\t\t\tcc2<-which((ccc[,4]<0.01)&ccc[,1]<0)\n\t\t\tdd1<-c()\n\t\t\tdd2<-c()\n\t\t\tif(length(cc1)>0)\n\t\t\t{\n\t\t\t\tdd1<-tg_c[cc1]\n\t\t\t}\n\t\t\tif(length(cc2)>0)\n\t\t\t{\n\t\t\t\tdd2<-tg_c[cc2]\n\t\t\t}\n\t\t\tdd3<-setdiff(tg_c,c(dd1,dd2))\n\t\t\tc_l<-c(tg_ids[j],dd2)\n\t\t\tc_r<-c(dd1)\n\t\t\tcc<-list(c_l,c_r)\n\t\t\tnames(cc)<-c(\"right\",\"left\")\n\t\t\tN<-N+1\n\t\t\tc_cell_depend[[N]]<-cc\n\t\t\tnn<-c(nn,tg_ids[j])\n\t\t}\n\t}\n\tnames(c_cell_depend)<-nn\n\tbaba<-c()\n\tdidi<-c()\n\tfor(i in 1:length(c_cell_depend))\n\t{\n\t\tcc<-c_cell_depend[[i]]\n\t\tif((length(cc[[1]])==1)&(length(cc[[2]])>1))\n\t\t{\n\t\t\tbaba<-c(baba,cc[[1]])\n\t\t\tdidi<-c(didi,cc[[2]])\n\t\t}\n\t}\n\tbaba<-unique(sort(baba))\n\tdidi<-setdiff(unique(sort(didi)),baba)\n\tdaye<-setdiff(1:length(tg_list),unique(sort(c(didi,baba))))\n\tccc<-list(didi,baba,daye,c_cell_depend)\n\tnames(ccc)<-c(\"Leaf_CT\",\"Root_CT\",\"Other_leat_CT\",\"Cell_dependency\")\n return(ccc)\n}\n\ncompute_CompRowspace_NN_uni<-function(tg_data_c,module_RB=Base_c,ROUNDS=3)\n{\n tg_list_c<-list()\n tg_list_c[[1]]<-rownames(tg_data_c)\n dd<-Compute_Rbase_SVD(tg_data_c,tg_list_c)\n dd<-dd/sd(dd)\n tg_list_selected<-c()\n ee_all<-c()\n ee_all<-c(ee_all,RMSE_row(dd))\n for(ii in 1:ROUNDS)\n {\n ccc<-cor(t(dd),t(module_RB))\n tg_id_c<-which(ccc==max(ccc))[1]\n if(ccc[tg_id_c]>0)\n {\n tg_list_selected<-c(tg_list_selected,tg_id_c)\n ttt_ccc<-module_RB[tg_id_c,]%*%t(module_RB[tg_id_c,])/sum((module_RB[tg_id_c,])^2)\n dd<-dd-dd%*%ttt_ccc\n ee_all<-c(ee_all,RMSE_row(dd))\n }\n }\n ee_all<-ee_all/ee_all[1]\n ee_all2<-ee_all[-1]\n if(length(ee_all2)>0)\n {\n for(i in 2:length(ee_all))\n {\n ee_all2[i-1]<-ee_all[i-1]-ee_all[i]\n }\n }\n ee_all<-ee_all[-1]\n ccc<-c()\n if(length(ee_all)>0)\n {\n ccc<-rbind(tg_list_selected,ee_all,ee_all2)\n colnames(ccc)<-1:ncol(ccc)\n } \n return(ccc)\n}\n\ncompute_CompRowspace_NN<-function(data_cc=tg_data,module_RB=module_selected[[4]],ROUNDS=3)\n{\n data_CORS_cancer<-data_cc\n for(ii in 1:ROUNDS)\n {\n ccc<-cor(t(data_CORS_cancer),t(module_RB))\n if(nrow(module_RB)>1)\n {\n ddd<-apply(ccc,1,order)\n eee<-ddd[nrow(ddd),]\n eee<-eee*(apply(ccc,1,max)>0)\n tg_ids<-sort(setdiff(unique(eee),0))\n if(length(tg_ids)>0)\n {\n for(i in 1:length(tg_ids))\n {\n ttt_ccc<-module_RB[tg_ids[i],]%*%t(module_RB[tg_ids[i],])/sum((module_RB[tg_ids[i],])^2)\n data_CORS_cancer[names(which(eee==tg_ids[i])),]<-data_CORS_cancer[names(which(eee==tg_ids[i])),]-data_CORS_cancer[names(which(eee==tg_ids[i])),]%*%ttt_ccc\n }\n }\n }\n else\n {\n ddd<-apply(ccc,1,order)\n eee<-ddd\n eee<-eee*(as.vector(ccc)>0)\n tg_ids<-sort(setdiff(unique(eee),0))\n if(length(tg_ids)>0)\n {\n for(i in 1:length(tg_ids))\n {\n ttt_ccc<-module_RB[tg_ids[i],]%*%t(module_RB[tg_ids[i],])/sum((module_RB[tg_ids[i],])^2)\n data_CORS_cancer[names(which(eee==tg_ids[i])),]<-data_CORS_cancer[names(which(eee==tg_ids[i])),]-data_CORS_cancer[names(which(eee==tg_ids[i])),]%*%ttt_ccc\n }\n }\n }\n }\n return(data_CORS_cancer)\n}\n\n\n\ncompute_IM_stat<-function(tg_list_c,immune_cell_uni_table=immune_cell_uni_table0_GS,IM_id_list0=ICTD::IM_id_list)\n{\n\tccc<-c()\n\tnn<-c()\n\tfor(i in 1:length(tg_list_c))\n\t{ \n ccc0<-c()\n for(j in 1:length(ICTD::IM_id_list))\n { \n if(length(ICTD::IM_id_list[[j]])>1)\n {\n cc0<-apply(immune_cell_uni_table[tg_list_c[[i]],IM_id_list0[[j]]],1,sum)/sum((1/(1:length(IM_id_list0[[j]]))))\n }\n else\n {\n cc0<-immune_cell_uni_table[tg_list_c[[i]],IM_id_list0[[j]]]\n }\n ccc0<-cbind(ccc0,cc0)\n }\n colnames(ccc0)<-names(ICTD::IM_id_list)\n ddd<-apply(ccc0,2,mean)\n ccc<-rbind(ccc,ddd)\n nn<-c(nn,colnames(ccc0)[which(ddd==max(ddd))[1]])\n\t}\n\trownames(ccc)<-nn\n\treturn(ccc)\n}\n\n\nidentify_max_base<-function(tg_R1_c,data.matrix,tProp)\n{\n\tcc<-Compute_Rbase_SVD(data.matrix,tg_R1_c)\n\ttg_R1_c_list<-list()\n\tdd<-cor(t(cc),t(tProp))\n\tfor(i in 1:ncol(dd))\n\t{\n\t\ttg_id<-which(dd[,i]==max(dd[,i]))[1]\n\t\ttg_R1_c_list[[i]]<-tg_R1_c[[tg_id]]\n\t}\n\tnames(tg_R1_c_list)<-colnames(dd)\n\treturn(tg_R1_c_list)\n}\n\nidentify_max_base2<-function(tg_R1_c,data.matrix,tProp)\n{\n\tcc<-Compute_Rbase_SVD(data.matrix,tg_R1_c)\n\ttg_R1_c_list<-list()\n\tdd<-cor(t(cc),t(tProp))\n\tff<-c()\n\tfor(i in 1:ncol(dd))\n\t{\n\t\ttg_id<-which(dd[,i]==max(dd[,i]))[1]\n\t\ttg_R1_c_list[[i]]<-tg_R1_c[[tg_id]]\n\t\tff<-c(ff,tg_id)\n\t}\n\tprint(ff)\n\tnames(tg_R1_c_list)<-colnames(dd)\n\treturn(tg_R1_c_list)\n}\n\nselect_R_base<-function(tg_R1_c,tg_id)\n{\n\ttg_R1_cc<-list()\n\tfor(i in 1:length(tg_id))\n\t{\n\t\ttg_R1_cc[[i]]<-tg_R1_c[[tg_id[i]]]\n\t}\n\tnames(tg_R1_cc)<-names(tg_R1_c)[tg_id]\n\treturn(tg_R1_cc)\n}", "meta": {"hexsha": "f8d0218e6a9de437d404360e4dc4c7d25a749d6f", "size": 7113, "ext": "r", "lang": "R", "max_stars_repo_path": "R/R4RR_selection_functions.r", "max_stars_repo_name": "changwn/ICTD", "max_stars_repo_head_hexsha": "acb0d5c2c859b4c756e1ff50e6624046a2f68d36", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-31T02:23:12.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-17T00:33:55.000Z", "max_issues_repo_path": "R/R4RR_selection_functions.r", "max_issues_repo_name": "changwn/ICTD", 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YES\n2. NO", "lm_q1_score": 0.734119526900183, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.34700617023012587}} {"text": "ef <- function(model='Schwab', from=NA, to=NA, efdata=NA, adjdates=NULL, period='months',\n annualize=TRUE, addline=TRUE, col='black', lty=1, pch=3) {\n ## create Efficient Frontier points to assess TWR vs. risk\n \n ## model = 'Schwab' uses a blend of US L, US S, Inter, Fixed, and Cash\n ## = 'SSP' uses a blend of US L and Fixed\n\n ## duration defined from one of the following\n ## addline == FALSE:\n ## if false, then execution is assumed primarily to obtain output\n ## for subsequent call with ef(symbols, efdata=efdata, from=xx, to=yy)\n ## from, to, and period:\n ## from = end of day to start\n ## to = end of day to end\n ## period = 'months' (default), 'days', 'weeks', 'years'\n ## efdata:\n ## efdata = output from prior execution of ef\n ## used to extract twri for benchmark so no call needed to yahoo\n ## efdata$twri needs to contain incremental twr values for:\n ## 'SPY', 'IWM', 'EFA', 'AGG', 'SHV'\n ## where:\n ## US L = SPY\n ## US S = IWM (iShares Russel 2000)\n ## Inter = EFA (iShares MSCI EAFE of large and mid cap\n ## in developed contries excluding US and Canada)\n ## Fixed = AGG\n ## Cash = SHV (iShares Short Treasury Bond, < 1 yr)\n\n ## to just get twri data for subsequent use\n ## twrief <- ef(period='months', addline=FALSE)$twri\n ## then to create ef line\n ## ef(model='Schwab', \n\n \n if (is.na(efdata[1])) {\n ## efdata is not provided so need to get it\n symbol <- c('SPY', 'IWM', 'EFA', 'AGG', 'SHV')\n if (isFALSE(addline)) {\n ## do not worry about dates and just get data for subsequent call to ef()\n out <- equity.twri(symbol, period=period)\n twri <- na.omit(out)\n } else if (is.null(adjdates)) {\n out <- equity.history(symbol, from=from, to=to, period=period)\n twri <- na.omit( out$twri )\n } else {\n out <- equity.twri(symbol, adjdates=adjdates)\n twri <- na.omit(out)\n }\n twri_in <- NA\n \n } else {\n ## efdata is provided so can use it directly\n if (class(efdata)[1] == 'xts') {\n ## input efdata is simply an xts object of twri values\n twri_in <- efdata\n } else {\n ## input efdata is full output of prior run of ef()\n twri_in <- efdata$twri\n }\n twri <- twri_in\n }\n \n ## restrict to duration\n if (is.na(from)) from <- zoo::index(twri)[1]\n if (is.na(to)) to <- zoo::index(twri)[nrow(twri)]\n xtsrange <- paste(noquote(from), '/', noquote(to), sep='')\n xtsrange\n twri <- twri[xtsrange]\n\n if (model == 'test') {\n twri <- head(twri, 4)\n twri[1,] <- c(0.1, 0.11, 0.1, 0.1, 0.1)\n twri[2,] <- c(0.1, 0.12, 0.1, 0.1, 0.1)\n twri[3,] <- c(0.1, 0.13, 0.1, 0.1, 0.1)\n twri[4,] <- c(0.1, 0.14, 0.11, 0.1, 0.1)\n }\n \n ## calculate twrc and standard deviation\n ## 1st date should have twrc = 0\n ## twrc_apply <- apply(twri, 2, function(x) { prod(x+1) - 1 }) / (twri[1,] + 1)\n twrc <- xts::as.xts( t(t(cumprod(twri+1)) / as.vector(twri[1,]+1) - 1) )\n twrcl <- tail(twrc, 1)\n std <- apply(twri[2:nrow(twri)], 2, sd)\n\n if (isTRUE(annualize)) {\n \n ## calculate annualized standard deviation from monthly standard deviation\n ## std(xi) = sqrt ( sum(xi-xbar)^2 / N ) for population which seems to be what finance world uses\n ## If have x = monthly TWR and want std for y = yearly TWR\n ## then set F * std(x) = std(y) and solve for F.\n ## If assume yi = 12*xi and give credit for the fact that 1 yearly entry is from 12 measurments, \n ## then can use Ny = 12*Nm and F = sqrt(12).\n ## std( TWR_monthly - avg_TWR_monthly )\n std <- std * 12^0.5 \n\n ## average annual return\n days.held <- as.numeric(as.Date(to) - as.Date(from))\n twrcl <- (1 + twrcl)^(365.25 / days.held) - 1\n }\n \n ## define asset class weights for requested benchmark model\n if (model == 'Schwab') {\n ## US L US S Inter Fixed Cash\n ## ----- ----- ----- ----- ----\n schwab_95_5 <- c(0.50, 0.20, 0.25, 0.00, 0.05) # bench - aggressive (95 / 5)\n schwab_80_20 <- c(0.45, 0.15, 0.20, 0.15, 0.05) # bench - mod agg (80 / 20)\n schwab_60_40 <- c(0.35, 0.10, 0.15, 0.35, 0.05) # bench - moderage (60 / 40)\n schwab_40_60 <- c(0.25, 0.05, 0.10, 0.50, 0.10) # bench - mod consv (40 / 60)\n schwab_20_80 <- c(0.15, 0.00, 0.05, 0.50, 0.30) # bench - consverv (20 / 80)\n schwab_0_100 <- c(0.00, 0.00, 0.00, 0.40, 0.60) # bench - short term ( 0 /100)\n weight <- rbind(schwab_95_5, schwab_80_20, schwab_60_40,\n schwab_40_60, schwab_20_80, schwab_0_100)\n\n } else if (model == 'test') {\n ## US L US S Inter Fixed Cash\n ## ----- ----- ----- ----- ----\n one <- c(0.2, 0.3, 0.2, 0.2, 0.2)\n two <- c(0, 1, 0, 1, 0 )\n weight <- rbind(one, two)\n \n } else {\n ## US L US S Inter Fixed Cash\n ## ----- ----- ----- ----- ----\n SSP_500 <- c(1.00, 0.00, 0.00, 0.00, 0.00)\n bench_80_20 <- c(0.80, 0.00, 0.00, 0.20, 0.00)\n bench_60_40 <- c(0.60, 0.00, 0.00, 0.40, 0.00)\n bench_40_60 <- c(0.40, 0.00, 0.00, 0.60, 0.00)\n bench_20_80 <- c(0.20, 0.00, 0.00, 0.80, 0.00)\n US_bonds <- c(0.00, 0.00, 0.00, 1.00, 0.00)\n weight <- rbind(SSP_500, bench_80_20, bench_60_40, bench_40_60, bench_20_80, US_bonds)\n }\n colnames(weight) <- c('US L', 'US S', 'Inter', 'Fixed', 'Cash')\n \n ## ## approximation (not too bad for twr;\n ## no good for sd since different benchmarks can compensate for eachother)\n ## ## calculate benchmark twr to define efficient frontier twr\n ## eftwr <- weight %*% twrc # column vector\n ## colnames(eftwr) <- 'EF TWR'\n\n ## calculate twri for efficient frontier model\n eftwri <- twri %*% t(weight) # matrix\n ## turn back into xts\n eftwri <- xts::as.xts( zoo::as.zoo( eftwri, zoo::index(twri)))\n\n ## calculate cumulative twr and standard deviation\n eftwrc <- t(t(cumprod(eftwri+1)) / as.vector(eftwri[1,]+1) - 1)\n eftwrcl <- t( xts::last(eftwrc) )\n colnames(eftwrcl) <- 'eftwrc'\n efstd <- as.matrix( apply(eftwri[2:nrow(eftwri),], 2, sd) ) # column vector\n colnames(efstd) <- 'efstd'\n\n if (isTRUE(annualize)) {\n ## calculate annualized standard deviation from monthly standard deviation\n eftwrcl <- (1 + eftwrcl)^(365.25 / days.held) - 1\n efstd <- efstd * 12^0.5 \n }\n \n ef <- as.data.frame( cbind(eftwrcl, efstd) )\n \n if (isTRUE(addline)) {\n ## plot lines for whatever period the data was supplied for\n lines(ef$efstd, ef$eftwrc, type='b', col=col, lty=lty, pch=pch)\n }\n \n return(list(model=model, weight=weight, twri_in=twri_in, twri=twri, twrc=twrc, std=std,\n eftwri=eftwri, ef=ef, from=from, to=to))\n}\n## ef(from='2020-12-31', to='2021-11-11')\n## ef(model='simple', from='2015-12-31', to='2021-11-30')\n", "meta": {"hexsha": "35c6ca24d5e10d1dbfe00d54b06293ec4aaf7356", "size": 7519, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/ef.r", "max_stars_repo_name": "dhjelmar/Finance", "max_stars_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/ef.r", "max_issues_repo_name": "dhjelmar/Finance", "max_issues_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/ef.r", "max_forks_repo_name": "dhjelmar/Finance", "max_forks_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.2294117647, "max_line_length": 108, "alphanum_fraction": 0.5182870063, "num_tokens": 2605, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7401743735019595, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.346986808366671}} {"text": "# A set of \"pluggable\" R functions for automated model fitting of glm models to presence/absence data #\n#\n# Modified 12-2-09 to: remove categorical covariates from consideration if they only contain one level\n# ID factor variables based on \"categorical\" prefix and look for tifs in subdir\n# Give progress reports\n# write large output tif files in blocks to alleviate memory issues\n# various bug fixes\n#\n#\n# Modified 3-4-09 to use a list object for passing of arguements and data\n#\n\n\n# Libraries required to run this program #\n# PresenceAbsence - for ROC plots\n# XML - for XML i/o\n# rgdal - for geotiff i/o\n# sp - used by rdgal library\n# raster for geotiff o\noptions(error=NULL)\n\nfit.glm.fct <- function(ma.name,tif.dir=NULL,output.dir=NULL,response.col=\"^response.binary\",make.p.tif=T,make.binary.tif=T,\n simp.method=\"AIC\",responseCurveForm=NULL,debug.mode=F,model.family=\"binomial\",script.name=\"glm.r\",opt.methods=2,save.model=TRUE,UnitTest=FALSE,MESS=FALSE){\n # This function fits a stepwise GLM model to presence-absence data.\n # written by Alan Swanson, 2008-2009\n ## Maintained and edited by Marian Talbert September 2010-\n # Arguements.\n # ma.name: is the name of a .csv file with a model array. full path must be included unless it is in the current\n # R working directory #\n # tif.dir: is the directory containing geotiffs for each covariate. only required if geotiffs output of the \n # response surface is requested # # cov.list.name: is the name of a text file with the names of covariates to be included in models (one per line).\n # output.dir: is the directory that output files will be stored in. if not given, files will go to the current working directory. \n # response.col: column number of the model array containing a binary 0/1 response. all other columns will be considered explanatory variables.\n # make.p.tif: T if a geotiff of the response surface is desired.\n # make.binary.tif: T if a geotiff of the response surface is desired.\n # simp.method: model simplification method. valid methods include: \"AIC\" and \"BIC\". \n # debug.mode: if T, output is directed to the console during the run. also, a pdf is generated which contains response curve plots and perspective plots\n # showing the effects of interactions deemed important. if F, output is diverted to a text file and the console is kept clear \n # except for final output of an xml file. in either case, a set of standard output files are created in the output directory.\n # \n\n # Value:\n # returns nothing but generates a number of output files in the directory\n # \"output.dir\" named above. These output files consist of:\n #\n # glm_output.txt: a text file with fairly detailed results of the final model.\n # glm_output.xml: an xml-formatted text file with results from the final model.\n # glm_response_curves.xml: an xml-formatted text file with response curves for\n # each covariate in the final model.\n # glm_prob_map.tif: a geotiff of the response surface\n # glm_bin_map.tif: a geotiff of the binary response surface. threhold is based on the roc curve at the point where sensitivity=specificity.\n # glm_log.txt: a file containing text output diverted from the console when debug.mode=F\n # glm_auc_plot.jpg: a jpg file of a ROC plot.\n # glm_response_curves.pdf: an pdf file with response curves for\n # each covariate in the final model and perspective plots showing the effect of interactions deemed significant.\n # only produced when debug.mode=T\n # seed=NULL # sets a seed for the algorithm, any inegeger is acceptable\n # opt.methods=2 # sets the method used for threshold optimization used in the\n # # the evaluation statistics module\n # save.model=FALSE # whether the model will be used to later produce tifs\n #\n # when debug.mode is true, these filenames will include a number in them so that they will not overwrite preexisting files. eg brt_1_output.txt.\n #\n times <- as.data.frame(matrix(NA,nrow=7,ncol=1,dimnames=list(c(\"start\",\"read data\",\"model fit\",\n \"model summary\",\"response curves\",\"tif output\",\"done\"),c(\"time\"))))\n times[1,1] <- unclass(Sys.time())\n t0 <- unclass(Sys.time())\n #simp.method <- match.arg(simp.method)\n out <- list(\n input=list(ma.name=ma.name,\n tif.dir=tif.dir,\n output.dir=output.dir,\n response.col=response.col,\n make.p.tif=make.p.tif,\n model.family=model.family,\n make.binary.tif=make.binary.tif,\n simp.method=simp.method,\n model.type=\"stepwise glm\",\n model.source.file=script.name,\n model.fitting.subset=NULL,\n save.model=save.model,\n run.time=paste(c(format(Sys.time(),\"%Y-%m-%d\"),format(Sys.time(),\"%H:%M:%S\")),collapse=\"T\"),\n sig.test=\"t-test p-value\",\n MESS=MESS),\n dat = list(missing.libs=NULL,\n output.dir=list(dname=NULL,exist=F,readable=F,writable=F),\n tif.dir=list(dname=NULL,exist=F,readable=F,writable=F),\n tiff.ind=NULL,\n tif.names=NULL,\n bname=NULL,\n bad.factor.covs=NULL, # factorchange\n ma=list( status=c(exists=F,readable=F),\n dims=c(NA,NA),\n resp.name=NULL,\n factor.levels=NA,\n used.covs=NULL,\n ma=NULL,\n train.weights=NULL,\n test.weights=NULL,\n train.xy=NULL,\n test.xy=NULL,\n ma.subset=NULL\n ),\n ma.test=NULL),\n mods=list(final.mod=NULL,\n r.curves=NULL,\n tif.output=list(prob=NULL,bin=NULL),\n auc.output=NULL,\n interactions=NULL, # not used #\n summary=NULL),\n time=list(strt=unclass(Sys.time()),end=NULL),\n error.mssg=list(NULL),\n ec=0 # error count #\n )\n # load libaries #\n out <- check.libs(list(\"PresenceAbsence\",\"rgdal\",\"XML\",\"sp\",\"survival\",\"tools\",\"raster\",\"tcltk2\",\"foreign\",\"ade4\"),out)\n \n # exit program now if there are missing libraries #\n if(!is.null(out$error.mssg[[1]])){\n cat(saveXML(glm.to.xml(out),indent=T),'\\n')\n return()\n }\n \n if(is.na(match(simp.method,c(\"AIC\",\"BIC\")))){\n return()\n }\n \n # check output dir #\n \n out$dat$output.dir <- check.dir(output.dir) \n if(out$dat$output.dir$writable==F) {out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: output directory\",output.dir,\"is not writable\")\n out$dat$output.dir$dname <- getwd()\n }\n \n # generate a filename for output #\n if(debug.mode==T){ #paste(bname,\"_summary.txt\",sep=\"\")\n outfile <- paste(bname<-paste(out$dat$output.dir$dname,\"/glm_\",n<-1,sep=\"\"),\"_output.txt\",sep=\"\")\n while(file.access(outfile)==0) outfile<-paste(bname<-paste(out$dat$output.dir$dname,\"/glm_\",n<-n+1,sep=\"\"),\"_output.txt\",sep=\"\")\n capture.output(cat(\"temp\"),file=outfile) # reserve the new basename #\n } else bname <- paste(out$dat$output.dir$dname,\"/glm\",sep=\"\")\n out$dat$bname <- bname \n \n # sink console output to log file #\n if(!debug.mode) {sink(logname <- paste(bname,\"_log.txt\",sep=\"\"));on.exit(sink)} else logname<-NULL\n options(warn=1)\n\n # check tif dir #\n if(!is.null(tif.dir)){\n out$dat$tif.dir <- check.dir(tif.dir)\n if(out$dat$tif.dir$readable==F & (out$input$make.binary.tif | out$input$make.p.tif)) {\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: tif directory\",tif.dir,\"is not readable\")\n if(!debug.mode) {sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))}\n cat(saveXML(brt.to.xml(out),indent=T),'\\n')\n return()\n }\n }\n\n\n # find .tif files in tif dir #\n \n if(out$dat$tif.dir$readable) out$dat$tif.names <- list.files(out$dat$tif.dir$dname,pattern=\".tif\",recursive=T)\n \n # check for model array #\n out$input$ma.name <- check.dir(out$input$ma.name)$dname\n if(UnitTest!=FALSE) options(warn=2)\n out <- read.ma(out)\n if(UnitTest==1) return(out)\n \n if(!is.null(out$error.mssg[[1]])){\n if(!debug.mode) {sink();on.exit();unlink(logname)}\n cat(saveXML(glm.to.xml(out),indent=T),'\\n')\n return()\n }\n #allowing site weights \n\n times[2,1] <- unclass(Sys.time())\n if(!debug.mode) {sink();cat(\"Progress:20%\\n\");flush.console();sink(logname,append=T)} else {cat(\"\\n\");cat(\"20%\\n\")} ### print time\n cat(\"\\nbegin processing of model array:\",out$input$ma.name,\"\\n\")\n cat(\"\\nfile basename set to:\",out$dat$bname,\"\\n\")\n if(debug.mode) assign(\"out\",out,envir=.GlobalEnv)\n \n \n \n \n ##############################################################################################################\n # Begin model fitting #\n ##############################################################################################################\n\n # Fit null GLM and run stepwise, then print results #\n cat(\"\\n\",\"Fitting stepwise GLM\",\"\\n\")\n flush.console()\n penalty <- if(out$input$simp.method==\"AIC\") 2 else log(nrow(out$dat$ma$ma))\n scope.glm <- list(lower=as.formula(paste(out$dat$ma$resp.name,\"~1\")),\n upper=as.formula(paste(out$dat$ma$resp.name,\"~\",paste(out$dat$ma$used.covs,collapse='+'))))\n\n mymodel.glm.step <- step(glm(as.formula(paste(out$dat$ma$resp.name,\"~1\")),family=out$input$model.family,data=out$dat$ma$ma,weights=out$dat$ma$train.weights,na.action=\"na.exclude\"),\n direction='both',scope=scope.glm,trace=0,k=penalty)\n \n out$mods$final.mod <- mymodel.glm.step\n\n out$dat$ma$used.covs <- attr(terms(formula(out$mods$final.mod)),\"term.labels\")\n #assign(\"out\",out,envir=.GlobalEnv)\n t3 <- unclass(Sys.time())\n cat(\"\\n\",\"Finished with stepwise GLM\",\"\\n\")\n cat(\"Summary of Model:\",\"\\n\")\n print(out$mods$summary <- summary(mymodel.glm.step))\n if(!is.null(out$dat$bad.factor.cols)){\n cat(\"\\nWarning: the following categorical response variables were removed from consideration\\n\",\n \"because they had only one level:\",paste(out$dat$bad.factor.cols,collapse=\",\"),\"\\n\\n\")\n }\n flush.console()\n\n flush.console()\n times[3,1] <- unclass(Sys.time())\n if(!debug.mode) {sink();cat(\"Progress:40%\\n\");flush.console();sink(logname,append=T)} else cat(\"40%\\n\")\n\n #r<-residuals(out$mods$final.mod, \"deviance\")\n\n if(length(coef(out$mods$final.mod))==1) stop(\"Null model was selected. \\nEvaluation metrics and plots will not be produced\")\n\n ##############################################################################################################\n # Begin model output #\n ##############################################################################################################\n \n # Store .jpg ROC plot #\n txt0 <- paste(\"Generalized Linear Results\\n\",out$input$run.time,\"\\n\\n\",\"Data:\\n\\t \",ma.name,\"\\n\\t \",\"n(pres)=\",\n out$dat$ma$n.pres[2],\"\\n\\t n(abs)=\",out$dat$ma$n.abs[2],\"\\n\\t number of covariates considered=\",(length(names(out$dat$ma$ma))-1),\n \"\\n\\n\",\"Settings:\\n\",\"\\n\\t model family=\",out$input$model.family,\n \"\\n\\n\",\"Results:\\n\\t \",\"number covariates in final model=\",length(out$dat$ma$used.covs),\n \"\\n\\t total time for model fitting=\",round((unclass(Sys.time())-t0)/60,2),\"min\\n\",sep=\"\")\n\n capture.output(cat(txt0),file=paste(bname,\"_output.txt\",sep=\"\"))\n\n capture.output(out$mods$summary,file=paste(bname,\"_output.txt\",sep=\"\"),append=TRUE)\n if(!is.null(out$dat$bad.factor.cols)){\n capture.output(cat(\"\\nWarning: the following categorical response variables were removed from consideration\\n\",\n \"because they had only one level:\",paste(out$dat$bad.factor.cols,collapse=\",\"),\"\\n\"),\n file=paste(bname,\"_output.txt\",sep=\"\"),append=T)\n }\n\n\n auc.output <- make.auc.plot.jpg(out$dat$ma$ma,pred=predict(mymodel.glm.step,type='response'),plotname=paste(bname,\"_auc_plot.jpg\",sep=\"\"),modelname=\"GLM\",opt.methods=opt.methods,\n weight=out$dat$ma$train.weights,out=out)\n\n out$mods$auc.output<-auc.output\n\n # if(out$input$model.family==\"poisson\"){\n # auc.output <- make.poisson.jpg(out$dat$ma$ma,pred=predict(mymodel.glm.step,type='response'),\n # plotname=paste(bname,\"_auc_plot.jpg\",sep=\"\"),modelname=\"BRT\",\n # weight=out$dat$ma$train.weights,out=out)\n\n # out$mods$auc.output<-auc.output\n # }\n\n # out$mods$auc.output<-auc.output\n\n\n\n\n times[4,1] <- unclass(Sys.time())\n if(!debug.mode) {sink();cat(\"Progress:70%\\n\");flush.console();sink(logname,append=T)} else cat(\"70%\\n\")\n \n # Response curves #\n \nif(is.null(responseCurveForm)){\nresponseCurveForm<-0} \n \nif(debug.mode | responseCurveForm==\"pdf\"){\n nvar <- length(coef(out$mods$final.mod))-1\n pcol <- min(ceiling(sqrt(nvar)),4)\n prow <- min(ceiling(nvar/pcol),3)\n \n pdf(paste(bname,\"_response_curves.pdf\",sep=\"\"),width=11,height=8.5,onefile=T)\n par(oma=c(2,2,4,2),mfrow=c(prow,pcol))\n r.curves <-my.termplot(out$mods$final.mod,plot.it=T)\n mtext(paste(\"GLM response curves for\",basename(ma.name)),outer=T,side=3,cex=1.3)\n par(mfrow=c(1,1))\n graphics.off()\n } else r.curves<-try(my.termplot(out$mods$final.mod,plot.it=F))\n \n \n if(class(r.curves)!=\"try-error\") {\n out$mods$r.curves <- r.curves\n } else {\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: problem fitting response curves\",r.curves)\n }\n \n \n t4 <- unclass(Sys.time())\n cat(\"\\nfinished with final model summarization, t=\",round(t4-t3,2),\"sec\\n\");flush.console()\n times[5,1] <- unclass(Sys.time())\n if(!debug.mode) {sink();cat(\"Progress:80%\\n\");flush.console();sink(logname,append=T)} else cat(\"80%\\n\")\n \n \n ##############################################################################################################\n # Make .tif of predictions #\n ##############################################################################################################\n out$mods$final.mod$contributions$var<-attr(terms(formula(out$mods$final.mod)),\"term.labels\")\n assign(\"out\",out,envir=.GlobalEnv)\n\n save.image(paste(output.dir,\"modelWorkspace\",sep=\"\\\\\"))\n if(out$input$make.p.tif==T | out$input$make.binary.tif==T){\n if((n.var <- length(coef(out$mods$final.mod)))<2){\n mssg <- \"Error producing geotiff output: null model selected by stepwise procedure - pointless to make maps\"\n class(mssg)<-\"try-error\"\n } else {\n cat(\"\\nproducing prediction maps...\",\"\\n\",\"\\n\");flush.console()\n mssg <- proc.tiff(model=out$mods$final.mod,vnames=attr(terms(formula(out$mods$final.mod)),\"term.labels\"),\n tif.dir=out$dat$tif.dir$dname,filenames=out$dat$tif.ind,pred.fct=glm.predict,factor.levels=out$dat$ma$factor.levels,make.binary.tif=make.binary.tif,\n thresh=out$mods$auc.output$thresh,make.p.tif=make.p.tif,outfile.p=paste(out$dat$bname,\"_prob_map.tif\",sep=\"\"),\n outfile.bin=paste(out$dat$bname,\"_bin_map.tif\",sep=\"\"),tsize=50,NAval=-3000,fnames=out$dat$tif.names,logname=logname,out=out) #\"brt.prob.map.tif\"\n }\n # model=out$mods$final.mod;vnames=attr(terms(formula(out$mods$final.mod)),\"term.labels\");\n# tif.dir=out$dat$tif.dir$dname;pred.fct=glm.predict;factor.levels=out$dat$ma$factor.levels;make.binary.tif=make.binary.tif;\n# thresh=out$mods$auc.output$thresh;make.p.tif=make.p.tif;outfile.p=paste(out$dat$bname,\"_prob_map.tif\",sep=\"\");\n# outfile.bin=paste(out$dat$bname,\"_bin_map.tif\",sep=\"\");tsize=50;NAval=-3000;fnames=out$dat$tif.names\n if(class(mssg)==\"try-error\"){\n if(!debug.mode) {sink();on.exit();unlink(logname)}\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"Error producing prediction maps:\",mssg)\n cat(saveXML(glm.to.xml(out),indent=T),'\\n')\n return()\n } else {\n if(make.p.tif) out$mods$tif.output$prob <- paste(out$dat$bname,\"_prob_map.tif\",sep=\"\")\n if(make.binary.tif) out$mods$tif.output$bin <- paste(out$dat$bname,\"_bin_map.tif\",sep=\"\")\n t5 <- unclass(Sys.time())\n cat(\"\\nfinished with prediction maps, t=\",round(t5-t4,2),\"sec\\n\");flush.console()\n }\n }\n times[6,1] <- unclass(Sys.time())\n if(!debug.mode) {sink();cat(\"Progress:90%\\n\");flush.console();sink(logname,append=T)} else cat(\"90%\\n\")\n \n # Evaluation Statistics on Test Data#\n\n if(!is.null(out$dat$ma$ma.test)) Eval.Stat<-EvaluationStats(out,thresh=auc.output$thresh,train=out$dat$ma$ma,\n train.pred=predict(mymodel.glm.step,type=\"response\"),opt.methods)\n\n \n # Write summaries to xml #\n assign(\"out\",out,envir=.GlobalEnv)\n doc <- glm.to.xml(out)\n \n #cat(paste(\"\\ntotal time=\",round((unclass(Sys.time())-t0)/60,2),\"min\\n\\n\\n\",sep=\"\"))\n if(!debug.mode) {\n sink();on.exit();unlink(logname)\n cat(\"Progress:100%\\n\");flush.console()\n cat(saveXML(doc,indent=T),'\\n')\n flush.console()\n } else {cat(\"100%\\n\")}\n capture.output(cat(saveXML(doc,indent=T)),file=paste(out$dat$bname,\"_output.xml\",sep=\"\"))\n assign(\"fit\",out$mods$final.mod,envir=.GlobalEnv)\n times[7,1] <- unclass(Sys.time())\n \n times$net <- times$time - times$time[1]\n times$pct <- round(times$net/times$net[7]*100,2)\n times$process <- c(0,times$time[-1]-times$time[-7])\n times$ppcnt <- round(times$process/times$net[7]*100,2)\n write.csv(times,paste(bname,\"times.csv\",sep=\"_\"))\n invisible(out)\n}\n\nglm.predict <- function(model,x) {\n # retrieve key items from the global environment #\n # make predictionss.\n \n y <- as.vector(predict(model,x,type=\"response\"))\n \n # encode missing values as -1.\n y[is.na(y)]<- NaN\n \n # return predictions.\n return(y)\n }\n\nlogit <- function(x) 1/(1+exp(-x))\n\n\nfile_path_as_absolute <- function (x){\n if (!file.exists(epath <- path.expand(x))) \n stop(gettextf(\"file '%s' does not exist\", x), domain = NA)\n cwd <- getwd()\n on.exit(setwd(cwd))\n if (file_test(\"-d\", epath)) {\n setwd(epath)\n getwd()\n }\n else {\n setwd(dirname(epath))\n file.path(getwd(), basename(epath))\n }\n}\n#file_path_as_absolute(\".\")\n\nglm.to.xml <- function(out){\n require(XML)\n schema.http=\"http://www.w3.org/2001/XMLSchema-instance\"\n schema.fname=\"file:/Users/isfs2/Desktop/Source/2008-04-09/src/gov/nasa/gsfc/quickmap/ModelBuilder/modelRun_output_v2.xsd\"\n xml.out <- newXMLDoc()\n mr <- newXMLNode(\"modelRunOutput\",doc=xml.out,namespaceDefinitions=c(xsi=schema.http,noNamespaceSchemaLocation=schema.fname))#,parent=xml.out\n sm <- newXMLNode(\"singleModel\",parent=mr)\n bg <- newXMLNode(\"background\",parent=sm)\n newXMLNode(\"mdsName\",out$input$ma.name,parent=bg)\n newXMLNode(\"runDate\",out$input$run.time,parent=bg)\n lc <- newXMLNode(\"layersConsidered\",parent=bg)#, parent = xml.out)\n kids <- lapply(paste(out$dat$tif.dir$dname,\"/\",out$dat$tif.names,sep=\"\"),function(x) newXMLNode(\"layer\", x))\n addChildren(lc, kids)\n mo <- newXMLNode(\"modelOutput\",parent=sm)\n newXMLNode(\"modelType\",out$input$model.type,parent=mo)\n newXMLNode(\"modelSourceFile\",out$input$model.source.file,parent=mo)\n newXMLNode(\"devianceExplained\",out$mods$auc.output$pct_dev_exp,parent=mo,attrs=list(type=\"percentage\"))\n newXMLNode(\"nativeOutput\",paste(out$dat$bname,\"_output.txt\",sep=\"\"),parent=mo)\n newXMLNode(\"binaryOutputFile\",out$mods$tif.output[[2]],parent=mo)\n newXMLNode(\"probOutputFile\",out$mods$tif.output[[1]],parent=mo)\n newXMLNode(\"auc\",out$mods$auc.output$auc,parent=mo)\n newXMLNode(\"rocGraphic\",out$mods$auc.output$plotname,parent=mo)\n newXMLNode(\"rocThresh\",out$mods$auc.output$thresh,parent=mo)\n newXMLNode(\"modelDeviance\",out$mods$auc.output$dev_fit,parent=mo)\n newXMLNode(\"nullDeviance\",out$mods$auc.output$null_dev,parent=mo)\n if(is.null(out$mods$r.curves)) rc.name <- NULL else rc.name <- paste(out$dat$bname,\"_response_curves.xml\",sep=\"\")\n newXMLNode(\"responsePlotsFile\",rc.name,parent=mo)\n newXMLNode(\"significanceDescription\",out$input$sig.test,parent=mo)\n mfp <- newXMLNode(\"modelFitParmas\",parent=mo)\n newXMLNode(\"simpMethod\",out$input$model.type,parent=mfp)\n newXMLNode(\"simpCriteria\",out$input$simp.method,parent=mfp)\n \n sv <- newXMLNode(\"significantVariables\",parent=mo)\n if(!is.null(out$mods$summary)) {\n t.table <- out$mods$summary$coefficients\n t.table <- t.table[order(t.table[,4]),]\n dimnames(t.table)[[2]] <- c(\"coefficient\",\"standardError\",\"testStatistic\",\"significanceMeasurement\")\n t.table <- as.data.frame(t.table)\n for(i in 1:nrow(t.table)){\n x <- newXMLNode(\"sigVar\",parent=sv)\n newXMLNode(name=\"name\", row.names(t.table)[i],parent=x)\n kids <- lapply(1:ncol(t.table),function(j) newXMLNode(name=names(t.table)[j], t.table[i,j]))\n addChildren(x, kids)\n }\n }\n \n if(!is.null(out$mods$r.curves)){\n r.curves <- out$mods$r.curves\n factor.levels <- out$dat$ma$factor.levels\n rc.out <- newXMLDoc()\n root <- newXMLNode(\"responseCurves\",doc=rc.out,namespaceDefinitions=c(xsi=schema.http,noNamespaceSchemaLocation=schema.fname))\n for(i in 1:length(r.curves$names)){\n if(!is.na(f.index<-match(r.curves$names[i],names(factor.levels)))){\n vartype <- \"factor\"} else vartype=\"continuous\"\n x <- newXMLNode(\"responseCurve\",attrs=list(covariate=r.curves$names[i],type=vartype),parent=root)\n kids <- lapply(1:length(r.curves$preds[[i]]),function(j){\n newXMLNode(name=\"responsePt\",parent=x,.children=list(\n newXMLNode(name=\"explanatory\",as.character(r.curves$preds[[i]])[j]),\n newXMLNode(name=\"response\",r.curves$resp[[i]][j])))})\n addChildren(x,kids)\n }\n saveXML(rc.out,rc.name,indent=T)\n \n } else { rc.name<-NULL }\n if(!is.null(out$error.mssg[[1]])) {\n kids <- lapply(out$error.mssg,function(j) newXMLNode(name=\"error\",j))\n addChildren(mo,kids)\n }\n return(xml.out)\n } \n\n\nget.cov.names <- function(model){\n return(attr(terms(formula(model)),\"term.labels\"))\n }\n\n\ncheck.dir <- function(dname){\n if(is.null(dname)) dname <- getwd()\n dname <- gsub(\"[\\\\]\",\"/\",dname)\n end.char <- substr(dname,nchar(dname),nchar(dname))\n if(end.char == \"/\") dname <- substr(dname,1,nchar(dname)-1)\n exist <- suppressWarnings(as.numeric(file.access(dname,mode=0))==0) # -1 if bad, 0 if ok #\n if(exist) dname <- file_path_as_absolute(dname)\n readable <- suppressWarnings(as.numeric(file.access(dname,mode=4))==0) # -1 if bad, 0 if ok #\n writable <- suppressWarnings(as.numeric(file.access(dname,mode=2))==0) # -1 if bad, 0 if ok #\n return(list(dname=dname,exist=exist,readable=readable,writable=writable))\n }\n\n\nget.image.info <- function(image.names){\n # this function creates a data.frame with summary image info for a set of images #\n require(rgdal)\n require(tools)\n n.images <- length(image.names)\n\n full.names <- image.names\n out <- data.frame(image=full.names,available=rep(F,n.images),size=rep(NA,n.images),\n type=factor(rep(\"unk\",n.images),levels=c(\"asc\",\"envi\",\"tif\",\"unk\")))\n out$type[grep(\".tif\",image.names)]<-\"tif\"\n out$type[grep(\".asc\",image.names)]<-\"asc\"\n for(i in 1:n.images){\n if(out$type[i]==\"tif\"){\n x <-try(GDAL.open(full.names[1],read.only=T))\n suppressMessages(try(GDAL.close(x)))\n if(class(x)!=\"try-error\") out$available[i]<-T\n x<-try(file.info(full.names[i]))\n } else {\n x<-try(file.info(full.names[i]))\n if(!is.na(x$size)) out$available[i]<-T\n }\n if(out$available[i]==T){\n out$size[i]<-x$size\n if(out$type[i]==\"unk\"){\n # if extension not known, look for envi .hdr file in same directory #\n if(file.access(paste(file_path_sans_ext(full.names[i]),\".hdr\",sep=\"\"))==0) \n out$type[i]<-\"envi\"\n }\n }\n }\n return(out)\n}\n\n#make.r.curves.glm <- function(model){\n# tms <- as.matrix(predict(model,type=\"terms\"))\n# mf <- model.frame(model)\n# preds <- list()\n# response <- list()\n# p.names <- colnames(tms)\n# is.fac <- sapply(p.names, function(i) is.factor(mf[, i]))\n# for(i in 1:length(p.names)){\n# if(is.fac[i]){\n# ff <- mf[, p.names[i]]\n# if (!is.null(model$na.action)) ff <- naresid(model$na.action,ff)\n# xx <- as.numeric(ff)\n# ll <- levels(ff)\n# out <- rep(NA,length(ll))\n# for (j in seq_along(ll)) {\n# ww <- which(ff == ll[j])[1]\n# out[j]<-tms[ww,i]\n# #out[j]<-tms[ff==ll[j],i][1]\n# }\n# \n# }\n# \n# \n# \n#data = NULL; envir = environment(formula(model));\n# partial.resid = FALSE; rug = FALSE; terms = NULL; se = FALSE;\n# xlabs = NULL; ylabs = NULL; main = NULL; col.term = 2; lwd.term = 1.5;\n# col.se = \"orange\"; lty.se = 2; lwd.se = 1; col.res = \"gray\";\n# cex = 1; pch = par(\"pch\"); col.smth = \"darkred\"; lty.smth = 2;\n# span.smth = 2/3; ask = dev.interactive() && nb.fig < n.tms;\n# use.factor.levels = TRUE; smooth = NULL; ylim = \"common\";plot.it=F;\n#terms=\"yell_250m_evi_16landcovermap_4ag05\"\nmy.termplot <- function (model, data = NULL, envir = environment(formula(model)),\n partial.resid = FALSE, rug = FALSE, terms = NULL, se = FALSE,\n xlabs = NULL, ylabs = NULL, main = NULL, col.term = 2, lwd.term = 1.5,\n col.se = \"orange\", lty.se = 2, lwd.se = 1, col.res = \"gray\",\n cex = 1, pch = 1, col.smth = \"darkred\", lty.smth = 2,\n span.smth = 2/3, ask = dev.interactive() && nb.fig < n.tms,\n use.factor.levels = TRUE, smooth = NULL, ylim = \"common\",plot.it=F,\n ...)\n{ # this function is borrowed from the stats library #\n\n which.terms <- terms\n terms <- if (is.null(terms))\n predict(model, type = \"terms\", se.fit = se)\n else predict(model, type = \"terms\", se.fit = se, terms = terms)\n n.tms <- ncol(tms <- as.matrix(if (se)\n terms$fit\n else terms))\n mf <- model.frame(model)\n if (is.null(data))\n data <- eval(model$call$data, envir)\n if (is.null(data))\n data <- mf\n if (NROW(tms) < NROW(data)) {\n use.rows <- match(rownames(tms), rownames(data))\n }\n else use.rows <- NULL\n nmt <- colnames(tms)\n cn <- parse(text = nmt)\n if (!is.null(smooth))\n smooth <- match.fun(smooth)\n if (is.null(ylabs))\n ylabs <- paste(\"Partial for\", nmt)\n if (is.null(main))\n main <- \"\"\n else if (is.logical(main))\n main <- if (main)\n deparse(model$call, 500)\n else \"\"\n else if (!is.character(main))\n stop(\"'main' must be TRUE, FALSE, NULL or character (vector).\")\n main <- rep(main, length.out = n.tms)\n pf <- envir\n carrier <- function(term) {\n if (length(term) > 1)\n carrier(term[[2]])\n else eval(term, data, enclos = pf)\n }\n carrier.name <- function(term) {\n if (length(term) > 1)\n carrier.name(term[[2]])\n else as.character(term)\n }\n if (is.null(xlabs))\n xlabs <- unlist(lapply(cn, carrier.name))\n if (partial.resid || !is.null(smooth)) {\n pres <- residuals(model, \"partial\")\n if (!is.null(which.terms))\n pres <- pres[, which.terms, drop = FALSE]\n }\n is.fac <- sapply(nmt, function(i) is.factor(mf[, i]))\n se.lines <- function(x, iy, i, ff = 2) {\n tt <- ff * terms$se.fit[iy, i]\n lines(x, tms[iy, i] + tt, lty = lty.se, lwd = lwd.se,\n col = col.se)\n lines(x, tms[iy, i] - tt, lty = lty.se, lwd = lwd.se,\n col = col.se)\n }\n if(plot.it) nb.fig <- prod(par(\"mfcol\"))\n if (ask & plot.it) {\n oask <- devAskNewPage(TRUE)\n on.exit(devAskNewPage(oask))\n }\n ylims <- ylim\n if (identical(ylims, \"common\")) {\n ylims <- if (!se)\n range(tms, na.rm = TRUE)\n else range(tms + 1.05 * 2 * terms$se.fit, tms - 1.05 *\n 2 * terms$se.fit, na.rm = TRUE)\n if (partial.resid)\n ylims <- range(ylims, pres, na.rm = TRUE)\n if (rug)\n ylims[1] <- ylims[1] - 0.07 * diff(ylims)\n }\n preds <- list()\n response <- list()\n p.names <- xlabs\n for (i in 1:n.tms) {\n if (identical(ylim, \"free\")) {\n ylims <- range(tms[, i], na.rm = TRUE)\n if (se)\n ylims <- range(ylims, tms[, i] + 1.05 * 2 * terms$se.fit[,\n i], tms[, i] - 1.05 * 2 * terms$se.fit[, i],\n na.rm = TRUE)\n if (partial.resid)\n ylims <- range(ylims, pres[, i], na.rm = TRUE)\n if (rug)\n ylims[1] <- ylims[1] - 0.07 * diff(ylims)\n }\n if (is.fac[i]) {\n ff <- mf[, nmt[i]]\n if (!is.null(model$na.action))\n ff <- naresid(model$na.action, ff)\n preds[[i]] <- ll <- levels(ff)\n xlims <- range(seq_along(ll)) + c(-0.5, 0.5)\n xx <- as.numeric(ff)\n response[[i]]<-rep(NA,length(ll))\n if (rug) {\n xlims[1] <- xlims[1] - 0.07 * diff(xlims)\n xlims[2] <- xlims[2] + 0.03 * diff(xlims)\n }\n\n if(plot.it){\n plot(1, 0, type = \"n\", xlab = xlabs[i], ylab = ylabs[i],\n xlim = xlims, ylim = ylims, main = main[i], xaxt = \"n\",\n ...)\n if (use.factor.levels)\n axis(1, at = seq_along(ll), labels = ll, ...)\n else axis(1)\n for (j in seq_along(ll)) {\n ww <- which(ff == ll[j])[c(1, 1)]\n jf <- j + c(-0.4, 0.4)\n lines(jf, response[[i]][j]<-tms[ww, i], col = col.term, lwd = lwd.term,...)\n if (se) se.lines(jf, iy = ww, i = i)\n }\n } else {\n for (j in seq_along(ll)) {\n ww <- which(ff == ll[j])[c(1, 1)]\n response[[i]][j]<-tms[ww, i]\n }\n }\n }\n else {\n xx <- carrier(cn[[i]])\n if (!is.null(use.rows))\n xx <- xx[use.rows]\n xlims <- range(xx, na.rm = TRUE)\n if (rug)\n xlims[1] <- xlims[1] - 0.07 * diff(xlims)\n \n response[[i]]<- logit(seq(min(tms[,i]),max(tms[,i]),length=100)) #aks\n preds[[i]]<-seq(min(xx),max(xx),length=100) #aks\n if(plot.it){\n oo <- order(xx)\n plot(xx[oo], logit(tms[oo, i]), type = \"l\", xlab = xlabs[i],\n ylab = ylabs[i], xlim = xlims, ylim = logit(ylims),\n main = main[i], col = col.term, lwd = lwd.term,\n ...)\n if (se)\n se.lines(xx[oo], iy = oo, i = i)\n }\n }\n if(plot.it){\n if (partial.resid) {\n if (!is.fac[i] && !is.null(smooth)) {\n smooth(xx, pres[, i], lty = lty.smth, cex = cex,\n pch = pch, col = col.res, col.smooth = col.smth,\n span = span.smth)\n }\n else points(xx, pres[, i], cex = cex, pch = pch,\n col = col.res)\n }\n if (rug) {\n n <- length(xx)\n lines(rep.int(jitter(xx), rep.int(3, n)), rep.int(ylims[1] +\n c(0, 0.05, NA) * diff(ylims), n))\n if (partial.resid)\n lines(rep.int(xlims[1] + c(0, 0.05, NA) * diff(xlims),\n n), rep.int(pres[, i], rep.int(3, n)))\n }\n }\n }\n invisible(list(names=p.names,preds=preds,resp=response))\n}\n\n\n# Interpret command line argurments #\n# Make Function Call #\n # Interpret command line argurments #\n# Make Function Call #\nmake.p.tif=T\nmake.binary.tif=T\nsimp.method=\"AIC\"\nopt.methods=2\nsave.model=FALSE\nMESS=FALSE\n\nArgs <- commandArgs(trailingOnly=FALSE)\n\n for (i in 1:length(Args)){\n if(Args[i]==\"-f\") ScriptPath<-Args[i+1]\n }\n\n print(Args)\n for (arg in Args) {\n \targSplit <- strsplit(arg, \"=\")\n \targSplit[[1]][1]\n \targSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"c\") csv <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"o\") output <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"rc\") responseCol <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"mpt\") make.p.tif <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"mbt\") make.binary.tif <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"om\") opt.methods <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"savm\") save.model <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"sm\") simp.method <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"mes\") MESS <- argSplit[[1]][2]\n }\n\tprint(csv)\n\tprint(output)\n\tprint(responseCol)\n\nScriptPath<-dirname(ScriptPath)\nsource(paste(ScriptPath,\"LoadRequiredCode.r\",sep=\"\\\\\"))\n\nmake.p.tif<-as.logical(make.p.tif)\nmake.binary.tif<-as.logical(make.binary.tif)\nsave.model<-make.p.tif | make.binary.tif\nopt.methods<-as.numeric(opt.methods)\nMESS<-as.logical(MESS)\n\n fit.glm.fct(ma.name=csv,\n tif.dir=NULL,output.dir=output,\n response.col=responseCol,make.p.tif=make.p.tif,make.binary.tif=make.binary.tif,\n simp.method=simp.method,debug.mode=F,responseCurveForm=\"pdf\",script.name=\"glm.r\",opt.methods=opt.methods,save.model=save.model,MESS=MESS)\n", "meta": {"hexsha": "a472b6dddcbcaeb9c52d85ff02d96af1e2c7ce0b", "size": 34758, "ext": "r", "lang": "R", "max_stars_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_GLM_pluggable.r", "max_stars_repo_name": "celiafish/VisTrails", "max_stars_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 83, "max_stars_repo_stars_event_min_datetime": "2015-01-05T14:50:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-17T19:45:26.000Z", "max_issues_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_GLM_pluggable.r", "max_issues_repo_name": "celiafish/VisTrails", "max_issues_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 254, "max_issues_repo_issues_event_min_datetime": "2015-01-02T20:39:19.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T17:16:44.000Z", "max_forks_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_GLM_pluggable.r", "max_forks_repo_name": "celiafish/VisTrails", "max_forks_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 40, "max_forks_repo_forks_event_min_datetime": "2015-04-17T16:46:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-28T22:43:24.000Z", "avg_line_length": 44.7912371134, "max_line_length": 184, "alphanum_fraction": 0.5600725013, "num_tokens": 9423, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7401743620390163, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.34698680299295065}} {"text": "score <- function(input) {\n var1 <- ((((((26.85177874216177) + ((subroutine0(input)) * (-0.12034962779157432))) + ((subroutine1(input)) * (1.0))) + ((subroutine2(input)) * (-1.0))) + ((subroutine3(input)) * (-1.0))) + ((subroutine4(input)) * (-1.0))) + ((subroutine5(input)) * (0.6171875007313155))\n var2 <- (subroutine6(input)) * (-1.0)\n var0 <- (((((((((((((((((((((((((var1) + (var2)) + ((subroutine7(input)) * (1.0))) + ((subroutine8(input)) * (-1.0))) + ((subroutine9(input)) * (1.0))) + ((subroutine10(input)) * (0.3164062486215933))) + ((subroutine11(input)) * (-1.0))) + ((subroutine12(input)) * (1.0))) + ((subroutine13(input)) * (-1.0))) + ((subroutine14(input)) * (1.0))) + ((subroutine15(input)) * (-1.0))) + ((subroutine16(input)) * (1.0))) + ((subroutine17(input)) * (-1.0))) + ((subroutine18(input)) * (-1.0))) + ((subroutine19(input)) * (-0.3201043650830524))) + ((subroutine20(input)) * (-1.0))) + ((subroutine21(input)) * (-1.0))) + ((subroutine22(input)) * (1.0))) + ((subroutine23(input)) * (-0.7715023625371545))) + ((subroutine24(input)) * (-1.0))) + ((subroutine25(input)) * (1.0))) + ((subroutine26(input)) * (-1.0))) + ((subroutine27(input)) * (-0.006346611962003479))) + ((subroutine28(input)) * (1.0))) + ((subroutine29(input)) * (-1.0))) + ((subroutine30(input)) * (-1.0))\n var3 <- (subroutine31(input)) * (-0.17130203879318218)\n return((((((((((((((((((((((((((var0) + (var3)) + ((subroutine32(input)) * (1.0))) + ((subroutine33(input)) * (1.0))) + ((subroutine34(input)) * (1.0))) + ((subroutine35(input)) * (-0.32034025068626093))) + ((subroutine36(input)) * (-0.9199503780639393))) + ((subroutine37(input)) * (1.0))) + ((subroutine38(input)) * (-1.0))) + ((subroutine39(input)) * (1.0))) + ((subroutine40(input)) * (-0.12010436508304956))) + ((subroutine41(input)) * (1.0))) + ((subroutine42(input)) * (1.0))) + ((subroutine43(input)) * (1.0))) + ((subroutine44(input)) * (-1.0))) + ((subroutine45(input)) * (-1.0))) + ((subroutine46(input)) * (1.0))) + ((subroutine47(input)) * (1.0))) + ((subroutine48(input)) * (0.816406250647308))) + ((subroutine49(input)) * (1.0))) + ((subroutine50(input)) * (1.0))) + ((subroutine51(input)) * (1.0))) + ((subroutine52(input)) * (-1.0))) + ((subroutine53(input)) * (1.0))) + ((subroutine54(input)) * (-1.0))) + ((subroutine55(input)) * (-1.0)))\n}\nsubroutine0 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((25.9406) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((5.304) - (input[6])) ^ (2))) + (((89.1) - (input[7])) ^ (2))) + (((1.6475) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((127.36) - (input[12])) ^ (2))) + (((26.64) - (input[13])) ^ (2)))))\n}\nsubroutine1 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((6.53876) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.631) - (input[5])) ^ (2))) + (((7.016) - (input[6])) ^ (2))) + (((97.5) - (input[7])) ^ (2))) + (((1.2024) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((392.05) - (input[12])) ^ (2))) + (((2.96) - (input[13])) ^ (2)))))\n}\nsubroutine2 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((22.5971) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.7) - (input[5])) ^ (2))) + (((5.0) - (input[6])) ^ (2))) + (((89.5) - (input[7])) ^ (2))) + (((1.5184) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((31.99) - (input[13])) ^ (2)))))\n}\nsubroutine3 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((45.7461) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((4.519) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.6582) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((88.27) - (input[12])) ^ (2))) + (((36.98) - (input[13])) ^ (2)))))\n}\nsubroutine4 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((11.8123) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.718) - (input[5])) ^ (2))) + (((6.824) - (input[6])) ^ (2))) + (((76.5) - (input[7])) ^ (2))) + (((1.794) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((48.45) - (input[12])) ^ (2))) + (((22.74) - (input[13])) ^ (2)))))\n}\nsubroutine5 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.08187) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((2.89) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.445) - (input[5])) ^ (2))) + (((7.82) - (input[6])) ^ (2))) + (((36.9) - (input[7])) ^ (2))) + (((3.4952) - (input[8])) ^ (2))) + (((2.0) - (input[9])) ^ (2))) + (((276.0) - (input[10])) ^ (2))) + (((18.0) - (input[11])) ^ (2))) + (((393.53) - (input[12])) ^ (2))) + (((3.57) - (input[13])) ^ (2)))))\n}\nsubroutine6 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((7.67202) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.747) - (input[6])) ^ (2))) + (((98.9) - (input[7])) ^ (2))) + (((1.6334) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((393.1) - (input[12])) ^ (2))) + (((19.92) - (input[13])) ^ (2)))))\n}\nsubroutine7 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((1.46336) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((19.58) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.605) - (input[5])) ^ (2))) + (((7.489) - (input[6])) ^ (2))) + (((90.8) - (input[7])) ^ (2))) + (((1.9709) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((403.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((374.43) - (input[12])) ^ (2))) + (((1.73) - (input[13])) ^ (2)))))\n}\nsubroutine8 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((20.0849) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.7) - (input[5])) ^ (2))) + (((4.368) - (input[6])) ^ (2))) + (((91.2) - (input[7])) ^ (2))) + (((1.4395) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((285.83) - (input[12])) ^ (2))) + (((30.63) - (input[13])) ^ (2)))))\n}\nsubroutine9 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((1.83377) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((19.58) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.605) - (input[5])) ^ (2))) + (((7.802) - (input[6])) ^ (2))) + (((98.2) - (input[7])) ^ (2))) + (((2.0407) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((403.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((389.61) - (input[12])) ^ (2))) + (((1.92) - (input[13])) ^ (2)))))\n}\nsubroutine10 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.5405) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.97) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.575) - (input[5])) ^ (2))) + (((7.47) - (input[6])) ^ (2))) + (((52.6) - (input[7])) ^ (2))) + (((2.872) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((264.0) - (input[10])) ^ (2))) + (((13.0) - (input[11])) ^ (2))) + (((390.3) - (input[12])) ^ (2))) + (((3.16) - (input[13])) ^ (2)))))\n}\nsubroutine11 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((73.5341) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((5.957) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.8026) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((16.45) - (input[12])) ^ (2))) + (((20.62) - (input[13])) ^ (2)))))\n}\nsubroutine12 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.33147) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((6.2) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.507) - (input[5])) ^ (2))) + (((8.247) - (input[6])) ^ (2))) + (((70.4) - (input[7])) ^ (2))) + (((3.6519) - (input[8])) ^ (2))) + (((8.0) - (input[9])) ^ (2))) + (((307.0) - (input[10])) ^ (2))) + (((17.4) - (input[11])) ^ (2))) + (((378.95) - (input[12])) ^ (2))) + (((3.95) - (input[13])) ^ (2)))))\n}\nsubroutine13 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((25.0461) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.987) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.5888) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((26.77) - (input[13])) ^ (2)))))\n}\nsubroutine14 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.57834) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.97) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.575) - (input[5])) ^ (2))) + (((8.297) - (input[6])) ^ (2))) + (((67.0) - (input[7])) ^ (2))) + (((2.4216) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((264.0) - (input[10])) ^ (2))) + (((13.0) - (input[11])) ^ (2))) + (((384.54) - (input[12])) ^ (2))) + (((7.44) - (input[13])) ^ (2)))))\n}\nsubroutine15 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((16.8118) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.7) - (input[5])) ^ (2))) + (((5.277) - (input[6])) ^ (2))) + (((98.1) - (input[7])) ^ (2))) + (((1.4261) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((30.81) - (input[13])) ^ (2)))))\n}\nsubroutine16 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.31533) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((6.2) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.504) - (input[5])) ^ (2))) + (((8.266) - (input[6])) ^ (2))) + (((78.3) - (input[7])) ^ (2))) + (((2.8944) - (input[8])) ^ (2))) + (((8.0) - (input[9])) ^ (2))) + (((307.0) - (input[10])) ^ (2))) + (((17.4) - (input[11])) ^ (2))) + (((385.05) - (input[12])) ^ (2))) + (((4.14) - (input[13])) ^ (2)))))\n}\nsubroutine17 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((67.9208) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.683) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.4254) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((384.97) - (input[12])) ^ (2))) + (((22.98) - (input[13])) ^ (2)))))\n}\nsubroutine18 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.18337) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((27.74) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.609) - (input[5])) ^ (2))) + (((5.414) - (input[6])) ^ (2))) + (((98.3) - (input[7])) ^ (2))) + (((1.7554) - (input[8])) ^ (2))) + (((4.0) - (input[9])) ^ (2))) + (((711.0) - (input[10])) ^ (2))) + (((20.1) - (input[11])) ^ (2))) + (((344.05) - (input[12])) ^ (2))) + (((23.97) - (input[13])) ^ (2)))))\n}\nsubroutine19 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((14.3337) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.7) - (input[5])) ^ (2))) + (((4.88) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.5895) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((372.92) - (input[12])) ^ (2))) + (((30.62) - (input[13])) ^ (2)))))\n}\nsubroutine20 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.20746) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((27.74) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.609) - (input[5])) ^ (2))) + (((5.093) - (input[6])) ^ (2))) + (((98.0) - (input[7])) ^ (2))) + (((1.8226) - (input[8])) ^ (2))) + (((4.0) - (input[9])) ^ (2))) + (((711.0) - (input[10])) ^ (2))) + (((20.1) - (input[11])) ^ (2))) + (((318.43) - (input[12])) ^ (2))) + (((29.68) - (input[13])) ^ (2)))))\n}\nsubroutine21 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((41.5292) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.531) - (input[6])) ^ (2))) + (((85.4) - (input[7])) ^ (2))) + (((1.6074) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((329.46) - (input[12])) ^ (2))) + (((27.38) - (input[13])) ^ (2)))))\n}\nsubroutine22 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((1.51902) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((19.58) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.605) - (input[5])) ^ (2))) + (((8.375) - (input[6])) ^ (2))) + (((93.9) - (input[7])) ^ (2))) + (((2.162) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((403.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((388.45) - (input[12])) ^ (2))) + (((3.32) - (input[13])) ^ (2)))))\n}\nsubroutine23 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((11.5779) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.7) - (input[5])) ^ (2))) + (((5.036) - (input[6])) ^ (2))) + (((97.0) - (input[7])) ^ (2))) + (((1.77) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((25.68) - (input[13])) ^ (2)))))\n}\nsubroutine24 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((14.2362) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((6.343) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.5741) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((20.32) - (input[13])) ^ (2)))))\n}\nsubroutine25 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((9.2323) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.631) - (input[5])) ^ (2))) + (((6.216) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.1691) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((366.15) - (input[12])) ^ (2))) + (((9.53) - (input[13])) ^ (2)))))\n}\nsubroutine26 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((9.91655) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.852) - (input[6])) ^ (2))) + (((77.8) - (input[7])) ^ (2))) + (((1.5004) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((338.16) - (input[12])) ^ (2))) + (((29.97) - (input[13])) ^ (2)))))\n}\nsubroutine27 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((22.0511) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.74) - (input[5])) ^ (2))) + (((5.818) - (input[6])) ^ (2))) + (((92.4) - (input[7])) ^ (2))) + (((1.8662) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((391.45) - (input[12])) ^ (2))) + (((22.11) - (input[13])) ^ (2)))))\n}\nsubroutine28 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.61154) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.97) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.647) - (input[5])) ^ (2))) + (((8.704) - (input[6])) ^ (2))) + (((86.9) - (input[7])) ^ (2))) + (((1.801) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((264.0) - (input[10])) ^ (2))) + (((13.0) - (input[11])) ^ (2))) + (((389.7) - (input[12])) ^ (2))) + (((5.12) - (input[13])) ^ (2)))))\n}\nsubroutine29 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((10.8342) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((6.782) - (input[6])) ^ (2))) + (((90.8) - (input[7])) ^ (2))) + (((1.8195) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((21.57) - (input[12])) ^ (2))) + (((25.79) - (input[13])) ^ (2)))))\n}\nsubroutine30 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((15.8603) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((5.896) - (input[6])) ^ (2))) + (((95.4) - (input[7])) ^ (2))) + (((1.9096) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((7.68) - (input[12])) ^ (2))) + (((24.39) - (input[13])) ^ (2)))))\n}\nsubroutine31 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((17.8667) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.671) - (input[5])) ^ (2))) + (((6.223) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.3861) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((393.74) - (input[12])) ^ (2))) + (((21.78) - (input[13])) ^ (2)))))\n}\nsubroutine32 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((8.26725) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.668) - (input[5])) ^ (2))) + (((5.875) - (input[6])) ^ (2))) + (((89.6) - (input[7])) ^ (2))) + (((1.1296) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((347.88) - (input[12])) ^ (2))) + (((8.88) - (input[13])) ^ (2)))))\n}\nsubroutine33 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.52693) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((6.2) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.504) - (input[5])) ^ (2))) + (((8.725) - (input[6])) ^ (2))) + (((83.0) - (input[7])) ^ (2))) + (((2.8944) - (input[8])) ^ (2))) + (((8.0) - (input[9])) ^ (2))) + (((307.0) - (input[10])) ^ (2))) + (((17.4) - (input[11])) ^ (2))) + (((382.0) - (input[12])) ^ (2))) + (((4.63) - (input[13])) ^ (2)))))\n}\nsubroutine34 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.0351) - (input[1])) ^ (2)) + (((95.0) - (input[2])) ^ (2))) + (((2.68) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.4161) - (input[5])) ^ (2))) + (((7.853) - (input[6])) ^ (2))) + (((33.2) - (input[7])) ^ (2))) + (((5.118) - (input[8])) ^ (2))) + (((4.0) - (input[9])) ^ (2))) + (((224.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((392.78) - (input[12])) ^ (2))) + (((3.81) - (input[13])) ^ (2)))))\n}\nsubroutine35 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((12.2472) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.584) - (input[5])) ^ (2))) + (((5.837) - (input[6])) ^ (2))) + (((59.7) - (input[7])) ^ (2))) + (((1.9976) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((24.65) - (input[12])) ^ (2))) + (((15.69) - (input[13])) ^ (2)))))\n}\nsubroutine36 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((14.4208) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.74) - (input[5])) ^ (2))) + (((6.461) - (input[6])) ^ (2))) + (((93.3) - (input[7])) ^ (2))) + (((2.0026) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((27.49) - (input[12])) ^ (2))) + (((18.05) - (input[13])) ^ (2)))))\n}\nsubroutine37 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.29819) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((6.2) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.504) - (input[5])) ^ (2))) + (((7.686) - (input[6])) ^ (2))) + (((17.0) - (input[7])) ^ (2))) + (((3.3751) - (input[8])) ^ (2))) + (((8.0) - (input[9])) ^ (2))) + (((307.0) - (input[10])) ^ (2))) + (((17.4) - (input[11])) ^ (2))) + (((377.51) - (input[12])) ^ (2))) + (((3.92) - (input[13])) ^ (2)))))\n}\nsubroutine38 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((38.3518) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.453) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.4896) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((30.59) - (input[13])) ^ (2)))))\n}\nsubroutine39 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.06129) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.33) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.4429) - (input[5])) ^ (2))) + (((7.645) - (input[6])) ^ (2))) + (((49.7) - (input[7])) ^ (2))) + (((5.2119) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((216.0) - (input[10])) ^ (2))) + (((14.9) - (input[11])) ^ (2))) + (((377.07) - (input[12])) ^ (2))) + (((3.01) - (input[13])) ^ (2)))))\n}\nsubroutine40 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((88.9762) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.671) - (input[5])) ^ (2))) + (((6.968) - (input[6])) ^ (2))) + (((91.9) - (input[7])) ^ (2))) + (((1.4165) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((17.21) - (input[13])) ^ (2)))))\n}\nsubroutine41 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.05602) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((2.46) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.488) - (input[5])) ^ (2))) + (((7.831) - (input[6])) ^ (2))) + (((53.6) - (input[7])) ^ (2))) + (((3.1992) - (input[8])) ^ (2))) + (((3.0) - (input[9])) ^ (2))) + (((193.0) - (input[10])) ^ (2))) + (((17.8) - (input[11])) ^ (2))) + (((392.63) - (input[12])) ^ (2))) + (((4.45) - (input[13])) ^ (2)))))\n}\nsubroutine42 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.01501) - (input[1])) ^ (2)) + (((90.0) - (input[2])) ^ (2))) + (((1.21) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.401) - (input[5])) ^ (2))) + (((7.923) - (input[6])) ^ (2))) + (((24.8) - (input[7])) ^ (2))) + (((5.885) - (input[8])) ^ (2))) + (((1.0) - (input[9])) ^ (2))) + (((198.0) - (input[10])) ^ (2))) + (((13.6) - (input[11])) ^ (2))) + (((395.52) - (input[12])) ^ (2))) + (((3.16) - (input[13])) ^ (2)))))\n}\nsubroutine43 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.02009) - (input[1])) ^ (2)) + (((95.0) - (input[2])) ^ (2))) + (((2.68) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.4161) - (input[5])) ^ (2))) + (((8.034) - (input[6])) ^ (2))) + (((31.9) - (input[7])) ^ (2))) + (((5.118) - (input[8])) ^ (2))) + (((4.0) - (input[9])) ^ (2))) + (((224.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((390.55) - (input[12])) ^ (2))) + (((2.88) - (input[13])) ^ (2)))))\n}\nsubroutine44 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((15.1772) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.74) - (input[5])) ^ (2))) + (((6.152) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.9142) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((9.32) - (input[12])) ^ (2))) + (((26.45) - (input[13])) ^ (2)))))\n}\nsubroutine45 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((18.0846) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((6.434) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.8347) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((27.25) - (input[12])) ^ (2))) + (((29.05) - (input[13])) ^ (2)))))\n}\nsubroutine46 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.01381) - (input[1])) ^ (2)) + (((80.0) - (input[2])) ^ (2))) + (((0.46) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.422) - (input[5])) ^ (2))) + (((7.875) - (input[6])) ^ (2))) + (((32.0) - (input[7])) ^ (2))) + (((5.6484) - (input[8])) ^ (2))) + (((4.0) - (input[9])) ^ (2))) + (((255.0) - (input[10])) ^ (2))) + (((14.4) - (input[11])) ^ (2))) + (((394.23) - (input[12])) ^ (2))) + (((2.97) - (input[13])) ^ (2)))))\n}\nsubroutine47 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((5.66998) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((1.0) - (input[4])) ^ (2))) + (((0.631) - (input[5])) ^ (2))) + (((6.683) - (input[6])) ^ (2))) + (((96.8) - (input[7])) ^ (2))) + (((1.3567) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((375.33) - (input[12])) ^ (2))) + (((3.73) - (input[13])) ^ (2)))))\n}\nsubroutine48 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.01538) - (input[1])) ^ (2)) + (((90.0) - (input[2])) ^ (2))) + (((3.75) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.394) - (input[5])) ^ (2))) + (((7.454) - (input[6])) ^ (2))) + (((34.2) - (input[7])) ^ (2))) + (((6.3361) - (input[8])) ^ (2))) + (((3.0) - (input[9])) ^ (2))) + (((244.0) - (input[10])) ^ (2))) + (((15.9) - (input[11])) ^ (2))) + (((386.34) - (input[12])) ^ (2))) + (((3.11) - (input[13])) ^ (2)))))\n}\nsubroutine49 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((4.89822) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.631) - (input[5])) ^ (2))) + (((4.97) - (input[6])) ^ (2))) + (((100.0) - (input[7])) ^ (2))) + (((1.3325) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((375.52) - (input[12])) ^ (2))) + (((3.26) - (input[13])) ^ (2)))))\n}\nsubroutine50 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((2.01019) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((19.58) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.605) - (input[5])) ^ (2))) + (((7.929) - (input[6])) ^ (2))) + (((96.2) - (input[7])) ^ (2))) + (((2.0459) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((403.0) - (input[10])) ^ (2))) + (((14.7) - (input[11])) ^ (2))) + (((369.3) - (input[12])) ^ (2))) + (((3.7) - (input[13])) ^ (2)))))\n}\nsubroutine51 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.52014) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.97) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.647) - (input[5])) ^ (2))) + (((8.398) - (input[6])) ^ (2))) + (((91.5) - (input[7])) ^ (2))) + (((2.2885) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((264.0) - (input[10])) ^ (2))) + (((13.0) - (input[11])) ^ (2))) + (((386.86) - (input[12])) ^ (2))) + (((5.91) - (input[13])) ^ (2)))))\n}\nsubroutine52 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((9.33889) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.679) - (input[5])) ^ (2))) + (((6.38) - (input[6])) ^ (2))) + (((95.6) - (input[7])) ^ (2))) + (((1.9682) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((60.72) - (input[12])) ^ (2))) + (((24.08) - (input[13])) ^ (2)))))\n}\nsubroutine53 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((0.03578) - (input[1])) ^ (2)) + (((20.0) - (input[2])) ^ (2))) + (((3.33) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.4429) - (input[5])) ^ (2))) + (((7.82) - (input[6])) ^ (2))) + (((64.5) - (input[7])) ^ (2))) + (((4.6947) - (input[8])) ^ (2))) + (((5.0) - (input[9])) ^ (2))) + (((216.0) - (input[10])) ^ (2))) + (((14.9) - (input[11])) ^ (2))) + (((387.31) - (input[12])) ^ (2))) + (((3.76) - (input[13])) ^ (2)))))\n}\nsubroutine54 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((24.8017) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.693) - (input[5])) ^ (2))) + (((5.349) - (input[6])) ^ (2))) + (((96.0) - (input[7])) ^ (2))) + (((1.7028) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((396.9) - (input[12])) ^ (2))) + (((19.77) - (input[13])) ^ (2)))))\n}\nsubroutine55 <- function(input) {\n var0 <- (0) - (0.07692307692307693)\n return(exp((var0) * (((((((((((((((13.6781) - (input[1])) ^ (2)) + (((0.0) - (input[2])) ^ (2))) + (((18.1) - (input[3])) ^ (2))) + (((0.0) - (input[4])) ^ (2))) + (((0.74) - (input[5])) ^ (2))) + (((5.935) - (input[6])) ^ (2))) + (((87.9) - (input[7])) ^ (2))) + (((1.8206) - (input[8])) ^ (2))) + (((24.0) - (input[9])) ^ (2))) + (((666.0) - (input[10])) ^ (2))) + (((20.2) - (input[11])) ^ (2))) + (((68.95) - (input[12])) ^ (2))) + (((34.02) - (input[13])) ^ (2)))))\n}\n", "meta": {"hexsha": "00982938b8ca63598ab66fb7f0b3887b572b169d", "size": 33210, "ext": "r", "lang": "R", "max_stars_repo_path": "generated_code_examples/r/regression/svm.r", "max_stars_repo_name": "yarix/m2cgen", "max_stars_repo_head_hexsha": "f1aa01e4c70a6d1a8893e27bfbe3c36fcb1e8546", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-28T06:59:21.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-28T06:59:21.000Z", "max_issues_repo_path": "generated_code_examples/r/regression/svm.r", "max_issues_repo_name": "yarix/m2cgen", "max_issues_repo_head_hexsha": "f1aa01e4c70a6d1a8893e27bfbe3c36fcb1e8546", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generated_code_examples/r/regression/svm.r", "max_forks_repo_name": "yarix/m2cgen", "max_forks_repo_head_hexsha": "f1aa01e4c70a6d1a8893e27bfbe3c36fcb1e8546", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 143.1465517241, "max_line_length": 965, "alphanum_fraction": 0.380277025, "num_tokens": 14673, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544335934766, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.34689580973590656}} {"text": "#calculates autofit(p,q)order all value in string/arma model for stationary models----> for LINUX\r\n\r\nrm(list=ls())\r\nlibrary(itsmr) #adding itsmr package\r\n\r\npandit <-read.table(\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_arma_stationary.txt\",skip=1)\r\nmadhur<-as.character(pandit$V1) #have all ids stat\r\n\r\nnaringi_firangi<-NULL #complete row information for Stationary model\r\nbunty_bubly<-NULL #for taking zero p & q\r\n\r\n\r\n\r\nfor (sangeet in madhur){\r\ncompendium<- read.table(paste(\"C:\\\\kindle_patrika\\\\PDB\\\\pdb_clf_potentials\\\\pdb_clf_stationary_pot\\\\\",sangeet,\".txt\",sep=\"\"))\r\nprint(paste(\"Reading-------------------------------->\",sangeet))\r\n\r\n#autofit(arma)model & checking for non stationarity\r\nred_money<-autofit(ts(compendium$V1)) #autofit(arma)model (max likelihood method)\r\n\r\n\r\n#checking p & q (both)zero\r\nif(red_money$phi[1]==0 && red_money$theta[1]==0 ){\r\n bunty_bubly<-append(bunty_bubly,sangeet) #p,q not zero else\r\n }\r\nelse{ \r\n \r\np_tunar<-paste(red_money$phi,collapse=\" \") #to decode jk<-strsplit(tunar,\" \") & pal<-jk[[1]][1]\r\nq_funar<-paste(red_money$theta,collapse=\" \")\r\n\r\nautofit_ar_p<-length(red_money$phi) #autoregressive p value\r\nautofit_ma_q<-length(red_money$theta) #moving average q value\r\nautofit_aicc<-red_money$aicc #aicc\r\nautofit_sigma2<-red_money$sigma2 # sigma2\r\n \r\n #joining single line\r\n bumro<-cbind(sangeet,autofit_ar_p,p_tunar,autofit_ma_q,q_funar,autofit_sigma2,autofit_aicc,deparse.level=0)\r\n naringi_firangi<-rbind(naringi_firangi,bumro)\r\n \r\n} #else\r\n\r\n\r\n\r\n} #last for 4 file iterating\r\n\r\n#writnig only stationary model\r\nwrite.table(naringi_firangi,\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_arma_stationary_rectified_linux.txt\",col.names=c(\"PDB_file\",\"Autof_p_order\",\"Autof_ar_p_value\",\"Autof_q_order\",\"AutoF_ma_q_value\",\"Autof_sigma2\",\"AutoF_aicc\"),row.names=F)\r\n\r\n#zero p & q models\r\nwrite.table(bunty_bubly,\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_arma_stat_p_q_zero.txt\",col.names=\"zero p & q models\",row.names=F)\r\n\r\n\r\n\r\n", "meta": {"hexsha": "93edc6a9981b4409735c72ae930a03ccedf8abfc", "size": 2060, "ext": "r", "lang": "R", "max_stars_repo_path": "I-dataset/Model/ARMA/stat_ARMA_all_p_q.r", "max_stars_repo_name": "sagarnikam123/bioinfoProject", "max_stars_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "I-dataset/Model/ARMA/stat_ARMA_all_p_q.r", "max_issues_repo_name": "sagarnikam123/bioinfoProject", "max_issues_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "I-dataset/Model/ARMA/stat_ARMA_all_p_q.r", "max_forks_repo_name": "sagarnikam123/bioinfoProject", "max_forks_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1481481481, "max_line_length": 265, "alphanum_fraction": 0.7174757282, "num_tokens": 674, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3467904083364927}} {"text": "genomewide_Genomewide_distribution_of_expressed_small_RNA_loci<- function(fprefix='input',wdir=\".\") {\r\n\r\nstart_time <- proc.time()\r\ncat(\"Genomewide scatter (Philadelphia) plot (M3.13) start\", date(), \"\\n\") \r\n \r\n# Plots for Module 3 Analysis and visualization of SPAR output \r\n# Session: Genomewide characteristics \r\n\r\n# Module_3_Figure_13(Figure 3.13) \t\r\n# Description: Genomewide scatter plot on RPM values (log10) for all loci \r\n# input: input_annot.with_conservation.xls, input.unannot.final.with_conservation.xls\r\n# output: Genomewide_distribution_of_expressed_small_RNA_loci.png / Genomewide_distribution_of_expressed_small_RNA_loci.pdf\r\n\r\n# parameters for the plot \r\ndatafile1=paste(wdir, \"/\", fprefix, \"_annot.with_conservation.xls\", sep=\"\")\r\ndatafile2=paste(wdir, \"/\", fprefix, \"_unannot.with_conservation.xls\", sep=\"\")\r\nBASENAME=\"Genomewide_distribution_of_expressed_small_RNA_loci\"\r\nPLOTTITLE=\"Genome wide scatter plot \\nof RPM of all loci\"\r\nXTITLE=\"Chromosome Number\"\r\nYTITLE=\"Log10(RPM)\"\r\n\r\n# libraries needed \r\nsuppressPackageStartupMessages(library(ggplot2))\r\nsuppressPackageStartupMessages(library(RColorBrewer))\r\nsuppressPackageStartupMessages(library(plyr))\r\nsuppressPackageStartupMessages(library(scales))\r\n\r\n#args<-commandArgs(TRUE)\r\n#datafile1=args[1] # input file 1\r\n#datafile2=args[2] # input file 2\r\n#wdir=args[3] # output / working directory\r\n#if (length(args)<1) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<2) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<3) { wdir=\".\" } \r\n#use current dir if no \r\n#working dir has been specified\r\n\r\n# output image file\r\npngfile= paste(wdir, \"/../figures/\", paste(BASENAME,\".png\",sep=\"\"), sep=\"\")\r\n#pdffile= paste(wdir, \"/\", paste(BASENAME,\".pdf\",sep=\"\"), sep=\"\")\r\n\r\n# Read in data\r\nD = read.table(datafile1,sep='\\t',header=T,comment.ch=\"\")\r\nE = read.table(datafile2,sep='\\t',header=T,comment.ch=\"\")\r\nE$annotRNAclass = \"unannot\"\r\n#DE = rbind(D[,c(1:20,25,29:31)],E) \r\nDE = rbind(D,E)\r\nDE$annotRNAclass2 = DE$annotRNAclass \r\nDE$annotRNAclass2 = revalue(DE$annotRNAclass2, c(\"mir-3p\"=\"miRNA\", \"mir-5p\"=\"miRNA\", \"mir-5p3pno\"=\"miRNA\", \"miRNAprimary\"=\"miRNA\", \"snoRNAnar\"=\"snoRNA\", \"tRF3\"=\"tRF\",\"tRF5\"=\"tRF\"))\r\n\r\nDE$plotchr = DE$X.peakChr\r\nDE$plotchr = revalue(DE$plotchr,c(\"chr1\"=1,\"chr2\"=2,\"chr3\"=3,\"chr4\"=4,\"chr5\"=5,\"chr6\"=6,\"chr7\"=7,\"chr8\"=8,\"chr9\"=9,\"chr10\"=10,\"chr11\"=11,\"chr12\"=12,\"chr13\"=13,\"chr14\"=14,\"chr15\"=15,\"chr16\"=16,\"chr17\"=17,\"chr18\"=18,\"chr19\"=19,\"chr20\"=20,\"chr21\"=21,\"chr22\"=22,\"chrX\"=\"X\",\"chrY\"=\"Y\",\"chrM\"=\"M\"))\r\nDE = DE[nchar(as.character(DE$plotchr))<=2,]\r\n\r\nDE$plotchr = droplevels(DE$plotchr)\r\n \r\nDE$plotpos = ifelse(DE$plotchr==1, DE$peakChrStart,\r\nifelse(DE$plotchr==2, 249250621 +DE$peakChrStart,\r\nifelse(DE$plotchr==3, 492449994 +DE$peakChrStart,\r\nifelse(DE$plotchr==4, 690472424 +DE$peakChrStart,\r\nifelse(DE$plotchr==5, 881626700 +DE$peakChrStart,\r\nifelse(DE$plotchr==6, 1062541960 +DE$peakChrStart,\r\nifelse(DE$plotchr==7, 1233657027 +DE$peakChrStart,\r\nifelse(DE$plotchr==8, 1392795690 +DE$peakChrStart,\r\nifelse(DE$plotchr==9, 1548066250 +DE$peakChrStart,\r\nifelse(DE$plotchr==10, 1694430272 +DE$peakChrStart,\r\nifelse(DE$plotchr==11, 1835643703 +DE$peakChrStart,\r\nifelse(DE$plotchr==12, 1971178450 +DE$peakChrStart,\r\nifelse(DE$plotchr==13, 2106184966 +DE$peakChrStart,\r\nifelse(DE$plotchr==14, 2240036861 +DE$peakChrStart,\r\nifelse(DE$plotchr==15, 2355206739 +DE$peakChrStart,\r\nifelse(DE$plotchr==16, 2462556279 +DE$peakChrStart,\r\nifelse(DE$plotchr==17, 2565087671 +DE$peakChrStart,\r\nifelse(DE$plotchr==18, 2655442424 +DE$peakChrStart,\r\nifelse(DE$plotchr==19, 2736637634 +DE$peakChrStart,\r\nifelse(DE$plotchr==20, 2814714882 +DE$peakChrStart,\r\nifelse(DE$plotchr==21, 2877740402 +DE$peakChrStart,\r\nifelse(DE$plotchr==22, 2937113968 +DE$peakChrStart,\r\nifelse(DE$plotchr==\"X\", 2996242951 +DE$peakChrStart,\r\nifelse(DE$plotchr==\"Y\", 3047547517 +DE$peakChrStart,\r\nifelse(DE$plotchr==\"M\", 3095677412 +DE$peakChrStart,\r\nDE$plotchr)))))))))))))))))))))))))\r\n\r\n\r\nDE$plotchr[DE$plotchr==\"23\"]<-\"X\"\r\nDE$plotchr[DE$plotchr==\"24\"]<-\"Y\"\r\nDE$plotchr[DE$plotchr==\"25\"]<-\"M\"\r\n\r\nChrLoc = c(1,249250621,492449994,690472424,881626700,1062541960,1233657027,1392795690,1548066250,1694430272,1835643703,1971178450,2106184966,2240036861,2355206739,2462556279,2565087671,2655442424,2736637634,2814714882,2877740402,2937113968,2996242951,3047547517,3095677412)\r\nDE2=DE[order(DE$plotpos),]\r\n\r\n# DE$plotpos = revalue(DE$plotchr,c(\"2\"=249250622,\"3\"=492449995,\"4\"=690472425,\"5\"=881626701,\"6\"=1062541961,\"7\"=1233657028,\"8\"=1392795691,\"9\"=1548066251,\"10\"=1694430273,\"11\"=1835643704,\"12\"=1971178451,\"13\"=2106184967,\"14\"=2240036862,\"15\"=2355206740,\"16\"=2462556280,\"17\"=2565087672,\"18\"=2655442425,\"19\"=2736637635,\"20\"=2814714883,\"21\"=2877740403,\"22\"=2937113969,\"X\"=2996242952,\"Y\"=3047547518,\"M\"=3095677413))\r\n\r\npng(pngfile,width = 14, height = 7, units = 'in', res = 300, type=\"cairo\")\r\n\r\nprint(ggplot(data = DE2, aes(x = plotpos, y = log10(peakRPM))) + \r\n\t\tgeom_point(aes(colour=annotRNAclass2)) + facet_wrap( ~ annotRNAclass2, shrink=FALSE)+\r\n\t\tscale_x_continuous(breaks=ChrLoc, labels=c(\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"X\",\"Y\",\"M\"), expand=c(0.02,0))+\r\n\t\tggtitle(PLOTTITLE) + xlab(XTITLE)+ylab(YTITLE)+theme_classic()+\r\n\t\tscale_color_discrete(name = \"sncRNA classes\")+\r\n\t\ttheme(legend.position=\"bottom\",legend.text=element_text(size=10),\r\n axis.text = element_text(size = 7),\r\n axis.text.x = element_text(size=6, angle = 90, hjust=1, vjust=0.5),\r\n axis.title = element_text(size =14),plot.title = element_text(size = 14)))\r\ndev.off()\r\n\r\ncat(\"Total time for RPM scatter/Phliadelphia plot (M3.13) analysis:\", (proc.time() - start_time)[['elapsed']], \"seconds (\", date(), \")\\n\")\r\n\r\n}\r\n# genomewide_Genomewide_distribution_of_expressed_small_RNA_loci(fprefix=fprefix,wdir=wdir)\r\n\r\n\r\n\r\n", "meta": {"hexsha": "746675ade5bebdf5bc5b39476a3c4a84c6918baa", "size": 5953, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/R/module3/M3.13_genomewide_Genomewide_distribution_of_expressed_small_RNA_loci.r", "max_stars_repo_name": "ConYel/spar_pipeline", "max_stars_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-12-03T10:07:54.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-03T10:07:54.000Z", "max_issues_repo_path": "scripts/R/module3/M3.13_genomewide_Genomewide_distribution_of_expressed_small_RNA_loci.r", "max_issues_repo_name": "ConYel/spar_pipeline", "max_issues_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-12-09T03:48:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-08T13:35:31.000Z", "max_forks_repo_path": "scripts/R/module3/M3.13_genomewide_Genomewide_distribution_of_expressed_small_RNA_loci.r", "max_forks_repo_name": "ConYel/spar_pipeline", "max_forks_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.2192982456, "max_line_length": 407, "alphanum_fraction": 0.7077103981, "num_tokens": 2148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5888891307678319, "lm_q1q2_score": 0.34679040833649266}} {"text": "\\name{reverse.circlize}\n\\alias{reverse.circlize}\n\\title{\nConvert to data coordinate system\n}\n\\description{\nConvert to data coordinate system\n}\n\\usage{\nreverse.circlize(x, y, sector.index = get.current.sector.index(),\n track.index = get.current.track.index())\n}\n\\arguments{\n\n \\item{x}{degree values. The value can also be a two-column matrix/data frame if you put x and y data points into one variable.}\n \\item{y}{distance to the circle center (the radius)}\n \\item{sector.index}{Index for the sector where the data coordinate is used}\n \\item{track.index}{Index for the track where the data coordinate is used}\n\n}\n\\details{\nThis is the reverse function of \\code{\\link{circlize}}. It transform data points from polar coordinate system to a specified data coordinate system.\n}\n\\value{\nA matrix with two columns (\\code{x} and \\code{y})\n}\n\\examples{\npdf(NULL)\nfactors = letters[1:4]\ncircos.initialize(factors, xlim = c(0, 1))\ncircos.trackPlotRegion(ylim = c(0, 1))\nreverse.circlize(c(30, 60), c(0.9, 0.8))\nreverse.circlize(c(30, 60), c(0.9, 0.8), sector.index = \"d\", track.index = 1)\nreverse.circlize(c(30, 60), c(0.9, 0.8), sector.index = \"a\", track.index = 1)\ncircos.clear()\ndev.off()\n\n}\n", "meta": {"hexsha": "fe84a9ff6125aea4a0530a9e83b1345321ebe591", "size": 1191, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/reverse.circlize.rd", "max_stars_repo_name": "calpan/circlize", "max_stars_repo_head_hexsha": "33f8f23663768367188e50e93d3f9b2b57edd0e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-09-16T12:30:42.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-16T12:30:47.000Z", "max_issues_repo_path": "man/reverse.circlize.rd", "max_issues_repo_name": "Nexller/circlize", "max_issues_repo_head_hexsha": "71df6b5316680dcee4d39d3ac7c224fcfc32439b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-08-16T14:55:12.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-16T14:55:12.000Z", "max_forks_repo_path": "man/reverse.circlize.rd", "max_forks_repo_name": "Nexller/circlize", "max_forks_repo_head_hexsha": "71df6b5316680dcee4d39d3ac7c224fcfc32439b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.5384615385, "max_line_length": 148, "alphanum_fraction": 0.7162048699, "num_tokens": 356, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34679039983869986}} {"text": "## Anatom\u00eda de las funciones\n\n# En esta lecci\u00f3n vamos a explorar las distintas partes que componen una funci\u00f3n.\n\n# Componentes de una funci\u00f3n\n# 1- Argumentos formales o par\u00e1metros formales (formals)\n# 2- Cuerpo de la funci\u00f3n\n# 3- Ambiente de la funci\u00f3n\n\n# Para comprender mejor estos componentes, empecemos por definir una funci\u00f3n 'f'\n# bien sencilla:\n\nf <- function(a, b = 5, ...) { # a partir de dos variables 'a' y 'b'\n x <- a * b + w # se crea un objeto 'x' y\n x / 2 # la funci\u00f3n devuelve el valor de 'x' dividido\n} # entre 2.\nw <- 24 # Vamos a definir tambi\u00e9n un valor 'w', que vamos a usar m\u00e1s adelante.\n\n# 1- Argumentos formales\n# Los argumentos de una funci\u00f3n son los valores que nosotros podemos manipular\n# en la entrada al ejecutar la misma. Vamos a distinguir entre los argumentos\n# formales y los argumentos reales (actual arguments). Los primeros son los que\n# componen la funci\u00f3n como nosotros la definimos. Indican qu\u00e9 valores vamos a\n# tener que definir para ejecutar la misma y muchas veces van a tener valores\n# por defecto. Veamos cu\u00e1les son estos argumentos formales en nuestra funci\u00f3n:\n\nformals(f)\n\n# Este comando devuelve una lista de los argumentos formales de cualquier\n# funci\u00f3n. En nuestro caso, nos dice que los argumentos formales de la funci\u00f3n\n# f son a y b, indicando adem\u00e1s que b tiene un valor por defecto de 5.\n# Los argumentos reales, o actuales, son los valores que le vamos a dar a los\n# argumentos formales al ejecutar la funci\u00f3n. Intentemos correr nuestra funci\u00f3n\n# con a = 2 y b = 4.\n\nf(2, 4)\n\n# Como dijimos antes, el argumento formal b tiene un valor por defecto por lo\n# que, si no nos interesa darle un valor particular, podemos dejarlo sin asignar\n# y la funci\u00f3n va a utilizar ese valor.\n\nf(2)\n\n# 2- Cuerpo de la funci\u00f3n\n# El cuerpo de una funci\u00f3n es el conjunto de las instrucciones que se van a eje-\n# cutar al llamar a la funci\u00f3n. En nuestra funci\u00f3n, podemos ver el cuerpo de la\n# misma, mediante el comando 'body'.\n\nbody(f)\n\n# 3- Ambiente de la funci\u00f3n\n# Este es el componente de las funciones que requiere m\u00e1s explicaci\u00f3n, ya que es\n# el componente m\u00e1s abstracto de las mismas.\n# El primer componente del ambiente de una funci\u00f3n es el marco (frame) de la\n# misma, y est\u00e1 compuesto por el conjunto de objetos presentes en R en el\n# momento que la funci\u00f3n es creada. El marco del ambiente de nuestra funci\u00f3n\n# est\u00e1 compuesto por los objetos presentes en el \u00e1rea de trabajo y los objetos\n# de los paquetes cargados. A los primeros podemos consultarlos mediante 'ls'\n# y a los segundos mediante 'search'\n\nls() # Que nos devuelve los objetos que tenemos asignados\nsearch() # Que nos devuelve '.GlobalEnv' y cada paquete actualmente cargado\n\n# '.GlobalEnv' es el ambiente correspondiente al interpretador de R, a la l\u00ednea\n# de comando. La funci\u00f3n 'environment', aplicada a una funci\u00f3n, nos dice en qu\u00e9\n# ambiente fue creada la misma.\n\nenvironment(f)\n\n# Nuestra funci\u00f3n fue creada directamente en el nivel superior, R_GlobalEnv,\n# que corresponde a la l\u00ednea de comando de R, pero veamos que ocurre con alguna\n# funci\u00f3n, por ejemplo 'mean'.\n\nenvironment(mean)\n\n# Esta funci\u00f3n fue creada dentro del ambiente correspondiente al paquete 'base'.\n# Los ambientes est\u00e1n organizados en forma jer\u00e1rquica dentro de R, de modo que\n# el funcionamiento de cualquier funci\u00f3n depende de que los objetos a los que\n# refiere se encuentren en el ambiente en que fue definida o en uno superior,\n# hasta R_GlobalEnv, que es el ambiente parental a todos. Esta pertenencia\n# jer\u00e1rquica entre ambientes es lo que se denomina el enclosure, y es el segundo\n# componente del ambiente. Si una funci\u00f3n depende de un objeto externo para su\n# funcionamiento, \u00e9ste deber\u00e1 estar en el ambiente en que fue definida o en\n# ambientes parentales a \u00e9ste. Como ejemplo, veamos qu\u00e9 sucede con nuestra\n# funci\u00f3n si eliminamos el objeto 'w' que hab\u00edamos creado al principio.\n\nrm (w)\nf(2, 4)\n\n# El objeto 'w', al que se hace referencia en el cuerpo de 'f', fue removido del\n# ambiente de \u00e9sta (del marco, concretamente), por lo que ahora no puede correr.\n# Volvamos a definirlo para seguir adelante.\n\nw <- 24\n\n# Adem\u00e1s, cuando se corre una funci\u00f3n se crea un ambiente dentro de la misma,\n# que es transitorio y s\u00f3lo existe mientras la funci\u00f3n est\u00e9 corriendo. En este\n# ambiente se van a ejecutar las instrucciones del cuerpo de la funci\u00f3n y no es\n# accesible desde el workspace, sino que corre en paralelo. El objetivo de esta\n# separaci\u00f3n, es evitar siempre que sea posible que una funci\u00f3n modifique alguna\n# variable de la propia sesi\u00f3n. Es decir, pase lo que pase dentro de la funci\u00f3n,\n# no se van a afectar los objetos que se encuentran en nuestra \u00e1rea de trabajo.\n# Si recordamos, en el cuerpo de nuestra funci\u00f3n una de las instrucciones crea\n# un objeto 'x'. Veamos qu\u00e9 sucede al correr esta funci\u00f3n si tenemos un objeto\n# llamado 'x' en el \u00e1rea de trabajo.\n\nx <- 1:5\nf(3, 4)\nx\n\n# El objeto 'x' de nuestra \u00e1rea de trabajo no fue modificado.\n# Este territorio puede parecer confuso, por lo que sugiero que experimenten\n# hasta entenderlo bien. A modo de gu\u00eda, veamos la lista de objetos visibles\n# para una funci\u00f3n de R.\n# - objetos definidos en los argumentos\n# - objetos creados en el cuerpo de la funci\u00f3n (hasta ac\u00e1 son los\n# pertenecientes al ambiente de la propia funci\u00f3n).\n# - objetos existentes en ambientes \"parentales\" (todos los \"antepasados\").\n\n# Para entender un poco mejor esto, n\u00f3tese que el objeto 'w', definido en para-\n# lelo a nuestra funci\u00f3n 'f', no es parte de los argumentos, y no est\u00e1 definido\n# en el cuerpo de 'f'. El 'w' es usado dentro de la funci\u00f3n para calcular 'x'\n# (el 'x' del cuerpo de 'f', no el que definimos en el workspace). De hecho, si\n# cambio a 'w' el resultado de evaluar 'f' ser\u00e1 distinto.\n\nf(3, 4)\nw <- 1:5\nf(3, 4)\n\n# Esperando que esta lecci\u00f3n complemente en buena forma lo visto en el video, la\n# pr\u00f3xima lecci\u00f3n ser\u00e1 acerca de la salida de las funciones.\n", "meta": {"hexsha": "3472a07245a6d5fc4fd046ba0e6367491db63957", "size": 5984, "ext": "r", "lang": "R", "max_stars_repo_path": "CODIGO CHURN/CODIGOS_UTILIZADOS/lecciones/5.2-anatomia-de-las-funciones.r", "max_stars_repo_name": "jcombari/Data-products-Course-Project", "max_stars_repo_head_hexsha": "bd3bc260d2bf3def34c8fc94bf847ca0482e7549", "max_stars_repo_licenses": ["FTL"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CODIGO CHURN/CODIGOS_UTILIZADOS/lecciones/5.2-anatomia-de-las-funciones.r", "max_issues_repo_name": "jcombari/Data-products-Course-Project", "max_issues_repo_head_hexsha": "bd3bc260d2bf3def34c8fc94bf847ca0482e7549", "max_issues_repo_licenses": ["FTL"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CODIGO CHURN/CODIGOS_UTILIZADOS/lecciones/5.2-anatomia-de-las-funciones.r", "max_forks_repo_name": "jcombari/Data-products-Course-Project", "max_forks_repo_head_hexsha": "bd3bc260d2bf3def34c8fc94bf847ca0482e7549", "max_forks_repo_licenses": ["FTL"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.3333333333, "max_line_length": 81, "alphanum_fraction": 0.7456550802, "num_tokens": 1598, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34679039983869986}} {"text": "# What is tidy data\n\n# A. Tidy data structure\n# 1. Multiple variables per column\nnetflix_df %>% \n # Split the duration column into value and unit columns. Using convert parameter creates the appropriate column data type conversion\n separate(duration, into = c(\"value\", \"unit\"), sep = \" \", convert = TRUE)\n\n# B. Columns with multiple values\n# 1. International phone numbers\nphone_nr_df %>%\n # Unite the country_code and national_number columns\n unite(\"international_number\", country_code, national_number, sep = \" \")\n\n# 2. Extracting observations from values\ntvshow_df %>% \n # Separate the actors in the cast column over multiple rows\n separate_rows(cast, sep = \", \") %>% \n rename(actor = cast) %>% \n count(actor, sort = TRUE) %>% \n head()\n\n# 3. Separating into columns and rows\ndrink_df %>% \n # Separate the ingredients over rows\n separate_rows(ingredients, sep = \"; \") %>% \n # Separate ingredients into three columns\n separate(\n ingredients, \n into = c(\"ingredient\", \"quantity\", \"unit\"), \n sep = \" \", \n convert = TRUE\n ) %>% \n # Group by ingredient and unit\n group_by(ingredient, unit) %>% \n # Calculate the total quantity of each ingredient\n summarize(quantity = sum(quantity))\n\n# C. Missing values\n# 1. Drop na values\ndirector_df %>% \n # Drop rows with NA values in the director column\n drop_na() %>% \n # Spread the director column over separate rows\n separate_rows(director, sep = \", \") %>% \n # Count the number of movies per director\n count(director, sort = TRUE)\n\n# 2. Imputing sales data\n# fill(): can be used to back and forward fill missing values for variables\nsales_df %>% \n # Impute the year column\n fill(year, .direction = \"up\") %>%\n # Create a line plot with sales per quarter colored by year.\n ggplot(aes(x = quarter, y = sales, color = year, group = year)) +\n geom_line()\n\n# 3. Working with replace_na\n# Method allows for missing values to be filled in for the DataFrame\ncountry_to_continent_df %>% \n left_join(nuke_df, by = \"country_code\") %>% \n # Impute the missing values in the n_bombs column with 0L\n replace_na(list(n_bombs = 0L)) %>% \n # Group the dataset by continent\n group_by(continent) %>% \n # Sum the number of bombs per continent\n summarize(n_bombs_continent = sum(n_bombs)) %>% \n # Plot the number of bombs per continent\n ggplot(aes(x = continent, y = n_bombs_continent)) +\n geom_col()\n", "meta": {"hexsha": "751a0cd57f337b672502e1d564ad727ad79bf31b", "size": 2368, "ext": "r", "lang": "R", "max_stars_repo_path": "R/DataAnalyst/Tidyr/tidy-data.r", "max_stars_repo_name": "James-McNeill/Learning", "max_stars_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/DataAnalyst/Tidyr/tidy-data.r", "max_issues_repo_name": "James-McNeill/Learning", "max_issues_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/DataAnalyst/Tidyr/tidy-data.r", "max_forks_repo_name": "James-McNeill/Learning", "max_forks_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3521126761, "max_line_length": 134, "alphanum_fraction": 0.6942567568, "num_tokens": 631, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3467903998386998}} {"text": "for(i in 10:0) {print(i)}\n", "meta": {"hexsha": "5f80b0d2102fd97812073f63a2b4fe7749a94639", "size": 26, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Loops-Downward-for/R/loops-downward-for.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Loops-Downward-for/R/loops-downward-for.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Loops-Downward-for/R/loops-downward-for.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 13.0, "max_line_length": 25, "alphanum_fraction": 0.5769230769, "num_tokens": 12, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727232, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34676992026641557}} {"text": "fuzzy_join <- function(df1,df2,col1,col2,mode,max_dist,method, q, p){\r\n# df1 the dataframe of the first concept\r\n# df2 the dataframe of the first concept\r\n# col1 column name from df1 to join\r\n# col2 column name from df12 to join\r\n# mode of the join: inner, left, right, full, semi, anti\r\n# max_dist maximum distance(lv,dl,qgram,osa,lcs,soundex) or minimum similarity(jw,cosine,jaccard) required to join the concept records\r\n# method for fuzzy matching\r\n# q the size of grams for q-gram matching\r\n# p - penalty for some methods for fuzzy matching\r\n# \r\nlibrary(fuzzyjoin)\r\n#write.csv(df1,\"C:\\\\Users\\\\rramirezjimenez\\\\workspace\\\\Semoss\\\\R\\\\FuzzyJoinTest\\\\Temp\\\\sourceDataFrame.csv\")\r\n#write.csv(df2,\"C:\\\\Users\\\\rramirezjimenez\\\\workspace\\\\Semoss\\\\R\\\\FuzzyJoinTest\\\\Temp\\\\targetDataFrame.csv\")\r\na<-paste(col1,\"=\",\"\\\"\",col2,\"\\\")\",sep=\"\")\r\nb<-paste(\",mode=\\\"\",mode,\"\\\",max_dist=\",max_dist,\",method=\\\"\",method,\"\\\",q=\",q,\",p=\",p,\")\",sep=\"\")\r\nc<-paste(\"r<-\",\"stringdist_join(df1,df2,by=c(\",a,b,sep=\"\")\r\neval(parse(text=c))\r\nr<-as.data.frame(r)\r\nif(ncol(df1) == 1){\r\nr<-as.data.frame(r)\r\nnames(r)[1]<-col1\r\n}\r\n#write.csv(r,\"C:\\\\Users\\\\rramirezjimenez\\\\workspace\\\\Semoss\\\\R\\\\FuzzyJoinTest\\\\Temp\\\\final.csv\")\r\nreturn(r)\r\n}\r\n\r\n#df1<-read.csv(\"C:/fuzzy1.csv\")\r\n#df2<-read.csv(\"C:/fuzzy2.csv\")\r\n\r\n# Jaro - Winkler\r\n#df<-fuzzy_join(df1,df2,\"t1c1\",\"t2c1\",\"inner\",0.1,method=\"jw\",q=1,p=0.1)\r\n# Damirau - Levenshtein\r\n#df<-fuzzy_join(df1,df2,\"t1c1\",\"t2c1\",\"inner\",1,method=\"dl\",q=1,p=0)\r\n# qgram\r\n#df<-fuzzy_join(df1,df2,\"t1c1\",\"t2c1\",\"inner\",2,method=\"qgram\",q=2,p=0)\r\n# cosine\r\n#df<-fuzzy_join(df1,df2,\"t1c1\",\"t2c1\",\"inner\",0.1,method=\"cosine\",q=2,p=0)\r\n# osa\r\n#df<-fuzzy_join(df1,df2,\"t1c1\",\"t2c1\",\"inner\",2,method=\"osa\",q=1,p=0)\r\n\r\n", "meta": {"hexsha": "bbf38bdf3c067c135d10470bc74fb0c288b37896", "size": 1717, "ext": "r", "lang": "R", "max_stars_repo_path": "R/FuzzyJoinTest/fuzzy_single_join.r", "max_stars_repo_name": "kunal0137/SEMOSS_Volume", "max_stars_repo_head_hexsha": "7870d743fa60dad3d6baaa1a61613ec2da3e141b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/FuzzyJoinTest/fuzzy_single_join.r", "max_issues_repo_name": "kunal0137/SEMOSS_Volume", "max_issues_repo_head_hexsha": "7870d743fa60dad3d6baaa1a61613ec2da3e141b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/FuzzyJoinTest/fuzzy_single_join.r", "max_forks_repo_name": "kunal0137/SEMOSS_Volume", "max_forks_repo_head_hexsha": "7870d743fa60dad3d6baaa1a61613ec2da3e141b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-01T19:30:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-01T19:30:33.000Z", "avg_line_length": 40.880952381, "max_line_length": 135, "alphanum_fraction": 0.6686080373, "num_tokens": 633, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727232, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34676992026641557}} {"text": "library(ggplot2)\r\nlibrary(ggwordcloud)\r\n\r\neinstein <- scan( \"http://www.gutenberg.org/files/5001/5001-h/5001-h.htm\" , what = character() )\r\ngalilei <- scan( \"http://www.gutenberg.org/cache/epub/37729/pg37729.txt\" , what = character() )\r\nhuygens <- scan( \"http://www.gutenberg.org/cache/epub/14725/pg14725.txt\" , what = character() )\r\ntesla <- scan( \"http://www.gutenberg.org/cache/epub/13476/pg13476.txt\" , what = character() )\r\n\r\nremove_punct <- function( str ) {\r\n tmp <- gsub( \"[[:punct:][:blank:]]+\" , \"\" , str )\r\n tmp <- unlist( apply( as.matrix( tmp ) , c(1,2) , strsplit , split = \"\\n\" ) )\r\n tmp <- tmp[ tmp != \"\" ]\r\n res <- tolower( tmp )\r\n return( res )\r\n}\r\n\r\ntmp_einstein <- remove_punct( einstein )\r\ntmp_galilei <- remove_punct( galilei )\r\ntmp_huygens <- remove_punct( huygens )\r\ntmp_tesla <- remove_punct( tesla )\r\n\r\nlength( tmp_einstein ); head( tmp_einstein , 20 )\r\nlength( tmp_galilei ); head( tmp_galilei , 20 )\r\nlength( tmp_huygens ); head( tmp_huygens , 20 )\r\nlength( tmp_tesla ); head( tmp_tesla , 20 )\r\n\r\ntab_einstein <- table( tmp_einstein ) ; tab_einstein <- sort( tab_einstein , decreasing = TRUE )\r\ntab_galilei <- table( tmp_galilei ) ; tab_galileo <- sort( tab_galilei , decreasing = TRUE )\r\ntab_huygens <- table( tmp_huygens ) ; tab_huygens <- sort( tab_huygens , decreasing = TRUE )\r\ntab_tesla <- table( tmp_tesla ) ; tab_tesla <- sort( tab_tesla , decreasing = TRUE )\r\n\r\n\r\n\r\nauthor <- rep( c( \"Einstein\" , \"Galilei\" , \"Huygens\" , \"Tesla\" ) , c( length( tab_einstein ) , length( tab_galilei ) , length( tab_huygens ) , length( tab_tesla ) ) )\r\nword <- c( names( tab_einstein ) , names( tab_galilei ) , names( tab_huygens ) , names( tab_tesla ) )\r\ncount <- c( tab_einstein , tab_galilei , tab_huygens , tab_tesla )\r\n\r\ntf_einstein <- tab_einstein / sum( tab_einstein )\r\ntf_galilei <- tab_galilei / sum( tab_galilei )\r\ntf_huygens <- tab_huygens / sum( tab_huygens )\r\ntf_tesla <- tab_tesla / sum( tab_tesla )\r\ntf <- c( tf_einstein , tf_galilei , tf_huygens , tf_tesla )\r\n\r\nphysics <- data.frame( author = author , word = word , count = count , tf = tf , stringsAsFactors = FALSE )\r\n\r\nidf_fun <- function( word ) {\r\n idx <- physics$word %in% word\r\n idf <- log( 4 / sum(idx) )\r\n return( idf )\r\n} #tf_idf \uac00\uc911\uce58 \ud568\uc218 \uc124\uc815\r\n\r\nidf <- sapply( physics$word , FUN = idf_fun )\r\n\r\nphysics$idf <- idf\r\nphysics$tf_idf <- physics$tf * physics$idf\r\n\r\nidx <- tapply( physics$tf_idf , physics$author , FUN = order , decreasing = TRUE )\r\n\r\ntf_idf_einstein <- physics[ physics$author == \"Einstein\" , ][ idx$Einstein[1:10] ,]\r\ntf_idf_galilei <- physics[ physics$author == \"Galilei\" , ][ idx$Galilei[1:10] ,]\r\ntf_idf_huygens <- physics[ physics$author == \"Huygens\" , ][ idx$Huygens[1:10] ,]\r\ntf_idf_tesla <- physics[ physics$author == \"Tesla\" , ][ idx$Tesla[1:10] ,]\r\n\r\nphysics <- rbind( tf_idf_einstein , tf_idf_galilei , tf_idf_huygens , tf_idf_tesla )\r\n\r\np <- ggplot( physics , aes( x = factor(word , levels = word ) , y = tf_idf , fill = author ) ) + geom_col( show.legend = FALSE )\r\np + facet_wrap( ~ author , scales = \"free\" ) + coord_flip() + theme( axis.title.y = element_blank() )", "meta": {"hexsha": "96c5c6e4ea6aba61915f2f432738feee827d40dd", "size": 3093, "ext": "r", "lang": "R", "max_stars_repo_path": "finalproejct_190529.r", "max_stars_repo_name": "Bonseong/TF-IDF-Algorithm", "max_stars_repo_head_hexsha": "8fb66aacfae57155bf78956d5ec8c1d65645ae56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "finalproejct_190529.r", "max_issues_repo_name": "Bonseong/TF-IDF-Algorithm", "max_issues_repo_head_hexsha": "8fb66aacfae57155bf78956d5ec8c1d65645ae56", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "finalproejct_190529.r", "max_forks_repo_name": "Bonseong/TF-IDF-Algorithm", "max_forks_repo_head_hexsha": "8fb66aacfae57155bf78956d5ec8c1d65645ae56", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.1641791045, "max_line_length": 167, "alphanum_fraction": 0.6563207242, "num_tokens": 1028, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3467699201100446}} {"text": "library(tidyverse)\nlibrary(RCurl)\nlibrary(xml2)\n\nurl_root = \"ftp://ftp-reg.cloud.bom.gov.au/fwo/IDV60920.xml\"\ndat=getURLContent(url_root,userpwd=\"bom893:bmaT94jN\",binary=FALSE)\ndat=read_xml(dat)\ndl = xml_find_all(dat,\".//observations\")\ndl = xml_find_all(dl,\".//station\")\n\nst_id = xml_attr(dl,\"wmo-id\")\nst_name = xml_attr(dl,\"stn-name\")\nst_lat = xml_attr(dl,\"lat\")\nst_lon = xml_attr(dl,\"lon\")\n\nget_var = function(dl,varname){\n x=map(dl,xml_find_all,xpath=sprintf(\"period/level/element[@type='%s']\",varname))\n x=map(x,xml_text)\n x[sapply(x, is_empty)] <- NA\n as.numeric(unlist(x))\n}\n\n\ndf = tibble(id = st_id,\n name = st_name,\n lat=as.numeric(st_lat),\n lon=as.numeric(st_lon),\n apparent_temp = get_var(dl,\"apparent_temp\"),\n air_temperature = get_var(dl,\"air_temperature\"),\n dew_point = get_var(dl,\"dew_point\"),\n pres = get_var(dl,\"pres\"),\n rel_humidity = get_var(dl,\"rel-humidity\"),\n wind_dir_deg = get_var(dl,\"wind_dir_deg\"),\n wind_spd_kmh = get_var(dl,\"wind_spd_kmh\"),\n rainfall_24hr = get_var(dl,\"rainfall_24hr\")\n )\n\nlibrary(tmap)\nlibrary(sf)\ndat = st_as_sf(df,coords=c(\"lon\",\"lat\"),crs=4326)\n\nm = tm_shape(dat) + tm_text(\"air_temperature\")\n", "meta": {"hexsha": "41c8d0fe642e70a644f30bf4bf22304d1c3c22a3", "size": 1277, "ext": "r", "lang": "R", "max_stars_repo_path": "met_xml.r", "max_stars_repo_name": "ozjimbob/aaqfx-render", "max_stars_repo_head_hexsha": "eebad98fe176d2abaa28b6c043a0c7852deb518d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "met_xml.r", "max_issues_repo_name": "ozjimbob/aaqfx-render", "max_issues_repo_head_hexsha": "eebad98fe176d2abaa28b6c043a0c7852deb518d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "met_xml.r", "max_forks_repo_name": "ozjimbob/aaqfx-render", "max_forks_repo_head_hexsha": "eebad98fe176d2abaa28b6c043a0c7852deb518d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6976744186, "max_line_length": 82, "alphanum_fraction": 0.6405638215, "num_tokens": 369, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647394, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.34676990327161705}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/sinking-marbles/results/\")\nsource(\"rscripts/summarySE.r\")\nsource(\"rscripts/helpers.r\")\nload(\"data/priors.RData\")\nr = read.table(\"data/sinking-marbles.tsv\", sep=\"\\t\", header=T)\nr$trial = r$slide_number_in_experiment - 2\nr = r[,c(\"workerid\", \"rt\", \"effect\", \"cause\",\"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\",\"quantifier\", \"object_level\", \"response\", \"object\",\"slider_id\",\"num_objects\",\"trial\")]\nrow.names(priors) = paste(priors$effect, priors$object)\nr$Prior = priors[paste(r$effect, r$object),]$response\nr$object_level = factor(r$object_level, levels=c(\"object_high\", \"object_mid\", \"object_low\"))\nr$Proportion = factor(ifelse(r$slider_id == \"all\",\"100\",ifelse(r$slider_id == \"lower_half\",\"1-50\",ifelse(r$slider_id == \"upper_half\",\"51-99\",\"0\"))),levels=c(\"0\",\"1-50\",\"51-99\",\"100\"))\nr$Half = as.factor(ifelse(r$trial < 16, 1, 2))\nr$Quarter = as.factor(ifelse(r$trial < 8, 1, ifelse(r$trial < 16, 2, ifelse(r$trial < 24, 3, 4))))\nsummary(r)\nr$Combination = as.factor(paste(r$cause,r$object,r$effect))\ntable(r$Combination)\n\n# compute normalized probabilities\nnr = ddply(r, .(workerid,trial), summarize, normresponse=response/(sum(response)),workerid=workerid,Proportion=Proportion)\nrow.names(nr) = paste(nr$workerid,nr$trial,nr$Proportion)\nr$normresponse = nr[paste(r$workerid,r$trial,r$Proportion),]$normresponse\n# test: sums should add to 1\nsums = ddply(r, .(workerid,trial), summarize, sum(normresponse))\ncolnames(sums) = c(\"workerid\",\"trial\",\"sum\")\nsums\n\nsave(r, file=\"data/r.RData\")\n\n##################\n\ns = summarySE(r, measurevar=\"response\", groupvars=c(\"effect\", \"object_level\", \"object\",\"Proportion\",\"Prior\",\"quantifier\"))\nhead(s[s$quantifier == \"Some\" & s$Proportion == \"100\",],20)\n\nggplot(s, aes(x=Prior, y=response))+#, colour=object_level)) +\n geom_point() +\n geom_smooth() +\n# geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n facet_grid(quantifier~Proportion) +\n scale_y_continuous(limits=c(0,1)) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\n\n\ns = summarySE(r, measurevar=\"response\", groupvars=c(\"effect\", \"object_level\", \"object\",\"Proportion\",\"Prior\",\"quantifier\",\"Quarter\"))\n\nggplot(s, aes(x=Prior, y=response,color=Quarter))+#, colour=object_level)) +\n geom_point() +\n geom_smooth() +\n # geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n facet_grid(quantifier~Proportion) +\n scale_y_continuous(limits=c(0,1)) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\n\n\n\n# see if first half different\nfirsthalf = subset(r, trial < 16)\ns = summarySE(r, measurevar=\"response\", groupvars=c(\"effect\", \"object_level\", \"object\",\"Proportion\",\"Prior\",\"quantifier\"))\n\nggplot(s, aes(x=Prior, y=response))+#, colour=object_level)) +\n geom_point() +\n geom_smooth() +\n # geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n facet_grid(quantifier~Proportion) +\n scale_y_continuous(limits=c(0,1)) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\n\n\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\ngraph_title = \"sinking-marbles-prior-with-labels\"\nggplot(s, aes(x=effect, y=response)) +\n geom_point(aes(colour=factor(object_level)), stat=\"identity\") +\n geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n geom_text(aes(label=object), size=3) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\ns$y = 0\ns$yobject = as.numeric(ifelse(s$object_level == \"object_high\",0.25,ifelse(s$object_level ==\"object_mid\",0,-0.25)))\nggplot(s, aes(x=response, y=yobject)) +\n geom_point(aes(colour=factor(object_level))) +\n geom_text(aes(label=effect,y=yobject-0.05),angle=45) +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\ngraph_title = \"sinking-marbles-prior-distribution\"\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\nggplot(s, aes(x=response, fill=factor(object_level))) +\n geom_histogram(position=\"dodge\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\ngraph_title = \"sinking-marbles-prior-histogram\"\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\n# ggplot(s, aes(x=sentence, y=response)) +\n# geom_bar(aes(fill=factor(sentence)), position=position_dodge(0.9), stat=\"identity\") +\n# geom_errorbar(aes(ymin=response-ci, ymax=response+ci, group=factor(sentence)), position=position_dodge(0.9), width=.3) +\n# ggtitle(graph_title) +\n# \n# graph_title = \"3a-dots\"\n# ggplot(r, aes(x=sentence, y=response)) +\n# geom_point(colour=\"black\", size=3, alpha=1/3, stat=\"identity\") +\n# geom_line(aes(group=workerid, colour=factor(workerid)), alpha=1) +\n# ggtitle(graph_title) +\n# ylab(\"\") +\n# xlab(\"\") +\n# theme_bw(18) +\n# theme(\n# axis.text.x = element_text(size=10, angle=-45, hjust=0)\n# ,legend.position=\"none\"\n# ,plot.background = element_blank()\n# ,panel.grid.minor = element_blank()\n# )\n# ggsave(file=paste(c(graph_title, \".png\"), collapse=\"\"), width=10, height=6, title=graph_title)\n# \n# r.z = ddply(r, .(workerid), transform, z.response = scale(response))\n# z = summarySE(r.z, measurevar=\"z.response\", groupvars=c(\"sentence\"))\n# graph_title = \"3a-z\"\n# ggplot(z, aes(x=sentence, y=z.response)) +\n# geom_bar(aes(fill=factor(sentence)), position=position_dodge(0.9), stat=\"identity\") +\n# geom_errorbar(aes(ymin=z.response-ci, ymax=z.response+ci, group=factor(sentence)), position=position_dodge(0.9), width=.3) +\n# ggtitle(graph_title) +\n# ylab(\"\") +\n# xlab(\"\") +\n# theme_bw(18) +\n# theme(\n# axis.text.x = element_text(size=10, angle=-45, hjust=0)\n# ,plot.background = element_blank()\n# ,panel.grid.minor = element_blank()\n# )\n# ggsave(file=paste(c(graph_title, \".png\"), collapse=\"\"), width=10, height=6, title=graph_title)\n# \n# r.z = ddply(r, .(workerid), transform, z.response = scale(response))\n# z = summarySE(r.z, measurevar=\"z.response\", groupvars=c(\"sentence\", \"workerid\"))\n# graph_title = \"3a-z-facet\"\n# ggplot(z, aes(x=sentence, y=z.response)) +\n# geom_bar(aes(fill=factor(sentence)), position=position_dodge(0.9), stat=\"identity\") +\n# geom_errorbar(aes(ymin=z.response-ci, ymax=z.response+ci, group=factor(sentence)), position=position_dodge(0.9), width=.3) +\n# ggtitle(graph_title) +\n# facet_wrap(~ workerid) +\n# ylab(\"\") +\n# xlab(\"\") +\n# theme_bw(18) +\n# theme(\n# axis.text.x = element_text(size=10, angle=-45, hjust=0)\n# ,plot.background = element_blank()\n# ,panel.grid.minor = element_blank()\n# )\n# ggsave(file=paste(c(graph_title, \".png\"), collapse=\"\"), width=10, height=6, title=graph_title)", "meta": {"hexsha": "9bf01915e4700a17db36fe8ba31061aece5f8f1d", "size": 7733, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/2_sinking-marbles/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/2_sinking-marbles/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/2_sinking-marbles/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3529411765, "max_line_length": 183, "alphanum_fraction": 0.679943101, "num_tokens": 2362, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.629774621301746, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34675854511590803}} {"text": "my_lottery_number <- c(37, 20, 48, 1, 13, 5)\ndays_count <- 0\n\nwhile (TRUE) {\n days_count <- days_count + 1\n lottery_number <- sample(1:49, 6)\n if (sum(sort(lottery_number) == sort(my_lottery_number)) == 6) {\n cat('Lottery number: ')\n cat(lottery_number)\n cat('\\n')\n break\n }\n}\n\ncat(sprintf('Took %s days to win the lottery\\n', days_count))\ncat(sprintf('Spent %s dollars to win the lottery\\n', days_count * 2))\n", "meta": {"hexsha": "63b60fc5cee4f03ba058ec1abd023e1fcb1e9837", "size": 426, "ext": "r", "lang": "R", "max_stars_repo_path": "homework-1/homework-1.r", "max_stars_repo_name": "phogbinh/NTUT2020FallAlgorithmicTrading", "max_stars_repo_head_hexsha": "1361716e0cde8bd0db318c89d85d8b75a2dcbee7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "homework-1/homework-1.r", "max_issues_repo_name": "phogbinh/NTUT2020FallAlgorithmicTrading", "max_issues_repo_head_hexsha": "1361716e0cde8bd0db318c89d85d8b75a2dcbee7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "homework-1/homework-1.r", "max_forks_repo_name": "phogbinh/NTUT2020FallAlgorithmicTrading", "max_forks_repo_head_hexsha": "1361716e0cde8bd0db318c89d85d8b75a2dcbee7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0588235294, "max_line_length": 69, "alphanum_fraction": 0.6455399061, "num_tokens": 143, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3467585374639343}} {"text": "InvertPreference <- function(data) {\n # This function takes some input data containing all the six preferences at\n # single level (no PCA) and it multiplies the ones with an opposite trend by\n # a -1\n \n # Define the original value of the gender to preserve the direction of the\n # change\n data[, genderOrig := gender] \n # Invert the sign for the known preferences\n data[preference %in% c(\"risktaking\", \"negrecip\", \"patience\"), \n gender := -1 * genderOrig]\n \n return(data)\n}", "meta": {"hexsha": "f02695610e9d7fa575169b4d0c00bcbaf1f546ea", "size": 490, "ext": "r", "lang": "R", "max_stars_repo_path": "ReproductionAnalysis/functions/helper_functions/InvertPreference.r", "max_stars_repo_name": "formozov/Global-Preferences-Survey", "max_stars_repo_head_hexsha": "03f26e15ddfdfa7fbcab8be1768fe7520681a56b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-02-08T10:31:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-30T07:20:58.000Z", "max_issues_repo_path": "ReproductionAnalysis/functions/helper_functions/InvertPreference.r", "max_issues_repo_name": "formozov/Global-Preferences-Survey", "max_issues_repo_head_hexsha": "03f26e15ddfdfa7fbcab8be1768fe7520681a56b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-02-07T13:00:42.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-07T13:02:15.000Z", "max_forks_repo_path": "ReproductionAnalysis/functions/helper_functions/InvertPreference.r", "max_forks_repo_name": "formozov/Global-Preferences-Survey", "max_forks_repo_head_hexsha": "03f26e15ddfdfa7fbcab8be1768fe7520681a56b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-02-09T07:09:04.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-30T07:32:19.000Z", "avg_line_length": 35.0, "max_line_length": 78, "alphanum_fraction": 0.6979591837, "num_tokens": 127, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3467585374639343}} {"text": "# install.packages(\"plotly\")\n\nlibrary(plotly)\n\ndata <- read.csv(\"./example-fingerprints/fingerprints.csv\")\ncolors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951', '#FAEF44', '#DEAF69')\n\np <- plot_ly(data, x = ~total_number_packets, y = ~no_incoming_packets, z = ~no_outgoing_packets, color = ~domain, size = ~total_incoming_sizes, colors = colors,\n marker = list(symbol = 'circle', sizemode = 'diameter'), sizes = c(5, 150),\n text = ~paste('Domain:', domain, '
# of packets:', total_number_packets)) %>%\n layout(title = 'Website Fingerprinting',\n scene = list(xaxis = list(title = 'Total Number of packets',\n gridcolor = 'rgb(255, 255, 255)',\n type = 'log',\n zerolinewidth = 1,\n ticklen = 5,\n gridwidth = 2),\n yaxis = list(title = 'Number of Incoming Packets',\n gridcolor = 'rgb(255, 255, 255)',\n zerolinewidth = 1,\n ticklen = 5,\n gridwith = 2),\n zaxis = list(title = 'Number of Outgoing Packets',\n gridcolor = 'rgb(255, 255, 255)',\n type = 'log',\n zerolinewidth = 1,\n ticklen = 5,\n gridwith = 2)),\n paper_bgcolor = 'rgb(243, 243, 243)',\n plot_bgcolor = 'rgb(243, 243, 243)')\n\np\n\n", "meta": {"hexsha": "a72fb0d1a54cfa8b866ad1dfc7cce359db8da2a5", "size": 1495, "ext": "r", "lang": "R", "max_stars_repo_path": "graph.r", "max_stars_repo_name": "chigakuishi/website-fingerprinting", "max_stars_repo_head_hexsha": "17a19ec1d972d8e6e0348979e286b940f8eab424", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "graph.r", "max_issues_repo_name": "chigakuishi/website-fingerprinting", "max_issues_repo_head_hexsha": "17a19ec1d972d8e6e0348979e286b940f8eab424", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graph.r", "max_forks_repo_name": "chigakuishi/website-fingerprinting", "max_forks_repo_head_hexsha": "17a19ec1d972d8e6e0348979e286b940f8eab424", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.9705882353, "max_line_length": 161, "alphanum_fraction": 0.4802675585, "num_tokens": 374, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3467585298119606}} {"text": "#!/usr/bin/env Rscript\n\n# Command line argument processing\nargs <- commandArgs(trailingOnly=TRUE)\n\nif (!require(\"stringr\")){\n install.packages(\"stringr\", dependencies=TRUE, repos='http://cloud.r-project.org/')\n library(\"stringr\")\n}\n\nif (!require(\"dplyr\")){\n install.packages(\"dplyr\", dependencies=TRUE, repos='http://cloud.r-project.org/')\n library(\"dplyr\")\n}\n\n### function\n\ncalc_Tcenter <- function(bed.row){\n \n if (bed.row[10]==1){\n new_position = as.integer(bed.row[2] + (bed.row[3]-bed.row[2])/2)\n return(new_position)\n }\n \n t_Length_set = as.integer(unlist(str_split(bed.row[11], \",\")))\n t_Length_set = t_Length_set[!is.na(t_Length_set)]\n t_length = as.integer(sum(t_Length_set)/2)\n \n t_Position_set = as.integer(unlist(str_split(bed.row[12], \",\")))\n t_Position_set = t_Position_set[!is.na(t_Position_set)]\n \n for(i in 1:length(t_Length_set)){\n S_i = sum(t_Length_set[1:i]) \n \n if(S_i >= t_length){\n k = i\n s = sum(t_Length_set[1:(i-1)]) \n new_position = as.integer(bed.row[2] + t_Position_set[k] + (t_length-s))\n return(new_position)\n }\n }\n}\n\ninputfile <- args[1]\nbed.raw = read.table(inputfile, sep=\"\\t\", header=F, check.names=FALSE, stringsAsFactors=F)\n\nbed.adjusted = bed.raw\nfor(i in 1:nrow(bed.raw)){\n if(bed.raw[i,7] == bed.raw[i,8]){\n bed.adjusted[i,7] = calc_Tcenter(bed.raw[i,])\n bed.adjusted[i,8] = bed.adjusted[i,7]\n }\n}\n\nwrite.table(bed.adjusted, 'adjusted.bed', sep=\"\\t\", col.names=F, row.names=F, quote=F)\n\n# Printing sessioninfo to standard out\nprint(\"adjusted bed script info:\")\nsessionInfo()\n\n\n\n\n\n\n\n", "meta": {"hexsha": "e882f230aee6c7f74f54df9203366b990f0bd6a6", "size": 1593, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/adjust_bed_noncoding.r", "max_stars_repo_name": "rikenbit/ramdaq.nf", "max_stars_repo_head_hexsha": "ccc22279244a9234fd30ead2b1b14cb8a1b3fe67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2019-09-30T08:46:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-09T02:12:50.000Z", "max_issues_repo_path": "bin/adjust_bed_noncoding.r", "max_issues_repo_name": "rikenbit/ramdaq.nf", "max_issues_repo_head_hexsha": "ccc22279244a9234fd30ead2b1b14cb8a1b3fe67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-04-08T04:53:51.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-07T01:38:39.000Z", "max_forks_repo_path": "bin/adjust_bed_noncoding.r", "max_forks_repo_name": "rikenbit/ramdaq.nf", "max_forks_repo_head_hexsha": "ccc22279244a9234fd30ead2b1b14cb8a1b3fe67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-07T06:15:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-07T06:15:01.000Z", "avg_line_length": 23.776119403, "max_line_length": 90, "alphanum_fraction": 0.6528562461, "num_tokens": 474, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3467084399388914}} {"text": "#' knots2kmh\n#'\n#' Conversion from knot per second to kilometers per hour.\n#'\n#' @param knots numeric Speed in knots per second.\n#' @return kilometers per hour\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords knots2kmh \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nknots2kmh=function(knots) {\n ct$assign(\"knots\", as.array(knots))\n ct$eval(\"var res=[]; for(var i=0, len=knots.length; i < len; i++){ res[i]=knots2kmh(knots[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "5cb6e3f0f1ca4a9adf8276cc7af7d2152e044be1", "size": 648, "ext": "r", "lang": "R", "max_stars_repo_path": "R/knots2kmh.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/knots2kmh.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/knots2kmh.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 25.92, "max_line_length": 122, "alphanum_fraction": 0.5601851852, "num_tokens": 195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.34670843128633855}} {"text": "#!/usr/bin/env Rscript\n#autor: Joao Sollari Lopes\n#local: University of Reading, Reading, UK\n#Rversion: 3.2.3\n#criado: 01.03.2010\n#modificado: 20.11.2017\n\n# @arg rej_file - file with rejection step results (.dat)\nplot_sstats <- function(data_file){\n\n #info on summstats\n\tlsstats <- c(\"pi1\",\"S1\",\"k_S1\",\"sH_S1\",\"avMFS1\",\"sdMFS1\")\n\tnsstats <- length(lsstats)\n\n\t#import the .dat file\n npoints <- 1000\n\tabc.data <- data.matrix(read.table(data_file))[1:npoints,]\n\n #normalize summstats\n\tsstats <- abc.data[,7:12]\n\tsstats.s <- sstats\n\tfor(i in 1:nsstats){\n\t\tsstats.s[,i] <- (sstats[,i] - mean(sstats[,i]))/sd(sstats[,i])\n\t}\n\n\t#plotting summstats\n fname <- \"../results/plot_sstats.eps\"\n postscript(file=fname,width=7,height=7,colormodel=\"rgb\",horizontal=FALSE,onefile=FALSE,paper=\"special\")\n\toldpar <- par(mfrow=c(2,3),xaxt=\"n\",yaxt=\"n\",mar=c(2,2,2,2))\n\tfor(i in 1:nsstats){\n\t\tplot(sstats[,i],log(abc.data[,6]),pch='.',main=lsstats[i])\n\t\tabline(lm(log(abc.data[,6])~sstats[,i]),lty=2,col=\"red\")\n\t}\n\tpar(oldpar)\n\tdev.off()\n\tprint(\"done plot sstats\")\n \n}\n\nargs = commandArgs(trailingOnly=TRUE)\nplot_sstats(args[1])\n", "meta": {"hexsha": "d5c7b70ec787fc416585292976b343fb18f9eaaf", "size": 1141, "ext": "r", "lang": "R", "max_stars_repo_path": "practicals/practical5/bin/practical5_part1.r", "max_stars_repo_name": "jsollari/IABC2017", "max_stars_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "practicals/practical5/bin/practical5_part1.r", "max_issues_repo_name": "jsollari/IABC2017", "max_issues_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "practicals/practical5/bin/practical5_part1.r", "max_forks_repo_name": "jsollari/IABC2017", "max_forks_repo_head_hexsha": "d69ba6a9b4fb25f8bdcb56a9dd9ecb7ad93e6545", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1666666667, "max_line_length": 107, "alphanum_fraction": 0.6555652936, "num_tokens": 410, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.34670843128633855}} {"text": "######################################################################################################\n# Triple plot\n#\n# Version 1.00, 28/08/2007 10:59:53\n#\n# Author: Mark Payne\n# DIFRES, Charlottenlund, DK\n#\n# Plots SSB, F.bar and Recruitment on a single stacked plot.\n# Functions here are called from elsewhere\n#\n# Changes:\n# V 1.00 - First release\n####################################################################################################\n\nplot.triple.panel<-function(x,label,xlimits,show.x.lbl=FALSE,legend.pos=NULL,legend.ncol=1) {\n yr <- as.numeric(dimnames(x)[[2]])\n n.runs <- dim(x)[3]\n plot(yr,x[sp,,1],ann=FALSE,type='b',ylim=c(0,max(x[sp,,],na.rm=T)),xlim=xlimits,\n lwd=3,pch=1,xaxt=if(show.x.lbl){\"s\"} else {\"n\"})\n mtext(label,las=0,side=2,padj=-6)\n grid()\n if (n.runs>1) for (yy in (2:n.runs)) {\n lines(yr,x[sp,,yy],type='b',pch=yy,col=1,lwd=1)\n }\n if(!is.null(legend.pos)) {\n run.names <- dimnames(x)[[3]]\n legend(x=legend.pos,legend=as.character(run.names),lwd=c(2,rep(1,n.runs-1)),\n pch=seq(1,n.runs),col=1,bg=\"white\",pt.bg=\"white\",ncol=legend.ncol)\n }\n }\n\nplot.triple<-function(legend.pos=NULL,legend.ncol=1) {\n layout(matrix(c(1,2,3),3,1,byrow=TRUE), height=c(1,1,1), width=1)\n oldpar <- par(oma=c(2.5,0.5,1.5,0.5),cex=1,las=1,mar=c(0,6,0,0))\n xlims <- range(pretty(as.numeric(c(dimnames(SSB)[[2]],dimnames(FI)[[2]],dimnames(REC)[[2]]))))\n for (sp in (1:nsp)) {\n #Not sure if this works in multispecies mode or not - needs to be checked. MRP 20070827\n if(nsp>1) {stop(\"Unsure whether this works in mulitspecies mode. Check it properly!! - MRP 20070827\")}\n plot.triple.panel(SSB,\"SSB (kt)\",xlims,legend.pos=legend.pos,legend.ncol=legend.ncol)\n plot.triple.panel(FI,\"F bar\",xlims)\n plot.triple.panel(REC,\"Recruits (10^6)\",xlims,show.x.lbl=TRUE)\n }\n par(oldpar)\n}\n\n", "meta": {"hexsha": "28bf3d9d73828ce861aae396a7c86dd75d4bd851", "size": 1949, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/function/plot_triple.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/function/plot_triple.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/function/plot_triple.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.4680851064, "max_line_length": 110, "alphanum_fraction": 0.543355567, "num_tokens": 640, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3466676140687355}} {"text": "# Clear workspace\nrm(list=ls())\n\n# load libraries\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(treemap)\nlibrary(RColorBrewer)\nlibrary(scales)\nlibrary(doBy)\n\n# Install trebuchet font to be used\n#library(devtools)\n#install_github(\"extrafont\", \"wch\")\n#font_import(pattern = \"trebuc\")\n\n# Define lab colors (TODO: Source this)\n# Lab RGB colors\nredL <- c(\"#B71234\")\ndredL \t<- c(\"#822443\")\ndgrayL \t<- c(\"#565A5C\")\nlblueL \t<- c(\"#7090B7\")\ndblueL \t<- c(\"#003359\")\nlgrayL\t<- c(\"#CECFCB\")\n\n#setwd(\"U:/FoodForPeace/R/\")\nsetwd(\"C:/Users/t/Box Sync/FoodForPeace/R/\")\nd <- read.csv(\"FFPdata0912.csv\", sep = \",\", header = TRUE)\nnames(d)\n\nd$Food.Aid <- round(d$decTotal/1000000, 0)\nd$Food.AidNom <- round(d$decTotalNom/1000000, 0)\n\n# d$Food.Aid <- format(d$tot.thous, big.mark=\",\", scientific=F)\n\n# Subset data into years; where x[1] = 1959, x[2] = 1953 etc...\n# Apply vector of names to new subset data\nx.split <- split(d, d$year)\nnew_names <- c(\"dfif\", \"dsix\", \"dsev\", \"degt\", \"dnin\", \"dzer\", \"dsub\")\nfor (i in 1:length(x.split)) {\n assign(new_names[i], x.split[[i]])\n}\n\n\n# Create a function to keep top X records; z controls graphic title -- linked to x input.\nmyBar <- function(x, y, z, a) {\n \n # Filter the data to select top N countries of NOMINAL FOOD Aid expenditures\n dplot <- head(arrange(x, desc(Food.AidNom)), n = y) \n \n # Initialize a ggplot bar chart using the dollars as stat\n c <- ggplot(dplot, aes(x = reorder(factor(country), Food.AidNom), \n y =Food.AidNom, fill = \"country\")) + geom_bar(stat = \"identity\") + scale_fill_manual(values = redL )\n \n # Touch up plot to remove formatting and add USAID lab colours\n pp <- c + coord_flip()+labs(x =\"\", title = z, y = \"($ Nominal US Millions)\") +\n theme(legend.position = \"none\", panel.background=element_rect(fill=\"white\"), \n axis.ticks.y=element_blank(),\n axis.text.y = element_text(hjust=1, size=10, colour = dblueL), \n axis.ticks.x=element_blank(),\n axis.text.x = element_text(hjust=1, size=10, colour = dblueL),\n axis.title.x = element_text(colour=dblueL, size=8),\n plot.title = element_text(lineheight=.8, colour = dblueL)) + \n scale_y_continuous(labels = dollar ) \n \n print(pp)\n \n # Save the plot in the working directory using the values in a (new_names)\n ggsave(pp, filename = paste(a, \".png\"), width=7.5, height=5.5)\n}\n\n# Define top N list and names for top of each graph\ntopList <- c(10)\ngraph.title <- c(\"Top 10 Receipients of Food Aid: 1954-1959\",\n \"Top 10 Receipients of Food Aid: 1960-1969\",\n \"Top 10 Receipients of Food Aid: 1970-1979\",\n \"Top 10 Receipients of Food Aid: 1980-1989\",\n \"Top 10 Receipients of Food Aid: 1990-1999\",\n \"Top 10 Receipients of Food Aid: 2000-2009\",\n \"Top 10 Receipients of Food Aid: 2010-2013\")\n\n\nfor (i in 1:length(new_names)) {\n myBar(get(new_names[i]), topList, graph.title[i], new_names[i])\n}\n\n\n### Bring in regional data\n\ndd <- read.csv(\"FFPdata0912_RegionTotals.csv\", sep = \",\", header = TRUE)\ndd$Food.Aid.R <- round(dd$decTotal/1000000, 0) # For real numbers\ndd$Food.Aid.RNom <- round(dd$decTotalNom/1000000, 0) # For nomimal numbers (default)\n\nx.splitreg <- split(dd, dd$year)\nnew_namesreg <- c(\"dfifreg\", \"dsixreg\", \"dsevreg\", \"degtreg\", \"dninreg\", \"dzerreg\", \"dsubreg\")\nfor (i in 1:length(x.split)) {\n assign(new_namesreg[i], x.splitreg[[i]])\n}\n\n# Create a function for regions\nmyReg <- function(x, z, a) {\n \n # Initialize a ggplot bar graph for showing regional totals by decade\n p <- ggplot(x , aes(x = reorder(factor(region), Food.Aid.RNom), \n y = Food.Aid.RNom, fill = \"region\")) + geom_bar(stat = \"identity\") + scale_fill_manual(values = dblueL )\n \n # Touch up plot to remove formatting and add USAID lab colours\n pp <- p + coord_flip()+labs(x =\"\", title = z, y = \"($ Nominal US Millions)\") +\n theme(legend.position = \"none\", panel.background=element_rect(fill=\"white\"), \n axis.ticks.y=element_blank(),\n axis.text.y = element_text(hjust=1, size=10, colour = dblueL), \n axis.ticks.x=element_blank(),\n axis.text.x = element_text(hjust=1, size=10, colour = dblueL),\n axis.title.x = element_text(colour=dblueL, size=8),\n plot.title = element_text(lineheight=.8, colour = dblueL)) + \n scale_y_continuous(labels = dollar ) \n print(pp)\n \n # Save plot using names in vector a (new_namesreg)\n ggsave(pp, filename = paste(a, \".png\"), width=7.5, height=5.5)\n}\n\n\ngraph.titlereg <- c(\"Regional Totals: 1954-1959\",\n \"Regional Totals: 1960-1969\",\n \"Regional Totals: 1970-1979\",\n \"Regional Totals: 1980-1989\",\n \"Regional Totals: 1990-1999\",\n \"Regional Totals: 2000-2009\",\n \"Regional Totals: 2010-2013\")\n\n\nfor (i in 1:length(new_namesreg)) {\n myReg(get(new_namesreg[i]), graph.titlereg[i], new_namesreg[i])\n}\n\n\n\n# Calculate totals Aggregates for decades\nsummaryBy(decTotalNom ~ year, data = d, FUN = sum)\n\ngrandT <- summaryBy(decTotalNom ~ region, data = d, FUN = sum)\ngrandD <- as.data.frame(grandT)\ngrandD$decTotalNom.sumR <- round(grandD$decTotalNom.sum/1000000, 0)\n\n# Plot grand totals\np <- ggplot(grandD , aes(x = reorder(factor(region), decTotalNom.sumR), \n y = decTotalNom.sumR, fill = \"region\")) + geom_bar(stat = \"identity\") + scale_fill_manual(values = dblueL )\npp <- p + coord_flip()+labs(x =\"\", title = \"Regional Totals: 1954-2013\", y = \"($ Nominal US Millions)\") +\n theme(legend.position = \"none\", panel.background=element_rect(fill=\"white\"), axis.ticks.y=element_blank(),\n axis.text.y = element_text(hjust=1, size=10, colour = dblueL), axis.ticks.x=element_blank(),\n axis.text.x = element_text(hjust=1, size=10, colour = dblueL),\n axis.title.x = element_text(colour=dblueL, size=8),\n plot.title = element_text(lineheight=.8, colour = dblueL)) + \n scale_y_continuous(labels = dollar ) \nprint(pp)\nggsave(pp, filename = paste(\"GrandTotNom\", \".png\"), width=7.5, height=5.5)\n\n\n# Calculate total Aggregates for countries\nsummaryBy(decTotal ~ country, data = d, FUN = sum)\ncountryTot <- as.data.frame(summaryBy(decTotal ~ country, data = d, FUN = sum))\ncountryTot$decTotal.sumD <- round(countryTot$decTotal.sum/1000000, 0)\n\n# Plot top 25 receipients of Food Aid\ndplot <- head(arrange(countryTot, desc(decTotal.sum)), n = 25) \nc <- ggplot(dplot, aes(x = reorder(factor(country), decTotal.sumD), \n y =decTotal.sumD, fill = \"country\")) + geom_bar(stat = \"identity\")+ scale_fill_manual(values = redL )\np <- c + coord_flip()+labs(x =\"\", title = \"Top 25 Recipients of Food Aid: 1954-2013\", y = \"($ 2013 US Millions)\") +\n theme(legend.position = \"none\", panel.background=element_rect(fill=\"white\"), axis.ticks.y=element_blank(),\n axis.text.y = element_text(hjust=1, size=10, colour = dblueL), axis.ticks.x=element_blank(),\n axis.text.x = element_text(hjust=1, size=10, colour = dblueL),\n axis.title.x = element_text(colour=dblueL, size=8),\n plot.title = element_text(lineheight=.8, colour = dblueL)) + \n scale_y_continuous(labels = dollar ) \nprint(p)\nggsave(p, filename = paste(\"CountryTot\", \".png\"), width=7.5, height=5.5)\n\n\n# Calculate total Aggregates for countries\nsummaryBy(decTotalNom ~ country, data = d, FUN = sum)\ncountryTot <- as.data.frame(summaryBy(decTotalNom ~ country, data = d, FUN = sum))\ncountryTot$decTotalNom.sumD <- round(countryTot$decTotalNom.sum/1000000, 0)\n\n\n# Plot top 25 receipients of Food Aid in nominal terms\ndplot <- head(arrange(countryTot, desc(decTotalNom.sum)), n = 25) \nc <- ggplot(dplot, aes(x = reorder(factor(country), decTotalNom.sumD), \n y =decTotalNom.sumD, fill = \"country\")) + geom_bar(stat = \"identity\")+ scale_fill_manual(values = redL )\np <- c + coord_flip()+labs(x =\"\", title = \"Top 25 Recipients of Food Aid: 1954-2013\", y = \"($ Nominal US Millions)\") +\n theme(legend.position = \"none\", panel.background=element_rect(fill=\"white\"), axis.ticks.y=element_blank(),\n axis.text.y = element_text(hjust=1, size=10, colour = dblueL), axis.ticks.x=element_blank(),\n axis.text.x = element_text(hjust=1, size=10, colour = dblueL),\n axis.title.x = element_text(colour=dblueL, size=8),\n plot.title = element_text(lineheight=.8, colour = dblueL)) + \n scale_y_continuous(labels = dollar ) \nprint(p)\nggsave(p, filename = paste(\"CountryTot\", \".png\"), width=7.5, height=5.5)\n\n\n# Basic treemap function to call with subsetted data above\nmyTree <- function(x) {\t\n tm <- treemap(x, index=c(\"region\", \"country\"),\n vSize = \"Food.Aid\",\n vColor =\"Food.Aid\",\n type = \"value\",\n palette=\"RdYlGn\",\n fontsize.labels=c(40,10),\n align.labels=list(c(\"center\", \"center\"), \n c(\"left\", \"top\")),\n lowerbound.cex.labels=.66,\n position.legend = \"bottom\",\n #inflate.labels = TRUE,\n title = \"Food Aid Decadal Totals\",\n #subtitle = \"US Dollars (000s)\",\n fontsize.title = 24,\n fontsize.legend = 16,\n bg.labels = 0, \n algorithm = \"pivotSize\",\n #sortID = \"size\"\n )\n}\nmyTree(d)\n", "meta": {"hexsha": "9f5f6364c710f6d8dd8134ed3ce560cd102136a8", "size": 9407, "ext": "r", "lang": "R", "max_stars_repo_path": "treeMaps.r", "max_stars_repo_name": "tessam30/FoodForPeace", "max_stars_repo_head_hexsha": "a96adeda7ddf9156acff82b7d62d9e9763fa4839", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "treeMaps.r", "max_issues_repo_name": "tessam30/FoodForPeace", "max_issues_repo_head_hexsha": "a96adeda7ddf9156acff82b7d62d9e9763fa4839", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "treeMaps.r", "max_forks_repo_name": "tessam30/FoodForPeace", "max_forks_repo_head_hexsha": "a96adeda7ddf9156acff82b7d62d9e9763fa4839", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.9955357143, "max_line_length": 132, "alphanum_fraction": 0.6302753269, "num_tokens": 2737, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982315512488, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.34663581577017105}} {"text": "\r\nc.activity_martin <- function(traj, min_pause_length=0.2) {\r\n\r\n\tactivities <- c()\r\n\tdistances <- c()\r\n\ttimestart <- c()\r\n\ttimestop <- c()\r\n\tXstart <- c()\r\n\tYstart <- c()\r\n\tXend <- c()\r\n\tYend <- c()\r\n\tspeedmax_all<- c()\r\n\r\n\t\t\r\n\tfor (oo in c(1:length(traj))) {\r\n\r\n\t\tdata = traj[[oo]]\r\n\t\tdt <- data$dt\r\n\r\n\t\t\r\n\t\txx <- data$x\r\n\t\tyy <- data$y\r\n\t\tddist <- data$dist\r\n\t\tif (length(ddist)<12) next\r\n\r\n\t\tdate <- data$date\r\n\r\n\t\t#moving <- data$dist\r\n\t\t#moving[is.na(moving)] <- 0\r\n\t\tdt[is.na(dt)] <- 0\r\n\r\n\t\tactivity <- c()\r\n\t\tDistance <- c()\r\n\t\tdatesstart <- c()\r\n\t\tdatesend <- c()\r\n\t\tdataXstart <- c()\r\n\t\tdataYstart <- c()\r\n\t\tdataXend <- c()\r\n\t\tdataYend <- c()\r\n\t\tspeedmax_burst <- c()\r\n\r\n\t\tpause_length <- 0\r\n\t\tactive_length <- 0\r\n\t\tDistance_traveled <- 0\r\n\t\tmovprec <- FALSE\r\n\r\n\t\tspeedmax_temp <- 0\r\n\t\tdtempP <- date [1]\r\n\t\tdtempA <-date [1]\r\n\t\txP<-xx [1]\r\n\t\tyP<-yy [1]\r\n\t\txA<-xx [1]\r\n\t\tyA<-yy [1]\r\n\t\tactive <- FALSE\r\n DDD=c()\r\n DD=c()\r\n\t\t# TODO: use rapply here!\r\n\t\tfor (i in c(6:(length(dt)-6))) {\r\n\r\n\t\tdisttrav1s = (ddist[i-5] +ddist[i-4] +ddist[i-3] +ddist[i-2] +ddist[i-1] +ddist[i] +ddist[i+1] +ddist[i+2] +ddist[i-+3] +ddist[i+4] +ddist[i+5])\r\n\t\tDD=c(DD,disttrav1s)\r\n\t\tmovprec =ifelse (disttrav1s <1.15, FALSE,movprec)\r\n\t\tmoving =ifelse (disttrav1s >2.5, TRUE,movprec) \r\n\t\t#moving is true if >4, false if <2 and the same as in the last loop otherwise\r\n\t\tmovprec = moving\r\n\t\t\t\r\n\t\t\tif (!moving) {\r\n\r\n\t\t\t\tpause_length <- pause_length + dt[i]\r\n\t\t\t\tdtempP= ifelse(pause_length > dt [i],dtempP,date [i])## end of bout temporary\r\n\t\t\t\txP = ifelse(pause_length > dt[i],xP,xx [i])\r\n\t\t\t\tyP = ifelse(pause_length > dt[i],yP,yy [i])\r\n\t\t\t\tif (active_length>0 && pause_length>=min_pause_length) {\r\n\t\t\t\t\tactivity <- c(activity,active_length)\r\n\t\t\t\t\tDistance <- c(Distance,Distance_traveled)\r\n\t\t\t\t\tspeedmax_burst <- c(speedmax_burst,speedmax_temp)\r\n\t\t\t\t\tdatesstart <- c(datesstart,dtempA)\r\n\t\t\t\t\tdatesend <- c(datesend,dtempP)\r\n\t\t\t\t\tdataXstart <- c(dataXstart,xA)\r\n\t\t\t\t\tdataYstart <- c(dataYstart,yA)\r\n\t\t\t\t\tdataXend <- c(dataXend,xP)\r\n\t\t\t\t\tdataYend <- c(dataYend,yP)\r\n\t\t\t\t\tactive_length <- 0\r\n\t\t\t\t\tDistance_traveled <- 0\r\n\t\t\t\t\tspeedmax_temp <-0\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t} else {\r\n\t\t\t\tactive_length <- active_length + dt[i]\r\n\t\t\t\tDistance_traveled <- ifelse(active_length > dt[i],Distance_traveled + dt[i+5], disttrav1s)\r\n\t\t\t\tspeedmax_temp <- ifelse (ddist[i]/dt[i] >speedmax_temp,ddist [i]/dt[i] ,speedmax_temp)\r\n\t\t\t\tif (pause_length>0) {\r\n\t\t\t\t\tdtempA= ifelse( active_length > dt [i],dtempA,date [i]) #start of bout temporary, change if active_length was 0(bout def depend on what happen next)\r\n\t\t\t\t\txA = ifelse(active_length > dt [i],xA,xx [i])\r\n\t\t\t\t\tyA = ifelse(active_length > dt [i],yA,yy [i])\r\n\t\t\t\t\tif (pause_length0,]\r\n########act_table = act_table[act_table$act>g_duration_slider/10*4,]\r\n#\r\n\r\n\r\n\r\n#calculation of linearity of curve for each bout\r\n#########################\r\n#act_table$lin = sqrt( (act_table$Xe-act_table$Xs)^2+(act_table$Ye-act_table$Ys)^2)/act_table$dist_traveled\r\n\r\n\r\n\r\n\r\n\r\n#calculate total activity time for each individual\r\nsum_act = c()\r\nmedian_act = c()\r\nmedian_pause = c()\r\nnumber_pause = c()\r\n#median_lin = c()\r\n#mean_lin = c()\r\nfor (i in c(1:nrow(id_table))) {\r\n\tact = act_table[act_table$id==id_table$id[i],]$act\r\n\tpause = pause_table[pause_table$id==id_table$id[i],]$pause\r\n\t#lin = act_table[act_table$id==id_table$id[i],]$lin\r\n\tsum_act = c(sum_act,sum(act))\r\n\tmedian_act = c(median_act,median(act))\r\n\tmedian_pause = c(median_pause, median(abs(pause)))\r\n\tnumber_pause = c(number_pause,length(pause))\r\n\t#median_lin = c(median_lin, quantile(lin, probs =0.5, na.rm=TRUE))\r\n\t\r\n\t\r\n}\r\n#act_table_m2 = data.frame(sum_act,median_act,median_pause,number_pause,id=id_table$id,group=id_table$group)\r\nnumber_pause=number_pause/f_table$length_experiment\nsum_act=sum_act/f_table$length_experiment\n\r\nf_table = data.frame (f_table,activitytime_permin_ST=sum_act,act_bouts_ST=median_act,\r\npause_duration_ST=median_pause,numb_pause_permin_ST=number_pause)\r\n\r\nf_table_positive= data.frame (f_table_positive,activitytime_permin_ST=sum_act,act_bouts_ST=median_act,\r\npause_duration_ST=median_pause,numb_pause_permin_ST=number_pause)\r\nmessage(\"starting writing activities log.txt\")\r\n\r\n\r\n\r\nsetwd(rgghome)\r\n\r\n#if (g_bin_size==0)\r\n#\t{g_bin_size = 0.5}\t\r\n#\t\r\n#v = act_table$act\r\n#bins = seq(min(v[!is.na(v)])-g_bin_size,max(v[!is.na(v)])+g_bin_size,g_bin_size)\r\n\r\n#create mean and sd table\r\n#mean_table = create.mean.table(act_table_m2,group_ids,data_cols=1:4)\r\n\r\n\r\n# # message(\"starting plots activity\")\r\n# ### create plots\r\n# mybarplot(mean_table$means$sum_act,mean_table$ses$sum_act,rownames(mean_table$means),\r\n\t# main=\"Total activity time Martin 27-10\",ylab=\"Total activity time [s]\", ylim= c(0,ylim_acttime))\r\n# mybarplot(mean_table$means$median_act,mean_table$ses$median_act,rownames(mean_table$means),\r\n\t# main=\"Mean of medians of bouts duration Martin 27-10\",ylab=\"median of bouts length [s]\",ylim = c(0,ylim_pausetime))\r\n\r\n# mybarplot(mean_table$means$number_pause,mean_table$ses$number_pause,rownames(mean_table$means),\r\n\t# main=\"Number of pauses Martin 27-10\",ylab=\"Number of pauses\", ylim = c(0,ylim_pauses))\r\n\r\n# mybarplot(mean_table$means$median_pause,mean_table$ses$median_pause,rownames(mean_table$means),\r\n\t# main=\"Mean of medians of pause duration Martin 27-10\",ylab=\"Median pause time [s]\", ylim =c(0, ylim_pausetime))\r\n\r\n# ##mybarplot(mean_table$means$median_lin,mean_table$ses$median_lin,rownames(mean_table$means),\r\n# ##\tmain=\"Mean of medians of linearity score Martin 2_1\",ylab=\"Lin score [0 to 1]\", ylim =c(0, 1))\r\n\r\n# #\r\n# #print(plot(hplot1, xlim=c(0,1),freq = FALSE))\r\n# #\r\n# #bins = 0.11\r\n# #\r\n# #hplot4 = hist(act_table$act,breaks= c(seq(0,max(act_table$act)+bins*5, bins*5)), plot=FALSE)\r\n# #print(plot(hplot4, freq = FALSE, xlim=c(0,100)))\r\n# #\r\n# #\r\n# #\r\n# #plot (act_table$lin, act_table$dist_traveled, ylim =c(0,1000))\r\n# #\r\n# #plot (act_table$lin, act_table$dist_traveled, ylim =c(0,35))\r\n# #\r\n# #plot (act_table_ori$speedmax_inbout, act_table_ori$dist_traveled)\r\n# #hist(act_table_ori$speedmax_inbout)\r\n# #\r\n# #hplot2 = hist(pause_table$pause,breaks= c(seq(0,200, 0.05), seq(220,1000,20)), plot=FALSE)\r\n# #print(plot(hplot2, xlim=c(0,100)))", "meta": {"hexsha": "2813b9e9dd0e6a6c17ae72c1ac6a296d3e0ebe20", "size": 8893, "ext": "r", "lang": "R", "max_stars_repo_path": "CeTrAn/scripts/activity_Martin.r", "max_stars_repo_name": "brembslab/CeTrAn", "max_stars_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2015-02-26T12:51:15.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-21T08:36:32.000Z", "max_issues_repo_path": "CeTrAn/scripts/activity_Martin.r", "max_issues_repo_name": "brembslab/CeTrAn", "max_issues_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2015-01-09T13:08:07.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-18T13:31:53.000Z", "max_forks_repo_path": "CeTrAn/scripts/activity_Martin.r", "max_forks_repo_name": "brembslab/CeTrAn", "max_forks_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-01-09T13:33:40.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-21T12:55:17.000Z", "avg_line_length": 30.9860627178, "max_line_length": 169, "alphanum_fraction": 0.6663668054, "num_tokens": 2812, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6825737473266734, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3466190471380007}} {"text": "library(plotly)\n\n\nx = Model_SDP_Mean$D\u00e9bit_CV004\ny = Model_SDP_Mean$Pression_PK16\nz = Model_SDP_Mean$X250\u00b5m\n\n\nfig <- plot_ly(x = ~x, y = ~y, z = ~z,intensity = ~z, type = 'mesh3d')\nfig %>% layout(\n scene = list(\n color =\"green\",\n xaxis = list(title = \"D\u00e9bit\"),\n yaxis = list(title = \"Pression_PK16\"),\n zaxis = list(title = \"Gros\")\n )\n ) \n", "meta": {"hexsha": "97d264b9736a06a7bbd4847b0cdb23d5c5199273", "size": 355, "ext": "r", "lang": "R", "max_stars_repo_path": "R/3D_plots.r", "max_stars_repo_name": "onlymujtaba/R", "max_stars_repo_head_hexsha": "1daef83485ece25f993cc3002d20c7f44717b643", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-20T20:58:28.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-20T20:58:28.000Z", "max_issues_repo_path": "R/3D_plots.r", "max_issues_repo_name": "onlymujtaba/R", "max_issues_repo_head_hexsha": "1daef83485ece25f993cc3002d20c7f44717b643", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-27T18:17:11.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-27T18:17:11.000Z", "max_forks_repo_path": "R/3D_plots.r", "max_forks_repo_name": "onlymujtaba/R", "max_forks_repo_head_hexsha": "1daef83485ece25f993cc3002d20c7f44717b643", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-27T18:06:02.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-27T18:06:02.000Z", "avg_line_length": 19.7222222222, "max_line_length": 70, "alphanum_fraction": 0.6084507042, "num_tokens": 130, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3466190405799409}} {"text": "## File downloaded from 'http://www.sthda.com/upload/rquery_cormat.r'\n## Date: 14/11/2016\n## All Credit to Original Authors for this Script\n\n#+++++++++++++++++++++++++\n# Computing of correlation matrix\n#+++++++++++++++++++++++++\n# Required package : corrplot\n\n# x : matrix\n# type: possible values are \"lower\" (default), \"upper\", \"full\" or \"flatten\";\n #display lower or upper triangular of the matrix, full or flatten matrix.\n# graph : if TRUE, a correlogram or heatmap is plotted\n# graphType : possible values are \"correlogram\" or \"heatmap\"\n# col: colors to use for the correlogram\n# ... : Further arguments to be passed to cor or cor.test function\n\n# Result is a list including the following components :\n # r : correlation matrix, p : p-values\n # sym : Symbolic number coding of the correlation matrix\nrquery.cormat<-function(x, type=c('lower', 'upper', 'full', 'flatten'),\n graph=TRUE, graphType=c(\"correlogram\", \"heatmap\"),\n col=NULL, ...)\n{\n library(corrplot)\n # Helper functions\n #+++++++++++++++++\n # Compute the matrix of correlation p-values\n cor.pmat <- function(x, ...) {\n mat <- as.matrix(x)\n n <- ncol(mat)\n p.mat<- matrix(NA, n, n)\n diag(p.mat) <- 0\n for (i in 1:(n - 1)) {\n for (j in (i + 1):n) {\n tmp <- cor.test(mat[, i], mat[, j], ...)\n p.mat[i, j] <- p.mat[j, i] <- tmp$p.value\n }\n }\n colnames(p.mat) <- rownames(p.mat) <- colnames(mat)\n p.mat\n }\n # Get lower triangle of the matrix\n getLower.tri<-function(mat){\n upper<-mat\n upper[upper.tri(mat)]<-\"\"\n mat<-as.data.frame(upper)\n mat\n }\n # Get upper triangle of the matrix\n getUpper.tri<-function(mat){\n lt<-mat\n lt[lower.tri(mat)]<-\"\"\n mat<-as.data.frame(lt)\n mat\n }\n # Get flatten matrix\n flattenCorrMatrix <- function(cormat, pmat) {\n ut <- upper.tri(cormat)\n data.frame(\n row = rownames(cormat)[row(cormat)[ut]],\n column = rownames(cormat)[col(cormat)[ut]],\n cor =(cormat)[ut],\n p = pmat[ut]\n )\n }\n # Define color\n if (is.null(col)) {\n col <- colorRampPalette(c(\"#67001F\", \"#B2182B\", \"#D6604D\", \n \"#F4A582\", \"#FDDBC7\", \"#FFFFFF\", \"#D1E5F0\", \"#92C5DE\", \n \"#4393C3\", \"#2166AC\", \"#053061\"))(200)\n col<-rev(col)\n }\n \n # Correlation matrix\n cormat<-signif(cor(x, use = \"complete.obs\", ...),2)\n pmat<-signif(cor.pmat(x, ...),2)\n # Reorder correlation matrix\n ord<-corrMatOrder(cormat, order=\"hclust\")\n cormat<-cormat[ord, ord]\n pmat<-pmat[ord, ord]\n # Replace correlation coeff by symbols\n sym<-symnum(cormat, abbr.colnames=FALSE)\n # Correlogram\n if(graph & graphType[1]==\"correlogram\"){\n corrplot(cormat, type=ifelse(type[1]==\"flatten\", \"lower\", type[1]),\n tl.col=\"black\", tl.srt=45,col=col,...)\n }\n else if(graphType[1]==\"heatmap\")\n heatmap(cormat, col=col, symm=TRUE)\n # Get lower/upper triangle\n if(type[1]==\"lower\"){\n cormat<-getLower.tri(cormat)\n pmat<-getLower.tri(pmat)\n }\n else if(type[1]==\"upper\"){\n cormat<-getUpper.tri(cormat)\n pmat<-getUpper.tri(pmat)\n sym=t(sym)\n }\n else if(type[1]==\"flatten\"){\n cormat<-flattenCorrMatrix(cormat, pmat)\n pmat=NULL\n sym=NULL\n }\n list(r=cormat, p=pmat, sym=sym)\n}\n", "meta": {"hexsha": "7fceae8742fcd6baf258b22a1ceecee3c36f8d82", "size": 3276, "ext": "r", "lang": "R", "max_stars_repo_path": "rquery_cormat.r", "max_stars_repo_name": "uhkniazi/BRC_SkinGut_Microbiome", "max_stars_repo_head_hexsha": "94de66ca25d40faaf567cdedd0604b1d22e3db77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "rquery_cormat.r", "max_issues_repo_name": "uhkniazi/BRC_SkinGut_Microbiome", "max_issues_repo_head_hexsha": "94de66ca25d40faaf567cdedd0604b1d22e3db77", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rquery_cormat.r", "max_forks_repo_name": "uhkniazi/BRC_SkinGut_Microbiome", "max_forks_repo_head_hexsha": "94de66ca25d40faaf567cdedd0604b1d22e3db77", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3333333333, "max_line_length": 85, "alphanum_fraction": 0.5873015873, "num_tokens": 1020, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.34660600443990675}} {"text": "# ' Functions for the detection of fixations in raw eye-tracking data.\n# '\n# ' Offers a function for detecting fixations in a stream of eye\n# ' positions recorded by an eye-tracker. The detection is done using\n# ' an algorithm for saccade detection proposed by Ralf Engbert and\n# ' Reinhold Kliegl (see reference below). Anything that happens\n# ' between two saccades is considered to be a fixation. This software\n# ' is therefore not suited for data sets with smooth-pursuit eye\n# ' movements.\n#'\n#' @name saccades\n#' @docType package\n#' @title Detection of Fixations in Raw Eye-Tracking Data\n#' @author Titus von der Malsburg \\email{malsburg@@posteo.de}\n#' @references\n#' Ralf Engbert, Reinhold Kliegl: Microsaccades uncover the\n#' orientation of covert attention, Vision Research, 2003.\n#' @importFrom zoom zm\n#' @keywords manip ts classif\n#' @seealso \\code{\\link{detect.fixations}},\n#' \\code{\\link{diagnostic.plot}}, \\code{\\link{calculate.summary}}\n\nNULL\n\n#' Samples of eye positions as recorded with an iViewX eye-tracker\n#' recording at approx. 250 Hz. The data quality is low on purpose\n#' and contains episodes of track-loss and blinks.\n#'\n#' @name samples\n#' @title Samples of Eye Positions as Recorded with an Eye-Tracker\n#' @docType data\n#' @usage samples\n#' @format a data frame containing one line per sample. The samples\n#' are sorted in chronological order. Time is given in milliseconds,\n#' x- and y-coordinates in screen pixels.\n#' @source Recorded with an iViewX Eye-Tracker by SMI at approximately\n#' 250 Hz.\n#' @author Titus von der Malsburg \\email{malsburg@@posteo.de}\n\nNULL\n\n#' Fixations detected in a stream of raw eye positions. The\n#' corresponding raw eye positions samples are found in the data frame\n#' \\code{\\link{samples}} also part of this package.\n#'\n#' @name fixations\n#' @title Fixations Detected in a Stream of Raw Positions\n#' @docType data\n#' @usage fixations\n#' @format a data frame containing one line per fixation. The\n#' fixations are sorted in chronological order. Time is given in\n#' milliseconds, x- and y-coordinates in screen pixels.\n#' @source Recorded with an iViewX Eye-Tracker by SMI at approximately\n#' 250 Hz.\n#' @author Titus von der Malsburg \\email{malsburg@@posteo.de}\n\nNULL\n\n#' Takes a data frame containing raw eye-tracking samples and returns a\n#' data frame containing fixations.\n#'\n#' @title Detect Fixations in a Stream of Raw Eye-Tracking Samples\n# ' @param samples a data frame containing the raw samples as recorded\n# ' by the eye-tracker. This data frame has four columns:\n# ' \\describe{\n# ' \\item{time:}{the time at which the sample was recorded}\n# ' \\item{trial:}{the trial to which the sample belongs}\n# ' \\item{x:}{the x-coordinate of the sample}\n# ' \\item{y:}{the y-coordinate of the sample}\n# ' }\n# ' Samples have to be listed in chronological order. The velocity\n# ' calculations assume that the sampling frequency is constant.\n# ' @param lambda a parameter for tuning the saccade\n# ' detection. It specifies which multiple of the standard deviation\n# ' of the velocity distribution should be used as the detection\n# ' threshold.\n# ' @param smooth.coordinates logical. If true the x- and y-coordinates will be\n# ' smoothed using a moving average with window size 3 prior to saccade\n# ' detection.\n# ' @param smooth.saccades logical. If true, consecutive saccades that\n# ' are separated only by a few samples will be joined. This avoids\n# ' the situation where swing-backs at the end of longer saccades are\n# ' recognized as separate saccades. Whether this works well, depends\n# ' to some degree on the sampling rate of the eye-tracker. If the\n# ' sampling rate is very high, the gaps between the main saccade and\n# ' the swing-back might become too large and look like genuine\n# ' fixations. Likewise, if the sampling frequency is very low,\n# ' genuine fixations may be regarded as spurious. Both cases are\n# ' unlikely to occur with current eye-trackers.\n#' @section Details: This function uses a velocity-based detection\n#' algorithm for saccades proposed by Engbert and Kliegl. Anything\n#' between two saccades is considered to be a fixation. Thus, the\n#' algorithm is not suitable for data sets containing episodes of\n#' smooth pursuit eye movements.\n#' @return a data frame containing the detected fixations. This data\n#' frame has the following columns:\n#' \\item{trial}{the trial to which the fixation belongs}\n#' \\item{start}{the time at which the fixation started}\n#' \\item{end}{the time at which the fixation ended}\n#' \\item{x}{the x-coordinate of the fixation}\n#' \\item{y}{the y-coordinate of the fixation}\n#' \\item{mad.x}{the standard deviation of the sample x-coordinates within the fixation}\n#' \\item{mad.y}{the standard deviation of the sample y-coordinates within the fixation}\n#' \\item{peak.vx}{the horizontal peak velocity that was reached within the fixation}\n#' \\item{peak.vy}{the vertical peak velocity that was reached within the fixation}\n#' \\item{dur}{the duration of the fixation}\n#' \\item{event}{the type of event, which could be 'fixation',\n#' 'blink', or artifacts which are labeled 'too dispersed' and 'too\n#' short'. Classification is based on simple heuristics that\n#' identify outliers with respect to dispersion and duration.}\n#' @author Titus von der Malsburg \\email{malsburg@@posteo.de}\n#' @references\n#' Ralf Engbert, Reinhold Kliegl: Microsaccades uncover the\n#' orientation of covert attention, Vision Research, 2003.\n#' @keywords manip ts classif\n#' @seealso \\code{\\link{diagnostic.plot}},\n#' \\code{\\link{calculate.summary}}\n#' @export\n#' @examples\n#' data(samples)\n#' head(samples)\n#' fixations <- detect.fixations(samples)\n#' head(fixations)\n#' \\dontrun{\n#' first.trial <- samples$trial[1]\n#' first.trial.samples <- subset(samples, trial==first.trial)\n#' first.trial.fixations <- subset(fixations, trial==first.trial)\n#' with(first.trial.samples, plot(x, y, pch=20, cex=0.2, col=\"red\"))\n#' with(first.trial.fixations, points(x, y, cex=1+sqrt(dur/10000)))\n#' }\ndetect.fixations <- function(samples, lambda=6, smooth.coordinates=FALSE, smooth.saccades=TRUE) {\n\n if (! all(c(\"x\", \"y\", \"trial\", \"time\") %in% colnames(samples)))\n stop(\"Input data frame needs columns 'x', 'y', 'trial', and 'time'.\")\n\n if (! all(with(samples, tapply(time, trial, function(x) all(diff(x) > 0)))))\n stop(\"Samples need to be in chronological order within trial.\")\n\n # Discard unnecessary columns:\n samples <- samples[c(\"x\", \"y\", \"trial\", \"time\")]\n\n if (smooth.coordinates) {\n # Keep and reuse original first and last coordinates as they can't\n # be smoothed:\n x <- samples$x[c(1,nrow(samples))]\n y <- samples$y[c(1,nrow(samples))]\n kernel <- rep(1/3, 3)\n samples$x <- stats::filter(samples$x, kernel)\n samples$y <- stats::filter(samples$y, kernel)\n # Plug in the original values:\n samples$x[c(1,nrow(samples))] <- x\n samples$y[c(1,nrow(samples))] <- y\n }\n \n samples <- detect.saccades(samples, lambda, smooth.saccades)\n \n if (all(!samples$saccade))\n stop(\"No saccades were detected. Something went wrong.\")\n\n fixations <- aggregate.fixations(samples)\n\n fixations$event <- label.blinks.artifacts(fixations)\n \n fixations\n \n}\n\n# EXPERIMENTAL: This function tries to detect blinks and artifacts\n# based on x- and y-dispersion and duration of fixations.\nlabel.blinks.artifacts <- function(fixations) {\n\n # Blink and artifact detection based on dispersion:\n lsdx <- log10(fixations$mad.x)\n lsdy <- log10(fixations$mad.y)\n median.lsdx <- stats::median(lsdx, na.rm=TRUE)\n median.lsdy <- stats::median(lsdy, na.rm=TRUE)\n mad.lsdx <- stats::mad(lsdx, na.rm=TRUE)\n mad.lsdy <- stats::mad(lsdy, na.rm=TRUE)\n\n # Dispersion too low -> blink:\n threshold.lsdx <- median.lsdx - 4 * mad.lsdx\n threshold.lsdy <- median.lsdy - 4 * mad.lsdy\n event <- ifelse((!is.na(lsdx) & lsdx < threshold.lsdx) &\n (!is.na(lsdy) & lsdy < threshold.lsdy),\n \"blink\", \"fixation\")\n\n # Dispersion too high -> artifact:\n threshold.lsdx <- median.lsdx + 4 * mad.lsdx\n threshold.lsdy <- median.lsdy + 4 * mad.lsdy\n event <- ifelse((!is.na(lsdx) & lsdx > threshold.lsdx) &\n (!is.na(lsdy) & lsdy > threshold.lsdy),\n \"too dispersed\", event)\n\n # Artifact detection based on duration:\n dur <- 1/fixations$dur\n median.dur <- stats::median(dur, na.rm=TRUE)\n mad.dur <- stats::mad(dur, na.rm=TRUE)\n\n # Duration too short -> artifact:\n threshold.dur <- median.dur + mad.dur * 5\n event <- ifelse(event!=\"blink\" & dur > threshold.dur, \"too short\", event)\n\n factor(event, levels=c(\"fixation\", \"blink\", \"too dispersed\", \"too short\"))\n}\n\n# This function takes a data frame of the samples and aggregates the\n# samples into fixations. This requires that the samples have been\n# annotated using the function detect.saccades.\naggregate.fixations <- function(samples) {\n \n # In saccade.events a 1 marks the start of a saccade and a -1 the\n # start of a fixation.\n\n saccade.events <- sign(c(0, diff(samples$saccade)))\n\n trial.numeric <- as.integer(factor(samples$trial))\n trial.events <- sign(c(0, diff(trial.numeric)))\n\n # New fixations start either when a saccade ends or when a trial\n # ends:\n samples$fixation.id <- cumsum(saccade.events==-1|trial.events==1)\n samples$t2 <- samples$time\n samples$t2 <- ifelse(trial.events==1, NA, samples$t2)\n samples$t2 <- samples$t2[2:(nrow(samples)+1)]\n # Set last t2 value in a trial to last time value to avoid -Inf dur\n # and end values when the last event has just one sample (see #13 on\n # Github). May produce zero-duration events but zero simply is our\n # most conservative guess in this case.\n samples$t2 <- with(samples, ifelse(is.na(t2), time, t2))\n\n # Discard samples that occurred during saccades:\n samples <- samples[!samples$saccade,,drop=FALSE]\n\n fixations <- with(samples, data.frame(\n trial = tapply(trial, fixation.id, function(x) x[1]),\n start = tapply(time, fixation.id, min),\n end = tapply(t2, fixation.id, function(x) max(x, na.rm=TRUE)),\n x = tapply(x, fixation.id, stats::median),\n y = tapply(y, fixation.id, stats::median),\n mad.x = tapply(x, fixation.id, stats::mad),\n mad.y = tapply(y, fixation.id, stats::mad),\n peak.vx = tapply(vx, fixation.id, function(x) x[which.max(abs(x))]),\n peak.vy = tapply(vy, fixation.id, function(x) x[which.max(abs(x))]),\n stringsAsFactors=FALSE))\n\n fixations$dur <- fixations$end - fixations$start\n \n fixations\n \n}\n\n# Implementation of the Engbert & Kliegl algorithm for the\n# detection of saccades. This function takes a data frame of the\n# samples and adds three columns:\n#\n# - A column named \"saccade\" which contains booleans indicating\n# whether the sample occurred during a saccade or not.\n# - Columns named vx and vy which indicate the horizontal and vertical\n# speed.\ndetect.saccades <- function(samples, lambda, smooth.saccades) {\n\n # Calculate horizontal and vertical velocities:\n vx <- stats::filter(samples$x, -1:1/2)\n vy <- stats::filter(samples$y, -1:1/2)\n\n # We don't want NAs, as they make our life difficult later\n # on. Therefore, fill in missing values:\n vx[1] <- vx[2]\n vy[1] <- vy[2]\n vx[length(vx)] <- vx[length(vx)-1]\n vy[length(vy)] <- vy[length(vy)-1]\n\n msdx <- sqrt(stats::median(vx**2, na.rm=TRUE) - stats::median(vx, na.rm=TRUE)**2)\n msdy <- sqrt(stats::median(vy**2, na.rm=TRUE) - stats::median(vy, na.rm=TRUE)**2)\n\n radiusx <- msdx * lambda\n radiusy <- msdy * lambda\n\n sacc <- ((vx/radiusx)**2 + (vy/radiusy)**2) > 1\n if (smooth.saccades) {\n sacc <- stats::filter(sacc, rep(1/3, 3))\n sacc <- as.logical(round(sacc))\n }\n samples$saccade <- ifelse(is.na(sacc), FALSE, sacc)\n samples$vx <- vx\n samples$vy <- vy\n\n samples\n\n}\n", "meta": {"hexsha": "2c7b71cd1e338dee39bd2de1889def53122dfdcf", "size": 11805, "ext": "r", "lang": "R", "max_stars_repo_path": "javascript/public/js/saccades.r", "max_stars_repo_name": "tejas67/Aankh_test", "max_stars_repo_head_hexsha": "c5ba588bb502ea1346ff134acd080e72dc4838e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "javascript/public/js/saccades.r", "max_issues_repo_name": "tejas67/Aankh_test", "max_issues_repo_head_hexsha": "c5ba588bb502ea1346ff134acd080e72dc4838e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "javascript/public/js/saccades.r", "max_forks_repo_name": "tejas67/Aankh_test", "max_forks_repo_head_hexsha": "c5ba588bb502ea1346ff134acd080e72dc4838e8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.5670103093, "max_line_length": 97, "alphanum_fraction": 0.6969080898, "num_tokens": 3252, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3466059957112453}} {"text": "library(parallel)\nlibrary(caret)\nlibrary(MASS)\nlibrary(xgboost)\n\nlibrary(OptimalCutpoints)\nlibrary(pROC)\nlibrary(ROCR)\n\nlibrary(ggplot2)\nlibrary(cowplot)\nlibrary(gridExtra)\nlibrary(viridis)\n\nlibrary(survival)\nlibrary(survminer)\n\nsetwd(\"/media/yuri/Data/home1/GNI_data/infection/ModelsSciRep\")\nload('COVID19_Ensemble.Rdat')\nXGB_model_bst <- xgb.load('COVID19_Ensemble.model')\nXGB_model_ntree <- XGB_model_bst$best_iteration\n\n# create xgb.DMatrix\ndtrain0 <- xgb.DMatrix(data = as.matrix(trn_x[,-1]), label = trn_y)\ndvalid <- xgb.DMatrix(data = as.matrix(val_x[,-1]), label = val_y)\n\n# predict discovery and validation cohorts (probs)\nYtrain_prob <- predict(XGB_model_bst, dtrain0, ntree = XGB_model_ntree, outputmargin=TRUE)\nYvalid_prob <- predict(XGB_model_bst, val_x[,-1], ntree = XGB_model_ntree, outputmargin=TRUE)\n\n################################################################################\n# Find optimal cutpoint from the ROC curve maximizing the product of Sensitivity and Specificity\np_cut <- optimal.cutpoints(y~x, status=trn_y, tag.healthy = 0, data= data.frame(y=Ytrain_prob, x = trn_y),\nmethods=\"MaxProdSpSe\", ci.fit=TRUE, control = control.cutpoints(ci.SeSp = \"AgrestiCoull\", ci.PV = \"AgrestiCoull\") )\ncutoff <- p_cut$MaxProdSpSe$Global$optimal.cutoff$cutoff[1]\n\n# predict discovery and validation cohorts (classes)\np_train_class <- factor(as.numeric(ifelse(Ytrain_prob <= cutoff, 0, 1)), levels = c(0,1))\np_valid_class <- factor(as.numeric(ifelse(Yvalid_prob <= cutoff, 0, 1)), levels = c(0,1))\n\n# Model summary bACC - balanced accuracy, Sensitivity, Specificity\nconf_train <- confusionMatrix(p_train_class, factor(trn_y), \"1\")\nconf_valid <- confusionMatrix(p_valid_class, factor(val_y), \"1\")\n\nconf_table <- data.frame( bACC = c( conf_train$byClass[[11]], conf_valid$byClass[[11]] ),\nSensitivity = c( conf_train$byClass[[1]], conf_valid$byClass[[1]] ),\nSpecificity = c( conf_train$byClass[[2]], conf_valid$byClass[[2]] )\n)\nconf_table <- t(conf_table)\ncolnames(conf_table) <- c(\"discovery\", \"validation\")\nconf_table <- round(conf_table, 3)\n\n################################################################################\n# Variable importance\n\nvar_imp <- xgb.importance(dimnames(dtrain0)[[2]], model = XGB_model_bst)\nvar_imp <- var_imp[order(var_imp$Gain, decreasing=T),][c(1,3,5,7,8,10,11,13:15),]\nvar_imp <- as.data.frame(var_imp)\nrownames(var_imp) <- var_imp[,1]\nvar_imp <- var_imp[,-1]\n\nvar_imp <- var_imp[order(var_imp$Gain, decreasing=F),]\n\n################################################################################\n# ROC analysis\nTrain_roc <- roc(trn_y, Ytrain_prob)\nTrain_auc <- lapply(pROC::ci(trn_y, Ytrain_prob), function(x) round(x, 3))\n\nValid_roc <- roc(val_y, Yvalid_prob)\nValid_auc <- lapply(pROC::ci(val_y, Yvalid_prob), function(x) round(x, 3))\n\nAll_roc <- roc(c(trn_y, val_y), c(Ytrain_prob, Yvalid_prob))\nAll_auc <- lapply(pROC::ci(c(trn_y, val_y), c(Ytrain_prob, Yvalid_prob)), function(x) round(x, 3))\n\n\n################################################################################\nsetwd(\"/media/yuri/Data/home1/GNI_data/infection/ResultsSciRep/Figures/\")\npdf(\"Figure6D.pdf\", width=6, height=3.5)\n\npar(mar=c(4,16,1,0.5))\nbarplot(var_imp[,1], horiz = T, names.arg = rownames(var_imp),\n xlab = \"Gain\", main = \"variable importance\",las=1, cex.axis=0.7, cex.names = 0.7)\n\ndev.off()\n################################################################################\npdf(\"Figure6C.pdf\", width=6, height=6)\n\nplot(Train_roc, lty = 1, lwd = 2, col=4, type=\"s\")\nplot(Valid_roc, add=TRUE, lty = 1, lwd = 2, col=2, type=\"s\")\nlegend(\"bottomright\", legend =\n c(paste(\"discovery cohort: AUC = \", Train_auc[[2]], \" (\", Train_auc[[1]], \", \", Train_auc[[3]], \")\", sep=\"\"),\n paste(\"validation cohort: AUC = \", Valid_auc[[2]], \" (\", Valid_auc[[1]], \", \", Valid_auc[[3]], \")\", sep=\"\")),\nlty=1, lwd=2, col=c(2,4), bty=\"n\")\n\ndev.off()\n################################################################################\n\n\n################################################################################\n\ntrn_res <- data.frame(trueY = trn_y, Yhat = p_train_class, Yp = Ytrain_prob,\n Acc = as.numeric(trn_y == p_train_class), hfd = COVID_pheno$HFD45[train_idx],\n cohort = \"discovery\")\nval_res <- data.frame(trueY = val_y, Yhat = p_valid_class, Yp = Yvalid_prob,\n Acc = as.numeric(val_y == p_valid_class), hfd = COVID_pheno$HFD45[test_idx],\n cohort = \"validation\")\n\nplot_res <- rbind(trn_res, val_res)\nplot_res$shape <- paste0(plot_res$cohort, plot_res$Acc)\n\np1 <- ggplot(plot_res, aes(y=Yp, x = as.factor(trueY) )) + geom_boxplot(aes(color = as.factor(trueY)), outlier.size=-1) +\ngeom_hline(yintercept=cutoff) +\ngeom_jitter(aes(color = as.factor(trueY), shape = shape), size=3, width = 0.1) +\nscale_shape_manual(values = c(3,2,4,17)) +\nscale_color_manual(values = c(\"#4591cc\", \"#21a883\")) +\nxlab(\"\") + ylab(\"model score\") +\ntheme_bw() +\ntheme(text = element_text(size=12),\n axis.text.y = element_text(size=12))\n\nsetwd(\"/media/yuri/Data/home1/GNI_data/infection/ResultsSciRep/Figures/\")\nggsave(\"FigureS12Aleft.pdf\", p1, width=4, height=6)\n", "meta": {"hexsha": "8557738bc22c2ec13307ff98fb2e30811eec5d2e", "size": 5082, "ext": "r", "lang": "R", "max_stars_repo_path": "ScriptsSciRep/plot_Figures/Figure6CD_S12AB_plot.r", "max_stars_repo_name": "Vityay/GeneEnsembleNoise", "max_stars_repo_head_hexsha": "6a85feb9bd3fea7ae465905383d7c0396a59af76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ScriptsSciRep/plot_Figures/Figure6CD_S12AB_plot.r", "max_issues_repo_name": "Vityay/GeneEnsembleNoise", "max_issues_repo_head_hexsha": "6a85feb9bd3fea7ae465905383d7c0396a59af76", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ScriptsSciRep/plot_Figures/Figure6CD_S12AB_plot.r", "max_forks_repo_name": "Vityay/GeneEnsembleNoise", "max_forks_repo_head_hexsha": "6a85feb9bd3fea7ae465905383d7c0396a59af76", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3170731707, "max_line_length": 121, "alphanum_fraction": 0.6300669028, "num_tokens": 1476, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7154240079185319, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.3465371412493565}} {"text": "rgbsum <- c(0,0)\nresget <- c(0,0)\n\nfor(i in 1:num) {\nprint(i)\na = 0\norgpic=readJPEG(v[i])\n\t\tfor(j in 1:num) {\n\t\tv4rgb <- orgpic[res4a[j],res4b[j],]\n\t\tprint(v4rgb)\n\t\trgbsum[j] = (v4rgb[1]+v4rgb[2]+v4rgb[3])/3\n\t\tprint (rgbsum[j])\n\t\tprint(\"__\")\n\t\ta = a+rgbsum[j]\n\t\t}\nprint(a)\nresget[i]=a\nprint(\"____________\")\n}\n\npicfun <- function(x){\n for(i in 1:x) {\n\tprint(resget[i])\n }\n}\n\npicfun(num)\nprint(\"____________\")", "meta": {"hexsha": "b955fc457d2ba4f04915bed8126735279569b08c", "size": 409, "ext": "r", "lang": "R", "max_stars_repo_path": "2_ImageCalculation.r", "max_stars_repo_name": "slvayf/ImageProcessing", "max_stars_repo_head_hexsha": "984610609ca2a7a091692de7f50049338396b065", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2_ImageCalculation.r", "max_issues_repo_name": "slvayf/ImageProcessing", "max_issues_repo_head_hexsha": "984610609ca2a7a091692de7f50049338396b065", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2_ImageCalculation.r", "max_forks_repo_name": "slvayf/ImageProcessing", "max_forks_repo_head_hexsha": "984610609ca2a7a091692de7f50049338396b065", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.6071428571, "max_line_length": 44, "alphanum_fraction": 0.5892420538, "num_tokens": 170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7154239957834734, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.3465371353713757}} {"text": "library(GEOquery)\n\n# get the data from GEO ( GSE matrix & annotation GPL)\n# define destination to save data, SO didn't need load data each time\nseries = \"GSE34670\"\nplatform = \"GPL96\"\ngset = getGEO(series, GSEMatrix =TRUE, AnnotGPL=TRUE, destdir = \"Data/\")\n# if the data have several platforms, choose the certain platform\nif (length(gset) > 1) idx <- grep(platform, attr(gset, \"names\")) else idx <- 1\ngset = gset[[idx]]\n\n# define groups for samples\ngr = c(rep(\"cALL_BM\", 5) , \"cALL_PB\" ,rep(\"cALL_BM\", 19), \"CL_cALL2\" , \"CL_697\" , \"CL_NALM6\" , rep(\"CD10\", 6), rep(\"CD10_pool\" , 3) )\n# elicit expression matrix from data\nex = exprs(gset)\n\n# log2 transformation\nex = log2(ex + 1 )\nexprs(gset) = ex\n\n#QC\n\n# normalization check\n#boxplot\njpeg(\"Results/boxplot.jpg\")\nboxplot(ex)\ndev.off()\n", "meta": {"hexsha": "83b40ffcbb6fd2b494b92c838f6fa74c20ba7229", "size": 783, "ext": "r", "lang": "R", "max_stars_repo_path": "Visualize data/boxplot.r", "max_stars_repo_name": "ZahraFarajollahi/Microarray-Data-Analysis", "max_stars_repo_head_hexsha": "fa8a9edcd14637560bd626bb6bb3a4cee2d9d326", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Visualize data/boxplot.r", "max_issues_repo_name": "ZahraFarajollahi/Microarray-Data-Analysis", "max_issues_repo_head_hexsha": "fa8a9edcd14637560bd626bb6bb3a4cee2d9d326", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Visualize data/boxplot.r", "max_forks_repo_name": "ZahraFarajollahi/Microarray-Data-Analysis", "max_forks_repo_head_hexsha": "fa8a9edcd14637560bd626bb6bb3a4cee2d9d326", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9642857143, "max_line_length": 133, "alphanum_fraction": 0.6896551724, "num_tokens": 264, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7154239836484144, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3465371294933949}} {"text": "######### citn07 analysis: look at immune cell gene expressions vs. time and cohort, and perform differential expression analyses\n\nrm(list=ls())\nlibrary(ggplot2)\nlibrary(pheatmap)\nsource(\"source_volcplot.r\")\n\n# coloring function\nfadecols = function(cols,fade=.5)\n{\n fcols = c()\n for(i in 1:length(cols))\n {\n tmp = as.vector(col2rgb(cols[i])/256)\n fcols[i] = rgb(tmp[1],tmp[2],tmp[3],fade)\n }\n \n return(fcols)\n}\n\n#### load normalized data and sample annotation: ####\nload(\"data/CITN07 data ready for analysis October2018.RData\")\n\n# use the log-transformed data:\nnorm = log2norm; rm(log2norm) #log2(pmax(norm, 1))\n#### screen out low-signal genes:\nlow.expression = names(which(max.counts < 25))\nnorm = norm[, setdiff(colnames(norm), low.expression)]\n\n\n### load the module-rene relationships, and format as a gene annotation matrix:\n#modules = read.csv(\"CITN_nano_mod_Gen2_NoHK[3].csv\", row.names = 1, stringsAsFactors = F)\n#gannot = matrix(0, ncol(norm), length(unique(modules$Function)), dimnames = list(colnames(norm), unique(modules$Function)))\n## fill it in:\n#for (m in unique(modules$Function)){\n# modgenes = modules$Symbol[modules$Function == m]\n# gannot[is.element(rownames(gannot), modgenes), m] = 1\n#}\n## remove modules with too few genes to be useful:\n#gannot = gannot[, colSums(gannot) >= 10]\n\n##### reformat the gene annotation matrix:\ngannot = gannot[match(colnames(norm), rownames(gannot)), ]\ngannot = replace(gannot, is.na(gannot), 0)\nrownames(gannot) = colnames(norm)\n\n# load additional gene sets:\ngenesets = read.csv(\"gene sets.csv\", stringsAsFactors = F)\ngannot2 = matrix(0, ncol(norm), length(unique(genesets$sig)), dimnames = list(colnames(norm), unique(genesets$sig)))\n# fill it in:\nfor (m in unique(genesets$sig)){\n modgenes = genesets$gene[genesets$sig == m]\n gannot2[is.element(rownames(gannot2), modgenes), m] = 1\n}\n# remove modules with too few genes to be useful:\ngannot2 = gannot2[, colSums(gannot2) >= 3]\n\ngannot = cbind(gannot, gannot2)\n\n\n######################################\n#### calculate cell type signatures: #### \n######################################\ncellmarkers = read.csv(\"data/cell type markers.csv\",row.names = 1)\n# function to calc cell scores from data and marker list:\ncalc.cellscores = function(e,markers)\n{\n cellscores = c()\n cells = unique(markers[,\"Cell.Type\"])\n for(cell in cells)\n {\n tempgenes = intersect(colnames(e),rownames(markers)[markers[,\"Cell.Type\"]==cell])\n lostgenes = setdiff(rownames(markers)[markers[,\"Cell.Type\"]==cell],colnames(e))\n if(length(lostgenes)>0){print(paste0(cell,\" genes missing:\"));print(lostgenes)}\n if(length(tempgenes)>0)\n {\n cellscores = cbind(cellscores,rowMeans(e[,tempgenes,drop=F]))\n colnames(cellscores)[ncol(cellscores)]=cell\n }\n }\n return(cellscores)\n}\ncellscores = calc.cellscores(norm,cellmarkers)\n\n\n\n######################################\n#### further transformations of data: \n# 1. get mean and se of expression/cell score within each cohort/time\n# 2. reformat expression/score matrices as deltas from baselines\n# 3. get mean and se of expression/score deltas within each cohort/time\n######################################\n\n## combined genes/scores data matrix:\nnormpluscells = cbind(cellscores, norm)\nis.cellscore = c(rep(TRUE, ncol(cellscores)), rep(FALSE, ncol(norm)))\n\n## timepoint/cohort variables:\n#timepoints = c(\"Pre-Tx\",\"C1D22\",\"C3D01\",\"C4D01\",\"FUW04\",\"FUW12\")\ntimepoints = levels(annot$time)\ntxtimes = paste0(rep(c(\"cohort1\",\"cohort2\"), each=length(timepoints)),\" \",rep(timepoints,2))\ntxtimes = setdiff(txtimes, \"cohort2 C1D01\")\nannot$txtime2 = paste0(\"cohort\",annot$cohort,\" \",annot$time)\n\n# 1. get mean and se of expression/cell score within each time point\n# means within each timepoint/cohort\nmeans = matrix(NA,length(txtimes),ncol(normpluscells),dimnames = list(txtimes,colnames(normpluscells)))\nfor(txtime in txtimes)\n{\n means[txtime,] = colMeans(normpluscells[annot$txtime2==txtime,])\n}\n# ses within each timepoint/cohort:\ngetse = function(x){return(sd(x,na.rm=T)/sqrt(sum(!is.na(x))))}\nses = matrix(NA, length(txtimes), ncol(normpluscells), dimnames = list(txtimes, colnames(normpluscells)))\nfor(txtime in txtimes)\n{\n ses[txtime,] = apply(normpluscells[annot$txtime2==txtime, ], 2, getse)\n}\n\n# 2. define a matrix of each patients' gene expression deltas from baseline:\ndeltas = normpluscells*NA\nfor(tp in timepoints)\n{\n # only look at the current timepoint's data:\n use = which(annot$time==tp)\n # for each observation, get the mean expression vector of the patient's time0 samples:\n for(i in use)\n {\n pt = annot$patient[i]\n baseline = which((annot$patient==pt)&(annot$time==\"baseline\"))\n if(length(baseline) > 0){\n deltas[i,] = normpluscells[i,] - colMeans(normpluscells[baseline,,drop=F],na.rm=T)\n }\n }\n}\n\n# 3. get the avg (and se) delta from pre-tx within each cohort*time:\nmeandeltas = matrix(NA, length(txtimes), ncol(normpluscells), dimnames = list(txtimes, colnames(normpluscells)))\nfor(txtime in txtimes)\n{\n meandeltas[txtime,] = colMeans(deltas[annot$txtime2==txtime,],na.rm=T)\n}\n\n# and ses of deltas:\nsedeltas = matrix(NA,length(txtimes),ncol(normpluscells),dimnames = list(txtimes,colnames(normpluscells)))\nfor(txtime in txtimes)\n{\n sedeltas[txtime,] = apply(deltas[annot$txtime2==txtime,],2,getse)\n}\n\n# get indices of cohorts:\ninc1 = grepl(\"cohort1\", rownames(means))\ninc2 = grepl(\"cohort2\", rownames(means))\n\n\n######################################\n#### trend plot of cells over time:\n######################################\n\n#cells = c(\"B-cells\",\"CD8 T cells\",\"T-cells\",\"Cytotoxic cells\",\"DC\",\"Macrophages\",\"NK cells\",\"Th1 cells\",\"Treg\")\n#cells = c(\"B-cells\",\"CD8 T cells\",\"T-cells\",\"Cytotoxic cells\",\"Macrophages\",\"NK cells\",\"Th1 cells\",\"Treg\")\n#cells = colnames(cellscores)\n#cellcols = c(\"darkblue\",\"orange\",\"gold\",\"chartreuse3\",\"purple\",\"blue\",\"forestgreen\",\"cornflowerblue\",\"red\",\"firebrick\",\"grey50\",\"lightblue\",\"pink\",\"green\")[1:length(cells)]\ncells = setdiff(colnames(cellscores), \"CD45\")\ncellcols = c(\"darkblue\",\"orange\",\"chartreuse3\",\"purple\",\"blue\",\"forestgreen\",\"cornflowerblue\",\"red\",\"firebrick\",\"grey50\",\"lightblue\",\"pink\",\"green\")[1:length(cells)]\nnames(cellcols)=cells\n\nsvg(\"manuscript - cells over time v1.svg\",width=10)\nnudge = 2\n#layout(matrix(1:2, nrow = 1), width = c(4,4))\npar(mar = c(5.5,4.5,2,1))\npar(mfrow = c(1,2))\nplot(0,0,xlim=c(1,sum(inc1)+1.5) + c(0,nudge),ylim=range(meandeltas[,cells]), xaxt=\"n\",xlab=\"\",\n ylab=\"Mean log2 fold-change from baseline\",main=\"cohort1\", cex.lab = 1.5)\nabline(h=0,col = \"grey20\", lty = 3, lwd = 2)\naxis(1,1:sum(inc1),timepoints,las=2, cex.axis = 1.5)\nfor(cell in cells)\n{\n lines(1:sum(inc1),meandeltas[inc1,cell],col=cellcols[cell],lwd=2)\n text(sum(inc1)+0.5,meandeltas[max(which(inc1)),cell],cell,col=cellcols[cell],cex=1.25)\n # points for changes sig at p=0.05:\n is.sig = (meandeltas[(inc1),cell] - 2*sedeltas[(inc1),cell] > 0)|(meandeltas[(inc1),cell] + 2*sedeltas[(inc1),cell] < 0)\n points((1:sum(inc1))[is.sig],meandeltas[which(inc1)[is.sig],cell],col=cellcols[cell],pch=16,cex=1.5)\n}\n\nnames(cellcols)=cells\nplot(0,0,xlim=c(1,sum(inc2)+1.5) + c(0,nudge),ylim=range(meandeltas[,cells]),xaxt=\"n\",xlab=\"\",\n ylab=\"\",main=\"cohort2\", cex.lab = 1.5)\nabline(h=0,col = \"grey20\", lty = 3, lwd = 2)\naxis(1,1:sum(inc2),setdiff(timepoints, \"C1D01\"),las=2, cex.axis = 1.5)\nfor(cell in cells)\n{\n lines(1:sum(inc2),meandeltas[inc2,cell],col=cellcols[cell],lwd=2)\n text(sum(inc2)+0.5,meandeltas[max(which(inc2)),cell],cell,col=cellcols[cell],cex=1.25)\n # points for changes sig at p=0.05:\n is.sig = (meandeltas[(inc2),cell] - 2*sedeltas[(inc2),cell] > 0)|(meandeltas[(inc2),cell] + 2*sedeltas[(inc2),cell] < 0)\n points((1:sum(inc2))[is.sig],meandeltas[which(inc2)[is.sig],cell],col=cellcols[cell],pch=16,cex=1.5)\n}\n\ndev.off()\n\nsvg(\"manuscript - cells over time v2.svg\", width=9, height = 6)\npar(mar = c(5.6,4.5,2,1))\nlayout(matrix(1:3, 1, 3), width = c(4,4,3))\nplot(0,0,xlim=c(1,sum(inc1)),ylim=range(meandeltas[,cells]), xaxt=\"n\",xlab=\"\",\n ylab=\"Mean log2 fold-change from baseline\",main=\"cohort1\", cex.lab = 1.8)\nabline(h=0,col = \"grey20\", lty = 3, lwd = 2)\naxis(1,1:sum(inc1),timepoints,las=2, cex.axis = 1.5)\nfor(cell in cells)\n{\n lines(1:sum(inc1),meandeltas[inc1,cell],col=cellcols[cell],lwd=2)\n #text(sum(inc1)+0.5,meandeltas[max(which(inc1)),cell],cell,col=cellcols[cell],cex=1.25)\n # points for changes sig at p=0.05:\n is.sig = (meandeltas[(inc1),cell] - 2*sedeltas[(inc1),cell] > 0)|(meandeltas[(inc1),cell] + 2*sedeltas[(inc1),cell] < 0)\n points((1:sum(inc1))[is.sig],meandeltas[which(inc1)[is.sig],cell],col=cellcols[cell],pch=16,cex=1.5)\n}\n\nnames(cellcols)=cells\nplot(0,0,xlim=c(1,sum(inc2)),ylim=range(meandeltas[,cells]),xaxt=\"n\",xlab=\"\",\n ylab=\"\",main=\"cohort2\", cex.lab = 1.5)\nabline(h=0,col = \"grey20\", lty = 3, lwd = 2)\naxis(1,1:sum(inc2),setdiff(timepoints, \"C1D01\"),las=2, cex.axis = 1.5)\nfor(cell in cells)\n{\n lines(1:sum(inc2),meandeltas[inc2,cell],col=cellcols[cell],lwd=2)\n #text(sum(inc2)+0.5,meandeltas[max(which(inc2)),cell],cell,col=cellcols[cell],cex=1.25)\n # points for changes sig at p=0.05:\n is.sig = (meandeltas[(inc2),cell] - 2*sedeltas[(inc2),cell] > 0)|(meandeltas[(inc2),cell] + 2*sedeltas[(inc2),cell] < 0)\n points((1:sum(inc2))[is.sig],meandeltas[which(inc2)[is.sig],cell],col=cellcols[cell],pch=16,cex=1.5)\n}\npar(mar = c(5.6,0,0,0))\nframe()\no.legend = order(meandeltas[3,names(cellcols)], decreasing = T)\nlegend(\"center\", col = c(\"black\", \"black\", NA, cellcols[o.legend]), \n legend = c(\"p > 0.05\", \"p < 0.05\", \"\", names(cellcols)[o.legend]), \n lty = 1, pch = c(NA, 16, NA, rep(NA, length(o.legend))), lwd = 3, bty = \"n\", cex = 1.5)\ndev.off()\n\n### table of cell type results:\n#times = as.character(unique(annot$time))\ncelltable = cbind(cells,\n paste0(round(meandeltas[\"cohort1 C1D08\", cells], 2),\n \" (\", \n round(sedeltas[\"cohort1 C1D08\", cells], 2),\n \")\"),\n paste0(round(meandeltas[\"cohort2 C1D08\", cells], 2),\n \" (\", \n round(sedeltas[\"cohort2 C1D08\", cells], 2),\n \")\"))[o.legend, ]\ncolnames(celltable) = c(\"Cell score\", \"Cohort 1 C1D08 log2 fold change from baseline\", \"Cohort 2 C1D08 log2 fold change from baseline\") \nwrite.csv(celltable, file = \"cell scores C1D08 results.csv\", row.names = F)\n\n#### perform differential expression analyses ####\n\n\n\n######\n# model delta from baseline within cohort 1\n######\n\n# initialize results matrices: e for estimated log2 fold-change; p for p-values:\ne = se = p = matrix(NA,ncol(norm),length(levels(annot$time))-1,\n dimnames=list(colnames(norm),setdiff(levels(annot$time),\"baseline\")))\n# at each timepoint, run the model:\nfor(time in setdiff(levels(annot$time),\"baseline\"))\n{\n use = (annot$cohort==\"1\")&(is.element(annot$time,c(\"baseline\",time)))\n for(gene in colnames(norm))\n {\n # paired t-test:\n mod = t.test(deltas[(annot$cohort==\"1\")&(is.element(annot$time,time)),gene])\n e[gene,time] = mod$estimate #mod[2,1]\n p[gene,time] = mod$p.value #mod[2,4]\n }\n}\n# get fdrs:\nf = p*NA\nfor(i in 1:ncol(p)){f[,i]=p.adjust(p[,i], method = \"BH\")}\n\n# save results for C1D08:\ne2 = e[,\"C1D08\"]\np2 = p[,\"C1D08\"]\nf2 = f[,\"C1D08\"]\nnames(e2) = names(p2) = names(f2) = rownames(e)\n\ntmp = cbind(e2,p2,f2)\ncolnames(tmp) = c(\"log2 fold-change from baseline\",\"p-value\",\"False Discovery Rate\")\nwrite.csv(tmp,file=\"DE results at C1D08.csv\")\n\n# summary of FDR (cohort 1):\ntable(rowSums(f < 0.05) > 0)\ntable(rowSums(f[, c(\"C3D01\", \"C4D01\", \"FUW04\", \"FUW12\")] < 0.05) > 0)\ntable(rowSums(f[, c(\"C3D01\", \"C4D01\", \"FUW04\", \"FUW12\")] < 0.50) > 0)\ntable(rowSums(p[, c(\"C3D01\", \"C4D01\", \"FUW04\", \"FUW12\")] < 0.05) > 0)\n\n######\n# model delta from baseline within cohort 2\n######\n\n# initialize results matrices: e for estimated log2 fold-change; p for p-values:\ne.c2 = se.c2 = p.c2 = matrix(NA,ncol(norm),length(levels(annot$time))-1,\n dimnames=list(colnames(norm),setdiff(levels(annot$time),\"baseline\")))\n# at each timepoint, run the model:\nfor(time in setdiff(levels(annot$time),c(\"baseline\",\"C1D01\")))\n{\n use = (annot$cohort==\"2\")&(is.element(annot$time,c(\"baseline\",time)))\n for(gene in colnames(norm))\n {\n # paired t-test:\n mod = t.test(deltas[(annot$cohort==\"2\")&(is.element(annot$time,time)),gene])\n e.c2[gene,time] = mod$estimate #mod[2,1]\n p.c2[gene,time] = mod$p.value #mod[2,4]\n }\n}\n# get fdrs:\nf.c2 = p.c2*NA\nfor(i in 1:ncol(p.c2)){f.c2[,i]=p.adjust(p.c2[,i], method = \"BH\")}\n# summary of FDR (cohort 1):\ntable(rowSums(f.c2 < 0.05, na.rm = T) > 0)\n\n######\n# for cohort 2 comparison, model delta from baseline at C1D08:\n######\ne2.c2 = p2.c2 = c()\nfor(gene in colnames(norm))\n{\n # paired t-test:\n mod = t.test(deltas[(annot$cohort==\"2\")&(is.element(annot$time,\"C1D08\")),gene])\n e2.c2[gene] = mod$estimate \n p2.c2[gene] = mod$p.value \n}\nf2.c2 = p.adjust(p2.c2, method = \"BH\")\n## and exceprt top genes for main body of paper:\ntop.up = rownames(tmp)[order(-log(p2) * (e2 > 0), decreasing = T)[1:10]]\ntop.dn = rownames(tmp)[order(-log(p2) * (e2 < 0), decreasing = T)[1:10]]\n# calc mean and SE of change from baseline at C1D08:\ntempc1 = deltas[(annot$cohort==\"1\")&(is.element(annot$time,\"C1D08\")), c(top.up, top.dn)]\ntempc2 = deltas[(annot$cohort==\"2\")&(is.element(annot$time,\"C1D08\")), c(top.up, top.dn)]\n\ncountnotna = function(x) sum(!is.na(x))\ngenetable = cbind(\n colnames(tempc1),\n paste0(round(colMeans(tempc1, na.rm=T),2 ), \" (\",\n round(apply(tempc1, 2, sd, na.rm=T) / sqrt(apply(tempc1, 2, countnotna)), 2),\n \")\"),\n paste0(round(colMeans(tempc2, na.rm=T),2 ), \" (\",\n round(apply(tempc2, 2, sd, na.rm=T) / sqrt(apply(tempc2, 2, countnotna)), 2),\n \")\")\n)\n# version with standard errors:\ncolnames(genetable) = c(\"Gene\", \"Cohort 1 C1D08 log2 fold-change from baseline\", \"Cohort 2 C1D08 log2 fold-change from baseline\")\nwrite.csv(genetable, file = \"top genes C1D08 results.csv\", row.names = F)\n# version with p-values:\nttestpval = function(x) {\n mod = t.test(x)\n return(mod$p.value)\n}\ngenetable.p = cbind(\n colnames(tempc1),\n paste0(round(colMeans(tempc1, na.rm=T),2 ), \" (p = \",\n signif(apply(tempc1, 2, ttestpval), 2),\n \")\"),\n paste0(round(colMeans(tempc2, na.rm=T),2 ), \" (p = \",\n signif(apply(tempc2, 2, ttestpval), 2),\n \")\")\n)\ncolnames(genetable.p) = c(\"Gene\", \n \"Cohort 1 C1D08 log2 fold-change from baseline (p-value)\", \n \"Cohort 2 C1D08 log2 fold-change from baseline (p-value)\")\nwrite.csv(genetable.p, file = \"top genes C1D08 results with p-value.csv\", row.names = F)\n# volc plot:\n#svg(\"volcanos - cohort 1 timepoints.svg\",height=4*ncol(e),width=7)\n#drawvolc(estmat=e,pvalmat=p,fdrmat=f,genesets=gannot,is.score = FALSE)\n#dev.off()\n\n\nsvg(\"C1D08 volcano plot.svg\", width = 10)\npar(mfrow = c(1,2))\npar(mar = c(14,4.5,2,0))\n# genes to highlight: FDR < 0.05\ntop = f2<0.05\nshow.text = f2<0.05\n\nplot(e2, -log10(p2),pch=16, cex = 0.5,col=c(rgb(0,0,0,.5),\"white\")[1 + show.text],\n xlab=paste0(\" \\n\\nlog2 fold-change\\nC1D08 vs. Pre-treatment\"),\n ylab = \"-log10(p-value)\",cex.lab=1.2)\n #main=\"cohort 1: C1D08 vs. baseline\",ylab=\"-log10(p-value)\",cex.lab=1.5)\n# text for top 50:\ntext(e2[show.text],-log10(p2[show.text]),colnames(norm)[show.text],cex=.5,col=c(\"darkblue\",\"firebrick\")[1+(e2[show.text]>0)]) #aquamarine4\n# draw lines for FDR cutoffs:\nfdr.cutoff = 0.05 #c(0.5,0.1,0.05)\nabline(h=-log10(max(p2[f20)])\n #col = c(rgb(0,0,1,0.7),rgb(1,0,0,0.7))[1+(e2[tempgenes]>0)])\n}\ndev.off()\n\n\n#### heatmap of cohort 1 DE results:\n\n# data to show: estimated fold-changes, but only where fdr< 0.5\nmat = e*(p<0.05)\n#mat = cbind(0, mat)\n#colnames(mat)[1] = \"Pre-tx\"\nmat = mat[rowSums(abs(mat))>0, ]\nclust = hclust(dist(mat))\nsvg(\"DE heatmaps sideways.svg\", height = 3, width = 6, onefile = F)\n#pheatmap(mat[order(mat[,\"C1D08\"]), ], cluster_cols = F, cluster_rows = F,\npheatmap(t(mat[as.character(clust$labels[clust$order]), ]), cluster_cols = F, cluster_rows = F,\n color = colorRampPalette(c(\"blue\",\"white\",\"red\"))(101), \n breaks = seq(-max(abs(mat)),max(abs(mat)),length.out = 100),\n show_colnames = F, main = \"Mean log2 fold-changes from baseline, cohort 1\")\ndev.off()\nsvg(\"DE heatmaps.svg\", height = 8, width = 4, onefile = F)\npheatmap(mat[as.character(clust$labels[clust$order]), ], cluster_cols = F, cluster_rows = F,\n color = colorRampPalette(c(\"blue\",\"white\",\"red\"))(101), \n breaks = seq(-max(abs(mat)),max(abs(mat)),length.out = 100),\n show_rownames = F) #, main = \"Mean log2 fold-changes from baseline, cohort 1\")\n#gannot.show = c(\"MHC2\",\"myleoid\",\"lymphoid\")\n#pheatmap(gannot[clust$labels[clust$order], gannot.show], cluster_cols = F, cluster_rows = F,\n# show_rownames = F, col = c(\"white\",\"darkblue\"))\ndev.off()\n\n\n#### trendplots:\n# individual genes: CD74, HLA-DRA, HLA-DPB1, FLT3LG, FLT3, CD1D\n# signatures: MHC2, TLRs\n\n\n### calculate scores:\n#scores = data.frame(rowMeans(norm[, rownames(gannot)[gannot[, \"MHC2\"]==1]]))\nscores = data.frame(rowMeans(norm[, setdiff(rownames(gannot)[gannot[, \"MHC2\"]==1], \"HLA-DQB1\")]))\ncolnames(scores)[1] = \"MHC2\"\nscores$APM = rowMeans(norm[, rownames(gannot)[gannot[, \"APM\"]==1]])\nscores$TLR = rowMeans(norm[, rownames(gannot)[gannot[, \"TLR\"]==1]])\n\n\nsvg(\"spaghetti plots.svg\", width = 10, height = 10)\n# genes:\npar(mfrow=c(3,3))\nname = \"HLA-DMA\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\n#name = \"IFI27\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"IL13RA1\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"CD74\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"FLT3LG\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"FLT3\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"SIGLEC1\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"CD1C\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\nname = \"CD1D\";trendplot(x=norm[,name],ylab=name,patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\n\n\n# scores:\n\nname = \"MHC2\";trendplot(x=scores[,name],ylab=\"mean of HLA-DRA, -DPB1, -DMA, -DPA1\",\n patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\n#name = \"APM\";trendplot(x=scores[,name],ylab=top5.altnames[name],patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\n#name = \"TLR\";trendplot(x=scores[,name],ylab=top5.altnames[name],patient=annot$patient,groups=annot$time,pointcols=annot$cohortcol)\ndev.off()\n\n", "meta": {"hexsha": "3c0f54bb44366df89ecfd4826e4d68a12698e4c4", "size": 21315, "ext": "r", "lang": "R", "max_stars_repo_path": "NanoString_cell_scores_and_differential_expression/cell_scores_and_differential_expression - old version.r", "max_stars_repo_name": "patrickjdanaher/CITN07-figure5-data-code-results", "max_stars_repo_head_hexsha": "e909eb74982efe00351dc1c57a8dc0f7ac753cb3", "max_stars_repo_licenses": ["MIT"], 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"max_forks_repo_forks_event_max_datetime": "2022-03-07T21:58:25.000Z", "avg_line_length": 41.8762278978, "max_line_length": 173, "alphanum_fraction": 0.6628665259, "num_tokens": 7338, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3464639096279216}} {"text": "\noptions(warn = -1) #ignore warnings\n\n# IMPORTANT: This assumes that all packages in \"Rstart.R\" are installed,\n# and the fonts \"Source Sans Pro\" and \"Open Sans Condensed Bold\" are installed\n# via extrafont. If ggplot2 charts fail to render, you may need to change/remove the theme call.\n\n\nsource(\"Rstart.R\")\nlibrary(ggmap)\n\n\n\nsessionInfo()\n\n#CDC information FLU\n#devtools::install_github(\"hrbrmstr/cdcfluview\") \n \n\nlibrary(cdcfluview)\nlibrary(statebins)\n# current verison\n# packageVersion(\"cdcfluview\")\n\n#df.flu <- get_flu_data(region = \"census\", sub_region = 1:9, data_source = \"all\", years = 2008:2015)\n#df.flu.census <- get_flu_data(region = \"census\", sub_region = 1:10, data_source = \"ilinet\", years = 2008:2015)\n\n# I am looking at the ILI activity\ndf.state <- get_state_data(years=2010:2015)\n\n#display the data \n#df.flu %>% head(10)\n#sprintf(\"# of Rows in Dataframe: %s\", nrow(df.flu))\n#sprintf(\"Dataframe Size: %s\", format(object.size(df.flu), units = \"MB\"))\n\n#display the data \ndf.state %>% head(10)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df.state))\nsprintf(\"Dataframe Size: %s\", format(object.size(df.state), units = \"MB\"))\n\n#display the data \n#df.flu.census %>% head(10)\n#sprintf(\"# of Rows in Dataframe: %s\", nrow(df.flu.census))\n#sprintf(\"Dataframe Size: %s\", format(object.size(df.flu.census), units = \"MB\"))\n\n#columns = c(\"REGION TYPE\",\"REGION\",\"YEAR\",\"WEEK\",\"AGE 0-4\",\"AGE 25-49\",\"AGE 25-64\",\"AGE 5-24\",\"AGE 50-64\",\"AGE 65\",\"ILITOTAL\",\"TOTAL PATIENTS\")\n\ncolumns = c(\"statename\",\"activity_level\",\"activity_level_label\",\"weekend\", \"season\", \"weeknumber\")\n\n# select() require column indices that we find trough which() \n\ndf <- df.state %>% select(which(names(df.state) %in% columns))\n\ndf %>% head(10)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df))\nsprintf(\"Dataframe Size: %s\", format(object.size(df), units = \"MB\"))\n\nproper_case <- function(x) {\n return (gsub(\"\\\\b([A-Z])([A-Z]+)\", \"\\\\U\\\\1\\\\L\\\\2\" , x, perl = TRUE))\n}\n\n\n\nfile <- \"data/state-lat-long.csv\"\ndf_state_location <- read_csv(file)\ncolnames(df_state_location)[1] <- 'statename'\n\ndf.location <- tbl_df(df_state_location)\n\ndf <- right_join(df,df_state_location) #join the dataframe yeah\n\n#save the dataframe \nwrite_csv(df, path = \"data/train.csv\")\n\ndf %>% head(10)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df))\n\n# grepl() is the best way to do in-text search\n#df_flu <- df %>% filter(grepl(\"Local Activity\", ACTIVITYESTIMATE))\n\ndf.flu.activity <- df %>% filter(activity_level > 0)\ndf.flu.activity %>% head(10)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df.flu.activity))\nsprintf(\"Dataframe Size: %s\", format(object.size(df.flu.activity), units = \"MB\"))\n\n as.Date(\"May-17-2014\",\"%b-%d-%Y\")\n\ndf.flu.weekend <- df.flu.activity %>%\n mutate(weekend = as.Date(weekend,\"%b-%d-%Y\")) %>% #convert to proper date for analysis\n group_by(weekend) %>%\n summarize(count = n()) %>%\n arrange(weekend)\n\ndf.flu.weekend %>% head(10)\n\nplot <- ggplot(df.flu.weekend, aes(x = weekend, y = count)) +\n geom_line(color = \"#F2CA27\", size = 0.1) +\n geom_smooth(color = \"#1A1A1A\") +\n fte_theme() +\n scale_x_date(breaks = date_breaks(\"2 years\"), labels = date_format(\"%Y\")) +\n labs(x = \"Weekend of the obseration\", y = \"Count of weekly activity > 0 \"\n , title = \"Weekly ILI Activity in the US from 2015 - 2017\")\n\nmax_save(plot, \"US-ILI-when-1\", \"US CDC Fluview Data\")\n\n# Returns the numeric hour component of a string formatted \"HH:MM\", e.g. \"09:40\" input returns 9\nget_month <- function(x) {\n date <- as.Date(x, \"%b-%d-%Y\") #format the data to date\n month <- format(date, \"%b\") # return the month\n return (month)\n}\n\ndf.flu.time <- df.flu.activity %>%\n mutate(month = sapply(weekend, get_month)) %>%\n group_by(month, season) %>% \n summarize(count = n())\n\n\ndf.flu.time %>% head(10)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df.flu.time))\nsprintf(\"Dataframe Size: %s\", format(object.size(df.flu.time), units = \"MB\"))\n\nmonth_format <- c(\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\")\nseason_format <- c(\"2010-11\",\"2011-12\",\"2012-13\",\"2013-14\",\"2014-15\",\"2015-16\")\n\ndf.flu.time$month <- factor(df.flu.time$month, level = rev(month_format))\ndf.flu.time$season <- factor(df.flu.time$season, level = rev(season_format))\n\ndf.flu.time %>% head(10)\n\nplot <- ggplot(df.flu.time, aes(x = month, y = season, fill = count)) +\n geom_tile() +\n fte_theme() +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.6),\n legend.title = element_blank(), legend.position=\"top\", \n legend.direction=\"horizontal\", legend.key.width=unit(2, \"cm\"), \n legend.key.height=unit(0.25, \"cm\"), legend.margin=unit(0.1,\"cm\"), \n panel.margin=element_blank()) +\n labs(x = \"Season of ILI activity \", y = \"Month\", \n title = \"# of ILI activity in U.S. from 2010 - 2015, by Season\") +\n scale_fill_gradient(low = \"white\", high = \"#27AE60\", labels = comma)\n\nmax_save(plot, \"US-ILI-when-2\", \"US CDC Fluview Data\", w=6)\n\ndf.flu.type <- df.flu.activity %>%\n group_by(activity_level_label) %>% \n summarize(count = n()) %>%\n arrange(desc(count))\n\ndf.flu.type %>% head(20)\nsprintf(\"# of Rows in Dataframe: %s\", nrow(df.flu.type))\n\n \n", "meta": {"hexsha": "7f81ce975b65d45360189b9a274e300c0cd6fe0c", "size": 5348, "ext": "r", "lang": "R", "max_stars_repo_path": "influenza_data_nm.r", "max_stars_repo_name": "smrtnrd/data-exploration-influenza", "max_stars_repo_head_hexsha": "0c6afc39b775f364d8bd6d5d493a40861ed25b53", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-11-06T17:10:11.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-06T17:10:11.000Z", "max_issues_repo_path": "influenza_data_nm.r", "max_issues_repo_name": "smrtnrd/data-exploration-influenza", "max_issues_repo_head_hexsha": "0c6afc39b775f364d8bd6d5d493a40861ed25b53", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "influenza_data_nm.r", "max_forks_repo_name": "smrtnrd/data-exploration-influenza", "max_forks_repo_head_hexsha": "0c6afc39b775f364d8bd6d5d493a40861ed25b53", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7272727273, "max_line_length": 144, "alphanum_fraction": 0.6327599102, "num_tokens": 1582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301064, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.34646266138565635}} {"text": "# Plot traffic data\n\nmainpath = \"D:/Dropbox/GitBit/recurrency-modelling2/dat/UKTraffic/\"\n\ngetdata <- function(flnm){\n data <- read.csv(flnm, header = FALSE, sep = \",\")\n colnames(data) <- c(\"Date\",\"Traffic\")\n data\n} \n#dat = getdata(paste(mainpath, \"internet-traffic-data-in-bits-5min.csv\", sep=\"\"))\n#mt <- read.table(\"D:/DATASETS/UKTraffic/labels.txt\", header = TRUE, sep = \",\")\n#abline(v = mt$ID, col = \"red\")\n\n#changes = read.table(paste(mainpath, \"TrueChanges.txt\", sep = \"\"), header = FALSE)$V1\n\npdf(file = \"D:/_tmp/20150423/TrafficData.pdf\", width = 2 * 5, height = 5)\npar(mfrow = c(2,1))\npar(mar = c(2, 4, 0.5, 2))\nplot(dat$Traffic, cex = 0.7, type = 'l', \n #xlab = \"Time in time units (1 time unit == 5 min)\", \n cex.main = 0.8, cex.lab = 0.7, cex.axis = 0.7, ann = FALSE)\n#mtext(side = 1, text = \"Time\", line = 2)\nmtext(side = 2, text = \"Traffic in bits\", line = 2, cex = 2)\n#mtext(side = 3, text = \"Aggregated traffic in the United Kingdom academic network backbone.\", line = 1, cex = 1.5)\nabline(v = changes, lty = 2)\n\npar(mar = c(5, 4, 1, 2))\nplot(dat$Traffic, xlim = c(5000, 1000), cex = 0.7, type = 'l', \n cex.main = 0.8, cex.lab = 0.7, cex.axis = 0.7, ann = FALSE)\nmtext(side = 1, text = \"Time (1 time unit == 5 min)\", line = 3, cex = 2)\nmtext(side = 2, text = \"Traffic in bits\", line = 2, cex = 2)\nabline(v = changes, lty = 2)\ndev.off()\n \n ", "meta": {"hexsha": "f82f9f9c81348addc88654294bfd53ac214d98f7", "size": 1370, "ext": "r", "lang": "R", "max_stars_repo_path": "v.1.0_clojure/src/r-code/4.2plotInputData.r", "max_stars_repo_name": "av-maslov/Recurrency", "max_stars_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "v.1.0_clojure/src/r-code/4.2plotInputData.r", "max_issues_repo_name": "av-maslov/Recurrency", "max_issues_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "v.1.0_clojure/src/r-code/4.2plotInputData.r", "max_forks_repo_name": "av-maslov/Recurrency", "max_forks_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.1428571429, "max_line_length": 115, "alphanum_fraction": 0.6072992701, "num_tokens": 532, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832804, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34646266076089943}} {"text": "##############################################################################\n# Create figures\n##############################################################################\nnrsq <- expression(\n\tpaste(\"Fraction of variance explained (Nagelkerke \", R^2, \")\", sep=\"\"))\n\n# Define common theme for Nagelkerke R^2 plots\ntheme_nrsq <- function(base_size = 12, base_family = \"\"){\n theme_bw(base_size = base_size) %+replace%\n theme(legend.text=element_text(size=14),\n axis.title.x=element_blank(),\n axis.title.y=element_text(size=16, angle=90),\n axis.text.x=element_blank(),\n axis.text.y=element_text(size=14))\n}\n\nrsq_full_dat <- combineddat %>%\n # filter(mod %in% mod[1:5])\n\tfilter(mod %in% mod[1:6])\n\nrsq_full_dat <- combineddat[1:6,]\n\nLv7v5v3_full <- ggplot(rsq_full_dat, aes(x=category, y=rsq, fill=mod))+\n geom_bar(stat=\"identity\", position=\"dodge\")+\n\t# scale_fill_manual(\"Model\", values=c(iwhPalette[c(1,3,4,5,9)]))+\n\tscale_fill_manual(\"Model\", values=c(iwhPalette[c(1,3,4,5,9,10)]))+\n\ttheme_nrsq()+\n\tylab(nrsq)\n# ggsave(paste0(parentdir, \"/images/Lv7v5v3_rsq_full.png\"), width=4, height=6)\n\n\nfd2 <- fulldat %>%\n\tfilter(mod %in% paste0(\"logmod\", c(3,5,7,\"L\")))\n\nfd3 <- fd2 %>%\n\tdplyr::select(-aic) %>%\n\tspread(mod, rsq)\n\nfd3$fold<-fd3[,5]/fd3[,4]\nfd3$fold2<-fd3[,5]/fd3[,3]\n\nbuildSegmentData <- function(){\n\tngroups <- 9\n\tnmodels <- 4\n\tnbars <- ngroups*nmodels\n\n\tst_once <- c(seq(1, nbars, nmodels), seq(nmodels, nbars, nmodels))\n\tst_twice <- setdiff(1:nbars, st_once)\n\tst_selection <- sort(c(st_once, rep(st_twice, each=2)))\n\n\tend_none <- seq(1, nbars, nmodels)\n\tend_twice <- setdiff(1:nbars, end_none)\n\tend_selection <- sort(c(rep(end_twice, each=2)))\n\n\tvertst <- sort(c(rep(c(1:ngroups-1/9), each=2),\n\t\trep(c(1:ngroups+1/9), each=2),\n\t\tc(1:ngroups+1/3),\n\t\tc(1:ngroups-1/3)))\n\tvertend <- vertst\n\n\thorizst <- sort(c(\n\t\tc(1:ngroups-1/3),\n\t\tc(1:ngroups-1/9),\n\t\tc(1:ngroups+1/9)))\n\thorizend <- sort(c(\n\t\tc(1:ngroups-1/9),\n\t\tc(1:ngroups+1/9),\n\t\tc(1:ngroups+1/3)))\n\n\t# Build data for geom_segment call in plot\n\tcorplot<-data.frame(\n\t\txst=vertst,\n\t xend=vertend,\n\t yst=fd2$rsq[st_selection]+.001,\n\t yend=fd2$rsq[end_selection]+.01)\n\n\tcorplot2 <- data.frame(\n\t\txst=horizst,\n\t xend=horizend,\n\t yst=fd2$rsq[unique(end_selection)]+.01,\n\t yend=fd2$rsq[unique(end_selection)]+.01,\n\t pval=signif(lrtestdat$pvals, 2))\n\n\tcorplot2$pval <- ifelse(corplot2$pval<0.001, \"***\",\n\t\tifelse(corplot2$pval<0.01, \"**\", ifelse(corplot2$pval<0.05, \"*\", \" \")))\n\n\tout <- list()\n\tout$corplot <- corplot\n\tout$corplot2 <- corplot2\n return(out)\n}\n\nsegment_data <- buildSegmentData()\ncorplot <- segment_data$corplot\ncorplot2 <- segment_data$corplot2\n\n# Plot pseudo-r^2 for 7-mers vs 5-mers vs 3-mers\nrsqdat <- fd2 %>%\n # fulldat %>%\n # filter(mod %in% c(\"7-mers\", \"5-mers\", \"3-mers\", \"7-mers+features\")) %>%\n filter(group==\"FULL\") %>%\n mutate(Category =\n factor(plyr::mapvalues(category, orderedcats2, orderedcats2),\n\t\t\t\tlevels=orderedcats2))\n\nLv7v5v3 <- ggplot(rsqdat)+\n geom_bar(aes(x=Category, y=rsq, fill=mod), stat=\"identity\", position=\"dodge\")+\n # scale_fill_manual(\"Model\", values=cbbPalette[c(4,6,7,8)])+\n\tscale_fill_manual(\"Model\", values=c(iwhPalette[c(3,4,5,9)]))+\n geom_segment(data=corplot, aes(x=xst, xend=xend, y=yst, yend=yend))+\n geom_segment(data=corplot2, aes(x=xst, xend=xend, y=yst, yend=yend))+\n geom_text(data=corplot2,\n\t\taes(x=xst+1/9, y=yst+.005, label=pval, angle=90), size=6)+\n scale_y_continuous(limits=c(0,0.1))+\n theme_bw()+\n theme(legend.text=element_text(size=14),\n axis.title.x=element_text(size=16),\n axis.title.y=element_text(size=16),\n axis.text.x=element_text(size=14, angle=45, hjust=1, vjust=1),\n axis.text.y=element_text(size=14))+\n xlab(\"Mutation Type\")+\n ylab(nrsq)\n# ggsave(paste0(parentdir, \"/images/Lv7v5v3_rsq.png\"), width=12, height=6)\n\nLv7v5v3_full <- ggplotGrob(Lv7v5v3_full)\nLv7v5v3 <- ggplotGrob(Lv7v5v3)\n\ng <- arrangeGrob(Lv7v5v3_full, Lv7v5v3, nrow=1, widths=c(1,2))\nggsave(paste0(parentdir, \"/images/Lv7v5v3_rsq_combined.svg\"),\n\twidth=12, height=6, g)\n\n# Plot pseudo-r^2 for 7-mers vs logit\nrsqdatL <- fulldat %>%\n filter(mod==\"7-mers\" | mod==\"7-mers+features\") %>%\n filter(group==\"FULL\") %>%\n mutate(Category =\n factor(plyr::mapvalues(category, orderedcats2, orderedcats2),\n\t\t\t\tlevels=orderedcats2))\n\nggplot(rsqdatL)+\n geom_bar(aes(x=Category, y=rsq, fill=mod),\n\t\tstat=\"identity\", position=\"dodge\")+\n scale_fill_manual(\"Model\", values=cbbPalette[c(7,8)])+\n\ttheme_nrsq()+\n\tylab(nrsq)+\n xlab(\"Mutation Type\")\nggsave(paste0(parentdir, \"/images/7v5v3_rsqL.png\"), width=12, height=6)\n\n# Plot pseudo-r^2 for ERVs vs Common\nrsqdatEC <- fulldat %>%\n\tfilter(mod==\"AV\" | mod==\"Common\" | mod==\"ERVs\") %>%\n filter(group==\"FULL\") %>%\n mutate(Category =\n factor(plyr::mapvalues(category, orderedcats2, orderedcats2),\n\t\t\t\tlevels=orderedcats2))\nggplot(rsqdatEC)+\n\tgeom_bar(aes(x=Category, y=rsq, fill=mod),\n\t\tstat=\"identity\", position=\"dodge\")+\n\tscale_fill_manual(\"Model\", values=c(\"grey30\", cbbPalette[c(3,7)]))+\n\ttheme_bw()+\n\ttheme(legend.text=element_text(size=14),\n\t\taxis.title.x=element_text(size=16),\n\t\taxis.title.y=element_text(size=16),\n\t\taxis.text.x=element_text(size=14, angle=45, hjust=1, vjust=1),\n\t\taxis.text.y=element_text(size=14))+\n\txlab(\"Mutation Type\")+\n ylab(nrsq)\nggsave(paste0(parentdir, \"/images/AvCvS_rsq.png\"), width=12, height=6)\n\noldmodnames <- unique(fulldat$mod)\n\nnewmodnames <- c(\"3-mers\", \"5-mers\", \"7-mers\", \"7-mers+features\",\n\t\"7-mers (downsampled BRIDGES ERVs)\",\n\t\"7-mers (BRIDGES MAC10+ variants)\",\n\t\"7-mers (1KG EUR intergenic variants)\")\n\nnewmodnamesord <- c(\"3-mers\", \"5-mers\", \"7-mers\",\n\t\"7-mers (downsampled BRIDGES ERVs)\",\n\t\"7-mers (BRIDGES MAC10+ variants)\",\n\t\"7-mers (1KG EUR intergenic variants)\",\n\t\"7-mers+features\")\n\nrsqdatFULL <- fulldat %>%\n filter(group==\"FULL\") %>%\n mutate(Category =\n factor(plyr::mapvalues(category, orderedcats2, orderedcats2),\n\t\t\t\tlevels=orderedcats2)) %>%\n\tmutate(mod=\n\t\tfactor(plyr::mapvalues(mod, oldmodnames, newmodnames),\n\t\t\tlevels=newmodnamesord))\n\n\nalldat <- combineddat[2:8,] %>%\n\tmutate(mod=factor(plyr::mapvalues(mod, oldmodnames, newmodnames),\n\t\tlevels=newmodnamesord))\n\nall_full <- ggplot(alldat)+\n geom_bar(aes(x=category, y=rsq, fill=mod),\n\t\tstat=\"identity\", position=\"dodge\")+\n\tscale_fill_manual(\"Model\", values=c(iwhPalette[c(3:9)]))+\n theme_nrsq(legend.position=\"none\")+\n ylab(nrsq)\n\nall <- ggplot(rsqdatFULL)+\n geom_bar(aes(x=Category, y=rsq, fill=mod),\n\t\tstat=\"identity\", position=\"dodge\")+\n\tscale_fill_manual(\"Model\", values=c(iwhPalette[c(3:9)]))+\n theme_bw()+\n theme(\n legend.text=element_text(size=14),\n\t\t\tlegend.position=c(0.4,0.7),\n\t\t\tlegend.background = element_rect(colour = \"black\"),\n axis.title.x=element_text(size=16),\n axis.title.y=element_text(size=16),\n axis.text.x=element_text(size=14, angle=45, hjust=1, vjust=1),\n axis.text.y=element_text(size=14))+\n xlab(\"Mutation Type\")+\n ylab(nrsq)\n\nall_full <- ggplotGrob(all_full)\nall <- ggplotGrob(all)\ng <- arrangeGrob(all_full, all, nrow=1, widths=c(1,2))\nggsave(paste0(parentdir, \"/images/all_rsq_combined.png\"), width=12, height=6, g)\n\nevcdat <- rsqdatFULL %>%\n\tfilter(grepl(\"BRIDGES\", mod))\nEvC <- ggplot(evcdat)+\n geom_bar(aes(x=Category, y=rsq, fill=mod),\n\t\tstat=\"identity\", position=\"dodge\")+\n # scale_fill_manual(\"Model\", values=brewer.pal(8, \"Set3\")[5:6])+\n\tscale_fill_manual(\"Model\",\n\t\tvalues=c(iwhPalette[c(6,7)]),\n\t\tguide = guide_legend(nrow=2))+\n theme_bw()+\n theme(\n legend.text=element_text(size=14),\n\t\t\tlegend.position=\"bottom\",\n axis.title.x=element_text(size=16),\n axis.title.y=element_text(size=16),\n axis.text.x=element_text(size=14, angle=45, hjust=1, vjust=1),\n axis.text.y=element_text(size=14))+\n xlab(\"Mutation Type\")+\n ylab(nrsq)\n# ggsave(paste0(parentdir, \"/images/EvC_rsq.png\"), width=12, height=6)\n\nEvC_full_dat <- combineddat %>%\n # filter(mod==\"AV\" | mod==\"Common\" | mod==\"ERVs\") %>%\n\tfilter(mod==\"Common\" | mod==\"ERVs\") %>%\n\tmutate(mod=plyr::mapvalues(mod, c(\"Common\", \"ERVs\"),\n\t\tc(\"7-mers (BRIDGES MAC10+ variants)\", \"7-mers (downsampled BRIDGES ERVs)\"))) %>%\n# combineddat %>%\n # filter(mod==\"AV\" | mod==\"Common\" | mod==\"ERVs\") %>%\n\t# filter(mod==\"Common\" | mod==\"ERVs\") %>%\n\tmutate(mod=factor(plyr::mapvalues(mod, oldmodnames, newmodnames),\n\t\tlevels=newmodnamesord)) %>%\n\tfilter(grepl(\"BRIDGES\", mod))\n\nEvC_full <- ggplot(EvC_full_dat)+\n geom_bar(aes(x=category, y=rsq, fill=mod), stat=\"identity\", position=\"dodge\")+\n # scale_fill_manual(\"Model\", values=brewer.pal(8, \"Set3\")[5:6])+\n\tscale_fill_manual(\"Model\", values=c(iwhPalette[c(6,7)]))+\n\ttheme_nrsq()+\n ylab(nrsq)\n# ggsave(paste0(parentdir, \"/images/EvC_rsq_full.png\"), width=8, height=6)\n\nEvC_full <- ggplotGrob(EvC_full)\nEvC <- ggplotGrob(EvC)\ng <- arrangeGrob(EvC_full, EvC, nrow=1, widths=c(1,2))\nggsave(paste0(parentdir, \"/images/EvC_rsq_combined.png\"), width=12, height=8, g)\n", "meta": {"hexsha": "519a0f6f61ecb824063c30ae5ea32948a2f2e442", "size": 8870, "ext": "r", "lang": "R", "max_stars_repo_path": "R/validation_figs.r", "max_stars_repo_name": "theandyb/smaug-genetics", "max_stars_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-09-18T20:54:24.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-16T05:30:06.000Z", "max_issues_repo_path": "R/validation_figs.r", "max_issues_repo_name": "theandyb/smaug-genetics", "max_issues_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-07-24T12:43:50.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-24T12:43:50.000Z", "max_forks_repo_path": "R/validation_figs.r", "max_forks_repo_name": "theandyb/smaug-genetics", "max_forks_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-07-16T20:50:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T10:41:40.000Z", "avg_line_length": 32.8518518519, "max_line_length": 82, "alphanum_fraction": 0.6647125141, "num_tokens": 3128, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34646265258178166}} {"text": "pdf_file<-\"pdf/tablecharts_symbol_signs.pdf\"\ncairo_pdf(bg=\"grey98\",pdf_file,width=9,height=4)\n\npar(omi=c(0.5,0.25,0.5,0.25),mai=c(0,0,0,0),family=\"Lato Light\",cex=1.2)\n\n# Import data\n\nlibrary(gdata)\nmyData<-read.xls(\"myData/leaking_pipeline.xlsx\", encoding=\"latin1\")\nattach(myData)\n\n# Create graphics\n\nb1<-barplot(Men+75,horiz=T,xlim=c(-175,175),border=NA,col=\"gainsboro\",axes=F)\nbarplot(-Women-75,horiz=T,border=NA,add=T,col=\"gainsboro\",axes=F)\nbarplot(rep(75,5),horiz=T,border=par(\"bg\"),add=T,col=par(\"bg\"),axes=F)\nbarplot(rep(-75,5),horiz=T,border=par(\"bg\"),add=T,col=par(\"bg\"),axes=F)\nabline(v=seq(-175,195,by=10),col=par(\"bg\"))\ntext(0,b1,Level)\n\n# Titling\n\nmtext(\"The 'Leaky Pipeline' 2005\",3,line=0.25,adj=0,cex=1.75,family=\"Lato Black\",outer=T)\nmtext(\"Source: Wissenschaftsrat, Drucksache Drs. 8036-07.\",1,line=0.25,adj=1.0,cex=0.65,outer=T,font=3)\n\n# Symbols\n\npar(family=\"Symbol Signs\")\nfor (i in 1:5) \n{\nMyMen_Number<-Men[i]\ntext(seq(10,10*round(MyMen_Number/10),by=10)+73.5,rep(b1[i],5),rep(\"M\",MyMen_Number),\n\tcex=2.75,col=\"cornflowerblue\")\nMyWomen_Number<-Women[i]\ntext(-seq(10,10*round(MyWomen_Number/10),by=10)-68,rep(b1[i],5),rep(\"F\",MyWomen_Number),\n\tcex=2.75,col=\"deeppink\")\n}\n\npar(family=\"Lato Bold\")\ntext(55,b1,paste(Men, \"%\", sep=\" \"))\ntext(-55,b1,paste(Women, \"%\", sep=\" \"))\ndev.off()\n", "meta": {"hexsha": "39f01fa7b960ab4cd08a3dd499aa214726b4623c", "size": 1306, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/tablecharts_symbol_signs.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/tablecharts_symbol_signs.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/tablecharts_symbol_signs.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3720930233, "max_line_length": 103, "alphanum_fraction": 0.6929555896, "num_tokens": 518, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3464626525817816}} {"text": "tab <- read.table(\"../../d5/metaphlan2_unpair_merge_Species_d5.txt\",sep=\"\\t\",header = T,row.names = 7)\r\ntab <- tab[,-c(1:6)]\r\n\r\nT_ID <- colnames(tab)[grep(\"T\",colnames(tab))]\r\nG_ID <- colnames(tab)[grep(\"G\",colnames(tab))]\r\n\r\nT_tab <- tab[,T_ID]\r\nG_tab <- tab[,G_ID]\r\n\r\nminus_tab <- (T_tab>0) - (G_tab>0)\r\n\r\n\r\nplot_data <- data.frame(ID=gsub(\"T\",\"\",colnames(minus_tab)),Tongue=colSums(minus_tab==1),Fluid=colSums(minus_tab==-1),Both= colSums((T_tab>0) & (G_tab>0)))\r\n\r\n\r\nlibrary(ggplot2)\r\nlibrary(reshape2)\r\n\r\nplot_data2 <- melt(plot_data)\r\ncolnames(plot_data2) <- c(\"ID\",\"site\",\"num\")\r\nplot_data2$site <- factor(plot_data2$site,levels = c(\"Tongue\",\"Both\",\"Fluid\"))\r\nggplot(plot_data2,aes(ID,num))+\r\n geom_col(aes(fill=site), width = 0.75)+\r\n theme_bw()+\r\n scale_fill_manual(values=c(\"Tongue\"=\"indianred1\",\"Fluid\"=\"cadetblue\",\"Both\"=\"darkolivegreen\"))+\r\n theme(panel.grid=element_blank(),axis.text.x=element_text(angle = 90),text = element_text(size=20))+\r\n ggsave(\"richness_overlap_species_num_overlap_d5.pdf\",width = 10,height = 6)\r\n \r\n\r\nrichness_overlap_tab <- plot_data\r\nrichness_overlap_tab$overlap_ratio_of_gastric <- plot_data$Both / (plot_data$Both + plot_data$Fluid)\r\nwrite.table(richness_overlap_tab,file=\"richness_overlap_tab.txt\",quote = F,sep = \"\\t\",row.names = F)\r\n", "meta": {"hexsha": "33559f5bb869976fbb88de5f925dca927ed74aa2", "size": 1285, "ext": "r", "lang": "R", "max_stars_repo_path": "richness_overlap.r", "max_stars_repo_name": "JiaxingCui/Tongue-coating-and-gastric-fluid-analysis", "max_stars_repo_head_hexsha": "edfaf2073200e55d774b9b6b4ea3beb32a369d31", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "richness_overlap.r", "max_issues_repo_name": "JiaxingCui/Tongue-coating-and-gastric-fluid-analysis", "max_issues_repo_head_hexsha": "edfaf2073200e55d774b9b6b4ea3beb32a369d31", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "richness_overlap.r", "max_forks_repo_name": "JiaxingCui/Tongue-coating-and-gastric-fluid-analysis", "max_forks_repo_head_hexsha": "edfaf2073200e55d774b9b6b4ea3beb32a369d31", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9393939394, "max_line_length": 156, "alphanum_fraction": 0.6941634241, "num_tokens": 413, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526660244837, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3462784773741619}} {"text": "pacman::p_load(\n tidyverse,\n tidymodels,\n here,\n ggthemes,\n lykkelib\n)\n\n# plot performance from short episodes\n# source(here(\"markdown\", \"performance_short.r\"))\n\n# plot performance from all episodes\n# source(here(\"markdown\", \"performance_all.r\"))\n\nall_metrics <- read_rds(here(\"data\", \"all_metrics_60.rds\"))\n\n# performance on all episode lengths --------------------------------------\n\n\nplot_short <-\n all_metrics %>%\n filter(.metric != \"accuracy\") %>% \n replace_na(replace = list(.estimate = 0)) %>%\n mutate(\n model = fct_relevel(model, c(\"syed_cnn\", \"sunda_rf\", \"tree_no_temp\", \"tree_imp6\", \"tree_full\", \"cz_60\", \"heuristic\")),\n .metric = fct_relevel(.metric, c(\"precision\", \"sensitivity\", \"f_meas\")),\n model = fct_reorder(model, .estimate)\n ) %>%\n ggplot(aes(model, .estimate, fill = dataset)) +\n geom_col(\n width = .75,\n alpha = .8,\n # color = \"grey50\",\n position = position_dodge(width = .8)\n ) +\n geom_text(aes(model, .estimate + .05,\n label = round(.estimate, 2)\n ),\n size = 3,\n position = position_dodge(width = .8)\n ) +\n expand_limits(y = c(0, 1.2)) +\n scale_fill_manual(\n values = c(\"#E8C15F\", \"lightblue\", \"#7CB07C\"),\n labels = c(\"Hip\", \"Thigh\", \"Wrist\")\n ) +\n # lykkelib::scale_fill_lykke() +\n labs(\n title = NULL,\n x = NULL,\n y = NULL,\n fill = NULL,\n ) +\n facet_wrap(~.metric,\n labeller = labeller(.metric = c(\n precision = \"Precision\",\n sensitivity = \"Sensitivity\",\n accuracy = \"Accuracy\",\n f_meas = \"F1 score\"\n )),\n nrow = 4\n ) +\n theme_bw() +\n theme(\n panel.border = element_rect(linetype = 1, size = .3),\n # axis.text.x = element_text(angle = 45, vjust = .65),\n legend.position = \"bottom\",\n # panel.spacing = unit(1, \"lines\"),\n strip.text.x = element_text(size = 12),\n strip.text.y = element_blank(),\n strip.background = element_blank()\n ) \n\n\n# ggsave(filename = here(\"figures\", \"performance_all_episodes.tiff\"), plot = p1, dpi = 600, height = 8, width = 12)\n# ggsave(filename = here(\"figures\", \"performance_short_episodes.tiff\"), plot = p1, dpi = 600, height = 8, width = 12)\n", "meta": {"hexsha": "7a29180375933986dc371492ef387ced73600b21", "size": 2190, "ext": "r", "lang": "R", "max_stars_repo_path": "code/plot_performance_metrics_short.r", "max_stars_repo_name": "esbenlykke/nonwear_project", "max_stars_repo_head_hexsha": "a9a57f5562d3ba749ca077a67dca91d3dd14cd54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/plot_performance_metrics_short.r", "max_issues_repo_name": "esbenlykke/nonwear_project", "max_issues_repo_head_hexsha": "a9a57f5562d3ba749ca077a67dca91d3dd14cd54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/plot_performance_metrics_short.r", "max_forks_repo_name": "esbenlykke/nonwear_project", "max_forks_repo_head_hexsha": "a9a57f5562d3ba749ca077a67dca91d3dd14cd54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4415584416, "max_line_length": 122, "alphanum_fraction": 0.5863013699, "num_tokens": 639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3462784765937602}} {"text": "city <- read.table(\"census-city.txt\", sep = \"|\", stringsAsFactors = F, na = \"X\")\n\nhas_comma <- function(x) length(grep(\",\", x)) > 0\ncomma_cols <- sapply(city, has_comma)\nstrip_comma <- function(x) as.numeric(gsub(\",\", \"\", x))\n\ncity[comma_cols] <- lapply(city[comma_cols], strip_comma)\ncity$V3 <- as.numeric(city$V3)\ncity$V13 <- as.numeric(city$V13)\n\nfields <- read.table(\"census-city.flds\", sep = \"|\", stringsAsFactors = F)\nnames(city) <- fields$V2\n\nfirms <- grep(\"firms\", names(city))\ncity[firms] <- lapply(city[firms], as.numeric)\n\nwrite.table(city, \"census-city.csv\", sep=\",\", row=F)", "meta": {"hexsha": "6f1ee1d7c917f452ed41e840af30398ffad442c8", "size": 586, "ext": "r", "lang": "R", "max_stars_repo_path": "census.r", "max_stars_repo_name": "hadley/sfhousing", "max_stars_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2015-08-02T06:56:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T14:24:40.000Z", "max_issues_repo_path": "census.r", "max_issues_repo_name": "hadley/sfhousing", "max_issues_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "census.r", "max_forks_repo_name": "hadley/sfhousing", "max_forks_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2015-02-26T12:57:23.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-04T05:18:22.000Z", "avg_line_length": 34.4705882353, "max_line_length": 80, "alphanum_fraction": 0.6604095563, "num_tokens": 184, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3462784684960046}} {"text": "##CTE534_Gr\u00e1ficos=group\n##Imagen=raster\n##Banda1=number 1\n##Banda2=number 2\n##Banda3=number 3\n##Banda4=number 4\n##showplots\npar(mfrow =c(2,2))\nplot(Imagen[[Banda1]], main = names(Imagen[[Banda1]]), col = gray(0:100 / 100))\nplot(Imagen[[Banda2]], main = names(Imagen[[Banda2]]), col = gray(0:100 / 100))\nplot(Imagen[[Banda3]], main = names(Imagen[[Banda3]]), col = gray(0:100 / 100))\nplot(Imagen[[Banda4]], main = names(Imagen[[Banda4]]), col = gray(0:100 / 100))", "meta": {"hexsha": "42d49f5717b28f8b9814f1b74bbc6bc7460a69ab", "size": 462, "ext": "rsx", "lang": "R", "max_stars_repo_path": "Rscripts/PlotBands2.rsx", "max_stars_repo_name": "klauswiese/QGIS-R", "max_stars_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/PlotBands2.rsx", "max_issues_repo_name": "klauswiese/QGIS-R", "max_issues_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/PlotBands2.rsx", "max_forks_repo_name": "klauswiese/QGIS-R", "max_forks_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5, "max_line_length": 79, "alphanum_fraction": 0.6666666667, "num_tokens": 186, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3462784684960045}} {"text": "library(dash)\nlibrary(dashCoreComponents)\nlibrary(dashHtmlComponents)\nlibrary(dashBootstrapComponents)\nlibrary(ggplot2)\nlibrary(plotly)\n\ndf = read.csv(\"data/Processed/HR_employee_Attrition_editted_processed.csv\")\n\napp = Dash$new(external_stylesheets = dbcThemes$BOOTSTRAP)\noptions(repr.plot.height = 200, repr.plot.width = 250)\np <- ggplot(df) +\n aes(x = Attrition,\n y = MonthlyIncome,\n fill = Attrition) +\n geom_boxplot(varwidth = TRUE) +\n theme_minimal(base_size = 16) +\n labs(y = 'Monthly Income', title = 'Monthly Income Distribution') +\n scale_y_continuous(labels = scales::label_dollar()) +\n coord_flip()\n\napp$layout(dccGraph(figure = ggplotly(p)))\n\n# app$layout(\n# dbcContainer(\n# htmlH1('Key Factors for Employee Attrition'),\n# list(\n# dccGraph(id='plot-area')\n # dccDropdown(\n # id = 'depart-widget',\n # options = purrr::map(df %>% colnames, function(col) list(label = col, value = col)),\n # value='Sales')\n # )\n #\n # ))\n # dbcRow(\n # list(\n # dbcCol(\n # list(\n # dccGraph(id='plot-area'),\n # htmlLabel('Department'),\n # dccDropdown(\n # id = 'depart-widget',\n # value = 'Sales'\n # options = list(list(label = col, value = col),\n # list(label = \"San Francisco\", value = \"SF\")),\n # )\n # )\n # ),\n # )))))\n\n\n# app$callback(\n# output('plot-area', 'figure'),\n# list(input('depart-widget', 'value')),\n#\n# function(xcol) {\n# options(repr.plot.height = 200, repr.plot.width = 250)\n# p <- ggplot(df %>% filter(Department = !!sym(xcol))) +\n# aes(x = MonthlyIncome,\n# y = Attrition,\n# fill = Attrition) +\n# geom_boxplot(varwidth = TRUE) +\n# # theme_minimal(base_siz=16) +\n# labs(x = 'Monthly Income', title = 'Monthly Income Distribution') +\n# scale_x_continuous(labels = scales::label_dollar())\n# ggplotly(p)\n# }\n# )\n\napp$run_server(debug = T)\n\n\n", "meta": {"hexsha": "415bdca7ec14721227c28d535fb6d87b6030c1b7", "size": 2045, "ext": "r", "lang": "R", "max_stars_repo_path": "src/app_plot1.r", "max_stars_repo_name": "AnitaLi-0371/532_Dashboard_Project_Group_14-R", "max_stars_repo_head_hexsha": "ba11b6fd5627011f86b08129e7d0f2fe2b91c9fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-29T05:00:47.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-29T05:00:47.000Z", "max_issues_repo_path": "src/app_plot1.r", "max_issues_repo_name": "AnitaLi-0371/532_Dashboard_Project_Group_14-R", "max_issues_repo_head_hexsha": "ba11b6fd5627011f86b08129e7d0f2fe2b91c9fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2021-01-27T20:54:53.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-31T05:37:49.000Z", "max_forks_repo_path": "src/app_plot1.r", "max_forks_repo_name": "AnitaLi-0371/532_Dashboard_Project_Group_14-R", "max_forks_repo_head_hexsha": "ba11b6fd5627011f86b08129e7d0f2fe2b91c9fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2021-01-27T21:00:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-30T16:59:57.000Z", "avg_line_length": 27.6351351351, "max_line_length": 94, "alphanum_fraction": 0.5701711491, "num_tokens": 549, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.34627845961784703}} {"text": "source(\"scRNA_func.r\")\n\nlibrary(dplyr)\nlibrary(Seurat)\nlibrary(ggplot2)\nlibrary(ggpubr)\nlibrary(DT)\nlibrary(data.table)\nlibrary(cowplot)\nlibrary(scales)\nlibrary(stringr)\nrequire(data.table)\n\noptions(future.globals.maxSize= 10779361280)\nrandom.seed=20200107\n\noptions_table<-read.table(parSampleFile1, sep=\"\\t\", header=F, stringsAsFactors = F)\nmyoptions<-split(options_table$V1, options_table$V2)\n\nby_sctransform<-ifelse(myoptions$by_sctransform == \"0\", FALSE, TRUE)\nprefix<-outFile\n\nspecies=myoptions$species\n\npca_dims<-1:as.numeric(myoptions$pca_dims)\n\nfinalListFile<-paste0(prefix, \".final.rds\")\n\nrawobj<-readRDS(parFile1)\n\nfinalList<-preprocessing_rawobj(rawobj, myoptions, prefix)\nrawobj<-finalList$rawobj\nfinalList<-finalList[names(finalList) != \"rawobj\"]\n\nif(by_sctransform){\n cat(\"performing SCTransform ...\\n\")\n nsamples=length(unique(rawobj$sample))\n if(nsamples > 1){\n objs<-SplitObject(object = rawobj, split.by = \"sample\")\n rm(rawobj)\n \n #perform sctransform\n objs<-lapply(objs, function(x){\n x <- SCTransform(x, verbose = FALSE)\n return(x)\n }) \n obj <- merge(objs[[1]], y = unlist(objs[2:length(objs)]), project = \"integrated\")\n VariableFeatures(obj[[\"SCT\"]]) <- rownames(obj[[\"SCT\"]]@scale.data)\n }else{\n obj=rawobj\n rm(rawobj)\n obj<-SCTransform(obj, verbose = FALSE)\n }\n assay=\"SCT\"\n}else{\n cat(\"performing NormalizeData/FindVariableFeatures ...\\n\")\n #perform standard workflow\n obj <-rawobj\n rm(rawobj)\n obj<-NormalizeData(obj, verbose = FALSE)\n obj<-FindVariableFeatures(obj, selection.method = \"vst\", nfeatures = 2000, verbose = FALSE) \n obj<-ScaleData(obj)\n assay=\"RNA\"\n}\n\ncat(\"run_pca ... \\n\")\nobj <- RunPCA(object = obj, assay=assay, verbose=FALSE)\n\npng(paste0(outFile, \".elbowplot.pca.png\"), width=1500, height=1200, res=300)\np<-ElbowPlot(obj, ndims = 20, reduction = \"pca\")\nprint(p)\ndev.off()\n\ncat(\"run_umap ... \\n\")\nobj <- RunUMAP(object = obj, dims=pca_dims, verbose = FALSE)\n\nfinalList$obj<-obj\nsaveRDS(finalList, file=finalListFile)\n\noutput_integraion_dimplot(obj, outFile, FALSE)\n", "meta": {"hexsha": "e155d40086907953f5888711eafb8f5c0f4c8ed8", "size": 2064, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/scRNA/seurat_merge.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/scRNA/seurat_merge.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/scRNA/seurat_merge.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 25.4814814815, "max_line_length": 95, "alphanum_fraction": 0.7122093023, "num_tokens": 630, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7090191214879992, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3462022639698301}} {"text": "args = commandArgs(trailingOnly=TRUE)\nif(length(args) < 3){\n stop(\"R --slave < this_code.r --args \")\n}\nsource(\"/home/yuanhao/single_cell/scripts/evaluation_pipeline/evaluation/generic_functions.r\")\nloom_path = args[1]\nmin_expressed_cell = as.integer(args[2])\nmin_expressed_cell_average_expression = as.numeric(args[3])\noutput_tsv = paste0(paste(delete_last_element(unlist(strsplit(loom_path, \".\", fixed = T))), collapse = \"/\"), \"_mc_\", min_expressed_cell, \"_mce_\", min_expressed_cell_average_expression, \"_hvg.tsv\")\ngene_bc_mat = t(h5read(loom_path, \"matrix\"))\ncolnames(gene_bc_mat) = h5read(loom_path, \"col_attrs/CellID\")\nrow.names(gene_bc_mat) = h5read(loom_path, \"row_attrs/Gene\")\nexpressed_cell = Matrix::rowSums(gene_bc_mat > 0)\ngene_expression = Matrix::rowSums(gene_bc_mat)\ngene_filter = expressed_cell >= min_expressed_cell & gene_expression > expressed_cell * min_expressed_cell_average_expression\nhvf.info = FindVariableFeatures_vst_by_genes(gene_bc_mat[gene_filter, ])\nhvg = rownames(hvf.info)[order(hvf.info$variance.standardized, decreasing = T)]\nwrite.table(hvf.info[order(hvf.info$variance.standardized, decreasing = T), ], output_tsv, sep = \"\\t\", quote = F, row.names = T, col.names = T)\n\n\n\n\n\n", "meta": {"hexsha": "61264ab6647885fc2dc6b9ee57e9d48d3b7ad403", "size": 1299, "ext": "r", "lang": "R", "max_stars_repo_path": "reproducibility/Data Preparation, Imputation and Computational Resource Evaluation/Data Pre-processing/BRAIN_1.3M/4.grep_feature_genes.r", "max_stars_repo_name": "iyhaoo/DISC", "max_stars_repo_head_hexsha": "42bcb570bc76ac28bba1681e905efc5189c15e39", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2019-12-13T06:20:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T07:48:27.000Z", "max_issues_repo_path": "reproducibility/Data Preparation, Imputation and Computational Resource Evaluation/Data Pre-processing/BRAIN_1.3M/4.grep_feature_genes.r", "max_issues_repo_name": "xie-lab/DISC", "max_issues_repo_head_hexsha": "f7e79c89fb3840f548cc093184edd53ffb3f57ca", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2019-12-13T11:25:06.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-21T01:23:55.000Z", "max_forks_repo_path": "reproducibility/Data Preparation, Imputation and Computational Resource Evaluation/Data Pre-processing/BRAIN_1.3M/4.grep_feature_genes.r", "max_forks_repo_name": "iyhaoo/DISC", "max_forks_repo_head_hexsha": "42bcb570bc76ac28bba1681e905efc5189c15e39", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-12-13T06:20:32.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-21T07:42:34.000Z", "avg_line_length": 54.125, "max_line_length": 196, "alphanum_fraction": 0.7767513472, "num_tokens": 359, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.34605352896925745}} {"text": "\n#------------------------------\n# library\n#------------------------------\n\nlibrary(Matrix)\nlibrary(magrittr)\nlibrary(data.table)\nlibrary(dtplyr)\nlibrary(dplyr)\n\n#------------------------------\n# functions\n#------------------------------\n\nremove_response <- function(data, dataset=\"train\"){\n\tif(dataset != \"train\") return(data)\n\tselect(data, -Response)\n}\n\nload_numeric <- function(data, dataset=\"train\"){\n\n\tcat(\"==> Load\", dataset, \"numeric\\n\")\n\tfread(file.path(data_dir, paste0(dataset, \"_numeric.csv\")), data.table=FALSE) %>%\n\n\t# remove unneeded columns\n\tselect(-Id) %>%\n\tremove_response(dataset=dataset) %>%\n\n\t# convert to sparse matrix\n\tdata.matrix %>%\n\treplace(.==0, 1e-10) %>%\n\treplace(is.na(.), 0) %>%\n\tMatrix(sparse=TRUE)\n}\n", "meta": {"hexsha": "9850fb3eeb234ca260d976b141e18b41bb71a301", "size": 732, "ext": "r", "lang": "R", "max_stars_repo_path": "source/02_xgboost/load_numeric.r", "max_stars_repo_name": "toshi-k/kaggle-bosch-production-line-performance", "max_stars_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:30:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T16:47:31.000Z", "max_issues_repo_path": "source/02_xgboost/load_numeric.r", "max_issues_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_issues_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-05-19T00:19:14.000Z", "max_issues_repo_issues_event_max_datetime": "2017-05-19T16:20:59.000Z", "max_forks_repo_path": "source/02_xgboost/load_numeric.r", "max_forks_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_forks_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2016-12-10T23:02:50.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-15T12:48:28.000Z", "avg_line_length": 20.3333333333, "max_line_length": 82, "alphanum_fraction": 0.5628415301, "num_tokens": 178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.34605352896925745}} {"text": "# importing all the libraries used in this project \nlibrary('readxl')\nlibrary(psych)\nlibrary(tidyverse)\nlibrary(sqldf) # <- make sure you have this install, inatall.packages('sqldf')\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(cowplot)\nlibrary(e1071) \nlibrary(car)\nlibrary(semTools)\nlibrary(pastecs)\nlibrary(sjstats) \nlibrary(userfriendlyscience)\nlibrary(generalhoslem)\nlibrary(regclass)\nlibrary(lm.beta)\nlibrary(stargazer)\nlibrary(broom)\nlibrary(Epi)\nlibrary(arm)\nlibrary(DescTools)\nlibrary(foreign)\nlibrary(olsrr)\nlibrary('dvmisc')\n\n# make sure the data is in local directory to this script\ndata <- read_excel('data_academic_performance.xlsx')\n# make copy of original dataset\ndf<-data\nnames(df)\n# to summarise the data properally, convert characters to factors\n# df[] <- lapply( df, factor)\ncol_names <- c('GENDER','EDU_FATHER','EDU_MOTHER','OCC_FATHER','OCC_MOTHER','STRATUM','SISBEN','PEOPLE_HOUSE','INTERNET','TV','COMPUTER','WASHING_MCH','MIC_OVEN','CAR','DVD','FRESH','PHONE','MOBILE','REVENUE','JOB','SCHOOL_NAME','SCHOOL_NAT','SCHOOL_TYPE','Cod_SPro','UNIVERSITY','ACADEMIC_PROGRAM')\n# convert character to factors using lapply\ndf[col_names] <- lapply(df[col_names] , factor)\ndf\n# to get statistical measures of the data\nsummary(df)\n\n# selecting only the variables of interest only using sql syntax, make sure to install sqldf\nfilter_data <- sqldf('select GENDER, STRATUM ,EDU_FATHER,EDU_MOTHER,OCC_FATHER,OCC_MOTHER,SISBEN,PEOPLE_HOUSE,INTERNET,TV,COMPUTER,WASHING_MCH,MIC_OVEN,CAR,DVD,PHONE,MOBILE,REVENUE,JOB,\n SCHOOL_NAT,SCHOOL_TYPE,MAT_S11,CR_S11,BIO_S11,ENG_S11,G_SC,CC_S11 FROM df')\n\n\n\nfilter_data\nsummary(filter_data)\n# only categorical data\ncat_data <- sqldf('select GENDER, EDU_FATHER,EDU_MOTHER,OCC_FATHER,OCC_MOTHER,SISBEN,PEOPLE_HOUSE,INTERNET,TV,COMPUTER,WASHING_MCH,MIC_OVEN,CAR,DVD,PHONE,MOBILE,REVENUE,JOB,\n SCHOOL_NAT,SCHOOL_TYPE from df')\n\ncat_data\n\n\n# Categorical data barcharts\nbar_cat_plot_list <- list()\ncol_names <- colnames(cat_data)\nfor(i in col_names){\n # print(i)\n gg <- ggplot(cat_data, aes_string(x = i)) + geom_bar() + theme(axis.text.x = element_text(angle = 45, hjust = 1, size=5))\n bar_cat_plot_list[[i]] <- gg\n} # end of loop\n\nplot_grid(plotlist = bar_cat_plot_list)\n\n\n# find null data, will remove later using dummy variables to isolate these\nsqldf('select count(JOB) from cat_data where JOB == 0')\nsqldf('select count(people_house) from cat_data where people_house == 0')\n\n# continous data only\ncont_data <- sqldf('select MAT_S11,CR_S11,BIO_S11,ENG_S11,CC_S11,G_SC from df')\n# get statistical measure\ndescribe(cont_data)\n\n# find missing value\nsqldf('select count(MAT_S11) from cont_data where MAT_S11 is null')\nsqldf('select count(CR_S11) from cont_data where CR_S11 is null')\nsqldf('select count(BIO_S11) from cont_data where BIO_S11 is null')\nsqldf('select count(ENG_S11) from cont_data where ENG_S11 is null')\nsqldf('select count(G_SC) from cont_data where G_SC is null')\n\n\n# create normal plot\ncol_names <- colnames(cont_data)\ncol_names\n# hold all the plots created in the loop\nplot_list <- list()\ncol_names <- colnames(cont_data)\nfor(i in col_names){\n print(i)\n print(mean(cont_data[,i]))\n gg <- ggplot(cont_data , aes_string(i)) \n gg <- gg + geom_histogram(binwidth=1, colour=\"black\", aes(y=..density.., fill=..count..))\n gg<-gg+scale_fill_gradient(\"Count\", low=\"#DCDCDC\", high=\"#7C7C7C\")\n gg<-gg+stat_function(fun=dnorm, color=\"red\",args=list(mean=mean(cont_data[,i], na.rm=TRUE), sd=sd(cont_data[,i], na.rm=TRUE)))\n plot_list[[i]] <- gg\n} # end of loop\nplot_grid(plotlist = plot_list)\n\n\n#Create boxplots\n# hold all theplots created in the loop\nbox_plot_list <- list()\nfor(i in col_names){\n print(i)\n gg <- ggplot(cont_data, aes_string(y=i)) + geom_boxplot() + theme(text = element_text(size=20))\n box_plot_list[[i]] <- gg\n} # end of loop\n\nplot_grid(plotlist = box_plot_list)\n# qq plot for each numerical data above\nqqnorm(cont_data$MAT_S11 ,main='MAT_S11')+qqline(cont_data$MAT_S11, col=2) #show a line on theplot\nqqnorm(cont_data$CR_S11 ,main='CR_S11')+qqline(cont_data$CR_S11, col=2) #show a line on theplot\nqqnorm(cont_data$BIO_S11 ,main='BIO_S11')+qqline(cont_data$BIO_S11, col=2) #show a line on theplot\nqqnorm(cont_data$ENG_S11 ,main='ENG_S11')+qqline(cont_data$ENG_S11, col=2) #show a line on theplot\nqqnorm(cont_data$CC_S11 ,main='CC_S11')+qqline(cont_data$CC_S11, col=2) #show a line on theplot\nqqnorm(cont_data$G_SC ,main='G_SC')+qqline(cont_data$G_SC, col=2) #show a line on theplot\n\n\n# removig data that is NA,using complete.cases in case i missed finding data with Null values\nhead(filter_data)\nfilter_data<-filter_data[complete.cases(filter_data),]\nfilter_data\n\n\n######################################################################################################### handling missing values #####################################################3\n# make copy\n#clean_data <- filter_data\n#clean_data <- clean_data[!(clean_data$JOB == \"0\"),]\n#clean_data <- clean_data[!(clean_data$PEOPLE_HOUSE == \"0\"),]\n#clean_data <- clean_data[!(clean_data$EDU_FATHER == \"0\"),]\nclean_data\n\n\n# removing outliers\n# removing outliers from maths, first find ranges, i use\nlowerbound <- 56-1.5 * IQR(cont_data$MAT_S11)\nupperbound = 72+1.5 * IQR(cont_data$MAT_S11)\n\n# found a total of 137 outliers, these need to be removed\nfn$sqldf(\"select count(MAT_S11) from clean_data where MAT_S11 <$lowerbound or MAT_S11 > $upperbound\")\nclean_data_v2<-clean_data[!(clean_data$MAT_S11 > upperbound | clean_data$MAT_S11 < lowerbound),]\n\n# removing outliers from critical, first find ranges\nlowerbound <- 54-1.5 * IQR(cont_data$CR_S11)\nupperbound = 67+1.5 * IQR(cont_data$CR_S11)\nfn$sqldf(\"select count(CR_S11) from clean_data_v2 where CR_S11 <$lowerbound or CR_S11 > $upperbound\")\n# found 146 in remaining set outliers\nclean_data_v2<-clean_data_v2[!(clean_data_v2$CR_S11 > upperbound | clean_data_v2$CR_S11 < lowerbound),]\n\n# removing outliers from biology, first find ranges\nlowerbound <- 56-1.5 * IQR(cont_data$BIO_S11)\nupperbound = 71+1.5 * IQR(cont_data$BIO_S11)\nlowerbound\nupperbound\nfn$sqldf(\"select count(BIO_S11) from clean_data_v2 where BIO_S11 <$lowerbound or BIO_S11 > $upperbound\")\n# found 121 in remaining set outliers\nclean_data_v2<-clean_data_v2[!(clean_data_v2$BIO_S11 > upperbound | clean_data_v2$BIO_S11 < lowerbound),]\n\n\n# removing outliers from english, first find ranges\nlowerbound <- 50-1.5 * IQR(cont_data$ENG_S11)\nupperbound = 72+1.5 * IQR(cont_data$ENG_S11)\nlowerbound\nupperbound\nfn$sqldf(\"select count(ENG_S11) from t where ENG_S11 <$lowerbound or ENG_S11 > $upperbound\")\n# found 0 in remaining set outliers\nclean_data_v2<-clean_data_v2[!(clean_data_v2$ENG_S11 > upperbound | clean_data_v2$ENG_S11 < lowerbound),]\n\n\n# removing outliers from CC, first find ranges\nlowerbound <- 54-1.5 * IQR(cont_data$CC_S11)\nupperbound = 67+1.5 * IQR(cont_data$CC_S11)\nlowerbound\nupperbound\nfn$sqldf(\"select count(CC_S11) from t where CC_S11 <$lowerbound or CC_S11 > $upperbound\")\n# found 151 in remaining set outliers\nclean_data_v2<-clean_data_v2[!(clean_data_v2$CC_S11 > upperbound | clean_data_v2$CC_S11 < lowerbound),]\n\n\n# removing outliers from G_SC, first find ranges\nlowerbound <- 147-1.5 * IQR(cont_data$CC_S11)\nupperbound = 179+1.5 * IQR(cont_data$CC_S11)\nlowerbound\nupperbound\nfn$sqldf(\"select count(G_SC) from t where G_SC <$lowerbound or G_SC > $upperbound\")\n# found 1258 in remaining set outliers\nclean_data_v2<-clean_data_v2[!(clean_data_v2$G_SC > upperbound | clean_data_v2$G_SC < lowerbound),]\n\n\n##################################################################################################### Normality testing ################################################33\n# use this to help you pick the numbers\nsummary(cont_data)\n# Getting Kurtosis and skew values of Maths, also standardised score:\nmath_skew <- semTools::skew(clean_data$MAT_S11)\nmath_kurt <- semTools::kurtosis(clean_data$MAT_S11)\n\n# standardise the values\nmath_skew[1]/math_skew[2]\nmath_kurt[1]/math_kurt[2]\n\nmath_score_range<- abs(scale(clean_data$MAT_S11))\n\nFSA::perc(as.numeric(math_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(math_score_range), 3.29, \"gt\") #0%\n\n# trimming maths to see if it reduces skew \ny <- trim(clean_data$MAT_S11, p = 0.05)\n\n# Getting Kurtosis and skew values of Maths, also standardised score:\nmath_skew <- semTools::skew(y)\nmath_kurt <- semTools::kurtosis(y)\n\n# standardise the values\nmath_skew[1]/math_skew[2]\nmath_kurt[1]/math_kurt[2]\n\nmath_score_range<- abs(scale(y))\n\nFSA::perc(as.numeric(math_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(math_score_range), 3.29, \"gt\") #0%\n\n# reduced by 8\n\n# performing same but this time with the removed outliers\ny <- trim(clean_data_v2$MAT_S11, p = 0.05)\n# Getting Kurtosis and skew values of Maths, also standardised score:\nmath_skew <- semTools::skew(y)\nmath_kurt <- semTools::kurtosis(y)\n\n# standardise the values\nmath_skew[1]/math_skew[2]\nmath_kurt[1]/math_kurt[2]\n\nmath_score_range<- abs(scale(y))\n\nFSA::perc(as.numeric(math_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(math_score_range), 3.29, \"gt\") #0%\n\n# Getting Kurtosis and skew values of Maths, also standardised score:\nmath_skew <- semTools::skew(clean_data$MAT_S11)\nmath_kurt <- semTools::kurtosis(clean_data$MAT_S11)\n\n# standardise the values\nmath_skew[1]/math_skew[2]\nmath_kurt[1]/math_kurt[2]\n\nmath_score_range<- abs(scale(clean_data$MAT_S11))\n\nFSA::perc(as.numeric(math_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(math_score_range), 3.29, \"gt\") #0%\n# reduced by another 2\n\n\n\n# Getting Kurtosis and skew values of Critical reading, also standardised score:\nreading_skew <- semTools::skew(clean_data$CR_S11)\nreading_kurt <- semTools::kurtosis(clean_data$CR_S11)\n\n# standardise the values\nreading_skew[1]/reading_skew[2]\nreading_kurt[1]/reading_kurt[2]\n\nreading_score_range<- abs(scale(clean_data$CR_S11))\n\nFSA::perc(as.numeric(reading_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(reading_score_range), 3.29, \"gt\") #0%\n\n\n\n# Getting Kurtosis and skew values of Critical reading, also standardised score:\n# This time removing outliers\nreading_skew <- semTools::skew(clean_data_v2$CR_S11)\nreading_kurt <- semTools::kurtosis(clean_data_v2$CR_S11)\n\n# standardise the values\nreading_skew[1]/reading_skew[2]\nreading_kurt[1]/reading_kurt[2]\n\nreading_score_range<- abs(scale(clean_data_v2$CR_S11))\n\nFSA::perc(as.numeric(reading_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(reading_score_range), 3.29, \"gt\") #0%\n# reduces skew by 7\n\n\n\n# Getting Kurtosis and skew values of Biology, also standardised score:\nbiology_skew <- semTools::skew(clean_data$BIO_S11)\nbiology_kurt <- semTools::kurtosis(clean_data$BIO_S11)\n\n# standardise the values\nbiology_skew[1]/biology_skew[2]\nbiology_kurt[1]/biology_kurt[2]\n\nbiology_score_range<- abs(scale(clean_data$BIO_S11))\n\nFSA::perc(as.numeric(biology_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(biology_score_range), 3.29, \"gt\") #0%\n\n# same as above but this time trimming the values\ny <- trim(clean_data$BIO_S11, p = 0.05)\n# Getting Kurtosis and skew values of Biology, also standardised score:\nbiology_skew <- semTools::skew(y)\nbiology_kurt <- semTools::kurtosis(y)\n\n# standardise the values\nbiology_skew[1]/biology_skew[2]\nbiology_kurt[1]/biology_kurt[2]\n\nbiology_score_range<- abs(scale(y))\n\nFSA::perc(as.numeric(biology_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(biology_score_range), 3.29, \"gt\") #0%\n# reduced skew by 10\n\n\n\n# Getting Kurtosis and skew values of English, also standardised score:\nenglish_skew <- semTools::skew(clean_data$ENG_S11)\nenglish_kurt <- semTools::kurtosis(clean_data$ENG_S11)\n\n# standardise the values\nenglish_skew[1]/english_skew[2]\nenglish_kurt[1]/english_kurt[2]\n\nenglish_score_range<- abs(scale(clean_data$ENG_S11))\n\nFSA::perc(as.numeric(english_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(english_score_range), 3.29, \"gt\") #0%\n\n# performing trim on above\ny <- trim(clean_data$ENG_S11, p = 0.05)\n\n# Getting Kurtosis and skew values of English, also standardised score:\nenglish_skew <- semTools::skew(y)\nenglish_kurt <- semTools::kurtosis(y)\n\n# standardise the values\nenglish_skew[1]/english_skew[2]\nenglish_kurt[1]/english_kurt[2]\n\nenglish_score_range<- abs(scale(y))\n\nFSA::perc(as.numeric(english_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(english_score_range), 3.29, \"gt\") #0%\n\n#reduced by 5\n\n\n# Getting Kurtosis and skew values of Global score, also standardised score:\nglobal_skew <- semTools::skew(clean_data$G_SC)\nglobal_kurt <- semTools::kurtosis(clean_data$G_SC)\n\n# standardise the values\nglobal_skew[1]/global_skew[2]\nglobal_kurt[1]/global_kurt[2]\n\nglobal_score_range<- abs(scale(clean_data$G_SC))\n\nFSA::perc(as.numeric(global_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(global_score_range), 3.29, \"gt\") #0%\n\n# performing trim\ny <- trim(clean_data$G_SC, p = 0.05)\n# Getting Kurtosis and skew values of Global score, also standardised score:\nglobal_skew <- semTools::skew(y)\nglobal_kurt <- semTools::kurtosis(y)\n\n# standardise the values\nglobal_skew[1]/global_skew[2]\nglobal_kurt[1]/global_kurt[2]\n\nglobal_score_range<- abs(scale(y))\n\nFSA::perc(as.numeric(global_score_range), 1.96, \"gt\")\nFSA::perc(as.numeric(global_score_range), 3.29, \"gt\") #0%\n\n# reduced by 2\n\n################################################################################Correlation testing#############################################################333\n\n#Scatterplot relationship, G_SC and MAT_S11\nscatter <- ggplot(clean_data, aes(clean_data$MAT_S11, clean_data$G_SC))\n\n#Add a regression line\nscatter + geom_point() + geom_smooth(method = \"lm\", colour = \"Red\", se = F) + labs(x = \"Maths scores(MAT_S11)\", y = \"Global score(G_SC)\") \n# Pearson Maths\nstats::cor.test(clean_data$G_SC, clean_data$MAT_S11, method='pearson')\n\n\n\n#Scatterplot relationship, G_SC and CR_S11\nscatter <- ggplot(clean_data, aes(clean_data$CR_S11, clean_data$G_SC))\n\n#Add a regression line\nscatter + geom_point() + geom_smooth(method = \"lm\", colour = \"Red\", se = F) + labs(x = \"Creative Reading scores(CR_S11)\", y = \"Global score(G_SC)\") \n# Pearson test Creative reading\nstats::cor.test(clean_data$G_SC, clean_data$CR_S11, method='pearson')\n# statistically significant result\n\n\n\n\n#Scatterplot relationship, G_SC and BIO_S11\nscatter <- ggplot(clean_data, aes(clean_data$BIO_S11, clean_data$G_SC))\n\n#Add a regression line\nscatter + geom_point() + geom_smooth(method = \"lm\", colour = \"Red\", se = F) + labs(x = \"Biology scores(BIO_S11)\", y = \"Global score(G_SC)\") \n\n\n# Pearson test Biology\nstats::cor.test(clean_data$G_SC, clean_data$BIO_S11, method='pearson')\n\n\n#Scatterplot relationship, G_SC and ENG_S11\nscatter <- ggplot(clean_data, aes(clean_data$ENG_S11, clean_data$G_SC))\n\n#Add a regression line\nscatter + geom_point() + geom_smooth(method = \"lm\", colour = \"Red\", se = F) + labs(x = \"English(ENG_S11)\", y = \"Global score(G_SC)\") \n# Pearsons Test\nstats::cor.test(clean_data$G_SC, clean_data$ENG_S11, method='pearson')\n\n\n\n############################################################################ T-test ############################################################################\n# global grade and gender\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$GENDER, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ GENDER, data=clean_data)\n\n# Levene's test showed unequal variance, therefore need to perform welsh modification to the t-test (will be rejected)\n# therefore set var.equal to false\n# Perfomring the T-test\nstats::t.test(G_SC~GENDER,var.equal=FALSE,data=clean_data)\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~GENDER,var.equal=FALSE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n\n\n# global grade and Internet \n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$INTERNET, mat=TRUE)\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ INTERNET, data=clean_data)\n\n\n\n# Levene's test showed unequal variance, therefore need to perform welsh modification to the t-test (wull be rejected)\n# therefore set var.equal to false\n# Perfomring the T-test\nstats::t.test(G_SC~INTERNET,var.equal=FALSE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~INTERNET,var.equal=FALSE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n# global grade and tv \n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$TV, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ TV, data=clean_data)\n\n# Levene's \n# therefore set var.equal to true\n# Perfomring the T-test\nstats::t.test(G_SC~TV,var.equal=TRUE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~TV,var.equal=TRUE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n\n# global grade and computer (will be rejected, failed levene test)\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$COMPUTER, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ COMPUTER, data=clean_data)\n\n\n\nstats::t.test(G_SC~COMPUTER,var.equal=FALSE,data=clean_data)\n\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~COMPUTER,var.equal=FALSE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n# global grade and washing machine \n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$WASHING_MCH, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ WASHING_MCH, data=clean_data)\n\nstats::t.test(G_SC~WASHING_MCH,var.equal=TRUE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~WASHING_MCH,var.equal=TRUE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n# global grade and car\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$CAR, mat=TRUE)\n\n\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ CAR, data=clean_data)\n\n\n\n\nstats::t.test(G_SC~CAR,var.equal=TRUE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~CAR,var.equal=TRUE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n# global grade and PHONE\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$PHONE, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ PHONE, data=clean_data)\n\n\n\n\nstats::t.test(G_SC~PHONE,var.equal=TRUE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~PHONE,var.equal=TRUE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n# global grade and MOBILE\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$MOBILE, mat=TRUE)\n\n\n\n# Using levene's test to test variance, pvalue needs to be greater than 0.05\ncar::leveneTest(G_SC ~ MOBILE, data=clean_data)\n\n\nstats::t.test(G_SC~MOBILE,var.equal=TRUE,data=clean_data)\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~MOBILE,var.equal=TRUE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n\n# testing on school nature\n# Describe the variables\npsych::describeBy(clean_data$G_SC, clean_data$SCHOOL_NAT, mat=TRUE)\n\n\n\ncar::leveneTest(G_SC ~ SCHOOL_NAT, data=clean_data)\n\n\nstats::t.test(G_SC~SCHOOL_NAT,var.equal=FALSE,data=clean_data)\n\n\n\n\n# Performing Cohen's d\nres <- stats::t.test(G_SC~SCHOOL_NAT,var.equal=FALSE,data=clean_data)\neffcd=round((2*res$statistic)/sqrt(res$parameter),2)\neffectsize::t_to_d(t = res$statistic, res$parameter)\n\n\n\n#################################################################################### Anova tests# ################################################33\n\ncolnames(clean_data)\n\nclean_data\n\n# Anova test for fathers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$EDU_FATHER, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ EDU_FATHER, data=clean_data)\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$EDU_FATHER),y=clean_data$G_SC,posthoc='games-howell')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ EDU_FATHER, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$EDU_MOTHER, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ EDU_MOTHER, data=clean_data)\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$EDU_MOTHER),y=clean_data$G_SC,posthoc='games-howell')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ EDU_MOTHER, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$OCC_FATHER, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ OCC_FATHER, data=clean_data)\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$OCC_FATHER),y=clean_data$G_SC,posthoc='games-howell')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ OCC_FATHER, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$OCC_MOTHER, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ OCC_MOTHER, data=clean_data)\n\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$OCC_MOTHER),y=clean_data$G_SC,posthoc='games-howell')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ OCC_MOTHER, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$PEOPLE_HOUSE, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ PEOPLE_HOUSE, data=clean_data)\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$PEOPLE_HOUSE),y=clean_data$G_SC,posthoc='Tukey')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ PEOPLE_HOUSE, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$REVENUE, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ REVENUE, data=clean_data)\n\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$REVENUE),y=clean_data$G_SC,posthoc='Tukey')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ REVENUE, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$SCHOOL_NAT, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ SCHOOL_NAT, data=clean_data)\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$SCHOOL_NAT),y=clean_data$G_SC,posthoc='Tukey')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ SCHOOL_NAT, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n\n# Anova test for mothers education and global grade \n# Check the statistical description of variable of interest\npsych::describeBy(clean_data$G_SC, clean_data$SCHOOL_TYPE, mat=TRUE)\n# performing Barrets test for homogenity of variance\nstats::bartlett.test(G_SC~ SCHOOL_TYPE, data=clean_data)\n\n\n\n\n\n# One-way Anova test \nanova_result<-userfriendlyscience::oneway(as.factor(clean_data$SCHOOL_TYPE),y=clean_data$G_SC,posthoc='games-howell')\nanova_result\n# access values in order to get f-statisitc on nect step\nres2<-stats::aov(G_SC~ SCHOOL_TYPE, data = clean_data)\nfstat<-summary(res2)[[1]][[\"F value\"]][[1]]\nfstat\n# Get the p-value\nanova_p_value<-summary(res2)[[1]][[\"Pr(>F)\"]][[1]]\nanova_p_value\n# Calculating the effect\naoveta<-sjstats::eta_sq(res2)[2]\naoveta\n\n\n\n####################################################################Building MLR#############################################################\n\n\n# making dummy variables to represent the categorical values. Starting with the simple 2 group variables\n# Gender\nclean_data_v2$dummyGender = ifelse(clean_data_v2$GENDER == \"M\", 0, ifelse(clean_data_v2$GENDER == \"F\", 1, NA))\n# internet\nclean_data_v2$dummyInternet = ifelse(clean_data_v2$INTERNET == \"Yes\", 0, ifelse(clean_data_v2$INTERNET == \"No\", 1, NA))\n# TV\nclean_data_v2$dummyTV = ifelse(clean_data_v2$TV == \"Yes\", 0, ifelse(clean_data_v2$TV == \"No\", 1, NA))\n# COMPUTER\nclean_data_v2$dummyComputer = ifelse(clean_data_v2$COMPUTER == \"Yes\", 0, ifelse(clean_data_v2$COMPUTER == \"No\", 1, NA))\n# WASHING_MACHINE\nclean_data_v2$dummyWmachine = ifelse(clean_data_v2$WASHING_MCH== \"Yes\", 0, ifelse(clean_data_v2$WASHING_MCH == \"No\", 1, NA))\n# MIC_OVEN\nclean_data_v2$dummyMicOven = ifelse(clean_data_v2$MIC_OVEN == \"Yes\", 0, ifelse(clean_data_v2$MIC_OVEN == \"No\", 1, NA))\n# Car\nclean_data_v2$dummyCar = ifelse(clean_data_v2$CAR == \"Yes\", 0, ifelse(clean_data_v2$CAR == \"No\", 1, NA))\n# DVD\nclean_data_v2$dummyDvd = ifelse(clean_data_v2$DVD == \"Yes\", 0, ifelse(clean_data_v2$DVD == \"No\", 1, NA))\n# PHONE\nclean_data_v2$dummyPhone = ifelse(clean_data_v2$PHONE == \"Yes\", 0, ifelse(clean_data_v2$PHONE == \"No\", 1, NA))\n# MOBILE\nclean_data_v2$dummyMobile = ifelse(clean_data_v2$MOBILE == \"Yes\", 0, ifelse(clean_data_v2$MOBILE == \"No\", 1, NA))\n# SCHOOL_NAT\nclean_data_v2$dummySchoolN = ifelse(clean_data_v2$SCHOOL_NAT == \"PRIVATE\", 0, ifelse(clean_data_v2$SCHOOL_NAT == \"PUBLIC\", 1, NA))\n\n\n# dummy data for school type\nclean_data_v2$dummySchoolAca= ifelse(clean_data_v2$SCHOOL_TYPE == \"ACADEMIC\", 1,0)\nclean_data_v2$dummySchoolTech= ifelse(clean_data_v2$SCHOOL_TYPE == \"TECHNICAL\", 1,0)\nclean_data_v2$dummySchoolTechAca = ifelse(clean_data_v2$SCHOOL_TYPE == \"TECHNICAL/ACADEMIC\", 1,0)\n\n#dummy data for job\nclean_data_v2$dummyJobNo= ifelse(clean_data_v2$JOB == \"No\", 1,0)\nclean_data_v2$dummyJobPT= ifelse(clean_data_v2$JOB == \"Yes, less than 20 hours per week\", 1,0)\nclean_data_v2$dummyJobFT= ifelse(clean_data_v2$JOB == \"Yes, 20 hours or more per week\", 1,0)\n\n\n# dummy data for revenue\nclean_data_v2$dummyRevenue1 = ifelse(clean_data_v2$REVENUE == \"less than 1 LMMW\", 1,\n ifelse(clean_data$REVENUE == \"Between 1 and less than 2 LMMW\", 1,0)) \n\nclean_data_v2$dummyRevenue2 = ifelse(clean_data_v2$REVENUE == \"Between 2 and less than 3 LMMW\", 1,\n ifelse(clean_data$REVENUE == \"Between 3 and less than 5 LMMW\", 1,0)) \n\nclean_data_v2$dummyRevenue3 = ifelse(clean_data_v2$REVENUE == \"Between 5 and less than 7 LMMW\", 1,\n ifelse(clean_data_v2$REVENUE == \"Between 7 and less than 10 LMMW\", 1,0)) \n\n\n# adding extra dummy variables\nclean_data_v2$dummyFatherOCSmallEnt = ifelse(clean_data_v2$OCC_FATHER == \"Small entrepreneur\", 1,0)\nclean_data_v2$dummyFatherOCTechOrProf = ifelse(clean_data_v2$OCC_FATHER == \"Technical or professional level employee\", 1,0)\nclean_data_v2$dummyFatherOCOperator = ifelse(clean_data_v2$OCC_FATHER == \"Operator\", 1,0)\nclean_data_v2$dummyFatherOCOther = ifelse(clean_data_v2$OCC_FATHER == \"Other occupation\", 1,0)\nclean_data_v2$dummyFatherOCIndi = ifelse(clean_data_v2$OCC_FATHER == \"Independent\", 1,0)\nclean_data_v2$dummyFatherOCENT = ifelse(clean_data_v2$OCC_FATHER == \"Entrepreneur\", 1,0)\n\n\n\n\n# adding extra dummies\nclean_data_v2$dummyEDUMotherIncProfEdu = ifelse(clean_data_v2$EDU_MOTHER == \"Incomplete Professional Education\", 1,0)\nclean_data_v2$dummyEDUMotherPostGrad = ifelse(clean_data_v2$EDU_MOTHER == \"Postgraduate education\", 1,0)\nclean_data_v2$dummyEDUMotherINCtech = ifelse(clean_data_v2$EDU_MOTHER == \"Incomplete technical or technological\", 1,0)\nclean_data_v2$dummyEDUMotherCompProfEdu = ifelse(clean_data_v2$EDU_MOTHER == \"Complete professional education\", 1,0)\n\n\n\n# adding extra dummies\nclean_data_v2$dummySTtech = ifelse(clean_data_v2$SCHOOL_TYPE == \"TECHNICAL\", 1,0)\nclean_data_v2$dummySTtechAca = ifelse(clean_data_v2$SCHOOL_TYPE == \"TECHNICAL/ACADEMIC\", 1,0)\n\n\n# dummy variable for Father significant job\nclean_data_v2$dummyFather = ifelse(clean_data_v2$OCC_FATHER == \"Entrepreneur\", 1,0) \n\n\n# dummy variable for mother significant job\nclean_data_v2$dummyMOTHER = ifelse(clean_data_v2$EDU_MOTHER == \"Ninguno\", 1,0) \n\n# dummy data for more than one group\n# people house\nclean_data_v2$dummyPhouseOne = ifelse(clean_data_v2$PEOPLE_HOUSE == \"One\", 1,ifelse(clean_data_v2$PEOPLE_HOUSE == \"Once\", 1,0) ) \nclean_data_v2$dummyPhouse2t3 = ifelse(clean_data_v2$PEOPLE_HOUSE == \"Two\", 1,ifelse(clean_data_v2$PEOPLE_HOUSE == \"Three\", 1,0)) \nclean_data_v2$dummyPhouseAbove3 = ifelse(clean_data_v2$PEOPLE_HOUSE == \"Four\", 1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Five\", 1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Six\", 1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Seven\",1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Eight\",1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Nueve\",1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Ten\",1,\n ifelse(clean_data_v2$PEOPLE_HOUSE == \"Twelve or more\",1,0)))))))) \n#\n# building the model baseline with all the variables of interest\ncolnames(clean_data_v2)\n\n\n\n\n############################################### model 1 ################################################\nbaseline_model1<-lm(formula = G_SC ~ MAT_S11 + CR_S11+BIO_S11+ENG_S11+CC_S11+OCC_FATHER+OCC_MOTHER+EDU_FATHER+EDU_MOTHER+dummyJobNo+dummyJobPT+dummyJobFT+SCHOOL_TYPE+\n dummyGender+dummyInternet+dummyTV+dummyComputer+dummyWmachine+dummyMicOven+\n dummyCar+dummyDvd+dummyPhone+dummyMobile+dummySchoolN, data= clean_data_v2)\n\nprint(\"Anova test\")\nanova(baseline_model1)\nprint(\"Summary of model\")\nsummary(baseline_model1)\nprint(\"model Info comparison\")\nstargazer(baseline_model1, type=\"text\") #Tidy output of all the required stats\nprint(\"Beta values of the model\")\nlm.beta(baseline_model1)\n\n\n# check for influencial outliers\ncooksd<-sort(cooks.distance(baseline_model1))\n# plotting the cooks model\nplot(cooksd, pch=\"*\", cex=2, main=\"Influential Obs by Cooks distance\") \nabline(h = 4*mean(cooksd, na.rm=T), col=\"red\") # add cutoff line\ntext(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),\"\"), col=\"red\") # add labels\n\n\n# find the rows that are influential to observation\ninfluential <- as.numeric(names(cooksd)[(cooksd > 4*mean(cooksd, na.rm=T))]) # influential row numbers\nstem(influential)\n\n\n# Bonferonni p-value for most extreme obs\ncar::outlierTest(baseline_model1)\n\n\n#Assess homocedasticity \nplot(baseline_model1,1)\nplot(baseline_model1, 3)\n\n#Create histogram and density plot of the residuals\nplot(density(resid(baseline_model1))) \n\n\n#Create a QQ plotqqPlot(model, main=\"QQ Plot\") #qq plot for studentized resid \ncar::qqPlot(baseline_model1, main=\"QQ Plot\") #qq plot for studentized resid\n\n\n# #Calculate Collinearity, will not run as you have na's in summary\nvifmodel<-car::vif(baseline_model1)\nvifmodel\n#Calculate tolerance\n1/vifmodel\n\n\n\n# better way of getting standardized residuals\nmax(stdres(baseline_model1))\nmin(stdres(baseline_model1))\n\n\n\n####################################################################################### Model 2 #############################################################\n\nbaseline_model12<-lm(formula = G_SC ~ MAT_S11 + CR_S11+BIO_S11+ENG_S11+CC_S11\n +dummyInternet+dummyWmachine+dummyMicOven+dummyCar+dummyMobile, data= clean_data_v2)\n\n\nprint(\"Anova test\")\nanova(baseline_model2)\nprint(\"Summary of model\")\nsummary(baseline_model2)\nprint(\"model Info comparison\")\nstargazer(baseline_model2, type=\"text\") #Tidy output of all the required stats\nprint(\"Beta values of the model\")\nlm.beta(baseline_model2)\n\n\n\n# get residuals\nmax(stdres(baseline_model2))\nmin(stdres(baseline_model2))\n\n\n\n# check for influencial outliers\ncooksd<-sort(cooks.distance(baseline_model2))\n# plotting the cooks model\nplot(cooksd, pch=\"*\", cex=2, main=\"Influential Obs by Cooks distance\") \nabline(h = 4*mean(cooksd, na.rm=T), col=\"red\") # add cutoff line\ntext(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),\"\"), col=\"red\") # add labels\n\n\n#Create a QQ plotqqPlot(model, main=\"QQ Plot\") #qq plot for studentized resid \ncar::qqPlot(baseline_model2, main=\"QQ Plot\") #qq plot for studentized resid\n\n\n#Assess homocedasticity \nplot(baseline_model2,1)\nplot(baseline_model2, 3)\n\n\n\n#Create histogram and density plot of the residuals\nplot(density(resid(baseline_model2))) \n\n\n# #Calculate Collinearity, will not run as you have na's in summary\nvifmodel<-car::vif(baseline_model2)\nvifmodel\n#Calculate tolerance\n1/vifmodel\n\n\n\n\n\n############################################################ model 3, selected model #################################################\nbaseline_model7<-lm(formula = G_SC ~ dummyFatherOCSmallEnt+dummyFatherOCTechOrProf+dummyFatherOCOperator\n +dummyFatherOCOther+dummyFatherOCIndi+dummyFatherOCENT+dummyEDUMotherIncProfEdu\n +dummyEDUMotherPostGrad+dummyEDUMotherINCtech+dummyEDUMotherCompProfEdu\n +dummySTtech+dummySTtechAca+dummyMicOven+dummyMobile+dummySchoolN\n +dummyGender+dummyInternet, data= clean_data_v2)\n\n# get residuals \nmax(stdres(baseline_model7))\nmin(stdres(baseline_model7))\n\n\n\nprint(\"Anova test\")\nanova(baseline_model7)\nprint(\"Summary of model\")\nsummary(baseline_model7)\nprint(\"model Info comparison\")\nstargazer(baseline_model7, type=\"text\") #Tidy output of all the required stats\nprint(\"Beta values of the model\")\nlm.beta(baseline_model7)\n\n\n\n\n# check for influencial outliers\ncooksd<-sort(cooks.distance(baseline_model7))\n# plotting the cooks model\nplot(cooksd, pch=\"*\", cex=2, main=\"Influential Obs by Cooks distance\") \nabline(h = 4*mean(cooksd, na.rm=T), col=\"red\") # add cutoff line\ntext(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),\"\"), col=\"red\") # add labels\n\n\n\nplot(baseline_model7,1)\nplot(baseline_model7, 3)\n\n\ncar::qqPlot(baseline_model7, main=\"QQ Plot\") \n\n\nplot(density(resid(baseline_model7))) \n\n\n# #Calculate Collinearity, will not run as you have na's in summary\nvifmodel<-car::vif(baseline_model7)\nvifmodel\n#Calculate tolerance\n1/vifmodel", "meta": {"hexsha": "257bf945af9a933e31dad8202944fd03ea186a0c", "size": 36920, "ext": "r", "lang": "R", "max_stars_repo_path": "Submission/submission.r", "max_stars_repo_name": "ValentinCiceu/PSI_Independent_research", "max_stars_repo_head_hexsha": "f48e7dc312c68c52cbab36022ee4d758ead6aa94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Submission/submission.r", "max_issues_repo_name": "ValentinCiceu/PSI_Independent_research", "max_issues_repo_head_hexsha": "f48e7dc312c68c52cbab36022ee4d758ead6aa94", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Submission/submission.r", "max_forks_repo_name": "ValentinCiceu/PSI_Independent_research", "max_forks_repo_head_hexsha": "f48e7dc312c68c52cbab36022ee4d758ead6aa94", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.747714808, "max_line_length": 299, "alphanum_fraction": 0.7235915493, "num_tokens": 10927, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.6513548714339144, "lm_q1q2_score": 0.34600582276648184}} {"text": "setwd(\"/Users/erhv/Data_Sets/Fuel Economy/1985-1997\")\nlibrary(reshape)\noptions(stringsAsFactors = FALSE)\n\n#FWF reported in data formats----------------------------------------------------------------------\nwidths <- c(4,1,2,3,28,4,2,6,2,12,6,3,5,5,32,1,6,5,5,5,5,4,6,8,10,10,10,10,10,10,1,4,3,2,15)\npaths <- dir(pattern = \"\\\\.DAT$\")\npaths <- paths[paths != \"95MFGUI.DAT\"]\n\n\n#read in data files--------------------------------------------------------------------------------\nd1995 <- read.fwf(file=\"95MFGUI.DAT\", widths=c(12,4,1,2,3,28,4,2,6,2,12,6,3,5,5,32,1,6,5,5,5,5,4,6,8,10,10,10,10,10,10,1,4,3,2,15))\nstr(d1995)\nd1995$V1<- NULL\nwrite.table(d1995, file=\"1995.CSV\",sep=\",\", col.names=FALSE, row.names=FALSE)\nd1995 <- read.csv(file=\"1995.csv\")\nfiles <- lapply(paths, read.fwf, width=widths, as.is = TRUE)\n\n\n# rbind(files[[1]], files[[2]], files[[3]], )---------------------------------------------------\ndata <- do.call(rbind, files)\n\n\n#Rename names of d1995 to match names of files----------------------------------------------------\ndatanames<- names(data)\nnames(d1995) <- datanames\n\n\n# Combine d1995 and data---------------------------------------------------------------------------\nmpg <- rbind(data, d1995)\ncolmap<- read.csv(file=\"col-map.csv\", header=TRUE)\n\n\n# Rename the names of mpg to match names of data frame create by Hadley for the years 1998-2007 ---\ncolmap<- read.csv(file=\"col-map.csv\", header=TRUE)\nnames(mpg) <- colmap$ABBR\n\n\n# Strip out extra whitespace on character columns--------------------------------------------------\nnakedmpg<- lapply(mpg, function(x) gsub(\"^ +| +$\", \"\", x))\nnakedmpg<- as.data.frame(nakedmpg)\n\n\n#Create Guzzler Vector-----------------------------------------------------------------------------\nnakedmpg$G <- F\nnakedmpg$G[grep(\"GUZZLER\", nakedmpg$eng.dscr.1)] <- T\nnakedmpg$G[grep(\"GUZZLER\", nakedmpg$eng.dscr.2)] <- T\nnakedmpg$G[grep(\"GUZZLER\", nakedmpg$eng.dscr.3)] <- T\ntable(nakedmpg$G, exclude=NULL)\n\n\n#Create TURBO Vector: for different years turbocharged was entered as TRBO, TURBO, and TC*--------\nnakedmpg$T <- F\nnakedmpg$T[grep(\"TRBO\", nakedmpg$eng.dscr.1)] <- T\nnakedmpg$T[grep(\"TRBO\", nakedmpg$eng.dscr.2)] <- T\nnakedmpg$T[grep(\"TRBO\", nakedmpg$eng.dscr.3)] <- T\n\nnakedmpg$T[grep(\"TURBO\", nakedmpg$eng.dscr.1)] <- T\nnakedmpg$T[grep(\"TURBO\", nakedmpg$eng.dscr.2)] <- T\nnakedmpg$T[grep(\"TURBO\", nakedmpg$eng.dscr.3)] <- T\n\nnakedmpg$T[grep(\"TC*\", nakedmpg$eng.dscr.1)] <- T\nnakedmpg$T[grep(\"TC*\", nakedmpg$eng.dscr.2)] <- T\nnakedmpg$T[grep(\"TC*\", nakedmpg$eng.dscr.3)] <- T\ntable(nakedmpg$T, exclude=NULL)\n\n\n#Clean up and recreate class variable--------------------------------------------------------------\ntable(nakedmpg$year, nakedmpg$class)\nclassname <- read.csv(file=\"classname.csv\")\nnames(nakedmpg)[3] <- \"cls\"\nnakedmpg <- merge(nakedmpg, classname,by=\"cls\")\n\n# Convert old mpg to new (post-2008) mpg ----------------------------------------------------------\nnakedmpg$year <- as.numeric(nakedmpg$year)\nnakedmpg$cty <- as.numeric(nakedmpg$cty)\nnakedmpg$hwy <- as.numeric(nakedmpg$hwy)\n\nnew_city <- function(mpg, year) {\n ifelse(year < 2008, round(1 / (0.003259 + (1.1805 / round(mpg / 0.9)))), mpg)\n}\nnew_highway <- function(mpg, year) {\n ifelse(year < 2008, round(1 / (0.001376 + (1.3466 / round(mpg / 0.78)))), mpg)\n}\n\nnakedmpg <- transform(nakedmpg,\n cty = new_city(cty, year),\n hwy = new_highway(hwy, year)\n)\n\n\n#remove uneeded and inconsistent variables---------------------------------------------------------\nnakedmpg$cmb <- NULL\nnakedmpg$cls <- NULL\nnakedmpg$ucty <- NULL\nnakedmpg$uhwy <- NULL\nnakedmpg$ucmb <- NULL\nnakedmpg$carline.Manufacturer.code <- NULL\nnakedmpg$state.code <- NULL\nnakedmpg$vi.Manufacturer.code <- NULL\nnakedmpg$date <- NULL\nnakedmpg$r.date <- NULL\nnakedmpg$pr <- NULL\nnakedmpg$b.eng.id <- NULL\nnakedmpg$sc <- NULL\nnakedmpg$c <- NULL\nnakedmpg$fl.system <- NULL\nnakedmpg$fcost<- NULL\nnakedmpg$opt.disp <- NULL\nnakedmpg$od <- NULL\n\nnakedmpg$S <- NA\nnakedmpg$bidx <- NA\nnakedmpg$x5c <- NA\nnakedmpg$vpc <- NA\nnakedmpg$trans.code.1 <- NA\nnakedmpg$trans.code.2 <- NA\nnakedmpg$sale.mtot <- NA\n\n#Rename m.trans into trans new format\nnakedmpg$m.trans<- gsub(\"A3\", \"auto(3)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"A4\", \"auto(4)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"A5\", \"auto(5)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"AV\", \"auto(V)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M3\", \"manual(m3)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M3/M4C\", \"manual(m3/m4C)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M4\", \"manual(m4)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M4C\", \"manual(m4C)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M5\", \"manual(m5)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M5C\", \"manual(m5C)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"M6\", \"manual(m6)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"S4\", \"semi-automatic(s4)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"S5\", \"semi-automatic(s5)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"C5\", \"creeper(C5)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"L3\", \"lock-up(s3)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"L4\", \"lock-up(s4)\",nakedmpg$m.trans)\nnakedmpg$m.trans<- gsub(\"L5\", \"lock-up(s5)\",nakedmpg$m.trans)\nnames(nakedmpg) [4] <- \"trans\"\nnames(nakedmpg) [9] <- \"eng.dscr\"\nnames(nakedmpg) [10] <- \"eng.dscr.1\"\nnames(nakedmpg) [11] <- \"eng.dscr.2\"\n\nnakedmpg$bt.2 <- as.character(nakedmpg$bt.2)\nnakedmpg$bt.4 <- as.character(nakedmpg$bt.4)\nnakedmpg$bt.hbk <- as.character(nakedmpg$bt.hbk)\n\nnakedmpg$twodhpv <- substr(nakedmpg$bt.2,6,7)\nnakedmpg$twolpv <- substr(nakedmpg$bt.2,9,10)\nnakedmpg$fourhpv<- substr(nakedmpg$bt.4,6,7)\nnakedmpg$fourlpv<- substr(nakedmpg$bt.4,9,10)\nnakedmpg$hbkhpv<- substr(nakedmpg$bt.hbk,6,7)\nnakedmpg$hbklpv<- substr(nakedmpg$bt.hbk,9,10)\n\nnames(nakedmpg) [25] <- \"twodoor.p\"\nnames(nakedmpg) [26] <- \"twodoor.l\"\nnames(nakedmpg) [27] <- \"fourdoor.p\"\nnames(nakedmpg) [28] <- \"fourdoor.l\"\nnames(nakedmpg) [29] <- \"hatch.p\"\nnames(nakedmpg) [30] <- \"hatch.l\"\n\nnakedmpg$bt.2 <- NULL\nnakedmpg$bt.4 <- NULL\nnakedmpg$bt.hbk <- NULL\n\nnames(nakedmpg) [2] <- \"model\"\nnakedmpg$hbkhpv <- NULL\nnakedmpg$hbklpv <- NULL\nnakedmpg$fourlpv <- NULL\n\n\n#Re-order the variables by year, ------------------------------------------------------------------\nnakedmpg <- nakedmpg[order(nakedmpg[,\"manufacturer\"]),]\nnakedmpg <- nakedmpg[order(nakedmpg[,\"carline.name\"]),]\nnakedmpg <- nakedmpg[order(nakedmpg[,\"class\"]),]\nnakedmpg <- nakedmpg[order(nakedmpg[,\"year\"]),]\n\n\n# Save as csv--------------------------------------------------------------------------------------\nwrite.table(nakedmpg, file=\"clean85-97.csv\",sep=\",\", col.names=TRUE, row.names=FALSE)\n \n\n\n\n\n#other code fit it in later to tell the story\n\n\n", "meta": {"hexsha": "16b40743569dea2c440478be24e31e8f5e20fcb0", "size": 6684, "ext": "r", "lang": "R", "max_stars_repo_path": "1985-1997/dat.r", "max_stars_repo_name": "hadley/data-fuel-economy", "max_stars_repo_head_hexsha": "7464a2defe7dd3ae84957385fa6e4f962b54207b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2015-02-03T17:35:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-04T15:32:01.000Z", "max_issues_repo_path": "1985-1997/dat.r", "max_issues_repo_name": "pparvina/data-fuel-economy", "max_issues_repo_head_hexsha": "7464a2defe7dd3ae84957385fa6e4f962b54207b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1985-1997/dat.r", "max_forks_repo_name": "pparvina/data-fuel-economy", "max_forks_repo_head_hexsha": "7464a2defe7dd3ae84957385fa6e4f962b54207b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 43, "max_forks_repo_forks_event_min_datetime": "2015-01-22T06:08:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-11T03:29:13.000Z", "avg_line_length": 35.5531914894, "max_line_length": 131, "alphanum_fraction": 0.6080191502, "num_tokens": 2413, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548782017745, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.346005816693504}} {"text": "library(tidyverse)\n\nd <- read_csv(\"data.csv\")\nd2 <- read_csv(\"data2.csv\")\nd3 <- read_csv(\"data3.csv\")\n\nd4 <- inner_join(d, d2, by = \"sp\") %>%\n inner_join(., d3)\n\nres <- lm(a ~ d, d4)\n\np <- ggplot(d4, aes(x = a, y = d)) +\n geom_point()\n\nggsave(\"fig1.png\", p)\n\nsave.image(\"fit.rda\")\n", "meta": {"hexsha": "1edad46620a45c0028bfd618f17bd9dce26d0308", "size": 283, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis.r", "max_stars_repo_name": "mattocci27/makeR", "max_stars_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis.r", "max_issues_repo_name": "mattocci27/makeR", "max_issues_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis.r", "max_forks_repo_name": "mattocci27/makeR", "max_forks_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.7222222222, "max_line_length": 38, "alphanum_fraction": 0.5759717314, "num_tokens": 106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548511303338, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3460058023129009}} {"text": "a<-read.table(\"diag-gene.txt\",header=F)\nb<-t(a)\nclass(b)\nb1<-as.vector(b)\nclass(b1)\nc<-diag(b1)\ndim(c)\nwrite.table(c,\"D-gene.txt\",sep=\"\\t\",row.names=F,col.names=F)\n\nn<-read.table(\"diag-mRNA.txt\",header=F)\nm<-t(n)\nclass(m)\nm1<-as.vector(m)\nclass(m1)\ny<-diag(m1)\ndim(y)\nwrite.table(y,\"D-mRNA.txt\",sep=\"\\t\",row.names=F,col.names=F)\n\nn<-read.table(\"diag-STRING.txt\",header=F)\nm<-t(n)\nclass(m)\nm1<-as.vector(m)\nclass(m1)\ny<-diag(m1)\ndim(y)\nwrite.table(y,\"D-STRING.txt\",sep=\"\\t\",row.names=F,col.names=F)\n\na<-read.table(\"diag-pearson.txt\",header=F)\nb<-t(a)\nclass(b)\nb1<-as.vector(b)\nclass(b1)\nc<-diag(b1)\ndim(c)\nwrite.table(c,\"D-pearson.txt\",sep=\"\\t\",row.names=F,col.names=F)\n", "meta": {"hexsha": "be1b6eaa8fe201aa7572f74b13ac6809365ab68e", "size": 669, "ext": "r", "lang": "R", "max_stars_repo_path": "source/diag.r", "max_stars_repo_name": "Crystal-JJ/maize", "max_stars_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-03-06T04:40:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-06T04:40:19.000Z", "max_issues_repo_path": "source/diag.r", "max_issues_repo_name": "Crystal-JJ/maize", "max_issues_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/diag.r", "max_forks_repo_name": "Crystal-JJ/maize", "max_forks_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5833333333, "max_line_length": 63, "alphanum_fraction": 0.6547085202, "num_tokens": 246, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3458206285736102}} {"text": "library(tidyverse)\n\n##############################################################################\n# Experiment 1 data\n\nnoise_exp <- read.csv(\"./outputs/simuls_nips/individual_spatial/scores.csv\",\n nrows = 6, header = F)\nfor (ii in c(2, 3, 4, 5, 6, 1)){\n noise_exp[ii,] <- noise_exp[ii,] / noise_exp[1,]\n}\nnoise_exp <- noise_exp[2:6,] %>%\n gather(key = \"estimator\", value = \"score\")\n\n# Identity + logDiag\n# Supervised + Logdiag\n# Identity + Wasserstein\n# Unsupervised + Geom\n# Supervised + Geom\n\nestimator_levels <- c(\"log-diag\", \"sup. log-diag\", \"Wasserstein\", \"geometric\",\n \"sup. geometric\")\nestimator <- estimator_levels %>% factor(levels = estimator_levels)\nnoise_exp$estimator <- rep(estimator, times = 10)\n\nnoises <- read.csv(\"./outputs/simuls_nips/individual_spatial/noises_A.csv\", header = F)\nnoise_exp$xaxis <- rep(noises[[\"V1\"]], each = 5)\nis_supervised <- noise_exp$estimator %>% grepl(\"sup\", .)\nnoise_exp$supervised <- ifelse(is_supervised, \"sup\", \"unsup\") %>%\n as.factor()\n\ncolor_cats <- c(\n \"#009D79\",# blueish green\n \"#009D79\",# blueish green\n \"#E36C2F\", #vermillon\n \"#EEA535\", # orange\n \"#EEA535\" # orange\n # \"#56B4E9\",# sky blue\n # \"#F0E442\", #yellow\n # \"#0072B2\", #blue\n # \"#CC79A7\" #violet\n)\n\nggplot(\n data = noise_exp,\n mapping = aes(y = score, x = xaxis, group = estimator,\n color = estimator, fill = estimator, linetype=supervised)) +\n geom_hline(yintercept = 1., color = \"black\", linetype = \"dotted\",\n size = 1) +\n geom_line(size = 1.5, alpha = 0.8) +\n geom_point(fill = \"white\", size = 3, shape = 21) +\n theme_minimal() +\n scale_x_log10() +\n scale_y_continuous(limits = c(0, 1.05)) +\n scale_color_manual(values = color_cats, name = NULL) +\n scale_fill_manual(values = color_cats, name = NULL) +\n scale_linetype_manual(values=c(\"dotdash\", \"solid\"), name = NULL) +\n annotate(geom = \"text\", x = 0.0045, y = 1.04, label = \"chance level\") +\n labs(x = expression(sigma),\n y = \"Normalized MAE\") +\n guides(\n color = guide_legend(\n nrow = 2,\n override.aes = list(linetype = c(\"solid\", \"dotdash\", \"solid\", \"solid\", \"dotdash\")))) +\n guides(linetype = F) +\n theme(text = element_text(family = \"Helvetica\", size = 16),\n legend.position = \"top\",\n legend.margin = margin(t = 0, r = 0, b = -.4, l = -1, unit=\"cm\"),\n legend.title = element_text(size = 14))\n\nggsave(\"./outputs/fig_1c_individual_noise.png\", width = 5, height = 3.5, dpi = 300)\nggsave(\"./outputs/fig_1c_individual_noise.pdf\", width = 5, height = 3.5, dpi = 300)\n", "meta": {"hexsha": "570183694b4103ba6d2cb7a6c7e778e1afe700e0", "size": 2585, "ext": "r", "lang": "R", "max_stars_repo_path": "debug/nips_simuls_plot_individual.r", "max_stars_repo_name": "DavidSabbagh/meeg_power_regression", "max_stars_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-18T06:10:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-18T06:10:16.000Z", "max_issues_repo_path": "debug/nips_simuls_plot_individual.r", "max_issues_repo_name": "DavidSabbagh/meeg_power_regression", "max_issues_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "debug/nips_simuls_plot_individual.r", "max_forks_repo_name": "DavidSabbagh/meeg_power_regression", "max_forks_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-01T01:36:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-01T13:44:02.000Z", "avg_line_length": 35.9027777778, "max_line_length": 96, "alphanum_fraction": 0.6027079304, "num_tokens": 817, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3458206285736102}} {"text": "# Experimental and Statistical Methods in Biological Sciences I\n# Demo 2: Descriptive statistics and plotting the data\n\n# Heini Saarim\u00e4ki 25.9.2014\n\n# -----\n\n# 1. Load in data\n\nsetwd('Z:/Desktop/Demo2')\nageweight <- read.csv(\"http://becs.aalto.fi/~heikkih3/age_weight2.csv\")\n\n# Examine your data\n\nhead(ageweight)\nsummary(ageweight)\n\n# Save the original data into a new dataframe\n\nageweight.orig <- ageweight\n\n# Change MALE into a factor\n\nageweight$MALE <- factor(ageweight$MALE)\n\n# Change weird (< 0 ) WEIGHT values to NA\n\nwhich(ageweight$WEIGHT <= 0)\nageweight[which(ageweight$WEIGHT <= 0), \"WEIGHT\"] <- NA\n\nsummary(ageweight)\n\n# Attach the ageweight data\n\nattach(ageweight)\n\n# -----\n\n# 2. Basics in plotting\n\n# --\n\n# 2.1 Basic structure of plotting functions\n\n\n# Using plot function...\n\n# For a numeric variable: scatterplot\nplot(WEIGHT)\n\n# For a factor (i.e., categorical variable): bar chart\nplot(MALE)\n\n# For 2 numeric variables: scatterplot\nplot(AGE, WEIGHT)\nplot(WEIGHT~AGE)\t\t# equivalents!\n\n# For a factor and a numeric variable: box plot\nplot(MALE, WEIGHT)\n\n# For 2 factors: bar chart\nplot(MALE, SMOKE1)\n\n\n# Other plotting functions..\n\n# Histograms:\nhist(WEIGHT)\n\n# Box plots:\nboxplot(WEIGHT)\n\n# --\n\n# 2.2 Modifying your plots\n\n# Build up a nice scatterplot by adding named arguments:\n\nplot(WEIGHT~AGE, main=\"Weight by Age\",\t\t# add main title\n\txlab=\"Age (yrs)\", ylab=\"Weight (kg)\",\t# add axis labels\n\tpch=16, col=\"blue\",\t\t\t\t# modify points\n\txlim=c(0,70), ylim=c(0,140))\t\t\t# scale axes\n\nabline(lm(WEIGHT~AGE),\t\t\t\t\t# draw regression line\n\tlty=\"dashed\",\t\t\t\t\t# modify line\n\tlwd=2,\n\tcol=\"red\")\n\n# Make a nice histogram:\n\nhist(WEIGHT, main=\"Weight\", xlab=\"Weight\", ylab=\"\")\n\n# Set total area size of histogram to 1:\n\nhist(WEIGHT, main=\"Weight\", xlab=\"Weight\", ylab=\"\", freq=F)\n\n# Change the bin size\n\nhist(WEIGHT)\t\t\t# automatic binning\n\nhist(WEIGHT, breaks=15)\t\t# suggest the number of bins\n\nbins=seq(20,140,by=20)\t\t# suggest the size of a bin\nhist(WEIGHT, breaks=bins)\n\n\n# Advanced graphics:\n\n# Sometimes you want to add a normal curve approximation to the plot.\n\n# First, create the histogram object:\nh <- hist(WEIGHT, freq=F, main=\"Distribution of Weights\", \n\txlab=\"Weight (kg)\", ylab=\"\")\n\n# Second, find highest and lowest values and create a sequence of them:\nx <- min(h$breaks):max(h$breaks)\n\n# Third, create vector y from normal distribution with same mean and sd as WEIGHT:\ny <- dnorm(x, \n\tmean=mean(WEIGHT, na.rm=T), \n\tsd=sd(WEIGHT, na.rm=T))\n\n# Finally, add the normal curve: \nlines(x,y,col=\"red\", lty=\"dashed\", lwd=3)\n\n# --\n\n# 2.3 Opening several plots in the same display\n\n# E.g., put 4 plots in the same display:\n\nlayout(matrix(c(1,2,3,4),2,2))\nhist(WEIGHT, main=\"Weight\")\nhist(HEIGHT, main=\"Height\")\nplot(SMOKE1, main=\"Smoking at time point 1\")\nplot(SMOKE2, main=\"Smoking at time point 2\")\n\n# --\n\n# 2.4 Saving your plots\n\n# First, open a device, then draw your plot, then close the device:\n\npdf(file=\"hist_weight.pdf\")\nhist(WEIGHT, main=\"Weight\", xlab=\"Weight (kg)\", ylab=\"\")\ndev.off()\n\n# --\n\n# 2.5 Exercises\n\n# Question 1:\n\nhist(WEIGHT, \n\txlim=c(0,150), \t\t\t# scales x axis\n\tbreaks=6, \t\t\t\t# sets number of bins\n\tcol=\"red\", \t\t\t\t# sets bar colour\n\txlab=\"Weight (kg)\", \t\t# sets x label\n\tbg=\"grey\")\t\t\t\t# OUGHT TO set background colour?\n\n# Note: bg looks like it should set the background colour,\n# BUT it does not actually work this way (see the arguments 'hist' approves of by\n# checking ?hist.\n# If you want to do it, you should use this instead:\n\npar(bg=\"grey\")\nhist(WEIGHT, xlim=c(0,150), breaks=6, col=\"red\", xlab=\"Weight (kg)\")\n\n# Here, we define the parameter background with function 'par'\n# The background will stay grey until we change it, so run this next:\npar(bg=\"white\")\n\n# NOTE: If you want to change the 'par' arguments, the defaults are somewhat tricky\n# to restore. If you are using 'par' arguments, it is good to save the defaults first\n# in an object:\nopar <- par() # stores whatever parameters you have currently\n# Now, you can change what ever you want:\npar(bg=\"green\")\nplot(WEIGHT, AGE) # see what changed\n# And restore the default values this way:\npar(opar)\n\n\n# -\n\n# Question 2:\n\nhist(WEIGHT, \n\txlim=c(0,150), \t\t\t\n\tbreaks=6, \t\t\t\t\n\tcol=\"red\", \t\t\t\t\n\txlab=\"Weight (kg)\",\n\tmain=\"\")\t\t\t\t# sets title to none\n\n# -\n\n# Question 3:\n \t\t\n# Hmm.. Maybe weight increases with age?\n\nplot(AGE, WEIGHT)\n\n# So it seems.\n\n# -\n\n# Question 4:\n\nplot(AGE, WEIGHT,\n\tpch=10,\t\t\t\t# set plot character type\n\tcol=\"green\")\t\t\t# set plot character colour\n\nabline(lm(WEIGHT~AGE))\t\t\t# add regression line\n\n\n# -\n\n# Question 5:\n\nplot(MALE, WEIGHT)\n\n# Weight seems to depend on gender too.\n\n# -\n\n# Question 6:\n\n# In question 3 both variables are numeric, so a scatterplot is produced.\n# In question 5 there is one factor and one numeric variable, so a boxplot is produced.\n# Output of plot function depends on input!\n\n# -\n\n# Question 7:\n\n# An example plot:\nplot(AGE, WEIGHT, pch=20, \n\tmain=\"Weight by Age\", xlab=\"Age (yrs)\", ylab=\"Weight (kg)\")\n\n# Changing font size larger for main title:\n\nplot(AGE, WEIGHT, pch=20, \n\tmain=\"Weight by Age\", xlab=\"Age (yrs)\", ylab=\"Weight (kg)\",\n\tcex.main=2)\n\n# Changing font style to bold-italic for axes labels:\n\nplot(AGE, WEIGHT, pch=20, \n\tmain=\"Weight by Age\", xlab=\"Age (yrs)\", ylab=\"Weight (kg)\",\n\tfont.lab=4)\n\n# Changing font type for the whole graph:\n\nplot(AGE, WEIGHT, pch=20, \n\tmain=\"Weight by Age\", xlab=\"Age (yrs)\", ylab=\"Weight (kg)\",\n\tfamily=\"mono\")\n\n\n# -----\n\n# 3. Descriptive statistics & plotting of categorical variables\n\n# -\n\n# 3.1 Contingency tables\n\n\n# One categorical variable (same information as with summary...)\n\ntable(MALE)\n\n# Two categorical variables:\n\ntable(MALE, SMOKE1)\n\n# Or even more... \n\ntable(MALE, SMOKE1, SMOKE2)\n\n# Express frequencies as percentages:\n\nround(prop.table(table(MALE, SMOKE1), margin=1)*100, 1)\n\n# -\n\n# 3.2 Plotting\n\n# A simple bar chart:\n\nplot(MALE)\n\n# A boosted bar chart:\n\nplot(MALE, col=c(\"red\", \"blue\"), xlab=\"Gender\")\n\n# A simple bar chart for displaying two categorical variables:\n\nplot(MALE, SMOKE1)\n\n# Again then same one boosted:\n\nplot(SMOKE1, MALE, col=c(\"red\", \"blue\"),\n\txlab=\"Smoking\", ylab=\"Gender\")\n\n# -\n\n# 3.3 Exercises\n\n\n# Question 8:\n\ntable(SMOKE1, SMOKE2)\n\n# 26 people quit smoking (smoked at time 1 but not at time 2)\n\n# -\n\n# Question 9\n\nplot(SMOKE1, SMOKE2, \n\tmain=\"Smoking\",\n\txlab=\"Smoking, time 1\", ylab=\"Smoking, time 2\",\n\tcol=c(\"green\", \"red\"))\n\n# Example: how to add frequency values to the plot:\n\nsmoke.table <- table(SMOKE1,SMOKE2)\t\t# save contingency table\n\ntext(0.25,0.2,smoke.table[1,1], cex=2)\ntext(0.25,0.8,smoke.table[1,2], cex=2)\ntext(0.77,0.2,smoke.table[2,1], cex=2)\ntext(0.77,0.8,smoke.table[2,2], cex=2)\n\n\n# -----\n\n# 4. Descriptive statistics & plotting of continuous variables\n\n# --\n\n# 4.1 Descriptive statistics\n\n# Summary:\n\nsummary(ageweight)\n\n# Use describe function from 'psych' package:\n\ninstall.packages('psych')\t\t# install if necessary\nlibrary('psych')\t\t\t\t# load for the use of this session\ndescribe(ageweight)\n\n# You can also take each descriptive statistic separately:\n\nmean(WEIGHT, na.rm=T)\t\t\t# na.rm=T removes missing values\nvar(WEIGHT, na.rm=T)\n\n# Take average weight for men and women separately:\n\ntapply(WEIGHT, MALE, mean, na.rm=T)\n\n# Take average weight for female and male non-smokers and smokers separately:\n\ntapply(WEIGHT, list(MALE, SMOKE1), mean, na.rm=T)\n\n# Save your table in .csv format:\n\nmy.table <- tapply(WEIGHT, list(MALE, SMOKE1), mean, na.rm=T)\nwrite.csv(my.table, \"my_table.csv\")\n\n# --\n\n# 4.2 Plotting\n\n# Box plot of a numeric variable:\n\nboxplot(WEIGHT)\t\t# remember to modify boxplots too! (add labels etc.)\n\n# Use box plot\n\nlayout(matrix(c(1,2)))\nboxplot(ageweight$WEIGHT, main=\"Missing values removed\")\nboxplot(ageweight.orig$WEIGHT, main=\"Original data\")\n\n# Box plots can be taken for categories separately:\n\nplot(MALE, WEIGHT, main=\"Weight by Gender\", ylab=\"Weight (kg)\")\n\n# Histogram shows the shape of the distribution efficiently:\n\nhist(WEIGHT)\nhist(WEIGHT, breaks=20)\t\t# adjust e.g. number of bins\n\n\n# --\n\n# 4.3 Exercises\n\n\n# Question 10\n\nmean(HEIGHT)\nsd(HEIGHT)\n\n# \n\n# -\n\n# Question 11\n\nheight_mean <- tapply(HEIGHT, MALE, mean, na.rm=T)\t# save table of means\nheight_sd <- tapply(HEIGHT, MALE, sd, na.rm=T)\t\t# save table of sd's\n\nheight_table <- rbind(height_mean, height_sd)\t\t# combine tables\nheight_table\n\n# (a) women: mean height 1.66 (sd 0.07)\n# (b) men: mean height 1.80 (sd 0.07)\n\n# Save final table:\n\nwrite.csv(height_table, \"Height_by_gender.csv\")\n\n# -\n\n# Question 12\n\nhist(HEIGHT)\n\n# -\n\n# Question 13\n\nh <- hist(HEIGHT, freq=F)\nx <- seq(min(h$breaks),max(h$breaks), by=0.025)\ny <- dnorm(x, mean=mean(HEIGHT, na.rm=T), sd=sd(HEIGHT, na.rm=T))\nlines(x,y,col=\"red\", lty=\"dashed\", lwd=2)\n\n# -\n\n# Question 14\n\nh <- hist(HEIGHT, freq=F, \n\tylim=c(0,5), \t\t\t\t\t# change y axis\n\tmain=\"Distribution of Height\", \t\t# add main title\n\txlab=\"Height (m)\", ylab=\"\",\t\t\t# add axis labels\n\tcol=\"grey\")\t\t\t\t\t\t# change colour\t\t\t\t\t\t\nx <- seq(min(h$breaks),max(h$breaks), by=0.025)\ny <- dnorm(x, mean=mean(HEIGHT, na.rm=T), sd=sd(HEIGHT, na.rm=T))\nlines(x,y,col=\"red\", lty=\"dashed\", lwd=2)\n\n# -\n\n# Question 15\n\npdf(file=\"Height_distribution.pdf\")\nh <- hist(HEIGHT, freq=F, \n\tylim=c(0,5), \t\t\t\t\t\n\tmain=\"Distribution of Height\", \t\t\n\txlab=\"Height (m)\", ylab=\"\",\t\t\t\n\tcol=\"grey\")\t\t\t\t\t\t\t\t\t\t\t\nx <- seq(min(h$breaks),max(h$breaks), by=0.025)\ny <- dnorm(x, mean=mean(HEIGHT, na.rm=T), sd=sd(HEIGHT, na.rm=T))\nlines(x,y,col=\"red\", lty=\"dashed\", lwd=2)\ndev.off()\n\n# -\n\n# Question 16\n\nboxplot(HEIGHT)\n\n# (a) median is a bit over 1.7 m\n# (b) distribution looks quite normal, maybe a bit more higher values than lower\n\n# -\n\n# Question 17\n\nboxplot(HEIGHT~MALE, main=\"Height by Gender\", ylab=\"Height (m)\", xlab=\"Gender\")\n\n# men seem to be taller than women\n\n# -\n\n# Question 18\n\nlayout(matrix(c(1,2)))\nboxplot(WEIGHT, main=\"Box plot of Weight\", ylab=\"Weight (kg)\", col=\"grey\")\nhist(WEIGHT, main=\"Histogram of Weight\", xlab=\"Weight (kg)\", ylab=\"\", col=\"grey\")\n\n\n# -----\n\n# 5. Exercises\n\n\n# Load in naming data\n\nnaming <- read.csv(\"http://becs.aalto.fi/~heikkih3/naming_new.csv\")\n\n# Check for missing values and that factors are factors\n\nsummary(naming)\n\n# Everything looks ok!\n# We have 2 factors (word.type, sex) already coded as factors\n# ... and 3 numeric variables (iq, hrs, ms)\n# Missing values in iq already coded as NA's\n# Other values look fine too.\n\n# Detach ageweight and attach naming:\ndetach(ageweight)\nattach(naming)\n\n# -\n\n# Question 20\n\nplot(word.type,ms)\n\n# Reading times for regular words seem to be faster than for exceptional words.\n\nlayout(matrix(c(1,2)))\nplot(word.type, ms, data=naming[which(sex==\"female\"),],\n\tmain=\"Female\")\nplot(word.type, ms, data=naming[which(sex==\"male\"),],\n\tmain=\"Male\")\n\n# Effect of word type on reading types seems similar in men and women.\n# I.e., no gender effects on differences in reading times between different word types.\n\n# -\n \n# Question 21\n\n# Take a subset of the data:\n\nnaming_subset <- naming[1:1000, c(\"iq\", \"hrs\", \"sex\")]\n\n# Check this new subset:\n\nhead(naming_subset)\nsummary(naming_subset)\n\n# Detach whole data and attach just the subset:\n\ndetach(naming)\nattach(naming_subset)\n\n# - \n\n# Question 22\n\n# Create factor reading group:\n\nnaming_subset$reading.group <- 'low'\nnaming_subset$reading.group[which(hrs>3.5)] <- 'medium'\nnaming_subset$reading.group[which(hrs>4.5)] <- 'high'\nnaming_subset$reading.group <- factor(naming_subset$reading.group)\n\n# Check out the data now:\n\nhead(naming_subset)\nsummary(naming_subset)\n\n# Need to attach the data again (cause we added a variable!)\n# Previously added variables will be masked automatically\n\nattach(naming_subset)\n\n# -\n\n# Question 23\n\ntable(sex,reading.group)\t\t# contingency table\nplot(table(sex,reading.group))\t# bar charts\n\n# No differences between men and women in how many participants are in which reading group.\n\n# -\n\n# Question 24\n\n# Plot reading hours by gender:\n\nboxplot(hrs~sex, main=\"Reading hours by gender\", ylab=\"Reading hours\")\n\n# Shows similar lack of differences as question 23.\n\n# -\n\n# Question 25\n\n# Plot intelligence by gender:\n\nboxplot(iq~sex, main=\"Intelligence by gender\", ylab=\"IQ\")\n\n# No differences in intelligence between men and women.\n\n# -\n\n# Question 26\n\n# Plot histogram of intelligence with a normal curve approximation:\n\nh <- hist(iq, freq=F, main=\"Distribution of IQ\", xlab=\"IQ\", ylab=\"\", col=\"grey\")\nx <- min(h$breaks):max(h$breaks)\ny <- dnorm(x, mean=mean(iq, na.rm=T), sd=sd(iq, na.rm=T))\nlines(x,y,col=\"red\", lty=\"dashed\", lwd=2)\n\n# Other ways to check for normality (see Section 6):\nqqnorm(iq)\t\t\t\t\t# QQ-plot\nqqline(iq, col=\"red\", lwd=2)\nshapiro.test(iq)\n\n# Both histogram, qq-plot and Shapiro-Wilk test for normality suggest the same:\n# intelligence in this data set follows normal distribution.\n\n# -\n\n# Question 27\n\n# Descriptive statistics for experimental manipulation (reading times by word type):\n\nmean <- tapply(naming$ms, naming$word.type, mean, na.rm=T)\nsd <- tapply(naming$ms, naming$word.type, sd, na.rm=T)\n\nreading_times <- rbind(mean, sd)\nreading_times\n\nwrite.csv(reading_times, \"reading_times.csv\")\n\n\n# Descriptive statistics for background variables: \n\nbg_variables <- describe(naming_subset)\nbg_variables\n\nwrite.csv(bg_variables, \"background_variables.csv\")\n\n\n# -----\n\n# 6. Basic tools for testing for normality\n\n# 6.1 Histogram\n\n# Resets the seed for randomization:\n\nset.seed(111)\n\n# Take a sample of 20 from normal distribution:\n\ndata_normal <- rnorm(20)\n\n# And a sample of 20 from exponential distribution:\n\ndata_exp <- rexp(20)\n\n# Look at the histograms:\n\nlayout(matrix(c(1,2), nrow=1))\nhist(data_normal)\nhist(data_exp)\n\n# -\n\n# 6.2 Q-Q plot\n\n?qqnorm\nlayout(matrix(c(1,2), nrow=1))\nqqnorm(data_normal)\nqqline(data_normal)\nqqnorm(data_exp)\nqqline(data_exp)\n\n# data_normal is relatively normally distributed,\n# but data_exp is clearly not\n\n# -\n\n# 6.3 Statistical tests for normality\n\n# Shapiro-Wilk test:\n\nshapiro.test(data_normal)\nshapiro.test(data_exp)\n\n# Kolmogorov-Smirnov test:\nks.test(data_normal, \"pnorm\", mean=mean(data_normal), sd=sd(data_normal))\nks.test(data_exp, \"pnorm\", mean=mean(data_exp), sd=sd(data_exp))\n\n# Normality tests confirm the observation from the Q-Q plots:\n# data_normal is normally distributed, data_exp is not.\n\n# ------\n\n", "meta": {"hexsha": "9e00e8c7d088e85a32338d63bf71dbb8f5abc0b4", "size": 14144, "ext": "r", "lang": "R", "max_stars_repo_path": "files/expmethodsI_demo2_exercise-solutions.r", "max_stars_repo_name": "hpsaarimaki/hpsaarimaki.github.io", "max_stars_repo_head_hexsha": "d3b17ae732d33606b7aba30619c812950227b45e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "files/expmethodsI_demo2_exercise-solutions.r", "max_issues_repo_name": "hpsaarimaki/hpsaarimaki.github.io", "max_issues_repo_head_hexsha": "d3b17ae732d33606b7aba30619c812950227b45e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "files/expmethodsI_demo2_exercise-solutions.r", "max_forks_repo_name": "hpsaarimaki/hpsaarimaki.github.io", "max_forks_repo_head_hexsha": "d3b17ae732d33606b7aba30619c812950227b45e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.234620887, "max_line_length": 91, "alphanum_fraction": 0.6920248869, "num_tokens": 4224, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632683808533, "lm_q2_score": 0.6370308082623217, "lm_q1q2_score": 0.3458206266325807}} {"text": "context('hi')\n\ntest_that('full_st histograms', {\n\n # ~.05 every 10ms, so 50ms = 25% change in activation\n setwd('/Users/Olive/Documents/GitHub/bc/stfeasibility')\n\n speeds <- sample(seq(0.05,1,length.out=100)) # this is the one that produced 100 speeds, unseeded with lipschitz constraints.\n speeds <- sample(seq(0.05,1,length.out=3))\n loop_update(1.0)\n destination <- \"/Volumes/GoogleDrive/My\\ Drive/outputs/apr18_outputs\" #don't include trailing slash\n destinations <- pblapply(seq(1,100),function(i){\n message(sprintf('CURRENT I: %s',1))\n out_filepath <- \"%s/ste_1e5_speed_%s_timefin_%s.rds\"%--%c(destination, i,format(Sys.time(), \"%H:%M:%OS3\"))\n message('Saving to %s' %--% out_filepath)\n my_H_matrix <- read.csv(\"data/fvc_hentz_2002.csv\", row.names=1) %>% as.matrix\n saveRDS(st_with_vel(my_H_matrix, speeds[i], har_n=1e5),out_filepath)\n # on MSI\n # system(\"rclone copy outputs remote:outputs\", wait=TRUE)\n system(sprintf(\"rm %s\",out_filepath), wait=TRUE)\n gc()\n return(out_filepath)\n })\n \ntest_that(\"we can plot things about the many tasks\", {\n library(data.table)\n six_speed_spatiotemporal_evaluations <- readRDS(\"/Volumes/GoogleDrive/My\\ Drive/outputs/100kvals_task_A_10N_mat_A.rds\")\n runplots(six_speed_spatiotemporal_evaluations)\n run_step_speed_distributions_plot(six_speed_spatiotemporal_evaluations)\n})\n\n\nskip('not today ')\ntest_that('minitest', { \n vector_out <- c(10,0,0,0)\n profvis({smallest_feasible_speedlimit <- bisection_method(1e-8, 0.25, 1e-5, f = force_cos_ramp_is_feasible, vector_out=vector_out)})\n velocity_constraint_options <- seq(smallest_feasible_speedlimit, 1.0, length.out = 1)\n har_n <- 1e3\n n_task_values <- 3\n pbmclapply(velocity_constraint_options, function(speed_limit){\n my_filename <- paste0(\"max_force_is_submaximal_10.0N_in_fx__n_task_values_\", n_task_values, \"_speed_limit_\",speed_limit, \"har_n_\",har_n, \".csv\")\n tall_segment <- har_dataframe_force_cos(H_matrix, bounds_tuple_of_numeric, speed_limit,speed_limit,n_task_values, har_n, vector_out)\n write.csv(tall_segment, my_filename)\n print('wrote to csv:')\n print(my_filename)\n }, mc.cores=detectCores(all.tests = FALSE, logical = TRUE)) \n})\n\nlibrary(pracma)\nskip('not today ')\ntest_that('very submaximal forces', { \n profvis({vector_out <- c(10,0,0,0)\n smallest_feasible_speedlimit <- bisection_method(1e-9, 0.10, 1e-5, f = force_cos_ramp_is_feasible, vector_out=vector_out)\n velocity_constraint_options <- c(logseq(smallest_feasible_speedlimit, 1.0, length.out = 8),1)\n har_n <- 1e5\n n_task_values <- 20\n pbmclapply(velocity_constraint_options, function(speed_limit){\n my_filename <- paste0(\"max_force_is_submaximal_10.0N_in_fx__n_task_values_\", n_task_values, \"_speed_limit_\",speed_limit, \"har_n_\",har_n, \".csv\")\n print('starting')\n print(my_filename)\n tall_segment <- har_dataframe_force_cos(H_matrix, bounds_tuple_of_numeric, speed_limit,speed_limit,n_task_values, har_n, vector_out)\n print('wroteCSV')\n write.csv(tall_segment, my_filename)\n print('wrote to csv:')\n print(my_filename)\n }, mc.cores=detectCores(all.tests = FALSE, logical = TRUE))}, interval=300)\n})\n\ncontext('fullst 20')\nskip('not today ')\ntest_that('full_ st histograms_20', { \n profvis({vector_out <- c(28.8,0,0,0)\n smallest_feasible_speedlimit <- bisection_method(1e-9, 10, 1e-5, f = force_cos_ramp_is_feasible, vector_out=vector_out)\n velocity_constraint_options <- c(logseq(smallest_feasible_speedlimit, 1.0, length.out = 8),1.0)\n har_n <- 1e5\n n_task_values <- 20\n pbmclapply(velocity_constraint_options, function(speed_limit){\n my_filename <- paste0(\"ramp_to_full_mvc_fx_28.8N_n_task_values_\", n_task_values, \"_speed_limit_\",speed_limit, \"har_n_\",har_n, \".csv\")\n print(my_filename)\n tall_segment <- har_dataframe_force_cos(H_matrix, bounds_tuple_of_numeric, speed_limit,speed_limit,n_task_values, har_n, vector_out)\n print('wroteCSV')\n write.csv(tall_segment, my_filename)\n }, mc.cores=detectCores(all.tests = FALSE, logical = TRUE))}, interval=300)\n})\n\n\nskip('not today ')\ntest_that('tailor visualization', {\n\n p <- ggplot(tall_df_st_and_no_st, aes(fill=st,frame=as.factor(task_index))) + geom_density(aes(activation), alpha=0.25) + facet_grid(~muscle, space=\"free\")\n p <- p + theme_classic() + xlab(\"Task Index\") + ylab(\"Number of Trajectories\") + ggtitle(\"ST of 0.5 max delta activation per 10ms vs degenerate case\")\n p <- p + scale_y_continuous(labels = scales::percent)\n gganimate::gganimate(p, output_subfolder_path(\"st_animation2\", \"st_animation2.html\"))\n\n site <- ggplotly(p)\n htmlwidgets::saveWidget(site, output_subfolder_path(\"st_histograms\", \"st_histograms.html\"))\n})\n\nskip('not today ')\ntest_that('tailor animation', { \np <- ggplot(tall_df_st_and_no_st, aes(fill=st, frame=as.factor(task_index))) + geom_histogram(aes(activation)) + facet_grid(~muscle, space=\"free\")\n p <- p + theme_classic() + xlab(\"Task Index\") + ylab(\"Number of Trajectories\") + ggtitle(\"ST of 0.5 max delta activation per 10ms vs degenerate case\")\n p <- p + scale_y_continuous(labels = scales::percent)\n p <- p + geom_segment(aes(x =max(activation), y = 0, xend = max(activation), yend = 1, group=muscle), linetype=\"dashed\", color=\"red\")\n gganimate::gganimate(p, output_subfolder_path(\"st_animation\", \"st_animation.html\"))\n show(p)\n})\n\n\nskip('not today ')\ntest_that('logical binary newton bisection_method works', {\n x_is_at_least_67 <- function(x) x>=67.98095181\n bisection_method(1.0,100.0,tol=1e-9,x_is_at_least_67)\n})\n\nskip('not today ')\ntest_that('boxplot significance testing', { \np <- ggplot(tall_df_st_and_no_st, aes(task_index,activation,fill=st, group=task_index)) + geom_boxplot() + facet_grid(st~muscle, space=\"free\")\nskip('not today')\n p <- p + theme_classic() + xlab(\"Task Index\") + ylab(\"Number of Trajectories\") + ggtitle(\"ST of 0.5 max delta activation per 10ms vs degenerate case\")\n p <- p + scale_y_continuous(labels = scales::percent)\n p <- p + geom_segment(aes(x =max(activation), y = 0, xend = max(activation), yend = 1, group=muscle), linetype=\"dashed\", color=\"red\")\n gganimate::gganimate(p, output_subfolder_path(\"st_animation\", \"st_animation.html\"))\n show(p)\n})\n\nskip('not today ')\ntest_that('animate sample trajectories', { \n\np <- ggplot(tall_df_st_and_no_st, aes(task_index,activation,fill=st, group=task_index)) + geom_boxplot() + facet_grid(st~muscle, space=\"free\")\n p <- p + theme_classic() + xlab(\"Task Index\") + ylab(\"Number of Trajectories\") + ggtitle(\"ST of 0.5 max delta activation per 10ms vs degenerate case\")\n p <- p + scale_y_continuous(labels = scales::percent)\n p <- p + geom_segment(aes(x =max(activation), y = 0, xend = max(activation), yend = 1, group=muscle), linetype=\"dashed\", color=\"red\")\n gganimate::gganimate(p, output_subfolder_path(\"st_animation\", \"st_animation.html\"))\n show(p)\n})\n\n", "meta": {"hexsha": "8e5ca9fff606a39c1b6c294af6e461270b52a051", "size": 7066, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_full_st.r", "max_stars_repo_name": "bc/stfeasibility", "max_stars_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-06-24T00:29:11.000Z", "max_stars_repo_stars_event_max_datetime": "2018-06-24T00:29:11.000Z", "max_issues_repo_path": "tests/testthat/test_full_st.r", "max_issues_repo_name": "bc/stfeasibility", "max_issues_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 32, "max_issues_repo_issues_event_min_datetime": "2018-06-20T19:10:32.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-18T23:07:18.000Z", "max_forks_repo_path": "tests/testthat/test_full_st.r", "max_forks_repo_name": "bc/stfeasibility", "max_forks_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.2028985507, "max_line_length": 160, "alphanum_fraction": 0.7086045853, "num_tokens": 2054, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307806984444, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3458206210919017}} {"text": "a <- c(1,2,3)\na\n\nb <- c(1,2,3)\n\na+b\n", "meta": {"hexsha": "4ba39aee2ded89316e474ca78954ca5676319277", "size": 36, "ext": "r", "lang": "R", "max_stars_repo_path": "test.r", "max_stars_repo_name": "sukkyungpark/test-repository", "max_stars_repo_head_hexsha": "460ce7250360394deba1fbfba7604055a89bfcec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test.r", "max_issues_repo_name": "sukkyungpark/test-repository", "max_issues_repo_head_hexsha": "460ce7250360394deba1fbfba7604055a89bfcec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test.r", "max_forks_repo_name": "sukkyungpark/test-repository", "max_forks_repo_head_hexsha": "460ce7250360394deba1fbfba7604055a89bfcec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 5.1428571429, "max_line_length": 13, "alphanum_fraction": 0.3611111111, "num_tokens": 25, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6757646140788307, "lm_q1q2_score": 0.3457999738956399}} {"text": "\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - In-text figures\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- in-text figures\"))\r\n\r\n# Methods\r\n\r\n# % of valid species\r\ndim(df_all) # includes subspecies\r\ndim(df_all[status %in% c(\"Valid species\", \"Synonym\")])\r\nround(prop.table(table(df_all$status))*100, 1)\r\n\r\n# Number of PTEs\r\ndim(df_describers)\r\ndim(df_describers[spp_N_1st_auth_s>=1])\r\n\r\n# Date of birth\r\ndim(df_describers[spp_N_1st_auth_s>=1 & !is.na(dob.describer)])\r\n\r\n", "meta": {"hexsha": "22862369910e7b22a2c80a069d76e5790f48e77d", "size": 531, "ext": "r", "lang": "R", "max_stars_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/in-text.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/in-text.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/in-text.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.2857142857, "max_line_length": 64, "alphanum_fraction": 0.549905838, "num_tokens": 163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.345799967212732}} {"text": "setwd(\"~/scratch\")\nlibrary(dplyr)\n\n# create dataset of Hypertension, Obesity, Diabetes for COVID patients 2020\n\n# import CDC data\ndf_raw <- read.csv(\"CDC_Conditions_Contributing_to_COVID-19_Deaths__by_State_and_Age.csv\", na.strings = c(\"\",\"NA\"))\n\n# select variables\ndf_of_interest <- df_raw %>% select(\"Group\",\"Year\",\"State\",\"Condition\",\"Age.Group\",\"COVID.19.Deaths\",\"Number.of.Mentions\")\n\n# filter for [Year, 2020, Conditions]\ndf_filtered <- df_of_interest %>% filter(Year == \"2,020\", \n Condition %in% c(\"Diabetes\",\"Hypertensive diseases\",\"Obesity\"), \n Group == \"By Year\", \n State != \"United States\"\n )\n# convert to numbers\ndf_filtered$COVID.19.Deaths <- as.numeric(df_filtered$COVID.19.Deaths)\ndf_filtered$Number.of.Mentions <- as.numeric(df_filtered$Number.of.Mentions)\n\n# remove commas\ndf_filtered$Year <- as.numeric(gsub(\",\",\"\",df_filtered$Year))\n\n# spread / pivot\nlibrary(tidyr)\ndf_spread <- df_filtered %>% spread(Condition, COVID.19.Deaths)\ndf_spread <- df_filtered %>% pivot_wider(\n names_from = c(Condition),\n values_from = c(COVID.19.Deaths, Number.of.Mentions)\n)\n\nwrite.csv(df_spread, \"Comorbidities_Mortality_CDC_2020.csv\")\n\n# explore\ndf_spread %>% summary()\n\n# amount of NA's for 0-24\ndf_spread %>% \n select(Age.Group\n ,`COVID.19.Deaths_Hypertensive diseases`\n ,COVID.19.Deaths_Diabetes\n ,COVID.19.Deaths_Obesity\n ,`Number.of.Mentions_Hypertensive diseases`\n ,Number.of.Mentions_Diabetes\n ,Number.of.Mentions_Obesity) %>% \n filter(Age.Group == \"0-24\") %>% summary()\n\ndf_spread_selected <- df_spread %>% \n select(\n `COVID.19.Deaths_Hypertensive diseases`\n ,COVID.19.Deaths_Diabetes\n ,COVID.19.Deaths_Obesity\n ,`Number.of.Mentions_Hypertensive diseases`\n ,Number.of.Mentions_Diabetes\n ,Number.of.Mentions_Obesity)\n\n\n\n\n\n\n# create a template for compare_summary_table\nsum1 <- do.call(cbind, lapply(df_spread_selected, summary))\ncompare_summary_table <- cbind(sum1)\n\n# loop for each one testing\n# create list for each\nlistOfAgeGroups <- unique(df_spread$Age.Group)\nlistOfAgeGroupsDFs <- paste0(\"df_\",listOfAgeGroups)\n\n# loop\nfor (i in 1:10) {\n print(i)\n DF <- df_spread %>% \n filter(Age.Group == listOfAgeGroups[i]) %>%\n select(`COVID.19.Deaths_Hypertensive diseases`\n ,COVID.19.Deaths_Diabetes\n ,COVID.19.Deaths_Obesity\n ,`Number.of.Mentions_Hypertensive diseases`\n ,Number.of.Mentions_Diabetes\n ,Number.of.Mentions_Obesity)\n print(paste0(\"df_\",listOfAgeGroups[i]))\n assign(listOfAgeGroupsDFs[i], do.call(cbind, lapply(DF, summary)))\n print(listOfAgeGroupsDFs[i])\n # use get to call variable from string\n summaryDF <- as.data.frame(get(listOfAgeGroupsDFs[i]))\n names(summaryDF) <- paste(names(summaryDF), listOfAgeGroups[i],sep=\".\")\n compare_summary_table <- cbind(compare_summary_table,summaryDF)\n}\n\nwrite.csv(compare_summary_table,\"compare_summary_table.csv\")\n\n\n\n# impute\ndf_spread_selected\n\ninstall.packages(\"devtools\")\nlibrary(devtools)\ninstall_github( \"decisionpatterns/tidyimport\")\nimpute(df_spread_selected)\n\n\n## scratch below\n\ninstall.packages(\"mlr\")\nlibrary(mlr)\ninstall.packages('tidyimpute')\n\ndf_spread %>% \n select(Age.Group\n ,`COVID.19.Deaths_Hypertensive diseases`\n ,COVID.19.Deaths_Diabetes\n ,COVID.19.Deaths_Obesity\n ,`Number.of.Mentions_Hypertensive diseases`\n ,Number.of.Mentions_Diabetes\n ,Number.of.Mentions_Obesity) %>% \n filter(Age.Group == \"0-24\") %>% summary()\n\n\nassign(\"sum1\",do.call(cbind, lapply(df_24, summary)))\nsum1 <- as.data.frame(sum1)\nnames(sum1) <- paste(names(sum1),\"0-24\",sep=\".\")\n\n\nassign(\"sum2\",do.call(cbind, lapply(df_spread, summary)))\n\nsum1 <- do.call(cbind, lapply(df_spread, summary))\ncompare_summary_table <- cbind(sum1)\n\n\n\n\n\n\n\n# loop each one\nlistOfAgeGroups <- unique(df_spread$Age.Group)\nfor (i in listOfAgeGroups) {\n DF <- df_spread %>% \n select(Age.Group\n ,`COVID.19.Deaths_Hypertensive diseases`\n ,COVID.19.Deaths_Diabetes\n ,COVID.19.Deaths_Obesity\n ,`Number.of.Mentions_Hypertensive diseases`\n ,Number.of.Mentions_Diabetes\n ,Number.of.Mentions_Obesity) %>% \n filter(Age.Group == i) %>% summary()\n \n assign(paste(\"\",i,sep=\"\"),data.frame(DF=matrix(DF),row.names=names(DF)))\n do.call(cbind, i)\n}\ntypeof(do.call(cbind, lapply(df_spread, summary)))\n\nDF <- data.frame(var=matrix(var),row.names=names(var))\n", "meta": {"hexsha": "b0d22c712a949bdcb0d927658ca9faaaf71a046d", "size": 4549, "ext": "r", "lang": "R", "max_stars_repo_path": "Data_Process/VincentLa/DATACODE/Comorbidities Cleanup and Exploration.r", "max_stars_repo_name": "STRIDES-Codes/A-Way-Out-Pandemic-preparedness-in-context-of-health-disparities-to-limit-disproportionate-morbidit", "max_stars_repo_head_hexsha": "42ef6d3003fea61a1bf4668d7d2995f5bccd56d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-06-16T16:59:08.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-22T02:03:11.000Z", "max_issues_repo_path": "Data_Process/VincentLa/DATACODE/Comorbidities Cleanup and Exploration.r", "max_issues_repo_name": "STRIDES-Codes/A-Way-Out-Pandemic-preparedness-in-context-of-health-disparities-to-limit-disproportionate-morbidit", "max_issues_repo_head_hexsha": "42ef6d3003fea61a1bf4668d7d2995f5bccd56d8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-06-22T19:05:22.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-22T19:05:22.000Z", "max_forks_repo_path": "Data_Process/VincentLa/DATACODE/Comorbidities Cleanup and Exploration.r", "max_forks_repo_name": "STRIDES-Codes/A-Way-Out-Pandemic-preparedness-in-context-of-health-disparities-to-limit-disproportionate-morbidit", "max_forks_repo_head_hexsha": "42ef6d3003fea61a1bf4668d7d2995f5bccd56d8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-06-16T16:59:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-22T18:43:13.000Z", "avg_line_length": 28.974522293, "max_line_length": 122, "alphanum_fraction": 0.6816882831, "num_tokens": 1271, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3457879473751622}} {"text": "require(Hmisc)\nx <- 1:10\ny <- (11:20) + .1\nlabel(y) <- 'Y'\nattributes(y)\nd <- data.frame(x, y)\nattributes(d$y)\nm <- model.frame(y ~ x, data=d)\nm$y\nattributes(m$y)\nmr <- model.response(m)\nattributes(mr)\nmr\n", "meta": {"hexsha": "44874d0867cd18742702ba4634a324068f6322a8", "size": 205, "ext": "r", "lang": "R", "max_stars_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/label.r", "max_stars_repo_name": "klodzikowski/phongames", "max_stars_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2019-04-27T10:26:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-04T09:57:34.000Z", "max_issues_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/label.r", "max_issues_repo_name": "klodzikowski/phongames", "max_issues_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2019-12-28T07:09:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:33:50.000Z", "max_forks_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/label.r", "max_forks_repo_name": "klodzikowski/phongames", "max_forks_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-03-05T05:52:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-18T07:52:04.000Z", "avg_line_length": 14.6428571429, "max_line_length": 31, "alphanum_fraction": 0.6146341463, "num_tokens": 77, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.34578793835137167}} {"text": "8 * .Machine$sizeof.long # e.g. 32\nEndianness\n.Platform$endian # e.g. \"little\"\n", "meta": {"hexsha": "6c701ccaedd57a1a91ab4b0a6881cd4dbdb25d19", "size": 95, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Host-introspection/R/host-introspection.r", "max_stars_repo_name": "djgoku/RosettaCodeData", "max_stars_repo_head_hexsha": "91df62d46142e921b3eacdb52b0316c39ee236bc", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Host-introspection/R/host-introspection.r", "max_issues_repo_name": "djgoku/RosettaCodeData", "max_issues_repo_head_hexsha": "91df62d46142e921b3eacdb52b0316c39ee236bc", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Host-introspection/R/host-introspection.r", "max_forks_repo_name": "djgoku/RosettaCodeData", "max_forks_repo_head_hexsha": "91df62d46142e921b3eacdb52b0316c39ee236bc", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 23.75, "max_line_length": 48, "alphanum_fraction": 0.6210526316, "num_tokens": 34, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.34578793835137167}} {"text": "\r\nget_close_stations <- function()\r\n{\r\n bigf <- readOGR(\"./data\", \"BIGF_UK_Locs\")\r\n uaplt <- readOGR(\"./data\", \"UAPLT\")\r\n uatmp <- readOGR(\"./data\", \"UATMP\")\r\n radiosonde <- rbind(uaplt, uatmp)\r\n \r\n df_bigf <- as.data.frame(bigf)\r\n df_bigf <- df_bigf[,c(4,1,3)]\r\n \r\n df_radiosonde <- as.data.frame(radiosonde)\r\n df_radiosonde <- df_radiosonde[,c(11,12,7)]\r\n \r\n df_bigf$name <- as.character(df_bigf$name)\r\n \r\n #browser()\r\n \r\n results <- data.frame(bigf=rep(NA,nrow(df_bigf)), radiosonde=rep(NA,nrow(df_bigf)),dist=rep(NA,nrow(df_bigf)))\r\n \r\n for (i in 1:nrow(df_bigf))\r\n {\r\n bigf_bit <- as.numeric(df_bigf[i,1:2])\r\n r <- spDistsN1(as.matrix(df_radiosonde[,1:2]), bigf_bit, longlat=TRUE)\r\n index <- which(r == min(r))[1]\r\n \r\n bigf_id <- df_bigf[i,]$name\r\n #print(df_bigf[i,])\r\n radiosonde_id <- df_radiosonde[index,]$Snippet\r\n dist <- r[index]\r\n \r\n if (dist < 25) print(paste(bigf_id, \"and\", radiosonde_id, \":\", dist, \"km\"))\r\n results[i,]$bigf <- bigf_id\r\n results[i,]$radiosonde <- as.character(radiosonde_id)\r\n results[i,]$dist <- dist\r\n }\r\n \r\n return(results)\r\n}\r\n", "meta": {"hexsha": "40b5413c7a664695bd40e7834c9e2197e143c5ab", "size": 1127, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/get_close_stations.r", "max_stars_repo_name": "robintw/BIGFVal", "max_stars_repo_head_hexsha": "ed5b48c6731dd7dee6e540d8922378fb39661fbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-11-16T08:34:09.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-16T08:34:09.000Z", "max_issues_repo_path": "lib/get_close_stations.r", "max_issues_repo_name": "robintw/BIGFVal", "max_issues_repo_head_hexsha": "ed5b48c6731dd7dee6e540d8922378fb39661fbf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/get_close_stations.r", "max_forks_repo_name": "robintw/BIGFVal", "max_forks_repo_head_hexsha": "ed5b48c6731dd7dee6e540d8922378fb39661fbf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-12-11T09:58:39.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-07T19:29:48.000Z", "avg_line_length": 28.175, "max_line_length": 113, "alphanum_fraction": 0.6015971606, "num_tokens": 388, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.34578793835137156}} {"text": "rm(list=ls())\nlibrary(viridis)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(scales)\nlibrary(gridExtra)\nlibrary(ggpubr)\n\n\n# The palette with grey:\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# The palette with black:\ncbbPalette <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n# To use for fills, add\n#scale_fill_manual(values=cbPalette)\n# To use for line and point colors, add\n#scale_colour_manual(values=cbPalette)\n\n#scale_color_viridis(discrete=TRUE) \n\ndGLSofAvgToxNum <- function(numeratorConstraint,maxLikes){\n # Rename column name for numberator in calculation. \n colnames(maxLikes)[colnames(maxLikes)==numeratorConstraint] <- \"numeratorConstraint\"\n \n # Start a list of dGLS values for all loci in dataset\n numLoci <- length(maxLikes$numeratorConstraint)\n x <- \"ToxAll\"\n # Make lists and add to data frame with locus names, constraint name, and 0 values\n numeratorList <- rep(c(x), times=numLoci)\n outdGLSList <- vector(mode=\"numeric\",length=numLoci)\n \n # Make dataframe \n outdGLSdf <- subset(maxLikes, select = c('Locus'))\n outdGLSdf$Hypothesis <- numeratorList\n outdGLSdf$dGLSavg <- outdGLSList\n outdGLSdf$dGLSmax <- outdGLSList\n \n # create a dataset of alternate constraints only to average over\n drops <- c(\"Locus\",\"numeratorConstraint\")\n df <- maxLikes[,!(names(maxLikes) %in% drops)]\n \n # for locus in dataset \n for (i in 1:nrow(outdGLSdf)){\n \n # Get all alternate maxinal likehoods \n othermaxLikes <- as.numeric(df[i,])\n \n # Maximum of alternate maxLH\n a <- max(othermaxLikes)\n \n # Calculate average across alternate constraints\n avgOthermaxLikes <- mean(othermaxLikes)\n # Calculate average across alternate constraints\n avgOtherMargLikes <- a+log(sum(exp(othermaxLikes-a)))-log(length(names(df)))\n \n # Calculate dGLS based on average of other LH values\n outdGLSdf[i,3] <- avgOthermaxLikes-maxLikes$numeratorConstraint[i]\n # Calculate dGLS based on maximum of other LH values\n outdGLSdf[i,4] <- a-maxLikes$numeratorConstraint[i]}\n \n return(outdGLSdf)\n}\n\ndGLSofAvg <- function(numeratorConstraint,maxLikes){\n \n # Rename column name for numberator in calculation. \n colnames(maxLikes)[colnames(maxLikes)==numeratorConstraint] <- \"numeratorConstraint\"\n \n # Start a list of dGLS values for all loci in dataset\n numLoci <- length(maxLikes$numeratorConstraint)\n \n # Make lists and add to data frame with locus names, constraint name, and 0 values\n numeratorList <- rep(c(numeratorConstraint), times=numLoci)\n outdGLSList <- vector(mode=\"numeric\",length=numLoci)\n \n # Make dataframe \n outdGLSdf <- subset(maxLikes, select = c('Locus'))\n outdGLSdf$Hypothesis <- numeratorList\n outdGLSdf$dGLSavg <- outdGLSList\n outdGLSdf$dGLSmax <- outdGLSList\n \n # create a dataset of alternate constraints only to average over\n drops <- c(\"Locus\",\"numeratorConstraint\")\n df <- maxLikes[,!(names(maxLikes) %in% drops)]\n \n # for locus in dataset \n for (i in 1:nrow(outdGLSdf)){\n \n # Get all alternate maxinal likehoods \n othermaxLikes <- as.numeric(df[i,])\n \n # Maximum of alternate maxLH\n a <- max(othermaxLikes)\n \n # Calculate average across alternate constraints\n avgOthermaxLikes <- mean(othermaxLikes)\n # Calculate average across alternate constraints\n avgOtherMargLikes <- a+log(sum(exp(othermaxLikes-a)))-log(length(names(df)))\n \n # Calculate dGLS based on average of other LH values\n outdGLSdf[i,3] <- maxLikes$numeratorConstraint[i]-avgOthermaxLikes\n # Calculate dGLS based on maximum of other LH values\n outdGLSdf[i,4] <- maxLikes$numeratorConstraint[i]-a}\n \n return(outdGLSdf)\n}\n\nwriteCSV <- function (outData,outFile){\n write.csv(outData, file=outFile, row.names=FALSE)\n}\n\n\nsetwd(\"/Users/ChatNoir/Projects/Squam/Graphs/dGLS\")\nmLdGLS <- read.table(\"SquamNTGdGLSSummary.txt\",header=TRUE)\n\n# Read in table of marginal Likelihoods\nmL <- read.table(\"SquamNTGdGLSSummary.txt\",header=TRUE)\nmLTox <- mL[,!(names(mL) %in% c(\"Sclero\"))]\nnames(mL)\nmLaiS <- mL[,!(names(mL) %in% c(\"ToxAngIg\"))]\nmLsaS <- mL[,!(names(mL) %in% c(\"ToxSnIg\"))]\nmLsiS <- mL[,!(names(mL) %in% c(\"ToxSnAng\"))]\n\n\n# Get list of dGLS for each relationship, using an average of all other constraints as a composite hypothesis. \n\nSclerodGLS <- dGLSofAvg(\"Sclero\",mL)\nSaidGLS <- dGLSofAvg(\"Sclero\",mLaiS)\nSsadGLS <- dGLSofAvg(\"Sclero\",mLsaS)\nSsidGLS <- dGLSofAvg(\"Sclero\",mLsiS)\n# differences only within tox constraints \nToxAngIgdGLStx <- dGLSofAvg(\"ToxAngIg\",mLTox)\nToxSnAngdGLStx <- dGLSofAvg(\"ToxSnAng\",mLTox) \nToxSnIgdGLStx <- dGLSofAvg(\"ToxSnIg\",mLTox)\n# all\nToxAngIgdGLS <- dGLSofAvg(\"ToxAngIg\",mL)\nToxSnAngdGLS <- dGLSofAvg(\"ToxSnAng\",mL) \nToxSnIgdGLS <- dGLSofAvg(\"ToxSnIg\",mL)\n\n# Combine and factor \nalldGLS <- bind_rows(SclerodGLS,ToxAngIgdGLS,ToxSnAngdGLS,ToxSnIgdGLS)\ntoxdGLS <- bind_rows(ToxAngIgdGLStx,ToxSnAngdGLStx,ToxSnIgdGLStx)\nalldGLS$Hypothesis <- factor(alldGLS$Hypothesis, levels=c(\"Sclero\",\"ToxAngIg\",\"ToxSnAng\",\"ToxSnIg\"))\ntoxdGLS$Hypothesis <- factor(toxdGLS$Hypothesis, levels=c(\"ToxAngIg\",\"ToxSnAng\",\"ToxSnIg\"))\ntoxdGLS <- toxdGLS %>% separate(Locus, c(\"LocusType\",\"LocusNumber\"), \"-\", remove = FALSE)\n\n# Absolute values, averages of differences \n\nmLablsAve <- mL %>% mutate(absAvg=(abs(Sclero-ToxAngIg)+\n abs(Sclero-ToxSnAng)+\n abs(Sclero-ToxSnIg)+\n abs(ToxAngIg-ToxSnAng)+\n abs(ToxAngIg-ToxSnIg)+\n abs(ToxSnAng-ToxSnIg))/6) %>% select(Locus,absAvg)\n\n\n\n# Support counts \n\n\n# SAve HERE \n\n#writeCSV(tvsSupport,\"tvsdGLSavg_support_avg.csv\")\n#writeCSV(toxSupport,\"toxdGLSavg_support_avg.csv\")\n#writeCSV(allSupport,\"alldGLSavg_support.csv\")\n\n# Loci Lists \n\n# Loci sig support for Sclero over Tox \n# Loci sig support for Tox over Sclero \n# Loci sig support for AI over other Tox \n# Loci sig support for SA over other Tox\n# Loci sig support for SI over other Tox\n\nalldGLS.SC.loci <- alldGLS %>% select(Locus, dGLSavg, Hypothesis, Percentile, locusRank) %>% \n filter(Hypothesis==\"Sclero\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis)\nalldGLS.AI.loci <- alldGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile, locusRank) %>% \n filter(Hypothesis==\"ToxAngIg\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis) \nalldGLS.SA.loci <- alldGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile, locusRank) %>% \n filter(Hypothesis==\"ToxSnAng\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis) \nalldGLS.SI.loci <- alldGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile, locusRank) %>% \n filter(Hypothesis==\"ToxSnIg\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis)\n\ntoxdGLS.AI.loci <- toxdGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile) %>% \n filter(Hypothesis==\"ToxAngIg\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis) \ntoxdGLS.SA.loci <- toxdGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile) %>% \n filter(Hypothesis==\"ToxSnAng\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis) \ntoxdGLS.SI.loci <- toxdGLS %>% select(Locus, dGLSavg,Hypothesis,Percentile) %>% \n filter(Hypothesis==\"ToxSnIg\") %>% \n filter(dGLSavg >= 0.5) %>% select(-Hypothesis)\n\n#writeCSV(tvsdGLS.SC.loci,\"tvsdGLS.SC.loci.avg.csv\")\n#writeCSV(tvsdGLS.TX.loci,\"tvsdGLS.TX.loci.avg.csv\")\n#writeCSV(toxdGLS.AI.loci,\"tvsdGLS.AI.loci.avg.csv\")\n#writeCSV(toxdGLS.SA.loci,\"tvsdGLS.SA.loci.avg.csv\")\n#writeCSV(toxdGLS.SI.loci,\"tvsdGLS.SI.loci.avg.csv\")\n\n############# Bar graphs Pair dGLS\ndGLSpairSupport <- pairdGLS %>% group_by(Hypothesis,Support) %>% dplyr::summarise(dGLSpairCounts=n())\ndGLSpairSupport$Support <- factor(dGLSpairSupport$Support, levels=c(\"Strong_Against\",\"Ambiguous\",\"Strong\"))\n\n\n############## Plots ############## ############## ############## ############## ############## \n\n\nquartz()\n\nmax <- ggplot(data=ToxSnIgdGLStx) + \n geom_bar(stat = \"identity\", position = position_dodge(), width = 1) + \n aes(x=reorder(Locus,-dGLSmax,sum),y=dGLSmax,color = dGLSmax < 0, fill = dGLSmax < 0) +\n scale_color_manual(values=c(\"#990000\",\"#482173FF\")) + \n scale_fill_manual(values=c(\"#990000\",\"#482173FF\"))+ \n labs(x=\"\",y=\"Support Value\",size=24) +\n theme_classic() + \n theme(\n axis.line=element_blank(),\n axis.title.x=element_blank(),\n axis.text.x= element_blank(),\n axis.ticks.x=element_blank(),\n axis.text.y = element_text(size=24, color=\"black\"),\n text = element_text(size=30),\n legend.position = \"none\", \n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid.major = element_blank(), # get rid of major grid\n panel.grid.minor = element_blank(), # get rid of minor grid\n legend.background = element_rect(fill = \"transparent\"), # get rid of legend bg\n legend.box.background = element_rect(fill = \"transparent\") # get rid of legend panel bg\n ) \nmax + scale_y_continuous(breaks=seq(20,20, 5))\nggsave(\"ToyExample_dGLSPlot2.pdf\", plot=max, \n width = 11, height = 4, units = \"in\", device = 'pdf',\n bg = \"transparent\")\n\n\n############## Saved Plots ############## ############## ############## ############## ############## \ntvsList <- c(\"orange\",\"#482173FF\")\nmyList <- c(\"orange\",\"#38598CFF\",\"#2BB07FFF\",\"#C2DF23FF\")\n#show_col(myList)\n\n# Singhal_dGLS_Histogram_TvS\n\nmyList <- c(\"orange\",\"#482173FF\")\navg <- ggplot(tvsdGLS, aes(x=dGLSavg, fill=Hypothesis, color=Hypothesis)) + \n geom_histogram(binwidth = 0.5, alpha=1, position=\"dodge\") +\n geom_vline(xintercept=c(0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5) + \n scale_color_manual(values=myList) + scale_fill_manual(values=myList) + \n labs(x=\"dGLS Hypoth vs average\")\nmax <- ggplot(tvsdGLS, aes(x=dGLSmax, fill=Hypothesis, color=Hypothesis)) + \n geom_histogram(binwidth = 0.5, alpha=1, position=\"dodge\") +\n geom_vline(xintercept=c(0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5) + \n scale_color_manual(values=myList) + scale_fill_manual(values=myList) + \n labs(x=\"dGLS Hypoth vs maximum\")\nall <- ggarrange(avg,max, ncol=2, nrow=1, common.legend = TRUE, legend=\"right\")\nall\nggsave(\"Singhal_dGLS_Histogram_TvS_avg.jpg\", plot=avg, width = 20, height = 20, units = \"cm\")\n\n# Singhal_dGLS_Histogram_All \n\nmyList <- c(\"orange\",\"#38598CFF\",\"#2BB07FFF\",\"#C2DF23FF\")\navg <- ggplot(alldGLS, aes(x=dGLSavg, fill=Hypothesis, color=Hypothesis)) + \n geom_histogram(binwidth = 0.5, alpha=1, position=\"dodge\") +\n geom_vline(xintercept=c(0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5) + \n scale_color_manual(values=myList) + scale_fill_manual(values=myList) + \n labs(x=\"dGLS Hypoth vs average\")\nmax <- ggplot(alldGLS, aes(x=dGLSmax, fill=Hypothesis, color=Hypothesis)) + \n geom_histogram(binwidth = 0.5, alpha=1, position=\"dodge\") +\n geom_vline(xintercept=c(0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5) + \n scale_color_manual(values=myList) + scale_fill_manual(values=myList) + \n labs(x=\"dGLS Hypoth vs maximum\")\nall <- ggarrange(avg,max, ncol=2, nrow=1, common.legend = TRUE, legend=\"right\")\nall\nggsave(\"Singhal_dGLS_Histogram_All_avgvmax.jpg\", plot=all, width = 30, height = 20, units = \"cm\")\n\nall <- ggplot(alldGLS, aes(x=dGLSavg, fill=Hypothesis, color=Hypothesis)) + \n geom_histogram(binwidth = 0.5, alpha=1, position=\"dodge\") +\n geom_vline(xintercept=c(0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5) + \n scale_color_manual(values=myList)+scale_fill_manual(values=myList)\nall\nggsave(\"Singhal_dGLS_Histo_all.jpg\", plot=all, width = 40, height = 20, units = \"cm\")\n\n\n# Singhal_dGLS_Violin_avg \n\ntvs <- ggplot(tvsdGLS, aes(x=Hypothesis, y=dGLSavg, color=Hypothesis)) + \n geom_violin(trim=TRUE) + scale_color_manual(values=tvsList) + stat_summary(fun.data=data_summary)\ntox <- ggplot(toxdGLS, aes(x=Hypothesis, y=dGLSavg, color=Hypothesis)) + \n geom_violin(trim=TRUE) + scale_color_manual(values=myList) + stat_summary(fun.data=data_summary)\ntox.trunc <- ggplot(toxdGLS, aes(x=Hypothesis, y=dGLSavg, color=Hypothesis)) + \n geom_violin(trim=TRUE) + scale_color_manual(values=myList) + stat_summary(fun.data=data_summary) +\n ylim(-2,2)\ntox.trunc\n\nggsave(\"Singhal_dGLS_Violin_TvS_avg.jpg\", plot=tvs, width = 20, height = 20, units = \"cm\")\nggsave(\"Singhal_dGLS_Violin_Tox_avg.jpg\", plot=tox, width = 20, height = 20, units = \"cm\")\nggsave(\"Singhal_dGLS_Violin_Tox_avgtrunc.jpg\", plot=tox.trunc, width = 20, height = 20, units = \"cm\")\n\n\n# Singhal_dGLS_Classic_avg\n\ntvsList <- c(\"orange\",\"#482173FF\")\n\nmax <- ggplot(data=SclerodGLS) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n theme(axis.title.x=element_blank(),\n axis.text.x=element_blank(),\n axis.ticks.x=element_blank()) + \n aes(x=reorder(Locus,-dGLSmax,sum),y=dGLSmax,color = dGLSmax < 0, fill = dGLSmax < 0) +\n scale_color_manual(values=c(\"orange\",\"#482173FF\")) + scale_fill_manual(values=c(\"orange\",\"#482173FF\"))+ \n labs(y=\"dGLS Hypoth vs maximum\")\navg <- ggplot(data=SclerodGLS) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n theme(axis.title.x=element_blank(),\n axis.text.x=element_blank(),\n axis.ticks.x=element_blank()) + \n aes(x=reorder(Locus,-dGLSavg,sum),y=dGLSavg,color = dGLSavg < 0, fill = dGLSavg < 0) +\n scale_color_manual(values=c(\"orange\",\"#482173FF\")) + scale_fill_manual(values=c(\"orange\",\"#482173FF\"))+\n labs(y=\"dGLS Hypoth vs average\")\navg\n\nall <- ggarrange(avg,max, ncol=1, nrow=2, common.legend = TRUE, legend=FALSE)\nall\nggsave(\"Singhal_dGLS_Classic_TvS_avg.jpg\", plot=avg, width = 30, height = 20, units = \"cm\")\n\n\ntoxList <- c(\"#38598CFF\",\"#2BB07FFF\",\"#C2DF23FF\")\n\navgAI <- ggplot(data=ToxAngIgdGLStx) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n theme(axis.title.x=element_blank(),\n axis.text.x=element_blank(),\n axis.ticks.x=element_blank()) + \n aes(x=reorder(Locus,-dGLSavg,sum),y=dGLSavg,color = dGLSavg < 0, fill = dGLSavg < 0) +\n scale_color_manual(values=c(\"#38598CFF\",\"grey40\")) + scale_fill_manual(values=c(\"#38598CFF\",\"grey40\"))+ \n labs(y=\"dGLS Hypoth vs Average\", title=\"(Anguimorph,Iguania)\") + scale_y_continuous(breaks=seq(-17,17, 2), limits=c(-17,17)) \n#avgAI\navgSA <- ggplot(data=ToxSnAngdGLStx) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n theme(axis.title.x=element_blank(),\n axis.text.x=element_blank(),\n axis.ticks.x=element_blank()) + \n aes(x=reorder(Locus,-dGLSavg,sum),y=dGLSavg,color = dGLSavg < 0, fill = dGLSavg < 0) +\n scale_color_manual(values=c(\"#2BB07FFF\",\"grey40\")) + scale_fill_manual(values=c(\"#2BB07FFF\",\"grey40\"))+ \n labs(y=\"dGLS Hypoth vs Average\", title=\"(Snakes,Anguimorph)\") + scale_y_continuous(breaks=seq(-17,17, 2), limits=c(-17,17))\n#maxSA\navgSI <- ggplot(data=ToxSnIgdGLStx) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n theme(axis.title.x=element_blank(),\n axis.text.x=element_blank(),\n axis.ticks.x=element_blank()) + \n aes(x=reorder(Locus,-dGLSavg,sum),y=dGLSavg,color = dGLSavg < 0, fill = dGLSavg < 0) +\n scale_color_manual(values=c(\"#C2DF23FF\",\"grey40\")) + scale_fill_manual(values=c(\"#C2DF23FF\",\"grey40\"))+ \n labs(y=\"dGLS Hypoth vs Average\", title=\"(Snakes,Iguania)\") + scale_y_continuous(breaks=seq(-17,17, 2), limits=c(-17,17))\n#avgSI\n\n\nall <- ggarrange(avgAI, avgSA, avgSI, ncol=1, nrow=3, common.legend = TRUE, legend=FALSE)\nall\nggsave(\"Singhal_dGLS_Classic_Tox_avg.jpg\", plot=all, width = 20, height = 40, units = \"cm\")\n\n\n\n", "meta": {"hexsha": "ea1b56843bb9681170db46b74b6ccf6c3fb3a8a8", "size": 15499, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/oldComparisons/dGLS_old/dGLScalcs.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/oldComparisons/dGLS_old/dGLScalcs.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, 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YES\n2. YES", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.34578793041811395}} {"text": "# preamble\nlibrary('mrgsolve')\n\n# load model\nmm_model <- mrgsolve::mread(model = 'mm', file = 'model.cpp')\n\n# run model\nsim <- mm_model %>%\n mrgsim(\n delta = 1,\n hmax = 0,\n maxsteps = 1e9,\n atol = 1e-7,\n rtol = 1e-4,\n end = 120\n )\n\n# plot results\nplot <- sim %>%\n plot(type='l')\n\n# show\nplot", "meta": {"hexsha": "bbbafa8735ce923371e9d118fe0dcd6f467925d5", "size": 314, "ext": "r", "lang": "R", "max_stars_repo_path": "cases/0-hello-world/master/mm_mrg/run.r", "max_stars_repo_name": "hetalang/heta-compiler", "max_stars_repo_head_hexsha": "5958ec77e03df9f54124b963391e92612a482a12", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-08-03T21:19:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-24T15:12:52.000Z", "max_issues_repo_path": "cases/0-hello-world/master/mm_mrg/run.r", "max_issues_repo_name": "hetalang/heta-compiler", "max_issues_repo_head_hexsha": "5958ec77e03df9f54124b963391e92612a482a12", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2020-11-01T17:42:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-11T15:52:16.000Z", "max_forks_repo_path": "cases/0-hello-world/master/mm_mrg/run.r", "max_forks_repo_name": "hetalang/heta-compiler", "max_forks_repo_head_hexsha": "5958ec77e03df9f54124b963391e92612a482a12", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-01T00:03:04.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-01T00:03:04.000Z", "avg_line_length": 13.652173913, "max_line_length": 61, "alphanum_fraction": 0.5541401274, "num_tokens": 116, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300573952052, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.34577695923716956}} {"text": "# Run init.r before other scripts\nrm(list=ls())\n # for use in R console.\n # set own relevant directory if working in R console, otherwise ignore if in terminal\nsetwd(\"/Users/davidbeauchesne/Dropbox/PhD/PhD_obj2/Structure_Comm_EGSL/EGSL_species_distribution/\")\n# -----------------------------------------------------------------------------\n# PROJECT:\n# Evaluating the structure of the communities of the estuary\n# and gulf of St.Lawrence\n# -----------------------------------------------------------------------------\n\n# -----------------------------------------------------------------------------\n# DETAILS:\n# The goal of this script is to run JSDMs from the HMSC package on data from\n# the annual northern Gulf DFO trawl survey\n# -----------------------------------------------------------------------------\nlibrary(reshape2)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(magrittr)\n# Dependencies\n library(Rcpp)\n library(RcppArmadillo)\n library(coda)\n\n# Installing the package\n # library(devtools)\n # install_github(\"guiblanchet/HMSC\")\n library(HMSC)\n\n# Other libraries\n # install.packages('beanplot', dependencies = T)\n # install.packages('corrplot', dependencies = T)\n # install.packages('circlize', dependencies = T)\n library(beanplot)\n library(corrplot)\n library(circlize)\n\n# Functions\n source('./Script/crossValid.r')\n\n# Importing data required data from RawData\n northPluri <- readRDS('./RData/northPluriCor.rds')\n fig <- \"../../../Wiki/docs/img/\"\n\n# Basic parameters needed for the analysis\n # Name of environmental covariables\n # , # add at some point\n envCov <- c('Prof','SSAL_MEAN','SalMoyMoy','TempMoyMoy','STEMMEAN','BTEMMEAN','O2_Sat_Mea','LaDeTow','LoDeTow')\n\n # Groups of environmental covariables, for variance partitioning\n # length(envGroup) == length(envCov) + 1; because of Intercept\n envGroup <- c('Intercept','Bathymetry','Salinity','Salinity','Temperature','Temperature','Temperature','Oxygen', 'Spatial', 'Spatial')\n\n# -------------------------------------------------------------\n# Remove NAs from environmental variables selected for analysis\n# -------------------------------------------------------------\n\n NAs <- northPluri[, envCov] %>%\n lapply(X = ., FUN = function(x) which(is.na(x))) %>%\n unlist(.) %>%\n unique(.)\n\n northPluri <- northPluri[-NAs, ]\n\n# ========================================\n# 1. Formatting the data for HMSC analysis\n# ========================================\n\n # Species list\n sp <- northPluri[, c('EspGen','N_EspSci')] %>%\n .[!duplicated(.), ] %>%\n .[order(.[, 'EspGen']), ]\n\n # ---------------------------------\n # Y: sample units by species matrix\n # ---------------------------------\n\n # The data has to be formatted so that lines are trawl sessions and columns are species\n # Creating wide version of dataset to have stations as rows and species captured as columns\n # Only for presence absence\n # Other fields could also be used for count or weight\n northPluri_wide <- dcast(northPluri,\n formula = No_Rel + No_Stn + DatDeTow + DatFiTow + HreDeb + HreFin + LaDeTow + LoDeTow + LaFiTow + LoFiTow + Prof + SSAL_MEAN + STEMMEAN + BTEMMEAN + HAB_C_E + O2_Sat_Mea + SalMoyMoy + TempMoyMoy + Megahabita + MHVar_3x3 ~ EspGen,\n value.var = 'EspGen',\n fun.aggregate = length)\n\n # Extracting only presence/absence data\n # This corresponds to the Y matrix for the HMSC package\n # A unique ID for each station is created by combining survey number (i.e. year) and station number\n Station <- paste(northPluri_wide[, 'No_Rel'], '_', northPluri_wide[, 'No_Stn'], sep = '')\n Y <- northPluri_wide[, as.character(sp[,'EspGen'])]\n rownames(Y) <- Station\n\n # -----------------------------------------\n # Pi: sample units by random effects matrix\n # -----------------------------------------\n\n # Create a dataframe for random effects, columns have to be factors\n # Using survey nomber, which correspond to years, as a random effect in the analysis\n # Ultimately, there is likely a correlation between stations done during a single year in a single strata\n # It would be a good thing to analyze spatial dependence between stations\n Pi <- data.frame(sampling_unit = as.factor(Station),\n survey_number = as.factor(northPluri_wide[, 'No_Rel']))\n\n # ----------------------------------------------------\n # X: sampling units by environmental covariates matrix\n # ----------------------------------------------------\n\n # Create a matrix for the values of environmental covariates at each sampling unit location\n # The values have to be numeric\n # Bathymetry used, extracted from northPluri rather than epipelagic and benthic habitat data\n\n X <- matrix(ncol = length(envCov),\n nrow = nrow(northPluri_wide))\n X <- northPluri_wide[, envCov]\n rownames(X) <- Station\n\n # ----------------------------\n # as.HMSCdata for HMSC package\n # ----------------------------\n # Creating HMSC dataset for analyses\n northPluri_HMSC <- as.HMSCdata(Y = Y, X = X, Random = Pi, interceptX = T, scaleX = T)\n saveRDS(northPluri_HMSC, file = './RData/northPluri_HMSC.rds')\n\n# ===============================\n# 2. Performing the MCMC sampling\n# ===============================\n\n # # Sampling of posterior distribution\n # # Simpler version when uninformative priors are sufficient and a priori parameters do not need to be set\n # model <- hmsc(northPluri_HMSC,\n # family = \"probit\",\n # niter = 10000,\n # nburn = 1000,\n # thin = 10)\n\n model <- hmsc(northPluri_HMSC,\n family = \"probit\",\n niter = 10000,\n nburn = 1000,\n thin = 100)\n\n # save model\n saveRDS(model, file = './RData/modelHSMC.rds')\n\n# =========================================\n# 3. Producing MCMC trace and density plots\n# =========================================\n\n # Mixing objects\n mixingParamX <- as.mcmc(model, parameters = \"paramX\")\n mixingMeansParamX <- as.mcmc(model, parameters = \"meansParamX\")\n mixingMeansVarX <- as.mcmc(model, parameters = \"varX\")\n mixingParamLatent <- as.mcmc(model, parameters = \"paramLatent\")\n\n # Save meanParamX for trace and density plots\n saveRDS(mixingMeansParamX, file = './RData/mixingMeansParamX.rds')\n\n # Trace and density plots to visually diagnose mcmc chains\n # Another way to check for convergence is to use diagnostic tests such as Geweke's convergence diagnostic (geweke.diag function in coda) and the Gelman and Rubin's convergence diagnostic (gelman.diag function in coda).\n paramModel <- colnames(mixingMeansParamX)\n nParam <- length(paramModel)\n\n jpeg(paste(fig,'MCMCTracePlot.jpeg',sep=''), width = 6, height = (1.5*nParam), res = 150, units = 'in')\n par(mfrow = c(nParam, 2), mar = rep(2, 4))\n for(i in 1:ncol(mixingMeansParamX)) {\n traceplot(mixingMeansParamX[,i], col = \"blue\", main = paste('Trace of ', paramModel[i]))\n densplot(mixingMeansParamX[,i], col = \"orange\", main = paste('Density of ', paramModel[i]))\n }\n dev.off()\n\n# ================================\n# 4. Producing posterior summaries\n# ================================\n # # Violin plot\n # par(mar=c(6,4,1,1))\n # mixingParamXDF <- as.data.frame(mixingParamX)\n # beanplot(mixingParamXDF, las = 2)\n # points(1:30, as.vector(param$param$paramX), pch=19, col=\"blue\", cex=2)\n #\n # # Box plot\n # par(mar=c(6,4,1,1))\n # boxplot(mixingParamXDF, las = 2)\n # points(1:30, as.vector(param$param$paramX), pch=19, col=\"blue\", cex=2)\n #\n # Average\n average <- apply(model$results$estimation$paramX, 1:2, mean)\n # 95% confidence intervals\n CI.025 <- apply(model$results$estimation$paramX, 1:2, quantile, probs = 0.025)\n CI.975 <- apply(model$results$estimation$paramX, 1:2, quantile, probs = 0.975)\n\n # Summary table\n paramXCITable <- cbind(unlist(as.data.frame(average)),\n unlist(as.data.frame(CI.025)),\n unlist(as.data.frame(CI.975)))\n colnames(paramXCITable) <- c(\"average\", \"lowerCI\", \"upperCI\")\n rownames(paramXCITable) <- paste(rep(colnames(average), each = nrow(average)), \"_\", rep(rownames(average), ncol(average)), sep=\"\")\n\n # Save summary table\n saveRDS(paramXCITable, file = './RData/modelPostSumm.rds')\n\n # Credible intervals\n paramXCITable_Full <- paramXCITable\n\n beg <- seq(1,nrow(paramXCITable_Full), by = 124)\n end <- seq(124,nrow(paramXCITable_Full), by = 124)\n sign <- abs(as.numeric(paramXCITable_Full[, 'lowerCI'] <= 0 & paramXCITable_Full[, 'upperCI'] >=0) - 3)\n sp <- northPluri[, c('EspGen','N_EspSci')] %>%\n .[!duplicated(.), ] %>%\n .[order(.[, 'EspGen']), ]\n\n # Export figure\n jpeg(paste(fig,'credibleInterval.jpeg',sep=''), width = 6, height = (1.5*nParam), res = 150, units = 'in')\n par(mfrow = c((length(beg)+1),1))\n for(i in 1:length(beg)) {\n paramXCITable <- paramXCITable_Full[beg[i]:end[i], ]\n cols <- sign[beg[i]:end[i]]\n par(mar=c(1,2,1,1))\n plot(0, 0, xlim = c(1, nrow(paramXCITable)), ylim = round(range(paramXCITable)), type = \"n\", xlab = \"\", ylab = \"\", main=paste(colnames(mixingMeansParamX)[i]), xaxt=\"n\", bty = 'n')\n axis(1,1:124,las=2, labels = rep('',124))\n abline(h = 0,col = 'grey')\n arrows(x0 = 1:nrow(paramXCITable), x1 = 1:nrow(paramXCITable), y0 = paramXCITable[, 2], y1 = paramXCITable[, 3], code = 3, angle = 90, length = 0.05, col = cols)\n points(1:nrow(paramXCITable), paramXCITable[,1], pch = 15, cex = 1, col = cols)\n }\n mtext(text = sp[,'N_EspSci'], side = 1, line = 1, outer = FALSE, at = 1:124, col = 1, las = 2, cex = 0.4)\n dev.off()\n\n# =========================\n# 5.1 Variance partitioning\n# =========================\n\n # for parameter names: colnames(mixingMeansParamX)\n nGroup <- length(unique(envGroup)) + 2\n variationPart <- variPart(model, envGroup)\n saveRDS(variationPart, file = './RData/variPart.rds')\n\n Colour <- rainbow(n = nGroup, s = 1, v = 1, start = 0, end = max(1, nGroup - 1)/nGroup, alpha = 1)\n\n jpeg(paste(fig,'variancePartitioning.jpeg',sep=''), width = 6, height = 4, res = 150, units = 'in')\n par(mfrow = c(1,1), mar = c(6,3,1,1))\n barplot(t(variationPart), col=Colour, names.arg = sp[, 'N_EspSci'], las = 2, cex.names = 0.4, cex.axis = 0.6)\n\n # Create legend elements\n legendVector <- character(nGroup)\n variPartLabel <- c(unique(envGroup), 'Random site', 'Random plot')\n\n for(i in 1:nGroup) {\n legendVector[i] <- paste(variPartLabel[i], ' (mean = ', round(mean(variationPart[, i]), 4)*100, \"%)\", sep=\"\")\n }\n\n legend('bottomleft', legend = legendVector, fill = Colour, bg = 'white', cex = 0.5)\n dev.off()\n\n# ========================================================================\n# 5.2 Variance partitioning for individual parameters (species diagnotics)\n# ========================================================================\n\n # Extract variance partitioning per parameters for individual taxa diagnostics\n nGroup <- length(envCov) + 2\n variationPart <- variPart(model, c('Intercept',envCov))\n saveRDS(variationPart, file = './RData/variPartInd.rds')\n\n# =======================\n# 6. Association networks\n# =======================\n # Extract all estimated associatin matrix\n assoMat <- corRandomEff(model)\n\n # Average\n siteMean <- apply(assoMat[, , , 1], 1:2, mean)\n plotMean <- apply(assoMat[, , , 2], 1:2, mean)\n colnames(plotMean) <- rownames(plotMean) <- sp[,'N_EspSci']\n saveRDS(plotMean, file = './RData/plotMean.rds')\n\n\n #--------------------\n ### Site level effect\n #--------------------\n # Build matrix of colours for chordDiagram\n siteDrawCol <- matrix(NA, nrow = nrow(siteMean), ncol = ncol(siteMean))\n siteDrawCol[which(siteMean > 0.4, arr.ind=TRUE)]<-\"red\"\n siteDrawCol[which(siteMean < -0.4, arr.ind=TRUE)]<-\"blue\"\n # Build matrix of \"significance\" for corrplot\n siteDraw <- siteDrawCol\n siteDraw[which(!is.na(siteDraw), arr.ind = TRUE)] <- 0\n siteDraw[which(is.na(siteDraw), arr.ind = TRUE)] <- 1\n siteDraw <- matrix(as.numeric(siteDraw), nrow = nrow(siteMean), ncol = ncol(siteMean))\n #--------------------\n ### Plot level effect\n #--------------------\n # Build matrix of colours for chordDiagram\n plotDrawCol <- matrix(NA, nrow = nrow(plotMean), ncol = ncol(plotMean))\n plotDrawCol[which(plotMean > 0.4, arr.ind=TRUE)]<-\"red\"\n plotDrawCol[which(plotMean < -0.4, arr.ind=TRUE)]<-\"blue\"\n # Build matrix of \"significance\" for corrplot\n plotDraw <- plotDrawCol\n plotDraw[which(!is.na(plotDraw), arr.ind = TRUE)] <- 0\n plotDraw[which(is.na(plotDraw), arr.ind = TRUE)] <- 1\n plotDraw <- matrix(as.numeric(plotDraw), nrow = nrow(plotMean), ncol = ncol(plotMean))\n\n # # plotDraw plots\n # par(mfrow=c(1,2))\n # # Matrix plot\n # Colour <- colorRampPalette(c(\"blue\", \"white\", \"red\"))(200)\n # corrplot::corrplot(siteMean, method = \"color\", col = Colour, type = \"lower\", diag = FALSE, p.mat = siteDraw, tl.srt = 45)\n # # Chord diagram\n # circlize::chordDiagram(siteMean, symmetric = TRUE, annotationTrack = c(\"name\", \"grid\"), grid.col = \"grey\", col = siteDrawCol)\n\n # siteDraw plots\n jpeg(paste(fig,'correlationPlot.jpeg',sep=''), width = 7, height = 7, res = 150, units = 'in')\n # Matrix plot\n Colour <- colorRampPalette(c(\"blue\", \"white\", \"red\"))(200)\n corrplot::corrplot(plotMean, method = \"color\", col = Colour, type = \"lower\", diag = FALSE, p.mat = plotDraw, tl.srt = 65, tl.cex = 0.4, pch.cex = 0.3, tl.col = 'black')\n dev.off()\n\n jpeg(paste(fig,'chordDiagram.jpeg',sep=''), width = 7, height = 7, res = 150, units = 'in')\n # Chord diagram\n circlize::chordDiagram(plotMean, symmetric = TRUE, annotationTrack = 'grid', grid.col = \"grey\", col = plotDrawCol, preAllocateTracks = 1)\n circlize::circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {\n xlim = circlize::get.cell.meta.data(\"xlim\")\n ylim = circlize::get.cell.meta.data(\"ylim\")\n sector.name = circlize::get.cell.meta.data(\"sector.index\")\n circlize::circos.text(mean(xlim), ylim[1] + .1, sector.name, facing = \"clockwise\", niceFacing = TRUE, adj = c(0, 0.5), cex = 0.4)\n # circlize::circos.axis(h = \"top\", labels.cex = 0.5, major.tick.percentage = 0.2, sector.index = sector.name, track.index = 2)\n }, bg.border = NA)\n dev.off()\n\n# ===============================================\n# 7. Computing the explanatory power of the model\n# ===============================================\n # Prevalence\n prevSp <- colSums(northPluri_HMSC$Y)\n # Coefficient of multiple determination\n R2 <- Rsquared(model, averageSp = FALSE)\n R2comm <- Rsquared(model, averageSp = TRUE)\n\n # Save R^2 calculation for individual summaries\n saveRDS(R2, file = './RData/modelR2.rds')\n saveRDS(R2comm, file = './RData/modelR2comm.rds')\n\n # Draw figure\n jpeg(paste(fig,'r2summaries.jpeg',sep=''), width = 6, height = 5, res = 150, units = 'in')\n plot(prevSp, R2, xlab = \"Prevalence\", ylab = expression(R^2), cex = 0.8, pch=19, las=1, cex.lab = 1, main = 'Explanatory power of the model')\n abline(h = R2comm, col = \"blue\", lwd = 2)\n dev.off()\n\n # Extract all MCMC of paramX\n # model$results$estimation$paramX\n\n ### Full joint probability distribution\n # fullPost <- jposterior(model)\n\n# =============================================\n# 8. Generating predictions for validation data\n# =============================================\n\n modelAUC <- crossValidation(data = northPluri_HMSC, nCV = 20, validPct = 0.2)\n saveRDS(modelAUC, file = './RData/modelAUC.rds')\n\n meanAUC <- colMeans(modelAUC)\n sdAUC <- apply(modelAUC, MARGIN = 2, FUN = sd)\n\n jpeg(paste(fig,'crossValidation.jpeg',sep=''), width = 6, height = 5, res = 150, units = 'in')\n plot(prevSp, meanAUC, ylim = c(0,1), xlab = \"Prevalence\", ylab = 'AUC', cex = 0.8, pch=19, las=1, cex.lab = 1, main = 'Monte Carlo cross-validation with AUC of ROC curves')\n abline(h = 0.5, col = 'grey')\n arrows(x0 = prevSp, x1 = prevSp, y0 = (meanAUC - sdAUC), y1 = (meanAUC + sdAUC), code = 3, angle = 90, length = 0.05)\n points(prevSp, meanAUC, pch = 19, cex = 0.8)\n dev.off()\n\n# # =============================================\n# # 9. Generating predictions from complete model\n# # =============================================\n#\n# modelPredictions <- predict(model)\n# saveRDS(modelPredictions, file = './RData/modelPredictions.rds')\n\n# =========================================\n# 10. Generating predictions study area grid\n# =========================================\n\n # Load HMSC model\n model <- readRDS('./RData/modelHSMC.rds')\n\n # Importing data for study grid that I wish to use for species distribution predictions\n egsl <- readRDS('./RData/egsl_grid.rds')\n\n # Use 'Bathy_Mean' instead of 'Prof', as 'Prof' comes from northPluri data\n colnames(egsl)[which(colnames(egsl) == 'Bathy_Mean')] <- 'Prof'\n\n # 'Prof' is positive, while 'Bathy_Mean' is negative, uniformize for model\n egsl[, 'Prof'] <- -egsl[, 'Prof']\n\n # 'x' and 'y' in egsl grid data, change to match environmental variables used\n colN <- colnames(egsl)\n colnames(egsl)[colN == 'x'] <- 'LoDeTow'\n colnames(egsl)[colN == 'y'] <- 'LaDeTow'\n\n # Remove NAs\n envCov <- c('Prof','SSAL_MEAN','SalMoyMoy','TempMoyMoy','STEMMEAN','BTEMMEAN','O2_Sat_Mea','LaDeTow','LoDeTow')\n NAs <- egsl[, envCov] %>%\n lapply(X = ., FUN = function(x) which(is.na(x))) %>%\n unlist(.) %>%\n unique(.)\n\n egsl <- egsl[-NAs, ]\n\n # New environmental covariables matrix (X matrix)\n Xnew <- egsl[, envCov]\n\n # New site- and plot-level random effect (Pi matrix)\n PiNew <- data.frame(sampling_unit = as.factor(egsl[, 'ID']),\n survey_number = as.factor(rep(1, nrow(egsl))))\n\n # Organize the data into an HMSCdata object\n dataEGSL <- as.HMSCdata(X = Xnew, Random = PiNew, scaleX = T, interceptX = T)\n\n # Generate predictions\n predEGSL <- predict(model, newdata = dataEGSL)\n\n # Replace NAs in grid\n data <- matrix(nrow = (length(NAs) + nrow(predEGSL)), ncol = ncol(predEGSL), data = 0)\n dimnames(data) = list(paste('ID',seq(1:nrow(data)), sep = ''), colnames(predEGSL))\n data[NAs, ] <- NA\n data[seq(1:nrow(data))[-NAs], ] <- predEGSL\n predEGSL <- data\n\n # Save predictions\n saveRDS(predEGSL, file = './RData/predEGSL.rds')\n", "meta": {"hexsha": "1fde8377382b24711ad187537d447b98d06bfcde", "size": 19606, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/3_northPluri_JSDM.r", "max_stars_repo_name": "david-beauchesne/EGSL_species_distribution", "max_stars_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-10T12:54:25.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-10T12:54:25.000Z", "max_issues_repo_path": "Script/3_northPluri_JSDM.r", "max_issues_repo_name": "david-beauchesne/EGSL_species_distribution", "max_issues_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Script/3_northPluri_JSDM.r", "max_forks_repo_name": "david-beauchesne/EGSL_species_distribution", "max_forks_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.5953488372, "max_line_length": 262, "alphanum_fraction": 0.5521779047, "num_tokens": 5377, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.34569497312102565}} {"text": "\n\nengScript_add <- function(engScript_a, engScript_b){\n return((engScript_a + engScript_b))\n}", "meta": {"hexsha": "f7d4c7a2198cbe510e9c548f0d9b756cde7677ee", "size": 96, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/EngScript_examples/R/basic_functions.r", "max_stars_repo_name": "jarble/Polyglot-code-generator", "max_stars_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2015-11-30T06:04:27.000Z", "max_stars_repo_stars_event_max_datetime": "2016-06-08T23:45:26.000Z", "max_issues_repo_path": "examples/EngScript_examples/R/basic_functions.r", "max_issues_repo_name": "jarble/Polyglot-code-generator", "max_issues_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/EngScript_examples/R/basic_functions.r", "max_forks_repo_name": "jarble/Polyglot-code-generator", "max_forks_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.2, "max_line_length": 52, "alphanum_fraction": 0.7395833333, "num_tokens": 27, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.34569497312102554}} {"text": "# Code to generate plots for manuscript\nlibrary(rstan)\nlibrary(plyr)\nlibrary(magrittr)\nlibrary(lubridate)\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(patchwork)\nlibrary(reshape2)\n\n# Loading and processing mosquito and case data\ntseries <- read.csv(\"Data/Vitoria.data.csv\")\n\npost <- ddply(tseries, .(tot.week), summarise,\n qobs = sum(Mosquitoes, na.rm = T),\n yobs = sum(Cases, na.rm = T),\n tau = sum(Trap, na.rm = T),\n week = unique(Week),\n year = unique(Year),\n epiweek = 0,\n epiyear = 0)\n\n# Generating epidemic weeks/years that count from peak to peak (week 16)\npost$epiweek[16:243] <- c(1:52)\npost$epiyear <- cumsum(post$epiweek == 1)\n\n# Population size\npop <- 327801\n\n# Loading and processing the weather data\nweather <- read.csv(\"Data/Vitoria.weather.csv\") %>% \n mutate(date = ymd(BRST), year = year(date), week = week(date))\n\nweather <- subset(weather, date < date[1] + weeks(243))\nweather$control <- rep(c(1:243), each = 7)\n\ntemp <- ddply(weather, .(control), summarise, temp = mean(Mean.TemperatureC, na.rm = T),\n humid = mean(Mean.Humidity, na.rm = T))\n\npost$temp <- temp$temp\npost$rov <- 7 * exp(0.21 * temp$temp - 7.9)\n\n# Loading MCMC results\nsim <- readRDS(\"Results/chain.rds\")\n\n# Effect of control in the number of cases in the following year\nmoving <- readRDS(\"Results/moving_control.rds\")\n\n# Figure 1: Observed and estimated time series =================================\n\n# Case reports\nyhat <- rstan::extract(sim, c(\"y_meas\"), permute = T)[[1]] %>% \n reshape2::melt(varnames = c(\"rep\", \"week\"), value.name = \"yhat\") %>% \n ddply(.(week), summarise,\n ymed = median(yhat),\n ymin = quantile(yhat, 0.1), \n ymax = quantile(yhat, 0.9))\n \n# Trapped mosquitoes\nqhat <- rstan::extract(sim, c(\"q_meas\"), permute = T)[[1]] %>%\n reshape2::melt(varnames = c(\"rep\", \"week\"), value.name = \"qhat\") %>% \n ddply(.(week), summarise,\n qmed = median(qhat),\n qmin = quantile(qhat, 0.1), \n qmax = quantile(qhat, 0.9))\n\n# Mosquito mortality rate\nsystem <- rstan::extract(sim, \"state\", permute = T)[[1]]\n\ndvmu <- data.frame(rep = 1:dim(system)[1],\n dvmu = 1.47 * rstan::extract(sim, \"dvmu_c\", permute = T)[[1]])\n\ndv <- system[, 2:244, 12] %>% \n reshape2::melt(varnames = c(\"rep\", \"control\"), value.name = \"dv\") %>% \n mutate(dvnat = exp(dv)) %>% \n join(dvmu) %>% \n mutate(dvnat = dvnat * dvmu) %>% \n join(temp)\n\n# Plotting\nyhat %>% \n ggplot(aes(week, ymin = ymin, ymax = ymax)) +\n geom_ribbon(fill = \"grey70\") + \n geom_line(aes(week, ymed)) + \n geom_point(aes(tot.week, yobs), data = post, inherit.aes = FALSE, size = 0.5) +\n theme_classic() +\n scale_y_continuous(expand = c(0, 0)) + \n scale_x_continuous(expand = c(0, 1), breaks = c(1, 54, 106, 158, 210), labels = c(2008, 2009, 2010, 2011, 2012)) +\n ylab(\"case reports\") + \n xlab(\"year\") +\n ggtitle(\"A\") -> fig1a\n\nqhat %>% \n ggplot(aes(week, ymin = qmin, ymax = qmax)) +\n geom_ribbon(fill = \"grey70\") + \n geom_line(aes(week, qmed)) + \n geom_point(aes(tot.week, qobs), data = post, inherit.aes = FALSE, size = 0.5) +\n theme_classic() +\n scale_y_continuous(expand = c(0, 0)) + \n scale_x_continuous(expand = c(0, 1), breaks = c(1, 54, 106, 158, 210), labels = c(2008, 2009, 2010, 2011, 2012)) +\n ylab(\"mosquitoes trapped\") + \n xlab(\"year\") +\n ggtitle(\"B\") -> fig1b\n\npost %>% \n ggplot(aes(tot.week, 1 / rov)) + \n geom_line() + \n theme_classic() +\n scale_y_continuous(expand = c(0.05, 0)) +\n scale_x_continuous(expand = c(0, 1), breaks = c(1, 54, 106, 158, 210), labels = c(2008, 2009, 2010, 2011, 2012)) +\n ylab(\"EIP (weeks)\") +\n xlab(\"year\") +\n ggtitle(\"C\") -> fig1c\n\ndv %>% \n ggplot(aes(control, dvnat)) + \n stat_summary(fun.ymin = function(x) quantile(x, 0.05),\n fun.ymax = function(x) quantile(x, 0.95),\n geom = \"ribbon\", fill = \"gray70\") +\n stat_summary(fun.y = \"median\", geom = \"line\") +\n theme_classic() +\n scale_y_continuous(expand = c(0.05, 0)) +\n scale_x_continuous(expand = c(0, 1), breaks = c(1, 54, 106, 158, 210), labels = c(2008, 2009, 2010, 2011, 2012)) +\n ylab(\"mosquito mortality rate\") +\n xlab(\"year\") +\n ggtitle(\"D\") -> fig1d\n\npostscript(\"Manuscript/figures/fig1.eps\",\n width = 5, height = 7, paper = \"special\", horizontal = FALSE,\n family = \"ArialMT\")\n\nfig1a + fig1b + fig1c + fig1d + plot_layout(ncol = 1)\n\ndev.off()\n\n# Figure 2: Estimates of latent mosquito mortality rate ========================\n\npostscript(\"Manuscript/figures/fig2.eps\",\n width = 4, height = 3, paper = \"special\", horizontal = FALSE,\n family = \"ArialMT\")\n\ndv %>%\n ddply(.(control), summarise,\n dvnat = median(dvnat),\n temp = median(temp)) %>%\n ggplot(aes(temp, dvnat)) +\n geom_point() +\n theme_classic() +\n scale_y_continuous(expand = c(0.05, 0)) +\n scale_x_continuous(expand = c(0, 1)) +\n ylab(\"mosquito mortality rate\") +\n xlab(\"weekly mean temperature\")\n\ndev.off()\n\n# Figure 3: Effect of adult control implemented in different weeks ===================\n\n# Adding week and year to moving window control results\nmoving <- moving %>% \n dplyr::rename(\"dv0\" = \"dvmu\") %>%\n mutate(week = week(ymd(\"2008-01-01\") + weeks(control - 1)),\n year = year(ymd(\"2008-01-01\") + weeks(control - 1))) %>% \n join(dv)\n\n# Computing the overall effect of control over 2008-2010\noverall <- moving %>% \n subset(year < 2011) %>% \n ddply(.(rep, week), summarise,\n tcases = sum(tcases),\n tcases0 = sum(tcases0), \n ratio = tcases / tcases0,\n diff = tcases0 - tcases,\n dv0 = mean(dv0),\n dvmu = mean(dvmu),\n delta = mean(delta),\n phi = mean(phi))\n\noverall <- dplyr::bind_rows(overall, moving) %>% \n mutate(year = factor(year, exclude = NULL, labels = c(\"2008\",\"2009\", \"2010\", \"2011\", \"2012\", \"overall\")))\n\n# Computing the median overall best week for control\noverall %>% \n ddply(.(rep, year), summarise,\n argmin = week[which.min(ratio)]) %>% \n ddply(.(year), summarise,\n argmin = median(argmin))\n\n# Plotting\n\npostscript(\"Manuscript/figures/fig3.eps\",\n width = 4, height = 5, paper = \"special\", horizontal = FALSE,\n family = \"ArialMT\")\n\noverall %>% \n subset(year %in% c(\"2008\", \"2009\", \"2010\", \"overall\") & week < 53) %>%\n ggplot(aes(week, ratio)) +\n stat_summary(fun.ymin = function(x) quantile(x, 0.1),\n fun.ymax = function(x) quantile(x, 0.9),\n geom = \"ribbon\", fill = \"gray70\") +\n stat_summary(fun.y = \"median\", geom = \"line\") +\n facet_grid(year ~.) +\n geom_hline(yintercept = 1.0, color = \"gray50\", linetype = 2) +\n theme_classic() + \n theme(strip.background = element_blank()) + \n scale_x_continuous(expand = c(0, 0)) + \n xlab(\"week of control\") +\n ylab(\"case ratio over following year\")\n\ndev.off()\n\n# Figure 4: effect of phi on effectiveness of control ==========================\n\noverall%>% \n subset(week == 34 & year == \"overall\") %>% \n ggplot(aes(dvmu, ratio)) + \n geom_point(size = 0.5) + \n theme_classic() + \n theme(strip.background = element_blank()) + \n labs(x = \"mosquito mortality rate\", y = \"case ratio\") -> fig4a\n\noverall%>% \n subset(week == 34 & year == \"overall\") %>% \n ggplot(aes(phi, ratio)) + \n geom_point(size = 0.5) + \n theme_classic() + \n theme(strip.background = element_blank()) + \n labs(x = \"case reporting probability\", y = \"case ratio\") -> fig4b\n\npostscript(\"Manuscript/figures/fig4.eps\",\n width = 6, height = 3, paper = \"special\", horizontal = FALSE,\n family = \"ArialMT\")\n\nfig4a + fig4b\n\ndev.off()\n\n# State var and control correlations ===========================================\n\n# Cases\ncases <- system[, , 11] %>% \n aaply(1, diff) %>% \n multiply_by(pop) %>% \n reshape2::melt(varnames = c(\"rep\", \"control\"), value.name = \"cases\") \n\n# Mosquitoes\nmosq <- system[, 2:244, 9] %>% \n reshape2::melt(varnames = c(\"rep\", \"control\"), value.name = \"mosq\") \n\n# Probability of surviving EIP\ninfmosq <- system[, , 17] %>% \n aaply(1, diff) %>% \n melt(varnames = c(\"rep\", \"control\"), value.name = \"inf\")\n\ndeadmosq <- system[, , 16] %>% \n aaply(1, diff) %>% \n reshape2::melt(varnames = c(\"rep\", \"control\"), value.name = \"dead\")\n\nsurv <- join(infmosq, deadmosq) %>%\n mutate(total_exits = dead + inf,\n psurv = inf / total_exits) \n\n# Joining everything\nmoving <- join(moving, cases) %>% \n join(mosq) %>% \n join(surv)\n\npostcorr <- moving %>% \n subset(control < 191) %>% \n ddply(.(rep), summarise,\n dv = cor(ratio, dvnat),\n mosq = cor(ratio, mosq), \n cases = cor(ratio, cases), \n psurv = cor(ratio, psurv),\n temp = cor(ratio, temp))\n\ncolwise(median)(postcorr)\n\n#===============================================================================\n# Supplemental figures\n#===============================================================================\n\n# Figure S1: epsilons ==========================================================\n\neps_dv <- rstan::extract(sim, \"eps_dv\", permute = T)[[1]] %>% \n adply(2, quantile, c(0.1, 0.5, 0.9)) %>% \n mutate(week = as.numeric(X1))\nnames(eps_dv) <- c(\"X1\", \"min\", \"med\", \"max\", \"week\")\n\neps_rv <- rstan::extract(sim, \"eps_rv\", permute = T)[[1]] %>% \n adply(2, quantile, c(0.1, 0.5, 0.9)) %>% \n mutate(week = as.numeric(X1))\nnames(eps_rv) <- c(\"X1\", \"min\", \"med\", \"max\", \"week\")\n\npostscript(\"Manuscript/figures/figS1.eps\",\n width = 5.2, height = 3,\n family = \"ArialMT\")\n\nggplot(eps_dv, aes(week, med)) + \n geom_ribbon(aes(ymin = min, ymax = max), fill = \"grey70\") +\n geom_line() + \n geom_hline(yintercept = 0, linetype = 2) + \n theme_classic() + \n scale_x_continuous(expand = c(0, 1)) +\n ylab(expression(epsilon[nu])) +\n ggtitle(\"A\") -> figs1.a\n\nggplot(eps_rv, aes(week, med)) + \n geom_ribbon(aes(ymin = min, ymax = max), fill = \"grey70\") +\n geom_line() + \n theme_classic() + \n scale_x_continuous(expand = c(0, 1)) +\n ylab(expression(epsilon[r])) +\n ggtitle(\"B\") -> figs2.b\n\ngrid.arrange(figs1.a, figs2.b, nrow = 1)\n\ndev.off()\n\n# Figure S2: sigmas ============================================================\n\npostscript(\"Manuscript/figures/figS2.eps\",\n width = 6, height = 4,\n family = \"ArialMT\")\n\npar(mfrow = c(1, 2))\nrstan::extract(sim, \"sigmadv\", permute = T)[[1]] %>% hist(main = \"A\", xlab = expression(sigma[d]), freq = F)\nrstan::extract(sim, \"sigmarv\", permute = T)[[1]] %>% hist(main = \"B\", xlab = expression(sigma[r]), freq = F)\n\ndev.off()\n\n# Figure S3: epidemiological parameters ===================================================\n\nepiparams <- rstan::extract(sim, c(\"ro_c\", \"gamma_c\", \"delta_c\", \"dvmu_c\"), permute = T)\n\npostscript(\"Manuscript/figures/figS3.eps\",\n width = 6, height = 6,\n family = \"ArialMT\")\n\npar(mfrow = c(2, 2))\n\nhist(epiparams[[1]], main = \"A\", xlab = \"scaled latent period\", freq = F, breaks = 30, ylim = c(0, 1.2))\ncurve(dgamma(x, 8.3, 8.3), add = T, lwd = 2)\n\nhist(epiparams[[2]], main = \"B\", xlab = \"scaled rate of infectious decay\", freq = F, breaks = 30)\ncurve(dgamma(x, 100, 100), add = T, lwd = 2)\n\nhist(epiparams[[3]], main = \"C\", xlab = \"scaled period of cross-immunity\", freq = F, breaks = 30, ylim = c(0, 1.3))\ncurve(dgamma(x, 10, 10), add = T, lwd = 2)\n\nhist(epiparams[[4]], main = \"D\", xlab = \"scaled mosquito mortality rate\", freq = F, breaks = 30)\ncurve(dgamma(x, 100, 10), add = T, lwd = 2)\n\ndev.off()\n\n# Figure S4: initial conditions ===========================================================\n\nics <- rstan::extract(sim, c(\"S0\", \"E0\",\"I0\", \"logNv\", \"dv0\", \"rv0\"), permute = T)\n\npostscript(\"Manuscript/figures/figS4.eps\",\n width = 6, height = 5,\n family = \"ArialMT\")\n\npar(mfrow = c(2, 3))\n\nhist(ics$S0, freq = F, main = \"A\", xlab = expression(S[0]))\ncurve(dbeta(x, 4, 6), add = T, lwd = 2)\n\nhist(ics$E0, freq = F, main = \"B\", xlab = expression(E[0]), ylim = c(0, 0.015))\ncurve(dgamma(x, 10, 0.1), add = T, lwd = 2)\n\nhist(ics$I0, freq = F, main = \"C\", xlab = expression(I[0]), ylim = c(0, 0.02))\ncurve(dgamma(x, 6, 0.1), add = T, lwd = 2)\n\nhist(ics$logNv, freq = F, main = \"D\", xlab = expression(log(V[N0])))\ncurve(dnorm(x, 0.7, 0.3), add = T, lwd = 2)\n\nhist(ics$dv0, freq = F, main = \"E\", xlab = expression(nu[0]))\ncurve(dnorm(x, 0, 0.5), add = T, lwd = 2)\n\nhist(ics$rv0, freq = F, main = \"F\", xlab = expression(r[0]))\ncurve(dnorm(x, 0, 0.5), add = T, lwd = 2)\n\ndev.off()\n\n# Figure S5: Measurement parameters ============================================\n\nlibrary(truncnorm)\n\nmeas <- rstan::extract(sim, c(\"log_phi_q\", \"phi_y\", \"eta_inv_q\", \"eta_inv_y\"), permute = T)\n\npostscript(\"Manuscript/figures/figS5.eps\",\n width = 6, height = 6,\n family = \"ArialMT\")\n\npar(mfrow = c(2, 2))\n\nhist(meas$log_phi_q, freq = F, main = \"A\", xlab = expression(log(phi[q])))\ncurve(dnorm(x, -13, 0.5), add = T, lwd = 2)\n\nhist(meas$phi_y, freq = F, main = \"B\", xlab = expression(phi[y]))\ncurve(dbeta(x, 7, 77), add = T, lwd = 2)\n\nhist(meas$eta_inv_q, freq = F, main = \"C\", xlab = expression(eta[q]))\ncurve(dtruncnorm(x, a = 0, b = Inf, mean = 0, sd = 5), add = T)\n\nhist(meas$eta_inv_y, freq = F, main = \"D\", xlab = expression(eta[y]))\ncurve(dtruncnorm(x, a = 0, b = Inf, mean = 0, sd = 5), add = T)\n\ndev.off()\n\n# Model checking ===============================================================\n\nyrep <- rstan::extract(sim, \"y_meas\", permute = T)[[1]]\nqrep <- rstan::extract(sim, \"q_meas\", permute = T)[[1]]\n\n# Figure S6: ACF plots =========================================================\n\n# Calculating the observed autocorrelation function\nyacf <- acf(post$yobs, lag.max = 55, plot = F)$acf\nqacf <- acf(post$qobs, lag.max = 55, plot = F)$acf\n\n# Summarizing the posterior acf\nyacfrange <- apply(yrep, 1, function(x){acf(x, lag.max = 55, plot = F)$acf}) %>% \n apply(1, quantile, probs = c(0.1,0.5, 0.9))\n\nqacfrange <- apply(qrep, 1, function(x){acf(x, lag.max = 55, plot = F)$acf}) %>% \n apply(1, quantile, probs = c(0.1,0.5, 0.9))\n\n# Assembling data frame for ggplot\nacfdf <- data.frame(\"lag\" = c(0:55), \n \"yobs\" = yacf,\n \"qobs\" = qacf,\n \"ymed\" = yacfrange[2, ], \n \"ymin\" = yacfrange[1, ], \n \"ymax\" = yacfrange[3, ],\n \"qmed\" = qacfrange[2, ], \n \"qmin\" = qacfrange[1, ], \n \"qmax\" = qacfrange[3, ]\n )\n\n# Plots\nggplot(acfdf, aes(lag, yobs)) + \n geom_hline(yintercept = 0, color = \"gray50\") +\n geom_linerange(aes(lag, ymin = ymin, ymax = ymax)) +\n geom_point() +\n theme_classic() +\n xlab(\"lag (weeks)\") +\n ylab(\"case autocorrelation\") + \n scale_x_continuous(expand = c(0, 0.1)) + \n ggtitle(\"A\") -> figs5.a\n\nggplot(acfdf, aes(lag, qobs)) + \n geom_hline(yintercept = 0, color = \"gray50\") +\n geom_linerange(aes(lag, ymin = qmin, ymax = qmax)) +\n geom_point() +\n theme_classic() +\n xlab(\"lag (weeks)\") +\n ylab(\"mosquito autocorrelation\") + \n scale_x_continuous(expand = c(0, 0.1)) + \n ggtitle(\"B\") -> figs5.b\n\npostscript(\"Manuscript/figures/figS6.eps\",\n width = 5.2, height = 3,\n family = \"ArialMT\")\n\ngrid.arrange(figs5.a, figs5.b, nrow = 1)\n\ndev.off()\n\n# Figure S7: Totals ============================================================\n\npostscript(\"Manuscript/figures/figS7.eps\",\n width = 6, height = 4,\n family = \"ArialMT\")\n\npar(mfrow = c(1, 2))\n\nhist(rowSums(yrep), main = \"A\", xlab = \"total cases\", freq = F, breaks = 20)\nabline(v = sum(post$yobs), lwd = 2)\n\nhist(rowSums(qrep), main = \"B\", xlab = \"total captured mosquitoes\", freq = F, breaks = 20)\nabline(v = sum(post$qobs), lwd = 2)\n\ndev.off()\n\n# Figure S8: Min and max ===========================================================\n\npostscript(\"Manuscript/figures/figS8.eps\",\n width = 6, height = 6,\n family = \"ArialMT\")\n\npar(mfrow = c(2, 2))\n\nhist(apply(yrep, 1, max), main = \"A\", xlab = \"maximum weekly cases\", freq = F)\nabline(v = max(post$yobs), lwd = 2)\n\nhist(apply(qrep, 1, max), main = \"B\", xlab = \"maximum weekly trap count\", freq = F)\nabline(v = max(post$qobs), lwd = 2)\n\nhist(apply(yrep, 1, min), main = \"C\", xlab = \"minimum weekly cases\", freq = F)\nabline(v = min(post$yobs), lwd = 2)\n\nhist(apply(qrep, 1, min), main = \"D\", xlab = \"minimum weekly trap count\", freq = F)\nabline(v = min(post$qobs), lwd = 2)\n\ndev.off()\n\n# Figure S9: Probability of surviving EIP ======================================\n\npostscript(\"Manuscript/figures/figS9.eps\",\n width = 6, height = 5,\n family = \"ArialMT\")\n\nmoving %>% \n ggplot(aes(control, psurv)) + \n stat_summary(fun.ymin = function(x) quantile(x, 0.1),\n fun.ymax = function(x) quantile(x, 0.9),\n geom = \"ribbon\", fill = \"gray70\") +\n stat_summary(fun.y = \"median\", geom = \"line\") + \n theme_classic() + \n scale_x_continuous(expand = c(0, 1), breaks = c(1, 54, 106, 158, 210), labels = c(2008, 2009, 2010, 2011, 2012)) +\n xlab(\"year\") + \n ylab(\"probability of surviving EIP\") -> figS9a\n\nfig1d + ggtitle(\"\") + figS9a + plot_layout(ncol = 1)\n \ndev.off()\n\n", "meta": {"hexsha": "d3844df07a440c888802b00fda3baba0847eedf0", "size": 17058, "ext": "r", "lang": "R", "max_stars_repo_path": "Analysis/plots.r", "max_stars_repo_name": "clint-leach/mosquito-recon", "max_stars_repo_head_hexsha": "57506d036258a0e556f6ae9c8e5a9975c4c70e39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-11-30T08:56:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-11T09:40:12.000Z", "max_issues_repo_path": "Analysis/plots.r", "max_issues_repo_name": "clint-leach/mosquito-recon", 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YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.34569497312102554}} {"text": "# All load of data goes here\n\nmoocs <- read.csv(\"./data/HMXPC13_DI_v2_5-14-14.csv\" ,header=TRUE, sep=\",\");\n# We keep only information from users that have obtained a certificate of completion\ndata <- moocs[moocs$certified == \"1\",]\n# We keep data only for registrants\ndata <- moocs[moocs$registered == \"1\",]\n# Years of students (we traduce year of birth to age)\nyears <- 2014 - data$YoB\n# Variable for range of studies\nstudies <- data$LoE_DI\n\n#tcertified <- table(studies,certified) # Number of#certified users by its LoE\n# t1 <- table(studies, courses) # Count ocurrences of level of studies per course\n# t2 <- table(studies) # Count ocurrences of level of studies in all courses\n\n#2\ncategories <- c(10,18,25,30,35,45,55,65,80,90)\nagecat <- cut(years, categories)\ntable(agecat)\n\n\n# 3\ngender <- table(data$gender)\ngenderpercent <- gender/sum(gender)*100\nlabels <- c(\"Missing\",\"Female\",\"Male\",\"Other\")\ngenderpercent <- round(genderpercent, digits=0)\nlabels <- paste(labels, genderpercent)\nlabels <- paste(labels,\"%\",sep=\"\") # ad % to labels\n\n# 4\ndataf <- table(data$final_cc_cname_DI)\nCountries <- as.data.frame(dataf)\nCountries[1] <- countrycode(Countries$Var1,\"country.name\", \"iso3c\")\n\nsPDF <- joinCountryData2Map(Countries, joinCode = \"NAME\", nameJoinColumn = \"Var1\")\nmapCountryData(sPDF, nameColumnToPlot=\"Freq\", mapTitle=\"N\u00ba de alumnos certificados atendiendo al Pa\u00eds\", colourPalette=\"terrain\")\n\n# Adding courses list\ncourse_id <- as.data.frame(unique(data$course_id))\ndf4 <- course_id\ndf4 <- cbind(fullname = names, df4)\n\nnames = c(\"CB22x, The Ancient Greek Hero (Launched Spring, 2013)\",\n \"CS50x, Introduction to Computer Science (Launched Fall, 2012)\",\n \"ER22x, Justice (Launched Spring, 2013)\",\n \"PH207x, Health in Numbers: Quantitative Methods in Clinical & Public Health Research (Launched Fall, 2012)\",\n \"PH278x, Human Health and Global Environmental Change (Launched Spring, 2013)\",\n \"6.002x, Circuits and Electronics (Launched Fall, 2012)\",\n \"6.002x, Circuits and Electronics (Launched Spring, 2013)\",\n \"14.73x, The Challenges of Global Poverty (Launched Spring, 2013)\",\n \"2.01x, Elements of Structures (Launched Spring, 2013)\",\n \"3.091x, Introduction to Solid State Chemistry (Launched Fall, 2012)\",\n \"3.091x, Introduction to Solid State Chemistry (Launched Spring, 2013)\",\n \"6.00x, Introduction to Computer Science Programming (Launched Fall, 2012)\",\n \"6.00x, Introduction to Computer Science Programming (Launched Spring, 2013)\",\n \"7.00x, Introduction to Biology - The Secret of Life (Launched Spring, 2013)\",\n \"8.02x, Electricity and Magnetism (Launched Spring, 2013)\",\n \"8.MReV, Mechanics ReView (Launched Summer, 2013)\")\n\ncolnames(df4)[2] <- \"course_id\"\n", "meta": {"hexsha": "d0b7ba2db42bf419de6a93e0e202dd1ebe1913d8", "size": 2816, "ext": "r", "lang": "R", "max_stars_repo_path": "helpers.r", "max_stars_repo_name": "Franjs88/MOOC-Visualization-Tool", "max_stars_repo_head_hexsha": "b7ccdeda7500d89b872865739cbc6a877e973fb9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "helpers.r", "max_issues_repo_name": "Franjs88/MOOC-Visualization-Tool", "max_issues_repo_head_hexsha": "b7ccdeda7500d89b872865739cbc6a877e973fb9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "helpers.r", "max_forks_repo_name": "Franjs88/MOOC-Visualization-Tool", "max_forks_repo_head_hexsha": "b7ccdeda7500d89b872865739cbc6a877e973fb9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.4193548387, "max_line_length": 128, "alphanum_fraction": 0.6963778409, "num_tokens": 790, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6859494421679929, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.34565415657943227}} {"text": "#### continuous_rqa_parameters-DCC.r: Part of `dual-conversation-constraints.Rmd` ####\n#\n# This script explores the parameters for the continuous cross-recurrence analysis\n# that we'll run over the movement data. Because this is a lengthy process,\n# we create a series of files along the way that can be re-run in pieces if needed.\n# This allows us to keep this file commented by default.\n#\n# Written by: A. Paxton (University of California, Berkeley)\n# Date last modified: 15 May 2017\n#####################################################################################\n\n#### 1. Preliminaries ####\n\n# read in libraries and functions\nsource('./supplementary-code/libraries_and_functions-DCC.r')\n\n# prep workspace and libraries\ninvisible(lapply(c('tseriesChaos',\n 'nonlinearTseries',\n 'crqa',\n 'quantmod',\n 'beepr'), \n require, \n character.only = TRUE))\n\n# read in coords dataset\ncoords = read.table('./data/DCC-trimmed-data.csv', sep=',', header = TRUE)\n\n#### 2. Determine delay with average mutual information (AMI) ####\n\n# set maximum AMI\nami.lag.max = 100\n\n# get AMI (lag and value) for both participants in each dyad\namis = coords %>% ungroup() %>%\n group_by(dyad,conv.type) %>%\n mutate(ami.val0 = min(as.numeric(mutual(euclid0, lag.max = ami.lag.max, plot = FALSE)),na.rm=TRUE)) %>%\n mutate(ami.val1 = min(as.numeric(mutual(euclid1, lag.max = ami.lag.max, plot = FALSE)),na.rm=TRUE)) %>%\n mutate(ami.loc0 = which.min(as.numeric(mutual(euclid0, lag.max = ami.lag.max, plot = FALSE)))-1) %>%\n mutate(ami.loc1 = which.min(as.numeric(mutual(euclid1, lag.max = ami.lag.max, plot = FALSE)))-1) %>%\n group_by(dyad,conv.num,conv.type,ami.val0,ami.val1,ami.loc0,ami.loc1) %>%\n distinct() %>%\n mutate(ami.selected = min(ami.loc1,ami.loc0))\n\n# write AMI information to file\nwrite.table(amis,'./data/crqa_parameters/ami_calculations-DCC.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n# if we've already run it, load it in\namis = read.table('./data/crqa_parameters/ami_calculations-DCC.csv', sep=',',header=TRUE)\n\n# join the AMI information to our whole dataframe\ncoords = join(coords,amis,by=c(\"dyad\",'conv.type','conv.num'))\n\n#### 3. Determing embedding dimension with FNN ####\n\n# set maximum percentage of false nearest neighbors\nfnnpercent = 10\n\n# create empty dataframe\nfnns = data.frame(dyad = numeric(),\n partic = numeric(),\n conv.type = numeric(),\n head.embed = numeric(),\n tail.embed = numeric())\n\n# cycle through both conversations for each dyad\nconvo.dfs = split(coords,list(coords$dyad,coords$conv.type))\nfor (conv.code in names(convo.dfs)){\n \n # cycle through participants\n for (partic in 0:1){\n \n # print update\n print(paste(\"Beginning FNN calculations for P\",partic,\" of Conversation \",conv.code,sep=\"\"))\n \n # grab next participant's data\n p.data = select(convo.dfs[[conv.code]],matches(paste(\"euclid\",partic,sep=\"\")))[,1]\n p.ami = unique(convo.dfs[[conv.code]]$ami.selected)\n \n # only proceed if we have the dyad's data\n if (length(p.data) > 0) {\n \n # calculate false nearest neighbors\n fnn = false.nearest(p.data,\n m = fnnpercent,\n d = p.ami,\n t = 0,\n rt = 10,\n eps = sd(p.data)/10)\n fnn = fnn[1,][complete.cases(fnn[1,])]\n threshold = fnn[1]/fnnpercent\n \n # identify the largest dimension after a large drop\n embed.dim.index = as.numeric(which(diff(fnn) < -threshold)) + 1\n head.embed = head(embed.dim.index,1)\n tail.embed = tail(embed.dim.index,1)\n if (length(embed.dim.index) == 0){\n head.embed = 1\n tail.embed = 1\n }\n \n # identify conversation type and dyad number from information\n conv.info = unlist(strsplit(conv.code,'[.]'))\n dyad = as.integer(conv.info[1])\n conv.type = as.integer(conv.info[2])\n \n # bind everything to data frame\n fnns = rbind.data.frame(fnns,\n cbind.data.frame(dyad, \n partic, \n conv.type, \n head.embed, \n tail.embed))\n \n }}}\n\n# change table configuration so that we get participants' embedding dimensions as columns, not rows\nfnn.partic = split(fnns,fnns$partic)\nfnn.partic.0 = plyr::rename(as.data.frame(fnn.partic[[\"0\"]]),c('head.embed' = 'embed.0')) %>%\n select(dyad,conv.type,embed.0)\nfnn.partic.1 = plyr::rename(as.data.frame(fnn.partic[[\"1\"]]),c('head.embed' = 'embed.1')) %>%\n select(dyad,conv.type,embed.1)\nfnn.merged = join(fnn.partic.0,fnn.partic.1,by=c(\"dyad\",\"conv.type\"))\n\n# choose the largest embedding dimension\nfnn.merged = fnn.merged %>% ungroup() %>%\n group_by(dyad,conv.type) %>%\n mutate(embed.selected = max(c(embed.0,embed.1)))\n\n# save false nearest neighbor calculations to file\nwrite.table(fnn.merged,'./data/crqa_parameters/fnn_calculations-DCC.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n# if we've already run it, load it in\nfnn.merged = read.table('./data/crqa_parameters/fnn_calculations-DCC.csv', sep=',',header=TRUE)\n\n# merge with coords dataset\ncoords = join(coords, fnn.merged, by = c(\"dyad\",\"conv.type\"))\n\n#### 4. Determine optimal radius ####\n\n# rescale by mean distance\ncoords_crqa = coords %>% ungroup() %>%\n dplyr::select(dyad,conv.num,conv.type,euclid0,euclid1,conv.num,ami.selected,embed.selected) %>%\n group_by(dyad,conv.num) %>%\n mutate(rescale.euclid0 = euclid0/mean(euclid0)) %>%\n mutate(rescale.euclid1 = euclid1/mean(euclid1))\n\n# create an empty dataframe to hold the parameter information\nradius_selection = data.frame(dyad = numeric(),\n conv.num = numeric(),\n chosen.delay = numeric(),\n chosen.embed = numeric(),\n chosen.radius = numeric(),\n rr = numeric())\n\n# identify radius for calculations\nradius.list = seq(.04,.26,by=.02)\n\n# cycle through all conversations of all dyads\ncrqa.data = split(coords_crqa,\n list(coords$dyad,\n coords$conv.type))\nfor (next.conv in crqa.data){\n \n # make sure we only proceed if we have data for the conversation\n if (dim(next.conv)[1] != 0){\n \n # reset `target` variables for new radius (above what RR can be)\n from.target = 101\n last.from.target = 102\n \n # cycle through radii\n for (chosen.radius in radius.list){\n \n # if we're still improving, keep going\n if (from.target < last.from.target){\n \n # keep the previous iteration's performance\n last.from.target = from.target\n \n # print update\n print(paste(\"Dyad \", unique(next.conv$dyad),\n \", conversation \",unique(next.conv$conv.num),\n \": radius \",chosen.radius,sep=\"\"))\n \n # identify parameters\n chosen.delay = unique(next.conv$ami.selected)\n chosen.embed = unique(next.conv$embed.selected)\n \n # run CRQA and grab recurrence rate (RR)\n rec_analysis = crqa(next.conv$rescale.euclid0, \n next.conv$rescale.euclid1,\n delay = chosen.delay, \n embed = chosen.embed, \n r = chosen.radius,\n normalize = 0, \n rescale = 0, \n mindiagline = 2,\n minvertline = 2, \n tw = 0, \n whiteline = FALSE,\n recpt=FALSE)\n rr = rec_analysis$RR\n \n # clear it so we don't take up too much memory (optional)\n rm(rec_analysis)\n \n # identify how far off the RR is from our target (5%)\n from.target = abs(rr - 5)\n \n # save individual radius calculations\n dyad = unique(next.conv$dyad)\n conv.num = unique(next.conv$conv.num)\n write.table(cbind.data.frame(dyad,\n conv.num,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target),\n paste('./data/crqa_parameters/radius_calculations-mean_scaled-r',chosen.radius,'-',dyad,'_',conv.num,'-DCC.csv', sep=''), \n sep=',',row.names=FALSE,col.names=TRUE)\n \n # append to dataframe\n radius_selection = rbind.data.frame(radius_selection,\n cbind.data.frame(dyad,\n conv.num,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target))\n } else {\n \n # if we're no longer improving, break\n break\n \n }}}}\n\n# save the radius explorations to file\nwrite.table(radius_selection,'./data/crqa_parameters/radius_calculations-mean_scaled-DCC.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n# let us know when it's finished\nbeepr::beep(\"fanfare\")\n\n# if we've already run it, load it in\nradius_selection = read.table('./data/crqa_parameters/radius_calculations-mean_scaled-DCC.csv', sep=',',header=TRUE)\n\n#### 5. Expand radius where necessary ####\n\n# load back in the data\nradius_selection = read.table('./data/crqa_parameters/radius_calculations-mean_scaled-DCC.csv', sep=',',header=TRUE)\nradius_stats = radius_selection %>% ungroup() %>%\n group_by(dyad,conv.num) %>%\n dplyr::filter(from.target==min(from.target)) %>%\n dplyr::arrange(dyad,conv.num)\n\n# check whether some conversations are further than 1% from our target recurrence rate\nrecheck_radii = radius_stats %>% ungroup() %>%\n dplyr::filter(from.target > 1) %>%\n dplyr::select(dyad,conv.num)\n\n# link conversation numbers to types\nchecking_numbers = coords_crqa %>%\n ungroup() %>%\n dplyr::select(dyad,conv.type,conv.num) %>%\n distinct()\nrecheck_radii = recheck_radii %>%\n dplyr::left_join(x=.,\n y=checking_numbers, \n by=c(\"dyad\"=\"dyad\",\"conv.num\"=\"conv.num\")) %>%\n mutate(recheck.conv = paste(dyad,conv.type,sep='.'))\n\n# create an empty dataframe to hold the parameter information\nrecheck_radius_selection = data.frame(dyad = numeric(),\n conv.num = numeric(),\n chosen.delay = numeric(),\n chosen.embed = numeric(),\n chosen.radius = numeric(),\n rr = numeric())\n\n# cycle through the conversations\nrecheck_radii = crqa.data[recheck_radii$recheck.conv]\nrecheck_radius_list = seq(.28,1.4,by=.02)\nfor (next.conv in recheck_radii){\n \n # make sure we only proceed if we have data for the conversation\n if (dim(next.conv)[1] != 0){\n \n # reset `target` variables for new radius (above what RR can be)\n from.target = 101\n last.from.target = 102\n \n # cycle through radii\n for (chosen.radius in recheck_radius_list){\n \n # if we're still improving, keep going\n if (from.target < last.from.target){\n \n # keep the previous iteration's performance\n last.from.target = from.target\n \n # print update\n print(paste(\"Dyad \", unique(next.conv$dyad),\n \", conversation \",unique(next.conv$conv.num),\n \": radius \",chosen.radius,sep=\"\"))\n \n # identify parameters\n chosen.delay = unique(next.conv$ami.selected)\n chosen.embed = unique(next.conv$embed.selected)\n \n # run CRQA and grab recurrence rate (RR)\n rec_analysis = crqa(next.conv$rescale.euclid0, \n next.conv$rescale.euclid1,\n delay = chosen.delay, \n embed = chosen.embed, \n r = chosen.radius,\n normalize = 0, \n rescale = 0, \n mindiagline = 2,\n minvertline = 2, \n tw = 0, \n whiteline = FALSE,\n recpt=FALSE)\n rr = rec_analysis$RR\n \n # clear it so we don't take up too much memory (optional)\n rm(rec_analysis)\n \n # identify how far off the RR is from our target (5%)\n from.target = abs(rr - 5)\n \n # save individual radius calculations\n dyad = unique(next.conv$dyad)\n conv.num = unique(next.conv$conv.num)\n write.table(cbind.data.frame(dyad,\n conv.num,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target),\n paste('./data/crqa_parameters/radius_calculations-mean_scaled-r',chosen.radius,'-',dyad,'_',conv.num,'-DCC.csv', sep=''), \n sep=',',row.names=FALSE,col.names=TRUE)\n \n # append to dataframe\n recheck_radius_selection = rbind.data.frame(recheck_radius_selection,\n cbind.data.frame(dyad,\n conv.num,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target))\n } else {\n \n # if we're no longer improving, break\n break\n \n }}}}\n\n# save the radius explorations to file\nwrite.table(recheck_radius_selection,'./data/crqa_parameters/radius_recheck_calculations-mean_scaled-DCC.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n# let us know when it's finished\nbeepr::beep(\"fanfare\")\n\n# if we've already run it, load it in\nrecheck_radius_selection = read.table('./data/crqa_parameters/radius_recheck_calculations-mean_scaled-DCC.csv', sep=',',header=TRUE)\n\n#### 6. Export chosen radii for all conversations ####\n\n# load in files\nradius_files = list.files('./data/crqa_parameters', \n pattern='radius_calculations-mean_scaled*',\n full.names = TRUE)\n\n# identify the target radii\nradius_stats = rbind.data.frame(rbindlist(lapply(radius_files, fread, sep=\",\"))) %>%\n ungroup() %>%\n group_by(dyad,conv.num) %>%\n dplyr::filter(from.target==min(from.target)) %>%\n dplyr::arrange(dyad,conv.num) %>%\n ungroup() %>%\n distinct()\n\n# rename our rescaled variables here\ncoords_crqa = coords_crqa %>% ungroup() %>%\n select(dyad,conv.num,rescale.euclid0,rescale.euclid1)\n\n# join the dataframes\ncoords_crqa = plyr::join(x=coords_crqa,\n y=radius_stats, by=c(\"dyad\"=\"dyad\",\"conv.num\"=\"conv.num\"))\n\n# save to file\nwrite.table(coords_crqa,'./data/crqa_data_and_parameters-DCC.csv', sep=',',row.names=FALSE,col.names=TRUE)", "meta": {"hexsha": "f585a7db878a6ebfd6a7d7e4db7d880d3095740c", "size": 15922, "ext": "r", "lang": "R", "max_stars_repo_path": "supplementary-code/continuous_rqa_parameters-DCC.r", "max_stars_repo_name": "a-paxton/dual-conversation-constraints", "max_stars_repo_head_hexsha": "a167f004c71d9637ec30082de13a1ba6283846bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-10-11T23:50:28.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-31T18:52:49.000Z", "max_issues_repo_path": "supplementary-code/continuous_rqa_parameters-DCC.r", "max_issues_repo_name": "a-paxton/dual-conversation-constraints", "max_issues_repo_head_hexsha": "a167f004c71d9637ec30082de13a1ba6283846bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "supplementary-code/continuous_rqa_parameters-DCC.r", "max_forks_repo_name": "a-paxton/dual-conversation-constraints", "max_forks_repo_head_hexsha": "a167f004c71d9637ec30082de13a1ba6283846bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2070707071, "max_line_length": 150, "alphanum_fraction": 0.539065444, "num_tokens": 3546, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.34548181972574316}} {"text": "# DATA CLEANING DAY 2\nSEER_Data<- read.csv(\"scratch/codeathon1.csv\", header=TRUE)\nlibrary(ggplot2)\n\n\n# DATA CLEANING \n\ndrops <- c(\"Site.recode.B.ICD.O.3.WHO.2008\", \"Lymphoma.subtype.recode.WHO.2008\", \"Laterality\", \"Histology.recode...Brain.groupings\", \n \"Summary.stage.2000..1998..\", \"SEER.summary.stage.1977..1995.2000.\", \"AJCC.stage.3rd.edition..1988.2003.\" , \"SEER.modified.AJCC.stage.3rd..1988.2003.\",\n \"RX.Summ..Surg.Prim.Site..1998..\", \"Site.specific.surgery..1973.1997.varying.detail.by.year.and.site.\", \"Radiation.to.Brain.or.CNS.Recode..1988.1997.\", \n \"CS.extension..2004.2015.\", \"Tumor.marker.1..1990.2003.\" , \"Tumor.marker.2..1990.2003.\" , \"Tumor.marker.3..1990.2003.\" ,\n \"Type.of.Reporting.Source\", \"Primary.Site...labeled\", \"Race.recode..W..B..AI..API.\" , \n \"Origin.recode.NHIA..Hispanic..Non.Hisp.\" )\nSEER_Data<- SEER_Data[ , !(names(SEER_Data) %in% drops)]\n\nSEER_Data<-SEER_Data[SEER_Data$Year.of.diagnosis>=1997 & SEER_Data$Year.of.diagnosis<=2016,]\n\nSEER_Data$Survival.months.flag.new<- ifelse(SEER_Data$Survival.months.flag==\"Complete dates are available and there are more than 0 days of survival\",1,0)\n\nSEER_Data$Survival.months.new<- as.integer(ifelse(SEER_Data$Survival.months.flag.new==0, NA, SEER_Data$Survival.months))\n\nSEER_Data$Radiation.code.new <- ifelse(SEER_Data$Radiation.recode!=\"None/Unknown\", 1,0)\n\nSEER_Data$Site.recode.ICD.O.3.WHO.2008 <- ifelse(SEER_Data$Site.recode.ICD.O.3.WHO.2008 %in% c(\"NHL - Nodal\",\"NHL - Extranodal\" ), \"NHL\", SEER_Data$Site.recode.ICD.O.3.WHO.2008)\nSEER_Data$Site.recode.ICD.O.3.WHO.2008 <- ifelse(SEER_Data$Site.recode.ICD.O.3.WHO.2008 %in% c(\"Rectum\", \"Sigmoid Colon\", \"Cecum\", \"Rectosigmoid Junction\", \"Ascending Colon\", \"Transverse Colon\", \"Descending Colon\", \"Large Intestine, NOS\",\n \"Hepatic Flexure\", \"Splenic Flexure\"), \"Colorectal\", SEER_Data$Site.recode.ICD.O.3.WHO.2008)\n\nSEER_Data<- SEER_Data[ , !(names(SEER_Data) %in% c(\"Survival.months.flag\", \"Survival.months\",\"Radiation.recode\"))]\n\nnames(SEER_Data)[names(SEER_Data) == 'Survival.months.flag.new'] <- 'Survival.months.flag'\nnames(SEER_Data)[names(SEER_Data) == 'Survival.months.new'] <- 'Survival.months'\nnames(SEER_Data)[names(SEER_Data) == 'Radiation.code.new'] <- 'Radiation.recode'\n\nwrite.csv(SEER_Data, \"scratch/codeathon_RECODE.csv\")\n\n### UPDATES ### \n\n## (\"scratch/codeathon_RECODE_Day3_v02.csv\") --> v01 is the same as v02 but I had to re-run it as I don't have permision to re-write the v01. \nSEER_Cleaned <- read_csv(\"scratch/codeathon_RECODE_Day3_v02.csv\")\nSEER_Cleaned_updated <- SEER_Cleaned\n#SEER_Cleaned_updated<- SEER_Cleaned[!SEER_Cleaned$X....High.school.education.ACS.2013.2017==\"Blank(s)\",]\n#SEER_Cleaned_updated<- SEER_Cleaned_updated[SEER_Cleaned_updated$Age.recode.with..1.year.olds %in% c(\"35-39 years\", \"25-29 years\", \"30-34 years\", \"20-24 years\", \"15-19 years\"),]\nSEER_Cleaned_updated$X..Unemployed.ACS.2013.2017 <- as.double(SEER_Cleaned_updated$X..Unemployed.ACS.2013.2017)/100\nSEER_Cleaned_updated$X....High.school.education.ACS.2013.2017<-as.double(SEER_Cleaned$X....High.school.education.ACS.2013.2017)/100\n\n## NEW VARIABLES \nSEER_Cleaned_updated<- SEER_Cleaned_updated %>% mutate(HighSchoolEdCat = cut(X....High.school.education.ACS.2013.2017, breaks = c(0,10,20,30, 100)),\n Unemployed_cat = cut(X..Unemployed.ACS.2013.2017, breaks = c(0,5,10,15, 100)),\n median_income_household_group = ntile(Median.household.income..in.tens..ACS.2013.2017 , 4)) \n\n\n# AGE GROUP COUNTS \n#SEER_Cleaned_updated %>% group_by(Decade,Age.recode.with..1.year.olds) %>% summarize(count=n()) %>% mutate(freq = count/sum(count)) \nwrite.csv(SEER_Cleaned_updated, \"scratch/codeathon_RECODE_v03.csv\")\n\n\n", "meta": {"hexsha": "393801f682146ebb7c7ca134930ca707ba432c99", "size": 3924, "ext": "r", "lang": "R", "max_stars_repo_path": "DataCleaningDay02.r", "max_stars_repo_name": "STRIDES-Codes/Racial-and-SES-Disparities-in-Adolescent-and-Young-Adult-Cancers", "max_stars_repo_head_hexsha": "826b7376ab03c6f85eb1cc9503b4dd8d0c1a8923", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-16T13:47:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-16T13:47:37.000Z", "max_issues_repo_path": "DataCleaningDay02.r", "max_issues_repo_name": "STRIDES-Codes/Racial-and-SES-Disparities-in-Adolescent-and-Young-Adult-Cancers", "max_issues_repo_head_hexsha": "826b7376ab03c6f85eb1cc9503b4dd8d0c1a8923", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-06-24T16:06:12.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-24T16:07:02.000Z", "max_forks_repo_path": "DataCleaningDay02.r", "max_forks_repo_name": "STRIDES-Codes/Racial-and-SES-Disparities-in-Adolescent-and-Young-Adult-Cancers", "max_forks_repo_head_hexsha": "826b7376ab03c6f85eb1cc9503b4dd8d0c1a8923", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 68.8421052632, "max_line_length": 238, "alphanum_fraction": 0.6817023445, "num_tokens": 1268, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.34548181972574316}} {"text": "#################################################\n#################################################\n# Shapefiles\n#################################################\n#################################################\n\n# We need:\n# ukcp18-uk-land-country-hires \n# contains the borders of the UK, and the internal borders between Wales, England and Scotland\n\nuk_borders <- st_read(\"/nfs/cfs/home4/rejb/rejbypa/UKCP18_R/ukcp-spatial-files/spatial-files/ukcp18-uk-land-country-hires/ukcp18-uk-land-country-hires.shp\")\nas_Spatial(uk_borders)\nplot(uk_borders)\n", "meta": {"hexsha": "3cd2e86d4fcde89c6e81971cefc394e07947cab0", "size": 550, "ext": "r", "lang": "R", "max_stars_repo_path": "shapefiles.r", "max_stars_repo_name": "ypalmeirosilva/HackingClimate---An-exposure-weighted-warming", "max_stars_repo_head_hexsha": "cd593c0d4ab8a4523a96c77a78127f85e87f4aba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shapefiles.r", "max_issues_repo_name": "ypalmeirosilva/HackingClimate---An-exposure-weighted-warming", "max_issues_repo_head_hexsha": "cd593c0d4ab8a4523a96c77a78127f85e87f4aba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "shapefiles.r", "max_forks_repo_name": "ypalmeirosilva/HackingClimate---An-exposure-weighted-warming", "max_forks_repo_head_hexsha": "cd593c0d4ab8a4523a96c77a78127f85e87f4aba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2857142857, "max_line_length": 156, "alphanum_fraction": 0.5018181818, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6187804337438502, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.3454818197257431}} {"text": "add_event_days <- function(df)\n{\n\tcolnames(df)[1] <- \"ds\"\n\tdf<- cbind(df, matrix(0, nrow=nrow(df), ncol=1))\n\tcolnames(df)[ncol(df)]<-\"lower_window\"\n\tdf<- cbind(df, matrix(0, nrow=nrow(df), ncol=1))\n\tcolnames(df)[ncol(df)]<-\"upper_window\"\n\n\n\tfor ( i in 1:nrow(holidays)){\n\t#i = 1\n\t\tclidx = grep(as.POSIXct(holidays$ds[i]), as.POSIXct(df$ds))\n\t#as.POSIXct(holidays$ds[i])\n\t#as.POSIXct(df$ds[clidx])\n\n\t\tif ( length(clidx) == 0 )\n\t\t{\n\t\t\tprint(as.POSIXct(holidays$ds[i]))\n\t\t\tnext\n\t\t}\n\t\t\n\t\tu = holidays$lower_window[i]\n\t\tdf$lower_window[clidx] = 1\n\t\tif ( u < 0 )\n\t\t{\n\t\t\tfor ( id in (clidx-u):clidx){\n\t\t\t\tif (clidx-u >= 1)df$lower_window[id] = 1\n\t\t\t}\n\t\t}\n\t\t\n\t\tu = holidays$upper_window[i]\n\t\tdf$upper_window[clidx] = 1\n\t\tif ( u > 0 )\n\t\t{\n\t\t\tfor ( id in clidx:(clidx+u)){\n\t\t\t\tif (clidx+u <=nrow(df))df$upper_window[id] = 1\n\t\t\t}\n\t\t}\n\t}\n\treturn(df)\n}\n", "meta": {"hexsha": "0b6ab045a0f90e716dcaac5c0599e8cbf2ef656b", "size": 840, "ext": "r", "lang": "R", "max_stars_repo_path": "Application/DDS2/script/add_event_days.r", "max_stars_repo_name": "Sanaxen/Data_analysis_tools", "max_stars_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2022-01-20T13:39:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-20T13:39:39.000Z", "max_issues_repo_path": "Application/DDS2/script/add_event_days.r", "max_issues_repo_name": "Sanaxen/Data_analysis_tools", "max_issues_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-11-09T13:16:29.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-09T13:16:52.000Z", "max_forks_repo_path": "Application/DDS2/script/add_event_days.r", "max_forks_repo_name": "Sanaxen/Data_analysis_tools", "max_forks_repo_head_hexsha": "be487ef5d011e1dd9af347a8c6f2b9347bcfabf9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-08T09:37:23.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-08T09:37:23.000Z", "avg_line_length": 20.0, "max_line_length": 61, "alphanum_fraction": 0.5857142857, "num_tokens": 338, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.34548181972574304}} {"text": "# Master Thesis Project - Extreme Value Theory\n# Introductory Task\n# Killian Martin--Horgassan\n# 19-02-2015\n\n# Clear the environment\nrm(list=ls())\n\n# Close all already open graphic windows\ngraphics.off()\n\n# Elementary test\n#x <- 5\n#print(x)\n\n# sourcing works\n\n# Loading function 'NormMax'\nsource(\"./NormMax2.r\")\nsource(\"./InputIntroductoryTask2.r\")\n\n# Input from keyboard\nDIST <- InputIntroductoryTask2()\nDIST_1 <- DIST[[1]]\nDIST_2 <- DIST[[2]]\n\n# Calling function NormMax\nlistMax <- NormMax2(length(DIST_1),DIST_1,DIST_2)\n", "meta": {"hexsha": "6a82abc772dac9830a09b61b16447f5c33b7c6b8", "size": 523, "ext": "r", "lang": "R", "max_stars_repo_path": "report/main/R_Files_1/IntroductoryTask2.r", "max_stars_repo_name": "CillianMH/pdmExtremeValueTheory", "max_stars_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "report/main/R_Files_1/IntroductoryTask2.r", "max_issues_repo_name": "CillianMH/pdmExtremeValueTheory", "max_issues_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "report/main/R_Files_1/IntroductoryTask2.r", "max_forks_repo_name": "CillianMH/pdmExtremeValueTheory", "max_forks_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.0344827586, "max_line_length": 49, "alphanum_fraction": 0.7284894837, "num_tokens": 149, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6548947425132315, "lm_q1q2_score": 0.3453368188016341}} {"text": " DGERFS Example Program Results\n\n Solution(s)\n 1 2\n 1 1.0000 3.0000\n 2 -1.0000 2.0000\n 3 3.0000 4.0000\n 4 -5.0000 1.0000\n\n Backward errors (machine-dependent)\n 9.4E-17 3.7E-17\n Estimated forward error bounds (machine-dependent)\n 2.4E-14 3.3E-14\n", "meta": {"hexsha": "f24c8c177144c4179264948c634de3fcf1859e95", "size": 318, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/baseresults/dgerfs_example.r", "max_stars_repo_name": "numericalalgorithmsgroup/LAPACK_examples", "max_stars_repo_head_hexsha": "0dde05ae4817ce9698462bbca990c4225337f481", "max_stars_repo_licenses": ["BSD-3-Clause-Open-MPI"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2018-01-28T15:48:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-18T09:26:43.000Z", "max_issues_repo_path": "examples/baseresults/dgerfs_example.r", "max_issues_repo_name": "numericalalgorithmsgroup/LAPACK_examples", "max_issues_repo_head_hexsha": "0dde05ae4817ce9698462bbca990c4225337f481", "max_issues_repo_licenses": ["BSD-3-Clause-Open-MPI"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/baseresults/dgerfs_example.r", "max_forks_repo_name": "numericalalgorithmsgroup/LAPACK_examples", "max_forks_repo_head_hexsha": "0dde05ae4817ce9698462bbca990c4225337f481", "max_forks_repo_licenses": ["BSD-3-Clause-Open-MPI"], "max_forks_count": 18, "max_forks_repo_forks_event_min_datetime": "2019-04-19T12:22:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-18T03:32:12.000Z", "avg_line_length": 22.7142857143, "max_line_length": 51, "alphanum_fraction": 0.5503144654, "num_tokens": 126, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6548947357776795, "lm_q1q2_score": 0.3453368152498662}} {"text": "# James Rekow\r\n\r\nintraCohortInteraction_optimizedBaseCase = function(chrt, M, N, intIx, lambda = 0, threshold = 10 ^ (-6),\r\n maxSteps = 10 ^ 4, tStep = 10 ^ (-2), intTime = 1,\r\n interSmplMult= 0.01){\r\n \r\n # ARGS: chrt - input cohort in the form list(list(abd, gr, imat)), where abundances have\r\n # already been integrated\r\n # lambda - rate of exponential random variable describing time between interactions\r\n # intTime - time over which interactions are to be simulated\r\n # interSmplMult - the fraction of a sample's abundances that get transmitted\r\n # upon contact with another sample (transmission is bi-directional\r\n # and does not deplete the abundance of the dog from which it is \r\n # transmitted)\r\n # intIx - the indices of the interacting samples in the cohort. It is assumed that any of these\r\n # samples can interact with any other one (e.g. form a completely connected subgraph). The\r\n # samples that are not interacting do not interact at all.\r\n #\r\n # RETURNS: abdList - list of interacted and re-integrated abundances from chrt\r\n # intIx - numerical vector containing the indices of all samples within the cohort\r\n # that are connected (capable of interacting) with at least one other sample.\r\n # Only returned if retIntIx == TRUE, in which case the output is of the form\r\n # list(abdList, intIx)\r\n #\r\n # NOTE: exactly one interaction occurs during each iteration of the loop. If cumTime is less\r\n # than intTime but the next wait time makes cumTime greater than intTime then the interaction\r\n # corresponding to that wait time does not occur.\r\n \r\n source(\"eulerIntegrate.r\")\r\n \r\n # precompute coefficient for use in interaction step\r\n tempSmplMult = 1 - interSmplMult\r\n \r\n # track cumulative time and terminate interaction step once cumulative time exceeds intTime\r\n cumTime = rexp(n = 1, rate = lambda)\r\n \r\n # interact samples until the cumulative interaction time exceeds the amount of time for the interaction\r\n # step\r\n while(cumTime < intTime){\r\n \r\n # select indices of the two samples which will interact during this interaction step\r\n iPair = sample(intIx, 2)\r\n \r\n # select the abundance vectors of the interacting samples\r\n abd1 = chrt[[iPair[1]]][[1]]\r\n abd2 = chrt[[iPair[2]]][[1]]\r\n \r\n # compute weighted sum\r\n tempSum = interSmplMult * {abd1 + abd2}\r\n \r\n # update sample abundances in the cohort (the net effect is that interSmplMult * abd1 is added to abd2,\r\n # and similarly interSmplMult * abd2 is added to abd1)\r\n chrt[[iPair[1]]][[1]] = tempSmplMult * abd1 + tempSum\r\n chrt[[iPair[2]]][[1]] = tempSmplMult * abd2 + tempSum\r\n \r\n # re-integrate samples that have interacted\r\n for(ii in iPair){\r\n chrt[[ii]][[1]] = eulerIntegrate(chrt[[ii]], threshold = threshold, maxSteps = maxSteps,\r\n tStep = tStep)\r\n } # end for\r\n \r\n # compute the cumulative time after waiting for another interaction\r\n cumTime = cumTime + rexp(n = 1, rate = lambda)\r\n \r\n } # end while loop\r\n \r\n # extract the list of abundance vectors from the cohort\r\n abdList = lapply(chrt, \"[[\", 1)\r\n \r\n return(abdList)\r\n \r\n} # end intraCohortInteraction_optimizedBaseCase function\r\n", "meta": {"hexsha": "78124448cad392b54d41c6230dab44a7c872815a", "size": 3579, "ext": "r", "lang": "R", "max_stars_repo_path": "intraCohortInteraction_optimizedBaseCase.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "intraCohortInteraction_optimizedBaseCase.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intraCohortInteraction_optimizedBaseCase.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.0273972603, "max_line_length": 109, "alphanum_fraction": 0.6177703269, "num_tokens": 882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947290421276, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3453368116980984}} {"text": "predict.EFSA<-function(model,dataset){\n\tdistance_matrix = compute_best_matching_distance(test_data,model$shapelets,model$shapelet_length)\n\tpredictions=predict(model$model,distance_matrix)\n\treturn(predictions)\n}\n", "meta": {"hexsha": "8615af54b60a16927832ea3af884092455c15bef", "size": 211, "ext": "r", "lang": "R", "max_stars_repo_path": "predict.EFSA.r", "max_stars_repo_name": "gorguluberk/EFSA", "max_stars_repo_head_hexsha": "c20d90216bd157d5584f5cb7cf6e1c98fd078004", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "predict.EFSA.r", "max_issues_repo_name": "gorguluberk/EFSA", "max_issues_repo_head_hexsha": "c20d90216bd157d5584f5cb7cf6e1c98fd078004", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "predict.EFSA.r", "max_forks_repo_name": "gorguluberk/EFSA", "max_forks_repo_head_hexsha": "c20d90216bd157d5584f5cb7cf6e1c98fd078004", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.1666666667, "max_line_length": 98, "alphanum_fraction": 0.8483412322, "num_tokens": 44, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3453368116980983}} {"text": "\n\n\n#TODO BC add functionality for pdf&kml outputs\n\nmap.set.information = function(p, outdir, variables, mapyears, interpolate.method='tps', theta=p$pres*25, ptheta=theta/2.3,\n idp=2, log.variable=TRUE, add.zeros=TRUE, minN=10, probs=c(0.025, 0.975) ) {\n\n set = snowcrab.db( DS=\"set.biologicals\")\n if(missing(variables)){\n variables = bio.snowcrab::snowcrab.variablelist(\"all.data\")\n variables = intersect( variables, names(set) )\n }\n\n # define compact list of variable year combinations for parallel processing\n if (missing(mapyears)) mapyears = sort( unique(set$yr) )\n if (exists( \"libs\", p)) RLibrary( p$libs )\n\n for ( v in variables ) {\n for ( y in mapyears ) {\n predlocs = bathymetry.db(p=p, DS=\"baseline\")\n ratio=FALSE\n outfn = paste( v,y, sep=\".\")\n outloc = file.path( outdir,v)\n ref=y\n\n if (grepl('ratio', v)) ratio=TRUE\n\n set_xyz = set[ which(set$yr==y), c(\"plon\",\"plat\",v) ]\n names( set_xyz) = c(\"plon\", \"plat\", \"z\")\n set_xyz = na.omit(subset(set_xyz,!duplicated(paste(plon,plat))))\n if(nrow(set_xyz) 0)\n if (length(withdata) < 3) print(paste(\"skipped\",v, y, \"<3 data points to create map\", sep=\".\"))\n if (length(withdata) < 3) next()\n S = set_xyz[ withdata, c(\"plon\", \"plat\") ]\n\n\n distances = rdist( predlocs[,c(\"plon\", \"plat\")], S)\n distances[ which(distances < ptheta) ] = NA\n ips = which( !is.finite( rowSums(distances) ) )\n plocs=predlocs[ips,]\n if(log.variable){\n set_xyz$z = log(set_xyz$z+offset)\n ler=log(er+offset)\n #if(offset<1)if(shift) xyz$z = xyz$z + abs(log(offset))\n }\n\n datarange = seq( ler[1], ler[2], length.out=50)\n #\n #if(logit.variable){\n # sr=set[,v]\n # sr=sr[sr>0&sr<1&!is.na(sr)]\n # lh=range(sr)\n # set_xyz$z[set_xyz$z==0] = lh[1]\n # set_xyz$z[set_xyz$z==1] = lh[2]\n # set_xyz$z = logit(set_xyz$z)\n # #if(offset<1)if(shift) xyz$z = xyz$z + abs(log(offset))\n # ler=logit(quantile(sr,probs))\n # datarange = seq( ler[1], ler[2], length.out=50)\n #}\n xyzi = na.omit(set_xyz)\n\n if(nrow(xyzi)ler[2]] = ler[2]\n if(ratio)xyz$z[xyz$zer[1]&labs\n\n## poscounts.r -- determine clinical parameters for structure file outputs\n## TP: True positive (count of \"positive\" results that are clinically positive)\n## FN: False negative (count of \"negative\" results that are clinically positive)\n## TN: True negative (count of \"negative\" results that are clinically negative)\n## FP: False positive (count of \"positive\" results that are clinically negative)\n\nusage <- function(){\n cat(\"usage: ./poscounts.r -s [options]\\n\");\n cat(\"\\nOther Options:\\n\");\n cat(\" -r : cutoff resolution (default 0.1)\\n\");\n cat(\" -min : minimum cutoff value (default 0)\\n\");\n cat(\" -max : maximum cutoff value (default 1)\\n\");\n cat(\" -flip : invert positive/negative labels\\n\");\n ## Should probably have 'flop' to invert case/control labels as well\n cat(\" -label : case/control labels\\n\");\n cat(\"\\n\");\n }\n\ninfile.name <- FALSE;\nsplitPoint <- FALSE; # location at which positives change to negatives\nminCO <- 0; # minimum cutoff value\nmaxCO <- 1; # maximum cutoff value\nCOResolution <- 0.1; # cutoff resolution\nflip <- FALSE; # whether to flip positive/negative labels\nccLabels <- NULL; # case/control labels\n\nargLoc <- 1;\nwhile(!is.na(commandArgs(TRUE)[argLoc])){\n if(file.exists(commandArgs(TRUE)[argLoc])){ # file existence check\n if(infile.name == FALSE){\n infile.name <- commandArgs(TRUE)[argLoc];\n } else{\n cat(\"Error: More than one input file specified\\n\");\n usage();\n quit(save = \"no\", status=1);\n }\n }\n if(commandArgs(TRUE)[argLoc] == \"-flip\"){\n flip <- TRUE;\n }\n if(commandArgs(TRUE)[argLoc] == \"-label\"){\n ccLabels <- c(commandArgs(TRUE)[argLoc+1],commandArgs(TRUE)[argLoc+2]);\n argLoc <- argLoc+2;\n }\n if(commandArgs(TRUE)[argLoc] == \"-s\"){\n splitPoint <- as.numeric(commandArgs(TRUE)[argLoc+1]);\n argLoc <- argLoc+1;\n }\n if(commandArgs(TRUE)[argLoc] == \"-r\"){\n COResolution <- as.numeric(commandArgs(TRUE)[argLoc+1]);\n argLoc <- argLoc+1;\n }\n if(commandArgs(TRUE)[argLoc] == \"-min\"){\n minCO <- as.numeric(commandArgs(TRUE)[argLoc+1]);\n argLoc <- argLoc+1;\n }\n if(commandArgs(TRUE)[argLoc] == \"-max\"){\n maxCO <- as.numeric(commandArgs(TRUE)[argLoc+1]);\n argLoc <- argLoc+1;\n }\n if(length(grep(\"-h\",commandArgs(TRUE)[argLoc])) > 0){\n usage();\n quit(save = \"no\", status = 0);\n }\n argLoc <- argLoc+1;\n}\n\nif(splitPoint == FALSE){\n cat(\"Error: No split point defined (use -s)\\n\");\n usage();\n quit(save = \"no\", status=1);\n}\n\na <- read.table(infile.name, row.names = 1);\n\ngetValues <- function(cutoff){\n if(length(cutoff)>1){\n return(data.frame(t(sapply(unique(cutoff),getValues))));\n }\n TP <- length(which(a$V2[1:splitPoint] >= cutoff));\n FN <- length(which(a$V2[1:splitPoint] < cutoff));\n FP <- length(which(a$V2[-(1:splitPoint)] >= cutoff));\n TN <- length(which(a$V2[-(1:splitPoint)] < cutoff));\n if(!flip){\n return (data.frame(cutoff = cutoff, TP = TP, TN = TN, FP = FP, FN = FN));\n } else {\n return (data.frame(cutoff = cutoff, TP = FN, TN = FP, FP = TN, FN = TP));\n }\n}\n\ncat(\"\\n\");\n\ncat(\"TP: Cases >= cutoff value\\n\");\ncat(\"FP: Controls >= cutoff value\\n\");\ncat(\"TN: Cases < cutoff value\\n\");\ncat(\"FN: Controls < cutoff value\\n\");\n\ncat(\"\\n\");\n\ngetValues(seq(minCO,maxCO,COResolution));\n\npdf(\"output_QHist.pdf\", paper = \"a4r\", width = 11, height = 8);\n\nif(length(ccLabels) == 0){\n ccLabels <- c(\"CASE\",\"CONTROL\");\n}\n\nhist(a$V2[1:splitPoint], breaks = 20, col = \"red\", xlab = \"Q value\",\n main = paste(\"Histogram of Q values for \",ccLabels[1],\"\",sep=\"\"));\nhist(a$V2[-(1:splitPoint)], breaks = 20, col = \"red\", xlab = \"Q value\",\n main = paste(\"Histogram of Q values for \",ccLabels[2],\"\",sep=\"\"));\n\ndummy <- dev.off();\n", "meta": {"hexsha": "488ab24dcdf2f9f5d012a482003cbd7ecf7c34bc", "size": 3912, "ext": "r", "lang": "R", "max_stars_repo_path": "poscounts.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "poscounts.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "poscounts.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_forks_repo_licenses": ["ISC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-02T11:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-02T11:22:10.000Z", "avg_line_length": 32.6, "max_line_length": 82, "alphanum_fraction": 0.6232106339, "num_tokens": 1143, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6406358685621721, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3452919837256914}} {"text": "#' Predict probability from DMBC model\n#'\n#' @description\n#' Based on a set of optimized features from training set, this function predicts the posterior probability for two class labels.\n#'\n#' @param data Validation dataset with rows are samples, columns are features. The first column should be the sample ID, second column group variable (Disease type, the label you want to classify on).\n#' @param testset Lable unknown testset without sample IDs.\n#' @param auc_out Output object of Cal_AUC() from a validation set.\n#' @param col_start An index indicating at which column is the beginning of bacteria (features) data in the validation set. The default is the 3rd column.\n#' @param type_col An index indicating at which column is group/type variable in the validation set. The default is the 2nd column.\n#' @param Prior1 Prevalence of label1 according to literature or experience. Default is 0.5.\n#' @param Prior2 Prevalence of label2 according to literature or experience. Default is 0.5.\n#'\n#' @return\n#'\n#' @examples\n#' #load the DMBC library\n#' library(DMBC)\n#'\n#' #load training dataset\n#' data(training)\n#'\n#' #load test dataset\n#' data(test)\n#'\n#'## calculate AUC based on training set using 10-fold cv ##\n#' auc_out <- Cal_AUC(tfcv(training))\n#'\n#'## calculate AUC based on training set using leave-one-out cv ##\n#' auc_out <- Cal_AUC(loocv(training))\n#'\n#' #predict unknown test set using training set and auc results.\n#' dmbc_predict(data=training,testset=test,auc_out=auc_out)\n#' @export\n\n\n\ndmbc_predict <- function(data=data,testset = testset,auc_out=auc_out,col_start =3,type_col=2,Prior1=0.5,Prior2=1-Prior1){\n namelist <- auc_out$Features[which.max(auc_out$AUC_Area)]\n NameList <- as.vector(unlist(strsplit(as.character(namelist),\";\")))\n\n Disease <- levels(data[,type_col])\n\n NewDF <- as.data.frame(data[,colnames(data) %in% NameList])\n colnames(NewDF) <- colnames(data)[colnames(data) %in% NameList]\n\n NewDF2 <- data[,!colnames(data)%in% NameList]\n\n col_end2 = dim(NewDF2)[2]\n NewDF2$Others = rowSums(NewDF2[, col_start:col_end2])\n NewDFTotal = data.frame(NewDF2[, 1:(col_start-1)],NewDF, NewDF2$Others)\n\n rep2_Type1 = NewDFTotal[NewDFTotal[,type_col] == Disease[1],]\n rep2_Type2 = NewDFTotal[NewDFTotal[,type_col] == Disease[2],]\n\n for (taxa in NameList){\n if(nrow(rep2_Type1) == sum(rep2_Type1[,taxa]<1)) {\n rep2_Type1[,taxa][1] =1\n }\n if(nrow(rep2_Type2) == sum(rep2_Type2[,taxa]<1)) {\n rep2_Type2[,taxa][1] =1\n }\n }\n\n\n ####fit3 <- dirmult(rep2_Type1[,-(1:(col_start-1))],epsilon=10^(-4),trace=FALSE)\n ####fit4 <- dirmult(rep2_Type2[,-(1:(col_start-1))],epsilon=10^(-4),trace=FALSE)\n ####alpha_Type1 = fit3$gamma\n ####alpha_Type2 = fit4$gamma\n tol=0.0001\n max.iter=1000\n\n Xc = as.matrix(rep(1, dim(rep2_Type1)[1]))\n Xa = Xc\n Xb = Xc\n c0 = as.matrix(rep(1, dim(rep2_Type1)[2]-1))\n alpha0 = c0\n beta0 = c0\n p_Type1 <- ZIGDM_prob(rep2_Type1[,-(1:(col_start-1))], Xc, Xa, Xb, c0, alpha0, beta0, tol=0.0001, max.iter=1000)\n\n Xc = as.matrix(rep(1, dim(rep2_Type1)[1]))\n Xa = Xc\n Xb = Xc\n c0 = as.matrix(rep(1, dim(rep2_Type1)[2]-1))\n alpha0 = c0\n beta0 = c0\n p_Type2 <- ZIGDM_prob(rep2_Type2[,-(1:(col_start-1))], Xc, Xa, Xb, c0, alpha0, beta0, tol=0.0001, max.iter=1000)\n ###### predict test set #####\n\n for (t in NameList){\n if (! (t %in% colnames(testset)) ){\n testset <- data.frame(testset,rep(0,nrow(testset)))\n colnames(testset)[ncol(testset)] <- t\n }\n }\n\n NewTestSignature = testset[, colnames(testset)%in% NameList]\n NewTestNonSignature = testset[, !colnames(testset)%in% NameList]\n NewTestNonSignature$Others = rowSums(NewTestNonSignature)\n NewTestTotal = data.frame(NewTestSignature, NewTestNonSignature$Others)\n\n test_res <- list()\n for (r in 1:nrow(testset)){\n ##### Calculate log of Dirichlet multinomial probability mass function P(x|Type1)\n #pdfln_Type1 <- ddirmn(NewTestTotal[r,], t(as.matrix(alpha_Type1)))\n #lh_Type1=exp(pdfln_Type1)\n #lhP_Type1 = lh_Type1*Prior1\n\n probability_Type1 = apply(NewTestTotal[r,],1,dmultinom, prob = p_Type1)\n lhP_Type1 = probability_Type1*Prior1\n\n ##### Calculate log of Dirichlet multinomial probability mass function P(x|Type2)\n #pdfln_Type2 <- ddirmn(NewTestTotal[r,], t(as.matrix(alpha_Type2)))\n #lh_Type2=exp(pdfln_Type2)\n #lhP_Type2 = lh_Type2*Prior2\n probability_Type2 = apply(NewTestTotal[r,],1,dmultinom, prob = p_Type2)\n lhP_Type12 = probability_Type2*Prior2\n\n #Pos_Type1 = lh_Type1/(lh_Type1+lh_Type2)\n #Pos_Type2 = lh_Type2/(lh_Type1+lh_Type2)\n\n PosP_Type1 = lhP_Type1/(lhP_Type1+lhP_Type2)\n PosP_Type2 = lhP_Type2/(lhP_Type1+lhP_Type2)\n\n test_res[[r]] <- as.matrix(t(c(rownames(testset)[r],Disease[1], PosP_Type1, Disease[2], PosP_Type2,paste(NameList,collapse=\";\"))))\n\n }\n\n out <- data.frame(t(sapply(test_res,function(x) x)))\n colnames(out) <- c(\"test_idx\",\"Group1\",\"Group1_prb\",\"Group2\",\"Group2_prb\",\"Features\")\n\n return(out)\n}\n", "meta": {"hexsha": "e519836e1a593aa7b1cc52e1f4c0b25672c5cfac", "size": 4943, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dmbc_predict.r", "max_stars_repo_name": "qunfengdong/BioMarkerClassifier", "max_stars_repo_head_hexsha": "06acc16d28de665119be76c529d7d07ad3ddfcf6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/dmbc_predict.r", "max_issues_repo_name": "qunfengdong/BioMarkerClassifier", "max_issues_repo_head_hexsha": "06acc16d28de665119be76c529d7d07ad3ddfcf6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/dmbc_predict.r", "max_forks_repo_name": "qunfengdong/BioMarkerClassifier", "max_forks_repo_head_hexsha": "06acc16d28de665119be76c529d7d07ad3ddfcf6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8880597015, "max_line_length": 200, "alphanum_fraction": 0.6965405624, "num_tokens": 1606, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.34529197632961595}} {"text": "# Import R Packages and Modules\nlibrary(\"ggplot2\")\nlibrary(\"dplyr\")\n\n# Import our datasets\narrests <- read.csv('data/arrests.csv', header = TRUE, stringsAsFactors = TRUE)\n\n# Exploring data\nhead(arrests) # Displays the first 6 rows of the dataset\ntail(arrests) # Display the last 6 rows of the datasets\nedit(arrests) # Open data editor to edit the datasets\nView(arrests) # View the datasets\nsummary(arrests) # Provides basic descriptive statistics and frequencies.\n\n# Arrest by Gender\narrests$Sex <- as.character(arrests$Sex)\narrests$Sex[arrests$Sex == \"M\"] <- \"Male\"\narrests$Sex[arrests$Sex == \"F\"] <- \"Female\"\n# Plot Bar Chart\nggplot(arrests, aes(Sex, fill=Sex) ) + geom_bar() + ggtitle(\"Graph showing arrests based on Gender.\") + xlab(\"Gender\") + ylab(\"Total Cases\") + labs(fill = \"Gender\")\n# Plot Pie Chart\nggplot(arrests, aes(x=factor(1), fill=factor(Sex)) ) + geom_bar(width = 1) + ggtitle(\"Graph showing arrests based on Gender.\") + labs(fill = \"Gender\") + coord_polar(theta = \"y\") # + theme_void()\narrests$Sex <- as.factor(arrests$Sex)\n\n# Arrest by Race\nggplot(arrests, aes(Race, fill=Race) ) + geom_bar() + ggtitle(\"Graph showing arrests based on Race.\") + xlab(\"Race\") + ylab(\"Total Cases\") + labs(fill = \"Race\")\n\n# Arrest by Districts\narrests$District <- as.character(arrests$District)\narrests$District[arrests$District == \"\"] <- NA\nggplot(arrests, aes(District, fill=District) ) + geom_bar() + ggtitle(\"Graph showing arrests based on District.\") + xlab(\"District\") + ylab(\"Total Cases\") + labs(fill = \"District\")\narrests$District <- as.factor(arrests$District)\n\n# Arrest by Age\narrests$Age_cat <- cut(arrests$Age, breaks = c(-Inf, 17, 24, 34, 44, 54, 64, Inf), labels = c(\"Below 18\", \"18 - 24\", \"25 - 34\", \"35 - 45\", \"45 - 54\", \"55 - 64\", \"65 Above\"), right = FALSE)\nggplot(arrests, aes(Age_cat, fill=Age_cat) ) + geom_bar() + ggtitle(\"Graph showing arrests based on Age.\") + xlab(\"Age Range\") + ylab(\"Total Cases\") + labs(fill = \"Age Range:\")\n\n# Arrest by Year\n# mutate(arrests, year = format(as.Date(ArrestDate, \"%d/%m/%Y\"), \"%Y\"))\nggplot(arrests, aes(factor(format(as.Date(ArrestDate, \"%d/%m/%Y\"), \"%Y\")), fill= factor(format(as.Date(ArrestDate, \"%d/%m/%Y\"), \"%Y\"))) ) + geom_bar() + ggtitle(\"Graph showing arrests based on year of arrest.\") + xlab(\"Year of arrest\") + ylab(\"Total Cases\") + labs(fill = \"Year\")\n\n# Determine the correlation between Age and the year of arrest\ncor.test(arrests$Age, as.numeric( factor(format(as.Date(arrests$ArrestDate, \"%d/%m/%Y\"), \"%Y\"))))\n\n# Arrest by hour of the day\narrests$ArrestTime_Rnd <- round(as.double(gsub(\":\",\".\", arrests$ArrestTime)))\nggplot(arrests, aes(ArrestTime_Rnd, fill= as.character(ArrestTime_Rnd)) ) + geom_bar() + ggtitle(\"Graph showing arrests based on hour of the day.\") + xlab(\"Hour of arrest\") + ylab(\"Total Cases\") + labs(fill = \"Hour\")\n\nView(arrests)\n", "meta": {"hexsha": "c1f1c15d0c1576db9eb34a3c5a05e8650a90bf3f", "size": 2826, "ext": "r", "lang": "R", "max_stars_repo_path": "baltimore_police_arrests/main.r", "max_stars_repo_name": "Kamparia/datascience_with_R", "max_stars_repo_head_hexsha": "04a70c1ea250dc4db383b3bf07d51c022037041d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "baltimore_police_arrests/main.r", "max_issues_repo_name": "Kamparia/datascience_with_R", "max_issues_repo_head_hexsha": "04a70c1ea250dc4db383b3bf07d51c022037041d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "baltimore_police_arrests/main.r", "max_forks_repo_name": "Kamparia/datascience_with_R", "max_forks_repo_head_hexsha": "04a70c1ea250dc4db383b3bf07d51c022037041d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-29T00:13:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-10T20:20:24.000Z", "avg_line_length": 56.52, "max_line_length": 279, "alphanum_fraction": 0.6882519462, "num_tokens": 882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.640635854839898, "lm_q1q2_score": 0.3452919763296159}} {"text": "\ndata(iris)\n\nsummary(iris)\n\nplot(iris)\n", "meta": {"hexsha": "87b026975548839362a41272b141a11007fb52f7", "size": 39, "ext": "r", "lang": "R", "max_stars_repo_path": "Section 3/R+Iris.r", "max_stars_repo_name": "PacktPublishing/Jupyter-Notebook-for-All-Part-I", "max_stars_repo_head_hexsha": "0d773cb7ea6f539b25e607e512ea241e888d0005", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-10-04T17:56:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-04T17:56:49.000Z", "max_issues_repo_path": "Section 3/R+Iris.r", "max_issues_repo_name": "PacktPublishing/Jupyter-Notebook-for-All-Part-I", "max_issues_repo_head_hexsha": "0d773cb7ea6f539b25e607e512ea241e888d0005", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Section 3/R+Iris.r", "max_forks_repo_name": "PacktPublishing/Jupyter-Notebook-for-All-Part-I", "max_forks_repo_head_hexsha": "0d773cb7ea6f539b25e607e512ea241e888d0005", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-09-25T18:33:54.000Z", "max_forks_repo_forks_event_max_datetime": "2020-09-25T18:33:54.000Z", "avg_line_length": 5.5714285714, "max_line_length": 13, "alphanum_fraction": 0.6923076923, "num_tokens": 13, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.34529196893354036}} {"text": "./tests.verify: test performed on 2004.08.17 \n\n\n#### Test: ./tests.verify running tmp.sh \nContent-type: text/html\n\nHello, World! The sine of 3.14 equals 0.00159265291649\nshould be 0.00159265291649\nCPU time of tmp.sh: 0.1 seconds on hplx30 i686, Linux\n\n\n#### Test: ./tests.verify running tmp.sh \nContent-type: text/html\n\n\n\n
\nHello, World! The sine of \n\n \n
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\n\n\n\nCPU time of tmp.sh: 0.6 seconds on hplx30 i686, Linux\n\n", "meta": {"hexsha": "8518d7fe7d5d3b84a1b368374fc72141dbd644f3", "size": 6843, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/cgi/tests.r", "max_stars_repo_name": "sniemi/SamPy", "max_stars_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2016-05-28T14:12:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-22T10:23:12.000Z", "max_issues_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/cgi/tests.r", "max_issues_repo_name": "sniemi/SamPy", "max_issues_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sandbox/src1/TCSE3-3rd-examples/src/py/cgi/tests.r", "max_forks_repo_name": "sniemi/SamPy", "max_forks_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-07-13T10:04:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-22T10:23:23.000Z", "avg_line_length": 21.1203703704, "max_line_length": 79, "alphanum_fraction": 0.4965658337, "num_tokens": 2132, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.6406358479787609, "lm_q1q2_score": 0.34529196314338934}} {"text": "library(data.table)\nlibrary(rstan)\nrstan_options(auto_write = TRUE)\noptions(mc.cores = parallel::detectCores())\n\ntype <- Sys.getenv('RUNLENGTH')\nif (type == '') type <- 'quick'\n\nnchains <- 3\n\n## * Data\nmodel_data <- new.env()\nload('../data.rdata', envir = model_data)\n\n## Different BETA0 between JAGS and Stan\nassign('BETA0', get('BETA0', model_data)^-0.5, envir = model_data)\n\n\n## * Run model\nfit <- stan(file = 'model.stan', \n model_name = \"SRAmodel\",\n pars = c('apf_t', 'q_t', 'q_o', 'q0', 'q_f0', 'q_g0', 'q_gf0',\n 'p_identified', 'p_live_cap', 'p_observable', 'p_survive_cap',\n 'live_captures_o', 'dead_captures_o', 'live_unident_o', 'dead_unident_o',\n 'incidents_t', 'ntot'),\n include = TRUE, ## TRUE to only store parameters in `pars`\n data = model_data, \n iter = 500,\n control = list(max_treedepth = 10),\n chains = nchains,\n diagnostic_file='diag.txt',\n verbose = F,\n seed = 3859285)\n\n\ncodasamples <- As.mcmc.list(fit)\nmcmc <- data.table(do.call('rbind', codasamples))\n\n\n## * Summarise samples\nmcsumm <- rbindlist(lapply(names(mcmc), function(x) data.table(var = x,\n mean = mean(mcmc[[x]]),\n lcl = quantile(mcmc[[x]], 0.025, names=F),\n ucl = quantile(mcmc[[x]], 0.975, names=F),\n n = length(mcmc[[x]]))))\nmcsumm[, vartype := sub('\\\\[.*\\\\]', '', var)]\nmcsumm[grep('.*\\\\[([0-9]+)\\\\].*', var), ind := as.numeric(sub('.*\\\\[([0-9]+)\\\\].*', '\\\\1', var))]\n\nsave(codasamples, mcsumm, fit,\n file = 'model-results.rdata')\n", "meta": {"hexsha": "be5ff6e62e762f36ee11844af1d44ecd9693e49c", "size": 1863, "ext": "r", "lang": "R", "max_stars_repo_path": "model/stan/run-model.r", "max_stars_repo_name": "dragonfly-science/seabird-risk-assessment", "max_stars_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-02-22T20:16:08.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-22T20:16:08.000Z", "max_issues_repo_path": "model/stan/run-model.r", "max_issues_repo_name": "seabird-risk-assessment/seabird-risk-assessment", "max_issues_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-10-09T08:18:07.000Z", "max_issues_repo_issues_event_max_datetime": "2017-10-29T22:13:43.000Z", "max_forks_repo_path": "model/stan/run-model.r", "max_forks_repo_name": "dragonfly-science/seabird-risk-assessment", "max_forks_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-04-18T22:56:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-16T13:57:07.000Z", "avg_line_length": 36.5294117647, "max_line_length": 99, "alphanum_fraction": 0.4847020934, "num_tokens": 504, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3452606306180124}} {"text": "#' Build and plot dendrogram from gene panel\n#'\n#' Build and plot a dendrogram using correlation-based average linkage hierarchical\n#' clustering and only using a specified set of genes. The output is the expected\n#' accuracy of mapping to each node in the tree, which gives an idea of the best-case\n#' expected results for mFISH analysis.\n#'\n#' @param dend dendrogram for mapping. Ignored if medianDat is passed\n#' @param refDat normalized data of the REFERENCE data set. Ignored if medianDat is passed\n#' @param mapDat normalized data of the MAPPING data set. Default is to map the data onto itself.\n#' @param medianDat representative value for each leaf and node. If not entered, it is calculated\n#' @param requiredGenes minimum number of genes required to be expressed in a cluster (column\n#' of medianDat) for the cluster to be included (default=2)\n#' @param clusters cluster calls for each cell\n#' @param mappedAsReference if TRUE, returns the fraction of cells mapped to a node which are\n#' were orginally clustered from that node; if FALSE (default) returns the fraction of cells\n#' clustered under a node which are mapped to the correct node.\n#' @param genesToMap which genes to include in the correlation mapping\n#' @param plotdendro should the dendrogram be plotted (default = TRUE)\n#' @param returnDendro should the dendrogram be returned (default = TRUE)\n#' @param mar margins (for use with par)\n#' @param main,ylab add title and labels to plot (default is NULL)\n#' @param use,... additional parameters for cor\n#'\n#' @return a list where the first entry is the resulting tree and the second entry is the\n#' fraction of cells correctly mapping to each node using the inputted gene panel.\n#'\n#' @export\nbuildTreeFromGenePanel <- function(dend = NA,\n refDat = NA,\n mapDat = refDat,\n medianDat = NA,\n requiredGenes = 2,\n clusters = NA,\n mappedAsReference = FALSE,\n genesToMap = rownames(mapDat),\n plotdendro = TRUE,\n returndendro = TRUE,\n mar = c(12, 5, 5, 5),\n main = NULL,\n ylab = NULL,\n use = \"p\",\n ...) {\n library(dendextend)\n\n # Calculate the median, if needed.\n if (is.na(medianDat[1])) {\n names(clusters) <- colnames(refDat)\n medianDat <- do.call(\"cbind\", tapply(\n names(clusters), clusters, function(x) rowMedians(refDat[, x])\n ))\n rownames(medianDat) <- rownames(refDat)\n if (!is.na(dend)) medianDat <- leafToNodeMedians(dend, medianDat)\n }\n gns <- intersect(genesToMap, intersect(rownames(mapDat), rownames(medianDat)))\n\n # Subset the data to relevant genes and clusters\n medianDat <- medianDat[gns, ]\n mapDat <- mapDat[gns, ]\n medianDat <- medianDat[, colSums(medianDat > 0) >= requiredGenes]\n kpDat <- (colSums(mapDat > 0) >= requiredGenes) & (is.element(clusters, colnames(medianDat)))\n mapDat <- mapDat[, kpDat]\n\n # Perform the correlation mapping\n facsCor <- corTreeMapping(medianDat = medianDat, mapDat = mapDat, use = use, ...)\n facsCl <- colnames(facsCor)[apply(facsCor, 1, which.max)]\n\n # Build a new tree based on mapping\n sCore <- function(x, use, ...) return(as.dist(1 - WGCNA::cor(x, use = use, ...)))\n dend <- getDend(medianDat, sCore, use = use, ...)\n\n # Which leaves have which nodes?\n has_any_labels <- function(sub_dend, the_labels) any(labels(sub_dend) %in% the_labels)\n node_labels <- NULL\n for (lab in labels(dend)) node_labels <- cbind(\n node_labels, noded_with_condition(dend, has_any_labels, the_labels = lab)\n )\n rownames(node_labels) <- get_nodes_attr(dend, \"label\")\n colnames(node_labels) <- labels(dend)\n\n # Swap the mapped and reference nodes if\n # mappedAsReference=TRUE\n clTmp <- as.character(clusters[kpDat])\n if (mappedAsReference) {\n temp <- clTmp\n clTmp <- facsCl\n facsCl <- temp\n }\n\n # Which clusters agree at the node level?\n agreeNodes <- apply(cbind(facsCl, clTmp), 1, function(lab, node_labels) {\n rowSums(node_labels[, lab]) == 2\n }, node_labels)\n colnames(agreeNodes) <- clTmp\n\n # Which clusters are in each nodes?\n isInNodes <- t(apply(node_labels, 1, function(node, cl, dend) {\n is.element(cl, labels(dend)[node])\n }, clTmp, dend))\n colnames(isInNodes) <- clTmp\n\n # For each node, plot the fraction of cells that\n # match if desired?\n fracAgree <- rowSums(agreeNodes) / rowSums(isInNodes)\n if (plotdendro) {\n par(mar = mar)\n dend %>% set(\"nodes_cex\", 0) %>% set(\"branches_col\", \"grey\") %>% plot()\n text(get_nodes_xy(dend)[, 1], get_nodes_xy(dend)[, 2], round(fracAgree * 100))\n title(main = main, ylab = ylab)\n }\n\n # Return the results (if desired)\n if (returndendro) return(list(dend, fracAgree))\n}\n\n\n#' Build a dendrogram from gene panel\n#'\n#' Build a dendrogram from an inputted data matrix.\n#'\n#' @param dat matrix of values (e.g., genes x clusters) for calculating the dendrogram\n#' @param distFun function for calculating distance matrix (default is correlation-based)\n#' @param ... additional variables for distFun\n#'\n#' @return dendrogram\n#'\n#' @export\ngetDend <- function(dat,\n distFun = function(x) return(as.dist(1 - WGCNA::cor(x))),\n ...) {\n distCor <- distFun(dat, ...)\n distCor[is.na(distCor)] <- max(distCor, na.rm = TRUE) * 1.2\n # Avoid crashing by setting NA values to hang off the side of the tree.\n avgClust <- hclust(distCor, method = \"average\")\n dend <- as.dendrogram(avgClust)\n dend <- labelDend(dend)[[1]]\n return(dend)\n}\n\n\n#' Label dendrogram nodes\n#'\n#' Add numeric node labels to a dendrogram.\n#'\n#' @param dend dendrogram object\n#' @param distFun starting numeric node value (default=1)\n#'\n#' @return a list where the first item is the new dendrogram object and the\n#' second item is the final numeric node value.\n#'\n#' @export\nlabelDend <- function(dend, n = 1) {\n if (is.null(attr(dend, \"label\"))) {\n attr(dend, \"label\") <- paste0(\"n\", n)\n n <- n + 1\n }\n if (length(dend) > 1) {\n for (i in 1:length(dend)) {\n tmp <- labelDend(dend[[i]], n)\n dend[[i]] <- tmp[[1]]\n n <- tmp[[2]]\n }\n }\n return(list(dend, n))\n}\n\n\n#' Summarize matrix\n#'\n#' Groups columns in a matrix by a specified group vector and summarizes using a specificed function.\n#' Optionally binarizes the matrix using a specified cutoff parameter. This is a wrapper for tapply.\n#'\n#' @param mat matrix where the columns (e.g., samples) are going to be grouped\n#' @param group vector of length dim(mat)[2] corresponding to the groups\n#' @param scale either 'none' (default),'row', or 'column'\n#' @param scaleQuantile what quantile of value should be set as 1 (default=1)\n#' @param binarize should the data be binarized? (default=FALSE)\n#' @param binMin minimum ON value for the binarized matrix (ignored if binarize=FALSE)\n#' @param summaryFunction function (or function name) to be used for summarization\n#' @param ... additional parameters for summaryFunction\n#'\n#' @return matrix of summarized values\n#'\n#' @export\nsummarizeMatrix <- function(mat,\n group,\n scale = \"none\",\n scaleQuantile = 1,\n binarize = FALSE,\n binMin = 0.5,\n summaryFunction = median,\n ...) {\n\n # Make sure the names match up\n if (is.null(colnames(mat))) colnames(mat) <- names(group)\n if (is.null(colnames(mat))) colnames(mat) <- 1:length(group)\n names(group) <- colnames(mat)\n\n # Calculate the summary\n summaryFunction <- match.fun(summaryFunction)\n runFunction <- function(x, ...) {\n if (length(x) > 1) {\n return(apply(mat[, x], 1, summaryFunction, ...))\n }\n return(mat[, x])\n }\n summarizedMat <- do.call(\"cbind\", tapply(names(group), group, runFunction, ...))\n if (is.factor(group)) summarizedMat <- summarizedMat[, levels(group)]\n rownames(summarizedMat) <- rownames(mat)\n\n # Scale the data if desired\n if (substr(scale, 1, 1) == \"r\") {\n for (i in 1:dim(summarizedMat)[1])\n summarizedMat[i, ] <- summarizedMat[i, ] /\n max(1e-06, quantile(summarizedMat[i, ], probs = scaleQuantile))\n }\n if (substr(scale, 1, 1) == \"c\") {\n for (i in 1:dim(summarizedMat)[2])\n summarizedMat[, i] <- summarizedMat[, i] /\n max(1e-06, quantile(summarizedMat[, i], probs = scaleQuantile))\n }\n\n # Binarize the data if desired\n if (binarize) {\n summarizedMat <- summarizedMat > binMin\n summarizedMat <- summarizedMat + 1 - 1\n }\n summarizedMat\n}\n\n\n#' Scale mFISH data and map to RNA-seq reference\n#'\n#' This function is a wrapper for several other functions which aim to scale mFISH data to\n#' more closely match RNA-seq data and then map the mFISH data to the closest reference\n#' classes. There are several parameters allowing flexability in filtering and analysis.\n#'\n#' @param mapDat normalized data of the MAPPING data set. Default is to map the data onto itself.\n#' @param refSummaryDat normalized summary data of the REFERENCE data set (e.g., what to map against)\n#' @param genesToMap which genes to include in the mapping (calculated in not entered)\n#' @param mappingFunction which function to use for mapping (default is cellToClusterMapping_byCor)\n#' The function must include at least two parameters with the first one being mapped data and the\n#' second data the reference. Additional parameters are okay. Output must be a data frame where\n#' the first value is a mapped class. Additional columns are okay and will be returned)\n#' @param transform function for transformation of the data (default in none)\n#' @param noiselevel scalar value at or below which all values are set to 0 (default is 0)\n#' @param scaleFunction which function to use for scaling mapDat to refSummaryDat (default is setting\n#' 90th quantile of mapDat to max of refSummaryDat and truncating higher mapDat values)\n#' @param omitGenes genes to be included in the data frames but excluded from the mapping\n#' @param metadata a data frame of possible metadata (additional columns are okay and ignored):\n#' \\describe{\n#' \\item{area}{a vector of cell areas for normalization}\n#' \\item{experiment}{a vector indicating if multiple experiments should be scaled separately}\n#' \\item{x,y}{x (e.g., parallel to layer) and y (e.g., across cortical layers) coordinates in tissue}\n#' }\n#' @param integerWeights if not NULL (default) a vector of integers corresponding to how many times\n#' each gene should be counted as part of the correlation. This is equivalent to calculating\n#' a weighted correlation, but only allows for integer weight values (for use with cor).\n#' @param binarize should the data be binarized? (default=FALSE)\n#' @param binMin minimum ON value for the binarized matrix (ignored if binarize=FALSE)\n#' @param ... additional parameters for passthrough into other functions\n#'\n#' @return a list with the following entrees:\n#' \\describe{\n#' \\item{mapDat}{mapDat data matrix is passed through}\n#' \\item{scaleDat}{scaled mapDat data matrix}\n#' \\item{mappingResults}{Results of the mapping and associated confidence values (if any)}\n#' \\item{metadata=metadata}{metadata is passed through unchanged}\n#' \\item{scaledX/Y}{scaled x and y coordinates (or unscaled if scaling was not performed)}\n#' }\n#'\n#' @export\nfishScaleAndMap <- function(mapDat,\n refSummaryDat,\n genesToMap = NULL,\n mappingFunction = cellToClusterMapping_byCor,\n transform = function(x) x,\n noiselevel = 0,\n scaleFunction = quantileTruncate,\n\t\t\t\t\t\t\tomitGenes = NULL,\n metadata = data.frame(experiment = rep(\"all\", dim(mapDat)[2])),\n integerWeights = NULL,\n binarize = FALSE,\n binMin = 0.5,\n ...) {\n\n # Setup\n mappingFunction <- match.fun(mappingFunction)\n scaleFunction <- match.fun(scaleFunction)\n transform <- match.fun(transform)\n if (is.null(genesToMap)) genesToMap <- colnames(mapDat)\n genesToMap <- intersect(genesToMap, rownames(refSummaryDat))\n params <- colnames(metadata)\n refSummaryDat <- refSummaryDat[genesToMap, ]\n mapDat <- mapDat[genesToMap, ]\n\n # Transform the data to be mapped\n scaleDat <- as.matrix(mapDat[genesToMap, ])\n scaleDat[scaleDat <= noiselevel] <- 0 # Set values less than or equal to noiselevel to 0\n if (is.element(\"area\", params)) {\n # Account for spot area in gene expression calculation\n scaleDat <- t(t(scaleDat) / metadata$area) * mean(metadata$area)\n }\n scaleDat <- transform(scaleDat)\n\n # Scale to the reference data\n for (ex in unique(metadata$experiment)) {\n isExp <- metadata$experiment == ex\n for (g in genesToMap) scaleDat[g, isExp] <- scaleFunction(scaleDat[\n g, isExp\n ], maxVal = max(refSummaryDat[g, ]), ...)\n }\n\n # Binarize, if desired\n if (binarize) {\n scaleDat <- scaleDat > binMin\n scaleDat <- scaleDat + 1 - 1\n }\n\n # Omit genes and weight scaling, if desired\n genesToMap2 <- genesToMap\n if (!is.null(integerWeights)) genesToMap2 <- rep(genesToMap2, integerWeights)\n genesToMap2 <- genesToMap2[!is.element(genesToMap2, omitGenes)]\n\n # Map the map data to the reference data\n scaleDat2 <- scaleDat[genesToMap2, ]\n refSummaryDat2 <- refSummaryDat[genesToMap2, ]\n mappingResults <- mappingFunction(refSummaryDat2, scaleDat2, ...)\n\n # Scale x and y coordinates to (0,1) within experiment, if desired\n for (ex in unique(metadata$experiment)) {\n isExp <- metadata$experiment == ex\n metadata$x[isExp] <- metadata$x[isExp] - min(metadata$x[isExp])\n metadata$x[isExp] <- metadata$x[isExp] / max(metadata$x[isExp])\n metadata$y[isExp] <- metadata$y[isExp] - min(metadata$y[isExp])\n metadata$y[isExp] <- metadata$y[isExp] / max(metadata$y[isExp])\n }\n\n # Return the results\n out <- list(\n mapDat = mapDat, scaleDat = scaleDat,\n mappingResults = mappingResults, metadata = metadata,\n scaledX = metadata$x, scaledY = metadata$y\n )\n}\n\n\n#' Filter (subset) fishScaleAndMap object\n#'\n#' Subsets all components in a fishScaleAndMap object\n#'\n#' @param datFish a fishScaleAndMap output list\n#' @param subset a boolean or numeric vector of the elements to retain\n#'\n#' @return a fishScaleAndMap output subsetted to the requested elements\n#'\n#' @export\nfilterCells <- function(datFish,\n subset) {\n ## Error checking\n if ((length(subset) != length(datFish$scaledX)) & (!is.numeric(subset))) {\n print(\"subset is incorrect format. Returning original entry.\")\n return(datFish)\n }\n if (is.numeric(subset)) subset <- intersect(subset, 1:length(datFish$scaledX))\n\n ## Subset all of the elements\n datFish$mapDat <- datFish$mapDat[, subset]\n datFish$scaleDat <- datFish$scaleDat[, subset]\n datFish$metadata <- datFish$metadata[subset, ]\n datFish$scaledX <- datFish$scaledX[subset]\n datFish$scaledY <- datFish$scaledY[subset]\n if (!is.null(datFish$mappingResults)) {\n datFish$mappingResults <- datFish$mappingResults[subset, ]\n }\n return(datFish)\n}\n\n\n#' Merge two fishScaleAndMap objects\n#'\n#' Merges all components of two fishScaleAndMap objects to create a new\n#' one. Note: only meta-data and mappingResults that is present in BOTH\n#' objects will be returned.\n#'\n#' @param datFish1 a fishScaleAndMap output list\n#' @param datFish2 a second fishScaleAndMap output list.\n#'\n#' @return a new fishScaleAndMap output list with the two original ones merged\n#'\n#' @export\nmergeFish <- function(datFish1,\n datFish2) {\n datFish <- datFish1\n datFish$mapDat <- cbind(datFish1$mapDat, datFish2$mapDat)\n datFish$scaleDat <- cbind(datFish1$scaleDat, datFish2$scaleDat)\n datFish$metadata <- rbind(datFish1$metadata, datFish2$metadata)\n datFish$scaledX <- c(datFish1$scaledX, datFish2$scaledX)\n datFish$scaledY <- c(datFish1$scaledY, datFish2$scaledY)\n if ((!is.null(datFish1$mappingResults)) & (!is.null(datFish2$mappingResults))) {\n datFish$mappingResults <- rbind(datFish1$mappingResults, datFish2$mappingResults)\n }\n return(datFish)\n}\n\n\n\n\n#' Rotate coordinates\n#'\n#' Rotates the scaledX and scaledY elements of a fishScaleAndMap output list so that the\n#' axis of interest (e.g., cortical layer) is paralled with the x cooridate plan.\n#' Rotation code is from https://stackoverflow.com/questions/15463462/rotate-graph-by-angle\n#'\n#' @param datFish a fishScaleAndMap output list\n#' @param flatVector a TRUE/FALSE vector ordred in the same way as the elements (e.g., cells)\n#' in datIn where all TRUE values correspond to cells who should have the same Y coordinate\n#' (e.g., be in the same layer). Alternatively a numeric vector of cell indices to include\n#' @param flipVector a numeric vector of values to ensure proper reflection on Y-axes (e.g.,\n#' layer; default=NULL)\n#' @param subset a boolean or numeric vector of the elements to retain\n#'\n#' @return a fishScaleAndMap output list with updated scaledX and scaleY coordinates\n#'\n#' @export\nrotateXY <- function(datFish,\n flatVector = NULL,\n flipVector = NULL,\n subset = NULL) {\n\n ## Error checking\n datFishIn <- datFish\n if ((length(flatVector) != length(datFish$scaledX)) & (!is.numeric(flatVector))) {\n print(\"flatVector is incorrect format. Returning original entry.\")\n return(datFish)\n }\n if (!is.null(subset)) {\n if ((length(subset) != length(datFish$scaledX)) & (!is.numeric(subset))) {\n print(\"subset is incorrect format. Returning original entry.\")\n return(datFish)\n }\n }\n if (((length(flipVector) != length(datFish$scaledX)) &\n (!is.numeric(flipVector))) & (!is.null(flipVector))) {\n print(\"flipVector is incorrect format. Returning original entry.\")\n return(datFish)\n }\n if (is.numeric(flatVector)) {\n flatVector <- intersect(flatVector, 1:length(datFish$scaledX))\n }\n\n ## Subset the data if needed\n datFish <- datFishIn\n if (!is.null(subset)) {\n datFish <- filterCells(datFishIn, subset)\n flatVector <- flatVector[subset]\n flipVector <- flipVector[subset]\n }\n\n ## Caculate best angle\n v <- prcomp(cbind(datFish$scaledX, datFish$scaledY)[flatVector, ])$rotation\n beta <- as.numeric(atan(-v[2, 1]/v[1, 1]))\n\n ## Rotate coordinates (internal function)\n rotCor <- function(datFish,\n beta) {\n M <- cbind(datFish$scaledX, datFish$scaledY)\n rotm <- matrix(c(cos(beta), sin(beta), -sin(beta), cos(beta)), ncol = 2) # rotation matrix\n M2.1 <- t(t(M) - c(M[1, 1], M[1, 2])) # shift points, so that turning point is (0,0)\n M2.2 <- t(rotm %*% (t(M2.1))) # rotate\n M2.3 <- t(t(M2.2) + c(M[1, 1], M[1, 2])) # shift back\n x <- M2.3[, 1]\n y <- M2.3[, 2]\n\n x <- x - min(x)\n x <- x / max(x)\n y <- y - min(y)\n y <- y / max(y)\n\n datFish$scaledX <- x\n datFish$scaledY <- y\n datFish\n }\n datFish2 <- rotCor(datFish, beta)\n\n if (!is.null(flipVector)) {\n if (sum(datFish2$scaledY * flipVector) < sum((1 - datFish2$scaledY) * flipVector)) {\n datFish2 <- rotCor(datFish, beta)# + pi) # Pi should not be needed\n }\n }\n datFish <- datFish2\n\n ## Unsubset and return the data\n if (is.null(subset)) return(datFish)\n\n datFishIn$scaledX[subset] <- datFish$scaledX\n datFishIn$scaledY[subset] <- datFish$scaledY\n datFishIn\n}\n\n\n\n\n#' Plot distributions\n#'\n#' Plot the distributions of cells across the tissue with overlaying color information. This is\n#' a wrapper function for plot\n#'\n#' @param datIn a fishScaleAndMap output list\n#' @param group a character vector (or factor) indicating how to split the data (e.g., cluster\n#' call) or a metadata/mappingResults column name\n#' @param groups a character vector of groups to show (default is levels of group)\n#' @param colors a character vector (or factor) indicating how to color the plots (e.g., layer\n#' or gene expression) or a metadata/mappingResults column name (default is all black)\n#' @param colormap function to use for the colormap for the data (default gray.colors)\n#' @param maxrow maximum number of plots to show in one row (default=12)\n#' @param xlim,ylim for plot, but will be calculated if not entered\n#' @param pch,cex for plot. Can be single values or vectors\n#' @param main,xlab,ylab,... other parameters for plot (must be single values)\n#' @param singlePlot should everything be plot on a single page (default=TRUE)\n#'\n#' @return Only returns if there is an error\n#'\n#' @export\nplotDistributions <- function(datIn,\n group,\n groups = NULL,\n colors = rep(\"black\", dim(datIn$mapDat)[2]),\n colormap = gray.colors,\n maxrow = 12,\n pch = 19,\n cex = 1.5,\n xlim = NULL, ylim = NULL,\n main = \"\",\n xlab = \"\", ylab = \"\",\n\t\t\t singlePlot = TRUE,\n ...) {\n colormap <- match.fun(colormap)\n meta <- cbind(datIn$metadata, datIn$mappingResults)\n if (length(group) == 1) {\n if (is.element(group, colnames(meta))) {\n group <- as.factor(meta[, group])\n if (is.null(groups)) groups <- levels(group)\n } else {\n return(paste(group, \"is not an available column name for division.\"))\n }\n }\n if (length(colors) == 1) {\n if (is.element(colors, colnames(meta))) {\n colors <- as.numeric(as.factor(meta[, colors]))\n colors <- colormap(length(unique(colors)))[colors]\n } else {\n return(paste(colors, \"is not an available column name for coloring.\"))\n }\n } else {\n colors <- as.numeric(as.factor(colors))\n colors <- colormap(length(unique(colors)))[colors]\n }\n\n if (is.null(xlim)) xlim <- range(datIn$scaledX)\n if (is.null(ylim)) ylim <- range(-datIn$scaledY)\n\n # Make the plot!\n if(singlePlot){\n ncolv <- min(length(groups), maxrow)\n nrowv <- ceiling(length(groups)/maxrow)\n par(mfrow = c(nrowv, ncolv))\n }\n for (gp in groups) {\n kp <- group == gp\n pch2 <- pch\n if (length(pch) > 1) pch2 <- pch[kp]\n cex2 <- cex\n if (length(cex) > 1) cex2 <- cex[kp]\n\n plot(datIn$scaledX[kp], -datIn$scaledY[kp],\n pch = pch2, col = colors[kp], xlim = xlim,\n ylim = ylim, main = paste(main, gp), xlab = xlab,\n ylab = ylab, cex = cex2, ...\n )\n }\n}\n\n\n#' Plot heatmap\n#'\n#' Plot the heatmap of cells ordering by a specified order. This is a wrapper for heatmap.2\n#'\n#' @param datIn a fishScaleAndMap output list\n#' @param group a character vector (or factor) indicating how to order the heatmap (e.g., cluster\n#' call) or a metadata/mappingResults column name\n#' @param groups a character vector of groups to show (default is levels of group)\n#' @param grouplab label for the grouping in the heatmap (default is 'Grouping' or the value for group)\n#' @param useScaled plot the scaled (TRUE) or unscaled (FALSE; default) values\n#' @param capValue values above capValue will be capped at capValue (default is none)\n#' @param colormap set of values to use for the colormap for the data (default heat_colors)\n#' @param Rowv,Colv,dendrogram,trace,margins,rowsep,colsep,key,... other parameters for heatmap.2\n#' (some default values are different)\n#'\n#' @return Only returns if there is an error\n#'\n#' @export\nplotHeatmap <- function(datIn,\n group,\n groups = NULL,\n grouplab = \"Grouping\",\n useScaled = FALSE,\n capValue = Inf,\n colormap = grey.colors(1000),\n pch = 19,\n xlim = NULL, ylim = NULL,\n Rowv = FALSE,\n Colv = FALSE,\n dendrogram = \"none\",\n trace = \"none\",\n margins = c(6, 10),\n rowsep = NULL,\n sepwidth=c(0.4,0.4),\n key = FALSE, ...) {\n library(gplots)\n \n if (useScaled) {\n plotDat <- datIn$scaleDat\n } else {\n plotDat <- datIn$mapDat\n }\n plotDat <- pmin(plotDat, capValue)\n \n meta <- cbind(datIn$metadata, datIn$mappingResults)\n if (length(group) == 1) {\n if (is.element(group, colnames(meta))) {\n if (grouplab == \"Grouping\") grouplab <- group\n group <- as.factor(meta[, group])\n if (is.null(groups)) groups <- levels(group)\n } else {\n return(paste(group, \"is not an available column name for division.\"))\n }\n }\n \n # Update the cell order\n groups <- c(groups, setdiff(levels(group), groups))\n ord <- order(factor(group, levels = groups), -colSums(plotDat))\n plotDat <- plotDat[, ord]\n group <- group[ord]\n \n # Append the cluster name to the plot data and find colseps\n cn <- rep(\"\", length(colnames(plotDat)))\n colseps <- NULL\n for (g in unique(as.character(group))){\n wg <- which(as.character(group)==g)\n cn[round(mean(wg))] <- g\n colseps <- c(colseps,min(wg)-1)\n }\n colnames(plotDat) <- paste(cn,colnames(plotDat))\n \n # Make the plot!\n heatmap.2(plotDat,\n Rowv = Rowv, Colv = Colv, dendrogram = dendrogram,\n trace = trace, margins = margins, rowsep = rowsep,\n colsep = colseps, key = key, col = colormap,\n ...\n )\n}\n\n\n#' Return top mapped correlation-based cluster and confidence\n#'\n#' Primary function for doing correlation-based mapping to cluster medians and also reporting the\n#' correlations and confidences. This is wrapper for getTopMatch and corTreeMapping.\n#'\n#' @param medianDat representative value for each leaf and node. If not entered, it is calculated\n#' @param mapDat normalized data of the MAPPING data set. Default is to map the data onto itself.\n#' @param refDat normalized data of the REFERENCE data set. Ignored if medianDat is passed\n#' @param clusters cluster calls for each cell. Ignored if medianDat is passed\n#' @param genesToMap which genes to include in the correlation mapping\n#' @param use additional parameter for cor (use='p' as default)\n#' @param method additional parameter for cor (method='p' as default)\n#' @param returnCor should the correlation matrix be appended to the return?\n#' @param ... not used\n#'\n#' @return data frame with the top match and associated correlation\n#'\n#' @export\ncellToClusterMapping_byCor <- function(medianDat,\n mapDat,\n refDat = NA,\n clusters = NA,\n genesToMap = rownames(mapDat),\n use = \"p\",\n method = \"p\",\n\t\t\t\t returnCor=FALSE,\n ...) {\n corVar <- corTreeMapping(\n medianDat = medianDat,\n mapDat = mapDat, refDat = refDat, clusters = clusters,\n genesToMap = genesToMap, use = use, method = method\n )\n corMatch <- getTopMatch(corVar)\n colnames(corMatch) <- c(\"Class\", \"Correlation\")\n\n dex <- apply(corVar, 1, function(x) return(diff(sort(-x)[1:2])))\n corMatch$DifferenceBetweenTopTwoCorrelations <- dex\n if(returnCor)\n corMatch <- cbind(corMatch,corVar)\n corMatch\n}\n\n\n#' Quantile normalize, truncate, and scale\n#'\n#' Quantile normalize, truncate, and scale a numeric vector (e.g. mFISH data from one gene)\n#'\n#' @param x input data vector\n#' @param qprob probs value to result from quantile (default=0.9)\n#' @param maxVal max value for scaling (default=1)\n#' @param truncate should data above the qprob threshold be truncated (default=yes)\n#' @param ... not used\n#'\n#' @return scaled vector\n#'\n#' @export\nquantileTruncate <- function(x,\n qprob = 0.9,\n maxVal = 1,\n truncate = TRUE,\n ...) {\n qs <- quantile(x[x > 0], probs = qprob, na.rm = TRUE)\n if (is.na(qs)) return(x)\n if (truncate) x[x > qs] <- qs\n x * maxVal / qs\n}\n\n\n#' Plot TSNE\n#'\n#' Plot a TSNE of the data, with assigned colors and labels from provided variables. Note that this\n#' function is a modification of code from Pabloc (https://www.r-bloggers.com/author/pabloc/) from\n#' https://www.r-bloggers.com/playing-with-dimensions-from-clustering-pca-t-sne-to-carl-sagan/\n#'\n#' @param datIn a fishScaleAndMap output list\n#' @param colorGroup a character vector (or factor) indicating how to color the Tsne (e.g., cluster\n#' call) or a metadata/mappingResults column name (default=NULL)\n#' @param labelGroup a character vector (or factor) indicating how to label the Tsne (e.g., cluster\n#' call) or a metadata/mappingResults column name (default=NULL)\n#' @param useScaled plot the scaled (TRUE) or unscaled (FALSE; default) values\n#' @param capValue values above capValue will be capped at capValue (default is none)\n#' @param perplexity,theta other parameters for Rtsne\n#' @param main title of the plot\n#' @param maxNchar what is the maximum number of characters to display in the plot for each entry?\n#' @param seed for reproducibility\n#'\n#' @return Only returns if there is an error\n#'\n#' @export\nplotTsne <- function(datIn,\n colorGroup = \"none\",\n labelGroup = \"none\",\n useScaled = FALSE,\n capValue = Inf,\n perplexity = 10,\n theta = 0.5,\n main = \"TSNE plot\",\n maxNchar = 1000,\n seed = 10) {\n library(Rtsne)\n library(ggplot2)\n\n # Get the data\n if (useScaled) {\n plotDat <- datIn$scaleDat\n } else {\n plotDat <- datIn$mapDat\n }\n plotDat <- pmin(plotDat, capValue)\n plotDat <- t(plotDat)\n\n # Color and label groups\n meta <- cbind(datIn$metadata, datIn$mappingResults)\n if (length(colorGroup) == 1) {\n if (is.element(colorGroup, colnames(meta))) {\n colorGroup <- as.factor(meta[, colorGroup])\n } else {\n colorGroup <- as.factor(rep(\"none\", dim(meta)[1]))\n }\n }\n if (length(labelGroup) == 1) {\n if (is.element(labelGroup, colnames(meta))) {\n labelGroup <- as.factor(meta[, labelGroup])\n } else {\n labelGroup <- as.factor(rep(\"*\", dim(meta)[1]))\n }\n }\n # Subset to maxNchar characters\n levs <- substr(levels(as.factor(labelGroup)), 1, maxNchar)\n labelGroup <- factor(as.character(substr(\n labelGroup, 1, maxNchar\n )), levels = as.character(unique(levs)))\n\n # Get the tsne corrdinates\n set.seed(seed)\n tsne_model_1 <- Rtsne(as.matrix(plotDat),\n check_duplicates = FALSE,\n pca = TRUE, perplexity = perplexity, theta = theta, dims = 2\n )\n d_tsne_1 <- as.data.frame(tsne_model_1$Y)\n\n # Make the plot!\n plot_k <- ggplot(d_tsne_1, aes_string(\n x = \"V1\", y = \"V2\",\n color = colorGroup, label = labelGroup\n )) +\n geom_text() + xlab(\"TSNE 1\") + ylab(\"TSNE 2\") +\n ggtitle(main) + theme(legend.title = element_blank())\n scale_colour_discrete()\n\n plot_k\n}\n", "meta": {"hexsha": "8fea2848db464356657faf09bf63cff357fc9a5a", "size": 31476, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mfishMapping.r", "max_stars_repo_name": "AllenInstitute/mfishtools", "max_stars_repo_head_hexsha": "f870c30eac12582073850835ca4bb9597abcf01b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2019-07-16T19:38:28.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T17:54:45.000Z", "max_issues_repo_path": "R/mfishMapping.r", "max_issues_repo_name": "AllenInstitute/mfishtools", "max_issues_repo_head_hexsha": "f870c30eac12582073850835ca4bb9597abcf01b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-02-06T18:31:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-27T19:04:15.000Z", "max_forks_repo_path": "R/mfishMapping.r", "max_forks_repo_name": "AllenInstitute/mfishtools", "max_forks_repo_head_hexsha": "f870c30eac12582073850835ca4bb9597abcf01b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-02-05T11:12:08.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-12T22:19:27.000Z", "avg_line_length": 38.4792176039, "max_line_length": 103, "alphanum_fraction": 0.6359766171, "num_tokens": 8236, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3452606306180124}} {"text": "\n# ==================================================================================================\n# This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This\n# project uses a tab-size of two spaces and a max-width of 100 characters per line.\n#\n# Author(s):\n# Cedric Nugteren \n#\n# This file implements the performance script for the Xsyrk routine\n#\n# ==================================================================================================\n\n# Includes the common functions\nargs <- commandArgs(trailingOnly = FALSE)\nthisfile <- (normalizePath(sub(\"--file=\", \"\", args[grep(\"--file=\", args)])))\nsource(file.path(dirname(thisfile), \"common.r\"))\n\n# ==================================================================================================\n\n# Settings\nroutine_name <- \"xsyrk\"\nparameters <- c(\"-n\",\"-k\",\"-layout\",\"-triangle\",\"-transA\",\n \"-num_steps\",\"-step\",\"-runs\",\"-precision\")\nprecision <- 32\n\n# Sets the names of the test-cases\ntest_names <- list(\n \"multiples of 128\",\n \"multiples of 128 (+1)\",\n \"around n=k=512\",\n \"around n=k=2048\",\n \"layouts and transposing (n=k=1024)\",\n \"powers of 2\"\n)\n\n# Defines the test-cases\ntest_values <- list(\n list(c( 128, 128, 102, 111, 111, 16, 128, num_runs, precision)),\n list(c( 129, 129, 102, 111, 111, 16, 128, num_runs, precision)),\n list(c( 512, 512, 102, 111, 111, 16, 1, num_runs, precision)),\n list(c(2048, 2048, 102, 111, 111, 16, 1, num_runs, precision)),\n list(\n c(1024, 1024, 101, 111, 111, 1, 0, num_runs, precision),\n c(1024, 1024, 101, 111, 112, 1, 0, num_runs, precision),\n c(1024, 1024, 101, 112, 111, 1, 0, num_runs, precision),\n c(1024, 1024, 101, 112, 112, 1, 0, num_runs, precision),\n c(1024, 1024, 102, 111, 111, 1, 0, num_runs, precision),\n c(1024, 1024, 102, 111, 112, 1, 0, num_runs, precision),\n c(1024, 1024, 102, 112, 111, 1, 0, num_runs, precision),\n c(1024, 1024, 102, 112, 112, 1, 0, num_runs, precision)\n ),\n list(\n c( 8, 8, 102, 111, 111, 1, 0, num_runs, precision),\n c( 16, 16, 102, 111, 111, 1, 0, num_runs, precision),\n c( 32, 32, 102, 111, 111, 1, 0, num_runs, precision),\n c( 64, 64, 102, 111, 111, 1, 0, num_runs, precision),\n c( 128, 128, 102, 111, 111, 1, 0, num_runs, precision),\n c( 256, 256, 102, 111, 111, 1, 0, num_runs, precision),\n c( 512, 512, 102, 111, 111, 1, 0, num_runs, precision),\n c(1024, 1024, 102, 111, 111, 1, 0, num_runs, precision),\n c(2048, 2048, 102, 111, 111, 1, 0, num_runs, precision),\n c(4096, 4096, 102, 111, 111, 1, 0, num_runs, precision),\n c(8192, 8192, 102, 111, 111, 1, 0, num_runs, precision)\n )\n)\n\n# Defines the x-labels corresponding to the test-cases\ntest_xlabels <- list(\n \"matrix sizes (n=k)\",\n \"matrix sizes (n=k)\",\n \"matrix sizes (n=k)\",\n \"matrix sizes (n=k)\",\n \"layout (row/col), triangle (u/l), transA (n/y)\",\n \"matrix sizes (n=k)\"\n)\n\n# Defines the x-axis of the test-cases\ntest_xaxis <- list(\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n list(1:8, c(\"row,u,n\", \"row,u,y\", \"row,l,n\", \"row,l,y\",\n \"col,u,n\", \"col,u,y\", \"col,l,n\", \"col,l,y\")),\n c(\"n\", \"x\")\n)\n\n# ==================================================================================================\n\n# Start the script\nmain(routine_name=routine_name, precision=precision, test_names=test_names, test_values=test_values,\n test_xlabels=test_xlabels, test_xaxis=test_xaxis, metric_gflops=TRUE)\n\n# ==================================================================================================", "meta": {"hexsha": "4ab46c9f299127eccbf999a832dac1c5d368e238", "size": 3593, "ext": "r", "lang": "R", "max_stars_repo_path": "third_party/coriander/src/CLBlast/scripts/graphs/xsyrk.r", "max_stars_repo_name": "pint1022/tensorflow", "max_stars_repo_head_hexsha": "92e437b36ae58ea22910dbed67e2c238c869c09f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-04-15T23:07:00.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-11T16:09:02.000Z", "max_issues_repo_path": "scripts/graphs/xsyrk.r", "max_issues_repo_name": "gcp/CLBlast", "max_issues_repo_head_hexsha": "7c13bacf129291e3e295ecb6e833788477085fa0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/graphs/xsyrk.r", "max_forks_repo_name": "gcp/CLBlast", "max_forks_repo_head_hexsha": "7c13bacf129291e3e295ecb6e833788477085fa0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-04T13:59:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-09T13:11:45.000Z", "avg_line_length": 38.2234042553, "max_line_length": 100, "alphanum_fraction": 0.5299192875, "num_tokens": 1222, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091673, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3451353225570901}} {"text": "setwd(\"P:/2019 0970 151 000/User Data/Faraz-Export IDAVE folders/Models2.5\")\nlibrary(tidyverse)\nlibrary(h2o)\nlibrary(inspectdf)\ntabna <- function(x){table(x, useNA = \"ifany\")}\n##adding cihi vars\ndfx <- readRDS(\"./dfx_v2.5.rds\")\n\nh2o.init()\ndfx %>% inspect_na() %>% print(n=10)\ndfx <- drop_na(dfx)\n\n###backward compatibility save \n# saveRDS(dfx, \"./dfx_cln_oldR.rds\", version = 2)\n###\n\n#############Train model on first 30 vars\n#DATA IS READY TO USE\n########################\n \n\nnew_resultFrame <- function(){\n\nreturn(data.frame(Model = character(),\nHit_Ratio = numeric(),\nMean_PerC_Error = numeric(),\nLogLoss = numeric(),\nAUC = numeric(),\nAUCPR = numeric(),\nRecall = numeric(),\nPrecision = numeric(),\nSpecificity = numeric(),\nmax_F1 = numeric(),\ntrshold = numeric(), \ntraining_AUC = numeric(),\ntraining_AUCPR = numeric(),\ntraining_Recall = numeric(),\ntraining_Precision = numeric(),\ntraining_Spec = numeric(),\nstringsAsFactors = FALSE))\n}\n\nadd_cv_results <- function (myModel, modH){\n# for tuning hyperparameters, using cross validation as \n\tidx <- nrow(modH) + 1\n\ttab2 <- myModel@model$cross_validation_metrics@metrics$cm$table\n\ttab1 <- myModel@model$training_metrics@metrics$cm$table\n\n\n\tmodH[idx, 1] <- myModel@model_id\n\tmodH[idx, 2] <- round(1- myModel@model$cross_validation_metrics@metrics$cm$table$Error[3], digits=3)\n\tmodH[idx, 3] <- round(myModel@model$cross_validation_metrics@metrics$mean_per_class_error, digits=3)\n\tmodH[idx, 4] <- round(myModel@model$cross_validation_metrics@metrics$logloss, digits=3)\n\tmodH[idx, 5] <- round(myModel@model$cross_validation_metrics@metrics$AUC, digits = 3)\n\tmodH[idx, 6] <- round(myModel@model$cross_validation_metrics@metrics$pr_auc, digits = 3)\n\tmodH[idx, 7] <- round(tab2[2,2] / (tab2[2,2] + tab2[2,1]),digits=3)#recall\n\tmodH[idx, 8] <- round(tab2[2,2] / (tab2[2,2] + tab2[1,2]),digits=3)# precision\n\tmodH[idx, 9] <- round(tab2[1,1] / (tab2[1,1] + tab2[2,1]),digits=3)# Specificity\n\tmodH[idx, 10] <- round(myModel@model$cross_validation_metrics@metrics$max_criteria_and_metric_scores$value[1], digits=3)\n\tmodH[idx, 11] <- round(myModel@model$cross_validation_metrics@metrics$max_criteria_and_metric_scores$threshold[1], digits=3)\n\tmodH[idx, 12] <- round(myModel@model$training_metrics@metrics$AUC, digits = 3)\n\tmodH[idx, 13] <- round(myModel@model$training_metrics@metrics$pr_auc, digits = 3)\n\tmodH[idx, 14] <- round(tab1[2,2] / (tab1[2,2] + tab1[2,1]),digits=3)#recall\n\tmodH[idx, 15] <- round(tab1[2,2] / (tab1[2,2] + tab1[1,2]),digits=3)# precision\n\tmodH[idx, 16] <- round(tab1[1,1] / (tab1[1,1] + tab1[2,1]),digits=3)# Specificity\n\n\treturn(modH)\n}\n\nadd_results <- function (myModel, modH){\n\tidx <- nrow(modH) + 1\n\ttab2 <- myModel@model$validation_metrics@metrics$cm$table\n\ttab1 <- myModel@model$training_metrics@metrics$cm$table\n\n\n\tmodH[idx, 1] <- myModel@model_id\n\tmodH[idx, 2] <- round(1- myModel@model$validation_metrics@metrics$cm$table$Error[3], digits=3)\n\tmodH[idx, 3] <- round(myModel@model$validation_metrics@metrics$mean_per_class_error, digits=3)\n\tmodH[idx, 4] <- round(myModel@model$validation_metrics@metrics$logloss, digits=3)\n\tmodH[idx, 5] <- round(myModel@model$validation_metrics@metrics$AUC, digits = 3)\n\tmodH[idx, 6] <- round(myModel@model$validation_metrics@metrics$pr_auc, digits = 3)\n\tmodH[idx, 7] <- round(tab2[2,2] / (tab2[2,2] + tab2[2,1]),digits=3)#recall\n\tmodH[idx, 8] <- round(tab2[2,2] / (tab2[2,2] + tab2[1,2]),digits=3)# precision\n\tmodH[idx, 9] <- round(tab2[1,1] / (tab2[1,1] + tab2[2,1]),digits=3)# Specificity\n\tmodH[idx, 10] <- round(myModel@model$validation_metrics@metrics$max_criteria_and_metric_scores$value[1], digits=3)\n\tmodH[idx, 11] <- round(myModel@model$validation_metrics@metrics$max_criteria_and_metric_scores$threshold[1], digits=3)\n\tmodH[idx, 12] <- round(myModel@model$training_metrics@metrics$AUC, digits = 3)\n\tmodH[idx, 13] <- round(myModel@model$training_metrics@metrics$pr_auc, digits = 3)\n\tmodH[idx, 14] <- round(tab1[2,2] / (tab1[2,2] + tab1[2,1]),digits=3)#recall\n\tmodH[idx, 15] <- round(tab1[2,2] / (tab1[2,2] + tab1[1,2]),digits=3)# precision\n\tmodH[idx, 16] <- round(tab1[1,1] / (tab1[1,1] + tab1[2,1]),digits=3)# Specificity\n\n\treturn(modH)\n}\n\nadd_test_results <- function (perf, modH){\n\tidx <- nrow(modH) + 1\n\ttab2 <- perf@metrics$cm$table\n\n\n\tmodH[idx, 1] <- perf@metrics$model$name\n\tmodH[idx, 2] <- round(1- perf@metrics$cm$table$Error[3], digits=3)\n\tmodH[idx, 3] <- round(perf@metrics$mean_per_class_error, digits=3)\n\tmodH[idx, 4] <- round(perf@metrics$logloss, digits=3)\n\tmodH[idx, 5] <- round(perf@metrics$AUC, digits = 3)\n\tmodH[idx, 6] <- round(perf@metrics$pr_auc, digits = 3)\n\tmodH[idx, 7] <- round(tab2[2,2] / (tab2[2,2] + tab2[2,1]),digits=3)#recall\n\tmodH[idx, 8] <- round(tab2[2,2] / (tab2[2,2] + tab2[1,2]),digits=3)# precision\n\tmodH[idx, 9] <- round(tab2[1,1] / (tab2[1,1] + tab2[2,1]),digits=3)# Specificity\n\tmodH[idx, 10] <- round(perf@metrics$max_criteria_and_metric_scores$value[1], digits=3)\n\tmodH[idx, 11] <- round(perf@metrics$max_criteria_and_metric_scores$threshold[1], digits=3)\n\n\n\treturn(modH)\n}\n\n\n\n#not using scu and patserv yet!\n# varx <- 5:42 \nvary <- \"alc_status\"\n\ndf.hex <- as.h2o(dfx)\n\ndf.split <- h2o.splitFrame(df.hex, ratios = c(0.8), seed = 1234)\ntrain <- df.split[[1]]\ntest <- df.split[[2]] #not using this until best hyperparameters are chosen\n\nsel_feat <- rownames(feat_rank)[1:30]\n\ndf.hex_gbmNew <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first30MutINF\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\n#score_each_iteration = T, #wrong if used with above argument (I guess!)\nseed = 1234\n)\n\n# modH <- new_resultFrame()\nmodH <- add_cv_results(df.hex_gbmNew, modH)\n\nsel_feat <- rownames(feat_rank)[1:40]\n\ndf.hex_gbmNew2 <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first40\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_cv_results(df.hex_gbmNew2, modH)\n\n##added\n\nsel_feat <- rownames(feat_rank)[1:45]\n\ndf.hex_gbmNew22 <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first45\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_cv_results(df.hex_gbmNew22, modH)\n###\n\nsel_feat <- rownames(feat_rank)[1:50]\n\ndf.hex_gbmNew3 <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first50\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_cv_results(df.hex_gbmNew3, modH)\n\nsel_feat <- rownames(feat_rank)[1:60]\n\ndf.hex_gbmNew4 <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first60\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_cv_results(df.hex_gbmNew4, modH)\n\nsel_feat <- rownames(feat_rank)[1:68]\n\ndf.hex_gbmNew5 <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first70\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_cv_results(df.hex_gbmNew5, modH)\n\n################\n# Using other algorithms, Random Forest etc., \n\nsel_feat <- rownames(feat_rank)[1:40] \n\ndf_rf1 <- h2o.randomForest(\ntraining_frame = train,\nx = sel_feat,\ny = vary,\nmodel_id = \"rf_first40\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_results(df_rf1, modH)\n\nsel_feat <- rownames(feat_rank)[1:50] \n\ndf_rf2 <- h2o.randomForest(\ntraining_frame = train,\nvalidation_frame = test,\nx = sel_feat,\ny = vary,\nmodel_id = \"rf_first50\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_results(df_rf2, modH)\n\n####Playing with sample per class a little bit\n#Up sample the minrotity (probably the default but let's see!)\nsel_feat <- rownames(feat_rank)[1:40]\n\ndf.hex_gbmNew2_2 <- h2o.gbm(\ntraining_frame = train,\nvalidation_frame = test,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first40_overSample\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nclass_sampling_factors = c(1, 8.3),\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_results(df.hex_gbmNew2_2, modH)\n\ndf.hex_gbmNew2_3 <- h2o.gbm(\ntraining_frame = train,\nvalidation_frame = test,\nx = sel_feat,\ny = vary,\nmodel_id = \"gbm_first40_UnderSample\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nclass_sampling_factors = c(0.12, 1),\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nmodH <- add_results(df.hex_gbmNew2_3, modH)\n\n##Not much difference between their results!----> use H2o balance_classes like before\n\n", "meta": {"hexsha": "83c22ca633bd218d35e151b142531cc3b5d31b8a", "size": 10620, "ext": "r", "lang": "R", "max_stars_repo_path": "Predective Modeling/h2o_ML.r", "max_stars_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_stars_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Predective Modeling/h2o_ML.r", "max_issues_repo_name": 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YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3451353147904489}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\nlibrary(stats)\nlibrary(tibble)\n\n# set dataset \n\ndataset <- \"SinghalOG\"\n\n# Colors \n# https://www.w3schools.com/colors/colors_picker.asp\ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"yellow4\" # 8b8b00\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n\nmetaData <- c(\"MEAN_COL_SCORE\",\"Sequences\",\"Columns\",\"Dist_Pat\",\"Pars_Info\",\"Sing_Sites\",\"Cons_Sites\" ,\"Chi2_Fail\",\"Gaps_Ambig\")\n\n# Add column to name type of support \nmLGL <- mutate(mLGL, supportType = case_when(TvS != 'a' ~ \"dGLS\"))\nmLBF <- mutate(mLBF, supportType = case_when(TvS != 'a' ~ \"BF\"))\n\n# Change from 2ln(BF) to ln(BF) by dividing all comparisons by 2 7-10\nmLBF[7:10] <- mLBF[7:10]/2\n# for singhal OG \nmLBF[4] <- mLBF[4]/2\n\n\n# Check number of NA \nsum(is.na(mLBF$MEAN_COL_SCORE))\n\n# Transform datasets - stack two comparisons, and add column for bf and gl \n# Each dataset one hypothesis \n\nreformatdf <- function(df,h,h1,s1,keepCols){\n # keepCols is a vector of the locus and support type column indices\n # Grab column number \n c1 <- match(h1,names(df))\n # Grab and rename column, add hypothesis column\n a.df <- df[,append(keepCols,c1)] \n names(a.df)[names(a.df) == h1] <- h\n a.df$Hypothesis <- rep(h1,length(a.df[,1]))\n c <- match(h,names(a.df))\n # Adjust direction of support if needed. ie if column is AIvSA, but you want to know SAvAI\n a.df[,c] <- a.df[,c]*(s1)\n return(a.df)\n}\n\n# reformat \nkeep <- c(1,seq(14,22))\nAI.g <- bind_rows(reformatdf(mLGL,\"AI\",\"AIvSA\",1,keep),\n reformatdf(mLGL,\"AI\",\"AIvSI\",1,keep))\n\nSA.g <- bind_rows(reformatdf(mLGL,\"SA\",\"AIvSA\",-1,keep),\n reformatdf(mLGL,\"SA\",\"SAvSI\",1,keep))\n\nSI.g <- bind_rows(reformatdf(mLGL,\"SI\",\"AIvSI\",-1,keep),\n reformatdf(mLGL,\"SI\",\"SAvSI\",-1,keep))\n\nAI.b <- bind_rows(reformatdf(mLBF,\"AI\",\"AIvSA\",1,keep),\n reformatdf(mLBF,\"AI\",\"AIvSI\",1,keep))\n\nSA.b <- bind_rows(reformatdf(mLBF,\"SA\",\"AIvSA\",-1,keep),\n reformatdf(mLBF,\"SA\",\"SAvSI\",1,keep))\n\nSI.b <- bind_rows(reformatdf(mLBF,\"SI\",\"AIvSI\",-1,keep),\n reformatdf(mLBF,\"SI\",\"SAvSI\",-1,keep))\n\nkeep <- c(1,seq(5,13)) # for SinghalOG \nTS.g <- reformatdf(mLGL,\"TS\",\"TvS\",-1,keep)\nTS.b <- reformatdf(mLBF,\"TS\",\"TvS\",-1,keep)\n\n# MERGE - yea i could have merged first, but the function was already written and i didnt want to mess with it. \n\nAI.a <- left_join(AI.g, AI.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nSA.a <- left_join(SA.g, SA.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nSI.a <- left_join(SI.g, SI.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nTS.a <- left_join(TS.g, TS.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\n\nsapply(AI.a,typeof)\n\n# Smash all together, then smash bf and gl together each in 1 column\nall <- bind_rows(bind_rows(bind_rows(AI.a,SA.a),SI.a),TS.a) %>% \n mutate(BF=coalesce(AI.b,SA.b,SI.b,TS.b)) %>% \n mutate(GL=coalesce(AI.g,SA.g,SI.g,TS.g)) %>%\n select(-c(AI.b,SA.b,SI.b,TS.b,AI.g,SA.g,SI.g,TS.g))\n\nTX <- bind_rows(bind_rows(AI.a,SA.a),SI.a) %>% \n mutate(BF=coalesce(AI.b,SA.b,SI.b)) %>% \n mutate(GL=coalesce(AI.g,SA.g,SI.g)) %>%\n select(-c(AI.b,SA.b,SI.b,AI.g,SA.g,SI.g))\n\n\nTS <- TS.a %>% \n mutate(BF=coalesce(TS.b)) %>% \n mutate(GL=coalesce(TS.g)) %>%\n select(-c(TS.b,TS.g))\n\n\n# Check number of NA \nsum(is.na(all$BF))\nsum(is.na(all$GL))\n\nrm(AI.g,AI.b,SA.b,SA.g,SI.b,SI.g,TS.b,TS.g)\nrm(AI.a,SA.a,SI.a,TS.a)\nrm(mLBF,mLGL)\n\n##################################################################################################\n## Test correlation across all hypotheses \n##################################################################################################\n# http://www.sthda.com/english/wiki/correlation-test-between-two-variables-in-r\n# https://rpubs.com/aaronsc32/spearman-rank-correlation\nrm(null,null_rs,new,sig2tp05,emp_rs)\n# Create null distribution \nall <- TS\nv <- \"ts\"\n\n# Making these all absolute values \n\nall$BF <- abs(all$BF)\nall$GL <- abs(all$GL)\n\n# start with all the meta data \n\nnull <- all %>% select(c(Locus,Hypothesis,metaData,BF,GL))\n\n# Permute BF and GL in 1,000 seperate columns\n\nfor (n in 1:1000) {\n newcolname1 <- paste('BF',n,sep='') \n null[, newcolname1] <- sample(all$BF, replace = FALSE)\n newcolname2 <- paste('GL',n,sep='') \n null[, newcolname2] <- sample(all$GL, replace = FALSE)\n}\n\n# Create new dataset for spearman rank coefficients (repeat later with kendall?)\n\nassign('null_rs', setNames(data.frame(1:1000), \"replicate\"))\n\n# for meta data \n\nfor (x in metaData) {\n null_rs[,paste(x,'.b.null',sep='')] <- NA\n null_rs[,paste(x,'.g.null',sep='')] <- NA\n}\n\n# then for bf and gl - these should end up the same \n\nnull_rs[,'GL.b.null'] <- NA # bf replicated, gl same\nnull_rs[,'BF.g.null'] <- NA # gl replicated, bf same\n\n# Add bf gl \n\nfor (n in 1:1000) {\n \n # Grab column names, calculate value \n colname1 <- paste('BF',n,sep='') \n cc1 <- cor(null[,'GL'],null[,colname1],method=\"spearman\") \n nullcol1 <- paste('GL','.b.null',sep='')\n null_rs[n,nullcol1] <- cc1\n colname2 <- paste('GL',n,sep='') \n cc2 <- cor(null[,'BF'],null[,colname2],method=\"spearman\") \n nullcol2 <- paste('BF','.g.null',sep='')\n null_rs[n,nullcol2] <- cc2\n \n}\n\n\nfor (i in metaData) {\n \n # Calculate rs for all permuted columns in null dataset\n \n for (n in 1:1000) {\n \n # Grab column names, calculate value \n colname1 <- paste('BF',n,sep='') \n cc1 <- cor(null[,i],null[,colname1],method=\"spearman\") \n nullcol1 <- paste(i,'.b.null',sep='')\n null_rs[n,nullcol1] <- cc1\n colname2 <- paste('GL',n,sep='') \n cc2 <- cor(null[,i],null[,colname2],method=\"spearman\") \n nullcol2 <- paste(i,'.g.null',sep='')\n null_rs[n,nullcol2] <- cc2\n \n }\n}\n\nsave(all,null,null_rs, file=paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_CorrNullDistAbs_\",v,\".RData\",sep=''))\nsave(null_rs, file=paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_CorrNullDistAbs_\",v,\"_rs.RData\",sep=''))\n\n#load(\"/Users/ChatNoir/Projects/Squam/scripts/Graphing/CorrelationNullDist_rs.RData\")\n\n# Add row of emp scores \n\nassign('emp_rs', setNames(data.frame('emp'), \"replicate\"))\nempRow <- 1\n\n# First bf and gl \n\n# Grab column names, calculate value for BF GL there is only one value for both columns\n\ni <- \"BF\"\ncc1 <- cor(all[,i],all[,'GL'],method=\"spearman\") \nempcol1 <- paste(i,'.g.emp',sep='')\nemp_rs[empRow,empcol1] <- cc1\ni <- \"GL\"\nempcol2 <- paste(i,'.b.emp',sep='')\nemp_rs[empRow,empcol2] <- cc1\n\n# Add all metadata emp for BF and GL \n\nmetaData <- c(\"MEAN_COL_SCORE\",\"Sequences\",\"Columns\",\"Dist_Pat\",\"Pars_Info\",\"Sing_Sites\",\"Cons_Sites\" ,\"Chi2_Fail\",\"Gaps_Ambig\")\n\nfor (i in metaData) {\n cc1 <- cor(all[,i],all[,'GL'],method=\"spearman\") \n empcol1 <- paste(i,'.g.emp',sep='')\n emp_rs[empRow,empcol1] <- cc1\n cc2 <- cor(all[,i],all[,'BF'],method=\"spearman\") \n empcol2 <- paste(i,'.b.emp',sep='')\n emp_rs[empRow,empcol2] <- cc2\n}\n\n# Add rows for quantile cut offs \n\nemp_rs$replicate <- as.character(emp_rs$replicate)\nemp_rs[2,1] <- \"p0.025\"\nemp_rs[3,1] <- \"p0.975\"\n\n# add gl and bf \n\nemp_rs[2,'GL.b.emp'] <- quantile(null_rs$BF.g.null, c(0.025, 0.975))[[1]]\nemp_rs[3,'GL.b.emp'] <- quantile(null_rs$BF.g.null, c(0.025, 0.975))[[2]]\nemp_rs[2,'BF.g.emp'] <- quantile(null_rs$GL.b.null, c(0.025, 0.975))[[1]]\nemp_rs[3,'BF.g.emp'] <- quantile(null_rs$GL.b.null, c(0.025, 0.975))[[2]]\n\nfor (i in metaData) {\n gl.n <- paste(i,'.g.null',sep='')\n gl.e <- paste(i,'.g.emp',sep='')\n g025 <- quantile(null_rs[,gl.n], c(0.025, 0.975))[[1]]\n g975 <- quantile(null_rs[,gl.n], c(0.025, 0.975))[[2]]\n emp_rs[2,gl.e] <- g025\n emp_rs[3,gl.e] <- g975\n bf.n <- paste(i,'.b.null',sep='')\n bf.e <- paste(i,'.b.emp',sep='')\n b025 <- quantile(null_rs[,bf.n], c(0.025, 0.975))[[1]]\n b975 <- quantile(null_rs[,bf.n], c(0.025, 0.975))[[2]]\n emp_rs[2,bf.e] <- b025\n emp_rs[3,bf.e] <- b975\n}\n\n# Create two boolean rows for significance \n\n# Transform dataset from rows to columns \n\nnew <- data.frame(t(emp_rs))\ncolnames(new) <- as.character(unlist(new[1,]))\nnew <- new[-1,]\nnew$emp <- as.numeric(as.character(new$emp))\nnew$p0.025 <- as.numeric(as.character(new$p0.025))\nnew$p0.975 <- as.numeric(as.character(new$p0.975))\nsig2tp05 <- new %>% rownames_to_column('metaData') %>%\n mutate(sig = case_when(emp < p0.025 ~ \"Lower\",\n emp > p0.975 ~ \"Upper\",\n emp >= p0.025 & emp <= p0.975 ~ \"False\"))\n\n# Save null and emp rs \nsave(null_rs, emp_rs, sig2tp05, file=paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_CorrAbs_\",v,\"_rsvals.RData\",sep=''))\nwrite.csv(sig2tp05,paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_CorrAbs_\",v,\"_rsvals.csv\",sep=''))\n#v <- 'tx'\n#load(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_Corr_\",v,\"_rsvals.RData\",sep=''))\n\n", "meta": {"hexsha": "f5fc9d984b66e4cabae861aed9fa8a95072597f0", "size": 9091, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/PrelimScripts/Calcs_Correlation_abs.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", 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0.6196238038, "num_tokens": 3125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3451353147904489}} {"text": "rm(list = ls())\n\nlibrary(parallel)\n\n# devtools::load_all()\n\nset.seed(1122)\n\nn <- 2e3\nparameters <- lapply(1:n, function(x) rbeta(5, 1, 10))# runif(5))\ndatasets <- lapply(parameters, function(x) {\n aphylo::raphylo(100, psi=x[1:2], mu=x[3:4], Pi = x[5])\n})\n\ntry_phylo_mle <- function(...)\n tryCatch(phylo_mle(...), error = function(e) NA)\n\ncl <- makeForkCluster(16)\n\n# Old Version\nsystem(\"git checkout master\")\ne0 <- clusterEvalQ(cl, {\n devtools::load_all()\n})\n\nans_old <- parLapply(cl, datasets, try_phylo_mle)\n\n# New version\nsystem(\"git checkout multiple-start\")\ne0 <- clusterEvalQ(cl, {\n devtools::load_all()\n })\n\nans_new <- parLapply(cl, datasets, try_phylo_mle)\n\n# Pi 0/1\nsystem(\"git checkout mlePi01\")\ne0 <- clusterEvalQ(cl, {\n devtools::load_all()\n })\n\nans_Pi01 <- parLapply(cl, datasets, try_phylo_mle)\n\n# Fixing pi\nsystem(\"git checkout fixpi\")\ne0 <- clusterEvalQ(cl, {\n devtools::load_all()\n})\n\nans_fixpi <- clusterMap(\n cl, try_phylo_mle,\n dat = datasets, \n Pi = lapply(1:n, function(x) parameters[[x]][5]))\n\nstopCluster(cl)\n# Tabulating results -----------------------------------------------------------\n\nextractme <- function(x) {\n if (inherits(x, \"phylo_mle\")) x[[\"ll\"]]\n else NA\n}\n\nmle_old <- unlist(sapply(ans_old, extractme))\nmle_new <- unlist(sapply(ans_new, extractme))\nmle_Pi01 <- unlist(sapply(ans_Pi01, extractme))\nmle_fixpi <- unlist(sapply(ans_fixpi, extractme))\n\nloglikes <- data.frame(\n old = mle_old,\n new = mle_new,\n Pi01 = mle_Pi01,\n fixpi = mle_fixpi,\n largest = apply(cbind(mle_old, mle_new, mle_Pi01, mle_fixpi), 1, function(x) {\n if (any(is.na(x))) return(\"-\")\n paste(c(\"old\", \"No fixed\", \"Pi 0/1\", \"Pi = Pi*\")[which(x == max(x))], collapse=\"-\")\n \n }), check.names = FALSE\n)\n\nbiascalc <- function(x, y) {\n if (inherits(x, \"phylo_mle\")) \n x[[\"par\"]] - y\n else rep(NA, 5)\n}\n\nbiases <- rbind(\n data.frame(do.call(rbind, Map(biascalc, ans_old, parameters)), model = \"MLE starting at {.05}^5\"),\n data.frame(do.call(rbind, Map(biascalc, ans_new, parameters)), model = \"MLE starting at {.05, .95}^5\"),\n data.frame(do.call(rbind, Map(biascalc, ans_Pi01, parameters)), model = \"MLE starting at {.05, .95}^4\\n and comparing Pi 0/1\"),\n data.frame(do.call(rbind, Map(biascalc, ans_fixpi, parameters)), model = \"MLE starting at {.05, .95}^4\\nandfixing Pi = Pi*\")\n)\n\n# The new is larger than the old one,\ntable(loglikes$largest)\n\n# But the size of the bias of the new one, is huge\noldpar <- par(no.readonly = TRUE)\npar(mfrow = c(2, 2), oma = c(2,0,2,0))\ninvisible(by(biases, biases$model, function(x) {\n boxplot(x[,-6], ylim = c(-1, 1), main = unique(x$model))\n abline(h=0, lty=\"dashed\", lwd=2)\n}))\npar(oldpar)\ntitle(\n main=\"Bias distribution\",\n sub = paste(\n paste(\"Each box represents\", n, \"simulations of annotated trees with 100 leafs.\"),\n \"All parameters follow a Beta(1,10) distribution.\",\n sep=\"\\n\"\n )\n)\n\n", "meta": {"hexsha": "0f1a7142ed94bd06145eb384bbc05ef9a6b0f1d4", "size": 2902, "ext": "r", "lang": "R", "max_stars_repo_path": "playground/new_mle_test.r", "max_stars_repo_name": "gvegayon/phylogenetic", "max_stars_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "playground/new_mle_test.r", "max_issues_repo_name": "gvegayon/phylogenetic", "max_issues_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2017-01-19T19:52:09.000Z", "max_issues_repo_issues_event_max_datetime": "2017-03-29T23:34:21.000Z", "max_forks_repo_path": "playground/new_mle_test.r", "max_forks_repo_name": "USCbiostats/phylogenetic", "max_forks_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6814159292, "max_line_length": 129, "alphanum_fraction": 0.6357684356, "num_tokens": 954, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7122321842389469, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.3449910854264418}} {"text": "library(phyloseq)\nlibrary('reshape2')\nlibrary('ggplot2')\n\n# chargement de mon fichier.\nload(system.file(\"data_test\", \"robjects_600.Rdata\", package=\"ExploreMetabar\"))\ntt <- sample_data(data)$SampleType %in% c('Feces', 'Freshwater', 'Skin' )\nphy_obj <- phyloseq::prune_samples(tt, data)\nphy_obj <- prune_taxa(taxa_sums(phy_obj) > 0, phy_obj)\n\nmetadata <- as.data.frame(as.matrix(sample_data(phy_obj)[, 'SampleType']))\n\no_table <- as.data.frame(as.matrix(otu_table(phy_obj)))\no_table <- t(o_table)\n\nlst <- c()\nfor (src in c('Feces', 'Freshwater')){\n lst[[src]] <- 'source'\n}\n\nmetadata$sourceSink <- metadata[[\"SampleType\"]]\nmetadata$sourceSink <- dplyr::recode(metadata$sourceSink, !!!lst)\n\nmetadata$sourceSink <- dplyr::recode(metadata$sourceSink, 'Skin' = 'sink')\n\n\nif(length(rownames(metadata)) != length(rownames(o_table))){\n stop('Number of samples in metadata differs from otu table.')\n}\n\nmetadata <- apply(metadata,2,function(x) gsub(\"[[:punct:]]\",'_',x))\nmetadata <- apply(metadata,2,function(x) gsub(\"[[:space:]]\",'_',x))\nmetadata <- as.data.frame(metadata)\n\ntrain <- which(metadata$sourceSink=='source')\ntest <- which(metadata$sourceSink=='sink')\n\nenvs <- metadata[[\"SampleType\"]]\n\nprint(envs)\nalpha1 <- alpha2 <- 0.001\n\nsource('R/SourceTracker.r')\n\nst <- sourcetracker(o_table[train,], envs[train], rarefaction_depth=1000)\n\nres <- predict(st, o_table[test,], alpha1=alpha1, alpha2=alpha2, rarefaction_depth=1000, burnin=10, nrestarts=4)\n\nprop <- as.data.frame(res$proportions)\nprop[,'SampleType'] <- as.vector(metadata[rownames(res$proportions), \"SampleType\"])\n\ntmp <- reshape2::melt(prop, id.vars='SampleType')\nggplot2::ggplot(tmp, aes(x=variable, y=value, fill=variable)) +geom_boxplot()\n", "meta": {"hexsha": "f78995bb61fc508437d038ae449e0771e81285bb", "size": 1700, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/test/test_sourceTracker.r", "max_stars_repo_name": "erifa1/ExploreMetabar", "max_stars_repo_head_hexsha": "fedd1546158162d690afb90e92d22326f251ea52", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/test/test_sourceTracker.r", "max_issues_repo_name": "erifa1/ExploreMetabar", "max_issues_repo_head_hexsha": "fedd1546158162d690afb90e92d22326f251ea52", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/test/test_sourceTracker.r", "max_forks_repo_name": "erifa1/ExploreMetabar", "max_forks_repo_head_hexsha": "fedd1546158162d690afb90e92d22326f251ea52", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4814814815, "max_line_length": 112, "alphanum_fraction": 0.7105882353, "num_tokens": 498, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.34489500140698126}} {"text": "#!/usr/bin/env Rscript\n#\n# phyloseq_ordination_p5.r - generate p5 ordination graphic through phyloseq\n#\n# Version 1.0.0 (December 24, 2015)\n#\n# Copyright (c) 2014-- Lela Andrews\n#\n# This software is provided 'as-is', without any express or implied\n# warranty. In no event will the authors be held liable for any damages\n# arising from the use of this software.\n#\n# Permission is granted to anyone to use this software for any purpose,\n# including commercial applications, and to alter it and redistribute it\n# freely, subject to the following restrictions:\n#\n# 1. The origin of this software must not be misrepresented; you must not\n# claim that you wrote the original software. If you use this software\n# in a product, an acknowledgment in the product documentation would be\n# appreciated but is not required.\n# 2. Altered source versions must be plainly marked as such, and must not be\n# misrepresented as being the original software.\n# 3. This notice may not be removed or altered from any source distribution.\n#\n\n## Load libraries\nlibrary(phyloseq)\nlibrary(ggplot2)\nlibrary(scales)\nlibrary(grid)\nlibrary(plyr)\ntheme_set(theme_bw())\n\n## Recieve input files from bash\nargs <- commandArgs(TRUE)\n\notufile=(args[1])\nmapfile=(args[2])\ntreefile=(args[3])\nfactor=(args[4])\n\n## Load data into phyloseq\nmap=import_qiime_sample_data(mapfile)\ntree=read_tree(treefile)\notus=import_biom(otufile,parseFunction=parse_taxonomy_greengenes)\nmergedata=merge_phyloseq(otus,tree,map)\nMD=mergedata\n\n## Filter taxa not present at least 5 times in at least 10% of samples\nmd0 = genefilter_sample(MD, filterfun_sample(function(x) x > 5), A = 0.1 * nsamples(MD))\nMD1=prune_taxa(md0, MD)\n\n## Ordinate command\nMD.ord <- ordinate(MD1, \"NMDS\", \"bray\")\n\n## Composite faceted ordination\ndist = \"bray\"\nord_meths = c(\"DCA\", \"CCA\", \"RDA\", \"DPCoA\", \"NMDS\", \"MDS\", \"PCoA\")\nplist = llply(as.list(ord_meths), function(i, physeq, dist) {\n ordi = ordinate(physeq, method = i, distance = dist)\n plot_ordination(physeq, ordi, \"samples\", color = \"Community\")\n}, MD1, dist)\nnames(plist) <- ord_meths\npdataframe = ldply(plist, function(x) {\n df = x$data[, 1:2]\n colnames(df) = c(\"Axis_1\", \"Axis_2\")\n return(cbind(df, x$data))\n})\nnames(pdataframe)[1] = \"method\"\np5 = ggplot(pdataframe, aes(Axis_1, Axis_2, color = \"Community\", fill = \"Community\"))\np5 = p5 + geom_point(size = 4) + geom_polygon()\np5 = p5 + facet_wrap(~method, scales = \"free\")\np5 = p5 + scale_fill_brewer(type = \"qual\", palette = \"Set1\")\np5 = p5 + scale_colour_brewer(type = \"qual\", palette = \"Set1\")\n\n## Output pdf graphic\npdf(paste0(factor, \"_composite_ordinations.pdf\"))\nplot(p5)\ndev.off()\n\n", "meta": {"hexsha": "6bb7d74ea3bee3d624cb5905c39f340fea140a90", "size": 2659, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/phyloseq_ordination_p5.r", "max_stars_repo_name": "lvandrews/amptools", "max_stars_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2016-02-01T20:25:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T16:52:49.000Z", "max_issues_repo_path": "scripts/phyloseq_ordination_p5.r", "max_issues_repo_name": "lvandrews/amptools", "max_issues_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_issues_repo_licenses": ["Zlib"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2016-06-29T18:01:54.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-18T18:47:09.000Z", "max_forks_repo_path": "scripts/phyloseq_ordination_p5.r", "max_forks_repo_name": "lvandrews/amptools", "max_forks_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_forks_repo_licenses": ["Zlib"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-05-02T18:37:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-13T22:22:27.000Z", "avg_line_length": 32.8271604938, "max_line_length": 88, "alphanum_fraction": 0.719819481, "num_tokens": 797, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6791786991753929, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3448950014069812}} {"text": "\nlibrary(plyr)\nsource('formant_functions.r')\n\n# LOAD THE VOWEL MEASUREMENTS\nall_formant_data <- list()\n\nall_formant_data[['PAC-Ayr']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-PAC-Ayr_formants.csv')\nall_formant_data[['ICE-Sco']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-ICE-Sco_formants.csv')\nall_formant_data[['LUCID']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-LUCID_formants.csv')\nall_formant_data[['UBC']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-UBC_formants.csv')\nall_formant_data[['Devon']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-Devon_formants.csv')\nall_formant_data[['Scottish-Polish']] <- read.csv('/projects/spade/datasets/datasets_static_formants/spade-Scottish-Polish_formants.csv')\n\nmeasurement_point <- 0.33\n\nfor (corpus_name in names(all_formant_data)){\n\tprint (corpus_name)\n\tformant_data <- all_formant_data[[corpus_name]]\n\n\t# NAME THE OUTPUT FILES\n\tprototypes_filename <- paste0('spade-',corpus_name,'/spade-',corpus_name,'_prototypes.csv')\n\tplot_filename <- paste0('spade-',corpus_name,'/corpus_means_for_prototypes_',corpus_name,'.pdf')\n\n\t# ADD AND RENAME DATA COLUMNS AS NEEDED\n\tformant_data$measurement <- measurement_point\n\tnames(formant_data) <- gsub('phone_label', 'phone', names(formant_data))\n\tnames(formant_data) <- gsub('phone_', '', names(formant_data))\n\tformant_data$A1A2diff <- with(formant_data, A1-A2) \n\tformant_data$A2A3diff <- with(formant_data, A2-A3) \n\n\t# THE PARAMETERS FOR THE PROTOTYPES\n\tproto_parameters <- c('F1','F2','F3','B1','B2','B3','A1A2diff','A2A3diff')\n\n\t# REMOVE ROWS WITH NA FOR COLUMNS WE NEED FOR PROTOTYPES AND OMIT PHONES WITH FEWER THAN 6 REMAINING TOKENS\n\tformant_data <- formant_data[complete.cases(formant_data[,proto_parameters]),]\n\tlofreq_phones <- names(table(formant_data$phone))[table(formant_data$phone)<6]\n\tif (length(lofreq_phones)){\n\t\tformant_data <- subset(formant_data, !phone%in%lofreq_phones)\n\t\tprint (paste('omitting low-frequency phone', lofreq_phones))\n\t}\n\n\t# CALCULATE THE MEANS AND COVARIANCE MATRICES\n\tcorpus_means_for_phones <- ddply(formant_data[,c('phone', proto_parameters)], .(phone), numcolwise(mean, na.rm=T))\n\tnames(corpus_means_for_phones)[names(corpus_means_for_phones)%in%proto_parameters] <- paste(names(corpus_means_for_phones)[names(corpus_means_for_phones)%in%proto_parameters], measurement_point, sep='_')\n\tcorpus_covmats_list <- findCovarianceMatrices(formant_data, parameters=proto_parameters, measurements=c(measurement_point), \n\t write.to.file=FALSE, filename=NULL, data.frame(vowel_means=corpus_means_for_phones, measurement=measurement_point), \n\t normalized=FALSE, pass_id='mixed', target_phones=unique(formant_data$phone))\n\n\t# FORMAT THE COVARIANCE MATRICES FOR THE OUTPUT AND COMBINE THEM WITH THE MEANS\n\tcorpus_covmats <- c()\n\tfor(p in names(corpus_covmats_list)){\n\t \tphone_matrix <- corpus_covmats_list[[p]]\n\t \tcorpus_covmats <- rbind(corpus_covmats, data.frame(phone=p, phone_matrix))\n\t}\n\tphones_for_polyglot <- rbind(data.frame(type='means', corpus_means_for_phones), data.frame(type='matrix',corpus_covmats))\n\n\t# MAKE THE PROTOTYPE FILE\n\twrite.table(phones_for_polyglot, file=prototypes_filename, row.names=F, sep=',', quote=FALSE)\n\n\t# PLOT THE MEANS\n\tcairo_pdf(plot_filename, h=6, w=6, onefile=T)\n\tfor (i in 1:(length(proto_parameters)-1)){\n\t\tparam1 <- paste(proto_parameters[i+1],'0.33',sep='_')\n\t\tparam2 <- paste(proto_parameters[i],'0.33',sep='_')\n\t\tplot(corpus_means_for_phones[,param1], corpus_means_for_phones[,param2], main=paste(corpus_name, param1, 'vs.', param2), type='n', xlab=param1, ylab=param2,\n\t\t xlim=rev(range(corpus_means_for_phones[,param1])), ylim=rev(range(corpus_means_for_phones[,param2])))\n\t\ttext(corpus_means_for_phones[,param1], corpus_means_for_phones[,param2], labels=corpus_means_for_phones$phone)\n\t}\n\tdev.off()\n}\n\n", "meta": {"hexsha": "2a994c6b782cb5bebb71c03aa24f85153079268f", "size": 3968, "ext": "r", "lang": "R", "max_stars_repo_path": "make_formant_prototypes.r", "max_stars_repo_name": "MontrealCorpusTools/SPADE", "max_stars_repo_head_hexsha": "e2b20506e09aeca8b37ab0eb5a382b6ab996a5c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-11T17:29:53.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-11T17:29:53.000Z", "max_issues_repo_path": "make_formant_prototypes.r", "max_issues_repo_name": "MontrealCorpusTools/SPADE", "max_issues_repo_head_hexsha": "e2b20506e09aeca8b37ab0eb5a382b6ab996a5c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 33, "max_issues_repo_issues_event_min_datetime": "2018-07-26T19:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-19T19:26:56.000Z", "max_forks_repo_path": "make_formant_prototypes.r", "max_forks_repo_name": "MontrealCorpusTools/SPADE", "max_forks_repo_head_hexsha": "e2b20506e09aeca8b37ab0eb5a382b6ab996a5c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-02-23T20:50:55.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-31T21:57:43.000Z", "avg_line_length": 54.3561643836, "max_line_length": 204, "alphanum_fraction": 0.7603326613, "num_tokens": 1126, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6791786861878392, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.34489499481174735}} {"text": "suppressPackageStartupMessages(library(float))\nsuppressPackageStartupMessages(library(utils))\n\nm = 10L\nn = 3L\n\nx = matrix(1:(m*n), m, n)\nxs = fl(x)\n\nstopifnot(identical(nrow(x), m))\nstopifnot(identical(ncol(x), n))\nstopifnot(identical(dim(x), c(m, n)))\n\np = paste(capture.output(print(xs)), collapse=\"\\n\")\ntruth = \"# A float32 matrix: 10x3\n [,1] [,2] [,3]\n [1,] 1 11 21\n [2,] 2 12 22\n [3,] 3 13 23\n [4,] 4 14 24\n [5,] 5 15 25\n [6,] 6 16 26\n [7,] 7 17 27\n [8,] 8 18 28\n [9,] 9 19 29\n[10,] 10 20 30\"\n\nstopifnot(identical(p, truth))\n", "meta": {"hexsha": "ab041764d9bbdac5894ce1fb308cfd59697a03a2", "size": 604, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/print.r", "max_stars_repo_name": "david-cortes/float", "max_stars_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2017-11-08T11:29:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-20T20:17:08.000Z", "max_issues_repo_path": "tests/print.r", "max_issues_repo_name": "david-cortes/float", "max_issues_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 37, "max_issues_repo_issues_event_min_datetime": "2017-09-02T11:14:09.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-19T15:11:19.000Z", "max_forks_repo_path": "tests/print.r", "max_forks_repo_name": "david-cortes/float", "max_forks_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2017-11-18T18:05:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-17T01:23:23.000Z", "avg_line_length": 20.8275862069, "max_line_length": 51, "alphanum_fraction": 0.5430463576, "num_tokens": 264, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.6688802603710085, "lm_q1q2_score": 0.3448879834812882}} {"text": "MergePathways <- function(file_dir, pattern = NULL) {\n # This function combine pathway files in a certain directory\n \n ## === input ===\n ### file_dir: pathway file directory (subnetworks)\n ### pattern: specific types of edges needed in the merged pathway; default as all edges\n \n ## == output ===\n ### merged pathway file\n \n # load files\n file_names = dir(file_dir,full.names = T)\n all_file = c()\n for (i in 1:length(file_names)) {\n tmp.file = read.delim(file_names[i],header = T,sep = \",\")\n all_file = rbind(all_file, tmp.file[,1:6])\n }\n # generate adjacency matrix and get rid of the redundant linkages\n new_file = c()\n multi_linkage = c()\n unique_nodes = unique(c(as.character(as.matrix(all_file$node1)), as.character(as.matrix(all_file$node2))))\n adj.matrix = matrix(0, nrow = length(unique_nodes), ncol = length(unique_nodes),\n dimnames = list(unique_nodes, unique_nodes))\n node1.list = as.character(as.matrix(all_file$node1))\n node2.list = as.character(as.matrix(all_file$node2))\n for (i in 1:dim(all_file)[1]) {\n current_state = adj.matrix[node1.list[i],node2.list[i]]\n if (current_state == 0) {\n adj.matrix[node1.list[i],node2.list[i]] = as.numeric(all_file$Subtype1[i])\n new_file = rbind(new_file, all_file[i,])\n } else {\n new_state = as.numeric(all_file$Subtype1[i])\n if (new_state != current_state) {\n if (is.null(dim(multi_linkage))) {\n multi_linkage = rbind(multi_linkage, c(node1.list[i], node2.list[i], new_state))\n new_file = rbind(new_file, all_file[i,])\n } else {\n flag = F\n for (j in 1:dim(multi_linkage)[1]) {\n if (multi_linkage[j,1] == node1.list[i] & node2.list[i] == multi_linkage[j,2] & new_state == multi_linkage[j,3]) {\n flag = T\n break\n }\n }\n if (!flag) {\n multi_linkage = rbind(multi_linkage, c(node1.list[i], node2.list[i], new_state))\n new_file = rbind(new_file, all_file[i,])\n }\n }\n }\n }\n }\n list(new_file,multi_linkage)\n}\n\n\n \n\n", "meta": {"hexsha": "924f8d6c89d4f42e1a4b0702d0fbac9636620ad8", "size": 2104, "ext": "r", "lang": "R", "max_stars_repo_path": "ProteinSpace/Networks/SignalPathway/src/MergePathways.r", "max_stars_repo_name": "ShaoGroup/PICheM", "max_stars_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-03-17T02:51:38.000Z", "max_stars_repo_stars_event_max_datetime": "2016-03-17T02:51:38.000Z", "max_issues_repo_path": "ProteinSpace/Networks/SignalPathway/src/MergePathways.r", "max_issues_repo_name": "ShaoGroup/PICheM", "max_issues_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ProteinSpace/Networks/SignalPathway/src/MergePathways.r", "max_forks_repo_name": "ShaoGroup/PICheM", "max_forks_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6610169492, "max_line_length": 126, "alphanum_fraction": 0.6097908745, "num_tokens": 572, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.668880247169804, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3448879766744842}} {"text": "#script resamples original soil texture data and converts to values neeed for moistureMap.r (etc)\n\nrm(list=ls())\nlibrary(raster)\n\n#read munis.r as latlong\nunzip(zipfile=\"Data/sim10_BRmunis_latlon_5km.zip\",exdir=\"Data\") #unzip\nmunis.r <- raster(\"Data/sim10_BRmunis_latlon_5km.asc\")\nlatlong <- \"+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs \"\ncrs(munis.r) <- latlong\n\n#note textura_wgs84.asc in zip file is 1.6GB!\nunzip(zipfile=\"Data/textura_wgs84.zip\",exdir=\"Data\") #unzip\nvsoil<-raster(\"Data/textura_wgs84.asc\")\n\n#resample soil maps from ~250m resolution to 5km \nvsoil.m<- resample(vsoil, munis.r, method='ngb') \nvsoil.m<- mask(x=vsoil.m, mask=munis.r) \nwriteRaster(vsoil.m, \"Data/soilT_2018-05-01\", \"ascii\", \"overwrite\"=T)\n\n#classify soil texture map for CRAFTY\n#classification is shown in SoilClassification.docx \n#plot(vsoil)\nvalues(vsoil.m)[values(vsoil.m)==1] = 0\nvalues(vsoil.m)[values(vsoil.m)==2] = 0\nvalues(vsoil.m)[values(vsoil.m)==3] = 1\nvalues(vsoil.m)[values(vsoil.m)==4] = 0.5\nvalues(vsoil.m)[values(vsoil.m)==5] = 0.1\n\n#write to file\nwriteRaster(vsoil.m, \"Data/vsoil_2018-05-08\", \"ascii\", \"overwrite\"=T) \n\nunlink(\"Data/textura_wgs84.asc\") #large file so delete! \nunlink(\"Data/sim10_BRmunis_latlon_5km.asc\")", "meta": {"hexsha": "ecb1179439e45d0ccdc7ac31223d843091d57dd4", "size": 1240, "ext": "r", "lang": "R", "max_stars_repo_path": "soilMap.r", "max_stars_repo_name": "jamesdamillington/CRAFTYInput", "max_stars_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "soilMap.r", "max_issues_repo_name": "jamesdamillington/CRAFTYInput", "max_issues_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "soilMap.r", "max_forks_repo_name": "jamesdamillington/CRAFTYInput", "max_forks_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4705882353, "max_line_length": 97, "alphanum_fraction": 0.7298387097, "num_tokens": 465, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7606506418255928, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.3447739156079578}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# set dataset \n\ndataset <- \"Singhal\"\n\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n\nmetaData <- c(\"MEAN_COL_SCORE\",\"Sequences\",\"Columns\",\"Dist_Pat\",\"Pars_Info\",\"Sing_Sites\",\"Cons_Sites\" ,\"Chi2_Fail\",\"Gaps_Ambig\")\n\n# Add column to name type of support \nmLGL <- mutate(mLGL, supportType = case_when(TvS != 'a' ~ \"dGLS\"))\nmLBF <- mutate(mLBF, supportType = case_when(TvS != 'a' ~ \"BF\"))\n\n# Change from 2ln(BF) to ln(BF) by dividing all comparisons by 2 7-10\nmLBF[7:10] <- mLBF[7:10]/2\n\n# Check number of NA \nsum(is.na(mLBF$MEAN_COL_SCORE))\n\n# Want to stack datasets so GL and BF columns have both comparisons for each hypothesis. \n\nreformatdf <- function(df,h,h1,s1,keepCols){\n # keepCols is a vector of the locus and support type column indices\n # Grab column number \n c1 <- match(h1,names(df))\n # Grab and rename column, add hypothesis column\n a.df <- df[,append(keepCols,c1)] \n names(a.df)[names(a.df) == h1] <- h\n a.df$Hypothesis <- rep(h1,length(a.df[,1]))\n c <- match(h,names(a.df))\n # Adjust direction of support if needed. ie if column is AIvSA, but you want to know SAvAI\n a.df[,c] <- a.df[,c]*(s1)\n return(a.df)\n}\n\n# reformat \nkeep <- c(1,seq(14,22))\nAI.g <- bind_rows(reformatdf(mLGL,\"AI\",\"AIvSA\",1,keep),\n reformatdf(mLGL,\"AI\",\"AIvSI\",1,keep))\n\nSA.g <- bind_rows(reformatdf(mLGL,\"SA\",\"AIvSA\",-1,keep),\n reformatdf(mLGL,\"SA\",\"SAvSI\",1,keep))\n\nSI.g <- bind_rows(reformatdf(mLGL,\"SI\",\"AIvSI\",-1,keep),\n reformatdf(mLGL,\"SI\",\"SAvSI\",-1,keep))\n\nAI.b <- bind_rows(reformatdf(mLBF,\"AI\",\"AIvSA\",1,keep),\n reformatdf(mLBF,\"AI\",\"AIvSI\",1,keep))\n\nSA.b <- bind_rows(reformatdf(mLBF,\"SA\",\"AIvSA\",-1,keep),\n reformatdf(mLBF,\"SA\",\"SAvSI\",1,keep))\n\nSI.b <- bind_rows(reformatdf(mLBF,\"SI\",\"AIvSI\",-1,keep),\n reformatdf(mLBF,\"SI\",\"SAvSI\",-1,keep))\n\nTS.g <- reformatdf(mLGL,\"TS\",\"TvS\",-1,keep)\nTS.b <- reformatdf(mLBF,\"TS\",\"TvS\",-1,keep)\n\n# MERGE - yea i could have merged first, but the function was already written and i didnt want to mess with it. \n\nAI.a <- left_join(AI.g, AI.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nSA.a <- left_join(SA.g, SA.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nSI.a <- left_join(SI.g, SI.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\nTS.a <- left_join(TS.g, TS.b, by=c(\"Locus\",\"Hypothesis\",metaData), suffix = c(\".g\", \".b\"))\n\nsapply(AI.a,typeof)\n\n# Smash all together, then smash bf and gl together each in 1 column\nall <- bind_rows(bind_rows(bind_rows(AI.a,SA.a),SI.a),TS.a) %>% \n mutate(BF=coalesce(AI.b,SA.b,SI.b,TS.b)) %>% \n mutate(GL=coalesce(AI.g,SA.g,SI.g,TS.g)) %>%\n select(-c(AI.b,SA.b,SI.b,TS.b,AI.g,SA.g,SI.g,TS.g))\n\nTX <- bind_rows(bind_rows(AI.a,SA.a),SI.a) %>% \n mutate(BF=coalesce(AI.b,SA.b,SI.b)) %>% \n mutate(GL=coalesce(AI.g,SA.g,SI.g)) %>%\n select(-c(AI.b,SA.b,SI.b,AI.g,SA.g,SI.g))\n\n\nTS <- TS.a %>% \n mutate(BF=coalesce(TS.b)) %>% \n mutate(GL=coalesce(TS.g)) %>%\n select(-c(TS.b,TS.g))\n\n\n# Check number of NA \nsum(is.na(all$BF))\nsum(is.na(all$GL))\n\nrm(AI.g,AI.b,SA.b,SA.g,SI.b,SI.g,TS.b,TS.g)\n\nv <- 'tx'\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/\",dataset,\"_Corr_\",v,\"_rsvals.RData\",sep=''))\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# Scatter ---------------------------------------------- Scatter BF vs dGLS --------------------------------------------------------------------------------------------------------\n\n#------------------------------------------------------------------------------------------------------------------------------------------------------\n\n## Scatter graph, colored by hypothesis \n\nS <- function(df,xcol,ycol,x.tic,y.tic,cc,xlab,ylab){\n scat <- ggplot(df, aes(x=df[[xcol]],y=df[[ycol]])) + \n geom_point(alpha=1, aes(color=df$Hypothesis), size=0.5) + theme_bw() + theme(panel.border = element_blank()) +\n theme_classic() + \n theme(\n axis.text = element_text(size=14, color=\"black\"),\n text = element_text(size=14),\n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid = element_blank(), # get rid of major grid\n plot.title = element_text(hjust = 0.5)\n ) +\n coord_cartesian(ylim=y.tic,xlim = x.tic) +\n scale_y_continuous(breaks = y.tic) + \n scale_x_continuous(breaks = x.tic) +\n labs(x=xlab,y=ylab,color=\"Hypothesis\") + \n scale_color_manual(values=cc) + \n guides(colour = guide_legend(override.aes = list(size=2))) \n \n return(scat)\n}\n\nS.abs <- function(df,xcol,ycol,x.tic,y.tic,cc,xlab,ylab){\n scat <- ggplot(df, aes(x=abs(df[[xcol]]),y=df[[ycol]])) + \n geom_point(alpha=1, aes(color=df$Hypothesis), size=0.5) + theme_bw() + theme(panel.border = element_blank()) +\n theme_classic() + \n theme(\n axis.text = element_text(size=14, color=\"black\"),\n text = element_text(size=14),\n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid = element_blank(), # get rid of major grid\n plot.title = element_text(hjust = 0.5)\n ) +\n coord_cartesian(ylim=y.tic,xlim = x.tic) +\n scale_y_continuous(breaks = y.tic) + \n scale_x_continuous(breaks = x.tic) +\n labs(x=xlab,y=ylab,color=\"Hypothesis\") + \n scale_color_manual(values=cc) + \n guides(colour = guide_legend(override.aes = list(size=2))) \n \n return(scat)\n}\n\n\n# col numbers for metadata \nhot <- 2 # alignment quality score \nseq <- 3 # total number of taxa\ncol <- 4 # sites in alignment\ndist <- 5 # distinct patterns\npi <- 6 # PI sites\nsing <- 7 # singleton sites\ncons <- 8 # constant sites\nchi2 <- 9 # number of sequences that failed composition chi2 test \ngaps <- 10 # number of sequences that contain 50% or more gaps/ambiguity\ngl <- 11\nhyp <- 12\nbf <- 13 # max 180 for tox, all but 3 btw -50 and 20, 70 for tvs\n\n\nyV <- gaps\nyL <- \"gaps\"\n#xV <- bf\n#xL <- \"ln(BF)\"\n#lines <- geom_vline(xintercept=c(-10,10),color=c(\"black\"), linetype=\"dashed\", size=0.5)\nxV <- gl\nxL <- \"dGLS\"\nlines <- geom_vline(xintercept=c(-0.5,0.5),color=c(\"black\"), linetype=\"dashed\", size=0.5)\n\n\ndf <- AI.a\n# Get max min for graph x = dgls y=bf\nmax(abs(df[[bf]]),na.rm = T)\nmax(abs(df[[gaps]]),na.rm = T)\nx.lim <- 180 \nx.t <- seq(-x.lim,x.lim,40)\ny.lim <- 8\ny.t <- seq(0,8,2)\n\n\n#quartz()\n# Set colors \ncc <- c('#1e7b59','#46d29f') # 3 up and 3 down from original\n# 11 = gl, 13 = bf\n \nAI.s <- S(AI.a,xV,yV,x.t,y.t,cc,xL,yL) + lines\n\nAI.s \n\n# Set colors \ncc <- c('#243a5b','#4975b6')\n\nSA.s <- S(SA.a,xV,yV,x.t,y.t,cc,xL,yL) + lines\n\nSA.s\n\n# Set colors \ncc <- c('#4d4d00','#cccc00')\n\nSI.s <- S(SI.a,xV,yV,x.t,y.t,cc,xL,yL) + lines\n\n\nSI.s\n\ncc = color_TP\nTS.s <- S(TS.a,xV,yV,x.t,y.t,cc,xL,yL) + lines\n\nTS.s\n\nSP <- ggarrange(TS.s, AI.s, SA.s, SI.s, ncol=1, nrow=4, align=\"h\")\nSP\n#yL <- \"gaps\"\nggsave(paste(dataset,\"_scatter_\",xL,\"_\",yL,\".pdf\",sep=\"\"), plot=SP,width = 6, height = 9, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n\n\n# Get max min for graph \n\nmax(abs(min(mL$BF)),abs(max(mL$BF)),abs(min(mL$dGLS)),abs(max(mL$dGLS)))\nlimit <- 90\ntic <- seq(-limit,limit,10)\n\n# Names \"TvS\", \"AIvSA\", \"AIvSI\", \"SAvAI\", \"SAvSI\", \"SIvAI\", \"SIvSA\", \"TvS_support\"\n\ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"#C2DF23FF\"\n\n\nquartz()\n# Set colors \ncolor_h0 <- color_AI\ncolor_h0 <- color_TP\n\ngraph_general <- ggplot(mL, aes(x=BF,y=MEAN_COL_SCORE)) + \n geom_point(alpha=0.5, color=color_h0, size=1) + theme_bw() + theme(panel.border = element_blank()) +\n theme_classic() + \n theme(\n axis.text = element_text(size=16, color=\"black\"),\n text = element_text(size=20),\n legend.position = \"none\", \n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid = element_blank(), # get rid of major grid\n plot.title = element_text(hjust = 0.5)\n ) +\n coord_cartesian(ylim=tic,xlim = tic) +\n scale_y_continuous(breaks = tic) + \n scale_x_continuous(breaks = tic) +\n labs(x='ln(BF)',y='dGLS')\n\ngraph_general\n\ngraph_custom <- graph_general + \n geom_vline(xintercept=c(10,-10),color=c(\"gray\"), linetype=\"dotted\", size=0.5) +\n geom_hline(yintercept=c(0.5,-0.5),color=c(\"gray\"), linetype=\"dotted\", size=0.5) +\n ggtitle(paste(dataset,\"\\n\",t,sep=\"\"))\n #geom_abline(color=c(\"gray\"), size=0.2, linetype=\"solid\") + \n \n\ngraph_custom\n\nggsave(paste(dataset,\"_scatter_\",hypoth,\".pdf\",sep=\"\"), plot=graph_custom,width = 9.5, height = 6, units = \"in\", device = 'pdf',bg = \"transparent\")\n", "meta": {"hexsha": "da5e8b65916d63f77735c4f1efff00f235f7e795", "size": 9137, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/oldComparisons/Graphs_Scatter_metaData.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/oldComparisons/Graphs_Scatter_metaData.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/oldComparisons/Graphs_Scatter_metaData.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6321428571, "max_line_length": 180, "alphanum_fraction": 0.6031520193, "num_tokens": 3007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34474859140422703}} {"text": "#########################################################################################################################\n# Routing with R #\n# #\n# Version: 0.1 #\n# Author: Michalis Pavlis #\n# Licence: MIT #\n# #\n# lines_to_graph: Function to create a graph representation (directed, undirected) of the lines network #\n# #\n# is_connected: Function to clean the line network by identifying the self-connected line segments #\n# #\n# shortest_route_cost: Function to calculate the shortest route cost either in kilometres or minutes #\n# #\n#########################################################################################################################\n\nlibrary(igraph)\nlibrary(FNN)\nlibrary(data.table)\nlibrary(sf)\nlibrary(parallel)\n\n#########################################################################################################################\n# Check functions' arguments #\n#########################################################################################################################\n\n.check_values <- function(sf_df, column_name){\n if (any(is.na(sf_df[[column_name]]))) stop(paste(\"NA values were found in column\", column_name))\n if (any(is.null(sf_df[[column_name]]))) stop(paste(\"NULL values were found in column\", column_name))\n}\n\n.check_sf <- function(sf_df, geom_column){\n \n if (! is(sf_df, \"sf\")) stop(\"object class should be 'sf'\")\n \n if (! geom_column %in% names(sf_df)) stop(\"provide geometry column\")\n \n .check_values(sf_df, geom_column)\n \n p4str <- st_crs(sf_df)$proj4string\n if (is.na(p4str) || !nzchar(p4str)) stop(\"specify coordinate reference system\")\n \n res <- grep(\"longlat\", p4str, fixed = TRUE)\n if (length(res) != 0) stop(\"use projected reference system\")\n \n}\n\n.check_line <- function(sf_df, geom_column){\n \n .check_sf(sf_df, geom_column)\n \n if (!is(sf_df[[geom_column]], \"sfc_LINESTRING\")){\n stop(\"the geometry column class should be 'sfc_LINESTRING'\")\n }\n \n}\n\n.check_is_connected <- function(lines_sf, geom_column, allpoints, area_id){\n \n .check_line(lines_sf, geom_column)\n \n if (! allpoints %in% c(T, F)) stop(\"allpoints should be either True or False\")\n \n if (!is.null(area_id)){\n if (! area_id %in% names(lines_sf)) stop(paste(\"the column\", area_id, \"was not found in lines_sf\"))\n .check_values(lines_sf, area_id)\n } \n \n}\n\n.check_lines_to_graph <- function(lines_sf, geom_column, allpoints, area_id, weighted_graph,\n speed_limit_column, direction_column, direction_lookup) {\n \n .check_is_connected(lines_sf, geom_column, allpoints, area_id)\n \n if (! allpoints %in% c(T, F)) stop(\"allpoints should either be True or False\")\n \n if (! weighted_graph %in% c(T, F)) stop(\"weighted_graph should either be True or False\")\n \n if (!is.null(speed_limit_column)){\n if (! speed_limit_column %in% names(lines_sf)) stop(paste(\"the column\", speed_limit_column, \"was not found in lines_sf\"))\n if (! is(lines_sf[[speed_limit_column]], \"numeric\")) stop(\"the class of the speed limit column should be numeric\")\n .check_values(lines_sf, speed_limit_column)\n }\n \n if (!is.null(direction_column)){\n if (! direction_column %in% names(lines_sf)) stop(paste(\"the column\", direction_column, \"was not found in lines_sf\"))\n .check_values(lines_sf, direction_column)\n if (length(unique(lines_sf[[direction_column]])) != 3) stop(paste(\"only three values are expected in column\", direction_column, \"that represent back, forward and both traffic direction\"))\n if (! length(direction_lookup[[1]]) == 3) stop(\"provide exactly three values for the first vector in the direction_lookup list: forward, opposite and both\")\n if (! length(direction_lookup[[2]]) == 3) stop(\"provide exactly three values for the second vector in the direction_lookup list\")\n if (! all(c(\"forward\",\"opposite\", \"both\") %in% direction_lookup[[1]])) stop(\"the only values expected in the first vector of direction_lookup are: forward, opposite and both\")\n if (! all(direction_lookup[[2]] %in% unique(lines_sf[[direction_column]]))) stop(paste(\"the values provided in the second column of the direction_lookup table do not match with the values in\", direction_column))\n }\n}\n\n\n.check_shortest_route <- function(origins_sf, destinations_sf, geom_column, id_column, lines_graph, lookup_table, join_by){\n \n .check_sf(origins_sf, geom_column)\n if (! is(origins_sf$geometry, \"sfc_POINT\")) stop(\"the class of the geometry column in origins_sf should be 'sfc_POINT'\")\n \n .check_sf(destinations_sf, geom_column)\n if (! is(destinations_sf$geometry, \"sfc_POINT\")) stop(\"the class of the geometry column in destinations_sf should be 'sfc_POINT'\")\n \n if (! identical(st_crs(origins_sf)$proj4string, st_crs(destinations_sf)$proj4string)){\n stop(\"origin and destination points are not in the same reference system\")\n }\n \n if (! identical(st_crs(origins_sf)$proj4string, attributes(lines_graph)$proj4string)){\n stop(\"the road network is not in the same reference system with the point data\")\n }\n \n if (! id_column %in% names(origins_sf)) stop(\"provide id field for origins_sf\")\n .check_values(origins_sf, id_column)\n \n if (! id_column %in% names(destinations_sf)) stop(\"provide id field for destinations_sf\")\n .check_values(destinations_sf, id_column)\n \n if (!is.null(lookup_table)){\n if (ncol(lookup_table) != 2){\n stop(\"the lookup table should have two columns\")\n }\n if (!any(unique(origins_sf[[id_column]]) %in% unique(lookup_table[,1]))){\n stop(paste(\"there are no matching values between the field\", id_column, \"in origins_sf and the first column in the lookup_table\"))\n }\n if (!any(unique(destinations_sf[[id_column]]) %in% unique(lookup_table[,2]))){\n stop(paste(\"there are no matching values between the\", id_column, \"field in destinations_sf and the second column in the lookup_table\"))\n }\n .check_values(lookup_table, colnames(lookup_table)[1])\n .check_values(lookup_table, colnames(lookup_table)[2])\n if (!is.null(join_by)){\n warning(\"both lookup_table and join_by were provided, ignoring join_by\")\n }\n }\n \n if (!is.null(join_by)){\n if (! join_by %in% names(origins_sf)){\n stop(paste(\"the field\", join_by, \"was not found in origins_sf\"))\n }\n if (! join_by %in% names(destinations_sf)){\n stop(paste(\"the field\", join_by, \"was not found in destinations_sf\"))\n }\n if (! any(origins_sf[[join_by]] %in% destinations_sf[[join_by]])){\n stop(paste(\"you are trying to join origins_sf and destinations_sf by\", join_by, \"but there are no common values\"))\n }\n .check_values(origins_sf, join_by)\n .check_values(destinations_sf, join_by)\n }\n}\n\n#########################################################################################################################\n# Create graph from lines network #\n#########################################################################################################################\n\nlines_to_graph <- function(lines_sf, geom_column, allpoints = T, area_id = NULL,\n weighted_graph = F, speed_limit_column = NULL, direction_column = NULL,\n direction_lookup = list(c(\"forward\",\"opposite\", \"both\"), c(\"F\",\"T\",\"B\"))){\n \n ##### 1. Edge list functions ##########################################################################################\n \n edge_list_endpoints <- function(coords){\n n <- nrow(coords)\n c(paste(coords[1, ], collapse = \" \"), paste(coords[n, ], collapse = \" \"))\n }\n \n edge_list_allpoints <- function(coords){\n n <- nrow(coords)\n cbind(coords[-n, 1], coords[-n, 2], coords[-1, 1], coords[-1, 2])\n }\n \n edge_list_allpoints_back <- function(coords){\n n <- nrow(coords)\n coords <- coords[n:1, ]\n cbind(coords[-n, 1], coords[-n, 2], coords[-1, 1], coords[-1, 2])\n }\n \n ##### 2. Check inputs #################################################################################################\n \n .check_lines_to_graph(lines_sf, geom_column, allpoints, area_id, weighted_graph, speed_limit_column, direction_column, direction_lookup)\n \n ##### 3. Build edge list ##############################################################################################\n \n if (is.null(direction_column)){\n if (! allpoints){\n\t edgelist <- do.call(rbind, lapply(1:nrow(lines_sf), function(x) edge_list_endpoints(unclass(lines_sf[[geom_column]][[x]]))))\n } else {\n edgelist <- do.call(rbind, lapply(1:nrow(lines_sf), function(x) cbind(x, edge_list_allpoints(unclass(lines_sf[[geom_column]][[x]])))))\n }\n } else {\n forward <- direction_lookup[[2]][which(direction_lookup[[1]] == \"forward\" | direction_lookup[[1]] == \"both\")]\n id_forward <- which(lines_sf[[direction_column]] %in% forward)\n \n opposite <- direction_lookup[[2]][which(direction_lookup[[1]] == \"opposite\" | direction_lookup[[1]] == \"both\")]\n id_opposite <- which(lines_sf[[direction_column]] %in% opposite)\n edgelist <- rbind(do.call(rbind, lapply(id_forward,\n function(x) cbind(x, edge_list_allpoints(unclass(lines_sf[[geom_column]][[x]]))))),\n do.call(rbind, lapply(id_opposite,\n function(x) cbind(x, edge_list_allpoints_back(unclass(lines_sf[[geom_column]][[x]]))))))\n }\n \n ##### 4. Build graph ###################################################################################################\n \n if (is.null(direction_column)){\n lines_graph <- graph.edgelist(cbind(paste(edgelist[, 2], edgelist[, 3]), paste(edgelist[, 4], edgelist[, 5])), directed = F)\n } else {\n lines_graph <- graph.edgelist(cbind(paste(edgelist[, 2], edgelist[, 3]), paste(edgelist[, 4], edgelist[, 5])), directed = T)\n }\n E(lines_graph)$id <- edgelist[,1]\n if (weighted_graph){\n edge_length <- sqrt((edgelist[, 2] - edgelist[, 4]) ^ 2 + (edgelist[, 3] - edgelist[, 5]) ^ 2) / 1000\n if (!is.null(speed_limit_column)){\n edge_length <- ifelse(edge_length < 0.00001, 0.00001, edge_length)\n E(lines_graph)$weight <- 60 * edge_length / lines_sf[[speed_limit_column]][edgelist[,1]]\n } else {\n E(lines_graph)$weight <- edge_length\n }\n }\n \n # set area id as graph attribute\n if (!is.null(area_id)){\n E(lines_graph)$area_id <- lines_sf[[area_id]][edgelist[, 1]]\n }\n \n # V(lines_graph)$name <- 1:length(V(lines_graph))\n \n attr(lines_graph, \"proj4string\") <- st_crs(lines_sf)$proj4string\n \n lines_graph\n}\n\n#########################################################################################################################\n# Get connected parts of the road network #\n#########################################################################################################################\n\nis_connected <- function(lines_sf, geom_column, allpoints = T, area_id = NULL, cores_nr = 1){\n \n ##### 1. Functions ####################################################################################################\n \n connected_edges <- function(G){\n graph_parts <- decompose.graph(G, min.vertices = 2)\n if (length(graph_parts) > 1){\n # Find road part with the highest number of vertices, extract id instead of name\n edges_id <- unique(E(graph_parts[[which.max(sapply(1:length(graph_parts), function(x) length(V(graph_parts[[x]]))))]])$id)\n return(edges_id)\n }\n return(unique(E(G)$id))\n }\n \n ##### 2. Check inputs #################################################################################################\n \n .check_is_connected(lines_sf, geom_column, allpoints, area_id)\n \n ##### 3. Main Function ################################################################################################\n \n if (!is.null(area_id)){\n lines_graph <- lines_to_graph(lines_sf = lines_sf, geom_column = geom_column, allpoints = allpoints, area_id = area_id)\n edges_id <- unlist(mclapply(unique(lines_sf[[area_id]]), \n function(x) connected_edges(subgraph.edges(lines_graph, which(E(lines_graph)$area_id == x))),\n mc.cores = cores_nr))\n } else {\n lines_graph <- lines_to_graph(lines_sf = lines_sf, geom_column = geom_column, allpoints = allpoints)\n edges_id <- connected_edges(lines_graph)\n }\n \n connected <- 1:nrow(lines_sf) %in% edges_id\n \n connected\n \n}\n\n\n#########################################################################################################################\n# Shortest Route Cost #\n#########################################################################################################################\n\nshortest_route_cost <- function(origins_sf, destinations_sf, lines_graph, geom_column, id_column, join_by = NULL, lookup_table = NULL, cores_nr = 1){\n \n ##### 1. Function #####################################################################################################\n \n calc_cost <- function(origins_id, destinations_id){\n \n o_node_ids <- origin_points[.(unique(origins_id)), .(get(id_column), origins_node_id), on = id_column, allow.cartesian = T]\n setnames(o_node_ids, 1, id_column)\n d_node_ids <- destination_points[.(unique(destinations_id)), .(get(id_column), destinations_node_id), on = id_column, allow.cartesian = T]\n setnames(d_node_ids, 1, id_column)\n \n o_unique_node_ids <- unique(o_node_ids$origins_node_id)\n d_unique_node_ids <- unique(d_node_ids$destinations_node_id)\n \n path_DT <- rbindlist(mclapply(o_unique_node_ids, function(o_id) list(rep(o_id, length(d_unique_node_ids)),\n d_unique_node_ids,\n as.numeric(shortest.paths(lines_graph, v = o_id, to = d_unique_node_ids))),\n mc.cores = cores_nr))\n setnames(path_DT, names(path_DT), c(\"origins_node_id\", \"destinations_node_id\", \"cost\"))\n path_DT <- path_DT[o_node_ids, on = \"origins_node_id\", allow.cartesian = T]\n path_DT <- path_DT[d_node_ids, on = \"destinations_node_id\", allow.cartesian = T]\n setkeyv(path_DT, c(id_column, paste0(\"i.\", id_column)))\n path_DT <- path_DT[, min(cost), by=c(id_column, paste0(\"i.\", id_column))]\n \n path_DT$V1\n }\n \n ##### 2. Check inputs #################################################################################################\n \n .check_shortest_route(origins_sf, destinations_sf, geom_column, id_column, lines_graph, lookup_table, join_by)\n \n if (nrow(origins_sf) > nrow(destinations_sf) & !is_directed(lines_graph) & is.null(lookup_table)){\n origin_points <- destinations_sf\n destination_points <- origins_sf\n } else {\n origin_points <- origins_sf\n destination_points <- destinations_sf\n }\n \n ##### 3. Map origins and destinations to road network #################################################################\n \n line_vertices <- do.call(cbind, tstrsplit(V(lines_graph)$name, \" \"))\n storage.mode(line_vertices) <- \"numeric\"\n # Map origin and destination nodes to closest nodes\n setDT(origin_points)\n setDT(destination_points)\n origin_points[, origins_node_id := get.knnx(line_vertices, do.call(rbind, unclass(origin_points[[geom_column]])), 1)$nn.index[,1]]\n destination_points[, destinations_node_id := get.knnx(line_vertices, do.call(rbind, unclass(destination_points[[geom_column]])), 1)$nn.index[,1]]\n \n V(lines_graph)$name <- seq_along(V(lines_graph))\n \n ##### 4. Calculate shortest route cost ################################################################################\n \n setkeyv(origin_points, id_column)\n setkeyv(destination_points, id_column)\n \n if (!is.null(lookup_table)){\n out_DT <- as.data.table(lookup_table)\n } else if (!is.null(join_by)){\n out_DT <- origin_points[, unique(id), keyby = join_by][destination_points[, unique(id), keyby = join_by], on = join_by, allow.cartesian = T]\n } else {\n unique_origins <- unique(origin_points[[id_column]])\n unique_destinations <- unique(destination_points[[id_column]])\n out_DT <- data.table(origins_id = rep(unique_origins, length(unique_destinations)),\n destinations_id = do.call(rbind, lapply(unique_destinations, \n function(x) cbind(rep(x, length(unique_origins))))))\n }\n \n setnames(out_DT, 1:2, c(\"origins_id\", \"destinations_id\"))\n setkeyv(out_DT, c(\"origins_id\", \"destinations_id\"))\n out_DT[, cost := calc_cost(origins_id, destinations_id), by = origins_id]\n \n if (nrow(origins_sf) > nrow(destinations_sf) & !is_directed(lines_graph) & is.null(lookup_table)){\n setnames(out_DT, c(\"origins_id\", \"destinations_id\"), c(\"destinations_id\", \"origins_id\"))\n }\n \n out_DT\n \n}\n", "meta": {"hexsha": "2ad80634d653040d3676275e4e611f7f8c37cdc0", "size": 18144, "ext": "r", "lang": "R", "max_stars_repo_path": "route.r", "max_stars_repo_name": "mpavlis/Route", "max_stars_repo_head_hexsha": "eac85bedca0db256f01addfd259e33dfa9064033", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "route.r", "max_issues_repo_name": "mpavlis/Route", "max_issues_repo_head_hexsha": "eac85bedca0db256f01addfd259e33dfa9064033", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "route.r", "max_forks_repo_name": "mpavlis/Route", "max_forks_repo_head_hexsha": "eac85bedca0db256f01addfd259e33dfa9064033", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.1098591549, "max_line_length": 215, "alphanum_fraction": 0.5353284832, "num_tokens": 3830, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.626124191181315, "lm_q1q2_score": 0.34474859140422703}} {"text": "card <- fread('card.csv',\r\n header = T, \r\n stringsAsFactors = F,\r\n data.table = F,\r\n encoding = 'UTF-8')\r\n\r\n## \ud55c\uae00 \uc5c6\uc560\uae30 ##\r\ndata <- card %>% filter(! (selng_cascnt %in% grep('[\u3131-\ud7a3]',unique(card$selng_cascnt), value = T)),\r\n ! (salamt %in% grep('[\u3131-\ud7a3]',unique(card$salamt), value = T))) %>% \r\n mutate(selng_cascnt = as.numeric(selng_cascnt),\r\n salamt = as.numeric(salamt)) %>%\r\n select(- c(adstrd_code, mrhst_induty_cl_code))\r\n\r\nrm(list = c('card'))\r\n\r\ndata$receipt_dttm=data$receipt_dttm %>% as.character() %>% as.Date('%Y%m%d')\r\n\r\n## \uc74c\uc218 \uac12 \ud655\uc778 - \uc591\uc218\ub9cc \ub123\uae30## \r\ndata$selng_cascnt %>% summary()\r\ndata$salamt %>% summary()\r\n\r\ndata = data %>% filter(selng_cascnt > 0, salamt > 0) %>% \r\n mutate(receipt_dttm = ymd(receipt_dttm),\r\n week = week(receipt_dttm))\r\n\r\ndata %>% glimpse()\r\n\r\n#\ucf54\ub85c\ub098 \uc2dc\uae30\ub97c \uc0c8\ub85c\uc6b4 period\ubcc0\uc218\ub85c \ub098\ud0c0\ub0b4 \uc90d\ub2c8\ub2e4.\r\nindex1 = which(data$receipt_dttm == '2020-02-22') %>% max() #\uae30 \r\nindex2 = which(data$receipt_dttm == '2020-03-08') %>% max() #\uc2b9\r\nindex3 = which(data$receipt_dttm == '2020-05-06') %>% max() #\uc804-1\r\nindex4 = nrow(data) #\uc804-2\r\n\r\ndata_period = data \r\ndata_period$period = c(rep(1, index1),\r\n rep(2, index2 - index1),\r\n rep(3, index3 - index2),\r\n rep(4, index4 - index3))\r\n\r\n##\uc774\uc0c1\uce58 \ubc0f \uacb0\uce21\uce58 \ucc98\ub9ac##\r\n\r\ndata_period %>% is.na() %>% colSums()\r\n\r\nmean_amount=data_period %>%\r\n group_by(mrhst_induty_cl_nm) %>% \r\n summarise(N_amount=mean(selng_cascnt)) %>% \r\n arrange(N_amount)\r\n\r\nmean_amount %>%\r\n ggplot(aes(x=1, y=N_amount))+\r\n geom_violin( color = \"#1E3269\",size=0.3)+theme_bw() +theme(plot.margin = margin(60,60,60,60)) \r\n\r\ncategories_new=mean_amount %>%\r\n filter(N_amount>=quantile(mean_amount$N_amount)[2]) %>% \r\n arrange(desc(N_amount)) %>% select(mrhst_induty_cl_nm)%>% \r\n ungroup()\r\n\r\ncategories_new <- as.data.frame(categories_new)\r\n\r\n\r\ndata_period <- data_period %>% \r\n filter(mrhst_induty_cl_nm%in%\r\n as.matrix(categories_new,nrow = 1))", "meta": {"hexsha": "f3f6e1f83e16e5bcbbc4f22afee880cd680e111c", "size": 2012, "ext": "r", "lang": "R", "max_stars_repo_path": "r/card_pre.r", "max_stars_repo_name": "AdonisHan/prep_r", "max_stars_repo_head_hexsha": "694eb1c31c3b5bbb4b93a1217039f58bf8574c1b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r/card_pre.r", "max_issues_repo_name": "AdonisHan/prep_r", "max_issues_repo_head_hexsha": "694eb1c31c3b5bbb4b93a1217039f58bf8574c1b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r/card_pre.r", "max_forks_repo_name": "AdonisHan/prep_r", "max_forks_repo_head_hexsha": "694eb1c31c3b5bbb4b93a1217039f58bf8574c1b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9365079365, "max_line_length": 98, "alphanum_fraction": 0.5849900596, "num_tokens": 673, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34474858372159606}} {"text": "# Create Figure 3 for the paper\n# SCA\n\nlibrary(plyr)\n\nload(\"../../vignettes/scalar_dat.rda\")\nload(\"../../vignettes/ts_dat.rda\")\n\n# Bring in F to scalars:\nf_merge <- subset(ts_dat, year == max(ts_dat$year))[ ,c(\"scenario\",\n \"replicate\", \"F_om\", \"F_em\")]\nscalar_dat <- join(scalar_dat, f_merge, by = c(\"scenario\",\n \"replicate\"))\n\nscalar_dat <- transform(scalar_dat,\n steep = (SR_BH_steep_om - SR_BH_steep_em)/SR_BH_steep_om,\n logR0 = (SR_LN_R0_om - SR_LN_R0_em)/SR_LN_R0_om,\n depletion = (depletion_om - depletion_em)/depletion_om,\n SSB_MSY = (SSB_MSY_em - SSB_MSY_om)/SSB_MSY_om,\n SR_sigmaR = (SR_sigmaR_em - SR_sigmaR_om)/SR_sigmaR_om,\n NatM = (NatM_p_1_Fem_GP_1_em - NatM_p_1_Fem_GP_1_om)/\n NatM_p_1_Fem_GP_1_om,\n Fmort = (F_em - F_om) / F_om)\n\n# add a tiny bit of noise for violin plotting:\n# N <- length(scalar_dat[scalar_dat$E == \"E0\", \"NatM\"])\n# scalar_dat[scalar_dat$E == \"E0\", \"NatM\"] <- rnorm(N, 0, 0.0001)\nscalar_dat[scalar_dat$E == \"E0\", \"NatM\"] <- NA\n\nts_dat <- transform(ts_dat,\n SpawnBio = (SpawnBio_em - SpawnBio_om)/SpawnBio_om,\n Recruit_0 = (Recruit_0_em - Recruit_0_om)/Recruit_0_om)\n\nscalar_dat_det <- subset(scalar_dat, E %in% c(\"E100\", \"E101\"))\nscalar_dat_sto <- subset(scalar_dat, E %in% c(\"E0\", \"E1\"))\nts_dat_det <- subset(ts_dat, E %in% c(\"E100\", \"E101\"))\nts_dat_sto <- subset(ts_dat, E %in% c(\"E0\", \"E1\"))\nts_dat_sto <- droplevels(ts_dat_sto)\nscalar_dat_sto <- droplevels(scalar_dat_sto)\n\nscalar_dat_sto <- subset(scalar_dat, E %in% c(\"E0\", \"E1\"))\nscalar_dat_long <- reshape2::melt(scalar_dat_sto[,c(\"scenario\", \"D\",\n \"E\", \"replicate\", \"max_grad\", \"depletion\", \"NatM\", \"SSB_MSY\",\n \"Fmort\")], id.vars = c(\"scenario\", \"D\", \"E\", \"replicate\",\n \"max_grad\"))\nscalar_dat_long <- plyr::rename(scalar_dat_long,\n c(\"value\" = \"relative_error\"))\n\nscalar_dat_long <- droplevels(scalar_dat_long)\n\nquant_dat <- ddply(ts_dat_sto, c(\"D\", \"E\", \"year\"), summarize,\n q05 = quantile(SpawnBio, probs = 0.05),\n q25 = quantile(SpawnBio, probs = 0.25),\n q50 = quantile(SpawnBio, probs = 0.50),\n q75 = quantile(SpawnBio, probs = 0.75),\n q95 = quantile(SpawnBio, probs = 0.95)\n)\n\ncols <- RColorBrewer::brewer.pal(8, \"Blues\")\ncols4 <- RColorBrewer::brewer.pal(8, \"Greys\")\n\nlabel_col <- \"grey40\"\nlabel_cex <- 0.75\naxis_col <- \"grey55\"\n\n# Need to load Arial font for PLOS:\n#library(\"extrafont\")\n#loadfonts(device = \"postscript\")\n\n#pdf(\"fig2-20131109.pdf\", width = 5, height = 4.5)\n\nArial <- Type1Font(family=\"Arial\", metrics=c(\"arial.afm\", \"arialbd.afm\",\n \"ariali.afm\", \"arialbi.afm\"))\npostscriptFonts(Arial=Arial)\n\npostscript(\"fig3.eps\", horizontal = FALSE, onefile = FALSE, paper =\n \"special\", width = 4.86, height = 4.374)\nlayout(rbind(c(1, 1, 2, 2),\n c(1, 1, 2, 2),\n c(3, 3, 4, 4),\n c(3, 3, 4, 4),\n c(5, 6, 7, 8),\n c(5, 6, 7, 8),\n c(5, 6, 7, 8)))\npar(mar = c(.2,.2,0,0))\npar(oma = c(3,3.5,2,3))\npar(cex = 0.7)\npar(tck = -0.03)\npar(mgp = c(2, 0.40, 0))\nii <<- 0 # for panel counting\n\nd_ply(quant_dat, c(\"D\", \"E\"), transform, {\n ii <<- ii + 1\n plot(1, 1, xlim = range(year), ylim = c(-0.25, 0.5), type = \"n\",\n axes = FALSE, ann = FALSE, xaxs = \"i\", yaxs = \"i\")\n polygon(c(year, rev(year)), c(q05, rev(q95)), col = cols[4], border = NA)\n polygon(c(year, rev(year)), c(q25, rev(q75)), col = cols[6], border = NA)\n lines(year, q50, col = cols[8], lwd = 1.4)\n if(i %in% c(1, 3)) axis(2, las = 1, at = seq(-0.2, 0.5, 0.2), col =\n axis_col, col.axis = axis_col)\n if(i %in% c(3, 4)) axis(1, col = axis_col, col.axis = axis_col, padj = -0.15)\n box(col = axis_col)\n abline(h = 0, col = \"#FFFFFF\", lty = 0, lwd = 1)\n mtext(paste(unique(D), unique(E), sep = \"-\"), side = 3, line =\n -1.15, cex = 0.7, col = label_col, adj = 0.043)\n #mtext(scen[ii], side = 3, line = -1.15, cex = 0.7, col = label_col, adj = 0.043)\n #mtext(\"SSB\", adj = 0.05, line = -1.3, col = \"grey50\", cex = 0.75)\n #mtext(LETTERS[ii], adj = 0.05, line = -1.5, col = axis_col, cex = 0.7)\n if(ii == 1) {\n text(1945, 0.15, \"Median\", pos = 4, col = cols[8])\n text(1959, 0.25, \"50% range\", pos = 4, col = cols[6])\n text(1978, 0.35, \"90% range\", pos = 4, col = cols[4])\n arrows(1955, 0.10, 1955, 0, length = 0.06, col = cols[8])\n arrows(1975, 0.20, 1975, 0.03, length = 0.06, col = cols[6])\n arrows(1995, 0.30, 1995, 0.06, length = 0.06, col = cols[4])\n }\n})\n\npanel_labs <- c(expression(Depletion), expression(italic(M)),\n expression(SSB[MSY]), expression(italic(F)))\n\npar(mar = c(.2,.2,1.7,0))\nd_ply(scalar_dat_long, \"variable\", function(x) {\n ii <<- ii + 1\n plot(1, 1, xlim = c(.6, 4.4), ylim = c(-0.6, 0.6), type = \"n\",\n axes = FALSE, ann = FALSE, yaxs = \"i\")\n cols_ii <- cols4\n abline(h = 0, col = axis_col, lty = 1, lwd = 1)\n beanplot::beanplot(relative_error ~ scenario, data = x, add = TRUE,\n border = NA, axes = FALSE, col = cols_ii[c(5, 3, 4, 8)], what =\n c(0, 1, 0, 0), log = FALSE)\n points(jitter(as.numeric(x$scenario), amount = 0.09),\n #x$relative_error, col = paste0(cols_ii[3], \"80\"), pch = 20, cex =\n x$relative_error, col = \"#D0D0D0\", pch = 20, cex =\n 0.17)\n if(ii %in% 5) axis(2, las = 1, at = seq(-0.6, 0.6, 0.3), col =\n axis_col, col.axis = axis_col)\n axis(1, col = axis_col, col.axis = label_col, at = 1:4, labels =\n substr(levels(x$scenario), 1, 5), las = 3)\n box(col = axis_col)\n #mtext(LETTERS[ii], adj = 0.05, line = -1.5, col = \"grey40\", cex = 0.8)\n text(0.5, 0.48, panel_labs[ii-4], col = label_col, pos = 4, cex = 1.05)\n})\n\n# Label the axes:\nmtext(\"Relative error in SSB\", side = 2, outer = TRUE, line = 2.2, cex\n = label_cex, col = label_col, adj = 0.8)\nmtext(\"Relative error\", side = 2, outer = TRUE, line = 2.2, cex =\n label_cex, col = label_col, adj = 0.1)\nmtext(expression(Fixed~italic(M)~(E0)), side = 3, outer =\n TRUE, cex = label_cex, adj = 0.18, line = 0.2, col = label_col)\nmtext(expression(Estimated~italic(M)~(E1)), side = 3, outer = TRUE,\n cex = label_cex, adj = 0.85, line = 0.2, col = label_col)\n\nmtext(expression(sigma[survey]==0.1~(D0)), side = 4, outer = TRUE, cex\n = label_cex, adj = 0.99, line = 0.6, col = label_col)\nmtext(expression(Higher~survey~effort), side = 4, outer = TRUE, cex =\n label_cex, adj = 1.025, line = 1.8, col = label_col)\n\nmtext(expression(sigma[survey]==0.4~(D1)), side = 4, outer = TRUE, cex\n = label_cex, adj = 0.58, line = 0.6, col = label_col)\nmtext(expression(Lower~survey~effort), side = 4, outer = TRUE, cex =\n label_cex, adj = 0.58, line = 1.8, col = label_col)\n\ndev.off()\n", "meta": {"hexsha": "4b230e9a0e61485d9f56743b2a5e4db05e66dba3", "size": 6514, "ext": "r", "lang": "R", "max_stars_repo_path": "make-eg-fig-base.r", "max_stars_repo_name": "ss3sim/ss3sim_andersonetal", "max_stars_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make-eg-fig-base.r", "max_issues_repo_name": "ss3sim/ss3sim_andersonetal", "max_issues_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "make-eg-fig-base.r", "max_forks_repo_name": "ss3sim/ss3sim_andersonetal", "max_forks_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2409638554, "max_line_length": 83, "alphanum_fraction": 0.6083819466, "num_tokens": 2643, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.34474858372159606}} {"text": "# Make aus mask\nlibrary(tidyverse)\nlibrary(sf)\nlibrary(raster)\n\ntemplate = raster(\"VPD/VPD_20181231.tif\")\n\nvalues(template)=0\naus <- read_sf(\"E:/geodata/Aus_Coastline/australia/aust.gpkg\")\naus <- aus %>% summarise()\naus <- st_buffer(aus,dist=0.1)\n\ntemp <- mask(template,aus)\ncrs(temp)<-CRS(SRS_string = \"EPSG:4326\")\nwriteRaster(temp,\"aus_mask.tif\",overwrite=TRUE)\n", "meta": {"hexsha": "29cb1c659b4d17c6115c7fd8d53fa8a600258675", "size": 364, "ext": "r", "lang": "R", "max_stars_repo_path": "make_aus_mask.r", "max_stars_repo_name": "ozjimbob/FireHub-DFMC", "max_stars_repo_head_hexsha": "478f765859d56198e3ec37ed54235ef3f695553f", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make_aus_mask.r", "max_issues_repo_name": "ozjimbob/FireHub-DFMC", "max_issues_repo_head_hexsha": "478f765859d56198e3ec37ed54235ef3f695553f", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "make_aus_mask.r", "max_forks_repo_name": "ozjimbob/FireHub-DFMC", "max_forks_repo_head_hexsha": "478f765859d56198e3ec37ed54235ef3f695553f", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.75, "max_line_length": 62, "alphanum_fraction": 0.7307692308, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5506073655352403, "lm_q1q2_score": 0.344748583721596}} {"text": "library(ggplot2)\nlibrary(reshape2)\nlibrary(plyr)\n\ndesign <- data.frame(n=c(202, 201, 204, 203), sample=c('1', '1', '2', '2'), condition=c('nonactivated', 'activated', 'nonactivated', 'activated'), stringsAsFactors=T)\nname <- mdply(data.frame(a=design$sample, b=design$condition), 'paste', sep='-')[,3]\ndesign <- cbind(design, data.frame(name=name))\n\nfct <- function(file, data, type, ytext, ltext) {\n mal <- cbind(data, design)\n mml <- melt(mal, id.vars=colnames(design))\n pdf(file)\n print(ggplot(mml, aes(x=name, y=value, fill=variable)) + xlab('Sample') + ylab(ytext) + guides(fill=guide_legend(title=ltext)) +\n\t\t geom_bar(stat=\"identity\", position=type))\n dev.off()\n}\n\nalr <- read.csv('data_aln.csv')\naln <- as.data.frame(data.matrix(alr)[,1] * data.matrix(alr)[,2:7])\nfct('data_anl.map.rel.pdf', aln, 'fill', 'Read ratio', 'Mapping class')\nfct('data_anl.map.abs.pdf', aln, 'stack', 'Read count', 'Mapping class')\n\nftc <- read.csv('data_ftc.csv')\nfct('data_anl.ftc.rel.pdf', ftc, 'fill', 'Read ratio', 'Feature class')\nfct('data_anl.ftc.abs.pdf', ftc, 'stack', 'Read count', 'Feature class')\n\nlibrary(Rsubread)\nlibrary(limma)\nlibrary(edgeR)\n\nalf <- paste('data_aln.', design$n, '.Aligned.out.bam', sep='')\nfc <- featureCounts(files=alf, nthreads=16, isPairedEnd=T, requireBothEndsMapped=T, strandSpecific=2, minMQS=40, countChimericFragments=F, \n annot.ext='/dunwich/scratch/ar756/.local/ensembl/75/Homo_sapiens.GRCh37.75.gtf', isGTFAnnotationFile=T)\nx <- DGEList(counts=fc$counts, genes=fc$annotation[,c(\"GeneID\",\"Length\")])\nisexpr <- rowSums(cpm(x) > 10) >= 2\nx <- x[isexpr,]\nd <- model.matrix(~design$condition + design$sample)\ny <- voom(x,d,plot=TRUE)\npdf('data_anl.mds.pdf')\nplotMDS(y)\ndev.off()\nfit <- eBayes(lmFit(y,d))\ntopTable(fit,coef=2)\nfc$counts[topTable(fit, coef=2, number=25)$GeneID,]\n\nc<- merge(ens, fc$counts, by.x='id', by.y='row.names')\nt<-merge(c, topTable(fit, coef=2, number=nrow(ens)), all=T, by.x='id', by.y='GeneID')\n", "meta": {"hexsha": "21ef19a0d1db709b5f61ac7177cfc3a8e668c5c2", "size": 1956, "ext": "r", "lang": "R", "max_stars_repo_path": "seq/rna/rna/analysis.r", "max_stars_repo_name": "chr1swallace/cd4-pchic", "max_stars_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "seq/rna/rna/analysis.r", "max_issues_repo_name": "chr1swallace/cd4-pchic", "max_issues_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "seq/rna/rna/analysis.r", "max_forks_repo_name": "chr1swallace/cd4-pchic", "max_forks_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.75, "max_line_length": 166, "alphanum_fraction": 0.6809815951, "num_tokens": 661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.34472384216266794}} {"text": "# 2. faza: Uvoz podatkov\nsource(\"lib/libraries.r\")\nlibrary(reshape2)\n#1. CSV TABELA\n#STOPNJA PRENASELJENOSTI STANOVANJA (2005-2016)\nsl <- locale(encoding = \"UTF-8\", decimal_mark = \".\", grouping_mark = \",\")\nslwin <- locale(encoding = \"Windows-1250\", decimal_mark = \".\", grouping_mark = \",\")\n\ndatoteka1 <- \"podatki/stopnja_pre1.csv\"\n\nstopnja_prenaseljenosti <-read_delim(datoteka1, \";\",skip = 3, trim_ws = TRUE, locale = slwin)%>%\n fill(1:2)%>% drop_na(3) %>% melt() %>% mutate(variable = parse_number(variable))\ncolnames(stopnja_prenaseljenosti)<- c(\"Kategorija\",\"Vsa gospodinjstva\",\"Spol\",\"Leto\",\"odstotek_oseb\")\nEncoding(stopnja_prenaseljenosti$Spol) <- \"UTF-8\"\ntabela2 <- stopnja_prenaseljenosti\n\n\n#melt je dal v stolpce, mutate spremeni leto v \u0161tevilo\n#trim_ws izbri\u0161e white space\n\n#2.CSV TABELA -STANOVANJSKA PRIKRAJSANOST\n\n#datoteka2 <- \"podatki/stanovanjska_prikrajsanost.csv\"\ndatoteka2 <- \"podatki/0867806Ss.csv\"\nstan.pri2 <- read_delim(datoteka2, \";\", skip = 4, n_max = 68, trim_ws = TRUE, locale=slwin)%>%\n fill(1:2) %>% drop_na(3)\nstolpci2 <- read_csv2(datoteka2, skip = 3, n_max = 1, col_names = FALSE,\n col_types = cols(.default = col_integer()))%>% t()\nstolpci3 <- read_csv2(datoteka2, skip = 4, n_max = 1, col_names = FALSE,\n locale = slwin) %>% t()\n\nEncoding(stolpci3) <- \"UTF-8\"\nstolpci <- data.frame(stolpci2, stolpci3) %>% fill(1) %>% apply(1, paste, collapse = \"\")\nstolpci[1:3] <- c(\"element\", \"starost\", \"spol\")\ncolnames(stan.pri2) <- stolpci\nstan.pri2 <- melt(stan.pri2, value.name = \"stopnja\", id.vars = 1:3, variable.name = \"stolpec\")%>%\n mutate(stolpec = parse_character(stolpec))%>%\n transmute(leto = stolpec %>% strapplyc(\"^([0-9]+)\") %>% unlist()%>% parse_number(),\n status = stolpec %>% strapplyc(\"([^0-9]+)$\") %>% unlist() %>% factor(),\n element, starost, spol, stopnja)\n\ntabela1 <- stan.pri2 \nEncoding(levels(tabela1$status)) <- \"UTF-8\" # faktor\nEncoding(tabela1$spol) <- \"UTF-8\" # znakovni stolpec\nEncoding(tabela1$element) <- \"UTF-8\"\n\n\n# 3.HTML TABELA (PRENASELJENOST PO DR\u017dAVAH)\n\npre.moski <- read_html(\"podatki/prenaseljenost_eurostat_moski.htm\") %>%\n html_node(xpath=\"//table[@class='infoData']\") %>% html_table() %>%\n melt(value.name = \"stopnja\", id.vars = \"timegeo\", variable.name = \"leto\")\n\n\npre.zenske <- read_html(\"podatki/prenaseljenost_eurostat_zenske.htm\") %>%\n html_node(xpath=\"//table[@class='infoData']\") %>% html_table() %>%\n melt(value.name = \"stopnja\", id.vars = \"timegeo\", variable.name = \"leto\")\nspoli <- c(\"moski\", \"zenske\")\npre.moski$spol <- factor(\"moski\", levels = spoli)\npre.zenske$spol <- factor(\"zenske\", levels = spoli)\n\nprenaseljenost <- rbind(pre.moski, pre.zenske) %>% mutate(stopnja = parse_number(stopnja, na = \":\"))\n\n# 4. tabela : Zadovoljstvo z \u017eivljenjem \n\nsl <- locale(encoding = \"Windows-1250\", decimal_mark = \".\", grouping_mark = \",\")\nzadovoljstvo <-\"podatki/zadovoljstvo2.csv\"\n\ntabela_zadovoljstvo <- read_csv2(zadovoljstvo, skip = 4, n_max= 7, trim_ws = TRUE, locale = sl) %>%\n drop_na(3) %>% select(-1, -2) %>% rename(ocena = X3) %>%melt(id.vars = \"ocena\", variable.name = \"leto\", value.name = \"odstotek\") %>%\n mutate(leto = parse_number(leto))\n#select(-1,-2) pobri\u0161e prvi in drugi stolpec \n\n# 5. tabela : breme stanovanjskih stro\u0161kov\nsl <- locale(encoding = \"Windows-1250\", decimal_mark = \".\", grouping_mark = \",\")\nstroski<-\"podatki/breme_stanovanjskih_stroskov.csv\"\nbreme_stanovanjskih_stroskov <- read_delim(stroski, \";\", skip = 3, n_max = 16, trim_ws = TRUE, locale=sl) %>%\n fill(1:2) %>% drop_na(3) %>%\n rename(gospodinjstvo = X1, velikost.bremena = X2)%>%\n melt(id.vars = c(\"gospodinjstvo\", \"velikost.bremena\"),\n variable.name = \"leto\", value.name = \"odstotek\") %>%\n mutate(leto = parse_number(leto))\n\n\n#5 Dele\u017e prebivalcev katerim so stanovanjski stro\u0161ki preveliko breme \n# delez<- read_html(\"http://ec.europa.eu/eurostat/tgm/web/_download/Eurostat_Table_tespm140HTMLNoDesc_bb759afd-69f5-4b14-89ed-c115cacfc96d.htm\") %>%\n# html_node(xpath=\"//table[@class='infoData']\") %>% html_table() %>%\n# melt(value.name = \"stopnja\", id.vars = \"timegeo\", variable.name = \"leto\")\n\n\ndelez<- read_html(\"podatki/ilc_lvho07a.html\") %>%\n html_node(xpath=\"//table\") %>% html_table()\ncolnames(delez) <- c(\"drzava\", 2003:2017)\ndelez <- melt(delez[-nrow(delez), ], value.name = \"stopnja\", id.vars = \"drzava\",\n variable.name = \"leto\") %>%\n mutate(leto = parse_number(leto),\n stopnja = parse_number(stopnja, na = \":\")) \n# sem gre datoteka ilc_lvho07a\nza2016 <- delez %>% filter(leto == \"2016\")\n\n", "meta": {"hexsha": "6bedc416dff56ddb5bf15148b9e759924ff77290", "size": 4562, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "ajdastare/APPR-2017-18", "max_stars_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "uvoz/uvoz.r", "max_issues_repo_name": "ajdastare/APPR-2017-18", "max_issues_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-12-14T16:18:12.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-14T09:19:16.000Z", "max_forks_repo_path": "uvoz/uvoz.r", "max_forks_repo_name": "ajdastare/APPR-2017-18", "max_forks_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.1683168317, "max_line_length": 148, "alphanum_fraction": 0.668128014, "num_tokens": 1647, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.6442251201477016, "lm_q1q2_score": 0.34472383992042627}} {"text": "# Some functions to make it easier to generate fake data!\n\nbases<-function(n){ return(ceiling(4*runif(n))) }\n\nsignal<-function(n){\n s1<-rbinom(n,1,0.5)+1\n s<-rbind(s1,rbinom(n,1,0.5)+1)\n s<-rbind(s,rbinom(n,1,0.5)+1)\n return(s)\n}\n\nsave_df<-function(df,name){ write.table(df,file=name,col.names=F,row.names=F,append=T)\n}\n\nmutate<-function(motif,r){\n n<-length(motif)\n rs<-runif(n)\n new<-bases(n)\n change<-which(rsinputId: \"SLIDERX\" for the x-axis; \"SLIDERY\" for the y-axis\n#\t\t\t>label: \"h5(\"x-axes\")\" for the x-axis; \"h5(\"y-axes\")\" for the y-axis\n#\t\t\t>min: subset the object foo (with the function \"subset\") for the minimum (use function \"min\") value of PC1 (for x-axis) or PC2 (for y-axis) when Var==input$var\n#\t\t\t>max: subset the object foo (with the function \"subset\") for the maximum (use function \"max\") value of PC1 (for x-axis) or PC2 (for y-axis) when Var==input$var\n#\t\t\t>value: the concatenated values of the two aforementioned min and max values: subset the object foo for the minimum/maximum value of PC1/PC2 when Var==input$var\n#\tc. For the scatterplot (plot) of PCA within the output$pcaplot function, change the plot parameters \"xlim\" and \"ylim\" to change with the input$SLIDERX and input$SLIDERY values.\n#2. After evaluating the plot function, add red (col = \"red\") dotted lines (lty = 2) at x=0 (h=0) & y=0 (v=0) with the function \"abline\". \n#3. Implement the percent variation explained into the plot axes. \n#\ta. First, within the output$pcaplot's renderPlot function, create an object \"pcaper\" to subset pca_percent for when pca_percent$Var==input$var\n#\tb. Within the plot function, adjust the xlab and ylab parameters. With the \"paste\" function, for xlab, add \"pcaper[2]\" as the x-axis percent variation explained: xlab=paste(\"PCA Axis 1 (\",pcaper[2],\"%)\")\n#\t\tDo the same for ylab, where instead use pcaper[3]\n#4. Use the R expressions instead to display labels. Paste the following code into your script:\n\t# output$pcaplot <- renderPlot({\n\t\t# ...\n\t\t# for(i in levels(fbar$param)){\n\t\t\t# foo2<-subset(fbar,param==i)\n\t\t\t# labs<-paste(\"c(\",paste(\"expression(\",foo2$Rexpression,\")\",sep=\"\",collapse=\",\"),\")\",sep=\"\")\n\t\t\t# text(PC2 ~ PC1, data=foo2, labels=eval(parse(text=labs)), col=cols[which(levels(fbar$param)==i)], cex=input$cexSlider)\n\t\t# }\n\t\t# ...\n\t# })\n\n#HINTS\n# 1.b \n# shinyServer(function(input, output) {\n\t# output$paramchkbxgrp <- renderUI({...})\n\t\n\t#********ADD THIS SCRIPT********\n\t# #X-axis\n\t \t# output$sliderX <-renderUI({\n\t\t\t# sliderInput(inputId=\"SLIDERX\", \n\t\t\t\t# label = h5(\"x-axes\"), \n\t\t\t\t# min = min(subset(foo,Var==input$var)[,\"PC1\"]), \n\t\t\t\t# max = max(subset(foo,Var==input$var)[,\"PC1\"]), \n\t\t\t\t# value = c(min(subset(foo,Var==input$var)[,\"PC1\"]),max(subset(foo,Var==input$var)[,\"PC1\"])))\n\t\t# })\n\t# #Y-axis\n\t \t# output$sliderY <-renderUI({\n\t\t\t# sliderInput(inputId=\"SLIDERY\", \n\t\t\t\t# label = h5(\"y-axes\"), \n\t\t\t\t# min = min(subset(foo,Var==input$var)[,\"PC2\"]), \n\t\t\t\t# max = max(subset(foo,Var==input$var)[,\"PC2\"]), \n\t\t\t\t# value = c(min(subset(foo,Var==input$var)[,\"PC2\"]),max(subset(foo,Var==input$var)[,\"PC2\"])))\n\t\t# })\n\t#*******************************\n\t\n\t# output$pcaplot <- renderPlot({...})\n# })\n\n#1.c.\t\n#\toutput$pcaplot <- renderPlot({\n#\t\t...\n#\tplot(PC2 ~ PC1, data=fbar, type=\"n\", xlab=\"PC1\", ylab=\"PC2\", xlim=input$SLIDERX, ylim=input$SLIDERY)\n#\t\t...\n# })\n#2.\t\n#\toutput$pcaplot <- renderPlot({\n#\t\t...\n#\tplot(PC2 ~ PC1, data=fbar, type=\"n\", xlab=\"PC1\", ylab=\"PC2\", xlim=input$SLIDERX, ylim=input$SLIDERY)\n#\tabline(h=0,v=0,col=\"red\",lty=2)\n#\t\t...\n# })\n#\n#3.\t\n#\toutput$pcaplot <- renderPlot({\n#\t\tfbar<-droplevels(...)\n\n#\t\tpcaper<-pca_percent[which(pca_percent$Var==input$var),]\n#\t\t\n#\t\tplot(PC2 ~ PC1, data=fbar, type=\"n\", xlim=input$SLIDERX, ylim=input$SLIDERY,\n#\t\t\txlab=paste(\"PCA Axis 1 (\",pcaper[2],\"%)\"),\n#\t\t\tylab=paste(\"PCA Axis 2 (\",pcaper[3],\"%)\"))\n#\t\t\n#\t\t...\n#\t})\n", "meta": {"hexsha": "27f33ce930e674ff12f961a478df36c7a1320ecd", "size": 6433, "ext": "r", "lang": "R", "max_stars_repo_path": "lessons/r/shiny/4/server.r", "max_stars_repo_name": "vmzhang/studyGroup", "max_stars_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 105, "max_stars_repo_stars_event_min_datetime": "2015-06-22T15:23:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T12:20:09.000Z", "max_issues_repo_path": "lessons/r/shiny/4/server.r", "max_issues_repo_name": "vmzhang/studyGroup", "max_issues_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 314, "max_issues_repo_issues_event_min_datetime": "2015-06-18T22:10:34.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T16:47:52.000Z", "max_forks_repo_path": "lessons/r/shiny/4/server.r", "max_forks_repo_name": "vmzhang/studyGroup", "max_forks_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 142, "max_forks_repo_forks_event_min_datetime": "2015-06-18T22:11:53.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-03T16:14:43.000Z", "avg_line_length": 44.986013986, "max_line_length": 278, "alphanum_fraction": 0.6632986165, "num_tokens": 2080, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.34455971855312756}} {"text": "#' Plot catch by fleet\r\n#' \r\n#' Barplot of total catch in weight with colors showing fleets.\r\n#' @param asap name of the variable that read in the asap.rdat file\r\n#' @param fleet.names names of fleets \r\n#' @param save.plots save individual plots\r\n#' @param od output directory for plots and csv files \r\n#' @param plotf type of plot to save\r\n#' @param liz.palette color definitions\r\n#' @export\r\n\r\nPlotCatchByFleet <- function(asap,fleet.names,save.plots,od,plotf,liz.palette){\r\n \r\n nfleets <- asap$parms$nfleets\r\n barplot(asap$catch.obs,xlab=\"Year\",ylab=\"Catch\",ylim=c(0,1.1*max(apply(asap$catch.obs,2,sum))),col=liz.palette[1:nfleets],space=0)\r\n if (nfleets > 1) legend('top',legend=fleet.names,horiz=T,pch=15,col=liz.palette[1:nfleets])\r\n if (save.plots==T) savePlot(paste0(od, 'catch.by.fleet.', plotf), type=plotf) \r\n \r\n # do proportions only if nfleets > 1\r\n if (nfleets > 1){\r\n catch.prop <- asap$catch.obs\r\n for (i in 1:length(catch.prop[1,])){\r\n catch.prop[,i] <- catch.prop[,i]/sum(catch.prop[,i])\r\n }\r\n barplot(catch.prop,xlab=\"Year\",ylab=\"Proportion of Catch\",ylim=c(0,1.1),col=liz.palette[1:nfleets],space=0)\r\n legend('top',legend=fleet.names,horiz=T,pch=15,col=liz.palette[1:nfleets])\r\n }\r\n if (save.plots==T) savePlot(paste0(od, 'catch.proportions.by.fleet.', plotf), type=plotf) \r\n return()\r\n}\r\n", "meta": {"hexsha": "de60c124e24c336f4efc506e7b475c8596814b99", "size": 1341, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_catch_by_fleet.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/plot_catch_by_fleet.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/plot_catch_by_fleet.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 43.2580645161, "max_line_length": 133, "alphanum_fraction": 0.6763609247, "num_tokens": 439, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032313, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3445597101099961}} {"text": "print(paste0(Sys.time(), \" --- Histogram of PTEs total spp described\"))\r\n\r\n# Tabulate statistics of years active\r\ntax <- df_describers[spp_N_1st_auth_s>=1]\r\n\r\nhighlight_auth <- tax[ns_spp_N>750]$full.name.of.describer.n\r\ntax_highlight <- tax[full.name.of.describer.n %in% highlight_auth][,c(\"ns_spp_N\", \"last.name\")]\r\n\r\nx_axis <- seq(0, max(tax$ns_spp_N), 1000)\r\nx_axis_minor <- seq(0, max(tax$ns_spp_N), 100)\r\n\r\nhist_tl_spp <- ggplot(tax, aes(x=ns_spp_N)) +\r\n geom_histogram(mapping=aes(y=..count../sum(..count..) * 100), fill='grey30', binwidth=100) + \r\n geom_vline(xintercept=median(tax$ns_spp_N), color='grey', size=.5) +\r\n xlab(\"\\nTotal number of valid species described, by PTE\") + \r\n ylab(\"Proportion of PTEs (%)\\n\") + \r\n geom_label_repel(data=tax_highlight, \r\n aes(x=ns_spp_N, y=.1, label=last.name),\r\n size=2, nudge_x=10, nudge_y=30,\r\n fontface='bold', color='black', segment.color='grey80', force=1,\r\n box.padding = unit(0.001, 'lines')) +\r\n scale_x_continuous(breaks=x_axis, minor_breaks=x_axis_minor) +\r\n # scale_y_continuous(breaks= seq(0, 12, 1), limits=c(0, 12)) +\r\n theme\r\n\r\nggsave(paste0(dir_plot, 'fig-4a.png'), hist_tl_spp, units=\"cm\", width=7, height=6, dpi=300)\r\n\r\n\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - Histogram of PTEs mean sp described per year\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- Histogram of PTEs mean sp described per year\"))\r\n\r\n# Tabulate statistics of years active\r\ntax <- df_describers[spp_N_1st_auth_s>=1]\r\n\r\n# Highlight auths from previous section\r\ntax_highlight <- tax[full.name.of.describer.n %in% highlight_auth][,c(\"ns_species_per_year_active\", \"last.name\")]\r\n\r\nhist_mean_spp <- ggplot(tax, aes(x=ns_species_per_year_active)) +\r\n geom_histogram(mapping=aes(y=..count../sum(..count..) * 100), fill='grey30', binwidth=1) + \r\n geom_vline(xintercept=median(tax$ns_species_per_year_active), color='grey', size=.5) +\r\n xlab(\"\\nMean number of species described per year, by PTE\") +\r\n ylab(\"Proportion of PTEs (%)\\n\") + \r\n scale_x_continuous(breaks= seq(0, max(tax$ns_species_per_year_active), 10)) +\r\n geom_label_repel(data=tax_highlight, \r\n aes(x=ns_species_per_year_active, y=.1, label=paste0(last.name, \" (\", round(ns_species_per_year_active, 0),\")\")), \r\n size=2, nudge_x=20, nudge_y=30,\r\n fontface='bold', color='black', segment.color='grey80', force=5,\r\n box.padding = unit(0.001, 'lines')) +\r\ntheme\r\nggsave(paste0(dir_plot, 'fig-4b.png'), hist_mean_spp, units=\"cm\", width=7, height=6, dpi=300)\r\n", "meta": {"hexsha": "b9dfb69ca6a1774ed7ad7e9551983f8754a883ea", "size": 2710, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch1/plots_main/fig-3.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch1/plots_main/fig-3.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch1/plots_main/fig-3.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.1320754717, "max_line_length": 136, "alphanum_fraction": 0.6239852399, "num_tokens": 821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34455971010999603}} {"text": "dataGaps<-function(years, minmod, maxmod, mindat, maxdat){\n gap_start <- mindat - minmod\n gap_end <- maxmod - maxdat\n actualyears <- (years-1) + minmod\n middleyears <- subset(actualyears,actualyears %in% seq(mindat,maxdat,1))\n middleyears <- sort(unique(middleyears))\n gap_middle <- max(diff(middleyears)-1)\n return(list(gap_start=gap_start,gap_end=gap_end,gap_middle=gap_middle))\n }", "meta": {"hexsha": "91386ce9ad955b34ab9d34a64100e5c24c478adc", "size": 413, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dataGaps.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/dataGaps.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-05-28T13:47:25.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-06T09:06:41.000Z", "max_forks_repo_path": "R/dataGaps.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.8888888889, "max_line_length": 77, "alphanum_fraction": 0.6973365617, "num_tokens": 121, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.34455971010999603}} {"text": "context(\"bed_fisher\")\n\nx <- tibble::tribble(\n ~ chrom, ~ start, ~ end,\n \"chr1\", 10, 20,\n \"chr1\", 30, 40,\n \"chr1\", 51, 52\n)\n\ny <- tibble::tribble(\n ~ chrom, ~ start, ~ end,\n \"chr1\", 15, 25,\n \"chr1\", 51, 52\n)\n\ngenome <- tibble::tribble(\n ~ chrom, ~ size,\n \"chr1\", 500\n)\n\ntest_that(\"fisher p.value is correct\", {\n res <- bed_fisher(x, y, genome)\n expect_equal(res$p.value, 0.003846154)\n})\n", "meta": {"hexsha": "715e624dc4c88cb35721cd90339701898e60778f", "size": 397, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_fisher.r", "max_stars_repo_name": "jimhester/valr", "max_stars_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 72, "max_stars_repo_stars_event_min_datetime": "2017-02-22T15:22:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T06:39:42.000Z", "max_issues_repo_path": "tests/testthat/test_fisher.r", "max_issues_repo_name": "jimhester/valr", "max_issues_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 178, "max_issues_repo_issues_event_min_datetime": "2016-12-06T15:42:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T00:10:30.000Z", "max_forks_repo_path": "tests/testthat/test_fisher.r", "max_forks_repo_name": "jimhester/valr", "max_forks_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 32, "max_forks_repo_forks_event_min_datetime": "2017-03-06T23:01:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-27T12:32:30.000Z", "avg_line_length": 15.88, "max_line_length": 40, "alphanum_fraction": 0.5743073048, "num_tokens": 161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3445597016668644}} {"text": "n <- 2000\ndataset <- data.frame(category = rep(LETTERS[1:5], n),\n x = rnorm(500, mean = rep(1:5, n)),\n y = rnorm(500, mean = rep(1:5, n)))\ndataset$fCategory <- factor(dataset$category)\nsubdata <- subset(dataset, category %in% c(\"A\", \"D\", \"E\"))\nsetwd('C:/Users/Isaias Prestes/Documents/Pesquisas/GitHub/R/Data')\nwrite.table(subdata, file = \"Data_IRF_20100312.dat\", sep = \"\\t\", row.names = 0)\n\n\nn <- 2000\ndataset <- data.frame(z = rnorm(500, mean = rep(1:5, n)),\n x = rnorm(500, mean = rep(1:5, n)),\n y = rnorm(500, mean = rep(1:5, n)))\nsubdata <- dataset\nsetwd('C:/Users/Isaias Prestes/Documents/Pesquisas/GitHub/R/Data')\nwrite.table(subdata, file = \"Data_IRF_20100312.dat\", sep = \"\\t\", row.names = FALSE)\n\n\n## License\n\nThis project is licensed under the terms of the MIT license, see LICENSE.", "meta": {"hexsha": "a9aa702933361759bc4e877e6a6c19780b682bad", "size": 804, "ext": "r", "lang": "R", "max_stars_repo_path": "Data/data_generator_002.r", "max_stars_repo_name": "isix/R", "max_stars_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Data/data_generator_002.r", "max_issues_repo_name": "isix/R", "max_issues_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Data/data_generator_002.r", "max_forks_repo_name": "isix/R", "max_forks_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5454545455, "max_line_length": 83, "alphanum_fraction": 0.6554726368, "num_tokens": 274, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34451893487286855}} {"text": "plot(FWEIGHT_B$WEIGHT2[FWEIGHT_B$YEAR==1978]~FWEIGHT_B$AGE[FWEIGHT_B$YEAR==1978],type=\"l\",ylim=c(0,2500))\nfor(i in 1979:2012){\n points(FWEIGHT_B$WEIGHT2[FWEIGHT_B$YEAR==i]~FWEIGHT_B$AGE[FWEIGHT_B$YEAR==i],col=(i-1978),type=\"l\")\n }\n \n \nplot(FWEIGHT_A$WEIGHT~FWEIGHT_A$AGE,col=as.numeric(FWEIGHT_B$PERIOD))\n #plot(FWEIGHT_A$pred[FWEIGHT_A$YEAR==1978]~FWEIGHT_A$AGE[FWEIGHT_A$YEAR==1978],type=\"l\",ylim=c(0,2500))\nfor(i in 1978:2012){\n points(FWEIGHT_A$pred[FWEIGHT_A$YEAR==i]~FWEIGHT_A$AGE[FWEIGHT_A$YEAR==i],col=as.numeric(FWEIGHT_A$PERIOD[FWEIGHT_A$YEAR==i]),type=\"l\")\n }\n \n for(i in 1978:2012){\n points(FWEIGHT_B$WEIGHT2[FWEIGHT_B$YEAR==i]~FWEIGHT_B$AGE[FWEIGHT_B$YEAR==i],col=as.numeric(FWEIGHT_B$PERIOD[FWEIGHT_B$YEAR==i]),type=\"l\",lwd=1)\n }\n \n \n points(data2$WEIGHT~data2$AGE,col=as.numeric(data2$PERIOD)cex=0.5,pch=1)\n\n", "meta": {"hexsha": "95080da5100da20c1bf16a00764288f707386dff", "size": 823, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/AI_Pollock/R_functions/Untitled8.r", "max_stars_repo_name": "NMFS-toolbox/AMAK", "max_stars_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-12T17:39:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-12T17:39:20.000Z", "max_issues_repo_path": "examples/AI_Pollock/R_functions/Untitled8.r", "max_issues_repo_name": "afsc-assessments/AMAK", "max_issues_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/AI_Pollock/R_functions/Untitled8.r", "max_forks_repo_name": "afsc-assessments/AMAK", "max_forks_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-05-21T18:18:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-12T04:18:42.000Z", "avg_line_length": 41.15, "max_line_length": 145, "alphanum_fraction": 0.7387606318, "num_tokens": 357, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.3445189262749576}} {"text": "source(\"simulation.R\")\nRNGkind(\"L'Ecuyer-CMRG\")\n\nseed <- 999983\ns_k <- 5000\ns_n <- 500\ns_m <- 5\n\nstephanie_5005 <- stephanie_type1(seed, s_k, s_n, s_m, L = 1000)\nsave(stephanie_5005, file = \"stephanie_5005.RData\")", "meta": {"hexsha": "29a359ee0c47487b6776344d91516f88141e0206", "size": 213, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation/others/type1/stephanie_5005.r", "max_stars_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_stars_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation/others/type1/stephanie_5005.r", "max_issues_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_issues_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation/others/type1/stephanie_5005.r", "max_forks_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_forks_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.3, "max_line_length": 64, "alphanum_fraction": 0.7042253521, "num_tokens": 92, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3445189262749576}} {"text": "# 3. faza: Vizualizacija podatkov\n\n\n\nlibrary(ggplot2)\nlibrary(ggvis)\nlibrary(dplyr)\nlibrary(rgdal)\nlibrary(mosaic)\nlibrary(maptools)\nlibrary(maps)\nlibrary(plotly)\n\nsource(file = 'lib/uvozi.zemljevid.r', encoding = 'UTF-8')\nsource('lib/libraries.r', encoding = 'UTF-8')\nsource('uvoz/uvoz.r', encoding = 'UTF-8')\n\n#struktura cestnih vozil skozi \u010das\ngraf_vozil <- ggplot(vrste_vozil, aes(x=leto, y=stevilo_vozil, fill=vrsta)) + \n geom_area()\n\n#graf stevila avtomobilov po regijah in letih\n\ngraf.st_avtomobilov <- ggplot((data=st_avtomobilov), aes(x=leto, y=st_avtomobilov, col=Regija)) + \n geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Stevilo avtomobilov na 10000 prebivalcev po regijah 2009-2018')\n\n#zemljevid razmerja med stevilom avtomobilov in umrlimi\nSlovenija <- uvozi.zemljevid(\"http://biogeo.ucdavis.edu/data/gadm2.8/shp/SVN_adm_shp.zip\",\n \"SVN_adm1\") %>% fortify()\ncolnames(Slovenija)[12] <- 'Regija'\nSlovenija$Regija <- gsub('Notranjsko-kra\u0139\u02c7ka', 'Primorsko-notranjska', Slovenija$Regija)\nSlovenija$Regija <- gsub('Spodnjeposavska', 'Posavska', Slovenija$Regija)\nSlovenija$Regija <- gsub(\"Koro\u0139\u02c7ka\", \"Koro\u0161ka\", Slovenija$Regija)\nSlovenija$Regija <- gsub(\"Gori\u0139\u02c7ka\", \"Gori\u0161ka\", Slovenija$Regija)\nSlovenija$Regija <- gsub(\"Obalno-kra\u0139\u02c7ka\", \"Obalno-kra\u0161ka\", Slovenija$Regija)\n\ngraf_slovenija <- ggplot(Slovenija, aes(x=long, y=lat, group=group, fill=Regija)) +\n geom_polygon() +\n labs(title=\"Slovenija\") +\n theme_classic()\n\n\npovprecno_stevilo <- tapply( st_avtomobilov$st_avtomobilov,st_avtomobilov$Regija, mean)\npovprecno_stevilo_avtomobilov <- data.frame(Regija=names(povprecno_stevilo), povprecno_stevilo_avtomobilov=povprecno_stevilo)\npovprecno_stevilo2 <- tapply(stevilo_umrlih$stevilo_umrlih, stevilo_umrlih$Regija, mean)\npovprecno_stevilo_umrlih <- data.frame(Regija=names(povprecno_stevilo2), povprecno_stevilo_umrlih=povprecno_stevilo2)\n\numrli_avtomobili <- povprecno_stevilo_avtomobilov %>% full_join(povprecno_stevilo_umrlih)\numrli_avtomobili$Razmerje <- (umrli_avtomobili$povprecno_stevilo_umrlih * 10000 / umrli_avtomobili$povprecno_stevilo_avtomobilov)\n\nzemljevid.razmerje_avtomobili_smrti <- ggplot() +\n geom_polygon(data = right_join(umrli_avtomobili,Slovenija, by = c('Regija')),\n aes(x = long, y = lat, group = group, fill = Razmerje))+\n xlab(\"\") + ylab(\"\") + ggtitle('Razmerje med \u0161tevilom umrlih v prometnih nesre\u010dah in \u0161tevilom avtomobilov') + \n theme(axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(), panel.background = element_blank()) + \n scale_fill_gradient(low = '#25511C', high='#2BFF00', limits = c(0.8, 1.93))\nzemljevid.razmerje_avtomobili_smrti$labels$fill <- 'Stevilo umrlih na 10.000 avtomobilov'\n\nplot(zemljevid.razmerje_avtomobili_smrti)\n\n\n#graf stevila umrlih po regijah in letih\n#graf.st_umrlih <- ggplot((data=stevilo_umrlih), aes(x=leto, y=stevilo_umrlih, col=Regija)) + \n# geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Stevilo umrlih v prometnih nesre\u010dah na 10000 prebivalcev po regijah 2009-2018')\n\n#plot(graf.st_umrlih)\n\n#povezava med stevilom umrlih v nesre\u010dah in \u0161tevilom avtomobilov po regijah\n#razmerje_avti_smrti <- stevilo_starost_smrti_migrantje$stevilo_umrlih / stevilo_starost_smrti_migrantje$st_avtomobilov\n#graf.povezava_nesrece_avtomobili <- ggplot((data=stevilo_starost_smrti_migrantje), aes(x=leto, y=(razmerje_avti_smrti), col=Regija)) + \n#geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Razmerje med stevilom umrlih v nesre\u010dah in \u0161tevilom avtomobilov po regijah 2009-2018')\n\n#graf starosti avtomobilov\n#graf.starost_avtomobilov <- ggplot((data=starost_avtomobilov), aes(x=leto, y=(starost_avtomobila), col=Regija)) + \n# geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Starost avtomobilov po regijah 2009-2018')\n#plot(graf.starost_avtomobilov)\n#graf delovne migracije\n#graf.delovni_migranti <- ggplot((data=delez_delovnih_migrantov), aes(x=leto, y=(migranti), col=Regija)) + \n# geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Delovni migranti po regijah 2009-2018')\n#plot(graf.delovni_migranti)\n\n#graf pla\u010de\n#graf.povprecne_place <- ggplot((data=povprecne_place), aes(x=leto, y=(place), col=Regija)) + \n# geom_point() + geom_line() + theme_classic() + scale_x_continuous('Leto',breaks = seq(2009, 2018, 1), limits = c(2009, 2018)) + labs(title='Povpre\u010dne pla\u010de po regijah 2009-2018')\n#plot(graf.povprecne_place)\n\n#povezana med delovnimi migracijami in stevilom avtomobilov\n\n#povezava med pla\u010dami in starostjo avtomobilov\n\ntabela_place_avti <- povprecne_place %>% full_join(starost_avtomobilov)\n\ngraf_place_avti <- ggplot(data = tabela_place_avti, aes(x=starost_avtomobila, y=place, color=Regija)) + geom_point(aes(frame=leto, ids=Regija)) + scale_x_log10()\ngraf_place_avti <- graf_place_avti + xlab('Starost avtomobila') + ylab('Povpre\u010dna mese\u010dna placa')\ngraf_place_avti <- ggplotly(graf_place_avti)\n\nplot(graf_place_avti)\n", "meta": {"hexsha": "c9776379c0da54b5aa8af4ef79bf27002c4db125", "size": 5342, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/test.r", "max_stars_repo_name": "evababnik/APPR-2019-20", "max_stars_repo_head_hexsha": "a0ae0af427210db651dd3f82eb4b7b6c35c3b0e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/test.r", "max_issues_repo_name": "evababnik/APPR-2019-20", "max_issues_repo_head_hexsha": "a0ae0af427210db651dd3f82eb4b7b6c35c3b0e9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-12-14T15:33:10.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-20T12:40:24.000Z", "max_forks_repo_path": "vizualizacija/test.r", "max_forks_repo_name": "evababnik/APPR-2019-20", "max_forks_repo_head_hexsha": "a0ae0af427210db651dd3f82eb4b7b6c35c3b0e9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.5102040816, "max_line_length": 228, "alphanum_fraction": 0.7581430176, "num_tokens": 2022, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3445189262749576}} {"text": "categories <- c(\"This program is for humans\", \"Still in Mama's arms\", \"Preschool Maniac\", \"Elementary school\", \"Middle school\", \"High school\", \"College\", \"Working for the man\", \"The Golden Years\")\nages <- c(0, 3, 5, 12, 15, 19, 23, 66, 101)\n\ncat(sapply(as.integer(readLines(tail(commandArgs(), n=1))), function(s) {\n i <- 1\n while (i <= length(ages) && s >= ages[i]) {\n i <- i + 1\n }\n if (i > length(ages)) {\n i <- 1\n }\n categories[i]\n}), sep=\"\\n\")\n", "meta": {"hexsha": "21eef971f578ff5c354739f1b4a666e06054113e", "size": 461, "ext": "r", "lang": "R", "max_stars_repo_path": "easy/age_distribution.r", "max_stars_repo_name": "IlkhamGaysin/ce-challenges", "max_stars_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-06-24T17:09:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-03T11:44:54.000Z", "max_issues_repo_path": "easy/age_distribution.r", "max_issues_repo_name": "IlkhamGaysin/ce-challenges", "max_issues_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "easy/age_distribution.r", "max_forks_repo_name": "IlkhamGaysin/ce-challenges", "max_forks_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.9285714286, "max_line_length": 196, "alphanum_fraction": 0.5856832972, "num_tokens": 159, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3445189262749576}} {"text": "\n\n#' @title makeonemodel generates a single model for use in dosingle\n#'\n#' @description makeonemodel puts together the formula needed to\n#' run a statistical model, but, different to makemodels, it\n#' only generates a single model.\n#'\n#' @param labelModel a vector of labels for each factor to be included\n#' in the analysis\n#' @param dependent the name of the dependent variable; defaults to\n#' LnCE\n#'\n#' @return a formula of 'dependent ~ labelModel components\n#' @export makeonemodel\n#'\n#' @examples\n#' labelM <- c(\"Year\",\"Vessel\",\"DepCat\",\"Zone:Month\")\n#' makeonemodel(labelM)\n#' makeonemodel(labelM,dependent=\"LnCE\")\nmakeonemodel <- function(labelModel,dependent = \"LnCE\") { # labelModel=labelM[1:i]; dependent = \"LnCE\"\n numvars <- length(labelModel)\n interterms <- grep(\":\", labelModel)\n ninter <- length(interterms)\n form <- paste0(dependent, \" ~ \", labelModel[1])\n if (numvars > 1) {\n for (i in 2:(numvars - ninter))\n form <- paste0(form, \" + \", labelModel[i])\n if (ninter > 0) for (i in interterms) {\n form <- paste0(form, \" + \", labelModel[i])\n }\n }\n form <- as.formula(form)\n return(form)\n} # end of makeonemodel\n\n#' @title dosingle conducts a standardization of indat using the inmodel\n#'\n#' @description dosingle conducts a standardization of indat using the\n#' inmodel.\n#'\n#' @param inmodel the formula used in the analysis; usually from the\n#' function makeonemodel.\n#' @param indat the data.frame containing the data to be analysed.\n#'\n#' @return a list with a similar structure to the out object, so not\n#' a outce class member but can be used with plotstand\n#' @export dosingle\ndosingle <- function(inmodel,indat) { # inmodel=mod; indat=sps2\n ans <- lm(inmodel,data=indat)\n bits <- unlist(strsplit(as.character(inmodel),\" \"))\n modcoef <- summary(ans)$coefficients\n years <- getfact(modcoef,bits[3])\n yrs <- sort(unique(indat[,bits[3]]))\n\n geo <- paste0(bits[2],\" ~ \",bits[3])\n ans2 <- lm(as.formula(geo),indat)\n modcoefG <- summary(ans2)$coefficients\n yearsG <- getfact(modcoefG,bits[3])\n Results <- cbind(\"Year\"=yearsG[,\"Scaled\"],\"optimum\"=years[,\"Scaled\"])\n rownames(Results) <- yrs\n StErr <- Results\n StErr[,1] <- yearsG[,\"SE\"]\n StErr[,2] <- years[,\"SE\"]\n optimum <- 2\n result <- list(Results=Results,StErr=StErr, Optimum=optimum,\n modelcoef=modcoef,optModel=ans,modelG=ans2,years=yrs)\n return(result)\n} # end of dosingle\n\n", "meta": {"hexsha": "7c48d5caa1268530c897b977c223ce27d0fc6e5d", "size": 2431, "ext": "r", "lang": "R", "max_stars_repo_path": "R/singleanalysis.r", "max_stars_repo_name": "haddonm/rforcpue", "max_stars_repo_head_hexsha": "34e385bbb7870ef2ead03e2089203c69cf83235c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/singleanalysis.r", "max_issues_repo_name": "haddonm/rforcpue", "max_issues_repo_head_hexsha": "34e385bbb7870ef2ead03e2089203c69cf83235c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/singleanalysis.r", "max_forks_repo_name": "haddonm/rforcpue", "max_forks_repo_head_hexsha": "34e385bbb7870ef2ead03e2089203c69cf83235c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.2394366197, "max_line_length": 102, "alphanum_fraction": 0.6746194981, "num_tokens": 711, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3445189262749576}} {"text": "library(tidyverse)\r\nlibrary(sf)\r\nlibrary(maptools)\r\nlibrary(cartogram)\r\nlibrary(patchwork)\r\nlibrary(showtext)\r\nlibrary(janitor)\r\nlibrary(colorspace)\r\nlibrary(CoordinateCleaner)\r\nlibrary(gggibbous)\r\nlibrary(ggthemes)\r\nlibrary(ggtext)\r\nlibrary(here)\r\n\r\ncaptured_vs_farmed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/capture-fisheries-vs-aquaculture.csv') %>% \r\n clean_names() %>% \r\n filter(year == 2018)\r\n\r\nconsumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/fish-and-seafood-consumption-per-capita.csv') %>% \r\n clean_names() %>% \r\n filter(year == 2017) %>% \r\n select(code, fish_seafood_food_supply_quantity_kg_capita_yr_fao_2020) %>% \r\n rename(fish_seafood_capita = fish_seafood_food_supply_quantity_kg_capita_yr_fao_2020)\r\n\r\nfont_add_google(\"Eczar\", \"Eczar\")\r\nfont_add_google(\"Playfair Display\", \"Playfair Display\")\r\nfont_add_google(\"Bitter\", \"Bitter\")\r\nfont_add_google(\"Roboto Mono\", \"Roboto Mono\")\r\n\r\nshowtext_auto()\r\n\r\ntheme_set(theme_map(base_family = \"Playfair Display\"))\r\ntheme_update(\r\n plot.title = element_markdown(family = \"Bitter\", size = 32, hjust = 0.5),\r\n plot.subtitle = element_markdown(family = \"Montserrat\", color = \"grey80\", size = 18, face = \"bold\", hjust = 0.5, margin = margin(b = 6)),\r\n plot.caption = element_markdown(family = \"Bitter\", color = \"grey60\", size = 9, hjust = 1, lineheight = 1.2),\r\n legend.position = \"bottom\",\r\n legend.justification = c(0.5, 0),\r\n legend.title = element_text(family = \"Bitter\", color = \"grey60\", face = \"bold\", size = 14),\r\n legend.text = element_text(family = \"Roboto Mono\", color = \"grey60\", size = 10)\r\n)\r\n\r\noptions(scipen=10000)\r\n\r\ndata(\"wrld_simpl\")\r\ndata(\"countryref\")\r\n\r\ncountry <- countryref %>% \r\n filter(is.na(source) & type == \"country\") %>% \r\n group_by(name) %>% \r\n slice(1) %>% \r\n ungroup()\r\n\r\ndf <- captured_vs_farmed %>% \r\n mutate(aquaculture_production_metric_tons = if_else(is.na(aquaculture_production_metric_tons), 0, aquaculture_production_metric_tons),\r\n capture_fisheries_production_metric_tons = if_else(is.na(capture_fisheries_production_metric_tons), 0, capture_fisheries_production_metric_tons)) %>% \r\n left_join(consumption, by = c(\"code\"=\"code\")) %>% \r\n mutate(total_production = aquaculture_production_metric_tons + capture_fisheries_production_metric_tons,\r\n prop_aquaculture = aquaculture_production_metric_tons / total_production,\r\n prop_fisheries = capture_fisheries_production_metric_tons / total_production) %>% \r\n drop_na() %>% \r\n mutate(code = if_else(code == \"UK\", \"GB\", code)) %>% \r\n mutate(max = max(fish_seafood_capita, na.rm = T),\r\n label_leg = if_else(entity == \"Iceland\", \"With 90.7 kg per person \\n per year, Icelandic people \\n consume far more seafood than any other country\", NA_character_))\r\n\r\ndf_final <-\r\n wrld_simpl %>%\r\n st_as_sf() %>%\r\n st_transform(crs = \"+proj=robin\") %>% \r\n mutate(code = as.character(ISO3)) %>%\r\n left_join(df, by =\"code\") %>% \r\n filter(NAME != \"Antarctica\")\r\n\r\ncountry_df <- country %>% \r\n mutate(code = as.character(iso3)) %>%\r\n left_join(df, by =\"code\") %>% \r\n filter(!is.na(total_production))\r\n\r\ncentroid_country <- st_as_sf(country_df, coords = c(\"centroid.lon\", \"centroid.lat\"), \r\n crs = 4326, agr = \"constant\")\r\n\r\noptions(scipen=1000000)\r\n\r\nmap <- ggplot(df_final) +\r\n geom_sf(aes(geometry = geometry,\r\n fill = fish_seafood_capita),\r\n color = \"grey80\", size = 0.1) +\r\n geom_point(data = centroid_country, \r\n aes(geometry = geometry, size = total_production),\r\n stat = StatSfCoordinates,\r\n fun.geometry = sf::st_centroid,\r\n color = lighten(\"white\", .65, space = \"combined\"),\r\n fill = \"transparent\",\r\n stroke = .3) +\r\n geom_moon(data = centroid_country, \r\n aes(geometry = geometry, ratio = prop_fisheries, size = total_production), \r\n stat = StatSfCoordinates,\r\n fun.geometry = sf::st_centroid,\r\n fill = \"white\", \r\n color = \"#855464\",\r\n stroke = .3,\r\n right = FALSE) +\r\n geom_moon(data = centroid_country, \r\n aes(geometry = geometry, ratio = prop_aquaculture, size = total_production), \r\n stat = StatSfCoordinates,\r\n fun.geometry = sf::st_centroid,\r\n fill = \"#855464\", \r\n color = \"#855464\",\r\n stroke = .3) +\r\n coord_sf(clip = \"on\", expand = TRUE) +\r\n rcartocolor::scale_fill_carto_c(palette = \"Darkmint\",\r\n direction = 1,\r\n limits = c(0, 100), ## max percent overall\r\n breaks = seq(0, 100, by = 25),\r\n labels = glue::glue(\"{seq(0, 100, by = 25)}kg\")) +\r\n scale_size(range = c(1, 20), guide = F) + \r\n guides(fill = guide_colorbar(barheight = unit(2.3, units = \"mm\"), \r\n barwidth = unit(230, units = \"mm\"),\r\n direction = \"horizontal\",\r\n title = \"Fish and seafood consumption per capita (kg)\",\r\n ticks.colour = \"grey20\",\r\n title.position = \"top\",\r\n label.position = \"top\",\r\n title.hjust = 0.5)) +\r\n theme(legend.direction = \"horizontal\")\r\n\r\nseafood_supply <- df %>% \r\n mutate(total_seafood_capita = sum(fish_seafood_capita, na.rm = TRUE)) %>% \r\n arrange(desc(fish_seafood_capita)) %>% \r\n mutate(fivefirst = if_else(row_number() <= 5, 1, 0)) %>% \r\n group_by(fivefirst) %>% \r\n summarize(prop_five = sum(fish_seafood_capita, na.rm = TRUE)/total_seafood_capita*100) %>% \r\n slice(n())\r\n\r\ndf_leg <- \r\n tribble(\r\n ~x, ~y, ~label, ~size, ~prop_aquaculture, ~prop_fisheries,\r\n .3, .18, \"40,000,000 tons\", 15, 0.8168214, 0.1831786,\r\n .35, .12, \"20,000,000 tons\", 8, 0.8168214, 0.1831786,\r\n .40, .075, \"10,000,000 tons\", 4, 0.8168214, 0.1831786,\r\n .43, .045, \"1.000,000 tons\", .4, 0.8168214, 0.1831786\r\n )\r\n\r\ndf_labs <-\r\n tribble(\r\n ~x, ~y, ~label, ~color,\r\n .06, .6, \"**China as a reference**
Production of more than
80 Millions of tons over 2018\", \"A\",\r\n .05, .3, \"**Total production of seafood**
per country\", \"A\",\r\n .32, .65, \"**Proportion of Aquaculture production**
(farming of aquatic organisms)\", \"A\",\r\n .5, .27, \"**Proportion of Capture fisheries production**
(fishing and catching wild fish and shellfish)\", \"grey88\"\r\n )\r\n\r\ndf_lines <-\r\n tribble(\r\n ~x, ~y, ~xend, ~yend, ~curv, ~color,\r\n .08, .57, .2, .43, .42, \"A\", ## *China as a reference\r\n .07, .33, .162, .39, .20, \"A\", ## production of seafood\r\n .34, .61, .21, .4, .38, \"A\", ## AquacultureCapture fisheries\r\n .32, .26, .170, .385, -.42, \"grey88\" ## Capture fisheries\r\n )\r\n\r\nlegend <-\r\n df_final %>%\r\n mutate(max = max(\r\n total_production,\r\n na.rm = T\r\n )) %>%\r\n filter(entity == \"China\") %>%\r\n ggplot(aes(x = .2,\r\n y = .4)) +\r\n # legend moon facet\r\n geom_point(\r\n size = 20,\r\n color = lighten(\"#855464\", .65, space = \"combined\"),\r\n shape = 21,\r\n fill = \"transparent\",\r\n stroke = 1.1\r\n ) +\r\n geom_moon(\r\n aes(ratio = prop_aquaculture), \r\n size = 20,\r\n fill = \"#855464\", \r\n color = \"#855464\",\r\n stroke = .3\r\n ) +\r\n geom_moon(\r\n aes(ratio = prop_fisheries), \r\n size = 20,\r\n fill = \"white\", \r\n color = \"#855464\",\r\n stroke = .3,\r\n right = FALSE\r\n ) +\r\n geom_richtext(\r\n data = df_labs,\r\n aes(x = x, y = y, label = label, color = color),\r\n family = \"sans\",\r\n size = 4,\r\n lineheight = .9,\r\n fill = NA,\r\n label.color = NA\r\n ) +\r\n geom_curve(\r\n data = df_lines,\r\n aes(\r\n x = x, xend = xend,\r\n y = y, yend = yend,\r\n color = color\r\n ),\r\n curvature = -.43\r\n ) +\r\n # legend moon facet (smallest ones)\r\n geom_point(data = df_leg, aes(x = x,\r\n y = y, size = size),\r\n #size = 15,\r\n color = lighten(\"#855464\", .65, space = \"combined\"),\r\n shape = 21,\r\n fill = \"transparent\",\r\n stroke = 1.1\r\n ) +\r\n geom_moon(data = df_leg, \r\n aes(x = x, y = y, size = size, ratio = prop_aquaculture),\r\n fill = \"#855464\", \r\n color = \"#855464\",\r\n stroke = .3\r\n ) +\r\n geom_moon(data = df_leg, \r\n aes(x = x, y = y, size = size, ratio = prop_fisheries), \r\n fill = \"white\", \r\n color = \"#855464\",\r\n stroke = .3,\r\n right = FALSE\r\n ) +\r\n geom_text(data = df_leg, \r\n aes(x = x-.035, y = y, label = label),\r\n size = 2.5,\r\n color = \"grey20\",\r\n family = \"Playfair Display\",\r\n hjust = 1) +\r\n coord_cartesian(clip = \"off\") +\r\n scale_x_continuous(limits = c(-0.5, 1.5)) +\r\n scale_y_continuous(limits = c(0, 1)) +\r\n scale_color_manual(\r\n values = c(\"#855464\", \"grey20\"),\r\n guide = F\r\n ) +\r\n scale_size_area(max_size = 20/1.3, guide = F) +\r\n theme(plot.margin = margin(0, 0, 0, 0))\r\n\r\n \r\n# Insert legend inside the map\r\nmap + annotation_custom(\r\n grob = ggplotGrob(legend),\r\n xmin = -23000000,\r\n xmax = 2300000,\r\n ymin = -6000000,\r\n ymax = 7000000\r\n) + \r\nlabs(title =\"The future of food from the sea in the World (Marine Capture Vs Aquaculture)\",\r\n subtitle = \"
While **China** is by far the major fish producer with more than 80,000,000 of tons in 2018, **Iceland** has the largest seafood consumption in the World in terms of kg/per capita.
The top 5 countries (others are Maldives, Kiribati, Hong Kong, and Malaysia) account for 11.89% of it.\",\r\n caption = \"
*Visualization by Guillaume Abgrall \u2022 Data by OurWorldinData.org*\")\r\n \r\npath <- here::here(\"global_fishing\")\r\nggsave(glue::glue(\"{path}.pdf\"), width = 24, height = 16, device = cairo_pdf)\r\npdftools::pdf_convert(pdf = glue::glue(\"{path}.pdf\"), \r\n filenames = glue::glue(\"{path}.png\"),\r\n format = \"png\", dpi = 450)", "meta": {"hexsha": "12bf044e2d721949e69433efe3a1936a93c6bd06", "size": 10293, "ext": "r", "lang": "R", "max_stars_repo_path": "2021/2021-Week42/fishmap.r", "max_stars_repo_name": "guigui351/tidytuesday", "max_stars_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2021-11-15T08:28:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-18T03:19:45.000Z", "max_issues_repo_path": "2021/2021-Week42/fishmap.r", "max_issues_repo_name": "jvilltolentino/tidytuesday", "max_issues_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2021/2021-Week42/fishmap.r", "max_forks_repo_name": "jvilltolentino/tidytuesday", "max_forks_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-11-15T12:00:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-15T17:28:01.000Z", "avg_line_length": 38.5505617978, "max_line_length": 446, "alphanum_fraction": 0.5907898572, "num_tokens": 3047, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5926665999540697, "lm_q1q2_score": 0.3445189262749575}} {"text": "# This script is intended to use corrected data originated in a names dataset from the Argentinian Goverment\r\n# https://datos.gob.ar/dataset/otros-nombres-personas-fisicas\r\n# BSD 3-Clause License\r\n# Copyright (c) 2021, CalmRott7915 (a pseudonym, author can be reached on Reddit under u/CalmRott7915 or on Github)\r\n# Redistributions of source code must retain the above copyright notice and the attached BSD-3 Licence\r\n\r\n\r\noptions(encoding = \"UTF-8\")\r\nlibrary(data.table)\r\nlibrary(ggplot2)\r\nlibrary(plotly)\r\nlibrary(cluster)\r\n\r\n\r\n######################################################################\r\n# TODO: Estandarizar nombre de variables #\r\n# Eliminar ppm y usar cantidades directamente #\r\n######################################################################\r\n\r\n\r\n# Carga los archivos como data.table ####\r\n\r\nNC <- fread(\"Nombres_Completos.csv\",\r\n encoding=\"UTF-8\",key=c(\"Nombre\",\"Yr\"))\r\n\r\nNS <- fread(\"Nombres_Simples.csv\",\r\n encoding=\"UTF-8\",key=\"Nombre\")\r\n\r\n\r\n###################################################\r\n# Variables comunes a varios ejemplos #\r\n###################################################\r\n\r\n# A\u00f1os m\u00ednimos y m\u00e1ximos usando data.table\r\nYrMin <- NC[,min(Yr)]\r\nYrMax <- NC[,max(Yr)]\r\n\r\n# Total de Inscriptos y Nombres simples en a\u00f1os y clase\r\nTI <- NC[,.(YrTotal=sum(N)),keyby=Yr]\r\nTI[,YrSimple:=NS[,.(YrSimple=sum(N)),keyby=Yr][,YrSimple]]\r\nTI[,ClassTotal:=frollsum(YrTotal,n=2,align=\"left\",fill=TI[J(YrMax),YrTotal])]\r\nTI[,ClassSimple:=frollsum(YrSimple,n=2,align=\"left\",fill=TI[J(YrMax),YrSimple])]\r\n\r\n\r\n# Partes por mill\u00f3n de Nombres simples\r\nNSp <- NS[,.(NameYrTotal=sum(N)),keyby=\"Nombre,Yr\"]\r\nNSp[,`:=`(ppm = NameYrTotal*1e6/TI[J(NSp[,Yr]),YrTotal],\r\n YrTotal =TI[J(NSp[,Yr]),YrTotal])]\r\n\r\n# Partes por mill\u00f3n de Nombres Completos\r\nNC[,`:=`(ppm=N*1e6/TI[J(NC[,Yr]),YrTotal],\r\n YrTotal =TI[J(NC[,Yr]),YrTotal])]\r\n\r\n#####################################################\r\n# Clases: El a\u00f1o Yr + el A\u00f1o siguiente: #\r\n# Ejemplo Yr= 1980 => Clase Julio 1980 a Junio 1981 #\r\n#####################################################\r\n\r\n# Nombres Completos\r\n\r\n # Para los nombres que no aparecen el a\u00f1o anterior, se crea una entrada con valores en Cero para la cantidad\r\n NCNoAnt <- NC[Yr%in%(YrMin+1):YrMax&\r\n (Nombre!=shift(Nombre,type=\"lag\")|(Yr-1)!=shift(Yr,type=\"lag\"))]\r\n NCNoAnt[,`:=`(ID=0,Yr=Yr-1,N=0,ppm=0)]\r\n NCNoAnt[,YrTotal:=TI[J(NCNoAnt[,Yr]),YrTotal]]\r\n \r\n NC <- rbindlist(list(NC,NCNoAnt))\r\n rm(NCNoAnt)\r\n setkey(NC,Nombre,Yr)\r\n \r\n # Copia los datos del a\u00f1o siguiente\r\n NC[,`:=`(YrTotal_1 = shift(YrTotal,type=\"lead\",fill=0),\r\n N_1 = shift(N,type=\"lead\",fill=0),\r\n Nombre_1 = shift(Nombre,type=\"lead\",fill=0),\r\n Yr_1 = shift(Yr,type=\"lead\",fill=YrMax)\r\n )]\r\n \r\n # Los nombres que no aparecen al a\u00f1o siguiente s\u00f3lo se promedia con el total de los dos a\u00f1os como denominador\r\n NC[,NoNext:=(Nombre!=Nombre_1|Yr_1!=Yr+1)]\r\n \r\n NC[NoNext==TRUE, `:=`(YrTotal_1 = TI[J(pmin(NC[NoNext==TRUE,Yr]+1,YrMax)),YrTotal],\r\n N_1 = 0)]\r\n \r\n \r\n # Calcula los totales de la Clase\r\n NC[,`:=`(ClassTotal = YrTotal+YrTotal_1,\r\n NClass = N +N_1)]\r\n \r\n # Elimina las Columnas Intermedias y calcula ppm de la Clase\r\n NC[,`:=`(YrTotal_1=NULL,N_1=NULL,Nombre_1=NULL,Yr_1=NULL,NoNext=NULL,\r\n cppm =NClass*1e6/ClassTotal )]\r\n\r\n setkey(NC,ID)\r\n\r\n# Nombres Simples\r\n\r\n # Para los nombres que no aparecen el a\u00f1o anterior, se crea una entrada con valores en Cero para la cantidad\r\n NSNoAnt <- NSp[Yr%in%(YrMin+1):YrMax&\r\n (Nombre!=shift(Nombre,type=\"lag\")|(Yr-1)!=shift(Yr,type=\"lag\"))]\r\n NSNoAnt[,`:=`(Yr=Yr-1,NameYrTotal=0,ppm=0)]\r\n NSNoAnt[,YrTotal:=TI[J(NSNoAnt[,Yr]),YrTotal]]\r\n \r\n NSp <- rbindlist(list(NSp,NSNoAnt))\r\n rm(NSNoAnt)\r\n setkey(NSp,Nombre,Yr)\r\n \r\n \r\n # Copia los datos del a\u00f1o siguiente\r\n NSp[,`:=`(YrTotal_1 = shift(YrTotal,type=\"lead\",fill=0),\r\n NameYrTotal_1 = shift(NameYrTotal,type=\"lead\",fill=0),\r\n Nombre_1 = shift(Nombre,type=\"lead\",fill=0),\r\n Yr_1 = shift(Yr,type=\"lead\",fill=YrMax)\r\n )]\r\n \r\n # Los nombres que no aparecen al a\u00f1o siguiente s\u00f3lo se promedia con el total de los dos a\u00f1os\r\n NSp[,NoNext:=(Nombre!=Nombre_1|Yr_1!=Yr+1)]\r\n \r\n NSp[NoNext==TRUE, `:=`(YrTotal_1 = TI[J(pmin(NSp[NoNext==TRUE,Yr]+1,YrMax)),YrTotal],\r\n NameYrTotal_1 = 0)]\r\n \r\n # Calcula los totales de la Clase\r\n NSp[,`:=`(ClassTotal = YrTotal+YrTotal_1,\r\n NameClassTotal = NameYrTotal+NameYrTotal_1)]\r\n \r\n # Elimina las Columnas Intermedias y calcula ppm de la Clase\r\n NSp[,`:=`(YrTotal_1=NULL,NameYrTotal_1=NULL,Nombre_1=NULL,Yr_1=NULL,NoNext=NULL,\r\n cppm =NameClassTotal*1e6/ClassTotal )]\r\n \r\n\r\n#####################################################\r\n# Un Conjunto de Nombres Simples (Suma de Todos) ####\r\n#####################################################\r\n\r\nListaNombres=c(\"Samanta\",\"Samantha\")\r\n\r\nAN <- NS[ListaNombres]\r\n\r\nA <- AN[,.(Total=sum(N)),Yr]\r\nCN <- NC[J(AN$ID),.(N = sum(N)),by=Nombre]\r\n\r\nCN <- setorder(CN,-N)\r\n\r\n#Plot\r\nCaption <- paste(head(CN,15)[,Nombre], collapse=\", \")\r\nCaption <- strwrap(Caption,width=80)\r\nCaption <- paste(Caption, collapse = \"\\n\")\r\nCaption <- paste(\"Mas Populares:\\n\", Caption,sep=\"\")\r\n\r\nTitle <- paste(\"Evoluci\u00f3n de los Nombres: \", paste(ListaNombres,collapse=\" o \"),sep=\"\")\r\n\r\nG <- ggplot(A)+\r\n geom_point(aes(x=Yr, y=Total),color=\"red\",size=2)+\r\n geom_line(aes(x=Yr,y=Total),color=\"blue\",size=1)+\r\n theme_minimal()+\r\n scale_x_continuous(breaks=seq(1920,2020,10))+\r\n theme(plot.caption = element_text(hjust=0)) +\r\n labs(x=\"A\u00f1o\",\r\n y=\"Total\",\r\n title=Title,\r\n caption=Caption)\r\nplot(G)\r\n\r\n# Limpieza\r\nrm(A,AN,CN,ListaNombres)\r\n\r\n\r\n#####################################################\r\n# Un Conjunto de Nombres Exactos (Suma de Todos) ####\r\n#####################################################\r\n\r\nListaNombres = c(\"Cristina Elizabeth\",\"N\u00e9stor Carlos\")\r\nB <- NC[Nombre%in%ListaNombres,.(Total=sum(N)),Yr]\r\n\r\n#Plot\r\nG <- ggplot(B)+\r\n geom_point(aes(x=Yr, y=Total),color=\"red\",size=2)+\r\n geom_line(aes(x=Yr,y=Total),color=\"blue\",size=1)+\r\n theme_minimal()+\r\n scale_x_continuous(breaks=seq(1920,2020,10))+\r\n labs(x=\"A\u00f1o\",y=\"Total\",\r\n title=paste(\"Evoluci\u00f3n de los Nombres: \", \r\n paste(ListaNombres,collapse=\"/\"),sep=\"\"))\r\nplot(G)\r\n\r\n#Limpieza\r\nrm(ListaNombres,B)\r\n\r\n\r\n#############################################################################\r\n# Nombres completos que contengan uno o m\u00e1s nombres de A y uno o m\u00e1s de B####\r\n#############################################################################\r\nA <- c(\"Raul\",\"Ra\u00fal\")\r\nB <- c(\"Ricardo\")\r\n\r\nAN <- NS[A]\r\nsetkey(AN,ID)\r\nBN <- NS[B]\r\nsetkey(BN,ID)\r\n\r\n#Por defecto es una intersecci\u00f3n y se une sobre las claves y con solo C <- merge(AN,BN) funciona bien.\r\nC <- merge(AN,BN, by=\"ID\",all=FALSE)\r\n\r\nCN <- NC[J(C$ID),.(N = sum(N)),by=Nombre]\r\nCN <- setorder(CN,-N)\r\n\r\nD <- C[,.(Total=sum(N.x)),.(Yr=Yr.x)]\r\n\r\n\r\n#Plot\r\n\r\nCaption <- paste(head(CN,15)[,Nombre], collapse=\", \")\r\nCaption <- strwrap(Caption,width=80)\r\nCaption <- paste(Caption, collapse = \"\\n\")\r\nCaption <- paste(\"Mas Populares:\\n\", Caption,sep=\"\")\r\n\r\nTitle <- paste(\"Evoluci\u00f3n de los Nombres: \", \r\n paste(paste(A,collapse=\" o \"),\r\n paste(B,collapse=\" o \"),\r\n sep=\" + \"))\r\n\r\nG <- ggplot(D)+\r\n geom_point(aes(x=Yr, y=Total), color=\"red\", size=2)+\r\n geom_line(aes(x=Yr,y=Total),color=\"blue\",size=1)+\r\n theme_minimal()+\r\n scale_x_continuous(breaks=seq(1920,2020,10))+\r\n theme(plot.caption = element_text(hjust=0)) +\r\n labs(x=\"A\u00f1o\",\r\n y=\"Total\",\r\n title=Title,\r\n caption=Caption)\r\nplot(G)\r\n\r\n# Limpieza\r\nrm (A, AN, B, BN, C, CN, D, Caption, Title)\r\n\r\n##########################################\r\n# 20 Nombres m\u00e1s populares de un a\u00f1o ####\r\n##########################################\r\n\r\nA\u00f1o = 1997\r\nNCi <- NSp[Yr==A\u00f1o]\r\nNCi <- setorder(NCi,-NameYrTotal)[1:20,]\r\n# Exportar sacando comentario de abajo\r\n# write.csv(NCi, file = Popular20Yr.csv, row.names=FALSE,fileEncoding = \"UTF-8\")\r\n\r\nG <- ggplot(NCi)+\r\n geom_bar(aes(x=reorder(Nombre,-NameYrTotal),\r\n y=NameYrTotal,\r\n fill=NameYrTotal),\r\n stat=\"identity\")+\r\n labs(x=\"Nombre\", y=\"Cantidad\",\r\n title=paste(\"20 Nombres m\u00e1s populares del a\u00f1o \",A\u00f1o,sep=\"\"))+\r\n theme_minimal()+\r\n theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))\r\nplot(G)\r\n \r\nrm(A\u00f1o, NCi)\r\n\r\n\r\n###########################\r\n# Total de Inscriptos ####\r\n###########################\r\n\r\nG <- ggplot(TI) + \r\n geom_point(aes(x=Yr,y=YrTotal/1e6))+\r\n geom_line(aes(x=Yr,y=YrTotal/1e6))+\r\n theme_minimal()+\r\n labs(title=\"N\u00famero de nombres inscriptos por a\u00f1o\",\r\n x=\"A\u00f1o\",\r\n y=\"Millones\") +\r\n scale_x_continuous(breaks = seq(1920,2020,10))\r\nplot(G)\r\n\r\n\r\n##########################################################\r\n# Funci\u00f3n X Nombres simples m\u00e1s populares de cada a\u00f1o ####\r\n##########################################################\r\n\r\nPopularX <- function(N_Popular, exclude=c(\"Del\",\"Los\",\"De\")){\r\n \r\n # Selecci\u00f3n de los 20 mayores de cada a\u00f1o que no est\u00e9n en los excluidos \r\n XPop <- NSp[!Nombre%in%exclude,\r\n last(.SD[order(NameYrTotal)],N_Popular),\r\n by=Yr]\r\n\r\n # Se recalculan todos los que algunas vez fueron top de un a\u00f1o para todos los a\u00f1os\r\n XPop <- NSp[Nombre%chin%XPop[,Nombre]]\r\n \r\n}\r\n \r\n \r\n \r\n############################################################ \r\n# Tabla de 20 Nombres M\u00e1s Populares por a\u00f1o para exportar #\r\n############################################################\r\n# NCtw <- dcast(PopularX(20),Yr~Nombre,value.var=\"NameYrTotal\", fill=0)\r\n# write.csv(NCtw,file=\"Popular20History.csv\",row.names = FALSE,fileEncoding = \"UTF-8\")\r\n\r\n\r\n############################################################\r\n# Probabilidad de haber sido de una clase particular #\r\n# Si tuviste compa\u00f1eros llamados .... #\r\n############################################################\r\n\r\n\r\nCompletos <- c(\"Nestor Fabio Damian\")\r\n# Primero las fracciones de todos los nombres como columnas, despu\u00e9s los multiplica y se queda con la multiplicaci\u00f3n \r\n# y al final pone en el listado de total de a\u00f1os siendo cero donde no hay ninguno\r\n\r\nif (length(Completos)==0){\r\n ProbC <- data.table(Yr=YrMin:YrMax,ProbC=1,key=\"Yr\")\r\n}else{\r\n ProbC <- dcast(NC[Nombre%in%Completos],Yr~Nombre,fill=0, fun=sum,value.var=\"NClass\")\r\n ProbC <- ProbC[,ProbC:=apply(.SD,1,prod),keyby=Yr][,.(Yr,ProbC)]\r\n ProbC <- merge(TI,ProbC,all.x=TRUE,by=\"Yr\")[is.na(ProbC),ProbC:=0]\r\n ProbC[,ProbC:=ProbC*ClassTotal^(-length(Completos))]\r\n}\r\n\r\nSimples <- c(\"Gabriela\", \"Silvina\", \"Daniel\", \"Ver\u00f3nica\",\r\n \"Laura\", \"Cecilia\", \"Nestor\", \"Ricardo\", \"Karina\", \"Carina\" )\r\n\r\nif (length(Simples)==0){\r\n ProbS <- data.table(Yr=YrMin:YrMax,ProbS=1,key=\"Yr\")\r\n}else{\r\n ProbS <- dcast(NSp[Nombre%in%Simples],Yr~Nombre,fill=0, fun=sum,value.var=\"NameClassTotal\")\r\n ProbS <- ProbS[,ProbS:=apply(.SD,1,prod),keyby=Yr][,.(Yr,ProbS)]\r\n ProbS <- merge(TI,ProbS,all.x=TRUE,by=\"Yr\")[is.na(ProbS),ProbS:=0]\r\n ProbS[,ProbS:=ProbS*ClassSimple^(-length(Simples))]\r\n}\r\n\r\n\r\n## Bayes\r\n\r\n###########################################################\r\n# TODO: Hay un error conceptual en dividir por total de #\r\n# nombres simples para calcular la probabilidad y despues #\r\n# ponderlos por el total de nombres completos. El efecto #\r\n# es m\u00ednimo porque la relaci\u00f3n Nombres Simples / Nombre #\r\n# completo se mantiene hist\u00f3ricamente en alrededor de 2 #\r\n###########################################################\r\n\r\n#P (A|B) = ProbT\r\nProbT <- merge(ProbS,\r\n ProbC[,`:=`(YrTotal =NULL,\r\n YrSimple=NULL,\r\n ClassTotal=NULL,\r\n ClassSimple=NULL)\r\n ],\r\n by=\"Yr\")[,ProbT:=ProbS*ProbC]\r\n\r\n#P(A) \u00f3 probabilidad de haber tenido esa combinaci\u00f3n en el total de los a\u00f1os\r\nPA <- sum(ProbT[,ProbT*ClassTotal/sum(.SD$ClassTotal)])\r\n\r\n# P(B) \u00f3 probabilidad de que un nombre al azar sea de una clase en particular\r\nPB <- ProbT[,ClassTotal/sum(.SD$ClassTotal)]\r\n\r\n# Probabilidades Porcentuales\r\nPBA <- ProbT[,.(Yr=Yr,PBA=ProbT*PB*100/PA)]\r\n\r\n# Imprime los a\u00f1os que suman el 90%\r\nsetorder(PBA,PBA)[,Acum:=cumsum(.SD$PBA)]\r\nsetorder(PBA,Yr)\r\nToPrint <- PBA[Acum>10,paste(\"Clase \",Yr,\"-\",Yr+1,\":\",signif(PBA,digits=3),\"%\\n\",sep=\"\")]\r\nToPrint <- paste(\"90% de Probabilidades en los a\u00f1os:\\n\", paste(ToPrint,collapse=\"\"),sep=\"\")\r\ncat(ToPrint)\r\n\r\n#Limpieza\r\nrm(Completos, Simples, ProbC, ProbS,ProbT, PA, PB, PBA, ToPrint)\r\n\r\n###################################################################\r\n# Gr\u00e1fico de evoluci\u00f3n de los n\u00f3mbres m\u00e1s populares #\r\n# Hecho en Plotly para poder hacer zoom y mirar con detalle #\r\n###################################################################\r\n\r\nNCht <- PopularX(5)\r\n\r\n# Para suavizar el gr\u00e1fico\r\nNCht[,ppms:=frollmean(ppm,c(1,2,rep(3,.N-2)), adaptive=TRUE), by=Nombre]\r\n\r\n\r\n# En plotly\r\nplot_ly(NCht,\r\n x=~Yr,\r\n y=~ppms,\r\n type=\"scatter\",\r\n mode=\"lines\",\r\n line=list(shape=\"spline\"),\r\n name=~Nombre,\r\n showlegend=FALSE,\r\n text = ~paste(\"Nombre: \", Nombre,\r\n \"\\nA\u00f1o:\", Yr,\"\\n\",\r\n format(round(ppm), nsmall=0,scientific=FALSE,big.mark=\" \"),\r\n \" por mill\u00f3n \",\r\n sep=\"\"),\r\n hoverinfo=\"all\") %>% \r\n layout(title=\"Evoluci\u00f3n de los nombres m\u00e1s populares\",\r\n yaxis = list(title=\"por mill\u00f3n de nacimientos\",\r\n fixedrange=FALSE),\r\n xaxis = list(title=\"A\u00f1o\",\r\n fixedrange=FALSE)) \r\n#Limpieza\r\nrm(NCht)\r\n\r\n\r\n######################################################\r\n# Clusters de Nombres que var\u00edan Juntos ####\r\n# Para los 100 Nombres m\u00e1s populares de cada a\u00f1o ####\r\n######################################################\r\n\r\n\r\nN100 <- PopularX(100)\r\nH <- as.matrix(dcast(N100,Yr~Nombre,value.var=\"NameYrTotal\", fill=0)[,Yr:=NULL])\r\n\r\n# Se utiliza la 1-correlaci\u00f3n como la distancia para el clustering \r\n# Correlaci\u00f3n -1 es distancia m\u00e1xima (2)\r\n# Correlaci\u00f3n 1 es distancia m\u00ednima (0)\r\ndistances <- as.dist(1-cor(H,H))\r\n\r\n# Divide en N Clusters\r\nCluster <- pam(distances,11)\r\nClusterTable <- data.table(Nombre =names(Cluster$clustering),\r\n Cluster=Cluster$clustering)\r\nsetkey(ClusterTable,Nombre)\r\n\r\n\r\n# Agrega el n\u00famero de cluster a cada entrada de N100\r\nN100[,Cluster:=ClusterTable[N100[,Nombre],Cluster]]\r\n\r\n#Plot\r\n\r\n# Esta secci\u00f3n es para generar la lista de nombre con un ancho de 30\r\n# Y que esa lista sea el \u00edndice de cada grupo (no el ID)\r\n\r\nClusterNames <- N100[,.(Nombres=list(.SD[,Nombre])),keyby=Cluster]\r\nClusterNames[,Nombres:=lapply(Nombres,unique)]\r\nClusterNames[,Nombres:=lapply(Nombres,paste,collapse=\" \")]\r\nClusterNames[,Nombres:=lapply(Nombres,strwrap,width=30)]\r\nClusterNames[,Nombres:=sapply(Nombres,paste,collapse=\"\\n\")] # Un Vector por cluster\r\n\r\nN100 <- N100[,.(ppmc=sum(.SD[,ppm])),keyby=\"Cluster,Yr\"]\r\nN100[,Text:=ClusterNames[J(N100[,Cluster]),Nombres]]\r\nN100[,Text:=as.factor(Text)]\r\n\r\n\r\nplot_ly(N100,\r\n x=~Yr,\r\n y=~ppmc,\r\n type=\"scatter\",\r\n mode=\"lines\",\r\n line=list(shape=\"spline\"),\r\n name=~Text,\r\n showlegend=FALSE,\r\n hovertemplate =\"Nombres\",\r\n hoverinfo=\"all\") %>% \r\n layout(title=\"Nombres por \u00c9poca en Argentina\",\r\n yaxis = list(title=\"Por mill\u00f3n de nacimientos\",\r\n fixedrange=FALSE),\r\n xaxis = list(title=\"A\u00f1o\",\r\n fixedrange=FALSE)) \r\n\r\n#Limpieza\r\nrm(N100,H,distances,Cluster,ClusterTable,ClusterNames)\r\n", "meta": {"hexsha": "2fee075809569c8bfe05bede26867ca718f7905b", "size": 16104, "ext": "r", "lang": "R", "max_stars_repo_path": "Nombres.r", "max_stars_repo_name": "CalmRott7915/Nombres_Argentina", "max_stars_repo_head_hexsha": "e2b004050ec029e6a6cd6ac6fb105a26d9cf6864", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Nombres.r", "max_issues_repo_name": "CalmRott7915/Nombres_Argentina", "max_issues_repo_head_hexsha": "e2b004050ec029e6a6cd6ac6fb105a26d9cf6864", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Nombres.r", "max_forks_repo_name": "CalmRott7915/Nombres_Argentina", "max_forks_repo_head_hexsha": "e2b004050ec029e6a6cd6ac6fb105a26d9cf6864", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7818574514, "max_line_length": 118, "alphanum_fraction": 0.5456408346, "num_tokens": 4581, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030761371502, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34451891767704657}} {"text": "context(\"readnet.r\")\n\ntest_that(\"Basic test\", {\n\n dat0 <- data.frame(\n ego = c(\"a\", \"b\", \"c\"),\n alter1 = c(\"b\", NA, NA),\n alter2 = NA,\n time = 0\n )\n\n dat1 <- data.frame(\n ego = c(\"a\", \"b\", \"c\"),\n alter1 = c(\"b\", \"a\", NA),\n alter2 = c(\"c\", NA, NA),\n time = 1\n )\n\n\n ans0 <- structure(list(\n `0` = structure(\n list(\n edgelist = structure(1:2, .Dim = 1:2, .Dimnames = list(NULL, c(\"ego\", \"alter\"))),\n labels = c(\"a\", \"b\", \"c\"),\n data = structure(\n list(\n ego = c(\"a\", \"b\", \"c\"),\n alter1 = c(\"b\", NA, NA),\n alter2 = c(NA_character_,\n NA_character_, NA_character_),\n time = c(\"0\", \"0\", \"0\")\n ),\n .Names = c(\"ego\",\n \"alter1\", \"alter2\", \"time\"),\n row.names = c(NA, 3L),\n class = \"data.frame\"\n ),\n graph.attrs=NULL\n ),\n class = \"rn_edgelist\"\n ),\n `1` = structure(\n list(\n edgelist = structure(\n c(1L, 2L, 1L, 2L, 1L, 3L),\n .Dim = c(3L,\n 2L),\n .Dimnames = list(NULL, c(\"ego\", \"alter\"))\n ),\n labels = c(\"a\",\n \"b\", \"c\"),\n data = structure(\n list(\n ego = c(\"a\", \"b\", \"c\"),\n alter1 = c(\"b\", \"a\", NA),\n alter2 = c(\"c\", NA, NA),\n time = c(\"1\",\n \"1\", \"1\")\n ),\n .Names = c(\"ego\", \"alter1\", \"alter2\", \"time\"),\n row.names = 4:6,\n class = \"data.frame\"\n ),\n graph.attrs = NULL\n ),\n class = \"rn_edgelist\"\n )\n ), .Names = c(\"0\", \"1\"))\n\n ans <- survey_to_edgelist(rbind(dat0, dat1), \"ego\", c(\"alter1\", \"alter2\"), time = \"time\")\n expect_equal(ans, ans0)\n})\n\ntest_that(\"igraph\", {\n\n set.seed(12)\n ans0 <- igraph::barabasi.game(10)\n igraph::vertex_attr(ans0, \"name\") <- as.character(1L:10L)\n igraph::V(ans0)$x <- runif(10)\n\n ans1 <- as_igraph(as_rn_edgelist(ans0))\n\n expect_equal(igraph::as_adj(ans0), igraph::as_adj(ans1))\n expect_equal(igraph::graph_attr(ans0), igraph::graph_attr(ans1))\n expect_equal(igraph::edge_attr(ans0), igraph::edge_attr(ans1))\n expect_equal(igraph::vertex_attr(ans0), igraph::vertex_attr(ans1))\n\n})\n", "meta": {"hexsha": "4b92b3d82fc807f8b99d513c54daab4a1b5b82c0", "size": 2273, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-readnet.r", "max_stars_repo_name": "USCCANA/readnet", "max_stars_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test-readnet.r", "max_issues_repo_name": "USCCANA/readnet", "max_issues_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-12-01T18:50:02.000Z", "max_issues_repo_issues_event_max_datetime": "2017-12-04T18:09:38.000Z", "max_forks_repo_path": "tests/testthat/test-readnet.r", "max_forks_repo_name": "USCCANA/readnet", "max_forks_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5393258427, "max_line_length": 91, "alphanum_fraction": 0.4544654641, "num_tokens": 720, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3444373862850612}} {"text": "library(data.table)\nlibrary(ggplot2)\nlibrary(patchwork)\n\nsource('../utils.r')\nsource('../config.r')\n\nold_age_path <- file.path(\n \"..\", \"figure-2\", \"inputs\",\n \"post_predictive_analysis_all_models_combined_imaging_non_imaging_age.csv\")\n\ndf_old <- fread(old_age_path)\ndf_old <- df_old[evaluation == \"Generalization\"]\n\n\nnew_age_path <- file.path(\n \"inputs\",\n \"post_predictive_analysis_all_models_combined_imaging_non_imaging_age_at_assessment.csv\")\n\ndf_new <- fread(new_age_path)\ndf_new <- df_new[evaluation == \"Generalization\"]\n\n\ncommon_eid <- intersect(unique(df_new$eid), unique(df_old$eid))\n\nsprintf(\n \"There are %d subjects in common betwee AGE at 1st visit and AGE at scan time\",\n length(common_eid))\n\ndf_old <- df_old[df_old$eid %in% common_eid][order(eid)]\ndf_new <- df_new[df_new$eid %in% common_eid][order(eid)]\n\nstopifnot(all(df_new$eid == df_old$eid))\n\nold_pred <- df_old[,.(predicted = mean(predicted), true = unique(true)), .(eid)]\n\nnew_pred <- df_new[,.(predicted = mean(predicted), true = unique(true)), .(eid)]\n\n\n\ndf_age_comp <- data.frame(old_age_pred = old_pred$predicted,\n new_age_pred = new_pred$predicted,\n old_age_true = old_pred$true,\n new_age_true = new_pred$true)\n\nc1 <- cor.test(old_pred$true, new_pred$true)\nc2 <- cor.test(old_pred$predicted, new_pred$predicted)\n\ncolor <- color_cats$`sky blue`\n\nget_corr_label <- function(c_res)\n{\n bquote(r == .(round(c_res$estimate, 3))*\n \", \"~italic(p)~.(format.pval(c_res$p.val)))\n}\n\n(\n fig1 <- ggplot(data = df_age_comp,\n mapping = aes(x = (new_age_true - old_age_true))) +\n geom_histogram(binwidth = 0.9, bins = 9, fill = color, color = color) +\n my_theme +\n coord_cartesian(xlim = c(0, 13)) +\n labs(x = \"difference in chronological age:\\nscan time - at first visit\",\n y = \"count\") +\n annotate('text', x = 4, y = 1400, label = get_corr_label(c1), size = 5)\n)\n\n(\n fig2 <- ggplot(data = df_age_comp,\n mapping = aes(x = old_age_pred, y = new_age_pred)) +\n geom_point(size = 0.3, alpha = 0.3, color = color) +\n my_theme +\n labs(x = \"predicted age (first visit)\", y = \"predicted age (scan time)\") +\n annotate('text', x = 59, y = 55, label = get_corr_label(c2), size = 5)\n)\n\n(fig1 | fig2) + plot_annotation(tag_levels = 'A') -> big_fig\n\nmy_ggsave('fig_sup_2', big_fig, width = 10, height = 5)\n", "meta": {"hexsha": "1a04252514192def6fb42b97187341e8b0dd9219", "size": 2409, "ext": "r", "lang": "R", "max_stars_repo_path": "figure-S2/plot_figure_S2.r", "max_stars_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_stars_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-05-03T13:33:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T11:09:38.000Z", "max_issues_repo_path": "figure-S2/plot_figure_S2.r", "max_issues_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_issues_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-09T11:05:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T11:05:01.000Z", "max_forks_repo_path": "figure-S2/plot_figure_S2.r", "max_forks_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_forks_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-12-29T20:16:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-29T20:16:45.000Z", "avg_line_length": 30.1125, "max_line_length": 91, "alphanum_fraction": 0.6537982565, "num_tokens": 684, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3444373858962672}} {"text": "#' Maps a function along an array preserving its structure\n#'\n#' @param X An n-dimensional array\n#' @param along Along which axis to apply the function\n#' @param FUN A function that maps a vector to the same length or a scalar\n#' @param subsets Whether to apply \\code{FUN} along the whole axis or subsets thereof\n#' @param drop Remove unused dimensions after mapping; default: TRUE\n#' @param ... Other arguments passed to \\code{FUN}\n#' @return An array where \\code{FUN} has been applied\n#' @export\nmap = function(X, along, FUN, subsets=base::rep(1,dim(X)[along]), drop=TRUE, ...) {\n subsets = as.factor(subsets)\n if (length(subsets) != dim(X)[along])\n stop(\"'subsets' needs to be same length as array along axis\")\n if (NA %in% subsets) {\n warning(\"NA found in subsets, those will be dropped\")\n X = subset(X, !is.na(subsets), along=along)\n subsets = subsets[!is.na(subsets)]\n }\n lsubsets = as.character(unique(subsets)) # levels() changes order!\n nsubsets = length(lsubsets)\n\n # create a list to index X with each subset\n subs_idx = base::rep(list(base::rep(list(TRUE), length(dim(X)))), nsubsets)\n for (i in 1:nsubsets)\n subs_idx[[i]][[along]] = (subsets==lsubsets[i])\n\n # for each subset, call map_one\n pb = pb(nsubsets)\n resultList = lapply(subs_idx, function(f) {\n re = map_one(subset(X, f), along, FUN, drop=FALSE, ...)\n pb$tick()\n re\n })\n\n # assemble results together\n Y = bind(resultList, along=along)\n if (dim(Y)[along] == dim(X)[along])\n base::dimnames(Y)[[along]] = base::dimnames(X)[[along]]\n else if (dim(Y)[along] == nsubsets)\n base::dimnames(Y)[[along]] = lsubsets\n\n drop_if(Y, drop)\n}\n\n#' Apply function that preserves order of dimensions\n#'\n#' @param X An n-dimensional array\n#' @param along Along which axis to apply the function\n#' @param FUN A function that maps a vector to the same length or a scalar\n#' @param pb progress bar object\n#' @param drop Remove unused dimensions after mapping; default: TRUE\n#' @param ... Arguments passed to the function\n#' @return An array where \\code{FUN} has been applied\nmap_one = function(X, along, FUN, pb, drop=TRUE, ...) {\n if (is.vector(X) || length(dim(X))==1)\n return(FUN(X, ...))\n\n preserveAxes = c(1:length(dim(X)))[-along]\n Y = as.array(apply(X, preserveAxes, FUN, ...))\n if (length(dim(Y)) < length(dim(X)))\n Y = array(Y, dim=c(1, dim(Y)), dimnames=c(list(NULL), dimnames(Y)))\n\n Y = aperm(Y, base::match(seq_along(dim(Y)), c(along, preserveAxes)))\n drop_if(Y, drop)\n}\n", "meta": {"hexsha": "fa6f4cd103277b5b569838068d1936a261c761ca", "size": 2659, "ext": "r", "lang": "R", "max_stars_repo_path": "R/map.r", "max_stars_repo_name": "cran/narray", "max_stars_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2016-12-07T16:03:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-20T09:10:42.000Z", "max_issues_repo_path": "R/map.r", "max_issues_repo_name": "cran/narray", "max_issues_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2016-11-21T09:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-11T16:08:02.000Z", "max_forks_repo_path": "R/map.r", "max_forks_repo_name": "cran/narray", "max_forks_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-06-21T03:17:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-21T03:17:21.000Z", "avg_line_length": 39.6865671642, "max_line_length": 86, "alphanum_fraction": 0.6250470102, "num_tokens": 724, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3444373776110126}} {"text": "# get_density_palette.r\n#\n# Copyright (c) 2020 VIB (Belgium) & Babraham Institute (United Kingdom)\n#\n# Software written by Carlos P. Roca, as research funded by the European Union.\n#\n# This software may be modified and distributed under the terms of the MIT\n# license. See the LICENSE file for details.\n\n\n# Returns a good color palette for a given density distribution.\n\nget.density.palette <- function( dens, asp )\n{\n rainbow.palette <- colorRampPalette( asp$density.palette.base.color )(\n asp$density.palette.base.n )\n\n dens.range <- range( dens, na.rm = TRUE )\n\n dens.grid <- seq( dens.range[ 1 ], dens.range[ 2 ],\n length.out = asp$density.palette.n )\n\n density.palette.idx <-\n round( ecdf( dens )( dens.grid ) * asp$density.palette.base.n )\n\n rainbow.palette[ density.palette.idx ]\n}\n\n", "meta": {"hexsha": "7094d22188594223b58ef6228735b413db2d30ba", "size": 826, "ext": "r", "lang": "R", "max_stars_repo_path": "R/get_density_palette.r", "max_stars_repo_name": "DillonHammill/autospill", "max_stars_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-07T21:48:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-12T03:00:59.000Z", "max_issues_repo_path": "R/get_density_palette.r", "max_issues_repo_name": "DillonHammill/autospill", "max_issues_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-09-10T08:08:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-29T23:41:00.000Z", "max_forks_repo_path": "R/get_density_palette.r", "max_forks_repo_name": "DillonHammill/autospill", "max_forks_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-09-05T14:15:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-12T14:36:42.000Z", "avg_line_length": 28.4827586207, "max_line_length": 79, "alphanum_fraction": 0.686440678, "num_tokens": 203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.34443737761101256}} {"text": "library(tidyverse)\nlibrary(viridis)\nlibrary(urbnmapr)\n\n# Add data\nirs.in <- read.csv(\"douglas-irs-in.csv\", header=TRUE, sep=\",\", quote=\"\\'\", colClass=c(\"character\", \"character\", \"numeric\", \"numeric\", \"numeric\"))\n# Join with urbnmapr\nirs.in.data <- left_join(irs.in, counties, by = \"county_fips\")\n\n#################################################\n# Pretty Breaks\npretty.breaks.in <- c(40, 60, 90, 135)\n# find min and max values of pop growth\nminVal.in <- min(irs.in.data$exemptions, na.rm = T)\nmaxVal.in <- max(irs.in.data$exemptions, na.rm = T)\n# compute pop growth labels\nlabels.in <- c()\nbrks.in <- c(minVal.in, pretty.breaks.in, maxVal.in)\n# round the labels (actually, only the extremes)\nlabels <- c()\nfor(idx in 1:length(brks.in)){\n labels.in <- c(labels.in, paste0(round(brks.in[idx], 2),\n \" \u2013 \",\n round(brks.in[idx + 1], 2)))\n}\n# Minus one label to remove the odd ending one\nlabels.in <- labels.in[1:length(labels.in)-1]\n\n# Create new variable for fill\nirs.in.data$brks.in <- cut(irs.in.data$exemptions,\n breaks = brks.in,\n labels = labels.in,\n include.lowest = T)\n\n#################################################\n# IRS In Migration\np <- ggplot() +\n # County Map\n geom_polygon(data = irs.in.data, mapping = aes(x = long, y = lat, group = group,\n fill = irs.in.data$brks.in), color = alpha(\"white\", 1 / 2), size = 0.2) +\n # State Map\n geom_polygon(data = urbnmapr::states, mapping = aes(long, lat, group = group),\n fill = NA, color = \"#7f7f7f\", size = 0.25, alpha=0.5) +\n# Projection\n coord_map(projection = \"polyconic\") +\n scale_fill_viridis(\n option = \"viridis\",\n name = \"In Migration, 2015-2016\",\n discrete = T,\n direction = 1,\n begin=0.1,\n #end=0.9,\n guide = guide_legend(\n keyheight = unit(5, units = \"mm\"),\n title.position = 'top',\n reverse = F\n )) +\n # Theming\n theme_minimal(base_family = \"Open Sans Condensed Light\") +\n theme(\n legend.position = \"bottom\",\n legend.text.align = 0,\n legend.title.align = 0.5,\n plot.margin = unit(c(.5,.5,.2,.5), \"cm\")) +\n theme(\n axis.line = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks = element_blank(),\n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n ) +\n theme(plot.title=element_text(family=\"Open Sans Condensed Bold\", margin=margin(b=15))) +\n theme(plot.subtitle=element_text(family=\"Open Sans Condensed Light Italic\")) +\n theme(plot.margin=unit(rep(0.5, 4), \"cm\")) +\n labs(x = \"\",\n y = \"\",\n title = \"Douglas County largely gains population locally\",\n subtitle = \"IRS Sources of Income Migration Data. Number of Exemptions. Douglas County, NE. 2015-2016\",\n caption = \"Author: Chris Goodman (@cbgoodman), Data: Internal Revenue Service SOI Migration Data\")\n\nggsave(plot=p, \"douglas-irs-in.png\", width=(5*2), height=(4*2), units=\"in\", dpi=\"retina\")\n", "meta": {"hexsha": "e8ab76286602b978b9dbbfb3e0d87f0695b5bd4b", "size": 2969, "ext": "r", "lang": "R", "max_stars_repo_path": "douglas-irs-in.r", "max_stars_repo_name": "cbgoodman/douglas-co-migration", "max_stars_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-12-12T16:48:25.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-12T16:48:25.000Z", "max_issues_repo_path": "douglas-irs-in.r", "max_issues_repo_name": "cbgoodman/douglas-co-migration", "max_issues_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "douglas-irs-in.r", "max_forks_repo_name": "cbgoodman/douglas-co-migration", "max_forks_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.9294117647, "max_line_length": 145, "alphanum_fraction": 0.6241158639, "num_tokens": 868, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.34443737761101256}} {"text": "\r\n\r\n######################################################################\r\n# PCCAT main menu #\r\n######################################################################\r\n\r\n###################################################################### \r\n# PCCAT is an add-on module of R. To use it please first change the #\r\n# path where all its modules locate. Then source the .pccat file. #\r\n###################################################################### \r\n\r\nsource(\"functions.r\") \r\n\r\n## basic R packages\r\nlibrary(stats);library(MASS);library(lattice);library(cluster);\r\n\r\n.checkpkg()\r\n\r\n\r\noptions(warn=1, keep.source=T)\r\n#, prompt=\"pccat: \")\r\n\r\n############## store the path for pccat ########\r\n.WD <- getwd()\r\n\r\n############### Welcome to use PCCAT ###########################\r\ncat(\"\\n\")\r\ncat(sprintf(\" %s\",date()), \"\\n\", \"\\n\",\r\nsprintf(\"***************************\"),\"\\n\",\r\nsprintf(\" Welcome to PCCAT! \"), \"\\n\", \r\nsprintf(\"***************************\"), \"\\n\", \"\\n\",\r\nsprintf(\"Please press Enter key to continue...\"), \"\\n\")\r\nreadline()\r\n##################################################################\r\n\r\ncat(\"The objects in the current workspace are:\\n\")\r\nprint(ls())\r\ncat(\"\\n which one do you choose as the data frame?\\n\")\r\ncat(\"1. I want to type the name of the object\\n\")\r\ncat(\"2. I want to use the training data set in PCCAT\\n\")\r\ncat(\"3. I want to import from csv/txt file\\n\")\r\n\r\n.ans <- .scanf(1,c(1:3))\r\n\r\nif(.ans == 1){\r\ncat(\"\\n please type the name:\\n\")\r\n.ansnam <- scan(\"\",character(0),nlines=1,quiet=T)\r\nwhile(all(.ansnam != ls())) {\r\n if(.ansnam[1] == 'quit') stop('Running stopped')\r\n cat(\"object not found. type again:\\n\"); \t \r\n .ansnam <- scan(\"\",character(0),nlines=1,quiet=T) }\r\ntrs <- get(.ansnam)\r\n}\r\n\r\nif(.ans == 2){\r\n cat(\"select the data set:\\n\")\r\n cat(\"1.TRSRSB_W_ND_0 2.TRSRSB_W_ND_05 3.T_BASF_Adjusted \\n\")\r\n cat(\"4.T_Trenton_Adjusted 5.T_Trenton_Given 6.T_Trenton_Qualifiers \\n\")\r\n .chos <- .scanf(1,c(1:6))\r\n if(.chos == 1) load(\"TRSRSB_W_ND_0\") #20*18\r\n if(.chos == 2) load(\"TRSRSB_W_ND_05\") #20*18\r\n if(.chos == 3) load(\"T_BASF_Adjusted\") #232*73\r\n\t if(.chos == 4) load(\"T_Trenton_Adjusted\") #232*69 \r\n if(.chos == 5) load(\"T_Trenton_Given\") #255*69\r\n if(.chos == 6) load(\"T_Trenton_Qualifiers\") #232*138 \r\n }\r\n\r\nif(.ans == 3){\r\ncat(\"\\n please type the path and filename, e.g.: C:/projects/MDEQ/pccat/example.txt \\n\")\r\n.ansnam2 <- scan(\"\",character(0),nlines=1,quiet=T)\r\ncat(\"\\n Is the first row the variable name? y/n\\n\")\r\n.ans1 <- .scanf(2)\r\n.ans2 <- (.ans1=='y')\r\nif(length(grep('.txt',.ansnam2))) trs <- read.table(.ansnam2, header=.ans2)\r\nif(length(grep('.csv',.ansnam2))) trs <- read.csv(.ansnam2, header=.ans2)\r\n}\r\n\r\nsource('data_preprocessing.r')\r\n\r\n\r\n.staypccat <- 1\r\nwhile(.staypccat == 1){\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\ncat(\"********************* pccat ********************\\n\")\r\ncat(\"\\n 1. visualization of data\\n\")\r\ncat(\"\\n 2. principle component analysis\\n\")\r\ncat(\"\\n 3. clustering analysis\\n\")\r\ncat(\"\\n 4. exit\\n\")\r\n.ans <- .scanf(1,c(1:4))\r\nif(.ans == 1) source('visualization.r')\r\nif(.ans == 2) source('principle component analysis.r')\r\nif(.ans == 3) source('clustering.r')\r\nif(.ans == 4) {.staypccat <- 0; cat(\"Thank you for using PCCAT!\\n\\n\")}\r\n}\r\n\r\n#rm(list=ls(all.names=T)[substr(ls(all.names=T),1,1)=='.'])\r\noptions(warn=1, keep.source=T)\r\n#, prompt=\"> \")\r\n", "meta": {"hexsha": "57ebbd1238af9314184e753a58b982287f6f1dd0", "size": 3564, "ext": "r", "lang": "R", "max_stars_repo_path": "pccat.r", "max_stars_repo_name": "jsanket123/PCCAT", "max_stars_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "pccat.r", "max_issues_repo_name": "jsanket123/PCCAT", "max_issues_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pccat.r", "max_forks_repo_name": "jsanket123/PCCAT", "max_forks_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-01-17T16:45:59.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-17T16:45:59.000Z", "avg_line_length": 35.64, "max_line_length": 89, "alphanum_fraction": 0.4938271605, "num_tokens": 1010, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.34443737761101256}} {"text": "# 3. faza: Vizualizacija podatkov\n\n##rentabilnost, cene in najemnine skozi cas glede na tip prostora\n\n\ntip = \"Stanovanje\"\n\ngraf.skozi.cas = function(tip, aspekt){\n if (tip != \"Vse\"){\n najemnine.skozi.cas = tabela.najemnin %>% filter(tip.prostora == tip) %>% group_by(leto.posla) %>%\n mutate(povprecna.najemnina = mean(mesecna.najemnina / povrsina)) %>% select(leto.posla, povprecna.najemnina) %>% distinct()\n \n cene.skozi.cas = tabela.nakupov %>% filter(tip.prostora == tip) %>% group_by(leto.posla) %>%\n mutate(povprecna.cena = mean(prodajna.cena / povrsina)) %>% select(leto.posla, povprecna.cena) %>% distinct()\n \n cas = cene.skozi.cas %>% left_join(najemnine.skozi.cas)\n cas = cas %>% mutate(\"rentabilnost (%)\" = 1200 * povprecna.najemnina / povprecna.cena)\n if (aspekt == \"rentabilnost\"){\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = `rentabilnost (%)`)) +\n geom_line() + labs(x = \"Leto\", y = \"Rentabilnost v %\", title = sprintf(\"Rentabilnost skozi \u010das (%s)\", tip))\n \n } else if (aspekt == \"najemnina\"){\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = povprecna.najemnina)) + \n geom_line() + labs(x = \"Leto\", y = bquote(\"Najemnina na \"~ m^2), title = sprintf(\"Vi\u0161ina najemnin skozi \u010das (%s)\", tip)) \n } else {\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = povprecna.cena)) + \n geom_line() + labs(x = \"Leto\", y = bquote(\"Cena na \"~m^2), title = sprintf(\"Cena skozi \u010das (%s)\", tip))\n }\n \n } else {\n najemnine.skozi.cas = tabela.najemnin %>% group_by(leto.posla) %>%\n mutate(povprecna.najemnina = mean(mesecna.najemnina / povrsina)) %>% select(leto.posla, povprecna.najemnina) %>% distinct()\n \n cene.skozi.cas = tabela.nakupov %>% group_by(leto.posla) %>%\n mutate(povprecna.cena = mean(prodajna.cena / povrsina)) %>% select(leto.posla, povprecna.cena) %>% distinct()\n \n cas = cene.skozi.cas %>% left_join(najemnine.skozi.cas)\n cas = cas %>% mutate(\"rentabilnost (%)\" = 1200 * povprecna.najemnina / povprecna.cena)\n \n if (aspekt == \"rentabilnost\"){\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = `rentabilnost (%)`)) +\n geom_line() + labs(x = \"Leto\", y = \"Rentabilnost v %\", title = sprintf(\"Rentabilnost skozi \u010das (%s)\", tip))\n \n } else if (aspekt == \"najemnina\"){\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = povprecna.najemnina)) + \n geom_line() + labs(x = \"Leto\", y = bquote(\"Najemnina na \"~ m^2), title = sprintf(\"Vi\u0161ina najemnin skozi \u010das (%s)\", tip)) \n } else {\n graf = cas %>% ggplot(mapping = aes(x = leto.posla, y = povprecna.cena)) + \n geom_line() + labs(x = \"Leto\", y = bquote(\"Cena na \"~m^2), title = sprintf(\"Cena skozi \u010das (%s)\", tip))\n }\n \n }\n return(graf)\n}\n\n\n\n# zemljevid rentabilnosti glede na statisti\u010dne regije\n\n\nsource(\"lib/uvozi.zemljevid.r\")\n\nslo.regije.sp = uvozi.zemljevid(\"http://kt.ijs.si/~ljupco/lectures/appr/zemljevidi/si/gadm36_SVN_shp.zip\", \"gadm36_SVN_1\", mapa = \"zemljevidi\", encoding=\"UTF-8\")\n\nslo.regije.map = slo.regije.sp %>% spTransform(CRS(\"+proj=longlat +datum=WGS84\"))\n\nslo.regije.poligoni = fortify(slo.regije.map)\n\n\nslo.regije.poligoni = slo.regije.poligoni %>% select(\n regija = NAME_1, long, lat, order, hole, piece, id, group\n ) %>%\n mutate(\n regija = replace(regija, regija == \"Notranjsko-kra\u0161ka\", \"Primorsko-notranjska\"),\n regija = replace(regija, regija == \"Spodnjeposavska\", \"Posavska\")\n )\n\nslo.regije.poligoni %>% write_csv(\"podatki/regije-poligoni.csv\")\n\n\nslo.regije.centroidi = slo.regije.map %>% coordinates %>% as.data.frame\ncolnames(slo.regije.centroidi) = c(\"long\", \"lat\")\n\nslo.regije.centroidi = slo.regije.centroidi %>% rownames_to_column() %>%\n left_join(\n rownames_to_column(slo.regije.map@data),\n by = \"rowname\"\n ) %>%\n select(\n regija = NAME_1, long, lat\n ) %>%\n mutate(\n regija = replace(regija, regija == \"Notranjsko-kra\u0161ka\", \"Primorsko-notranjska\"),\n regija = replace(regija, regija == \"Spodnjeposavska\", \"Posavska\")\n )\nslo.regije.centroidi %>% write_csv(\"podatki/regije-centroidi.csv\")\n\n#Pripadnost ob\u010din regijam\n\nobcine.regije = read_csv(\"podatki/obcine-regije (1).csv\")\n\nobcine.regije = obcine.regije %>% mutate(obcina = str_to_sentence(obcina)) %>%\n mutate(obcina = str_replace(obcina, \"([:alpha:]*)/[:alpha:]*\", \"\\\\1\")) %>%\n mutate(obcina = str_replace(obcina, \"([:alpha:]*)\\\\s-\\\\s([:alpha:]*)\", \"\\\\1-\\\\2\"))\n\npovprecna.najemnina.obcina = tabela.najemnin %>% group_by(obcina) %>%\n mutate(povprecna.najemnina = mean(mesecna.najemnina / povrsina)) %>% \n select(obcina, povprecna.najemnina) %>% distinct()\n\npovprecna.cena.obcina = tabela.nakupov %>% group_by(obcina) %>%\n mutate(povprecna.cena = mean(prodajna.cena / povrsina)) %>%\n select(obcina, povprecna.cena) %>% distinct()\n\nregije.s.pripadnostjo = left_join(povprecna.najemnina.obcina, obcine.regije) %>% \n left_join(povprecna.cena.obcina) %>% filter(obcina != \"Slovenija\") %>%\n group_by(regija) %>% mutate(povprecna.najemnina.regija = mean(povprecna.najemnina)) %>%\n mutate(povprecna.cena.regija = mean(povprecna.cena)) %>%\n select(regija, povprecna.najemnina = povprecna.najemnina.regija, povprecna.cena = povprecna.cena.regija) %>%\n distinct() %>% na.omit() %>% mutate(\"povprecna.rentabilnost\" = 1200 * povprecna.najemnina / povprecna.cena) \n\nregije.zemljevid.rentabilnost = left_join(regije.s.pripadnostjo, slo.regije.poligoni) %>%\n ggplot() + \n geom_polygon(\n mapping = aes(long, lat, group = group, fill = povprecna.rentabilnost),\n color = \"grey\"\n ) + \n labs(fill = \"Rentabilnost v %\") + \n coord_map() +\n theme_classic() +\n theme(\n axis.line = element_blank(),\n axis.ticks = element_blank(),\n axis.text = element_blank(),\n axis.title = element_blank()\n ) +\n geom_text(\n data = slo.regije.centroidi,\n mapping = aes(x = long, y = lat, label = regija),\n size = 3,\n fontface =\"bold.italic\",\n color = \"black\"\n ) +\n labs(title = \"Rentabilnost glede na regijo\")\n\nregije.zemljevid.najemnine = left_join(regije.s.pripadnostjo, slo.regije.poligoni) %>%\n ggplot() + \n geom_polygon(\n mapping = aes(long, lat, group = group, fill = povprecna.najemnina),\n color = \"grey\"\n ) + \n labs(fill = bquote(\"Najemnina na\"~ m^2)) + \n coord_map() +\n theme_classic() +\n theme(\n axis.line = element_blank(),\n axis.ticks = element_blank(),\n axis.text = element_blank(),\n axis.title = element_blank()\n ) +\n geom_text(\n data = slo.regije.centroidi,\n mapping = aes(x = long, y = lat, label = regija),\n size = 3,\n fontface =\"bold.italic\",\n color = \"black\"\n ) +\n labs(title = \"Vi\u0161ina najemnin glede na regijo\")\n\n#Povprecna donostnost glede na tip prostora\n\npovprecna.najemnina.tip.prostora = tabela.najemnin %>% group_by(tip.prostora) %>%\n mutate(povprecna.najemnina.na.kv.meter = mean(mesecna.najemnina / povrsina)) %>% select(tip.prostora, povprecna.najemnina.na.kv.meter) %>% distinct()\n\npovprecna.cena.tip.prostora = tabela.nakupov %>% group_by(tip.prostora) %>%\n mutate(povprecna.cena.na.kv.meter = mean(prodajna.cena / povrsina)) %>% select(tip.prostora, povprecna.cena.na.kv.meter) %>% distinct()\n\npovprecna.rentabilnost = povprecna.cena.tip.prostora %>% left_join(povprecna.najemnina.tip.prostora) %>% \n mutate(povprecna.rentabilnost = 1200 * povprecna.najemnina.na.kv.meter / povprecna.cena.na.kv.meter)\n\ngraf.rentabilnost.tip.prostora = povprecna.rentabilnost %>% arrange(povprecna.rentabilnost) %>% ggplot(mapping = aes(x = reorder(tip.prostora, povprecna.rentabilnost), y = povprecna.rentabilnost)) + \n geom_bar(stat=\"identity\") + labs(x = \"Tip prostora\", y = \"Letna rentabilnost (%)\", title = \"Rentabilnost glede na tip prostora\") + \n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "8a54d70adff76bb4f5b38d52010305c1539c1122", "size": 7796, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "LeonBahovec/APPR-2021-22", "max_stars_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "LeonBahovec/APPR-2021-22", "max_issues_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-03T10:36:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-05T07:49:07.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "LeonBahovec/APPR-2021-22", "max_forks_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.3737373737, "max_line_length": 199, "alphanum_fraction": 0.6552077989, "num_tokens": 2738, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.34443737761101256}} {"text": "# 3. faza: Vizualizacija podatkov\n\n# Uvozimo funkcije za pobiranje in uvoz zemljevida.\nsource('lib/uvozi.zemljevid.r')\nsource('lib/libraries.r', encoding = 'UTF-8')\n\n#UVOZIM ZEMLJEVID SLOVENIJE\n\nSlovenija <- uvozi.zemljevid(\"http://biogeo.ucdavis.edu/data/gadm2.8/shp/SVN_adm_shp.zip\",\n \"SVN_adm1\") %>% fortify()\ncolnames(Slovenija)[12] <- 'Regija'\nSlovenija$Regija <- gsub('Notranjsko-kra\u0161ka', 'Primorsko-notranjska', Slovenija$Regija)\nSlovenija$Regija <- gsub('Spodnjeposavska', 'Posavska', Slovenija$Regija)\n\ngraf_slovenija <- ggplot(Slovenija, aes(x=long, y=lat, group=group, fill=Regija)) +\n geom_polygon() +\n labs(title=\"Slovenija\") +\n theme_classic()\n\n#tabela za zemljevid:\nnajsrecnejsi_2018 <- filter(zadovoljstvo_regije, leto == '2018')\n\n#zemljevid: procent ljud, ki so zadovoljstvo v letu 2018 ocenili z 8 ali ve\u010d\nzemljevid.zadovoljstvo <- ggplot() +\n geom_polygon(data = right_join(najsrecnejsi_2018,Slovenija, by = c('Regija')),\n aes(x = long, y = lat, group = group, fill = vrednost))+\n xlab(\"\") + ylab(\"\") + ggtitle('PROCENT LJUDI, KI SO V LETU 2018 SRE\u010cO OZNA\u010cILI Z VE\u010c KOT 8') + \n theme(axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(), panel.background = element_blank()) + \n scale_fill_gradient(low = '#2f5163', high='#71c0eb', limits = c(50,80))\nzemljevid.zadovoljstvo$labels$fill <- 'procent najsre\u010dnej\u0161ih'\n\n#zemljevid: procent ljudi, ki imajo vi\u0161je\u0161olsko zobrazbo ali ve\u010d v letu 2018\nzemljevid.izobrazba <- ggplot() +\n geom_polygon(data = right_join(izobrazba_regije,Slovenija, by = c('Regija')),\n aes(x = long, y = lat, group = group, fill = povprecje))+\n xlab(\"\") + ylab(\"\") + ggtitle('PROCENT LJUDI Z VI\u0160JE\u0160OLSKO IZOBRAZBO ALI VE\u010c V LETU 2018') + \n theme(axis.title=element_blank(), axis.text=element_blank(), axis.ticks=element_blank(), panel.background = element_blank()) + \n scale_fill_gradient(low = '#2f5163', high='#71c0eb', limits = c(7,16))\nzemljevid.izobrazba$labels$fill <- 'procent izobra\u017eenih'\n\n#stolpi\u010dni grafikon: procent ludi po kvantilih in letih, ki so svojo sre\u010do ozna\u010dili ve\u010d kot 8\nprvi_kvintil1 <- zadovoljstvo_dohodek %>% filter(DOHODEK=='1.kvintil')\ndrugi_kvintil1 <- zadovoljstvo_dohodek %>% filter(DOHODEK=='2.kvintil')\ntretji_kvintil1 <- zadovoljstvo_dohodek %>% filter(DOHODEK=='3.kvintil')\ncetrti_kvintil1 <- zadovoljstvo_dohodek %>% filter(DOHODEK=='4.kvintil')\npeti_kvintil1 <- zadovoljstvo_dohodek %>% filter(DOHODEK=='5.kvintil')\n\ngraf_sreca <- plot_ly(zadovoljstvo_dohodek, x = ~2013:2018, y = ~prvi_kvintil1$vrednost, type = 'bar', name = '1.kvintil', marker = list(color = 'rgb(225,52,139)')) %>%\n add_trace(y = ~drugi_kvintil1$vrednost, name = '2.kvintil', marker = list(color = 'rgb(176,58,117)')) %>%\n add_trace(y = ~tretji_kvintil1$vrednost, name = '3.kvintil', marker = list(color = 'rgb(131,46,88)')) %>%\n add_trace(y = ~cetrti_kvintil1$vrednost, name = '4.kvintil', marker = list(color = 'rgb(121,23,73)')) %>%\n add_trace(y = ~peti_kvintil1$vrednost, name = '5.kvintil', marker = list(color = 'rgb(102,0,51)')) %>%\n layout(title = 'PROCENT LJUDI PO DOHODKU, KI SO SVOJO SRE\u010cO OZNA\u010cILI VE\u010c KOT 8',\n xaxis = list(title = \"\", tickangle = -45),\n yaxis = list(title = \"\"),\n margin = list(b = 100),\n barmode = 'group')\n\n#stolpi\u010dni grafikon: procent ludi po kvantilih in letih, ki so svoje zdravje ocenili z Dobro ali Zelo dobro\nprvi_kvintil2 <- zdravstvo_dohodek %>% filter(DOHODEK=='1. kvintil')\ndrugi_kvintil2 <- zdravstvo_dohodek %>% filter(DOHODEK=='2. kvintil')\ntretji_kvintil2 <- zdravstvo_dohodek %>% filter(DOHODEK=='3. kvintil')\ncetrti_kvintil2 <- zdravstvo_dohodek %>% filter(DOHODEK=='4. kvintil')\npeti_kvintil2 <- zdravstvo_dohodek %>% filter(DOHODEK=='5. kvintil')\n\ngraf_zdravje <- plot_ly(zdravstvo_dohodek, x = ~2013:2018, y = ~prvi_kvintil2$vrednost, type = 'bar', name = '1.kvintil', marker = list(color = 'rgb(225,52,139)')) %>%\n add_trace(y = ~drugi_kvintil2$vrednost, name = '2.kvintil', marker = list(color = 'rgb(176,58,117)')) %>%\n add_trace(y = ~tretji_kvintil2$vrednost, name = '3.kvintil', marker = list(color = 'rgb(131,46,88)')) %>%\n add_trace(y = ~cetrti_kvintil2$vrednost, name = '4.kvintil', marker = list(color = 'rgb(121,23,73)')) %>%\n add_trace(y = ~peti_kvintil2$vrednost, name = '5.kvintil', marker = list(color = 'rgb(102,0,51)')) %>%\n layout(title = 'PROCENT LJUDI PO DOHODKU, KI SO SVOJE ZDRAVJE OZNA\u010cILI KOT ZELO DOBRO',\n xaxis = list(title = \"\", tickangle = -45),\n yaxis = list(title = \"\"),\n margin = list(b = 100),\n barmode = 'group')\n\n#graf: zadovoljstvo z \u0161ivljenjem po starosti\ngraf_starost <- ggplot((data = zadovoljstvo_starost), aes(x= as.numeric(leto), y=vrednost, col=STAROST)) + geom_point() + geom_line() +\n scale_x_continuous('leto', breaks = seq(2013, 2018, 1), limits = c(2013,2018)) +\n ggtitle('PROCENT LJUDI PO STAROSTI, KI SO SVOJO SRE\u010cO OZNA\u010cILI VE\u010c KOT 8')+\n theme(plot.title = element_text(size = 8, face = \"bold\"))\n\n#GRAF: ocena zdravstvenega stanja po starosti\ngraf_zdravstvo <- ggplot((data = zdravstvo_starost), aes(x= as.numeric(leto), y=vrednost, col=STAROST)) + geom_point() + geom_line() +\n scale_x_continuous('leto', breaks = seq(2013, 2018, 1), limits = c(2013,2018)) +\n ggtitle('PROCENT LJUDI PO STAROSTI, KI SO SVOJE ZDRAVJE OCENILI Z ZELO DOBRO')+\n theme(plot.title = element_text(size = 8, face = \"bold\"))\n", "meta": {"hexsha": "032c0cae15ee58b4331642dff1c0001958ecc41a", "size": 5420, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "AnaMarijaB/APPR-2019-20", "max_stars_repo_head_hexsha": "504359c6cb6fc13fef657374c8111525de7f2fb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "AnaMarijaB/APPR-2019-20", "max_issues_repo_head_hexsha": "504359c6cb6fc13fef657374c8111525de7f2fb8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2019-12-14T15:39:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-05T10:03:58.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "AnaMarijaB/APPR-2019-20", "max_forks_repo_head_hexsha": "504359c6cb6fc13fef657374c8111525de7f2fb8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 61.5909090909, "max_line_length": 168, "alphanum_fraction": 0.6904059041, "num_tokens": 2102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.34443737761101245}} {"text": "##############################################################################\n# dci_calc_fx.r\n\n# Edited by: G Oldford\n # Last modified: August, 2020\n # Inputs: \n # sum_table \n # lengths\n # all_sections (t/f)\n #\n # Output:\n # DCI\n #\n # Output example: \n # DCIp DCId\n # 30.03119 44.29823\n #\n # Notes:\n # \n #\n # to-do: \n \n #\n # Notes from previous coding work: \n\ndci_calc_fx<-function(sum_table,\n lengths,\n all_sections=F){\n\n # Old Notes\n #sum.table is a variable that is used in the dci.calc.r function, \n #where we describe sum.table as sum.table.all or sum.table.nat\n #where: sum.table.nat<-read.csv(\"summary table natural.csv\") and \n #sum.table.all<-read.csv(\"summary table all.csv\") - from the \n #graph.and.data.setup.for.DCI.r function\n\n #WHAT THIS FUNCTION DOES: it calculates the DCIp and DCId \n #values for the riverscape (includes both natural and \n #artificial barriers)\n\n #this contains the length of each section\n\n #use this number for the DCIp calculation - i\n # interested in movements in all directions from all segments\n p_nrows<-dim(sum_table)[1]\n \n d_nrows<-subset(sum_table, \n start==\"sink\")\n #for diadromous fish we are only interested in the movement \n # from the segment which is closest to the ocean\n d_sum_table<-d_nrows\n\n DCIp<-0\n DCId<-0\n\n #DCIp calculation\n for (k in 1:p_nrows){\n # Old notes: \n #to get the riverscape connectivity index for potadromous fish, \n #use the given formula: DCIp= Cij*(li/L)*(lj/L)\n #Cij = passability for pathway (product of all barrier passabilities \n #in the pathway), li & lj = length of start and finish sections, \n #L = total length of all sections\n\n lj<-sum_table$start_section_length[k]/sum(lengths$Shape_Length)\n lk<-sum_table$finish_section_length[k]/sum(lengths$Shape_Length)\n pass<-sum_table$pathway_pass[k]\n DCIp<-DCIp+lj*lk*pass*100\n \n #add DCIp at the beginning to keep a running total of DCIp values\n }\n\n #DCId calculation\n for (a in 1:dim(d_nrows)[1]){\n # Old notes:\n #to get the DCI for diadromous fish, use the following formula: \n # DCId= li/L*Cj (where j= the product of the passability in the pathway)\n \n la<-d_sum_table$finish_section_length[a]/sum(lengths$Shape_Length)\n pass_d<-d_sum_table$pathway_pass[a]\n DCId<-DCId+la*pass_d*100\n }\n\n DCI<-t(c(DCIp,DCId))\n DCI<-as.data.frame(DCI)\t\n\n names(DCI)<-c(\"DCIp\",\"DCId\")\n \n # Old notes\n ######### ALL SECTION ANLAYSIS ######\n ## if desired, one can calculate the DCI_d starting with every sections. This\n ## gives a \"section-level\" DCI score for each section in the watershed\n if(all_sections==T){\n sections<-as.vector(unique(sum_table$start))\n # store the all section results in DCI.as\n DCI_as<-NULL\n \n for(s in 1:length(sections)){\n DCI_s<-0\n # Old notes:\n # select out only the data that corresponds to pathways from one sectino \n # to all other sections\n d_nrows<-subset(sum_table, start==sections[s])\n d_sum_table<-d_nrows\n \n for (a in 1:dim(d_nrows)[1]){\n # Old note:\n #to get the DCI for diadromous fish, use the following formula: \n # DCId= li/L*Cj (where j= the product of the passability in the pathway)\n la<-d_sum_table$finish_section_length[a]/sum(lengths$Shape_Length)\n pass_d<-d_sum_table$pathway_pass[a]\n DCI_s<-round(DCI_s+la*pass_d*100, digits=2)\n } # end loop over sections for dci calc\n \n DCI_as[s]<-DCI_s\n } # end loop over \"first\" sections\t\n\n # STORE RESULTS IN .CSV file\n res<-data.frame(sections,DCI_as)\n write.table(x=res,\n file=\"DCI_all_sections.csv\",\n sep=\",\",\n row.names=F)\n\n } # end if statement over all.sections\n\n print(DCI)\n\n #write.table(DCI,\"DCI.csv\", row.names=F, sep=\",\")\n\n return(DCI)\n # Old note:\n #returns the results (but you can't do anything after this, so \"return\" \n # must always be at the end of a function)\n\n}", "meta": {"hexsha": "a9bda9a5dd1a8683611bed8273fb13cda98bd508", "size": 4389, "ext": "r", "lang": "R", "max_stars_repo_path": "2021 Debug/dci_calc_fx.r", "max_stars_repo_name": "aligharouni/DCI-R-Code-2020", "max_stars_repo_head_hexsha": "1784dcf0907832ff9a6798948f1aa23ba55613c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2021 Debug/dci_calc_fx.r", "max_issues_repo_name": "aligharouni/DCI-R-Code-2020", "max_issues_repo_head_hexsha": "1784dcf0907832ff9a6798948f1aa23ba55613c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2021 Debug/dci_calc_fx.r", "max_forks_repo_name": "aligharouni/DCI-R-Code-2020", "max_forks_repo_head_hexsha": "1784dcf0907832ff9a6798948f1aa23ba55613c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-17T17:27:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-17T17:27:40.000Z", "avg_line_length": 32.2720588235, "max_line_length": 88, "alphanum_fraction": 0.5848712691, "num_tokens": 1148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.8006920020959544, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.3444155417354649}} {"text": "# 0_set_up_environment.r\n#20191002\n\nmessage('Setting up environment.')\n\noptions(width = 100)\n\nconflict_prefer('filter', 'dplyr')\nconflict_prefer('recode', 'dplyr')\nconflict_prefer('select', 'dplyr')\n\nget_percentage <- function(x, y, return_rounded = TRUE) {\n ratio <- x/y\n percentage <- ratio*100\n if (return_rounded) {\n return(round(percentage, 2))\n }\n else {\n return(percentage)\n }\n}\n\nget_p_from_t_test <- function(variable, group) {\n return(\n t.test(abcd_continuous[[variable]] ~ abcd_continuous[[group]]) %>% \n tidy() %>% \n select(c(p.value)) %>% \n as.numeric())\n}\n\nget_p_from_fisher_test <- function(variable, group) {\n return(\n fisher.test(abcd_categorical[[variable]], abcd_categorical[[group]]) %>% \n tidy() %>% \n select(c(p.value)) %>% \n as.numeric())\n}\n\n# Sources my hacked-up version of ggcoef\nsource(here('r', 'functions', 'my_ggcoef.r'))\n\nmessage('Environment setup complete.')\n", "meta": {"hexsha": "07b8dd95ba7bc505863630575ec847a718035883", "size": 985, "ext": "r", "lang": "R", "max_stars_repo_path": "r/0_set_up_environment.r", "max_stars_repo_name": "amandeepjutla/2019-abcd-asd", "max_stars_repo_head_hexsha": "06860acbc83af4ccccb41b8ba6bb3d0678a39a3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-23T19:56:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-23T19:56:58.000Z", "max_issues_repo_path": "r/0_set_up_environment.r", "max_issues_repo_name": "amandeepjutla/2019-abcd-asd", "max_issues_repo_head_hexsha": "06860acbc83af4ccccb41b8ba6bb3d0678a39a3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r/0_set_up_environment.r", "max_forks_repo_name": "amandeepjutla/2019-abcd-asd", "max_forks_repo_head_hexsha": "06860acbc83af4ccccb41b8ba6bb3d0678a39a3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9069767442, "max_line_length": 81, "alphanum_fraction": 0.6243654822, "num_tokens": 263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.6297746143530797, "lm_q1q2_score": 0.34432180881822566}} {"text": "plotit <- function(x,prefix_nm,parname,basenm,xlb=\"Tagger\",anon=F,condor,ymax=1.5,pltype=\"png\") {\r\n #if(parname==\"cradle2\") browser()\r\n pos <- grep(parname,dimnames(x$coefficients)[[1]])\r\n namelen <- nchar(parname)\r\n if(length(pos) >1) {\r\n cf <- data.frame(x$coefficients[pos,])\r\n dimnames(cf)[[1]] <- substr(dimnames(cf)[[1]],namelen+1,namelen+20)\r\n } else {\r\n cf <- data.frame(rbind(x$coefficients[pos,],c(0,2,0,0)))\r\n dimnames(cf)[[1]][1] <- substr(dimnames(x$coefficients)[[1]][pos],namelen+1,namelen+20)\r\n }\r\n b <- cf[cf$Std..Error<2,]\r\n L <- b[,1]-1.96*b[,2]; U <- b[,1]+1.96*b[,2]\r\n labs <- dimnames(b)[[1]]\r\n if(anon) basenm <- \"Reference\"\r\n if(anon & parname%in% c(\"tagger\",\"assist\")) labs <- seq(2:(length(dimnames(b)[[1]])+1))\r\n if(anon & parname==\"tagger.tagtype\") {\r\n labs <- dimnames(b)[[1]]\r\n k <- 0\r\n for(i in 1:length(labs)) {\r\n if(substring(labs[i],5,8)==\"conv\") k <- k + 1\r\n labs[i] <- paste(k,substring(labs[i],5,10),sep=\".\")\r\n }\r\n }\r\n #if(io_agg) b_tagger <- \"JDD\" else b_tagger <- \"Tony Lewis\"\r\n if(parname %in% c(\"tagger.tagtype\",\"assist\")) windows(width=9,height=7) else windows()\r\n plot(b[,1],ylim=c(min(L),max(U)),xlab=xlb,ylab=paste(\"logit Return rate (vs\",basenm,\"at 0)\"))\r\n X <- seq(1,length(b[,1]))\r\n segments(X,L,X,U)\r\n abline(h=0)\r\n text(X,b[,1]-0.1,labels=labs,cex=0.8)\r\n if(anon) prefix_nm <- paste(prefix_nm,\"_anon\",sep=\"\")\r\n if(condor==F) savePlot(paste(prefix_nm,parname,\"logit\",sep=\"_\"),type=pltype)\r\n pr <- (exp(b[,1])/(1+exp(b[,1])))/0.5\r\n L <- (exp(L)/(1+exp(L)))/0.5\r\n U <- (exp(U)/(1+exp(U)))/0.5\r\n plot(pr,xlim=c(0.5,length(pr)+0.5),ylim=c(0,ymax),xaxt=\"n\",xlab=xlb,ylab=paste(\"Relative Return rate (vs\",basenm,\"at 1)\"))\r\n text(X,pr-0.03,labels=labs,cex=0.75)\r\n segments(X,L,X,U)\r\n abline(h=1)\r\n pars <- cbind(b,pr,L,U)\r\n if(anon & parname %in% c(\"tagger\",\"tagger.tagtype\",\"assist\")) rownames(pars) <- labs\r\n write.csv(pars,file=paste(prefix_nm,parname,\"parests.csv\",sep=\"_\"))\r\n if(condor==F) savePlot(paste(prefix_nm,parname,\"probs\",sep=\"_\"),type=pltype)\r\n }\r\n\r\nplot_tagger.tagtype <- function(x,prefix_nm,parname,basenm,xlb=\"Tagger\",anon=F,condor,ymax=1.5,pltype=\"png\") {\r\n pos <- grep(parname,dimnames(x$coefficients)[[1]])\r\n namelen <- nchar(parname)\r\n cf <- data.frame(x$coefficients[pos,])\r\n dimnames(cf)[[1]] <- substr(dimnames(cf)[[1]],namelen+1,namelen+20)\r\n b <- cf[cf$Std..Error<2,]\r\n L1a <- b[,1]-1.96*b[,2]; L2a <- b[,1] - 0.01\r\n U1a <- b[,1]+1.96*b[,2]; U2a <- b[,1] + 0.01\r\n labs <- dimnames(b)[[1]]\r\n k <- 0\r\n for(i in 1:length(labs)) {\r\n if(substring(labs[i],5,8)==\"conv\") k <- k + 1\r\n labs[i] <- paste(k,substring(labs[i],5,10),sep=\".\")\r\n }\r\n numlabs <- gsub(\".intern\",\"\",gsub(\".conv\",\"\",labs))\r\n windows(width=9,height=7)\r\n ttype <- rep(7,length(labs))\r\n ttype[grep(\"conv\",labs)] <- 1\r\n ltype <- ttype\r\n ltype[ltype==7] <- 2\r\n X <- seq(1,length(b[,1]))\r\n if(anon) prefix_nm <- paste(prefix_nm,\"_anon\",sep=\"\")\r\n pr <- (exp(b[,1])/(1+exp(b[,1])))/0.5\r\n L1 <- (exp(L1a)/(1+exp(L1a)))/0.5; L2 <- pr - 0.01\r\n U1 <- (exp(U1a)/(1+exp(U1a)))/0.5; U2 <- pr + 0.01\r\n plot(pr,xlim=c(0.5,length(pr)+0.5),ylim=c(0,ymax),xaxt=\"n\",xlab=xlb,ylab=paste(\"Relative Return rate (vs Reference at 1)\"),pch=ttype,cex=1)\r\n text(X,L1-0.03,labels=numlabs,cex=1)\r\n segments(X,U1,X,U2,lty=ltype)\r\n segments(X,L1,X,L2,lty=ltype)\r\n abline(h=1)\r\n legend(\"topleft\",legend=c(\"Conventional tag\",\"Internal tag\"),pch=c(1,7),lty=c(1,2))\r\n }\r\n\r\nplot_tagger <- function(x,prefix_nm,parname,basenm,xlb=\"Tagger\",anon=F,condor,ymax=1.5,pltype=\"png\") {\r\n pos <- grep(parname,dimnames(x$coefficients)[[1]])\r\n namelen <- nchar(parname)\r\n cf <- data.frame(x$coefficients[pos,])\r\n dimnames(cf)[[1]] <- substr(dimnames(cf)[[1]],namelen+1,namelen+20)\r\n b <- cf[cf$Std..Error<2,]\r\n L1a <- b[,1]-1.96*b[,2]; L2a <- b[,1] - 0.01\r\n U1a <- b[,1]+1.96*b[,2]; U2a <- b[,1] + 0.01\r\n labs <- dimnames(b)[[1]]\r\n k <- 0\r\n for(i in 1:length(labs)) {\r\n labs[i] <- i\r\n }\r\n numlabs <- gsub(\".intern\",\"\",gsub(\".conv\",\"\",labs))\r\n windows()\r\n ttype <- rep(2,length(labs))\r\n ttype[grep(\"conv\",labs)] <- 2\r\n ltype <- 1\r\n ltype[ltype==7] <- 2\r\n X <- seq(1,length(b[,1]))\r\n if(anon) prefix_nm <- paste(prefix_nm,\"_anon\",sep=\"\")\r\n pr <- (exp(b[,1])/(1+exp(b[,1])))/0.5\r\n L1 <- (exp(L1a)/(1+exp(L1a)))/0.5; L2 <- pr - 0.01\r\n U1 <- (exp(U1a)/(1+exp(U1a)))/0.5; U2 <- pr + 0.01\r\n plot(pr,xlim=c(0.5,length(pr)+0.5),ylim=c(0,ymax),xaxt=\"n\",xlab=xlb,ylab=paste(\"Relative Return rate (vs Reference at 1)\"),pch=ttype,cex=1)\r\n text(X,L1-0.03,labels=numlabs,cex=1)\r\n segments(X,U1,X,U2,lty=ltype)\r\n segments(X,L1,X,L2,lty=ltype)\r\n abline(h=1)\r\n }\r\n\r\nplot_event <- function(res,dat=indat,prefix_nm,condor,basenm,pltype=\"png\") {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"sp_id\",\"len5\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n if(length(grep(\"xprt\",nms))>0) nmlist$xprt <- 2500\r\n if(length(grep(\"assist\",nms))>0) nmlist$assist <- levels(dat$assist)[1]\r\n if(length(grep(\"cradle2\",nms))>0) nmlist$cradle2 <- levels(dat$cradle2)[1]\r\n if(length(grep(\"tagger.tagtype\",nms))>0) {\r\n nmlist$tagger.tagtype <- basenm\r\n } else nmlist$tagger <- basenm\r\n nmlist$tag_sch_id <- unique(dat$tag_sch_id)\r\n nmlist$len5 <- 50\r\n nmlist$Qual <- levels(dat$Qual)[1]\r\n spp <- sort(unique(dat$sp_id))\r\n if(length(spp) > 1) nmlist$sp_id <- spp[2] else nmlist$sp_id <- spp\r\n pd <- expand.grid(nmlist)\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=F)\r\n windows()\r\n hist(p,breaks=seq(0,1,0.01),xlim=c(0,1),xlab=\"Return rate\",ylab=\"Frequency\",main=\"\")\r\n if(condor==F) savePlot(paste(prefix_nm,\"event\",sep=\"_\"),type=pltype)\r\n }\r\n\r\n\r\nplotsz <- function(res,dat=indat,prefix_nm,xlb=\"Length\",condor,basenm,do_ci=T,pltype=\"png\") {\r\nif(length(unique(dat$sp_id))>1) {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"sp_id\",\"len5\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n if(length(grep(\"tagger.tagtype\",nms))>0) {\r\n nmlist$tagger.tagtype <- basenm\r\n } else nmlist$tagger <- basenm\r\n if(length(grep(\"assist\",nms))>0) nmlist$assist <- levels(dat$assist)[1]\r\n if(length(grep(\"OTC\",nms))>0) nmlist$OTC <- levels(dat$OTC)[1]\r\n if(length(grep(\"xprt\",nms))>0) nmlist$xprt <- 2500\r\n if(length(grep(\"cradle2\",nms))>0) nmlist$cradle2 <- levels(dat$cradle2)[1]\r\n if(nmlist$cradle2==\"ARC\") nmlist$cradle2 <- levels(dat$cradle2)[2]\r\n\r\n nmlist$len5 <- sort(unique(dat$len5))\r\n nmlist$Qual <- levels(dat$Qual)[1]\r\n nmlist$sp_id <- unique(dat$sp_id)\r\n pd <- expand.grid(nmlist)\r\n pd <- pd[paste(pd$sp_id,pd$len5) %in% unique(paste(dat$sp_id,dat$len5)),]\r\n par(mfrow=c(2,2))\r\n p <- predict(res,newdata=pd,type=\"link\",se.fit=T)\r\n pr <- inv.logit(cbind(p$fit,p$fit + 2*p$se.fit,p$fit - 2*p$se.fit))\r\n if(do_ci) yl <- c(0,max(pr)) else yl <- c(0,max(pr[,1]))\r\n if(length(grep(\"S\",dat$sp_id)) > 0) {\r\n plot(pd[pd$sp_id==\"S\",]$len5,pr[pd$sp_id==\"S\",1],ylim=yl,type=\"l\",xlab=\"Skipjack length (cm)\",ylab=\"Return rate\")\r\n if(do_ci) lines(pd[pd$sp_id==\"S\",]$len5,pr[pd$sp_id==\"S\",2])\r\n if(do_ci) lines(pd[pd$sp_id==\"S\",]$len5,pr[pd$sp_id==\"S\",3])\r\n }\r\n if(length(grep(\"B\",dat$sp_id)) > 0) {\r\n plot(pd[pd$sp_id==\"B\",]$len5,pr[pd$sp_id==\"B\",1],ylim=yl,type=\"l\",xlab=\"Bigeye length (cm)\",ylab=\"Return rate\")\r\n if(do_ci) lines(pd[pd$sp_id==\"B\",]$len5,pr[pd$sp_id==\"B\",2])\r\n if(do_ci) lines(pd[pd$sp_id==\"B\",]$len5,pr[pd$sp_id==\"B\",3])\r\n }\r\n if(length(grep(\"Y\",dat$sp_id)) > 0) {\r\n plot(pd[pd$sp_id==\"Y\",]$len5,pr[pd$sp_id==\"Y\",1],ylim=yl,type=\"l\",xlab=\"Yellowfin length (cm)\",ylab=\"Return rate\")\r\n if(do_ci) lines(pd[pd$sp_id==\"Y\",]$len5,pr[pd$sp_id==\"Y\",2])\r\n if(do_ci) lines(pd[pd$sp_id==\"Y\",]$len5,pr[pd$sp_id==\"Y\",3])\r\n }\r\n if(condor==F) savePlot(paste(prefix_nm,\"length\",sep=\"_\"),type=pltype)\r\n } else {\r\n \r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"len5\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n nmlist$len5 <- sort(unique(dat$len5))\r\n if(length(grep(\"tagger.tagtype\",nms))>0) {\r\n nmlist$tagger.tagtype <- basenm\r\n } else nmlist$tagger <- basenm\r\n if(length(grep(\"assist\",nms))>0) nmlist$assist <- levels(dat$assist)[1]\r\n if(length(grep(\"OTC\",nms))>0) nmlist$OTC <- levels(dat$OTC)[1]\r\n if(length(grep(\"xprt\",nms))>0) nmlist$xprt <- 2500\r\n if(length(grep(\"cradle2\",nms))>0) nmlist$cradle2 <- levels(dat$cradle2)[1]\r\n nmlist$Qual <- levels(dat$Qual)[1]\r\n spp <- sort(unique(dat$sp_id))\r\n if(length(spp) > 1) nmlist$sp_id <- spp[2] else nmlist$sp_id <- spp\r\n\r\n pd <- expand.grid(nmlist)\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=T)\r\n p2 <- predict(res,newdata=pd,type=\"terms\",se.fit=T)\r\n plot(pd$len5,p$fit,ylim=c(0,max(p$fit+2*p$se.fit)),type=\"b\",xlab=\"Length (cm)\",ylab=\"Return rate\")\r\n sd2 <- p2$se.fit[,2]\r\n# lines(pd$len5,p$fit+2*p$se.fit)\r\n# lines(pd$len5,p$fit-2*p$se.fit)\r\n lines(pd$len5,inv.logit(logit(p$fit) - 1.96*sd2))\r\n lines(pd$len5,inv.logit(logit(p$fit) + 1.96*sd2))\r\n if(condor==F) savePlot(paste(prefix_nm,\"length\",sep=\"_\"),type=pltype)\r\n }}\r\n \r\nplotsz2 <- function(res,dat=indat,prefix_nm,xlb=\"Length\",condor,pltype=\"png\") {\r\nif(length(unique(dat$sp_id))>1) {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"sp_id\",\"len5\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n nmlist$len5 <- sort(unique(dat$len5))\r\n nmlist$sp_id <- unique(dat$sp_id)\r\n pd <- expand.grid(nmlist)\r\n pd <- pd[paste(pd$sp_id,pd$len5) %in% unique(paste(dat$sp_id,dat$len5)),]\r\n par(mfrow=c(2,2))\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=F)\r\n pter <- predict(res,newdata=pd,type=\"terms\",se.fit=T)\r\n loc <- grep(\":sp_id\",colnames(pter$fit))\r\n plgt <- logit(p)\r\n pr_adj <- inv.logit(cbind(plgt,plgt + 2*pter$se.fit[,loc],plgt - 2*pter$se.fit[,loc]))\r\n plot(pd[pd$sp_id==\"S\",]$len5,pr_adj[pd$sp_id==\"S\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Skipjack length (cm)\",ylab=\"Return rate\")\r\n lines(pd[pd$sp_id==\"S\",]$len5,pr_adj[pd$sp_id==\"S\",2])\r\n lines(pd[pd$sp_id==\"S\",]$len5,pr_adj[pd$sp_id==\"S\",3])\r\n plot(pd[pd$sp_id==\"B\",]$len5,pr_adj[pd$sp_id==\"B\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Bigeye length (cm)\",ylab=\"Return rate\")\r\n lines(pd[pd$sp_id==\"B\",]$len5,pr_adj[pd$sp_id==\"B\",2])\r\n lines(pd[pd$sp_id==\"B\",]$len5,pr_adj[pd$sp_id==\"B\",3])\r\n plot(pd[pd$sp_id==\"Y\",]$len5,pr_adj[pd$sp_id==\"Y\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Yellowfin length (cm)\",ylab=\"Return rate\")\r\n lines(pd[pd$sp_id==\"Y\",]$len5,pr_adj[pd$sp_id==\"Y\",2])\r\n lines(pd[pd$sp_id==\"Y\",]$len5,pr_adj[pd$sp_id==\"Y\",3])\r\n if(condor==F) savePlot(paste(prefix_nm,\"length\",sep=\"_\"),type=pltype)\r\n } else {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"len5\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n nmlist$len5 <- sort(unique(dat$len5))\r\n pd <- expand.grid(nmlist)\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=T)\r\n plot(pd$len5,p$fit,ylim=c(0,max(p$fit+2*p$se.fit)),type=\"b\",xlab=\"Length (cm)\",ylab=\"Return rate\")\r\n lines(pd$len5,p$fit+2*p$se.fit)\r\n lines(pd$len5,p$fit-2*p$se.fit)\r\n if(condor==F) savePlot(paste(prefix_nm,\"length\",sep=\"_\"),type=pltype)\r\n }}\r\n \r\nplot_xprt<- function(res,dat=indat,prefix_nm,plxprt,inmodel,condor,pltype=\"png\") {\r\n if(plxprt & inmodel$xprt & inmodel$xptype) {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"xprt\",\"xptype\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n nmlist$xprt <- sort(unique(dat$xprt))\r\n nmlist$xptype <- unique(dat$xptype)\r\n pd <- expand.grid(nmlist)\r\n pd <- pd[paste(pd$xptype,pd$xprt) %in% unique(paste(dat$xptype,dat$xprt)),]\r\n par(mfrow=c(2,2))\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=F)\r\n pter <- predict(res,newdata=pd,type=\"terms\",se.fit=T)\r\n loc <- grep(\"xptype:\",colnames(pter$fit))\r\n plgt <- logit(p)\r\n maxp <- max(plgt)\r\n pr_adj <- inv.logit(cbind(plgt,plgt + 2*pter$se.fit[,loc],plgt - 2*pter$se.fit[,loc]))\r\n pr_adj[pd$xptype==\"N\",] <- pr_adj[pd$xptype==\"N\",] / max(pr_adj[pd$xptype==\"N\",1])\r\n pr_adj[pd$xptype==\"O\",] <- pr_adj[pd$xptype==\"O\",] / max(pr_adj[pd$xptype==\"O\",1])\r\n pr_adj[pd$xptype==\"T\",] <- pr_adj[pd$xptype==\"T\",] / max(pr_adj[pd$xptype==\"T\",1])\r\n plot(pd[pd$xptype==\"N\",]$xprt,pr_adj[pd$xptype==\"N\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Tuna tagged\",ylab=\"Relative return rate\")\r\n lines(pd[pd$xptype==\"N\",]$xprt,pr_adj[pd$xptype==\"N\",2])\r\n lines(pd[pd$xptype==\"N\",]$xprt,pr_adj[pd$xptype==\"N\",3])\r\n plot(pd[pd$xptype==\"O\",]$xprt,pr_adj[pd$xptype==\"O\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Tuna tagged\",ylab=\"Relative return rate\")\r\n lines(pd[pd$xptype==\"O\",]$xprt,pr_adj[pd$xptype==\"O\",2])\r\n lines(pd[pd$xptype==\"O\",]$xprt,pr_adj[pd$xptype==\"O\",3])\r\n plot(pd[pd$xptype==\"T\",]$xprt,pr_adj[pd$xptype==\"T\",1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Tuna tagged\",ylab=\"Relative return rate\")\r\n lines(pd[pd$xptype==\"T\",]$xprt,pr_adj[pd$xptype==\"T\",2])\r\n lines(pd[pd$xptype==\"T\",]$xprt,pr_adj[pd$xptype==\"T\",3])\r\n if(condor==F) savePlot(paste(prefix_nm,\"xprt_xptype\",sep=\"_\"),type=pltype)\r\n } else if(plxprt & inmodel$xprt & !inmodel$xptype) {\r\n nms <- names(dat)\r\n nms <- nms[!nms %in% c(\"xprt\",\"wts\")]\r\n nmlist <- as.list(dat[1,nms])\r\n nmlist$xprt <- sort(unique(dat$xprt))\r\n pd <- expand.grid(nmlist)\r\n pd <- pd[paste(pd$xprt) %in% unique(paste(dat$xprt)),]\r\n par(mfrow=c(1,1))\r\n p <- predict(res,newdata=pd,type=\"response\",se.fit=F)\r\n pter <- predict(res,newdata=pd,type=\"terms\",se.fit=T)\r\n loc <- grep(\"xprt\",colnames(pter$fit))\r\n plgt <- logit(p)\r\n pr_adj <- inv.logit(cbind(plgt,plgt + 2*pter$se.fit[,loc],plgt - 2*pter$se.fit[,loc]))\r\n pr_adj <- pr_adj / max(pr_adj[,1])\r\n plot(pd$xprt,pr_adj[,1],ylim=c(0,max(pr_adj)),type=\"b\",xlab=\"Tuna tagged\",ylab=\"Relative return rate\")\r\n lines(pd$xprt,pr_adj[,2])\r\n lines(pd$xprt,pr_adj[,3])\r\n if(condor==F) savePlot(paste(prefix_nm,\"xprt\",sep=\"_\"),type=pltype)\r\n } else if(plxprt==2) {\r\n cf.xprt <- data.frame(a$coefficients[grep(\"xprt\",dimnames(a$coefficients)[[1]]),])\r\n dimnames(cf.xprt)[[1]] <- substr(dimnames(cf.xprt)[[1]],16,44)\r\n b <- cf.xprt[cf.xprt$Std..Error<2,]\r\n L <- b[,1]-1.96*b[,2]; U <- b[,1]+1.96*b[,2]\r\n labs <- dimnames(b)[[1]]\r\n plot(b[,1],xlim=c(0,3),ylim=c(min(L),max(c(0,max(U)))),xlab=\"Expertise\",ylab=\"logit Return rate (vs Good at 0)\")\r\n X <- seq(1,length(b[,1]))\r\n segments(X,L,X,U)\r\n abline(h=0)\r\n text(X,b[,1]-0.1,labels=labs,cex=0.8)\r\n }\r\n if(condor==F) savePlot(paste(prefix_nm,\"xprt\",sep=\"_\"),type=pltype)\r\n }\r\n\r\n\r\naggdata <- function(dat=tag_all,minTperSch=50,archival=T,xptype=F,tagger.tagtype=F,assist=F,cond=F,cradle=F) {\r\n if(!cond & !cradle) {\r\n if(!tagger.tagtype & !xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + Cond2 + OTC + tag_type + xprt + tag_sch_id,data=dat,length)\r\n if(!tagger.tagtype & xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + Cond2 + OTC + tag_type + xprt + tag_sch_id + xptype,data=dat,FUN=length)\r\n if(tagger.tagtype & !xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + Cond2 + OTC + xprt + tag_sch_id,data=dat,length)\r\n if(tagger.tagtype & xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + Cond2 + OTC + xprt + tag_sch_id + xptype,data=dat,FUN=length)\r\n if(!tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + Cond2 + OTC + tag_type + xprt + assist + tag_sch_id,data=dat,length)\r\n if(!tagger.tagtype & xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + Cond2 + OTC + tag_type + xprt + assist + tag_sch_id + xptype,data=dat,FUN=length)\r\n if(tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + Cond2 + OTC + xprt + assist + tag_sch_id,data=dat,length)\r\n if(tagger.tagtype & xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + Cond2 + OTC + xprt + assist + tag_sch_id + xptype,data=dat,FUN=length)\r\n }\r\n if(cond==1 & !cradle) {\r\n if(tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + OTC + xprt + assist + tag_sch_id,data=dat,length)\r\n if(!tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + OTC + tag_type + xprt + assist + tag_sch_id,data=dat,length)\r\n }\r\n if(cond==1 & cradle) {\r\n if(tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + cradle2 + xprt + assist + tag_sch_id,data=dat,length)\r\n if(!tagger.tagtype & !xptype & assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + cradle2 + tag_type + xprt + assist + tag_sch_id,data=dat,length)\r\n }\r\n if(!cond & cradle) {\r\n if(tagger.tagtype & !xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger.tagtype + len5 + sp_id + Qual + Cond + Cond2 + OTC + xprt + cradle2 + tag_sch_id,data=dat,length)\r\n if(!tagger.tagtype & !xptype & !assist) tag_agg <- aggregate(recap ~ eval(recap==T) + tagger + len5 + sp_id + Qual + Cond + Cond2 + OTC + tag_type + xprt + cradle2 + tag_sch_id,data=dat,length)\r\n }\r\n\r\n names(tag_agg)[c(1,3)] <- c(\"recap\",\"len5\")\r\n names(tag_agg)[grep(\"recap\",names(tag_agg))[2]] <- c(\"wts\")\r\n if(!archival) tag_agg <- tag_agg[tag_agg$tag_type %in% c(\"ST\",\"DT\",\"a.Y13\",\"b.Y11\"),]\r\n a <- tapply(tag_agg$wts,tag_agg$tag_sch_id,sum,na.rm=T)\r\n a <- a[a > minTperSch]\r\n tagsub <- tag_agg[tag_agg$tag_sch_id %in% names(a),]\r\n tagsub$tag_sch_id <- as.factor(as.character(tagsub$tag_sch_id))\r\n tagsub <- tagsub[tagsub$sp_id != \"U\",]\r\n tagsub <- tagsub[tagsub$Qual != \"Unknown\",]\r\n tagsub$Qual <- as.factor(as.character(tagsub$Qual))\r\n tagsub$Qual <- relevel(tagsub$Qual,ref=\"Good\")\r\n tagsub$sp_id <- as.factor(as.character(tagsub$sp_id))\r\n if(!tagger.tagtype) {\r\n tagsub$tag_type <- as.factor(as.character(tagsub$tag_type))\r\n if(!is.na(match(\"ST\",levels(tagsub$tag_type)))) tagsub$tag_type <- relevel(tagsub$tag_type,ref=\"ST\") else tagsub$tag_type <- relevel(tagsub$tag_type,ref=\"a.Y13\")\r\n }\r\n if(length(grep(\"OTC\",names(tagsub))) > 0) tagsub$OTC <- as.factor(tagsub$OTC)\r\n tagsub$recap <- na.omit(tagsub$recap)\r\n return(tagsub)\r\n }\r\n\r\nplot_assocs <- function(model) {\r\n a <- summary(model)@coefs\r\n coefs <- a[grep(\"assoc\",rownames(a)),]\r\n passc <- exp(coefs)\r\n nass <- dim(coefs)[1]+1\r\n assc <- factor(1:nass,levels=1:nass,labels=c(\"Unassociated\",\"Log\",\"Anchored FAD\",\"Drifting FAD\")[1:nass])\r\n plotCI(1:nass,exp(c(0,coefs[,1])),ui=exp(c(0,coefs[,1]+2*coefs[,2])), li=exp(c(0,coefs[,1]-2*coefs[,2])),sfrac=0.01, col=1,lwd=1,xaxt=\"n\",xlab=\"\",\r\n ylab=\"Relative recapture rate\",ylim=c(0,max(c(exp(coefs[,1]+2*coefs[,2])),1)))\r\n abline(h=1,lty=2)\r\n axis(1, at = 1:nass, labels = assc)\r\n }\r\n\r\n", "meta": {"hexsha": "804da71c3df30dd039b9127309ca1185f5aabafd", "size": 18977, "ext": "r", "lang": "R", "max_stars_repo_path": "support_functions.r", "max_stars_repo_name": "hoyles/tagmort", "max_stars_repo_head_hexsha": "2fb7fdd1883dde6438c8c9cb28a66b02b297a933", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "support_functions.r", "max_issues_repo_name": "hoyles/tagmort", "max_issues_repo_head_hexsha": "2fb7fdd1883dde6438c8c9cb28a66b02b297a933", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "support_functions.r", "max_forks_repo_name": "hoyles/tagmort", "max_forks_repo_head_hexsha": "2fb7fdd1883dde6438c8c9cb28a66b02b297a933", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.7138888889, "max_line_length": 208, "alphanum_fraction": 0.6131106076, "num_tokens": 7217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3443218050191247}} {"text": "\nrm(list=ls())\n\nlibrary(raster)\nlibrary(tidyverse)\nlibrary(readxl)\n\n######\n#FUNCTIONS\nextractXYZ <- function(raster, nodata = FALSE, addCellID = TRUE){\n \n vals <- raster::extract(raster, 1:ncell(raster)) #specify raster otherwise dplyr used\n xys <- rowColFromCell(raster,1:ncell(raster))\n combine <- cbind(xys,vals)\n \n if(addCellID){\n combine <- cbind(1:length(combine[,1]), combine)\n }\n \n if(!nodata){\n combine <- combine[!rowSums(!is.finite(combine)),] #from https://stackoverflow.com/a/15773560\n }\n \n return(combine)\n}\n\n\ngetLCs <- function(data)\n{\n #calculates proportion of each LC in the muni (ignoring NAs, help from https://stackoverflow.com/a/44290753)\n data %>%\n group_by(muniID) %>%\n dplyr::summarise(LC1 = round(sum(lc == 1, na.rm = T) / sum(!is.na(lc)), 3),\n LC2 = round(sum(lc == 2, na.rm = T) / sum(!is.na(lc)), 3),\n LC3 = round(sum(lc == 3, na.rm = T) / sum(!is.na(lc)), 3),\n LC4 = round(sum(lc == 4, na.rm = T) / sum(!is.na(lc)), 3),\n LC5 = round(sum(lc == 5, na.rm = T) / sum(!is.na(lc)), 3),\n NonNAs = sum(!is.na(lc)),\n NAs = sum(is.na(lc))\n ) -> LCs\n\n return(LCs)\n}\n######\n\nyears <- seq(2001,2018,1)\n \ninput_path <- \"C:/Users/k1076631/Google Drive/Shared/Crafty Telecoupling/Data/\"\n\n#load the rasters\nmunis.r <- raster(paste0(input_path,\"CRAFTYInput/Data/sim10_BRmunis_latlon_5km.asc\"))\n\nlcname <- paste0(input_path,\"CRAFTYInput/Data/ObservedLCmaps/LandCover2015_PastureB_Disagg.asc\")\nlc <- raster(lcname)\nlc.t <- extractXYZ(lc, addCellID = F)\n\n\nstate_weights <- read_excel(paste0(input_path,\"CRAFTYInput/Data/HumanDev/StateWeightedCaps.xlsx\"), sheet = \"Human\", range=\"A1:U11\", col_names=T) \n\nstate_weights <- state_weights %>%\n mutate_if(is.numeric, round, digits=3)\n\nmunis.t <- extractXYZ(munis.r, addCellID = F)\nmunis.t <- as.data.frame(munis.t)\nmunis.t <- plyr::rename(munis.t, c(\"vals\" = \"muniID\"))\n\n#set NA in both rasters\nlc[is.na(munis.r)] <- NA\nmunis.r[is.na(lc)] <- NA\n \nlc_munis <- left_join(as.data.frame(munis.t), as.data.frame(lc.t), by = c(\"row\" = \"row\", \"col\" = \"col\"))\n\n#add state label\n#add state ID\nlc_munis <- lc_munis %>%\n mutate(state = (muniID %/% 100000)) %>%\n mutate(state = if_else(state == 17, \"TO\", \n if_else(state == 29, \"BA\",\n if_else(state == 31, \"MG\",\n if_else(state == 35, \"SP\",\n if_else(state == 41, \"PR\",\n if_else(state == 42, \"SC\",\n if_else(state == 43, \"RS\", \n if_else(state == 50, \"MS\",\n if_else(state == 51, \"MT\",\n if_else(state == 52, \"GO\", \"NA\"\n ))))))))))\n )\n\n\n\nnew_munis <- left_join(lc_munis, state_weights, by = c(\"state\" = \"state\"))\n\n#set emptyto a raster with same extent as inputs (to the same) with help from https://gis.stackexchange.com/questions/250149/assign-values-to-a-subset-of-cells-of-a-raster)\nempty.r <- raster(munis.r)\nempty.r[] <- NA_real_\ncells <- cellFromRowCol(empty.r, new_munis$row, new_munis$col)\n\n\nfor(year in years){\n \n year_dat <- new_munis %>%\n select(paste0(year))\n\n final.r <- empty.r \n final.r[cells] <- year_dat[[1]]\n \n #plot(final.r, main=paste0(year))\n \n writeRaster(final.r, paste0(input_path,\"CRAFTYInput/Data/HumanDev/HumanCapital\",year,\".asc\"), format = 'ascii', overwrite=T)\n}\n\n\n\n\n", "meta": {"hexsha": "a029135f4b14979228486d6634ea8c3ac45b10e2", "size": 3309, "ext": "r", "lang": "R", "max_stars_repo_path": "humdevMap.r", "max_stars_repo_name": "jamesdamillington/CRAFTYInput", "max_stars_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "humdevMap.r", "max_issues_repo_name": "jamesdamillington/CRAFTYInput", "max_issues_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "humdevMap.r", "max_forks_repo_name": "jamesdamillington/CRAFTYInput", "max_forks_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.525862069, "max_line_length": 172, "alphanum_fraction": 0.6158960411, "num_tokens": 1086, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3443218050191247}} {"text": "library(rotp)\nkey = \"asdf\"\n\ntest = hotp(key, 1)\ntruth = 679366\nstopifnot(all.equal(test, truth))\n\ntest = hotp(key, 10)\ntruth = 795860\nstopifnot(all.equal(test, truth))\n\ntest = hotp(key, 10000)\ntruth = 350230\nstopifnot(all.equal(test, truth))\n\ntest = hotp(key, 10000000)\ntruth = 620310\nstopifnot(all.equal(test, truth))\n\n\n\ntest = hotp(key, 0, digits=8)\ntruth = 96788213\nstopifnot(all.equal(test, truth))\n", "meta": {"hexsha": "da5787c3cceb73deec2b765822050aceb78d9dea", "size": 403, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/hotp.r", "max_stars_repo_name": "wrathematics/rotp", "max_stars_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_stars_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-06-08T09:54:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-26T13:04:01.000Z", "max_issues_repo_path": "tests/hotp.r", "max_issues_repo_name": "wrathematics/rotp", "max_issues_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_issues_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/hotp.r", "max_forks_repo_name": "wrathematics/rotp", "max_forks_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_forks_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.12, "max_line_length": 33, "alphanum_fraction": 0.7047146402, "num_tokens": 138, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34431511031717515}} {"text": "#==========================================================================================================================\n#==========================================================================================================================\n\n# 3. faza: Vizualizacija podatkov\n\n#==========================================================================================================================\n#==========================================================================================================================\n\n# a) delovno aktivno prebivalstvo po starostnih skupinah, letno\n\ngraf_25_29_65 <- starost_spol1 %>%\n ggplot(mapping = aes(x=leto, y=stevilo, color=STAROST)) + \n geom_line(size = 0.8) + \n xlab(\"leto\") + \n ylab(\"\u0161tevilo delovno aktivnih\") + \n labs(title=\"Delovno aktivni v starostnih skupinah od 25 do 29 let \\nin 65 let in ve\u010d v obdobju med letoma 2005 in 2020\") +\n facet_grid(vars(STAROST), vars(SPOL), scales = \"free_y\") + \n scale_x_continuous(breaks=seq(2005, 2020, 2)) +\n scale_color_manual(name = \"Legenda\", \n values = c(\"25-29 let\" = \"orange2\", \"65 let in ve\u010d\" = \"steelblue1\")) +\n expand_limits(y=0)\n\n# za leto 2020 bom nardila se diagram, da bo videt, kok je zastopano delovno \n# aktivno prebivalstvo po posameznih skupinah7\n\ngraf_starostne_skupine_2018 <- ggplot(starostne_skupine_2018, \n aes(starost, stevilo), \n group = 1) + \n geom_col(fill=\"orange1\") + \n labs(x = \"starostna skupina\", y = \"\u0161tevilo delovno aktivnih\", title = \"Delovno aktivno prebivalstvo v letu 2018 po starostnih skupinah\")\n\n#==========================================================================================================================\n\n# b) delovno aktivno prebivalstvo glede na stopnjo dose\u017eene izobrazbe, letno\n\ngraf_spol_izobrazba <- ggplot(group = 1) + \n geom_line(data = zenske_izobrazba1 %>% filter(zenske_izobrazba1 == \"Brez izobrazbe, nepopolna osnovno\u0161olska\"), \n aes(x=leto, y=stevilo, color=\"\u017eenske brez izobrazbe, \\nnepopolna osnovno\u0161olska\"), size=0.8) + \n geom_line(data = moski_izobrazba1 %>% filter(moski_izobrazba1 == \"Brez izobrazbe, nepopolna osnovno\u0161olska\"), \n aes(x=leto, y=stevilo, color=\"mo\u0161ki brez izobrazbe, \\nnepopolna osnovno\u0161olska\"), size=0.8) + \n geom_line(data = zenske_izobrazba1 %>% filter(zenske_izobrazba1 == \"Vi\u0161je\u0161olska, visoko\u0161olska\"), \n aes(x=leto, y=stevilo, color=\"\u017eenske vi\u0161je\u0161olska, \\nvisoko\u0161olska izobrazba\"), size=0.8) + \n geom_line(data = moski_izobrazba1 %>% filter(moski_izobrazba1 == \"Vi\u0161je\u0161olska, visoko\u0161olska\"), \n aes(x=leto, y=stevilo, color=\"mo\u0161ki vi\u0161je\u0161olska, \\nvisoko\u0161olska izobrazba\"), size=0.8) + \n ggtitle(\"Stopnja delovne aktivnosti pri osebah brez izobrazbe oziroma \\npri osebah z vi\u0161je\u0161olsko, visoko\u0161olsko izobrazbo, \\npo spolu, v obdobju med letoma 2005 in 2020\") +\n xlab(\"leto\") + ylab(\"stopnja delovne aktivnosti v %\") + \n scale_x_continuous(breaks = c(2008:2020, 2)) +\n scale_color_manual(name = \"Legenda\", \n values = c(\"\u017eenske brez izobrazbe, \\nnepopolna osnovno\u0161olska\" = \"violetred2\", \n \"mo\u0161ki brez izobrazbe, \\nnepopolna osnovno\u0161olska\" = \"steelblue2\", \n \"\u017eenske vi\u0161je\u0161olska, \\nvisoko\u0161olska izobrazba\" = \"orange2\", \n \"mo\u0161ki vi\u0161je\u0161olska, \\nvisoko\u0161olska izobrazba\" = \"seagreen3\")) \n\n#==========================================================================================================================\n\n# c) delovno aktivno prebivalstvo glede po dejavnostih, letno (oblika csv)\n\nf <- DEJAVNOSTI_osnovne_2020 %>% group_by(DEJAVNOST)\ntortnidiagram <- ggplot(f) + aes(x=\"\", y=stevilo, fill= DEJAVNOST) +\n ggtitle(\"Delovno aktivni glede na dejavnost, v kateri so zaposleni, v letu 2020\") + \n geom_col(width=1) + \n coord_polar(theta=\"y\") + \n xlab(\"\") + ylab(\"\") +\n theme(axis.text = element_blank(),\n axis.ticks = element_blank(),\n panel.grid = element_blank(),\n legend.key.size = unit(0.3, 'cm'), #change legend key size\n legend.key.height = unit(0.3, 'cm'), #change legend key height\n legend.key.width = unit(0.3, 'cm'), #change legend key width\n legend.text = element_text(size=7)) + \n scale_fill_discrete(name = \"Legenda\") + \n theme(legend.position = \"right\")\n\ngraf_predelovalne_2020 <- ggplot(predelovalne_2020, aes(x=predelovalne, y=stevilo)) + \n geom_col(position = 'dodge', fill=\"royalblue1\") + \n coord_flip() + \n labs(x = \"Vrsta predelovalne dejavnosti\", y = \"\u0161tevilo delovno aktivnih\", \n title = \"Delovno aktivni v posameznih \\npodkategorijah predelovalne dejavnosti, v letu 2020\") \n\n#==========================================================================================================================\n\n# d) delovno aktivno prebivalstvo po starostnih skupinah in statisti\u010dnih regijah, letno (oblika xlsx)\n\nzemljevid3 <- uvozi.zemljevid(\"https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_SVN_shp.zip\", \"gadm36_SVN_1\",\n encoding=\"UTF-8\")\n\nzem_REG<- merge(zemljevid3, regija_2 , by.x=\"NAME_1\", by.y=\"REGIJA\" )\nzemljevid1 <- tm_shape(zem_REG) + \n tm_polygons(\"stevilo\",\n title = \"Legenda: \u0161tevilo delovno aktivnih\", \n palette = \"YlOrRd\") + \n tm_style(\"white\") +\n tm_text(\"NAME_1\", size = 0.5) +\n tm_layout(main.title = \"Delovno aktivni v starostni skupini 40-44 let v letu 2018 \",\n main.title.size = 1.2,\n legend.outside = TRUE)\n\n", "meta": {"hexsha": "10e13151b17dab32a9c1a6c9040b465b0d201dd1", "size": 5502, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "NikaFurlan/APPR-2020-21", "max_stars_repo_head_hexsha": "1e7a5a8fa540ea03cb544bd057b6fb6f5263d6b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "NikaFurlan/APPR-2020-21", "max_issues_repo_head_hexsha": "1e7a5a8fa540ea03cb544bd057b6fb6f5263d6b4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-08-23T09:32:51.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-25T11:00:04.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "NikaFurlan/APPR-2020-21", "max_forks_repo_head_hexsha": "1e7a5a8fa540ea03cb544bd057b6fb6f5263d6b4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 56.1428571429, "max_line_length": 173, "alphanum_fraction": 0.562522719, "num_tokens": 1732, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34431511031717515}} {"text": "library(tidyverse)\nlibrary(urbnmapr)\n\n# Add data\nnet <- read.csv(\"douglas-acs-net.csv\", header=TRUE, sep=\",\", quote=\"\\'\", colClass=c(\"character\", \"character\", \"numeric\", \"numeric\"))\n# Join with urbnmapr\nnet.data <- left_join(net, counties, by = \"county_fips\")\n\n#################################################\n# Pretty Breaks\npretty.breaks.net <- c(-50,-25,0,25,50)\n# find min and max values of pop growth\nminVal.net <- min(net.data$net_migration, na.rm = T)\nmaxVal.net <- max(net.data$net_migration, na.rm = T)\n# compute pop growth labels\nlabels.net <- c()\nbrks.net <- c(minVal.net, pretty.breaks.net, maxVal.net)\n# round the labels (actually, only the extremes)\nlabels <- c()\nfor(idx in 1:length(brks.net)){\n labels.net <- c(labels.net, paste0(round(brks.net[idx], 2),\n \" \u2013 \",\n round(brks.net[idx + 1], 2)))\n}\n# Minus one label to remove the odd ending one\nlabels.net <- labels.net[1:length(labels.net)-1]\n\n# Create new variable for fill\nnet.data$brks.net <- cut(net.data$net_migration,\n breaks = brks.net,\n labels = labels.net,\n include.lowest = T)\n\n#################################################\n# City Labels\nannot <- read.table(text=\n \"lat|long|just|city\n 41.257160|-95.995102|0|Omaha\n 41.881832|-87.623177|1|Chicago\",\n sep=\"|\", header=TRUE, stringsAsFactors=FALSE)\n\n#################################################\n# Net Migration Map\np <- ggplot() +\n # County Map\n geom_polygon(data = net.data, mapping = aes(x = long, y = lat, group = group,\n fill = net.data$brks.net), color = alpha(\"white\", 1 / 2), size = 0.2) +\n # State Map\n geom_polygon(data = urbnmapr::states, mapping = aes(long, lat, group = group),\n fill = NA, color = \"#7f7f7f\", size = 0.25, alpha=0.5) +\n # Projection\n coord_map(projection = \"polyconic\") +\n scale_fill_brewer(\n type = \"div\",\n palette = \"PRGn\",\n name = \"ACS Net Migration, 2012-2016\",\n #discrete = T,\n direction = -1,\n #begin=0.1,\n #end=0.9,\n guide = guide_legend(\n keyheight = unit(5, units = \"mm\"),\n title.position = 'top',\n reverse = F\n )) +\n # City labels -- experimental\n #geom_label(data=annot, aes(x=long, y=lat, label=city, hjust=just),\n # family=\"Open Sans Condensed Light\", lineheight=0.95,\n # size=3.5, label.size=0, color=\"#2b2b2b\", fill = \"transparent\") +\n # Theming\n theme_minimal(base_family = \"Open Sans Condensed Light\") +\n theme(\n legend.position = \"bottom\",\n legend.text.align = 0,\n legend.title.align = 0.5,\n plot.margin = unit(c(.5,.5,.2,.5), \"cm\")) +\n theme(\n axis.line = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks = element_blank(),\n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n ) +\n theme(plot.title=element_text(family=\"Open Sans Condensed Bold\", margin=margin(b=15))) +\n theme(plot.subtitle=element_text(family=\"Open Sans Condensed Light Italic\")) +\n theme(plot.margin=unit(rep(0.5, 4), \"cm\")) +\n labs(x = \"\",\n y = \"\",\n title = \"Douglas County loses the most population to warm places\",\n subtitle = \"Net Migration for Douglas County, Nebraska. 2012-2016\",\n caption = \"Author: Chris Goodman (@cbgoodman), Data: U.S. Census Bureau American Community Survey\")\n\n ggsave(plot=p, \"douglas-acs-net.png\", width=10, height=8, units=\"in\", dpi=\"retina\")\n", "meta": {"hexsha": "76d7f7ef652416c27dce554f151bc2074c001e21", "size": 3398, "ext": "r", "lang": "R", "max_stars_repo_path": "douglas-acs-net.r", "max_stars_repo_name": "cbgoodman/douglas-co-migration", "max_stars_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-12-12T16:48:25.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-12T16:48:25.000Z", "max_issues_repo_path": "douglas-acs-net.r", "max_issues_repo_name": "cbgoodman/douglas-co-migration", "max_issues_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "douglas-acs-net.r", "max_forks_repo_name": "cbgoodman/douglas-co-migration", "max_forks_repo_head_hexsha": "43b5404d1db93db24878d0d4e0eee4f9172cd7e8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0309278351, "max_line_length": 132, "alphanum_fraction": 0.619776339, "num_tokens": 994, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883592602049, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34431510211192906}} {"text": "context(\"data set functions\")\n\ntest_that(\"random splitting a data set works\", {\n data(iris)\n \n total <- nrow(iris)\n test_fraction <- 0.10\n \n ret <- random_split_dataset(iris, test_fraction)\n \n test <- ret[\"test\"]$test\n \n observed <- nrow(test)\n expected <- total * test_fraction\n \n expect_equal(expected, observed)\n})", "meta": {"hexsha": "b69ea81fbfd64041a59d13fbdb12935b1ff7e7c4", "size": 330, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_dataset_functions.r", "max_stars_repo_name": "mmadsen/mmadsenr", "max_stars_repo_head_hexsha": "1a7ac5431d87628a32fee0453a7d2a289dd5975b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test_dataset_functions.r", "max_issues_repo_name": "mmadsen/mmadsenr", "max_issues_repo_head_hexsha": "1a7ac5431d87628a32fee0453a7d2a289dd5975b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test_dataset_functions.r", "max_forks_repo_name": "mmadsen/mmadsenr", "max_forks_repo_head_hexsha": "1a7ac5431d87628a32fee0453a7d2a289dd5975b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.4117647059, "max_line_length": 50, "alphanum_fraction": 0.6727272727, "num_tokens": 86, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6001883449573377, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.344315093906683}} {"text": "segmentation_Genomic_length_distribution_of_identified_small_RNA_loci<- function(fprefix='input',wdir=\".\") {\r\nstart_time <- proc.time()\r\ncat(\"Length distribution of loci after segmentation (M3.02) start\", date(), \"\\n\")\r\n\r\n# Plots for Module 3 Analysis and visualization of SPAR output \r\n# Session: Segmentation characteristics \r\n\r\n# Module_3_Figure_2(Figure 3.02) \t\r\n# Description: Length distribution of loci after segmentation\r\n# input: input_annot.with_conservation.xls, input.unannot.final.with_conservation.xls\r\n# output: Genomic_length_distribution_of_identified_small_RNA_loci.png\r\n# output: Genomic_length_distribution_of_identified_small_RNA_loci.pdf\r\n\r\n# parameters for the plot \r\ndatafile1=paste(wdir, \"/\", fprefix, \"_annot.with_conservation.xls\", sep=\"\")\r\ndatafile2=paste(wdir, \"/\", fprefix, \"_unannot.with_conservation.xls\", sep=\"\")\r\nBASENAME=\"Genomic_length_distribution_of_identified_small_RNA_loci\"\r\nPLOTTITLE=\"Length distribution of all loci \\n after segmentation\"\r\nXTITLE=\"Peak_length(nt)\"\r\nYTITLE=\"Percentage\"\r\n\r\n# libraries needed \r\nsuppressPackageStartupMessages(library(reshape2))\r\nsuppressPackageStartupMessages(library(ggplot2))\r\nsuppressPackageStartupMessages(library(plyr))\r\n\r\n#args<-commandArgs(TRUE)\r\n#datafile1=args[1] # input file 1\r\n#datafile2=args[2] # input file 2\r\n#wdir=args[3] # output / working directory\r\n#if (length(args)<1) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<2) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<3) { wdir=\".\" } \r\n#use current dir if no \r\n#working dir has been specified\r\n\r\n# output image file\r\npngfile = paste(wdir, \"/../figures/\", paste(BASENAME,\".png\",sep=\"\"), sep=\"\")\r\n#pdffile= paste(wdir, \"/\", paste(BASENAME,\".pdf\",sep=\"\"), sep=\"\")\r\n\r\n# Read in data\r\nD = read.table(datafile1,sep='\\t',header=T,comment.ch=\"\")\r\nE = read.table(datafile2,sep='\\t',header=T,comment.ch=\"\")\r\nE$annotRNAclass = \"unannot\"\r\n#DE = rbind(D[,c(1:20,25,29:31)],E) \r\nDE = rbind(D,E)\r\n\r\nDE$Peak_length = DE$peakChrEnd-DE$peakChrStart\r\nDX = DE[DE$Peak_length<=44,]\r\nDXS = table(DX$Peak_length)\r\nDXS = data.frame(DXS)\r\nDXS$norm = DXS$Freq/sum(DXS$Freq)*100 \r\n\r\n# make plot \r\npng(pngfile,width = 7, height = 7, units = 'in', res = 300, type=\"cairo\")\r\noptions(scipen=10000)\r\nprint(\tggplot(data = DXS, aes(x = Var1, y = norm)) + \r\n\t\tgeom_bar(stat=\"identity\") + theme_classic()+\r\n\t\tggtitle(PLOTTITLE) + xlab(XTITLE)+ylab(YTITLE)+\r\n\t\tylim(0,30)+\r\n\t\ttheme(text = element_text(size=16),axis.text.x = element_text(angle=90, vjust = 0.5, hjust=1)))\r\ndev.off()\r\n\r\ncat(\"Total time for length distribution of loci after segmentation (M3.02) analysis:\", (proc.time() - start_time)[['elapsed']], \"seconds (\", date(), \")\\n\")\r\n}\r\n\r\n# segmentation_Genomic_length_distribution_of_identified_small_RNA_loci(fprefix=fprefix,wdir=wdir)\r\n", "meta": {"hexsha": "a090b3a2288a0dc608c31897e83f6e7867b35a65", "size": 2829, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/R/module3/M3.02_segmentation_Genomic_length_distribution_of_identified_small_RNA_loci.r", "max_stars_repo_name": "ConYel/spar_pipeline", "max_stars_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-12-03T10:07:54.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-03T10:07:54.000Z", "max_issues_repo_path": "scripts/R/module3/M3.02_segmentation_Genomic_length_distribution_of_identified_small_RNA_loci.r", "max_issues_repo_name": "ConYel/spar_pipeline", "max_issues_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-12-09T03:48:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-08T13:35:31.000Z", "max_forks_repo_path": "scripts/R/module3/M3.02_segmentation_Genomic_length_distribution_of_identified_small_RNA_loci.r", "max_forks_repo_name": "ConYel/spar_pipeline", "max_forks_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.6029411765, "max_line_length": 156, "alphanum_fraction": 0.721456345, "num_tokens": 820, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736783928749127, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3443150933626251}} {"text": "#!/usr/bin/env Rscript\n\nargs=commandArgs(trailingOnly=TRUE)\n\nlibrary(GSEABase)\nlibrary(xCell)\nrnaseq=as.logical(toupper(args[1]))\nscale=as.logical(toupper(args[2]))\nalpha=as.numeric(args[3])\nnperm=as.numeric(args[4])\nparallel.sz=as.numeric(args[5])\nverbose=as.logical(toupper(args[6]))\ntempdir=args[7]\nbeta_pval=as.logical(toupper(args[8]))\nperm_pval=as.logical(toupper(args[9]))\n\nmat = read.csv(file.path(tempdir,'expr.csv'),header=TRUE,row.names=1,check.names=FALSE)\n\noutput = xCellAnalysis(mat,\n\t rnaseq=rnaseq,\n\t scale=scale,\n\t alpha=alpha,\n parallel.sz=parallel.sz\n )\nofile = file.path(tempdir,\"pathways.csv\")\nwrite.csv(output,ofile)\n\nif(beta_pval) {\n distroBeta = xCellSignifcanceBetaDist(output,rnaseq=rnaseq)\n ofileBeta = file.path(tempdir,\"beta.csv\")\n write.csv(distroBeta,ofileBeta)\n}\nif(perm_pval) {\n distroRandom = xCellSignifcanceRandomMatrix(scores = output, expr = mat, spill = NULL, alpha=alpha,nperm=nperm)\n ofileRandomP = file.path(tempdir,\"randomP.csv\")\n ofileRandomD = file.path(tempdir,\"randomD.csv\")\n write.csv(distroRandom$pvals,ofileRandomP)\n write.csv(distroRandom$shuff_xcell,ofileRandomD)\n}\n", "meta": {"hexsha": "a0bdf60845ef6a320bc5c7ff98b6006cfde54644", "size": 1161, "ext": "r", "lang": "R", "max_stars_repo_path": "xcell/xcell.r", "max_stars_repo_name": "jason-weirather/xCell", "max_stars_repo_head_hexsha": "3cbb3c0ff271cefeac011e187e67d278abdb3a5b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-13T23:57:47.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-13T23:57:47.000Z", "max_issues_repo_path": "xcell/xcell.r", "max_issues_repo_name": "jason-weirather/xCell", "max_issues_repo_head_hexsha": "3cbb3c0ff271cefeac011e187e67d278abdb3a5b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "xcell/xcell.r", "max_forks_repo_name": "jason-weirather/xCell", "max_forks_repo_head_hexsha": "3cbb3c0ff271cefeac011e187e67d278abdb3a5b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-13T23:57:52.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-13T23:57:52.000Z", "avg_line_length": 29.025, "max_line_length": 115, "alphanum_fraction": 0.7347114556, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544335934766, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.34416473606487413}} {"text": "setwd(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion\") \n\n###############################################################################\n\n# Cargar librerias que se van a utilizar\n\nlibrary(\"leaflet\") #Paquete para usar cosas de mapas\nlibrary(\"rgdal\") #Paquete para leer archivos shapefiles .shp\nlibrary(\"dplyr\") #Paquete para filtrar datos de dataframes\nlibrary(\"fontawesome\") #Paquete para \u00edconos de marcadores\nlibrary(\"htmlwidgets\") #Paquete para salvar mapa en html\nlibrary(\"leaflet.extras\") #Paquete para poder buscar en los mapas\nlibrary(\"sf\")\nlibrary(\"raster\")\nlibrary(\"geojsonsf\")\nlibrary('geojsonio')\nlibrary(\"spdplyr\")\nlibrary(\"tidyverse\")\nlibrary(\"DBI\")\nlibrary('RPostgres')\nsetwd(paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/\")) \n###############################################################################\n# SQl database\n\ndb <- 'ZMVM_Urbanizacion'\nhost_db <- 'localhost' \ndb_port <- '5432'\ndb_user <- 'postgres' \ndb_key <- readLines('./database_key.txt')\ncon <- dbConnect(Postgres(), dbname = db, host=host_db, port=db_port, user=db_user, password=db_key)\n\n###############################################################################\n# Initiate map, get data, create empty map and groups list\ngrupo_d=\"L\u00edmite distrito\"\ngrupos<-c()\nmap <- leaflet()\n\n###############################################################################\n#CDMX\n\ndistritos<-c(1:85)\n\nfor(i in 1:length(distritos)){\n if(str_length(as.character(distritos[i]))==1){\n distritos[i]<-paste0(\"00\",distritos[i])\n }\n if(str_length(as.character(distritos[i]))==2){\n distritos[i]<-paste0(\"0\",distritos[i])\n }\n}\n\nubicacion_dist <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/DistritosEODHogaresZMVM2017/DistritosEODHogaresZMVM2017.shp\") \ndist_todos <- readOGR(ubicacion_dist, layer = paste0(\"DistritosEODHogaresZMVM2017\"), verbose = FALSE, GDAL1_integer64_policy=TRUE) \ndist_todos <- spTransform(dist_todos,CRS(\"+proj=longlat +ellps=WGS84 +no_defs\"))\n\n\npb <- txtProgressBar(min = 0, max = length(distritos), style = 3)\ni<-0\nfor(distrito_str in distritos){\n i<-i+1\n if (sample(1:1, 1)==sample(1:1, 1) || i==length(distritos)){\n setTxtProgressBar(pb, i)\n }\n \n distrito_int<-as.numeric(distrito_str)\n query= paste0(\"select * from fid where distrito = \",distrito_str)\n datos<-dbGetQuery(con, query)\n\n ###############################################################################\n #Distritos\n\n\n dist <- subset(dist_todos, dist_todos$Distrito %in% distrito_str)\n\n map <- map %>%\n\n addPolygons(data= dist,\n stroke = TRUE,\n color='grey',\n opacity=1,\n smoothFactor = 0.5,\n weight = 4,\n fillColor = \"#26b8e8\",\n fillOpacity = 0,\n group = grupo_d,\n popup = paste(\n '
Clave distrito: ', paste(dist$Distrito),\n '

Estado: ', paste(\"CDMX\")\n ))\n\n area_dist <-area(dist)\n radius_dist <- sqrt(area_dist/pi)\n point_dist <- dist %>% geojson_json() %>% geojson_sf() %>% st_centroid()\n\n mixto_data <- subset(datos, uso_suelo==\"Habitacional y comercial\")\n area_mixto <- sum(mixto_data$area)\n\n trabajo_data <- subset(datos, uso_suelo %in% c(\"Industrial\",\"Industrial y comercial\",\"Equipamiento\"))\n area_trabajo <- sum(trabajo_data$area)+area_mixto\n #area_trabajo <- 0\n radius_trabajo <- sqrt((area_trabajo)/pi)\n\n residencia_data <- subset(datos, uso_suelo==\"Habitacional\")\n #area_residencia <- sum(residencia_data$area)+area_mixto\n area_residencia <- 0\n radius_residencia <- sqrt((area_residencia+area_trabajo)/pi)\n\n ###############################################################################\n #sin zonificaci\u00f3n\n\n grupo=\"Sin Zonificaci\u00f3n\"\n\n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_dist,\n stroke = TRUE,\n color='black',\n opacity=1,\n weight = 4,\n fillColor = \"#26b8e8\",\n fillOpacity = 0,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"CDMX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round(((area_dist-(area_residencia-area_mixto+area_trabajo))/area_dist)*100,2),'%')\n ))\n\n\n ###############################################################################\n #residencia\n\n grupo=\"Residencia\"\n\n if(nrow(residencia_data)+nrow(mixto_data)>0){\n\n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_residencia,\n stroke = TRUE,\n color=\"#105166\",\n weight = 4,\n fillColor = \"#26b8e8\",\n group = grupo,\n fillOpacity = 0.7,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"CDMX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_residencia/area_dist)*100,2),'%')\n ))\n\n if(!(grupo %in% grupos)){\n grupos<-c(grupos,grupo)\n }\n }\n\n ###############################################################################\n #Trabajo\n\n grupo=\"Trabajo\"\n\n if(nrow(trabajo_data)+nrow(mixto_data)>0){\n \n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_trabajo,\n stroke = TRUE,\n color=\"#170f42\",\n weight = 4,\n fillColor = \"#3c29ab\",\n group = grupo,\n fillOpacity = 1,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"CDMX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_trabajo/area_dist)*100,2),'%')\n ))\n\n if(!(grupo %in% grupos)){\n grupos<-c(grupos,grupo)\n }\n }\n}\n\n###############################################################################\n#MEX\n\ndistritos<-c(100:207)\n\nfor(i in 1:length(distritos)){\n if(str_length(as.character(distritos[i]))==1){\n distritos[i]<-paste0(\"00\",distritos[i])\n }\n if(str_length(as.character(distritos[i]))==2){\n distritos[i]<-paste0(\"0\",distritos[i])\n }\n}\n\n###############################################################################\n# Get data, create empty map and groups list\n\n\nubicacion_datos <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/ZMVM_municipios.csv\") \ndatos <- ubicacion_datos %>% read.csv(header = TRUE) \ndatos <- datos %>% lapply(as.character) %>% as.data.frame(stringsAsFactors = FALSE)\nmex_data <- subset(datos, state_abbr==\"MEX\")\n\nubicacion_dist <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/DistritosEODHogaresZMVM2017/DistritosEODHogaresZMVM2017.shp\") \ndist_todos <- readOGR(ubicacion_dist, layer = paste0(\"DistritosEODHogaresZMVM2017\"), verbose = FALSE, GDAL1_integer64_policy=TRUE) \ndist_todos <- spTransform(dist_todos,CRS(\"+proj=longlat +ellps=WGS84 +no_defs\"))\n\nubicacion_zh_mex_todos <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/igecemTipologiaahA2015Cg/igecemTipologiaahA2015Cg.shp\") #Ubicaci\u00f3n del archivo shapefile \nzh_mex_todos <- ubicacion_zh_mex_todos %>% readOGR(layer = paste0(\"igecemTipologiaahA2015Cg\"), verbose = FALSE, GDAL1_integer64_policy=TRUE) #Leer shapefile\nzh_mex_todos <- zh_mex_todos %>% spTransform(CRS(\"+proj=longlat +ellps=WGS84 +no_defs\"))\nzh_mex_todos <- subset(zh_mex_todos,zh_mex_todos$cveinegi %in% mex_data$region)\nzh_mex_todos_sf <- st_as_sf(zh_mex_todos)\nzh_mex_todos_cent <- st_centroid(zh_mex_todos_sf)\n\npb <- txtProgressBar(min = 0, max = nrow(zh_mex_todos_cent), style = 3)\nfor (i in 1:nrow(zh_mex_todos_cent)){\n if (sample(1:40, 1)==sample(1:40, 1) || i==length(zh_mex_todos_cent)){\n setTxtProgressBar(pb, i)\n }\n point <- SpatialPoints(cbind(as.numeric(unlist(zh_mex_todos_cent$geometry[i]))[1],as.numeric(unlist(zh_mex_todos_cent$geometry[i]))[2]),proj4string=CRS(as.character(\"+proj=longlat +ellps=WGS84 +no_defs\")))\n zh_mex_todos$distrito[i]<-as.character(unlist(over(point,dist_todos,returnList = FALSE)$Distrito))\n zh_mex_todos$area[i]<-area(zh_mex_todos[i,1])\n}\n\npb <- txtProgressBar(min = 0, max = length(distritos), style = 3)\nii<-0\nfor(distrito_str in distritos){\n ii<-ii+1\n if (sample(1:1, 1)==sample(1:1, 1) || ii==length(distritos)){\n setTxtProgressBar(pb, ii)\n }\n zh_mex<-zh_mex_todos\n distrito_int<-as.numeric(distrito_str)\n\n #Distritos\n\n dist <- subset(dist_todos, dist_todos$Distrito %in% distrito_str)\n\n k <- 0\n\n\n\n\n zh_dist <- subset(zh_mex,zh_mex$distrito==distrito_str)\n \n area_dist <-area(dist)\n radius_dist <- sqrt(area_dist/pi)\n point_dist <- dist %>% geojson_json() %>% geojson_sf() %>% st_centroid()\n\n trabajo_data <- subset(zh_dist@data, tipologia %in% c(\"Industrial\",\"Comercial\",\"Equipamiento\"))\n area_trabajo <- sum(trabajo_data$area)\n #area_trabajo <- 0\n radius_trabajo <- sqrt(area_trabajo/pi)\n\n residencia_data <- subset(zh_dist@data, tipologia==\"Habitacional\")\n #area_residencia <- sum(residencia_data$area)\n area_residencia <- 0\n radius_residencia <- sqrt((area_residencia+area_trabajo)/pi)\n\n ###############################################################################\n #distrito\n\n map <- map %>%\n\n addPolygons(data= dist,\n stroke = TRUE,\n color='grey',\n opacity=1,\n smoothFactor = 0.5,\n weight = 4,\n fillColor = \"#26b8e8\",\n fillOpacity = 0,\n group=grupo_d,\n popup = paste(\n '
Clave distrito: ', paste(dist$Distrito),\n '

Estado: ', paste(\"CDMX\")\n ))\n\n\n ###############################################################################\n #sin zonificaci\u00f3n\n\n grupo=\"Sin Zonificaci\u00f3n\"\n\n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_dist,\n stroke = TRUE,\n color='black',\n opacity=1,\n weight = 4,\n fillColor = \"#26b8e8\",\n fillOpacity = 0,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"MEX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round(((area_dist-(area_residencia+area_trabajo))/area_dist)*100,2),'%')\n ))\n\n\n ###############################################################################\n #residencia\n\n grupo=\"Residencia\"\n\n if(nrow(residencia_data)>0){\n\n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_residencia,\n stroke = TRUE,\n color=\"#105166\",\n weight = 4,\n fillColor = \"#26b8e8\",\n group = grupo,\n fillOpacity = 0.7,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"MEX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_residencia/area_dist)*100,2),'%')\n ))\n\n if(!(grupo %in% grupos)){\n grupos<-c(grupos,grupo)\n }\n }\n\n ###############################################################################\n #Trabajo\n\n grupo=\"Trabajo\"\n\n if(nrow(trabajo_data)>0){\n\n map <- map %>% \n\n addCircles(lng=unlist(point_dist$geometry)[1],\n lat=unlist(point_dist$geometry)[2],\n radius=radius_trabajo,\n stroke = TRUE,\n color=\"#170f42\",\n weight = 4,\n fillColor = \"#3c29ab\",\n group = grupo,\n fillOpacity = 1,\n popup = paste(\n '
Distrito: ', paste(distrito_str),\n '

Estado: ', paste(\"MEX\"),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_trabajo/area_dist)*100,2),'%')\n ))\n\n if(!(grupo %in% grupos)){\n grupos<-c(grupos,grupo)\n }\n }\n###############################################################################\n}\n\n###############################################################################\n\nmap <- addTiles(map)\nmap<- map %>% addLayersControl( #Agrega control sobre visulizaci\u00f3n de Oxxos en el mapa\noverlayGroups = grupo_d,\noptions = layersControlOptions(collapsed = TRUE)\n)\nmap <- map %>% addResetMapButton() \n\n###############################################################################\nsetwd(paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/Mapas_distritos/\")) \nsaveWidget(map, file=paste0(\"000_circle_work_map.html\"))\n", "meta": {"hexsha": "0b161f4f82327c348662c2a6bdd099946ea7a328", "size": 14125, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/distritos_circulos.r", "max_stars_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_stars_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/distritos_circulos.r", "max_issues_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_issues_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/distritos_circulos.r", "max_forks_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_forks_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.2179487179, "max_line_length": 209, "alphanum_fraction": 0.5186548673, "num_tokens": 3604, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544210587585, "lm_q2_score": 0.4921881357207955, "lm_q1q2_score": 0.3441647298954345}} {"text": "#' Plot a lagr object\n#'\n#' @export\nplot.lagr <- function(obj, target, type=c(\"raw\", \"coef\", \"is.zero\"), id=\"id\") {\n \n \n #Follow here for a one-dimensional effect-modifying parameter:\n if (obj$dim == 1) {\n #Put the target in a plotting slot:\n if (type == \"coef\") {\n val = obj$coefs[[target]]\n } else if (type == \"is.zero\") {\n val = obj$is.zero[[target]]\n } else if (type == \"raw\") {\n val = obj$data[[target]]\n }\n \n plot(x=obj$coords, y=val, xlab=\"location\", ylab=target, type='l', bty='n')\n } \n \n #Follow here for a two-dimensional effect-modifying parameter:\n if (obj$dim == 2) {\n \n \n if (is(obj$data, \"Spatial\")) {\n polygons = obj$data\n if (!(id %in% colnames(polygons@data))) {\n polygons@data[[id]] = rownames(polygons@data)\n }\n } else {\n #If the data was not specified as a spatial data frame, make a Voronoi diagram:\n crds = obj$coords\n z = deldir(crds[,1], crds[,2])\n w = tile.list(z)\n polys = vector(mode='list', length=length(w))\n \n for (i in seq(along=polys)) {\n pcrds = cbind(w[[i]]$x, w[[i]]$y)\n pcrds = rbind(pcrds, pcrds[1,])\n polys[[i]] = Polygons(list(Polygon(pcrds)), id=as.character(i))\n }\n SP = SpatialPolygons(polys)\n polygons = SpatialPolygonsDataFrame(SP, data=data.frame(x=crds[,1], \n y=crds[,2], row.names=sapply(slot(SP, 'polygons'), \n function(x) slot(x, 'id'))))\n \n polygons@data$id = rownames(polygons@data)\n } \n \n #Put the target in a plotting slot:\n if (type == \"coef\") {\n polygons@data[[paste(\".\", target, sep=\"\")]] = obj$coefs[[target]]\n } else if (type == \"is.zero\") {\n polygons@data[[paste(\".\", target, sep=\"\")]] = obj$is.zero[[target]]\n } else if (type == \"raw\") {\n polygons@data[[paste(\".\", target, sep=\"\")]] = obj$data[[target]]\n }\n \n #Repair any holes in the shapefile:\n slot(polygons, \"polygons\") <- lapply(slot(polygons, \"polygons\"), checkPolygonsHoles)\n polygons2 <- unionSpatialPolygons(polygons, as.character(polygons@data[[id]]))\n \n points = fortify(polygons2, region=id)\n# names(points)[which(names(points)=='id')] = id\n df = inner_join(points, polygons@data, by=id) \n \n #Plot the shapes:\n ggplot(df, aes_string(\"long\", \"lat\", group='group', fill=paste(\".\", target, sep=\"\"))) + geom_polygon() +\n scale_fill_gradient2(low=muted(\"blue\"), mid=\"white\", high=\"orange\", limits=range(polygons@data[[paste(\".\", target, sep=\"\")]], na.rm=TRUE), name=\"\")\n\n }\n\n\n}\n", "meta": {"hexsha": "5006e04f3e15d7a09b45733a93ce7d7d7eb88eb6", "size": 2859, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot.lagr.r", "max_stars_repo_name": "wrbrooks/lagr", "max_stars_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/plot.lagr.r", "max_issues_repo_name": "wrbrooks/lagr", "max_issues_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/plot.lagr.r", "max_forks_repo_name": "wrbrooks/lagr", "max_forks_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.12, "max_line_length": 159, "alphanum_fraction": 0.5099685205, "num_tokens": 751, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832805, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.34415216484672406}} {"text": "library(ConjunctionStats)\n\nsim <- ReadSummary(list.files(pattern = 'og.out'))\nGradTable <- ReadSetting()\nGradTable <- FillSetting(sim,GradTable)\n\nn <- 5\npalette <- brewer.pal(8,\"Spectral\")\npalette <- palette[c(1,3,6,7,8)]\n\nplot(c(),xlab='centered index',ylab='logit(Hybrid index)',xlim = c(-25,25),ylim = c(-9,9),cex.lab=2, cex.axis=2)\ni = 0\nfor(s in which(GradTable$L == 2)){\n\ti = i + 1\n\tsim[[s]]$centered <- sim[[s]]$order - GradTable$center[s]\n\tord <- order(sim[[s]]$centered)\n\tpoints(logit(meanHI) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\tlines(logit(meanHI[ord]) ~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n}\nlegend(-25, 9, c(expression(1 / 16),expression(1 / 4),1,4,16), col = palette, pch = 20, title = expression(beta),cex=1)\n\n\npdf('Beta_effect_simmula_l2_zoom.pdf')\n\tpar(mar= c(4, 4.3, 0, 0) + 0.5)\n\tplot(c(),xlab='centered index',ylab='logit(Hybrid index)',xlim = c(-5,5),ylim = c(-5,5),cex.lab=2, cex.axis=2)\n\n\ti = 0\n\tfor(s in which(GradTable$L == 2)){\n\t\ti = i + 1\n\t\tsim[[s]]$centered <- sim[[s]]$order - GradTable$center[s]\n\t\tord <- order(sim[[s]]$centered)\n\t\tpoints(logit(meanHI) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\t\tlines(logit(meanHI[ord])~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n\t}\n# \tlegend(-23, 8, c(expression(1 / 16),expression(1 / 4),1,4,16), col = palette, pch = 20, title = expression(beta),cex=1.3)\ndev.off()\n\npdf('Beta_effect_simmula_l8.pdf')\n\tpar(mar= c(4, 4.3, 0, 0) + 0.5)\n\tplot(c(),xlab='centered index',ylab='logit(Hybrid index)',xlim = c(-60,60),ylim = c(-10,10),cex.lab=2, cex.axis=2)\n\n\ti = 0\n\tfor(s in which(GradTable$L == 8)){\n\t\ti = i + 1\n\t\tord <- order(sim[[s]]$centered)\n\t\tpoints(logit(meanHI) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\t\tlines(logit(meanHI[ord])~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n\t}\n# \tlegend(-50, 10, c(expression(1 / 16),expression(1 / 4),1,4,16), col = palette, pch = 20, title = expression(beta),cex=1.3)\ndev.off()\n\npdf('Beta_effect_simmula_l8_zoom.pdf')\n\tpar(mar= c(4, 4.3, 0, 0) + 0.5)\n\tplot(c(),xlab='centered index',ylab='logit(Hybrid index)',xlim = c(-8,8),ylim = c(-5,5),cex.lab=2, cex.axis=2)\n\n\ti = 0\n\tfor(s in which(GradTable$L == 8)){\n\t\ti = i + 1\n\t\tord <- order(sim[[s]]$centered)\n\t\tpoints(logit(meanHI) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\t\tlines(logit(meanHI[ord])~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n\t}\n# \tlegend(-50, 10, c(expression(1 / 16),expression(1 / 4),1,4,16), col = palette, pch = 20, title = expression(beta),cex=1.3)\ndev.off()\n\n# normalize\n\npdf('Beta_effect_l8_norm.pdf')\n\tpar(mar= c(4, 4.3, 0, 0) + 0.5)\n\tplot(c(),xlab='centered index',ylab='logit(Hybrid index) / slope',xlim = c(-8,8),ylim = c(-5,5),cex.lab=2, cex.axis=2)\n\n\ti = 0\n\tslopes <- 2 * sqrt(GradTable$ss) / (sqrt(0.5))\n\n\tfor(s in which(GradTable$L == 8)){\n\t\ti = i + 1\n\t\tnormC <- slopes[s]\n\t\tord <- order(sim[[s]]$centered)\n\t\tpoints((logit(meanHI) / normC) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\t\tlines((logit(meanHI[ord]) / normC) ~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n\t}\n\n# \t\tslopes[9] = slopes[9] - 0.2\n# \t\tslopes[10] = slopes[10] - 0.3\n# \t\tnormC <- slopes[9]\n# \t\tord <- order(sim[[9]]$centered)\n# \t\tpoints((logit(meanHI) / normC) ~ centered, data = sim[[9]], pch = 2,col=1)\n# \t\tlines((logit(meanHI[ord]) / normC) ~ centered[ord], data = sim[[9]], lwd = 2, lty = 2, col=1)\n# \t\tnormC <- slopes[10]\n# \t\tord <- order(sim[[10]]$centered)\n# \t\tpoints((logit(meanHI) / normC) ~ centered, data = sim[[10]], pch = 3,col=1)\n# \t\tlines((logit(meanHI[ord]) / normC) ~ centered[ord], data = sim[[10]], lwd = 2, lty = 3, col=1)\n\ndev.off()\n\n\n\npdf('Beta_effect_l2_norm.pdf')\n\tpar(mar= c(4, 4.3, 0, 0) + 0.5)\n\tplot(c(),xlab='centered index',ylab='logit(Hybrid index) / slope',xlim = c(-8,8),ylim = c(-5,5),cex.lab=2, cex.axis=2)\n\n\ti = 0\n\tslopes <- 2 * sqrt(GradTable$ss) / (sqrt(0.5))\n\tfor(s in which(GradTable$L == 2)){\n\t\ti = i + 1\n\t\tnormC <- slopes[s]\n\t\tord <- order(sim[[s]]$centered)\n\t\tpoints((logit(meanHI) / normC) ~ centered, data = sim[[s]], pch = 20,col=palette[i])\n\t\tlines((logit(meanHI[ord]) / normC) ~ centered[ord], data = sim[[s]], lwd = 2,col=palette[i])\n\t}\ndev.off()\n", "meta": {"hexsha": "df3c6fcb76c355d3c79536d90ecd592ec21a6139", "size": 4184, "ext": "r", "lang": "R", "max_stars_repo_path": "V_verification/V06_epistasis/masters/STEPclines_epi.r", "max_stars_repo_name": "KamilSJaron/ConjunctionExamples", "max_stars_repo_head_hexsha": "d96e9ba2f94bfc6525a5b58e0d7b3b65b436c630", "max_stars_repo_licenses": ["OLDAP-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "V_verification/V06_epistasis/masters/STEPclines_epi.r", "max_issues_repo_name": "KamilSJaron/ConjunctionExamples", "max_issues_repo_head_hexsha": "d96e9ba2f94bfc6525a5b58e0d7b3b65b436c630", "max_issues_repo_licenses": ["OLDAP-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "V_verification/V06_epistasis/masters/STEPclines_epi.r", "max_forks_repo_name": "KamilSJaron/ConjunctionExamples", "max_forks_repo_head_hexsha": "d96e9ba2f94bfc6525a5b58e0d7b3b65b436c630", "max_forks_repo_licenses": ["OLDAP-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3571428571, "max_line_length": 125, "alphanum_fraction": 0.6063575526, "num_tokens": 1695, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3441521567221512}} {"text": "# author Jens Georg\r\n# selects candidates for copraRNA based on a phylogenetic tree of the input sRNAs\r\n# selects a pre-defined set of organisms around the ooi, the pre-selected organisms and the organsims selected based uon the tree by the script\r\n\r\n#call: R --slave -f GLASSgo2CopraRNA_all_neighbours.r --args wildcard=NC_000913,NC_003197 exclude=NC_020260 max_number=5 outfile_prefix=sRNA ooi=NC_000913 sim=3\r\n\r\nargs <- commandArgs(trailingOnly = TRUE)\r\n\r\nooi<-\"NC_000913\"\r\nwildcard<-c(\"NC_000913\",\"NC_000911\",\"NC_003197\",\"NC_016810\",\"NC_000964\",\"NC_002516\",\"NC_003210\",\"NC_007795\",\"NC_003047\")\r\nmax_number<-5\r\noutfile_prefix<-\"sRNA\"\r\nexclude<-c(\"NZ_CP009781.1\",\"NZ_LN681227.1\")\r\nsim<-3\r\n\r\nfor(i in 1:length(args)){\r\n\ttemp<-strsplit(args[i],\"=\")\r\n\ttemp<-temp[[1]]\r\n\ttemp1<-temp[1]\r\n\ttemp2<-temp[2]\r\n\tassign(as.character(temp1),temp2)\r\n }\r\n\r\nwil<-grep(\",\",wildcard)\r\nif(length(wil)>0){\r\n\twildcard<-strsplit(wildcard,\",\")[[1]]\r\n} \r\n \r\nmax_number<-as.numeric(max_number)\r\nsim<-as.numeric(sim)\r\nrequire(ape)\r\nload(\"refined_GLASSgo_table.Rdata\")\r\n\r\n\r\ntemp<-coor2\r\nif(length(exclude)>0){\r\n\ttemp_ex<-c()\r\n\tfor(i in 1:length(exclude)){\r\n\t\ttemp_ex1<-grep(exclude[i], coor2[,\"fin\"])\r\n\t\tif(length(temp_ex1)>0){\r\n\t\t\ttemp_ex<-c(temp_ex,temp_ex1)\r\n\t\t}\r\n\t}\r\n\tif(length(temp_ex)>0){\r\n\t\r\n\ttemp<-temp[-temp_ex,]\r\n\t}\r\n}\r\ncoor2<-temp\r\n\r\n\r\n\r\n# clustal omega call with kimura distance matrix for tree generation\r\nclustalo3<-function(coor, positions){\r\n\tfasta<-c()\r\n\tfor(i in 1:length(positions)){\r\n\t\tfasta<-c(fasta, paste(\">\",coor[positions[i],\"fin\"],sep=\"\"))\r\n\t\tfasta<-c(fasta, as.character(coor[positions[i],\"sequence\"]))\r\n\t}\r\n\twrite.table(fasta, file=\"temp_fasta\", row.names=F, col.names=F, quote=F)\r\n\twd<-getwd()\r\n\tcommand<-paste(\"clustalo -i \", \"temp_fasta\", \" --distmat-out=distmatout.txt --full --output-order=input-order --use-kimura --force --max-hmm-iterations=-1\", sep=\"\")\r\n\tsystem(command)\r\n\tna<-grep(\">\", fasta)\r\n\tna<-gsub(\">\",\"\",fasta[na])\r\n\ttemp<-read.delim(\"distmatout.txt\",sep=\"\",header=F, , skip=1)\r\n\tunlink(\"distmatout.txt\")\r\n\tunlink(\"temp_fasta\")\r\n\ttemp<-temp[,2:ncol(temp)]\r\n\tcolnames(temp)<-na\r\n\trownames(temp)<-na\r\n\ttemp\r\n}\r\n\r\n# clustal omega call with percent identity matrix \r\nclustalo4<-function(coor, positions){\r\n\twd<-getwd()\r\n\tfasta<-c()\r\n\tfor(i in 1:length(positions)){\r\n\t\tfasta<-c(fasta, paste(\">\",coor[positions[i],\"fin\"],sep=\"\"))\r\n\t\tfasta<-c(fasta, as.character(coor[positions[i],\"sequence\"]))\r\n\t}\r\n\twrite.table(fasta, file=\"temp_fasta\", row.names=F, col.names=F, quote=F)\r\n\tcommand<-paste(\"clustalo -i \", \"temp_fasta\", \" --distmat-out=distmatout.txt --full --percent-id --output-order=input-order --force --max-hmm-iterations=-1\", sep=\"\")\r\n\tsystem(command)\r\n\tna<-grep(\">\", fasta)\r\n\tna<-gsub(\">\",\"\",fasta[na])\r\n\ttemp<-read.delim(\"distmatout.txt\",sep=\"\",header=F, , skip=1)\r\n\tunlink(\"distmatout.txt\")\r\n\ttemp<-temp[,2:ncol(temp)]\r\n\tcolnames(temp)<-na\r\n\trownames(temp)<-na\r\n\ttemp\r\n}\r\n\r\nif(nrow(coor2)0){\r\n\t\tfasta<-c(paste(\">\",as.character(coor2[ooi_pos,\"fin\"],sep=\"\")),as.character(coor2[ooi_pos,\"sequence\"]))\r\n\t\tcoor2<-coor2[-ooi_pos,]\r\n\t}\r\n\t\r\n\tfor(i in 1:nrow(coor2)){\r\n\t\tfasta<-c(fasta, paste(\">\",coor2[i,\"fin\"],sep=\"\"))\r\n\t\tfasta<-c(fasta, as.character(coor2[i,\"sequence\"]))\r\n\t}\r\n\tnam<-paste(outfile_prefix,\"CopraRNA_input_balanced.fasta\", sep=\"_\" )\r\n\twrite.table(fasta, file=nam, row.names=F, col.names=F, quote=F)\r\n}\r\n\r\nif(nrow(coor2)>max_number){\r\n\t\r\n\t\r\n\twildcard<-c(ooi,wildcard)\r\n\tpos_wild<-c()\r\n\tpos_ooi<-grep(ooi,coor2[,\"fin\"])[1]\r\n\tfor(i in 1:length(wildcard)){\r\n\t\tpos_wild<-c(pos_wild,grep(wildcard[i],coor2[,\"fin\"])[1])\r\n\t}\r\n\tpos_wild<-unique(na.omit(pos_wild))\r\n\t\r\n\t\r\n\t\r\n\tmax_number2<-max_number-length(pos_wild)+1\r\n\tpos<-seq(1,nrow(coor2))\r\n\tif(length(pos_wild)>0){\r\n\t\tpos<-pos[-pos_wild]\r\n\t}\r\n\tpos<-unique(c(pos_ooi,pos))\r\n\tdis<-clustalo3(coor2, pos)\r\n\tdis2<-clustalo4(coor2, pos)\r\n\tdis<-as.dist(dis)\r\n\tclus<-(hclust(dis,method=\"average\"))\r\n\tplot(clus)\r\n\tknum<-min(max_number2,length(clus$labels)-1)\r\n\tif(knum<2){\r\n\t\tknum<-2\r\n\t}\r\n\tclus2<-rect.hclust(clus,k=knum)\r\n\t\r\n\tout<-c()\r\n\tfor(i in 1:length(clus2)){\r\n\t\ttemp<-clus2[[i]]\r\n\t\ttemp_ooi<-grep(ooi,names(temp))\r\n\t\tif(length(temp_ooi)==0){\r\n\t\t\ttemp2<-sample(length(temp),1)\r\n\t\t\tout<-c(out, names(temp)[temp2])\r\n\t\t}\r\n\t}\r\n\tout_old<-out\r\n\tout<-c(coor2[pos_wild,\"fin\"],out)\r\n\t\r\n\t\r\n\tdis<-clustalo3(coor2, seq(1,nrow(coor2)))\r\n\tdis<-as.dist(dis)\r\n\tclus<-(hclust(dis,method=\"average\"))\r\n\tdis2<-clustalo4(coor2, seq(1,nrow(coor2)))\r\n\t\r\n\t\r\n\t\r\n\tsel2<-c()\r\n\tfor(i in 1:length(out)){\r\n\t\tsel<-c()\r\n\t\tooil<-sort(dis2[grep(out[i], colnames(dis2)),], decreasing=T)\r\n\t\tident<-which(ooil==100)\r\n\t\tif(length(ident)>0){\r\n\t\t\tooil<-ooil[-ident]\r\n\t\t}\r\n\t\tclose_orgs<-c()\r\n\t\tiii<-0\r\n\t\twhile(length(close_orgs)sim){\r\n\t\t\tn<-length(close_orgs)%/%sim\r\n\t\t\tn<-seq(1,n*sim,by=n)\r\n\t\t\tn<-names(close_orgs)[n]\r\n\t\t\tsel<-unique(c(sel,n))\r\n\t\t}\r\n\r\n\r\n\t\tif(length(close_orgs)<=sim){\r\n\t\tsel<-unique(c(sel,names(close_orgs)))\r\n\r\n\t\t}\r\n\t\tsel2<-c(sel2,sel)\r\n\t}\r\n\tsel2<-unique(sel2)\r\n\t\r\n\t\r\n\t\r\n\t\r\n\r\n\t\r\n\tout<-c(coor2[pos_wild,\"fin\"],sel2,out)\r\n\tout<-match(out,coor2[,\"fin\"])\r\n\tfasta<-c()\r\n\tfor(i in 1:length(out)){\r\n\t\tfasta<-c(fasta, paste(\">\",coor2[out[i],\"fin\"],sep=\"\"))\r\n\t\tfasta<-c(fasta, as.character(coor2[out[i],\"sequence\"]))\r\n\t}\r\n\tnam<-paste(outfile_prefix,\"CopraRNA_input_neighbourhood.fasta\", sep=\"_\" )\r\n\tfasta<-gsub(\"\\\\..*\",\"\",fasta)\r\n\twrite.table(fasta, file=nam, row.names=F, col.names=F, quote=F)\r\n\tdis<-clustalo3(coor2, seq(1,nrow(coor2)))\r\n\tdis<-as.dist(dis)\r\n\tclus<-(hclust(dis,method=\"average\"))\r\n\tclus<-as.phylo(clus)\r\n\tlab<-clus$tip.label\r\n\r\n\tnam_selected<-match(out_old,lab)\r\n\tnam_wildcard<-match(coor2[pos_wild,\"fin\"],lab)\r\n\tnam_neighbourhood<-match(sel2,lab)\r\n\tnam_ooi<-grep(ooi,lab)\r\n\r\n\tlab<-match(lab,coor2[,\"fin\"])\r\n\tlab<-coor2[lab,\"nam2\"]\r\n\tclus$tip.label<-lab\r\n\tnam<-paste(outfile_prefix,\"tree_coprarna_candidates_neighbourhood.pdf\", sep=\"_\" )\r\n\tpdf(nam)\r\n\tcolo<-rep(\"1\",length(lab))\r\n\tcolo[nam_neighbourhood]<-\"orangered\"\r\n\tcolo[nam_selected]<-\"dodgerblue1\"\r\n\tcolo[nam_wildcard]<-\"olivedrab2\"\r\n\tcolo[nam_ooi]<-\"purple1\"\r\n\tpar(mar=c(3, 1, 1, 1), xpd=TRUE)\r\n\tplot(clus,tip.color=colo, cex=0.5 )\r\n\r\n\tlegend(\"bottom\", inset=c(-0.05),bty=\"n\", legend=c(\"organism of interst (ooi)\",\"pre-selected organisms\",\"selected organisms\",\"close to initial organisms\"), text.col=c(\"purple1\",\"olivedrab2\",\"dodgerblue1\",\"orangered\"),cex=0.6)\r\n\tpar(xpd=FALSE)\r\n\tdev.off()\r\n\t\r\n}\r\n\r\nunlink(\"Rplots.pdf\")\r\n", "meta": {"hexsha": "765247e1e9affc67d1375ca8345732268e7a5789", "size": 6855, "ext": "r", "lang": "R", "max_stars_repo_path": "GLASSgo2CopraRNA_all_neighbours.r", "max_stars_repo_name": "JensGeorg/GLASSgo_postprocessing", "max_stars_repo_head_hexsha": "63d4151e9028734cc1d5d8569192b5ad2289f97c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "GLASSgo2CopraRNA_all_neighbours.r", "max_issues_repo_name": "JensGeorg/GLASSgo_postprocessing", "max_issues_repo_head_hexsha": "63d4151e9028734cc1d5d8569192b5ad2289f97c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GLASSgo2CopraRNA_all_neighbours.r", "max_forks_repo_name": "JensGeorg/GLASSgo_postprocessing", "max_forks_repo_head_hexsha": "63d4151e9028734cc1d5d8569192b5ad2289f97c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-02-08T16:30:56.000Z", "max_forks_repo_forks_event_max_datetime": "2019-09-03T08:50:10.000Z", "avg_line_length": 26.77734375, "max_line_length": 227, "alphanum_fraction": 0.6463894967, "num_tokens": 2355, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.34415215672215116}} {"text": "suppressMessages(library(\"FRESA.CAD\"))\nsuppressMessages(library(readxl))\n\nTADPOLE_D3 <- read.csv(\"data/TADPOLE_D3.csv\", na.strings=c(\"NA\",-4,\"-4.0\",\"\",\" \",\"NaN\"))\ntrain_df <- read.csv(\"data/temp/train_df.csv\", na.strings=c(\"NA\",-4,\"-4.0\",\"\",\" \"))\nTADPOLE_D1_D2_Dict <- read.csv(\"data/TADPOLE_D1_D2_Dict.csv\", na.strings=c(\"NA\",-4,\"-4.0\",\"\",\" \"))\nsubmissionTemplate <- as.data.frame(read_excel(\"data/TADPOLE_Simple_Submission_TeamName.xlsx\"))\n\nsubmissionTemplate$`Forecast Date` <- as.Date(paste(submissionTemplate$`Forecast Date`,\"-01\",sep=\"\"))\nsubmissionTemplate$`CN relative probability` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`MCI relative probability` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`AD relative probability` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$ADAS13 <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`ADAS13 50% CI lower` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`ADAS13 50% CI upper` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$Ventricles_ICV <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`Ventricles_ICV 50% CI lower` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate$`Ventricles_ICV 50% CI upper` <- as.numeric(nrow(submissionTemplate))\nsubmissionTemplate <- submissionTemplate[order(submissionTemplate$`Forecast Month`),]\n\nTADPOLE_D3$EXAMDATE <- as.Date(TADPOLE_D3$EXAMDATE)\n\nTrainingSet <- subset(train_df,D1==1)\n\nrownames(TrainingSet) <- paste(TrainingSet$RID,TrainingSet$VISCODE,sep=\"_\")\nrownames(TADPOLE_D3) <- paste(TADPOLE_D3$RID,TADPOLE_D3$VISCODE,sep=\"_\")\n\nTrainingSet <- TrainingSet[order(TrainingSet$EXAMDATE),]\nTrainingSet <- TrainingSet[order(as.numeric(TrainingSet$RID)),]\n\n\n#D3 Cross sectional\n## First Remove D2 subjects from Training Set\nD3IDS <- TADPOLE_D3$RID\nD3TrainingSet <- TrainingSet[!(TrainingSet$RID %in% D3IDS),]\n ignore = TRUE\n if(ignore == TRUE) { \n ## Conditioning the data sets\n source('R_scripts/dataPreprocessing.R')\n dataTadpoleD3 <- dataTADPOLEPreprocesing(D3TrainingSet,\n TADPOLE_D3,\n TADPOLE_D1_D2_Dict,\n MinVisit=18,\n colImputeThreshold=0.15,\n rowImputeThreshold=0.10,\n includeID=FALSE)\n save(dataTadpoleD3,file=\"data/temp/D3DataFrames.RDATA\")\n }else{\n load(\"data/temp/D3DataFrames.RDATA\")\n }\n if (ignore == TRUE){\n source('R_scripts/TADPOLE_Train.R')\n ## Build the 35 predictive models of cognitive status\n D3CognitiveClassModels <- TrainTadpoleClassModels(dataTadpoleD3$AdjustedTrainFrame,\n predictors=c(\"AGE\",\"PTGENDER\",colnames(dataTadpoleD3$AdjustedTrainFrame)[-c(1:22)]),\n numberOfRandomSamples=25,\n MLMethod=BSWiMS.model,\n NumberofRepeats = 1)\n save(D3CognitiveClassModels,file=\"data/temp/D3CognitiveClassModels_25.RDATA\")\n } else{\n load(\"data/temp/D3CognitiveClassModels_25.RDATA\")\n }\n \n\n## Predict all D3 congnitive status\n source('R_scripts/predictCognitiveStatus.R')\npredictADNID3 <- forecastCognitiveStatus(D3CognitiveClassModels,dataTadpoleD3$testingFrame)\n\n\n## Train D3 Correlations ADAS 13 and Ventricles\ndataTadpoleD3$AdjustedTrainFrame$Ventricles <- D3TrainingSet[rownames(dataTadpoleD3$AdjustedTrainFrame),\"Ventricles\"]/D3TrainingSet[rownames(dataTadpoleD3$AdjustedTrainFrame),\"ICV\"]\ndataTadpoleD3$AdjustedTrainFrame$ADAS13 <- D3TrainingSet[rownames(dataTadpoleD3$AdjustedTrainFrame),\"ADAS13\"]\n if(ignore == TRUE){\n source('R_scripts/TADPOLE_Train_ADAS_ICV.R')\n D3RegresModels <- TrainTadpoleRegresionModels(dataTadpoleD3$AdjustedTrainFrame,\n predictors=c(\"AGE\",\"PTGENDER\",colnames(dataTadpoleD3$AdjustedTrainFrame)[-c(1:22)]),\n numberOfRandomSamples=50,\n MLMethod=BSWiMS.model,\n NumberofRepeats = 1)\n \n save(D3RegresModels,file=\"data/temp/D3RegresModelss_50_Nolog.RDATA\")\n }else{\n load(\"data/temp/D3RegresModelss_50_Nolog.RDATA\")\n}\n## Predict the D3 ADAS13 and Ventricles\n source('R_scripts/predictTADPOLERegresions.R')\n \ndataTadpoleD3$testingFrame$Ventricles <- dataTadpoleD3$Test_Imputed[rownames(dataTadpoleD3$testingFrame),\"Ventricles\"]/dataTadpoleD3$Test_Imputed[rownames(dataTadpoleD3$testingFrame),\"ICV\"]\ndataTadpoleD3$testingFrame$ADAS13 <- dataTadpoleD3$Test_Imputed[rownames(dataTadpoleD3$testingFrame),\"ADAS13\"]\n\n### The last time D3 point\nltptf <- dataTadpoleD3$testingFrame\nltptf <- ltptf[order(ltptf$EXAMDATE),]\nltptf <- ltptf[order(as.numeric(ltptf$RID)),]\nrids <- ltptf$RID\nltptf <- ltptf[c(rids[1:(length(rids)-1)] != rids[-1],TRUE),]\nrownames(ltptf) <- ltptf$RID\n\n source('R_scripts/FiveYearForecast.R')\n ## Forecast the testing set\n forecastD3 <- FiveYearForeCast(predictADNID3,\n testDataset=ltptf,\n ADAS_Ventricle_Models=D3RegresModels,\n Subject_datestoPredict=submissionTemplate)\n write.csv(forecastD3,file=\"data/temp/_ForecastD3_BORREGOS_TEC.csv\")\n", "meta": {"hexsha": "3404fb2864528a9231b49292fa4ab89ebb46e5a3", "size": 5455, "ext": "r", "lang": "R", "max_stars_repo_path": "R_scripts/Preproc_Forecast_D3.r", "max_stars_repo_name": "EiderDiaz/R_Python_interop_2", "max_stars_repo_head_hexsha": "38713c83696f8800c9aead080bbbb9d81df8e00c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R_scripts/Preproc_Forecast_D3.r", "max_issues_repo_name": "EiderDiaz/R_Python_interop_2", "max_issues_repo_head_hexsha": "38713c83696f8800c9aead080bbbb9d81df8e00c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R_scripts/Preproc_Forecast_D3.r", "max_forks_repo_name": "EiderDiaz/R_Python_interop_2", "max_forks_repo_head_hexsha": "38713c83696f8800c9aead080bbbb9d81df8e00c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.4622641509, "max_line_length": 189, "alphanum_fraction": 0.6758936755, "num_tokens": 1503, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7341195269001831, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.34414835303409846}} {"text": "pdf_file<-\"pdf/columncharts_shares_1x4.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=11,height=7)\n\npar(cex=0.9,omi=c(0.75,0.5,1.25,0.5),mai=c(0.5,1,0.75,1),mgp=c(3,2,0),family=\"Lato Light\",las=1)\n\n# Import data\n\nsource(\"scripts/inc_data_dfg.r\", encoding=\"latin1\")\n\n# Create charts and other elements\n\nbarplot(x,col=c(myC1a,myC1a,myC2a,myC2a,myC3a,myC3a,myC4a,myC4a),beside=T,border=NA,axes=F,names.arg=c(\"\",\"\",\"\",\"\"))\nbarplot(2*y,col=c(myC1a,myC1b,myC2a,myC2b,myC3b,myC3b,myC4a,myC4b),beside=T,border=NA,axes=F,add=T,names.arg=labelling,cex.names=1.25)\nz<-1\nfor (i in 1:4)\n{\ntext(z+0.25,x[1,i]/2,format(round(x[1,i],1),nsmall=1),adj=0)\ntext(z+1.25,y[2,i],format(round(y[2,i],1),nsmall=1),adj=0,col=\"white\")\ntext(z+0.65,x[1,i]+50,paste(format(round(100*y[2,i]/x[1,i],1),\n\tnsmall=1),\"%\",sep=\" \"),adj=0,cex=1.5,xpd=T)\nz<-z+3\n}\n\n# Titling\n\nmtext(\"DFG grants in 2010\",3,line=4,adj=0,family=\"Lato Black\",outer=T,cex=2)\nmtext(\"Individual grants by science sector, values in million Euro. Percent value: approval quota\",3,line=1,adj=0,cex=1.35,font=3,outer=T)\nmtext(\"Source: DFG Information Cards, www.dfg.de\",1,line=2,adj=1.0,cex=1.1,font=3,outer=T)\ndev.off()\n", "meta": {"hexsha": "4489c7742019f6172fab8048a6c9b8da81537e72", "size": 1150, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/columncharts_shares_1x4.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/columncharts_shares_1x4.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/columncharts_shares_1x4.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.3333333333, "max_line_length": 138, "alphanum_fraction": 0.6930434783, "num_tokens": 513, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982315512488, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34411649265496635}} {"text": "source(\"_common.r\")\n\nx_ob = readLines(\"../runs/summit/dgemm/openblas_cuda.txt\")\ndf_ob = process(x_ob, gpu=TRUE)\n\nx_essl = readLines(\"../runs/summit/dgemm/essl_cuda.txt\")\ndf_essl = process(x_essl, gpu=TRUE)\n\ndf = rbind(\n cbind(subset(df_ob, tile==\"Tile = 8192\"), Backend=\"NVBLAS with OpenBLAS (Tile = 8192)\"),\n cbind(df_essl, Backend=\"ESSL\")\n)\n\n\nplotter(df, color=ngpu) +\n facet_wrap(.~Backend) +\n labs(color=\"Number of GPUs\") +\n ggtitle(\"Square Matrix Product\", subtitle=\"NVBLAS with OpenBLAS vs ESSL from R\")\n\nggsave(last_plot(), file=\"nvblas_openblas_vs_essl.pdf\")\n", "meta": {"hexsha": "c7b2e315cd6dc0ba386cd454efc105fc3755f13f", "size": 572, "ext": "r", "lang": "R", "max_stars_repo_path": "plot/essl_cuda.r", "max_stars_repo_name": "wrathematics/matprodbench", "max_stars_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-04T16:08:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-04T16:08:53.000Z", "max_issues_repo_path": "plot/essl_cuda.r", "max_issues_repo_name": "wrathematics/matprodbench", "max_issues_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot/essl_cuda.r", "max_forks_repo_name": "wrathematics/matprodbench", "max_forks_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.2380952381, "max_line_length": 90, "alphanum_fraction": 0.708041958, "num_tokens": 190, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34411648543097656}} {"text": "#' Plot SDM map\n#'\n#' @param raster_data A raster dataset containing the occurrence data.\n#' @param bmr_models A list of models extracted from the benchmarking bmr object.\n#' @param model_id A character string indicating the model id of interest.\n#' @param model_iteration A numeric value indicating the model iteration of interest.\n#' @param map_type A logical indicating if the map should be static or interactive.\n#'\n#' @return An interactive leaflet map, showing the species distribution.\n#' @examples\n#' \\dontrun{\n#' # download benchmarking data\n#' benchmarking_data <- get_benchmarking_data(\"Lynx lynx\",\n#' limit = 1500,\n#' climate_resolution = 10)\n#'\n#' # create a list of algorithms to compare\n#' learners <- list(mlr::makeLearner(\"classif.randomForest\",\n#' predict.type = \"prob\"),\n#' mlr::makeLearner(\"classif.logreg\",\n#' predict.type = \"prob\"))\n#'\n#' # run the model benchmarking process\n#' bmr <- benchmark_sdm(benchmarking_data$df_data,\n#' learners = learners,\n#' dataset_type = \"default\",\n#' sample = FALSE)\n#'\n#' # get best model results\n#' # you should obtain a dataframe containing the highest performing (by AUC)\n#' # algorithm name, iteration and associated AUC\n#' best_results <- get_best_model_results(bmr)\n#'\n#' # plot the SDM map of the best performing model\n#' # change the map_type argument if you want a dynamic leaflet map\n#' plot_sdm_map(raster_data = benchmarking_data$raster_data$climate_variables,\n#' bmr_models = bmr$learners,\n#' model_id = best_results$learner.id[1],\n#' model_iteration = best_results$iter[1],\n#' map_type = \"static\")\n#'}\n#'@export\nplot_sdm_map <- function(raster_data, bmr_models, model_id, model_iteration, map_type = \"static\") {\n if (model_id == \"classif.logreg\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunLogreg)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n } else if (model_id == \"classif.gbm\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunGBM)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n\n } else if (model_id == \"classif.multinom\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunMultinom)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n } else if (model_id == \"classif.naiveBayes\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunNB)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n } else if (model_id == \"classif.xgboost\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunXGB)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n } else if (model_id == \"classif.ksvm\") {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFunKSVM)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n }\n else {\n model <- bmr_models$benchmarking_data[[model_id]][[model_iteration]]$learner.model\n pr <- dismo::predict(raster_data$climate_variables, model, fun = customPredictFun)\n if (map_type == \"static\") {\n raster::plot(pr, main = model_id)\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n\n }\n}\n", "meta": {"hexsha": "df120d2acf850a779dfe993a43edfc00896836fc", "size": 7553, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_sdm_map.r", "max_stars_repo_name": "boyanangelov/sdmbench", "max_stars_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2018-06-25T19:55:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-06T08:36:48.000Z", "max_issues_repo_path": "R/plot_sdm_map.r", "max_issues_repo_name": "boyanangelov/sdmbench", "max_issues_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2018-08-01T01:31:09.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-12T16:02:07.000Z", "max_forks_repo_path": "R/plot_sdm_map.r", "max_forks_repo_name": "boyanangelov/sdmbench", "max_forks_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-10-12T06:07:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-02T17:52:53.000Z", "avg_line_length": 54.3381294964, "max_line_length": 122, "alphanum_fraction": 0.6009532636, "num_tokens": 1885, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34411648543097656}} {"text": "#!/usr/bin/env Rscript\n\n# R scripts for calculating NSC and RSC based on output files from phantompeakqualtools\n# Author @chuan-wang https://github.com/chuan-wang\n\n# Command line argument processing\nargs <- commandArgs(trailingOnly=TRUE)\n\n# Check input args\nif (length(args) != 1) {\n stop(\"Usage: calculateNSCRSC.r [ cross-correlation-file ]\", call.=FALSE)\n}\n\ndata<-read.table(args[1], header=FALSE)\n\ndata[,12]<-NA\ndata[,13]<-NA\ndata[,14]<-NA\ndata[,15]<-NA\n\ncolnames(data)[14]<-\"NSC\"\ncolnames(data)[15]<-\"RSC\"\n\nfor (i in 1:nrow(data)){\n data[i,12]<-as.numeric(unlist(strsplit(as.character(data[i,4]),\",\"))[1])\n data[i,13]<-as.numeric(unlist(strsplit(as.character(data[i,6]),\",\"))[1])\n data[i,14]<-round(data[i,12]/as.numeric(data[i,8]),2)\n data[i,15]<-round((data[i,12]-as.numeric(data[i,8]))/(data[i,13]-as.numeric(data[i,8])),2)\n}\n\nwrite.table(data, file=\"cross_correlation_processed.txt\", quote=FALSE, sep='\\t', row.names=FALSE)", "meta": {"hexsha": "2ac7ed35b4dc43641b5c47798f4373c86ff4406b", "size": 956, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/calculateNSCRSC.r", "max_stars_repo_name": "DoaneAS/chipseq", "max_stars_repo_head_hexsha": "520cef50801bc953487691624e768cee1ca27029", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2016-06-15T21:09:46.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-02T02:03:34.000Z", "max_issues_repo_path": "bin/calculateNSCRSC.r", "max_issues_repo_name": "apeltzer/chipseq", "max_issues_repo_head_hexsha": "deb604a0c6e8ca593161ed7869f5ba8b5d1266de", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 47, "max_issues_repo_issues_event_min_datetime": "2016-06-16T14:23:56.000Z", "max_issues_repo_issues_event_max_datetime": "2018-06-13T15:14:20.000Z", "max_forks_repo_path": "bin/calculateNSCRSC.r", "max_forks_repo_name": "apeltzer/chipseq", "max_forks_repo_head_hexsha": "deb604a0c6e8ca593161ed7869f5ba8b5d1266de", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 18, "max_forks_repo_forks_event_min_datetime": "2016-06-15T14:26:11.000Z", "max_forks_repo_forks_event_max_datetime": "2019-12-31T21:52:26.000Z", "avg_line_length": 30.8387096774, "max_line_length": 97, "alphanum_fraction": 0.6705020921, "num_tokens": 303, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.34411648543097645}} {"text": "# CREATING A TIMELINE GRAPHIC USING R AND GGPLOT2\n# https://benalexkeen.com/creating-a-timeline-graphic-using-r-and-ggplot2/\n#\nlibrary(ggplot2)\nlibrary(scales)\nlibrary(lubridate)\n\nevents <- data.frame(\n year = c(2021, 2021, 2021),\n month = c(1, 3, 4),\n event = c(\"Riapertura scuole (Inizio Zona Gialla)\", \"Chiusura scuole (Inizio Zona Rossa)\", \"Riapertura scuole (Inizio Zona Arancione)\"), \n restriction = c(\"Zona Gialla\", \"Zona Rossa\", \"Zona Arancione\")\n )\n\n# Add date column\nevents$date <- with(events, ymd(sprintf('%04d%02d%02d', year, month, 1)))\nevents <- events[with(events, order(date)), ]\n\n# convert the status to an ordinal categorical variable, in order of criticality ranging from \u201cComplete\u201d to \u201cCritical\u201d. \n# We\u2019ll also define some hexadecimal colour values to associate with these statuses.\nrestriction_levels <- c(\"Zona Rossa\", \"Zona Arancione\", \"Zona Gialla\")\nrestriction_colors <- c(\"#C00000\", \"#f5a905\", \"#FFC000\")\n\nevents$restriction <- factor(events$restriction, levels=restriction_levels, ordered = TRUE)\n\npositions <- c(-0.5, 1.0, 0.5)\ndirections <- c(1, -1)\n\nline_pos <- data.frame(\n \"date\" = unique(df$date),\n \"position\" = rep(positions, length.out=length(unique(df$date))),\n \"direction\" = rep(directions, length.out=length(unique(df$date)))\n)\n\nevents <- merge(x = events, y = line_pos, by=\"date\", all = TRUE)\nevents <- events[with(df, order(date, restriction)), ]\n\ntext_offset <- 0.05\nevents$text_position <- (events$month_count * text_offset * events$direction) # events$position * text_offset)\n\n# Because we want to display all months on our timelines, not just the months we have events for, we\u2019ll create a data frame containing all of our months.\nmonth_buffer <- 2\nmonth_date_range <- seq(min(events$date) - months(month_buffer), max(events$date) + months(month_buffer), by='month')\nmonth_format <- format(month_date_range, '%b')\nmonth_df <- data.frame(month_date_range, month_format)\n\n# #### PLOT ####\ntimeline_plot <- ggplot(events, aes(x = date, y = 0, col = restriction, label = event))\ntimeline_plot <- timeline_plot + labs(col=\"event\")\ntimeline_plot <- timeline_plot + scale_color_manual(values = restriction_colors, labels = restriction_levels, drop = FALSE)\ntimeline_plot <- timeline_plot + theme_classic()\n\n# # Plot horizontal black line for timeline\ntimeline_plot <- timeline_plot + geom_hline(yintercept=0, color = \"black\", size=0.3)\n\n# # Plot vertical segment lines for milestones\n# timeline_plot <- timeline_plot + geom_segment(data = events[events$position == 1,], aes(y = 1, yend = 0, xend = date), color='black', size=0.2)\ntimeline_plot <- timeline_plot + geom_segment(data = events[events$position == 1,], aes(y = position, yend = 0, xend = date), color='black', size=0.2)\n# Plot scatter points at zero and date\ntimeline_plot <- timeline_plot + geom_point(aes(y = 0), size = 3)\n\n# Don't show axes, appropriately position legend\ntimeline_plot <- timeline_plot + theme(axis.line.y=element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n axis.ticks.y = element_blank(), \n axis.ticks.x =element_blank(),\n axis.line.x = element_blank(),\n legend.position = \"bottom\"\n)\n\n# Show text for each month\ntimeline_plot <- timeline_plot + geom_text(data = month_df, aes(x = month_date_range, y=-0.1, label=month_format), size=2.5, vjust=0.5, color='black', angle=90)\n\n# Show text for each milestone\ntimeline_plot <- timeline_plot + geom_text(aes(y = text_position, label = event), size=2.5)\n\n## End! \nprint(timeline_plot)\n\n# ##### RENDER DAILY CHART #####\n# \n# https://www.datanovia.com/en/blog/how-to-create-a-ggplot-with-multiple-lines/\n# https://www.r-graph-gallery.com/279-plotting-time-series-with-ggplot2.html\n# \n\n# library(ggplot2)\n# library(hrbrthemes)\n# aggregateData <- read.csv(file = '/Users/andrea/Documents/Works/GitHub/RExercises/Covid19/aggregate.csv') \n\nggplot(aggregateData, aes(x = as.Date(date))) +\n scale_colour_manual(name='', values=c(\"Numero positivi\" = \"darkgray\", \"Positivi su 1000 abitanti (Comune di Minerbe)\" = \"steelblue\", \"Positivi su 1000 abitanti (Provincia di Verona)\" = \"#69b3a2\", \"Contatti scolastici\" = \"coral2\")) +\n geom_line(aes(y = casiPostivi, color=\"Numero positivi\"), size = 1) + \n geom_point(aes(y = casiPostivi, color=\"Numero positivi\"), shape=21, fill=\"#69b3a2\", size = 2) + \n # stat_smooth(aes(y = casiPostivi), method = \"lm\", formula = y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5), se = FALSE, color = \"lightred\", size = 0.7) + \n stat_smooth(aes(x = as.Date(date), y = casiPostivi), formula = y ~ x, inherit.aes = FALSE, se = FALSE, color = \"darkorange2\", size = 0.2) +\n # geom_smooth(aes(y = casiPostivi), method = \"lm\", formula = y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5), color = \"black\", fill = \"firebrick\") +\n # geom_line(aes(y = maxSuPopolazione, color=\"Max Positivi su 1000 abitanti (Provincia)\")) +\n geom_line(aes(y = scolastici, color=\"Contatti scolastici\")) +\n geom_line(aes(y = meanSuPopolazione, color=\"Positivi su 1000 abitanti (Provincia di Verona)\"), size = 1) +\n geom_line(aes(y = positiviSuMille, color = \"Positivi su 1000 abitanti (Comune di Minerbe)\"), size = 1) +\n scale_x_date(date_breaks = \"4 day\", date_labels = \"%d-%m\") +\n theme_ipsum(plot_margin = margin(30, 30, 30, 30)) +\n labs(x = \"Linea temporale - Anno 2021\", y = \"Numero casi\", title = \"Evoluzione contagi COVID19\", subtitle = \"Comune di Minerbe\", caption = \"Data source: ULSS9 Scaligera\") +\n theme(\n axis.text.x = element_text(angle=45, hjust=1, face=3, color=\"black\"), \n axis.text.y = element_text(color=\"black\"), \n # legend.position=c(-.8500,-.10), \n legend.position='top', \n legend.justification='left',\n legend.direction='horizontal',\n panel.background = element_rect(colour = \"black\", size = 0.2) \n ) +\n geom_vline(xintercept = as.Date('2021-01-31'), color=\"yellow\", size=0.5) + geom_label(aes(as.Date('2021-01-31'), 25), label = \"Riapertura scuole (Inizio Zona Gialla)\", show.legend = FALSE) +\n geom_vline(xintercept = as.Date('2021-03-15'), color=\"darkred\", size=0.5) + geom_label(aes(as.Date('2021-03-15'), 28), label = \"Chiusura scuole (Inizio Zona Rossa)\", show.legend = FALSE) +\n geom_vline(xintercept = as.Date('2021-04-07'), color=\"darkorange\", size=0.5) + geom_label(aes(as.Date('2021-04-07'), 25), label = \"Riapertura scuole (Inizio Zona Arancione)\", show.legend = FALSE) +\n geom_vline(xintercept = as.Date('2021-04-26'), color=\"yellow\", size=0.5) + geom_label(aes(as.Date('2021-04-26'), 27), label = \"Riapertura scuole 70 % (Inizio Zona Gialla)\", show.legend = FALSE)\n \n\n", "meta": {"hexsha": "cb169a03999a31cc8cdcb7bd9dab880877916a42", "size": 6740, "ext": "r", "lang": "R", "max_stars_repo_path": "Covid19/renderChart.r", "max_stars_repo_name": "giandrea77/RExercises", "max_stars_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Covid19/renderChart.r", "max_issues_repo_name": "giandrea77/RExercises", "max_issues_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Covid19/renderChart.r", "max_forks_repo_name": "giandrea77/RExercises", "max_forks_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 55.2459016393, "max_line_length": 236, "alphanum_fraction": 0.6808605341, "num_tokens": 2025, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.647798211152541, "lm_q1q2_score": 0.3441164818189816}} {"text": "## This script was used to perform the meta-analysis combining the p values from EWAS performed in three different cohorts\n## Filepaths and sample IDs have been removed for security reasons, therfore it serves as a guide to the analysis\n\nlibrary(IlluminaHumanMethylation450kanno.ilmn12.hg19)\nlibrary(qqman)\n\nsetwd(\"\")\nload(\"\") ## load seed.SS object\nload(\"\") ## load simons.SS object\nminerva<-read.csv(\"Analysis/CaseControl_Sex_Batch_HousemanCellComp_SmokingScore_BY_BM_GA_Urbanicity.csv\", stringsAsFactors = FALSE, row.names = 1)\n\nprobes<-table(c(rownames(minerva), rownames(seed.SS), rownames(simons.SS)))\ntable(probes)\n\nminerva<-minerva[names(probes)[which(probes > 1)],]\nseed.SS<-seed.SS[names(probes)[which(probes > 1)],]\nsimons.SS<-simons.SS[names(probes)[which(probes > 1)],]\n\n### look at overlap of top probes\n\npar(mfrow = c(3,2))\nplot(minerva$MeanDiff[which(minerva$P.value < 5e-5)], seed.SS$MeanDiff[which(minerva$P.value < 5e-5)], xlab = \"Minerva\", ylab = \"seed\", main = paste(length(which(sign(minerva$MeanDiff[which(minerva$P.value < 5e-5)]) == sign(seed.SS$MeanDiff[which(minerva$P.value < 5e-5)]))), \"/\", length(which(minerva$P.value < 5e-5)), \" P = \", signif(binom.test(length(which(sign(minerva$MeanDiff[which(minerva$P.value < 5e-5)]) == sign(seed.SS$MeanDiff[which(minerva$P.value < 5e-5)]))), length(which(minerva$P.value < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\nplot(minerva$MeanDiff[which(minerva$P.value < 5e-5)], simons.SS$MeanDiff[which(minerva$P.value < 5e-5)], xlab = \"Minerva\", ylab = \"simons\", main = paste(length(which(sign(minerva$MeanDiff[which(minerva$P.value < 5e-5)]) == sign(simons.SS$MeanDiff[which(minerva$P.value < 5e-5)]))), \"/\", length(which(minerva$P.value < 5e-5)), \" P = \", signif(binom.test(length(which(sign(minerva$MeanDiff[which(minerva$P.value < 5e-5)]) == sign(simons.SS$MeanDiff[which(minerva$P.value < 5e-5)]))), length(which(minerva$P.value < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\n\nplot(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)], minerva$MeanDiff[which(seed.SS$pvalue < 5e-5)], xlab = \"seed\", ylab = \"Minerva\", main = paste(length(which(sign(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]) == sign(minerva$MeanDiff[which(seed.SS$pvalue < 5e-5)]))), \"/\", length(which(seed.SS$pvalue < 5e-5)), \" P = \", signif(binom.test(length(which(sign(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]) == sign(minerva$MeanDiff[which(seed.SS$pvalue < 5e-5)]))), length(which(seed.SS$pvalue < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\nplot(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)], simons.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)], xlab = \"seed\", ylab = \"simons\", main = paste(length(which(sign(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]) == sign(simons.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]))), \"/\", length(which(seed.SS$pvalue < 5e-5)), \" P = \", signif(binom.test(length(which(sign(seed.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]) == sign(simons.SS$MeanDiff[which(seed.SS$pvalue < 5e-5)]))), length(which(seed.SS$pvalue < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\n\nplot(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)], minerva$MeanDiff[which(simons.SS$pvalue < 5e-5)], xlab = \"simons\", ylab = \"Minerva\", main = paste(length(which(sign(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]) == sign(minerva$MeanDiff[which(simons.SS$pvalue < 5e-5)]))), \"/\", length(which(simons.SS$pvalue < 5e-5)), \" P = \", signif(binom.test(length(which(sign(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]) == sign(minerva$MeanDiff[which(simons.SS$pvalue < 5e-5)]))), length(which(simons.SS$pvalue < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\nplot(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)], seed.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)], xlab = \"simons\", ylab = \"seed\", main = paste(length(which(sign(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]) == sign(seed.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]))), \"/\", length(which(simons.SS$pvalue < 5e-5)), \" P = \", signif(binom.test(length(which(sign(simons.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]) == sign(seed.SS$MeanDiff[which(simons.SS$pvalue < 5e-5)]))), length(which(simons.SS$pvalue < 5e-5)))$p.value,3), sep = \"\"), pch = 16)\nabline(v = 0)\nabline(h = 0)\n\n\n\npar(mfrow = c(3,2))\nplot(-log10(minerva$P.value[which(minerva$P.value < 5e-5)]), -log10(seed.SS$pvalue[which(minerva$P.value < 5e-5)]), xlab = \"Minerva\", ylab = \"seed\", pch = 16)\nabline(v = 0)\nabline(h = 0)\nplot(-log10(minerva$P.value[which(minerva$P.value < 5e-5)]), -log10(simons.SS$pvalue[which(minerva$P.value < 5e-5)]), xlab = \"Minerva\", ylab = \"simons\", pch = 16)\nabline(v = 0)\nabline(h = 0)\n\n\n### use fisher's method to combine p-values\n\nres<-cbind(minerva[match(names(probes)[which(probes == 3)], rownames(minerva)), c(\"MeanDiff\", \"SE\", \"P.value\")], seed.SS[match(names(probes)[which(probes == 3)], rownames(seed.SS)) , c(\"MeanDiff\", \"SE\", \"pvalue\")], simons.SS[match(names(probes)[which(probes == 3)], rownames(simons.SS)), c(\"MeanDiff\", \"SE\", \"pvalue\")])\nres<-cbind(res, NA, 3)\ncolnames(res)<-c(\"Minerva:MeanDiff\", \"Minerva:SE\", \"Minerva:P\", \"seed:MeanDiff\", \"seed:SE\", \"seed:P\",\"simons:MeanDiff\", \"simons:SE\", \"simons:P\",\"Fishers_P\", \"n_studies\")\nrownames(res)<-names(probes)[which(probes ==3)]\n\n### start with probes annotated in all 3 datasets\nfor(each in names(probes)[which(probes == 3)]){\n\n\tpval<-res[each,c(3,6,9)]\n\n\t### compare to fishers p value\n\tfisher<-(sum(-log(pval))*2)\n\tfisherp<-1-pchisq(fisher, 2*length(pval))\n\t\n\tres[each,10]<-fisherp\n}\n\nwrite.csv(res, \"Analysis/CaseControl_ASD_MetaAnalysis_FishersP.csv\")\n\n### then consider probes tested in 2 datasets\n\nres.2<-cbind(minerva[match(names(probes)[which(probes == 2)], rownames(minerva)), c(\"MeanDiff\", \"SE\", \"P.value\")], seed.SS[match(names(probes)[which(probes == 2)], rownames(seed.SS)) , c(\"MeanDiff\", \"SE\", \"pvalue\")], simons.SS[match(names(probes)[which(probes == 2)], rownames(simons.SS)), c(\"MeanDiff\", \"SE\", \"pvalue\")])\nres.2<-cbind(res.2, NA, 2)\ncolnames(res.2)<-c(\"Minerva:MeanDiff\", \"Minerva:SE\", \"Minerva:P\", \"seed:MeanDiff\", \"seed:SE\", \"seed:P\",\"simons:MeanDiff\", \"simons:SE\", \"simons:P\",\"Fishers_P\", \"n_studies\")\nrownames(res.2)<-names(probes)[which(probes == 2)]\nres<-rbind(res,res.2)\n\nfor(each in names(probes)[which(probes == 2)]){\n\tif(is.na(minerva$MeanDiff[match(each, rownames(minerva))])){\n\tpval<-res[each,c(6,9)]\n\t} else {\n\t\tif(is.na(seed.SS$MeanDiff[match(each, rownames(seed.SS))])){\t\t\n\t\t\tpval<-res[each,c(3,9)]\n\t\t} else {\n\t\t\tif(is.na(simons.SS$MeanDiff[match(each, rownames(simons.SS))])){\n\t\t\t\t\n\t\t\t\tpval<-res[each,c(3,6)]\n\t\t\t}\n\t\t}\t\n\t}\n\tfisher<-(sum(-log(pval))*2)\n\tfisherp<-1-pchisq(fisher, 2*length(pval))\n\tres[each,10]<-fisherp\n}\n\n\nwrite.csv(res, \"Analysis/CaseControl_ASD_MetaAnalysis_FishersP.csv\")\n\n\ndata(IlluminaHumanMethylation450kanno.ilmn12.hg19)\nprobeAnnot<-as.data.frame(IlluminaHumanMethylation450kanno.ilmn12.hg19@data$Locations)\nprobeAnnot<-probeAnnot[rownames(res),]\nres<-cbind(res, probeAnnot)\nprobeAnnot<-as.data.frame(IlluminaHumanMethylation450kanno.ilmn12.hg19@data$Other)\nprobeAnnot<-probeAnnot[rownames(res),]\nres<-cbind(res, probeAnnot)\nwrite.csv(res, \"Analysis/CaseControl_ASD_MetaAnalysis_FishersP.csv\")\n\nqq(res$Fishers_P)\nres$chr<-gsub(\"chr\", \"\", res$chr)\nres$chr[which(res$chr == \"X\")]<-23\nres$chr<-as.numeric(res$chr)\n\n##For manhattan plot redefine function to use symbools to indicate when all same direction of effects\nmanhattan<-function (x, chr = \"CHR\", bp = \"BP\", p = \"P\", snp = \"SNP\", col = c(\"gray10\",\n \"gray60\"), chrlabs = NULL, suggestiveline = -log10(1e-05),\n genomewideline = -log10(5e-08), highlight = NULL, logp = TRUE,\n annotatePval = NULL, annotateTop = TRUE, pch = 20, ...)\n{\n CHR = BP = P = index = NULL\n if (!(chr %in% names(x)))\n stop(paste(\"Column\", chr, \"not found!\"))\n if (!(bp %in% names(x)))\n stop(paste(\"Column\", bp, \"not found!\"))\n if (!(p %in% names(x)))\n stop(paste(\"Column\", p, \"not found!\"))\n if (!(snp %in% names(x)))\n warning(paste(\"No SNP column found. OK unless you're trying to highlight.\"))\n if (!is.numeric(x[[chr]]))\n stop(paste(chr, \"column should be numeric. Do you have 'X', 'Y', 'MT', etc? If so change to numbers and try again.\"))\n if (!is.numeric(x[[bp]]))\n stop(paste(bp, \"column should be numeric.\"))\n if (!is.numeric(x[[p]]))\n stop(paste(p, \"column should be numeric.\"))\n d = data.frame(CHR = x[[chr]], BP = x[[bp]], P = x[[p]])\n if (!is.null(x[[snp]]))\n d = transform(d, SNP = x[[snp]])\n d <- subset(d, (is.numeric(CHR) & is.numeric(BP) & is.numeric(P)))\n d <- d[order(d$CHR, d$BP), ]\n if (logp) {\n d$logp <- -log10(d$P)\n }\n else {\n d$logp <- d$P\n }\n d$pos = NA\n d$index = NA\n ind = 0\n for (i in unique(d$CHR)) {\n ind = ind + 1\n d[d$CHR == i, ]$index = ind\n }\n nchr = length(unique(d$CHR))\n if (nchr == 1) {\n d$pos = d$BP\n ticks = floor(length(d$pos))/2 + 1\n xlabel = paste(\"Chromosome\", unique(d$CHR), \"position\")\n labs = ticks\n }\n else {\n lastbase = 0\n ticks = NULL\n for (i in unique(d$index)) {\n if (i == 1) {\n d[d$index == i, ]$pos = d[d$index == i, ]$BP\n }\n else {\n lastbase = lastbase + tail(subset(d, index ==\n i - 1)$BP, 1)\n d[d$index == i, ]$pos = d[d$index == i, ]$BP +\n lastbase\n }\n ticks = c(ticks, (min(d[d$index == i, ]$pos) + max(d[d$index ==\n i, ]$pos))/2 + 1)\n }\n xlabel = \"Chromosome\"\n labs <- unique(d$CHR)\n }\n xmax = ceiling(max(d$pos) * 1.03)\n xmin = floor(max(d$pos) * -0.03)\n def_args <- list(xaxt = \"n\", bty = \"n\", xaxs = \"i\", yaxs = \"i\",\n las = 1, pch = pch, xlim = c(xmin, xmax), ylim = c(0,\n ceiling(max(d$logp))), xlab = xlabel, ylab = expression(-log[10](italic(p))))\n dotargs <- list(...)\n do.call(\"plot\", c(NA, dotargs, def_args[!names(def_args) %in%\n names(dotargs)]))\n if (!is.null(chrlabs)) {\n if (is.character(chrlabs)) {\n if (length(chrlabs) == length(labs)) {\n labs <- chrlabs\n }\n else {\n warning(\"You're trying to specify chromosome labels but the number of labels != number of chromosomes.\")\n }\n }\n else {\n warning(\"If you're trying to specify chromosome labels, chrlabs must be a character vector\")\n }\n }\n if (nchr == 1) {\n axis(1, ...)\n }\n else {\n axis(1, at = ticks, labels = labs, ...)\n }\n col = rep(col, max(d$CHR))\n if (nchr == 1) {\n with(d, points(pos, logp, pch = pch, col = col[1], ...))\n }\n else {\n icol = 1\n for (i in unique(d$index)) {\n with(d[d$index == unique(d$index)[i], ], points(pos,\n logp, col = col[icol], pch = pch, ...))\n icol = icol + 1\n }\n }\n if (suggestiveline)\n abline(h = suggestiveline, col = \"blue\")\n if (genomewideline)\n abline(h = genomewideline, col = \"red\")\n if (!is.null(highlight)) {\n if (any(!(highlight %in% d$SNP)))\n warning(\"You're trying to highlight SNPs that don't exist in your results.\")\n d.highlight = d[which(d$SNP %in% highlight), ]\n with(d.highlight, points(pos, logp, col = \"green3\", pch = pch,\n ...))\n }\n if (!is.null(annotatePval)) {\n topHits = subset(d, P <= annotatePval)\n par(xpd = TRUE)\n if (annotateTop == FALSE) {\n with(subset(d, P <= annotatePval), textxy(pos, -log10(P),\n offset = 0.625, labs = topHits$SNP, cex = 0.45),\n ...)\n }\n else {\n topHits <- topHits[order(topHits$P), ]\n topSNPs <- NULL\n for (i in unique(topHits$CHR)) {\n chrSNPs <- topHits[topHits$CHR == i, ]\n topSNPs <- rbind(topSNPs, chrSNPs[1, ])\n }\n textxy(topSNPs$pos, -log10(topSNPs$P), offset = 0.625,\n labs = topSNPs$SNP, cex = 0.5, ...)\n }\n }\n par(xpd = FALSE)\n}\n\npchInd<-rep(2, nrow(res))\npchInd[which(abs(rowSums(sign(res[,c(1,4,7)]), na.rm = TRUE)) == rowSums(!is.na(res[,c(1,4,7)])))]<-16\n\nmanhattan(res, p = \"Fishers_P\", chr = \"chr\", bp = \"pos\", suggestiveline = -log10(5e-5), genomewideline = -log10(1e-7), pch = pchInd)\n", "meta": {"hexsha": "99a3c50634b0e844a3af71643f46cff7a781a17e", "size": 12501, "ext": "r", "lang": "R", "max_stars_repo_path": "ASD_EWAS_MetaAnalysis.r", "max_stars_repo_name": "ejh243/MinervaASDEWAS", "max_stars_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-05-17T01:53:35.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T07:22:14.000Z", "max_issues_repo_path": "ASD_EWAS_MetaAnalysis.r", "max_issues_repo_name": "ejh243/MinervaASDEWAS", "max_issues_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ASD_EWAS_MetaAnalysis.r", "max_forks_repo_name": "ejh243/MinervaASDEWAS", "max_forks_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.0807692308, "max_line_length": 561, "alphanum_fraction": 0.6052315815, "num_tokens": 4290, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.34404329433900654}} {"text": "\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(shiny)\nlibrary(plotly)\nlibrary(ggplot2)\nlibrary(markdown)\ndevtools::install_github(\"jcheng5/bubbles\")\n\nlibrary(bubbles)\n\n#Get the right amount of Apprehended data \napprehended <- read.csv(\"data/apprehended.csv\", stringsAsFactors = FALSE)\napprehended <- apprehended[3:5, 2:6 ] \ncolnames(apprehended) <- c(\"2006\",\"2007\",\"2008\",\"2009\",\"2010\")\napprehended <- apprehended[-c(2), ] \napprehended <- gather(apprehended,\"Year\", \"Apprehended\",1:5)\napprehended <- apprehended[-c(1,3,5,7,9), ]\n#Same with Immigrant\nimmigrant <- read.csv(\"data/us.immigrant.csv\", stringsAsFactors = FALSE)\ncols <- c(\"Year\",\"Drug\",\"Immigration\",\"Other\")\ncolnames(immigrant) <- cols\nimmigrant <- immigrant[31:35, ]\n#Join together to plot later\nimmigrant <- full_join(immigrant,apprehended)\n#Get rid of commas in the values\nimmigrant$Drug<-as.numeric(gsub(\",\", \"\", immigrant$Drug))\nimmigrant$Immigration<-as.numeric(gsub(\",\", \"\", immigrant$Immigration))\nimmigrant$Other<-as.numeric(gsub(\",\", \"\", immigrant$Other))\nimmigrant$Apprehended<-as.numeric(gsub(\",\", \"\", immigrant$Apprehended))\n#Create extra tables based on the data\nimmigrant <- mutate(immigrant,Total = Drug+Immigration)\nimmigrant <- mutate(immigrant,Ratio = Total/Apprehended)\n\n#Present graph \nmain.plot <- ggplot(data = immigrant)+\n geom_line(mapping = aes(x= Year, y= Ratio, group=1))+\n ggtitle(\"Percentage of People who are Arrested/Apprehended\") \n \n#Call above graph and use this function at the bottom \nbar.it <- function(year){\nyear <- as.numeric(year)\n\n#How big youd want the bubble graph to be\nn<-1000\nrandom<-1:n\nims <- filter(immigrant,Year == year) \nims<- gather(ims,\"Reason\",\"Amount\", 1:7) %>% \n filter(Reason == \"Ratio\")\nratio<- as.numeric(ims$Amount)*n\nsample <- sample(random,ratio)\n\n#use bubbles!\nbubble.frame <- data.frame(random,0)\ncolnames(bubble.frame) <- c(\"Set\", \"Hit\")\n\nbubble.frame$Hit <- \"green\"\nbubble.frame$Hit[sample] <- \"blue\"\n\n\nbubbles(value = sample(10), label=\" \", key = , tooltip = \"One out of A Thousand\", color = bubble.frame$Hit,\n textColor = \"#333333\", width = NULL, height = NULL)\n\n\n \n}\n\n", "meta": {"hexsha": "313f522c7df49647b4da6904dab3a11ba42d2cb6", "size": 2107, "ext": "r", "lang": "R", "max_stars_repo_path": "usinfo.r", "max_stars_repo_name": "mholmes2-1562497/immigration", "max_stars_repo_head_hexsha": "98090d50b3b204f067e8a141c857d09fd184c95b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-12-05T04:53:17.000Z", "max_stars_repo_stars_event_max_datetime": "2017-12-05T04:53:17.000Z", "max_issues_repo_path": "usinfo.r", "max_issues_repo_name": "mholmes2-1562497/immigration", "max_issues_repo_head_hexsha": "98090d50b3b204f067e8a141c857d09fd184c95b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2017-11-18T05:05:53.000Z", "max_issues_repo_issues_event_max_datetime": "2017-12-07T12:12:41.000Z", "max_forks_repo_path": "usinfo.r", "max_forks_repo_name": "mholmes2-1562497/immigration", "max_forks_repo_head_hexsha": "98090d50b3b204f067e8a141c857d09fd184c95b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.9852941176, "max_line_length": 107, "alphanum_fraction": 0.7081158045, "num_tokens": 609, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316860482762, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.34404328761965075}} {"text": "\nNMF_method1<-function(tg_list,data_ng,data_normalized,max_ES_cut=0.3)\n{\n\ttg_selected_R4_RR<-tg_list\n\tdata.matrix0_s<-data_ng\n\tdata_23_s<-data_normalized\n\tRbase_selected_R4_RR<-Compute_Rbase_SVD(data.matrix0,tg_selected_R4_RR)\n\tstat_selected_R4_RR<-compute_IM_stat(tg_list_c=tg_selected_R4_RR)\n\tstat_selected_R4_RR_max<-apply(stat_selected_R4_RR,1,max)\n\ttg_I_id<-which(stat_selected_R4_RR_max==max(stat_selected_R4_RR_max))[1]\n\ttg_selected_id<-tg_I_id\n\tX_I<-data.matrix0[tg_selected_R4_RR[[tg_I_id]],]\n\tN<-0\n\tNMF_list_c<-list()\n\tN<-N+1\n\tNMF_list_c[[N]]<-tg_selected_R4_RR[[tg_I_id]]\n\tnames(NMF_list_c)<-\"T\"\n\tNMF_self_c1<-Building_NMF_input_no_cancer(tg_data=X_I,tg_list=NMF_list_c,tg_list_add=list())\n\tV_I<-t(as.matrix(Rbase_selected_R4_RR[tg_I_id,]))\n\tV_I_n<-normalize_data2(V_I)\n\tV_c<-V_I_n\n\tccc<-compute_CompRowspace_NN(data_23_s,module_RB=V_c,ROUNDS=min(nrow(V_I),3))\n\tES_I<-RMSE_row(ccc)/RMSE_row(data_23_s)\n\tES_base_c<-Compute_base_ES_score(ES_I,tg_selected_R4_RR)\n\tES_max<-max(ES_base_c)\n\twhile(ES_max>max_ES_cut)\n\t{\n\t\tN<-N+1\n\t\ttg_id<-which(abs(ES_base_c-max(ES_base_c))<0.01)\n\t\ttg_id<-setdiff(tg_id,tg_selected_id)\n\t\ttg_id_add<-tg_id[which(stat_selected_R4_RR_max[tg_id]==max(stat_selected_R4_RR_max[tg_id]))[1]]\n\t\ttg_selected_id<-c(tg_selected_id,tg_id_add)\n\t\tNMF_list_c[[N]]<-tg_selected_R4_RR[[tg_id_add]]\n\t\tnames(NMF_list_c)<-names(tg_selected_R4_RR)[tg_selected_id]\n\t\tNMF_self_c1<-Building_NMF_input_no_cancer(tg_data=data.matrix0_s,tg_list=NMF_list_c,tg_list_add=list())\n\t\tqnmf_result_c <- run_NMF(NMF_self_c1,RR0=0.8, maxIter=2000)\n\t\tX1 = qnmf_result_c[[\"X1\"]]\n\t\tU = qnmf_result_c[[\"U\"]]\n\t\tV = qnmf_result_c[[\"V\"]]\n\t\tttt1 = qnmf_result_c[[\"ttt1\"]]\n\t\t########check correlation between: V and true Proportion\n\t\tictd.ccc= t(cor(t(tProp),V) ) \n\t\to6_predict_ture = apply(ictd.ccc, 1, max)\n\t\to6_predict_ture2 = apply(ictd.ccc, 2, max)\n\t\tprint(N)\n\t\tprint(o6_predict_ture2)\n\t\tprint(apply(cor(t(tProp),t(Rbase_selected_R4_RR[tg_selected_id,])),1,max))\n\t\tV_c<-normalize_data2(t(V))\n\t\tccc<-compute_CompRowspace_NN(data_23_s,module_RB=V_c,ROUNDS=min(nrow(V_I),3))\n\t\tES_I<-RMSE_row(ccc)/RMSE_row(data_23_s)\n\t\tES_base_c<-Compute_base_ES_score(ES_I,tg_selected_R4_RR)\n\t\tES_max<-max(ES_base_c)\n\t}\n\treturn(qnmf_result_c)\n}\n\nCompute_base_ES_score<-function(ES_c,tg_selected_c)\n{\n\tccc<-c()\n\tfor(i in 1:length(tg_selected_c))\n\t{\t\n\t\tccc<-c(ccc,mean(ES_c[tg_selected_c[[i]]]))\n\t}\n\tnames(ccc)<-names(tg_selected_c)\n\treturn(ccc)\n}\n\n\ncompute_CompRowspace_NN_selflist<-function(tg_data,tg_data_ng,tg_list,ROUNDS=3)\n{\n\tRbase_selected_R4_og<-Compute_Rbase_SVD(tg_data_ng,tg_list)\n\tRbase_selected_R4<-Compute_Rbase_SVD(tg_data,tg_list)\n\taaa<-matrix(0,length(tg_list),ROUNDS)\n\tbbb<-matrix(0,length(tg_list),ROUNDS)\n\tddd<-matrix(0,length(tg_list),ROUNDS)\n for(j in 1:length(tg_list))\n {\n\t\ttg_data_c<-tg_data[tg_list[[j]],]\n \tBase_c<-Rbase_selected_R4[-j,]\n\t\t#print(tg_data_c[,1:5])\n\t\t#print(j)\n\t\t#print(Base_c[,1:5])\n\t\t#print(j)\n\t\ttg_id_kk<-setdiff(1:length(tg_list),j)\n\t\tccc<-c()\n\t\tccc<-compute_CompRowspace_NN_uni(tg_data_c,module_RB=Base_c,ROUNDS=3)\n\t\t#print(ccc)\n\t\tif(length(ccc)>0)\n\t\t{\n\t\t\ttg_id1<-tg_id_kk[ccc[1,]]\n\t\t\taaa[j,1:length(tg_id1)]<-tg_id1\n\t\t\tbbb[j,1:length(tg_id1)]<-ccc[2,]\n\t\t\tddd[j,1:length(tg_id1)]<-ccc[3,]\n\t\t}\n \t}\n\ttg_ids<-which(apply(bbb,1,min)<0.2)\n\tc_cell_depend<-list()\n\tN<-0\n\tnn<-c()\n\tfor(j in 1:length(tg_ids))\n\t{\n\t\ttg_c<-aaa[tg_ids[j],which(ddd[tg_ids[j],]>0.1)]\n\t\tif(length(tg_c)>0)\n\t\t{\n\t\t\tdata_ccc<-t(Rbase_selected_R4_og[c(tg_ids[j],tg_c),])\n\t\t\tcolnames(data_ccc)<-c(\"Y\",tg_c)\n\t\t\trownames(data_ccc)<-1:nrow(data_ccc)\n\t\t\tdata_ccc<-as.data.frame(data_ccc)\n\t\t\tlm_cc<-lm(Y~.+0,data=data_ccc)\n\t\t\tccc<-summary(lm_cc)$coefficients\n\t\t\tcc1<-which((ccc[,4]<0.01)&ccc[,1]>0)\n\t\t\tcc2<-which((ccc[,4]<0.01)&ccc[,1]<0)\n\t\t\tdd1<-c()\n\t\t\tdd2<-c()\n\t\t\tif(length(cc1)>0)\n\t\t\t{\n\t\t\t\tdd1<-tg_c[cc1]\n\t\t\t}\n\t\t\tif(length(cc2)>0)\n\t\t\t{\n\t\t\t\tdd2<-tg_c[cc2]\n\t\t\t}\n\t\t\tdd3<-setdiff(tg_c,c(dd1,dd2))\n\t\t\tc_l<-c(tg_ids[j],dd2)\n\t\t\tc_r<-c(dd1)\n\t\t\tcc<-list(c_l,c_r)\n\t\t\tnames(cc)<-c(\"right\",\"left\")\n\t\t\tN<-N+1\n\t\t\tc_cell_depend[[N]]<-cc\n\t\t\tnn<-c(nn,tg_ids[j])\n\t\t}\n\t}\n\tnames(c_cell_depend)<-nn\n\tbaba<-c()\n\tdidi<-c()\n\tfor(i in 1:length(c_cell_depend))\n\t{\n\t\tcc<-c_cell_depend[[i]]\n\t\tif((length(cc[[1]])==1)&(length(cc[[2]])>1))\n\t\t{\n\t\t\tbaba<-c(baba,cc[[1]])\n\t\t\tdidi<-c(didi,cc[[2]])\n\t\t}\n\t}\n\tbaba<-unique(sort(baba))\n\tdidi<-setdiff(unique(sort(didi)),baba)\n\tdaye<-setdiff(1:length(tg_list),unique(sort(c(didi,baba))))\n\tccc<-list(didi,baba,daye,c_cell_depend)\n\tnames(ccc)<-c(\"Leaf_CT\",\"Root_CT\",\"Other_leat_CT\",\"Cell_dependency\")\n return(ccc)\n}\n\ncompute_CompRowspace_NN<-function(data_cc=tg_data,module_RB=module_selected[[4]],ROUNDS=3)\n{\n data_CORS_cancer<-data_cc\n for(ii in 1:ROUNDS)\n {\n ccc<-cor(t(data_CORS_cancer),t(module_RB))\n if(nrow(module_RB)>1)\n {\n ddd<-apply(ccc,1,order)\n eee<-ddd[nrow(ddd),]\n eee<-eee*(apply(ccc,1,max)>0)\n tg_ids<-sort(setdiff(unique(eee),0))\n if(length(tg_ids)>0)\n {\n for(i in 1:length(tg_ids))\n {\n ttt_ccc<-module_RB[tg_ids[i],]%*%t(module_RB[tg_ids[i],])/sum((module_RB[tg_ids[i],])^2)\n data_CORS_cancer[names(which(eee==tg_ids[i])),]<-data_CORS_cancer[names(which(eee==tg_ids[i])),]-data_CORS_cancer[names(which(eee==tg_ids[i])),]%*%ttt_ccc\n }\n }\n }\n else\n {\n ddd<-apply(ccc,1,order)\n eee<-ddd\n eee<-eee*(as.vector(ccc)>0)\n tg_ids<-sort(setdiff(unique(eee),0))\n if(length(tg_ids)>0)\n {\n for(i in 1:length(tg_ids))\n {\n ttt_ccc<-module_RB[tg_ids[i],]%*%t(module_RB[tg_ids[i],])/sum((module_RB[tg_ids[i],])^2)\n data_CORS_cancer[names(which(eee==tg_ids[i])),]<-data_CORS_cancer[names(which(eee==tg_ids[i])),]-data_CORS_cancer[names(which(eee==tg_ids[i])),]%*%ttt_ccc\n }\n }\n }\n }\n return(data_CORS_cancer)\n}\n\n\n\ncompute_IM_stat<-function(tg_list_c,immune_cell_uni_table=immune_cell_uni_table0_GS,IM_id_list0=ICTD::IM_id_list)\n{\n\tccc<-c()\n\tnn<-c()\n\tfor(i in 1:length(tg_list_c))\n\t{ \n ccc0<-c()\n for(j in 1:length(ICTD::IM_id_list))\n { \n if(length(ICTD::IM_id_list[[j]])>1)\n {\n cc0<-apply(immune_cell_uni_table[tg_list_c[[i]],IM_id_list0[[j]]],1,sum)/sum((1/(1:length(IM_id_list0[[j]]))))\n }\n else\n {\n cc0<-immune_cell_uni_table[tg_list_c[[i]],IM_id_list0[[j]]]\n }\n ccc0<-cbind(ccc0,cc0)\n }\n colnames(ccc0)<-names(ICTD::IM_id_list)\n ddd<-apply(ccc0,2,mean)\n ccc<-rbind(ccc,ddd)\n nn<-c(nn,colnames(ccc0)[which(ddd==max(ddd))[1]])\n\t}\n\trownames(ccc)<-nn\n\treturn(ccc)\n}\n\n#######################\nrun_NMF<-function(NMF_input1=NMF_input1,RR0=0.8, maxIter=20000, tProp){\n\n###########Preprocess the data\nP_fix=NMF_input1$S_indi\nNMF_indi_all = NMF_input1$NMF_indi_all\n\ndata_t=NMF_input1$NMF_data\naddP=NMF_input1$NMF_P_pre\nNMF_indi_all0<-NMF_indi_all[which(apply(NMF_indi_all,1,sum)<=2),]\nNMF_indi_all01<-NMF_indi_all0[,which(apply(NMF_indi_all0,2,sum)>0 | P_fix==0)] #?????0#######if 5 is okay, originally it was 10\nNMF_indi_all01<-NMF_indi_all01[which(apply(NMF_indi_all01,1,sum)>0),]\nNMF_indi_all=NMF_indi_all01[rownames(NMF_indi_all01)%in%rownames(data_t),]\nX1=data_t[match(rownames(NMF_indi_all),rownames(data_t)),]###take only those rows that correspond to rows in NMF_indi_all\nK=ncol(NMF_indi_all)\nindiS=1-NMF_indi_all\nindiS[,which(P_fix==0)]=0*indiS[,which(P_fix==0)]\n###########\n \n###########Parameter settings\nRR=RR0##penalty parameter for constraints on NMF_indi_all\nindiS_method=\"nonprdescent\" ##the updating scheme for the structural constraints\niter=maxIter\nalpha=beta=gamma=roh=0\n#gamma=0.1\n#roh=0.1 ##roh has to be equal to 0 in order to implement addP\nUM=VM=NULL\nqq=1\nepslog=8\nnPerm=2\ninitial_U=initial_V=NULL\nmscale=0###########1\ncnormalize=1\n###########\n\n###########Run the constrained qNMF\nttt1=qnmf_indisS_all_revise_addP_RR(X1,initial_U,initial_V,NMF_indi_all,indiS_method,UM,VM,alpha,beta,gamma,roh,RR,qq,iter,epslog,mscale,cnormalize,addP, P_fix, tProp)\n#ttt1=qnmf_indisS_all_revise_addP_old(X1,initial_U,initial_V,NMF_indi_all,indiS_method,UM,VM,alpha,beta,gamma,roh,theta,qq,iter,epslog,mscale,cnormalize,addP, P_fix)\n###########\nX1=ttt1$X1\nU=ttt1$U\nV=ttt1$V\nextraOut=ttt1$extraOut\n#sum((X1-(ttt1$U)%*%t(ttt1$V))^2)\n#diag(cor(ttt1$U,NMF_indi_all, method=\"spearman\"))\n###########Run cross validation of the constrained qNMF\n#devs1=cv_qnmf(X1,indiS_method,NMF_indi_all,alpha,beta,gamma,roh,theta,qq,iter,epslog,nPerm,mscale,cnormalize)\n#############################################\n\nreturn(list(X1=X1, U=U, V=V, extraOut=extraOut, ttt1=ttt1))\n\n\n}\n\nidentify_max_base<-function(tg_R1_c,data.matrix,tProp)\n{\n\tcc<-Compute_Rbase_SVD(data.matrix,tg_R1_c)\n\ttg_R1_c_list<-list()\n\tdd<-cor(t(cc),t(tProp))\n\tfor(i in 1:ncol(dd))\n\t{\n\t\ttg_id<-which(dd[,i]==max(dd[,i]))[1]\n\t\ttg_R1_c_list[[i]]<-tg_R1_c[[tg_id]]\n\t}\n\tnames(tg_R1_c_list)<-colnames(dd)\n\treturn(tg_R1_c_list)\n}\n\nidentify_max_base2<-function(tg_R1_c,data.matrix,tProp)\n{\n\tcc<-Compute_Rbase_SVD(data.matrix,tg_R1_c)\n\ttg_R1_c_list<-list()\n\tdd<-cor(t(cc),t(tProp))\n\tff<-c()\n\tfor(i in 1:ncol(dd))\n\t{\n\t\ttg_id<-which(dd[,i]==max(dd[,i]))[1]\n\t\ttg_R1_c_list[[i]]<-tg_R1_c[[tg_id]]\n\t\tff<-c(ff,tg_id)\n\t}\n\tprint(ff)\n\tnames(tg_R1_c_list)<-colnames(dd)\n\treturn(tg_R1_c_list)\n}\n\nselect_R_base<-function(tg_R1_c,tg_id)\n{\n\ttg_R1_cc<-list()\n\tfor(i in 1:length(tg_id))\n\t{\n\t\ttg_R1_cc[[i]]<-tg_R1_c[[tg_id[i]]]\n\t}\n\tnames(tg_R1_cc)<-names(tg_R1_c)[tg_id]\n\treturn(tg_R1_cc)\n}\n", "meta": {"hexsha": "6819473eb7a07cd2ee112202223b08dd67646f5d", "size": 9820, "ext": "r", "lang": "R", "max_stars_repo_path": "R/NMF_functions_new.r", "max_stars_repo_name": "changwn/ICTD", "max_stars_repo_head_hexsha": "acb0d5c2c859b4c756e1ff50e6624046a2f68d36", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-31T02:23:12.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-17T00:33:55.000Z", "max_issues_repo_path": "R/NMF_functions_new.r", "max_issues_repo_name": "changwn/ICTD", "max_issues_repo_head_hexsha": "acb0d5c2c859b4c756e1ff50e6624046a2f68d36", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/NMF_functions_new.r", "max_forks_repo_name": "changwn/ICTD", "max_forks_repo_head_hexsha": "acb0d5c2c859b4c756e1ff50e6624046a2f68d36", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8805031447, "max_line_length": 186, "alphanum_fraction": 0.6560081466, "num_tokens": 3586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.746138993030751, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.34398259548637133}} {"text": "## Run colocalisation analysis of genetic signals for DNA methylation and ASD following method in Giambartolomei et al.\n## Tests ASD against all DNA methylation sites found within 250 kb of a ASD GWAS loci as identified by the PGC-iPSYCH meta-analysis.\n\nremoveAlleles<-function(text){\n\ttmp<-unlist(strsplit(unlist(strsplit(gsub(\"X\", \"\", text), \"\\\\.\")), \"_\"))\n\tif(length(tmp) == 3){\n\t\treturn(tmp)\n\t} else {\n\t\treturn(c(tmp[1:2], paste(tmp[3:length(tmp)], collapse = \"_\")))\n\t}\n}\n\nlibrary(coloc)\nlibrary(data.table)\nlibrary(IlluminaHumanMethylation450kanno.ilmn12.hg19)\ndata(IlluminaHumanMethylation450kanno.ilmn12.hg19)\n\nsetwd(\"\")\n\nprobeAnnot<-as.data.frame(IlluminaHumanMethylation450kanno.ilmn12.hg19@data$Locations)\n\ngwas<-fread(\"\") ## Load ASD GWAS results\noutput<-NULL\n\nregions<-read.table(\"Analysis/AdditionalAutismRegions.txt\", header = TRUE, row.names = 1)\nregions<-regions[1:3,] ## only first 3 are genome-wide significant\n\nfor(i in 1:3){\n\n\tchr<-regions$CHR[i]\n\tstart<-regions$BP[i]-250000\n\tstop<-regions$BP[i]+250000\n\texpression_file_name<-paste(\"MatrixEQTL/Methylation_ProbesForASDColocAnalysis_Chr\", chr, \"_\", start, \"_\", stop, \".txt\", sep = \"\")\n\tSNP_file_name = paste(\"MatrixEQTL/Genotypes_MinGenoCount5_Chr\", chr, \".txt\", sep = \"\")\n\tsnps_location_file_name = paste(\"MatrixEQTL/Genotypes_MapInfo_Chr\", chr, \".txt\", sep = \"\")\n\t\n ## load mQTL results\n\tmQTL<-read.table(paste(\"MatrixEQTL/Output/AllmQTLsforASDColocAnalysis_mQTL_chr\", chr,\"_\", start, \"_\", stop, \".txt\", sep = \"\"), stringsAsFactors = FALSE, header = TRUE)\n\tmQTL<-cbind(unlist(lapply(strsplit(mQTL$SNP, \"_\"), head, n = 1)), mQTL)\n\t\n ## load variants frequencies\n\tfreq<-read.table(\"01_Genotypes/FilteredGenotypes/GT_hwe_mind_geno_maf_freq.frq\", header = TRUE, stringsAsFactors = FALSE)\n\tgwas.sub<-gwas[which(gwas$CHR == chr),]\n\t\n\tmQTL<-cbind(mQTL, paste(freq$SNP, apply(apply(freq[,c(\"A1\", \"A2\")], 1, sort), 2, paste, collapse = \"_\"), sep = \"_\")[match(mQTL[,7], freq$SNP)])\n\tcolnames(mQTL)[ncol(mQTL)]<-\"SNP2\"\n\tmQTL$SNP2<-as.character(mQTL$SNP2)\n\t\n\tdataset2<-list(beta= log(gwas.sub$OR), varbeta= (gwas.sub$SE^2), type = \"cc\", snp = gwas.sub$SNP, MAF = (gwas.sub$FRQ_A_18381*18381+gwas.sub$FRQ_U_27969*27969)/(18381+27969), N=46350, s = 0.397)\n\n\tfor(each in unique(mQTL$gene)){\n\t\tmQTL.sub<-mQTL[which(mQTL$gene == each),]\n\t\tif(nrow(mQTL.sub) > 1){\n\t\t\tdataset1<-list(beta=mQTL.sub$beta,varbeta=(mQTL.sub$beta/mQTL.sub$t.stat)^2,type = \"quant\",snp = mQTL.sub[,1], MAF = freq$MAF[match(mQTL.sub[,1], freq$SNP)], N=1157)\n\t\t\tmy.res<-coloc.abf(dataset1, dataset2)\n\t\t\toutput<-rbind(output, c(\"trait2\"=each, my.res$summary, i))\n\t\t\t}\n\t\t}\n\t}\n}\n\n\nsum<-as.numeric(output[,6])+as.numeric(output[,7])\nratio<-as.numeric(output[,7])/as.numeric(output[,6])\noutput<-cbind(output, sum, ratio)\n\n## Annotate results\nprobeAnnot<-as.data.frame(IlluminaHumanMethylation450kanno.ilmn12.hg19@data$Locations)\nprobeAnnot<-probeAnnot[match(output[,1], rownames(probeAnnot)),c(\"chr\", \"pos\")]\noutput<-cbind(output, probeAnnot)\nprobeAnnot<-as.data.frame(IlluminaHumanMethylation450kanno.ilmn12.hg19@data$Other)\nprobeAnnot<-probeAnnot[match(output[,1], rownames(probeAnnot)),5:14]\noutput<-cbind(output, probeAnnot)\n\nwrite.csv(output, \"Coloc/ColocAnalysis_ASD_GenomeWideSigRegionsOnly.csv\")\n\n", "meta": {"hexsha": "d44a80f45d5dd807e60573823f5aa92ecd6faa49", "size": 3230, "ext": "r", "lang": "R", "max_stars_repo_path": "RunColocASDGWASRegions.r", "max_stars_repo_name": "ejh243/MinervaASDEWAS", "max_stars_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-05-17T01:53:35.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T07:22:14.000Z", "max_issues_repo_path": "RunColocASDGWASRegions.r", "max_issues_repo_name": "ejh243/MinervaASDEWAS", "max_issues_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RunColocASDGWASRegions.r", "max_forks_repo_name": "ejh243/MinervaASDEWAS", "max_forks_repo_head_hexsha": "6df862f006532388c81b2d13d3840c9f5dfcbe48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.9480519481, "max_line_length": 195, "alphanum_fraction": 0.719504644, "num_tokens": 1106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7461389817407017, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.3439825902814692}} {"text": "library(tidyverse)\n\ntrain <- \n read_csv(\"./0_raw/train.csv\")\n\ntest <- \n read_csv(\"./0_raw/test.csv\")\n\nfeatures <- \n readRDS('./2_model/final_features.rds')\n\nfill_numeric_na <- function(x){\n \n}\n\n# new_train <- \n# train[c(features$features, 'NU_NOTA_MT')] %>%\n# mutate_at(vars(c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO', 'NU_NOTA_MT')), function(x){as.integer(ifelse(is.na(x), 0, x))}) %>%\n# mutate_at(vars(c('TP_PRESENCA_CH', 'TP_PRESENCA_CN', 'TP_PRESENCA_LC', 'TP_STATUS_REDACAO')), function(x){as.factor(ifelse(is.na(x), -1, x))})\n# \n# new_test <- \n# test[c(features$features, 'NU_INSCRICAO')] %>%\n# mutate_at(vars(c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO')), \n# function(x){as.integer(ifelse(is.na(x), 0, x))}) %>%\n# mutate_at(vars(c('TP_PRESENCA_CH', 'TP_PRESENCA_CN', 'TP_PRESENCA_LC', 'TP_STATUS_REDACAO')), function(x){as.factor(ifelse(is.na(x), -1, x))}) #%>%\n# #mutate(NU_NOTA_MT = NA)\n\nnew_train <- \n train[c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO', 'NU_NOTA_MT')] %>%\n mutate_at(vars(c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO', 'NU_NOTA_MT')), \n function(x){as.integer(ifelse(is.na(x), -1, x))})\n\nnew_test <- \n test[c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO', 'NU_INSCRICAO')] %>%\n mutate_at(vars(c('NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_LC', 'NU_NOTA_REDACAO')), \n function(x){as.integer(ifelse(is.na(x), -1, x))})\n\nrecipe <- \n recipe(NU_NOTA_MT ~ ., data = new_train) %>%\n step_normalize(all_numeric(), -all_outcomes()) %>%\n #remove_role(NU_INSCRICAO, old_role = \"predictor\") %>%\n #update_role(NU_INSCRICAO, new_role = \"id variable\") %>%\n prep(.)\n\ntrain_baked <- \n recipe %>%\n bake(new_data = new_train)\n\n#summary(recipe)\n\nmodl_ml <-\n rand_forest(mode = \"regression\", trees = 100) %>%\n set_engine(\"ranger\", importance = \"impurity\")\n\nfit_ml <-\n modl_ml %>%\n fit(NU_NOTA_MT ~ ., data = train_baked)\n\ntest_baked <-\n recipe %>%\n bake(new_data = new_test) %>%\n bind_cols(., new_test %>% select(NU_INSCRICAO))\n \ntest_final <- test_baked\n\ntest_final$NU_NOTA_MT <- predict(fit_ml, new_test)$.pred\ntest_final <- test_final %>% select(NU_INSCRICAO, NU_NOTA_MT)\nwrite_csv(test_final, './2_model/prediction_celso.csv')\n\n", "meta": {"hexsha": "5917bcc2b126f83a751124d18ed884d723737fb5", "size": 2300, "ext": "r", "lang": "R", "max_stars_repo_path": "3_improved/fitting_test.r", "max_stars_repo_name": "matth3us/desafio_codenation_jun2020", "max_stars_repo_head_hexsha": "fa97633dbb38219a5733cec96be2edc78280a4a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "3_improved/fitting_test.r", "max_issues_repo_name": "matth3us/desafio_codenation_jun2020", "max_issues_repo_head_hexsha": "fa97633dbb38219a5733cec96be2edc78280a4a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3_improved/fitting_test.r", "max_forks_repo_name": "matth3us/desafio_codenation_jun2020", "max_forks_repo_head_hexsha": "fa97633dbb38219a5733cec96be2edc78280a4a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.8571428571, "max_line_length": 151, "alphanum_fraction": 0.6552173913, "num_tokens": 789, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7217432062975979, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3439681254148879}} {"text": "#script rasterizes land protection shapefiles \n\n\nrm(list=ls())\n\nlibrary(raster)\nlibrary(sf)\n\n\n#unzip(zipfile=\"Data/sim10_BRmunis_latlon_5kmzip\",exdir=\"Data\") #unzip if needed\nmunis.r <- raster(\"Data/sim10_BRmunis_latlon_5km.asc\") #for rasterization\n\n#LandProtect is for all services\nLandProtect<- st_read(\"Data/landProtection/protected_areas_clip.shp\")\nLp2001<- LandProtect[\"FID_ucsmi\"]\nLpr<-rasterize(Lp2001, munis.r, field='FID_ucsmi') \n\n#Indigenous is for all services\nIndigenous <- st_read(\"Data/landProtection/indigenous.shp\")\nIndigenous <- Indigenous[\"GID0\"]\nIndig <-rasterize(Indigenous, munis.r, field='GID0') \n\n#Amazon is for Soy and Pasture \nAmazon <- st_read(\"Data/landProtection/Amazon_area.shp\")\nAmazon <- Amazon['NOME']\nAmzn <- rasterize(Amazon, munis.r, field='NOME') \n\n#Land Protection\n#set all values to 1 (not protected) or 0 (protected)\nLP_bin <- Lpr\nLP_bin[!is.na(Lpr)] <- 0\nLP_bin[is.na(Lpr)] <- 1\n\n#add indig at \nLP_bin[Indig == 1] <- 0.1 \n\n#LandProtect currently is the only restriction on OAgri and Maize\n#output data\nwriteRaster(LP_bin, \"Data/All_ProtectionMap.asc\", format = 'ascii', overwrite=T)\n\n#lc 2006 needed for Soy Moratorium\nlc2006 <- raster(\"Data/ObservedLCmaps/LandCover2006_PastureB_Disagg.asc\")\n\n#where 2006 nature AND Amazon Biome, Soy should be prevented\nSoy_bin <- Amzn\nSoy_bin[lc2006 != 1] <- NA\nSoy_bin[!is.na(Soy_bin)] <- 0 #protected\nSoy_bin[is.na(Soy_bin)] <- 1 #not protected\n\n#then combine with 'standard' land protection\nSoy_bin[LP_bin == 0] <- 0 #protected\nSoy_bin[LP_bin == 0.1] <- 0.1 #indigenous\n\n#output data\nwriteRaster(Soy_bin, \"Data/Soy_ProtectionMap_2006.asc\", format = 'ascii', overwrite=T)\n\n#lc 2008 needed for Beef Moratorium\nlc2009 <- raster(\"Data/ObservedLCmaps/LandCover2009_PastureB_Disagg.asc\")\n\n#where 2008 nature AND Amazon Biome, Pature should be prevented\nPas_bin <- Amzn\nPas_bin[lc2009 != 1] <- NA\nPas_bin[!is.na(Pas_bin)] <- 0 #protected\nPas_bin[is.na(Pas_bin)] <- 1 #not protected\n\n#then combine with 'standard' land protection\nPas_bin[LP_bin == 0] <- 0 #protected\nPas_bin[LP_bin == 0.1] <- 0.1 #indigenous\n\n#output data\nwriteRaster(Pas_bin, \"Data/Pasture_ProtectionMap_2009.asc\", format = 'ascii', overwrite=T)\n\n", "meta": {"hexsha": "c89bc37452768a7fa20c7910e9bf6c198902e588", "size": 2208, "ext": "r", "lang": "R", "max_stars_repo_path": "LandProtectionMap.r", "max_stars_repo_name": "jamesdamillington/CRAFTYInput", "max_stars_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "LandProtectionMap.r", "max_issues_repo_name": "jamesdamillington/CRAFTYInput", "max_issues_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LandProtectionMap.r", "max_forks_repo_name": "jamesdamillington/CRAFTYInput", "max_forks_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2465753425, "max_line_length": 90, "alphanum_fraction": 0.7386775362, "num_tokens": 741, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7217431943271999, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3439681197100398}} {"text": "library(ape)\nupham <- read.nexus(\"data/upham_tree.nex\")\nD <- cophenetic(upham)\n\ndf <- data.frame(D[\"Homo_sapiens\",])\ncolnames(df) <- c(\"phylodist\")\nwrite.csv(df, \"artifacts/phylo_distance_to_human.csv\")\n", "meta": {"hexsha": "71340053e52785c65ae04dc892f03723918a15da", "size": 203, "ext": "r", "lang": "R", "max_stars_repo_path": "R/readphylo.r", "max_stars_repo_name": "viralemergence/trefle", "max_stars_repo_head_hexsha": "67856d35787a19026420582d936f66ef52fa4b5c", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-01T20:48:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-01T20:48:41.000Z", "max_issues_repo_path": "R/readphylo.r", "max_issues_repo_name": "viralemergence/trefle", "max_issues_repo_head_hexsha": "67856d35787a19026420582d936f66ef52fa4b5c", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/readphylo.r", "max_forks_repo_name": "viralemergence/trefle", "max_forks_repo_head_hexsha": "67856d35787a19026420582d936f66ef52fa4b5c", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-20T20:47:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-20T20:47:50.000Z", "avg_line_length": 25.375, "max_line_length": 54, "alphanum_fraction": 0.7192118227, "num_tokens": 67, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3439531166114479}} {"text": "# Exploratory plots for TSM\n\nrequire(mgcv)\nrequire(data.table)\nrequire(ggplot2)\nrequire(lattice)\n\ndat <- fread('temp/warmingtolerance_byspecies.csv')\n\n\n#############################\n##### Thab only plots ###\n#############################\n# plot un-adjusted thab vs. adjustments for elevation (all from climatology)\n\tcex = 0.5\n\tquartz(width=10,height=4)\n\t# pdf(width=10, height=4, file='figures/thab.adj_vs_thab.pdf')\n\tpar(mfrow=c(1,3), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,1,0))\n\n\tdat[Realm=='Terrestrial',plot(thabsum, thabsum.adj-thabsum, xlim=c(-10,45), ylim=c(-16,16), col='green', main='thabsum elevation adjustment', cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial',plot(thabmaxmo, thabmaxmo.adj-thabmaxmo, xlim=c(-10,45), ylim=c(-16,16), col='green', main='thabmaxmo elevation adjustment', cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial',plot(thabmaxhr, thabmaxhr.adj-thabmaxhr, xlim=c(-10,45), ylim=c(-16,16), col='green', main='thabmaxhr elevation adjustment', cex=cex)]\n\tabline(h=0)\n\n\tdev.off()\n\n\n# plot NM vs. Thab_maxhr (climatology, elevation-corrected). Terrestrial only.\n\tcex = 0.5\n\tquartz(width=10,height=4)\n\t# pdf(width=10, height=4, file='figures/NM_vs_thabmaxhr.adj.pdf')\n\tpar(mfrow=c(1,4), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,1,0), omi=c(0,0,0.25,0))\n\n\tdat[Realm=='Terrestrial',plot(thabmaxhr.adj, NM_1cm_airshade, main='1 cm shade', cex=cex)]\n\tabline(0,1)\n\n\tdat[Realm=='Terrestrial',plot(thabmaxhr.adj, NM_2m_airshade, main='2 m shade', cex=cex)]\n\tabline(0,1)\n\n\tdat[Realm=='Terrestrial',plot(thabmaxhr.adj, NM_exposed_bodytemp, main='Exposed body', cex=cex)]\n\tabline(0,1)\n\n\tdat[Realm=='Terrestrial',plot(thabmaxhr.adj, NM_exposed_air, main='Exposed air', cex=cex)]\n\tabline(0,1)\n\n\tmtext('NicheMapper vs. air temperatures', side=3, outer=TRUE)\n\n\tdev.off()\n\t\n\n# plot NM and Thab vs. latitude (no elevation correction)\n\t# ylims <- dat[,range(thabsum, thabmaxmo, thabmaxhr, NM_exposed_air, NM_exposed_bodytemp, na.rm=TRUE)]\n\tylims <- dat[,range(thabsum, thabmaxmo, NM_1cm_airshade, NM_2m_airshade, NM_exposed_air, NM_exposed_bodytemp, na.rm=TRUE)]\n\txlims <- c(-80, 80) \n\tcex=0.5\n\tsetkey(dat, lat)\n\n\tquartz(width=8, height=4)\n\t# pdf(width=8, height=4, file='figures/thab_vs_lat.pdf')\n\tpar(mfrow=c(1,2))\n\t\n\tdat[Realm=='Terrestrial', plot(lat, thabsum, ylim=ylims, xlim=xlims, cex=cex, main='Terrestrial', xlab='Latitude', ylab='Temperature \u00b0C')]\n\t\tdat[Realm=='Terrestrial' & !is.na(lat) & !is.na(thabsum),lines(lat, predict(loess(thabsum~lat)))]\n\tdat[Realm=='Terrestrial', points(lat, thabmaxmo, col='pink', cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(lat) & !is.na(thabmaxmo),lines(lat, predict(loess(thabmaxmo~lat)), col='pink')]\n\tdat[Realm=='Terrestrial', points(lat, NM_2m_airshade, col='red', cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(lat) & !is.na(NM_1cm_airshade),lines(lat, predict(loess(NM_1cm_airshade~lat)), col='red')]\n\tdat[Realm=='Terrestrial', points(lat, NM_exposed_air, col='purple', cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(lat) & !is.na(NM_exposed_air),lines(lat, predict(loess(NM_exposed_air~lat)), col='purple')]\n\tdat[Realm=='Terrestrial', points(lat, NM_exposed_bodytemp, col='darkslateblue', cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(lat) & !is.na(NM_exposed_bodytemp),lines(lat, predict(loess(NM_exposed_bodytemp~lat)), col='darkslateblue')]\n\n\tdat[Realm=='Marine', plot(lat, thabsum, ylim=ylims, xlim=xlims, cex=cex, main='Marine', xlab='Latitude', ylab='Temperature \u00b0C')]\n\t\tdat[Realm=='Marine' & !is.na(lat) & !is.na(thabsum),lines(lat, predict(loess(thabsum~lat)))]\n\tdat[Realm=='Marine', points(lat, thabmaxmo, col='pink', cex=cex)]\n\t\tdat[Realm=='Marine' & !is.na(lat) & !is.na(thabmaxmo),lines(lat, predict(loess(thabmaxmo~lat)), col='pink')]\n\tdat[Realm=='Marine', points(lat, thabmaxhr, col='red', cex=cex)]\n\t\tdat[Realm=='Marine' & !is.na(lat) & !is.na(thabmaxhr),lines(lat, predict(loess(thabmaxhr~lat)), col='red')]\n\n\n\tlegend('topleft', col=c('black', 'pink', 'red', 'purple', 'darkslateblue'), legend=c('Summer', 'Warmest month', 'Daily max (land: 2m shade)', 'Air - exposed', 'Body - exposed'), pch=1, cex=0.6)\n\n\tdev.off()\n\n#############################\n##### Tmax plots ###\n#############################\n# Examine Thab values used for Tmax adjustments: plot tmax_acc with thabsum, thabmaxmo, thabmaxhr (like Fig. S6C in Sunday et al. 2014 PNAS)\n\tsetkey(dat, thabmaxhr.adj)\n\tcex1 <- 0.6\n\tcex2 <- 0.4\n\n\tquartz(width=8, height=4)\n\t# pdf(width=8, height=4, file='figures/acclimation_corrections.pdf')\n\tpar(mfrow=c(1,2))\n\tdat[Realm=='Terrestrial',plot(1:.N, tmax_acc, col='grey', cex=cex1, main='Terrestrial', ylab='\u00b0C', xlab='Rank order', ylim=c(-10,45))]\n\tdat[Realm=='Terrestrial',points(1:.N, thabmaxhr.adj, col='black', cex=cex1)]\n\tdat[Realm=='Terrestrial',points(1:.N, thabmaxmo.adj, col='red', cex=cex1)]\n\tdat[Realm=='Terrestrial',points(1:.N, thabsum.adj, col='pink', cex=cex2)]\n\n\tdat[Realm=='Marine',plot(1:.N, tmax_acc, col='grey', cex=cex1, main='Marine', ylab='\u00b0C', xlab='Rank order', ylim=c(-10,45))]\n\tdat[Realm=='Marine',points(1:.N, thabmaxhr.adj, col='black', cex=cex1)]\n\tdat[Realm=='Marine',points(1:.N, thabmaxmo.adj, col='red', cex=cex1)]\n\tdat[Realm=='Marine',points(1:.N, thabsum.adj, col='pink', cex=cex2)]\n\n\tlegend('topleft', legend=c('tmax_acc', 'thabmaxhr.adj', 'thabmaxmo.adj', 'thabsum.adj'), col=c('grey', 'black', 'red', 'pink'), pch=1, cex=0.5)\n\n\tdev.off()\n\n# plot adjustments for acclimation and elevation and duration vs un-adjusted tmax\n# acclimation: accounts for difference between experimental acclimation temperature and max monthly habitat temperature\n# duration: accounts for difference between 30 or 90 days and acclimation duration time\n\tcex=0.7\n\t\n\tquartz(width=5,height=6.5)\n\t# pdf(width=5, height=6.5, file='figures/tmax.adjDelta_vs_tmax.pdf')\n\tpar(mfrow=c(3,2), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,1,0))\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accsum-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thabsum', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accsum-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accsum-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accsum-tmax, col='blue', pch=2, cex=cex)]\n\n\tlegend('topright', legend=c('Terrestrial', 'Marine', 'crit', 'leth'), col=c('green', 'blue', 'black', 'black'), pch=c(1,1,1,2), cex=cex)\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accmo-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thab_maxmo', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accmo-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accmo-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accmo-tmax, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accsum.elev-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thabsum\\nand elevation adjustment', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accsum.elev-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accsum.elev-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accsum.elev-tmax, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accmo.elev-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thab_maxmo\\nand elevation adjustment', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accmo.elev-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accmo.elev-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accmo.elev-tmax, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accsum.elev.dur-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thabsum & time\\nand elevation adjustment', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accsum.elev.dur-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accsum.elev.dur-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accsum.elev.dur-tmax, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tmax, tmax.accmo.elev.dur-tmax, xlim=c(3,60), ylim=c(-6,6), col='green', main='acclimation to thab_maxmo & time\\nand elevation adjustment', cex=cex, cex.main=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tmax, tmax.accmo.elev.dur-tmax, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tmax, tmax.accmo.elev.dur-tmax, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tmax, tmax.accmo.elev.dur-tmax, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\n\tdev.off()\n\n\n# Examine acclimation, elevation, and duration adjustments by plotting adjusted vs. unadjusted Tmax: \n\n\tquartz(width=5, height=6.5)\n\t# pdf(width=5, height=6.5, file='figures/tmax.adj_vs_tmax.pdf')\t\n\tpar(mfrow=c(3,2), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,1,0), omi=c(0,0,0.3,0))\n\t\t\n\t\t# plot tmax.acc vs. tmax\n\tdat[, plot(tmax, tmax.accmo, type='n', main='Acclimation to max month')] # set up axes\n\tdat[Realm=='Terrestrial', points(tmax, tmax.accmo, col='green')]\n\tdat[Realm=='Marine', points(tmax, tmax.accmo, col='blue')]\n\tabline(0,1)\n\n\tdat[, plot(tmax, tmax.accsum, type='n', main='Acclimation to summer')]\n\tdat[Realm=='Terrestrial', points(tmax, tmax.accsum, col='green')]\n\tdat[Realm=='Marine', points(tmax, tmax.accsum, col='blue')]\n\tabline(0,1)\n\n\t\t# plot tmax.acc.elev vs. tmax.acc\n\tdat[, plot(tmax.accmo, tmax.accmo.elev, type='n', main='Adjust for elevation')]\n\tdat[Realm=='Terrestrial', points(tmax.accmo, tmax.accmo.elev, col='green')]\n\tdat[Realm=='Marine', points(tmax.accmo, tmax.accmo.elev, col='blue')]\n\tabline(0,1)\n\n\tdat[, plot(tmax.accsum, tmax.accsum.elev, type='n', main='Adjust for elevation')]\n\tdat[Realm=='Terrestrial', points(tmax.accsum, tmax.accsum.elev, col='green')]\n\tdat[Realm=='Marine', points(tmax.accsum, tmax.accsum.elev, col='blue')]\n\tabline(0,1)\n\n\t\t# plot tmax.acc.elev.dur vs. tmax.acc.elev\n\tdat[, plot(tmax.accmo.elev, tmax.accmo.elev.dur, type='n', main='Adjust for duration')]\n\tdat[Realm=='Terrestrial', points(tmax.accmo.elev, tmax.accmo.elev.dur, col='green')]\n\tdat[Realm=='Marine', points(tmax.accmo.elev, tmax.accmo.elev.dur, col='blue')]\n\tabline(0,1)\n\n\tdat[, plot(tmax.accsum.elev, tmax.accsum.elev.dur, type='n', main='Adjust for duration')]\n\tdat[Realm=='Terrestrial', points(tmax.accsum.elev, tmax.accsum.elev.dur, col='green')]\n\tdat[Realm=='Marine', points(tmax.accsum.elev, tmax.accsum.elev.dur, col='blue')]\n\tabline(0,1)\n\n\tmtext('Tmax adjustments', side=3, outer=TRUE, line=0.5)\n\n\tdev.off()\n\n\n# plot tmax.adj with NM_2m_airshade (land) & thab_maxhr (ocean) against latitude\n\tsetkey(dat, lat)\n\tylims <- dat[,c(0,max(c(thabmaxhr.adj, thabmaxhr, tmax, tmax.accsum.elev, predict(loess(NM_exposed_bodytemp~lat, na.action=na.exclude))), na.rm=TRUE))]\n\txlims <- c(-80, 80)\n\n\tquartz(width=8, height=4)\n\t# pdf(width=8, height=4, file='figures/tmax_and_thabmaxhr_vs_lat.pdf')\n\tpar(mfrow=c(1,2), mai=c(0.7,0.7,0.5,0.2), mgp=c(2,0.7,0), cex.axis=0.8)\n\tdat[Realm=='Terrestrial',plot(lat, NM_2m_airshade, ylim=ylims, xlim=xlims, main='Terrestrial', ylab='Temperature (\u00b0C)', xlab='Latitude (\u00b0N)', cex=0.7)]\n\tdat[Realm=='Terrestrial',points(lat, thabmaxhr, col='grey', cex=0.7)] # w/out elevation adjustment\n\tdat[Realm=='Terrestrial',points(lat, tmax.accsum.elev, col='red', cex=0.7)]\n\tdat[Realm=='Terrestrial',points(lat, tmax, col='pink', cex=0.5)] # w/out tmax_acc and tmax_metric adjustment\n\n\t\tdat[Realm=='Terrestrial',lines(lat, predict(loess(NM_2m_airshade~lat, na.action=na.exclude)))]\n\t\tdat[Realm=='Terrestrial',lines(lat, predict(loess(thabmaxhr~lat, na.action=na.exclude)), col='grey')]\n\t\tdat[Realm=='Terrestrial',lines(lat, predict(loess(NM_exposed_bodytemp~lat, na.action=na.exclude)), col='purple')]\n\n\t\tdat[Realm=='Terrestrial' & !is.na(tmax.accsum.elev),lines(lat, predict(loess(tmax.accsum.elev~lat, na.action=na.exclude)), col='red')]\n\t\tdat[Realm=='Terrestrial' & !is.na(tmax.accsum.elev.dur),lines(lat, predict(loess(tmax.accsum.elev.dur~lat, na.action=na.exclude)), col='red', lty=2)] # with tmax duration adjustment as well (fewer data points)\n\t\tdat[Realm=='Terrestrial',lines(lat, predict(loess(tmax~lat, na.action=na.exclude)), col='pink')]\n\n\tdat[Realm=='Marine',plot(lat, thabmaxhr.adj, ylim=ylims, xlim=xlims, main='Marine', ylab='Temperature (\u00b0C)', xlab='Latitude (\u00b0N)', cex=0.7)]\n\tdat[Realm=='Marine',points(lat, tmax.accsum.elev, col='red', cex=0.7)]\n\tdat[Realm=='Marine',points(lat, tmax, col='pink', cex=0.5)] # w/out tmax_acc adjustment\n\n\t\tdat[Realm=='Marine',lines(lat, predict(loess(thabmaxhr.adj~lat, na.action=na.exclude)))]\n\t\tdat[Realm=='Marine',lines(lat, predict(loess(tmax.accsum.elev~lat, na.action=na.exclude)), col='red')]\n\t\tdat[Realm=='Marine' & !is.na(tmax.accsum.elev.dur),lines(lat, predict(loess(tmax.accsum.elev.dur~lat, na.action=na.exclude)), col='red', lty=2)]\n\t\tdat[Realm=='Marine',lines(lat, predict(loess(tmax~lat, na.action=na.exclude)), col='pink')]\n\n\tlegend('topleft', legend=c('NM_2m_airshade/thabmaxhr', 'thabmaxhr', 'NM_exposed_bodytemp', 'tmax.accsum.elev (tmax_acc&elevation)', 'tmax.accsum.elev.dur (tmax_acc&elevation&duration)', 'tmax'), col=c('black', 'grey', 'purple', 'red', 'red', 'pink'), pch=c(1,1,NA,1,NA,1), lty=c(1,1,1,1,2,1), cex=0.5, bty='n')\n\n\tdev.off()\n\n\n\n#############################\n##### TSM plots ###\n#############################\n# plot un-adjusted tsm vs. adjustments for acclimation, elevation, and duration (only using air temperature from climatologies)\n# former accounts for difference between experimental acclimation temperature and max monthly habitat temperature\n\tcex=0.7\n\tylims=c(-15,12)\n\txlims=c(-10,30)\n\t\n\tquartz(width=6,height=6)\n\t# pdf(width=6, height=6, file='figures/tsm_adj_vs_tsm.pdf')\n\tpar(mfrow=c(3,3), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,0.7,0), cex.main=0.8, cex.lab=0.8)\n\n\tplot(0,0,bty='n', xaxt='n', yaxt='n', xlab='', ylab='', col='white')\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accsum-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nno elevation correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accsum-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tlegend('topright', legend=c('Terrestrial', 'marine', 'crit', 'leth'), col=c('green', 'blue', 'black', 'black'), pch=c(1,1,1,2), cex=0.7)\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accmo-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thab_maxmo\\nno elevation correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accmo-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\t\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_elev-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='no acclimation\\nw/ elevation correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_elev-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_elev-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_elev-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accsum.elev-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nw/ elevation correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accsum.elev-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum.elev-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum.elev-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accmo.elev-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thab_maxmo\\nw/ elevation correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accmo.elev-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo.elev-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo.elev-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tplot(0,0,bty='n', xaxt='n', yaxt='n', xlab='', ylab='', col='white')\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accsum.elev.dur-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nw/ elevation&duration correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accsum.elev.dur-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum.elev.dur-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accsum.elev.dur-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_maxhr_accmo.elev.dur-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thab_maxmo\\nw/ elevation&duration correction', cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='crit',points(tsm_maxhr, tsm_maxhr_accmo.elev.dur-tsm_maxhr, col='blue', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo.elev.dur-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tdat[Realm=='Marine' & tmax_metric=='leth',points(tsm_maxhr, tsm_maxhr_accmo.elev.dur-tsm_maxhr, col='blue', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdev.off()\n\n\n# plot un-adjusted tsm vs. adjustments for acclimation, elevation, and duration (for NicheMapper)\n# former accounts for difference between experimental acclimation temperature and max monthly habitat temperature\n\tcex=0.7\n\tylims=c(-10,10)\n\txlims=c(-10,30)\n\t\n\tquartz(width=6,height=6)\n\t# pdf(width=6, height=6, file='figures/tsm_NM_1cm_airshade_vs_tsm.pdf')\n\tpar(mfrow=c(3,3), las=1, mai=c(0.5, 0.5, 0.3, 0.05), mgp=c(2,0.7,0), cex.main=0.8, cex.lab=0.7)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='no acclimation\\nno elevation correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accsum-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nno elevation correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accsum-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accmo-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabmaxmo\\nno elevation correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accmo-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tplot(0,0,bty='n', xaxt='n', yaxt='n', xlab='', ylab='', col='white')\n\tlegend('topright', legend=c('Terrestrial', 'marine', 'crit', 'leth'), col=c('green', 'blue', 'black', 'black'), pch=c(1,1,1,2), cex=0.7, bty='n')\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accsum.elev-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nw/elevation correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accsum.elev-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accmo.elev-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabmaxmo\\nw/ elevation correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accmo.elev-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tplot(0,0,bty='n', xaxt='n', yaxt='n', xlab='', ylab='', col='white')\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accsum.elev.dur-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabsum\\nw/elevation&duration correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accsum.elev.dur-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\tdat[Realm=='Terrestrial' & tmax_metric=='crit',plot(tsm_maxhr, tsm_NM_1cm_airshade_accmo.elev.dur-tsm_maxhr, xlim=xlims, ylim=ylims, col='green', main='acclimation to thabmaxmo\\nw/ elevation&duration correction', cex=cex)]\n\tdat[Realm=='Terrestrial' & tmax_metric=='leth',points(tsm_maxhr, tsm_NM_1cm_airshade_accmo.elev.dur-tsm_maxhr, col='green', pch=2, cex=cex)]\n\tabline(h=0)\n\n\n\tdev.off()\n\n\n# tsm_maxhr by Class boxplot\n\tylims=c(-7,30)\n\tquartz(width=8, height=4)\n\t# pdf(width=8, height=4, file='figures/tsm_maxhr_vs_Class.pdf')\n\tpar(mfrow=c(1,2), las=2)\n\tboxplot(tsm_maxhr_adjadjsum ~ Class, cex.axis=0.5, data=droplevels(dat[Realm=='Terrestrial',]), main='terrestrial\\ntsm_maxhr_adjadjsum', ylim=ylims, ylab='Thermal Safety Margin \u00b0C')\n\t\tabline(h=0, lty=2, col='grey')\n\tboxplot(tsm_maxhr_adjadjsum ~ Class, cex.axis=0.5, data=droplevels(dat[Realm=='Marine',]), main='marine\\ntsm_maxhr_adjadjsum', ylim=ylims)\n\t\tabline(h=0, lty=2, col='grey')\n\n\tdev.off()\n\t\n\n# tsm_NM_2m_airshade (terrestrial non-amphibian) and tsm_NM_shade_body_wet (amphibian) and tsm_maxhr (marine) by lat\n\tsetkey(dat, lat)\n\txlims <- c(-80,80)\n\tylims <- dat[,range(tsm_NM_favorable, tsm_maxhr_accsum.elev, na.rm=TRUE)]\n\tcols1 <- c('#b2df8a70', '#a6cee370') # land, ocean light transparent\n\tcols2 <- c('#33a02c', '#1f78b4') # land, ocean dark\n\tcex=0.5\n\n\tl1 <- dat[Realm=='Terrestrial' & !is.na(tsm_NM_favorable),loess(tsm_NM_favorable~lat, na.action=na.exclude)]\n\tl2 <- dat[Realm=='Marine' & !is.na(tsm_maxhr_accsum.elev),loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)]\n\tp1 <- predict(l1, se=TRUE)\n\tp2 <- predict(l2, se=TRUE)\n\tp1$lat <- dat[Realm=='Terrestrial' & !is.na(tsm_NM_favorable), lat]\n\tp2$lat <- dat[Realm=='Marine' & !is.na(tsm_maxhr_accsum.elev), lat]\n\n\tquartz(width=5, height=4)\n\t# pdf(width=5, height=4, file='figures/tsm_vs_lat_loess_noexposed.pdf')\n\tpar(las=1, mai=c(0.7, 0.7, 0.3, 0.1), mgp=c(2.5,0.7,0))\n\t\n\tplot(-100,0, xlim=xlims, ylim=ylims, main='Thermal Safety Margins', xlab='Latitude (\u00b0N)', ylab='Thermal safety margin (\u00b0C)') # set up\n\tabline(h=0, lty=2, col='grey')\n\n\t\t# terrestrial shade or wet skin (favorable microclimate)\n\tdat[Realm=='Terrestrial', points(lat, tsm_NM_favorable, col=cols1[1], cex=cex)]\n\t\twith(p1, polygon(c(lat, rev(lat)), c(fit+1.96*se.fit, rev(fit-1.96*se.fit)), col=cols1[1], border=NA)) # 95%CI\n\t\twith(p1, lines(lat, fit, col=cols2[1], lwd=2)) # mean\n\n\t\t# marine\n\tdat[Realm=='Marine', points(lat, tsm_maxhr_accsum.elev, col=cols1[2], cex=cex)]\n\t\twith(p2, polygon(c(lat, rev(lat)), c(fit+1.96*se.fit, rev(fit-1.96*se.fit)), col=cols1[2], border=NA))\n\t\twith(p2, lines(lat, fit, col=cols2[2], lwd=2))\n\n\tlegend('bottomleft', legend=c('Terrestrial shade (+wet skin for amphibians)', 'Marine surface'), pch=1, col=cols1, lty=1, bty='n', cex=0.5)\n\n\tdev.off()\n\n\n\n# tsm_NM_2m_airshade (terrestrial non-amphibian) and tsm_NM_shade_body_wet (amphibian) and tsm_maxhr (marine) by lat\n# also show tsm_NM_exposed_bodytemp for comparison\n\tsetkey(dat, lat)\n\txlims <- c(-80,80)\n\tylims <- dat[,range(tsm_NM_favorable, tsm_NM_exposed_bodytemp_accsum.elev, tsm_maxhr_accsum.elev, na.rm=TRUE)]\n\tcols1 <- c('#AAAAAA70', '#b2df8a70', '#a6cee370') # exposed, land, ocean light transparent\n\tcols2 <- c('#808080', '#33a02c', '#1f78b4') # exposed, land, ocean dark\n\tcex=0.5\n\t\t\n\n\tquartz(width=5, height=4)\n\t# pdf(width=5, height=4, file='figures/tsm_vs_lat_loess.pdf')\n\t# pdf(width=5, height=4, file='figures/tsm_vs_lat_loess_only_landshade.pdf')\n\t# pdf(width=5, height=4, file='figures/tsm_vs_lat_loess_only_landshade&exposed.pdf')\n\tpar(las=1, mai=c(0.7, 0.7, 0.3, 0.1), mgp=c(2.5,0.7,0))\n\t\n\tplot(-100,0, xlim=xlims, ylim=ylims, main='Thermal Safety Margins', xlab='Latitude (\u00b0N)', ylab='Thermal safety margin (\u00b0C)') # set up\n\tabline(h=0, lty=2, col='grey')\n\n\t\t# exposed\n\tdat[Realm=='Terrestrial', points(lat, tsm_NM_exposed_bodytemp_accsum.elev, col=cols1[1], cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(tsm_NM_exposed_bodytemp_accsum.elev),lines(lat, predict(loess(tsm_NM_exposed_bodytemp_accsum.elev~lat, na.action=na.exclude)), col=cols2[1], lwd=2)]\n\t\t\t# could add polygon of loess +/- SE\n\n\t\t# terrestrial shade or wet skin\n\tdat[Realm=='Terrestrial', points(lat, tsm_NM_favorable, col=cols1[2], cex=cex)]\n\t\tdat[Realm=='Terrestrial' & !is.na(tsm_NM_favorable),lines(lat, predict(loess(tsm_NM_favorable~lat, na.action=na.exclude)), col=cols2[2], lwd=2)]\n\t\t\t# could add polygon of loess +/- SE\n\n\t\t# marine\n\tdat[Realm=='Marine', points(lat, tsm_maxhr_accsum.elev, col=cols1[3], cex=cex)]\n\t\tdat[Realm=='Marine' & !is.na(tsm_maxhr_accsum.elev),lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols2[3], lwd=2)]\n\t\t\t# could add polygon of loess +/- SE\n\n\tlegend('bottomleft', legend=c('Terrestrial exposed body', 'Terrestrial shade (wet skin for amphibians)', 'Marine surface'), pch=1, col=cols1, lty=1, bty='n', cex=0.5)\n\n\tdev.off()\n\n\n\n# tsm_NM_1cm_airshade (terrestrial) and tsm_maxhr (marine) by lat NO AMPHIBIANS\n\tsetkey(dat, lat)\n\txlims <- c(-80,80)\n\tylims <- dat[Class != 'Amphibia',range(tsm_NM_1cm_airshade_accsum.elev, tsm_NM_exposed_bodytemp_accsum.elev, tsm_maxhr_accsum.elev, na.rm=TRUE)]\n\n\tquartz(width=5, height=4)\n\t# pdf(width=5, height=4, file='figures/tsm_vs_lat_loess_no_amphib.pdf')\n\tpar(las=1, mai=c(0.7, 0.7, 0.3, 0.1), mgp=c(2.5,0.7,0))\n\t\n\tdat[Realm=='Terrestrial' & Class != 'Amphibia', plot(lat, tsm_NM_exposed_bodytemp_accsum.elev, col='grey', xlim=xlims, ylim=ylims, main='Thermal Safety Margins no Amphibians')]\n\t\tdat[Realm=='Terrestrial' & !is.na(tsm_NM_exposed_bodytemp_accsum.elev) & Class != 'Amphibia',lines(lat, predict(loess(tsm_NM_exposed_bodytemp_accsum.elev~lat, na.action=na.exclude)), col='grey')]\n\t\tabline(h=0, lty=2, col='grey')\n\n\tdat[Realm=='Terrestrial' & Class != 'Amphibia', points(lat, tsm_NM_1cm_airshade_accsum.elev, col='green')]\n\t\tdat[Realm=='Terrestrial' & !is.na(tsm_NM_1cm_airshade_accsum.elev) & Class != 'Amphibia',lines(lat, predict(loess(tsm_NM_1cm_airshade_accsum.elev~lat, na.action=na.exclude)), col='green')]\n\tdat[Realm=='Marine' & Class != 'Amphibia', points(lat, tsm_maxhr_accsum.elev, col='blue')]\n\t\tdat[Realm=='Marine' & !is.na(tsm_maxhr_accsum.elev) & Class != 'Amphibia',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col='blue')]\n\n\tlegend('bottomleft', legend=c('Terrestrial 1cm shade', 'Marine surface', 'Terrestrial exposed body'), pch=1, col=c('green', 'blue', 'grey'), lty=1, bty='n', cex=0.5)\n\n\tdev.off()\n\n\n# Amphibians: tsm_NM_2m_airshade vs. tsm_NM_exposed_body_wet\n\tdat[animal.type=='amphibian', plot(tsm_NM_2m_airshade, tsm_NM_exposed_body_wet)]\n\t\tabline(0,1, lty=2)\n\t\t\n\tdat[animal.type=='amphibian', mean(tsm_NM_2m_airshade - tsm_NM_exposed_body_wet)] #\n\n\n\n# tsm_maxhr vs. lat by Class\n\trequire(RColorBrewer)\n\tsetkey(dat, lat) # sort by lat\n\tylims <- dat[,range(c(tsm_maxhr, tsm_maxhr_adjadjsum), na.rm=TRUE)]\n\txlims <- c(-90,90)\n\tcols <- brewer.pal(12, 'Paired')[c(1:12, 1:8)] # repeat to get to 20\n\tpchs <- c(rep(1, 10), rep(4,10))\n\ti1 <- dat$Realm=='Terrestrial' & !is.na(dat$tsm_maxhr)\n\ti2 <- dat$Realm=='Marine' & !is.na(dat$tsm_maxhr)\n\ti3 <- dat$Realm=='Terrestrial' & !is.na(dat$tsm_maxhr_adjadjsum)\n\ti4 <- dat$Realm=='Marine' & !is.na(dat$tsm_maxhr_adjadjsum)\n\t\n\tquartz(width=8, height=6)\n\t# pdf(width=8, height=6, file='figures/tsm_maxhr_vs_lat_by_Class.pdf')\n\tpar(mfrow=c(2,2), mgp=c(2,0.7,0), las=1, cex.axis=0.8, mai=c(0.7, 0.7, 0.2, 0.1))\n\t\n\t\t# tsm_maxhr\n\tdat[i1, plot(tsm_maxhr ~ lat_max, main='Terrestrial', ylim=ylims, xlim=xlims, ylab='Thermal Safety Margin \u00b0C \\n(tsm_maxhr)', col=cols[as.numeric(Class)], pch=pchs[as.numeric(Class)])]\n\t\tdat[i1 & Class=='Reptilia',lines(lat, predict(loess(tsm_maxhr~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Reptilia'])]\n\t\tdat[i1 & Class=='Amphibia',lines(lat, predict(loess(tsm_maxhr~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Amphibia'])]\n\t\tdat[i1 & Class=='Insecta',lines(lat, predict(loess(tsm_maxhr~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Insecta'])]\n\tlegend('topright', legend=levels(dat$Class), col=cols, pch=pchs, cex=0.3)\n\n\tdat[i2, plot(tsm_maxhr ~ lat_max, main='Marine', ylim=ylims, xlim=xlims, col=cols[as.numeric(Class)], pch=pchs[as.numeric(Class)])]\n\t\tdat[i2 & Class=='Actinopterygii',lines(lat, predict(loess(tsm_maxhr~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Actinopterygii'])]\n\t\tdat[i2 & Class=='Bivalvia',lines(lat, predict(loess(tsm_maxhr~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Bivalvia'])]\n\n\t\t# tsm_maxhr_adjadjsum\n\tplot(tsm_maxhr_adjadjsum ~ lat_max, data=dat[i3,], main='Terrestrial', ylim=ylims, xlim=xlims, ylab='Thermal Safety Margin \u00b0C \\n(tsm_maxhr_adjadjsum)', col=cols[as.numeric(dat$Class[i3])], pch=pchs[as.numeric(dat$Class[i3])])\n\t\tdat[i3 & Class=='Reptilia',lines(lat, predict(loess(tsm_maxhr_adjadjsum~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Reptilia'])]\n\t\tdat[i3 & Class=='Amphibia',lines(lat, predict(loess(tsm_maxhr_adjadjsum~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Amphibia'])]\n\t\tdat[i3 & Class=='Insecta',lines(lat, predict(loess(tsm_maxhr_adjadjsum~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Insecta'])]\n\n\tplot(tsm_maxhr_adjadjsum ~ lat_max, data=dat[i4,], main='Marine', ylim=ylims, xlim=xlims, col=cols[as.numeric(dat$Class[i4])], pch=pchs[as.numeric(dat$Class[i4])])\n\t\tdat[i4 & Class=='Actinopterygii',lines(lat, predict(loess(tsm_maxhr_adjadjsum~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Actinopterygii'])]\n\t\tdat[i4 & Class=='Bivalvia',lines(lat, predict(loess(tsm_maxhr_adjadjsum~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Bivalvia'])]\n\n\n\tdev.off()\n\n\n# tsm_NM_1cm_airshade vs. lat by Class\n\trequire(RColorBrewer)\n\tsetkey(dat, lat) # sort by lat\n\tylims <- dat[,range(c(tsm_NM_1cm_airshade, tsm_maxhr_accsum.elev), na.rm=TRUE)]\n\txlims <- c(-90,90)\n\tcols <- brewer.pal(12, 'Paired')[c(1:12, 1:8)] # repeat to get to 20\n\tpchs <- c(rep(1, 10), rep(4,10))\n\ti1 <- dat$Realm=='Terrestrial' & !is.na(dat$tsm_maxhr_accsum.elev)\n\ti2 <- dat$Realm=='Marine' & !is.na(dat$tsm_maxhr_accsum.elev)\n\ti3 <- dat$Realm=='Terrestrial' & !is.na(dat$tsm_NM_1cm_airshade)\n\ti2 <- dat$Realm=='Marine' & !is.na(dat$tsm_maxhr_accsum.elev)\n\t\n\tquartz(width=8, height=6)\n\t# pdf(width=8, height=6, file='figures/tsm_maxhr_w_NM_vs_lat_by_Class.pdf')\n\tpar(mfrow=c(2,2), mgp=c(2,0.7,0), las=1, cex.axis=0.8, mai=c(0.7, 0.7, 0.2, 0.1))\n\t\n\t\t# tsm_maxhr_accsum.elev\n\tplot(tsm_maxhr_accsum.elev ~ lat_max, data=dat[i1,], main='Terrestrial', ylim=ylims, xlim=xlims, ylab='Thermal Safety Margin \u00b0C \\n(tsm_maxhr_accsum.elev)', col=cols[as.numeric(dat$Class[i1])], pch=pchs[as.numeric(dat$Class[i1])])\n\t\tdat[i1 & Class=='Reptilia',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Reptilia'])]\n\t\tdat[i1 & Class=='Amphibia',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Amphibia'])]\n\t\tdat[i1 & Class=='Insecta',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Insecta'])]\n\n\t\tabline(h=0, col='grey', lty=2)\n\n\tplot(tsm_maxhr_accsum.elev ~ lat_max, data=dat[i2,], main='Marine', ylim=ylims, xlim=xlims, col=cols[as.numeric(dat$Class[i2])], pch=pchs[as.numeric(dat$Class[i2])])\n\t\tdat[i2 & Class=='Actinopterygii',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Actinopterygii'])]\n\t\tdat[i2 & Class=='Bivalvia',lines(lat, predict(loess(tsm_maxhr_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Bivalvia'])]\n\n\t\tabline(h=0, col='grey', lty=2)\n\n\t\t# tsm_NM_1cm_airshade_accsum.elev\n\tplot(tsm_NM_1cm_airshade_accsum.elev ~ lat_max, data=dat[i1,], main='terrestrial NicheMapper', ylim=ylims, xlim=xlims, ylab='Thermal Safety Margin \u00b0C \\n(tsm_NM_1cm_airshade_accsum.elev)', col=cols[as.numeric(dat$Class[i1])], pch=pchs[as.numeric(dat$Class[i1])])\n\t\tdat[i1 & Class=='Reptilia',lines(lat, predict(loess(tsm_NM_1cm_airshade_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Reptilia'])]\n\t\tdat[i1 & Class=='Amphibia',lines(lat, predict(loess(tsm_NM_1cm_airshade_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Amphibia'])]\n\t\tdat[i1 & Class=='Insecta',lines(lat, predict(loess(tsm_NM_1cm_airshade_accsum.elev~lat, na.action=na.exclude)), col=cols[levels(dat$Class)=='Insecta'])]\n\n\t\tabline(h=0, col='grey', lty=2)\n\n\tplot(0,0,bty='n', xaxt='n', yaxt='n', xlab='', ylab='', col='white')\n\tlegend('topright', legend=levels(dat$Class), col=cols, pch=pchs, cex=0.7, ncol=3, bty='n')\n\n\n\tdev.off()\n\n\n# tsm_maxhr: plot model result on top of data (unfolded lat)\n\tcols <- c('#b2df8a', '#1f78b4') # land, ocean\n\tcols2 <- c('#b2df8a88', '#1f78b488') # land, ocean faded somewhat\n\tcols3 <- c('#b2df8a55', '#1f78b455') # land, ocean faded\n\n\tquartz(width=5, height=5)\n\tplot(dat$tsm_maxhr~dat$lat, type=\"n\", ylab=\"Thermal safety margin\", xlab=\"latitude\")\n\n\twith(dat[dat$Realm==\"marine\",], points(tsm_maxhr~lat, col=cols2[2], pch=16))\n\twith(dat[dat$Realm==\"terrestrial\",], points(tsm_maxhr~lat, col=cols2[1], pch=16))\n\n#\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], polygon(c(lat, rev(lat)), c(tsm+se, rev(tsm-se)), col=cols3[2], border=FALSE))\n#\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], polygon(c(lat, rev(lat)), c(tsm+se, rev(tsm-se)), col=cols3[1], border=FALSE))\n\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_maxhr~lat, col=cols[2], lwd=3)) # current TSM\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_maxhr~lat, col=cols[1], lwd=3))\n\n#\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], polygon(c(lat, rev(lat)), c(tsm_fut+sefut, rev(tsm_fut-sefut)), col=cols3[2], border=FALSE))\n#\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], polygon(c(lat, rev(lat)), c(tsm_fut+sefut, rev(tsm_fut-sefut)), col=cols3[1], border=FALSE))\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_fut85~lat, col=cols[2], lwd=3, lty=2)) # future TSM\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_fut85~lat, col=cols[1], lwd=3, lty=2))\n\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_sum~lat, col=cols[2], lwd=1, lty=1)) # current TSM summer\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_sum~lat, col=cols[1], lwd=1, lty=1))\n\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_fut85sum~lat, col=cols[2], lwd=1, lty=2)) # future TSM summer\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_fut85sum~lat, col=cols[1], lwd=1, lty=2))\n\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_futacc85~lat, col=cols[2], lwd=3, lty=2)) # future TSM acc\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_futacc85~lat, col=cols[1], lwd=3, lty=2))\n\n\twith(newdat[newdat$Realm=='Marine' & newdat$lat > -80,], lines(tsm_futacc85sum~lat, col=cols[2], lwd=1, lty=2)) # future TSM acc summer\n\twith(newdat[newdat$Realm=='Terrestrial' & newdat$lat > -55 & newdat$lat < 70,], lines(tsm_futacc85sum~lat, col=cols[1], lwd=1, lty=2))\n\n\n\tlegend('topleft', legend=c('land', 'ocean'), col=cols, lwd=1, bty='n')\n\n\n######################################\n## Combined Tmax, Thab, TSM plots\n######################################\n\n# TSM vs. Tb\npar(mfrow=c(1,2))\ndat[Realm=='Terrestrial', plot(tb_favorableGHCND95, tsm_favorableGHCND95)]\ndat[Realm=='Marine', plot(tb_favorableGHCND95, tsm_favorableGHCND95)]\n\n\t# lattice plot\n\tlatshingle <- shingle(dat$lat, intervals=matrix(c(seq(-90,60,by=15), seq(-60,90,by=15)),byrow=FALSE, ncol=2))\n\txyplot(tsm_favorableGHCND95 ~ tb_favorableGHCND95 | latshingle,\n\t\tdata = dat[Realm=='Terrestrial',])\n\t\t\n# TSM vs. Tmax\n\t# lattice plot\n\tlatshingle <- shingle(dat$lat, intervals=matrix(c(seq(-90,60,by=15), seq(-60,90,by=15)),byrow=FALSE, ncol=2))\n\txyplot(tsm_favorableGHCND95 ~ tmax.accsum.elev | latshingle,\n\t\tdata = dat[Realm=='Terrestrial',])", "meta": {"hexsha": "1faf68043e5f863c67fc524dea4f85dbf3b439c5", "size": 37756, "ext": "r", "lang": "R", "max_stars_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/thermal_safety_vs_lat_bylatlon_plots.r", "max_stars_repo_name": "HuckleyLab/phyto-mhw", "max_stars_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-13T02:37:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-27T04:41:09.000Z", "max_issues_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/thermal_safety_vs_lat_bylatlon_plots.r", "max_issues_repo_name": 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YES\n2. YES", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3439531166114479}} {"text": "rm(list=ls())\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(lubridate)\nlibrary(stringr)\n\nremove_acentos <- function(x) iconv(x, to = \"ASCII//TRANSLIT\")\n\ninfo_munic <- read.csv2('data/informacoes_municipais_seade.csv') %>%\n mutate(munic = tolower(munic),\n munic = remove_acentos(munic),\n munic = str_replace_all(munic, '-', ' '))\n\narquivo_xlsx <- 'data/Municipios informacoes dia.xlsx'\n\ndf <- excel_sheets(arquivo_xlsx) %>%\n map(function(x){\n tabela <- read_excel(arquivo_xlsx, x)\n\n if(length(tabela) < 3) {\n n <- str_which(tabela[[1]], \"Cidade\")\n\n tabela_casos <- tabela %>%\n slice(1:(n-2))\n\n names(tabela_casos) <- c('munic', 'casos')\n\n tabela_obitos <- tabela %>%\n slice((n+1):n())\n\n names(tabela_obitos) <- c('munic', 'obitos')\n\n tabela <- full_join(tabela_casos, tabela_obitos, by = 'munic')\n }\n\n tabela <- tabela %>%\n mutate(dia_mes = x,\n dia = as.numeric(substr(dia_mes, 1,2)),\n mes = substr(dia_mes, 4, 6),\n mes = replace(mes, mes == 'mar', 3),\n mes = replace(mes, mes == 'abr', 4),\n mes = as.numeric(mes)) %>%\n select(-dia_mes)\n\n names(tabela) <- c('munic', 'casos', 'obitos', 'dia', 'mes')\n\n tabela <- tabela %>%\n mutate(munic = tolower(munic),\n munic = remove_acentos(munic),\n munic = str_replace_all(munic, '-', ' '),\n munic = str_replace_all(munic, '\\\\?', ''),\n casos = as.numeric(casos),\n obitos = as.numeric(obitos)) %>%\n filter(!is.na(munic)) %>%\n filter(munic != 'total')\n\n }) %>%\n reduce(bind_rows) %>%\n left_join(info_munic, by = 'munic')\n\ndf %>%\n write_csv2('data/dados_covid_sp.csv')\n\ntail(df)\n", "meta": {"hexsha": "e1631a1b62808578e65a62865249d4e23eecc2f6", "size": 1741, "ext": "r", "lang": "R", "max_stars_repo_path": "fixtures/r/file2.r", "max_stars_repo_name": "nvuillam/jscpd", "max_stars_repo_head_hexsha": "aec15511efe5fffda10960e26a140acb4f704731", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2489, "max_stars_repo_stars_event_min_datetime": "2015-01-06T22:06:43.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T09:02:55.000Z", "max_issues_repo_path": "fixtures/r/file2.r", "max_issues_repo_name": "nvuillam/jscpd", "max_issues_repo_head_hexsha": "aec15511efe5fffda10960e26a140acb4f704731", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 397, "max_issues_repo_issues_event_min_datetime": "2015-01-08T20:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T12:11:33.000Z", "max_forks_repo_path": "fixtures/r/file2.r", "max_forks_repo_name": "nvuillam/jscpd", "max_forks_repo_head_hexsha": "aec15511efe5fffda10960e26a140acb4f704731", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 173, "max_forks_repo_forks_event_min_datetime": "2015-02-18T22:30:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-24T02:02:25.000Z", "avg_line_length": 26.7846153846, "max_line_length": 68, "alphanum_fraction": 0.5605973578, "num_tokens": 519, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.34394862617845895}} {"text": "#Code for Figure 4A: Compares neoantigens/nonsynonymous mutation rates\n#Also provides correlation between mutation and neoantigen load (used in manuscript)\n#Query at the top joins analysis.neoag_freq materialized view to other tables to make figure that shows subtype and timepoint-specific fractions\n#-----------------------------------------------------\nlibrary(DBI)\nlibrary(odbc)\nlibrary(ggplot2)\nlibrary(reshape)\n\nrm(list=ls())\n\ncon <- DBI::dbConnect(odbc::odbc(), \"VerhaakDB\")\n\nq = \"SELECT gs.tumor_pair_barcode, \nCOALESCE(neo1.neoag_count::numeric,0) AS neoag_count_a, \nCOALESCE(neo1.mt_count::numeric,0) AS mt_count_a, \nCOALESCE(neo1.prop_immunogenic::numeric,0) AS prop_immunogenic_a, \nCOALESCE(neo2.neoag_count::numeric,0) AS neoag_count_b, \nCOALESCE(neo2.mt_count::numeric,0) AS mt_count_b, \nCOALESCE(neo2.prop_immunogenic::numeric,0) AS prop_immunogenic_b, \nclin.idh_codel_subtype AS subtype, \nCASE WHEN mf.coverage_adj_mut_freq >= 10 THEN 1 WHEN mf.coverage_adj_mut_freq < 10 THEN 0 END AS hypermutator\nFROM analysis.gold_set gs\nLEFT JOIN analysis.neoag_freq neo1 ON neo1.aliquot_barcode = gs.tumor_barcode_a \nLEFT JOIN analysis.neoag_freq neo2 ON neo2.aliquot_barcode = gs.tumor_barcode_b\nLEFT JOIN clinical.subtypes clin ON clin.case_barcode = gs.case_barcode\nINNER JOIN analysis.mut_freq mf ON mf.aliquot_barcode = gs.tumor_barcode_b\nORDER BY gs.tumor_pair_barcode\"\n\nneo <- dbGetQuery(con,q)\n\n#get the n's for this plot\ntmp <- neo[,\"subtype\"]\ntmp <- tmp[order(tmp)]\nrle(tmp)\n\n#get average proportion of mutations giving rise to neoantigens\nmean(c(neo[,\"prop_immunogenic_a\"],neo[,\"prop_immunogenic_b\"]))\t\t#42%\n\n#get correlation between mutation and neoantigen load\ncor(c(neo[,\"neoag_count_a\"],neo[,\"neoag_count_b\"]),c(neo[,\"mt_count_a\"],neo[,\"mt_count_b\"]),method=\"s\")\t#0.89\n\n\nprop <- c(neo[,\"prop_immunogenic_a\"],neo[,\"prop_immunogenic_b\"])\npair <- rep(neo[,\"tumor_pair_barcode\"],2)\nsubtype <- rep(neo[,\"subtype\"],2)\ntimepoint <- c(rep(\"Initial\",nrow(neo)),rep(\"Recurrent\",nrow(neo)))\nres <- data.frame(pair,prop,subtype,timepoint)\n\n#Figure 4A\npdf(\"/projects/varnf/GLASS/Figures/resubmission/final/Figure4A_points.pdf\",width=2,height=2)\np1 <- ggplot(res,aes(y = prop, x = timepoint, fill = subtype)) +\n\tgeom_bar(position=\"dodge\",stat=\"summary\",fun.y=\"mean\",color=\"black\") +\n\tgeom_point(position=position_dodge(width = .9),size=0.01) +\n\tgeom_errorbar(stat=\"summary\",fun.data=\"mean_sdl\", fun.args=list(mult=1) ,size=0.4,width=0.35, position=position_dodge(.9))+\n\ttheme_bw() +\n\tlabs(x = \"\", y = \"Neoantigens/nonsynonymous\") +\n\ttheme(axis.text.x=element_text(size=7),axis.text.y = element_text(size=7),\n\taxis.title.x = element_blank(),axis.title.y = element_text(size=7),\n\tpanel.grid.major=element_blank(),panel.grid.minor=element_blank(),\n\tlegend.position=\"none\") +\n\tcoord_cartesian(ylim=c(0,0.9))\np1\ndev.off()\n\nsubtypes <- unique(neo[,\"subtype\"])\nprop_pri <- prop_rec <- neoag_sd_pri <- neoag_sd_rec <- rep(0,length(subtypes))\nfor(i in 1:length(subtypes))\n{\n\tsub_res <- res[which(res[,\"subtype\"]==subtypes[i]),]\n\tprop_pri[i] <- mean(sub_res[which(sub_res[,\"timepoint\"]==\"Initial\"),\"prop\"])\n\tprop_rec[i] <- mean(sub_res[which(sub_res[,\"timepoint\"]==\"Recurrent\"),\"prop\"])\n\tneoag_sd_pri[i] <- sd(sub_res[which(sub_res[,\"timepoint\"]==\"Initial\"),\"prop\"])\n\tneoag_sd_rec[i] <- sd(sub_res[which(sub_res[,\"timepoint\"]==\"Recurrent\"),\"prop\"])\n\n}\n\n#Significance tests\ng1 <- res[which(res[,\"timepoint\"]==\"Initial\" & res[,\"subtype\"] == \"IDHmut-codel\"),\"prop\"]\ng2 <- res[which(res[,\"timepoint\"]==\"Initial\" & res[,\"subtype\"] == \"IDHmut-noncodel\"),\"prop\"]\ng3 <- res[which(res[,\"timepoint\"]==\"Initial\" & res[,\"subtype\"] == \"IDHwt\"),\"prop\"]\n\ng4 <- res[which(res[,\"timepoint\"]==\"Recurrent\" & res[,\"subtype\"] == \"IDHmut-codel\"),\"prop\"]\ng5 <- res[which(res[,\"timepoint\"]==\"Recurrent\" & res[,\"subtype\"] == \"IDHmut-noncodel\"),\"prop\"]\ng6 <- res[which(res[,\"timepoint\"]==\"Recurrent\" & res[,\"subtype\"] == \"IDHwt\"),\"prop\"]\n\nwilcox.test(g1,g4)\t\t#0.35\nwilcox.test(g2,g5)\t\t#0.82\nwilcox.test(g3,g6)\t\t#0.90\n\nwilcox.test(g1,g2)\t\t#0.19\nwilcox.test(g1,g3)\t\t#0.38\nwilcox.test(g2,g3)\t\t#0.61\n\nwilcox.test(g4,g5)\t\t#0.66\nwilcox.test(g4,g6)\t\t#0.92\nwilcox.test(g5,g6)\t\t#0.26\n\n#Plot A (old)\nplot_res <- data.frame(c(prop_pri,prop_rec),\n\t\t\tc(neoag_sd_pri,neoag_sd_rec),\n\t\t\tc(rep(\"Initial\",3),rep(\"Recurrent\",3)),\n\t\t\tc(rep(\"prop_immunogenic\",6)),\n\t\t\trep(subtypes,2))\ncolnames(plot_res) <- c(\"fraction\",\"sd\",\"status\",\"neoag\",\"subtypes\")\n\npdf(\"/projects/varnf/GLASS/Figures/resubmission/final/Figure4A_old.pdf\",width=2,height=2)\np1 <- ggplot(plot_res,aes(y = fraction, x = status, fill = subtypes)) +\n\tgeom_bar(position=\"dodge\",stat=\"identity\",color=\"black\") +\n\tgeom_point(size=0.5,position=\"dodge\") +\n\tgeom_errorbar(aes(ymin=fraction-sd,ymax=fraction+sd),size=0.4,width=0.35,position=position_dodge(.9))+\n\ttheme_bw() +\n\tlabs(x = \"\", y = \"Neoantigens/nonsynonymous\") +\n\ttheme(axis.text.x=element_text(size=7),axis.text.y = element_text(size=7),\n\taxis.title.x = element_blank(),axis.title.y = element_text(size=7),\n\tpanel.grid.major=element_blank(),panel.grid.minor=element_blank(),\n\tlegend.position=\"none\") +\n\tcoord_cartesian(ylim=c(0,0.60))\np1\ndev.off()\n\n#Other analyses\n#-------------------------------------\n\n#Mut and neoag load comparisons\nneo_box <- neo\nneo_box[,\"neoag_count\"] <- as.numeric(neo_box[,\"neoag_count\"])\nneo_box[,\"mt_count\"] <- as.numeric(neo_box[,\"mt_count\"])\nneo_box <- neo_box[which(neo_box[,\"hypermutator\"]==0),]\npdf(\"/projects/varnf/GLASS/Figures/resubmission/neaog_load_boxplot.pdf\",width=4,height=2)\np1 <- ggplot(neo_box,aes(y = neoag_count, x = sample_type,fill=subtype)) +\n\tgeom_boxplot(outlier.shape=NA) +\n\tgeom_line(aes(group=case_barcode),linetype=2,colour=\"gray50\",size=0.1) +\n\tgeom_point(size=0.5) +\n\ttheme_bw() +\n\tfacet_grid(.~subtype) +\n\tlabs(x = \"\", y = \"Mutation count\") +\n\ttheme(axis.text.x=element_text(size=7),axis.text.y = element_text(size=7),\n\taxis.title.x = element_blank(),axis.title.y = element_text(size=7),\n\tpanel.grid.major=element_blank(),panel.grid.minor=element_blank(),\n\tlegend.position=\"none\")\np1\ndev.off()\n\npdf(\"/projects/varnf/GLASS/Figures/resubmission/mut_load_boxplot.pdf\",width=4,height=2)\np1 <- ggplot(neo_box,aes(y = mt_count, x = sample_type,fill=subtype)) +\n\tgeom_boxplot(outlier.shape=NA) +\n\tgeom_line(aes(group=case_barcode),linetype=2,colour=\"gray50\",size=0.1) +\n\tgeom_point(size=0.5) +\n\ttheme_bw() +\n\tfacet_grid(.~subtype) +\n\tlabs(x = \"\", y = \"Neoantigen count\") +\n\ttheme(axis.text.x=element_text(size=7),axis.text.y = element_text(size=7),\n\taxis.title.x = element_blank(),axis.title.y = element_text(size=7),\n\tpanel.grid.major=element_blank(),panel.grid.minor=element_blank(),\n\tlegend.position=\"none\")\np1\ndev.off()\n\n\n#Hypermutator plot\ng1 <- neo[which(neo[,\"hypermutator\"]==1),\"prop_immunogenic\"]\ng2 <- neo[which(neo[,\"hypermutator\"]==0 & neo[,\"sample_type\"]==\"R\"),\"prop_immunogenic\"]\n\n\nhm <- neo[which(neo[,\"hypermutator\"]==1),]\nsubtypes <- unique(neo[,\"subtype\"])\nprop_immunogenic_pri <- prop_immunogenic_rec <- neoag_sd_pri <- neoag_sd_rec <- rep(0,length(subtypes))\nfor(i in 1:length(subtypes))\n{\n\tsub_neo <- neo[which(neo[,\"subtype\"]==subtypes[i]),]\n\tprop_immunogenic_pri[i] <- mean(sub_neo[which(sub_neo[,\"sample_type\"]==\"P\"),\"prop_immunogenic\"])\n\tprop_immunogenic_rec[i] <- mean(sub_neo[which(sub_neo[,\"sample_type\"]==\"R\"),\"prop_immunogenic\"])\n\tneoag_sd_pri[i] <- sd(sub_neo[which(sub_neo[,\"sample_type\"]==\"P\"),\"prop_immunogenic\"])\n\tneoag_sd_rec[i] <- sd(sub_neo[which(sub_neo[,\"sample_type\"]==\"R\"),\"prop_immunogenic\"])\n\n}\n\nplot_res <- data.frame(c(prop_immunogenic_pri,prop_immunogenic_rec),\n\t\t\tc(neoag_sd_pri,neoag_sd_rec),\n\t\t\tc(rep(\"Primary\",3),rep(\"Recurrent\",3)),\n\t\t\tc(rep(\"prop_immunogenic\",6)),\n\t\t\trep(subtypes,2))\ncolnames(plot_res) <- c(\"fraction\",\"sd\",\"status\",\"neoag\",\"subtypes\")\n", "meta": {"hexsha": "b42c6c658dd4e76a10ccdf95c4519cd7bfe86b10", "size": 7732, "ext": "r", "lang": "R", "max_stars_repo_path": "R/neoantigens/figures/Fig4_neoag_nonsyn_rate.r", "max_stars_repo_name": "Kcjohnson/SCGP", "max_stars_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2020-11-09T14:23:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T07:10:15.000Z", "max_issues_repo_path": "R/neoantigens/figures/Fig4_neoag_nonsyn_rate.r", "max_issues_repo_name": "Kcjohnson/SCGP", "max_issues_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-01-16T06:07:35.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-01T15:51:32.000Z", "max_forks_repo_path": "R/neoantigens/figures/Fig4_neoag_nonsyn_rate.r", "max_forks_repo_name": "Kcjohnson/SCGP", "max_forks_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2020-12-14T02:51:42.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-18T07:15:55.000Z", "avg_line_length": 41.7945945946, "max_line_length": 144, "alphanum_fraction": 0.7096482152, "num_tokens": 2450, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.343948625324705}} {"text": "##' Plot infection prevalence as predicted by EpiNow2\n##'\n##' @return ggplot object\n##' @importFrom EpiNow2 calc_summary_measures\n##' @param counties counties to plot, including the possibilities of \"pilot\" (for all pilot counties), \"non-pilot\" (for all non-pilot counties) and \"all\"\nplot_predicted_prevalence <- function(counties = NULL) {\n prev <- prevalence.samples\n if (!is.null(counties)) {\n prev <- prev %>%\n filter(county %in% counties)\n }\n\n prev_summary <- calc_summary_measures(prev[, value := prev_ratio],\n summarise_by = \"county\",\n CrIs = 0.95)\n}\n", "meta": {"hexsha": "8ac65309f8cef262e8e3eb88334041dc315ee37b", "size": 648, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_predicted_prevalence.r", "max_stars_repo_name": "kevinvzandvoort/covid19.slovakia.mass.testing", "max_stars_repo_head_hexsha": "03b60024ff2bf17c0061c9e5e2a7877b27ca99de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-30T09:48:06.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T09:48:06.000Z", "max_issues_repo_path": "R/plot_predicted_prevalence.r", "max_issues_repo_name": "kevinvzandvoort/covid19.slovakia.mass.testing", "max_issues_repo_head_hexsha": "03b60024ff2bf17c0061c9e5e2a7877b27ca99de", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-11-30T11:13:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T11:42:39.000Z", "max_forks_repo_path": "R/plot_predicted_prevalence.r", "max_forks_repo_name": "kevinvzandvoort/covid19.slovakia.mass.testing", "max_forks_repo_head_hexsha": "03b60024ff2bf17c0061c9e5e2a7877b27ca99de", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-11-30T10:56:25.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-24T09:21:53.000Z", "avg_line_length": 38.1176470588, "max_line_length": 153, "alphanum_fraction": 0.6203703704, "num_tokens": 155, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.6076631698328917, "lm_q1q2_score": 0.34394861728143317}} {"text": "# This is an example code to run Quantile function on a Hadoop Cluster\n# Prior to running the R Script, launch the H2O hadoop jar on the cluster\n\n# Detach and remove old H2O package if it exists\ndetach(\"package:h2o\", unload=TRUE)\nremove.packages(\"h2o\",.libPaths())\n\n# Install the same version of H2O from online repository that is running on your Hadoop Cluster \ninstall.packages(\"h2o\", repos=(c(\"http://h2o-release.s3.amazonaws.com/h2o/rel-kahan/2/R\", getOption(\"repos\")))) \n\n#Load H2O library\nlibrary(h2o)\n\n#Connect to H2O instance by specifying ip and port\nmyIP = \"192.168.1.153\" \nmyPort = 54321\nremote.h2o = h2o.init( ip = myIP, port = myPort )\n\n#Import the data file from HDFS and parse it into H2O memory\nqfile = h2o.importHDFS.FV(remote.h2o,\"hdfs://abc.loc:2312/datasets/quant_file.csv\",key=\"qfile\")\n#If the data file sits on your local machine, upload and parse it into H2O memory\n#qfile=h2o.uploadFile.FV(remote.h2o,\"/Users/nidhimehta/Desktop/quant_file.csv\",key=\"qfile\")\n\n#Check dimensions of data file\ndim(qfile)\n\n#Run summary\nsummary(qfile)\n\n#Print column names \ncname = colnames(qfile)\nprint(cname)\n\n# Get a single quantile for a column\nquantile(qfile$M1, probs = 0.60, na.rm = T)\n\n#Get qunatiles for specified multiple probabilites for a column\nquantile(qfile$M11,probs=seq(0,1,.1))\nquantile(qfile$M1,probs=seq(0,1,.01))\n\n#Get quartiles of a column\nquantile(qfile$M10,probs=seq(0,1,.25), na.rm = T)\n\n#Print 99.9 quantile for each column in the data file\nfor(i in 16:length(cname)){\n print(paste(cname[i],quantile(qfile[,i],prob = .999, na.rm = T)))\n}\n\n", "meta": {"hexsha": "28035346278aaa5df627e178ef9ddb8980fd38f7", "size": 1570, "ext": "r", "lang": "R", "max_stars_repo_path": "R/examples/quantile_example.r", "max_stars_repo_name": "gigliovale/h2o", "max_stars_repo_head_hexsha": "be350f3f2c2fb6f135cc07c41f83fd0e4f521ac1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 882, "max_stars_repo_stars_event_min_datetime": "2015-05-22T02:59:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T05:02:48.000Z", "max_issues_repo_path": "R/examples/quantile_example.r", "max_issues_repo_name": "VonRosenchild/h2o-2", "max_issues_repo_head_hexsha": "be350f3f2c2fb6f135cc07c41f83fd0e4f521ac1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-22T12:15:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:15:02.000Z", "max_forks_repo_path": "R/examples/quantile_example.r", "max_forks_repo_name": "VonRosenchild/h2o-2", "max_forks_repo_head_hexsha": "be350f3f2c2fb6f135cc07c41f83fd0e4f521ac1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 392, "max_forks_repo_forks_event_min_datetime": "2015-05-22T17:04:11.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T09:04:39.000Z", "avg_line_length": 32.0408163265, "max_line_length": 112, "alphanum_fraction": 0.7369426752, "num_tokens": 509, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3439486172814331}} {"text": "#' Draw meanfield model attractor\n#'\n#' @param model\n#' @param times\n#' @param parms\n#' @param method\n#' @param rho\n#' @param colors\n#'\n#' @import foreach\n#'\n#' @return\n#' @export\n#' @examples\n#'\n#' p <- set_parms(livestock$parms, set = list(b = 0.1, f = 0.5, p = 0.9, L = 1.5))\n#' par(mfrow = c(1,2))\n#' plot_pairapproximation(livestock, parms = p) -> out\n#' plot_pairapproximation(out, parms = p, side = \"plain\")\n\nplot_pairapproximation <- function(\n model,\n parms = model$parms,\n side = \"rho\",\n rho_1_ini = seq(0,1, length = 11),\n rho_11_ini = seq(0,1, length = 11),\n times = c(0,1000),\n method = \"ode45\",\n rho = seq(0,1,length = 100),\n colors = c(\"#000000\",\"#009933\"),\n #fog = TRUE,\n new = TRUE,\n ...\n) {\n\n #par(mfrow = c(1,3))\n # open new base plot if none exists\n if(dev.cur() == 1 | new == TRUE) plot_base(ylim = switch(side, plain = c(0,1), c(0,0.25) ),\n ylab = switch(side, plain = \"local cover\", \"plant mortality/growth\" ),\n xlab = switch(side, q = \"local cover\",\"vegetation cover\"), ...)\n\n # draw trajectories of mortality and growth\n if(class(model) == \"attractor\") {\n trajectories <- model$trajectories\n } else {\n trajectories <- sim_trajectories(model = model, parms = parms, rho_1_ini = rho_1_ini, times = times, method = method)\n }\n\n # visualize trajectories to the attractor\n sapply(trajectories, function(x){\n rho <- ini_rho(x$rho_1, x$rho_11)\n mort <- limit(mortality(rho, parms))\n grow <- limit(growth(rho, parms))\n q_11_vec <- q_11(rho)\n #fog_m <- highlight(q_11_vec, colrange = c(paste0(colors[1],\"88\"),colors[1]))\n #fog_g <- highlight(q_11_vec, colrange = c(paste0(colors[2],\"88\"),colors[2]))\n\n\n switch(side,\n rho = {\n lines(rho$rho_1, mort)\n #arrows(tail(rho$rho_1,2)[1],tail(mort,2)[1],tail(rho$rho_1,1),tail(mort,1), length = 0.1 )\n lines(rho$rho_1, grow, col = \"#009933\")\n #arrows(tail(rho$rho_1,2)[1],tail(grow,2)[1],tail(rho$rho_1,1),tail(grow,1), length = 0.1 , col = \"#009933\")\n\n },\n q = {\n lines(q_11_vec, mort)\n #arrows(tail(q_11_vec,2)[1],tail(mort,2)[1],tail(q_11_vec,1),tail(mort,1), length = 0.1 )\n lines(q_11_vec, grow, col = \"#009933\")\n #arrows(tail(q_11_vec,2)[1],tail(grow,2)[1],tail(q_11_vec,1),tail(grow,1), length = 0.1 , col = \"#009933\")\n\n },\n plain = {\n lines(rho$rho_1, q_11_vec)\n #arrows(tail(rho$rho_1,2)[1],tail(q_11_vec,2)[1],tail(rho$rho_1,1),tail(q_11_vec,1), length = 0.1 )\n lines(rho$rho_1+0.002, q_11_vec+0.002, col = \"#009933\")\n #arrows(tail(rho$rho_1,2)[1],tail(q_11_vec,2)[1],tail(rho$rho_1,1),tail(q_11_vec,1), length = 0.1 , col = \"#009933\")\n\n }\n\n )\n\n\n }\n )\n\n\n if(class(model) == \"attractor\") {\n eq <- model$eq\n } else {\n eq <- get_equilibria(y = model$template, func = model$pair, parms = parms, method = method, t_max = 130)\n }\n\n rho_steady <- ini_rho(c(eq$lo[1],eq$hi[1]),c(eq$lo[2],eq$hi[2]))\n q_steady <- q_11(rho_steady)\n switch(side,\n rho = {\n points(rho_steady$rho_1, mortality(rho_steady, parms = parms), xpd = TRUE, pch = 20, cex = 2)\n\n },\n q = {\n points(q_steady, mortality(rho_steady, parms = parms), xpd = TRUE, pch = 20, cex = 2)\n\n },\n plain = {\n points(rho_steady$rho_1, q_steady, xpd = TRUE, pch = 20, cex = 2)\n\n }\n\n )\n\n\n output <- list(trajectories = trajectories,\n eq = eq\n )\n class(output) <- \"attractor\"\n return(output)\n\n}\n\n\n\n\n\n", "meta": {"hexsha": "253488614f4eb9cb909c744296825fa6e3fd24e4", "size": 3633, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_pairapproximation.r", "max_stars_repo_name": "fdschneider/livestock", "max_stars_repo_head_hexsha": "d7f8767f1bfd447f6f885bcce527ca2fcc723be4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-01T02:59:02.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-01T02:59:02.000Z", "max_issues_repo_path": "R/plot_pairapproximation.r", "max_issues_repo_name": "fdschneider/livestock", "max_issues_repo_head_hexsha": "d7f8767f1bfd447f6f885bcce527ca2fcc723be4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/plot_pairapproximation.r", "max_forks_repo_name": "fdschneider/livestock", "max_forks_repo_head_hexsha": "d7f8767f1bfd447f6f885bcce527ca2fcc723be4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8333333333, "max_line_length": 126, "alphanum_fraction": 0.5598678778, "num_tokens": 1260, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.34394860923816123}} {"text": "#' Plots segments on underlying point data\n#'\n#' @param segs segment data\n#' @param aes ggplot mapping (required: y)\n#' @param fml formula for mapping between pts and segs\n#' @param breaks dashed line breaks indicating secondary axis\n#' @param name name of the secondary axis\n#' @return ggplot2 object\nsegs = function(segs, aes, fml, ..., breaks=NULL, name=waiver()) {\n if (\"GRanges\" %in% class(segs))\n segs = as.data.frame(segs)\n\n default_segs = aes(x=start, xend=end)\n default_segs[['yend']] = aes[['y']]\n aes_segs = utils::modifyList(default_segs, aes)\n\n args = list(...)\n defaults = list(size=1.5, color=\"green\")\n defaults = defaults[!names(defaults) %in% names(aes_segs)]\n args = utils::modifyList(defaults, args)\n\n fscale = eval(fml[[2]][[3]], envir=environment(fml))\n if (is.null(breaks) && nrow(segs) > 0)\n breaks = 1:ceiling(max(segs[[as.character(aes[['y']][[2]])]])/fscale)\n breaks = breaks * fscale\n\n list(geom_hline(yintercept=breaks, color=\"grey\", linetype=\"dashed\"),\n do.call(geom_segment, c(list(data=segs, mapping=aes_segs), args)),\n facet_grid(. ~ seqnames, scales=\"free_x\"),\n scale_y_continuous(sec.axis=sec_axis(fml, breaks=breaks, name=name)))\n}\n", "meta": {"hexsha": "4ed5244a96d304b4b0f3184a9fbfa80c3c91e0c5", "size": 1247, "ext": "r", "lang": "R", "max_stars_repo_path": "plot/genome/segs.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "plot/genome/segs.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "plot/genome/segs.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 38.96875, "max_line_length": 78, "alphanum_fraction": 0.6503608661, "num_tokens": 370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.34388305442149597}} {"text": "# For use with baseballdatawrangling2.r\r\n\r\nbrett <- bavg %>%\r\n filter(batterid=='bretg001') %>%\r\n select(H,AB,RDate) %>%\r\n arrange(RDate) %>%\r\n mutate(Total_H=cumsum(H), Total_AB=cumsum(AB)) %>%\r\n mutate(AVG = round((Total_H / Total_AB),3)) %>%\r\n mutate(player='George Brett')", "meta": {"hexsha": "00b20796ca42d2ed1d17d0c7a77360fe00025f85", "size": 282, "ext": "r", "lang": "R", "max_stars_repo_path": "brett.r", "max_stars_repo_name": "KT12/R", "max_stars_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "brett.r", "max_issues_repo_name": "KT12/R", "max_issues_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "brett.r", "max_forks_repo_name": "KT12/R", "max_forks_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3333333333, "max_line_length": 53, "alphanum_fraction": 0.634751773, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3438830544214959}} {"text": "### tripsAndDip uses read counts for biallelic SNPS to determine if a sample is diploid or triploid\n### Input\n### counts is either a matrix or a dataframe with each row corresponding to a different sample\n### the columns correspond to the read counts for each locus, in a two column per locus format\n### So, column 1 is the read counts for locus1Allele1, column two is the read counts for locus1Allele2, locus2Allele1, locus2Allele2, ...\n### the rownames of the matrix or dataframe should be the sample names\n### h is a list of h values for each locus in the same order that the loci are ordered in counts\n### eps is a list of epsilon (error rate per read) values for each locus in the same order that the loci are ordered in counts\n### min_reads is the minimum number of reads needed to use a locus in the algorithm\n### min_loci is the minimum number of loci needed to attempt to calculate an LLR\n### Output\n### a dataframe with column 1 containing sample names, column 2 containing calculated LLRs (larger means more likely triploid)\n### and column 3 containing the number of loci used to calculate the LLR\n######\n### note that constant values for h and epsilon can be easily implemented usign the rep() function in R, for example\n###### constant_h <- rep(1, (ncol(allele_counts))/2)\n###### constant_eps <- rep(.01, (ncol(allele_counts))/2)\n###### results <- tripsAndDip(allele_counts, constant_h, constant_eps)\n\ntripsAndDip <- function(counts, h, eps, min_reads = 30, min_loci = 15, binom_p_value = .05){\n\t### input error checking\n\tif(!is.matrix(counts) && !is.data.frame(counts)){\n\t\tcat(\"\\nError. Counts must be either a matrix or a dataframe.\\n\")\n\t\treturn()\n\t}\n\tif((ncol(counts)/2) != length(h)){\n\t\tcat(\"\\nError. The number of columns of counts is not equal to twice the length of h.\\n\")\n\t\treturn()\n\t}\n\tif(length(eps) != length(h)){\n\t\tcat(\"\\nError. The length of h is not equal to the length of eps.\\n\")\n\t\treturn()\n\t}\n\tif(min_reads < 1){\n\t\tcat(\"\\nError. min_reads must be 1 or greater.\\n\")\n\t\treturn()\n\t}\n\tif(min_loci < 1){\n\t\tcat(\"\\nError. min_loci must be 1 or greater.\\n\")\n\t\treturn()\n\t}\n\tif(binom_p_value < 0 || binom_p_value > 1){\n\t\tcat(\"\\nError. binom_p_value must be between 0 and 1.\\n\")\n\t\treturn()\n\t}\n\t\n\t### calculate llr for each sample\n\tnum_counts <- ncol(counts)\n\tllr <- apply(counts, 1, function(x){\n\t\t#separate allele1 and allele2\n\t\tcount1 <- x[seq(1, (num_counts - 1), 2)]\n\t\tcount2 <- x[seq(2, num_counts, 2)]\n\t\t#caluculate total read count and which is larger\n\t\tn <- count1 + count2\n\t\t#determine whether to include loci based on read count\n\t\tcount1 <- count1[n >= min_reads]\n\t\tcount2 <- count2[n >= min_reads]\n\t\th_corr <- h[n >= min_reads]\n\t\teps_corr <- eps[n >= min_reads]\n\t\tn <- n[n >= min_reads]\n\t\t# determine if enough loci\n\t\tif (length(n) < min_loci){\n\t\t\treturn(c(NA, length(n)))\n\t\t}\n\t\t\n\t\tk <- mapply(max, count1, count2)\n\t\t#flip h as necessary\n\t\th_corr[count2 > count1] <- 1/h_corr[count2 > count1]\n\t\t\n\t\t#calculate probabilities\n\t\t#based on model with error and allelic bias from Gerard et al. 2018\n\t\tp_temp <- (0.6666667*(1 - eps_corr) + (0.3333333)*eps_corr)\n\t\tprob_trip <- p_temp / (h_corr*(1 - p_temp) + p_temp)\n\t\tp_temp <- 0.5 # (0.5*(1 - eps_corr) + (0.5)*eps_corr) simplifies to 0.5\n\t\tprob_dip <- p_temp / (h_corr*(1 - p_temp) + p_temp)\n\t\t\n\t\t#determine which loci to include using binomial test\n\t\tbinom_results <- mapply(function(x,y,z){\n\t\t\t\treturn(binom.test(x, y, z, \"greater\")$p.value)\n\t\t\t}, k, n, prob_trip)\n\t\tbinom_results <- binom_results > binom_p_value\n\t\t\n\t\th_corr <- h_corr[binom_results]\n\t\teps_corr <- eps_corr[binom_results]\n\t\tn <- n[binom_results]\n\t\tk <- k[binom_results]\n\t\tprob_trip <- prob_trip[binom_results]\n\t\tprob_dip <- prob_dip[binom_results]\n\t\t# determine if enough loci\n\t\tif (length(n) < min_loci){\n\t\t\treturn(c(NA, length(n)))\n\t\t}\n\t\t\n\t\t#calculate log likelihoods\n\t\ttrip <- (k*log(prob_trip)) + ((n-k)*log(1-prob_trip))\n\t\tdip <- (k*log(prob_dip)) + ((n-k)*log(1-prob_dip))\n\t\t#sum accros loci\n\t\ttrip <- sum(trip)\n\t\tdip <- sum(dip)\n\t\n\t\treturn(c(trip - dip, length(n)))\n\t\t\t\n\t})\n\t# associate llr's with sample names and return\n\treturn(data.frame(sample_name = rownames(counts),\n\t\t\t LLR = llr[1,], loci_used=llr[2,], stringsAsFactors = F, row.names = NULL))\n}\n", "meta": {"hexsha": "6df204265a0e85516bce546c665cd7222332e1c2", "size": 4208, "ext": "r", "lang": "R", "max_stars_repo_path": "tripsAndDip.r", "max_stars_repo_name": "delomast/tripsAndDip", "max_stars_repo_head_hexsha": "9ca40bea33b1afde1b8c84526d62f03605234cfe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tripsAndDip.r", "max_issues_repo_name": "delomast/tripsAndDip", "max_issues_repo_head_hexsha": "9ca40bea33b1afde1b8c84526d62f03605234cfe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tripsAndDip.r", "max_forks_repo_name": "delomast/tripsAndDip", "max_forks_repo_head_hexsha": "9ca40bea33b1afde1b8c84526d62f03605234cfe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.962962963, "max_line_length": 137, "alphanum_fraction": 0.6839353612, "num_tokens": 1300, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3438830474919201}} {"text": "#' The ATA.BoxCoxAttr function works with many different types of inputs.\n#'\n#' @param bcMethod Choose method to be used in calculating lambda. \"guerrero\" (Guerrero, V.M. (1993) is default. Other method is \"loglik\").\n#' @param bcLower Lower limit for possible lambda values. The lower value is limited by -5. Default value is 0.\n#' @param bcUpper Upper limit for possible lambda values. The upper value is limited by 5. Default value is 5.\n#' @param bcBiasAdj Use adjusted back-transformed mean for Box-Cox transformations.\n#' If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts.\n#' If bcBiasAdj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.\n#' If bcBiasAdj=TRUE. Can either be the forecast variance, or a list containing the interval \\code{level}, the corresponding \\code{upper} and \\code{lower} intervals.\n#'\n#' @return An object of class \\code{ataoptim}.\n#'\n#' @author Ali Sabri Taylan and Hanife Taylan Selamlar\n#'\n#' @seealso \\code{\\link{BoxCox}}, \\code{\\link{InvBoxCox}}, \\code{\\link{BoxCox.lambda}}\n#'\n#' @references\n#'\n#' #'\\insertRef{boxcox1964}{ATAforecasting}\n#'\n#' #'\\insertRef{guerrero1993}{ATAforecasting}\n#'\n#'\n#'\n#' @export\nATA.BoxCoxAttr <- function(bcMethod = \"guerrero\", bcLower = 0, bcUpper = 5, bcBiasAdj = FALSE)\n{\n if ((bcMethod != \"guerrero\" & bcMethod != \"loglik\") | !is.character(bcMethod)){\n warning(\"Selected method for calculating lambda must be string. guerrero or loglik for calculating lambda.\")\n bcMethod <- \"guerrero\"\n }\n if(bcLower < 0){\n warning(\"Specified lower value is less than the minimum, setting bcLower=0\")\n bcLower <- 0\n }else if(bcUpper > 5){\n warning(\"Specified upper value is larger than the maximum, setting bcUpper=5\")\n bcUpper <- 5\n }\n if (!is.logical(bcBiasAdj)) {\n warning(\"bcBiasAdj information not found, defaulting to FALSE.\")\n biasadj <- FALSE\n }\n mylist <- list(\"bcMethod\"=bcMethod, \"bcLower\"=bcLower, \"bcUpper\"=bcUpper, \"bcBiasAdj\"=bcBiasAdj)\n attr(mylist, \"class\") <- \"ataoptim\"\n return(mylist)\n}\n", "meta": {"hexsha": "fea4535cc19434f59ad24ac09d1ae8005a441fa4", "size": 2104, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ATA_BoxCoxAttributes.r", "max_stars_repo_name": "cran/ATAforecasting", "max_stars_repo_head_hexsha": "383d55b20ac008028d60e6d3f7259a67427ef830", "max_stars_repo_licenses": ["FTL"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/ATA_BoxCoxAttributes.r", "max_issues_repo_name": "cran/ATAforecasting", "max_issues_repo_head_hexsha": "383d55b20ac008028d60e6d3f7259a67427ef830", "max_issues_repo_licenses": ["FTL"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ATA_BoxCoxAttributes.r", "max_forks_repo_name": "cran/ATAforecasting", "max_forks_repo_head_hexsha": "383d55b20ac008028d60e6d3f7259a67427ef830", "max_forks_repo_licenses": ["FTL"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.7659574468, "max_line_length": 165, "alphanum_fraction": 0.716730038, "num_tokens": 590, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.63341026367784, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.34385517533531446}} {"text": "Square <- function(x) {\n return(x^2)\n}\n\ncat(\"R program running\")\nprint(Square(4))\n\nwhile (TRUE) {\n Sys.sleep(3)\n}\n", "meta": {"hexsha": "d18d6fd878bdefd3d10d0448036f09b4f3911fd1", "size": 122, "ext": "r", "lang": "R", "max_stars_repo_path": "fixtures/simple/simple.r", "max_stars_repo_name": "Rushikesh-m/r-buildpack", "max_stars_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2018-07-05T20:52:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-17T22:28:19.000Z", "max_issues_repo_path": "fixtures/simple/simple.r", "max_issues_repo_name": "Rushikesh-m/r-buildpack", "max_issues_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 107, "max_issues_repo_issues_event_min_datetime": "2018-02-06T18:21:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-11T00:44:13.000Z", "max_forks_repo_path": "fixtures/simple/simple.r", "max_forks_repo_name": "Rushikesh-m/r-buildpack", "max_forks_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 23, "max_forks_repo_forks_event_min_datetime": "2018-03-27T05:17:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-07T21:57:00.000Z", "avg_line_length": 11.0909090909, "max_line_length": 24, "alphanum_fraction": 0.5819672131, "num_tokens": 37, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154240079185318, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3437459815684315}} {"text": "library(Cairo)\nlibrary(ape)\n\ntreefiles = dir()\ntreefiles = treefiles[grep('(phylip|nexus)',treefiles)]\n\nCairoSVG( 'trees.svg', width=3*length(treefiles),height=3)\npar(mfrow=c(1,length(treefiles)))\nfor( treefile in treefiles ){\n outname = gsub('(phylip|nexus)','svg',treefile)\n tree = read.tree( file=treefile )\n \n plot( tree )\n \n \n}\ndev.off()\n", "meta": {"hexsha": "821e8ea59a961461a9e4fe2cd8b0374835b1e550", "size": 352, "ext": "r", "lang": "R", "max_stars_repo_path": "FIGS/STRUCTURES/WNT/trees.r", "max_stars_repo_name": "adam-coster/dissertation", "max_stars_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FIGS/STRUCTURES/WNT/trees.r", "max_issues_repo_name": "adam-coster/dissertation", "max_issues_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FIGS/STRUCTURES/WNT/trees.r", "max_forks_repo_name": "adam-coster/dissertation", "max_forks_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.5555555556, "max_line_length": 58, "alphanum_fraction": 0.6761363636, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.34370457845116037}} {"text": "\r\nmnum=40000\t\t\r\nooi<-\"NC_000913\"\r\nmRNA_only<-TRUE\r\n\r\nda<-read.csv(\"CopraRNA2_prep_anno_addhomologs_padj_amountsamp.csv\") ## edit prw changed file name \r\noptions <- read.table(\"CopraRNA_option_file.txt\", sep=\":\") \r\nroot<-as.numeric(as.character(options[14,2]))\r\n\r\n\r\n\r\nen<-grep(\"Annotation\", colnames(da))\r\nda2<-da[,3:(en-1)]\r\nnamegenomes<-colnames(da2)\r\ngenomes<-list()\r\n\r\nmyfun<-function(da2){\r\n\tint_table<-gsub(\"^([^\\\\|]*\\\\|[^\\\\|]*\\\\|)\", \"\",da2)\r\n\tint_table<-gsub(\"\\\\|.*\",\"\",int_table)\r\n\tint_table\r\n}\r\n\r\n\r\nint_table<-apply(da2, 2, myfun)\r\n\r\n\r\nint_table<-matrix(as.numeric(int_table),nrow(int_table),ncol(int_table))\r\ncolnames(int_table)<-colnames(da2)\r\nrownames(int_table)<-seq(1,nrow(int_table))\r\n\r\nweight<-read.csv(\"zscore.weight\", header=F, sep=\";\")\r\n\r\nweight<-weight[ match(colnames(da2),toupper(weight[,1])),]\r\n\r\n\r\nif(root==0){\r\n\tweight[,2]<-1\r\n}\r\n\r\nif(root>0){\r\n\tweight[,2]<-weight[,2]^(1/root)\r\n}\r\n\r\n\r\nqtrans<-function(dat2){\r\n\t\tif(is(dat2)[1]==\"numeric\"){\r\n\t\t\tdat2<-matrix(dat2,1,length(dat2))\r\n\t\t}\r\n for(i in 1:ncol(dat2)){\r\n dat2[,i]<-qnorm(dat2[,i], lower.tail=FALSE)\r\n }\r\n dat2\r\n}\r\n\r\n\r\nif(ooi_close_analysis==TRUE){\r\n\r\nmnum<-40000\r\nif(chooseclose==TRUE){\r\n\tclose_name_vector<-c()\r\n}\r\n\r\nif(auto_close==TRUE){\r\n\tcommand<-paste(\"clustalo -i \", \"input_sRNA.fa\", \" --distmat-out=distmatout.txt --full --percent-id --output-order=input-order --force --max-hmm-iterations=-1\", sep=\"\")\r\n\tsystem(command)\r\n\ttemp<-read.delim(\"distmatout.txt\",sep=\"\",header=F, , skip=1)\r\n\tunlink(\"distmatout.txt\")\r\n\tna<-temp[,1]\r\n\ttemp<-temp[,2:ncol(temp)]\r\n\tcolnames(temp)<-na\r\n\trownames(temp)<-na\r\n\tooi_pos<-grep(ooi, colnames(temp))\r\n\tooi_col<-temp[,ooi_pos]\r\n\tnames(ooi_col)<-na\r\n\tooi_col<-sort(ooi_col,decreasing = TRUE)\r\n\tnum<-floor(length(ooi_col)*close)\r\n\tooi_col<-ooi_col[1:4]\r\n}\r\n\r\nload(\"conservation_table.Rdata\")\r\n\r\n\r\n\r\ncon_table<-conservation_table[[3]]\r\ncon_table_sub<-conservation_table[[4]]\r\n\r\np_table<-con_table\r\np_table[]<-NA\r\n\r\nmRNA_test<-function(x){\r\n\tout<-NA\r\n\tif(is.na(x[1])==F){\r\n\t\tif(length(x)==4){\r\n\t\t\tif(x[2]==\"TRUE\"){\r\n\t\t\t\tout<-as.numeric(x[1])\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tout\r\n}\r\nmRNA_and_sRNA_test<-function(x){\r\n\tout<-NA\r\n\tif(is.na(x[1])==F){\r\n\tif(length(x)==4){\r\n\t\tif(x[2]==\"TRUE\"){\r\n\t\t\tif(x[3]==\"TRUE\" | x[4]==\"TRUE\"){\r\n\t\t\t\tout<-as.numeric(x[1])\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t}\r\n\tout\r\n}\r\n\r\n\r\nfor(i in 1:nrow(p_table)){\r\n\t\r\n\tif(mRNA_only==TRUE){\r\n\t\tcon_temp<-con_table[i,]\r\n\t\tcon_temp<-strsplit(con_temp, \"\\\\|\")\r\n\t\tcon_temp_sub<-con_table_sub[i,]\r\n\t\tcon_temp_sub<-strsplit(con_temp_sub, \"\\\\|\")\r\n\t\tcon_temp<-unlist(lapply(con_temp,mRNA_test))\r\n\t\tcon_temp_sub<-unlist(lapply(con_temp_sub,mRNA_test))\r\n\t\tna<-which(is.na(con_temp))\r\n\t\tif(length(na)>0){\r\n\t\t\tcon_temp[na]<-con_temp_sub[na]\r\n\t\t}\r\n\t\t\r\n\t\tp_table[i,]<-con_temp\r\n\t}\r\n\t\r\n\tif(mRNA_only==FALSE){\r\n\t\tcon_temp<-con_table[i,]\r\n\t\tcon_temp<-strsplit(con_temp, \"\\\\|\")\r\n\t\tcon_temp_sub<-con_table_sub[i,]\r\n\t\tcon_temp_sub<-strsplit(con_temp_sub, \"\\\\|\")\r\n\t\tcon_temp<-unlist(lapply(con_temp,mRNA_and_sRNA_test))\r\n\t\tcon_temp_sub<-unlist(lapply(con_temp_sub,mRNA_and_sRNA_test))\r\n\t\tna<-which(is.na(con_temp))\r\n\t\tif(length(na)>0){\r\n\t\t\tcon_temp[na]<-con_temp_sub[na]\r\n\t\t}\r\n\t\t\r\n\t\tp_table[i,]<-con_temp\r\n\t}\r\n}\r\nnan<-colnames(p_table)\r\np_table<-matrix(as.numeric(p_table),nrow(p_table),ncol(p_table))\r\ncolnames(p_table)<-nan\r\n\r\n\r\nclose_pos<-match(names(ooi_col),colnames(p_table))\r\np_table<-p_table[,close_pos]\r\n\r\nposvec<-rep(NA,10^7)\r\nvari<-rep(NA,10^7)\r\ncount<-0\r\nfor(i in 1:nrow(p_table)){\r\n\ttemp<-order(p_table[i,], na.last=NA)\r\n\t\r\n\tif(length(temp)>2){ \r\n\t\tfor(j in 1:(length(temp)-2)){\r\n\t\t\tcount<-count+1\r\n\t\t\tvari[count]<-paste(sort(temp[1:(j+2)]), collapse=\"_\")\r\n\t\t\tposvec[count]<-i\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvari<-na.omit(vari)\r\nposvec<-na.omit(posvec)\r\ndups<-which(duplicated(vari))\r\nfirst_occurence<-match(vari[dups], vari)\r\nposvec2<-posvec\r\n\r\nfor(i in 1:length(dups)){\r\n\tposvec2[first_occurence[i]]<-paste(posvec2[first_occurence[i]],posvec[dups[i]], sep=\"_\")\r\n\r\n}\r\nposvec2<-posvec2[-dups]\r\nvari<-unique(vari)\r\n\r\n\r\nspa<-floor(length(vari)/mnum)\r\nprint(length(vari))\r\nrest<-length(vari)-(mnum*spa)\r\nspal<-rep(mnum,spa)\r\nif(rest>0){\r\nspa<-spa+1\r\nspal<-c(spal,rest)\r\n}\r\n\r\nooilist<-rep((list(rep(NA,nrow(int_table)))), spa)\r\n\r\nna<-paste(\"_\",vari,\"_\",sep=\"\")\r\ncount<-1\r\nooi_pos<-grep(ooi, colnames(p_table))\r\nfor(jj in 1:spa){\r\n\r\nout<-rep((list(rep(NA,nrow(int_table)))), spal[jj])\r\n\r\nfor(i in count:(count+spal[jj]-1)){\r\n\t\r\n\ttemp1<-as.numeric(strsplit(vari[i],\"_\")[[1]])\r\n\ttemp<-na.omit(int_table[,temp1])\r\n\twtemp<-weight[temp1,2]\r\n\tposition<-as.numeric(strsplit(posvec2[i],\"_\")[[1]])\r\n\tprint(i)\r\n\tdat2<-qtrans(temp)\r\n a<-rowSums(t(t(dat2)*wtemp))\r\n b<-sum(wtemp)^2\r\n d<-sum(wtemp^2)\r\n\tl<-length(a)\r\n k<-1/l\r\n\ty<-k * seq(1,l)\r\n dat3<-data.frame(y,a,d,b)\r\n \r\n\trhotemp<-coef(nls(y~sort(pnorm(a/sqrt((1-rho)*d+rho*b), lower.tail=F)), data=dat3, start=list(rho=0), control=list(minFactor = 1/128, tol = 1e-05, warnOnly = TRUE)))\r\n\t\r\n\ttemp<-na.omit(p_table[position,temp1])\r\n\twtemp<-weight[temp1,2]\r\n\tposition<-as.numeric(strsplit(posvec2[i],\"_\")[[1]])\r\n\t\r\n\tdat2<-qtrans(temp)\r\n a<-rowSums(t(t(dat2)*wtemp))\r\n b<-sum(wtemp)^2\r\n d<-sum(wtemp^2)\r\n\t\r\n\tout[[i-count+1]][position]<-pnorm(a/sqrt((1-rhotemp)*d+rhotemp*b),lower.tail=F)\r\n\t\r\n\t\r\n}\r\n\tnames(out)<-na[count:(count+spal[jj]-1)]\r\n\tsave(out, file=paste(\"ooi_ooi_cons_full_table_\",jj,\".Rdata\",sep=\"\"))\r\n\ttemp<-grep(paste(\"_\",ooi_pos,\"_\",sep=\"\"), na[count:(count+spal[jj]-1)])\r\n\tif(length(temp)>0){\r\n\tooilist[[jj]]<-do.call(pmin, c(out[temp],list(na.rm=T)))\r\n\t}\r\n\r\ncount<-count+spal[jj]\r\n\r\n}\r\n################################\r\n\r\nout_ooi<-do.call(pmin, c(ooilist,list(na.rm=T)))\r\n\r\nout_ooi_fdr<-p.adjust(out_ooi, method=\"BH\")\r\nout_ooi<-cbind(out_ooi_fdr,out_ooi)\r\ncolnames(out_ooi)<-c(\"fdr\",\"p-value\")\r\nout_ooi<-cbind(out_ooi, da[,3:ncol(da)])\r\n\r\ninitial_sorting<-seq(1,nrow(p_table))\r\nout_ooi<-cbind(out_ooi, initial_sorting)\r\nout_ooi<-out_ooi[order(as.numeric(out_ooi[,2])),]\r\n\r\nwrite.table(out_ooi, file=\"ooi_ooi_cons_close_orgs.csv\",sep=\",\", quote=F, row.names=F)\r\n\r\n\r\n}", "meta": {"hexsha": "74c4b2e2e631c79b483add6595d1f87eb91f7450", "size": 6016, "ext": "r", "lang": "R", "max_stars_repo_path": "CopraRNA2_pvalue_combination_on_a_subset.r", "max_stars_repo_name": "JensGeorg/CopraRNA_postprocessing", "max_stars_repo_head_hexsha": "4eeed7229b85e69dc467ad6dc0f5ad04b1af61b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CopraRNA2_pvalue_combination_on_a_subset.r", "max_issues_repo_name": "JensGeorg/CopraRNA_postprocessing", "max_issues_repo_head_hexsha": "4eeed7229b85e69dc467ad6dc0f5ad04b1af61b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CopraRNA2_pvalue_combination_on_a_subset.r", "max_forks_repo_name": "JensGeorg/CopraRNA_postprocessing", "max_forks_repo_head_hexsha": "4eeed7229b85e69dc467ad6dc0f5ad04b1af61b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.1384615385, "max_line_length": 169, "alphanum_fraction": 0.6268284574, "num_tokens": 2039, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.34370457845116026}} {"text": "\n\nlibrary(tidyverse)\nlibrary(data.table)\nlibrary(USAboundaries)\nlibrary(USAboundariesData)\nlibrary(tigris)\nlibrary(albersusa)\nlibrary(scales)\nlibrary(viridisLite)\n\nlarge_text_theme = theme(\n plot.title = element_text(size = 24),\n plot.subtitle = element_text(size = 18, face = 'italic'),\n plot.caption = element_text(size = 13, face = 'italic', hjust = 0),\n axis.text = element_text(size = 16),\n axis.title = element_text(size = 18)\n) \nlibrary(cowplot)\n\nsetwd(\"~/Public_Policy/Projects/Affordable Care Act/data\")\nlist.files()\nmedicaid_expansion_data = read_csv('medicaid_expansion_states.csv')\nnational_uninsured_stats_files = list.files(\"national characteristics of uninsured\", pattern = '_data.*.csv', full.names = T)\ncounty_uninsured_stats_files = list.files(\"county characteristics of uninsured, 5-year\", pattern = '_data.*.csv', full.names = T) \nstate_uninsured_stats_files = list.files(\"state characteristics of uninsured, 5-year\", pattern = '_data.*.csv', full.names = T)\n\n\nget_stacked_acs_data = function(filenames) {\n \n all_stacked_uninsured_data = map(filenames, function(the_file){\n print(the_file)\n \n headers = read_csv(the_file, n_max = 1)\n # t(headers) %>% View()\n uninsured_data = read_csv(the_file, col_names = F, skip = 2, na = c('', 'NA', 'null'))\n names(uninsured_data) = names(headers) \n \n uninsured_data = rename(uninsured_data, total_population = S2702_C01_001E, uninsured = S2702_C02_001E) %>%\n mutate(\n total_population = as.numeric(total_population), \n uninsured = as.numeric(uninsured),\n year = str_extract(the_file, 'ACSST[0-9+]{1}Y[0-9]{4}') %>% str_remove('ACSST[0-9]{1}Y') %>% as.numeric()\n ) %>%\n select(GEO_ID, NAME, year, total_population, uninsured)\n }) %>% \n bind_rows() %>%\n mutate(\n percent_uninsured = uninsured / total_population,\n fips = str_extract(GEO_ID, 'US[0-9]+') %>% str_remove('US')\n )\n return(all_stacked_uninsured_data)\n}\n\nnational_uninsured_data = get_stacked_acs_data(national_uninsured_stats_files)\nstate_uninsured_data = get_stacked_acs_data(state_uninsured_stats_files) %>% filter(!is.na(total_population))\ncounty_uninsured_data = get_stacked_acs_data(county_uninsured_stats_files) %>% filter(!is.na(total_population))\n\n# View(state_uninsured_data)\n# View(county_uninsured_data)\n\n##### Plot overall uninsured stats #####\n\nggplot(national_uninsured_data, aes(year, percent_uninsured)) +\n theme_bw() +\n large_text_theme + \n theme(panel.grid.minor = element_blank()) +\n geom_bar(stat = 'identity', fill = 'steelblue', colour = 'black', width = 0.75) +\n geom_text(\n aes(label = paste0(comma(uninsured/1e6, accuracy = 0.1), 'M')),\n vjust = -0.5, size = 5\n ) +\n labs(\n x = '', y = 'Uninsured Population (%)', \n title = 'Trend in the U.S. Uninsured Population',\n subtitle = 'The Affordable Care Act (Obamacare) became law in 2010 and went into effect in 2014.',\n caption = 'Chart: Taylor G. White\\nData: Census ACS 1-Year Estimates'\n ) + \n scale_y_continuous(labels = percent, breaks = seq(0, 0.15, by = 0.025)) +\n scale_x_continuous(breaks = unique(stacked_uninsured_data$year))\n\nsetwd(\"~/Public_Policy/Projects/Affordable Care Act/output\")\n\nggsave('total_effects_uninsured.png', height = 9, width = 12, units = 'in', dpi = 600)\n\n\n## get shapefile data ## \n# us_counties_shp = us_counties()\n# us_states_shp = us_states()\n# us_map = USAboundaries::us_boundaries()\n# us_states_tigris = tigris::states()\n# us_counties_tigris = tigris::counties()\n\nus_sf <- usa_sf(\"laea\")\n# plot(us_sf)\ncty_sf <- counties_sf(\"aeqd\")\n\nView(state_uninsured_data)\n\nstate_uninsured_stats = group_by(state_uninsured_data, GEO_ID, fips, NAME ) %>%\n summarize(\n obs = n(),\n latest_year = max(year),\n delta_uninsured_pct = percent_uninsured[year == max(year)] - percent_uninsured[year == min(year)],\n delta_uninsured = uninsured [year == max(year)] - uninsured [year == min(year)],\n ) %>%\n ungroup() %>%\n left_join(medicaid_expansion_data, by = c('NAME' = 'Name')) %>%\n arrange(delta_uninsured_pct) %>%\n mutate(\n state_name_factor = factor(NAME, levels = NAME)\n )\nstate_uninsured_stats$NAME\n\ncounty_uninsured_stats = group_by(county_uninsured_data, GEO_ID, fips) %>%\n summarize(\n obs = n(),\n latest_year = max(year),\n delta_uninsured_pct = percent_uninsured[year == max(year)] - percent_uninsured[year == min(year)],\n delta_uninsured = uninsured [year == max(year)] - uninsured [year == min(year)],\n ) %>%\n ungroup() \n\nView(county_uninsured_data)\n\ncounty_map_data = left_join(cty_sf, county_uninsured_stats)\nstate_map_data = left_join(us_sf, state_uninsured_stats, by = c('fips_state' = 'fips')) \n\n\nggplot(state_uninsured_stats, aes(NAME, delta_uninsured_pct)) +\n geom_bar(stat = 'identity')\n\nggplot(state_map_data) +\n large_text_theme +\n theme_map() +\n theme(\n plot.title = element_text(size = 24),\n plot.subtitle = element_text(size = 18, face = 'italic'),\n plot.caption = element_text(size = 13, face = 'italic', hjust = 0),\n ) +\n geom_sf(aes(fill = delta_uninsured_pct)) +\n geom_sf_text(data = filter(state_map_data, Status == 'Not Adopted'), aes(label = 'No Exp.'), fontface = 'bold', size = 3.5) + \n scale_fill_viridis_c(direction = -1, option = 'D', labels = percent, name = 'Change in Uninsured\\nPopulation') +\n labs(\n title = 'Percent Change in Uninsured Population, 2013-2018',\n subtitle = 'States that did not expand Medicaid are marked \"No Exp.\"',\n caption = 'Chart: Taylor G. White\\nData: Census ACS 5-Year Estimates, Kaiser Family Foundation'\n )\nggsave('change_in_uninsured_2013_2018_map.png', height = 9, width = 12, units = 'in', dpi = 600)\n\n \nggplot(county_map_data) +\n geom_sf(aes(fill = delta_uninsured_pct)) +\n scale_fill_viridis_c()\n\n\n \n\n\n##### create map for all 50 states #####\nUS_state_data = left_join(us_sf, latest_state_data, by = c('iso_3166_2' = 'location')) %>% left_join(state_geo_center, by = c('iso_3166_2' = 'state_abbr'))\n\nggplot() +\n geom_sf(data = US_state_data, aes(fill = log(cases_per_100k)), alpha = 0.75, size = 0.25) +\n scale_fill_viridis(guide = F, option = 'C') +\n geom_sf_text(data = US_state_data, aes(long, lat, label = comma(cases_per_100k, accuracy = 1)),\n colour = 'black', fontface='bold', size = 2) +\n theme_map() +\n labs(x = '', y = '',\n caption = 'Chart: Taylor G. White\\nData: covidtracking.com',\n title = 'U.S. COVID-19 Cases by State, Per 100k Population', subtitle = sprintf('As of %s',\n unique(format(latest_state_data$date, '%B %d')))) +\n theme(\n axis.ticks = element_blank(),\n axis.text = element_blank(),\n title = element_text(size = 16),\n plot.subtitle = element_text(size = 11),\n plot.caption = element_text(hjust = 0, face = 'italic', size = 10)\n )\n", "meta": {"hexsha": "8c64d4ec6654278b4e605b430a2c3859f8a427c5", "size": 6865, "ext": "r", "lang": "R", "max_stars_repo_path": "Projects/Affordable Care Act/scripts/analyze_aca_impacts.r", "max_stars_repo_name": "vishalbelsare/Public_Policy", "max_stars_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-03-09T01:39:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-08T19:11:44.000Z", "max_issues_repo_path": "Projects/Affordable Care Act/scripts/analyze_aca_impacts.r", "max_issues_repo_name": "vishalbelsare/Public_Policy", "max_issues_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-06-03T20:11:43.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-07T00:03:58.000Z", "max_forks_repo_path": "Projects/Affordable Care Act/scripts/analyze_aca_impacts.r", "max_forks_repo_name": "vishalbelsare/Public_Policy", "max_forks_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-04T22:48:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-09T14:00:22.000Z", "avg_line_length": 38.1388888889, "max_line_length": 155, "alphanum_fraction": 0.6830298616, "num_tokens": 1968, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621764862150634, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.34370456948173755}} {"text": "install.packages(\"devtools\")\ninstall.packages(\"devtools\", lib=\"~/R/lib\")\nlibrary(devtools)\ndevtools::use_rcpp()\ndevtools::install_github(\"cran/rgdal\")\n\n\ninstall.packages(\"RcppCCTZ\") #for parseDatetime\ninstall.packages(\"ggplot2\") \ninstall.packages(\"spatstat\")\ninstall.packages(\"RColorBrewer\")\ninstall.packages(\"raster\") #for loading and plotting geotiff\ninstall.packages(\"png\")\ninstall.packages(\"stringr\") # for srt_pad\n\nlibrary(RcppCCTZ)\nlibrary(ggplot2)\nlibrary(spatstat)\nlibrary(RColorBrewer)\nlibrary(raster)\nlibrary(png)\nlibrary(stringr)\n\n\nsetwd(\"~/projects/stay-safe/heatmap-creation/\")\n\ndata <- read.csv('../data/objectstream.csv', header = T)\ndata$timestamp2 <- parseDatetime(data$timestamp, \"%Y-%m-%dT%H:%M:%SZ\");\nmin_x <- min(data$x)\nmax_x <- max(data$x)\nmin_y <- min(data$y)\nmax_y <- max(data$y)\n\n#\n# constant colors\n#\nnorm_palette <- colorRampPalette(c(\"white\", \"yellow\", \"orange\",\"red\"))\npal_opaque <- norm_palette(4)\npal_trans <- norm_palette(4)\npal_trans[1] <- \"#FFFFFF00\" #was originally \"#FFFFFF\" \npal_trans2 <- paste(pal_opaque,\"50\",sep = \"\")\n\n#\n# find max quantiles\n#\n\nfrom_timestamp <- \"2018-06-21T16:19:50Z\"\nto_timestamp <- \"2018-06-21T16:20:00Z\"\nfilter_from <- parseDatetime(from_timestamp, \"%Y-%m-%dT%H:%M:%SZ\");\nfilter_to <- parseDatetime(to_timestamp, \"%Y-%m-%dT%H:%M:%SZ\"); \n\ndata_subset <- subset(data, timestamp2 >= filter_from & timestamp2 <= filter_to)\ndata_points <- data.frame(x=data_subset$x, y=data_subset$y)\n\nP <- as.ppp(data_points, c(min_x,max_x,min_y,max_y), checkdup=F)\nZ <- density(P, bw=.5)\n\ncol_breakes\ncol_breakes <- as.vector(quantile(Z, probs = (0:4)/4))\n\n#\n# filtering data\n#\nfor (from_hour in 6:23){\n for (from_minute in 0:59){\n for (from_second in 0:5){\n \n #from_hour <- 0\n #from_minute <- 59\n #from_second <- 5\n \n from_second <- from_second * 10\n \n to_hour <- from_hour\n to_minute <- from_minute\n to_second <- from_second + 10\n \n if(from_second == 50){\n to_second <- 0\n to_minute <- from_minute +1\n if(from_minute == 59){\n to_minute <- 0\n to_hour <- from_hour + 1\n }\n }\n \n from_hour_str <- str_pad(from_hour, 2, pad = \"0\")\n to_hour_str <- str_pad(to_hour, 2, pad = \"0\")\n from_minute_str <- str_pad(from_minute, 2, pad = \"0\")\n to_minute_str <- str_pad(to_minute, 2, pad = \"0\")\n from_second_str <- str_pad(from_second, 2, pad = \"0\")\n to_second_str <- str_pad(to_second, 2, pad = \"0\")\n \n from_time<- paste(from_hour_str, \":\", from_minute_str, \":\", from_second_str, sep=\"\")\n to_time<- paste(to_hour_str, \":\", to_minute_str, \":\", to_second_str, sep=\"\")\n \n print(paste(\"from\", from_time, \"to\", to_time))\n \n from_timestamp <- paste(\"2018-06-21T\",from_time,\"Z\", sep=\"\")\n to_timestamp <- paste(\"2018-06-21T\",to_time,\"Z\", sep=\"\")\n filter_from <- parseDatetime(from_timestamp, \"%Y-%m-%dT%H:%M:%SZ\");\n filter_to <- parseDatetime(to_timestamp, \"%Y-%m-%dT%H:%M:%SZ\"); \n \n data_subset <- subset(data, timestamp2 >= filter_from & timestamp2 <= filter_to)\n data_points <- data.frame(x=data_subset$x, y=data_subset$y)\n \n #\n # plot\n #\n filename <- paste(\"plots/\", from_time,\".png\", sep = \"\")\n png(filename=filename, width = 800, height = 180)\n \n P <- as.ppp(data_points, c(min_x,max_x,min_y,max_y), checkdup=F)\n Z <- density(P, bw=.5)\n \n par(mar=c(0,0,1.5,0))\n if(nrow(data_subset) == 0){\n next\n plot(P, \n #col = pal_opaque,\n breaks = col_breakes,\n legend = col_breakes,\n zlim=c(min(col_breakes), max(col_breakes))\n )\n }else{\n Q <- quadratcount(P, nx= 6, ny=3) \n plot(intensity(Q, image=TRUE), main = from_timestamp,\n col = pal_opaque,\n #breaks = c(0.001,0.002,0.003,0.004,0.005) #col_breakes\n breaks = col_breakes,\n legend = col_breakes,\n zlim=c(min(col_breakes), max(col_breakes))\n )\n plot(P, pch=20, cex=0.3, add=T)\n }\n \n dev.off()\n \n }\n }\n}\n\n\n\n#\n# geotiff -> skipped!\n#\ngeotiff <- raster(\"../data/aerial-photo.geotiff\")\naerial_photo <- readPNG(\"../data/aerial-photo.png\")\nplot(aerial_photo)\n", "meta": {"hexsha": "278b84a566c75b57587677ece5c268b06677b2df", "size": 4287, "ext": "r", "lang": "R", "max_stars_repo_path": "heatmap-creation/script.r", "max_stars_repo_name": "tursics/hacktrain-stay-safe", "max_stars_repo_head_hexsha": "41201cb0d157f9ee4d54c4cdc5ee54fe0d310c1f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-09-25T06:33:13.000Z", "max_stars_repo_stars_event_max_datetime": "2018-09-28T20:18:16.000Z", "max_issues_repo_path": "heatmap-creation/script.r", "max_issues_repo_name": "tursics/hacktrain-stay-safe", "max_issues_repo_head_hexsha": "41201cb0d157f9ee4d54c4cdc5ee54fe0d310c1f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "heatmap-creation/script.r", "max_forks_repo_name": "tursics/hacktrain-stay-safe", "max_forks_repo_head_hexsha": "41201cb0d157f9ee4d54c4cdc5ee54fe0d310c1f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-09-22T21:27:50.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-22T21:27:50.000Z", "avg_line_length": 28.3907284768, "max_line_length": 90, "alphanum_fraction": 0.6078843014, "num_tokens": 1258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548782017745, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.34347018985968114}} {"text": "\n{% case args.infmt %}\n# full\n{% when 'full' %}\nmat = read.table({{i.infile | quote}}, row.names = 1, header = T, check.names = F, sep = \"\\t\")\n\n# upper\n{% when 'upper' %}\nmat = read.table({{i.infile | quote}}, row.names = 1, header = T, check.names = F, sep = \"\\t\", fill = T)\nmat[lower.tri(mat)] <- t(mat)[lower.tri(mat)]\n\n# lower\n{% when 'lower' %}\nmat = read.table({{i.infile | quote}}, row.names = 1, header = T, check.names = F, sep = \"\\t\", fill = T)\nmat[upper.tri(mat)] <- t(mat)[upper.tri(mat)]\n\n# pair\n{% when 'pair' %}\nmat0 <- read.table({{i.infile | quote}}, header=F, row.names=NULL, stringsAsFactor=F, check.names=F)\nname1 = as.character (mat0[, 1])\nname2 = as.character (mat0[, 2])\nnames = unique(union(name1, name2))\nnlen = length(names)\nmat = matrix(NA, ncol = nlen, nrow = nlen)\nrownames(mat) = names\ncolnames(mat) = names\ndiag(mat) = 0\nmat[as.matrix(mat0[,1:2,drop=F])] = mat0[,3]\n\nut = upper.tri(mat)\nlt = lower.tri(mat)\n\nutdata = mat[ut]\nltdata = mat[lt]\n\nuttdata = t(mat)[ut]\nlttdata = t(mat)[lt]\n\nutdata[is.na(utdata)] = uttdata[!is.na(uttdata)]\nltdata[is.na(ltdata)] = lttdata[!is.na(lttdata)]\n\nmat[ut] = utdata\nmat[lt] = ltdata\n\n{% endcase %}\n\ncoords = cmdscale(mat, k={{args.k}})\ncoords = round(coords, 3)\nwrite.table (coords, {{o.outfile | quote}}, col.names=T, row.names=T, quote=F, sep=\"\\t\", na=\"\")\n", "meta": {"hexsha": "04bd9dc95da85f2b6a13822294a289fdf1076d53", "size": 1332, "ext": "r", "lang": "R", "max_stars_repo_path": "bioprocs/scripts/cluster/pDist2Feats.r", "max_stars_repo_name": "LeaveYeah/bioprocs", "max_stars_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-10T00:17:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-10T09:53:09.000Z", "max_issues_repo_path": "bioprocs/scripts/cluster/pDist2Feats.r", "max_issues_repo_name": "LeaveYeah/bioprocs", "max_issues_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-02-15T22:59:49.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-15T23:03:09.000Z", "max_forks_repo_path": "bioprocs/scripts/cluster/pDist2Feats.r", "max_forks_repo_name": "LeaveYeah/bioprocs", "max_forks_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-10T00:17:54.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-10T09:56:40.000Z", "avg_line_length": 26.64, "max_line_length": 104, "alphanum_fraction": 0.6133633634, "num_tokens": 471, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.34342015362247647}} {"text": "# calculate_compensation_website.r\n#\n# Copyright (c) 2020 VIB (Belgium) & Babraham Institute (United Kingdom)\n#\n# Software written by Carlos P. Roca, as research funded by the European Union.\n#\n# This software may be modified and distributed under the terms of the MIT\n# license. See the LICENSE file for details.\n\n\n# Runs a calculation of compensation with autospill, creating all figures and\n# tables used in autospill website.\n#\n# Requires being called as a batch script, and assumes fixed values for the\n# following two variables (see below):\n# control.dir directory with the set of single-color controls\n# control.def.file csv file defining the names and channels of the\n# single-color controls\n\n\nlibrary( autospill )\n\n\n# set parameters\n\nasp <- get.autospill.param( \"website\" )\n\nasp\n\n\n# read flow controls\n\ncontrol.dir <- \"./samples/\"\ncontrol.def.file <- \"./fcs_control.csv\"\n\nflow.control <- read.flow.control( control.dir, control.def.file, asp )\n\nnames( flow.control )\n\nflow.control$antigen\nflow.control$autof.marker.idx\nstr( flow.control$event )\nflow.control$event.n\nflow.control$event.number.width\ntable( flow.control$event.sample )\nflow.control$expr.data.max\nflow.control$expr.data.max.ceil\nflow.control$expr.data.min\nstr( flow.control$expr.data.tran )\nstr( flow.control$expr.data.untr )\nflow.control$figure.scatter.dir\nstr( flow.control$flow.set, max.level = 1 )\nstr( flow.control$gate.parameter )\nflow.control$marker\nflow.control$marker.n\nflow.control$marker.original\nflow.control$sample\nflow.control$scatter.and.marker\nflow.control$scatter.and.marker.label\nflow.control$scatter.and.marker.original\nflow.control$scatter.parameter\nstr( flow.control$transform )\nstr( flow.control$transform.inv )\nflow.control$wavelength\n\n\n# gate events before calculating spillover\n\nflow.gate <- gate.flow.data( flow.control, asp )\n\nstr( flow.gate )\n\n\n# get initial spillover matrices from untransformed and transformed data\n\nmarker.spillover.unco.untr <- get.marker.spillover( TRUE, flow.gate,\n flow.control, asp )\nmarker.spillover.unco.tran <- get.marker.spillover( FALSE, flow.gate,\n flow.control, asp )\n\nstr( marker.spillover.unco.untr$inte )\nstr( marker.spillover.unco.untr$coef )\n\nstr( marker.spillover.unco.tran$inte )\nstr( marker.spillover.unco.tran$coef )\n\n\n# refine spillover matrix iteratively\n\nrefine.spillover.result <- refine.spillover( marker.spillover.unco.untr,\n marker.spillover.unco.tran, flow.gate, flow.control, asp )\n\nstr( refine.spillover.result )\n\n\n# output session info\n\nsessionInfo()\n\n", "meta": {"hexsha": "20f4afd57787d6e7db6aa5707ddc44e2879fc0d5", "size": 2535, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/batch/calculate_compensation_website.r", "max_stars_repo_name": "DillonHammill/autospill", "max_stars_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-07T21:48:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-12T03:00:59.000Z", "max_issues_repo_path": "inst/batch/calculate_compensation_website.r", "max_issues_repo_name": "DillonHammill/autospill", "max_issues_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-09-10T08:08:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-29T23:41:00.000Z", "max_forks_repo_path": "inst/batch/calculate_compensation_website.r", "max_forks_repo_name": "DillonHammill/autospill", "max_forks_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-09-05T14:15:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-12T14:36:42.000Z", "avg_line_length": 25.35, "max_line_length": 79, "alphanum_fraction": 0.7597633136, "num_tokens": 631, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.34342014574354185}} {"text": "## to be sourced from main analysis script\n\ngee_multi_plate <- function(data, levels) {\n fit <- geeglm(\n rel_lum ~ factor(allele, levels=levels) + factor(plate_id),\n data=data,\n id=factor(plate_id):factor(clone_id),\n family=gaussian(link=\"log\"),\n corstr=\"exchangeable\",\n std.err=\"san.se\",\n zcor=NULL\n )\n return(fit)\n}\n\ngee_single_plate <- function(data, levels) {\n fit <- geeglm(\n rel_lum ~ factor(allele, levels=levels),\n data=data,\n id=factor(clone_id),\n family=gaussian(link=\"log\"),\n corstr=\"exchangeable\",\n std.err=\"san.se\",\n zcor=NULL\n )\n return(fit)\n}\n\nfit_gee <- function(data) {\n if (length(levels(factor(data$plate_id))) > 1) {\n cat(\"testing multi plate experiment\\n\")\n if (length(levels(factor(data$allele))) == 3) {\n cat(\"testing tri alleleic site\\n\")\n levs <- levels(factor(data$allele))\n fit1 <- gee_multi_plate(data, levs)\n idx <- data$allele %in% levs[2:3]\n fit2 <- gee_multi_plate(data[idx, ], levs[2:3])\n fit <- setNames(list(fit1, fit2), c(\"default\", \"extra\"))\n } else {\n cat(\"testing bi alleleic site\\n\")\n levs <- levels(factor(data$allele))\n fit <- gee_multi_plate(data, levs)\n }\n } else {\n cat(\"testing single plate experiment\\n\")\n if (length(levels(factor(data$allele))) == 3) {\n cat(\"testing tri alleleic site\\n\")\n levs <- levels(factor(data$allele))\n fit1 <- gee_single_plate(data, levs)\n idx <- data$allele %in% levs[2:3]\n fit2 <- gee_single_plate(data[idx, ], levs[2:3])\n fit <- setNames(list(fit1, fit2), c(\"default\", \"extra\"))\n } else {\n cat(\"testing bi alleleic site\\n\")\n levs <- levels(factor(data$allele))\n fit <- gee_single_plate(data, levs)\n }\n }\n return(fit)\n}\n\nfit_parser <- function(fit) {\n df <- summary(fit)$coefficients\n df <- df[2:nrow(df), ]\n colnames(df) <- c(\"Effect\", \"SE\", \"Wald_Stat\", \"pval\")\n df$Locus_Cell <- paste(unname(unlist(\n fit$data[1, c(\"locus\", \"orientation\", \"cell_line\")]\n )), collapse=\"_\")\n df$A1 <- levels(fit$model[ , \"factor(allele, levels = levels)\"])[1]\n df$A2 <- unlist(lapply(strsplit(rownames(df), \")\"), \"[[\", 2))\n df$Label <- paste(df$Locus_Cell, paste(df$A1, df$A2, sep=\"_\"), sep=\"::\")\n col_order <- c(\n \"Label\", \"Locus_Cell\", \"A1\", \"A2\", \"Effect\", \"SE\", \"Wald_Stat\", \"pval\"\n )\n df <- df[col_order]\n i <- suppressWarnings(which(is.na(as.numeric(df$A2))))\n df <- df[i, ]\n return(df)\n}\n\nparse_gee <- function(fit) {\n if (class(fit)[1] == \"list\") {\n cat(\"extracting coefficient summary for tri alleleic site\\n\")\n df <- lapply(fit, fit_parser)\n } else {\n cat(\"extracting coefficient summary for bi alleleic site\\n\")\n df <- fit_parser(fit)\n }\n return(df)\n}\n\nwrite_gee <- function(sums, target) {\n obj <- lapply(sums, FUN <- function(sum) {\n if (class(sum) == \"list\") {\n df <- do.call(rbind, sum)\n } else {\n df <- sum\n }\n return(df)\n })\n df <- do.call(rbind, obj)\n write.table(\n df, file=target, quote=FALSE, sep=\"\\t\", row.names=FALSE, col.names=TRUE\n )\n cat(\"table written to\", target, \"\\n\")\n}\n", "meta": {"hexsha": "21b0a644b563c7136b79a1f4cda1168b308ef7f4", "size": 3412, "ext": "r", "lang": "R", "max_stars_repo_path": "code/luciferase/gee.r", "max_stars_repo_name": "dampierch/glioma", "max_stars_repo_head_hexsha": "2d418ff02b19b48d1f73b89756c4d31c7b913499", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/luciferase/gee.r", "max_issues_repo_name": "dampierch/glioma", "max_issues_repo_head_hexsha": "2d418ff02b19b48d1f73b89756c4d31c7b913499", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/luciferase/gee.r", "max_forks_repo_name": "dampierch/glioma", "max_forks_repo_head_hexsha": "2d418ff02b19b48d1f73b89756c4d31c7b913499", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.8878504673, "max_line_length": 79, "alphanum_fraction": 0.5457209848, "num_tokens": 945, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3431612818573753}} {"text": "install.packages(c(\"statebins\", \"viridis\"))\n\nlibrary(statebins) # https://rpubs.com/sdtanner/Statebin_project\nlibrary(viridis)\n\nsource(\"../utility_scripts/data_prep.r\")\nsource(\"../utility_scripts/annotation_defaults.r\")\n\ndata <- scent_data %>%\n select(state, trial) %>%\n distinct(trial, .keep_all=TRUE) %>%\n group_by(state) %>%\n summarize(number_of_trials = n()) %>%\n ungroup() %>%\n arrange(desc(number_of_trials))\nhead(data)\n\nplot <- statebins(\n state_data = data,\n value_col = \"number_of_trials\",\n ggplot2_scale_function = scale_fill_viridis,\n light_label = \"white\",\n dark_label = \"white\",\n font_size = 5,\n round = TRUE) +\n scale_fill_viridis(option=\"turbo\", name=\"Number of trials\") +\n theme_statebins() +\n theme(\n legend.position = 'right',\n legend.text = element_text(size=9, color=\"black\"),\n legend.key.height = unit(1.2, \"cm\")\n )\n\nannotated_plot <- getAnnotatedPlot(\n plot,\n title=\"Number of NACSW Trials by State\",\n subtitle=\"State-wise count of all trials since 2009\")\nannotated_plot\n\nggsave(\"trials_across_space_states.png\", plot=annotated_plot, path=\".\", width=4194, height=3226, units=\"px\",\n bg=\"white\")\n\n", "meta": {"hexsha": "9648c063fede22a64dc5edaf415a9b14ced9306f", "size": 1166, "ext": "r", "lang": "R", "max_stars_repo_path": "trials_across_space/trials_across_space_states.r", "max_stars_repo_name": "saylibenadikar/snoot-scoop", "max_stars_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "trials_across_space/trials_across_space_states.r", "max_issues_repo_name": "saylibenadikar/snoot-scoop", "max_issues_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "trials_across_space/trials_across_space_states.r", "max_forks_repo_name": "saylibenadikar/snoot-scoop", "max_forks_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1162790698, "max_line_length": 108, "alphanum_fraction": 0.6912521441, "num_tokens": 319, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.34309548260233946}} {"text": "#' Create a new (empty) medley object\n#'\n#' @param x matrix of predictors\n#' @param y vector of response values\n#' @param label a unique label for this medley (used in status messages)\n#' @param errfunc an error metric for this medley\n#' @param base.model a function to use as a baseline model\n#' @param folds the default number of cross-validation folds to use\n#' @export\n#' @examples\n#' require(e1071);\n#' data(swiss);\n#' x <- swiss[,1:5];\n#' y <- swiss[,6];\n#' train <- sample(nrow(swiss), 30);\n#' m <- create.medley(x[train,], y[train]);\n#' for (gamma in c(1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 1e-1, 2e-1, 5e-1, 1)) {\n#' m <- add.medley(m, svm, list(gamma=gamma));\n#' }\n#' p <- predict(m, x[-train,]);\n#' rmse(p, y[-train]);\ncreate.medley <- function (x, y, label='', errfunc=rmse, base.model=NULL, folds=8) {\n if (!is.null(base.model)) {\n base.y <- predict(base.model, x);\n } else {\n base.y <- rep(0, length(y));\n }\n object <- list(\n x=as.matrix(x), \n y=y,\n base.y=base.y,\n mod.y=y - base.y, \n errfunc=errfunc, \n models=list(), \n args=list(), \n predict.args=list(),\n feature.subset=list(), \n fitted=list(), \n cv=list(),\n base.model=base.model,\n label=label,\n folds=folds,\n postprocess=list()\n );\n class(object) <- 'medley';\n return(object);\n}\n\n#' Recursively combine two or more medley objects for the same problem\n#'\n#' @param e1 the first medley to combine\n#' @param e2 the second medley to combine\n#' @param ... further medleys to combine\n#' @export\ncombine.medley <- function (e1, e2=NULL, ...) {\n if (is.null(e2)) return(e1);\n \n e1$models <- c(e1$models, e2$models);\n e1$args <- c(e1$args, e2$args);\n e1$predict.args <- c(e1$predict.args, e2$predict.args);\n e1$feature.subset <- c(e1$feature.subset, e2$feature.subset);\n e1$fitted <- c(e1$fitted, e2$fitted);\n e1$cv <- c(e1$cv, e2$cv);\n e1$postprocess <- c(e1$postprocess, e2$postprocess);\n return(combine.medley(e1, ...));\n}\n\n#' Add a new model to an existing medley\n#'\n#' @param object the medley to be added to\n#' @param model the model fitting function\n#' @param args a list of extra arguments to \\code{model}\n#' @param predict.args a list of extra arguments to the predict function for \\code{model}\n#' @param feature.subset a subset of the features to use\n#' @param folds the number of cross-validation folds to use\n#' @param postprocess an optional function to apply to the predicted values\n#' @export\nadd.medley <- function (object, model, args=list(), predict.args=list(), feature.subset=NULL, folds=object$folds, postprocess=c()) {\n if (is.null(feature.subset)) {\n feature.subset <- c(1:ncol(object$x));\n }\n n <- length(object$models) + 1;\n object$models[[n]] <- model;\n object$args[[n]] <- args;\n object$predict.args[[n]] <- predict.args;\n object$feature.subset[[n]] <- feature.subset;\n object$postprocess[[n]] <- function(x) x;\n\n pt <- proc.time()[[3]];\n # Fit model to all data\n data <- list(x=object$x[,feature.subset], y=object$mod.y);\n object$fitted[[n]] <- do.call(model, c(data, args));\n \n # Do cross-validation\n k <- length(object$y);\n cv.sets <- (seq_len(k) %% folds) + 1;\n \n pred <- numeric(k);\n for (i in 1:folds) {\n holdout <- which(cv.sets == i);\n data <- list(x=object$x[-holdout,feature.subset], y=object$mod.y[-holdout]);\n fitted <- do.call(model, c(data, args));\n preddata <- list(fitted, newdata=object$x[holdout,feature.subset])\n pred[holdout] <- do.call(predict, c(preddata, predict.args));\n }\n object$cv[[n]] <- pred + object$base.y;\n pt <- proc.time()[[3]] - pt;\n cat(object$label, 'CV model', n, class(object$fitted[[n]]), substring(deparse(args, control=NULL), 5), 'time:', pt, 'error:', object$errfunc(object$y, object$cv[[n]]), '\\n');\n \n nn <- n;\n for (pp in postprocess) {\n nn <- nn + 1;\n object$models[[nn]] <- model;\n object$args[[nn]] <- args;\n object$predict.args[[nn]] <- predict.args;\n object$feature.subset[[nn]] <- feature.subset;\n object$postprocess[[nn]] <- pp;\n object$fitted[[nn]] <- object$fitted[[n]];\n object$cv[[nn]] <- pp(pred) + object$base.y;\n cat(object$label, 'PP error:', object$errfunc(object$y, object$cv[[nn]]), '\\n');\n }\n \n return(object);\n}\n\n#' Prune the models in a medley\n#'\n#' @param object the medley to prune\n#' @param prune.factor the proportion of the models to keep\n#' @export\nprune.medley <- function (object, prune.factor=0.2) {\n n <- ceiling(length(object$models) * prune.factor);\n errs <- sapply(object$cv, function(pred) object$errfunc(object$y, pred));\n keep <- order(errs)[1:n];\n object$models <- object$models[keep];\n object$args <- object$args[keep];\n object$predict.args <- object$predict.args[keep];\n object$feature.subset <- object$feature.subset[keep];\n object$fitted <- object$fitted[keep];\n object$cv <- object$cv[keep];\n return(object);\n}\n\n#' Make a prediction based on a medley\n#'\n#' @param object the medley to predict from\n#' @param newdata a matrix of new predictor data\n#' @param seed indices of models which should be used for an initial ensemble\n#' @param min.members the minimum number of models in an ensemble (selected randomly)\n#' @param max.members the maximum number of models in an ensemble\n#' @param baggs the number of bagging iterations to perform\n#' @param mixer a function to combine model predictions\n#' @param ... other arguments (unused)\n#' @method predict medley\n#' @export\npredict.medley <- function (object, newdata, seed=c(), min.members=5, max.members=100, baggs=1, mixer=mean, ...) {\n newdata <- as.matrix(newdata);\n best.errs <- c();\n big.mix <- c();\n for (i in 1:baggs) {\n if (baggs == 1) {\n s <- 1:length(object$y);\n } else {\n s <- sample(length(object$y), replace=T);\n }\n model.sample <- sample(length(object$models), length(object$models) * .8);\n cat('Sampled...');\n if (length(seed) == 0) {\n errs <- sapply(object$cv[model.sample], function(pred) object$errfunc(object$y[s], pred[s]));\n mix <- model.sample[order(errs)[1:min.members]];\n \n } else {\n mix <- seed;\n }\n mixpred <- simplify2array(object$cv[mix]);\n \n best.err <- object$errfunc(object$y[s], apply(mixpred, 1, mixer)[s]);\n \n while(length(mix) < max.members) {\n errs <- sapply(object$cv[model.sample], function(pred) object$errfunc(object$y[s], apply(cbind(mixpred, pred), 1, mixer)[s]));\n best <- which.min(errs);\n if (best.err <= errs[best]) break;\n mix <- c(mix, model.sample[best]);\n best.err <- errs[best];\n mixpred <- simplify2array(object$cv[mix]);\n }\n predictions <- list();\n for (i in unique(sort(mix))) {\n preddata <- list(object$fitted[[i]], newdata=newdata[,object$feature.subset[[i]]]);\n predictions[[i]] <- object$postprocess[[i]](do.call(predict, c(preddata, object$predict.args[[i]])));\n frac <- mean(mix == i);\n cat(format(frac*100, digits=1, nsmall=2), '%: ', i, class(object$fitted[[i]]), substring(deparse(object$args[[i]], control=NULL), 5), '\\n');\n }\n best.err <- object$errfunc(object$y, apply(mixpred, 1, mixer));\n cat('CV error:', best.err, '\\n');\n if (length(unique(mix)) == 1) {pred <- predictions[[mix]]}\n else {pred <- apply(simplify2array(predictions[mix]), 1, mixer)}\n best.errs <- c(best.errs, best.err);\n big.mix <- cbind(big.mix, pred);\n }\n pred <- apply(big.mix, 1, mixer);\n if (!is.null(object$base.model)) {\n pred <- pred + predict(object$base.model, newdata);\n }\n attr(pred, 'cv.err') <- mean(best.errs);\n return(pred);\n}", "meta": {"hexsha": "e81e4df307cc19d9cc765736a7a6e170545311c1", "size": 7515, "ext": "r", "lang": "R", "max_stars_repo_path": "R/medley.r", "max_stars_repo_name": "valexandersaulys/medley", "max_stars_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2015-03-18T19:20:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T20:45:40.000Z", "max_issues_repo_path": "R/medley.r", "max_issues_repo_name": "valexandersaulys/medley", "max_issues_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/medley.r", "max_forks_repo_name": "valexandersaulys/medley", "max_forks_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2015-02-17T03:56:14.000Z", "max_forks_repo_forks_event_max_datetime": "2018-06-14T10:56:34.000Z", "avg_line_length": 36.3043478261, "max_line_length": 176, "alphanum_fraction": 0.6304723886, "num_tokens": 2244, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438502, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3430954748063675}} {"text": "# Functions for calculating a project's Bus Factor\n\nlibrary(plyr)\n\n# Get a vector of the files in a repo\nenumerate_files_in_repo <- function(repo_path) {\n system(paste(\"cd\", repo_path, \"; git ls-tree HEAD -r | awk -F '\\t' '{print $2}'\"), intern=T)\n}\n\n# Get a vector of author names - one for each line in a file\nget_author_for_lines_in_file <- function(repo_path, filename) {\n system(paste(\"cd\", repo_path, \"; git blame --line-porcelain\", filename, \"| grep '^author ' | sed -e 's/^author //'\"), intern=T)\n}\n\n# Count the lines by author for a vector of names\ncount_line_authors <- function(author) {\n as.data.frame(table(author), responseName=\"line_count\")\n}\n\n# Count the lines by author for a given file/ repo\ncount_of_lines_by_author_in_file <- function(repo_path, filename) {\n line_authors <- get_author_for_lines_in_file(repo_path, filename)\n file_blame <- count_line_authors(line_authors)\n file_blame$filename <- rep(filename,nrow(file_blame))\n file_blame\n}\n\n# Count the lines by author for a whole repo\ncount_of_lines_by_author_in_repo <- function(repo_path) {\n repo_tree <- enumerate_files_in_repo(repo_path)\n lines_by_file <- adply(repo_tree, 1, count_of_lines_by_author_in_file, repo_path=repo_path, .progress = \"text\")\n ddply(lines_by_file, .(author), summarise, line_count=sum(line_count))\n}\n\n# Calculate the cumulative line count of authors in order of contribution\ncalculate_author_contribution <- function(author_lines) {\n author_lines$author <- reorder(as.factor(author_lines$author), -author_lines$line_count)\n sorted_contributions <- author_lines[order(author_lines$line_count, decreasing=T),]\n sorted_contributions$cumulative_line_count <- cumsum(sorted_contributions$line_count)\n sorted_contributions$cumulative_author_count <- 1:nrow(sorted_contributions)\n sorted_contributions$cumulative_line_percent <- sorted_contributions$cumulative_line_count/max(sorted_contributions$cumulative_line_count)\n sorted_contributions$cumulative_author_percent <- sorted_contributions$cumulative_author_count/max(sorted_contributions$cumulative_author_count)\n return(sorted_contributions)\n}\n\n# Calculate the number of authors required to reach a given contribution\ncalculate_bus_factor <- function(author_contribution, critical_threshold=0.5) {\n critical_contributions <- author_contribution[author_contribution$cumulative_line_percent < critical_threshold, ]\n nrow(critical_contributions)\n}\n\n# Overall calculation of bus factor for a given repo\ncalculate_bus_factor_for_repo <- function(repo) {\n lba <- count_of_lines_by_author_in_repo(repo)\n ac <- calculate_author_contribution(lba)\n calculate_bus_factor(ac)\n}", "meta": {"hexsha": "017a2378ffe3b101a76c39255cdbb2d571240499", "size": 2636, "ext": "r", "lang": "R", "max_stars_repo_path": "code/bus-factor.r", "max_stars_repo_name": "Robsteranium/bus-factor", "max_stars_repo_head_hexsha": "78533db8c99ddfa2d69cec0136f295dafabcf9f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/bus-factor.r", "max_issues_repo_name": "Robsteranium/bus-factor", "max_issues_repo_head_hexsha": "78533db8c99ddfa2d69cec0136f295dafabcf9f0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/bus-factor.r", "max_forks_repo_name": "Robsteranium/bus-factor", "max_forks_repo_head_hexsha": "78533db8c99ddfa2d69cec0136f295dafabcf9f0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.2456140351, "max_line_length": 146, "alphanum_fraction": 0.7951441578, "num_tokens": 617, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.34309547480636743}} {"text": "## Copyright 2016 Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone\n##\n## Licensed under the Apache License, Version 2.0 (the \"License\");\n## you may not use this file except in compliance with the License.\n## You may obtain a copy of the License at\n##\n## http://www.apache.org/licenses/LICENSE-2.0\n##\n## Unless required by applicable law or agreed to in writing, software\n## distributed under the License is distributed on an \"AS IS\" BASIS,\n## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n## See the License for the specific language governing permissions and\n## limitations under the License.\n\n## Code to produce the plots in the paper - comparing MCMC samples with samples from the variational posterior in a two-layer DGP model with a Gaussian likelihood \n\nh = function(x) { exp(-x^2) * 2 * x }\n\n## Set page size\nps.options(width=20, height=7.5, paper=\"special\", horizontal=F, pointsize=36)\npdf.options(width=20, height=7.5, pointsize=36)\n\n## Load data\nX = as.matrix(read.table(\"X.txt\"))\nXtest = as.matrix(read.table(\"Xtest.txt\"))\nY = as.matrix(read.table(\"Y.txt\"))\n\n## Load predictions layer 1\npredictions_MCMC = as.matrix(read.table(\"predictions_MCMC_F1.txt\"))\npredictions_var_NRFF_10 = as.matrix(read.table(\"predictions_variational_F1_NRFF_10.txt\"))\npredictions_var_NRFF_50 = as.matrix(read.table(\"predictions_variational_F1_NRFF_50.txt\"))\n\nnsamples = dim(predictions_MCMC)[2]\n\ncolor_MCMC = rgb(1,0,0,alpha=0.02) \ncolor_var = rgb(0,0,1,alpha=0.02) \n\n## Open figure\npdf(\"figure_compare_MCMC_var.pdf\")\npar(mfrow=c(2,3),\n oma = c(1.1,2.1,1.5,0.2),\nmar=c(0.3,0.5,0.0,0.0), mgp=c(1.1,0.1,0)\n ## mar = c(0,0,0,0) + 0.1\n )\n\n## Produce plots layer 1\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_var_NRFF_10[,1], type=\"l\", lwd=10, col=color_var, xlab=\"\", ylab=\"Layer 1\", ylim = c(-3,3), yaxt=\"n\", xaxt=\"n\") # , main=\"Variational - 10 RFF\")\naxis(2, at=c(-2, 0, 2), tck=0.02)\nfor(i in 2:nsamples) {\n points(Xtest, predictions_var_NRFF_10[,i], type=\"l\", lwd=10, col=color_var)\n}\npoints(Xtest, h(Xtest), type=\"l\")\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_var_NRFF_50[,1], type=\"l\", lwd=10, col=color_var, xlab=\"\", ylab=\"\", ylim = c(-3,3), yaxt=\"n\", xaxt=\"n\") # , main=\"Variational - 50 RFF\")\nfor(i in 2:nsamples) {\n points(Xtest, predictions_var_NRFF_50[,i], type=\"l\", lwd=10, col=color_var)\n}\npoints(Xtest, h(Xtest), type=\"l\")\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_MCMC[,1], type=\"l\", lwd=10, col=color_MCMC, xlab=\"\", ylab=\"\", ylim = c(-3,3), yaxt=\"n\", xaxt=\"n\") #, main=\"MCMC\")\nfor(i in 2:nsamples) {\n points(Xtest, predictions_MCMC[,i], type=\"l\", lwd=10, col=color_MCMC)\n}\npoints(Xtest, h(Xtest), type=\"l\")\n\n## ******************************\n\n## Load predictions layer 2\npredictions_MCMC = as.matrix(read.table(\"predictions_MCMC_F2.txt\"))\npredictions_var_NRFF_10 = as.matrix(read.table(\"predictions_variational_F2_NRFF_10.txt\"))\npredictions_var_NRFF_50 = as.matrix(read.table(\"predictions_variational_F2_NRFF_50.txt\"))\n\n## Produce plots layer 2\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_var_NRFF_10[,1], type=\"l\", lwd=10, col=color_var, xlab=\"\", ylab=\"Layer 2\", ylim = c(-1,1), yaxt=\"n\", tck=0.02) ##, main=\"Layer 2 - Variational - 10 RFF\", ylim = c(-1,1))\naxis(2, at=c(-1, 0, 1), tck=0.02)\nfor(i in 2:nsamples) {\n points(Xtest, predictions_var_NRFF_10[,i], type=\"l\", lwd=10, col=color_var)\n}\npoints(X, Y, pch=20)\npoints(Xtest, h(h(Xtest)), type=\"l\")\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_var_NRFF_50[,1], type=\"l\", lwd=10, col=color_var, xlab=\"\", ylab=\"\", ylim = c(-1,1), yaxt=\"n\", tck=0.02) ##, main=\"Layer 2 - Variational - 50 RFF\", ylim = c(-1,1))\nfor(i in 2:nsamples) {\n points(Xtest, predictions_var_NRFF_50[,i], type=\"l\", lwd=10, col=color_var)\n}\npoints(X, Y, pch=20)\npoints(Xtest, h(h(Xtest)), type=\"l\")\n\n## par(\"mar\"=c(1.5,1.5,0.3,0.3), \"mgp\"=c(1.8,0.6,0))\nplot(Xtest, predictions_MCMC[,1], type=\"l\", lwd=10, col=color_MCMC, xlab=\"\", ylab=\"\", ylim = c(-1,1), yaxt=\"n\", tck=0.02) ##, main=\"Layer 2 - MCMC\", ylim = c(-1,1))\nfor(i in 2:nsamples) {\n points(Xtest, predictions_MCMC[,i], type=\"l\", lwd=10, col=color_MCMC)\n}\npoints(X, Y, pch=20)\npoints(Xtest, h(h(Xtest)), type=\"l\")\n\n## ******************************\n\n## Add legend text / titles\nmtext('Variational - 10 RFF', side = 3, outer = TRUE, line = 0.3, at=0.18)\nmtext('Variational - 50 RFF', side = 3, outer = TRUE, line = 0.3, at=0.51)\nmtext('MCMC', side = 3, outer = TRUE, line = 0.3, at=0.85)\nmtext('Layer 1', side = 2, outer = TRUE, line = 0.8, at=0.8)\nmtext('Layer 2', side = 2, outer = TRUE, line = 0.8, at=0.25)\n\n\ndev.off()\n", "meta": {"hexsha": "e8b9cf7b1f1f3267ee696fc07d2582ac0b64fc89", "size": 4748, "ext": "r", "lang": "R", "max_stars_repo_path": "code/mcmc/plot.r", "max_stars_repo_name": "mauriziofilippone/deep_gp_random_features", "max_stars_repo_head_hexsha": "766da8a92eb9e19fe8962c572801e5fe1f8b505a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2017-01-10T00:23:43.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-29T16:58:59.000Z", "max_issues_repo_path": "code/mcmc/plot.r", "max_issues_repo_name": "mauriziofilippone/deep_gp_random_features", "max_issues_repo_head_hexsha": "766da8a92eb9e19fe8962c572801e5fe1f8b505a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-01-02T21:46:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-25T22:55:20.000Z", "max_forks_repo_path": "code/mcmc/plot.r", "max_forks_repo_name": "mauriziofilippone/deep_gp_random_features", "max_forks_repo_head_hexsha": "766da8a92eb9e19fe8962c572801e5fe1f8b505a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2017-01-10T00:14:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-02T20:30:09.000Z", "avg_line_length": 41.2869565217, "max_line_length": 194, "alphanum_fraction": 0.652064027, "num_tokens": 1804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.34309547480636743}} {"text": "\n#command line a args for RScript\nargs <- commandArgs(trailingOnly = TRUE)\n\n#read input + chip pairs of data for 2 samples\nrmi <- read.table(args[1], header=T) #sample1 input\nrmc <- read.table(args[2], header=T) #sample1 chip\n\nrwi <- read.table(args[3], header=T) #sample2 input\nrwc <- read.table(args[4], header=T) #sample2 chip\n\ncols <- c(\"CEN1\",\"CEN2\",\"CEN3\",\"CEN4\",\"CEN5\",\"CEN6\",\"CEN7\",\"CEN8\",\"CEN9\",\"CEN10\",\"CEN11\",\"CEN12\",\"CEN13\",\"CEN14\",\"CEN15\",\"CEN16\")\n\n#create a plot for each CEN\nfor(i in cols){\n #each plot names after cen\n #create a svg file for each CEN\n svg(paste(args[1],i,\".svg\", sep=\"\"))\n \n par(cex.lab=1.4)\n # init plot \n plot(rwc$Position, log2(rwc[[i]]/rwi[[i]] ), \n xlim=c(-10000,10000), \n ylim=c(-2,5), \n pch='.', xlab=\"Position\", ylab=\"Sample/Input\")\n# add lines for each\n lines(rwc$Position, log2( rwc[[i]] / rwi[[i]] ), col=\"red\", lwd=3)\n lines(rwc$Position, log2( rmc[[i]] / rmi[[i]] ), col=\"blue\", lwd=3)\n\n\n axis(1, lwd=2)\n axis(2, lwd=2)\n} \n \n", "meta": {"hexsha": "894463816143301f4875fc0098c84d9dbb89ab4a", "size": 1018, "ext": "r", "lang": "R", "max_stars_repo_path": "plot-every-cen.r", "max_stars_repo_name": "AlastairKerr/Vincenten2015", "max_stars_repo_head_hexsha": "5dac2209695728f389256744fab059cdf6f0fb4e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot-every-cen.r", "max_issues_repo_name": "AlastairKerr/Vincenten2015", "max_issues_repo_head_hexsha": "5dac2209695728f389256744fab059cdf6f0fb4e", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot-every-cen.r", "max_forks_repo_name": "AlastairKerr/Vincenten2015", "max_forks_repo_head_hexsha": "5dac2209695728f389256744fab059cdf6f0fb4e", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0857142857, "max_line_length": 129, "alphanum_fraction": 0.6031434185, "num_tokens": 386, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6406358685621719, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3428032465894626}} {"text": "#setwd(\"D:/Dropbox/Data analyze/Rdocuments/datas\")\nlibrary(xlsx)\n library(RColorBrewer)\n\n\n\n fv<-5:3500 #\u7535\u538b\u9891\u7387\n\nq<-c(2.5e-14, 4.5e-13, 9e-13, 3e-12) #\u4e0d\u540c\u7684\u6d41\u91cf\u4e0b\n\nk<-c(0.5,0.6,0.7,0.8) #\u4e0d\u540c\u5360\u7a7a\u6bd4\u4e0b\u65e0\u7535\u573a\u65f6\u95f4\u6bd4\u4f8b\uff0c\u53731-k_i\n\nv<-4.66e-13 #\u6cf0\u52d2\u9525\u7684\u4f53\u79ef\u603b\u91cf\n\nmycolors<-c(\"red\", \"blue\", \"darkgreen\", \"yellow3\")\n\npchall<-c(21,22,23,24)\n\n######\npar(fig=c(0,1,0,1), mar = c(3,3,1,1), oma = c(1,1,1,1),new=FALSE)\nplot(fv, (k[1]*q[1]/fv+v), mgp = c(1, 0, 0),tck=0.01,col=mycolors[1], log=\"x\", type=\"b\", xlab = expression(italic(f[\"v\"])(Hz)),\n ylab = expression(italic(V[\"ne\"]+V[\"m\"](m^3))), main=\"\", lwd=2, pch=pchall[1], lty=2,cex.lab=1,cex.axis=1, ylim=c(4.4e-13, 8e-13))\n\n#\u753b\u51fa\u5360\u7a7a\u6bd4\u4e3a0.5\uff0c\u6d41\u91cf\u4e3a1.5nl/min\u65f6\u7684\u5f2f\u6708\u9762\u4f53\u79ef\uff0c\u8bf4\u660e\u6700\u5c0f\u503c\u662f\u4ec0\u4e48\n\n###\u6d41\u91cf\u4e3a1.5nl/min\u65f6####\nfor (i in 1:3){\nlines(fv, (k[i+1]*q[1]/fv+v), lwd=1.5, type=\"b\",col=\"red\", pch=pchall[i+1],lty=2,cex=0.6)\n}\n#\u753b\u51fa\uff0c\u6d41\u91cf\u4e3a\u6700\u5c0f\uff0c\u5360\u7a7a\u6bd4\u4ece\u5927\u5230\u5c0f\uff0c\u4f53\u79ef\u4ece\u5c0f\u5230\u5927\u53d8\u5316\u7684\u66f2\u7ebf\uff0c\u5176\u4e2d\uff1a\u989c\u8272\u5747\u4e3ared\uff0cpch\u7684\u6539\u53d8\u4ee3\u8868\u7740\u5360\u7a7a\u6bd4\u7684\u6539\u53d8\n\n###\u6d41\u91cf\u4e3a27nl/min\u65f6####\npar(fig=c(0,1,0,1))\nfor (i in 1:4){\nlines(fv, (k[i]*q[2]/fv+v), lwd=1.5, type=\"b\", col=\"blue\", pch=pchall[i] ,lty=2,cex=0.6)\n}\n\n###\u6d41\u91cf\u4e3a54nl/min\u65f6####\nfor (i in 1:4){\nlines(fv, (k[i]*q[3]/fv+v), lwd=1.5, type=\"b\", col=\"darkgreen\", pch=pchall[i] ,lty=2,cex=0.6)\n}\n\n###\u6d41\u91cf\u4e3a180nl/min\u65f6####\nfor (i in 1:4){\nlines(fv, (k[i]*q[4]/fv+v), lwd=1.5, type=\"b\", col=\"yellow3\", pch=pchall[i] ,lty=2,cex=0.6)\n}\n\nda<-c(\"kv0.5-Q1.5nlmin\", \"kv0.4-Q1.5nlmin\", \"kv0.3-Q1.5nlmin\", \"kv0.2-Q1.5nlmin\", \"kv0.5-Q27nlmin\", \"kv0.4-Q27nlmin\", \"kv0.3-Q27nlmin\", \"kv0.2-Q27nlmin\",\"kv0.5-54nlmin\", \"kv0.4-Q54nlmin\", \"kv0.3-Q54nlmin\", \"kv0.2-Q54nlmin\",\"kv0.5-Q180nlmin\", \"kv0.4-Q180nlmin\", \"kv0.3-Q180nlmin\", \"kv0.2-Q180nlmin\")\n\nmycolorsss<-c(\"red\",\"red\",\"red\",\"red\",\"blue\",\"blue\",\"blue\",\"blue\",\"darkgreen\",\"darkgreen\",\"darkgreen\",\"darkgreen\",\"yellow3\",\"yellow3\",\"yellow3\",\"yellow3\")\n\npchss<-c(21,22,23,24,21,22,23,24,21,22,23,24,21,22,23,24)\n\nlegend(\"topright\", da, inset=0, col=mycolorsss, pch=pchss,lwd=1.5, lty=2, cex=0.8, bty=\"n\")\n\nabline(v=20, col=\"red\", lwd=2,lty=3)\nabline(v=45, col=\"blue\", lwd=2,lty=3)\n#abline(v=125, col=\"darkgreen\", lwd=2,lty=3)\n\n#################################################\npar(fig=c(0.4, 0.98,0.20,0.98), new=TRUE)\n\nplot(fv, (k[1]*q[1]/fv+v),bty=\"n\", col=mycolors[1], mgp = c(1, 0, 0),tck=0.01,log=\"x\", type=\"l\", xlab = \"125Hz ~ 1KHz\", ylab =\"\", lwd=2, lty=2, xlim=c(125,1000), ylim=c(4.66e-13, 5e-13))\n\n\n#\u753b\u51fa\u5360\u7a7a\u6bd4\u4e3a0.5\uff0c\u6d41\u91cf\u4e3a1.5nl/min\u65f6\u7684\u5f2f\u6708\u9762\u4f53\u79ef\uff0c\u8bf4\u660e\u6700\u5c0f\u503c\u662f\u4ec0\u4e48\n\n###\u6d41\u91cf\u4e3a1.5nl/min\u65f6####\nfor (i in 1:3){\nlines(fv, (k[i+1]*q[1]/fv+v), lwd=2, col=\"red\", lty=1)\n}\n#\u753b\u51fa\uff0c\u6d41\u91cf\u4e3a\u6700\u5c0f\uff0c\u5360\u7a7a\u6bd4\u4ece\u5927\u5230\u5c0f\uff0c\u4f53\u79ef\u4ece\u5c0f\u5230\u5927\u53d8\u5316\u7684\u66f2\u7ebf\uff0c\u5176\u4e2d\uff1a\u989c\u8272\u5747\u4e3ared\uff0cpch\u7684\u6539\u53d8\u4ee3\u8868\u7740\u5360\u7a7a\u6bd4\u7684\u6539\u53d8\n\n###\u6d41\u91cf\u4e3a27nl/min\u65f6####\nfor (i in 1:4){\nlines(fv, (k[i]*q[2]/fv+v), lwd=2, col=\"blue\", lty=1)\n}\n\n###\u6d41\u91cf\u4e3a54nl/min\u65f6####\nfor (i in 1:4){\nlines(fv, (k[i]*q[3]/fv+v), lwd=2,col=\"darkgreen\", lty=1)\n}\n\n###\u6d41\u91cf\u4e3a180nl/min\u65f6####\nfor (i in 1:4){\nlines(fv, (k[i]*q[4]/fv+v), lwd=2,col=\"yellow3\", lty=1)\n}\n\nabline(h=5.126e-13, col=\"red\", lwd=1.5, lty=4)\nabline(h=5.592e-13, col=\"blue\", lwd=1.5, lty=4)\n", "meta": {"hexsha": "3c181cee0aa624bb53e7f10400e41d34176c827e", "size": 2873, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap4/fig4-2.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap4/fig4-2.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap4/fig4-2.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.5714285714, "max_line_length": 298, "alphanum_fraction": 0.5938043857, "num_tokens": 1573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.3428032392466955}} {"text": "#install data from github repo /RamiKrispin/coronavirus\ninstall.packages(\"devtools\")\ndevtools::install_github(\"RamiKrispin/coronavirus\")\n#install coronavirus library data bulk package\nlibrary(coronavirus)\nupdate_dataset()\n\n#data\ncovid19_df <- refresh_coronavirus_jhu()\nhead(covid19_df)\n\n#data.frame\ncovid19_df<-data.frame(covid19_df)\n\n#plotting packages\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(GGally)\n\n#plots\nadvanced_countries<-unique(covid19_df[,\"contries\"])\ncovid19_advanced_co_df<-covid19_df%>%\nfilter(country=advanced_countries))\nplot<-ggplot(data=covid19_df, aes(date,cases))\n", "meta": {"hexsha": "d036fb9b84401386bcd8eadbc8c0424d432f6804", "size": 583, "ext": "r", "lang": "R", "max_stars_repo_path": "coronavirus 2019.r", "max_stars_repo_name": "olgaklischuk/conda", "max_stars_repo_head_hexsha": "4fdb04519e435f5ab74a7efca8ae1619feb7d892", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "coronavirus 2019.r", "max_issues_repo_name": "olgaklischuk/conda", "max_issues_repo_head_hexsha": "4fdb04519e435f5ab74a7efca8ae1619feb7d892", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "coronavirus 2019.r", "max_forks_repo_name": "olgaklischuk/conda", "max_forks_repo_head_hexsha": "4fdb04519e435f5ab74a7efca8ae1619feb7d892", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.32, "max_line_length": 55, "alphanum_fraction": 0.8164665523, "num_tokens": 164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3428032392466954}} {"text": "#' Function that transforms an alignment result in matrix form into an alignment result in data frame form\n#'\n#'@param alignM An alingment matrix.\n#'@param distM An distance matrix of same dimensions as alignM.\n#'@param text1_indel_dist What distance should be assigned if an particular line of this text has been deleted respectively inserted?\n#'@param text2_indel_dist See above.\n\nalign_matrix_to_align_df <- function(alignM,\n distM=NULL,\n text1_indel_dist=NA,\n text2_indel_dist=NA){\n # preparation and option handling\n if(is.null(distM)){\n distM <- matrix( rep(NA, length(alignM)), dim(alignM)[1], dim(alignM)[2])\n }\n unused_rows <- seq_len(dim(alignM)[1])[!apply(alignM,1,sum)>0]\n unused_cols <- seq_len(dim(alignM)[2])[!apply(alignM,2,sum)>0]\n\n if ( length(unused_rows)>0 ) {\n rows_indel_dist <- text1_indel_dist[unused_rows]\n }\n if ( length(unused_cols)>0 ) {\n cols_indel_dist <- text2_indel_dist[unused_cols]\n }\n\n # matrix to data frame (matches)\n df <- cbind(which(alignM==TRUE, arr.ind=T), dist=distM[alignM==TRUE])\n\n # add insertions and deletions from text1 and text2\n if ( length(unused_rows)>0 ){\n df <- rbind(df, cbind(unused_rows, NA, rows_indel_dist))\n }\n if ( length(unused_cols)>0 ){\n df <- rbind(df, cbind(NA, unused_cols, cols_indel_dist))\n }\n df <- as.data.frame(df)\n names(df) <- c(\"lnr1\",\"lnr2\",\"distance\")\n\n # compute type (insertion, deletion, change, no change, ignore)\n align_type <- function(alignRow){\n lnr1 <- alignRow[1]\n lnr2 <- alignRow[2]\n distance <- alignRow[3]\n if( !any(is.na(alignRow)) ){\n if( distance==0 ) return(\"equal\")\n if( distance> 0 ) return(\"mod\")\n }\n if( all(is.na(alignRow)) ){\n return(\"empty!\")\n }\n if( is.na(lnr1) & is.na(distance) ){\n return(\"ignore\")\n }\n if( is.na(lnr2) & is.na(distance) ){\n return(\"ignore\")\n }\n if( is.na(lnr1) ){\n return(\"ins\")\n }\n if( is.na(lnr2) ){\n return(\"del\")\n }\n }\n df$type = apply(df, 1, align_type)\n\n # return results\n return(df)\n}\n\n\n\n\n\n", "meta": {"hexsha": "902ba97e6ec6260fc6e8fb6aac4ef03ae54c118b", "size": 2248, "ext": "r", "lang": "R", "max_stars_repo_path": "R/align_matrix_to_align_df.r", "max_stars_repo_name": "wagbr/diffrr", "max_stars_repo_head_hexsha": "c9d767cac40d953b4737a3a8661ffd4e6683ffb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_stars_repo_stars_event_min_datetime": "2015-01-16T14:16:00.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-17T15:00:59.000Z", "max_issues_repo_path": "R/align_matrix_to_align_df.r", "max_issues_repo_name": "wagbr/diffrr", "max_issues_repo_head_hexsha": "c9d767cac40d953b4737a3a8661ffd4e6683ffb8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2015-12-09T18:12:39.000Z", "max_issues_repo_issues_event_max_datetime": "2016-07-13T13:25:28.000Z", "max_forks_repo_path": "R/align_matrix_to_align_df.r", "max_forks_repo_name": "wagbr/diffrr", "max_forks_repo_head_hexsha": "c9d767cac40d953b4737a3a8661ffd4e6683ffb8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2015-08-01T08:47:18.000Z", "max_forks_repo_forks_event_max_datetime": "2015-08-01T08:47:18.000Z", "avg_line_length": 30.3783783784, "max_line_length": 133, "alphanum_fraction": 0.587633452, "num_tokens": 629, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3428032392466954}} {"text": "library(readxl)\n\naulas <- read_excel(\"X:/Development/github.com/r_study/Alura/Analise de dados - Introducao com R/data/aulas.xlsx\")\n# View(aulas)\n\nattach(aulas)\noptions(max.print=400000)\nprint(sort(section_id))\n\naulas[33137,3] <- 3255\n\nprint(sort(aulas$section_id))\n\nunique(aulas$section_id)\n\nlength(unique(aulas$section_id))\n\ntable(aulas$section_id)\n\nsort(table(aulas$section_id))\n\nlibrary(plyr)\n\ndf_edit <- count(aulas, vars = \"course_id\" )\n\nwrite.csv(df_edit,\"X:/Development/github.com/r_study/Alura/Analise de dados - Introducao com R/data/popularidade.csv\", row.names=FALSE)\n\n\n", "meta": {"hexsha": "29ea310d8c11585efc396f87c7034296cd5f3eb6", "size": 582, "ext": "r", "lang": "R", "max_stars_repo_path": "Alura/Analise de dados - Introducao com R/analise_01.r", "max_stars_repo_name": "BrunoASNascimento/r_study", "max_stars_repo_head_hexsha": "537ad4e4518b22b19e60bf6b2ff6f888c9b4be3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Alura/Analise de dados - Introducao com R/analise_01.r", "max_issues_repo_name": "BrunoASNascimento/r_study", "max_issues_repo_head_hexsha": "537ad4e4518b22b19e60bf6b2ff6f888c9b4be3f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Alura/Analise de dados - Introducao com R/analise_01.r", "max_forks_repo_name": "BrunoASNascimento/r_study", "max_forks_repo_head_hexsha": "537ad4e4518b22b19e60bf6b2ff6f888c9b4be3f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.0689655172, "max_line_length": 135, "alphanum_fraction": 0.7594501718, "num_tokens": 165, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.6406358411176238, "lm_q1q2_score": 0.34280322240475003}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of betabinomial prior wonky world model predictions\"\n#' author: \"Judith Degen\"\n#' date: \"January 19, 2014\"\n#' ---\n\nlibrary(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/models/wonky_world/results/\")\nsource(\"rscripts/helpers.r\")\n\n#' get model predictions\nload(\"data/mp-betabinomial.RData\")\nd = read.table(\"data/parsed_betabinomial_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\ntable(d$Item)\nnrow(d)\nhead(d)\nd[d$Item == \"ate the seeds birds\" & d$QUD==\"how-many\" & d$Alternatives==\"0_basic\" & d$SpeakerOptimality == 1,]\nd[d$Item == \"stuck to the wall baseballs\" & d$QUD==\"how-many\" & d$Alternatives==\"0_basic\" & d$SpeakerOptimality == 2 & d$Wonky == \"true\",]\nmp = ddply(d, .(Item, QUD, State, Alternatives, Quantifier, SpeakerOptimality, WonkyWorldPrior), summarise, PosteriorProbability=sum(PosteriorProbability))\nhead(mp)\n#mp[mp$Item == \"ate the seeds birds\" & mp$QUD==\"how-many\" & mp$Alternatives==\"0_basic\" & mp$SpeakerOptimality == 1,]\nwr = ddply(d, .(Item, QUD, Wonky, Alternatives, Quantifier, SpeakerOptimality, WonkyWorldPrior), summarise, PosteriorProbability=sum(PosteriorProbability))\nwr[wr$Item == \"ate the seeds birds\" & wr$QUD==\"how-many\" & wr$Alternatives==\"0_basic\" & wr$SpeakerOptimality == 1,]\n\n\n# get prior expectations\npriorexpectations = read.table(file=\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorexpectations) = paste(priorexpectations$effect,priorexpectations$object)\nmp$PriorExpectation = priorexpectations[as.character(mp$Item),]$expectation\nwr$PriorExpectation = priorexpectations[as.character(wr$Item),]$expectation\n\n# get smoothed prior probabilities\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nhead(priorprobs)\nrow.names(priorprobs) = paste(priorprobs$effect,priorprobs$object)\nmpriorprobs = melt(priorprobs, id.vars=c(\"effect\", \"object\"))\nhead(mpriorprobs)\nrow.names(mpriorprobs) = paste(mpriorprobs$effect,mpriorprobs$object,mpriorprobs$variable)\nmp$PriorProbability = mpriorprobs[paste(as.character(mp$Item),\" X\",mp$State,sep=\"\"),]$value\nmp$AllPriorProbability = priorprobs[paste(as.character(mp$Item)),]$X15\nhead(mp)\n\n# get empirical state posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/3_sinking-marbles-nullutterance/results/data/r.RData\")\nhead(r)\nr$Item = as.factor(paste(r$effect,r$object))\n# because posteriors come in 4 bins, make Bin variable for model prediction dataset:\nmp$Proportion = as.factor(ifelse(mp$State == 0, \"0\", ifelse(mp$State == 15, \"100\", ifelse(mp$State < 8, \"1-50\", \"51-99\"))))\n\nagr = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=mean)\n#agr$CILow = aggregate(normresponse ~ Item + quantifier + Proportion,data=r, FUN=ci.low)$normresponse\n#agr$CIHigh = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=ci.high)$normresponse\n#agr$YMin = agr$normresponse - agr$CILow\n#agr$YMax = agr$normresponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Proportion, agr$Quantifier)\nmp$PosteriorProbability_empirical = agr[paste(mp$Item,mp$Proportion,mp$Quantifier),]$normresponse\n\n#plot empirical against predicted distributions for \"some\"\nsome = ddply(mp, .(Item, QUD, Alternatives, Quantifier, SpeakerOptimality, PriorExpectation, Proportion, WonkyWorldPrior, PosteriorProbability_empirical), summarise, PosteriorProbability_predicted=sum(PosteriorProbability), PriorProbability_smoothed=sum(PriorProbability))\nsome= subset(some, Quantifier == \"some\")\nnrow(some)\nhead(some)\nmsome = melt(some, measure.vars=c(\"PosteriorProbability_empirical\",\"PosteriorProbability_predicted\",\"PriorProbability_smoothed\"))\nmsome$ptype = as.factor(ifelse(msome$variable == \"PosteriorProbability_empirical\", \"posterior (empirical)\",ifelse(msome$variable == \"PosteriorProbability_predicted\",\"posterior (model)\", \"prior\")))\nhead(msome)\nnrow(msome)\nsummary(msome)\n\ntoplot = droplevels(subset(msome, QUD == \"how-many\" & SpeakerOptimality == 2 & Alternatives == \"0_basic\"))#\"0_basic1_lownum2_extra4_twowords5_threewords\"))\nnrow(toplot)\ntoplot$Probability = as.factor(ifelse(toplot$ptype == \"prior\",\"prior\",\"posterior\"))\ntoplot$Prop = factor(toplot$Proportion, levels=c(\"1-50\",\"51-99\",\"100\"))\nggplot(toplot, aes(x=Prop, y=value,color=ptype, group=ptype, size=Probability)) +\n geom_point() +\n geom_line() +\n scale_size_discrete(range=c(1,2)) +\n scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_wrap(WonkyWorldPrior~Item)\nggsave(\"graphs/model-empirical-betabinomial-howmany-2-basic.pdf\",width=35,height=30)\n\n#plot empirical against predicted expectations for \"some\"\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nsummary(r)\nr$Item = as.factor(paste(r$effect, r$object))\nagr = aggregate(ProportionResponse ~ Item + quantifier, data=r, FUN=mean)\n#agr$CILow = aggregate(ProportionResponse ~ Item + quantifier,data=r, FUN=ci.low)$ProportionResponse\n#agr$CIHigh = aggregate(ProportionResponse ~ Item + quantifier,data=r,FUN=ci.high)$ProportionResponse\n#agr$YMin = agr$ProportionResponse - agr$CILow\n#agr$YMax = agr$ProportionResponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Quantifier)\nmp$PosteriorExpectation_empirical = agr[paste(mp$Item,mp$Quantifier),]$ProportionResponse\nmp$PriorExpectation_smoothed = mp$PriorExpectation/15\n\npexpectations = ddply(mp, .(Item, QUD, Alternatives, Quantifier, SpeakerOptimality,PriorExpectation_smoothed, WonkyWorldPrior, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability)/15)\nhead(pexpectations)\nsome = droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ncors = ddply(some, .(Alternatives, QUD, SpeakerOptimality, WonkyWorldPrior), summarise, r=cor(PosteriorExpectation_predicted, PosteriorExpectation_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n\nggplot(some, aes(x=PosteriorExpectation_predicted, y=PosteriorExpectation_empirical,color=as.factor(SpeakerOptimality), shape=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-betabinomial-expectations.pdf\",width=30,height=10)\n\n#plot empirical against predicted allstate-prbabilities for \"some\"\nallstate = droplevels(subset(mp, State == 15 & Quantifier == \"some\"))\ncors = ddply(allstate, .(Alternatives, QUD, SpeakerOptimality, WonkyWorldPrior), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n# .59 correlation despite being shitty model\n\nggplot(allstate, aes(x=PosteriorProbability, y=PosteriorProbability_empirical,color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-betabinomial-allstateprobs.pdf\",width=30,height=10)\n\n#maybe COGSCI plot basis? plot predicted allstate-prbabilities for \"some\" as a function of prior allstate-probabilities\n\nggplot(allstate, aes(x=PriorProbability, y=PosteriorProbability,color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-betabinomial-allstateprobs.pdf\",width=30,height=10)\n\n\n# get empirical wonkiness posteriors\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/11_sinking-marbles-normal/results/data/r.RData\")\nhead(r)\nnrow(r)\nr$Item = as.factor(paste(r$effect,r$object))\n\nt = as.data.frame(prop.table(table(r$Item, r$quantifier, r$response), mar=c(1,2)))\nhead(t)\ncolnames(t) = c(\"Item\",\"Quantifier\",\"NormalMarbles\",\"Proportion\")\nt[t$Var1==\"ate the seeds birds\",]\nt$Quantifier = tolower(t$Quantifier)\ntail(t)\nt$Wonky = as.factor(ifelse(t$NormalMarbles == \"yes\",\"false\",\"true\"))\nrow.names(t) = paste(t$Item, t$Quantifier, t$Wonky)\n\nwr$PosteriorProbability_empirical = t[paste(wr$Item, wr$Quantifier, wr$Wonky),]$Proportion\nhead(wr)\nwonky = droplevels(subset(wr, Wonky == \"true\"))\n\ncors = ddply(wonky, .(Alternatives, QUD, SpeakerOptimality, Quantifier, WonkyWorldPrior), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors,15)\n\nggplot(wonky, aes(x=PosteriorProbability, y=PosteriorProbability_empirical, color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(Quantifier~Alternatives)\nggsave(\"graphs/model-empirical-betabinomial-wonkiness.pdf\", width=30,height=10)\n\n\n\nggplot(wonky, aes(x=PriorExpectation, y=PosteriorProbability, color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(Quantifier~Alternatives) \nggsave(\"graphs/model-betabinomial-wonkiness.pdf\", width=30,height=10)\n\nsave(mp, file=\"data/mp-betabinomial.RData\")\nsave(wr, file=\"data/wr-betabinomial.RData\")\n\n\n\n\n\n\n################\n\nexpectations = ddply(mp, .(Item, QUD, Alternatives, Quantifier, SpeakerOptimality, PriorExpectation), summarise, Expectation=sum(PosteriorProbability*State))\nhead(expectations)\ntail(expectations)\nexpectations$ExpectationProportion = expectations$expectation/15\nexpectations$QUDOpt = as.factor(paste(expectations$QUD,expectations$SpeakerOptimality))\n\nggplot(expectations, aes(x=NormAllPrior,y=ExpectationProportion,color=as.factor(SpeakerOptimality),shape=QUD,group=QUDOpt)) +\n geom_point() +\n geom_smooth() +\n facet_grid(SmoothingBW~Alternatives)\nggsave(\"graphs/marble_expectations.pdf\",width=25,height=7)\n\nggplot(expectations, aes(x=ExpectationProportion,fill=QUD)) +\n geom_histogram(position=\"dodge\") +\n facet_grid(SpeakerOptimality~Alternatives,scales=\"free_y\")\nggsave(\"graphs/expectation_histograms.pdf\",width=12)\n\n \nsubexp = subset(expectations, SpeakerOptimality == 2 & QUD == \"how-many\" & SmoothingBW == \"bwdf\")\nggplot(subexp, aes(x=NormAllPrior,y=ExpectationProportion)) +\n geom_point() +\n geom_smooth() +\n facet_wrap(~Alternatives)\nggsave(\"graphs/expectations_spopt2_qudhowmany_bwdf.pdf\",width=12,height=7)\n\nggplot(subset(subexp,Alternatives==\"0_basic\"), aes(x=PriorExpectation,y=ExpectationProportion)) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n# geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Prior expectation\") +\n scale_y_continuous(name=\"Posterior expectation\") \nggsave(\"graphs/expectations_bypriorexp_spopt2_qudhowmany_alternativesbasic.pdf\",width=12,height=7)\nggsave(\"graphs/modelpredictions_expectations.pdf\")\n\ndd = droplevels(subset(subexp,Alternatives==\"0_basic\"))\nm = lm(ExpectationProportion ~ PriorExpectation, data=dd)\nsummary(m)\n\nggplot(subset(mp, Alternatives==\"0_basic\" & SpeakerOptimality == 2 & QUD == \"how-many\" & NumState == 15), aes(x=NormAllPrior,y=PosteriorProbability,color=SmoothingBW)) +\n geom_point() +\n #geom_smooth() +\n #geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Prior probability of all-state\") +\n scale_y_continuous(name=\"Posterior probability of all-state\") \nggsave(\"graphs/modelpredictions_allstate.pdf\")\n\n\n\n# get empirical posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/3_sinking-marbles-nullutterance/results/data/r.RData\")\nhead(r)\nsome = subset(r, quantifier == \"Some\" & Proportion == 100)\nsome$Item = as.factor(paste(some$effect, some$object))\nhead(some$normresponse)\n# get model predictions for all-state with basic alts, spopt==2, and qud==how-many\nmp_some_allstate = subset(mp, Alternatives==\"0_basic\" & SpeakerOptimality == 2 & QUD == \"how-many\" & NumState == 15)\nhead(mp_some_allstate)\nnrow(mp_some_allstate)\n\nagr = aggregate(normresponse ~ Item,data=some,FUN=mean)\nagr$CILow = aggregate(normresponse ~ Item,data=some, FUN=ci.low)$normresponse\nagr$CIHigh = aggregate(normresponse ~ Item,data=some,FUN=ci.high)$normresponse\nagr$YMin = agr$normresponse - agr$CILow\nagr$YMax = agr$normresponse + agr$CIHigh\nrow.names(agr) = agr$Item\nhead(agr)\nmp_some_allstate$EmpiricalPosterior = agr[as.character(mp_some_allstate$Item),]$normresponse\nmp_some_allstate$YMin = agr[as.character(mp_some_allstate$Item),]$YMin\nmp_some_allstate$YMax = agr[as.character(mp_some_allstate$Item),]$YMax\n\nggplot(mp_some_allstate, aes(x=PosteriorProbability, y=EmpiricalPosterior)) +\n geom_point() +\n geom_smooth() +\n# geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(x=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Model predicted posterior all-state probability\") +\n scale_y_continuous(name=\"Empirical mean all-state probability\") +\n facet_wrap(~SmoothingBW)\nggsave(\"graphs/model-empirical.pdf\") \ncor(mp_some_allstate$PosteriorProbability,mp_some_allstate$EmpiricalPosterior) # .51\n\nhead(subexp)\n# get empirical expectations:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nhead(r)\nsome = subset(r, quantifier == \"Some\")\nsome$Item = as.factor(paste(some$effect, some$object))\nrownames(some) = some$Item\n# plot subject variability\nggplot(some, aes(x=ProportionResponse)) +\n geom_histogram() + \n facet_wrap(~workerid)\nggsave(\"graphs/some_subjectvariability.pdf\",width=12,height=10)\n\n\n# mark the people who chose at most 3 middle values to respond to \"some\" with\nsome$NoVariance = as.factor(ifelse(some$workerid %in% c(\"8\",\"24\",\"27\",\"37\",\"52\",\"54\",\"61\",\"72\",\"79\",\"108\",\"113\"),1,0))\n\nagr = aggregate(ProportionResponse ~ Item,data=some,FUN=mean)\nagr$CILow = aggregate(ProportionResponse ~ Item,data=some, FUN=ci.low)$ProportionResponse\nagr$CIHigh = aggregate(ProportionResponse ~ Item,data=some,FUN=ci.high)$ProportionResponse\nagr$YMin = agr$ProportionResponse - agr$CILow\nagr$YMax = agr$ProportionResponse + agr$CIHigh\nrow.names(agr) = agr$Item\n\nsubexp$EmpiricalMean = agr[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin = agr[as.character(subexp$Item),]$YMin\nsubexp$YMax = agr[as.character(subexp$Item),]$YMax\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions.pdf\",width=12,height=7)\n\ncors = ddply(subexp, .(Alternatives), summarize, Correlation=cor(ExpectationProportion,EmpiricalMean))\ncors # best correlation is with basic alternatives (some, none, all -- .6) and basic+many/most/few/afew (-- .59)\n\n# split up by subject vairance on \"some\" trials\nagr = aggregate(ProportionResponse ~ Item + NoVariance,data=some,FUN=mean)\nagr$CILow = aggregate(ProportionResponse ~ Item + NoVariance,data=some, FUN=ci.low)$ProportionResponse\nagr$CIHigh = aggregate(ProportionResponse ~ Item + NoVariance,data=some,FUN=ci.high)$ProportionResponse\nagr$YMin = agr$ProportionResponse - agr$CILow\nagr$YMax = agr$ProportionResponse + agr$CIHigh\nagrgood = subset(agr, NoVariance == 0)\nagrbad = subset(agr, NoVariance == 1)\nrow.names(agrgood) = agrgood$Item\nrow.names(agrbad) = agrbad$Item\nnrow(agrgood)\nnrow(agrbad)\nsubexp$EmpiricalMean_good = agrgood[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin_good = agrgood[as.character(subexp$Item),]$YMin\nsubexp$YMax_good = agrgood[as.character(subexp$Item),]$YMax\n\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean)) +\n geom_point() +\n #geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions_good.pdf\",width=12,height=7)\n\n\n\nsubexp$EmpiricalMean_bad = agrbad[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin_bad = agrbad[as.character(subexp$Item),]$YMin\nsubexp$YMax_bad = agrbad[as.character(subexp$Item),]$YMax\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean_bad)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin_bad,ymax=YMax_bad)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions_bad.pdf\",width=12,height=7)\n\ncors = ddply(subexp, .(Alternatives), summarize, Correlation=cor(ExpectationProportion,EmpiricalMean))\ncors # best correlation is with basic alternatives (some, none, all -- .6) and basic+many/most/few/afew (.22)\n\n# plot wonkiness\nwonk = unique(mp[,c(\"PriorExpectation\",\"WonkyProbability\",\"Item\",\"SmoothingBW\",\"Alternatives\",\"SpeakerOptimality\",\"QUD\")])\nnrow(wonk)\nggplot(wonk, aes(x=PriorExpectation,y=WonkyProbability, color=as.factor(SpeakerOptimality), shape=QUD)) +\n geom_point() +\n geom_smooth() +\n facet_grid(SmoothingBW~Alternatives,scales=\"free\")\nggsave(\"graphs/wonky_probabilities.pdf\",width=20,height=7)\n\nwonk = unique(mp[,c(\"NormAllPrior\",\"WonkyProbability\",\"Item\",\"SmoothingBW\",\"Alternatives\",\"SpeakerOptimality\",\"QUD\")])\nnrow(wonk)\nggplot(wonk, aes(x=NormAllPrior,y=WonkyProbability, color=as.factor(SpeakerOptimality), shape=QUD)) +\n geom_point() +\n geom_smooth() +\n facet_grid(SmoothingBW~Alternatives,scales=\"free\")\nggsave(\"graphs/wonky_probabilities_byallprior.pdf\",width=20,height=7)\n\n", "meta": {"hexsha": "4bd62e1e1abae7f9323ee73309408effc8a41ef5", "size": 18742, "ext": "r", "lang": "R", "max_stars_repo_path": "models/wonky_world/results/rscripts/modelpredictions-betabinomial.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "models/wonky_world/results/rscripts/modelpredictions-betabinomial.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/wonky_world/results/rscripts/modelpredictions-betabinomial.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.8072916667, "max_line_length": 272, "alphanum_fraction": 0.7676341906, "num_tokens": 5557, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3427307002184103}} {"text": "de_array_limma = function(path, meta_path, out_path, bNormal,bFilter,bPaired){\n \n pacman::p_load(limma,oligo, genefilter)\n\n #Read DF \n eset = read.csv(path, check.names = F, header = TRUE, row.names = 1)\n metadata = read.csv(meta_path, check.names = F, header = TRUE, row.names = 1) \n \n if(bPaired){\n colnames(metadata) = c(\"sample\",\"condition\",\"patient\")\n \n }else{\n colnames(metadata) = c(\"sample\",\"condition\")\n\n }\n \n ##Intersect\n lSample = intersect(metadata$sample, colnames(eset))\n \n \n if (bNormal) {\n print(\"Applyin RMA\")\n eset = limma::voom(eset)[[1]]\n \n }\n \n if (bFilter){\n eset = eset[rowSums(eset) > 5,]\n }\n \n #Filter by Sample\n eset = eset[lSample]\n metadata = metadata[metadata$sample %in% lSample,]\n \n #Design Matrix\n if (bPaired){\n design <- model.matrix(~metadata$patient + metadata$condition)\n } else{\n design <- model.matrix(~metadata$condition)\n }\n #Limma\n fit <- lmFit(eset, design.mat)\n fit<- eBayes(fit)\n \n #Get DE Table\n if (bPaired){\n res = topTable(fit, coef=5, adjust.method = \"BH\", sort.by=\"P\", number=Inf)\n } else{\n res = topTable(fit, coef=1, adjust.method = \"BH\", sort.by=\"P\", number=Inf)\n }\n #Save File\n write.csv(res, out_path)\n \n}\n\n\narguments = function(){\n pacman::p_load(optparse)\n option_list = list(\n make_option(c(\"-f\", \"--file\"), type=\"character\", default=NULL, \n help=\"dataset file name\", metavar=\"character\"),\n \n make_option(c(\"-m\", \"--metadata\"), type=\"character\", default=NULL, \n help=\"Metadata file name\", metavar=\"character\"),\n \n make_option(c(\"-o\", \"--out\"), type=\"character\", default=NULL, \n help=\"Out Res file name\", metavar=\"character\"),\n \n make_option(c(\"-n\", \"--normalize\"), type=\"logical\", default=NULL, \n help=\"Normalize data. If True the script applies TMM normalziation\", metavar=\"bool\"),\n \n make_option(c(\"-t\", \"--filter\"), type=\"logical\", default=NULL, \n help=\"Filter Low Expression\", metavar=\"character\")\n make_option(c(\"-p\", \"--paired\"), type=\"logical\", default=FALSE, \n help=\"Paired Samples\", metavar=\"character\")\n ); \n \n opt_parser = OptionParser(option_list=option_list);\n opt = parse_args(opt_parser);\n return(opt)\n}\n\n\narg = arguments()\n\nde_array_limma(path = arg$file, meta_path = arg$metadata, out_path = arg$out, bNormal = arg$normalize, bFilter = arg$filter, , bPaired = arg$paired)", "meta": {"hexsha": "a2c90f376f4e6d0e134e0895e6012aafbfdd9715", "size": 2444, "ext": "r", "lang": "R", "max_stars_repo_path": "miopy/Rscript/get_de_array_limma.r", "max_stars_repo_name": "icbi-lab/miopy", "max_stars_repo_head_hexsha": "1bf23d9c69347070fa6b57f02de9cc1d259f50cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "miopy/Rscript/get_de_array_limma.r", "max_issues_repo_name": "icbi-lab/miopy", "max_issues_repo_head_hexsha": "1bf23d9c69347070fa6b57f02de9cc1d259f50cc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "miopy/Rscript/get_de_array_limma.r", "max_forks_repo_name": "icbi-lab/miopy", "max_forks_repo_head_hexsha": "1bf23d9c69347070fa6b57f02de9cc1d259f50cc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4186046512, "max_line_length": 149, "alphanum_fraction": 0.6198854337, "num_tokens": 682, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091671, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34273070021841023}} {"text": "##' estimate missing quantiles\n##'\n##' @param x a vector of quantiles, with levels as names and some missing (NA) values\n##' @return a vector of quantiles, with levels as names and all values\n##' @importFrom quantgen quantile_extrapolate\n##' @author Sebastian Funk\nestimate_quantiles <- function(x) {\n vec <- unlist(x)\n if (any(is.na(x))) {\n tau <- as.numeric(names(vec[!is.na(vec)]))\n x[1, order(as.numeric(names(vec)))] <-\n quantgen::quantile_extrapolate(\n tau, vec[!is.na(vec)], sort(as.numeric(names(vec))),\n nonneg = TRUE, round = TRUE)\n }\n return(x)\n}\n\n", "meta": {"hexsha": "df33a983bcdbfdea0f6a65ecd89dac5fa815f837", "size": 608, "ext": "r", "lang": "R", "max_stars_repo_path": "R/estimate_quantiles.r", "max_stars_repo_name": "epiforecasts/covid19.forecasts.uk", "max_stars_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-13T15:57:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-13T15:57:27.000Z", "max_issues_repo_path": "R/estimate_quantiles.r", "max_issues_repo_name": "epiforecasts/covid19.forecasts.uk", "max_issues_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/estimate_quantiles.r", "max_forks_repo_name": "epiforecasts/covid19.forecasts.uk", "max_forks_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0, "max_line_length": 85, "alphanum_fraction": 0.6299342105, "num_tokens": 173, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3427306925058808}} {"text": "#for matrixizing Solidity files\nlibrary(stringr)\nsolList <- list.files(path = \"~/set-protocol-contracts/contracts\", pattern = \"\\\\.sol$\", recursive = TRUE, full.names = TRUE)\n#flatten the Solidity files representing each as a single character vector\nflatCode <- lapply(solList, readLines)\n#extract all numbers in the code\nflatCodeNumbers <- str_extract_all(flatCode,\"[0-9]+\")\nlibrary(ggplot2)\nlibrary(ggpubr)\ntheme_set(theme_pubr())\n#total numbers: table(unlist(flatCodeNumbers))\nflatCodeTable<-lapply(flatCodeNumbers, table)\nz <- do.call(cbind, flatCodeTable[3])\nvector <- unlist(flatCodeNumbers)\nnumbers <- as.numeric(vector)\nsortedNumbers <- sort.int(numbers)\n#library(plyr)\ncountedNumbers <- count(numbers)\n#library(dplyr)\nstr_count(flatCode, \"withdraw\")\n#find all functions with \"withdraw\" in them\nstr_count(flatCode, \"deposit\")\n#find all functions with \"deposit\" in them\nstr_count(flatCode, \"transfer\")\n#find all functions with \"transfer\" in them\n#str_count(flatCode, \"token\")\n#str_count(flatCode, \"set\")\nsortedNumbers\nsortedXyData()\nplot(sortedNumbers)\nas.data.frame(sortedNumbers)\nflattenedCodeDf<-as.data.frame(sortedNumbers)\nplot(flattenedCodeDf)\nas.numeric(flattenedCodeDf)\nas.integer(flattenedCodeDf)\nas.vector(flattenedCodeDf)\nflattenedCodeVector<-as.vector(flattenedCodeDf)\nflattenedCodeVector\nas.data.frame(flattenedCodeVector)\nflattenedCodeDf<-as.data.frame(flattenedCodeVector)\nflattenedCodeDf\nsummary(flattenedCodeDf)\nplot(flattenedCodeDf)\npower()\npower(flattenedCodeDf)\nunlist(flattenedCodeDf)\nunlist(flatCodeNumbers)\nunlistedNumbers<-unlist(flatCodeNumbers)\nplot(unlistedNumbers)\nsort(unlistedNumbers)\nsortNumbers<-sort(unlistedNumbers)\nplot(sortNumbers)\nas.data.frame(sortNumbers)\ndfNumbers<-as.data.frame(sortNumbers)\nplot(dfNumbers)\nplot(dfNumbers)\nplot(dfNumbers)\nsavehistory(\"~/scripts/R/hist.r\")\n", "meta": {"hexsha": "809c14f2040ac36e5786bd56b1cbe73779547316", "size": 1822, "ext": "r", "lang": "R", "max_stars_repo_path": "R/hist.r", "max_stars_repo_name": "metameshllc/quantEthSec", "max_stars_repo_head_hexsha": "086cabb3189d6c379f2bbb985fc8cc9310e418bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/hist.r", "max_issues_repo_name": "metameshllc/quantEthSec", "max_issues_repo_head_hexsha": "086cabb3189d6c379f2bbb985fc8cc9310e418bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/hist.r", "max_forks_repo_name": "metameshllc/quantEthSec", "max_forks_repo_head_hexsha": "086cabb3189d6c379f2bbb985fc8cc9310e418bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8813559322, "max_line_length": 124, "alphanum_fraction": 0.8040614709, "num_tokens": 502, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34273069250588073}} {"text": "p <- ggplot(ledger_data, aes(date, total)) +\n geom_line() +\n geom_smooth(method=\"loess\") +\n scale_x_date(breaks = \"1 month\", minor_breaks = \"1 week\", labels = date_format(\"%b\")) +\n scale_y_continuous(name = commodity) +\n facet_wrap(~year) +\n theme(axis.title.x = element_blank()) # No title on the x-axis\n\nsuppressMessages(ggsave(file=\"line-total-vs-date-facet-year.svg\", plot=p, width=12, height=3))\n", "meta": {"hexsha": "f7335b342a500092c7ae76d4ba1da0d3f448b7fc", "size": 467, "ext": "r", "lang": "R", "max_stars_repo_path": "Support/lib/r/line-total-vs-date-facet-year.r", "max_stars_repo_name": "lifepillar/Ledger.tmbundle", "max_stars_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2015-11-05T08:56:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-07T00:19:12.000Z", "max_issues_repo_path": "Support/lib/r/line-total-vs-date-facet-year.r", "max_issues_repo_name": "lifepillar/Ledger.tmbundle", "max_issues_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-10-02T05:58:02.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-02T07:14:12.000Z", "max_forks_repo_path": "Support/lib/r/line-total-vs-date-facet-year.r", "max_forks_repo_name": "lifepillar/Ledger.tmbundle", "max_forks_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2016-09-08T18:30:38.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T06:21:52.000Z", "avg_line_length": 46.7, "max_line_length": 99, "alphanum_fraction": 0.6038543897, "num_tokens": 116, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5506073655352403, "lm_q1q2_score": 0.34273069250588073}} {"text": "library(tmod)\n\ndn.out = file.path(PROJECT_DIR, \"generated_data/fgsea_with_wgcna_modules/\")\ndir.create(dn.out, showWarnings = F)\ndn.fig = file.path(PROJECT_DIR, \"figure_generation/SLE-Sig\")\ndir.create(dn.fig, showWarnings = F)\n\n\nisv.seq = c(0.5,0.75)\n\n# load FGSEA resuls for BTM modules\nmset = \"LI\"\nfn.res = file.path(PROJECT_DIR, \"generated_data/fgsea_with_btm_modules\", \n sprintf(\"BTM.%s_genes_fgsea_results.rds\", mset))\nres = readRDS(fn.res)\n\niuse = names(res) %in% paste0(\"ISV.\",isv.seq)\nres = res[iuse]\n\n# extract p-values and AUC as effect size\ndf.pv = data.frame(pathway = res[[1]]$pathway, stringsAsFactors = F)\ndf.padj = df.pv\nfor(i in 1:length(res)) {\n tmp = res[[i]] %>% dplyr::select(pathway, pval)\n names(tmp)[2] = names(res)[i]\n df.pv = full_join(df.pv, tmp, by=\"pathway\")\n tmp = res[[i]] %>% dplyr::select(pathway, padj)\n names(tmp)[2] = names(res)[i]\n df.padj = full_join(df.padj, tmp, by=\"pathway\")\n}\ndf.btm = df.pv %>% \n tibble::remove_rownames() %>% tibble::column_to_rownames(\"pathway\") %>% \n data.matrix()\ndf.btm.padj = df.padj %>% \n tibble::remove_rownames() %>% tibble::column_to_rownames(\"pathway\") %>% \n data.matrix()\n\n# load FGSEA resuls for WGCNA\nfn.res = file.path(PROJECT_DIR, \"generated_data\", \"fgsea_with_wgcna_modules\", \n \"WGCNA_genes_fgsea_results.rds\")\nres = readRDS(fn.res)\n\n# select ISV threshold to plot and filter the loaded results\niuse = names(res) %in% paste0(\"ISV.\",isv.seq)\nres = res[iuse]\n\n# extract p-values and AUC as effect size\ndf.pv = data.frame(pathway = res[[1]]$pathway, stringsAsFactors = F)\ndf.padj = df.pv\nfor(i in 1:length(res)) {\n tmp = res[[i]] %>% dplyr::select(pathway, pval)\n names(tmp)[2] = names(res)[i]\n df.pv = full_join(df.pv, tmp, by=\"pathway\")\n tmp = res[[i]] %>% dplyr::select(pathway, padj)\n names(tmp)[2] = names(res)[i]\n df.padj = full_join(df.padj, tmp, by=\"pathway\")\n}\ndf.wgcna = df.pv %>% \n dplyr::filter(pathway %in% \"brown\") %>%\n tibble::remove_rownames() %>% tibble::column_to_rownames(\"pathway\") %>% \n data.matrix()\ndf.wgcna.padj = df.padj %>% \n dplyr::filter(pathway %in% \"brown\") %>%\n tibble::remove_rownames() %>% tibble::column_to_rownames(\"pathway\") %>% \n data.matrix()\n\ndf.comb = rbind(df.wgcna, df.btm)\ndf.comb.padj = rbind(df.wgcna.padj, df.btm.padj)\ndf.comb = apply(df.comb, 2, p.adjust, method=\"BH\")\n\n# filter modules\npval.row.in = 0.05 # p threshold for modules to be included\npval.col.in = 0.05 # p threshold for columns to be included\npval.min = 0.1 # p threshold for an enrichment to be shown\n\ni.row.in = apply(df.comb, 1, function(x) any(x <= pval.row.in, na.rm = T))\n\ndf.comb = df.comb[i.row.in, ]\n\nrownm = rownames(df.comb)\nibrown = rownm %in% \"brown\"\ndata(tmod)\nrowttl = tmod$MODULES$Title[match(rownm, tmod$MODULES$ID)]\nrownm2 = sprintf(\"%s (%s)\", rownm, rowttl)\nrownm2[ibrown] = \"WGCNA brown module\"\n\ni.hide = df.comb > pval.min\ndf.comb[i.hide] = NA\n\ndf.comb = df.comb %>% as.data.frame() %>% \n mutate(module = rownm2) %>% \n gather(\"comparison\",\"P.Value\", -module)\n\ndf.mod = df.comb %>% \n dplyr::filter(comparison == \"ISV.0.5\", !grepl(\"TBA\", module), !is.na(P.Value))\n\niord = order(-log10(df.mod$P.Value[-1]))\niord = c(iord+1, 1)\ndf.mod = df.mod %>% \n mutate(module = factor(module, levels=module[iord]))\n\np = ggplot(df.mod, aes(module, -log10(P.Value))) +\n geom_bar(stat=\"identity\") +\n geom_hline(yintercept = -log10(c(0.05, 0.01)), col=\"red\", lty=2) +\n geom_text(data=data.frame(y=-log10(c(0.05,0.01)), x=nrow(df.mod), label=c(\"5% FDR\", \"1% FDR\")), \n aes(x=x,y=y,label=label), col=\"red\", size=2, vjust=-2, hjust=0.5) +\n xlab(\"\") + ylab(\"adjusted p-value,-log10\") +\n coord_flip() +\n theme_bw() +\n theme(\n # panel.border = element_blank(), \n panel.grid.major.y = element_blank(),\n axis.ticks.y = element_blank(),\n plot.margin = margin(5,5,0,0,\"mm\"))\n# Disable clip-area.\ngt <- ggplot_gtable(ggplot_build(p))\ngt$layout$clip[gt$layout$name == \"panel\"] <- \"off\"\n\nlibrary(grid)\nlibrary(gridExtra)\ngrid.draw(gt)\n\nfn.fig = file.path(dn.fig, \"brown_BTM_modules_enrichment_CD38.cor.ranked_ISV0.5_barplot\")\nggsave(paste0(fn.fig, \".png\"), gt, w=5.5, h=3)\nggsave(paste0(fn.fig, \".pdf\"), gt, w=5.5, h=3)\n\n", "meta": {"hexsha": "ed51b3a4d73e9ec2ca620a26e08071e6742a8595", "size": 4184, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fgsea_with_wgcna_modules/chi_CD38_cor_by_ISV_fgsea_FIGURE.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/fgsea_with_wgcna_modules/chi_CD38_cor_by_ISV_fgsea_FIGURE.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/fgsea_with_wgcna_modules/chi_CD38_cor_by_ISV_fgsea_FIGURE.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 32.9448818898, "max_line_length": 98, "alphanum_fraction": 0.6532026769, "num_tokens": 1464, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.34273069250588073}} {"text": "#!/usr/bin/Rscript\n\nlibrary(ape)\n\ntree <- read.nexus('mcc.tree')\nflows <- read.csv(\"shipment-flows-origins-on-rows-dests-on-columns.csv\", row.names=1)\nwrite.tree(tree, file='mcc.nh')\n\nnms <- tree$tip.label\nabs <- sapply(strsplit(nms, '_'), '[[', 1)\nabsF <- factor(abs)\nlevs <- levels(absF)\n\nalph <- LETTERS[seq_along(levs)]\nlevels(absF) <- alph\n\nregDNA <- lapply(absF, as.character)\nnames(regDNA) <- nms\n\nwrite.dna(regDNA, file='sim.fasta', format='fasta')\nsave(regDNA, file='regDNA.RData')\n\npairs <- expand.grid(to=levs, from=levs)\ntest <- pairs$from != pairs$to\npairs <- pairs[test,]\npairs <- pairs[, c('from', 'to')]\n\ntest <- abs %in% state.abb\npmf <- table(abs[test])\npmf <- pmf/sum(pmf)\npnms <- names(pmf)\n\nusaRow <- colSums(flows[pnms, ] * as.numeric(pmf))\nusaCol <- rowSums(t(t(flows[, pnms]) * as.numeric(pmf)))\n\nM <- cbind(flows, 'USA'=usaCol)\nM <- rbind(M, 'USA'=c(usaRow, 0))\n\naggFlow <- function(from, to, sym=TRUE){\n tot <- M[from, to]\n if(sym){\n tot <- tot + M[to, from]\n }\n log10(tot + 1)\n}\n\npairFlows <- mapply(aggFlow, from=pairs$from, to=pairs$to)\nZ <- cbind(\"(Intercept)\"=1, pairFlows)\ncat(t(Z), file='designMat2')\n\nn <- length(alph)\nmodFile <- 'init.mod'\ncat(\"ALPHABET:\", alph, \"\\n\", file=modFile)\ncat(\"ORDER: 0\", \"\\n\", file=modFile, append=TRUE)\ncat(\"SUBST_MOD: UNREST\", \"\\n\", file=modFile, append=TRUE)\ncat(\"TRAINING_LNL: 0\", \"\\n\", file=modFile, append=TRUE) ## Not sure all of these tags are needed\ncat(\"BACKGROUND:\", rep(1, times=n)/n, \"\\n\", file=modFile, append=TRUE)\ncat(\"RATE_MAT:\", \"\\n\", file=modFile, append=TRUE)\nM <- matrix(1/(n-1), nrow=n, ncol=n)\ndiag(M) <- -1L\nfor(i in 1:n){\n cat(M[i,], sep=\"\\t\", file=modFile, append=TRUE)\n cat(\"\\n\", file=modFile, append=TRUE)\n}\ncat(\"TREE:\", write.tree(tree), \"\\n\", file=modFile, append=TRUE)\n\n\n", "meta": {"hexsha": "1ab744b55d36160c63c1e930e515c2b61c817ba3", "size": 1790, "ext": "r", "lang": "R", "max_stars_repo_path": "src/make-regression-inputs.r", "max_stars_repo_name": "e3bo/2015phylo", "max_stars_repo_head_hexsha": "1721e4f06fc826244d5b9732048d58c2004d81c1", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/make-regression-inputs.r", "max_issues_repo_name": "e3bo/2015phylo", "max_issues_repo_head_hexsha": "1721e4f06fc826244d5b9732048d58c2004d81c1", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 44, "max_issues_repo_issues_event_min_datetime": "2015-02-19T22:54:04.000Z", "max_issues_repo_issues_event_max_datetime": "2015-09-20T21:10:04.000Z", "max_forks_repo_path": "src/make-regression-inputs.r", "max_forks_repo_name": "e3bo/2015phylo", "max_forks_repo_head_hexsha": "1721e4f06fc826244d5b9732048d58c2004d81c1", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.3235294118, "max_line_length": 96, "alphanum_fraction": 0.6329608939, "num_tokens": 630, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583124210896, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3425424243299271}} {"text": "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Fitting generalized additive models to fraction feeding data\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nrm(list=ls()) # clears workspace\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nlibrary(plyr)\nlibrary(mgcv) # for GAM\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Load saved 'fdat' w/ tWS\nload('../Data/SatMeta_FracFDataPhylo.Rdata') # load full post-ToL Frac Feeding dataset\nnrow(fdat)\nlength(unique(fdat$Consumer.identity))\nfDat<-fdat # We'll create multiple datasets, to save original to resuse\n\n########################################################################\n# Restrict data to those that have information for all desired variables\n########################################################################\npdf('../Output/Plots/SatMeta-VarCors.pdf',height=20,width=20)\n pairs(fdat[,c('fF','tWS','Lat','DR','Year','TG','EE','TA','SAlog','BMlog','GT','Eco','FD','RLS','ST')],pch=21,cex=0.5)\ndev.off()\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# fF - Fraction feeding\n# TSc - Total stomach count\n# tWS - Days since winter solstice\n# Lat - Latitude\n# DR - Diet richness minimum\n# DRlog - Diet richness minimum - log10 transformed\n# TG - Taxon group\n# EE - Endo vs. ectotherm\n# TA - Time averaging\n# SA - Space averaging (as factor)\n# SAlog - Space averaging - (as numeric) log-10 transformed\n# BM - Body mass (g)\n# BMlog - body mass (g) - log10 transformed \n# GT - Generation time (days)\n# Eco - Ecosystem\n# FD - Feeding data type\n# RLS - Rate limiting step\n# ST - Space-time replicate\n# PS - Population split\n# Tmp - Temperature\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~\n# NOTES: \n# Population.split - not much variation, and causes problems in estimation of smoothed variables, so leave out.\n# Temperature - unavailable for most surveys, so leave out.\n# Diet richness and rate-limiting step might be correlated (so consider leaving latter out)\n# Same with Ecosystem (!?)\n# Dropped Space-Time despite being significant in case it sucked up variation from other variables and because there's no expectation that they would differ other than by taxonomic focus.\n# Dropped Feeding data type for same reasons\n# Dropped Rate-limiting step since could be confounded (correlated) for molluscs with biology\n# ~~~~~~~~~~~~~~~~~~~~~~~~~\n# Restrict data set to non-NA values for following variables\nvars<-vars1<-c('FSc','TSc','tWS','Lat','DR','DRlog','Year','TG','EE','TA','SA','SAlog','Eco')\nfdat<-fDat[-which(apply(fdat[,vars],1,function(x) any(is.na(x)))==TRUE),]\nnrow(fdat) # => matches count following 'na.omit'\nnrow(fdat)/nrow(fDat) # Fraction of used surveys\n# Note: Dropping FD and RLS (see below) doesn't alter number of used surveys\nnSpp<-length(unique(fdat$Consumer.identity)); nSpp\nsave(fdat,file='../Data/SatMeta_gamData_FullModel.Rdata')\n\n# Second dataset that drops tWS to include larger dataset\nvars<-vars2<-vars1[-3]\nfdat<-fDat[-which(apply(fDat[,vars],1,function(x) any(is.na(x)))==TRUE),]\nnrow(fdat); nrow(fdat)/nrow(fDat)\nnSpp<-length(unique(fdat$Consumer.identity)); nSpp\nsave(fdat,file='../Data/SatMeta_gamData_SubModel.Rdata')\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Fullmodel data set\nload(file='../Data/SatMeta_gamData_FullModel.Rdata')\n############################\n# Quasi-binomial with logit\n############################\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With Endo/Ecto and Taxon Group\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfit1q_l<-fit<-gam((FSc/TSc)~s(tWS,bs='cc',k=5)+s(Lat,k=6)+s(DRlog,k=4)+s(Year,k=5)+EE+TG+TA+SA+Eco,family=quasibinomial(link='logit'),weights=TSc,data=fdat)\n\nsave(fit,file='../Output/GAM/GAM_fit_qBinom_logit_wTaxInf.Rdata')\nlapply(concurvity(fit,full=FALSE),round,2) # Variables exhibit very low \"co-linearity\"\nanova(fit) # Variable significance\nsummary(fit) # Parameter estimates and significance\n\npdf(paste0('../Output/GAM/GAM_fit_qBinom_logit_wTaxInf.pdf'),height=8,width=10)\n plot(fit,residuals=FALSE,se=1.96,seWithMean=TRUE,shade=TRUE,rug=TRUE,all.terms=TRUE,pch=21,pages=1,scale=-1,too.far=0,pers=TRUE)\n par(mfrow=c(2,2));gam.check(fit)\ndev.off()\n\n# Use 'predict' to plot on probability scale\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Without Endo/Ecto and Taxon Group\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfit2q_l<-fit<-gam((FSc/TSc)~s(tWS,bs='cc',k=5)+s(Lat,k=6)+s(DRlog,k=4)+s(Year,k=5)+TA+SA+Eco,family=quasibinomial(link='logit'),weights=TSc,data=fdat)\n\nsave(fit,file='../Output/GAM/GAM_fit_qBinom_logit_noTaxInf.Rdata')\nlapply(concurvity(fit,full=FALSE),round,2) # Variables exhibit very low \"co-linearity\"\nanova(fit) # Variable significance\nsummary(fit) # Parameter estimates and significance\n\npdf(paste0('../Output/GAM/GAM_fit_qBinom_logit_noTaxInf.pdf'),height=8,width=10)\n plot(fit,residuals=FALSE,se=1.96,seWithMean=TRUE,shade=TRUE,rug=TRUE,all.terms=TRUE,pch=21,pages=1,scale=-1,too.far=0,pers=TRUE)\n par(mfrow=c(2,2));gam.check(fit)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With log10(Body Mass)\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nfit1q_l<-fit<-gam((FSc/TSc)~s(tWS,bs='cc',k=5)+s(Lat,k=6)+s(DR,k=4)+s(Year,k=5)+BMlog+SAlog+TA+EE+TG+Eco,family=quasibinomial(link='logit'),weights=TSc,data=fdat)\n\nsave(fit,file='../Output/GAM/GAM_fit_qBinom_logit_wTaxInf_wBM.Rdata')\nlapply(concurvity(fit,full=FALSE),round,2) # Variables exhibit very low \"co-linearity\"\nanova(fit) # Variable significance\nsummary(fit) # Parameter estimates and significance\n\npdf(paste0('../Output/GAM/GAM_fit_qBinom_logit_wTaxInf_wBM.pdf'),height=8,width=10)\npar(cex.axis=0.8,tcl=-0.1,mgp=c(1.5,0.2,0))\nplot(fit,residuals=FALSE,se=1.96,seWithMean=TRUE,shade=TRUE,rug=TRUE,all.terms=TRUE,pch=21,pages=1,scale=-1,too.far=0,pers=TRUE)\npar(mfrow=c(2,2));gam.check(fit)\ndev.off()\n\n\n", "meta": {"hexsha": "6ec79afd170ebf790a17d09f1217aa9e701d5fae", "size": 6090, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/zOld/SatMeta-Analyses-GAM.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/zOld/SatMeta-Analyses-GAM.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/zOld/SatMeta-Analyses-GAM.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.3333333333, "max_line_length": 187, "alphanum_fraction": 0.5972085386, "num_tokens": 1720, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7185943805178139, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.34246745912349813}} {"text": "body <- setRefClass('body',\n fields = c(\n 'mass',\n 'position',\n 'velocity',\n 'acceleration'\n ),\n methods = list(\n initialize = function(m, p, v, a){\n setMass(m)\n setPosition(p)\n setVelocity(v)\n setAcceleration(a)\n },\n setMass = function(m){\n validateMass(m)\n mass <<- m\n },\n setPosition = function(p){\n validateVector(p)\n position <<- p\n },\n setVelocity = function(v){\n validateVector(v)\n velocity <<- vect(v)\n },\n setAcceleration = function(a){\n validateVector(a)\n acceleration <<- vect(a)\n },\n validateMass = function(m){\n if(!is.numeric(m)){\n stop(\"Mass is not numeric\")\n }\n if(any(is.nan(m))){\n stop(\"Mass is NaN\")\n }\n if(is.infinite(m)){\n stop(\"Mass cannot be infinite\")\n }\n if(length(m) > 1){\n stop(\"Mass must be a single value\")\n }\n },\n validateVector = function(v){\n if(!is.numeric(v)){\n stop(\"Input is not numeric\")\n }\n if(any(is.nan(v))){\n stop(\"Input contains NaN\")\n }\n if(any(is.infinite(v))){\n stop(\"Input cannot be infinite\")\n }\n }\n ))", "meta": {"hexsha": "69085a2bcec1df975b1b84a788f5c87304314014", "size": 2176, "ext": "r", "lang": "R", "max_stars_repo_path": "body.r", "max_stars_repo_name": "danVatnik/gravitation-simulation", "max_stars_repo_head_hexsha": "fabf0af789962279ad27cda503d69eba99162121", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "body.r", "max_issues_repo_name": "danVatnik/gravitation-simulation", "max_issues_repo_head_hexsha": "fabf0af789962279ad27cda503d69eba99162121", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "body.r", "max_forks_repo_name": "danVatnik/gravitation-simulation", "max_forks_repo_head_hexsha": "fabf0af789962279ad27cda503d69eba99162121", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.8571428571, "max_line_length": 61, "alphanum_fraction": 0.2752757353, "num_tokens": 291, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5851011542032313, "lm_q1q2_score": 0.34234336064995335}} {"text": "library(png)\nrequire(animation)\npngs <- list.files(\"img/\",\"coin\",full = TRUE)\nimgs <- lapply(pngs,png::readPNG)\n\nani.options(interval = 0.5,ani.width = 1800, ani.height = 900)\n\nsaveGIF({\n layout(matrix(c(0,0,2,2,2,3,3,3,1,1,2,2,2,3,3,3,1,1,2,2,2,3,3,3,0,0,2,2,2,3,3,3),nrow = 4,byrow = TRUE))\n par(xpd = TRUE)\n for(i in 1:2){\n plot(1,type = \"n\", axes = FALSE,xlim = c(-0.6,0.6),xlab =\"\",ylim = c(-0.6,0.6),ylab = \"\")\n rasterImage(imgs[[i]],-0.5,-0.5,0.5,0.5)\n text(0,0.6,\"Throw a coin 10 times\",cex = 4)\n text(0,-0.6,\"Is it fair?\",cex = 4)\n num <- seq(0,40,4)\n plot(1,type = \"n\", axes = FALSE,xlim = c(-1,42),xlab =\"\",ylim = c(-0.1,2.2), ylab = \"\")\n ## sleeping\n for(i in num[2:11]){ rasterImage(imgs[[1]],i - 2,0.1,i + 2,0.3)}\n for(i in num[3:11]){ rasterImage(imgs[[1]],i - 2,0.3,i + 2,0.5)}\n for(i in num[4:11]){ rasterImage(imgs[[1]],i - 2,0.5,i + 2,0.7)}\n for(i in num[5:11]){ rasterImage(imgs[[1]],i - 2,0.7,i + 2,0.9)}\n for(i in num[6:11]){ rasterImage(imgs[[1]],i - 2,0.9,i + 2,1.1)}\n for(i in num[7:11]){ rasterImage(imgs[[1]],i - 2,1.1,i + 2,1.3)}\n for(i in num[8:11]){ rasterImage(imgs[[1]],i - 2,1.3,i + 2,1.5)}\n for(i in num[9:11]){ rasterImage(imgs[[1]],i - 2,1.5,i + 2,1.7)}\n for(i in num[10:11]){ rasterImage(imgs[[1]],i - 2,1.7,i + 2,1.9)}\n for(i in num[11]){ rasterImage(imgs[[1]],i - 2,1.9,i + 2,2.1)}\n mtext(paste(0:10,\"\\n sleeping \\n cats\"),line = 0,side = 1, at = num, cex = 0.9)\n ## behind\n for(i in num[1:10]){ rasterImage(imgs[[2]],i - 2,1.9,i + 2,2.1)}\n for(i in num[1:9]){ rasterImage(imgs[[2]],i - 2,1.7,i + 2,1.9)}\n for(i in num[1:8]){ rasterImage(imgs[[2]],i - 2,1.5,i + 2,1.7)}\n for(i in num[1:7]){ rasterImage(imgs[[2]],i - 2,1.3,i + 2,1.5)}\n for(i in num[1:6]){ rasterImage(imgs[[2]],i - 2,1.1,i + 2,1.3)}\n for(i in num[1:5]){ rasterImage(imgs[[2]],i - 2,0.9,i + 2,1.1)}\n for(i in num[1:4]){ rasterImage(imgs[[2]],i - 2,0.7,i + 2,0.9)}\n for(i in num[1:3]){ rasterImage(imgs[[2]],i - 2,0.5,i + 2,0.7)}\n for(i in num[1:2]){ rasterImage(imgs[[2]],i - 2,0.3,i + 2,0.5)}\n for(i in num[1]){ rasterImage(imgs[[2]],i - 2,0.1,i + 2,0.3)}\n mtext(paste(10:0,\"\\n teasing \\n cats\"),line = 0,side = 3, at = num, cex = 0.9)\n ## boxes\n rect(xleft = c(-2,38),ybottom = 0.1,xright = c(2,42),ytop = 2.1,border = \"red\",lwd = 3)\n rect(xleft = c(2.1,34),ybottom = 0.1,xright = c(6,37.9),ytop = 2.1,border = \"red\",lwd = 2)\n rect(xleft = c(6.1,34),ybottom = 0.1,xright = c(10,30),ytop = 2.1,border = \"red\",lwd = 1)\n mtext(\"more \\n weird\",line = -4,side = 3,at = num[c(1,11)],cex = 1,col = \"red\")\n mtext(\"kind of \\n weird\",line = -4,side = 3,at = num[c(2,10)],cex = 0.8,col = \"red\")\n mtext(\"less \\n weird\",line = -4,side = 3,at = num[c(3,9)],cex = 0.6,col = \"red\")\n ## line\n arrows(-2,-0.01,42,-0.01,length = 0.1,code = 3,lwd = 2)\n text(20,0.05,\"expected\",cex = 1.7)\n text(c(8,32),0.05,\"weirder\",cex = 1.7)\n ## probabilities\n ps <- dbinom(0:10,10,0.5)\n par(xpd = TRUE,mar = c(13.1, 4.1, 4.1, 2.1))\n bar <- barplot(ps,axes = FALSE,border = NA,ylab = \"Probability of getting x cats\",cex.lab = 1.5)\n barplot(c(ps[1],0,0,0,0,0,0,0,0,0,ps[11]),axes = FALSE,border = c(\"red\",NA,NA,NA,NA,NA,NA,NA,NA,NA,\"red\"),\n col = NA,lwd = 3,add = TRUE)\n barplot(c(0,ps[2],0,0,0,0,0,0,0,ps[10],0),axes = FALSE,border = c(NA,\"red\",NA,NA,NA,NA,NA,NA,NA,\"red\",NA),\n col = NA,lwd = 2,add = TRUE)\n barplot(c(0,0,ps[3],0,0,0,0,0,ps[9],0,0),axes = FALSE,border = c(NA,NA,\"red\",NA,NA,NA,NA,NA,\"red\",NA,NA),\n col = NA,lwd = 1,add = TRUE)\n ## line\n arrows(0,-0.01,13,-0.01,length = 0.1,code = 3,lwd = 2)\n text(7.1,-0.007,\"expected\",cex = 1.7)\n text(c(3.1,10.7),-0.007,\"weirder\",cex = 1.7)\n ## text\n text(bar[c(1,11)],0.01,\"more \\n weird\",cex = 1.9,col = \"red\")\n text(bar[c(2,10)],0.02,\"kind of \\n weird\",cex = 1.7,col = \"red\")\n text(bar[c(3,9)],0.05,\"less \\n weird\",cex = 1.5,col = \"red\")\n ## axis\n axis(2,las = 2,at = seq(0,0.25,0.05),cex.axis = 1.3,line = -1)\n mtext(paste(0:10,\"\\n teasing \\n or \\n sleeping \\n cats\"),line = 9,side = 1, at = bar, cex = 0.9)\n par(mar = c(5.1, 4.1, 4.1, 2.1))\n }\n},movie.name = \"binomial_cat.gif\")\n\n", "meta": {"hexsha": "37d3d095ca58304050a0fdea4ddb40563258b6ca", "size": 4488, "ext": "r", "lang": "R", "max_stars_repo_path": "r_scripts/weird_coin.r", "max_stars_repo_name": "statbiscuit/swots", "max_stars_repo_head_hexsha": "005da6fdb980e6cbb40f407e059623256f9c0626", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-02-22T02:58:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-22T03:01:46.000Z", "max_issues_repo_path": "r_scripts/weird_coin.r", "max_issues_repo_name": "cmjt/statbiscuits", "max_issues_repo_head_hexsha": "005da6fdb980e6cbb40f407e059623256f9c0626", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_scripts/weird_coin.r", "max_forks_repo_name": "cmjt/statbiscuits", "max_forks_repo_head_hexsha": "005da6fdb980e6cbb40f407e059623256f9c0626", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 57.5384615385, "max_line_length": 114, "alphanum_fraction": 0.5031194296, "num_tokens": 2035, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3423433606499533}} {"text": "library(devtools)\ndevtools::load_all()\n\nlibrary(parallel)\nRNGkind(\"L'Ecuyer-CMRG\")\nset.seed(999983)\n\n\n# para setup\ns_q <- 2L\nncores <- 16\ns_n <- 128L\ns_k <- 32L\n\n\ncv1 <- cv_simulation(ncores, s_n, s_k, s_q,\n maxit = 1e4, thresh = 1e-4,\n s_range = 5, l_range = 10, lambda_l = 5)\n\n\nsave(cv1, file = \"cv1.RData\")\n", "meta": {"hexsha": "5dd40621677b75ea7013693335509daeb6afdff6", "size": 340, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation/cv_simulation1.r", "max_stars_repo_name": "ZhuolinSong/Length-penalized-Principal-Curves", "max_stars_repo_head_hexsha": "29426aeaf378ae930441bb2f36fb77068ebd8418", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation/cv_simulation1.r", "max_issues_repo_name": "ZhuolinSong/Length-penalized-Principal-Curves", "max_issues_repo_head_hexsha": "29426aeaf378ae930441bb2f36fb77068ebd8418", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation/cv_simulation1.r", "max_forks_repo_name": "ZhuolinSong/Length-penalized-Principal-Curves", "max_forks_repo_head_hexsha": "29426aeaf378ae930441bb2f36fb77068ebd8418", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.4545454545, "max_line_length": 56, "alphanum_fraction": 0.6029411765, "num_tokens": 129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3423433606499533}} {"text": "library(caTools)\n\n# Given a vector of seats, smear across the vector.\nvisible <- function(x) {\n # It assumes the values in x are 1 for occupied, 0 for empty seat,\n # and -1 for floor.\n # Smearing stops when it arrives at an unoccupied seat.\n \n # We start at the edge of the boat, so nothing is visible in this\n # direction:\n smearing <- 0\n\n # For every seat in the vector:\n for (i in 1:length(x)) {\n if (x[i] == 0) {\n # Empty seat, which blocks our view; so will not smear\n # anything past it.\n next_smearing <- 0\n } else if (x[i] == 1) {\n # Occupied seat, smear from it (regardless of current\n # smearing status)\n next_smearing <- 1\n } else {\n # Else it's the floor, which doesn't change our smearing status.\n next_smearing <- smearing\n }\n\n # Smear a 0 or 1 to the current element.\n x[i] <- smearing\n\n smearing <- next_smearing\n }\n return(x)\n} # visible\n\n\n# These functions do a column-wise shift up or down on a matrix.\n# Every column is shifted by (col_num-1) rows. So,\n# The first column is not shifted. The second column is shifted\n# up or down by 1 row, the third by 2, and so on. This has the\n# effect of transforming a matrix that represents rectangular\n# relationships between seats into one that represents diagonal\n# relationships.\nshift_diag_down <- function(mat) {\n nr <- nrow(mat)\n nc <- ncol(mat)\n\n for( i in 2:nc ){\n mat[,i] <- c(rep(0,i-1), head(mat[,i], nr+1-i))\n }\n\n return(mat)\n}\nshift_diag_up <- function(mat) {\n nr <- nrow(mat)\n nc <- ncol(mat)\n\n for( i in 2:nc ){\n mat[,i] <- c(tail(mat[,i], nr+1-i), rep(0,i-1))\n }\n\n return(mat)\n}\n\n# These helper functions accomplish what I'm calling \"smearing,\" with a series\n# of gross-looking matrix operations, which boil down to applying the\n# `visible` function row-wise or column-wise. For everything but top-down\n# column-wise, this requires us to apply and then reverse a series of\n# reversing and transposition actions.\nsmear_right <- function(mat) {\n t(apply(mat, 1, visible)) # Smear any 1s we find all the way RIGHT\n}\nsmear_left <- function(mat) {\n t(apply(apply(apply(mat, 1, rev), 2, visible), 2, rev)) # Smear LEFT\n}\nsmear_up <- function(mat) {\n apply(apply(apply(mat, 2, rev), 2, visible), 2, rev)\n}\nsmear_down <- function(mat) {\n apply(mat, 2, visible) # Smear any 1s we find all the way DOWN\n}\n\n###############\n# Here begins the program logic,\n# which really should be in a procedure. Oh well.\n\nlines <- scan(\"input.txt\", character(), quote=\"\")\nchars <- strsplit(lines, \"\")\nseats <- do.call(rbind, chars)\n\n# The boat's dimensions:\nr <- nrow(seats)\nc <- ncol(seats)\n\n# Now we're playing a WEIRD game of Life, where adjacency is defined VERY\n# differently. The neighbor count is based on whether there are ANY 1s\n# visible in each cardinal direction, and in diagonal directions,\n# except that our \"visibility\" may be blocked by an empty seat.\n# Then, we use these rules:\n# If its neighbor count is 0, it becomes full (1)\n# If its neighbor count is 5+, it empties (0)\n# Otherwise, no change.\n# Floors are always empty and never change.\n\n# Let's make a MASK out of the floors, in which 0 is not a floor, and\n# 1 is a floor.\nfloor <- matrix(0, r, c)\nfloor[seats==\".\"] <- 1\n\ncurr_layout <- matrix(0, r, c) # Boats start empty.\nprev_layout <- matrix(2, r, c) # Create an invalid \"previous\" layout\n\n# Now we've got a floor mask (floor), and a matrix of where people are\n# sitting (curr_layout), and a matrix of where people were previously\n# sitting (prev_layout), which is invalid for the first run so we'll always\n# loop at least once.\n\ngif_storage = array(0, c(r,c,100))\nsteps <- 0\n\n# So, we're off to see the wizard.\nwhile (!all(curr_layout == prev_layout)) {\n\n steps <- steps+1\n # Ok, we're going to do something similar to part 1 here. For each\n # cardinal direction, we need to produce a matrix where a 1 means\n # there's an occupied cell in that direction. I'm going to call this\n # \"smearing\" because we're looking for 1s and then smearing them in\n # some direction until we find an empty seat to mark with it.\n\n # Annotate the floor with a -1. Our smearing visibility function depends\n # upon the following key:\n # Empty seat = 0\n # Full seat = 1\n # Floor = -1\n curr_layout[floor == 1] <- -1\n\n # Cardinal directions.\n cnt_from_u <- smear_down(curr_layout)\n cnt_from_l <- smear_right(curr_layout)\n cnt_from_r <- smear_left(curr_layout)\n cnt_from_d <- smear_up(curr_layout)\n\n # Now, it's time for the diagonal ones. These are a little complicated.\n # We solve these by transforming the matrix: we double its vertical size,\n # then shift each column up or down according to its index. In this\n # transformed matrix, a left/right or up/down relationship between\n # entries corresponds to a DIAGONAL relationship in the source matrix.\n # After the transformation, we apply a horizontal smearing function to\n # the matrix.\n # Then, we reverse the diagonal transformation and trim off the excess\n # rows.\n #\n # For example, take the following grid, and assume we wish to smear\n # diagonally down and to the right to achieve the Goal grid:\n #\n # Original Goal\n # 111 000\n # 000 011\n # 001 001\n #\n # To achieve this, we expand it vertically and shift each column but\n # the first up according to its position; then we smear to the right,\n # then reverse the shift:\n #\n # Shifted Smeared Unshifted and truncated\n # 001 000 000\n # 010 001 011\n # 101 011 001\n # 000 000\n # 000 000\n\n cnt_from_lu <- rbind(matrix(0,r,c), curr_layout)\n cnt_from_lu <- shift_diag_down(smear_right(shift_diag_up(cnt_from_lu)))[-1:-r,]\n\n cnt_from_ru <- rbind(curr_layout, matrix(0,r,c))\n cnt_from_ru <- shift_diag_up(smear_left(shift_diag_down(cnt_from_ru)))[1:r,]\n\n cnt_from_rd <- rbind(matrix(0,r,c), curr_layout)\n cnt_from_rd <- shift_diag_down(smear_left(shift_diag_up(cnt_from_rd)))[-1:-r,]\n\n cnt_from_ld <- rbind(curr_layout, matrix(0,r,c))\n cnt_from_ld <- shift_diag_up(smear_right(shift_diag_down(cnt_from_ld)))[1:r,]\n\n # Sum these up.\n neighbor_cnt <- cnt_from_l + cnt_from_r + cnt_from_u + cnt_from_d +\n cnt_from_lu + cnt_from_ld + cnt_from_ru + cnt_from_rd\n\n gif_storage[,,steps] <- neighbor_cnt\n gif_storage[,,steps][floor == 1] <- 0\n\n # Use our current layout as the starting point for the next one:\n next_layout <- curr_layout\n\n # Logical subscripting makes this next part easy!\n # If a seat's neighbor count is 0, it becomes full (1)\n next_layout[neighbor_cnt == 0] <- 1\n # If its neighbor count is 5+, it empties (0)\n next_layout[neighbor_cnt >= 5] <- 0\n # Otherwise, no change.\n\n # However! Nobody is allowed to sit on the floor. Mask it off.\n next_layout[floor == 1] <- 0\n curr_layout[floor == 1] <- 0 # Clean up our -1s\n\n prev_layout <- curr_layout\n curr_layout <- next_layout\n}\n\ngif_storage <- gif_storage[,,1:steps]/max(gif_storage)\nwrite.gif(gif_storage, \"part2.gif\", col=\"jet\", delay=10)\n\n# How many butts are in seats once this thing stabilizes?\nprint(sum(curr_layout==1))\n", "meta": {"hexsha": "3476ea173ddabac5bcc7080261e6acfe492969df", "size": 7604, "ext": "r", "lang": "R", "max_stars_repo_path": "2020/11/advent11b.r", "max_stars_repo_name": "duplico/adventofcode", "max_stars_repo_head_hexsha": "c68c7a994549bb5320dff44aedf0bcbfbfcc79a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020/11/advent11b.r", "max_issues_repo_name": "duplico/adventofcode", "max_issues_repo_head_hexsha": "c68c7a994549bb5320dff44aedf0bcbfbfcc79a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020/11/advent11b.r", "max_forks_repo_name": "duplico/adventofcode", "max_forks_repo_head_hexsha": "c68c7a994549bb5320dff44aedf0bcbfbfcc79a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.3674418605, "max_line_length": 84, "alphanum_fraction": 0.6371646502, "num_tokens": 2060, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.34232597557068944}} {"text": "#' @export\n#' @title colour.names\n#' @description This function returns all of the named colours used by R with their associated R, G and B values \n#' @param \\code{colour.order} = the sort order. Valid values are \\code{r}, \\code{g} and \\code{b} (\\code{r} is the default)\n#' @examples\n#' color.code(type='colourblind1',n=2)\n#' [1] \"#FFFFD8\" \"#071D58\"\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\n colour.names = function( colour.order=\"r\" ) {\n h = t( col2rgb(colors()) )\n rownames(h) = colors()\n if (colour.order==\"r\") i = order( h[,1], h[,2], h[,3])\n if (colour.order==\"g\") i = order( h[,2], h[,3], h[,1])\n if (colour.order==\"b\") i = order( h[,3], h[,1], h[,2])\n return( h[i,] )\n }\n\n\n", "meta": {"hexsha": "9f1cd40cf4297cda827865026b0c8759e4010667", "size": 725, "ext": "r", "lang": "R", "max_stars_repo_path": "R/colour.names.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/colour.names.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/colour.names.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.25, "max_line_length": 123, "alphanum_fraction": 0.5889655172, "num_tokens": 247, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3423259679420455}} {"text": "#' Filter peaks that do not pass a posterior probability threshold\n#'\n#' @param model Chromstar model object\n#' @param pp Posterior probability cutoff\nfilter_peaks = function(model, pp=1e-4) {\n if (is.list(model) && !grepl(\"HMM\", class(model)))\n lapply(model, chromstaR::changeFDR, fdr=pp)\n else\n chromstaR::changeFDR(model, fdr=pp)\n}\n", "meta": {"hexsha": "6e76330f60f6e482c30dbdc57d1a71d612553ade", "size": 360, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/chromstar/filter_peaks.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "tools/chromstar/filter_peaks.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "tools/chromstar/filter_peaks.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 32.7272727273, "max_line_length": 66, "alphanum_fraction": 0.6777777778, "num_tokens": 104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3423259679420455}} {"text": "library(ape)\nlibrary(phytools)\nargs = commandArgs(trailingOnly=TRUE)\n\ndat <- read.table(args[1], header=T, sep='\\t', row.names=1)\nd <- dist(dat)\nnjt <- nj(d)\nmid.njt <- midpoint.root(njt)\nwrite.tree(mid.njt, file=args[2])", "meta": {"hexsha": "28fcf217566f6695bd402e4a4530f5a6c76285aa", "size": 221, "ext": "r", "lang": "R", "max_stars_repo_path": "Helper_Scripts/constructNJDendro.r", "max_stars_repo_name": "raufs/Rotation-in-Anantharaman-Lab", "max_stars_repo_head_hexsha": "2b21392b129a9af1eff07e6b05540daa450d3931", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Helper_Scripts/constructNJDendro.r", "max_issues_repo_name": "raufs/Rotation-in-Anantharaman-Lab", "max_issues_repo_head_hexsha": "2b21392b129a9af1eff07e6b05540daa450d3931", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Helper_Scripts/constructNJDendro.r", "max_forks_repo_name": "raufs/Rotation-in-Anantharaman-Lab", "max_forks_repo_head_hexsha": "2b21392b129a9af1eff07e6b05540daa450d3931", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.5555555556, "max_line_length": 59, "alphanum_fraction": 0.6877828054, "num_tokens": 70, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3423230717621839}} {"text": "# This file contains some utility functions.\n\nplot_values <- function(df, title=NA, ylab='Implied Volatility'){\n # Draw a plot that compares the implied volatility\n #\n # Args:\n # df (data.frame): A dataframe containing the strikes (in column 'K') and either prices or implied volatilities.\n # title (Optional[string]): The title for the plot and file name. Defaults to NA (no file saved in this case).\n # ylab (Optional[string]): The label for the y-axis. Defaults to 'Implied Volatility'.\n\n # Draw the plot\n matplot(df$K, df[names(df)!='K'], main=title, xlab='K', ylab=ylab, type='l', lty=1)\n legend('topright', inset=.05, legend=names(df)[names(df)!='K'], lty=1, col=1:(ncol(df)-1))\n\n # Save plot to a pdf file in the \"results\" folder. Replace spaces in file name with underscores\n if (!is.na(title)) dev.copy2pdf(file=paste('results/', gsub(\" \", \"_\", title), '.pdf', sep=''))\n}\n\nrowProds <- function(x, N_T){\n # More efficient row product based on r.789695.n4.nabble.com/matrix-row-product-and-cumulative-product-tp841548.html\n #\n # Args:\n # x (array-like): A matrix or vector or scalar to do a row product over.\n # N_T (int): The number of time steps not including 0 and T.\n # Returns:\n # (scalar or vector): The product of each row.\n\n # If N_T==1, then there is only one malliavin weight per row. To avoid mistakenly doing a product over the column, return x as is.\n if (N_T == 1) return(x)\n\n # If there is only one one row, return the product of that row.\n if(is.vector(x)) return(prod(x))\n\n # Otherwise, return the product of each row in the matrix.\n y <- x[,1]\n for (j in 2:ncol(x)) y <- y*x[,j]\n return(y)\n}\n\nfeller <- function(params)\n # Args:\n # params (list): A list containing the parameters to plug into the Heston model\n # Returns:\n # (boolean): Do the parameters meet the Feller Condition?\n 2*params$lambda*params$vbar > params$eta^2", "meta": {"hexsha": "6d503fd678662157c008ea59d49515bd0a65b364", "size": 1975, "ext": "r", "lang": "R", "max_stars_repo_path": "utility_functions.r", "max_stars_repo_name": "scotthounsell/exact-simulation", "max_stars_repo_head_hexsha": "71ea16268e07e23ef99ad5586844a843bb1cf48a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-12-21T18:46:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-21T18:46:57.000Z", "max_issues_repo_path": "utility_functions.r", "max_issues_repo_name": "scotthounsell/exact-simulation", "max_issues_repo_head_hexsha": "71ea16268e07e23ef99ad5586844a843bb1cf48a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utility_functions.r", "max_forks_repo_name": "scotthounsell/exact-simulation", "max_forks_repo_head_hexsha": "71ea16268e07e23ef99ad5586844a843bb1cf48a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.8888888889, "max_line_length": 135, "alphanum_fraction": 0.6506329114, "num_tokens": 553, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5888891307678319, "lm_q1q2_score": 0.34232306321907324}} {"text": "rm(list = setdiff(ls(),c('DATADIR','RESDIR','MAIN_DIR')))\r\n\r\n# Run eid_time_table_unrounded.py to create editor-vs-edit timestamp file before executing this script\r\n\r\n# Raw Input Files:\r\n# \t'wikichallenge_example_entry.csv'\r\n#\t'regdates.tsv'\r\n# Derived Input files:\r\n#\t'edit_times_unrounded.csv'\r\n\r\n\r\nNE = 44514\r\nNT = 22126031\r\n# DATADIR = 'E:\\\\public\\\\ICDM11\\\\wikichallenge_data_all\\\\'\r\n# RESDIR = 'D:\\\\ICDM11\\\\Honourable Mention\\\\AllFeatureSets\\\\'\r\nt0 = strptime(\"2001-01-01 0:0:0\", \"%Y-%m-%d %H:%M:%S\"); \r\n\r\n# First create EID | FSDT | RegDate Table\r\nX = read.csv(paste(DATADIR,'wikichallenge_example_entry.csv',sep=''),header=T)\r\nRD <- read.csv(paste(DATADIR,'regdates.tsv',sep=''),header=T,sep='\\t')\r\nET = scan(paste(RESDIR,'edit_times_unrounded.csv',sep=''),what='character');\r\nstopifnot(length(ET) == NE, all(dim(X) == (c(NE,2))))\r\n\r\nEFR = array(0,c(NE,3))\r\n\r\nfor (i in 1:NE){\r\n\tix <- which(RD[,1] == X[i,1])\r\n\tEFR[i,1] = X[i,1]\r\n\tEFR[i,2] <- min(as.numeric(strsplit(ET[i],',',fixed=T)[[1]]))\r\n\tEFR[i,3] <- as.numeric(strptime(RD[ix,2],\"%Y-%m-%d\") - t0)\r\n}\r\n\r\ncolnames(EFR) = c('user_id','FSDTnum','RegDateNum')\r\nwrite.csv(EFR,file=paste(RESDIR,'eid_fsdt_regdate_table.csv',sep=''),row.names=F)", "meta": {"hexsha": "277761d1e3af7ef7a9d16bf95c50cb5e703368b6", "size": 1201, "ext": "r", "lang": "R", "max_stars_repo_path": "FeatureCreation/eid_fsdt_regdate_table_creation.r", "max_stars_repo_name": "kvdesai/wikipedia-challenge", "max_stars_repo_head_hexsha": "6d558b0b50c67cc37410ccefeba9471c1374ff94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FeatureCreation/eid_fsdt_regdate_table_creation.r", "max_issues_repo_name": "kvdesai/wikipedia-challenge", "max_issues_repo_head_hexsha": "6d558b0b50c67cc37410ccefeba9471c1374ff94", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FeatureCreation/eid_fsdt_regdate_table_creation.r", "max_forks_repo_name": "kvdesai/wikipedia-challenge", "max_forks_repo_head_hexsha": "6d558b0b50c67cc37410ccefeba9471c1374ff94", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.3235294118, "max_line_length": 103, "alphanum_fraction": 0.6486261449, "num_tokens": 424, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.342277142545966}} {"text": "# R code\n# Luis P. F. Garcia 2018\n# Config File \n\n# Packages\nrequire(e1071)\nrequire(kknn)\nrequire(randomForest)\nrequire(rJava)\nrequire(rpart)\nrequire(RWeka)\nrequire(xgboost)\nrequire(parallel)\nrequire(wavelets)\n\n# list of classifiers\nCLASSIFIERS = c(\"Adaboost\", \"ANN\", \"C4.5\", \"CART\", \"kNN\", \"RF\", \"SVM\", \"XGBoost\")\n\n# datasets\nFILES = list.files(path=\"datasets\", full.names=TRUE)\n\n# mlp classifier\nMLP = make_Weka_classifier(\"weka/classifiers/functions/MultilayerPerceptron\")\n", "meta": {"hexsha": "92e6d8d89ead0315db0685bef4d2b272328502df", "size": 476, "ext": "r", "lang": "R", "max_stars_repo_path": "exp/config.r", "max_stars_repo_name": "QROWD/ar", "max_stars_repo_head_hexsha": "c3a6aafd0252700215f1f98c096428d5b6d4a8c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exp/config.r", "max_issues_repo_name": "QROWD/ar", "max_issues_repo_head_hexsha": "c3a6aafd0252700215f1f98c096428d5b6d4a8c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exp/config.r", "max_forks_repo_name": "QROWD/ar", "max_forks_repo_head_hexsha": "c3a6aafd0252700215f1f98c096428d5b6d4a8c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.8333333333, "max_line_length": 81, "alphanum_fraction": 0.7352941176, "num_tokens": 149, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.342277142545966}} {"text": "## Produce results for WPRO\n\nsource(\"utils/extract-results.r\")\n\n##JOBIDs\ndateDir <- \"2020-05-11\"\nJOBID <- \"1103566\"\nStanModel <- \"base_wpro\"\n# Load files and results\nresultsDir <- paste0(\"results/DateRep-\", dateDir, \"/\")\nfiguresDir <- paste0(\"figures/DateRep-\", dateDir, \"/\")\nfilename <- paste0(StanModel, '-',JOBID,\"-stanfit.Rdata\")\n# filename <- paste0('base-',JOBID,\".Rdata\")\nprint(sprintf(\"loading: %s\",paste0(\"results/DateRep-\", dateDir, \"/\", \n filename)))\nload(paste0(\"results/DateRep-\", dateDir, \"/\", filename))\n\n# system(paste0(\"Rscript covariate-size-effects.r \", \"DateRep-\", dateDir, \"/\", filename,\n # '-stanfit.Rdata'))\nresults <- StanResults(countries,JOBID,out,resultsDir)\nplot_covariate_effects(resultsDir, out)\n\npredictionR0 <- resultsR0(stan_data, out)\n\n# Sort out intervention dates\ncovariates <- read.csv(\"data/interventions.csv\", \n stringsAsFactors = FALSE)\n\nif ((length(countries) == 2) & (countries[1] == countries[2])) {\n nCountries <- 1\n} else {\n nCountries <- length(countries) \n}\n\ncovariates <- TidyCovariates(nCountries, covariates)\n\nresults <- vector(mode = \"list\", length = nCountries)\n\nfor(ii in 1:nCountries) {\n print(ii)\n N <- length(dates[[ii]])\n country <- countries[[ii]]\n print(country)\n \n data_country <- CountryOutputs(ii,country,dates[[ii]],reported_cases,\n deaths_by_country,prediction,estimated.deaths,out$Rt)\n \n write.csv(data_country, paste0(figuresDir, 'SummaryResults-',JOBID,\n '-',countries[[ii]],'.csv'))\n \n country_covariates <- CountryCovariates(country, covariates,\n data_country$rt_max)\n \n write.csv(country_covariates, paste0(figuresDir, 'Interventions-',\n JOBID, '-',countries[[ii]],'.csv'))\n \n make_plots(data_country = data_country, \n covariates_country_long = country_covariates,\n filename = filename,\n figuresDir = figuresDir, \n country = country)\n \n results[[ii]] <- data_country\n}\n\nforecast <- 10\n\nfor(ii in 1:nCountries) {\n N <- length(dates[[ii]])\n country <- countries[[ii]]\n \n data_country <- CountryOutputs(ii,country,dates[[ii]],reported_cases,\n deaths_by_country,prediction,estimated.deaths,rt)\n \n data_country_forecast <- CountryForecast(ii,country,dates[[ii]],\n forecast,prediction, estimated.deaths)\n \n make_single_plot(data_country, data_country_forecast, filename,\n figuresDir, country, logy = FALSE, \n ymax = round(max(data_country$deaths), digits = -1))\n}\n\nforecastR0 <- vector(mode = \"list\", length = nCountries)\n\nfor(ii in 1:nCountries) {\n N <- length(dates[[ii]])\n country <- countries[[ii]]\n \n forecastR0[[ii]] <- CountryForecastR0(ii,country,dates[[ii]],\n forecast,predictionR0)\n \n write.csv(forecastR0[[ii]], paste0(figuresDir, 'R0_Results-',JOBID,\n '-',countries[[ii]],'.csv'))\n \n data_countryTest <- CountryOutputs(ii,country,dates[[ii]],reported_cases,\n deaths_by_country, prediction, estimated.deaths, out$Rt)\n \n data_country_forecastTest <- CountryForecast(ii,country,dates[[ii]],\n forecast, prediction, estimated.deaths)\n \n make_comparison_plot(data_countryTest, data_country_forecastTest, \n forecastR0[[ii]], \"infections\", filename, figuresDir, country, \n logy = TRUE, include_cases = TRUE, \n ymax = max(forecastR0[[ii]]$predicted_max))\n \n make_comparison_plot(data_countryTest, data_country_forecastTest, \n forecastR0[[ii]], \"infections\", paste0(filename, \"linear\"), \n figuresDir, country, logy = FALSE, include_cases = FALSE, \n ymax = max(forecastR0[[ii]]$predicted_max))\n \n make_comparison_plot(data_countryTest, data_country_forecastTest, \n forecastR0[[ii]], \"deaths\", filename, figuresDir, country, \n logy = TRUE, include_cases = TRUE, \n ymax = max(forecastR0[[ii]]$death_max))\n \n make_comparison_plot(data_countryTest, data_country_forecastTest, \n forecastR0[[ii]], \"deaths\", paste0(filename, \"linear\"), \n figuresDir, country, logy = FALSE, include_cases = FALSE, \n ymax = max(forecastR0[[ii]]$death_max))\n}\n\n\n", "meta": {"hexsha": "80abec8fc2bff0a2bd97a43779675b1fd0dfee28", "size": 4007, "ext": "r", "lang": "R", "max_stars_repo_path": "wpro-results.r", "max_stars_repo_name": "leftygray/covid19model-wpro", "max_stars_repo_head_hexsha": "1d557ee36d803f34bf555995a7f80f419a8a53ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-10T01:04:29.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-10T01:04:29.000Z", "max_issues_repo_path": "wpro-results.r", "max_issues_repo_name": "The-Kirby-Institute/covid19model-wpro", "max_issues_repo_head_hexsha": "1d557ee36d803f34bf555995a7f80f419a8a53ba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "wpro-results.r", "max_forks_repo_name": "The-Kirby-Institute/covid19model-wpro", "max_forks_repo_head_hexsha": "1d557ee36d803f34bf555995a7f80f419a8a53ba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5772357724, "max_line_length": 88, "alphanum_fraction": 0.6900424258, "num_tokens": 1093, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.342277142545966}} {"text": "# test funciones Web\r\n\r\n\r\ntestthat::test_that(\r\n \"mostrar grado: devuelva grado\",{\r\n source(here:::here(\"funcs\",\"funciones.r\"),encoding = \"UTF-8\") # asi toma la ultima version\r\n \r\n # setup \r\n cota_seccion <- c(\"Trabajos Originales\")\r\n db_limpia <- \"db_raab_grafos.sqlite\"\r\n cota_anio <- c(1996:2016)\r\n art_full <- articulos_todos_grafo(db_limpia,anios = cota_anio,secciones = cota_seccion)\r\n gb_ok <- armado_grafo_bipartito(art_full)\r\n g_aut <- extraccion_grafo_coautoria(gb_ok,art_full,width_multiplier = 3)\r\n \r\n in_value <- \"a0185\"\r\n g <- g_aut\r\n \r\n vertice <- get_vertex_from_click_vertex(g,in_value)\r\n \r\n grado <- igraph:::degree(g,vertice)\r\n #glimpse(grado)\r\n actual <- as.numeric(grado)\r\n expected <- 0\r\n \r\n expect_equal(actual, expected)\r\n \r\n})\r\n", "meta": {"hexsha": "35619dc89e2b7e5b67b0cb6d33c0eaa4eff42a67", "size": 918, "ext": "r", "lang": "R", "max_stars_repo_path": "test/test_funciones_web.r", "max_stars_repo_name": "jas1/raab_coaut_tesis", "max_stars_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-01-26T03:25:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T03:25:05.000Z", "max_issues_repo_path": "test/test_funciones_web.r", "max_issues_repo_name": "jas1/raab_coaut_tesis", "max_issues_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-12-05T19:22:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-05T19:22:29.000Z", "max_forks_repo_path": "test/test_funciones_web.r", "max_forks_repo_name": "jas1/raab_coaut_tesis", "max_forks_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.6551724138, "max_line_length": 99, "alphanum_fraction": 0.5751633987, "num_tokens": 251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.34226222288368036}} {"text": "# Import R Packages and Modules\nlibrary(\"ggplot2\")\nlibrary(\"dplyr\")\nlibrary(\"leaflet\")\n\n# Import Crimes_2015 datasets\ncrimes <- read.csv('Crimes_2015.csv', header = TRUE, stringsAsFactors = TRUE)\n\n# Plot Bar Chart showing crimes bases on Offense types\nggplot(crimes, aes(Offense, fill=Offense) ) + geom_bar() + ggtitle(\"Graph showing arrests based on types of offense.\") + xlab(\"Offense\") + ylab(\"Total Crimes\") + labs(fill = \"Offense types\")\n\n# Plot Bar Chart showing crimes bases on neighborhood\nggplot(crimes, aes(Neighborhood, fill=Neighborhood) ) + geom_bar() + ggtitle(\"Graph showing arrests based on Neighborhood.\") + xlab(\"Neighborhood\") + ylab(\"Total Crimes\") + labs(fill = \"Neighborhood\")\n\n# Plot Bar Chart showing crimes bases on Month\nggplot(crimes, aes(Month, fill=Month) ) + geom_bar(width = 0.5) + ggtitle(\"Graph showing arrests based on Month of crime.\") + xlab(\"Month\") + ylab(\"Total Crimes\") + labs(fill = \"Month\")\n\n# Plot Bar Chart showing crimes bases on Precinct\nggplot(crimes, aes(Precinct, fill=Precinct) ) + geom_bar(width = 0.5) + ggtitle(\"Graph showing arrests based on Precinct.\") + xlab(\"Precinct\") + ylab(\"Total Crimes\") + labs(fill = \"Precinct\")\n\n# Plot a map showing the first 20 rows from the `crimes` dataset\nleaflet(data = crimes[1:20,]) %>% addTiles() %>%\n addMarkers(~Longitude, ~Latitude, popup = ~as.character(Description))\n\n# Plot a clustered map of the first 1000 rows\nleaflet(data = crimes[1:1000,]) %>% addProviderTiles(\"CartoDB.Positron\") %>% addMarkers(\n ~Longitude, \n ~Latitude, \n popup = ~as.character(Description),\n clusterOptions = markerClusterOptions()\n)\n\n# Plot a pallete map \npal <- colorFactor(c(\"navy\", \"red\", \"green\", \"blue\", \"orange\", \"purple\"), domain = c(\"1\", \"2\", \"3\", \"4\", \"5\", \"18\"))\nleaflet(data = crimes[1:100,]) %>% addTiles() %>% addCircleMarkers(\n ~Longitude, \n ~Latitude, \n popup = ~as.character(Description),\n radius = crimes$Precinct,\n color = ~pal(Precinct),\n stroke = FALSE, \n fillOpacity = 0.5\n)\n", "meta": {"hexsha": "fdf32af626303462fa5bc0fb472835b7ecfc64b7", "size": 1998, "ext": "r", "lang": "R", "max_stars_repo_path": "eda/data_exploration.r", "max_stars_repo_name": "Kamparia/minneapolis_crimedata_mapping_with_r", "max_stars_repo_head_hexsha": "d04d6f13bb7d643a3c710e66df9394024b5e2094", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-01-06T15:04:42.000Z", "max_stars_repo_stars_event_max_datetime": "2017-01-19T01:03:18.000Z", "max_issues_repo_path": "eda/data_exploration.r", "max_issues_repo_name": "Kamparia/minneapolis_crimedata_mapping_with_r", "max_issues_repo_head_hexsha": "d04d6f13bb7d643a3c710e66df9394024b5e2094", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "eda/data_exploration.r", "max_forks_repo_name": "Kamparia/minneapolis_crimedata_mapping_with_r", "max_forks_repo_head_hexsha": "d04d6f13bb7d643a3c710e66df9394024b5e2094", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-07-26T15:40:32.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-01T00:36:53.000Z", "avg_line_length": 45.4090909091, "max_line_length": 200, "alphanum_fraction": 0.6911911912, "num_tokens": 570, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3422622228836803}} {"text": "## reference: https://github.com/kevinblighe/EnhancedVolcano\nVolcanoSC <- function(DEs, AdjustedCutoff, FCCutoff, LabellingCutoff, main)\n{\n DEs$Significance <- \"NS\"\n DEs$Significance[(abs(DEs$avg_logFC) > FCCutoff)] <- \"FC\"\n DEs$Significance[(DEs$p_valFCCutoff)] <- \"FC_max_pval\"\n #table(DEs$Significance)\n \n DEs$Significance <- factor(DEs$Significance, levels=c(\"NS\", \"FC\", \"max_pval\", \"FC_max_pval\"))\n \n plot <- ggplot(DEs, aes(x=avg_logFC, y=-log10(p_val))) +\n #Add points:\n #\u00a0\u00a0\u00a0\u00a0\u00a0 Colour based on factors set a few lines up\n #\u00a0\u00a0\u00a0\u00a0\u00a0 'alpha' provides gradual shading of colour\n #\u00a0\u00a0\u00a0\u00a0\u00a0 Set size of points\n geom_point(aes(color=factor(Significance)), alpha=1/2, size=0.8) +\n \n #Choose which colours to use; otherwise, ggplot2 choose automatically (order depends on how factors are ordered in DEs$Significance)\n \n scale_color_manual(values=c(NS=\"grey80\", FC=\"#AEC7E8\", max_pval=\"royalblue\", FC_max_pval=\"red2\"), labels=c(NS=\"NS\", FC=paste(\"LogFC>|\", FCCutoff, \"|\", sep=\"\"), max_pval=paste(\"max_pval <\", AdjustedCutoff, sep=\"\"), FC_max_pval=paste(\"max_pval <\", AdjustedCutoff, \" & LogFC>|\", FCCutoff, \"|\", sep=\"\"))) +\n \n #Set the size of the plotting window\n \n theme_bw(base_size=24) +\n \n #Modify various aspects of the plot text and legend\n \n theme(legend.background=element_rect(),\n plot.title=element_text(angle=0, size=12, face=\"bold\", vjust=1),\n panel.grid.major=element_blank(), #Remove gridlines\n panel.grid.minor=element_blank(), #Remove gridlines\n axis.text.x=element_text(angle=0, size=12, vjust=1),\n axis.text.y=element_text(angle=0, size=12, vjust=1),\n axis.title=element_text(size=12),\n \n \n #Legend\n legend.position=\"top\",\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #Moves the legend to the top of the plot\n legend.key=element_blank(),\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #removes the border\n legend.key.size=unit(0.5, \"cm\"), #Sets overall area/size of the legend\n legend.text=element_text(size=8), #Text size\n title=element_text(size=8),\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #Title text size\n legend.title=element_blank()) +\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 #Remove the title\n \n \n #Change the size of the icons/symbols in the legend\n guides(colour = guide_legend(override.aes=list(size=2.5))) +\n \n #Set x- and y-axes labels\n xlab(bquote(~Log[2]~ \"fold change\")) +\n ylab(bquote(~-Log[10]~max~italic(Pval))) +\n \n #Set the axis limits\n #xlim(-6.5, 6.5) +\n #ylim(0, 100) +\n \n #Set title\n ggtitle(main) +\n \n #Tidy the text labels for a subset of genes\n \n geom_text(data=subset(DEs, p_valFCCutoff),\n aes(label=row.names(subset(DEs, p_valFCCutoff))),\n size=2.25,\n #segment.color=\"black\", #This and the next parameter spread out the labels and join them to their points by a line\n #segment.size=0.01,\n check_overlap=TRUE,\n vjust=1.0) +\n \n #Add a vertical line for fold change cut-offs\n geom_vline(xintercept=c(-FCCutoff, FCCutoff), linetype=\"longdash\", colour=\"black\", size=0.4) +\n \n #Add a horizontal line for P-value cut-off\n geom_hline(yintercept=-log10(AdjustedCutoff), linetype=\"longdash\", colour=\"black\", size=0.4)\n \n return(plot)\n \n}", "meta": {"hexsha": "12ee42d5d106d040ef988c13aab14df13dce816d", "size": 3463, "ext": "r", "lang": "R", "max_stars_repo_path": "script/VolcanoSC_integrativeAnalysis.r", "max_stars_repo_name": "Koehler-Lab/Lee_et_al_2020_Nature", "max_stars_repo_head_hexsha": "a0ee9c94a73091452fde092c9cf59fd77e1a7d6e", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-04T08:46:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-30T09:17:19.000Z", "max_issues_repo_path": "script/VolcanoSC_integrativeAnalysis.r", "max_issues_repo_name": "Koehler-Lab/Lee_et_al_2020_Nature", "max_issues_repo_head_hexsha": "a0ee9c94a73091452fde092c9cf59fd77e1a7d6e", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "script/VolcanoSC_integrativeAnalysis.r", "max_forks_repo_name": "Koehler-Lab/Lee_et_al_2020_Nature", "max_forks_repo_head_hexsha": "a0ee9c94a73091452fde092c9cf59fd77e1a7d6e", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-06-09T22:05:37.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-09T22:05:37.000Z", "avg_line_length": 43.835443038, "max_line_length": 306, "alphanum_fraction": 0.6321108865, "num_tokens": 1003, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.34226221457390893}} {"text": "\n\n## Two-class sample data for classification metrics ----------------------------\n\n\nrm(list = ls())\nset.seed(7)\noptions(scipen = 999)\npred <- runif(1000)\nact <- round(pred)\npred[sample(1000, 300)] <- runif(300) # noise\n\nwrite.csv(data.frame(act, pred),\n file = \"two-class-sample-data.csv\",\n row.names = FALSE)\n\n## Multi-class sample data for classification metrics --------------------------\n\n\n\n\n\n## Sample data for regression metrics ------------------------------------------\n\n\nrm(list = ls())\ndata(swiss)\nfit <- lm(Fertility ~ ., data = swiss)\npred <- predict(fit)\n\nwrite.csv(data.frame(act = swiss$Fertility,\n pred = pred),\n file = \"regression-sample-data.csv\",\n row.names = FALSE)\n", "meta": {"hexsha": "080374fca83ddeb805764bdc6f24ee00d491770f", "size": 743, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/tinytest/helper-data.r", "max_stars_repo_name": "maiing/metrics", "max_stars_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/tinytest/helper-data.r", "max_issues_repo_name": "maiing/metrics", "max_issues_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/tinytest/helper-data.r", "max_forks_repo_name": "maiing/metrics", "max_forks_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.2285714286, "max_line_length": 80, "alphanum_fraction": 0.5397039031, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34226220595463785}} {"text": "canMakeNoRecursion <- function(x) {\n x <- toupper(x)\n charList <- strsplit(x, character(0))\n getCombos <- function(chars) {\n charBlocks <- data.matrix(expand.grid(lapply(chars, function(char) which(blocks == char, arr.ind=TRUE)[, 1L])))\n charBlocks <- charBlocks[!apply(charBlocks, 1, function(row) any(duplicated(row))), , drop=FALSE]\n if (dim(charBlocks)[1L] > 0L) {\n t(apply(charBlocks, 1, function(row) apply(blocks[row, , drop=FALSE], 1, paste, collapse=\"\")))\n } else {\n character(0)\n }\n }\n setNames(lapply(charList, getCombos), x)\n}\ncanMakeNoRecursion(c(\"A\",\n \"BARK\",\n \"BOOK\",\n \"TREAT\",\n \"COMMON\",\n \"SQUAD\",\n \"CONFUSE\"))\n", "meta": {"hexsha": "92e478a134ba17723d1e046bd4ecf96bc0afd5ad", "size": 718, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/ABC-Problem/R/abc-problem-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 139, "max_stars_repo_stars_event_min_datetime": "2016-12-14T19:36:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T05:41:52.000Z", "max_issues_repo_path": "Task/ABC-Problem/R/abc-problem-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": 133, "max_issues_repo_issues_event_min_datetime": "2017-01-19T10:19:36.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-13T21:07:07.000Z", "max_forks_repo_path": "Task/ABC-Problem/R/abc-problem-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 66, "max_forks_repo_forks_event_min_datetime": "2017-01-21T21:36:16.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-18T10:12:38.000Z", "avg_line_length": 32.6363636364, "max_line_length": 116, "alphanum_fraction": 0.5821727019, "num_tokens": 210, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6442251064863698, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3422184151282659}} {"text": "setwd(\"./\")\nresult_path_save <- \"./\"\n\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func1_SA.R\")\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func2_GA.R\")\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func3_PSO.R\")\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func4_Bee.R\")\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func5_Fish.R\")\nsource(\"https://raw.githubusercontent.com/liyujiao1026/pmediansolvers/master/Func6_compare.R\")\n\n\n# 1. Read IKEA data ===================================================#\nlibrary(h5)\nloadhdf5data <- function(h5File, dataset) {\n f <- h5file(h5File)\n nblocks <- h5attr(f[dataset], \"nblocks\")\n data <- do.call(cbind, lapply(seq_len(nblocks) - 1, function(i) {\n data <- as.data.frame(f[paste0(dataset, \"/block\", i, \"_values\")][])\n colnames(data) <- f[paste0(dataset, \"/block\", i, \"_items\")][]\n data}))\n h5close(f)\n data\n}\n\n\n\nDistance.matrix <- loadhdf5data(\"./data/PHD_MatrixDist.h5\", \"matrix\")\n# #List of Settlements (X,Y, ID of node). 1938 lines\nstore_candidates <- read.csv(\"./data/PHD_Settlements.txt\", header = F, sep = \" \")\n# List of population points : (X,Y, number of persons), 187679 lines\ncustomer <- read.csv(\"./data/PHD_Pop.txt\", header = F, sep = \" \") \nweight_customer <- customer[,3]\n#-------------------------------------------#\n\n\n# Check whether data is correct\ndim(Distance.matrix) == c(187679, 1938) #(187679 lines, 1938 columns)\ndim(customer) == c(187679, 3) # population points : (X,Y, number_of_persons), 187679 lines\n\n# 2. Settings =========================================#\n\nset.seed(1)\n# (1). P-median problem settings\nN = nrow(store_candidates) # Number of candidates\np = 19 # Number of selected facilities\n\n# (2). Iteration settings\nni <- 500 # Number of iterations\npop_size <- 100\n\n# (3). Parameters settings\nSA_temparature <- 500 # better the same as iteration time\nSA_cool_rate <- 0.95\n\n\nGA_prob_refine <- 0.9 # top 0.9 are selected \nGA_prob_cross <- 0.8\nGA_prob_mutation <- 0.2\n\n\nPSO_c1 <- 0.7 # updating rate for pbest\nPSO_c2 <- 0.3 # updating rate for gbest\n\nBEE_maxtrial <- 100\n\nFISH_crowdness <- 5 # how many peers are allowed in one group at most\nFISH_visual_mutual <- 10 # how many common elements in solutions are assumed as one group: p/2\nFISH_try_number <- 5\n\n\n# 2. Running algorithms---------------------------------------#\n\n\nresult_compare <- Compare_Algorithm_Func(\n N,p,ni,pop_size,\n Distance.matrix, weight_customer,\n algorithms = c(\"SA\",\"GA\",\"PSO\",\"BEE\",\"FISH\"),\n SA_temparature, SA_cool_rate,\n GA_prob_refine, GA_prob_cross,GA_prob_mutation,\n PSO_c1, PSO_c2,\n BEE_maxtrial,\n FISH_crowdness, FISH_visual_mutual, FISH_try_number,\n path_save = result_path_save)\n\n\n\n\n", "meta": {"hexsha": "bffc17b4b468e38734b1f91c5527080da274d3ef", "size": 3026, "ext": "r", "lang": "R", "max_stars_repo_path": "ikeaRun.r", "max_stars_repo_name": "liyujiao1026/pmediansolvers", "max_stars_repo_head_hexsha": "b2124d4517da9118d54b5ce7cd815dcd30be17cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ikeaRun.r", "max_issues_repo_name": "liyujiao1026/pmediansolvers", "max_issues_repo_head_hexsha": "b2124d4517da9118d54b5ce7cd815dcd30be17cd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-08-19T20:00:57.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-24T21:46:12.000Z", "max_forks_repo_path": "ikeaRun.r", "max_forks_repo_name": "liyujiao1026/pmediansolvers", "max_forks_repo_head_hexsha": "b2124d4517da9118d54b5ce7cd815dcd30be17cd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.3863636364, "max_line_length": 95, "alphanum_fraction": 0.6331791143, "num_tokens": 830, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863698, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3422184151282659}} {"text": "#Test code \n\npi <- 3.1415", "meta": {"hexsha": "91d576f5e635b2d3961b491f893efe405ee96fbf", "size": 25, "ext": "r", "lang": "R", "max_stars_repo_path": "r.r", "max_stars_repo_name": "ShinkaM/test", "max_stars_repo_head_hexsha": "6aa50a3d56359c4e789170fdfa004470b77a4f93", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r.r", "max_issues_repo_name": "ShinkaM/test", "max_issues_repo_head_hexsha": "6aa50a3d56359c4e789170fdfa004470b77a4f93", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-07-02T01:46:43.000Z", "max_issues_repo_issues_event_max_datetime": "2017-07-02T01:51:46.000Z", "max_forks_repo_path": "r.r", "max_forks_repo_name": "ShinkaM/test", "max_forks_repo_head_hexsha": "6aa50a3d56359c4e789170fdfa004470b77a4f93", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.3333333333, "max_line_length": 12, "alphanum_fraction": 0.6, "num_tokens": 11, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34221841512826584}} {"text": "\r\n\r\n#install.packages(\"FLCore\", repo = \"http://flr-project.org/R\")\r\n#library(devtools)\r\n#install_github(\"ices-tools-prod/msy\")\r\n\r\nlibrary(msy)\r\n\r\nnsamp <- 100 # number of stochatic runs for each option\r\n\r\nMstk<-SMS2FLStocks(sumfile=file.path(data.path,'summary.out'),\r\n bio.interact=F, read.input=TRUE, read.output=TRUE,control=read.FLSMS.control())\r\nlapply(Mstk,function(x) x@name) # just check\r\n\r\nSSB.R.year.first<-SMS.control@SSB.R.year.first\r\nSSB.R.year.last <-SMS.control@SSB.R.year.last\r\nSSB.R.year.first[SSB.R.year.first==-1]<-SMS.control@first.year.model\r\nSSB.R.year.last[SSB.R.year.last==-1]<-SMS.control@last.year.model\r\n\r\n# read recruiment years used\r\nrecruit.years<-matrix(head(scan(file='recruitment_years.in',comment.char='#'),-1),ncol=SMS.control@last.year.model-SMS.control@first.year.model+1 ,byrow=T)\r\ncolnames(recruit.years)<-c(as.character(seq(SMS.control@first.year,SMS.control@last.year)))\r\n\r\ndev<-'png'\r\nnox<-3; noy<-3; \r\nnoxy<-nox*noy\r\ni<-noxy\r\n\r\nFITs<-list()\r\n\r\n\r\nfor (s in (1:length(Mstk))) {\r\n i<-i+1 \r\n stk<-Mstk[[s]]\r\n class(stk)<-'FLStock'\r\n\r\n # move recruitment to first Quarter\r\n stk@stock.n[1,,,1,,]<-as.vector(stock.n(stk)[1,,,3,,])\r\n \r\n stk<-trim(stk,season=1,year=SSB.R.year.first[s]:(SSB.R.year.last[s]-1)) # Delete the most recent year (as driven by used S/R relation). SSB and recruit are there, but the rest of data is crap (first half-year only)\r\n \r\n harvest.spwn(stk)<-0\r\n m.spwn(stk)<-0\r\n #stk@stock.n<-stk@stock.n/1000\r\n cat('\\n',sp.names[s+first.VPA-1],'\\n SSB:\\n');print(ssb(stk))\r\n \r\n models<- c(\"Ricker\", \"Segreg\", \"Bevholt\")\r\n if ( Mstk[[s]]@name %in% c('Herring')) models<- c(\"Ricker\", \"Segreg\") # does not work with Ricker ?\r\n\r\n \r\n if (0==length(excl.years<-as.numeric(dimnames(recruit.years)[[2]][0==recruit.years[s,]]))) excl.years<-NULL\r\n FIT<-eqsr_fit(stk, nsamp = nsamp, models = models,\r\n method = \"Buckland\", \r\n id.sr = paste(sp.names[s+first.VPA-1],', ',SSB.R.year.first[s],'-',SSB.R.year.last[s],sep=''), \r\n remove.years = excl.years)\r\n \r\n FITs[[s]]<-FIT\r\n if (i>=noxy) {\r\n if (dev=='png') cleanup()\r\n newplot(dev,filename=paste('equisim',s,sep='_'),nox,noy,Portrait=T);\r\n i<-0\r\n }\r\n eqsr_plot(FIT,Scale=0.001)\r\n}\r\n\r\nif (dev=='png') cleanup()\r\n\r\nnames(FITs[[1]])\r\nFITs[[1]]$sr.det\r\nFITs[[1]]$id.sr\r\nlapply(FITs,function(x){ print(x$sr.det) })\r\n\r\nout<-file.path(data.path,'op_eqsim.in');unlink(out)\r\nsr<-function(x) {\r\n a<-x$sr.det\r\n dummy<-data.frame(model=c(\"Ricker\", \"Segreg\", \"Bevholt\"))\r\n a<-merge(x=a,y=dummy,all.y=T)\r\n a[is.na(a$a),'a']<- 1\r\n a[is.na(a$b),'b']<- 1\r\n a[is.na(a$cv),'cv']<- 1\r\n a[is.na(a$n),'n']<- 0\r\n a[is.na(a$prop),'prop']<- 0\r\n print(a)\r\n \r\n a$model2<-ifelse(a$model=='Ricker',1,ifelse(a$model=='Segreg',3,ifelse(a$model==\"Bevholt\",2,NA)))\r\n a$Species.n<-first.VPA-1+match(substr(x$id.sr,1,3),substr(sp.names[first.VPA:nsp],1,3))\r\n a$n<-NULL\r\n write.table(a,file=out,append=T,col.names=F,row.names=F)\r\n\r\n}\r\na<-lapply(FITs,sr)\r\na<-read.table(file=out,header=F)\r\nnames(a)<-c('m','a','b','cv','prop','model','Species.n')\r\n\r\n\r\na<-a[order(a$Species.n,a$model),]\r\na[a$m=='Segreg','a']<-log(a[a$m=='Segreg','a']) # to fit to SMS\r\nhead(a)\r\n\r\nunlink(out)\r\ncat('### parameters from Eqsim (a, b,cv, proportion) (1=Ricker, 2=Beverton&Holt, 3 = segmented regression)\\n',file=out)\r\nfor (i in (1:dim(a)[[1]])) {\r\n cat(a[i,'a'],a[i,'b'],a[i,'cv'],a[i,'prop'],'#',sp.names[a[i,'Species.n']],as.character(a[i,'m']),'\\n',file=out,append=T)\r\n} \r\n\r\n\r\n### stochastic\r\nhead(FITs[[1]]$sr.sto)\r\ndim((FITs[[1]]$sr.sto))\r\n\r\nout<-file.path(data.path,'op_eqsim_stoch.in');unlink(out)\r\nsr<-function(x) {\r\n a<-x$sr.sto\r\n a$model2<-ifelse(a$model=='Ricker',1,ifelse(a$model=='Segreg',100,ifelse(a$model==\"Bevholt\",2,NA)))\r\n a$iter<-1:nsamp\r\n a$Species.n<-first.VPA-1+match(substr(x$id.sr,1,3),substr(sp.names[first.VPA:nsp],1,3))\r\n #print(head(a))\r\n write.table(a,file=out,append=T,col.names=F,row.names=F)\r\n}\r\na<-lapply(FITs,sr)\r\na<-read.table(file=out,header=F)\r\nnames(a)<-c('a','b','cv','m','model','iter','Species.n')\r\n\r\n\r\na<-a[order(a$Species.n,a$iter),]\r\na[a$m=='Segreg','a']<-log(a[a$m=='Segreg','a']) # to fit to SMS\r\nhead(a)\r\n\r\nunlink(out)\r\n\r\ncat('### parameters from Eqsim\\n',nsamp,'### number of samples\\n### parameters,(a, b,cv, model) (1=Ricker, 2=Beverton&Holt, 100 = segmented regression)\\n',file=out)\r\na$label<-paste(\" #\",sp.names[a$Species.n], a$m,a$iter)\r\nwrite.table(subset(a,select=c(a,b,cv,model,label)),file=out,append=T,col.names=F,row.names=F,quote=F)\r\n\r\n\r\n", "meta": {"hexsha": "37872b39c0730a262fd478ccad34a3a1a9cfc398", "size": 4577, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/EQUISIM.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/EQUISIM.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/EQUISIM.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.6544117647, "max_line_length": 218, "alphanum_fraction": 0.6135022941, "num_tokens": 1588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.34221840787123814}} {"text": "require(reshape2) # For data handling\nrequire(lme4) # Linear mixed-effects models\nrequire(DHARMa) # Evaluate model fit\nrequire(car) # Anova() function [instead of base R anova()]\nrequire(emmeans) # Post-hoc analysis on the model\n\nrm(list = ls()) # Remove variables/objects\ngraphics.off() # Close any open graphics\n\nELP_10 = read.csv(\"./Data/ELP_10_GABA_side_learning_Y_maze.csv\", header = TRUE, stringsAsFactors = FALSE, sep = \";\")\nhead(ELP_10, n = 1) # Check if data was imported correctly\n\nELP_10$Solution = ifelse(ELP_10$Solution == \"G\", \"0.734mM GABA\", \"Control\")\n\nELP_10$Initial_Decision_Binary = ifelse(ELP_10$Initial_Decision == ELP_10$Reward_Side, 1, 0)\nELP_10$Final_Decision_Binary = ifelse(ELP_10$Final_Decision == ELP_10$Reward_Side, 1, 0)\nELP_10$Switched_Decision_Binary = ifelse(ELP_10$Initial_Decision_Binary == ELP_10$Final_Decision_Binary, 0, 1)\npaste0(\"Ants switched their final decision in \", round(sum(ELP_10$Switched_Decision_Binary) / nrow(ELP_10) * 100, 0), \"% of the visits!\")\n\ntable(ELP_10$Solution, ELP_10$Reward_Side) / 4\n\nfor (i in 1:nrow(ELP_10)) {\n if (ELP_10$Visit[i] == \"2\" & !is.na(ELP_10$Bridge_Nest_Duration[i])) {\n if (ELP_10$Bridge_Nest_Duration[i] != ELP_10$Time_Since_Marking[i]) {\n print(paste0(\"Warning: Row \", i, \" was changed from \", ELP_10$Bridge_Nest_Duration[i], \"s to \", ELP_10$Time_Since_Marking[i], \"s!\"))\n ELP_10$Bridge_Nest_Duration[i] = ELP_10$Time_Since_Marking[i]\n }\n }\n else if (ELP_10$Visit[i] == \"2\" & is.na(ELP_10$Bridge_Nest_Duration[i])) {\n ELP_10$Bridge_Nest_Duration[i] = ELP_10$Time_Since_Marking[i]\n print(paste0(\"Warning: Row \", i, \" was changed and Bridge_Nest_Duration was NA!\"))\n }\n}\n\nfor (i in 1:nrow(ELP_10)) {\n if (ELP_10$Time_Since_Marking[i] <= 1800) {\n ELP_10$TSM_Bin[i] = \"0-30\"\n }\n else if (ELP_10$Time_Since_Marking[i] > 1800 & ELP_10$Time_Since_Marking[i] <= 3600) {\n ELP_10$TSM_Bin[i] = \"30-60\"\n }\n else {\n print(\"Warning: Undefined bins!\")\n }\n}\n\ntable(ELP_10$Solution, ELP_10$TSM_Bin, ELP_10$Reward_Side) / 4\n\nELP_10$Collection_Date = as.factor(ELP_10$Collection_Date)\nELP_10$Time_Collection = as.factor(ELP_10$Time_Collection)\nELP_10$Experimentor = as.factor(ELP_10$Experimentor)\nELP_10$Starvation_Period = as.factor(ELP_10$Starvation_Period)\nELP_10$Donor_Colony = as.factor(ELP_10$Donor_Colony)\nELP_10$Recipient_Colony = as.factor(ELP_10$Recipient_Colony)\n\nELP_10$Visit = as.factor(ELP_10$Visit)\nELP_10$Solution = as.factor(ELP_10$Solution)\nELP_10$Solution = relevel(ELP_10$Solution, \"Control\")\nELP_10$Reward_Side = as.factor(ELP_10$Reward_Side)\nELP_10$TSM_Bin = as.factor(ELP_10$TSM_Bin)\n\nELP_10$Initial_Decision_Binary = as.factor(ELP_10$Initial_Decision_Binary)\nELP_10$Final_Decision_Binary = as.factor(ELP_10$Final_Decision_Binary)\n\ninitial_final_diff = melt(ELP_10, measure.vars = c(\"Initial_Decision_Binary\", \"Final_Decision_Binary\"))\ninitial_final_diff$value = as.factor(initial_final_diff$value)\nhead(initial_final_diff, n = 1) # Check if data was imported correctly\n\ndiff_model = glmer(value ~ variable + (1|Starvation_Period) + (1|Collection_Date), data = initial_final_diff, family = binomial, glmerControl(optimizer = \"bobyqa\", optCtrl = list(maxfun = 1000000000)))\nAnova(diff_model)\n\ne = emmeans(diff_model, ~variable, type = \"response\")\npairs(e)\n\nmod1 = glmer(Final_Decision_Binary ~ (Reward_Side + Solution + TSM_Bin + Visit)^2 +\n (1|Starvation_Period) + (1|Collection_Date), \n data = ELP_10, family = \"binomial\", glmerControl(optimizer = \"bobyqa\", optCtrl = list(maxfun = 1000000000)))\n\nsimres = simulateResiduals(mod1)\nplot(simres, asFactor = T)\n\nsummary(mod1)\nAnova(mod1)\ndrop.scope(mod1)\n\nmod2 = update(mod1, . ~ . - Reward_Side:Visit)\nanova(mod1, mod2)\n\nsummary(mod2)\nAnova(mod2)\ndrop.scope(mod2)\n\nmod3 = update(mod2, . ~ . - TSM_Bin:Visit)\nanova(mod2, mod3)\n\nsummary(mod3)\nAnova(mod3)\ndrop.scope(mod3)\n\nmod4 = update(mod3, . ~ . - Solution:TSM_Bin)\nanova(mod3, mod4)\n\nsummary(mod4)\nAnova(mod4)\ndrop.scope(mod4)\n\nmod5 = update(mod4, . ~ . - Reward_Side:TSM_Bin)\nanova(mod4, mod5)\n\nsummary(mod5)\nAnova(mod5)\ndrop.scope(mod5)\n\nmod6 = update(mod5, . ~ . - Solution:Visit)\nanova(mod5, mod6)\n\nsummary(mod6)\nAnova(mod6)\ndrop.scope(mod6)\n\nmod7 = update(mod6, . ~ . - Reward_Side:Solution)\nanova(mod6, mod7)\n\nsummary(mod7)\nAnova(mod7)\ndrop.scope(mod7)\n\nanova(mod1, mod7)\nsimres = simulateResiduals(mod7)\nplot(simres, asFactor = T)\n\nmeanobj = emmeans(mod7, ~Visit, type = \"response\")\npairs(meanobj, adjust = \"bonferroni\")\n\nmeanobj = emmeans(mod7, ~TSM_Bin * Visit, type = \"response\")\ntest(meanobj)\n\nmeanobj = emmeans(mod7, ~Solution, type = \"response\")\ntest(meanobj)\npairs(meanobj, adjust = \"bonferroni\")\n\nmeanobj = emmeans(mod7, ~1, type = \"response\")\ntest(meanobj)\n\nsessionInfo()\n", "meta": {"hexsha": "92a6259bdcbc417225cbbf8c0c7bc0eec10f655f", "size": 4742, "ext": "r", "lang": "R", "max_stars_repo_path": "content/_build/jupyter_execute/ELP_10_analysis.r", "max_stars_repo_name": "ACElab-UR/ACEBook", "max_stars_repo_head_hexsha": "2b27bb8a71f8df1c647125cb08d7d1b80df9b395", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "content/_build/jupyter_execute/ELP_10_analysis.r", "max_issues_repo_name": "ACElab-UR/ACEBook", "max_issues_repo_head_hexsha": "2b27bb8a71f8df1c647125cb08d7d1b80df9b395", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "content/_build/jupyter_execute/ELP_10_analysis.r", "max_forks_repo_name": "ACElab-UR/ACEBook", "max_forks_repo_head_hexsha": "2b27bb8a71f8df1c647125cb08d7d1b80df9b395", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4794520548, "max_line_length": 201, "alphanum_fraction": 0.7311261071, "num_tokens": 1661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5736784074525098, "lm_q1q2_score": 0.3421608175009802}} {"text": "test_that(\"Gerador de grades\", {\n\n # metodos entregam mesmo resultado\n grade_1 <- coordgrade(colinadummy, 20, 20)\n grade_2 <- coordgrade(colinadummy$CC, 20, 20)\n grade_3 <- coordgrade(as.data.frame(colinadummy$CC), 20, 20)\n expect_true(all(grade_1 == grade_2))\n expect_true(all(grade_2 == grade_3))\n\n # grade gerada com dhl e dpot inteiros\n expect_equal(length(unique(grade_1$hl)), 20)\n expect_equal(min(grade_1$hl), min(colinadummy$CC$hl))\n expect_equal(max(grade_1$hl), max(colinadummy$CC$hl))\n\n expect_equal(length(unique(grade_1$pot)), 20)\n expect_equal(min(grade_1$pot), min(colinadummy$CC$pot))\n expect_equal(max(grade_1$pot), max(colinadummy$CC$pot))\n\n grade_2 <- coordgrade(colinadummy, 30, 30, expande = c(.1, .1))\n\n deltahl <- .1 * diff(range(colinadummy$CC$hl))\n deltapot <- .1 * diff(range(colinadummy$CC$pot))\n\n expect_equal(length(unique(grade_2$hl)), 30)\n expect_equal(min(grade_2$hl), min(colinadummy$CC$hl) - deltahl)\n expect_equal(max(grade_2$hl), max(colinadummy$CC$hl) + deltahl)\n\n expect_equal(length(unique(grade_2$pot)), 30)\n expect_equal(min(grade_2$pot), min(colinadummy$CC$pot) - deltapot)\n expect_equal(max(grade_2$pot), max(colinadummy$CC$pot) + deltapot)\n\n # grade gerada com dhl e dpot vetores\n\n grade_5 <- coordgrade(colinadummy, 20:40, 200:300)\n\n expect_equal(unique(grade_5$hl), 20:40)\n expect_equal(unique(grade_5$pot), 200:300)\n\n grade_5 <- coordgrade(colinadummy, 20:40, 200:300, expand = c(.1, .1))\n\n expect_equal(unique(grade_5$hl), 20:40)\n expect_equal(unique(grade_5$pot), 200:300)\n\n # grade gerada com byhl e bypot\n # os limites dos expect_equal aqui e no proximo teste foram pre calculados\n\n grade_3 <- coordgrade(colinadummy, byhl = .5, bypot = 5)\n\n expect_equal(min(grade_3$hl), 34)\n expect_equal(max(grade_3$hl), 61.5)\n expect_equal(seq(34, 61.5, by = .5), unique(grade_3$hl))\n\n expect_equal(min(grade_3$pot), 115)\n expect_equal(max(grade_3$pot), 455)\n expect_equal(seq(115, 455, by = 5), unique(grade_3$pot))\n\n # grade gerada com byhl e bypot e expand\n\n grade_4 <- coordgrade(colinadummy, byhl = .5, bypot = 5, expand = c(.1, .1))\n\n expect_equal(min(grade_4$hl), 31)\n expect_equal(max(grade_4$hl), 64)\n expect_equal(seq(31, 64, by = .5), unique(grade_4$hl))\n\n expect_equal(min(grade_4$pot), 85)\n expect_equal(max(grade_4$pot), 485)\n expect_equal(seq(85, 485, by = 5), unique(grade_4$pot))\n})", "meta": {"hexsha": "968ce770eee0df5b2b9d4dc716e01013e304451c", "size": 2476, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-utils.r", "max_stars_repo_name": "lkhenayfis/gtdp-curvacolina", "max_stars_repo_head_hexsha": "a3583255f8dfeb9ef14a6195055deac2b8938ba4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test-utils.r", "max_issues_repo_name": "lkhenayfis/gtdp-curvacolina", "max_issues_repo_head_hexsha": "a3583255f8dfeb9ef14a6195055deac2b8938ba4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-01-29T15:10:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-30T16:13:23.000Z", "max_forks_repo_path": "tests/testthat/test-utils.r", "max_forks_repo_name": "lkhenayfis/gtdp-curvacolina", "max_forks_repo_head_hexsha": "a3583255f8dfeb9ef14a6195055deac2b8938ba4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4117647059, "max_line_length": 80, "alphanum_fraction": 0.6829563813, "num_tokens": 874, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3421608175009801}} {"text": "#' Local ATE label\n#'\n#' @param object object with \"local_lm\" or \"global_lm\" class\n#' @param label string of label format\n#' @param digits decimal place\n#'\n#' @importFrom generics tidy\n#' @importFrom dplyr case_when\n#' \nlabel_maker <- function(object, label, digits = 3) {\n decompose <- strsplit(label, \"\\\\{|\\\\}\")[[1]]\n textpart <- decompose[grep(\"[^'']\", decompose)]\n numform <- paste0(\"%1.\", digits, \"f\")\n\n res <- generics::tidy(object)\n\n textpart[grep(\"estimate\", textpart)] <- sprintf(numform, res$estimate)\n textpart[grep(\"std.error\", textpart)] <- sprintf(numform, res$std.error)\n textpart[grep(\"statistic\", textpart)] <- sprintf(numform, res$statistic)\n textpart[grep(\"p.value\", textpart)] <- sprintf(numform, res$p.value)\n textpart[grep(\"star\", textpart)] <- dplyr::case_when(\n res$p.value <= .01 ~ \"***\",\n res$p.value < .05 ~ \"**\",\n res$p.value < .1 ~ \"*\",\n TRUE ~ \"\"\n )\n\n paste0(textpart, collapse = \"\")\n}\n\n#' Basic RD plot\n#'\n#' @param aggregate observed data aggregated by mass points\n#' @param predict1 predicted data of treated\n#' @param predict0 predicted data of control\n#' @param cutoff cutoff value\n#' @param ate_label In-plot text about local ATE estimates\n#' @param ate_label_size In-plot text size\n#' @param outcome_label Outcome label in plot title\n#' @param ylim numeric vector of limits of y-axis\n#' @param vjust numeric. Adjust in-plot text vertically\n#' @param hjust numeric. Adjust in-plot text horizontally\n#' @param xlab label of x-axis\n#' @param ylab label of y-axis\n#' @param \\dots arguments of [simplegg()]\n#'\n#' @importFrom ggplot2 ggplot\n#' @importFrom ggplot2 aes\n#' @importFrom ggplot2 geom_point\n#' @importFrom ggplot2 geom_line\n#' @importFrom ggplot2 geom_vline\n#' @importFrom ggplot2 annotate\n#' @importFrom ggplot2 labs\n#' @importFrom ggplot2 ylim\n#'\n#'\nrdplot_internal_cutoff <- function(aggregate,\n predict1,\n predict0,\n cutoff,\n ate_label,\n ate_label_size = 5,\n outcome_label,\n ylim,\n vjust = 0,\n hjust = 0,\n xlab = \"Running variable\",\n ylab = \"Average\",\n ...) {\n x <- outcome <- d <- yhat1 <- yhat0 <- NULL\n\n title <- if (missing(outcome_label)) NULL else outcome_label\n\n g <- ggplot2::ggplot(aggregate, ggplot2::aes(x = x, y = outcome)) +\n ggplot2::geom_point(ggplot2::aes(shape = d), size = 2) +\n ggplot2::geom_line(ggplot2::aes(x = x + cutoff, y = yhat1), predict1) +\n ggplot2::geom_line(ggplot2::aes(x = x + cutoff, y = yhat0), predict0) +\n ggplot2::geom_vline(ggplot2::aes(xintercept = cutoff), linetype = 3) +\n ggplot2::annotate(\n \"text\", x = -Inf, y = Inf, label = ate_label,\n vjust = 1 + vjust, hjust = 0 + hjust, size = ate_label_size,\n family = getOption(\"discRD.plot_family\")\n ) +\n ggplot2::labs(\n x = xlab,\n y = ylab,\n title = title,\n shape = NULL\n )\n\n if (missing(ylim)) {\n g + simplegg(font_family = getOption(\"discRD.plot_family\"), ...)\n } else {\n g + ggplot2::ylim(ylim) +\n simplegg(font_family = getOption(\"discRD.plot_family\"), ...)\n }\n\n}", "meta": {"hexsha": "1aacd8b3d81b98db243424331f03afe01a149e47", "size": 3407, "ext": "r", "lang": "R", "max_stars_repo_path": "R/internal-plot.r", "max_stars_repo_name": "KatoPachi/discreteRD", "max_stars_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/internal-plot.r", "max_issues_repo_name": "KatoPachi/discreteRD", "max_issues_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/internal-plot.r", "max_forks_repo_name": "KatoPachi/discreteRD", "max_forks_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7653061224, "max_line_length": 75, "alphanum_fraction": 0.5785148224, "num_tokens": 907, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3421608175009801}} {"text": "## ----echo = FALSE----------------------------------------------------------------------\nknitr::opts_chunk$set(\n message = FALSE,\n warning = FALSE,\n collapse = TRUE,\n comment = \"#>\",\n fig.height = 4,\n fig.width = 8,\n fig.align = \"center\",\n cache = FALSE\n)\n\n\n## /* custom.css */\n\n## .left-code {\n\n## color: #777;\n\n## width: 48%;\n\n## height: 92%;\n\n## float: left;\n\n## }\n\n## .right-plot {\n\n## width: 50%;\n\n## float: right;\n\n## padding-left: 1%;\n\n## }\n\n## ----echo=FALSE------------------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(plotly)\nlibrary(gganimate)\nlibrary(datasauRus)\n\n\n## ---- include = F----------------------------------------------------------------------\n# Download cran data from metacran\nlibrary(cranlogs)\nlibrary(lubridate)\ncran_dls <- cran_downloads(c(\"ggplot2\", \"plotly\", \"leaflet\", \"ggvis\", \"animint2\", \"rCharts\", \"gridSVG\", \"R2D3\", \"shiny\", \"crosstalk\"), \n from = \"2019-01-01\", to = today())\nwrite_csv(cran_dls, file = \"../../data/package-info-July-2021.csv\")\n\n\n## ---- echo=FALSE, fig.width=10, fig.height = 8-----------------------------------------\ncran_dls <- read_csv(here::here(\"data/package-info-July-2021.csv\"))\ncran_summary <- cran_dls %>%\n mutate(date = ymd(date) %>% floor_date(\"week\")) %>%\n group_by(package, date) %>%\n summarise(totaldown = sum(count))\n\nlabel_summary <- cran_summary %>%\n ungroup() %>%\n group_by(package) %>%\n filter(totaldown == max(totaldown))\n\ncran_summary %>% \n ggplot(aes(x = date, y = totaldown, colour=package)) +\n geom_line() +\n theme_bw() +\n theme(legend.position=\"none\") +\n geom_text(\n aes(x = date, y = 1.05*totaldown, label=package),\n data = label_summary) +\n ylab(\"Monthly downloads\") +\n xlab(\"Time\") + \n scale_y_log10()\n\n\n\n\n## ----plotly----------------------------------------------------------------------------\nlibrary(plotly)\nplot_ly(data = economics, x = ~date, y = ~unemploy / pop)\n\n\n## --------------------------------------------------------------------------------------\ngg <- ggplot(data=economics, aes(x = date, y = unemploy / pop)) + \n geom_point() + geom_line()\n\nggplotly(gg)\n\n\n## ----fig.width=6, fig.height=6---------------------------------------------------------\nlibrary(GGally)\np <- ggpairs(economics[,3:6])\nggplotly(p)\n\n\n## --------------------------------------------------------------------------------------\ndata(canada.cities, package = \"maps\")\nviz <- ggplot(canada.cities, aes(long, lat)) +\n borders(regions = \"canada\") +\n coord_equal() +\n geom_point(aes(text = name, size = log2(pop)), colour = \"red\", alpha = 1/4)\n ggplotly(viz)\n\n\n## ----eval=FALSE------------------------------------------------------------------------\n## sd <- highlight_key(txhousing, ~year)\n## \n## p <- ggplot(sd, aes(month, median)) +\n## geom_line(aes(group = year)) +\n## geom_smooth(data = txhousing, method = \"gam\") +\n## facet_wrap(~ city)\n## \n## gg <- ggplotly(p, height = 600, width = 1000) %>%\n## layout(title = \"Click on a line to highlight a year\")\n## \n## highlight(gg)\n\n\n## ----echo=FALSE------------------------------------------------------------------------\nsd <- highlight_key(txhousing, ~year)\n\np <- ggplot(sd, aes(month, median)) +\n geom_line(aes(group = year)) + \n geom_smooth(data = txhousing, method = \"gam\") + \n facet_wrap(~ city)\n\ngg <- ggplotly(p, height = 600, width = 1000) %>%\n layout(title = \"Click on a line to highlight a year\")\n\nhighlight(gg)\n\n\n## ---- echo=FALSE, fig.width = 8, fig.height = 6----------------------------------------\nlibrary(gapminder)\n\nggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +\n geom_point(alpha = 0.7) +\n scale_colour_manual(values = country_colors, guide=FALSE) +\n scale_size(\"Population size\", range = c(2, 12), breaks=c(1*10^8, 2*10^8, 5*10^8, 10^9, 2*20^9)) +\n scale_x_log10() +\n facet_wrap(~continent) +\n theme(legend.position = \"bottom\") +\n # Here comes the gganimate specific bits\n labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +\n gganimate::transition_time(year) +\n gganimate::ease_aes('linear')\n\n\n## ----plot1, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(economics) #<<\n\n\n## ----output1, ref.label=\"plot1\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot2, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(economics) +\n## aes(date, unemploy) #<<\n\n\n## ----output2, ref.label=\"plot2\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot3, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(economics) +\n## aes(date, unemploy) +\n## geom_line() #<<\n\n\n## ----output3, ref.label=\"plot3\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot5-anim, eval=FALSE, echo=TRUE-------------------------------------------------\n## ggplot(economics) +\n## aes(date, unemploy) +\n## geom_line() +\n## transition_reveal(date) #<<\n\n\n## ----output5-anim, ref.label=\"plot5-anim\", echo=FALSE, cache=TRUE, fig.height = 6------\n\n\n## ----plot5, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(datasaurus_dozen)#<<\n\n\n## ----output5, ref.label=\"plot5\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot6, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(datasaurus_dozen) +\n## aes(x, y, color=dataset)#<<\n\n\n## ----output6, ref.label=\"plot6\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot7, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(datasaurus_dozen) +\n## aes(x, y, color=dataset) +\n## geom_point() #<<\n\n\n## ----output7, ref.label=\"plot7\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot8, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(datasaurus_dozen) +\n## aes(x, y, color=dataset) +\n## geom_point() +\n## facet_wrap(~dataset)#<<\n\n\n## ----output8, ref.label=\"plot8\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## ----plot9, eval=FALSE, echo=TRUE------------------------------------------------------\n## ggplot(datasaurus_dozen) +\n## aes(x, y) +\n## geom_point() +\n## transition_states(dataset, 3, 1) + #<<\n## labs(title = \"Dataset: {closest_state}\") #<<\n## \n\n\n## ----output9, ref.label=\"plot9\", echo=FALSE, cache=TRUE, fig.height = 6----------------\n\n\n## --------------------------------------------------------------------------------------\nlibrary(gapminder)\n\nggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +\n geom_point(alpha = 0.7) +\n scale_colour_manual(values = country_colors, guide=FALSE) +\n scale_size(\"Population size\", range = c(2, 12), breaks=c(1*10^8, 2*10^8, 5*10^8, 10^9, 2*20^9)) +\n scale_x_log10() +\n facet_wrap(~continent) +\n theme(legend.position = \"bottom\")\n\n", "meta": {"hexsha": "11376682d97ccf3cd3bd7234318772ff062e93d0", "size": 6949, "ext": "r", "lang": "R", "max_stars_repo_path": "code/3.1-interactive-plots.r", "max_stars_repo_name": "TengMCing/SISBID", "max_stars_repo_head_hexsha": "54bb601e51271e8a81029915cfeeb5e1f7f77a11", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/3.1-interactive-plots.r", "max_issues_repo_name": "TengMCing/SISBID", "max_issues_repo_head_hexsha": "54bb601e51271e8a81029915cfeeb5e1f7f77a11", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/3.1-interactive-plots.r", "max_forks_repo_name": "TengMCing/SISBID", "max_forks_repo_head_hexsha": "54bb601e51271e8a81029915cfeeb5e1f7f77a11", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.5967078189, "max_line_length": 135, "alphanum_fraction": 0.4891351274, "num_tokens": 1815, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736783928749126, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.34216080880641797}} {"text": "#' mrt_thorsson\n#'\n#' Calculated the mean radiant temperature from the solar radiation. Modified based on direct and diffuse ratio. Assumes a uniform surround temperature of Ta && short wave solar radiation only;\n#'\n#' @param t numeric air temperature in degC.\n#' @param tg numeric global short solar irradiance in Watt on mq.\n#' @param wind numeric windspeed in meter per second.\n#' @param diam numeric diameter of the sphere in millimeter. Input example 50 mm.\n#' @return Mean Radiant temperature in degC\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @references Thorsson et al, Different methods for estimating the mean radiant temperature in an outdoor urban setting, Int. J. Climatol.27 1983 1993 (2007)\n#' @keywords MRT \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nmrt_thorsson=function(t,tg,wind,diam) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"tg\", as.array(tg))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"diam\", as.array(diam))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=mrt_thorsson(t[i],tg[i],wind[i],diam[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n#' mrt_globe\n#'\n#' Given air temperature , the globe Temeperature, the wind speed and diameter mean radiant temperature is done according ISO 7726 1998.\n#'\n#' @param t numeric air temperature in degC.\n#' @param tg numeric global short solar irradiance in Watt on mq.\n#' @param wind numeric windspeed in meter per second.\n#' @param diam numeric diameter of the sphere in millimeters. Input example 50 mm.\n#' @return Mean Radiant temperature in degC\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords globe temperature \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nmrt_globe=function(t,tg,wind,diam) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"tg\", as.array(tg))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"diam\", as.array(diam))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=mrt_globe(t[i],tg[i],wind[i],diam[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n#' mrt_solar_proj\n#'\n#' Calculated the mean radiant temperature from the short solar irradiance with human projection factor. it assumes a uniform surround temperature of air temperature and short wave solar radiation.\n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Relative humidity in percentage.\n#' @param solar numeric Global solar radiation in Watt on mq.\n#' @param sunelev numeric Sun elevation angle in decimal degrees.\n#' @param alb_sfc numeric Mean albedo of surroundings. Default is 0.4.\n#' @param emis_sfc numeric Surface emissivity.\n#' @param fdir numeric Ratio between directed ad diffuse radiation.\n#' @return Mean Radiant temperature projected in degC.\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords fMRT \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nmrt_solar_proj=function(t,rh,solar,sunelev,alb_sfc,fdir,emis_sfc) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n ct$assign(\"solar\", as.array(solar))\n ct$assign(\"sunelev\", as.array(sunelev))\n ct$assign(\"albedo\", as.array(alb_sfc))\n ct$assign(\"emis_sfc\", as.array(emis_sfc))\n ct$assign(\"fdir\", as.array(fdir))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=mrt_solar_proj(t[i],rh[i],solar[i],sunelev[i],albedo[i],emis_sfc[0],fdir[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n\n", "meta": {"hexsha": "013e50d30372bc859272b7ca2d9c669453bdeca5", "size": 4111, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mrt_functions.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/mrt_functions.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mrt_functions.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 41.5252525253, "max_line_length": 198, "alphanum_fraction": 0.6061785454, "num_tokens": 1077, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3420619302881985}} {"text": "## setwd('stan')\nlibrary(data.table)\nlibrary(ggplot2)\n\nsource('../../functions.r')\nsource('../../data/parameters.r')\n\nspplist <- fread('../../data/species-info/species-list.csv')\nsetkey(spplist, code)\nnspp <- nrow(spplist)\n\nmodeldat <- new.env()\nload('../../generated/data.rdata', modeldat)\n\nload('assets/mcmc-results.rdata')\n\nplot_prior_posterior <- function(mc, mainlab=NULL, sublab=NULL, panel_scales = 'free_y', xlims = NULL, trans_x = NULL) {\n g <- ggplot(mc) +\n geom_density(aes(x = value, y = ..scaled.., fill = type), size = 0.1, colour = 'black') +\n facet_wrap(~ label, scales = panel_scales) +\n scale_x_continuous(limits = xlims, trans = trans_x) +\n scale_fill_manual(name = NULL, values = c(Prior = '#55555555', Posterior = \"#238B4599\")) +\n labs(x = 'log(Vulnerability)', y = 'Density') +\n theme_minimal() +\n theme(panel.grid.major.y=element_blank(),\n panel.grid.minor.y=element_blank(),\n strip.text=element_text(size = 6),\n axis.text = element_text(size = 6))\n if (!is.null(mainlab)) {\n if (!is.null(sublab)) {\n g <- g + ggtitle(mainlab, sublab)\n } else {\n g <- g + ggtitle(mainlab)\n }\n }\n return(g)\n}\n\n\n## * Table of estimates\n\nqs <- mcsumm[grepl('^q', variable, perl=T) & !grepl('prior', variable, perl=T)]\n\nqs[grepl('^q0', variable, perl=T),\n par_lab := 'Intercept']\nqs[grepl('^q_f0', variable, perl=T),\n par_lab := 'Fishery group']\nqs[grepl('^q_g0', variable, perl=T),\n par_lab := 'Species group']\nqs[grepl('^q_gf0', variable, perl=T),\n par_lab := 'Species x fishery group']\n\nqs[, fg_lab := flags[as.character(fishery_group)]]\n\nqs[species_group == 'ANT', sp_lab := 'Antipodeans']\nqs[species_group == 'RYL', sp_lab := 'Royals']\nqs[species_group == 'WND', sp_lab := 'Wanderers']\nqs[species_group == 'DIP', sp_lab := 'S. royal']\nqs[species_group == 'DIQ', sp_lab := 'N. royal']\nqs[species_group == 'DIW', sp_lab := 'Gibson\\'s']\nqs[species_group == 'DQS', sp_lab := 'Antipodean']\nqs[species_group == 'TAM', sp_lab := 'Tristan + Amst.']\n\nqs[vartype %in% 'q_gf0', sp_lab := upper1st(spplist[match(species, code), common_name])]\nqs[vartype %in% 'q_gf0', lab := sprintf('%s in %s', sp_lab, fg_lab)]\nqs[vartype %in% 'q_g0', lab := sp_lab]\nqs[vartype %in% 'q_f0', lab := fg_lab]\n\nqs_export <- qs[, .(par_lab, lab, mean, lcl, ucl, min, max)]\nqs_export[, par_lab := factor(par_lab, levels = c('Intercept', 'Fishery group', 'Species group', 'Species x fishery group'))]\nsetorder(qs_export, par_lab, -mean)\n\n\nfwrite(qs, 'assets/vulnerabilities-all.csv')\nfwrite(qs_export, 'assets/vulnerabilities.csv')\n\n\n\n## * Plot of prior/posterior\n\n## ** Posteriors\nmc <- mcmc[grepl('^q', variable, perl=T) & !grepl('prior', variable, perl=T)]\nmc <- merge(mc, mc_attributes, by = c('variable'), all.x=T, all.y=F)\nmc[, type := 'Posterior']\n\n## ** Priors\npriors <- mcmc[grepl('^q', variable, perl=T) & grepl('prior', variable, perl=T)]\npriors_d <- rbindlist(lapply(qs$variable, function(v) {\n if (grepl('^q0|^q_f0|^q_g0', v)) {\n p <- priors[variable == 'q_prior']\n } else {\n p <- priors[variable == 'q_gf_prior']\n }\n p[, variable := v]\n p[, type := 'Prior']\n return(p)\n}))\npriors_d <- merge(priors_d, mc_attributes, by = c('variable'), all.x=T, all.y=F)\n\n\n## ** Labels\nprior_posterior <- rbind(priors_d, mc)\n\nprior_posterior[grepl('^q0', variable, perl=T),\n par_lab := 'Intercept']\nprior_posterior[grepl('^q_f0', variable, perl=T),\n par_lab := 'Fishery']\nprior_posterior[grepl('^q_g0', variable, perl=T),\n par_lab := 'Species']\nprior_posterior[grepl('^q_gf0', variable, perl=T),\n par_lab := 'Species x fishery']\n\nprior_posterior[, fg_lab := flags[as.character(fishery_group)]]\n\nprior_posterior[species_group == 'ANT', sp_lab := 'Antipodeans']\nprior_posterior[species_group == 'RYL', sp_lab := 'Royals']\nprior_posterior[species_group == 'WND', sp_lab := 'Wanderers']\nprior_posterior[species_group == 'DIP', sp_lab := 'S. royal']\nprior_posterior[species_group == 'DIQ', sp_lab := 'N. royal']\nprior_posterior[species_group == 'DIW', sp_lab := 'Gibson\\'s']\nprior_posterior[species_group == 'DQS', sp_lab := 'Antipodean']\nprior_posterior[species_group == 'TAM', sp_lab := 'Tristan + Amst.']\n\nprior_posterior[, lab := paste(na.omit(c(sp_lab, fg_lab)), collapse=' in '), by = 1:nrow(prior_posterior)]\nprior_posterior[, label := paste(setdiff(c(par_lab, lab), ''), collapse='\\n'), by = 1:nrow(prior_posterior)]\nprior_posterior[, label := factor(label, levels = unique(label))]\n\nprior_posterior[, type := factor(type, levels = c('Prior', 'Posterior'))]\n\ng <- plot_prior_posterior(prior_posterior, panel_scales='free', trans_x = 'log')\n\nggsave('assets/vulnerabilities_comparison-prior-posterior.png', g, width = 9, height = 7)\n\n", "meta": {"hexsha": "4e007f0a189bc8b23114e8a65722e010979cd2c4", "size": 4796, "ext": "r", "lang": "R", "max_stars_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/vulnerabilities.r", "max_stars_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_stars_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/vulnerabilities.r", "max_issues_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_issues_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/vulnerabilities.r", "max_forks_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_forks_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.7910447761, "max_line_length": 125, "alphanum_fraction": 0.642618849, "num_tokens": 1454, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3420619302881985}} {"text": "permuted.difference <- function(){\n\nsource('/data/Dropbox/phenocam_classification/code/image.plot.R')\n\nlabels.2006 <- read.table('../all-labels-2006.txt')\nlabels.2007 <- read.table('../all-labels-2007.txt')\nlabels.2008 <- read.table('../all-labels-2008.txt')\nlabels.2009 <- read.table('../all-labels-2009.txt')\nlabels.2010 <- read.table('../all-labels-2010.txt')\n\nnrows <- dim(labels.2006)[1]\nncols <- dim(labels.2006)[2]\n\noutputmatrix <- matrix(NA,nrows,ncols)\n\n# for every class extract col from matrics\n\nfor (k in 1:ncols){\n\ntmp <- matrix(NA,nrows,25)\nx <- matrix(NA,nrows,5)\n\nx[,1] <- labels.2006[,k]\nx[,2] <- labels.2007[,k]\nx[,3] <- labels.2008[,k]\nx[,4] <- labels.2009[,k]\nx[,5] <- labels.2010[,k]\n\n#x[x == 15] <- NA \n\n\tfor (i in 1:5){\n\t\tfor (j in 1:5){\n\t\tdiff <- abs(x[,i] - x[,j])\n\t\tdiff[diff > 0 ] <- 1\n\t\ttmp[,i*j] <- diff\n\t\t}\n\t}\n\ntmp <- as.matrix(rowSums(tmp,na.rm=T)/25)\noutputmatrix[,k] <- tmp\n\n}\n\noutputmatrix <- apply(outputmatrix,1,function(x)1-mean(x,na.rm=T))\noutputmatrix <- matrix(outputmatrix,480,640)\nmyImagePlot(outputmatrix)\nwrite.table(outputmatrix,'permuted.differences.txt',quote=F,row.names=F,col.names=F)\n\n}\npermuted.difference()\n\n\n", "meta": {"hexsha": "8bac49cf12de9c3d5161d6295b2187edf052bd55", "size": 1161, "ext": "r", "lang": "R", "max_stars_repo_path": "code/permuted.difference.r", "max_stars_repo_name": "id175196/PhenocamProject", "max_stars_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-02-08T22:12:02.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-08T22:12:02.000Z", "max_issues_repo_path": "code/permuted.difference.r", "max_issues_repo_name": "id175196/PhenocamProject", "max_issues_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/permuted.difference.r", "max_forks_repo_name": "id175196/PhenocamProject", "max_forks_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.9056603774, "max_line_length": 84, "alphanum_fraction": 0.6528854436, "num_tokens": 391, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.34206193028819837}} {"text": "## ============================================================================================\n##\n## predictDiagnosis\n##\n## ============================================================================================\n\ncomputePredResMat <- function(predRes, predRes0, followUpTime, followUpTime0) {\n selPeriodStrs <- c(\"Year 1\", \"Year 1-2\", \"Year 3-4\", \"Year 5-6\")\n selPeriodInterval <- matrix(NA, nrow=4, ncol=2)\n selPeriodInterval[1,] <- c(1,365)\n selPeriodInterval[2,] <- c(1,2*365)\n selPeriodInterval[3,] <- c(2*365+1,4*365)\n selPeriodInterval[4,] <- c(4*365+1,6*365)\n \n resMat <- matrix(NA, nrow=5, ncol=5)\n rownames(resMat) <- c(\"All years\", selPeriodStrs)\n colnames(resMat) <- c(\"FN\", \"TP\", \"FP\", \"TN\", \"P-value\")\n \n for (i in 0:length(selPeriodStrs)) {\n fn <- sum(!predRes>0)\n tp <- sum(predRes>0)\n fp <- sum(!predRes0<0)\n tn <- sum(predRes0<0)\n if (i>0) {\n sel <- followUpTime>selPeriodInterval[i,1] & followUpTime<=selPeriodInterval[i,2]\n sel0 <- followUpTime0>selPeriodInterval[i,1] & followUpTime0<=selPeriodInterval[i,2]\n fn <- sum(!predRes[sel]>0)\n tp <- sum(predRes[sel]>0)\n fp <- sum(!predRes0[sel0]<0)\n tn <- sum(predRes0[sel0]<0)\n }\n \n resMat[i+1,] <-\n c(fn, tp, fp, tn,\n fisher.test(matrix(c(tp, fn, fp, tn), ncol=2, byrow=F),\n alternative=\"g\")$p.value)\n }\n\n resMat\n}\n\n\npredictDiagnosis <- function(dataList, n, nofGenes, strFollowUpTime, strPlotFracCorr, strResMat,\n m, m2, selPeriodStr, selPeriodInterval, selPeriodFromWith,\n hasInsitu=FALSE) {\n\n ## Predict diagnosis using a leave-one-out approach, n=periodLength\n caseStr <- \"Weight\"\n caseN <- \"N1\"\n \n curDataList <- selectDataset(dataList, caseN, dataList$withSpread)\n data <- curDataList$data\n followUpTime <- curDataList$followUpTime\n weights <- curDataList$weights\n curDataList0 <- selectDataset(dataList, caseN, dataList$withoutSpread)\n data0 <- curDataList0$data\n followUpTime0 <- curDataList0$followUpTime\n weights0 <- curDataList0$weights\n if (hasInsitu) {\n curDataListInsitu <- selectDataset(dataList, caseN, dataList$insitu)\n dataInsitu <- curDataListInsitu$data\n followUpTimeInsitu <- curDataListInsitu$followUpTime\n weightsInsitu <- curDataListInsitu$weights\n }\n selFollowUpTime <- followUpTime0\n notSelFollowUpTime <- followUpTime\n if (selPeriodFromWith) {\n selFollowUpTime <- followUpTime\n notSelFollowUpTime <- followUpTime0\n }\n nofPeriods <- length(selFollowUpTime)-n+1\n \n ## Select genes for predictor\n ## Find sorting of genes from analyses for \"Weights\" in predictor\n ## Weights with sign for genes selected for predictor: Number of genes selected?\n \n predRes <- matrix(NA, ncol=length(nofGenes), nrow=ncol(data))\n predRes0 <- matrix(NA, ncol=length(nofGenes), nrow=ncol(data0))\n rownames(predRes) <- colnames(data)\n rownames(predRes0) <- colnames(data0)\n colnames(predRes) <- colnames(predRes0) <- nofGenes\n if (hasInsitu) {\n predResInsitu <- matrix(NA, ncol=length(nofGenes), nrow=ncol(dataInsitu))\n colnames(predResInsitu) <- nofGenes\n }\n \n for (p in 1:nofPeriods)\n print(c(selFollowUpTime[p+n-1]-selFollowUpTime[p],\n sum(notSelFollowUpTime %in% selFollowUpTime[p]:selFollowUpTime[p+n-1])))\n \n meanTimeForPeriod <- rep(NA, nofPeriods)\n for (p in 1:nofPeriods)\n meanTimeForPeriod[p] <- (selFollowUpTime[p+n-1]+selFollowUpTime[p])/2\n \n bestPeriod <- rep(NA, length(followUpTime))\n bestPeriod0 <- rep(NA, length(followUpTime0))\n bestPeriod[followUpTime < mean(meanTimeForPeriod[1:2])] <- 1\n bestPeriod0[followUpTime0 < mean(meanTimeForPeriod[1:2])] <- 1\n bestPeriod[followUpTime > mean(meanTimeForPeriod[(length(meanTimeForPeriod)-1):\n (length(meanTimeForPeriod))])] <- nofPeriods\n bestPeriod0[followUpTime0 > mean(meanTimeForPeriod[(length(meanTimeForPeriod)-1):\n (length(meanTimeForPeriod))])] <- nofPeriods\n if (hasInsitu) {\n bestPeriodInsitu <- rep(NA, length(followUpTimeInsitu))\n bestPeriodInsitu[followUpTimeInsitu < mean(meanTimeForPeriod[1:2])] <- 1\n bestPeriodInsitu[followUpTimeInsitu >\n mean(meanTimeForPeriod[(length(meanTimeForPeriod)-1):\n (length(meanTimeForPeriod))])] <- nofPeriods\n } else {\n bestPeriodInsitu <- c()\n }\n\n for (p in 2:(nofPeriods-1)) {\n bestPeriod[followUpTime >= mean(meanTimeForPeriod[(p-1):p]) &\n followUpTime <= mean(meanTimeForPeriod[(p):(p+1)])] <- p\n bestPeriod0[followUpTime0 >= mean(meanTimeForPeriod[(p-1):p]) &\n followUpTime0 <= mean(meanTimeForPeriod[(p):(p+1)])] <- p\n if (hasInsitu) {\n bestPeriodInsitu[followUpTimeInsitu >= mean(meanTimeForPeriod[(p-1):p]) &\n followUpTimeInsitu <= mean(meanTimeForPeriod[(p):(p+1)])] <- p\n }\n }\n \n for (p in unique(sort(c(bestPeriod, bestPeriod0, bestPeriodInsitu)))) {\n selInd <- (1:length(followUpTime))[followUpTime>=selFollowUpTime[p] &\n followUpTime<=selFollowUpTime[p+n-1]]\n selInd0 <- (1:length(followUpTime0))[followUpTime0>=selFollowUpTime[p] &\n followUpTime0<=selFollowUpTime[p+n-1]]\n \n leaveOut <- which(bestPeriod==p)\n leaveOut0 <- which(bestPeriod0==p)\n if (hasInsitu) {\n leaveOutInsitu <- which(bestPeriodInsitu==p)\n }\n \n ## Predict for group1\n if (length(leaveOut)>0)\n for (i in leaveOut) { \n curData <- data[, selInd[selInd!=i]]\n curWeights <- weights[, selInd[selInd!=i]]\n curFollowUpTime <- followUpTime[selInd[selInd!=i]]\n curData0 <- data0[, selInd0]\n curWeights0 <- weights0[, selInd0]\n curFollowUpTime0 <- followUpTime0[selInd0]\n \n mu1 <- computeMean(curData, curWeights)\n mu0 <- computeMean(curData0, curWeights0)\n var1 <- computeVar(curData, curWeights)\n var0 <- computeVar(curData0, curWeights0)\n \n weight <- as.numeric((mu1-mu0)/sqrt(var1 + var0))\n sortList <- sort.list(abs(weight), decreasing=TRUE)\n \n for (j in 1:length(nofGenes)) {\n selGenes <- sortList[1:nofGenes[j]]\n predRes[i, j] <- sum(weight[selGenes]*data[selGenes,i])\n }\n }\n \n ## Predict for group0\n if (length(leaveOut0)>0)\n for (i in leaveOut0) { \n curData <- data[, selInd] \n curWeights <- weights[, selInd] \n curFollowUpTime <- followUpTime[selInd]\n curData0 <- data0[, selInd0[selInd0!=i]]\n curWeights0 <- weights0[, selInd0[selInd0!=i]]\n curFollowUpTime0 <- followUpTime0[selInd0[selInd0!=i]]\n \n mu1 <- computeMean(curData, curWeights)\n mu0 <- computeMean(curData0, curWeights0)\n var1 <- computeVar(curData, curWeights)\n var0 <- computeVar(curData0, curWeights0)\n \n weight <- as.numeric((mu1-mu0)/sqrt(var1+var0))\n sortList <- sort.list(abs(weight), decreasing=TRUE)\n for (j in 1:length(nofGenes)) {\n selGenes <- sortList[1:nofGenes[j]]\n predRes0[i, j] <- sum(weight[selGenes]*data0[selGenes,i])\n }\n }\n \n ## Predict for groupInsitu\n if (hasInsitu) {\n if (length(leaveOutInsitu)>0) {\n curData <- data[, selInd] \n curWeights <- weights[, selInd] \n curFollowUpTime <- followUpTime[selInd]\n curData0 <- data0[, selInd0]\n curWeights0 <- weights0[, selInd0]\n curFollowUpTime0 <- followUpTime0[selInd0]\n \n mu1 <- computeMean(curData, curWeights)\n mu0 <- computeMean(curData0, curWeights0)\n var1 <- computeVar(curData, curWeights)\n var0 <- computeVar(curData0, curWeights0)\n \n weight <- as.numeric((mu1-mu0)/sqrt(var1+var0))\n sortList <- sort.list(abs(weight), decreasing=TRUE)\n \n for (i in leaveOutInsitu) { \n for (j in 1:length(nofGenes)) {\n selGenes <- sortList[1:nofGenes[j]]\n predResInsitu[i, j] <- sum(weight[selGenes]*dataInsitu[selGenes,i])\n }\n }\n }\n }\n }\n\n for (nGenes in nofGenes) {\n\n resMat <- computePredResMat(predRes[,paste(nGenes)], predRes0[,paste(nGenes)],\n followUpTime, followUpTime0)\n write.table(resMat, paste(strResMat, nGenes, \".txt\", sep=\"\"), sep=\"\\t\")\n \n ## === Print correctly and wrongly classified and p-value ===\n print(paste(\"Number of genes selected:\", nGenes))\n ##\n fn <- sum(!predRes[,paste(nGenes)]>0)\n tp <- sum(predRes[,paste(nGenes)]>0)\n fp <- sum(!predRes0[,paste(nGenes)]<0)\n tn <- sum(predRes0[,paste(nGenes)]<0)\n print(\"FN TP FP TN:\")\n print(c(fn, tp, fp, tn))\n print(\"p-value in Fisher test, all years:\")\n print(fisher.test(matrix(c(tp, fn, fp, tn), ncol=2, byrow=F), alternative=\"g\")$p.value)\n ## \n sel <- followUpTime>selPeriodInterval[1] & followUpTime<=selPeriodInterval[2]\n sel0 <- followUpTime0>selPeriodInterval[1] & followUpTime0<=selPeriodInterval[2]\n fn <- sum(!predRes[,paste(nGenes)][sel]>0)\n tp <- sum(predRes[,paste(nGenes)][sel]>0)\n fp <- sum(!predRes0[,paste(nGenes)][sel0]<0)\n tn <- sum(predRes0[,paste(nGenes)][sel0]<0)\n print(\"FN TP FP TN:\")\n print(c(fn, tp, fp, tn))\n print(paste(\"p-value in Fisher test, \", selPeriodStr, \":\", sep=\"\")) \n print(fisher.test(matrix(c(tp, fn, fp, tn), ncol=2, byrow=F), alternative=\"g\")$p.value)\n ##\n print(\"\")\n \n ## === Plot correctly and wrongly classified ===\n plotDataList <- dataList\n plotCorrectPredWithS <- predRes[,paste(nGenes)]>0\n plotCorrectPredWithoutS <- predRes0[,paste(nGenes)]<=0\n ##save(plotDataList, plotCorrectPredWithS, plotCorrectPredWithoutS,\n ## file=paste(strFollowUpTime, nGenes, \".pdf.R\", sep=\"\"))\n plotFollowUpTimesWithPredRes(dataList, predRes[,paste(nGenes)]>0,\n predRes0[,paste(nGenes)]<=0, \n paste(strFollowUpTime, nGenes, \".pdf\", sep=\"\"))\n \n ## === Plot fraction of correctly classified ===\n \n pdf(paste(strPlotFracCorr, nGenes, \".pdf\", sep=\"\"), height=7, width=15)\n \n tVecStart <- min(followUpTime):(max(followUpTime)-m2)\n tVecStop <- tVecStart+m2\n tVecMean <- tVecStart+m\n isCorr <- predRes[,paste(nGenes)]>0\n fracCorr <- rep(NA, length(tVecStart))\n for (i in 1:length(tVecStart))\n fracCorr[i] <- mean(isCorr[followUpTime>=tVecStart[i] & followUpTime<=tVecStop[i]])\n keep <- !is.na(fracCorr)\n ##\n plotX <- tVecMean[keep]\n plotY <- runmed(fracCorr[keep],m2)\n plotXlim <- rev(range(dataList$followUpTime))\n ##save(plotX, plotY, plotXlim, file=paste(strPlotFracCorr, nGenes, \".pdf.red.R\", sep=\"\"))\n ##\n plot(tVecMean[keep], runmed(fracCorr[keep],m2), xlim=rev(range(dataList$followUpTime)),\n type=\"l\", ylim =c(0,1), xlab=\"Follow up time (days)\", ylab=\"Fraction correctly classified\",\n cex=1.4, cex.axis=1.4, cex.lab=1.4, col=2)\n \n tVecStart <- min(followUpTime0):(max(followUpTime0)-m2)\n tVecStop <- tVecStart+m2\n tVecMean <- tVecStart+m\n isCorr <- predRes0[,paste(nGenes)]<=0\n fracCorr <- rep(NA, length(tVecStart))\n for (i in 1:length(tVecStart))\n fracCorr[i] <- mean(isCorr[followUpTime0>=tVecStart[i] & followUpTime0<=tVecStop[i]])\n keep <- !is.na(fracCorr)\n ##\n plotX <- tVecMean[keep]\n plotY <- runmed(fracCorr[keep],m2)\n ##save(plotX, plotY, plotXlim, file=paste(strPlotFracCorr, nGenes, \".pdf.black.R\", sep=\"\"))\n ##\n lines(tVecMean[keep], runmed(fracCorr[keep], m2), cex=1.4, cex.axis=1.4, cex.lab=1.4, col=1)\n \n abline(h=0.5, lty=2)\n for (i in 0:7)\n abline(v=365*i)\n \n dev.off()\n }\n}\n\n", "meta": {"hexsha": "d4a1e9947538c51532ae4fa0b672f6f9a3b7c862", "size": 11857, "ext": "r", "lang": "R", "max_stars_repo_path": "predictDiagnosis.r", "max_stars_repo_name": "theresenoest/Local_in_time_statistics", "max_stars_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2017-12-07T11:16:23.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-22T19:17:48.000Z", "max_issues_repo_path": "predictDiagnosis.r", "max_issues_repo_name": "theresenoest/Local_in_time_statistics", "max_issues_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "predictDiagnosis.r", "max_forks_repo_name": "theresenoest/Local_in_time_statistics", "max_forks_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.9225589226, "max_line_length": 100, "alphanum_fraction": 0.6060554946, "num_tokens": 3647, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3420619233238505}} {"text": "#!/usr/bin/env Rscript\r\n#----------------------------------------------------------------------------------------------------------------#\r\n# Filename:\r\n# ppa_sibgc_v1.r\r\n# Description:\r\n# R implementation of the Perfect Plasticity Approximation (PPA) with simply biogeochemistry (PPA-SiBGC).\r\n# Version:\r\n# 1.0\r\n# Authors:\r\n# Adam Erickson, Washington State University\r\n# Nikolay Strigul, Washington State University\r\n# Modified:\r\n# June 30, 2018\r\n# Procedure:\r\n# (1) Generate constant growth, mortality, and regeneration coefficients from plot data\r\n# (2) Generate above- and below-ground stoichiometric coefficients from databases\r\n# (3) Run the Sortie-PPA model:\r\n# (*) Generate cohorts from tree inventory data\r\n# (*) Initialize values for allometry, biomass, C, and N from species, type, DBH\r\n# (a) Soil respiration\r\n# (b) Regeneratation\r\n# (c) Sort by height descending\r\n# (d) Calculate cumulative crown area (CCA)\r\n# (e) Calculate Z* height and nearest cohort\r\n# (f) Mortality\r\n# (g) Growth\r\n# (h) Allometry\r\n# (i) Biomass fractions\r\n# (j) Carbon fractions\r\n# (k) Nitrogen fractions\r\n# (l) Save yearly results to CSV\r\n# (m) Save final results to CSV\r\n# (4) Compare model results against empirical data\r\n# Notes:\r\n# Sortie-PPA is based on the assumption that tree canopies are perfectly plastic. This reduces\r\n# the variance in canopy join heights to zero, creating a single canopy join height known as z*.\r\n# The approximation allows for model simplification and mathematical tractability. The spatial\r\n# location of trees is discarded. Growth and mortality are modeled by their mean values for each\r\n# species and type (adults = above z*; saplings = below z*). Coefficients are used to modify\r\n# growth rates per the fraction of light received by trees above and below the z* threshold.\r\n# Aboveground biomass is modeled using equations from Chojnacky, Heath, and Jenkins (2014).\r\n# Belowground biomass as well as C,N dynamics are modeled using allometry and stoichiometry.\r\n# Examples:\r\n# Rscript --vanilla ppa_v50.r\r\n# Rscript --vanilla ppa_v50.r --wd /Users/null/test --verbose\r\n#----------------------------------------------------------------------------------------------------------------#\r\nVersion <- \"5.0\"\r\n\r\n# Debugging\r\noptions(error=quote({dump.frames(to.file=TRUE); q()}))\r\n\r\n# Print banner and pause\r\nBanner <- readLines(\"sortie_ppa_ansi_shadow.txt\")\r\ncat(Banner, sep=\"\\n\")\r\n\r\n# Print version\r\nmessage(\"\")\r\nmessage(\"Authors: Adam Erickson, Nikolay Strigul\")\r\nmessage(paste(\"Version:\", Version, \"\\n\"))\r\nSys.sleep(0)\r\n\r\n# Remove existing outputs\r\noutputs_files <- dir(\"outputs\", recursive=FALSE, full.names=TRUE)\r\noutputs_deleted <- file.remove(outputs_files)\r\n\r\n# Fetch trailing command line arguments\r\nargs <- commandArgs(trailingOnly=TRUE)\r\n\r\n# Default verbosity setting\r\nVerbose <- FALSE\r\n\r\n# Set working directory\r\nif (length(args) == 0) {\r\n message(paste0(\"Working directory: \", getwd()))\r\n} else if (length(args) == 1) {\r\n Verbose <- args[1] %in% c(\"--v\", \"--verbose\")\r\n stopifnot(Verbose)\r\n} else if (length(args) == 2) {\r\n flag <- args[1]\r\n stopifnot(flag %in% c(\"--wd\", \"--dir\"))\r\n folder <- args[2]\r\n if (flag==\"--wd\" || flag==\"--dir\") {\r\n setwd(folder)\r\n message(paste(\"Working directory:\", getwd()))\r\n } else {\r\n message(\"Flag not recognized.\")\r\n }\r\n} else if (length(args) == 3) {\r\n flag <- args[1]\r\n stopifnot(flag %in% c(\"--wd\", \"--dir\"))\r\n folder <- args[2]\r\n setwd(folder)\r\n message(paste(\"Working directory:\", getwd()))\r\n Verbose <- args[3] %in% c(\"--v\", \"--verbose\")\r\n stopifnot(Verbose)\r\n} else {\r\n stop(\"Error: Only 0 or 2 arguments may be passed.\", call.=FALSE)\r\n}\r\n\r\n# Libraries #\r\n\r\n# Functions #\r\n\r\n# Round to the nearest integer by step\r\nround_int <- function(x, step) { \r\n return(step * round(x / step))\r\n}\r\n\r\n# Bin trees into cohorts by rounding DBH to the nearest cm; check n-trees\r\n# Expects CSV file with species, type, dbh, age (optional)\r\nGenerateCohorts <- function(Trees, interval=1) {\r\n n_trees <- nrow(Trees)\r\n Trees$dbh <- round_int(Trees$dbh, interval)\r\n if(\"age\" %in% tolower(colnames(Trees))) {\r\n age <- aggregate(Trees$age, by=list(Trees$species, Trees$type, Trees$dbh), FUN=mean)\r\n colnames(age) <- c(\"species\",\"type\",\"dbh\",\"age\")\r\n Trees <- subset(Trees, select= -c(age))\r\n Trees <- merge(Trees, age, by=c(\"species\",\"type\",\"dbh\"))\r\n }\r\n cohorts <- data.frame(table(Trees$species, Trees$type, Trees$dbh))\r\n colnames(cohorts) <- c(\"species\",\"type\",\"dbh\",\"n_trees\")\r\n cohorts$n_trees <- cohorts$n_trees + 1\r\n Trees <- merge(Trees, cohorts, by=c(\"species\",\"type\",\"dbh\"))\r\n Trees <- Trees[!duplicated(Trees[,c(\"species\",\"type\",\"dbh\")]), ]\r\n Trees <- Trees[Trees$dbh > 0 & !is.na(Trees$n_trees), ]\r\n Trees$id <- 1:nrow(Trees)\r\n if (\"age\" %in% tolower(colnames(Trees))) {\r\n Trees <- Trees[, c(\"id\",\"species\",\"type\",\"age\",\"dbh\",\"n_trees\")]\r\n } else {\r\n Trees <- Trees[, c(\"id\",\"species\",\"type\",\"dbh\",\"n_trees\")]\r\n }\r\n message(paste(n_trees, \"trees merged to\", nrow(Trees), \"cohorts containing\",\r\n sum(Trees$n_trees), \"trees\"))\r\n return(Trees)\r\n}\r\n\r\n# Create biomass, C, and N compartments\r\nInitialize <- function(Trees, ...) {\r\n # Create columns\r\n Trees$height <- NA\r\n Trees$crown_l <- NA\r\n Trees$crown_r <- NA\r\n Trees$crown_a <- NA\r\n Trees$ba <- NA\r\n Trees$crown_a_cumsum <- NA\r\n Trees$biomass_ag <- NA\r\n Trees$biomass_stem <- NA\r\n Trees$biomass_branch <- NA\r\n Trees$biomass_leaf <- NA\r\n Trees$biomass_root_coarse <- NA\r\n Trees$biomass_root_fine <- NA\r\n Trees$biomass_soil <- NA\r\n Trees$biomass_bg <- NA\r\n Trees$biomass_total <- NA\r\n Trees$c_ag <- NA\r\n Trees$c_bg <- NA\r\n Trees$c_total <- NA\r\n Trees$anpp <- NA\r\n Trees$n_ag <- NA\r\n Trees$n_bg <- NA\r\n Trees$n_total <- NA\r\n Trees$annp <- NA\r\n # Update each species and type; vectorized\r\n for (species in unique(Trees$species)) {\r\n if (!species %in% AllometryLUT$species) { stop(\"Species not found in allometry table\") }\r\n s <- Trees$species==species\r\n for (type in unique(Trees[s,]$type)) {\r\n st <- Trees$species==species & Trees$type==type\r\n index <- AllometryLUT$species==species & AllometryLUT$type==type\r\n allometry <- AllometryLUT[index,]\r\n Trees[st,]$height <- 1.35 + (30-1.35) * (1-exp(-(allometry$h_coeff * Trees[st,]$dbh)))\r\n Trees[st,]$height <- ifelse(Trees[st,]$height > 30, 30, Trees[st,]$height) # limit height to 30m\r\n Trees[st,]$type <- ifelse(Trees[st,]$dbh > 10, \"adult\", \"sapling\")\r\n Trees[st,]$crown_l <- allometry$cd * Trees[st,]$dbh\r\n Trees[st,]$crown_r <- allometry$cr1 * Trees[st,]$dbh^allometry$cr2\r\n Trees[st,]$crown_a <- Trees[st,]$crown_r^2 * pi\r\n Trees[st,]$ba <- Trees[st,]$dbh^2 * 0.00007854 # forester's constant for DBH (cm); m2\r\n }\r\n # Biomass allocation or partitioning\r\n if (!species %in% BiomassLUT$species) { stop(\"Species not found in biomass table\") }\r\n index <- BiomassLUT$species==species\r\n biomass <- BiomassLUT[index,]\r\n if (CohortMode) {\r\n Trees[s,]$biomass_ag <- exp(biomass$b0 + biomass$b1 * log(Trees[s,]$dbh)) * Trees[s,]$n_trees\r\n } else {\r\n Trees[s,]$biomass_ag <- exp(biomass$b0 + biomass$b1 * log(Trees[s,]$dbh))\r\n }\r\n Trees[s,]$biomass_stem <- Trees[s,]$biomass_ag * biomass$fraction_stem\r\n Trees[s,]$biomass_branch <- Trees[s,]$biomass_ag * biomass$fraction_branch\r\n Trees[s,]$biomass_leaf <- Trees[s,]$biomass_ag * biomass$fraction_leaf\r\n Trees[s,]$biomass_root_coarse <- Trees[s,]$biomass_ag * exp(-1.4485 - 0.03476 * log(Trees[s,]$dbh))\r\n Trees[s,]$biomass_root_fine <- Trees[s,]$biomass_ag * exp(-1.8629 - 0.77534 * log(Trees[s,]$dbh))\r\n Trees[s,]$biomass_soil <- Trees[s,]$biomass_ag * biomass$fraction_soil\r\n Trees[s,]$biomass_bg <- Trees[s,]$biomass_root_coarse + Trees[s,]$biomass_root_fine +\r\n Trees[s,]$biomass_soil\r\n Trees[s,]$biomass_total <- Trees[s,]$biomass_ag + Trees[s,]$biomass_bg\r\n # C fraction\r\n #index <- CarbonLUT$species==species\r\n carbon <- CarbonLUT #[index,]\r\n Trees[s,]$c_ag <- Trees[s,]$biomass_ag *\r\n ((biomass$fraction_stem * carbon$fraction_stem) +\r\n (biomass$fraction_branch * carbon$fraction_branch) +\r\n (biomass$fraction_leaf * carbon$fraction_leaf))\r\n Trees[s,]$c_bg <- Trees[s,]$biomass_ag *\r\n ((biomass$fraction_root * carbon$fraction_root) +\r\n (biomass$fraction_soil * carbon$fraction_soil))\r\n Trees[s,]$c_total <- Trees[s,]$c_ag + Trees[s,]$c_bg\r\n Trees[s,]$anpp <- NA\r\n # C:N stoichiometry\r\n if (!species %in% StoichiometryLUT$species) { stop(\"Species not found in stoichiometry table\") }\r\n index <- StoichiometryLUT$species==species\r\n stoichiometry <- StoichiometryLUT[index,]\r\n Trees[s,]$n_ag <- Trees[s,]$c_ag /\r\n ((biomass$fraction_stem * stoichiometry$cn_stem) +\r\n (biomass$fraction_branch * stoichiometry$cn_branch) +\r\n (biomass$fraction_leaf * stoichiometry$cn_leaf))\r\n Trees[s,]$n_bg <- Trees[s,]$c_bg /\r\n ((biomass$fraction_root * stoichiometry$cn_root) +\r\n (biomass$fraction_soil * stoichiometry$cn_soil))\r\n Trees[s,]$n_total <- Trees[s,]$n_ag + Trees[s,]$n_bg\r\n Trees[s,]$annp <- NA\r\n }\r\n return(Trees)\r\n}\r\n\r\n# Ensure column names and tree type are lower case\r\nensure_lowercase <- function(Trees) {\r\n colnames(Trees) <- tolower(colnames(Trees))\r\n Trees$type <- tolower(Trees$type)\r\n return(Trees)\r\n}\r\n\r\n# Check if year is a leap year (366 days instead of 365)\r\nleap_year <- function(year) {\r\n return(ifelse((year %%4 == 0 & year %%100 != 0) | year %%400 == 0, TRUE, FALSE))\r\n}\r\n\r\n# Soil respiration, monthly mean; heterotrophic + autotrophic\r\n# g C m-2 day-1; kg C m-2 day-1\r\n# Parameters:\r\n# Ta = monthly mean temperature (\u00b0C)\r\n# P = monthly mean precipitation (cm)\r\n# Raich, Potter, Bhagawati, 2002, Interannual variability in global soil respiration, 1980-94, Global Change Biology, 8, pp. 800-12.\r\n# https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1365-2486.2002.00511.x\r\nrespiration_soil <- function(Ta, P) {\r\n if (Ta < -13.3) {\r\n Rs <- 0\r\n } else {\r\n if (Ta > 33.5) { Ta <- 33.5 } # bound the temperature response\r\n e <- exp(1) # Euler's constant\r\n Q <- 0.05452 # soil respiration rate change wrt temperature (\u00b0C-1)\r\n F <- 1.250 # soil respiration rate at 0\u00b0C (g C m-2 day-1)\r\n K <- 4.259 # hyperbolic respiration-rainfall curve half-saturation constant (mm month-1)\r\n Rs <- F * e^(Q * Ta) * (P / (K + P))\r\n }\r\n return(Rs / 1000)\r\n}\r\n\r\n# Simple SOC model\r\n# Mg C ha-1 cm-1; kg C m-2; midpoint of profile depth; Approximates integral in 1 cm steps\r\n# Domke et al. (2017) Toward inventory-based estimates of soil organic carbon in forests of the United States, Ecological Applications, 27(4), pp. 1223\u20131235.\r\n# https://www.fs.fed.us/nrs/pubs/jrnl/2017/nrs_2017_domke_001.pdf\r\nsoc_depth = function(order, depth_cm=100) {\r\n soc_table = data.frame(\r\n order = c(\"All\",\"Alfisols\",\"Andisols\",\"Aridisols\",\"Entisols\",\"Histosols\",\"Inceptisols\",\r\n \"Mollisols\",\"Spodosols\",\"Ultisols\",\"Vertisols\"),\r\n intercept = c(1.1795,1.1122,1.3837,0.2065,0.9300,1.6227,1.1631,1.0163,1.4262,1.1576,0.5145),\r\n slope = c(-0.8228,-0.8330,-0.8425,-0.1300,-0.7207,-1.0109,-0.7331,-0.6214,-0.9801,-0.8867,-0.2427)\r\n )\r\n coeffs = as.numeric(soc_table[soc_table$order==order,][,c(\"intercept\",\"slope\")])\r\n soc = sapply(seq(1, depth_cm, 1), function(x) 10^(coeffs[1] + coeffs[2] * log10(x)))\r\n soc = sum(soc) / 10 # convert to kg m-2 integrated over depth\r\n return(soc)\r\n}\r\n\r\n# Time #\r\nstart_time <- proc.time()\r\nloop_times <- c()\r\n\r\n# Data #\r\n\r\n# Load lookup tables (LUTs) into memory; expects CSV files in lut folder\r\nConfiguration <- read.csv(\"configuration.csv\", stringsAsFactors=FALSE, nrows=1)\r\nTrees <- read.csv(\"trees.csv\", stringsAsFactors=FALSE)\r\nClimate <- read.csv(\"climate.csv\", stringsAsFactors=FALSE)\r\nGrowthLUT <- read.csv(\"lut/growth.csv\", stringsAsFactors=FALSE)\r\nMortalityLUT <- read.csv(\"lut/mortality.csv\", stringsAsFactors=FALSE)\r\nRegenerationLUT <- read.csv(\"lut/regeneration.csv\", stringsAsFactors=FALSE)\r\nAllometryLUT <- read.csv(\"lut/allometry.csv\", stringsAsFactors=FALSE)\r\nBiomassLUT <- read.csv(\"lut/biomass.csv\", stringsAsFactors=FALSE)\r\nStoichiometryLUT <- read.csv(\"lut/stoichiometry.csv\", stringsAsFactors=FALSE)\r\nCarbonLUT <- read.csv(\"lut/carbon.csv\", stringsAsFactors=FALSE, nrows=1)\r\n\r\n# Model #\r\n\r\n# Optional: process in parallel\r\n#nCores <- as.integer(Configuration$n_cores)\r\n#if (nCores > 1) {\r\n# library(doParallel)\r\n# cl <- parallel::makeCluster(nCores)\r\n# doParallel::registerDoParallel(cl)\r\n#}\r\n\r\n# Optional: Generate cohorts from initial tree list\r\nCohortMode <- as.logical(Configuration$cohort_mode)\r\nif (CohortMode == TRUE) {\r\n message(\"Generating cohorts...\")\r\n Trees <- GenerateCohorts(Trees)\r\n}\r\n\r\n# Initialize tree biomass\r\nmessage(\"Initializing...\")\r\nTrees <- Initialize(Trees)\r\n\r\n# Ensure column names and tree type are lower case\r\nTrees <- ensure_lowercase(Trees)\r\n\r\n# Number of years to run model; e.g., 2000-2049 (50 years)\r\nStartYear <- as.numeric(Configuration$start_year) \r\nEndYear <- as.numeric(Configuration$end_year)\r\nYears <- StartYear:EndYear # 200 years\r\n\r\n# Amount of light suppressed cohorts receive\r\nUnderstoryAdult <- as.numeric(Configuration$understory_adult) # 0.1\r\nUnderstorySapling <- as.numeric(Configuration$understory_sapling) # 0.7\r\n\r\n# id placeholder\r\nid_holder <- nrow(Trees)\r\n\r\n# Run the simulation\r\nmessage(\"Starting simulation...\")\r\nmessage(\"=========================================================================\")\r\nfor (year in Years) {\r\n \r\n loop_start_time <- proc.time()\r\n \r\n if (Verbose) {\r\n message(\"=========================================================================\")\r\n message(paste(\"Year:\", year))\r\n message(paste(\"n-Trees:\", nrow(Trees)))\r\n message(\"=========================================================================\")\r\n }\r\n\r\n # Append or update year; vectorized\r\n Trees$year <- year\r\n\r\n # Increment tree age; vectorized; regeneration yields age 0\r\n Trees$age <- Trees$age + 1\r\n\r\n # Calculate soil respiration\r\n if (Verbose) {\r\n message(\"Computing soil respiration...\")\r\n }\r\n climate <- Climate[Climate$year==year,]\r\n n_days <- list(\r\n \"1\" = 31,\r\n \"2\" = ifelse(leap_year(year), 29, 28),\r\n \"3\" = 31,\r\n \"4\" = 30,\r\n \"5\" = 31,\r\n \"6\" = 30,\r\n \"7\" = 31,\r\n \"8\" = 31,\r\n \"9\" = 30,\r\n \"10\" = 31,\r\n \"11\" = 30,\r\n \"12\" = 31\r\n )\r\n Rs_month <- c()\r\n for (month in climate$month) {\r\n month_days <- as.numeric(n_days[as.character(month)])\r\n month_i <- climate[climate$month==month,]\r\n Rs <- respiration_soil(Ta=month_i$temperature, P=month_i$precipitation)\r\n Rs <- Rs * month_days\r\n Rs_month <- c(Rs_month, Rs)\r\n }\r\n Rs_year <- sum(Rs_month) # * Configuration$field_area\r\n respiration_df <- data.frame(year=year, r_soil=Rs_year)\r\n\r\n # Calculate soil organic C and N\r\n c_so <- soc_depth(order=Configuration$soil_order, depth_cm=100)\r\n n_so <- c_so / mean(StoichiometryLUT$cn_soil, na.rm=TRUE)\r\n som_df <- data.frame(year=year, c_so=c_so, n_so=n_so)\r\n\r\n # Sort trees by descending height; vectorized\r\n if (Verbose) {\r\n message(\"Sorting...\")\r\n }\r\n Trees <- Trees[order(Trees$height, decreasing=TRUE), ]\r\n\r\n # Calculate cumulative crown area; vectorized\r\n if (Verbose) {\r\n message(\"Calculating culumative crown area...\")\r\n }\r\n if (CohortMode) {\r\n Trees$crown_a_cumsum <- cumsum(Trees$crown_a * Trees$n_trees)\r\n } else {\r\n Trees$crown_a_cumsum <- cumsum(Trees$crown_a)\r\n }\r\n\r\n # Calculate z* and closest cohort; vectorized\r\n if (Verbose) {\r\n message(\"Calculating Z*...\")\r\n }\r\n index <- which.min(abs(Trees$crown_a_cumsum - Configuration$field_area))\r\n zstar_df <- Trees[index,]\r\n if (Verbose) {\r\n message(paste(\"Z* height:\", zstar_df$height))\r\n }\r\n\r\n # Dataframe placeholders\r\n mortality_df <- data.frame(matrix(ncol=nrow(Trees), nrow=0))\r\n colnames(mortality_df) <- colnames(Trees)\r\n regeneration_df <- data.frame(matrix(ncol=nrow(Trees), nrow=0))\r\n colnames(regeneration_df) <- colnames(Trees)\r\n\r\n # Loop over each species\r\n for (species in unique(Trees$species)) {\r\n\r\n # Skip species not found in lookup tables\r\n if (!species %in% GrowthLUT$species) {\r\n next\r\n }\r\n\r\n # Species index\r\n s <- Trees$species==species\r\n \r\n # Regeneration; species\r\n if (Verbose) {\r\n message(\"Applying regeneration...\")\r\n }\r\n n_replicates <- length(which(s))\r\n index <- RegenerationLUT$species==species\r\n n_saplings <- RegenerationLUT[index,]$mean\r\n # new sapling used in regeneration\r\n new_sapling <- data.frame(species=species, type=\"sapling\", age=0, dbh=1,\r\n height=2, crown_r=0.1, crown_l=0.845, crown_a=0.0314, ba=0.00008,\r\n crown_a_cumsum=NA, biomass_ag=NA, biomass_stem=NA, biomass_branch=NA,\r\n biomass_leaf=NA, biomass_root_coarse=NA, biomass_root_fine=NA,\r\n biomass_soil=NA, biomass_bg=NA, biomass_total=NA, c_ag=NA, c_bg=NA,\r\n c_total=NA, anpp=NA, n_ag=NA, n_bg=NA, n_total=NA, annp=NA, year=year)\r\n if (CohortMode & n_replicates > 0) {\r\n new_sapling$id <- id_holder\r\n new_sapling$n_trees <- n_saplings\r\n Trees <- rbind(Trees, new_sapling)\r\n regeneration_df <- rbind(regeneration_df, new_sapling)\r\n id_holder <- id_holder + 1 #+ n_replicates\r\n } else if (n_replicates > 0){\r\n n_trees <- nrow(trees)\r\n new_saplings <- do.call(rbind, replicate(n_replicates * n_saplings, new_sapling, simplify=FALSE))\r\n new_saplings$id <- id_holder:(id_holder + (n_replicates * n_saplings) - 1)\r\n Trees <- rbind(Trees, new_saplings)\r\n regeneration_df <- rbind(regeneration_df, new_saplings)\r\n id_holder <- id_holder + (n_replicates * n_saplings)\r\n }\r\n\r\n # Recalculate species index after regeneration\r\n s <- Trees$species==species\r\n\r\n # Loop over each type within each species\r\n for (type in unique(Trees[s,]$type)) {\r\n\r\n # Species and type index\r\n st <- Trees$species==species & Trees$type==type\r\n\r\n # Mortality; species & type\r\n if (Verbose) {\r\n message(\"Applying mortality...\")\r\n }\r\n index <- MortalityLUT$species==species & MortalityLUT$type==type\r\n mortality <- MortalityLUT[index,]\r\n random_uniform <- runif(1, min=0, max=1)\r\n if (random_uniform < mortality$probability) {\r\n kill_fraction <- runif(1, min=0, max=1) # random uniform\r\n if (CohortMode) {\r\n kill_n <- round(nrow(Trees[st,]) * kill_fraction)\r\n if (kill_n > 0) {\r\n # Kill fraction of cohorts\r\n kill_index <- sample(which(st), kill_n, replace=FALSE)\r\n mortality_df <- rbind(mortality_df, Trees[kill_index,])\r\n Trees <- Trees[-kill_index,] # kill trees\r\n # Optional: kill fraction within cohorts and then remove dead cohorts\r\n #Trees[st,]$n_trees <- Trees[st,]$n_trees - kill_n # kill trees\r\n #mortality_df <- cbind(data.frame(year=year), Trees[st,])\r\n #mortality_df$n_trees <- round(mortality_df$n_trees * kill_fraction)\r\n }\r\n #dead_cohorts <- Trees$n_trees < 1 | is.na(Trees$n_trees) | is.na(Trees$dbh)\r\n #if(any(dead_cohorts)) {\r\n # Trees <- Trees[-which(dead_cohorts),] # remove dead trees/cohorts\r\n # message(\"Removal\")\r\n # stopifnot(!anyNA(Trees[st,]$dbh) | !anyNA(Trees[st,]$n_trees)) # \"PIAB\" \"sapling\"\r\n #}\r\n } else {\r\n kill_n <- round(nrow(Trees[st,]) * kill_fraction)\r\n if (kill_n > 0) {\r\n kill_index <- sample(which(st), kill_n, replace=FALSE)\r\n mortality_df <- rbind(mortality_df, Trees[kill_index,])\r\n Trees <- Trees[-kill_index,] # kill trees\r\n }\r\n }\r\n }\r\n\r\n # Recalculate species and type index after mortality\r\n st <- Trees$species==species & Trees$type==type\r\n\r\n # Skip ahead if species or type is all dead\r\n if (nrow(Trees[st,]) < 1) {\r\n next\r\n }\r\n\r\n # Growth; species & type\r\n index <- GrowthLUT$species==species & GrowthLUT$type==type\r\n growth <- GrowthLUT[index,]\r\n if (type==\"adult\") {\r\n Trees[st,]$dbh <- ifelse(Trees[st,]$crown_a_cumsum < Configuration$field_area,\r\n Trees[st,]$dbh + growth$mean,\r\n Trees[st,]$dbh + growth$mean * UnderstoryAdult)\r\n } else if (type==\"sapling\") {\r\n Trees[st,]$dbh <- Trees[st,]$dbh + growth$mean * UnderstorySapling\r\n } else {\r\n message(\"Error: Type not recognized\")\r\n }\r\n\r\n # Allometry; species & type\r\n if (Verbose) {\r\n message(\"Calculating allometry...\")\r\n }\r\n index <- AllometryLUT$species==species & AllometryLUT$type==type\r\n allometry <- AllometryLUT[index,]\r\n Trees[st,]$height <- 1.35 + (30-1.35) * (1-exp(-(allometry$h_coeff * Trees[st,]$dbh)))\r\n Trees[st,]$height <- ifelse(Trees[st,]$height > 30, 30, Trees[st,]$height) # limit height to 30m\r\n Trees[st,]$type <- ifelse(Trees[st,]$dbh > 10, \"adult\", \"sapling\")\r\n Trees[st,]$crown_l <- allometry$cd * Trees[st,]$dbh\r\n Trees[st,]$crown_r <- allometry$cr1 * Trees[st,]$dbh^allometry$cr2\r\n Trees[st,]$crown_a <- Trees[st,]$crown_r^2 * pi\r\n Trees[st,]$ba <- Trees[st,]$dbh^2 * 0.00007854 # forester's constant for DBH (cm); m2\r\n } # end type loop\r\n\r\n # Calculate species index after regeneration and mortality\r\n s <- Trees$species==species\r\n\r\n # Skip ahead if species is all dead\r\n if (nrow(Trees[s,]) < 1) {\r\n next\r\n }\r\n\r\n # Biomass expansion factors and allocation or partitioning; species\r\n # DBH to AGB and AGB to BGB (Chojnacky, Heath, Jenkins, 2014)\r\n # To add...? BGB to leaf and steam (Enquist and Niklas, 2002)\r\n if (Verbose) {\r\n message(\"Calculating biomass...\")\r\n }\r\n index <- BiomassLUT$species==species\r\n biomass <- BiomassLUT[index,]\r\n if (CohortMode) {\r\n Trees[s,]$biomass_ag <- exp(biomass$b0 + biomass$b1 * log(Trees[s,]$dbh)) * Trees[s,]$n_trees\r\n } else {\r\n Trees[s,]$biomass_ag <- exp(biomass$b0 + biomass$b1 * log(Trees[s,]$dbh))\r\n }\r\n Trees[s,]$biomass_stem <- Trees[s,]$biomass_ag * biomass$fraction_stem\r\n Trees[s,]$biomass_branch <- Trees[s,]$biomass_ag * biomass$fraction_branch\r\n Trees[s,]$biomass_leaf <- Trees[s,]$biomass_ag * biomass$fraction_leaf\r\n Trees[s,]$biomass_root_coarse <- Trees[s,]$biomass_ag * exp(-1.4485 - 0.03476 * log(Trees[s,]$dbh))\r\n Trees[s,]$biomass_root_fine <- Trees[s,]$biomass_ag * exp(-1.8629 - 0.77534 * log(Trees[s,]$dbh))\r\n Trees[s,]$biomass_soil <- Trees[s,]$biomass_ag * biomass$fraction_soil\r\n Trees[s,]$biomass_bg <- Trees[s,]$biomass_root_coarse + Trees[s,]$biomass_root_fine +\r\n Trees[s,]$biomass_soil\r\n Trees[s,]$biomass_total <- Trees[s,]$biomass_ag + Trees[s,]$biomass_bg\r\n\r\n # Year naught total C and N for ANPP and ANNP calculations\r\n c_total_previous <- Trees[s,]$c_total\r\n n_total_previous <- Trees[s,]$n_total\r\n\r\n # C fraction; species\r\n if (Verbose) {\r\n message(\"Calculating C fraction...\")\r\n }\r\n #index <- CarbonLUT$species==species\r\n carbon <- CarbonLUT #[index,]\r\n Trees[s,]$c_ag <- Trees[s,]$biomass_ag * \r\n (biomass$fraction_stem * carbon$fraction_stem) +\r\n (biomass$fraction_branch * carbon$fraction_branch) +\r\n (biomass$fraction_leaf * carbon$fraction_leaf)\r\n Trees[s,]$c_bg <- Trees[s,]$biomass_ag *\r\n (biomass$fraction_root * carbon$fraction_root) +\r\n (biomass$fraction_soil * carbon$fraction_soil)\r\n Trees[s,]$c_total <- Trees[s,]$c_ag + Trees[s,]$c_bg\r\n if (year > min(Years)) {\r\n Trees[s,]$anpp <- Trees[s,]$c_total - c_total_previous\r\n }\r\n # C:N stoichiometry; species\r\n if (Verbose) {\r\n message(\"Calculating N fraction...\")\r\n }\r\n index <- StoichiometryLUT$species==species\r\n stoichiometry <- StoichiometryLUT[index,]\r\n Trees[s,]$n_ag <- Trees[s,]$c_ag /\r\n (biomass$fraction_stem * stoichiometry$cn_stem) +\r\n (biomass$fraction_branch * stoichiometry$cn_branch) +\r\n (biomass$fraction_leaf * stoichiometry$cn_leaf)\r\n Trees[s,]$n_bg <- Trees[s,]$c_bg /\r\n (biomass$fraction_root * stoichiometry$cn_root) +\r\n (biomass$fraction_soil * stoichiometry$cn_soil)\r\n Trees[s,]$n_total <- Trees[s,]$n_ag + Trees[s,]$n_bg\r\n if (year > min(Years)) {\r\n Trees[s,]$annp <- Trees[s,]$n_total - n_total_previous\r\n }\r\n } # end species loop\r\n\r\n # Fix order of regeneration_df columns\r\n regeneration_df <- regeneration_df[,colnames(Trees)]\r\n\r\n # Calculate NEE and add to fluxes; NEE calculation based on Equation 1 in:\r\n # Clark et al. (2001) Measuring Net Primary Production in Forests: Concepts and Field Methods,\r\n # Ecological Applications, 11(2), pp. 356-370.\r\n # and\r\n # Reichstein et al. (2005) On the separation of net ecosystem exchange into assimilation and\r\n # ecosystem respiration: review and improved algorithm, Global Change Biology, 11, pp. 1424\u20131439.\r\n # Convert C to CO2 in ANPP\r\n fluxes_df <- data.frame(\r\n year = year,\r\n nee = (respiration_df$r_soil * Configuration$field_area) - sum(Trees$anpp, na.rm=TRUE),\r\n r_soil = (respiration_df$r_soil * Configuration$field_area)\r\n )\r\n\r\n # Save annual tree/cohort results\r\n write.csv(Trees, file=paste0(\"outputs/trees_\", year, \".csv\"), row.names=FALSE,\r\n fileEncoding=\"UTF-8\")\r\n\r\n # Append results to CSV\r\n header_boolean <- year == Years[1]\r\n\r\n suppressWarnings(write.table(som_df, file=\"outputs/som.csv\",\r\n append=TRUE, col.names=header_boolean, row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\"))\r\n suppressWarnings(write.table(fluxes_df, file=\"outputs/fluxes.csv\",\r\n append=TRUE, col.names=header_boolean, row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\"))\r\n suppressWarnings(write.table(zstar_df, file=\"outputs/zstar.csv\",\r\n append=TRUE, col.names=header_boolean, row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\"))\r\n suppressWarnings(write.table(regeneration_df, file=\"outputs/regeneration.csv\",\r\n append=TRUE, col.names=header_boolean, row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\"))\r\n # Mortality is non-deterministic\r\n if (nrow(mortality_df) > 0 | header_boolean) {\r\n suppressWarnings(write.table(mortality_df, file=\"outputs/mortality.csv\",\r\n append=TRUE, col.names=header_boolean, row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\"))\r\n }\r\n\r\n # Calculate loop time\r\n loop_time <- proc.time() - loop_start_time\r\n loop_time <- as.numeric(loop_time[\"elapsed\"])\r\n message(paste(paste0(\"Year: \", year, \",\"), \"Duration (sec):\", round(loop_time, 2)))\r\n loop_times <- c(loop_times, loop_time)\r\n} # end year loop\r\n\r\n# Calculate total time\r\ntotal_time <- proc.time() - start_time\r\ntotal_time <- as.numeric(total_time[\"elapsed\"])\r\nmessage(\"=========================================================================\")\r\nmessage(\"Simulation complete\")\r\nmessage(paste(\"Years:\", length(Years)))\r\nmessage(paste(\"Duration (sec):\", round(total_time, 2)))\r\nmessage(\"=========================================================================\")\r\n\r\n# Export loop times to CSV\r\nloop_times_df <- data.frame(year=Years, time=loop_times)\r\nwrite.table(loop_times_df, file=\"outputs/loop_times.csv\", col.names=TRUE,\r\n row.names=FALSE, sep=\",\", fileEncoding=\"UTF-8\")\r\n\r\n# End #\r\n", "meta": {"hexsha": "e16b2be27b96000284a9f43460eeee579b88d320", "size": 27944, "ext": "r", "lang": "R", "max_stars_repo_path": "models/ppa_sibgc/jerc_rd/ppa_sibgc_v1.r", "max_stars_repo_name": "adam-erickson/ecosystem-model-comparison", "max_stars_repo_head_hexsha": "4eb34532a0cef99e5a556c0fc1157096d44d23e5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-03-02T13:21:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-27T11:36:59.000Z", "max_issues_repo_path": "models/ppa_sibgc/jerc_rd/ppa_sibgc_v1.r", "max_issues_repo_name": "adam-erickson/ecosystem-model-comparison", "max_issues_repo_head_hexsha": "4eb34532a0cef99e5a556c0fc1157096d44d23e5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/ppa_sibgc/jerc_rd/ppa_sibgc_v1.r", "max_forks_repo_name": "adam-erickson/ecosystem-model-comparison", "max_forks_repo_head_hexsha": "4eb34532a0cef99e5a556c0fc1157096d44d23e5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.7698056801, "max_line_length": 158, "alphanum_fraction": 0.6168408245, "num_tokens": 8083, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573377, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.34201891972181364}} {"text": "# Use this script to assemble statistics for:\n# 1. raw\n# 2. stepwise f0, f1, f2\n# 3. intensity features\n#\n# all extracted from the IDS dataset using PraatMasterScript.praat for further analysis\n# Script written by Courtney Hilton & Cody Moser\n\n# libraries ---------------------------------------------------------------\n\nlibrary(tidyverse)\nlibrary(here)\n\n# load data ---------------------------------------------------------------\n\ndata <- read_csv(here(\"data\", \"Praat_MasterData.csv\"))\nmirrri <- read_csv(here(\"data\", \"mir_roughness_rolloff_inharm\"))\nmirras <- read_csv(here(\"data\", \"mir_reconcatenated_attack_summary\"))\nmirtp <- read_csv(here(\"data\", \"mir_tempo_pulseclarity\"))\nnpvi <- read_csv(here(\"data\", \"npvi_summary\"))\ntm <- read_csv(here(\"data\", \"tm_summary\"))\n\n# Processing --------------------------------------------------------------\n\ndata <- data %>%\n group_by(id) %>%\n mutate(\n # Calculate stepwise euclidian travel distance for intensity\n praat_intensitytravel = sqrt((intensity - lag(intensity))^2),\n # Calculate stepwise euclidian travel distance for intensity\n praat_f0travel = sqrt((f0 - lag(f0))^2),\n # Calculate stepwise euclidian travel distance for vowel space\n praat_voweltravel = sqrt((sqrt(f1^2 + f2^2) - lag(sqrt(f1^2 + f2^2)))^2),\n # Change 0 to NA\n across(c(f0,f1,f2, intensity), ~ ifelse(.x == 0, NA, .x)),\n # remove extra bit on ID var\n id = str_sub(id, 1,6)\n )\n\n# set column names for reference\ncolnames(data) <- c(\n \"id\",\n \"time\",\n \"praat_f0\",\n \"praat_f1\",\n \"praat_f2\",\n \"praat_intensity\",\n \"praat_intensity_travel\",\n \"praat_f0_travel\",\n \"praat_vowel_travel\"\n)\n\n# Caclulate summary data --------------------------------------------------\n\nsummarydata <- data %>%\n group_by(id) %>%\n summarize(\n praat_f0_mean = mean(praat_f0, na.rm = TRUE),\n praat_f0_median = median(praat_f0, na.rm = TRUE),\n praat_f0_std = sd(praat_f0, na.rm = TRUE),\n praat_f0_min = min(praat_f0, na.rm = TRUE),\n praat_f0_max = max(praat_f0, na.rm = TRUE),\n praat_f0_first_quart = quantile(praat_f0, probs = 0.25, na.rm = TRUE),\n praat_f0_third_quart = quantile(praat_f0, probs = 0.75, na.rm = TRUE),\n praat_f0_IQR = IQR(praat_f0, na.rm = TRUE),\n praat_f1_mean = mean(praat_f1, na.rm = TRUE),\n praat_f1_median = median(praat_f1, na.rm = TRUE),\n praat_f1_std = sd(praat_f1, na.rm = TRUE),\n praat_f1_min = min(praat_f1, na.rm = TRUE),\n praat_f1_max = max(praat_f1, na.rm = TRUE),\n praat_f1_first_quart = quantile(praat_f1, probs = 0.25, na.rm = TRUE),\n praat_f1_third_quart = quantile(praat_f1, probs = 0.75, na.rm = TRUE),\n praat_f1_IQR = IQR(praat_f1, na.rm = TRUE), praat_f2_mean = mean(praat_f2, na.rm = TRUE),\n praat_f2_median = median(praat_f2, na.rm = TRUE),\n praat_f2_std = sd(praat_f2, na.rm = TRUE),\n praat_f2_min = min(praat_f2, na.rm = TRUE),\n praat_f2_max = max(praat_f2, na.rm = TRUE),\n praat_f2_first_quart = quantile(praat_f2, probs = 0.25, na.rm = TRUE),\n praat_f2_third_quart = quantile(praat_f2, probs = 0.75, na.rm = TRUE),\n praat_f2_IQR = IQR(praat_f2, na.rm = TRUE),\n praat_intensity_mean = mean(praat_intensity, na.rm = TRUE),\n praat_intensity_median = median(praat_intensity, na.rm = TRUE),\n praat_intensity_std = sd(praat_intensity, na.rm = TRUE),\n praat_intensity_min = min(praat_intensity, na.rm = TRUE),\n praat_intensity_max = max(praat_intensity, na.rm = TRUE),\n praat_intensity_first_quart = quantile(praat_intensity, probs = 0.25, na.rm = TRUE),\n praat_intensity_third_quart = quantile(praat_intensity, probs = 0.75, na.rm = TRUE),\n praat_intensity_IQR = IQR(praat_intensity, na.rm = TRUE),\n praat_intensitytravel_mean = mean(praat_intensity_travel, na.rm = TRUE),\n praat_intensitytravel_median = median(praat_intensity_travel, na.rm = TRUE),\n praat_intensitytravel_std = sd(praat_intensity_travel, na.rm = TRUE),\n praat_intensitytravel_min = min(praat_intensity_travel, na.rm = TRUE),\n praat_intensitytravel_max = max(praat_intensity_travel, na.rm = TRUE),\n praat_intensitytravel_first_quart = quantile(praat_intensity_travel, probs = 0.25, na.rm = TRUE),\n praat_intensitytravel_third_quart = quantile(praat_intensity_travel, probs = 0.75, na.rm = TRUE),\n praat_intensitytravel_IQR = IQR(praat_intensity_travel, na.rm = TRUE),\n praat_voweltravel_mean = mean(praat_vowel_travel, na.rm = TRUE),\n praat_voweltravel_median = median(praat_vowel_travel, na.rm = TRUE),\n praat_voweltravel_std = sd(praat_vowel_travel, na.rm = TRUE),\n praat_voweltravel_min = min(praat_vowel_travel, na.rm = TRUE),\n praat_voweltravel_max = max(praat_vowel_travel, na.rm = TRUE),\n praat_voweltravel_first_quart = quantile(praat_vowel_travel, probs = 0.25, na.rm = TRUE),\n praat_voweltravel_third_quart = quantile(praat_vowel_travel, probs = 0.75, na.rm = TRUE),\n praat_voweltravel_IQR = IQR(praat_vowel_travel, na.rm = TRUE),\n praat_f0travel_mean = mean(praat_f0_travel, na.rm = TRUE),\n praat_f0travel_median = median(praat_f0_travel, na.rm = TRUE),\n praat_f0travel_std = sd(praat_f0_travel, na.rm = TRUE),\n praat_f0travel_min = min(praat_f0_travel, na.rm = TRUE),\n praat_f0travel_max = max(praat_f0_travel, na.rm = TRUE),\n praat_f0travel_first_quart = quantile(praat_f0_travel, probs = 0.25, na.rm = TRUE),\n praat_f0travel_third_quart = quantile(praat_f0_travel, probs = 0.75, na.rm = TRUE),\n praat_f0travel_IQR = IQR(praat_f0_travel, na.rm = TRUE),\n praat_f0_range = praat_f0_max - praat_f0_min,\n praat_f1_range = praat_f1_max - praat_f1_min,\n praat_f2_range = praat_f2_max - praat_f2_min,\n praat_intensity_range = praat_intensity_max - praat_intensity_min,\n praat_intensitytravel_range = praat_intensitytravel_max - praat_intensitytravel_min,\n praat_voweltravel_range = praat_voweltravel_max - praat_voweltravel_min,\n praat_f0travel_range = praat_f0travel_max - praat_f0travel_min,\n praat_f0travel_rate_median = median(praat_f0_travel / time, na.rm = TRUE),\n praat_f0travel_rate_IQR = IQR(praat_f0_travel / time, na.rm = TRUE),\n praat_voweltravel_rate_median = median(praat_vowel_travel / time, na.rm = TRUE),\n praat_voweltravel_rate_IQR = IQR(praat_vowel_travel / time, na.rm = TRUE),\n praat_intensitytravel_rate_median = median(praat_intensity_travel / time, na.rm = TRUE),\n praat_intensitytravel_rate_IQR = IQR(praat_intensity_travel / time, na.rm = TRUE),\n # sum file length, f0, vowel space, and intensity for gross travel rates\n timesum = sum(time),\n f0sum = sum(praat_f0_travel, na.rm = TRUE),\n vowelsum = sum(praat_vowel_travel, na.rm = TRUE),\n intensitysum = sum(praat_intensity_travel, na.rm = TRUE),\n # calculate gross travel rates\n praat_f0_travel_rate_default = (f0sum / timesum),\n praat_voweltravel_rate_default = (vowelsum / timesum),\n praat_intensitytravel_rate_default = (intensitysum / timesum)\n ) %>% \n ungroup() %>% \n # Remove unnecessary variables (minimums and sums)\n select(-c(\n praat_voweltravel_min,\n praat_intensitytravel_min,\n praat_f0travel_min,\n timesum,\n f0sum,\n vowelsum,\n intensitysum\n ))\n\n\n# Add song info to files --------------------------------------------------\n\nsummarydata <- summarydata %>%\n transmute(\n id = id,\n voc_type = substr(id,6,6),\n song = case_when(\n endsWith(id, \"A\") ~ 1 ,\n endsWith(id, \"B\") ~ 0 ,\n endsWith(id, \"C\") ~ 1 ,\n endsWith(id, \"D\") ~ 0 ,\n ),\n infdir = case_when(\n endsWith(id, \"A\") ~ 1 ,\n endsWith(id, \"B\") ~ 1 ,\n endsWith(id, \"C\") ~ 0 ,\n endsWith(id, \"D\") ~ 0 ,\n ),\n id_site = substr(id,1,3),\n id_person = substr(id,1,5),\n ) %>% \n merge(summarydata, by = \"id\")\n\n# Combine summary statistics from Praat with additional files -------------\n\ndf_list <- list(summarydata, mirrri, mirras, mirtp, npvi, tm)\nsummarydata %>% reduce(full_join, by='id')\n\n# Save output -------------------------------------------------------------\n\nwrite_csv(summarydata, here(\"results\", \"praatsummary.csv\"))\n", "meta": {"hexsha": "2321b14f75be9e78c5b3f57013af4b516036d5f2", "size": 8086, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/acoustics_processing/7_SummaryStatistics.r", "max_stars_repo_name": "themusiclab/infant-speech-song", "max_stars_repo_head_hexsha": "7e4095a484435b5a685cedadcdcfd175e99f24e3", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/acoustics_processing/7_SummaryStatistics.r", "max_issues_repo_name": "themusiclab/infant-speech-song", "max_issues_repo_head_hexsha": "7e4095a484435b5a685cedadcdcfd175e99f24e3", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/acoustics_processing/7_SummaryStatistics.r", "max_forks_repo_name": "themusiclab/infant-speech-song", "max_forks_repo_head_hexsha": "7e4095a484435b5a685cedadcdcfd175e99f24e3", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9222222222, "max_line_length": 101, "alphanum_fraction": 0.6674499134, "num_tokens": 2394, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056167854461, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3419602631069061}} {"text": "# IDEAL POINTS --- \n\n\nsel.trunc <- function(i){\n if (!is.na(north) & i==north){\n draw ~ dnorm(x.mean[i], x.tau[i])T(,0)\n\n for(i 1:n.members)){\n theta[i] ~ sel.trunc (x.mean[i], x.tau[i]); # auto-regressive process\n}\n\n\n setdiff(1:9, c(NA,NA))\n\n theta[north] ~ ifelse (is.na(north)==FALSE, dnorm(x.mean[north], x.tau[north])T(0,) # normal + truncada\n ifelse (is.na(south)==FALSE) theta[south] ~ dnorm(x.mean[south], x.tau[south])T(,0) # normal - truncada\n", "meta": {"hexsha": "16bb7799359115ae5bbb2e6a8f50af1f562d7b76", "size": 527, "ext": "r", "lang": "R", "max_stars_repo_path": "code/tmp.r", "max_stars_repo_name": "grosasballina/ife-update", "max_stars_repo_head_hexsha": "174e3bfdffa6e84bff9fe70defe1e8afff05c7d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/tmp.r", "max_issues_repo_name": "grosasballina/ife-update", "max_issues_repo_head_hexsha": "174e3bfdffa6e84bff9fe70defe1e8afff05c7d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/tmp.r", "max_forks_repo_name": "grosasballina/ife-update", "max_forks_repo_head_hexsha": "174e3bfdffa6e84bff9fe70defe1e8afff05c7d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-02T19:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-08T13:31:15.000Z", "avg_line_length": 31.0, "max_line_length": 115, "alphanum_fraction": 0.5313092979, "num_tokens": 175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7879311956428947, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.3419475344497906}} {"text": "guess_if_intercept = function(x){\n\tx = as.data.frame(x)\n\tis_intercept = rep(F,ncol(x))\n\tfor(i in 1:ncol(x)){\n\t\tvals = sort(unique(x[,i]))\n\t\tif(length(vals)==1){\n\t\t\tif(vals==1){\n\t\t\t\tis_intercept[i] = T\n\t\t\t}\n\t\t}else{\n\t\t\tif(length(vals)==2){\n\t\t\t\tif(all(vals==c(0,1))){\n\t\t\t\t\tis_intercept[i] = T\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn(is_intercept)\n}\n", "meta": {"hexsha": "b31b17c4b993f4a706419e8204b61dba7fece374", "size": 333, "ext": "r", "lang": "R", "max_stars_repo_path": "r_helpers/guess_if_intercept.r", "max_stars_repo_name": "mike-lawrence/hierarchical_mvn_vs_pariwise", "max_stars_repo_head_hexsha": "b33c1cf6136c359125fc02bd7d969ae9d23ee4b2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r_helpers/guess_if_intercept.r", "max_issues_repo_name": "mike-lawrence/hierarchical_mvn_vs_pariwise", "max_issues_repo_head_hexsha": "b33c1cf6136c359125fc02bd7d969ae9d23ee4b2", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_helpers/guess_if_intercept.r", "max_forks_repo_name": "mike-lawrence/hierarchical_mvn_vs_pariwise", "max_forks_repo_head_hexsha": "b33c1cf6136c359125fc02bd7d969ae9d23ee4b2", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.65, "max_line_length": 33, "alphanum_fraction": 0.5525525526, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632979641571, "lm_q2_score": 0.6297746213017459, "lm_q1q2_score": 0.3418815278939939}} {"text": "context(\"Test formatOccData\")\n\n# Create data\nn <- 15000 #size of dataset\nnyr <- 20 # number of years in data\nnSamples <- 100 # set number of dates\nnSites <- 50 # set number of sites\nset.seed(125)\n\n# Create somes dates\nfirst <- as.Date(strptime(\"2010/01/01\", \"%Y/%m/%d\")) \nlast <- as.Date(strptime(paste(2010+(nyr-1),\"/12/31\", sep=''), \"%Y/%m/%d\")) \ndt <- last-first \nrDates <- first + (runif(nSamples)*dt)\n\n# taxa are set as random letters\ntaxa <- sample(letters, size = n, TRUE)\n\n# three sites are visited randomly\nsite <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)\n\n# the date of visit is selected at random from those created earlier\nsurvey <- sample(rDates, size = n, TRUE)\n\n# set the closure period to be in 2 year bins\nclosure_period <- ceiling((as.numeric(format(survey,'%Y')) - 2009)/2)\n\n# create survey variable that is not a date and a closure period to match\nsurvey_numbered <- as.integer(as.factor(survey))\n\n\nreplicate <- rep(1, n)\n\ntest_that(\"Test formatOccData\", {\n\n expect_warning(visitData <- formatOccData(taxa = taxa, site = site, survey = survey),\n '871 out of 15000 observations will be removed as duplicates')\n \n head_spp_vis <- structure(list(visit = c(\"A102010-04-141\", \"A102010-04-221\", \"A102010-08-291\", \n \"A102010-11-041\", \"A102011-02-091\", \"A102011-03-091\"), a = c(FALSE, \n FALSE, FALSE, FALSE, FALSE, FALSE), b = c(FALSE, FALSE, FALSE, \n FALSE, FALSE, FALSE), c = c(FALSE, FALSE, FALSE, FALSE, FALSE, \n FALSE), d = c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE), e = c(FALSE, \n TRUE, FALSE, FALSE, FALSE, FALSE), f = c(FALSE, FALSE, FALSE, \n FALSE, FALSE, FALSE), g = c(FALSE, FALSE, TRUE, FALSE, FALSE, \n FALSE), h = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), i = c(FALSE, \n FALSE, FALSE, FALSE, FALSE, FALSE), j = c(FALSE, FALSE, FALSE, \n FALSE, FALSE, FALSE), k = c(FALSE, FALSE, FALSE, FALSE, FALSE, \n FALSE), l = c(TRUE, FALSE, FALSE, FALSE, FALSE, TRUE), m = c(FALSE, \n FALSE, FALSE, FALSE, FALSE, FALSE), n = c(FALSE, TRUE, TRUE, \n FALSE, FALSE, FALSE), o = c(TRUE, FALSE, FALSE, FALSE, FALSE, \n FALSE), p = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE), q = c(FALSE, \n TRUE, FALSE, FALSE, FALSE, FALSE), r = c(TRUE, FALSE, FALSE, \n FALSE, FALSE, FALSE), s = c(FALSE, FALSE, FALSE, TRUE, TRUE, \n FALSE), t = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), u = c(FALSE, \n FALSE, FALSE, FALSE, FALSE, FALSE), v = c(FALSE, FALSE, FALSE, \n FALSE, FALSE, FALSE), w = c(TRUE, FALSE, FALSE, FALSE, FALSE, \n FALSE), x = c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE), y = c(FALSE, \n FALSE, FALSE, FALSE, FALSE, FALSE), z = c(TRUE, FALSE, FALSE, \n FALSE, FALSE, FALSE)), .Names = c(\"visit\", \"a\", \"b\", \"c\", \"d\", \n \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \n \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\"), row.names = c(NA, \n 6L), class = \"data.frame\")\n \n\n head_occDetdata <- structure(list(visit = c(\"A102010-04-141\", \"A102010-04-221\", \n \"A102010-08-291\", \"A102010-11-041\",\n \"A102011-02-091\", \"A102011-03-091\"),\n site = c(\"A10\", \"A10\", \"A10\", \"A10\", \"A10\",\n \"A10\"),\n L = c(5L, 5L, 2L, 2L, 1L, 2L),\n TP = c(2010, 2010, 2010, 2010, 2011, 2011)),\n row.names = c(1L, 6L, 11L, 13L, 15L, 16L),\n class = \"data.frame\")\n \n expect_identical(head(visitData$spp_vis), head_spp_vis)\n expect_identical(head(visitData$occDetdata), head_occDetdata)\n \n})\n\ntest_that(\"Test formatOccData errors\", {\n \n expect_error(visitData <- formatOccData(taxa = head(taxa), site = site, survey = survey, closure_period = closure_period, replicate = replicate),\n 'The following arguements are not of equal length: taxa, site, survey, closure_period, replicate')\n expect_error(visitData <- formatOccData(taxa = taxa, site = head(site), survey = survey, closure_period = closure_period, replicate = replicate),\n 'The following arguements are not of equal length: taxa, site, survey, closure_period, replicate')\n expect_error(visitData <- formatOccData(taxa = taxa, site = site, survey = head(survey), closure_period = closure_period, replicate = replicate),\n 'The following arguements are not of equal length: taxa, site, survey, closure_period, replicate')\n expect_error(visitData <- formatOccData(taxa = taxa, site = site, survey = survey, closure_period=head(closure_period), replicate = replicate),\n 'The following arguements are not of equal length: taxa, site, survey, closure_period, replicate')\n expect_error(visitData <- formatOccData(taxa = taxa, site = site, survey = survey, closure_period=closure_period, replicate = head(replicate)),\n 'The following arguements are not of equal length: taxa, site, survey, closure_period, replicate')\n \n})\n\ntest_that(\"Test formatOccData date requirement errors\", {\n \n expect_warning(visitData <- formatOccData(taxa = taxa, site = site, survey = survey, closure_period = closure_period),\n '871 out of 15000 observations will be removed as duplicates')\n expect_error(suppressWarnings(visitData <- formatOccData(taxa = taxa, site = site, survey = survey_numbered)),\n 'survey must be a date if closure_period not supplied')\n expect_error(suppressWarnings(visitData <- formatOccData(taxa =taxa, site = site, survey = survey_numbered, includeJDay = TRUE, closure_period = closure_period)),\n 'survey must be a date if Julian Date is to be included')\n})\n\ntest_that(\"Test formatOccData specified closure period\", {\n \n expect_warning(visitData <- formatOccData(taxa = taxa, site = site, survey = survey, closure_period = closure_period),\n '871 out of 15000 observations will be removed as duplicates')\n \n head_occDetdata_cp <- structure(list(visit = c(\"A102010-04-141\", \"A102010-04-221\", \n \"A102010-08-291\", \"A102010-11-041\",\n \"A102011-02-091\", \"A102011-03-091\"),\n site = c(\"A10\", \"A10\", \"A10\", \"A10\", \"A10\", \"A10\"),\n L = c(5L, 5L, 2L, 2L, 1L, 2L),\n TP = c(1L, 1L, 1L, 1L, 1L, 1L)),\n row.names = c(1L, 6L, 11L, 13L, 15L, 16L),\n class = \"data.frame\")\n \n expect_identical(head(visitData$occDetdata), head_occDetdata_cp)\n \n})", "meta": {"hexsha": "12086a3e7c0c63850e98c7dd83ff58ac0ff11bef", "size": 7256, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/testformatOccData.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "tests/testthat/testformatOccData.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "tests/testthat/testformatOccData.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 59.4754098361, "max_line_length": 164, "alphanum_fraction": 0.5519570011, "num_tokens": 1911, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3418815185785869}} {"text": "library(tidyverse)\n\nd = read_csv('data/02_TVJ_pretest/data_raw.csv') %>% \n mutate(response = ifelse(response == \"TRUE\", 1, 0))\n\nd = d[-which(d$comments == \"Test run by MF\"),]\n\nd_summary = d %>% group_by(trigger, condition, question) %>% \n summarize(mean_true = mean(response)) %>% arrange(condition)\n\nd_summary_id = d %>% group_by(submission_id, trigger, condition) %>% \n summarize(mean_true = mean(response)) %>% arrange(submission_id) %>% \n filter(condition == \"implicature\") \n\nd_summary_id %>% filter(trigger != \"number\") %>% \n group_by(submission_id) %>% \n summarize(mean_true = mean(mean_true)) %>% \n ggplot(aes(x = mean_true)) + geom_histogram()\n\n", "meta": {"hexsha": "bbc80462a6dfe74547343febbfeb729f52debffe", "size": 660, "ext": "r", "lang": "R", "max_stars_repo_path": "analyses/02_TVJ_pretest/explore_data.r", "max_stars_repo_name": "michael-franke/misleading_with_underinfo", "max_stars_repo_head_hexsha": "bb0f303858e0dee6f68ac316e0948768c3204d2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analyses/02_TVJ_pretest/explore_data.r", "max_issues_repo_name": "michael-franke/misleading_with_underinfo", "max_issues_repo_head_hexsha": "bb0f303858e0dee6f68ac316e0948768c3204d2d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analyses/02_TVJ_pretest/explore_data.r", "max_forks_repo_name": "michael-franke/misleading_with_underinfo", "max_forks_repo_head_hexsha": "bb0f303858e0dee6f68ac316e0948768c3204d2d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0, "max_line_length": 71, "alphanum_fraction": 0.6742424242, "num_tokens": 185, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6297746074044135, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3418815110342354}} {"text": "# 4. faza: Analiza podatkov\n\n# Uvozimo funkcijo za uvoz spletne strani.\nsource(\"lib/xml.r\")\n\n# # Preberemo spletno stran v razpredelnico.\n# cat(\"Uva\u017eam spletno stran...\\n\")\n# tabela <- preuredi(uvozi.obcine(), obcine)\n# \n# # Nari\u0161emo graf v datoteko PDF.\n# cat(\"Ri\u0161em graf...\\n\")\n# pdf(\"slike/naselja.pdf\", width=6, height=4)\n# plot(tabela[[1]], tabela[[4]],\n# main = \"\u0160tevilo naselij glede na povr\u0161ino ob\u010dine\",\n# xlab = \"Povr\u0161ina (km^2)\",\n# ylab = \"\u0160t. naselij\")\n# dev.off()\n\n#######################################################################################################\n\n# # CLUSTERING\n# \n# data <- CHI\n# data$Team <- NULL\n# data$Pos <- NULL\n# data$Naziv <- NULL\n# data$Coutry <- NULL\n# data$Birth.City <- NULL\n# data$FO. <- NULL\n# data$TOI.GP <- NULL\n# \n# data1 <- scale(data)\n# \n# #Nari\u0161emo grafe\n# \n# rezultati <- kmeans(data1, centers = 3)\n# center <- rezultati$centers\n# skupina <- rezultati$cluster\n# barve <- c(\"red\", \"green\", \"blue\")\n# \n# pdf(\"slike/clustering_1.pdf\")\n# \n# \n# plot(data[,\"P\"],data[,\"X...\"], col = barve[skupina],\n# xlab = \"\u0160tevilo to\u010dk\", ylab = \"+/-\",\n# main = \"Razvr\u0161\u010danje - Odvisnost \u0161tevila to\u010dk in +/-\")\n# \n# # plot(iris2[c(\"G\", \"X...\")], col = rezultati$cluster)\n# # points(rezultati$centers[,c(\"G\", \"X...\")], col = 1:3, \n# # pch = 8, cex=2)\n# \n# dev.off()\n# \n# \n# pdf(\"slike/clustering_2.pdf\")\n# plot(data[,\"G\"],data[,\"S\"], col = barve[skupina],\n# xlab = \"\u0160tevilo zadetkov\", ylab = \"\u0160tevilo strelov\",\n# main = \"Razvr\u0161\u010danje - Odvisnost \u0161tevila golov in \u0161tevilo strelov\")\n# dev.off()\n\n#########################################################################################################\n\n# Napovedovanje\n\npdf(\"slike/place.pdf\")\n\nleto <- salary$YEAR\nplace <- salary$AVERAGE.SALARY/1000000\n\nplot(leto, place, xlab = \"Leto\", ylab = \"Pla\u010de v mio $\", main = \"Linearna, kvadratna in gam metoda\")\n\nlegend(2000, 1, c(\"Linerana metoda\", \"Gam metoda\"), lty=c(1,1), \n col = c(\"blue\", \"green\"))\n\n#Napi\u0161emo funkcijo za linearno rast\n\nlinearna <- lm(place ~ leto)\nabline(linearna, col=\"blue\")\n\n#Preverimo \u010de so pla\u010de kvadratna funkcija\n\nkvadratna <- lm(place ~ I(leto^2) + leto)\n# curve(predict(kvadratna, data.frame(leto=x)), add = TRUE, col = \"red\") \n\n#Loess model za primerjavo (model loess uporablja lokalno prilagajanje)\n\nloess <- loess(place ~ leto)\n\n# Gam model\nlibrary(mgcv)\ngam <- gam(place~s(leto))\ncurve(predict(gam, data.frame(leto=x)), add = TRUE, col = \"green\")\n#Pogledamo ostanke pri modelih. Tisti, ki ima manj\u0161i ostanek je bolj natan\u010den\n\nvsota.kvadratov <- sapply(list(linearna, kvadratna, loess, gam), function(x) sum(x$residuals^2))\n# najmanj\u0161a vrednost je z metodo gam - 0.44273517 0.44272328 0.26843681 0.06716553\n\ndev.off()\n################################\n\n#Narisali bomo napoved za rast pla\u010d do leta 2030 po modelpo treh modelih - linearni, kvadratni in gam\n\npdf(\"slike/napoved.pdf\")\n\nplot(salary$YEAR, place, xlim = c(1989, 2040), ylim = c(0, 10), \n xlab = \"Leto\", ylab = \"Pla\u010de v mio $\",\n main = \"Napovedana rast pla\u010d igralcev lige MLB\") \nabline(h = 3.818923,col = \"black\", lwd = 1.5, lty = 1)\nabline(h = 5.98,col = \"black\", lwd = 1.5, lty = 1)\nabline(h = 7.3,col = \"black\", lwd = 1.5, lty = 1)\n\n# Prese\u010di\u0161\u010da\n\npoints(2030, 5.98, col = \"black\", pch = 21)\npoints(2030, 7.3, col = \"black\", pch = 21)\n\n# V prese\u010di\u0161\u010dih potegnemo navpi\u010dne \u010drte\n\nabline(v=2014, col = \"magenta\", lty = 1)\nabline(v=2030, col = \"darkblue\", lty = 1)\nabline(v=2040, col = \"lightblue2\", lty = 1)\n\nnapoved <- function(x,model){predict(model, data.frame(leto=x))}\n\ncurve(napoved(x, linearna), add= TRUE, lwd = 1.5, col = \"blue\")\n# curve(napoved(x, kvadratna), add = TRUE, lwd = 1.5, col = \"red\")\ncurve(napoved(x, gam), add=TRUE, col=\"green\")\n#curve(napoved(x, loess), add = TRUE, lwd = 1.5, col = \"black\")\n\n\n# Legenda\nlegend(2015, 3, c(\"Linearna\", \"Gam\"),\n lty=c(1,1), col = c(\"blue\", \"green\"))\n\ndev.off()\n\n#################################################################################################\n# Hierarhi\u010dno razvr\u0161\u010danje\n\n\npdf(\"slike/hierarhija.pdf\")\n\nX <- scale(as.matrix(ekipe[1:18]))\nt <- hclust(dist(X), method = \"ward.D\")\n\nplot(t, hang=-1, cex=0.4, main = \"Skupine klubov\")\n\nlegend(\"topright\", \n c(\"Skupina 1\", \"Skupina 2\",\"Skupina 3\"),\n lty=c(1,1,1), col = c(\"blue\",\"green\",\"red\"))\n\nrect.hclust(t,k=3,border=c(\"blue\",\"red\",\"green\"))\n\np1 <- cutree(t, k=3)\n\ndev.off()\n\n#########################################\n\n# Poiskati \u017eelim najbolj\u0161e igralce v klubu po vseh statisti\u010dnih spremenljivkah.\n\nCHI$TOI.GP <- NULL\nnormaliziran <- scale(as.matrix(CHI[c(6:18)]))\nmatrikarazdalj <- dist(normaliziran)\nrazdelitev <- hclust(matrikarazdalj, method = \"complete\")\n\npdf(\"slike/najigralci.pdf\")\nplot(razdelitev, hang=-1, cex=0.6, main = \"Uspe\u0161nost igralcev ekipe Chicago Blackhawks\")\nrect.hclust(razdelitev,k = 4,border=\"orange\")\ndev.off()\n\np <- cutree(razdelitev, k=4)\nbarve=c(\"red\", \"green\", \"blue\",\"cyan\")\ntable(p)\nbarve\n\npdf(\"slike/najigralci1.pdf\")\npairs(normaliziran, col = barve[p])\ndev.off()\n\n# Graf prikazuje polo\u017eaje osamelcev v odvisnosti od ostalih igralcev.\n\n###############################################################################\n\n# Metoda enojnega povezovanja\n\nrazdelitev1 <- hclust(matrikarazdalj, method = \"single\")\n\npdf(\"slike/chicago.pdf\")\nplot(razdelitev1, hang=-1, cex=0.6, main = \"Uspe\u0161nost igralcev z metodo enojnega povezovanja\")\nrect.hclust(razdelitev1,k=4,border=\"red\")\ndev.off()\n", "meta": {"hexsha": "8850cba42362318d517a15447af1065ede6087c7", "size": 5421, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "filiplenarcic/APPR-2014-15", "max_stars_repo_head_hexsha": "4a8a84dbe8c4144bc526c45c1ced435f82f84841", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "filiplenarcic/APPR-2014-15", "max_issues_repo_head_hexsha": "4a8a84dbe8c4144bc526c45c1ced435f82f84841", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-01-14T20:55:44.000Z", "max_issues_repo_issues_event_max_datetime": "2015-03-01T19:00:13.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "filiplenarcic/APPR-2014-15", "max_forks_repo_head_hexsha": "4a8a84dbe8c4144bc526c45c1ced435f82f84841", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0880829016, "max_line_length": 105, "alphanum_fraction": 0.5930640103, "num_tokens": 1975, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6297746074044135, "lm_q1q2_score": 0.3418815110342354}} {"text": "#' Overall nucleotides frequency\r\n#'\r\n#' Calculate the overall nucleotides frequency composition\r\n#'\r\n#' @param x a list of KZsqns objects.\r\n#' @return a n-by-4 numeric matrix, where n is the length of list x\r\n#'\r\n#' @examples\r\n#' data(CodonTable0)\r\n#' x = vector('list', 5) # Creating an empty list of length 5\r\n#'\r\n#' for(i in 1:5){\r\n#' x[[i]] = CodonTable0[sample(1:64, 10*i, TRUE),1]\r\n#' attr(x[[i]], 'class') = 'KZsqns'\r\n#' }\r\n#'\r\n#' cat(base_freq(x))\r\n#' @export\r\n\r\nbase_freq<- function(x){\r\n if(!is.list(x)){\r\n cat(\"Just one perhap very long sequence?\\n\")\r\n x = list(x)\r\n }\r\n\r\n ans = matrix(0, length(x), 4)\r\n for(i in 1:length(x)){\r\n if(class(x[[i]])!='KZsqns') warning(\"KZsqns objects are expected\")\r\n freq_table = table(strsplit(paste0(paste0(x[[i]], collapse = ''),'ATGC', sep=''),''))\r\n ans[i,] = (freq_table-rep(1,4))/length(x[[i]])/3\r\n }\r\n\r\n colnames(ans) <- c('A', 'C', 'G', 'T')\r\n rownames(ans) <- paste0('s_', 1:length(x), sep='')\r\n return(ans)\r\n}\r\n", "meta": {"hexsha": "427e98c7f857efec6a211d945f7651c1e9da0447", "size": 989, "ext": "r", "lang": "R", "max_stars_repo_path": "R/base_freq.r", "max_stars_repo_name": "HVoltBb/kondonz", "max_stars_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/base_freq.r", "max_issues_repo_name": "HVoltBb/kondonz", "max_issues_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-01-07T00:23:48.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-07T18:19:25.000Z", "max_forks_repo_path": "R/base_freq.r", "max_forks_repo_name": "HVoltBb/kodonz", "max_forks_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7297297297, "max_line_length": 90, "alphanum_fraction": 0.5733063701, "num_tokens": 329, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.34188151103423536}} {"text": "require(maptools)\r\nrequire(sp)\r\nrequire(raster)\r\nrequire(rgeos)\r\n\r\n\r\n# this little script fixes a small problem with the origin of the RAD data being different than the origin of the downscaled outputs. this is creating an issue where I \r\n# cannot perform any calculation of the 2 maps until the origins match. \r\n\r\nl <- list.files(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/girr_radiation/\", pattern=\".tif\", full.names=T)\r\n# now stack that list of files\r\nrad.monthly <- stack(l)\r\n\r\n# this is the template map used to rasterize the data to a new origin\r\nout_template <- raster(\"/Data/Base_Data/ALFRESCO_formatted/ALFRESCO_Master_Dataset/ALFRESCO_Model_Input_Datasets/AK_CAN_Inputs/Climate/cru_TS31/historical/pr/pr_total_mm_alf_cru_TS31_01_1901.tif\")\r\n\r\nfor (i in 1:nlayers(rad.monthly)){\r\n\tprint(i)\r\n\trad.monthly.current <- subset(rad.monthly, i , drop=T)\r\n\txyz <- cbind(coordinates(rad.monthly.current), getValues(rad.monthly.current))\r\n\tpts <- SpatialPoints(xyz[,1:2])\r\n\tpts.spdf <- SpatialPointsDataFrame(pts, as.data.frame(xyz), match.ID=TRUE,proj4string=projection(out_template))\r\n\trasterize(pts.spdf, out_template, field=pts.spdf$V3, fun=mean,filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/girr_radiation/version2/\",rad.monthly@layernames[i],\".tif\",sep=\"\"), overwrite=TRUE)\r\n}\r\n", "meta": {"hexsha": "e700e316af1937724c9f55020972d34add94c117", "size": 1405, "ext": "r", "lang": "R", "max_stars_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/IEM_CalculateRad_MLindgren_Sept2012_fixORIGIN.r", "max_stars_repo_name": "ua-snap/downscale", "max_stars_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-24T21:55:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T16:32:54.000Z", "max_issues_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/IEM_CalculateRad_MLindgren_Sept2012_fixORIGIN.r", "max_issues_repo_name": "ua-snap/downscale", "max_issues_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17, "max_issues_repo_issues_event_min_datetime": "2016-01-04T23:37:47.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-17T20:57:02.000Z", "max_forks_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/IEM_CalculateRad_MLindgren_Sept2012_fixORIGIN.r", "max_forks_repo_name": "ua-snap/downscale", "max_forks_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-09-16T04:48:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-25T03:46:00.000Z", "avg_line_length": 56.2, "max_line_length": 258, "alphanum_fraction": 0.7758007117, "num_tokens": 380, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.34188151103423536}} {"text": "library(data.table)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(sf)\n\nsource('../../functions.r')\nsource('../../parameters.r')\n\nspplist <- fread('../../input-data/species-list.csv')\nsetkey(spplist, code)\nnspp <- nrow(spplist)\n\nload('mcmc-results.rdata')\nmcsumm <- readRDS('mcmc-results-summary.rds')\n\ngrid <- read_sf('../../input-data/southern-hemisphere-5-degree.shp')\nworld <- read_sf('../../input-data/world.shp')\n\n\n## * APF by species\n\nmcsel <- mcmc[variable %in% mc_attributes[role == 'month0_spatial1' & vartype == 'apf_t', variable]]\nmcsel <- merge(mcsel, mc_attributes, by = 'variable', all.x=T, all.y=F)\napf_sp <- mcsel[, .(value = sum(value)), .(species, grid_id, sample)][\n , .(mean = mean(value),\n median = median(value),\n sd = sd(value),\n lcl = quantile(value, 0.025, names=F),\n ucl = quantile(value, 0.975, names=F),\n nsamples = .N), .(code = species, grid_id)]\n\napf_sp <- apf_sp[spplist[base_species == T], on = 'code'][, .(common_name = upper1st(common_name), code, grid_id, mean , sd, lcl, ucl)]\napf_sp\n\nspp <- spplist[base_species == T]\nfor (i in 1:nrow(spp)) {\n apf1 <- apf_sp[code == spp[i, code]]\n sp_apf <- apf1[match(grid$GRID_ID, grid_id), mean]\n sp_apf[sp_apf == 0] <- NA\n grid[[spp[i, code]]] <- sp_apf\n}\n\ngrid2 <- sf::st_transform(grid, \"+proj=laea +y_0=0 +lon_0=179 +lat_0=-90 +ellps=WGS84 +no_defs\")\nworld2 <- sf::st_transform(world, \"+proj=laea +y_0=0 +lon_0=179 +lat_0=-90 +ellps=WGS84 +no_defs\")\nw2 <- st_as_sf(raster::crop(as(world, 'Spatial'), raster::extent(as(grid, 'Spatial'))))\nw2 <- sf::st_transform(w2, \"+proj=laea +y_0=0 +lon_0=179 +lat_0=-90 +ellps=WGS84 +no_defs\")\n\n\nsp='DQS'\nfor (sp in spp$code) {\n cat(sp, '\\n')\n grid2$sp <- grid2[[sp]]\n g <- ggplot() +\n geom_sf(data = grid2, aes(fill = sp), colour = '#CCCCCC', size = 0.1, na.rm=T) +\n geom_sf(data = w2, fill = '#AAAAAA', colour = NA, size = 0.1, na.rm=T) +\n scale_fill_gradientn(name = 'APF', colours = brewer.pal(9, 'BuPu'), na.value=NA, limits = c(0, NA)) +\n theme_void() +\n theme(legend.position=c(0.95, 0.15))\n ggsave(sprintf('assets/apf-map-%s.png', sp), width = 7, height = 7)\n}\n", "meta": {"hexsha": "0f28921bece620d902a858bcb9cfcf97eb8e4d4e", "size": 2193, "ext": "r", "lang": "R", "max_stars_repo_path": "model/apf-maps.r", "max_stars_repo_name": "dragonfly-science/seabird-risk-assessment", "max_stars_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-02-22T20:16:08.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-22T20:16:08.000Z", "max_issues_repo_path": "model/apf-maps.r", "max_issues_repo_name": "seabird-risk-assessment/seabird-risk-assessment", "max_issues_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-10-09T08:18:07.000Z", "max_issues_repo_issues_event_max_datetime": "2017-10-29T22:13:43.000Z", "max_forks_repo_path": "model/apf-maps.r", "max_forks_repo_name": "dragonfly-science/seabird-risk-assessment", "max_forks_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-04-18T22:56:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-16T13:57:07.000Z", "avg_line_length": 35.9508196721, "max_line_length": 135, "alphanum_fraction": 0.6146830825, "num_tokens": 755, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.34188151103423536}} {"text": "library(readr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(\"cowplot\")\n\nuser <- read_csv('Users.csv')\nglimpse(user)\n\ntable(user$ group)\n\nlibrary(rlang)\nmyplot <- function(mydf, myxcol, myycol, ymin=0,ymax=1) {\n ggplot2::ggplot(data = mydf, aes(x=reorder({{ myxcol }}, \n {{ myycol }}), y= {{ myycol }},col = factor(group))) +\n geom_jitter(height = 0.1, width = 0.5,alpha = .08, shape = 16,aes(colour = factor(group)))+\n geom_violin(trim=TRUE,alpha=0.35, position = position_dodge(width = 1),colour=NA,aes( fill = factor(group)))+\n geom_boxplot(notch = FALSE,width=.4, outlier.size = -1, lwd=.6,outlier.shape = NA,fill = NA,\n col=c('steelblue4','deeppink4'),lty=1)+ #,'',''\n ylim(ymin,ymax)+\n scale_x_discrete(limits=c('NP',\"CP\"),label=c('Control', 'Experiment' ))+ coord_flip()+ \n stat_summary(fun.y=mean, geom=\"point\", shape=18, size=4)+# color=c('orange4','darkolivegreen4','purple4','forestgreen'), fill=c('orange4','darkolivegreen4','purple4','forestgreen')) +\n# scale_colour_manual(limits=c('TR','AI',\"NR\", \"SR\",'SAI'),values = aes(colour = factor(Condition)))#c('darkkhaki','deeppink4','snow4','blue4','orchid4')) +\n# scale_fill_manual(limits=c('TR','AI',\"NR\", \"SR\",'SAI'),values = c('olivedrab4','maroon4','navajowhite4','steelblue4','violetred4')) +\n# scale_linetype_manual(limits=c('AI',\"NR\", \"SR\",'SAI'),values = c(1,1,1,1)) +\n theme(plot.title = element_text(hjust = 0.5),plot.margin = unit(c(0.1, 0.1, 0, 0), \"cm\"),axis.title.x=element_blank(), axis.title.y=element_blank(), \n panel.grid=element_line(colour=\"lightgrey\", size = (.05)) ,panel.background =element_blank() ,legend.position=\"none\")\n\n}\n\nbx1=myplot(user, group, words,0,max(user$words))\nbx1\n\npdf(\"words.pdf\", width=4, height=3)\nbx1\ndev.off()\n\n\nbx1=myplot(user, group, likeRate ,0,max(user$likeRate ))\nbx1\n\npdf(\"likeRate.pdf\", width=4, height=3)\nbx1\ndev.off()\n\n\nbx1=myplot(user, group, Decision ,min(user$Decision ),max(user$Decision ))\nbx1\n\npdf(\"Decision.pdf\", width=4, height=3)\nbx1\ndev.off()\n\n\nbx1=myplot(user, group, Confidence ,min(user$Confidence ),max(user$Confidence ))\nbx1\n\npdf(\"Confidence.pdf\", width=4, height=3)\nbx1\ndev.off()\n\n", "meta": {"hexsha": "918f1d7e53d29bfc19ab29ce34c2ac58a5966b1b", "size": 2218, "ext": "r", "lang": "R", "max_stars_repo_path": "1-Submission/Evaluation_Fig4_Comparing length and like.r", "max_stars_repo_name": "ali-darvishi/BJET_Trustworthy-Peer-Assessment", "max_stars_repo_head_hexsha": "034232b05a8ccdc416dd17301ef0ce781baa1466", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "1-Submission/Evaluation_Fig4_Comparing length and like.r", "max_issues_repo_name": "ali-darvishi/BJET_Trustworthy-Peer-Assessment", "max_issues_repo_head_hexsha": "034232b05a8ccdc416dd17301ef0ce781baa1466", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1-Submission/Evaluation_Fig4_Comparing length and like.r", "max_forks_repo_name": "ali-darvishi/BJET_Trustworthy-Peer-Assessment", "max_forks_repo_head_hexsha": "034232b05a8ccdc416dd17301ef0ce781baa1466", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.3606557377, "max_line_length": 192, "alphanum_fraction": 0.6460775473, "num_tokens": 731, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6297746074044134, "lm_q1q2_score": 0.34188151103423536}} {"text": "# #-------------------------------------------------------------------------------------------------------\n# #-------------------------------------------------------------------------------------------------------\n# # This file replicates the figures from Errickson et al. (2020) using the results from \"main.jl\"\n# #-------------------------------------------------------------------------------------------------------\n# #-------------------------------------------------------------------------------------------------------\n\nrenv::restore()\n\n# Suppress warnings.\noptions(warn = -1)\n\n# Load required R packages.\nlibrary(data.table)\nlibrary(egg)\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(reshape2)\nlibrary(msm)\nlibrary(scales)\n\n# Name of folder containing replication results produced by \"main.jl\" file.\nresults_folder_name = \"my_results\"\n\n#####################################################################################################\n#####################################################################################################\n# Run Social Cost of Methane Figure Replication Code\n#####################################################################################################\n#####################################################################################################\n\n# Load generic helper functions file.\nsource(file.path(\"src\", \"utils\", \"figure_helper_functions.r\"))\n\n# Load calibration observations for hindcasts.\nobs = read.csv(file.path(\"data\", \"calibration_data\", \"calibration_data_combined.csv\"))\n\n# Set default colors for different versions of SNEASY+CH4.\nfairch4_color = \"#10BBFD\"\nfundch4_color = \"#785EF0\"\nhectorch4_color = \"#DC267F\"\nmagiccch4_color = \"#FE6100\"\n\n# Set default colors for different SC-CH4 estimation scenarios.\nbase_color = \"#2bc3a1\"\nforcing_color = \"#2f74ec\"\ncorr_color = \"#079818\"\nus_ecs_color = \"#cb67e5\"\n\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n# Figure 1 - Temperature and Methane Hindcasts.\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n\n# Load temperature and methane credible interval results for baseline projections.\nfair_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_temperature.csv\"))\nfund_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_temperature.csv\"))\nhector_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_temperature.csv\"))\nmagicc_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_temperature.csv\"))\n\nfair_ch4_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_ch4.csv\"))\nfund_ch4_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_ch4.csv\"))\nhector_ch4_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_ch4.csv\"))\nmagicc_ch4_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_ch4.csv\"))\n\n# Load temperature credible interval results for projections using U.S. climate sensitivity values.\nfair_temperature_ci_ecs = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_fair\", \"ci_temperature.csv\"))\nfund_temperature_ci_ecs = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_fund\", \"ci_temperature.csv\"))\nhector_temperature_ci_ecs = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_hector\", \"ci_temperature.csv\"))\nmagicc_temperature_ci_ecs = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_magicc\", \"ci_temperature.csv\"))\n\n# Set some common figure settings.\nmean_width = 0.3\npoint_size = 1.0\npoint_stroke = 0.2\nouter_ci_width = 0.2\ninner_ci_width = 0.2\nplot_years = c(1850, 2022)\npoint_color = \"yellow\"\necs_color = \"gray70\"\n\n#-------------------------\n# Temperature Hindcasts.\n#-------------------------\n\nfair_temperature_hindcast = climate_projection(fair_temperature_ci_base, obs, \"hadcrut_temperature_obs\", fairch4_color, c(-0.5,2.03), plot_years, \"\", \"Surface Temperature \\n Increase (*C)\", c(-0.5, 0.0, 0.5, 1.0, 1.5, 2.0), c(\"-0.5\", \"0\", \"0.5\", \"1\", \"1.5\", \"2.0\"), FALSE, TRUE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FAIR\", c(1856, 1.82), TRUE, fair_temperature_ci_ecs, ecs_color)\n# Override margins to allow room for title\nfair_temperature_hindcast = fair_temperature_hindcast + theme(plot.margin = unit(c(0.1,0.1,0.1,0.4), \"cm\"))\n\nfund_temperature_hindcast = climate_projection(fund_temperature_ci_base, obs, \"hadcrut_temperature_obs\", fundch4_color, c(-0.5,2.03), plot_years, \"\", \"\", c(-0.5, 0.0, 0.5, 1.0, 1.5, 2.0), c(\"-0.5\", \"0\", \"0.5\", \"1\", \"1.5\", \"2.0\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FUND\", c(1856, 1.82), TRUE, fund_temperature_ci_ecs, ecs_color)\n\nhector_temperature_hindcast = climate_projection(hector_temperature_ci_base, obs, \"hadcrut_temperature_obs\", hectorch4_color, c(-0.5,2.03), plot_years, \"\", \"\", c(-0.5, 0.0, 0.5, 1.0, 1.5, 2.0), c(\"-0.5\", \"0\", \"0.5\", \"1\", \"1.5\", \"2.0\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-Hector\", c(1856, 1.82), TRUE, hector_temperature_ci_ecs, ecs_color)\n\nmagicc_temperature_hindcast = climate_projection(magicc_temperature_ci_base, obs, \"hadcrut_temperature_obs\", magiccch4_color, c(-0.5,2.03), plot_years, \"\", \"\", c(-0.5, 0.0, 0.5, 1.0, 1.5, 2.0), c(\"-0.5\", \"0\", \"0.5\", \"1\", \"1.5\", \"2.0\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-MAGICC\", c(1856, 1.82), TRUE, magicc_temperature_ci_ecs, ecs_color)\n\n#----------------------------------\n# Methane Concentration Hindcasts.\n#----------------------------------\n\nfair_ch4_hindcast = climate_projection(fair_ch4_ci_base, obs, \"noaa_ch4_obs\", fairch4_color, c(0, 2200), plot_years, \"Year\", expression(paste(\" Methane \\n Concentration (ppb)\")), c(0, 500, 1000, 1500, 2000), c(\"0\", \"500\", \"1000\", \"1500\", \"2000\"), TRUE, TRUE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FAIR\", c(1855, 2017), FALSE, NULL, NULL)\nfair_ch4_hindcast = fair_ch4_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_ch4_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\nfair_ch4_hindcast = fair_ch4_hindcast + theme(plot.margin=unit(c(0.1,0.1,0.1,0.4), \"cm\"))\n\nfund_ch4_hindcast = climate_projection(fund_ch4_ci_base, obs, \"noaa_ch4_obs\", fundch4_color, c(0,2200), plot_years, \"Year\", \"\", c(0, 500, 1000, 1500, 2000), c(\"0\", \"500\", \"1000\", \"1500\", \"2000\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FUND\", c(1855, 2017), FALSE, NULL, NULL)\nfund_ch4_hindcast = fund_ch4_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_ch4_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\nhector_ch4_hindcast = climate_projection(hector_ch4_ci_base, obs, \"noaa_ch4_obs\", hectorch4_color, c(0,2200), plot_years, \"Year\", \"\", c(0, 500, 1000, 1500, 2000), c(\"0\", \"500\", \"1000\", \"1500\", \"2000\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-Hector\", c(1855, 2017), FALSE, NULL, NULL)\nhector_ch4_hindcast = hector_ch4_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_ch4_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\nmagicc_ch4_hindcast = climate_projection(magicc_ch4_ci_base, obs, \"noaa_ch4_obs\", magiccch4_color, c(0,2200), plot_years, \"Year\", \"\", c(0, 500, 1000, 1500, 2000), c(\"0\", \"500\", \"1000\", \"1500\", \"2000\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-MAGICC\", c(1855, 2017), FALSE, NULL, NULL)\nmagicc_ch4_hindcast = magicc_ch4_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_ch4_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\n# Arranage individual hindcast figures into panel.\npanel_labels = c(\"a\",\"b\",\"c\",\"d\", \"e\", \"f\", \"g\", \"h\")\nlabel_design = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=1.1, hjust=c(-0.2, 0.2, 0.2, 0.2, -0.2, 0.2, 0.2, 0.2))\n\nfig_1 = ggarrange(fair_temperature_hindcast, fund_temperature_hindcast, hector_temperature_hindcast, magicc_temperature_hindcast,\n\t \t\t fair_ch4_hindcast, fund_ch4_hindcast, hector_ch4_hindcast, magicc_ch4_hindcast,\n\t\t\t\t nrow=2, ncol=4, labels=panel_labels, label.args = label_design)\n\n# Save a .jpg and .pdf version of Figure 1.\nggsave(fig_1, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Figure_1.jpg\"), device=\"jpeg\", type=\"cairo\", width=183, height=70, unit=\"mm\", dpi=300)\nggsave(fig_1, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Figure_1.pdf\"), device=\"pdf\", width=183, height=70, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n# Figure 2 - Various SC-CH4 Distributions.\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n\n# Load baseline SC-CH4 results for DICE.\ndice_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load baseline SC-CH4 results for FUND.\nfund_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load different scenario SC-CH4 estimates for FUND + SNEASY-MAGICC.\nfund_scch4_base = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_base) = \"scch4\"\nfund_scch4_oldrf = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_oldrf) = \"scch4\"\nfund_scch4_nocorr = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_nocorr) = \"scch4\"\nfund_scch4_ecs = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_ecs) = \"scch4\"\n\n# Load different scenario SC-CH4 estimates for DICE + SNEASY-MAGICC.\ndice_scch4_base = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_base) = \"scch4\"\ndice_scch4_oldrf = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_oldrf) = \"scch4\"\ndice_scch4_nocorr = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_nocorr) = \"scch4\"\ndice_scch4_ecs = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_ecs) = \"scch4\"\n\n#-------------------------------\n# Baseline SC-CH4 Distributions\n#-------------------------------\n\n# Merge baseline data into a data.frame for plotting.\nscch4_fairch4 = data.frame(fund=fund_scch4_fairch4[,1], dice=dice_scch4_fairch4[,1])\nscch4_fundch4 = data.frame(fund=fund_scch4_fundch4[,1], dice=dice_scch4_fundch4[,1])\nscch4_hectorch4 = data.frame(fund=fund_scch4_hectorch4[,1], dice=dice_scch4_hectorch4[,1])\nscch4_magiccch4 = data.frame(fund=fund_scch4_magiccch4[,1], dice=dice_scch4_magiccch4[,1])\n\n# Calculate baseline SC-CH4 means for each IAM.\nbase_dice_mean = mean(c(dice_scch4_fairch4[,1], dice_scch4_fundch4[,1], dice_scch4_hectorch4[,1], dice_scch4_magiccch4[,1]))\nbase_fund_mean = mean(c(fund_scch4_fairch4[,1], fund_scch4_fundch4[,1], fund_scch4_hectorch4[,1], fund_scch4_magiccch4[,1]))\n\n# Create a vector of alpha values to set color transparency levels.\nalphas = rep(1.0, 4)\n\n# Order colors in terms of increasing SC-CH4 estimates by model.\nscch4_colors = c(fundch4_color, hectorch4_color, fairch4_color, magiccch4_color)\n\n# Create Figure 2a and add points identifying each IAM's mean SC-CH4 estimate.\nfig_2a = scch4_pdf_baseline(scch4_fundch4, scch4_hectorch4, scch4_fairch4, scch4_magiccch4, alphas, scch4_colors, 0.4, c(-100,3300), c(0,500,1000,1500,2000,2500,3000), c(\"0\",\"500\",\"1000\",\"1500\",\"2000\",\"2500\",\"3000\"), \"Social Cost of Methane ($/t-CH4)\", c(0,0.00255))\nfig_2a = fig_2a + geom_point(aes(x=c(base_fund_mean, base_dice_mean), y=c(0,0)), shape=c(21,23), size=2.25, fill=\"white\", stroke=0.3)\n\n#-------------------------------\n# SC-CH4 Scenario Distributions\n#-------------------------------\n\n# Set alpha values and order of different scenario colors.\nscenario_colors = c(forcing_color, base_color, corr_color, us_ecs_color)\n\n# Calculate baseline SC-CH4 means for each scenario - IAM pair.\nscenario_mean_data = data.frame(zeros = c(0,0,0,0),\n fund = c(mean(fund_scch4_oldrf[,1]), mean(fund_scch4_base[,1]), mean(fund_scch4_nocorr[,1]), mean(fund_scch4_ecs[,1])),\n dice = c(mean(dice_scch4_oldrf[,1]), mean(dice_scch4_base[,1]), mean(dice_scch4_nocorr[,1]), mean(dice_scch4_ecs[,1])))\n\n# Create Figure 2b plots for FUND.\nfig_2b_top = scch4_pdf_scenario(fund_scch4_oldrf, fund_scch4_base, fund_scch4_nocorr, fund_scch4_ecs, alphas, scenario_colors, 0.3, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"\", FALSE, c(0,0.0026), \"solid\", c(0.5,0.2,0.2,0))\nfig_2b_top = fig_2b_top + geom_point(data=scenario_mean_data, aes_string(x=\"fund\", y=\"zeros\"), shape=21, size=1.4, fill=scenario_colors, stroke=0.17)\n\n# Create Figure 2b plots for DICE.\nfig_2b_bottom = scch4_pdf_scenario(dice_scch4_oldrf, dice_scch4_base, dice_scch4_nocorr, dice_scch4_ecs, alphas, scenario_colors, 0.3, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"Social Cost of Methane ($/t-CH4)\", TRUE, c(0,0.0026), \"22\", c(-2.2,0.2,0.2,0))\nfig_2b_bottom = fig_2b_bottom + geom_point(data=scenario_mean_data, aes_string(x=\"dice\", y=\"zeros\"), shape=23, size=1.4, fill=scenario_colors, stroke=0.17)\n\n# Set panel design.\nfig_2a_design = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), hjust=-5.0, vjust=1.5)\nfig_2b_design = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), hjust=0.0, vjust=1.5)\n\n# Arrange all Figure 2 panels together.\nfig_2a_panel = ggarrange(fig_2a, labels=c(\"a\"), label.args=fig_2a_design)\nfig_2b_panel = ggarrange(fig_2b_top, fig_2b_bottom, ncol=1, labels=c(\"b\",\"\"), label.args = fig_2b_design)\nfig_2 = grid.arrange(fig_2a_panel, fig_2b_panel, widths=c(3,2.5), nrow=1, ncol=2)\n\n# Save a .jpg and .pdf version of Figure 2.\nggsave(fig_2, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Figure_2.jpg\"), device=\"jpeg\", type=\"cairo\", width=136, height=70, unit=\"mm\", dpi=300)\nggsave(fig_2, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Figure_2.pdf\"), device=\"pdf\", width=136, height=70, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n# Figure 3 - Posterior Parameter and SC-CH4 Correlations.\n#----------------------------------------------------------------------\n#----------------------------------------------------------------------\n\n# Load S-MAGICC temperature projections and confidence intervals (baseline case and scenario without posterior correlations).\nmagicc_temperature_baseline = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"base_temperature.csv\"), data.table=FALSE)\nmagicc_temperature_no_corr = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_magicc\", \"base_temperature.csv\"), data.table=FALSE)\nmagicc_temperature_ci_baseline = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_temperature.csv\"))\nmagicc_temperature_ci_no_corr = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_magicc\", \"ci_temperature.csv\"))\n\n# Load S-MAGICC posterior parameters and corresponding SC-CH4 estimates for scatter plots.\npost_param_magiccch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_magicc\", \"parameters_100k.csv\"), data.table=FALSE)\ndice_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n#-------------------------------\n# Temperature Projection\n#-------------------------------\n\n# Settings for projection plot\nmean_width = 0.4\nci_width = 0.25\npoint_size = 1.0\npoint_stroke = 0.2\nno_corr_color = \"gray80\"\n\n# Plot temperature projections (with and without posterior correlations).\nfig_3a_main = climate_projection(magicc_temperature_ci_baseline, obs, \"hadcrut_temperature_obs\", magiccch4_color, c(-0.5,12.5), c(1850,2210), \"Year\", \"Surface Temperature (C)\", seq(0,12,by=2), as.character(seq(0,12,by=2)), TRUE, TRUE, point_size, point_stroke, ci_width, \"dashed\", mean_width, \"\", c(1856, 15.5), TRUE, magicc_temperature_ci_no_corr, no_corr_color)\nfig_3a_main = fig_3a_main + theme(plot.margin = unit(c(3,1,5,1), \"mm\"))\n\n# Get years for temperature pdf inset.\nsneasy_years = 1765:2300\npdf_years = c(2050, 2100)\n\n# Isolate baseline projections.\nbaseline_pdf_data = magicc_temperature_baseline[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]\ncolnames(baseline_pdf_data) = c(\"Year_1\", \"Year_2\")\n\n# Isolate no correlation projections.\nno_corr_pdf_data = magicc_temperature_no_corr[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]\ncolnames(no_corr_pdf_data) = c(\"Year_1\", \"Year_2\")\n\n# Create temperature pdf inset.\nfig_3a_inset = ggarrange(inset_nocorr_pdfs(baseline_pdf_data, no_corr_pdf_data, c(magiccch4_color, \"gray75\"), c(0.8,0.6), 0.2, c(0,8), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Surface Temperature (C)\", c(0,1.71), c(0,0,0,0)), nrow=1, ncol=1)\n\n# Join Fig 3a with inset\nfig_3a = fig_3a_main + annotation_custom(grob=fig_3a_inset, xmin=1860, xmax=2045, ymin=3.5, ymax=10.5)\n\n#-----------------------------------\n# Parameter & SC-CH4 Scatter Plots\n#-----------------------------------\n\n# Set an upper value on point size to create a \"max_val+\" bin for graph legibiity (99th quantile, rounded to next integer).\nupper_bound_size = ceiling(as.numeric(quantile(post_param_magiccch4$Q10, 0.99)))\npost_param_magiccch4$Q10_size = post_param_magiccch4$Q10\npost_param_magiccch4[which(post_param_magiccch4$Q10 > upper_bound_size), \"Q10_size\"] = upper_bound_size\n\n# Create plot data dataframe.\nscatter_data = data.frame(ECS=post_param_magiccch4$ECS, aerosol=post_param_magiccch4$alpha, Q10_size = post_param_magiccch4$Q10_size, heat_diffusion=post_param_magiccch4$kappa, dice=dice_scch4_magiccch4[,1], fund=fund_scch4_magiccch4[,1])\n\n# Create parameter and SC-CH4 scatter plots.\nfig_3b = scatter_4way(scatter_data, c(\"aerosol\", \"ECS\", \"Q10_size\", \"heat_diffusion\"), 5000, 21, 0.5, c(\"blue\", \"dodgerblue\", \"cyan\", \"yellow\"), c(2.0, 4.0), c(2.0, 4.0), c(\"< 2.0\", \"> 4.0\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,2.03), c(0,0.5,1,1.5,2), c(\"0\",\"0.5\",\"1\",\"1.5\",\"2\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Aerosol Radiative Forcing Scale Factor\", \"Equilibirium Climate Sensitivity (C)\", c(\"Ocean Heat \\n Diffusivity\", \"Carbon Sink Respiration \\n Temperature Sensitivity\"), c(6,2,3,3), FALSE, \"\", \"\", TRUE, TRUE)\nfig_3c = scatter_4way(scatter_data, c(\"ECS\", \"dice\", \"Q10_size\", \"aerosol\"), 5000, 23, 0.5, c(\"yellow\", \"red\", \"blue\"), c(0.6, 1.5), c(0.6,1.5), c(\"< 0.6\", \"> 1.5\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), c(0,3600), c(0,1000,2000,3000), c(\"0\",\"1000\",\"2000\",\"3000\"), \"Equilibirium Climate Sensitivity (C)\", \"Social Cost of Methane ($/t-CH4)\", c(\"Aerosol Forcing \\n Scale Factor\", \"Carbon Sink Respiration \\n Temperature Sensitivity\"), c(6,3,3,2), TRUE, c(\"ECS\", \"fund\", \"Q10_size\", \"aerosol\"), 21, TRUE, TRUE)\n\n# Arrange all panels into figure 3.\nfig_3_top_panel = ggarrange(fig_3a, labels=c(\"a\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\", vjust=-3)))\nfig_3_bottom_panel = ggarrange(fig_3b, fig_3c, labels=c(\"b\", \"c\"), nrow=1, ncol=2, label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\")))\nfig_3 = grid.arrange(fig_3_top_panel, fig_3_bottom_panel, nrow=2, ncol=1, heights=c(1.5,2))\n\n# Save a .jpg and .pdf version of Figure 3.\nggsave(fig_3, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Figure_3.jpg\"), device=\"jpeg\", type=\"cairo\", width=136, height=150, unit=\"mm\", dpi=300)\nggsave(fig_3, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Figure_3.pdf\"), device=\"pdf\", width=136, height=150, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n# Figure 4 - Temperature and Discounted Climate Damage Impulse Responses\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n# Load temperature and discounted damage projections (take transpose for plotting convenience -> row = year, column = new model run).\ntemperature_base = transpose(fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"base_temperature.csv\"), data.table=FALSE))\ntemperature_pulse = transpose(fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"pulse_temperature.csv\"), data.table=FALSE))\n\n# Load DICE marginal discounted damages for different discount rates.\ndice_damages_25 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"discounted_damages_25.csv\"), data.table=FALSE))\ndice_damages_30 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"discounted_damages_30.csv\"), data.table=FALSE))\ndice_damages_50 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"discounted_damages_50.csv\"), data.table=FALSE))\ndice_damages_70 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"discounted_damages_70.csv\"), data.table=FALSE))\n\n# Load FUND marginal discounted damages for different discount rates.\nfund_damages_25 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"discounted_damages_25.csv\"), data.table=FALSE))\nfund_damages_30 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"discounted_damages_30.csv\"), data.table=FALSE))\nfund_damages_50 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"discounted_damages_50.csv\"), data.table=FALSE))\nfund_damages_70 = transpose(fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"discounted_damages_70.csv\"), data.table=FALSE))\n\n#-----------------------------------\n# Temperature Impulse Response\n#-----------------------------------\n\n# Calculate temperature impulse responses.\ntemperature_response = temperature_pulse - temperature_base\n\n# Calculate mean and 95% interval responses\npercentile = 0.95\nlower = (1-percentile) / 2\nupper = 1 - lower\n\ntemperature_response_CI = data.frame(Year = 1765:2300,\n Mean = rowMeans(temperature_response),\n Lower_CI = apply(temperature_response, 1, quantile, lower),\n Upper_CI = apply(temperature_response, 1, quantile, upper))\n\n# Set years for inset distributions.\nsneasy_years = 1765:2300\npdf_years = c(2030,2040,2050,2070)\n\n# Get mean temperature values for years corresponding to inset distributions.\ncircle_indices = which(sneasy_years == pdf_years[1] | sneasy_years == pdf_years[2] | sneasy_years == pdf_years[3] | sneasy_years == pdf_years[4])\nmean_circle_data = data.frame(x=pdf_years, y=temperature_response_CI$Mean[circle_indices])\n\n# Create temperature impulse response plot.\nfig_4a_main = spaghetti_single(temperature_response, temperature_response_CI, \"#efab23\", 0.15, c(1765, 2300), 2020, 500, c(0,6.5e-11), c(0,2e-11,4e-11,6e-11), c(\"0\", \"2e-11\", \"4e-11\", \"6e-11\"), \"Year\", \"Temperature Response (C/t-CH4)\", c(2010,2160), c(2020, 2060, 2100, 2140), c(\"2020\", \"2060\", \"2100\", \"2140\"))\n\n# Add cirlces for years with distribution inset plots.\nfig_4a_main = fig_4a_main + geom_point(data=mean_circle_data, aes(x=x, y=y), shape=21, colour=\"black\", fill=\"black\", size=2.0)\nfig_4a_main = fig_4a_main + geom_point(data=mean_circle_data, aes(x=x, y=y), shape=21, colour=\"black\", fill=c(\"#ff7272\", \"#b67be0\", \"#86e0a9\", \"#42c5f4\"), size=1.75)\n\n# Isolate data for temperature distribution inset.\ntemperature_pdf_data = transpose(temperature_response[which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2] | sneasy_years==pdf_years[3] | sneasy_years==pdf_years[4]), ])\ncolnames(temperature_pdf_data) = c(\"Year_1\", \"Year_2\", \"Year_3\", \"Year_4\")\n\n# Create temperature distribution inset.\nfig_4a_inset = inset_4pdfs(temperature_pdf_data, c(\"#ff7272\", \"#b67be0\", \"#86e0a9\", \"#42c5f4\"), rep(0.8,4), rep(1,4), 0.25, c(0,6.5e-11), c(0, 3e-11, 6e-11), c(\"0.0\", \"3e-11\", \"6e-11\"), \"Temperature\", c(0,1.6e11), 6, c(-0.1,0.0,0.0,0.0))\n\n# Add inset to temperature impulse response figure.\nfig_4a = fig_4a_main + annotation_custom(grob=ggplotGrob(fig_4a_inset), xmin=2065, xmax=Inf, ymin=(32/75)*6.5e-11, ymax=Inf)\n\n#-----------------------------------\n# Climate Damage Impulse Response\n#-----------------------------------\n\n# Set damage impulse colors and transparency.\nimpulse_colors = c(\"#d585ae\", \"#de9b18\", \"#57b3e2\", \"#1c8967\")\nimpulse_alphas = c(0.7,0.3,0.25,0.07)\n\n# Set years for each IAM.\ndice_years = 2010:2300\nfund_years = 1950:2300\n\n# Get mean response for DICE across different discount rates.\ndice_damages_mean = data.frame(Year = 2010:2300,\n Mean1 = rowMeans(dice_damages_25),\n Mean2 = rowMeans(dice_damages_30),\n Mean3 = rowMeans(dice_damages_50),\n Mean4 = rowMeans(dice_damages_70))\n\n# Get mean response for FUND across different discount rates.\nfund_damages_mean = data.frame(Year = 1950:2300,\n Mean1 = rowMeans(fund_damages_25),\n Mean2 = rowMeans(fund_damages_30),\n Mean3 = rowMeans(fund_damages_50),\n Mean4 = rowMeans(fund_damages_70))\n\n# Create damage impulse response graphs.\nfig_4b_main = spaghetti_multi(dice_damages_25, dice_damages_30, dice_damages_50, dice_damages_70, dice_damages_mean, impulse_colors, impulse_alphas, c(2010, 2300), 2020, 250, c(-10,65), c(0, 30, 60), c(\"0\", \"30\", \"60\"), \"Year\", \"Damage Response ($/t-CH4)\", c(2015,2160), c(2020,2060,2100,2140), c(\"2020\",\"2060\",\"2100\",\"2140\"))\nfig_4c_main = spaghetti_multi(fund_damages_25, fund_damages_30, fund_damages_50, fund_damages_70, fund_damages_mean, impulse_colors, impulse_alphas, c(1950, 2300), 2020, 250, c(-10,65), c(0, 30, 60), c(\"0\", \"30\", \"60\"), \"Year\", \"Damage Response ($/t-CH4)\", c(2015,2160), c(2020,2060,2100,2140), c(\"2020\",\"2060\",\"2100\",\"2140\"))\n\n# Isolate damage distribution inset data for DICE and FUND.\ndice_pdf_data_25 = transpose(dice_damages_25[which(dice_years==pdf_years[1] | dice_years==pdf_years[2] | dice_years==pdf_years[3] | dice_years==pdf_years[4]), ])\ndice_pdf_data_50 = transpose(dice_damages_50[which(dice_years==pdf_years[1] | dice_years==pdf_years[2] | dice_years==pdf_years[3] | dice_years==pdf_years[4]), ])\ncolnames(dice_pdf_data_25) = c(\"Year_1\", \"Year_2\", \"Year_3\", \"Year_4\")\ncolnames(dice_pdf_data_50) = c(\"Year_1\", \"Year_2\", \"Year_3\", \"Year_4\")\n\nfund_pdf_data_25 = transpose(fund_damages_25[which(fund_years==pdf_years[1] | fund_years==pdf_years[2] | fund_years==pdf_years[3] | fund_years==pdf_years[4]), ])\nfund_pdf_data_50 = transpose(fund_damages_50[which(fund_years==pdf_years[1] | fund_years==pdf_years[2] | fund_years==pdf_years[3] | fund_years==pdf_years[4]), ])\ncolnames(fund_pdf_data_25) = c(\"Year_1\", \"Year_2\", \"Year_3\", \"Year_4\")\ncolnames(fund_pdf_data_50) = c(\"Year_1\", \"Year_2\", \"Year_3\", \"Year_4\")\n\n# Create DICE inset and add to main panel.\ndice_25_pdf = inset_4pdfs(dice_pdf_data_25, rep(impulse_colors[1], 4), c(0.5,0.4,0.35,0.3), c(1,3,2,4), 0.25, c(-1,51), c(0,25,50), c(\"0\",\"25\",\"50\"), \"Discounted Damages\", c(0,0.29), 6, c(-1.5,0.0,0.0,0.0))\ndice_50_pdf = inset_4pdfs(dice_pdf_data_50, rep(impulse_colors[3] ,4), c(0.5,0.4,0.35,0.3), c(1,3,2,4), 0.25, c(-1,51), c(0,25,50), c(\"0\",\"25\",\"50\"), NULL, c(0,0.29), 6, c(-0.01,0.0,0.0,0.0))\nfig_4b_inset = ggarrange(dice_50_pdf, dice_25_pdf, nrow=2, ncol=1)\nfig_4b = fig_4b_main + annotation_custom(grob=fig_4b_inset, xmin=2080, xmax=Inf, ymin=22, ymax=Inf)\n\n# Create FUND inset and add to main panel.\nfund_25_pdf = inset_4pdfs(fund_pdf_data_25, rep(impulse_colors[1], 4), c(0.5,0.4,0.35,0.3), c(1,3,2,4), 0.25, c(-1,20), c(0,10,20), c(\"0\",\"10\",\"20\"), \"Discounted Damages\", c(0,1.05), 6, c(-1.5,0.0,0.0,0.0))\nfund_50_pdf = inset_4pdfs(fund_pdf_data_50, rep(impulse_colors[3] ,4), c(0.5,0.4,0.35,0.3), c(1,3,2,4), 0.25, c(-1,20), c(0,10,20), c(\"0\",\"10\",\"20\"), NULL, c(0,1.05), 6, c(-0.01,0.0,0.0,0.0))\nfig_4c_inset = ggarrange(fund_50_pdf, fund_25_pdf, nrow=2, ncol=1)\nfig_4c = fig_4c_main + annotation_custom(grob=fig_4c_inset, xmin=2080, xmax=Inf, ymin=22, ymax=Inf)\n\n# Set Figure 4 panel design.\nlabel_design = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=1.1, hjust=c(0.0, 0.2, 0.2))\n\n# Combine all panels into Figure 4.\nfigure_4 = ggarrange(fig_4a, fig_4b, fig_4c, nrow=1, ncol=3, labels=c(\"a\",\"b\",\"c\"), label.args = label_design)\n\n# Save a .jpg and .pdf version of Figure 4.\nggsave(figure_4, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Figure_4.jpg\"), device=\"jpeg\", type=\"cairo\", width=183, height=70, unit=\"mm\", dpi=300)\nggsave(figure_4, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Figure_4.pdf\"), device=\"pdf\", width=183, height=70, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n# Figure 5 - Equity-Weighted SC-CH4 Estimates using FUND.\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n# Load credible interval results for all FUND regions and eta values.\neq_ci_00 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_00.csv\"), data.table=FALSE)\neq_ci_01 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_01.csv\"), data.table=FALSE)\neq_ci_02 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_02.csv\"), data.table=FALSE)\neq_ci_03 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_03.csv\"), data.table=FALSE)\neq_ci_04 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_04.csv\"), data.table=FALSE)\neq_ci_05 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_05.csv\"), data.table=FALSE)\neq_ci_06 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_06.csv\"), data.table=FALSE)\neq_ci_07 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_07.csv\"), data.table=FALSE)\neq_ci_08 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_08.csv\"), data.table=FALSE)\neq_ci_09 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_09.csv\"), data.table=FALSE)\neq_ci_10 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_10.csv\"), data.table=FALSE)\neq_ci_11 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_11.csv\"), data.table=FALSE)\neq_ci_12 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_12.csv\"), data.table=FALSE)\neq_ci_13 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_13.csv\"), data.table=FALSE)\neq_ci_14 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_14.csv\"), data.table=FALSE)\neq_ci_15 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_15.csv\"), data.table=FALSE)\n\n# Load credible intervals for sensitivity analyses that sets regional inequality aversion to 0.\neq_no_reg_ci_01 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_01.csv\"), data.table=FALSE)\neq_no_reg_ci_02 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_02.csv\"), data.table=FALSE)\neq_no_reg_ci_03 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_03.csv\"), data.table=FALSE)\neq_no_reg_ci_04 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_04.csv\"), data.table=FALSE)\neq_no_reg_ci_05 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_05.csv\"), data.table=FALSE)\neq_no_reg_ci_06 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_06.csv\"), data.table=FALSE)\neq_no_reg_ci_07 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_07.csv\"), data.table=FALSE)\neq_no_reg_ci_08 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_08.csv\"), data.table=FALSE)\neq_no_reg_ci_09 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_09.csv\"), data.table=FALSE)\neq_no_reg_ci_10 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_10.csv\"), data.table=FALSE)\neq_no_reg_ci_11 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_11.csv\"), data.table=FALSE)\neq_no_reg_ci_12 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_12.csv\"), data.table=FALSE)\neq_no_reg_ci_13 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_13.csv\"), data.table=FALSE)\neq_no_reg_ci_14 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_14.csv\"), data.table=FALSE)\neq_no_reg_ci_15 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"ci_scch4_equity_no_regional_15.csv\"), data.table=FALSE)\n\n# Combine all regions into plot-friendly data format for full range of eta values.\nfund_regions = c(\"usa\", \"canada\", \"western_europe\", \"japan_south_korea\", \"australia_new_zealand\", \"central_eastern_europe\", \"former_soviet_union\", \"middle_east\", \"central_america\", \"south_america\", \"south_asia\", \"southeast_asia\", \"china_plus\", \"north_africa\", \"sub_saharan_africa\", \"small_island_states\")\nequity_ci_data = list()\nequity_noreg_ci_data = list()\n\nfor(r in 1:16){\n\tequity_ci_data[[fund_regions[r]]] = cbind(seq(0,1.5,by=0.1), rbind(eq_ci_00[r,2:4], eq_ci_01[r,2:4], eq_ci_02[r,2:4], eq_ci_03[r,2:4], eq_ci_04[r,2:4], eq_ci_05[r,2:4], eq_ci_06[r,2:4], eq_ci_07[r,2:4], eq_ci_08[r,2:4], eq_ci_09[r,2:4], eq_ci_10[r,2:4], eq_ci_11[r,2:4], eq_ci_12[r,2:4], eq_ci_13[r,2:4], eq_ci_14[r,2:4], eq_ci_15[r,2:4]))\n\tcolnames(equity_ci_data[[fund_regions[r]]]) = c(\"eta\", \"mean\", \"lower_ci\", \"upper_ci\")\n}\n\nfor(r in 1:16){\n\tequity_noreg_ci_data[[fund_regions[r]]] = cbind(seq(0,1.5,by=0.1), rbind(eq_ci_00[r,2:4], eq_no_reg_ci_01[r,2:4], eq_no_reg_ci_02[r,2:4], eq_no_reg_ci_03[r,2:4], eq_no_reg_ci_04[r,2:4], eq_no_reg_ci_05[r,2:4], eq_no_reg_ci_06[r,2:4], eq_no_reg_ci_07[r,2:4], eq_no_reg_ci_08[r,2:4], eq_no_reg_ci_09[r,2:4], eq_no_reg_ci_10[r,2:4], eq_no_reg_ci_11[r,2:4], eq_no_reg_ci_12[r,2:4], eq_no_reg_ci_13[r,2:4], eq_no_reg_ci_14[r,2:4], eq_no_reg_ci_15[r,2:4]))\n\tcolnames(equity_noreg_ci_data[[fund_regions[r]]]) = c(\"eta\", \"mean\", \"lower_ci\", \"upper_ci\")\n}\n\n# Load individual equity-weighted SC-CH4 estimates for eta = 1.0 and a sensitivity analysis where regional inequality aversion = 0.\nequity_10 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"scch4_equity_10.csv\"), data.table=FALSE)\nequity_no_regional_10 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"equity_weighting\", \"fund\", \"s_magicc\", \"scch4_equity_no_regional_10.csv\"), data.table=FALSE)\n\n#------------------------------------------------------\n# Equity-Weighted SC-CH4 Across Different Elasticities\n#------------------------------------------------------\n\n# Set colors and indices for different regions.\nusa_color = \"#5BC0EB\"\nwestern_europe_color = \"#FDE74C\"\ncentral_america_color = \"#9BC53D\"\nchina_plus_color = \"#FAA421\"\nsub_saharan_africa_color = \"#E55934\"\n\nplot_region_colors = c(usa_color, western_europe_color, central_america_color, china_plus_color, sub_saharan_africa_color)\nplot_regions = c(\"usa\", \"western_europe\", \"central_america\", \"china_plus\", \"sub_saharan_africa\")\n\nplot_region_indices = match(plot_regions, colnames(equity_10))\n\n# Set axis settings for Figure 5a.\naxis_5a_settings = list(x_lim=c(0,1.5), x_breaks=seq(0,1.5,by=0.3), x_labels=as.character(seq(0,1.5,by=0.3)), x_title=\"Consumption Elasticity of Marginal Utility\",\n\t\t\t\t y_lim=c(-300,56000), y_breaks=c(0,10000,20000,30000,40000,50000), y_labels=c(\"0\",\"10,000\",\"20,000\",\"30,000\", \"40,000\", \"50,000\"), y_title=\"Equity-Weighted Social Cost \\n of Methane ($/t-CH4)\")\n\n# Create Figure 5a.\nfig_5a = equity_ci(equity_ci_data, plot_regions, plot_region_colors, c(0.8,0.5,0.6,0.55,0.55), axis_5a_settings, 0.7, c(5,7,1,1), FALSE, 0.0, FALSE)\n\n# Add line segments to Figure 5a.\nfig_5a = fig_5a + geom_hline(yintercept=4000, linetype=\"32\", color=\"gray50\", size=0.25)\nfig_5a = fig_5a + geom_segment(aes(x=1.5, y=0, xend=1.5, yend=4000), linetype=\"32\", color=\"gray50\", size=0.25)\nfig_5a = fig_5a + geom_vline(xintercept=1.0, linetype=\"solid\", color=\"red\", size=0.2)\n\n# Set axis settigns for Figure 5b.\naxis_5b_settings = list(x_lim=c(0,1.5), x_breaks=seq(0,1.5,by=0.3), x_labels=as.character(seq(0,1.5,by=0.3)), x_title=\"Consumption Elasticity of Marginal Utility\",\n\t\t\t\t y_lim=c(0,4000), y_breaks=c(0,1000,2000,3000,4000), y_labels=c(\"0\",\"1,000\",\"2,000\",\"3,000\", \"4,000\"), y_title=\"\")\n\nfig_5b = equity_ci(equity_ci_data, plot_regions, plot_region_colors, c(0.8,0.7,0.6,0.55,0.55), axis_5b_settings, 1.0, c(5,7,1,1), FALSE, 0, TRUE)\n\n# Add dashed line for sensitivity results where regional inequality aversion = 0.\nfig_5b = fig_5b + geom_line(data=equity_noreg_ci_data[[plot_regions[1]]], aes_string(x=\"eta\", y=\"mean\"), size=0.25, colour=\"black\", linetype=\"22\")\n\n\n#----------------------------------------\n# Equity-Weighted SC-CH4 Distributions\n#----------------------------------------\n\n# Isolate equity-weghted estimates for eta = 1.0.\nequity_pdf_data = list(region1=equity_10[,plot_region_indices[5]], region2=equity_10[,plot_region_indices[4]], region3=equity_10[,plot_region_indices[3]], region4=equity_10[,plot_region_indices[2]], region5=equity_10[,plot_region_indices[1]])\n\n# Get mean values.\nmean_vals = data.frame(zeros=rep(0,6), scch4=c(mean(equity_10[,plot_region_indices[5]]), mean(equity_10[,plot_region_indices[4]]), mean(equity_no_regional_10$usa), mean(equity_10[,plot_region_indices[3]]), mean(equity_10[,plot_region_indices[2]]), mean(equity_10[,plot_region_indices[1]])))\n\n# Set distribution colors.\npdf_region_colors = c(sub_saharan_africa_color, china_plus_color, central_america_color, western_europe_color, usa_color)\nmean_colors = c(sub_saharan_africa_color, china_plus_color, \"white\", central_america_color, western_europe_color, usa_color)\n\n# Set axis settings for Figure 5c.\naxis_5c_settings = list(x_lim=c(-450,14000), x_breaks=seq(0,13500,by=3000), x_labels=c(\"0\", \"3,000\", \"6,000\", \"9,000\", \"12,000\"), x_title=\"Equity-Weighted Social Cost of Methane ($/t-CH4)\",\n\t\t\t\t y_lim=c(0,0.0018), y_breaks=c(0,0.0005,0.001, 0.0015), y_labels=c(\"0\",\"0.0005\",\"0.001\", \"0.0015\"), y_title=\"Density\")\n\n# Create Figure 5c.\nfig_5c = equity_pdfs(equity_pdf_data, pdf_region_colors, rep(0.7, 5), axis_5c_settings, 0.25, c(2,7,1,4), \"red\", FALSE, 0)\n\n# Add density for sensitivity without inequality aversion (intertemporal = 0.0) and all mean estimate points.\nfig_5c = fig_5c + geom_density(aes(x=equity_no_regional_10$usa), colour=\"black\", size=0.25, linetype=\"22\")\nfig_5c = fig_5c + geom_point(data=mean_vals, aes(x=scch4, y=zeros), shape=21, fill=mean_colors, size=1.4, stroke=0.3)\n\n# Combine figures for top and bottom part of Figure 5.\nfig_5_top = ggarrange(fig_5a, fig_5b, labels=c(\"a\", \"b\"), nrow=1, ncol=2, label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=1.5))\nfig_5_bottom = ggarrange(fig_5c, labels=c(\"c\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\")))\n\n# Combine all panels.\nfig_5 = grid.arrange(fig_5_top, fig_5_bottom, nrow=2, ncol=1, heights=c(2,1.2))\n\n# Save a .jpg and .pdf version of Figure 5.\nggsave(fig_5, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Figure_5.jpg\"), device=\"jpeg\", type=\"cairo\", width=130, height=90, unit=\"mm\", dpi=300)\nggsave(fig_5, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Figure_5.pdf\"), device=\"pdf\", width=130, height=90, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n# Extended Data Figure 1 - Additional Climate Model Hindcasts.\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n# Load credible interval results for baseline projections.\nfair_co2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_co2.csv\"))\nfund_co2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_co2.csv\"))\nhector_co2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_co2.csv\"))\nmagicc_co2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_co2.csv\"))\n\nfair_oceanco2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_oceanco2_flux.csv\"))\nfund_oceanco2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_oceanco2_flux.csv\"))\nhector_oceanco2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_oceanco2_flux.csv\"))\nmagicc_oceanco2_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_oceanco2_flux.csv\"))\n\nfair_oceanheat_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_ocean_heat.csv\"))\nfund_oceanheat_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_ocean_heat.csv\"))\nhector_oceanheat_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_ocean_heat.csv\"))\nmagicc_oceanheat_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"ci_ocean_heat.csv\"))\n\n\n# Set some common figure settings.\nmean_width = 0.3\npoint_size = 0.8\npoint_stroke = 0.2\nouter_ci_width = 0.2\ninner_ci_width = 0.2\nplot_years = c(1850, 2022)\npoint_color = \"yellow\"\necs_color = \"gray70\"\n\n#----------------------------------\n# CO2 Concentration Hindcasts.\n#----------------------------------\n\n# Create atmospheric CO2 concentration hindcasts for each for each version of SNEASY+CH4 and add in ice core observations.\n\nfair_co2_hindcast = climate_projection(fair_co2_ci_base, obs, \"maunaloa_co2_obs\", fairch4_color, c(250, 450), plot_years, \"Year\", expression(paste(\" Carbon Dioxide \\n Concentration (ppm)\")), c(250, 300, 350, 400, 450), c(\"250\", \"300\", \"350\", \"400\", \"450\"), FALSE, TRUE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FAIR\", c(1856, 436), FALSE, NULL, NULL)\nfair_co2_hindcast = fair_co2_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_co2_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n# Override margins to allow room for title\nfair_co2_hindcast = fair_co2_hindcast + theme(plot.margin = unit(c(0.4,0.1,0.1,0.5), \"cm\"))\n\nfund_co2_hindcast = climate_projection(fund_co2_ci_base, obs, \"maunaloa_co2_obs\", fundch4_color, c(250, 450), plot_years, \"Year\", expression(paste(\"Carbon Dioxide \\n Concentration (ppm)\")), c(250, 300, 350, 400, 450), c(\"250\", \"300\", \"350\", \"400\", \"450\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FUND\", c(1856, 436), FALSE, NULL, NULL)\nfund_co2_hindcast = fund_co2_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_co2_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\nhector_co2_hindcast = climate_projection(hector_co2_ci_base, obs, \"maunaloa_co2_obs\", hectorch4_color, c(250, 450), plot_years, \"Year\", expression(paste(\"Carbon Dioxide \\n Concentration (ppm)\")), c(250, 300, 350, 400, 450), c(\"250\", \"300\", \"350\", \"400\", \"450\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-Hector\", c(1856, 436), FALSE, NULL, NULL)\nhector_co2_hindcast = hector_co2_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_co2_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\nmagicc_co2_hindcast = climate_projection(magicc_co2_ci_base, obs, \"maunaloa_co2_obs\", magiccch4_color, c(250, 450), plot_years, \"Year\", expression(paste(\"Carbon Dioxide \\n Concentration (ppm)\")), c(250, 300, 350, 400, 450), c(\"250\", \"300\", \"350\", \"400\", \"450\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-MAGICC\", c(1856, 436), FALSE, NULL, NULL)\nmagicc_co2_hindcast = magicc_co2_hindcast + geom_point(data=obs, aes_string(x=\"year\", y=\"lawdome_co2_obs\"), shape=24, size=point_size*0.9, stroke=point_stroke, color=\"black\", fill=point_color)\n\n#----------------------------------\n# Ocean Carbon Flux Hindcasts.\n#----------------------------------\n\n# Create ocean carbon flux hindcasts for each version of SNEASY+CH4.\n\nfair_oceanco2_hindcast = climate_projection(fair_oceanco2_ci_base, obs, \"oceanco2_flux_obs\", fairch4_color, c(-8.5, 8.5), plot_years, \"Year\", expression(paste(\" Ocean Carbon \\n Uptake (GtC/yr)\")), c(-8, -4, 0, 4, 8), c(\"-8\", \"-4\", \"0\", \"4\", \"8\"), FALSE, TRUE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FAIR\", c(1856, 7.31), FALSE, NULL, NULL)\nfair_oceanco2_hindcast = fair_oceanco2_hindcast + theme(plot.margin = unit(c(0.4,0.1,0.1,0.5), \"cm\"))\n\nfund_oceanco2_hindcast = climate_projection(fund_oceanco2_ci_base, obs, \"oceanco2_flux_obs\", fundch4_color, c(-8.5, 8.5), plot_years, \"Year\", expression(paste(\" Ocean Carbon \\n Uptake (GtC/yr)\")), c(-8, -4, 0, 4, 8), c(\"-8\", \"-4\", \"0\", \"4\", \"8\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FUND\", c(1856, 7.31), FALSE, NULL, NULL)\n\nhector_oceanco2_hindcast = climate_projection(hector_oceanco2_ci_base, obs, \"oceanco2_flux_obs\", hectorch4_color, c(-8.5, 8.5), plot_years, \"Year\", expression(paste(\" Ocean Carbon \\n Uptake (GtC/yr)\")), c(-8, -4, 0, 4, 8), c(\"-8\", \"-4\", \"0\", \"4\", \"8\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-Hector\", c(1856, 7.31), FALSE, NULL, NULL)\n\nmagicc_oceanco2_hindcast = climate_projection(magicc_oceanco2_ci_base, obs, \"oceanco2_flux_obs\", magiccch4_color, c(-8.5, 8.5), plot_years, \"Year\", expression(paste(\" Ocean Carbon \\n Uptake (GtC/yr)\")), c(-8, -4, 0, 4, 8), c(\"-8\", \"-4\", \"0\", \"4\", \"8\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-MAGICC\", c(1856, 7.31), FALSE, NULL, NULL)\n\n#----------------------------------\n# Ocean Heat Content Hindcasts.\n#----------------------------------\n\n# Create ocean heat content hindcasts for each version of SNEASY+CH4.\n\nfair_oceanheat_hindcast = climate_projection(fair_oceanheat_ci_base, obs, \"ocean_heat_obs\", fairch4_color, c(-70, 70), plot_years, \"Year\", expression(paste(\"Global Ocean Heat \\n Content (10^22 J)\")), c(-60,0,60), c(\"-60\", \"0\", \"60\"), TRUE, TRUE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FAIR\", c(1856, 60.2), FALSE, NULL, NULL)\nfair_oceanheat_hindcast = fair_oceanheat_hindcast + theme(plot.margin = unit(c(0.4,0.1,0.1,0.5), \"cm\"))\n\nfund_oceanheat_hindcast = climate_projection(fund_oceanheat_ci_base, obs, \"ocean_heat_obs\", fundch4_color, c(-70, 70), plot_years, \"Year\", expression(paste(\"Global Ocean Heat \\n Content (10^22 J)\")), c(-60,0,60), c(\"-60\", \"0\", \"60\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-FUND\", c(1856, 60.2), FALSE, NULL, NULL)\n\nhector_oceanheat_hindcast = climate_projection(hector_oceanheat_ci_base, obs, \"ocean_heat_obs\", hectorch4_color, c(-70, 70), plot_years, \"Year\", expression(paste(\"Global Ocean Heat \\n Content (10^22 J)\")), c(-60,0,60), c(\"-60\", \"0\", \"60\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-Hector\", c(1856, 60.2), FALSE, NULL, NULL)\n\nmagicc_oceanheat_hindcast = climate_projection(magicc_oceanheat_ci_base, obs, \"ocean_heat_obs\", magiccch4_color, c(-70, 70), plot_years, \"Year\", expression(paste(\"Global Ocean Heat \\n Content (10^22 J)\")), c(-60,0,60), c(\"-60\", \"0\", \"60\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, \"solid\", mean_width, \"S-MAGICC\", c(1856, 60.2), FALSE, NULL, NULL)\n\n# Create some panel settings.\nextended_fig_1_labels = c(\"a\",\"b\",\"c\",\"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\")\nextended_fig_1_label_design = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"))\n\n# Arranage individual hindcasts into a single figure.\nextended_fig_1 = ggarrange(fair_co2_hindcast, fund_co2_hindcast, hector_co2_hindcast, magicc_co2_hindcast,\n\t\t \t\t fair_oceanco2_hindcast, fund_oceanco2_hindcast, hector_oceanco2_hindcast, magicc_oceanco2_hindcast,\n \t\t\t\t\t fair_oceanheat_hindcast, fund_oceanheat_hindcast, hector_oceanheat_hindcast, magicc_oceanheat_hindcast,\n\t\t\t\t\t nrow=3, ncol=4, labels=extended_fig_1_labels, label.args = extended_fig_1_label_design)\n\n# Save a .jpg and .pdf version of Extended Data Figure 1.\nggsave(extended_fig_1, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_1.jpg\"), device=\"jpeg\", type=\"cairo\", width=183, height=105, unit=\"mm\", dpi=300)\nggsave(extended_fig_1, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_1.pdf\"), device=\"pdf\", width=183, height=105, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n# Extended Data Figure 2 - SC-CH4 Scenario PDFs For Other Models.\n#--------------------------------------------------------------------------\n#--------------------------------------------------------------------------\n\n# Load different scenario SC-CH4 estimates for FUND.\nfund_scch4_base_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_base_fairch4) = \"scch4\"\nfund_scch4_oldrf_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_oldrf_fairch4) = \"scch4\"\nfund_scch4_nocorr_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_nocorr_fairch4) = \"scch4\"\nfund_scch4_ecs_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_ecs_fairch4) = \"scch4\"\n\nfund_scch4_base_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_base_fundch4) = \"scch4\"\nfund_scch4_oldrf_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_oldrf_fundch4) = \"scch4\"\nfund_scch4_nocorr_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_nocorr_fundch4) = \"scch4\"\nfund_scch4_ecs_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_ecs_fundch4) = \"scch4\"\n\nfund_scch4_base_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_base_hectorch4) = \"scch4\"\nfund_scch4_oldrf_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_oldrf_hectorch4) = \"scch4\"\nfund_scch4_nocorr_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_nocorr_hectorch4) = \"scch4\"\nfund_scch4_ecs_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(fund_scch4_ecs_hectorch4) = \"scch4\"\n\n# Load different scenario SC-CH4 estimates for DICE.\ndice_scch4_base_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_base_fairch4) = \"scch4\"\ndice_scch4_oldrf_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_oldrf_fairch4) = \"scch4\"\ndice_scch4_nocorr_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_nocorr_fairch4) = \"scch4\"\ndice_scch4_ecs_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_ecs_fairch4) = \"scch4\"\n\ndice_scch4_base_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_base_fundch4) = \"scch4\"\ndice_scch4_oldrf_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_oldrf_fundch4) = \"scch4\"\ndice_scch4_nocorr_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_nocorr_fundch4) = \"scch4\"\ndice_scch4_ecs_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_ecs_fundch4) = \"scch4\"\n\ndice_scch4_base_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_base_hectorch4) = \"scch4\"\ndice_scch4_oldrf_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"outdated_ch4_forcing\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_oldrf_hectorch4) = \"scch4\"\ndice_scch4_nocorr_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"remove_correlations\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_nocorr_hectorch4) = \"scch4\"\ndice_scch4_ecs_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE); colnames(dice_scch4_ecs_hectorch4) = \"scch4\"\n\n# Set colors and transparency values for all panels.\nscenario_colors = c(forcing_color, base_color, corr_color, us_ecs_color)\nalphas = rep(1.0, 4)\nline_size = 0.3\n\n#----------------------------------\n# SNEASY+FAIR-CH4 Distributions\n#----------------------------------\n\n# Calculate means SC-CH4 estimates for each model-scenario pair.\nscenario_means_fairch4 = data.frame(zeros = c(0,0,0,0),\n fund = c(mean(fund_scch4_oldrf_fairch4[,1]), mean(fund_scch4_base_fairch4[,1]), mean(fund_scch4_nocorr_fairch4[,1]), mean(fund_scch4_ecs_fairch4[,1])),\n dice = c(mean(dice_scch4_oldrf_fairch4[,1]), mean(dice_scch4_base_fairch4[,1]), mean(dice_scch4_nocorr_fairch4[,1]), mean(dice_scch4_ecs_fairch4[,1])))\n\n# Plot SC-CH4 distributions for FUND + FAIR-CH4.\nextended_fig_2a_top = scch4_pdf_scenario(fund_scch4_oldrf_fairch4, fund_scch4_base_fairch4, fund_scch4_nocorr_fairch4, fund_scch4_ecs_fairch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"\", FALSE, c(0,0.003), \"solid\", c(0.5,0.4,0.2,0))\nextended_fig_2a_top = extended_fig_2a_top + geom_point(data=scenario_means_fairch4, aes_string(x=\"fund\", y=\"zeros\"), shape=21, size=1.75, fill=scenario_colors, stroke=0.2)\n\n# Plot SC-CH4 distributions for DICE + FAIR-CH4.\nextended_fig_2a_bottom = scch4_pdf_scenario(dice_scch4_oldrf_fairch4, dice_scch4_base_fairch4, dice_scch4_nocorr_fairch4, dice_scch4_ecs_fairch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"Social Cost of Methane ($/t-CH4)\", TRUE, c(0,0.0033), \"22\", c(-3.2,0.4,0.2,0))\nextended_fig_2a_bottom = extended_fig_2a_bottom + geom_point(data=scenario_means_fairch4, aes_string(x=\"dice\", y=\"zeros\"), shape=23, size=1.75, fill=scenario_colors, stroke=0.2)\n\n#----------------------------------\n# SNEASY+FUND-CH4 Distributions\n#----------------------------------\n\n# Calculate means SC-CH4 estimates for each model-scenario pair.\nscenario_means_fundch4 = data.frame(zeros = c(0,0,0,0),\n fund = c(mean(fund_scch4_oldrf_fundch4[,1]), mean(fund_scch4_base_fundch4[,1]), mean(fund_scch4_nocorr_fundch4[,1]), mean(fund_scch4_ecs_fundch4[,1])),\n dice = c(mean(dice_scch4_oldrf_fundch4[,1]), mean(dice_scch4_base_fundch4[,1]), mean(dice_scch4_nocorr_fundch4[,1]), mean(dice_scch4_ecs_fundch4[,1])))\n\n# Plot SC-CH4 distributions for FUND + FUND-CH4.\nextended_fig_2b_top = scch4_pdf_scenario(fund_scch4_oldrf_fundch4, fund_scch4_base_fundch4, fund_scch4_nocorr_fundch4, fund_scch4_ecs_fundch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"\", FALSE, c(0,0.003), \"solid\", c(0.5,0.3,0.2,0.1))\nextended_fig_2b_top = extended_fig_2b_top + geom_point(data=scenario_means_fundch4, aes_string(x=\"fund\", y=\"zeros\"), shape=21, size=1.75, fill=scenario_colors, stroke=0.2)\n\n# Plot SC-CH4 distributions for DICE + FUND-CH4.\nextended_fig_2b_bottom = scch4_pdf_scenario(dice_scch4_oldrf_fundch4, dice_scch4_base_fundch4, dice_scch4_nocorr_fundch4, dice_scch4_ecs_fundch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"Social Cost of Methane ($/t-CH4)\", TRUE, c(0,0.0033), \"22\", c(-3.2,0.3,0.2,0.1))\nextended_fig_2b_bottom = extended_fig_2b_bottom + geom_point(data=scenario_means_fundch4, aes_string(x=\"dice\", y=\"zeros\"), shape=23, size=1.75, fill=scenario_colors, stroke=0.2)\n\n#----------------------------------\n# SNEASY+MAGICC-CH4 Distributions\n#----------------------------------\n\n# Calculate means SC-CH4 estimates for each model-scenario pair.\nscenario_means_hectorch4 = data.frame(zeros = c(0,0,0,0),\n fund = c(mean(fund_scch4_oldrf_hectorch4[,1]), mean(fund_scch4_base_hectorch4[,1]), mean(fund_scch4_nocorr_hectorch4[,1]), mean(fund_scch4_ecs_hectorch4[,1])),\n dice = c(mean(dice_scch4_oldrf_hectorch4[,1]), mean(dice_scch4_base_hectorch4[,1]), mean(dice_scch4_nocorr_hectorch4[,1]), mean(dice_scch4_ecs_hectorch4[,1])))\n\n# Plot SC-CH4 distributions for FUND + Hector-CH4.\nextended_fig_2c_top = scch4_pdf_scenario(fund_scch4_oldrf_hectorch4, fund_scch4_base_hectorch4, fund_scch4_nocorr_hectorch4, fund_scch4_ecs_hectorch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"\", FALSE, c(0,0.003), \"solid\", c(0.5,0.2,0.2,0.2))\nextended_fig_2c_top = extended_fig_2c_top + geom_point(data=scenario_means_hectorch4, aes_string(x=\"fund\", y=\"zeros\"), shape=21, size=1.75, fill=scenario_colors, stroke=0.2)\n\n# Plot SC-CH4 distributions for DICE + Hector-CH4.\nextended_fig_2c_bottom = scch4_pdf_scenario(dice_scch4_oldrf_hectorch4, dice_scch4_base_hectorch4, dice_scch4_nocorr_hectorch4, dice_scch4_ecs_hectorch4, alphas, scenario_colors, line_size, c(-200,5200), c(0,1000,2000,3000,4000,5000), c(\"0\",\"1000\",\"2000\",\"3000\",\"4000\",\"5000\"), \"Social Cost of Methane ($/t-CH4)\", TRUE, c(0,0.0033), \"22\", c(-3.2,0.2,0.2,0.2))\nextended_fig_2c_bottom = extended_fig_2c_bottom + geom_point(data=scenario_means_hectorch4, aes_string(x=\"dice\", y=\"zeros\"), shape=23, size=1.75, fill=scenario_colors, stroke=0.2)\n\n# Create pannel design settings.\nextended_fig_2_label_deisgn = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"))#, hjust=0.0, vjust=1.5)\n\n# Combine DICE and FUND distributions for each version of SNEASY-CH4.\nextended_fig_2a = ggarrange(extended_fig_2a_top, extended_fig_2a_bottom, ncol=1, labels=c(\"a\", \"\"), label.args = extended_fig_2_label_deisgn)\nextended_fig_2b = ggarrange(extended_fig_2b_top, extended_fig_2b_bottom, ncol=1, labels=c(\"b\", \"\"), label.args = extended_fig_2_label_deisgn)\nextended_fig_2c = ggarrange(extended_fig_2c_top, extended_fig_2c_bottom, ncol=1, labels=c(\"c\", \"\"), label.args = extended_fig_2_label_deisgn)\n\n# Combine all panels into Extended Data Figure 2.\nextended_fig_2 = grid.arrange(extended_fig_2a, extended_fig_2b, extended_fig_2c, widths=c(1,1,1), nrow=1, ncol=3)\n\n# Save a .jpg and .pdf version of Extended Data Figure 2.\nggsave(extended_fig_2, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_2.jpg\"), device=\"jpeg\", type=\"cairo\", width=180, height=95, unit=\"mm\", dpi=300)\nggsave(extended_fig_2, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_2.pdf\"), device=\"pdf\", width=180, height=95, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#---------------------------------------------------------------------------------\n#---------------------------------------------------------------------------------\n# Extended Data Figure 3 - RCP2.6 Temperature Hindcasts and SC-CH4 Distributions.\n#---------------------------------------------------------------------------------\n#---------------------------------------------------------------------------------\n\n# Load RCP 2.6 temperature projection credible intervals.\nfair_rcp26_ci_temperature = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"rcp26\", \"s_fair\", \"ci_temperature.csv\"))\nfund_rcp26_ci_temperature = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"rcp26\", \"s_fund\", \"ci_temperature.csv\"))\nhector_rcp26_ci_temperature = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"rcp26\", \"s_hector\", \"ci_temperature.csv\"))\nmagicc_rcp26_ci_temperature = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"rcp26\", \"s_magicc\", \"ci_temperature.csv\"))\n\n# Load RCP 2.6 SC-CH4 results for DICE.\ndice_scch4_fairch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_fundch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_hectorch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_magiccch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load RCP 2.6 SC-CH4 results for FUND.\nfund_scch4_fairch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_fundch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_hectorch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_magiccch4_rcp26 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"rcp26\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n#----------------------------------\n# RCP 2.6 Temperature Projections\n#----------------------------------\n\n# Set some common figure settings.\nmean_width = 0.3\npoint_size = 1.25\npoint_stroke = 0.2\nouter_ci_width = 0.25\ninner_ci_width = 0.2\n\n# Make RCP 2.6 tempertaure projections.\nextended_fig_3a = rcp26_projection(fair_rcp26_ci_temperature, obs, \"hadcrut_temperature_obs\", fairch4_color, 0.8, c(-1.0, 2.5), c(1850,2300), \"Year\", expression(paste(\"Surface Temperature (\"~degree*C*\")\")), c(-1.0, 0.0, 1.0, 2.0), c(\"-1\", \"0\", \"1\", \"2\"), FALSE, TRUE, point_size, point_stroke, outer_ci_width, mean_width, \"S-FAIR\", c(1860, 2.35), c(0.25,0.2,0.2,0.3))\nextended_fig_3b = rcp26_projection(fund_rcp26_ci_temperature, obs, \"hadcrut_temperature_obs\", fundch4_color, 0.8, c(-1.0, 2.5), c(1850,2300), \"Year\", expression(paste(\"Surface Temperature (\"~degree*C*\")\")), c(-1.0, 0.0, 1.0, 2.0), c(\"-1\", \"0\", \"1\", \"2\"), FALSE, FALSE, point_size, point_stroke, outer_ci_width, mean_width, \"S-FUND\", c(1860, 2.35), c(0.25,0.2,0.2,0.2))\nextended_fig_3c = rcp26_projection(hector_rcp26_ci_temperature, obs, \"hadcrut_temperature_obs\", hectorch4_color, 0.8, c(-1.0, 2.5), c(1850,2300), \"Year\", expression(paste(\"Surface Temperature (\"~degree*C*\")\")), c(-1.0, 0.0, 1.0, 2.0), c(\"-1\", \"0\", \"1\", \"2\"), TRUE, TRUE, point_size, point_stroke, outer_ci_width, mean_width, \"S-Hector\", c(1860, 2.35),c(0.25,0.2,0.2,0.3))\nextended_fig_3d = rcp26_projection(magicc_rcp26_ci_temperature, obs, \"hadcrut_temperature_obs\", magiccch4_color, 0.8, c(-1.0, 2.5), c(1850,2300), \"Year\", expression(paste(\"Surface Temperature (\"~degree*C*\")\")), c(-1.0, 0.0, 1.0, 2.0), c(\"-1\", \"0\", \"1\", \"2\"), TRUE, FALSE, point_size, point_stroke, outer_ci_width, mean_width, \"S-MAGICC\", c(1860, 2.35), c(0.25,0.2,0.2,0.2))\n\n#----------------------------------\n# RCP 2.6 SC-CH4 Distributions\n#----------------------------------\n\n# Set colors and transparencies for different versions of SNEASY+CH4.\nscch4_colors = c(fundch4_color, hectorch4_color, fairch4_color, magiccch4_color)\nalphas = rep(1.0, 4)\n\n# Merge baseline data into a data.frame for plotting.\nscch4_rcp26_fairch4 = data.frame(fund=fund_scch4_fairch4_rcp26[,1], dice=dice_scch4_fairch4_rcp26[,1])\nscch4_rcp26_fundch4 = data.frame(fund=fund_scch4_fundch4_rcp26[,1], dice=dice_scch4_fundch4_rcp26[,1])\nscch4_rcp26_hectorch4 = data.frame(fund=fund_scch4_hectorch4_rcp26[,1], dice=dice_scch4_hectorch4_rcp26[,1])\nscch4_rcp26_magiccch4 = data.frame(fund=fund_scch4_magiccch4_rcp26[,1], dice=dice_scch4_magiccch4_rcp26[,1])\n\n\n# Calculate IAM mean SC-CH4 estimates.\nrcp26_dice_mean = mean(c(dice_scch4_fairch4_rcp26[,1], dice_scch4_fundch4_rcp26[,1], dice_scch4_hectorch4_rcp26[,1], dice_scch4_magiccch4_rcp26[,1]))\nrcp26_fund_mean = mean(c(fund_scch4_fairch4_rcp26[,1], fund_scch4_fundch4_rcp26[,1], fund_scch4_hectorch4_rcp26[,1], fund_scch4_magiccch4_rcp26[,1]))\n\n# Combine with baseline SC-CH4 means from Figure 2.\nrcp_mean_data = data.frame(zeros=c(0,0,0,0), means=c(rcp26_fund_mean, base_fund_mean, rcp26_dice_mean, base_dice_mean))\n\n# Create RCP 2.6 SC-CH4 distributions and add points for mean estimates.\nextended_fig_3e = scch4_pdf_baseline(scch4_rcp26_fundch4, scch4_rcp26_hectorch4, scch4_rcp26_fairch4, scch4_rcp26_magiccch4, alphas, scch4_colors, 0.35, c(-100,2100), c(0,500,1000,1500,2000), c(\"0\",\"500\",\"1000\",\"1500\",\"2000\"), \"Social Cost of Methane ($/t-CH4)\", c(-0.00035,0.003))\nextended_fig_3e = extended_fig_3e + geom_point(data=rcp_mean_data, aes_string(x=\"means\", y=\"zeros\"), shape=c(21,21,23,23), size=2.75, fill=\"white\", stroke=0.5)\n\n# Merge temperature projections into a single panel.\nextended_fig_3_top = ggarrange(extended_fig_3a, extended_fig_3b, extended_fig_3c, extended_fig_3d, nrow=2, ncol=2, labels=c(\"a\", \"b\", \"c\", \"d\"), label.args = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\")))\n\n# Turn distribution plot into a labeled panel.\nextended_fig_3_bottom = ggarrange(extended_fig_3e, labels=c(\"e\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=0.5))\n\n# Combine everything into Extended Data Figure 3.\nextended_fig_3 = grid.arrange(extended_fig_3_top, extended_fig_3_bottom, heights=c(2,1.3), nrow=2, ncol=1)\n\n# Save a .jpg and .pdf version of Extended Data Figure 3.\nggsave(extended_fig_3, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_3.jpg\"), device=\"jpeg\", type=\"cairo\", width=130, height=160, unit=\"mm\", dpi=600)\nggsave(extended_fig_3, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_3.pdf\"), device=\"pdf\", width=130, height=160, unit=\"mm\", useDingbats = FALSE)\n\n\n#-------------------------------------------------------------------------------------------\n#-------------------------------------------------------------------------------------------\n# Extended Data Figure 4 - Wider Prior SC-CH4 Distributions and Methane Cycle Correlations\n#-------------------------------------------------------------------------------------------\n#-------------------------------------------------------------------------------------------\n\n# Load baseline SC-CH4 results for DICE.\ndice_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load baseline SC-CH4 results for FUND.\nfund_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_magiccch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load SC-CH4 results for DICE using wider prior parameter distributions.\ndice_scch4_fairch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_fundch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_hectorch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\ndice_scch4_magiccch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Load SC-CH4 results for FUND using wider prior parameter distributions.\nfund_scch4_fairch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_fundch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_hectorch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)\nfund_scch4_magiccch4_wider = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"wider_priors\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)\n\n# Pool baseline and wider prior SC-CH4 results by IAM.\nbaseline_dice = data.frame(scch4=c(dice_scch4_fairch4[,1], dice_scch4_fundch4[,1], dice_scch4_hectorch4[,1], dice_scch4_magiccch4[,1]))\nbaseline_fund = data.frame(scch4=c(fund_scch4_fairch4[,1], fund_scch4_fundch4[,1], fund_scch4_hectorch4[,1], fund_scch4_magiccch4[,1]))\n\nwider_dice = data.frame(scch4=c(dice_scch4_fairch4_wider[,1], dice_scch4_fundch4_wider[,1], dice_scch4_hectorch4_wider[,1], dice_scch4_magiccch4_wider[,1]))\nwider_fund = data.frame(scch4=c(fund_scch4_fairch4_wider[,1], fund_scch4_fundch4_wider[,1], fund_scch4_hectorch4_wider[,1], fund_scch4_magiccch4_wider[,1]))\n\n#Load baseline posterior parameters for scatter plot.\nposterior_param_magiccch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_magicc\", \"parameters_100k.csv\"), data.table=FALSE)\n\n#----------------------------------\n# Wider Prior SC-CH4 Distribution\n#----------------------------------\n\n# Calculate mean SC-CH4 estimates for baseline and wider prior scenarios.\nwider_mean = mean(c(wider_dice[,1], wider_fund[,1]))\nbase_mean = mean(c(baseline_dice[,1], baseline_fund[,1]))\n\n# Create distribution plot and add points multi-model mean estimates.\nextended_fig_4a = scch4_wider_pdfs(baseline_dice, baseline_fund, wider_dice, wider_fund, \"red\", \"dodgerblue\", 0.5, 0.8, c(-200,3600), c(0,1000,2000,3000,4000), c(\"0\",\"1,000\",\"2,000\",\"3,000\",\"4,000\"), \"Social Cost of Methane ($/t-CH4)\", c(0,0.0023))\nextended_fig_4a = extended_fig_4a + geom_point(aes(x=c(wider_mean,base_mean), y=c(0,0)), shape=21, color=\"black\", stroke=0.5, size=2.5, fill=c(\"dodgerblue\", \"red\"))\n\n#---------------------------------------\n# Methane Cycle Parameter Scatter Plots\n#---------------------------------------\n\n# Select methane cycle parameters to plot.\nscatter_data_ch4cycle = data.frame(lifetime=post_param_magiccch4$tau_0, natural_emiss=post_param_magiccch4$CH4_nat, dice=dice_scch4_magiccch4[,1], fund=fund_scch4_magiccch4[,1])\n\n# Create scatter plot between intitial CH4 tropospheric lifetime and natural CH4 emission rates.\nextended_fig_4b = ch4cycle_scatter(scatter_data_ch4cycle, c(\"natural_emiss\", \"lifetime\"), 5000, 21, 1.0, \"mediumorchid1\", 0.8, 1,1, \"1\", c(175,320), c(180, 220, 260, 300), c(\"180\", \"220\", \"260\", \"300\"), c(6.6,9.1), c(7,8,9), c(\"7\",\"8\",\"9\"), \"Natural methane emissions (Mt/yr)\", \"Initial tropospheric methane lifetime (years)\", \"N/A\", c(6,2,3,3), TRUE, 2, FALSE, \"\", \"\", \"\")\n\n# Create scatter plot between methane cycle parameters and SC-CH4.\nextended_fig_4c = ch4cycle_scatter(scatter_data_ch4cycle, c(\"natural_emiss\", \"dice\", \"lifetime\"), 5000, 23, 1.0, \"dodgerblue1\", \"\", c(0.2,3.8), c(7,7.5,8,8.5), c(\"7\", \"7.5\", \"8\", \"8.5\"), c(175,320), c(180, 220, 260, 300), c(\"180\", \"220\", \"260\", \"300\"), c(0,3600), c(0,1000,2000,3000), c(\"0\",\"1,000\",\"2,000\",\"3,000\"), \"Natural methane emissions (Mt/yr)\", \"Social cost of methane ($/t-CH4)\", c(\"Initial tropospheric \\nmethane lifetime (years)\"), c(6,2,3,3), FALSE, \"\", TRUE, c(\"natural_emiss\", \"fund\", \"lifetime\"), 21, \"indianred1\")\n\n# Create panels and combine into Extended Data Figure 4.\nextended_fig_4_top = ggarrange(extended_fig_4a, labels=c(\"a\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\", vjust=-3)))\nextended_fig_4_bottom = ggarrange(extended_fig_4b, extended_fig_4c, labels=c(\"b\", \"c\"), nrow=1, ncol=2, label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\")))\nextended_fig_4 = grid.arrange(extended_fig_4_top, extended_fig_4_bottom, nrow=2, ncol=1, heights=c(1,0.8))\n\n# Save a .jpg and .pdf version of Extended Data Figure 4.\nggsave(extended_fig_4, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_4.jpg\"), device=\"jpeg\", type=\"cairo\", width=136, height=160, unit=\"mm\", dpi=300)\nggsave(extended_fig_4, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_4.pdf\"), device=\"pdf\", width=136, height=160, unit=\"mm\", useDingbats = FALSE)\n\n\n#--------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------\n# Extended Data Figure 5 - Parameter Scatterplots for Other Models.\n#--------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------\n\n# Load climate model indices that successfully ran.\nfairch4_climate_indices = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"good_indices.csv\"))[,1]\nfundch4_climate_indices = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"good_indices.csv\"))[,1]\nhectorch4_climate_indices = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"good_indices.csv\"))[,1]\nmagiccch4_climate_indices = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_magicc\", \"good_indices.csv\"))[,1]\n\n# Load SC-CH4 model indices that successfully ran.\nfairch4_scch4_indices_dice = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fair\", \"good_indices.csv\"))[,1]\nfundch4_scch4_indices_dice = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fund\", \"good_indices.csv\"))[,1]\nhectorch4_scch4_indices_dice = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_hector\", \"good_indices.csv\"))[,1]\nmagiccch4_scch4_indices_dice = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_magicc\", \"good_indices.csv\"))[,1]\n\nfairch4_scch4_indices_fund = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fair\", \"good_indices.csv\"))[,1]\nfundch4_scch4_indices_fund = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fund\", \"good_indices.csv\"))[,1]\nhectorch4_scch4_indices_fund = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_hector\", \"good_indices.csv\"))[,1]\nmagiccch4_scch4_indices_fund = read.csv(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_magicc\", \"good_indices.csv\"))[,1]\n\n# Combine indices (all successful climate runs that also ran for both DICE and FUND).\nfairch4_indices = fairch4_climate_indices[unique(c(fairch4_scch4_indices_dice, fairch4_scch4_indices_fund))]\nfundch4_indices = fundch4_climate_indices[unique(c(fundch4_scch4_indices_dice, fundch4_scch4_indices_fund))]\nhectorch4_indices = hectorch4_climate_indices[unique(c(hectorch4_scch4_indices_dice, hectorch4_scch4_indices_fund))]\nmagiccch4_indices = magiccch4_climate_indices[unique(c(magiccch4_scch4_indices_dice, magiccch4_scch4_indices_fund))]\n\n# Load S-FAIR posterior parameters and corresponding SC-CH4 estimates for scatter plots.\npost_param_fairch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_fair\", \"parameters_100k.csv\"), data.table=FALSE)[fairch4_indices, ]\ndice_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)[fairch4_indices, ]\nfund_scch4_fairch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)[fairch4_indices, ]\n\n# Load S-FUND posterior parameters and corresponding SC-CH4 estimates for scatter plots.\npost_param_fundch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_fund\", \"parameters_100k.csv\"), data.table=FALSE)[fundch4_indices, ]\ndice_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)[fundch4_indices, ]\nfund_scch4_fundch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)[fundch4_indices, ]\n\n# Load S-Hector posterior parameters and corresponding SC-CH4 estimates for scatter plots.\npost_param_hectorch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_hector\", \"parameters_100k.csv\"), data.table=FALSE)[hectorch4_indices, ]\ndice_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)[hectorch4_indices, ]\nfund_scch4_hectorch4 = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"baseline_run\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)[hectorch4_indices, ]\n\n# Get upper bound point size value from Figure 3.\npost_param_magiccch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_magicc\", \"parameters_100k.csv\"), data.table=FALSE)[magiccch4_indices, ]\nupper_bound_size = ceiling(as.numeric(quantile(post_param_magiccch4$Q10, 0.99)))\n\n# Set Figure 3's upper bound point size value across all other models.\npost_param_fairch4$Q10_size = post_param_fairch4$Q10\npost_param_fairch4[which(post_param_fairch4$Q10 > upper_bound_size), \"Q10_size\"] = upper_bound_size\n\npost_param_fundch4$Q10_size = post_param_fundch4$Q10\npost_param_fundch4[which(post_param_fundch4$Q10 > upper_bound_size), \"Q10_size\"] = upper_bound_size\n\npost_param_hectorch4$Q10_size = post_param_hectorch4$Q10\npost_param_hectorch4[which(post_param_hectorch4$Q10 > upper_bound_size), \"Q10_size\"] = upper_bound_size\n\n# Create data.frames of each model's parameters and SC-CH4 values for plotting.\nscatter_data_fairch4 = data.frame(ECS=post_param_fairch4$ECS, aerosol=post_param_fairch4$alpha, Q10_size = post_param_fairch4$Q10_size, heat_diffusion=post_param_fairch4$kappa, dice=dice_scch4_fairch4, fund=fund_scch4_fairch4)\nscatter_data_fundch4 = data.frame(ECS=post_param_fundch4$ECS, aerosol=post_param_fundch4$alpha, Q10_size = post_param_fundch4$Q10_size, heat_diffusion=post_param_fundch4$kappa, dice=dice_scch4_fundch4, fund=fund_scch4_fundch4)\nscatter_data_hectorch4 = data.frame(ECS=post_param_hectorch4$ECS, aerosol=post_param_hectorch4$alpha, Q10_size = post_param_hectorch4$Q10_size, heat_diffusion=post_param_hectorch4$kappa, dice=dice_scch4_hectorch4, fund=fund_scch4_hectorch4)\n\n#----------------------------------\n# SNEASY+FAIR-CH4 Scatter Plots\n#----------------------------------\n\n# Create scatter plots for S-FAIR.\nextended_fig_5a = scatter_4way(scatter_data_fairch4, c(\"aerosol\", \"ECS\", \"Q10_size\", \"heat_diffusion\"), 5000, 21, 0.8, c(\"blue\", \"dodgerblue\", \"cyan\", \"yellow\"), c(2.0, 4.0), c(2.0, 4.0), c(\"< 2.0\", \"> 4.0\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,2.03), c(0,0.5,1,1.5,2), c(\"0\",\"0.5\",\"1\",\"1.5\",\"2\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Aerosol radiative forcing scale factor\", \"Equilibirium climate sensitivity (C)\", c(\"Ocean heat \\ndiffusivity\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,2,3,3), FALSE, \"\", \"\", FALSE, TRUE)\nextended_fig_5d = scatter_4way(scatter_data_fairch4, c(\"ECS\", \"dice\", \"Q10_size\", \"aerosol\"), 5000, 23, 0.9, c(\"yellow\", \"red\", \"blue\"), c(0.6, 1.5), c(0.6,1.5), c(\"< 0.5\", \"> 1.5\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), c(0,3600), c(0,1000,2000,3000), c(\"0\",\"1,000\",\"2,000\",\"3,000\"), \"Equilibirium climate sensitivity (C)\", \"Social cost of methane ($/t-CH4)\", c(\"Aerosol forcing \\nscale factor\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,3,3,2), TRUE, c(\"ECS\", \"fund\", \"Q10_size\", \"aerosol\"), 21, FALSE, TRUE)\n\n#----------------------------------\n# SNEASY+FUND-CH4 Scatter Plots\n#----------------------------------\n\n# Create scatter plots for S-FUND.\nextended_fig_5b = scatter_4way(scatter_data_fundch4, c(\"aerosol\", \"ECS\", \"Q10_size\", \"heat_diffusion\"), 5000, 21, 0.8, c(\"blue\", \"dodgerblue\", \"cyan\", \"yellow\"), c(2.0, 4.0), c(2.0, 4.0), c(\"< 2.0\", \"> 4.0\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,2.03), c(0,0.5,1,1.5,2), c(\"0\",\"0.5\",\"1\",\"1.5\",\"2\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Aerosol radiative forcing scale factor\", \"Equilibirium climate sensitivity (C)\", c(\"Ocean heat \\ndiffusivity\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,2,3,3), FALSE, \"\", \"\", FALSE, TRUE)\nextended_fig_5e = scatter_4way(scatter_data_fundch4, c(\"ECS\", \"dice\", \"Q10_size\", \"aerosol\"), 5000, 23, 0.9, c(\"yellow\", \"red\", \"blue\"),c(0.6, 1.5), c(0.6,1.5), c(\"< 0.5\", \"> 1.5\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), c(0,3600), c(0,1000,2000,3000), c(\"0\",\"1,000\",\"2,000\",\"3,000\"), \"Equilibirium climate sensitivity (C)\", \"Social cost of methane ($/t-CH4)\", c(\"Aerosol forcing \\nscale factor\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,3,3,2), TRUE, c(\"ECS\", \"fund\", \"Q10_size\", \"aerosol\"), 21, FALSE, TRUE)\n\n#----------------------------------\n# SNEASY+Hector-CH4 Scatter Plots\n#----------------------------------\n\n# Create scatter plots for S-Hector.\nextended_fig_5c = scatter_4way(scatter_data_hectorch4, c(\"aerosol\", \"ECS\", \"Q10_size\", \"heat_diffusion\"), 5000, 21, 0.8, c(\"blue\", \"dodgerblue\", \"cyan\", \"yellow\"), c(2.0, 4.0), c(2.0, 4.0), c(\"< 2.0\", \"> 4.0\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,2.03), c(0,0.5,1,1.5,2), c(\"0\",\"0.5\",\"1\",\"1.5\",\"2\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Aerosol radiative forcing scale factor\", \"Equilibirium climate sensitivity (C)\", c(\"Ocean heat \\ndiffusivity\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,2,3,3), FALSE, \"\", \"\", TRUE, TRUE)\nextended_fig_5f = scatter_4way(scatter_data_hectorch4, c(\"ECS\", \"dice\", \"Q10_size\", \"aerosol\"), 5000, 23, 0.9, c(\"yellow\", \"red\", \"blue\"), c(0.6, 1.5), c(0.6,1.5), c(\"< 0.5\", \"> 1.5\"), c(0.5,4.0), c(1.01,2.0,3, 4), c(\"1\", \"2\", \"3\", \"4+\"), c(0,9.5), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), c(0,3600), c(0,1000,2000,3000), c(\"0\",\"1,000\",\"2,000\",\"3,000\"), \"Equilibirium climate sensitivity (C)\", \"Social cost of methane ($/t-CH4)\", c(\"Aerosol forcing \\nscale factor\", \"Carbon sink respiration \\ntemperature sensitivity\"), c(6,3,3,2), TRUE, c(\"ECS\", \"fund\", \"Q10_size\", \"aerosol\"), 21, TRUE, TRUE)\n\n# Merge all scatter plots into a single panel.\nextended_fig_5 = ggarrange(extended_fig_5a, extended_fig_5d, extended_fig_5b, extended_fig_5e, extended_fig_5c, extended_fig_5f, nrow=3, ncol=2, labels=c(\"a\",\"d\",\"b\",\"e\",\"c\",\"f\"), label.args = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=2))\n\n# Save a .jpg and .pdf version of Extended Data Figure 5.\nggsave(extended_fig_5, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_5.jpg\"), device=\"jpeg\", type=\"cairo\", width=136, height=190, unit=\"mm\", dpi=300)\nggsave(extended_fig_5, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_5.pdf\"), device=\"pdf\", width=136, height=190, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#----------------------------------------------------------------------------------\n#----------------------------------------------------------------------------------\n# Extended Data Figure 6 - Temperature Projections Without Posterior Correlations.\n#----------------------------------------------------------------------------------\n#----------------------------------------------------------------------------------\n\n# Load individual baseline temperature projections.\nfair_temperature_base = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"base_temperature.csv\"), data.table=FALSE)\nfund_temperature_base = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"base_temperature.csv\"), data.table=FALSE)\nhector_temperature_base = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"base_temperature.csv\"), data.table=FALSE)\n\n# Load baseline temperature projection credible intervals.\nfair_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fair\", \"ci_temperature.csv\"))\nfund_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_fund\", \"ci_temperature.csv\"))\nhector_temperature_ci_base = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"baseline_run\", \"s_hector\", \"ci_temperature.csv\"))\n\n# Load individual temperature projections for scenario without posterior correlations.\nfair_temperature_no_corr = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_fair\", \"base_temperature.csv\"), data.table=FALSE)\nfund_temperature_no_corr = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_fund\", \"base_temperature.csv\"), data.table=FALSE)\nhector_temperature_no_corr = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_hector\", \"base_temperature.csv\"), data.table=FALSE)\n\n# Load temperature projection credible intervals for scenario without posterior correlations.\nfair_temperature_ci_no_corr = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_fair\", \"ci_temperature.csv\"))\nfund_temperature_ci_no_corr = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_fund\", \"ci_temperature.csv\"))\nhector_temperature_ci_no_corr = read.csv(file.path(\"results\", results_folder_name, \"climate_projections\", \"remove_correlations\", \"s_hector\", \"ci_temperature.csv\"))\n\n# Set years for pdf temperature plots.\nsneasy_years = 1765:2300\npdf_years = c(2050, 2100)\n\n# Isolate individual temperature projections for specific years.\nfair_pdf_data_base = fair_temperature_base[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(fair_pdf_data_base) = c(\"Year_1\", \"Year_2\")\nfair_pdf_data_no_corr = fair_temperature_no_corr[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(fair_pdf_data_no_corr) = c(\"Year_1\", \"Year_2\")\n\nfund_pdf_data_base = fund_temperature_base[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(fund_pdf_data_base) = c(\"Year_1\", \"Year_2\")\nfund_pdf_data_no_corr = fund_temperature_no_corr[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(fund_pdf_data_no_corr) = c(\"Year_1\", \"Year_2\")\n\nhector_pdf_data_base = hector_temperature_base[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(hector_pdf_data_base) = c(\"Year_1\", \"Year_2\")\nhector_pdf_data_no_corr = hector_temperature_no_corr[ , which(sneasy_years==pdf_years[1] | sneasy_years==pdf_years[2])]; colnames(hector_pdf_data_no_corr) = c(\"Year_1\", \"Year_2\")\n\n# Set some plot settings.\nmean_width = 0.4\nci_width = 0.25\npoint_size = 1.0\npoint_stroke = 0.2\nplot_years = c(1850, 2210)\npoint_color = \"yellow\"\nno_corr_color = \"gray75\"\n\n#-------------------------------------------\n# SNEASY+FAIR-CH4 Temperature Projections\n#-------------------------------------------\n\n# Plot S-FAIR temperature projections (with and without posterior correlations).\nextended_fig_6a_main = climate_projection(fair_temperature_ci_base, obs, \"hadcrut_temperature_obs\", fairch4_color, c(-0.5,12.5), plot_years, \"Year\", \"Surface Temperature Increase (*C)\", seq(0,12,by=2), as.character(seq(0,12,by=2)), FALSE, TRUE, point_size, point_stroke, ci_width, \"dashed\", mean_width, \"S-FAIR\", c(1856, 11.75), TRUE, fair_temperature_ci_no_corr, no_corr_color)\n\n# Create temperature distribution inset for S-FAIR and merge into main panel.\nextended_fig_6a_inset = ggarrange(inset_nocorr_pdfs(fair_pdf_data_base, fair_pdf_data_no_corr, c(fairch4_color, no_corr_color), c(0.7,0.6), 0.2, c(0,8), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Surface Temperature Increase (*C)\", c(0,1.69), c(0,0,0,0)), nrow=1, ncol=1)\nextended_fig_6a = extended_fig_6a_main + annotation_custom(grob=extended_fig_6a_inset, xmin=1860, xmax=2045, ymin=3.5, ymax=11.5)\n\n#-------------------------------------------\n# SNEASY+FUND-CH4 Temperature Projections\n#-------------------------------------------\n\n# Plot S-FUND temperature projections (with and without posterior correlations).\nextended_fig_6b_main = climate_projection(fund_temperature_ci_base, obs, \"hadcrut_temperature_obs\", fundch4_color, c(-0.5,12.5), plot_years, \"Year\", \"Surface Temperature Increase (*C)\", seq(0,12,by=2), as.character(seq(0,12,by=2)), FALSE, TRUE, point_size, point_stroke, ci_width, \"dashed\", mean_width, \"S-FUND\", c(1856, 11.75), TRUE, fund_temperature_ci_no_corr, no_corr_color)\n\n# Create temperature distribution inset for S-FUND and merge into main panel.\nextended_fig_6b_inset = ggarrange(inset_nocorr_pdfs(fund_pdf_data_base, fund_pdf_data_no_corr, c(fundch4_color, no_corr_color), c(0.7,0.6), 0.2, c(0,8), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Surface Temperature Increase (*C)\", c(0,1.69), c(0,0,0,0)), nrow=1, ncol=1)\nextended_fig_6b = extended_fig_6b_main + annotation_custom(grob=extended_fig_6b_inset, xmin=1860, xmax=2045, ymin=3.5, ymax=11.5)\n\n#-------------------------------------------\n# SNEASY+Hector-CH4 Temperature Projections\n#-------------------------------------------\n\n# Plot S-Hector temperature projections (with and without posterior correlations).\nextended_fig_6c_main = climate_projection(hector_temperature_ci_base, obs, \"hadcrut_temperature_obs\", hectorch4_color, c(-0.5,12.5), plot_years, \"Year\", \"Surface Temperature Increase (*C)\", seq(0,12,by=2), as.character(seq(0,12,by=2)), TRUE, TRUE, point_size, point_stroke, ci_width, \"dashed\", mean_width, \"S-Hector\", c(1856, 11.75), TRUE, hector_temperature_ci_no_corr, no_corr_color)\n\n# Create temperature distribution inset for S-Hector and merge into main panel.\nextended_fig_6c_inset = ggarrange(inset_nocorr_pdfs(hector_pdf_data_base, hector_pdf_data_no_corr, c(hectorch4_color, no_corr_color), c(0.7,0.6), 0.2, c(0,8), c(0,2,4,6,8), c(\"0\",\"2\",\"4\",\"6\",\"8\"), \"Surface Temperature Increase (*C)\", c(0,1.69), c(0,0,0,0)), nrow=1, ncol=1)\nextended_fig_6c = extended_fig_6c_main + annotation_custom(grob=extended_fig_6c_inset, xmin=1860, xmax=2045, ymin=3.5, ymax=11.5)\n\n# Merge all plots into a single panel.\nextended_fig_6 = ggarrange(extended_fig_6a, extended_fig_6b, extended_fig_6c, nrow=3, ncol=1, labels=c(\"a\", \"b\", \"c\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\")))\n\n# Save a .jpg and .pdf version of Extended Data Figure 6.\nggsave(extended_fig_6, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_6.jpg\"), device=\"jpeg\", type=\"cairo\", width=130, height=170, unit=\"mm\", dpi=300)\nggsave(extended_fig_6, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_6.pdf\"), device=\"pdf\", width=130, height=170, unit=\"mm\", useDingbats = FALSE)\n\n\n\n#--------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------\n# Extended Data Figure 7 - ECS Distributions and SC-CH4 vs. EPA ECS Correlation.\n#--------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------\n\n# Load DICE SC-CH4 estimates that sample the U.S. climate sensivitiy distribution.\ndice_scch4_fairch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)[,1]\ndice_scch4_fundch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)[,1]\ndice_scch4_hectorch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)[,1]\ndice_scch4_magiccch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)[,1]\n\n# Load FUND SC-CH4 estimates that sample the U.S. climate sensivitiy distribution.\nfund_scch4_fairch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fair\", \"scch4_30.csv\"), data.table=FALSE)[,1]\nfund_scch4_fundch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fund\", \"scch4_30.csv\"), data.table=FALSE)[,1]\nfund_scch4_hectorch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_hector\", \"scch4_30.csv\"), data.table=FALSE)[,1]\nfund_scch4_magiccch4_us = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_magicc\", \"scch4_30.csv\"), data.table=FALSE)[,1]\n\n# Load DICE indices to ensure ECS sample and corresponding SC-CH4 estimates align (i.e. incase an individual parameter combination produced a model error).\ndice_fairch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fair\", \"good_indices.csv\"), data.table=FALSE)[,1]\ndice_fundch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_fund\", \"good_indices.csv\"), data.table=FALSE)[,1]\ndice_hectorch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_hector\", \"good_indices.csv\"), data.table=FALSE)[,1]\ndice_magiccch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"dice\", \"s_magicc\", \"good_indices.csv\"), data.table=FALSE)[,1]\n\n# Load FUND indices to ensure ECS sample and corresponding SC-CH4 estimates align (i.e. incase an individual parameter combination produced a model error).\nfund_fairch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fair\", \"good_indices.csv\"), data.table=FALSE)[,1]\nfund_fundch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_fund\", \"good_indices.csv\"), data.table=FALSE)[,1]\nfund_hectorch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_hector\", \"good_indices.csv\"), data.table=FALSE)[,1]\nfund_magiccch4_indices = fread(file.path(\"results\", results_folder_name, \"scch4_estimates\", \"us_climate_sensitivity\", \"fund\", \"s_magicc\", \"good_indices.csv\"), data.table=FALSE)[,1]\n\n# Load sampled U.S. climate sensivity values used for each SC-CH4 point estimate.\nus_ecs_fairch4 = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_fair\", \"ecs_sample.csv\"), data.table=FALSE)[,1]\nus_ecs_fundch4 = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_fund\", \"ecs_sample.csv\"), data.table=FALSE)[,1]\nus_ecs_hectorch4 = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_hector\", \"ecs_sample.csv\"), data.table=FALSE)[,1]\nus_ecs_magiccch4 = fread(file.path(\"results\", results_folder_name, \"climate_projections\", \"us_climate_sensitivity\", \"s_magicc\", \"ecs_sample.csv\"), data.table=FALSE)[,1]\n\n# Load posterior climate sensitivity values from each climate model calibration.\nposterior_ecs_fairch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_fair\", \"parameters_100k.csv\"), data.table=FALSE)$ECS\nposterior_ecs_fundch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_fund\", \"parameters_100k.csv\"), data.table=FALSE)$ECS\nposterior_ecs_hectorch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_hector\", \"parameters_100k.csv\"), data.table=FALSE)$ECS\nposterior_ecs_magiccch4 = fread(file.path(\"results\", results_folder_name, \"calibrated_parameters\", \"s_magicc\", \"parameters_100k.csv\"), data.table=FALSE)$ECS\n\n#-------------------------------------------\n# Climate Sensitivity Distributions\n#-------------------------------------------\n\n# Create a sample from U.S. climate sensitivity distribution.\nus_ecs_sample = data.frame(us_ecs = 1.2 / (1 - rtnorm(500000, 00.6198, 0.1841, -0.2, 0.88)))\n\n# Combine posterior climate sensitivity samples into dataframe.\nposterior_ecs_samples = data.frame(fair=posterior_ecs_fairch4, fund=posterior_ecs_fundch4, hector=posterior_ecs_hectorch4, magicc=posterior_ecs_magiccch4)\ncolnames(posterior_ecs_samples) = c(\"fair\", \"fund\", \"hector\", \"magicc\")\n\n# Calculate means of each climate sensitivity sample.\necs_means = data.frame(x=c(colMeans(posterior_ecs_samples), mean(us_ecs_sample$us_ecs)), y=rep(0, 5))\n\n# Set some figure settings.\necs_colors = c(fairch4_color, fundch4_color, hectorch4_color, magiccch4_color)\nalpha = c(0.8, 0.8, 0.8, 0.8)\nsize = 0.4\nx_range=c(0,10)\nx_breaks = c(0,2,4,6,8,10)\nx_labels=c(\"0\", \"2\", \"4\", \"6\", \"8\", \"10\")\ny_range = c(0,0.53)\nshape=21\nshape_size = 2.8\n\n# Create plot of climate sensitivity distributions.\nextended_fig_7a = ecs_pdf(posterior_ecs_samples, us_ecs_sample, alpha, ecs_colors, size, x_range, x_breaks, x_labels, y_range, expression(paste(\"Equilibrium Climate Sensitvity (\"*degree*C*\")\")), c(0.0,0.2,0.5,0.1))\n\n# Add points for mean climate sensitivity estimates.\nextended_fig_7a = extended_fig_7a + geom_point(data=ecs_means, aes_string(x=\"x\", y=\"y\"), shape=8, size=1.5, stroke=0.25, colour=c(ecs_colors, \"black\"))\n\n#-------------------------------------------------\n# U.S. Climate Sensitivity & SC-CH4 Scatter Plots\n#-------------------------------------------------\n\n# Combine plot data for each model (for convenience).\ndice_fairch4_data = data.frame(ecs = us_ecs_fairch4[dice_fairch4_indices], scch4 = dice_scch4_fairch4_us)\nfund_fairch4_data = data.frame(ecs = us_ecs_fairch4[fund_fairch4_indices], scch4 = fund_scch4_fairch4_us)\n\ndice_fundch4_data = data.frame(ecs = us_ecs_fundch4[dice_fundch4_indices], scch4 = dice_scch4_fundch4_us)\nfund_fundch4_data = data.frame(ecs = us_ecs_fundch4[fund_fundch4_indices], scch4 = fund_scch4_fundch4_us)\n\ndice_hectorch4_data = data.frame(ecs = us_ecs_hectorch4[dice_hectorch4_indices], scch4 = dice_scch4_hectorch4_us)\nfund_hectorch4_data = data.frame(ecs = us_ecs_hectorch4[fund_hectorch4_indices], scch4 = fund_scch4_hectorch4_us)\n\ndice_magiccch4_data = data.frame(ecs = us_ecs_magiccch4[dice_magiccch4_indices], scch4 = dice_scch4_magiccch4_us)\nfund_magiccch4_data = data.frame(ecs = us_ecs_magiccch4[fund_magiccch4_indices], scch4 = fund_scch4_magiccch4_us)\n\n# Create individual scatter plots\nextended_fig_7b = us_ecs_scatter(dice_fairch4_data, fund_fairch4_data, 0.12, 1.5, 4000, c(0,5800), c(0,10.1), expression(paste(\"Equilibrium Climate Sensitvity (\"*degree*C*\")\")), \"Social Cost of Methane ($/t-CH4)\", FALSE, TRUE, \"S-FAIR\", c(0.4, 5600), c(0.3,0.3,0.3,0.3))\nextended_fig_7c = us_ecs_scatter(dice_fundch4_data, fund_fundch4_data, 0.12, 1.5, 4000, c(0,5800), c(0,10.1), expression(paste(\"Equilibrium Climate Sensitvity (\"*degree*C*\")\")), \"Social Cost of Methane ($/t-CH4)\", FALSE, FALSE, \"S-FUND\", c(0.4, 5600), c(0.3,0.3,0.3,0.3))\nextended_fig_7d = us_ecs_scatter(dice_hectorch4_data, fund_hectorch4_data, 0.12, 1.5, 4000, c(0,5800), c(0,10.1), expression(paste(\"Equilibrium Climate Sensitvity (\"*degree*C*\")\", sep=\"\")), \"Social Cost of Methane ($/t-CH4)\", TRUE, TRUE, \"S-Hector\", c(0.4, 5600), c(0.3,0.3,0.3,0.3))\nextended_fig_7e = us_ecs_scatter(dice_magiccch4_data, fund_magiccch4_data, 0.12, 1.5, 4000, c(0,5800), c(0,10.1), expression(paste(\"Equilibrium Climate Sensitvity (\"*degree*C*\")\")), \"Social Cost of Methane ($/t-CH4)\", TRUE, FALSE, \"S-MAGICC\", c(0.4, 5600), c(0.3,0.3,0.3,0.3))\n\n# Create labeled panel for climate sensitivity distributions.\nextended_fig_7_top = ggarrange(extended_fig_7a, labels=c(\"a\"), label.args=list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=1.0))\n\n# Combine all scatter plots into a single panel.\nextended_fig_7_bottom = ggarrange(extended_fig_7b, extended_fig_7c, extended_fig_7d, extended_fig_7e, nrow=2, ncol=2, labels= c(\"b\", \"c\", \"d\", \"e\"), label.args = list(gp = grid::gpar(fontsize = 8, fontface=\"bold\"), vjust=1))\n\n# Merge all panels to create Extended Data Figure 7.\nextended_fig_7 = grid.arrange(extended_fig_7_top, extended_fig_7_bottom, heights=c(1,2), nrow=2, ncol=1)\n\n# Save a .jpg and .pdf version of Extended Data Figure 7.\nggsave(extended_fig_7, file=file.path(\"results\", results_folder_name, \"figures\", \"jpg_figures\", \"Extended_Data_Figure_7.jpg\"), device=\"jpeg\", type=\"cairo\", width=130, height=180, unit=\"mm\", dpi=300)\nggsave(extended_fig_7, file=file.path(\"results\", results_folder_name, \"figures\", \"pdf_figures\", \"Extended_Data_Figure_7.pdf\"), device=\"pdf\", width=130, height=180, unit=\"mm\", useDingbats = FALSE)\n\n# Finished creating all figures.\nprint(\"All done.\")\n", "meta": {"hexsha": "838f1eea6627e6ccccaa72f1d6f530dfcbb55923", "size": 110868, "ext": "r", "lang": "R", "max_stars_repo_path": "src/create_figures.r", "max_stars_repo_name": "anthofflab/paper-2021-scch4", "max_stars_repo_head_hexsha": "5de54910b6b0e908fc4a50764c7f7c017a35c807", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, 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YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3418682112651253}} {"text": "library(EpiEstim)\nlibrary(optparse)\nlibrary(gsubfn)\nlibrary(plyr)\nlibrary(data.table) \nsource(\"covidmap/read_data.r\")\nsource(\"covidmap/utils.r\")\n\n\nepiestim_options = function(\n first_day_modelled = NULL,\n last_day_modelled = NULL,\n weeks_modelled = NULL,\n days_ignored = NULL,\n days_per_step = 7,\n days_predicted = 0,\n num_steps_forecasted = 3,\n\n num_samples = 20,\n num_iterations = 5000,\n num_chains = 1,\n\n data_directory = \"data/\",\n results_directory = \"fits/test\",\n produce_plots = FALSE,\n approximation = \"epiestim\",\n area_index = 0\n) {\n as.list(environment())\n}\n\n\nepiestim_run = function(area_index = 0, opt = epiestim_options()) {\n if (area_index==0) {\n stop(\"Area index 0.\")\n }\n\n print(opt)\n env = covidmap_read_data(environment())\n\n numiters = opt$iterations\n\n list[Nstep, Tstep, Tcond, Tlik, Tcur, Tignore] = process_dates_modelled(\n dates, \n first_day_modelled = opt$first_day_modelled,\n last_day_modelled = opt$last_day_modelled,\n days_ignored = opt$days_ignored,\n weeks_modelled = opt$weeks_modelled,\n days_per_step = opt$days_per_step\n )\n message(\"Nproj = \", opt$num_steps_forecasted)\n\n area = areas[area_index]\n message(\"Area = \",area)\n\n Nsample <- opt$num_samples\n\n # Count = unlist(AllCount[area,(Tcond):(Tcond+(Tstep*Nstep))], use.names = FALSE)\n # Note extra day at the start to allow epiestim to work, should be Tcond+1\n Count = AllCount[area,(Tcond):(Tcond+(Tstep*Nstep))]\n dates = colnames(Count)\n Count = transpose(Count)\n starts = seq(from=2,by=Tstep,length.out=Nstep) \n ends = seq(from=Tstep+1,by=Tstep,length.out=Nstep)\n\n# starts = seq(from=2,by=1,length.out=(Nstep*Tstep)-Tstep-1) \n# ends = seq(from=Tstep+1,by=1,length.out=(Nstep*Tstep)-Tstep-1)\n\n modified_infprofile = replicate(length(infprofile)+1, 0.0)\n modified_infprofile[2:(length(infprofile)+1)] = infprofile\n modified_infprofile = unlist(modified_infprofile)\n\n config = make_config(\n t_start = starts, \n t_end = ends, \n si_distr=modified_infprofile,\n mean_prior=1.0\n )\n\n fit = estimate_R(\n Count, \n method=\"non_parametric_si\",\n config = config\n )\n wt = wallinga_teunis(\n Count, \n method=\"non_parametric_si\",\n config = config\n )\n\n fit$dates = dates\n dir.create(paste(opt$results_directory,'/epiestim/fits',sep=''), recursive=TRUE) \n saveRDS(fit, paste(opt$results_directory, '/epiestim/fits/',area,'.rds',sep=''))\n\n}\n\n\nepiestim_combine = function(opt = epiestim_options()) {\n covidmap_read_data(environment())\n\n # work out days to be modelled\n list[Nstep, Tstep, Tcond, Tlik, Tcur, Tignore] = process_dates_modelled(\n dates, \n first_day_modelled = opt$first_day_modelled,\n last_day_modelled = opt$last_day_modelled,\n days_ignored = opt$days_ignored,\n weeks_modelled = opt$weeks_modelled,\n days_per_step = opt$days_per_step\n )\n\n area_date_dataframe <- function(areas,dates,provenance,data,data_names) {\n numareas <- length(areas)\n numdates <- length(dates)\n dates <- rep(dates,numareas)\n dim(dates) <- c(numareas*numdates)\n provenance <- rep(provenance,numareas)\n dim(provenance) <- c(numareas*numdates)\n areas <- rep(areas,numdates)\n dim(areas) <- c(numareas,numdates)\n areas <- t(areas)\n dim(areas) <- c(numareas*numdates)\n df <- data.frame(area=areas,Date=dates,data=data,provenance=provenance)\n colnames(df)[3:(ncol(df)-1)] <- data_names\n df\n }\n\n Nproj = opt$num_steps_forecasted\n Tproj = Nproj * Tstep\n Tpred = opt$days_predicted\n provenance <- c(rep('inferred',Tlik),rep('projected',Tproj))\n days_likelihood = dates[(Tcond+1):Tcur]\n days_all <- c(days_likelihood,seq(days_likelihood[Tlik]+1,by=1,length.out=Tproj))\n message(\"Nstep = \",Nstep)\n message(\"Nproj = \",Nproj)\n\n Count <- AllCount[, 1:Tcur]\n\n numiters <- opt$num_iterations \n Nsample <- opt$num_samples\n\n # Initialize dummy arrays\n Clatent_sample <- array(0, c(N, Tcur+Tproj, Nsample))\n Clatent_mean <- array(0, c(N, Tcur+Tproj))\n Clatent_median <- array(0, c(N, Tcur+Tproj))\n Crecon_sample <- array(0, c(N, Tcur, Nsample))\n Crecon_median <- array(0, c(N, Tcur))\n Clatent_mean[, 1:Tcond] <- as.matrix(Count[, 1:Tcond])\n Clatent_median[, 1:Tcond] <- as.matrix(Count[, 1:Tcond])\n for (i in 1:Nsample) {\n Clatent_sample[, 1:Tcond, i] <- as.matrix(Count[, 1:Tcond])\n Crecon_sample[, 1:Tcond, i] <- as.matrix(Count[, 1:Tcond])\n }\n logpred = array(0.0, c(N, Tpred))\n\n # C_percentiles = c(.025,.25,.5,.75,.975)\n # C_str_percentiles = c(\"2.5%\",\"25%\",\"50%\",\"75%\",\"97.5%\")\n # num_C_percentiles = length(C_percentiles)\n\n \n\n # Initialise actual arrays\n normal_percentiles = c(.025,.1,.2,.25,.3,.4,.5,.6,.7,.75,.8,.9,.975)\n normal_str_percentiles = c(\"2.5%\",\"10%\",\"20%\",\"25%\",\"30%\",\"40%\",\"50%\",\"60%\",\"70%\",\"75%\",\"80%\",\"90%\",\"97.5%\")\n num_normal_percentiles = length(normal_percentiles)\n percentiles = c(.025,.05,.25,.5,.75,.095,.975)\n str_percentiles = c(\"2.5%\",\"5%\",\"25%\",\"50%\",\"75%\",\"95%\",\"97.5%\")\n num_percentiles = length(percentiles)\n epiestim_percentiles = c(\"Quantile.0.025(R)\",\"Quantile.0.05(R)\",\"Quantile.0.25(R)\",\"Median(R)\",\"Quantile.0.75(R)\",\"Quantile.0.95(R)\",\"Quantile.0.975(R)\")\n # pad_percentiles = c(\"10%\", \"20%\", \"30%\", \"40%\", \"60%\", \"70%\", \"80%\", \"90%\")\n pad_percentiles = c()\n num_samples = opt$num_iterations\n Rt_percentiles = array(0.0, c(N, Nstep+Nproj, num_percentiles + length(pad_percentiles)))\n Rt_samples = array(0.0, c(N, Nstep+Nproj, num_samples))\n\n Cpred = array(0.0, c(N, Nstep*Tstep, num_normal_percentiles))\n Cproj = array(0.0, c(N, Nproj*Tstep, num_normal_percentiles))\n\n rev_infprofile = rev(infprofile)\n L = length(infprofile)\n\n # Loop over areas, loading area RDS files and filling the arrays\n for (area_index in 1:N) {\n area <- areas[area_index]\n print(area)\n \n fit <- readRDS(paste(opt$results_directory, '/epiestim/fits/',area,'.rds',sep=''))\n for (p in 1:num_percentiles) {\n Rt_percentiles[area_index,1:Nstep, p] = fit$R[,epiestim_percentiles[p]]\n Rt_proj = fit$R[Nstep,epiestim_percentiles[p]]\n\n for (i in 1:Nproj) {\n Rt_percentiles[area_index,Nstep + i, p] = Rt_proj\n }\n }\n\n Rt_proj = sample_posterior_R(fit, n = opt$num_samples, window = Nstep) \n\n C = array(0.0, (Nstep+Nproj)*Tstep)\n C[1:(Nstep*Tstep)] = as.numeric(Count[area,(Tcond+1):(Tcond+(Tstep*Nstep))])\n C_samples = array(0.0, c((Nstep+Nproj)*Tstep, opt$num_samples))\n C_samples[1:(Nstep*Tstep),] = as.numeric(Count[area,(Tcond+1):(Tcond+(Tstep*Nstep))])\n\n for (t in 1:(Nproj*Tstep)) {\n C_samples[(Nstep*Tstep) + t,] = Rt_proj * colSums(C_samples[((Nstep*Tstep) + t - L) : ((Nstep*Tstep) + t - 1),] * rev_infprofile)\n for (i in 1:opt$num_samples) {\n C_samples[(Nstep*Tstep) + t, i] = rpois(1, C_samples[(Nstep*Tstep) + t, i])\n }\n }\n\n C_percentiles = t(apply(C_samples,1,quantile,\n probs=normal_percentiles\n ))\n\n Cpred[area_index,,] = C_percentiles[1:(Nstep*Tstep),]\n Cproj[area_index,,] = C_percentiles[((Nstep*Tstep)+1):((Nstep+Nproj)*Tstep),]\n }\n\n Xpred = Cpred\n Xproj = Cproj\n\n days <- colnames(Count)\n days_proj <- c(days,as.character(seq(days_likelihood[Tlik]+1,by=1,length.out=Tproj),format='%Y-%m-%d'))\n\n # Save real data\n Rt = Rt_percentiles[,rep(c(1:(Nstep+Nproj)),each=Tstep),]\n Rt = aperm(Rt, c(2,1,3))\n dim(Rt) <- c(N*(Nstep+Nproj)*Tstep, num_percentiles + length(pad_percentiles))\n Rt <- area_date_dataframe(\n quoted_areas,\n days_all,\n provenance,\n format(round(Rt,2),nsmall=2),\n c(\"Rt_2_5\",\"Rt_5\",\"Rt_25\",\"Rt_50\",\"Rt_75\",\"Rt_95\",\"Rt_97_5\") #, \"Rt_10\", \"Rt_20\", \"Rt_30\", \"Rt_40\", \"Rt_60\", \"Rt_70\", \"Rt_80\", \"Rt_90\")\n )\n write.csv(Rt, paste(opt$results_directory, \"/epiestim/Rt.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n\n # Save dummy data\n thresholds = c(.8, .9, 1.0, 1.1, 1.2, 1.5, 2.0)\n numthresholds = length(thresholds)\n Pexceedance = array(0.0, dim=c(Nstep+Nproj,N,numthresholds))\n Pexceedance = Pexceedance[c(1:(Nstep+Nproj)),,]\n Pexceedance <- Pexceedance[sapply(1:(Nstep+Nproj),function(k)rep(k,Tstep)),,]\n dim(Pexceedance) <- c(Tstep*(Nstep+Nproj)*N,numthresholds)\n Pexceedance <- area_date_dataframe(\n quoted_areas, \n days_all,\n provenance,\n format(round(Pexceedance,2),nsmall=2),\n c(\"P_08\",\"P_09\",\"P_10\",\"P_11\",\"P_12\",\"P_15\",\"P_20\")\n )\n write.csv(Pexceedance, paste(opt$results_directory, \"/epiestim/Pexceed.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n\n logpred = data.frame(area = areas, logpred = logpred, provenance=rep('inferred', length(areas)))\n for (i in 1:Tpred)\n colnames(logpred)[i+1] <- sprintf('logpred_day%d',i)\n write.csv(logpred, paste(opt$results_directory, \"/epiestim/logpred.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n Cweekly = array(0.0, c(N, (Nstep + Nproj), Tstep))\n actuals <- as.matrix(AllCount[,(Tcond+1):(Tcond+Tlik)])\n dim(actuals) <- c(N,Tstep,Nstep)\n actuals <- aperm(actuals, c(1,3,2))\n # preds = Cpred[,,3]\n # dim(preds) <- c(N, Nstep, Tstep)\n Cweekly[,1:Nstep,] = actuals\n Cweekly = apply(Cweekly, c(1,2), sum) \n Cweekly = Cweekly[,sapply(1:(Nstep+Nproj),function(k)rep(k,Tstep))]\n Cweekly = aperm(Cweekly, c(2,1))\n dim(Cweekly) <- c(N*(Nstep+Nproj)*Tstep)\n Cweekly <- area_date_dataframe(\n quoted_areas,\n days_all,\n provenance,\n Cweekly,\n c(\"C_weekly\")\n )\n write.csv(Cweekly, paste(opt$results_directory, \"/epiestim/Cweekly.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n Cpred = aperm(Cpred, c(2,1,3))\n dim(Cpred) <- c(N*Nstep*Tstep, num_normal_percentiles)\n Cpred <- area_date_dataframe(\n quoted_areas,\n days_likelihood,\n rep('inferred',Nstep*Tstep),\n Cpred,\n c(\"C_025\",\"C_10\",\"C_20\",\"C_25\",\"C_30\",\"C_40\",\"C_50\",\"C_60\",\"C_70\",\"C_75\",\"C_80\",\"C_90\",\"C_975\")\n )\n write.csv(Cpred, paste(opt$results_directory, \"/epiestim/Cpred.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n Cproj = aperm(Cproj, c(2,1,3))\n dim(Cproj) <- c(N*Nproj*Tstep, num_normal_percentiles)\n Cproj <- area_date_dataframe(\n quoted_areas,\n seq(days_likelihood[Tlik]+1,by=1,length.out=Tproj),\n rep('projected',Tproj),\n Cproj,\n c(\"C_025\",\"C_10\",\"C_20\",\"C_25\",\"C_30\",\"C_40\",\"C_50\",\"C_60\",\"C_70\",\"C_75\",\"C_80\",\"C_90\",\"C_975\")\n )\n write.csv(Cproj, paste(opt$results_directory, \"/epiestim/Cproj.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n Xpred = aperm(Xpred, c(2,1,3))\n dim(Xpred) <- c(N*Nstep*Tstep, num_normal_percentiles)\n Xpred <- area_date_dataframe(\n quoted_areas,\n days_likelihood,\n rep('inferred',Nstep*Tstep),\n Xpred,\n c(\"X_025\",\"X_10\",\"X_20\",\"X_25\",\"X_30\",\"X_40\",\"X_50\", \"X_60\", \"X_70\",\"X_75\",\"X_80\",\"X_90\",\"X_975\")\n )\n write.csv(Xpred, paste(opt$results_directory, \"/epiestim/Xpred.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n\n Xproj = aperm(Xproj, c(2,1,3))\n dim(Xproj) <- c(N*Nproj*Tstep, num_normal_percentiles)\n Xproj <- area_date_dataframe(\n quoted_areas,\n seq(days_likelihood[Tlik]+1,by=1,length.out=Tproj),\n rep('projected',Tproj),\n Xproj,\n c(\"X_025\",\"X_10\",\"X_20\",\"X_25\",\"X_30\",\"X_40\",\"X_50\", \"X_60\", \"X_70\",\"X_75\",\"X_80\",\"X_90\",\"X_975\")\n )\n write.csv(Xproj, paste(opt$results_directory, \"/epiestim/Xproj.csv\", sep=\"\"), quote=FALSE, row.names=FALSE)\n\n}\n\nepiestim_cmdline_options = function(opt = epiestim_options()) {\n list(\n make_option(\n c(\"--area_index\"),\n type=\"integer\",\n default=0,\n help=\"Area index (required argument).\"\n ),\n make_option(\n c(\"--num_samples\"),\n type=\"integer\",\n default=opt$num_samples,\n help=\"Number of samples to output.\"\n ),\n make_option(\n c(\"--num_iterations\"),\n type=\"integer\",\n default=opt$num_iterations,\n help=paste(\"Number of MCMC iterations, default\",opt$num_iterations)\n ),\n make_option(\n c(\"--first_day_modelled\"),\n type=\"character\",\n default=opt$first_day_modelled,\n help=paste(\"Date of first day to model; default =\",opt$first_day_modelled)\n ),\n make_option(\n c(\"--weeks_modelled\"),\n type=\"integer\",\n default=opt$weeks_modelled,\n help=paste(\"Number of weeks to model; default =\",opt$weeks_modelled)\n ),\n make_option(\n c(\"--last_day_modelled\"),\n type=\"character\",\n default=opt$last_day_modelled,\n help=paste(\"Date of last day to model; default =\",opt$last_day_modelled)\n ),\n make_option(\n c(\"--days_ignored\"),\n type=\"integer\",\n default=opt$days_ignored,\n help=paste(\"Number of recent days ignored, default\",opt$days_ignored)\n ),\n make_option(\n c(\"--days_per_step\"),\n type=\"integer\",\n default=opt$days_per_step,\n help=paste(\"Days per modelling step, default\",opt$days_per_step)\n ),\n make_option(\n c(\"--days_predicted\"),\n type=\"integer\",\n default=opt$days_predicted,\n help=paste(\"Days predicted; default =\",opt$days_predicted)\n ),\n make_option(\n c(\"--num_steps_forecasted\"),\n type=\"integer\",\n default=opt$num_steps_forecasted,\n help=paste(\"Number of steps to forecast, default\",opt$num_steps_forecasted)\n ),\n make_option(\n c(\"--results_directory\"), \n type=\"character\", \n default=opt$results_directory, \n help=paste(\"Directory to put cleaned results in, default \",opt$results_directory)\n ),\n make_option(\n c(\"--data_directory\"), \n type=\"character\", \n default=opt$data_directory, \n help=paste(\"Directory to get data from, default \",opt$data_directory)\n ),\n make_option(\n c(\"--produce_plots\"), \n type=\"logical\", \n default=opt$produce_plots, \n help=paste(\"Whether to produce plots; default\",opt$produce_plots)\n ),\n make_option(\n c(\"--limit_area\"), \n type=\"character\", \n default=opt$limit_area, \n help=paste(\"If not NULL, center the radius of regions on this region\",opt$limit_area)\n ),\n make_option(\n c(\"--limit_radius\"), \n type=\"double\", \n default=opt$limit_radius, \n help=paste(\"If not NULL, the radius of regions to limit the data to; default\",opt$limit_radius)\n )\n )\n}\n\nepiestim_get_cmdline_options = function(opt=epiestim_options()) {\n cmdline_opt = epiestim_cmdline_options(opt)\n opt_parser = OptionParser(option_list=cmdline_opt)\n parsed_opt = parse_args(opt_parser)\n for (o in names(parsed_opt)) {\n opt[o] = parsed_opt[o]\n }\n opt\n}\n\n", "meta": {"hexsha": "945627fa39d2ab6cc3b480f189167f936167a7de", "size": 14366, "ext": "r", "lang": "R", "max_stars_repo_path": "alternate_methods/epiestim.r", "max_stars_repo_name": "oxcsml/Rmap", "max_stars_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-03T10:25:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-03T10:25:31.000Z", "max_issues_repo_path": "alternate_methods/epiestim.r", "max_issues_repo_name": "oxcsml/Rmap", "max_issues_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "alternate_methods/epiestim.r", "max_forks_repo_name": "oxcsml/Rmap", "max_forks_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2546296296, "max_line_length": 155, "alphanum_fraction": 0.6499373521, "num_tokens": 4620, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3418682112651253}} {"text": "# ----------------------------------------------------------------------\n# Function: mapReduce\n# mapReduce pattern implemented on an R data structure \n# (array data.frame matrix) \n#\n# map: the map step evaluated on the data structure\n# ...: The Reduce step can be one or more functions with optional names \n# evaluated on the split data. name=function.\n# data: the R data structure \n# Returns a matrix or data.frame.\n# ----------------------------------------------------------------------\n\nmapReduce <- function( map, ..., data=NULL, apply=sapply ) {\n\n if( is.null(data) ) \n stop( \"You must explicitly provide data using the data argument\" ) \n\n m <- match.call( expand.dots=FALSE )\n map <- eval( m$map, data )\n\n # The dots will be the expression that gets evaluated.\n expr = m$`...`\n\n # Split data ... this is important since each of the features\n # will operate on the split data. This is also the most time\n # consuming part of the process.\n # because split is a poor function\n if( class(data) == \"list\" ) {\n split.data <- data\n } else {\n split.data <- split( data, map )\n }\n\n # innerFun: Evaluates expression \n innerFun = function( entity.data, expr ) {\n eval( expr, entity.data )\n } \n\n # outerFun: Split data based on map \n outerFun = function( expr, split.data ) {\n sapply(\n # split( data, map ) , \n split.data ,\n innerFun ,\n expr\n )\n }\n\n # RETURN:\n ret=apply( \n expr , # Elimanates call\n outerFun , # Contains inner function\n split.data \n )\n\n # detach(data)\n return(ret)\n}\n\n\n\n", "meta": {"hexsha": "d400d5a20d3e2c5dfc9b7517178e51afbdf828c2", "size": 1630, "ext": "r", "lang": "R", "max_stars_repo_path": "mapreduce.r", "max_stars_repo_name": "Aurametrix/HDFS", "max_stars_repo_head_hexsha": "25d19048f3ecae24ec0515c93c78235b26c9a2b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-21T23:59:13.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-21T23:59:13.000Z", "max_issues_repo_path": "mapreduce.r", "max_issues_repo_name": "Aurametrix/HDFS", "max_issues_repo_head_hexsha": "25d19048f3ecae24ec0515c93c78235b26c9a2b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mapreduce.r", "max_forks_repo_name": "Aurametrix/HDFS", "max_forks_repo_head_hexsha": "25d19048f3ecae24ec0515c93c78235b26c9a2b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2903225806, "max_line_length": 73, "alphanum_fraction": 0.5539877301, "num_tokens": 386, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.34183661188386244}} {"text": "library(reshape)\nlibrary(wordcloud)\nlibrary(RColorBrewer)\n\namcat.lda.addMeta <- function(m, meta){\n if('meta' %in% names(m)) {m$meta = meta[match(rownames(m$dtm), meta$id),]\n } else m = c(m, list(meta=meta[match(rownames(m$dtm), meta$id),]))\n m\n}\n\n## PLOTTING\namcat.plot.lda.alltopics <- function(m, time_var=m$meta$date, category_var=m$meta$medium, date_interval='day', path='/tmp/clouds/'){\n for(topic_nr in 1:nrow(m$document_sums)){\n print(paste('Plotting:',topic_nr))\n fn = paste(path, topic_nr, \".png\", sep=\"\")\n if (!is.null(fn)) png(fn, width=1280,height=800)\n amcat.plot.lda.topic(m, topic_nr, time_var, category_var, date_interval)\n if (!is.null(fn)) dev.off()\n }\n par(mfrow=c(1,1))\n}\n\namcat.plot.lda.topic <- function(m, topic_nr, time_var=m$meta$date, category_var=m$meta$medium, date_interval='day', pct=F, value='total'){\n par(mar=c(4.5,3,2,1), cex.axis=1.7)\n layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), widths=c(2.5,1.5), heights=c(1,2))\n amcat.plot.lda.time(m, topic_nr, time_var, date_interval, pct=pct, value=value)\n amcat.plot.lda.wordcloud(m, topic_nr)\n amcat.plot.lda.category(m, topic_nr, category_var, pct=pct, value=value)\n par(mfrow=c(1,1))\n}\n\namcat.prepare.time.var <- function(time_var, date_interval){\n if(class(time_var) == 'Date'){\n if(date_interval == 'day') time_var = as.Date(format(time_var, '%Y-%m-%d'))\n if(date_interval == 'month') time_var = as.Date(paste(format(time_var, '%Y-%m'),'-01',sep=''))\n if(date_interval == 'week') time_var = as.Date(paste(format(time_var, '%Y-%W'),1), '%Y-%W %u')\n if(date_interval == 'year') time_var = as.Date(paste(format(time_var, '%Y'),'-01-01',sep=''))\n } \n time_var\n}\n\namcat.fill.time.gaps <- function(d, date_interval){\n if(class(d$time) == 'numeric'){\n for(t in min(d$time):max(d$time)) \n if(!t %in% d$time) d = rbind(d, data.frame(time=t, value=0))\n }\n if(class(d$time) == 'Date'){\n date_sequence = seq.Date(from=min(d$time), to=max(d$time), by=date_interval)\n for(i in 1:length(date_sequence)){\n t = date_sequence[i]\n if(!t %in% d$time) d = rbind(d, data.frame(time=t, value=0))\n }\n }\n d[order(d$time),]\n}\n\namcat.prepare.plot.values <- function(m, break_var, topic_nr, pct=F, value='total'){\n d = data.frame(value=m$document_sums[topic_nr,], break_var=break_var)\n if(value == 'relative') d$value= d$value / colSums(m$document_sums)\n if(pct == T) d$value = d$value / sum(d$value)\n d = aggregate(d[,c('value')], by=list(break_var=d$break_var), FUN='sum') \n d\n}\n\namcat.plot.lda.time <- function(m, topic_nr, time_var=m$meta$date, date_interval='day', pct=F, value='total'){\n par(mar=c(3,3,3,1))\n time_var = amcat.prepare.time.var(time_var, date_interval) \n d = amcat.prepare.plot.values(m, break_var=time_var, topic_nr=topic_nr, pct=pct, value=value)\n colnames(d) = c('time','value')\n d = amcat.fill.time.gaps(d, date_interval)\n plot(d$time, d$value, type='l', xlab='', main='', ylab='', xlim=c(min(d$time), max(d$time)), ylim=c(0, max(d$value)), bty='L', lwd=5, col='darkgrey')\n d\n}\n\namcat.plot.lda.category <- function(m, topic_nr, category_var=m$meta$medium, pct=F, value='total'){\n par(mar=c(10,0,1,2))\n d = amcat.prepare.plot.values(m, break_var=category_var, topic_nr=topic_nr, pct=pct, value=value)\n colnames(d) = c('category','value')\n barplot(as.matrix(t(d[,c('value')])), main='', beside=TRUE,horiz=FALSE,\n density=NA,\n col='darkgrey',\n xlab='',\n ylab=\"\",\n axes=T, names.arg=d$category, cex.names=1.5, cex.axis=1.5, adj=1, las=2)\n d\n}\n\namcat.plot.lda.wordcloud <- function(m, topic_nr){\n x = m$topics[topic_nr,]\n x = sort(x[x>5], decreasing=T)[1:100]\n x = x[!is.na(x)]\n names = sub(\"/.*\", \"\", names(x))\n freqs = x**.5\n pal <- brewer.pal(6,\"YlGnBu\")\n wordcloud(names, freqs, scale=c(6,.5), min.freq=1, max.words=Inf, random.order=FALSE, rot.per=.15, colors=pal)\n}\n\n## GRAPHS\n\namcat.ucmatrix.to.simmatrix<- function(ucmatrix, similarity.measure){\n if(similarity.measure=='pearson_cor') sim.matrix = cor(t(ucmatrix))\n if(similarity.measure=='spearman_cor') sim.matrix = cor(t(ucmatrix), method='spearman')\n if(similarity.measure=='hellinger_dist') {\n library(topicmodels)\n sim.matrix = distHellinger(as.matrix(ucmatrix))\n }\n if(similarity.measure=='cosine') {\n library(lsa)\n sim.matrix = cosine(t(as.matrix(ucmatrix))) \n }\n sim.matrix\n}\n\namcat.simmatrix.to.simgraph <- function(m){\n m[lower.tri(m, diag=T)] = NA\n m.indices = which(!is.na(m),arr.ind=T)\n m.values = na.omit(as.vector(m))\n data.frame(node.X=m.indices[,1], node.Y=m.indices[,2], similarity=m.values)\n}\n\namcat.ucmatrix.to.simgraph <- function(ucmatrix, similarity.measure='pearson_cor'){\n sim = amcat.ucmatrix.to.simmatrix(ucmatrix, similarity.measure)\n amcat.simmatrix.to.simgraph(sim)\n}\n\namcat.unit.similarity.graph <- function(ucmatrix, unit.id.list, similarity.measure='pearson_cor'){\n d = aggregate(ucmatrix, by=unit.id.list, FUN='sum')\n ucmatrix = d[,(length(unit.id.list)+1):ncol(d)]\n simgraph = amcat.ucmatrix.to.simgraph(ucmatrix, similarity.measure)\n \n meta = as.data.frame(d[,names(unit.id.list)], stringsAsFactors=F)\n colnames(meta) = names(unit.id.list)\n meta = cbind(id=1:nrow(meta), meta)\n meta$topic_totals = rowSums(ucmatrix)\n \n list(graph_df=simgraph, meta=meta)\n}\n\namcat.lda.similarity.graph <- function(m, unit.id.list, vertex_label=names(unit.id.list)[1], similarity.measure='pearson_cor'){\n library(igraph)\n doc_topic_matrix = t(m$document_sums)\n unit.similarity = amcat.unit.similarity.graph(doc_topic_matrix, unit.id.list, similarity.measure)\n g = graph.data.frame(unit.similarity$graph_df, directed=F, vertices=unit.similarity$meta)\n E(g)$weight = E(g)$similarity\n g\n}\n\n\n", "meta": {"hexsha": "0c1d68d24434971611d6e6b1b4957f4c38659611", "size": 5727, "ext": "r", "lang": "R", "max_stars_repo_path": "lda.r", "max_stars_repo_name": "amcat/amcat-r-tools", "max_stars_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-02-18T04:45:08.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-18T04:45:08.000Z", "max_issues_repo_path": "lda.r", "max_issues_repo_name": "amcat/amcat-r-tools", "max_issues_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lda.r", "max_forks_repo_name": "amcat/amcat-r-tools", "max_forks_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4362416107, "max_line_length": 151, "alphanum_fraction": 0.6657936092, "num_tokens": 1805, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.34183661188386244}} {"text": "#' Extract the information from the simulation data frame to analyse the naive causal effects\n#'\n#' @param allsim dataset with all simulations values\n#' @param dataset dataset with all variables\n#' @param exposures a vector with exposures\n#' @param delta a vector with two values\n#' @param ic_dis choose between ic (interval confidences) and dis (distribution)\n#' @param st summary table from general function\n#' @return a data frame with naive ace and confident intervals\n#' @examples\n#' data(expose_data)\n#' data(simu)\n#' data(gen)\n#' delta=c(1,0)\n#' Exposures<- c('Var1','Var2','Var3','Var4','Var5')\n#' summary_table_lines <- gen[[2]]\n#' ace.df.g <- naive_ace (allsim = simu[[1]], dataset = expose_data,\n#' ic_dis = 'IC', st = summary_table_lines,\n#' exposures = Exposures, delta = delta)\n#' @export\n\n\nnaive_ace <- function(allsim, dataset, exposures, delta = c(0, 1), ic_dis = \"IC\", st) {\n dataset <- data.frame(dataset)\n len_exp <- length(exposures)\n df_ace <- data.frame(matrix(NA, len_exp, 4))\n names(df_ace) <- c(\"Group\", \"Mean\", \"ICa\", \"ICb\")\n h <- 0\n for (ex in 1:len_exp) {\n h <- h + 1\n df_ace[h, \"Group\"] <- paste0(exposures[ex])\n stp <- st[st$Group == \"ACE\" & st$Case == exposures[ex], c(4, 5)]\n from <- as.numeric(stp[1, 1])\n to <- as.numeric(stp[2, 2])\n mdata <- allsim[from:to, ]\n b <- naive_ace_ind(allsim = mdata, dataset = dataset, ic_dis = \"IC\")\n df_ace[h, \"Mean\"] <- b[[1]]\n df_ace[h, \"ICa\"] <- b[[2]]\n df_ace[h, \"ICb\"] <- b[[3]]\n }\n return(df_ace)\n}\n", "meta": {"hexsha": "6edf6d5a65d4db8fdd1809f3774f43dc1adedc07", "size": 1564, "ext": "r", "lang": "R", "max_stars_repo_path": "R/naive_ace.r", "max_stars_repo_name": "itamuria/expose", "max_stars_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/naive_ace.r", "max_issues_repo_name": "itamuria/expose", "max_issues_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-13T02:01:15.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-13T02:01:15.000Z", "max_forks_repo_path": "R/naive_ace.r", "max_forks_repo_name": "itamuria/expose", "max_forks_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.3720930233, "max_line_length": 93, "alphanum_fraction": 0.6170076726, "num_tokens": 481, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185205547239, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3418366030414684}} {"text": "library(tidyverse)\n\ne <- rnorm(200)\nf <- rnorm(200, e)\ndat <- data_frame(e,f) %>%\n mutate(sp = paste0(\"sp\", rep(1:20, each = 10)))\n\nwrite.csv(dat, \"data3.csv\", row.names = F)\n", "meta": {"hexsha": "3912f9c6c1760d3312d0b7dde6c4383142b434f0", "size": 176, "ext": "r", "lang": "R", "max_stars_repo_path": "csv_script3.r", "max_stars_repo_name": "mattocci27/makeR", "max_stars_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "csv_script3.r", "max_issues_repo_name": "mattocci27/makeR", "max_issues_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "csv_script3.r", "max_forks_repo_name": "mattocci27/makeR", "max_forks_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.5555555556, "max_line_length": 49, "alphanum_fraction": 0.6022727273, "num_tokens": 64, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.6076631698328917, "lm_q1q2_score": 0.3416139545338275}} {"text": "print(\"--------------- File plot-explore.r\")\n\n# load environment from all-IL run, post-simulation\n# manual stuff\n# load(\"../modelOutput/results/run_2/us_base-488236.Rdata\") # -> prev - 4000 iterations\n# load(\"../modelOutput/results/big_sim/us_base-2225348.Rdata\") # -> prev - 8000 iterations (\"big sim\")\n# load(\"../modelOutput/results/five_county_big/us_base-1028756.Rdata\") # -> 24K iterations on 5 counties with most data\n# load(\"../modelOutput/results/nine_county_big/us_base-606037.Rdata\") # -> 24K iterations on 9 counties with most data\n\n# automating ..\nargs <- commandArgs(trailingOnly = TRUE)\nfilename2 <- args[1]\nload(paste0(\"../modelOutput/results/\", filename2))\n\nexploreNames <- c(\n \"County\",\n\n \"Rt\",\n \"R0\",\n \"Prop_Reduction_in_Rt\", # (R0 - Rt) / R0\n\n \"Modeled_Cases\",\n \"Reported_Cases\",\n\n \"Modeled_Deaths\",\n \"Reported_Deaths\"\n)\n\nexplore <- data.frame(matrix(0, ncol = length(exploreNames)))\ncolnames(explore) <- exploreNames\n\nfor (i in 1:length(countries)) {\n N <- length(dates[[i]])\n\n country <- countries[[i]]\n\n dimensions <- dim(out$Rt)\n Rt <- mean(out$Rt[, dimensions[2], i])\n R0 <- mean(out$mu[, i])\n\n total_predicted_cases <- sum(colMeans(prediction[, 1:N, i]))\n total_predicted_cases_cf <- sum(colMeans(out$prediction0[, 1:N, i]))\n total_reported_cases <- sum(reported_cases[[i]])\n\n total_estimated_deaths <- sum(colMeans(estimated.deaths[, 1:N, i]))\n total_estimated_deaths_cf <- sum(colMeans(estimated.deaths.cf[, 1:N, i]))\n total_reported_deaths <- sum(deaths_by_country[[i]])\n\n countyStats <- c(\n country,\n Rt,\n R0,\n (R0 - Rt) / R0,\n log(total_predicted_cases),\n log(total_reported_cases),\n log(total_estimated_deaths),\n log(total_reported_deaths)\n )\n\n explore <- rbind(explore, countyStats)\n}\n\n# take away initial row which is just a zero vector placeholder\nexplore <- explore[-1, ]\n\n# separate df without cook county\n# here -> watch this\nexploreNoCook <- explore[explore$County != \"84017031\", ]\n\n# remove county column (it's not a variable)\nexplore$County <- NULL\nexploreNoCook$County <- NULL\n\n## plots -> save them, name them, easily readable axes\n\n# look at everything\npng(filename = \"../modelOutput/explorePlots/exploreVars.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# todo: fix this manual toggling\n# plot(exploreNoCook)\nplot(explore)\ndev.off()\n\n# NOTE: make it clear in each diagram if cook county is included or not\n# assume cook county is included\n# if exluded - explicitly state this in the title\n# NOTE: I haven't done this yet\n\n# td: REALLY gotta fix the manual toggling between including or not including cook county in these plots\n\n#### distributions of interest\n\n# Rt\npng(filename = \"../modelOutput/explorePlots/freq_Rt.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\nhist(as.numeric(explore$Rt), breaks = 8, main = \"Rt\", xlab = \"Rt\")\ndev.off()\npng(filename = \"../modelOutput/explorePlots/freq_R0.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\nhist(as.numeric(explore$R0), breaks = 8, main = \"R0\", xlab = \"R0\")\ndev.off()\npng(filename = \"../modelOutput/explorePlots/freq_ReductionInRt.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\nhist(as.numeric(explore$Prop_Reduction_in_Rt), main = \"Reduction in Rt\", xlab = \"Reduction in Rt\")\ndev.off()\n\n# Reported Cases\npng(filename = \"../modelOutput/explorePlots/freq_ReportedCases_log.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# hist(as.numeric(exploreNoCook$Reported_Cases), main=\"log(Reported Cases)\", xlab=\"log(Reported Cases)\")\nhist(as.numeric(explore$Reported_Cases), main = \"log(Reported Cases)\", xlab = \"log(Reported Cases)\")\ndev.off()\npng(filename = \"../modelOutput/explorePlots/freq_ReportedCases.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# hist(exp(as.numeric(exploreNoCook$Reported_Cases)), main=\"Reported Cases\", xlab=\"Reported Cases\")\nhist(exp(as.numeric(explore$Reported_Cases)), main = \"Reported Cases\", xlab = \"Reported Cases\")\ndev.off()\n\n\n# Reported Deaths\npng(filename = \"../modelOutput/explorePlots/freq_ReportedDeaths_log.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# hist(as.numeric(exploreNoCook$Reported_Deaths), main=\"log(Reported Deaths)\", xlab=\"log(Reported Deaths)\")\nhist(as.numeric(explore$Reported_Deaths), main = \"log(Reported Deaths)\", xlab = \"log(Reported Deaths)\")\ndev.off()\npng(filename = \"../modelOutput/explorePlots/freq_ReportedDeaths.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# hist(exp(as.numeric(exploreNoCook$Reported_Deaths)), main=\"Reported Deaths\", xlab=\"Reported Deaths\")\nhist(exp(as.numeric(explore$Reported_Deaths)), main = \"Reported Deaths\", xlab = \"Reported Deaths\")\ndev.off()\n\n\n#### highlight some plots\n\n# Reduction in Rt vs. Reported Deaths\npng(filename = \"../modelOutput/explorePlots/ReductionInRt_vs_ReportedDeaths.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# plot(exp(as.numeric(exploreNoCook$Reported_Deaths)), exploreNoCook$Prop_Reduction_in_Rt,\n# main=\"Reduction in Rt vs. Reported Deaths\",\n# xlab=\"Reported Deaths\", ylab=\"Reduction in Rt\")\nplot(exp(as.numeric(explore$Reported_Deaths)), explore$Prop_Reduction_in_Rt,\n main = \"Reduction in Rt vs. Reported Deaths\",\n xlab = \"Reported Deaths\", ylab = \"Reduction in Rt\"\n)\ndev.off()\n\n# Reported Deaths vs. Reported Cases\n# y is reported deaths -> \"x vs. y\"\npng(filename = \"../modelOutput/explorePlots/ReportedDeaths_vs_ReportedCases.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# plot(exploreNoCook$Reported_Cases, exploreNoCook$Reported_Deaths,\n# main=\"Reported Deaths vs. Reported Cases\",\n# xlab=\"log(Reported Cases)\", ylab=\"log(Reported Deaths)\")\nplot(explore$Reported_Cases, explore$Reported_Deaths,\n main = \"Reported Deaths vs. Reported Cases\",\n xlab = \"log(Reported Cases)\", ylab = \"log(Reported Deaths)\"\n)\ndev.off()\n\n# Rt vs. Reported Deaths\npng(filename = \"../modelOutput/explorePlots/ReportedDeaths_vs_Rt.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\n# plot(exploreNoCook$Rt, exploreNoCook$Reported_Deaths,\n# main=\"Reported Deaths vs. Rt\",\n# xlab=\"Rt\", ylab=\"log(Reported Deaths)\")\nplot(explore$Rt, explore$Reported_Deaths,\n main = \"Reported Deaths vs. Rt\",\n xlab = \"Rt\", ylab = \"log(Reported Deaths)\"\n)\ndev.off()\n\n# R0 vs. Rt\npng(filename = \"../modelOutput/explorePlots/Rt_vs_R0.png\", width = 1600, height = 1600, units = \"px\", pointsize = 36)\nplot(explore$R0, explore$Rt, main = \"Rt vs. R0\", xlab = \"R0\", ylab = \"Rt\")\ndev.off()\n\n#### todo! : fetch soc-ec vars, pop dens, etc. -> plot reduction in Rt, and Rt, or whatever, against these other soc-ec vars by county\n", "meta": {"hexsha": "14ee3b1f26fb26108290b53fb04a50701958d6e3", "size": 6659, "ext": "r", "lang": "R", "max_stars_repo_path": "covid19model/r/plot-explore.r", "max_stars_repo_name": "uc-cdis/covid19model", "max_stars_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "covid19model/r/plot-explore.r", "max_issues_repo_name": "uc-cdis/covid19model", "max_issues_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2021-03-22T21:16:32.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-29T22:58:33.000Z", "max_forks_repo_path": "covid19model/r/plot-explore.r", "max_forks_repo_name": "uc-cdis/covid19model", "max_forks_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-05-12T23:53:08.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-22T15:08:21.000Z", "avg_line_length": 39.874251497, "max_line_length": 140, "alphanum_fraction": 0.710016519, "num_tokens": 1996, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3416139545338274}} {"text": "library(rbenchmark)\nlibrary(tau)\nlibrary(ngram)\n\nx <- ngram::rcorpus(50000)\n\nreps <- 15\ncols <- c(\"test\", \"replications\", \"elapsed\", \"relative\")\n\nbenchmark(tau=textcnt(x, n=3, split=\" \", method=\"string\"), ngram=get.phrasetable(ngram(x, n=3)), replications=reps, columns=cols)\n\n", "meta": {"hexsha": "85781cb9303e39187d399919b4b0d798dde3e667", "size": 277, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/benchmarks/rbenchmark.r", "max_stars_repo_name": "russey/ngram", "max_stars_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2015-03-10T17:47:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:26:08.000Z", "max_issues_repo_path": "inst/benchmarks/rbenchmark.r", "max_issues_repo_name": "russey/ngram", "max_issues_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2015-06-23T14:59:00.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-22T17:14:00.000Z", "max_forks_repo_path": "inst/benchmarks/rbenchmark.r", "max_forks_repo_name": "russey/ngram", "max_forks_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 22, "max_forks_repo_forks_event_min_datetime": "2015-02-09T14:21:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T00:58:48.000Z", "avg_line_length": 23.0833333333, "max_line_length": 129, "alphanum_fraction": 0.6931407942, "num_tokens": 88, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.34159470414198595}} {"text": "##--------------------------------------------------\n##\n## LDA-link analysis of asthma patient\n##\n##--------------------------------------------------\n\n\n\n\n##################################################\n##\n## data -1.simple_cor.r data processing\n##################################################\n\n\n###the last one \nwd = \"\" ###change here to data folder\n\nsetwd(wd)\n####setwd(\"/ysm-gpfs/pi/gerstein/from_louise/sl2373/asthma/p152ds\")\n\n\n###exo.genus, genus level abundance of microbes\nload(\"../exogenous/exo.genus.wRowName.rdata\")\n\n###clinical expression\nload(\"../exogenous/ExoAsthma_humanGenes_clinical.RData\")\n\n###update Jan 31\n\nload(\"counts.rpm.protein.rpkm.clinical.Rdata\")\n\nbulk=all.mats.protein$rpm\ncolnames(bulk)=gsub(\"\\\\.fq\",\"\", colnames(bulk))\n\nexo<-exo.genus\n\ncolnames(exo)=gsub(\"\\\\.fq\",\"\", colnames(exo))\nexo=exo[,colnames(bulk)]\n\n\nbulk=apply(bulk, 2, function(x){ x[is.na(x)]=0.0; x;})\n\nexo=apply(exo, 2, function(x){ x[is.na(x)]=0.0; x;})\n\nexo=exo[-which(rowSums(exo)==0),]\n\n\n##################################################\n## get correlation\nbce.cor=cor(t(bulk), t(exo))\nbce.cor.vec=as.vector(bce.cor)\n\nbce.cor.log2=cor(log2(t(bulk)+1), log2(t(exo)+1))\nbce.corlog2.vec=as.vector(bce.cor.log2)\n##17201 x 498\n\nif (FALSE){\n###Fig 4A\npdf(\"bulk_exo_cor_distr.pdf\")\nplot(density(as.vector(bce.cor)), xlab=\"correlation\", ylab=\"density\")\ndev.off()\n}\n\n\nbulk.quant=apply(bulk,2, function(x){ y=rank(x); })\nquartnum= floor(nrow(bulk)*0.5)\n\nbulk.exprcnt= apply(bulk.quant, 1, function(x){ sum(x>quartnum);})\nbulk.filt=bulk[which(bulk.exprcnt > 70), ]\n\n\nbulk.quant2=apply(bulk,2, function(x){ y=rank(x); })\nquartnum= floor(nrow(bulk)*0.5)\nbulk.exprcnt2= apply(bulk.quant2, 1, function(x){ sum(x>quartnum);})\n\n###filter exo data\nexo.gt0 = apply(exo, 1, function(x){ sum(x>0);})\nexo.filt=exo[which(exo.gt0>10),] ##130x172\nexo.filt2 = exo.filt[,which(colSums(exo.filt)>0)] ##130x170\n\n\ncutoff=30 ##for p152ds\nbulk.filt2=bulk[which(bulk.exprcnt2 > cutoff), ]\n###correlation \nbce.corfilt2=cor(t(bulk.filt2), t(exo.filt))\n\n\nb.cols=split(t(bulk.filt2), col(t(bulk.filt2)))\ne.cols= split(t(exo.filt), col(t(exo.filt)))\n\n\n\nbe.cortest=outer(b.cols, e.cols,Vectorize(function(x,y){cor.test(x,y)$p.value;}))\n\n\ncor.vec=as.vector(bce.corfilt2)\nmx = \"fdr\" ###can also try BH, hochberg, hommel etc\nbe.cor.padj_fdr = p.adjust(as.vector(be.cortest), method = mx)\n#summary(be.cor.padj_fdr[which( abs(cor.vec)> 0.4)])\n# Min. 1st Qu. Median Mean 3rd Qu. Max. \n#0.000e+00 1.369e-07 3.203e-05 2.608e-04 3.756e-04 1.599e-03\n\n\n\ndiv=10\ndata=ceiling(bulk.filt2/div)\ndata=as.matrix(t(data))\ndata[which(data>1000)]=1000\n\nntopic=10\nlibrary(\"topicmodels\")\nlda.bulk10= LDA(data, k = ntopic, method = \"Gibbs\")\n#lda.d2t.bulkfilt2= lda.noCtrl.model@gamma\n\nsave(lda.bulk10, file=\"bulk_ldaout10.rdata\")\n\n\nfin=\"exo_signal2_ldad10.txt\"\nSEED=123\ndata = read.table(fin, sep=\"\\t\", header=T, row.names=1, stringsAsFactors=F)\n\ndata.matrix=as.matrix(data)\n\n\nlda.exo10 = LDA(data, k = ntopic, method = \"Gibbs\", control = list(seed = SEED, burnin = 1000,\n thin = 100, iter = 1000))\n\n\nsave(lda.exo10, file=\"exo_ldaout10.rdata\")\n\n\n\n\n###this is the final version\n\nbulk.ldaout=\"0rpmlogF_noCtrl/bulk.filt2.lda10.txt\"\nexo.ldaout=\"0rpmlogF_noCtrl/exo.filt2.lda10.txt\"\n\nload(\"bulk_ldaout10.rdata\")\nload(\"exo_ldaout10.rdata\")\n\n####################################################################################################\n## exogerous \n## lda.bulk10=lda.noCtrl.model from rpm1logF.rpm.10.expc10.log_FALSE1000.lda.model.rdata\n##link from exogenous\nload(\"ExoAsthma_humanGenes_clinical.RData\")\nsev = df[,c(1,46)]\nlda.d2t.bulkfilt2= lda.bulk10@gamma\nrownames(lda.d2t.bulkfilt2) = gsub(\"\\\\.\", \"-\", lda.bulk10@documents)\nsample.comm=intersect(rownames(lda.d2t.bulkfilt2), sev[,1])\n\nsev=sev[which(sev[,1] %in% sample.comm),]\nlda.d2t.bf2sev=lda.d2t.bulkfilt2[sev[,1],]\n\n#sev = df[,c(1,46)]\nlda.d2t.exofilt2= lda.exo10@gamma\nrownames(lda.d2t.exofilt2) = gsub(\"\\\\.\", \"-\", lda.exo10@documents)\n\nlda.d2t.ef2sev=lda.d2t.exofilt2[sev[,1],]\n\n\n\n###distribution of cor- beta=log(prob) or ln(prob)\nbulk.filt2.lda10 = lda.bulk10@beta\ncolnames(bulk.filt2.lda10) = lda.bulk10@terms\n##write.table(bulk.filt2.lda10, file=bulk.ladout,sep=\"\\t\", quote=F, row.names=F, col.names=T)\n\n##10x149\nexo.filt2.lda10=lda.exo10@beta\ncolnames(exo.filt2.lda10)=lda.exo10@terms\n##write.table(exo.filt2.lda10, file=exo.ldaout,sep=\"\\t\", quote=F, row.names=F, col.names=T)\n\n\n###Fig 4D-F and Fig S6, S7\n\npdf(\"top20_bulk_gene2topic_dist.pdf\", width=12,height=14)\npar(mfrow=c(4,3))\n\npar(cex=0.7)\npar(lend=2)\npar(tcl= -0.15) \npar(mar=c(3,3,3,1)+0.1)\npar(mgp=c(1.1, 0.15, 0))\n\nfor ( i in 1:10){\n\n i.order=order(bulk.filt2.lda10[i,], decreasing=T)\n yy=exp(bulk.filt2.lda10[i,i.order[1:20]])\n xx=gsub(\"\\\\.protein_coding\",\"\",colnames(bulk.filt2.lda10)[i.order[1:20]])\n \n xbar=barplot(yy, xaxt=\"n\", xlab=\"\", ylab=\"G2T Prob\",main=paste(\"topic\",i),col=\"#C0C0C0\")\n\n\n axis(1, labels=xx, at=xbar,cex=0.5,las=2)\n\n \n}\ndev.off()\n\n\n\n\npdf(\"top10_exo_microbe2topic_dist.pdf\", width=12,height=14)\npar(mfrow=c(4,3))\n\npar(cex=0.7)\npar(lend=2)\n#par(tcl= -0.15) \npar(mar=c(7,3,3,1)+0.1)\n#par(mgp=c(1.1, 0.15, 0))\n\nfor ( i in 1:10){\n\n i.order=order(exo.filt2.lda10[i,], decreasing=T)\n yy=exp(exo.filt2.lda10[i,i.order[1:10]])\n xx=gsub(\"X(\\\\d+)\",\"\",colnames(exo.filt2.lda10)[i.order[1:10]])\n \n xbar=barplot(yy, xaxt=\"n\", xlab=\"\", ylab=\"M2T Prob\",main=paste(\"topic\",i),col=\"#C0C0C0\")\n\n\n axis(1, labels=xx, at=xbar,cex=0.5,las=2)\n\n \n}\ndev.off()\n\n\n##########################################################################################\n## #\n## randomForest\n##########################################################################################\n##define positive and negative based on correlation\n\n\n##bulk.filt2.lda10=read.table(bulk.ldaout, sep=\"\\t\", header=T)\n##exo.filt2.lda10=read.table(exo.ldaout, sep=\"\\t\", header=T)\n\ncor.cut=0.4 #0.4, 0.5, 0.6, 0.7\n###log2cor test #cor.cut=0.35\n\n\n\npos.idx = which(abs(bce.corfilt2) > cor.cut)\npos.b= pos.idx %% nrow(bulk.filt2)\npos.b[which(pos.b == 0)] = nrow(bulk.filt2)\npos.e = ceiling(pos.idx /nrow(bulk.filt2))\n\nneg.tmp.idx = which(abs(bce.corfilt2) < 0.05)\nneg.tmp.b = neg.tmp.idx %% nrow(bulk.filt2)\nneg.tmp.b[which(neg.tmp.b == 0 )] = nrow(bulk.filt2)\n\nneg.tmp.e = ceiling(neg.tmp.idx/ nrow(bulk.filt2))\n\nneg.b = neg.tmp.b[which( !neg.tmp.b %in% pos.b & !neg.tmp.e %in% pos.e)]\nneg.e = neg.tmp.e[which( !neg.tmp.b %in% pos.b & !neg.tmp.e %in% pos.e)]\n\n#colnames(bce.corfilt2)=paste(\"X\", colnames(bce.corfilt2),sep=\"\")\nneg.rand.idx = sample(length(neg.b), length(pos.b), rep=F)\npos.ds = cbind( t(bulk.filt2.lda10[ , pos.b]) , t(exo.filt2.lda10[, pos.e]))\nneg.ds = cbind( t(bulk.filt2.lda10 [, neg.b[neg.rand.idx]]) , t(exo.filt2.lda10[, neg.e[neg.rand.idx]]))\n\nds.xx = rbind(pos.ds, neg.ds)\nds.xx =cbind(data.frame(y=rep(c(\"pos\",\"neg\"), c(nrow(pos.ds), nrow(neg.ds)))), ds.xx)\nds.xx$y= as.factor(ds.xx$y)\ncolnames(ds.xx)[-1] = paste(\"X\", colnames(ds.xx)[-1], sep=\"\")\n\n##################################################\n####todo: here for cross-validation\n##################################################\n\n\n###where is the model for randomForest\n###need to remove the overlapping idx\n## pos.b unique, then remove pos.e dup, similar to negative set\n\npos.b.grp0=split(pos.b, as.factor(pos.e))\npos.b.grp = pos.b.grp0[sample(length(pos.b.grp0), length(pos.b.grp0), rep=F)]\npos.b.grplen= unlist(lapply(pos.b.grp, length))\npos.b.grpcumsum = cumsum(pos.b.grplen)\n\nneg.b.grp0 = split(neg.b, as.factor(neg.e))\nneg.b.grp = neg.b.grp0[sample(length(neg.b.grp0), length(neg.b.grp0),rep=F)]\nneg.b.grplen =unlist(lapply(neg.b.grp, length))\nneg.b.grpcumsum = cumsum(neg.b.grplen)\n\n\nk0=10\npsize = ceiling(sum(pos.b.grplen)/k0)\npsize_neg = ceiling(sum(neg.b.grplen)/k0)\n\nmethod=\"rf\"\nres = NULL\nntree=500\nuprate=1\nfor (k in 1:k0){\n cat(k,'\\r')\n ###positive\n cid = ((k-1)*psize+1):min(k*psize, sum(pos.b.grplen))\n start=which(pos.b.grpcumsum >= cid[1])[1]\n end = which(pos.b.grpcumsum <=cid[length(cid)])\n end = end[length(end)]\n \n pos.tmp = pos.b.grp[start:end]\n pos.b0id = unique(unlist(pos.tmp))\n pos.e0id = as.integer(names(pos.tmp))\n\n te.idx = which(pos.b %in% pos.b0id & pos.e %in% pos.e0id)\n tr.idx = which(!pos.b %in% pos.b0id & !pos.e %in% pos.e0id)\n \n pte = cbind ( t(bulk.filt2.lda10[ , pos.b[te.idx]]), t(exo.filt2.lda10[, pos.e[te.idx]]))\n ptr = cbind ( t(bulk.filt2.lda10[ , pos.b[tr.idx]]), t(exo.filt2.lda10[, pos.e[tr.idx]]))\n\n if(FALSE){\n te.idx = sample(length(neg.b), nrow(pte),rep=F)\n neg.b.trtmp = neg.b[-te.idx]\n neg.e.trtmp = neg.e[-te.idx]\n\n \n neg.b.trtmp0 = neg.b.trtmp[which(!neg.b.trtmp %in% neg.b[te.idx] & !neg.e.trtmp %in% neg.e[te.idx])]\n neg.e.trtmp0 = neg.e.trtmp[which(!neg.b.trtmp %in% neg.b[te.idx] & !neg.e.trtmp %in% neg.e[te.idx])]\n\n if (length(neg.b.trtmp0) < 1/2 * nrow(ptr)){\n next\n }\n tr.idx = sample(length(neg.b.trtmp0), nrow(ptr), rep=F)\n \n\n nte = cbind ( t(bulk.filt2.lda10[ , neg.b[te.idx]]), t(exo.filt2.lda10[, neg.e[te.idx]]))\n ntr = cbind ( t(bulk.filt2.lda10[ , neg.b[tr.idx]]), t(exo.filt2.lda10[, neg.e[tr.idx]]))\n}\n\n ###neg\n cid = ((k-1)*psize_neg+1):min(k*psize_neg, sum(neg.b.grplen))\n start=which(neg.b.grpcumsum >= cid[1])[1]\n end = which(neg.b.grpcumsum <=cid[length(cid)])\n end = end[length(end)]\n \n neg.tmp = neg.b.grp[start:end]\n neg.b0id = unique(unlist(neg.tmp))\n neg.e0id = as.integer(names(neg.tmp))\n te.idx = which(neg.b %in% neg.b0id & neg.e %in% neg.e0id)\n tr.idx = which(!neg.b %in% neg.b0id & !neg.e %in% neg.e0id)\n \n nte = cbind ( t(bulk.filt2.lda10[ , neg.b[te.idx]]), t(exo.filt2.lda10[, neg.e[te.idx]]))\n ntr = cbind ( t(bulk.filt2.lda10[ , neg.b[tr.idx]]), t(exo.filt2.lda10[, neg.e[tr.idx]]))\n\n ptr = ptr[sample(nrow(ptr), ceiling(uprate*nrow(ntr)),rep=T),]\n pte = pte[sample(nrow(pte), ceiling(uprate*nrow(nte)),rep=T),]\n\n print(paste('nrow pte, nte', nrow(pte),nrow(nte), 'nrow ptr,ntr', nrow(ptr), nrow(ntr),'\\r'))\n te = rbind(pte,nte)\n tr = rbind(ptr, ntr)\n colnames(te)=paste(\"X\", 1:ncol(te),sep=\"\")\n colnames(tr)=paste(\"X\", 1:ncol(tr), sep=\"\")\n\n\n te<-cbind(data.frame(y=rep(c(1,0), c(nrow(pte), nrow(nte)))), te)\n tr<-cbind(data.frame(y=rep(c(1,0), c(nrow(ptr), nrow(ntr)))), tr)\n\n te$y=as.factor(te$y)\n tr$y= as.factor(tr$y)\n\n tmp<-NULL\n if ( method==\"rf\"){\n ##stratify\n rfmodel<- randomForest(y~., data=tr, ntree=ntree) #,\n\n tmp<- predict(rfmodel, te[,-1],type=\"prob\")\n tmp = data.frame(te[,1], tmp[,2])\n ##print(head(tmp))\n}else if (method==\"logit\"){\n logist = glm(y~., data=tr, family=binomial(link='logit'))\n tmp= predict(logist, te[,-1], type=\"response\")\n print(sum(tmp[1:nrow(pte)]>0.5)/nrow(pte))\n tmp = data.frame(te[,1], tmp) \n}else if (method==\"svm\"){\n svm.model=svm(y~., data=tr,probability=TRUE, scale=F)\n tmp=predict(svm.model, newdata=te[,-1], probability = TRUE)\n tmp=data.frame(te[,1], attr(tmp,\"probabilities\")[,2])\n}else if (method==\"lasso\"){\n\n las.model=glmnet(as.matrix(tr[,-1]), tr[,1], alpha=1, family=\"binomial\", intercept=TRUE, lambda=lbd0)\n tmp=predict(las.model, newx=as.matrix(te[,-1]), type=\"response\")\n tmp=data.frame(te[,1], tmp)\n\n}else if (method==\"svr\"){\n svr.model=svm(y~., data=tr, scale=T)\n tmp=predict(svr.model, newdata=te[,-1])\n tmp=data.frame(te[,1], tmp)\n}\n\n\nres=rbind(res,tmp)\n\n\n\n\n}\nlibrary(\"PRROC\")\nroc1=roc.curve(scores.class0=res[which(res[,1]==1),2], scores.class1=res[which(res[,1]==0),2])$auc\nprc1=pr.curve(scores.class0=res[which(res[,1]==1),2], scores.class1=res[which(res[,1]==0),2])\n\n\nroc1\nprc1\n\n\n\nlibrary(\"randomForest\")\nlibrary(\"glmnet\")\nlibrary(\"e1071\")\n\n\n##################################################\n##cross validation\n\nrf.b2e = randomForest(y~., data=ds.xx)\nrf.b2e.impt=importance(rf.b2e)\n\npdf(\"0rpmlogF_noCtrl/rf_varimp.pdf\")\nvarImpPlot(rf.b2e)\ndev.off()\n\n\n\nsummary(as.vector(b2e.prob))\n\ntest.p = c(1:length(bce.corfilt2)) %% nrow(bulk.filt2)\ntest.p[which(test.p==0)] = nrow(bulk.filt2)\ntest.e = ceiling(c(1:length(bce.corfilt2)) / nrow(bulk.filt2))\n\ntest.ds = cbind( t(bulk.filt2.lda10[, test.p]), t(exo.filt2.lda10[, test.e]))\ntest.ds = as.data.frame(test.ds)\n\ncolnames(test.ds)=colnames(ds.xx)[-1]\n#test.pred = predict(rf.b2e, newdata=test.ds, type=\"prob\")\n##Error in z[keep, ] <- out.class.votes :\n## NAs are not allowed in subscripted assignments\ntest.pred=rbind(predict(rf.b2e, newdata=test.ds[c(1:1200000),], type=\"prob\"), predict(rf.b2e, newdata=test.ds[-c(1:1200000),], type=\"prob\"))\nb2e.prob= matrix(test.pred[,2], nrow = nrow(bce.corfilt2), ncol=ncol(bce.corfilt2),byrow=F)\n#b2e.prob=as.matrix(b2e.prob)\n\n\n\n###todo: double check \nprob.cut=0.9\n#suf2=\"rpmlas1se\"\n\n\nsuf2 = \"corcut0.8.rpm.las1se\"\nb2e.links.idx=which(b2e.prob>prob.cut) #0.95\n###0.7 for lasso\n\nb2e.links.p = b2e.links.idx %% nrow(bulk.filt2)\nb2e.links.p[which(b2e.links.p ==0)]=nrow(bulk.filt2)\n\nb2e.links.e = ceiling(b2e.links.idx / nrow(bulk.filt2))\n\nlength(unique(b2e.links.p))\nlength(unique(b2e.links.e))\n\n\nb2e.links.c0.9=data.frame(gene=gsub(\"([^:]+):(.+)\",\"\\\\1\", rownames(bulk.filt2)[b2e.links.p]), exo=gsub(\"(\\\\d+)(.+)\", \"\\\\1:\\\\2\", rownames(exo.filt2)[b2e.links.e]))\n\nwrite.table(unique(gsub(\"([^:]+):(.+)\",\"\\\\1\", rownames(bulk.filt2)[b2e.links.p])), file=paste(\"0rpmlogF_noCtrl/b2e.links.c\",prob.cut,\"_\",suf2, \".gene.txt\",sep=\"\"),sep=\"\\t\", quote=F, row.names=F, col.names=F)\n\nwrite.table(b2e.links.c0.9, file=paste(\"0rpmlogF_noCtrl/b2e.links.probcut\",prob.cut,\"_\",suf2,\".txt\",sep=\"\"),sep=\"\\t\", quote=F, row.names=F, col.names=F)\n\nsave(list=ls(),file=\"rf.link.2ndRun.rdata\")\n\n\n\n\n###fig 4B\n\nGAD.raw = read.table(\"fig4a_corlinks.gene.david.GAD.txt\", \n header=T,stringsAsFactors=F, sep=\"\\t\")\n\nterms = c(\"Asthma\", \"Arrhythmias, Cardiac|Long QT Syndrome\", \"Celiac Disease|\", \"Atrial Fibrillation|\",\n \"respiratory syncytial virus bronchiolitis\", \"gastrointestinal symptoms\", \"Bone Density\",\n \"Asthma|Bronchial Hyperreactivity|Hypersensitivity, Immediate\", \"depression | long QT syndrome\",\n \"Long QT Syndrome|Sinus Tachycardia|Tachycardia, Sinus\" , \"Sudden Infant Death\")\nterms.id = rev(which(GAD.raw$Term %in% terms))\n\npdf(\"fig4B.pdf\", height = 4, width = 6)\npar(mar=c(3,22,1,1))\nxbar=barplot(-log(GAD.raw[terms.id,\"PValue\"]),horiz=T,col=\"orange\", xlim=c(2,6), xpd=FALSE)\naxis(2, at=xbar, labels=GAD.raw[terms.id, \"Term\"], las=2)\ndev.off()\n\npdf(\"Fig4c.pdf\")\n\n##x1-x10: bulk 10topic; x11-x20: exo 10topics\nvarImpPlot(rfmodel,type=2)\ndev.off()\n\n\n####tool for cross-validation\nctool<-function(pdata0, ndata0, k0,lbd0, kk=1, method=c(\"rf\",\"logit\",\"svm\",\"lasso\",\"softmax\"), ntree=500){\n\n\nrequire(\"randomForest\")\nrequire(\"e1071\")\nrequire(\"PRROC\")\nrequire(\"ROCR\")\nrequire(\"glmnet\")\nrequire(\"AUC\")\n\n\nna.p= apply(pdata0[,-1],1,function(x){ y=sum(is.na(x)); y;})\nna.n= apply(ndata0[,-1],1,function(x){y=sum(is.na(x)); y;})\n\nnap.idx= which(na.p==0)\nnan.idx=which(na.n==0)\naucs=matrix(rep(0,kk*3),ncol=3);\nprcs=matrix(rep(0,kk*3),ncol=3);\n\n\nif (colnames(pdata0)[1] != \"y\" || colnames(ndata0)[1] != \"y\"){\nprint(\"The first colnames should be the target variable and named as y\")\nreturn;\n}\n\nif(sum(colnames(pdata0) == colnames(ndata0)) != ncol(pdata0) || ncol(pdata0) != ncol(ndata0)){\nprint(\"the pdata and ndata has different number of column\")\nreturn\n}\n\n\nxx.col=grep(\"^[1-9]\", colnames(pdata0[,-1]))\nif(length(xx.col)>0){\nprint(\"The colnames of input file should not start with a number, change it and try again\");\nreturn;\n}\nrocs=NULL\n\nif(length(nap.idx)==0 | length(nan.idx)==0){\n\nprint(\"No data left after removing missing data\")\nreturn\n}\n\n\npdata0=pdata0[nap.idx,]\nndata0=ndata0[nan.idx,]\n\npsize=ceiling(nrow(pdata0)/k0)\nnsize=ceiling(nrow(ndata0)/k0)\n\n\nres.out=NULL\nfor(k1 in 1:kk){ ##n time\n\n#pdata[,1]=as.factor(pdata[,1])\n#ndata[,1]=factor(ndata[,1])\npdata=pdata0[sample(nrow(pdata0),rep=F),]\nndata=ndata0[sample(nrow(ndata0),rep=F),]\n\n\nres=NULL\n\nfor (k in 1:k0){ ## n fold\ncat(k,'\\r')\n\ncid =((k-1)*psize+1):min(k*psize, nrow(pdata))\nptr=pdata[-cid,]\npte=pdata[cid,]\n\ncid=((k-1)*nsize+1):min(k*nsize, nrow(ndata))\nntr=ndata[-cid,]\nnte=ndata[cid,]\n\ntr=rbind(ptr,ntr)\nte=rbind(pte,nte)\n####\n#tr$y=as.factor(as.character(tr$y))\n#te$y=as.factor(as.character(te$y))\ntmp<-NULL\nif ( method==\"rf\"){\n##stratify\nrfmodel<- randomForest(y~., data=tr, ntree=ntree) #, sampsize=c(nrow(ptr), nrow(ptr)), strata=tr$y)\n\ntmp<- predict(rfmodel, te[,-1],type=\"prob\")\ntmp = data.frame(te[,1], tmp[,2])\n#print(head(tmp))\n}else if (method==\"logit\"){\nlogist = glm(y~., data=tr, family=binomial(link='logit'))\ntmp= predict(logist, te[,-1], type=\"response\")\nprint(sum(tmp[1:nrow(pte)]>0.5)/nrow(pte))\ntmp = data.frame(te[,1], tmp) \n}else if (method==\"svm\"){\nsvm.model=svm(y~., data=tr,probability=TRUE, scale=F)\ntmp=predict(svm.model, newdata=te[,-1], probability = TRUE)\ntmp=data.frame(te[,1], attr(tmp,\"probabilities\")[,2])\n}else if (method==\"lasso\"){\n\nlas.model=glmnet(as.matrix(tr[,-1]), tr[,1], alpha=1, family=\"binomial\", intercept=TRUE, lambda=lbd0)\ntmp=predict(las.model, newx=as.matrix(te[,-1]), type=\"response\")\ntmp=data.frame(te[,1], tmp)\n\n}else if (method==\"svr\"){\nsvr.model=svm(y~., data=tr, scale=T)\ntmp=predict(svr.model, newdata=te[,-1])\ntmp=data.frame(te[,1], tmp)\n}\n\n\nres=rbind(res,tmp)\n\n\n}\n\nprint(dim(res))\n\n\n#print(AUC::auc(roc(res[,2], res[,1])))\n###convert class as 1, 0 integer\n\n\nres.out=rbind(res.out, cbind(res, data.frame(run=rep(k1,nrow(res)))))\n\n\n}\n\n\nout=list()\n#out[[\"auc\"]]=aucs\n#out[[\"roc\"]]=rocs\n\n#out[[\"res\"]]=res.out\n#out\nres.out\n}\n\n\n\n\n####end of to del\n", "meta": {"hexsha": "9082a2d7fee75f8b2649569812c9d80bc68c86d4", "size": 17414, "ext": "r", "lang": "R", "max_stars_repo_path": "Figure 4-LDA-Link/fig4.LDA_link.r", "max_stars_repo_name": "dspak/decoasthma", "max_stars_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-04T15:59:58.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-04T15:59:58.000Z", "max_issues_repo_path": "Figure 4-LDA-Link/fig4.LDA_link.r", "max_issues_repo_name": "dspak/decoasthma", "max_issues_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Figure 4-LDA-Link/fig4.LDA_link.r", "max_forks_repo_name": "dspak/decoasthma", "max_forks_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T21:05:20.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T21:05:20.000Z", "avg_line_length": 27.209375, "max_line_length": 207, "alphanum_fraction": 0.6284024348, "num_tokens": 6262, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.34159470414198595}} {"text": "context(\"Test simOccData\")\n\ntest_that(\"Test simOccData\", {\n \n sink(file=ifelse(Sys.info()[\"sysname\"] == \"Windows\",\n \"NUL\",\n \"/dev/null\"))\n set.seed(125)\n \n results <- simOccData(nvisit=200, nsite=10, nTP=5, psi=0.5, beta1=182, beta2=20,\n beta3=100, JD_range = c(100,300))\n \n head_spp_vis<-structure(list(visit = c(1L, 2L, 3L, 4L, 5L, 6L), mysp = c(1L, 1L, 1L, 1L, 0L, 1L)),\n .Names = c(\"visit\", \"mysp\"), row.names = c(NA, 6L), class = \"data.frame\")\n \n head_occDetdata<-structure(list(visit = c(1L, 2L, 3L, 4L, 5L, 6L), site = c(1L, 10L, 7L, 4L, 1L, 7L), \n L = c(4, 1, 4, 2, 1, 2), TP = c(2L, 2L, 5L, 2L, 5L, 1L),\n Jul_date = c(171L, 283L, 299L, 133L, 280L, 298L)),\n .Names = c(\"visit\", \"site\", \"L\", \"TP\", \"Jul_date\"), row.names = c(NA, 6L), class = \"data.frame\")\n \n head_Z <-matrix(c(1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L), ncol = 5, nrow = 6)\n \n head_p <-c(0.9999603, 0.1705370, 0.9998911, 0.8201487, 0.2942406, 0.8923152)\n \n expect_identical(names(results), c('spp_vis','occDetdata','Z','p'))\n expect_equal(head(results$spp_vis), head_spp_vis)\n expect_equal(head(results$occDetdata), head_occDetdata)\n expect_equal(head(results$Z), head_Z)\n expect_equal(head(results$p), head_p, tolerance = 1e-7)\n \n # Test positive trend with unrestricted Julian date\n set.seed(125)\n resultsB<-simOccData(nvisit=200, nsite=10, nTP=5, psi=0.5, beta1=182, beta2=20,\n beta3=100,trend = +0.2)\n \n head_spp_visB<-structure(list(visit = c(1L, 2L, 3L, 4L, 5L, 6L), mysp = c(1L, 1L, 1L, 1L, 0L, 1L)),\n .Names = c(\"visit\", \"mysp\"), row.names = c(NA, 6L), class = \"data.frame\")\n \n head_occDetdataB<-structure(list(visit = c(1L, 2L, 3L, 4L, 5L, 6L), site = c(1L, 10L, 7L, 4L, 1L, 7L), \n L = c(4, 1, 4, 2, 1, 2), TP = c(2L, 2L, 5L, 2L, 5L, 1L),\n Jul_date = c(72L, 184L, 200L, 290L, 199L, 91L)),\n .Names = c(\"visit\", \"site\", \"L\", \"TP\", \"Jul_date\"), row.names = c(NA, 6L), class = \"data.frame\")\n \n head_ZB <-matrix(c(1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L), ncol = 5, nrow = 6)\n \n head_pB <-c(0.9997792, 0.5993916, 0.9999712, 0.8050512, 0.6259862, 0.8923213)\n \n expect_equal(head(resultsB$spp_vis), head_spp_visB)\n expect_equal(head(resultsB$occDetdata), head_occDetdataB)\n expect_equal(head(resultsB$Z), head_ZB)\n expect_equal(head(resultsB$p), head_pB, tolerance = 1e-7)\n \n expect_error(results <- simOccData(nvisit=200, nsite=10, nTP=5, psi=0.5, beta1=182, beta2=20, beta3=100, JD_range = c(100,400)),\n 'Invalid Julian date range')\n \n sink()\n})", "meta": {"hexsha": "d831ab542e9133496c19680f85ddc4b15280bbc6", "size": 2957, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/testsimOccData.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "tests/testthat/testsimOccData.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "tests/testthat/testsimOccData.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 51.8771929825, "max_line_length": 161, "alphanum_fraction": 0.5515725397, "num_tokens": 1312, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3415946969709353}} {"text": "\n\nsnowcrab.timeseries.db = function( DS=\"default\", p=NULL, regions=c( \"cfa4x\", \"cfanorth\", \"cfasouth\", \"cfaall\" ), trim=0, vn=NULL, sdci=F ) {\n\n if (is.null(p)) p = bio.snowcrab::snowcrab_parameters()\n \n tsoutdir = file.path( p$project.outputdir, \"timeseries\" )\n dir.create(tsoutdir, showWarnings=FALSE, recursive=TRUE)\n\n if (DS == \"default\") return( snowcrab.timeseries.db( DS=\"biologicals\", p=p) )\n\n if (DS == \"biologicals.direct\" ) {\n # \\\\ no saving .. just a direct one-off\n dat = snowcrab.db( DS =\"set.biologicals\", p=p )\n dat$year = as.character(dat$yr)\n\n if (is.null(vn)) vn = c( \"R0.mass\", \"t\", \"R1.mass\" )\n yrs = sort(unique(dat$yr))\n\n #area designations\n for (a in regions) {\n dat[,a] = NA\n ai = NULL\n ai = polygon_inside(dat, a)\n if (length(ai) > 0) dat[ai,a] = a\n }\n tsdata = expand.grid( region=regions, year=yrs, variable=vn, stringsAsFactors=FALSE, KEEP.OUT.ATTRS=FALSE )\n tsdata$year = as.character( tsdata$year)\n tsdata$mean = NA\n tsdata$se = NA\n tsdata$sd = NA\n tsdata$n = NA\n tsdata$ub = NA\n tsdata$lb = NA\n\n lookup.table = snowcrab.db( p=p, DS=\"data.transforms\" )\n\n for (vi in 1:length(vn) ) {\n v = vn[vi]\n if ( !is.numeric( dat[,v] ) ) next()\n print( paste( vi, v) )\n XX = bio.snowcrab::variable.recode( dat[,v], v, direction=\"forward\", lookup.table=lookup.table ) # transform variables where necessary\n for (r in regions) {\n ri = which( dat[,r] == r)\n if (length(ri)==0) next()\n XXmean = tapply( XX[ri], INDEX=dat$year[ri], FUN=mean, na.rm=TRUE )\n XXn = tapply( XX[ri], INDEX=dat$year[ri], FUN=function(x) length(which(is.finite(x))) )\n XXse = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE ) / XXn\n XXsd = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE )\n tsi = which(tsdata$variable==v & tsdata$region==r)\n\n tsdata[ tsi,\"mean\"] = bio.snowcrab::variable.recode (XXmean[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\", lookup.table=lookup.table )\n tsdata[ tsi,\"n\"] = XXn[ tsdata[ tsi, \"year\"] ]\n tsdata[ tsi,\"se\"] = bio.snowcrab::variable.recode (XXse[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"sd\"] = bio.snowcrab::variable.recode (XXsd[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n XXlb = XXmean - XXse* 1.96\n XXub = XXmean + XXse* 1.96\n if(sdci){\n XXlb = XXmean - XXsd* 1.96\n XXub = XXmean + XXsd* 1.96\n }\n tsdata[ tsi,\"lb\"] = bio.snowcrab::variable.recode (XXlb[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"ub\"] = bio.snowcrab::variable.recode (XXub[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n }\n }\n tsdata$year = as.numeric( tsdata$year)\n return(tsdata)\n }\n\n\n # -------------------\n\n\n if (DS %in% c( \"biologicals\", \"biologicals.redo\" ) ) {\n fn = file.path( tsoutdir, \"snowcrab.timeseries.rdata\" )\n if (DS==\"biologicals\") {\n tsdata = NULL\n if (file.exists( fn) ) load(fn)\n return(tsdata)\n }\n\n dat = snowcrab.db( DS =\"set.biologicals\", p=p )\n dat$year = as.character(dat$yr)\n\n if (is.null(vn)) vn = setdiff( colnames(dat), c(\"trip\", \"set\", \"set_type\", \"station\", \"lon1\", \"lat1\", \"towquality\", \"lon\", \"lat\", \"plon\", \"plat\", \"seabird_uid\", \"minilog_uid\", \"netmind_uid\" ) )\n yrs = sort(unique(dat$yr))\n\n #area designations\n for (a in regions) {\n dat[,a] = NA\n ai = NULL\n ai = polygon_inside(dat, a)\n if (length(ai) > 0) dat[ai,a] = a\n }\n\n tsdata = expand.grid( region=regions, year=yrs, variable=vn, stringsAsFactors=FALSE, KEEP.OUT.ATTRS=FALSE )\n tsdata$year = as.character( tsdata$year)\n tsdata$mean = NA\n tsdata$se = NA\n tsdata$sd = NA\n tsdata$n = NA\n tsdata$ub = NA\n tsdata$lb = NA\n\n lookup.table = snowcrab.db( p=p, DS=\"data.transforms\" )\n\n for (vi in 1:length(vn) ) {\n v = vn[vi]\n if ( !is.numeric( dat[,v] ) ) next()\n print( paste( vi, v) )\n XX = bio.snowcrab::variable.recode( dat[,v], v, direction=\"forward\", lookup.table=lookup.table ) # transform variables where necessary\n for (r in regions) {\n ri = which( dat[,r] == r)\n if (length(ri)==0) next()\n XXmean = tapply( XX[ri], INDEX=dat$year[ri], FUN=mean, na.rm=TRUE )\n XXn = tapply( XX[ri], INDEX=dat$year[ri], FUN=function(x) length(which(is.finite(x))) )\n XXse = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE ) / XXn\n XXsd = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE )\n tsi = which(tsdata$variable==v & tsdata$region==r)\n\n tsdata[ tsi,\"mean\"] = bio.snowcrab::variable.recode (XXmean[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\", lookup.table=lookup.table )\n tsdata[ tsi,\"n\"] = XXn[ tsdata[ tsi, \"year\"] ]\n tsdata[ tsi,\"se\"] = bio.snowcrab::variable.recode (XXse[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"sd\"] = bio.snowcrab::variable.recode (XXsd[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n XXlb = XXmean - XXse* 1.96\n XXub = XXmean + XXse* 1.96\n if(sdci){\n XXlb = XXmean - XXsd* 1.96 # confidence intervals for population instead of mean\n XXub = XXmean + XXsd* 1.96\n }\n tsdata[ tsi,\"lb\"] = bio.snowcrab::variable.recode (XXlb[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"ub\"] = bio.snowcrab::variable.recode (XXub[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n }\n }\n tsdata$year = as.numeric( tsdata$year)\n save( tsdata, file=fn, compress=TRUE )\n return( fn)\n }\n\n\n # -------------------\n\n\n if (DS %in% c( \"biologicals.2014\", \"biologicals.2014.redo\" ) ) {\n #\\\\ \"reduced\" subset of stations found in 2014 ... to be comparable with smaller survey\n fn = file.path( tsoutdir, \"snowcrab.timeseries.2014.rdata\" )\n if (DS==\"biologicals.2014\") {\n tsdata = NULL\n if (file.exists( fn) ) load(fn)\n return(tsdata)\n }\n\n dat = snowcrab.db( DS =\"set.biologicals\", p=p )\n dat$year = as.character(dat$yr)\n stations.in.2014 = unique( dat$station[ which(dat$yr==2014) ] )\n dat = dat[ which(dat$station %in% stations.in.2014 ),]\n\n vn = setdiff( colnames(dat), c(\"trip\", \"set\", \"set_type\", \"station\", \"lon1\", \"lat1\", \"towquality\",\n \"lon\", \"lat\", \"plon\", \"plat\", \"seabird_uid\", \"minilog_uid\", \"netmind_uid\" ) )\n yrs = sort(unique(dat$yr))\n\n print( \"This will take a bit of time... \" )\n print( paste( \"There are\", length(vn), \"variables\" ) )\n\n #area designations\n for (a in regions) {\n dat[,a] = NA\n ai = NULL\n ai = polygon_inside(dat, a)\n if (length(ai) > 0) dat[ai,a] = a\n }\n\n tsdata = expand.grid( region=regions, year=yrs, variable=vn, stringsAsFactors=FALSE, KEEP.OUT.ATTRS=FALSE )\n tsdata$year = as.character( tsdata$year)\n tsdata$mean = NA\n tsdata$se = NA\n tsdata$n = NA\n tsdata$ub = NA\n tsdata$lb = NA\n lookup.table = snowcrab.db( p=p, DS=\"data.transforms\" )\n\n for (vi in 1:length(vn) ) {\n v = vn[vi]\n if ( !is.numeric( dat[,v] ) ) next()\n print( paste( vi, v) )\n XX = bio.snowcrab::variable.recode( dat[,v], v, direction=\"forward\", lookup.table=lookup.table ) # transform variables where necessary\n for (r in regions) {\n ri = which( dat[,r] == r)\n if (length(ri)==0) next()\n XXmean = tapply( XX[ri], INDEX=dat$year[ri], FUN=mean, na.rm=TRUE )\n XXn = tapply( XX[ri], INDEX=dat$year[ri], FUN=function(x) length(which(is.finite(x))) )\n XXse = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE ) / XXn\n tsi = which(tsdata$variable==v & tsdata$region==r)\n\n tsdata[ tsi,\"mean\"] = bio.snowcrab::variable.recode (XXmean[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\", lookup.table=lookup.table )\n tsdata[ tsi,\"n\"] = XXn[ tsdata[ tsi, \"year\"] ]\n tsdata[ tsi,\"se\"] = bio.snowcrab::variable.recode (XXse[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n XXlb = XXmean - XXse* 1.96\n XXub = XXmean + XXse* 1.96\n tsdata[ tsi,\"lb\"] = bio.snowcrab::variable.recode (XXlb[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"ub\"] = bio.snowcrab::variable.recode (XXub[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n }\n }\n tsdata$year = as.numeric( tsdata$year)\n save( tsdata, file=fn, compress=TRUE )\n return( fn)\n }\n\n\n # -------------------\n\n\n if (DS %in% c( \"observer\", \"observer.redo\" ) ) {\n #\\\\ \"reduced\" subset of stations found in 2014 ... to be comparable with smaller survey\n fn = file.path( tsoutdir, \"snowcrab.observer.timeseries.rdata\" )\n if (DS==\"observer\") {\n tsdata = NULL\n if (file.exists( fn) ) load(fn)\n return(tsdata)\n }\n\n dat = observer.db( DS=\"odb\" )\n dat$yr=dat$fishyr #Bz March 2019- We want the fishing year (2018/19= 2018), not the calendar year of catch\n dat$year = as.character(dat$yr)\n #dat = dat[ which( dat$cw >= 95),] #BZ March 2019- We want to keep all observed animals, not just cw>95\n vn = c( \"cw\", \"totmass\", \"abdomen\", \"chela\", \"shell\", \"durometer\", \"cpue.kg.trap\", \"mass\", \"mat\" )\n yrs = sort(unique(dat$yr))\n\n # print( \"This will take a bit of time... \" )\n # print( paste( \"There are\", length(vn), \"variables\" ) )\n\n #area designations\n for (a in regions) {\n dat[,a] = NA\n ai = NULL\n ai = polygon_inside(dat, a)\n if (length(ai) > 0) dat[ai,a] = a\n }\n\n tsdata = expand.grid( region=regions, year=yrs, variable=vn, stringsAsFactors=FALSE, KEEP.OUT.ATTRS=FALSE )\n tsdata$mean = NA\n tsdata$se = NA\n tsdata$n = NA\n tsdata$ub = NA\n tsdata$lb = NA\n tsdata$year = as.character( tsdata$year)\n lookup.table = snowcrab.db( p=p, DS=\"data.transforms\" )\n\n for (vi in 1:length(vn) ) {\n v = vn[vi]\n if ( !is.numeric( dat[,v] ) ) next()\n print( paste( vi, v) )\n XX = bio.snowcrab::variable.recode( dat[,v], v, direction=\"forward\", lookup.table=lookup.table ) # transform variables where necessary\n for (r in regions) {\n ri = which( dat[,r] == r)\n if (length(ri)==0) next()\n XXmean = tapply( XX[ri], INDEX=dat$year[ri], FUN=mean, na.rm=TRUE )\n XXn = tapply( XX[ri], INDEX=dat$year[ri], FUN=function(x) length(which(is.finite(x))) )\n XXse = tapply( XX[ri], INDEX=dat$year[ri], FUN=sd, na.rm=TRUE ) / XXn\n tsi = which(tsdata$variable==v & tsdata$region==r)\n\n tsdata[ tsi,\"mean\"] = bio.snowcrab::variable.recode (XXmean[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"n\"] = XXn[ tsdata[ tsi, \"year\"] ]\n tsdata[ tsi,\"se\"] = bio.snowcrab::variable.recode (XXse[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n XXlb = XXmean - XXse* 1.96\n XXub = XXmean + XXse* 1.96\n tsdata[ tsi,\"lb\"] = bio.snowcrab::variable.recode (XXlb[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n tsdata[ tsi,\"ub\"] = bio.snowcrab::variable.recode (XXub[ tsdata[ tsi, \"year\"] ], v, direction=\"backward\" , lookup.table=lookup.table)\n }\n }\n tsdata$year = as.numeric( tsdata$year)\n save( tsdata, file=fn, compress=TRUE )\n return( fn)\n }\n\n # -------------------\n\n\n if (DS %in% c( \"groundfish.t\", \"groundfish.t.redo\" ) ) {\n #\\\\ \"reduced\" subset of stations found in 2014 ... to be comparable with smaller survey\n fn = file.path( tsoutdir, \"groundfish.t.rdata\" )\n if (DS==\"groundfish.t\") {\n tsdata = NULL\n if (file.exists( fn) ) load(fn)\n return(tsdata)\n }\n tsdata = data.frame(r=NA,yrs=NA,V3='t',meanval=NA,se=NA,n=NA,ub=NA,lb=NA)\n h = groundfish.db( DS='gshyd')\n g = groundfish.db(DS='gsinf')\n g = g[,c('id','sdate','lon','lat','bottom_temperature')]\n h = h[,c('id','temp')]\n names(h)[2] <- 'bottom_temperature'\n f <- merge(g,h,by='id',all.x=T)\n i <- which(is.na(f$bottom_temperature.x) & !is.na(f$bottom_temperature.y))\n f[i,'bottom_temperature.x'] <- f[i,'bottom_temperature.y']\n f$yr <- as.numeric(format(f$sdate,'%Y'))\n f <- fishing.area.designations(f)\n ar <- unique(f$cfa)\n yy <- unique(f$yr)\n yy <- yy[order(yy)]\n for (r in ar) {\n for (yrs in yy) {\n y = f[which(f$yr == yrs & f$cfa ==r & !is.na(f$bottom_temperature.x)),]\n if(nrow(y)>3) {\n ym <- min(y$bottom_temperature.x[y$bottom_temperature.x>0])\n q = log(y$bottom_temperature.x+ym)\n m = mean (q, na.rm=T)\n n = length(q)\n se = sd(q, na.rm=T)/ sqrt(n-1)\n meanval = exp(m)-ym\n ub = exp(m+se*1.96)-ym\n lb = exp(m-se*1.96)-ym\n j = as.data.frame(cbind(r, yrs, 't',meanval, se, n, ub, lb))\n tsdata <- rbind(tsdata,j)\n }\n }\n }\n #browser()\n colnames(tsdata) = c(\"region\", \"year\", \"variable\",\"mean\", \"se\",\"n\", \"ub\", \"lb\")\n numbers = c(\"year\", \"mean\", \"se\", \"n\", \"ub\", \"lb\")\n tsdata = factor2number(tsdata, numbers)\n tsdata <- tsdata[!is.na(tsdata$year),]\n\n save(tsdata, file=fn, compress=T)\n return(fn)\n }\n\n}\n", "meta": {"hexsha": "b75cc973ae51da73e10634df0f05213421be6451", "size": 13457, "ext": "r", "lang": "R", "max_stars_repo_path": "R/snowcrab.timeseries.db.r", "max_stars_repo_name": "PEDsnowcrab/bio.snowcrab", "max_stars_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/snowcrab.timeseries.db.r", "max_issues_repo_name": "PEDsnowcrab/bio.snowcrab", "max_issues_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/snowcrab.timeseries.db.r", "max_forks_repo_name": "PEDsnowcrab/bio.snowcrab", "max_forks_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9027355623, "max_line_length": 197, "alphanum_fraction": 0.5809615813, "num_tokens": 4483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544210587585, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.3414343228901938}} {"text": "context(\"rep\")\n\ntest_that(\"vector\", {\n ref = rbind(1:2, 1:2)\n expect_equal(ref, rep(1:2, 2, along=1))\n expect_equal(ref, t(rep(1:2, 2, along=2)))\n expect_equal(ref, rrep(1:2, 2))\n expect_equal(ref, t(crep(1:2, 2)))\n})\n\ntest_that(\"keep names\", {\n x = setNames(1:2, letters[1:2])\n m = rep(x, 2, along=2)\n\n expect_equal(names(x), rownames(m))\n\n colnames(m) = LETTERS[1:2]\n expect_equal(rbind(m, m), rep(m, 2, along=1))\n expect_equal(rbind(m, m), rrep(m, 2))\n\n expect_equal(cbind(m, m), rep(m, 2, along=2))\n expect_equal(cbind(m, m), crep(m, 2))\n})\n", "meta": {"hexsha": "771ed3ac9e250af454697a6f701396f862c66009", "size": 584, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-rep.r", "max_stars_repo_name": "cran/narray", "max_stars_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2016-12-07T16:03:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-20T09:10:42.000Z", "max_issues_repo_path": "tests/testthat/test-rep.r", "max_issues_repo_name": "cran/narray", "max_issues_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2016-11-21T09:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-11T16:08:02.000Z", "max_forks_repo_path": "tests/testthat/test-rep.r", "max_forks_repo_name": "cran/narray", "max_forks_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-06-21T03:17:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-21T03:17:21.000Z", "avg_line_length": 24.3333333333, "max_line_length": 49, "alphanum_fraction": 0.5856164384, "num_tokens": 225, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6723317057447908, "lm_q1q2_score": 0.3414180169082797}} {"text": "#Plot the memory model, first argument is the upperbound, the the dataset containing the plotting data\n#Load Data \nargs<-commandArgs(TRUE)\nupperbound <- as.integer(args[1])\ndataset <- read.csv(args[2])\nfilename = gsub(\".csv\", \"\", args[2])\nfileNameEt <- paste(filename, \"_AVG_B.png\", sep=\"\")\ngraphtitle1 <- \"MEMORY BEFORE RESULT EN0-EN9\"\ngraphtitle2 <- \"MEMORY BEFORE AVG RESULT \"\npng(paste(fileNameEt), 1827, 1458)\npng(paste(filename, \"_AVG_B_THUMBNAIL.png\"), 150, 121)\n\n#upperbound <- max(cbind(dataset$MEMB0,dataset$MEMB1 ,dataset$MEMB2 ,dataset$MEMB3 ,dataset$MEMB4,dataset$MEMB5,dataset$MEMB6,dataset$MEMB7,dataset$MEMB8,dataset$MEMB9))\nxmean <- rowMeans(cbind(dataset$MEMB0,dataset$MEMB1 ,dataset$MEMB2 ,dataset$MEMB3 ,dataset$MEMB4,dataset$MEMB5,dataset$MEMB6,dataset$MEMB7,dataset$MEMB8,dataset$MEMB9))\nplotsummary = summary(xmean)\n\n#Plot the AVG\nplot(xmean ~ dataset$ENUM, type=\"l\", col=\"brown\", ylab = \"memory B\", xlab = \"Events\", yaxt=\"n\", ylim=c(0,upperbound), ,axes=FALSE)\npar(new=TRUE)\n\nabline(h = mean(xmean), v = 0 , col = \"gray60\")\npar(new=TRUE)\nabline(h = mean(xmean[200:999]), v = 0, col = \"red2\")\npar(new=TRUE)\n\n#Better Axis\naxis(1, at = seq(min(dataset$ENUM), max(dataset$ENUM)+1, by = 25))\naxis(2, at = seq(0, 10 + (upperbound - upperbound %% 10), by = 10))\n\n\n#Grid Behind\ngrid(nx = NULL, ny = NULL, col = \"gray1\", lty = \"dotted\",lwd = par(\"lwd\"), equilogs = TRUE)\n\n#Usefult informations\nfor(j in seq(1,6,1)){\n text(800, upperbound -(40*(j-1)), (paste(names(plotsummary)[j],as.vector(plotsummary)[j])) , cex = 1, adj = 0)\n}\ntext(300, upperbound - 55, paste(\"Stationary Mean\",mean(xmean[200:999])) , cex = 1, adj = 0)\n\ntitle(graphtitle1,filename)\n\n\ndev.off()\n\n\n", "meta": {"hexsha": "c4d31c298a0513e88972340300ad278ca11613d6", "size": 1683, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/avg/PlotAVGExecutionMemoryBCLI.r", "max_stars_repo_name": "streamreasoning/HeavenTeststand", "max_stars_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/avg/PlotAVGExecutionMemoryBCLI.r", "max_issues_repo_name": "streamreasoning/HeavenTeststand", "max_issues_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/avg/PlotAVGExecutionMemoryBCLI.r", "max_forks_repo_name": "streamreasoning/HeavenTeststand", "max_forks_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5869565217, "max_line_length": 169, "alphanum_fraction": 0.696969697, "num_tokens": 591, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.34141801357423857}} {"text": "library(ggplot2)\nlibrary(data.table)\n\nevec<-fread(\"../../Results/Genetic_pca/ADAPT_1000G_NoPalin_LDPrune_Euro_NoFins_ADAPTRef.pca.evec\",header=T)\neval<-fread(\"../../Results/Genetic_pca/ADAPT_1000G_NoPalin_LDPrune_Euro_NoFins_ADAPTRef.pca.eval\")\ncolnames(eval)<-c(\"Eigenvalue\")\neval$gPC<-seq(1,nrow(eval),1)\n\nscree<-ggplot(data=eval[which(eval$gPC<11),],aes(gPC,Eigenvalue))+\n geom_point()+\n geom_line()+\n theme_bw()+\n scale_x_continuous(breaks=seq(1,10))+\n ylim(c(1,4))\n\nggsave(\"../../Results/Genetic_pca/gPC_screeplot_1_10_09102018.pdf\",scree,height=5,width=7)\n\n\n", "meta": {"hexsha": "c40000b53d5ff71a1f6171e91b373f72d3694895", "size": 569, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/PCA_screeplot/geneticPCA_screeplot.r", "max_stars_repo_name": "Arslan-Zaidi/Facial_masculinity_MHC", "max_stars_repo_head_hexsha": "b62d295b4ce4657390ac6199c4c9b079f9e99077", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-03-27T13:55:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-01T08:27:39.000Z", "max_issues_repo_path": "Scripts/PCA_screeplot/geneticPCA_screeplot.r", "max_issues_repo_name": "Arslan-Zaidi/Facial_masculinity_MHC", "max_issues_repo_head_hexsha": "b62d295b4ce4657390ac6199c4c9b079f9e99077", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/PCA_screeplot/geneticPCA_screeplot.r", "max_forks_repo_name": "Arslan-Zaidi/Facial_masculinity_MHC", "max_forks_repo_head_hexsha": "b62d295b4ce4657390ac6199c4c9b079f9e99077", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9473684211, "max_line_length": 107, "alphanum_fraction": 0.7539543058, "num_tokens": 209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3414180135742385}} {"text": "#' Calculate percentage change between two years using Bayesian output\n#' \n#' Using the data returned from occDetModel/occDetFunc this function models a \n#' trend between two years for each iteration of the models. Several options are\n#' available for the method used to calculate the trend. This distribution of the results is used to\n#' calculate the mean estimate and the 95% credibale intervals. \n#'\n#' @param bayesOut occDet object as returned from occDetModel or occDetFunc. \n#' @param firstYear numeric, the first year over which the change is to be estimated. Defaults to the final year in the dataset\n#' @param lastYear numeric, the last year over which the change is to be estimated. Defaults to the first year in the dataset\n#' @param change A character string that specifies the type of change to be calculated, the default\n#' is annual growth rate. See details for options.\n#' @param region A character string specifying the region name if change is to be determined regional estimates of occupancy.\n#' Region names must match those in the model output.\n#' \n#' \n#' @details \\code{change} is used to specify which change measure to be calculated.\n#' There are four options to choose from: difference, percentdif, growthrate and\n#' lineargrowth.\n#' \n#' \\code{difference} calculates the simple difference between the first and last year.\n#' \n#' \\code{percentdif} calculates the percentage difference between the first and last year.\n#' \n#' \\code{growthrate} calculates the annual growth rate across years.\n#' \n#' \\code{lineargrowth} calculates the linear growth rate from a linear model.\n#' \n#' @return A list giving the mean, median, credible intervals and raw data from the\n#' estimations.\n#' @examples\n#' \\dontrun{\n#' \n#' #' # Create data\n#' n <- 15000 #size of dataset\n#' nyr <- 20 # number of years in data\n#' nSamples <- 100 # set number of dates\n#' nSites <- 50 # set number of sites\n#' \n#' # Create somes dates\n#' first <- as.Date(strptime(\"1980/01/01\", \"%Y/%m/%d\")) \n#' last <- as.Date(strptime(paste(1980+(nyr-1),\"/12/31\", sep=''), \"%Y/%m/%d\")) \n#' dt <- last-first \n#' rDates <- first + (runif(nSamples)*dt)\n#' \n#' # taxa are set as random letters\n#' taxa <- sample(letters, size = n, TRUE)\n#' \n#' # three sites are visited randomly\n#' site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)\n#' \n#' # the date of visit is selected at random from those created earlier\n#' survey <- sample(rDates, size = n, TRUE)\n#'\n#' # run the model with these data for one species\n#' results <- occDetModel(taxa = taxa,\n#' site = site,\n#' survey = survey,\n#' species_list = c('a','m','g'),\n#' write_results = FALSE,\n#' n_iterations = 1000,\n#' burnin = 10,\n#' thinning = 2)\n#'\n#' # estimate the change for one species \n#' change <- occurrenceChange(firstYear = 1990,\n#' lastYear = 1999,\n#' bayesOut = results$a) \n#' } \n#' @export\n\noccurrenceChange <- function(bayesOut, firstYear=NULL, lastYear=NULL, change = 'growthrate', region = NULL){\n\n # error checks for years (or set to defaults)\n if(is.null(firstYear)) \n firstYear <- bayesOut$min_year\n else\n if(!firstYear %in% bayesOut$min_year:bayesOut$max_year) stop('firstYear must be in the year range of the data')\n \n if(is.null(lastYear)) \n lastYear <- bayesOut$max_year\n else \n if(!lastYear %in% bayesOut$min_year:bayesOut$max_year) stop('lastYear must be in the year range of the data')\n \n # error checks for change\n if(!class(change) == 'character') stop('Change must be a character string identifying the change metric. Either: difference, percentdif, growthrate or lineargrowth')\n if(!change %in% c('difference', 'percentdif', 'growthrate', 'lineargrowth')) stop('The change metric must be one of the following: difference, percentdif, growthrate or lineargrowth')\n \n # error check for region\n if(!is.null(region)){\n if(!class(region) == 'character') stop('region must be a character string identifying the regional estimates that change is to be calculated for.')\n if(!region %in% bayesOut$regions) stop('region must match that used in the model output file, check spelling.')\n }\n \n \n # extract the sims list, if there is a region code, use the psi.fs for that region\n if(!is.null(region)){\n reg_code <- paste(\"psi.fs.r_\", region, sep = \"\")\n\n occ_it <- bayesOut$BUGSoutput$sims.list\n occ_it <- occ_it[[grep(reg_code, names(occ_it))]]\n\n }else{\n occ_it <- bayesOut$BUGSoutput$sims.list$psi.fs\n \n }\n \n \n colnames(occ_it) <- bayesOut$min_year:bayesOut$max_year\n years <- firstYear:lastYear\n \n ## edit values that are 0 or 1 to prevent estimates of inf later on\n #occ_it[occ_it == 0] <- 0.0001\n #occ_it[occ_it == 1] <- 0.9999\n \n \n ### loops depend on which change metric has been specified\n \n if(change == 'lineargrowth'){\n prediction <- function(years, series){\n\n # cut data\n data_table <- data.frame(occ = series[as.character(years)], year = (years - min(years) + 1))\n \n # run model\n model <- glm(occ ~ year, data = data_table, family = 'quasibinomial')\n \n # create predicted values\n predicted <- plogis(predict(model))\n names(predicted) <- years\n \n # build results\n results <- data.frame(predicted[1], predicted[length(predicted)], row.names = NULL)\n colnames(results) <- as.character(c(min(years), max(years)))\n results$change = (results[,2] - results[,1]) / results[,1]\n \n return(results)\n \n }\n\n res_tab <- do.call(rbind, apply(X = occ_it, MARGIN = 1, years = years, FUN = prediction))\n \n } # end of loop for linear growth rate\n \n \n if(change == 'difference'){\n first <- years[1]\n last <- years[length(years)]\n res_tab <- data.frame(occ_it[, colnames(occ_it) == first],\n occ_it[, colnames(occ_it) == last],\n row.names = NULL)\n colnames(res_tab) <- as.character(c(min(years), max(years)))\n res_tab$change = res_tab[,2] - res_tab[,1]\n } # end of loop for simple difference\n \n \n if(change == 'percentdif'){\n first <- years[1]\n last <- years[length(years)]\n res_tab <- data.frame(occ_it[, colnames(occ_it) == first],\n occ_it[, colnames(occ_it) == last],\n row.names = NULL)\n\n ## edit 0 in the first year with some small value to prevent Infinite trends\n res_tab[,1][res_tab[,1] == 0] <- 0.0000001\n \n colnames(res_tab) <- as.character(c(min(years), max(years)))\n res_tab$change = ((res_tab[,2] - res_tab[,1])/res_tab[,1])*100\n } # end of loop for percentage difference\n \n \n if(change == 'growthrate'){\n nyr <- length(years)\n first <- years[1]\n last <- years[length(years)]\n res_tab <- data.frame(occ_it[, colnames(occ_it) == first],\n occ_it[, colnames(occ_it) == last],\n row.names = NULL)\n\n ## edit 0 in the first year with some small value to prevent Infinite trends\n res_tab[,1][res_tab[,1] == 0] <- 0.0000001\n \n colnames(res_tab) <- as.character(c(min(years), max(years)))\n res_tab$change = (((res_tab[,2]/res_tab[,1])^(1/nyr))-1)*100\n } # end of loop for growth rate\n\n # return the mean, quantiles, and the data\n return(list(mean = mean(res_tab$change),\n median = median(res_tab$change),\n CIs = quantile(res_tab$change, probs = c(0.025, 0.975)),\n data = res_tab))\n\n}\n", "meta": {"hexsha": "fe50502785ea1014258e0848cab48c07db43ea46", "size": 7614, "ext": "r", "lang": "R", "max_stars_repo_path": "R/occurrenceChange.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/occurrenceChange.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-05-28T13:47:25.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-06T09:06:41.000Z", "max_forks_repo_path": "R/occurrenceChange.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2474226804, "max_line_length": 185, "alphanum_fraction": 0.6334384029, "num_tokens": 1978, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3414180135742385}} {"text": "# plotrocs_query200.r - plot ROCs for different methods on query200 data set\n#\n# Alex Stivala, March 2009\n#\n# Plot ROC curves for different methods on the query200 query set in\n# ASTRAL SCOP 95% sequence nr data set\n#\n# Requires the ROCR package from CRAN (developed with version 1.0-2)\n# (ROCR in turn requires gplots, gtools, gdata)\n#\n# Run this on the output of e.g. tsevalfn.py with the -l option,\n# it is a table with one column of scores from classifier, and second\n# column of true class label (0 or 1)\n#\n# The citation for the ROCR package is\n# Sing et al 2005 \"ROCR: visualizing classifier performance in R\"\n# Bioinformatics 21(20):3940-3941\n# \n# \n# $Id: plotrocs_query200.r 2376 2009-05-14 01:40:32Z astivala $\n \n\nlibrary(ROCR)\n\n#\n# globals\n#\n\ncolorvec=c('deepskyblue4','brown','red','turquoise','blue','purple','green','cyan','gray20','magenta','darkolivegreen2','midnightblue','magenta3','darkseagreen','violetred3','darkslategray3')\nltyvec=c(1,2,4,5,6,1,2,1,5,6,1,2,4,5,6,1,2)\nnamevec=c('QP tableau search (norm2)','VAST','SHEBA','TableauSearch (norm2)', 'TOPS')\nslrtabs=c('query200/norm2/query200_roc.slrtab','../other_results/vast/vast_query200_res/vast_query200_roc.slrtab','../other_results/sheba/query200-pdbstyle-sel-gs-bib-95-1.73/sheba_query200_roc.slrtab','../other_results/TableauSearch/query200/norm2/tabsearch_query200_roc.slrtab','../other_results/tops/query200/tops_query200_roc.slrtab')\n\n#\n# functions\n#\n\n#\n# Return the ROCR performance object for plotting ROC curve\n#\n# Parameters:\n# tab : data frame with score and label columns\n# \n# Return value:\n# ROCR performance object with FPR and TPR for plotting ROC curve\n#\ncompute_perf <- function(tab)\n{\n # tab is a data frame with score and label columns\n pred <- prediction(tab$score, tab$label)\n perfroc <- performance(pred, measure=\"tpr\",x.measure=\"fpr\")\n return(perfroc)\n}\n\n#\n# main\n#\n\n\n\n# EPS suitable for inserting into LaTeX\n\npostscript('rocs_query200.eps',\n onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\nfor (i in 1:length(slrtabs)) {\n tab <- read.table(slrtabs[i], header=TRUE)\n perfroc <- compute_perf(tab)\n plot(perfroc, lty=ltyvec[i], col=colorvec[i], add=(i>1),\n downsampling=0.5,\n# main='ROC for 200 queries in ASTRAL SCOP 95% sequence identity nonredundant data set' # no title since including in paper with caption\n )\n}\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec)\n#lines(c(0,1),c(0,1),type='l',lty=3)\ndev.off()\n\n", "meta": {"hexsha": "1c914350fa14fb4d8a297d0815ccc315f21a7be8", "size": 2523, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/plotrocs_query200.r", "max_stars_repo_name": "stivalaa/cuda_satabsearch", "max_stars_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/plotrocs_query200.r", "max_issues_repo_name": "stivalaa/cuda_satabsearch", "max_issues_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/plotrocs_query200.r", "max_forks_repo_name": "stivalaa/cuda_satabsearch", "max_forks_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3461538462, "max_line_length": 338, "alphanum_fraction": 0.7118509711, "num_tokens": 794, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316860482762, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3414180069061559}} {"text": "\nsum.2.pos.number <- function(x, y){\n if(x > 0 & y > 0){\n z <- x + y\n return(z)\n }else{\n stop('at least one value is negative')\n }\n}\n\n{\n a <- 3\n b <- -2\n \n c <- sum.2.pos.number(a, b)\n print(c)\n \n d <- a + as.numeric(c)\n print(d)\n}\n", "meta": {"hexsha": "7fc8d22063e0dfd7e4c8c76be8674a074e027b76", "size": 248, "ext": "r", "lang": "R", "max_stars_repo_path": "5.exercices.corrections/0.previous.courses/functions.building.r", "max_stars_repo_name": "GuillaumeBal/r.training.mnhn", "max_stars_repo_head_hexsha": "45d86ad46f53d60fe63c6f1c4f5f048ab1526687", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "5.exercices.corrections/0.previous.courses/functions.building.r", "max_issues_repo_name": "GuillaumeBal/r.training.mnhn", "max_issues_repo_head_hexsha": "45d86ad46f53d60fe63c6f1c4f5f048ab1526687", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "5.exercices.corrections/0.previous.courses/functions.building.r", "max_forks_repo_name": "GuillaumeBal/r.training.mnhn", "max_forks_repo_head_hexsha": "45d86ad46f53d60fe63c6f1c4f5f048ab1526687", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.8095238095, "max_line_length": 42, "alphanum_fraction": 0.4677419355, "num_tokens": 97, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.3413975113934068}} {"text": "# This file is Wei Wu's code in participating\n# Kaggle \"American Epilepsy Society Seizure Prediction Challenge\" Competition\n# \n# Copyright (c) 2014, Wei Wu\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy \n# of this software and associated documentation files (the \"Software\"), to deal \n# in the Software without restriction, including without limitation the rights \n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell \n# copies of the Software, and to permit persons to whom the Software is \n# furnished to do so, subject to the following conditions: \n# \n# The above copyright notice and this permission notice shall be included in all \n# copies or substantial portions of the Software. \n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR \n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, \n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE \n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER \n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, \n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS \n# IN THE SOFTWARE. \n\nrequire(R.matlab)\n\nread_one_matfile <- function (filename) {\n\tretval=list()\n\ta=readMat(filename)\n\tretval[[\"mat\"]]=a[[1]][[1]]\n\tretval[[\"seconds\"]]=as.numeric(a[[1]][[2]])\n\tretval[[\"freq\"]]=as.numeric(a[[1]][[3]])\n\tretval[[\"labels\"]]=unlist(a[[1]][[4]])\n\tif (length(a[[1]]) > 4) {\n\t\tretval[[\"seq\"]]=as.numeric(a[[1]][[5]])\n\t} else {\n\t\tretval[[\"seq\"]]=-1\n\t}\n\trm(a);gc()\n\tretval\n}\n\ndown_sampling <- function (data, factor = 1) {\n\tif (factor <= 1) {\n\t\treturn(data)\n\t}\n\tnot_multi=is.null(nrow(data))\n\tif (not_multi) {\n\t\tnewlen=floor(length(data)/factor)\n\t\tif (newlen > 1) {\n\t\t\tnewdata=sapply(1:newlen, FUN=function(i, d, f) { mean(d[round((i-1)*f+1):round(i*f)]) },\n\t\t\t\td=data, f=factor)\n\t\t}\n\t} else {\n\t\tnewlen=floor(ncol(data)/factor)\n\t\tif (newlen > 1) {\n\t\t\tnewdata=sapply(1:newlen,\n\t\t\t\tFUN=function(i, d, f) {\n\t\t\t\t\tcol_start=round((i-1)*factor+1);\n\t\t\t\t\tcol_end=round(i*factor);\n\t\t\t\t\tif (col_end > col_start) {\n\t\t\t\t\t\trowMeans(d[,col_start:col_end])\n\t\t\t\t\t} else {\n\t\t\t\t\t\td[,col_start]\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t\td=data, f=factor)\n\t\t} else {\n\t\t\tnewdata=data\n\t\t}\n\t}\n\tnewdata\n}\n\ngen_features_onearray <- function (indata, pre, freqs) {\n\tfeats=c()\n\tsumheads=c('Min','1stQrt','Med','Mean','3rdQrt','Max')\n\tsumindex=c(4,6)\n\tsumheads=sumheads[sumindex]\n\ta=summary(indata)[sumindex]\n\theads=paste0(pre,\"_\",sumheads)\n\tfeats[heads]=a\n\tfeats[paste0(pre,\"_\",\"Sd\")]=sd(abs(indata))\n\ta=summary(abs(indata))[sumindex]\n\theads=paste0(pre, \"Amp\", \"_\",sumheads)\n\tfeats[heads]=a\n\tfeats[paste0(pre,\"Amp_Sd\")]=sd(abs(indata))\n\tfreqs=freqs[1:length(indata)]\n#\tcat(paste(\"In gen_features_onearray(), before fft size is:\",paste(dim(indata),collapse=','),\"\\n\"))\n#\tflush.console()\n#\tb=Sys.time()\n\tif (dolog == 1) {\n\t\tmyfft=log(abs(fft(indata))+1)\n\t} else {\n\t\tmyfft=abs(fft(indata))\n\t}\n\ta=summary(myfft)[sumindex]\n#\tcat(paste(\"After fft, time used:\",format(Sys.time()-b),\"\\n\"));flush.console()\n\theads=paste0(pre,\"FFT_\",sumheads)\n\tfeats[heads]=a\n\tfeats[paste0(pre,\"FFT_\",\"Sd\")]=sd(myfft)\n\tfeats[paste0(pre,\"FFT_\",\"MaxFreq\")]=freqs[which.max(myfft)]\n\tif (addFFT == 1 || addFFT == 2) {\n\t\tif (addFFT == 2) {\n\t\t\tmyfft=(myfft-mean(myfft))/sd(myfft)\n\t\t}\n\t\tmyfft=myfft[1:round(FFTratio*length(myfft))]\n\t\tmyfft=down_sampling(myfft, round(length(myfft)/FFTavglen))\n\t\tcolnames=paste0(pre,\"FFT_\",sprintf(\"%02d\",1:length(myfft)))\n\t\tfeats[colnames]=myfft\n\t}\n\tfeats\n}\n\ngen_features_oneseries <- function (indata, pre, freqs) {\n\tfeats=c()\n\tindata=as.vector(indata)\n\tdelta1=rep(0,length(indata))\n\tdelta1[1:(length(indata)-1)]=indata[2:length(indata)]-indata[1:(length(indata)-1)]\n\tdelta2=rep(0,length(indata))\n\tdelta2[1:(length(delta1)-2)]=delta1[2:(length(delta1)-1)]-delta1[1:(length(delta1)-2)]\n\tfeats=c(feats, gen_features_onearray(indata, paste0(pre,\"\"), freqs))\n\tfeats=c(feats, gen_features_onearray(delta1, paste0(pre,\"Del1\"), freqs))\n\tfeats=c(feats, gen_features_onearray(delta2, paste0(pre,\"Del2\"), freqs))\n\tfeats\n}\n\ngen_features_onefile <- function (indata, f, t) {\n\tb=Sys.time()\n\tfeats=c()\n\tchans=nrow(indata)\n\tnc=ncol(indata)\n\tif (round(f*t) != nc) {\n\t\tstop(paste(\"Stop in gen_features_onefile(), f*t=\",f*t,\"!= number of data points,\",nc))\n\t}\n\tfreqs=(1:nc)*1/t\n\tfor (i in 1:chans) {\n\t\tprename=paste0(\"chan\",i)\n\t\tfeats=c(feats,gen_features_oneseries (indata[i,], pre=prename, freqs))\n\t}\n\tfnames=names(feats)\n#\tcat(paste(\"Total number of features before global features\",length(feats),format(Sys.time()-b),\"\\n\"))\n#\tflush.console()\n#\tuniq_postfix=grep('^chan[0-9]*FFT_[0-9][0-9]',fnames,invert=T,value=T)\n\tuniq_postfix=fnames\n\tuniq_postfix=unique(gsub('^chan[0-9]*','',uniq_postfix))\n\tfor (mypost in uniq_postfix) {\n\t\tpre=paste0(\"All\",mypost)\n\t\tmynames=grep(paste0('^chan[0-9]*',mypost),fnames,value=T)\n\t\tmydata=feats[mynames]\n\t\tsumheads=c('Min','1stQrt','Med','Mean','3rdQrt','Max')\n\t\tsumindex=c(4,6)\n\t\tsumheads=sumheads[sumindex]\n\t\ta=summary(mydata)[sumindex]\n\t\theads=paste0(pre, \"_\",sumheads)\n\t\tfeats[heads]=a\n\t\tfeats[paste0(pre,\"_\",\"Sd\")]=sd(abs(mydata))\n\t}\n#\tcat(paste(\"Total number of features after AllCh features\",length(feats),format(Sys.time()-b),\"\\n\"))\n#\tflush.console()\n\tfeats=c(feats,gen_features_oneseries (colMeans(indata), pre=\"chanAvg\", freqs))\n#\tcat(paste(\"Total number of features after chAvg features\",length(feats),format(Sys.time()-b),\"\\n\"))\n#\tflush.console()\n\tmydata=indata\n\tfor (del in 0:2) {\n\t\tif (del != 0) {\n\t\t\tmydata_tmp=matrix(rep(0,length(indata)), nrow=nrow(indata))\n\t\t\tmydata_tmp[,1:(ncol(mydata)-del)]=mydata[,2:(ncol(mydata)-del+1)]-mydata[,1:(ncol(mydata)-del)]\n\t\t\tmydata=mydata_tmp\n\t\t\trm(mydata_tmp)\n\t\t\tpre=paste0(\"Del\",del)\n\t\t} else {\n\t\t\tmydata=indata\n\t\t\tpre=\"\"\n\t\t}\n\t\tcov=cov(t(mydata))\n#\t\tcat(paste(\"Size of data is:\", paste(dim(mydata),collapse=','),\n#\t\t\t\"Size of covar is:\",paste(dim(cov),collapse=','),\"\\n\"));flush.console()\n\t\tcovarray=abs(cov[upper.tri(cov)])\n\t\tfeats[paste0(pre,'CovMean')]=mean(covarray)\n\t\tfeats[paste0(pre,'CovSd')]=sd(covarray)\n\t\tcovarray=covarray[order(covarray,decreasing=T)]\n\t\tfeats[paste0(pre,'CovMax')]=covarray[1]\n\t\tfeats[paste0(pre,'Cov2nd')]=covarray[2]\n\t\tfeats[paste0(pre,'Cov3rd')]=covarray[3]\n#\t\tcat(paste(\"Before FFT\", format(Sys.time()-b),\"\\n\"));flush.console()\n\t\tmyfft=matrix(rep(0,length(mydata)),nrow=nrow(mydata))\n\t\tfor (i in 1:chans) {\n\t\t\tif (dolog == 1) {\n\t\t\t\tmyfft[i,]=log(abs(fft(mydata[i,]))+1)\n\t\t\t} else {\n\t\t\t\tmyfft[i,]=abs(fft(mydata[i,]))\n\t\t\t}\n\t\t}\n#\t\tcat(paste(\"After FFT\", format(Sys.time()-b),\"\\n\"));flush.console()\n\t\tpre=paste0(pre,\"FFT\")\n\t\tcov=cov(t(myfft))\n#\t\tcat(paste(\"Size of FFT data is:\", paste(dim(myfft),collapse=','),\n#\t\t\t\"Size of FFT covar is:\",paste(dim(cov),collapse=','),\"\\n\"));flush.console()\n\t\tcovarray=abs(cov[upper.tri(cov)])\n\t\tfeats[paste0(pre,'CovMean')]=mean(covarray)\n\t\tfeats[paste0(pre,'CovSd')]=sd(covarray)\n\t\tcovarray=covarray[order(covarray,decreasing=T)]\n\t\tfeats[paste0(pre,'CovMax')]=covarray[1]\n\t\tfeats[paste0(pre,'Cov2nd')]=covarray[2]\n\t\tfeats[paste0(pre,'Cov3rd')]=covarray[3]\n#\t\tgc()\n#\t\tcat(paste(\"Total number of features after Del\",del,\"covar features\",length(feats),\n#\t\t\tformat(Sys.time()-b),\"\\n\"))\n#\t\tflush.console()\n\t}\n#\tcat(paste(\"Total number of features after covar features\",length(feats),format(Sys.time()-b),\"\\n\"))\n\tflush.console()\n\tfeats\n}\n\nsplit_mat <- function (mymat, nsplit) {\n\tif (ncol(mymat) %% nsplit != 0) {\n\t\tstop(paste(\"In split_mat(), the nsplit\",nsplit,\"and column number\",ncol(mymat),\"do not match to even blocks.\"))\n\t}\n\tretdata=list()\n\tmysize=ncol(mymat) / nsplit\n\tfor (i in 1:nsplit) {\n\t\tretdata[[i]]=mymat[,((i-1)*mysize+1):(i*mysize)]\n\t} \n\tretdata\n}\n\ntypes=c('Dog_1','Dog_2','Dog_3','Dog_4','Dog_5','Patient_1','Patient_2')\ntypenums=c(Dog_1=1,Dog_2=2,Dog_3=3,Dog_4=4,Dog_5=5,Patient_1=6,Patient_2=7)\n\nnv=rep(NA,length(types)*1000)\nsummary_df=data.frame(fname=nv, second=nv,freq=nv,ch=nv,label=nv,seq=nv,datalen=nv, time1=nv,time2=nv)\nfixfreq=0\nfixfreq=200\ndolog=1\naddFFT=2\nFFTratio=0.2\nnsplit=10\t# original clip is 10min=600sec, nsplit=10, new length=60sec, nsplit=20, newlength=30sec, nsplit=40 newlength=15sec\nFFTavglen=24\ndo_holdout=1\n\ninum=1\nfor (mytype in types) {\nbegTime0=Sys.time()\n# datadir=paste0(\"Data/\",mytype)\ndatadir=paste0(\"F:/Work/Kaggle/SeizurePrediction/Data/\",mytype)\nholdoutdir=paste0(datadir,\"_holdout\")\n\ninterfiles=dir(datadir, \".*_interictal_segment_.*.mat\")\nprefiles=dir(datadir, \".*_preictal_segment_.*.mat\")\ntestfiles=dir(datadir, \".*_test_segment_.*.mat\")\nif (mytype %in% c(\"Dog_1\",\"Dog_2\",\"Dog_3\",\"Dog_4\") && do_holdout) {\n holdoutfiles=dir(holdoutdir, \".*_holdout_segment_.*.mat\")\n}\n\ntrainmat=NULL\nbegTime=Sys.time()\nisub=1\nfor (myfile in interfiles) {\n\tbegTime0=Sys.time()\n\tfilename=paste0(datadir,\"/\",myfile)\n\tretval=read_one_matfile(filename)\n\torimat=retval[[\"mat\"]]\n\tseconds=retval[[\"seconds\"]]\n\tfreq=retval[[\"freq\"]]\n\tlabels=retval[[\"labels\"]]\n\tseq=retval[[\"seq\"]]\n\tif (fixfreq > 0 && fixfreq < freq) {\n\t\torimat=down_sampling(orimat, freq/fixfreq)\n\t\tfreq=fixfreq\n\t}\n\tnewmats=split_mat(orimat, nsplit)\n\tfor (mi in 1:nsplit) {\n\t\tmymat=newmats[[mi]]\n\t\tf=gen_features_onefile(mymat, freq, seconds/nsplit)\n\t\tf['seq']=seq\n\t\tf['flag']=0\n\t\tf['id']=typenums[mytype]*100000+isub*100+mi\n\t\tf['si']=mi\n\t\tif(is.null(trainmat)) {\n\t\t\ttrainmat=f\n\t\t} else {\n\t\t\ttrainmat=rbind(trainmat, f)\n\t\t}\n\t}\n\tendTime=Sys.time()\n\ttime1=difftime(endTime, begTime0, units=\"secs\")\n\ttime2=difftime(endTime, begTime, units=\"secs\")\n\tcat(paste(inum, myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),\n\t\tretval[[\"labels\"]][1], retval[[\"seq\"]],ncol(orimat), time1, time2,\"\\n\"))\n\tsummary_df[inum,]=c(myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),\n\t\t\t\tretval[[\"labels\"]][1], retval[[\"seq\"]],ncol(orimat), time1, time2)\n\tinum=inum+1\n\tisub=isub+1\n\tflush.console()\n}\n\nfor (myfile in prefiles) {\n\tbegTime0=Sys.time()\n\tfilename=paste0(datadir,\"/\",myfile)\n\tretval=read_one_matfile(filename)\n\torimat=retval[[\"mat\"]]\n\tseconds=retval[[\"seconds\"]]\n\tfreq=retval[[\"freq\"]]\n\tlabels=retval[[\"labels\"]]\n\tseq=retval[[\"seq\"]]\n\tif (fixfreq > 0 && fixfreq < freq) {\n\t\torimat=down_sampling(orimat, freq/fixfreq)\n\t\tfreq=fixfreq\n\t}\n\tnewmats=split_mat(orimat, nsplit)\n\tfor (mi in 1:nsplit) {\n\t\tmymat=newmats[[mi]]\n\t\tf=gen_features_onefile(mymat, freq, seconds/nsplit)\n\t\tf['seq']=seq\n\t\tf['flag']=1\n\t\tf['id']=typenums[mytype]*100000+isub*100+mi\n\t\tf['si']=mi\n\t\tif(is.null(trainmat)) {\n\t\t\ttrainmat=f\n\t\t} else {\n\t\t\ttrainmat=rbind(trainmat, f)\n\t\t}\n\t}\n\tendTime=Sys.time()\n\ttime1=difftime(endTime, begTime0, units=\"secs\")\n\ttime2=difftime(endTime, begTime, units=\"secs\")\n\tcat(paste(inum, myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),retval[[\"labels\"]][1],\n\t\t\tretval[[\"seq\"]],ncol(orimat),time1, time2,\"\\n\"))\n\tsummary_df[inum,]=c(myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),\n\t\t\t\tretval[[\"labels\"]][1], retval[[\"seq\"]],ncol(orimat),time1, time2)\n\tinum=inum+1\n\tisub=isub+1\n\tflush.console()\n}\n\ntestmat=NULL\nfor (myfile in testfiles) {\n\tbegTime0=Sys.time()\n\tfilename=paste0(datadir,\"/\",myfile)\n\tretval=read_one_matfile(filename)\n\torimat=retval[[\"mat\"]]\n\tseconds=retval[[\"seconds\"]]\n\tfreq=retval[[\"freq\"]]\n\tlabels=retval[[\"labels\"]]\n\tseq=retval[[\"seq\"]]\n\tif (fixfreq > 0 && fixfreq < freq) {\n\t\torimat=down_sampling(orimat, freq/fixfreq)\n\t\tfreq=fixfreq\n\t}\n\tnewmats=split_mat(orimat, nsplit)\n\tfor (mi in 1:nsplit) {\n\t\tmymat=newmats[[mi]]\n\t\tf=gen_features_onefile(mymat, freq, seconds/nsplit)\n\t\tf['id']=typenums[mytype]*100000+isub*100+mi\n\t\tf['si']=mi\n\t\tif(is.null(testmat)) {\n\t\t\ttestmat=f\n\t\t} else {\n\t\t\ttestmat=rbind(testmat, f)\n\t\t}\n\t}\n\tendTime=Sys.time()\n\ttime1=difftime(endTime, begTime0, units=\"secs\")\n\ttime2=difftime(endTime, begTime, units=\"secs\")\n\tcat(paste(inum, myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),retval[[\"labels\"]][1],\n\t\tretval[[\"seq\"]],ncol(orimat),time1, time2,\"\\n\"))\n\tsummary_df[inum,]=c(myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),\n\t\t\t\tretval[[\"labels\"]][1], retval[[\"seq\"]],ncol(orimat),time1, time2)\n\tinum=inum+1\n\tisub=isub+1\n\tflush.console()\n}\n\nif (mytype %in% c(\"Dog_1\",\"Dog_2\",\"Dog_3\",\"Dog_4\") && do_holdout) {\nholdoutmat=NULL\nfor (myfile in holdoutfiles) {\n\tbegTime0=Sys.time()\n\tfilename=paste0(holdoutdir,\"/\",myfile)\n\tretval=read_one_matfile(filename)\n\torimat=retval[[\"mat\"]]\n\tseconds=retval[[\"seconds\"]]\n\tfreq=retval[[\"freq\"]]\n\tlabels=retval[[\"labels\"]]\n\tseq=retval[[\"seq\"]]\n\tif (fixfreq > 0 && fixfreq < freq) {\n\t\torimat=down_sampling(orimat, freq/fixfreq)\n\t\tfreq=fixfreq\n\t}\n\tnewmats=split_mat(orimat, nsplit)\n\tfor (mi in 1:nsplit) {\n\t\tmymat=newmats[[mi]]\n\t\tf=gen_features_onefile(mymat, freq, seconds/nsplit)\n\t\tf['id']=typenums[mytype]*100000+isub*100+mi\n\t\tf['si']=mi\n\t\tif(is.null(holdoutmat)) {\n\t\t\tholdoutmat=f\n\t\t} else {\n\t\t\tholdoutmat=rbind(holdoutmat, f)\n\t\t}\n\t}\n\tendTime=Sys.time()\n\ttime1=difftime(endTime, begTime0, units=\"secs\")\n\ttime2=difftime(endTime, begTime, units=\"secs\")\n\tcat(paste(inum, myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),retval[[\"labels\"]][1],\n\t\tretval[[\"seq\"]],ncol(orimat),time1, time2,\"\\n\"))\n\tsummary_df[inum,]=c(myfile,retval[[\"seconds\"]],retval[[\"freq\"]],length(retval[[\"labels\"]]),\n\t\t\t\tretval[[\"labels\"]][1], retval[[\"seq\"]],ncol(orimat),time1, time2)\n\tinum=inum+1\n\tisub=isub+1\n\tflush.console()\n}\n}\n# datadir=paste0(\"Data/\",mytype)\ndatadir=paste0(\"F:/Work/Kaggle/SeizurePrediction/Kaggle-SeizureDetection-Official/Data/\",mytype)\nif (addFFT>=1) {\n\tfreq=paste0(freq,\"FFT\",addFFT,\"-\",FFTratio)\n}\nfilename=paste0(datadir,\"/\",mytype,\"Split\",nsplit,\"_Freq\",freq,ifelse(dolog==1,paste0(\"Log\",dolog),\"\"),\".RData\")\nsummary_df=summary_df[!is.na(summary_df$fname),]\nif (mytype %in% c(\"Dog_1\",\"Dog_2\",\"Dog_3\",\"Dog_4\") && do_holdout) {\n save(trainmat, testmat, holdoutmat, summary_df, file=filename, compress='bzip2')\n} else {\n save(trainmat, testmat, summary_df, file=filename, compress='bzip2')\n}\nendTime=Sys.time()\ncat(paste(\"Total time used for\",mytype,\":\",format(endTime-begTime0),\"\\n\"))\ncat(paste(\"File saved:\",filename,\"\\n\"))\nflush.console()\n}\n\nsummary_df=summary_df[!is.na(summary_df$fname),]\n\ntotaltime=(Sys.time()-begTime)\nprint(tail(summary_df, n=20))\nprint(totaltime)\n\n\n", "meta": {"hexsha": "467ac2e87a5b5aff9dee6fc196ea68621945c5b1", "size": 14201, "ext": "r", "lang": "R", "max_stars_repo_path": "read_data_split.r", "max_stars_repo_name": "wei-wu-nyc/Kaggle-SeizureDetection-Official", "max_stars_repo_head_hexsha": "f89b532e615100333118d3fa798f42a61ef00c50", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2015-03-11T14:26:44.000Z", "max_stars_repo_stars_event_max_datetime": "2017-12-15T15:32:43.000Z", "max_issues_repo_path": "read_data_split.r", "max_issues_repo_name": "wei-wu-nyc/Kaggle-SeizureDetection-Official", "max_issues_repo_head_hexsha": "f89b532e615100333118d3fa798f42a61ef00c50", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "read_data_split.r", "max_forks_repo_name": "wei-wu-nyc/Kaggle-SeizureDetection-Official", "max_forks_repo_head_hexsha": "f89b532e615100333118d3fa798f42a61ef00c50", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2016-09-03T07:38:02.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-19T08:59:22.000Z", "avg_line_length": 32.3485193622, "max_line_length": 125, "alphanum_fraction": 0.6779100063, "num_tokens": 4947, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3413975039337175}} {"text": "# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html\n# https://covidtracking.com/api/\n# https://covid.ourworldindata.org\n\nlibrary(rnaturalearth)\nlibrary(rnaturalearthdata)\nlibrary(rgeos)\nlibrary(data.table)\nlibrary(countrycode)\nlibrary(viridisLite)\nlibrary(gganimate)\nlibrary(gifski)\nlibrary(tidyverse)\nlibrary(lmtest)\nlibrary(WDI)\nlibrary(plotly)\nlibrary(USAboundaries)\nlibrary(sf)\nlibrary(fuzzyjoin)\nlibrary(tigris)\n\n# install.packages('tigris')\n\n# install.packages(c('tidyverse', 'data.table', 'rnaturalearth', 'rgeos', \n# 'WDI', 'lmtest', 'gifski', 'gganimate', 'viridisLite'))\n# install.packages(c('plotly', 'rnaturalearthdata', 'USAboundaries', 'countrycode', 'quantmod'))\n# install.packages(\"remotes\")\n# remotes::install_github(\"ropensci/USAboundariesData\")\n\nus_cities_shp = us_cities() %>%\n mutate(\n province_state_city = paste0(city, ', ', state_abbr)\n )\n\nus_counties_shp = us_counties() %>%\n mutate(\n province_state_city = ifelse(state_abbr == 'LA', paste0(name, ' Parish, ', state_abbr), paste0(name, ' County, ', state_abbr))\n )\nus_counties_shp$geometry[1]\n\ncounty_shp = tigris::counties()\n\nus_covid_data = read_csv('https://covidtracking.com/api/us/daily.csv') %>%\n mutate(\n date = as.Date(as.character(date), format = '%Y%m%d'),\n location = 'United States',\n location_type = 'country', \n data_source = 'covidtracking.com',\n location_key = paste(location, location_type, data_source, sep = '|')\n ) %>%\n rename(\n total_cases = positive,\n total_deaths = death,\n total_tests = total\n ) %>%\n mutate(\n new_cases = total_cases - lag(total_cases, 1),\n new_deaths = total_deaths - lag(total_deaths, 1)\n )\n\nus_states_covid_data = read_csv('http://covidtracking.com/api/states/daily.csv') %>%\n mutate(\n date = as.Date(as.character(date), format = '%Y%m%d'),\n location_type = 'US State', \n data_source = 'covidtracking.com'\n ) %>%\n rename(\n total_cases = positive,\n total_deaths = death,\n total_tests = total,\n location = state\n ) %>%\n mutate(\n location_key = paste(location, location_type, data_source, sep = '|'),\n new_cases = total_cases - lag(total_cases, 1),\n new_deaths = total_deaths - lag(total_deaths, 1)\n )\n\nall_covid_data_stacked = bind_rows(us_covid_data, us_states_covid_data) %>%\n arrange(location_key, date) %>%\n pivot_longer(cols = c('new_cases', 'new_deaths', 'total_cases', 'total_deaths'),\n names_to = c('measure'), values_to = 'value') %>%\n data.table()\n\n\n\n# compute first differences, pct changes, etc. \nall_covid_data_diffs = \n all_covid_data_stacked[, {\n lag_value = lag(value, 1)\n diff_value = value - lag_value\n pct_change_value = diff_value / lag_value\n \n lag_4_value = lag(value, 4)\n lag_5_value = lag(value, 5)\n lag_6_value = lag(value, 6)\n \n list(\n date = date,\n value = value,\n lag_value = lag_value, \n diff_value = diff_value, \n pct_change_value = pct_change_value,\n lag_4_value = lag_4_value, \n lag_5_value = lag_5_value,\n lag_6_value = lag_6_value, \n first_value = date[date == min(date)],\n last_value = date[date == max(date)],\n value_past_100 = min(date[value >= 100])\n )\n \n }, by = list(location_key, location, location_type, data_source, measure)] %>%\n pivot_wider(\n id_cols = c('location_key', 'location', 'location_type','data_source', 'date'),\n names_from = 'measure', \n values_from = c('value', 'lag_value', 'diff_value', \n 'pct_change_value', 'lag_4_value', 'lag_5_value',\n 'lag_6_value')\n )\n\ncase_100_dates = group_by(all_covid_data_diffs, location_key) %>%\n summarize(\n date_case_100 = min(date[value_total_cases >= 100])\n )\n\nall_covid_data_diffs_dates = left_join(all_covid_data_diffs, case_100_dates) %>%\n filter(date >= date_case_100) %>%\n mutate(\n days_since_case_100 = as.numeric(date - date_case_100)\n )\n\ndaily_growth_stats = group_by(all_covid_data_diffs_dates, days_since_case_100) %>%\n summarize(\n n_countries = n_distinct(location),\n median_daily_change_cases = median(pct_change_value_total_cases, na.rm = T),\n mean_daily_change_cases = mean(pct_change_value_total_cases, na.rm = T),\n mean_daily_change_deaths = mean(pct_change_value_total_deaths, na.rm = T),\n median_daily_change_deaths = median(pct_change_value_total_deaths, na.rm = T)\n )\n\n\nggplot(all_covid_data_diffs_dates %>% filter(location_type == 'US State'), aes(days_since_case_100, pct_change_value_total_cases)) +\n geom_line(aes(alpha = days_since_case_100, group = location)) +\n geom_line(data = filter(all_covid_data_diffs_dates, location == 'United States', data_source == 'covidtracking.com'),\n colour = 'red', size = 1) +\n scale_y_continuous(limits = c(0, 1)) +\n # geom_line(data = daily_growth_stats %>% filter(n_countries >= 5), aes(x = days_since_case_100, y = median_daily_change_cases), size = 1, colour = 'blue')\n scale_alpha(range = c(0.1, 1)) \n \n\nggplotly(a)\n\nggplot(all_covid_data_diffs_dates, aes(pct_change_value_total_cases)) +\n stat_density(position = 'identity')\nsummary(all_covid_data_diffs_dates$pct_change_value_total_cases)\n\n\n# (7000/104)^(1/15) - 1\n\n# are new cases just the first difference? mostly \nwith(all_covid_data_diffs, summary(diff_value_total_cases - value_new_cases))\n\n\ntop_countries = \n group_by(all_covid_data_diffs, location) %>%\n summarize(\n last_value_cases = tail(value_total_cases, 1)\n ) %>%\n arrange(-last_value_cases) %>% head(20)\n\n\nall_covid_data_diffs_top = filter(all_covid_data_diffs_dates, location %in% top_countries$location)\n\n\na = ggplot(all_covid_data_diffs_top, aes(date, value_total_cases, colour = location)) + \n geom_line(size = 1) +\n geom_point()\n\nggplotly(a)\n# estimate crude r0 \ncrude_r0 = lm(value_total_cases ~ lag_5_value_total_cases, data = all_covid_data_diffs)\nsummary(crude_r0)\n\nnames(all_covid_data_diffs)\nhead(all_covid_data_diffs)\nnames(all_covid_data_diffs)\nggplot(all_covid_data_diffs, aes())\n\nacf(all_covid_data_diffs$value_new_cases)\nacf(all_covid_data_diffs$value_new_deaths)\n\nccf(all_covid_data_diffs$value_new_cases, all_covid_data_diffs$value_new_deaths)\nccf(all_covid_data_diffs$value_new_deaths, all_covid_data_diffs$value_new_cases)\n\nccfvalues = ccf(all_covid_data_diffs$value_new_cases, all_covid_data_diffs$value_new_deaths)\ngrangertest(value_new_deaths ~ value_new_cases, order = 10, data = all_covid_data_diffs)\ngrangertest(value_new_cases ~ value_new_deaths, order = 10, data = all_covid_data_diffs)\n\nggplot(all_covid_data_diffs %>% filter(location != 'World'), aes(value_new_cases, value_new_deaths)) +\n geom_point() +\n stat_smooth(method = 'lm')\n\n\nggplot(all_covid_data_diffs %>% filter(location == 'United States', date >= as.Date('2020-02-01')), \n aes(date, value)) +\n facet_wrap(~measure, scales = 'free_y') +\n geom_line()\n # geom_point() +\n # geom_linerange(aes(ymin = 0, ymax = pct_change_value))\n\n\n\n\nsetdiff(world$geounit, latest_data$location)\nsetdiff(latest_data$location, world$geounit)\n\n\nhead(all_data)\n\n#### get world bank data on each country #####\n\n\n#### map everything ####\n\nworld <- ne_countries(scale = \"medium\", returnclass = \"sf\")\nggplot(data = world) +\n geom_sf()\n", "meta": {"hexsha": "3355fc7b50b4e83cdf113e94581b3d103e6e5639", "size": 7220, "ext": "r", "lang": "R", "max_stars_repo_path": "Projects/COVID-19/scripts/archive/analyze_world_covid_data.r", "max_stars_repo_name": "vishalbelsare/Public_Policy", "max_stars_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-03-09T01:39:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-08T19:11:44.000Z", "max_issues_repo_path": "Projects/COVID-19/scripts/archive/analyze_world_covid_data.r", "max_issues_repo_name": "vishalbelsare/Public_Policy", "max_issues_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-06-03T20:11:43.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-07T00:03:58.000Z", "max_forks_repo_path": "Projects/COVID-19/scripts/archive/analyze_world_covid_data.r", "max_forks_repo_name": "vishalbelsare/Public_Policy", "max_forks_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-04T22:48:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-09T14:00:22.000Z", "avg_line_length": 31.6666666667, "max_line_length": 157, "alphanum_fraction": 0.7130193906, "num_tokens": 2033, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583270090337583, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.34135106852648045}} {"text": "library(gdata) # load gdata package\nlibrary(ggplot2)\nlibrary(dplyr)\n\ndf <- read.csv(\"./benchmark.csv\")\ndf <- df %>% mutate(time = timeNs / 1e9)\n\nplotSubset <- function (vec, y_axis_scale = 1) {\n ggplot(df %>% filter(type %in% vec)) + scale_x_discrete() + geom_point(aes(x = input, y = time, col = type)) +\n scale_y_continuous(trans = \"log10\", labels = scales::comma) +\n labs(y = \"Time in seconds\", x = \"Input ID\")\n # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n \n \n}\n\n\nplotSubset(c(\"naive\", \"qbf\", \"muser\"))\nplotSubset(c(\"naive\", \"muser\"))\nplotSubset(c(\"naive\", \"qbf\"))\nplotSubset(c(\"muser\", \"qbf\"))\n", "meta": {"hexsha": "3a02f6c1c0266fff25ed98f646fba4178ea83656", "size": 652, "ext": "r", "lang": "R", "max_stars_repo_path": "plots/Plots.r", "max_stars_repo_name": "fendor/sat-solving", "max_stars_repo_head_hexsha": "9a3011b86e8a4cfe19aa407825606d2d3161081a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plots/Plots.r", "max_issues_repo_name": "fendor/sat-solving", "max_issues_repo_head_hexsha": "9a3011b86e8a4cfe19aa407825606d2d3161081a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plots/Plots.r", "max_forks_repo_name": "fendor/sat-solving", "max_forks_repo_head_hexsha": "9a3011b86e8a4cfe19aa407825606d2d3161081a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6363636364, "max_line_length": 112, "alphanum_fraction": 0.6165644172, "num_tokens": 207, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3413510595401536}} {"text": "require(\"ggplot2\")\nrequire(\"tidyr\")\n\nresults <- read.csv(\"results.csv\",header=TRUE,quote=\"\\\"\")\nresults$means <- rowMeans(results[, c(\"controls\",\"understandability\",\"difficulty\",\"stress_level\",\"aesthetics\")])\nresults$pre_tune <- sapply(results$pre_tune, function(x) (if (x == \"true\") {TRUE} else {FALSE}))\n\nhead(results)\n\nmeanVGeneral <- ggplot(results, aes(x=means,y=general_gameplay,color=pre_tune, shape=first_played)) +\n geom_point(size=3) +\n scale_x_continuous(breaks=c(0,2,4,6,8,10), limits = c(0, 10)) +\n scale_y_continuous(breaks=c(0,2,4,6,8,10), limits = c(0, 10)) +\n labs(x=\"Mean of initial 5 aspects\"\n ,y=\"Overall gameplay\"\n ,color=\"Pre-Tune?\"\n ,shape=\"First version played\")\n\nggsave(meanVGeneral, file=\"meanVGeneral.png\")\n\nresults2 <- results\nresults2$pre_tune <- NULL\nresults2$first_played <- NULL\nresults2$means <- NULL\nresults2$general_gameplay <- NULL\nresults2 <- gather(results2, key=\"key\", value=\"value\")\nprint(results2)\n\nboxes <- ggplot(results2, aes(x=key, y=value)) +\n geom_boxplot() +\n labs(x=\"Aspect of gameplay\", y=\"Similarity Score\") +\n ggtitle(\"Ratings including pre-tune\")\n\nggsave(boxes, file=\"boxes.png\")\n\n\nresults3 <- subset(results, pre_tune == TRUE)\nresults3$pre_tune <- NULL\nresults3$first_played <- NULL\nresults3$means <- NULL\nresults3$general_gameplay <- NULL\nresults3 <- gather(results3, key=\"key\", value=\"value\")\nprint(results3)\n\nboxesPost <- ggplot(results3, aes(x=key, y=value)) +\n geom_boxplot() +\n labs(x=\"Aspect of gameplay\", y=\"Similarity Score\") +\n ggtitle(\"Ratings post-tune\")\n\nggsave(boxesPost, file=\"boxesPost.png\")\n\n\n", "meta": {"hexsha": "e1adc8259cc562e5c2a34d3aacc11885c902a52a", "size": 1728, "ext": "r", "lang": "R", "max_stars_repo_path": "Docs/Testing/Processing/proc.r", "max_stars_repo_name": "ScottSedgwick/Dissertation", "max_stars_repo_head_hexsha": "b222ad865f480a15f34818718287283a1ba39dfe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-03-25T00:19:26.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-01T22:40:12.000Z", "max_issues_repo_path": "Docs/Testing/Processing/proc.r", "max_issues_repo_name": "ScottSedgwick/Dissertation", "max_issues_repo_head_hexsha": "b222ad865f480a15f34818718287283a1ba39dfe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Docs/Testing/Processing/proc.r", "max_forks_repo_name": "ScottSedgwick/Dissertation", "max_forks_repo_head_hexsha": "b222ad865f480a15f34818718287283a1ba39dfe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-01T23:37:58.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-01T23:37:58.000Z", "avg_line_length": 32.6037735849, "max_line_length": 112, "alphanum_fraction": 0.6481481481, "num_tokens": 477, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3413510595401536}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\n\nsetwd(\"/Users/mengmengjiang/all datas/conductivity\")\n\nlibrary(xlsx)\n\n# reading datasets\n\n# compare qd and conductivitys\n# Gly1,18nl/min - 1.8kv; 180nl/min - 2kv;\n# Gly2,18nl/min - 2kv; 180nl/min - 1.8kv;\n#\n\nk1<-read.xlsx(\"gly1.xlsx\",sheetName=\"qd1-18\",header=TRUE)\nk2<-read.xlsx(\"gly1.xlsx\",sheetName=\"qd2-20\",header=TRUE)\n\nk3<-read.xlsx(\"gly2.xlsx\",sheetName=\"qd1-20\",header=TRUE)\nk4<-read.xlsx(\"gly2.xlsx\",sheetName=\"qd2-18\",header=TRUE)\n\n#\nyan<-c(\"red\",\"blue\",\"black\",\"green3\")\npcc<-c(0,1,2,5)\n\n#error\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\n if(length(x) != length(y) | length(y) !=length(lower) | length(lower) !=\n length(upper))\n stop(\"vectors must be same length\")\n arrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n#\nplot(k1$fv,k1$deva, col=0,xlab = expression(italic(f[\"v\"]) (Hz)),\n ylab = expression(italic(d[\"d\"]) (um)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(0,4000),ylim=c(0,60))\n\n mtext(\"Small droplets of Q-V\",3,line=-1.2,font=2,cex=1)\n\npoints(k1$fv,k1$deva,col=yan[1],lty=2,pch=pcc[1],cex=0.8)\npoints(k2$fv,k2$deva,col=yan[2],lty=2,pch=pcc[2],cex=0.8)\npoints(k3$fv,k3$deva,col=yan[3],lty=2,pch=pcc[3],cex=0.8)\npoints(k4$fv,k4$deva,col=yan[4],lty=2,pch=pcc[4],cex=0.8)\n\n\nerror.bar(k1$fv,k1$deva,k1$stdd/2,col=yan[1])\nerror.bar(k2$fv,k2$deva,k2$stdd/2,col=yan[2])\nerror.bar(k3$fv,k3$deva,k3$stdd/2,col=yan[3])\nerror.bar(k4$fv,k4$deva,k4$stdd/2,col=yan[4])\n\nfit1=lm(k1$deva~k1$fv)\nfit2=lm(k2$deva~k2$fv)\nfit3=lm(k3$deva~k3$fv)\nfit4=lm(k4$deva~k4$fv)\n\nabline(fit1,col=yan[1],lwd=1.5,lty=2)\nabline(fit2,col=yan[2],lwd=1.5,lty=2)\nabline(fit3,col=yan[3],lwd=1.5,lty=2)\nabline(fit4,col=yan[4],lwd=1.5,lty=2)\n\nleg<-c(\"Gly1:18nl/min-1.8kv\",\"Gly1:180nl/min-2kv\",\"Gly2:18nl/min-2kv\",\"Gly2:180nl/min-1.8kv\")\n\nlegend(\"topright\",legend=leg,col=yan,pch=pcc,lwd=1.5,inset=.01,cex=0.8,bty=\"n\")\n\ninter<-c(coef(fit1)[1],coef(fit2)[1],coef(fit3)[1],coef(fit4)[1])\nslope<-c(coef(fit1)[2],coef(fit2)[2],coef(fit3)[2],coef(fit4)[2])\n", "meta": {"hexsha": "a16eba989b87ac808d6498f531a63d5804beeb16", "size": 2147, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap6/fig6-19.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap6/fig6-19.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap6/fig6-19.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5303030303, "max_line_length": 104, "alphanum_fraction": 0.6567303214, "num_tokens": 959, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3413510595401535}} {"text": "print(paste0(Sys.time(), \" --- Describers profiles (for appendix)\"))\r\n\r\n# For describer metrics, see df01.r in the cleaning scripts.\r\n# Data for describers is \"basefile\" + \"-describers_2.csv\"\r\n\r\n# Filter describers with dod/dob and\r\n# those who described at least 1 syn or valid sp.\r\n\r\ndes <- df_describers[\r\n (ns_spp_N + syn_spp_N) >= 1 & \r\n !(is.na(dod.describer) | is.na(dob.describer)), \r\n c(\r\n \"full.name.of.describer\", \"last.name\", \r\n \"dob.describer\", \"dod.describer\", \"max\", \"spp_N\", \r\n \"spp_per_pub_mean\", \"n_pubs\",\r\n \"spp_per_pub_mean_20y\", \"n_pubs_20y\",\r\n \"spp_per_pub_mean_5y\", \"n_pubs_5y\",\r\n \"n_spp_20y\", \"n_spp_5y\"\r\n )\r\n]\r\n\r\n# missing variables:\r\n# spp_per_pub_mean, n_pubs, \r\n# n_spp_20y, \r\n\r\n# n_spp_5y, pub_years\r\n# spp_per_pub_mean_20y, n_pubs_20y, \r\n# spp_per_pub_mean_5y, n_pubs_5y, \r\n\r\ndes$years_last_pub_death <- des$dod.describer - des$max\r\ndes$age_at_death <- des$dod.describer - des$dob.describer\r\n\r\nmround <- function(x,base) base*round(x/base)\r\n\r\ndes$date.century <- substr(\r\n as.character((des$dod.describer - des$dob.describer)/2+des$dob.describer),\r\n 1, 3\r\n)\r\n\r\ndes[((des$dod.describer - des$dob.describer)/2+des$dob.describer) <1000]\r\n\r\ndes$date.century <- mround(as.numeric(des$date.century), 5)\r\ndes$date.century <- paste0(des$date.century, \"0s\")\r\n\r\ndes$check_young <- ifelse(des$age_at_death >=40, \"T\", \"F\") # young\r\ndes$check_few.spp <- ifelse(des$spp_N <= 12, \"T\", \"F\") # few spp (than median)\r\ndes$check_few.spp.pub <- ifelse(des$spp_per_pub_mean <=10, \"T\", \"F\") # few sp/pb\r\ndes$check_few.pub <- ifelse(des$n_pubs <=3, \"T\", \"F\") # few pub\r\n\r\ntable(is.na(des$check_young))\r\ntable(is.na(des$check_few.spp))\r\ntable(is.na(des$check_few.spp.pub))\r\n\r\ndes$check_few.pub.near.death <- ifelse(\r\n is.na(des$n_spp_20y), \"T\", ifelse(\r\n des$n_spp_20y/des$spp_N >=0.8, \"F\", \"T\")\r\n) # few pub\r\n\r\nsummary(des$age_at_death)\r\n\r\ndes1 <- des[, \r\n list(\r\n names=paste0(full.name.of.describer, collapse=\"; \"),\r\n med_age_at_death=median(as.numeric(age_at_death), na.rm=T),\r\n med_spp_N = median(spp_N, na.rm=T),\r\n med_spp_per_pub_mean = median(spp_per_pub_mean, na.rm=T),\r\n med_prop_20y = median(n_spp_20y/spp_N, na.rm=T)\r\n ),\r\n by = c(\r\n \"check_young\", \"check_few.spp\", \r\n \"check_few.spp.pub\", \"check_few.pub.near.death\"\r\n )\r\n]\r\n\r\ndes2 <- des[, list(.N), by=c(\r\n \"check_young\", \"check_few.spp\", \r\n \"check_few.spp.pub\", \"check_few.pub.near.death\", \r\n \"date.century\"\r\n)]\r\n\r\ndes3 <- dcast(\r\n des2, \r\n \r\n check_young + check_few.spp.pub + \r\n check_few.pub.near.death + check_few.spp ~ date.century, \r\n\r\n value.var=\"N\"\r\n)\r\n\r\ndes3[is.na(des3)] <- 0\r\n\r\ndes4 <- merge(\r\n des1, des3, by=c(\r\n \"check_young\", \"check_few.spp.pub\", \r\n \"check_few.pub.near.death\", \"check_few.spp\"\r\n )\r\n)\r\n\r\nfwrite(\r\n des4, paste0(dir_table_ch1, '2019-10-02-taxonomist-one-large-mono.csv'),\r\n row.names=F\r\n)\r\n\r\n# Checks\r\ntable(is.na(des$check_young))\r\ntable(is.na(des$check_few.spp.pub))\r\ntable(is.na(des$check_few.spp))\r\ntable(is.na(des$check_few.pub.near.death))\r\n\r\n# Copy and paste into report\r\n\r\n# Table 1\r\n\r\n# Caption\r\ndim(des)\r\ndim(des[check_young == \"T\"])\r\ndim(des[check_young == \"T\" & check_few.pub.near.death == \"T\"])\r\nauth_throughout_life <- table(des[check_young==\"T\",]$check_few.pub.near.death)\r\nprop.table(auth_throughout_life)*100\r\n\r\n# First row\r\ndes[check_young == \"F\", \r\n list(\r\n med_age_at_death=median(age_at_death, na.rm=T),\r\n med_spp_N = median(as.numeric(spp_N), na.rm=T),\r\n med_spp_per_pub_mean = median(spp_per_pub_mean, na.rm=T),\r\n med_prop_20y = median(n_spp_20y/spp_N, na.rm=T),\r\n N = .N\r\n ),\r\nby=c(\"check_young\")]\r\n\r\n# Second row\r\ndes[check_young == \"T\" & check_few.spp.pub == \"F\", \r\n list(\r\n med_age_at_death=as.numeric(median(age_at_death, na.rm=T)),\r\n med_spp_N = as.numeric(median(spp_N, na.rm=T)),\r\n med_spp_per_pub_mean = as.numeric(median(spp_per_pub_mean, na.rm=T)),\r\n med_prop_20y = as.numeric(median(n_spp_20y/spp_N, na.rm=T)),\r\n N = .N\r\n ),\r\n by=c(\"check_few.pub.near.death\")\r\n]\r\n\r\n# Group that died young, few publications, few species, average number of species per pub\r\n# Group that did not die young, few publications, few species, average number of species per pub\r\n# Group that did not die young, large number of publications, large number of species, large number of species per pub\r\n# Group that did not die young, few publications, large number of species {N and when}\r\n\r\n", "meta": {"hexsha": "c70c1d88ee40974bed59ab3503c347764cc76a72", "size": 4563, "ext": "r", "lang": "R", "max_stars_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/si-methods-table-1.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/si-methods-table-1.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020-08-31-jsa-type-v2-ch1/01-main/si-methods-table-1.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6241610738, "max_line_length": 119, "alphanum_fraction": 0.6399298707, "num_tokens": 1492, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.3413510595401535}} {"text": "training = read.csv(\"UCI HAR Dataset/train/X_train.txt\", sep=\"\", header=FALSE)\ntraining[,562] = read.csv(\"UCI HAR Dataset/train/Y_train.txt\", sep=\"\", header=FALSE)\ntraining[,563] = read.csv(\"UCI HAR Dataset/train/subject_train.txt\", sep=\"\", header=FALSE)\n\ntesting = read.csv(\"UCI HAR Dataset/test/X_test.txt\", sep=\"\", header=FALSE)\ntesting[,562] = read.csv(\"UCI HAR Dataset/test/Y_test.txt\", sep=\"\", header=FALSE)\ntesting[,563] = read.csv(\"UCI HAR Dataset/test/subject_test.txt\", sep=\"\", header=FALSE)\n\nactivityLabels = read.csv(\"UCI HAR Dataset/activity_labels.txt\", sep=\"\", header=FALSE)\n\n# Read features and make the feature names better suited for R with some substitutions\nfeatures = read.csv(\"UCI HAR Dataset/features.txt\", sep=\"\", header=FALSE)\nfeatures[,2] = gsub('-mean', 'Mean', features[,2])\nfeatures[,2] = gsub('-std', 'Std', features[,2])\nfeatures[,2] = gsub('[-()]', '', features[,2])\n\n# Merge training and test sets together\nallData = rbind(training, testing)\n\n# Get only the data on mean and std. dev.\ncolsWeWant <- grep(\".*Mean.*|.*Std.*\", features[,2])\n# First reduce the features table to what we want\nfeatures <- features[colsWeWant,]\n# Now add the last two columns (subject and activity)\ncolsWeWant <- c(colsWeWant, 562, 563)\n# And remove the unwanted columns from allData\nallData <- allData[,colsWeWant]\n# Add the column names (features) to allData\ncolnames(allData) <- c(features$V2, \"Activity\", \"Subject\")\ncolnames(allData) <- tolower(colnames(allData))\n\ncurrentActivity = 1\nfor (currentActivityLabel in activityLabels$V2) {\n allData$activity <- gsub(currentActivity, currentActivityLabel, allData$activity)\n currentActivity <- currentActivity + 1\n}\n\nallData$activity <- as.factor(allData$activity)\nallData$subject <- as.factor(allData$subject)\n\ntidy = aggregate(allData, by=list(activity = allData$activity, subject=allData$subject), mean)\ntidy[,90] = NULL\ntidy[,89] = NULL\nwrite.table(tidy, \"tidy.txt\", sep=\"\\t\")\n", "meta": {"hexsha": "0ed64e7d6127dda1365b0190d9d4c4043481debe", "size": 1941, "ext": "r", "lang": "R", "max_stars_repo_path": "anothersimplecode.r", "max_stars_repo_name": "vj-ug/Data-Analysis-of-Human-Activity-", "max_stars_repo_head_hexsha": "1d3c1af0cae7db8599facc993772a7105997d3f3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-07-19T19:45:08.000Z", "max_stars_repo_stars_event_max_datetime": "2015-07-19T19:45:08.000Z", "max_issues_repo_path": "anothersimplecode.r", "max_issues_repo_name": "vj-ug/Data-Analysis-of-Human-Activity-", "max_issues_repo_head_hexsha": "1d3c1af0cae7db8599facc993772a7105997d3f3", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "anothersimplecode.r", "max_forks_repo_name": "vj-ug/Data-Analysis-of-Human-Activity-", "max_forks_repo_head_hexsha": "1d3c1af0cae7db8599facc993772a7105997d3f3", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.1333333333, "max_line_length": 94, "alphanum_fraction": 0.7223080886, "num_tokens": 523, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369904, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.34135105055382653}} {"text": "library(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\n\n# read in the first n_files (+1) CSV files\nlink = \"https://github.com/gumdropsteve/datasets/raw/master/zillow/properties_2016_part_0.csv\"\nn_files = 4\nfor(n in 0:n_files){\n link = substr(link, start=1, stop=80)\n link = paste0(link, n, '.csv')\n # if it's the first file, create the dataframe\n if(n == 0){\n properties = read.csv(link)\n }else{\n # not the first file, missing column names, add it to the bottom of the dataframe\n new_properties = read.csv(link)\n colnames(new_properties) = colnames(properties)\n properties = rbind(properties, new_properties)\n }\n}\nproperties = as_tibble(properties)\nproperties\n\n# load in train data (transactions)\nlink <- 'https://github.com/gumdropsteve/datasets/raw/master/zillow/train_2016_v2.csv'\ntransactions <- read.csv(link)\ntransactions <- as_tibble(transactions)\ntransactions\n\n# check for matches in properties and transactions data\nmatches = inner_join(transactions, properties, by='parcelid')\n\n# convert all NULL values to 0\nmatches[is.na(matches)] = 0\nmatches\n\n# load sample submissions file\nsample_submissions = read.csv('https://raw.githubusercontent.com/gumdropsteve/datasets/master/zillow/sample_submission.csv')\n\n\n# reference: https://www.kaggle.com/philippsp/exploratory-analysis-zillow\n# rename columns so they're more understandable\nproperties <- properties %>% rename(\n id_parcel = parcelid,\n build_year = yearbuilt,\n area_basement = basementsqft,\n area_patio = yardbuildingsqft17,\n area_shed = yardbuildingsqft26, \n area_pool = poolsizesum, \n area_lot = lotsizesquarefeet, \n area_garage = garagetotalsqft,\n area_firstfloor_finished = finishedfloor1squarefeet,\n area_total_calc = calculatedfinishedsquarefeet,\n area_base = finishedsquarefeet6,\n area_live_finished = finishedsquarefeet12,\n area_liveperi_finished = finishedsquarefeet13,\n area_total_finished = finishedsquarefeet15, \n area_unknown = finishedsquarefeet50,\n num_unit = unitcnt, \n num_story = numberofstories, \n num_room = roomcnt,\n num_bathroom = bathroomcnt,\n num_bedroom = bedroomcnt,\n num_bathroom_calc = calculatedbathnbr,\n num_bath = fullbathcnt, \n num_75_bath = threequarterbathnbr, \n num_fireplace = fireplacecnt,\n num_pool = poolcnt, \n num_garage = garagecarcnt, \n region_county = regionidcounty,\n region_city = regionidcity,\n region_zip = regionidzip,\n region_neighbor = regionidneighborhood, \n tax_total = taxvaluedollarcnt,\n tax_building = structuretaxvaluedollarcnt,\n tax_land = landtaxvaluedollarcnt,\n tax_property = taxamount,\n tax_year = assessmentyear,\n tax_delinquency = taxdelinquencyflag,\n tax_delinquency_year = taxdelinquencyyear,\n zoning_property = propertyzoningdesc,\n zoning_landuse = propertylandusetypeid,\n zoning_landuse_county = propertycountylandusecode,\n flag_fireplace = fireplaceflag, \n flag_tub = hashottuborspa,\n quality = buildingqualitytypeid,\n framing = buildingclasstypeid,\n material = typeconstructiontypeid,\n deck = decktypeid,\n story = storytypeid,\n heating = heatingorsystemtypeid,\n aircon = airconditioningtypeid,\n architectural_style= architecturalstyletypeid\n)\ntransactions <- transactions %>% rename(\n id_parcel = parcelid,\n date = transactiondate\n)\n\nproperties <- properties %>% \n mutate(tax_delinquency = ifelse(tax_delinquency==\"Y\",1,0),\n flag_fireplace = ifelse(flag_fireplace==\"Y\",1,0),\n flag_tub = ifelse(flag_tub==\"Y\",1,0))\n\n# view the renamed dataframe\nView(properties)\n\n\n# visualize missing values\nmissing_values <- properties %>% summarize_each(funs(sum(is.na(.))/n()))\n\nmissing_values <- gather(missing_values, key=\"feature\", value=\"missing_pct\")\nmissing_values %>% \n ggplot(aes(x=reorder(feature, -missing_pct), y=missing_pct)) +\n geom_bar(stat=\"identity\",fill=\"red\")+\n coord_flip()+theme_bw()\n\n\n# when were the houses built?\nproperties %>% \n ggplot(aes(x=build_year))+geom_line(stat=\"density\", color=\"red\", size=1.2)+theme_bw()\n\n\n# extract census tractnumber and block number from rawcensustractandblock column\n# note: unfinished\n# converting from python: https://github.com/eswar3/Zillow-prediction-models/blob/master/Step%202a-Approach1.ipynb\nmatches$rawcensustractandblock\nmatches$census_tractnumber <- substring(matches$rawcensustractandblock,\n first=5,\n last=11)\nmatches$block_number = substring(matches$rawcensustractandblock, first=12)\nmatches$block_number \nmatches$block_number = paste0(substring(matches$block_number, first=1, last=5), '.', substring(matches$block_number, first=6))\n#df_train['block_number']=df_train['block_number'].apply(lambda x: int(round(float(x),0)) )\n#df_train['block_number']=df_train['block_number'].apply(lambda x: str(x).ljust(4,'0') )\n", "meta": {"hexsha": "a5c9bb209ed4c1ce8357568e0075b925b73f2211", "size": 4771, "ext": "r", "lang": "R", "max_stars_repo_path": "stack_2/day_10/zillow_munging.r", "max_stars_repo_name": "mpHarm88/learn_datascience", "max_stars_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-12-08T09:23:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T19:02:17.000Z", "max_issues_repo_path": "stack_2/day_10/zillow_munging.r", "max_issues_repo_name": "mpHarm88/learn_datascience", "max_issues_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-12-08T05:07:24.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-08T08:00:40.000Z", "max_forks_repo_path": "stack_2/day_10/zillow_munging.r", "max_forks_repo_name": "mpHarm88/learn_datascience", "max_forks_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-12-10T08:25:19.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-14T13:14:52.000Z", "avg_line_length": 35.0808823529, "max_line_length": 126, "alphanum_fraction": 0.7520435967, "num_tokens": 1266, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369904, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.3413510505538265}} {"text": "##BarBIQ_final_fitting_OD.r\n\n#####Author#####\n#Jianshi Frank Jin\n\n#####Version#####\n#V1.001\n#2018.12.02\n\n###Before running, please change the following information which labeled by \"CHECK1-4\".\n\nsetwd(\"/yourpath\") ### \"CHECK1\" path of your data\nlibrary (plotrix)\n\ngroup1 <- read.table(\"output_filename_step15\", ## \"CHECK2\" your data file name\n header=T, sep=\"\")\n\noutputname<- \"EDrops.txt\" ## \"CHECK3\" your output file name\n\nwrite.table(t(c(\"ID\", \"EDrop\", \"SE\")),file=outputname,sep=\"\\t\",col.names = F, row.names = F, quote = F)\n\n## ID: the index ID;\n## EDrop: operational droplet (OD);\n## SE: standard error.\n\n### For the sample SX\nfor (i in c(\"S1\",\"S2\")) ## \"CHECK4\" Indexes you want to analysis\n {\n sample<- i\n sampleA<-paste(c(sample,\"A\"),collapse = \"_\")\n sampleB<-paste(c(sample,\"B\"),collapse = \"_\")\n sampleO<-paste(c(sample,\"O\"),collapse = \"_\")\n group2 <- group1[(group1[,sampleA]>0 & group1[,sampleB]>0), ]\n x=log10(group2[,sampleA]*group2[,sampleB])\n y=group2[,sampleO]\n\ndata <- cbind(x,y)\ndata <- data.frame(data)\n### Calculate the median\nstock<-c()\nfor(i in seq(from=-0.4, to=10, by=0.2))\n {\n low=i;\n up=i+0.4;\n mid=low+0.2;\n data1<-data[data$x >low, ];\n data1<-data1[data1$x <= up, ];\n # print(nrow(data1));\n if(nrow(data1)>2)\n {\n mean<-mean(data1[,2]);\n media<-median(data1[,2]);\n stock=rbind(stock,c(mid,mean,media));\n }\n }\nstock <- data.frame(stock)\ncolnames(stock)=c(\"mid\",\"mean\",\"media\")\nstock$media<-log10(stock$media)\nstock$mean<-log10(stock$mean)\nstock[stock == \"-Inf\"]<- -2\n\nselect=stock[stock$media > -2, ];\nx=select$mid\ny=select$media\n\n## Fitting\nmodel <- lm(y ~ 1 + offset(x))\nprint(p<-summary(model))\npredicted.intervals <- predict(model,data.frame(x=x),interval='confidence',\n level=0.99)\n \n# EDrop <- read.table(outputname, header=F, sep=\"\", colClasses=c(\"character\"))\nnewdata<-t(c(sample, -model$coeff,p$coefficients[,2]))\n# EDrop<-rbind(EDrop,newdata)\nwrite.table(newdata,file=outputname,sep=\"\\t\",col.names = F, row.names = F, quote = F, append = T)\n \n }\n\n##end##\n\n\n\n\n\n\n", "meta": {"hexsha": "940c73310503e19607756b4bddcdd555cfca8f7f", "size": 2151, "ext": "r", "lang": "R", "max_stars_repo_path": "BarBIQ_code_1_0_0.0/BarBIQ_final_fitting_OD.r", "max_stars_repo_name": "Shiroguchi-Lab/BarBIQ", "max_stars_repo_head_hexsha": "25a3219c67a4ad7f1c1dbcc03b0f207106f8eee5", "max_stars_repo_licenses": ["Artistic-1.0-Perl"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-24T03:32:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T03:32:56.000Z", "max_issues_repo_path": "BarBIQ_code_1_0_0.0/BarBIQ_final_fitting_OD.r", "max_issues_repo_name": "Shiroguchi-Lab/BarBIQ", "max_issues_repo_head_hexsha": "25a3219c67a4ad7f1c1dbcc03b0f207106f8eee5", "max_issues_repo_licenses": ["Artistic-1.0-Perl"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "BarBIQ_code_1_0_0.0/BarBIQ_final_fitting_OD.r", "max_forks_repo_name": "Shiroguchi-Lab/BarBIQ", "max_forks_repo_head_hexsha": "25a3219c67a4ad7f1c1dbcc03b0f207106f8eee5", "max_forks_repo_licenses": ["Artistic-1.0-Perl"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.011627907, "max_line_length": 103, "alphanum_fraction": 0.6034402603, "num_tokens": 653, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.34130062433844754}} {"text": "library(raster)\nlibrary(Matrix)\nlibrary(dplyr)\nlibrary(parallel)\noptions(future.globals.maxSize = 4000 * 1024^2)\n\nlabelling<- function(mat){\n tmp<-data.frame(matrix(ncol=3, nrow=0))\n colnames(tmp)<-c(\"x\",\"y\",\"name\")\n for(i in (1:max(mat))){\n ind<-center_spark(mat,i)\n tmp<-rbind(tmp, c(ind[2],nrow(mat)-ind[1], i))\n }\n return(tmp)\n}\naffichage <- function(img_mat_fun,mat){\n res_img<-matrix(0,dim(img_mat_fun)[1],dim(img_mat_fun)[2])\n if(max(mat)!=0){\n for(i in (1:max(mat))){\n ind<-which(mat==i, arr.ind=TRUE)\n for(el in 1:nrow(ind)){\n res_img[ind[el,1],ind[el,2]]<-img_mat_fun[ind[el,1],ind[el,2]]\n }\n \n }\n }\n return(res_img)\n}\n\n\ncenter_spark<-function(mat_spark, sparknumber){\n ind<-which(mat_spark==sparknumber, arr.ind=TRUE)\n x<-mean(ind[,1])\n y<-mean(ind[,2])\n return(c(x,y))\n}\nresults_dir<-paste0(\"results/\", file_name)\nif(!dir.exists(results_dir)){\n dir.create(results_dir)\n}\n\n\nimg=raster(paste0(\"data/\",file_name,\".tif\"))\nimg_mat=as.matrix(img)\n\nfiltre<- function(pixel){\n if (pixel<(moyenne+85)){\n return(0L)\n }else{\n return(1L)\n }\n}\n\nmoyenne=mean(img_mat)\nimg_mat_ft=matrix(unlist(mclapply(img_mat, filtre, mc.cores=3)), nrow=dim(img_mat)[1], ncol=dim(img_mat)[2])\nimg_mat_ft[dim(img_mat_ft)[1],dim(img_mat_ft)[2]]=1\n\nfind_transient<- function(mat){\n transient_x_coord<-c()\n for(ix in 1:nrow(mat)){\n if(sum(mat[ix,]!=0)/ncol(mat)>0.5){\n transient_x_coord<-c(transient_x_coord,ix)\n }\n }\n transients<-c()\n for(el in transient_x_coord){\n transients<-c(transients, el:(min(el+30,nrow(mat))))\n }\n transients<-unique(transients)\n return(transients)\n}\n\n\n\nfind_blanks<- function(mat){\n blanks<-c()\n for(ix in 1:nrow(mat)){\n if(sum(mat[ix,]!=0)/ncol(mat)<0.16){\n blanks<-c(blanks,ix)\n }\n }\n return(blanks)\n}\n\n\n\ntransient <- find_transient(img_mat_ft)\n\n\nimg_mat_ft[transient,]=0\nblanks<-find_blanks(img_mat_ft)\nblanks<-sort(unique(c(1,nrow(img_mat_ft),blanks)))\n\nwhile(max(blanks[2:length(blanks)]-blanks[1:length(blanks)-1])>200){\n ind<-which.max(blanks[2:length(blanks)]-blanks[1:length(blanks)-1])\n ind2<-which.min(rowSums(img_mat_ft[(blanks[ind]+1):(blanks[ind+1]-1),]))\n blanks<<-c(blanks,(ind2+blanks[ind]))\n blanks<<-sort(blanks)\n print((ind2+blanks[ind]))\n}\n\n\nanalyse<-function(img_mat_ft_fun,img_mat_fun,renum2){\n img_mat_ft_fun[dim(img_mat_ft_fun)[1],dim(img_mat_ft_fun)[2]]=1\n img_mat_ft_fun <- Matrix(img_mat_ft_fun, sparse = TRUE) \n \n sm = summary(img_mat_ft_fun)\n d <- dist(sm, \"manhattan\")\n if(!(dim(as.matrix(d))[[1]]<=1 | dim(as.matrix(d))[[2]]<=1)){\n gr = cutree(hclust(d, \"single\"), h = 1)\n mat_sparks=sparseMatrix(i = sm[, \"i\"], j = sm[, \"j\"], x = gr)\n rm(gr)\n }else {\n mat_sparks<- Matrix(0,nrow=nrow(img_mat_ft_fun),ncol=ncol(img_mat_ft_fun), sparse = TRUE)\n }\n\n rm(sm)\n rm(d)\n \n renum=1\n for(i in (1:max(mat_sparks))){\n if(sum(mat_sparks==i)<40 ){\n mat_sparks[mat_sparks==i]=0\n }else{\n mat_sparks[mat_sparks==i]=renum\n renum=renum+1\n }\n }\n mat_sparks=as.matrix(mat_sparks)\n \n \n \n \n \n \n \n \n distance_pixel<-function(x1,y1,x2,y2){\n distance<-sqrt((x1-x2)^2+(y1-y2)^2)\n return(distance)\n }\n \n \n subspark<- function(mat, sparknumber, mat_img_fun){\n mat[mat!=sparknumber]=0\n mat[mat==sparknumber]=1\n spark<-affichage(mat_img_fun, mat)\n moy_sp=mean(spark)\n std_sp=sd(spark)\n filtre_sp<- function(pixel){\n if (pixel>(moy_sp+3*std_sp)){\n return(1L)\n }else{\n return(0L)\n }\n }\n peaks=matrix(unlist(mclapply(spark, filtre_sp, mc.cores=3)), nrow=dim(spark)[1], ncol=dim(spark)[2])\n peaks=Matrix(peaks, sparse = TRUE)\n sm = summary(peaks)\n d <<- dist(sm, \"manhattan\")\n if(!(dim(as.matrix(d))[[1]]<=1 | dim(as.matrix(d))[[2]]<=1)){\n gr = cutree(hclust(d, \"single\"), h = 1)\n res=sparseMatrix(i = sm[, \"i\"], j = sm[, \"j\"], x = gr)\n rm(gr)\n }else {\n res<- Matrix(0,nrow=nrow(peaks),ncol=ncol(peaks), sparse = TRUE)\n }\n\n rm(d)\n rm(sm)\n renum=1\n for(i in (1:max(res))){\n if(sum(res==i)<15 ){\n res[res==i]=0\n }else{\n res[res==i]=renum\n renum=renum+1\n }\n }\n res=as.matrix(res)\n if(max(res)==0){\n return(mat)\n }else{\n newspark_center<-list()\n for(newspark in 1:max(res)){\n newspark_center[length(newspark_center)+1]<-list(center_spark(res,newspark))\n }\n inds<-which(mat==1, arr.ind=TRUE)\n for(i in 1:nrow(inds)){\n distances<-c()\n for(newspark in 1:max(res)){\n distances<-c(distances,(distance_pixel(newspark_center[[newspark]][1],newspark_center[[newspark]][2],inds[i,1],inds[i,2])))\n }\n mat[inds[i,1],inds[i,2]]<-which(distances==min(distances), arr.ind=TRUE)\n }\n return(mat)\n }\n }\n \n \n mat_subsparks<-matrix(0,dim(mat_sparks)[1],dim(mat_sparks)[2])\n if(max(mat_sparks)!=0){\n for(spark in 1:max(mat_sparks)){\n ressub<-subspark(mat_sparks,spark,img_mat_fun)\n for(i in 1:max(ressub)){\n ind<-which(ressub==i, arr.ind=TRUE)\n renum2=renum2+1\n for(el in 1:nrow(ind)){\n mat_subsparks[ind[el,1],ind[el,2]]<-renum2\n \n }\n }\n }\n }\n \n return(list(mat_subsparks,renum2))\n}\n\nres_assemblee<-matrix(0,dim(img_mat_ft)[1],dim(img_mat_ft)[2])\nif(blanks[1]!=1){\n temp<-analyse(img_mat_ft[1:blanks[1],],img_mat[1:blanks[1],],0)\n res_assemblee[1:blanks[1],]<-temp[[1]]\n \n}else{\n temp<-c(0,0,0)\n}\n\nfor(i in 1:(length(blanks)-1)){\n print(i)\n print((length(blanks)-1))\n if((blanks[i]+1)!=(blanks[i+1])){\n temp<-analyse(img_mat_ft[blanks[i]:blanks[i+1],],img_mat[blanks[i]:blanks[i+1],],temp[[2]])\n res_assemblee[blanks[i]:blanks[i+1],]<-temp[[1]]\n }\n}\n\nrenum=1\nfor(i in (1:max(res_assemblee))){\n if(sum(res_assemblee==i)>1000 ){\n res_assemblee[res_assemblee==i]=0\n }else{\n res_assemblee[res_assemblee==i]=renum\n renum=renum+1\n }\n}\n\n\n# resa = raster(res_assemblee)\n# extent(resa) = c(1,ncol(res_assemblee),1,nrow(res_assemblee))\n# plot(resa)\n# textimg_mat<-labelling(res_assemblee)\n# colnames(textimg_mat)<-c(\"x\",\"y\",\"name\")\n# text(x = textimg_mat$x, y = textimg_mat$y, labels = textimg_mat$name,cex=0.5)\n\n\n\n\n\nabc<-affichage(img_mat,res_assemblee)\nabc[transient,]=0\nif(max(res_assemblee)!=0){\n try({\n png(paste0(results_dir,\"/plot.png\"),height=nrow(abc)*0.3,width = ncol(abc), unit=\"px\")\n resa=raster(abc)\n extent(resa) = c(1,ncol(abc),1,nrow(abc))\n par(mar = c(1, 1, 1, 1)) \n plot(resa)\n textimg_mat<-labelling(res_assemblee)\n colnames(textimg_mat)<-c(\"x\",\"y\",\"name\")\n text(x = textimg_mat$x, y = textimg_mat$y, labels = textimg_mat$name)\n \n dev.off()\n })\n}\n\n\nwriteRaster(resa,paste0(results_dir,\"/res_assemblee.tif\"), overwrite=TRUE)\nsparks_params<-as.data.frame(matrix(ncol = 15))\ncolnames(sparks_params)<-c(\"taille\",\"duree\", 'debut','fin', 'distance','Transitoire avant','Transitoire apr\u00e8s','distance_entre_transitoire','Distance normalis\u00e9e','surface','f','f0','fsurf0', 'fsurf0v2','Profile Plot')\nif(max(res_assemblee)!=0){\n for(i in 1:max(res_assemblee)){\n ind<-which(res_assemblee==i, arr.ind=TRUE)\n debut<-min(ind[,1])\n fin<-max(ind[,1])\n duree<-(max(ind[,1])-min(ind[,1]))*1.88\n taille<-(max(ind[,2])-min(ind[,2]))*0.26\n x<-center_spark(res_assemblee,i)[[1]]\n tmp<-transient-x\n distance<-min(tmp[tmp>=0])*1.88\n transient_apres<-min(tmp[tmp>=0])+x\n transient_avant<-max(tmp[tmp<=0])+x\n distance_entre_transitoire<-(transient_apres-transient_avant)*1.88\n distance_norm<-distance/(transient_apres-transient_avant)\n surface<-length(which(res_assemblee==i))\n profile_plot<-c()\n all_values_of_spark<-c()\n for(i2 in 0:(max(ind[,1])-min(ind[,1]))){\n ind2<-filter(as.data.frame(ind), row==(min(ind[,1])+i2))\n tmp2<-0L\n for(i3 in ind2[,2]){\n tmp2<-tmp2+abc[(min(ind[,1])+i2),i3]\n all_values_of_spark<-c(all_values_of_spark,abc[(min(ind[,1])+i2),i3])\n }\n profile_plot<-c(profile_plot,tmp2/length(ind2[,2]))\n }\n f<-max(profile_plot[!is.nan(profile_plot)])\n f0<-min(profile_plot[!is.nan(profile_plot)])\n fsurf0<-f/f0\n fsurf0v2<-mean(sort(all_values_of_spark, decreasing = TRUE)[1:5])/mean(sort(all_values_of_spark)[1:5])\n sparks_params[i,]<-c(taille,duree,debut,fin,distance,transient_avant,transient_apres,distance_entre_transitoire,distance_norm,surface,f,f0,fsurf0,fsurf0v2, 0)\n sparks_params$'Profile Plot'[i]<-list(profile_plot)\n }\n}\n\n\n# plot(sparks_params$taille)\n# plot(sparks_params$duree)\n# plot(sparks_params$distance)\n# plot(sparks_params$surface)\n# plot(1:length(sparks_params$'Profile Plot'[[1]])*1.88,sparks_params$'Profile Plot'[[1]], type='b')\n\nsaveRDS(sparks_params, paste(results_dir,\"/sparks_params.rds\",sep=\"\"))\nsaveRDS(res_assemblee, paste(results_dir,\"/res_assemblee.rds\", sep=\"\"))\nwrite.csv(x = subset(sparks_params, select=-`Profile Plot`), file = paste(results_dir, \"/sparks_params.csv\", sep=\"\"))\nconn<-file(paste(results_dir,\"/resultats.txt\", sep=\"\"))\ndata_to_write<-c(\n paste(\"Nombre de sparks\", max(res_assemblee)),\n paste(\"Nb sparks normalis\u00e9 pour 1s et 20\u00b5m\", max(res_assemblee)*1000*1.88/dim(res_assemblee)[1]*20*0.26/dim(res_assemblee)[2]),\n paste(\"Moyenne de la taille (\u00b5m)\", mean(sparks_params$taille[!is.infinite(sparks_params$taille)])),\n paste(\"Moyenne de la dur\u00e9e (ms)\", mean(sparks_params$duree[!is.infinite(sparks_params$duree)])),\n paste(\"Moyenne de la distance aux transients (si pr\u00e9sent)(ms)\", mean(sparks_params$distance[!is.infinite(sparks_params$distance)])),\n paste(\"F/F0 \", mean(sparks_params$fsurf0)),\n paste(\"F/F0v2 \", mean(sparks_params$fsurf0v2)),\n paste(\"Nb sparks dist <20 \", nrow(sparks_params %>% filter(distance < 20))),\n paste(\"Nb sparks dist <30 \", nrow(sparks_params %>% filter(distance < 30))),\n paste(\"Nb sparks dist <40 \", nrow(sparks_params %>% filter(distance < 40))),\n paste(\"Nb sparks dist/dist entre transitoire <0.1 \", nrow(sparks_params %>% filter((distance/distance_entre_transitoire) < 0.1))),\n paste(\"Nb sparks dist/dist entre transitoire <0.2 \", nrow(sparks_params %>% filter((distance/distance_entre_transitoire) < 0.2))),\n paste(\"Nb sparks dist/dist entre transitoire <0.3 \", nrow(sparks_params %>% filter((distance/distance_entre_transitoire) < 0.3)))\n) \nwriteLines(data_to_write, conn)\nclose(conn)\n", "meta": {"hexsha": "2e5b69ea70af115b0bbe928aff3e60dce7c73da6", "size": 10326, "ext": "r", "lang": "R", "max_stars_repo_path": "sparks analysis.r", "max_stars_repo_name": "olivierbortolotti/lcr-analysis-san", "max_stars_repo_head_hexsha": "283ddf502083f0e752d01c1ee0e5d29e1d552aa9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sparks analysis.r", "max_issues_repo_name": "olivierbortolotti/lcr-analysis-san", "max_issues_repo_head_hexsha": "283ddf502083f0e752d01c1ee0e5d29e1d552aa9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sparks analysis.r", "max_forks_repo_name": "olivierbortolotti/lcr-analysis-san", "max_forks_repo_head_hexsha": "283ddf502083f0e752d01c1ee0e5d29e1d552aa9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1049562682, "max_line_length": 217, "alphanum_fraction": 0.6450706953, "num_tokens": 3444, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7341195385342971, "lm_q2_score": 0.4649015713733884, "lm_q1q2_score": 0.34129332704050147}} {"text": "library(\"future\")\nfuture::plan(future.batchtools::batchtools_slurm,\n template = \".batchtools.slurm.tmpl\",\n label = \"sim_test_1\",\n resources = list(\n job.name = \"sim_test_1\",\n walltime = 1,\n memory = \"5G\",\n ncpus = 1,\n output = \"/work/%u/%j-%x.log\",\n email = \"sagouis@pm.me\"\n )\n)\n\n### Community\nsource(\"./analysis/parameters/community_v1.r\")\n\ncomm <- mobsim::sim_thomas_community(100, 100000, fix_s_sim = TRUE)\ncomm <- sRealm::jitter_species(comm = comm, sd = 0.01)\ncomm <- sRealm::torusify(comm)\n\nx0 <- y0 <- 0.45\nxsize <- ysize <- 0.1\n\nres <- mobsim::abund_rect(comm = comm, x0 = x0, y0 = y0, xsize = xsize, ysize = ysize)\nsaveRDS(object = res, file = \"~/simRealm/data/simulations/sim_test_1.rds\")\n", "meta": {"hexsha": "da0e4ff00902982c1ad05396a8fea9581da6d79b", "size": 846, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/test_job_on_EVE.r", "max_stars_repo_name": "sRealmWG/simRealm", "max_stars_repo_head_hexsha": "e2d8825099051233580e1f131f4d61657b7d5e83", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/test_job_on_EVE.r", "max_issues_repo_name": "sRealmWG/simRealm", "max_issues_repo_head_hexsha": "e2d8825099051233580e1f131f4d61657b7d5e83", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/test_job_on_EVE.r", "max_forks_repo_name": "sRealmWG/simRealm", "max_forks_repo_head_hexsha": "e2d8825099051233580e1f131f4d61657b7d5e83", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3333333333, "max_line_length": 86, "alphanum_fraction": 0.5543735225, "num_tokens": 252, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3412868672061621}} {"text": "library(ggvis)\n\nshinyServer(function(input, output, session) {\n reactive({\n mtcars %>% ggvis(~disp, ~mpg) %>%\n layer_points() %>%\n scale_numeric(\"x\", domain = input$x_domain, nice = FALSE, clamp = TRUE) %>%\n scale_numeric(\"y\", domain = input$y_domain, nice = FALSE, clamp = TRUE)\n }) %>%\n bind_shiny(\"zoom\")\n})\n", "meta": {"hexsha": "398f1629a25217ac5359fc65ef5d4f8ac49f93a8", "size": 332, "ext": "r", "lang": "R", "max_stars_repo_path": "demo/apps/zoom/server.r", "max_stars_repo_name": "romainfrancois/ggvis", "max_stars_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 469, "max_stars_repo_stars_event_min_datetime": "2015-01-07T19:21:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-18T00:46:01.000Z", "max_issues_repo_path": "demo/apps/zoom/server.r", "max_issues_repo_name": "romainfrancois/ggvis", "max_issues_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 178, "max_issues_repo_issues_event_min_datetime": "2015-01-02T16:37:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T23:17:53.000Z", "max_forks_repo_path": "demo/apps/zoom/server.r", "max_forks_repo_name": "romainfrancois/ggvis", "max_forks_repo_head_hexsha": "435ca190326b082597e3d3060f0a4daac4fbc57f", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 134, "max_forks_repo_forks_event_min_datetime": "2015-01-07T20:47:08.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-16T23:39:06.000Z", "avg_line_length": 27.6666666667, "max_line_length": 81, "alphanum_fraction": 0.6114457831, "num_tokens": 97, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3412868672061621}} {"text": "length2<-subset(length, length$STRATUM<700)\n\n\nlength3<-aggregate(list(FREQ=length2$FREQUENCY),by=list(YEAR=length2$YEAR,LENGTH=length2$LENGTH),FUN=sum)\ngrid<-expand.grid(YEAR=c(2002,2004,2006,2010,2012),LENGTH=seq(1,100,1)\n\nLENGTH=merge(grid,length3,all=T)\n\n LENGTH$FREQ[is.na(LENGTH$FREQ)==T]<-0\n \nLENGTH$PROB[LENGTH$YEAR==2012]<-LENGTH$FREQ[LENGTH$YEAR==2012]/sum(LENGTH$FREQ[LENGTH$YEAR==2012])\n LENGTH$PROB[LENGTH$YEAR==2010]<-LENGTH$FREQ[LENGTH$YEAR==2010]/sum(LENGTH$FREQ[LENGTH$YEAR==2010])\n LENGTH$PROB[LENGTH$YEAR==2006]<-LENGTH$FREQ[LENGTH$YEAR==2006]/sum(LENGTH$FREQ[LENGTH$YEAR==2006])\n LENGTH$PROB[LENGTH$YEAR==2004]<-LENGTH$FREQ[LENGTH$YEAR==2004]/sum(LENGTH$FREQ[LENGTH$YEAR==2004])\n LENGTH$PROB[LENGTH$YEAR==2002]<-LENGTH$FREQ[LENGTH$YEAR==2002]/sum(LENGTH$FREQ[LENGTH$YEAR==2002])\n \n \n \nHL12<-HL\nHL12$counts<-LENGTH$FREQ[LENGTH$YEAR==2012]\nHL12$density<-LENGTH$PROB[LENGTH$YEAR==2012]\nplot(HL12,col=\"brown\",freq=F,ylab=\"Proportion\",xlab=\"Fork length(cm)\",main=2012,cex.lab=1.2,cex.axis=1.2,cex.main=1.5)\n\nHL10<-HL\nHL10$counts<-LENGTH$FREQ[LENGTH$YEAR==2010]\nHL10$density<-LENGTH$PROB[LENGTH$YEAR==2010]\nplot(HL10,col=\"brown\",freq=F,ylab=\"Proportion\",xlab=\"Fork length(cm)\",main=2010,cex.lab=1.2,cex.axis=1.2,cex.main=1.5)\n\n\nHL06<-HL\nHL06$counts<-LENGTH$FREQ[LENGTH$YEAR==2006]\nHL06$density<-LENGTH$PROB[LENGTH$YEAR==2006]\nplot(HL06,col=\"brown\",freq=F,ylab=\"Proportion\",xlab=\"Fork length(cm)\",main=2006,cex.lab=1.2,cex.axis=1.2,cex.main=1.5)\n\nHL04<-HL\nHL04$counts<-LENGTH$FREQ[LENGTH$YEAR==2004]\nHL04$density<-LENGTH$PROB[LENGTH$YEAR==2004]\nplot(HL04,col=\"brown\",freq=F,ylab=\"Proportion\",xlab=\"Fork length(cm)\",main=2004,cex.lab=1.2,cex.axis=1.2,cex.main=1.5)\n\nHL02<-HL\nHL02$counts<-LENGTH$FREQ[LENGTH$YEAR==2002]\nHL02$density<-LENGTH$PROB[LENGTH$YEAR==2002]\nplot(HL02,col=\"brown\",freq=F,ylab=\"Proportion\",xlab=\"Fork length(cm)\",main=2002,cex.lab=1.2,cex.axis=1.2,cex.main=1.5)\n", "meta": {"hexsha": "c953426249e4038249510a1e19523d54b61fb325", "size": 1894, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/AI_Pollock/R_functions/Length_figures.r", "max_stars_repo_name": "NMFS-toolbox/AMAK", "max_stars_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-12T17:39:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-12T17:39:20.000Z", "max_issues_repo_path": "examples/AI_Pollock/R_functions/Length_figures.r", "max_issues_repo_name": "afsc-assessments/AMAK", "max_issues_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/AI_Pollock/R_functions/Length_figures.r", "max_forks_repo_name": "afsc-assessments/AMAK", "max_forks_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-05-21T18:18:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-12T04:18:42.000Z", "avg_line_length": 43.0454545455, "max_line_length": 118, "alphanum_fraction": 0.7397043295, "num_tokens": 755, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3412868672061621}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# set dataset \n\ndataset <- \"Singhal\"\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n\n# Colors \ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"#C2DF23FF\"\n\n\n# you'll have to graph first, then reset this \nmaxloci <- 1200\nymax <- seq(0,maxloci,100)\n\n#Streicher 350,50\n#Singhal 1200,100\n#quartz()\n\n# Set title and input data \ntitle.g <- paste(dataset,\" - Tox vs Sclero\",sep=\"\") \ncc.g <- color_TP\ndf.g <- mLGL\nxval.g <- mLGL$TvS\nxlablab.g <- 'dGLS values'\n\n# Get max min for graph to set x axis values\nlimit.g <- 5 + round(max(abs(min(xval.g)),abs(max(xval.g))),-1)\nlimit.g\ntic.g <- seq(-limit.g,limit.g,10)\nlines.g <- c(0.5,-0.5)\n\n\nGL_hist <- ggplot(data=df.g, aes(x=xval.g)) + \n geom_histogram(binwidth = limit.g*0.01, alpha=1, position=\"dodge\", color=cc.g, fill=cc.g)+ \n theme_classic() + \n theme(plot.title = element_text(hjust = 0.5, size=16),\n axis.text = element_text(size=10, color=\"black\"),\n text = element_text(size=14),\n legend.title = element_text(size = 12),\n legend.text = element_text(size = 10)) +\n labs(y=\"Number of Loci\",x=xlablab.g) + \n ggtitle(title.g) +\n coord_cartesian(ylim=ymax, xlim = tic.g) +\n scale_x_continuous(breaks = c(0,tic.g)) +\n scale_y_continuous(breaks = ymax) + \n # geom_vline(xintercept=lines.g,color=c(\"black\"), linetype=\"dashed\", size=0.5) +\n geom_vline(xintercept=c(0),color=c(\"black\"), linetype=\"dashed\", size=0.2)\nGL_hist\n\n \n \n\n\n\n# Set title and input data \ntitle <- \"\"\ncc <- color_TP\ndf <- mLBF\nxval <- mLBF$TvS\nxlablab <- '2ln(BF) values'\n\n# Get max min for graph to set x axis values\nlimit <- 10 + round(max(abs(min(xval)),abs(max(xval))),-1)\nlimit\ntic <- seq(-limit,limit,20)\nlines <- c(10,-10)\n\n\nBF_hist <- ggplot(data=df, aes(x=xval)) + \n geom_histogram(binwidth = limit*0.01, alpha=1, position=\"dodge\", color=cc, fill=cc)+ \n theme_classic() + \n theme(plot.title = element_text(hjust = 0.5, size=16),\n axis.text = element_text(size=10, color=\"black\"),\n text = element_text(size=14),\n legend.title = element_text(size = 12),\n legend.text = element_text(size = 10)) +\n labs(y=\"Number of Loci\",x=xlablab) + \n ggtitle(title) +\n coord_cartesian(ylim=ymax, xlim = tic) +\n scale_x_continuous(breaks = c(0,tic)) +\n scale_y_continuous(breaks = ymax) + \n #geom_vline(xintercept=lines,color=c(\"black\"), linetype=\"dashed\", size=0.5) +\n geom_vline(xintercept=c(0),color=c(\"black\"), linetype=\"dashed\", size=0.2)\n\n\nBF_hist\n\n\n\n\ngraph_combined <- ggarrange(GL_hist, BF_hist, ncol=1, nrow=2, align=\"v\")\ngraph_combined\n\nggsave(paste(dataset,\"_histo.pdf\",sep=\"\"), plot=graph_combined,width = 9, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n", "meta": {"hexsha": "470b849c02224d6297f6ab8f499588a2622cf55c", "size": 2938, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/TvS_v0410/Graphs_Histogram.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/TvS_v0410/Graphs_Histogram.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/TvS_v0410/Graphs_Histogram.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7090909091, "max_line_length": 134, "alphanum_fraction": 0.659632403, "num_tokens": 975, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737214979745, "lm_q2_score": 0.5, "lm_q1q2_score": 0.34128686074898723}} {"text": "\n\n### UNITS\n# Costs are in million USD2010 per year\n# Water flows in million cubic meters per day (MCM/day)\n# power flows in MW \n# land in Mha (million hectares)\n# yields are in kton\n# Note that most parameters are defined as rates as opposed to absolute volumes\n# This avoids some of the headache associated with unequal timesteps (i.e., months)\n\n# Power plant cost and performance data from Parkinson et al. (2016) - Impacts of groundwater constraints on Saudi Arabia's low-carbon electricity supply strategy\n# Additional power plant technologies not included in Parkinson et al. (2016) are estimated from Black & Veatch (2012) \"Cost and performance data for Power generation\"\n# Emission factors in metric tons per day per MW are default IPCC numbers or matched to MESSAGE IAM\n# Fuel usage rates are estimated from the power plant heat rate\n# flexibility rates for electricity technologies from Sullivan et al. (2013)\n\n# Gas combined cycle with once through freshwater cooling\n\nmsg_tec = 'gas_cc' # tec name in MESSAGE IAM\nvtgs = year_all\nnds = bcus\nlft = 20\ndCF = 0.9 # capacity factor drop in flexibility mode, modelled as output decrease and var_cost increase actually (1-(CF0-CF))\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_cc_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00096 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00096 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00094 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00094 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.88 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 2.00 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.0002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.023\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_cc_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# Gas combined cycle with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_cc_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.92 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 2.04 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.04 / 1.92 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ), \t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.064\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.92 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 1.92 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_cc_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\n# Gas combined cycle with air cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_cc_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 2.06 / 1.94 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.94 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 2.06 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.06 / 1.94 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.105\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.017\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.94 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 2.06 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_cc_ac\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Gas combined cycle with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_cc_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.88 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.94 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.023\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.88 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 1.94 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_cc_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Gas turbine\nvtgs = year_all\nmsg_tec = 'gas_ct'\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_gt = list( \t\tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 2.86 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.03 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( ., gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( ., emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.088 , digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.102 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_gt\",]\n\n\t\t\t\t\t)\n\n# Gas single cycle with once through freshwater cooling\nvtgs = year_all\nmsg_tec = 'gas_ppl'\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_st_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00319 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00319 * 3.37 / 3.18, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00315 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00315 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00004 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.159\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_st_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Gas single cycle with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_st_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00006 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00006 * 3.43 / 3.23, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00001 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00001 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.23 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.43 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00005 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00005 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.205\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.017\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.43/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_st_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# Gas single cycle with air cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngas_st_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * 3.74 / 3.53, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.53 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.74 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.251\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.53/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.74/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_st_ac\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n# Gas single cycle with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\ngas_st_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.159\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.18/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"gas\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.37/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"gas_st_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# oil combined cycle with once through freshwater cooling\nvtgs = year_all\nmsg_tec = 'loil_cc'\nnds = bcus\noil_cc_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00096 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00096 * ( 2.00/1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00094 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00094 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 1.88 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 2.00 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.023\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_cc_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\n# oil combined cycle with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\noil_cc_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 1.92 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 2.04 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 2.00 / 1.88 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.064\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.92 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 1.92 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_cc_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\n# oil combined cycle with air cooling\nvtgs = year_all\nnds = bcus\noil_cc_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 2.06 / 1.94 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 1.94 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 2.06 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.105\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.017\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.94 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 2.06 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_cc_ac\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\n# oil combined cycle with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\noil_cc_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 1.88 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.07333 * 1.94 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.023\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.026 * ( 1.88 / 1.88 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.046 * ( 1.94 / 1.88 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_cc_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\n# Oil turbine\nvtgs = year_all\nmsg_tec = 'loil_ppl'\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \noil_gt = list( \t\tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 2.86 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.03 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.088 , digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.102 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_gt\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 10\t)\n\n\t\t\t\t\t)\n\n# Oil single cycle with once through freshwater cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \noil_st_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00319 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00319 * 3.37 / 3.18, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00315 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00315 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00004 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.159\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_st_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Oil single cycle with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \noil_st_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00006 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00006 * 3.43 / 3.23, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00001 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00001 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.23 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.43 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00005 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00005 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.205\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.017\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.43/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_st_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\n# Oil single cycle with air cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \noil_st_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * 3.74 / 3.53, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.53 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.74 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.251\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.53/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.74/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_st_ac\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n# Oil single cycle with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\noil_st_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0733 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.159\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.18/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.37/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"oil_st_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 1e-6\t) %>% \n\t\t\t\t\t mutate(value = if_else(!grepl('PAK',node),value,\n\t\t\t\t\t if_else(year_all %in% c(2020,2030),50,value ) ) )\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\n# pulverized coal with once through freshwater cooling\nvtgs = year_all\nmsg_tec = 'coal_ppl'\nnds = bcus\ncoal_st_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2, 3, 4 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00318 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00318 * 3.37 / 3.18, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(3,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'solid',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round(1/ 0.31 * 0.2 * 3600 * 15 * 1e-6 , digits = 5) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00316 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00316 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 3, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 4, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2,3,4),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.60\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.023\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.43/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"coal_st_ot\",]\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\t# growth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\n# pulverized coal with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ncoal_st_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2, 3, 4 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00006 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00006 * 3.43 / 3.23, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(3,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'solid',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round(1/ 0.31 * 0.2 * 3600 * 15 * 1e-6 , digits = 5) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00001 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00001 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.23 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.43 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 3, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.23 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 4, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.43 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00005 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00005 * ( 3.43 / 3.23 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2,3,4),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.860\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.024\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.053 * (3.43/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"coal_st_cl\",]\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\t# growth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# Pilverized coal with air cooling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ncoal_st_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2, 3, 4 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * 3.74 / 3.53, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(3,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'solid',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round(1/ 0.31 * 0.2 * 3600 * 15 * 1e-6 , digits = 5) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.53 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.74 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 3, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.53 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 4, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.74 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.74 / 3.53 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2,3,4),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 7.145\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.025\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.53/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.056 * (3.74/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"coal_st_ac\",]\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\t# growth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n# Pulveized coal with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\ncoal_st_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 3, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.18 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 4, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.8 * 0.0961 * 3.37 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * ( 3.37 / 3.18 ), digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2,3,4),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.600\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.023\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,3) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.035 * ( 3.18/3.18 ), digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2,4) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.056 * (3.37/3.18 ) / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"coal_st_sw\",]\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\t# growth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# integrated gasification with once through freshwater cooling\nvtgs = year_all\nnds = bcus\nmsg_tec = 'igcc'\nigcc_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00136 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00136 * 2.83 / 2.65, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00135 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00135 * 2.83 / 2.65, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.65 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.83 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00001 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00001 * 2.83 / 2.65, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) ,\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 4.010\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.031\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.057, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.077 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"igcc_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\n# integrated gasifcation with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus\nigcc_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00004 * 2.89 / 2.71, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * 2.89 / 2.71, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.71 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.89 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * 2.89 / 2.71, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 4.131\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.032\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.057, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.077 * 2.89 / 2.71 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"igcc_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\t\t\t\t\t\n# integrated gasification with air cooling\nvtgs = year_all\nnds = bcus\nigcc_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.95 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 3.07 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 5.105\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.037\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.057 * 2.95 / 2.71, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.077 * 3.07 / 2.71 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"igcc_ac\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# integrated gasification combined cycle with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\nigcc_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.65 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0961 * 2.83 * 60 * 60 * 24 / 1e3, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Other air pollutant emissions\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( air_pollution_factors.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == msg_tec ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\texpand( node = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnesting(emission,val) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>% rename( value = val ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t) %>% # aggregate CO2eq emissions\n\t\t\t\t\t\tleft_join( . , gwp.df ) %>% mutate( CO2eq = value * gwp ) %>% dplyr::select( -gwp ) %>%\n\t\t\t\t\t\tbind_rows( ., gather( . , emission, value, -node, -vintage, -year_all, -mode, -emission, -value, CO2eq) ) %>%\n\t\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = sum( value, na.rm=TRUE ) ) %>%\n\t\t\t\t\t\tungroup( ) %>% data.frame ( ) %>%\n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 4.010\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.031\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.057, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"coal\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.077 / dCF, digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"igcc_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# nuclear with once through freshwater cooling\nvtgs = year_all\nnds = bcus[!grepl('AFG',bcus)]\nnuclear_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00424 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00433, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00420 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00420, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00013, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 5.530\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.093\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.036 / dCF, digits = 5 ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"nuclear_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1e-6\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# nuclear with closed-loop freshwater cooling\nvtgs = year_all\nnds = bcus[!grepl('AFG',bcus)]\nnuclear_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00014 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00020, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00001 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00001, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00013 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00019, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 5.751\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.097\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.021\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.039 / dCF, digits = 5 ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"nuclear_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1e-6\t)\n\n\t\t\t\t\t)\n\t\t\t\t\n# nuclear with once-through sea cooling\nvtgs = year_all\nnds = coast_pid[grepl('PAK',coast_pid)]\nnuclear_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 5.530\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.093\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.036 / dCF, digits = 5 ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"nuclear_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1e-6\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# geothermal with once-through freshwater\nvtgs = year_all\nnds = bcus\ngeothermal_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00424 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00433, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00420 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00420, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00013, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.243\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.132\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"geothermal_ot\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_new_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030), value = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.05\t)\n\n\n\t\t\t\t\t)\n\n# geothermal with closed loop cooling\nvtgs = year_all\nnds = bcus\ngeothermal_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00014 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00023, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.0010 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0010, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00004 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00013, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.343\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.135\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"geothermal_cl\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_new_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030), value = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.05\t)\n\n\t\t\t\t\t)\n\n# geothermal with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\ngeothermal_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 6.243\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.132\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.018\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"geothermal_sw\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_new_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030), value = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2040,2050,2060), value = 0.05\t)\n\n\n\t\t\t\t\t)\n\n# Biomass steam plant with once-through freshwater cooling\nvtgs = year_all\nnds = bcus\nbiomass_st_ot = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00318 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00318 * 3.63 / 3.45, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00316 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00316 * 3.63 / 3.45, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.3 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00002 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00002 * 3.63 / 3.45, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.830\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.095\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.118\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.130\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"biomass_st_ot\",]\n\n\t\t\t\t\t)\n\n\n# Biomass steam plant with closed loop cooling\nvtgs = year_all\nnds = bcus\nbiomass_st_cl = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00018 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00023, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.0005 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0005, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.3 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.00013 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.00018 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.930\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.096\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.120\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.135\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"biomass_st_cl\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\n# Biomass steam plant with air cooling\nvtgs = year_all\nnds = bcus\nbiomass_st_ac = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0, digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.3 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\n\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 4.130\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.098\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.125\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.140),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"biomass_st_ac\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Biomass plant with once-through sea cooling\nvtgs = year_all\nnds = coast_pid\nbiomass_st_sw = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.3 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.930\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.096\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.120\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.135\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t mutate(value = if_else(vintage < 2030 , 1,value) ) %>% \n\t\t\t\t\t dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"biomass_st_sw\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# solar PV (utility-scale)\nvtgs = year_all\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"solar_1\"]))\nsolar_pv_1 = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.05 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'solar_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"solar_1\",],\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.873\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.003\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[ hist_new_cap.df$tec == \"solar_pv_1\", ],\n\n\t\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'solar_1' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\t\n\t\t\t\t\t)\n\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"solar_2\"]))\nsolar_pv_2 = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.05 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'solar_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"solar_2\",],\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.873\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.003\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"solar_pv_2\",],\n\n\t\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'solar_2' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\t\n\t\t\t\t\t)\n\t\t\t\t\t\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"solar_3\"]))\nsolar_pv_3 = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.05 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'solar_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"solar_3\",],\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.873\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.003\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"solar_pv_3\",],\n\n\t\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'solar_3' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\t\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\t\t\t\t\n# Wind (utility-scale)\nvtgs = year_all\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"wind_1\"]))\nwind_1 = list( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.08 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'wind_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"wind_1\",],\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 7.000\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.008\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"wind_1\",],\n\n\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'wind_1' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\n\t\t\t\t)\n\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"wind_2\"]))\nwind_2 = list( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.08 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'wind_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"wind_2\",],\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 7.000\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.008\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"wind_2\",],\n\n\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'wind_2' ) %>% \n\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\n\t\t\t\t)\n\nnds = as.character(unique(capacity_factor_sw.df$node[capacity_factor_sw.df$tec == \"wind_3\"]))\nwind_3 = list( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.08 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'wind_credit', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \tcapacity_factor_sw.df[capacity_factor_sw.df$tec == \"wind_3\",],\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 7.000\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.008\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"wind_3\",],\n\n\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\tbound_total_capacity_up = max_potential_sw.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( tec == 'wind_3' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\texpand( data.frame( node, value ), year_all ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, year_all, value )\n\t\t\t\t\n\t\t\t\t)\n\n# Old hydropower \nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = as.character( water_resources.df$node[ which( water_resources.df$existing_MW > 0 | water_resources.df$planned_MW > 0 ) ] )\n\nbound_total_capacity_lo_hydro = water_resources.df %>% select(node,existing_MW,planned_MW) %>% \n tidyr::crossing(year_all = year_all[ year_all > baseyear ]) %>% \n mutate(value = if_else(year_all == 2020, existing_MW, existing_MW + planned_MW)) %>% \n filter(value != 0) %>% dplyr::select(node,year_all,value)\n\nhydro_old = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'hydro_potential_old', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.95 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# Bound total capacity based on existing and planned MW\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t# bound_total_capacity_lo = bound_total_capacity_lo_hydro,\n\t\t\t\t\t\n\t\t\t\t\t# Upper bound equal to lower bound to prevent building in future years beyond planned - \n\t\t\t\t\t# add additional 0.1 % to allow for some room beteweeen bounds for optimization efficiency\n\t\t\t\t\tbound_total_capacity_up = bound_total_capacity_lo_hydro %>% mutate( value = value * 1.001 ),\t\t\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all) \n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df %>%\n\t\t\t\t\t\tfilter( tec == 'hydro')\t\n\t\t\t\t\t\n\t\t\t\t\t)\n\n\n# New hydropower - river technologies, highly dependent on upstream flow_routes\nnds = as.character( water_resources.df$node[ which( water_resources.df$river_max_mw > 0 ) ] )\nvtgs = year_all[ year_all > baseyear ]\nhydro_river = list( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1,2 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'hydro_potential_river', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.95 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 10\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 50\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# Bound total capacity based on existing and planned MW\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\tbound_total_capacity_up = do.call( rbind, lapply( nds, function( nn ){ \n\t\t\t\t\t\tdata.frame( node = rep( nn, length( vtgs ) ), \n\t\t\t\t\t\t\t\t\tyear_all = vtgs, \n\t\t\t\t\t\t\t\t\tvalue = rep( ( water_resources.df$river_max_mw[ which( water_resources.df$node == nn ) ] ), length( vtgs ) ) ) \n\t\t\t\t\t\t} ) )\n\t\t\t\t\n\t\t\t\t)\n\n# New hydropower - canal technologies, independent of upstream flow \t\t\t\t\nnds = as.character( water_resources.df$node[ which( water_resources.df$canal_max_mw > 0 ) ] )\nvtgs = year_all[ year_all > baseyear ]\nhydro_canal = list( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1, 2 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = NULL,\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * dCF ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\t# Canal plant capacity factor is determined based on internal run off\n\t\t\t\tcapacity_factor = left_join( bind_rows( lapply( nds, function(nn){ do.call( rbind, lapply( vtgs, function( yy ){ \n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tdata.frame( node = rep( nn, length(time) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tyear_all = rep( yy, length(time) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = unlist( sapply( unlist( water_resources.df[ which( as.character( water_resources.df$node ) == nn ), grepl( paste0( 'runoff_km3_per_day_', yy ), names(water_resources.df) ) ] ) *\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tc( water_resources.df[ which( as.character( water_resources.df$node ) == nn ), 'canal_mw_per_km3_per_day' ] ) / \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tc( water_resources.df[ which( as.character( water_resources.df$node ) == nn ), 'canal_max_mw' ] ), function(iii){ min( 1, round( iii, digits = 3) ) } ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trow.names = 1:length(time) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t} ) ) } ) ),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.000\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# Bound total capacity based on existing and planned MW\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\tbound_total_capacity_up = do.call( rbind, lapply( nds, function( nn ){ \n\t\t\t\t\t\tdata.frame( node = rep( nn, length( vtgs ) ), \n\t\t\t\t\t\t\t\t\tyear_all = vtgs, \n\t\t\t\t\t\t\t\t\tvalue = rep( ( water_resources.df$river_max_mw[ which( water_resources.df$node == nn ) ] ), length( vtgs ) ) ) \n\t\t\t\t\t\t} ) )\n\t\t\t\t\n\t\t\t\t)\n\n# electricity_distribution_urban - generation to end-use within the same spatial unit\nvtgs = year_all\nnds = bcus\ntmp = demand.df %>% filter(commodity == 'electricity' & level == 'urban_final' & year_all == 2015) %>% \n\t\tmutate(year_all = as.numeric(year_all)) %>% \n\t\tgroup_by(node,year_all,units) %>% \n\t\tsummarise(value = sum(value)) %>% \n\t\tmutate(tec = 'electricity_distribution_urban') %>% \n\t\tdplyr::select(node,tec,year_all,value) %>% ungroup() %>% \n\t\tmutate(value = 0.5 * value)\nelectricity_distribution_urban = \n\t\tlist( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 1.12\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.036\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.025\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = tmp %>% \n\t\t\t\t\t\t\t\t\t\t\tbind_rows(tmp %>% mutate(year_all = 2010))\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n# electricity_distribution_industry - generation to end-use within the same spatial unit\nvtgs = year_all\nnds = bcus\ntmp = demand.df %>% filter(commodity == 'electricity' & level == 'industry_final' & year_all == 2015) %>% \n\t\tmutate(year_all = as.numeric(year_all)) %>% \n\t\tgroup_by(node,year_all,units) %>% \n\t\tsummarise(value = sum(value)) %>% \n\t\tmutate(tec = 'electricity_distribution_industry') %>% \n\t\tdplyr::select(node,tec,year_all,value) %>% ungroup() %>% \n\t\tmutate(value = 0.5 * value)\t\t\t\t\nelectricity_distribution_industry = \n\t\tlist( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 1.12\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.036\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.025\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = tmp %>% \n\t\t\t\t\t\t\t\t\t\t\tbind_rows(tmp %>% mutate(year_all = 2010))\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\t\t\t\t\n\n# electricity_distribution_rural - generation to end-use within the same spatial unit\nvtgs = year_all\nnds = bcus\ntmp = demand.df %>% filter(commodity == 'electricity' & level == 'rural_final' & year_all == 2015) %>% \n\t\tmutate(year_all = as.numeric(year_all)) %>% \n\t\tgroup_by(node,year_all,units) %>% \n\t\tsummarise(value = sum(value)) %>% \n\t\tmutate(tec = 'electricity_distribution_rural') %>% \n\t\tdplyr::select(node,tec,year_all,value) %>% ungroup() %>% \n\t\tmutate(value = 0.5 * value)\nelectricity_distribution_rural = \n\t\tlist( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 1.12\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.036\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.025\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = tmp %>% \n\t\t\t\t\t\t\t\t\t\t\tbind_rows(tmp %>% mutate(year_all = 2010))\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n# electricity_distribution_irrigation - generation to end-use within the same spatial unit\nvtgs = year_all\nnds = bcus\ntmp = demand.df %>% filter(commodity == 'electricity' & level == 'irrigation_final' & year_all == 2015) %>% \n\tmutate(year_all = as.numeric(year_all)) %>% \n\tgroup_by(node,year_all,units) %>% \n\tsummarise(value = sum(value)) %>% \n\tmutate(tec = 'electricity_distribution_irrigation') %>% \n\tdplyr::select(node,tec,year_all,value) %>% ungroup() %>% \n\tmutate(value = 0.5 * value)\nelectricity_distribution_irrigation = \n\t\tlist( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = -0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 1.12\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.036\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.025\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = tmp %>% \n\t\t\t\t\t\t\t\t\t\t\tbind_rows(tmp %>% mutate(year_all = 2010))\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n# electricity_shtort term storage\nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nelectricity_short_strg = \n\t\tlist( \tnodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 3.000\t),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.015\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = NULL\t\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\n\n# Electricity transmission\nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nfor( jjj in 1:length(adjacent_routes) ){ # add\n\n\troutes_names <- paste0('trs_',adjacent_routes)\n\tnds = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1]\n\tassign( routes_names[jjj], \n\t\t\n\t\tlist( \tnodes = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1,2 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnode_in = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnode_in = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[2],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnode_out = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[2],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 - trs_eff.df$value[trs_eff.df$tec == adjacent_routes[jjj]] ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tnode_out = unlist( strsplit( adjacent_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 - trs_eff.df$value[trs_eff.df$tec == adjacent_routes[jjj]] ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 , digits = 5 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = trs_inv_cost.df$value[trs_inv_cost.df$tec == adjacent_routes[jjj]] ),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.05 * trs_inv_cost.df$value[trs_inv_cost.df$tec == adjacent_routes[jjj]] ),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = trs_hurdle.df$hurdle[as.character(trs_hurdle.df$tec) == adjacent_routes[jjj]]\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = transmission_routes.df[transmission_routes.df$tec == routes_names[jjj],]\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\t\n\t\t) \n \n\t}\n\n# externl routes no input or output for countries\n#extimate of elec export to Karachi area # 0.791 ratio of population that is not in the basin\n# we scale pakistan electricity demand accordingly, then remove 5.7GW * 0.75 (CF) * 30*24 /12\nelec_exp =demand.df %>% filter(grepl('PAK',node), commodity == 'electricity') %>% \n mutate(value = value * (1 - 0.791) / 0.791 ) %>% #in MW month\n group_by(level,year_all,time) %>% summarise(value = sum(value)) %>% ungroup() %>% \n #subtract external generation capacity to esternal industry demand\n mutate(value = if_else(level == 'industry_final',value - (5700/12), value)) %>% \n group_by(year_all,time) %>% summarise(value = sum(value))\n\nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ntrs_hurdle.df$hurdle[trs_hurdle.df$tec == 'PAK_4|PAK'] = 0\nfor( jjj in 1:length(electricity_export_routes) ){ # add\n\t\n\texp_routes_names <- paste0('trs_',electricity_export_routes)\n\tnds = unlist( strsplit( electricity_export_routes[jjj], '[|]' ) )[1]\n\t\n\tif (electricity_export_routes[jjj] == \"PAK_4|PAK\"){ \n\t tmp_bound_act_lo = elec_exp %>% \n\t crossing(node = \"PAK_4\",\n\t mode = 1) %>% \n\t select(node,year_all,mode,time,value)\n\t \n\t tmp_bound_act_up = crossing(node = \"PAK_4\",\n\t mode = c('2'),\n\t year_all = as.character(year_all),\n\t time = time,\n\t value = 1e-6) %>% \n\t select(node,year_all,mode,time,value)\n\t \n\t} else { \n\t tmp_bound_act_lo = NULL \n\t tmp_bound_act_up = NULL\n\t}\n\t\n\tassign( exp_routes_names[jjj], \n\t\n\t\tlist( \tnodes = unlist( strsplit( electricity_export_routes[jjj], '[|]' ) )[1],\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = time,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('power'),\n\t\t\t\tmodes = c( 1,2 ),\n\t\t\t\tlifetime = lft,\n\t\t\t\t\n\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = unlist( strsplit( electricity_export_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 / ( 1 - trs_eff.df$value[trs_eff.df$tec == adjacent_routes[jjj]] ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = unlist( strsplit( electricity_export_routes[jjj], '[|]' ) )[1],\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 - trs_eff.df$value[trs_eff.df$tec == adjacent_routes[jjj]] ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t),\t\n\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0561 * 1.88 * 60 * 60 * 24 / 1e3, digits = 5 ) ) , # test with ccgt ef\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( value ) or data,frame( node, year_all, time, value )\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = trs_inv_cost.df$value[trs_inv_cost.df$tec == electricity_export_routes[jjj]] ),\n\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.05 * trs_inv_cost.df$value[trs_inv_cost.df$tec == electricity_export_routes[jjj]] ),\n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = trs_hurdle.df$hurdle[ as.character(trs_hurdle.df$tec) == electricity_export_routes[jjj] ]\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\thistorical_new_capacity = transmission_routes.df[transmission_routes.df$tec == exp_routes_names[jjj],] %>% mutate( value = 0.1 * value ),\n\t\t\t\t\n\t\t\t\t# growth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 0.0005\t),\n\t\t\t\t\n\t\t\t\tbound_activity_lo = tmp_bound_act_lo,\n\t\t\t\t\n\t\t\t\tbound_activity_up = tmp_bound_act_up\n\t\t\t\t\n\t\t\t)\n\t\t)\n \n\t}\n\n#### Water technologies\n\n# sw_extract - generalized surface water extraction technology \t\n# Note that for the sw_extract and gw_extract, no costs are included because these technologies are just aggregating the sectoral withdrawals\n# which makes it easier to constrain based on historical capacity\t\t\t\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nsw_extract = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_in',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\n# gw_extract\t\t\t\t\t\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ngw_extract = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = c('groundwater','water_consumption'), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nrenew_gw_extract = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'renewable_gw', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'groundwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n# urban_sw_diversion \nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 15\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 57\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.2\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_sw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\n# urban_gw_diversion \nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 13 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 15\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 8.5\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_gw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\n# urban_piped_distribution \nvtgs = year_all\nnds = bcus\nlft = 50\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_piped_distribution = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1013\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 252\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_piped_distribution\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# urban_unimproved_distribution \nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_unimproved_distribution = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n# urban_wastewater_collection\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_wastewater_collection = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 785\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 251\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_wastewater_collection\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\n# urban_wastewater_release\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_wastewater_release = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# urban_wastewater_treatment\nvtgs = year_all\nnds = bcus\nlft = 25\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_wastewater_treatment = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 25,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 12.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 431\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 37\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_wastewater_treatment\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# industry_sw_diversion \nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 15\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 57\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.2\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_sw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\n# industry_gw_diversion \nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 13 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 15\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 8.5\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_gw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\n# industry_distribution \nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_distribution = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\n# industry_wastewater_collection\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_collection = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_wastewater_collection\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\n# industry_wastewater_release\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_release = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# industry_wastewater_treatment\nvtgs = year_all\nnds = bcus\nlft = 25\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_treatment = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 25,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 12.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 431\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 37\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_wastewater_treatment\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# industry_wastewater_collection\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_collection = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_wastewater_collection\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\n# industry_wastewater_release\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_release = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# industry_wastewater_treatment\nvtgs = year_all\nnds = bcus\nlft = 25\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_treatment = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 25,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 12.5 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 431\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 37\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_wastewater_treatment\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# rural_sw_diversion \nvtgs = year_all\nlft = 15\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 15,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 57\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_sw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\n# rural_gw_diversion \nvtgs = year_all\nlft = 15\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 15,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 15 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 8.5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_gw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# rural_piped_distribution \nvtgs = year_all\nlft = 15\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_piped_distribution = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 326\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 18\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_piped_distribution\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# rural_unimproved_distribution \nvtgs = year_all\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_unimproved_distribution = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_unimproved_distribution\",]\n\n\t\t\t\t\t)\n\n# rural_wastewater_collection\nvtgs = year_all\nlft = 15\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_wastewater_collection = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 15,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_wastewater_collection\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\t\t\n# rural_wastewater_release\nvtgs = year_all\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_wastewater_release = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_wastewater_release\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\t\t\t\t\n# rural_wastewater_treatment\nas.character( basin.spdf@data$DOWN[ iii ] )\nvtgs = year_all\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nrural_wastewater_treatment = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 759\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 77\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_wastewater_treatment\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# irrigation_sw_diversion - conventional\nvtgs = year_all\nnds = bcus\nlft = 50\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nirrigation_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.75 ) , # Average losses for canals from Wu et al. World Bank (2013)\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t# Seepage to groundwater\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'renewable_gw', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.25 ) , # Assumed inverse of the above\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 57\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 1\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"irrigation_sw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# irrigation_sw_diversion - smart\nvtgs = year_all\nnds = bcus\nlft = 50\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nsmart_irrigation_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.75 + 0.03 ) , # Average losses for canals from Wu et al. World Bank (2013) + assumed efficiency increase from smart tech\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t# Seepage to groundwater\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'renewable_gw', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.25 - 0.03 ) , # Assumed inverse of the above\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t# Electricity flexibility\t\t\t\n\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 * 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 57 * 1.1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3 * 1.1\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\n# irrigation_gw_diversion - conv\nvtgs = year_all\nnds = bcus\nirrigation_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 16 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.95 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 8.5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"irrigation_gw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# irrigation_gw_diversion - smart\nvtgs = year_all\nnds = bcus\nsmart_irrigation_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 16 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'flexibility', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 16 * 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 20\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 8.5 * 1.1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 * 1.1\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t vintage = vtgs,\n\t\t\t\t\t value = 0.8\t),\n\t\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# energy_sw_diversion\nvtgs = year_all\nnds = bcus\nenergy_sw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\n\t\t\t\t\t\n\t\t\t\t\t# min_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t# vintage = vtgs,\n\t\t\t\t\t# value = 0.8\t),\n\t\t\t\t\t# vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"energy_sw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# energy_gw_diversion \nvtgs = year_all\nnds = bcus\nenergy_gw_diversion = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\n\t\t\t\t\t\n\t\t\t\t\t# min_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t# vintage = vtgs,\n\t\t\t\t\t# value = 0.8\t),\n\t\t\t\t\t# vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"energy_gw_diversion\",]\n\n\t\t\t\t\t)\n\t\t\t\t\n# transfer surface to groundwater - backstop option for meeting flow constraints\t\t\t\t\t\nvtgs = year_all\nnds = bcus\nsurface2ground = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t\t)\n\n\n# transfer groundwater to suface water - backstop option for meeting flow constraints\nvtgs = year_all\nnds = bcus\nground2surface = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 6 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_div',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t\t)\n\t\t\t\n# urban_desal_seawater \nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = coast_pid\nurban_desal_seawater = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 167 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1650\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 165\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_desal_seawater\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2030,2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# industry_desal_seawater \nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = coast_pid\nindustry_desal_seawater = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 167 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1650\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 165\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_desal_seawater\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2030,2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\n# urban_wastewater_recycling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_wastewater_recycling = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 42 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.8 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1350\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 99\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_wastewater_recycling\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# industry_wastewater_recycling\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nindustry_wastewater_recycling = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 42 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'industry_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.8 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1350\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 99\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"industry_wastewater_recycling\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# urban_wastewater_irrigation\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nurban_wastewater_irrigation = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 80 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.8 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1350\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 99\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"urban_wastewater_irrigation\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# rural_wastewater_recycling\nvtgs = year_all\nnds = bcus\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nrural_wastewater_recycling = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 42 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\n\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'wastewater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.8 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1350\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 99\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"rural_wastewater_recycling\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n\t\t\t\t\t\n# irrigation_desal_seawater - reverse osmosis\nvtgs = year_all\nnds = coast_pid\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nirrigation_desal_seawater = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 30,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 167 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1650\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 165\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"irrigation_desal_seawater\",],\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,inv_tec,year)\n\t\t\t\t\tgrowth_new_capacity_up = expand.grid( \tnode = nds, year_all = c(2030,2040,2050,2060), value = 0.5\t)\n\n\t\t\t\t\t)\n\t\n# Distributed diesel gensets for water pumping in agriculture sector\nvtgs = year_all\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nirri_diesel_genset = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0741 * 2.86 * 60 * 60 * 24 / 1e3, digits = 3 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = left_join( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.088 , digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"irri_diesel_genset\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Distributed diesel gensets for machinery in agriculture sector\nvtgs = year_all\nlft = 20\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nagri_diesel_genset = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'energy', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'agriculture_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0741 * 2.86 * 60 * 60 * 24 / 1e3, digits = 3 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = left_join( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfossil_fuel_cost_var %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(commodity == \"crudeoil\") %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( year_all, value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( (1 + value) * 0.088 , digits = 5 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"agri_diesel_genset\",]\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t\t\n# # Distributed solar PV for water pumping in agriculture sector - assume pumping flexible to output (i.e., flexible load)\nvtgs = year_all\nnds = bcus\nagri_pv = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = NULL,\n\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0 * 2.86 * 60 * 60 * 24 / 1e3, digits = 3 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.25 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 3.873\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"agri_pv\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# ##### Network technologies\n\n## Technology to represent environmental flows - movement of water from river_in to river_out within the same PID\nvtgs = year_all\nnds = bcus\nenvironmental_flow = list( \tnodes = nds,\n\t\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c( 1 ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_in',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 - ( 0.6 * 0.1 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t# left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\t\t\t\t\tcommodity = 'renewable_gw', \n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\t\t\t\t\tlevel = 'aquifer',\n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\t\t\t\t\tvalue = 0.6 * 0.1 ) , # test value of 0.6 base flow index; 0.1 represents 10% baseflow as indicator for renewable GW availability from Gleeson and Richter 2017\n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t# \t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'env_flow', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ) \n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\t\tfix_cost = NULL, \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t\t\t)\n\n## Technology to represent internal inflows in the node, move water to the river_in level and assign internal hydropower runoff potential\nvtgs = year_all\nnds = bcus\ninitial_nodes = as.character(basin.spdf@data$PID[!basin.spdf@data$PID %in% basin.spdf@data$DOWN])\ntmp = bind_rows( lapply( initial_nodes, function( ii ){ data.frame(node = ii,\n qnt = quantile( unlist( water_resources.df[ which( water_resources.df$node == ii), \n grepl( '2015', names(water_resources.df) ) ] ) , 0.75 )\n\t) } ) )\ninternal_runoff = list( \tnodes = nds,\n years = year_all,\n times = time,\n vintages = vtgs,\t\n types = c('water'),\n modes = c( 1 ),\n lifetime = 1,\n \n input = bind_rows( left_join( expand.grid( \tnode = nds,\n vintage = vtgs,\n mode = c( 1 ), \n commodity = 'freshwater', \n level = 'inflow',\n value = 1 ) ,\n vtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n dplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\t\t\t\n \n ),\n \n output = bind_rows( left_join( expand.grid( \tnode = nds,\n vintage = vtgs,\n mode = 1, \n commodity = 'freshwater', \n level = 'river_in',\n value = 1 ),\n vtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n dplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ) #,\t\n \n # left_join( expand.grid( \tnode = nds,\n # vintage = vtgs,\n # mode = 1, \n # commodity = 'hydro_potential_river', \n # level = 'energy_secondary' ),\n # vtg_year_time ) %>% left_join(\n # water_resources.df %>%\n # mutate( value = river_mw_per_km3_per_day / 1e3 ) %>%\n # dplyr::select( node,value )) %>% \n # filter(value > 0) %>% \n # mutate( node_out = node, time_out = time ) %>% \n # dplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n \n # left_join( expand.grid( \tnode = initial_nodes,\n # vintage = vtgs,\n # mode = 1, \n # commodity = 'hydro_potential_old', \n # level = 'energy_secondary' ),\n # vtg_year_time ) %>% left_join(\n # water_resources.df %>% filter(node %in% initial_nodes) %>% \n # left_join(tmp) %>% \n # mutate( value = as.numeric(( existing_MW + planned_MW ) / qnt / 1e3)) %>% \n # dplyr::select(node,value) ) %>% \n # filter(value > 0) %>% \n # mutate( node_out = node, time_out = time ) %>% \n # dplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n \n ),\t\n \n \n \n # data.frame( node,vintage,year_all,mode,emission) \n emission_factor = NULL,\t\t\t\t\t\t\t\t\n \n \n # data.frame(node,vintage,year_all,time)\n capacity_factor = left_join( \texpand.grid( \tnode = nds,\n vintage = vtgs,\n value = 1 ) ,\n vtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n \n # data.frame( vintages, value )\n construction_time = expand.grid( \tnode = nds,\n vintage = vtgs,\n value = 0\t),\n \n # data.frame( vintages, value )\n technical_lifetime = expand.grid( \tnode = nds,\n vintage = vtgs,\n value = 1\t),\n \n # data.frame( vintages, value )\n inv_cost = expand.grid( node = nds,\n vintage = vtgs,\n value = 0\t),\n \n # data.frame( node, vintages, year_all, value )\n fix_cost = NULL, \n \n # data.frame( node, vintage, year_all, mode, time, value ) \n var_cost = NULL,\t\t\t\t\t\t\t\n \n # data.frame(node,year_all,value)\n historical_new_capacity = NULL\n \n)\n\n## River network - technologies that move surface water between PIDs\nvtgs = year_all\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nriver_names = NULL\nfor( iii in 1:length( basin.spdf ) )\n\t{\n\t\n\t# Use the network mapping contained with the basin shapefile\n\tbi = as.character( basin.spdf@data$PID[ iii ] )\n\tbo = as.character( basin.spdf@data$DOWN[ iii ] )\n\t\n\tif( !is.na( bo ) ){ # A downstream basin exists\n\t\t\n\t\t# Output to downstream basin while producing hydropower potential \n\t\toutx = bind_rows( \texpand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater',\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_in',\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.75 ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tmutate( node_out = bo, time_out = time, year_all = vintage ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\texpand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = 'hydro_potential_old',\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\tvalue = water_resources.df %>%\n\t\t\t\t\t\t\t\t\t\t\t\tfilter( node == bi ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = as.numeric(( existing_MW + planned_MW ) / \n\t\t\t\t\t\t\t\t\t\t\t\t quantile( unlist( water_resources.df[ which( water_resources.df$node == bo), \n\t\t\t\t\t\t\t\t\t\t\t\t grepl( '2015', names(water_resources.df) ) ] ) , 0.75 ) / 1e3 )) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% unlist() ) %>%\t\n\t\t\t\t\t\t\t\tmutate( node_out = bi, time_out = time, year_all = vintage ) %>%\n\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\texpand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\ttime = time,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = 'hydro_potential_river',\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\tvalue = water_resources.df %>%\n\t\t\t\t\t\t\t\t\t\t\t\tfilter( node == bi ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = river_mw_per_km3_per_day / 1e3 ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% unlist() ) %>%\t\n\t\t\t\t\t\t\t\tmutate( node_out = node, time_out = time, year_all = vintage ) %>%\n\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t)\n\t\t\t\n\t\t}else{ # No downstream basin - sink or outlet, i.e., no downstream node in the core model\n\t\t\n\t\tbo = 'SINK' \n\t\t\n\t\toutx = bind_rows( \tleft_join( data.frame( commodity = 'hydro_potential_old', \n\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary', \n\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = water_resources.df %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( node == bi ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = ( existing_MW + planned_MW ) / quantile( get( names(water_resources.df)[ grepl( '2015', names(water_resources.df) ) ] ) , 0.75 ) / 1e3 ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% unlist() ),\n\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>%\n\t\t\t\t\t\t\t\t\tmutate( node_out = node, time_out = time ) %>%\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tleft_join( data.frame( commodity = 'hydro_potential_river', \n\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary', \n\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = water_resources.df %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( node == bi ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = river_mw_per_km3_per_day / 1e3 ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% unlist() ),\n\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>%\n\t\t\t\t\t\t\t\t\tmutate( node_out = node, time_out = time ) %>%\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t)\n\t\t\n\t\t}\n\t\n\triver_name = paste( 'river', bi, bo, sep = '|' ) \n\t\n\triver_names = c( river_names, river_name )\n\t\n\ttlst = list( \tnodes = bi,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\n\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = outx,\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL\n\n\t\t\t\t\t)\n\t\n\tassign( river_name, tlst )\n\t\t\t\n\t}\n\nriver_routes = gsub('river\\\\|','',river_names)\n## Conveyance - technologies that move surface water between PIDs\nvtgs = year_all\nlft = 50\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \ncanal_names = NULL\n\n# Need to provide options for lined and unlined canals \nfor( hhh in c( 'conv', 'lined' ) )\n\t{\n\t\n\tif( hhh == 'conv' ){ eff = 0; cst = 1; flg = 1 }else{ eff = 0.15; cst = 1.5; flg = 0 } # set adders/mulitpliers for efficiency and costs between lined and unlined systems\n\t\n\t# Need to provide option for both directions as invididual investments as opposed to a single investment / bi-directional system - conveyance not typically operated bi-directionally\n\tadjacent_routes2 = c( adjacent_routes, paste( unlist( strsplit( adjacent_routes, '[|]' ) )[ seq( 2,2*length(adjacent_routes),by=2 ) ], unlist( strsplit( adjacent_routes, '[|]' ) )[ seq( 1,2*length(adjacent_routes),by=2 ) ], sep = '|' ) )\n\t\n\t# # Remove options that are across borders \n\t# adjacent_routes2 = adjacent_routes2[ which( unlist( strsplit( unlist( strsplit( adjacent_routes2, '[|]' ) )[ seq( 1,2*length(adjacent_routes2), by=2 ) ], '_' ) )[ seq( 1,2*length(adjacent_routes2), by=2 ) ] ==\n\t# \t\t\t\t\t\t\t\t\t\t\tunlist( strsplit( unlist( strsplit( adjacent_routes2, '[|]' ) )[ seq( 2,2*length(adjacent_routes2), by=2 ) ], '_' ) )[ seq( 1,2*length(adjacent_routes2), by=2 ) ] ) ]\n\t# \n\t# Cost data\n\tadjacent_routes2 = setdiff(adjacent_routes2,river_routes)\n\t\n\tif (!FULL_COOPERATION) {\n\t# # Remove options that are across borders\n\tadjacent_routes2 = adjacent_routes2[gsub('_.*', '',gsub('.*\\\\|', '',adjacent_routes2)) == \tgsub('_.*', '',gsub('\\\\|.*', '',adjacent_routes2))]\n\t} else{\n\t} # end if\n\tcan_inv_cost.df = rbind( can_inv_cost.df, can_inv_cost.df %>% mutate( tec = paste( unlist( strsplit( tec, '[|]' ) )[ seq( 2,2*length(tec),by=2 ) ], unlist( strsplit( tec, '[|]' ) )[ seq( 1,2*length(tec),by=2 ) ], sep = '|' ) ) )\n\t\n\t# Go through routes\n\tfor( iii in 1:length(adjacent_routes2) )\n\t\t{\n\t\t\n\t\tbi = unlist( strsplit( as.character( adjacent_routes2[iii] ), '[|]' ) )[1]\n\t\tbo = unlist( strsplit( as.character( adjacent_routes2[iii] ), '[|]' ) )[2]\n\t\t\n\t\tif( adjacent_routes2[iii] %in% canals_agg.df$route ){ hc = round( canals_agg.df$capacity_m3_per_sec[ which( canals_agg.df$route == adjacent_routes2[iii] ) ] * 60 * 60 * 24 / 1e6, digits = 1 ) }else{ hc = 0 }\n\t\t\n\t\tcanal_name = paste0( hhh, '_canal|', adjacent_routes2[iii] )\n\t\t\n\t\tcanal_names = c( canal_names, canal_name )\n\t\t\n\t\ttlst = list( \tnodes = bi,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\tlifetime = 20,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_out',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) , \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'urban_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 12 ) , # ! Should be updated to unique value for each route\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = bi,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_in',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.75 + eff ) , # assuming 25% losses - should be updated to align with seepage estimates\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = bo, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\t\n\t\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\temission_factor = NULL,\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = cst * can_inv_cost.df$value[ which( can_inv_cost.df$tec == adjacent_routes2[iii] | can_inv_cost.df$tec == paste( unlist( strsplit( adjacent_routes2[iii], '[|]' ))[c(2,1)] ,collapse='|') ) ] ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.05 * cst * can_inv_cost.df$value[ which( can_inv_cost.df$tec == adjacent_routes2[iii] | can_inv_cost.df$tec == paste( unlist( strsplit( adjacent_routes2[iii], '[|]' ))[c(2,1)] ,collapse='|') ) ]\t),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\thistorical_new_capacity = expand.grid( node = bi, year_all = year_all, value = hc * flg ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\tbound_total_capacity_lo = expand.grid( node = bi, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tyear_all = year_all, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = flg * max( c( 0, round( canals_agg.df %>% filter( route == adjacent_routes2[iii] ) %>% select( capacity_m3_per_sec )%>% unlist( . ) * 60 * 60 * 24 / 1e6, digits = 1 ) ), na.rm = TRUE ) ),\n\t\t\t\t\t\t\n\t\t\t\t\t\tbound_activity_lo = expand.grid( \tnode = bi, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tyear_all = year_all, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttime = as.character( time ), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = flg * 0.2 * max( c( 0, round( canals_agg.df %>% filter( route == adjacent_routes2[iii] ) %>% select( capacity_m3_per_sec ) %>% unlist( . ) * 60 * 60 * 24 / 1e6, digits = 1 ) ), na.rm = TRUE ) )\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t)\n\t\t\t\t\n\t\tassign( canal_name, tlst )\n\t\t\t\t\n\t\t}\t\n\t\n\t}\n\t\n# interbasin canal for tranfers to India potentially missed in irrigation implementation\n# Identified manually as 1) node that transfers water out to India via Indira Ghandhi canal etc.; and \n# 2) node the contains Keenjhar Lake and 583 MGD link to Karachi for potable water supply\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = interbasin_transfer.df$node \ninterbasin_canal = list( \tnodes = nds,\n\t\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\t\ttypes = c('water'),\n\t\t\t\t\t\t\tmodes = c( 1 ),\n\t\t\t\t\t\t\tlifetime = 1,\n\t\t\t\t\t\n\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'river_in',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\t\n\t\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\toutput = NULL, # transferred outside the basin\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t), # test w/ small penalty \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = NULL,\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = NULL,\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\tbound_total_capacity_lo = bind_rows( lapply( nds, function( nnn ){\n\t\t\t\t\t\texpand.grid( \tnode = nnn, \n\t\t\t\t\t\t\t\t\t\tyear_all = year_all[ year_all>baseyear ], \n\t\t\t\t\t\t\t\t\t\tvalue = unlist( interbasin_transfer.df[nnn,'min_flow'] )\t)\n\t\t\t\t\t\t} ) ),\n\t\t\t\t\t\n\t\t\t\t\tbound_activity_lo = bind_rows( lapply( nds, function( nnn ) { \n\t\t\t\t\t\texpand.grid( \tnode = nnn, \n\t\t\t\t\t\t\t\t\t\tyear_all = year_all, \n\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\ttime = as.character( time ), \n\t\t\t\t\t\t\t\t\t\tvalue = unlist( interbasin_transfer.df[nnn,'min_flow'] ) * 0.95 ) \n\t\t\t\t\t\t} ) )\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t)\t\n\t\n#------------\n# CROPS and irrigation technologies\n\n# the number of crops and names can be decided by the user, it is basin specific \n# to select the main crops, and in this way many crops can be easily added\ncrp = crop_names\n# ittigation technologies are just 3 at the moment, and rain-fed irrigation\n# is considered in addition\n\nmachinery_ei_kwh_per_kg = 0.13 # energy in per unit of crop production - Average from Rao et al. (2018)\nirrigation_tecs = unique( irr_tech_data.df$irr_tech )\n# Define average efficiency for water once it reaches the farm-gate \n# Assuming all irrigation is basically flood irrigation based on disucssions with stakeholders\n# Also aligns closely with asssumptions in Wu et al. (2013, World Bank) figure 5.4 \nfield_efficiency = 0.85\nwater_course_efficiency = 0.55 # these numbers need to match the efficiencies used for calibration in crop_yields.r so perhaps good to explicility link later\nfield_efficiency_conv = field_efficiency * water_course_efficiency\ngwp_ch4 = gwp.df$gwp[gwp.df$emission == 'CH4']\ncrop_ch4 = data.frame( crop = 'rice', value = 1300 ) # default IPCC CH4 emission factor converted to metric tons per Mha per day\ncrop_tech_names = NULL\nrainfed_crop_names = NULL\nirr_tech_names = NULL\nresidue_data.d = residue_data %>% bind_rows(\n\tdata.frame(crop = c('fruit','vegetables'), res_yield = c(0,0) , mode = c(8,9), liquid = c(0,0),\n ethanol_ratio = c(NA,NA), var_eth_cost = c(NA,NA) ,stringsAsFactors = F)\n\t)\nfor( ii in seq_along( crop_names ) )\n\t{\n\tvtgs = year_all\n\tnds = bcus\n\tcrop_tech_name = paste0('crop_',crop_names[ii])\n\tcrop_tech_names = c( crop_tech_names, crop_tech_name )\n\t\n\tlft = 1\n\tvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \n\tvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \n\tnode_mode_vtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( node = bcus, mode = 1, vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) )\n\tgrowing_season = crop_tech_data.df %>% filter(crop == crop_names[ii] & par == 'crop_coef') %>% filter( value > 0 ) %>% select( time ) %>% unlist()\n\tvtg_year_time = vtg_year_time %>% filter( time %in% growing_season )\n\t\n\ttmp = crop_input_data.df %>% filter(crop == crop_names[ii] & par == 'rain-fed_yield') %>% dplyr::select(node,value)\n\ttmp = tmp %>% expand(tmp,time) %>% filter( time %in% growing_season ) %>%\n left_join(crop_tech_data.df %>% \n filter(crop == crop_names[ii] & par == 'crop_coef') %>% \n rename(ccf = value) %>% \n dplyr::select(time,ccf) ) %>% #from yearly yield to monthly and scaled with crop coefficient\n mutate(value = value * ccf ) %>% #kton/Mha\n filter(!is.na(value)) %>% \n mutate(commodity = paste0(crop_names[ii],'_yield'))\n\n\ttlst = list(nodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = growing_season,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('land'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = 1,\n\t\t\t\t\n\t\t\t\tinput = left_join( tmp %>% expand(tmp, level = c('agriculture_final'), vintage = vtgs ) %>% # on-farm energy requirements for machinery\n\t\t\t\t\t\t\t\t\tmutate( mode = 1, time_in = time, node_in = node, commodity = 'energy' ) %>%\n\t\t\t\t\t\t\t\t\tmutate( value = round( machinery_ei_kwh_per_kg * value * ( 1 / 8760 ) * ( 1 / 1e3 ) * ( 1e6 / 1 ) , digits = 3 ) )\t, # convert the intensity to MW per Mha - assumed it is evenly distributed across the year\n\t\t\t\t\t\t\t\tvtg_year ) %>% \n\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( \n\t\t\t\t\t\n\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = paste0(crop_names[ii],'_land'), \n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'crop', \n\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ), # crop area in Mha\n\t\t\t\t\t\tvtg_year_time )\t%>%\tmutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ) \n\t\t\t\t\t\n\t\t\t\t\t),\n\t\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\temission_factor = \n\t\t\t\t rbind( left_join( fertilizer_emissions.df %>% filter( crop == crop_names[ii] ) %>%\n\t\t\t\t\t\tfilter( if( length( which( irrigation == 'rainfed' ) ) == 0 ) irrigation == 'irrigated' else irrigation == 'rainfed' ) %>%\n\t\t\t\t\t\tgroup_by( PID, crop, emission ) %>% # 0.8 reflects that about 80% farmers using fertilizers at recommended rates\n\t\t\t\t\t\tsummarise( value = round( sum ( value ) * 1 / 365 * 1 / 1e3 * 1e6 / 1 * 0.8, digits = 3 ) ) %>% # convert from kg per year per hectare to metric tons per day per Mha\n\t\t\t\t\t\tungroup() %>% data.frame() %>%\n\t\t\t\t\t\trename( node = PID ),\n\t\t\t\t\tnode_mode_vtg_year ) %>%\n\t\t\t\t\tselect( node, vintage, year_all, mode, emission, value ), # add methane emissions\n\t\t\t\t\tleft_join( \tnode_mode_vtg_year,\n\t\t\t\t\t\t\t\trbind( \texpand.grid( node = bcus, emission = 'CH4', value = max( 0, unlist( crop_ch4 %>% filter(crop==crop_names[ii]) %>% select(value) ), na.rm=TRUE ) ),\n\t\t\t\t\t\t\t\t\t\texpand.grid( node = bcus, emission = 'CO2eq', value = max( 0, unlist( crop_ch4 %>% filter(crop==crop_names[ii]) %>% select(value) ), na.rm=TRUE ) * gwp_ch4 ) ) ) ) %>%\n\t\t\t\t\tgroup_by( node, vintage, year_all, mode, emission ) %>% summarise( value = round( sum(value), digits = 3 ) ) %>%\n\t\t\t\t\tungroup() %>% data.frame(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = crop_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( crop == crop_names[ii], par == 'inv_cost' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tunlist() ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = crop_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\tfilter( crop == crop_names[ii], par == 'fix_cost' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tunlist() ), \n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = crop_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( crop == crop_names[ii], par == 'var_cost' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( value ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tunlist() ), \n\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value )\t,\t\n\t\t\t\t\n\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t vintage = vtgs,\n\t\t\t\t value = 0\t),\n\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\n\t\t\t\thistorical_new_capacity = \n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\texpand.grid(node = nds, year_all = c(2015) ) %>%\n\t\t\t\t\t\t\t\t\t\tleft_join(\tcrop_input_data.df %>%\n\t\t\t\t\t\t\t\t\t\tfilter(crop == crop_names[ii] & par %in% c('crop_irr_land_2015','crop_rainfed_land_2015') ) %>%\n\t\t\t\t\t\t\t\t\t\tmutate(crop = crop_tech_name, value = value ) %>%\n\t\t\t\t\t\t\t\t\t\tdplyr::rename( tec = crop ) ) %>%\n\t\t\t\t group_by(node,tec,year_all) %>% \n\t\t\t\t summarise(value = sum(value)) %>% ungroup() %>% \n\t\t\t\t\t\t\t\t\t\tdplyr::select(node,tec,year_all,value)\n\t\t\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n\t\t\t\t\t\n\tassign( crop_tech_name, tlst )\n \n\t# rain-fed crops, will have particular parametrization, like yield,\n\t# for now we keep it separated from other irrigation technologies\n\t\n\trainfed_crop_name = paste0('rainfed_',crop_names[ii])\n\trainfed_crop_names = c( rainfed_crop_names, rainfed_crop_name )\n\n\ttmp = crop_input_data.df %>% filter(crop == crop_names[ii] & par == 'rain-fed_yield') %>% dplyr::select(node,value)\n\ttmp = tmp %>% expand(tmp,time) %>% filter( time %in% growing_season ) %>%\n left_join(crop_tech_data.df %>% \n filter(crop == crop_names[ii] & par == 'crop_coef') %>% \n rename(ccf = value) %>% \n dplyr::select(time,ccf) ) %>% #from yearly yield to monthly and scaled with crop coefficient\n mutate(value = value * ccf ) %>% #kton/Mha\n filter(!is.na(value)) %>% \n mutate(commodity = paste0(crop_names[ii],'_yield'))\n\t\n\tnds = unique((tmp %>% filter(value > 0))$node)\n\t\t\n\ttlst = list(nodes = nds,\n\t\t\t\tyears = year_all,\n\t\t\t\ttimes = growing_season,\n\t\t\t\tvintages = vtgs,\t\n\t\t\t\ttypes = c('land'),\n\t\t\t\tmodes = c( 1 ),\n\t\t\t\tlifetime = 1,\n\t\t\t\t\n\t\t\t\tinput = left_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = paste0(crop_names[ii],'_land'), \n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'crop', \n\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ), \n\t\t\t\t\t\tvtg_year_time )\t%>%\tmutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\n\t\t\t\toutput = bind_rows( \n\t\t\t\t\t\n\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tcommodity = 'crop_land', \n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tlevel = 'area', \n\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ), \n\t\t\t\t\t\tvtg_year_time )\t%>%\tmutate( node_out = node, time_out = as.character(time) ) %>% \n\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ) ,\n\n\t\t\t\t\t # for the next df the time_output is yearly (annual yield demand)\n left_join( tmp %>% expand(tmp, level = c('raw','residue'), vintage = vtgs ) %>% \n\t\t\t\t\t\t\t\t\tmutate(mode = 1, time = time) %>% \n\t\t\t\t\t\t\t\t\tmutate(time_out = if_else(level == 'raw','year',time)) %>% \n\t\t\t\t\t\t\t\t\tmutate(node_out = node) %>%\n\t\t\t\t\t\t\t\t\tmutate(value = if_else(value == 0 , value, if_else(level == 'residue', ( residue_data.d$res_yield[ residue_data.d$crop == crop_names[ii] ] ) *ccf , value ) ) ),\n\t\t\t\t\t\t\t\tvtg_year ) %>% filter( time %in% growing_season ) %>%\n\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\n\t\t\t\t\t\n\t\t\t\t\t),\n\t\n\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) emissions allocated to crop land area and to irrigation tec\n\t\t\t\temission_factor = NULL,\n\t\t\t\t\t\n\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\n\t\t\t\t# data.frame( vintages, value )\n\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\tvalue = 0 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ), \n\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\n\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\tvar_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0), \n\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\n\t\t\t\t\n\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t vtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\t\n\t\t\t\t\t\t\n\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\thistorical_new_capacity = expand.grid(node = nds, year_all = c(2015) ) %>%\n\t\t\t\t left_join( crop_input_data.df %>%\n\t\t\t\t dplyr::filter(crop == crop_names[ii] & par == 'crop_rainfed_land_2015') %>%\n\t\t\t\t mutate( crop = rainfed_crop_name, value = round( value, digits = 5 ) ) %>%\n\t\t\t\t dplyr::rename( tec = crop )\n\t\t\t\t ) %>% dplyr::select(node,tec,year_all,value)\n\t\t\t\t\t\t\n\t\t\t\t)\n\t\t\t\t\n\tassign( rainfed_crop_name, tlst )\n \n\tfor (jj in seq_along(irrigation_tecs)){\n\t\t\n\t\tirr_tech_name = paste0(irrigation_tecs[jj],'_',crop_names[ii])\n\t\tirr_tech_names = c( irr_tech_names, irr_tech_name)\n\t\ttmp = crop_input_data.df %>% filter(crop == crop_names[ii]) %>% \n\t\t spread(par,value) %>% \n\t\t mutate(irrigation_yield = if_else(is.na(irrigation_yield),0,irrigation_yield),\n\t\t `rain-fed_yield` = if_else(is.na(`rain-fed_yield` ),0,`rain-fed_yield` ) ) %>% \n\t\t mutate(value = irrigation_yield) %>% \n\t\t dplyr::select(node,value) %>% \n\t\t group_by(node) %>% summarise(value = max(value)) %>% ungroup()\n\t\ttmp = tmp %>% expand(tmp,time) %>% \n\t\t\tleft_join(\tcrop_tech_data.df %>% \n\t\t\t\t\t\tfilter(crop == crop_names[ii] & par == 'crop_coef') %>% \n\t\t\t\t\t\trename(ccf = value) %>% \n\t\t\t\t\t\tdplyr::select(time,ccf) ) %>% \n\t\t\tmutate(value = value * ccf) %>% #kton/Mha\n\t\t\tdplyr::filter(!is.na(value)) %>% \n\t\t\tmutate(commodity = paste0(crop_names[ii],'_yield'))\n\t\t\n\t\tif (grepl('irr_flood_',irr_tech_name)){\n\t\t tmp_hist_new_cap = expand.grid(node = nds, year_all = c(2015) ) %>%\n\t\t left_join( crop_input_data.df %>%\n\t\t dplyr::filter(crop == crop_names[ii] & par == 'crop_irr_land_2015') %>%\n\t\t mutate( crop = irr_tech_name, value = round( value, digits = 5 ) ) %>%\n\t\t dplyr::rename( tec = crop )\n\t\t ) %>% dplyr::select(node,tec,year_all,value)\n\t\t} else {\n\t\t tmp_hist_new_cap = NULL\n\t\t}\n\t\t\n\t\tnds = unique((tmp %>% filter(value > 0))$node)\n\t\tnds = nds[nds %in% unique((crop_water.df %>% filter(crop == crop_names[ii]))$node)]\n \n\t\ttlst = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = growing_season,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('land'),\n\t\t\t\t\t\tmodes = 1,\n\t\t\t\t\t\tlifetime = 9,\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( \n\t\t\t\t\t\t\t\t\t\t\t\texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = paste0(crop_names[ii],'_land'), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'crop',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t# crop water requirement scaled to represent water taken from irrigation diversions \n\t\t\t\t\t\t\t\t\t\t\t# need to divide by the field efficiency multiplied by the efficiency of the irrigation tech to estimate water requirement at the crop-level\n\t\t\t\t\t\t\t\t\t\t\t# Note that additional efficiency losses are accounted for in 'irrigation_sw_diversion', to account for the distribution losses\n\t\t\t\t\t\t\t\t\t\t\tleft_join( crop_water.df %>% filter(crop == crop_names[ii]) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::rename( year_all = year ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = ( 1 / unlist( \tirr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par == 'water_efficiency' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) * \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfield_efficiency_conv ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t) * value, time = as.character( time ) ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( commodity = 'freshwater', level = 'irrigation_final', mode = 1, node_in = node, time_in = time ),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>%\t\n\t\t\t\t\t\t\t\t\t\t\t filter(year_all <= lastyear) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( value, digits = 3 ) ),\n\n\t\t\t\t\t\t\t\t\t\t\t# Operational electricity for the irrigation tech - varies based on water use\n\t\t\t\t\t\t\t\t\t\t\tleft_join( crop_water.df %>% filter(crop == crop_names[ii]) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::rename( year_all = year ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = ( 1 / unlist( \tirr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par == 'water_efficiency' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) * \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfield_efficiency_conv ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t) * value, time = as.character( time ) ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( commodity = 'electricity', level = 'irrigation_final', mode = 1, node_in = node, time_in = time ),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>%\t\n\t\t\t\t\t\t\t\t\t\t\t\tfilter(year_all <= lastyear) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = value * irr_tech_data.df %>% # multiply water use per area by electricity use per water to get electricity per activity\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par == 'electricity_intensity' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( value, digits = 3 ) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t) %>% filter(node %in% nds), \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'crop_land', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'area',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = as.character(time) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t# for the next df the time_output is yearly (annual yield demand)\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( tmp %>% expand(tmp, level = c('raw','residue'), vintage = vtgs ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate(mode = 1, time = time) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate(time_out = if_else(level == 'raw','year',time)) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate(node_out = node) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate(value = if_else(value == 0 , value, if_else(level == 'residue', ( residue_data.d$res_yield[ residue_data.d$crop == crop_names[ii] ] ) *ccf , value ) ) ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% filter( time %in% growing_season ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t# irrigation losses contribute to groundwater recharge\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( crop_water.df %>% filter(crop == crop_names[ii]) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::rename( year_all = year ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = (( 1 / unlist( irr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par == 'water_efficiency' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) * field_efficiency_conv ) )-1 ) * value, time = as.character( time ) ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( commodity = 'renewable_gw', level = 'aquifer', mode = 1, node_out = node, time_out = time ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>%\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(year_all <= lastyear) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t# Operational electricity flexibility for the smart irrigation tech - varies based on water use\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( crop_water.df %>% filter(crop == crop_names[ii]) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::rename( year_all = year ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = ( 1 / unlist( \tirr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par == 'water_efficiency' ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) * \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfield_efficiency_conv ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t) * value, time = as.character( time ) ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( commodity = 'flexibility', level = 'energy_secondary', mode = 1, node_in = node, time_in = time ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>%\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter(year_all <= lastyear) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = value * irr_tech_data.df %>% # multiply water use per area by electricity use per water to get electricity per area \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[jj], par %in% c( 'electricity_intensity', 'electricity_flexibility' ) ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist( . ) %>% min( c( prod( . ), 0 ) ) ) %>% # electricity flexibility impacts depend on electricity demand level - avoid double counting flexibility impacts included in distribution techs\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmutate( value = round( value, digits = 3 ) ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\trename( node_out = node_in, time_out = time_in )\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t) %>% filter(node %in% nds),\t\n\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) - difference between the irrigated and rain fed to avoid doible counting\n\t\t\t\t\t\temission_factor = right_join(\n\t\t\t\t\t\t\tleft_join( fertilizer_emissions.df %>% filter( crop == crop_names[ii] ) %>%\n\t\t\t\t\t\t\t\tfilter( if( length( which( irrigation == 'rainfed' ) ) == 0 ) irrigation == 'irrigated' else irrigation == 'rainfed' ) %>%\n\t\t\t\t\t\t\t\tgroup_by( PID, crop, emission ) %>%\n\t\t\t\t\t\t\t\tsummarise( value = sum ( value ) * 1 / 365 * 1 / 1e3 * 1e6 / 1 * 0.8 ) %>% ungroup() %>% data.frame() %>%\n\t\t\t\t\t\t\t\trename( node = PID ), node_mode_vtg_year ) %>%\n\t\t\t\t\t\t\tselect( node, vintage, year_all, mode, emission, value ) %>% rename( value2 = value ),\n\t\t\t\t\t\t\tleft_join( fertilizer_emissions.df %>% filter( crop == crop_names[ii] ) %>%\n\t\t\t\t\t\t\t\t\tfilter( irrigation == 'irrigated') %>%\n\t\t\t\t\t\t\t\t\tgroup_by( PID, crop, emission ) %>%\n\t\t\t\t\t\t\t\t\tsummarise( value = sum ( value ) * 1 / 365 * 1 / 1e3 * 1e6 / 1 * 0.8 ) %>% # 0.8 reflects that about 80% farmers using fertilizers at recommended rates\n\t\t\t\t\t\t\t\t\tungroup() %>% data.frame() %>%\n\t\t\t\t\t\t\t\trename( node = PID ), node_mode_vtg_year ) %>%\n\t\t\t\t\t\t\tselect( node, vintage, year_all, mode, emission, value ) ) %>%\n\t\t\t\t\t\t\t\tmutate( value2 = ifelse( is.na(value2), 0, value2 ) ) %>%\n\t\t\t\t\t\t\t\tmutate( value = round( value-value2 , digits = 3 ) ) %>%\n\t\t\t\t\t\t\t\tselect( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = irr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[ jj ], par == 'inv_cost' ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist() ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = irr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[ jj ], par == 'fix_cost' ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist() ), \n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = irr_tech_data.df %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfilter( irr_tech == irrigation_tecs[ jj ], par == 'var_cost' ) %>%\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tselect( value ) %>% unlist() ),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ) ,\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\t\n\t\t\t\t\t\thistorical_new_capacity = tmp_hist_new_cap\n\t\t\t)\n\t\n\t\tassign( irr_tech_name, tlst )\n \n\t\t}\n\t\n\t}\n\n# fellow crops, do not consume of produce anything, no costs, just consume land,\n# it is required to not necessarily use all the available land and still having\n# a full commodity balance\nvtgs = year_all\nnds = bcus\nlft = 1\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nfallow_crop\t = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('land'),\n\t\t\t\t\t\tmodes = 1,\n\t\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tinput = NULL, \n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'crop_land', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'area',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\t\tvar_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue =0 ) ,\t # 5.2E-3 M$/kt transport costs\t\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\t\n\t\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"fallow_crop\",]\n\n\t\t\t\t\t\t)\n\t\t\t\t\t\n## biomass converions into ethanol or solid, including transport\nvtgs = year_all\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nnds = bcus\nsolid_biom = list( \tnodes = nds,\n\t\t\t\t\tyears = year_all,\n\t\t\t\t\ttimes = time,\n\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\tmodes = residue_data$mode,\n\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\n\t\t\t\t\tinput = left_join( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = residue_data$mode, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'residue',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\tdata.frame( mode = residue_data$mode, commodity = paste0( residue_data$crop, '_yield' ) ) \n\t\t\t\t\t\t\t\t\t\t\t), vtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ), \n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = residue_data$mode, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'solid',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = residue_data$mode, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = residue_data$mode,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5.2E-3 ) ,\t # 5.2E-3 M$/kt transport costs\t\n\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"solid_biom\",]\n\n\t\t\t\t\t)\n\t\t\t\t\t\n#solid biomass tratment, and trnasport (drying is usually done at the power plant, using waste heat)\nvtgs = year_all\nnds = bcus\nlft = 30\nvtg_year = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ] ) } ) ) \nvtg_year_time = bind_rows( lapply( vtgs, function( vv ){ expand.grid( vintage = vv, year_all = year_all[ year_all %in% vv:(vv+lft) ], time = time ) } ) ) \nliq_modes = unlist( ( residue_data %>% filter(liquid > 0))['mode'] )\nethanol_prod = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\t\tmodes = liq_modes,\n\t\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( \tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'residue' ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.frame( mode = liq_modes, value = round( 1 / ( residue_data %>% filter(liquid > 0) %>% dplyr::select( ethanol_ratio ) %>% unlist() ), digits = 5 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t commodity = paste0(residue_data$crop[residue_data$liquid > 0],'_yield') ) \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t), vtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value ),\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'freshwater', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'energy_secondary',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'ethanol',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'water_consumption', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 3 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 1.064\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.016\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = left_join( \tleft_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = liq_modes ),\n\t\t\t\t\t\t\t\t\t\t\t\tdata.frame( mode = liq_modes, value = 5.2E-3 +( residue_data %>% filter(liquid > 0) %>% dplyr::select( var_eth_cost ) %>% unlist() ) ) ),\t # 5.2E-3 M$/kt transport costs\t\n\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value ),\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"ethanol_prod\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_new_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1\t)\n\n\t\t\t\t\t)\t\t\t\n\n# Ethanol generator, rural areas only\nethanol_genset = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\t\tmodes = c(1,2),\n\t\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'ethanol',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1/0.33 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'irrigation_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value ),\n\t\t\t\t\t\t\t\t\t\t\tleft_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 2, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'electricity', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'rural_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0741 * 2.86 * 60 * 60 * 24 / 1e3, digits = 3 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"ethanol_genset\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_total_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1\t)\n\n\t\t\t\t\t)\n\t\t\t\t\t\n# Ethanol generator, on-farm machinery\nethanol_agri_genset = list( \tnodes = nds,\n\t\t\t\t\t\tyears = year_all,\n\t\t\t\t\t\ttimes = time,\n\t\t\t\t\t\tvintages = vtgs,\t\n\t\t\t\t\t\ttypes = c('power'),\n\t\t\t\t\t\tmodes = c(1,2),\n\t\t\t\t\t\tlifetime = lft,\n\t\t\t\t\t\t\n\t\t\t\t\t\tinput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1,2), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'biomass', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'ethanol',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1/0.33 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_in = node, time_in = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_in, commodity, level, time, time_in, value )\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\n\t\t\t\t\t\t\n\t\t\t\t\t\toutput = bind_rows( left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = 1, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tcommodity = 'energy', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlevel = 'agriculture_final',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 1 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% mutate( node_out = node, time_out = time ) %>% \n\t\t\t\t\t\t\t\t\t\t\t\t\tdplyr::select( node, vintage, year_all, mode, node_out, commodity, level, time, time_out, value )\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t),\t\n\n\t\t\t\t\t# data.frame( node,vintage,year_all,mode,emission) \n\t\t\t\t\temission_factor = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\temission = 'CO2', \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = round( 0.0741 * 2.86 * 60 * 60 * 24 / 1e3, digits = 3 ) ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, mode, emission, value )\n\n\t\t\t\t\t\t),\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,time)\n\t\t\t\t\tcapacity_factor = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.9 ) ,\n\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, time, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tconstruction_time = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 5\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\ttechnical_lifetime = expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = lft\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( vintages, value )\n\t\t\t\t\tinv_cost = expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\tvalue = 0.676\t),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintages, year_all, value )\n\t\t\t\t\tfix_cost = left_join( \texpand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.007\t),\n\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ), \n\t\t\t\t\t\n\t\t\t\t\t# data.frame( node, vintage, year_all, mode, time, value ) \n\t\t\t\t\tvar_cost = bind_rows( \tleft_join( expand.grid( node = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmode = c(1),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0 ),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year_time ) %>% dplyr::select( node, vintage, year_all, mode, time, value )\n\t\t\t\t\t\t\t\t\t\t\t),\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,vintage,year_all,value)\n\t\t\t\t\tmin_utilization_factor = left_join( expand.grid( \tnode = nds,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvintage = vtgs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tvalue = 0.2\t),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tvtg_year ) %>% dplyr::select( node, vintage, year_all, value ),\n\t\t\t\t\t\n\t\t\t\t\t# data.frame(node,year_all,value)\n\t\t\t\t\thistorical_new_capacity = hist_new_cap.df[hist_new_cap.df$tec == \"ethanol_agri_genset\",],\n\t\t\t\t\t\n\t\t\t\t\t# bound_new_capacity_up(node,inv_tec,year)\n\t\t\t\t\tbound_total_capacity_up = expand.grid( \tnode = nds, year_all = c(2020,2030,2040,2050,2060), value = 1\t)\n\n\t\t\t\t\t)\t\t\t\t\t\n\t\t\t\t", "meta": {"hexsha": "88dfdb5c9740e486557a6c8ad95436d5d3694ddd", "size": 572496, "ext": "r", "lang": "R", "max_stars_repo_path": "MESSAGEix/basin_msggdx_technologies.r", "max_stars_repo_name": "amirsarikhani/NEST", "max_stars_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-07-15T19:28:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-24T04:45:43.000Z", "max_issues_repo_path": "MESSAGEix/basin_msggdx_technologies.r", "max_issues_repo_name": "amirsarikhani/NEST", "max_issues_repo_head_hexsha": 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YES\n2. NO", "lm_q1_score": 0.7279754607093178, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.34126807239444956}} {"text": "\nfile.name<-'ICES stock summary sum' # graphical output file if paper<-TRUE\n\nfirst.year<-2004 #first year on plot, negative value means value defined by data\nlast.year<- 2013 #last year on plot\n\n##########################################################################\n\n\nref<-Read.reference.points.OP()\nInit.function()\nBlim<-as.vector(ref[,'Blim'])\nBpa<-as.vector(ref[,'Bpa'])\nFpa<-as.vector(ref[,'Fpa'])\n\na<-subset(Read.summary.data(), select=c(Species.n,Species,Year,Age,Yield))\nprice<-Read.price()\na<-merge(a,price)\na$Value<-a$Price*a$Yield\na<-aggregate(Value~Species.n+Species+Year,data=a,sum)\n\nb<-Read.summary.table()\nb<-merge(a,b)\n\nb<-droplevels(subset(b,Species !='Plaice' & Species !='Sole' & Year>=first.year & Year<=last.year))\nb$belowBlim<-ifelse(b$SSBFpa[b$Species.n-first.VPA+1],1,0)\n \n\nminY<-min(b$Year)\nmaxY<-max(b$Year)\nny<-maxY-minY+1\n \n a<-aggregate(cbind(Yield,SOP,Value,SSB,mean.F)~Species.n+Species,mean,data=b)\n\n aa<-aggregate(cbind(belowBlim,belowBpa,aboveFpa)~Species.n+Species,sum,data=b)\n \n aa$belowBlim<- aa$belowBlim/ny*100\n aa$belowBpa<- aa$belowBpa/ny*100\n aa$aboveFpa<- aa$aboveFpa/ny*100\n \n aa<-merge(a,aa)\n aa$labels<-paste(aa$Species.n-first.VPA+1,aa$Species)\n \n a<-cbind(\n t(t(tapply(aa$Yield,aa$labels,sum)/1000)),\n t(t(tapply(aa$SOP,aa$labels,sum)/1000)),\n t(t(tapply(aa$Value,aa$labels,sum)/1000)),\n t(t(tapply(aa$SSB,aa$labels,sum)/1000)) ,\n t(t(tapply(aa$mean.F,aa$labels,sum))),\n t(t(tapply(aa$belowBlim,aa$labels,sum))),\n t(t(tapply(aa$belowBpa,aa$labels,sum))),\n t(t(tapply(aa$aboveFpa,aa$labels,sum))))\n \n all<-colSums(a) \n all[5:8]<-NA \n \n a<-rbind(a,all)\n \n my.dimnames<-c('Yield','Catch','Value','SSB','Fbar','SSB below Blim','SSB below Bpa','F above Fpa') \n \n dimnames(a)[[2]]<-my.dimnames \n \n print(a)\n my.units<-c('(kt)','(kt)','(m Euro)','(kt)',' ','(%)','(%)','(%)') \n my.dec<-c(0,0,0,0,2,0,0,0)\n\n xtab(a, caption='', cornername=' ',\n file=file.path(paste('hist_performance_',first.year,'-',last.year,'.html',sep='')),\n dec=my.dec, width='\"100%\"',units=my.units)\n\n ", "meta": {"hexsha": "b09a2231e065845fb78829e8f4ea75e1452fc4a1", "size": 2272, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/hcr_op_hist_compare.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/hcr_op_hist_compare.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/hcr_op_hist_compare.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1232876712, "max_line_length": 102, "alphanum_fraction": 0.6087147887, "num_tokens": 782, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7279754371026368, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.3412680613278588}} {"text": "#Code that uses Cox PH models to associate observed-to-expected neoantigen ratio in initial or recurent tumors with overall survival and surgical interval\n#Cox PH models that associate overall survival with observed-to-expected neoantigen ratio are reported in the manuscript\n#Query at the top adapted from Kevin, joins neoantigen depletion with other tables to perform survival analyses\n#Other analyses (not reported in manuscript): \n#\tCorrelates surgical interval with observed-to-expected neoantigen ratio (no association)\n#\tQuery and then Cox model to examine association between neoantigen load with overall survival (no association, reviewer request)\n#\tQuery to look at observed-to-expected neoantigen ratio in samples treated with immunotherapy (nothing of note)\n#------------------------------------------------------------------------------\n\nlibrary(odbc)\nlibrary(DBI)\nlibrary(ggplot2)\nlibrary(survival)\n\ncon <- DBI::dbConnect(odbc::odbc(), \"VerhaakDB\") \n\nq <- \"WITH roman_to_int (grade, grade_int)\nAS\n(\n VALUES ('I', 1),\n ('II', 2),\n ('III', 3),\n ('IV', 4)\n)\nSELECT\n tp.tumor_pair_barcode,\n tp.case_barcode,\n tp.tumor_barcode_a,\n tp.tumor_barcode_b,\n\tclin.idh_codel_subtype AS subtype,\n\tcas1.case_age_diagnosis_years AS age,\n (CASE\n WHEN s1.surgery_location = s2.surgery_location AND (s1.surgery_laterality = s2.surgery_laterality OR (s1.surgery_laterality IS NULL AND s2.surgery_laterality IS NULL)) THEN 'Local'\n WHEN s1.surgery_location <> s2.surgery_location OR s1.surgery_laterality <> s2.surgery_laterality THEN 'Distal'\n END) AS recurrence_location,\n (SELECT bool_or(treatment_tmz) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_tmz,\n (SELECT sum(treatment_tmz_cycles) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number)::integer AS received_tmz_sum_cycles,\n (SELECT bool_or(treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt,\n (SELECT sum(treatment_radiation_dose_gy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt_sum_gy,\n (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_alk,\n (SELECT bool_or(treatment_alkylating_agent or treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_treatment,\n (CASE\n WHEN r2.grade_int > r1.grade_int THEN 'Grade up'\n WHEN r2.grade_int = r1.grade_int THEN 'Grade stable'\n WHEN r2.grade_int < r1.grade_int THEN 'Grade down'\n END) AS grade_change,\n s2.surgical_interval_mo - s1.surgical_interval_mo AS surgical_interval,\n 1 AS surgery,\n cas1.case_overall_survival_mo AS OS,\n CASE WHEN cas1.case_vital_status='alive' THEN 0 WHEN cas1.case_vital_status='dead' THEN 1 END AS case_vital_status,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 THEN false\n ELSE NULL\n END) hypermutator_status,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN false\n ELSE NULL\n END) alk_assoc_hypermutator_status,\n CASE WHEN nd1.rneo < 1 THEN 1 WHEN nd1.rneo >=1 THEN 0 END AS depletion_initial,\n CASE WHEN nd2.rneo < 1 THEN 1 WHEN nd2.rneo >=1 THEN 0 END AS depletion_recurrent\nFROM analysis.tumor_pairs tp\nLEFT JOIN biospecimen.aliquots a1 ON a1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN biospecimen.aliquots a2 ON a2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.surgeries s1 ON s1.sample_barcode = a1.sample_barcode\nLEFT JOIN clinical.surgeries s2 ON s2.sample_barcode = a2.sample_barcode\nLEFT JOIN roman_to_int r1 ON r1.grade = s1.grade\nLEFT JOIN roman_to_int r2 ON r2.grade = s2.grade\nLEFT JOIN analysis.mut_freq mf1 ON mf1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.mut_freq mf2 ON mf2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN analysis.neoantigen_depletion nd1 ON nd1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.neoantigen_depletion nd2 ON nd2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.subtypes clin ON clin.case_barcode = s1.case_barcode\nLEFT JOIN clinical.cases cas1 ON cas1.case_barcode = s1.case_barcode\nINNER JOIN analysis.gold_set gs ON gs.case_barcode = tp.case_barcode\nWHERE nd1.rneo IS NOT NULL AND nd2.rneo IS NOT NULL AND (nd1.nobs >= 3 AND nd2.nobs >= 3)\n\"\n\nres <- dbGetQuery(con, q)\n\n#surg_cox_a <- coxph(Surv(surgical_interval,surgery) ~ nd_a + subtype, data = res)\n#surg_cox_b <- coxph(Surv(surgical_interval,surgery) ~ nd_b + subtype, data = res)\n\nos_cox_a <- summary(coxph(Surv(os,case_vital_status) ~ depletion_initial + age + subtype, data = res))\nos_cox_b <- summary(coxph(Surv(os,case_vital_status) ~ depletion_recurrent + age + subtype, data = res))\n\nsurg_cox_a <- coxph(Surv(surgical_interval,surgery) ~ nd_a + subtype + received_treatment, data = res)\nsurg_cox_b <- coxph(Surv(surgical_interval,surgery) ~ nd_b + subtype + received_treatment, data = res)\n\n\ncor(res[,\"surgical_interval\"],res[,\"nd_b\"],method=\"s\",use=\"complete\")\n\n#Neoantigen load\n#----------------\nlibrary(odbc)\nlibrary(DBI)\nlibrary(ggplot2)\nlibrary(survival)\n\ncon <- DBI::dbConnect(odbc::odbc(), \"VerhaakDB\") \n\nq <- \"WITH roman_to_int (grade, grade_int)\nAS\n(\n VALUES ('I', 1),\n ('II', 2),\n ('III', 3),\n ('IV', 4)\n)\nSELECT\n tp.tumor_pair_barcode,\n tp.case_barcode,\n tp.tumor_barcode_a,\n tp.tumor_barcode_b,\n\tclin.idh_codel_subtype AS subtype,\n\tcas1.case_age_diagnosis_years AS age,\n (CASE\n WHEN s1.surgery_location = s2.surgery_location AND (s1.surgery_laterality = s2.surgery_laterality OR (s1.surgery_laterality IS NULL AND s2.surgery_laterality IS NULL)) THEN 'Local'\n WHEN s1.surgery_location <> s2.surgery_location OR s1.surgery_laterality <> s2.surgery_laterality THEN 'Distal'\n END) AS recurrence_location,\n (SELECT bool_or(treatment_tmz) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_tmz,\n (SELECT sum(treatment_tmz_cycles) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number)::integer AS received_tmz_sum_cycles,\n (SELECT bool_or(treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt,\n (SELECT sum(treatment_radiation_dose_gy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt_sum_gy,\n (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_alk,\n (SELECT bool_or(treatment_alkylating_agent or treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_treatment,\n (CASE\n WHEN r2.grade_int > r1.grade_int THEN 'Grade up'\n WHEN r2.grade_int = r1.grade_int THEN 'Grade stable'\n WHEN r2.grade_int < r1.grade_int THEN 'Grade down'\n END) AS grade_change,\n s2.surgical_interval_mo - s1.surgical_interval_mo AS surgical_interval,\n 1 AS surgery,\n cas1.case_overall_survival_mo AS OS,\n CASE WHEN cas1.case_vital_status='alive' THEN 0 WHEN cas1.case_vital_status='dead' THEN 1 END AS case_vital_status,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 THEN false\n ELSE NULL\n END) hypermutator_status,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN false\n ELSE NULL\n END) alk_assoc_hypermutator_status,\n CAST(COALESCE(nd1.neoag_count,0) AS numeric) AS neoag_load_a,\n CAST(COALESCE(nd2.neoag_count,0) AS numeric) AS neoag_load_b\nFROM analysis.tumor_pairs tp\nLEFT JOIN biospecimen.aliquots a1 ON a1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN biospecimen.aliquots a2 ON a2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.surgeries s1 ON s1.sample_barcode = a1.sample_barcode\nLEFT JOIN clinical.surgeries s2 ON s2.sample_barcode = a2.sample_barcode\nLEFT JOIN roman_to_int r1 ON r1.grade = s1.grade\nLEFT JOIN roman_to_int r2 ON r2.grade = s2.grade\nLEFT JOIN analysis.mut_freq mf1 ON mf1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.mut_freq mf2 ON mf2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN analysis.neoag_freq nd1 ON nd1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.neoag_freq nd2 ON nd2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.subtypes clin ON clin.case_barcode = s1.case_barcode\nLEFT JOIN clinical.cases cas1 ON cas1.case_barcode = s1.case_barcode\nINNER JOIN analysis.gold_set gs ON gs.case_barcode = tp.case_barcode\n\"\n\nres <- dbGetQuery(con, q)\n\nos_cox_a <- summary(coxph(Surv(os,case_vital_status) ~ neoag_load_a + age + subtype, data = res))\nos_cox_b <- summary(coxph(Surv(os,case_vital_status) ~ neoag_load_b + age+ subtype, data = res))\n\n\n#Immunotherapy\n#----------------\n\n\nlibrary(odbc)\nlibrary(DBI)\nlibrary(ggplot2)\nlibrary(survival)\n\ncon <- DBI::dbConnect(odbc::odbc(), \"VerhaakDB\") \n\nq <- \"\nWITH roman_to_int (grade, grade_int)\nAS\n(\n VALUES ('I', 1),\n ('II', 2),\n ('III', 3),\n ('IV', 4)\n)\nSELECT\n tumor_pair_barcode,\n tp.case_barcode,\n tumor_barcode_a,\n tumor_barcode_b,\n\tclin.idh_codel_subtype AS subtype,\n (CASE\n WHEN s1.surgery_location = s2.surgery_location AND (s1.surgery_laterality = s2.surgery_laterality OR (s1.surgery_laterality IS NULL AND s2.surgery_laterality IS NULL)) THEN 'Local'\n WHEN s1.surgery_location <> s2.surgery_location OR s1.surgery_laterality <> s2.surgery_laterality THEN 'Distal'\n END) AS recurrence_location,\n (SELECT bool_or(treatment_tmz) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_tmz,\n (SELECT sum(treatment_tmz_cycles) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number)::integer AS received_tmz_sum_cycles,\n (SELECT bool_or(treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt,\n (SELECT sum(treatment_radiation_dose_gy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_rt_sum_gy,\n (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_alk,\n (SELECT bool_or(treatment_alkylating_agent or treatment_radiotherapy) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) AS received_treatment,\n (CASE\n WHEN r2.grade_int > r1.grade_int THEN 'Grade up'\n WHEN r2.grade_int = r1.grade_int THEN 'Grade stable'\n WHEN r2.grade_int < r1.grade_int THEN 'Grade down'\n END) AS grade_change,\n s2.surgical_interval_mo - s1.surgical_interval_mo AS surgical_interval,\n 1 AS surgery,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 THEN false\n ELSE NULL\n END) hypermutator_status,\n (CASE\n WHEN mf2.coverage_adj_mut_freq > mf1.coverage_adj_mut_freq AND mf2.coverage_adj_mut_freq > 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN true \n WHEN mf2.coverage_adj_mut_freq < 10 AND (SELECT bool_or(treatment_alkylating_agent) FROM clinical.surgeries ss WHERE ss.case_barcode = tp.case_barcode AND ss.surgery_number >= s1.surgery_number AND ss.surgery_number < s2.surgery_number) IS TRUE THEN false\n ELSE NULL\n END) alk_assoc_hypermutator_status,\n\t s1.treatment_chemotherapy_other AS immunotherapy_a,\n\t s2.treatment_chemotherapy_other AS immunotherapy_b,\t \n nd1.rneo AS nd_a,\n nd2.rneo AS nd_b\nFROM analysis.tumor_pairs tp\nLEFT JOIN biospecimen.aliquots a1 ON a1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN biospecimen.aliquots a2 ON a2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.surgeries s1 ON s1.sample_barcode = a1.sample_barcode\nLEFT JOIN clinical.surgeries s2 ON s2.sample_barcode = a2.sample_barcode\nLEFT JOIN roman_to_int r1 ON r1.grade = s1.grade\nLEFT JOIN roman_to_int r2 ON r2.grade = s2.grade\nLEFT JOIN analysis.mut_freq mf1 ON mf1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.mut_freq mf2 ON mf2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN analysis.neoantigen_depletion nd1 ON nd1.aliquot_barcode = tp.tumor_barcode_a\nLEFT JOIN analysis.neoantigen_depletion nd2 ON nd2.aliquot_barcode = tp.tumor_barcode_b\nLEFT JOIN clinical.subtypes clin ON clin.case_barcode = s1.case_barcode\nWHERE (nd1.rneo IS NOT NULL AND nd2.rneo IS NOT NULL AND (nd1.nobs >= 3 AND nd2.nobs >= 3)) AND\n\ts1.treatment_chemotherapy_other LIKE '%embrolizumab%' OR s2.treatment_chemotherapy_other LIKE '%embrolizumab%' OR\n\ts1.treatment_chemotherapy_other LIKE 'DC%' OR s2.treatment_chemotherapy_other LIKE 'DC%' OR\n\ts1.treatment_chemotherapy_other LIKE '%endritic%' OR s2.treatment_chemotherapy_other LIKE '%endritic%' OR\n\ts1.treatment_chemotherapy_other LIKE '%accine%' OR s2.treatment_chemotherapy_other LIKE '%accine%' OR\n\ts1.treatment_chemotherapy_other LIKE '%dc%ax%' OR s2.treatment_chemotherapy_other LIKE '%dc%ax%'\n\"\n\nres <- dbGetQuery(con, q)\n", "meta": {"hexsha": "728f48e2eb12b90d818af1b3b32ac1aebe6526ad", "size": 15875, "ext": "r", "lang": "R", "max_stars_repo_path": "R/neoantigens/analysis/neoantigen_depletion_survival_cox.r", "max_stars_repo_name": "Kcjohnson/SCGP", "max_stars_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2020-11-09T14:23:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T07:10:15.000Z", "max_issues_repo_path": "R/neoantigens/analysis/neoantigen_depletion_survival_cox.r", "max_issues_repo_name": "Kcjohnson/SCGP", "max_issues_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-01-16T06:07:35.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-01T15:51:32.000Z", 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YES\n2. NO", "lm_q1_score": 0.7217432062975979, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.34115608797992686}} {"text": "# Data Wrangling in R\n# Austin Water Quality Case Study\n\n# Load in the libraries that we'll need\nlibrary(tidyverse)\nlibrary(stringr)\nlibrary(lubridate)\n\n# Read in the dataset\nwater <- read_csv('http://594442.youcanlearnit.net/austinwater.csv')\n\n# Let's take a look at what we have\nglimpse(water)\n\n# First, let's get rid of a lot of columns that we don't need\n# I'm going to do that by building a new tibble with just\n# siteName, siteType, parameter, result and unit\n\nwater <- tibble('siteName'=water$SITE_NAME,\n 'siteType'=water$SITE_TYPE,\n 'sampleTime'=water$SAMPLE_DATE,\n 'parameterType'=water$PARAM_TYPE,\n 'parameter'=water$PARAMETER,\n 'result'=water$RESULT,\n 'unit'=water$UNIT)\n\nglimpse(water)\n\n# Now let's start finding the rows that we need.\n# First, we need pH. I might start by trying to look at all unique parameter names\n\nunique(water$parameter)\n\n# but that's way too long... what if we try searching for names that contain PH?\n\nunique(water[which(str_detect(water$parameter,'PH')),]$parameter)\n\n# still a mess... let's backtrack and look at parameter types\n\nunique(water$parameterType)\n\n# OK, what if I filter this down to look only at parameter types of Alkalinity/Hardness/pH\n# and Conventionals\n\nfiltered_water <- subset(water,(parameterType=='Alkalinity/Hardness/pH') |\n parameterType=='Conventionals')\n\n# Notice that this is much smaller in size. let's check what parameters we have now\n\nunique(filtered_water$parameter)\n\n# I want only two of these, (discuss PH and temp choices), so let's filter those\n\nfiltered_water <- subset(filtered_water, ((parameter=='PH') |\n (parameter=='WATER TEMPERATURE')))\n\nglimpse(filtered_water)\n\n# Let's take a look at the data a different way\nsummary(filtered_water)\n\n# It would be helpful to convert some of these to factors\nfiltered_water$siteType <- as.factor(filtered_water$siteType)\nfiltered_water$parameterType <- as.factor(filtered_water$parameterType)\nfiltered_water$parameter <- as.factor(filtered_water$parameter)\nfiltered_water$unit <- as.factor(filtered_water$unit)\n\nsummary(filtered_water)\n\n# And sampleTime should be a date/time object\nfiltered_water$sampleTime <- mdy_hms(filtered_water$sampleTime)\n\nsummary(filtered_water)\n\n# Why are some of these measurements in feet?\nsubset(filtered_water,unit=='Feet')\n\n# Looks like that is supposed to be Farenheit\nconvert <- which(filtered_water$unit=='Feet')\nfiltered_water$unit[convert] <- 'Deg. Fahrenheit'\n\nsummary(filtered_water)\n\n# What about the MG/L?\nsubset(filtered_water,unit=='MG/L')\nsubset(filtered_water,unit=='MG/L' & parameter=='PH')\n\nconvert <- which(filtered_water$unit=='MG/L' & filtered_water$parameter=='PH')\nfiltered_water$unit[convert] <- 'Standard units'\n\nsubset(filtered_water,unit=='MG/L')\nsubset(filtered_water,unit=='MG/L' & filtered_water$result>70)\nconvert <- which(filtered_water$unit=='MG/L' & filtered_water$result>70)\nfiltered_water$unit[convert] <- 'Deg. Fahrenheit'\n\nsubset(filtered_water,unit=='MG/L')\nconvert <- which(filtered_water$unit=='MG/L')\nfiltered_water$unit[convert] <- 'Deg. Celsius'\n\nsummary(filtered_water)\n\n# Let's just take a quick and dirty look at all of our results\nggplot(filtered_water,mapping=aes(x=sampleTime, y=result)) +\n geom_point()\n\n# There's clearly one large outlier\nsubset(filtered_water,result>1000000)\n\n# I don't know how to correct that, so I'm going to remove it. I also want to remove the NA result\nremove <- which(filtered_water$result>1000000 | is.na(filtered_water$result))\nfiltered_water <- filtered_water[-remove,]\n\nsummary(filtered_water)\n\n# Still some very high values, so let's repeat\nsubset(filtered_water,result>1000)\n\nremove <- which(filtered_water$result>1000 | is.na(filtered_water$result))\nfiltered_water <- filtered_water[-remove,]\n\nsummary(filtered_water)\n\n# That looks better. Let's drill into some boxplots now\n\nggplot(data=filtered_water, mapping = aes(x=unit,y=result)) + geom_boxplot()\n\n# Those Celsius values over 60 should probably be Fahrenheit\n# Because 60 degrees Celsius is 140 degrees Fahrenheit!\n\nconvert <- which(filtered_water$result>60 & filtered_water$unit=='Deg. Celsius')\nfiltered_water$unit[convert] <- 'Deg. Fahrenheit'\n\n# Let's look at the boxplots again\n\nggplot(data=filtered_water, mapping = aes(x=unit,y=result)) + geom_boxplot()\n\n# Let's find our Fahrenheit values in the dataset\nfahrenheit <- which(filtered_water$unit=='Deg. Fahrenheit')\n\n# And convert them to Celsius\nfiltered_water$result[fahrenheit] <- (filtered_water$result[fahrenheit] - 32) * (5/9)\n\n# Now how do our boxplots look?\nggplot(data=filtered_water, mapping = aes(x=unit,y=result)) + geom_boxplot()\n\n# We just need to fix up the unit values\nfiltered_water$unit[fahrenheit] <- 'Deg. Celsius'\n\n# And check the plots again\nggplot(data=filtered_water, mapping = aes(x=unit,y=result)) + geom_boxplot()\n\n# Let's look at a final summary of the data\nsummary(filtered_water)\n\n# There are some empty factor levels in there, let's get rid of them\nfiltered_water$unit <- droplevels(filtered_water$unit)\nsummary(filtered_water)\n\n\n", "meta": {"hexsha": "e9bde0c0d488368ae8699070c2bbb01a9d632862", "size": 5174, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_08/water_8_start.r", "max_stars_repo_name": "vvpn9/Handy-Tools", "max_stars_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_08/water_8_start.r", "max_issues_repo_name": "vvpn9/Handy-Tools", "max_issues_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_08/water_8_start.r", "max_forks_repo_name": "vvpn9/Handy-Tools", "max_forks_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3375, "max_line_length": 99, "alphanum_fraction": 0.7388867414, "num_tokens": 1239, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548782017746, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.34093239762974975}} {"text": "#!/usr/bin/env Rscript\n\n##======================\n## filters input interactions according to the distance range\n## allows circukler genome\n##======================\n\nargs <- commandArgs(TRUE)\nInpFile <- as.character(args[1])\nOutFile <- as.character(args[2])\nLowDistThres <- as.integer(args[3])\nUppDistThres <- as.integer(args[4])\nCircularGenome <- as.integer(args[5])\nChrSizeFile <- as.character(args[6])\n\nif (CircularGenome == 0) {\n\t## default interaction filtering based on the absolute genomic distance\n\tsystem(paste0(\"awk -v l=\", LowDistThres, \" -v u=\", UppDistThres, \" -F[\\'\\t\\'] \\'function abs(v) {return v < 0 ? -v : v} {if ((NR==1) || (($1==$4) && ($7>0) && (abs($2-$5)>=l) && (abs($2-$5)<=u))) {print $0}}\\' \", InpFile, \" > \", OutFile))\n} else {\n\t## use circular genome and chromosome size information\n\tsystem(paste0(\"awk \\'(NR==1)\\' \", InpFile, \" > \", OutFile))\n\n\tChrSizeData <- read.table(ChrSizeFile, header=F, sep=\"\\t\", stringsAsFactors=F)\n\tChrList <- unique(ChrSizeData[,1])\n\tfor (j in 1:length(ChrList)) {\n\n\t\tcurrchr <- ChrList[j]\n\t\tcat(sprintf(\"\\n ==>> circular genome - genomic distance based filtering - processing chromosome : %s \", currchr))\n\t\t## chromosome size for this chromosome\n\t\tmax_coord <- ChrSizeData[which(ChrSizeData[,1] == currchr), 2]\n\t\tcat(sprintf(\" -->> max_coord (from ChrSizeFile) for this chromosome : %s \", max_coord))\n\n\t\t## analyze the circular genomic distance\n\t\t## include cis-pairs, with distance within the specified distance thresholds\t\t\n\t\tsystem(paste0(\"awk -F\\'[\\t]\\' -v R=\", max_coord, \" -v c=\\\"\", currchr, \"\\\" -v l=\", LowDistThres, \" -v u=\", UppDistThres, \" \\'function abs(v) {return v < 0 ? -v : v}; function min(x,y) {return x < y ? x : y} {if ((NR>1) && ($1==c) && ($1==$4) && ($7>0)) {d=min(min(abs($5-$2),abs((R-$5)+$2)),abs((R-$2)+$5)); if ((d>=l) && (d<=u)) {print $0}}}\\' \", InpFile, \" >> \", OutFile))\n\t\n\t}\t# end chromosome loop\n}\n", "meta": {"hexsha": "643e09c18d2c00123d21612bd832e312045db453", "size": 1886, "ext": "r", "lang": "R", "max_stars_repo_path": "src/Filt_Loop_DistThr.r", "max_stars_repo_name": "gahanleeo/FitHiChIP", "max_stars_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "max_stars_repo_stars_event_min_datetime": "2017-11-07T09:39:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T05:35:24.000Z", "max_issues_repo_path": "src/Filt_Loop_DistThr.r", "max_issues_repo_name": "gahanleeo/FitHiChIP", "max_issues_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 66, "max_issues_repo_issues_event_min_datetime": "2018-03-28T02:36:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T10:44:28.000Z", "max_forks_repo_path": "src/Filt_Loop_DistThr.r", "max_forks_repo_name": "gahanleeo/FitHiChIP", "max_forks_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2017-12-08T22:02:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T06:49:25.000Z", "avg_line_length": 48.358974359, "max_line_length": 375, "alphanum_fraction": 0.6086956522, "num_tokens": 603, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3409101664010405}} {"text": "library(Seurat)\nlibrary(Seurat)\nllibrary(data.table)\nlibrary(dplyr) \nlibrary('org.Hs.eg.db')\nlibrary(limma)\n\nwrite_csv = function(vec, fn){write.table(vec, file = fn, append = FALSE, quote = FALSE, sep = \",\",\n eol = \"\\n\", na = \"NA\", dec = \".\", row.names = TRUE,\n col.names = TRUE, qmethod = c(\"escape\", \"double\"),\n fileEncoding = \"\")}\n\nseurat_recipet = function(expr){\nexpr_obj = CreateSeuratObject(expr)\nexpr_obj = NormalizeData(expr_obj, normalization.method = \"LogNormalize\", scale.factor = 10000)\nexpr_obj = ScaleData(expr_obj)\nexpr_obj = FindVariableFeatures(expr_obj, selection.method = \"vst\", nfeatures = 2000)\nexpr_obj = RunPCA(expr_obj, features = VariableFeatures(object = expr_obj), npcs = 100)\nexpr_obj\n}\n\nfolder_to_save = './seurat/'\nexprs=read.csv('expr_v10k.csv', row.names=1, header=T , check.names = F)\nexprs=t(exprs)\n\nmeta=read.csv('meta_v10k', row.names=1, header=T, check.names = F)\nabrain = seurat_recipet(exprs)\nwrite_csv(Embeddings(object = abrain, reduction = \"pca\"), paste0(folder_to_save, 'seurat_pca.csv'))\n \nfor (npc in c(20, 50, 100))\n{\n abrain1=FindNeighbors(object = abrain, dims = 1:npc, k.param = 100) \n abrain1=FindClusters(object = abrain1, resolution = 1., graph.name = 'RNA_snn')\n abrain1=RunUMAP(object = abrain1, min.dist = 1.5, graph = 'RNA_snn') \n\n write_csv(abrain1@meta.data$seurat_clusters, paste0(folder_to_save, \"seurat_clusters_pc\", npc, '.csv') )\n write_csv(Embeddings(object = abrain1, reduction = \"umap\"), paste0(folder_to_save, 'seurat_umap_pc', npc, '.csv') )\n}\n\nnpc = 50\n\nfn_cluster = fn_hpca = paste0('./seurat/harmony_cluster', npc, '.csv')\nfn_topgene = 'seurat/topgene_r1.csv'\nfn_marker = 'seurat/markers_r1.csv'\n\nfn_hpca = paste0('./seurat/harmony', npc, '.csv')\n\nfn_umap = paste0('./seurat/umap_harmony', npc, '.csv')\n\ndf_harmony = read.csv(fn_hpca, row.names = 1)\n\nabrain[[\"harmony\"]] <- CreateDimReducObject(embeddings = as.matrix(df_harmony), \n key = \"harmony_\", assay = DefaultAssay(abrain))\n \nabrain1=FindNeighbors(object = abrain, dims = 1:npc, k.param = 15, reduction = \"harmony\") \nabrain1=FindClusters(object = abrain1, resolution = .1, graph.name = 'RNA_snn')\nwrite_csv(abrain1@meta.data$seurat_clusters, fn_cluster)\n\nabrain1=RunUMAP(object = abrain1, min.dist = .3, graph = 'RNA_snn', n.epochs = 200) \nwrite_csv(Embeddings(object = abrain1, reduction = \"umap\"), fn_umap)\n\n\nabrain.markers=FindAllMarkers(object = abrain1, min.pct= 0.3, return.thresh = 1.)\ntopgene=abrain.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)\nwrite_csv(topgene,fn_topgene)\nwrite_csv(as.data.frame(abrain.markers), fn_marker)\n\nabrain.markers=FindAllMarkers(object = abrain1, min.pct= 0.3, return.thresh = 1.)\ntopgene=abrain.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)\nwrite_csv(topgene,fn_topgene)\nwrite_csv(as.data.frame(abrain.markers), fn_marker)\n\n", "meta": {"hexsha": "c92e6aa98e620868e530b398a4bc9d3316cf11a5", "size": 2928, "ext": "r", "lang": "R", "max_stars_repo_path": "MOMIC_subcluster/marker_v3.r", "max_stars_repo_name": "howchihlee/covid_brain_sc", "max_stars_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-29T02:40:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T02:40:23.000Z", "max_issues_repo_path": "MOMIC_subcluster/marker_v3.r", "max_issues_repo_name": "howchihlee/covid_brain_sc", "max_issues_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MOMIC_subcluster/marker_v3.r", "max_forks_repo_name": "howchihlee/covid_brain_sc", "max_forks_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.1095890411, "max_line_length": 119, "alphanum_fraction": 0.6967213115, "num_tokens": 906, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3409101664010405}} {"text": "# Example for \n# http://stackoverflow.com/questions/25085537/permute-groups-in-a-data-frame-r#\n#\n#DqStr <- \"Group q Dq SD.Dq\n# 1 -3.0 0.7351 0.0067\n# 1 -2.5 0.6995 0.0078\n# 1 -2.0 0.6538 0.0093\n# 2 -3.0 0.7203 0.0081\n# 2 -2.5 0.6829 0.0094\n# 2 -2.0 0.6350 0.0112\"\n# \n# Dq1 <- read.table(textConnection(DqStr), header=TRUE)\n# \n# g <-unique(Dq1$q)\n# Dq2<- data.frame()\n# for(n in g)\n# {\n# Dqq <- Dq1[Dq1$q==n,]\n# Dqq$Group <-sample(Dqq$Group)\n# Dq2 <- rbind(Dq2,Dqq) \n# }\n#\n# n <- -3\n# print(n)\n# Dqq <- Dq1[Dq1$q==g,]\n# Dqq$Group <-sample(Dqq$Group)\n# Dq2 <- rbind(Dq2,Dqq) \n# print(Dq2[,1:2])\n# library(plyr)\n# ddply(Dq1,.(q), function(x) { x$Group <- sample(x$Group)\n# data.frame(x)})\n#\n#Dq1$Group <- with(Dq1, ave(Group, q, FUN = sample))\n#Dq1\n\n# Test for comparing two curves with sd\n#\n\nsource(\"R/Test_Dq_fun.r\")\n\n\n# Test with other data\n#\nsetwd(\"Simul\")\nDq1 <- readNeutral_calcDq(paste0(bName,\"T100mfSAD.txt\"))\nDq1$Time <- rep( 1:(nrow(Dq1)/35),each=35)\nDq1$n <- 6\n\nplotDq_ReplaceR(Dq1,0.2,0.04,0.0001)\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,1)\nnames(Dqq)\n\n\nDqq<- Dqq[1:70,c(5:8,10)]\nplotDq(Dqq,\"as.factor(Time)\")\n\n#undebug(compareTwoGCurves)\n\n# Compare using permutations\n#\ncompareTwoGCurves(Dqq$Time,Dqq[,3:5],Dqq$q,1000)\n\n\nDq2 <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.01)\nDq2<- Dq2[1:70,c(5:8,10)]\nplotDq(Dq2,\"as.factor(Time)\")\n\nDq2 <- rbind(Dq2[1:35,],Dqq[1:35,])\nplotDq(Dq2,\"as.factor(Time)\")\nnames(Dq2)\n\ncompareTwoGCurves(Dq2$Time,Dq2[,3:5],Dq2$q,1000)\n\n\n# Compare using differences an Bartlett test\n#\n\ng <- unique(Dq2$Time)\nDiq1 <- with(Dq2, Dq2[Time==g[1],])\nDiq2 <- with(Dq2, Dq2[Time==g[2],]) \ndiffNormT(Diq1[,3:5],Diq2[,3:5])\n\n\ng <- unique(Dqq$Time)\nDiq1 <- with(Dqq, Dqq[Time==g[1],])[,3:5]\nDiq2 <- with(Dqq, Dqq[Time==g[2],])[,3:5]\ndiffNormT(Diq1,Diq2)\n\n\n# Compare using monte carlo\n#\nundebug(compareTwoCurvesMC)\ncompareTwoCurvesMC(Dqq$Time,Dqq$Dq,Dqq$SD.Dq)\ncompareTwoCurvesMC(Dq2$Time,Dq2$Dq,Dq2$SD.Dq,10000)\nDiq2 <- with(Dq2, Dq2[q<0,])\ncompareTwoCurvesMC(Diq2$Time,Diq2$Dq,Diq2$SD.Dq,10000)\n\n# Another dataset\n\nDq1 <- readNeutral_calcDq(paste0(bName,\"T100mfOrd.txt\"))\nDq1$Time <- rep( 1:(nrow(Dq1)/35),each=35)\nDq1$n <- 6\n\nplotDq_ReplaceR(Dq1,0.2,0.04,0.0001)\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,1)\nnames(Dqq)\n\n\nDqq<- Dqq[1:70,c(5:8,10)]\nplotDq(Dqq,\"as.factor(Time)\")\n\n#undebug(compareTwoGCurves)\n\ncompareTwoGCurves(Dqq$Time,Dqq[,3:5],Dqq$q,1000)\n\n\nDq2 <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.01)\nDq2<- Dq2[1:70,c(5:8,10)]\nplotDq(Dq2,\"as.factor(Time)\")\n\nDq2 <- rbind(Dq2[1:35,],Dqq[1:35,])\nplotDq(Dq2,\"as.factor(Time)\")\n\n\ncompareTwoGCurves(Dq2$Time,Dq2[,3:5],Dq2$q,1000)\n\nDq2$SD.Dq <- Dq2$SD.Dq/10 \nplotDq(Dq2,\"as.factor(Time)\")\ncompareTwoGCurves(Dq2$Time,Dq2[,3:5],Dq2$q,1000)\n\n\ng <- unique(Dqq$Time)\n\nDiq1 <- with(Dqq, Dqq[Time==g[1],])\nDiq2 <- with(Dqq, Dqq[Time==g[2],]) \ndiffNormT(Diq1[,3:5],Diq2[,3:5])\n\n\ng <- unique(Dq2$Time)\n\nDiq1 <- with(Dq2, Dq2[Time==g[1],])\nDiq2 <- with(Dq2, Dq2[Time==g[2],]) \n\n\nnames(Dq2)\ndiffNormT(Diq1[,3:5],Diq2[,3:5])\n\n\n\nDq2$SD.Dq[is.nan(Dq2$SD.Dq)] <- 0\nc <- compareTwoCurvesMC(Dq2$Time,Dq2$Dq,Dq2$SD.Dq,1000)\nhist(c$dist)\nc$stat\nc\ndebug(compareTwoCurvesMC)\n\n## COMPARE USING MULTIPLE T TEST\n\n\ndebug(compareTwoCurvesT)\nc <- compareTwoCurvesT(Dq2$Time,Dq2$Dq,Dq2$SD.Dq,6)\nDq2$p.value <- c\nDq2[Dq2$p.value<0.05,]\n\nFisher.test(na.omit(c)) # p-value = 0.017\n\nc <- compareTwoCurvesT(Dqq$Time,Dqq$Dq,Dqq$SD.Dq,6)\nc[c<0.05]\n\nFisher.test(na.omit(c)) # p-value = 0.017\n\nc[c<0.05]\n\n\n\n\n### TEST KS for Dq comparison \n#\n# THIS Discard the SD but works the way it should!\n#\nDq1 <- readNeutral_calcDq(paste0(bName,\"T100mfSAD.txt\"))\n\n\nnames(Dq1)\nplotDq_ReplaceR(Dq1,0.2,0.04,0.0001)\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,1)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\n\nmk1 <- pairKS_DqByRep(Dqq$Dq,Dqq$rep)\npropNotDiffSAD(mk1) # =.36\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.1)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nmk1 <- pairKS_DqByRep(Dqq$Dq,Dqq$rep)\npropNotDiffSAD(mk1) # =1\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.01)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nmk1 <- pairKS_DqByRep(Dqq$Dq,Dqq$rep)\npropNotDiffSAD(mk1) # =1\n\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.001)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nmk1 <- pairKS_DqByRep(Dqq$Dq,Dqq$rep)\npropNotDiffSAD(mk1) # =1\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nmk1 <- pairKS_DqByRep(Dqq$Dq,Dqq$rep)\npropNotDiffSAD(mk1) # =1\n\n# Para DqSAD solamente replacementRate=1 da que los que provienen de simulaciones iguales \n# son distintos\n\n# Dq1 <- readNeutral_calcDq(paste0(bName,\"T100mfOrd.txt\"))\n\n# RELACIONAR VARAIBILIDADES TEMPORALES - ESPACIALES con PARAMETROS\n# Como varia SAD DqSAD DqSRS en tiempo en relacion de con parametros/procesos \n# (nicho vs no nicho)\n#\n\n\nrequire(plyr)\nDq2 <- ddply(Dq1,.(MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate,q),summarise,Dq=mean(Dq))\n\nplotDq_ReplaceR(Dq2,0.2,0.04,0.0001)\n\n\n# Test pairwise diferences in SAD\n#\nmk1 <- pairKS_Dq(Dq2)\npropNotDiffSAD(mk1) # 0.84 is not so good \n\n# Select 1 from each combination\n#\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nDiq1 <- Dqq[Dqq$rep==sample(10,1),]\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.1)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nDiq1 <- rbind(Diq1,Dqq[Dqq$rep==sample(10,1),])\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.01)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nDiq1 <- rbind(Diq1,Dqq[Dqq$rep==sample(10,1),])\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,0.001)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nDiq1 <- rbind(Diq1,Dqq[Dqq$rep==sample(10,1),])\n\nDqq <- sel_ReplaceR(Dq1,0.2,0.04,0.001,1)\nDqq$rep <- rep( 1:(nrow(Dqq)/35),each=35)\nDiq1 <- rbind(Diq1,Dqq[Dqq$rep==sample(10,1),])\n\nmk1 <- pairKS_Dq(Diq1)\npropNotDiffSAD(mk1) # 0.3 is very good \n\n# TO DO a function to compare one of each parameter combination.\n\nhh <- function(x) {\nn <- nrow(x)/35\nrp <- rep( 1:n,each=35)\nreturn(x[rp==sample(n,1),])\n}\ndebug(hh)\nDq2 <- ddply(Dq1,.(MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate),hh)\n\ndebug(pairKS_Dq)\nmk1 <- pairKS_Dq(Dq2,35)\nmk1 <- pairKS_Dq(Dq1,35)\npropNotDiffSAD(mk1) # 0.3 is very good \ndebug(compMethod_DqKS_Time)\nm <- compMethod_DqKS_Time(bName,100,spMeta) \n\n", "meta": {"hexsha": "063f2197f07dcbf4d2439a19fc1ae603776209d6", "size": 6319, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Test_DqComp.r", "max_stars_repo_name": "lsaravia/SpeciesRankSurface", "max_stars_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Test_DqComp.r", "max_issues_repo_name": "lsaravia/SpeciesRankSurface", "max_issues_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-05-09T12:48:54.000Z", "max_issues_repo_issues_event_max_datetime": "2015-05-09T12:48:54.000Z", "max_forks_repo_path": "R/Test_DqComp.r", "max_forks_repo_name": "lsaravia/SpeciesRankSurface", "max_forks_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-05-08T01:29:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-26T13:41:06.000Z", "avg_line_length": 22.5678571429, "max_line_length": 109, "alphanum_fraction": 0.6575407501, "num_tokens": 2889, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.6370307944803831, "lm_q1q2_score": 0.34087418655893825}} {"text": "# General operators (from R documentation `?Syntax`)\n\n:: :::\n$ @\n[ [[\n^\n- +\n:\n%any%\n* /\n+ -\n< > <= >= == !=\n!\n& &&\n| ||\n~\n-> ->>\n<- <<-\n=\n?\n\n# Subset extraction\n\nx[3]\nx[[\"a\"]]\nx$y\nx$`a a`\nx$\"a b\"\n\n# Operators\n\n2-2, 2+2, 2~2, 2*2, 2/2, 2^2, 2<2, 2>2, 2==2, 2>=2, 2<=2, 2!=2, a<-2, a=2, a<<-2, a:=2, 2->a, 2->>a, 1:2\n~a+b\n!TRUE\n?help, ?`?`, methods?show, ??topic\nTRUE&FALSE, T|F\nTRUE&&FALSE, T||F\nbase::sum, base:::sum\n\n# Custom operators\n\n2%*%3\na%<>%b\n2%in%y\na %`tick`% b\na %'quot'% b\na %\"quot\"% b\na %for% b\na %\\% b\na %`% b\n\n`% %` = paste\n\"foo\"`% %`\"bar\"\n", "meta": {"hexsha": "11a7a1e88917b0a37c411e25901920739e88ab59", "size": 555, "ext": "r", "lang": "R", "max_stars_repo_path": "test/markup/r/ops.r", "max_stars_repo_name": "mortie/highlight.js", "max_stars_repo_head_hexsha": "a6083b24e244ac63624ada4ae6e409e5c77424ac", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-03-10T21:01:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-06T14:55:53.000Z", "max_issues_repo_path": "test/markup/r/ops.r", "max_issues_repo_name": "mortie/highlight.js", "max_issues_repo_head_hexsha": "a6083b24e244ac63624ada4ae6e409e5c77424ac", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 263, "max_issues_repo_issues_event_min_datetime": "2021-02-20T20:14:34.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:53:56.000Z", "max_forks_repo_path": "test/markup/r/ops.r", "max_forks_repo_name": "mortie/highlight.js", "max_forks_repo_head_hexsha": "a6083b24e244ac63624ada4ae6e409e5c77424ac", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-12-08T14:59:47.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-08T15:00:59.000Z", "avg_line_length": 10.2777777778, "max_line_length": 104, "alphanum_fraction": 0.4342342342, "num_tokens": 300, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.34087418448790857}} {"text": "library(tidyverse)\n\n# simple custom data\n# https://r4ds.had.co.nz/relational-data.html#understanding-joins\n\nx <- tribble(\n ~key, ~val_x,\n 1, \"x1\",\n 2, \"x2\",\n 3, \"x3\"\n)\ny <- tribble(\n ~key, ~val_y,\n 1, \"y1\",\n 2, \"y2\",\n 4, \"y3\"\n)\n\nx %>% \n inner_join(y, by = \"key\")\n\nx %>% \n left_join(y, by = \"key\")\n\nx %>% \n right_join(y, by = \"key\")\n\nx %>% \n full_join(y, by = \"key\")\n", "meta": {"hexsha": "a539445c7da6c35c1cd78ef1bd717fbe3752df65", "size": 378, "ext": "r", "lang": "R", "max_stars_repo_path": "stack_2/day_08/03_joins.r", "max_stars_repo_name": "mpHarm88/learn_datascience", "max_stars_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-12-08T09:23:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T19:02:17.000Z", "max_issues_repo_path": "stack_2/day_08/03_joins.r", "max_issues_repo_name": "mpHarm88/learn_datascience", "max_issues_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-12-08T05:07:24.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-08T08:00:40.000Z", "max_forks_repo_path": "stack_2/day_08/03_joins.r", "max_forks_repo_name": "mpHarm88/learn_datascience", "max_forks_repo_head_hexsha": "8a509bed737b715d5e492af931693cab79163640", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-12-10T08:25:19.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-14T13:14:52.000Z", "avg_line_length": 12.6, "max_line_length": 65, "alphanum_fraction": 0.5291005291, "num_tokens": 164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6370308082623217, "lm_q1q2_score": 0.34087418448790857}} {"text": "createSurvivalFrame <- function(f.survfit){ # initialise frame variable\nf.frame <- NULL\n# check if more then one strata\nif(length(names(f.survfit$strata)) == 0){\n# create data.frame with data from survfit\nf.frame <- data.frame(time=f.survfit$time, n.risk=f.survfit$n.risk, n.event=f.survfit$n.event, n.censor = f.survfit $n.censor, surv=f.survfit$surv, upper=f.survfit$upper, lower=f.survfit$lower)\n# create first two rows (start at 1)\nf.start <- data.frame(time=c(0, f.frame$time[1]), n.risk=c(f.survfit$n, f.survfit$n), n.event=c(0,0), n.censor=c(0,0), surv=c(1,1), upper=c(1,1), lower=c(1,1))\n# add first row to dataset\nf.frame <- rbind(f.start, f.frame)\n# remove temporary data\nrm(f.start) }\nelse {\n# create vector for strata identification\nf.strata <- NULL\nfor(f.i in 1:length(f.survfit$strata)){\n# add vector for one strata according to number of rows of strata\nf.strata <- c(f.strata, rep(names(f.survfit$strata)[f.i], f.survfit$strata[f.i])) }\n# create data.frame with data from survfit (create column for strata)\nf.frame <- data.frame(time=f.survfit$time, n.risk=f.survfit$n.risk, n.event=f.survfit$n.event, n.censor = f.survfit $n.censor, surv=f.survfit$surv, upper=f.survfit$upper, lower=f.survfit$lower, strata=factor(f.strata))\n# remove temporary data\nrm(f.strata)\n# create first two rows (start at 1) for each strata\nfor(f.i in 1:length(f.survfit$strata)){\n# take only subset for this strata from data\nf.subset <- subset(f.frame, strata==names(f.survfit$strata)[f.i])\n# create first two rows (time: 0, time of first event)\nf.start <- data.frame(time=c(0, f.subset$time[1]), n.risk=rep(f.survfit[f.i]$n, 2), n.event=c(0,0), n.censor=c(0,0), surv=c(1,1), upper=c(1,1), lower=c(1,1), strata=rep(names(f.survfit$strata)[f.i], 2))\n# add first two rows to dataset\nf.frame <- rbind(f.start, f.frame)\n# remove temporary data\nrm(f.start, f.subset) }\n# reorder data\nf.frame <- f.frame[order(f.frame$strata, f.frame$time), ]\n# rename row.names\nrownames(f.frame) <- NULL }\n# return frame\nreturn(f.frame) }", "meta": {"hexsha": "eb4eee59fd7b6b0861b1626f8801691ec5561b6d", "size": 2007, "ext": "r", "lang": "R", "max_stars_repo_path": "CIR/Figure_1/createSurvFrame.r", "max_stars_repo_name": "eduardporta/domainXplorer", "max_stars_repo_head_hexsha": "d04d2341963aac5db9f12a56021bfe2208059856", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-07-29T20:58:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-26T09:05:41.000Z", "max_issues_repo_path": "CIR/Figure_1/createSurvFrame.r", "max_issues_repo_name": "eduardporta/domainXplorer", "max_issues_repo_head_hexsha": "d04d2341963aac5db9f12a56021bfe2208059856", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CIR/Figure_1/createSurvFrame.r", "max_forks_repo_name": "eduardporta/domainXplorer", "max_forks_repo_head_hexsha": "d04d2341963aac5db9f12a56021bfe2208059856", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2016-07-25T18:04:57.000Z", "max_forks_repo_forks_event_max_datetime": "2016-07-25T18:04:57.000Z", "avg_line_length": 52.8157894737, "max_line_length": 218, "alphanum_fraction": 0.7075236672, "num_tokens": 657, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6370308013713525, "lm_q1q2_score": 0.3408741808005618}} {"text": "data = read.csv(file=\"edmonton.csv\", header=FALSE, sep=\",\")\ncolnames(data)=NULL\nplot(data[,1])\nfor (count in c(1:dim(data)[1]))\n{\n lines(predict(smooth.spline(data[,count],spar=0.01),seq(1,length(data[,count]),len=1000)))\n}\n", "meta": {"hexsha": "9f73bfbf4cb2bcd56de8192496a5bda7c6ba1370", "size": 227, "ext": "r", "lang": "R", "max_stars_repo_path": "plotedmonton.r", "max_stars_repo_name": "rohitner/weatherscrap", "max_stars_repo_head_hexsha": "a789f5b1261fe98c30c691fe6fc1666dfe95d0f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plotedmonton.r", "max_issues_repo_name": "rohitner/weatherscrap", "max_issues_repo_head_hexsha": "a789f5b1261fe98c30c691fe6fc1666dfe95d0f0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plotedmonton.r", "max_forks_repo_name": "rohitner/weatherscrap", "max_forks_repo_head_hexsha": "a789f5b1261fe98c30c691fe6fc1666dfe95d0f0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.375, "max_line_length": 94, "alphanum_fraction": 0.6651982379, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.340874177113215}} {"text": "#' Dispatch the fitting of local models to parallel cores, if registered\n#' \n#' Loops through estimation locations with foreach, sending each local model to a core for fitting. If zero or one cores are registered, then foreach computes the local models sequentially.\n#' \n#' @param x matrix of observed covariates\n#' @param y vector of observed responses\n#' @param family exponential family distribution of the response\n#' @param coords matrix of locations, with each row giving the location at which the corresponding row of data was observed\n#' @param fit.loc matrix of locations where the local models should be fitted\n#' @param kernel kernel function for generating the local observation weights\n#' @param bw bandwidth parameter\n#' @param bw.type type of bandwidth - options are \\code{dist} for distance (the default), \\code{knn} for nearest neighbors (bandwidth a proportion of \\code{n}), and \\code{nen} for nearest effective neighbors (bandwidth a proportion of the sum of squared residuals from a global model)\n#' @param tol.loc tolerance for the tuning of an adaptive bandwidth (e.g. \\code{knn} or \\code{nen})\n#' @param varselect.method criterion to minimize in the regularization step of fitting local models - options are \\code{AIC}, \\code{AICc}, \\code{BIC}, \\code{GCV}\n#' @param tuning logical indicating whether this model will be used to tune the bandwidth, in which case only the tuning criteria are returned\n#' @param D pre-specified matrix of distances between locations\n#' @param verbose print detailed information about our progress?\n#' \nlagr.dispatch = function(x, y, family, coords, fit.loc, oracle, D, bw, bw.type, verbose, varselect.method, prior.weights, tuning, predict, simulation, kernel, min.bw, max.bw, min.dist, max.dist, tol.loc, lambda.min.ratio, n.lambda, lagr.convergence.tol, lagr.max.iter, jacknife=FALSE, bootstrap.index=NULL) {\n if (!is.null(fit.loc)) { coords.fit = fit.loc }\n else { coords.fit = coords }\n n = nrow(coords.fit)\n\n vcr.model = list()\n\n #For the adaptive bandwith methods, use a default tolerance if none is specified:\n if (is.null(tol.loc)) {tol.loc = bw / 1000}\n\n #The knn bandwidth is a proportion of the total prior weight, so compute the total prior weight:\n if (bw.type == 'knn') {\n prior.weights = drop(prior.weights)\n total.weight = sum(prior.weights)\n }\n \n group.id = attr(x, 'assign')\n \n#models = list()\n models = foreach(i=1:n, .errorhandling='stop') %dopar% {\n#for (i in 1:n) {\n if (!is.null(fit.loc)) {\n dist = drop(D[nrow(coords)+i,1:nrow(coords)])\n } else { dist = drop(D[i,]) }\n loc = coords.fit[i,]\n\n #If we are seeking the bandwidth via the jacknife, then remove any observations with zero distance.\n if (jacknife==TRUE || jacknife=='anti') {\n indx = which(dist!=0)\n } else {\n indx = 1:nrow(x)\n }\n\n if (!is.null(bootstrap.index)) {\n indx = indx[bootstrap.index]\n }\n\n if (jacknife=='anti') {\n indx = c(indx, which(indx==0))\n }\n \n #Use a prespecified distance as the bandwidth?\n if (bw.type == 'dist') {\n bandwidth = bw\n kernel.weights = drop(kernel(dist, bandwidth))\n\n #Compute the bandwidth that sets the sum of weights around location i equal to bw?\n } else if (bw.type == 'knn') {\n opt = optimize(\n lagr.knn,\n lower=min.dist,\n upper=max.dist,\n maximum=FALSE,\n tol=tol.loc,\n loc=loc,\n coords=coords[indx,],\n kernel=kernel,\n verbose=verbose,\n dist=dist[indx],\n total.weight=total.weight,\n prior.weights=prior.weights[indx],\n target=bw\n )\n bandwidth = opt$minimum\n kernel.weights = drop(kernel(dist, bandwidth))\n \n #Compute the bandwidth so that the sum of the local weighted squared error equals the bw?\n } else if (bw.type == 'nen') {\n opt = optimize(\n lagr.ssr,\n lower=min.dist,\n upper=max.dist, \n maximum=FALSE,\n tol=tol.loc,\n x=x[indx,],\n y=y[indx],\n group.id=group.id,\n family=family,\n loc=loc,\n coords=coords[indx,],\n dist=dist[indx],\n kernel=kernel,\n target=bw,\n varselect.method=varselect.method,\n oracle=oracle,\n prior.weights=prior.weights[indx],\n verbose=verbose,\n lambda.min.ratio=lambda.min.ratio,\n n.lambda=n.lambda, \n lagr.convergence.tol=lagr.convergence.tol,\n lagr.max.iter=lagr.max.iter\n )\n bandwidth = opt$minimum\n kernel.weights = drop(kernel(dist, bandwidth))\n }\n \n #If we have specified covariates via an oracle, then use those\n if (!is.null(oracle)) {oracle.loc = oracle[[i]]}\n else {oracle.loc = NULL}\n\n #Fit the local model\n m = list(tunelist=list('ssr-loc'=list('pearson'=Inf, 'deviance'=Inf), 'df-local'=1), 'sigma2'=0, 'nonzero'=vector(), 'weightsum'=sum(kernel.weights))\n try(m <- lagr.fit.inner(\n x=x[indx,, drop=FALSE],\n y=y[indx],\n group.id=group.id,\n family=family,\n coords=coords[indx,, drop=FALSE],\n loc=loc,\n varselect.method=varselect.method,\n tuning=tuning,\n predict=predict,\n simulation=simulation,\n lambda.min.ratio=lambda.min.ratio,\n n.lambda=n.lambda, \n lagr.convergence.tol=lagr.convergence.tol,\n lagr.max.iter=lagr.max.iter,\n verbose=verbose,\n kernel.weights=kernel.weights[indx],\n prior.weights=prior.weights[indx],\n oracle=oracle.loc)\n )\n if (verbose) {\n cat(paste(\"For i=\", i, \"; location=(\", paste(round(loc,3), collapse=\",\"), \"); bw=\", round(bandwidth,3), \"; s=\", m$s, \"; dispersion=\", round(tail(m$dispersion, 1), 3), \"; nonzero=\", paste(m$nonzero, collapse=\",\"), \"; weightsum=\", round(m$weightsum, 3), \".\\n\", sep=''))\n } \n m$bw = bandwidth\n return(m)\n#models[[i]] = m\n }\n \n vcr.model$fits = models\n \n #Calculate information criteria:\n fitted = vector()\n df = 0\n n = nrow(x)\n \n #Compute model-average fitted values and degrees of freedom:\n for (x in models) {\n #Compute the model-averaging weights:\n crit = x$tunelist$criterion\n if (varselect.method %in% c(\"AIC\", \"AICc\", \"BIC\")) {\n crit.weights = as.numeric(crit==min(crit))\n } else if (varselect.method %in% c(\"wAIC\", \"wAICc\")) {\n crit.weights = -crit\n }\n \n fitted = c(fitted, sum(x$tunelist$localfit * crit.weights) / sum(crit.weights))\n df = df + sum((1 + x$model$results$df) * crit.weights / x$weightsum ) / sum(crit.weights)\n }\n\n dev.resids = family$dev.resids(y, fitted, prior.weights)\n ll = family$aic(y, n, fitted, prior.weights, sum(dev.resids))\n\n #Compute the information criteria:\n vcr.model$AICc = ll + 2*df + 2*df*(df+1)/(n-df-1)\n vcr.model$AIC = ll + 2*df\n vcr.model$GCV = ll\n vcr.model$BIC = ll + log(n)*df\n\n vcr.model$df = df\n \n return(vcr.model)\n}\n", "meta": {"hexsha": "293523ec8915bc88b92b28df3b360779d022483b", "size": 7566, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lagr.dispatch.r", "max_stars_repo_name": "wrbrooks/lagr", "max_stars_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/lagr.dispatch.r", "max_issues_repo_name": "wrbrooks/lagr", "max_issues_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lagr.dispatch.r", "max_forks_repo_name": "wrbrooks/lagr", "max_forks_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3442622951, "max_line_length": 308, "alphanum_fraction": 0.5785091197, "num_tokens": 1844, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.340874177113215}} {"text": "#! /usr/bin/env Rscript\nd<-scan(\"stdin\", quiet=TRUE)\ncat(\"Min:\", min(d), \"Max:\", max(d), \"Median:\", median(d), \"Mean:\", mean(d), sep=\"\\n\")\n", "meta": {"hexsha": "874a84e695c70196b8541050b9aeefb99c37b5dd", "size": 139, "ext": "r", "lang": "R", "max_stars_repo_path": "basic_statistics.r", "max_stars_repo_name": "tothebeat/noaa-gsod-data-munging", "max_stars_repo_head_hexsha": "89761fb23ef0b24967344146aba93a54412723bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2015-01-23T06:58:11.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-06T09:15:35.000Z", "max_issues_repo_path": "basic_statistics.r", "max_issues_repo_name": "BStudent/NOAA-GSOD-GET", "max_issues_repo_head_hexsha": "e3ef1d2a6b6ce6e9060bc6e7c73ceb44495317f8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-01-23T07:00:26.000Z", "max_issues_repo_issues_event_max_datetime": "2015-02-04T01:45:51.000Z", "max_forks_repo_path": "basic_statistics.r", "max_forks_repo_name": "BStudent/NOAA-GSOD-GET", "max_forks_repo_head_hexsha": "e3ef1d2a6b6ce6e9060bc6e7c73ceb44495317f8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2015-08-12T05:23:20.000Z", "max_forks_repo_forks_event_max_datetime": "2018-01-24T10:31:13.000Z", "avg_line_length": 34.75, "max_line_length": 85, "alphanum_fraction": 0.5611510791, "num_tokens": 47, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7520125626441471, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.34085860387487243}} {"text": "churn <- read.csv(file.choose(), na.strings = \"\")\nView(churn)\nlibrary(dplyr)\nlibrary(tidyverse)\n\n#________________________________Cleaning___________________________________#\n\n#Checking for NA's\nsummary(churn)\nchurn <- na.omit(churn)\nchurn = churn[,c(-1)]\n#Changing Data Type\nchurn$MonthlyCharges <- as.double(churn$MonthlyCharges)\nchurn$TotalCharges <- as.double(churn$TotalCharges)\nView(churn)\n#0utliers\n\noutliers <- function(dataframe){\n dataframe %>%\n select_if(is.numeric) %>% \n map(~ boxplot.stats(.x)$out) \n \n \n}\nz <- outliers(churn)\nz <- t(as.matrix(z))\nz\n\n#Cleaning data\nchurn$Contract <- gsub(\"-\", \"\", churn$Contract) \nchurn$Contract <- gsub(\" \", \"\", churn$Contract)\nchurn$Contract <- tolower(churn$Contract)\n\nchurn$PaymentMethod <- tolower(churn$PaymentMethod)\nchurn$PaymentMethod <- gsub(\" \", \"\", churn$PaymentMethod)\nchurn$PaymentMethod <- gsub(\"automatic|[[:punct:]]\", \"\", churn$PaymentMethod)\n\nnew.function <- function(a, b=\"No internet service\")\n{\n c <- gsub(b, \"no\", a)\n tolower(c)\n}\n\nchurn$MultipleLines <- new.function(churn$MultipleLines,\"No phone service\")\nchurn$OnlineSecurity <- new.function(churn$OnlineSecurity)\nchurn$OnlineBackup <- new.function(churn$OnlineBackup)\nchurn$DeviceProtection <- new.function(churn$DeviceProtection)\nchurn$TechSupport <- new.function(churn$TechSupport)\nchurn$StreamingTV <- new.function(churn$StreamingTV)\nchurn$StreamingMovies <- new.function(churn$StreamingMovies)\nchurn$Partner <- new.function(churn$Partner)\nchurn$Dependents <- new.function(churn$Dependents)\nchurn$PhoneService <- new.function(churn$PhoneService)\nchurn$PaperlessBilling <- new.function(churn$PaperlessBilling)\nchurn$Churn <- new.function(churn$Churn)\n\nchurn$SeniorCitizen <- ifelse(churn$SeniorCitizen == 0, \"n\", \"y\")\n\n#Subset\nmale_cust <- churn[which(churn$gender=='Male'),]\nfemale_cust <- churn[which(churn$gender=='Female'),]\nelder_male <- male_cust[which(male_cust$SeniorCitizen=='y'),]\nelder_Female <- female_cust[which(female_cust$SeniorCitizen=='y'),]\n\n\n#_____________________________________Modelling_______________________________________#\n\n#_________________________________Logistic Model_____________________________________#\n#Creating Dummy Variables\ninstall.packages(\"fastDummies\")\nlibrary(fastDummies)\nresults <- fastDummies::dummy_cols(churn)\nknitr::kable(results)\nView(results)\nchurn_dummy <- results[,c(5,18:56)]\nnames(churn_dummy)[18] <- \"InternetService_Fiber\"\n\n#.1 logistic Regression\ntable(churn_dummy$Churn)\nstr(churn_dummy)\nchurn_dummy$Churn <- ifelse(churn$Churn == 'no', 1, 0)\na <- colnames(churn_dummy)\na <- a[c(1,5:40)]\nchurn_dummy[a] <- sapply(churn_dummy[a], as.numeric)\nView(churn_dummy)\n#.1.2 Subsetting\nchurn_yes <- churn_dummy[which(churn_dummy$Churn==0),]\nchurn_no <- churn_dummy[which(churn_dummy$Churn==1),]\n\n#.1.3 Training sets\nset.seed(100)\ntrain_rows_churn_yes <- sample(1:nrow(churn_yes), 0.7*nrow(churn_yes))\ntrain_rows_churn_no <- sample(1:nrow(churn_no), 0.7*nrow(churn_no))\ntraining_churn_yes <- churn_yes[train_rows_churn_yes, ]\ntraining_churn_no <- churn_no[train_rows_churn_no, ]\ntrainingData <- rbind(training_churn_yes, training_churn_no)\nView(trainingData)\n#.1.4 Test data\ntest_churn_yes <- churn_yes[-train_rows_churn_yes, ]\ntest_churn_no <- churn_no[-train_rows_churn_no, ]\ntestData <- rbind(test_churn_yes, test_churn_no)\n\n#.1.5 Build model\nlibrary(MASS)\nmodel <- glm(Churn ~ ., data = trainingData, family = binomial(link=\"logit\"))\nstepAIC(model, direction = 'backward')\nmodel <- glm(Churn ~ tenure + MonthlyCharges + TotalCharges + \n SeniorCitizen_n + PhoneService_no + MultipleLines_no + InternetService_DSL + \n InternetService_Fiber + OnlineBackup_no + DeviceProtection_no + \n StreamingTV_no + StreamingMovies_no + Contract_monthtomonth + \n Contract_oneyear + PaperlessBilling_no + PaymentMethod_electroniccheck, \n family = binomial(link = \"logit\"), data = trainingData)\npredicted <- predict(model, testData, type=\"response\")\nstepAIC(model, direction = 'backward')\n\n#.1.6 Optimal cut off\ninstall.packages(\"InformationValue\")\nlibrary(InformationValue)\noptCutOff <- optimalCutoff(testData$Churn, predicted)[1]\nsummary(model)\n\n#.1.7 Comparison between actual vs predicted\nmisClassError(testData$Churn, predicted, threshold = optCutOff)\n\n#.1.8 true positive detection using ROC\nplotROC(testData$Churn, predicted)\n\n#1.9 Confusion matrix\nConcordance(testData$Churn, predicted)\np <- InformationValue::confusionMatrix(testData$Churn, predicted, threshold = optCutOff)\n\n# Accuracy of the Model\naccuracy = ((p[1,1] + p[2,2])/sum(p))*100\naccuracy\n\n#VIF\nlibrary(car)\nvif(model) #Used to check multicollinearity\n\n#k-cross validation\n# Define training control\ninstall.packages(\"e1071\")\nlibrary(e1071)\nlibrary(caret)\nset.seed(123) \ntrain.control <- trainControl(method = \"cv\", number = 10)\n# Train the model\nmodel <- train(Churn ~ tenure + MonthlyCharges + TotalCharges + \n SeniorCitizen_n + PhoneService_no + MultipleLines_no + InternetService_DSL + \n InternetService_Fiber + OnlineBackup_no + DeviceProtection_no + \n StreamingTV_no + StreamingMovies_no + Contract_monthtomonth + \n Contract_oneyear + PaperlessBilling_no + PaymentMethod_electroniccheck, data = trainingData, method = \"glm\",\n trControl = train.control)\n# Summarize the results\nprint(model)\n\n#________________Forward Selection______________________#\n#.1.5 Build model\nlibrary(MASS)\nmodel <- glm(Churn ~ ., data = trainingData, family = binomial(link=\"logit\"))\nstepAIC(model, direction = 'forward')\nmodel <- glm(Churn ~ tenure + MonthlyCharges + TotalCharges + gender_Female + \n SeniorCitizen_n + Partner_no + Dependents_no + PhoneService_no + \n MultipleLines_no + InternetService_DSL + InternetService_Fiber + \n OnlineSecurity_no + OnlineBackup_no + DeviceProtection_no + \n TechSupport_no + StreamingMovies_no + Contract_monthtomonth + \n Contract_oneyear + PaperlessBilling_no + PaymentMethod_banktransfer + \n PaymentMethod_creditcard + PaymentMethod_electroniccheck, family = binomial(link = \"logit\"), data = trainingData)\npredicted <- plogis(predict(model, testData))\nstepAIC(model, direction = 'forward')\n\n#.1.6 Optimal cut off\ninstall.packages(\"InformationValue\")\nlibrary(InformationValue)\noptCutOff <- optimalCutoff(testData$Churn, predicted)[1]\nsummary(model)\n\n#.1.7 Comparison between actual vs predicted\nmisClassError(testData$Churn, predicted, threshold = optCutOff)\n\n#.1.8 true positive detection using ROC\nplotROC(testData$Churn, predicted)\n\n#1.9 Confusion matrix\nConcordance(testData$Churn, predicted)\np <- InformationValue::confusionMatrix(testData$Churn, predicted, threshold = optCutOff)\n# Accuracy of the Model\naccuracy = ((p[1,1] + p[2,2])/sum(p))*100\naccuracy\n\n#VIF validation\nvif(model)\n\n#k-cross validation\n# Define training control\ninstall.packages(\"e1071\")\nlibrary(e1071)\nlibrary(caret)\nset.seed(123) \ntrain.control <- trainControl(method = \"cv\", number = 10)\n# Train the model\nmodel <- train(Churn ~ tenure + MonthlyCharges + TotalCharges + gender_Female + \n SeniorCitizen_n + Partner_no + Dependents_no + PhoneService_no + \n MultipleLines_no + InternetService_DSL + InternetService_Fiber + \n OnlineSecurity_no + OnlineBackup_no + DeviceProtection_no + \n TechSupport_no + StreamingMovies_no + Contract_monthtomonth + \n Contract_oneyear + PaperlessBilling_no + PaymentMethod_banktransfer + \n PaymentMethod_creditcard + PaymentMethod_electroniccheck, data = trainingData, method = \"glm\",\n trControl = train.control)\n# Summarize the results\nprint(model)\n\n#_________________________random Forest___________________________#\n\nlibrary(randomForest)\n\nrandom_forest <- churn\ns <- random_forest[,c(5,8,18,19)]\nrandom_forest <- random_forest[,-c(5,8,18,19)]\nView(random_forest)\nc <- colnames(random_forest)\nrandom_forest[c] <- lapply(random_forest[c], as.factor)\nstr(random_forest)\nrandom_forest <- as.data.frame(random_forest)\nrandom_forest <- cbind(s, random_forest)\n\nset.seed(123)\nindex = sample(1:nrow(random_forest), size=0.7*nrow(random_forest))\ntrain_data = random_forest[index,]\ntest_data = random_forest[-index,]\n\n#Without tuning\nmodel_r <- randomForest(Churn ~ ., data = train_data,importance = TRUE)\n\n#optimal mtry\ntune_r <- tuneRF(train_data[,-20], train_data[,20], stepFactor = 0.5, plot = TRUE, ntreeTry = 500, trace = TRUE, improve = 0.001)\n\n#With Tuning\nmodel_r <- randomForest(Churn ~ ., data = train_data, mtry = 2, importance = TRUE)\n\n#Prediction for trainig set\npredTrain <- predict(model_r, train_data, type = \"class\")\ntable(predTrain, train_data$Churn)\n\n#Prediction for testin set\npredtest <- predict(model_r, test_data, type = \"class\")\np_r <- table(predtest, test_data$Churn)\naccuracy = ((p_r[1,1] + p_r[2,2])/sum(p_r))*100\naccuracy\n\n#Importance\nimportance(model_r)\nvarImpPlot(model_r)\n\n#Plot\nplot(model_r, main = \"Forest Model with Tuning\" )\nlegend(\"topright\", colnames(model_r$err.rate), col = 1:3, fill=1:3)\n\n\n\n# We will compare model 1 of Random Forest with Decision Tree model\n\nmodel_dt <- train(Churn ~ ., data = train_data, method = \"rpart\")\nmodel_dt_1 <- predict(model_dt, data = train_data)\ntable(model_dt_1, train_data$Churn)\nmean(model_dt_1 == train_data$Churn)\n\nmodel_dt_vs <- predict(model_dt, newdata = test_data)\ntable(model_dt_vs, test_data$Churn)\n\nmean(model_dt_vs == test_data$Churn)\nacc_r <- table(model_dt_vs, test_data$Churn)\naccuracy = ((acc_r[1,1] + acc_r[2,2])/sum(acc_r))*100\naccuracy\n\n\n", "meta": {"hexsha": "b9851ca6a652641717d5cba9bb7051aaa7094573", "size": 9633, "ext": "r", "lang": "R", "max_stars_repo_path": "TelecoCustomerChrun.r", 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"alphanum_fraction": 0.7401640195, "num_tokens": 2568, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3407050644684546}} {"text": "library(ggplot2)\nlibrary(odbc)\nlibrary(DBI)\nlibrary(rjson)\n\nrm(list=ls())\n\ncon <- DBI::dbConnect(odbc::odbc(), \"GLASSv3\")\n\nq <- \"SELECT kq.*\nFROM analysis.kallisto_qc kq\"\nka <- dbGetQuery(con,q)\n\nunique_thr <- mean(ka[,\"p_unique\"]) - 2*sd(ka[,\"p_unique\"])\n\ncomplexity_exclusion <- ifelse(ka[,\"p_unique\"] < mean(ka[,\"p_unique\"])-2*sd(ka[,\"p_unique\"]),\"block\",\"allow\")\ncomplexity_exclusion_reason <- ifelse(ka[,\"p_unique\"] < mean(ka[,\"p_unique\"])-2*sd(ka[,\"p_unique\"]),\"low_complexity\",NA)\n\nres <- data.frame(ka[,\"aliquot_barcode\"],complexity_exclusion,complexity_exclusion_reason)\ncolnames(res) <- c(\"aliquot_barcode\",\"complexity_exclusion\",\"complexity_exclusion_reason\")\n\ndbWriteTable(con, Id(schema=\"analysis\", table=\"rna_blocklist\"), res, overwrite=TRUE)\n", "meta": {"hexsha": "8469e8714cdf9e9169edf716626c7476738b2238", "size": 757, "ext": "r", "lang": "R", "max_stars_repo_path": "R/expression/qc/rnaseq_blocklist.r", "max_stars_repo_name": "Kcjohnson/SCGP", "max_stars_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/expression/qc/rnaseq_blocklist.r", "max_issues_repo_name": "Kcjohnson/SCGP", "max_issues_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/expression/qc/rnaseq_blocklist.r", "max_forks_repo_name": "Kcjohnson/SCGP", "max_forks_repo_head_hexsha": "e757b3b750ce8ccf15085cb4bc60f2dfd4d9a285", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.9130434783, "max_line_length": 120, "alphanum_fraction": 0.7212681638, "num_tokens": 242, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.34066857611173373}} {"text": "pdf_file<-\"pdf/maps_tunisia_symbols.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=10,height=10)\n\npar(bg=\"lightskyblue1\",mai=c(0,0,0,0),oma=c(0,0,0,0),family=\"Lato Light\",las=1)\nlibrary(maptools)\nlibrary(rgdal)\nlibrary(gdata)\n\n# Import data and prepare chart\n\nmyTun<-readShapeSpatial(\"myData/TUN_adm/TUN_adm0.shp\",proj4string=CRS(\"+proj=longlat\"))\nplot(myTun,col=\"mintcream\",border=\"white\",lwd=3, xlim=c(8,14), ylim=c(32,38))\nmyDza<-readShapeSpatial(\"myData/DZA_adm/DZA_adm0.shp\",proj4string=CRS(\"+proj=longlat\"))\nplot(myDza,col=\"burlywood1\",border=\"white\",lwd=3, add=T)\nmyLby<-readShapeSpatial(\"myData/LBY_adm/LBY_adm0.shp\",proj4string=CRS(\"+proj=longlat\"))\n\n# Create chart and other elements\n\nplot(myLby,col=\"burlywood1\",border=\"white\",lwd=3, add=T)\n\nmyLocations<-read.xls(\"myData/tunisia.xlsx\", encoding=\"latin1\")\nattach(myLocations)\nn<-nrow(myLocations)\nfor (i in 1:n)\n{\ntext(long[i]+hoffset[i], lat[i]+voffset[i], place[i], cex=1.75,col=\"black\", adj=adjust[i])\n}\ntext(10.12, 36.43, \"Tunis\", cex=3, family=\"Lato Black\")\ntext(12.5, 33.5, \"Mediterranean\", adj=0, cex=2, family=\"Lato Regular\", col=\"darkblue\")\ntext(7.25, 32, \"ALGERIA\", adj=0, cex=2, family=\"Lato Black\")\ntext(9,33, \"TUNISIA\", adj=0, cex=2, family=\"Lato Black\")\ntext(12, 32, \"LIBYA\", adj=0, cex=2, family=\"Lato Black\")\n\npar(family=\"Datendesign\")\ntext(long, lat, \"a\", cex=size, col=\"red\")\n\n# Titling\n\nmtext(\"Unrest in Tunisia\",side=3,line=-4,adj=0.05,cex=2.7,family=\"Lato Black\",col=\"black\")\n\n# Separate figure \n\npar(mai=c(6,6,0,0), bg=\"white\",new=T)\ndata(wrld_simpl) \nw <- wrld_simpl[wrld_simpl@data[, \"NAME\"] != \"Antarctica\",]\nm <- spTransform(w, CRS=CRS(\"+proj=merc\"))\nplot(m,xlim=c(-900000,2800000),ylim=c(3300000,7000000),col=rgb(160,160,160,100,maxColorValue=255),border=F)\nw <- wrld_simpl[wrld_simpl@data[, \"NAME\"] == \"Tunisia\",]\nm <- spTransform(w, CRS=CRS(\"+proj=merc\"))\nplot(m, add=T,col=\"red\",border=F)\ndev.off()\n", "meta": {"hexsha": "35c9ce15ffdf4dabbae97fe86ef47465f8ca296e", "size": 1886, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/maps_tunisia_symbols.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/maps_tunisia_symbols.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/maps_tunisia_symbols.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.2692307692, "max_line_length": 107, "alphanum_fraction": 0.7025450689, "num_tokens": 727, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6757645879592642, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.34052194569771477}} {"text": "theme_set(theme_bw(18))\n\nsetwd(\"/home/caroline/cocolab/overinformativeness/models/basic_level_reference/modelExploration/rscripts\")\n#setwd(\"~/cogsci/projects/stanford/projects/overinformativeness/models/basic_level_reference/modelExploration/rscripts\")\n\nsource(\"helpers.R\")\n\n#load(\"data/r.RData\")\nunlogged = read.table(file=\"../litLisOutput_unloggedTyps.csv\",sep=\",\", header=T, quote=\"\")\nhead(unlogged)\nlogged = read.table(file=\"../litLisOutput_loggedTyps.csv\",sep=\",\", header=T, quote=\"\")\nhead(logged)\nnrow(logged)\n\nagr = unlogged %>%\n select(p_l_t,p_l_d1,p_l_d2, t_d1, t_t) %>%\n gather(Utterance,Mentioned,-t_t, -t_d1) %>%\n group_by(Utterance,t_t, t_d1) %>%\n summarise(Probability=mean(Mentioned),ci.low=ci.low(Mentioned),ci.high=ci.high(Mentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$Probability - agr$ci.low\nagr$YMax = agr$Probability + agr$ci.high\nhead(agr)\n\n#ggplot(agr, aes(x=Utterance,y=Probability)) +\n# geom_bar(stat=\"identity\") +\n# geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n# facet_grid(t_d1~t_t) +\n# #facet_wrap(~condition) + \n# theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))\n#ggsave(\"../graphs/litLisProb_unlogged_by_t_t_by_t_d.pdf\",width=10,height=10)\n\nggplot(agr, aes(x=t_t,y=Probability,color=t_d1,group=t_d1)) +\n# geom_bar(stat=\"identity\") +\n geom_point() +\n geom_line() +\n# geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_wrap(~Utterance) +\n #facet_wrap(~condition) + \n theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))\nggsave(\"../graphs/litLisProb2_unlogged_by_t_t_by_t_d.pdf\",width=10,height=10)\n\n\n# do same thing for logged typicalities\nagr = logged %>%\n select(p_l_t,p_l_d1,p_l_d2, t_d1, t_t) %>%\n gather(Utterance,Mentioned,-t_t, -t_d1) %>%\n group_by(Utterance,t_t, t_d1) %>%\n summarise(Probability=mean(Mentioned),ci.low=ci.low(Mentioned),ci.high=ci.high(Mentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$Probability - agr$ci.low\nagr$YMax = agr$Probability + agr$ci.high\n\n#ggplot(agr, aes(x=Utterance,y=Probability)) +\n# geom_bar(stat=\"identity\") +\n# geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n# facet_grid(t_d1~t_t) +\n# #facet_wrap(~condition) + \n# theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))\n#ggsave(\"litLisProb_logged_by_t_t_by_t_d.pdf\",width=10,height=10)\n\nggplot(agr, aes(x=t_t,y=Probability,color=t_d1,group=t_d1)) +\n # geom_bar(stat=\"identity\") +\n geom_point() +\n geom_line() +\n # geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_wrap(~Utterance) +\n #facet_wrap(~condition) + \n theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))\nggsave(\"../graphs/litLisProb2_logged_by_targetTyp_by_t_sub_d1.pdf\",width=10,height=10)\n\n# t_t = targetTypicality \n# t_d1 = distr1Typicality (=distr2Typicality)\n# p_l_t = literal listener probability to pick target \n# p_l_d1 = literal listener probability to pick distractor1 \n# p_l_d2 = literal listener probability to pick distractor2 \n\n", "meta": {"hexsha": "7571691f7d0730798086a8f4e8a4f8aaef0d81da", "size": 2963, "ext": "r", "lang": "R", "max_stars_repo_path": "models/old/basic_level_reference/modelExploration/rscripts/log_unlog_litlisOutput.r", "max_stars_repo_name": "thegricean/overinformativeness", "max_stars_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-10-27T18:41:57.000Z", "max_stars_repo_stars_event_max_datetime": "2016-10-27T18:41:57.000Z", "max_issues_repo_path": "models/old/basic_level_reference/modelExploration/rscripts/log_unlog_litlisOutput.r", "max_issues_repo_name": "thegricean/overinformativeness", "max_issues_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2015-11-30T21:44:31.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-21T01:26:05.000Z", "max_forks_repo_path": "models/old/basic_level_reference/modelExploration/rscripts/log_unlog_litlisOutput.r", "max_forks_repo_name": "thegricean/overinformativeness", "max_forks_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-11-25T09:53:20.000Z", "max_forks_repo_forks_event_max_datetime": "2017-03-17T21:51:18.000Z", "avg_line_length": 37.9871794872, "max_line_length": 120, "alphanum_fraction": 0.7229159636, "num_tokens": 984, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.34050163185622123}} {"text": "#' @title\n#' Plot the predicted response trajectory\n#'\n#' @description\n#' This function plots the predicted response trajectory based on the output of \\code{\\link{NRRR.pred}}.\n#'\n#'\n#' @usage\n#' NRRR.plot.pred(Ypred, Y = NULL, i_ind = 1, yi_ind = 1,\n#' tseq, t_index = NULL, x_lab = NULL,\n#' y_lab = NULL)\n#'\n#'\n#' @param Ypred an array of dimension \\code{(n, d, length(tseq))} where n is\n#' the sample size and d is the number of components in the\n#' multivariate response. The response trajectory is predicted at\n#' a set of time points \\code{tseq}.\n#' @param Y an array of dimension \\code{(n, d, length(tseq))}. It is the truly\n#' observed response trajectory in the form of discrete observations at\n#' time points \\code{tseq}. This term is only available\n#' when the function is applied to a testing set.\n#' @param i_ind a user-specified sample ID for which the plot is to be drawn.\n#' If the prediction is only conducted on one sample,\n#' then set \\code{i_ind = 1}; otherwise it should be an integer\n#' less than or equal to n. Default is 1.\n#' @param yi_ind a user-specified response component index for which the plot\n#' is to be drawn. It should satisfy \\eqn{0 < yi_ind \\le d}.\n#' Default is 1.\n#' @param tseq a sequence of time points at which the predicted response values\n#' are obtained.\n#' @param x_lab,y_lab the user-specified x-axis and y-axis label,\n#' and it should be given as a character string, e.g., x_lab = \"Time\".\n#' Default is NULL.\n#' @param t_index the user-specified x-axis tick marks, and it should be\n#' given as a vector of character strings of the same length as tseq.\n#' Default is NULL.\n#'\n#' @return A ggplot2 object. If the truly observed response trajectory is available, then\n#' it is plotted as a black line while the predicted trajectory is in red.\n#'\n#'\n#' @details\n#' An example of its usage can be found in the vignette of electricity demand analysis.\n#'\n#' @references\n#' Liu, X., Ma, S., & Chen, K. (2020). Multivariate Functional Regression via Nested Reduced-Rank Regularization.\n#' arXiv: Methodology.\n#'\n#' @import ggplot2\n#' @export\n\nNRRR.plot.pred <- function(Ypred, Y = NULL, i_ind = 1, yi_ind = 1,\n tseq, t_index = NULL, x_lab = NULL,\n y_lab = NULL){\n\n if (i_ind > dim(Ypred)[1]) stop(\"i_ind cannot be greater than n\")\n if (yi_ind > dim(Ypred)[2]) stop(\"yi_ind cannot be greater than d\")\n\n iy <- i_ind\n l <- yi_ind\n\n tseq <- factor(tseq)\n if (!is.null(t_index)) {\n levels(tseq) <- t_index\n } else {\n t_index <- tseq\n }\n\n if (is.null(x_lab)) x_lab <- \"t\"\n if (is.null(y_lab)) y_lab <- \"Predicted Value\"\n\n if (is.null(Y)) {\n b <- Ypred[iy,l,]\n y.range <- range(b)\n cur <- data.frame(b,tseq)\n ggplot(data = cur, aes(x = tseq)) +\n geom_line(aes(y = b,group = 1), colour = \"red\", linetype = 2,size=1.5) +\n scale_x_discrete(breaks = t_index[seq(1,length(t_index),2)]) +\n xlab(x_lab) +\n ylab(y_lab) +\n ylim(y.range) +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n theme(axis.text=element_text(size=12),axis.title=element_text(size=20),\n plot.title = element_text(hjust = 0.5, size = 20))\n } else {\n a <- Y[iy,l,]\n b <- Ypred[iy,l,]\n y.range <- range(a,b)\n cur <- data.frame(a,b,tseq)\n ggplot(data = cur, aes(x = tseq)) +\n geom_line(aes(y = a,group = 1), colour = \"black\",size=1.5) +\n geom_line(aes(y = b,group = 1), colour = \"red\", linetype = 2,size=1.5) +\n scale_x_discrete(breaks = t_index[seq(1,length(t_index),2)]) +\n xlab(x_lab) +\n ylab(y_lab) +\n ylim(y.range) +\n theme_bw() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n theme(axis.text=element_text(size=12),axis.title=element_text(size=20),\n plot.title = element_text(hjust = 0.5, size = 20))\n }\n}\n\n\n", "meta": {"hexsha": "6782c027c5417a97a86356976181b7761af92f7c", "size": 4104, "ext": "r", "lang": "R", "max_stars_repo_path": "R/NRRR.plot.Pred.r", "max_stars_repo_name": "xliu-stat/NRRR", "max_stars_repo_head_hexsha": "e51d9df7500b287f8c4daa8839f4cc1782525cef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/NRRR.plot.Pred.r", "max_issues_repo_name": "xliu-stat/NRRR", "max_issues_repo_head_hexsha": "e51d9df7500b287f8c4daa8839f4cc1782525cef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/NRRR.plot.Pred.r", "max_forks_repo_name": "xliu-stat/NRRR", "max_forks_repo_head_hexsha": "e51d9df7500b287f8c4daa8839f4cc1782525cef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.7169811321, "max_line_length": 113, "alphanum_fraction": 0.6020955166, "num_tokens": 1151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555713, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.340322273621201}} {"text": "# generate gene list from genes highly correlated with CD38-high B cells \n\nsource(\"R/functions/load_sig.r\")\n\ndir.create(file.path(PROJECT_DIR, \"generated_data\", \"signatures\"), showWarnings = F)\n\nfn.cd38.cor = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"robust_corr_genes.txt\")\ngene.sig = load_sig(fn.cd38.cor, \"cor.mean.sd.ratio\", ntop=10)\nfn.cd38.sig = file.path(PROJECT_DIR, \"generated_data\", \"signatures\", \"CD38_ge_sig.txt\")\ngene.sig %>% as.data.frame() %>% \n fwrite(fn.cd38.sig, col.names = F)\n\n\n# generate gene lists from BTMs related to plasma cells \n\nlibrary(tmod)\ndata(tmod)\nmod.id.li = tmod$MODULES %>% dplyr::filter(grepl(\"plasma\",Title, ignore.case = F), Category==\"immune\") %>% \n dplyr::select(ID) %>% \n unlist(use.names=F)\nmod.id.dc = c(\"DC.M4.11\",\"DC.M7.7\",\"DC.M7.32\")\nmod.id = c(mod.id.dc, mod.id.li)\nmod.gene = tmod$MODULES2GENES[mod.id]\n\nfor(k in mod.id) {\n fn.pb.sig = file.path(PROJECT_DIR, \"generated_data\", \"signatures\", \n sprintf(\"PB_%s_ge_sig.txt\", k))\n mod.gene[[k]] %>% as.data.frame() %>% \n fwrite(fn.pb.sig, col.names = F)\n}\n\n", "meta": {"hexsha": "f2de879b4521f593bb0dd47940fd9c73315b9834", "size": 1091, "ext": "r", "lang": "R", "max_stars_repo_path": "R/chi_signature_analysis/ge_sig_prep.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/chi_signature_analysis/ge_sig_prep.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/chi_signature_analysis/ge_sig_prep.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 34.09375, "max_line_length": 107, "alphanum_fraction": 0.6654445463, "num_tokens": 335, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.546738151984614, "lm_q1q2_score": 0.34032226442688074}} {"text": "library(zoo) \nlibrary(ggplot2)\nlibrary(plotly)\nlibrary(htmlwidgets)\nlibrary(readr)\n\nlibrary(grid)\n\nSys.setlocale(\"LC_TIME\", \"hr_HR.UTF-8\")\n\n# num days to plot\nn <- 100\n\n# read latest data\ndiff_df <- read_csv('data/latest/diff_df.csv')\n\nload('data/latest/percentage_change.Rda')\nload('data/latest/cumulative_cases.Rda')\nload('data/latest/avg7_df.Rda')\n\nload('data/latest/last_date.Rda')\nload('data/latest/last_date_.Rda')\n\n\nplot_df <- merge(diff_df, percentage_change, by=c(\"Datum\"))\n\n\n\ndata_to_plot <- tail(plot_df, n=n)\n\nf <- list(size = 18, color = \"black\")\nf2 <- list(size = 16, color = \"black\")\n\nfor(use_log_scale in c(FALSE, TRUE)) {\n p <- list()\n a <- list()\n \n for(i in 1:22) {\n title <- colnames(data_to_plot)[1 + i]\n \n if (i == 21) {\n title <- gsub(\"[.]\", \"\", title)\n }\n else \n {\n title <- gsub(\"[.]\", \"-\", title)\n }\n \n if ((i - 1) %% 5 == 0) {\n ylab <- ylab(\"Broj slu\u010dajeva\")\n }\n else {\n ylab <- ylab('')\n }\n \n p[[i]] <- ggplot(data=data_to_plot, aes_string(x='Datum', y=colnames(data_to_plot)[1 + i])) +\n ylab +\n geom_bar(stat=\"identity\") +\n geom_col(aes_string(fill=colnames(data_to_plot)[1 + i + 22])) +\n scale_fill_distiller(palette = \"RdYlGn\", limits = c(-50, 50), oob = scales::oob_squish_any, name='') +\n geom_line(data=tail(avg7_df, n=n), aes_string(x='Datum', y=colnames(data_to_plot)[1 + i]), size=1, colour='blue') + #\n theme_minimal() +\n theme(axis.text.x = element_text(size = 10)) +\n theme(plot.margin = unit(c(1,1,1,1), \"cm\"))\n \n if (use_log_scale) {\n p[[i]] <- p[[i]] + scale_y_continuous(trans=scales::pseudo_log_trans(base = 10))\n }\n \n p[[i]] <- ggplotly(p[[i]])\n \n a[[i]] <- list(\n text = paste('\\n', title, ' (', diff_df[nrow(diff_df), i], ')\\nTjedna razlika ', sprintf(\"%+d\", round(data_to_plot[nrow(data_to_plot), 1 + i + 22], 0)), '%', sep=''),\n font = f,\n xref = \"paper\",\n yref = \"paper\",\n yanchor = \"bottom\",\n yshift = 10,\n xanchor = \"center\",\n align = \"center\",\n cliponaxis = FALSE,\n x = 0.5,\n y = 0.92, # 0.92\n showarrow = FALSE\n )\n \n p[[i]] <- p[[i]] %>%\n layout(annotations = a[i])\n }\n \n s <- subplot(p, nrows = 5, margin=c(0.01,0.0,0.04,0.05), titleY = TRUE, heights=c(0.17, 0.22, 0.22, 0.22, 0.17)) %>% # 2 0 5 4\n add_annotations(x = 0.65,\n y = 0.07,\n text = paste('COVID 19 u Hrvatskoj: Pregled broja zara\u017eenih po \u017eupanijama (', last_date ,')\\n\\nBoje prikazuju promjenu broja slu\u010dajeva zadnjih tjedan dana u usporedbi s brojem slu\u010dajeva prethodnog tjedna.\\nBroj novih slu\u010dajeva u pro\u0161lih 24 sata prikazan je u zagradi kraj imena \u017eupanije.\\n\\nGenerirano: ', format(Sys.time() + as.difftime(1, units=\"hours\"), '%d.%m.%Y. %H:%M:%S h'), '\\nIzvor podataka: koronavirus.hr\\n\\nAutor: Petar Pala\u0161ek, ppalasek.github.io', sep=''),\n font = f2,\n xref = \"paper\",\n yref = \"paper\",\n align='left',\n ax = 0,\n ay = 0)\n \n if (use_log_scale) {\n saveWidget(s, file = \"html/index_log.html\", title = \"COVID 19 u Hrvatskoj: Pregled broja zara\u017eenih po \u017eupanijama\")\n }\n else {\n saveWidget(s, file = \"html/index.html\", title = paste(\"COVID 19 u Hrvatskoj: Pregled broja zara\u017eenih po \u017eupanijama (\", last_date, \")\", sep=\"\"))\n \n \n orca(s, file = paste('img/', last_date_, '_line_plots.png', sep = ''), width = 27 * 72, height = 13 * 72)\n }\n}\n", "meta": {"hexsha": "bef22ca1b123d3fd20b23fad678b5f10126a6c97", "size": 3559, "ext": "r", "lang": "R", "max_stars_repo_path": "generate_cases_line_plots.r", "max_stars_repo_name": "ppalasek/covid_plots_croatia", "max_stars_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate_cases_line_plots.r", "max_issues_repo_name": "ppalasek/covid_plots_croatia", "max_issues_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate_cases_line_plots.r", "max_forks_repo_name": "ppalasek/covid_plots_croatia", "max_forks_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.7767857143, "max_line_length": 497, "alphanum_fraction": 0.5633604945, "num_tokens": 1209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3403117782996635}} {"text": "pdf_file<-\"pdf/networks_directed_network.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=6,height=6)\n\npar(mai=c(0.25,0.25,0.25,0.5),omi=c(0.25,0.25,0.25,0.25),\n\tfamily=\"Lato Light\",las=1)\nlibrary(igraph)\nlibrary(RColorBrewer)\n\n# Import data and prepare chart\n\nnodes <- read.csv(\"myData/reg_plot.csv\", header=T, as.is=T)\nlinks <- read.csv(\"myData/reg_flow.csv\", header=T, as.is=T)\n\nlinks <- links[order(links$orig_reg, links$dest_reg),]\ncolnames(links)[3] <- \"weight\"\nrownames(links) <- NULL\n\nbinnen<-links[links$orig_reg==links$dest_reg, ]\nnodes$inside<-binnen$weight[match(nodes$region, binnen$orig_reg)]\n\nnet <- graph_from_data_frame(d=links, vertices=nodes, directed=T)\nnet <- simplify(net, remove.multiple = F, remove.loops = T) \n\nE(net)$width <- E(net)$weight*5\nV(net)$size <- sqrt(V(net)$inside*100)\n\ncolrs <- brewer.pal(9, \"Paired\")\nV(net)$color <- colrs[V(net)$order1]\n\nedge.start <- ends(net, es=E(net), names=F)[,1] \nedge.col <- V(net)$color[edge.start]\n\n# Create chart\n\nplot(net, edge.arrow.size=0, edge.color=edge.col,layout=layout_in_circle(net),\n vertex.color=colrs, vertex.frame.color=\"#ffffff\", edge.curved=.1,\n vertex.label=V(net)$media, vertex.label.color=\"black\", vertex.label.family=\"Lato Light\") \n \nlegend(x=0.8, y=1.25, c(\"\", \" 2 M\",\"\", \" 1 M\"), pch=19,xpd=T,title=\"Internal Migration:\",\n col=\"#777777\", pt.cex=c(0, sqrt(4),0,sqrt(2)), cex=.8, bty=\"n\", ncol=1)\n\nlegend(x=-1.25, y=-1.15, c(\" 3 M\",\" 2 M\", \" 1 M\"), pch=15,xpd=T,horiz=T,\n col=\"#777777\", pt.cex=c(sqrt(3),sqrt(2),sqrt(1)), cex=.8, bty=\"n\", ncol=1)\n\n# Titling\n\nmtext(\"Migration 2010-2015\", line=-1.5, adj=0, cex=2, family=\"Lato Black\", col=\"grey40\", outer=T)\nmtext(\"Data Source: https://github.com/cran/migest/tree/master/inst/vidwp\", side=1, line=-1, adj=1, cex=0.9, font=3, outer=T)\ndev.off()\n", "meta": {"hexsha": "0f4b0f21ad179d97d8ffbd666a19ec44a9089d6d", "size": 1798, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/networks_directed_network.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/networks_directed_network.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/networks_directed_network.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.96, "max_line_length": 125, "alphanum_fraction": 0.6662958843, "num_tokens": 632, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3403117782996635}} {"text": "subroutine testeq(a,b,eps,value)\n\n# Test for the equality of a and b in a fairly\n# robust way.\n# Called by trifnd, circen, stoke.\n\nimplicit double precision(a-h,o-z)\nlogical value\n\n# Define constants.\none = 1.d0\nten = 1.d10\n\n# If b is essentially 0, check whether a is essentially zero also.\n# The following is very sloppy! Must fix it!\nif(abs(b)<=eps) {\n if(abs(a)<=eps) value = .true.\n else value = .false.\n return\n}\n\n# Test if a is a `lot different' from b. (If it is\n# they're obviously not equal.) This avoids under/overflow\n# problems in dividing a by b.\nif(abs(a)>ten*abs(b)|abs(a)\n# \n#\n# In this version, the square root of the aligned reads is used.\n#\n#==============================================================================#\n\ncargs = commandArgs(trailingOnly=TRUE)\ngroup_1 = scan(cargs[1], what='character')\ngroup_2 = scan(cargs[2], what='character')\n\ntrtmnt = c(rep('a', length(group_1)),\n rep('b', length(group_2)))\n\ndata_sets = lapply(c(group_1, group_2),\n function(x) read.csv(x, sep='\\t',\n stringsAsFactors=FALSE,\n check.names=FALSE,\n comment.char='#',\n header=TRUE))\nn_genes = nrow(data_sets[[1]])\n\nsame_gene = sapply(1:n_genes,\n function(i) {\n genes = sapply(data_sets,\n function(x) x[i, 'name'])\n all(genes == genes[1])\n })\nif(!all(same_gene))\n stop('Gene names do not match')\n\n# Get the weights - based on the number of aligned reads. Divide the\n# count of aligned reads by the count of aligned reads million to get\n# the number of aligned reads in millions. This only has to be done\n# once per pool.\nwts = sapply(data_sets, function(x) {\n r = which(x$count > 10)[1]\n sqrt(x[r, 'count'] / x[r, 'count (CPM)'])\n })\ncat(wts, '\\n', file=stderr())\n\ncat('contig\\tstart\\tend\\tgene\\tp\\teffect\\trpkm1\\trpkm2\\n', file=stdout())\nfor(i in 1:n_genes) {\n contig = data_sets[[1]][i, 'chrom']\n start_pos = data_sets[[1]][i, 'start']\n end_pos = data_sets[[1]][i, 'end']\n gene = data_sets[[1]][i, 'name']\n\n raw_counts = sapply(data_sets, function(x) x[i, 'count'])\n cpm = sapply(data_sets, function(x) x[i, 'count (CPM)'])\n rpkm = sapply(data_sets, function(x) x[i, 'RPKM'])\n\n # A previous version of this script had an \"and\" instead of an\n # \"or\", but I think it's okay to do a test if one treatment has\n # no reads, as long as the other one has reads.\n if(sum(raw_counts[trtmnt == 'a'] != 0) > 0 ||\n sum(raw_counts[trtmnt == 'b'] != 0) > 0) {\n # In a previous version of this script, I eliminated replicates\n # with zero reads. I have no idea why I did that - it seems\n # silly.\n test_results = lm(rpkm ~ trtmnt, weights=wts)\n effect = coefficients(test_results)['trtmntb']\n p = coefficients(summary(test_results))['trtmntb', 'Pr(>|t|)']\n } else {\n p = NaN\n effect = NaN\n }\n cat(paste(contig, start_pos, end_pos, gene, p, effect, sep='\\t'),\n '\\t', paste(round(rpkm[trtmnt == 'a'], 3), collapse=','),\n '\\t', paste(round(rpkm[trtmnt == 'b'], 3), collapse=','),\n '\\n', sep='', file=stdout())\n}\n", "meta": {"hexsha": "89951d8caec0d8a3563fe8dca1396391f6c819f3", "size": 3057, "ext": "r", "lang": "R", "max_stars_repo_path": "read_depth_test3.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "read_depth_test3.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "read_depth_test3.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.6962025316, "max_line_length": 80, "alphanum_fraction": 0.5456329735, "num_tokens": 804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.34029529195850444}} {"text": "comp208 <- read.table(\"Comp_Data.txt\", header = T, sep = \"\\t\")\r\n\r\n#Compute the largest y and x in the data\r\nmax_y <- max(comp208$Compara\u00e7\u00f5es_AVL, comp208$Compara\u00e7\u00f5es_ABB)\r\nmax_x <- max(comp208$Tamanho_AVL, comp208$Tamanho_ABB)\r\n\r\n#Define colors to be used for AVL and BST\r\nplot_colors <- c(\"black\", 'red')\r\n\r\n#Start PNG device to save output to Fig_Plot.png\r\npng(\"Fig_Plot.png\", height = 720, width = 1280, bg = \"white\")\r\n\r\n#Plotting\r\nplot(comp208$Tamanho_ABB, comp208$Compara\u00e7\u00f5es_ABB, type = \"p\", pch = 20, col = plot_colors[1],\r\n ylim = c(0, max_y), xlim = c(0, max_x), axes = FALSE, ann = FALSE)\r\n\r\n#Change interval scale\r\naxis(1, las = 1, at = ((max_x %% 1000000)/10)*0:max_x)\r\n\r\naxis(2, las = 1, at = ((max_y %% 1000000)/10)*0:max_y)\r\n\r\nbox()\r\n\r\n#Graph BST\r\nlines(comp208$Tamanho_AVL, comp208$Compara\u00e7\u00f5es_AVL, type = \"p\", pch = 20, lty = 2, col = plot_colors[2])\r\n\r\n#Create a title with red, bold/italic font\r\ntitle(main = \"Search Algorithm: AVL x BST\", col.main = \"black\", font.main = 4)\r\n\r\n#Label the x and y axes with dark green text\r\ntitle(xlab = \"Size\", col.lab = rgb(0, 0, 0), cex.lab = 1.75, line = 2.5)\r\ntitle(ylab = \"Number of Comparisons\", col.lab = rgb(0, 0, 0), cex.lab = 1.75, line = -2)\r\n\r\nnameStruct <- c(\"ABB\", \"AVL\")\r\n\r\nlegend(1, max_y, nameStruct, cex = 0.8, col = plot_colors, pch = 20:20, lty = 1:2);\r\n\r\n#turn off device driver (to flush output to png)\r\ndev.off()\r\n\r\n", "meta": {"hexsha": "6c6f08a8a090bb8ad2d1fb6774498c6d84d34116", "size": 1396, "ext": "r", "lang": "R", "max_stars_repo_path": "Plotagem/plotagem.r", "max_stars_repo_name": "enrickgsilva/PROJETO", "max_stars_repo_head_hexsha": "e935da897f00b333f1dcdd5041b14665325f58d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Plotagem/plotagem.r", "max_issues_repo_name": "enrickgsilva/PROJETO", "max_issues_repo_head_hexsha": "e935da897f00b333f1dcdd5041b14665325f58d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-09-03T03:23:46.000Z", "max_issues_repo_issues_event_max_datetime": "2019-09-03T03:24:28.000Z", "max_forks_repo_path": "Plotagem/plotagem.r", "max_forks_repo_name": "enrickgsilva/PROJECT", "max_forks_repo_head_hexsha": "e935da897f00b333f1dcdd5041b14665325f58d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-01-29T04:31:36.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-29T04:31:36.000Z", "avg_line_length": 34.0487804878, "max_line_length": 105, "alphanum_fraction": 0.6475644699, "num_tokens": 487, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.6548947290421275, "lm_q1q2_score": 0.3402317753714238}} {"text": "library(shiny)\nlibrary(shinydashboard)\n\nshinyServer(\n \n function(input, output) {\n output$histogram <- renderPlot(\n {\n hist(faithful$eruptions, breaks = input$bins)\n }\n )\n \n }\n)", "meta": {"hexsha": "9db5262a6e23bf98202d88d45f7be6870da3ca99", "size": 206, "ext": "r", "lang": "R", "max_stars_repo_path": "R/tutorial_2/server.r", "max_stars_repo_name": "harsh52/Assignments-competitive_coding", "max_stars_repo_head_hexsha": "bec05217ca3d67f71d5209498dc5e6bfae9d4a93", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2018-09-28T19:15:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-22T11:17:24.000Z", "max_issues_repo_path": "R/tutorial_2/server.r", "max_issues_repo_name": "harsh52/Assignments-competitive_coding", "max_issues_repo_head_hexsha": "bec05217ca3d67f71d5209498dc5e6bfae9d4a93", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/tutorial_2/server.r", "max_forks_repo_name": "harsh52/Assignments-competitive_coding", "max_forks_repo_head_hexsha": "bec05217ca3d67f71d5209498dc5e6bfae9d4a93", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-09-14T21:33:52.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-13T11:06:08.000Z", "avg_line_length": 14.7142857143, "max_line_length": 53, "alphanum_fraction": 0.6019417476, "num_tokens": 57, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.340121117766051}} {"text": "student<-c(0:849)\r\n# we can sample number of students according to our wish using sample function\r\nsample(student , 20)\r\n# We would repeat the process in subsequent weeks .\r\n ", "meta": {"hexsha": "d4c6649a3cde0c05f56930ca63b0350f9118ebad", "size": 176, "ext": "r", "lang": "R", "max_stars_repo_path": "An_Introduction_To_Statistical_Methods_And_Data_Analysis_by_R_Lyman_Ott_And_Michael_Longnecker/CH4/EX4.21/Ex4_21.r", "max_stars_repo_name": "prashantsinalkar/R_TBC_Uploads", "max_stars_repo_head_hexsha": "b3f3a8ecd454359a2e992161844f2fb599f8238a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "An_Introduction_To_Statistical_Methods_And_Data_Analysis_by_R_Lyman_Ott_And_Michael_Longnecker/CH4/EX4.21/Ex4_21.r", "max_issues_repo_name": "prashantsinalkar/R_TBC_Uploads", "max_issues_repo_head_hexsha": "b3f3a8ecd454359a2e992161844f2fb599f8238a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "An_Introduction_To_Statistical_Methods_And_Data_Analysis_by_R_Lyman_Ott_And_Michael_Longnecker/CH4/EX4.21/Ex4_21.r", "max_forks_repo_name": "prashantsinalkar/R_TBC_Uploads", "max_forks_repo_head_hexsha": "b3f3a8ecd454359a2e992161844f2fb599f8238a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-04-07T16:44:56.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-13T06:35:28.000Z", "avg_line_length": 35.2, "max_line_length": 79, "alphanum_fraction": 0.7443181818, "num_tokens": 41, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.34012110927789324}} {"text": "#FAN PLOT\nlibrary(plotrix)\nslices<-c(10,12,4,16,8)\nlbls<-c(\"US\", \"UK\", \"Australia\", \"Germany\", \"France\")\nfan.plot(slices,labels=lbls,main=\"Fan Plot\")\n", "meta": {"hexsha": "98be4d69b573b401f8a155245a2004ee929278af", "size": 150, "ext": "r", "lang": "R", "max_stars_repo_path": "Basic Graph/fan.r", "max_stars_repo_name": "loangelak/Data-Analysis-and-Graphics", "max_stars_repo_head_hexsha": "b48b0fb3f2cb2a69ae453532381ea4f2d98161f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Basic Graph/fan.r", "max_issues_repo_name": "loangelak/Data-Analysis-and-Graphics", "max_issues_repo_head_hexsha": "b48b0fb3f2cb2a69ae453532381ea4f2d98161f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Basic Graph/fan.r", "max_forks_repo_name": "loangelak/Data-Analysis-and-Graphics", "max_forks_repo_head_hexsha": "b48b0fb3f2cb2a69ae453532381ea4f2d98161f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0, "max_line_length": 53, "alphanum_fraction": 0.6733333333, "num_tokens": 59, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.34012110927789324}} {"text": "#load libraries\nlibrary(tidyverse)\nlibrary(datasets)\n#make a tibble\nTestDataFrame <- as_tibble(diamonds)\n#plot in ggplot2\nggplot(data=TestDataFrame) +\n aes(x = cut, y = color) +\n geom_count()", "meta": {"hexsha": "cc759b4dd8528694e53a5f88ea2e7852a4286dd9", "size": 193, "ext": "r", "lang": "R", "max_stars_repo_path": "data/test.r", "max_stars_repo_name": "UCSBCarpentry/2020-08-17-Summer-R", "max_stars_repo_head_hexsha": "0bd1420e0e4ea1fc2132b6b664997110a9854366", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data/test.r", "max_issues_repo_name": "UCSBCarpentry/2020-08-17-Summer-R", "max_issues_repo_head_hexsha": "0bd1420e0e4ea1fc2132b6b664997110a9854366", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-04T16:39:49.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-04T16:39:49.000Z", "max_forks_repo_path": "data/test.r", "max_forks_repo_name": "UCSBCarpentry/2020-08-17-Summer-R", "max_forks_repo_head_hexsha": "0bd1420e0e4ea1fc2132b6b664997110a9854366", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.4444444444, "max_line_length": 36, "alphanum_fraction": 0.7461139896, "num_tokens": 59, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.34012110927789324}} {"text": "#' mbtommHG\n#'\n#' Conversion from Millibar [hPa] to mmHG.\n#'\n#' @param mb numeric Air pressure in Millibar [hPa]. \n#' @return mmHG\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords mbtommHG \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nmbtommHG=function(mb) {\n ct$assign(\"mb\", as.array(mb))\n ct$eval(\"var res=[]; for(var i=0, len=mb.length; i < len; i++){ res[i]=mbtommHG(mb[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "e9032e40575f1d81bce5194ede0c3bedae61df2b", "size": 602, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mbtommHG.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/mbtommHG.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mbtommHG.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 24.08, "max_line_length": 115, "alphanum_fraction": 0.5249169435, "num_tokens": 189, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.34012110927789324}} {"text": "#' ms2mph\n#'\n#' Conversion speed in meters per second to miles per hour. \n#'\n#' @param ms numeric Speed in meters per second.\n#' @return miles per hour\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords ms2mph \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nms2mph=function(ms) {\n ct$assign(\"ms\", as.array(ms))\n ct$eval(\"var res=[]; for(var i=0, len=ms.length; i < len; i++){ res[i]=ms2mph(ms[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "a5245e0cca0825bf370e3cce46f527a8b4cdbbcf", "size": 616, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ms2mph.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/ms2mph.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ms2mph.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 24.64, "max_line_length": 113, "alphanum_fraction": 0.5340909091, "num_tokens": 180, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3401211007897354}} {"text": "# Unit tets for RNGseq\n# \n# Author: Renaud Gaujoux\n###############################################################################\n\nlibrary(parallel)\n\ntest.RNGseq_seed <- function(){\n\t\n\t# actual testing function\n\t.test_loc <- function(.msg, ..., .change=FALSE){\n\t\tmsg <- function(...) paste(.msg, ':', ...)\n\t\tos <- RNGseed()\n\t\ton.exit(RNGseed(os))\n\t\ts <- RNGseq_seed(...)\n\t\tcheckTrue(length(s) == 7L && s[1] %% 100 == 7L, msg(\"RNGseq_seed returns a value of .Random.seed for L'Ecuyer-CMRG\"))\n\t\tcheckIdentical(RNGseed()[1], os[1], msg(\"RNGseq_seed does not change the type of RNG\"))\n\t\t\n\t\tif( !.change ) checkIdentical(RNGseed(), os, msg(\"RNGseq_seed does not change the value of .Random.seed\"))\n\t\telse checkTrue( !identical(RNGseed(), os), msg(\"RNGseq_seed changes the value of .Random.seed\"))\n\t\ts\n\t}\n\t\n\t# test in two RNG settings: default and L'Ecuyer\n\t.test <- function(.msg, ..., ss=NULL, .change=FALSE, Dchange=.change, Lchange=.change){\n\t\tos <- RNGseed()\n\t\ton.exit(RNGseed(os))\n\t\t\n\t\t# default RNG\n\t\tRNGkind('default')\n\t\tif( !is.null(ss) ) set.seed(ss)\n\t\ts1 <- .test_loc(paste(.msg, '- default'), ..., .change=Dchange)\n\t\t\n\t\tRNGkind(\"L'Ecuyer\")\n\t\tif( !is.null(ss) ) set.seed(ss)\n\t\ts2 <- .test_loc(paste(.msg, \"- CMRG\"), ..., .change=Lchange)\n\t\t\n\t\tlist(s1, s2)\n\t}\n\t\n\tos <- RNGseed()\n\ton.exit(RNGseed(os))\n\t\n\tRNGkind('default', 'default')\n\t\n\t# test different arguments\n\ts1 <- .test(\"seed=missing\", ss=1, Dchange=TRUE, Lchange=FALSE)\n\trunif(10)\n\ts2 <- .test(\"seed=NULL\", NULL, ss=1, Dchange=TRUE, Lchange=FALSE)\n\tcheckIdentical(s1, s2, \"set.seed(1) + seed=missing and seed=NULL return identical results\")\n\t\n\t# doRNG seed with single numeric\n\trunif(10)\n\ts3 <- .test(\"seed=single numeric\", 1)\n\tcheckIdentical(s1[[1]], s3[[1]], \"v1.4 - set.seed(1) + seed=missing and seed=1 return identical results when current RNG is NOT CMRG\")\n\tcheckIdentical(s1[[2]], s3[[2]], \"v1.4 - set.seed(1) + seed=missing and seed=1 return identical results when current RNG is CMRG\")\n\tcheckTrue( !identical(s1[[1]], s1[[2]]), \"v1.4 - set.seed(1) + seed=missing return NON identical results in different RNG settings\")\n\tcheckTrue( !identical(s3[[1]], s3[[2]]), \"v1.4 - seed=num return NON identical results in different RNG settings\")\n\t\n\t# version < 1.4\n#\tdoRNGversion(\"1.3.9999\")\n\ts1 <- .test(\"v1.3 - seed=missing\", ss=1, Dchange=TRUE, Lchange=TRUE, version=1)\n\ts3 <- .test(\"v1.3 - seed=single numeric\", 1, version=1)\n\tcheckIdentical(s1[[1]], s3[[1]], \"v1.3 - set.seed(1) + seed=missing and seed=1 return identical results when current RNG is NOT CMRG\")\n\tcheckTrue( !identical(s1[[2]], s3[[2]]), \"v1.3 - set.seed(1) + seed=missing and seed=1 return NON identical results when current RNG is CMRG\")\n\tcheckTrue( !identical(s1[[1]], s1[[2]]), \"v1.3 - set.seed(1) + seed=missing return NON identical results in different RNG settings\")\n\tcheckTrue( !identical(s3[[1]], s3[[2]]), \"v1.4 - seed=num return NON identical results in different RNG settings\")\n#\tdoRNGversion(NULL) \n\t##\n\t\n\t.test(\"seed=single integer\", 10L)\n\t# directly set doRNG seed with a 6-length\n\t.test(\"seed=6-length integer\", 1:6)\n\t.test(\"seed=6-length numeric\", as.numeric(1:6))\n\ts <- 1:6\n\tcheckIdentical(RNGseq_seed(s)[2:7], s, \"RNGseq_seed(6-length) returns stream to the given value\")\n\t# directly set doRNG seed with a full 7-length .Random.seed\n\t.test(\"seed=7-length integer\", c(407L,1:6))\n\t.test(\"seed=7-length numeric\", as.numeric(c(107L,1:6)))\n\ts <- c(407L,1:6)\n\tcheckIdentical(RNGseq_seed(s), s, \"RNGseq_seed(7-length) returns complete seed with the given value\")\n\t\n\t# errors\n\tos <- RNGseed()\n\tcheckException(RNGseq_seed(NA), \"seed=NA throws an exception\")\n\tcheckIdentical(os, RNGseed(), \"RNGseq_seed(NA) does not change the value of .Random.seed [error]\")\n\t\n\t# Current CMRG is L'Ecuyer\n\tRNGkind(\"L'Ecuyer\")\n\tset.seed(456)\n\ts <- RNGseed()\n\tr <- RNGseq_seed(NULL)\n\tcheckIdentical(s, r, \"Current is CMRG: seed=NULL return current stream\")\n\trunif(10)\n\tcheckIdentical(s, RNGseq_seed(456), \"Current is CMRG: seed=numeric return stream seeded with value\")\n\t\n}\n\ntest.RNGseq <- function(){\n\t\n\tos <- RNGseed()\n\ton.exit(RNGseed(os))\n\t\n\t# actual testing function\n\t.test_loc <- function(.msg, n, ..., .list=TRUE, .change=FALSE){\n\t\tmsg <- function(...) paste(.msg, ':', ...)\n\t\tos <- RNGseed()\n\t\ton.exit(RNGseed(os))\n\t\n\t\ts <- RNGseq(n, ...)\n\t\t\n\t\tif( !.change ) checkIdentical(RNGseed(), os, msg(\"the value of .Random.seed is not changed\"))\n\t\telse checkTrue( !identical(RNGseed(), os), msg(\"the value of .Random.seed does change\"))\n\t\t\n\t\tif( .list )\tcheckTrue(is.list(s), msg(\"result is a list\"))\n\t\telse{\n\t\t\tcheckTrue(is.integer(s), msg(\"result is an integer vector\"))\n\t\t\ts <- list(s)\n\t\t}\n\t\t\n\t\tcheckTrue(length(s) == n, msg(\"result has correct length\"))\n\t\tcheckTrue(all(sapply(s, length) == 7L), msg(\"each element has length 7\"))\n\t\tcheckTrue(all(sapply(s, function(x) x[1] %% 100) == 7L), msg(\"each element has correct RNG kind\"))\n\t\ts\n\t}\n\t\n\t.test <- function(msg, n, ...){\n\t\tset.seed(1)\n\t\ts1 <- .test_loc(paste(msg, '- no seed'), n, ..., .change=TRUE)\n\t\trunif(1)\n\t\ts2 <- .test_loc(paste(msg, '- seed=1'), n, 1, ..., .change=FALSE)\n\t\t#checkIdentical(s1, s2, paste(msg, \" - set.seed(1) + no seed is identical to seed=1\"))\n\t\t.test_loc(paste(msg, '- seed=1:6'), n, 1:6, ...)\n\t}\n\t.test(\"n=1\", 1, .list=FALSE)\n\t.test(\"n=2\", 2)\n\t.test(\"n=5\", 5)\n\t\n\t# with full list\n\ts <- RNGseq(3)\n\tcheckIdentical(RNGseq(length(s), s), s, \"If passing a complete list: returns the list itself\")\n\ts3 <- RNGseq(5)\n\ts <- structure(s, rng=s3)\n\tcheckIdentical(RNGseq(length(s3), s), s3, \"If passing a complete list in rng S3 slot: returns the complete slot\")\n\t#\n\n\t# Current RNG is CMRG\n\tset.seed(456, \"L'Ec\")\n\ts <- .Random.seed\n\tref <- list(s, nextRNGStream(s), nextRNGStream(nextRNGStream(s)))\n\trs <- RNGseq(3, 456)\n\tcheckIdentical(rs, ref, \"Current RNG is CMRG: RNGseq(n, num) returns RNG streams that start with stream as set.seed\")\n\tcheckIdentical(s, .Random.seed, \"Current RNG is CMRG: RNGseq(n, num) did not change random seed\")\n\t\n\trunif(10)\n\ts <- .Random.seed\n\tref <- list(s, nextRNGStream(s), nextRNGStream(nextRNGStream(s)))\n\trs2 <- RNGseq(3)\n\tcheckIdentical(rs2, ref, \"Current RNG is CMRG: RNGseq(n) returns RNG streams that start with current stream\")\n\tcheckIdentical(.Random.seed, nextRNGStream(tail(rs2,1)[[1]]), \"Current RNG is CMRG: RNGseq(n) changes current random seed to next stream of last stream in sequence\")\n\t\n}\n", "meta": {"hexsha": "674012eaf5917345769213bdf80f9daa01b90208", "size": 6331, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNGseq.r", "max_stars_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_stars_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNGseq.r", "max_issues_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_issues_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-12T14:06:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-10T23:26:27.000Z", "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNGseq.r", "max_forks_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_forks_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.8404907975, "max_line_length": 166, "alphanum_fraction": 0.6548728479, "num_tokens": 2033, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807521910589}} {"text": "# cerner_2^5_2018\nnumerals <- readline(prompt=\"Enter roman numerals: \")\n\nprint(as.integer(as.roman(numerals)))", "meta": {"hexsha": "5af1adb38e2ffb955ad78f65478c36681cf1a03c", "size": 110, "ext": "r", "lang": "R", "max_stars_repo_path": "TwentyOne/FromRomanNumerals.r", "max_stars_repo_name": "jgrieger/2-to-the-5th", "max_stars_repo_head_hexsha": "b085d4f19c635054b47034f48b8fc848e0ae9182", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "TwentyOne/FromRomanNumerals.r", "max_issues_repo_name": "jgrieger/2-to-the-5th", "max_issues_repo_head_hexsha": "b085d4f19c635054b47034f48b8fc848e0ae9182", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "TwentyOne/FromRomanNumerals.r", "max_forks_repo_name": "jgrieger/2-to-the-5th", "max_forks_repo_head_hexsha": "b085d4f19c635054b47034f48b8fc848e0ae9182", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5, "max_line_length": 53, "alphanum_fraction": 0.7545454545, "num_tokens": 33, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807436267244}} {"text": "##functions providng various diagnostics for evaluating model assumptions and stability and\n#for helping with setting an appropriate random slopes structure for (G)LMMs\n#written by Roger Mundry, last modified early 2016\nranef.diagn.plot<-function(model.res, QQ=F, col=grey(0.5, alpha=0.75)){\n old.par = par(no.readonly = TRUE)\n\tif(class(model.res)[[1]]!=\"glmmTMB\"){\n\t\tn.plots=sum(unlist(lapply(ranef(model.res), length)))\n\t}else{\n\t\tn.plots=sum(unlist(lapply(ranef(model.res)[[\"cond\"]], ncol)))+sum(unlist(lapply(ranef(model.res)[[\"zi\"]], ncol)))\n\t}\n\tx=ifelse(n.plots%%2==1,n.plots+1,n.plots)\n\txmat=outer(1:x, 1:(1+x/2), \"*\")-n.plots\n\tcolnames(xmat)=1:(1+x/2)\n\trownames(xmat)=1:x\n\txmat=as.data.frame(as.table(xmat))\n\txmat=subset(xmat, as.numeric(as.character(xmat$Var1))>=as.numeric(as.character(xmat$Var2)) & xmat$Freq>=0)\n\tsum.diff=as.numeric(as.character(xmat$Var1))-as.numeric(as.character(xmat$Var2))+xmat$Freq\n\txmat=xmat[sum.diff==min(sum.diff),]\n\txmat=xmat[which.min(xmat$Freq),]\n\tpar(mfrow=c(xmat$Var2, xmat$Var1))\n\tpar(mar=c(rep(2, 3), 1))\n\tpar(mgp=c(1, 0.5, 0))\n\txnames=lapply(ranef(model.res), names)\n\txnames=lapply(xnames, gsub, pattern=\"(Intercept)\", replacement=\"icpt\", fixed=T)\n\tif(class(model.res)[[1]]!=\"glmmTMB\"){\n\t\tfor(i in 1:length(ranef(model.res))){\n\t\t\tto.plot=ranef(model.res)[[i]]\n\t\t\tfor(k in 1:ncol(to.plot)){\n\t\t\t\tif(QQ){\n\t\t\t\t\tqqnorm(to.plot[,k], main=\"\", tcl=-0.25, xlab=\"\", ylab=\"\", pch=19, col=col)\n\t\t\t\t\tqqline(to.plot[,k])\n\t\t\t\t}else{\n\t\t\t\t\thist(to.plot[,k], main=\"\", tcl=-0.25, xlab=\"\", ylab=\"\", col=NA)\n\t\t\t\t}\n\t\t\t\tmtext(text=paste(c(names(ranef(model.res)[i]), xnames[[i]][k]), collapse=\"_\"), side=3)\n\t\t\t}\n\t\t}\n\t}else{\n\t\tfor(i in c(\"cond\", \"zi\")){\n\t\t\tif(length(ranef(model.res)[[i]])>0){\n\t\t\t\tfor(j in 1:length(ranef(model.res)[[i]])){\n\t\t\t\t\tto.plot=ranef(model.res)[[i]][[j]]\n\t\t\t\t\tfor(k in 1:ncol(to.plot)){\n\t\t\t\t\t\tif(QQ){\n\t\t\t\t\t\t\tqqnorm(to.plot[,k], main=\"\", tcl=-0.25, xlab=\"\", ylab=\"\", pch=19, col=col)\n\t\t\t\t\t\t\tqqline(to.plot[,k])\n\t\t\t\t\t\t}else{\n\t\t\t\t\t\t\thist(to.plot[,k], main=\"\", tcl=-0.25, xlab=\"\", ylab=\"\", col=NA)\n\t\t\t\t\t\t}\n\t\t\t\t\t\tmtext(text=paste(c(names(ranef(model.res)[i]), names(ranef(model.res)[[i]][j]), colnames(to.plot)[k]), collapse=\", \"), side=3)\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\tpar(old.par)\n}\n\ndiagnostics.plot<-function(mod.res, col=grey(level=0.25, alpha=0.5), size.fac=1, weights=NULL){\n\tif(is.null(weights)){\n\t\tif(class(mod.res)[[1]]==\"lm\" | class(mod.res)[[1]]==\"glm\"){\n\t\t\tweights=mod.res$model$\"(weights)\"\n\t\t\tif(is.null(weights)){weights=rep(1, length(residuals(mod.res)))}\n\t\t}else if(class(mod.res)[[1]]==\"lmerMod\"){\n\t\t\tweights=mod.res@resp$weights\n\t\t}else{\n\t\t\tweights=rep(1, length(residuals(mod.res)))\n\t\t}\n\t}\n\tweights=size.fac*weights/max(weights)\n old.par = par(no.readonly = TRUE)\n par(mfrow=c(2, 2))\n par(mar=c(3, 3, 1, 0.5))\n hist(residuals(mod.res), probability=T, xlab=\"\", ylab=\"\", main=\"\", col=NA)\n mtext(text=\"histogram of residuals\", side=3, line=0)\n x=seq(min(residuals(mod.res)), max(residuals(mod.res)), length.out=100)\n lines(x, dnorm(x, mean=0, sd=sd(residuals(mod.res))))\n qqnorm(residuals(mod.res), main=\"\", pch=19, col=col, cex=sqrt(weights))\n qqline(residuals(mod.res))\n mtext(text=\"qq-plot of residuals\", side=3, line=0)\n plot(fitted(mod.res), residuals(mod.res), pch=19, col=col, cex=sqrt(weights))\n abline(h=0, lty=2)\n mtext(text=\"residuals against fitted values\", side=3, line=0)\n par(old.par)\n}\n\nlev.thresh<-function(model.res){\n\tk=length(coefficients(model.res))\n\tn=length(residuals(model.res))\n return(2*(k+1)/n)\n}\n\noverdisp.test<-function(x){\n\t##last updated Nov 26 2019\n\t##additions/changes: bugfix for zeroinfl; deals with glmmTMB (beta and nbinom family)\n\t##function needed for Gamma family:\n\tgamma.par.transf<-function(mean=NULL, var=NULL, shape=NULL, scale=NULL){\n\t\t##parameterizations are\n\t\t\t##shape and scale\n\t\t\t##shape and mean\n\t\t\t##var and mean\n\t\tif(is.null(mean) & is.null(var) & !is.null(shape) & !is.null(scale)){\n\t\t\tmean=shape*scale\n\t\t\tvar=shape*scale^2\n\t\t}else if(is.null(shape) & is.null(scale) & !is.null(mean) & !is.null(var)){\n\t\t\tscale=var/mean\n\t\t\tshape=mean/scale\n\t\t}else if(is.null(scale) & is.null(var) & !is.null(shape) & !is.null(mean)){\n\t\t\tscale=mean/shape\n\t\t\tvar=shape*scale^2\n\t\t}else{\n\t\t\tstop(\"unsupported parameter combination\")\n\t\t}\n\t\treturn(list(shape=as.vector(shape), scale=as.vector(scale), mean=as.vector(mean), var=as.vector(var)))\n\t}\n\t\n if(class(x)[[1]]!=\"glmmTMB\"){\n\t\tpr=residuals(x, type =\"pearson\")\n\t\tsum.dp=sum(pr^2)\n\t}\n n.model.terms=NULL\n if(class(x)[[1]]==\"glmerMod\"){\n n.model.terms=length(fixef(x))+nrow(as.data.frame(summary(x)$varcor))\n if(grepl(x=as.character(x@call)[4], pattern=\"negative.binomial\") | grepl(x=as.character(x@call)[4], pattern=\"Gamma\")){n.model.terms=n.model.terms+1}\n\t}else if(class(x)[[1]]==\"glm\" | class(x)[[1]]==\"negbin\"){\n n.model.terms=length(x$coefficients)\n n.model.terms=n.model.terms+length(summary(x)$theta)\n }else if(class(x)[[1]]==\"zeroinfl\"){\n\t\tn.model.terms=sum(unlist(lapply(x$coefficients, length)))\n\t\tn.model.terms=n.model.terms+length(summary(x)$theta)\n }else if(class(x)[[1]]==\"betareg\"){\n\t\tn.model.terms=sum(unlist(lapply(x$coefficients, length)))\n }else if(class(x)[[1]]==\"glmmTMB\"){\n\t\tprint(\"caution: support for models of class glmmTMB is experimental and embryonic\")\n\t\tprint(\"and don't worry if it takes some time\")\n\t\t#browser()\n\t\t#n.model.terms=sum(unlist(lapply(fixef(x), length)))+nrow(extract.ranef.from.glmmTB(x))\n\t\tn.model.terms=sum(unlist(lapply(fixef(x), length)))##fixed effects\n\t\tn.model.terms=n.model.terms+sum(unlist(lapply(summary(x)$varcor, function(y){\n\t\t\tif(!is.null(y)){\n\t\t\t\tlapply(y, function(yy){\n\t\t\t\t\txx=dim(attr(yy, \"correlation\"))\n\t\t\t\t\treturn((xx[1]^2-xx[1])/2+length(attr(yy, \"stddev\")))\n\t\t\t\t})\n\t\t\t}else{\n\t\t\t\t0\n\t\t\t}\n\t\t})))\n\t\tif((grepl(x=x$modelInfo$family$family, pattern=\"nbinom\") | x$modelInfo$family$family==\"beta\") &\n\t\t\tlength(attr(terms(x$call$dispformula), \"term.labels\"))==0){\n\t\t\t#n.model.terms=n.model.terms+1\n\t\t\t##but maybe it needs to be done for negbin??? need to check this\n\t\t}\n\t\tpr=residuals(x, type =\"response\")\n\t\tif(x$modelInfo$family$family==\"poisson\"){\n\t\t\tfitted.var = fitted(x)\n\t\t}else{\n\t\t\t#model.terms=attr(terms(x$call$dispformula), \"term.labels\")#attr(terms(x$call$dispformula), \"intercept\")\n\t\t\tsigma.f=exp(as.vector(model.matrix(object=x$call$dispformula, data=x$frame)[, names(fixef(x)$disp), drop=F]%*%fixef(x)$disp))\n\t\t\tif(x$modelInfo$family$family==\"nbinom2\"){\n\t\t\t\tfitted.var = fitted(x)*(1+fitted(x)/sigma.f)#fitted(x) + fitted(x)^2/sigma(x)\n\t\t\t}else if(x$modelInfo$family$family==\"nbinom1\"){\n\t\t\t\tfitted.var = fitted(x)*(1+sigma.f)#fitted(x) + fitted(x)/sigma(x)\n\t\t\t}else if(x$modelInfo$family$family==\"beta\"){\n\t\t\t\txfitted=fitted(x)\n\t\t\t\tshape1 = xfitted * sigma.f\n\t\t\t\tshape2 = (1 - xfitted) * sigma.f\n\t\t\t\t#xfitted(1-xfitted)/(1+sigma(x))#according to the glmmTMB help page for sigma\n\t\t\t\tfitted.var = shape1*shape2/((shape1+shape2)^2*(shape1+shape2+1))\n\t\t\t}else if(x$modelInfo$family$family==\"Gamma\"){\n\t\t\t\t#x.disp=exp(as.vector(model.matrix(object=x$call$dispformula, data=x$frame)%*%log(sigma(x))))\n\t\t\t\txx=gamma.par.transf(mean=fitted(x), var=NULL, shape=1/sigma.f, scale=NULL)\n\t\t\t\tfitted.var = xx$var\n\t\t\t\t#xdf=length(residuals(x))-(length(coef(x))+1)\n\t\t\t\t#n.model.terms=sum(unlist(lapply(fixef(x), length)))+sum(unlist(lapply(summary(x)$varcor, function(y){length(as.data.frame(y))})))\n\t\t\t\txdf=length(residuals(x))-n.model.terms\n\t\t\t}\n\t\t}\n\t\t#n.model.terms=sum(unlist(lapply(fixef(x), length)))+sum(unlist(lapply(summary(x)$varcor, function(y){length(as.data.frame(y))})))\n\t\t#xdf=length(pr)-n.model.terms\n\t\tsum.dp=sum((pr/sqrt(fitted.var))^2)\n\t\t#if(as.numeric(substr(sessionInfo()[[\"otherPkgs\"]][[\"glmmTMB\"]]$Version, start=1, stop=1))>0){\n\t\t\t#sum.dp=sum(residuals(x, type =\"pearson\")^2)\n\t\t#}\n }\n if(!is.null(n.model.terms)){\n\t\txdf=length(pr)-n.model.terms\n\t\treturn(data.frame(chisq=sum.dp, df=xdf, P=1-pchisq(sum.dp, xdf), dispersion.parameter=sum.dp/xdf))\n\t}else{\n\t\tprint(\"model isn't of any of the currently supported classes (glm, negbin, zeroinfl, glmerMod, glmmTMB)\")\n\t}\n}\n\nfe.re.tab<-function(fe.model, re, other.vars=NULL, data, treat.covs.as.factors=c(NA, F, T)){\n\tprint(\"please read the documentation in the beginning of the script\")\n\t#function helping in determinining which random slopes are needed\n\t#last updated: 2018, June 6\n\t#latest updates:\n\t\t#major revision of how interactions are treated; reveals in the summary\n\t\t\t#for combinations factors whether the number of observations is >1 (seperately for each level of the random effect)\n\t\t\t#for combination of covariates whether the number of unique combinations per level of random effect is >2\n\t\t\t#for combinations of factors and covariates whether the number unique values per covariate is larger than 2 (when treated as covariate)\n\t\t\t\t#or larger than 1 (when treated as a factor), separately for each commbination of levels of fixed and random effects factors\n\t\t#treat.covs.as.factors can be NA in which case covariates aretreates as such and as factors\n\t\t\t#this is the default now\n\t\t#output gives information about which fixed effects are covariates and factors aso for interactions\n\t\t\t#and also how covariates were treated\n\t#input/arguments:\n\t\t#data: a dataframe with all relevant variables (including the response)\n\t\t#fe.model: character; the model wrt the fixed effect (including the response); e.g., \"r~f1*c*f2\"\n\t\t#re: character; either a vector with the names of the random effects (e.g., c(\"re1\", \"re2\")) or a random intercepts expression (e.g., \"(1|re1)+(1|re2)\")\n\t\t#other.vars: character, optional; a vector with the names of variables which are to be kept in the data considered and returned\n\t\t#treat.covs.as.factors: logical, when set to TRUE covariates will be treated like factors (see value/summary for details)\n\t#value: list with the following entries:\n\t\t#detailed: list with cross-tabulations ffor each combination of (main) fixed and random effect \n\t\t#summary: list tables...\n\t\t\t#telling for each combination of (main) fixed and random effect...\n\t\t\t\t#the number of levels of the random effect with a given number of unique values of the fixed effect (in case of a covariate)\n\t\t\t\t#the number of levels of the random effect with a given number of levels of the fixed effect for which at least two cases do exist (in case of a factor)\n\t\t\t#telling for each combination of interaction and random effect...\n\t\t\t\t#the combination of the above two informations, i.e., the number of individuals with a given number of unique values of the covariate\n\t\t\t\t\t#and a given number of factor levels for which at least two cases exist\n\t\t#data: data frame containing all relevant variables (i.e., response, fixed and random effects as well as those indicated in other.vars (e.g., offset terms)\n\t\t\t#also includes columns for dummy variables coding the levels (except the reference level) of all factors\n\t\t#pot.terms: length one vector comprising the model wrt the random slopes (for all combinations of fixed and random effects, i.e., most likely some will need to be omitted)\n\t\t\t#note that this comprises only the random slopes but not the correlations between random slopes and intercepts not the random intercept itself\n\t\t#pot.terms.with.corr: length one vector comprising the model wrt the random slopes (for all combinations of fixed and random effects, i.e., most likely some will need to be omitted)\n\t\t\t#note that this comprises the random slopes and intercepts and also all correlations among them\n\ttreat.covs.as.factors=treat.covs.as.factors[1]\n\tif(sum(grepl(x=re, pattern=\"\", fixed=T))>0 & length(re)==1){#if random effects are handed over as formula\n\t\tre=gsub(x=re, pattern=\"(1|\", replacement=\"\", fixed=T)\n\t\tre=gsub(x=re, pattern=\")\", replacement=\"\", fixed=T)\n\t\tre=gsub(x=re, pattern=\" \", replacement=\"\", fixed=T)\n\t\tre=unlist(strsplit(re, split=\"+\", fixed=T))\n\t}\n\tfe.model=gsub(x=fe.model, pattern=\" \", replacement=\"\", fixed=T)#remove spaces\n\tmodel.terms=attr(terms(as.formula(fe.model)), \"term.labels\")#get individual terms from fixed effects model\n\tfe.me=model.terms[!grepl(x=model.terms, pattern=\":\", fixed=T)]#remove interactions\n\tfe.me=fe.me[!grepl(x=fe.me, pattern=\"^\", fixed=T)]#remove squares terms\n\tresp=unlist(strsplit(fe.model, split=\"~\", fixed=T))[1]#determine response\n\tif(substr(resp, start=1, stop=6)==\"cbind(\"){\n\t\tresp=gsub(x=resp, pattern=\"cbind(\", replacement=\"\", fixed=T)\n\t\tresp=gsub(x=resp, pattern=\")\", replacement=\"\", fixed=T)\n\t\tresp=gsub(x=resp, pattern=\" \", replacement=\"\", fixed=T)\n\t\tresp=unlist(strsplit(resp, split=\",\", fixed=T))\n\t}\n\txx=setdiff(c(resp, fe.me, re, other.vars), names(data))\n\tif(length(xx)>0){\n\t\tstop(paste(c(\"error: preditor(s) missing in the data is/are \", paste(xx, collapse=\", \")), collapse=\"\"))\n\t}\n\tdata=droplevels(as.data.frame(na.omit(data[, c(resp, fe.me, re, other.vars)])))#keep complete data wrt all relevant variables\n\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"^\", fixed=T)]#remove squares terms\n\tmodes=rep(NA, length(model.terms))#initialize vector storing whether PVs are factors or not\n\teffect=c(rep(\"main\", length(fe.me)), rep(\"int\", length(model.terms)-length(fe.me)))#create vector telling for each model term whether it is a main effect of not\n\tfor(i in 1:ncol(data)){#for all columns in data\n\t\tif(is.character(data[, i])){data[, i]=as.factor(data[, i])}#turn character column into factor\n\t\tmodes[i]=class(data[, i])#and determine its class\n\t}\n\tnames(modes)=names(data)#name 'm\n\tto.do=data.frame(expand.grid(re=re, fe=fe.me))#create data frame with one column for each combination of fixed main and random effect\n\tto.do$re=as.character(to.do$re)#reformat to character (for later addressing by name)\n\tto.do$fe=as.character(to.do$fe)#reformat to character (for later addressing by name)\n\tres.detailed=lapply(1:nrow(to.do), function(xrow){#create detailed results by lapply-ing over the rows of to.do\n\t\ttable(data[,to.do$re[xrow]], data[,to.do$fe[xrow]])#cross tabulate the respective fixed and random effect\n\t})\n\tnames(res.detailed)=paste(to.do$fe, to.do$re, sep=\"_within_\")#name it\n\tmodes=modes[as.character(to.do$fe)]\n\tmodes=gsub(x=modes, pattern=\"integer\", replacement=\"numeric\")\n\tto.do$modes.cons=modes\n\tto.do$modes=modes\n\tif(is.na(treat.covs.as.factors) & any(to.do[, \"modes.cons\"]==\"numeric\")){\n\t\txx=to.do[to.do[, \"modes.cons\"]==\"numeric\", ]\n\t\txx[\"modes.cons\"]=\"factor\"\n\t\tto.do=rbind(to.do, xx)\n\t\tto.do=to.do[order(to.do[, \"re\"], to.do[, \"fe\"]), ]\n\t}else if(!is.na(treat.covs.as.factors) & treat.covs.as.factors){\n\t\tto.do$modes.cons=\"factor\"\n\t}\n\tto.do=to.do[order(to.do$fe), ]\n\tres.summary=lapply(1:nrow(to.do), function(xrow){#begin with creating summary results by lapply-ing over the rows of to.do\n\t\t#browser()\n\t\txwhat=paste(to.do[xrow, c(\"fe\", \"re\")], collapse=\"_within_\")\n\t\tif(to.do$modes.cons[xrow]!=\"factor\"){#if fixed effect is not a factor\n\t\t\tires=table(apply(res.detailed[[xwhat]]>0, 1, sum))#determine number of levels of the random effect per number of unique cases of the fixed effect\n\t\t}else{#if fixed effect is a factor\n\t\t\tires=table(apply(res.detailed[[xwhat]]>1, 1, sum))#determine number of levels of the random effect per number of unique cases of the fixed effect with at least two observations\n\t\t}\n\t\tires=c(ires, tot=nrow(res.detailed[[xwhat]]))\n\t})\n\txx=to.do[, \"modes.cons\"]\n\txx[xx==\"numeric\"]=\"covariate\"\n\txx[xx==\"integer\"]=\"covariate\"\n\txx[to.do[, \"modes.cons\"]!=to.do[, \"modes\"]]=\"covariate as factor\"\n\txx=paste(\"(\", xx, \")\", sep=\"\")\n\tnames(res.summary)=paste(paste(to.do$fe, to.do$re, sep=\"_within_\"), xx, sep=\" \")#name it\n\t#append 'factor' or 'covariate' to the names:\n\t#names(res.summary)[modes[to.do$fe]==\"factor\"]=paste(names(res.summary)[modes[to.do$fe]==\"factor\"], \"(factor)\", sep=\" \")\n\t#names(res.summary)[!modes[to.do$fe]==\"factor\"]=paste(names(res.summary)[!modes[to.do$fe]==\"factor\"], \"(covariate)\", sep=\" \")\n\tto.do=data.frame(expand.grid(re=re, int=setdiff(model.terms, fe.me)))#create data frame with one row for each combination of interaction and random effect\n\tif(nrow(to.do)>0){\n\t\t#add two columns denoting original mode:\n\t\tto.do$mode=unlist(lapply(strsplit(as.character(to.do$int), split=\":\", fixed=T), function(x){\n\t\t\tpaste(modes[x], collapse=\":\")\n\t\t}))\n\t\tto.do$mode.cons=to.do$mode\n\t\tif(is.na(treat.covs.as.factors)){#if is.na(treat.covs.as.factors)\n\t\t\txx=to.do[grepl(x=to.do$mode, pattern=\"numeric\"), ]#add rows duplicating interactions involving covariates (to treat them as covariate and factor\n\t\t\txx$mode.cons=gsub(x=xx$mode.cons, pattern=\"numeric\", replacement=\"factor\")\n\t\t\tto.do=rbind(to.do, xx)\n\t\t\tto.do=to.do[order(to.do$re, to.do$int), ]\n\t\t}else if(treat.covs.as.factors){#if treat.covs.as.factors\n\t\t\tto.do$mode.cons=gsub(x=to.do$mode.cons, pattern=\"numeric\", replacement=\"factor\")#change mode to be considered\n\t\t}\n\t\tto.do[, \"int\"]=as.character(to.do[, \"int\"])#reformat to character (for later addressing by name)\n\t\tto.do[, \"re\"]=as.character(to.do[, \"re\"])#reformat to character (for later addressing by name)\n\t\t##this needs to be rewritten!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\t\tto.add=lapply(1:nrow(to.do), function(xrow){#treat combinations of fixed and random effect by lapply-ing over the rows of to.do\n\t\t\t#browser()\n\t\t\titerms=unlist(strsplit(to.do[xrow, \"int\"], split=\":\", fixed=T))#determine main effects involved in the interaction...\n\t\t\timodes=unlist(strsplit(to.do[xrow, \"mode.cons\"], split=\":\", fixed=T))#... and their classes\n\t\t\timodes.orig=unlist(strsplit(to.do[xrow, \"mode\"], split=\":\", fixed=T))#... and their classes\n\t\t\tif(all(imodes.orig==\"factor\")){\n\t\t\t\tcomb.fac=lapply(iterms[imodes==\"factor\"], function(x){paste(x, data[, x], sep=\"\")})\n\t\t\t\tcomb.fac=matrix(unlist(comb.fac), ncol=length(comb.fac), byrow=F)\n\t\t\t\tcomb.fac=apply(comb.fac, 1, paste, collapse=\"__\")\n\t\t\t\tires=table(data[,to.do$re[xrow]], comb.fac)\n\t\t\t\tclass(ires)=\"matrix\"\n\t\t\t\tires.summary=aggregate(1:nrow(ires), data.frame(ires>1), length)\n\t\t\t\txx=as.matrix(ires.summary[, -ncol(ires.summary)])\n\t\t\t\txx[xx]=\">1\"\n\t\t\t\txx[xx!=\">1\"]=\"!>1\"\n\t\t\t\tires.summary=data.frame(xx, freq=ires.summary$x)\n\t\t\t\tires=list(detailed=ires, summary=ires.summary)\n\t\t\t}else if(any(imodes.orig==\"factor\")){\n\t\t\t\tcomb.fac=lapply(iterms[imodes.orig==\"factor\"], function(x){paste(x, data[, x], sep=\"\")})\n\t\t\t\tcomb.fac=matrix(unlist(comb.fac), ncol=length(comb.fac), byrow=F)\n\t\t\t\tcomb.fac=apply(comb.fac, 1, paste, collapse=\"__\")\n\t\t\t\tcovs=lapply(iterms[imodes.orig==\"numeric\"], function(x){data[, x]})\n\t\t\t\tif(all(imodes==\"factor\")){\n\t\t\t\t\tires=lapply(covs, function(x){\n\t\t\t\t\t\tx=tapply(x, list(data[, to.do[xrow, \"re\"]], comb.fac), function(x){\n\t\t\t\t\t\t\tsum(table(x)>1)\n\t\t\t\t\t\t})\n\t\t\t\t\t\tx[is.na(x)]=0\n\t\t\t\t\t\treturn(x)\n\t\t\t\t\t})\n\t\t\t\t}else{\n\t\t\t\t\tires=lapply(covs, function(x){\n\t\t\t\t\t\tx=tapply(x, list(data[, to.do[xrow, \"re\"]], comb.fac), function(x){\n\t\t\t\t\t\t\tlength(unique(x))\n\t\t\t\t\t\t})\n\t\t\t\t\t\tx[is.na(x)]=0\n\t\t\t\t\t\treturn(x)\n\t\t\t\t\t})\n\t\t\t\t}\n\t\t\t\txx=matrix(unlist(lapply(ires, c)), ncol=length(ires), byrow=F)\n\t\t\t\txx=apply(xx, 1, paste, collapse=\"/\")\n\t\t\t\txx=matrix(xx, ncol=ncol(ires[[1]]), byrow=F)\n\t\t\t\tcolnames(xx)=colnames(ires[[1]])\n\t\t\t\trownames(xx)=rownames(ires[[1]])\n\t\t\t\tires.detailed=xx\n\t\t\t\tif(all(imodes==\"factor\")){\n\t\t\t\t\txx=matrix(unlist(lapply(ires, c))>=2, ncol=length(ires), byrow=F)\n\t\t\t\t\txx[xx]=\">1\"\n\t\t\t\t\txx[xx!=\">1\"]=\"!>1\"\n\t\t\t\t}else{\n\t\t\t\t\txx=matrix(unlist(lapply(ires, c))>=3, ncol=length(ires), byrow=F)\n\t\t\t\t\txx[xx]=\">2\"\n\t\t\t\t\txx[xx!=\">2\"]=\"!>2\"\n\t\t\t\t}\n\t\t\t\txx=apply(xx, 1, paste, collapse=\"/\")\n\t\t\t\txx=matrix(xx, ncol=ncol(ires[[1]]), byrow=F)\n\t\t\t\tcolnames(xx)=colnames(ires[[1]])\n\t\t\t\trownames(xx)=rownames(ires[[1]])\n\t\t\t\tires.summary=aggregate(1:nrow(xx), data.frame(xx), length)\n\t\t\t\tcolnames(ires.summary)[ncol(ires.summary)]=\"freq\"\n\t\t\t\tires=list(detailed=ires.detailed, summary=ires.summary)\n\t\t\t}else{\n\t\t\t\txx=lapply(iterms, function(x){\n\t\t\t\t\tapply(res.detailed[[paste(c(x, to.do[xrow, \"re\"]), collapse=\"_within_\")]]>0, 1, sum)\n\t\t\t\t})\n\t\t\t\tyy=matrix(unlist(xx), ncol=length(xx), byrow=F)\n\t\t\t\tcolnames(yy)=iterms\n\t\t\t\trownames(yy)=names(xx[[1]])\n\t\t\t\tires=yy\n\t\t\t\tires.summary=aggregate(1:nrow(ires), data.frame(ires), length)\n\t\t\t\tcolnames(ires.summary)=c(colnames(ires), \"freq\")#[ncol(ires.summary)]\n\t\t\t\tires=list(detailed=ires, summary=ires.summary)\n\t\t\t\t#browser()\n\t\t\t}\n\t\t\treturn(ires)\n\t\t})\n\t\txx=paste(to.do$int, to.do$re, sep=\"_within_\")#gsub(x=paste(to.do$int, to.do$re, sep=\"_within_\"), pattern=\":\", replacement=\"_\", fixed=T)#name it\n\t\tmode.mat=to.do[, \"mode\"]\n\t\tmode.mat=gsub(x=mode.mat, pattern=\"numeric\", replacement=\"COV\")\n\t\tmode.mat=gsub(x=mode.mat, pattern=\"factor\", replacement=\"FAC\")\n\t\tmode.mat=strsplit(mode.mat, split=\":\", fixed=T)#), nrow=nrow(to.do), byrow=T)\n\t\tmode.cons.mat=to.do[, \"mode.cons\"]\n\t\tmode.cons.mat=gsub(x=mode.cons.mat, pattern=\"numeric\", replacement=\"COV\")\n\t\tmode.cons.mat=gsub(x=mode.cons.mat, pattern=\"factor\", replacement=\"FAC\")\n\t\tmode.cons.mat=strsplit(mode.cons.mat, split=\":\", fixed=T)#), nrow=nrow(to.do), byrow=T)\n\t\tfor(i in 1:length(mode.mat)){\n\t\t\tmode.mat[[i]][mode.mat[[i]]!=mode.cons.mat[[i]]]=\"COVasFAC\"\n\t\t}\n\t\t#mode.mat[mode.mat==\"numeric\"]=\"COV\"\n\t\t#mode.mat[mode.mat==\"factor\"]=\"FAC\"\n\t\t#mode.mat[mode.mat!=mode.cons.mat]=\"COVasFAC\"\n\t\txx=paste(xx, paste(\"(\", unlist(lapply(mode.mat, paste, collapse=\":\")), \")\", sep=\"\"), sep=\" \")\n\t\tto.add.2=lapply(to.add, \"[[\", \"summary\")\n\t\tnames(to.add.2)=xx\n\t\tres.summary=c(res.summary, to.add.2)#and append to.add to res.summary\n\t\tto.add.2=lapply(to.add, \"[[\", \"detailed\")\n\t\tnames(to.add.2)=xx\n\t\tres.detailed=c(res.detailed, to.add.2)\n\t}\n\t#add columns with the dummy coded factor levels to data:\n\tto.code=fe.me[modes[fe.me]==\"factor\"]#determine factors among the fixed effects:\n\tif(length(to.code)>0){\n\t\tcoded=lapply(to.code, function(xc){#for each factor\n\t\t\tlapply(levels(data[, xc])[-1], function(xl){#for all levels except the reference level\n\t\t\t\tas.numeric(data[, xc]==xl)#code it\n\t\t\t})\n\t\t})\n\t\tcoded=matrix(unlist(coded), nrow=nrow(data), byrow=F)#reformat to matrix\n\t\txnames=unlist(lapply(to.code, function(xc){#determine column names to be given to the matrix...\n\t\t\tpaste(xc, levels(data[, xc])[-1], sep=\".\")\n\t\t}))\n\t\tcolnames(coded)=xnames#... and use 'm\n\t\tdata=data.frame(data, coded)#and append 'm to data\n\t}else{\n\t\txnames=\"\"\n\t}\n\t#now the model expression wrt random slopes; first no correllations between random slopes and intercepts:\n\tpot.terms=c(xnames, fe.me[modes[fe.me]!=\"factor\"])#create vector with names of dummy variables and names of fixed main effects not being factors\n\tpot.terms=outer(pot.terms, re, Vectorize(function(x, y){#create matrix with separate model term for each combination of fixed and random effect\n\t\tpaste(c(\"(0+\", x, \"|\", y, \")\"), collapse=\"\")\n\t}))\n\tpot.terms=paste(c(t(pot.terms)), collapse=\"+\")#put them all in a single entry\n\t#now the random slopes part of the model including random intercepts and slopes and their correlation:\n\tpot.terms.with.corr=paste(c(xnames, fe.me[modes[fe.me]!=\"factor\"]), collapse=\"+\")#get all fixed effects terms together...\n\tpot.terms.with.corr=paste(paste(\"(1+\", pot.terms.with.corr, \"|\", re, \")\", sep=\"\"), collapse=\"+\")#... and paste random effects, brackets and all that \n\t\t#(and everything in a single entry)\n\treturn(list(detailed=res.detailed, summary=res.summary, data=data, pot.terms=pot.terms, pot.terms.with.corr=pot.terms.with.corr))\n}\n\nwrite.fe.re.summary2file<-function(x, file){\n\tc.tab<-function(x, incl.fst=F, incl.rownames=T){\n\t\tres=format(as.matrix(x))\n\t\tres=gsub(x=res, pattern=\" \", replacement=\"\", fixed=T)\n\t\tres=rbind(colnames(res), res)\n\t\trownames(res)=NULL\n\t\tcolnames(res)=NULL\n\t\tres[is.na(res)]=\"NA\"\n\t\tn.char.range=apply(res, 2, function(x){range(nchar(x))})\n\t\t#res[, 1]=unlist(lapply(res[, 1], function(x){paste(c(x, rep(\" \", n.char.range[2, 1]-nchar(x))), collapse=\"\")}))\n\t\tres=matrix(unlist(lapply(1:ncol(res), function(x){\n\t\t\tunlist(lapply(res[, x], function(y){paste(c(rep(\" \", n.char.range[2, x]-nchar(y)), y), collapse=\"\")}))\n\t\t})), nrow=nrow(res), byrow=F)\n\t\tres=apply(res, 1, paste, collapse=paste(rep(\" \", 1), collapse=\"\"))\n\t\treturn(res)\n\t}\n\n\tappend=F\n\tfor(i in 1:length(x)){\n\t\twrite.table(x=names(x[i]), file=file, row.names=F, col.names=F, sep=\"\\t\", quote=F, append=append)\n\t\tappend=T\n\t\txx=x[[i]]\n\t\tif(is.null(dim(xx))){\n\t\t\txx=cbind(names(xx), xx)\n\t\t\tcolnames(xx)=NULL\n\t\t\txx=c.tab(xx)\n\t\t}else{\n\t\t\trownames(xx)=NULL\n\t\t\txx=c.tab(x=xx)\n\t\t}\n\t\txx=matrix(xx, ncol=1)\n\t\twrite.table(x=xx, file=file, row.names=F, col.names=F, sep=\"\\t\", quote=F, append=append)\n\t\twrite.table(x=\"\", file=file, row.names=F, col.names=F, sep=\"\\t\", quote=F, append=append)\n\t}\n}\n\nm.stab.plot<-function(est, lower=NULL, upper=NULL, xnames=NULL, col=\"black\", center.at.null=F, reset.par=T, pch=18, xlim=NULL){\n\t#version from Aug 12 2018\n\t#function to plot model stability or CIs (not really supposed to be nice, but to give a quick overview/rapid diagnostic)\n\t#input:\n\t\t#either a three columns data frame or matrix (with rownames) handed over to argument est (first column needs to comprise the original estimate)\n\t\t#or several vectors handed over as follows:\n\t\t\t#est: numeric; estimated coefficients of the model\n\t\t\t#lower: numeric; lower limits of the estimates (either from model stability of from bootstrap)\n\t\t\t#upper: numeric; upper limits of the estimates (either from model stability of from bootstrap)\n\t\t\t#xnames: character; names to be depicted besides the error bars\n\t\t#col: character; name of the color with which data should be depicted\n\tif(ncol(est)==3){\n\t\tlower=est[, 2]\n\t\tupper=est[, 3]\n\t\txnames=rownames(est)\n\t\test=est[, 1]\n\t\tx.at=est\n\t}\n\tif(is.null(xlim)){\n\t\txlim=range(c(0, lower, upper))\n\t}\n old.par = par(no.readonly = TRUE)\n\tpar(mar=c(3, 0.5, 0.5, 0.5), mgp=c(1, 0.4, 0), tcl=-0.2)\n\tplot(x=est, y=1:length(est), pch=pch, xlab=\"estimate\", ylab=\"\", yaxt=\"n\", xlim=xlim, type=\"n\", ylim=c(1, length(est)+1))\n\tabline(v=0, lty=3)\n\tif(center.at.null){\n\t\tx.at=rep(0, length(est))\n\t\ttext(labels=xnames, x=x.at, y=(1:length(est))+0.3, cex=0.8)\n\t}else{\n\t\txx=range(est)#c(lower, upper))\n\t\tfor(i in 1:length(est)){\n\t\t\ttext(labels=xnames[i], x=x.at[i], y=i+0.3, cex=0.8, adj=c((est[i]-min(xx))/diff(xx), 0))#pos=c(2, 4)[1+as.numeric(est<0)])\n\t\t}\n\t}\n\tpoints(x=est, y=1:length(est), pch=pch, col=col)\n\tsegments(x0=lower, x1=upper, y0=1:length(est), y1=1:length(est), col=col)\n\tif(reset.par){par(old.par)}\n}\n\nfe.re.tab.old<-function(fe.model, re, other.vars=NULL, data, treat.covs.as.factors=F){\n\t#function helping in determinining which random slopes are needed\n\t#last updated: 2015, Nov 25\n\t#input/arguments:\n\t\t#data: a dataframe with all relevant variables (including the response)\n\t\t#fe.model: character; the model wrt the fixed effect (including the response); e.g., \"r~f1*c*f2\"\n\t\t#re: character; either a vector with the names of the random effects (e.g., c(\"re1\", \"re2\")) or a random intercepts expression (e.g., \"(1|re1)+(1|re2)\")\n\t\t#other.vars: character, optional; a vector with the names of variables which are to be kept in the data considered and returned\n\t\t#treat.covs.as.factors: logical, when set to TRUE covariates will be treated like factors (see value/summary for details)\n\t#value: list with the following entries:\n\t\t#detailed: list with cross-tabulations ffor each combination of (main) fixed and random effect \n\t\t#summary: list tables...\n\t\t\t#telling for each combination of (main) fixed and random effect...\n\t\t\t\t#the number of levels of the random effect with a given number of unique values of the fixed effect (in case of a covariate)\n\t\t\t\t#the number of levels of the random effect with a given number of levels of the fixed effect for which at least two cases do exist (in case of a factor)\n\t\t\t#telling for each combination of interaction and random effect...\n\t\t\t\t#the combination of the above two informations, i.e., the number of individuals with a given number of unique values of the covariate\n\t\t\t\t\t#and a given number of factor levels for which at least two cases exist\n\t\t#data: data frame containing all relevant variables (i.e., response, fixed and random effects as well as those indicated in other.vars (e.g., offset terms)\n\t\t\t#also includes columns for dummy variables coding the levels (except the reference level) of all factors\n\t\t#pot.terms: length one vector comprising the model wrt the random slopes (for all combinations of fixed and random effects, i.e., most likely some will need to be omitted)\n\t\t\t#note that this comprises only the random slopes but not the correlations between random slopes and intercepts not the random intercept itself\n\t\t#pot.terms.with.corr: length one vector comprising the model wrt the random slopes (for all combinations of fixed and random effects, i.e., most likely some will need to be omitted)\n\t\t\t#note that this comprises the random slopes and intercepts and also all correlations among them\n\tif(sum(grepl(x=re, pattern=\"\", fixed=T))>0 & length(re)==1){#if random effects are handed over as formula\n\t\tre=gsub(x=re, pattern=\"(1|\", replacement=\"\", fixed=T)\n\t\tre=gsub(x=re, pattern=\")\", replacement=\"\", fixed=T)\n\t\tre=gsub(x=re, pattern=\" \", replacement=\"\", fixed=T)\n\t\tre=unlist(strsplit(re, split=\"+\", fixed=T))\n\t}\n\tfe.model=gsub(x=fe.model, pattern=\" \", replacement=\"\", fixed=T)#remove spaces\n\tmodel.terms=attr(terms(as.formula(fe.model)), \"term.labels\")#get individual terms from fixed effects model\n\tfe.me=model.terms[!grepl(x=model.terms, pattern=\":\", fixed=T)]#remove interactions\n\tfe.me=fe.me[!grepl(x=fe.me, pattern=\"^\", fixed=T)]#remove squares terms\n\tresp=unlist(strsplit(fe.model, split=\"~\", fixed=T))[1]#determine response\n\tif(substr(resp, start=1, stop=6)==\"cbind(\"){\n\t\tresp=gsub(x=resp, pattern=\"cbind(\", replacement=\"\", fixed=T)\n\t\tresp=gsub(x=resp, pattern=\")\", replacement=\"\", fixed=T)\n\t\tresp=gsub(x=resp, pattern=\" \", replacement=\"\", fixed=T)\n\t\tresp=unlist(strsplit(resp, split=\",\", fixed=T))\n\t}\n\txx=setdiff(c(resp, fe.me, re, other.vars), names(data))\n\tif(length(xx)>0){\n\t\tstop(paste(c(\"error: preditor(s) missing in the data is/are \", paste(xx, collapse=\", \")), collapse=\"\"))\n\t}\n\tdata=droplevels(as.data.frame(na.omit(data[, c(resp, fe.me, re, other.vars)])))#keep complete data wrt all relevant variables\n\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"^\", fixed=T)]#remove squares terms\n\tmodes=rep(NA, length(model.terms))#initialize vector storing whether PVs are factors or not\n\teffect=c(rep(\"main\", length(fe.me)), rep(\"int\", length(model.terms)-length(fe.me)))#create vector telling for each model term whether it is a main effect of not\n\tfor(i in 1:ncol(data)){#for all columns in data\n\t\tif(is.character(data[, i])){data[, i]=as.factor(data[, i])}#turn character column into factor\n\t\tmodes[i]=class(data[, i])#and determine its class\n\t}\n\tnames(modes)=names(data)#name 'm\n\tto.do=data.frame(expand.grid(re=re, fe=fe.me))#create data frame with one column for each combination of fixed main and random effect\n\tto.do$re=as.character(to.do$re)#reformat to character (for later addressing by name)\n\tto.do$fe=as.character(to.do$fe)#reformat to character (for later addressing by name)\n\tres.detailed=lapply(1:nrow(to.do), function(xrow){#create detailed results by lapply-ing over the rows of to.do\n\t\t\ttable(data[,to.do$re[xrow]], data[,to.do$fe[xrow]])#cross tabulate the respective fixed and random effect\n\t})\n\tnames(res.detailed)=paste(to.do$fe, to.do$re, sep=\"_within_\")#name it\n\tres.summary=lapply(1:nrow(to.do), function(xrow){#begin with creating summary results by lapply-ing over the rows of to.do\n\t\tif(modes[to.do[xrow, \"fe\"]]!=\"factor\" & !treat.covs.as.factors){#if fixed effect is not a factor\n\t\t\tires=table(apply(res.detailed[[xrow]]>0, 1, sum))#determine number of levels of the random effect per number of unique cases of the fixed effect\n\t\t}else{#if fixed effect is a factor\n\t\t\tires=table(apply(res.detailed[[xrow]]>1, 1, sum))#determine number of levels of the random effect per number of unique cases of the fixed effect with at least two observations\n\t\t}\n\t\tires=c(ires, tot=nrow(res.detailed[[xrow]]))\n\t})\n\tnames(res.summary)=paste(to.do$fe, to.do$re, sep=\"_within_\")#name it\n\t#append 'factor' or 'covariate' to the names:\n\tnames(res.summary)[modes[to.do$fe]==\"factor\"]=paste(names(res.summary)[modes[to.do$fe]==\"factor\"], \"(factor)\", sep=\" \")\n\tnames(res.summary)[!modes[to.do$fe]==\"factor\"]=paste(names(res.summary)[!modes[to.do$fe]==\"factor\"], \"(covariate)\", sep=\" \")\n\tto.do=data.frame(expand.grid(re=re, int=setdiff(model.terms, fe.me)))#create data frame with one row for each combination of interaction and random effect\n\tif(nrow(to.do)>0){\n\t\tto.do[, \"int\"]=as.character(to.do[, \"int\"])#reformat to character (for later addressing by name)\n\t\tto.do[, \"re\"]=as.character(to.do[, \"re\"])#reformat to character (for later addressing by name)\n\t\tto.add=lapply(1:nrow(to.do), function(xrow){#treat combinations of fixed and random effect by lapply-ing over the rows of to.do\n\t\t\titerms=unlist(strsplit(to.do[xrow, \"int\"], split=\":\", fixed=T))#determine main effects involved in the interaction...\n\t\t\timodes=modes[iterms]#... and their classes\n\t\t\tall.tabs=lapply(1:length(iterms), function(yrow){#for each term\n\t\t\t\t#if its not a factor, determine determine the number of unique cases of the fixed effect per level of the random effect\n\t\t\t\t#otherwise, determine per level of thee random effect the number of levels of the fixed effect with at least two observations\n\t\t\t\tapply(res.detailed[[paste(c(iterms[yrow], to.do[xrow, \"re\"]), collapse=\"_within_\")]]>ifelse(imodes[iterms[yrow]]==\"factor\" | treat.covs.as.factors, 1, 0), 1, sum)\n\t\t\t})\n\t\t\tall.tabs=matrix(unlist(all.tabs), ncol=length(all.tabs), byrow=F)#reformat to matrix (one row per level of the random effect, one column per term)\n\t\t\tall.tabs=aggregate(1:nrow(all.tabs), c(data.frame(all.tabs)), length)#summarize it ((wrt the number of levels of the random effec per combination of values)\n\t\t\tcolnames(all.tabs)=c(iterms, paste(c(\"n\", to.do[xrow, \"re\"]), collapse=\".\"))#name it\n\t\t\treturn(all.tabs)\n\t\t})\n\t\tnames(to.add)=gsub(x=paste(to.do$int, to.do$re, sep=\"_within_\"), pattern=\":\", replacement=\"_\", fixed=T)#name it\n\t\tres.summary=c(res.summary, to.add)#and append to.add to res.summary\n\t}\n\t#add columns with the dummy coded factor levels to data:\n\tto.code=fe.me[modes[fe.me]==\"factor\"]#determine factors among the fixed effects:\n\tif(length(to.code)>0){\n\t\tcoded=lapply(to.code, function(xc){#for each factor\n\t\t\tlapply(levels(data[, xc])[-1], function(xl){#for all levels except the reference level\n\t\t\t\tas.numeric(data[, xc]==xl)#code it\n\t\t\t})\n\t\t})\n\t\tcoded=matrix(unlist(coded), nrow=nrow(data), byrow=F)#reformat to matrix\n\t\txnames=unlist(lapply(to.code, function(xc){#determine column names to be given to the matrix...\n\t\t\tpaste(xc, levels(data[, xc])[-1], sep=\".\")\n\t\t}))\n\t\tcolnames(coded)=xnames#... and use 'm\n\t\tdata=data.frame(data, coded)#and append 'm to data\n\t}else{\n\t\txnames=\"\"\n\t}\n\t#now the model expression wrt random slopes; first no correllations between random slopes and intercepts:\n\tpot.terms=c(xnames, fe.me[modes[fe.me]!=\"factor\"])#create vector with names of dummy variables and names of fixed main effects not being factors\n\tpot.terms=outer(pot.terms, re, Vectorize(function(x, y){#create matrix with separate model term for each combination of fixed and random effect\n\t\tpaste(c(\"(0+\", x, \"|\", y, \")\"), collapse=\"\")\n\t}))\n\tpot.terms=paste(c(t(pot.terms)), collapse=\"+\")#put them all in a single entry\n\t#now the random slopes part of the model including random intercepts and slopes and their correlation:\n\tpot.terms.with.corr=paste(c(xnames, fe.me[modes[fe.me]!=\"factor\"]), collapse=\"+\")#get all fixed effects terms together...\n\tpot.terms.with.corr=paste(paste(\"(1+\", pot.terms.with.corr, \"|\", re, \")\", sep=\"\"), collapse=\"+\")#... and paste random effects, brackets and all that \n\t\t#(and everything in a single entry)\n\treturn(list(detailed=res.detailed, summary=res.summary, data=data, pot.terms=pot.terms, pot.terms.with.corr=pot.terms.with.corr))\n}\n", "meta": {"hexsha": "40c00d40ebc3e68d59048bf9d96fd0c67ddfea21", "size": 35572, "ext": "r", "lang": "R", "max_stars_repo_path": "diagnostic_fcns.r", "max_stars_repo_name": "heidibaum/mb1-cdi-followup", "max_stars_repo_head_hexsha": "88d3e71e85b2c64ecf2be9abbd56ba022c2a5eea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "diagnostic_fcns.r", "max_issues_repo_name": "heidibaum/mb1-cdi-followup", "max_issues_repo_head_hexsha": "88d3e71e85b2c64ecf2be9abbd56ba022c2a5eea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "diagnostic_fcns.r", "max_forks_repo_name": 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YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3400807436267244}} {"text": "###############################################################\n# oml4spark_function_create_balanced_input.r \n# \n# Function to create a Balanced Dataset based on Input \n# Dataset and formula. \n# \n# Input can be HDFS ID, HIVE, IMPALA, Spark DF or R dataframe \n# \n# It allows for a range to be choosen \n# for the TARGET proportion so that if one thinks the \n# proportion is within that range, then the returned Spark DF \n# is the original input \n# \n# Usage: createBalancedInput( input_bal , \n# formula_bal , \n# feedback = FALSE , \n# rangeForNoProcess = c(0.45,0.55) \n# ) \n# \n# \n# Copyright (c) 2020 Oracle Corporation \n# The Universal Permissive License (UPL), Version 1.0 \n# \n# https://oss.oracle.com/licenses/upl/ \n# \n###############################################################\n\n#######################################\n### GENERATE A BALANCED SAMPLE\n### FROM ANY INPUT\n### HDFS ID, SPARK, HIVE, R Dataframe\n#######################################\n\ncreateBalancedInput <- function(input_bal, formula_bal, reduceToFormula=FALSE, \n feedback = FALSE, rangeForNoProcess = c(0.45,0.55),\n sampleSize = 0) {\n # Extract the Target variable from the formula\n targetFromFormula <- strsplit(deparse(formula_bal), \" \")[[1]][1] \n # If the Target has an \"as.factor\", remove it for processing\n if (startsWith(targetFromFormula,\"as.factor(\")) \n { targetFromFormula <- regmatches(targetFromFormula,\n gregexpr(\"(?<=\\\\().+?(?=\\\\))\", \n targetFromFormula,\n perl = T))[[1]]\n }\n # If the user wants to run a full verbose mode, store the info\n if (grepl(feedback, \"FULL\", fixed = TRUE)) \n {verbose_user <- TRUE\n } else {verbose_user <- FALSE} \n \n # Find the ideal number of Partitions to use when creating the Spark DF\n # To Maximize Spark parallel utilization\n sparkXinst <- as.numeric(spark.property('spark.executor.instances'))\n sparkXcores <- as.numeric(spark.property('spark.executor.cores'))\n ideal_partitions <- sparkXinst*sparkXcores\n \n # Push the INPUT DATA to Spark (if it's not already)\n # In Case it is a Spark DF already we don't do anything\n if (!((spark.connected()) && (class(input_bal)[1]==\"jobjRef\"))) {\n # Check if the input if a DFS ID (HDFS)\n if (is.hdfs.id(input_bal)) {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) print('Input is HDFS...processing')\n dat_df <- orch.df.fromCSV(input_bal, \n minPartitions = ideal_partitions, \n verbose = FALSE ) # Convert the input HDFS to Spark DF\n } else\n # Check if the input is HIVE and load it into Spark DF\n if ( ore.is.connected(type='HIVE') && (is.ore.frame(input_bal)) ) {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) print('Input is HIVE Table...processing')\n dat_df <- ORCHcore:::.ora.getHiveDF(table=input_bal@sqlTable)\n } else\n # Check if the input is IMPALA\n if ( ore.is.connected(type='IMPALA') && (is.ore.frame(input_bal)) ) {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) print('Input is IMPALA Table...processing')\n dat_df <- ORCHcore:::.ora.getHiveDF(table=input_bal@sqlTable)\n } else\n # For R Dataframe it is a two-step process for now\n if (is.data.frame(input_bal)){\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) print('Input is R Dataframe...processing')\n dat_hdfs <- hdfs.put(input_bal)\n dat_df <- orch.df.fromCSV(dat_hdfs, \n minPartitions = ideal_partitions, \n verbose = FALSE ) # Convert the input HDFS to Spark DF\n }\n } else \n # If it's already a Spark DF then just point to it\n { if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) print('Input is already Spark DF')\n dat_df <- input_bal}\n \n # Persist the Spark DF for added performance\n orch.df.persist(dat_df, storageLevel = \"MEMORY_ONLY\", verbose = verbose_user)\n \n # Extract Original terms from formula to reduce the original Dataset (if indicated)\n formulaTerms <- terms(x=formula_bal, data=orch.df.collect(dat_df$limit(1L)))\n \n # Extract Var names from formula\n tempVars <- gsub(\".*~\",\"\",Reduce(paste, deparse(formulaTerms)))\n tempVars <- gsub(\" \", \"\", tempVars)\n tempVars <- gsub(\"-1\",\"\", tempVars)\n \n # Final list\n finalVarList <- strsplit( tempVars , \"+\", fixed = TRUE)[[1]]\n # In case the user added \"as.factor()\" to the variables\n removeAsFactor <- function(x) {\n if (startsWith(x,\"as.factor(\")) {\n regmatches(x, gregexpr(\"(?<=\\\\().+?(?=\\\\))\", x, perl = T))[[1]]\n } else x\n }\n finalVarList <- unlist(lapply(finalVarList,removeAsFactor))\n \n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) {\n print('List of Variables from Formula that are going to be in the output Spark DataFrame:')\n print(c(targetFromFormula,finalVarList))\n }\n \n if (reduceToFormula==TRUE) {\n # Select only the columns used by the formula plus the target\n dat_df <- dat_df$selectExpr(append(targetFromFormula,finalVarList))\n # Persist the Reduced Spark DF for added performance\n orch.df.persist(dat_df, storageLevel = \"MEMORY_ONLY\", verbose = verbose_user)\n }\n \n # Prepare to create a SQL View with random name for the Spark DF\n op <- options(digits.secs = 6)\n time <- as.character(Sys.time())\n options(op)\n tempViewName <- paste0(\"tmp_view_\",\n paste(regmatches(time,\n gregexpr('\\\\(?[0-9]+', \n time))[[1]],\n collapse = ''), \n collapse = \" \")\n orch.df.createView(dat_df , tempViewName)\n \n # Capture the proportion of Target=1 in order to balance the Data into 50/50\n targetInfo <- orch.df.collect(orch.df.sql(paste0(\"select \",targetFromFormula,\n \" as target, count(*) as num_rows from \",\n tempViewName,\" group by \",targetFromFormula, \n \" order by \",targetFromFormula)))\n proportionTarget <- targetInfo[2,2]/sum(targetInfo$num_rows)\n # Not needed, maybe future use: names(proportionTarget) <- c(as.character(targetInfo[2,1]),as.character(targetInfo[1,1]))\n \n # Only need to Sample from Target = 0 if the proportion is outside of the Range given by the user.\n # Default is 0.45 to 0.55, so if the proportion is already close enough to 0.5 we should not waste time sampling\n if (findInterval(proportionTarget, rangeForNoProcess)) {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) \n cat(paste0('\\nTarget proportion in Input Data already within range ',\n paste0(rangeForNoProcess, collapse = ' <-> '),' . No change done. \\nTarget proportion is : ', \n format(proportionTarget,digits=6), '\\nNum Rows is : ',format(sum(targetInfo$num_rows),big.mark=',')))\n balanced <- dat_df \n } else {\n cat(paste0('\\nTarget proportion is outside the range ',paste0(rangeForNoProcess, collapse = ' <-> '),\n ' . Processing...\\nTarget proportion is : ', format(proportionTarget,digits=6)))\n # Select all Target = 1 records and put them into a Spark DF \"input_1\"\n input_1 <- dat_df$filter(c(paste0(targetFromFormula,\" == '\",targetInfo$target[which.min(targetInfo$num_rows)],\"'\")))\n target_1_count <- targetInfo[2,2] # or input_1$count()\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nTarget = ',targetInfo[2,1],' count : ', \n format(target_1_count,big.mark = \",\")))\n \n # Select a sample of Target = 0 records and put them into a Spark DF \"input_0\"\n input_0 <- dat_df$filter(c(paste0(targetFromFormula,\" == '\",targetInfo$target[which.max(targetInfo$num_rows)],\"'\")))\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nTarget = ',targetInfo[1,1],' count : ', \n format(targetInfo[1,2],big.mark = \",\"))) # or input_0$count()\n \n # Prepare the settings needed for the Sampling function of Spark on Data Frames \"$sample\"\n samp_rate <- min(1,targetInfo[2,2]/targetInfo[1,2])\n seed_long <- .jlong(12345L)\n # Runs the sample of Target = 0 records a little bigger (with an offset) to avoid limitations \n # by Spark DF \"$sample\" function sometimes sampling smaller than desired samples\n offset <- 10*(1/target_1_count)\n if ((samp_rate+offset)>=1) {offset <- 1 - samp_rate -0.01}\n sample_0 <- input_0$sample(FALSE,samp_rate+offset,seed_long)\n \n # Trims the sample of Target = 0 records to the ideal size (same as te target = 1) using the function \"$limit\"\n input_0_samp <- sample_0$limit(as.integer(target_1_count))\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nSampled Target = ',\n targetInfo[1,1],' count : ', \n format(input_0_samp$count(),big.mark = \",\")))\n \n # Use the function \"$union\" from Spark Data Frame to join both Target and non-Target portions \n balanced <- input_1$union(input_0_samp)\n \n if (sampleSize >0) {\n newSampRate <- (sampleSize/balanced$count())\n if (newSampRate < 1) {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nSampling final balanced count from ',\n format(balanced$count(),big.mark = \",\"),' down to ',\n format(sampleSize,big.mark = \",\"),' records'))\n sample_final <- balanced$sample(FALSE,newSampRate+offset,seed_long)\n balanced <- sample_final$limit(as.integer(sampleSize))\n orch.df.unpersist(sample_final)\n } else {\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nSampling requested of ',\n format(sampleSize,big.mark = \",\"),\n ' is larger than final balanced count and was ignored.'\n ))\n }\n }\n \n orch.df.unpersist(dat_df)\n orch.df.unpersist(input_1)\n orch.df.unpersist(input_0)\n orch.df.unpersist(input_0_samp)\n orch.df.persist(balanced, storageLevel = 'MEMORY_ONLY', verbose = FALSE)\n if (grepl(feedback, \"FULL|TRUE\", fixed = TRUE)) cat(paste0('\\nBalanced Final count : ', \n format(balanced$count(),big.mark = \",\"),\n '\\n'))\n }\n return(balanced)\n}", "meta": {"hexsha": "21d573d1660639c4501b285709643d58046f558d", "size": 11672, "ext": "r", "lang": "R", "max_stars_repo_path": "oml4spark_function_create_balanced_input.r", "max_stars_repo_name": "marancibia/ORAAH-Tutorials", "max_stars_repo_head_hexsha": "97fa0d5919b519391c36627d780ecc00c7846823", "max_stars_repo_licenses": ["UPL-1.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-07T22:50:32.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T19:54:00.000Z", "max_issues_repo_path": "oml4spark_function_create_balanced_input.r", "max_issues_repo_name": "marancibia/ORAAH-Tutorials", "max_issues_repo_head_hexsha": "97fa0d5919b519391c36627d780ecc00c7846823", "max_issues_repo_licenses": ["UPL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "oml4spark_function_create_balanced_input.r", "max_forks_repo_name": "marancibia/ORAAH-Tutorials", "max_forks_repo_head_hexsha": "97fa0d5919b519391c36627d780ecc00c7846823", "max_forks_repo_licenses": ["UPL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 55.580952381, "max_line_length": 124, "alphanum_fraction": 0.5374400274, "num_tokens": 2612, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807436267244}} {"text": "##\r\n# Copyright (c) 2011 LabKey Corporation\r\n#\r\n# Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0\r\n##\r\n\r\n# This R script will calculate and store kinship coefficients (aka. relatedness) for all animals in the colony. This is a large, sparse matrix.\r\n# The matrix is converted into a very long 3-column dataframe (animal1, animal2, coefficient). This dataframe is compared to the data\r\n# already present in the system. The minimal number of inserts/updates/deletes are then performed. This script is designed\r\n# to run as a daily cron job. If it runs frequently enough, the number of updates should remain small. If more than 5000 inserts need to be performed,\r\n# the script will output a TSV file to /usr/local/labkey/kinship/. This file can be manually imported into LabKey. The threshold of 5000 is probably\r\n# too conservative, but large imports using the HTTP API can be problematic.\r\n\r\n# When the script runs it outputs the log to /usr/local/labkey/kinship/kinshipOut.txt. This file is monitored by monit and an alert will be through\r\n# if the timestamp does not change once per day. This monitors whether the script is running, but does not directly monitor for errors.\r\n# Monit also monitors checksum changes on the TSV output file. If this file changes, an alert email will be triggered, in which case someone\r\n# should import this file into the DB.\r\n\r\n#options(echo=TRUE);\r\nlibrary(kinship)\r\nlibrary(Rlabkey)\r\n\r\nlibrary(Matrix)\r\n\r\n#print('Labkey.data:')\r\n#str(labkey.data);\r\n#remove(labkey.data)\r\n\r\n#NOTE: to run directly in R instead of through labkey, uncomment this:\r\nlabkey.url.base = \"https://ehr.primate.wisc.edu/\"\r\n\r\n\r\n#this section queries labkey to obtain the pedigree data\r\n#you could replace it with a command that loads from TSV if you like\r\nallPed <- labkey.selectRows(\r\n baseUrl=labkey.url.base,\r\n folderPath=\"/WNPRC/EHR\",\r\n schemaName=\"study\",\r\n queryName=\"Pedigree\",\r\n colSelect=c('Id', 'Dam','Sire', 'Gender'),\r\n showHidden = TRUE,\r\n colNameOpt = 'fieldname', #rname\r\n #showHidden = FALSE\r\n)\r\ncolnames(allPed)<-c('Id', 'Dam', 'Sire', 'Gender')\r\n\r\n\r\nis.na(allPed$Id)<-which(allPed$Id==\"\")\r\nis.na(allPed$Dam)<-which(allPed$Dam==\"\")\r\nis.na(allPed$Sire)<-which(allPed$Sire==\"\")\r\nis.na(allPed$Gender)<-which(allPed$Gender==\"\")\r\n#print(\"All ped2\");\r\n#str(allPed)\r\n#allPedfile=read.delim(\"demographics_2011-07-01.txt\",header=TRUE,na.strings=\"\")\r\n#allPedfile=allPedfile[,1:4]\r\n#str(allPed[(is.na(allPed$Dam))&(!is.na(allPed$Sire)),])\r\n#str(allPed[(!is.na(allPed$Dam))&(is.na(allPed$Sire)),])\r\n\r\n##### These code are commented out after checking data\r\n# duplicatedId=allPed$Id[duplicated(allPed$Id)]\r\n# allPed[allPed$Id%in%duplicatedId,]# Check for duplication: passed\r\n# Id.Dam=unique(allPed$Dam)\r\n# allPed[(allPed$Id%in%Id.Dam)&(allPed$Gender!=2),]#Check for Dam that is not female\r\n# Id.Sire=unique(allPed$Sire)\r\n# allPed[(allPed$Id%in%Id.Sire)&(allPed$Gender!=1),]#Check for Sire that is not male:1 not passed\r\n\r\n#this function adds missing parents to the pedigree\r\n#it is similar to add.Inds from kinship; however, we retain gender\r\n`addMissing` <-\r\nfunction(ped)\r\n {\r\n if(ncol(ped)<4)stop(\"pedigree should have at least 4 columns\")\r\n head <- names(ped)\r\n\r\n nsires <- match(ped[,3],ped[,1])# [Quoc] change ped,2 to ped,3\r\n nsires <- as.character(unique(ped[is.na(nsires),3]))\r\n nsires <- nsires[!is.na(nsires)]\r\n if(length(nsires)){\r\n ped <- rbind(ped, data.frame(Id=nsires, Dam=rep(NA, length(nsires)), Sire=rep(NA, length(nsires)), Gender=rep(1, length(nsires))));\r\n }\r\n\r\n ndams <- match(ped[,2],ped[,1])# [Quoc] change ped,3 to ped,2\r\n ndams <- as.character(unique(ped[is.na(ndams),2]))\r\n ndams <- ndams[!is.na(ndams)];\r\n\r\n if(length(ndams)){\r\n ped <- rbind(ped,data.frame(Id=ndams, Dam=rep(NA, length(ndams)), Sire=rep(NA, length(ndams)), Gender=rep(2, length(ndams))));\r\n }\r\n\r\n names(ped) <- head\r\n return(ped)\r\n }\r\n\r\n#str(allPed)\r\nallPed <- addMissing(allPed)\r\n#print(\"All ped 3\");\r\n#str(allPed)\r\n#[Quoc: new code to collapse sparse matrix]\r\n#[makefamid separate the giant dataset to unrelated families]\r\n#[It resizes the biggest matrix from 12000^2 to 8200^2 thus reduces the memory used by half ]\r\nfami=makefamid(id=allPed$Id,father.id=allPed$Sire,mother.id=allPed$Dam)\r\nfamid=unique(fami)\r\nfamid=famid[famid!=0]\r\nnewRecords=NULL\r\nfor (fam.no in famid){\r\n familytemp=allPed[fami==fam.no,]\r\n temp.kin=kinship(familytemp$Id,familytemp$Sire,familytemp$Dam)\r\n sparse.kin=as(temp.kin,\"dsTMatrix\") #change kinship matrix to symmetric triplet sparse matrix\r\n #[Quoc this more efficient than do it manually, sparse matrix is S4 object from library Matrix]\r\n temp.tri=data.frame(Id=colnames(temp.kin)[sparse.kin@i+1],Id2=colnames(temp.kin)[sparse.kin@j+1],coefficient=sparse.kin@x,stringsAsFactors=FALSE)\r\n newRecords=rbind(newRecords,temp.tri)\r\n #str(newRecords)\r\n}\r\n\r\n# [Quoc: Untouched code frome here]\r\n#these keys are created in order to subset this dataframe\r\n#it is possible a more efficient approach could be used\r\nnewRecords$key1 <- paste(newRecords$Id, newRecords$Id2, sep=\":\")\r\nnewRecords$key2 <- paste(newRecords$key1, newRecords$coefficient, sep=\":\")\r\n\r\n#we set date=now() as a timestamp\r\n#the purpose of this is so we have a record when we save to the DB of when this was calculated.\r\nnewRecords$date <- c(date())\r\nnewRecords$date <- as.character(newRecords$date)\r\n\r\n\r\n#in the next sections we will compare the newly created dataframe to the data already in the DB\r\n#the first time this script is run, the DB will be blank\r\n#on subsequent runs, we perform these steps to minimize the amount of add/deletes against this data\r\n\r\n#find old records first by querying labkey\r\n#this can be replaced with a TSV load for testing if needed\r\noldRecords <- labkey.selectRows(\r\n baseUrl=labkey.url.base,\r\n folderPath=\"/WNPRC/EHR\",\r\n schemaName=\"ehr\",\r\n queryName=\"kinship\",\r\n colSelect=c('rowid', 'Id', 'Id2', 'coefficient'),\r\n showHidden = TRUE,\r\n #stringsAsFactors = FALSE,\r\n colNameOpt = 'fieldname' #rname\r\n)\r\ncolnames(oldRecords)<-c('rowid', 'Id', 'Id2', 'coefficient');\r\n\r\nprint(\"New Records\")\r\nstr(newRecords);\r\n\r\n#these keys are created in order to subset this dataframe\r\n#it is possible a more efficient approach could be used\r\noldRecords$key1 <- paste(oldRecords$Id, oldRecords$Id2, sep=\":\")\r\noldRecords$key2 <- paste(oldRecords$key1, oldRecords$coefficient, sep=\":\")\r\n\r\nprint(\"Old Records\")\r\nstr(oldRecords);\r\n\r\n\r\n#first we find any cases where an Id existing in oldRecords, but not newRecords. These need to be deleted\r\nIdxToDelete <- setdiff(oldRecords$key1, newRecords$key1);\r\ntoDelete <- oldRecords[match(IdxToDelete, oldRecords$key1),]\r\nprint('Total To Delete: ')\r\nlength(toDelete$Id)\r\n\r\nif(length(toDelete$Id)){\r\n toDelete <- data.frame(rowid=toDelete$rowid)\r\n del <- labkey.deleteRows(\r\n baseUrl=labkey.url.base,\r\n folderPath=\"/WNPRC/EHR\",\r\n schemaName=\"ehr\",\r\n queryName=\"kinship\",\r\n toDelete=toDelete\r\n );\r\n}\r\nremove(IdxToDelete)\r\nremove(toDelete)\r\n\r\n\r\n#next we find any cases where an Id/Id2 pair exists in both oldRecords and newRecords, but the coefficient is different.\r\n#These need to be updated\r\n\r\nSharedIdPairs <- intersect(oldRecords$key1, newRecords$key1);\r\ncoefficient1 <- oldRecords[match(SharedIdPairs, oldRecords$key1),]\r\ncoefficient2 <- newRecords[match(SharedIdPairs, newRecords$key1),]\r\n\r\n#find records where the old coefficient does not equal the new one:\r\ntoGet <- (!is.na(coefficient1$coefficient) & is.na(coefficient2$coefficient)) | (is.na(coefficient1$coefficient) & !is.na(coefficient2$coefficient)) | (!is.na(coefficient1$coefficient) & !is.na(coefficient2$coefficient) & coefficient1$coefficient != coefficient2$coefficient)\r\ntoUpdate <- coefficient1[toGet,];\r\ntoUpdate$coefficient <- coefficient2$coefficient[toGet];\r\n\r\nprint('Total To Update: ')\r\nlength(toUpdate$Id)\r\n\r\nif(length(toUpdate$Id)){\r\n update <- labkey.updateRows(\r\n baseUrl=labkey.url.base,\r\n folderPath=\"/WNPRC/EHR\",\r\n schemaName=\"ehr\",\r\n queryName=\"kinship\",\r\n toUpdate=toUpdate\r\n );\r\n}\r\n\r\n#next we find any cases where an Id/Id2 pair exists in newRecords, but not oldRecords.\r\n#These need to be inserted\r\n\r\nIdxToInsert <- setdiff(newRecords$key1, oldRecords$key1);\r\ntoInsert <- newRecords[match(IdxToInsert, newRecords$key1),]\r\nprint(\"Total To Insert:\");\r\nlength(toInsert$Id)\r\n\r\nif(length(toInsert$Id) > 5000){\r\n toInsert$container = c('29e3860b-02b5-102d-b524-493dbd27b599');\r\n toInsert <- subset(toInsert, select = -c(key1,key2,date) )\r\n\r\n write.table(toInsert, file = \"/usr/local/labkey/kinship/kinship.tsv\", quote = FALSE, sep = \"\\t\");\r\n print(\"NOTE: There are too many rows to import using the API.\")\r\n print(\"A TSV file has been written to /usr/local/labkey/kinship.tsv\")\r\n print(\"It can be imported here: https://ehr.primate.wisc.edu/query/WNPRC/EHR/import.view?schemaName=ehr&query.queryName=kinship\")\r\n stop()\r\n #toInsert <- toInsert[1:500,];\r\n #length(toInsert$Id);\r\n}\r\n\r\nif(length(toInsert$Id) <= 5000){\r\n if(length(toInsert$Id)){\r\n ins <- labkey.insertRows(\r\n baseUrl=labkey.url.base,\r\n folderPath=\"/WNPRC/EHR\",\r\n schemaName=\"ehr\",\r\n queryName=\"kinship\",\r\n toInsert=toInsert\r\n );\r\n }\r\n remove(IdxToInsert)\r\n remove(toInsert)\r\n}\r\n", "meta": {"hexsha": "10c9411c4036ed055ab1e0605044975e6cfd75df", "size": 9450, "ext": "r", "lang": "R", "max_stars_repo_path": "WNPRC_EHR/tools/kinship/populateKinship.r", "max_stars_repo_name": "LabKey/wnprc-modules", "max_stars_repo_head_hexsha": "15780ee3caca02b66c59890a05347b4143425751", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-25T18:03:49.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-25T18:03:49.000Z", "max_issues_repo_path": "WNPRC_EHR/tools/kinship/populateKinship.r", "max_issues_repo_name": "WNPRC-EHR-Services/wnprc-modules", "max_issues_repo_head_hexsha": "9513649b49e76c76a7fe7acfcf495ebe6dbcdf8a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 51, "max_issues_repo_issues_event_min_datetime": "2018-03-20T16:38:40.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-08T21:57:22.000Z", "max_forks_repo_path": "WNPRC_EHR/tools/kinship/populateKinship.r", "max_forks_repo_name": "LabKey/wnprc-modules", "max_forks_repo_head_hexsha": "15780ee3caca02b66c59890a05347b4143425751", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-02-02T00:34:57.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-28T22:54:47.000Z", "avg_line_length": 40.0423728814, "max_line_length": 276, "alphanum_fraction": 0.6975661376, "num_tokens": 2639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678319, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.34008074362672436}} {"text": "#' precision_matrix_match.r\n#' takes a a dataframe of x values, and a dataframe of precision\n#' Renames columns of precision matrix to match x_mu matrix, so long as some of x label is in precision label (mat to mat_SD)\n#' Add columns to precision matrix for x_mu columns not present, sets precision to 10000 (v high).\n#' makes sure columns of x_mu and x_precision are in the same order.\n#'\n#' @param a x_mu matrix as a dataframe with column names.\n#' @param b x_precision matrix as a dataframe with column names.\n#'\n#' @return returns precision matrix as dataframe with columns added for x_mu columns not already in it.\n#' @export\n#'\n#' @examples\n#' a <- data.frame(rnorm(100),rnorm(100),rnorm(100))\n#' b <- data.frame(rnorm(100),rnorm(100))\n#' colnames(a) <- c('intercept','mat','map')\n#' colnames(b) <- c('mat_sd','map_sd')\n#' precision_matrix_match(a,b)\n#' \nprecision_matrix_match <- function(a,b){\n #gotta drop any data.table formatting if present.\n #if supplied as matrices need to be dataframes.\n a <- as.data.frame(a)\n b <- as.data.frame(b)\n \n \n #rename columns to match x_mu\n for(i in 1:ncol(a)){\n position <- grep(colnames(a)[i], colnames(b))\n if(length(position) > 0){\n position <- position[1]\n colnames(b)[position] <- colnames(a)[i]\n }\n }\n \n #add columns for x values not present in precision matrix.\n #give these columns a sd value of 0.01, which corresponds to a precision of 10000\n to_add <- colnames(a)[!(colnames(a) %in% colnames(b))]\n new.precision <- matrix(ncol = length(to_add), nrow = nrow(b))\n new.precision[,] <- 0.0001\n colnames(new.precision) <- to_add\n b <- cbind(b,new.precision)\n #order the precision matrix columns to match x_mu matrix.\n b <- b[,colnames(a)]\n b <- as.data.frame(b)\n colnames(b) <- colnames(a)\n \n #return new precision matrix.\n return(b)\n \n} ##end function.\n", "meta": {"hexsha": "edddd91170323473b46233adc22090b4506d26bb", "size": 1848, "ext": "r", "lang": "R", "max_stars_repo_path": "NEFI_functions/precision_matrix_match.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "NEFI_functions/precision_matrix_match.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "NEFI_functions/precision_matrix_match.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 35.5384615385, "max_line_length": 125, "alphanum_fraction": 0.6845238095, "num_tokens": 507, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807350623898}} {"text": "#!/usr/bin/env Rscript\n\n#' @author Noah Ollikainen, Charlotte A Lai, Peter Chovanec\n\n# This program plots SPRITE clusters as a barplot, with each bar corresponding\n# to the read-coverage over a chromosome. If passed only a single clusters\n# file, this program will plot absolute coverage \nif(!require(ggplot2)){\n install.packages(\"ggplot2\", repos='http://cran.us.r-project.org')\n library(ggplot2)\n}\nif(!require(optparse)){\n install.packages(\"optparse\", repos='http://cran.us.r-project.org')\n library(optparse)\n}\n\n\nrefs <- c(\"chr1\", \"chr2\", \"chr3\", \"chr4\", \"chr5\", \"chr6\", \"chr7\", \"chr8\",\n \"chr9\", \"chr10\", \"chr11\", \"chr12\", \"chr13\", \"chr14\", \"chr15\",\n \"chr16\", \"chr17\", \"chr18\", \"chr19\", \"chrX\")\n\nparseArgs <- function() {\n \n option_list <- list(\n make_option(c(\"-i\", \"--input\"),\n action = \"store\",\n dest = \"input\",\n metavar = \"FILE\",\n type = \"character\",\n help = \"blah\"),\n\n make_option(c(\"-n\", \"--normalize\"),\n action = \"store\",\n dest = \"normalize\",\n metavar = \"FILE\",\n type = \"character\",\n default = NULL,\n help = \"blah\"),\n \n make_option(c(\"-o\", \"--output\"),\n action = \"store\",\n dest = \"output\",\n metavar = \"FILE\",\n type = \"character\",\n help = \"blah\")\n )\n\n args <- parse_args(OptionParser(option_list = option_list))\n\n if (!file.exists(args$input)) {\n stop(paste0(\"Cannot open file \", args$input, \"!\"))\n }\n\n if (!is.null(args$normalize) && !file.exists(args$normalize)) {\n stop(paste0(\"Cannot open file \", args$normalize, \"!\"))\n }\n \n args\n}\n\nprocessFile <- function(input) {\n hash <- new.env()\n sapply(refs, function(x) hash[[x]] <- 0)\n \n tryCatch({\n con <- file(input, open = \"r\")\n while (length(oneLine <- readLines(con, n = 1, warn = FALSE)) > 0) {\n fields <- unlist(strsplit(oneLine, \"\\t\"))[-1]\n for (i in 1:length(fields)) {\n chrom <- getChrom(fields[i])\n count <- hash[[chrom]]\n if (!is.null(count)) {\n hash[[chrom]] <- count + 1\n }\n }\n }\n }, finally = {\n close(con)\n })\n \n hash\n}\n\nplotHash <- function(hash, output) {\n chroms <- names(hash)\n df <- data.frame(matrix(ncol = 2, nrow = length(chroms)))\n colnames(df) <- c(\"chromosome\", \"count\")\n for (i in 1:length(chroms)) {\n df[i, ] <- c(chroms[i], hash[[chroms[i]]])\n }\n \n df$chromosome <- factor(df$chromosome, levels = refs)\n \n p <- ggplot(df, aes(x = chromosome, y = as.numeric(count)))\n p <- p + geom_bar(stat = \"identity\")\n p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))\n p <- p + ylab(\"coverage (absolute)\")\n\n ggsave(paste0(output, \".pdf\"), plot = p)\n ggsave(paste0(output, \".eps\"), plot = p)\n}\n\nplotHashes <- function(numHash, denomHash, output) {\n chroms <- names(numHash)\n df <- data.frame(matrix(ncol = 3, nrow = length(chroms)))\n colnames(df) <- c(\"chromosome\", \"num\", \"denom\")\n for (i in 1:length(chroms)) {\n df[i, ] <- c(chroms[i], numHash[[chroms[i]]], denomHash[[chroms[i]]])\n }\n\n df$num <- as.numeric(df$num)\n df$denom <- as.numeric(df$denom)\n df$num.frac <- df$num / sum(df$num)\n df$denom.frac <- df$denom / sum(df$denom)\n df$oe <- df$num.frac / df$denom.frac\n\n df$chromosome <- factor(df$chromosome, levels = refs)\n\n p <- ggplot(df, aes(x = chromosome, y = oe))\n p <- p + geom_bar(stat = \"identity\")\n p <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))\n p <- p + ylab(\"O/E\")\n\n ggsave(paste0(output, \".pdf\"), plot = p)\n ggsave(paste0(output, \".eps\"), plot = p)\n}\n\ngetChrom <- function(s) unlist(strsplit(s, \":\"))[1]\n\nmain <- function() {\n args <- parseArgs()\n hash <- processFile(args$input)\n if (is.null(args$normalize)) {\n plotHash(hash, args$output)\n } else {\n hash2 <- processFile(args$normalize)\n plotHashes(hash, hash2, args$output)\n }\n}\n\nmain()\n", "meta": {"hexsha": "0baeaa7379d194554ff0f5adeddfe72aa54d780e", "size": 4270, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/r/plot_chromosome_coverage.r", "max_stars_repo_name": "Princeton-LSI-ResearchComputing/sprite-pipeline", "max_stars_repo_head_hexsha": "5e11a588df9a70a98d167956af471880669a18f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2018-05-04T16:15:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-07T20:15:11.000Z", "max_issues_repo_path": "scripts/r/plot_chromosome_coverage.r", "max_issues_repo_name": "Princeton-LSI-ResearchComputing/sprite-pipeline", "max_issues_repo_head_hexsha": "5e11a588df9a70a98d167956af471880669a18f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-07-03T19:30:10.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-25T18:29:51.000Z", "max_forks_repo_path": "scripts/r/plot_chromosome_coverage.r", "max_forks_repo_name": "Princeton-LSI-ResearchComputing/sprite-pipeline", "max_forks_repo_head_hexsha": "5e11a588df9a70a98d167956af471880669a18f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-11-05T12:40:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-07T20:15:29.000Z", "avg_line_length": 30.0704225352, "max_line_length": 78, "alphanum_fraction": 0.5210772834, "num_tokens": 1171, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807350623898}} {"text": "# author: Cheng Min\n# date: 2020-01-30\n\n\"Creates eda plots and tables for the un-pre-processed and pre-processed training data of the online shoppers intetion data from UCI website (https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset).\nSaves the plots as png files and save the tables as .csv files\n\nUsage: src/eda.r --input_dir= --out_dir=\n \nOptions:\n--input_dir= Path to training data (filename should not be included)\n--out_dir= Path to directory where the plots and tables should be saved\n\" -> doc\n\nlibrary(data.table, quietly = TRUE)\nlibrary(tidyverse, quietly = TRUE)\nlibrary(plotly, quietly = TRUE)\nlibrary(cowplot, quietly = TRUE)\nlibrary(scales, quietly = TRUE)\nlibrary(pheatmap, quietly = TRUE)\nlibrary(testthat, quietly = TRUE)\nlibrary(docopt)\n\nopt <- docopt(doc)\n\nmain <- function(input_dir, out_dir) {\n \n # Read the training data without pre-processing\n mydata <- suppressMessages(read_csv(paste0(input_dir, \"/training_for_eda.csv\")))\n setDT(mydata)\n \n # Define the numerical and categorical features\n num_vars_1 <- c(\"Administrative\", \"Informational\", \"ProductRelated\")\n num_vars_2 <- c(\"Administrative_Duration\", \"Informational_Duration\", \"ProductRelated_Duration\", \n \"BounceRates\", \"ExitRates\", \n \"PageValues\", \"ProductRelated\")\n\n num_vars_2_units <- c(\"sec\", \"sec\", \"sec\", \n \"percent\", \"percent\", \n \"dollars\", \"Count\")\n units <- tibble(num_vars = num_vars_2, num_vars_2_units=num_vars_2_units)\n\n target <- \"Revenue\"\n cat_vars <- setdiff(names(mydata), c(num_vars_1, num_vars_2, target))\n \n # Create a table for quantile distribution and save it in a .csv file\n quantile_dist <- sapply(num_vars_2, FUN=function(x) {\n print(paste(\"Mean of\", x, \"is\", round(mydata[, mean(get(x))], digits=3), \n \"and standard deviation is\", round(mydata[, sd(get(x))], digits=3)))\n mydata[, round(quantile(get(x), probs=seq(0,1,0.05)), digits=3)]\n })\n \n quantile_dist <- as_tibble(quantile_dist)\n \n quantile_dist$probs <- seq(0,1,0.05)\n \n quantile_dist <- quantile_dist %>% \n select(probs, num_vars_2)\n write_csv(quantile_dist, paste0(out_dir, \"/quantile_dist.csv\"))\n \n # Save percentage of rows for categories of categorical variables in a .rds file\n cat_vars_expo <- sapply(c(cat_vars, target), FUN=function(x) {\n round(mydata[, table(get(x))]/nrow(mydata), digits=3)*100\n })\n \n saveRDS(cat_vars_expo, paste0(out_dir, \"/cat_vars_expo.rds\"))\n \n # Save the distribution plot for numerical variables\n temp <- mydata %>% \n select(num_vars_2, Revenue) %>% \n gather(\"num_vars\", \"values\", -Revenue) %>% \n mutate(values = values+0.001) \n\n temp <- left_join(temp, units) \n temp <- temp %>% mutate(num_vars = paste(num_vars, num_vars_2_units, sep=\", \"))\n\n num_vars_dist_plot <- temp %>%\n ggplot(aes(y=values, x=Revenue)) +\n geom_violin(mapping = aes(fill = Revenue), show.legend = FALSE) +\n scale_y_log10(labels = scales::comma) + \n facet_wrap(~num_vars, scales = \"free\", nrow = 3) +\n labs(\n title = \"Distributions of the numerical variables\",\n x = \"\",\n y = \"Log Scaled Value\"\n ) + \n theme_bw()\n \n ggsave(paste0(out_dir, \"/img/num_vars_dist_plot.png\"), num_vars_dist_plot)\n \n # Save the distribution plot for categorical variables\n my_level <- c(seq(0,0.8,0.2),seq(1,20),c(\"Feb\", \"Mar\", \"May\", \"June\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\", \"Returning_Visitor\", \"New_Visitor\", \"Other\", \"FALSE\", \"TRUE\"))\n \n cat_vars_dist_plot <- mydata %>% \n select(cat_vars, Revenue) %>%\n gather(\"num_vars\", \"values\", -Revenue) %>% \n group_by(Revenue, values, num_vars) %>% \n summarise(n = n()) %>% \n group_by(Revenue, num_vars) %>% \n mutate(freq = n / sum(n),\n values = factor(values, levels = my_level)) %>% \n ggplot(aes(x=values, y=freq, fill=Revenue)) +\n geom_col(position = \"dodge\")+\n scale_y_continuous(labels = scales::percent) +\n facet_wrap(~num_vars, scales =\"free\", nrow = 4) +\n labs(\n title = \"Distributions of the categorical variables\",\n x = \"Categorical variables\",\n y = \"Frequency\"\n ) + \n theme_bw()\n \n ggsave(paste0(out_dir, \"/img/cat_vars_dist_plot.png\"), cat_vars_dist_plot, width = 22, height = 15, units = \"cm\")\n \n # Plot the correlation after pre-process and save the plot\n X_train <- read_csv(paste0(input_dir, \"/X_train.csv\"))\n y_train <- read_csv(paste0(input_dir, \"/y_train.csv\"))\n \n training <- cbind(X_train, y_train)\n\n corr_mat <- training %>% \n select(num_vars_1, num_vars_2, Revenue) %>% \n cor()\n\n corr_mat[lower.tri(corr_mat)] <- NA\n\n melt_corr_mat <- melt(corr_mat, na.rm=TRUE)\n melt_corr_mat <- melt_corr_mat %>% filter(Var1!=Var2)\n\n corr_plot <- melt_corr_mat %>%\n ggplot(aes(Var2, Var1, fill = value))+\n geom_tile(color = \"white\")+\n scale_fill_gradient(low = \"#56B1F7\", high = \"#132B43\", name=\"Pearson\\nCorrelation\") +\n labs(x=\"\", y=\"\")+\n ggtitle(\"Correlation between target and numerical variables\")+\n theme_minimal()+ \n theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 12, hjust = 1), axis.text.y = element_text(size = 12))+\n coord_fixed()\n\n ggsave(paste0(out_dir, \"/img/corr_plot.png\"), corr_plot)\n}\n\ntest_inputs <- function(input, output){\n test_that(\"The path to training data is wrong!\", {\n expect_true(dir.exists(input))\n })\n \n test_that(\"The path to store the results is wrong!\", {\n expect_true(dir.exists(output))\n })\n \n test_that(\"The file needed does not exist! You may need to run fetch_data.py and pre_process.py first.\", {\n expect_true(file.exists(paste0(input, \"/training_for_eda.csv\")))\n })\n}\n\ntest_inputs(opt[[\"--input_dir\"]], opt[[\"--out_dir\"]])\n\nmain(opt[[\"--input_dir\"]], opt[[\"--out_dir\"]])", "meta": {"hexsha": "1bc673aaa32a951f9372790d45d9a36b8383d177", "size": 5823, "ext": "r", "lang": "R", "max_stars_repo_path": "src/eda.r", "max_stars_repo_name": "vermashivam679/DSCI522_309", "max_stars_repo_head_hexsha": "65675ccb7b6f73daa438c438fa2791acf068c7b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/eda.r", "max_issues_repo_name": "vermashivam679/DSCI522_309", "max_issues_repo_head_hexsha": "65675ccb7b6f73daa438c438fa2791acf068c7b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/eda.r", "max_forks_repo_name": "vermashivam679/DSCI522_309", "max_forks_repo_head_hexsha": "65675ccb7b6f73daa438c438fa2791acf068c7b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8544303797, "max_line_length": 228, "alphanum_fraction": 0.663232011, "num_tokens": 1673, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3400807350623898}} {"text": "#!/usr/bin/env Rscript\n# Time line plots\n# plot two identical timeline plots counts vs. counts to see relative growth\n# scott hendrickson\n# @drskippy\n#\nlibrary(ggplot2)\n##############\n# Args are infile, outfile, title, time_period_string\nargs <- commandArgs(trailingOnly = TRUE)\nif (length(args) != 3) {\n print(args)\n stop(\"Error! 3 arguments required (infile1[.csv], infile2[.csv], outfile[.csv], title). Don't include the csv.\")\n}\n##############\nY1 = read.delim(paste(sep=\"\", \"./examples/\", args[1], \".csv\"), sep=\",\", header=TRUE)\ncolnames(Y1) <- c(\"time\",\"ts\",\"count\")\nY1$date <- as.POSIXct(Y1$time, format=\"%Y-%m-%dT%H:%M:%S\")\nY1$series <- as.factor(args[1])\n##############\nY2 = read.delim(paste(sep=\"\", \"./examples/\", args[2], \".csv\"), sep=\",\", header=TRUE)\ncolnames(Y2) <- c(\"time\",\"ts\",\"count\")\nY2$date <- as.POSIXct(Y2$time, format=\"%Y-%m-%dT%H:%M:%S\")\nY2$series <- as.factor(args[2])\n##############\nY <- Y1\nY$count2 <- Y2$count\n##############\npng(filename = paste(sep=\"\", args[3], \".png\"), width = 550, height = 550, units = 'px')\n ggplot(data=Y) +\n\tgeom_point(aes(count, count2), size=1, color=\"red\") + \n\tstat_smooth(aes(count, count2), method=\"lm\", color=\"blue\") + \n\tgeom_abline(intercept=0, slope=1, size=1, color=\"green\") + \n labs(title = args[3]) +\n xlab(args[1]) +\n ylab(paste(args[2])) +\n theme(legend.position = 'none', text = element_text(size=20))\ndev.off()\n##############\nY$count <- (Y$count - min(Y$count))/(max(Y$count) - min(Y$count))\nY$count2 <- (Y$count2 - min(Y$count2))/(max(Y$count2) - min(Y$count2))\npng(filename = paste(sep=\"\", args[3], \"_norm.png\"), width = 550, height = 550, units = 'px')\n ggplot(data=Y) +\n\tgeom_point(aes(count, count2), size=1, color=\"red\") + \n\tstat_smooth(aes(count, count2), method=\"lm\", size=1, color=\"blue\") + \n\tgeom_abline(intercept=0, slope=1, size=1, color=\"green\") + \n labs(title = args[3]) +\n xlab(args[1]) +\n ylab(paste(args[2])) +\n theme(legend.position = 'none', text = element_text(size=20))\ndev.off()\n##############\n", "meta": {"hexsha": "3bd0e5ab9af50268b2c10d96ed241563cd21e368", "size": 2018, "ext": "r", "lang": "R", "max_stars_repo_path": "timeline_plots/xvy.r", "max_stars_repo_name": "twitterdev/Gnip-Search-API-Utilities", "max_stars_repo_head_hexsha": "0f88746e806c6c0306eb9961f96fc3bcc39a63c8", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-02-17T22:06:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-13T22:31:54.000Z", "max_issues_repo_path": "timeline_plots/xvy.r", "max_issues_repo_name": "twitterdev/Gnip-Search-API-Utilities", "max_issues_repo_head_hexsha": "0f88746e806c6c0306eb9961f96fc3bcc39a63c8", "max_issues_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "timeline_plots/xvy.r", "max_forks_repo_name": "twitterdev/Gnip-Search-API-Utilities", "max_forks_repo_head_hexsha": "0f88746e806c6c0306eb9961f96fc3bcc39a63c8", "max_forks_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-03-31T20:02:33.000Z", "max_forks_repo_forks_event_max_datetime": "2019-12-17T19:19:22.000Z", "avg_line_length": 38.0754716981, "max_line_length": 116, "alphanum_fraction": 0.5976214073, "num_tokens": 643, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301064, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3400000398515991}} {"text": "#### MAIN FUNCTION 3.\n\n\n#' @title\n#' Plot a Z-score heatmap with events overlaid.\n#'\n#' @description\n#' Plot a heatmap of z-scores, masking the z-scores at insignificant p-values. Rows in the heatmap correspond to clusters, and columns in the heatmap correspond to time intervals. Overlaid on this heatmap are events, where the increasing events are shown in red, and the decreasing events are shown in blue.\n#'\n#' @param list_concTpSummary Z-scores and p-values extracted from tukey-constrasts of consecutive time intervals for each cluster. Obtained by running the \\code{summaryGetZP} function.\n#' @param mat_events A matrix containing the event information generated by running the \\code{calcEvents} function.\n#' @param significanceTh P-value cutoff of the Tukey contrasts.\n#'\n#'\n#'\n#' @return Displays a plot and returns a matrix, with z-scores masked (NA) according to the signficance threshold specified. The plot also displays events calculated for the centroids.\n#'\n#' @importFrom methods is\n#' @importFrom gplots heatmap.2\n#' @importFrom grDevices colorRampPalette\n#' @importFrom graphics segments\n#' @importFrom shape Arrows\n#'\n#' @seealso \\code{\\link{summaryGetZP}}, \\code{\\link{calcEvents}}, \\code{\\link{plotZP}}\n#'\n#' @export\nplotZP_withEvents <- function(list_concTpSummary, mat_events, significanceTh=0.05){\n\n\tstopifnot(is(list_concTpSummary, \"summary.clusterChange\"), is(mat_events, \"matrix\"), (significanceTh >0 && significanceTh <= 1))\n\n\tsegs <- calcSegsOfCentroids(mat_events) # need to add to global, for graphics::segments to find.\n\tassign(\"segs\", segs, envir=.GlobalEnv)\n\ttheSignifs <- list_concTpSummary[[2]] < significanceTh\n\n\tmatToPlot <- list_concTpSummary[[1]]\n\n\trwb <- grDevices::colorRampPalette(colors = c(\"blue\", \"#cfcfcf\", \"red\"))(n=299)\n\n\n\tif (!all(theSignifs)){\n\t\tzscore_largest <- ceiling(max(c(abs(min(matToPlot)), abs(max(matToPlot)))))\n\t\ttheBreaks <- seq(-zscore_largest, zscore_largest, length.out=299)\n\n\t\tzscore_largest_noSignif <- max(c(abs(max(matToPlot[!theSignifs])), abs(min(matToPlot[!theSignifs]))))\n\t\tidx_forGray50 <- theBreaks <= zscore_largest_noSignif & theBreaks >= -zscore_largest_noSignif\n\n\n\t\trwb[idx_forGray50] <- \"#cfcfcf\"\n\n\t\tmatToPlot[!theSignifs] <- NA\n\t}\n\n\n\ttitleTxt = paste(\"Significant changes\\n(z-values of intervals,\\n with significance p \\u003C\", significanceTh, \")\", sep=\"\")\n\t# print(titleTxt)\n\n\tidx_up = segs[[5]] == \"#8B0000\"\n\tassign(\"idx_up\", idx_up, envir=.GlobalEnv)\n\tidx_down = segs[[5]] == \"#00008B\"\n\tassign(\"idx_down\", idx_down, envir=.GlobalEnv)\n\n\n\tgplots::heatmap.2(matToPlot, main=titleTxt, xlab=\"Time interval\", ylab=\"Cluster\", Rowv=FALSE, Colv=FALSE, dendrogram=\"none\", col=rwb, na.color=\"#cfcfcf\", tracecol=NA, density.info=\"none\", sepcolor=\"#cfcfcf\", sepwidth=c(0.001, 0.001), colsep=0:ncol(matToPlot), rowsep=0:nrow(matToPlot), srtCol=45, add.expr=c(shape::Arrows(x0=segs[[1]][idx_up], x1=segs[[1]][idx_up], y0=segs[[2]][idx_up]-0.1, y1=segs[[2]][idx_up]+0.5, arr.type=\"triangle\", arr.length=0.2, arr.width=0.2, col=\"#8B0000\", arr.adj=0, segment=FALSE), shape::Arrows(x0=segs[[1]][idx_down], x1=segs[[1]][idx_down], y0=segs[[2]][idx_down]+2, y1=segs[[2]][idx_down]+0.5, arr.type=\"triangle\", arr.length=0.2, arr.width=0.2, col=\"#00008B\", arr.adj=0, segment=FALSE), graphics::segments(x0=segs[[1]], y0=segs[[2]]+0.3, x1=segs[[1]], y1=segs[[4]]-0.3, col=segs[[5]], lwd=3, lend=2)), cexCol=0.8)\n\n\n\treturn (matToPlot)\n}\n\naddToMatrix <- function(theMat, clusNum, x_50, y_50, direction){\n\ttheMat <- rbind(theMat, c(clusNum, x_50, y_50, direction))\n\treturn (theMat)\n}\n\n\ncalcSegsOfCentroids <- function(mat_fiftyPoints){\n\n\ttotalNumCluster <- max(mat_fiftyPoints[,cols_matFifty$col_clus]) + 0.5\n\ty_val = totalNumCluster\n\n\tx <- c(); y <- c(); x1 <- c(); y1 <- c(); colors <- c();\n\n\tfor (rowNum in 1:nrow(mat_fiftyPoints)){\n\t\tx[rowNum] <- mat_fiftyPoints[rowNum, cols_matFifty$col_x] - 0.5\n\t\tx1[rowNum] <- mat_fiftyPoints[rowNum, cols_matFifty$col_x] - 0.5\n\t\ty[rowNum] <- ((totalNumCluster - (mat_fiftyPoints[rowNum, cols_matFifty$col_clus])) * 1)\n\t\ty1[rowNum] <- ((totalNumCluster - (mat_fiftyPoints[rowNum, cols_matFifty$col_clus] - 1)) * 1)\n\n\t\tif (mat_fiftyPoints[rowNum, cols_matFifty$col_dir] == 1){\n\t\t\tcolors[rowNum] <- \"#8B0000\"\n\t\t}\n\t\telse{\n\t\t\tcolors[rowNum] <- \"#00008B\"\n\t\t}\n\t}\n\n\treturn(list(x, y, x1, y1, colors))\n}\n\n\n\ngetMidX <- function(x1, y1, x2, y2, y_50){\n\tm <- (y2 - y1)/(x2 - x1)\n\tx_50 <- x1 + ((y_50 - y1)/m)\n\treturn (x_50)\n}\n\n\n#### MAIN FUNCTION 2.\n\n\n#' @title\n#' Calculate events for the cluster centroids.\n#'\n#'\n#' @param timeRegions A list containing the time regions information.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#' @param Tc A matrix containing time course data (from this centroids are computed, if not provided below).\n#' @param centroids A matrix containing centroids. If no matrix is provided, the centroid are then compuated, and are a simple average of all the profiles within each cluster. Alternatively, for example, centroids from FCM clustering can be provided where profiles are weighted by the membership score.\n#' @param phosEventTh Value between 0 and 1, to define the threshold at which a phosphorylation (or increasing) event occurs. Here, a threshold of 0 corresponds to the minimum value within an interval. (Such an event is designated as 1).\n#' @param dephosEventTh Value between 0 and 1, to define the threshold at which a dephosphorylation (or decreasing) event occurs. A threshold of 0 corresponds to the maximum value within an interval. (Such an event is designated as -1).\n#'\n#'\n#'\n#' @return A matrix containing the details of the calculated events for the cluster centroids (event window information is appended to the matrix).\n#'\n#' @importFrom methods is\n#'\n#' @seealso \\code{\\link{getTimeRegionsWithMaximalChange}} to generate \\code{timeRegions}.\n#'\n#' @export\ncalcEvents <- function(timeRegions, clusters, Tc=NA, centroids=NA, phosEventTh=0.5, dephosEventTh=0.5){\n\n\tstopifnot(is(timeRegions, \"list\"), (length(clusters) == nrow(Tc)), (phosEventTh >= 0 && phosEventTh <= 1), (dephosEventTh >= 0 && dephosEventTh <= 1), ( (!is.na(centroids)) || (is.na(centroids) && !is.na(Tc))) )\n\n\tmat_fiftyPoints <- matrix(ncol=length(Col_events), data=NA) # see Col_events\n\n\tif (is.na(centroids)){\n\t\tcentroids = calcAvgOfClusProf(Tc, clusters)\n\t}\n\n\t# centroids = calcAvgOfClusProf()\n\tfor (clusNum in 1:max(clusters)){\n\t\tif (nrow(timeRegions[[clusNum]]) > 0){\n\t\t\tfor (i in 1:nrow(timeRegions[[clusNum]])){\n\t\t\t\tstartTp = as.integer(min(timeRegions[[clusNum]][i, cols_timeReg$tpStart], timeRegions[[clusNum]][i, cols_timeReg$tpEnd]))\n\t\t\t\tendTp = as.integer(max(timeRegions[[clusNum]][i, cols_timeReg$tpStart], timeRegions[[clusNum]][i, cols_timeReg$tpEnd]))\n\n\t\t\t\t# print(paste(\"StartTp: \", startTp, endTp, timeRegions[[clusNum]][i, cols_timeReg$tpStart], timeRegions[[clusNum]][i, cols_timeReg$tpEnd]))\n\n\t\t\t\tcrossPt <- calcCrossing_v3(centroids[clusNum, startTp:endTp], timeRegions[[clusNum]][i, cols_timeReg$dir], (startTp -1), phosEventTh, dephosEventTh)\n\n\t\t\t\t# if (crossPt[1] == -1){\n\t\t\t\t\t# print(clustered$center[clusNum, startTp:endTp])\n\t\t\t\t# }\n\t\t\t\tmat_fiftyPoints <- addToMatrixWithRegionInfo(mat_fiftyPoints, clusNum, crossPt[1], crossPt[2], timeRegions[[clusNum]][i, cols_timeReg$dir], startTp, endTp)\n\t\t\t}\n\t\t}\n\t}\n\n\tmat_fiftyPoints <- mat_fiftyPoints[-1,,drop=FALSE]\n\n\t# print(mat_fiftyPoints)\n\tmat_fiftyPoints <- mat_fiftyPoints[order(mat_fiftyPoints[,Col_events$clus],mat_fiftyPoints[,Col_events$x]),] # order by cluster, then time; else issue in downstream function (as only subsequent events are checked over there)\n\n\tcolnames(mat_fiftyPoints) <- c(\"Cluster\", \"Time\", \"Th. abundance\", \"Direction\", \"Start interval\", \"End interval\", \"Order\", \"CombinedDatasetNum\")\n\n\treturn (mat_fiftyPoints)\n}\n\naddToMatrixWithRegionInfo <- function(theMat, clusNum, x_50, y_50, direction, tpStart, tpEnd){\n\ttheRow <- c(clusNum, x_50, y_50, direction, tpStart, tpEnd, NA, NA)\n\ttheMat <- rbind(theMat, theRow)\n\trownames(theMat)[nrow(theMat)] <- ''\n\treturn (theMat)\n}\n\n\n\ncalcCrossing_v3 <- function(region, dir, offset, phosTh, dephosTh){\n\n\tx_50 <- -1 # initial time\n\ty_50 <- -1 # initial th_point\n\n\ty_max <- max(region)\n\ty_min <- min(region)\n\n\tif (dir == 1){\n\t\ty_10Percent <- (y_max - y_min) * phosTh\n\t\ty_50 <- y_min + y_10Percent\n\t}\n\telse{\n\t\ty_10Percent <- (y_max - y_min) * (1- dephosTh)\n\t\ty_50 <- y_min + y_10Percent\n\t}\n\n\n\te = 0.0001 # due to calculation some round error when threshold is 0.\n\t# find intervals and store\n\tfor (j in 2:length(region)){\n\n\t\tif (dir == 1 && ( (y_50 >= region[j-1] && y_50 <= region[j]) || abs(y_50 - region[j-1]) < e || abs(y_50 - region[j]) < e )){\n\t\t\t# 50 is crossed here in phos direction\n\t\t\tx_50 <- getMidX((offset+j-1), region[j-1], offset+j, region[j], y_50)\n\t\t\t# mat_fiftyPoints <- addToMatrix(mat_fiftyPoints, clusNum, x_50, y_50, 1)\n\t\t}\n\t\telse if (dir == -1 && ( (y_50 <= region[j-1] && y_50 >=region[j]) || abs(y_50 - region[j-1]) < e || abs(y_50 - region[j]) < e ) ){\n\t\t\t# 50 is crossed here in dephos direction\n\t\t\t# getMidX()\n\t\t\tx_50 <- getMidX((offset+j-1), region[j-1], offset+j, region[j], y_50)\n\t\t\t# mat_fiftyPoints <- addToMatrix(mat_fiftyPoints, clusNum, x_50, y_50, -1)\n\n\t\t}\n\t}\n\n\treturn (c(x_50, y_50))\n}\n\n\n#' @title\n#' A summary, providing percentage of profiles with missing events in each cluster.\n#'\n#' @description\n#' Summary of clusters: providing information regarding the percentage of profiles which do not contain events within the identified time regions.\n#'\n#' @param Tc A matrix containing time course data.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#' @param mat_events A matrix containing the event information generated by running the \\code{calcEvents} function.\n#' @param phosEventTh Value between 0 and 1, to define the threshold at which a phosphorylation (or increasing) event occurs. Here, a threshold of 0 corresponds to the minimum value within an interval. (Such an event is designated as 1).\n#' @param dephosEventTh Value between 0 and 1, to define the threshold at which a dephosphorylation (or decreasing) event occurs. A threshold of 0 corresponds to the maximum value within an interval. (Such an event is designated as -1).\n#'\n#'\n#'\n#' @return A matrix providing the number of profiles missing events (1 events per row) for each cluster.\n#'\n#' @importFrom methods is\n#'\n#' @seealso \\code{\\link{getTimeRegionsWithMaximalChange}}, \\code{\\link[e1071]{cmeans}}.\n#'\n#' @export\nmissingStats <- function(Tc, clusters, mat_events, phosEventTh, dephosEventTh){\n\n\tstopifnot(is(Tc, \"matrix\"), (length(clusters) == nrow(Tc)), is(mat_events, \"matrix\"), (phosEventTh >= 0 && phosEventTh <= 1), (dephosEventTh >= 0 && dephosEventTh <= 1))\n\n\n\tlist_matrices <- splitIntoSubMatrices(Tc, clusters)\n\n\tlist_distributions <- getDistOfAllEvents_v2(mat_events, list_matrices, phosEventTh, dephosEventTh)\n\n\n\tlist_distributions\n\tmat_missingStats <- matrix(ncol=7) #cluster, time, th.abun., dir, numNa, numTotal, percentNa\n\n\tfor (eventNum in 1:length(list_distributions)){\n\n\t\tnumNa <-length(which(is.na(list_distributions[[eventNum]][,cols_matFifty$col_x])))\n\n\t\tnumTotal <- nrow(list_distributions[[eventNum]])\n\n\t\tpercentNa <- (numNa * 100) / numTotal\n\n\t\tmat_missingStats <- rbind(mat_missingStats, c(mat_events[eventNum, cols_matFifty_v2$clus:cols_matFifty_v2$dir], numNa, numTotal, percentNa))\n\n\t\t# print(paste(eventNum, numNa, numTotal, percentNa, sep=\" \"))\n\t}\n\n\tcolnames(mat_missingStats) <- c(\"Cluster\", \"Time\", \"Th. abun.\", \"Dir.\", \"Num. NA\", \"Num. total\", \"Percent NA\")\n\n\tmat_missingStats <- mat_missingStats[-1,,drop=FALSE]\n\treturn(mat_missingStats)\n}\n\n\n#### MAIN FUNCTION 1.\n\n#' @title\n#' Calculate time regions\n#'\n#' @description\n#' Utilize the Tukey significantly constrasting intervals, and determine time regions. Time regions are intervals such that:\n#' 1. the z-score is maximal\n#' 2. if a region consists of any sub-intervals, they too have the same sign\n#' 3. intervals are non-overlapping.\n#'\n#'\n#' @param glmTukeyForEachClus A list (with same length as number of clusters) containing the GLM post-hoc Tukey contrasts for all time points. Obtained by running the \\code{calcClusterChng} function.\n#' @param numTps Total number of time points in the data.\n#' @param signifTh P-value cutoff of the Tukey contrasts.\n#' @param phosZscoreTh A z-score value filter to remove phosphorylation (or increasing) events, such that all events with a z-score <= phosZscoreTh are removed.\n#' @param dephosZscoreTh A z-score value filter to remove dephosphorylation (or decreasing) events, such that all events with a z-score >= dephosZscoreTh are removed.\n#'\n#' @return A List of matrices, one for each cluster, containing the time regions information. The columns in each matrix are cluster, zscore, start time point, end time point, and direction.\n#'\n#' @importFrom methods is\n#'\n#' @seealso \\code{\\link{calcClusterChng}}, \\code{\\link{missingStats}}\n#'\n#' @export\ngetTimeRegionsWithMaximalChange <- function(glmTukeyForEachClus, numTps, signifTh=0.05, phosZscoreTh=0, dephosZscoreTh=0){\n\n\t# Add checks\n\tstopifnot(is(glmTukeyForEachClus, \"clusterChange\"), numTps > 0, (signifTh > 0 && signifTh <= 1), phosZscoreTh >= 0, dephosZscoreTh <= 0)\n\n\tlist_timeRegNoOvrlp <- list()\n\n\tfor (i in 1:length(glmTukeyForEachClus)){ # for each cluster\n\n\t\t# convert z-scores to matrix (by time * time)\n\t\tcombined <- convertToMatAndAddNAs(glmTukeyForEachClus[[i]], numTps, signifTh)\n\n\t\t# Convert regions to 0 which cannot statisfy zscoreTh.\n\t\tcombined <- filterByZscore(combined, phosZscoreTh, dephosZscoreTh)\n\n\t\t# print(combined)\n\t\t# find time regions\n\t\ttimeRegions <- findMonotonicRegions(combined, i)\n\n\t\tif (nrow(timeRegions) == 0){ # no time regions here.\n\t\t\t# print('yes recognized, can skip')\n\t\t\tlist_timeRegNoOvrlp[[i]] <- timeRegions\n\t\t}\n\t\telse{\n\t\t\ttimeRegions_noOverlaps<- removeAnyOverlaps(timeRegions)\n\t\t\tlist_timeRegNoOvrlp[[i]] <- timeRegions_noOverlaps\n\t\t}\n\n\n\n\t}\n\n\n\n\treturn(list_timeRegNoOvrlp)\n}\n\nfilterByZscore <- function(combined, phosZscoreTh, dephosZscoreTh){\n\t# combined_local <- combined\n\n\n\tfor (rowNum in 1:nrow(combined)){\n\t\tfor (colNum in 1:ncol(combined)){\n\n\t\t\tif (!is.na(combined[rowNum, colNum]) && combined[rowNum, colNum] > 0 && combined[rowNum, colNum] < phosZscoreTh){\n\t\t\t\tcombined[rowNum, colNum] <- 0\n\n\t\t\t}\n\n\t\t\tif (!is.na(combined[rowNum, colNum]) && combined[rowNum, colNum] < 0 && combined[rowNum, colNum] > dephosZscoreTh){\n\n\t\t\t\tcombined[rowNum, colNum] <- 0\n\n\t\t\t}\n\t\t}\n\t}\n\n\n\n\treturn (combined)\n}\n\nisAnyOverlap <- function(noOverlaps, currTp1, currTp2){\n\n\tif (nrow(noOverlaps) == 0){\n\t\treturn (FALSE)\n\t}\n\n\tfor (i in 1: nrow(noOverlaps)) {\n\t\tif (noOverlaps[i,cols_timeReg$tpStart] > min(c(currTp1,currTp2)) && noOverlaps[i,cols_timeReg$tpStart] < max(c(currTp1, currTp2))) {\n\t\t\treturn (TRUE)\n\t\t}\n\t\tif ((noOverlaps[i,cols_timeReg$tpEnd] > min(c(currTp1,currTp2)) && noOverlaps[i,cols_timeReg$tpEnd] < max(c(currTp1, currTp2))) || (noOverlaps[i,cols_timeReg$tpEnd] > currTp1 && noOverlaps[i,cols_timeReg$tpEnd] < currTp2)){\n\t\t\treturn (TRUE)\n\t\t}\n\t\tif (currTp1 > min(c(noOverlaps[i,cols_timeReg$tpStart],noOverlaps[i,cols_timeReg$tpEnd])) && currTp2 < max(c(noOverlaps[i,cols_timeReg$tpStart], noOverlaps[i,cols_timeReg$tpEnd]) )) {\n\t\t\treturn (TRUE)\n\t\t}\n\t\tif (currTp2 > min(c(noOverlaps[i,cols_timeReg$tpStart],noOverlaps[i,cols_timeReg$tpEnd])) && currTp2 < max(c(noOverlaps[i,cols_timeReg$tpStart], noOverlaps[i,cols_timeReg$tpEnd]) ) ){\n\t\t\treturn (TRUE)\n\t\t}\n\t}\n\n\treturn (FALSE)\n}\n\nremoveAnyOverlaps <- function(timeRegions){\n\n\tnoOverlaps_phos <- matrix(ncol=5)\n\tnoOverlaps_dephos <- matrix(ncol=5)\n\tisFirstAdded_phos <- FALSE\n\tisFirstAdded_dephos <- FALSE\n\tfor (i in 1:nrow(timeRegions)){\n\t\tif (nrow(timeRegions) == 0){\n\t\t\tbreak;\n\t\t}\n\t\tif ( any(timeRegions[,cols_timeReg$zScore] > 0)){\n\t\t\tmaxIdx <- which.max(timeRegions[,cols_timeReg$zScore])\n\t\t\tif (isFirstAdded_phos == FALSE){\n\t\t\t\tnoOverlaps_phos <- rbind(noOverlaps_phos, timeRegions[maxIdx,])\n\t\t\t\tisFirstAdded_phos = TRUE\n\t\t\t\tnoOverlaps_phos <- noOverlaps_phos[-1,,drop=FALSE ]\n\t\t\t\t# print(timeRegions)\n\t\t\t}\n\t\t\telse{\n\t\t\t\tif (isAnyOverlap(noOverlaps_phos, timeRegions[maxIdx,cols_timeReg$tpStart], timeRegions[maxIdx,cols_timeReg$tpEnd]) == FALSE){\n\t\t\t\t\tnoOverlaps_phos <- rbind(noOverlaps_phos, timeRegions[maxIdx,])\n\t\t\t\t}\n\t\t\t}\n\t\t\ttimeRegions <- timeRegions[-maxIdx,,drop=F]\n\t\t}\n\n\t\tif (nrow(timeRegions) > 0 && any(timeRegions[,cols_timeReg$zScore] < 0)){\n\t\t\tminIdx <- which.min(timeRegions[,cols_timeReg$zScore])\n\t\t\tif (isFirstAdded_dephos == FALSE){\n\t\t\t\tnoOverlaps_dephos <- rbind(noOverlaps_dephos, timeRegions[minIdx,])\n\t\t\t\tisFirstAdded_dephos = TRUE\n\t\t\t\tnoOverlaps_dephos <- noOverlaps_dephos[-1,,drop=FALSE]\n\t\t\t}\n\t\t\telse{\n\t\t\t\tif (isAnyOverlap(noOverlaps_dephos, timeRegions[minIdx,cols_timeReg$tpStart], timeRegions[minIdx,cols_timeReg$tpEnd]) == FALSE){\n\t\t\t\t\tnoOverlaps_dephos <- as.matrix(rbind(noOverlaps_dephos, timeRegions[minIdx,]))\n\t\t\t\t}\n\t\t\t}\n\t\t\ttimeRegions <- timeRegions[-minIdx,,drop=FALSE]\n\t\t}\n\t}\n\n\tif (is.na(noOverlaps_phos[1,1] )){\n\t\treturn (noOverlaps_dephos)\n\t}\n\tif (is.na(noOverlaps_dephos[1,1])){\n\t\treturn (noOverlaps_phos)\n\t}\n\n\toverlaps <- rbind(noOverlaps_phos, noOverlaps_dephos)\n\n\treturn (overlaps)\n\n}\n\n\n\n\naddToTimeRegionsMat <- function(timeRegions, cluster, zScore, tpStart, tpEnd, direction){\n\t# dir = -1\n\t# if (zScore > 0){\n\t#\tdir = 1\n\t# }\n\ttpStart = tpStart + 1\n\t# startReg = min(tpStart, tpEnd)\n\t# endReg = max(tpStart, tpEnd)\n\n\ttimeRegions <- rbind(timeRegions, c(cluster, zScore, tpStart, tpEnd, direction))\n\n\treturn(timeRegions)\n}\n\n\n\n########################### AUX\nfindMonotonicRegions <- function(combined, cluster){\n\ttimeRegions <- matrix(ncol=5) # cluster, z-score, tpStart, tpEnd, direction, z-score\n\n\tdirection = 0\n\tcurrMaxMin = 0\n\trowStart = 0\n\tcolStart = 0\n\trowEnd = 0\n\tcolEnd = 0\n\n\n\t# trial 3\n\trowNum =1\n\twhile (rowNum <= nrow(combined)){\n\t\tcolNum = 1\n\t\tisBreak = FALSE\n\t\twhile (colNum <= rowNum){\n\t\t\tif (combined[rowNum, colNum] > 0 && direction != 1) {\n\t\t\t\t# save any prev value ...\n\t\t\t\tif (currMaxMin < 0){\n\t\t\t\t\ttimeRegions <- addToTimeRegionsMat(timeRegions, cluster, combined[rowEnd, colEnd], rowEnd, colEnd, direction)\n\n\t\t\t\t\t# print(\"Set 1: \")\n\t\t\t\t\t# print(paste(cluster, rowStart, colStart, rowEnd+1, colEnd, combined[rowEnd, colEnd], direction))\n\t\t\t\t}\n\t\t\t\t# new one encountered in + dir.\n\t\t\t\tdirection = 1\n\t\t\t\tcurrMaxMin = combined[rowNum, colNum]\n\t\t\t\trowStart = rowNum; rowEnd = rowNum; colStart = colNum; colEnd = colNum;\n\n\n\t\t\t}\n\t\t\telse if (combined[rowNum, colNum] > 0 && direction == 1 ){\n\t\t\t\t# encountered in same direction\n\n\t\t\t\tif (combined[rowNum, colNum] > currMaxMin){\n\t\t\t\t\tcurrMaxMin = combined[rowNum, colNum]\n\t\t\t\t\trowEnd = rowNum; colEnd = colNum;\n\t\t\t\t}\n\t\t\t}\n\n\n\t\t\tif (combined[rowNum, colNum] < 0 && direction != -1) {\n\t\t\t\t# save any prev value ...\n\t\t\t\tif (currMaxMin > 0){\n\t\t\t\t\t# print(paste(rowStart, colStart, rowEnd+1, colEnd, combined[rowEnd, colEnd]))\n\t\t\t\t\ttimeRegions <- addToTimeRegionsMat(timeRegions, cluster, combined[rowEnd, colEnd], rowEnd, colEnd, direction)\n\n\t\t\t\t\t# print(\"Set 2: \")\n\t\t\t\t\t# print(paste(cluster, rowStart, colStart, rowEnd+1, colEnd, combined[rowEnd, colEnd], direction))\n\t\t\t\t}\n\t\t\t\t# new one encountered in + dir.\n\t\t\t\tdirection = -1\n\t\t\t\tcurrMaxMin = combined[rowNum, colNum]\n\t\t\t\trowStart = rowNum; rowEnd = rowNum; colStart = colNum; colEnd = colNum;\n\t\t\t\t#print(\"here! \")\n\t\t\t\t#print(combined[rowNum, colNum])\n\t\t\t}\n\t\t\telse if (combined[rowNum, colNum] < 0 && direction == -1 ){\n\t\t\t\t# encountered in same direction\n\t\t\t\tif (combined[rowNum, colNum] < currMaxMin){\n\t\t\t\t\tcurrMaxMin = combined[rowNum, colNum]\n\t\t\t\t\trowEnd = rowNum; colEnd = colNum;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tcolNum = colNum + 1\n\t\t}\n\n\n\t\trowNum = rowNum + 1\n\n\n\t}\n\n\n\tif ( rowEnd > 0 && colEnd > 0 && rowEnd <= nrow(combined) && colEnd <= ncol(combined) && combined[rowEnd, colEnd] != 0) {\n\t\ttimeRegions <- addToTimeRegionsMat(timeRegions, cluster, combined[rowEnd, colEnd], rowEnd, colEnd, direction)\n\n\t\t# print(paste(rowStart, colStart, rowEnd+1, colEnd, combined[rowEnd, colEnd]))\n\t}\n\n\n\ttimeRegions <- timeRegions[-1,, drop=FALSE]\n\treturn (timeRegions)\n}\n\n\nconvertToMatAndAddNAs <- function(glmTukeyForAClus, numTps, signifTh){\n\tmat_zscore <- matrix(nrow=(numTps-1), ncol=(numTps-1))\n\tmat_pvalue <- matrix(nrow=(numTps-1), ncol=(numTps-1))\n\tcombined <- matrix(nrow=(numTps-1), ncol=(numTps-1)) # the row ranges from (2, 9), the col ranges from (1,8)\n\n\n\tfor (val1 in seq(1, numTps)){\n\t\tif ((val1+1) <= numTps){\n\t#\t\tfor (val2 in seq((val1+1), (numTps))){\n\t\t\tfor (val2 in seq((val1+1), numTps)) {\n\n\t\t\t\tname = paste(val2, \"-\", (val1))\n\t\t\t\t# print(name)\n\n\t\t\t\tidx = which(names(glmTukeyForAClus$test$tstat) == name )\n\t\t\t\t# print(paste(idx , name))\n\t\t\t\t# print(glmTukeyForAClus$test$tstat[idx])\n\t\t# \t\tprint(glmTukeyForAClus$contrasts[idx,]$z.ratio)\n\n\n\t\t\t\tmat_zscore[val2-1, val1] <- as.numeric(glmTukeyForAClus$test$tstat[idx]) # * -1\n\n\t\t\t\t# idx = which(names(zscores) == name)\n\n\t\t\t\tmat_pvalue[val2-1, val1] <- glmTukeyForAClus$test$pvalues[[idx]]\n\n\t\t\t\tif(as.numeric(mat_pvalue[val2-1, val1]) < signifTh){\n\t\t\t\t\tcombined[val2-1, val1] <- mat_zscore[val2-1, val1]\n\t\t\t\t}\n\t\t\t\telse{\n\t\t\t\t\tcombined[val2-1, val1] <- 0\n\t\t\t\t}\n\t\t\t\t# print(name)\n\t\t\t\t# print(idx)\n\n\t\t\t}\n\t\t}\n\t}\n\n\n\n\treturn (combined)\n}\n", "meta": {"hexsha": "0155840aa05fb3dafd6dd882a69c050694c44053", "size": 21252, "ext": "r", "lang": "R", "max_stars_repo_path": "R/events.r", "max_stars_repo_name": "ODonoghueLab/Minardo-Model", "max_stars_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-11-25T03:08:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T10:14:50.000Z", "max_issues_repo_path": "R/events.r", "max_issues_repo_name": "ODonoghueLab/Minardo-Model", "max_issues_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-08T09:47:48.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-23T02:36:36.000Z", "max_forks_repo_path": "R/events.r", "max_forks_repo_name": "ODonoghueLab/Minardo-Model", "max_forks_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0203389831, "max_line_length": 851, "alphanum_fraction": 0.6992283079, "num_tokens": 6693, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5736784074525098, "lm_q1q2_score": 0.34000003946679247}} {"text": "#\r\n# convert original Aglink .gdx files into a more appropriate format\r\n#\r\n# output: MTO20XX.gdx file, to be read by scripts/convert_to_gdx.gms\r\n#\r\n\r\n\r\n\r\n# set library path on local computer\r\n.libPaths( c(.libPaths(), \"c:/Users/Public/R/libraries_x64_64\"))\r\n\r\n\r\nlibrary(dplyr)\r\nlibrary(gdxrrw)\r\nlibrary(tidyverse)\r\nigdx(\"c:/Program Files (x86)/GAMS/win64/28.2/\")\r\n\r\n\r\nsetwd(\"c:/Users/Public/jrc_bl\")\r\n\r\n\r\nagri19 <- rgdx.param(\"data/result_EUNMerge_PostM.gdx\", \"p_dbvar\")\r\n\r\nagri19 <- as_tibble(agri19)\r\n\r\n\r\ncolnames(agri19) <- c(\"countries\", \"commodities\", \"attributes\", \"aglinkYears\", \"value\")\r\n\r\nagri19 <- agri19 %>% select(countries, attributes, commodities, aglinkYears, value)\r\n\r\n# filter out undefined data points (Undf alredy in the input .gdx)\r\nagri19 <- agri19 %>% filter(!is.na(value))\r\n\r\n\r\n\r\n# write to .gdx\r\n#--------------\r\n\r\nDATAOUT <- as.data.frame(agri19)\r\n\r\nDATAOUT$aglinkYears <- factor(DATAOUT$aglinkYears)\r\nattr(DATAOUT, \"symName\") <- \"DATAOUT\"\r\nattr(DATAOUT, \"ts\") <- \"output cube from Aglink\"\r\n\r\n# prepare GAMS sets\r\nx <- agri19 %>% group_by(countries) %>% summarise(value = sum(value))\r\ncountries <- x$countries\r\ncountries <- as.data.frame(countries)\r\nattr(countries, \"symName\") <- \"countries\"\r\nattr(countries, \"ts\") <- \"countries in the output cube from Aglink\"\r\n\r\n\r\nx <- agri19 %>% group_by(attributes) %>% summarise(value = sum(value))\r\nattributes <- x$attributes\r\nattributes <- as.data.frame(attributes)\r\nattr(attributes, \"symName\") <- \"attributes\"\r\nattr(attributes, \"ts\") <- \"attributes in the output cube from Aglink\"\r\n\r\n\r\nx <- agri19 %>% group_by(commodities) %>% summarise(value = sum(value))\r\ncommodities <- x$commodities\r\ncommodities <- as.data.frame(commodities)\r\nattr(commodities, \"symName\") <- \"commodities\"\r\nattr(commodities, \"ts\") <- \"commodities in the output cube from Aglink\"\r\n\r\n\r\nx <- agri19 %>% group_by(aglinkYears) %>% summarise(value = sum(value))\r\naglinkYears <- x$aglinkYears\r\naglinkYears <- as.data.frame(aglinkYears)\r\nattr(aglinkYears, \"symName\") <- \"aglinkYears\"\r\nattr(aglinkYears, \"ts\") <- \"aglinkYears in the output cube from Aglink\"\r\n\r\n\r\n# 'tuple' will hold the combinations of (countries, attributes, commodities) with data\r\nx <- agri19 %>% group_by(countries, attributes, commodities) %>% summarise(value = sum(value))\r\ntuple <- x %>% select(-value)\r\ntuple <- as.data.frame(tuple)\r\nattr(tuple, \"symName\") <- \"tuple\"\r\nattr(tuple, \"ts\") <- \"tuples in the output cube from Aglink\"\r\n\r\nrm(x)\r\n\r\nwgdx.lst(\"data/MTO_2019.gdx\", list(DATAOUT, commodities, attributes, aglinkYears, countries, tuple))\r\n\r\n", "meta": {"hexsha": "24d5e14ade603fb331c07e6fa8b25404948df18a", "size": 2582, "ext": "r", "lang": "R", "max_stars_repo_path": "R/adjust_gdx.r", "max_stars_repo_name": "trialsolution/convert_aglink", "max_stars_repo_head_hexsha": "2e59d5833cd4dac2382c8ddafc1d7797e5af9061", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/adjust_gdx.r", "max_issues_repo_name": "trialsolution/convert_aglink", "max_issues_repo_head_hexsha": "2e59d5833cd4dac2382c8ddafc1d7797e5af9061", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/adjust_gdx.r", "max_forks_repo_name": "trialsolution/convert_aglink", "max_forks_repo_head_hexsha": "2e59d5833cd4dac2382c8ddafc1d7797e5af9061", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3764705882, "max_line_length": 101, "alphanum_fraction": 0.6793183579, "num_tokens": 720, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.34000003121194444}} {"text": "rm(list = ls())\n\nlibrary(readnet)\nlibrary(dplyr)\n\nedgelist <- readxl::read_excel(\"playground/kayla/GROW_Edgelist clean for George.xlsx\")\ndat <- readr::read_tsv(\"playground/kayla/GROW Attribute Data for George.csv\")\nids2work <- readxl::read_excel(\"playground/kayla/GROW_Edgelist clean for George.xlsx\", \"NetMembersN400\")\n\n\n# Catched an error:\n# Error: Missing ids in `data`. There are 610 observations in `data` and 613\n# identified ids. The observations that are missing in `data` are: '812398',\n# '812660', '831086'.\n\n# The same error shows up when I run it filtering the data:\n#\n# Error: Missing ids in `data`. There are 394 observations in `data` and 397\n# identified ids. The observations that are missing in `data` are: '812398',\n# '812660', '831086'. So\n#\n# So I'll just drop it!\n\ndids <- unique(dat$study_id)\nedgelist <- filter(edgelist, (Sender %in% dids) & (Receiver %in% dids))\n\nans <- edgelist_to_adjmat_w_attributes(\n edgelist = edgelist,\n data = dat,\n data.idvar = \"study_id\",\n edgelist.timevar = \"wave\",\n ids.to.keep = ids2work$study_id\n )\n", "meta": {"hexsha": "586cab2a6d244147d6bbec1e68f6ba74eb123bea", "size": 1097, "ext": "r", "lang": "R", "max_stars_repo_path": "playground/kayla/grow.r", "max_stars_repo_name": "USCCANA/readnet", "max_stars_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "playground/kayla/grow.r", "max_issues_repo_name": "USCCANA/readnet", "max_issues_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-12-01T18:50:02.000Z", "max_issues_repo_issues_event_max_datetime": "2017-12-04T18:09:38.000Z", "max_forks_repo_path": "playground/kayla/grow.r", "max_forks_repo_name": "USCCANA/readnet", "max_forks_repo_head_hexsha": "63c9eef3330bd565235845dcf81d56ccb0eb6fbd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2647058824, "max_line_length": 104, "alphanum_fraction": 0.6937101185, "num_tokens": 323, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3400000312119444}} {"text": "timestamp()\n\nargs=(commandArgs(TRUE))\nidirBase = args[1]\nlabel = args[2]\nofile = args[3]\nstrand = args[4]\nprobabilityFile = args[5]\n \n# idirBase = \"products/_06_seesawFitting_bin_5k\"\n# label = \"chr6:94050766-94283009\"\n# ofile = \"\"\n\nthPval = 1e-5\nthProbability = 0.95\n\nifiles = list.files(idirBase,\n sprintf(\"%s.res.txt.gz\", label), \n recursive=TRUE, full.names=TRUE)\n\npvalfiles = list.files(idirBase,\n sprintf(\"%s.pval.txt\", label), \n recursive=TRUE, full.names=TRUE)\n\npath_rdata = sprintf(\"%s.rda\", ofile)\n\n\nsource(\"scripts/r14_replot_fitting_bin_5k_coloringByPvalue/base_prepare.r\", echo = TRUE)\nsource(\"scripts/r14_replot_fitting_bin_5k_coloringByPvalue/base.r\", echo = TRUE)\n\nsessionInfo()\ntimestamp()\n\n", "meta": {"hexsha": "2f9142307977fcb41703d039881447f1e30f610c", "size": 794, "ext": "r", "lang": "R", "max_stars_repo_path": "recursive_splicing/02_replot_fitting/run.r", "max_stars_repo_name": "yuifu/Hayashi2018", "max_stars_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2018-02-14T15:41:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-01T03:05:11.000Z", "max_issues_repo_path": "recursive_splicing/02_replot_fitting/run.r", "max_issues_repo_name": "yuifu/Hayashi2018", "max_issues_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-01-26T05:13:23.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-21T02:39:44.000Z", "max_forks_repo_path": "recursive_splicing/02_replot_fitting/run.r", "max_forks_repo_name": "yuifu/Hayashi2018", "max_forks_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.3529411765, "max_line_length": 88, "alphanum_fraction": 0.6536523929, "num_tokens": 230, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3400000312119444}} {"text": "library(\"dplyr\")\nlibrary(\"shiny\")\nlibrary(\"leaflet\")\nlibrary(\"plotly\")\n\nwashington_covid <- read.csv(\"data/washington_unemployment/crosstab_real.csv\",\n stringsAsFactors = FALSE)\n\nclaims_data <- read.csv(\"data/washington_unemployment/initial_claims.csv\",\n stringsAsFactors = FALSE)\n\nclaims_data$Initial.Claims <- gsub(\",\", \"\", claims_data$Initial.Claims)\nclaims_data$Initial.Claims <- as.numeric(claims_data$Initial.Claims)\n\n#Converted all relevant columns from type char to type numeric\nwashington_covid$ADAMS <- as.numeric(washington_covid$ADAMS)\nwashington_covid$ASOTIN <- as.numeric(washington_covid$ASOTIN)\nwashington_covid$BENTON <- as.numeric(washington_covid$BENTON)\nwashington_covid$CHELAN <- as.numeric(washington_covid$CHELAN)\nwashington_covid$CLALLAM <- as.numeric(washington_covid$CLALLAM)\nwashington_covid$CLARK <- as.numeric(washington_covid$CLARK)\nwashington_covid$COLUMBIA <- as.numeric(washington_covid$COLUMBIA)\nwashington_covid$COWLITZ <- as.numeric(washington_covid$COWLITZ)\nwashington_covid$DOUGLAS <- as.numeric(washington_covid$DOUGLAS)\nwashington_covid$FERRY <- as.numeric(washington_covid$FERRY)\nwashington_covid$FRANKLIN <- as.numeric(washington_covid$FRANKLIN)\nwashington_covid$GARFIELD <- as.numeric(washington_covid$GARFIELD)\nwashington_covid$GRANT <- as.numeric(washington_covid$GRANT)\nwashington_covid$GRAYS.HARBOR <- as.numeric(washington_covid$GRAYS.HARBOR)\nwashington_covid$ISLAND <- as.numeric(washington_covid$ISLAND)\nwashington_covid$JEFFERSON <- as.numeric(washington_covid$JEFFERSON)\nwashington_covid$KING <- as.numeric(washington_covid$KING)\nwashington_covid$KITSAP <- as.numeric(washington_covid$KITSAP)\nwashington_covid$KITTITAS <- as.numeric(washington_covid$KITTITAS)\nwashington_covid$KLICKITAT <- as.numeric(washington_covid$KLICKITAT)\nwashington_covid$LEWIS <- as.numeric(washington_covid$LEWIS)\nwashington_covid$LINCOLN <- as.numeric(washington_covid$LINCOLN)\nwashington_covid$MASON <- as.numeric(washington_covid$MASON)\nwashington_covid$OKANOGAN <- as.numeric(washington_covid$OKANOGAN)\nwashington_covid$PACIFIC <- as.numeric(washington_covid$PACIFIC)\nwashington_covid$PEND.OREILLE <- as.numeric(washington_covid$PEND.OREILLE)\nwashington_covid$PIERCE <- as.numeric(washington_covid$PIERCE)\nwashington_covid$SAN.JUAN <- as.numeric(washington_covid$SAN.JUAN)\nwashington_covid$SKAGIT <- as.numeric(washington_covid$SKAGIT)\nwashington_covid$SKAMANIA <- as.numeric(washington_covid$SKAMANIA)\nwashington_covid$SNOHOMISH <- as.numeric(washington_covid$SNOHOMISH)\nwashington_covid$SPOKANE <- as.numeric(washington_covid$SPOKANE)\nwashington_covid$STEVENS <- as.numeric(washington_covid$STEVENS)\nwashington_covid$THURSTON <- as.numeric(washington_covid$THURSTON)\nwashington_covid$WAHKIAKUM <- as.numeric(washington_covid$WAHKIAKUM)\nwashington_covid$WALLA.WALLA <- as.numeric(washington_covid$WALLA.WALLA)\nwashington_covid$WASHINGTON <- as.numeric(washington_covid$WASHINGTON)\nwashington_covid$WHATCOM <- as.numeric(washington_covid$WHATCOM)\nwashington_covid$WHITMAN <- as.numeric(washington_covid$WHITMAN)\nwashington_covid$YAKIMA <- as.numeric(washington_covid$YAKIMA)\n\n#theme(axis.ticks.x = element_blank(), axis.text.x = element_blank())\n\n####################################################################\n\nimpacted_industry <- mutate(washington_covid, Total =\n rowSums(Filter(is.numeric, washington_covid),\n na.rm = TRUE))\n\nimpacted_industry <- impacted_industry %>% arrange(Industry) %>% mutate(Row = seq(1,94))\nindustries <- length(impacted_industry$Total)\n\nserver <- function(input, output) {\n output$plot <- renderPlotly({\n plot_data <- impacted_industry %>%\n filter(Row > input$industry_choice[1], Row < input$industry_choice[2] )\n p <- ggplot(plot_data) +\n geom_col(aes(x = Industry, y = Total, group=1)) +\n scale_y_continuous(limits = c(0, 35000)) +\n labs(\n title = \"Amount of Initial Claims Per Industry\",\n x = \"Industry\",\n y = \"Initial Claims\"\n ) + \n scale_x_discrete(labels=seq(1,94)) +\n theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 2))\n ggplotly(p)\n return(p)\n})\n}\n\nindustry_input <- sliderInput(\n inputId = \"industry_choice\",\n label = \"Please Choose a Range of Industries (1-94), all ordered by Alphabetical Order (A-Z)\",\n min = head(impacted_industry$Row, 1),\n max = industries,\n value = c(1,94)\n)\n\nwashington_page <- tabPanel(\n \"Washington Unemployment Data\",\n titlePanel(\"Washington Unemployment Data\"),\n sidebarLayout(\n sidebarPanel(industry_input),\n mainPanel(\n h1(\"Plot of Initial Claims Per Industry\"),\n plotlyOutput(\"plot\")\n )\n )\n)\n\nui <- navbarPage(\n \"Final Deliverable\",\n washington_page\n)\n\nshinyApp(ui = ui, server = server)\n\n###################################################################\n\n\nclaims_data$Initial.Claims <- gsub(\",\", \"\", claims_data$Initial.Claims)\nclaims_data$Initial.Claims <- as.numeric(claims_data$Initial.Claims)\nclaims_plot <- ggplot(data = claims_data, aes(x = Week, y = Initial.Claims, group=1)) +\n geom_line() +\n geom_point() +\n scale_y_continuous(limits = c(5000, 185000)) +\n labs(\n title = \"Amount of Initial Claims of Unemployment Per Week\",\n x = \"Week of Quarantine\",\n y = \"Initial Claims\"\n )\nwa_claims_plot <- ggplotly(claims_plot)\nprint(wa_claims_plot)\n", "meta": {"hexsha": "51afdaa69ac835cbab96aa5c2c2459046101deeb", "size": 5401, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/washington_shiny_page.r", "max_stars_repo_name": "info-201a-sp20/Unemployment-During-COVID-19", "max_stars_repo_head_hexsha": "ac872307110e0379a3df469ba824379742e2a0c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/washington_shiny_page.r", "max_issues_repo_name": "info-201a-sp20/Unemployment-During-COVID-19", "max_issues_repo_head_hexsha": "ac872307110e0379a3df469ba824379742e2a0c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/washington_shiny_page.r", "max_forks_repo_name": "info-201a-sp20/Unemployment-During-COVID-19", "max_forks_repo_head_hexsha": "ac872307110e0379a3df469ba824379742e2a0c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.5461538462, "max_line_length": 96, "alphanum_fraction": 0.7341233105, "num_tokens": 1533, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.34000003121194433}} {"text": "#https://cran.r-project.org/web/packages/pals/pals.pdf\n#https://cran.r-project.org/web/packages/pals/vignettes/pals_examples.html\n\nmakeDf = function(pal, group){\n return(data.frame(pal, group))\n}\n\nfirstUp <- function(x) {\n substr(x, 1, 1) = toupper(substr(x, 1, 1))\n return(x)\n}\n\n\npalOut = '../pals'\npalInfo = read.csv(\"pals_info.csv\", stringsAsFactors = F)\ncolnames(palInfo)\ngroups = unique(palInfo$group)\n\nfor(g in 1:length(groups)){\n print(groups[g])\n these = which(palInfo$group == groups[g])\n lines = vector()\n for(r in 1:length(these)){\n hex = eval(parse(text=paste0(palInfo[these[r],1],'(n=7)')))\n hex = paste0(\"\\'\",paste(hex, collapse=\"\\', \"),\"\\'\")\n lines = c(lines, paste0(palInfo[these[r],4],': {7:{[',hex,']}'))\n \n }\n lines = paste(lines, collapse = ',\\n')\n cat(lines)\n cat('\\n\\n\\n')\n}\n\n\nfor(i in 1:nrow(palInfo)){\n outFile = paste0(palOut,'/',palInfo$jsName[i],'.png')\n png(outFile, height = 3, width = 2.75, units = 'in', res=300)\n eval(parse(text=paste0('pal.bands(',palInfo$palName[i],', n=10000, labels=\"\")')))\n dev.off()\n}\n\n\nfiles = list.files(palOut, '.png', full.names=T)\nlength(files)\nfor(i in 1:length(files)){\n cmd = paste0('magick ',files[i],' -crop 417x25+263+403 +repage ',files[i])\n system(cmd)\n}\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "a70d8ba80c45d500f2583307e066008ff901e5e6", "size": 1261, "ext": "r", "lang": "R", "max_stars_repo_path": "misc/pals_maker.r", "max_stars_repo_name": "gee-community/ee-palettes", "max_stars_repo_head_hexsha": "6c2c25b94272efb065f34d8d088d468b68b3b77c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 175, "max_stars_repo_stars_event_min_datetime": "2019-01-19T00:40:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T09:28:08.000Z", "max_issues_repo_path": "misc/pals_maker.r", "max_issues_repo_name": "ytmartins/ee-palettes", "max_issues_repo_head_hexsha": "6c2c25b94272efb065f34d8d088d468b68b3b77c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "misc/pals_maker.r", "max_forks_repo_name": "ytmartins/ee-palettes", "max_forks_repo_head_hexsha": "6c2c25b94272efb065f34d8d088d468b68b3b77c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 39, "max_forks_repo_forks_event_min_datetime": "2019-02-27T01:30:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T11:53:59.000Z", "avg_line_length": 21.7413793103, "max_line_length": 83, "alphanum_fraction": 0.6225218081, "num_tokens": 415, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.339899834098418}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# set dataset \n\ndataset <- \"Singhal\"\n\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n\n# Rename \nmL <- mLGL\nytxt <- \"dGLS\"\nyint <- c(0.5,-0.5)\n\n\nmL <- mLBF\nytxt <- \"2ln(BF)\"\nyint <- c(10,-10)\n\n\n# Names \"TvS\", \"AIvSA\", \"AIvSI\", \"SAvAI\", \"SAvSI\", \"SIvAI\", \"SIvSA\", \"TvS_support\"\n\ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"#C2DF23FF\"\n\n\nquartz()\n# Set colors \ncolor_h0 <- color_TP\ncolor_h1 <- color_S\n\ngraph_general <- ggplot(data=mL) + \n geom_bar(stat = \"identity\", position = position_dodge()) +\n aes(x=reorder(Locus,-TvS,sum),y=TvS, color = TvS < 0, fill = TvS < 0) +\n theme(axis.text.x = element_blank(),\n axis.ticks.x = element_blank(),\n axis.text = element_text(size=16, color=\"black\"),\n panel.background = element_blank(),\n text = element_text(size=20),\n legend.position = \"none\",\n plot.title = element_text(hjust = 0.5)) + \n scale_color_manual(values=c(color_h0,color_h1)) + \n scale_fill_manual(values=c(color_h0,color_h1)) \n\n# Customize per dataset\n\n# only need to adjust the max limits of the graph based on the largest value\nmax(abs(min(mL$TvS)),abs(max(mL$TvS)))\nlimit <- 150\n#ytic <- c(seq(-limit,limit,10),0.5,-0.5)\n#ytic <- seq(-limit,limit,10)\n#ytic <- c(seq(-limit,limit,50),10,-10)\nytic <- c(seq(-limit,limit,20))\n\n\ngraph_custom <- graph_general + \n coord_cartesian(ylim=ytic) +\n scale_y_continuous(breaks = ytic) + \n ggtitle(paste(dataset,\"\\nToxPoly vs. Sclero\",sep=\"\")) + \n labs(y=ytxt,x='') +\n geom_hline(yintercept=yint,color=c(\"black\"), linetype=\"dashed\", size=0.5) \ngraph_custom\n\n# Write to file \n# change axis text to 24 element texts to 30 \nggsave(paste(dataset,\"_BF_Bar.pdf\",sep=\"\"), plot=graph_custom,width = 9.5, height = 6, units = \"in\", device = 'pdf',bg = \"transparent\")\n\nggsave(paste(dataset,\"_dGLS_Bar.pdf\",sep=\"\"), plot=graph_custom,width = 9.5, height = 6, units = \"in\", device = 'pdf',bg = \"transparent\")\n", "meta": {"hexsha": "5fbf695c17aadf5ee45485723bb6ef0947a6657b", "size": 2171, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/TvS_v0410/Graphs_LocusValues.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/TvS_v0410/Graphs_LocusValues.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/TvS_v0410/Graphs_LocusValues.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.4810126582, "max_line_length": 137, "alphanum_fraction": 0.6600644864, "num_tokens": 718, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.339899834098418}} {"text": "\n\ncontext(\"Test against sklearn's calibration_curve()\")\n\nread_pydata = function(py_path) {\n ## depending on if testing one at a time or running\n ## CTRL+SHIFT+T\n if(interactive()) {\n ## interactive + local use. devtools::test() interactively\n if (Sys.getenv(\"NOT_CRAN\") == \"true\") {\n path = \"../sklearn-compare/\"\n } else {\n path = \"tests/sklearn-compare/\"\n }\n } else {\n path = \"../sklearn-compare/\"\n }\n read.csv(paste0(path, py_path))\n}\n\n\npy_unf_clbr = read_pydata(\"unf_df.csv\")\npy_unf_clbr = py_unf_clbr[, c(2, 1)]\npy_qtl_clbr = read_pydata(\"qtl_df.csv\")\npy_qtl_clbr = py_qtl_clbr[, c(2, 1)]\n\ndata(two_class_example, package = \"yardstick\")\n\nr_unf_clbr = calibration_curve(two_class_example, truth, Class1, discretise_strategy = \"uniform\")\nr_qtl_clbr = calibration_curve(two_class_example, truth, Class1, discretise_strategy = \"quantile\")\n\nexpect_equal(r_unf_clbr, py_unf_clbr)\nexpect_equal(r_qtl_clbr, py_qtl_clbr)\n", "meta": {"hexsha": "cf83a09dfeef92e327c933d0f2c4846591947133", "size": 991, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-calibration-curve.r", "max_stars_repo_name": "chuvanan/calibcurve", "max_stars_repo_head_hexsha": "c623efecbaa2edb0c71233205cc680c1336071cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-06-19T01:50:39.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-21T16:13:21.000Z", "max_issues_repo_path": "tests/testthat/test-calibration-curve.r", "max_issues_repo_name": "chuvanan/calibcurve", "max_issues_repo_head_hexsha": "c623efecbaa2edb0c71233205cc680c1336071cd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test-calibration-curve.r", "max_forks_repo_name": "chuvanan/calibcurve", "max_forks_repo_head_hexsha": "c623efecbaa2edb0c71233205cc680c1336071cd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1470588235, "max_line_length": 98, "alphanum_fraction": 0.6730575177, "num_tokens": 289, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.626124191181315, "lm_q1q2_score": 0.339899834098418}} {"text": "library(dplyr)\noptions(scipen=999)\n\nMode <- function(x){\n\tux <- unique(x)\n\treturn(ux[which.max(tabulate(match(x, ux)))])\n}\n\nparse <- function(str){\n\treturn(as.numeric(strsplit(str, split=\",\")[[1]]))\n}\n\ntest = read.csv(\"test.csv\", stringsAsFactors=FALSE)\n# print(head(test))\n# for(i in 1:nrow(test)){\n# \tprint(test$Sequence[i])\n# \tprint(Mode(parse(test$Sequence[i])))\n# \tbreak\n# }\n\n# x = c(1,2,2,2)\n# print(Mode(x))\n\ntest %>%\nrowwise() %>%\nprint(Sequence) %>%\nmutate(Last = Mode(parse(Sequence))) %>%\nselect(Id, Last) %>%\narrange(Id) %>%\nwrite.csv(., \"mode_benchmark.csv\", row.names=FALSE)", "meta": {"hexsha": "a77c86efe252fedbd7773e7729347abbbfe0560b", "size": 588, "ext": "r", "lang": "R", "max_stars_repo_path": "Mode_Benchmark/submit.r", "max_stars_repo_name": "aayush26/Integer_Sequence_Learning", "max_stars_repo_head_hexsha": "06121529f4947c29580b30078d14955f9ccd46b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Mode_Benchmark/submit.r", "max_issues_repo_name": "aayush26/Integer_Sequence_Learning", "max_issues_repo_head_hexsha": "06121529f4947c29580b30078d14955f9ccd46b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Mode_Benchmark/submit.r", "max_forks_repo_name": "aayush26/Integer_Sequence_Learning", "max_forks_repo_head_hexsha": "06121529f4947c29580b30078d14955f9ccd46b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6, "max_line_length": 51, "alphanum_fraction": 0.6343537415, "num_tokens": 186, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.5428632831725051, "lm_q1q2_score": 0.33989983409841795}} {"text": "library(kimisc)\r\nlibrary(tidyverse)\r\nsource('generalCode/estimationFunctions.r')\r\n\r\n#if(!exists('ya')) ya <- FALSE\r\n\r\nabvs <- c(HS='hs',Associates='cc',Bachelors='bach')\r\n\r\n\r\nest1 <- function(edLev,des,raw=FALSE){\r\n abv <- if(edLev%in%names(abvs)) abvs[edLev] else 'lev'\r\n subsets <- list('AGEP','ageRange','raceEth','SEX',c('SEX','raceEth'))\r\n\r\n dat <- des$variables[,c('DEAR','attain',unique(unlist(subsets)))]\r\n dat$pwgtp <- des$pweights\r\n dat <- cbind(dat,des$repweights)\r\n dat[[abv]] <- dat$attain>=edLev\r\n\r\n if(raw){\r\n dat$ya <- dat$AGEP<30\r\n subsets <- c('DEAR','ya',subsets)\r\n }\r\n subsets <- lapply(subsets,function(sss)\r\n if(raw) unique(c('DEAR',sss)) else unique(c('DEAR','AGEP',sss)))\r\n\r\n outfun <- function(sss){\r\n out <- dat%>%group_by(!!! syms(sss))%>%do(x=factorProps(abv,.))\r\n ests <- out$x\r\n out$x <- NULL\r\n out <- bind_cols(out,do.call('bind_rows',ests))\r\n out <- out[,-grep('FALSE',colnames(out))]\r\n out <- rename(out,`I(attain >= edLev)TRUE`=`% TRUE`,se2=`TRUE SE`,Freq=n)\r\n out\r\n }\r\n\r\n\r\n out <- lapply(subsets, outfun)\r\n\r\n names(out) <- paste0(abv,c(if(raw) c('Tot','25.29','ByAge') else 'Tot',\r\n 'ByAgeCat','Race','Sex','RaceSex'))\r\n\r\n if(!raw) out[[paste0(abv,'25.29')]] <- subset(out[[paste0(abv,'Tot')]],AGEP<30)\r\n\r\n out\r\n}\r\n\r\n\r\n\r\n\r\nsampSize <- function(des){\r\n out <- list()\r\n out$bachByAge <- out$hsByAge <- xtabs(~AGEP+DEAR,des$variables,drop=TRUE)\r\n out$bach25.29 <- out$hs25.29 <- xtabs(~DEAR+I(AGEP<30),des$variables,drop=TRUE)\r\n out$bachTot <- out$hsTot <- xtabs(~DEAR,des$variables,drop=TRUE)\r\n out$bachByAgeCat <- out$hsByAgeCat <- xtabs( ~ageRange+DEAR,des$variables,drop=TRUE)\r\n\r\n out$bachRace <- out$hsRace <- xtabs(~raceEth+DEAR,des$variables,drop=TRUE)\r\n\r\n out$bachSex <- out$hsSex <- xtabs(~SEX+DEAR,des$variables,drop=TRUE)\r\n\r\n out$bachRaceSex <- out$hsRaceSex <- xtabs(~SEX+raceEth+DEAR,des$variables,drop=TRUE)\r\n\r\n out\r\n}\r\n\r\nyearInfo <- function(year,des){\r\n print(year)\r\n if(missing(des)) load(paste0(DIR,'design',year,'.RData'))\r\n des <- subset(des,AGEP>24 & AGEP<65) ## changed this to change age range\r\n# des <- update(des,raceEth=ifelse(raceEth%in%c('American Indian','Asian/PacIsl'),'Other',raceEth))\r\n\r\n levs <- c('HS','Associates','Bachelors')\r\n\r\n outRaw <- do.call('c',lapply(levs,est1,des=des,raw=TRUE))\r\n outAdj <- do.call('c',lapply(levs,est1,des=des,raw=FALSE))\r\n outSS <- sampSize(des)\r\n\r\n rm(des);gc()\r\n\r\n list(outRaw=outRaw,outAdj=outAdj,outSS=outSS)\r\n}\r\n\r\n\r\n", "meta": {"hexsha": "dd6e90b0a8b361c0341eaecdb2ed4ba3ce685a88", "size": 2601, "ext": "r", "lang": "R", "max_stars_repo_path": "R/estByYear.r", "max_stars_repo_name": "nationalDeafCenter/educationalAttainmentTrends", "max_stars_repo_head_hexsha": "86a81c98dff19b6cf9c5a56733f3abb830db7cbd", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/estByYear.r", "max_issues_repo_name": "nationalDeafCenter/educationalAttainmentTrends", "max_issues_repo_head_hexsha": "86a81c98dff19b6cf9c5a56733f3abb830db7cbd", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/estByYear.r", "max_forks_repo_name": "nationalDeafCenter/educationalAttainmentTrends", "max_forks_repo_head_hexsha": "86a81c98dff19b6cf9c5a56733f3abb830db7cbd", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-01T17:53:14.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-01T17:53:14.000Z", "avg_line_length": 30.9642857143, "max_line_length": 103, "alphanum_fraction": 0.5959246444, "num_tokens": 859, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3398998265238403}} {"text": "library(plyr)\nlibrary(ggplot2)\n\nall_data <- data.frame(V1=integer(), V2=integer(),\n V3=integer(), V4=integer(),\n middlebox=character())\n\nnetfilter <- read.table(\"netfilter-latency.txt\")\nnetfilter[\"middlebox\"] <- \"NetFilter\"\nvignat <- read.table(\"vignat-latency.txt\")\nvignat[\"middlebox\"] <- \"VigNAT\"\ncounted_chains <- read.table(\"counted-chains-latency.txt\")\ncounted_chains [\"middlebox\"] <- \"counted-chains\"\nnop <- read.table(\"nop-latency.txt\")\nnop[\"middlebox\"] <- \"NOP\"\nunverified <- read.table(\"unverified-latency.txt\")\nunverified[\"middlebox\"] <- \"DPDK-unverified\"\n\nall_data <- rbind(all_data, netfilter)\nall_data <- rbind(all_data, vignat)\nall_data <- rbind(all_data, counted_chains)\nall_data <- rbind(all_data, nop)\nall_data <- rbind(all_data, unverified)\n\ncbbPalette <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\npd <- position_dodge(2)\n\np <- ggplot(all_data, aes(x=V1/1e3, y=V3/1e3,\n group=middlebox,\n color=middlebox,\n shape=middlebox)) +\n geom_point(size=3,position=pd) +\n geom_line() +\n geom_errorbar(aes(ymin=(V3-V4)/1e3, ymax=(V3+V4)/1e3), width=.01,\n position=pd) +\n labs(title=\"1 way latency for ~8Kpkt/s. No churns\") +\n xlab(\"# concurrent flows (K)\") +\n ylab(bquote(\"1-way latency, \"+mu+\"s\")) +\n theme_bw() +\n expand_limits(x=0,y=0) +\n coord_cartesian(ylim=c(0,25)) +\n theme( plot.margin = unit( c(0,0,0,0) , \"in\" ) )\n\nggsave(filename=\"latency.png\")\nprint(p)", "meta": {"hexsha": "5f80868655f02c1d190e700b59c3857fb695163b", "size": 1596, "ext": "r", "lang": "R", "max_stars_repo_path": "bench/util/plot/lat.r", "max_stars_repo_name": "pmdm56/vigor", "max_stars_repo_head_hexsha": "0a65733a2b7bf48fc7d6071ea89c1af36f1cba80", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2017-08-23T17:01:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T06:03:19.000Z", "max_issues_repo_path": "dpdk-nfs/nf/testbed/hard/util/plot/lat.r", "max_issues_repo_name": "dslab-epfl/pix", "max_issues_repo_head_hexsha": "bab9226ad307ec7f1f548e00f216de9c80b7be48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2019-10-03T10:33:19.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-10T13:06:01.000Z", "max_forks_repo_path": "dpdk-nfs/nf/testbed/hard/util/plot/lat.r", "max_forks_repo_name": "dslab-epfl/pix", "max_forks_repo_head_hexsha": "bab9226ad307ec7f1f548e00f216de9c80b7be48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 10, "max_forks_repo_forks_event_min_datetime": "2017-09-28T13:14:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-26T09:18:11.000Z", "avg_line_length": 34.6956521739, "max_line_length": 103, "alphanum_fraction": 0.6109022556, "num_tokens": 493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3398998265238403}} {"text": "#'\n#'@title Plot comparison of observed and model size comps averaged over time\n#'\n#'@param sizebins - \n#'@param obs.comps - \n#'@param prd.comp - \n#'@param cols - \n#'@param ymx - \n#'@param xlab - \n#'@param ylab -\n#'@param pch - \n#'@param lty - \n#'@param lwd - \n#'@param clr - \n#'@param CI - \n#'@param bar.width - \n#'@param addToPlot - \n#'\n#'@export\n#'@import graphics\n#'\n#source(\"plotErrorBars.V.r\")\nplotMeanSizeComps<-function(size.bins,\n obs.comp,\n prd.comp,\n cols=NULL,\n ymx=NULL,\n xlab=\"year\",\n ylab=\"size mmCW\",\n pch=21,\n lty=1,\n lwd=1,\n clr='black',\n CI=0.8,\n bar.width=1,\n addToPlot=FALSE){\n #set columns to extract\n if (is.null(cols)){cols<-1:ncol(obs.comp)}\n \n #calculate means and std. dev.s by size bin\n mns.obs<-colMeans(obs.comp,na.rm=TRUE)[cols];\n std.obs<-apply(obs.comp,2,sd,na.rm=TRUE)[cols]/sqrt(length(cols));#standard errors of mean\n mns.prd<-colMeans(prd.comp,na.rm=TRUE)[cols];\n std.prd<-apply(prd.comp,2,sd,na.rm=TRUE)[cols]/sqrt(length(cols));#standard errors of mean\n \n if (!addToPlot){\n if (is.null(ymx)){\n ymx<-max(mns.obs+std.obs);\n }\n plot(length.bins, mns.obs,\n type=\"n\",pch=pch,col=clr,\n ylim=c(0,ymx),\n xlab=xlab,ylab=ylab);\n }\n plotErrorBars.V(size.bins,\n mns.obs,\n sigma=std.obs,\n CI=CI,\n width=bar.width,\n pch=pch,col=clr)\n points(size.bins,mns.obs,pch=pch,col=clr)\n lines(size.bins,mns.prd,lty=lty,lwd=lwd,col=clr)\n}", "meta": {"hexsha": "c5d2fc80728cf0b2b3f8fdc0d72db978193f84ab", "size": 1908, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plotMeanSizeComps.r", "max_stars_repo_name": "wStockhausen/rTCSAM2015", "max_stars_repo_head_hexsha": "7cfbe7fd5573486c6d5721264c9d4d6696830a31", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/plotMeanSizeComps.r", "max_issues_repo_name": "wStockhausen/rTCSAM2015", "max_issues_repo_head_hexsha": "7cfbe7fd5573486c6d5721264c9d4d6696830a31", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2016-01-20T15:20:42.000Z", "max_issues_repo_issues_event_max_datetime": "2016-01-20T15:30:33.000Z", "max_forks_repo_path": "R/plotMeanSizeComps.r", "max_forks_repo_name": "wStockhausen/rTCSAM2015", "max_forks_repo_head_hexsha": "7cfbe7fd5573486c6d5721264c9d4d6696830a31", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2857142857, "max_line_length": 94, "alphanum_fraction": 0.4669811321, "num_tokens": 507, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3398998265238403}} {"text": "i=0\nwhile(i<=10)\n{\n i=i+1\n if(i==5)\n break\n print(i)\n}", "meta": {"hexsha": "ea6b683c0df7a910965fd0d5e8e371cf44b0d374", "size": 60, "ext": "r", "lang": "R", "max_stars_repo_path": "Basics/control_structures/break.r", "max_stars_repo_name": "DivyaMaddipudi/R-Programming-Basics", "max_stars_repo_head_hexsha": "28546e2f159b98bd8e94503b2a2e07aef68e24f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Basics/control_structures/break.r", "max_issues_repo_name": "DivyaMaddipudi/R-Programming-Basics", "max_issues_repo_head_hexsha": "28546e2f159b98bd8e94503b2a2e07aef68e24f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Basics/control_structures/break.r", "max_forks_repo_name": "DivyaMaddipudi/R-Programming-Basics", "max_forks_repo_head_hexsha": "28546e2f159b98bd8e94503b2a2e07aef68e24f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 7.5, "max_line_length": 12, "alphanum_fraction": 0.4666666667, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.3398998265238403}} {"text": "\npaper<-F # output on paper (=TRUE) or screen (=FALSE)\n\nPortrait<-F # graphical output orientation\ninclude.terminal.year <- T # plot terminal year (last assessment year +1) as well?\ninclude.last.assess.year.recruit <- T # plot recruits terminal year as well?\n\nfirst.year<- 1974 #first year on plot, negative value means value defined by data\nlast.year<- 2011 #last year on plot\n\n##########################################################################\n\ncleanup()\n \nif (paper) dev<-\"wmf\" else dev<-\"screen\"\n\nInit.function()\n\ndat<-Read.summary.data.areas(extend=include.terminal.year,read.init.function=F)\ndat<-subset(dat,Year<=last.year )\nif (first.year>0) dat<-subset(dat,Year>=first.year )\n\n# Dead in numbers caused by predation\nnox<-2; noy<-3;\nnoxy<-nox*noy\n\nfor (sp in (first.VPA:nsp)){\n sp.name<-sp.names[sp]\n\n newplot(dev,nox,noy,filename=paste(file.name,'_',sp.name,sep=''),Portrait=Portrait)\n par(mar=c(3,4,3,2))\n\n s<-subset(dat,Species.n==sp)\n for (a in (SMS.control@first.age :min(SMS.control@species.info[sp,'last-age'],5))){\n s1<-subset(s,Age==a )\n if (sum(s1$DeadM2)>0) {\n deadM2<-tapply(s1$DeadM2,list(s1$Area,s1$Year),sum)/1000\n cat(\"\\nsp:\",sp,\" Age:\",a,\"\\n\"); print(ftable(deadM2))\n barplot(deadM2,space=1,xlab='',ylab='millions',main=paste(sp.name,' age:',a,sep=''),ylim=c(0,max(apply(deadM2,2,sum))))\n }\n }\n}\n \n##############################\n# M2 by quarter\ncleanup()\nnox<-2; noy<-3;\nnoxy<-nox*noy\n\ndat0<-Read.summary.data(extend=include.terminal.year,read.init.function=F)\ndat0<-subset(dat0,Year>=min(dat$Year) & Year<=max(dat$Year),select=c(Species,Year,Quarter,Species.n,Age,M2))\ndat0$Area<-0\n\n\nfor (sp in (first.VPA:nsp)){\n sp.name<-sp.names[sp]\n s <-subset(dat, Species.n==sp,select=c(Area,Species,Year,Quarter,Species.n,Age,M2))\n s0<-subset(dat0,Species.n==sp,select=c(Area,Species,Year,Quarter,Species.n,Age,M2))\n for (q in (1:SMS.control@last.season)) {\n s1<-subset(s,Quarter==q )\n newplot(dev,nox,noy,filename=paste(file.name,'_',sp.name,sep=''),Portrait=Portrait)\n par(mar=c(3,4,3,2))\n\n for (a in (SMS.control@first.age :min(SMS.control@species.info[sp,'last-age'],5))){\n s2<-subset(s1,Age==a )\n s01<-subset(s0,Age==a & Quarter==q)\n s2<-rbind(s01,s2)\n \n b<-tapply(s2$M2,list(s2$Year,s2$Area),sum)\n if (sum(b,na.rm=T)>=0.01) {\n #cat(\"\\nsp:\",sp,\" Age:\",a,\"\\n\"); print(ftable(b))\n\n y<-as.numeric(dimnames(b)[[1]])\n plot(y,b[,1],main=paste(sp.name,\" Q:\",q,\" Age:\",a),xlab=\"\",ylab='local M2',type='b',pch='0',lwd=1.5,ylim=c(0,max(b,na.rm=T)))\n for (aa in (2:(dim(b)[2]))) if(max(b[,aa],na.rm=T)>0.001) lines(y,b[,aa],col=aa,lwd=1.5,type='b',pch=as.character(aa-1))\n }\n }\n }\n}\n\n##############################\n# sum of quarterly M2\ncleanup()\nnox<-2; noy<-2;\nnoxy<-nox*noy\n\ndat0<-Read.summary.data(extend=include.terminal.year,read.init.function=F)\ndat0<-subset(dat0,Year>=min(dat$Year) & Year<=max(dat$Year),select=c(Species,Year,Quarter,Species.n,Age,M2))\ndat0$Area<-0\ncleanup()\npalette(rainbow(4))\n\nfor (sp in (first.VPA:nsp)){\n sp.name<-sp.names[sp]\n s <-subset(dat, Species.n==sp,select=c(Area,Species,Year,Quarter,Species.n,Age,M2))\n s0<-subset(dat0,Species.n==sp,select=c(Area,Species,Year,Quarter,Species.n,Age,M2))\n newplot(dev,nox,noy,filename=paste(file.name,'_',sp.name,sep=''),Portrait=Portrait)\n par(mar=c(3,4,3,2))\n\n for (a in (SMS.control@first.age :min(SMS.control@species.info[sp,'last-age'],3))){\n s2<-rbind(subset(s,Age==a ),subset(s0,Age==a))\n\n b<-tapply(s2$M2,list(s2$Year,s2$Area),sum)\n if (sum(b,na.rm=T)>=0.01) {\n y<-as.numeric(dimnames(b)[[1]])\n plot(y,b[,1],main=paste(sp.name,\" Age:\",a),xlab=\"\",ylab='local M2',type='b',pch='0',lwd=2.5,ylim=c(0,max(b,na.rm=T)))\n # for (aa in (2:(dim(b)[2]))) if(max(b[,aa],na.rm=T)>0.001) lines(y,b[,aa],col=aa,lwd=1.5,type='b',pch=as.character(aa-1))\n pchs<-c('5','6','8')\n for (aa in (2:(dim(b)[2]))) if(max(b[,aa],na.rm=T)>0.001) lines(y,b[,aa],col=c('grey','red','blue','green')[aa],lwd=2.0,type='b',pch=pchs[aa-1])\n\n }\n }\n}\n", "meta": {"hexsha": "1efe12bd92ec409f42f5d4aa027fa79029c8e5a7", "size": 4275, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_summary_areas.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_summary_areas.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_summary_areas.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5, "max_line_length": 152, "alphanum_fraction": 0.5838596491, "num_tokens": 1420, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.33989981894926247}} {"text": "# Jinliang Yang\n# purpose: calculate the GCA and SCA\n# updated: 3.7.2012\n# .7\n\n#################\nsetwd(\"~/Documents/Heterosis_GWAS/pheno2011\")\nrawdata <- read.csv(\"pheno_diallel_master_raw_040612.csv\")\nrawdata <- subset(trait, !is.na(pop2))\n# 2461 22\n\ndata2SAS <- function(input=trait, mytrait=\"TKW\"){\n tkw <- input[, c(\"Farm\", \"pop1\", \"pop2\", \"Genotype2\",\"Barcode\",mytrait)];\n names(tkw) <- c(\"block\",\"female\", \"male\",\"cross\",\"Tree\",\"Height\");\n outfile <- paste(\"pheno_diallel_\", mytrait, \"_4SAS.csv\", sep=\"\");\n write.table(tkw, outfile, row.names=FALSE, col.names=FALSE, quote=FALSE, sep=\",\")\n}\n\n#############################\ndata2SAS(input=rawdata, mytrait=\"KRN\")\ndata2SAS(input=rawdata, mytrait=\"KC\")\ndata2SAS(input=rawdata, mytrait=\"AKW\")\ndata2SAS(input=rawdata, mytrait=\"TKW\")\ndata2SAS(input=rawdata, mytrait=\"CD\")\ndata2SAS(input=rawdata, mytrait=\"CL\")\ndata2SAS(input=rawdata, mytrait=\"CW\")\n\n\n############################\nsetwd(\"/Users/yangjl/Documents/Heterosis_GWAS/pheno2011/temoutput\")\nsca1 <- read.csv(\"SCA_AKW_BLUP.csv\")\nsca2 <- read.csv(\"SCA_KC_BLUP.csv\")\nsca3 <- read.csv(\"SCA_KRN_BLUP.csv\")\nsca4 <- read.csv(\"SCA_TKW_BLUP.csv\")\nsca5 <- read.csv(\"SCA_CD_BLUP.csv\")\nsca6 <- read.csv(\"SCA_CW_BLUP.csv\")\nsca7 <- read.csv(\"SCA_CL_BLUP.csv\")\n\nscatot <- merge(sca1[,2:3], sca2[, 2:3], by=\"cross\")\nscatot <- merge(scatot, sca3[, 2:3], by=\"cross\")\nscatot <- merge(scatot, sca4[, 2:3], by=\"cross\")\nscatot <- merge(scatot, sca5[, 2:3], by=\"cross\")\nscatot <- merge(scatot, sca6[, 2:3], by=\"cross\")\nscatot <- merge(scatot, sca7[, 2:3], by=\"cross\")\n\nidx <- grep(\"x\", scatot$cross)\n\nSCA <- scatot[idx,]\nGCA <- scatot[-idx,]\n\nnames(GCA) <- c(\"Genotype\", \"AKW\", \"KC\", \"KRN\", \"TKW\", \"CD\", \"CW\", \"CL\")\nnames(SCA) <- c(\"Genotype\", \"AKW\", \"KC\", \"KRN\", \"TKW\", \"CD\", \"CW\", \"CL\")\n\nsetwd(\"/Users/yangjl/Documents/Heterosis_GWAS/pheno2011/\")\nwrite.table(GCA, \"cache/pheno_diallel_master_BLUP_GCA.csv\", sep=\",\", row.names=FALSE, quote=FALSE)\nwrite.table(SCA, \"cache/pheno_diallel_master_BLUP_SCA.csv\", sep=\",\", row.names=FALSE, quote=FALSE)\n\n", "meta": {"hexsha": "78c873776c08691c6834bfca80336daa969dfcd3", "size": 2034, "ext": "r", "lang": "R", "max_stars_repo_path": "profiling/pheno2011/02-D.pheno_diallel_SCA_SAS.r", "max_stars_repo_name": "yangjl/Heterosis-GWAS", "max_stars_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-04-16T08:27:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-31T13:00:43.000Z", "max_issues_repo_path": "profiling/pheno2011/02-D.pheno_diallel_SCA_SAS.r", "max_issues_repo_name": "yangjl/Heterosis-GWAS", "max_issues_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_issues_repo_licenses": ["RSA-MD"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "profiling/pheno2011/02-D.pheno_diallel_SCA_SAS.r", "max_forks_repo_name": "yangjl/Heterosis-GWAS", "max_forks_repo_head_hexsha": "454208509c22b1269f17ba63452ef19a9c3d13f8", "max_forks_repo_licenses": ["RSA-MD"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-01-03T14:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-03T01:34:08.000Z", "avg_line_length": 35.0689655172, "max_line_length": 98, "alphanum_fraction": 0.651425762, "num_tokens": 746, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6261241632752915, "lm_q1q2_score": 0.33989981894926247}} {"text": "# \n# getProjRaster<-function(data, res){\n# \tgrid<-getRegularGrid(data,res)\n# \t\n# \n# \n# }\n# \n# llon<-length(sort(unique(sp_pj$lon)))\n# lon_large<-sort(rep(unique(grid$lon),length.out=llon))\n# lon_table<-cbind(sort(unique(sp_pj$lon)),lon_large)\n# llat<-length(sort(unique(sp_pj$lat)))\n# lat_large<-sort(rep(unique(grid$lat),length.out=llat))\n# lat_table<-cbind(sort(unique(sp_pj$lat)),lat_large)\n# \n# # plot(lon_table[,1][1:100],type=\"l\")\n# # lines(lon_table[,2])\n# # x11()\n# # plot(lat_table[,1],type=\"l\")\n# # lines(lat_table[,2])\n# # lon<-array(NA,21632)\n# # lat<-array(NA,21632)\n# # val<-array(NA,21632)\n# # k<-1\n# # for(j in 1:length(unique(grid$lat))){\n# # \tfor(i in 1:length(unique(grid$lon))){\n# # \t\tlon[k]<-grid$lon[i]\n# # \t\tlat[k]<-grid$lat[j]\n# # \t\tdis<-(sp_pj$lon-grid$lon[i])^2+(sp_pj$lat-grid$lat[j])^2\n# # \t\t\n# # \t\t\n# # \n# # \t\tval[k]<-sp_pj[dis==min(dis),3]\n# # \t\tcat(lon[k],lat[k],sp_pj$lon[dis==min(dis),],\"\\n\")\n# # \t\tk<-k+1\n# # \t\t\n# # \t}\n# # }\n# p<-data.frame(lon,lat,val)\n# \n# # for(i in 1:length(sp_pj$lon)){\n# # \tlon_new<-lon_table[,2][lon_table[,1]==sp_pj$lon[i]]\n# # \t\n# # \tlat_new<-lat_table[,2][lat_table[,1]==sp_pj$lat[i]]\n# # \t#out[out$lon==lon_new&out$lat==lat_new,3]<-sp_pj$val[i]\n# # \n# # }\n# # \n# \n# # out2<-out\n# # for(i in 1:length(out[,1])){\n# # \tif(is.na(out2[,3][i])){\n# # \t\tout2[,3][i]<-out2[,3][i-1]\n# # \t}\n# # }\n# names(p)<-c(\"lon\",\"lat\",\"val\")\n# ggplot(p,aes(lon,lat,fill=val))+geom_raster()+scale_alpha_manual(values=c(0,1),guide=\"none\")", "meta": {"hexsha": "429cfa84414908366b932de07f9b06c991dc75d9", "size": 1481, "ext": "r", "lang": "R", "max_stars_repo_path": "R/recycle/getProjRaster.r", "max_stars_repo_name": "sinanshi/visotmed", "max_stars_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T02:17:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T10:32:36.000Z", "max_issues_repo_path": "R/recycle/getProjRaster.r", "max_issues_repo_name": "sinanshi/visotmed", "max_issues_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/recycle/getProjRaster.r", "max_forks_repo_name": "sinanshi/visotmed", "max_forks_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.9824561404, "max_line_length": 95, "alphanum_fraction": 0.571910871, "num_tokens": 598, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3398790179657902}} {"text": "setwd('/home/pc-752828/Dev/results-search-master/results-csv')\n\nlibrary(dplyr)\nlibrary(gridExtra)\nlibrary(grid)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(extrafont)\nlibrary(dplyr)\nlibrary('EnvStats')\nlibrary(gtable)\n\ndata <- read.csv(file = 'output-n.out', sep = ',', header = T)\n\ndata_f1 <- data[data['ite'] == 20 & data['target'] == 0,]\ndata_f2 <- data[data['ite'] == 20 & data['target'] == 1,]\ndata_f3 <- data[data['ite'] == 20 & data['target'] == 2,]\ndata_f4 <- data[data['ite'] == 20 & data['target'] == 6,]\n\nnrow(data_f3)\nnrow(data_f1)\nnrow(unique(data_f1['name']))\nnrow(unique(data_f3['name']))\n\nunique(data_f3['name'])\ndata_f4a <- data_f4 %>% filter(grepl('-A', name))\ndata_f3a <- data_f3 %>% filter(grepl('-A', name))\ndata_f2a <- data_f2 %>% filter(grepl('-A', name))\ndata_f1a <- data_f1 %>% filter(grepl('-A', name))\n\ndata_f4b <- data_f4 %>% filter(grepl('-B', name))\ndata_f3b <- data_f3 %>% filter(grepl('-B', name))\ndata_f2b <- data_f2 %>% filter(grepl('-B', name))\ndata_f1b <- data_f1 %>% filter(grepl('-B', name))\n\ndata_f4c <- data_f4 %>% filter(grepl('-C', name))\ndata_f3c <- data_f3 %>% filter(grepl('-C', name))\ndata_f2c <- data_f2 %>% filter(grepl('-C', name))\ndata_f1c <- data_f1 %>% filter(grepl('-C', name))\n\nvalue <- function(x, myname) {\n mym = mean(x)\n mysd = sd(x)\n print(mysd)\n mysqrt = sqrt(length(x))\n return(c(mn = mym, sd = mysd/mysqrt, name=myname))\n}\n\nrun_plot <- function(data, title) {\n bo1 <- aggregate(x = data$best,\n by = list(data$name),\n FUN = function(x) return(value(x, title)))\n bo1 <- as.data.frame(as.list(bo1))\n bo1$x.mn = as.numeric(as.character(bo1$x.mn))\n bo1$x.sd = as.numeric(as.character(bo1$x.sd))\n return(bo1)\n}\n\nbo1 <- run_plot(data_f1a, \"BO-6rnd-EIdef\")\nbo2 <- run_plot(data_f2a, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo4 <- run_plot(data_f4a, \"BO com Fun\u00e7\u00e3o de Aquisi\u00e7\u00e3o Nova\")\nbo3 <- run_plot(data_f3a, \"PB3Opt\")\nbo <- rbind(bo1, bo3)\n\nggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n coord_cartesian(ylim=c(0,3))+\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.8, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n labs(fill = \"Modo de Busca\", x=\"Entrada A\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(.95, .95),legend.position=c(.95, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))+ ggtitle('b) Zoom da Figura a')\n\n\nbo1 <- run_plot(data_f1a, \"BO-6rnd-EIdef\")\nbo2 <- run_plot(data_f2a, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo4 <- run_plot(data_f4a, \"BO com Fun\u00e7\u00e3o de Aquisi\u00e7\u00e3o Nova\")\nbo3 <- run_plot(data_f3a, \"PB3Opt\")\nbo <- rbind(bo1, bo3)\n\np1 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n #coord_cartesian(ylim=c(1,3))+\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.8, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n labs(fill = \"Modo de Busca\", x=\"Entrada A\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(.95, .95),legend.position=c(.95, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))+ ggtitle('a) Melhor Custo Encontrado')\n\n\n\ngrid.arrange(p1, p2, nrow = 1, ncol=2, top=\"\", bottom=\"Carga de Trabalho\", \n left=\"M\u00e9dia do Melhor Custo Normalizado\")\n\n\n\nbo1 <- run_plot(data_f1b, \"BO-6rnd-EIdef\")\nbo2 <- run_plot(data_f2b, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo4 <- run_plot(data_f4b, \"BO com Fun\u00e7\u00e3o de Aquisi\u00e7\u00e3o Nova\")\nbo3 <- run_plot(data_f3b, \"PB3Opt\")\nbo <- rbind(bo1, bo3)\n\np2 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.8, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n labs(fill = \"Modo de Busca\", x=\"Carga de Trabalho com Entrada B\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(.5, .5),legend.position=c(.5, .5),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))\n\n\nbo1 <- run_plot(data_f1c, \"BO-6rnd-EIdef\")\nbo2 <- run_plot(data_f2c, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo4 <- run_plot(data_f4c, \"BO com Fun\u00e7\u00e3o de Aquisi\u00e7\u00e3o Nova\")\nbo3 <- run_plot(data_f3c, \"PB3Opt\")\nbo <- rbind(bo1, bo3)\n\np3 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.8, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n labs(fill = \"Modo de Busca\", x=\"Carga de Trabalho com Entrada C\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(.5, .5),legend.position=c(.5, .5),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))\n\n\n\ngrid.arrange(p1, p2, p3, nrow = 3, ncol=1, top=\"\", bottom=\"Carga de Trabalho\", \n left=\"M\u00e9dia do Melhor Custo Normalizado\")\n\n\n\n\n\n\n\n\n\n #scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n #scale_y_continuous(minor_breaks = seq(0, 800, 100))+ ggtitle('a) Melhor Custo Encontrado')\n #coord_cartesian(ylim=c(1,1.5))\n #theme(legend.position = \"top\",\n # axis.title.x=element_blank(),\n # axis.title.y=element_blank(),\n # axis.text.y=element_blank(),\n # axis.ticks.y=element_blank())\n#dev.off()\n\nbo1 <- run_plot(data_f1a, \"BO com In\u00edcio Aleat\u00f3rio\")\nbo2 <- run_plot(data_f2a, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo3 <- run_plot(data_f3a, \"Abordagem Proposta\")\nbo <- rbind(bo1, bo3)\n#pdf(\"rplot.pdf\") \np1 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.4, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n theme(legend.position = \"none\",\n axis.title.x=element_blank(),\n axis.title.y=element_blank(),\n axis.text.y=element_blank(),\n axis.ticks.y=element_blank())\n# labs(fill = \"Modo de Busca\", x=\"Carga de Trabalho\", y=\"M\u00e9dia do Melhor Custo Normalizado\")\n#dev.off()\n\nbo1 <- run_plot(data_f1b, \"BO com In\u00edcio Aleat\u00f3rio\")\nbo2 <- run_plot(data_f2b, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo3 <- run_plot(data_f3b, \"Abordagem Proposta\")\nbo <- rbind(bo1, bo3)\n#pdf(\"rplot.pdf\") \np2 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.4, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n theme(legend.position = \"none\",\n axis.title.x=element_blank(),\n axis.title.y=element_blank(),\n axis.text.y=element_blank(),\n axis.ticks.y=element_blank())\n# labs(fill = \"Modo de Busca\", x=\"none\", y=\"none\")\n#dev.off()\n\nbo1 <- run_plot(data_f1a, \"BO com In\u00edcio Aleat\u00f3rio\")\nbo2 <- run_plot(data_f2a, \"BO com In\u00edcio Estrat\u00e9gico\")\nbo3 <- run_plot(data_f3a, \"Abordagem Proposta\")\nbo <- rbind(bo1, bo3)\np3 <- ggplot(bo, aes(x=Group.1, y=x.mn, fill = x.name))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7)+\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+\n expand_limits(y = 1) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd), width=0.4, alpha=.9, size=.8, position=position_dodge(.9),\n colour=\"orange\") +\n geom_hline(yintercept=1, linetype=\"dashed\", color = \"black\", size=.5)+\n theme(axis.title.x=element_blank(),\n axis.title.y=element_blank())\n #labs(fill = \"Modo de Busca\", x=\"\", y=\"\")\n\n#legend = gtable_filter(ggplot_gtable(ggplot_build(p3)), \"guide-box\")\n\n#pdf(\"rplot.pdf\") \ngrid.arrange(p1, p2, p3, nrow = 3, ncol=1, top=\"\", bottom=\"Carga de Trabalho\", \n left=\"M\u00e9dia do Melhor Custo Normalizado\")\n#dev.off()\n", "meta": {"hexsha": "d9e2edb65098e3ea57807792edb42457814da353", "size": 10120, "ext": "r", "lang": "R", "max_stars_repo_path": "results-dissertation/chart-bar.r", "max_stars_repo_name": "lmcad-unicamp/PB3Opt", "max_stars_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "results-dissertation/chart-bar.r", "max_issues_repo_name": 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YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3398790179657902}} {"text": "library(igraph)\nlibrary(dplyr)\nlibrary(data.table)\n\n# turns an igraph into an edge list with community column\ngraphToEdgelist <- function(graph, community, k) {\n frame <- igraph::as_data_frame(graph, what = \"edges\")\n \n # sample vertex with lowest degree in the graph\n halfEdges <- c(frame$from, frame$to)\n endpoint <- data.frame(table(halfEdges))\n endpoint <-\n 100 * community + as.numeric(endpoint[which.min(endpoint$Freq), 1])\n \n # k is the number of branches per arm\n starPoint <- ceiling(community / k)\n newEdge <-\n data.table(from = endpoint,\n to = starPoint,\n community = community)\n \n frame <- frame %>% mutate(from = as.numeric(from), to = as.numeric(to))\n frame <-\n frame %>% mutate(\n from = from + 100 * community %>% as.numeric(),\n to = to + 100 * community %>% as.numeric(),\n community = community %>% as.numeric()\n )\n frame <- bind_rows(frame, newEdge)\n \n \n return(frame)\n}\n# create the centre as edgelist with community column\nstarList <- function(arms) {\n frame <-\n data.table(\n from = rep(0, arms),\n to = 1:arms,\n community = rep(0, arms)\n )\n \n return(frame)\n}\n\n\nwriteFinal <- function(frame, name) {\n fwrite(frame, paste0(\"graphs/artificial/\", name, \".csv\"))\n}\n\n\nprocessArtGraphstest <- function(frame, graphName) {\n frame <-\n frame %>% mutate(\n intercommunity = ifelse(from %/% 100 == to %/% 100, TRUE, FALSE),\n intracommunity = ifelse(from %/% 100 == to %/% 100, FALSE, TRUE)\n )\n \n # Create the graph\n graph <-\n frame %>% select(from, to, intercommunity, intracommunity) %>% graph_from_data_frame(directed = FALSE)\n \n community <- as.numeric(V(graph)$name) %/% 100\n \n graph <- set_vertex_attr(graph, \"community\", value = community)\n graph <- set_vertex_attr(graph, \"graphName\", value = graphName)\n \n path <- paste0(\"test graphs/\", graphName, \".xml\")\n write_graph(graph, path, format = \"graphml\")\n \n return(graph)\n}\n\n\nfull <- function(n, community, k = 2) {\n edgelist <-\n make_full_graph(n, directed = FALSE) %>% graphToEdgelist(community, k)\n return(edgelist)\n}\nbipartite <- function(m, n, community, k = 2) {\n edgelist <-\n make_full_bipartite_graph(m, n, directed = FALSE) %>% graphToEdgelist(community, k)\n return(edgelist)\n}\n\n\n# add item to list\naddTo <- function(item, list) {\n list[[length(list) + 1]] <- item\n return(list)\n \n}\n\n\n\n#\n#\n# Test graphs\n#\n#\n\ntest1 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 6\n b <- 3\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- full(10, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <- full(10, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <- full(10, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test1\")\n processArtGraphstest(frame, \"test1\")\n return(frame)\n}\ntest2 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 7\n b <- 6\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- full(15, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <- full(15, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <- full(15, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n branchlist <- full(15, b * (arms - 1) + 4, b) %>% addTo(branchlist)\n branchlist <- full(15, b * (arms - 1) + 5, b) %>% addTo(branchlist)\n branchlist <- full(15, b * (arms - 1) + 6, b) %>% addTo(branchlist)\n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test2\")\n processArtGraphstest(frame, \"test2\")\n return(frame)\n}\ntest3 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 8\n b <- 7\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- bipartite(8, 3, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(8, 3, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(8, 3, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(18, 2, b * (arms - 1) + 4, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(18, 2, b * (arms - 1) + 5, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(18, 2, b * (arms - 1) + 6, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(18, 2, b * (arms - 1) + 7, b) %>% addTo(branchlist)\n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test3\")\n processArtGraphstest(frame, \"test3\")\n \n return(frame)\n}\ntest4 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 10\n b <- 4\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- bipartite(10, 4, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(10, 4, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <- full(6, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n branchlist <- full(6, b * (arms - 1) + 4, b) %>% addTo(branchlist)\n \n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test4\")\n processArtGraphstest(frame, \"test4\")\n \n return(frame)\n}\ntest5 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 12\n b <- 4\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- bipartite(6, 3, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(6, 3, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(6, 3, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(6, 3, b * (arms - 1) + 4, b) %>% addTo(branchlist)\n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test5\")\n processArtGraphstest(frame, \"test5\")\n \n return(frame)\n}\ntest6 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 15\n b <- 6\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <-\n bipartite(10, 10, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(10, 10, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n branchlist <-\n bipartite(10, 10, b * (arms - 1) + 3, b) %>% addTo(branchlist)\n branchlist <- full(8, b * (arms - 1) + 4, b) %>% addTo(branchlist)\n branchlist <- full(8, b * (arms - 1) + 5, b) %>% addTo(branchlist)\n branchlist <- full(8, b * (arms - 1) + 6, b) %>% addTo(branchlist)\n \n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test6\")\n processArtGraphstest(frame, \"test6\")\n \n return(frame)\n}\ntest7 <- function(void) {\n # a is the number of arms, b is the number of branches per arm\n a <- 40\n b <- 2\n star <- starList(a)\n \n branchlist <- list()\n \n for (arms in 1:a) {\n branchlist <- full(5, b * (arms - 1) + 1, b) %>% addTo(branchlist)\n branchlist <- full(5, b * (arms - 1) + 2, b) %>% addTo(branchlist)\n \n }\n \n frame <- rbindlist(branchlist) %>% bind_rows(star)\n frame <- frame %>% mutate(graphName = \"test7\")\n processArtGraphstest(frame, \"test7\")\n \n return(frame)\n}\n\n\n\n\na <- test1(1)\nb <- test2(1)\nc <- test3(1)\ne <- test4(1)\nf <- test5(1)\ng <- test6(1)\nh <- test7(1)\n#\n#\n# Training graphs\n#\n#\n\nart1 <- function(void) {\n star <- starList(6)\n b11 <- full(5, 1)\n b12 <- full(5, 2)\n b21 <- full(5, 3)\n b22 <- full(5, 4)\n b31 <- full(5, 5)\n b32 <- full(5, 6)\n b41 <- full(5, 7)\n b42 <- full(5, 8)\n b51 <- full(5, 9)\n b52 <- full(5, 10)\n b61 <- full(5, 11)\n b62 <- full(5, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art1\")\n writeFinal(frame, \"art1\")\n return(frame)\n}\nart2 <- function(void) {\n star <- starList(6)\n b11 <- full(6, 1)\n b12 <- full(6, 2)\n b21 <- full(6, 3)\n b22 <- full(6, 4)\n b31 <- full(6, 5)\n b32 <- full(6, 6)\n b41 <- full(6, 7)\n b42 <- full(6, 8)\n b51 <- full(6, 9)\n b52 <- full(6, 10)\n b61 <- full(6, 11)\n b62 <- full(6, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art2\")\n writeFinal(frame, \"art2\")\n \n return(frame)\n}\nart3 <- function(void) {\n star <- starList(6)\n b11 <- full(7, 1)\n b12 <- full(7, 2)\n b21 <- full(7, 3)\n b22 <- full(7, 4)\n b31 <- full(7, 5)\n b32 <- full(7, 6)\n b41 <- full(7, 7)\n b42 <- full(7, 8)\n b51 <- full(7, 9)\n b52 <- full(7, 10)\n b61 <- full(7, 11)\n b62 <- full(7, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art3\")\n writeFinal(frame, \"art3\")\n return(frame)\n}\nart4 <- function(void) {\n star <- starList(6)\n b11 <- full(8, 1)\n b12 <- full(8, 2)\n b21 <- full(8, 3)\n b22 <- full(8, 4)\n b31 <- full(8, 5)\n b32 <- full(8, 6)\n b41 <- full(8, 7)\n b42 <- full(8, 8)\n b51 <- full(8, 9)\n b52 <- full(8, 10)\n b61 <- full(8, 11)\n b62 <- full(8, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art4\")\n writeFinal(frame, \"art4\")\n return(frame)\n}\nart5 <- function(void) {\n star <- starList(6)\n b11 <- full(5, 1)\n b12 <- full(8, 2)\n b21 <- full(5, 3)\n b22 <- full(8, 4)\n b31 <- full(5, 5)\n b32 <- full(8, 6)\n b41 <- full(5, 7)\n b42 <- full(8, 8)\n b51 <- full(5, 9)\n b52 <- full(8, 10)\n b61 <- full(5, 11)\n b62 <- full(8, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art5\")\n writeFinal(frame, \"art5\")\n return(frame)\n}\nart6 <- function(void) {\n star <- starList(6)\n b11 <- full(5, 1)\n b12 <- full(10, 2)\n b21 <- full(6, 3)\n b22 <- full(9, 4)\n b31 <- full(7, 5)\n b32 <- full(8, 6)\n b41 <- full(8, 7)\n b42 <- full(7, 8)\n b51 <- full(9, 9)\n b52 <- full(6, 10)\n b61 <- full(10, 11)\n b62 <- full(5, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art6\")\n writeFinal(frame, \"art6\")\n return(frame)\n}\nart7 <- function(void) {\n star <- starList(6)\n b11 <- full(5, 1)\n b12 <- full(10, 2)\n b21 <- full(5, 3)\n b22 <- full(10, 4)\n b31 <- full(5, 5)\n b32 <- full(10, 6)\n b41 <- full(5, 7)\n b42 <- full(10, 8)\n b51 <- full(5, 9)\n b52 <- full(10, 10)\n b61 <- full(5, 11)\n b62 <- full(10, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art7\")\n writeFinal(frame, \"art7\")\n return(frame)\n}\nart8 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(4, 5, 1)\n b12 <- bipartite(6, 3, 2)\n b21 <- bipartite(7, 2, 3)\n b22 <- bipartite(3, 4, 4)\n b31 <- bipartite(8, 1, 5)\n b32 <- bipartite(4, 2, 6)\n b41 <- bipartite(4, 5, 7)\n b42 <- bipartite(3, 7, 8)\n b51 <- full(6, 9)\n b52 <- full(8, 10)\n b61 <- full(5, 11)\n b62 <- full(10, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art8\")\n writeFinal(frame, \"art8\")\n return(frame)\n}\nart9 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 3, 1)\n b12 <- bipartite(1, 3, 2)\n b21 <- bipartite(2, 2, 3)\n b22 <- bipartite(2, 2, 4)\n b31 <- bipartite(2, 3, 5)\n b32 <- bipartite(2, 3, 6)\n b41 <- bipartite(4, 1, 7)\n b42 <- bipartite(3, 1, 8)\n b51 <- full(7, 9)\n b52 <- full(5, 10)\n b61 <- full(5, 11)\n b62 <- full(6, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art9\")\n writeFinal(frame, \"art9\")\n return(frame)\n}\nart10 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 6, 1)\n b12 <- bipartite(7, 2, 2)\n b21 <- bipartite(7, 2, 3)\n b22 <- bipartite(4, 3, 4)\n b31 <- bipartite(8, 1, 5)\n b32 <- bipartite(9, 1, 6)\n b41 <- bipartite(4, 2, 7)\n b42 <- bipartite(2, 2, 8)\n b51 <- full(3, 9)\n b52 <- full(11, 10)\n b61 <- full(7, 11)\n b62 <- full(5, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art10\")\n writeFinal(frame, \"art10\")\n return(frame)\n}\nart11 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 1, 1)\n b12 <- bipartite(7, 7, 2)\n b21 <- bipartite(2, 2, 3)\n b22 <- bipartite(6, 6, 4)\n b31 <- bipartite(3, 3, 5)\n b32 <- bipartite(5, 5, 6)\n b41 <- bipartite(4, 4, 7)\n b42 <- bipartite(4, 4, 8)\n b51 <- full(14, 9)\n b52 <- full(5, 10)\n b61 <- full(10, 11)\n b62 <- full(6, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art11\")\n writeFinal(frame, \"art11\")\n return(frame)\n}\nart12 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(4, 5, 1)\n b12 <- bipartite(6, 3, 2)\n b21 <- bipartite(7, 2, 3)\n b22 <- bipartite(3, 4, 4)\n b31 <- bipartite(8, 1, 5)\n b32 <- bipartite(4, 2, 6)\n b41 <- bipartite(4, 5, 7)\n b42 <- bipartite(3, 7, 8)\n b51 <- full(6, 9)\n b52 <- full(8, 10)\n b61 <- full(5, 11)\n b62 <- full(10, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art12\")\n writeFinal(frame, \"art12\")\n return(frame)\n}\nart13 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 9, 1)\n b12 <- bipartite(3, 5, 2)\n b21 <- bipartite(1, 9, 3)\n b22 <- bipartite(3, 5, 4)\n b31 <- bipartite(1, 9, 5)\n b32 <- bipartite(3, 5, 6)\n b41 <- bipartite(1, 9, 7)\n b42 <- bipartite(3, 5, 8)\n b51 <- bipartite(1, 9, 9)\n b52 <- bipartite(3, 5, 10)\n b61 <- bipartite(1, 9, 11)\n b62 <- bipartite(3, 5, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art13\")\n writeFinal(frame, \"art13\")\n return(frame)\n}\nart14 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(3, 7, 1)\n b12 <- bipartite(2, 4, 2)\n b21 <- bipartite(3, 7, 3)\n b22 <- bipartite(2, 4, 4)\n b31 <- bipartite(3, 7, 5)\n b32 <- bipartite(2, 4, 6)\n b41 <- bipartite(3, 7, 7)\n b42 <- bipartite(2, 4, 8)\n b51 <- bipartite(3, 7, 9)\n b52 <- bipartite(2, 4, 10)\n b61 <- bipartite(3, 7, 11)\n b62 <- bipartite(2, 4, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art14\")\n writeFinal(frame, \"art14\")\n return(frame)\n}\nart15 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 8, 1)\n b12 <- bipartite(4, 2, 2)\n b21 <- bipartite(1, 8, 3)\n b22 <- bipartite(4, 2, 4)\n b31 <- bipartite(1, 8, 5)\n b32 <- bipartite(4, 2, 6)\n b41 <- bipartite(1, 8, 7)\n b42 <- bipartite(4, 2, 8)\n b51 <- bipartite(1, 8, 9)\n b52 <- bipartite(4, 2, 10)\n b61 <- bipartite(1, 8, 11)\n b62 <- bipartite(4, 2, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art15\")\n writeFinal(frame, \"art15\")\n return(frame)\n}\nart16 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(2, 2, 1)\n b12 <- bipartite(2, 2, 2)\n b21 <- bipartite(2, 2, 3)\n b22 <- bipartite(2, 2, 4)\n b31 <- bipartite(2, 2, 5)\n b32 <- bipartite(2, 2, 6)\n b41 <- bipartite(2, 2, 7)\n b42 <- bipartite(2, 2, 8)\n b51 <- bipartite(2, 2, 9)\n b52 <- bipartite(2, 2, 10)\n b61 <- bipartite(2, 2, 11)\n b62 <- bipartite(2, 2, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art16\")\n writeFinal(frame, \"art16\")\n return(frame)\n}\nart17 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(1, 4, 1)\n b12 <- bipartite(1, 4, 2)\n b21 <- bipartite(1, 4, 3)\n b22 <- bipartite(1, 4, 4)\n b31 <- bipartite(1, 4, 5)\n b32 <- bipartite(1, 4, 6)\n b41 <- bipartite(1, 4, 7)\n b42 <- bipartite(1, 4, 8)\n b51 <- bipartite(1, 4, 9)\n b52 <- bipartite(1, 4, 10)\n b61 <- bipartite(1, 4, 11)\n b62 <- bipartite(1, 4, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art17\")\n writeFinal(frame, \"art17\")\n return(frame)\n}\nart18 <- function(void) {\n star <- starList(6)\n b11 <- bipartite(3, 3, 1)\n b12 <- bipartite(4, 4, 2)\n b21 <- bipartite(3, 3, 3)\n b22 <- bipartite(4, 4, 4)\n b31 <- bipartite(3, 3, 5)\n b32 <- bipartite(4, 4, 6)\n b41 <- bipartite(3, 3, 7)\n b42 <- bipartite(4, 4, 8)\n b51 <- bipartite(3, 3, 9)\n b52 <- bipartite(4, 4, 10)\n b61 <- bipartite(3, 3, 11)\n b62 <- bipartite(4, 4, 12)\n frame <-\n bind_rows(star, b11, b12, b21, b22, b31, b32, b41, b42, b51, b52, b61, b62)\n frame <- frame %>% mutate(graphName = \"art18\")\n writeFinal(frame, \"art18\")\n return(frame)\n}\n\n\nart1()\nart2()\nart3()\nart4()\nart5()\nart6()\nart7()\nart8()\nart9()\nart10()\nart11()\nart12()\nart13()\nart14()\nart15()\nart16()\nart17()\nart18()\nart19()\n", "meta": {"hexsha": "d7c495a3d8e91607ed9b10ba3a13e70cc21fd01b", "size": 17282, "ext": "r", "lang": "R", "max_stars_repo_path": "artificialgraph.r", "max_stars_repo_name": "J-JJJJJJ/sentinelClassification", "max_stars_repo_head_hexsha": "6b6cffdeec6a00303542612bf1227b29189cd281", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "artificialgraph.r", "max_issues_repo_name": "J-JJJJJJ/sentinelClassification", "max_issues_repo_head_hexsha": "6b6cffdeec6a00303542612bf1227b29189cd281", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "artificialgraph.r", "max_forks_repo_name": "J-JJJJJJ/sentinelClassification", "max_forks_repo_head_hexsha": "6b6cffdeec6a00303542612bf1227b29189cd281", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.7172619048, "max_line_length": 106, "alphanum_fraction": 0.565501678, "num_tokens": 7165, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.3398790097901846}} {"text": "source('2021-06-02-jsa-type-v2-china/init.r')\r\n\r\n# Create map dataset\r\n\r\ndf_all <- get_df()\r\n\r\ndf_all <- df_all[duplicated == FALSE]\r\n\r\ndf_all$lat_old <- df_all$lat\r\ndf$lon_old <- df_all$lon\r\n\r\ndf_all$lat <- as.numeric(df_all$lat)\r\ndf_all$lon <- as.numeric(df_all$lon)\r\n\r\n\r\ndf <- df_all[\r\n status %in% c(\"Valid species\", \"Synonym\"), \r\n c(\"idx\", \"genus\", \"species\", \"status\", \"date\", \"lat\", \"lon\", \r\n \"global.mapper_n\", \"global.mapper\", \"merged.global.mapper\", \"type.country_n\", \"type.state\")\r\n]\r\n# note: \"type.state\" field was not cleaned!\r\n\r\n# Checks\r\ndf_all[type.country_n == \"CH\", .N, by=status]\r\ndf_all[type.country_n == \"CH\" & (is.na(lat) | is.na(lon)), .N, by=status]\r\ndf_all[type.country_n == \"CH\" & !(is.na(lat) | is.na(lon)), .N, by=status]\r\n\r\n# Note: mapped/ plotted in species description curves for China, using lat/lon\r\n# which means 160 valid species (of 665 species; 24.1%) were omitted from the map and curves\r\n# (these with country China, but no lat/lon)\r\n# but were included in 04-flow and 05-type-repo (also including synonyms)\r\nround((160/665*100), 1) \r\n\r\n\r\ndir_geo_chn <- \"data/geo_processed/gadm/china/gadm36_CHN_shp/\"\r\nlist.files(dir_geo_chn)\r\nv_chn <- st_read(paste0(dir_geo_chn, \"gadm36_CHN_0.shp\"))\r\nv_chn_pri <- st_read(paste0(dir_geo_chn, \"gadm36_CHN_1.shp\"))\r\n\r\nv_df <- st_as_sf(df[!(is.na(lat) | is.na(lon))], coords = c(\"lon\", \"lat\"), crs = wgs84)\r\nv_df <- st_transform(v_df, st_crs(v_chn_pri))\r\nv_df <- st_join(v_df, v_chn_pri, join = st_intersects)\r\n\r\ndf_merged <- data.table(v_df)[, c(\"idx\", \"GID_0\", \"NAME_1\")]\r\nnames(df_merged) <- c(\"idx\", \"china\", \"pri\")\r\n\r\ndf <- merge(df, df_merged, by=\"idx\", all.x=T, all.y=F)\r\ndim(df)\r\n\r\n\r\nv_chn_pri <- st_read(paste0(v2_dir_china, \"01-map/Prov_ann/Prov_ann.shp\"))\r\n\r\nv_df <- st_as_sf(df[!(is.na(lat) | is.na(lon))], coords = c(\"lon\", \"lat\"), crs = wgs84)\r\nv_df <- st_transform(v_df, st_crs(v_chn_pri))\r\nv_df <- st_join(v_df, v_chn_pri, join = st_intersects)\r\n\r\ndf_merged <- data.table(v_df)[, c(\"idx\", \"Eng_NAME\")]\r\nnames(df_merged) <- c(\"idx\", \"pri\")\r\n\r\ndf <- merge(df, df_merged, by=\"idx\", all.x=T, all.y=F, suffixes=c(\"\", \"_max\"))\r\ndim(df)\r\n\r\nwfile <- paste0(v2_dir_china, \"01-map/lat-lon.csv\")\r\nfwrite(df, wfile, na=\"\")\r\n\r\n\r\n\r\n\r\n\r\n# Mismatches \r\n# fortunately these are few can be trivialised (/ignored)\r\n\r\ndf[type.country_n==\"CH\" & is.na(china)] \r\n# due to boundary issues of GADM shp or plotting in the sea\r\n# (should be rectified on the GADM shp, or lat/lon should be modified)\r\n\r\ndf[type.country_n!=\"CH\" & china==\"CHN\"] \r\n# erroneous georeferencing (should be excluded)\r\n\r\ndf[type.country_n == \"CH\", .N, by=status]\r\n# tally with values above", "meta": {"hexsha": "e6199ff5be0fb1e5bb23f916df7f7ef81aeaf9ea", "size": 2637, "ext": "r", "lang": "R", "max_stars_repo_path": "2021-06-02-jsa-type-v2-china/01-map.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2021-06-02-jsa-type-v2-china/01-map.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2021-06-02-jsa-type-v2-china/01-map.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5555555556, "max_line_length": 100, "alphanum_fraction": 0.6549108836, "num_tokens": 853, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.3398790097901846}} {"text": "# (8) #########\n\nprint(\"Same as above but explicitly 'return' a value as columns\")\n\n##\nct <- makeCluster(cores) \nregisterDoParallel(ct)\n##\n\nnt<-10\nmymat<-matrix(nrow=nt,ncol=nt)\nmymat[1:nt,]<-0\nbsum=0\nbsum<-foreach(ijk = 1:nt, .combine=cbind ) %dopar% { \n\tset.seed(ijk) \n\tmymat[,ijk]<-rnorm(mymat[,ijk])\n\tjunk<-sum(mymat[,ijk])\n}\nprint(bsum)\nprint(mymat)\n\nstopCluster(ct)\n#readline(prompt = \"NEXT>\")\n\n", "meta": {"hexsha": "fb800987043d436b50c7a8a6276700aceae2a3f1", "size": 402, "ext": "r", "lang": "R", "max_stars_repo_path": "r/semantics/08.r", "max_stars_repo_name": "timkphd/examples", "max_stars_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-11-01T00:29:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T19:09:47.000Z", "max_issues_repo_path": "r/semantics/08.r", "max_issues_repo_name": "timkphd/examples", "max_issues_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-09T01:59:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T01:59:47.000Z", "max_forks_repo_path": "r/semantics/08.r", "max_forks_repo_name": "timkphd/examples", "max_forks_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.08, "max_line_length": 65, "alphanum_fraction": 0.6467661692, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.33987900161457885}} {"text": "require(matrixStats) #install.packages('matrixStats')\nsetwd(\"~/Dokumenty/studijni_materialy/Edu-hoc/src\")\n\ndat1 = read.delim(\"RssiApp/values4.txt\", header = FALSE, sep = \";\", comment.char = \"&\")\ndat2 = read.delim(\"RssiApp2/values4.txt\", header = FALSE, sep = \";\", comment.char = \"&\")\ndat3 = read.delim(\"RssiAppSniffer/values4.txt\", header = FALSE, sep = \";\", comment.char = \"&\")\n\nbasicStats <- function(x){\n m = mean(x)\n sd = sd(x)\n \n qp = m + alpha * sd\n qm = m - alpha * sd\n \n result <- list(\"mean\"=m, \"sd\"=sd, \"qp\"=qp, \"qm\"=qm)\n return(result)\n}\n\nquantization <- function(stats, values){\n y = c(NA)\n x = c(NA)\n for(i in 1:length(values)){\n if(values[i] > stats$qp){\n y <- c(y,1)\n x <- c(x,i)\n } else if (values[i] < stats$qm){\n y <- c(y,0)\n x <- c(x,i)\n }\n }\n return(list(\"x\"=na.omit(x),\"y\"=na.omit(y)))\n}\n\nmapLocations <- function(locations, bits){\n result = c(NA)\n \n for(i in locations){\n result = c(result, bits$y[which(bits$x == i)])\n }\n return(na.omit(result))\n}\n\ninc <- function(x){\n eval.parent(substitute(x <- x + 1))\n}\n\ndistance <- function( a, b){\n d <- 0;\n if(length(a) == 0 || length(b) == 0){\n return(max(length(a), length(b)))\n }\n a <- na.omit(a)\n b <- na.omit(b)\n if(length(a) <= length(b)){\n for(i in 1:length(a)){\n if(a[i] != b[i]){\n inc(d)\n }\n }\n } else {\n for(i in 1:length(b)){\n if(a[i] != b[i]){\n inc(d)\n }\n }\n }\n return(d)\n}\n\nlengths <- c(NA)\nh <- c(NA)\nhS <- c(NA)\n\nalpha <- 0.5\nfor (i in 1:140){ \n#i<-1\n values1 <- as.numeric(dat1[i,1:1000]) -256\n values2 <- as.numeric(dat2[i,1:1000]) -256\n values3 <- as.numeric(dat3[i,1:1000]) -256\n \n s1 <- basicStats(values1)\n r1 <- quantization(s1, values1)\n \n s2 <- basicStats(values2)\n r2 <- quantization(s2, values2)\n \n s3 <- basicStats(values3)\n r3 <- quantization(s3, values3)\n \n #plot(r1$x, r1$y, col = \"red\", pch=0, ylim=c(-0.1,1.1))\n #points(r2$x, r2$y+0.02, col = \"blue\", pch=1)\n #points(r3$x, r3$y+0.04, col = \"green\", pch=2)\n \n \n x_locations = intersect(r1$x,r2$x)\n s_locations = intersect(x_locations,r1$x)\n \n bits1 <- mapLocations(x_locations, r1)\n bits2 <- mapLocations(x_locations, r2)\n bits3 <- mapLocations(s_locations, r3)\n \n lengths <- c(lengths, length(bits1))\n if(length(bits1) != 0){\n h <- c(h, distance(bits1, bits2))\n hS <- c(hS, distance(bits1, bits3))\n }\n \n if(length(bits1) > 5){\n sink(\"outfile.txt\", append=TRUE)\n for(i in 1:length(bits1)){\n cat(bits1[i])\n }\n cat(\"\\n\")\n sink()\n }\n \n #points(x_locations, bits1 -0.02, col = \"red\", pch=15)\n #points(x_locations, bits2 -0.04, col = \"blue\", pch=16)\n #points(s_locations, bits3 -0.06, col = \"green\", pch=17)\n\n}\nhist(h, breaks = 50, main = \"histogram of distances for non zero vectors between A and B\", col = \"grey\")\nhist(hS, breaks = 50, main = \"histogram of distances for non zero vectors between AB and sniffer\", col = \"grey\")\nhist(lengths, breaks = 50, main = \"histogram of lenghts of final vectors\", col = \"grey\")\n\nboxplot(h, hS, names=c(\"distances between A and B\",\"distances between AB and Sniffer\"))\n \nprint(table(lengths))", "meta": {"hexsha": "d979051d9f9249de03e502f376c1e9e5abb21cad", "size": 3144, "ext": "r", "lang": "R", "max_stars_repo_path": "code/quant.r", "max_stars_repo_name": "LukeMcNemee/thesis2", "max_stars_repo_head_hexsha": "83dce1965e79b3971302665344313085d72963a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-02-25T11:54:39.000Z", "max_stars_repo_stars_event_max_datetime": "2017-02-25T11:54:39.000Z", "max_issues_repo_path": "code/quant.r", "max_issues_repo_name": "LukeMcNemee/thesis2", "max_issues_repo_head_hexsha": "83dce1965e79b3971302665344313085d72963a8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/quant.r", "max_forks_repo_name": "LukeMcNemee/thesis2", "max_forks_repo_head_hexsha": "83dce1965e79b3971302665344313085d72963a8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.5625, "max_line_length": 112, "alphanum_fraction": 0.5798346056, "num_tokens": 1094, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7310585669110202, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.3398702765229108}} {"text": "setwd('/home/pc-752828/Dev/results-search-master/outputs-normal')\n\ndata_norm <- read.csv(file = 'output-n-price.csv', sep = ',', header = T)\n\nget_df <- function(seed, data) {\n my_ite <- 10\n bo1 <- list()\n bo2 <- list()\n bo3 <- list()\n bo4 <- list()\n rs <- list()\n app <- list()\n seed_l <- list()\n \n for(i in levels(factor(data$name))) {\n r_bo1 <- data[data['name'] == i & data['bo'] == 'bo1' & data['ite'] == my_ite,][seed,]\n r_bo4 <- data[data['name'] == i & data['bo'] == 'bo4' & data['ite'] == my_ite,][seed,]\n r_bo3 <- data[data['name'] == i & data['bo'] == 'bo3' & data['ite'] == my_ite,][seed,]\n r_bo2 <- data[data['name'] == i & data['bo'] == 'bo5' & data['ite'] == my_ite,][seed,]\n r_rs <- data[data['name'] == i & data['bo'] == 'rs' & data['ite'] == my_ite,][seed,]\n bo1 <- append(bo1, levels(factor(r_bo1$best)))\n bo2 <- append(bo2, levels(factor(r_bo2$best)))\n bo3 <- append(bo3, levels(factor(r_bo3$best)))\n bo4 <- append(bo4, levels(factor(r_bo4$best)))\n rs <- append(rs, levels(factor(r_rs$best)))\n app <- append(app, i)\n seed_l <- append(seed_l, seed)\n }\n \n df <- cbind(bo1, bo2, bo3, bo4, rs, app)\n df <- data.frame(df)\n df$bo1 = as.numeric(as.character(df$bo1))\n df$bo2 = as.numeric(as.character(df$bo2))\n df$bo3 = as.numeric(as.character(df$bo3))\n df$bo4 = as.numeric(as.character(df$bo4))\n df$rs = as.numeric(as.character(df$rs))\n \n return(df)\n}\n\ndf_1 <- get_df(1, data_norm)\ndf_2 <- get_df(2, data_norm)\ndf_3 <- get_df(3, data_norm)\ndf_4 <- get_df(4, data_norm)\ndf_5 <- get_df(5, data_norm)\n\nsummary(df_norm$bo4)\nsummary(df_norm$rs)\n\ndf_norm <- rbind(df_1, df_2, df_3, df_4, df_5)\ndf <- df_norm\ndf <- df_5\n\ncols <- c(\"BO-6rnd-EIdef\",\"BO-6rnd-EInova\",\"BO-6sel-EIdef\", \"PB3Opt\", \"Ranking Search\", \"ID\")\ncolnames(df) <- cols\ndf$ID = as.character(df$ID)\ndf$ID <- factor(df$ID)\ncols <- c(\"BO-6rnd-EIdef\",\"BO-6rnd-EInova\",\"BO-6sel-EIdef\", \"PB3Opt\", \"Ranking Search\")\nnew_df <- melt(df, id = c(\"ID\"), measured = cols)\nfriedman.test(value ~ variable | ID, data = new_df)\n\nnew_df %>% friedman_effsize(value ~ variable | ID)\n\n\ncolnames(new_df) <- c(\"ID\", \"Abordagem\", \"Best\")\n\nnew_df %>% group_by(Abordagem) %>%\n get_summary_stats(Best, type = \"median_iqr\")\n\n\n\n\n", "meta": {"hexsha": "fc6ff15dc15ee15428e451a4374d52499ca53b6b", "size": 2230, "ext": "r", "lang": "R", "max_stars_repo_path": "results-dissertation/train-cost.r", "max_stars_repo_name": "lmcad-unicamp/PB3Opt", "max_stars_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "results-dissertation/train-cost.r", "max_issues_repo_name": "lmcad-unicamp/PB3Opt", "max_issues_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "results-dissertation/train-cost.r", "max_forks_repo_name": "lmcad-unicamp/PB3Opt", "max_forks_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.5479452055, "max_line_length": 93, "alphanum_fraction": 0.6044843049, "num_tokens": 800, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3397177359502127}} {"text": "# Rscript gobench.r gobench.out output.png\n\n# Parse the command line args.\nargs = commandArgs(trailingOnly=TRUE) # this line only works when you run this script from the command line.\n\nlibrary(tidyverse)\nlibrary(drlib)\n\nplot = readr::read_delim(args[1], \"\\t\", skip = 3, col_names = FALSE) %>%\n set_names(\"Name\", \"Ops\", \"NsPerOp\", \"MBPerS\", \"AllocatedBytesPerOp\", \"AllocationsPerOp\") %>%\n\n # Clean up the text so we get numbers\n mutate(NsPerOp = as.numeric(str_replace(NsPerOp, \" ns/op\", \"\")),\n MBPerS = as.numeric(str_replace(MBPerS, \" MB/s\", \"\")),\n AllocatedBytesPerOp = as.numeric(str_replace(AllocatedBytesPerOp, \" B/op\", \"\")),\n AllocationsPerOp = as.numeric(str_replace(AllocationsPerOp, \" allocs/op\", \"\")),\n Ops = as.numeric(Ops)) %>%\n filter(!is.na(NsPerOp)) %>%\n\n # Strip out the \"Benchmark\" prefix from the benchmark name.\n mutate (Name = str_replace(Name, \"Benchmark\", \"\")) %>%\n\n # Split out the cores.\n separate(Name, c(\"Name\", \"Cores\"), \"-\") %>%\n mutate(Cores = as.numeric(Cores)) %>%\n \n # Split out the bytes.\n separate(Name, c(\"Name\", \"Bytes\"), \"/\") %>%\n mutate(Bytes = as.numeric(Bytes)) %>%\n\n # Generate the plots.\n ggplot(aes(Name, NsPerOp)) + \n geom_col() + \n rotate_x_labels(vjust = .5) +\n # labs(title=\"Title\", subtitle=\"Sub title\") +\n xlab(\"name\") +\n ylab(\"nanoseconds per operation\") +\n facet_wrap(~ Bytes, scales=\"free_y\")\n\nggsave(args[2], plot, width = 16, height = 9)", "meta": {"hexsha": "bbf22c473a8bc54a455999b324c09f81dafb6384", "size": 1444, "ext": "r", "lang": "R", "max_stars_repo_path": "plotting/gobench_multi_nsop.r", "max_stars_repo_name": "pltr/go-benchmarks", "max_stars_repo_head_hexsha": "a019a11ff8e0ad21361a6975130e68668c5264fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plotting/gobench_multi_nsop.r", "max_issues_repo_name": "pltr/go-benchmarks", "max_issues_repo_head_hexsha": "a019a11ff8e0ad21361a6975130e68668c5264fa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plotting/gobench_multi_nsop.r", "max_forks_repo_name": "pltr/go-benchmarks", "max_forks_repo_head_hexsha": "a019a11ff8e0ad21361a6975130e68668c5264fa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.1, "max_line_length": 108, "alphanum_fraction": 0.6447368421, "num_tokens": 440, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3397177359502127}} {"text": "#' @title make.list\n#' @description Make a simple expanded list of parameter values with which parallel runs can be made.\n#' @family abysmally documented\n#' @author Jae Choi, \\email{jae.choi@dfo-mpo.gc.ca}\n#' @export\n# define compact list of variable (e.g., year, var, etc) combinations for parallel processing\n# which can be accessed within a parallel run to recover variable comnbinations using a single index \n# outside of parallel run\n# p = make.list( list(p$vars.to.model, p$years.to.model, p$regions.to.model), Y=list() )\n# ...\n# inside of a parallel run\n# i = 1 ... parallel nids or \"nruns\"\n# v = p$runs[i,1]\n# y = p$runs[i,2]\n# r = p$runs[i,3]\n \n\n make.list = function( Z, Y=list(), delimit=\"~\" ) { \n \n nvars = length(Z) # Z must be a list\n sm = NULL\n for (i in 1:nvars) {\n sm = c( sm, mode( Z[[i]] ) )\n }\n \n Q = expand.grid(Z)\n X = Q[,1]\n if ( nvars > 1 ) {\n for (i in 2:nvars) { X = paste( X, Q[,i], sep=delimit ) }\n } \n \n Y$process.list = list( data=X, sm=sm, delimit=delimit, nvars=nvars, varnames=names(Q), Z=Z )\n Y$runs = break.list( Y$process.list )\n Y$nruns = nrow( Y$runs )\n \n return( Y)\n \n }\n\n\n", "meta": {"hexsha": "764653c45a13db1022faba7f9eed82a360534b7e", "size": 1213, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make.list.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/make.list.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/make.list.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5853658537, "max_line_length": 101, "alphanum_fraction": 0.582852432, "num_tokens": 395, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3397177278545247}} {"text": "mcmcMH <- function(target, init.theta, proposal.sd = NULL,\n n.iterations, covmat = NULL,\n limits=list(lower = NULL, upper = NULL),\n adapt.size.start = NULL, adapt.size.cooling = 0.99,\n adapt.shape.start = NULL, adapt.shape.stop = NULL,\n print.info.every = n.iterations/100,\n verbose = FALSE, max.scaling.sd = 50) {\n \n # initialise theta\n theta.current <- init.theta\n theta.propose <- init.theta\n \n # extract theta of gaussian proposal\n covmat.proposal <- covmat\n lower.proposal <- limits$lower\n upper.proposal <- limits$upper\n \n # reorder vector and matrix by names, set to default if necessary\n theta.names <- names(init.theta)\n if (!is.null(proposal.sd) && is.null(names(proposal.sd))) {\n names(proposal.sd) <- theta.names\n }\n \n if (is.null(covmat.proposal)) {\n if (is.null(proposal.sd)) {\n proposal.sd <- init.theta/10\n }\n covmat.proposal <-\n matrix(diag(proposal.sd[theta.names]^2, nrow = length(theta.names)),\n nrow = length(theta.names),\n dimnames = list(theta.names, theta.names))\n } else {\n covmat.proposal <- covmat.proposal[theta.names,theta.names]\n }\n \n if (is.null(lower.proposal)) {\n lower.proposal <- init.theta\n lower.proposal[] <- -Inf\n } else {\n lower.proposal <- lower.proposal[theta.names]\n }\n \n if (is.null(upper.proposal)) {\n upper.proposal <- init.theta\n upper.proposal[] <- Inf\n } else {\n upper.proposal <- upper.proposal[theta.names]\n }\n \n # covmat init\n covmat.proposal.init <- covmat.proposal\n \n adapting.size <- FALSE # will be set to TRUE once we start\n # adapting the size\n \n adapting.shape <- 0 # will be set to the iteration at which\n # adaptation starts\n \n # find estimated theta\n theta.estimated.names <- names(which(diag(covmat.proposal) > 0))\n \n # evaluate target at theta init\n target.theta.current <- target(theta.current)\n \n if (!is.null(print.info.every)) {\n message(Sys.time(), \", Init: \", printNamedVector(theta.current[theta.estimated.names]),\n \", target: \", target.theta.current)\n }\n \n # trace\n trace <- matrix(ncol=length(theta.current)+1, nrow=n.iterations, 0)\n colnames(trace) <- c(theta.estimated.names, \"log.density\")\n \n # acceptance rate\n acceptance.rate <- 0\n \n # scaling factor for covmat size\n scaling.sd <- 1\n \n # scaling multiplier\n scaling.multiplier <- 1\n \n # empirical covariance matrix (0 everywhere initially)\n covmat.empirical <- covmat.proposal\n covmat.empirical[,] <- 0\n \n # empirical mean vector\n theta.mean <- theta.current\n \n # if print.info.every is null never print info\n if (is.null(print.info.every)) {\n print.info.every <- n.iterations + 1\n }\n \n start_iteration_time <- Sys.time()\n \n for (i.iteration in seq_len(n.iterations)) {\n \n # adaptive step\n if (!is.null(adapt.size.start) && i.iteration >= adapt.size.start &&\n (is.null(adapt.shape.start) || acceptance.rate*i.iteration < adapt.shape.start)) {\n if (!adapting.size) {\n message(\"\\n---> Start adapting size of covariance matrix\")\n adapting.size <- TRUE\n }\n # adapt size of covmat until we get enough accepted jumps\n scaling.multiplier <- exp(adapt.size.cooling^(i.iteration-adapt.size.start) * (acceptance.rate - 0.234))\n scaling.sd <- scaling.sd * scaling.multiplier\n scaling.sd <- min(c(scaling.sd,max.scaling.sd))\n # only scale if it doesn't reduce the covariance matrix to 0\n covmat.proposal.new <- scaling.sd^2*covmat.proposal.init\n if (!(any(diag(covmat.proposal.new)[theta.estimated.names] <\n .Machine$double.eps))) {\n covmat.proposal <- covmat.proposal.new\n }\n \n } else if (!is.null(adapt.shape.start) &&\n acceptance.rate*i.iteration >= adapt.shape.start &&\n (adapting.shape == 0 || is.null(adapt.shape.stop) ||\n i.iteration < adapting.shape + adapt.shape.stop)) {\n if (!adapting.shape) {\n message(\"\\n---> Start adapting shape of covariance matrix\")\n # flush.console()\n adapting.shape <- i.iteration\n }\n \n ## adapt shape of covmat using optimal scaling factor for multivariate target distributions\n scaling.sd <- 2.38/sqrt(length(theta.estimated.names))\n \n covmat.proposal <- scaling.sd^2 * covmat.empirical\n } else if (adapting.shape > 0) {\n message(\"\\n---> Stop adapting shape of covariance matrix\")\n adapting.shape <- -1\n }\n \n # print info\n if (i.iteration %% ceiling(print.info.every) == 0) {\n message(Sys.time(), \", Iteration: \",i.iteration,\"/\", n.iterations,\n \", acceptance rate: \",\n sprintf(\"%.3f\",acceptance.rate), appendLF=FALSE)\n if (!is.null(adapt.size.start) || !is.null(adapt.shape.start)) {\n message(\", scaling.sd: \", sprintf(\"%.3f\", scaling.sd),\n \", scaling.multiplier: \", sprintf(\"%.3f\", scaling.multiplier),\n appendLF=FALSE)\n }\n message(\", state: \",(printNamedVector(theta.current)))\n message(\", logdensity: \", target.theta.current)\n }\n \n # propose another parameter set\n if (any(diag(covmat.proposal)[theta.estimated.names] <\n .Machine$double.eps)) {\n print(covmat.proposal[theta.estimated.names,theta.estimated.names])\n stop(\"non-positive definite covmat\",call.=FALSE)\n }\n if (length(theta.estimated.names) > 0) {\n theta.propose[theta.estimated.names] <-\n as.vector(rtmvnorm(1,\n mean =\n theta.current[theta.estimated.names],\n sigma =\n covmat.proposal[theta.estimated.names,theta.estimated.names],\n lower =\n lower.proposal[theta.estimated.names],\n upper = upper.proposal[theta.estimated.names]))\n }\n \n # evaluate posterior of proposed parameter\n target.theta.propose <- target(theta.propose)\n # if return value is a vector, set log.density and trace\n \n if (!any(is.finite(target.theta.propose))) { # GK: changed to include \"any\" as getting error with list\n # if posterior is 0 then do not compute anything else and don't accept\n log.acceptance <- -Inf\n \n }else{\n \n # compute Metropolis-Hastings ratio (acceptance probability)\n log.acceptance <- target.theta.propose - target.theta.current\n log.acceptance <- log.acceptance +\n dtmvnorm(x = theta.current[theta.estimated.names],\n mean =\n theta.propose[theta.estimated.names],\n sigma =\n covmat.proposal[theta.estimated.names,\n theta.estimated.names],\n lower =\n lower.proposal[theta.estimated.names],\n upper =\n upper.proposal[theta.estimated.names],\n log = TRUE)\n log.acceptance <- log.acceptance -\n dtmvnorm(x = theta.propose[theta.estimated.names],\n mean = theta.current[theta.estimated.names],\n sigma =\n covmat.proposal[theta.estimated.names,\n theta.estimated.names],\n lower =\n lower.proposal[theta.estimated.names],\n upper =\n upper.proposal[theta.estimated.names],\n log = TRUE)\n \n }\n \n if (verbose) {\n message(\"Propose: \", theta.propose[theta.estimated.names],\n \", target: \", target.theta.propose,\n \", acc prob: \", exp(log.acceptance), \", \",\n appendLF = FALSE)\n }\n \n if (is.accepted <- (log(runif (1)) < log.acceptance)) {\n # accept proposed parameter set\n theta.current <- theta.propose\n target.theta.current <- target.theta.propose\n if (verbose) {\n message(\"accepted\")\n }\n } else if (verbose) {\n message(\"rejected\")\n }\n trace[i.iteration, ] <- c(theta.current, target.theta.current)\n \n # update acceptance rate\n if (i.iteration == 1) {\n acceptance.rate <- is.accepted\n } else {\n acceptance.rate <- acceptance.rate +\n (is.accepted - acceptance.rate) / i.iteration\n }\n \n # update empirical covariance matrix\n if (adapting.shape >= 0) {\n tmp <- updateCovmat(covmat.empirical, theta.mean,\n theta.current, i.iteration)\n covmat.empirical <- tmp$covmat\n theta.mean <- tmp$theta.mean\n }\n \n }\n \n return(list(trace = trace,\n acceptance.rate = acceptance.rate,\n covmat.empirical = covmat.empirical))\n}\n\n\n#'Print named vector\n#'\n#'Print named vector with format specified by \\code{fmt} (2 decimal places by default).\n#' @param x named vector\n#' @inheritParams base::sprintf\n#' @inheritParams base::paste\n#' @export\n#' @seealso \\code{\\link[base]{sprintf}}\n#' @keywords internal\nprintNamedVector <- function(x, fmt=\"%.2f\", sep=\" | \") {\n \n paste(paste(names(x),sprintf(fmt,x),sep=\" = \"),collapse=sep)\n \n}\n\n#' Simulate forward a stochastic model\n#'\n#' This function uses the function \\code{\\link[adaptivetau]{ssa.adaptivetau}} to simulate the model and returns the trajectories in a valid format for the class \\code{\\link{fitmodel}}.\n#' @param theta named vector of model parameters.\n#' @param init.state named vector of initial state of the model.\n#' @param times time sequence for which state of the model is wanted; the first value of times must be the initial time.\n#' @inheritParams adaptivetau::ssa.adaptivetau\n#' @export\n#' @import adaptivetau\n#' @return a data.frame of dimension \\code{length(times)x(length(init.state)+1)} with column names equal to \\code{c(\"time\",names(init.state))}.\nsimulateModelStochastic <- function(theta,init.state,times,transitions,rateFunc) {\n \n \n stoch <- as.data.frame(ssa.adaptivetau(init.state,transitions,rateFunc,theta,tf=diff(range(times))))\n \n # rescale time as absolute value\n stoch$time <- stoch$time + min(times)\n \n # interpolate\n traj <- cbind(time=times,apply(stoch[,-1],2,function(col){approx(x=stoch[,1],y=col,xout=times,method=\"constant\")$y}))\n \n return(as.data.frame(traj))\n \n}\n\n\n\n#'Simulate several replicate of the model\n#'\n#'Simulate several replicate of a fitmodel using its function simulate\n#' @param times vector of times at which you want to observe the states of the model.\n#' @param n number of replicated simulations.\n#' @param observation logical, if \\code{TRUE} simulated observation are generated by \\code{rTrajObs}.\n#' @inheritParams testFitmodel\n#' @export\n#' @import plyr\n#' @return a data.frame of dimension \\code{[nxlength(times)]x[length(init.state)+2]} with column names equal to \\code{c(\"replicate\",\"time\",names(init.state))}.\nsimulateModelReplicates <- function(fitmodel,theta, init.state, times, n, observation=FALSE) {\n \n stopifnot(inherits(fitmodel,\"fitmodel\"),n>0)\n \n if(observation && is.null(fitmodel$dPointObs)){\n stop(\"Can't generate observation as \",sQuote(\"fitmodel\"),\" doesn't have a \",sQuote(\"dPointObs\"),\" function.\")\n }\n \n rep <- as.list(1:n)\n names(rep) <- rep\n \n if (n > 1) {\n progress = \"text\"\n } else {\n progress = \"none\"\n }\n \n traj.rep <- ldply(rep,function(x) {\n \n if(observation){\n traj <- rTrajObs(fitmodel, theta, init.state, times)\n } else {\n traj <- fitmodel$simulate(theta,init.state,times)\n }\n \n return(traj)\n \n },.progress=progress,.id=\"replicate\")\n \n return(traj.rep)\n}\n\n\n#'Simulate model until extinction\n#'\n#'Return final state at extinction\n#' @param extinct character vetor. Simulations stop when all these state are extinct.\n#' @param time.init numeric. Start time of simulation.\n#' @param time.step numeric. Time step at which extinction is checked\n#' @inheritParams testFitmodel\n#' @inheritParams simulateModelReplicates\n#' @inheritParams particleFilter\n#' @export\n#' @import plyr parallel doParallel\nsimulateFinalStateAtExtinction <- function(fitmodel, theta, init.state, extinct=NULL ,time.init=0, time.step=100, n=100, observation=FALSE, n.cores = 1) {\n \n stopifnot(inherits(fitmodel,\"fitmodel\"),n>0)\n \n if(observation && is.null(fitmodel$dPointObs)){\n stop(\"Can't generate observation as \",sQuote(\"fitmodel\"),\" doesn't have a \",sQuote(\"dPointObs\"),\" function.\")\n }\n \n if(is.null(n.cores)){\n n.cores <- detectCores()\n }\n \n if(n.cores > 1){\n registerDoParallel(cores=n.cores)\n }\n \n rep <- as.list(1:n)\n names(rep) <- rep\n \n if (n > 1 && n.cores==1) {\n progress = \"text\"\n } else {\n progress = \"none\"\n }\n \n times <- c(time.init, time.step)\n \n final.state.rep <- ldply(rep,function(x) {\n \n if(observation){\n traj <- rTrajObs(fitmodel, theta, init.state, times)\n } else {\n traj <- fitmodel$simulate(theta,init.state,times)\n }\n \n current.state <- unlist(traj[nrow(traj),fitmodel$state.names])\n current.time <- last(traj$time)\n \n while(any(current.state[extinct]>=0.5)){\n \n times <- times + current.time\n \n if(observation){\n traj <- rTrajObs(fitmodel, theta, current.state, times)\n } else {\n traj <- fitmodel$simulate(theta, current.state,times)\n }\n \n current.state <- unlist(traj[nrow(traj),fitmodel$state.names])\n current.time <- last(traj$time)\n }\n \n return(data.frame(t(c(time=current.time,current.state))))\n \n },.progress=progress,.id=\"replicate\",.parallel=(n.cores > 1),.paropts=list(.inorder=FALSE))\n \n return(final.state.rep)\n}\n\n\n#'Update covariance matrix\n#'\n#'Update covariance matrix using a stable one-pass algorithm. This is much more efficient than using \\code{\\link{cov}} on the full data.\n#' @param covmat covariance matrix at iteration \\code{i-1}. Must be numeric, symmetrical and named.\n#' @param theta.mean mean vector at iteration \\code{i-1}. Must be numeric and named.\n#' @param theta vector of new value at iteration \\code{i}. Must be numeric and named.\n#' @param i current iteration.\n#' @references \\url{http://en.wikipedia.org/wiki/Algorithms\\%5Ffor\\%5Fcalculating\\%5Fvariance#Covariance}\n#' @export\n#' @keywords internal\n#' @return A list of two elements\n#' \\itemize{\n#' \\item \\code{covmat} update covariance matrix\n#' \\item \\code{theta.mean} updated mean vector\n#' }\nupdateCovmat <- function(covmat,theta.mean,theta,i) {\n \n if(is.null(names(theta))){\n stop(\"Argument \",sQuote(\"theta\"),\" must be named.\",.call=FALSE)\n }\n if(is.null(names(theta.mean))){\n stop(\"Argument \",sQuote(\"theta.mean\"),\" must be named.\",.call=FALSE)\n }\n if(is.null(rownames(covmat))){\n stop(\"Argument \",sQuote(\"covmat\"),\" must have named rows.\",.call=FALSE)\n }\n if(is.null(colnames(covmat))){\n stop(\"Argument \",sQuote(\"covmat\"),\" must have named columns.\",.call=FALSE)\n }\n \n covmat <- covmat[names(theta),names(theta)]\n theta.mean <- theta.mean[names(theta)]\n \n residual <- as.vector(theta-theta.mean)\n covmat <- (covmat*(i-1)+(i-1)/i*residual%*%t(residual))/i\n theta.mean <- theta.mean + residual/i\n \n return(list(covmat=covmat,theta.mean=theta.mean))\n}\n\n\n\n#'Burn and thin MCMC chain\n#'\n#'Return a burned and thined trace of the chain.\n#' @param trace either a \\code{data.frame} or a \\code{list} of \\code{data.frame} with all variables in column, as outputed by \\code{\\link{mcmcMH}}. Accept also an \\code{mcmc} or \\code{mcmc.list} object.\n#' @param burn proportion of the chain to burn.\n#' @param thin number of samples to discard per sample that is being kept\n#' @export\n#' @import coda\n#' @return an object with the same format as \\code{trace} (\\code{data.frame} or \\code{list} of \\code{data.frame} or \\code{mcmc} object or \\code{mcmc.list} object)\nburnAndThin <- function(trace, burn = 0, thin = 0) {\n \n convert_to_mcmc <- FALSE\n \n if(class(trace)==\"mcmc\"){\n convert_to_mcmc <- TRUE\n trace <- as.data.frame(trace)\n } else if(class(trace)==\"mcmc.list\"){\n convert_to_mcmc <- TRUE\n trace <- as.list(trace)\n }\n \n if(is.data.frame(trace) || is.matrix(trace)){\n \n # remove burn\n if (burn > 0) {\n trace <- trace[-(1:burn), ]\n }\n # thin\n trace <- trace[seq(1, nrow(trace), thin + 1), ]\n \n if(convert_to_mcmc){\n trace <- mcmc(trace)\n }\n \n } else {\n \n trace <- lapply(trace, function(x) {\n \n # remove burn\n if (burn > 0) {\n x <- x[-(1:burn), ]\n }\n # thin\n x <- x[seq(1, nrow(x), thin + 1), ]\n \n if(convert_to_mcmc){\n x <- mcmc(x)\n }\n \n return(x)\n }) \n \n if(convert_to_mcmc){\n trace <- mcmc.list(trace) \n }\n }\n \n return(trace)\n}\n\n\n#'Distance weighted by number of oscillations\n#'\n#'This positive distance is the mean squared differences between \\code{x} and the \\code{y}, divided by the square of the number of times the \\code{x} oscillates around the \\code{y} (see note below for illustration).\n#' @param x,y numeric vectors of the same length.\n#' @note To illustrate this distance, suppose we observed a time series \\code{y = c(1,3,5,7,5,3,1)} and we have two simulated time series \\code{x1 = (3,5,7,9,7,5,3)} and \\code{x2 = (3,5,3,5,7,5,3)}; \\code{x1} is consistently above \\code{y} and \\code{x2} oscillates around \\code{y}. While the squared differences are the same, we obtain \\eqn{d(y, x1) = 4} and \\eqn{d(y, x2) = 1.3}.\n#' @export\ndistanceOscillation <- function(x, y) {\n \n # check x and y have same length\n if(length(x)!=length(y)){\n stop(sQuote(\"x\"),\" and \",sQuote(\"y\"),\" must be vector of the same length\")\n }\n \n # 1 + number of times x oscillates around y\n n.oscillations <- 1+length(which(diff((x-y)>0)!=0))\n \n dist <- sum((x-y)^2)/(length(x)*n.oscillations)\n \n return(dist)\n}\n\n\n#'Export trace in Tracer format\n#'\n#'Print \\code{trace} in a \\code{file} that can be read by the software Tracer.\n#' @param trace a \\code{data.frame} with one column per estimated parameter, as returned by \\code{\\link{burnAndThin}}\n#' @inheritParams utils::write.table\n#' @note Tracer is a program for analysing the trace files generated by Bayesian MCMC runs. It can be dowloaded at \\url{http://tree.bio.ed.ac.uk/software/tracer}.\n#' @export\n#' @seealso burnAndThin\n#' @keywords internal\nexport2Tracer <- function(trace, file) {\n \n if(is.mcmc(trace)){\n trace <- as.data.frame(trace)\n }\n \n if(!\"iteration\"%in%names(trace)){\n trace$iteration <- (1:nrow(trace) - 1)\n }\n \n trace <- trace[c(\"iteration\",setdiff(names(trace),c(\"iteration\",\"weight\")))]\n write.table(trace,file=file,quote=FALSE,row.names=FALSE,sep=\"\\t\")\n \n}", "meta": {"hexsha": "8a7a26ccd0e582d2b68c18680cedb19cdc129981", "size": 18628, "ext": "r", "lang": "R", "max_stars_repo_path": "code/mcmcmh.r", "max_stars_repo_name": "gwenknight/piglet_transfer_rates", "max_stars_repo_head_hexsha": "25b015ee67538419f77c15e915867533d867e024", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/mcmcmh.r", "max_issues_repo_name": "gwenknight/piglet_transfer_rates", "max_issues_repo_head_hexsha": "25b015ee67538419f77c15e915867533d867e024", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/mcmcmh.r", "max_forks_repo_name": "gwenknight/piglet_transfer_rates", "max_forks_repo_head_hexsha": "25b015ee67538419f77c15e915867533d867e024", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7463768116, "max_line_length": 380, "alphanum_fraction": 0.6257783981, "num_tokens": 4824, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.33971054342622387}} {"text": "#' @title\n#' Prepare for plotting the bootstrapped metrics at the species level of one or\n#' multiple networks.\n#'\n#' @description\n#' Takes a list of network interactions (each interaction being repeated as\n#' many times as it was observed), and bootstraps the given species level\n#' metric (`index`) for each network. Runs `boot_specieslevel_n()` for a list\n#' of network interactions and prepares the data for plotting with `ggplot`.\n#' The output list can be passed to\n#' \\code{\\link[bootstrapnet]{gg_specieslevel_compare_webs}} or to\n#' \\code{\\link[bootstrapnet]{get_stats_multi}} and then its output to\n#' \\code{\\link[bootstrapnet]{gg_specieslevel_web_by_web}}. See examples below.\n#'\n#' @param lst\n#' A list of one or multiple data frames of interactions from which to build\n#' and sample web matrices. Each interaction (row in the data frame) must be\n#' repeated as many times as it was observed. E.g. if the interaction species_1\n#' x species_2 was observed 5 times, then repeat that row 5 times within the\n#' data frame.\n#'\n#' @param col_lower\n#' Quoted column name in `data` for lower trophic level species (plants).\n#'\n#' @param col_higher\n#' Quoted column name in `data` for higher trophic level species (insects).\n#'\n#' @param index\n#' Passed to \\code{\\link[bipartite]{networklevel}}. See\n#' `?bipartite::networklevel` for details.\n#'\n#' @param level\n#' Passed to \\code{\\link[bipartite]{networklevel}}. See\n#' `?bipartite::networklevel` for details. For which level should the\n#' level-specific indices be computed: 'both' (default), 'lower' or 'higher'?\n#'\n#' @param start\n#' Integer. The sample size (number of interactions) to start the bootstrap\n#' with. If the start sample size is small (e.g. 5 or 10), then first\n#' iterations might results in NaN-s and warning messages are displayed.\n#' Consider to set `start` to maybe 10\\\\% of your total unique interactions.\n#'\n#' @param step\n#' Integer. Sample size (number of interactions) used to increase gradually the\n#' sampled network until all interactions are sampled. If `step` is too small\n#' (e.g. 1) then the computation time is very long depending on your total\n#' number of interactions from which samples are taken. Consider to set `step`\n#' to maybe 5-10\\\\% of your total unique interactions.\n#'\n#' @param n_boot\n#' Number of desired bootstraps (50 or 100 can be enough).\n#'\n#' @param n_cpu\n#' Number of CPU-s to use for parallel processing.\n#'\n#' @param probs\n#' A numeric vector of two probabilities in `[0, 1]`. Passed to\n#' \\code{\\link[matrixStats]{rowQuantiles}} and used for building the lower and\n#' upper bounds of the confidence intervals. Defaults to `c(0.025, 0.975)`,\n#' which corresponds to a 95\\\\% confidence interval.\n#'\n#' @param ...\n#' Other arguments passed to \\code{\\link[bipartite]{specieslevel}}.\n#'\n#' @return\n#' A 3 levels list of the same length as `lst`. The list can be passed to\n#' \\code{\\link[bootstrapnet]{gg_specieslevel_compare_webs}} or to\n#' \\code{\\link[bootstrapnet]{get_stats_multi}} and then its output to\n#' \\code{\\link[bootstrapnet]{gg_specieslevel_web_by_web}}. Each element of the\n#' list is a sub-list corresponding to each network. Each sub-list contains\n#' further sub-lists with the bootstrapped metric values at 'lower', 'higher'\n#' or 'both' levels. Each sub-sub-lists contains two data frames: `stats_df`\n#' and `lines_df`, which can be used by the `ggplot2::geom_line()` function.\n#' See the return section of \\code{\\link[bootstrapnet]{get_stats_single}} for\n#' more details about `stats_df` and `lines_df` data frames.\n#'\n#' @examples\n#'\n#' library(bootstrapnet)\n#' library(bipartite)\n#' library(magrittr)\n#' data(Safariland)\n#'\n#' set.seed(321)\n#' Safariland_1 <- Safariland[, sort(sample.int(ncol(Safariland), 20))]\n#' set.seed(123)\n#' Safariland_2 <- Safariland[, sort(sample.int(ncol(Safariland), 20))]\n#'\n#' lst <- list(s1 = Safariland_1, s2 = Safariland_2) %>%\n#' lapply(web_matrix_to_df) %>%\n#' boot_specieslevel(col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"betweenness\",\n#' level = \"both\",\n#' start = 50,\n#' step = 20,\n#' n_boot = 10,\n#' n_cpu = 2)\n#'\n#' lst %>%\n#' get_stats_multi() %>%\n#' gg_specieslevel_compare_webs(sp_lower = \"Alstroemeria aurea\",\n#' sp_higher = \"Allograpta.Toxomerus\")\n#'\n#' lst %>%\n#' gg_specieslevel_web_by_web(sp_lower = c(\"Alstroemeria aurea\",\n#' \"Aristotelia chilensis\"))\n#'\n#' @export\n#'\n#' @md\nboot_specieslevel <- function(lst,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n n_boot,\n n_cpu,\n probs = c(0.025, 0.975),\n ...){\n\n webs_stats <- vector(mode = \"list\", length = length(lst))\n names(webs_stats) <- names(lst)\n\n for (i in 1:length(webs_stats)){\n webs_stats[[i]] <- lst[[i]] %>%\n boot_specieslevel_n(col_lower = col_lower,\n col_higher = col_higher,\n index = index,\n level = level,\n start = start,\n step = step,\n n_boot = n_boot,\n n_cpu = n_cpu,\n ...) %>%\n lapply(FUN = get_stats_single, probs = probs)\n }\n\n return(webs_stats)\n}\n\n\n#' @title\n#' Bootstrap metric at the species level multiple times.\n#'\n#' @description\n#' Bootstrap a single network of interactions n times in parallel. This\n#' function is used for network metrics at the species level. Starts with a\n#' small sample size of interactions (e.g. `start = 30`), builds the\n#' corresponding web matrix/network, computes its metric (e.g. `index = \"d\"`)\n#' using \\code{\\link[bipartite]{specieslevel}}, then adds new interactions\n#' (e.g. `step = 20`) until all interactions are sampled. The last sample is\n#' actually the entire network. Repeats n times (as given in `n_boot`) these\n#' steps in parallel on multiple CPUs.\n#'\n#' @param data\n#' Data frame of interactions from which to build and sample web matrices. Each\n#' interaction (row in the data frame) must be repeated as many times as it was\n#' observed. E.g. if species_1 x species_2 was observed 5 times, then repeat\n#' that row 5 times within the data frame. See examples below.\n#'\n#' @param col_lower\n#' Quoted column name in `data` for lower trophic level species (plants).\n#'\n#' @param col_higher\n#' Quoted column name in `data` for higher trophic level species (insects).\n#'\n#' @param index\n#' Passed to \\code{\\link[bipartite]{specieslevel}}. See\n#' `?bipartite::specieslevel` for details.\n#'\n#' @param level\n#' Passed to \\code{\\link[bipartite]{specieslevel}}. See\n#' `?bipartite::specieslevel` for details. For which level should the\n#' level-specific indices be computed: 'both' (default), 'lower' or 'higher'?\n#'\n#' @param start\n#' Integer. The sample size (number of interactions) to start the bootstrap\n#' with. If the start sample size is small (e.g. 5 or 10), then first\n#' iterations might results in NaN-s and warning messages are displayed.\n#' Consider to set `start` to maybe 10\\\\% of your total unique interactions.\n#'\n#' @param step\n#' Integer. Sample size (number of interactions) used to increase gradually the\n#' sampled network until all interactions are sampled. If `step` is too small\n#' (e.g. 1) then the computation time is very long depending on your total\n#' number of interactions from which samples are taken. Consider to set `step`\n#' to maybe 5-10\\\\% of your total unique interactions.\n#'\n#' @param n_boot\n#' Number of desired bootstraps (50 or 100 can be enough).\n#'\n#' @param n_cpu\n#' Number of CPU-s to use for parallel processing.\n#'\n#' @param ...\n#' Other arguments passed to \\code{\\link[bipartite]{specieslevel}} like\n#' `logbase`, etc.\n#'\n#' @return\n#' Returns a list of 1, 2 or 4 arrays of matrices. The species names are stored\n#' as the row names of each matrix. The number of columns of a matrix indicates\n#' how many iterations took place. This is decided internally based on the given\n#' values to `start`, `step` and the total number of rows (interactions) in\n#' `data`. The column names give the sample size at each iteration. The last\n#' iteration (last column name) is always the entire network (total number of\n#' interactions in `data`). The 3rd dimension (number of matrices in the array)\n#' corresponds to `n_boot` (number of bootstraps).\n#'\n#' @examples\n#'\n#' library(bootstrapnet)\n#' library(magrittr)\n#' library(bipartite)\n#' data(Safariland)\n#'\n#' Safariland %>%\n#' web_matrix_to_df() %>%\n#' boot_specieslevel_n(col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"d\",\n#' level = \"both\",\n#' start = 100,\n#' step = 100,\n#' n_boot = 10,\n#' n_cpu = 2)\n#'\n#' @import data.table\n#'\n#' @importFrom foreach foreach %dopar% registerDoSEQ\n#' @importFrom parallel splitIndices makeCluster stopCluster\n#' @importFrom doParallel registerDoParallel\n#' @importFrom iterators iter\n#'\n#' @export\n#'\n#' @md\nboot_specieslevel_n <- function(data,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n n_boot,\n n_cpu,\n ...){\n test_data(data, col_lower, col_higher)\n cls_data <- class(data)\n if (! \"data.table\" %in% class(data)) data.table::setDT(data)\n\n test_index_specieslevel(index)\n\n test_level_value(level)\n\n test_data_species_names(data, col_lower, col_higher)\n\n # Get sample sizes. Column names of the resulting bootstrap matrices will\n # carry information about the sample size at each iteration/bootstrap step.\n # This is run also before the parallel processing initiation because it can\n # throw error messages if the start and step values are not adequate. No need\n # to initiate parallel processing for something that will fail.\n iter_spl_size <- sample_indices(data = data, start = start, step = step, seed = 42) %>%\n sapply(length)\n\n { # Start parallel processing\n chunks <- parallel::splitIndices(n_boot, n_cpu)\n cl <- parallel::makeCluster(n_cpu)\n doParallel::registerDoParallel(cl)\n i <- NULL # to avoid 'Undefined global functions or variables: i' in R CMD check\n\n boot_lst <- foreach::foreach(i = iterators::iter(chunks),\n .errorhandling = 'pass',\n .packages = c(\"data.table\",\n \"bipartite\",\n \"magrittr\"),\n .export = c(\"boot_specieslevel_once\",\n \"split_in_chunks\",\n \"sample_indices\",\n \"boot_specieslevel_lower_or_higher\",\n \"boot_specieslevel_both_levels\")) %dopar%\n {\n lapply(i, FUN = function(x)\n boot_specieslevel_once(data = data,\n col_lower = col_lower,\n col_higher = col_higher,\n index = index,\n level = level,\n start = start,\n step = step,\n seed = x,\n ...) )\n }\n parallel::stopCluster(cl)\n remove(cl)\n foreach::registerDoSEQ()\n } # End of parallel processing\n\n boot_lst <- unlist(boot_lst, recursive = FALSE)\n\n metric_names <- names(boot_lst[[1]])\n n <- length(metric_names)\n\n if (n == 1) {\n results <- get_list_of_arrays(boot_lst, metric_names, n, iter_spl_size)\n # Convert data back to data frame if applicable\n if (! \"data.table\" %in% cls_data) data.table::setDF(data)\n return(results)\n\n } else if (n == 2) {\n results <- get_list_of_arrays(boot_lst, metric_names, n, iter_spl_size)\n if (! \"data.table\" %in% cls_data) data.table::setDF(data)\n return(results)\n\n } else if (n == 4) {\n results <- get_list_of_arrays(boot_lst, metric_names, n, iter_spl_size)\n if (! \"data.table\" %in% cls_data) data.table::setDF(data)\n return(results)\n }\n}\n\n\n#' @title\n#' Bootstrap metric at the species level once.\n#'\n#' @description\n#' You will rarely use this function alone. It was designed to be executed in\n#' parallel by \\code{\\link[bootstrapnet]{boot_specieslevel_n}}. See more\n#' details there.\n#'\n#' @param data\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param col_lower\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param col_higher\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param index\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param level\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param start\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param step\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @param seed\n#' Set seed to get reproducible random results. Passed to `set.seed()`.\n#'\n#' @param ...\n#' See \\code{\\link[bootstrapnet]{boot_specieslevel_n}}.\n#'\n#' @return\n#' A list of 1, 2 or 4 matrices. This depends on the provided `index` metric\n#' and `level` value.\n#'\n#' @references\n#' This function is a wrapper of \\code{\\link[bipartite]{specieslevel}}.\n#'\n#' @import data.table\n#'\n#' @importFrom bipartite specieslevel\n#'\n#' @export\n#'\n#' @md\nboot_specieslevel_once <- function(data,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n seed,\n ...){\n\n # List of sampled indices as vectors for bootstrapping. Each vector of indices\n # is used to build a web matrix/network from data.\n ids_lst <- sample_indices(data = data, start = start, step = step, seed = seed)\n\n # Allocate memory for a list of outputs (data frames) from\n # bipartite::specieslevel()\n metric_lst <- vector(mode = \"list\", length = length(ids_lst))\n\n # Sample interactions with the indices build above and compute the species\n # level metric until all interactions are sampled. In case of warnings or\n # errors (most probably related to small sample size of a network at first\n # iterations), then return NA-s for the metric so it doesn't break the\n # simulation.\n for (i in 1:length(ids_lst)){\n metric_lst[[i]] <- try({\n # Some metrics may have random processes in their computation, so set seed\n # here also for reproducibility.\n web <- data[ids_lst[[i]], table(get(col_lower), get(col_higher))]\n set.seed(42)\n bipartite::specieslevel(web = web, index = index, level = level, ...)\n })\n }\n\n if (level == \"lower\") {\n boot_results <- boot_specieslevel_lower_or_higher(data = data,\n col = col_lower,\n metric_lst = metric_lst,\n level)\n return(boot_results)\n\n } else if (level == \"higher\") {\n boot_results <- boot_specieslevel_lower_or_higher(data = data,\n col = col_higher,\n metric_lst = metric_lst,\n level)\n return(boot_results)\n\n } else if (level == \"both\") {\n boot_results <- boot_specieslevel_both_levels(data = data,\n col_lower = col_lower,\n col_higher = col_higher,\n metric_lst = metric_lst)\n return(boot_results)\n }\n}\n", "meta": {"hexsha": "decbf3b60328366e817482482e7b5f0cc4354920", "size": 17175, "ext": "r", "lang": "R", "max_stars_repo_path": "R/boot_specieslevel.r", "max_stars_repo_name": "valentinitnelav/bootstrapnet", "max_stars_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-12-13T14:09:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-25T13:53:11.000Z", "max_issues_repo_path": "R/boot_specieslevel.r", "max_issues_repo_name": "valentinitnelav/bootstrapnet", "max_issues_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 34, "max_issues_repo_issues_event_min_datetime": "2019-11-06T00:07:03.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T16:57:00.000Z", "max_forks_repo_path": "R/boot_specieslevel.r", "max_forks_repo_name": "valentinitnelav/bootstrapnet", "max_forks_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-12-03T15:41:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-03T15:41:38.000Z", "avg_line_length": 40.034965035, "max_line_length": 96, "alphanum_fraction": 0.5771761281, "num_tokens": 3994, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.33966533870228965}} {"text": "#!/usr/bin/env Rscript\n\n\n#library(tikzDevice)\n#tikz(file=\"plot-results.tex\", width=5, height=5)\n\n\ntransform.proportions = function(x, from, to) {\n\tbase = 1 + 1/(to - 1)\n\tbase ^ (x / from) * from\n}\n\nfrom.prop = 10\nto.prop = 5\n\nxtrans = function(x) {\n\ttransform.proportions(x, from.prop, to.prop)\n}\n\n\ncolumn <- \"time\"\ntimeout <- 60\n#xlim <- c(xtrans(0),xtrans(100))\nxlim <- NULL\nylim <- c(0.001,timeout)\nmergeBy <- \"query\"\n\ncut.timeout = function(x) {\n\tsapply(x, function(e) min(e,timeout))\n}\n\n\nargs <- commandArgs(TRUE)\n\nrequiredArgCount <- 3\nif(length(args) < requiredArgCount) {\n\tcat(sprintf(\"Expected %d arguments!\\n\", requiredArgCount))\n\tq(status=1)\n}\n\n\nxvalues = list()\nyvalues = list()\ncolors = list()\n\nargIndex <- 1\ntitle <- args[argIndex]\n\nargIndex <- argIndex + 1\nlegend <- args[argIndex]\nargIndex <- argIndex + 1\nX <- read.csv(args[argIndex])\n\ndataIndex <- 1\nnames(X) <- paste0(names(X), \".\", dataIndex)\nM <- X\n\nwhile (argIndex + 1 < length(args)) {\n\targIndex <- argIndex + 1\n\tlegend <- c(legend, args[argIndex])\n\targIndex <- argIndex + 1\n\tX <- read.csv(args[argIndex])\n\n\tdataIndex <- dataIndex + 1\n\tnames(X) <- paste0(names(X), \".\", dataIndex)\n\tM <- merge(M, X, by.x=paste0(mergeBy, \".1\"), by.y=paste0(mergeBy, \".\", dataIndex))\n}\n\nmin.na.rm <- function(...) {\n\tmin(..., na.rm=TRUE)\n}\nM[[column]] <- apply(M[,paste0(column, \".\", seq(dataIndex))], 1, min.na.rm)\ndata <- M[[column]]\ntimeOrder <- order(data)\nstep <- 100 / length(data)\nxvalues <- seq(step, 100, step)\nplot(xtrans(xvalues), cut.timeout(data[timeOrder] / 1000), type=\"l\", log=\"y\", axes=FALSE, main=title, xlab=\"\\\\% of queries\", ylab=\"time in seconds\", xlim=xlim, ylim=ylim)\nxticks = seq(0, 100, 10)\naxis(1, at=xtrans(xticks), labels=xticks)\nyticks = c(0.001, 0.01, 0.1, 1, 10, 60, 100)\nylabels = c(\"0.001\", \"0.01\", \"0.1\", \"1\", \"10\", \"60\", \"100\")\naxis(2, at=yticks, labels=ylabels)\nabline(h=yticks, v=xtrans(xticks), col=\"lightgray\", lty=3)\nbox()\n\ncolorIndex <- 2\nlegendStyle <- c()\nfor (i in seq(dataIndex)) {\n\tdata <- M[[paste0(column, \".\", i)]]\n\ttimeOrder <- order(data)\n\tstep <- 100 / length(data)\n\txvalues <- seq(step, 100, step)\n\tlines(xtrans(xvalues), cut.timeout(data[timeOrder] / 1000), col=colorIndex, lty=colorIndex)\n\n\tlegendStyle <- c(legendStyle, colorIndex)\n\tcolorIndex <- colorIndex + 1\n}\n\nlegend <- c(legend, \"minimal\")\nlegendStyle <- c(legendStyle, 1)\nlegend(xtrans(0), ylim[2], legend, col=legendStyle, lty=legendStyle, bg=\"white\")\n\n\ndev.off()\n\n", "meta": {"hexsha": "f36059430c0d0b706f56616e37c9b7b176cc12c3", "size": 2433, "ext": "r", "lang": "R", "max_stars_repo_path": "src/main/docker/prunpinpointing/scripts/plot_common_queries.r", "max_stars_repo_name": "marnadir/docker-pruning-experiments", "max_stars_repo_head_hexsha": "e10d8d2fb8e540162c6eb54207084bd680832fe2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/main/docker/prunpinpointing/scripts/plot_common_queries.r", "max_issues_repo_name": "marnadir/docker-pruning-experiments", "max_issues_repo_head_hexsha": "e10d8d2fb8e540162c6eb54207084bd680832fe2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-01-22T11:25:03.000Z", "max_issues_repo_issues_event_max_datetime": "2018-01-23T11:50:01.000Z", "max_forks_repo_path": "src/main/docker/pinpointing/scripts/plot_common_queries.r", "max_forks_repo_name": "liveontologies/docker-pinpointing-experiments", "max_forks_repo_head_hexsha": "80c3b0cbae2cfaf8fd3beebe4e37ef9cc746a2e0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9528301887, "max_line_length": 170, "alphanum_fraction": 0.6448828607, "num_tokens": 803, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3396653319985615}} {"text": "library('RMySQL')\nlibrary('lme4')\n\nargs = commandArgs(trailingOnly=TRUE)\n\ncon = dbConnect(RMySQL::MySQL(), \n host = \"db.tychio.net\",\n user = \"psychio\",\n password = args[1],\n dbname = \"psychio\",\n port = 62992\n)\n\nres = dbGetQuery(con, \"\n SELECT * FROM experiment_trials WHERE kind = 0 LIMIT 10000\n\")\n\nfm = lmer(key ~ speed + 1 | name, REML = FALSE, data = res)\n\nanova(fm)", "meta": {"hexsha": "81c129934d6e0f98cd479c3cab527f2d7ba0d59a", "size": 387, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis.r", "max_stars_repo_name": "tychio/psychio-lme4", "max_stars_repo_head_hexsha": "a754aa414f02b67de642aed6e1e9497563fe01a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis.r", "max_issues_repo_name": "tychio/psychio-lme4", "max_issues_repo_head_hexsha": "a754aa414f02b67de642aed6e1e9497563fe01a2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis.r", "max_forks_repo_name": "tychio/psychio-lme4", "max_forks_repo_head_hexsha": "a754aa414f02b67de642aed6e1e9497563fe01a2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.35, "max_line_length": 62, "alphanum_fraction": 0.6304909561, "num_tokens": 120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.782662489091802, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.33966101346312544}} {"text": "#' Function for mapping ie results onto a phylogenetic tree\n#'\n#' This function maps the results of an ie analysis onto a tree\n#' @param ieObject object comprising the output of an ie analysis\n#' @return phylogenetic tree with mapped change and ancestral states\n#' @export\n\niePlot<-function(ieObject,tree,branchWidth,branchCutoff,labelSize,scaling){\n\n par(bg=\"black\")\n #Branches\n cutoff<-(sd(ieObject$change)*branchCutoff)\n branch_widths_ieObject<-sqrt(ieObject$change*ieObject$change)*branchWidth\n branch_col_ieObject<-rep(\"white\",length(ieObject$change))\n for(i in 1:length(branch_col_ieObject)){\n if(ieObject$change[i]>cutoff)branch_col_ieObject[i]<-\"green\" else (if(ieObject$change[i]<=-cutoff)branch_col_ieObject[i]<-\"red\")}\n plot(tree,edge.col=branch_col_ieObject,edge.width=branch_widths_ieObject,label.offset=0.01,cex=0.9,tip.col=\"white\")\n axisPhylo(cex=2,col=\"white\",col.axis=\"white\")\n mtext(\"Mya\",side=1,adj=-0.05,col=\"white\")\n #Add labels\n #Internal nodes\n rootvalue<-ieObject$value_anc[1]\n results_ordered<-ieObject[order(ieObject$node_desc),]\n internalnodesize<-results_ordered$value_desc[(length(tree$tip.label)+1):((length(tree$tip.label)*2)-2)]\n nodelabelsize<-(c(rootvalue,internalnodesize)*labelSize)^scaling\n nodelabels(pch=21,node=which(nodelabelsize>=0)+length(tree$tip.label),cex=abs(nodelabelsize[which(nodelabelsize>=0)]),bg=\"white\")\n nodelabels(pch=21,node=which(nodelabelsize<0)+length(tree$tip.label),cex=abs(nodelabelsize[which(nodelabelsize<0)]),bg=\"white\")\n #Terminal nodes\n tiplabelsize<-(results_ordered$value_desc[1:length(tree$tip.label)]*labelSize)^scaling\n tiplabels(pch=21,tip=which(tiplabelsize>=0),cex=abs(tiplabelsize[which(tiplabelsize>=0)]),bg=\"white\")\n tiplabels(pch=21,tip=which(tiplabelsize<=0),cex=abs(tiplabelsize[which(tiplabelsize<=0)]),bg=\"white\")\n par(bg=\"white\")\n }\n", "meta": {"hexsha": "11b2905adc54c42926d8ada9259e6aefda689197", "size": 2458, "ext": "r", "lang": "R", "max_stars_repo_path": "Evomap_Previous Version/V2/R/iePlot.r", "max_stars_repo_name": "JeroenSmaers/evomap", "max_stars_repo_head_hexsha": "dfa7dfdc560d1fd04414dffedab7b6be765d8175", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-01-07T04:20:28.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-15T19:42:12.000Z", "max_issues_repo_path": "Evomap_Previous Version/V2/R/iePlot.r", "max_issues_repo_name": "JeroenSmaers/evomap", "max_issues_repo_head_hexsha": "dfa7dfdc560d1fd04414dffedab7b6be765d8175", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Evomap_Previous Version/V2/R/iePlot.r", "max_forks_repo_name": "JeroenSmaers/evomap", "max_forks_repo_head_hexsha": "dfa7dfdc560d1fd04414dffedab7b6be765d8175", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2015-10-14T18:26:29.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-10T04:57:55.000Z", "avg_line_length": 72.2941176471, "max_line_length": 165, "alphanum_fraction": 0.5724165989, "num_tokens": 582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3395893495876965}} {"text": "a <- 1\nb <- 2\nx <- rnorm(100, mean = 3, sd = 1)\nplot(1:100, (1:100)^2, main = \"plot(1:100, (1:100) ^ 2)\")\n\nmtcars0 <- mtcars\ny <- 1:10\nres <- lapply(1:10, rnorm)\n\ntest1 <- rnorm(100)\n\ntest2 <- function(x, y) {\n x + y\n}\n\ntest2(1, 2)\n\ndata1 <- list(a = 1, b = 2)\n\nplot(rnorm(100))\nabline(h = 0, col = \"blue\")\n\nlibrary(ggplot2)\nggplot(mpg, aes(displ, hwy, colour = class)) +\n geom_point()\n\nlibrary(plotly)\n\nlibrary(shiny)\nshiny::runExample(\"01_hello\")", "meta": {"hexsha": "4ac8d3ca5d016e5bfb25ab737b6e4473d820449c", "size": 454, "ext": "r", "lang": "R", "max_stars_repo_path": "simulator/rcode.r", "max_stars_repo_name": "subercui/DeepVelo", "max_stars_repo_head_hexsha": "5f6f7be89b29e62ec4c1e80c8ed3347d94c4a15a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulator/rcode.r", "max_issues_repo_name": "subercui/DeepVelo", "max_issues_repo_head_hexsha": "5f6f7be89b29e62ec4c1e80c8ed3347d94c4a15a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2021-02-22T00:43:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-13T22:33:39.000Z", "max_forks_repo_path": "simulator/rcode.r", "max_forks_repo_name": "subercui/DeepVelo", "max_forks_repo_head_hexsha": "5f6f7be89b29e62ec4c1e80c8ed3347d94c4a15a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-08T06:12:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-08T06:12:06.000Z", "avg_line_length": 15.1333333333, "max_line_length": 57, "alphanum_fraction": 0.5837004405, "num_tokens": 192, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.3395162939177265}} {"text": "\n## Author: Bill Duncan\n## Project: OHD\n## Date: Dec. 1, 2015\n##\n## Summary: Statics on dental proceedures\n\nsource(\"load.r\")\n\n## NB: this lines comes after sourcing above files!\n.GlobalEnv[[\"interpret_type\"]]=interpret_rdf_type;\n\n\nrestoration.counts.by.tooth <- function (limit=0, print.query=FALSE) \n{\n ## get dataframe holding results\n df <- query.from.file(\"sparql/restoration-count-by-tooth.sparql\", limit)\n \n ## when res is coverted to a data frame, the data types of the values are chars\n ## so, convert the count column into numeric data type\n mode(df$count) <- \"numeric\"\n \n ## add a column to the data frame the contains the integer that represents the tooth number\n ## e.g., for tooth type \"Tooth 12\", this column contains the number 12\n ## this is needed for ordering the data properly (i.e., by tooth number)\n df$toothnum <- match.tooth.position(df$tooth)\n \n ## build summary table\n info <- tapply(df$count, df$toothnum, sum)\n \n ## create barplot of info\n barplot(info, \n main=\"Number of restorations per tooth\",\n xlab=\"Tooth number of patient\", \n ylab=\"Number of restoration procedures\",\n col=\"blue\")\n \n ## return info\n info\n}\n\nnon.failed.restorations <- function (limit=0, print.query=FALSE) {\n \n ## build sparql query, note: use querystring function to add prefixes\n file <- \"sparql/non-failed-restorations.sparql\"\n query.string <- read.sparql.file(file, limit, print.query)\n \n ## get results and convert to dataframe; note: stringsAsFactors must be false\n res <- queryc(query.string)\n df <- as.data.frame(res, stringsAsFactors = F) \n}\n\naverage.time.to.restoration.failure <- function (limit=0, print.query=FALSE, days=FALSE)\n{\n\n ## build sparql query\n file <- \"sparql/first-and-second-restoration.sparql\"\n query.string <- read.sparql.file(file, limit, print.query)\n \n ## get results\n res <- queryc(query.string)\n \n ## build matrix with only the restoration procedure dates\n dates <- matrix(c(res[,\"date1\"], res[,\"date2\"]), ncol=2)\n\n ## add column to dates matrix that contains the difference\n ## between the dates in days\n dates <- cbind(dates, abs(floor(difftime(res[,\"date2\"], res[,\"date1\"], units = \"days\"))))\n\n ## determine average number of days to failure\n ave.failure.days <- as.numeric(mean(as.numeric(dates[,3])))\n\n ## determine average number of years to failure\n if(ave.failure.days > 0) {\n ave.failure.years <- ave.failure.days/365.25\n } else {\n ave.failure.years <- 0\n }\n\n ## if days is true return average in days\n if (days) {\n ave.failure.days\n } else {\n ave.failure.years\n }\n}\n\n\naverage.time.to.restoration.failure.by.sex <- function (limit=0, print.query=FALSE, days=FALSE)\n{\n\n ## build sparql query\n file <- \"sparql/first-and-second-restoration.sparql\"\n query.string <- read.sparql.file(file, limit, print.query)\n \n ## get results and convert to dataframe; note: stringsAsFactors must be false\n res <- queryc(query.string)\n df <- as.data.frame(res, stringsAsFactors = F)\n \n ## add column to data frame that contains the difference\n ## between the date1 and date2 in days\n df$datediff <- as.numeric(abs(floor(difftime(df$date2, df$date1, units = \"days\"))))\n\n ## build table data of the mean time for restoration differences by sex\n ## if days is false calculate average in years\n ## set up custome labels for barplot\n if (days==FALSE) {\n info <- tapply(df$datediff, df$patienttype, function(x) { mean(x) / 365.25 })\n y.label <- \"mean years until failure\"\n main.label <- \"mean time in years until restoration failure by sex\" \n } else {\n info <- tapply(df$datediff, df$patienttype, mean) \n y.label <- \"mean days until failure\"\n main.label <- \"mean time in days until restoration failure by sex\"\n }\n \n ## common labels\n x.label <- \"sex of patient\"\n y.lim <- ceiling(max(info[[1]], info[[2]]))\n# print(info[[1]])\n# print(info[[2]])\n# print(y.lim)\n\n ## draw barplot of info\n barplot(info, \n main=main.label,\n xlab=x.label,\n ylab=y.label,\n ylim=c(0, as.numeric(y.lim)),\n names.arg=c(\"female\", \"male\"), \n col=c(\"pink\", \"blue\"))\n\n ## return info about mean time to failure\n info\n }\n\naverage.time.to.restoration.failure.by.tooth <- function (limit=0, print.query=FALSE, days=FALSE, by.sex=FALSE)\n{\n ## build sparql query\n file <- \"sparql/first-and-second-restoration.sparql\"\n query.string <- read.sparql.file(file, limit, print.query)\n \n ## get results and convert to dataframe; note: stringsAsFactors must be false\n res <- queryc(query.string)\n df <- as.data.frame(res, stringsAsFactors = F)\n \n ## add column the data frame that contains the difference\n ## between the date1 and date2 in days\n df$datediff <- as.numeric(abs(floor(difftime(df$date2, df$date1, units = \"days\"))))\n \n ## add a column to the data frame the contains the integer that represents the tooth number\n ## e.g., for tooth type \"Tooth 12\", this column contains the number 12\n ## this is needed for ordering the data properly (i.e., by tooth number)\n df$toothnum <- match.tooth.position(df$toothtype)\n \n ## build table data of the mean time for restoration differences by tooth\n ## determine if results should be differentiated by sex\n if (by.sex==FALSE) {\n ## note: if days is false calculate average in years\n info <- tapply(df$datediff, df$toothnum, \n function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n } else {\n ## get subsets bases on sex\n females <- subset(df, patienttype == \"female dental patient\")\n males <- subset(df, patienttype == \"male dental patient\")\n \n ## get means for subsets\n females.mean <- tapply(females$datediff, females$toothnum,\n function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n males.mean <- tapply(males$datediff, males$toothnum,\n function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n \n ## combine male and female means\n info <- rbind(females.mean, males.mean)\n }\n \n ## set up custome labels for barplot\n x.label <- \"tooth number of patient\"\n \n if (days==FALSE) {\n y.label <- \"mean years until failure\"\n main.label <- \"mean time in years until restoration failure by tooth\" \n } else {\n y.label <- \"mean days until failure\"\n main.label <- \"mean time in days until restoration failure by tooth\"\n }\n \n ## draw barplot of info\n if (by.sex==FALSE) {\n barplot(info, \n main=main.label,\n xlab=x.label,\n ylab=y.label,\n col=\"red3\")\n } else {\n barplot(info, \n main=main.label,\n xlab=x.label,\n ylab=y.label,\n col=c(\"pink\", \"blue\"),\n beside=TRUE)\n legend(\"topleft\", c(\"female\",\"male\"), pch=15, \n col=c(\"pink\",\"blue\"), bty=\"n\")\n }\n \n ## return info about mean time to failure\n info\n}\n\naverage.time.to.restoration.failure.by.surface <- function (limit=0, print.query=FALSE, days=FALSE, by.sex=FALSE, by.surface.letter=FALSE)\n{\n \n ## build sparql query\n file <- \"sparql/first-and-second-restoration.sparql\"\n query.string <- read.sparql.file(file, limit, print.query)\n \n ## get results and convert to dataframe; note: stringsAsFactors must be false\n res <- queryc(query.string)\n df <- as.data.frame(res, stringsAsFactors = F)\n \n ## add column the data frame that contains the difference\n ## between the date1 and date2 in days\n df$datediff <- as.numeric(abs(floor(difftime(df$date2, df$date1, units = \"days\"))))\n \n ## add a column to the data frame the contains the normalized surface\n ## e.g., \"Occlusial surface enamel of tooth\" -> \"occlusal\" or \"o\"\n ## NB: you must call \"unlist\" for surfacenaem to be an atomic vector\n ## this necessary for tapply to work (below)\n if (by.surface.letter==FALSE) {\n df$surfacename <- unlist(lapply(df$surfacetype, FUN = function(x) { match.surface.name(x)}))\n } else {\n df$surfacename <- unlist(lapply(df$surfacetype, FUN = function(x) { match.surface.letter(x)}))\n }\n \n ## build table data of the mean time for restoration differences by surface\n ## determine if results should be differentiated by sex\n if (by.sex==FALSE) {\n ## note: if days is false calculate average in years\n #info <- tapply(df$datediff, df$surfacename, \n # function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n print(str(df$surfacename))\n \n info <- tapply(df$datediff, df$surfacename, mean)\n } else {\n ## get subsets bases on sex\n females <- subset(df, patienttype == \"female dental patient\")\n males <- subset(df, patienttype == \"male dental patient\")\n \n print(head(females$surfacename))\n print(length(females$datediff))\n \n ## get means for subsets\n females.mean <- tapply(females$datediff, females$surfacename,\n function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n \n males.mean <- tapply(males$datediff, males$surfacename,\n function(x) { ifelse(days==FALSE, mean(x) / 365.25, mean(x)) })\n \n ## combine male and female means\n info <- rbind(females.mean, males.mean)\n }\n \n ## set up custome labels for barplot\n x.label <- \"surface of tooth\"\n \n if (days==FALSE) {\n y.label <- \"mean years until failure\"\n main.label <- \"mean time in years until restoration failure by tooth\" \n } else {\n y.label <- \"mean days until failure\"\n main.label <- \"mean time in days until restoration failure by tooth\"\n }\n \n ## draw barplot of info\n if (by.sex==FALSE) {\n barplot(info, \n main=main.label,\n xlab=x.label,\n ylab=y.label,\n col=\"red3\")\n } else {\n barplot(info, \n main=main.label,\n xlab=x.label,\n ylab=y.label,\n col=c(\"pink\", \"blue\"),\n beside=TRUE)\n legend(\"topleft\", c(\"female\",\"male\"), pch=15, \n col=c(\"pink\",\"blue\"), bty=\"n\")\n }\n \n ## return info about mean time to failure\n info\n}\n\nmatch.tooth.position <- function(toothtype.string)\n{\n ## vector of teeth\n teeth <- c(\"tooth 1\", \"tooth 2\", \"tooth 3\", \"tooth 4\", \"tooth 5\", \"tooth 6\", \"tooth 7\", \"tooth 8\",\n \"tooth 9\", \"tooth 10\", \"tooth 11\", \"tooth 12\", \"tooth 13\", \"tooth 14\", \"tooth 15\", \"tooth 16\", \n \"tooth 17\", \"tooth 18\", \"tooth 19\", \"tooth 20\", \"tooth 21\", \"tooth 22\", \"tooth 23\", \"tooth 24\", \n \"tooth 25\", \"tooth 26\", \"tooth 27\", \"tooth 28\", \"tooth 29\", \"tooth 30\", \"tooth 31\", \"tooth 32\")\n \n ## get vector of postions for toothtypes\n positions <- match(tolower(toothtype.string), teeth, nomatch = 0)\n \n ## return position\n positions\n}\n\nmatch.surface.letter <- function (surfacetype.string) {\n ## cast to lower case\n surfacetype.string <- tolower(surfacetype.string)\n \n #### ***** Occlusal is MISPELLED\n if (surfacetype.string == \"occlusial surface enamel of tooth\") {\n surface.letter <- \"o\"\n } else if (surfacetype.string == \"distal surface enamel of tooth\") {\n surface.letter <- \"d\"\n } else if (surfacetype.string == \"mesial surface enamel of tooth\") {\n surface.letter <- \"m\"\n } else if (surfacetype.string == \"buccal surface enamel of tooth\") {\n surface.letter <- \"b\"\n } else if (surfacetype.string == \"labial surface enamel of tooth\") {\n surface.letter <- \"f\"\n } else if (surfacetype.string == \"lingual surface enamel of tooth\") {\n surface.letter <- \"l\"\n } else if (surfacetype.string == \"incisal surface enamel of tooth\") {\n surface.letter <- \"i\" \n } else {\n surface.letter <- surfacetype.string\n }\n \n ## return the surface letter\n surface.letter\n}\n\nmatch.surface.name <- function (surfacetype.string) {\n ## cast to lower case\n surfacetype.string <- tolower(surfacetype.string)\n \n #### ***** Occlusal is MISPELLED\n if (surfacetype.string == \"occlusial surface enamel of tooth\") {\n surface.name <- \"occlusal\"\n } else if (surfacetype.string == \"distal surface enamel of tooth\") {\n surface.name <- \"distal\"\n } else if (surfacetype.string == \"mesial surface enamel of tooth\") {\n surface.name <- \"mesial\"\n } else if (surfacetype.string == \"buccal surface enamel of tooth\") {\n surface.name <- \"buccal\"\n } else if (surfacetype.string == \"labial surface enamel of tooth\") {\n surface.name <- \"labial\"\n } else if (surfacetype.string == \"lingual surface enamel of tooth\") {\n surface.name <- \"lingual\"\n } else if (surfacetype.string == \"incisal surface enamel of tooth\") {\n surface.name <- \"incisal\"\n } else {\n surface.name <- surfacetype.string\n }\n \n ## return the surface name\n surface.name\n}\n\n", "meta": {"hexsha": "c9af41dfbf4111531f2e1107fb2500745577b683", "size": 12670, "ext": "r", "lang": "R", "max_stars_repo_path": "src/analysis/restoration-time-intervals.r", "max_stars_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_stars_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-08T16:11:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T16:11:01.000Z", "max_issues_repo_path": "src/analysis/restoration-time-intervals.r", "max_issues_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_issues_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-05-13T19:04:16.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-31T18:34:56.000Z", "max_forks_repo_path": "src/analysis/restoration-time-intervals.r", "max_forks_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_forks_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7123287671, "max_line_length": 138, "alphanum_fraction": 0.6389897395, "num_tokens": 3493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3395162850576029}} {"text": "library(iotools)\n\ncol_types <- c(rep(\"integer\", 8), \"character\", \"integer\", \"character\",\n rep(\"integer\", 5), \"character\", \"character\",\n rep(\"integer\", 4), \"character\", rep(\"integer\", 6))\n\ncol_names <- c(\"Year\", \"Month\", \"DayofMonth\", \"DayOfWeek\", \"DepTime\", \n \"CRSDepTime\", \"ArrTime\", \"CRSArrTime\", \"UniqueCarrier\", \"FlightNum\", \n \"TailNum\", \"ActualElapsedTime\", \"CRSElapsedTime\", \"AirTime\", \"ArrDelay\", \n \"DepDelay\", \"Origin\", \"Dest\", \"Distance\", \"TaxiIn\", \"TaxiOut\", \"Cancelled\", \n \"CancellationCode\", \"Diverted\", \"CarrierDelay\", \"WeatherDelay\", \"NASDelay\", \n \"SecurityDelay\", \"LateAircraftDelay\")\n\n# A mutable closure to skip the header line.\nmake_airline_df_gen <- function() {\n first_run <- TRUE\n function(chunk) {\n x <- dstrsplit(chunk, col_types=col_types, sep=\",\", \n skip=as.integer(first_run))\n colnames(x) <- col_names\n first_run <<- FALSE\n x\n }\n}\n\n#######\n# One pass to get the unique character types.\n#######\n\nmake_airline_df <- make_airline_df_gen()\n\nus <- chunk.apply(\"airline.csv\",\n # Get the unique values for each of the character columns.\n function(chunk) {\n x <- make_airline_df(chunk)\n list(carrier=unique(x$UniqueCarrier),\n tail_num=unique(x$TailNum),\n origin=unique(x$Origin),\n dest=unique(x$Dest),\n cancel_code=unique(x$CancellationCode))\n }, CH.MERGE=list)\n\n########\n# Make the maps between the character values and an integer.\n########\n\nunique_reduce <- function(x, y) unique(c(x, y))\n\ncarrier_names <- \n sort(Reduce(unique_reduce, Map(function(x) x$carrier, us)))\ncarrier <- 1:length(carrier_names)\nnames(carrier) <- carrier_names\n\ntail_num_names <- sort(Reduce(unique_reduce, Map(function(x) x$tail_num, us)))\ntail_num_names <- tail_num_names[tail_num_names != \"NA\"]\ntail_num <- 1:length(tail_num_names)\nnames(tail_num) <- tail_num_names\n\norigin_names <- sort(Reduce(unique_reduce, Map(function(x) x$origin, us)))\norigin <- 1:length(origin_names)\nnames(origin) <- origin_names\n\ndest_names <- sort(Reduce(unique_reduce, Map(function(x) x$dest, us)))\ndest <- 1:length(dest_names)\nnames(dest) <- dest_names\n\ncancel_names <- sort(Reduce(unique_reduce, Map(function(x) x$cancel_code, us)))\ncancel_names <- cancel_names[cancel_names != \"\" & cancel_names != \"NA\"] \ncancel_code <- 1:length(cancel_names)\nnames(cancel_code) <- cancel_names\n\nsave(carrier, tail_num, origin, dest, cancel_code, \n file=\"airline_character_maps.RData\", ascii=TRUE)\n\n#######\n# Now create the output file. \n#######\n\nout_file <- file(\"airline_int_cols.csv\", \"wb\")\nwriteBin(as.output(matrix(col_names, nrow=1), sep=\",\"), out_file)\n\n# Make sure to create a new make_airline_df mutable closure. first_run\n# is FALSE in the old one.\nmake_airline_df <- make_airline_df_gen()\n\nchunk.apply(\"airline.csv\",\n function(chunk) {\n x <- make_airline_df(chunk)\n x$UniqueCarrier <- as.vector(as.integer(carrier[x$UniqueCarrier]))\n x$TailNum <- as.vector(as.integer(carrier[x$TailNum]))\n x$Origin <- as.vector(as.integer(carrier[x$Origin]))\n x$Dest <- as.vector(as.integer(carrier[x$Dest]))\n x$CancellationCode <- as.vector(as.integer(carrier[x$CancellationCode]))\n writeBin(as.output(x, sep=\",\"), out_file)\n NULL\n })\n\nclose(out_file)\n\n", "meta": {"hexsha": "735679bb3453f9f244b9b9c457c24acd51184b4e", "size": 3226, "ext": "r", "lang": "R", "max_stars_repo_path": "Case Studies/05_make_airline.r", "max_stars_repo_name": "bmoretz/Statistical-Computing", "max_stars_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-27T08:18:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-09T09:20:49.000Z", "max_issues_repo_path": "Case Studies/05_make_airline.r", "max_issues_repo_name": "bmoretz/Statistical-Computing", "max_issues_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Case Studies/05_make_airline.r", "max_forks_repo_name": "bmoretz/Statistical-Computing", "max_forks_repo_head_hexsha": "606e6bb222013c38867c1aee7e79fae762e7a445", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-07-27T08:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-02T11:31:27.000Z", "avg_line_length": 32.26, "max_line_length": 79, "alphanum_fraction": 0.6850588965, "num_tokens": 873, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33951628505760284}} {"text": "library(magrittr)\n\n##############################\n#Estimates the TRW model for fixed lambda\n#Parallelizes by connected components\n#\n##############################\n\nparallelTrwFit<-function(edge_coef,node_coef,edge_mean,node_mean,\n edgelist,weights,lambda,alpha_start,\n g,cores=1){\n clust<- clusters(g)$membership\n d<-dim(node_coef)[1]\n res<-dim(legen.Vec)[2]\n m<-dim(edge_mean)[1]\n out_list=list()\n out_list$beliefs<-matrix(nrow=d,ncol=res)\n out_list$edge_den<- array(dim=c(res,res,dim(edgelist)[1]))\n out_list$vert_coef<-matrix(nrow=d,ncol=m)\n out_list$vert_quasi<-matrix(nrow=d,ncol=m)\nout_list$vert_gradient<-matrix(nrow=d,ncol=m)\n\n out_list$mutual_info<- vector(length=dim(edgelist)[1])\n \n out_list$edge_coef<- array(dim=c(m,m,dim(edgelist)[1]))\n out_list$edge_quasi<- array(dim=c(m,m,dim(edgelist)[1]))\n out_list$edge_gradient<- array(dim=c(m,m,dim(edgelist)[1]))\n out_list$mes <- array(dim=c(2*dim(edgelist)[1],res))\n clust_out<-mclapply(unique(clust),\n function(c){\n sub<- induced.subgraph(g,vids=(clust==c))\n sub_edgelist<- get.edgelist(sub)\n ekeep<- apply(edgelist,1,function(x)any( (clust==c)[x]) )\n out<-rwrapper(\n edge_coef[,,ekeep,drop=F],\n node_coef[c==clust,,drop=F],\n edge_mean[,,ekeep,drop=F],\n node_mean[c==clust,,drop=F],\n sub_edgelist,\n weights[ekeep],\n legen.Vec,\n legen.array, \n lambda,\n alpha_start\n )\n return(out)\n },\n mc.cores=cores,\n mc.preschedule=F\n )\n out_list$part_fn<-0\n out_list$nll<-0\n for(c in unique(clust)){\n out_list$part_fn<-out_list$part_fn+clust_out[[c]]$part_fn\n out_list$nll<-out_list$nll+clust_out[[c]]$nll\n\n sub<- induced.subgraph(g,vids=(clust==c))\n sub_edgelist<- get.edgelist(sub)\n ekeep<- apply(edgelist,1,function(x)any( (clust==c)[x]) )\n out_list$beliefs[c==clust,]<-clust_out[[c]]$beliefs\n out_list$edge_den[,,ekeep]<- clust_out[[c]]$edge_den\n out_list$vert_coef[c==clust,]<-clust_out[[c]]$vert_coef\n out_list$edge_coef[,,ekeep]<- clust_out[[c]]$edge_coef\n out_list$mutual_info[ekeep]<- clust_out[[c]]$mutual_info\n out_list$vert_quasi[c==clust,]<- clust_out[[c]]$vert_quasi\n out_list$edge_quasi[,,ekeep]<- clust_out[[c]]$edge_quasi\n\n out_list$vert_gradient[c==clust,]<- clust_out[[c]]$vert_gradient\n out_list$edge_gradient[,,ekeep]<- clust_out[[c]]$edge_gradient\n out_list$mes[c(ekeep,ekeep),]<-clust_out[[c]]$mes\n }\n return(out_list)\n}\n\n\n\n##############################\n#Fits TRW on path of lambdas\n#using warm starts\n##############################\n\ntrw_path<-function(edge_coef_start,node_coef_start,node_mean,edge_mean,\n edgelist,weights=NULL,alpha_start=1,\n g,cores=1,limit=100,relax=T,edge.opt=T,out_len=50,\n lambda_range=NULL){\n #lambda_path<- apply(edge_mean,3,norm,\"F\")\n lambda_seq<- lapply(1:dim(edgelist)[1],\n \t function(x){\n \t norm(edge_mean[,,x]-tcrossprod(node_mean[edgelist[x,1],],node_mean[edgelist[x,2],]),\"F\")\n \t })\n lambda_seq<-simplify2array(lambda_seq)\n if(!is.null(lambda_range)){\n \tlambda_path <- exp( seq(from = log(max(lambda_seq)), to = log(max(lambda_seq)*.02), length.out = out_len) )\n }else{\n \tlambda_path<- lambda_seq\n }\n edge_coef<-edge_coef_start\n node_coef<-node_coef_start\n out<-list()\n n=0\n seqlp<-round( seq(from=1,to=min(limit,length(lambda_path)),length.out=out_len) )\n lp<-(lambda_path%>%sort(decreasing=T))[seqlp]\n for(i in lp){\n lam<-ifelse(relax==T,.0001,i)\n n=n+1\n eind<- which(lambda_seq > i)\n elist_new<-edgelist[eind,,drop=F]\n sub<-subgraph.edges(g,eids=eind,delete.vertices=F)\n if(edge.opt==T){\n\t tree_obj<-edge.Start(sub,burn.in=10)\n wt<-E(tree_obj)$weight\n if(is.null(wt))wt<-logical(0)\n }else{\n wt<- weights[eind]\n }\n out[[n]]<- parallelTrwFit(edge_coef[,,eind,drop=F],node_coef,edge_mean[,,eind,drop=F],node_mean,\n elist_new,wt,lambda=lam,alpha_start,\n sub,cores=cores)\n out[[n]]$edgelist<-elist_new\n out[[n]]$tree_obj<-tree_obj\n out[[n]]$eind<- eind\n out[[n]]$weight<- wt\n #node_coef<-out[[n]]$vert_coef\n #edge_coef[,,eind]<-out[[n]]$edge_coef\n #out[[n]]$edge_coef<-edge_coef\n out[[n]]$lambda<-i\n print(paste(\"DONE:\",n))\n }\n return(out)\n}\n\n\n##############################\n#Kruskal's algorithm\n#\n##############################\nkruskal.min.span<-function (graph.obj) \n{\n #weights for Kruskal's algorithm in rand.weight\n #inputs a sparse adjacency matrix\n nv <- vcount(graph.obj)\n sum.mat<-get.edges(graph.obj,\n E(graph.obj)\n )\n em <- t(sum.mat)\n ne <- dim(sum.mat)[1]\n eW <- E(graph.obj)$rand.weight\n ans <- .Call(\n \"BGL_KMST_D\", \n as.integer(nv), \n as.integer(ne), \n as.integer(em - 1), \n as.double(eW), \n PACKAGE = \"RBGL\"\n )\n adj.out<-sparseMatrix(\n i=c(ans[[1]][1,],ans[[1]][2,])+1,\n j=c(ans[[1]][2,],ans[[1]][1,])+1,x=1,dims=c(nv,nv))\n \n #adj.out<-forceSymmetric(adj.out,uplo=\"L\")\n \n g<-graph.adjacency(adjmatrix=adj.out)\n \n E(g)$weight<-1\n g\n}\n\n\n##############################\n#Randomly generates a\n#collection of spanning trees\n#to create edge weights\n##############################\nedge.Start<-function(graph.obj,burn.in=5){\n #adj.Mat<-forceSymmetric(adj.Mat,uplo=\"L\")\n g<-get.edgelist(graph.obj)\n \n dd<-vcount(graph.obj)\n ff<-ecount(graph.obj)\n ret<-sparseMatrix(i=c(),j=c(),dims=c(dd,dd),symmetric=T)\n ct<-0\n tree.list<-list()\n weight.list<-vector(length=0)\n while(TRUE){\n ct<-ct+1\n #edgeMatrix<-sparseMatrix(i=g[,1],j=g[,2],x=runif(ff,-1000,-999),dims=c(dd,dd),symmetric=T)\n #random edge weights\n E(graph.obj)$rand.weight<-lapply(E(graph.obj),function(e) runif(1,-1000,-999))\n \n new<-kruskal.min.span(graph.obj)[]\n tree.match=F\n if(length(tree.list)==0){\n #E(new)$weight<-1\n tree.list[[1]]<-new\n weight.list[1]<-1\n tree.match=T\n }else if(length(tree.list)>0){\n for(j in 1:length(tree.list)){\n if(all(tree.list[[j]][]==new[])){\n weight.list = weight.list*(ct-1)/ct\n weight.list[j] = weight.list[j] + 1/ct\t\t\t\t\t\n tree.match=T\n break\n }\n }\n }\n if(tree.match==F){\n tree.list[[length(tree.list)+1]]<-new\n weight.list = weight.list*(ct-1)/ct\n weight.list[length(tree.list)]<-1/ct\n }\n \n ret<- ret + new\n if(all(sign(ret)==sign(graph.obj[])) & burn.in<=ct) break\n }\n E(graph.obj)$weight<-\tsummary(forceSymmetric(ret/ct))$x\n graph.obj$tree.list=tree.list\n graph.obj$weight.list=weight.list\n \n return(graph.obj)\n}\n\n\n##############################\n#Add a new edge to graph correcting edge weights\n#\n##############################\nedge.add<-function(graph.obj,edg){\n ge<-get.edges(graph.obj,edg)\n s=ge[1];t=ge[2]\n for(j in 1:length(graph.obj$tree.list)){\n g<-graph.adjacency(graph.obj$tree.list[[j]],mode=\"undirected\",weighted=T)\n if(shortest.paths(g,s,t)==Inf){\n graph.obj$tree.list[[j]][s,t]=graph.obj$tree.list[[j]][t,s]=1\n E(graph.obj)[from(s) & to(t)]$weight<-E(graph.obj)[from(s) & to(t)]$weight+graph.obj$weight.list[[j]]\n }\n \n }\n return(graph.obj)\n}\n\n\n##############################\n#Takes a single edge weight step\n#\n#\n##############################\nedge.weight.optim<-function(graph.obj,mutual_info,delta=.1){\n #Takes a single edge weight step\n \n E(graph.obj)$rand.weight<- -mutual_info\n \n tree.match=F\n new.tree<-\tkruskal.min.span(graph.obj)\n for(j in 1:length(graph.obj$tree.list)){\n if(all(graph.obj$tree.list[[j]]==new.tree[])){\n graph.obj$weight.list = graph.obj$weight.list*(1-delta)\n graph.obj$weight.list[j] = graph.obj$weight.list[j] + delta\t\t\t\t\n tree.match=T\n break\n }\n }\n \n if(tree.match==F){\n graph.obj$tree.list[[length(graph.obj$tree.list)+1]]<-new.tree[]\n graph.obj$weight.list = graph.obj$weight.list*(1-delta)\n graph.obj$weight.list[length(graph.obj$tree.list)]<-delta\n }\n new.weight<-graph.obj[]*(1-delta)+new.tree[]*delta\n E(graph.obj)$weight<-\tE(graph.adjacency(new.weight,mode=\"undirected\",weighted=T))$weight\n return(graph.obj)\n \n}\n\n##############################\n\nnew.edges<-function(enew, edge.coef,edge.mean, graph.obj)\n{\n #For given edges, adds edge to current set of trees if\n #it doesn't create a cycle, then adds a new tree\n enew<- matrix(enew,ncol=2)\n #add to vertlist\n graph.obj<- add.edges(graph.obj, enew)\n #new edge means\n em.new<- edge.means(dat=data,vertlist=enew,m.set)\n #new edge coefs: all zero\n ec.new<- array(0,c(m.set,m.set,dim(enew)[1]))\n \n edge.coef<- abind(edge.coef, ec.new, along=1)\n edge.mean<- abind(edge.mean, em.new, along=1)\n \n #next: modify trees\n #E(graph.obj)$weight<-1\n #graph.obj<- edge.Start(graph.obj)\n E(graph.obj)$weight<-1\n if(is.null(graph.obj$tree.list)){\n graph.obj<-edge.Start(graph.obj)\n }else{\n lapply(\n 1:dim(enew)[1],\n function(edg){\n lapply(\n 1:length(graph.obj$tree.list),\n function(x){\n if( shortest.paths(\n graph.adjacency(graph.obj$tree.list[[x]]), \n v=enew[edg,1],to=enew[edg,2]\n )==Inf\n )\n {\n graph.obj$tree.list[enew[edg,1],e[edg,2]] <<- 1 \n graph.obj$tree.list[enew[edg,2],e[edg,1]] <<- 1\n }\n }\n )\n }\n )\n }\n new<- Matrix(0,d,d)\n lapply(\n 1:length(graph.obj$tree.list),\n function(x){\n new<<- new + graph.obj$tree.list[[x]]*graph.obj$weight.list[[x]]\n }\n )\n \n #add a random tree\n #not worked out yet\n #E(graph.obj)$rand.weight<-lapply(E(graph.obj),function(e) runif(1,-1000,-999))\n \n #new<-kruskal.min.span(graph.obj)[]\n \n \n E(graph.obj)$weight<- summary(forceSymmetric(new))$x\n \n return(list(edge.coef=edge.coef,edge.mean=edge.mean,graph.obj=graph.obj))\n}\n\n\n\n", "meta": {"hexsha": "3ebe1d89afbe3f18572c38cebdfd0ca9ce5d16f2", "size": 10287, "ext": "r", "lang": "R", "max_stars_repo_path": "trw_fit.r", "max_stars_repo_name": "geb5101h/trw", "max_stars_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "trw_fit.r", "max_issues_repo_name": "geb5101h/trw", "max_issues_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-04-28T00:31:07.000Z", "max_issues_repo_issues_event_max_datetime": "2016-04-28T00:31:24.000Z", "max_forks_repo_path": "trw_fit.r", "max_forks_repo_name": "geb5101h/trw", "max_forks_repo_head_hexsha": "2037bdedb1c6c38151abdcbcb44b2b18c2906a6c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1671554252, "max_line_length": 114, "alphanum_fraction": 0.5700398561, "num_tokens": 3058, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3394931761501736}} {"text": "# Experiment 1\n# Four fixed regimes, from small/frequent through to large/infrequent\n# No shift in regime\n\n## Launch model\nlibrary(lhs)\nlibrary(doParallel)\nlibrary(foreach)\n\nsource(\"cat_face_mortality_pfire.r\")\n\nregisterDoParallel(cores=16)\n\n\n######################################################\n######################################################\n\n# Experiment 1: fire interval 1 \n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ=100 # percentage of years\nFIRE_PROB=5 # Size as %age of field\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=100\n\noutput_dir=\"e1_l1\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ=FIRE_FREQ\nsim_def_frame$FIRE_PROB=FIRE_PROB\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim(i)\n\n######################################################\n######################################################\n\n# Experiment 1: fire interval 2 \n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ=20 # percentage of years\nFIRE_PROB=25 # Size as %age of field\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=100\n\noutput_dir=\"e1_l2\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ=FIRE_FREQ\nsim_def_frame$FIRE_PROB=FIRE_PROB\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim(i)\n\n######################################################\n######################################################\n\n# Experiment 1: fire interval 3 \n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ=10 # percentage of years\nFIRE_PROB=50 # Size as %age of field\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=100\n\noutput_dir=\"e1_l3\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ=FIRE_FREQ\nsim_def_frame$FIRE_PROB=FIRE_PROB\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim(i)\n\n######################################################\n######################################################\n\n# Experiment 1: fire interval 4 \n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ=5 # percentage of years\nFIRE_PROB=100 # Size as %age of field\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=100\n\noutput_dir=\"e1_l4\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ=FIRE_FREQ\nsim_def_frame$FIRE_PROB=FIRE_PROB\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim(i)\n\n####################################################\n####################################################\n##\n##\n##\n##\n\n", "meta": {"hexsha": "4c9531ccd90cfcf549555abf2f85fe21d326acf1", "size": 8554, "ext": "r", "lang": "R", "max_stars_repo_path": "experiment_1.r", "max_stars_repo_name": "ozjimbob/FireScar", "max_stars_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-03T05:25:44.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-03T05:47:55.000Z", "max_issues_repo_path": "experiment_1.r", "max_issues_repo_name": "ozjimbob/FireScar", "max_issues_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiment_1.r", "max_forks_repo_name": "ozjimbob/FireScar", "max_forks_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9090909091, "max_line_length": 69, "alphanum_fraction": 0.6236848258, "num_tokens": 2476, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3394931761501735}} {"text": "# Usage: After running R, run latex on /tmp/z.tex\nrequire(Hmisc)\nsource('~/R/Hmisc/R/latexTherm.s')\nf <- '/tmp/lt.tex'\ncat('', file='/tmp/z.tex'); cat('', file=f)\nct <- function(...) cat(..., sep='', file='/tmp/z.tex', append=TRUE)\nct('\\\\documentclass{report}\\\\begin{document}\\n')\nlatexTherm(c(1, 1, 1, 1), name='lta', file=f)\nlatexTherm(c(.5, .7, .4, .2), name='ltb', file=f)\nlatexTherm(c(.5, NA, .75, 0), w=.3, h=1, name='ltc', extra=0, file=f)\nlatexTherm(c(.5, NA, .75, 0), w=.3, h=1, name='ltcc', file=f)\nlatexTherm(c(0, 0, 0, 0), name='ltd', file=f)\nct('\\\\input{/tmp/lt}\\n')\nct('This is a the first:\\\\lta and the second:\\\\ltb\\\\\\\\ and the third without extra:\\\\ltc END\\\\\\\\\\nThird with extra:\\\\ltcc END\\\\\\\\ \\n\\\\vspace{2in}\\\\\\\\ \\n')\nct('All data = zero, frame only:\\\\ltd\\\\\\\\')\nct('\\\\end{document}\\n')\n", "meta": {"hexsha": "dc0e43e09d8b2faf5ab2c48e192f74e46512afab", "size": 803, "ext": "r", "lang": "R", "max_stars_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/latexTherm.r", "max_stars_repo_name": "klodzikowski/phongames", "max_stars_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2019-04-27T10:26:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-04T09:57:34.000Z", "max_issues_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/latexTherm.r", "max_issues_repo_name": "klodzikowski/phongames", "max_issues_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2019-12-28T07:09:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:33:50.000Z", "max_forks_repo_path": ".checkpoint/2018-05-06/lib/x86_64-apple-darwin15.6.0/3.5.1/Hmisc/tests/latexTherm.r", "max_forks_repo_name": "klodzikowski/phongames", "max_forks_repo_head_hexsha": "7dcc3cec90d44342653a53e8e3738b9ed3cb844a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-03-05T05:52:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-18T07:52:04.000Z", "avg_line_length": 47.2352941176, "max_line_length": 154, "alphanum_fraction": 0.5940224159, "num_tokens": 305, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3392747591150776}} {"text": "#' Histogram of records over time\n#' \n#' This is a useful function for visualising your data and how the number of records\n#' change over time. This is key for understanding biases that may be present in your\n#' data (Isaac et al, 2014). \n#' \n#' @param time_period A numeric vector of user defined time periods, or a date vector,\n#' as long as the number of observations.\n#' @param Log Logical, should the y-axis be on a log scale?\n#' @param col Passed to \\link{barplot}, the colour of bars\n#' @param xlab Passed to \\link{barplot}, the x-axis label\n#' @param ylab Passed to \\link{barplot}, the y-axis label\n#' @param ... other arguements to pass to \\link{barplot}\n#' \n#' @return A plot\n#' @examples\n#' \\dontrun{\n#' \n#' # Create data\n#' n <- 3000 #size of dataset\n#' nyr <- 10 # number of years in data\n#' nSamples <- 30 # set number of dates\n#' nSites <- 15 # set number of sites\n#' \n#' # Create somes dates\n#' first <- as.POSIXct(strptime(\"2010/01/01\", \"%Y/%m/%d\")) \n#' last <- as.POSIXct(strptime(paste(2010+(nyr-1),\"/12/31\", sep=''), \"%Y/%m/%d\")) \n#' dt <- last-first \n#' rDates <- first + (runif(nSamples)*dt)\n#' \n#' # taxa are set as random letters\n#' taxa <- sample(letters, size = n, TRUE)\n#' \n#' # three sites are visited randomly\n#' site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)\n#' \n#' # the date of visit is selected at random from those created earlier\n#' time_period <- sample(rDates, size = n, TRUE)\n#' \n#' # combine this to a dataframe (adding a final row of 'bad' data)\n#' df <- data.frame(taxa = c(taxa,'bad'),\n#' site = c(site,'A1'),\n#' time_period = c(time_period, as.POSIXct(strptime(\"1200/01/01\", \"%Y/%m/%d\"))))\n#' \n#' # This reveals the 'bad data' \n#' recsOverTime(df$time_period)\n#' \n#' # remove and replot\n#' df <- df[format(df$time_period, '%Y') > 2000, ]\n#' recsOverTime(df$time_period)\n#' \n#' # plot with style\n#' recsOverTime(df$time_period, col = 'blue', main = 'Records of Species A',\n#' ylab = 'log(number of records)', Log = TRUE) \n#' \n#' }\n#' @export\n\nrecsOverTime <- function(time_period, Log = FALSE, col = 'black', xlab = 'Year',\n ylab = ifelse(Log, 'log(Frequency)', 'Frequency'), ...){\n \n # Do some error checks\n errorChecks(time_period = time_period)\n \n # Make if we have dates convert to year for plotting\n if('POSIXct' %in% class(time_period) | 'Date' %in% class(time_period)){ \n time_period <- format(time_period, format='%Y') \n }\n \n # This may not include all values needed since a year in the middle might be missing\n year_range <- range(as.numeric((time_period)))\n # nil counts are filled in as 0.1 if using log to avoid errors\n frequencies_full <- data.frame(all_years = year_range[1]:year_range[2],\n freq = NA)\n \n # Get our frequencies\n frequencies <- as.data.frame(table(time_period))\n frequencies_full$freq <- frequencies$Freq[match(x = frequencies_full$all_years,\n table = frequencies$time_period)]\n \n # Plot\n if(Log){\n barplot(height = log(frequencies_full$freq), names.arg = frequencies_full$all_years,\n col = col, xlab = xlab, ylab = ylab, ...) \n } else {\n barplot(height = frequencies_full$freq, names.arg = frequencies_full$all_years,\n col = col, xlab = xlab, ylab = ylab, ...) \n } \n}", "meta": {"hexsha": "3be421dc83dc3df06366abfc8b7fdba96638661a", "size": 3418, "ext": "r", "lang": "R", "max_stars_repo_path": "R/recsOverTime.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "R/recsOverTime.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "R/recsOverTime.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 38.404494382, "max_line_length": 97, "alphanum_fraction": 0.6146869514, "num_tokens": 941, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.33927475118110456}} {"text": "# clean wd\nrm(list=ls())\n\n#load lib\nlibrary(raster)\nlibrary(doParallel)\n\n# load ref raster\nload(\"./data/0000_land_rs.Robj\")\n\n# Band extent\next_study_area <- c(-74.6,-74,45.6,50.3)\n\n# crop band\nrs <- crop(rs,ext_study_area)\n\n# create empty raster with res of 100 meters\next_lcc <- extent(projectExtent(rs,\"+proj=lcc +lat_0=45.6 +lon_0=-74.3 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs;\"))\nref_rs <- raster(xmn=ext_lcc[1],xmx=ext_lcc[2]+400,ymn=ext_lcc[3],ymx=ext_lcc[4],res=100,crs=\"+proj=lcc +lat_0=45.6 +lon_0=-74.3 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs;\")\nlcc_proj <- proj4string(ref_rs)\n\n# create ouptput dir\nsystem(\"mkdir -p ./data/futClimGrid/rasterClim/res100\")\nsystem(\"mkdir -p ./data/futClimGrid/rasterClim/res1000\")\nsystem(\"mkdir -p ./data/futClimGrid/stmClim\")\n\nlist_rs <- list.files(\"./data/futClimRaw/\",recursive=TRUE,full.names=TRUE)\n\ncl <- makeCluster(35)\nregisterDoParallel(cl)\n\nforeach(i=1:length(list_rs),.packages=c('raster','rgdal'))%dopar%{\n\n # read csv\n fut_clim <- read.csv(list_rs[i],stringsAsFactors=FALSE)\n\n # set NA\n fut_clim[which(fut_clim[,\"val\"]==-9999),\"val\"] <- NA\n\n # get metadata\n clim_mod <- unique(fut_clim$mod)\n year_max <- unique(fut_clim$max_yr)\n year_min <- unique(fut_clim$min_yr)\n\n ###### CREATE CLIM PROJ #####\n\n # subset bio1\n bio1_clim <- subset(fut_clim, var == \"bio1\")[,c(\"lon\",\"lat\",\"val\")]\n coordinates(bio1_clim) <- ~ lon + lat\n gridded(bio1_clim) <- TRUE\n bio1_clim <- raster(bio1_clim)\n\n # subset bio12\n bio12_clim <- subset(fut_clim, var == \"bio12\")[,c(\"lon\",\"lat\",\"val\")]\n coordinates(bio12_clim) <- ~ lon + lat\n gridded(bio12_clim) <- TRUE\n bio12_clim <- raster(bio12_clim)\n\n # stack raster\n bioclim <- stack(bio1_clim,bio12_clim)\n names(bioclim) <- c('tp','pp')\n projection(bioclim) <- \"+proj=longlat +ellps=GRS80 +datum=NAD83 +no_defs;\"\n bioclim_lcc <- projectRaster(bioclim,crs=lcc_proj)\n\n # transform to a res of 1000 meters\n bioclim_lcc <- resample(bioclim_lcc,ref_rs)\n bioclim_10_lcc <- aggregate(bioclim_lcc,10)\n\n ###### SAVE CLIM PROJ #####\n # save proj climate with 1000 meters of res\n saveRDS(bioclim_lcc,file=paste0(\"./data/futClimGrid/rasterClim/res100/rcp85-\",clim_mod,\"-\",year_min,\"-\",year_max,\".rda\"))\n saveRDS(bioclim_10_lcc,file=paste0(\"./data/futClimGrid/rasterClim/res1000/rcp85-\",clim_mod,\"-\",year_min,\"-\",year_max,\".rda\"))\n\n ###### CREATE CLIM STM GRID (UNPROJ) #####\n\n stm_clim_grid <- as.data.frame(bioclim_10_lcc,xy=TRUE)\n stm_clim_grid$x <- as.numeric(as.factor(stm_clim_grid$x))-1\n stm_clim_grid$y <- as.numeric(as.factor(stm_clim_grid$y))-1\n\n #### RESCALE\n load(\"./data/scale_info.robj\")\n stm_clim_grid$tp <- (stm_clim_grid$tp-vars.means['annual_mean_temp'])/vars.sd['annual_mean_temp']\n stm_clim_grid$pp <- (stm_clim_grid$pp-vars.means['tot_annual_pp'])/vars.sd['tot_annual_pp']\n stm_clim_grid$year <- 0\n\n # Manage NA\n stm_clim_grid[which(is.na(stm_clim_grid$tp)),\"tp\"] <- -9999\n stm_clim_grid[which(is.na(stm_clim_grid$pp)),\"pp\"] <- -9999\n\n # reformat names and columns order\n stm_clim_grid <- stm_clim_grid[,c('x','y','year','tp','pp')]\n names(stm_clim_grid)[4:5] <- c('env1','env2')\n\n ###### SAVE STM CLIM GRID UNPROJ #####\n write.csv(stm_clim_grid,file=paste0(\"./data/futClimGrid/stmClim/rcp85-\",clim_mod,\"-\",year_min,\"-\",year_max,\".csv\"),row.names=FALSE,quote=FALSE)\n\n}\n", "meta": {"hexsha": "d0e8eb84e39de32cac9488cac9ceabbc9e3a6572", "size": 3341, "ext": "r", "lang": "R", "max_stars_repo_path": "2_prepClimGrid.r", "max_stars_repo_name": "QUICC-FOR/STModel-Band", "max_stars_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2_prepClimGrid.r", "max_issues_repo_name": "QUICC-FOR/STModel-Band", "max_issues_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2_prepClimGrid.r", "max_forks_repo_name": "QUICC-FOR/STModel-Band", "max_forks_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0918367347, "max_line_length": 189, "alphanum_fraction": 0.6926070039, "num_tokens": 1222, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3392680464675018}} {"text": "## NOTE: the function in current file is not perceptron at all\n## The intention we do this is to distinguish python & R\n## You will find the model of lm or sgd in R gives a confused function for given data\n## And this function is so different with Python's\n## Why, does R make mistake?\n## No! The package we used here, although it is called linear model, But Please Note That It Is Regression Model Rather Than Classification Model.\n## So it surely will give a different model. This is for regression!!!\n## On the following topics&algorithms, I will write R script only when we talk about regression algorithms, because I think R is not good at do classification or something else. And one more thing, maybe I love python more.\n\n\nlibrary(sgd)\n\n## This function will calculate the linear function using SGD\ncalModelInSGD <- function (features, classes) {\n\n model <- sgd(y ~ ., data=data.frame(y=classes, x=features), model=\"lm\")\n summary(model)\n model$coefficients[1]\n ''\n\n}\n\n## This function will calculate the linear function using lm\ncalModelInLm <- function(features, classes) {\n\n ## A simple linear model\n model <- lm(classes ~ features$x + features$y)\n summary(model)\n\n sprintf(\n 'The function of the model is:\n %f*feature_x + %f*feature_y + %f = 0' ,\n model$coefficients['features$x'],\n model$coefficients['features$y'],\n model$coefficients['(Intercept)'])\n}\n\n\n#The features&classes are same to the data in Py\nfeatures <- data.frame(x=c(-1, -2, 1, 2, 0), y=c(-1, -1, 1, 1, 0))\nclasses <- c(1,1,2,2,1)\n\ncat(calModelInLm(features, classes))\n", "meta": {"hexsha": "cc3afe822361fa565ca6755694a3ce00c35fc16b", "size": 1601, "ext": "r", "lang": "R", "max_stars_repo_path": "algorithms/perceptron/perceptron.r", "max_stars_repo_name": "Marcnuth/DataScienceInterestGroup", "max_stars_repo_head_hexsha": "cae73aa9f6ab1588b565492225a1086b93e121f6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "algorithms/perceptron/perceptron.r", "max_issues_repo_name": "Marcnuth/DataScienceInterestGroup", "max_issues_repo_head_hexsha": "cae73aa9f6ab1588b565492225a1086b93e121f6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "algorithms/perceptron/perceptron.r", "max_forks_repo_name": "Marcnuth/DataScienceInterestGroup", "max_forks_repo_head_hexsha": "cae73aa9f6ab1588b565492225a1086b93e121f6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.3863636364, "max_line_length": 223, "alphanum_fraction": 0.698313554, "num_tokens": 412, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3392680464675018}} {"text": "#'Compute progeny pathway scores and assesses significance based on permutations\n#'\n#'@param df A data.frame of n*m+1 dimension, where n is the number of omic\n#'features to be considered and m is the number of samples/contrasts.\n#'The first column should be the identifiers of the omic features. \n#'These identifiers must be coherent with the identifiers of the weight matrix.\n#'@param weight_matrix A progeny coefficient matrix. the first column should be\n#'the identifiers of the omic features and should be coherent with \n#'the identifiers provided in df.\n#'@param k The number of permutations to be performed to generate\n#'the null-distribution used to estimate the significance of progeny scores. \n#'The default value is 10000.\n#'@param z_scores if true, the z-scores will be returned for \n#'the pathway activity estimations. Else, the function returns \n#'a normalized z-score value between -1 and 1.\n#'@param get_nulldist if true, the null score distribution used for \n#'normalization will be returned along with the actual normalized score data \n#'frame.\n#'@importFrom stats complete.cases sd ecdf\n#'@export \n#'@return This function returns a list of two elements. The first element is \n#'a data frame of p*m+1 dimensions, where p is the number of progeny pathways,\n#'and m is the number of samples/contrasts. Each cell represents the \n#'significance of a progeny pathway score for one sample/contrast. The \n#'significance ranges between -1 and 1. The significance is equal to x*2-1, x \n#'being the quantile of the progeny pathway score with respect to the null \n#'distribution. Thus, this significance can be interpreted as the equivalent of \n#'1-p.value two-sided test over an empirical distribution) with the sign \n#'indicating the direction of the regulation. The second element is the null \n#'distribution list (a null distribution is generated for each sample/contrast).\n#'@examples\n#' # use example gene expression matrix\n#' gene_expression <- as.matrix(read.csv(system.file(\"extdata\", \n#' \"human_input.csv\", package = \"progeny\"), row.names = 1))\n#'\n#' # calculate pathway activities\n#' progeny(gene_expression, scale=TRUE, organism=\"Human\", top=100, perm=10000)\n#'@export\nprogenyPerm <- \n function(df,weight_matrix,k = 10000, z_scores = TRUE, get_nulldist = FALSE)\n{\n resList <- list()\n if(get_nulldist) {\n nullDist_list <- list()\n }\n \n for(i in 2:length(df[1,])) {\n current_df <- df[,c(1,i)]\n current_df <- current_df[complete.cases(current_df),]\n t_values <- current_df[,2]\n current_weights <- weight_matrix\n names(current_df)[1] <- \"ID\"\n names(current_weights)[1] <- \"ID\"\n \n common_ids <- merge(current_df, current_weights, by = \"ID\")\n common_ids <- as.character(common_ids$ID)\n\n row.names(current_df) <- current_df$ID \n current_df <- as.data.frame(current_df[common_ids,-1])\n row.names(current_weights) <- current_weights$ID\n current_weights <- as.data.frame(current_weights[common_ids,-1])\n current_mat <- as.matrix(current_df)\n current_weights <- t(current_weights)\n \n scores <- as.data.frame(current_weights %*% current_mat)\n null_dist_t <- replicate(k, sample(t_values,length(current_mat[,1]), \n replace = FALSE))\n null_dist_scores <- current_weights %*% null_dist_t\n \n if(get_nulldist) {\n nullDist_list[[i-1]] <- null_dist_scores\n }\n \n if(z_scores) {\n scores$mean <- apply(null_dist_scores,1,mean)\n scores$sd <- apply(null_dist_scores,1,sd)\n resListCurrent <- (scores[,1]-scores[,2])/scores[,3]\n names(resListCurrent) <- names(weight_matrix[,-1])\n resList[[i-1]] <- resListCurrent\n } else {\n for(j in seq(1, length(weight_matrix[,-1]))) {\n ecdf_function <- ecdf(null_dist_scores[j,])\n scores[j,1] <- ecdf_function(scores[j,1])\n }\n score_probas <- scores*2-1\n resListCurrent <- score_probas[,1]\n names(resListCurrent) <- names(weight_matrix[,-1])\n resList[[i-1]] <- resListCurrent\n }\n }\n names(resList) <- colnames(df)[-1]\n resDf <- as.data.frame(resList)\n if(get_nulldist) {\n names(nullDist_list) <- names(df[,-1])\n return(list(resDf, nullDist_list))\n } else {\n return(t(resDf))\n }\n}\n", "meta": {"hexsha": "6302594c0dfac0c875bfe247ecfd2452b7015ac0", "size": 4392, "ext": "r", "lang": "R", "max_stars_repo_path": "R/progenyPermutations.r", "max_stars_repo_name": "jan-glx/progeny", "max_stars_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 41, "max_stars_repo_stars_event_min_datetime": "2017-11-19T18:07:44.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T13:39:31.000Z", "max_issues_repo_path": "R/progenyPermutations.r", "max_issues_repo_name": "jan-glx/progeny", "max_issues_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 36, "max_issues_repo_issues_event_min_datetime": "2018-02-03T11:43:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T08:58:32.000Z", "max_forks_repo_path": "R/progenyPermutations.r", "max_forks_repo_name": "jan-glx/progeny", "max_forks_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2017-10-23T06:54:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T16:41:06.000Z", "avg_line_length": 43.92, "max_line_length": 80, "alphanum_fraction": 0.6739526412, "num_tokens": 1075, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7549149868676283, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.3392532347453729}} {"text": "args <- commandArgs(TRUE)\n\nif (length(args) != 3) stop (\"Required command line arugments are: input_tree_file outgroup_name output_name\")\n\nlibrary(phybase, warn.conflicts=FALSE, quietly=TRUE)\n\ngenetrees<-read.tree.string(file=args[1],format=\"phylip\")\n\n#read.tree.string only extract taxa names from the first tree. Add any additional names from other trees \nfor (input_tree in genetrees$tree){\n\tinput_tree_nodes = read.tree.nodes(input_tree)\n\tfor (input_name in input_tree_nodes$names){\n\t\tif(!input_name %in% genetrees$names){\n\t\t\tgenetrees$names = append(genetrees$names, input_name)\n\t\t}\n\t}\n}\n\n#define a species matrix. This assumes that there is one terminal for each species. STAR can accomodate mutliple terminals per species, but the following code would have to be modified to specify that in the matrix.\nnumtax=length(genetrees$names)\nspecies.structure<-matrix(0,numtax,numtax)\ndiag(species.structure)<-1\n\n#generate species tree with STAR based on all gene trees. Note this requires specification of a single terminal as an outgroup for rooting the resulting trees. This is specificied by the user on the command line \nfull_sptree = star.sptree(genetrees$tree, speciesname=genetrees$names, taxaname=genetrees$names, species.structure=species.structure, outgroup=args[2], method=\"nj\")\n\n#print to output file\nsink(args[3], append=TRUE)\ncat (c(full_sptree, \"\\n\"), sep=\"\")\n", "meta": {"hexsha": "9649ef9c2600c16e24e196dcbaabc2303d14d1d4", "size": 1375, "ext": "r", "lang": "R", "max_stars_repo_path": "r_code/star.r", "max_stars_repo_name": "dbsloan/msc_tree_resampling", "max_stars_repo_head_hexsha": "430f00adedde4cfcc49c99200fee3b9e0c39623e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-27T10:53:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-27T10:53:01.000Z", "max_issues_repo_path": "r_code/star.r", "max_issues_repo_name": "dbsloan/msc_tree_resampling", "max_issues_repo_head_hexsha": "430f00adedde4cfcc49c99200fee3b9e0c39623e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_code/star.r", "max_forks_repo_name": "dbsloan/msc_tree_resampling", "max_forks_repo_head_hexsha": "430f00adedde4cfcc49c99200fee3b9e0c39623e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.8333333333, "max_line_length": 215, "alphanum_fraction": 0.7796363636, "num_tokens": 346, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3391146571611324}} {"text": "library(Cairo)\n\n\nCairoSVG( 'expression', width=3, height=4.5 )\nlayout(matrix(c(1,1,1,1,2,2,2,\n 3,3,3,3,4,4,4),nrow=2,byrow=T))\npar(mai=c(.75,.75,.75,0))\n\npValCutoff = .05\n\nhcec = read.table('W:/2013_06_wnt_tgfb_crosstalk/qPCR/HCEC/HCEC_2hr.csv',sep=',',header=T,stringsAsFactors=F)\nhcec$Celltype = rep('HCEC',dim(hcec)[1])\nskmel = read.table(\"W:/2013_06_wnt_tgfb_crosstalk/qPCR/SKMEL2+MALME3/SKEML2+MALME3M.csv\",sep=',',header=T,stringsAsFactors=F)\nskmel = skmel[skmel$Celltype=='SKMEL2',]\n\ndata = rbind(hcec,skmel)\n\n\n###### PLOT SMAD7 ######\ntreatments = list(tgfb=c('ctrl+ctrl','ctrl+TGFB3','BMP4+ctrl'),\n wnt =c('ctrl+ctrl','Wnt3A+ctrl'))\noutNames = list(tgfb=c('ctrl','TGFB3','BMP4'),\n wnt =c('ctrl','Wnt3A'))\nreadouts = list(tgfb='SMAD7',wnt='AXIN2')\n\ncolors = c('black','darkred','blue','darkgreen')\nnames(colors) = c('ctrl+ctrl','Wnt3A+ctrl','ctrl+TGFB3','BMP4+ctrl')\n\nfor( celltype in c('HCEC','SKMEL2')){\n for( input in c('tgfb','wnt')){\n readout = readouts[[input]]\n subdata = data[data$Detector==readouts[input] &\n data$Celltype==celltype, ] \n subdata$RNA = 2^(-subdata$dCT)\n ctrls = subdata[subdata$Treatment == 'ctrl+ctrl', 'RNA']\n subdata$RNA = subdata$RNA / mean(ctrls) \n ctrls = ctrls / mean(ctrls) \n \n subdata$RNA = log2(subdata$RNA)\n \n ylims = range(subdata$RNA[subdata$Treatment %in% treatments[[input]]])*1.1\n yvals = 0:(floor(ylims[2]))\n \n plot( NA,NA, xlim=c(.5,length(treatments[[input]])+.5), xlab='',\n ylim=ylims,\n main=celltype, yaxt='n',\n ylab=paste('log2(relative',readout,'mRNA)'),xaxt='n')\n axis( 1, at=1:length(treatments[[input]]),treatments[[input]],las=3)\n axis( 2, at=yvals )\n abline(h=0,col='gray',lty=3)\n \n for( x in 1:length(treatments[[input]])){\n values = subdata$RNA[subdata$Treatment==treatments[[input]][x]]\n lines( c(x,x), c(-1,1)*sd(values)+mean(values), col=colors[treatments[[input]][x]] )\n points( x, mean(values), pch=16, col=colors[treatments[[input]][x]])\n \n # SIGNIFICANCE\n isEqCtrl = t.test(subdata$RNA[subdata$Treatment==treatments[[input]][x]],\n subdata$RNA[subdata$Treatment=='ctrl+ctrl'])$p.value\n if( isEqCtrl < pValCutoff){\n isEqCtrl = '*'\n }else{\n isEqCtrl = ''\n }\n \n axis(1,at=x,isEqCtrl,tick=F,col.axis=colors[treatments[[input]][x]])\n }\n }\n}\n \n\n\n\ndev.off()\n\n\n\n", "meta": {"hexsha": "5c7f3c97961eb779b11abdf2e4ff664db45c4388", "size": 2521, "ext": "r", "lang": "R", "max_stars_repo_path": "FIGS/insulation/expression.r", "max_stars_repo_name": "adam-coster/dissertation", "max_stars_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FIGS/insulation/expression.r", "max_issues_repo_name": "adam-coster/dissertation", "max_issues_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FIGS/insulation/expression.r", "max_forks_repo_name": "adam-coster/dissertation", "max_forks_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3205128205, "max_line_length": 125, "alphanum_fraction": 0.5854819516, "num_tokens": 887, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3391146571611324}} {"text": "#Plot the memory model, first argument is the upperbound, the the dataset containing the plotting data\n\nargs<-commandArgs(TRUE)\nlowerbound <- as.integer(args[1])\nupperbound <- as.integer(args[2])\ndataset <-read.csv(args[3])\nfilename = gsub(\".csv\", \"\", args[3])\nfileNameEt <- paste(filename, \"_FREQUENCE\", sep=\"\")\ngraphtitle1 <- paste(paste(\"RESULT EN0-EN9 \", lowerbound), upperbound)\ngraphtitle2 <- \"FREQ AVG RESULT \"\nbreaks <- c(11,117,223,329,435,541,647,753,859,965,1071)\nbreaksSS <- c(16.4,111.13,205.86,300.59,395.32,490.05,584.78,679.51,774.24,868.97,963.7)\n\nxmean <- rowMeans(cbind(dataset$MEMA0,dataset$MEMA1 ,dataset$MEMA2 ,dataset$MEMA3 ,dataset$MEMA4,dataset$MEMA5,dataset$MEMA6,dataset$MEMA7,dataset$MEMA8,dataset$MEMA9))\n\n\npng(paste(fileNameEt,\"_FULL_DENSITY_B.png\") , 1827, 1458)\nd<- density(xmean)\nplot(d, main = \"Density\" , col=\"brown\", type=\"h\",ylim=c(0,0.05), xlim=range(breaks))\n\npng(paste(fileNameEt,\"_STEADY_DENSITY_B.png\") , 1827, 1458)\nd<- density(xmean[lowerbound:upperbound])\nplot(d, main = \"Density Steady\" ,col=\"brown\",type=\"h\", ylim=c(0,0.05), xlim=range(breaksSS))\n\npng(paste(fileNameEt,\"_FULL_HIST_B.png\") , 1827, 1458)\nhist(xmean, main=\"Histogram\", col=\"brown\",xlim=range(breaks), ylim=c(0,20000), labels=TRUE, breaks=breaks)\n\npng(paste(fileNameEt,\"_STEADY_HIST_B.png\") , 1827, 1458)\nhist(main=\"Histogram Steady\", col=\"brown\",xmean[lowerbound:upperbound], xlim=range(breaksSS), ylim=c(0,5000), labels=TRUE, breaks=breaksSS)\n\n\npng(paste(fileNameEt,\"_SUMMARY_B.png\") , 1827, 1458)\nsplit.screen(c(2,1))\nsplit.screen(c(1,2), screen=1)\nscreen(3)\nd<- density(xmean)\nplot(d, main = \"Density\" , col=\"brown\", type=\"h\",ylim=c(0,0.05), xlim=range(breaks))\nscreen(4)\nd<- density(xmean[lowerbound:upperbound])\nplot(d, main = \"Density Steady\" ,col=\"brown\", type=\"h\", ylim=c(0,0.05), xlim=range(breaksSS))\nscreen(2)\nsplit.screen(c(1,2), screen=2)\nhist(xmean, main=\"Histogram\", col=\"brown\",xlim=range(breaks), ylim=c(0,20000), labels=TRUE, breaks=breaks)\nscreen(6)\nhist(main=\"Histogram Steady\", col=\"brown\",xmean[lowerbound:upperbound], xlim=range(breaksSS), ylim=c(0,5000), labels=TRUE, breaks=breaksSS)\n\ndev.off()\n", "meta": {"hexsha": "51e4553d78aa95932acaf3ce9fb66fe3ee46cca8", "size": 2137, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/distribution/PlotAVGExecutionMemoryDistributionB.r", "max_stars_repo_name": "streamreasoning/HeavenTeststand", "max_stars_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/distribution/PlotAVGExecutionMemoryDistributionB.r", "max_issues_repo_name": "streamreasoning/HeavenTeststand", "max_issues_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/distribution/PlotAVGExecutionMemoryDistributionB.r", "max_forks_repo_name": "streamreasoning/HeavenTeststand", "max_forks_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.5208333333, "max_line_length": 168, "alphanum_fraction": 0.7182966776, "num_tokens": 765, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850154599563, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.33911465121422146}} {"text": "# Function 'firstElement'\n# Killian Martin--Horgassan\n# 08-06-2015\n\n# Takes a logical vector and find the first FALSE element \n# in it, returns the index of that element\n\n\nfirstElement <- function(x) {\n\tindex <- 1\n\tN <- length(x)\n\twhile(x[index]) {\n\t\tindex <- index +1\n\t}\n\toutput <- index\n}", "meta": {"hexsha": "f3293cbe33495c556030bd79b01147a59d20dccc", "size": 290, "ext": "r", "lang": "R", "max_stars_repo_path": "r_files_gbm_task7/firstElement.r", "max_stars_repo_name": "CillianMH/pdmExtremeValueTheory", "max_stars_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r_files_gbm_task7/firstElement.r", "max_issues_repo_name": "CillianMH/pdmExtremeValueTheory", "max_issues_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_files_gbm_task7/firstElement.r", "max_forks_repo_name": "CillianMH/pdmExtremeValueTheory", "max_forks_repo_head_hexsha": "f7a7504c2eca0c6be665bcfc3d98dfee6c02de41", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.125, "max_line_length": 58, "alphanum_fraction": 0.675862069, "num_tokens": 84, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.6477982247516797, "lm_q1q2_score": 0.3390707728398555}} {"text": "library(monocle)\ndiff_test_res <- differentialGeneTest(HSMM,fullModelFormulaStr = \"~State\")\ntop50<-head(diff_test_res[order(diff_test_res$qval),],n=50)$gene_short_name\nmarker_genes<-top50\npdf(\"pseudotime.heatmap.50.pdf\")\nplot_pseudotime_heatmap(HSMM[marker_genes,],num_clusters = 3,cores = 1,show_rownames = T)\ndev.off()\n", "meta": {"hexsha": "dce02175a340c6b27a2e4dce82ef8f2ebad03944", "size": 321, "ext": "r", "lang": "R", "max_stars_repo_path": "functions/pseudotime.heatmap.r", "max_stars_repo_name": "TongZhou2017/scTools", "max_stars_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "functions/pseudotime.heatmap.r", "max_issues_repo_name": "TongZhou2017/scTools", "max_issues_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "functions/pseudotime.heatmap.r", "max_forks_repo_name": "TongZhou2017/scTools", "max_forks_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.125, "max_line_length": 89, "alphanum_fraction": 0.800623053, "num_tokens": 100, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33907076928082247}} {"text": "#-------------------------------------------------------------------\n# iniPars() -- Returns default parameter values for generating\n# model spectra from our standard 30-parameter model.\n#\n# Notes: This function declares separate VT and SRC parameter\n# vectors and assigns names to each element, but then\n# concatenates the two vectors into a single output.\n#\n# 1/10/20 -- htb\n#-------------------------------------------------------------------\niniPars <- function() {\n vtPars <- c(500,80,60,\n 1500,100,60,\n 2500,120,60,\n 3500,240,60,\n 4500,480,60,\n 5500,500,60,\n 6500,500,60,\n 7500,500,60)\n names(vtPars) <- c(\"f1f\",\"f1b\",\"f1a\",\n \"f2f\",\"f2b\",\"f2a\",\n \"f3f\",\"f3b\",\"f3a\",\n \"f4f\",\"f4b\",\"f4a\",\n \"f5f\",\"f5b\",\"f5a\",\n \"f6f\",\"f6b\",\"f6a\",\n \"f7f\",\"f7b\",\"f7a\",\n \"f8f\",\"f8b\",\"f8a\")\n srcPars <- c(.6,.667,.0002,24,0,0)\n names(srcPars) <- c(\"oq\",\"tprat\",\"ta\",\"av\",\"af\",\"ah\")\n \n pars <- c(srcPars,vtPars)\n pars\n}\n#-------------------------------------------------------------------\n# lbPars() -- experimental vector with elements corresponding to the\n# pars vector that lists lower bounds on the allowed\n# value of each parameter. These need to be considered\n# much more carefully.\n#-------------------------------------------------------------------\nlbPars <- function() {\n vtPars <- c(150,20,0,\n 750,20,0,\n 1750,30,0,\n 2750,30,0,\n 3750,40,0,\n 4750,40,0,\n 5750,50,0,\n 6750,50,0)\n names(vtPars) <- c(\"f1f\",\"f1b\",\"f1a\",\n \"f2f\",\"f2b\",\"f2a\",\n \"f3f\",\"f3b\",\"f3a\",\n \"f4f\",\"f4b\",\"f4a\",\n \"f5f\",\"f5b\",\"f5a\",\n \"f6f\",\"f6b\",\"f6a\",\n \"f7f\",\"f7b\",\"f7a\",\n \"f8f\",\"f8b\",\"f8a\")\n srcPars <- c(.1,.6,0.0,0,0,0)\n names(srcPars) <- c(\"oq\",\"tprat\",\"ta\",\"av\",\"af\",\"ah\")\n \n pars <- c(srcPars,vtPars)\n pars\n}\n#-------------------------------------------------------------------\n# ubPars() -- experimental vector with elements corresponding to the\n# pars vector that lists upper bounds on the allowed\n# value of each parameter. These need to be considered\n# much more carefully.\n#-------------------------------------------------------------------\nubPars <- function() {\n vtPars <- c(1000,1000,66,\n 3000,1000,66,\n 4000,1000,66,\n 5000,1000,66,\n 6000,1000,66,\n 7000,1000,66,\n 8000,1000,66,\n 10000,1000,66)\n names(vtPars) <- c(\"f1f\",\"f1b\",\"f1a\",\n \"f2f\",\"f2b\",\"f2a\",\n \"f3f\",\"f3b\",\"f3a\",\n \"f4f\",\"f4b\",\"f4a\",\n \"f5f\",\"f5b\",\"f5a\",\n \"f6f\",\"f6b\",\"f6a\",\n \"f7f\",\"f7b\",\"f7a\",\n \"f8f\",\"f8b\",\"f8a\")\n srcPars <- c(.95,.95,.0009,68,68,68)\n names(srcPars) <- c(\"oq\",\"tprat\",\"ta\",\"av\",\"af\",\"ah\")\n\n pars <- c(srcPars,vtPars)\n pars\n}\n\n#-------------------------------------------------------------------\n# estSpect -- Function to generate a spectrum from parameters for\n# use in fitting parameters.\n# Args:\n# pars -- Vector of parameters for both source function and VT\n# response function. Each element of the vector needs to\n# be named and is selected by name. See iniPars() for the\n# default values and names of each parameter.\n# np -- Number of points in the output Log Magnitude spectrum.\n# The actual output will have np+1 values (DC is included)\n# fs -- Sampling frequency in Hz.\n# vcd -- The voicing status for the output to be generated. This\n# only effects source spectrum calculation.\n# t0 -- The epoch duration (i.e. 1/f0) in seconds. This only\n# directly effects source spectrum, however, \"np\" should\n# be chosen to cover at least fs*t0*2 samples of data\n# and must be the same for both voiced and voiceless\n# frames.\n# mode -- One of \"vt\", \"src\", or \"comb\" to determine if the output\n# spectrum is based on only vocal tract, only source, or\n# both combined. This makes it possible to separately\n# display, or fit, the two contributions independetly.\n#-------------------------------------------------------------------\nestSpect <- function(pars, np=512, fs=16000, vcd=1, t0=0.01,\n mode=\"vt\") {\n \n if (!(mode %in% c(\"src\",\"vt\",\"comb\")))\n stop(\"estSpect: error - mode must be one of 'vt', 'src', or 'comb'\")\n \n if (mode %in% c(\"src\",\"comb\")) {\n src <- srcfn(pars, vcd, t0, fs)\n np2 <- 2 * np\n if (length(src) < (np2))\n src <- c(src, rep(0,np2-length(src)))\n else if (length(src > (np2))) # Hope we don't need this!\n src <- src[1:np2]\n srcsp <- 20 * log10(1/np * abs(fft(src)[1:(np+1)]) + 1)\n } else {\n srcsp <- rep(0, np+1)\n }\n if (mode %in% c(\"vt\",\"comb\")) {\n vt <- vtfn(pars, np, fs)\n vtsp <- 20 * log10(vt)\n } else {\n vtsp <- rep(0, np+1)\n }\n return(srcsp+vtsp)\n}\n\n#\n# Returns a vector of residual errors for each\n# point in the target spectrum\n#\nerrSpect <- function(pars, targ, np=512, fs=16000, vcd=1, t0=0.01,\n mode=\"vt\") {\n est <- estSpect(pars, np, fs, vcd, t0, mode)\n return(targ - est)\n}\n\n#\n# Returns a Scaler sum-squared error value to\n# express the distance from predicted to target.\n#\nerrSpectS <- function(pars, targ, np=512, fs=16000, vcd=1, t0, mode) {\n est <- estSpect(pars, np, fs, vcd, t0, mode)\n return(sum((targ - est)^2))\n}", "meta": {"hexsha": "ba6a15d2f38974c540dd61a81a0ca0df2c0bc165", "size": 5871, "ext": "r", "lang": "R", "max_stars_repo_path": "R/estSpect.r", "max_stars_repo_name": "NemoursResearch/FormantTracking", "max_stars_repo_head_hexsha": "7053e6add8672dc00aa70f6549d90ff935f96748", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-01T14:22:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T03:47:04.000Z", "max_issues_repo_path": "R/estSpect.r", "max_issues_repo_name": "NemoursResearch/FormantTracking", "max_issues_repo_head_hexsha": "7053e6add8672dc00aa70f6549d90ff935f96748", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/estSpect.r", "max_forks_repo_name": "NemoursResearch/FormantTracking", "max_forks_repo_head_hexsha": "7053e6add8672dc00aa70f6549d90ff935f96748", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-31T18:20:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-31T18:20:28.000Z", "avg_line_length": 36.9245283019, "max_line_length": 74, "alphanum_fraction": 0.4653381025, "num_tokens": 1721, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33907076928082247}} {"text": "# This code is for mapping North American hummingbird banding data from the Bird Banding \n# Laboratory (BBL). Recapture data is available through 2013 and was last updated 11 April 2014. \n# Miscellaneous old code at end (to be cleaned/deleted)\n#(c) 2013 Marisa Lim, Stony Brook University\n\n# required packages\nrequire(maps)\nrequire(geosphere)\nrequire(raster)\nrequire(ggplot2)\n\n--------------------------------------------------------------------------------------------\n# To Do:\n# Clean up code\n--------------------------------------------------------------------------------------------\n\n# set working directory\n\nwd = \"C:/Users/mcwlim/Dropbox/NASA_Anusha/MarisaRAstuff/BBLrecapproj\"\nsetwd(wd)\n\n################# Updated BBL 11April2014 #################\nBBLdat <- read.csv(\"11April14_BBLdata.csv\", sep=\",\", header=T)\nhead(BBLdat)\nnames(BBLdat)\ndim(BBLdat)\n\n# First, let's clean up the data. Information that needs to be cleaned: (quick filter check of excel file shows that these need cleaning)\n# 1. incorrect months of year (in ENCOUNTER_MONTH)\n# 2. incorrect days of month (in ENCOUNTER_DAY)\n# 3. unidentified species (in B_SPECIES_NAME)\n# 4. missing lat/long information (in E_LAT_DECIMAL_DEGREES, E_LON_DECIMAL_DEGREES)\n\nBBLdat1 <- subset(BBLdat, BBLdat$ENCOUNTER_MONTH <= 12)\nBBLdat2 <- subset (BBLdat1, BBLdat1$ENCOUNTER_DAY <= 31)\nBBLdat3 <- subset(BBLdat2, BBLdat2$B_SPECIES_NAME != \"Unidentified Hummingbird\")\nBBLdat4 <- subset(BBLdat3, BBLdat3$E_LAT_DECIMAL_DEGREES != \"NA\")\ndim(BBLdat4)\n\n# based on 11April14 data, there were 28 entries removed, 1293 records used \n\n# set extent\nxlim <- c(-171.738281, -56.601563)\nylim <- c(8, 71.856229)\n# set color for connecting lines\ncolors <- \"#FF3300\"\n\n# maps of each species, banding and recapture points connected with a line\nspecies <- unique(BBLdat4$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n jpeg(paste(\"species_\", species[i], \".jpg\"), height=5, width=5, units=\"in\", res=500)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- BBLdat4[BBLdat4$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n bandnums <- unique(birdies$BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.35)\n title(sub=paste(\"# unique birds = \", length(bandnums)), cex.sub=1.5, line=0.5)\n for(j in 1:nrow(birdies)){\n inter <- gcIntermediate(c(birdies[j,]$B_LON_DECIMAL_DEGREES, birdies[j,]$B_LAT_DECIMAL_DEGREES),\n c(birdies[j,]$E_LON_DECIMAL_DEGREES, birdies[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=2)\n points(birdies$B_LAT_DECIMAL_DEGREES ~ birdies$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"black\") \n }\n dev.off()\n}\n\n\n#--------------- plot rufous that are intially captured east of 103 (outside western flyway)\nrufous <- BBLdat4[BBLdat4$B_SPECIES_NAME == \"Rufous Hummingbird\", ]\nbands <- unique(rufous$BAND_NUM)\neastern <- rufous[rufous$B_LON_DECIMAL_DEGREES > -103,]\nwestern <- rufous[rufous$B_LON_DECIMAL_DEGREES <= -103,]\nalleast <- rufous[rufous$B_LON_DECIMAL_DEGREES > -103 & rufous$E_LON_DECIMAL_DEGREES > -103,]\n\n#-------------- plot histogram of months rufous captured outside of Western Flyway\nggplot(alleast, aes(ENCOUNTER_MONTH)) + xlab(\"encounter month\") + \n geom_histogram(fill = \"grey40\", col=\"black\", binwidth=1) +\n theme_classic() + theme(text=element_text(size=20)) + \n scale_x_continuous(breaks = seq(1, 12, 2), limits = c(1,12))\n\n#-------------- plot histogram of rufous captured outside of Western Flyway\nggplot(eastern, aes(BANDING_YEAR)) + ylab(\"Number birds banded\") +\n xlab(\"Banding Year\") + geom_histogram(fill = \"grey40\", col = \"black\", binwidth=1) + \n theme_classic() + theme(text=element_text(size=20), axis.text.x = element_text(angle = 60, hjust=1)) + \n scale_x_continuous(breaks = seq(1980, 2013, 5), limits = c(1980,2013))\n\n#-------------- plot where these eastern captures went\nplot(NA, NA, xlim=c(-170,-50), ylim=c(15,75), xlab=\"\", ylab=\"\", main = \"Rufous Hummingbird recaptures\")\nfor(k in 1:nrow(western)){\n if(western[k,]$E_LON_DECIMAL_DEGREES <= -103) { color = \"black\"}\n else { color = \"indianred\" }\n inter <- gcIntermediate(c(western[k,]$B_LON_DECIMAL_DEGREES, western[k,]$B_LAT_DECIMAL_DEGREES),\n c(western[k,]$E_LON_DECIMAL_DEGREES, western[k,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=color, lwd=2)\n}\nfor(j in 1:nrow(eastern)){\n if(eastern[j,]$E_LON_DECIMAL_DEGREES > -103) { color = \"cadetblue\"}\n else { color = \"indianred\" }\n inter <- gcIntermediate(c(eastern[j,]$B_LON_DECIMAL_DEGREES, eastern[j,]$B_LAT_DECIMAL_DEGREES),\n c(eastern[j,]$E_LON_DECIMAL_DEGREES, eastern[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=color, lwd=2)\n}\nmap(\"worldHires\", c(\"usa\", \"canada\", \"mexico\"), add=TRUE, cex = 0.5)\n\n\n#-------------------------- \n# plot black-chinned that are captured east of 103 (outside western flyway)\n#--------------------------\nbc <- BBLdat4[BBLdat4$B_SPECIES_NAME == \"Black-chinned Hummingbird\", ]\nbands <- unique(bc$BAND_NUM)\neastern <- bc[bc$B_LON_DECIMAL_DEGREES > -103,]\nwestern <- bc[bc$B_LON_DECIMAL_DEGREES <= -103,]\nalleast <- bc[bc$B_LON_DECIMAL_DEGREES > -103 & bc$E_LON_DECIMAL_DEGREES > -103,]\n\n#-------------- plot histogram of months bc captured outside of Western Flyway\nggplot(alleast, aes(ENCOUNTER_MONTH)) + xlab(\"encounter month\") + \n geom_histogram(fill = \"grey40\", col=\"black\", binwidth=1) +\n theme_classic() + theme(text=element_text(size=20)) + \n scale_x_continuous(breaks = seq(1, 12, 2), limits = c(1,12))\n\n#-------------- plot histogram of bc captured outside of Western Flyway\nggplot(eastern, aes(BANDING_YEAR)) + ylab(\"Number birds banded\") +\n xlab(\"Banding Year\") + geom_histogram(fill = \"grey40\", col = \"black\", binwidth=1) + \n theme_classic() + theme(text=element_text(size=20), axis.text.x = element_text(angle = 60, hjust=1)) + \n scale_x_continuous(breaks = seq(1980, 2013, 5), limits = c(1980,2013))\n\n#-------------- plot where these eastern captures went\nplot(NA, NA, xlim=c(-130,-60), ylim=c(15,50), xlab=\"\", ylab=\"\", main = \"Black-chinned Hummingbird recaptures\")\nfor(k in 1:nrow(western)){\n if(western[k,]$E_LON_DECIMAL_DEGREES <= -103) { color = \"black\"}\n else { color = \"indianred\" }\n inter <- gcIntermediate(c(western[k,]$B_LON_DECIMAL_DEGREES, western[k,]$B_LAT_DECIMAL_DEGREES),\n c(western[k,]$E_LON_DECIMAL_DEGREES, western[k,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=color, lwd=2)\n}\nfor(j in 1:nrow(eastern)){\n if(eastern[j,]$E_LON_DECIMAL_DEGREES > -103) { color = \"cadetblue\"}\n else { color = \"indianred\" }\n inter <- gcIntermediate(c(eastern[j,]$B_LON_DECIMAL_DEGREES, eastern[j,]$B_LAT_DECIMAL_DEGREES),\n c(eastern[j,]$E_LON_DECIMAL_DEGREES, eastern[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=color, lwd=2)\n}\nmap(\"worldHires\", c(\"usa\", \"mexico\"), add=TRUE, cex = 0.5)\n\n\n\n\n\n\n\n#--------------------------------------------------------------------------------------\n\n\n################# Test data with time, connections, sex, and age #################\n\nhumtest <- read.csv(\"humtest27aug.csv\", sep=\",\", header=T)\nhumtest <- humtest[order(humtest$B_SPECIES_NAME, humtest$B_BAND_NUM, humtest$BANDING_YEAR, humtest$BANDING_MONTH, humtest$BANDING_DATE),]\nhead(humtest)\nnames(humtest)\ndim(humtest)\n\n# set extent\nxlim <- c(-171.738281, -56.601563)\nylim <- c(8, 71.856229)\n# set color for connecting lines\ncolors <- \"#FF3300\"\n\n# maps of each species, banding and recapture points connected with a line\nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n bandnums <- unique(birdies$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n for(j in 1:nrow(birdies)){\n inter <- gcIntermediate(c(birdies[j,]$B_LON_DECIMAL_DEGREES, birdies[j,]$B_LAT_DECIMAL_DEGREES),\n c(birdies[j,]$E_LON_DECIMAL_DEGREES, birdies[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=2)\n points(birdies$B_LAT_DECIMAL_DEGREES ~ birdies$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"black\") }\n dev.off()\n}\n\n# maps of each species, points colored by sex (classes are defined as female or male) \nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"Sp_\", species[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n females <- birdies[birdies$B_SEX_CODE == \"5\" | birdies$B_SEX_CODE == \"7\", ]\n males <- birdies[birdies$B_SEX_CODE == \"4\" | birdies$B_SEX_CODE == \"6\", ]\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"red\")\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"blue\")\n legend(\"left\", ncol=2, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=20, cex=1, col=c(\"red\", \"blue\"), c(\"Female\", \"Male\"))\n title(main=species[i])\n dev.off()\n}\n\n# maps connecting recaptures, with 1st banding and recapture in same row and,\n# unique individuals (male or female) are marked as points, they are shown for the original banding location only\nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n bandnums <- unique(birdies$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n for(j in 1:nrow(birdies)){\n inter <- gcIntermediate(c(birdies[j,]$B_LON_DECIMAL_DEGREES, birdies[j,]$B_LAT_DECIMAL_DEGREES),\n c(birdies[j,]$E_LON_DECIMAL_DEGREES, birdies[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=2)\n females <- birdies[birdies$B_SEX_CODE == \"5\" | birdies$B_SEX_CODE == \"7\", ]\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"red\")\n males <- birdies[birdies$B_SEX_CODE == \"4\" | birdies$B_SEX_CODE == \"6\", ]\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"blue\")\n }\n dev.off()\n}\n\n# mapping males and females with the lines, separately\n# Males only.\nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \"_m\", \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n males <- birdies[birdies$B_SEX_CODE == \"4\" | birdies$B_SEX_CODE == \"6\", ] #all rows for a specificed sex\n bandnums <- unique(males$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"blue\")\n for(j in 1:nrow(males)){\n if(nrow(males) != 0){\n inter <- gcIntermediate(c(males[j,]$B_LON_DECIMAL_DEGREES, males[j,]$B_LAT_DECIMAL_DEGREES),\n c(males[j,]$E_LON_DECIMAL_DEGREES, males[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=0.5) \n }\n }\n dev.off()\n}\n# Females only. \n# note: had to add the if statement because some species have no female records\nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \"_f\", \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n females <- birdies[birdies$B_SEX_CODE == \"5\" | birdies$B_SEX_CODE == \"7\", ] #all rows for a specificed sex\n bandnums <- unique(females$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=1, col=\"red\")\n for(j in 1:nrow(females)){\n if(nrow(females) != 0){\n inter <- gcIntermediate(c(females[j,]$B_LON_DECIMAL_DEGREES, females[j,]$B_LAT_DECIMAL_DEGREES),\n c(females[j,]$E_LON_DECIMAL_DEGREES, females[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=0.5)\n }\n }\n dev.off()\n}\n\n# age data\n# Males and age\nspecies <- unique(humtest$B_SPECIES_NAME)\n\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \"_m\", \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n males <- birdies[birdies$B_SEX_CODE == \"4\" | birdies$B_SEX_CODE == \"6\", ] #all rows for a specificed sex\n males <- males[order(males$B_AGE_CODE), ]\n bandnums <- unique(males$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n for(j in 1:nrow(males)){\n if(nrow(males) != 0){\n inter <- gcIntermediate(c(males[j,]$B_LON_DECIMAL_DEGREES, males[j,]$B_LAT_DECIMAL_DEGREES),\n c(males[j,]$E_LON_DECIMAL_DEGREES, males[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=0.5) \n ages <- unique(males$B_AGE_CODE)\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=ages, col=\"blue\")\n legend(\"left\", col=\"blue\", pch=ages, legend=ages, bg=\"whitesmoke\")\n }\n }\n dev.off()\n}\n# Females & age\n# note: had to add the if statement because some species have no female records\nspecies <- unique(humtest$B_SPECIES_NAME)\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \"_f\", \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n females <- birdies[birdies$B_SEX_CODE == \"5\" | birdies$B_SEX_CODE == \"7\", ] #all rows for a specificed sex\n females <- females[order(females$B_AGE_CODE), ]\n bandnums <- unique(females$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n for(j in 1:nrow(females)){\n if(nrow(females) != 0){\n inter <- gcIntermediate(c(females[j,]$B_LON_DECIMAL_DEGREES, females[j,]$B_LAT_DECIMAL_DEGREES),\n c(females[j,]$E_LON_DECIMAL_DEGREES, females[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=colors, lwd=0.5)\n ages <- unique(females$B_AGE_CODE)\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=ages, col=\"red\")\n legend(\"left\", col=\"red\", pch=ages, legend=ages, bg=\"whitesmoke\")\n }\n }\n dev.off()\n}\n\n# For RUHU: showing how points change within year, can track the migration pattern, also showing ages \nRUHU <- humtest[humtest$B_SPECIES_NAME == \"RUFOUS HUMMINGBIRD\", ]\nRUHUorder <- RUHU[order(RUHU$BANDING_MONTH), ]\nRUHUmonth <- unique(RUHUorder$BANDING_MONTH)\nfor(i in 1:length(RUHUmonth)){\n pdf(paste(\"Month_\", RUHUmonth[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=RUHUmonth[i])\n mopts <- RUHU[RUHU$BANDING_MONTH == RUHUmonth[i], ]\n mopts <- mopts[order(mopts$B_AGE_CODE), ]\n ages <- unique(mopts$B_AGE_CODE)\n points(mopts$B_LAT_DECIMAL_DEGREES ~ mopts$B_LON_DECIMAL_DEGREES, col=\"black\", cex=1, pch=ages)\n legend(\"left\", col=\"black\", pch=ages, legend=ages, bg=\"whitesmoke\")\n dev.off()\n}\n\n# For RUHU: same as above, but adding points to show male vs. female - is there a difference in timing of migration between the sexes?\nRUHU <- humtest[humtest$B_SPECIES_NAME == \"RUFOUS HUMMINGBIRD\", ]\nRUHUorder <- RUHU[order(RUHU$BANDING_MONTH), ]\nRUHUmonth <- unique(RUHUorder$BANDING_MONTH)\nfor(i in 1:length(RUHUmonth)){\n pdf(paste(\"Month_\", RUHUmonth[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=RUHUmonth[i])\n mopts <- RUHU[RUHU$BANDING_MONTH == RUHUmonth[i], ]\n mopts <- mopts[order(mopts$B_AGE_CODE), ]\n ages <- unique(mopts$B_AGE_CODE)\n females <- mopts[mopts$B_SEX_CODE == \"5\" | mopts$B_SEX_CODE == \"7\", ]\n males <- mopts[mopts$B_SEX_CODE == \"4\" | mopts$B_SEX_CODE == \"6\", ]\n points(mopts$B_LAT_DECIMAL_DEGREES ~ mopts$B_LON_DECIMAL_DEGREES, col=\"black\", cex=1, pch=ages)\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"red\")\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"blue\")\n legend(\"left\", col=\"black\", pch=ages, legend=ages, bg=\"whitesmoke\")\n dev.off()\n}\n\n# maps for each species by month (pts are colored by sex and shaped by age)\nspecies <- unique(humtest$B_SPECIES_NAME)\nhummonth <- unique(humtest$BANDING_MONTH)\nfor(i in 1:length(species)){\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n for(j in 1:length(hummonth)){\n pdf(paste(\"Sp_\", species[i], \"_Month_\", hummonth[j], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=hummonth[j])\n mopts <- birdies[birdies$BANDING_MONTH == hummonth[j], ]\n mopts <- mopts[order(mopts$B_AGE_CODE), ]\n females <- mopts[mopts$B_SEX_CODE == \"5\" | mopts$B_SEX_CODE == \"7\", ]\n males <- mopts[mopts$B_SEX_CODE == \"4\" | mopts$B_SEX_CODE == \"6\", ]\n points(females$B_LAT_DECIMAL_DEGREES ~ females$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"red\")\n points(males$B_LAT_DECIMAL_DEGREES ~ males$B_LON_DECIMAL_DEGREES, cex=1, pch=20, col=\"blue\")\n ages <- unique(mopts$B_AGE_CODE)\n if(length(ages) != 0){\n points(mopts$B_LAT_DECIMAL_DEGREES ~ mopts$B_LON_DECIMAL_DEGREES, col=\"black\", cex=1, pch=ages)\n colors <- c(rep(\"black\", length(ages)), \"red\", \"blue\")\n legend(\"left\", col=colors, pch=c(ages, 20, 20), legend=c(ages, \"Female\", \"Male\"), bg=\"whitesmoke\") \n }\n dev.off()\n }\n}\n\n# by year for RUHU, RTHU, BCHU - where banded and recap'ed in same year\n# pattern with el nino years? are hummers doing something different in each year? \ncolors <- rainbow(12, start=0.4, end=1, alpha=0.8)\nBCHUpts <- humtest[humtest$B_SPECIES_NAME == \"BLACK-CHINNED HUMMINGBIRD\", ]\nBCHUpts <- BCHUpts[order(BCHUpts$BANDING_YEAR), ]\n# new df of all birds that were recaptured in the same year that they were banded\nBCHUpts <- BCHUpts[BCHUpts$BANDING_YEAR == BCHUpts$ENCOUNTER_YEAR, ] \nBCHUyr <- unique(BCHUpts$BANDING_YEAR)\nfor(i in 1:length(BCHUyr)){\n yrpts <- BCHUpts[BCHUpts$BANDING_YEAR == BCHUyr[i], ]\n pdf(paste(\"BCHU_\", BCHUyr[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=BCHUyr[i])\n for(j in 1:nrow(yrpts)){\n inter <- gcIntermediate(c(yrpts[j,]$B_LON_DECIMAL_DEGREES, yrpts[j,]$B_LAT_DECIMAL_DEGREES),\n c(yrpts[j,]$E_LON_DECIMAL_DEGREES, yrpts[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=\"#FF3300\", lwd=2)\n points(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, col=\"black\", bg=colors, cex=1.5, pch=21)\n legend(\"left\", ncol=4, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=21, cex=1, col=\"black\", pt.bg=colors, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n }\n dev.off()\n}\nRUHUpts <- humtest[humtest$B_SPECIES_NAME == \"RUFOUS HUMMINGBIRD\", ]\nRUHUpts <- RUHUpts[order(RUHUpts$BANDING_YEAR), ]\nRUHUpts <- RUHUpts[RUHUpts$BANDING_YEAR == RUHUpts$ENCOUNTER_YEAR, ] \nRUHUyr <- unique(RUHUpts$BANDING_YEAR) \nfor(i in 1:length(RUHUyr)){\n yrpts <- RUHUpts[RUHUpts$BANDING_YEAR == RUHUyr[i], ]\n pdf(paste(\"RUHU_\", RUHUyr[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=RUHUyr[i])\n for(j in 1:nrow(yrpts)){\n inter <- gcIntermediate(c(yrpts[j,]$B_LON_DECIMAL_DEGREES, yrpts[j,]$B_LAT_DECIMAL_DEGREES),\n c(yrpts[j,]$E_LON_DECIMAL_DEGREES, yrpts[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=\"#FF3300\", lwd=2)\n points(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, col=\"black\", bg=colors, cex=1.5, pch=21)\n legend(\"left\", ncol=4, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=21, cex=1, col=\"black\", pt.bg=colors, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n }\n dev.off()\n} \nRTHUpts <- humtest[humtest$B_SPECIES_NAME == \"RUBY-THROATED HUMMINGBIRD\", ]\nRTHUpts <- RTHUpts[order(RTHUpts$BANDING_YEAR), ]\nRTHUpts <- RTHUpts[RTHUpts$BANDING_YEAR == RTHUpts$ENCOUNTER_YEAR, ] \nRTHUyr <- unique(RTHUpts$BANDING_YEAR)\nfor(i in 1:length(RTHUyr)){\n yrpts <- RTHUpts[RTHUpts$BANDING_YEAR == RTHUyr[i], ]\n pdf(paste(\"RTHU_\", RTHUyr[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n title(main=RTHUyr[i])\n for(j in 1:nrow(yrpts)){\n inter <- gcIntermediate(c(yrpts[j,]$B_LON_DECIMAL_DEGREES, yrpts[j,]$B_LAT_DECIMAL_DEGREES),\n c(yrpts[j,]$E_LON_DECIMAL_DEGREES, yrpts[j,]$E_LAT_DECIMAL_DEGREES),\n n=50, addStartEnd=TRUE,)\n lines(inter, col=\"#FF3300\", lwd=2)\n points(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, col=\"black\", bg=colors, cex=1.5, pch=21)\n legend(\"left\", ncol=4, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=21, cex=1, col=\"black\", pt.bg=colors, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n }\n dev.off()\n}\n\n################# BBL basic metrics of the data #################\n# counting number of individuals, females, males, and juveniles per species\nnumbers <- function(){\n indiv.v <- c()\n species <- unique(humtest$B_SPECIES_NAME)\n for(i in 1:length(species)){\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n indivs <- length(unique(birdies$B_BAND_NUM))\n indiv.v[i] <- indivs\n }\n fem.v <- c()\n for(i in 1:length(species)){\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n females <- birdies[birdies$B_SEX_CODE == \"5\" | birdies$B_SEX_CODE == \"7\", ]\n females <- length(unique(females$B_BAND_NUM))\n fem.v[i] <- females\n }\n male.v <- c()\n for(i in 1:length(species)){\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n males <- birdies[birdies$B_SEX_CODE == \"4\" | birdies$B_SEX_CODE == \"6\", ]\n males <- length(unique(males$B_BAND_NUM))\n male.v[i] <- males\n }\n #assuming juveniles are AHY, HY, or J\n juv.v <- c()\n for(i in 1:length(species)){\n birdies <- humtest[humtest$B_SPECIES_NAME == species[i], ]\n juvs <- birdies[birdies$B_AGE_CODE == \"1\" | birdies$B_AGE_CODE == \"2\" | birdies$B_AGE_CODE == \"3\", ]\n juvs <- length(unique(juvs$B_BAND_NUM))\n juv.v[i] <- juvs\n }\n allnums <- data.frame(species, indiv.v, fem.v, male.v, juv.v)\n write.table(allnums, file=\"24sep13_BBLdata.csv\", sep=\",\")\n}\nnumbers()\n\n# calculating the distance between points in km, Euclidian distance\nA <- SpatialPointsDataFrame(coords=cbind(humtest$B_LON_DECIMAL_DEGREES,humtest$B_LAT_DECIMAL_DEGREES),humtest)\nB <- SpatialPointsDataFrame(coords=cbind(humtest$E_LON_DECIMAL_DEGREES,humtest$E_LAT_DECIMAL_DEGREES),humtest)\ndistances <- pointDistance(A, B, longlat=TRUE)/1000 # /1000 to get km instead of meters\nhist(distances, col=\"plum4\")\nnewhumtest <- data.frame(A, distances)\nnewhumtest2 <- data.frame(humtest, distances)\n\ndistcalcs <- function(){\n species <- unique(newhumtest2$B_SPECIES_NAME)\n meandist.v <- c()\n sddist.v <- c()\n meddist.v <- c()\n rangedist.m <- matrix(nrow=14, ncol=2)\n for(i in 1:length(species)){\n birdies <- newhumtest2[newhumtest2$B_SPECIES_NAME == species[i], ]\n #for each species, i want to calculate the mean distance, sd, median + include the range of values\n meandist <- mean(birdies$distances)\n sddist <- sd(birdies$distances)\n meddist <- median(birdies$distances)\n rangedist <- range(birdies$distances)\n meandist.v[i] <- meandist\n sddist.v[i] <- sddist\n meddist.v[i] <- meddist \n rangedist.m[i, ] <- rangedist\n }\n allcalcs <- data.frame(meandist.v, sddist.v, meddist.v, rangedist.m)\n write.table(allcalcs, file=\"25sep13_distcalcs.csv\", sep=\",\")\n}\n\ndistcalcs()\n\n################# SDM test code #################\n# not working yet\nhum.sp<-SpatialPointsDataFrame(coords=cbind(humtest$B_LON_DECIMAL_DEGREES,humtest$B_LAT_DECIMAL_DEGREES),humtest)\n\nrequire(dismo)\nUSA<-getData('GADM', country='USA', level=1)\n\nplot(USA)\n\nrequire(raster)\nu<-raster()\nr<-rasterize(USA,u)\nplot(r)\nr[r>0]<-1\nplot(r)\nplot(r,ext=extent(hum.sp))\npoints(hum.sp)\n\npredictors <- stack(list.files(path=paste(system.file(package=\"dismo\"), '/ex', sep=''), \n pattern='grd', full.names=TRUE ))\nme<-maxent(predictors,hum.sp)\n\n################# OLD/test code for recap connections by species #################\n# need to clean up/delete? \n\nlibrary(maps)\nlibrary(geosphere)\nlibrary(adegenet)\n\nxlim <- c(-171.738281, -56.601563)\nylim <- c(8, 71.856229)\n\nallhum <- read.csv(\"testhummerdata.csv\", sep=\",\", header=TRUE)\nnames(allhum)\nhead(allhum)\nallhum2 <- data.frame(allhum[,1:9]) #allhum has 6 empty columns for some reason, got rid of here\nhead(allhum2)\n#putting data in order by species, bandnumber, year, month, and then day\nallhum2 <- allhum2[order(allhum2$B_SPECIES_NAME, allhum2$B_BAND_NUM, allhum2$BANDING_YEAR, allhum2$BANDING_MONTH, allhum2$BANDING_DATE), ]\nhead(allhum2)\nspecies <- unique(allhum2$B_SPECIES_NAME)\ncolors <- \"#FF3300\"\n# pal <- colorRampPalette(c(\"orange\", \"green\"))\n# colors <- pal(50)\n\n# ##test plotting of datapoints by species\n# plot.new()\n# map(\"world\", xlim=xlim, ylim=ylim, col=\"whitesmoke\", fill=T, bg=\"azure3\", lwd=0.05)\n# birdies <- allhum2[allhum2$B_SPECIES_NAME == species[11], ]\n# points(birdies$B_LAT_DECIMAL_DEGREES ~ birdies$B_LON_DECIMAL_DEGREES, cex=2, col=\"red\", pch=20)\n\n\nfor(i in 1:length(species)){\n pdf(paste(\"species_\", species[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n birdies <- allhum2[allhum2$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n bandnums <- unique(birdies$B_BAND_NUM)\n par(adj=0.5)\n title(main=species[i], col.main=\"black\")\n par(adj=0.55)\n title(sub=length(bandnums), cex.sub=1.5, line=0.5)\n par(adj=0.35)\n title(sub=\"# unique birds =\", line=0.5, cex.sub=1.5)\n for(j in 1:length(bandnums)){\n bird <- subset(birdies, B_BAND_NUM == bandnums[j])\n # bird <- bird[order(bird$BANDING_DATE), ] #how does R read dates?\n for(k in 1:nrow(bird)){\n if((k+1) <= nrow(bird)){\n temp <- bird[k:(k+1), ]\n inter <- gcIntermediate(c(temp[1,]$B_LON_DECIMAL_DEGREES, temp[1,]$B_LAT_DECIMAL_DEGREES), \n c(temp[2,]$B_LON_DECIMAL_DEGREES, temp[2,]$B_LAT_DECIMAL_DEGREES), \n n=50, addStartEnd=TRUE, )\n lines(inter, col=colors, lwd=2)\n }\n } \n }\n dev.off()\n}\n\n#new code gets rid of this: Error in if (antipodal(p1, p2)) { : missing value where TRUE/FALSE needed\n\n#code below goes through individual species at a time for recap maps\nALHU <- allhum2[allhum2$B_SPECIES_NAME == \"ALLEN'S HUMMINGBIRD\", ] #df with just data for Allen's hbird\nANHU <- allhum2[allhum2$B_SPECIES_NAME == \"ANNA'S HUMMINGBIRD\", ]\nBCHU <- allhum2[allhum2$B_SPECIES_NAME == \"BLACK-CHINNED HUMMINGBIRD\", ]\nBLUH <- allhum2[allhum2$B_SPECIES_NAME == \"BLUE-THROATED HUMMINGBIRD\", ]\nBBLH <- allhum2[allhum2$B_SPECIES_NAME == \"BROAD-BILLED HUMMINGBIRD\", ]\nBTLH <- allhum2[allhum2$B_SPECIES_NAME == \"BROAD-TAILED HUMMINGBIRD\", ] #problematic\nBUFH <- allhum2[allhum2$B_SPECIES_NAME == \"BUFF-BELLIED HUMMINGBIRD\", ]\nCAHU <- allhum2[allhum2$B_SPECIES_NAME == \"CALLIOPE HUMMINGBIRD\", ]\nCOHU <- allhum2[allhum2$B_SPECIES_NAME == \"COSTA'S HUMMINGBIRD\", ]\nMAHU <- allhum2[allhum2$B_SPECIES_NAME == \"MAGNIFICENT HUMMINGBIRD\", ]\nRTHU <- allhum2[allhum2$B_SPECIES_NAME == \"RUBY-THROATED HUMMINGBIRD\", ] #problematic\nRUHU <- allhum2[allhum2$B_SPECIES_NAME == \"RUFOUS HUMMINGBIRD\", ]\nVCHU <- allhum2[allhum2$B_SPECIES_NAME == \"VIOLET-CROWNED HUMMINGBIRD\", ]\nWEHU <- allhum2[allhum2$B_SPECIES_NAME == \"WHITE-EARED HUMMINGBIRD\", ]\n\n# prints out the number of samples/species\nfor(i in 1:length(species)){\n birdies <- allhum2[allhum2$B_SPECIES_NAME == species[i], ]\n bands <- unique(birdies$B_BAND_NUM)\n print(length(bands))\n}\n\n#returns rows with same band number -> so it shows the original and recapture\n# bird1 <- allhum2[allhum2$B_BAND_NUM == allens[20, ]$B_BAND_NUM, ] \n\n#tutorial testdrive data\n# plot.new()\n# map(\"world\", col=\"#f2f2f2\", fill=TRUE, bg=\"white\", lwd=0.05, xlim=xlim, ylim=ylim)\n# \n# allenslato <- 37.91667\n# allenslongo <- -122.75\n# allenslatr <- 29.00\n# allenslongr <- -120.00\n# inter <- gcIntermediate(c(allenslongo, allenslato), c(allenslongr, allenslatr), n=50, addStartEnd=TRUE)\n# lines(inter, col=\"green\")\n# lat_tx <- 29.954935\n# lon_tx <- -98.701172\n# inter2 <- gcIntermediate(c(allenslongo, allenslato), c(lon_tx, lat_tx), n=50, addStartEnd=TRUE)\n# lines(inter2, col=\"red\")\n# inter3 <- gcIntermediate(c(allenslongr, allenslatr), c(lon_tx, lat_tx), n=50, addStartEnd=TRUE)\n# lines(inter3, col=\"blue\")\n\n############################### Old temporal data by species code\n# all points\nplot.new()\nmap(\"world\", col=\"#f2f2f2\", fill=TRUE, bg=\"steelblue\", lwd=0.05, xlim=xlim, ylim=ylim)\npoints(allhum2$B_LAT_DECIMAL_DEGREES ~ allhum2$B_LON_DECIMAL_DEGREES, pch=20, cex=0.5)\n\n# pal <- colorRampPalette(c(\"orange\", \"blue\"))\n# colors <- pal(14)\n# plot.new()\n# map(\"world\", col=\"#f2f2f2\", fill=TRUE, bg=\"steelblue\", lwd=0.05, xlim=xlim, ylim=ylim)\n# points(RUHU$B_LAT_DECIMAL_DEGREES ~ RUHU$B_LON_DECIMAL_DEGREES, pch=20, cex=1, col=transp(colors))\n\n#maps out points by species, colors by month\n# birdyears <- unique(birdies$BANDING_YEAR)\n# for(i in 1:length(species)){\n# pdf(paste(\"species_\", species[i], \".pdf\", sep=\"\"), width=11, height=7)\n# map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n# birdies <- allhum2[allhum2$B_SPECIES_NAME == species[i], ] #all rows for a specified species i\n# par(adj=0.5)\n# title(main=species[i], col.main=\"black\")\n# # legend(\"bottomleft\", ncol=2, y.intersp=0.8, x.intersp=0.4, xjust=0, bg=\"whitesmoke\", col=birdies$BANDING_MONTH, pch=20, cex=2, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n# for(j in 1:length(birdyears)){\n# bird <- subset(birdies, BANDING_YEAR == birdyears[j])\n# points(birdies$B_LAT_DECIMAL_DEGREES ~ birdies$B_LON_DECIMAL_DEGREES, col=birdies$BANDING_MONTH, cex=1.5, pch=21)\n# } \n# dev.off()\n# }\n\n# pal <- colorRampPalette(c(\"orange\", \"blue\"))\n# colors <- pal(12)\n\n#loop goes through each species and plots all points for unique years of data of that species\nplot.new()\nspecies <- unique(allhum2$B_SPECIES_NAME)\ncolors <- rainbow(12, start=0.4, end=1, alpha=0.8)\nfor(i in 1:length(species)){\n birdies <- allhum2[allhum2$B_SPECIES_NAME == species[i], ]\n birdyears <- unique(birdies$BANDING_YEAR)\n for(j in 1:length(birdyears)){\n yrpts <- birdies[birdies$BANDING_YEAR == birdyears[j], ]\n pdf(paste(\"Sp_\", species[i], \"_Year_\", birdyears[j], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n points(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, cex=1.5, pch=20, col=colors)\n title(main=birdyears[j])\n legend(\"left\", ncol=4, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=21, cex=1, col=\"black\", pt.bg=colors, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n dev.off()\n }\n}\n\n#for one species at a time\nplot.new()\nRUHUorder <- RUHU[order(RUHU$BANDING_YEAR), ]\nRUHUyears <- unique(RUHUorder$BANDING_YEAR)\nfor(i in 1:length(RUHUyears)){\n yrpts <- RUHU[RUHU$BANDING_YEAR == RUHUyears[i], ]\n pdf(paste(\"Year_\", RUHUyears[i], \".pdf\", sep=\"\"), width=11, height=7)\n map(\"world\", col=\"whitesmoke\", fill=TRUE, bg=\"azure3\", lwd=0.05, xlim=xlim, ylim=ylim)\n points(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, col=colors, cex=1.5, pch=20)\n title(main=RUHUyears[i])\n legend(\"left\", ncol=4, y.intersp=1, x.intersp=0.5, xjust=0, bg=\"whitesmoke\", pch=21, cex=1, col=\"black\", pt.bg=colors, c(\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"))\n dev.off()\n}\n\n#manual way of plotting different years, have to change ALHUyears number\nplot.new()\nmap(\"world\", xlim=xlim, ylim=ylim)\nyrpts <- ALHU[ALHU$BANDING_YEAR == ALHUyears[4], ]\npoints(yrpts$B_LAT_DECIMAL_DEGREES ~ yrpts$B_LON_DECIMAL_DEGREES, col=\"pink\", cex=1.5, pch=20)\n", "meta": {"hexsha": "aa73ef535845bb53dffcbea3c4ec44cc0f0dc256", "size": 34345, "ext": "r", "lang": "R", "max_stars_repo_path": "Project-1/BBLmapcode.r", 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YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33907076928082247}} {"text": "#!/usr/bin/Rscript\n\nargs = commandArgs(TRUE)\ndata = read.csv(args[1],sep = ',', dec = '.', header = TRUE, stringsAsFactors = FALSE)\n\nframe = data.frame(data)\nframe$Fitness <- as.numeric(frame$Fitness)\nframe$Generation <- as.numeric(frame$Generation)\n\n\npng('result.png', width = 1024)\nboxplot(Fitness ~ Generation, frame)\ndev.off()\n\ndev.new()\npng('result-victories-scatter.png', width = 1024)\nplot(frame$Generation, frame$Fitness, col=frame$Victory+2, ylab=\"Victories\", xlab = \"Generations\")\ndev.off()\n\ndev.new()\npng('result-victories.png', width = 1024)\nplot(aggregate(frame$Victory, by=list(frame$Generation), FUN=sum), ylab=\"Victories\", xlab = \"Generations\")\ndev.off()", "meta": {"hexsha": "f84a0ab06fc0c51d2d5e36c90863cb0224c83402", "size": 671, "ext": "r", "lang": "R", "max_stars_repo_path": "core/assets/plotter.r", "max_stars_repo_name": "sci10n/HaxeAI", "max_stars_repo_head_hexsha": "02acbac5a5f8cb89dcae35ba0bbd8902cee51689", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-16T14:36:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T00:37:34.000Z", "max_issues_repo_path": "core/assets/plotter.r", "max_issues_repo_name": "CaffeineViking/qk2d", "max_issues_repo_head_hexsha": "cdef33884f78205502539f79a73b49eca3496a54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2017-09-04T03:57:58.000Z", "max_issues_repo_issues_event_max_datetime": "2018-03-17T16:06:21.000Z", "max_forks_repo_path": "core/assets/plotter.r", "max_forks_repo_name": "CaffeineViking/qk2d", "max_forks_repo_head_hexsha": "cdef33884f78205502539f79a73b49eca3496a54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-08-15T12:42:25.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-15T12:42:25.000Z", "avg_line_length": 29.1739130435, "max_line_length": 106, "alphanum_fraction": 0.7108792846, "num_tokens": 191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819874558604, "lm_q2_score": 0.5544704649604274, "lm_q1q2_score": 0.33899325485308107}} {"text": "###################################################################\n#\n# Mark Cembrowski, Janelia Research Campus, Nov 11 2016\n#\n# This single-serving script plots the results of looking for\n# barcoded genes across all hippocampal populations.\n#\n###################################################################\n\ngeneBar125Heatmap <- function(label=F){\n\t# Identify probes that are part of pop'n RNA-seq dataset.\n\tprobesInPop <- genesBar125%in%unlist(idToSym(rownames(fpkmPoolMat)))\n\n\t# Extract these genes.\n\tprobesInPop <- genesBar125[probesInPop]\n\n\t# Get maximum FPKM values for these genes.\n\tmaxFpkm <- apply(subFpkmMatrix(probesInPop),1,max)\n\n\t# Sort by FPKM.\n\tmaxSort <- probesInPop[order(maxFpkm)]\n\n\t# Plot.\n\tfpkmHeatmap(maxSort,rMax=10,label=label)\n}\n", "meta": {"hexsha": "4e1552e487c4e16ffcf004e593b28cf771ba2d82", "size": 758, "ext": "r", "lang": "R", "max_stars_repo_path": "geneBar125Heatmap.r", "max_stars_repo_name": "cembrowskim/hippXValidate", "max_stars_repo_head_hexsha": "090e8bee4393ac70cd633922a1665899290ea6dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "geneBar125Heatmap.r", "max_issues_repo_name": "cembrowskim/hippXValidate", "max_issues_repo_head_hexsha": "090e8bee4393ac70cd633922a1665899290ea6dc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "geneBar125Heatmap.r", "max_forks_repo_name": "cembrowskim/hippXValidate", "max_forks_repo_head_hexsha": "090e8bee4393ac70cd633922a1665899290ea6dc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1538461538, "max_line_length": 69, "alphanum_fraction": 0.6200527704, "num_tokens": 191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3389932470008315}} {"text": "library(testthat)\nlibrary(digest)\n\ntest_1 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(reg1$coefficients[1], 3)), \"56f04106b0d09f24d95010b891573eea\")\n })\n print(\"Success!\")\n}\n\n\ntest_2 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(reg2$coefficients[1], 3)), \"a4dd2bedaafa1f6b52eb1e6b4820fbde\")\n })\n print(\"Success!\")\n}\n\ntest_3 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(reg3$coefficients[1], 3)), \"f02741b40f9113aeb5a5e87fb9b2ab23\")\n })\n print(\"Success!\")\n}\n\n\ntest_4 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(reg4$coefficients[1], 3)), \"f02741b40f9113aeb5a5e87fb9b2ab23\")\n })\n print(\"Success!\")\n}\n\ntest_5 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(reg5$residuals[1], 3)), \"02cba6d8393bb07ad6a077228a380f34\")\n })\n print(\"Success!\")\n}\n\n\ntest_6 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(census_data$resid[1], 3)), \"bbfd349bb38017dca76e637fb2dc5373\")\n })\n print(\"Success!\")\n}\n\ntest_7 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(WT$coefficients[1], 2)), \"0da67aa7287597c3912d21e461eb1e44\")\n })\n print(\"Success!\")\n}\n\n\ntest_8 <- function() {\n test_that(\"Solution is incorrect\", {\n expect_equal(digest(round(BPstat,1)), \"b5063b59d220cc2a6d8e7a6a56eb9568\")\n })\n print(\"Success!\")\n}\n", "meta": {"hexsha": "3565500fdae8b1c0a6e3c613bbe87451f1615dd6", "size": 1482, "ext": "r", "lang": "R", "max_stars_repo_path": "hands_on_4/hands_on_tests_4.r", "max_stars_repo_name": "jlgraves-ubc/econ-326-student", "max_stars_repo_head_hexsha": "3b7b549afca03564d50ddcddce007c2b14552939", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-14T22:20:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-14T22:20:57.000Z", "max_issues_repo_path": "hands_on_4/hands_on_tests_4.r", "max_issues_repo_name": "jlgraves-ubc/econ-326-student", "max_issues_repo_head_hexsha": "3b7b549afca03564d50ddcddce007c2b14552939", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "hands_on_4/hands_on_tests_4.r", "max_forks_repo_name": "jlgraves-ubc/econ-326-student", "max_forks_repo_head_hexsha": "3b7b549afca03564d50ddcddce007c2b14552939", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5238095238, "max_line_length": 92, "alphanum_fraction": 0.69365722, "num_tokens": 500, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3389932470008315}} {"text": "#' Split array, apply function, and return results in an array.\n#'\n#' For each slice of an array, apply function, keeping results as an array.\n#'\n#' This function is very similar to \\code{\\link{apply}}, except that it will\n#' always return an array, and when the function returns >1 d data structures,\n#' those dimensions are added on to the highest dimensions, rather than the\n#' lowest dimensions. This makes \\code{aaply} idempotent, so that\n#' \\code{aaply(input, X, identity)} is equivalent to \\code{aperm(input, X)}.\n#'\n#' @section Warning:Passing a data frame as first argument may lead to\n#' unexpected results, see \\url{https://github.com/hadley/plyr/issues/212}.\n#'\n#' @template ply\n#' @template a-\n#' @template -a\n#' @export\n#' @examples\n#' dim(ozone)\n#' aaply(ozone, 1, mean)\n#' aaply(ozone, 1, mean, .drop = FALSE)\n#' aaply(ozone, 3, mean)\n#' aaply(ozone, c(1,2), mean)\n#'\n#' dim(aaply(ozone, c(1,2), mean))\n#' dim(aaply(ozone, c(1,2), mean, .drop = FALSE))\n#'\n#' aaply(ozone, 1, each(min, max))\n#' aaply(ozone, 3, each(min, max))\n#'\n#' standardise <- function(x) (x - min(x)) / (max(x) - min(x))\n#' aaply(ozone, 3, standardise)\n#' aaply(ozone, 1:2, standardise)\n#'\n#' aaply(ozone, 1:2, diff)\naaply <- function(.data, .margins, .fun = NULL, ..., .expand = TRUE,\n .progress = \"none\", .inform = FALSE, .drop = TRUE,\n .parallel = FALSE, .paropts = NULL) {\n pieces <- splitter_a(.data, .margins, .expand)\n\n laply(.data = pieces, .fun = .fun, ...,\n .progress = .progress, .inform = .inform, .drop = .drop,\n .parallel = .parallel, .paropts = .paropts)\n}\n", "meta": {"hexsha": "5dab5dcc76da130a1f73c2e106049b0176722b7e", "size": 1602, "ext": "r", "lang": "R", "max_stars_repo_path": "source/gdaexperience6/plyr/R/aaply.r", "max_stars_repo_name": "lalaithan/developer-immersion-data", "max_stars_repo_head_hexsha": "b48d291ad5a03d56c0228d00e0b290b638d50194", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 82, "max_stars_repo_stars_event_min_datetime": "2017-05-24T22:55:14.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-31T00:56:05.000Z", "max_issues_repo_path": "source/gdaexperience6/plyr/R/aaply.r", "max_issues_repo_name": "lalaithan/developer-immersion-data", "max_issues_repo_head_hexsha": "b48d291ad5a03d56c0228d00e0b290b638d50194", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2017-05-20T16:10:54.000Z", "max_issues_repo_issues_event_max_datetime": "2018-09-30T18:04:46.000Z", "max_forks_repo_path": "source/gdaexperience6/plyr/R/aaply.r", "max_forks_repo_name": "lalaithan/developer-immersion-data", "max_forks_repo_head_hexsha": "b48d291ad5a03d56c0228d00e0b290b638d50194", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 55, "max_forks_repo_forks_event_min_datetime": "2017-05-20T12:42:19.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-26T16:38:16.000Z", "avg_line_length": 35.6, "max_line_length": 78, "alphanum_fraction": 0.6348314607, "num_tokens": 519, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3389932470008315}} {"text": "folder <- \"E:\\\\ML_Samples_new_2\\\\features\"\r\nall_features <- read_features_performance(folder = folder, sample_size = 250)\r\nall_features <- make_median_artificial(all_features)\r\nall_features <- make_balanced(all_features)\r\n\r\n\r\nbest_data <- table(as.factor(all_features$best))\r\n#folder <- \"E:\\\\gecco_ml_samples_new\\\\features\"\r\nfolder <- \"E:\\\\gecco_ml_samples\\\\features\"\r\ncoco_features <- read_features_performance_coco(folder = folder, sample_size = 250)\r\ncoco_features <- make_median_coco(coco_features)\r\ncoco_features <- as.data.frame(coco_features$data)\r\n\r\ncnames <- colnames(coco_features)\r\ncnames <- str_remove(cnames, \"data.\")\r\ncolnames(coco_features)<-cnames\r\n\r\nbest_coco_multiple <- readMat(\"E:\\\\matlab_results\\\\10\\\\all_best5.mat\")$allBest\r\nbest_coco <- best_coco_multiple[,1]\r\nbest_coco_multiple <- transform_best_coco(best_coco_multiple)\r\n\r\n\r\nalgs <- c(\"ABC\",\"ACO\",\"CMAES\",\"CSO\",\"DE\",\"FEP\",\"GA\",\"PSO\",\"SA\",\"Rand\")\r\nbest_coco <- algs[best_coco]\r\n\r\ncombined_cols <- intersect(colnames(all_features$data), colnames(coco_features))\r\n\r\nall_features$data <- all_features$data[,combined_cols]\r\ncoco_features <- coco_features[,combined_cols]\r\nall_features$data <- rbind(all_features$data, coco_features)\r\nall_features$best <- c(all_features$best, best_coco)\r\n\r\ntotal = nrow(all_features$data)\r\n\r\n\r\n\r\nX_temp <- all_features$data[1:total,]\r\nbest <- all_features$best[1:total]\r\n\r\n\r\nX_temp <- as.data.frame(X_temp)\r\nX_temp$basic.objective_min <- NULL\r\nX_temp$basic.objective_max <- NULL\r\nX_temp$basic.upper_max <- NULL\r\nX_temp$basic.upper_min <- NULL\r\nX_temp$basic.lower_min <- NULL\r\nX_temp$basic.lower_max<- NULL\r\nX_temp$basic.blocks_min <- NULL\r\nX_temp$basic.blocks_max <- NULL\r\nX_temp$basic.cells_total <- NULL\r\nX_temp$basic.cells_filled <- NULL\r\nX_temp$basic.minimize_fun <- NULL\r\nX_temp$basic.costs_fun_evals <- NULL\r\nX_temp$basic.dim <- NULL\r\nX_temp$basic.observations <- NULL\r\n\r\n\r\nX_temp$basic.lower_max <- NULL\r\nX_temp$basic.lower_min <- NULL\r\nX_temp$basic.costs_runtime <- NULL\r\nX_temp$pca.costs_runtime <- NULL\r\nX_temp$pca.costs_fun_evals <- NULL\r\nX_temp$nbc.costs_fun_evals <- NULL\r\nX_temp$nbc.costs_runtime<- NULL\r\nX_temp$limo.costs_runtime<- NULL\r\nX_temp$limo.costs_fun_evals<- NULL\r\nX_temp$disp.costs_fun_evals<- NULL\r\nX_temp$disp.costs_runtime<- NULL\r\nX_temp$ic.costs_fun_evals<- NULL\r\nX_temp$ic.costs_runtime<- NULL\r\nX_temp$ela_meta.costs_fun_evals<- NULL\r\nX_temp$ela_level.costs_fun_evals<- NULL\r\nX_temp$ela_meta.costs_runtime<- NULL\r\nX_temp$ela_level.costs_runtime<- NULL\r\nX_temp$cm_grad.costs_fun_evals <- NULL\r\nX_temp$cm_grad.costs_runtime<- NULL\r\nX_temp$cm_angle.costs_runtime <- NULL\r\nX_temp$cm_angle.costs_fun_evals<- NULL\r\nX_temp$cm_grad.costs_fun_evals <- NULL\r\n\r\n\r\nX_temp$ic.eps.ratio <- NULL #contians infinite values\r\n\r\ncombined_cols <- colnames(X_temp)\r\ncoco_features <- coco_features[,combined_cols]\r\n\r\n\r\n\r\nX_temp <- sapply(X_temp, as.numeric)\r\nX_temp <- as.data.frame(X_temp)\r\n\r\nbest <- as.numeric(as.factor(best))\r\nclasses <- best\r\nclasses <- as.factor(classes)\r\nX_temp$class <- classes\r\nX_temp <- X_temp[ , colSums(is.na(X_temp)) == 0]\r\nX_temp <- X_temp[ , colSums(is.nan(as.matrix(X_temp))) == 0]\r\nX_temp <- X_temp[ , colSums(is.infinite(as.matrix(X_temp))) == 0]\r\n\r\nX_temp$func <- NULL\r\n\r\ntrain_rows <- 1:500\r\ntest_rows <- 501:860\r\ntrain <- X_temp[train_rows,]\r\ntest <- X_temp[test_rows,]\r\ncolumns_2 <- intersect(colnames(train), colnames(test))\r\ntest <- test[columns_2]\r\ntrain <- train[columns_2]\r\n\r\n\r\nsaveRDS(test, \"landscape_features/processed/coco.RDS\")\r\nsaveRDS(train, \"landscape_features/processed/artificial.RDS\")\r\n\r\n", "meta": {"hexsha": "73f678d2333bd88bb2cd5f84dc491508a2117728", "size": 3555, "ext": "r", "lang": "R", "max_stars_repo_path": "machine_learning/preprocess.r", "max_stars_repo_name": "UrbanSkv/ela-transfer-learning", "max_stars_repo_head_hexsha": "0d71c4e90750347ce7ce1988bb7310124d41f4dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "machine_learning/preprocess.r", "max_issues_repo_name": "UrbanSkv/ela-transfer-learning", "max_issues_repo_head_hexsha": "0d71c4e90750347ce7ce1988bb7310124d41f4dc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "machine_learning/preprocess.r", "max_forks_repo_name": "UrbanSkv/ela-transfer-learning", "max_forks_repo_head_hexsha": "0d71c4e90750347ce7ce1988bb7310124d41f4dc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1842105263, "max_line_length": 84, "alphanum_fraction": 0.7476793249, "num_tokens": 980, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324418, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3389932391485819}} {"text": "# forecast to NEON sites using delgao-ramirez calibration models.\n\nrm(list=ls())\nlibrary(data.table)\nsource('paths.r')\nsource('paths_fall2019.r')\nsource('NEFI_functions/tic_toc.r')\nsource('NEFI_functions/precision_matrix_match.r')\nsource('NEFI_functions/ddirch_forecast_noLogMap.r')\n\n# source ddirch_forecast\nlibrary(RCurl)\nscript <- getURL(\"https://raw.githubusercontent.com/colinaverill/NEFI_microbe/master/NEFI_functions/ddirch_forecast.r\", ssl.verifypeer = FALSE)\neval(parse(text = script))\n\n#set output path.----\noutput.path <- NEON_cps_fcast_ddirch_16S.path\n\n#load prior model results.----\nall.mod <- readRDS(prior_delgado_ddirch_16S.path)\n\n#get core-level covariate means and sd.----\ndat <- readRDS(hierarch_filled_data.path)\ndat <- lapply(dat, function(x) x[!(names(x) %in% c(\"pH\", \"conifer\"))])\ndat <- lapply(dat, function(x) setnames(x, old = \"pH_water\", new = \"pH\", skip_absent = TRUE))\ndat <- mapply(cbind, dat, \"study_id\"=200)\n\ncore_mu <- dat$core.core.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#site_mu$map <- log(site_mu$map) # the function already takes the log of map\n\n#merge together.\nplot_mu$siteID <- NULL\ncore.preds <- merge(core_mu , plot_mu)\ncore.preds <- merge(core.preds, site_mu)\ncore.preds$relEM <- NULL\nnames(core.preds)[names(core.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get core-level SD.\ncore_sd <- dat$core.core.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n\n#merge together.\nplot_sd$siteID <- NULL\ncore.sd <- merge(core_sd , plot_sd)\ncore.sd <- merge(core.sd, site_sd)\ncore.sd$relEM <- NULL\nnames(core.sd)[names(core.sd)==\"b.relEM\"] <- \"relEM\"\n\n#get plot-level covariate means and sd.----\ncore_mu <- dat$core.plot.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#merge together, .\nplot_mu$siteID <- NULL\nplot.preds <- merge(core_mu,plot_mu)\nplot.preds <- merge(plot.preds,site_mu)\nplot.preds$relEM <- NULL\nnames(plot.preds)[names(plot.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get plot-level SD.\ncore_sd <- dat$core.plot.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together\nplot_sd$siteID <- NULL\nplot.sd <- merge(core_sd,plot_sd)\nplot.sd <- merge(plot.sd,site_sd)\nplot.sd$relEM <- NULL\nnames(plot.sd)[names(plot.sd)=='b.relEM'] <- \"relEM\"\n\n#get site-level covariate means and sd.----\ncore_mu <- dat$core.site.mu\nplot_mu <- dat$plot.site.mu\nsite_mu <- dat$site.site.mu\n#merge together\nsite.preds <- merge(core_mu, plot_mu)\nsite.preds <- merge(site.preds,site_mu)\nnames(site.preds)[names(site.preds)=='b.relEM'] <- \"relEM\"\n\n#get site-level SD.\ncore_sd <- dat$core.site.sd\nplot_sd <- dat$plot.site.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nsite.sd <- merge(core_sd,plot_sd)\nsite.sd <- merge(site.sd,site_sd)\nnames(site.sd)[names(site.sd)=='b.relEM'] <- \"relEM\"\n\n# # add dummy vars for study effects\n# fcast.preds <- list()\n# for (h in 1:3){\n# hier <- list(core.preds, plot.preds, site.preds)\n# nrow <- nrow(hier[[h]])\n# \n# all_vars <- unique(all.mod[[1]]$species_parameter_output[[1]]$predictor)\n# dummy_vars <- all_vars[grep(\"study_id_\", all_vars)]\n# dummy_mat <- matrix(0, nrow = nrow, ncol = length(dummy_vars))\n# colnames(dummy_mat) <- dummy_vars\n# out <- cbind(hier[[h]], dummy_mat)\n# fcast.preds[[h]] <- out\n# }\n# \n# \n# fcast.sd <- list()\n# for (h in 1:3){\n# hier <- list(core.sd, plot.sd, site.sd)\n# nrow <- nrow(hier[[h]])\n# \n# all_vars <- unique(all.mod[[1]]$species_parameter_output[[1]]$predictor)\n# dummy_vars <- all_vars[grep(\"study_id_\", all_vars)]\n# dummy_mat <- matrix(0.01, nrow = nrow, ncol = length(dummy_vars))\n# colnames(dummy_mat) <- dummy_vars\n# out <- cbind(hier[[h]], dummy_mat)\n# fcast.sd[[h]] <- out\n# }\n\n#Get forecasts from ddirch_forecast.----\nfcast.output <- list()\n\ncat('Making forecasts...\\n')\nfor(i in 1:length(all.mod)){\n\n tic()\n mod <- all.mod[[i]]\n #mod$jags_model$mcmc <- runjags::combine.mcmc(mod$jags_model, return.samples = 2000, collapse.chains = FALSE)\n core.fit <- ddirch_forecast_noLogMap(mod=mod, cov_mu=core.preds, cov_sd=core.sd, names=core.preds$sampleID, n.samp = 1000)\t\n plot.fit <- ddirch_forecast_noLogMap(mod=mod, cov_mu=plot.preds, cov_sd=plot.sd, names=plot.preds$plotID , n.samp = 1000)\t\n site.fit <- ddirch_forecast_noLogMap(mod=mod, cov_mu=site.preds, cov_sd=site.sd, names=site.preds$siteID , n.samp = 1000)\n \n #store output as a list and save.----\n output <- list(core.fit,plot.fit,site.fit,core.preds,plot.preds,site.preds,core.sd,plot.sd,site.sd)\n names(output) <- c('core.fit','plot.fit','site.fit',\n 'core.preds','plot.preds','site.preds', \n 'core.sd','plot.sd','site.sd')\n fcast.output[[i]] <- output\n cat(paste0(i,' of ',length(all.mod),' forecasts complete. '))\n toc()\n}\ncat('All forecasts complete.')\ntoc()\n\n\n#Save output.----\nnames(fcast.output) <- names(all.mod)\nsaveRDS(fcast.output, output.path)\n", "meta": {"hexsha": "21d59f45179e217729a8786a290292e89b6fbce5", "size": 4846, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/spatial_analysis/forecasts_delgado-to-NEON/1._delgado-to-NEON_fcast_cps_16S.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "data_analysis/16S/02._global_validation_fcast_16S.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data_analysis/16S/02._global_validation_fcast_16S.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 32.5234899329, "max_line_length": 143, "alphanum_fraction": 0.6861328931, "num_tokens": 1505, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.33899323914858187}} {"text": "## Author: Yunchuan Kong\r\n## Copyright Reserved 2018\r\n\r\nlibrary(igraph)\r\n\r\n# Match GO terms with GO IDs\r\n# library(GO.db)\r\n# GO.names<-mget(names(GO.select), GOTERM, ifnotfound=NA)\r\n# findTerm=function(obj){\r\n# if (is.na(obj)!=T)\r\n# return(Term(obj))\r\n# else\r\n# return(NA)\r\n# }\r\n# GOdict<-as.vector(unlist(lapply(GO.names, findTerm)))\r\n\r\n##############################################################################################\r\n## Supervised section\r\n\r\n## INPUT: net, label, GO.select\r\nplot_entrie_sup <- function(net, label, GO.select, folds=10){\r\n\r\n # pick up those larger than f fold change\r\n k=dim(net)[1] # net is fully normalized \r\n f=folds # fold change\r\n net_vec_sel=as.vector(net)\r\n net_vec_sel[net_vec_sel0,arr.ind=TRUE)\r\n ewgt=net_sel[elist]\r\n elist=cbind(elist,ewgt)\r\n V=length(unique(as.vector(elist[,1:3]))) # num of modules that contain selected edges\r\n # print(V)\r\n \r\n # fold as weight, allow multi-edge\r\n el=rbind(elist[,c(1,2,4)],elist[,c(1,3,4)],elist[,c(2,3,4)])\r\n g=graph.edgelist(apply(el[,1:2],2,as.character), directed = F)\r\n E(g)$weight=el[,3]\r\n degrees=table(el[,1:2]) # number of connections for each node\r\n dlevels=sort(unique(degrees),decreasing=T) # degree levels\r\n nocol=length(dlevels) # number of colors\r\n rgb.palette <- colorRampPalette(c(\"red\",\"orange\",\"yellow\" \r\n # ,\"green\",\"cyan\",\"blue\"\r\n ), space = \"rgb\",bias=0.5)\r\n col<-rgb.palette(nocol)\r\n color=rep(NA,V)\r\n ind=match(names(degrees),names(V(g)))\r\n for (i in 1:nocol){\r\n color[ind[which(degrees==dlevels[i])]]=col[i]\r\n }\r\n V(g)$color=color\r\n vlabels=names(GO.select)[as.numeric(names(V(g)))]\r\n\r\n # ewidth=(E(g)$weight-f)/(mean(E(g)$weight)-f)\r\n ewidth = log(E(g)$weight-f+1)\r\n\r\n pdf(paste(\"full_sup (f=\",f,\").pdf\",sep=''))\r\n# png(\"Figure1.png\")\r\n plot(g, # http://igraph.org/r/doc/plot.common.html\r\n edge.width=ewidth,\r\n edge.color='grey75',\r\n vertex.label=vlabels,\r\n vertex.label.cex=0.3,\r\n vertex.label.color='black',\r\n vertex.frame.color=NA,\r\n vertex.size=5)\r\n dev.off()\r\n cat(\"A full hypergraph plot (2-D version) for the supervised approach has been saved.\\n\")\r\n res <- NULL\r\n res$elist <- elist\r\n res$g <- g\r\n res$folds <- f\r\n return(res)\r\n}\r\n\r\n\r\n## INPUT: g, elist from plot_entire_sup(), and folds should be consistent\r\nplot_top_sup <- function(g, elist, label, GO.select, folds, n_keep=15){\r\n library(GO.db)\r\n f = folds\r\n hdegrees=table(elist[,1:3])\r\n nokeep=n_keep # number of nodes to keep\r\n keep=as.numeric(names(sort(hdegrees,decreasing = T)[1:nokeep]))\r\n find_keep=function(x)return(all(x %in% keep))\r\n found_keep=apply(elist[,1:3],1,find_keep)\r\n helist=elist[found_keep,]\r\n noe=dim(helist)[1]\r\n hel=NULL\r\n hedge_type=NULL\r\n for (i in 1:noe){\r\n he1=c(as.character(helist[i,1]),paste(\"E\",i,sep=''),helist[i,4])\r\n he2=c(as.character(helist[i,2]),paste(\"E\",i,sep=''),helist[i,4])\r\n he3=c(as.character(helist[i,3]),paste(\"E\",i,sep=''),helist[i,4])\r\n if (length(unique(helist[i,1:3]))==1)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"blue\"))\r\n else if (length(unique(helist[i,1:3]))==2)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"cyan\"))\r\n else\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"green\"))\r\n hel=rbind(hel,he1,he2,he3)\r\n }\r\n rownames(hel)=NULL\r\n hg=graph.edgelist(hel[,1:2], directed = F)\r\n hind=match(names(V(hg)),names(V(g)))\r\n V(hg)$color=V(g)$color[hind]\r\n for (i in 1:length(hind)){\r\n if (is.na(hind[i]))\r\n V(hg)$color[i]=hedge_type[which(hedge_type==names(V(hg)[i]),arr.ind = T)[1],2]\r\n }\r\n V(hg)$shape='circle'\r\n V(hg)$shape[is.na(hind)]='csquare'\r\n V(hg)$size[is.na(hind)]=4\r\n hvlabels=rep(NA,length(hind))\r\n hvlabels[!is.na(hind)]=names(GO.select)[as.numeric(names(V(hg))[!is.na(hind)])]\r\n hvlabels=as.vector(Term(hvlabels))\r\n hewgt=as.numeric(hel[,3])\r\n hewidth=log(hewgt-f+1)\r\n noc=ncol(label)-1 # num of clusters\r\n labelSum=apply(label[,1:noc],2,sum)\r\n hvsizes=labelSum[as.numeric(names(V(hg))[!is.na(hind)])]\r\n hvsizes=(hvsizes-min(hvsizes))/(mean(hvsizes)-min(hvsizes))\r\n hvsizes=4 * (hvsizes+2)\r\n V(hg)$size[!is.na(hind)]=hvsizes\r\n \r\n pdf(paste(\"Top\",nokeep,\"Connected_sup (f=\",f,\").pdf\",sep=''))\r\n# png(\"Figure1.png\")\r\n plot(hg, # http://igraph.org/r/doc/plot.common.html\r\n edge.width=hewidth,\r\n edge.color='grey80',\r\n vertex.label=hvlabels,\r\n vertex.label.cex=1,\r\n vertex.label.color='black',\r\n vertex.frame.color=NA)\r\n \r\n legend(\"bottomleft\"\r\n # 0.85,-0.75\r\n ,c(\"type1\",\"type2\",\"type3\")\r\n ,col=c(\"blue\",\"cyan\",\"green\")\r\n ,pch=15 # square\r\n # ,lwd=4\r\n # ,title=\"legend\"\r\n ) \r\n dev.off()\r\n cat(\"A hypergraph plot of the\",nokeep,\"most connected vertices for the supervised approach has been saved.\\n\")\r\n}\r\n\r\n## INPUT: g, elist from plot_entire_sup(), and folds should be consistent\r\nplot_one_sup <- function(g, elist, label, GO.select, folds, GOID, max_num_display=15){\r\n library(GO.db)\r\n f = folds\r\n term = Term(GOID)\r\n groupID = which(names(GO.select)==GOID)\r\n find_keep=function(x)return(all(groupID %in% x))\r\n found_keep=apply(elist[,1:3],1,find_keep)\r\n if (length(which(found_keep==T))==0){\r\n cat(\"WARNING: the group is not involved under the current fold change threshold!\\n\")\r\n return(NULL)\r\n }\r\n helist=elist[found_keep,]\r\n if (length(which(found_keep==T))==1){\r\n cat(\"WARNING: the group has only one edge. No hypergraph will be drawn.\\n\")\r\n cat(\"The edge connects:\\n\",Term(names(GO.select)[helist[1]]),\"\\n\",\r\n Term(names(GO.select)[helist[2]]),\"\\n\",\r\n Term(names(GO.select)[helist[3]]),\"\\n\")\r\n return(NULL)\r\n }\r\n ## If want a limited number of connections per plot: \r\n else if (nrow(helist)>max_num_display){\r\n ord = order(helist[,4],decreasing = T)\r\n helist = helist[ord[1:15],]\r\n noe=dim(helist)[1]\r\n cat(\"Only display at most\", max_num_display, \"of connections, according to the fold change rank.\\n\")\r\n } else {\r\n noe=dim(helist)[1]\r\n }\r\n \r\n hel=NULL\r\n hedge_type=NULL\r\n for (i in 1:noe){\r\n he1=c(as.character(helist[i,1]),paste(\"E\",i,sep=''),helist[i,4])\r\n he2=c(as.character(helist[i,2]),paste(\"E\",i,sep=''),helist[i,4])\r\n he3=c(as.character(helist[i,3]),paste(\"E\",i,sep=''),helist[i,4])\r\n if (length(unique(helist[i,1:3]))==1)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"blue\"))\r\n else if (length(unique(helist[i,1:3]))==2)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"cyan\"))\r\n else\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"green\"))\r\n hel=rbind(hel,he1,he2,he3)\r\n }\r\n rownames(hel)=NULL\r\n hg=graph.edgelist(hel[,1:2], directed = F)\r\n hind=match(names(V(hg)),names(V(g)))\r\n V(hg)$color=V(g)$color[hind]\r\n for (i in 1:length(hind)){\r\n if (is.na(hind[i]))\r\n V(hg)$color[i]=hedge_type[which(hedge_type==names(V(hg)[i]),arr.ind = T)[1],2]\r\n }\r\n V(hg)$shape='circle'\r\n V(hg)$shape[is.na(hind)]='csquare'\r\n V(hg)$size[is.na(hind)]=6\r\n hvlabels=rep(NA,length(hind))\r\n hvlabels[!is.na(hind)]=names(GO.select)[as.numeric(names(V(hg))[!is.na(hind)])]\r\n hvlabels=as.vector(Term(hvlabels))\r\n hewgt=as.numeric(hel[,3])\r\n # hewidth=(hewgt-f)/(mean(hewgt)-f)\r\n hewidth=log(hewgt-f+1)\r\n \r\n noc=ncol(label)-1 # num of clusters\r\n labelSum=apply(label[,1:noc],2,sum)\r\n hvsizes=labelSum[as.numeric(names(V(hg))[!is.na(hind)])]\r\n hvsizes=(hvsizes-min(hvsizes))/(mean(hvsizes)-min(hvsizes))\r\n hvsizes=4 * (hvsizes+2)\r\n V(hg)$size[!is.na(hind)]=hvsizes\r\n\r\n pdf(paste(term,\"_sup (f=\",f,\").pdf\",sep=''))\r\n plot(hg, # http://igraph.org/r/doc/plot.common.html\r\n edge.width=hewidth,\r\n edge.color='grey80',\r\n vertex.label=hvlabels,\r\n vertex.label.cex=1,\r\n vertex.label.color='black',\r\n vertex.frame.color=NA,\r\n margin=c(0,0,0,0)\r\n )\r\n \r\n legend(\"bottomleft\"\r\n # 0.85,-0.75\r\n ,c(\"type1\",\"type2\",\"type3\") \r\n ,col=c(\"blue\",\"cyan\",\"green\")\r\n ,pch=15 # square\r\n # ,lwd=4\r\n # ,title=\"legend\"\r\n ) \r\n dev.off()\r\n cat(\"A hypergraph plot of\",GOID,\"and its connected vertices has been saved.\\n\")\r\n}\r\n\r\n##############################################################################################\r\n## plot gene level hypergraph for a given module level hyper-edge\r\n\r\nquery <- function(triplet, c1, c2, c3){\r\n n=dim(triplet)[1] \r\n k1 <- length(c1)\r\n k2 <- length(c2)\r\n k3 <- length(c3)\r\n res <- rep(c(0,0,0), k1*k2*k3)\r\n .C('query',tri=as.integer(as.vector(t(triplet))),\r\n n=as.integer(n),\r\n c1=as.integer(c1),\r\n c2=as.integer(c2),\r\n c3=as.integer(c3),\r\n k1=as.integer(k1),\r\n k2=as.integer(k2),\r\n k3=as.integer(k3),\r\n res=as.integer(res))$res\r\n}\r\n\r\nfind_Vhg <- function(str){\r\n if (unlist(strsplit(str,\"\"))[1] == \"E\" ){\r\n return(NA)\r\n } else {\r\n return(1)\r\n }\r\n}\r\n\r\nplot_gene_level <- function(hyperedge, module_names, \r\n net, label, triplets, glist, \r\n folds){\r\n net_sel <- net\r\n net_sel[net0,arr.ind=TRUE)\r\n ewgt=net_sel[elist]\r\n elist=cbind(elist,ewgt)\r\n V=length(unique(as.vector(elist[,1:3]))) # num of modules that contain selected edges\r\n # print(V)\r\n \r\n # fold as weight, allow multi-edge\r\n el=rbind(elist[,c(1,2,4)],elist[,c(1,3,4)],elist[,c(2,3,4)])\r\n g=graph.edgelist(apply(el[,1:2],2,as.character), directed = F)\r\n E(g)$weight=el[,3]\r\n degrees=table(el[,1:2]) # number of connections for each node\r\n dlevels=sort(unique(degrees),decreasing=T) # degree levels\r\n nocol=length(dlevels) # number of colors\r\n rgb.palette <- colorRampPalette(c(\"red\",\"orange\",\"yellow\" \r\n # ,\"green\",\"cyan\",\"blue\"\r\n ), space = \"rgb\",bias=0.5)\r\n col<-rgb.palette(nocol)\r\n color=rep(NA,V)\r\n ind=match(names(degrees),names(V(g)))\r\n for (i in 1:nocol){\r\n color[ind[which(degrees==dlevels[i])]]=col[i]\r\n }\r\n V(g)$color=color\r\n # ewidth=(E(g)$weight-f)/(mean(E(g)$weight)-f)\r\n ewidth = log(E(g)$weight-f+1)\r\n \r\n pdf(paste(\"full_unsup (f=\",f,\").pdf\",sep=''))\r\n plot(g, # http://igraph.org/r/doc/plot.common.html\r\n edge.width=ewidth,\r\n edge.color='grey75',\r\n # vertex.label=vlabels,\r\n vertex.label.cex=0.5,\r\n vertex.label.color='black',\r\n vertex.frame.color=NA,\r\n vertex.size=5)\r\n dev.off()\r\n cat(\"A full hypergraph plot (2-D version) for the unsupervised approach has been saved.\\n\")\r\n res <- NULL\r\n res$elist <- elist\r\n res$g <- g\r\n res$folds <- f\r\n return(res)\r\n}\r\n\r\n\r\n## INPUT: g, elist from plot_entire_unsup(), and folds should be consistent\r\nplot_top_unsup <- function(g, elist, label, folds, n_keep=15){\r\n f = folds\r\n hdegrees=table(elist[,1:3])\r\n nokeep=n_keep # number of nodes to keep\r\n keep=as.numeric(names(sort(hdegrees,decreasing = T)[1:nokeep]))\r\n find_keep=function(x)return(all(x %in% keep))\r\n found_keep=apply(elist[,1:3],1,find_keep)\r\n length(which(found_keep==T))\r\n helist=elist[found_keep,]\r\n noe=dim(helist)[1]\r\n hel=NULL\r\n hedge_type=NULL\r\n for (i in 1:noe){\r\n he1=c(as.character(helist[i,1]),paste(\"E\",i,sep=''),helist[i,4])\r\n he2=c(as.character(helist[i,2]),paste(\"E\",i,sep=''),helist[i,4])\r\n he3=c(as.character(helist[i,3]),paste(\"E\",i,sep=''),helist[i,4])\r\n if (length(unique(helist[i,1:3]))==1)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"blue\"))\r\n else if (length(unique(helist[i,1:3]))==2)\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"cyan\"))\r\n else\r\n hedge_type=rbind(hedge_type,c(paste(\"E\",i,sep=''),\"green\"))\r\n hel=rbind(hel,he1,he2,he3)\r\n }\r\n rownames(hel)=NULL\r\n hg=graph.edgelist(hel[,1:2], directed = F)\r\n hind=match(names(V(hg)),names(V(g)))\r\n V(hg)$color=V(g)$color[hind]\r\n for (i in 1:length(hind)){\r\n if (is.na(hind[i]))\r\n V(hg)$color[i]=hedge_type[which(hedge_type==names(V(hg)[i]),arr.ind = T)[1],2]\r\n }\r\n V(hg)$shape='circle'\r\n V(hg)$shape[is.na(hind)]='csquare'\r\n V(hg)$size[is.na(hind)]=5\r\n hvlabels=rep(NA,length(hind))\r\n hvlabels[!is.na(hind)]=names(V(hg))[!is.na(hind)]\r\n # hvlabels=as.vector(Term(hvlabels))\r\n hewgt=as.numeric(hel[,3])\r\n # hewidth=(hewgt-f)/(mean(hewgt)-f)\r\n hewidth = log(hewgt-f+1)\r\n noc=ncol(label)-1 # num of clusters\r\n labelSum=apply(label[,1:noc],2,sum)\r\n hvsizes=labelSum[as.numeric(names(V(hg))[!is.na(hind)])]\r\n hvsizes=(hvsizes-min(hvsizes))/(mean(hvsizes)-min(hvsizes))\r\n hvsizes=6 * (hvsizes+2)\r\n V(hg)$size[!is.na(hind)]=hvsizes\r\n \r\n pdf(paste(\"Top\",nokeep,\"Connected_unsup (f=\",f,\").pdf\",sep=''))\r\n plot(hg, # http://igraph.org/r/doc/plot.common.html\r\n edge.width=hewidth,\r\n edge.color='grey80',\r\n vertex.label=hvlabels,\r\n vertex.label.cex=1,\r\n vertex.label.color='black',\r\n vertex.frame.color=NA)\r\n \r\n legend(\"bottomleft\"\r\n # 0.85,-0.75\r\n ,c(\"type1\",\"type2\",\"type3\")\r\n ,col=c(\"blue\",\"cyan\",\"green\")\r\n ,pch=15 # square\r\n # ,lwd=4\r\n # ,title=\"legend\"\r\n ) \r\n dev.off()\r\n cat(\"A hypergraph plot of the\",nokeep,\"most connected vertices for the unsupervised approach has been saved.\\n\")\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "58eaebb6d79bcd9e8fe3cf1dc45adad0d23a5d98", "size": 16681, "ext": "r", "lang": "R", "max_stars_repo_path": "visualize.r", "max_stars_repo_name": "kyccw55555/HypergraphDynamicCorrelation", "max_stars_repo_head_hexsha": "acdeb9cedd1df98590441e052561533870561acd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-03-28T02:04:22.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-09T20:19:23.000Z", "max_issues_repo_path": "visualize.r", "max_issues_repo_name": "kyccw55555/HypergraphDynamicCorrelation", "max_issues_repo_head_hexsha": "acdeb9cedd1df98590441e052561533870561acd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "visualize.r", "max_forks_repo_name": "kyccw55555/HypergraphDynamicCorrelation", "max_forks_repo_head_hexsha": "acdeb9cedd1df98590441e052561533870561acd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-27T07:21:53.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-27T07:21:53.000Z", "avg_line_length": 33.6310483871, "max_line_length": 115, "alphanum_fraction": 0.5882141358, "num_tokens": 5486, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.33893683676998}} {"text": "#' @title\n#' Calculate changes at all intervals within each cluster\n#'\n#' @description\n#' Create and run generalised linear models and post-hoc tukey contrasts at all time points for each cluster.\n#'\n#' @param Tc A matrix containing time course data.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#'\n#' @return A list (with same length as number of clusters) containing the Tukey summaries.\n#'\n#'\n#' @importFrom multcomp glht mcp\n#' @importFrom stats glm\n#' @importFrom methods is\n#'\n#'\n#' @seealso \\code{\\link[e1071]{cmeans}} for clustering.\n#'\n#' @export\ncalcClusterChng <- function(Tc, clusters){\n\n\n\t# 1. split into matrix for each cluster...\n\t# 2. convert to df. of required format for glm\n\t# 3. for each cluster run glm.\n\t# 4. for each glm result run tukey contrast. (which can give the summary directly or which can initially give the full matrix (between each time point))\n\t# another function to do the plotting.\n\n\tstopifnot(is(Tc, \"matrix\"), (length(clusters) == nrow(Tc)))\n\n\tlist_matrices <- splitIntoSubMatrices(Tc, clusters)\n\tlist_dfsGlm <- convertMatToDfForGlmFormat(list_matrices)\n\n\tlist_lm <- runGlmForEachCluster(list_dfsGlm)\n\t# print(\"Note: finished creating glm's\")\n\n\tlist_resSummaries <- runPostHocTukeyForEachClus(list_lm)\n\t# print(\"Note: finished post-hoc tukey\")\n\n\t# list_resSummaries <- createGlmTukeyForEachClus(list_dfsGlm)\n\t# return (list_resSummaries)\n\n\n\tclass(list_resSummaries) <- \"clusterChange\"\n\treturn (list_resSummaries)\n}\n\n#' @title\n#' Summarize the Tukey constrasting results for consecutive intervals.\n#'\n#' @description\n#' Summarize results obtained by running the \\code{calcClusterChng} function. Specifically this function extracts z-scores and p-values for consecutive intervals.\n#'\n#' @param list_resSummaries A list returned by the \\code{calcClusterChng} function.\n#' @param Tc The time course data\n#'\n#' @return A list containing two items, where the first item is a z-score matrix, and the second item is a p-value matrix.\n#'\n#' @importFrom methods is\n#'\n#' @seealso \\code{\\link{calcClusterChng}}\n#'\n#' @export\nsummaryGetZP <- function(list_resSummaries, Tc){\n\n\tstopifnot(is(list_resSummaries,\"clusterChange\"), is(Tc, \"matrix\"))\n\n\n\tlist_concTpSummary <- list()\n\tcolNames = c()\n\n\tzScores <- matrix(nrow=length(list_resSummaries), ncol=ncol(Tc)-1)\n\tpValues <- matrix(nrow=length(list_resSummaries), ncol=ncol(Tc)-1)\n\n\n\n\tfor(clustNum in 1:length(list_resSummaries)){\n\t\tfor(tp in 2:ncol(Tc)){\n\n\t\t\tselName = paste(tp, \"-\", (tp-1))\n\t\t\tif (!is.null(colnames(Tc))){\n\t\t\t\tname = paste(colnames(Tc)[tp], \"-\", colnames(Tc)[(tp-1)])\n\t\t\t}\n\t\t\telse{\n\t\t\t\tname = selName\n\t\t\t}\n\n\n\t\t\t# print(name)\n\t\t\tif(clustNum == 1){\n\t\t\t\tcolNames <- c(colNames, name)\n\t\t\t}\n\n\n\t\t\tzScores[clustNum, tp-1] <- list_resSummaries[[clustNum]]$test$tstat[selName]\n\n\t\t\tidx = which(names(list_resSummaries[[clustNum]]$test$tstat) == selName)\n\n\t\t\tpValues[clustNum, tp-1] <- list_resSummaries[[clustNum]]$test$pvalues[[idx]]\n\n\t\t}\n\t}\n\n\n\tcolnames(zScores) <- colNames\n\tcolnames(pValues) <- colNames\n\n\tlist_concTpSummary[[1]] <- zScores\n\tlist_concTpSummary[[2]] <- pValues\n\n\tclass(list_concTpSummary) <- \"summary.clusterChange\"\n\n\treturn(list_concTpSummary)\n}\n\n#' @title\n#' Print changes in consecutive contrasting intervals\n#'\n#' @description\n#' Prints two matrices, the first containing z-scores, and the second, p-values, where each row corresponds to each cluster, and each column corresponds to the time-interval.\n#'\n#' @param list_concTpSummary Z-scores and p-values extracted from tukey-constrasts for glm's of each cluster.\n#'\n#'\n#' @seealso \\code{\\link{summaryGetZP}}\n#'\n#' @export\nprintZP <- function(list_concTpSummary){\n\tcat('===== list_[[1]] - zScores\\n')\n\tprint(list_concTpSummary[[1]])\n\tcat('\\n\\n')\n\n\tcat('===== list_[[2]] - pValues\\n')\n\tprint(list_concTpSummary[[2]])\n\tcat('\\n\\n')\n}\n\n#' @title\n#' Plot a Z-score heatmap\n#'\n#' @description\n#' Plot a heatmap of z-scores, masking the z-scores at insignificant p-values. Rows in the heatmap correspond to clusters, and columns in the heatmap correspond to time intervals.\n#'\n#' @param list_concTpSummary Z-scores and p-values extracted from tukey-constrasts of consecutive time intervals for each cluster. Obtained by running the \\code{summaryGetZP} function.\n#' @param significanceTh Tukey's p-value cutoff to be applied for significance.\n#'\n#' @return Displays a plot and returns a matrix, with z-scores masked (NA) according to the signficance (p-value) threshold specified.\n#'\n#'\n#' @seealso \\code{\\link{summaryGetZP}}\n#'\n#' @importFrom gplots heatmap.2\n#' @importFrom grDevices colorRampPalette\n#' @importFrom methods is\n#'\n#' @export\nplotZP <- function(list_concTpSummary, significanceTh=0.05){\n\n\tstopifnot(is(list_concTpSummary, \"summary.clusterChange\"), (significanceTh >0 && significanceTh <= 1))\n\n\ttheSignifs <- list_concTpSummary[[2]] < significanceTh\n\n\tmatToPlot <- list_concTpSummary[[1]]\n\n\trwb <- grDevices::colorRampPalette(colors = c(\"blue\", \"#cfcfcf\", \"red\"))(n=299)\n\n\n\tif (!all(theSignifs)){\n\t\tzscore_largest <- ceiling(max(c(abs(min(matToPlot)), abs(max(matToPlot)))))\n\t\ttheBreaks <- seq(-zscore_largest, zscore_largest, length.out=299)\n\n\t\tzscore_largest_noSignif <- max(c(abs(max(matToPlot[!theSignifs])), abs(min(matToPlot[!theSignifs]))))\n\t\tidx_forGray50 <- theBreaks <= zscore_largest_noSignif & theBreaks >= -zscore_largest_noSignif\n\n\n\t\trwb[idx_forGray50] <- \"#cfcfcf\"\n\n\t\tmatToPlot[!theSignifs] <- NA\n\t}\n\n\n\ttitleTxt = paste(\"Significant changes\\n(z-values of intervals,\\n with significance p \\u003C\", significanceTh, \")\", sep=\"\")\n\n\tgplots::heatmap.2(matToPlot, main=titleTxt, xlab=\"Time interval\", ylab=\"Cluster\", Rowv=FALSE, Colv=FALSE, dendrogram=\"none\", col=rwb, na.color=\"#cfcfcf\", tracecol=NA, density.info=\"none\", sepcolor=\"#cfcfcf\", sepwidth=c(0.001, 0.001), colsep=0:ncol(matToPlot), rowsep=0:nrow(matToPlot), srtCol=45, cexCol=0.8)\n\n\treturn (matToPlot)\n}\n\nplotZP_eachCluster <- function(list_resSummaries, Tc, glmTukeyForEachClus){\n\n\n\t# stopifnot(is(list_resSummaries,\"clusterChange\"), is(Tc, \"matrix\"))\n\n\tlist_matsToPlot <- list()\n\n\n\tfor (clusNum in 1:length(list_resSummaries)){\n\t\tmat_aClus <- matrix(nrow=ncol(Tc), ncol=ncol(Tc))\n\n\t\t# names_r <- c()\n\t\t# names_c <- c()\n\t\t# for (i in length(glmTukeyForEachClus[[clusNum]]$test$tstat)){\n\n\t\t# }\n\n\t\tfor (tp2 in 1:ncol(Tc)){\n\t\t\tfor (tp1 in tp2:ncol(Tc)){\n\t\t\t\tif (tp2 != tp1){\n\t\t\t\t\t# print(paste(tp1, '-', tp2))\n\t\t\t\t\tselName = paste(tp1, \"-\", (tp2))\n\n\t\t\t\t\t# names_r <- c(names_r, paste(colnames(Tc)[tp1], '-', colnames(Tc)[tp2]))\n\n\t\t\t\t\tmat_aClus[tp1, tp2] = as.numeric(glmTukeyForEachClus[[clusNum]]$test$tstat[selName])\n\n\n\t\t\t\t\tmat_aClus[tp2, tp1] = -1 * as.numeric(glmTukeyForEachClus[[clusNum]]$test$tstat[selName])\n\t\t\t\t}\n\t\t\t\telse{\n\t\t\t\t\tmat_aClus[tp1, tp2] = 0\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t# print(names_r)\n\t\tcolnames(mat_aClus) <- colnames(Tc)\n\t\trownames(mat_aClus) <- colnames(Tc)\n\t\tlist_matsToPlot[[clusNum]] <- mat_aClus\n\n\t}\n\tdoThePlotting(list_matsToPlot)\n\treturn (list_matsToPlot)\n}\n\ngetAbsMaxValue <- function(list_matsToPlot){\n\n\tlargestAbsVal <- 0\n\tfor (clusNum in 1:length(list_matsToPlot)){\n\t\tnewMax <- max(abs(list_matsToPlot[[clusNum]]))\n\n\t\tif (newMax > largestAbsVal){\n\t\t\tlargestAbsVal <- newMax\n\t\t}\n\t}\n\n\treturn (largestAbsVal)\n}\n\ndoThePlotting <- function(list_matsToPlot){\n\n\tpdf(\"allByAllHeatmaps.pdf\", height=10, width=10)\n\n\tpar(mfrow=c(ceiling(length(list_matsToPlot)/8), 8))\n\n\trwb <- grDevices::colorRampPalette(colors = c(\"blue\", \"#cfcfcf\", \"red\"))(n=99)\n\n\tlargestAbsVal <- getAbsMaxValue(list_matsToPlot)\n\tzscore_largest <- ceiling(largestAbsVal)\n\n\ttheBreaks <- sort(seq(-zscore_largest, zscore_largest, length.out=100))\n\n\tprint(theBreaks)\n\n\tfor (clusNum in 1:length(list_matsToPlot)){\n\n\n\n\n\t\t# zscore_largest_noSignif <- max(c(abs(max(matToPlot[!theSignifs])), abs(min(matToPlot[!theSignifs]))))\n\t\t# idx_forGray50 <- theBreaks <= zscore_largest_noSignif & theBreaks >= -zscore_largest_noSignif\n\n\n\t\t# rwb[idx_forGray50] <- \"#cfcfcf\"\n\n\t\t# matToPlot[!theSignifs] <- NA\n\n\t\ttitleTxt = paste(\"Cluster \", clusNum, sep=\"\")\n\n\t\tgplots::heatmap.2(list_matsToPlot[[clusNum]], main=titleTxt, xlab=\"Time interval\", ylab=\"Time interval\", Rowv=FALSE, Colv=FALSE, dendrogram=\"none\", col=rwb, na.color=\"#cfcfcf\", tracecol=NA, density.info=\"none\", sepcolor=\"#cfcfcf\", sepwidth=c(0.001, 0.001), colsep=0:ncol(list_matsToPlot[[clusNum]]), rowsep=0:nrow(list_matsToPlot[[clusNum]]), srtCol=45, cexCol=0.8, breaks=theBreaks)\n\t}\n\tdev.off()\n}\n\n\n\n\n##################################### PRIVATE FUNCTIONS\n#' Run tukey post-hoc evaluations for the glm's.\n#'\n#' @param list_lms A list containing a lm for each cluster.\n#' @return A list of tukey evaluations\n#'\nrunPostHocTukeyForEachClus <- function(list_lms){\n\tlist_tukeys <- list()\n\n\tfor (clustNum in 1:length(list_lms)){\n\t\t# list_tukeys[[clustNum]] <- emmeans(list_lms[[clustNum]], pairwise ~ fac_timepoint, adjust=\"tukey\")\n\n\t\tlist_tukeys[[clustNum]] <- summary(multcomp::glht(list_lms[[clustNum]], multcomp::mcp(fac_timepoint=\"Tukey\")))\n\t}\n\n\treturn (list_tukeys)\n}\n\n#' Run glm for each cluster.\n#'\n#' @param list_dfsGlm A list of dataframes (for each cluster) of the time series data.\n#' @return A list of glm's for each cluster.\n#'\nrunGlmForEachCluster <- function(list_dfsGlm){\n\n\tlist_lm <- list()\n\n\tfor (dfNum in 1:length(list_dfsGlm)){\n\t\taGlm <- list_dfsGlm[[dfNum]]\n\t\tlist_lm[[dfNum]] <- stats::glm(formula=experimentalObs ~ fac_profileNum + fac_timepoint, data=aGlm)\n\t}\n\n\treturn (list_lm)\n}\n\n\n#' Convert a list of matrices of each cluster, to a list of data frames (in the required glm format) for each cluster. Timepoint, and profileNum are factors, and experimentalObs (ratio) is y.\n#'\n#' @param list_matrices A list of matrices for each cluster\n#' @return A list containing glm's for each cluster.\n#'\nconvertMatToDfForGlmFormat <- function(list_matrices){\n\tlist_dfsGlm <- list()\n\n\t# converting each matrix to a data_frame for GLM.\n\tfor (clustNum in 1:length(list_matrices)){\n\n\t\tfac_timepoint = c()\n\t\tfac_profileNum = c()\n\t\texperimentalObs = c()\n\n\t\tfor (tp in 1:ncol(list_matrices[[clustNum]])){\n\n\t\t\tfac_timepoint <- append(fac_timepoint, c(rep(tp, nrow(list_matrices[[clustNum]]))))\n\n\n\t\t\tfac_profileNum <- append(fac_profileNum, 1:nrow(list_matrices[[clustNum]]))\n\n\t\t\texperimentalObs <- append(experimentalObs, list_matrices[[clustNum]][,tp])\n\n\t\t}\n\n\t\tglmDf <- data.frame(fac_timepoint, fac_profileNum, experimentalObs)\n\t\t# add this df to the list\n\t\t# print(glmDf$fac_timepoint)\n\t\tglmDf$fac_timepoint <- factor(glmDf$fac_timepoint, ordered=TRUE)\n\t\tglmDf$fac_profileNum <- factor(glmDf$fac_profileNum) # , ordered=TRUE)\n\n\t\tlist_dfsGlm[[clustNum]] <- glmDf\n\n\t}\n\n\treturn(list_dfsGlm)\n}\n\n\n#' Split a matrix into cluster based submatrices.\n#'\n#' @param Tc A matrix containing time course data.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#'\n#' @return A list of matrices\n#'\nsplitIntoSubMatrices <- function(Tc, clusters){\n\n\tlist_matrices <- list()\n\n\tfor (i in 1:max(clusters)){\n\t\tlist_matrices[[i]] <- matrix(nrow=0, ncol=ncol(Tc))\n\t}\n\n\tfor (i in 1:nrow(Tc)){\n\t\trn <- rownames(Tc)[i]\n\t\t# print(rn)\n\t\tclusterOfRow <- clusters[[rn]]\n\n\t\tlist_matrices[[clusterOfRow]] <- rbind(list_matrices[[clusterOfRow]], Tc[i,])\n\n\t\trownames(list_matrices[[clusterOfRow]])[nrow(list_matrices[[clusterOfRow]])] <- rownames(Tc)[i]\n\t}\n\n\treturn(list_matrices)\n}\n", "meta": {"hexsha": "9580fac21a00dc1b0411dcb69daa283fb4e589b6", "size": 11384, "ext": "r", "lang": "R", "max_stars_repo_path": "R/calcClusterChng.r", "max_stars_repo_name": "ODonoghueLab/Minardo-Model", "max_stars_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-11-25T03:08:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T10:14:50.000Z", "max_issues_repo_path": "R/calcClusterChng.r", "max_issues_repo_name": "ODonoghueLab/Minardo-Model", "max_issues_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-08T09:47:48.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-23T02:36:36.000Z", "max_forks_repo_path": "R/calcClusterChng.r", "max_forks_repo_name": "ODonoghueLab/Minardo-Model", "max_forks_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1897435897, "max_line_length": 385, "alphanum_fraction": 0.7100316233, "num_tokens": 3456, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33893683676997993}} {"text": "#!/usr/bin/Rscript\n\n## data2plot.r -- takes as input a space-separated text file, converts\n## it into a plot\n\n## Author: David Eccles (gringer), 2009 \n\nusage <- function(){\n cat(\"usage: cat | ./data2plot.r( p )*\\n\");\n}\n\npopNames <- NULL;\npopLimits <- NULL;\n\nargLoc <- 1;\nwhile(!is.na(commandArgs(TRUE)[argLoc])){\n if(substr(commandArgs(TRUE)[argLoc],1,1) == \"p\"){\n inName <- substring(commandArgs(TRUE)[argLoc],2);\n popNames <- append(popNames, sub(\"_\",\" \",inName));\n popLimits <- cbind(popLimits,c(as.numeric(commandArgs(TRUE)[argLoc+1]),\n as.numeric(commandArgs(TRUE)[argLoc+2])));\n argLoc <- argLoc + 2;\n }\n argLoc <- argLoc + 1;\n}\n\n\ndata.df <- read.table(file(\"stdin\"));\npdf(\"output.pdf\", paper = \"a4r\", height = 8, width = 11);\nplot(data.df$V2);\ndummy <- dev.off();\n\ndm <- abs(mean(data.df$V2[popLimits[1,1]:popLimits[2,1]]) -\n mean(data.df$V2[popLimits[1,2]:popLimits[2,2]]));\n\nif(dim(popLimits)[2] == 2){\n cat(sprintf(\"Difference of means: %f (adjusted = %f)\\n\",\n dm, dm / diff(range(data.df$V2))));\n}\n", "meta": {"hexsha": "bc0a852cb0a7ce4328090c6cf7d6bafb90792ca0", "size": 1143, "ext": "r", "lang": "R", "max_stars_repo_path": "data2plot.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data2plot.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data2plot.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_forks_repo_licenses": ["ISC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-02T11:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-02T11:22:10.000Z", "avg_line_length": 28.575, "max_line_length": 80, "alphanum_fraction": 0.6036745407, "num_tokens": 352, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33893683676997993}} {"text": "#library(dplyr)\n#library(readr)\n#library(tidyr)\n\ntitanicCsvImport <- function(filename) {\n data <- read_csv(filename, col_names = T, skip_empty_rows = T)\n if('Survived' %in% colnames(data)) {\n data <- data %>%\n #select(Survived, Age, Sex, Fare, Pclass, SibSp, Ticket, Parch, Cabin) %>%\n mutate( Survived = as.factor(Survived) )\n } else {\n #data <- data %>% \n # select(Age, Sex, Fare, Pclass, SibSp, Ticket, Parch, Cabin)\n }\n \n data <- data %>%\n replace(., is.na(.), \"\" ) %>% \n #drop_na() %>%\n mutate( Age = as.numeric(Age) ) %>%\n mutate( Sex = as.factor(Sex) ) %>%\n mutate( Pclass = as.factor(Pclass) ) %>%\n mutate( Embarked = as.factor(Embarked) ) %>%\n mutate( Ticket = as.factor(Ticket) ) %>%\n mutate( Name = as.factor(Name) ) %>%\n mutate( CabinClass = substring(Cabin,1,1) ) %>%\n mutate( CabinClass = as.factor(CabinClass) ) %>%\n mutate( Cabin = as.factor(Cabin) ) %>%\n mutate( Fare = as.numeric(Fare) ) %>%\n mutate( hasTicket = Ticket != '') %>%\n mutate( knowsCabin = Cabin != '') %>%\n mutate( knowsAge = !is.na(Age) )\n \n cabins <- data %>%\n select(Cabin) %>%\n group_by(Cabin) %>%\n summarise( peopleInCabin = n() ) %>%\n arrange(desc(peopleInCabin))\n cabins[cabins$peopleInCabin > 50, ]$peopleInCabin <- 0\n data <- data %>% left_join(cabins, by = 'Cabin') \n \n return(data)\n}\n", "meta": {"hexsha": "d1b3d946a75d0cbdd15f225289d71f375ab8a1e8", "size": 1371, "ext": "r", "lang": "R", "max_stars_repo_path": "R/import.r", "max_stars_repo_name": "viadee/webinar-intelligente-prozesse-verstehen", "max_stars_repo_head_hexsha": "bdef4edf21b2808b198110fdcccff40a537488f8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-03T18:07:56.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-06T12:08:41.000Z", "max_issues_repo_path": "R/import.r", "max_issues_repo_name": "viadee/webinar-intelligente-prozesse-verstehen", "max_issues_repo_head_hexsha": "bdef4edf21b2808b198110fdcccff40a537488f8", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/import.r", "max_forks_repo_name": "viadee/webinar-intelligente-prozesse-verstehen", "max_forks_repo_head_hexsha": "bdef4edf21b2808b198110fdcccff40a537488f8", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.8837209302, "max_line_length": 80, "alphanum_fraction": 0.5769511306, "num_tokens": 443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33893683676997993}} {"text": "library(\"ggplot2\")\n\nsims <- read.table(\"all_te_similarity_0106.txt\",sep=\"\\t\",header=F)\nnames(sims) <- c(\"type\",\"element\",\"length\",\"similarity\")\nsims$type <- factor(sims$type, \n\t \t levels = c(\"copia\",\"gypsy\",\"unclassified-ltr\",\"trim\",\"hAT\",\n\t\t \"mutator\",\"tc1-mariner\",\"unclassified-tir\"), \n\t\t labels = c(\"Copia\",\"Gypsy\",\"Unclassified-LTR\",\"TRIM\",\"hAT\",\n\t\t \"Mutator\",\"Tc1-Mariner\",\"Unclassified-TIR\"))\n\nggplot(sims, aes(similarity, y=..density..)) + \n\t geom_histogram(binwidth=.3,fill=\"cornsilk\",colour=\"grey60\",size=.2) + \n\t geom_density(size=1,alpha=.3) + \n\t scale_x_reverse() + \n\t theme_bw() + \n\t facet_grid(type ~ .) + \n\t ylab(\"Density\") + \n\t xlab(\"LTR/TIR similarity\")", "meta": {"hexsha": "16db16bafe7277e04c743ecdfe301493ac10ae5b", "size": 707, "ext": "r", "lang": "R", "max_stars_repo_path": "transposon_annotation/r_scripts/plot_te_similarity.r", "max_stars_repo_name": "sestaton/sesbio", "max_stars_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2015-01-14T17:25:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-09T01:15:18.000Z", "max_issues_repo_path": "transposon_annotation/r_scripts/plot_te_similarity.r", "max_issues_repo_name": "sestaton/sesbio", "max_issues_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "transposon_annotation/r_scripts/plot_te_similarity.r", "max_forks_repo_name": "sestaton/sesbio", "max_forks_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-10-09T02:56:51.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-02T12:07:39.000Z", "avg_line_length": 39.2777777778, "max_line_length": 76, "alphanum_fraction": 0.6195190948, "num_tokens": 224, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6723317123102955, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3387920984755405}} {"text": "#' Create cylinders.\n#'\n#' Create cylinders to cover events encoded in \\code{observation.matrix}\n#' and evaluate their exceedances with respect to \\code{baseline}.\n\n#' If \\code{observation.matrix} and \\code{baseline} have the same matrix dimension,\n#' the exceedances are computed calling \\code{compute}. Otherwise the function\n#' this function assumes that the baseline is an \\code{expand.grid} data.frame and \n#' exceedancies are computed with \\code{compute.from.tab.baseline}.\n#' @param observation.matrix A 2D \\code{Matrix} or {sparseMatrix} object.\n#' @param baseline A 2D \\code{matrix} or a Nx4 \\code{expand.grid} data frame.\n#' @param week.range A \\code{numeric} vector to set the lower and upper limit to the heigh of the cylinders.\n#' @param n.cylinders An \\code{integer}, total number of drawn cylinders.\n#' @param p.val.threshold A \\code{numeric}. If the probability of observed exceedance is < \\code{p.val.threshold}, flag the cylinder as anomalous.\n#' @param size_factor A \\code{numeric} multiplier to increase or reduce the cylinder heights and radia.\n#' @examples\n#' CreateCylinders(observation.matrix, baseline.matrix, week.range = c(0,99), n.cylinders = 10000)\n#' CreateCylinders(observation.matrix, baseline.tab, week.range = c(0,99), n.cylinders = 100)\nCreateCylinders<-function(observation.matrix, baseline, week.range,\n n.cylinders=1000,\n p.val.threshold=0.05,\n size_factor=1, coord.df=NULL, only.last=FALSE){\n \n # load(\"~/Documents/Rancovr/Data/postcode2coord.RData\")\n if (is.null(coord.df)){\n coord.df = PostcodeMap(observation.matrix)\n }\n #postcode2coord\n test = all(dim(baseline) == dim(observation.matrix))\n if (test){\n baseline = baseline[!(rownames(baseline) == 'NA'),]\n baseline = baseline[,!(colnames(baseline) == 'NA')]\n\n radia_and_heights = f_radia_and_heights(baseline, 1:24) * size_factor \n if (sum(baseline) > 2 * sum(observation.matrix)){\n print(sum(baseline))\n print(sum(observation.matrix)) \n warning(\n sprintf(\n \"Warning: the baseline might be too high. If you are working with typed data, please check that it is scaled by type factor.\"\n )\n )\n }\n }else{\n radia_and_heights = f_radia_and_heights_(baseline, 1:24) * size_factor \n }\n\n observation.matrix = observation.matrix[!(rownames(observation.matrix) == 'NA'),]\n observation.matrix = observation.matrix[,!(colnames(observation.matrix) == 'NA')]\n\n init = Sys.time()\n coord.df = coord.df[!is.na(coord.df$latitude),]\n coord.km.df = coord.df\n coord.km.df[,c('y','x')]= vlatlong2km(coord.df[,c(\"latitude\",\"longitude\")])\n week.range = range(as.integer(week.range))\n if ((week.range[1] < min(as.integer(colnames(observation.matrix)))) | (week.range[2] > max(as.integer(colnames(observation.matrix))))){\n A = sprintf(\"%d-%d\", week.range[1], week.range[2])\n B = range(as.integer(colnames(observation.matrix)))\n B = sprintf(\"%d-%d\", B[1], B[2])\n stop(paste0(\"`week.range`` is \", A, \", while the range of `observation.matrix` is \", B, \".\" ))\n }\n # week.range = range(\n # as.integer(week.range)) + 1 - min(as.integer(colnames(observation.matrix))\n # )\n\n cat(\"Evaluating cylinder exceedances from \",\n as.character(week2Date(week.range[1])), # this interactive output doesnt work in the prospective mode\n \" to \",\n as.character(week2Date(week.range[2])), \".\\n\")\n \n # # generate cylinders\n # print(week.range)\n # week.range = week.range + 2 - min(as.integer(colnames(observation.matrix)), na.rm=T)\n\n cylinders = rcylinder(n.cylinders, observation.matrix, week.range, radia_and_heights, coord.km.df, only.last)\n# print(any(cylinders$t.low == cylinders$t.upp))\n if (NROW(cylinders) > 0){\n if (test){\n tmp = t(apply(cylinders, 1, compute, observation.matrix, baseline, coord.km.df))\n }else{\n tmp = t(apply(cylinders, 1, compute.from.tab.baseline, observation.matrix, baseline, coord.km.df))\n }\n\n cylinders[,c('n_obs', 'mu', 'p.val')] = tmp\n cylinders$warning = cylinders$p.val < p.val.threshold\n }else{\n cat(\"No cases in the selected week range.\\n\")\n }\n print(Sys.time() - init)\n return(cylinders)\n}\n\n\n#' \n#' n.cylinders=10000 takes around 3 hours for the whole dataset, to end up with 300 non-empty cylinders\n#' observation.matrix and baseline.matrix have dimension \n#' This function scans the matrices\n#' -1 in observation.matrix index means that we are excluding from week NA\n#' @inheritParams CreateCylinders\n#' @param observation.matrix.untyped A 2D matrix.\n#' @param baseline.matrix.untyped A 2dD matrix.\nCreateCylinders.delay<-function(observation.matrix.typed, baseline.matrix.typed,\n observation.matrix.untyped, baseline.matrix.untyped,\n emmtype,\n week.range, n.cylinders=1000, \n p.val.threshold=0.05, coord.df=postcode2coord, size_factor=1){\n observation.matrix.typed = as.matrix(observation.matrix.typed[!(rownames(observation.matrix.typed) == 'NA'),])\n baseline.matrix.typed = as.matrix(baseline.matrix.typed[!(rownames(baseline.matrix.typed) == 'NA'),])\n observation.matrix.untyped = as.matrix(observation.matrix.untyped[!(rownames(observation.matrix.untyped) == 'NA'),])\n baseline.matrix.untyped = as.matrix(baseline.matrix.untyped[!(rownames(baseline.matrix.untyped) == 'NA'),])\n\n week.range = range(as.integer(week.range))\n c1 = sum(baseline.matrix.typed) > 2 * sum(observation.matrix.typed)\n c2 = sum(baseline.matrix.untyped) > 2 * sum(observation.matrix.untyped)\n if (c1){\n warning(\"Warning: the typed baseline is very high.\")\n }\n if (c2){\n warning(\"Warning: the untyped baseline seems is very high.\")\n }\n init = Sys.time()\n coord.df = coord.df[!is.na(coord.df$latitude),]\n coord.km.df = coord.df\n coord.km.df[,2:3] = vlatlong2km(coord.df[,2:3])\n if ((week.range[1] < min(as.integer(colnames(observation.matrix.typed)))) | (week.range[2] > max(as.integer(colnames(observation.matrix.typed))))){\n # line = sprintf(\"Try with `starting.week` < %d\", ncol(observation.matrix)-2)\n # writeLines(c(\"`starting.week` is bigger than the matrix length.\", line))\n A = sprintf(\"%d-%d\", week.range[1], week.range[2])\n B = range(as.integer(colnames(observation.matrix.typed)))\n B = sprintf(\"%d-%d\", B[1], B[2])\n writeLines(paste0(\"Error: `week.range` is \", A, \", while the range of `observation.matrix` is \", B, \".\" ))\n return(NA)\n }\n # weeks = as.integer(colnames(observation.matrix)[-seq(1:(starting.week+2))])\n # weeks = weeks[seq(1, length(weeks), 20)]\n # cylinders0 = data.frame(x=double(), y=double(), rho=double(), t.low=integer(), t.upp=integer(), n_obs=integer(), mu=double(), lower=integer(), upper=integer(), p.val=double(), warning=logical())\n radia_and_heights = f_radia_and_heights(baseline.matrix.typed, 1:24) * size_factor\n cat(\"Evaluating cylinder exceedances from \",\n as.character(week2Date(week.range[1])), # this interactive output doesnt work in the prospective mode\n \" to \",\n as.character(week2Date(week.range[2])), \n \" for emm type \", emmtype, \".\\n\")\n\n # generate cylinders\n cylinders = rcylinder(n.cylinders, observation.matrix.typed + observation.matrix.untyped, week.range, radia_and_heights, coord.km.df)\n if (NROW(cylinders) > 0){\n cylinders[,c('n_obs.typed', 'mu.typed', 'p.val.typed')] = t(apply(cylinders, 1, compute,\n observation.matrix.typed,\n baseline.matrix.typed,\n coord.km.df))\n cylinders[,c('n_obs.untyped', 'mu.untyped', 'p.val.untyped')] = t(apply(cylinders, 1, compute,\n observation.matrix.untyped,\n baseline.matrix.untyped,\n coord.km.df))\n }else{\n cat(\"No (typed) cases in the selected week range.\\n\")\n }\n cylinders$warning = (cylinders$p.val.typed < p.val.threshold) | (cylinders$p.val.untyped < p.val.threshold)\n print(Sys.time() - init)\n return(cylinders)\n}\n\n\nSaveCylinders<-function(cylinders, file.basename){\n write.csv(cylinders, paste0(file.basename,'.csv'), quote = F, row.names = F)\n save.and.tell(\"cylinders\", file = paste0(file.basename,'.RData'))\n}\n\nEvaluate<-function(case.file, cylinders, emmtype, p.val.threshold = 0.05,\n warning.score.name = 'warning.score', date.time.field = 'SAMPLE_DT_numeric'){\n case.df<-tryCatch({\n load(paste0(case.file, \".RData\"))\n case.df\n },\n error = function(e){\n case.df = Init(case.file)\n return(case.df$case.df)\n })\n if (!('x' %in% names(case.df) & ('y' %in% names(case.df)))){\n writeLines(\"Inserting coordinates...\")\n if (!('latitude' %in% names(case.df) & ('longitude' %in% names(case.df)))){\n case.df[,c(\"latitude\", \"longitude\")] = t(apply(case.df, 1, postcode.to.location2)) \n }\n case.df[,c(\"y\", \"x\")] = vlatlong2km(case.df[,c(\"latitude\", \"longitude\")])\n writeLines(\"...Done.\")\n save.and.tell('case.df', file=paste0(case.file, \".RData\"))\n }\n idx = case.df$emmtype == emmtype\n writeLines(paste0(\"Computing warning scores for emmtype \", emmtype, \"...\"))\n case.df[idx, warning.score.name] = apply(case.df[idx,], 1, FUN=warning.score, cylinders, date.time.field)\n writeLines(\"...Done.\")\n return(case.df)\n}\n\n\nCluster<-function(case.df, emmtype, makeplot=FALSE, warning.score='warning.score'){\n if(requireNamespace(\"tsne\")){\n # idx = rownames(observation.matrix) != 'NA'\n # observation.matrix = observation.matrix[idx,]\n # idx = rownames(postcode2coord.km) != 'NA'\n # postcode2coord.km = postcode2coord.km[idx,]\n # cases = which(observation.matrix>0, arr.ind=TRUE)\n # cases = as.data.frame(cases)\n # c3 = apply(cases, 1, function(x){postcode2coord.km[x['row'], 3]})\n # c2 = apply(cases, 1, function(x){postcode2coord.km[x['row'], 2]})\n # cases2 = cbind(cases,c2,c3)\n idx = ((case.df$emmtype == emmtype) & !is.na(case.df$x))\n case.df = case.df[idx, ]\n X = tsne(case.df[,c('x', 'y', 'SAMPLE_DT_numeric')])\n # palette = colorRampPalette(c('blue', 'red'))(max(case.df$SAMPLE_DT_numeric))\n if (makeplot == TRUE){\n if(requireNamespace(viridisLite)){\n palette = viridis(max(case.df$SAMPLE_DT_numeric), begin = 0, end = 1)\n }else{\n \n }\n warning.marker = 20 #ifelse(case.df[,warning.score] > 0.9, 20, 1)\n plot(X[,1],X[,2], xlab='C1', ylab='C2', col=palette[case.df$SAMPLE_DT_numeric], pch=warning.marker,\n main=emmtype)\n \n palette = viridis(max(case.df$SAMPLE_DT_numeric), alpha = 1, begin = 0, end = 1)\n idx = ifelse(case.df[,warning.score] > 0.9, T, F)\n points(X[idx,1],X[idx,2], xlab='C1', ylab='C2', col=palette[case.df[idx, \"SAMPLE_DT_numeric\"]], pch=1, cex=2)\n }\n return(X)\n }else{\n writeLines(\"This function requires tsne package.\") \n }\n}\n", "meta": {"hexsha": "3eedcd1a34a69f430d7b0805ca527768646becaf", "size": 11183, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Evaluate.r", "max_stars_repo_name": "mcavallaro/rancovr", "max_stars_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Evaluate.r", "max_issues_repo_name": "mcavallaro/rancovr", "max_issues_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Evaluate.r", "max_forks_repo_name": "mcavallaro/rancovr", "max_forks_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.6217391304, "max_line_length": 198, "alphanum_fraction": 0.6340874542, "num_tokens": 3013, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6723316860482762, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.33879208524194687}} {"text": "#!/usr/bin/env Rscript\n#\n# Use WGCNA to make a network.\n#\n\nlibrary(data.table)\nlibrary(WGCNA)\n\noptions(stringsAsFactors=FALSE)\n\nargv = commandArgs(trailingOnly=TRUE)\n\ngeno_file = argv[1]\nld_var_file = argv[2]\nblock_size = as.numeric(argv[3])\nn_threads = as.numeric(argv[4])\ntrans_pow = as.numeric(argv[5])\n\nenableWGCNAThreads(n_threads)\n\n# Get list of variants\nvars_to_use = scan(ld_var_file, what='character')\n\n# Read in genotype data\ngeno_data = fread(geno_file, header=TRUE)\n\n# Remove unwanted variants\ngeno_data = geno_data[geno_data$rs %in% vars_to_use, ]\n\n# Transform into WGCNA format and eliminate variants with no\n# variation\ngeno_mat = t(geno_data[, -(1:3)])\ngeno_sd = apply(geno_data, 2, sd, na.rm=TRUE)\ngeno_mat = geno_mat[, geno_sd > 0]\n\n# Check for excessive missing data and remove samples and variants\n# if necessary\ngsg = goodSamplesGenes(geno_mat, verbose=3)\ncat('No missing data issues?', gsg$allOK, '\\n')\nif(!gsg$allOK) {\n # Optionally, print the gene and sample names that were removed:\n if (sum(!gsg$goodGenes)>0)\n printFlush(paste('Removing genes:', paste(names(geno_mat)[!gsg$goodGenes], collapse=', ')))\n if (sum(!gsg$goodSamples)>0)\n printFlush(paste('Removing samples:', paste(rownames(geno_mat)[!gsg$goodSamples], collapse=', ')))\n # Remove the offending genes and samples from the data:\n geno_mat = geno_mat[gsg$goodSamples, gsg$goodGenes]\n}\n\n# Make the network; there are lots of options to tinker with\n# in this function\nnet = blockwiseModules(geno_mat, maxBlockSize=block_size,\n power=trans_pow,\n TOMType='unsigned', minModuleSize=10,\n reassignThreshold=0, mergeCutHeight=0.25,\n numericLabels=TRUE,\n saveTOMs=TRUE,\n saveTOMFileBase=\"output.wgcna\",\n verbose=3)\n\n# Plotting\npdf('output.wgnca.dendrogram.pdf', width=12, height=9)\nclrs = labels2colors(net$colors)\nplotDendroAndColors(net$dendrograms[[1]], clrs[net$blockGenes[[1]]],\n 'Module colors',\n dendroLabels=FALSE, hang=0.03,\n addGuide=TRUE, guideHang=0.05)\ndev.off()\n", "meta": {"hexsha": "89d1253ed536911af52936fe49c0e53d6c85107a", "size": 2207, "ext": "r", "lang": "R", "max_stars_repo_path": "make_wgcna_network.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make_wgcna_network.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "make_wgcna_network.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9855072464, "max_line_length": 106, "alphanum_fraction": 0.661078387, "num_tokens": 606, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3387169266059758}} {"text": "Edge Path 0:\n1 -> 2\n2 -> 4\n4 -> 1\n1 -> 3\n3 -> 1\n1 -> 4\nEdge Path 1:\n1 -> 2\n2 -> 4\n4 -> 1\n1 -> 3\n3 -> 3\n3 -> 4\n", "meta": {"hexsha": "2c8fcd71bc15d3df6fdb939827a8c5ee5028879f", "size": 110, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/EdgePredecessor/Paths/03.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/EdgePredecessor/Paths/03.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/EdgePredecessor/Paths/03.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 7.3333333333, "max_line_length": 12, "alphanum_fraction": 0.3818181818, "num_tokens": 70, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.6619228691808012, "lm_q1q2_score": 0.33871692319323893}} {"text": "library(randomForest)\nlibrary(caret)\nlibrary(doMC)\nlibrary(mmadsenr)\nlibrary(futile.logger)\nlibrary(dplyr)\nlibrary(ggthemes)\n\n\n\n# Train and tune random forest classifiers for each of the three data sets coming out of the experiment\n# \"equifinality-4\", for binary analysis. \n#\n# Assumes that data-preparation.r has previously loaded CSV files and created binary \n# data files, stored in a local data directory. \n#\n# Reduces the four class data into a two class problem for basic classifier analysis, and it splits off\n# a test data set, placing it in the environment. \n# \n# NOTE: This analysis takes a LONG time to run, so it is kept separate from the Rmarkdown\n# analysis script. At the completion of the analysis, it saves an environment image, which\n# other scripts, such as RMarkdown documents, can load, and access the fitted model popsampled_results. \n#\n# Only the model training and fitting is done in this script. The test data set is not analyzed\n# here, since it can be done quickly enough that I want to be working in RMarkdown to examine different\n# options. \n\n\nlog_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"population-classification.log\")\nflog.appender(appender.file(log_file), name='cl')\n\nclargs <- commandArgs(trailingOnly = TRUE)\nif(length(clargs) == 0) {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-population-data.rda\")\n} else {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-population-data.rda\", args = clargs)\n}\n\nload(pop_data_file)\n\nflog.info(\"Loaded data file: %s\", pop_data_file, name='cl')\n\nflog.info(\"Beginning classification analysis of equifinality-4 data sets\", name='cl')\n\n# set up parallel processing - use all the cores (unless it's a dev laptop under OS X) - from mmadsenr\nnum_cores <- get_parallel_cores_given_os(dev=TRUE)\nflog.info(\"Number of cores used in analysis: %s\", num_cores, name='cl')\nregisterDoMC(cores = num_cores)\n\n\n########### Training and Tuning Variables ##############\n\n#\n# Common training and tuning parameters for ctmixtures analysis\n#\n\ngbm_grid <- expand.grid(.interaction.depth = (1:6)*2,\n .n.trees = (2:10)*50, \n .shrinkage = 0.05)\n\ntraining_control <- trainControl(method=\"repeatedcv\", \n number=10, repeats=5)\n\n\n\n\n# make this repeatable - comment this out or change it to get a fresh analysis result\nseed_value <- 58132133\nset.seed(seed_value)\nflog.info(\"RNG seed to replicate this analysis: %s\", seed_value, name='cl')\n\n\n# Set up sampling of train and test data sets\ntraining_set_fraction <- 0.8\ntest_set_fraction <- 1.0 - training_set_fraction\n\n\n# set up popsampled_results data frames\nexperiment_names <- c(\"Population Census\")\npopsampled_results <- data.frame()\n\nresults_roc <- NULL\nresults_model <- NULL\nresults_cm <- NULL\n\n###### Population Data ######\n\nflog.info(\"Starting analysis of population census data\", name='cl')\n\n# first row of results\ni <- 1\nexp_name <- experiment_names[i]\n\n# prepare data\n# create a label combining the biased models into one\n# then, split into training and test sets, with balanced samples for each of the binary classes\neq4_pop_df$two_class_label <- factor(ifelse(eq4_pop_df$model_class_label == 'allneutral', 'neutral', 'biased'))\n\n\n# remove fields from analysis that aren't predictors, and the detailed label with 4 classes\nexclude_columns <- c(\"simulation_run_id\", \"model_class_label\", \"innovation_rate\")\n\n\n####### \n\n#model <- train_randomforest(df, training_set_fraction, fit_grid, fit_control, exclude_columns)\nmodel <- train_gbm_classifier(eq4_pop_df, training_set_fraction, \"two_class_label\", gbm_grid, training_control, exclude_columns, verbose=FALSE)\n\n\nresults_model[[\"population_census\"]] <- model$tunedmodel\n\n# use the test data split by the train_randomforest function and calculate tuned model predictions\n# and then get the confusion matrix and fitting metrics\npredictions <- predict(model$tunedmodel, newdata=model$test_data)\ncm <- confusionMatrix(predictions, model$test_data$two_class_label)\nresults <- get_parsed_binary_confusion_matrix_stats(cm)\nresults$experiments <- exp_name\nresults$elapsed <- model$elapsed\nresults_cm[[\"population_census\"]] <- cm\n\n# calculate a ROC curve\npopulation_roc <- calculate_roc_binary_classifier(model$tunedmodel, model$test_data, \"two_class_label\", exp_name)\nresults$auc <- unlist(population_roc$auc@y.values)\nresults_roc[[\"population_census\"]] <- population_roc\n\nresults <- rbind(popsampled_results, results)\n\n\n\n\n############## Complete Processing and Save popsampled_results ##########3\n\n# we can now use plot_multiple_roc() to plot all the ROC curves on the same plot, etc. \n# as well as graph various of the metrics as they vary across sample size and TA duratio\n#plot_multiple_roc_from_list(popsampled_results_roc)\n\nif(length(clargs) == 0) {\n \n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"classification-population-results-gbm.RData\")\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"classification-population-results-gbm-dfOnly.RData\")\n \n \n} else {\n \n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"classification-population-results-gbm.RData\", args = clargs)\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"classification-population-results-gbm-dfOnly.RData\", args = clargs)\n}\n\nflog.info(\"Saving results of analysis to R environment snapshot: %s\", image_file, name='cl')\nsave(results, results_model, results_roc, results_cm, file=image_file)\n\nflog.info(\"Saving just data frame of results of analysis to R environment snapshot: %s\", image_file_results, name='cl')\nsave(results, file=image_file_results)\n\n# End\nflog.info(\"Analysis complete\", name='cl')\n\n\n", "meta": {"hexsha": "76cf01a2421b238b6102b7f8cc265a9c04f2d373", "size": 6096, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-4/modelfitting/population-classification.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-4/modelfitting/population-classification.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-4/modelfitting/population-classification.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3987730061, "max_line_length": 147, "alphanum_fraction": 0.7360564304, "num_tokens": 1457, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6992544210587585, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.33870491540349534}} {"text": "library(tidyr)\nlibrary(reticulate)\nlibrary(ggbeeswarm)\nsource('config.r')\nnp <- import(\"numpy\")\n\ndata <- np$load(\n \"./outputs/all_scores_models_tuh_mae_shuffle-split.npy\",\n # \"./outputs/all_scores_mag_models_mnecommonsubjects_interval_rep-kfold.npy\",\n# \"all_scores_mag_models_mnecommonsubjects.npy\",\n allow_pickle = T)[[1]] %>%\n as.data.frame()\n\ndata_comp_scores <- read.csv(\n \"outputs/tuh_component_scores.csv\",\n stringsAsFactor = T\n)\n\ndata_comp_scores_long <- data.frame(\n score = c(data_comp_scores$spoc, data_comp_scores$riemann),\n estimator = factor(rep(c(\"SPoC\", \"Riemann\"),\n each = nrow(data_comp_scores))),\n n_components = rep(data_comp_scores$n_components, times = 2),\n fold_idx = factor(rep(data_comp_scores$fold_idx, times = 2))\n)\n\nagg_scores <- aggregate(cbind(spoc, riemann) ~ n_components,\n data = data_comp_scores, FUN = mean)\n\n\ndata_ <- data[, (!names(data) %in% c(\"dummy\"))]\nn_splits <- nrow(data_)\n\ndata_long <- data_ %>% gather(key = \"estimator\", value = \"score\")\n# move to long format\ndata_long$estimator <- factor(data_long$estimator)\n\nest_types <- c(\n \"naive\",\n \"diag\",\n \"SPoC\",\n \"Riemann\",\n \"SPoC\",\n \"Riemann\"\n)\n\nest_names <- c(\n \"upper\",\n \"diag\",\n \"SPoC\",\n \"Riemann\",\n sprintf(\"SPoC[%d]\", which.min(agg_scores$spoc)),\n sprintf(\"Riemann[%d]\", which.min(agg_scores$riemann))\n)\n\nest_labels <- setNames(\n c(\"upper\", est_types[c(-1, -5, -6)]),\n est_types[c(-5, -6)]\n)\n\n# categorical colors based on: https://jfly.uni-koeln.de/color/\n# beef up long data\ndata_long$est_type <- factor(rep(est_types, each = n_splits))\n\ndata_long$fold <- rep(1:n_splits, times = length(est_types))\n\n# prepare properly sorted x labels\nsort_idx <- order(apply(data_, 2, mean))\nlevels_est <- est_names[sort_idx]\n\nmy_color_cats <- setNames(\n with(\n color_cats,\n c(`sky blue`, `blueish green`, vermillon, orange)),\n c(\"naive\", \"diag\", \"SPoC\", \"Riemann\"))\n\nggplot(data = subset(data_long, estimator != \"dummy\"),\n mapping = aes(y = score, x = reorder(estimator, I(score)))) +\n geom_beeswarm(\n priority = 'density',\n mapping = aes(color = est_type),\n size = 2.5,\n alpha = 0.2,\n show.legend = T, cex = 1) +\n scale_size_continuous(range = c(0.5, 2)) +\n scale_alpha_continuous(range = c(0.4, 0.7)) +\n geom_boxplot(mapping = aes(fill = est_type, color = est_type),\n alpha = 0.3,\n outlier.fill = NA, outlier.colour = NA) +\n geom_jitter(width = 0.2)+\n stat_summary(geom = 'text',\n mapping = aes(label = sprintf(\"%1.2f\",\n ..y..)),\n fun.y = mean, size = 3.2, show.legend = FALSE,\n position = position_nudge(x = -0.49)) +\n my_theme +\n labs(y = \"MAE\", x = NULL, parse = T) +\n guides(size = F, alpha = F) +\n theme(legend.position = c(0.8, 0.25)) +\n # legend.position = \"top\", legend.text = element_text(size = 18)) +\n coord_flip(ylim=c(5,15)) +\n scale_fill_manual(values = my_color_cats, \n breaks = names(my_color_cats),\n labels = est_labels,\n name = NULL) +\n scale_color_manual(\n values = my_color_cats,\n breaks = names(my_color_cats),\n labels = est_labels,\n name = NULL) +\n scale_x_discrete(labels = parse(text = levels_est)) +\n geom_hline(yintercept = mean(data$dummy), linetype = 'dashed') +\n annotate(geom = \"text\",\n y = mean(data$dummy) + 0.2, x = 3, label = 'predicting~bar(age)',\n size = annotate_text_size,\n parse = T, angle = 270)\n\nfname <- \"./outputs/fig_tuh_model_comp\"\nggsave(paste0(fname, \".png\"),\n width = 8, height = 5, dpi = 300)\n\nggsave(paste0(fname, \".pdf\"),\n useDingbats = F,\n width = 8, height = 5, dpi = 300)\nembedFonts(file = paste0(fname, \".pdf\"), outfile = paste0(fname, \".pdf\"))\n\n\ncomponent_labels <- setNames(\n rev(parse(text = est_names[-c(1:4, 7)])),\n c(\"Riemann\", \"SPoC\"))\n\nfig_components <- ggplot(data = data_comp_scores_long,\n mapping = aes(\n x = n_components, y = score,\n # group = interaction(estimator, fold_idx),\n color = estimator, fill = estimator)) +\n stat_summary(inherit.aes = F,\n mapping = aes(fill = estimator, x = n_components,\n y = score),\n fun.ymin = function(x) mean(x) - sd(x),\n fun.ymax = function(x) mean(x) + sd(x),\n geom = 'ribbon', alpha = 0.2) +\n stat_summary(fun.y = mean, geom = 'line', size = 1.5) +\n my_theme +\n theme(legend.position = c(0.8, 0.5)) +\n scale_color_manual(\n values = my_color_cats[c(\"Riemann\", \"SPoC\")],\n breaks = c(\"Riemann\", \"SPoC\"),\n labels = component_labels,\n name = NULL) +\n scale_fill_manual(\n values = my_color_cats[c(\"Riemann\", \"SPoC\")],\n breaks = c(\"Riemann\", \"SPoC\"),\n labels = component_labels,\n name = NULL) +\n labs(x='#components', y = \"MAE\") +\n geom_vline(\n xintercept = which.min(agg_scores$spoc),\n size = 0.6,\n color = my_color_cats['SPoC'], linetype = 'dashed') +\n geom_vline(\n xintercept = which.min(agg_scores$riemann),\n size = 0.6,\n color = my_color_cats['Riemann'], linetype = 'dashed') +\n scale_x_continuous(breaks = seq(0, 100, 10))\n # scale_y_continuous(breaks = seq(0, .8, .1)) +\n # coord_cartesian(ylim = c(0, 0.8))\n\nfname <- \"./outputs/fig_tuh_component_selection\"\nggsave(paste0(fname, \".png\"),\n width = 8, height = 5, dpi = 300)\n\nggsave(paste0(fname, \".pdf\"),\n useDingbats = F,\n width = 8, height = 5, dpi = 300)\nembedFonts(file = paste0(fname, \".pdf\"), outfile = paste0(fname, \".pdf\"))\n", "meta": {"hexsha": "23892790da78fd1dd989585282668f9c82cad410", "size": 5615, "ext": "r", "lang": "R", "max_stars_repo_path": "debug/plot_figure_tuh_model_comp.r", "max_stars_repo_name": "DavidSabbagh/meeg_power_regression", "max_stars_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-18T06:10:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-18T06:10:16.000Z", "max_issues_repo_path": "debug/plot_figure_tuh_model_comp.r", "max_issues_repo_name": "DavidSabbagh/meeg_power_regression", "max_issues_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "debug/plot_figure_tuh_model_comp.r", "max_forks_repo_name": "DavidSabbagh/meeg_power_regression", "max_forks_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-01T01:36:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-01T13:44:02.000Z", "avg_line_length": 31.7231638418, "max_line_length": 79, "alphanum_fraction": 0.6074799644, "num_tokens": 1668, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33867191146841946}} {"text": "#!/usr/bin/Rscript\n\n\n# week olera kolera cholera epidemi pest farsot\n# olera is the some of kolera and cholera\n\nseries <- read.table(file = \"weekly_counts.txt\",\n col.names = c(\"week\",\"olera\",\"kolera\",\"cholera\",\"epidemi\",\"pest\",\"farsot\"),\n sep = \"\",\n quote = \"\\\"\",\n dec = \".\",\n fill = TRUE,\n comment.char = \"#\")\n\npdf( \"weekly_mentions.pdf\", width = 7, height = 5 )\n\n# cairo_ps(\"image.eps\")\n\nplot(x = series$week,\n y = series$olera,\n type = \"l\",\n ylab = \"Occurence in corpus\",\n xlab = \"Week\")\n", "meta": {"hexsha": "de1d15d3d058cb203a3edc720e176661a312f915", "size": 627, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_weekly.r", "max_stars_repo_name": "siglun/etudes", "max_stars_repo_head_hexsha": "d78a76d9e6a58192041483b3ac90e9d2f5eb84fd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_weekly.r", "max_issues_repo_name": "siglun/etudes", "max_issues_repo_head_hexsha": "d78a76d9e6a58192041483b3ac90e9d2f5eb84fd", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_weekly.r", "max_forks_repo_name": "siglun/etudes", "max_forks_repo_head_hexsha": "d78a76d9e6a58192041483b3ac90e9d2f5eb84fd", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.125, "max_line_length": 96, "alphanum_fraction": 0.4912280702, "num_tokens": 161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.33867190369842193}} {"text": "library(filesampler)\n\nsystem.time(x <- wc_l(\"big.csv\"))\n\nx\n", "meta": {"hexsha": "183fe70f35c8e2902672262d11cafe83e30ce8bc", "size": 59, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/benchmarks/wc.r", "max_stars_repo_name": "wrathematics/lineSampler", "max_stars_repo_head_hexsha": "b3683ea15888b0da6e1f983233c395d23cf9e2b6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2015-02-25T23:05:19.000Z", "max_stars_repo_stars_event_max_datetime": "2018-05-27T14:27:22.000Z", "max_issues_repo_path": "inst/benchmarks/wc.r", "max_issues_repo_name": "wrathematics/filesampler", "max_issues_repo_head_hexsha": "b3683ea15888b0da6e1f983233c395d23cf9e2b6", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/benchmarks/wc.r", "max_forks_repo_name": "wrathematics/filesampler", "max_forks_repo_head_hexsha": "b3683ea15888b0da6e1f983233c395d23cf9e2b6", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-02-14T18:26:40.000Z", "max_forks_repo_forks_event_max_datetime": "2017-02-14T18:26:40.000Z", "avg_line_length": 9.8333333333, "max_line_length": 33, "alphanum_fraction": 0.6779661017, "num_tokens": 16, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5506073655352403, "lm_q1q2_score": 0.3386718959284242}} {"text": "library(data.table)\n\nsource('../../functions.r')\n\nload('../generated/model/mcmc-results.rdata', v=F)\n\nspplist <- fread('../acap-species-list.csv')[base_species == T]\nsetkeyv(spplist, 'code.sra')\n### APF by genus\nmcsel_genus <- mcmc[variable %in% mc_attributes[role == 'quarter0_spatial1' & vartype == 'apf_t', variable]]\nmcsel_genus <- merge(mcsel_genus, mc_attributes, by = 'variable', all.x=T, all.y=F)\napf_samples_genus <- merge(mcsel_genus, spplist[, .(species_code=code.sra, genus=vulgroup)], by='species_code', all.x=T)\napf_genus <- apf_samples_genus[, .(apf=sum(value)), .(sample, genus)][, .(amean=mean(apf), ali=quantile(apf, 0.025), aui=quantile(apf, 0.975)), genus]\nwrite.csv(apf_genus, row.names=FALSE, file='../generated/apf-genus.csv')\n\n", "meta": {"hexsha": "39c4a1a3932c31362c9f4ad6fa9db9b3e9b3c76c", "size": 751, "ext": "r", "lang": "R", "max_stars_repo_path": "12-genus-tracking-interaction-fleet/model/genus-summary.r", "max_stars_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_stars_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12-genus-tracking-interaction-fleet/model/genus-summary.r", "max_issues_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_issues_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12-genus-tracking-interaction-fleet/model/genus-summary.r", "max_forks_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_forks_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.9375, "max_line_length": 150, "alphanum_fraction": 0.7017310253, "num_tokens": 243, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.5506073655352403, "lm_q1q2_score": 0.3386718959284241}} {"text": "cl.out.path <- \"/outputs\"\nsetwd(cl.out.path)\n\nlibrary(scrattch.hicat)\nlibrary(Matrix)\nlibrary(scrattch.io)\nlibrary(mfishtools)\nlibrary(matrixStats)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(scrattch.vis)\nlibrary(RANN)\nlibrary(WGCNA)\nlibrary(gridExtra)\nlibrary(data.table)\nlibrary(R.utils)\nlibrary(Seurat)\nlibrary(cowplot)\n\noptions(stringsAsFactors=FALSE)\n\n############################################################################\n## Read in 10x data and additional information\n\nload(\"data/hippocampus_10x.RData\")\nnorm.dat.10X <- logCPM(datExpr)\nrow.names(anno) <- colnames(norm.dat.10X)\nsamp.dat.10X <- anno\n\nexclude.genes <- scan(\"data/exclusiongenes_mito_sex_tissue.txt\", what=\"character\")\nuse.genes <- sort(setdiff(row.names(norm.dat.10X), exclude.genes))\nsave(use.genes, exclude.genes, file=\"use.genes.rda\")\n\nionChannels <- scan(\"data/ion_channels.txt\",what=\"character\")\nepState <- setNames(c(\"WG1\",\"WG1\",\"WG4\",\"WG4\"), c(\"H16.06.008\",\"H17.06.015\",\"H16.06.009\",\"H16.06.010\")) \n\n\n############################################################################\n## Assign classes using k-means clustering on all cells based on marker genes from HBA and Cell types database\n\n# Read in predetermined genes from Allen Brain Map\nhighDG <- intersect(scan(\"data/topDG_HBA.txt\",what=\"character\",sep=\"\\n\"),rownames(norm.dat.10X))[1:100]\nhighGlut <- intersect(scan(\"data/topGlut_CT.txt\",what=\"character\",sep=\"\\n\"),rownames(norm.dat.10X))\nhighGABA <- intersect(scan(\"data/topGABA_CT.txt\",what=\"character\",sep=\"\\n\"),rownames(norm.dat.10X))\nhighGlia <- intersect(scan(\"data/topGlia_CT.txt\",what=\"character\",sep=\"\\n\"),rownames(norm.dat.10X))\ndexGenes <- unique(c(highDG,highGlut,highGABA,highGlia)) \n\n# Run K-means clustering on cells with enough genes detected\nset.seed(1) # For reproducibility\nkpCells <- samp.dat.10X$gene.counts.0 > 1000 \ndat <- t(norm.dat.10X[dexGenes,kpCells])\nrcl <- kmeans(as.matrix(as.data.frame(dat)),4)\nrcl <- kmeans(as.matrix(as.data.frame(dat)),4)\nrcl <- kmeans(as.matrix(as.data.frame(dat)),4)\n# NOTE: this above line of code needs to be run at least 3 times to reproduce the results\n# There is no reason why this should be the case, but it is.\ncluster <- norm.dat.10X[1,]*0\ncluster[kpCells] <- rcl$cluster\n\n# Assign clusters to appropriate classes based on marker gene expression\noutVal <- NULL\nfor(i in 1:4) {\n outVal <- cbind(outVal,Matrix::rowMeans(norm.dat.10X[c(\"PROX1\",\"GAD1\",\"SLC17A7\",\"SLC1A3\"),cluster==i]>0))\n}\nval <- 1\nfor (i in 1:4) {\n v <- which.max(outVal[i,])\n val <- c(val, v+1)\n outVal[,v] = 0\n}\nclasses <- c(\"Low quality\", \"Dentate Gyrus\", \"Inhibitory\", \"Excitatory\", \"Non-neuronal\")[order(val)]\nclasses <- classes[cluster+1] \nsamp.dat.10X$class_label <- classes\nprint(table(classes[kpCells]))\n#Dentate Gyrus Excitatory Inhibitory Non-neuronal \n# 12011 6386 5302 3543 # These are the values you should get.\n\n\n############################################################################\n## Plot relevant ion channels - This is figure 4B\nionch <- c(\"KCNJ2\",\"KCNA1\",\"KCNB1\",\"KCNC4\",\"KCND2\",\"KCNQ2\",\"CACNA1D\",\"CACNA1B\",\"CACNA1H\",\"KCNMA1\",\"KCNN2\",\"SCN1A\",\"HCN1\")\nionDat <- 2^as.matrix(t(norm.dat.10X[ionch,]))-1 # Convert to linear CPM\nplotDat <- data.frame(sample_name = colnames(norm.dat.10X),ionDat) \nannoDat <- data.frame(sample_name = colnames(norm.dat.10X),cluster = samp.dat.10X$class)\nannoDat <- annotate_cat(annoDat,\"cluster\")\n\np <- sample_fire_plot(plotDat,annoDat,ionch,\"cluster\",log_scale=TRUE, max_width=25,label_height=15)\np\nggsave(\"class_markers_new.pdf\",p,device=\"pdf\",height=5,width=4)\n\n\n############################################################################\n## Subset only the cells in dentate gyrus\nkpDG <- samp.dat.10X$class_label==\"Dentate Gyrus\"\ndatDG <- as.matrix(norm.dat.10X[,kpDG])\nannoDG <- samp.dat.10X[kpDG,]\n\n# Reorder donor by WG\nannoDG$Donor = factor(annoDG$Donor, levels=c(\"H16.06.008\",\"H17.06.015\",\"H16.06.009\",\"H16.06.010\"))\nannoDG <- annotate_factor(annoDG,\"Donor\")\n\nprint(table(annoDG$Donor_label))\n# H16.06.008 H16.06.009 H16.06.010 H17.06.015 \n# 5035 1398 1853 3725\n\nprint(table(annoDG$Donor_label)/table(samp.dat.10X$Donor)[names(table(annoDG$Donor_label))])\n# H16.06.008 H16.06.009 H16.06.010 H17.06.015 \n# 0.5678998 0.2992935 0.2318569 0.4848367\n# There is a significantly higher fraction of DG cells in the WG1 cases vs. the WG4 cases.\n\nkpGn <- rowSums(datDG>1) >= (dim(datDG)[2]/20) # Keep genes expressed in at least 5% of DG cells\nuseGenes <- sort(rownames(datDG)[kpGn])\ndatDG <- datDG[useGenes,]\n\nfwrite(signif(datDG,3), file = \"DG_data_new.csv\", row.names=TRUE)\nfile.remove('DG_data_new.csv.gz')\ngzip('DG_data_new.csv',destname='DG_data_new.csv.gz')\n\n\n############################################################################\n## Compute statistics per donor and find potentially significant genes and output\ncl <- setNames(annoDG$Donor_label,colnames(datDG))\nmeansDG <- get_cl_means(datDG,cl)\ne1 <- epState[colnames(meansDG)] == \"WG1\"\ne4 <- epState[colnames(meansDG)] == \"WG4\"\ndexDG <- rowMeans(meansDG[,e4]) - rowMeans(meansDG[,e1])\nsigDG <- dexDG / (1+apply(meansDG[,e4],1,sd)+apply(meansDG[,e1],1,sd))\n\nstats <- signif(data.frame(meansDG,dexDG,sigDG)[order(sigDG),],3)\ncolnames(stats) <- c(paste(epState[colnames(meansDG)],names(epState[colnames(meansDG)])),\"Fold Change\", \"Score\")\n\nfwrite(stats, file = \"DG_stats_new.csv\", row.names=TRUE)\n\n\n############################################################################\n## Plot all the ion channels\n\n# The first three genes in this plot is part of Figure 4E\nionChannels <- c(\"KCNMA1\",\"CACNA1B\",\"KCNJ2\",intersect(names(sort(sigDG)), ionChannels))\n\nplotDat <- data.frame(donor = as.factor(paste(epState[annoDG$Donor_label],annoDG$Donor_label)),\n WG = epState[annoDG$Donor_label])\n\npdf(\"ionChannels_new.pdf\", onefile = TRUE)\nfor (i in ionChannels){\n plotDat$log2CPM_UMI <- datDG[i,]\n main <- paste0(i,\"; mean(WG4-WG1) = \",signif(dexDG[i],3),\"; score = \",signif(sigDG[i],3))\n p <- ggplot(plotDat, aes(x=donor, y=log2CPM_UMI, fill=WG)) + \n\t geom_boxplot(width=0.1, fill=\"white\") +\n\t labs(title=main) + \n\t ylim(0,15)\n grid.arrange(p)\n}\ndev.off()\t \n\n\n############################################################################\n## Plot the best ion channels\n\n# This plot is part of Figure 1B\nionch2 <- c(intersect(rownames(stats),ionChannels)[1:10],\n intersect(rownames(stats)[dim(stats)[1]:1],ionChannels)[10:1])\nionDG <- 2^as.matrix(t(datDG[ionch2,]))-1 # Convert to linear CPM\nplotDG <- data.frame(sample_name = colnames(datDG),ionDG) \nannoDG$sample_name = colnames(datDG)\nannoDG <- annoDG[,c(\"sample_name\",setdiff(colnames(annoDG),\"sample_name\"))]\n\np = sample_fire_plot(plotDG,annoDG,ionch2,\"Donor\",log_scale=TRUE, max_width=35,label_height=15)\np\nggsave(\"ionChannel_DG_markers_new.pdf\",p,device=\"pdf\",height=5,width=2.5)\n\n\n############################################################################\n## Plot the best neuron projection genes\n\n# This plot is part of Figure 1B\nnpGenes <- scan(\"data/neuron_projection_genes.txt\", what=\"character\")\nnp <- c(intersect(rownames(stats),npGenes)[1:10],\n intersect(rownames(stats)[dim(stats)[1]:1],npGenes)[10:1])\nnpDG <- 2^as.matrix(t(datDG[np,]))-1 # Convert to linear CPM\nplotDG <- data.frame(sample_name = colnames(datDG),npDG) \n\np = sample_fire_plot(plotDG,annoDG,np,\"Donor\",log_scale=TRUE, max_width=35,label_height=15)\np\nggsave(\"neuronProjection_DG_markers_new.pdf\",p,device=\"pdf\",height=5,width=2.5)\n\n\n############################################################################\n## Compare WG4/WG1 DEX genes to other combos.\n\ndexR1 <- rowMeans(meansDG[,c(3,4)]) - rowMeans(meansDG[,c(1,2)])\ndexR2 <- rowMeans(meansDG[,c(2,4)]) - rowMeans(meansDG[,c(1,3)])\n\nout_dex <- data.frame(\n real_dex = c(sum(dexDG >= 1),sum(dexDG <= -1)),\n\trand_dex1 = c(sum(dexR1 >= 1),sum(dexR1 <= -1)),\n\trand_dex2 = c(sum(dexR2 >= 1),sum(dexR2 <= -1)))\nrownames(out_dex) <- c(\"Higher in WG4\", \"Lower in WG4\")\nprint(out_dex)\n# real_dex rand_dex1 rand_dex2\n# Higher in WG4 397 634 303\n# Lower in WG4 341 176 289\n# The number of genes is no different from what we'd expect by chance. What if we put all the cells on a PC vector?\n\n\n############################################################################\n## Run Seurat on the data and output plots for PCs and UMAP\n\nhuman_metadata <- data.frame(donor = annoDG$Donor_label,\n state = epState[annoDG$Donor_label])\nrownames(human_metadata) <- colnames(datDG)\nhuman <- CreateSeuratObject(datDG, meta.data = human_metadata)\nhuman <- NormalizeData(human, verbose = FALSE)\nhuman <- FindVariableFeatures(human, selection.method = \"vst\", nfeatures = 2000, verbose = FALSE) # 750\nhuman <- ScaleData(object = human, verbose = FALSE)\nhuman <- RunPCA(object = human, npcs = 30, verbose = FALSE)\nhuman <- RunUMAP(object = human, reduction = \"pca\", dims = 1:30, verbose = FALSE)\n\np1 <- DimPlot(object = human, group.by = \"donor\", reduction = \"pca\", do.return = TRUE, \n pt.size = 0.5, label=TRUE) + NoLegend() \np2 <- DimPlot(object = human, group.by = \"state\", reduction = \"pca\", do.return = TRUE, \n pt.size = 0.5, label=TRUE, cols=c(\"blue\",\"red\")) + NoLegend()\np3 <- DimPlot(object = human, group.by = \"donor\", reduction = \"umap\", do.return = TRUE, \n pt.size = 0.5, label=TRUE) + NoLegend() \np4 <- DimPlot(object = human, group.by = \"state\", reduction = \"umap\", do.return = TRUE, \n pt.size = 0.5, label=TRUE, cols=c(\"blue\",\"red\")) + NoLegend()\nplot_grid(p1, p2, p3, p4, ncol = 2)\nggsave(\"umap_pca_plots.pdf\",device=\"pdf\",height=9,width=9)\n\n# This plot is part of Figure 1B\nDimPlot(object = human, group.by = \"state\", reduction = \"pca\", do.return = TRUE, \n pt.size = 0.4, label=FALSE, cols=c(\"blue\",\"red\")) + NoLegend()\nggsave(\"umap_pca_plots_PC_byState.pdf\",device=\"pdf\",height=5,width=5)\n\n", "meta": {"hexsha": "3e02945a0d07200d495b0793bc0ce81cb4ddab59", "size": 10018, "ext": "r", "lang": "R", "max_stars_repo_path": "Fig_4/RNAseq_analysis/epilepsy_RNAseq_analysis_R_script.r", "max_stars_repo_name": "abuchin/epilepsy_human_dg", "max_stars_repo_head_hexsha": "b2478ee9b5412dfa823575b0fdf37c48ba7d0fbc", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-07-18T03:15:57.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-18T03:15:57.000Z", "max_issues_repo_path": "Fig_4/RNAseq_analysis/epilepsy_RNAseq_analysis_R_script.r", "max_issues_repo_name": "AllenInstitute/epilepsy_human_dg_public", "max_issues_repo_head_hexsha": "b09dcdada4ed6b94c4141ae2cab5cff9b50897f9", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2021-03-31T20:00:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-12T00:48:01.000Z", "max_forks_repo_path": "Fig_4/RNAseq_analysis/epilepsy_RNAseq_analysis_R_script.r", "max_forks_repo_name": "AllenInstitute/epilepsy_human_dg_public", "max_forks_repo_head_hexsha": "b09dcdada4ed6b94c4141ae2cab5cff9b50897f9", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-07-09T23:35:32.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-09T23:35:32.000Z", "avg_line_length": 42.4491525424, "max_line_length": 123, "alphanum_fraction": 0.6363545618, "num_tokens": 3058, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.33862061780087627}} {"text": "#' Function that corrects chronologies for sudden jumps in time\n#' \n#' Some occurrences in the model results can lead the CumDY function\n#' to detect extra year transitions, resulting in sudden jumps in\n#' the shell chronology or a start of the chronology at an age\n#' beyond 1 year. This function removes these sharp transitions\n#' and late onset by adding or subtracting whole years to the age\n#' result.\n#' @param resultarray Array containing the full results of\n#' the optimized growth model\n#' @param T_per The period length of one year (in days)\n#' @param agecorrection Correct for jumps in age (/code{TRUE}) or\n#' only for starting time (/code{FALSE})\n#' @param plot Should the results be plotted? (/code{TRUE/FALSE})\n#' @return An updated and corrected version of \\code{resultarray}\n#' @references package dependencies: ggplot2 3.2.1\n#' @examples\n#' testarray <- array(NA, dim = c(20, 16, 9)) # Create empty array\n#' # with correct third dimension\n#' windowfill <- seq(10, 100, 10) # Create dummy simulation data \n#' # (ages) to copy through the array\n#' for(i in 6:length(testarray[1, , 1])){\n#' testarray[, i, 3] <- c(windowfill,\n#' rep(NA, length(testarray[, 1, 3]) - length(windowfill)))\n#' windowfill <- c(NA, windowfill + 31)\n#' }\n#' testarray2 <- age_corr(testarray, 365, FALSE, FALSE) # Apply function on \n#' array\n#' @export\nage_corr <- function(resultarray,\n T_per = 365,\n plot = TRUE,\n agecorrection = TRUE\n ){\n Age <- Age_corr <- NULL # Predefine variables to circumvent global variable binding error\n # Check on glitches where consecutive windows are placed (almost) 1 year apart.\n # These are repaired by adding one T_per to the cumulative Day values of those windows in\n # resultarray[, , 3]\n mean_window_age <- data.frame(\n 1:(length(resultarray[1,,1]) - 5),\n unname(colMeans(resultarray[, 6:length(resultarray[1, , 1]), 3], na.rm = TRUE)))\n colnames(mean_window_age) <- c(\"window\", \"Age\")\n\n # Plot mean window ages\n dev.new() # Plot Window age to check for strange jumps in age\n ageplot <- ggplot2::ggplot(mean_window_age, ggplot2::aes(window, Age)) +\n ggplot2::geom_line() +\n ggplot2::geom_point() +\n ggplot2::ggtitle(\"Plot of average ages of modelling windows\") +\n ggplot2::xlab(\"Window #\") +\n ggplot2::scale_y_continuous(\"Age (days)\", seq(0, 365 * ceiling(max(unname(colMeans(\n resultarray[, 6:length(resultarray[1,,1]),3], na.rm = TRUE)),\n na.rm = TRUE) / 365), 365))\n plot(ageplot)\n\n if(agecorrection == TRUE){\n agecorr <- rep(0, length(mean_window_age[, 2])) # Create vector to store corrections\n # (in days) to the mean ages of modelling windows\n for(i in 1:(length(mean_window_age[,2]) - 1)){ # Loop through all windows\n if(mean_window_age[i + 1, 2] - mean_window_age[i, 2] > T_per / 2){ # If there is a\n # large (> half a year) positive step between consecutive windows...\n # ...subtract one year from the subsequent windows\n agecorr[(i + 1):length(agecorr)] <- agecorr[(i + 1):length(agecorr)] - T_per\n }else if(mean_window_age[i + 1, 2] - mean_window_age[i, 2] < T_per / -2){ # If there is\n # a large (> half a year) negative drop between consecutive windows, add one year\n # from the subsequent windows\n agecorr[(i + 1):length(agecorr)] <- agecorr[(i + 1):length(agecorr)] + T_per \n }\n }\n\n if(min(mean_window_age$Age + agecorr) > T_per){\n agecorr <- agecorr - 365 # Subtract whole number of years from correction if the\n # smallest value is more than one year old\n }\n\n mean_window_age$Age_corr <- mean_window_age$Age + agecorr\n mean_window_age$correction <- agecorr\n\n if(plot == TRUE){ # Plot updated result\n ageplot <- ggplot2::ggplot(mean_window_age, ggplot2::aes(window, Age)) + # Plot Window\n # age to check for strange jumps in age\n ggplot2::geom_line() +\n ggplot2::geom_point() +\n ggplot2::ggtitle(\"Plot of average ages of modelling windows\") +\n ggplot2::xlab(\"Window #\") +\n ggplot2::scale_y_continuous(\"Age (days)\", seq(0, 365 * ceiling(max(unname(colMeans(\n resultarray[, 6:length(resultarray[1,,1]),3], na.rm = TRUE)),\n na.rm = TRUE) / 365), 365)) +\n ggplot2::geom_line(ggplot2::aes(window, Age_corr), col = \"red\") # Add corrected window\n # age to plot\n plot(ageplot)\n }\n \n resultarray[, 6:length(resultarray[1, , 1]), 3] <- resultarray[, 6:length(\n resultarray[1, , 1]), 3] + matrix(agecorr, nrow = length(resultarray[, 1, 1]),\n ncol = length(agecorr), byrow = TRUE) # Apply correction on the results of\n # age modelling\n }else{\n resultarray[, 6:length(resultarray[1, , 1]), 3] <- resultarray[, 6:length(\n resultarray[1, , 1]), 3] - floor(min(mean_window_age$Age) / 365) * 365 # Correct age if\n # all windows occur past year one\n }\n return(resultarray)\n}", "meta": {"hexsha": "ad88aed87be6334fdaee3d818c6d55c8d14490f7", "size": 5169, "ext": "r", "lang": "R", "max_stars_repo_path": "R/age_corr.r", "max_stars_repo_name": "nhoeche/ShellChron.jl", "max_stars_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/age_corr.r", "max_issues_repo_name": "nhoeche/ShellChron.jl", "max_issues_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/age_corr.r", "max_forks_repo_name": "nhoeche/ShellChron.jl", "max_forks_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.7019230769, "max_line_length": 99, "alphanum_fraction": 0.6225575547, "num_tokens": 1383, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.33862061780087627}} {"text": "#assert existence of\n#commonfile\nsource(commonfile)\n#bamfile\nload(bamfile)\n#pwmfile\n#load(paste0(pwmdir,pwmid,'.pwmout.RData'))\n#tmpdir\n\nbamnames = names(allreads[[1]])\n\nobschrnames=names(allreads)\npreads=allreads[[obschrnames[1]]][[1]]$plus\ncutat=10\n\n#stablize the variance (helps when there are few high coverage sites).\ntfun <- function(x){\n y = x\n x[x>cutat]=cutat\n y[x>0] = sqrt(x[x>0])\n y\n}\n\nunlink(paste0(tmpdir,'/',pwmid,'/','*tf',pwmid,'*'))\n\nuse.w=!is.null(whitelist)\nif(use.w){\n wtable=read.table(whitelist)\n white.list=lapply(levels(wtable[,1]),function(i){\n wtchr=wtable[wtable[,1]==i,]\n ir=IRanges(wtchr[,2],wtchr[,3])\n })\n names(white.list)=levels(wtable[,1])\n}\n\ndir.create(paste0(tmpdir,'/',pwmid),recursive=T)\nmakeTFmatrix <- function(coords,prefix='',offset=0){\n cwidth = width(coords[[1]][1])\n obschrnames=names(allreads)\n validchr = obschrnames[which(obschrnames%in%ncoords)]\n readcov=lapply(1:length(bamnames),function(j){\n sapply(validchr,function(i){length(allreads[[i]][[j]]$plus)+length(allreads[[i]][[j]]$minus)})/seqlengths(genome)[validchr]\n })\n readfact = lapply(readcov,function(i){i/i[1]})\n slen = seqlengths(genome)\n scrd =sapply(coords,length)\n minbgs=floor(max(10000,sum(scrd))*(scrd/sum(scrd)));\n for(chr in validchr){\n chrlen = slen[chr]\n print(chr)\n chrcoord=coords[[chr]]\n pos.mat = do.call(rBind,lapply(1:length(bamnames),function(i){\n pluscoord=allreads[[chr]][[i]]$plus\n if(length(pluscoord)>0){\n rre = rle(sort(pluscoord))\n irp=IRanges(start=rre$values,width=1)\n fos=findOverlaps(chrcoord,irp)\n uquery=queryHits(fos)\n querycoord=rre$values[subjectHits(fos)]\n uoffset = querycoord-start(chrcoord)[uquery]+1\n rval= rre$lengths[subjectHits(fos)] / readfact[[i]][chr]\n pos.triple = cbind(round(uquery),round(uoffset),tfun(rval))\n pos.mat=sparseMatrix(i=round(uoffset),j=round(uquery),x=tfun(rval),dims=c(2*wsize,length(chrcoord)),giveCsparse=T)\n }else{\n pos.triple=cbind(1,1,0)\n pos.mat=Matrix(0,nrow=2*wsize,ncol=length(chrcoord))\n }\n pos.mat\n }))\n #\n neg.mat = do.call(rBind,lapply(1:length(bamnames),function(i){\n minuscoord=allreads[[chr]][[i]]$minus\n if(length(minuscoord)>0){\n rre = rle(sort(minuscoord))\n irp=IRanges(start=rre$values,width=1)\n fos=findOverlaps(chrcoord,irp)\n uquery=queryHits(fos)\n querycoord=rre$values[subjectHits(fos)]\n uoffset = querycoord-start(chrcoord)[uquery]+1\n rval= rre$lengths[subjectHits(fos)] / readfact[[i]][chr]\n neg.triple = cbind(round(uquery),round(uoffset),tfun(rval))\n neg.mat=sparseMatrix(i=round(uoffset),j=round(uquery),x=tfun(rval),dims=c(2*wsize,length(chrcoord)),giveCsparse=T)\n }else{\n neg.triple=cbind(1,1,0)\n neg.mat=Matrix(0,nrow=2*wsize,ncol=length(chrcoord))\n }\n neg.mat\n }))\n#\n save(pos.mat,neg.mat,file=paste0(tmpdir,'/',pwmid,'/',prefix,'tf',pwmid,'-',chr,'.RData'))\n\tgc()\n }\n}\n\nload(paste0(pwmdir,pwmid,'.pwmout.RData'))\ncoords2=sapply(coords.short,flank,width=wsize,both=T)\nmakeTFmatrix(coords2,'positive.')\nload(paste0(pwmdir.bg,pwmid,'.pwmout.RData'))\ncoords2=sapply(coords.short,flank,width=wsize,both=T)\nmakeTFmatrix(coords2,'background.',10000)\n\nload(paste0(pwmdir,pwmid,'.pwmout.RData'))\n\n#\n#####\n", "meta": {"hexsha": "128708e3bf29d8d6ba6b8211b33d384cbe2889d5", "size": 3680, "ext": "r", "lang": "R", "max_stars_repo_path": "loadbam.sup.r", "max_stars_repo_name": "wakilsarfaraz/PIQ", "max_stars_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-21T04:19:43.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-21T04:19:43.000Z", "max_issues_repo_path": "loadbam.sup.r", "max_issues_repo_name": "wakilsarfaraz/PIQ", "max_issues_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "loadbam.sup.r", "max_forks_repo_name": "wakilsarfaraz/PIQ", "max_forks_repo_head_hexsha": "4b78caf686b995af2ef8e7d82e94fc4c14c90633", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7169811321, "max_line_length": 131, "alphanum_fraction": 0.6035326087, "num_tokens": 1090, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7341195152660687, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.3384414145582815}} {"text": "source('~/project/main.r')\nraw_tmp <-arrange(filter(row_refine,\u8eca\u7a2e=='B03'))\nraw_tmp <-arrange(filter(raw_tmp,\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b==9))\nraw_tmp <-arrange(filter(raw_tmp, \u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b==15)) #\u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b\u610f\u7fa9=\u8eca\u8207\u8eca-\u8def\u53e3\u4ea4\u5c94\u649e - 15\n\nraw_tmp <- select(raw_tmp,\u5340,\u6b7b,\u53d7\u50b7)\nraw_tmp$count <- 1\nraw_tmp[is.na(raw_tmp)] <- 0\nraw_tmp_summary <- (raw_tmp %>% group_by(\u5340) %>%\n summarise_all(sum)\n)\n\nraw_tmp_summary$\u6b7b\u6bd4\u7387 <- raw_tmp_summary$\u6b7b / sum(raw_tmp_summary$count) * 100\nraw_tmp_summary$\u53d7\u50b7\u6bd4\u7387 <- raw_tmp_summary$\u53d7\u50b7 / sum(raw_tmp_summary$count) * 100\nraw_tmp_summary$\u610f\u5916\u6bd4\u7387 <- raw_tmp_summary$count / sum(raw_tmp_summary$count) * 100\n\nhighchart() %>%\n hc_chart(type =\"column\") %>%\n hc_xAxis(categories = raw_tmp_summary$\u5340) %>%\n hc_add_series(data = raw_tmp_summary$\u6b7b\u6bd4\u7387, name = \"\u6b7b/\u7e3d\u610f\u5916\u6bd4\u7387\") %>%\n hc_add_series(data = raw_tmp_summary$\u53d7\u50b7\u6bd4\u7387, name = \"\u53d7\u50b7/\u7e3d\u610f\u5916\u6bd4\u7387\") %>%\n hc_add_series(data = raw_tmp_summary$\u610f\u5916\u6bd4\u7387, name = \"\u610f\u5916/\u7e3d\u610f\u5916\u6bd4\u7387\")\n", "meta": {"hexsha": "6e204e34102b944c2861980e72a261a05badffb0", "size": 898, "ext": "r", "lang": "R", "max_stars_repo_path": "bigdata-and-r/data/project/private-car-insight/go-straight/zone.r", "max_stars_repo_name": "vinnson/nkust", "max_stars_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bigdata-and-r/data/project/private-car-insight/go-straight/zone.r", "max_issues_repo_name": "vinnson/nkust", "max_issues_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bigdata-and-r/data/project/private-car-insight/go-straight/zone.r", "max_forks_repo_name": "vinnson/nkust", "max_forks_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.0434782609, "max_line_length": 80, "alphanum_fraction": 0.7048997773, "num_tokens": 381, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7341195152660687, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.3384414145582815}} {"text": "library(rrBLUP)\nlibrary(data.table)\nargs = commandArgs(trailingOnly=TRUE)\n\nX_file <- args[1] # geno matrix\nY_file <- args[2] # pheno matrix\ntest_file <- args[3] # individuals in test set\n\n\ngeno <- fread(X_file)\ntest <- scan(test_file, what='character')\ntraining <- geno[geno$ID %in% setdiff(geno$ID,test),]\nwrite.csv(training,'geno_training.csv',quote=F,row.names=F)\n\nY <- read.csv(Y_file, row.names=1) \nY <- Y[!rownames(Y) %in% test,]\nwrite.csv(Y,'pheno_training.csv',row.names=T,quote=F)\n", "meta": {"hexsha": "ed939c462d92de2248d328e85b77875e24b3e8c6", "size": 490, "ext": "r", "lang": "R", "max_stars_repo_path": "11_split_geno_pheno_fread.r", "max_stars_repo_name": "peipeiwang6/Genomic_prediction_in_Switchgrass", "max_stars_repo_head_hexsha": "1fba3508c0d81d16e0629e3cf94ff4d174a85b13", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "11_split_geno_pheno_fread.r", "max_issues_repo_name": "peipeiwang6/Genomic_prediction_in_Switchgrass", "max_issues_repo_head_hexsha": "1fba3508c0d81d16e0629e3cf94ff4d174a85b13", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "11_split_geno_pheno_fread.r", "max_forks_repo_name": "peipeiwang6/Genomic_prediction_in_Switchgrass", "max_forks_repo_head_hexsha": "1fba3508c0d81d16e0629e3cf94ff4d174a85b13", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.2222222222, "max_line_length": 59, "alphanum_fraction": 0.7081632653, "num_tokens": 153, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3383967291557105}} {"text": "# $ r --file=analyze.r\n\nlibrary(lme4)\n\ntasks <- read.csv(\"tasks.csv\", header = TRUE)\n\n# 'data.frame': 208 obs. of 27 variables:\n# $ participantNumber : int 1 1 1 1 1 1 1 1 1 1 ...\n# $ usedMouse : Factor w/ 2 levels \"no\",\"yes\": 1 1 1 1 1 1 1 1 1 1 ...\n# $ taskNumber : int 1 2 3 4 5 6 7 8 9 10 ...\n# $ secondEncounter : Factor w/ 2 levels \"no\",\"yes\": 1 1 1 1 2 2 2 2 1 1 ...\n# $ task : Factor w/ 6 levels \"Four Squares\",..: 5 3 4 6 5 4 6 3 1 2 ...\n# $ treatment : Factor w/ 3 levels \"BoxSelectOnly\",..: 3 1 1 1 1 3 3 3 2 2 ...\n# $ allEventsLogged : Factor w/ 2 levels \"no\",\"yes\": 2 2 2 2 2 2 2 2 2 2 ...\n# $ completed : Factor w/ 2 levels \"no\",\"yes\": 2 2 2 2 2 2 2 1 2 1 ...\n# $ timedOut : Factor w/ 2 levels \"no\",\"yes\": 1 1 1 1 1 1 1 2 1 2 ...\n# $ grossTime : num 141.6 201.2 118.1 72 59.9 ...\n# $ interactionTime : num 91.4 178.8 94.6 52.2 29.2 ...\n# $ textSelectInteractionsStartedCount : int 3 0 0 0 0 7 4 19 4 10 ...\n# $ textSelectInteractingTime : num 71.6 0 0 0 0 ...\n# $ textSelectRefactoringsCount : int 2 0 0 0 0 6 3 5 3 6 ...\n# $ singleArgTextSelectRefactoringsCount: int 1 0 0 0 0 5 1 0 3 6 ...\n# $ multiArgTextSelectRefactoringsCount : int 1 0 0 0 0 1 2 5 0 0 ...\n# $ boxSelectInteractionsStartedCount : int 0 9 4 3 2 0 0 0 12 54 ...\n# $ boxSelectInteractingTime : num 0 96.2 68.2 38.6 22.8 ...\n# $ boxSelectRefactoringsCount : int 0 9 4 2 2 0 0 0 10 33 ...\n# $ singleArgBoxSelectRefactoringsCount : int 0 2 2 0 1 0 0 0 3 6 ...\n# $ multiArgBoxSelectRefactoringsCount : int 0 7 2 2 1 0 0 0 7 27 ...\n# $ interactionsStartedCount : int 3 9 4 3 2 7 4 19 16 64 ...\n# $ interactingTime : num 71.6 96.2 68.2 38.6 22.8 ...\n# $ refactoringsCount : int 2 9 4 2 2 6 3 5 13 39 ...\n# $ undoCount : int 0 4 0 0 0 0 0 5 2 39 ...\n# $ redoCount : int 0 0 0 0 0 0 0 0 0 3 ...\n# $ invocations : Factor w/ 155 levels \"\",\"Abstract (rect \\\"yellowgree... over its constants; Rename 'rect2' to 'oneCorner'; Undo; Undo; Merge 4 rects by \"| __truncated__,..: 72 147 111 15 72 109 21 135 81 61 ...\n\ntasks[ , \"completionProbability\"] <- sapply(tasks$completed, function (yn) { as.double(yn == \"yes\") })\ntasks[ , \"firstEncounter\"] <- sapply(tasks$secondEncounter, function (yn) { if(yn==\"yes\") \"no\" else \"yes\" })\ntasks[ , \"logInteractionTime\"] <- sapply(tasks$interactionTime, log)\n\nheadToHeadTasks <- subset(tasks, treatment != \"CodeToolsOnly\")\n\ncompletedHeadToHeadTasks <- subset(headToHeadTasks, completed == \"yes\")\n\ninteractionTimeModel <- lmer(interactionTime ~ treatment + (1 | participantNumber) + (1 + secondEncounter | task) + secondEncounter + treatment:secondEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(interactionTimeModel)\n\nlogInteractionTimeModel <- lmer(logInteractionTime ~ treatment + (1 | participantNumber) + (1 + secondEncounter | task) + secondEncounter + treatment:secondEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(logInteractionTimeModel)\n\n# Swap firstEncounter for secondEncounter to get an easy to interpret p value for treatment on second encounter.\ninteractionTimeModel <- lmer(interactionTime ~ treatment + (1 | participantNumber) + (1 + firstEncounter | task) + firstEncounter + treatment:firstEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(interactionTimeModel)\n\n# Swap firstEncounter for secondEncounter to get an easy to interpret p value for treatment on second encounter.\nlogInteractionTimeModel <- lmer(logInteractionTime ~ treatment + (1 | participantNumber) + (1 + firstEncounter | task) + firstEncounter + treatment:firstEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(logInteractionTimeModel)\n\ncompletionProbabilityModel <- glmer(completionProbability ~ treatment + (1 | participantNumber) + (1 + secondEncounter | task) + secondEncounter + treatment:secondEncounter + taskNumber + usedMouse + usedOwnComputer, headToHeadTasks, family=binomial(link=\"logit\"), control=glmerControl(optimizer=\"bobyqa\"))\nsummary(completionProbabilityModel)\n\n# Swap firstEncounter for secondEncounter to get an easy to interpret p value for treatment on second encounter.\ncompletionProbabilityModel <- glmer(completionProbability ~ treatment + (1 | participantNumber) + (1 + firstEncounter | task) + firstEncounter + treatment:firstEncounter + taskNumber + usedMouse + usedOwnComputer, headToHeadTasks, family=binomial(link=\"logit\"), control=glmerControl(optimizer=\"bobyqa\"))\nsummary(completionProbabilityModel)\n\ninvocationsModel <- lmer(refactoringsCount ~ treatment + (1 | participantNumber) + (1 + secondEncounter | task) + secondEncounter + treatment:secondEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(invocationsModel)\n\n# Swap firstEncounter for secondEncounter to get an easy to interpret p value for treatment on second encounter.\ninvocationsModel <- lmer(refactoringsCount ~ treatment + (1 | participantNumber) + (1 + firstEncounter | task) + firstEncounter + treatment:firstEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(invocationsModel)\n\ninteractingTimeModel <- lmer(interactingTime ~ treatment + (1 | participantNumber) + (1 + secondEncounter | task) + secondEncounter + treatment:secondEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(interactingTimeModel)\n\n# Swap firstEncounter for secondEncounter to get an easy to interpret p value for treatment on second encounter.\ninteractingTimeModel <- lmer(interactingTime ~ treatment + (1 | participantNumber) + (1 + firstEncounter | task) + firstEncounter + treatment:firstEncounter + taskNumber + usedMouse + usedOwnComputer, completedHeadToHeadTasks)\nsummary(interactingTimeModel)\n\n# summaryToBootstrap <- function(.) { fixef(.) }\n#\n# set.seed(101)\n# ## 3.8s (on a 5600 MIPS 64bit fast(year 2009) desktop \"AMD Phenom(tm) II X4 925\"):\n# boot01 <- bootMer(interactionTimeModel, summaryToBootstrap, nsim = 100)\n#\n# ## to \"look\" at it\n# require(\"boot\") ## a recommended package, i.e. *must* be there\n# boot01", "meta": {"hexsha": "76d6b9056ffb68d923055f306b485df02ec0970e", "size": 6547, "ext": "r", "lang": "R", "max_stars_repo_path": "analyze.r", "max_stars_repo_name": "digitalsatori/sketch-n-sketch", "max_stars_repo_head_hexsha": "d69577e27049059fb6b701d4f71548bc25adb07b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 565, "max_stars_repo_stars_event_min_datetime": "2015-07-18T14:02:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T10:31:18.000Z", "max_issues_repo_path": "analyze.r", "max_issues_repo_name": "digitalsatori/sketch-n-sketch", "max_issues_repo_head_hexsha": "d69577e27049059fb6b701d4f71548bc25adb07b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 49, "max_issues_repo_issues_event_min_datetime": "2015-07-17T18:11:22.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-15T03:05:42.000Z", "max_forks_repo_path": "analyze.r", "max_forks_repo_name": "digitalsatori/sketch-n-sketch", "max_forks_repo_head_hexsha": "d69577e27049059fb6b701d4f71548bc25adb07b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 44, "max_forks_repo_forks_event_min_datetime": "2015-08-07T16:25:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-27T01:17:20.000Z", "avg_line_length": 75.2528735632, "max_line_length": 306, "alphanum_fraction": 0.6761875668, "num_tokens": 1976, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3383927403512351}} {"text": "clump<-function(dat, window = 5000, thres1 = 1e-7, thres2 = 5e-5, listProbes = TRUE){\n#' takes all significant association less than thres1 in order of significance as an index site and identifies all other associations within specified window less than thres2. If a less significant probe is clumped with a more significant probe it is excluded for consideration as an index site. \n#'\n#' @param dat A matrix with the columns ids, pval, chr and pos,\n#' @param window Distance in bp of region around site to look for other sinificant associations.\n#' @param thres1 P value threshold to select significant sites to clump.\n#' @param thres2 P value threshold to select additional sites to clump with site under consideration\n#' @return a ma \n\n\ttmp<-dat[order(dat$pval),]\n\tout<-NULL\n\twhile(tmp[1,2] < thres1){\n\t\tchrom<-tmp[1,3]\n\t\tstart<-tmp[1,4]-window\n\t\tend<-tmp[1,4]+window\n\t\tindex<-which(tmp$chr == chrom & tmp$pos <= end & tmp$pos >= start & tmp$pos != tmp[1,4])\t## remove self from consideration\n\n\t\tn.sig<-length(which(tmp[index,2] < thres2))\n\t\tif(n.sig > 0){\n\t\t\tprobes<-tmp[index,1][which(tmp[index,2] < thres2)]\n\t\t\tif(n.sig > 1){\n\t\t\t\tprobes<-paste(probes, collapse = \";\")\n\t\t\t\t}\n\t\t} else {\n\t\t\tprobes<-NA\n\t\t}\n\t\tout<-rbind(out, c(unlist(tmp[1,]), length(index), n.sig, probes))\n\t\t## remove these probes from furture consideration\n\t\ttmp<-tmp[-c(1,index),]\n\t}\n\tcolnames(out)<-c(\"Index\", \"Pvalue\", \"Chr\", \"Position\", \"nSitesinWindow\", \"nSigSites\", \"SigDNAmSites\")\n\treturn(out)\n\t\n}\n", "meta": {"hexsha": "e77aeb3b485f9a99bf22d814d56c738a63ef966d", "size": 1477, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/functions.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DNAm/functions.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "DNAm/functions.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.2, "max_line_length": 296, "alphanum_fraction": 0.692620176, "num_tokens": 446, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7401743620390163, "lm_q2_score": 0.45713671682749485, "lm_q1q2_score": 0.3383608777424015}} {"text": "#### continuous_rqa_parameters-pac.r: Part of `politeness_and_coordination.Rmd` ####\n#\n# This script explores the parameters for the continuous cross-recurrence analysis\n# that we'll run over the movement data. Because this is a lengthy process,\n# we create a series of files along the way that can be re-run in pieces if needed.\n# This allows us to keep this file commented by default.\n#\n# Written by: A. Paxton (University of California, Berkeley)\n# Date last modified: 18 July 2018\n#####################################################################################\n\n#### 1. Preliminaries ####\n\n# read in libraries and functions\nsource('./scripts/03-data_analysis/libraries_and_functions-pac.r')\n\n# prep workspace and libraries\ninvisible(lapply(c('tseriesChaos',\n 'nonlinearTseries',\n 'crqa',\n # 'beepr',\n 'quantmod'), \n require, \n character.only = TRUE))\n\n# read in conversation_df dataset\nconversation_df = read.table('./data/pac-filtered_movement_data.csv',\n sep=',',header=TRUE)\n\n# spread data to wide form\nconversation_df = spread(conversation_df, interlocutor, movement) %>%\n rename(movement_0 = '0',\n movement_1 = '1')\n\n#### 2. Determine delay with average mutual information (AMI) ####\n\n# set maximum AMI\nami.lag.max = 200\n\n# get AMI lag for both participants in each conversation\nconversation_df = conversation_df %>% ungroup() %>%\n group_by(participant_id, task, partner_type) %>%\n mutate(ami.loc0 = first_local_minimum(\n mutual(movement_0, lag.max = ami.lag.max, plot = FALSE))\n ) %>%\n mutate(ami.loc1 = first_local_minimum(\n mutual(movement_1, lag.max = ami.lag.max, plot = FALSE))\n ) %>%\n group_by(participant_id, task, partner_type, ami.loc0, ami.loc1) %>%\n distinct() %>%\n mutate(ami.selected = min(ami.loc1, ami.loc0, na.rm = TRUE)) %>%\n ungroup()\n\n# write AMI information to file\namis = conversation_df %>%\n select(participant_id, task, partner_type, contains('ami')) %>%\n distinct()\nwrite.table(amis,'./data/crqa_parameters/ami_calculations-pac.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n##### 2b. Merging (if we already have amis) #####\n\n# if we've already run it, load it in\namis = read.table('./data/crqa_parameters/ami_calculations-pac.csv', sep=',',header=TRUE)\nconversation_df = full_join(conversation_df, amis,\n by=c('participant_id',\n 'partner_type',\n 'task'))\n\n#### 3. Determing embedding dimension with FNN ####\n\n# set maximum percentage of false nearest neighbors\nfnnpercent = 10\n\n# create empty dataframe\nfnn_dataframe = data.frame()\n\n# cycle through each conversation\nconvo.dfs = split(conversation_df,\n list(conversation_df$participant_id, \n conversation_df$task,\n conversation_df$partner_type))\nfor (next_conv in convo.dfs){\n if (dim(data.frame(next_conv))[1] > 0){\n \n # clean up conversation data\n next_conv_df = data.frame(next_conv) \n\n # print update\n print(paste0(\"Beginning FNN calculations for Participant \", unique(next_conv_df$participant_id),\n \", Task `\", unique(next_conv_df$task),\"`, Partner `\",unique(next_conv_df$partner_type),\"`\"))\n \n # get the AMI value for that conversation\n next_ami = unique(next_conv_df$ami.selected)\n \n # calculate false nearest neighbors for participant 0\n fnn_0 = false.nearest(next_conv_df$movement_0,\n m = fnnpercent,\n d = next_ami,\n t = 0,\n rt = 10,\n eps = sd(next_conv_df$movement_0) / 10)\n fnn_0 = fnn_0[1,][complete.cases(fnn_0[1,])]\n threshold_0 = as.numeric(fnn_0[1]/fnnpercent)\n \n # calculate false nearest neighbors for participant 1\n fnn_1 = false.nearest(next_conv_df$movement_1,\n m = fnnpercent,\n d = next_ami,\n t = 0,\n rt = 10,\n eps = sd(next_conv_df$movement_1) / 10)\n fnn_1 = fnn_1[1,][complete.cases(fnn_1[1,])]\n threshold_1 = as.numeric(fnn_1[1]/fnnpercent)\n \n # identify the largest dimension after a large drop for each participant\n embed_0 = max(as.numeric(which(diff(fnn_0) < -threshold_0))) + 1\n embed_1 = max(as.numeric(which(diff(fnn_1) < -threshold_1))) + 1\n \n # bind everything to data frame\n next_conv_df = next_conv_df %>% ungroup() %>%\n mutate(embed.0 = embed_0) %>%\n mutate(embed.1 = embed_1) %>%\n mutate(embed.selected = max(embed_0, embed_1))\n fnn_dataframe = rbind.data.frame(fnn_dataframe, next_conv_df)\n}}\n\n# rename it to save over the old conversation_df\nconversation_df = fnn_dataframe\n\n# save false nearest neighbor calculations to file\nfnn.merged = conversation_df %>% ungroup() %>%\n select(participant_id, task, partner_type, contains('embed')) %>%\n distinct()\nwrite.table(fnn.merged,'./data/crqa_parameters/fnn_calculations-pac.csv', sep=',',row.names=FALSE,col.names=TRUE)\n\n##### 3b. Merging (if we already have fnns) #####\n\n# if we've already run it, load it in\nfnn.merged = read.table('./data/crqa_parameters/fnn_calculations-pac.csv', sep=',',header=TRUE)\nconversation_df = full_join(conversation_df, fnn.merged,\n by=c('participant_id',\n 'partner_type',\n 'task'))\n\n#### 4. Determine optimal radius ####\n\n# rescale by mean distance\nconversation_df_crqa = conversation_df %>% ungroup() %>%\n dplyr::select(participant_id, task, partner_type,\n movement_0, movement_1, ami.selected, embed.selected) %>%\n group_by(participant_id, task, partner_type) %>%\n mutate(rescale.movement_0 = movement_0/mean(movement_0)) %>%\n mutate(rescale.movement_1 = movement_1/mean(movement_1))\n\n# cycle through all conversations of all dyads\ncrqa.data = split(conversation_df_crqa,\n list(conversation_df$participant_id,\n conversation_df$task,\n conversation_df$partner_type))\nfor (next.conv in crqa.data){\n \n # make sure we only proceed if we have data for the conversation\n if (dim(next.conv)[1] > 0){ \n \n # reset `target` variables for new radius (above what RR can be)\n from.target = 101\n last.from.target = 102\n \n # identify parameters for the conversation\n chosen.delay = unique(next.conv$ami.selected)\n chosen.embed = unique(next.conv$embed.selected)\n \n # start the radius\n chosen.radius = .00\n \n # arbitrary starting point for `rr`\n rr = 100\n \n # if we're still improving, keep going \n while ((from.target < last.from.target) | rr == 0){\n\n # keep the previous iteration's performance\n last.from.target = from.target\n \n # don't let it continue forever\n if (chosen.radius > 2) {\n break\n }\n \n # increment radius size\n chosen.radius = chosen.radius + .01\n \n # print update\n print(paste(\"Participant \", unique(next.conv$participant_id),\n \", partner \",unique(next.conv$partner_type),\n \", task \", unique(next.conv$task),\n \": radius \",chosen.radius,sep=\"\"))\n \n # run CRQA and grab recurrence rate (RR)\n rec_analysis = crqa(next.conv$rescale.movement_0, \n next.conv$rescale.movement_1,\n delay = chosen.delay, \n embed = chosen.embed, \n r = chosen.radius,\n normalize = 0, \n rescale = 0, \n mindiagline = 2,\n minvertline = 2, \n tw = 0, \n whiteline = FALSE,\n recpt=FALSE)\n rr = rec_analysis$RR\n \n # clear it so we don't take up too much memory (optional)\n rm(rec_analysis)\n \n # identify how far off the RR is from our target (5%)\n from.target = abs(rr - 5)\n \n # save individual radius calculations\n participant_id = unique(next.conv$participant_id)\n task = unique(next.conv$task)\n partner_type = unique(next.conv$partner_type)\n write.table(cbind.data.frame(participant_id,\n task,\n partner_type,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target),\n paste('./data/crqa_parameters/radius_calculations/radius_calculations-mean_scaled-r',\n chosen.radius, '-', participant_id ,'_', task, '_', partner_type, '-pac.csv', sep=''), \n sep=',',row.names=FALSE,col.names=TRUE)\n }}}\n\n# # let us know when it's finished\n# beepr::beep(\"fanfare\")\n\n# concatenate the radius files\nradius_files = list.files('./data/crqa_parameters/radius_calculations',\n pattern='radius_calculations',\n full.names = TRUE)\nradius_selection = data.frame()\nfor (next_file in radius_files){\n radius_selection = rbind.data.frame(radius_selection,\n read.table(next_file, header = TRUE, sep = ','))\n}\nwrite.table(radius_selection,'./data/crqa_parameters/radius_calculations-mean_scaled-pac.csv', \n sep=',', row.names=FALSE, col.names=TRUE)\n\n#### 4b. Reload datasets (if we've already run things) ####\n\n# rescale by mean distance\nconversation_df_crqa = conversation_df %>% ungroup() %>%\n dplyr::select(participant_id, task, partner_type,\n movement_0, movement_1, ami.selected, embed.selected) %>%\n group_by(participant_id, task, partner_type) %>%\n mutate(rescale.movement_0 = movement_0/mean(movement_0)) %>%\n mutate(rescale.movement_1 = movement_1/mean(movement_1))\n\n#### 5. Expand radius ####\n\n# load back in the data\nradius_selection = read.table('./data/crqa_parameters/radius_calculations-mean_scaled-pac.csv', \n sep=',',header=TRUE)\n\n# get radius yielding closest to 5% (only the largest radius, if more than one)\nradius_stats = radius_selection %>% ungroup() %>%\n group_by(participant_id, partner_type, task) %>%\n dplyr::filter(from.target==min(from.target)) %>%\n dplyr::arrange(participant_id, partner_type, task) %>%\n dplyr::arrange(desc(from.target)) %>%\n slice(1) %>%\n distinct()\n \n# link conversation numbers to types\nchecking_numbers = conversation_df_crqa %>%\n ungroup() %>%\n dplyr::select(participant_id, task, partner_type) %>%\n distinct()\nrecheck_radii = radius_stats %>%\n dplyr::left_join(x=.,\n y=checking_numbers,\n by=c(\"participant_id\"=\"participant_id\",\n \"task\"=\"task\",\n \"partner_type\"=\"partner_type\")) %>%\n mutate(recheck.conv = paste(participant_id,\n task,\n partner_type,\n sep='.')) %>%\n mutate(from.target = rr - 5)\n\n# cycle through all conversations of all dyads to refine\nrecheck_conversation_df = dplyr::left_join(recheck_radii,\n conversation_df_crqa)\n\n# split the data\ncrqa.data = split(recheck_conversation_df,\n list(recheck_conversation_df$participant_id,\n recheck_conversation_df$task,\n recheck_conversation_df$partner_type))\n\n# cycle through the conversations\nrecheck_radii = crqa.data[recheck_radii$recheck.conv]\nfor (next.conv in recheck_radii){\n\n # make sure we only proceed if we have data for the conversation\n if (dim(next.conv)[1] != 0){\n\n # figure out whether we need to have a larger or smaller radius\n original.from.target = unique(next.conv$from.target)\n chosen.radius = unique(next.conv$chosen.radius)\n rr = unique(next.conv$rr)\n\n # reset `target` variables for new radius (above what RR can be)\n from.target = abs(original.from.target)\n last.from.target = 102\n\n # identify parameters\n chosen.delay = unique(next.conv$ami.selected)\n chosen.embed = unique(next.conv$embed.selected)\n\n # if we haven't hit our target threshold or we're still improving, keep going\n while ((from.target > 1) | (from.target < last.from.target)){\n\n # set some bounds for our radius explorations\n if (chosen.radius < 0 | chosen.radius > 2) { break } # if we've already a bunch\n if (from.target > (last.from.target) + .5) { break } # if we're not improving\n\n # keep the previous iteration's performance\n last.from.target = from.target\n\n # update radius based on whether we overshot or undershot target RR\n if (original.from.target > 0){\n chosen.radius = chosen.radius - .001\n } else {\n chosen.radius = chosen.radius + .001\n }\n\n # print update\n print(paste(\"Participant \", unique(next.conv$participant_id),\n \", partner \",unique(next.conv$partner_type),\n \", task \", unique(next.conv$task),\n \": radius \",chosen.radius,sep=\"\"))\n\n # run CRQA and grab recurrence rate (RR)\n rec_analysis = crqa(next.conv$rescale.movement_0,\n next.conv$rescale.movement_1,\n delay = chosen.delay,\n embed = chosen.embed,\n r = chosen.radius,\n normalize = 0,\n rescale = 0,\n mindiagline = 2,\n minvertline = 2,\n tw = 0,\n whiteline = FALSE,\n recpt=FALSE)\n rr = rec_analysis$RR\n\n # clear it so we don't take up too much memory (optional)\n rm(rec_analysis)\n\n # identify how far off the RR is from our target (5%)\n from.target = abs(rr - 5)\n\n # save individual radius calculations\n participant_id = unique(next.conv$participant_id)\n task = unique(next.conv$task)\n partner_type = unique(next.conv$partner_type)\n write.table(cbind.data.frame(participant_id,\n task,\n partner_type,\n chosen.delay,\n chosen.embed,\n chosen.radius,\n rr,\n from.target),\n paste('./data/crqa_parameters/radius_calculations/radius_calculations-mean_scaled-r',\n chosen.radius, '-', participant_id ,'_', task, '_', partner_type, '-pac.csv', sep=''),\n sep=',',row.names=FALSE,col.names=TRUE)\n }}}\n\n# # let us know when it's finished\n# beepr::beep(\"fanfare\")\n\n# concatenate the radius files\nradius_files = list.files('./data/crqa_parameters/radius_calculations',\n pattern='radius_calculations',\n full.names = TRUE)\nradius_selection = data.frame()\nfor (next_file in radius_files){\n radius_selection = rbind.data.frame(radius_selection,\n read.table(next_file, header = TRUE, sep = ','))\n}\n\n# save the new version\nwrite.table(radius_selection,'./data/crqa_parameters/radius_recheck_calculations-mean_scaled-pac.csv',\n sep=',', row.names=FALSE, col.names=TRUE)\n\n#### 6. Export chosen radii for all conversations ####\n\n# load in files\nradius_selection = read.table('./data/crqa_parameters/radius_recheck_calculations-mean_scaled-pac.csv',\n sep=',',header=TRUE)\n\n# identify the target radii\nradius_stats = radius_selection %>% ungroup() %>%\n group_by(participant_id, partner_type, task) %>%\n dplyr::filter(from.target==min(from.target)) %>%\n dplyr::arrange(participant_id, partner_type, task) %>%\n dplyr::arrange(desc(from.target)) %>%\n slice(1) %>%\n distinct()\n\n# join the dataframes\nconversation_df_crqa = full_join(radius_stats,\n conversation_df_crqa,\n by = c(\"participant_id\",\n \"task\",\n \"partner_type\",\n \"chosen.embed\" = \"embed.selected\",\n \"chosen.delay\" = \"ami.selected\"))\n\n# save to files\nwrite.table(conversation_df_crqa,'./data/crqa_data_and_parameters-pac.csv', \n sep=',',row.names=FALSE,col.names=TRUE)\nwrite.table(radius_stats, './data/crqa_parameters-pac.csv',\n sep=',',row.names=FALSE,col.names=TRUE)", "meta": {"hexsha": "31457c31a947176e6814f6a6ea2f367a3e82fcd2", "size": 16894, "ext": "r", "lang": "R", "max_stars_repo_path": "cogsci2018/scripts/03-data_analysis/continuous_rqa_parameters-pac.r", "max_stars_repo_name": "a-paxton/politeness-and-coordination", "max_stars_repo_head_hexsha": "e8c5c6cbcc7bdc23e851eab610ac3e55b85ded40", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-10-23T06:33:47.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-23T06:33:47.000Z", "max_issues_repo_path": "cogsci2018/scripts/03-data_analysis/continuous_rqa_parameters-pac.r", "max_issues_repo_name": "a-paxton/politeness-and-coordination", "max_issues_repo_head_hexsha": "e8c5c6cbcc7bdc23e851eab610ac3e55b85ded40", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cogsci2018/scripts/03-data_analysis/continuous_rqa_parameters-pac.r", "max_forks_repo_name": "a-paxton/politeness-and-coordination", "max_forks_repo_head_hexsha": "e8c5c6cbcc7bdc23e851eab610ac3e55b85ded40", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.288372093, "max_line_length": 113, "alphanum_fraction": 0.5845862436, "num_tokens": 3804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.33831087851660446}} {"text": "library(lme4)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(reshape2)\nlibrary(ggplot2)\n\ndir_separator = '/'\n\n# dedupe function\ndedupe = function(data, verbose = TRUE){\n # Deduplicates columns in data\n # Parameters:\n # data: data you want deduplicated\n # Returns: deduplicated dataframe \n \n deduped <- data[!duplicated(as.list(data))]\n if (verbose) {\n print(\"Number of duplicate columns:\")\n print((ncol(data) - ncol(deduped)))\n }\n return(deduped)\n}\n\nget_qq = function(x) {\n # Returns x values of each data point from a qq plt\n \n return(qqnorm(x)$x)\n}\ncombine_category = function(f, local_title, save_path) {\n # Combines multiple samples into a single data point\n # For instances in which there are zero values,\n # runs a mixed effect model that imputes the actual value\n # based on the bias of the sample and of other values for that \n # period that are not zero.\n # Parameters:\n # f: filename of csv with samples\n # local_title: region name\n # save_path: directory to save results in\n \n # print basic setup information\n print(local_title)\n print(paste(\"Loading: \", f))\n \n # Loading\n data = read.table(f, header=TRUE, sep = \",\", quote = \"'\")\n \n # reorder columns\n # API code places the period in a somewhat random spot\n # It also may save the index in the first column, not any actual data\n # So first check if the first column is an integer; if so, drop it\n if (class(data[,1]) == \"integer\") {\n data = data[, -1]\n }\n # And then reorder\n df = data %>% select(period, everything())\n # and dedupe just in case\n df = dedupe(df)\n \n # get the region and topic\n filesplit = strsplit(f, dir_separator)\n fn = filesplit[[1]][length(filesplit[[1]])]\n local_fn = substr(fn, 1, nchar(fn)-4)\n print(paste(\"Working on:\", local_fn))\n \n # report the number of samples; excluding the period column\n print(paste(\"# samples:\", ncol(df)-1 )) \n \n # Some search terms will simply not return any values\n # So after deduplication there will just be the date column and a single value column\n # No need to do any additional processing on that\n if (ncol(df) == 2) {\n print(\"Found all zeroes. Just saving this.\")\n colnames(df) = c('period_date', 'value')\n write.csv(df, paste0(save_path,local_fn, '_combined.csv'))\n }\n else\n {\n # convert data to long format\n print(\"Converting to long format\")\n print(head(df))\n long = df %>% gather(sample, value, -period)\n long = long %>% group_by(period) %>%\n mutate(qq = get_qq(value),\n value2 = na_if(value, 0),\n log_value = log(value2)) \n long$sample = factor(long$sample)\n long$timeperiod = long$period\n \n # Fit mixed effects model allowing intercept and slope to vary by sample and period\n print(\"Running Mixed effects model\")\n lm1 = lmer(log_value~qq + (1 + qq|period) + (1 + qq|sample), REML = F, data=long)\n \n # Get coefficients in order to calculate predicted values conditional on week and sample\n print(\"Determining period and sample effects\")\n period_effects = as.data.frame(ranef(lm1)$period) %>% select(int_period=1, qq_period=2)\n period_effects$period = row.names(period_effects)\n sample_effects = as.data.frame(ranef(lm1)$sample) %>% select(int_sample=1, qq_sample=2)\n sample_effects$sample = row.names(sample_effects)\n \n print(\"Running Fixed effect model\")\n fixed_effects = fixef(lm1)\n \n long$int_fixed = fixed_effects[1] \n long$qq_fixed = fixed_effects[2]\n \n print(\"Imputing..\")\n long_betas = long %>% left_join(period_effects) %>% left_join(sample_effects) %>%\n mutate(int_full = int_fixed + int_sample + int_period,\n qq_full = qq_fixed + qq_sample + qq_period,\n pred = int_full + qq_full*qq, # Predicted value on the log scale\n pred_exp = exp(pred), # Exponentiated predicted value\n imputed_value = ifelse(is.na(value2), pred_exp, value)) # Use true value if available, else pred_exp\n # save this output\n write.csv(long_betas, paste0(save_path, local_fn, '_imputed_samples.csv'))\n \n # and convert and take the mean\n print(\"Taking the mean..\")\n condensed = long_betas %>% group_by(period) %>%\n summarize(imp_value=mean(imputed_value),\n avg_value=mean(value)) # take the mean of these\n condensed$period_date = as.Date(condensed$period, format=\"%Y-%m-%d\")\n condensed = arrange(condensed, period_date)\n \n final = condensed %>% select(period_date, imp_value, avg_value) %>%\n melt(id=\"period_date\")\n print(save_path)\n save_title = paste(save_path, \"-\", local_title)\n plt = ggplot(final, aes(x = period_date, y=value, color=variable)) + geom_line() + \n ggtitle(local_title) \n plt\n ggsave(paste0(save_path, local_fn, '.png'),device='png')\n write.csv(final, paste0(save_path, local_fn, '_combined.csv'))\n } # else\n \n}\n\nprocess_region = function(data_directory, region, results_directory=\"\") {\n # Using a list of samples of Google Health calls contained in csv files in data_dictionary, \n # combine multiple samples into single data point and save results\n # Most of the combining work is actually done in combine_category\n # Parameters:\n # data_directory: parent directory above where data is stored\n # region: shorthand for region. DMA code for DMAs, state (e.g. US-CA) or country (e.g. US) code\n # results_directory: where to save the combined results\n \n current_wd <- getwd()\n \n # Scan directory for files\n setwd(data_directory)\n if (results_directory == \"\") { results_directory = paste0(region, '/combined/') }\n filenames = list.files(region, pattern='*.csv', full.names=TRUE)\n \n # run each file\n for (f in filenames) {\n if (startsWith(f, paste0(region, '/'))) {\n fn = substring(f, nchar(region)+2, nchar(f))\n }\n else { fn = f }\n local_title = substr(fn, 1, nchar(f)-4) # take the .csv off\n if (!dir.exists(results_directory)) { dir.create(results_directory) }\n combine_category(f, local_title, results_directory)\n print(f)\n } # for loop\n setwd(current_wd)\n}\n\n# run code\n# Assumptions: \n# - Starting directory (below) is the parent directory in which data is stored\n# - for each region, data is stored in a folder named after the region - either the DMA or the state/country code\n# - under that, each file is a series of samples run by the Search Sampler python package\nstarting_dir = '~/repos/flint_water/Search/DATA/'\n\nprocess_region(starting_dir, '513')\nprocess_region(starting_dir, 'US-MI')\nprocess_region(starting_dir, 'US')\n", "meta": {"hexsha": "011b1b5bd35f9958cea334f0121895ad7e4b2e83", "size": 6518, "ext": "r", "lang": "R", "max_stars_repo_path": "impute_samples.r", "max_stars_repo_name": "gserapio/searching_for_news", "max_stars_repo_head_hexsha": "741aeba0d6305aabf48b95895fb955e94980b0c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-09T19:50:15.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-09T19:50:15.000Z", "max_issues_repo_path": "impute_samples.r", "max_issues_repo_name": "gserapio/searching_for_news", "max_issues_repo_head_hexsha": "741aeba0d6305aabf48b95895fb955e94980b0c1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "impute_samples.r", "max_forks_repo_name": "gserapio/searching_for_news", "max_forks_repo_head_hexsha": "741aeba0d6305aabf48b95895fb955e94980b0c1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8248587571, "max_line_length": 113, "alphanum_fraction": 0.6793494937, "num_tokens": 1703, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.33831087851660446}} {"text": "library(ggplot2)\nqplot(factor(names(prob), levels = names(prob)), hebrew, geom = \"histogram\")\n", "meta": {"hexsha": "d3a11a2a7c0fdc3850156e1498747f007fcbfd18", "size": 94, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Probabilistic-choice/R/probabilistic-choice-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Probabilistic-choice/R/probabilistic-choice-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Probabilistic-choice/R/probabilistic-choice-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 31.3333333333, "max_line_length": 76, "alphanum_fraction": 0.7234042553, "num_tokens": 26, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.546738151984614, "lm_q1q2_score": 0.3383108708293504}} {"text": "library(sf)\nlibrary(mapview)\nlibrary(gdtools)\noptions(stringsAsFactors = FALSE)\nap = \"Lelystad Airport\"\ncd = \"LEY\"\nclass(cd)\n\nlat = 52.460278\nlon = 5.527222\nclass(lon)\n\nairport = data.frame(ap, cd, lat, lon, stringsAsFactors = FALSE)\n\n\nNL_Airports = read.csv(file = 'http://www.twiav.nl/files/NL_Airports.csv')\n\nnames(airport) = names(NL_Airports)\nNL_Airports = rbind(NL_Airports, airport)\nstr(NL_Airports)\n\n\nNL_Airports = st_as_sf(NL_Airports, coords = c('longitude', 'latitude'), crs = 4326)\nclass(NL_Airports)\nNL_Airports\n\nplot(st_geometry(NL_Airports), main = \"Airports in the Netherlands\", pch = 17)\n\nst_write(NL_Airports, \"NL_Airports.geojson\", append = FALSE)\n\nmapview(NL_Airports, color = \"red\", col.regions = \"orange\", alpha.regions = 1, label =NL_Airports$airport)\n\nGR_Airports = read.csv(file = \"C:\\\\Users\\\\kougi\\\\Downloads\\\\gr-airports.csv\")\nGR_Airports = GR_Airports[-1,]\nGR_Airports_sm = GR_Airports[, c(\"name\",\"iata_code\", \"longitude_deg\",\"latitude_deg\")]\n\nnames(GR_Airports_sm)[names(GR_Airports_sm) == 'longitude_deg'] <- 'longitude'\nnames(GR_Airports_sm)[names(GR_Airports_sm) == 'latitude_deg'] <- 'latitude'\nstr(GR_Airports_sm)\n\nGR_Airports_sm = st_as_sf(GR_Airports_sm, coords = c('longitude', 'latitude'), crs = 4326)\nGR_Airports_sm\n\n\nmapview(GR_Airports_sm, color = \"red\", col.regions = \"orange\", alpha.regions = 1, label =GR_Airports_sm$name)\n", "meta": {"hexsha": "935c1d037460882d849a2eb9d1a99b5cd608ec75", "size": 1367, "ext": "r", "lang": "R", "max_stars_repo_path": "lectures/lecture-4-3.r", "max_stars_repo_name": "kougianos/R-code", "max_stars_repo_head_hexsha": "9e582d7f44e151242bb02f8dbf386eac0e703c9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lectures/lecture-4-3.r", "max_issues_repo_name": "kougianos/R-code", "max_issues_repo_head_hexsha": "9e582d7f44e151242bb02f8dbf386eac0e703c9a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lectures/lecture-4-3.r", "max_forks_repo_name": "kougianos/R-code", "max_forks_repo_head_hexsha": "9e582d7f44e151242bb02f8dbf386eac0e703c9a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7173913043, "max_line_length": 109, "alphanum_fraction": 0.741038771, "num_tokens": 451, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.6187804267137442, "lm_q1q2_score": 0.3383108669857234}} {"text": "#===============================================================================\n# File: 06-additional-results.R\n# Date: Feb 3, 2021\n# Purpose: replicate appendix analyses: Sections 5 and 6\n# Data In: \n# ./data/survey_data.csv\n# ./data/daily_pulse_data.csv\n#===============================================================================\n\n# PACKAGES\n#===============================================================================\nlibrary(tidyverse)\nlibrary(estimatr)\nlibrary(glmnet)\nlibrary(powerLATE)\nlibrary(haven)\nsource('code/functions.r')\n\n# DATA\n#===============================================================================\npulse <- read_csv(\"data/daily_pulse_data.csv\")\nsvy <- read_csv(\"data/survey_data.csv\")\n\n# Dropping observations where treatment is missing\nsvy <- svy[!is.na(svy$W3_PATA306_treatment_w3),]\n\n# Analysis ----------------------------------------------------------------\n\n# https://declaredesign.org/blog/biased-fixed-effects.html\n\nvars <- c(\"party7\", \"age\", \"agesq\", \"female\", \"raceeth\", \"educ\",\n \"ideo\", \"income\", \"employ\", \"state\", \"polint\", \"freq_tv\", \"freq_np\", \n \"freq_rad\", \"freq_net\", \"freq_disc\", \"log_news_pre\", \"diet_mean_pre\")\n\n########################################################\n### Issue H1: issue opinions, Fox News treatment\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"issue_scale_w5\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1: Conservatism\")\n\n\n\n########################################################\n### Issue H2: issue opinions, HuffPost treatment\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"issue_scale_w5\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2: Conservatism\")\n\n########################################################\n### Issue H3: immigration attitudes, FoxNews treatment\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"imm_issue_scale_w5\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt, more_vars = \"policy14_imm_pre\")\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H3: Pro-immigration\")\n\n########################################################\n### Issue H4: immigration attitudes, HuffPost treatment\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"imm_issue_scale_w5\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt, more_vars = \"policy14_imm_pre\")\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4: Pro-immigration\")\n\n########################################################\n### Issue RQ1: heterogeneous treatment effects\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H2\nheterogeneous_effect(dv = \"issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n# H3\nheterogeneous_effect(dv = \"imm_issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"imm_issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H4\nheterogeneous_effect(dv = \"imm_issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"imm_issue_scale_w5\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n\n########################################################\n### Agenda H1: FoxNews treatment\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"agenda_lean_w5\"\ndv_pre <- \"agenda_lean_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1: Agenda setting\")\n\n########################################################\n### Agenda H2: HuffPost treatment\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"agenda_lean_w5\"\ndv_pre <- \"agenda_lean_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2: Agenda setting\")\n\n########################################################\n### Agenda RQ1: heterogeneous treatment effects by party ID / ideology\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"agenda_lean_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"agenda_lean_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n\n# H2\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"agenda_lean_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"agenda_lean_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n########################################################\n### Agenda RQ2: heterogeneous treatment effects by pre-treatment habits\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"log_fn_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H2\nheterogeneous_effect(dv = \"agenda_lean_w5\", dv_pre = \"log_hp_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n\n########################################################\n### Approval H1a: President\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"trump_approve_w5\"\ndv_pre <- \"trump_approve_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1a: Pres. approval\")\n\n\n########################################################\n### Approval H2a: President\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"trump_approve_w5\"\ndv_pre <- \"trump_approve_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2a: Pres. approval\")\n\n\n########################################################\n### Approval RQ1: heterogeneous treatment effects by party ID / ideology\n########################################################\n\n# H1a\nheterogeneous_effect(dv = \"trump_approve_w5\", dv_pre = \"trump_approve_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"trump_approve_w5\", dv_pre = \"trump_approve_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n\n# H2a\nheterogeneous_effect(dv = \"trump_approve_w5\", dv_pre = \"trump_approve_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"trump_approve_w5\", dv_pre = \"trump_approve_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n########################################################\n### Turnout RQ1a: Fox News\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"vote_after\"\ndv_pre <- \"vote_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"RQ1a: Turnout\")\n\n########################################################\n### Turnout RQ1a: Huff Post\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"vote_after\"\ndv_pre <- \"vote_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"RQ1b: Turnout\")\n\n########################################################\n### Media Trust H1a: Trust in Fox News for FN treatment\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"trust_fox_w5\"\ndv_pre <- \"\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1a: Trust in Fox News\")\n\n########################################################\n### Media Trust H1b: Trust in HuffPost for FN treatment\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"trust_hp_w5\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1b: Trust in HuffPost\")\n\n########################################################\n### Media Trust H2a: Trust in HuffPost for HP treatment\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"trust_hp_w5\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2a: Trust in HuffPost\")\n\n########################################################\n### Media Trust H2b: Trust in Fox News for HP treatment\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"trust_fox_w5\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2b: Trust in Fox News\")\n\n\n########################################################\n### Media Trust RQ1\n########################################################\n\n# H1a\nheterogeneous_effect(dv = \"trust_fox_w5\", \n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"trust_fox_w5\", \n moderator = \"ideo\", trt = \"FoxNews\")\n# H1b\nheterogeneous_effect(dv = \"trust_hp_w5\", \n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"trust_hp_w5\", \n moderator = \"ideo\", trt = \"FoxNews\")\n# H2a\nheterogeneous_effect(dv = \"trust_hp_w5\", \n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"trust_hp_w5\", \n moderator = \"ideo\", trt = \"HuffPost\")\n# H2b\nheterogeneous_effect(dv = \"trust_fox_w5\", \n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"trust_fox_w5\", \n moderator = \"ideo\", trt = \"HuffPost\")\n\n\n########################################################\n### Media Trust H4a: Media trust for FN group (w7)\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"trust_w7\"\ndv_pre <- \"\"\nD <- \"comp_fn\"\n\n# svy$trust_w7 <- zap_labels(svy$trust_w7)\n\n# resetting after het fx analysis\nvars <- c(\"party7\", \"age\", \"agesq\", \"female\", \"raceeth\", \"educ\",\n \"ideo\", \"income\", \"employ\", \"state\", \"polint\", \"freq_tv\", \"freq_np\", \n \"freq_rad\", \"freq_net\", \"freq_disc\", \"log_news_pre\", \"diet_mean_pre\")\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4a: Media trust (W7)\")\n\n########################################################\n### Media Trust H4b: Media trust for HP group (w7)\n########################################################\n\n# svy$trust_w7 <- svy$trust_w7/sd(svy$trust_w7[which(svy$W3_PATA306_treatment_w3 == \"Control\")], na.rm = TRUE)\n\ntrt <- \"HuffPost\"\ndv <- \"trust_w7\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4b: Media trust (W7)\")\n\n\n\n########################################################\n### Factual knowledge RQ1a: news reception for HP group\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"event_mokken_w5\"\ndv_pre <- \"event_pre_mokken\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt, dv_pre = dv_pre)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"RQ1a: Event knowledge\")\n\n########################################################\n### Factual knowledge RQ1b: news reception for FN group\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"event_mokken_w5\"\ndv_pre <- \"event_pre_mokken\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt, dv_pre = dv_pre)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"RQ1b: Event knowledge\")\n\n# RQ1a\nheterogeneous_effect(dv = \"event_mokken_w5\", dv_pre = \"event_pre_mokken\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"event_mokken_w5\", dv_pre = \"event_pre_mokken\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n# RQ1b\nheterogeneous_effect(dv = \"event_mokken_w5\", dv_pre = \"event_pre_mokken\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"event_mokken_w5\", dv_pre = \"event_pre_mokken\",\n moderator = \"ideo\", trt = \"FoxNews\")\n\n\n#### WAVE 7 ####\n\n########################################################\n### Issue H1: issue opinions, Fox News treatment (w7)\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"issue_scale_w7\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1: Conservatism\")\n\n########################################################\n### Issue H2: issue opinions, HuffPost treatment (w7)\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"issue_scale_w7\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2: Conservatism\")\n\n########################################################\n### Issue H3: immigration attitudes, FoxNews treatment (w7)\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"imm_issue_scale_w7\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt, more_vars = \"policy14_imm_pre\")\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H3: Pro-immigration\")\n\n########################################################\n### Issue H4: immigration attitudes, HuffPost treatment (w7)\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"imm_issue_scale_w7\"\ndv_pre <- \"issue_scale_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt, more_vars = \"policy14_imm_pre\")\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4: Pro-immigration\")\n\n########################################################\n### Issue RQ1: heterogeneous treatment effects (w7)\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H2\nheterogeneous_effect(dv = \"issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n# H3\nheterogeneous_effect(dv = \"imm_issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"imm_issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H4\nheterogeneous_effect(dv = \"imm_issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"imm_issue_scale_w7\", dv_pre = \"issue_scale_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n\n\n########################################################\n### Agenda H1: FoxNews treatment (w8)\n########################################################\n\ntrt <- \"FoxNews\"\ndv <- \"agenda_lean_w8\"\ndv_pre <- \"agenda_lean_pre\"\nD <- \"comp_fn\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H1: Agenda setting\")\n\n########################################################\n### Agenda H2: HuffPost treatment (w8)\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"agenda_lean_w8\"\ndv_pre <- \"agenda_lean_pre\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, dv_pre = dv_pre, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H2: Agenda setting\")\n\n########################################################\n### Agenda RQ1: heterogeneous treatment effects by party ID / ideology (w8)\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"agenda_lean_pre\",\n moderator = \"party7\", trt = \"FoxNews\")\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"agenda_lean_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n\n# H2\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"agenda_lean_pre\",\n moderator = \"party7\", trt = \"HuffPost\")\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"agenda_lean_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n########################################################\n### Agenda RQ2: heterogeneous treatment effects by pre-treatment habits (w8)\n########################################################\n\n# H1\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"log_fn_pre\",\n moderator = \"ideo\", trt = \"FoxNews\")\n# H2\nheterogeneous_effect(dv = \"agenda_lean_w8\", dv_pre = \"log_hp_pre\",\n moderator = \"ideo\", trt = \"HuffPost\")\n\n\n\n\n########################################################\n### Media Trust H4a: Media trust for FN group (w8)\n########################################################\n\n# svy$trust_w8 <- svy$trust_w8/sd(svy$trust_w8[which(svy$W3_PATA306_treatment_w3 == \"Control\")], na.rm = TRUE)\n\ntrt <- \"FoxNews\"\ndv <- \"trust_w8\"\nD <- \"comp_fn\"\n\n# svy$trust_w8 <- zap_labels(svy$trust_w8)\n\n# resetting after het fx analysis\nvars <- c(\"party7\", \"age\", \"agesq\", \"female\", \"raceeth\", \"educ\",\n \"ideo\", \"income\", \"employ\", \"state\", \"polint\", \"freq_tv\", \"freq_np\", \n \"freq_rad\", \"freq_net\", \"freq_disc\", \"log_news_pre\", \"diet_mean_pre\")\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4a: Media trust (W8)\")\n\n########################################################\n### Media Trust H4b: Media trust for HP group (w8)\n########################################################\n\ntrt <- \"HuffPost\"\ndv <- \"trust_w8\"\nD <- \"comp_hp\"\n\n## DIM\n(dim <- difference_in_means(formula(paste0(dv, \" ~ W3_PATA306_treatment_w3\")), \n blocks = W3_Browser_treatment_w3, \n data = svy, \n condition1 = \"Control\", \n condition2 = trt))\n## ITT, with Lin's covariate adjustment\nitt <- run_model(dv = dv, trt = trt)\ncompute_proportion_missing_covars(itt)\n## CACE\ncace <- estimate_cace(Y=dv, D = D, Z = \"W3_PATA306_treatment_w3\",\n X = extract_covariates(itt), trt=trt)\n\n# power analysis\npwr <- power2(dv, dim, itt, cace, covariates = TRUE, D = D, trt = trt)\n\nformat_latex2(dim, itt, cace, trt, pwr,\n dv = \"H4b: Media trust (W8)\")", "meta": {"hexsha": "e1e9407f0beffac8c84923807fb6a0a42d2536d8", "size": 34403, "ext": "r", "lang": "R", "max_stars_repo_path": "code/06-additional-results.r", "max_stars_repo_name": "NetDem-USC/homepage-experiment", "max_stars_repo_head_hexsha": "e5d205f28a315262cb3754433df3eb483643f8ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-03-30T03:38:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-29T14:21:25.000Z", "max_issues_repo_path": "code/06-additional-results.r", "max_issues_repo_name": "NetDem-USC/homepage-experiment", "max_issues_repo_head_hexsha": "e5d205f28a315262cb3754433df3eb483643f8ad", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/06-additional-results.r", "max_forks_repo_name": "NetDem-USC/homepage-experiment", "max_forks_repo_head_hexsha": "e5d205f28a315262cb3754433df3eb483643f8ad", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.2900843882, "max_line_length": 110, "alphanum_fraction": 0.5163793855, "num_tokens": 9168, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419958239132, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.33820558544538365}} {"text": "ANCOM <- function (real.data, sig = 0.05, multcorr = 3, tau = 0.02, theta = 0.1) \n{\n #no_cores <- detectCores() - 1\n\n colnames(real.data)[dim(real.data)[2]] <- \"Group\"\n real.data$Group <- factor(real.data$Group)\n real.data <- data.frame(real.data[which(is.na(real.data$Group) == \n F), ], row.names = NULL)\n num_OTU <- ncol(real.data) - 1\n W.detected <- ancom.detect(real.data, num_OTU, sig, multcorr, \n ncore = ncores)\n W_stat <- W.detected\n if (num_OTU < 10) {\n detected <- colnames(real.data)[which(W.detected > num_OTU - \n 1)]\n }\n else {\n if (max(W.detected)/num_OTU >= theta) {\n c.start <- max(W.detected)/num_OTU\n cutoff <- c.start - c(0.05, 0.1, 0.15, 0.2, 0.25)\n prop_cut <- rep(0, length(cutoff))\n for (cut in 1:length(cutoff)) {\n prop_cut[cut] <- length(which(W.detected >= num_OTU * \n cutoff[cut]))/length(W.detected)\n }\n del <- rep(0, length(cutoff) - 1)\n for (ii in 1:(length(cutoff) - 1)) {\n del[ii] <- abs(prop_cut[ii] - prop_cut[ii + 1])\n }\n if (del[1] < tau & del[2] < tau & del[3] < tau) {\n nu = cutoff[1]\n }\n else if (del[1] >= tau & del[2] < tau & del[3] < \n tau) {\n nu = cutoff[2]\n }\n else if (del[2] >= tau & del[3] < tau & del[4] < \n tau) {\n nu = cutoff[3]\n }\n else {\n nu = cutoff[4]\n }\n up_point <- min(W.detected[which(W.detected >= nu * \n num_OTU)])\n W.detected[W.detected >= up_point] <- 99999\n W.detected[W.detected < up_point] <- 0\n W.detected[W.detected == 99999] <- 1\n detected <- colnames(real.data)[which(W.detected == \n 1)]\n }\n else {\n W.detected <- 0\n detected <- \"No significant OTUs detected\"\n }\n }\n results <- list(W = W_stat, detected = detected, dframe = real.data)\n class(results) <- \"ancom\"\n return(results)\n}\n\n", "meta": {"hexsha": "195fcf60f8959ed1a48be0c2fac97ed558908f41", "size": 2174, "ext": "r", "lang": "R", "max_stars_repo_path": "akutils_resources/ANCOM.akutils.r", "max_stars_repo_name": "lvandrews/amptools", "max_stars_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2016-02-01T20:25:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T16:52:49.000Z", "max_issues_repo_path": "akutils_resources/ANCOM.akutils.r", "max_issues_repo_name": "lvandrews/amptools", "max_issues_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_issues_repo_licenses": ["Zlib"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2016-06-29T18:01:54.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-18T18:47:09.000Z", "max_forks_repo_path": "akutils_resources/ANCOM.akutils.r", "max_forks_repo_name": "lvandrews/amptools", "max_forks_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_forks_repo_licenses": ["Zlib"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-05-02T18:37:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-13T22:22:27.000Z", "avg_line_length": 35.064516129, "max_line_length": 81, "alphanum_fraction": 0.4632014719, "num_tokens": 629, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3382055792494692}} {"text": "#Usage:\n#Rscript cell_cluster.r cluster_number\nlibrary(\"monocle\")\nlibrary(\"reshape\")\nlibrary('ggplot2')\nargs <- commandArgs(trailingOnly=TRUE)\nif(length(args)==0)\n{\n\tprint(\"Usage: Rscript cell_cluster.r cluster_number cluster_labels\")\n}\ncluster_number <- args[1]\nHSMM <- readRDS('post_quality_control_data.rds')\nHSMM <- clusterCells(HSMM, num_clusters=as.numeric(cluster_number), tol = 1e-6,max_components = 10, verbose=TRUE,param.gamma=300)\nsaveRDS(HSMM,'post_quality_control_data.rds')\nprint(plot_cell_trajectory(HSMM, color=\"Cluster\"))\nggsave(\"cell_cluster_plot.png\")\nggsave(\"cell_cluster_plot.tiff\", width = 7, height = 7,dpi=600)\npData(HSMM)$Size_Factor <- NULL\nrownames(pData(HSMM)) <- NULL\npData(HSMM)$Total_mRNAs <- Matrix::colSums(exprs(HSMM))\nwrite.table(pData(HSMM), 'cluster_table_after.txt', sep=\"\\t\",quote=FALSE)\ndev.off() ", "meta": {"hexsha": "78e103582ba1d364b2d2d15b70033882f737630a", "size": 837, "ext": "r", "lang": "R", "max_stars_repo_path": "RScripts/cell_cluster.r", "max_stars_repo_name": "nikhil/RAS", "max_stars_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RScripts/cell_cluster.r", "max_issues_repo_name": "nikhil/RAS", "max_issues_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RScripts/cell_cluster.r", "max_forks_repo_name": "nikhil/RAS", "max_forks_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.0454545455, "max_line_length": 129, "alphanum_fraction": 0.7670250896, "num_tokens": 248, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7154239957834733, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.33816910158461405}} {"text": "# From Trevor Thompson tkt2@cdc.gov\n\nrequire(rms)\nif(FALSE) {\nrequire(rms)\n\n## simulate competing risk data with 2 predictors\nset.seed(115)\ndat1<- crisk.sim(n=2000, foltime=200, dist.ev=c(\"weibull\",\"weibull\"), anc.ev=c(0.8,0.9), beta0.ev=c(2,2), dist.cens=\"weibull\", \n anc.cens=1,beta0.cens=1, z=NULL, beta=list(c(-0.69,0), c(-0.35, -0.5)), x=list(c(\"bern\", 0.3), c(\"bern\", 0.6)), nsit=2)\n\ndat1$event<-factor(ifelse(is.na(dat1$cause), 0, dat1$cause))\ndat1$x<-factor(dat1$x)\ndat1$x.1<-factor(dat1$x.1)\n\n## set some predictor data to missing\nmiss1<-rbinom(2000, 1, .15)\nmiss2<-rbinom(2000, 1, .05)\n \ndat1[miss1==1,]$x<-NA\ndat1[miss2==1,]$x.1<-NA\n\ndescribe(dat1)\n\n## impute missing data\nimp.obj <- aregImpute(~ x + x.1 + event + time, n.impute=20, data=dat1, x=TRUE)\nimp.obj\n\n## create Fine-Gray function for dtrans\ndtrans.fg <-\n function(data) finegray(Surv(time, event) ~ ., data=data, etype=1)\n\nchk <- dtrans.fg(dat1)\ndim(chk)\n\n## fit model with imputed data\nmod.fg <-\n fit.mult.impute(Surv(fgstart, fgstop, fgstatus) ~ x + x.1,\n cph, data=dat1, xtrans=imp.obj, dtrans=dtrans.fg,\n weights=fgwt, fit.reps=TRUE, x=TRUE, y=TRUE, surv=TRUE)\n# Problem: fgwt is in data created by dtrans, not in dat1\n}\n\nx <- runif(10)\ny <- runif(10)\nx[1] <- NA\nww = seq(0.01, 1, length=10)\nd <- data.frame(x, y, ww)\na <- aregImpute(~ x + y, data=d, nk=0)\n\ndt <- function(dat) cbind(dat, ww=1:10)\n\nf <- fit.mult.impute(Surv(y) ~ x, cph, weights=ww,\n data=d, xtrans=a, dtrans=dt)\n", "meta": {"hexsha": "15552c0cd08490627d3228c66ecffea49a2d8d3b", "size": 1529, "ext": "r", "lang": "R", "max_stars_repo_path": "SilveR/R/library/Hmisc/tests/aregImpute5.r", "max_stars_repo_name": "robalexclark/SilveR-Dev", "max_stars_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SilveR/R/library/Hmisc/tests/aregImpute5.r", "max_issues_repo_name": "robalexclark/SilveR-Dev", "max_issues_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SilveR/R/library/Hmisc/tests/aregImpute5.r", "max_forks_repo_name": "robalexclark/SilveR-Dev", "max_forks_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-31T18:42:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-31T18:42:06.000Z", "avg_line_length": 27.8, "max_line_length": 136, "alphanum_fraction": 0.6245912361, "num_tokens": 586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7520125737597972, "lm_q2_score": 0.4493926344647597, "lm_q1q2_score": 0.3379489116725397}} {"text": "library(ggplot2)\n#setwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/sinking-marbles-prior/results/\")\nsetwd(\"~/Dropbox/sinking_marbles/sinking-marbles/experiments/1_sinking-marbles-prior/results/\")\nsource(\"rscripts/summarySE.r\")\nr = read.table(\"data/sinking-marbles-prior.tsv\", sep=\"\\t\", header=T)\nr = r[,c(\"workerid\", \"rt\", \"effect\", \"cause\", \"object_level\", \"response\", \"object\")]\n\nr$object_level = factor(r$object_level, levels=c(\"object_high\", \"object_mid\", \"object_low\"))\ns = summarySE(r, measurevar=\"response\", groupvars=c(\"effect\", \"object_level\", \"object\"))\npriors = s\nsave(priors, file=\"data/priors.RData\")\nload(\"data/priors.RData\")\n\ngraph_title = \"sinking-marbles-prior\"\nggplot(s, aes(x=effect, y=response)) +\n geom_point(aes(colour=factor(object_level)), stat=\"identity\") +\n geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\ngraph_title = \"sinking-marbles-prior-with-labels\"\nggplot(s, aes(x=effect, y=response)) +\n geom_point(aes(colour=factor(object_level)), stat=\"identity\") +\n geom_errorbar(aes(ymin=response-ci, ymax=response+ci, colour=factor(object_level)), width=.3) +\n geom_text(aes(label=object), size=3) +\n ylab(\"\") +\n xlab(\"\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\ns$y = 0\ns$yobject = as.numeric(ifelse(s$object_level == \"object_high\",0.25,ifelse(s$object_level ==\"object_mid\",0,-0.25)))\nggplot(s, aes(x=response, y=yobject)) +\n geom_point(aes(colour=factor(object_level))) +\n geom_text(aes(label=effect,y=yobject-0.05),angle=45) +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\ngraph_title = \"sinking-marbles-prior-distribution\"\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\nggplot(s, aes(x=response, fill=factor(object_level))) +\n geom_histogram(position=\"dodge\") +\n theme_bw(18) +\n theme(\n axis.text.x = element_text(size=10, angle=-45, hjust=0)\n ,plot.background = element_blank()\n ,panel.grid.minor = element_blank()\n )\ngraph_title = \"sinking-marbles-prior-histogram\"\nggsave(file=paste(c(\"graphs/\",graph_title, \".png\"), collapse=\"\"), width=15, height=6, title=graph_title)\n\n## plot variance histogram\nggplot(priors, aes(x=sd)) +\n geom_histogram()\n\n## plot individual items\nr$Combo = as.factor(paste(r$effect,r$object))\nggplot(r, aes(x=response)) +\n geom_histogram() +\n facet_wrap(~Combo)\nggsave(file=\"graphs/item_variability.pdf\",width=20,height=15)\n\n\nr$Bin = as.factor(ifelse(r$response == 0, 0, ifelse(r$response < 51, 50, ifelse(r$response < 100, 99, 100))))\nggplot(r, aes(x=Bin)) +\n geom_histogram() +\n facet_wrap(~Combo)\nggsave(file=\"graphs/item_variability_binned.pdf\",width=20,height=15)\n\nt = as.data.frame(prop.table(table(r$Bin,r$Combo),mar=2))\ncolnames(t) = c(\"Bin\",\"Combo\",\"Proportion\")\ntail(t)\nt$Proportion = round(t$Proportion,digits=3)\nt$SmoothedProportion = t$Proportion + 0.001\nlibrary(reshape2)\ncasted = dcast(t, Combo ~ Bin, value.var=\"Proportion\")\nwrite.table(casted,file=\"data/binned_priors_norownames.txt\",row.names=F,quote=F,sep=\"\\t\")\nwrite.table(casted[,c(\"0\",\"50\",\"99\",\"100\")],file=\"data/binned_priors.txt\",row.names=F,quote=F,sep=\"\\t\")\nwrite.table(casted[,c(\"0\",\"50\",\"99\",\"100\")],file=\"data/binned_priors.txt\",row.names=F,quote=F,sep=\" \")\nnrow(casted) # 90 different priors\n# get smoothed probs (no zero-probs) so model doesn't freak out\ncasted = dcast(t, Combo ~ Bin, value.var=\"SmoothedProportion\")\nwrite.table(casted[,c(\"0\",\"50\",\"99\",\"100\")],file=\"data/binned_priors_smoothed.txt\",row.names=F,quote=F,sep=\" \")\nrow.names(casted) = as.character(casted$Combo)\nhead(casted)\n\nhead(priors)\nrow.names(priors) = paste(priors$effect, priors$object)\npriors$r_all = casted[paste(priors$effect, priors$object),5]\npriors$r_upper_half = casted[paste(priors$effect, priors$object),4]\npriors$r_lower_half = casted[paste(priors$effect, priors$object),3]\npriors$r_none = casted[paste(priors$effect, priors$object),2]\n\n# plot all-prior mean vs proportion all against each other\npriors$resp = priors$response/100\nggplot(priors, aes(x=resp,y=r_all)) +\n geom_point() +\n xlab(\"all-prior (mean)\") +\n ylab(\"all-prior (proportion)\") +\n geom_abline(intercept=0,slope=1)\nggsave(\"~/Dropbox/sinking_marbles/sinking-marbles/experiments/1_sinking-marbles-prior/results/graphs/mean-vs-proportion-allprior.pdf\")\n\nsank = subset(r, effect == \"sank\" & object == \"balloons\")\nsank\nds = density(sank$response,n=101,from=0,to=100)#,bw=5)#,bw=2)\nplot(ds$x,ds$y)\nsum(ds$x*ds$y)\nstr(ds)\n\nds = density(sank$response,n=16,from=0,to=15)#,bw=5)#,bw=2)\nplot(ds$x,ds$y)\n\nseeds = subset(r, effect == \"ate the seeds\" & object == \"birds\")\nseeds\nd = density(seeds$response,n=101,from=0,to=100)#,bw=2)\nplot(d$x,d$y)\nsum(d$x*d$y)\n\nfelldown = subset(r, effect == \"fell down\" & object == \"card towers\")\nfelldown\ndf = density(felldown$response,n=101,from=0,to=100)#,bw=2)\nplot(df$x,df$y)\nsum(df$x*df$y)\n\nexpectations = ddply(r, .(effect,object), summarize, expectation=sum(density(response,n=101,from=0,to=100)$x*density(response,n=101,from=0,to=100)$y))\nexpectations5 = ddply(r, .(effect,object), summarize, expectation=sum(density(response,n=101,from=0,to=100,bw=5)$x*density(response,n=101,from=0,to=100,bw=5)$y))\nmean(expectations$expectation)\nmedian(expectations$expectation)\nmean(expectations5$expectation)\nmedian(expectations5$expectation)\n\nggplot(expectations, aes(x=expectation)) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,100))\n\nggplot(expectations5, aes(x=expectation)) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,100))\n\nhead(expectations)\nnrow(priors)\nnrow(expectations)\nwrite.table(expectations,row.names=F,sep=\"\\t\",quote=F,file=\"data/expectations.txt\")\nwrite.table(expectations5,row.names=F,sep=\"\\t\",quote=F,file=\"data/expectations5.txt\")\n\nsmoothedpriors = ddply(r, .(effect,object), summarize, smoothprior=density(response,n=101,from=0,to=100)$x*density(response,n=101,from=0,to=100)$y)\n", "meta": {"hexsha": "9aaff93a1bc626b95f1dc605673587db5bd4bd6d", "size": 6558, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/1_sinking-marbles-prior/results/rscripts/sinking-marbles-prior.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/1_sinking-marbles-prior/results/rscripts/sinking-marbles-prior.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/1_sinking-marbles-prior/results/rscripts/sinking-marbles-prior.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2331288344, "max_line_length": 161, "alphanum_fraction": 0.7163769442, "num_tokens": 2042, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3379132916256894}} {"text": "\n\nview(CO2)\nnames(CO2)\nCO2\nCO2 %>% \n ggplot( aes(conc, uptake, \n colour = Treatment))+\n geom_point(size = 3,alpha=0.5)+\n geom_smooth(method = lm, se = F)+\n facet_wrap(~Type)+\n labs(title = \"Concentration of CO2\")\n", "meta": {"hexsha": "a5a0520970e7d853758ecc268fdfb425befb5c26", "size": 230, "ext": "r", "lang": "R", "max_stars_repo_path": "co2.r", "max_stars_repo_name": "Gobz1994/ggplotCO2", "max_stars_repo_head_hexsha": "92fc927848887767afa7d9536a0027c2927f0599", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "co2.r", "max_issues_repo_name": "Gobz1994/ggplotCO2", "max_issues_repo_head_hexsha": "92fc927848887767afa7d9536a0027c2927f0599", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "co2.r", "max_forks_repo_name": "Gobz1994/ggplotCO2", "max_forks_repo_head_hexsha": "92fc927848887767afa7d9536a0027c2927f0599", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.6923076923, "max_line_length": 38, "alphanum_fraction": 0.6086956522, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.33791329162568934}} {"text": "saa = read.csv(\"sa-scpa1.txt\", header = FALSE, sep = \";\")\nsab = read.csv(\"sa-scpb1.txt\", header = FALSE, sep = \";\")\nsac = read.csv(\"sa-scpc1.txt\", header = FALSE, sep = \";\")\nsad = read.csv(\"sa-scpd1.txt\", header = FALSE, sep = \";\")\nacoa = read.csv(\"aco-scpa1.txt\", header = FALSE, sep = \";\")\nacob = read.csv(\"aco-scpb1.txt\", header = FALSE, sep = \";\")\nacoc = read.csv(\"aco-scpc1.txt\", header = FALSE, sep = \";\")\nacod = read.csv(\"aco-scpd1.txt\", header = FALSE, sep = \";\")\n\nsaa$V3 <- 100*(1-((saa$V2-253)/253))\nsab$V3 <- 100*(1-((sab$V2-69)/69))\nsac$V3 <- 100*(1-((sac$V2-227)/227))\nsad$V3 <- 100*(1-((sad$V2-60)/60))\nacoa$V3 <- 100*(1-((acoa$V2-253)/253))\nacob$V3 <- 100*(1-((acob$V2-69)/69))\nacoc$V3 <- 100*(1-((acoc$V2-227)/227))\nacod$V3 <- 100*(1-((acod$V2-60)/60))\n\ninterpsaa <- approx(saa$V1, saa$V3)\ninterpsab <- approx(sab$V1, sab$V3)\ninterpsac <- approx(sac$V1, sac$V3)\ninterpsad <- approx(sad$V1, sad$V3)\ninterpacoa <- approx(acoa$V1, acoa$V3)\ninterpacob <- approx(acob$V1, acob$V3)\ninterpacoc <- approx(acoc$V1, acoc$V3)\ninterpacod <- approx(acod$V1, acod$V3)\n\nmodsaa <- lm(interpsaa$y ~ poly(interpsaa$x,5))\nmodsab <- lm(interpsab$y ~ poly(interpsab$x,5))\nmodsac <- lm(interpsac$y ~ poly(interpsac$x,5))\nmodsad <- lm(interpsad$y ~ poly(interpsad$x,5))\nmodacoa <- lm(interpacoa$y ~ poly(interpacoa$x,5))\nmodacob <- lm(interpacob$y ~ poly(interpacob$x,5))\nmodacoc <- lm(interpacoc$y ~ poly(interpacoc$x,5))\nmodacod <- lm(interpacod$y ~ poly(interpacod$x,5))\n\npredicted.intervals.saa <- predict(model,data.frame(x=interpsaa$x),interval='confidence',level=0.99)\npredicted.intervals.sab <- predict(model,data.frame(x=interpsab$x),interval='confidence',level=0.99)\npredicted.intervals.sac <- predict(model,data.frame(x=interpsac$x),interval='confidence',level=0.99)\npredicted.intervals.sad <- predict(model,data.frame(x=interpsad$x),interval='confidence',level=0.99)\npredicted.intervals.acoa <- predict(model,data.frame(x=interpacoa$x),interval='confidence',level=0.99)\npredicted.intervals.acob <- predict(model,data.frame(x=interpacob$x),interval='confidence',level=0.99)\npredicted.intervals.acoc <- predict(model,data.frame(x=interpacoc$x),interval='confidence',level=0.99)\npredicted.intervals.acod <- predict(model,data.frame(x=interpacod$x),interval='confidence',level=0.99)\n\nplot(1, main=\"title\", sub=\"subtitle\", xlab=\"X-axis label\", ylab=\"y-axix label\", xlim=c(0, 50000), ylim=c(85, 100))\n\nlines(interpsaa$x,predicted.intervals.saa[,1],col='green')\nlines(interpsab$x,predicted.intervals.sab[,1],col='blue')\nlines(interpsac$x,predicted.intervals.sac[,1],col='red')\nlines(interpsad$x,predicted.intervals.sad[,1],col='black')\nlines(interpacoa$x,predicted.intervals.acoa[,1],col='green', lwd=3)\nlines(interpacob$x,predicted.intervals.acob[,1],col='blue', lwd=3)\nlines(interpacoc$x,predicted.intervals.acoc[,1],col='red', lwd=3)\nlines(interpacod$x,predicted.intervals.acod[,1],col='black', lwd=3)\n\n\nplot(1, main=\"Quality - Time\", xlab=\"CPU - Time (milliseconds)\", ylab=\"Quality (%)\", xlim=c(0, 55000), ylim=c(85, 100))\n\nlines(interpsaa$x,predicted.intervals.saa[,1],col='green')\nlines(interpsab$x,predicted.intervals.sab[,1],col='blue')\nlines(interpsac$x,predicted.intervals.sac[,1],col='red')\nlines(interpsad$x,predicted.intervals.sad[,1],col='black')\nlines(interpacoa$x,predicted.intervals.acoa[,1],col='green', type=\"c\")\nlines(interpacob$x,predicted.intervals.acob[,1],col='blue', type=\"c\")\nlines(interpacoc$x,predicted.intervals.acoc[,1],col='red', type=\"c\")\nlines(interpacod$x,predicted.intervals.acod[,1],col='black', type=\"c\")\n\n\nlegend(1, 95, legend=c(\"SA - SCPA1\", \"SA - SCPB1\", \"SA - SCPC1\", \"SA - SCPD1\", \"ACO - SCPA1\", \"ACO - SCPB1\", \"ACO - SCPC1\", \"ACO - SCPD1\"), col=c(\"green\", \"blue\", \"red\", \"black\", \"green\", \"blue\", \"red\", \"black\"), lty=c(1,1,1,1,2,2,2,2), cex=0.8)", "meta": {"hexsha": "d405bdedd3970f4b6d815418915ad201ea058354", "size": 3787, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/r-chart.r", "max_stars_repo_name": "fabioanh/set-covering-problem", "max_stars_repo_head_hexsha": "b66761f2834deffb2fe3f4b8636ad56e0025d983", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/r-chart.r", "max_issues_repo_name": "fabioanh/set-covering-problem", "max_issues_repo_head_hexsha": "b66761f2834deffb2fe3f4b8636ad56e0025d983", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/r-chart.r", "max_forks_repo_name": "fabioanh/set-covering-problem", "max_forks_repo_head_hexsha": "b66761f2834deffb2fe3f4b8636ad56e0025d983", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.1, "max_line_length": 245, "alphanum_fraction": 0.6886717719, "num_tokens": 1377, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3379132831926307}} {"text": "test_that(\"dates in segment annotation work\", {\n dt <- structure(list(month = structure(c(1364774400, 1377993600),\n class = c(\"POSIXct\", \"POSIXt\"), tzone = \"UTC\"), total = c(-10.3,\n 11.7)), .Names = c(\"month\", \"total\"), row.names = c(NA, -2L), class =\n \"data.frame\")\n\n p <- ggplot(dt, aes(month, total)) +\n geom_point() +\n annotate(\"segment\",\n x = as.POSIXct(\"2013-04-01\"),\n xend = as.POSIXct(\"2013-07-01\"),\n y = -10,\n yend = 10\n )\n\n expect_true(all(c(\"xend\", \"yend\") %in% names(layer_data(p, 2))))\n})\n\ntest_that(\"segment annotations transform with scales\", {\n # Line should match data points\n df <- data_frame(x = c(1, 10), y = c(10, 1))\n plot <- ggplot(df, aes(x, y)) +\n geom_point() +\n annotate(\"segment\", x = 1, y = 10, xend = 10, yend = 1, colour = \"red\") +\n scale_y_reverse(NULL, breaks = NULL) +\n scale_x_continuous(NULL, breaks = NULL)\n\n expect_doppelganger(\"line matches points\", plot)\n})\n\ntest_that(\"annotation_* has dummy data assigned and don't inherit aes\", {\n skip_if(packageVersion(\"base\") < \"3.5.0\")\n custom <- annotation_custom(zeroGrob())\n logtick <- annotation_logticks()\n library(maps)\n usamap <- map_data(\"state\")\n map <- annotation_map(usamap)\n rainbow <- matrix(hcl(seq(0, 360, length.out = 50 * 50), 80, 70), nrow = 50)\n raster <- annotation_raster(rainbow, 15, 20, 3, 4)\n dummy <- dummy_data()\n expect_equal(custom$data, dummy)\n expect_equal(logtick$data, dummy)\n expect_equal(map$data, dummy)\n expect_equal(raster$data, dummy)\n\n expect_false(custom$inherit.aes)\n expect_false(logtick$inherit.aes)\n expect_false(map$inherit.aes)\n expect_false(raster$inherit.aes)\n})\n", "meta": {"hexsha": "6a5f36c128abd33223371d0da4daea0f7f34c8df", "size": 1671, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-annotate.r", "max_stars_repo_name": "PPaccioretti/ggplot2", "max_stars_repo_head_hexsha": "c89c265a57fd71f8a0288ce81037296aadc0a012", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-11-08T11:08:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-02T22:18:34.000Z", "max_issues_repo_path": "tests/testthat/test-annotate.r", "max_issues_repo_name": "PPaccioretti/ggplot2", "max_issues_repo_head_hexsha": "c89c265a57fd71f8a0288ce81037296aadc0a012", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-07-02T04:11:29.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-02T04:11:29.000Z", "max_forks_repo_path": "tests/testthat/test-annotate.r", "max_forks_repo_name": "PPaccioretti/ggplot2", "max_forks_repo_head_hexsha": "c89c265a57fd71f8a0288ce81037296aadc0a012", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.7647058824, "max_line_length": 78, "alphanum_fraction": 0.6433273489, "num_tokens": 523, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3379132831926307}} {"text": "\nml.datasets.df <- as.data.frame(fread(paste(output.local.data.dir,\"ml-dataset-summaries-clustered.csv\",sep=\"\")))\ndataset.names <- sort(unique(ml.datasets.df$dataset))\n\nfinal.weights.viz.df <- NULL\nfor(this.dataset in dataset.names){\n \n # this.dataset <- dataset.names[1]\n print(paste0(\"Preparing: \", this.dataset))\n \n this.dataset.metadata <- subset(ml.datasets.df, dataset == this.dataset)\n\n load(file=paste0(output.data.dir, \"saved-weights/\", this.dataset, \"-weights-list.RData\"))\n load(file=paste0(output.data.dir, \"saved-weights/\", this.dataset, \"-dropout-weights-list.RData\"))\n\n activation.hash <- hash()\n .set(activation.hash, \"rectifier\", dnn.weights.list)\n .set(activation.hash, \"RectifierWithDropout\", dnn.dropout.weights.list)\n \n this.weights.df <- NULL\n activation.types <- keys(activation.hash)\n for(activation.type in activation.types){\n \n layer.index <- 0\n dnn.weights.list <- activation.hash[[activation.type]]\n n.layers <- length(dnn.weights.list)\n \n for(dnn.layer.weights in dnn.weights.list){\n \n layer.index <- layer.index + 1\n if(layer.index == 1 || layer.index == n.layers){\n next\n }else{\n n.cols <- ncol(dnn.layer.weights); n.rows <- nrow(dnn.layer.weights)\n prefix <- paste0(\"h\",layer.index,\"_w\")\n for(col in 1:n.cols){\n this.label <- paste0(prefix, (col-1))\n these.labels <- paste(this.label, 0:(n.rows-1), sep=\"\")\n these.values <- dnn.layer.weights[,col]\n this.weights.df <- rbind(this.weights.df, data.frame(weightid=these.labels, weight=these.values,activation.type=activation.type))\n }\n }\n }\n }\n \n this.weights.df <- cbind(this.weights.df, this.dataset.metadata)\n \n final.weights.viz.df <- rbind(final.weights.viz.df, this.weights.df)\n \n}\n\nfinal.weights.viz.df$layer[grepl(\"h2\", final.weights.viz.df$weightid, fixed = TRUE)] <- \"Hidden2\"\nfinal.weights.viz.df$layer[grepl(\"h3\", final.weights.viz.df$weightid, fixed = TRUE)] <- \"Hidden3\"\nfinal.weights.viz.df$layer[grepl(\"h4\", final.weights.viz.df$weightid, fixed = TRUE)] <- \"Hidden4\"\n\nwrite.table(final.weights.viz.df, file = paste(output.local.data.dir, \"annotated-weights-data.csv\",sep=\"\"),row.names=FALSE, sep=\",\", na=\"\")\n\nfinal.weights.viz.df.metadata <- subset(final.weights.viz.df, select=-weight)\nfinal.weights.viz.df.weights <- subset(final.weights.viz.df, select=c(weightid, weight))\n\nfinal.weights.viz.df.weights$one <- 1\nfinal.weights.viz.df.weights.wide <- final.weights.viz.df.weights %>% group_by(weightid) %>% mutate(cumsum = cumsum(one))\nfinal.weights.viz.df.weights.wide <- subset(final.weights.viz.df.weights.wide, select=-one)\nfinal.weights.viz.df.weights.wide <- arrange(final.weights.viz.df.weights.wide, weightid)\nfinal.weights.viz.df.weights.wide <- as.data.frame(final.weights.viz.df.weights.wide)\nfinal.weights.viz.df.weights.wide <- reshape(final.weights.viz.df.weights.wide, timevar = \"cumsum\",idvar = \"weightid\",v.names = \"weight\",direction = \"wide\")\nhead(final.weights.viz.df.weights.wide)\n\nfinal.weights.viz.df.weights.wide$layer[grepl(\"h2\", final.weights.viz.df.weights.wide$weightid, fixed = TRUE)] <- \"Hidden2\"\nfinal.weights.viz.df.weights.wide$layer[grepl(\"h3\", final.weights.viz.df.weights.wide$weightid, fixed = TRUE)] <- \"Hidden3\"\nfinal.weights.viz.df.weights.wide$layer[grepl(\"h4\", final.weights.viz.df.weights.wide$weightid, fixed = TRUE)] <- \"Hidden4\"\n\nn.cols <- ncol(final.weights.viz.df.weights.wide)\nfinal.weights.viz.df.weights.wide <- final.weights.viz.df.weights.wide[,c(1,n.cols, 2:(n.cols-1))]\n\n \n ", "meta": {"hexsha": "a046d3f3b317905e70f1e8f017088b6cf07d5cd5", "size": 3558, "ext": "r", "lang": "R", "max_stars_repo_path": "modeling/visualize-weights.r", "max_stars_repo_name": "weiwang2330/DL-with-R", "max_stars_repo_head_hexsha": "301bfa6c83d5afd70ce4bcf91c3a1ce8a965f63d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2015-09-10T19:12:04.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-07T14:59:37.000Z", "max_issues_repo_path": "modeling/visualize-weights.r", "max_issues_repo_name": "celloharper/Deep-Learning-with-h2o-in-R", "max_issues_repo_head_hexsha": "301bfa6c83d5afd70ce4bcf91c3a1ce8a965f63d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modeling/visualize-weights.r", "max_forks_repo_name": "celloharper/Deep-Learning-with-h2o-in-R", "max_forks_repo_head_hexsha": "301bfa6c83d5afd70ce4bcf91c3a1ce8a965f63d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2015-09-15T20:42:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-23T15:34:15.000Z", "avg_line_length": 46.2077922078, "max_line_length": 156, "alphanum_fraction": 0.6939291737, "num_tokens": 951, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3379103237816563}} {"text": "#' Return dimension names of an array respecting the number of dimensions\n#'\n#' Act on each element if 'x' is a list\n#'\n#' @param x An n-dimensional array\n#' @param along Limit to dimension (default: all) \n#' @param null_as_integer Whether nameless dimensions should be \\code{NULL} or\n#' numbered\n#' @param drop Drop list of only one axis requested (default: if not\n#' returning all dimensions)\n#' @return A list of dimension names with length \\code{length(ndim(X))}\n#' @export\ndimnames = function(x, along=TRUE, null_as_integer=FALSE, drop=!identical(along, TRUE)) {\n if (is.list(x) && !is.data.frame(x))\n return(lapply(x, function(x1) dimnames(x1, along=along,\n null_as_integer=null_as_integer, drop=drop)))\n if (is.data.frame(x))\n x = as.matrix(x)\n if (is.vector(x)) {\n x = as.array(x)\n if (!identical(along, TRUE))\n along = 1\n }\n\n dn = base::dimnames(x)\n\n if (is.null(dn))\n dn = base::rep(list(NULL), length(dim(x)))\n\n if (null_as_integer == TRUE && length(dn) > 0)\n dn = lapply(1:length(dn), function(i) {\n if (is.null(dn[[i]]))\n seq_len(dim(x)[i])\n else\n dn[[i]]\n })\n\n if (length(along) == 1 && drop==TRUE)\n dn[[along]]\n else\n dn[along]\n}\n", "meta": {"hexsha": "b53f6a7a466e1c08a98fc95748cb563d35a3a460", "size": 1363, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dimnames.r", "max_stars_repo_name": "cran/narray", "max_stars_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2016-12-07T16:03:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-20T09:10:42.000Z", "max_issues_repo_path": "R/dimnames.r", "max_issues_repo_name": "cran/narray", "max_issues_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2016-11-21T09:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-11T16:08:02.000Z", "max_forks_repo_path": "R/dimnames.r", "max_forks_repo_name": "cran/narray", "max_forks_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-06-21T03:17:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-21T03:17:21.000Z", "avg_line_length": 31.6976744186, "max_line_length": 89, "alphanum_fraction": 0.5627292737, "num_tokens": 364, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.3379103161776006}} {"text": "setwd(\"/home/remoteuser/Code/ROnAzure\")\r\nsource(\"SetComputeContext.r\")\r\n\r\n# For local compute context, skip the following line\r\nstartRxSpark()\r\n\r\nfinalData <- RxXdfData(file.path(dataDir, \"airDFXDFSubset\"))\r\n\r\n################################################\r\n# Split out Training and Test Datasets\r\n################################################\r\n\r\n# split out the training data\r\n\r\ntrainDS <- RxXdfData( file.path(dataDir, \"finalDataTrainSubset\" ))\r\n\r\nrxDataStep( inData = finalData, outFile = trainDS,\r\n rowSelection = ( Year != 2012 ), overwrite = T )\r\n\r\n# split out the testing data\r\n\r\ntestDS <- RxXdfData( file.path(dataDir, \"finalDataTestSubset\" ))\r\n\r\nrxDataStep( inData = finalData, outFile = testDS,\r\n rowSelection = ( Year == 2012 ), overwrite = T )\r\n\r\n\r\n# system('hadoop fs -ls /user/RevoShare/remoteuser/Data')\r\n\r\n################################################\r\n# Train and Test a Logistic Regression model\r\n################################################\r\n\r\nformula <- as.formula(ArrDel15 ~ Month + DayofMonth + DayOfWeek + Carrier + OriginAirportID + \r\n DestAirportID + CRSDepTime + CRSArrTime)\r\n\r\n# Use the scalable rxLogit() function\r\n\r\nlogitModel <- rxLogit(formula, data = trainDS)\r\n\r\noptions(max.print = 100)\r\nbase::summary(logitModel)\r\n\r\n# Predict over test data (Logistic Regression).\r\n\r\nlogitPredict <- RxXdfData(file.path(dataDir, \"logitPredictSubset\"))\r\n\r\n# Use the scalable rxPredict() function\r\n\r\nrxPredict(logitModel, data = testDS, outData = logitPredict,\r\n extraVarsToWrite = c(\"ArrDel15\"),\r\n type = 'response', overwrite = TRUE)\r\n\r\n# Calculate ROC and Area Under the Curve (AUC).\r\n\r\nlogitRoc <- rxRoc(\"ArrDel15\", \"ArrDel15_Pred\", logitPredict)\r\nlogitAuc <- rxAuc(logitRoc)\r\n\r\nplot(logitRoc)\r\n\r\nsave(logitModel, file = \"logitModelSubset.RData\")\r\n\r\n# For local compute context, skip the following line\r\nrxSparkDisconnect(rxGetComputeContext())\r\n", "meta": {"hexsha": "b9d9241e020419ed69073f247e92834990e94d68", "size": 1940, "ext": "r", "lang": "R", "max_stars_repo_path": "Applied Machine Learning for Developers/R server Tools for scaling R using Azure/code/ROnAzure/2-Train-Test-Subset.r", "max_stars_repo_name": "Bhaskers-Blu-Org2/AI-Immersion-Workshop", "max_stars_repo_head_hexsha": "63ee5b6c26cfbbda84fa34c2e7d7435ae0f7656f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Applied Machine Learning for Developers/R server Tools for scaling R using Azure/code/ROnAzure/2-Train-Test-Subset.r", "max_issues_repo_name": "Bhaskers-Blu-Org2/AI-Immersion-Workshop", "max_issues_repo_head_hexsha": "63ee5b6c26cfbbda84fa34c2e7d7435ae0f7656f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Applied Machine Learning for Developers/R server Tools for scaling R using Azure/code/ROnAzure/2-Train-Test-Subset.r", "max_forks_repo_name": "Bhaskers-Blu-Org2/AI-Immersion-Workshop", "max_forks_repo_head_hexsha": "63ee5b6c26cfbbda84fa34c2e7d7435ae0f7656f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-07-30T11:53:14.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-30T11:53:14.000Z", "avg_line_length": 29.8461538462, "max_line_length": 95, "alphanum_fraction": 0.6211340206, "num_tokens": 468, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.33791031617760053}} {"text": "#' Expand a PhenoCam time series from 3-day to a 1-day time step\n#' \n#' Necessary step to guarantee consistent data processing between 1 and 3-day\n#' data products. .\n#'\n#' @param data a PhenoCam dataframe from API\n#' @param truncate year (numerical), limit the time series\n#' to a particular year (default = NULL)\n#' (\\code{TRUE} = default/ \\code{FALSE} )\n#' @return Expanded PhenoCam data structure or file, including 90 day padding\n#' if requested.\n#' @keywords time series, post-processing, phenocam\n\n\nexpand_phenocam = function(phenocam_data,\n truncate = NULL,\n internal = TRUE,\n out_dir = tempdir()) {\n \n # convert dates\n phenocam_data$dates = as.Date(phenocam_data$date)\n \n ## # remove dates that were filled before expanding again\n ## # this is similar to phenocam_contract() but includes\n ## # the removal of the padding\n \n # truncate the data if necessary\n max_date = max(phenocam_data$date)\n \n # pad with 90 days (regardless)\n min_range = min(phenocam_data$date) - 90\n max_range = max(phenocam_data$date) + 90\n \n \n # create vectors to populate final output with\n all_dates = seq(as.Date(min_range), as.Date(max_range), \"days\")\n all_years = as.integer(format(all_dates, \"%Y\"))\n all_doy = as.integer(format(all_dates, \"%j\"))\n \n # create data frame with dates to merge with original data\n all_dates = as.data.frame(all_dates)\n colnames(all_dates) = \"date\"\n \n \n output=left_join(all_dates, phenocam_data)\n output$year = all_years\n output$doy = all_doy\n \n # stuff expanded data back into original data structure\n data = output\n \n#return data\n \n return(data)\n \n}\n", "meta": {"hexsha": "4ae7166250199d6fa4180eb01d7ac6714314d56b", "size": 1683, "ext": "r", "lang": "R", "max_stars_repo_path": "functions/expand_phenocam.r", "max_stars_repo_name": "katharynduffy/phenoSpline", "max_stars_repo_head_hexsha": "8c7af1264fc7a985c21c79e0f5bc072795a8c8a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "functions/expand_phenocam.r", "max_issues_repo_name": "katharynduffy/phenoSpline", "max_issues_repo_head_hexsha": "8c7af1264fc7a985c21c79e0f5bc072795a8c8a2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "functions/expand_phenocam.r", "max_forks_repo_name": "katharynduffy/phenoSpline", "max_forks_repo_head_hexsha": "8c7af1264fc7a985c21c79e0f5bc072795a8c8a2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5263157895, "max_line_length": 77, "alphanum_fraction": 0.6755793226, "num_tokens": 433, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725051, "lm_q2_score": 0.6224593241981982, "lm_q1q2_score": 0.3379103123755726}} {"text": "#' @export\n#' @title Bounding box of zeros around Cardinaldata\n#' @description Adds zeros around \"MSImageSet\" object to so that datasets can be combined after registration\n#' @param cardinaldata an object of class \"MSImageSet\"\n#' @param xmax maximum of bounding box in x\n#' @param ymax maximum of bounding box in y\n#' @param boolean, whether to center the imaging data in the bounding box\n#' @return msset an object of class \"MSImageSet\"\n\nboundBox <- function(cardinaldata, xmax=300, ymax=250, center=F){\n \n #size of zero box\n xseq <- c(1:xmax)\n yseq <- c(1:ymax)\n \n #grab pixel and intensity data\n intensity_data <- data.frame(pData(cardinaldata)[1:2],t(iData(cardinaldata)))\n \n #make empty data.frame expanded to size of bounding box\n empty_df<-data.frame(expand.grid(xseq,yseq),sampleNames(cardinaldata))\n empty_df[4:(nrow(cardinaldata)+3)] <- 0\n colnames(empty_df) <- c(\"x\",\"y\",\"sample\")\n \n #sort pixels by x then y\n empty_df <- arrange(empty_df,x,y)\n intensity_data <- arrange(intensity_data,x,y)\n \n #make character vector of pixel names combining x & y\n zero_pixels <- paste0(\"X\",empty_df$x,\"Y\",empty_df$y) \n \n #center data in bounding box\n if(center == T){\n mpx <- round(max(pData(cardinaldata)$x) / 2, 0)\n mpy <- round(max(pData(cardinaldata)$y) / 2, 0)\n \n mpx <- (xmax / 2) - mpx\n mpy <- (xmax / 2) - mpy\n \n cdata_pixels <- paste0(\"X\",intensity_data$x + mpx,\"Y\",intensity_data$y + mpy)\n \n } else {\n \n #bounding box extends after the data\n \n cdata_pixels <- paste0(\"X\",intensity_data$x,\"Y\",intensity_data$y)\n \n }\n \n #fill empty pixels with intensity data\n non_zero_idx = which(zero_pixels %in% cdata_pixels)\n empty_df[non_zero_idx, 4:length(empty_df)] <- intensity_data[3:length(intensity_data)]\n \n #make cardinal data\n msset <- MSImageSet(spectra=t(empty_df[,4:length(empty_df)]), coord=empty_df[c(\"x\",\"y\",\"sample\")], \n mz=Cardinal::mz(cardinaldata),\n processingData = processingData(cardinaldata),\n protocolData = protocolData(cardinaldata),\n experimentData = experimentData(cardinaldata))\n \n msset$binary_mask = 0\n msset$binary_mask[non_zero_idx] = 1\n \n return(msset)\n}", "meta": {"hexsha": "c5682b9014e49a9cd1e4d20c723f46a80eb2cbec", "size": 2240, "ext": "r", "lang": "R", "max_stars_repo_path": "R/boundBox.r", "max_stars_repo_name": "NHPatterson/RegComb_IMS", "max_stars_repo_head_hexsha": "41a29418070851c703f7dce5ea8eb0a1f61076c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-11-07T08:53:17.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-07T08:53:17.000Z", "max_issues_repo_path": "R/boundBox.r", "max_issues_repo_name": "NHPatterson/RegCombIMS", "max_issues_repo_head_hexsha": "41a29418070851c703f7dce5ea8eb0a1f61076c9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/boundBox.r", "max_forks_repo_name": "NHPatterson/RegCombIMS", "max_forks_repo_head_hexsha": "41a29418070851c703f7dce5ea8eb0a1f61076c9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0, "max_line_length": 108, "alphanum_fraction": 0.6678571429, "num_tokens": 637, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3379103085735447}} {"text": "#!/bin/env Rscript\n\n# Copyright (c) 2015 Hanspeter Portner (dev@open-music-kontrollers.ch)\n#\n# This is free software: you can redistribute it and/or modify\n# it under the terms of the Artistic License 2.0 as published by\n# The Perl Foundation.\n#\n# This source is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# Artistic License 2.0 for more details.\n#\n# You should have received a copy of the Artistic License 2.0\n# along the source as a COPYING file. If not, obtain it from\n# http://www.perlfoundation.org/artistic_license_2_0.\n\npdf('stat.pdf')\n#svg('33_%02d.svg', width=8, height=8, pointsize=16)\n\n{\n\tf <- file('stat.dump.osc', 'rb')\n\n\thead <- readChar(f, 8)\n\tsrate <- readBin(f, 'integer', 1, 4, endian='big')\n\n\tprint(head)\n\tprint(srate)\n\n\ta <- NULL\n\ti <- 0\n\twhile(i < 3000)\n\t{\n\t\ttime <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tif(!length(time))\n\t\t\tbreak;\n\t\tsize <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tpath <- readChar(f, 8) # '/dump000'\n\t\tfmt <- readChar(f, 4) # ',ib0'\n\t\tfid <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tlen <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tblob <- readBin(f, 'integer', len/2, 2, endian='big')\n\t\ta <- rbind(a, c(blob))\n\n\t\ti <- i + 1\n\t}\n\n\tclose(f)\n}\n\n{\n\tmavg <- apply(a, c(2), mean)\n\tmsd <- apply(a, c(2), sd)\n\tprint(summary(msd))\n\n\tplot(mavg, type='l', ylim=c(-5,5), col=2, xlab='Sensor', ylab='Mean')\n\tplot(msd, type='l', ylim=c(0,5), col=2, xlab='Sensor', ylab='Standard deviation')\n}\n\nfor(path in c('stat.none.osc', 'stat.quadratic.osc', 'stat.catmullrom.osc', 'stat.lagrange.osc'))\n{\n\tprint(path)\n\tf <- file(path, 'rb')\n\n\thead <- readChar(f, 8)\n\tsrate <- readBin(f, 'integer', 1, 4, endian='big')\n\n\tprint(head)\n\tprint(srate)\n\n\ta <- NULL\n\ti <- 0\n\twhile(i< 48000)\n\t{\n\t\ttime <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tif(!length(time))\n\t\t\tbreak;\n\t\tsize <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tpath <- readChar(f, 8) # '/set0000'\n\t\tfmt <- readChar(f, 8) # ',iiiff00'\n\t\tsid <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tgid <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tpid <- readBin(f, 'integer', 1, 4, endian='big')\n\t\tx <- readBin(f, 'numeric', 1, 4, endian='big')\n\t\ty <- readBin(f, 'numeric', 1, 4, endian='big')\n\t\ta <- rbind(a, c(x, y))\n\n\t\ti <- i + 1\n\t}\n\n\tclose(f)\n\n\tlen <- length(a[,1])\n\ts <- function(o)\n\t{\n\t\treturn(c(mean(o), sd(o)))\n\t}\n\n\tdx <- abs(a[2:len,1] - a[1:(len-1),1])\n\tdx <- dx[dx > 0]\n\tbx <- log(1/dx) / log(2)\n\tprint(s(bx))\n\thist(bx, border=2, breaks=1000, xlab='x-dimension bitdepth', main='', xlim=c(5,20))\n\n#\tdz <- abs(a[2:len,2] - a[1:(len-1),2])\n#\tdz <- dz[dz > 0]\n#\tbz <- log(1/dz) / log(2)\n#\tprint(s(bz))\n#\thist(bz, border=2, breaks=1000, xlab='z-dimension bitdepth', main='', xlim=c(5,20))\n\n\tplot(a[,1], type='l', ylim=c(0,1), col=2, xlab='Sample', ylab='x-dimension')\n\tplot(a[,2], type='l', ylim=c(0,1), col=2, xlab='Sample', ylab='z-dimension')\n\tplot(a[,2] ~ a[,1], type='l', xlim=c(0,1), ylim=c(0,1), col=2, xlab='x-dimension', ylab='z-dimension')\n}\n\ndev.off()\n", "meta": {"hexsha": "4133d303a9073faa4909ef7b5b43c1f058d7f9c5", "size": 3084, "ext": "r", "lang": "R", "max_stars_repo_path": "stat.r", "max_stars_repo_name": "OpenMusicKontrollers/chimaera_tjost", "max_stars_repo_head_hexsha": "e244a8c6b9bf0374fc16d9a7675c64d90ea8681f", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "stat.r", "max_issues_repo_name": "OpenMusicKontrollers/chimaera_tjost", "max_issues_repo_head_hexsha": "e244a8c6b9bf0374fc16d9a7675c64d90ea8681f", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stat.r", "max_forks_repo_name": "OpenMusicKontrollers/chimaera_tjost", "max_forks_repo_head_hexsha": "e244a8c6b9bf0374fc16d9a7675c64d90ea8681f", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.358974359, "max_line_length": 103, "alphanum_fraction": 0.6037613489, "num_tokens": 1141, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3379103085735447}} {"text": "# This is the script for processing Buckingham CGMS data into the common format. \n# Author: Rucha Bhat, edited by Shaun Cass\n# Date: April 16, 2021\n\nlibrary(tidyverse)\nlibrary(magrittr)\n\n# This study downloads as a folder containing data tables and forms\n# First download the entire dataset. Do not rename the downloaded folder\n# Place the downloaded folder into a folder of your creation specific for this dataset\n# You may name your created folder however you like\n# Here we have named the created folder by first author last name and date of the original paper\ndataset <- \"Buckingham2007\"\n# If you have a different naming method, you will need to adjust this, eg.\n# dataset <- \"insert_your_name\"\n\n# We will set the working directory here in the created folder\nsetwd(dataset)\n\n# This is the relative path to the Medtronic CGMS CGM file with original names\nfile.path <- \"DirecNetNavigatorPilotStudy/DataTables/tblFNavGlucose.csv\"\n# Alternatively, if the file structure has been changed, simply place the CGM.txt file into the created folder\n# Then run the file path as follows:\n# file.path <- \"tblCDataCGMS.csv\"\n\n# Read the raw data in \ncurr = read.csv(file.path, header = TRUE, stringsAsFactors = FALSE)\n\n# combine date and time into standard format (POSIX1t format)\ncurr$time = strptime(paste(as.Date(curr$NavReadDt), curr$NavReadTm),\n format = \"%Y-%m-%d %H:%M:%S\")\n\n# reorder and select only id, time, gl columns\ncurr = curr[, c(2,6,5)]\n\n# Renaming the columns with the standard format names\ncolnames(curr) = c(\"id\",\"time\",\"gl\")\n\n#Ensure glucose values are recorded as numeric\ncurr$gl = as.numeric(curr$gl)\n\n# Change all values less than 32 mg/dL and above 450 mg/dL to NA\ncurr$gl[curr$gl <= 32] <- NA\ncurr$gl[curr$gl > 450] <- NA\n\n# This dataset has some NA values for glucose readings, \n# If you would like to filter them out, simply uncomment this code:\n# curr = na.omit(curr)\n\n# The following function is used to remove regions of zero variability\n# These regions likely arise from cgm sensor errors.\n\nzero.remove = function(tab){\n tab %>%\n mutate(diff = gl - lag(gl)) %>% \n filter(diff != 0, diff != lag(diff), diff != lag(diff, 2)) %>%\n select(-diff)\n}\n\ncurr = curr %>% group_split(id) %>% map_dfr(zero.remove)\n\n# Save the cleaned data to the created dataset folder\n# The cleaned file will be named \"dataset\"_processed.csv\nwrite.table(curr, file = paste(dataset, \"_processed.csv\", sep = \"\"), row.names = F, \n col.names = !file.exists(paste(dataset, \"_processed.csv\", sep = \"\")), \n sep = \",\")\n\n# If instead you want to save the cleaned dataset as an .RData file uncomment:\n# save(curr, file = paste(dataset, \"_processed.RData\", sep = \"\"))\n", "meta": {"hexsha": "d9519f38840a743d5d90258ae550cbaa9eb47fdb", "size": 2700, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Buckingham2007/preprocessor.r", "max_stars_repo_name": "ruchabhat/Awesome-CGM", "max_stars_repo_head_hexsha": "0689609e56b2eff6c3443e2b7c56b1b52a164b6a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 56, "max_stars_repo_stars_event_min_datetime": "2020-07-20T02:14:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T19:19:17.000Z", "max_issues_repo_path": "R/Buckingham2007/preprocessor.r", "max_issues_repo_name": "ruchabhat/Awesome-CGM", "max_issues_repo_head_hexsha": "0689609e56b2eff6c3443e2b7c56b1b52a164b6a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-09-13T19:24:50.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-23T08:28:03.000Z", "max_forks_repo_path": "R/Buckingham2007/preprocessor.r", "max_forks_repo_name": "ruchabhat/Awesome-CGM", "max_forks_repo_head_hexsha": "0689609e56b2eff6c3443e2b7c56b1b52a164b6a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2020-05-29T16:56:36.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-25T20:48:45.000Z", "avg_line_length": 38.5714285714, "max_line_length": 110, "alphanum_fraction": 0.7159259259, "num_tokens": 689, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3378932047172349}} {"text": "require(tfestimators)\nrequire(nzr)\n\nnzConnectDSN('DBM_SANDBOX')\n\nnzdata <- nzQuery(paste('SELECT * FROM MODEL_12MO_VAL'))\n\nnzDisconnect()\n\nhead(nzdata)\n\nvars <- c('SALES_R12','SALES_R24','SALES_R48','SALES_RLT',\n 'FISHING_MARINE_SALES_LT','CAMPING_WATERSPORTS_SALES_LT','HUNTING_HUNTCLOTH_SALES_LT',\n 'FOOTWEAR_SALES_LT','SHOOTING_SALES_LT','GIFT_SALES_LT','APPAREL_SALES_LT',\n 'OTHER_SALES_LT','VISITS_LT','FISH_MARINE_PERC','CAMPING_WATERSPORTS_PERC',\n 'HUNTING_PERC','FOOTWEAR_PERC','SHOOTING_PERC','GIFT_PERC','APPAREL_PERC',\n 'OTHER_PERC','DAYS_SINCE_PURCHASE','DAYS_AS_CUSTOMER','REWARDS_CUSTOMER', \n 'YOY_TREND','SALES_TARGET_N12')\n\nvars.x <- c('SALES_R12','SALES_R24','SALES_R48','SALES_RLT',\n 'FISHING_MARINE_SALES_LT','CAMPING_WATERSPORTS_SALES_LT','HUNTING_HUNTCLOTH_SALES_LT',\n 'FOOTWEAR_SALES_LT','SHOOTING_SALES_LT','GIFT_SALES_LT','APPAREL_SALES_LT',\n 'OTHER_SALES_LT','VISITS_LT','FISH_MARINE_PERC','CAMPING_WATERSPORTS_PERC',\n 'HUNTING_PERC','FOOTWEAR_PERC','SHOOTING_PERC','GIFT_PERC','APPAREL_PERC',\n 'OTHER_PERC','DAYS_SINCE_PURCHASE','DAYS_AS_CUSTOMER','REWARDS_CUSTOMER', \n 'YOY_TREND')\nvars.y <- c('SALES_TARGET_N12')\n\ndatasub <- nzdata[, vars]\n\ndatasub_input_fn <- function(data, num_epochs = 1) {\n input_fn(data,\n features = vars.x,\n response = vars.y,\n batch_size = 64,\n num_epochs = num_epochs)\n}\n\ncols <- feature_columns(\n column_numeric('SALES_R12','SALES_R24','SALES_R48','SALES_RLT',\n 'FISHING_MARINE_SALES_LT','CAMPING_WATERSPORTS_SALES_LT','HUNTING_HUNTCLOTH_SALES_LT',\n 'FOOTWEAR_SALES_LT','SHOOTING_SALES_LT','GIFT_SALES_LT','APPAREL_SALES_LT',\n 'OTHER_SALES_LT','VISITS_LT','FISH_MARINE_PERC','CAMPING_WATERSPORTS_PERC',\n 'HUNTING_PERC','FOOTWEAR_PERC','SHOOTING_PERC','GIFT_PERC','APPAREL_PERC',\n 'OTHER_PERC','DAYS_SINCE_PURCHASE','DAYS_AS_CUSTOMER', \n 'YOY_TREND','REWARDS_CUSTOMER')\n)\n\n\nmod = dnn_regressor(hidden_units = c(10, 20, 10),\n feature_columns = cols,\n optimizer = \"Adagrad\",\n activation_fn = \"relu\")\n\n\nindices <- sample(1:nrow(datasub), size=0.80 *nrow(datasub))\ntrain <- datasub[indices, ]\ntest <- datasub[-indices, ]\n\nmod %>% train(datasub_input_fn(train, num_epochs=10))\n\n\n\n", "meta": {"hexsha": "232b79ee9c1b50fbb915d6ca858a4120a934f307", "size": 2476, "ext": "r", "lang": "R", "max_stars_repo_path": "TensorflowExample.r", "max_stars_repo_name": "FloobtheGreat/RAnalyses", "max_stars_repo_head_hexsha": "d018d6fa1c15c011226f0466f2d2170013c77ac4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "TensorflowExample.r", "max_issues_repo_name": "FloobtheGreat/RAnalyses", "max_issues_repo_head_hexsha": "d018d6fa1c15c011226f0466f2d2170013c77ac4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "TensorflowExample.r", "max_forks_repo_name": "FloobtheGreat/RAnalyses", "max_forks_repo_head_hexsha": "d018d6fa1c15c011226f0466f2d2170013c77ac4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.6875, "max_line_length": 103, "alphanum_fraction": 0.6607431341, "num_tokens": 770, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3378932047172349}} {"text": "##' Produce a table of prevalence ratios in counties\n##'\n##' @importFrom dplyr mutate select arrange matches left_join\n##' @importFrom kableExtra kable add_header_above save_kable\n##' @importFrom tools file_ext\n##' @param file file name to save the table to (pdf)\n##' @return NULL (invisibly)\n##' @export\ncounty_table <- function(file = NULL) {\n\n options(knitr.kable.NA = \"\")\n\n ms.R <- ms.tst %>%\n dplyr::select(county, region, pop,\n dplyr::matches(\"attendance_[0-9]\"),\n dplyr::matches(\"positive_[0-9]\")) %>%\n simplify_names() %>%\n dplyr::left_join(Rt.county %>%\n rename(simple_name = county), by = \"simple_name\") %>%\n dplyr::select(-simple_name) %>%\n mutate(pop = round(pop))\n\n additional_rows <- ms.R %>%\n mutate(county = if_else(!is.na(attendance_1),\n \"Pilot and Round 1 & 2\", NA_character_),\n county = if_else(is.na(county) & !is.na(attendance_3),\n \"Round 1 & 2\", county),\n R = NA_real_) %>%\n replace_na(list(county = \"Round 1 only\")) %>%\n group_by(county) %>%\n summarise_if(is.numeric, sum) %>%\n mutate(level = \"zaggregate\")\n\n print_table <- ms.R %>%\n mutate(level = \"acounty\") %>%\n bind_rows(additional_rows) %>%\n mutate(pilot_prev = round(positive_1 / attendance_1 * 100, 2),\n round2_prev = round(positive_2 / attendance_2 * 100, 2),\n round3_prev = round(positive_3 / attendance_3 * 100, 2),\n R = round(R, 1)) %>%\n select(level, county, region, pop, R,\n ends_with(\"_1\"), pilot_prev,\n ends_with(\"_2\"), round2_prev,\n ends_with(\"_3\"), round3_prev) %>%\n arrange(level, county) %>%\n select(-level)\n\n if (!is.null(file)) {\n col.names <-\n c(\"County\", \"Region\", \"Population\", \"R\",\n rep(c(\"Positive\", \"Attendance\", \"%\"), times = 3))\n\n if (file_ext(file) == \"pdf\") {\n format <- \"latex\"\n } else if (file_ext(file) == \"html\") {\n format <- \"html\"\n }\n\n k <- kable(print_table,\n format = format,\n col.names = col.names,\n align = c('l', 'l', 'r', 'r', rep('r', 3), 'r',\n rep('r', 2), 'r', rep('r', 2)),\n booktabs = TRUE) %>%\n pack_rows(index = c(\"County level\" = nrow(ms.R),\n \"Aggregate level\" = nrow(additional_rows)))\n\n k <-\n add_header_above(k, c(\" \" = 4, \"Pilot\" = 3,\n \"Round 1\" = 3, \"Round 2\" = 3))\n\n save_kable(k, file)\n invisible(NULL)\n } else {\n return(print_table)\n }\n}\n", "meta": {"hexsha": "d79ea86ee493ad68cf500ab5274d05eff2d745a5", "size": 2605, "ext": "r", "lang": "R", "max_stars_repo_path": "R/county_table.r", "max_stars_repo_name": "sbfnk/covid19.slovakia.mass.testing", "max_stars_repo_head_hexsha": "eea691276701e8fb24e882b395767c7051878b28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-30T09:48:06.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T09:48:06.000Z", "max_issues_repo_path": "R/county_table.r", "max_issues_repo_name": "epiforecasts/covid19.slovakia.mass.testing", "max_issues_repo_head_hexsha": "eea691276701e8fb24e882b395767c7051878b28", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-11-30T11:13:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-29T11:42:39.000Z", "max_forks_repo_path": "R/county_table.r", "max_forks_repo_name": "sbfnk/covid19.slovakia.mass.testing", "max_forks_repo_head_hexsha": "eea691276701e8fb24e882b395767c7051878b28", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-11-30T10:56:25.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-24T09:21:53.000Z", "avg_line_length": 33.3974358974, "max_line_length": 74, "alphanum_fraction": 0.5312859885, "num_tokens": 743, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.33789319620798963}} {"text": "library(changepoint)\r\nlibrary(jsonlite)\r\nlibrary(\"rjson\")\r\n\r\n\r\nfile_names = list.files(path = \"E:\\\\project\\\\20160517_testfile_device_id\", pattern = NULL, all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = FALSE, include.dirs = FALSE, no.. = FALSE)\r\n\r\ni = 1\r\nid = vector()\r\ndevice_cpt = vector()\r\ngps_lat = vector()\r\ngps_lon = vector()\r\nfor (json_file in file_names){\r\n\t#-----------------$device_id $gps_lat $gps_lon $s_d0(pm2.5\u503c) $time------------------#\r\n\tmy_path = paste0(\"E:\\\\project\\\\20160517_testfile_device_id\\\\\", json_file)\r\n\tjson_data <- fromJSON(file=my_path)\r\n\tif(length(json_data$s_d0) < 30) next\r\n\t#-------------change in mean and variance--------------#\r\n\tansmeanvar=cpt.meanvar(json_data$s_d0)\r\n\r\n\tch_id <- cpts(ansmeanvar) #\u56de\u50b3\u7de8\u865f\r\n\tch_time <- json_data$time[ch_id]\r\n\r\n\t#print(ch_time)\r\n\t\r\n\tid[i] = json_data$device_id\r\n\tgps_lat[i] = json_data$gps_lat\r\n\tgps_lon[i] = json_data$gps_lon\r\n\tdevice_cpt[i] = ch_time\r\n\ti = i+1\r\n\t\r\n\t#pic\r\n\tif(is.na(ch_id)) {\r\n\t\t\tprint(i)\r\n\t\t\tnext\r\n\t\t}\r\n\tzz_b = 1:ch_id\r\n\txx_b = json_data$time[zz_b]\r\n\tyy_b = rep(mean(json_data$s_d0[zz_b]), times = length(zz_b))\r\n\tzz_f = (ch_id+1):length(json_data$s_d0)\r\n\txx_f = json_data$time[zz_f]\r\n\tyy_f = rep(mean(json_data$s_d0[zz_f]), times = length(zz_f))\r\n\tplot(as.POSIXct(strptime(json_data$time, \"%H:%M:%S\")), json_data$s_d0 ,type = \"l\", xlab = \"PM2.5\", ylab = \"Time\")\r\n\tlines(xx_b,yy_b,col=\"red\")\r\n\tlines(xx_f,yy_f,col=\"red\")\r\n\t\r\n\t#curve(x=sensor_of_day$PM25[ch_id],as.numeric(sensor_of_day$f_time[1]), as.numeric(sensor_of_day$f_time[ch_id]))\r\n\t\t\r\n\tpic_name <- paste0(\"E:\\\\LASS_Graph\\\\\", i,\".png\")\r\n\tdev.copy(png,pic_name)\r\n\tdev.off()\r\n}\r\n\r\ndevice_order <- as.integer( as.POSIXct(strptime(device_cpt, \"%H:%M:%S\")) )\r\ndata_end <- data.frame(id, gps_lat, gps_lon, device_cpt, device_order)\r\nwrite.table(data_end, file = \"E:\\\\20160517_testfile_device_id\\\\test.CSV\", sep = \",\")", "meta": {"hexsha": "601bfe93c2b92de8f7185505c598cd5d8a50cd10", "size": 1880, "ext": "r", "lang": "R", "max_stars_repo_path": "json_out.r", "max_stars_repo_name": "ninetf135246/R-notes", "max_stars_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "json_out.r", "max_issues_repo_name": "ninetf135246/R-notes", "max_issues_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "json_out.r", "max_forks_repo_name": "ninetf135246/R-notes", "max_forks_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.5714285714, "max_line_length": 206, "alphanum_fraction": 0.6553191489, "num_tokens": 597, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646140788307, "lm_q2_score": 0.5, "lm_q1q2_score": 0.33788230703941535}} {"text": "print(paste0(Sys.time(), \" --- Describers profiles (for appendix)\"))\r\ndes <- df_describers[, c(\"full.name.of.describer.n\", \"last.name\", \r\n \"spp_per_pub_mean\", \"n_pubs\",\r\n \"spp_per_pub_mean_20y\", \"n_pubs_20y\",\r\n \"spp_per_pub_mean_5y\", \"n_pubs_5y\",\r\n \"n_spp_20y\", \"n_spp_5y\",\r\n \"dob.describer.n\", \"dod.describer.n\",\r\n \"spp_N\",\r\n \"max\", \"pub_years\")]\r\ndes$dod.describer.n <- as.numeric(des$dod.describer.n)\r\ndes$dob.describer.n <- as.numeric(des$dob.describer.n)\r\ndes$max <- as.numeric(des$max)\r\n\r\ndes <- des[!(is.na(dod.describer.n) | is.na(dob.describer.n))]\r\ndim(des)\r\ndes$years_last_pub_death <- des$dod.describer.n - des$max\r\ndes$age_at_death <- des$dod.describer.n - des$dob.describer.n\r\n\r\nmround <- function(x,base) base*round(x/base)\r\ndes$date.century <- substr(as.character((des$dod.describer.n - des$dob.describer.n)/2 + \r\n des$dob.describer.n), 1, 3)\r\n# des$date.century <- substr(as.character(des$dob.describer.n), 1, 3)\r\ndes$date.century <- mround(as.numeric(des$date.century), 5)\r\ndes$date.century <- paste0(des$date.century, \"0s\")\r\ntable(des$date.century)\r\n\r\ndes$check_young <- ifelse(des$age_at_death >=40, \"T\", \"F\") #young\r\ndes$check_few.spp <- ifelse(des$spp_N <= 12, \"T\", \"F\") #few spp (than median)\r\ndes$check_few.spp.pub <- ifelse(des$spp_per_pub_mean <=10, \"T\", \"F\") #few spp/pub\r\ndes$check_few.pub <- ifelse(des$n_pubs <=3, \"T\", \"F\") # few pub\r\ndes$check_few.pub.near.death <- ifelse(is.na(des$n_spp_20y), \"T\", ifelse(\r\n des$n_spp_20y/des$spp_N >=0.8, \"F\", \"T\")) # few pub\r\n\r\nsummary(des$age_at_death)\r\n\r\ndes0 <- des[, list(names=paste0(full.name.of.describer.n, collapse=\"; \"),\r\n med_age_at_death=median(age_at_death, na.rm=T),\r\n med_spp_N = median(as.numeric(spp_N), na.rm=T),\r\n med_spp_per_pub_mean = median(spp_per_pub_mean, na.rm=T),\r\n med_prop_20y = median(n_spp_20y/spp_N, na.rm=T)\r\n ),\r\n by=c(\"check_young\")]\r\n\r\ndes1 <- des[, list(names=paste0(full.name.of.describer.n, collapse=\"; \"),\r\n med_age_at_death=median(age_at_death, na.rm=T),\r\n med_spp_N = median(as.numeric(spp_N), na.rm=T),\r\n med_spp_per_pub_mean = median(spp_per_pub_mean, na.rm=T),\r\n med_prop_20y = median(n_spp_20y/spp_N, na.rm=T)\r\n ),\r\n by=c(\"check_young\", \"check_few.spp\", \"check_few.spp.pub\", \"check_few.pub.near.death\")]\r\n\r\ndes2 <- des[, list(.N), \r\n by=c(\"check_young\", \"check_few.spp\", \"check_few.spp.pub\", \"check_few.pub.near.death\", \"date.century\")]\r\n\r\ndes3 <- dcast(des2, \r\n check_young + check_few.spp.pub + check_few.pub.near.death + check_few.spp ~ date.century, \r\n value.var=\"N\", fun=sum)\r\ndes4 <- merge(des1, des3,\r\n by=c(\"check_young\", \"check_few.spp.pub\", \"check_few.pub.near.death\", \"check_few.spp\"))\r\n\r\nwrite.csv(des4, paste0(dir_data_ch1, '2019-10-02-taxonomist-one-large-mono.csv'),\r\n row.names=F)\r\n\r\n# Checks\r\ntable(is.na(des$check_young))\r\ntable(is.na(des$check_few.spp))\r\ntable(is.na(des$check_few.spp.pub))\r\ntable(is.na(des$check_few.pub.near.death))\r\n\r\n# Throughout life proportion\r\nauth_throughout_life <- table(des[check_young==\"T\",]$check_few.pub.near.death)\r\nprop.table(auth_throughout_life)*100\r\n\r\n# Group that died young, few publications, few species, average number of species per pub\r\n# Group that did not die young, few publications, few species, average number of species per pub\r\n# Group that did not die young, large number of publications, large number of species, large number of species per pub\r\n# Group that did not die young, few publications, large number of species {N and when}\r\n", "meta": {"hexsha": "c89c6e2efce70639117beb5a9f73c55e13146a3e", "size": 3838, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch1/plots_main/si-D-table-1.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch1/plots_main/si-D-table-1.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch1/plots_main/si-D-table-1.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.2051282051, "max_line_length": 119, "alphanum_fraction": 0.6227201668, "num_tokens": 1148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.33783298728957095}} {"text": "###Analysis to see the model assumptions.\r\n# Question to ask ? is it necessary to run a complex model or a simple linear model??\r\n# \r\n# Y=a+b+e or Y=a+b+c*e is c necessary? is this a constant error or not??\r\n#\r\n# ANSWER: 1.definitely the array effects due to primary or secondary antibodies\r\n#\t\tare multiplicative instead of simply additive. Please see the results\r\n#\t\tin Analysis_proteinWithControl.r \r\n# \t\t2. the block effects are checked in this analysis.\r\n#\r\n# only if you install a Bioconductor package for the first time\r\n# source(\"http://www.bioconductor.org/biocLite.R\")\r\n# # else\r\n# library(\"BiocInstaller\")\r\n# biocLite(\"PAA\", dependencies=TRUE)\r\n \r\n #library(PAA)\r\n\r\nlibrary(ARPPA) #this is the my own package to run analysis\r\n\r\n#now start reading the text exported data\r\n#this is the batch done on 10/08/2015\r\ndatapath<-system.file(\"extdata\", package=\"ARPPA\")\r\ntargets <- list.files(system.file(\"extdata\", package=\"ARPPA\"),\r\n\t pattern = \"targets_text_Batch1\", full.names=TRUE) \r\nelist2<-importTextData(dataFilePath=datapath, targetFile=targets, start.data=51, nrows.data=18803-53,\r\n\t\t\tstart.control=18803, nrows.control=23286,aggregation=\"geoMean\",\r\n\t\t\tas.is=TRUE, header=TRUE,sep=\"\\t\",na.strings=\"\", quote=\"\")\r\n\r\n#===>background correction\r\nlibrary(limma)\r\n\r\nelist2 <- backgroundCorrect(elist2, method=\"normexp\",\r\n normexp.method=\"saddle\")\r\n \r\n #now we need to rearrange the control and testing proteins to background correct control\r\n #it works this way that background correction only working on elist$E\r\n #so we have to do the rearrangement\r\n elist_c<-elist2\r\n elist_c$E<-elist2$C\r\n elist_c$Eb<-elist2$Cb\r\n #this is good enough, we don't have to change elistC$C, since other fields are not touched\r\n elist_c<-backgroundCorrect(elist_c, method=\"normexp\",\r\n normexp.method=\"saddle\")\r\n \r\n #now change it back\r\n elist2$C<-elist_c$E\r\n elist2$Cb<-elist_c$Eb\r\n ##=====>end of background correction\r\n \r\n indices<-grep(\"HumanIgG\", elist2$cgenes$Name)\r\n elist2$cgenes[indices,]\r\n \r\n#now we need to start checking the error models\r\n# we need to check the block effects\r\n\r\n setwd(\"E:\\\\feng\\\\LAB\\\\MSI\\\\AbSynthesis\\\\proteinArray\\\\Run2015_10_08\\\\AnalysisResults\");\r\ngroups<-c(\"HumanIgG\",\r\n\t\t\t\"HumanIgG\", \"Anti-HumanIgG\", \r\n\t\t\t\"Anti-HumanIgG1\",\"Anti-HumanIgG2\",\"Anti-HumanIgG3\",\"Anti-HumanIgG4\",\r\n\t\t\t\"HumanIgG1\",\"HumanIgG2\",\"HumanIgG3\",\"HumanIgG4\"\r\n\t\t\t)\r\n #first plot the all IgGs\r\nindices<-grep(groups[1], elist2$cgenes$Name)\r\nexpByBlock<-cbind(elist2$C[indices,],elist2$cgenes[indices,c(\"Block\",\"Name\")])\r\n\r\npdf(\"BlockEffects_100082015_poolAllIgGs.pdf\")\r\nop<-par(mfrow=c(3,2),\r\n\tpty=\"m\" #\"s\" for square, \"m\" for maximal plot area\r\n\t)\r\nnumArrays<-length(colnames(elist2$C))\t\r\n#by each array now\r\nfor(i in c(1:length(colnames(elist2$C))))#<-1 ##array one\r\n{\r\n\tarrayName<-colnames(elist2$C)[i]\r\n\t#colnames(expByBlock)<-paste(\"a\",colnames(expByBlock),sep=\"\")\r\n\tboxplot(expByBlock[,i]~Block, data=expByBlock,log=\"y\",\r\n\t\tmain=paste(\"Array \",arrayName, sep=\"\"),\r\n\t\txlab=\"Block\",ylab=\"Expression (log)\",\r\n\t\tylim=c(min(expByBlock[,c(1:numArrays)]),max(expByBlock[,c(1:numArrays)])),\r\n\t\tcol=i+1\r\n\t\t)\r\n } \r\n par(op)\r\n dev.off()\r\n\r\n \r\n#second plot the all anti-IgGs\r\nfor(k in c(2:length(groups)))\r\n{\r\n\tindices<-grep(paste(\"^\",groups[k],sep=\"\"), elist2$cgenes$Name)\r\n\texpByBlock<-cbind(elist2$C[indices,],elist2$cgenes[indices,c(\"Block\",\"Name\")])\r\n\tnumArrays<-length(colnames(elist2$C))\t\r\n\t\r\n\tpdf(paste(\"BlockEffects_100082015_\",groups[k],\".pdf\",sep=\"\"))\r\n\top<-par(mfrow=c(3,2),\r\n\t\tpty=\"m\" #\"s\" for square, \"m\" for maximal plot area\r\n\t\t)\r\n\t\t\r\n\t#by each array now\r\n\tfor(i in c(1:length(colnames(elist2$C))))#<-1 ##array one\r\n\t{\r\n\t\tarrayName<-colnames(elist2$C)[i]\r\n\t\t#colnames(expByBlock)<-paste(\"a\",colnames(expByBlock),sep=\"\")\r\n\t\tboxplot(expByBlock[,i]~Block, data=expByBlock,log=\"y\",\r\n\t\t\tmain=paste(\"Array \",arrayName, sep=\"\"),\r\n\t\t\txlab=\"Block\",ylab=\"Expression (log)\",\r\n\t\t\tylim=c(min(expByBlock[,c(1:numArrays)]),max(expByBlock[,c(1:numArrays)])),\r\n\t\t\tcol=i+1\r\n\t\t\t)\r\n\t } \r\n\t par(op)\r\n\t dev.off()\r\n} \r\n \r\n \r\n ####code to \"debugging\"\r\n ctrData_IgG<-elist3_c$E[indices,]\r\n indices<-grep(\"HumanIgG1\", elist3_c$genes$Name)\r\n ctrData_IgG1<-elist3_c$E[indices,]\r\nboxplot(ctrData_IgG1)\r\n indices<-grep(\"HumanIgG2\", elist3_c$genes$Name)\r\n ctrData_IgG2<-elist3_c$E[indices,]\r\nboxplot(ctrData_IgG2)\r\nindices<-grep(\"HumanIgG3\", elist3_c$genes$Name)\r\n ctrData_IgG3<-elist3_c$E[indices,]\r\n ctrData_IgG3N<-cbind(ctrData_IgG3,elist3_c$genes$Name[indices])\r\nboxplot(ctrData_IgG3)\r\nindices<-grep(\"HumanIgG4\", elist3_c$genes$Name)\r\n ctrData_IgG4<-elist3_c$E[indices,]\r\nboxplot(ctrData_IgG4)", "meta": {"hexsha": "c0cd8051c8d0605883fbd041a01ec0a188fb7b22", "size": 4566, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/Analysis_model.r", 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0.6988611476, "num_tokens": 1498, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.33783298728957095}} {"text": "#DECKER_INFO640_Final_Project_Code\n#John Decker\n#INFO 640\n#Prof. McSweeney\n#Fall, 2020\n#Pratt Institute\n\n#R-code\n#load libraries\nlibrary(lubridate)\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(gmodels)\nlibrary(dplyr)\nlibrary(ggthemes)\n\n#load congress information\ncongress <- read.csv(\"~/Documents/GitHub/R_work/data/Congressional_Majorities_1970_2020.csv\")\nhead(congress)\n\n#read in dataset with NEA data\nnea_funding_data <- read.csv(\"~/Documents/GitHub/R_work/data/State_Arts_Funding_w_NEA_data_and_Majority.csv\")\nnames(nea_funding_data)\n\n#strip dollar signs from dollar amount columns\nnea_funding_data$Total.Legislative.Appropriations <- gsub(\"\\\\$\", \"\", nea_funding_data$Total.Legislative.Appropriations)\nnea_funding_data$Total.Other.State.funding <- gsub(\"\\\\$\", \"\", nea_funding_data$Total.Other.State.funding)\nnea_funding_data$Total.National.Endowment.for.the.Arts.Funding <- gsub(\"\\\\$\", \"\", nea_funding_data$Total.National.Endowment.for.the.Arts.Funding)\nnea_funding_data$Total.Private.and.Misc..Funding <- gsub(\"\\\\$\", \"\", nea_funding_data$Total.Private.and.Misc..Funding)\nnea_funding_data$Total.Revenue <- gsub(\"\\\\$\", \"\", nea_funding_data$Total.Revenue)\n\n#strip commas from dollar amount columns\nnea_funding_data$Total.Legislative.Appropriations <- gsub(\",\", \"\", nea_funding_data$Total.Legislative.Appropriations)\nnea_funding_data$Total.Other.State.funding <- gsub(\",\",\"\", nea_funding_data$Total.Other.State.funding)\nnea_funding_data$Total.National.Endowment.for.the.Arts.Funding <- gsub(\",\", \"\", nea_funding_data$Total.National.Endowment.for.the.Arts.Funding)\nnea_funding_data$Total.Private.and.Misc..Funding <- gsub(\",\", \"\", nea_funding_data$Total.Private.and.Misc..Funding)\nnea_funding_data$Total.Revenue <- gsub(\",\", \"\", nea_funding_data$Total.Revenue)\n\n#convert dollar amount columns to numeric\nnea_funding_data$Total.Legislative.Appropriations <- as.numeric(nea_funding_data$Total.Legislative.Appropriations, rm.na = TRUE)\u00a0\nnea_funding_data$Total.Other.State.funding <- as.numeric(nea_funding_data$Total.Other.State.funding, rm.na = TRUE)\nnea_funding_data$Total.National.Endowment.for.the.Arts.Funding <- as.numeric(nea_funding_data$Total.National.Endowment.for.the.Arts.Funding, rm.na = TRUE)\nnea_funding_data$Total.Private.and.Misc..Funding <- as.numeric(nea_funding_data$Total.Private.and.Misc..Funding, rm.na=TRUE)\nnea_funding_data$Total.Revenue <- as.numeric(nea_funding_data$Total.Revenue, rm.na=TRUE)\n\n#initial analysis of funding data\nsummary(nea_funding_data)\n\n#separate out states with high, medium, and low state-wide arts funding\nnea_just_cali <- nea_funding_data %>% filter(State.or.Jurisdiction == \"California\")\nnea_just_tx <- nea_funding_data %>% filter(State.or.Jurisdiction == \"Texas\")\nnea_just_ny <- nea_funding_data %>% filter(State.or.Jurisdiction == \"New York\")\nnea_just_il <- nea_funding_data %>% filter(State.or.Jurisdiction == \"Illinois\")\nnea_just_ak <- nea_funding_data %>% filter(State.or.Jurisdiction == \"Alaska\")\nnea_just_nv <- nea_funding_data %>% filter(State.or.Jurisdiction == \"Nevada\")\n\n#calculate mean for all of the test states\ntotal_test_state_mean <-(mean(nea_just_cali$Total.National.Endowment.for.the.Arts.Funding) + mean(nea_just_tx$Total.National.Endowment.for.the.Arts.Funding) + mean(nea_just_ny$Total.National.Endowment.for.the.Arts.Funding) + mean(nea_just_il$Total.National.Endowment.for.the.Arts.Funding) + mean(nea_just_ak$Total.National.Endowment.for.the.Arts.Funding) + mean(nea_just_nv$Total.National.Endowment.for.the.Arts.Funding))/6\n\n#t test for all states in unadjusted dollars\nt.test(nea_funding_data$Total.National.Endowment.for.the.Arts.Funding~nea_funding_data$Congress.Majority)\n\n#subset all state data for culture wars period\nculture_wars_states_rep_maj <- nea_funding_data %>% filter(Fiscal.Year >= 1995)\n\n#t test for all states in unadjusted dollars from 1995 to 2020\nt.test(culture_wars_states_rep_maj$Total.National.Endowment.for.the.Arts.Funding~culture_wars_states_rep_maj$Congress.Majority)\n\n# move to dataset with inflation adjusted dollars to refine testing\nnea_adjusted_data <-read.csv(\"~/Documents/GitHub/R_work/data/nea_adjusted_to_2020_w_majority_and_gdp.csv\")\u00a0\nhead(nea_adjusted_data)\n#t test for aggregated appropriations in unadjusted dollars\nt.test(nea_adjusted_data$Total.NEA.Appropriation~nea_adjusted_data$Congress.Majority)\n\n#t test for aggregated appropriations in adjusted dollars\nt.test(nea_adjusted_data$In.2020.Dollars~nea_adjusted_data$Congress.Majority)\n\n#t test for culture wars period in unadjusted dollars\nculture_wars <- nea_adjusted_data %>% filter(Fiscal.Year >= 1995)\nt.test(culture_wars$Total.NEA.Appropriation~culture_wars$Congress.Majority)\n\n#t test for culture wars period in adjusted dollars\nculture_wars <- nea_adjusted_data %>% filter(Fiscal.Year >= 1995)\nt.test(culture_wars$In.2020.Dollars~culture_wars$Congress.Majority)\n\nnea_adjusted_data_national <- read.csv(\"~/Documents/GitHub/R_work/data/national_NEA_appropriations_w_Majority.csv\")\nhead(nea_adjusted_data_national)\n\n#examine national appropriations to test hypothesis\n#t test for all states in unadjusted dollars (Congress)\nt.test(nea_adjusted_data_national$National.NEA.Appropriation~nea_adjusted_data_national$Congress.Majority)\n\n#examine national appropriations to test hypothesis\n#t test for all states in 2020 dollars (House)\nt.test(nea_adjusted_data_national$Adjusted.National.NEA.Appropriation~nea_adjusted_data_national$Congress.Majority)\n\n#t test for culture wars period in unadjusted dollars for national appropriations (House)\nculture_wars_2 <- nea_adjusted_data_national %>% filter(Fiscal.Year >= 1995)\nt.test(culture_wars_2$National.NEA.Appropriation~culture_wars_2$Congress.Majority)\n\n#t test for culture wars period in 2020 dollars for national appropriations (House)\nculture_wars_2 <- nea_adjusted_data_national %>% filter(Fiscal.Year >= 1995)\nt.test(culture_wars_2$Adjusted.National.NEA.Appropriation~culture_wars_2$Congress.Majority)\n\n# Discover for why national nea appropriations show strong congressional effect but weak economic effect and state nea appropriations show the opposite\n\n# linear models for funding by larger economy\nfunding_lm_just_GDP <- lm(formula = Total.NEA.Appropriation ~ GDP.Per.Capita, data = nea_adjusted_data)\n\nfunding_lm_just_GrowthRate <- lm(formula = Total.NEA.Appropriation ~ Growth.Rate, data = nea_adjusted_data)\n\n# multivariable linear model for funding by larger economy\nfunding_lm_GDP_and_GrowthRate <- lm(formula = Total.NEA.Appropriation ~ Growth.Rate + GDP.Per.Capita, data = nea_adjusted_data)\n\n# multivariable linear model for funding by larger economy with inflation\nfunding_lm_GDP_GrowthRate_Inflation <- lm(formula = Total.NEA.Appropriation ~ Growth.Rate + GDP.Per.Capita + Avg.Yearly.Inflation, data = nea_adjusted_data)\n\n#report statistics for each model\nsummary(funding_lm_just_GDP)\nsummary(funding_lm_just_GrowthRate)\nsummary(funding_lm_GDP_and_GrowthRate)\nsummary(funding_lm_GDP_GrowthRate_Inflation)\n\n#use best linear model for test prediction\nunknown_funding <- data.frame(Growth.Rate = 3.62, GDP.Per.Capita = 47975.97, Avg.Yearly.Inflation = 2.8)\npredict(funding_lm_GDP_GrowthRate_Inflation, unknown_funding)\n\n#code for figures used in paper\n#Figure 1\nggplot(nea_adjusted_data, aes(x=Fiscal.Year, y=Total.NEA.Appropriation, color=Congress.Majority)) + geom_line() + scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(nea_adjusted_data$Total.NEA.Appropriation), color = \"dark green\", linetype=\"dashed\") +\n\u00a0\u00a0\ntheme_tufte() +\nlabs(title = \"Total State NEA Funding per Year (in unadjusted dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 1\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nGreen = Mean Funding Level\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure1.jpg\", width = 6, height = 4)\n\n#Figure 2\nggplot(culture_wars, aes(x=Fiscal.Year, y=Total.NEA.Appropriation, color=Congress.Majority)) + geom_line() + scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(culture_wars$Total.NEA.Appropriation), color = \"dark green\", linetype=\"dashed\") +\n\u00a0\u00a0\ntheme_tufte() +\nlabs(title = \"State NEA Funding per Year 1995-2020 (in unadjusted dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 2\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nGreen = Mean Funding Level\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure2.jpg\", width = 6, height = 4)\n\n#Figure 3\nggplot(nea_adjusted_data, aes(x=Fiscal.Year, y=In.2020.Dollars, color=Congress.Majority)) + geom_line() + scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(nea_adjusted_data$In.2020.Dollars), color = \"dark green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"Total State NEA Funding per Year (in 2020 Dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 3\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nGreen = Mean Funding Level\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure3.jpg\", width = 6, height = 4)\n\n#Figure 4\nggplot(culture_wars, aes(x=Fiscal.Year, y=In.2020.Dollars, color=Congress.Majority)) + geom_line() + scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(nea_adjusted_data$In.2020.Dollars), color = \"dark green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"State NEA Funding 1995-2020 (in 2020 dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 4\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nGreen = Mean Funding Level (in 2020 dollars)\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure4.jpg\", width = 6, height = 4)\n\n#Figure 5\nggplot(nea_adjusted_data_national, aes(x=Fiscal.Year, y=National.NEA.Appropriation, color=Congress.Majority)) + geom_line() +\u00a0\n\u00a0\u00a0scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(nea_adjusted_data_national$National.NEA.Appropriation), color = \"dark green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"National NEA Funding per Year (in unadjusted dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 5\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nHorizontal Green Line = Mean of National NEA Appropriations\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure5.jpg\", width = 6, height = 4)\n\n#Figure 6\nggplot(culture_wars_2, aes(x=Fiscal.Year, y=National.NEA.Appropriation, color=Congress.Majority)) + geom_line() +\u00a0\n\u00a0\u00a0scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(culture_wars_2$National.NEA.Appropriation), color = \" dark green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"National NEA Funding 1995-2020 (in unadjusted dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 6\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nHorizontal Green Line = Mean of National NEA Appropriations\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure6.jpg\", width = 6, height = 4)\n\n#Figure 7\nggplot(nea_adjusted_data_national, aes(x=Fiscal.Year, y=Adjusted.National.NEA.Appropriation, color=Congress.Majority)) + geom_line() +\n\u00a0\u00a0scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(nea_adjusted_data_national$Adjusted.National.NEA.Appropriation), color = \"green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"National NEA Funding per Year (in 2020 dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 7\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nHorizontal Green Line = Mean of National NEA Appropriations\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure7.jpg\", width = 6, height = 4)\n\n#Figure 8\nggplot(culture_wars_2, aes(x=Fiscal.Year, y=Adjusted.National.NEA.Appropriation, color=Congress.Majority)) + geom_line() +\n\u00a0\u00a0scale_y_continuous(labels = scales::comma) +\n\ngeom_hline(yintercept = mean(culture_wars_2$Adjusted.National.NEA.Appropriation), color = \"green\", linetype=\"dashed\") +\n\ntheme_tufte() +\nlabs(title = \"National NEA Funding 1995-2020 (in 2020 dollars)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 8\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Light Blue = Democratic Majority \\nDark Blue = Republican Majority \\nHorizontal Green Line = Mean of National NEA Appropriations\") +\ntheme(legend.position = \"none\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.caption = element_text(hjust = 0.0)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure8.jpg\", width = 6, height = 4)\n\n\n#Figure 9\n#plot several states with vertical lines showing majority change in congress\nggplot(nea_just_cali, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) + geom_line() + scale_y_continuous(labels = scales::comma) +\ngeom_line(data = nea_just_tx, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_ny, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_il, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_ak, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_nv, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\u00a0\n\n#vertical lines for congressional majorities (red = repub, blue = dem)\u00a0\ngeom_vline(xintercept = 1995, color = \"red\", linetype = \"dotted\") +\ngeom_vline(xintercept = 2007, color = \"blue\", linetype = \"dotted\") +\ngeom_vline(xintercept = 2011, color = \"red\", linetype = \"dotted\") +\ngeom_vline(xintercept = 2019, color = \"blue\", linetype = \"dotted\") +\n\n#mean of funding for all states in sample\u00a0\u00a0\ngeom_hline(yintercept = total_test_state_mean, linetype = \"dashed\", color = \"brown\") +\n\ntheme_tufte() +\nlabs(title=\"NEA Funding by State\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"NEA Funding Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0color = \"State\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 9\",\n\u00a0\u00a0\u00a0\u00a0\u00a0subtitle = \"Source: National Assembly of State Arts Agencies, \\nAnnual Appropriations and Revenue Survey Data\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Red Vertical Lines = Start of Republican Majorty \\nBlue Vertical Lines = Start of Democratic Majority\\nBrown Horizontal Line = Mean Funding for all Test States\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.subtitle = element_text(hjust = 0.5, size=10)) +\ntheme(plot.caption = element_text(hjust = 0.0, size=6)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure9.jpg\", width = 6, height = 4)\n\n#Figure 10\n#plot several states with vertical lines showing majority change in congress\nggplot(nea_just_cali, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) + geom_line() + scale_y_continuous(labels = scales::comma) +\ngeom_line(data = nea_just_tx, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_ny, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_il, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_ak, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\ngeom_line(data = nea_just_nv, aes(x=Fiscal.Year, y=Total.National.Endowment.for.the.Arts.Funding, color = State.or.Jurisdiction)) +\u00a0\n\n#Vertical lines for major economic events.\n#1970s recession 1\ngeom_vline(xintercept = 1972, color = \"gray\") +\n#1970s recession 2\ngeom_vline(xintercept = 1976, color = \"gray\") +\n#1980s recession\ngeom_vline(xintercept = 1982, color = \"gray\") +\n#black monday\ngeom_vline(xintercept = 1987, color = \"gray\") +\n#1990-1991 recession\ngeom_vline(xintercept = 1991, color = \"gray\") +\n#major recession tied to Asian Banking Crisis\ngeom_vline(xintercept = 1997, color = \"gray\") +\n#dot com crash\ngeom_vline(xintercept = 1999, color = \"gray\") +\n#september 11\ngeom_vline(xintercept = 2001, color = \"gray\") +\n#2008 recession\ngeom_vline(xintercept = 2008, color = \"gray\") +\n#global oil drop\ngeom_vline(xintercept = 2014, color = \"gray\") +\n\u00a0\u00a0\n#mean of funding for all states in sample\u00a0\u00a0\ngeom_hline(yintercept = total_test_state_mean, linetype = \"dashed\", color = \"brown\") +\n\ntheme_tufte() +\nlabs(title=\"NEA Funding by State\",\n\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0y = \"NEA Funding Amount\",\n\u00a0\u00a0\u00a0\u00a0\u00a0color = \"State\",\n\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 10\",\n\u00a0\u00a0\u00a0\u00a0\u00a0subtitle = \"Source: National Assembly of State Arts Agencies, \\nAnnual Appropriations and Revenue Survey Data\",\n\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Brown Horizontal Line = Mean Funding for all Test States \\nGray Vertical Lines = Major Economic Events \\n\\t\\tMajor Recession: 1970, Major Recession: 1976, Major Recession: 1982 \\n\\t\\tBlack Monday: 1987, Major Recession: 1990-91, Asian Banking Crisis: 1997, Dot Com Crash: 1999 \\n\\t\\t9/11: 2001, Great Recession: 2008, Global Oil Drop: 2014\") +\ntheme(plot.title = element_text(hjust = 0.5)) +\ntheme(plot.subtitle = element_text(hjust = 0.5, size=10)) +\ntheme(plot.caption = element_text(hjust = 0.0, size=6)) +\ntheme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure10.jpg\", width = 6, height = 4)\n\n#Figure 11\n\n# scale amount used to adjust total dollars to amount in thousands of dollars so that both measures are the same scale\nscale_amount = 1000\n\nggplot(nea_adjusted_data, aes(x=Fiscal.Year, y= GDP.Per.Capita)) + geom_line(color=\"blue\") +\n\u00a0\u00a0geom_line(data=nea_adjusted_data, aes(x=Fiscal.Year, y=Total.NEA.Appropriation/scale_amount, color = \"red\")) +\n\u00a0\u00a0stat_smooth(method=\"lm\", formula = y~x, linetype = \"dashed\", color = \"brown\", size = 0.3) +\n\u00a0\u00a0theme_tufte() +\n\u00a0\u00a0labs(title = \"GDP Per Capita & State NEA Allocation by Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0subtitle = \"NEA Scaled to GDP: (NEA/1000)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Blue = GDP Per Capita\\nRed = NEA Appropriation\\nBrown Dashed = Line of Best Fit\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 11\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount\") +\n\u00a0\u00a0theme(legend.position = \"none\") +\n\u00a0\u00a0theme(plot.title = element_text(hjust = 0.5)) +\n\u00a0\u00a0theme(plot.subtitle = element_text(hjust = 0.5, size=10)) +\n\u00a0\u00a0theme(plot.caption = element_text(hjust = 0.0)) +\n\u00a0\u00a0theme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure11.jpg\", width = 6, height = 4)\n\n#Figure 12\nscale_amount = 1000\n\nggplot(nea_adjusted_data, aes(x=Fiscal.Year, y=GDP.Per.Capita/Growth.Rate)) + geom_line(color = \"blue\") +\n\u00a0\u00a0geom_line(data=nea_adjusted_data, aes(x=Fiscal.Year, y=Total.NEA.Appropriation/(scale_amount*Growth.Rate), color = \"red\")) +\n\u00a0\u00a0stat_smooth(method=\"lm\", formula = y~x, linetype = \"dashed\", color = \"brown\", size = 0.3) +\n\u00a0\u00a0\n\u00a0\u00a0theme_tufte() +\n\u00a0\u00a0labs(title = \"GDP Per Capita & State NEA Allocation by Year & Growth Rate\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0subtitle = \"Formula: GDP/Growth Rate ~= NEA/(1,000*Growth Rate)\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Blue = GDP Per Capita\\nRed = NEA Appropriation\\nBrown Dashed = Line of Best Fit\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 12\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount in Thousands of Dollars\") +\n\u00a0\u00a0theme(legend.position = \"none\") +\n\u00a0\u00a0theme(plot.title = element_text(hjust = 0.5)) +\n\u00a0\u00a0theme(plot.subtitle = element_text(hjust = 0.5, size=8)) +\n\u00a0\u00a0theme(plot.caption = element_text(hjust = 0.0)) +\n\u00a0\u00a0theme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure12.jpg\", width = 6, height = 4)\n\n#Figure 13\n#shows GDP and funding in relation to economic health indicators, specifically Growth Rate and Inflation.\n\nscale_amount = 1000\n\nggplot(nea_adjusted_data, aes(x=Fiscal.Year, y=(GDP.Per.Capita/Growth.Rate)*Avg.Yearly.Inflation)) + geom_line(color=\"blue\") +\n\u00a0\u00a0geom_line(data=nea_adjusted_data, aes(x=Fiscal.Year, y=Total.NEA.Appropriation/(scale_amount*Growth.Rate)*Avg.Yearly.Inflation, color = \"red\")) +\n\u00a0\u00a0stat_smooth(method=\"lm\", formula=y~x, linetype = \"dashed\", color = \"brown\", size = 0.3) +\n\u00a0\u00a0\n\u00a0\u00a0theme_tufte() +\n\u00a0\u00a0labs(title = \"GDP Per Capita & State NEA Allocation by Year\\nMapped by Growth Rate and Average Yearly Inflation\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0subtitle = \"Formula: (GDP/Growth Rate)*Inflation ~= (NEA/(1,000*Growth Rate)*Inflation\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0caption = \"Blue = GDP Per Capita\\nRed = NEA Appropriation\\nBrown Dashed = Line of Best Fit\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0tag = \"Figure 13\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0x = \"Fiscal Year\",\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0y = \"Dollar Amount in Thousands of Dollars\") +\n\u00a0\u00a0theme(legend.position = \"none\") +\n\u00a0\u00a0theme(plot.title = element_text(hjust = 0.5)) +\n\u00a0\u00a0theme(plot.subtitle = element_text(hjust = 0.5, size=8)) +\n\u00a0\u00a0theme(plot.caption = element_text(hjust = 0.0)) +\n\u00a0\u00a0theme(plot.tag = element_text(size = 10))\n\n#ggsave(\"Figure13.jpg\", width = 6, height = 4)\n", "meta": {"hexsha": "0b5777793034f83e11e5c2e76327f6004e4480fc", "size": 21752, "ext": "r", "lang": "R", "max_stars_repo_path": "DECKER_INFO640_Final_Project_Code.r", "max_stars_repo_name": "john-decker/NEA-Funding-Project", "max_stars_repo_head_hexsha": "da7f6c50316586112d0c5168a664cea627e0e9ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DECKER_INFO640_Final_Project_Code.r", "max_issues_repo_name": "john-decker/NEA-Funding-Project", "max_issues_repo_head_hexsha": "da7f6c50316586112d0c5168a664cea627e0e9ef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, 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YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3378329787049826}} {"text": "require(dplyr)\ndata = read.csv(\"merge.csv\")\n\nplotMerge = function(n, yaxt = 's') {\n if (yaxt == 'n') {\n ylab = NA\n old = par(mar=c(3.5, 0.5, 2, 0.2))\n } else {\n ylab = \"Absolute error (ppm)\"\n old = par(mar=c(3.5, 4.0, 2, 0.2))\n }\n par(las=1)\n par(lwd=0.5)\n par(cex.lab=1.3)\n par(cex.axis=0.9)\n par(mgp=c(2.4, 0.5, 0))\n par(tcl=-0.3)\n\n our.data = data %>% filter(type == \"quantile\", parts == n)\n boxplot(e1*1e6 ~ q, at=(1:6)-0.23, xaxt='n', boxwex=0.19, our.data,\n ylim=c(-3000, 3000), cex=0.5, yaxt = yaxt,\n col=rgb(0.95, 0.95, 0.95), \n xlab=NA, ylab=NA)\n title(xlab=expression('Quantile '(italic(q))), mgp=c(2.2, 0.5, 0))\n title(ylab=ylab, mgp=c(2.8, 0.0, 0))\n boxplot(e3*1e6 ~ q, at=1:6, xaxt='n', boxwex=0.19, add=T, our.data,\n col=rgb(0.7, 0.7, 0.7), cex=0.5, yaxt = yaxt)\n boxplot(e2*1e6 ~ q, at=1:6+0.23, xaxt='n', boxwex=0.19, add=T, our.data,\n col=rgb(0.4, 0.4, 0.4), cex=0.5, yaxt = yaxt)\n axis(side=1, at=1:6,\n labels=c(expression(10^-3), expression(10^-2), 0.1, 0.2, 0.3, 0.5),\n )\n legend(0.13, -1300,\n expression(\"Direct \"(delta==100),\n \"Stratified merge \"(delta==200,100),\n \"Flat merge \"(delta==100,100)), \n fill = c(rgb(0.95, 0.95, 0.95), rgb(0.7, 0.7, 0.7), rgb(0.4, 0.4, 0.4)),\n cex=0.75)\n abline(h=0, col=rgb(0.4, 0.4, 0.4))\n title(paste(n, \" parts\"), cex.main=1.3)\n box()\n par(old)\n}\n\n#setEPS()\npdf(\"merge.pdf\", width=6, height=2.4, pointsize=9, family='serif')\nlayout(matrix(c(1,2,3), 1, 3, byrow=T), widths=c(1.285,1,1))\npar(cex=1)\n\nplotMerge(5, 's')\nplotMerge(20, 'n')\nplotMerge(100, 'n')\n\ndev.off()\n", "meta": {"hexsha": "5a71cc34e8a68aa866ebe9489390d35bee4bf49c", "size": 1675, "ext": "r", "lang": "R", "max_stars_repo_path": "quality/merge.r", "max_stars_repo_name": "slandelle/t-digest", "max_stars_repo_head_hexsha": "8630c2b678ae5d2c055ee26bea6cf0af79c55fa6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1479, "max_stars_repo_stars_event_min_datetime": "2015-01-05T09:42:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T04:20:21.000Z", "max_issues_repo_path": "quality/merge.r", "max_issues_repo_name": "slandelle/t-digest", "max_issues_repo_head_hexsha": "8630c2b678ae5d2c055ee26bea6cf0af79c55fa6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 141, "max_issues_repo_issues_event_min_datetime": "2015-01-02T14:02:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-03T22:54:11.000Z", "max_forks_repo_path": "quality/merge.r", "max_forks_repo_name": "slandelle/t-digest", "max_forks_repo_head_hexsha": "8630c2b678ae5d2c055ee26bea6cf0af79c55fa6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 222, "max_forks_repo_forks_event_min_datetime": "2015-01-08T23:52:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T04:50:59.000Z", "avg_line_length": 30.4545454545, "max_line_length": 81, "alphanum_fraction": 0.527761194, "num_tokens": 787, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3378329704266836}} {"text": "# Copyright (c) 2020, Zolisa Bleki\n# SPDX-License-Identifier: BSD-3-Clause\n\n\nhypertruncated_mvn_data <- function() {\n k1 <- 100\n k2 <- 20\n out <- list()\n out$mean <- rnorm(k1)\n cov <- matrix(rnorm(k1 * k1), ncol = k1)\n out$cov <- cov %*% t(cov)\n out$g <- matrix(rep(k2, k1), ncol = k1)\n out$r <- rnorm(k2)\n out$gnrow <- k2\n out$gncol <- k1\n\n out\n}\n\nstructured_mvn_data <- function() {\n out <- hypertruncated_mvn_data()\n k1 <- out$gncol\n k2 <- out$gnrow\n eig <- eigen(out$cov)\n out$phi <- eig$vector[1:k2, ]\n out$omega <- diag(eig$values[1:k2])\n out$a <- diag(runif(k1))\n\n out\n}\n\ntest_that(\"wrong generator name parameter\", {\n expect_that(HTNGenerator(gen = \"blah\"), throws_error())\n expect_that(HTNGenerator()$rng, is_a(\"externalptr\"))\n expect_that(HTNGenerator(gen = \"xrs128p\"), is_a(\"HTNGenerator\"))\n expect_that(HTNGenerator(gen = \"pcg64\"), is_a(\"HTNGenerator\"))\n})\n\n\ntest_that(\"hyperplane truncated norm method\", {\n out <- hypertruncated_mvn_data()\n mean <- out$mean\n cov <- out$cov\n g <- out$g\n r <- out$r\n\n gen <- HTNGenerator(10)\n hpmvn <- gen$hyperplane_truncated_mvnorm\n expect_that(hpmvn(matrix(mean), cov, g, r), throws_error())\n expect_that(hpmvn(mean[2:out$gncol], cov, g, r), throws_error())\n expect_that(hpmvn(mean, cov, g[, 2:out$gncol], r), throws_error())\n # test consistency of output when `diag=True` is used for same seed\n cov_diag <- diag(runif(out$gncol))\n gen1 <- HTNGenerator(10)$hyperplane_truncated_mvnorm(mean, cov_diag, g, r)\n gen2 <- HTNGenerator(10)$hyperplane_truncated_mvnorm(\n mean, cov_diag, g, r, diag = TRUE\n )\n expect_equal(gen1, gen2)\n # test results of passing output array through the `out` parameter\n res <- rep(1, out$gncol)\n hpmvn(mean, cov, g, r, out = res)\n expect_false(length(sum(res == rep(1, out$gncol))) == length(res))\n # test results of samples truncated on the hyperplane sum(x) = 0\n gg <- matrix(rep(1, out$gncol), ncol = out$gncol)\n r <- c(0)\n expect_equal(sum(hpmvn(mean, cov, gg, r)), 0)\n # test for non-SPD covariance input\n c <- diag(rnorm(out$gncol))\n expect_that(hpmvn(mean, c, g, r), throws_error())\n})\n\n\ntest_that(\"structured precision normal\", {\n out <- structured_mvn_data()\n mean <- out$mean\n a <- out$a\n phi <- out$phi\n omega <- out$omega\n\n gen <- HTNGenerator(10)\n spmvn <- gen$structured_precision_mvnorm\n expect_that(spmvn(matrix(mean), a, phi, omega), throws_error())\n expect_that(spmvn(mean, a, phi[2:nrow(phi), ], omega), throws_error())\n # raise error if invalid matrix structure is specified\n expect_that(spmvn(mean, a, phi, omega, a_type = -1000), throws_error())\n # test consistency of output when `a_type` or `o_type` is given\n gen1 <- HTNGenerator(10)$structured_precision_mvnorm(mean, a, phi, omega)\n gen2 <- HTNGenerator(10)$structured_precision_mvnorm(\n mean, a, phi, omega, a_type = \"diagonal\", o_type = \"diagonal\"\n )\n expect_equal(gen1, gen2)\n # test results of passing output array through the `out` parameter\n m <- length(mean)\n res <- rep(0, m)\n spmvn(mean, a, phi, omega, out = res)\n expect_false(length(sum(res == rep(0, m))) == length(res))\n # test for non-SPD a input\n aa <- diag(rnorm(ncol(phi)))\n expect_that(hpmvn(mean, aa, g, r), throws_error())\n})\n\n\ntest_that(\"reproducability via seeding\", {\n out <- hypertruncated_mvn_data()\n mean <- out$mean\n cov <- out$cov\n g <- out$g\n r <- out$r\n\n gen1 <- HTNGenerator(10)\n res1 <- gen1$hyperplane_truncated_mvnorm(mean, cov, g, r)\n gen2 <- HTNGenerator(10)\n res2 <- gen2$hyperplane_truncated_mvnorm(mean, cov, g, r)\n gen3 <- HTNGenerator(233)\n res3 <- gen3$hyperplane_truncated_mvnorm(mean, cov, g, r)\n expect_equal(res1, res2)\n expect_false(length(sum(res1 == res3)) == length(res1))\n # test if the errors are raised when the wrong input for seed is given\n expect_that(HTNGenerator(-100), throws_error(\"cannot be negative\"))\n expect_that(HTNGenerator(\"100\"), throws_error(\"non-numeric\"))\n})\n", "meta": {"hexsha": "aefe6a50e7e40ed465188d717f00e989df66127c", "size": 4113, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_rhtnorm.r", "max_stars_repo_name": "zoj613/htnorm", "max_stars_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-12-06T22:29:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T06:10:16.000Z", "max_issues_repo_path": "tests/testthat/test_rhtnorm.r", "max_issues_repo_name": "zoj613/htnorm", "max_issues_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-07-20T18:43:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-23T07:48:11.000Z", "max_forks_repo_path": "tests/testthat/test_rhtnorm.r", "max_forks_repo_name": "zoj613/htnorm", "max_forks_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-02-01T16:15:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-23T20:29:13.000Z", "avg_line_length": 33.7131147541, "max_line_length": 78, "alphanum_fraction": 0.6457573547, "num_tokens": 1276, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3378178789624539}} {"text": "n <- function(x) function()x\n\nA <- function(k, x1, x2, x3, x4, x5) {\n B <- function() A(k <<- k-1, B, x1, x2, x3, x4)\n if (k <= 0) x4() + x5() else B()\n}\n\nA(10, n(1), n(-1), n(-1), n(1), n(0))\n", "meta": {"hexsha": "9d354d1f2c70fa708fe87d9d04d1a54126b180b4", "size": 195, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Man-or-boy-test/R/man-or-boy-test-1.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Man-or-boy-test/R/man-or-boy-test-1.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Man-or-boy-test/R/man-or-boy-test-1.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 21.6666666667, "max_line_length": 49, "alphanum_fraction": 0.4461538462, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3378178717264721}} {"text": "#!/usr/bin/env Rscript\n\nlibrary(modules, warn.conflicts = FALSE, quietly = TRUE)\nsys = import('sys')\n\nplot_te = function (data, summary) {\n import_package('ggplot2', attach = TRUE)\n\n data$Which = factor(data$Which,\n levels = c('All', 'Upregulated', 'GO', 'Housekeeping', 'Ribosomal', 'Proliferation'),\n ordered = TRUE)\n\n plot = ggplot(data, aes(x = Mode, y = TE)) +\n (if (summary)\n geom_boxplot(width = 0.4)\n else\n geom_boxplot(width = 0.2, outlier.size = 0)) +\n scale_y_continuous(limits = c(0.1, 0.85)) +\n facet_wrap(~ Which, nrow = 1) +\n theme_bw()\n\n if (summary)\n plot\n else {\n import_package('ggbeeswarm', attach = TRUE)\n plot +\n geom_point(aes(color = mRNA, shape = tRNA),\n position = position_beeswarm()) +\n scale_color_manual(limits = names(config$celltype_colors),\n values = config$celltype_colors) +\n scale_shape_manual(limits = names(config$celltype_colors),\n values = c(15, 4, 5, 16))\n }\n}\n\nmean_center = function (all_te) {\n import_package('dplyr', attach = TRUE)\n trna_zscores = all_te %>%\n filter(Mode == 'Match') %>%\n group_by(Which) %>%\n mutate(AllMedian = median(TE)) %>%\n group_by(tRNA, add = TRUE) %>%\n mutate(tRNAMedian = median(TE)) %>%\n summarize(Scaling = first(tRNAMedian / AllMedian))\n\n inner_join(all_te, trna_zscores, by = c('Which', 'tRNA')) %>%\n mutate(TE = TE / Scaling)\n}\n\nsys$run({\n valid_te_methods = c('simple-te', 'wobble-te', 'tai')\n args = sys$cmdline$parse(opt('t', 'te',\n do.call(sprintf,\n c('the method to calculate translation efficiency (%s, %s or %s)',\n lapply(valid_te_methods, dQuote))),\n valid_te_methods[1],\n function (x) x %in% valid_te_methods),\n opt('c', 'mean-center', 'center tRNA strata before plotting?', FALSE),\n opt('s', 'summary', 'plot summary rather than detailed points?', FALSE),\n opt('i', 'ramp-up', 'use codons at start of transcript (\u201cramp up\u201d) only', FALSE),\n arg('species', 'the species'),\n arg('outfile', 'the filename of the PDF output'))\n\n config = import(sprintf('../config_%s', args$species))\n te = readRDS(sprintf('results/%s%s-%s.rds',\n if (args$ramp_up) 'init-' else '',\n args$te, args$species))\n if (args$mean_center)\n te = mean_center(te)\n\n # Required by ggplot2, see \n library(methods)\n on.exit(dev.off())\n pdf(args$outfile)\n plot(plot_te(te, args$summary))\n})\n\n# vim: ft=r\n", "meta": {"hexsha": "d4b9d902a1067c3bd1412e5e8e8f22cf845f9e96", "size": 3017, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/plot-te-boxplot.r", "max_stars_repo_name": "klmr/codons", "max_stars_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-12-19T00:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2015-12-19T00:54:46.000Z", "max_issues_repo_path": "scripts/plot-te-boxplot.r", "max_issues_repo_name": "klmr/codons", "max_issues_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-03-06T14:47:12.000Z", "max_issues_repo_issues_event_max_datetime": "2016-03-06T14:47:12.000Z", "max_forks_repo_path": "scripts/plot-te-boxplot.r", "max_forks_repo_name": "klmr/codons", "max_forks_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1898734177, "max_line_length": 110, "alphanum_fraction": 0.5144182963, "num_tokens": 730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635841117624, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3378178644904902}} {"text": "#08/14/2018\r\n#christina bandaragoda\r\n\r\n##Installations (if needed)\r\n#install.packages(\"xlsx\")\r\n## installing/loading the package:\r\n#if(!require(installr)) {\r\n# install.packages(\"installr\"); \r\n# require(installr)\r\n#} #load / install+load installr\r\n## using the package:\r\n#updateR()\r\n\r\n\r\n# change param options\r\noptions(java.parameters = \"-Xmx1024m\")\r\nlibrary(xlsx)\r\nlibrary(data.table)\r\nlibrary(dplyr)\r\nlibrary(hydroGOF)\r\n\r\n# home directories\r\nif(dir.exists('C:/Users/Jimmy/Google Drive/Watershed Dynamics Group/Projects/Bertrand Creek/2016 Project')){ # for jim\r\n Project2016 <- 'C:/Users/Jimmy/Google Drive/Watershed Dynamics Group/Projects/Bertrand Creek/2016 Project'\r\n} else if (dir.exists('C:/Users/cband/Nooksack/Bertrand Creek/2016 Project')) { # for Christina\r\n Project2016 <- 'C:/Users/cband/Nooksack/Bertrand Creek/2016 Project'\r\n}\r\n# home directories\r\nif(dir.exists('C:/Users/Jimmy/Google Drive/Watershed Dynamics Group/Projects/Bertrand Creek/2016 Project')){ # for jim\r\n Project2018 <- 'C:/Users/Jimmy/Google Drive/Watershed Dynamics Group/Projects/Bertrand Creek/2016 Project'\r\n} else if (dir.exists('C:/Users/cband/Nooksack/Bertrand Creek/2018 Project')) { # for Christina\r\n Project2018 <- 'C:/Users/cband/Nooksack/Bertrand Creek/2018 Project'\r\n}\r\n# subdirectories\r\n\r\nWMoff2018_Models <- paste(Project2018, 'modelruns_1952WRIA1_081418/modelruns_1952WRIA1_110912_WMoff', sep='/')\r\nWMon2012_Models <- paste(Project2018, 'modelruns_1952WRIA1_081418/modelruns_1952WRIA1_110912_WMon', sep='/')\r\n#WMon2018_Models <- paste(Project2018, 'modelruns_1952WRIA1_081418', sep='/')\r\n# set working directory\r\n\r\n# for (ModelSet_folder in c(Test1, Test2, Test3, Test4, Test5)){\r\n#for (ModelSet_folder in c(Test2, Test3, Test4, Test5)){\r\n#ModelSet_folder=WMoff2018_Models\r\nModelSet_folder=WMon2012_Models\r\nfor (ModelSet_folder in c(WMoff2018_Models,WMon2012_Models)){\r\n\r\n print(paste('start' ModelSet_folder))\r\n\r\n setwd(ModelSet_folder)\r\n getwd()\r\n # read in topsbd_v8\r\n topsbd <- read.table('topsbd_v8.txt', skip = 1, header = T) %>% data.table()\r\n \r\n # if statement for colnames that have been removed\r\n if (!'TimeStep' %in% colnames(topsbd)){\r\n topsbd <- read.table('topsbd_v8.txt', skip = 0, header = F) %>%\r\n data.table() %>%\r\n setnames(., colnames(.), c('Basin', 'TimeStep', 'IrrDrainCat', 'Afrac', 'SWInput_mm', 'Qlat_mm', 'Qtot_mm', 'Qb_mm', 'Recharge_mm', 'SatEx_mm', 'InfEx_mm', 'SurfRo_mm', 'SatAfrac', 'InfAfrac', 'IntStore_mm', 'WTDepth_mm', 'SoilStore_mm', 'Pet_mm', 'Aet_mm', 'Irrig_mm', 'GWTake_mm', 'IrrDem_mm', 'Prec_mm', 'SWE_mm', 'Sublim_mm', 'Tave_C', 'Tdew_C', 'Trange_C', 'ErrClosure_mm'))\r\n }\r\n \r\n # generate TimeStep by subbasin matrices\r\n Zbar <- dcast(topsbd %>% select(TimeStep, Basin, WTDepth_mm), TimeStep~Basin, value.var = 'WTDepth_mm', fill = 0)\r\n PET <- dcast(topsbd %>% select(TimeStep, Basin, Pet_mm), TimeStep~Basin, value.var = 'Pet_mm', fill = 0)\r\n AET <- dcast(topsbd %>% select(TimeStep, Basin, Aet_mm), TimeStep~Basin, value.var = 'Aet_mm', fill = 0)\r\n Rain <- dcast(topsbd %>% select(TimeStep, Basin, Prec_mm), TimeStep~Basin, value.var = 'Prec_mm', fill = 0)\r\n SatEx <- dcast(topsbd %>% select(TimeStep, Basin, SatEx_mm), TimeStep~Basin, value.var = 'SatEx_mm', fill = 0)\r\n InfEx <- dcast(topsbd %>% select(TimeStep, Basin, InfEx_mm), TimeStep~Basin, value.var = 'InfEx_mm', fill = 0)\r\n SurfRO <- dcast(topsbd %>% select(TimeStep, Basin, SurfRo_mm), TimeStep~Basin, value.var = 'SurfRo_mm', fill = 0)\r\n SoilStore <- dcast(topsbd %>% select(TimeStep, Basin, SoilStore_mm), TimeStep~Basin, value.var = 'SoilStore_mm', fill = 0)\r\n \r\n # load Recharge inputs\r\n Basins <- read.table(\"basin.txt\", header=T)\r\n Precipitation_mm <- Rain\r\n Evaporation_mm = read.table(\"Evaporation_mm.txt\", header=T)\r\n Depth_to_Water_mm <- Zbar\r\n # Make if statement here\r\n #Surface_runoff_cms = read.table(\"TotalRunoff_cms.txt\", header=T)\r\n Surface_runoff_cms = read.table(\"TotalRunoff_noWithdrawal_cms.txt\", header=T)\r\n\r\n # print these outputs to xlsx using these names\r\n for (mat in c('InfEx', 'PET', 'AET', 'SatEx', 'Rain', 'Zbar', 'SoilStore', 'Surface_runoff_cms')){\r\n \r\n print(paste('starting to print', mat))\r\n write.table(mat %>% get() %>% data.frame(), file = paste0(mat, '.txt'), col.names = T, row.names = F, append = F)\r\n print(paste(mat, 'printed to', paste0(mat, '.txt')))\r\n }\r\n rm(mat)\r\n \r\n # source the RechargeCalculator_kbcb.R script\r\n # output is the object totalRecharge\r\n source(paste(WMon2018_Models, \"RechargeCalculator_kbcb_jp.R\", sep = '/'))\r\n}\r\n", "meta": {"hexsha": "64fc539f1416e77c53a9aab9d652f2b1fe77024b", "size": 4550, "ext": "r", "lang": "R", "max_stars_repo_path": "archive/RechargeOperator_cband.r", "max_stars_repo_name": "ChristinaB/Topnet-WM", "max_stars_repo_head_hexsha": "abfed11a3792a43ad23000cbf3b2cb9161f19218", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "archive/RechargeOperator_cband.r", "max_issues_repo_name": "ChristinaB/Topnet-WM", "max_issues_repo_head_hexsha": "abfed11a3792a43ad23000cbf3b2cb9161f19218", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17, "max_issues_repo_issues_event_min_datetime": "2018-03-19T17:26:04.000Z", "max_issues_repo_issues_event_max_datetime": "2019-06-06T00:15:35.000Z", "max_forks_repo_path": "archive/RechargeOperator_cband.r", "max_forks_repo_name": "ChristinaB/Topnet-WM", "max_forks_repo_head_hexsha": "abfed11a3792a43ad23000cbf3b2cb9161f19218", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-03-25T01:55:20.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-09T22:12:50.000Z", "avg_line_length": 48.9247311828, "max_line_length": 386, "alphanum_fraction": 0.6984615385, "num_tokens": 1454, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.33773263338844023}} {"text": "### Finding the best hospital in a state\n\nbest <- function(state, outcome) {\n ## Read outcome caredata\n caredata <- read.csv(\"outcome-of-care-measures.csv\", colClasses = \"character\")\n caredataframe <- as.data.frame(cbind(caredata[, 2], # col 2 for hospital\n caredata[, 7], # col 7 for state\n caredata[, 11], # col 11 for heart attack\n caredata[, 17], # col 17 for heart failure\n caredata[, 23]), # col 23 for pneumonia\n stringsAsFactors = FALSE)\n colnames(caredataframe) <- c(\"hospital\", \"state\", \"heart attack\", \"heart failure\", \"pneumonia\")\n \n ## Check that state and outcome are valid\n if(!state %in% caredataframe[, \"state\"]){\n stop('invalid state')\n } else if(!outcome %in% c(\"heart attack\", \"heart failure\", \"pneumonia\")){\n stop('invalid outcome')\n } else {\n si <- which(caredataframe[, \"state\"] == state)\n ts <- caredataframe[si, ] # extracting data for the called state\n oi <- as.numeric(ts[, eval(outcome)])\n min_val <- min(oi, na.rm = TRUE)\n result <- ts[, \"hospital\"][which(oi == min_val)]\n output <- result[order(result)]\n }\nreturn(output)\n}", "meta": {"hexsha": "fbeba65ab91af7ca002daacc52fbd0084ea42631", "size": 1395, "ext": "r", "lang": "R", "max_stars_repo_path": "ProgrammingAssignment3/best.r", "max_stars_repo_name": "NikTheDexter/r-programming", "max_stars_repo_head_hexsha": "3dc949ba7a51c0e56c51efe784c519377f914082", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ProgrammingAssignment3/best.r", "max_issues_repo_name": "NikTheDexter/r-programming", "max_issues_repo_head_hexsha": "3dc949ba7a51c0e56c51efe784c519377f914082", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ProgrammingAssignment3/best.r", "max_forks_repo_name": "NikTheDexter/r-programming", "max_forks_repo_head_hexsha": "3dc949ba7a51c0e56c51efe784c519377f914082", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.8214285714, "max_line_length": 99, "alphanum_fraction": 0.5096774194, "num_tokens": 325, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.33773263338844023}} {"text": "########################################################################\n########################## Exp1: GLMMS #############################\n########################################################################\n\ngetwd()\n# setwd(\"D:/Uni-Stuttgart/thesis/Corpora/Exp3\")\nsetwd(\"/mount/projekte50/projekte/semrel/Users/reem/thesis/Corpora/Exp3/\")\ngetwd()\n\n# load libs\nlibrary(lsr)\nlibrary(lme4)\nlibrary(ggplot2) # load the package\n\n##STEP 0: set gloabal option to ignore scientific notation (numbers with E at the end)\noptions(scipen = 999)\n\n## STEP 1: Load data frames\nlibrary(readr)\nsample.grain.10k <- read.delim(\"samples/sample-grain-unigrams-10K-1.tsv\", encoding=\"UTF8\")\n\nsample.kidko.10k <- read.delim(\"samples/sample-kidko-unigrams-10K-1.tsv\", encoding=\"UTF8\")\n\n## STEP 1.1: Concatenate data frames\n# use row bind | requires both dataframes to have the same columns\ndata.20k.sampled.df <- rbind(sample.grain.10k, sample.kidko.10k)\n\n# STEP 1.2 shuffle rows \n# By default sample() randomly reorders the elements passed as the first argument. \n# This means that the default size is the size of the passed array. \n# Passing parameter replace=FALSE (the default) to sample(...) \n# ensures that sampling is done without replacement which accomplishes a row wise shuffle.\ndata.20k.sampled.df <- data.20k.sampled.df[sample(nrow(data.20k.sampled.df)),]\n\n# Rename column where names is \"isKD\"\nnames(data.20k.sampled.df)[names(data.20k.sampled.df) == \"isKD\"] <- \"corpus_type\"\n\n#View(data.20k.sampled.df)\n# summary(data.20k.sampled.df)\n\nprint(length(unique(data.20k.sampled.df[[\"pos_seq\"]])))\n\n# STEP 1.3 factor our predictors and outcome\ndata.20k.sampled.df$pos_seq <- factor(data.20k.sampled.df$pos_seq)\nlvls <- levels(data.20k.sampled.df$pos_seq)\nwrite.table(lvls, file = \"output/unigrams-20k-grand-mean-ngram-levels.csv\", sep = \"\\t\")\n\n# STEP 2 create null/baseline model\n# model only the intercept\nnull.glm = glm(corpus_type ~ 1, family = \"binomial\", data = data.20k.sampled.df)\nsummary(null.glm)\n\n# STEP 3 create POSt model and compare to previous model\n# set contrast to \"contr.sum\"\n# sum contrasts compare the mean of each group\\level to the grand mean/mean of all levels\n# https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/#DEVIATION\n# see novarro book p.536 \n# contrasts(data.20k.sampled.df$pos_seq) <- \"contr.sum\"\nmy.contrasts <- list(pos_seq = contr.sum)\n\n# use family=binomial to turn our factor columns into numerical values\nstart_time <- Sys.time()\n\n#POSt.glm <- glm(corpus_type ~ 1 + pos_seq, contrasts = my.contrasts,\n# family = \"binomial\", data = data.20k.sampled.df)\n\nlibrary(boot)\nlibrary(parallel)\n\n# function to return bootstrapped coefficients\nmyLogitCoef <- function(data, indices, formula, contrs) {\n d <- data[indices,]\n fit <- glm(formula, data=d, family = binomial(link = \"logit\"))\n return(coef(fit))\n}\n\n# set up cluster of 4 CPU cores\ncl<-makeCluster(4)\nclusterExport(cl, 'myLogitCoef')\n\n# set random seed to ensure reproducibility \nset.seed(333)\n# bootstrap data 500 times\ncoef.boot <- boot(data=data.20k.sampled.df, statistic=myLogitCoef, R=500, \n formula= corpus_type ~ 1 + pos_seq,\n # process in parallel across 4 CPU cores\n parallel = 'snow', ncpus=4, cl=cl)\nstopCluster(cl)\n\nend_time <- Sys.time()\ntotal_time <- end_time - start_time\n\nprint(total_time)\n\n\n# STEP 3.2 analyze model\n\n# STEP 3.2.1 summary of the model\nmodel.summary <- coef.boot$t0\nwrite.table(model.summary, file = \"output/unigrams-20k-grand-mean-coeff.csv\", sep = \"\\t\")\n\n# STEP 3.2.2 summary of the model\nboots.summary <- coef.boot$t\nwrite.table(boots.summary, file = \"output/unigrams-20k-grand-mean-coeff-boots.csv\", sep = \"\\t\")\n\n# STEP 3.3 confidence intervals\ncon-int <- boot.ci(myBootstrap, index=1, type='norm')$norm\nwrite.table(con-int, file = \"output/unigrams-20k-grand-mean-boots-conf-int.csv\", sep = \"\\t\")\n\n# STEP 4 get the estimate for the last level (VERB) using emmeans\n# Estimated Marginal Means, aka Least-Squares Means (emmeans)\nlibrary(emmeans)\ngroups <- emmeans(POSt.glm, \"pos_seq\")\n\nem.df <- summary(groups)\n\n# save emmeans\nwrite.table(em.df, file = \"output/unigrams-20k-grand-mean-emmeans.csv\", sep = \"\\t\")\n\n# plot distribution of bootstrap realizations\n# https://www.datacamp.com/community/tutorials/bootstrap-r\nsvg(filename = 'output/pos-unigram-grand-mean-boots.svg')\nplot(myBootstrap, index=1)\ndev.off()\n", "meta": {"hexsha": "02df6ed86da08efd7cb822b53cc76453db71da88", "size": 4452, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/exp3-unigrams-20k-grand-mean-bootstrap.r", "max_stars_repo_name": "Reem-Alatrash/German-Dialect-Variation", "max_stars_repo_head_hexsha": "e847374407c7d7cd52e018e90037939244382f04", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-07-30T11:28:09.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-30T11:28:09.000Z", "max_issues_repo_path": "experiments/exp3-unigrams-20k-grand-mean-bootstrap.r", "max_issues_repo_name": "Reem-Alatrash/German-Dialect-Variation", "max_issues_repo_head_hexsha": "e847374407c7d7cd52e018e90037939244382f04", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/exp3-unigrams-20k-grand-mean-bootstrap.r", "max_forks_repo_name": "Reem-Alatrash/German-Dialect-Variation", "max_forks_repo_head_hexsha": "e847374407c7d7cd52e018e90037939244382f04", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.616, "max_line_length": 110, "alphanum_fraction": 0.6902515723, "num_tokens": 1225, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.33773263338844023}} {"text": "# Working with data that is taken from statistical packages\n# Examples are shown for SAS, STATA and SPSS\n\n# A. haven\n# This is one of the packages that can be used to import data\n# A very fast package for working with external data types\n# 1. Import SAS data\n# Load the haven package\nlibrary(haven)\n\n# Import sales.sas7bdat: sales\nsales <- read_sas(\"sales.sas7bdat\")\n\n# Display the structure of sales\nstr(sales)\n\n# 2. Import STATA data\n# haven is already loaded\n\n# Import the data from the URL: sugar\nsugar <- read_dta(\"http://assets.datacamp.com/production/course_1478/datasets/trade.dta\")\n\n# Structure of sugar\nstr(sugar)\n\n# Convert values in Date column to dates. When character variables are imported then convert the data type to the unique levels.\n# This method helps to ensure that the variable is converted to the correct format\nsugar$Date <- as.Date(as_factor(sugar$Date))\n\n# Structure of sugar again\nstr(sugar)\n\n# 3. Import SPSS data\n# haven is already loaded\n\n# Import person.sav: traits\ntraits <- read_sav(\"person.sav\")\n\n# Summarize traits\nsummary(traits)\n\n# Print out a subset. Method allows us to pass filters to the second positional parameter (subset)\nsubset(traits, traits$Extroversion > 40 & traits$Agreeableness > 40)\n\n# 4. Factorize, round two\n# Import SPSS data from the URL: work\nwork <- read_sav(\"http://s3.amazonaws.com/assets.datacamp.com/production/course_1478/datasets/employee.sav\")\n\n# Display summary of work$GENDER\nsummary(work$GENDER)\n\n# Convert work$GENDER to a factor\nwork$GENDER <- as_factor(work$GENDER)\n\n# Display summary of work$GENDER again\nsummary(work$GENDER)\n\n# B. foreign\n# 1. Import STATA data\n# Load the foreign package\nlibrary(foreign)\n\n# Import florida.dta and name the resulting data frame florida\nflorida <- read.dta(\"florida.dta\")\n\n# Check tail() of florida\ntail(florida)\n\n# 2. Import STATA data (2)\n# foreign is already loaded\n\n# Specify the file path using file.path(): path\npath <- file.path(\"worldbank/edequality.dta\")\n\n# Create and print structure of edu_equal_1\nedu_equal_1 <- read.dta(path)\nstr(edu_equal_1)\n\n# Create and print structure of edu_equal_2\nedu_equal_2 <- read.dta(path, convert.factors = FALSE)\nstr(edu_equal_2)\n\n# Create and print structure of edu_equal_3\nedu_equal_3 <- read.dta(path, convert.underscore = TRUE)\nstr(edu_equal_3)\n\n# Review the number of rows that match the subset filtering\nnrow(subset(edu_equal_1, ethnicity_head == \"Bulgaria\" & income > 1000))\n\n# 3. Import SPSS\n# foreign is already loaded\n\n# Import international.sav as a data frame: demo\ndemo <- read.spss(\"international.sav\", to.data.frame = TRUE)\n\n# Create boxplot of gdp variable of demo\nboxplot(demo$gdp)\n\n# 4. Import SPSS\n# foreign is already loaded\n\n# Import international.sav as demo_1\ndemo_1 <- read.spss(\"international.sav\", to.data.frame = TRUE)\n\n# Print out the head of demo_1\nhead(demo_1)\n\n# Import international.sav as demo_2\ndemo_2 <- read.spss(\"international.sav\", to.data.frame = TRUE, use.value.labels = FALSE)\n\n# Print out the head of demo_2\nhead(demo_2)\n", "meta": {"hexsha": "53dce973ff1308481b9ee2209133b36fde73cb5b", "size": 3014, "ext": "r", "lang": "R", "max_stars_repo_path": "R/DataAnalyst/ImportingData/Intermediate/from-statistical-package.r", "max_stars_repo_name": "James-McNeill/Learning", "max_stars_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/DataAnalyst/ImportingData/Intermediate/from-statistical-package.r", "max_issues_repo_name": "James-McNeill/Learning", "max_issues_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/DataAnalyst/ImportingData/Intermediate/from-statistical-package.r", "max_forks_repo_name": "James-McNeill/Learning", "max_forks_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.6725663717, "max_line_length": 128, "alphanum_fraction": 0.7554744526, "num_tokens": 818, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.33773263338844023}} {"text": " #SNOPSIS\n\n #runs ANOVA.\n \n #AUTHOR\n # Isaak Y Tecle (iyt2@cornell.edu)\n\n\noptions(echo = FALSE)\n\n\n#library(dplyr)\nlibrary(data.table)\nlibrary(phenoAnalysis)\nlibrary(methods)\n\n\nallArgs <- commandArgs()\n\noutputFiles <- scan(grep(\"output_files\", allArgs, value = TRUE),\n what = \"character\")\n\ninputFiles <- scan(grep(\"input_files\", allArgs, value = TRUE),\n what = \"character\")\n\nphenoDataFile <- grep(\"phenotype_data\", inputFiles, value = TRUE)\nmessage('pheno file: ', phenoDataFile)\n\ntraitsFile <- grep(\"traits\", inputFiles, value = TRUE)\nmessage('traits file: ', traitsFile)\n\nmetadataFile <- grep(\"metadata\", inputFiles, value = TRUE)\nmessage('metadata file: ', metadataFile)\n\nmetaData <- scan(metadataFile, what=\"character\")\n\ndesignFactors <- c('germplasmName','studyYear', 'studyDesign', 'blockNumber', 'locationName', 'replicate')\ndropCols <- metaData[! metaData %in% designFactors]\n\nphenoData <- fread(phenoDataFile,\n header = TRUE,\n sep=\"\\t\",\n drop=dropCols,\n na.strings=c(\"NA\", \"-\", \" \", \".\", \"..\"))\n\nphenoData <- data.frame(phenoData)\n\ntraits <- scan(traitsFile, what = \"character\")\ntraits <- strsplit(traits, \"\\t\")\n\n\n#needs more work for multi traits anova\nfor (trait in traits) {\n \n message('trait: ', trait)\n anovaFiles <- grep(\"anova_table\",\n outputFiles,\n value = TRUE)\n\n message('anova file: ', anovaFiles)\n anovaHtmlFile <- grep(\"html\",\n anovaFiles,\n value = TRUE)\n\n message('anova html file: ', anovaHtmlFile)\n anovaTxtFile <- grep(\"txt\",\n anovaFiles,\n value = TRUE)\n\n message('anova txt file: ', anovaTxtFile)\n modelSummFile <- grep(\"anova_model\",\n outputFiles,\n value = TRUE)\n\n message('model file: ', modelSummFile)\n adjMeansFile <- grep(\"adj_means\",\n outputFiles,\n value = TRUE)\n\n message('means file: ', adjMeansFile)\n\n\n diagnosticsFile <- grep(\"anova_diagnostics\",\n outputFiles,\n value = TRUE)\n\n errorFile <- grep(\"anova_error\",\n outputFiles,\n value = TRUE)\n\n anovaOut <- runAnova(phenoData, trait)\n \n if (class(anovaOut) == 'character') {\n cat(anovaOut, file=errorFile)\n } else if (is.null(anovaOut)) {\n \n cat('Error occured fitting anova model to this trait data.\n Please check the trait data and design factors.',\n file=errorFile)\n \n } else if (class(anovaOut)[1] == 'lmerModLmerTest' ||\n class(anovaOut)[1] == 'merModLmerTest') {\n \n png(diagnosticsFile, 960, 480)\n par(mfrow=c(1,2))\n plot(fitted(anovaOut), resid(anovaOut),\n xlab=\"Fitted values\",\n ylab=\"Residuals\",\n main=\"Fitted values vs Residuals\") \n abline(0,0)\n qqnorm(resid(anovaOut)) \n dev.off()\n \n anovaTable <- getAnovaTable(anovaOut,\n tableType=\"html\",\n traitName=trait,\n out=anovaHtmlFile)\n \n anovaTable <- getAnovaTable(anovaOut,\n tableType=\"text\",\n traitName=trait,\n out=anovaTxtFile)\n \n \n adjMeans <- getAdjMeans(traitName=trait, modelOut=anovaOut)\n \n fwrite(adjMeans,\n file = adjMeansFile,\n row.names = FALSE,\n sep = \"\\t\",\n quote = FALSE,\n )\n\n sink(modelSummFile)\n print(anovaOut)\n sink()\n }\n \n}\n\n\nq(save = \"no\", runLast = FALSE)\n", "meta": {"hexsha": "3a89c028b7321efb7f377e79afd3dd9cc774a56e", "size": 3974, "ext": "r", "lang": "R", "max_stars_repo_path": "R/solGS/anova.r", "max_stars_repo_name": "TriticeaeToolbox/sgn", "max_stars_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2015-02-03T15:47:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T13:34:05.000Z", "max_issues_repo_path": "R/solGS/anova.r", "max_issues_repo_name": "TriticeaeToolbox/sgn", "max_issues_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2491, "max_issues_repo_issues_event_min_datetime": "2015-01-07T05:49:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:05.000Z", "max_forks_repo_path": "R/solGS/anova.r", "max_forks_repo_name": "TriticeaeToolbox/sgn", "max_forks_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-06-30T19:10:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T13:34:09.000Z", "avg_line_length": 27.7902097902, "max_line_length": 106, "alphanum_fraction": 0.5108203322, "num_tokens": 903, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3376767649961489}} {"text": "# Differential expression analysis with limma\nsource(\"http://www.bioconductor.org/biocLite.R\")\nlibrary(\"Biobase\")\nlibrary(\"GEOquery\")\nlibrary(\"limma\")\n\n# load series and platform data from GEO\nsetwd(\"~/work/abe516/project1\")\ncwd = getwd()\n\ngset <- getGEO(\"GSE95636\", GSEMatrix = TRUE, AnnotGPL = TRUE)\nif (length(gset) > 1) idx <- grep(\"GPL200\", attr(gset, \"names\")) else idx <- 1\ngset <- gset[[idx]]\n\n# make proper column names to match toptable \nfvarLabels(gset) <- make.names(fvarLabels(gset))\n\n# group names for all samples\ngsms <- \"3311100022233\"\nsml <- c()\nfor (i in 1:nchar(gsms)) { sml[i] <- substr(gsms,i,i) }\n\n# log2 transform\nex <- exprs(gset)\nqx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))\nLogC <- (qx[5] > 100) ||\n (qx[6]-qx[1] > 50 && qx[2] > 0) ||\n (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)\nif (LogC) { ex[which(ex <= 0)] <- NaN\nexprs(gset) <- log2(ex) }\n\n# set up the data and proceed with analysis\ngroups = c(\"C\", \"Bs\", \"Efs\", \"Efm\")\nfl <- factor(c(rep('0', 2), rep('1', 3), rep('2', 3), rep('3', 3), rep('0', 2)), \n labels=groups)\n# sml <- paste(\"G\", sml, sep=\"\") # set group names\n# fl <- as.factor(sml)\ngset$description <- fl\ndesign <- model.matrix(~ description + 0, gset)\ncolnames(design) <- levels(fl)\nfit <- lmFit(gset, design)\ncontrast.matrix <- makeContrasts(Efm-C, Efs-C, Bs-C, Efs-Bs, Efm-Efs, levels = design)\nfit2 = contrasts.fit(fit, contrast.matrix)\nfit2 = eBayes(fit2, 0.01)\ntT = topTable(fit2, adjust = \"fdr\", sort.by = \"B\", number = 250)\ncnames = c(\"ID\",\"adj.P.Val\",\"P.Value\",\"Gene.symbol\",\"Gene.title\",\"GO.Function\",\"GO.Process\",\"GO.Component\")\ntT = subset(tT, select = cnames)\n\nwrite.table(tT, file=file.path(cwd, \"top250.tab\"), row.names=F, sep=\"\\t\")\n\nresults = decideTests(fit2, adjust=\"fdr\", p=0.05)\nsummary(results)\ntable(efm.c=results[,1], bs.c=results[,2])\n\npng(filename = file.path(cwd, \"venn.png\"), width = 400, height = 800)\npar(mfrow = c(3, 1))\nvennDiagram(results, main=\"Diff Exp Genes\")\nvennDiagram(results, include=\"down\", main=\"Down\")\nvennDiagram(results, include=\"up\", main=\"Up\")\ndev.off()\n\npng(filename = file.path(cwd, \"volcano.png\"), width = 800, height = 800)\npar(mfrow = c(2,2))\nvolcanoplot(fit2,\n coef=1, \n main=\"E_faecium-Control\",\n names=row.names(fit2$coefficients), \n highlight=10)\n\nvolcanoplot(fit2,\n coef=2, \n main=\"B_subtilis-Control\",\n names=row.names(fit2$coefficients), \n highlight=10)\n\nvolcanoplot(fit2,\n coef=3, \n main=\"E_faecium-B_subtilis\",\n names=row.names(fit2$coefficients), \n highlight=10)\n\nvolcanoplot(fit2,\n coef=3, \n main=\"E_faecium-E_faecalis\",\n names=row.names(fit2$coefficients), \n highlight=10)\ndev.off()", "meta": {"hexsha": "ae0217b79c3629b414d50e49039587e36c4002b9", "size": 2830, "ext": "r", "lang": "R", "max_stars_repo_path": "project1/de-limma.r", "max_stars_repo_name": "kyclark/abe516", "max_stars_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "project1/de-limma.r", "max_issues_repo_name": "kyclark/abe516", "max_issues_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "project1/de-limma.r", "max_forks_repo_name": "kyclark/abe516", "max_forks_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5287356322, "max_line_length": 107, "alphanum_fraction": 0.6088339223, "num_tokens": 957, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.337616183459178}} {"text": "pattern_matching <- function(pattern, genome) {\n pattern_length <- nchar(pattern)\n for (i in 0:nchar(genome) - pattern_length) {\n if (substr(genome, i + 1, i + pattern_length) == pattern)\n print(i)\n }\n}\n\n\nmessage(\"File\")\nfile <- scan(\"stdin\", what=\"character\", nlines=1)\npattern <- scan(file, what=\"character\", nlines=1)\ngenome <- scan(file, what=\"character\", skip=1, nlines=1)\n\npattern_matching(pattern, genome)\n", "meta": {"hexsha": "a2a0dc9a0f0bd6e30455bdd33205260f0a4269b3", "size": 424, "ext": "r", "lang": "R", "max_stars_repo_path": "pattern_matching.r", "max_stars_repo_name": "rjcc/bioinformatics_algorithms", "max_stars_repo_head_hexsha": "99a5e564f8c02e6da5ee9e186bf8d5d3f1effb92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-05-13T13:44:57.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T13:44:57.000Z", "max_issues_repo_path": "pattern_matching.r", "max_issues_repo_name": "rjcc/bioinformatics_algorithms", "max_issues_repo_head_hexsha": "99a5e564f8c02e6da5ee9e186bf8d5d3f1effb92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pattern_matching.r", "max_forks_repo_name": "rjcc/bioinformatics_algorithms", "max_forks_repo_head_hexsha": "99a5e564f8c02e6da5ee9e186bf8d5d3f1effb92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-07T12:47:12.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-07T12:47:12.000Z", "avg_line_length": 26.5, "max_line_length": 61, "alphanum_fraction": 0.6745283019, "num_tokens": 118, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.33759222391170346}} {"text": "context(\"Kmeans\")\n#source('../../h2o-runit.R')\nlibrary(h2o)\n\nprint(Sys.getenv(\"H2O_IP\"))\nprint(Sys.getenv(\"H2O_PORT\"))\n\n#conn <- h2o.init(ip=Sys.getenv(\"H2O_IP\"), port=as.integer(Sys.getenv(\"H2O_PORT\"), startH2O=FALSE))\nconn <- h2o.init( startH2O=FALSE )\nhex <- as.h2o( conn, iris )\n\n#model <- tryCatch({\n#\th2o.kmeans(data=hex, centers=5)\n#}, error = function(err) {\n#\treturn(err)\n#})\n\nmodel <- h2o.kmeans(data=hex, centers=5)\n\nif(is(model, \"H2OKMeansModel\")) {\n\ttest_that(\"Correct # of centers returned: \", {\n\t\texpect_equal(5, length(model@model$centers))\n\t})\n} else {\n\ttest_that(\"Input permutation foo: \", fail(message=toString(model)))\n}\n", "meta": {"hexsha": "b5ba7f016bc7170683b341c680850f65aa18d358", "size": 641, "ext": "r", "lang": "R", "max_stars_repo_path": "h2o-r/src/test/R/acceptance/test-kmeans.r", "max_stars_repo_name": "PawarPawan/h2o-v3", "max_stars_repo_head_hexsha": "cf569a538c9e2ec16ba9fc1a75d14beda8f40c18", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "h2o-r/src/test/R/acceptance/test-kmeans.r", "max_issues_repo_name": "PawarPawan/h2o-v3", "max_issues_repo_head_hexsha": "cf569a538c9e2ec16ba9fc1a75d14beda8f40c18", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "h2o-r/src/test/R/acceptance/test-kmeans.r", "max_forks_repo_name": "PawarPawan/h2o-v3", "max_forks_repo_head_hexsha": "cf569a538c9e2ec16ba9fc1a75d14beda8f40c18", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-12-18T19:20:02.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-18T19:20:02.000Z", "avg_line_length": 23.7407407407, "max_line_length": 99, "alphanum_fraction": 0.6708268331, "num_tokens": 207, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3375922076705073}} {"text": "#' dim\n#' \n#' Dimension information for a float vector/matrix.\n#' \n#' @param x\n#' A float vector/matrix.\n#' @param value\n#' The right hand side for the \"setter\" (\\code{dim<-}).\n#' \n#' @return\n#' The requested integer values.\n#' \n#' @examples\n#' library(float)\n#' \n#' s = flrunif(10, 3)\n#' dim(s)\n#' nrow(s)\n#' ncol(s)\n#' \n#' @name dims\n#' @rdname dims\nNULL\n\n\n\nDATA = function(x) x@Data\nisavec = function(x) !is.matrix(DATA(x))\n\n\n\n#' @rdname dims\n#' @export\nsetMethod(\"nrow\", signature(x=\"float32\"), function(x) NROW(DATA(x)))\n\n#' @rdname dims\n#' @export\nsetMethod(\"ncol\", signature(x=\"float32\"), function(x) NCOL(DATA(x)))\n\n#' @rdname dims\n#' @export\nsetMethod(\"NROW\", signature(x=\"float32\"), function(x) NROW(DATA(x)))\n\n#' @rdname dims\n#' @export\nsetMethod(\"NCOL\", signature(x=\"float32\"), function(x) NCOL(DATA(x)))\n\n#' @rdname dims\n#' @export\nsetMethod(\"dim\", signature(x=\"float32\"), function(x) c(nrow(x), ncol(x)))\n\n#' @rdname dims\n#' @export\nsetMethod(\"length\", signature(x=\"float32\"), \n function(x)\n {\n len = tryCatch(nrow(x) * ncol(x), warning=identity)\n if (inherits(len, \"warning\"))\n len = as.double(nrow(x)) * as.double(ncol(x))\n \n len\n }\n)\n\n\n\ndimset_float32 = function(x, value)\n{\n dim(x@Data) = value\n x\n}\n\n#' @rdname dims\n#' @export\nsetReplaceMethod(\"dim\", signature(x=\"float32\"), dimset_float32)\n", "meta": {"hexsha": "9db514979cf77b53fee446fd5e7bf8790c05ef71", "size": 1332, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dims.r", "max_stars_repo_name": "david-cortes/float", "max_stars_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2017-11-08T11:29:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-20T20:17:08.000Z", "max_issues_repo_path": "R/dims.r", "max_issues_repo_name": "david-cortes/float", "max_issues_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 37, "max_issues_repo_issues_event_min_datetime": "2017-09-02T11:14:09.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-19T15:11:19.000Z", "max_forks_repo_path": "R/dims.r", "max_forks_repo_name": "david-cortes/float", "max_forks_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2017-11-18T18:05:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-17T01:23:23.000Z", "avg_line_length": 17.5263157895, "max_line_length": 73, "alphanum_fraction": 0.6238738739, "num_tokens": 418, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3375922076705073}} {"text": "#https://www.mbr-pwrc.usgs.gov/site/communitymodeling/software-code/\n#The model code below is written for program R and uses the R2WinBUGS package\n#to run WinBUGS as well as the reshape package to format the occurrence data.\n\n#It is designed to estimate static species-specific occupancy and detection,\n#constant across sampling locations using the community model. The model also\n#estimates the total species richness N, using data augmentation.\n#The data are found in the file \"occ data.csv\".\n\n#Read in the occurence data\ndata1 <- read.table(\"basic_MSOM_example/occ data.csv\", header=TRUE,sep=\",\",na.strings=TRUE)\ndata1$Occ <- rep(1, dim(data1)[1])\n#See the first ten lines of data\ndata1[1:10,]\n#How many citings for each species\ntotal.count = tapply(data1$Occ, data1$Species, sum)\n\n#Find the number of unique species\nuspecies = as.character(unique(data1$Species))\n#n is the number of observed species\nn=length(uspecies)\n\n#Find the number of unique sampling locations\nupoints = as.character(unique(data1$Point))\n#J is the number of sampled points\nJ=length(upoints)\n\n#Reshape the data using the R package \"reshape\"\nlibrary(reshape)\n\n#The detection/non-detection data is reshaped into a three dimensional\n#array X where the first dimension, j, is the point; the second\n#dimension, k, is the rep; and the last dimension, i, is the species.\njunk.melt=melt(data1,id.var=c(\"Species\", \"Point\", \"Rep\"), measure.var=\"Occ\")\nX=cast(junk.melt, Point ~ Rep ~ Species)\n\n#Add in the missing lines with NAs\nfor (i in 1: dim(X)[3]) {\n b = which(X[,,i] > 0)\n X[,,i][b] = 1\n X[,,i][-b] = 0\n X[,,i][1:36,4] = NA; X[,,i][38:56,4] = NA;\n X[,,i][59:61,4] = NA; X[,,i][66:70,4] = NA;\n}\n\n#Create all zero encounter histories to add to the detection array X\n#as part of the data augmentation to account for additional\n#species (beyond the n observed species).\n\n#nzeroes is the number of all zero encounter histories to be added\n nzeroes = 50\n#X.zero is a matrix of zeroes, including the NAs for when a point has not been sampled\n X.zero = matrix(0, nrow=70, ncol=4)\n X.zero[1:36,4] = NA; X.zero[38:56,4] = NA;\n X.zero[59:61,4] = NA; X.zero[66:70,4] = NA;\n#Xaug is the augmented version of X. The first n species were actually observed\n#and the n+1 through nzeroes species are all zero encounter histories\n Xaug <- array(0, dim=c(dim(X)[1],dim(X)[2],dim(X)[3]+nzeroes))\n Xaug[,,(dim(X)[3]+1):dim(Xaug)[3]] = rep(X.zero, nzeroes)\n dimnames(X)=NULL\n Xaug[,,1:dim(X)[3]] <- X\n\n#K is a vector of length J indicating the number of reps at each point j\nKK <- X.zero\na=which(KK==0); KK[a] <- 1\nK=apply(KK,1,sum, na.rm=TRUE)\nK=as.vector(K)\n\n################\n\n#Write the model code to a text file (used to run WinBUGS)\ncat(\"\n\tmodel{\n\n#Define prior distributions for community-level model parameters\nomega ~ dunif(0,1)\n\nu.mean ~ dunif(0,1)\nmu.u <- log(u.mean) - log(1-u.mean)\n\nv.mean ~ dunif(0,1)\nmu.v <- log(v.mean) - log(1-v.mean)\n\ntau.u ~ dgamma(0.1,0.1)\ntau.v ~ dgamma(0.1,0.1)\n\nfor (i in 1:(n+nzeroes)) {\n\n#Create priors for species i from the community level prior distributions\n w[i] ~ dbern(omega)\n u[i] ~ dnorm(mu.u, tau.u) # prob that species i occurs in the community on logit scale which varies normally among species\n v[i] ~ dnorm(mu.v, tau.v) # prob that species i is detected on logit scale and varies normally among species\n\n#Create a loop to estimate the Z matrix (true occurrence for species i\n#at point j.\n for (j in 1:J) {\n logit(psi[j,i]) <- u[i]\n\n mu.psi[j,i] <- psi[j,i]*w[i]\n Z[j,i] ~ dbern(mu.psi[j,i])\n\n#Create a loop to estimate detection for species i at point k during #sampling period k.\n for (k in 1:K[j]) {\n \tlogit(p[j,k,i]) <- v[i]\n mu.p[j,k,i] <- p[j,k,i]*Z[j,i]\n X[j,k,i] ~ dbern(mu.p[j,k,i])\n} \t}\t\t}\n\n#Sum all species observed (n) and unobserved species (n0) to find the\n#total estimated richness\nn0 <- sum(w[(n+1):(n+nzeroes)])\nN <- n + n0\n\n#Finish writing the text file into a document called basicmodel.txt\n}\n\",file=\"basicmodel.txt\")\n\n#Load the R2Winbugs library\nlibrary(R2WinBUGS)\n\n#Create the necessary arguments to run the bugs() command\n#Load all the data\nsp.data = list(n=n, nzeroes=nzeroes, J=J, K=K, X=Xaug)\n\n#Specify the parameters to be monitored\nsp.params = list('u', 'v', 'mu.u', 'mu.v', 'tau.u', 'tau.v', 'omega', 'N')\n\n#Specify the initial values\n sp.inits = function() {\n omegaGuess = runif(1, n/(n+nzeroes), 1)\n psi.meanGuess = runif(1, .25,1)\n list(omega=omegaGuess,w=c(rep(1, n), rbinom(nzeroes, size=1, prob=omegaGuess)),\n u=rnorm(n+nzeroes), v=rnorm(n+nzeroes),\n Z = matrix(rbinom((n+nzeroes)*J, size=1, prob=psi.meanGuess),\n\t\t nrow=J, ncol=(n+nzeroes))\n )\n }\n\n#Run the model and call the results ?fit?\nfit = bugs(sp.data, sp.inits, sp.params, \"basicmodel.txt\", debug=TRUE,\n n.chains=3, n.iter=10000, n.burnin=5000, n.thin=5)\n\n\n#The model code below is written in the R language and designed to be run\n#after the ?covariate model code? for summary of the model results\n\n#See a summary of the parameter estimates\nfit$summary\n\n#See baseline estimates of species-specific occupancy and detection in one of\n#the habitat types (CATO)\nspecies.occ = fit$sims.list$u\nspecies.det = fit$sims.list$v\n\n#Show occupancy and detection estimates for only the observed species (1:n)\npsi = plogis(species.occ[,1:n])\np = plogis(species.det[,1:n])\n\nocc.matrix <- cbind(apply(psi,2,mean),apply(psi,2,sd))\ncolnames(occ.matrix) = c(\"mean occupancy\", \"sd occupancy\")\nrownames(occ.matrix) = uspecies\ndet.matrix <- cbind(apply(p,2,mean),apply(p,2,sd))\ncolnames(det.matrix) = c(\"mean detection\", \"sd detection\")\n\nround(occ.matrix, digits=2)\n\n#See estimates of total richness (N)\nN = fit$sims.list$N\nmean(N)\nsummary(N)\ntable(N)\nplot(table(N))\n", "meta": {"hexsha": "b18454f12532cf9de889f938785b74fcb6599a4d", "size": 5779, "ext": "r", "lang": "R", "max_stars_repo_path": "basic_MSOM_example/basic model code.r", "max_stars_repo_name": "jclbrooks/MD_Stream_Salamanders", "max_stars_repo_head_hexsha": "493647045e57261b3aa83976c6a9a93866ed285c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "basic_MSOM_example/basic model code.r", "max_issues_repo_name": "jclbrooks/MD_Stream_Salamanders", "max_issues_repo_head_hexsha": "493647045e57261b3aa83976c6a9a93866ed285c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 20, "max_issues_repo_issues_event_min_datetime": "2018-09-29T03:52:36.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-17T02:57:23.000Z", "max_forks_repo_path": "basic_MSOM_example/basic model code.r", "max_forks_repo_name": "jclbrooks/MD_Stream_Salamanders", "max_forks_repo_head_hexsha": "493647045e57261b3aa83976c6a9a93866ed285c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4046242775, "max_line_length": 126, "alphanum_fraction": 0.6822979754, "num_tokens": 1816, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514778, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3375507192511419}} {"text": "#script to create 'access' capital maps\n\n#requires maps output from createSingleLCMap.r \n#note: for singleLC_Nature_2001_PastureB_Disagg.asc this took approx 8 hours to complete (2.5 hours for Agri)\n\n\nlibrary(raster)\n\n#assumes focal value = 1\nbuf_width <- 5000 #width of buffer in m\nBKGs <- list(0.05,0.05,0.0) #background valeus\nBUFs <- list(0.95,0.95,0.75) #buffer values\nLCs <- list(\"Agri\",\"OAgri\",\"Nature\") #the maps to work through\nyears <- c(2005, 2010)\nsuffix <- \"_PastureB_Disagg.asc\"\n\n\nfor(yr in years){\n \n for(i in seq_along(LCs)){\n \n print(yr)\n print(LCs[[i]])\n print(paste0(\"Start: \",Sys.time()))\n \n #create single LC map for the appropriate LC createSingleLCMap.r\n lc <- raster(paste0(\"Data/ObservedLCmaps/singleLC_\",LCs[[i]],\"_\",yr,suffix)) #land cover from LandCoverMap.r (or ClassifyDisaggregateMap.r)\n \n crs(lc) <- latlong <- \"+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs \"\n \n #crop (for testing)\n #e <- extent(-60,-55,-15,-10)\n #lc <- crop(lc,e)\n #plot(lc)\n \n lc[lc == 0] <- NA #set 0 to NA for buffer\n \n bf <- buffer(lc, width=buf_width, doEdge=T) #buffer width of 5km\n \n s <- stack(lc, bf)\n \n s[is.na(s)] <- BKGs[[i]]\n #plot(s)\n \n setB <- function(a , b){\n ifelse(a != 1 & b == 1, BUFs[[i]], a)\n }\n \n out <- overlay(s, fun=setB)\n \n writeRaster(out, paste0(\"Data/\",LCs[[i]],\"Access_\",yr,suffix), format = 'ascii', overwrite=T)\n \n print(paste0(\"End: \",Sys.time()))\n }\n}\n", "meta": {"hexsha": "23836fa57a66ae6e24b8407f8f62d3e27fde561b", "size": 1507, "ext": "r", "lang": "R", "max_stars_repo_path": "accessMap.r", "max_stars_repo_name": "jamesdamillington/CRAFTYInput", "max_stars_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "accessMap.r", "max_issues_repo_name": "jamesdamillington/CRAFTYInput", "max_issues_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "accessMap.r", "max_forks_repo_name": "jamesdamillington/CRAFTYInput", "max_forks_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.9107142857, "max_line_length": 144, "alphanum_fraction": 0.6104844061, "num_tokens": 523, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514778, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.33755071925114183}} {"text": "#Title: phastcons_score_heatmap\r\n#Auther: Naoto Imamachi\r\n#ver: 1.0.0\r\n#Date: 2015-06-10\r\n\r\n#library\r\nrequire(ggplot2)\r\nrequire(data.table)\r\nrequire(reshape2)\r\n\r\n#file_path\r\nsetwd(\"C:/Users/Naoto/Documents/github/MIRAGE/data/PhastCons46Ways/\")\r\ninput_file_path = \"C:/Users/Naoto/Documents/github/MIRAGE/data/PhastCons46Ways/phastCons46way_miRBase_v21_hg38Tohg19.txt\"\r\noutput_file_path = \"C:/Users/Naoto/Documents/github/MIRAGE/data/PhastCons46Ways/phastCons46way_miRBase_v21_hg38Tohg19_heatmap.png\"\r\n\r\n#read_files\r\nlabel <- c('name','1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28')\r\nsetnames(input_data <- fread(input_file_path,header=F)[1:1000],label)\r\ninput_data.melt <- melt(input_data)\r\n\r\np1 <- ggplot(input_data.melt, aes(variable,name)) \r\np1 <- p1 + geom_tile(aes(fill=value),colour=\"white\") \r\np1 <- p1 + scale_fill_gradient(low=\"black\",high=\"steelblue\")\r\nplot(p1)", "meta": {"hexsha": "8505822c6e160d5e873c58cf7e3d299f5bf034ab", "size": 953, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/module/preparation/phastcons_score_heatmap_ver1.r", "max_stars_repo_name": "Naoto-Imamachi/MIRAGE", "max_stars_repo_head_hexsha": "448d7f2b62f0830c0abd3eb1435d16baffc5d3f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-01-16T03:31:38.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-04T16:00:59.000Z", "max_issues_repo_path": "scripts/module/preparation/phastcons_score_heatmap_ver1.r", "max_issues_repo_name": "Imamachi-n/MIRAGE", "max_issues_repo_head_hexsha": "448d7f2b62f0830c0abd3eb1435d16baffc5d3f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/module/preparation/phastcons_score_heatmap_ver1.r", "max_forks_repo_name": "Imamachi-n/MIRAGE", "max_forks_repo_head_hexsha": "448d7f2b62f0830c0abd3eb1435d16baffc5d3f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7083333333, "max_line_length": 150, "alphanum_fraction": 0.7093389297, "num_tokens": 322, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3374704255929373}} {"text": "# Read in ITRDB data\r\n\r\nadjustLW <- function(lw, ew){\r\n cores = names(lw)\r\n lwadj = data.frame(matrix(ncol = dim(lw)[2], nrow = dim(lw)[1]))\r\n names(lwadj) <- names(lw)\r\n row.names(lwadj) <- row.names(lw)\r\n for(j in 1:length(cores)){\r\n if(names(lw)[j] %in% names(ew)){\r\n idx = match(names(lw)[j], names(ew))\r\n mdl1 = lm(lw[,j]~ew[,idx])\r\n mdl2 = lm(lw[,j]~ew[,idx]+I(ew[,idx]^2))\r\n if(AIC(mdl2)0 & mdl2$coefficients[3]>0){\r\n lwadj[!is.na(lw[,j])&!is.na(ew[,idx]), j] = mdl2$residuals+1\r\n } else lwadj[!is.na(lw[,j])&!is.na(ew[,idx]), j] = mdl1$residuals+1\r\n }\r\n }\r\n return(lwadj)\r\n}\r\n\r\n\r\n\r\n##### ITRDB operations #####\r\n\r\nsetwd(\"C:\\\\Users\\\\dannenberg\\\\Documents\\\\Data_Analysis\\\\ITRDB_update092717\") ## Change to ITRDB directory on local machine!!!\r\nlibrary(dplR)\r\n\r\n# List text files\r\nfiles <- list.files() # List all files\r\ntxtfiles <- files[grep(glob2rx(\"*.txt\"), files)] # List txt files\r\n\r\nn = length(txtfiles)\r\ntrdata <- data.frame(matrix(ncol = 10, nrow = n))\r\nnames(trdata) <- c(\"SITE\", \"START\", \"END\", \"PI\", \"NAME\", \"LOCATION\", \"SPECIES\", \"LAT\", \"LON\", \"ELEV\")\r\n\r\nfor(i in 1:n){\r\n file = txtfiles[i]\r\n site = unlist(strsplit(file, \"[.]\"))[1]\r\n site = unlist(strsplit(site, \"[_]\"))[1] # some with \"_gap\"\r\n fc = file(file)\r\n mylist <- strsplit(readLines(fc), \": \")\r\n e = \"great!\"\r\n idx = 1\r\n idxn = length(mylist)\r\n repeat{\r\n testline = mylist[[idx]][1]\r\n test = grepl(\"Beginning\", testline)\r\n if(idx==idxn){\r\n e = \"crap!\"\r\n break()\r\n }\r\n if(test==TRUE) break()\r\n idx = idx+1\r\n }\r\n trdata$SITE[i] = site\r\n if(e != \"crap!\"){\r\n trdata$START[i] = as.numeric(mylist[[idx]][2])\r\n trdata$END[i] = as.numeric(mylist[[idx+1]][2])\r\n trdata$PI[i] = mylist[[idx+2]][2]\r\n trdata$NAME[i] = mylist[[idx+3]][2]\r\n trdata$LOCATION[i] = mylist[[idx+4]][2]\r\n trdata$SPECIES[i] = substring(mylist[[idx+5]][2], 1, 4)\r\n \r\n if(grepl(\"S\", mylist[[idx+6]][2])==TRUE){ lat = -1*as.numeric(gsub(\"\\\\D\", \"\", mylist[[idx+6]][2])) / 100\r\n } else lat = as.numeric(gsub(\"\\\\D\", \"\", mylist[[idx+6]][2])) / 100\r\n \r\n if(!is.na(lat)){\r\n dsign = sign(lat)\r\n degree = floor(abs(lat))\r\n minute = round(100*(abs(lat)-degree))\r\n \r\n if(minute < 60) trdata$LAT[i] = dsign*degree + dsign*(minute/60)\r\n else trdata$LAT[i] = lat\r\n } else trdata$LAT[i] = NA\r\n \r\n if(grepl(\"W\", mylist[[idx+7]][2])==TRUE){ lon = -1*as.numeric(gsub(\"\\\\D\", \"\", mylist[[idx+7]][2])) / 100\r\n } else lon = as.numeric(mylist[[idx+7]][2]) / 100\r\n \r\n if(!is.na(lon)){\r\n dsign = sign(lon)\r\n degree = floor(abs(lon))\r\n minute = round(100*(abs(lon)-degree))\r\n \r\n if(minute < 60) trdata$LON[i] = dsign*degree + dsign*(minute/60)\r\n else trdata$LON[i] = lon\r\n } else trdata$LON[i] = NA\r\n \r\n trdata$ELEV[i] = as.numeric(gsub(\"\\\\D\", \"\", mylist[[idx+8]][2]))\r\n }\r\n \r\n \r\n ## Insert code to detrend, chronologize, and export to csv\r\n # Include exceptions for when it can't find the *.rwl file (write names to txt file)\r\n rwlFile = paste(c(site, \".rwl\"), collapse=\"\")\r\n if(!length(grep(rwlFile, files))){\r\n txtToWrite = paste(c(site, \"\\n\"), collapse=\"\")\r\n } else{\r\n \r\n scratchRWL <- tryCatch(\r\n {\r\n rwl = read.rwl(rwlFile)\r\n spl = detrend(rwl, method=\"Spline\")\r\n \r\n ids = read.ids(spl)\r\n window.length = min(50, nrow(spl))\r\n window.overlap = window.length - 10\r\n stats <- tryCatch(\r\n {\r\n stats = rwi.stats.running(spl, ids=ids, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, error=function(cond){\r\n stats = rwi.stats.running(spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, warning=function(cond){\r\n stats = rwi.stats.running(spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, finally = {\r\n temp = NULL\r\n }\r\n )\r\n \r\n write.csv(stats, paste(c(site, \"w_stats.csv\"), collapse=\"\"))\r\n \r\n res = chron(spl, prefix=\"\", prewhiten=TRUE)\r\n write.csv(res, paste(c(site, \"w_crn.csv\"), collapse=\"\"))\r\n }, error=function(e){})\r\n \r\n \r\n ## EW\r\n site = unlist(strsplit(site, \"[w]\"))[1]\r\n rwlFile = paste(c(site, \"e.rwl\"), collapse=\"\")\r\n scratchEW <- tryCatch(\r\n {\r\n \r\n ew = read.rwl(rwlFile)\r\n ew_spl = detrend(ew, method=\"Spline\")\r\n \r\n ids = read.ids(ew_spl)\r\n window.length = min(50, nrow(ew_spl))\r\n window.overlap = window.length - 10\r\n stats <- tryCatch(\r\n {\r\n stats = rwi.stats.running(ew_spl, ids=ids, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, error=function(cond){\r\n stats = rwi.stats.running(ew_spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, warning=function(cond){\r\n stats = rwi.stats.running(ew_spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, finally = {\r\n temp = NULL\r\n }\r\n )\r\n \r\n write.csv(stats, paste(c(site, \"e_stats.csv\"), collapse=\"\"))\r\n \r\n ewres = chron(ew_spl, prefix=\"\", prewhiten=TRUE)\r\n write.csv(ewres, paste(c(site, \"e_crn.csv\"), collapse=\"\"))\r\n }, error=function(e){})\r\n \r\n \r\n ## LW\r\n rwlFile = paste(c(site, \"l.rwl\"), collapse=\"\")\r\n scratchLW <- tryCatch(\r\n {\r\n \r\n lw = read.rwl(rwlFile)\r\n lw_spl = detrend(lw, method=\"Spline\")\r\n \r\n ids = read.ids(lw_spl)\r\n window.length = min(50, nrow(lw_spl))\r\n window.overlap = window.length - 10\r\n stats <- tryCatch(\r\n {\r\n stats = rwi.stats.running(lw_spl, ids=ids, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, error=function(cond){\r\n stats = rwi.stats.running(lw_spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, warning=function(cond){\r\n stats = rwi.stats.running(lw_spl, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, finally = {\r\n temp = NULL\r\n }\r\n )\r\n \r\n write.csv(stats, paste(c(site, \"l_stats.csv\"), collapse=\"\"))\r\n \r\n lwres = chron(lw_spl, prefix=\"\", prewhiten=TRUE)\r\n write.csv(lwres, paste(c(site, \"l_crn.csv\"), collapse=\"\"))\r\n \r\n \r\n ## Adjusted LW (following Griffin et al. 2011, Tree-Ring Res)\r\n if(dim(lw_spl)[1]==dim(ew_spl)[1] & dim(lw_spl)[2]==dim(ew_spl)[2]){\r\n lwadj = adjustLW(lw_spl, ew_spl)\r\n ids = read.ids(lwadj)\r\n window.length = min(50, nrow(lwadj))\r\n window.overlap = window.length - 10\r\n stats <- tryCatch(\r\n {\r\n stats = rwi.stats.running(lwadj, ids=ids, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, error=function(cond){\r\n stats = rwi.stats.running(lwadj, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, warning=function(cond){\r\n stats = rwi.stats.running(lwadj, window.length=window.length, window.overlap=window.overlap)\r\n \r\n }, finally = {\r\n temp = NULL\r\n }\r\n )\r\n write.csv(stats, paste(c(site, \"la_stats.csv\"), collapse=\"\"))\r\n \r\n lwadjres = chron(lwadj, prefix=\"\", prewhiten=TRUE)\r\n write.csv(lwadjres, paste(c(site, \"la_crn.csv\"), collapse=\"\"))\r\n \r\n } else {\r\n commonYrs = intersect(row.names(lwres), row.names(ew))\r\n lwadjres = data.frame(matrix(nrow = length(commonYrs), ncol=3))\r\n names(lwadjres) <- names(lwres)\r\n row.names(lwadjres) <- commonYrs\r\n lwadjres[,3] = lwres[row.names(lwres) %in% commonYrs,3]\r\n \r\n # STD adjustment\r\n lwtemp = lwres[row.names(lwres) %in% commonYrs,1]\r\n ewtemp = ewres[row.names(ewres) %in% commonYrs,1]\r\n mdl = lm(lwtemp~ewtemp)\r\n lwadjres[,1] = mdl$residuals +1 \r\n \r\n # RES adjustment\r\n lwtemp = lwres[row.names(lwres) %in% commonYrs,2]\r\n ewtemp = ewres[row.names(ewres) %in% commonYrs,2]\r\n naidx = !is.na(lwtemp)&!is.na(ewtemp)\r\n mdl = lm(lwtemp~ewtemp, na.action = na.exclude)\r\n lwadjres[naidx,2] = mdl$residuals +1 \r\n \r\n write.csv(lwadjres, paste(c(site, \"la_crn.csv\"), collapse=\"\"))\r\n \r\n }\r\n \r\n }, error=function(e){})\r\n \r\n \r\n }\r\n\r\n \r\n \r\n close(fc)\r\n}\r\n\r\n\r\n\r\n", "meta": {"hexsha": "b0886e19c157dd17e2bde45ca756c66a552c59f1", "size": 8843, "ext": "r", "lang": "R", "max_stars_repo_path": "read_itrdb.r", "max_stars_repo_name": "mpdannenberg/environmental-stress-conus", "max_stars_repo_head_hexsha": "77d3244a83f81022d68cd0520241789d421c0ed1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "read_itrdb.r", "max_issues_repo_name": "mpdannenberg/environmental-stress-conus", "max_issues_repo_head_hexsha": "77d3244a83f81022d68cd0520241789d421c0ed1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "read_itrdb.r", "max_forks_repo_name": "mpdannenberg/environmental-stress-conus", "max_forks_repo_head_hexsha": "77d3244a83f81022d68cd0520241789d421c0ed1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.54296875, "max_line_length": 126, "alphanum_fraction": 0.5235779713, "num_tokens": 2524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3374704255929373}} {"text": "#coverage/inst/shiny/coverage1/server.r\n#*current version*\n#andy south 12/5/16\n\n#https://andysouth.shinyapps.io/coverage1/\n\nlibrary(shiny)\n#library(devtools)\n#install_github('AndySouth/coverage')\nlibrary(coverage)\nlibrary(png)\n\nshinyServer(function(input, output, session) {\n\n\n ################################\n output$plot_feed <- renderPlot({\n\n #add dependency on the button\n #if ( input$aButtonRun > 0 )\n #{\n #isolate reactivity of other objects\n # isolate({\n\n plot_feeding( man = input$feed_man,\n #cow = 1-input$feed_man,\n indoor = input$feed_in,\n #outdoor = 1-input$feed_in,\n intervene_indoor = input$intervene_indoor,\n intervene_outdoor = input$intervene_outdoor,\n intervene_cow = input$intervene_cow )\n #intervention = input$intervention,\n #coverage = input$target_coverage )\n\n\n #}) #end isolate\n #} #end if ( input$aButtonRun > 0 )\n })\n\n\n ####################################\n output$plot_pie_feed <- renderPlot(width = 150, height = 150,{\n #output$plot_pie_feed <- renderPlot({\n\n plot_pie_feeding( man = input$feed_man,\n #cow = 1-input$feed_man,\n indoor = input$feed_in,\n #outdoor = 1-input$feed_in,\n intervene_indoor = input$intervene_indoor,\n intervene_outdoor = input$intervene_outdoor,\n intervene_cow = input$intervene_cow )\n })\n\n\n ####################################\n output$plot_pie_expose <- renderPlot(width = 150, height = 150,{\n #output$plot_pie_expose <- renderPlot({\n\n\n plot_pie_exposure(man = input$feed_man,\n #cow = 1-input$feed_man,\n indoor = input$feed_in,\n #outdoor = 1-input$feed_in,\n intervene_indoor = input$intervene_indoor,\n intervene_outdoor = input$intervene_outdoor,\n intervene_cow = input$intervene_cow )\n #intervention = input$intervention,\n #coverage = input$target_coverage )\n })\n\n\n #to update values based on changes in others\n\n #stop feed_man going below feed_indoors\n #not needed now that human feed is a proportion of indoors\n #observe({ if ( input$feed_man < input$feed_in ) updateSliderInput(session, \"feed_man\", value = input$feed_in ) })\n\n #stop feedindoors going above feed_man\n # observe({ updateNumericInput(session, \"feed_man\", value = 1-input$feed_cow) })\n # observe({ updateNumericInput(session, \"feed_cow\", value = 1-input$feed_man) })\n # observe({ updateNumericInput(session, \"feed_in\", value = 1-input$feed_out) })\n # observe({ updateNumericInput(session, \"feed_out\", value = 1-input$feed_in) })\n\n\n})\n", "meta": {"hexsha": "7e1973d7a5ffd962d73fbf325f945f0ddbd99cd0", "size": 2818, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/shiny/coverage1/server.r", "max_stars_repo_name": "AndySouth/coverage", "max_stars_repo_head_hexsha": "e6b009e506da89dfba2d72d8ae77474c9be2e93b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/shiny/coverage1/server.r", "max_issues_repo_name": "AndySouth/coverage", "max_issues_repo_head_hexsha": "e6b009e506da89dfba2d72d8ae77474c9be2e93b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/shiny/coverage1/server.r", "max_forks_repo_name": "AndySouth/coverage", "max_forks_repo_head_hexsha": "e6b009e506da89dfba2d72d8ae77474c9be2e93b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.7674418605, "max_line_length": 116, "alphanum_fraction": 0.5908445706, "num_tokens": 692, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3374118004864944}} {"text": "# wahpenayo at gmail dot com\n# 2018-01-21\n#-----------------------------------------------------------------\n# explore the data\n#-----------------------------------------------------------------\nif (file.exists('e:/porta/projects/taigabench')) {\n setwd('e:/porta/projects/taigabench')\n} else {\n setwd('c:/porta/projects/taigabench')\n}\nsource('src/scripts/r/functions.r')\n#-----------------------------------------------------------------\ndataset <- 'ontime'\ndataf <- ontime.data\n#suffixes <- c('32768','131072','524288','2097152','8388608','33554432')\nsuffix <- '8388608' # OOM with 33554432 records\ntrainfile <- train.file(dataset=dataset,suffix=suffix)\ndtrain <- dataf(trainfile)\n#-----------------------------------------------------------------\nsummary(dtrain)\n#-----------------------------------------------------------------\nfor (col in colnames(dtrain)) {\n print(col)\n if (is.numeric(dtrain[[col]])) {\n hist.numeric(\n data=dtrain,col=col,dataset='ontime',problem='l2')\n } else {\n hist.factor(\n data=dtrain,col=col,dataset='ontime',problem='l2')\n }\n}\n#-----------------------------------------------------------------\nquantile(\n x=dtrain$arrdelay,\n probs=seq(from=0.01,to=0.99,by=0.01))\nquantile(\n x=dtrain$arrdelay,\n probs=seq(from=0.90,to=0.999,by=0.001)) \n#-----------------------------------------------------------------\n# => use 3 hours =180 min for cancelled/delayed\nfiltered <- dtrain[dtrain$arrdelay<2*60,]\nsummary(filtered)\nquantile(\n x=filtered$arrdelay,\n probs=seq(from=0.90,to=0.999,by=0.001))\ndev.on(\n file=plot.file(\n dataset='ontime',\n problem='l2',\n prefix='arrdelay-filtered'),\n aspect=0.5,\n width=1280)\nggplot(data=filtered, aes(filtered$arrdelay)) + \n geom_histogram(bins=1000) +\n # scale_y_log10() +\n scale_x_continuous(breaks = scales::pretty_breaks(n = 20))\ndev.off()\n#-----------------------------------------------------------------\n", "meta": {"hexsha": "bbc1993b945179d4a11f011c85b92c8920a29f2a", "size": 1908, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/r/attributes.r", "max_stars_repo_name": "wahpenayo/taigabench", "max_stars_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/r/attributes.r", "max_issues_repo_name": "wahpenayo/taigabench", "max_issues_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/r/attributes.r", "max_forks_repo_name": "wahpenayo/taigabench", "max_forks_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3389830508, "max_line_length": 72, "alphanum_fraction": 0.4926624738, "num_tokens": 479, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3374117997220261}} {"text": "\r\n\r\n# Goal: Make pictures in PDF files that can be put into a paper.\r\n\r\nxpts <- seq(-3,3,.05)\r\n\r\n# Here is my suggested setup for a two-column picture --\r\npdf(\"demo2.pdf\", width=5.6, height=2.8, bg=\"cadetblue1\", pointsize=8)\r\npar(mai=c(.6,.6,.2,.2))\r\nplot(xpts, sin(xpts*xpts), type=\"l\", lwd=2, col=\"cadetblue4\",\r\n xlab=\"x\", ylab=\"sin(x*x)\")\r\ngrid(col=\"white\", lty=1, lwd=.2)\r\nabline(h=0, v=0)\r\n\r\n# My suggested setup for a square one-column picture --\r\npdf(\"demo1.pdf\", width=2.8, height=2.8, bg=\"cadetblue1\", pointsize=8)\r\npar(mai=c(.6,.6,.2,.2))\r\nplot(xpts, sin(xpts*xpts), type=\"l\", lwd=2, col=\"cadetblue4\",\r\n xlab=\"x\", ylab=\"sin(x*x)\")\r\ngrid(col=\"white\", lty=1, lwd=.2)\r\nabline(h=0, v=0)\r\n\r\n\r\n", "meta": {"hexsha": "de855d26f4ca5fc052db81823a0213b9574a59a1", "size": 706, "ext": "r", "lang": "R", "max_stars_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g2.r", "max_stars_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_stars_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g2.r", "max_issues_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_issues_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g2.r", "max_forks_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_forks_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4166666667, "max_line_length": 70, "alphanum_fraction": 0.6104815864, "num_tokens": 269, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3374117916812903}} {"text": "#' Network Delineation\n#'\n#' Identifies flowlines upstream from a COMID\n#'\n#' @param comid COMID at network outlet\n#' @param flow NHDPlusV2 flow table, PlusFlow.dbf\n#' @param vaa NHDPlusV2 value added attributes table, PlusFlowlineVAA.dbf\"\n\n#' @return vector of upstream COMIDs\n#'\n#' @export\n\nnet_delin <- function(comid, flow = flow, vaa = vaa){\n\n names(flow) <- toupper(names(flow))\n names(vaa) <- toupper(names(vaa))\n\n fcomid <- comid\n VPUOUT <- vaa[vaa$COMID %in% fcomid, c(\"VPUOUT\")]\n net <- data.frame(fcomid, VPUOUT)\n\n # delineate upstream until the full net is delineated or 5 wbs are found\n while (any(flow$TOCOMID %in% fcomid) & #the fcomid has somthing upstream\n all(!is.na(net$fcomid)) & #na might occur if missmatch\n all(net$VPUOUT == 0))# if network leaves the vpu stop the loop\n {\n #upstream comid's\n fcomid <- flow[flow[, \"TOCOMID\"] %in% fcomid, \"FROMCOMID\"] #upstream comids\n fcomid <- unique(fcomid[fcomid != 0])\n\n #vaa of upstream COMID\n VPUOUT <- vaa[vaa$COMID %in% fcomid, c(\"VPUOUT\")]\n\n if(length(VPUOUT) > 0){\n #Identify whether lake network travels outside vpu\n if(any(VPUOUT == 1)){VPUOUT = 1} else {VPUOUT = 0}\n }\n\n temp <- data.frame(VPUOUT, fcomid)\n net <- rbind(temp[!temp$fcomid %in% net$fcomid, ], net)\n\n if (nrow(net) > 5e+05) {\n stop(print(\"There is a problem\"))\n }\n }\n\n if(any(net$VPUOUT != 0)){\n warning(\"Stream Network Left VPU\")\n }\n\n net <- net$fcomid\n\n return(net)\n}\n\n\n\n#' Basin Shape Metrics\n#'\n#' Calculates metrics the describe the shape of a networks waterhsed.\n#'\n#' @param network a single network extracted from NHDPlusV2\n#' @param NHDCatchments NHDPlusV2 catchment coverage, Catchment.shp\n#'\n#' @return sf object of watershed attributed with\n#' width (\\code{width}), maximum length (\\code{mxln}),\n#' area (\\code{area}), parimeter (\\code{prmt}),\n#' compactedness coefficient (\\code{CmpC}),\n#' circularoty ratio (\\code{CrcR}), and elongation\n#' ratio (\\code{ElnR}). Where applicable, units = m\n#'\n#' @details Compactness coefficient (CmpC) is defined as the ratio of the\n#' watershed perimeter to the circumference of equivalent circular area.\n#'\n#' Elongation ratio (ElnR) is defined as the ratio of diameter of a circle of the same area as\n#' the watershed to the maximum watershed length. The numerical value varies\n#' from 0 (in highly elongated shape) to 1 (in circular shape).\n#'\n#' Circulatory ratio (CrcR) is defined as the ratio of watershed area to the area of the circle\n#' having the same perimeter as the watershed perimeter. The numeric value\n#' may vary in between 0 (in line) and 1 (in a circle).\n#'\n#' @export\n\ncat_shp <- function(network, NHDCatchments){\n\n cats <- NHDCatchments[NHDCatchments$FEATUREID%in%network$COMID,]\n #project to albers\n\n cats <- sf::st_transform(cats, 5070)\n cats <- sf::st_cast(sf::st_union(cats), \"POLYGON\")\n\n box <- sf::st_bbox(cats)\n\n vert <- sf::st_linestring(as.matrix(rbind(c(box$xmin,box$ymin), c(box$xmin,box$ymax))))\n vert <- sf::st_length(vert)\n\n horiz <- sf::st_linestring(as.matrix(rbind(c(box$xmin,box$ymin),c(box$xmax,box$ymin))))\n horiz <- sf::st_length(horiz)\n\n width <- min(horiz, vert)\n mxln <- max(horiz, vert)\n area <- sf::st_area(cats)\n prmt <- lwgeom::st_perimeter(cats)\n\n #Perimeter of watershed/Perimeter of circle of watershed area\n #Circle Perimeter (P) = \u221a(4\u03c0A)\n #circle area (A) = P^2 / 4\u03c0\n #circle diameter D = 2r; r=\u221a(A/\u03c0)\n\n #Compactness coefficient is defined as the\n #ratio of the watershed perimeter to the\n #circumference of equivalent circular area.\n CmpC <- prmt/sqrt(4* base::pi *area)\n\n #Circulatory ratio is defined as the\n #ratio of watershed area to the area of\n #the circle having the same perimeter as the watershed perimeter\n CrcR <- area/(prmt^2 / 4 * base::pi)\n\n #Elongation ratio is defined as the\n #ratio of diameter of a circle of the same area as the watershed\n #to the maximum watershed length.\n #The numerical value varies from 0 (in highly elongated shape) to\n #1 (in circular shape). These values can be\n ElnR <- (2*sqrt(area / base::pi))/mxln\n\n cats <- sf::st_as_sf(data.frame(cbind(width = width/1000, mxln = mxln/1000,\n area = area/1e6, prmt = prmt/1000,\n CmpC, CrcR, ElnR), geometry = cats),\n crs = 5070)\n\n #transform back into network crs\n cats <- sf::st_transform(cats, crs = sf::st_crs(network))\n\n return(cats)\n}\n\n\n\n#' Network Shape Metrics\n#'\n#' Calculates metrics the describe the shape of a network\n#'\n#' @param network a single network extracted from NHDPlusV2\n#' @param vaa NHDPlusV2 value added attributes table, PlusFlowlineVAA.dbf\"\n#'\n#' @return data.frame with network stream order (\\code{Ord}),\n#' Mainstem Length (\\code{Ord}), total length (\\code{TL}), Drainage\n#' Density (\\code{Dd}), Horton Laws: bifurcation ratio (\\code{Rb}),\n#' length Ratio (\\code{Rl}) and area Ratio (\\code{Ord})\n#'\n#' @details Horton ratios estimated as semilog relationships\n#'\n#' @export\n\nnet_shp<-function (network, vaa){\n\n net <- sf::st_set_geometry(network, NULL)\n names(net) <- toupper(names(net))\n names(vaa) <- toupper(names(vaa))\n\n\n #remove diveregences\n vaa <- vaa[vaa$STREAMORDE == vaa$STREAMCALC & vaa$COMID %in% net$COMID,]\n\n #split each path - more accurately represents stream reaches in calcuations\n #z<-dat[[1]][,c(\"COMID\", \"STREAMORDE\", \"LEVELPATHI\")]\n #plot(st_geometry(network))\n #plot(st_geometry(network[network$COMID%in%z$COMID,]), col = \"red\", add = T)\n\n dat <- split(vaa, as.character(vaa$LEVELPATHI))\n\n #calculate Mainstem Length (while we are here)\n MSL <- max(unlist(lapply(dat, function(x) sum(x$LENGTHKM))))\n\n dat <- do.call(rbind, lapply(dat, function(z)\n do.call(rbind, lapply(split(z, z$STREAMORDE), function(x)\n data.frame(STREAMORDE = unique(x$STREAMORDE),\n TOTDASQKM = max(x$TOTDASQKM),\n LENGTHKM = sum(x$LENGTHKM))))))\n\n\n #summarise by stream order\n dat <- split(dat, as.character(dat$STREAMORDE))\n dat <- do.call(rbind, lapply(dat, function(x)\n data.frame(STREAMORDE = unique(x$STREAMORDE),\n N = nrow(x),logN = log(nrow(x)) ,\n A = mean(x$TOTDASQKM), logA = log(mean(x$TOTDASQKM)),\n L = mean(x$LENGTHKM), logL = log(mean(x$LENGTHKM)))))\n\n\n #y = log(c(60,13,9,4,1))\n #x = c(1:5)\n #1/exp(coef(lm(y~x))[2])\n\n # estimate log(Rb) as slope\n #plot(dat$STREAMORDE,dat$logN)\n #abline(lm(dat$logN~dat$STREAMORDE))\n #fit semilog model\n lmRb <- stats::lm(logN ~ STREAMORDE, data = dat)\n # slove for Rb w/base e\n Rb <- 1/exp(stats::coef(lmRb)[\"STREAMORDE\"])\n Rb_rsq <- summary(lmRb)$r.squared\n\n #length\n #plot(dat$STREAMORDE, dat$logL)\n #abline(lm(dat$logL~dat$STREAMORDE))\n lmRl <- stats::lm(logL ~ STREAMORDE, data = dat)\n # slove for Rb w/base e\n Rl <- exp(stats::coef(lmRl)[\"STREAMORDE\"])\n Rl_rsq <- summary(lmRl)$r.squared\n\n #area\n #plot(dat$STREAMORDE, dat$logA)\n #abline(lm(dat$logA~dat$STREAMORDE))\n lmRa <- stats::lm(logA ~ STREAMORDE, data = dat)\n # slove for Rb w/base e\n Ra <- exp(stats::coef(lmRa)[\"STREAMORDE\"])\n Ra_rsq <- summary(lmRa)$r.squared\n\n TL <- sum(vaa$LENGTHKM)\n Dd <- sum(vaa$LENGTHKM)/max(vaa$TOTDASQKM)\n Ord <- max(vaa$STREAMORDE)\n\n out <- data.frame(Ord, MSL, TL, Dd, Rb,Rb_rsq, Rl, Rl_rsq, Ra, Ra_rsq, row.names = NULL)\n\n return(out)\n}\n\n", "meta": {"hexsha": "b08c0eee47fdfdb9722846640953d619df6ce711", "size": 7374, "ext": "r", "lang": "R", "max_stars_repo_path": "R/WatershedScale_Functions.r", "max_stars_repo_name": "dkopp3/MMASN", "max_stars_repo_head_hexsha": "9b7be92d8eda65076eeaaa1b8763e195eee947f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/WatershedScale_Functions.r", "max_issues_repo_name": "dkopp3/MMASN", "max_issues_repo_head_hexsha": "9b7be92d8eda65076eeaaa1b8763e195eee947f0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/WatershedScale_Functions.r", "max_forks_repo_name": "dkopp3/MMASN", "max_forks_repo_head_hexsha": "9b7be92d8eda65076eeaaa1b8763e195eee947f0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9220779221, "max_line_length": 95, "alphanum_fraction": 0.6623270952, "num_tokens": 2365, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3374117916812903}} {"text": "source('2020-08-31-jsa-type-v2-ch2/02-model/init.r')\r\n\r\nmodel_analyse <- \"BGY-E0-C4-I8000-A0.8-T12-F25-V0\"\r\nmodel_predict <- \"BGY-E1-C4-I20000-A0.9-T12-F10-V0\"\r\n\r\n\r\n# Get graph from model_predict\r\ndir_model_folder <- paste0(dir_analysis_edie_model, model_predict, \"/\")\r\n\r\n# Load posterior simulation\r\nload(paste0(dir_model_folder, \"post.data\")) # as \"allsim\"\r\n\r\n# Load forecast predictions\r\nload(paste0(dir_model_folder, \"forecast.data\")) # as \"forecast\"\r\n\r\n# Original data\r\ndata_raw <- read.csv(\r\n paste0(dir_model_folder, \"data.csv\"), \r\n na.strings=c(\"\"),\r\n stringsAsFactor = TRUE\r\n) \r\n\r\n# R data\r\ndata <- read_rdump(paste0(dir_model_folder, \"count_info.data.R\")) \r\n\r\n\r\n# Map model indices to original variables\r\nmapping <- unique(data.frame(\r\n groupname = as.character(data_raw$group),\r\n group = as.numeric(data_raw$group)\r\n))\r\n\r\n# Forecast information\r\n\r\nforsim <- data.table(convert_forecast_to_df(data, data_raw, forecast))\r\n\r\nhead(forsim)\r\n\r\nforsim <- forsim[, list(\r\n for_cml_value_pred_median = as.integer(median(value)),\r\n for_cml_value_pred_lwrCI95 = as.integer(quantile(value, 0.025)),\r\n for_cml_value_pred_uprCI95 = as.integer(quantile(value, 0.925)),\r\n for_cml_value_pred_mean = as.integer(mean(value))\r\n), by=c(\"index\", \"group\")]\r\n\r\nnames(forsim)[names(forsim) == \"index\"] <- \"year\"\r\n\r\n# Up till year 2019\r\n\r\nli_df <- summarize_simulations_observed(data, allsim)\r\nZ <- li_df$Z # Counts for each year for sim and actual, df\r\n\r\nY <- data.table(Z)\r\nsim_median <- Y[sim != 0, list(\r\n cml_value_pred_median = as.integer(median(cml_value)),\r\n cml_value_pred_lwrCI95 = as.integer(quantile(cml_value, 0.025)),\r\n cml_value_pred_uprCI95 = as.integer(quantile(cml_value, 0.925)),\r\n cml_value_pred_mean = as.integer(mean(cml_value))\r\n), by=c(\"year\", \"group\")]\r\nobs <- Y[sim == 0, c(\"year\", \"group\", \"cml_value\")]\r\nnames(obs)[3] <- \"cml_value_obs\"\r\n\r\n# sanity check\r\ncheck <- data.table(data_raw)[, .N, by=c(\"group\", \"year\")][order(group, year)]\r\ncheck[, cml:= cumsum(N), by=\"group\"]\r\nhead(check[group==\"NA\"], 20)\r\n\r\nall <- merge(obs, sim_median, by=c(\"year\", \"group\"))\r\n\r\n\r\n# Combine with forecasts\r\nhead(all)\r\nhead(forsim)\r\n\r\nfinal_counts <- all[year==max(year),list(\r\n count = max(cml_value_obs)\r\n), by= c(\"year\", \"group\")]\r\nfinal_counts$year <- NULL\r\n\r\nforsim <- merge(forsim, final_counts, by=c(\"group\"), all.x=T, all.y=T)\r\n\r\nforsim$for_cml_value_pred_median <- \r\n forsim$for_cml_value_pred_median + forsim$count\r\nforsim$for_cml_value_pred_lwrCI95 <- \r\n forsim$for_cml_value_pred_lwrCI95 + forsim$count\r\nforsim$for_cml_value_pred_uprCI95 <-\r\n forsim$for_cml_value_pred_uprCI95 + forsim$count\r\nforsim$for_cml_value_pred_mean <-\r\n forsim$for_cml_value_pred_mean + forsim$count\r\nforsim$count <- NULL\r\n\r\nhead(all)\r\n\r\n\r\nall <- merge(all, forsim, by=c(\"year\", \"group\"), all.x=T, all.y=T)\r\n\r\nall <- merge(all, mapping, by=\"group\", all.x=T, all.y=F)\r\nall$group <- NULL\r\n\r\nwfile <- paste0(v2_dir_data_webapp, \"ch2-fig-01-data.csv\")\r\nfwrite(all, wfile, na=\"\")\r\n\r\n\r\n# show the mean!\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Get results.csv from model_analyse\r\ndir_model_folder <- paste0(dir_analysis_edie_model, model_analyse, \"/\")\r\nrfile <- paste0(dir_model_folder, \"output/results.csv\")\r\nresults <- fread(rfile)\r\n\r\nresults$expected_median <- NULL\r\nresults$expected_CI_lower <- NULL\r\nresults$expected_CI_higher <- NULL\r\nresults$fore_mu <- NULL\r\nresults$fore_lower <- NULL\r\nresults$fore_upper <- NULL\r\nresults$group <- NULL\r\n\r\ndir_model_folder <- paste0(dir_analysis_edie_model, model_predict, \"/\")\r\nrfile <- paste0(dir_model_folder, \"output/results.csv\")\r\nresults_pred <- fread(rfile)\r\n\r\nresults_pred$expected_median <- NULL\r\nresults_pred$expected_CI_lower <- NULL\r\nresults_pred$expected_CI_higher <- NULL\r\nresults_pred$slowdown<- NULL\r\nresults_pred$slowdown_CI_lower <- NULL\r\nresults_pred$slowdown_CI_higher <- NULL\r\nresults_pred$group <- NULL\r\n\r\nresults <- merge(results, results_pred, on=\"groupname\")\r\n\r\nnames(results)[grep(\"for_\", names(results))] <- \r\n paste0(names(results)[grep(\"for_\", names(results))], \"_10y\")\r\n\r\nwfile <- paste0(v2_dir_data_webapp, \"ch2-fig-02-data.csv\")\r\nfwrite(results, wfile, na=\"\")\r\n\r\n\r\n", "meta": {"hexsha": "5f033f8c5aa415a40c692590f51db1f867cf0870", "size": 4170, "ext": "r", "lang": "R", "max_stars_repo_path": "2020-08-31-jsa-type-v2-ch2/99-ancillary/bitesized.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020-08-31-jsa-type-v2-ch2/99-ancillary/bitesized.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020-08-31-jsa-type-v2-ch2/99-ancillary/bitesized.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7586206897, "max_line_length": 80, "alphanum_fraction": 0.6942446043, "num_tokens": 1191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3374117916812903}} {"text": "args <- commandArgs(TRUE)\n \nN <- args[1]\nx <- rnorm(N,0,1)\n \npng(filename=\"C:/xampp/htdocs/recommender/temp.png\", width=500, height=500)\nhist(x, col=\"lightblue\")\ndev.off()", "meta": {"hexsha": "746362e2659da9ba04221e9db7447cc1f6d6ab09", "size": 171, "ext": "r", "lang": "R", "max_stars_repo_path": "my_rscript.r", "max_stars_repo_name": "vaibhavvar/recommender_system", "max_stars_repo_head_hexsha": "4165c73845c31edc25af4169cea99c72e78d7792", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "my_rscript.r", "max_issues_repo_name": "vaibhavvar/recommender_system", "max_issues_repo_head_hexsha": "4165c73845c31edc25af4169cea99c72e78d7792", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "my_rscript.r", "max_forks_repo_name": "vaibhavvar/recommender_system", "max_forks_repo_head_hexsha": "4165c73845c31edc25af4169cea99c72e78d7792", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.375, "max_line_length": 75, "alphanum_fraction": 0.6725146199, "num_tokens": 57, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251201477015, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3372005371812552}} {"text": "############################################################\n# For macaque paper, 08.19\n# Compares chromosome lengths and # of CNVs\n# Gregg Thomas\n############################################################\n\nthis.dir <- dirname(parent.frame(2)$ofile)\nsetwd(this.dir)\n\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(plyr)\nlibrary(grid)\nlibrary(ggpubr)\nlibrary(cowplot)\n\nsource(\"../lib/read_svs.r\")\nsource(\"../lib/filter_svs.r\")\nsource(\"../lib/subset_svs.r\")\nsource(\"../lib/design.r\")\n\n############################################################\n\nsavefiles = T\nrm_alus = T\nin_data = read.csv(\"../cnv-calls/macaque-cnv-chrome-counts.csv\")\n\nif(rm_alus){\n in_data = subset(in_data, Length < 275 | Length > 325)\n}\n# For Alu stuff\n\n######################\n\n######################\n# All CNVs and chromosome length\nfigS2a = ggplot(in_data, aes(Length, Num.CNVs)) +\n geom_point(size=3, color=\"#666666\") +\n geom_smooth(method=\"lm\", fullrange=T, size=0.75, linetype=\"dashed\", alpha=0, color=\"#333333\") +\n labs(x=\"Chromosome length\", y=\"# CNVs\") +\n bartheme()\nprint(figS2a)\n######################\n\n######################\n# Deletions and chromosome length\nfigS2b = ggplot(in_data, aes(Length, Num.dels)) +\n geom_point(size=3, color=\"#666666\") +\n geom_smooth(method=\"lm\", fullrange=T, size=0.75, linetype=\"dashed\", alpha=0, color=\"#333333\") +\n labs(x=\"Chromosome length\", y=\"# Deletions\") +\n bartheme()\nprint(figS2b)\n######################\n\n######################\n# Duplications and chromosome length\nfigS2c = ggplot(in_data, aes(Length, Num.dups)) +\n geom_point(size=3, color=\"#666666\") +\n geom_smooth(method=\"lm\", fullrange=T, size=0.75, linetype=\"dashed\", alpha=0, color=\"#333333\") +\n #ggtitle(\"Gene family changes vs SV duplications\") +\n #scale_color_manual(name=\"\", values=c('#490092','#920000'), labels=c(\"Macaque\",\"Human\"), drop=FALSE) +\n #scale_y_continuous(limits=c(0, 5)) +\n labs(x=\"Chromosome length\", y=\"# Duplications\") +\n bartheme()\nprint(figS2c)\n######################\n\n######################\n# Making the figure\n\np = plot_grid(figS2a, figS2b, figS2c, nrow=1, labels=c(\"A\",\"B\",\"C\"), label_size=24)\n\nprint(p)\n\nif(savefiles){\n outfile = \"figS2.pdf\"\n cat(\" -> \", outfile, \"\\n\")\n ggsave(filename=outfile, p, width=14, height=4, units=\"in\")\n}\n\n######################", "meta": {"hexsha": "d1b94e14fcc47d459b905e55da0043ddaece00d9", "size": 2277, "ext": "r", "lang": "R", "max_stars_repo_path": "figS2/figS2.r", "max_stars_repo_name": "gwct/macaque-cnv-figs", "max_stars_repo_head_hexsha": "1767306693e27834650dede03ace4122c4ed5a45", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "figS2/figS2.r", "max_issues_repo_name": "gwct/macaque-cnv-figs", "max_issues_repo_head_hexsha": "1767306693e27834650dede03ace4122c4ed5a45", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "figS2/figS2.r", "max_forks_repo_name": "gwct/macaque-cnv-figs", "max_forks_repo_head_hexsha": "1767306693e27834650dede03ace4122c4ed5a45", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.1111111111, "max_line_length": 104, "alphanum_fraction": 0.5669740887, "num_tokens": 639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251201477015, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3372005371812552}} {"text": "# Libraries\nlibrary(raster)\nlibrary(maptools)\n\n\n# Params\ndirectory = 'data/geo'\nmap_crs = '+proj=longlat +datum=WGS84'\n\n# Initialize\ndata(wrld_simpl)\n\n\n################ Geospatial functions ################\n\n\n\n\n# Read master grid\nfile <- paste0(directory, '/0_manual/anthropogenic_biomes/anthromes-v2-2000-global-geotif/a2000_global.tif')\nx1 <- raster::raster(file)\n\n\n\n# Read shp 1\nfile <- c('/0_manual/soil_map/DSMW/DSMW.shp')\nx2 <- raster::shapefile(paste0(directory, file[1]))\nraster::projection(x2) <- map_crs\nplot(x2)\n\n\n# Rasterize shp\nfile <- paste0(directory, '/0_manual/plant_wcmc/Centres_of_Plant_Diversity_2013/Centres_of_Plant_Diversity_2013/CPD_2013.shp')\nx4 <- raster::shapefile(file)\n\nrst <- raster(matrix(runif(20), 5, 4))\nextent(rst) <- extent(-180, 180, -90, 90)\nres(rst) <- 0.5 # 1 deg cell size\n\nrasterized <- raster::rasterize(x4, rst)\n# attribute_t <- levels(rasterized)[[1]]\nhead(levels(rasterized)[[1]]); dim(levels(rasterized)[[1]])\nhead(unique(levels(rasterized)[[1]])); unique(dim(levels(rasterized)[[1]]))\n# rasterized <- raster::ratify(rasterized) # reset raster attribute table\n\n\n# Resample to same extent/resolution/grid (for categorical)\nx5 <- raster::resample(rasterized, x1, method='ngb')\npar(mfrow=c(2,1)); plot(rasterized, col=rev(topo.colors(50))); plot(x3, col=rev(heat.colors(50)))\n\nplot(x3)\nplot(wrld_simpl, add=T)\n\nraster::stack(x1, x5) # now it is able to stack \n\n\n# Resample to same extent/resolution/grid (for continuous)\n# aggregate (fun=mean) -> resample\n# TODO:\n\n\n\n\n# Tutorial on vector\n# https://cengel.github.io/rspatial/2_spDataTypes.nb.html\n\n# sp package\nlibrary(sp)\nln <- sp::Line(matrix(runif(6), ncol=2))\nstr(ln)\n\nlns <- sp::Lines(list(ln), ID = \"a\") # this contains just one Line!\nstr(lns)\n\nsp_lns <- sp::SpatialLines(list(lns))\nstr(sp_lns)\n\ndfr <- data.frame(id = \"a\", use = \"road\", cars_per_hour = 10) # note how we use the ID from above!\nsp_lns_dfr <- sp::SpatialLinesDataFrame(sp_lns, dfr, match.ID = \"id\")\nstr(sp_lns_dfr)\n\n# sf package\nlibrary(sf)\nlnstr_sfg <- sf::st_linestring(matrix(runif(6), ncol=2)) \nclass(lnstr_sfg)\n\n(lnstr_sfc <- sf::st_sfc(lnstr_sfg)) # just one feature here\nclass(lnstr_sfc) \n\n(lnstr_sf <- st_sf(dfr, lnstr_sfc))\nclass(lnstr_sf)\n\n\n\n# Test\nfolders <- list.files(directory)\nfolder_dir <- paste0(directory, \"/\", folders[1])\nlist.files(folder_dir)", "meta": {"hexsha": "96fe8e6eb5c4d3644296e4484d8394d493eb4b60", "size": 2329, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-12-convert-geo-files/read_shp.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-12-convert-geo-files/read_shp.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-12-convert-geo-files/read_shp.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.7653061224, "max_line_length": 126, "alphanum_fraction": 0.7020180335, "num_tokens": 746, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832804, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.33719145346791324}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\n\nsetwd(\"/Users/mengmengjiang/all datas/voltage\")\nlibrary(xlsx)\n\n##raeding datas of flow rates\neq<-read.xlsx(\"voltage.xls\",sheetName=\"ethanol_q\",header=TRUE)\naq<-read.xlsx(\"voltage.xls\",sheetName=\"acetone_q\",header=TRUE)\niq<-read.xlsx(\"voltage.xls\",sheetName=\"iso_q\",header=TRUE)\n\n##\nyan<-c(\"red\",\"blue\",\"green3\")\n\n## layout\n\npar(mfrow = c(2,2), mar = c(2.4,3.4,1,1), oma = c(1,1,1,1))\nlayout(matrix(c(1,2,3),3,1,byrow = TRUE))\n\n## flow rates\nplot(eq$f, eq$va, col=0, xaxs=\"i\", xlim=c(-0.002, 0.032), ylim=c(0.4,1.2),\n xlab=expression(italic(Q)(ml/min)),mgp=c(1.8, 0.5, 0),tck=0.01,cex.lab=1.6,cex.axis=1.6,\n ylab=expression(italic(V)(kv)))\n\nmtext(\"Flow rate\",col=\"black\",3,line=-1.4,font=2,cex=1.2)\n\nlines(eq$f,eq$seva,col=yan[1],pch=0,lwd=2,lty=4,type=\"b\")\nlines(aq$f,aq$seva,col=yan[2],pch=1,lwd=2,lty=4,type=\"b\")\nlines(iq$f,iq$seva,col=yan[3],pch=2,lwd=2,lty=4,type=\"b\")\n\nleg<-c(\"ethanol\",\"acetone\",\"iso\")\n\nlegend(\"bottomright\",legend=leg, col=yan, pch=c(0,1,2),bty=\"n\",lwd=2,lty=2,inset=.01,cex=1.5)\n\n## Distance\n\neq<-read.xlsx(\"voltage.xls\",sheetName=\"ethanol_d\",header=TRUE)\naq<-read.xlsx(\"voltage.xls\",sheetName=\"acetone_d\",header=TRUE)\niq<-read.xlsx(\"voltage.xls\",sheetName=\"iso_d\",header=TRUE)\n\nplot(eq$d, eq$va, col=0, xaxs=\"i\", xlim=c(0.5,4.5), ylim=c(0.6,1.4),xlab=expression(italic(Distance)(mm)),mgp=c(1.7, 0.5,0),tck=0.01,cex.lab=1.6,cex.axis=1.6,ylab=expression(italic(V)(kv)))\n\nmtext(\"Distance\",col=\"black\",3,line=-1.4,font=2,cex=1.2)\n\nlines(eq$d,eq$seva,col=yan[1],pch=0,lwd=2,lty=4,type=\"b\")\nlines(aq$d,aq$seva,col=yan[2],pch=1,lwd=2,lty=4,type=\"b\")\nlines(iq$d,iq$seva,col=yan[3],pch=2,lwd=2,lty=4,type=\"b\")\n\nleg<-c(\"ethanol\",\"acetone\",\"iso\")\n\nlegend(\"bottomleft\",legend=leg, col=yan, pch=c(0,1,2),bty=\"n\",lwd=2,lty=2,inset=.01,cex=1.5)\n\n## Nozzle diameter ###\n\neq<-read.xlsx(\"voltage.xls\",sheetName=\"ethanol_r\",header=TRUE)\naq<-read.xlsx(\"voltage.xls\",sheetName=\"acetone_r\",header=TRUE)\niq<-read.xlsx(\"voltage.xls\",sheetName=\"iso_r\",header=TRUE)\n\nplot(eq$r, eq$va, col=0, xaxs=\"i\", xlim=c(0.25,0.85), ylim=c(0.6,1.4),xlab=expression(paste(italic(Nozzle),\" \",italic(diameter),(mm))),mgp=c(1.7, 0.5,0),tck=0.01,cex.lab=1.6,cex.axis=1.6,ylab=expression(italic(V)(kv)))\n\nmtext(\"Nozzles\",col=\"black\",3,line=-1.4,font=2,cex=1.2)\n\nlines(eq$r,eq$seva,col=yan[1],pch=0,lwd=2,lty=4,type=\"b\")\nlines(aq$r,aq$seva,col=yan[2],pch=1,lwd=2,lty=4,type=\"b\")\nlines(iq$r,iq$seva,col=yan[3],pch=2,lwd=2,lty=4,type=\"b\")\ng\nleg<-c(\"ethanol\",\"acetone\",\"iso\")\n\nlegend(\"topleft\",legend=leg, col=yan, pch=c(0,1,2),bty=\"n\",lwd=2,lty=2,inset=.01,cex=1.5)\n", "meta": {"hexsha": "4b19f3bbb5ecda79c539716f0ae961ea001d51c5", "size": 2661, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap5/chap5-fig5-24.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap5/chap5-fig5-24.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap5/chap5-fig5-24.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5652173913, "max_line_length": 219, "alphanum_fraction": 0.668169861, "num_tokens": 1156, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.3371914455076655}} {"text": "#function to process the simulated data, \r\n# feng@BU 12/15/2016\r\n#\r\n#1) read the data from the disk\r\n#2) process to see the linear regress results\r\n#\t\tpick the top portions to see the identification performance\r\n#\t\tfdr\r\n#\t\troc\r\n#\t\t\r\n#3) do the ordinary t test to compare the performance of linear regression model\r\n#4) sumarize the results\r\n#5) plotting and reporting\r\n###############################\r\nlibrary(ARPPA)\r\nlibrary(qvalue)\r\n#install.packages(\"pROC\")\r\nlibrary(pROC)\r\n\r\n\r\n#filename is the base name of the file. the file should be \r\n#\t\tr data file (\".RData\"). filename=\"result_\"\r\n#sample.size, used for t test, the sample size for each group\r\n#percent.nonZero, indicate the proportion of nonzero interaction in the simulation \r\n#object.load, used to indicate the loaded object name\r\n#mode, 1 for comparison with either negative control or isotype control, other values for no control\r\nanalyzeData<-function(path,filename=\"result_\", repeats=1, \r\n\t\t\t\t\tsample.size=5,\r\n\t\t\t\t\tproportion.nonZero=0.02,\r\n\t\t\t\t\tobject.load=\"lstToSave\",\r\n\t\t\t\t\tmode=1\r\n\t\t\t)\r\n\t\t\t\r\n{\r\n#path<-filePath\r\nsampleSize=sample.size;\r\nprob.nonZero=proportion.nonZero;\r\ncat(\"start doing the data reading......\\n\");\r\nflush.console();\r\nrepeats<-repeats\r\nsetwd(path)\r\n#(\"E:\\\\feng\\\\LAB\\\\hg\\\\proteinArray_Masa\\\\ARPPA\\\\data\\\\EqualVar_NegativeCon\");\r\n#setwd(\"~/Desktop/arppr/EqualVar_NegativeCon/\")\r\ni<-19\r\nportions<-seq(0.000, 1.0, 0.001)\r\nstats_df<-data.frame(\"portions\"=portions);\r\nstats_qval_tpr<-data.frame(\"cutoff\"=portions)\r\nstats_qval_fpr<-data.frame(\"cutoff\"=portions)\r\n\r\nstats_qval_tpr_ordT<-data.frame(\"cutoff\"=portions)\r\nstats_qval_fpr_ordT<-data.frame(\"cutoff\"=portions)\r\n\r\nroc.auc<-rep(0,repeats)\r\nroc_ordT.auc<-rep(0,repeats)\r\nfor(i in c(1:repeats))\r\n{\r\n\t\r\n\tcat(\"reading the \",i, \"/\", repeats,\" data sets.....\\n\")\r\n\t#load(paste(filename, i,\"_rlm.RData\",sep=\"\"))\r\n\tload(paste(filename, i,\".RData\",sep=\"\"))\r\n\t\r\n\tcat(\"count the correct ones.....\\n\");\r\n\tflush.console();\r\n\t#depending on which data,\r\n\tlstData<-get(object.load);#lstToSave\r\n\teval(paste(\"rm(\",object.load,\")\",sep=\"\"))\r\n\t\r\n\t#try to know which one is nonZero as the true values\r\n\tgammaTrue<-lstData$data$gamma\r\n\tif(mode==1)\r\n\t{\r\n\t\tgammaTrueIndex<-which(gammaTrue[,2]!=0)\r\n\t} else\r\n\t{\r\n\t\tgammaTrueIndex<-which(gammaTrue[,2]!=0|gammaTrue[,1]!=0)\r\n\t}\r\n\t\r\n\t#now need to sort the p-value of the \r\n\t#and pick top n genes to be the significant ones as the analysis results\r\n\t#\r\n\tlr_coe<-lstData$lregCoef\r\n\tlr_coe_names<-rownames(lr_coe)\r\n\tlr_coe_gamma_index<-which(grepl(\"gene\\\\d*:group2\",lr_coe_names)); #indices for interaction terms only\r\n\tlr_coe_int<-lr_coe[lr_coe_gamma_index,];#get entries for interaction terms\r\n\tlr_coe_int_names<-lr_coe_names[lr_coe_gamma_index];\r\n\r\n\t#sort the array according to the prob\r\n\tlr_coe_int_prob<-lr_coe_int[,\"Pr(>|t|)\"]\r\n\t#lr_coe_int_prob<-(1-pt(abs(lr_coe_int[,\"t value\"]),df=2000*2*5-4000))*2\r\n\t\r\n\tlr_coe_int_sort_order<-order(lr_coe_int_prob) #NOTE:the names is one more than its index, eg. 811 is gene812:group2\r\n\t#lr_coe_int_sort<-lr_coe_int[lr_coe_int_sort_order,]\r\n\t\r\n\t#lr_coe_int_prob_qvalue<-qvalue(lr_coe_int_prob[lr_coe_int_sort_order])$qvalue\r\n\tlr_coe_int_prob_qvalue<-tryCatch(\r\n\t\tqvalue(lr_coe_int_prob)$qvalue, #(lr_coe_int_prob[lr_coe_int_sort_order])$qvalue,\r\n\t\terror=function(c){\r\n\t\tcat(\"******ERROR in calling \\\"qvalue\\\"\\n\")\r\n\t\tcat(\"\\t\",c$message,\"\\n\")\r\n\t\tcat(\"calling p.adjust instead\\n\")\r\n\t\tlr_coe_int_prob_qvalue<- p.adjust(lr_coe_int_prob, method=\"BH\")#(lr_coe_int_prob[lr_coe_int_sort_order], method=\"BH\")\t\t\r\n\t\t},\r\n\t\twarning = function(c) c$message,\r\n\t\tmessage = function(c) c$message\r\n\t)\r\n\t\r\n\troc_response<-rep(0, length(gammaTrue[,1]))\r\n\troc_response[gammaTrueIndex]<-1\r\n\t\r\n\troc_predict<-lr_coe_int_prob_qvalue #qvalue(lr_coe_int_prob)$qvalue\r\n\troc.obj<-roc(roc_response[-1], roc_predict)\r\n\troc.auc[i]<-roc.obj$auc\r\n\t\r\n\r\n\t#now we have everything, just need to collect statistics\r\n\tp_correct<-portions;#initialize the vector\r\n\tp_correct_byQ<-portions;#initialize the vector\r\n\tp_fpr_byQ<-portions;#initilize\r\n\t\r\n\tp_correct_byQ_ordT<-portions;#initialize the vector\r\n\tp_fpr_byQ_ordT<-portions;#initialize\r\n\t\r\n\t\r\n\t#now we are doing -->ordinary t tests<--\r\n\t#get the data first\r\n\tdtExp<-lstData$data$exp\r\n\tprob_ordT<-rep(0,length(dtExp[,1]))\r\n\t#run t.test\r\n\tfor(nn in c(1:length(dtExp[,1])))\r\n\t{\r\n\t\tprob_ordT[nn]<-t.test(dtExp[nn,c(1:sampleSize)],dtExp[nn,c((1+sampleSize):(sampleSize+sampleSize))])$p.value\r\n\t}\r\n\tprob_ordT_Q<-\ttryCatch(\r\n\t\tqvalue(prob_ordT)$qvalue,\r\n\t\terror=function(c){\r\n\t\tcat(\"******ERROR in calling \\\"qvalue\\\"\\n\")\r\n\t\tcat(\"\\t\",c$message,\"\\n\")\r\n\t\tcat(\"calling p.adjust instead\\n\")\r\n\t\tprob_ordT_Q<- p.adjust(prob_ordT, method=\"BH\")\t\t\r\n\t\t},\r\n\t\twarning = function(c) c$message,\r\n\t\tmessage = function(c) c$message\r\n\t)\r\n\troc_ordT<-roc(roc_response, prob_ordT_Q)\r\n\t\r\n\troc_ordT.auc[i]<-roc_ordT$auc\r\n\tj<-1\r\n\tfor(j in c(1:length(portions)))\r\n\t{\r\n\t\tif(mode==1)\r\n\t\t\tfactors<-1\r\n\t\telse\r\n\t\t\tfactors<-2\r\n\t\t#for each portion, we need to check what is percentage to be correct\r\n\t\tnumToPick<-floor(lstData$data$params[1]*portions[j]*factors)\r\n\t\t#picking from the order array\r\n\t\tgenesToPick_byProb<-lr_coe_int_sort_order[c(1:numToPick)]+1 #see NOTE above, index is one more less than the name\r\n\t\t#now we got the genes, just need to check whether the genes so far are the TRUE ones\r\n\t\tnumOfCorrect<-sum(is.element(genesToPick_byProb, gammaTrueIndex));\r\n\t\tp_correct[j]<-numOfCorrect/numToPick\r\n\t\t\r\n\t\t#now doing the q value, using the portion as the qvalue cutoff\r\n\t\tnumToPick_byQ<-floor(portions[j]*lstData$data$params[1]*factors);#sum(lr_coe_int_prob_qvalue<=portions[j])\r\n\t\tlr_coe_int_Q_sort_order<-order(lr_coe_int_prob_qvalue)\t\r\n\t\tgenesToPick_byQ<-lr_coe_int_Q_sort_order[c(1:numToPick_byQ)]+1\r\n\t\t#now we got the genes, just need to check whether the genes so far are the TRUE ones\r\n\t\tnumOfCorrect_byQ<-sum(is.element(genesToPick_byQ, gammaTrueIndex));\r\n\t\tp_correct_byQ[j]<-numOfCorrect_byQ/floor(lstData$data$params[1]*lstData$data$params[3]*factors)\r\n\t\tp_fpr_byQ[j]<-(length(genesToPick_byQ)-numOfCorrect_byQ)/floor(lstData$data$params[1]*(1-lstData$data$params[3]*factors))\r\n\t\t\r\n\t\t#now doing ordinary T tests<--------\r\n\t\tnumToPick_byQ_ordT<-numToPick_byQ;#sum(prob_ordT_Q<=portions[j])\r\n\t\tprob_ordT_Q_sort<-order(prob_ordT_Q)\r\n\t\tgeneToPick_byQ_OrdT<-prob_ordT_Q_sort[c(1:numToPick_byQ_ordT)]#this is different, there is +1 here!!!!!!!\r\n\t\tnumOfCorrect_byQ_ordT<-sum(is.element(geneToPick_byQ_OrdT, gammaTrueIndex));\r\n\t\t\r\n\t\tp_correct_byQ_ordT[j]<-numOfCorrect_byQ_ordT/floor(lstData$data$params[1]*lstData$data$params[3]*factors)\r\n\t\tp_fpr_byQ_ordT[j]<-(length(geneToPick_byQ_OrdT)-numOfCorrect_byQ_ordT)/floor(lstData$data$params[1]*(1-lstData$data$params[3]*factors))\r\n\t}\r\n\tstats_df[,paste(\"repeat_\",i, sep=\"\")]<-p_correct;\r\n\tstats_qval_tpr[,paste(\"repeat_\",i,sep=\"\")]<-p_correct_byQ;\r\n\tstats_qval_fpr[,paste(\"repeat_\",i,sep=\"\")]<-p_fpr_byQ;\r\n\t\r\n\tstats_qval_tpr_ordT[,paste(\"repeat_\",i,sep=\"\")]<-p_correct_byQ_ordT;\r\n\tstats_qval_fpr_ordT[,paste(\"repeat_\",i,sep=\"\")]<-p_fpr_byQ_ordT;\r\n\tcat(\"done!\\n\")\r\n}\r\ncat(\"start doing the plotting and reporting...\\n\")\r\nflush.console();\r\n#statistics with the stats_df array\r\nmean_correct<-portions\r\nmax_correct<-portions\r\nmin_correct<-portions\r\nstd_correct<-portions\r\nm_stats_df<-as.matrix(stats_df)\r\nfor(k in c(1:length(portions)))\r\n{\r\n\tmean_correct[k]<-mean(m_stats_df[k,c(-1)])\r\n\tmax_correct[k]<-max(m_stats_df[k,c(-1)])\r\n\tmin_correct[k]<-min(m_stats_df[k,c(-1)])\r\n\tstd_correct[k]<-sqrt(var(m_stats_df[k,c(-1)]))\r\n}\r\n\r\nstats_df[,\"mean\"]<-mean_correct\r\nstats_df[,\"min\"]<-min_correct\r\nstats_df[,\"max\"]<-max_correct\r\nstats_df[,\"std\"]<-std_correct\r\n#op<-par(mfrow=c(2,1),\r\n#\tpty=\"s\" #\"s\" for square, \"m\" for maximal plot area\r\n#\t)\r\nplot(c(0.001,0.51),c(0.02, 1.1), type=\"n\", main=\"true discovery rate for protein selection\", \r\n\txlab=\"portion of protein selected\", ylab=\"portion of true discovery\", log=\"x\")\r\nlines(stats_df[,1], stats_df[,\"mean\"], col=1, lty=1, lwd=2)\r\nlines(stats_df[,1], stats_df[,\"min\"], col=\"grey\", lty=2, lwd=1)\r\nlines(stats_df[,1], stats_df[,\"max\"], col=\"grey\", lty=2, lwd=1)\r\nlines(c(prob.nonZero, prob.nonZero), c(0,1.2), col=2, lty=3)\r\n\r\n#show stats\r\nstats_df[c(1:30),c(\"portions\", \"mean\", \"min\", \"max\", \"std\")]\r\n############adding code to do FDR\r\n\r\n#statistics with the roc array\r\nmean_tpr<-portions\r\nmean_fpr<-portions\r\nfor(k in c(1:length(portions)))\r\n{\r\n\tmean_tpr[k]<-median(as.matrix(stats_qval_tpr[k,c(-1)]))\r\n\tmean_fpr[k]<-median(as.matrix(stats_qval_fpr[k,c(-1)]))\r\n}\r\n\r\n\r\nplot(c(0,1), c(0,1),type=\"n\", main=\"C: No Control\", ylab=\"True Positive Rate\", xlab=\"False Positive Rate\")\r\n#for(ii in c(1:repeats))\r\n#{\r\n#\tlines( stats_qval_fpr[,ii+1],stats_qval_tpr[,ii+1],col=\"red\", lty=\"dotted\")\r\n#}\r\nlines(mean_fpr,mean_tpr, col=\"red\",lty=1,lwd=2)\r\n#legend(0.7,0.2,c(\"mean ROC\"),col=c(\"red\"), lty=c(2),lwd=c(2))\r\n#text(0.7,0.3,labels=paste(\"AUC:\",mean(roc.auc)))\r\n\r\n#statistics with the roc array for ---->ordT\r\nmean_tpr_ordT<-portions\r\nmean_fpr_ordT<-portions\r\nfor(k in c(1:length(portions)))\r\n{\r\n\tmean_tpr_ordT[k]<-median(as.matrix(stats_qval_tpr_ordT[k,c(-1)]))\r\n\tmean_fpr_ordT[k]<-median(as.matrix(stats_qval_fpr_ordT[k,c(-1)]))\r\n}\r\n\r\n\r\n#plot(c(0,1), c(0,1),type=\"n\", main=\"ROC\", ylab=\"True Positive Rate\", xlab=\"False Positive Rate\")\r\n#for(ii in c(1:repeats))\r\n#{\r\n#\tlines( stats_qval_fpr_ordT[,ii+1],stats_qval_tpr_ordT[,ii+1],col=\"light green\", lty=\"dotted\")\r\n#}\r\nlines(mean_fpr_ordT,mean_tpr_ordT, col=\"green\",lty=1,lwd=2)\r\nlegend(0.7,0.2,c(\"model\", \"ordinary t\"),col=c(\"red\", \"green\"), lty=c(2, 2),lwd=c(2,2))\r\ntext(0.7,0.25,labels=paste(\"AUC ordinary t:\",mean(roc_ordT.auc)))\r\ntext(0.7,0.35,labels=paste(\"AUC model:\",mean(roc.auc)))\r\n#par(op);\r\n\r\n####starting here, prepare the output\r\n\t\r\n\tretDataList<-list(\"roc.auc.model\"=roc.auc,\"roc.auc.t\"=roc_ordT.auc, \r\n\t\t\t\t\"qvalule.tpr.model\"=stats_qval_tpr, \"qvalule.fpr.model\"=stats_qval_fpr,\r\n\t\t\t\t\"qvalule.tpr.t\"=stats_qval_tpr_ordT, \"qvalule.fpr.t\"=stats_qval_fpr_ordT,\r\n\t\t\t\t\"fdr\"=stats_df \r\n\t\t\t\t#, \"param.alpha\"=lstData$data$alpha,\"param.beta\"=lstData$data$beta,\r\n\t\t\t\t#\"param.gamma\"=lstData$data$gamma,\r\n\t\t\t\t);\r\n\treturn(retDataList)\r\n}#end of function\r\n", "meta": {"hexsha": "f093d38705e2140789e393df21504e17e914ef61", "size": 10066, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/functionAnalysis.r", "max_stars_repo_name": "ffeng23/ARPPA", "max_stars_repo_head_hexsha": "28128313b22cb1362341c3459c799cffd6967181", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/functionAnalysis.r", "max_issues_repo_name": "ffeng23/ARPPA", "max_issues_repo_head_hexsha": "28128313b22cb1362341c3459c799cffd6967181", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/functionAnalysis.r", "max_forks_repo_name": "ffeng23/ARPPA", "max_forks_repo_head_hexsha": "28128313b22cb1362341c3459c799cffd6967181", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.0073529412, "max_line_length": 138, "alphanum_fraction": 0.7001788198, "num_tokens": 3329, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.33719144550766533}} {"text": "nb=function(y=1930) {\n debut=1816\n MatDFemale=matrix(D$Female,nrow=111)\n colnames(MatDFemale)=debut+0:198\n cly=(y-debut+1):111\n deces=diag(MatDFemale[:,cly[cly%in%1:199]])\n return(c(B$Female[B$Year==y],deces))\n}\n", "meta": {"hexsha": "8fcad91d4168d95bc614e1c85585004a09dd72d1", "size": 230, "ext": "r", "lang": "R", "max_stars_repo_path": "_unittests/ut_languages/data/r18.r", "max_stars_repo_name": "mohamedelkansouli/Ensae_py2", "max_stars_repo_head_hexsha": "e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2015-07-19T21:20:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-16T11:50:53.000Z", "max_issues_repo_path": "_unittests/ut_languages/data/r18.r", "max_issues_repo_name": "mohamedelkansouli/Ensae_py2", "max_issues_repo_head_hexsha": "e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 34, "max_issues_repo_issues_event_min_datetime": "2015-06-16T15:38:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-29T11:04:01.000Z", "max_forks_repo_path": "_unittests/ut_languages/data/r18.r", "max_forks_repo_name": "mohamedelkansouli/Ensae_py2", "max_forks_repo_head_hexsha": "e54a05f90c6aa6e2a5065eac9f9ec10aca64b46a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27, "max_forks_repo_forks_event_min_datetime": "2015-01-13T08:24:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T14:51:23.000Z", "avg_line_length": 25.5555555556, "max_line_length": 47, "alphanum_fraction": 0.652173913, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.33719143754741754}} {"text": "library(lpSolve)\nlibrary(irr)\nlibrary(dplyr)\nlibrary(tidyr)\n\noptions(stringsAsFactors = FALSE)\n\ngetResult <- function(name) {\n con <- gzcon(url(paste(\"https://pyt.blob.core.windows.net/data/results/v2/latest/\", name, \".csv.gz\", sep=\"\")))\n txt <- readLines(con)\n return(read.csv(textConnection(txt), header=TRUE, quote=\"\\\"\"))\n}\n\n# helpfull articles\n# simple explanation http://neoacademic.com/2011/11/16/computing-intraclass-correlations-icc-as-estimates-of-interrater-reliability-in-spss/\n# specific to irr https://www.r-bloggers.com/k-is-for-cohens-kappa/\n# another irr one.maybe cappa is better http://www.cookbook-r.com/Statistical_analysis/Inter-rater_reliability/\n\nresults = data.frame(tag=character(), subjects=integer(), raters=integer(), \n kap=numeric(), kap_p=numeric(), \n icc=numeric(), icc_p=numeric(), icc_lbound=numeric(),icc_ubound=numeric(), \n agreement=numeric())\n\naddResult <- function(name, ratings) {\n agree = agree(ratings)\n kap = kappam.fleiss(ratings)\n icc = icc(ratings, model=\"twoway\", type=\"agreement\")\n df = data.frame(name=name, subjects=kap$subjects, raters=kap$raters, \n kap = kap$value, kap_p = kap$p.value,\n icc = icc$value, icc_p = icc$p.value, icc_lbound=icc$lbound, icc_ubound=icc$ubound,\n agreement = agree$value)\n rbind(results, df)\n}\n\n\n\n#\n# lr ICC\n#\n\nlr = getResult(\"icc_lr\")\n\nreviwers = c(\"Ac\", \"os\", \"zY\") # filter out reviewers with only a handfull of classifications\nlrCodes = c( \"L\" = -1, \"C\" = 0, \"R\" = 1)\n\nlrRatings = lr %>% \n filter(REVIEWER %in% reviwers) %>%\n mutate(REVIEWER_LR = lrCodes[REVIEWER_LR]) %>%\n spread(REVIEWER, REVIEWER_LR, sep=\"_\") %>%\n select(starts_with(\"REVIEWER\"))\n\n\nresults = addResult(\"Left/Center/Right\", lrRatings)\n\n\n#\n# tag ICC\n#\ntag = getResult(\"icc_tags\")\nuniqueTags = unique(tag$TAG)\n\nfor(t in uniqueTags) {\n tagRatings = tag %>%\n filter(REVIEWER %in% reviwers & TAG == t) %>%\n mutate(REVIEWER_HAS_TAG = as.logical(REVIEWER_HAS_TAG)) %>%\n spread(REVIEWER, REVIEWER_HAS_TAG, sep=\"_\") %>%\n select(starts_with(\"REVIEWER\"))\n \n results = addResult(t, tagRatings)\n}\n\nprint(results)\n\nwrite.csv(results, \"reviewer_reliability.csv\", row.names = FALSE, sep = \",\")\n\n", "meta": {"hexsha": "03687e1620bbb8e124bd0c1171a63acf22eec06a", "size": 2272, "ext": "r", "lang": "R", "max_stars_repo_path": "Analysis/ClassificationReliability.r", "max_stars_repo_name": "pendulumfn/Recfluence", "max_stars_repo_head_hexsha": "8531468c323b6eedba02f751faaf25d44fda3df9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 77, "max_stars_repo_stars_event_min_datetime": "2020-01-18T17:18:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T10:36:55.000Z", "max_issues_repo_path": "Analysis/ClassificationReliability.r", "max_issues_repo_name": "pendulumfn/Recfluence", "max_issues_repo_head_hexsha": "8531468c323b6eedba02f751faaf25d44fda3df9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 15, "max_issues_repo_issues_event_min_datetime": "2020-05-26T23:03:37.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-27T21:35:00.000Z", "max_forks_repo_path": "Analysis/ClassificationReliability.r", "max_forks_repo_name": "pendulumfn/Recfluence", "max_forks_repo_head_hexsha": "8531468c323b6eedba02f751faaf25d44fda3df9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2020-06-25T17:14:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-08T01:30:38.000Z", "avg_line_length": 29.8947368421, "max_line_length": 140, "alphanum_fraction": 0.661971831, "num_tokens": 657, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334527, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.33710835213297274}} {"text": "source(\"make_precip_inputs.r\")\r\n\r\n\"\r\nMADD = Mean Annual Dry Days\r\nMADDM = Mean Annual Dry Days of the Driest Month\r\nMADM = Mean Annual Prciep of Dryiest Month\r\nMConc = Mean Annual Seasonal Concentration\r\n\"\r\nextent = c(-180, 180, -30, 30)\r\n\r\ndata_dir = \"../LimFIRE/outputs/\"\r\nvariables = c(\"TreeCover\" = \"treecover2000-2014.nc\",\r\n \"nonTreeCover\" = \"nontree2000-2014.nc\",\r\n \"MaxWind\" = \"../../savanna_fire_feedback_test/data/CRUNCEP.wspeed.r0d5.1997.2013.nc\",\r\n \"MAT\" = \"Tas2000-2014.nc\",\r\n \"MTWM\" = \"../data/cru_ts4.03/cru_ts4.03.1901.2018.tmx.dat.nc\",\r\n \"MTCM\" = \"../data/cru_ts4.03/cru_ts4.03.1901.2018.tmn.dat.nc\",\r\n\t \"SW\" = \"cld2000-2014.nc\",\r\n \"BurntArea_GFED_four_s\" = \"fire2000-2014.nc\",\r\n \"BurntArea_GFED_four\" = \"../../fireMIPbenchmarking/data/benchmarkData/GFED4.nc\",\r\n \"BurntArea_meris\" = \"../../fireMIPbenchmarking/data/benchmarkData/meris_v2.nc\",\r\n \"BurntArea_MODIS\" = \"../../fireMIPbenchmarking/data/benchmarkData/MODIS250_q_BA_regridded0.5.nc\",\r\n \"BurntArea_MCD_forty_five\" = \"../../fireMIPbenchmarking/data/benchmarkData/MCD45.nc\",\r\n\t \"urban\" = \"urban_area2000-2014.nc\", \"crop\" = \"cropland2000-2014.nc\",\r\n \"pas\" = \"pasture2000-2014.nc\", \r\n\t \"PopDen\" = \"population_density2000-2014.nc\", \r\n\t \"MAP_CRU\" = \"Prc2000-2014.nc\",\r\n \"MADD_CRU\" = \"Wet2000-2014.nc\", \"MDDM_CRU\" = \"Wet2000-2014.nc\",\r\n\t \"MADM_CRU\" = \"Prc2000-2014.nc\", \"MConc_CRU\" = \"Prc2000-2014.nc\",\r\n \"buffalo\" = \"../../savanna_fire_feedback_test/data/livestock/buffaloLivestock0.5.nc\",\r\n \"cattle\" = \"../../savanna_fire_feedback_test/data/livestock/cattleLivestock0.5.nc\",\r\n \"goat\" = \"../../savanna_fire_feedback_test/data/livestock/goatLivestock0.5.nc\",\r\n \"sheep\" = \"../../savanna_fire_feedback_test/data/livestock/sheepLivestock0.5.nc\")\r\n\t\t\t \r\nannualAverage <- function(...) mean(...)\r\n\r\nannualAverage12 <- function(...) 12 * annualAverage(...)\r\n\r\nannualAverageMax <- function(r, ...) {\r\n\tnyr = nlayers(r)/12\r\n\tannualMax <- function(yr) {\r\n\t\tmn = ((yr-1)*12 + 1):(yr*12)\r\n\t\treturn(max(r[[mn]]))\r\n\t}\r\n\t\r\n\tra = layer.apply(1:nyr, annualMax)\r\n\treturn(mean(ra))\r\n}\r\n\r\ntemp_max <- function(r, ...) \r\n annualAverageMax(r[[1195:1362]])\r\n\r\n\r\ntemp_min <- function(r, ...)\r\n annualAverageMax(r[[1195:1362]]*(-1))*(-1)\r\n\r\n\r\nAllMax <- function(r, ...) r = max(r)\r\n\r\nsunshineHours <- function(r, Q00 = 1360, ...) {\r\n\tmidDay = 2 * pi * seq(15, 345, 30)/360\r\n\tQ0 = Q00 * (1 + 2 * 0.01675 * cos(midDay))\r\n\t\r\n\tlat = r[[1]]\r\n\tlat[] = yFromCell(lat, 1:length(lat))\r\n\tlat = 2 * lat * pi / 360\r\n\t\r\n\tdelta = -23.4 * cos(midDay + pi * 2 * 10/360) * 2 * pi / 360\r\n\t\r\n\tcz = layer.apply(sin(delta) , function(i) i * sin(lat)) + layer.apply(cos(delta) , function(i) i * cos(lat))/pi\r\n\tcz[cz < 0] = 0\r\n\tcz = cz * Q0\r\n\t\r\n r = r / 100\r\n\tSW2 = mean(cz * r) # defuse\r\n\tSW1 = mean(cz * (1 - r)) # direct\r\n\treturn(list(SW1, SW2))\r\n}\r\n\r\nMADD <- function(r, ...) {\r\n\t#out = PolarConcentrationAndPhase(r)[[2]]\r\n\tout = annualAverageMax(r*(-1))\r\n\tout = 1-(out * (-1)/mean(r))\r\n\t\r\n\tnames(out) = \"layer\"\r\n\treturn(out)\r\n}\r\n\r\nMDDM = function(r, ...) {\r\n\tout = 1 + annualAverageMax(r * (-1))\r\n\treturn(out)\r\n}\r\n\r\nMADM = function(r, ...) {\r\n\tout = annualAverageMax(r * (-1))\r\n\tout = (out/annualAverage(r)) + 1\r\n\treturn(out)\r\n}\r\n\r\nMConc = function(r, ...) {\r\n\tprint(\"MConc\")\r\n\tout = PolarConcentrationAndPhase(r)[[2]]\r\n\treturn(out)\r\n}\r\n\r\nmakeWind <- function(r, ...) \r\n max(r[[55:198]])\r\n\r\nlayer1GT0 <- function(r, ...) {\r\n r = r[[1]]\r\n r[r<0] = 0.0\r\n return(r)\r\n}\r\n\r\nFUNS = c(\"TreeCover\" = annualAverage, \"nonTreeCover\" = annualAverage,\r\n \"MaxWind\" = makeWind,\r\n \"MAT\" = annualAverage,\r\n \"MTWM\" = temp_max,\r\n \"MTCM\" = temp_min, \r\n\t \"sunshine\" = sunshineHours,\r\n \"BurntArea_GFED_four_s\" = annualAverage12,\r\n \"BurntArea_GFED_four\" = annualAverage12,\r\n \"BurntArea_meris\" = annualAverage12,\r\n \"BurntArea_MODIS\" = annualAverage12,\r\n \"BurntArea_MCD_forty_five\" = annualAverage12,\r\n\t \"urban\" = annualAverage, \"crop\" = annualAverage, \"pas\" = annualAverage,\r\n \"PopDen\" = annualAverage, \r\n\t \"MAP_CRU\" = annualAverage12, \"MADD_CRU\" = MADD, \"MDDM_CRU\" = MDDM,\r\n\t \"MADM_CRU\" = MADM, \"MConc_CRU\" = MConc,\r\n \"buffalo\" = layer1GT0, \"goat\" = layer1GT0, \"cattle\" = layer1GT0, \"sheep\" = layer1GT0)\r\n\t\t\t \r\n\t\t\t \r\nscaling = c(\"TreeCover\" = 1, \"nonTreeCover\" = 1, \"MaxWind\" = 1, \"MAT\" = 1, \"MTWM\" = 1,\r\n \"MTCM\" = 1,\r\n\t \"sunshine\" = 1,\r\n \"BurntArea_GFED_four_s\" = 1,\r\n \"BurntArea_GFED_four\" = 1,\r\n \"BurntArea_meris\" = 1,\r\n \"BurntArea_MODIS\" = 1,\r\n \"BurntArea_MCD_forty_five\" = 1,\r\n\t \"urban\" = 1, \"crop\" = 1, \"pas\" = 1, \"PopDen\" = 1,\r\n\t \"MAP_CRU\" = 1, \"MADD_CRU\" = 1, \"MDDM_CRU\" = 1,\r\n\t \"MADM_CRU\" = 1, \"MConc_CRU\" = 1,\r\n \"buffalo\" = 1, \"goat\" = 1, \"cattle\" = 1, \"sheep\" = 1)\r\n\t\t\t \r\nMinPoint = c(\"TreeCover\" = 0, \"nonTreeCover\" = 0, \"MaxWind\" = 0, \"MAT\" = 0, \"MTWM\" = 0,\r\n \"MTCM\" = 0,\r\n\t \"sunshine\" = 0,\r\n \"BurntArea_GFED_four_s\" = 0,\r\n \"BurntArea_GFED_four\" = 0,\r\n \"BurntArea_meris\" = 0,\r\n \"BurntArea_MODIS\" = 0,\r\n \"BurntArea_MCD_forty_five\" = 0,\r\n\t \"urban\" = 0, \"crop\" = 0, \"pas\" = 0, \"PopDen\" = 0,\r\n\t \"MAP_CRU\" = 0, \"MADD_CRU\" = 1, \"MDDM_CRU\" = 1,\r\n\t \"MADM_CRU\" = 1, \"MConc_CRU\" = 1,\r\n \"buffalo\" = 0, \"goat\" = 0, \"cattle\" = 0, \"sheep\" = 0)\r\n\t\t\t \r\nmakeVar <- function(filename, FUN) {\r\n\tprint(filename)\r\n\tr = brick(paste(data_dir, filename, sep = '/'))\r\n\tr = FUN(r)\r\n\treturn(r)\r\n}\r\n\r\nins = mapply(makeVar, variables, FUNS)\r\n\r\nins_all = c(unlist(ins))#, unlist(pr_ins))\r\n\r\nmask = is.na(ins_all[[1]][[1]])\r\n\r\nfor (i in ins_all[-1]) {\r\n i0 = i\r\n mask = raster::crop(mask, i)\r\n i = raster::crop(i, mask)\r\n i = raster::resample(i, mask)\r\n mask = mask + is.na(i)\r\n}\r\nmask = mask > 3 \r\n\r\nn96_mask = raster('../UKESM-ConFire/data/n96e_orca1_mask.nc')\r\nwriteVar <- function(nme, r, sc, mp = 0.0) {\r\n\t\r\n\twriteSub <- function(nmei, ri) {\r\n ri = raster::crop(ri, mask)\r\n ri = raster::resample(ri, mask)\r\n\t\tri[mask] = NaN\r\n\t\tri[!mask & is.na(ri)] = mp\r\n\t\tnames(ri) = NULL\r\n\t\tfname = paste('data/driving_Data/', nmei, '.nc', sep = '')\r\n\t\tprint(fname)\r\n\t\tri = crop(ri, extent(extent))\r\n\t\tri = ri * sc\r\n \r\n\t\tri = writeRaster.gitInfo(ri, fname, varname = \"layer\",\r\n comment = list(src_file = 'make_inputs.r'), \r\n overwrite = TRUE)\r\n\r\n rr = convert_regular_2_pacific_centric(ri)\r\n rr = raster::resample(rr, n96_mask)\r\n\r\n\t\tfnamer = paste('data/driving_Data/N96/', nmei, '.nc', sep = '')\r\n \r\n rr = writeRaster.gitInfo(rr, fnamer, varname = nme,\r\n comment = list(src_file = 'make_inputs.r'), \r\n overwrite = TRUE)\r\n\t\treturn(ri)\r\n\t}\r\n\tif (is.raster(r)) r = writeSub(nme, r)\r\n\telse {\r\n\t\tnme = paste(nme, 1:length(r), sep = '')\r\n\t\tr = mapply(writeSub, nme, r)\r\n\t}\r\n\treturn(r)\r\n}\r\n\r\nmapply(writeVar, names(variables), ins, scaling, MinPoint)\r\n\r\nmaskAndReout_pr <- function(r) {\r\n fname = filename(r)\r\n r = raster::crop(r, mask)\r\n r[mask] = NaN\r\n r = writeRaster.gitInfo(r, fname, zname = 'layer',\r\n comment = list(source='Based on data the amazing Li G processed for me. Regridded for 0.25 to 0.5', \r\n src_file = 'make_inputs.r'), \r\n overwrite = TRUE)\r\n return(r)\r\n}\r\n\r\nlapply(unlist(pr_ins), maskAndReout_pr)\r\n", "meta": {"hexsha": "aefc08c7c648583368951529ad9613e33aa70f7f", "size": 7841, "ext": "r", "lang": "R", "max_stars_repo_path": "make_inputs.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make_inputs.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "make_inputs.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 33.9437229437, "max_line_length": 129, "alphanum_fraction": 0.5406198189, "num_tokens": 2626, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583124210896, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3371083460237952}} {"text": "# James Rekow\r\n\r\naggDensityComparativeMultiplot = function(aggDOCDF, densLim = 20){\r\n \r\n # ARGS: aggDOCDF - an aggregate data frame containing DOC points from multiple replicates labeled\r\n # by replicate number and the number of interacting samples corresponding to each\r\n # point\r\n # densLim - maximimum density displayed in the output density multiplot. Regions where the density\r\n # exceeds the maximum are displayed in grey\r\n #\r\n # RETURNS: a plot of six 2D DOC density plots arranged into two columns and three rows. Each row\r\n # corresponds to a distinct replicate (for 3 total replicates). The left column plots 2D\r\n # densities of DOC points corresponding to 0 interacting samples, and the right column\r\n # plots 2D densities of DOC points corresponding to 2 interacting samples\r\n \r\n library(ggplot2)\r\n library(gridExtra)\r\n library(grid)\r\n source(\"DOCDensity_2d.r\")\r\n \r\n # create vector with all replicate IDs present in the aggregate data frame\r\n replicateIDs = unique(aggDOCDF$replicateID)\r\n \r\n # randomly select the replicate IDs of the three replicates used in the density multiplot\r\n displayedReplicateIDs = sample(replicateIDs, 3)\r\n\r\n # select only DOC points corresponding to 0 or 2 interacting samples, respectively\r\n aggDOCDF_IC0 = aggDOCDF[aggDOCDF$intCount == 0, ]\r\n aggDOCDF_IC2 = aggDOCDF[aggDOCDF$intCount == 2, ]\r\n\r\n replicateDensity = function(n){\r\n \r\n # ARGS: n - replicate ID\r\n #\r\n # RETURNS: replicateDensList - list of two ggplot objects of 2D DOC density plots. The first element\r\n # in the list, repDensGrob_IC0, is the density plot for DOC points from replicate n\r\n # corresponding to 0 interacting samples. The second element, repDensGrob_IC2, is the\r\n # density plot for DOC points from replicate n corresponding to 2 interacting samples\r\n \r\n # select DOC points from replicate n\r\n replicateDOCDF_IC0 = subset(aggDOCDF_IC0, replicateID == n, select = c(\"over\", \"diss\"))\r\n replicateDOCDF_IC2 = subset(aggDOCDF_IC2, replicateID == n, select = c(\"over\", \"diss\"))\r\n \r\n # create the ggplot objects corresponding to the 2D DOC density plots for the points from replicate n\r\n # with 0 and 2 interacting samples, respectively\r\n repDensGrob_IC0 = DOCDensity_2d(doc = replicateDOCDF_IC0, densLim = densLim)\r\n repDensGrob_IC2 = DOCDensity_2d(doc = replicateDOCDF_IC2, densLim = densLim)\r\n \r\n # create a list to store the two ggplot objects\r\n replicateDensList = list(repDensGrob_IC0, repDensGrob_IC2)\r\n \r\n return(replicateDensList)\r\n \r\n } # end replicateDensity function\r\n\r\n # create a replicate density list for each of the three displayed replicates\r\n densGrobList = lapply(as.list(displayedReplicateIDs), replicateDensity)\r\n \r\n # put all 6 ggplot objects in a single list\r\n densGrobList = unlist(densGrobList, recursive = FALSE)\r\n \r\n # create title object for the multiplot (must manually change this to include correct M, lambda values)\r\n titleText = textGrob(\"DOC Densities of 3 Replicates, M = 400, 1 - exp(-lambda/M) = 0.5\")\r\n \r\n # arrange and plot all 6 density plots in a new graphics device\r\n dev.new()\r\n grid.arrange(arrangeGrob(densGrobList[[1]], densGrobList[[3]], densGrobList[[5]], \r\n top = \"0 Interacting Samples\"),\r\n arrangeGrob(densGrobList[[2]], densGrobList[[4]], densGrobList[[6]],\r\n top = \"2 Interacting Samples\"), ncol = 2, top = titleText)\r\n \r\n} # end aggDensityComparativeMultiplot function\r\n", "meta": {"hexsha": "714015129969de06e60973e740d863ae43335812", "size": 3647, "ext": "r", "lang": "R", "max_stars_repo_path": "aggDensityComparativeMultiplot.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "aggDensityComparativeMultiplot.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "aggDensityComparativeMultiplot.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.9589041096, "max_line_length": 108, "alphanum_fraction": 0.6874143131, "num_tokens": 945, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3369914102474836}} {"text": "library(ggplot2)\n\ntable <- read.table(\"stacked.data\", header = TRUE, sep = \"\", quote = \"\\\"\")\ncounts <- table(table$Status, table$Engine)\n\nvector <- c(counts[\"2-Confirmed\",\"Chakra\"],\n counts[\"2-Confirmed\",\"JSC\"],\n counts[\"2-Confirmed\",\"V8\"],\n counts[\"2-Confirmed\",\"Hermes\"],\n counts[\"2-Confirmed\",\"SpiderMonkey\"],\n counts[\"3-Fixed\",\"Chakra\"],\n counts[\"3-Fixed\",\"JSC\"],\n counts[\"3-Fixed\",\"V8\"],\n counts[\"3-Fixed\",\"Hermes\"],\n counts[\"3-Fixed\",\"SpiderMonkey\"],\n counts[\"1-New\",\"Chakra\"],\n counts[\"1-New\",\"JSC\"],\n counts[\"1-New\",\"V8\"],\n counts[\"1-New\",\"Hermes\"], \n counts[\"1-New\",\"SpiderMonkey\"])\n\ndf <- data.frame(status=rep(c(\"2-Confirmed\", \"3-Fixed\", \"1-New\"), each=5),\n engine=rep(c(\"Chakra\", \"JSC\", \"V8\", \"Hermes\", \"SpiderMonkey\"), each=1),\n number=vector)\n\nggplot(data=df, aes(x=engine, y=number, fill=status)) +\n geom_bar(stat=\"identity\") +\n scale_fill_grey(start = 0, end = .9) + \n theme_bw() +\n theme(axis.text=element_text(size=14), axis.title.x=element_blank(), axis.title.y=element_blank(), legend.text=element_text(size=14), aspect.ratio=0.3, panel.border = element_blank())\n\n\n", "meta": {"hexsha": "535bff3f6b4c954810386e645a93e5e139bc811d", "size": 1276, "ext": "r", "lang": "R", "max_stars_repo_path": "paper/R/stackedbar/stacked-engine.r", "max_stars_repo_name": "gustavopinto/entente", "max_stars_repo_head_hexsha": "19b65d8cafd77c198c9c441f4f5e01503360309b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-03-20T21:53:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-28T21:08:47.000Z", "max_issues_repo_path": "paper/R/stackedbar/stacked-engine.r", "max_issues_repo_name": "gustavopinto/entente", "max_issues_repo_head_hexsha": "19b65d8cafd77c198c9c441f4f5e01503360309b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2018-04-09T20:16:00.000Z", "max_issues_repo_issues_event_max_datetime": "2019-06-11T12:31:10.000Z", "max_forks_repo_path": "paper/R/stackedbar/stacked-engine.r", "max_forks_repo_name": "gustavopinto/entente", "max_forks_repo_head_hexsha": "19b65d8cafd77c198c9c441f4f5e01503360309b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2018-04-06T00:52:24.000Z", "max_forks_repo_forks_event_max_datetime": "2018-07-10T19:44:16.000Z", "avg_line_length": 38.6666666667, "max_line_length": 185, "alphanum_fraction": 0.5564263323, "num_tokens": 347, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.33699140281104284}} {"text": "# Edge_Simulate_Visualize\n# A simple program to generate 50-1000 Edge Nodes, so we can \n# (a) Create some 'medium' simulation data (b) play with Visualization Methods (c) Start to get a feel for policy Driven State\n\n\nlibrary(ggplot2) # for the viz\nlibrary(wesanderson) # for GGPLOT colors # # Install # install.packages(\"wesanderson\") # for GGPLOT colors\nlibrary(treemapify) # for tree map example way down below\n\nlibrary(kohonen) # old but good. Self organizing maps\nlibrary(SOMbrero) # first time I've tried this package - looks pretty cool\n\n\n\n## 1 START WITH MATRIX BONES - node_count number of rows\nnode_count <- 1001# later, this will be a user input , for \"size of test set to gen\"\n\nnodes_df <- as.data.frame(matrix(1:node_count, nrow = node_count, ncol = 13))# let's get started\ncolnames(nodes_df) <- c(\"index\",\"type\",\"name\",\"size\",\"servers\",\"cpus\",\"cores\",\"range_x\",\"range_y\",\"magnitude\",\"angle\",\"capacity_utilization\",\"policy_noncompliant\") # name columns\n\nhead(nodes_df,10) # what do we have? show first 10 rows \n\n## 2 LET'S GROUP the set into N Groups (1=SMALL (e.g. Raspi); 2=Medium (e.g. Edge Gateway), (3) = Large Edge Device (e.g. ICP on Prem))\n# See: https://wiki.akraino.org/pages/viewpage.action?pageId=1147248 for model outlining Cruiser, Rover, Tricycle concept\n# CAVEAT - THIS iS FOR DISCUSSION ONLY - handle with care!\nnode_type_lookup <- data.frame(\"type\" = 1:5, \n \"name\" = c(\"rover\",\"satellite\", \"unicycle\", \"tricycle\", \"cruiser\"), \n \"size\" = c(1,5,50,100,1000), \n \"servers\" = c(0,2,42,126,252), ## Approx. SME needs to REVIEW THIS (!)\n \"cpus\" = c(1,2,200,800,1600), ## Approx. SME needs to REVIEW THIS (!)\n \"cores\" = c(0,16,1600,6400,10000), ## Approx. SME needs to REVIEW THIS (!)\n \"pct_allocation\" = c(0.944,0.04,0.01,0.005,0.001), # later; make more configurable\n \"count\" = c(0,0,0,0,0) # will calculate this later\n )\n# \" Can we relate to processors and architecture? Like rover = Armv6, Armv7hf, Arm Cortex 4, up to four cores. Next class up = Atom, next class = Xeon-class x86_64, and so on?\"\n\n# check our Allocation % add up to 100%\nsum(node_type_lookup$pct_allocation)\nif (sum(node_type_lookup$pct_allocation)==1){print(\"pct_allocation = OK - sums to 1 \")} else {print(\"pct_allocation Check = BAD - node sums dont line up\")}\n\n\n# calculate number of whole number nodes in each TYPE category given user input\nnode_type_lookup$count <- round(node_type_lookup$pct_allocation * node_count)\nnode_type_lookup\nsum(node_type_lookup$count)\nnode_count\nnode_count == sum(node_type_lookup$count) # True if we've done work well here (and rounded well)\n\n# different ways to count types in table (5 here)\ndim(node_type_lookup)[1] # how many types /rows\nnrow(node_type_lookup) # also 5\n\n\n## BIG MOMENT OF TYPE ASSIGMENT - DID this with loop in case you have lots of types (you can hand code this sectio nif hless than 10)\nindex_prior = 1\nindex_new = 1\nfor (i in 1:nrow(node_type_lookup)){\n print(\"---\")\n print(i)\n index_new <- (node_type_lookup$count[i]+index_prior) # add our new range to old range\n \n ## DO THE ALLOCATION - BIG MOMENT\n print(index_prior)\n print(index_new-1)\n nodes_df$type[index_prior:(index_new-1)] <- node_type_lookup$type[i] # SETS THE TYPE \n nodes_df$name[index_prior:(index_new-1)] <- paste(node_type_lookup$name[i]) # SETS THE NAME \n\n nodes_df$size[index_prior:(index_new-1)] <- paste(node_type_lookup$size[i]) # SETS THE SIZE \n nodes_df$servers[index_prior:(index_new-1)] <- paste(node_type_lookup$servers[i]) # SETS THE SERVERS \n nodes_df$cpus[index_prior:(index_new-1)] <- paste(node_type_lookup$cpus[i]) # SETS THE CPU (LATER MAY BE VARIETY HERE)\n nodes_df$cores[index_prior:(index_new-1)] <- paste(node_type_lookup$cores[i]) # SETS THE CORES (LATER MAY BE VARIETY HERE)\n \n ## Set up for next time\n index_prior <- index_new # set up for next loop\n}\n\n## CHECK\nnode_table <- table(nodes_df$type) # check this worked like you expect\nnode_table\nsum(table(nodes_df$type))\nsum(table(nodes_df$type)) == node_count # hopefully true - if you did this right\ntable(nodes_df$name) # check this worked like you expect\nplot(sort(table(nodes_df$name),decreasing=TRUE))\n\n## PRIME Number fucntion - purely to allocate \"Red\" state (Non Compiance)\nis.prime <- function(num) {\n if (num == 2) {TRUE} else if (any(num %% 2:(num-1) == 0)) {FALSE} \n else {TRUE}}\n## test - is.prime(8)\n\n\n###\n\n\n\n## 4 LOCATION - SEMI RANDOM - ZONING FOR VIZ CORE = BIG, Cruiser - EDGES = Rovers\nhead(nodes_df,20) # check first 20\ntail(nodes_df,20) # check last 20\n\n## let's get stared with some plus and minus coordinate data (scale by type later)\nnodes_df$range_x <- sample(-100:100,node_count,replace=TRUE)\nnodes_df$range_y <- sample(-100:100,node_count,replace=TRUE)\n\nhead(nodes_df,20) # check first 20\ntail(nodes_df,20) # check last 20\n\n\n#nodes_df$range_x <- nodes_df$range_x*1000/nodes_df$size\n\n# SCALE RANGE TO EDGE - there's a more elegant way to do this in R - so improve later ()\n# for (i in 1:node_count)\n# {\n# nodes_df$range_x[i] <- nodes_df$range_x[i]*100/as.numeric(nodes_df$size[i])\n# nodes_df$range_y[i] <- nodes_df$range_y[i]*100/as.numeric(nodes_df$size[i])\n# } # this centralizes the big stuff\n\n\n\n\n# rnorm(1, mean=10, sd=1)/10 # provide our own mean and standard deviation\n\n# ANGLES For Polar coordinates - not sure we need this (may remove later if not used)\nnodes_df$angle <- sample(0:359,node_count,replace=TRUE)\n\nnodes_df$capacity_utilization <- sample(0:100,node_count,replace=TRUE)\n\n# Magnutude seeds for polar coords (like the length of the Minutes hand in clock)\nnodes_df$magnitude <- sample(50:100,node_count,replace=TRUE)\n\n\n# TRY ANOTHER METHOD FOR DONUT POINT CLOUD GENERATE\nfor (i in 1:node_count)\n{\n nodes_df$magnitude[i] <- nodes_df$magnitude[i]/as.numeric(nodes_df$type[i]) # divide them down by 'bands'\n nodes_df$range_x[i] <- round(nodes_df$magnitude[i] * sin(pi*nodes_df$angle[i]/180)/as.numeric(nodes_df$type[i]))\n nodes_df$range_y[i] <- round(nodes_df$magnitude[i] * cos(pi*nodes_df$angle[i]/180)/as.numeric(nodes_df$type[i]))\n\n #for some color - let's tag all PRIME Number magnitudes as non-compliant (Paint Red)\n nodes_df$policy_noncompliant[i] <- is.prime(nodes_df$index[i])\n} # \n\n\n\n## PLOT - Light BLUES are problems (works)\nggplot(nodes_df, aes(x=range_x, y=range_y, size=type, color=policy_noncompliant)) + geom_point()\n\n\n# Shades of Life Aquatic (works but hard to read)\npal <- wes_palette(\"Zissou1\", 100, type = \"continuous\")\n# Add some color \nggplot(nodes_df, aes(x = range_x, y = range_y , size=type, fill = type)) +\n geom_tile() + \n scale_fill_gradientn(colours = pal) + \n coord_equal() \n\n# Add some color \nggplot(nodes_df, aes(x = range_x, y = range_y , size=type, fill = policy_noncompliant)) +\n geom_tile() + \n scale_fill_gradientn(colours = pal) + \n coord_equal() \n\n\n# works (by size)\nggplot2::ggplot(nodes_df, ggplot2::aes(area = type, fill = size)) +\n geom_treemap() \n\n# works (by policy_noncompliant)\nsp <- ggplot2::ggplot(nodes_df, \n ggplot2::aes(area = type, \n fill = policy_noncompliant))+\n geom_treemap() \nsp\n\n\n# works (by CPU)\nggplot2::ggplot(nodes_df, ggplot2::aes(area = type, size = cpus, fill = cpus)) +\n geom_treemap() \n\n# works - by LFEdge/Akraino Label \nggplot2::ggplot(nodes_df, ggplot2::aes(area = type, size = type, fill = name )) +\n geom_treemap() \n\n\n# works - by LFEdge/Akraino Label (sort of works - but labels EVERY box, so zone lable better if we can figure out)\n# ggplot2::ggplot(nodes_df, ggplot2::aes(area = type, size = type, fill = name, label = name)) +\n# geom_treemap() + \n# geom_treemap_text()\n\n \n######### KOHONEN \nnamelist <- data.frame(names(nodes_df))\nnamelist # in case you want to see which numbers map (for selection) \n### OK - for now, use this line below to CHERRY PICK your TRAITS/VALUES for explirng \ndata <- nodes_df[c(2,4,5,6,7,12,13)] # the countable and sortable stuff \nnames(data) # OK - here is what we selected (from the 52 traits/values)\n\nhead(data)\ndata <- data.matrix(data)\n\n## what do we have?\nhead(data)\ndata.sc <- scale(data)\nhead(data.sc)\n\nset.seed(7)\ndata.som <- som(data.sc, grid = somgrid(10, 8, \"hexagonal\"))\n\n# add rows / node count to TITLE\ntitle_cluster = paste(\"\\n System Summary \\n\", nrow(data), \"ICP/Edge Nodes Clustered\")\ntitle_count = paste(\"\\n System Summary \\n\", nrow(data), \"ICP/Edge Nodes COUNT\")\n\n## Fancy Colors\ncoolBlueHotRed <- function(n, alpha = 1) { rainbow(n, end=4/6, alpha=alpha)[n:1] } ## palette for nicer color scheme \n\n## Let's Plot\nplot(data.som, main = title_cluster, palette.name = coolBlueHotRed)\nplot(data.som, main = title_count, type = \"counts\", palette.name = coolBlueHotRed, heatkey = TRUE)\nplot(data.som, type = \"mapping\", pchs = 20, main = \"Mapping Type SOM\")\n\n\n# Good documentation here https://clarkdatalabs.github.io/soms/SOM_NBA \n\n\n## GOOD TO HERE\nsommap <- som(scale(data), grid = somgrid(6, 4, \"hexagonal\"))\nplot(data.som, type = \"mapping\", property = getCodes(sommap, 1)[,1],\n main = colnames(getCodes(sommap, 1))[1])\n\nplot(kohmap, type=\"codes\", main = c(\"Codes X\", \"Codes Y\"))\nadd.cluster.boundaries(sommap, som.hc)\n\n\n##\n# temp <- as.factor(nodes_df$name) # where vintages used to go\n# kohmap <- xyf(scale(data), temp,\n# grid = somgrid(10, 10, \"hexagonal\"), rlen=100)\n# plot(kohmap, type=\"changes\")\n# counts <- plot(kohmap, type=\"counts\", shape = \"straight\")\n# add.cluster.boundaries(sommap, som.hc)\n\n## show both sets of codebook vectors in the map\n#par(mfrow = c(1,2))\n\n\n## add background colors to units according to their predicted class labels\nxyfpredictions <- classmat2classvec(getCodes(kohmap, 2))\nbgcols <- c(\"gray\", \"pink\", \"lightgreen\")\nplot(kohmap, type=\"mapping\", \n col = as.integer(temp),\n pchs = as.integer(temp), \n bgcol = bgcols[as.integer(xyfpredictions)],\n main = \"another mapping plot\", shape = \"straight\", border = NA)\n\n \nbgcols <- c(\"gray\", \"pink\", \"lightgreen\")\nplot(data.som, type=\"mapping\", \n col = as.integer(temp),\n pchs = as.integer(temp), \n bgcol = bgcols[as.integer(xyfpredictions)],\n main = \"another mapping plot\", shape = \"straight\", border = NA)\n\n##\nname_lfedge <- as.factor(nodes_df$name) # where vintages used to go\nplot(data.som, \n type = \"mapping\", \n pchs = as.integer(name_lfedge), \n col = as.integer(name_lfedge),\n main = \"Mapping Type SOM\",\n bgcols <- c(\"gray\", \"pink\", \"lightgreen\"), \n palette.name = coolBlueHotRed, \n heatkey = TRUE)\n\n\n## FRESH TRY\ndata.som <- som(data.sc, grid = somgrid(10, 8, \"hexagonal\"))\nplot(data.som, \n main = title_cluster, \n # type = \"mapping\", \n pchs = as.integer(name_lfedge), \n col = as.integer(name_lfedge),\n palette.name = coolBlueHotRed, \n heatkey = TRUE)\n\n", "meta": {"hexsha": "abf424388609f65eae0d740b592f8b8e8fce6e7b", "size": 10941, "ext": "r", "lang": "R", "max_stars_repo_path": "edge_simulate_visualize.r", "max_stars_repo_name": "rustyoldrake/R_Scripts_for_Watson", "max_stars_repo_head_hexsha": "7e97f2170d3516d0f702c14b14f70cdf7902eace", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 52, "max_stars_repo_stars_event_min_datetime": "2016-02-09T15:41:25.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-25T13:25:46.000Z", "max_issues_repo_path": "edge_simulate_visualize.r", "max_issues_repo_name": "rustyoldrake/R_Scripts_for_Watson", "max_issues_repo_head_hexsha": "7e97f2170d3516d0f702c14b14f70cdf7902eace", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2016-10-10T10:23:03.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-26T12:12:53.000Z", "max_forks_repo_path": "edge_simulate_visualize.r", "max_forks_repo_name": "rustyoldrake/R_Scripts_for_Watson", "max_forks_repo_head_hexsha": "7e97f2170d3516d0f702c14b14f70cdf7902eace", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 38, "max_forks_repo_forks_event_min_datetime": "2015-10-08T19:13:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-17T16:05:47.000Z", "avg_line_length": 38.5246478873, "max_line_length": 178, "alphanum_fraction": 0.6810163605, "num_tokens": 3205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070808, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33699139537460193}} {"text": "suppressPackageStartupMessages(library(edgeR))\nsuppressPackageStartupMessages(library(dplyr))\nsuppressPackageStartupMessages(library(tibble))\nsuppressPackageStartupMessages(library(sva))\n\nsource(\"/mnt/webrepo/fr-s-bsg-onc-d/htdocs/clinomics/app/scripts/ComBat_seq.R\")\n\nmakeMatrix <- function(x){\n #print(paste(x))\n #count_table <- read.csv(paste(x, sep=\"\"), sep=\"\\t\", header=FALSE, stringsAsFactors = FALSE)\n #data <- read.table(x, head=T, sep=\"\\t\")\n data <- as.data.frame(data.table::fread(x, sep=\"\\t\", header = TRUE))\n colnames(data) <- c(gsub(\"\\\\.rsem.*.\", \"\", basename(x))) \n return(data) \n}\n\n\nfpkmToTpm <- function(fpkm){\n exp(log(fpkm) - log(sum(fpkm)) + log(1e6))\n }\n\n\nnormalize <- function(countObj, workdir=\"\", annotName = \"\", method=\"\", fileName=\"\", annotationRDS=\"\", Refcol =1, priorCount=0, merge=T, saveFiles=F,\n condition=\"\",batch=NULL, group=NULL){\n\n Annotation <- data.frame(readRDS(annotationRDS)) \n\n #drop <- which( apply(countObj,1,sum) <= 1); print(paste(\"Droping \", length(drop))) ; countObj <- countObj[-drop,]\n #Annotation <- Annotation[-drop,]\n \n if(priorCount == 0) { tolog = FALSE} else{ tolog = TRUE }\n \n ########################################### Choose the Annotation\n #print(paste(\"Chooseing the Annotation\"))\n if( annotName == \"exon\")\n {\n #print(paste(\"I am Exon\"))\n rownames(Annotation) <- Annotation$ExonID\n Annotation$ExonID <- factor(Annotation$ExonID, levels=rownames(countObj))\n Annotation <- Annotation %>% dplyr::arrange(ExonID)\n \n genesObj <- Annotation[,c(\"ExonID\", \"Length\")]\n }\n else if(annotName == \"trans\"){\n rownames(Annotation) <- Annotation$TranscriptID\n Annotation$TranscriptID <- factor(Annotation$TranscriptID, levels=rownames(countObj))\n Annotation <- Annotation %>% dplyr::arrange(TranscriptID) \n genesObj <- Annotation[,c(\"TranscriptID\", \"Length\")]\n }\n else{\n \t genesObj <- Annotation %>% dplyr::select(GeneName, Length) %>% group_by(GeneName) %>% summarize(Length=mean(Length)) %>% dplyr::arrange(GeneName) \n genesObj <- genesObj[match(rownames(countObj), genesObj$GeneName), ]\n write.table(genesObj, \"genesObj.txt\", quote=F,sep=\"\\t\")\n \t #rownames(Annotation) <- Annotation$GeneID\n #Annotation$GeneID <- factor(Annotation$GeneID, levels=rownames(countObj))\n #Annotation <- Annotation %>% dplyr::arrange(GeneID)\n #Annotation <- Annotation %>% dplyr::filter(GeneID %in% rownames(countObj))\n #genesObj <- Annotation[,c(\"GeneID\", \"Length\")]\n }\n \n ########################################### Choose the Method \n #print(paste(\"Chooseing the Normalization Method\"))\n if( method==\"EdgeR\") {\n \n #### EdgeR\n ## Make EdgeR Object\n colnames(genesObj) <- c(\"GeneID\", \"Length\")\n #print(nrow(countObj))\n #print(nrow(genesObj))\n GeneDF_EdgeR <- DGEList(counts=countObj, genes=genesObj)\n ## Estimate Normalising Factors\n GeneDF.Norm <- calcNormFactors(GeneDF_EdgeR, refColumn = Refcol) ; \n ## Regularized Log Transformation using CPM, FPKM & TPM values\n #GeneDF.tpm <- as.data.frame(cpm(GeneDF.Norm, normalized.lib.sizes = TRUE,log = tolog, prior.count = priorCount))\n GeneDF.rpkm <- as.data.frame(rpkm(GeneDF.Norm, normalized.lib.sizes = T, gene.length=genesObj$Length))\n GeneDF.lrpkm <- log2(GeneDF.rpkm + 1)\n GeneDF.tpm <- apply(rpkm(GeneDF.Norm, normalized.lib.sizes = T, gene.length=genesObj$Length), 2 , fpkmToTpm)\n GeneDF.lcpm <- cpm(GeneDF.Norm, log=T)\n #print(nlevels(batch))\n if (!is.null(batch) && nlevels(batch) > 1) {\n print(\"removing library type effect\") \n design <- matrix(1,ncol(GeneDF.Norm),1)\n if (!is.null(group) && nlevels(group) > 1) {\n x <- data.frame(\"group\"=as.factor(as.character(group)))\n design <- model.matrix(~group, data=x) \n }\n #GeneDF.voom <- voom(GeneDF.Norm, design=design) \n #GeneDF.lcpm <- log2(GeneDF.cpm + 1) \n GeneDF.lcpm <- removeBatchEffect(GeneDF.lcpm, batch, design=design)\n GeneDF.lrpkm <- removeBatchEffect(GeneDF.lrpkm, batch, design=design)\n GeneDF.lrpkm <- ifelse(GeneDF.lrpkm < 0, 0, GeneDF.lrpkm)\n GeneDF.rpkm <- 2^GeneDF.lrpkm - 1\n\n } \n GeneDF.lrpkm <- round(GeneDF.lrpkm, 2)\n GeneDF.rpkm <- round(GeneDF.rpkm, 2)\n #GeneDF.ScaledTpm <- t(t(GeneDF.tpm) / GeneDF.Norm$samples$norm.factors)\n #GeneDF.ScaledTpm <- GeneDF.tpm\n }\n else if( method == \"DESeq\") {\n #### DESeq\n ##Make DESeq Object\n GeneDF_DESeq <- DESeqDataSetFromMatrix(countData = as.matrix(countObj), colData = DataFrame(condition), design = ~ condition)\n mcols(GeneDF_DESeq)$basepairs <- genesObj$Length-175+1\n ## Estimate SizeFactors\n GeneDF.Norm <- estimateSizeFactors(GeneDF_DESeq)\n ## Regularized Log Transformation using CPM, FPKM & TPM values\n GeneDF.fpm <- as.data.frame(fpm(object = GeneDF.Norm, robust = TRUE))\n GeneDF.rpkm <- as.data.frame(fpkm(object = GeneDF.Norm, robust = TRUE))\n GeneDF.tpm <- apply(GeneDF.rpkm, 2 , fpkmToTpm)\n }\n \n ########################################### Prepare final files\n if( annotName == \"Exon\")\n {\n #GeneDF_Norm_CPM <- cbind(data.frame(Annotation[,c(\"Chr\",\"Start\",\"End\",\"GeneID\", \"GeneName\",\"TranscriptID\",\"ExonID\")]), GeneDF.CPM )\n GeneDF_Norm_rpkm <- cbind(data.frame(Annotation[,c(\"Chr\",\"Start\",\"End\",\"GeneID\", \"GeneName\",\"TranscriptID\",\"ExonID\")]), GeneDF.rpkm )\n GeneDF_Norm_tpm <- cbind(data.frame(Annotation[,c(\"Chr\",\"Start\",\"End\",\"GeneID\", \"GeneName\",\"TranscriptID\",\"ExonID\")]), GeneDF.tpm )\n \n }\n else if(annotName == \"trans\"){\n \n #GeneDF_Norm_CPM <- cbind(data.frame(Annotation[,c(\"Chr\",\"Start\",\"End\",\"GeneID\",\"GeneName\",\"TranscriptID\")]), GeneDF.CPM )\n GeneDF_Norm_rpkm <- cbind(data.frame(Annotation[,c(\"TranscriptID\", \"GeneID\", \"GeneName\")]), GeneDF.rpkm )\n GeneDF_Norm_tpm <- cbind(data.frame(Annotation[,c(\"TranscriptID\", \"GeneID\", \"GeneName\")]), GeneDF.tpm)\n \n }\n else{\n #GeneDF_Norm_CPM <- cbind(data.frame(Annotation[,c(\"Chr\",\"Start\",\"End\",\"GeneID\",\"GeneName\")]), GeneDF.CPM)\n #GeneDF_Norm_rpkm <- cbind(data.frame(Annotation[,c(\"GeneID\", \"GeneID\", \"GeneName\")]), GeneDF.rpkm)\n GeneDF_Norm_rpkm <- GeneDF.rpkm\n #GeneDF_Norm_tpm <- cbind(data.frame(Annotation[,c(\"GeneID\", \"GeneID\", \"GeneName\")]), GeneDF.tpm)\n GeneDF_Norm_tpm <- GeneDF.tpm\n }\n \n ########################################### Choose approprite folder and write files \n if(merge) { RawCount=FPKM=CPM=TPM=\"/MergedFiles/\" }\n else { RawCount=\"/RawCount/\"; FPKM=\"/FPKM/\" ; CPM=\"/CPM/\" ; TPM=\"/TPM\"}\n \n if(saveFiles)\n {\n #write.table(countObj, paste(workdir,fileName,\"_Count_\",annotName,\".txt\", sep= \"\"), sep=\"\\t\",row.names = FALSE, quote = FALSE)\n #write.table(GeneDF_Norm_rpkm, paste(workdir,fileName,\"_Norm_rpkm_\",annotName,\".txt\", sep= \"\"), sep=\"\\t\",row.names = FALSE, quote = FALSE)\n #write.table(GeneDF_Norm_CPM, paste(workdir, fileName,\"_Norm_cpm_\",annotName,\".txt\", sep= \"\"), sep=\"\\t\",row.names = FALSE, quote = FALSE)\n #write.table(GeneDF_Norm_tpm, paste(workdir,fileName,\"_Norm_tpm_\",annotName,\".txt\", sep= \"\"), sep=\"\\t\",row.names = FALSE, quote = FALSE)\n }\n #print(paste(\"lcpm row\",nrow(GeneDF.lcpm)))\n #print(paste(\"lcpm col\",ncol(GeneDF.lcpm)))\n return(list(\"tpm\" = GeneDF_Norm_tpm, \"rpkm\" = GeneDF_Norm_rpkm, \"lrpkm\"=GeneDF.lrpkm, \"lcpm\"=GeneDF.lcpm))\n \n}\n\nrunPCA <- function(lcpm, file_prefix, var_gene_num=2000) {\n\n print(\"Running PCA\")\n mat <- as.matrix(lcpm)\n mat<-t(mat)\n class(mat)<-\"numeric\" \n lib_type <- \"all\"\n #norm_type <- \"tmm-rpkm\"\n sd_gene <- apply(mat, 2, sd)\n\n mat <- mat[,which(sd_gene > sort(sd_gene, decreasing=T)[var_gene_num])]\n res<-prcomp(mat)\n loading_file<-paste(file_prefix, \"-loading.tsv\", sep=\"\");\n coord_file<-paste(file_prefix, \"-coord.tsv\", sep=\"\");\n std_file<-paste(file_prefix, \"-std.tsv\", sep=\"\");\n z_loading_file<-paste(file_prefix, \"-loading.zscore.tsv\", sep=\"\");\n z_coord_file<-paste(file_prefix, \"-coord.zscore.tsv\", sep=\"\");\n z_std_file<-paste(file_prefix, \"-std.zscore.tsv\", sep=\"\"); \n\n s <- min(30, ncol(res$rotation))\n if (s > 3) {\n write.table(res$rotation[,1:s], file=loading_file, sep='\\t', col.names=FALSE, quote = FALSE);\n write.table(res$x[,1:3], file=coord_file, sep='\\t', col.names=FALSE, quote = FALSE);\n write.table(res$sdev, file=std_file, sep='\\t', col.names=FALSE, quote = FALSE);\n }\n\n res_z<-prcomp(mat, center=T, scale=T)\n s <- min(30, ncol(res_z$rotation))\n if (s > 3) {\n write.table(res_z$rotation[,1:s], file=z_loading_file, sep='\\t', col.names=FALSE, quote = FALSE);\n write.table(res_z$x[,1:3], file=z_coord_file, sep='\\t', col.names=FALSE, quote = FALSE);\n write.table(res_z$sdev, file=z_std_file, sep='\\t', col.names=FALSE, quote = FALSE);\n }\n}\n\ngetColName <- function(x){\n sample <- gsub(\"Sample_\", \"\", gsub(\".*\\\\.(.*)\\\\.star_.*\", \"\\\\1\", c(x)))\n idx <- grep(sample,f[,1])\n if ( identical(idx, integer(0)) ) {\n return (sample)\n }\n return (f[idx[1],2])\n}\n\nArgs<-commandArgs(trailingOnly=T)\ninFile<-Args[1]\ngeneID_file <- Args[2]\ncoding_file <- Args[3]\nannotationRDS <- Args[4]\nannotationType <- Args[5]\noutDIR <- Args[6]\noutFile <- Args[7]\n\nf<-read.table(inFile, header=F, sep=\"\\t\", fill=T)\nbatch <- f$V4\ntissue_type <- f$V5\nf<-as.matrix(f)\nprint(\"merging count files\")\ncountObj<- do.call(cbind,lapply(paste(f[,3], \".count.txt\", sep=\"\"),makeMatrix))\ncolnames(countObj) <- f[,2]\nprint(\"merging TPM files\")\ntpmObj<- do.call(cbind,lapply(paste(f[,3], \".tpm.txt\", sep=\"\"),makeMatrix))\ncolnames(countObj) <- f[,2]\ngeneIDs <- read.table(geneID_file, head=T, sep=\"\\t\")\ncoding_genes <- read.table(coding_file, head=F, sep=\"\\t\")\nrownames(countObj) <- geneIDs[,1]\nrownames(tpmObj) <- geneIDs[,1]\ncoding_list <- as.character(coding_genes$V1)\ncountCodingObj <- na.omit(subset(countObj, rownames(countObj) %in% coding_list))\ntpmCodingObj <- na.omit(subset(tpmObj, rownames(tpmObj) %in% coding_list))\n#countCodingObj <- na.omit(countObj[coding_list,])\n#tpmCodingObj <- na.omit(tpmObj[coding_list,])\ncountCodingObj <- countCodingObj[ order(row.names(countCodingObj)), ]\ntpmCodingObj <- tpmCodingObj[ order(row.names(tpmCodingObj)), ]\n#ltpmObj <- round(log2(tpmObj+1),2)\n#ltpmCodingObj <- round(log2(tpmCodingObj+1),2)\n#fileList=c(\"/mnt/webrepo/fr-s-bsg-onc-d/htdocs/onco.data/ProcessedResults/clinomics/CL0045/20160415/CL0045_T2R_T/TPM_UCSC/CL0045_T2R_T_counts.Gene.fc.RDS\",\"/mnt/webrepo/fr-s-bsg-onc-d/htdocs/onco.data/ProcessedResults/clinomics/CL0045/20160415/CL0045_T1R_T4/TPM_UCSC/CL0045_T1R_T4_counts.Gene.fc.RDS\")\n#annotationRDS = \"/mnt/webrepo/fr-s-bsg-onc-d/htdocs/clinomics_dev/app/storage/data/AnnotationRDS/annotation_UCSC_gene.RDS\"\n#countObj <- ComBat_seq(countObj, batch=batch, group=tissue_type)\nGeneDF_Norm<-normalize(countObj=countCodingObj, workdir=outDIR, annotName=annotationType, method=\"EdgeR\", annotationRDS=annotationRDS , \n priorCount=0, fileName=outFile, merge=T, saveFiles=T, batch=batch, group=tissue_type)\n\nrunPCA(GeneDF_Norm$lcpm, paste(outDIR,outFile,sep=\"\"))\nold_cols <- colnames(GeneDF_Norm$tpm, do.NULL = FALSE)\nnew_cols <-lapply(old_cols, getColName) \ncolnames(GeneDF_Norm$tpm) <- new_cols\ncolnames(GeneDF_Norm$lrpkm) <- new_cols\n\nprint(\"Saving RDS files\")\nsaveRDS(tpmCodingObj, paste(outDIR,outFile,\".coding.tpm.RDS\", sep= \"\"))\nsaveRDS(GeneDF_Norm$rpkm, paste(outDIR,outFile,\".coding.tmm-rpkm.RDS\", sep= \"\"))\n#write.table(GeneDF_Norm$tpm, paste(outDIR,outFile,\".tpm.tsv\", sep= \"\"), sep=\"\\t\",row.names = FALSE, quote = FALSE)\nprint(paste(\"Saving \", outDIR,outFile,\".all.count.tsv\", sep= \"\"))\nwrite.table(countObj, paste(outDIR,outFile,\".all.count.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\nprint(paste(\"Saving \", outDIR,outFile,\".coding.count.tsv\", sep= \"\"))\nwrite.table(countCodingObj, paste(outDIR,outFile,\".coding.count.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\nprint(paste(\"Saving \", outDIR,outFile,\".all.tpm.tsv\", sep= \"\"))\nwrite.table(tpmObj, paste(outDIR,outFile,\".all.tpm.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\n#print(paste(\"Saving \", outDIR,outFile,\".all.tpm.log2.tsv\", sep= \"\"))\n#write.table(ltpmObj, paste(outDIR,outFile,\".all.tpm.log2.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\nprint(paste(\"Saving \", outDIR,outFile,\".coding.tpm.tsv\", sep= \"\"))\nwrite.table(tpmCodingObj, paste(outDIR,outFile,\".coding.tpm.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\n#print(paste(\"Saving \", outDIR,outFile,\".coding.tpm.log2.tsv\", sep= \"\"))\n#write.table(ltpmCodingObj, paste(outDIR,outFile,\".coding.tpm.log2.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\nprint(paste(\"Saving \", outDIR,outFile,\".coding.tmm-rpkm.tsv\", sep= \"\"))\nwrite.table(GeneDF_Norm$rpkm, paste(outDIR,outFile,\".coding.tmm-rpkm.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\n#print(paste(\"Saving \", outDIR,outFile,\".coding.tmm-rpkm.log2.tsv\", sep= \"\"))\n#write.table(GeneDF_Norm$lrpkm, paste(outDIR,outFile,\".coding.tmm-rpkm.log2.tsv\", sep= \"\"), sep=\"\\t\",row.names = T, quote = FALSE)\n\n", "meta": {"hexsha": "cb51b351839f6d5f01a09956e505743445014e56", "size": 13340, "ext": "r", "lang": "R", "max_stars_repo_path": "app/scripts/tmmNormalize_RSEM.r", "max_stars_repo_name": "hsienchao/oncogenomics", "max_stars_repo_head_hexsha": "4e05579f42e1322e6f13f7d679f68998c5c89818", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "app/scripts/tmmNormalize_RSEM.r", "max_issues_repo_name": "hsienchao/oncogenomics", "max_issues_repo_head_hexsha": "4e05579f42e1322e6f13f7d679f68998c5c89818", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "app/scripts/tmmNormalize_RSEM.r", "max_forks_repo_name": "hsienchao/oncogenomics", "max_forks_repo_head_hexsha": "4e05579f42e1322e6f13f7d679f68998c5c89818", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.9160305344, "max_line_length": 302, "alphanum_fraction": 0.6456521739, "num_tokens": 4078, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7371581626286834, "lm_q2_score": 0.45713671682749485, "lm_q1q2_score": 0.3369820622466648}} {"text": "\nsize.at.maturity = function(r) {\n \n require (doBy)\n\n\toutputvector = NULL\n\n res = coef(summary(r))\n# res2 = confint(r)\n# fin = cbind(res[,c(\"Estimate\",\"Std. Error\")],res2)\n# colnames(fin) = c(\"Estimate\",\"Std. Error\", colnames(res2))\n cw50 = dose.LD50(r, lambda=c(1,NA)) # lambda is a vector of model coef, with NA for inverse prediction variable.\n names(cw50) = c(\"cw50\", \"cw50lower\", \"cw50upper\")\n olist = c(\"Estimate\",\"Std. Error\")\n r1 = res[1,olist] ; names(r1) = c(\"a0\", \"a0.se\" )\n r2 = res[2,olist] ; names(r2) = c(\"a1\", \"a1.se\" )\n r3 = c(length(i), r$deviance, r$aic); names(r3)=c(\"n\", \"deviance\", \"aic\")\n r4 = c(floor(r$converged)); names(r4)=\"converged\"\n\n outputvector = c( r1, r2, r3, r4, cw50)\n \n}\n\n\n\n", "meta": {"hexsha": "06e16f1e496928bd9396d3db3dbf6d5a1444afbc", "size": 773, "ext": "r", "lang": "R", "max_stars_repo_path": "R/size.at.maturity.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/size.at.maturity.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/size.at.maturity.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 29.7307692308, "max_line_length": 118, "alphanum_fraction": 0.5666235446, "num_tokens": 276, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3369363617689983}} {"text": "#' Use binary tree metrics to compare two inferred ancestries (ARGs) over a genomic region\n#'\n#' Runs genome.trees.dist(..., randomly.resolve.polytomies=TRUE) multiple times on the same\n#' two tree sequences, breaking polytomies in the second tree sequence differentially (at random)\n#' each time.\n#' See ?genome.trees.dist for more information.\n#' @param treeseq.a The base multiPhylo object, or a path to a .nex file\n#' @param treeseq.b The multiPhylo object (or path to .nex files) containing a nonbinary tree \n#' to compare to treeseq.a.\n#' @param replicates The number of times to run genome.trees.dist()\n#' @param seed The random seed to give to set.seed before starting random polytomy breaking\n#' @param acceptable.length.diff.pct How much difference in sequence length is allowed between the 2 trees? (Default: 0.1 percent)\n#' @param variant.positions A list of positions of each variant (not implemented)\n#' @return an average of the metrics over each of the replicates \n#' @export\n#' @examples\n#' genome.trees.dist.forcebin()\n\ngenome.trees.dist.forcebin.b <- function(treeseq.a, treeseq.b, replicates=1, seed=NA, acceptable.length.diff.pct = 0.1, variant.positions=NULL, threads=1) { \n require(ape)\n require(parallel) #always available, as this is in R base\n if (class(treeseq.a) != \"multiPhylo\") {\n a <- read.nexus(treeseq.a, force.multi=TRUE)\n } else {\n a <- treeseq.a\n }\n if (class(treeseq.b) != \"multiPhylo\") {\n b <- read.nexus(treeseq.b, force.multi=TRUE)\n } else {\n b <- treeseq.b\n }\n \n if (!is.na(seed)) {\n set.seed(seed)\n seeds <- sample.int(2^31-1, replicates) #pick an integer seed - see ?sample.int\n } else {\n seeds <- TRUE\n }\n return(colMeans(do.call(rbind,mcmapply(\n function(r, seed) genome.trees.dist(treeseq.a, treeseq.b, randomly.resolve.b=seed,\n acceptable.length.diff.pct = acceptable.length.diff.pct,\n variant.positions = variant.positions), \n 1:replicates,\n seeds, \n SIMPLIFY = FALSE, mc.cores=threads))))\n}\n", "meta": {"hexsha": "c005dc7ff8e552774612fd7b141e6bbfb5d54fc8", "size": 2111, "ext": "r", "lang": "R", "max_stars_repo_path": "ARGmetrics/R/genome_trees_dist_forcebin_b.r", "max_stars_repo_name": "HDRUK/treeseq-inference", "max_stars_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2018-11-01T21:07:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-19T15:47:20.000Z", "max_issues_repo_path": "ARGmetrics/R/genome_trees_dist_forcebin_b.r", "max_issues_repo_name": "HDRUK/treeseq-inference", "max_issues_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2018-09-26T13:27:01.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-02T10:58:21.000Z", "max_forks_repo_path": "ARGmetrics/R/genome_trees_dist_forcebin_b.r", "max_forks_repo_name": "HDRUK/treeseq-inference", "max_forks_repo_head_hexsha": "0cbbb062c96ad4433d8b4d0f120f93ac2d985345", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2018-09-26T13:21:04.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-15T18:24:09.000Z", "avg_line_length": 44.914893617, "max_line_length": 157, "alphanum_fraction": 0.6669824728, "num_tokens": 560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3369363617689983}} {"text": "#' Perform FET for all elements in needle, haystack\n#'\n#' @param set1\n#' @param set2\n#' @param valid\nfishers_exact_test = function(set1, set2, valid) {\n stop(\"this is not working yet\")\n\n tp = length(intersect(set1, set2))\n fp = length(setdiff(set1, set2))\n tn = length(intersect(set1, setdiff(set2, valid)))\n fn = length(setdiff(set2, set1))\n stats::fisher.test(matrix(c(tp, fp, fn, tn), ncol=2))\n}\n\nif (is.null(module_name())) {\n re = fishers_exact_test(1:10, 2:11, 1:100)\n}\n", "meta": {"hexsha": "c954afc6d4b66462f1a2a07e572a3e6f7dadf8e3", "size": 497, "ext": "r", "lang": "R", "max_stars_repo_path": "stats/fishers_exact_test.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "stats/fishers_exact_test.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "stats/fishers_exact_test.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 26.1578947368, "max_line_length": 57, "alphanum_fraction": 0.6478873239, "num_tokens": 160, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6791786861878392, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.336936355325953}} {"text": "#!/usr/bin/env R\n#\n# r_bonferroni.R\n#\n# author = thomas huber\n# mail = thomas.huber@evobio.eu\n#\n# script to analyze HyDe (python package phyde) outputs\n# previously set p_values will be corrected after bonferroni\n#\n\nargs = commandArgs(trailingOnly = TRUE)\nip_name = as.character(args[1L])\ndato = read.table(ip_name, header = T)\nplato = dato\nplato$Pvalue = p.adjust(dato$Pvalue, method = 'bonferroni')\nop_name = paste(gsub('.txt','',ip_name),'-bonf.txt', sep = '')\nwrite.table(plato, op_name, sep=\"\\t\", quote = FALSE, row.names = FALSE)\n\n", "meta": {"hexsha": "c082cbd06f83381cb145f6e6f3f3d51e082b3194", "size": 537, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/hyde/r_bonferroni.r", "max_stars_repo_name": "biothomme/--valencene", "max_stars_repo_head_hexsha": "a1327c886d2c7e6f9709c0c502f3936abd01e452", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/hyde/r_bonferroni.r", "max_issues_repo_name": "biothomme/--valencene", "max_issues_repo_head_hexsha": "a1327c886d2c7e6f9709c0c502f3936abd01e452", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/hyde/r_bonferroni.r", "max_forks_repo_name": "biothomme/--valencene", "max_forks_repo_head_hexsha": "a1327c886d2c7e6f9709c0c502f3936abd01e452", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.85, "max_line_length": 71, "alphanum_fraction": 0.7039106145, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3369312803165705}} {"text": "reference = \"AGCATCGATCGATCGATCGATCGATTGTCGATCGATCGATGT\"\npattern = \"AGT\"\n\nnaive <- function(ref, patt, thresh){\n occ <- vector()\n for(i in 0:(nchar(ref) - nchar(patt) + 1)){\n match = TRUE\n mismatch = 0\n for(j in 1:nchar(patt)){\n if(substr(ref, i+j, i+j) != substr(patt, j, j)){\n mismatch = mismatch + 1\n if(mismatch > thresh){\n match = FALSE\n break\n }\n }\n }\n if(match){\n occ <- c(occ, i)\n }\n }\n return(occ)\n}\nprint(paste(naive(reference, pattern, 1), collapse = \",\"))\n\n", "meta": {"hexsha": "a76842dea04a4e9dc8d19b4f929d595061a5d36e", "size": 646, "ext": "r", "lang": "R", "max_stars_repo_path": "naives.r", "max_stars_repo_name": "mschemmel/naives", "max_stars_repo_head_hexsha": "d1d8bcc42676f85c11cd897ff3160d8b41c86e98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-06T14:33:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-06T14:33:05.000Z", "max_issues_repo_path": "naives.r", "max_issues_repo_name": "mschemmel/naives", "max_issues_repo_head_hexsha": "d1d8bcc42676f85c11cd897ff3160d8b41c86e98", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "naives.r", "max_forks_repo_name": "mschemmel/naives", "max_forks_repo_head_hexsha": "d1d8bcc42676f85c11cd897ff3160d8b41c86e98", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.8461538462, "max_line_length": 61, "alphanum_fraction": 0.4551083591, "num_tokens": 178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3369312803165705}} {"text": "#------------------------------------------------------------------------------\n# The threshold value of nuber of rows by which calculate.residual() method\n# separates large and small dataset.\n#------------------------------------------------------------------------------\nTHRESHOLD_NROW_FOR_SMALL_DATASET = 1000\n\n\n#------------------------------------------------------------------------------\n#'\t(Internal) A reference class calculate partial residuals.\n#'\n#'\t@field settings\n#'\t\tpp.settings object having settings of the class.\n#'\n#'\t@field data\n#'\t\ta numeric vector having calculated partial residual data.\n#'\n#'\t@include pp.settings.r\n#------------------------------------------------------------------------------\npartial.residual <- setRefClass(\n\t\"partial.residual\",\n\tfields = list(settings = \"pp.settings\", data = \"numeric\")\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\tinitialize = function(settings) {\n\t\t\"\n\t\tInitialize class and calculate residuals.\n\n\t\t\\\\describe{\n\t\t\t\\\\item{\\\\code{settings}}{\\\\code{\\\\link{pp.settings}} object.}\n\t\t}\n\t\t\"\n\t\t# Initialize settings.\n\t\tif (missing(settings)) {\n\t\t\treturn()\n\t\t}\n\t\tinitFields(settings = settings)\n\t\t# Calculate partial residual if possible.\n\t\tif (identical(settings$adapter$link, binomial()$linkfun)) {\n\t\t\tmessage <- paste(\n\t\t\t\t\"Currently, partial residual can't be calculated\",\n\t\t\t\t\"for logit link function.\"\n\t\t\t)\n\t\t\twarning(message)\n\t\t} else {\n\t\t\tlsmeans.compatible <- !any(\n\t\t\t\tclass(settings$model) %in% LSMEANS_INCOMPATIBLE_MODELS\n\t\t\t)\n\t\t\tif (lsmeans.compatible) {\n\t\t\t\t.self$calculate.residuals.lsmeans()\n\t\t\t} else {\n\t\t\t\t.self$calculate.residuals()\n\t\t\t}\n\t\t\tsettings$set.residual(.self)\n\t\t}\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tAssume that creating a model predicts petal length (PL) by petal width (PW)\n#\tand sepal length (SL) and species (SP) and their interactions.\n#\t\ti: intercept\t\t\tr: residual\n#\n#\tPL = i + PW + SL + SP + PW:SP + SL:SP + r\t\t# Response variable\n#\tPL = i + PW + SL + SP + PW:SP + SL:SP\t\t\t# FULL MODEL\n#\tPL = + r\t\t# Residual\n#\tpredict = i + PW + SL + SP + PW:SP + SL:SP\t\t\t# Result of predict()\n#\n#\tTo see the partial relationship between PL and SL...\n#\n#\tIf we want to plot contrast plot, partial residual is:\n#\tPL = i + PW 0 + SP + PW:SP 0 + r\n#\n#\tTo see this, we need to remove:\n#\tPL = + SL + SL:SP\t\t # Remove these effects\n#\n#\tlsmeans() calculates:\n#\tLSMEANS = i + MM + SL + SP + MM:SP + SL:SP\n#\t\twhere MM is mean of petal width (PW).\n#\n#\tSo when using lsmeans(), i.e., conditional plot, partial residual is:\n#\tLSMEANS = i + MM + SL + SP + MM:SP + SL:SP + r\n#\n#\tThis can be achieved using predict() with using mean values for\n#\tpetal width (PW) and adding residual for it.\n#\tHowever, becase MCMCglmm show deviated values from this calculation,\n#\tcurrently partial residual for MCMCglmm (and models compatible with\n#\tlsmeans) are calculated by special method.\n#\n#\tFor non-linear models with interaction such as Random Forest, population\n#\tpredicted values for given value of focal explanatory variable cannot be\n#\tcalculated by fixing values of other explanatory variables to their\n#\tmean/median.\n#\tTherefore, the predicted values for given set of values of focal\n#\texplanatory variable are calculated using all dataset as similar way to\n#\tcalculate partial relationship.\n#\n#\tFor performance issue, this method change the method used for calculating\n#\tfitted value. For small dataset, get.fit.for.small.data() method is called.\n#\tFor large dataset, get.fit.for.large.data() method is called.\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\tcalculate.residuals = function() {\n\t\t\"\n\t\tCalculate partial residuals.\n\t\t\"\n\t\t# Prepare data for partial residual.\n\t\tnewdata <- lapply(\n\t\t\t1:nrow(.self$settings$data), \"[.data.frame\",\n\t\t\tx = .self$settings$data,\n\t\t)\n\t\t# Change calculation method based on the sample size.\n\t\tif (nrow(.self$settings$data) < THRESHOLD_NROW_FOR_SMALL_DATASET) {\n\t\t\tfit <- .self$settings$cluster.apply(\n\t\t\t\tnewdata, .self$get.fit.for.small.data\n\t\t\t)\n\t\t} else {\n\t\t\trelationship <- .self$extended.partial.relationship()\n\t\t\tfit <- .self$settings$cluster.apply(\n\t\t\t\tnewdata, .self$get.fit.for.large.data, relationship\n\t\t\t)\n\t\t}\n\t\tfit <- unlist(fit)\n\t\tresidual <- fit + .self$settings$adapter$residuals(.self$settings$type)\n\t\tif (.self$settings$type == \"response\") {\n\t\t\tresidual <- .self$settings$adapter$linkinv(residual)\n\t\t}\n\t\t.self$data <- residual\n\t}\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\tget.fit.for.small.data = function(x) {\n\t\t\"\n\t\tCalculate fitted value for a observation.\n\n\t\t\\\\describe{\n\t\t\t\\\\item{x}{a data.frame of one row representing one observation.}\n\t\t}\n\t\t\"\n\t\ton.exit(gc())\n\t\tnewdata <- .self$settings$data\n\t\tfor (i in .self$settings$x.names) {\n\t\t\tnewdata[[i]] <- x[[i]]\n\t\t}\n\t\tfit <- .self$settings$adapter$predict(newdata = newdata, type = \"link\")\n\t\tfit <- mean(fit$fit[, \"fit\"])\n\t\treturn(fit)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\tget.fit.for.large.data = function(x, relationship) {\n\t\t\"\n\t\tCalculate fitted value for a observation.\n\n\t\tThis function uses approximation using estimated relationship\n\t\tfor calculating fitted value.\n\n\t\t\\\\describe{\n\t\t\t\\\\item{x}{a data.frame of one row representing one observation.}\n\t\t\t\\\\item{relationship}{a data.frame having predicted relationship.}\n\t\t}\n\t\t\"\n\t\ton.exit(gc())\n\t\tfor (i in .self$settings$x.names.factor) {\n\t\t\trelationship <- relationship[relationship[[i]] == x[[i]], ]\n\t\t}\n\t\tindex <- relationship[.self$settings$x.names.numeric]\n\t\tfor (i in .self$settings$x.names.numeric) {\n\t\t\tindex[[i]] <- index[[i]] > x[[i]]\n\t\t}\n\t\tindex <- Position(function(x) x, apply(index, 1, all))\n\t\tif (length(settings$x.names.numeric) == 1) {\n\t\t\tf <- as.formula(sprintf(\"fit ~ %s\", .self$settings$x.names.numeric))\n\t\t\td <- relationship[c(index - 1, index),]\n\t\t} else {\n\t\t\tx.names <- paste(.self$settings$x.names.numeric, collapse = \"+\")\n\t\t\tf <- as.formula(sprintf(\"fit ~ %s\", x.names))\n\t\t\tres <- .self$settings$resolution\n\t\t\td <- relationship[\n\t\t\t\tc(index, index - 1, index - res, index - res - 1),\n\t\t\t]\n\t\t}\n\t\tr <- lm(f, data = d)\n\t\tfit <- predict(r, newdata = x)\n\t\treturn(fit)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\textended.partial.relationship = function() {\n\t\tsequences <- .self$extend.sequences(.self$settings$numeric.sequences)\n\t\tgrid <- do.call(\n\t\t\texpand.grid, c(sequences, .self$settings$factor.levels)\n\t\t)\n\t\tnew.settings <- .self$settings$copy()\n\t\tpr <- partial.relationship(new.settings)\n\t\tresult <- pr$calculate.relationship(grid)\n\t\treturn(result)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\textend.sequences = function(sequences) {\n\t\t\"\n\t\tReturn extended 'numeric.sequences' of pp.settings.\n\t\t\"\n\t\tfor (i in names(sequences)) {\n\t\t\tlen <- length(sequences[[i]])\n\t\t\tnew.value <- 2 * sequences[[i]][len] - sequences[[i]][len - 1]\n\t\t\tsequences[[i]] <- c(sequences[[i]], new.value)\n\t\t}\n\t\treturn(sequences)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\npartial.residual$methods(\n\tcalculate.residuals.lsmeans = function() {\n\t\t\"\n\t\tCalculate partial residuals for lsmeans compatible models.\n\t\t\"\n\t\t# Prepare names of numeric variables.\n\t\tx.names <- .self$settings$adapter$x.names(type = \"base\")\n\t\tall.numerics <- x.names[\n\t\t\tsapply(.self$settings$data[x.names], is.numeric)\n\t\t]\n\t\tother.numerics <- all.numerics[\n\t\t\t!all.numerics %in% .self$settings$x.names.numeric\n\t\t]\n\t\t# Calculate prediction 1.\n\t\tdata1 <- .self$settings$data\n\t\tfor (i in other.numerics) {\n\t\t\tdata1[[i]] <- data1[[i]] - mean(data1[[i]])\n\t\t}\n\t\tdata1[.self$settings$x.names.numeric] <- 0\n\t\tpred1 <- .self$settings$adapter$predict(\n\t\t newdata = data1, type = \"link\", interval = \"prediction\"\n\t\t)\n\t\tpred1 <- pred1$fit[, \"fit\"]\n\t\t# Calculate prediction 2.\n\t\tdata2 <- .self$settings$data\n\t\tdata2[all.numerics] <- 0\n\t\tpred2 <- .self$settings$adapter$predict(\n\t\t newdata = data2, type = \"link\", interval = \"prediction\"\n\t\t)\n\t\tpred2 <- pred2$fit[, \"fit\"]\n\t\t# Calculate partial residual.\n\t\tresult <- (\n\t\t\t.self$settings$adapter$link(\n\t\t\t\t.self$settings$data[[.self$settings$adapter$y.names()]]\n\t\t\t)\n\t\t\t- (pred1 - pred2)\n\t\t)\n\t\tif (.self$settings$type == \"response\") {\n\t\t\tresult <- .self$settings$adapter$linkinv(result)\n\t\t}\n\t\t.self$data <- result\n\t}\n)\n", "meta": {"hexsha": "9c54d6a35519c4d6a842f4930427da34535c2a5e", "size": 8638, "ext": "r", "lang": "R", "max_stars_repo_path": "R/partial.residual.r", "max_stars_repo_name": "Marchen/partial.plot", "max_stars_repo_head_hexsha": "2b4c909335caf87b6e4ced390e2361732f896a0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-12T16:31:55.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-12T16:31:55.000Z", "max_issues_repo_path": "R/partial.residual.r", "max_issues_repo_name": "Marchen/partial.plot", "max_issues_repo_head_hexsha": "2b4c909335caf87b6e4ced390e2361732f896a0e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-11-28T01:41:55.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T01:42:23.000Z", "max_forks_repo_path": "R/partial.residual.r", "max_forks_repo_name": "Marchen/partial.plot", "max_forks_repo_head_hexsha": "2b4c909335caf87b6e4ced390e2361732f896a0e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-04T04:46:16.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-04T04:46:16.000Z", "avg_line_length": 31.5255474453, "max_line_length": 79, "alphanum_fraction": 0.5906459829, "num_tokens": 2250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3369312724373997}} {"text": "#SNOPSIS\n#calculates genomic estimated breeding values (GEBVs) using rrBLUP,\n#GBLUP method\n\n#AUTHOR\n# Isaak Y Tecle (iyt2@cornell.edu)\n\noptions(echo = FALSE)\n\nlibrary(methods)\nlibrary(rrBLUP)\nlibrary(plyr)\nlibrary(stringr)\n#library(lme4)\nlibrary(randomForest)\nlibrary(parallel)\nlibrary(genoDataFilter)\nlibrary(phenoAnalysis)\nlibrary(caret)\nlibrary(dplyr)\nlibrary(tibble)\nlibrary(rlang)\nlibrary(jsonlite)\nlibrary(data.table)\n\n\nallArgs <- commandArgs()\n\n\ninputFiles <- scan(grep(\"input_files\", allArgs, value = TRUE),\n what = \"character\")\n\noutputFiles <- scan(grep(\"output_files\", allArgs, value = TRUE),\n what = \"character\")\n\ntraitsFile <- grep(\"traits\", inputFiles, value = TRUE)\nmodelInfoFile <- grep(\"model_info\", inputFiles, value = TRUE)\nmessage('model_info_file ', modelInfoFile)\n\nmodelInfo <- read.table(modelInfoFile,\n header=TRUE, sep =\"\\t\",\n as.is = c('Value'))\n\nmodelInfo <- column_to_rownames(modelInfo, var=\"Name\")\ntraitId <- modelInfo[\"trait_id\", 1]\ntraitAbbr <- modelInfo[\"trait_abbr\", 1]\nmodelId <- modelInfo[\"model_id\", 1]\nprotocolId <- modelInfo[\"protocol_id\", 1]\n\nmessage('class ', class(traitAbbr))\nmessage('trait_id ', traitId)\nmessage('trait_abbr ', traitAbbr)\nmessage('protocol_id ', protocolId)\nmessage('model_id ', modelId)\n\ndatasetInfoFile <- grep(\"dataset_info\", inputFiles, value = TRUE)\ndatasetInfo <- c()\n\nif (length(datasetInfoFile) != 0 ) {\n datasetInfo <- scan(datasetInfoFile, what = \"character\")\n datasetInfo <- paste(datasetInfo, collapse = \" \")\n } else {\n datasetInfo <- c('single population')\n }\n\n#validationTrait <- paste(\"validation\", trait, sep = \"_\")\nvalidationFile <- grep('validation', outputFiles, value = TRUE)\n\nif (is.null(validationFile)) {\n stop(\"Validation output file is missing.\")\n}\n\n#kinshipTrait <- paste(\"rrblup_training_gebvs\", trait, sep = \"_\")\nblupFile <- grep('rrblup_training_gebvs', outputFiles, value = TRUE)\n\nif (is.null(blupFile)) {\n stop(\"GEBVs file is missing.\")\n}\n\n#markerTrait <- paste(\"marker_effects\", trait, sep = \"_\")\nmarkerFile <- grep('marker_effects', outputFiles, value = TRUE)\n\n#traitPhenoFile <- paste(\"trait_phenotype_data\", traitId, sep = \"_\")\nmodelPhenoFile <- grep('model_phenodata', outputFiles, value = TRUE)\nmessage('model input trait pheno file ', modelPhenoFile)\ntraitRawPhenoFile <- grep('trait_raw_phenodata', outputFiles, value = TRUE)\nvarianceComponentsFile <- grep(\"variance_components\", outputFiles, value = TRUE)\nfilteredGenoFile <- grep(\"filtered_genotype_data\", outputFiles, value = TRUE)\nformattedPhenoFile <- grep(\"formatted_phenotype_data\", inputFiles, value = TRUE)\n\ngenoFile <- grep(\"genotype_data_\", inputFiles, value = TRUE)\n\nif (is.null(genoFile)) {\n stop(\"genotype data file is missing.\")\n}\n\nif (file.info(genoFile)$size == 0) {\n stop(\"genotype data file is empty.\")\n}\n\nreadFilteredGenoData <- c()\nfilteredGenoData <- c()\nformattedPhenoData <- c()\nphenoData <- c()\ngenoData <- c()\n\nif (length(filteredGenoFile) != 0 && file.info(filteredGenoFile)$size != 0) {\n filteredGenoData <- fread(filteredGenoFile,\n na.strings = c(\"NA\", \"\", \"--\", \"-\"),\n header = TRUE)\n\n genoData <- data.frame(filteredGenoData)\n genoData <- column_to_rownames(genoData, 'V1')\n readFilteredGenoData <- 1\n}\n\n\nif (is.null(filteredGenoData)) {\n genoData <- fread(genoFile,\n na.strings = c(\"NA\", \"\", \"--\", \"-\"),\n header = TRUE)\n\n genoData <- unique(genoData, by='V1')\n genoData <- data.frame(genoData)\n genoData <- column_to_rownames(genoData, 'V1')\n\n #genoDataFilter::filterGenoData\n genoData <- convertToNumeric(genoData)\n genoData <- filterGenoData(genoData, maf=0.01)\n genoData <- roundAlleleDosage(genoData)\n\n filteredGenoData <- genoData\n\n}\n\ngenoData <- genoData[order(row.names(genoData)), ]\n\nif (length(formattedPhenoFile) != 0 && file.info(formattedPhenoFile)$size != 0) {\n formattedPhenoData <- data.frame(fread(formattedPhenoFile,\n header = TRUE,\n na.strings = c(\"NA\", \"\", \"--\", \"-\", \".\")\n ))\n\n} else {\n\n if (datasetInfo == 'combined populations') {\n\n phenoFile <- grep(\"model_phenodata\", inputFiles, value = TRUE)\n } else {\n\n phenoFile <- grep(\"\\\\/phenotype_data\", inputFiles, value = TRUE)\n }\n\n if (is.null(phenoFile)) {\n stop(\"phenotype data file is missing.\")\n }\n\n if (file.info(phenoFile)$size == 0) {\n stop(\"phenotype data file is empty.\")\n }\n\n phenoData <- data.frame(fread(phenoFile,\n sep = \"\\t\",\n na.strings = c(\"NA\", \"\", \"--\", \"-\", \".\"),\n header = TRUE))\n\n\n}\n\nphenoTrait <- c()\ntraitRawPhenoData <- c()\n\nif (datasetInfo == 'combined populations') {\n\n if (!is.null(formattedPhenoData)) {\n phenoTrait <- subset(formattedPhenoData, select = traitAbbr)\n phenoTrait <- na.omit(phenoTrait)\n\n } else {\n\n if (any(grepl('Average', names(phenoData)))) {\n phenoTrait <- phenoData %>% select(V1, Average) %>% data.frame\n } else {\n phenoTrait <- phenoData\n }\n\n colnames(phenoTrait) <- c('genotypes', traitAbbr)\n }\n } else {\n\n if (!is.null(formattedPhenoData)) {\n phenoTrait <- subset(formattedPhenoData, select = c('V1', traitAbbr))\n phenoTrait <- as.data.frame(phenoTrait)\n phenoTrait <- na.omit(phenoTrait)\n print(head(phenoTrait))\n colnames(phenoTrait)[1] <- 'genotypes'\n\n } else if (length(grep('list', phenoFile)) != 0) {\n message('phenoTrait traitAbbr ', traitAbbr)\n phenoTrait <- averageTrait(phenoData, traitAbbr)\n\n } else {\n print(head(phenoTrait))\n print(head(phenoData))\n message('phenoTrait trait_abbr ', traitAbbr)\n print(class(traitAbbr))\n print(traitAbbr)\n phenoTrait <- getAdjMeans(phenoData,\n traitName = traitAbbr,\n calcAverages = TRUE)\n }\n\n keepMetaCols <- c('observationUnitName', 'germplasmName', 'studyDbId', 'locationName',\n 'studyYear', 'replicate', 'blockNumber')\n\n traitRawPhenoData <- phenoData %>%\n select(c(keepMetaCols, traitAbbr))\n\n\n}\n\nprint('phenoTrait')\nprint(head(phenoTrait))\nmeanType <- names(phenoTrait)[2]\nnames(phenoTrait) <- c('genotypes', traitAbbr)\n\nselectionTempFile <- grep(\"selection_population\", inputFiles, value = TRUE)\n\nselectionFile <- c()\nfilteredPredGenoFile <- c()\nselectionAllFiles <- c()\n\nif (length(selectionTempFile) !=0 ) {\n selectionAllFiles <- scan(selectionTempFile, what = \"character\")\n\n selectionFile <- grep(\"\\\\/genotype_data\", selectionAllFiles, value = TRUE)\n\n #filteredPredGenoFile <- grep(\"filtered_genotype_data_\", selectionAllFiles, value = TRUE)\n}\n\nselectionPopGEBVsFile <- grep(\"rrblup_selection_gebvs\", outputFiles, value = TRUE)\n\nselectionData <- c()\nreadFilteredPredGenoData <- c()\nfilteredPredGenoData <- c()\n\n## if (length(filteredPredGenoFile) != 0 && file.info(filteredPredGenoFile)$size != 0) {\n## selectionData <- fread(filteredPredGenoFile, na.strings = c(\"NA\", \" \", \"--\", \"-\"),)\n## readFilteredPredGenoData <- 1\n\n## selectionData <- data.frame(selectionData)\n## rownames(selectionData) <- selectionData[, 1]\n## selectionData[, 1] <- NULL\n\n## } else\nif (length(selectionFile) != 0) {\n\n selectionData <- fread(selectionFile,\n header = TRUE,\n na.strings = c(\"NA\", \"\", \"--\", \"-\"))\n\n selectionData <- unique(selectionData, by='V1')\n selectionData <- data.frame(selectionData)\n selectionData <- column_to_rownames(selectionData, 'V1')\n\n selectionData <- convertToNumeric(selectionData)\n selectionData <- filterGenoData(selectionData, maf=0.01)\n selectionData <- roundAlleleDosage(selectionData)\n\n filteredPredGenoData <- selectionData\n}\n\n\n#impute genotype values for obs with missing values,\ngenoDataMissing <- c()\n\nif (sum(is.na(genoData)) > 0) {\n genoDataMissing<- c('yes')\n\n genoData <- na.roughfix(genoData)\n genoData <- data.frame(genoData)\n}\n\n#create phenotype and genotype datasets with\n#common stocks only\n\n#extract observation lines with both\n#phenotype and genotype data only.\ncommonObs <- intersect(phenoTrait$genotypes, row.names(genoData))\ncommonObs <- data.frame(commonObs)\nrownames(commonObs) <- commonObs[, 1]\n\n#include in the genotype dataset only phenotyped lines\ngenoDataFilteredObs <- genoData[(rownames(genoData) %in% rownames(commonObs)), ]\n\n#drop phenotyped lines without genotype data\nphenoTrait <- phenoTrait[(phenoTrait$genotypes %in% rownames(commonObs)), ]\n\nphenoTraitMarker <- data.frame(phenoTrait)\nrownames(phenoTraitMarker) <- phenoTraitMarker[, 1]\nphenoTraitMarker[, 1] <- NULL\n\n#impute missing data in prediction data\nselectionDataMissing <- c()\nif (length(selectionData) != 0) {\n #purge markers unique to both populations\n commonMarkers <- intersect(names(data.frame(genoDataFilteredObs)), names(selectionData))\n selectionData <- subset(selectionData, select = commonMarkers)\n genoDataFilteredObs <- subset(genoDataFilteredObs, select= commonMarkers)\n\n if (sum(is.na(selectionData)) > 0) {\n selectionDataMissing <- c('yes')\n selectionData <- na.roughfix(selectionData)\n selectionData <- data.frame(selectionData)\n }\n}\n\n#change genotype coding to [-1, 0, 1], to use the A.mat ) if [0, 1, 2]\ngenoTrCode <- grep(\"2\", genoDataFilteredObs[1, ], value = TRUE)\nif(length(genoTrCode) != 0) {\n genoData <- genoData - 1\n genoDataFilteredObs <- genoDataFilteredObs - 1\n}\n\nif (length(selectionData) != 0 ) {\n genoSlCode <- grep(\"2\", selectionData[1, ], value = TRUE)\n if (length(genoSlCode) != 0 ) {\n selectionData <- selectionData - 1\n }\n}\n\nordered.markerEffects <- c()\ntrGEBV <- c()\nvalidationAll <- c()\ncombinedGebvsFile <- c()\nallGebvs <- c()\nmodelPhenoData <- c()\nrelationshipMatrix <- c()\n\n#additive relationship model\n#calculate the inner products for\n#genotypes (realized relationship matrix)\nrelationshipMatrixFile <- grep(\"relationship_matrix_table\", outputFiles, value = TRUE)\nrelationshipMatrixJsonFile <- grep(\"relationship_matrix_json\", outputFiles, value = TRUE)\n\ntraitRelationshipMatrixFile <- grep(\"relationship_matrix_adjusted_table\", outputFiles, value = TRUE)\ntraitRelationshipMatrixJsonFile <- grep(\"relationship_matrix_adjusted_json\", outputFiles, value = TRUE)\n\ninbreedingFile <- grep('inbreeding_coefficients', outputFiles, value=TRUE)\naveKinshipFile <- grep('average_kinship', outputFiles, value=TRUE)\n\ninbreeding <- c()\naveKinship <- c()\n\nif (length(relationshipMatrixFile) != 0) {\n if (file.info(relationshipMatrixFile)$size > 0 ) {\n relationshipMatrix <- data.frame(fread(relationshipMatrixFile,\n \t\t\t header = TRUE))\n\n rownames(relationshipMatrix) <- relationshipMatrix[, 1]\n relationshipMatrix[, 1] <- NULL\n colnames(relationshipMatrix) <- rownames(relationshipMatrix)\n relationshipMatrix <- data.matrix(relationshipMatrix)\n\n } else {\n relationshipMatrix <- A.mat(genoData)\n diag(relationshipMatrix) <- diag(relationshipMatrix) + 1e-6\n\n inbreeding <- diag(relationshipMatrix)\n inbreeding <- inbreeding - 1\n\n inbreeding <- inbreeding %>% replace(., . < 0, 0)\n inbreeding <- data.frame(inbreeding)\n\n inbreeding <- inbreeding %>%\n rownames_to_column('genotypes') %>%\n rename(Inbreeding = inbreeding) %>%\n arrange(Inbreeding) %>%\n mutate_at('Inbreeding', round, 3) %>%\n column_to_rownames('genotypes')\n }\n}\n\nrelationshipMatrix <- data.frame(relationshipMatrix)\ncolnames(relationshipMatrix) <- rownames(relationshipMatrix)\n\nrelationshipMatrix <- rownames_to_column(relationshipMatrix, var=\"genotypes\")\nrelationshipMatrix <- relationshipMatrix %>% mutate_if(is.numeric, round, 3)\nrelationshipMatrix <- column_to_rownames(relationshipMatrix, var=\"genotypes\")\n\ntraitRelationshipMatrix <- relationshipMatrix[(rownames(relationshipMatrix) %in% rownames(commonObs)), ]\ntraitRelationshipMatrix <- traitRelationshipMatrix[, (colnames(traitRelationshipMatrix) %in% rownames(commonObs))]\n\ntraitRelationshipMatrix <- data.matrix(traitRelationshipMatrix)\n\n#relationshipMatrixFiltered <- relationshipMatrixFiltered + 1e-3\n\nnCores <- detectCores()\n\nif (nCores > 1) {\n nCores <- (nCores %/% 2)\n} else {\n nCores <- 1\n}\n\n\nif (length(selectionData) == 0) {\n\n trModel <- kin.blup(data = phenoTrait,\n geno = 'genotypes',\n pheno = traitAbbr,\n K = traitRelationshipMatrix,\n n.core = nCores,\n PEV = TRUE\n )\n\n trGEBV <- trModel$g\n trGEBVPEV <- trModel$PEV\n trGEBVSE <- sqrt(trGEBVPEV)\n trGEBVSE <- data.frame(round(trGEBVSE, 2))\n\n trGEBV <- data.frame(round(trGEBV, 2))\n\n colnames(trGEBVSE) <- c('SE')\n colnames(trGEBV) <- traitAbbr\n\n trGEBVSE <- rownames_to_column(trGEBVSE, var=\"genotypes\")\n trGEBV <- rownames_to_column(trGEBV, var=\"genotypes\")\n\n trGEBVSE <- full_join(trGEBV, trGEBVSE)\n\n trGEBVSE <- trGEBVSE %>% arrange_(.dots= paste0('desc(', traitAbbr, ')'))\n\n trGEBVSE <- column_to_rownames(trGEBVSE, var=\"genotypes\")\n\n trGEBV <- trGEBV %>% arrange_(.dots = paste0('desc(', traitAbbr, ')'))\n trGEBV <- column_to_rownames(trGEBV, var=\"genotypes\")\n\n phenoTraitMarker <- data.matrix(phenoTraitMarker)\n genoDataFilteredObs <- data.matrix(genoDataFilteredObs)\n\n markerEffects <- mixed.solve(y = phenoTraitMarker,\n Z = genoDataFilteredObs\n )\n\n ordered.markerEffects <- data.matrix(markerEffects$u)\n ordered.markerEffects <- data.matrix(ordered.markerEffects [order (-ordered.markerEffects[, 1]), ])\n ordered.markerEffects <- round(ordered.markerEffects, 5)\n\n colnames(ordered.markerEffects) <- c(\"Marker Effects\")\n ordered.markerEffects <- data.frame(ordered.markerEffects)\n\n\n modelPhenoData <- data.frame(round(phenoTraitMarker, 2))\n\n heritability <- round((trModel$Vg/(trModel$Ve + trModel$Vg)), 2)\n additiveVar <- round(trModel$Vg, 2)\n errorVar <- round(trModel$Ve, 2)\n\n cat(\"\\n\", file = varianceComponentsFile, append = FALSE)\n cat('Additive genetic variance', additiveVar , file = varianceComponentsFile, sep = '\\t', append = TRUE)\n cat(\"\\n\", file = varianceComponentsFile, append = TRUE)\n cat('Error variance', errorVar, file = varianceComponentsFile, sep = \"\\t\", append = TRUE)\n cat(\"\\n\", file = varianceComponentsFile, append = TRUE)\n cat('SNP heritability (h)', heritability, file = varianceComponentsFile, sep = '\\t', append = TRUE)\n\n combinedGebvsFile <- grep('selected_traits_gebv', outputFiles, ignore.case = TRUE,value = TRUE)\n\n if (length(combinedGebvsFile) != 0) {\n fileSize <- file.info(combinedGebvsFile)$size\n if (fileSize != 0 ) {\n combinedGebvs <- data.frame(fread(combinedGebvsFile,\n header = TRUE))\n\n rownames(combinedGebvs) <- combinedGebvs[,1]\n combinedGebvs[,1] <- NULL\n\n allGebvs <- merge(combinedGebvs, trGEBV,\n by = 0,\n all = TRUE\n )\n\n rownames(allGebvs) <- allGebvs[,1]\n allGebvs[,1] <- NULL\n }\n }\n\n#cross-validation\n\n if (is.null(selectionFile)) {\n genoNum <- nrow(phenoTrait)\n\n if (genoNum < 20 ) {\n warning(genoNum, \" is too small number of genotypes.\")\n }\n\n set.seed(4567)\n\n k <- 10\n times <- 2\n cvFolds <- createMultiFolds(phenoTrait[, 2], k=k, times=times)\n\n for ( r in 1:times) {\n re <- paste0('Rep', r)\n\n for (i in 1:k) {\n fo <- ifelse(i < 10, 'Fold0', 'Fold')\n\n trFoRe <- paste0(fo, i, '.', re)\n trG <- cvFolds[[trFoRe]]\n slG <- as.numeric(rownames(phenoTrait[-trG,]))\n\n kblup <- paste(\"rKblup\", i, sep = \".\")\n\n result <- kin.blup(data = phenoTrait[trG,],\n geno = 'genotypes',\n pheno = traitAbbr,\n K = traitRelationshipMatrix,\n n.core = nCores,\n PEV = TRUE\n )\n\n assign(kblup, result)\n\n #calculate cross-validation accuracy\n valBlups <- result$g\n\n valBlups <- data.frame(valBlups)\n\n slG <- slG[which(slG <= nrow(phenoTrait))]\n\n slGDf <- phenoTrait[(rownames(phenoTrait) %in% slG),]\n rownames(slGDf) <- slGDf[, 1]\n slGDf[, 1] <- NULL\n\n valBlups <- rownames_to_column(valBlups, var=\"genotypes\")\n slGDf <- rownames_to_column(slGDf, var=\"genotypes\")\n\n valCorData <- inner_join(slGDf, valBlups, by=\"genotypes\")\n valCorData$genotypes <- NULL\n\n accuracy <- try(cor(valCorData))\n validation <- paste(\"validation\", trFoRe, sep = \".\")\n cvTest <- paste(\"CV\", trFoRe, sep = \" \")\n\n if ( class(accuracy) != \"try-error\")\n {\n accuracy <- round(accuracy[1,2], digits = 3)\n accuracy <- data.matrix(accuracy)\n\n colnames(accuracy) <- c(\"correlation\")\n rownames(accuracy) <- cvTest\n\n assign(validation, accuracy)\n\n if (!is.na(accuracy[1,1])) {\n validationAll <- rbind(validationAll, accuracy)\n }\n }\n }\n }\n\n validationAll <- data.frame(validationAll[order(-validationAll[, 1]), ])\n colnames(validationAll) <- c('Correlation')\n }\n}\n\nselectionPopResult <- c()\nselectionPopGEBVs <- c()\nselectionPopGEBVSE <- c()\n\nif (length(selectionData) != 0) {\n\n genoDataTrSl <- rbind(genoDataFilteredObs, selectionData)\n rTrSl <- A.mat(genoDataTrSl)\n\n selectionPopResult <- kin.blup(data = phenoTrait,\n geno = 'genotypes',\n pheno = traitAbbr,\n K = rTrSl,\n n.core = nCores,\n PEV = TRUE\n )\n\n selectionPopGEBVs <- round(data.frame(selectionPopResult$g), 2)\n colnames(selectionPopGEBVs) <- traitAbbr\n selectionPopGEBVs <- rownames_to_column(selectionPopGEBVs, var=\"genotypes\")\n\n selectionPopPEV <- selectionPopResult$PEV\n selectionPopSE <- sqrt(selectionPopPEV)\n selectionPopSE <- data.frame(round(selectionPopSE, 2))\n colnames(selectionPopSE) <- 'SE'\n genotypesSl <- rownames(selectionData)\n\n selectionPopSE <- rownames_to_column(selectionPopSE, var=\"genotypes\")\n selectionPopSE <- selectionPopSE %>% filter(genotypes %in% genotypesSl)\n\n selectionPopGEBVs <- selectionPopGEBVs %>% filter(genotypes %in% genotypesSl)\n\n selectionPopGEBVSE <- inner_join(selectionPopGEBVs, selectionPopSE, by=\"genotypes\")\n\n sortVar <- parse_quosure(traitAbbr)\n selectionPopGEBVs <- selectionPopGEBVs %>% arrange(desc((!!sortVar)))\n selectionPopGEBVs <- column_to_rownames(selectionPopGEBVs, var=\"genotypes\")\n\n selectionPopGEBVSE <- selectionPopGEBVSE %>% arrange(desc((!!sortVar)))\n selectionPopGEBVSE <- column_to_rownames(selectionPopGEBVSE, var=\"genotypes\")\n}\n\nif (!is.null(selectionPopGEBVs) & length(selectionPopGEBVsFile) != 0) {\n fwrite(selectionPopGEBVs,\n file = selectionPopGEBVsFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\nif(!is.null(validationAll)) {\n fwrite(validationAll,\n file = validationFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\n\nif (!is.null(ordered.markerEffects)) {\n fwrite(ordered.markerEffects,\n file = markerFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\n\nif (!is.null(trGEBV)) {\n fwrite(trGEBV,\n file = blupFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\nif (length(combinedGebvsFile) != 0 ) {\n if(file.info(combinedGebvsFile)$size == 0) {\n fwrite(trGEBV,\n file = combinedGebvsFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n } else {\n fwrite(allGebvs,\n file = combinedGebvsFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n }\n}\n\n\nif (!is.null(modelPhenoData) & length(modelPhenoFile) != 0) {\n\n if (!is.null(meanType)) {\n colnames(modelPhenoData) <- meanType\n }\n\n fwrite(modelPhenoData,\n file = modelPhenoFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\nif (!is.null(traitRawPhenoData) & length(traitRawPhenoFile) != 0) {\n\n fwrite(traitRawPhenoData,\n file = traitRawPhenoFile,\n row.names = FALSE,\n sep = \"\\t\",\n na = 'NA',\n quote = FALSE,\n )\n}\n\n\n\nif (!is.null(filteredGenoData) && is.null(readFilteredGenoData)) {\n fwrite(filteredGenoData,\n file = filteredGenoFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n\n}\n\n## if (length(filteredPredGenoFile) != 0 && is.null(readFilteredPredGenoData)) {\n## fwrite(filteredPredGenoData,\n## file = filteredPredGenoFile,\n## row.names = TRUE,\n## sep = \"\\t\",\n## quote = FALSE,\n## )\n## }\n\n## if (!is.null(genoDataMissing)) {\n## write.table(genoData,\n## file = genoFile,\n## sep = \"\\t\",\n## col.names = NA,\n## quote = FALSE,\n## )\n\n## }\n\n## if (!is.null(predictionDataMissing)) {\n## write.table(predictionData,\n## file = predictionFile,\n## sep = \"\\t\",\n## col.names = NA,\n## quote = FALSE,\n## )\n## }\n\n\nif (file.info(relationshipMatrixFile)$size == 0) {\n\n fwrite(relationshipMatrix,\n file = relationshipMatrixFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\nif (file.info(relationshipMatrixJsonFile)$size == 0) {\n\n relationshipMatrixJson <- relationshipMatrix\n relationshipMatrixJson[upper.tri(relationshipMatrixJson)] <- NA\n\n\n relationshipMatrixJson <- data.frame(relationshipMatrixJson)\n\n relationshipMatrixList <- list(labels = names(relationshipMatrixJson),\n values = relationshipMatrixJson)\n\n relationshipMatrixJson <- jsonlite::toJSON(relationshipMatrixList)\n\n\n write(relationshipMatrixJson,\n file = relationshipMatrixJsonFile,\n )\n}\n\n\nif (file.info(traitRelationshipMatrixFile)$size == 0) {\n\n inbre <- diag(traitRelationshipMatrix)\n inbre <- inbre - 1\n\n diag(traitRelationshipMatrix) <- inbre\n\n traitRelationshipMatrix <- data.frame(traitRelationshipMatrix) %>% replace(., . < 0, 0)\n\n fwrite(traitRelationshipMatrix,\n file = traitRelationshipMatrixFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n\n if (file.info(traitRelationshipMatrixJsonFile)$size == 0) {\n\n traitRelationshipMatrixJson <- traitRelationshipMatrix\n traitRelationshipMatrixJson[upper.tri(traitRelationshipMatrixJson)] <- NA\n\n traitRelationshipMatrixJson <- data.frame(traitRelationshipMatrixJson)\n\n traitRelationshipMatrixList <- list(labels = names(traitRelationshipMatrixJson),\n values = traitRelationshipMatrixJson)\n\n traitRelationshipMatrixJson <- jsonlite::toJSON(traitRelationshipMatrixList)\n\n write(traitRelationshipMatrixJson,\n file = traitRelationshipMatrixJsonFile,\n )\n }\n}\n\n\nif (file.info(inbreedingFile)$size == 0) {\n\n fwrite(inbreeding,\n file = inbreedingFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\n\nif (file.info(aveKinshipFile)$size == 0) {\n\n aveKinship <- data.frame(apply(traitRelationshipMatrix, 1, mean))\n\n aveKinship<- aveKinship %>%\n rownames_to_column('genotypes') %>%\n rename(Mean_kinship = contains('traitRe')) %>%\n arrange(Mean_kinship) %>%\n mutate_at('Mean_kinship', round, 3) %>%\n column_to_rownames('genotypes')\n\n fwrite(aveKinship,\n file = aveKinshipFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\n\nif (file.info(formattedPhenoFile)$size == 0 && !is.null(formattedPhenoData) ) {\n fwrite(formattedPhenoData,\n file = formattedPhenoFile,\n row.names = TRUE,\n sep = \"\\t\",\n quote = FALSE,\n )\n}\n\nmessage(\"Done.\")\n\nq(save = \"no\", runLast = FALSE)\n", "meta": {"hexsha": "e973c2606008c22ecc9f9a544a60a7fe5485409e", "size": 25552, "ext": "r", "lang": "R", "max_stars_repo_path": "R/solGS/gs.r", "max_stars_repo_name": "TriticeaeToolbox/sgn", "max_stars_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2015-02-03T15:47:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T13:34:05.000Z", "max_issues_repo_path": "R/solGS/gs.r", "max_issues_repo_name": "TriticeaeToolbox/sgn", "max_issues_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2491, "max_issues_repo_issues_event_min_datetime": "2015-01-07T05:49:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:05.000Z", "max_forks_repo_path": "R/solGS/gs.r", "max_forks_repo_name": "TriticeaeToolbox/sgn", "max_forks_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-06-30T19:10:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T13:34:09.000Z", "avg_line_length": 30.3828775268, "max_line_length": 114, "alphanum_fraction": 0.6015184721, "num_tokens": 6542, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.712232184238947, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3366603850070301}} {"text": "library(cummeRbund)\n\nargs = commandArgs(trailingOnly=TRUE)\n\nsetwd(args[1])\n\ncuff = readCufflinks(args[2])\n\npdf(file=\"density-19f-dapto-t1.pdf\")\ncsDensity(genes(cuff))\ndev.off()\n\npdf(file=\"scatter-19f-dapto-t1.pdf\")\ncsScatter(genes(cuff), 'X19f_dapto','X19f_t1')\ndev.off()\n\npdf(file=\"scatter-matrix-19f-dapto-t1.pdf\")\ncsScatterMatrix(genes(cuff))\ndev.off()\n\npdf(file=\"volcano-19f-dapto-t1.pdf\")\ncsVolcanoMatrix(genes(cuff))\ndev.off()\n\ngene_diff_data=diffData(genes(cuff))\nsig_gene_data = subset(gene_diff_data,significant=='yes')\nsig_genes = getGenes(cuff,sig_gene_data$gene_id)\n\nwrite.table(sig_gene_data,'sig-diff-genes.csv',sep=',',quote=F)\n\npdf(file=\"sig-gene-diff-barplot-19f-dapto-t1.pdf\")\nexpressionBarplot(sig_genes,logMode=T,showErrorbars=T)\ndev.off()\n\npdf(file=\"sig-gene-diff-heatmap-19f-dapto-t1.pdf\")\ncsHeatmap(sig_genes,cluster='both')\ndev.off()\n\nplot_list = list()\nfor (i in 1:length(sig_gene_data$gene_id)) {\n ex_gene = getGene(cuff,sig_gene_data$gene_id[i])\n plot_list[[i]] = expressionBarplot(ex_gene,logMode=T,showErrorbars=T)\n}\n\npdf(file=\"sig-genes-individual.pdf\")\nfor (i in 1:length(sig_gene_data$gene_id)) {\n print(plot_list[[i]])\n}\ndev.off()\n", "meta": {"hexsha": "d99861037b82c8f00757b30da0e99c65454d95ad", "size": 1173, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/cummerbund.r", "max_stars_repo_name": "jsa-aerial/aerobio", "max_stars_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-01-23T16:08:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-17T19:40:12.000Z", "max_issues_repo_path": "Scripts/cummerbund.r", "max_issues_repo_name": "jsa-aerial/aerobio", "max_issues_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2017-06-08T19:17:52.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-04T21:26:30.000Z", "max_forks_repo_path": "Scripts/cummerbund.r", "max_forks_repo_name": "jsa-aerial/aerobio", "max_forks_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-03T02:18:02.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-03T02:18:02.000Z", "avg_line_length": 23.46, "max_line_length": 73, "alphanum_fraction": 0.7442455243, "num_tokens": 380, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33663142544877056}} {"text": "library(tidyverse)\nlibrary(reticulate)\nlibrary(ggbeeswarm)\nsource('config.r')\nnp <- import(\"numpy\")\n\ndata <- np$load(\n \"./outputs/all_scores_models_fieldtrip_spoc_test.npy\",\n# \"all_scores_mag_models_mnecommonsubjects.npy\",\n allow_pickle = T)[[1]]$'neg_mean_absolute_error-10folds' %>%\n as.data.frame()\n\ndata_ <- data[, (names(data) %in% c(\"sensor_logdiag\",\n \"sup_logdiag\",\n \"sensor_naivevec\",\n \"sensor_riemannwass\",\n \"sensor_riemanngeo\"))]\n\ndata_long <- data_ %>% gather(key = \"estimator\", value = \"score\")\n# move to long format\ndata_long$estimator <- factor(data_long$estimator)\n\n# set distance types\nest_types <- c(\n \"log-diag\",\n \"log-diag\",\n \"euclidean\",\n \"Wasserstein\",\n \"geometric\"\n)\n\n# categorical colors based on: https://jfly.uni-koeln.de/color/\n\nmy_color_cats <- with(\n color_cats,\n c(`blueish green`, `blueish green`, `sky blue`, vermillon, orange))\n\n# beef up long data\ndata_long$est_type <- rep(est_types, each = 10) %>%\n factor(., levels = c(\"log-diag\", \"euclidean\", \"Wasserstein\", \"geometric\"))\ndata_long$fold <- rep(1:10, times = length(est_types))\n\n# prepare properly sorted x labels\nsort_idx <- apply(data_, 2, mean) %>% order()\nlevels_est <- c(\n \"identity\",\n \"supervised\",\n \"identity\",\n \"identity\",\n \"identity\"\n)[rev(sort_idx)]\n\nggplot(data = data_long %>% subset(estimator != \"dummy\"),\n mapping = aes(y = score, x = reorder(estimator, I(-score)))) +\n geom_beeswarm(\n priority = 'density',\n mapping = aes(color = est_type, size = 1 - score,\n alpha = 1 - score),\n show.legend = T, cex = 0.65) +\n scale_size_continuous(range = c(0.5, 2)) +\n scale_alpha_continuous(range = c(0.4, 0.7)) +\n geom_boxplot(mapping = aes(fill = est_type, color = est_type),\n alpha = 0.4,\n outlier.fill = NA, outlier.colour = NA) +\n stat_summary(geom = 'text',\n mapping = aes(label = sprintf(\"%1.2f\",\n ..y..)),\n fun.y= mean, size = 3.2, show.legend = FALSE,\n position = position_nudge(x=-0.49)) +\n my_theme +\n labs(y = expression(MAE), x = NULL, parse = T) +\n guides(size = F, alpha = F) +\n theme(text = element_text(family = \"Helvetica\", size = 18),\n legend.position = \"top\", legend.text = element_text(size = 18)) +\n coord_flip() +\n scale_fill_manual(values = my_color_cats[2:6], name = NULL) +\n scale_color_manual(values = my_color_cats[2:6], name = NULL) +\n scale_x_discrete(labels = parse(text = levels_est))\n\n\nfname <- \"./figures_nimg_2019/fig_fieldtrip_model_comp_testMAE\"\nggsave(paste0(fname, \".png\"),\n width = 8, height = 4, dpi = 300)\nggsave(paste0(fname, \".pdf\"),\n useDingbats = F,\n width = 8, height = 4, dpi = 300)\nembedFonts(file = paste0(fname, \".pdf\"), outfile = paste0(fname, \".pdf\"))\n", "meta": {"hexsha": "9ced8e255b65302ee6e8604f6e8c22f46f8c0cb4", "size": 2932, "ext": "r", "lang": "R", "max_stars_repo_path": "debug/plot_figure_fieldtrip_results_intervals_test.r", "max_stars_repo_name": "DavidSabbagh/meeg_power_regression", "max_stars_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-18T06:10:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-18T06:10:16.000Z", "max_issues_repo_path": "debug/plot_figure_fieldtrip_results_intervals_test.r", "max_issues_repo_name": "DavidSabbagh/meeg_power_regression", "max_issues_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "debug/plot_figure_fieldtrip_results_intervals_test.r", "max_forks_repo_name": "DavidSabbagh/meeg_power_regression", "max_forks_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-01T01:36:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-01T13:44:02.000Z", "avg_line_length": 33.3181818182, "max_line_length": 76, "alphanum_fraction": 0.6016371078, "num_tokens": 821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33663142544877056}} {"text": "# Exercise 8\n> library(ISLR)\n\n# Locad College\n> ", "meta": {"hexsha": "f8ce994e640867fc19e05a46c550336fd5d5c40b", "size": 48, "ext": "r", "lang": "R", "max_stars_repo_path": "ISLR/Rsample.r", "max_stars_repo_name": "giandrea77/RExercises", "max_stars_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ISLR/Rsample.r", "max_issues_repo_name": "giandrea77/RExercises", "max_issues_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ISLR/Rsample.r", "max_forks_repo_name": "giandrea77/RExercises", "max_forks_repo_head_hexsha": "d435e303775b154d4cbbc25f990eb4b23272039d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.6, "max_line_length": 15, "alphanum_fraction": 0.6666666667, "num_tokens": 17, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3366314176512292}} {"text": "library(readr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(xtable)\n\nload_file <- function(bmName){\n df <- read_csv(paste(\"aws-scripts/results-\",bmName,\"-knarr-z3.csv\",sep = \"\"))\n df['successRate.charHint'] <- 100*df['inputsSavedBy_CharHint']/df['inputsCreatedBy_CharHint']\n df['successRate.singleStringHint'] <- 100*df['inputsSavedBy_StrHint']/df['inputsCreatedBy_StrHint']\n df['successRate.multiStringHint'] <- 100*df['inputsSavedBy_MultipleStrHint']/df['inputsCreatedBy_MultipleStrHint']\n df['successRate.extendedDictionary'] <- 100*df['countOfSavedInputsWithExtendedDictionaryHints']/df['countOfCreatedInputsWithExtendedDictionaryHints']\n df['successRate.random'] <- 100*df['inputsSavedBy_Random']/df['inputsCreatedBy_Random']\n df['successRate.z3'] <- 100*df['inputsSavedBy_Z3']/df['inputsCreatedBy_Z3']\n\n return(df)\n}\ngetLastRow <- function(dat){\n return(subset(dat,`# unix_time` == max(dat$`# unix_time`)))\n}\nbms <- c('ant', 'bcelgen', 'maven', 'closure', 'rhino')\n\n#data <- lapply(bms,load_file)\nfinal_stats <- as.data.frame(t(sapply(data,getLastRow)), row.names=bms)\n# successStats <- pivot_longer(final_stats, cols=starts_with(\"successRate\"), names_to=\"measure\", values_to = \"successRate\", names_prefix=\"successRate.\")\n", "meta": {"hexsha": "31f0f58bcf8390cc6421e14d26c02321a4bd5dbf", "size": 1232, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/graph-knarr-mutators.r", "max_stars_repo_name": "neu-se/CONFETTI-artifact", "max_stars_repo_head_hexsha": "05e32377407340604a6adcbe9f04a355a132a281", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-14T11:26:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-14T11:26:27.000Z", "max_issues_repo_path": "scripts/graph-knarr-mutators.r", "max_issues_repo_name": "neu-se/CONFETTI-artifact", "max_issues_repo_head_hexsha": "05e32377407340604a6adcbe9f04a355a132a281", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/graph-knarr-mutators.r", "max_forks_repo_name": "neu-se/CONFETTI-artifact", "max_forks_repo_head_hexsha": "05e32377407340604a6adcbe9f04a355a132a281", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.28, "max_line_length": 152, "alphanum_fraction": 0.7483766234, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33663140985368767}} {"text": "#' mdi_index\n#'\n#' Modified discomfort index MDI.\n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Relative humidity in percentage.\n#' @param wind numeric Windspeed in meters per second.\n#' @param pair numeric Air pressure in hPa.\n#' @return mdi_index \n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords mdi_index \n#' @references Moran DS, Shitzer A, Pandolf KB , 1998, A physiological strain index to evaluate heat stress. Am J Physiol 275, R 129 34.\n#' \n#'\n#'\n#'\n#' @export\n\nmdi_index<-function(t,rh,wind=0.2,pair=1010) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"pair\", as.array(pair))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=mdi_index(t[i],rh[i],wind[0],pair[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n\n", "meta": {"hexsha": "5862150ccbdb61a38d70d9036a51f6e001346a17", "size": 1102, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mdi_index.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/mdi_index.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mdi_index.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 33.3939393939, "max_line_length": 137, "alphanum_fraction": 0.5644283122, "num_tokens": 299, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.3365764931934892}} {"text": "[2, 1 -> 2]\n[3, 2 -> 3]\n[4, 2 -> 4]\n[5, 7 -> 5]\n[1, 3 -> 1]\n[6, 4 -> 6]\n[7, 5 -> 7]\n[8, 7 -> 8]\n", "meta": {"hexsha": "cadc13073ccb1ac1006169fbb3650bc05a0bcc4f", "size": 96, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Search/BestFirstFrontier/05.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Search/BestFirstFrontier/05.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Search/BestFirstFrontier/05.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.6666666667, "max_line_length": 11, "alphanum_fraction": 0.25, "num_tokens": 72, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6477982247516796, "lm_q1q2_score": 0.33654499008121164}} {"text": "context(\"irep iterator\")\n\ntest_that(\"irep matches first example from base::rep\", {\n it <- irep(1:4, 2)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 3)\n expect_equal(nextElem(it), 4)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 3)\n expect_equal(nextElem(it), 4)\n expect_error(nextElem(it), \"StopIteration\")\n})\n\ntest_that(\"irep matches second example from base::rep\", {\n it <- irep(1:4, each=2)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 3)\n expect_equal(nextElem(it), 3)\n expect_equal(nextElem(it), 4)\n expect_equal(nextElem(it), 4)\n expect_error(nextElem(it), \"StopIteration\")\n})\n\ntest_that(\"irep matches fifth example from base::rep\", {\n it <- irep(1:4, each=2, length.out=4)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 2)\n expect_error(nextElem(it), \"StopIteration\")\n})\n\ntest_that(\"irep replicates a list and matches tenth example from base::rep\", {\n # 8 integers plus two recycled 1's.\n fred <- list(happy=1:10, name=\"squash\")\n it <- irep(fred, times=5)\n expect_equal(nextElem(it), 1:10)\n expect_equal(nextElem(it), \"squash\")\n expect_equal(nextElem(it), 1:10)\n expect_equal(nextElem(it), \"squash\")\n expect_equal(nextElem(it), 1:10)\n expect_equal(nextElem(it), \"squash\")\n expect_equal(nextElem(it), 1:10)\n expect_equal(nextElem(it), \"squash\")\n expect_equal(nextElem(it), 1:10)\n expect_equal(nextElem(it), \"squash\")\n expect_error(nextElem(it), \"StopIteration\")\n})\n\ntest_that(\"irep replicates a factor and matches last example from base::rep\", {\n # 8 integers plus two recycled 1's.\n x <- factor(LETTERS[1:4])\n it <- irep(x, 2)\n expect_equal(nextElem(it), x[1])\n expect_equal(nextElem(it), x[2])\n expect_equal(nextElem(it), x[3])\n expect_equal(nextElem(it), x[4])\n expect_equal(nextElem(it), x[1])\n expect_equal(nextElem(it), x[2])\n expect_equal(nextElem(it), x[3])\n expect_equal(nextElem(it), x[4])\n expect_error(nextElem(it), \"StopIteration\")\n})\n\ntest_that(\"irep_len works on numeric vectors\", {\n it <- irep_len(1:4, length.out=3)\n expect_equal(nextElem(it), 1)\n expect_equal(nextElem(it), 2)\n expect_equal(nextElem(it), 3)\n expect_error(nextElem(it), \"StopIteration\")\n})\n\n# Related to Issue #33\ntest_that(\"irep matches base::rep() when both times and each args are given\", {\n it <- irep(1:4, times=2, each=3)\n expected_vector <- rep(1:4, times=2, each=3)\n expect_equal(unlist(as.list(it)), expected_vector)\n})\n", "meta": {"hexsha": "ab543563e74efcfe619df251464c0f14b4210f07", "size": 2649, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-irep.r", "max_stars_repo_name": "ramhiser/itertools2", "max_stars_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2015-02-02T02:54:54.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-20T12:07:34.000Z", "max_issues_repo_path": "tests/testthat/test-irep.r", "max_issues_repo_name": "ramhiser/itertools2", "max_issues_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_issues_repo_licenses": ["Apache-2.0", "MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2015-01-07T15:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2017-02-18T18:01:36.000Z", "max_forks_repo_path": "tests/testthat/test-irep.r", "max_forks_repo_name": "ramhiser/itertools2", "max_forks_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_forks_repo_licenses": ["Apache-2.0", "MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-02-02T05:04:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T02:13:12.000Z", "avg_line_length": 31.5357142857, "max_line_length": 79, "alphanum_fraction": 0.7010192525, "num_tokens": 815, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6477982179521105, "lm_q1q2_score": 0.3365449865486905}} {"text": "#V2 for Final\nsearch()\nlibrary(igraph)\nlibrary(rgexf)\n\nwriting.dir <- c(\"/home/sdal/mann2/mann/analysis/\")\nwd <- c(\"/home/sdal/mann2/mann/multidisciplinary-diffusion-model-experiments/results/simulations\")\nsetwd(wd)\n\n#ITERATION LISTS\nbatch.dir.list <- c(\"02-lens_batch_2015-11-30_16-09-28_457_750\",\n \"02-lens_batch_2015-12-01_19-09-26_495_750\",\n \"02-lens_batch_2015-12-02_08-30-37_740_750\",\n \"02-lens_batch_2015-12-02_17-25-33_776_750\",\n \"02-lens_batch_2015-12-02_22-45-10_646_750\",\n \"02-lens_batch_2015-12-04_19-59-57_776_750\",\n \"02-lens_batch_2015-12-05_15-26-02_776_750\",\n \"02-lens_batch_2015-12-06_02-25-29_756_750\",\n \"02-lens_batch_2015-12-06_13-14-04_770_750\",\n \"02-lens_batch_2015-12-07_13-38-26\",\n \"02-lens_batch_2015-12-07_23-06-53\",\n \"02-lens_batch_2015-12-08_21-42-32\")\n\nbtwn.list <- c(21:29,3,31:39,4,41:45)\n\nwn.list <- c(5,51:59,6,61:69,7,71:79,8)\n\nmaster.matrix <- matrix(rep(NA,(length(btwn.list)*length(wn.list))),nrow=length(btwn.list),ncol=length(wn.list))\n \nfor(i in 1:length(btwn.list)){ #BETWEEN\n\n for(j in 1:length(wn.list)){ #WITHIN\n\n rm(catch.across.runs)\n catch.across.runs <- rep(NA,length(batch.dir.list))\n \n for(k in 1:length(batch.dir.list)){\n setwd(wd)\n if (file.exists(paste(batch.dir.list[k],\"/a250_bm-0.\",btwn.list[i],\"_bs0.1_wm0.\",wn.list[j],\"_ws0.2_c0.25_r000/output\",sep=\"\"))) { #IF N0 1\n\n setwd(paste(batch.dir.list[k],\"/a250_bm-0.\",btwn.list[i],\"_bs0.1_wm0.\",wn.list[j],\"_ws0.2_c0.25_r000/output\",sep=\"\"))\n\n if (file.exists(\"network_of_agents.pout\")) { #IF NO 2\n d <- read.csv(\"./network_of_agents.pout\",header=F)\n\n if(tail(d$V1,n=1)==99 & tail(d$V2,n=1)==249) { #IF NO 3\n #GET MEAN, SD AND RANGE\n mean.for.d <- apply(d[,4:13],1,mean)\n sd.for.d <- apply(d[,4:13],1,sd)\n min.for.d <- apply(d[,4:13],1,min)\n max.for.d <- apply(d[,4:13],1,max)\n mean.for.d.neg <- apply(d[,4:8],1,mean)\n mean.for.d.pos <- apply(d[,9:13],1,mean)\n \n d <- cbind(d,mean.for.d,mean.for.d.neg,mean.for.d.pos,sd.for.d,min.for.d,max.for.d)\n \n d$diff.means.pos.neg <- d$mean.for.d.pos-d$mean.for.d.neg\n \n d$node.name <- NA\n d$node.name <- paste(\"A\",d$V2,sep=\"\")\n \n for(v in 99:99){ #v is tick number\n assign(paste(\"d.t.\",v,sep=\"\"), d[d$V1==v,])\n \n #LOAD NETWORK\n e.list.in <- read.table(gzfile(\"edge_list.gz\"))\n e.list.use <- e.list.in[,1:2]\n gg <- graph.data.frame(e.list.use,directed=FALSE)\n \n V(gg)$state <- get(paste(\"d.t.\",v,sep=\"\"))$diff.means.pos.neg[match(V(gg)$name,get(paste(\"d.t.\",v,sep=\"\"))$node.name)]\n \n #DATA WRITING HERE #WITNIN RUNS\n write(c(date(),\"\",c(i,\"\",j,\"\",k),\"ERR: NONE\"),file=paste(writing.dir,\"AnalysisTracker.txt\",sep=\"\"),ncolumns=8,append=TRUE)\n catch.across.runs[k] <- assortativity(gg,V(gg)$state,directed=FALSE)\n } #v\n \n } #IF NO 3\n else {\n write(c(date(),\"\",c(i,\"\",j,\"\",k),\"ERR: NO 3\"),file=paste(writing.dir,\"AnalysisTracker.txt\",sep=\"\"),ncolumns=8,append=TRUE) \n } #ELSE NO 3\n } #IF NO 2\n \n else {\n write(c(date(),\"\",c(i,\"\",j,\"\",k),\"ERR: NO 2\"),file=paste(writing.dir,\"AnalysisTracker.txt\",sep=\"\"),ncolumns=8,append=TRUE)\n } #ELSE NO 2\n \n } #IF NO 1\n \n else {\n write(c(date(),\"\",c(i,\"\",j,\"\",k),\"ERR: NO 1\"),file=paste(writing.dir,\"AnalysisTracker.txt\",sep=\"\"),ncolumns=8,append=TRUE)\n } #ELSE NO 1\n\n } #k\n\n master.matrix[i,j] <- mean(catch.across.runs,na.rm=TRUE)\n \n } #j\n} #i\n\n\nsetwd(writing.dir)\nsave.image()\n\n#EOF\n", "meta": {"hexsha": "17f46dcb23aebb0cebe08e9773c7cd95f2d0c4dc", "size": 4545, "ext": "r", "lang": "R", "max_stars_repo_path": "ExtractingAssortInParamSpace_v2.r", "max_stars_repo_name": "chendaniely/mann_assortativity_analysis", "max_stars_repo_head_hexsha": "c41529b1bbd7de585772a4da25381269ae443670", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ExtractingAssortInParamSpace_v2.r", "max_issues_repo_name": "chendaniely/mann_assortativity_analysis", "max_issues_repo_head_hexsha": "c41529b1bbd7de585772a4da25381269ae443670", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ExtractingAssortInParamSpace_v2.r", "max_forks_repo_name": "chendaniely/mann_assortativity_analysis", "max_forks_repo_head_hexsha": "c41529b1bbd7de585772a4da25381269ae443670", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.2857142857, "max_line_length": 151, "alphanum_fraction": 0.4723872387, "num_tokens": 1334, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3365449865486904}} {"text": "source(\"http://www.bioconductor.org/biocLite.R\")\nlibrary(\"affy\")\nlibrary(\"simpleaffy\")\nlibrary(\"scales\")\nlibrary(\"R.utils\")\n\nsetwd(\"~/work/abe516/project1\")\ncwd = getwd()\n\ndat = ReadAffy(celfile.path = \"data\")\nfeatureNames(dat)[1:10]\nnum_samples = length(dat)\nprintf(\"There are %s features in %s samples\\n\", len(featureNames(dat)), num_samples)\n\nhead(pm(dat, \"172682_x_at\"))\nhead(mm(dat, \"172682_x_at\"))\nhist(dat)\n\nsnames = c('c1', 'c2', 'bs1', 'bs2', 'bs3', 'efs1', 'efs2', 'efs3', 'efm1', 'efm2', 'efm3', 'c3', 'c4')\ngroups = paste0(\"G\", unlist(strsplit(\"3311100022233\", '')))\nngroups = len(unique(groups))\n\ngroups = c(\"C\", \"Bs\", \"Efs\", \"Efm\")\nfl <- factor(c(rep('0', 2), rep('1', 3), rep('2', 3), rep('3', 3), rep('0', 2)), \n labels=groups)\npalette(brewer_pal(type = \"seq\", palette = \"Set2\")(len(groups)))\nboxplot(dat, names = fl, main = \"Raw\", las = 2, col = fl)\nplot(probeset(dat, geneNames(dat)[1]) [[1]])\n\nRNAdeg = AffyRNAdeg(dat)\nplotAffyRNAdeg(RNAdeg)\n\npng(filename = file.path(cwd, \"ma-raw.png\"), width = 600, height = 800)\npar(mfrow = c(5,3))\nMAplot(dat, plot.method = \"smoothScatter\")\ndev.off()\n\ndat.rmabg = bg.correct(dat, \"rma\")\npng(filename = file.path(cwd, \"ma-bg-corrected.png\"), width = 600, height = 800)\npar(mfrow = c(5,3))\nMAplot(dat.rmabg, plot.method = \"smoothScatter\")\ndev.off()\n\ndat.norm = normalize(dat.rmabg, \"quantiles\")\npng(filename = file.path(cwd, \"ma-normalized.png\"), width = 600, height = 800)\npar(mfrow = c(5,3))\nMAplot(dat.norm, plot.method = \"smoothScatter\")\ndev.off()\n\nboxplot(dat.norm, \n names = snames, \n main = \"Normalized\", \n las = 2,\n col = as.factor(groups))\n\n# Nothing from here on works\n# what can I do with this?\nexpr.dat <- expresso(dat, \n bgcorrect.method = \"rma\", \n normalize.method = \"constant\", \n pmcorrect.method = \"pmonly\", \n summary.method = \"avgdiff\")\n\n# does not compute\nhist(expr.dat)\nMAplot(expr.dat, plot.method = \"smoothScatter\")\n#top250 = read.table(\"top250.txt\", header = T)\n", "meta": {"hexsha": "3870c1d576f705c3073c389b275a673ca16631ca", "size": 2060, "ext": "r", "lang": "R", "max_stars_repo_path": "project1/elegans.r", "max_stars_repo_name": "kyclark/abe516", "max_stars_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "project1/elegans.r", "max_issues_repo_name": "kyclark/abe516", "max_issues_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "project1/elegans.r", "max_forks_repo_name": "kyclark/abe516", "max_forks_repo_head_hexsha": "755d9c49fc2f66159c57e5eb623908ae1640c0b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2941176471, "max_line_length": 103, "alphanum_fraction": 0.617961165, "num_tokens": 656, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.33654497948364787}} {"text": "#' sun_data\n#'\n#' Calculate sun parameters for specific location and time.\n#'\n#' @param datetime character Datetime as in YYYY-MM-DDTHH:MM:SS considering local time\n#' @param lat numeric Latitude in decimal degrees\n#' @param lon numeric Longitude in decimal degrees\n#' @param parameter character Six solar parameter are available by name \"azimuth\",\"zenith\",\"solarZenith\",\"elevation\",\"declination\"or \"JD\"\n#' @param tz character time zone.\n#' @return value of parameter indicated\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords sun parameters \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nsun_data=function(datetime,lat,lon,parameter=\"azimuth\",tz=\"GMT\") {\n ct$assign(\"datetime\", as.array(datetime))\n ct$assign(\"lat\", as.array(lat))\n ct$assign(\"lon\", as.array(lon))\n ct$assign(\"parameter\", as.array(parameter))\n ct$eval(\"var res=[]; for(var i=0, len=lat.length; i < len; i++){ res[i]=sun_data(datetime[0],lat[i],lon[i],parameter[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "51766235e15d6a5726153e409dc913d2d14e9c50", "size": 1263, "ext": "r", "lang": "R", "max_stars_repo_path": "R/sun_data.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/sun_data.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/sun_data.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 39.46875, "max_line_length": 149, "alphanum_fraction": 0.5890736342, "num_tokens": 309, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3364734692568112}} {"text": "r-pkgconfig\nr-knitr\nr-plyr\nr-stringr\nr-rgl\nr-data.table\nr-ggplot2\nr-lme4\nr-reshape\nr-rcolorbrewer\nr-gplots\nr-png\nr-lattice\nr-tm\nr-igraph\nr-rjsonio\nr-curl\nr-devtools\n", "meta": {"hexsha": "2abbd89a6e7f8ed0042b5540d6cf3e55d66dab95", "size": 165, "ext": "r", "lang": "R", "max_stars_repo_path": "narrbase-image/conda-requirements/r.r", "max_stars_repo_name": "pranjan77/narrative", "max_stars_repo_head_hexsha": "5714d199c7ca3d65cbfc1110b3d0641e250e62f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2015-01-09T08:14:23.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-03T14:55:28.000Z", "max_issues_repo_path": "narrbase-image/conda-requirements/r.r", "max_issues_repo_name": "pranjan77/narrative", "max_issues_repo_head_hexsha": "5714d199c7ca3d65cbfc1110b3d0641e250e62f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2006, "max_issues_repo_issues_event_min_datetime": "2015-01-04T01:18:31.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T21:08:22.000Z", "max_forks_repo_path": "narrbase-image/conda-requirements/r.r", "max_forks_repo_name": "pranjan77/narrative", "max_forks_repo_head_hexsha": "5714d199c7ca3d65cbfc1110b3d0641e250e62f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 78, "max_forks_repo_forks_event_min_datetime": "2015-01-06T19:34:53.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-04T20:37:14.000Z", "avg_line_length": 8.6842105263, "max_line_length": 14, "alphanum_fraction": 0.7757575758, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6334102567576901, "lm_q1q2_score": 0.33647346558076263}} {"text": "stanfit_one <- function(gdat, dz, nnfits, which.spax,\n prep_data = prep_data_mod,\n init_opt = init_opt_mod,\n init_sampler = init_sampler_mod,\n stan_model=NULL,\n stan_file=\"spm_dust_mod_simpl.stan\", stan_filedir=\"~/spmcode/\",\n iter_opt=5000, \n jv=1.e-4,\n iter=1000, warmup=250, thin=1, chains=4, \n OP=FALSE, ...) {\n \n require(rstan)\n if (is.null(stan_model)) {\n stan_model <- rstan::stan_model(file=file.path(stan_filedir, stan_file))\n }\n \n spm_data <- prep_data(gdat, dz, nnfits, which.spax)\n inits <- init_opt(spm_data, nnfits, which.spax, jv)\n spm_opt <- optimizing(stan_model, data=spm_data, init=inits, as_vector=FALSE, verbose=TRUE, iter=iter_opt)\n \n init_pars <- lapply(X=1:chains, init_sampler, stan_opt=spm_opt$par, jv=jv)\n \n stanfit <- sampling(stan_model, data=spm_data,\n chains=chains, iter=iter, warmup=warmup, thin=thin,\n cores=min(chains, getOption(\"mc.cores\")),\n init=init_pars, open_progress=OP, ...)\n \n list(spm_data=spm_data, stanfit=stanfit, \n norm_g=spm_data$norm_g, norm_st=spm_data$norm_st, norm_em=spm_data$norm_em, in_em=spm_data$in_em)\n}\n\n\n \nstanfit_batch <- function(gdat, dz, nnfits,\n init_tracked = init_tracked_mod,\n update_tracked = update_tracked_mod,\n return_tracked = return_tracked_mod,\n prep_data = prep_data_mod,\n init_opt = init_opt_mod,\n init_sampler = init_sampler_mod,\n stan_file=\"spm_dust_mod_simpl.stan\", stan_filedir=\"~/spmcode/\",\n iter_opt=5000, \n jv=1.e-4,\n iter=1000, warmup=250, chains=4,\n OP=FALSE,\n start=NULL, end=NULL, fpart=\"bfits.rda\", ...) {\n dims <- dim(gdat$flux)\n dz <- dz$dz\n nsim <- (iter-warmup)*chains\n nt <- length(ages)\n nr <- dims[1]\n n_st <- ncol(lib.ssp)-1\n n_em <- length(emlines)\n nl <- length(gdat$lambda)\n smodel <- rstan::stan_model(file.path(stan_filedir, stan_file))\n if (is.null(start) || !file.exists(fpart)) {\n init_tracked(nsim, n_st, n_em, nr)\n start <- 1\n } else {\n load(fpart)\n }\n if (is.null(end)) {\n end <- nr\n }\n for (i in start:end) {\n if (is.na(dz[i]) || is.na(nnfits$Mstar[i])) next\n sfit <- stanfit_one(gdat, dz, nnfits, which.spax=i,\n prep_data,\n init_opt,\n init_sampler,\n stan_model=smodel,\n iter_opt=iter_opt, jv=jv,\n iter = iter, warmup = warmup, chains = chains, \n OP=OP, ...)\n plot(plotpp(sfit)+ggtitle(paste(\"fiber =\", i)))\n update_tracked(i, sfit, fpart)\n rm(sfit)\n }\n return_tracked()\n}\n\n## star formation history, mass growth history, etc.\n\nget_sfh <- function(..., z, fibersinbin=1, density=TRUE, tsf=0.1) {\n ins <- list(...)\n if (is.list(ins[[1]])) {\n ins <- ins[[1]]\n post <- rstan::extract(ins$stanfit)\n b_st <- post$b_st\n norm_st <- ins$norm_st\n if (exists(\"norm_g\", ins)) {\n b_st <- b_st*ins$norm_g\n norm_st <- 1/norm_st\n }\n } else {\n b_st <- ins$b_st\n norm_st <- ins$norm_st\n }\n nsim <- nrow(b_st)\n nt <- length(ages)\n nz <- ncol(b_st)/nt\n T.gyr <- 10^(ages-9)\n isf <- which.min(abs(tsf-T.gyr))\n if (density) {\n binarea <- log10(pi*fibersinbin*cosmo::ascale(z)^2)\n } else {\n binarea <- 0\n }\n b_st <- t(t(b_st)*norm_st)*cosmo::lum.sol(1, z)\n rmass <- t(t(b_st) * mstar)\n sfh_post <- matrix(0, nsim, nt)\n mgh_post <- matrix(0, nsim, nt)\n for (i in 1:nz) {\n sfh_post <- sfh_post + b_st[,((i-1)*nt+1):(i*nt)]\n mgh_post <- mgh_post + rmass[,((i-1)*nt+1):(i*nt)]\n }\n totalmg_post <- cbind(rowSums(mgh_post), rowSums(mgh_post) - t(apply(mgh_post, 1, cumsum)))\n mgh_post <- cbind(rep(1, nsim), 1 - (t(apply(mgh_post, 1, cumsum))/rowSums(mgh_post)))\n sfr <- log10(rowSums(sfh_post[,1:isf])/T.gyr[isf])-9.\n relsfr <- sfr - log10(rowSums(sfh_post)/(cosmo::dcos(Inf)$dT-cosmo::dcos(z)$dT)) + 6\n sigma_sfr <- sfr - binarea\n sigma_mstar <- log10(b_st %*% mstar) - binarea\n ssfr <- sigma_sfr - sigma_mstar\n list(sfh_post=sfh_post, mgh_post=mgh_post, totalmg_post=totalmg_post,\n sigma_mstar=sigma_mstar, sigma_sfr=sigma_sfr, ssfr=ssfr, relsfr=relsfr)\n}\n\nbatch_sfh <- function(gdat, sfits, tsf=0.1) {\n b_st <- sfits$b_st\n norm_st <- sfits$norm_st\n dims <- dim(b_st)\n nsim <- dims[1]\n nt <- length(ages)\n nz <- dims[2]/nt\n nf <- dims[3]\n if (exists(\"norm_g\", sfits)) {\n norm_g <- sfits$norm_g\n norm_st <- 1/norm_st\n } else {\n norm_g <- rep(1, nf)\n }\n z <- gdat$meta$z\n if (!exists(\"fibersinbin\", gdat)) {\n fibersinbin <- rep(1, nf)\n } else {\n fibersinbin <- gdat$fibersinbin\n }\n \n sfh_post <- array(NA, dim=c(nsim, nt, nf))\n mgh_post <- array(0, dim=c(nsim, nt+1, nf))\n totalmg_post <- matrix(0, nsim, nt+1)\n sigma_mstar <- matrix(NA, nsim, nf)\n sigma_sfr <- matrix(NA, nsim, nf)\n ssfr <- matrix(NA, nsim, nf)\n relsfr <- matrix(NA, nsim, nf)\n \n for (i in 1:nf) {\n if (is.na(b_st[1, 1, i])) next\n sfi <- get_sfh(b_st=b_st[,,i]*norm_g[i], norm_st=norm_st[,i], z=z, fibersinbin=fibersinbin[i])\n sfh_post[,,i] <- sfi$sfh_post\n mgh_post[,,i] <- sfi$mgh_post\n totalmg_post <- totalmg_post + sfi$totalmg_post\n sigma_mstar[, i] <- sfi$sigma_mstar\n sigma_sfr[, i] <- sfi$sigma_sfr\n ssfr[, i] <- sfi$ssfr\n relsfr[,i] <- sfi$relsfr\n }\n list(sfh_post=sfh_post, mgh_post=mgh_post, totalmg_post=totalmg_post,\n sigma_mstar=sigma_mstar, sigma_sfr=sigma_sfr, ssfr=ssfr, relsfr=relsfr)\n}\n \n \n## some sorta useful summary measures\n\nget_proxies <- function(...) {\n ins <- list(...)\n if (is.list(ins[[1]])) {\n ins <- ins[[1]]\n post <- rstan::extract(ins$stanfit)\n b_st <- post$b_st\n norm_st <- ins$norm_st\n if (exists(\"a\", post)) {\n b_st <- b_st*ins$norm_g\n norm_st <- 1/norm_st\n }\n } else {\n b_st <- ins$b_st\n norm_st <- ins$norm_st\n }\n b_st <- t(t(b_st)*norm_st)\n nz <- length(Z)\n nt <- length(ages)\n T.gyr <- 10^(ages-9)\n tbar <- log10((b_st %*% rep(T.gyr, nz))/rowSums(b_st)) + 9\n tbar_lum <- log10((b_st %*% (rep(T.gyr, nz) * gri.ssp[\"r\",]))/\n ((b_st %*% gri.ssp[\"r\",]))) + 9\n g_i <- 2.5*log10(b_st %*% gri.ssp[\"i\",]) - 2.5*log10(b_st %*% gri.ssp[\"g\",])\n data.frame(tbar=tbar, tbar_lum=tbar_lum, g_i=g_i)\n}\n\n## emission line fluxes, luminosity density, equivalent width\n\nget_em <- function(..., z, fibersinbin=1, ew_width=15) {\n ins <- list(...)\n if (is.list(ins[[1]])) {\n ins <- ins[[1]]\n post <- rstan::extract(ins$stanfit)\n b_st <- post$b_st\n b_em <- post$b_em\n norm_st <- ins$norm_st\n norm_em <- ins$norm_em\n if (exists(\"norm_g\", ins)) {\n b_st <- b_st*ins$norm_g\n b_em <- b_em*ins$norm_g\n norm_st <- 1/norm_st\n }\n } else {\n b_st <- ins$b_st\n b_em <- ins$b_em\n norm_st <- ins$norm_st\n norm_em <- ins$norm_em\n }\n emlines <- emlines[ins$in.em]\n b_st <- t(t(b_st)*norm_st)\n ne <- ncol(b_em)\n nsim <- nrow(b_em)\n binarea <- log10(pi*fibersinbin*cosmo::ascale(z)^2)\n em.mult <- emlines*log(10)/10000\n\n flux_em <- matrix(NA, nsim, ne)\n sigma_logl_em <- matrix(NA, nsim, ne)\n ew_em <- matrix(NA, nsim, ne)\n \n flux_em <- t(t(b_em)*em.mult)*norm_em\n sigma_logl_em <- cosmo::loglum.ergs(flux_em, z) - binarea\n \n mu_st <- tcrossprod(b_st, as.matrix(lib.ssp[, -1]))\n il_em <- findInterval(emlines, lib.ssp$lambda)\n for (i in 1:ne) {\n intvl <- (il_em[i]-ew_width):(il_em[i]+ew_width)\n fc <- rowMeans(mu_st[, intvl])\n ew_em[, i] <- flux_em[, i]/fc\n }\n colnames(flux_em) <- names(emlines)\n colnames(sigma_logl_em) <- names(emlines)\n colnames(ew_em) <- names(emlines)\n list(flux_em=flux_em, sigma_logl_em=sigma_logl_em, ew_em=ew_em)\n}\n\nbatch_em <- function(gdat, sfits, ew_width=15) {\n nsim <- dim(sfits$b_em)[1]\n ne <- length(emlines)\n nf <- length(gdat$xpos)\n norm_st <- sfits$norm_st\n if (exists(\"norm_g\", sfits)) {\n norm_g <- sfits$norm_g\n norm_st <- 1/sfits$norm_st\n } else {\n norm_g <- rep(1, nf)\n }\n \n flux_em <- array(NA, dim=c(nsim, ne, nf))\n sigma_logl_em <- array(NA, dim=c(nsim, ne, nf))\n ew_em <- array(NA, dim=c(nsim, ne, nf))\n \n for (i in 1:nf) {\n if (is.na(sfits$b_st[1, 1, i])) next\n in.em <- sfits$in_em[!is.na(sfits$in_em[,i]), i]\n if(is.null(dim(norm_st))) {\n nst <- norm_st[i]\n } else {\n nst <- norm_st[,i]\n }\n emi <- get_em(b_em=sfits$b_em[,in.em,i]*norm_g[i], b_st=sfits$b_st[,,i]*norm_g[i], \n norm_em=sfits$norm_em[i], norm_st=nst, \n in.em=in.em, z=gdat$meta$z,\n fibersinbin=gdat$fibersinbin[i], ew_width=ew_width)\n flux_em[,in.em,i] <- emi$flux_em\n sigma_logl_em[,in.em,i] <- emi$sigma_logl_em\n ew_em[,in.em,i] <- emi$ew_em\n }\n dimnames(flux_em)[[2]] <- names(emlines)\n dimnames(sigma_logl_em)[[2]] <- names(emlines)\n dimnames(ew_em)[[2]] <- names(emlines)\n list(flux_em=flux_em, sigma_logl_em=sigma_logl_em, ew_em=ew_em)\n}\n\n## bpt class from [N II]/Halpha\n\nbatch_bptclass <- function(flux_em, snthresh=3) {\n nb <- dim(flux_em)[3]\n bpt <- as.factor(rep(\"NO EM\", nb))\n levels(bpt) <- c(\"NO EM\", \"EL\", \"SF\", \"COMP\", \"LINER\", \"AGN\")\n f_m <- apply(flux_em, c(2, 3), mean)\n f_sd <- apply(flux_em, c(2, 3), sd)\n for (i in 1:nb) {\n if (all(is.na(f_m[, i]))) {\n bpt[i] <- NA\n next\n }\n if(any(f_m[, i]/f_sd[, i] > snthresh, na.rm=TRUE)) bpt[i] <- \"EL\"\n if (any(is.na(f_m[c(\"h_beta\", \"oiii_5007\", \"h_alpha\", \"nii_6584\"), i]))) next\n if (f_m[\"h_beta\", i]/f_sd[\"h_beta\", i] > snthresh &&\n f_m[\"oiii_5007\", i]/f_sd[\"oiii_5007\", i] > snthresh &&\n f_m[\"h_alpha\", i]/f_sd[\"h_alpha\", i] > snthresh &&\n f_m[\"nii_6584\", i]/f_sd[\"nii_6584\", i] > snthresh) {\n o3hbeta <- log10(f_m[\"oiii_5007\", i]/f_m[\"h_beta\", i])\n n2halpha <- log10(f_m[\"nii_6584\", i]/f_m[\"h_alpha\", i])\n if ((o3hbeta <= 0.61/(n2halpha-0.05)+1.3) &&\n (n2halpha <= 0.05)) {\n bpt[i] <- \"SF\"\n next\n }\n if ((o3hbeta > 0.61/(n2halpha-0.05)+1.3 || n2halpha > 0.05) &&\n (o3hbeta <= 0.61/(n2halpha-0.47)+1.19)) {\n bpt[i] <- \"COMP\"\n next\n }\n if ((o3hbeta > 0.61/(n2halpha-0.47)+1.19 || n2halpha > 0.47) &&\n (o3hbeta > 1.05*n2halpha+0.45)) {\n bpt[i] <- \"AGN\"\n } else {\n bpt[i] <- \"LINER\"\n }\n }\n }\n bpt\n}\n\n\n## emission line ratios and various \"strong line\" metallicity calibrations\n \nget_lineratios <- function(flux_em, tauv, delta=0, tauv_mult=1, alaw=calzetti_mod) {\n o3hbeta <- log10(flux_em[,\"oiii_5007\"]/flux_em[,\"h_beta\"])\n o1halpha <- log10(flux_em[,\"oi_6300\"]/flux_em[,\"h_alpha\"])\n n2halpha <- log10(flux_em[,\"nii_6584\"]/flux_em[,\"h_alpha\"])\n s2halpha <- log10((flux_em[,\"sii_6717\"]+flux_em[,\"sii_6730\"])/flux_em[,\"h_alpha\"])\n \n \n o2 <- (flux_em[,\"oii_3727\"]+flux_em[,\"oii_3729\"])*alaw(3728., -tauv*tauv_mult, delta)\n o3 <- (flux_em[,\"oiii_4959\"]+flux_em[,\"oiii_5007\"])*alaw(4980., -tauv*tauv_mult, delta)\n hb <- flux_em[,\"h_beta\"]*alaw(4863., -tauv*tauv_mult, delta)\n \n r23 <- log10((o2+o3)/hb)\n o3n2 <- o3hbeta-n2halpha\n\n ## log(O/H) estimates from Pettini & Pagel 2004, Tremonti et al. 2004, or Dopita et al. 2016\n \n oh_n2 <- 9.37+2.03*n2halpha+1.26*n2halpha^2+0.32*n2halpha^3\n oh_o3n2 <- 8.73-0.32*o3n2\n oh_r23 <- 9.185-0.313*r23-0.264*r23^2-0.321*r23^3\n oh_n2s2ha <- 8.77+n2halpha-s2halpha+0.264*n2halpha\n \n data.frame(o3hbeta=o3hbeta, o1halpha=o1halpha, n2halpha=n2halpha, s2halpha=s2halpha,\n r23=r23, o3n2=o3n2, \n oh_n2=oh_n2, oh_o3n2=oh_o3n2, oh_r23=oh_r23, oh_n2s2ha=oh_n2s2ha)\n}\n\n \nsum_batchfits <- function(gdat, nnfits, sfits, drpcat, alaw=calzetti_mod, intr_bd=2.86, clim=0.95) {\n \n tauv.bd <- function(flux_em, intr_bd, delta=0, alaw) {\n bd <- flux_em[,'h_alpha']/flux_em[,'h_beta']\n bd[!is.finite(bd)] <- NA\n tauv <- log(bd/intr_bd)/(log(alaw(6562.8,1, delta))-log(alaw(4861.3,1, delta)))\n tauv[tauv<0] <- 0\n tauv\n }\n \n logl.ha.cor <- function(logl.halpha, tauv, delta=0, alaw) {\n att <- alaw(lambda=6562.8, tauv, delta)\n logl.halpha - log10(att)\n }\n \n nf <- length(gdat$xpos)\n fibersinbin <- rep(1, nf)\n if (exists(\"bin.fiber\", gdat)) {\n if (!exists(\"fibersinbin\", gdat)) {\n bin.fiber <- gdat$bin.fiber\n bin.no <- unique(bin.fiber[!is.na(bin.fiber)])\n for (i in seq_along(bin.no)) {\n fibersinbin[i] <- length(which(bin.fiber == bin.no[i]))\n }\n } else {\n fibersinbin <- gdat$fibersinbin\n }\n }\n \n nsim <- nrow(sfits$b_st)\n nt <- length(ages)\n fiberarea <- pi*cosmo::ascale(gdat$meta$z)^2\n plateifu <- rep(gdat$meta$plateifu, nf)\n \n ## projected distance in kpc and relative to effective radius\n \n d_kpc <- cosmo::ascale(gdat$meta$z)*sqrt(gdat$xpos^2+gdat$ypos^2)\n d_re <- sqrt(gdat$xpos^2+gdat$ypos^2)/\n drpcat$nsa_petro_th50[match(gdat$meta$plateifu,drpcat$plateifu)]\n \n \n ## stuff taken from nnfits\n \n d4000_n <- nnfits$d4000_n\n d4000_n_err <- nnfits$d4000_n_err\n lick_hd_a <- nnfits$lick[,'HdeltaA']\n lick_hd_a_err <- nnfits$lick.err[,'HdeltaA_err']\n mgfe <- sqrt(nnfits$lick[,'Mg_b']*(0.72*nnfits$lick[,'Fe5270']+0.28*nnfits$lick[,'Fe5335']))\n mgfe[is.nan(mgfe)] <- NA\n \n ## tauv from batch fits\n \n tauv_m <- colMeans(sfits$tauv)\n tauv_std <- apply(sfits$tauv, 2, sd)\n \n if (exists(\"delta\", sfits)) {\n delta <- sfits$delta\n } else {\n delta <- matrix(0, nsim, nf)\n }\n delta_m <- colMeans(delta)\n delta_std <- apply(delta, 2, sd)\n \n if (exists(\"ll\", sfits)) {\n ll_m <- colMeans(sfits$ll)\n } else {\n ll_m <- rep(NA, nf)\n }\n \n mgh_post <- array(NA, dim=c(nsim, nt+1, nf))\n sfh_post <- array(NA, dim=c(nsim, nt, nf))\n totalmg_post <- matrix(0, nsim, nt+1)\n \n varnames <- c(\"sigma_mstar\", \"sigma_sfr\", \"ssfr\", \"relsfr\",\n \"tbar\", \"tbar_lum\", \"g_i\",\n \"tauv_bd\", \"sigma_logl_ha\", \"sigma_logl_ha_ctauv\", \"sigma_logl_ha_ctauv_bd\",\n \"eqw_ha\", \"o3hbeta\", \"o1halpha\", \"n2halpha\", \"s2halpha\",\n \"r23\", \"o3n2\", \"oh_n2\", \"oh_o3n2\", \"oh_r23\", \"oh_n2s2ha\")\n suffixes <- c(\"m\", \"std\", \"lo\", \"hi\")\n \n for (i in seq_along(varnames)) {\n for (j in seq_along(suffixes)) {\n assign(paste(varnames[i], suffixes[j], sep=\"_\"), numeric(nf))\n }\n }\n \n sfh_all <- batch_sfh(gdat, sfits)\n \n for (i in 1:4) {\n assign(paste(varnames[i], suffixes[1], sep=\"_\"), colMeans(sfh_all[[varnames[i]]]))\n assign(paste(varnames[i], suffixes[2], sep=\"_\"), apply(sfh_all[[varnames[i]]], 2, sd))\n }\n \n \n em_all <- batch_em(gdat, sfits)\n bpt <- batch_bptclass(em_all$flux_em)\n \n \n for (i in 1:nf) {\n \n ## star formation history, etc.\n \n quants <- hdiofmcmc(sfh_all$sigma_mstar[,i])\n sigma_mstar_lo[i] <- quants[1]\n sigma_mstar_hi[i] <- quants[2]\n \n quants <- hdiofmcmc(sfh_all$sigma_sfr[,i])\n sigma_sfr_lo[i] <- quants[1]\n sigma_sfr_hi[i] <- quants[2]\n \n quants <- hdiofmcmc(sfh_all$ssfr[,i])\n ssfr_lo[i] <- quants[1]\n ssfr_hi[i] <- quants[2]\n \n quants <- hdiofmcmc(sfh_all$relsfr[,i])\n relsfr_lo[i] <- quants[1]\n relsfr_hi[i] <- quants[2]\n \n if (is.null(dim(sfits$norm_st))) {\n norm_st <- sfits$norm_st[i]\n } else {\n norm_st <- 1/sfits$norm_st[,i]\n }\n proxi <- get_proxies(b_st=sfits$b_st[,,i], norm_st=norm_st)\n \n tbar_m[i] <- mean(proxi$tbar)\n tbar_std[i] <- sd(proxi$tbar)\n quants <- hdiofmcmc(proxi$tbar, credmass=clim)\n tbar_lo[i] <- quants[1]\n tbar_hi[i] <- quants[2]\n \n tbar_lum_m[i] <- mean(proxi$tbar_lum)\n tbar_lum_std[i] <- sd(proxi$tbar_lum)\n quants <- hdiofmcmc(proxi$tbar_lum, credmass=clim)\n tbar_lum_lo[i] <- quants[1]\n tbar_lum_hi[i] <- quants[2]\n \n g_i_m[i] <- mean(proxi$g_i)\n g_i_std[i] <- sd(proxi$g_i)\n quants <- hdiofmcmc(proxi$g_i, credmass=clim)\n g_i_lo[i] <- quants[1]\n g_i_hi[i] <- quants[2]\n \n linesi <- get_lineratios(em_all$flux_em[,,i], sfits$tauv[,i], delta[,i], alaw=alaw)\n \n tauv_bd <- tauv.bd(em_all$flux_em[,,i], intr_bd=intr_bd, delta[,i], alaw=alaw)\n \n tauv_bd_m[i] <- mean(tauv_bd)\n tauv_bd_std[i] <- sd(tauv_bd)\n quants <- hdiofmcmc(tauv_bd, credmass=clim)\n tauv_bd_lo[i] <- quants[1]\n tauv_bd_hi[i] <- quants[2]\n \n ## uncorrected Halpha luminosity from stan fits\n \n sigma_logl_ha_m[i] <- mean(em_all$sigma_logl_em[,\"h_alpha\", i])\n sigma_logl_ha_std[i] <- sd(em_all$sigma_logl_em[,\"h_alpha\", i])\n quants <- hdiofmcmc(em_all$sigma_logl_em[,\"h_alpha\", i], credmass=clim)\n sigma_logl_ha_lo[i] <- quants[1]\n sigma_logl_ha_hi[i] <- quants[2]\n \n ## correct from stan fit estimate of tauv\n \n logl_ha_c <- logl.ha.cor(em_all$sigma_logl_em[, \"h_alpha\", i], sfits$tauv[,i], delta[,i], alaw=alaw)\n \n sigma_logl_ha_ctauv_m[i] <- mean(logl_ha_c)\n sigma_logl_ha_ctauv_std[i] <- sd(logl_ha_c)\n quants <- hdiofmcmc(logl_ha_c, credmass=clim)\n sigma_logl_ha_ctauv_lo[i] <- quants[1]\n sigma_logl_ha_ctauv_hi[i] <- quants[2]\n \n \n ## correct from balmer decrement\n \n logl_ha_c <- logl.ha.cor(em_all$sigma_logl_em[, \"h_alpha\", i], tauv_bd, delta[,i], alaw=alaw)\n \n sigma_logl_ha_ctauv_bd_m[i] <- mean(logl_ha_c)\n sigma_logl_ha_ctauv_bd_std[i] <- sd(logl_ha_c)\n quants <- hdiofmcmc(logl_ha_c, credmass=clim)\n sigma_logl_ha_ctauv_bd_lo[i] <- quants[1]\n sigma_logl_ha_ctauv_bd_hi[i] <- quants[2]\n \n eqw_ha_m[i] <- mean(em_all$ew_em[,\"h_alpha\", i])\n eqw_ha_std[i] <- sd(em_all$ew_em[,\"h_alpha\", i])\n quants <- hdiofmcmc(em_all$ew_em[,\"h_alpha\", i], credmass=clim)\n eqw_ha_lo[i] <- quants[1]\n eqw_ha_hi[i] <- quants[2]\n \n ## some emission line ratios\n \n o3hbeta_m[i] <- mean(linesi$o3hbeta)\n o3hbeta_std[i] <- sd(linesi$o3hbeta)\n quants <- hdiofmcmc(linesi$o3hbeta, credmass=clim)\n o3hbeta_lo[i] <- quants[1]\n o3hbeta_hi[i] <- quants[2]\n \n o1halpha_m[i] <- mean(linesi$o1halpha)\n o1halpha_std[i] <- sd(linesi$o1halpha)\n quants <- hdiofmcmc(linesi$o1halpha, credmass=clim)\n o1halpha_lo[i] <- quants[1]\n o1halpha_hi[i] <- quants[2]\n \n n2halpha_m[i] <- mean(linesi$n2halpha)\n n2halpha_std[i] <- sd(linesi$n2halpha)\n quants <- hdiofmcmc(linesi$n2halpha, credmass=clim)\n n2halpha_lo[i] <- quants[1]\n n2halpha_hi[i] <- quants[2]\n \n s2halpha_m[i] <- mean(linesi$s2halpha)\n s2halpha_std[i] <- sd(linesi$s2halpha)\n quants <- hdiofmcmc(linesi$s2halpha, credmass=clim)\n s2halpha_lo[i] <- quants[1]\n s2halpha_hi[i] <- quants[2]\n \n r23_m[i] <- mean(linesi$r23)\n r23_std[i] <- sd(linesi$r23)\n quants <- hdiofmcmc(linesi$r23, credmass=clim)\n r23_lo[i] <- quants[1]\n r23_hi[i] <- quants[2]\n \n o3n2_m[i] <- mean(linesi$o3n2)\n o3n2_std[i] <- sd(linesi$o3n2)\n quants <- hdiofmcmc(linesi$o3n2, credmass=clim)\n o3n2_lo[i] <- quants[1]\n o3n2_hi[i] <- quants[2]\n\n oh_n2_m[i] <- mean(linesi$oh_n2)\n oh_n2_std[i] <- sd(linesi$oh_n2)\n quants <- hdiofmcmc(linesi$oh_n2, credmass=clim)\n oh_n2_lo[i] <- quants[1]\n oh_n2_hi[i] <- quants[2]\n \n oh_o3n2_m[i] <- mean(linesi$oh_o3n2)\n oh_o3n2_std[i] <- sd(linesi$oh_o3n2)\n quants <- hdiofmcmc(linesi$oh_o3n2, credmass=clim)\n oh_o3n2_lo[i] <- quants[1]\n oh_o3n2_hi[i] <- quants[2]\n \n oh_r23_m[i] <- mean(linesi$oh_r23)\n oh_r23_std[i] <- sd(linesi$oh_r23)\n quants <- hdiofmcmc(linesi$oh_r23, credmass=clim)\n oh_r23_lo[i] <- quants[1]\n oh_r23_hi[i] <- quants[2]\n \n oh_n2s2ha_m[i] <- mean(linesi$oh_n2s2ha)\n oh_n2s2ha_std[i] <- sd(linesi$oh_n2s2ha)\n quants <- hdiofmcmc(linesi$oh_n2s2ha, credmass=clim)\n oh_n2s2ha_lo[i] <- quants[1]\n oh_n2s2ha_hi[i] <- quants[2]\n }\n \n data.frame(plateifu, d4000_n, d4000_n_err,\n lick_hd_a, lick_hd_a_err, mgfe,\n d_kpc, d_re,\n tauv_m, tauv_std, \n delta_m, delta_std, ll_m=ll_m,\n sigma_mstar_m , sigma_mstar_std , sigma_mstar_lo , sigma_mstar_hi , \n sigma_sfr_m , sigma_sfr_std , sigma_sfr_lo , sigma_sfr_hi , \n ssfr_m , ssfr_std , ssfr_lo , ssfr_hi , \n relsfr_m , relsfr_std , relsfr_lo , relsfr_hi , \n tbar_m , tbar_std , tbar_lo , tbar_hi , \n tbar_lum_m , tbar_lum_std , tbar_lum_lo , tbar_lum_hi , \n g_i_m , g_i_std , g_i_lo , g_i_hi , \n tauv_bd_m , tauv_bd_std , tauv_bd_lo , tauv_bd_hi , \n sigma_logl_ha_m , sigma_logl_ha_std , sigma_logl_ha_lo , sigma_logl_ha_hi , \n sigma_logl_ha_ctauv_m , sigma_logl_ha_ctauv_std , sigma_logl_ha_ctauv_lo , sigma_logl_ha_ctauv_hi , \n sigma_logl_ha_ctauv_bd_m , sigma_logl_ha_ctauv_bd_std , sigma_logl_ha_ctauv_bd_lo , sigma_logl_ha_ctauv_bd_hi ,\n eqw_ha_m, eqw_ha_std, eqw_ha_lo, eqw_ha_hi,\n o3hbeta_m , o3hbeta_std , o3hbeta_lo , o3hbeta_hi , \n o1halpha_m , o1halpha_std , o1halpha_lo , o1halpha_hi , \n n2halpha_m , n2halpha_std , n2halpha_lo , n2halpha_hi , \n s2halpha_m , s2halpha_std , s2halpha_lo , s2halpha_hi , \n r23_m , r23_std , r23_lo , r23_hi , \n o3n2_m , o3n2_std , o3n2_lo , o3n2_hi , \n oh_n2_m , oh_n2_std , oh_n2_lo , oh_n2_hi , \n oh_o3n2_m , oh_o3n2_std , oh_o3n2_lo , oh_o3n2_hi , \n oh_r23_m , oh_r23_std , oh_r23_lo , oh_r23_hi , \n oh_n2s2ha_m , oh_n2s2ha_std , oh_n2s2ha_lo , oh_n2s2ha_hi , \n bpt=bpt)\n}\n\n## estimated mean mass fraction in broad ages bins\n\nsum_binnedmass <- function(mgh_post, ages, ages.bins = c(0.1, 2.5, 5)) {\n T.gyr <- 10^(ages-9)\n ind.bins <- findInterval(ages.bins, T.gyr)\n dims <- dim(mgh_post)\n nsim <- dims[1]\n nfib <- dims[3]\n nt <- length(ages.bins)+2\n mgh.binned <- mgh_post[, c(1, ind.bins, dims[2]), ]\n mdiff <- mgh.binned[, 1:(nt-1),] - mgh.binned[, 2:nt, ]\n mb_m <- apply(mdiff, c(2, 3), mean)\n mb_sd <- apply(mdiff, c(2, 3), sd)\n df <- data.frame(cbind(t(mb_m), t(mb_sd)))\n df[df==0] <- NA\n T.ind <- c(0, T.gyr[ind.bins], T.gyr[length(T.gyr)])\n nt <- length(T.ind)\n bnames <- paste(formatC(T.ind[1:(nt-1)], format=\"f\", digits=1), \" < T < \",\n formatC(T.ind[2:nt], format=\"f\", digits=1), sep=\"\")\n names(df) <- c(paste(bnames, \"_m\", sep=\"\"), paste(bnames, \"_sd\", sep=\"\"))\n df\n}\n \n\n## computes highest density interval from a sample of representative values,\n## estimated as shortest credible interval.\n## arguments:\n## samplevec\n## is a vector of representative values from a probability distribution.\n## credmass\n## is a scalar between 0 and 1, indicating the mass within the credible\n## interval that is to be estimated.\n## value:\n## hdilim is a vector containing the limits of the hdi\n\n\nhdiofmcmc <- function(samplevec , credmass=0.95) {\n sortedpts <- sort(samplevec)\n ciidxinc <- floor(credmass * length(sortedpts))\n ncis <- length(sortedpts) - ciidxinc\n ciwidth <- rep(0 , ncis)\n for (i in 1:ncis) {\n ciwidth[i] <- sortedpts[i + ciidxinc] - sortedpts[i]\n }\n hdimin <- sortedpts[which.min(ciwidth)]\n hdimax <- sortedpts[which.min(ciwidth) + ciidxinc]\n hdilim <- c(hdimin , hdimax)\n return(hdilim)\n}\n\n", "meta": {"hexsha": "76f3939831bc16029f27e551987c1b180a3bdb49", "size": 24155, "ext": "r", "lang": "R", "max_stars_repo_path": "spmutils/R/spm_stan.r", "max_stars_repo_name": "mlpeck/spmutils", "max_stars_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "spmutils/R/spm_stan.r", "max_issues_repo_name": "mlpeck/spmutils", "max_issues_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "spmutils/R/spm_stan.r", "max_forks_repo_name": "mlpeck/spmutils", "max_forks_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.9565846599, "max_line_length": 126, "alphanum_fraction": 0.5786793625, "num_tokens": 8681, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7057850402140659, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.33636278819118204}} {"text": "hanoimove <- function(ndisks, from, to, via) {\n if (ndisks == 1) {\n cat(\"move disk from\", from, \"to\", to, \"\\n\")\n } else {\n hanoimove(ndisks - 1, from, via, to)\n hanoimove(1, from, to, via)\n hanoimove(ndisks - 1, via, to, from)\n }\n}\n\nhanoimove(4, 1, 2, 3)\n", "meta": {"hexsha": "1938fbb85da052710e5f54cdb57f4a7c464812ae", "size": 270, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Towers-of-Hanoi/R/towers-of-hanoi.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-15T00:56:39.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-15T00:56:39.000Z", "max_issues_repo_path": "Task/Towers-of-Hanoi/R/towers-of-hanoi.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Towers-of-Hanoi/R/towers-of-hanoi.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.5, "max_line_length": 47, "alphanum_fraction": 0.5592592593, "num_tokens": 114, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.6150878555160666, "lm_q1q2_score": 0.3362919974330334}} {"text": "#!/usr/bin/Rscript\n\nlibrary(optparse)\nlibrary(DESeq2)\nlibrary(BiocParallel)\n\n###############################################################################\n### SCRIPT ARGUMENTS\n\n# arguments\nrda.opt <- make_option( c(\"-r\", \"--rda-se\"), type=\"character\", default=NULL )\nfml.opt <- make_option( c(\"-f\", \"--formula\"), type=\"character\", default=\"~ 1\" )\ncor.opt <- make_option( c(\"-c\", \"--ncores\"), type=\"integer\", default=30 )\nout.opt <- make_option( c(\"-o\", \"--rda-out\"), type=\"character\", default=NULL )\n\n# parsing\noptions <- list(rda.opt, fml.opt, cor.opt, out.opt)\nparser <- OptionParser(option_list=options)\nargs <- parse_args(parser)\n\n# affectation\nrda_se <- args[[\"rda-se\"]]\nformula <- args[[\"formula\"]]\nncores <- args[[\"ncores\"]]\nrda_out <- args[[\"rda-out\"]]\n\n###############################################################################\n### PARALLELIZATION\n\nregister(MulticoreParam(ncores))\n\n###############################################################################\n### LOADING DATA\n\nload(rda_se)\nse <- data\n\n###############################################################################\n### DESEQ2 MODELING\n\nSummarizedExperiment::assay(se) <- round( SummarizedExperiment::assay(se) )\ndds <- DESeq2::DESeqDataSet(se, design=as.formula(formula))\ndds <- DESeq2::estimateSizeFactors(dds)\ndds <- DESeq2::DESeq(dds, parallel=T)\n\n###############################################################################\n### SAVING\n\ndata <- dds\nsave(data, file=rda_out)\n\n", "meta": {"hexsha": "0370d120fabad8a9d705df89905b2c407e46b629", "size": 1468, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/r/dds.r", "max_stars_repo_name": "nicholas-owen/BABS-RNASeq", "max_stars_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2018-12-04T15:36:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-02T19:43:56.000Z", "max_issues_repo_path": "scripts/r/dds.r", "max_issues_repo_name": "nicholas-owen/BABS-RNASeq", "max_issues_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/r/dds.r", "max_forks_repo_name": "nicholas-owen/BABS-RNASeq", "max_forks_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-02-14T12:06:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T13:46:43.000Z", "avg_line_length": 28.2307692308, "max_line_length": 79, "alphanum_fraction": 0.4938692098, "num_tokens": 322, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3361658495896431}} {"text": "#' oxford_index\n#'\n#' Computes the oxford index of thermal discomfort. \n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Relative humidity in percentage.\n#' @param wind numeric Windspeed in meters per second.\n#' @param pair numeric Air pressure in hPa.\n#' @return oxford index\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords oxford_index \n#' @references Lind AR, Hallon RF,1957, Assessment of physiologic severity of hot climate. J Appl Physiol 11, 35 40.\n#' @export\n#'\n#'\n#'\n#'\n\noxford_index=function(t,rh,wind=0.2,pair=1010) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"pair\", as.array(pair))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=oxford_index(t[i],rh[i],wind[0],pair[0])};\")\n res=ct$get(\"res\")\n return(res)\n}\n\n\n\n", "meta": {"hexsha": "fa06a3fa45aa02bd879d3ba47d7dd71c183a292c", "size": 1081, "ext": "r", "lang": "R", "max_stars_repo_path": "R/oxford_index.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/oxford_index.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/oxford_index.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 32.7575757576, "max_line_length": 139, "alphanum_fraction": 0.5763182239, "num_tokens": 293, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.5, "lm_q1q2_score": 0.336165849589643}} {"text": "#' Calculate mass based on MT3D Model Run\n#'\n#' This function reads in a btn file, hds file, and ucn file to calculate\n#' the total mass (in units of lbs) in the model at each times step.\n#'\n#' @param BTN This is the name of the .btn file (without the file extension)\n#' @param HDS This is the name of the .hds file (without the file extension)\n#' @param UCN This is the name of the .ucn file (without the file extension)\n#' @export\n#' @examples\n#' masscalc(BTN = \"T04\", HDS = \"F95\", UCN = \"T041\")\n#' \n#' # A tibble: 32,325,120 x 7\n#' YEARS LAY ROW COL THICK CONC MASS\n#' \n#' 1 1 1 1 1 0 0 0\n#' 2 1 1 1 2 0 0 0\n#' 3 1 1 1 3 0 0 0\n#' 4 1 1 1 4 0 0 0\n#' 5 1 1 1 5 0 0 0\n#' 6 1 1 1 6 0 0 0\n#' 7 1 1 1 7 0 0 0\n#' 8 1 1 1 8 0 0 0\n#' 9 1 1 1 9 0 0 0\n#' 10 1 1 1 10 0 0 0\n#' # ... with 32,325,110 more rows\n#' \n#' # Determine the total mass in the top layer of model for each timestep:\n#' m <- masscalc(BTN = \"T04\", HDS = \"F95\", UCN = \"T041\")\n#' m %>% filter(LAY == 1) %>%\n#' group_by(YEARS) %>%\n#' summarise(TOT = sum(MASS))\n#' # A tibble: 16 x 2\n#' YEARS TOT\n#' \n#' 1 1 4866.663\n#' 2 2 4896.831\n#' 3 3 4926.615\n#' 4 4 4954.909\n#' 5 5 4981.363\n#' 6 6 5005.803\n#' 7 7 5028.133\n#' 8 8 5048.347\n#' 9 9 5066.414\n#' 10 10 5082.366\n#' 11 11 5096.217\n#' 12 12 5107.996\n#' 13 13 5117.772\n#' 14 14 5125.564\n#' 15 15 5101.810\n#' 16 100 1442.831\n#' \n#' # Note: the total mass is in units of lbs\n\nmasscalc <- function(BTN, HDS, UCN){\n b <- readbtn(BTN)\n NLAY <- b$NLAY\n NCOL <- b$NCOL\n NROW <- b$NROW\n NSP <- b$NPER\n NPRS <- b$NPRS\n POR <- b$TRANS$PRSITY\n THICK <- b$TRANS$dZ\n dX <- b$dX\n dY <- b$dY\n dZ <- b$TRANS %>% select(LAY, ROW, COL, dZ) \n TOP <- b$TOP$TOP\n BOTL1 <- b$TOP$TOP - dZ[dZ$LAY == 1, ]$dZ\n rm(TOP)\n gc()\n if(toupper(b$LUNIT) == \"FT\"){\n from_L <- 1000. / 2.54^3 / 12^3\n } else if(toupper(b$LUNIT) == \"FEET\"){\n from_L <- 1000. / 2.54^3 / 12^3\n } else if(toupper(b$LUNIT) == \"FOOT\"){\n from_L <- 1000. / 2.54^3 / 12^3\n } else if(toupper(b$LUNIT) == \"M\"){\n from_L_ <- 1000. / 100^3\n } else if(toupper(b$LUNIT) == \"METER\"){\n from_L_ <- 1000. / 100^3\n } else if(toupper(b$LUNIT) == \"METERS\"){\n from_L_ <- 1000. / 100^3\n } else if(toupper(b$LUNIT) == \"CM\"){\n from_L_ <- 1000.\n } else{\n print(\"LENGTH UNITS CAN BE ft, m, cm\")\n stop(\"LENGTH UNITS DON'T MATCH\\n CHECK THE BTN FILE\")\n }\n \n if(toupper(b$MUNIT) == \"MG\"){\n to_lbs <- 1. / 1000 * 2.2046226218\n } else if(toupper(b$MUNIT) == \"LB\"){\n to_lbs <- 1.\n } else if(toupper(b$MUNIT) == \"LBS\"){\n to_lbs <- 1.\n } else{\n to_lbs <- 1. / 10^6 / 1000. * 2.2046226218\n print(\"MASS UNITS ARE ASSUMED TO BE micrograms\")\n print(\"IF THIS IS NOT THE CASE, MAKE THE CORRECTION\")\n print(\"IN THE MASS DATA FRAME\")\n }\n \n if(toupper(b$TUNIT) == \"D\"){\n to_yrs <- 1. / 365\n } else if(toupper(b$TUNIT) == \"DAYS\"){\n to_yrs <- 1. / 365\n } else if(toupper(b$TUNIT) == \"DAY\"){\n to_yrs <- 1. / 365\n } else if(toupper(b$TUNIT) == \"H\"){\n to_yrs <- 1. / 24 / 365\n } else if(toupper(b$TUNIT) == \"HOUR\"){\n to_yrs <- 1. / 24 / 365\n } else if(toupper(b$MUNIT) == \"HOURS\"){\n to_yrs <- 1. / 24 / 365\n } else if(toupper(b$TUNIT) == \"S\"){\n to_yrs <- 1. / 60 / 24 / 365\n } else if(toupper(b$TUNIT) == \"SEC\"){\n to_yrs <- 1. / 60 / 24 / 365\n } else if(toupper(b$MUNIT) == \"SECS\"){\n to_yrs <- 1. / 60 / 24 / 365\n } else if(toupper(b$TUNIT) == \"Y\"){\n to_yrs <- 1.\n } else if(toupper(b$TUNIT) == \"YRS\"){\n to_yrs <- 1.\n } else if(toupper(b$MUNIT) == \"YEAR\"){\n to_yrs <- 1.\n } else{\n stop(\"TIME UNITS DON'T MATCH\\n CHECK THE BTN FILE\")\n }\n \n TIMPRS_YEARS <- b$TIMPRS * to_yrs\n rm(b) \n gc()\n h <- MFtools::readhds(HDS, NLAY, NSP) %>%\n dplyr::mutate(YEARS = TIME * to_yrs) \n \n h$GWE[h$GWE == 999] <- -10^6 # Index and filter this way to improve speed\n \n h %<>% dplyr::mutate(THICK = ifelse(LAY == 1, GWE - BOTL1, dZ$dZ)) %>% \n dplyr::mutate(THICK = ifelse(THICK < 0, 0, THICK))\n \n FLWYRS <- h %>% dplyr::group_by(YEARS) %>% dplyr::summarise(TIME = mean(YEARS)) %>% dplyr::select(YEARS)\n h %<>% dplyr::select(STP, LAY, ROW, COL, THICK) %>% dplyr::rename(PER = STP)\n \n TRANSYRS <- TIMPRS_YEARS %>% tibble::data_frame(YEARS = .)\n NTTS <- dplyr::full_join(FLWYRS, TRANSYRS, by = \"YEARS\") %>% nrow() %>% as.integer()\n dX <- dX %>% rep(NROW) %>% rep(NLAY) %>% rep(NTTS)\n dY <- dY %>% rep(each = NCOL) %>% rep(NLAY) %>% rep(NTTS)\n POR <- POR %>% rep(NTTS)\n \n MASS <- MFtools::readucn(UCN, NLAY, NTTS) %>%\n dplyr::mutate(YEARS = TIME * to_yrs) %>% \n dplyr::left_join(h, by = c(\"PER\", \"LAY\", \"ROW\", \"COL\")) %>%\n dplyr::mutate(dX = dX) %>%\n dplyr::mutate(dY = dY) %>%\n dplyr::mutate(POR = POR) %>%\n dplyr::mutate(CONC = ifelse(CONC < 0 , 0, CONC)) %>%\n dplyr::mutate(MASS = dX * dY * POR * THICK * CONC / from_L * to_lbs) %>%\n dplyr::select(YEARS, LAY, ROW, COL, THICK, CONC, MASS) \n \n rm(h)\n rm(dX) \n rm(dY)\n rm(POR) \n rm(BOTL1)\n rm(THICK)\n gc() \n return(MASS)\n }\n \n \n", "meta": {"hexsha": "576a0ae8cda19aaab8b69cad5546b6c9eb1fd61f", "size": 5827, "ext": "r", "lang": "R", "max_stars_repo_path": "R/masscalc.r", "max_stars_repo_name": "dpphat/MFtools", "max_stars_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-12-23T21:35:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-23T14:52:25.000Z", "max_issues_repo_path": "R/masscalc.r", "max_issues_repo_name": "dpphat/MFtools", "max_issues_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/masscalc.r", "max_forks_repo_name": "dpphat/MFtools", "max_forks_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-10-21T18:07:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-21T18:07:26.000Z", "avg_line_length": 33.8779069767, "max_line_length": 108, "alphanum_fraction": 0.477604256, "num_tokens": 2336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.33613228959443797}} {"text": "#!/usr/bin/Rscript\nlibrary(ggplot2)\nlibrary(Rsubread)\nlibrary(limma)\nlibrary(edgeR)\n\n# read in target file\noptions(digits=2)\ntargets <- readTargets(\"../../data/rnaseq/Targets.txt\")\n\n# create a design matrix\ncelltype <- factor(targets$CellType)\ndesign <- model.matrix(~celltype)\n\n# build an index for reference sequence (Chr1 in hg19)\n# buildindex(basename=\"chr1\",reference=\"../../data/rnaseq/hg19_chr1.fa\")\n#\n# # align reads\n# align(\n# ndex=\"chr1\",readfile1=targets$InputFile,input_format=\"gzFASTQ\",\n# output_format=\"BAM\",output_file=targets$OutputFile,unique=TRUE,indels=5\n# )\n\n# count numbers of reads mapped to NCBI Refseq genes\n# the file is obtained from:\n# https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.gtf.gz\n# our version is slightly modified from the original gtf version\n# gzip -cd \"${INFILE_DIR}/GCF_000001405.25_GRCh37.p13_genomic.gtf.gz\" | \\\n# grep -P \"^#|^NC_000001.10\\t\" | sed \"s|NC_000001.10\t|chr1\t|\" > \\\n# \"${INFILE_DIR}/hg19_chr1.gtf\"\nfc <- featureCounts(\n files=paste(\n \"../../data/rnaseq/\", c(\"A_1.bam\", \"A_2.bam\", \"B_1.bam\", \"B_2.bam\"), sep=\"\"\n ),\n isGTFAnnotationFile=TRUE,\n annot.ext=\"../../data/rnaseq/hg19_chr1.gtf.gz\"\n)\n# write.table(fc$counts, sep=\"\\t\", quote=F, \"original.tsv\")\nx <- DGEList(counts=fc$counts, genes=fc$annotation[,c(\"GeneID\",\"Length\")])\n\n# filter out low-count genes\nisexpr <- rowSums(cpm(x) > 10) >= 2\nx <- x[isexpr,]\n\n# generate CPM values for plotting\nx_cpm <- cpm(x)\n\n# make pca plots\ny <- prcomp(t(x_cpm), scale=TRUE)\ndf_out <- as.data.frame(y$x)\ndf_out$group <- sapply(\n strsplit(as.character(row.names(df_out)), \".\", fixed=TRUE), \"[[\", 8\n)\ndf_out$group <- sapply( strsplit(df_out$group, \"_\", fixed=TRUE), \"[[\", 1 )\n\np <- ggplot(df_out, aes(x=PC1, y=PC2, color=group)) + geom_point(size=8) + theme_bw()\nggsave(\"../../results/ismb_plots_2021/original.pca.pdf\", p)\n\n# clear environment to avoid variable clash\nrm(list=ls())\n\n# read in target file\ntargets <- readTargets(\"../../data/rnaseq/Targets.txt\")\n\n# create a design matrix\ncelltype <- factor(targets$CellType)\ndesign <- model.matrix(~celltype)\n\n# make pca plot from reformatted data\n# data was reformatted by passing it through our pipeline\nx <- read.table(\"../../results/rnaseq/24000.joined.tsv\")\nx <- DGEList(x)\n\n# filter out low-count genes\nisexpr <- rowSums(cpm(x) > 10) >= 2\nx <- x[isexpr,]\n\n# generate CPM values for plotting\nx_cpm <- cpm(x)\n\n# make pca plots\ny <- prcomp(t(x_cpm), scale=TRUE)\ndf_out <- as.data.frame(y$x)\ndf_out$group <- sapply( strsplit(row.names(df_out), \"_\", fixed=TRUE), \"[[\", 1 )\n\np <- ggplot(df_out, aes(x=PC1, y=PC2, color=group)) + geom_point(size=8) + theme_bw()\nggsave(\"../../results/ismb_plots_2021/reformat.pca.pdf\", p)\n", "meta": {"hexsha": "cc56c090149ae4c383c73b9ab3408f10be86d88b", "size": 2796, "ext": "r", "lang": "R", "max_stars_repo_path": "src/other/ismb_plots_2021.r", "max_stars_repo_name": "tyronechen/universal_data_format", "max_stars_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/other/ismb_plots_2021.r", "max_issues_repo_name": "tyronechen/universal_data_format", "max_issues_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/other/ismb_plots_2021.r", "max_forks_repo_name": "tyronechen/universal_data_format", "max_forks_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.7727272727, "max_line_length": 174, "alphanum_fraction": 0.6963519313, "num_tokens": 903, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544210587585, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3359768398494668}} {"text": "\n\nPortrait<-F # graphical output orientation\n\nfirst.year<- 1963 #first year on plot, negative value means value defined by data\nlast.year<- 2100 #last year on plot\n\n\nHindcastModel<-T; # plot values from the hindcast\nForecastModel<-F; # plot values from the scenarios\n\nredefine.scenario.manually<-T # Define the scenario dir used explecitely in this script or do it externally\n\nop.dir<-data.path\nif (ForecastModel==T & redefine.scenario.manually) {\n scenario<-\"Opti-2_HCR_4_stoch_penBlim_atAgeW__\"; \n output.dir<-data.path \n op.dir<-file.path(data.path,scenario)\n} else if (ForecastModel==T ) {\n output.dir<-scenario.dir \n op.dir<-scenario.dir\n} \n\n\n##########################################################################\n\n\nmy.dev<-'png' # output device: 'screen', 'wmf', 'png', 'pdf'\n\n#my.dev<-'screen'\ncleanup()\nfile.name<-'summary'\n\n\nnox<-3; noy<-1;\nnoxy<-nox*noy\n\nref<-Read.reference.points()\n\nInit.function()\n\npel.bio<<-NULL\ndem.bio<-NULL\ndeadM<-NULL\ndeadM1<-NULL\ndeadM2<-NULL\nCWsum<-NULL\nExpectancy<-NULL\nLFI<-NULL\n\n\nif (HindcastModel) {\n dat1<-Read.summary.data(dir=data.path)\n \n dat1<-subset(dat1,Year<=last.year )\n if (first.year>0) dat1<-subset(dat1,Year>=first.year )\n bio<-subset(dat1,select=c(Species.n,Year,Age,BIO),Quarter==1)\n \n round(tapply(bio$BIO,list(bio$Year,bio$Species.n),sum)/1000)\n \n MSFD<-read.FLOP.MSFD.control(file=file.path(op.dir,\"op_msfd.dat\"),n.VPA=nsp-first.VPA+1,n.other.pred=first.VPA-1)\n \n pl<-strmacro(opt,out,n1=1,\n expr={\n incl<-NULL\n for (s in (n1:nsp)) { \n fa.i<-MSFD@opt['first.age',s]\n la.i<-MSFD@opt['last.age',s] \n if (la.i>=fa.i) incl<-rbind(incl,data.frame(Species.n=s,Age=seq(fa.i,la.i)))\n }\n out<-merge(incl,bio)\n out<-tapply(out$BIO,list(out$Year),sum)\n } )\n \n pl(community.biomass.pelagic.ages,pel.bio) # creates pel.bio\n pl(community.biomass.demersal.ages,dem.bio) # creates dem.bio\n pl(community.biomass.forage.ages,forage.bio) # creates forage.bio\n \n bio2<-subset(bio,Species.n>=first.VPA)\n tot.bio<-tapply(bio2$BIO,list(bio2$Year),sum)\n \n\n #community M and F\n bio<-subset(dat1,select=c(Species.n,Year,Quarter,Age,BIO,M,M1,M2,CWsum,west, N.bar ))\n incl<-data.frame(Species.n=first.VPA:nsp,incl=c(MSFD@community.M.sp))\n a<-merge(incl,bio)\n a<-subset(a,incl==1)\n a[a$M1==-1,'M1']<-a[a$M1==-1,'M']\n a$deadM1<-a$N.bar*a$west*a$M1\n a$deadM2<-a$N.bar*a$west*a$M2\n bio2<-subset(a,Quarter==1); \n bio2<-tapply(bio2$BIO,list(bio2$Year),sum) \n deadM1<-tapply(a$deadM1,list(a$Year),sum)/bio2\n deadM2<-tapply(a$deadM2,list(a$Year),sum)/bio2\n\n incl<-data.frame(Species.n=first.VPA:nsp,incl=c(MSFD@community.F.sp))\n a<-merge(incl,bio)\n a<-subset(a,incl==1)\n bio2<-subset(a,Quarter==1); \n bio2<-tapply(bio2$BIO,list(bio2$Year),sum) \n CWsum<-tapply(a$CWsum,list(a$Year),sum)/bio2\n \n\n # life expectancy\n bio<-subset(dat1,select=c(Species.n,Year,Quarter,Age,Z))\n incl<-data.frame(Species.n=first.VPA:nsp,incl=c(MSFD@community.life.expectancy.options['first.age',]))\n incl<-NULL\n for (s in (first.VPA:nsp)) { \n fa.i<-MSFD@community.life.expectancy.options['first.age',s-first.VPA+1]\n la.i<-SMS.control@species.info[s,'last-age']\n if (la.i>=fa.i) incl<-rbind(incl,data.frame(Species.n=s,Age=seq(fa.i,la.i)))\n }\n bio<-merge(bio,incl)\n a<-data.frame(Species.n=first.VPA:nsp,w=c(MSFD@community.life.expectancy.options['weighting',]))\n bio<-merge(bio,a)\n \n bio<-bio[order(bio$Species.n,bio$Year,bio$Age,bio$Quarter),]\n min.Year<-min(bio$Year)\n bio$first<- !duplicated(paste(bio$Species.n,bio$Year))\n \n a<-unique(subset(bio,first,select=c(Species.n,Age,Quarter)))\n a$ini.age<-a$Age+(a$Quarter-1)*0.25\n a<-subset(a,select=c(Species.n,ini.age))\n \n bio<-merge(bio,a)\n bio$sumZ<-bio$Z\n bio<-bio[order(bio$Species.n,bio$Year,bio$Age,bio$Quarter),]\n \n \n for (x in (1:dim(bio)[1])) {\n if (!bio[x,'first']) bio[x,'sumZ']<-bio[x,'sumZ']+(bio[x-1,'sumZ'])\n }\n bio$p<-exp(-bio$sumZ)\n \n a<-aggregate(p~Year+Species.n+ini.age+w,sum,na.rm=T,data=bio)\n a$p<-a$p/4 + a$ini.age+0.25/2 # to expected life in years (from quarters)\n a$pw<-a$p*a$w\n \n a<-aggregate(cbind(pw, w)~Year,sum,na.rm=T,data=a)\n a$Expectancy<-a$pw/a$w\n Expectancy<-tapply(a$Expectancy,list(a$Year),sum)\n \n # LFI\n bio<-subset(dat1,Quarter==1,select=c(Species.n,Year,Age,BIO))\n incl<-data.frame(Species.n=1:nsp,incl=MSFD@LFI.sp['include',])\n bio<-merge(bio,incl)\n bio<-subset(bio,incl==1,select=-incl)\n NW.all<-tapply(bio$BIO,list(bio$Year),sum)\n \n incl<-NULL\n for (s in (1:nsp)) { \n fa.i<-MSFD@LFI.age['first.age',s]\n la.i<-SMS.control@species.info[s,'last-age']\n if (la.i>=fa.i) incl<-rbind(incl,data.frame(Species.n=s,Age=seq(fa.i,la.i)))\n }\n bio<-merge(bio,incl)\n NW.LFI<-tapply(bio$BIO,list(bio$Year),sum)\n \n LFI<-NW.LFI/NW.all\n\n}\n\n\nif (ForecastModel) {\n dat<-Read.OP.community.indicator(dir=op.dir)\n} else dat<-NULL\n\nadd.set<-strmacro(in1,in2,out,\n expr={\n if (HindcastModel & ForecastModel) out<-c(in1,in2) else if (HindcastModel) out<-c(in1) else if (ForecastModel) out<-c(in2)\n } )\n\n# biomass plot\nb.factor<-1000\nif (ForecastModel) {\n pel.b<-tapply(dat$bio.pelag,list(dat$Year),mean)\n dem.b<-tapply(dat$bio.demer,list(dat$Year),mean)\n}\nadd.set(in1=pel.bio,in2=pel.b,pel.b)\nadd.set(in1=dem.bio,in2=dem.b,dem.b)\nadd.set(in1=forage.bio,in2=forage.b,forage.b)\nadd.set(in1=tot.bio,in2=tot.b,tot.b)\ntot.b<-tot.b/b.factor\npel.b<-pel.b/b.factor\ndem.b<-dem.b/b.factor\nforage.b<-forage.b/b.factor\nmax.bio<-max(c(pel.b,dem.b,forage.b))\nmin.bio<-min(c(pel.b,dem.b,forage.b))\nratio<-pel.b/dem.b\nyear<-as.numeric(unlist(names(ratio)))\n\n\n\n \ncleanup()\nplotfile<-function(dev='screen',out) {\n if (dev=='screen') X11(width=8, height=12, pointsize=12)\n if (dev=='wmf') win.metafile(filename = paste(out,'.wmf',sep=''), width=8, height=10, pointsize=12)\n if (dev=='png') png(filename =paste(out,'.png',sep=''), width = 1000, height = 1400,units = \"px\", pointsize = 30, bg = \"white\")\n if (dev=='pdf') pdf(file =paste(out,'.pdf',sep=''), width = 8, height = 10,pointsize = 12,onefile=FALSE)\n}\n\n\nplotfile(dev=my.dev,out=file.path(op.dir,'community_indicator_year'));\npar(mfcol=c(nox,noy))\npar(mar=c(3,4,3,2)) # bottom, left, top, right\npar(mar=c(2,4,3,5)+.1) #bottom, left, top, right\n\nplot(year,dem.b, ylim=c(min.bio,max.bio),type='b',xlab='Year',ylab='1000 t',lty=1,pch='d',lwd=2,main='Biomass: Pelagic (p), Demersal (d) and p:d ratio(r)')\nlines(year,pel.b,lty=2,pch='p',lwd=2,type='b',col=2)\n#lines(year,forage.b,lty=2,pch='f',lwd=2,type='b',col=3)\n\n\n#legend('topright',\n# c('Pelagic','Demersal','Ratio'),\n# pch=\"pdr\",lty=c(0,0,0),col=c(1,2,3),lwd=rep(2,3))\n \npar(new=T)\nplot(year,ratio,lty=3,pch='r',lwd=2,ylab=' ',xlab=' ',type='b',axes=F,col=4) \naxis(side=4)\nmtext(side=4,line=3.0,\"Ratio Pelagic:Demersal\",cex=0.65)\npar(xaxs=\"r\")\n\n# community F and M2\n\n\nif (ForecastModel) {\n FF<-tapply(dat$comm.Fall,list(dat$Year),mean)\n M<-tapply(dat$comm.M,list(dat$Year),mean)\n M2<-tapply(dat$comm.M2,list(dat$Year),mean)\n}\nadd.set(in1=CWsum,in2=FF,FF)\nadd.set(in1=deadM1,in2=M,M1)\nadd.set(in1=deadM2,in2=M2,M2)\n\n\nmax.D<-max(c(FF,M1,M2))\nmin.D<-min(c(FF,M1,M2))\nyear<-as.numeric(unlist(names(M1)))\n\n\n\nplot(year,FF,ylim=c(min.D,max.D),lty=1,pch='F',lwd=2,ylab='',xlab=' ',type='b',axes=T,col=1,main='Removed biomass relative to TSB 1. January') \n\nlegend('topright',\n c('Fishing','M2 predation','M1 residual mort.'),\n pch=\"F21\",lty=c(0,0,0),col=c(1,2,4),lwd=rep(2,3))\n\nlines(year,M1,lty=2,pch='1',lwd=2,type='b',col=4)\nlines(year,M2,lty=3,pch='2',lwd=2,type='b',col=2)\n\n\n\n\n# community.life.expect\nif (ForecastModel) { \n ex<-tapply(dat$community.life.expect,list(dat$Year),mean)\n}\nadd.set(in1=Expectancy,in2=ex,ex)\nyear<-as.numeric(unlist(names(ex)))\nplot(year,ex,type='b',xlab=' ',pch='e',ylab='Life expectancy (year)',lty=1,lwd=2,main='Life expectancy (e) and LFI (l)')\n\n\n# LFI\nif (ForecastModel) {\n LFI2<-tapply(dat$LFI,list(dat$Year),mean)\n}\nadd.set(in1=LFI,in2=LFI2,LFI)\nLFI<-LFI*100\nyear<-as.numeric(unlist(names(LFI)))\n\n \npar(new=T)\nplot(year,LFI,lty=3,pch='l',lwd=2,ylab=' ',xlab=' ',type='b',axes=F,col=4) \naxis(side=4)\nmtext(side=4,line=3.0,\"LFI (%)\",cex=0.65)\npar(xaxs=\"r\")\n\n\nif (my.dev %in% c('png','wmf','pdf')) dev.off() \n", "meta": {"hexsha": "e175c645bbccafabbb06ef663512edaf2f0d80d5", "size": 8335, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/plot_op_community_indicator.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/plot_op_community_indicator.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/plot_op_community_indicator.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6619217082, "max_line_length": 155, "alphanum_fraction": 0.6434313137, "num_tokens": 3095, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.33595469383238324}} {"text": "################################################################\n## Title: Data Science For Database Professionals\n## Description:: Main R file\n## Author: Microsoft\n################################################################\n\n####################################################################################################\n##Compute context\n####################################################################################################\nconnection_string <- \"Driver=SQL Server;Server=.;Database=telcoedw2;Trusted_Connection=yes;\"\nsql <- RxInSqlServer(connectionString = connection_string, autoCleanup = FALSE, consoleOutput = TRUE)\nlocal <- RxLocalParallel()\nrxOptions(reportProgress = 0)\n\n####################################################################################################\n##Connect to the data\n####################################################################################################\nrxSetComputeContext(local)\n\n##SQL data source\nmyDataTb <- RxSqlServerData(\n connectionString = connection_string,\n table = \"edw_cdr\",\n colInfo = col_info)\n\ntrainDataTb <- RxSqlServerData(\n connectionString = connection_string,\n table = \"edw_cdr_train\",\n colInfo = col_info)\n\ntestDataTb <- RxSqlServerData(\n connectionString = connection_string,\n table = \"edw_cdr_test\",\n colInfo = col_info)\n\nrxGetInfo(myDataTb, getVarInfo = T)\nrxGetInfo(trainDataTb, getVarInfo = T)\nrxGetInfo(testDataTb, getVarInfo = T)\n\n##Data frame\nmyData <- rxDataStep(myDataTb, overwrite = TRUE)\ntrainData <- rxDataStep(trainDataTb, overwrite = TRUE)\ntestData <- rxDataStep(testDataTb, overwrite = TRUE)\nstr(myData)\nstr(trainData)\nstr(testData)\n\n####################################################################################################\n##Data exploration and visualization on the data frame myData\n####################################################################################################\n#impact of callfailure rate (%) on churn\nmyData %>%\n group_by(month, callfailurerate) %>%\n summarize(countofchurn = sum(as.numeric(churn))) %>%\n ggplot(aes(x = month,\n y = countofchurn,\n group = factor(callfailurerate),\n fill = factor(callfailurerate))) +\n geom_bar(stat = \"identity\", position = position_dodge()) +\n labs(x = \"month\",\n y = \"Counts of churn\") +\n theme_minimal()\n\n####################################################################################################\n##Data exploration and visualization on the SQL data source myDataTb\n####################################################################################################\nrxSetComputeContext(sql)\n\n#Counts of churned customer by age and state (Interactive HeatMap)\ntmp <- rxCrossTabs(N(churn) ~ F(age):state, myDataTb, means = FALSE)\nResults_df <- rxResultsDF(tmp, output = \"sums\")\ncolnames(Results_df) <- substring(colnames(Results_df), 7)\nlibrary(Rcpp)\nlibrary(d3heatmap)\nd3heatmap(data.matrix(Results_df[, -1]), scale = \"none\", labRow = Results_df[, 1], dendrogram = \"none\",\n color = cm.colors(255))\n\n####################################################################################################\n## Extreme gradient boost with xgboost on the data frame trainData & testData\n####################################################################################################\n\n##Step 1- Train Model\nlibrary(Matrix)\nlibrary(xgboost)\nsystem.time(\nxgboost_model <- xgboost(data = dtrainData$data, label = dtrainData$label, max.depth = 32, eta = 1, nthread = 2, nround = 2, objective = \"binary:logistic\")\n)\nimportance <- xgb.importance(feature_names = dtrainData$data@Dimnames[[2]], model = xgboost_model)\nlibrary(Ckmeans.1d.dp)\nxgb.plot.importance(importance)\n\n##Step 2- Score Model\npredictions <- predict(xgboost_model, dtestData$data)\nthreshold <- 0.5\nxgboost_Probability <- predictions\nxgboost_Prediction <- ifelse(xgboost_Probability > threshold, 1, 0)\ntestPredData <- cbind(testData[, -27], dtestData$label, xgboost_Prediction, xgboost_Probability)\nnames(testPredData)[names(testPredData) == \"dtestData$label\"] <- \"churn\"\nhead(testPredData)\n\n##Step 3- Evaluate Model\nxgboost_metrics <- evaluate_model(data = testPredData,\n observed = \"churn\",\n predicted = \"xgboost_Prediction\")\nxgboost_metrics\n\nthreshold = 0.5\nS = testPredData$xgboost_Probability\nY = testPredData$churn\nroc.curve(s = threshold)\nROC.curve = Vectorize(roc.curve)\nM.ROC.xgboost = ROC.curve(s = seq(0, 1, by = .01))\nlibrary(AUC)\nlibrary(pROC)\nxgboost.auc <- auc(testPredData$xgboost_Prediction, testPredData$churn)\nplot(M.ROC.xgboost[1,], M.ROC.xgboost[2,], main = \"ROC Curves for Xgboost\", col = \"blue\", lwd = 4, type = \"l\", xlab = \"False Positive Rate\", ylab = \"True Positive Rate\")\ntext(0.5, 0, paste(\"AUC=\", round(xgboost.auc, 2)))\n\n####################################################################################################\n## Decision forest with rxDForest on SQL data source trainDataTb & testDataTb\n####################################################################################################\n\n##Step 1- Train Model\nrxSetComputeContext(sql)\ntrain_vars <- rxGetVarNames(trainDataTb)\ntrain_vars <- train_vars[!train_vars %in% c(\"churn\")]\ntemp <- paste(c(\"churn\", paste(train_vars, collapse = \"+\")), collapse = \"~\")\nformula <- as.formula(temp)\n\nsystem.time(\n rx_forest_model <- rxDForest(formula = formula,\n data = trainDataTb,\n nTree = 8,\n maxDepth = 32,\n mTry = 2,\n minBucket = 1,\n replace = TRUE,\n importance = TRUE,\n seed = 8,\n parms = list(loss = c(0, 4, 1, 0))))\nrx_forest_model\nplot(rx_forest_model)\nrxVarImpPlot(rx_forest_model)\n\n##Step 2- Score Model\nrxSetComputeContext(local)\nsystem.time(\n predictions <- rxPredict(modelObject = rx_forest_model,\n data = testData,\n type = \"prob\",\n overwrite = TRUE))\nthreshold <- 0.5\npredictions$X0_prob <- NULL\npredictions$churn_Pred <- NULL\nnames(predictions) <- c(\"Forest_Probability\")\npredictions$Forest_Prediction <- ifelse(predictions$Forest_Probability > threshold, 1, 0)\npredictions$Forest_Prediction <- factor(predictions$Forest_Prediction, levels = c(1, 0))\ntestPredData <- cbind(testData, predictions)\nhead(testPredData)\n\n##Step 3- Evaluate Model\nrx_forest_metrics <- evaluate_model(data = testPredData,\n observed = \"churn\",\n predicted = \"Forest_Prediction\")\nrx_forest_metrics\n\nroc_curve(data = testPredData,\n observed = \"churn\",\n predicted = \"Forest_Probability\")", "meta": {"hexsha": "069321798bfb5f2c6b5d8ca76f157053bda4c43a", "size": 6845, "ext": "r", "lang": "R", "max_stars_repo_path": "samples/features/r-services/telco-customer-churn/R/TelcoChurn-Main.r", "max_stars_repo_name": "manikanth/sql-server-samples", 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"max_forks_repo_forks_event_min_datetime": "2019-05-07T00:07:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T23:31:15.000Z", "avg_line_length": 40.5029585799, "max_line_length": 169, "alphanum_fraction": 0.5415631848, "num_tokens": 1503, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3359168710985631}} {"text": "# NOTE: Run 1-Clean-Join.r in the same R session before running 2-Train-Test.r\n\n################################################\n# Split out Training and Test Datasets\n################################################\n\n# split out the training data\n\nairWeatherTrainDF <- airWeatherDF %>% filter(Year < 2012) \nairWeatherTrainDF <- airWeatherTrainDF %>% sdf_register(\"flightsweathertrain\")\n\n# split out the testing data\n\nairWeatherTestDF <- airWeatherDF %>% filter(Year == 2012 & Month == 1)\nairWeatherTestDF <- airWeatherTestDF %>% sdf_register(\"flightsweathertest\")\n\n\n# Create ScaleR data source objects\n\ncolInfo <- list(\n ArrDel15 = list(type=\"numeric\"),\n CRSArrTime = list(type=\"integer\"),\n Year = list(type=\"factor\"),\n Month = list(type=\"factor\"),\n DayOfMonth = list(type=\"factor\"),\n DayOfWeek = list(type=\"factor\"),\n Carrier = list(type=\"factor\"),\n OriginAirportID = list(type=\"factor\"),\n DestAirportID = list(type=\"factor\")\n)\n\nfinalData <- RxHiveData(table = \"flightsweather\", colInfo = colInfo)\ncolInfoFull <- rxCreateColInfo(finalData)\n\ntrainDS <- RxHiveData(table = \"flightsweathertrain\", colInfo = colInfoFull)\ntestDS <- RxHiveData(table = \"flightsweathertest\", colInfo = colInfoFull)\n\n\n################################################\n# Train and Test a Logistic Regression model\n################################################\n\nformula <- as.formula(ArrDel15 ~ Month + DayOfMonth + DayOfWeek + Carrier + OriginAirportID + \n DestAirportID + CRSDepTime + CRSArrTime + RelativeHumidityOrigin + \n AltimeterOrigin + DryBulbCelsiusOrigin + WindSpeedOrigin + \n VisibilityOrigin + DewPointCelsiusOrigin + RelativeHumidityDest + \n AltimeterDest + DryBulbCelsiusDest + WindSpeedDest + VisibilityDest + \n DewPointCelsiusDest\n)\n\n# Use the scalable rxLogit() function\n\nlogitModel <- rxLogit(formula, data = trainDS)\n\nsummary(logitModel)\n\nsave(logitModel, file = \"logitModelSubset.RData\")\n\n# Predict over test data (Logistic Regression).\n\nlogitPredict <- RxXdfData(file.path(dataDir, \"logitPredictSubset\"))\n\n# Use the scalable rxPredict() function\n\nrxPredict(logitModel, data = testDS, outData = logitPredict,\n extraVarsToWrite = c(\"ArrDel15\"),\n type = 'response', overwrite = TRUE)\n\n# Calculate ROC and Area Under the Curve (AUC).\n\nlogitRoc <- rxRoc(\"ArrDel15\", \"ArrDel15_Pred\", logitPredict)\nlogitAuc <- rxAuc(logitRoc)\n# 0.645261\n\nplot(logitRoc)\n\n\n#####################################\n# rxEnsemble of fastTrees\n#####################################\n\ntrainers <- list(fastTrees(numTrees = 50))\n\nfastTreesEnsembleModelTime <- system.time(\n fastTreesEnsembleModel <- rxEnsemble(formula, data = trainDS,\n type = \"regression\", trainers = trainers, modelCount = 16, splitData = TRUE)\n)\n\nsummary(fastTreesEnsembleModel)\n\nsave(fastTreesEnsembleModel, file = \"fastTreesEnsembleModelSubset.RData\")\n\n# Test\nfastTreesEnsemblePredict <- RxXdfData(file.path(dataDir, \"fastTreesEnsemblePredictSubset\"))\n\n# Experimental feature to parallelize rxPredict when using a MicrosoftML model\nassign(\"predictMethod\", \"useDataStep\", envir = MicrosoftML:::rxHashEnv)\n\nfastTreesEnsemblePredictTime <- system.time(\n rxPredict(fastTreesEnsembleModel, data = testDS, outData = fastTreesEnsemblePredict,\n extraVarsToWrite = c(\"ArrDel15\"),\n overwrite = TRUE)\n)\n\n# Calculate ROC and Area Under the Curve (AUC).\n\nfastTreesEnsembleRoc <- rxRoc(\"ArrDel15\", \"Score\", fastTreesEnsemblePredict)\nfastTreesEnsembleAuc <- rxAuc(fastTreesEnsembleRoc)\n# 0.6662082\n\nplot(fastTreesEnsembleRoc)\n\nrxSparkDisconnect(cc)\n\n# Note - Restart R to switch to default HDFS settings:\n# \n# rxOptions(hdfsHost = \"default\")\n# rxOptions(fileSystem = RxNativeFileSystem())\n\n# See the following blog post for more sparklyr/rsparkling/H2O interop examples:\n#\n# https://blogs.msdn.microsoft.com/microsoftrservertigerteam/2017/04/19/new-features-in-9-1-microsoft-r-server-with-sparklyr-interoperability\n#\n", "meta": {"hexsha": "f0fe770d82f63770dbb41cf1a9ee89de1f234749", "size": 4004, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/MRS/2-Train-Test.r", "max_stars_repo_name": "Azure/SparkMLADS", "max_stars_repo_head_hexsha": "2ad7f8204489ae66030a94d56d06e4b9e4985c70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2017-06-17T18:31:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-01T14:57:55.000Z", "max_issues_repo_path": "Code/MRS/2-Train-Test.r", "max_issues_repo_name": "Azure/SparkMLADS", "max_issues_repo_head_hexsha": "2ad7f8204489ae66030a94d56d06e4b9e4985c70", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-06-08T04:48:49.000Z", "max_issues_repo_issues_event_max_datetime": "2017-06-08T16:39:34.000Z", "max_forks_repo_path": "Code/MRS/2-Train-Test.r", "max_forks_repo_name": "Azure/SparkMLADS", "max_forks_repo_head_hexsha": "2ad7f8204489ae66030a94d56d06e4b9e4985c70", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-06-07T15:44:04.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-10T11:55:04.000Z", "avg_line_length": 32.2903225806, "max_line_length": 141, "alphanum_fraction": 0.6810689311, "num_tokens": 1016, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307806984444, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3359168565636752}} {"text": "\r\n## compare effects of normalisation across all samples and within cell type.\r\n## use metrics from original wateRmelon paper to do comparision\r\n\r\nargs<-commandArgs(trailingOnly = TRUE)\r\nsource(args[1])\r\n\r\nlibrary(bigmelon)\r\n\r\nsetwd(dataDir)\r\nnormgdsFile<-sub(\"\\\\.gds\", \"Norm.gds\", gdsFile)\r\ngfile<-openfn.gds(normgdsFile, readonly = FALSE)\r\n\r\nnormbetas<-index.gdsn(gfile, \"normbeta\")[,]\r\ncelltypenormbeta<-index.gdsn(gfile, \"celltypenormbeta\")[,]\r\nrawbetas<-betas(gfile)[,]\r\n\r\n## remove NAs \r\nrawbetas<-na.omit(rawbetas)\r\nnormbetas<-na.omit(normbetas)\r\ncelltypenormbeta<-na.omit(celltypenormbeta)\r\n\r\nQCmetrics<-read.gdsn(index.gdsn(gfile, \"QCdata\"))\r\nQCmetrics<-QCmetrics[match(colnames(rawbetas), QCmetrics$Basename),]\r\n\r\n## apply wateRmelon metrics to compare normalisation\r\nmatOut<-matrix(data = NA, nrow = 3,ncol = 3)\r\nrownames(matOut)<-c(\"raw\", \"normTog\", \"normSep\")\r\ncolnames(matOut)<-c(\"iDMR\", \"genki\", \"seabi\")\r\n\r\nif(length( grep(\"rs\", rownames(rawbetas))) > 0){\r\n\tmatOut[1,2]<-mean(genki(rawbetas))\r\n}\r\nif(length( grep(\"rs\", rownames(normbetas))) > 0){\r\n\tmatOut[2,2]<-mean(genki(normbetas))\r\n}\r\nif(length( grep(\"rs\", rownames(celltypenormbeta))) > 0){\r\n\tmatOut[3,2]<-mean(genki(celltypenormbeta))\r\n}\r\n\r\n\r\n## filter to common probes\r\nprobes<-intersect(intersect(rownames(rawbetas), rownames(normbetas)), rownames(celltypenormbeta))\r\nrawbetas<-rawbetas[probes,]\r\nnormbetas<-normbetas[probes,]\r\ncelltypenormbeta<-celltypenormbeta[probes,]\r\n\r\nprobeAnno<-fData(gfile)\r\nprobeAnno<-probeAnno[probes,]\r\nx.probes<-probeAnno$chr == \"chrX\"\r\n\r\n\r\nidmr<-intersect(iDMR(), rownames(rawbetas))\r\n\r\nmatOut[1,1]<-dmrse_row(rawbetas, idmr)\r\nmatOut[2,1]<-dmrse_row(normbetas, idmr)\r\nmatOut[3,1]<-dmrse_row(celltypenormbeta, idmr)\r\n\r\nmatOut[1,3]<-seabi(rawbetas, sex = QCmetrics$Sex, X = x.probes)\r\nmatOut[2,3]<-seabi(normbetas, sex = QCmetrics$Sex, X = x.probes)\r\nmatOut[3,3]<-seabi(celltypenormbeta, sex = QCmetrics$Sex, X = x.probes)\r\n\r\nwrite.csv(matOut, paste0(qcOutFolder, \"CompareNormalisationStrategies.csv\"))\r\n\r\npheno<-QCmetrics\r\n\r\n\r\nqualDat.tog<-qual(rawbetas, normbetas)\r\nqualDat.sep<-qual(rawbetas, celltypenormbeta)\r\n\r\ncellCols<-c(\"orange\", \"darkblue\", \"darkmagenta\", \"deeppink\", \"darkgray\") ## assumes celltypes are order alphabetically\r\n\r\n\r\n## look at normalisation effects\r\npdf(paste0(qcOutFolder, \"CompareNormalisationStrategies.pdf\"), height = 6, width = 10)\r\npar(mfrow = c(1,2))\r\nboxplot(qualDat.tog$rmsd ~ QCmetrics$Cell.type, ylab = \"root mean square error\", main = \"Normalised together\", xlab = \"Cell type\")\r\nboxplot(qualDat.sep$rmsd ~ QCmetrics$Cell.type, ylab = \"root mean square error\", main = \"Normalised separately\", xlab = \"Cell type\")\r\n\r\n## look at distribution of beta values\r\ndensityPlot(normbetas, sampGroups = QCmetrics$Cell.type,pal = cellCols)\r\ndensityPlot(celltypenormbeta, sampGroups = QCmetrics$Cell.type)\r\ndev.off()\r\n\r\n\r\n## reextract cell norm corrected betas to save in bespoke DNAm data object\r\ncelltypenormbeta<-index.gdsn(gfile, \"celltypenormbeta\")[,]\r\nclosefn.gds(gfile)\r\n\r\n\r\n## remove cross-hybridising & snp probes\r\ncrosshyb<-read.table(paste(refFiles, \"/EPICArray/CrossHydridisingProbes_McCartney.txt\", sep = \"\"), stringsAsFactors = FALSE)\r\nsnpProbes<-read.table(paste(refFiles, \"/EPICArray/SNPProbes_McCartney.txt\", sep = \"\"), stringsAsFactors = FALSE, header = TRUE)\r\ncrosshyb2<-read.csv(paste(refFiles, \"/EPICArray/Pidsley_SM1.csv\", sep = \"\"), stringsAsFactors = FALSE)\r\nsnpProbes2<-read.csv(paste(refFiles, \"/EPICArray/Pidsley_SM4.csv\", sep = \"\"), stringsAsFactors = FALSE)\r\nsnpProbes3<-read.csv(paste(refFiles, \"/EPICArray/Pidsley_SM5.csv\", sep = \"\"), stringsAsFactors = FALSE)\r\nsnpProbes4<-read.csv(paste(refFiles, \"/EPICArray/Pidsley_SM6.csv\", sep = \"\"), stringsAsFactors = FALSE)\r\nsnpProbes<-snpProbes[which(snpProbes$DIST_FROM_MAPINFO < 10 & snpProbes$AF > 0.01),]\r\nsnpProbes2<-snpProbes2[which(snpProbes2$AF > 0.01),]\r\nsnpProbes3<-snpProbes3[which(snpProbes3$AF > 0.01),]\r\nsnpProbes4<-snpProbes4[which(snpProbes4$AF > 0.01),]\r\n\r\ndist<-cbind(abs(snpProbes4$VARIANT_END - snpProbes4$MAPINFO), abs(snpProbes4$VARIANT_START - snpProbes4$MAPINFO))\r\ndist<-apply(dist, 1, min)\r\nsnpProbes4<-snpProbes4[which(dist <=10),]\r\n\r\nremove<-intersect(rownames(celltypenormbeta),unique(c(crosshyb[,1], snpProbes$IlmnID, snpProbes2$PROBE, snpProbes3$PROBE, snpProbes4$PROBE)))\r\n\r\ncelltypenormbeta<-celltypenormbeta[!rownames(celltypenormbeta) %in% remove,]\r\n\r\nsave(celltypenormbeta, pheno, file = normData)\r\n\r\n", "meta": {"hexsha": "5069a0506bcdb8f7d764bbcfa7d872ba866a71be", "size": 4435, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/compareNormalisation.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DNAm/compareNormalisation.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "DNAm/compareNormalisation.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2477876106, "max_line_length": 142, "alphanum_fraction": 0.7321307779, "num_tokens": 1471, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307806984444, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3359168565636752}} {"text": "#' The application User-Interface and server\r\n#' \r\n#' @param input,output,session Internal parameters for {shiny}. \r\n#' DO NOT REMOVE.\r\n#' @import shiny shinydashboard shinyWidgets plotly ggplot2 waiter ggpubr reshape2 fastmatch xml2 dplyr tidyr ggfortify\r\n#' @importFrom WriteXLS WriteXLS\r\n#' @importFrom openxlsx read.xlsx\r\n#' @importFrom stringr str_c str_detect\r\n#' @importFrom janitor clean_names\r\n#' @importFrom stats prcomp\r\n#' @importFrom utils read.csv read.csv2 unzip write.csv\r\n#' @importFrom grDevices dev.off pdf\r\n#' \r\n#' @noRd\r\n\r\n\r\nstartProjects<-c()\r\n#Script group_plotter----\r\nplotter_grouped<-function(data,group,columns,stats) {\r\n data[data==0] <- NA\r\n columns<-c(group,columns)\r\n data<-select(data,all_of(columns))\r\n metabolites<-names(data)\r\n varx<-metabolites[1]\r\n \r\n \r\n if(stats==\"none\"){\r\n pltg<- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inheriet.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\",axis.text.x=element_text(angle=45,hjust=1))+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])\r\n })\r\n }\r\n if(stats==\"ttest\"){\r\n pltg<- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inheriet.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\",axis.text.x=element_text(angle=45,hjust=1))+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_compare_means(method=\"t.test\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n if(stats==\"wilcox\"){\r\n pltg<- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inheriet.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\",axis.text.x=element_text(angle=45,hjust=1))+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_compare_means(method=\"wilcox.text\",aes(label=..p.signif..),hide.ns = TRUE)\r\n \r\n })\r\n }\r\n if(stats==\"anova\"){\r\n pltg<- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inheriet.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\",axis.text.x=element_text(angle=45,hjust=1))+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_compare_means(method=\"anova\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n if(stats==\"kruskal\"){\r\n pltg<- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inheriet.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\",axis.text.x=element_text(angle=45,hjust=1))+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_compare_means(method=\"kruskal.text\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n pltg<-pltg[-1]\r\n plot_combined<-ggarrange(plotlist=pltg)\r\n plot<-append(plot,list(plot_combined))\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n#Script xmler----\r\nxmler<-function(data2,type){\r\n\r\n l<-data2\r\n\r\n if(type==\"Metabolites\"){\r\n\r\n #reading in the xml file\r\n all_measurements<-lapply(seq_len(length(data2)), FUN=function(x) {\r\n l<-data2[[x]]\r\n\r\n\r\n #numbner of parameters\r\n parameters_xml<-xml_find_all(l,\".//VALUE\")\r\n\r\n #Find the Sample name and clean it up\r\n Sample<-xml_find_all(l,\".//SAMPLE\")\r\n Sample<-xml_attr(Sample,\"name\")\r\n Sample<-sub(\"_e.*\",\"\",Sample)\r\n\r\n #create data frame\r\n xmlasdataframe<-as.data.frame(lapply(seq_len(length(parameters_xml)), FUN=function(x) {\r\n names<-xml_attrs(xml_child(xml_child(l, 4), x))[[\"name\"]]\r\n conc<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 1))[[\"conc\"]]\r\n errConc<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 2))[[\"errConc\"]]\r\n rawConc<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 2))[[\"rawConc\"]]\r\n sigCorr<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 2))[[\"sigCorr\"]]\r\n lod<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 1))[[\"lod\"]]\r\n #loq<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 1))[[\"loq\"]]\r\n rbind(names,conc,errConc,rawConc,sigCorr,lod)\r\n }\r\n ))\r\n df1<-data.frame(t(xmlasdataframe[,-1]))\r\n rownames(df1)<-c()\r\n melty<-melt(df1,id.vars = \"names\")\r\n melty$names<-str_c(melty$names,\"_\",melty$variable)\r\n melty<-subset(melty,select=-c(variable))\r\n final_table<-melty[order(melty$name),]\r\n final_table<-pivot_wider(final_table,names_from=\"names\",values_from=\"value\")\r\n final_table<-cbind(Sample,final_table)\r\n if(x==1){\r\n thisisit<<-final_table[FALSE,]\r\n }\r\n thisisit<-rbind(thisisit,final_table)\r\n thisisactuallyit<-thisisit\r\n\r\n })\r\n test<-do.call(rbind.data.frame,all_measurements)\r\n }\r\n\r\n if(type==\"Lipids\"){\r\n\r\n all_measurements<-lapply(seq_len(length(data2)), FUN=function(x) {\r\n\r\n l<-data2[[x]]\r\n\r\n\r\n #numbner of parameters\r\n parameters_xml<-xml_find_all(l,\".//VALUE\")\r\n\r\n #Find the Sample name and clean it up\r\n Sample<-xml_find_all(l,\".//SAMPLE\")\r\n Sample<-xml_attr(Sample,\"name\")\r\n Sample<-sub(\"_e.*\",\"\",Sample)\r\n\r\n\r\n xmlasdataframe<-as.data.frame(lapply(seq_len(length(parameters_xml)), FUN=function(x) {\r\n names<-xml_attrs(xml_child(xml_child(l, 4), x))[[\"name\"]]\r\n values<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 1))[[\"value\"]]\r\n unit<-xml_attrs(xml_child(xml_child(xml_child(l, 4), x), 1))[[\"unit\"]]\r\n rbind(names,values,unit)\r\n }))\r\n df1<-data.frame(t(xmlasdataframe[,-1]))\r\n rownames(df1)<-c()\r\n df1$names<-str_c(df1$names,\"_\",df1$unit)\r\n df1<-subset(df1,select=-c(unit))\r\n #remove duplicated value inoriginal xml file\r\n df1<-df1[!duplicated(df1),]\r\n\r\n final_table<-df1[order(df1$name),]\r\n final_table<-distinct(pivot_wider(final_table,names_from=\"names\",values_from=\"values\"))\r\n final_table<-cbind(Sample,final_table)\r\n if(x==1){\r\n thisisit<<-final_table[FALSE,]\r\n }\r\n thisisit<-rbind(thisisit,final_table)\r\n thisisactuallyit<-thisisit\r\n\r\n })\r\n test<-do.call(rbind.data.frame,all_measurements)\r\n }\r\n test<-test\r\n\r\n\r\n}\r\n\r\n\r\n#Script Plotter_single-----\r\nplotter_single<-function(data,group,column,grouped,stat){\r\n\r\n ata_original<-data\r\n if(length(column)>1){\r\n column<-column[1]\r\n }\r\n columns<-c(group,column)\r\n data<-select(data,all_of(columns))\r\n data[data==0] <- NA\r\n #if length ofcolumn vector >1 then show message....\r\n plot<-ggplot(data,aes(x = data[ ,group], y = data[ ,column],inherit.aes=FALSE,group= data[ ,group],fill= data[ ,group])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n labs(x=\"\", y=\"\", title = column)\r\n plot<-plot\r\n}\r\n#SCRIPT ExperimentPicker----\r\nexperimentpicker<-function(solvent,size){\r\n\r\n if(solvent==\"Urine\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-c(\"N PROF_URINE_NOESY\",\"N PROF_URINE_JRES\",\"N PROF_URINE_DAS_A\",\"N PROF_URINE_DAS_E\")\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<-c(\"N PROF_URINE_NOESY_3mm\",\"N PROF_URINE_JRES_3mm\",\"N PROF_URINE_DAS_A\",\"N PROF_URINE_DAS_E\")\r\n }\r\n }\r\n if(solvent==\"Urine_Neo\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-c(\"N PROF_URINE_NOESY\",\"N PROF_URINE_JRES\",\"N PROF_URINE_DAS_N\")\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<- c(\"N PROF_URINE_NOESY_3mm\",\"N PROF_URINE_JRES_3mm\",\"N PROF_URINE_DAS_N\")\r\n }\r\n }\r\n if(solvent==\"Plasma\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-c(\"N PROF_PLASMA_NOESY\",\"N PROF_PLASMA_JRES\",\"N PROF_PLASMA_CPMG\",\"N PROF_PLASMA_DIFF\",\"N PROF_PLASMA_DAS_A\",\"N PROF_PLASMA_DAS_L\")\r\n }\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-c(\"N PROF_PLASMA_NOESY_3mm\",\"N PROF_PLASMA_JRES_3mm\",\"N PROF_PLASMA_CPMG_3mm\",\"N PROF_PLASMA_DIFF_3mm\",\"N PROF_PLASMA_DAS_A\",\"N PROF_PLASMA_DAS_L\")\r\n }\r\n }\r\n if(solvent==\"CSF\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-c(\"N PROF_PLASMA_NOESY\",\"N PROF_PLASMA_JRES\")\r\n }\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-c(\"N PROF_PLASMA_NOESY_3mm\",\"N PROF_PLASMA_JRES_3mm\")\r\n }\r\n }\r\n if(solvent==\"MEOH\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-c(\"N PROF_MEOH_ZG30\",\"N PROF_MEOH_ZGPS\",\"N PROF_PLASMA_NOESY\",\"N PROF_PLASMA_JRES\")\r\n }\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-c(\"N PROF_MEOH_ZG30_3mm\",\"N PROF_MEOH_ZGPS_3mm\",\"N PROF_PLASMA_NOESY_3mm\",\"N PROF_PLASMA_JRES_3mm\")\r\n }\r\n }\r\n EXPERIMENTE<-EXPERIMENTE\r\n}\r\n\r\n#SCRIPT Plotter----\r\nplotter_v2<-function(data,group,columns,stat){\r\n data_original<-data\r\n columns<-c(group,columns)\r\n data<-select(data,all_of(columns))\r\n data[data==0] <- NA\r\n metabolites<-names(data)\r\n varx<-metabolites[1]\r\n \r\n if(stat==\"none\"){\r\n plot <- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inherit.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n stat_boxplot(geom=\"errorbar\",width=0.2)+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])\r\n })\r\n }\r\n if(stat==\"ttest\"){\r\n plot <- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inherit.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_boxplot(geom=\"errorbar\",width=0.2)+\r\n stat_compare_means(method=\"t.test\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n \r\n }\r\n if(stat==\"anova\"){\r\n plot <- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inherit.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_boxplot(geom=\"errorbar\",width=0.2)+\r\n stat_compare_means(method=\"anova\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n if(stat==\"wilcox\"){\r\n plot <- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inherit.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_boxplot(geom=\"errorbar\",width=0.2)+\r\n stat_compare_means(method=\"wilcox.text\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n if(stat==\"kruskal\"){\r\n plot <- lapply(seq_len(ncol(data)), FUN = function(x) {\r\n ggplot(data,aes(x = .data[[varx]], y = data[ , x],inherit.aes=FALSE,fill=.data[[varx]],group=.data[[varx]])) +\r\n geom_boxplot()+\r\n theme_classic()+\r\n theme(legend.position=\"none\")+\r\n labs(x=\"\", y=\"\", title = colnames(data)[x])+\r\n stat_boxplot(geom=\"errorbar\",width=0.2)+\r\n stat_compare_means(method=\"kruskal.text\",aes(label=..p.signif..),hide.ns = TRUE)\r\n })\r\n }\r\n \r\n metabolites<-metabolites[-1]\r\n plot<-plot[-1]\r\n plot<-plot\r\n \r\n}\r\n#SCRIPT Normalizer----\r\nnormalizor<- function(data,log,center){\r\n\r\n data[data==0] <- NA\r\n df1<-reshape::melt.data.frame(data)\r\n\r\n if(log==\"Yes\"){\r\n df1$value<-log10(df1$value)\r\n }\r\n if(center==\"Yes\"){\r\n df1$value<-scale(df1$value,center=TRUE, scale=FALSE)\r\n }\r\n\r\n data2<-pivot_wider(df1,names_from=\"variable\",values_from=\"value\")\r\n}\r\n\r\n#SCRIPT Excel Manual----\r\nexcel_manual<-function(data,solvent,size,rack,slot,experiments,path){\r\n\r\n #set up the parameters\r\n SOLVENT<-solvent\r\n standard_path<-path\r\n EXPERIMENTE<-experiments\r\n\r\n #create Experiments from input\r\n if(solvent==\"Urine\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine_3mm\")\r\n }\r\n }\r\n if(solvent==\"Urine_Neo\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine\")\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine_3mm\")\r\n }\r\n }\r\n if(solvent==\"Plasma\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Plasma_3mm\")\r\n }\r\n }\r\n if(solvent==\"CSF\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"CSF\")}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"CSF_3mm\")\r\n }\r\n }\r\n if(solvent==\"MEOH\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"MEOH\")}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"MEOH_3mm\")\r\n }\r\n }\r\n #read in rest from data\r\n Date<-Sys.Date()\r\n\r\n #FilePath\r\n YEAR<-format(as.Date(Date, format=\"%Y/%m/%d\"),\"%Y\")\r\n DAY<-format(as.Date(Date, format=\"%Y/%m/%d\"),\"%d\")\r\n datapath<-c(\"data\")\r\n nmr<-c(\"nmr\")\r\n DISK<-file.path(standard_path,YEAR,datapath,Date,nmr)\r\n\r\n #Names\r\n NAME<-select(data,Name)\r\n NAME<- NAME %>%\r\n rename(NAME = Name)\r\n TITLE<-NAME\r\n TITLE<- TITLE %>%\r\n rename(TITLE = NAME)\r\n\r\n\r\n POSI<-slot\r\n HOLDER<-paste(rack, sprintf(\"%02d\",POSI), sep=\"\")\r\n\r\n #Combine Experiments\r\n data<-data[order(data$Name),]\r\n EXPERIMENT<-EXPERIMENTE\r\n data_final<-cbind(DISK,NAME,SOLVENT,HOLDER,TITLE)\r\n data_final<-merge(x=data_final,y=EXPERIMENTE)\r\n data_final<-data_final[order(data_final$HOLDER),]\r\n data_final<- data_final %>%\r\n rename(EXPERIMENT = y)\r\n data_final<-data_final[,c(1,2,3,6,4,5)]\r\n}\r\n#SCRIPT Excel Upload-------------------------------------------\r\nexcel_creator<-function(data,solvent,size,rack,experiments,path){\r\n\r\n #set up the parameters\r\n SOLVENT<-solvent\r\n standard_path<-path\r\n EXPERIMENTE<-experiments\r\n #create Experiments from input\r\n if(solvent==\"Urine\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine_3mm\")\r\n }\r\n }\r\n if(solvent==\"Urine_Neo\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine\")\r\n }\r\n else if(size==\"3mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Urine_3mm\")\r\n }\r\n }\r\n if(solvent==\"Plasma\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Plasma_3mm\")\r\n }\r\n }\r\n if(solvent==\"Media\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Plasma\")}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"Plasma_3mm\")\r\n }\r\n }\r\n if(solvent==\"CSF\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"CSF\")}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"CSF_3mm\")\r\n }\r\n }\r\n if(solvent==\"MEOH\"){\r\n if(size==\"5mm\"){\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"MEOH\")}\r\n else if(size==\"3mm\") {\r\n EXPERIMENTE<-experiments\r\n SOLVENT<-c(\"MEOH_3mm\")\r\n }\r\n }\r\n #read in rest from data\r\n Date<-Sys.Date()\r\n\r\n #FilePath\r\n YEAR<-format(as.Date(Date, format=\"%Y/%m/%d\"),\"%Y\")\r\n DAY<-format(as.Date(Date, format=\"%Y/%m/%d\"),\"%d\")\r\n nmr<-c(\"nmr\")\r\n datapath<-c(\"data\")\r\n DISK<-file.path(standard_path,YEAR,datapath,Date,nmr)\r\n\r\n #Names\r\n NAME<-select(data,Name)\r\n NAME<- NAME %>%\r\n rename(NAME = Name)\r\n TITLE<-NAME\r\n TITLE<- TITLE %>%\r\n rename(TITLE = NAME)\r\n\r\n #for sorting purposes only\r\n order<-nrow(data)\r\n dataorder<-cbind(data,order)\r\n\r\n #Positions, create 2nd rack if necessary\r\n if (nrow(data)>192){\r\n rack_temp2<-data %>%\r\n slice(193:n())\r\n data<-data %>%\r\n slice(1:192)\r\n rack_temp1<-data %>%\r\n slice(97:n())\r\n data<-data %>%\r\n slice(1:96)\r\n POSI<-seq(1, nrow(data), by = 1)\r\n POSI2<-seq(1, nrow(rack_temp1), by = 1)\r\n POSI3<-seq(1, nrow(rack_temp2), by = 1)\r\n HOLDER<-paste(rack, sprintf(\"%02d\",POSI), sep=\"\")\r\n HOLDER2<-paste(rack+1, sprintf(\"%02d\",POSI2), sep=\"\")\r\n HOLDER3<-paste(rack+2, sprintf(\"%02d\",POSI3), sep=\"\")\r\n if(rack==4) {\r\n HOLDER2<-paste(rack+1, sprintf(\"%02d\",POSI2), sep=\"\")\r\n HOLDER3<-paste(rack-3, sprintf(\"%02d\",POSI3), sep=\"\")\r\n }\r\n if(rack==5){\r\n HOLDER2<-paste(rack-4, sprintf(\"%02d\",POSI2), sep=\"\")\r\n HOLDER3<-paste(rack-3, sprintf(\"%02d\",POSI3), sep=\"\")\r\n }\r\n POSI<-c(POSI,POSI2,POSI3)\r\n HOLDER<-c(HOLDER,HOLDER2,HOLDER3)\r\n\r\n } else\r\n if (nrow(data)>96){\r\n rack_temp<- data %>%\r\n slice(97:n())\r\n data<-data %>%\r\n slice(1:96)\r\n POSI<-seq(1, nrow(data), by = 1)\r\n HOLDER<-paste(rack, sprintf(\"%02d\",POSI), sep=\"\")\r\n POSI2<-seq(1, nrow(rack_temp), by = 1)\r\n if(rack==5){\r\n HOLDER2<-paste(1, sprintf(\"%02d\",POSI2), sep=\"\")\r\n } else {\r\n HOLDER2<-paste(rack+1, sprintf(\"%02d\",POSI2), sep=\"\")\r\n }\r\n POSI<-c(POSI,POSI2)\r\n HOLDER<-c(HOLDER,HOLDER2)\r\n #cat(paste(\"\",\"More than 96 samples (1 rack) were submitted.\",\"A 2nd rack with a higher number (unless 5 then 1) was used from sample 97+\",sep=\"\\n\"))\r\n } else {\r\n POSI<-seq(1, nrow(data), by = 1)\r\n HOLDER<-paste(rack, sprintf(\"%02d\",POSI), sep=\"\")\r\n }\r\n\r\n #Combine Experiments\r\n data<-data[order(data$Name),]\r\n EXPERIMENT<-EXPERIMENTE\r\n data_final<-cbind(DISK,NAME,SOLVENT,HOLDER,TITLE)\r\n data_final<-merge(x=data_final,y=EXPERIMENTE)\r\n data_final<-data_final[order(data_final$HOLDER),]\r\n data_final<- data_final %>%\r\n rename(EXPERIMENT = y)\r\n # data_final<-sort(data_final$dataorder)\r\n # data_final=subset(data_final,select=-c(dataorder))\r\n data_final<-data_final[,c(1,2,3,6,4,5)]\r\n data_final<-data_final\r\n}\r\n#SCRIPT Extractor----\r\nclean_csv<-function(names,data){\r\n\r\n if(names==\"Yes\"){\r\n clean_csv<-clean_names(data)\r\n if(\"instrument\" %in% colnames(clean_csv)){\r\n clean_csv<-subset(clean_csv, select = -c(directory,exp_no,proc_no,experiment,pulse_program,aunm,aunmp,instrument,probehead,ns,ds,p1,pld_b_1,pld_b9,rg,swh,td,si,te,phc0,phc1,o1,sf,sr,date,date_2,name,type))\r\n }\r\n if(\"is_b_i_quant_ps\" %in% colnames(clean_csv)){\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ps,is_quant_ps_2_0_0))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_l\"))\r\n clean_csv<-clean_csv[seq(1, ncol(clean_csv),3)]\r\n clean_csv<-cbind(ID,clean_csv)\r\n }\r\n if(\"is_b_i_quant_ur_ne\" %in% colnames(clean_csv)){\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ur_ne,is_quant_ur_ne_1_1_0))\r\n clean_csv<-clean_names(data)\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n crea<-select(clean_csv, contains(\"creatinine_conc_\"))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_mol_crea\"))\r\n clean_csv<-cbind(ID,crea,clean_csv)\r\n }\r\n if(\"is_b_i_quant_ur_e\" %in% colnames(clean_csv)){\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ur_e,is_quant_ur_e_1_1_0))\r\n clean_csv<-clean_names(data)\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n crea<-select(clean_csv, contains(\"creatinine_conc_\"))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_mol_crea\"))\r\n clean_csv<-cbind(ID,crea,clean_csv)\r\n }\r\n\r\n #Main Parameters\r\n names(clean_csv)[names(clean_csv) == 'tptg_mg_d_l'] <- 'TG_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'tpch_mg_d_l'] <- 'CHOL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldch_mg_d_l'] <- 'LDL-CHOL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hdch_mg_d_l'] <- 'HDL-CHOL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'tpa1_mg_d_l'] <- 'Apo-A1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'tpa2_mg_d_l'] <- 'Apo-A2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'tpab_mg_d_l'] <- 'Apo-B100_mg_dl'\r\n\r\n #Calculated Figues\r\n names(clean_csv)[names(clean_csv) == 'ldhd'] <- 'LDL-CHOL_HDL-CHOL'\r\n names(clean_csv)[names(clean_csv) == 'aba1'] <- 'Apo-B100_Apo-A1'\r\n\r\n #Total Concentration of ApoB carryin particles\r\n names(clean_csv)[names(clean_csv) == 'tbpn_nmol_l'] <- 'Total_Particles_ApoB_nmol_l'\r\n\r\n #Lipoprotein Main fractions\r\n names(clean_csv)[names(clean_csv) == 'vlpn_nmol_l'] <- 'VLDL-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'idpn_nmol_l'] <- 'IDL-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'ldpn_nmol_l'] <- 'LDL-Particles_nmol_l'\r\n\r\n #LDL Subfractions\r\n names(clean_csv)[names(clean_csv) == 'l1pn_nmol_l'] <- 'LDL-1-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'l2pn_nmol_l'] <- 'LDL-2-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'l3pn_nmol_l'] <- 'LDL-3-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'l4pn_nmol_l'] <- 'LDL-4-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'l5pn_nmol_l'] <- 'LDL-5-Particles_nmol_l'\r\n names(clean_csv)[names(clean_csv) == 'l6pn_nmol_l'] <- 'LDL-6-Particles_nmol_l'\r\n\r\n #Lipoprotein Main fractions\r\n #TG\r\n names(clean_csv)[names(clean_csv) == 'vltg_mg_d_l'] <- 'TG_VLDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'idtg_mg_d_l'] <- 'TG_IDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldtg_mg_d_l'] <- 'TG_LDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hdtg_mg_d_l'] <- 'TG_HDL_mg_dl'\r\n #CHOL\r\n names(clean_csv)[names(clean_csv) == 'vlch_mg_d_l'] <- 'CHOL_VLDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'idch_mg_d_l'] <- 'CHOL_IDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldch_mg_d_l'] <- 'CHOL_LDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hdch_mg_d_l'] <- 'CHOL_HDL_mg_dl'\r\n #free CHOL\r\n names(clean_csv)[names(clean_csv) == 'vlfc_mg_d_l'] <- 'fCHOL_VLDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'idfc_mg_d_l'] <- 'fCHOL_IDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldfc_mg_d_l'] <- 'fCHOL_LDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hdfc_mg_d_l'] <- 'fCHOL_HDL_mg_dl'\r\n #Phospholipid\r\n names(clean_csv)[names(clean_csv) == 'vlpl_mg_d_l'] <- 'PHOSL_VLDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'idpl_mg_d_l'] <- 'PHOSL_IDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldpl_mg_d_l'] <- 'PHOSL_LDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hdpl_mg_d_l'] <- 'PHOSL_HDL_mg_dl'\r\n\r\n names(clean_csv)[names(clean_csv) == 'hda1_mg_d_l'] <- 'ApoA1_HDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'hda2_mg_d_l'] <- 'ApoA2_HDL_mg_dl'\r\n\r\n names(clean_csv)[names(clean_csv) == 'vlab_mg_d_l'] <- 'ApoB_VLDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'idab_mg_d_l'] <- 'ApoB_IDL_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'ldab_mg_d_l'] <- 'ApoB_LDL_mg_dl'\r\n\r\n #VLDL Subfractions\r\n #TG\r\n names(clean_csv)[names(clean_csv) == 'v1tg_mg_d_l'] <- 'TG_VLDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v2tg_mg_d_l'] <- 'TG_VLDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v3tg_mg_d_l'] <- 'TG_VLDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v4tg_mg_d_l'] <- 'TG_VLDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v5tg_mg_d_l'] <- 'TG_VLDL-5_mg_dl'\r\n\r\n #CHOL\r\n names(clean_csv)[names(clean_csv) == 'v1ch_mg_d_l'] <- 'CHOL_VLDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v2ch_mg_d_l'] <- 'CHOL_VLDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v3ch_mg_d_l'] <- 'CHOL_VLDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v4ch_mg_d_l'] <- 'CHOL_VLDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v5ch_mg_d_l'] <- 'CHOL_VLDL-5_mg_dl'\r\n\r\n #free CHOL\r\n names(clean_csv)[names(clean_csv) == 'v1fc_mg_d_l'] <- 'fCHOL_VLDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v2fc_mg_d_l'] <- 'fCHOL_VLDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v3fc_mg_d_l'] <- 'fCHOL_VLDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v4fc_mg_d_l'] <- 'fCHOL_VLDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v5fc_mg_d_l'] <- 'fCHOL_VLDL-5_mg_dl'\r\n\r\n #PHOSpholipids\r\n names(clean_csv)[names(clean_csv) == 'v1pl_mg_d_l'] <- 'PHOSL_VLDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v2pl_mg_d_l'] <- 'PHOSL_VLDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v3pl_mg_d_l'] <- 'PHOSL_VLDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v4pl_mg_d_l'] <- 'PHOSL_VLDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'v5pl_mg_d_l'] <- 'PHOSL_VLDL-5_mg_dl'\r\n\r\n #LDL Subfractions\r\n #TG\r\n names(clean_csv)[names(clean_csv) == 'l1tg_mg_d_l'] <- 'TG_LDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l2tg_mg_d_l'] <- 'TG_LDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l3tg_mg_d_l'] <- 'TG_LDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l4tg_mg_d_l'] <- 'TG_LDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l5tg_mg_d_l'] <- 'TG_LDL-5_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l6tg_mg_d_l'] <- 'TG_LDL-6_mg_dl'\r\n\r\n #CHOL\r\n names(clean_csv)[names(clean_csv) == 'l1ch_mg_d_l'] <- 'CHOL_LDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l2ch_mg_d_l'] <- 'CHOL_LDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l3ch_mg_d_l'] <- 'CHOL_LDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l4ch_mg_d_l'] <- 'CHOL_LDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l5ch_mg_d_l'] <- 'CHOL_LDL-5_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l6ch_mg_d_l'] <- 'CHOL_LDL-6_mg_dl'\r\n\r\n #free CHOL\r\n names(clean_csv)[names(clean_csv) == 'l1fc_mg_d_l'] <- 'fCHOL_LDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l2fc_mg_d_l'] <- 'fCHOL_LDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l3fc_mg_d_l'] <- 'fCHOL_LDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l4fc_mg_d_l'] <- 'fCHOL_LDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l5fc_mg_d_l'] <- 'fCHOL_LDL-5_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l6fc_mg_d_l'] <- 'fCHOL_LDL-6_mg_dl'\r\n\r\n #PHOSpholipids\r\n names(clean_csv)[names(clean_csv) == 'l1pl_mg_d_l'] <- 'PHOSL_LDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l2pl_mg_d_l'] <- 'PHOSL_LDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l3pl_mg_d_l'] <- 'PHOSL_LDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l4pl_mg_d_l'] <- 'PHOSL_LDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l5pl_mg_d_l'] <- 'PHOSL_LDL-5_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l6pl_mg_d_l'] <- 'PHOSL_LDL-6_mg_dl'\r\n\r\n #ApoB\r\n names(clean_csv)[names(clean_csv) == 'l1ab_mg_d_l'] <- 'ApoB_LDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l2ab_mg_d_l'] <- 'ApoB_LDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l3ab_mg_d_l'] <- 'ApoB_LDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l4ab_mg_d_l'] <- 'ApoB_LDL-4_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l5ab_mg_d_l'] <- 'ApoB_LDL-5_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'l6ab_mg_d_l'] <- 'ApoB_LDL-6_mg_dl'\r\n\r\n #HDL Subfractions\r\n #TG\r\n names(clean_csv)[names(clean_csv) == 'h1tg_mg_d_l'] <- 'TG_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2tg_mg_d_l'] <- 'TG_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3tg_mg_d_l'] <- 'TG_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4tg_mg_d_l'] <- 'TG_HDL-4_mg_dl'\r\n\r\n #CHOL\r\n names(clean_csv)[names(clean_csv) == 'h1ch_mg_d_l'] <- 'CHOL_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2ch_mg_d_l'] <- 'CHOL_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3ch_mg_d_l'] <- 'CHOL_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4ch_mg_d_l'] <- 'CHOL_HDL-4_mg_dl'\r\n\r\n #free CHOL\r\n names(clean_csv)[names(clean_csv) == 'h1fc_mg_d_l'] <- 'fCHOL_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2fc_mg_d_l'] <- 'fCHOL_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3fc_mg_d_l'] <- 'fCHOL_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4fc_mg_d_l'] <- 'fCHOL_HDL-4_mg_dl'\r\n\r\n #Phospholipids\r\n names(clean_csv)[names(clean_csv) == 'h1pl_mg_d_l'] <- 'PHOSL_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2pl_mg_d_l'] <- 'PHOSL_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3pl_mg_d_l'] <- 'PHOSL_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4pl_mg_d_l'] <- 'PHOSL_HDL-4_mg_dl'\r\n\r\n #ApoA1\r\n names(clean_csv)[names(clean_csv) == 'h1a1_mg_d_l'] <- 'ApoA1_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2a1_mg_d_l'] <- 'ApoA1_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3a1_mg_d_l'] <- 'ApoA1_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4a1_mg_d_l'] <- 'ApoA1_HDL-4_mg_dl'\r\n\r\n #ApoA2\r\n names(clean_csv)[names(clean_csv) == 'h1a2_mg_d_l'] <- 'ApoA2_HDL-1_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h2a2_mg_d_l'] <- 'ApoA2_HDL-2_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h3a2_mg_d_l'] <- 'ApoA2_HDL-3_mg_dl'\r\n names(clean_csv)[names(clean_csv) == 'h4a2_mg_d_l'] <- 'ApoA2_HDL-4_mg_dl'\r\n #end----\r\n\r\n #plasma names----\r\n names(clean_csv)[names(clean_csv) == 'ethanol_conc_mmol_l'] <- 'Ethanol'\r\n names(clean_csv)[names(clean_csv) == 'trimethylamine_n_oxide_conc_mmol_l'] <- 'Trimethylamine N-oxide'\r\n names(clean_csv)[names(clean_csv) == 'x2_aminobutyric_acid_conc_mmol_l'] <- '2-Aminobutyrate'\r\n names(clean_csv)[names(clean_csv) == 'alanine_conc_mmol_l'] <- 'Alanine'\r\n names(clean_csv)[names(clean_csv) == 'asparagine_conc_mmol_l'] <- 'Asparagine'\r\n names(clean_csv)[names(clean_csv) == 'creatine_conc_mmol_l'] <- 'Creatine'\r\n names(clean_csv)[names(clean_csv) == 'creatinine_conc_mmol_l'] <- 'Creatinine'\r\n names(clean_csv)[names(clean_csv) == 'glutamic_acid_conc_mmol_l'] <- 'Glutamate'\r\n names(clean_csv)[names(clean_csv) == 'glutamine_conc_mmol_l'] <- 'Glutamine'\r\n names(clean_csv)[names(clean_csv) == 'glycine_conc_mmol_l'] <- 'Glycine'\r\n names(clean_csv)[names(clean_csv) == 'histidine_conc_mmol_l'] <- 'Histidine'\r\n names(clean_csv)[names(clean_csv) == 'isoleucine_conc_mmol_l'] <- 'Isoleucine'\r\n names(clean_csv)[names(clean_csv) == 'leucine_conc_mmol_l'] <- 'Leucine'\r\n names(clean_csv)[names(clean_csv) == 'lysine_conc_mmol_l'] <- 'Lysine'\r\n names(clean_csv)[names(clean_csv) == 'methionine_conc_mmol_l'] <- 'Methionine'\r\n names(clean_csv)[names(clean_csv) == 'n_n_dimethylglycine_conc_mmol_l'] <- 'N,N-Dimethylglycine'\r\n names(clean_csv)[names(clean_csv) == 'ornithine_conc_mmol_l'] <- 'Ornithine'\r\n names(clean_csv)[names(clean_csv) == 'phenylalanine_conc_mmol_l'] <- 'Phenylalanine'\r\n names(clean_csv)[names(clean_csv) == 'proline_conc_mmol_l'] <- 'Proline'\r\n names(clean_csv)[names(clean_csv) == 'sarcosine_conc_mmol_l'] <- 'Sarcosine'\r\n names(clean_csv)[names(clean_csv) == 'threonine_conc_mmol_l'] <- 'Threonine'\r\n names(clean_csv)[names(clean_csv) == 'tyrosine_conc_mmol_l'] <- 'Tyrosine'\r\n names(clean_csv)[names(clean_csv) == 'valine_conc_mmol_l'] <- 'Valine'\r\n names(clean_csv)[names(clean_csv) == 'x2_hydroxybutyric_acid_conc_mmol_l'] <- '2-Hydroxybutyrate'\r\n names(clean_csv)[names(clean_csv) == 'acetic_acid_conc_mmol_l'] <- 'Acetate'\r\n names(clean_csv)[names(clean_csv) == 'citric_acid_conc_mmol_l'] <- 'Citrate'\r\n names(clean_csv)[names(clean_csv) == 'formic_acid_conc_mmol_l'] <- 'Formate'\r\n names(clean_csv)[names(clean_csv) == 'lactic_acid_conc_mmol_l'] <- 'Lactate'\r\n names(clean_csv)[names(clean_csv) == 'succinic_acid_conc_mmol_l'] <- 'Succinate'\r\n names(clean_csv)[names(clean_csv) == 'choline_conc_mmol_l'] <- 'Choline'\r\n names(clean_csv)[names(clean_csv) == 'x2_oxoglutaric_acid_conc_mmol_l'] <- '2-Oxoglutarate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxybutyric_acid_conc_mmol_l'] <- '3-Hydroxybutyrate'\r\n names(clean_csv)[names(clean_csv) == 'acetoacetic_acid_conc_mmol_l'] <- 'Acetoacetate'\r\n names(clean_csv)[names(clean_csv) == 'acetone_conc_mmol_l'] <- 'Acetone'\r\n names(clean_csv)[names(clean_csv) == 'pyruvic_acid_conc_mmol_l'] <- 'Pyruvate'\r\n names(clean_csv)[names(clean_csv) == 'd_galactose_conc_mmol_l'] <- 'Galactose'\r\n names(clean_csv)[names(clean_csv) == 'glucose_conc_mmol_l'] <- 'Glucose'\r\n names(clean_csv)[names(clean_csv) == 'glycerol_conc_mmol_l'] <- 'Glycerol'\r\n names(clean_csv)[names(clean_csv) == 'dimethylsulfone_conc_mmol_l'] <- 'Dimethylsufone'\r\n names(clean_csv)[names(clean_csv) == 'ca_edta_conc_mmol_l'] <- 'Ca_EDTA'\r\n names(clean_csv)[names(clean_csv) == 'k_edta_conc_mmol_l'] <- 'K_EDTA'\r\n\r\n #urine names ----\r\n names(clean_csv)[names(clean_csv) == 'creatinine_conc_mmol_l'] <- 'Creatinine'\r\n names(clean_csv)[names(clean_csv) == 'ethanol_conc_mmol_mol_crea'] <- 'Ethanol'\r\n names(clean_csv)[names(clean_csv) == 'isopropanol_conc_mmol_mol_crea'] <- 'Isopropanol'\r\n names(clean_csv)[names(clean_csv) == 'methanol_conc_mmol_mol_crea'] <- 'Methanol'\r\n names(clean_csv)[names(clean_csv) == 'propylene_glycol_conc_mmol_mol_crea'] <- 'Propylene glycol'\r\n names(clean_csv)[names(clean_csv) == 'x1_methylguanidine_conc_mmol_mol_crea'] <- 'Methylguanidine'\r\n names(clean_csv)[names(clean_csv) == 'dimethylamine_conc_mmol_mol_crea'] <- 'Dimethylamine'\r\n names(clean_csv)[names(clean_csv) == 'trimethylamine_conc_mmol_mol_crea'] <- 'Trimethylamine'\r\n names(clean_csv)[names(clean_csv) == 'tyramine_conc_mmol_mol_crea'] <- 'Tyramine'\r\n names(clean_csv)[names(clean_csv) == 'x1_methylhistidine_conc_mmol_mol_crea'] <- '1-Methylhistidine'\r\n names(clean_csv)[names(clean_csv) == 'x2_furoylglycine_conc_mmol_mol_crea'] <- '2-Furoylglycine'\r\n names(clean_csv)[names(clean_csv) == 'x3_aminoisobutyric_acid_conc_mmol_mol_crea'] <- '3-Aminoisobutyrate'\r\n names(clean_csv)[names(clean_csv) == 'x3_methylcrotonylglycine_conc_mmol_mol_crea'] <- '3-Methylcrotonylglycine'\r\n names(clean_csv)[names(clean_csv) == 'x4_aminobutyric_acid_conc_mmol_mol_crea'] <- '4-Aminobutyrate'\r\n names(clean_csv)[names(clean_csv) == 'x5_aminopentanoic_acid_conc_mmol_mol_crea'] <- '5-Aminopentanoate'\r\n names(clean_csv)[names(clean_csv) == 'alanine_conc_mmol_mol_crea'] <- 'Alanine'\r\n names(clean_csv)[names(clean_csv) == 'arginine_conc_mmol_mol_crea'] <- 'Arginine'\r\n names(clean_csv)[names(clean_csv) == 'argininosuccinic_acid_conc_mmol_mol_crea'] <- 'Argininosuccinate'\r\n names(clean_csv)[names(clean_csv) == 'betaine_conc_mmol_mol_crea'] <- 'Betaine'\r\n names(clean_csv)[names(clean_csv) == 'citrulline_conc_mmol_mol_crea'] <- 'Citrulline'\r\n names(clean_csv)[names(clean_csv) == 'creatine_conc_mmol_mol_crea'] <- 'Creatine'\r\n names(clean_csv)[names(clean_csv) == 'cystine_conc_mmol_mol_crea'] <- 'Cystine'\r\n names(clean_csv)[names(clean_csv) == 'dl_alloisoleucine_conc_mmol_mol_crea'] <- 'DL-Alloisoleucine'\r\n names(clean_csv)[names(clean_csv) == 'dl_tyrosine_conc_mmol_mol_crea'] <- 'Tyrosine'\r\n names(clean_csv)[names(clean_csv) == 'glutamic_acid_conc_mmol_mol_crea'] <- 'Glutamate'\r\n names(clean_csv)[names(clean_csv) == 'glutamine_conc_mmol_mol_crea'] <- 'Glutamine'\r\n names(clean_csv)[names(clean_csv) == 'glycine_conc_mmol_mol_crea'] <- 'Glycine'\r\n names(clean_csv)[names(clean_csv) == 'guanidinoacetic_acid_conc_mmol_mol_crea'] <- 'Guanidinoacetate'\r\n names(clean_csv)[names(clean_csv) == 'isobutyrylglycine_conc_mmol_mol_crea'] <- 'Isobutyrylglycine'\r\n names(clean_csv)[names(clean_csv) == 'l_carnosine_conc_mmol_mol_crea'] <- 'L-Carnosine'\r\n names(clean_csv)[names(clean_csv) == 'l_homocystine_conc_mmol_mol_crea'] <- 'L-Homocystine'\r\n names(clean_csv)[names(clean_csv) == 'l_isoleucine_conc_mmol_mol_crea'] <- 'L-Isoleucine'\r\n names(clean_csv)[names(clean_csv) == 'l_pyroglutamic_acid_conc_mmol_mol_crea'] <- 'L-Pyroglutamate'\r\n names(clean_csv)[names(clean_csv) == 'l_tryptophan_conc_mmol_mol_crea'] <- 'L-Tryptophan'\r\n names(clean_csv)[names(clean_csv) == 'leucine_conc_mmol_mol_crea'] <- 'Leucine'\r\n names(clean_csv)[names(clean_csv) == 'methionine_conc_mmol_mol_crea'] <- 'Methionine'\r\n names(clean_csv)[names(clean_csv) == 'n_n_dimethylglycine_conc_mmol_mol_crea'] <- 'N,N-Dimethylglycine'\r\n names(clean_csv)[names(clean_csv) == 'n_acetylaspartic_acid_conc_mmol_mol_crea'] <- 'N-Acetylaspartate'\r\n names(clean_csv)[names(clean_csv) == 'n_acetylglutamate_conc_mmol_mol_crea'] <- 'N-Acetylglutamate'\r\n names(clean_csv)[names(clean_csv) == 'n_acetylphenylalanine_conc_mmol_mol_crea'] <- 'N-Acetylphenylalanine'\r\n names(clean_csv)[names(clean_csv) == 'n_acetyltyrosine_conc_mmol_mol_crea'] <- 'N-Acetyltyrosine'\r\n names(clean_csv)[names(clean_csv) == 'n_isovaleroylglycine_conc_mmol_mol_crea'] <- 'N-Isovaleroylglycine'\r\n names(clean_csv)[names(clean_csv) == 'phenylalanine_conc_mmol_mol_crea'] <- 'Phenylalanine'\r\n names(clean_csv)[names(clean_csv) == 'proline_betaine_conc_mmol_mol_crea'] <- 'Proline betaine'\r\n names(clean_csv)[names(clean_csv) == 'propionylglycine_conc_mmol_mol_crea'] <- 'Propionylglycine'\r\n names(clean_csv)[names(clean_csv) == 'sarcosine_conc_mmol_mol_crea'] <- 'Sarcosine'\r\n names(clean_csv)[names(clean_csv) == 'taurine_conc_mmol_mol_crea'] <- 'Taurine'\r\n names(clean_csv)[names(clean_csv) == 'tiglylglycine_conc_mmol_mol_crea'] <- 'Tiglylglycine'\r\n names(clean_csv)[names(clean_csv) == 'valine_conc_mmol_mol_crea'] <- 'Valine'\r\n names(clean_csv)[names(clean_csv) == 'x2_hydroxyphenylacetic_acid_conc_mmol_mol_crea'] <- '2-Hydroxyphenylacetate'\r\n names(clean_csv)[names(clean_csv) == 'x3_phenyllactic_acid_conc_mmol_mol_crea'] <- '3-Phenyllactate'\r\n names(clean_csv)[names(clean_csv) == 'x4_aminohippuric_acid_conc_mmol_mol_crea'] <- '4-Aminohippurate'\r\n names(clean_csv)[names(clean_csv) == 'x4_ethylphenol_conc_mmol_mol_crea'] <- '4-Ethylphenol'\r\n names(clean_csv)[names(clean_csv) == 'x4_hydroxyhippuric_acid_conc_mmol_mol_crea'] <- '4-Hydroxyhippurate'\r\n names(clean_csv)[names(clean_csv) == 'x4_hydroxyphenylacetic_acid_conc_mmol_mol_crea'] <- '4-Hydroxyphenylacetate'\r\n names(clean_csv)[names(clean_csv) == 'x4_hydroxyphenyllactic_acid_conc_mmol_mol_crea'] <- '4-Hyxroxyphenyllactate'\r\n names(clean_csv)[names(clean_csv) == 'benzoic_acid_conc_mmol_mol_crea'] <- 'Benzoate'\r\n names(clean_csv)[names(clean_csv) == 'd_mandelic_acid_conc_mmol_mol_crea'] <- 'D-Mandelicate'\r\n names(clean_csv)[names(clean_csv) == 'hippuric_acid_conc_mmol_mol_crea'] <- 'Hippurate'\r\n names(clean_csv)[names(clean_csv) == 'phenylacetic_acid_conc_mmol_mol_crea'] <- 'Phenylacetate'\r\n names(clean_csv)[names(clean_csv) == 'phenylpyruvic_acid_conc_mmol_mol_crea'] <- 'Phenylpyruvate'\r\n names(clean_csv)[names(clean_csv) == 'pyrocatechol_conc_mmol_mol_crea'] <- 'Pyrocatechol'\r\n names(clean_csv)[names(clean_csv) == 'syringic_acid_conc_mmol_mol_crea'] <- 'Syringate'\r\n names(clean_csv)[names(clean_csv) == 'x5_aminolevulinic_acid_conc_mmol_mol_crea'] <- '5-Aminolevulinate'\r\n names(clean_csv)[names(clean_csv) == 'acetic_acid_conc_mmol_mol_crea'] <- 'Acetate'\r\n names(clean_csv)[names(clean_csv) == 'citric_acid_conc_mmol_mol_crea'] <- 'Citrate'\r\n names(clean_csv)[names(clean_csv) == 'e_glutaconic_acid_conc_mmol_mol_crea'] <- 'Glutaconate'\r\n names(clean_csv)[names(clean_csv) == 'ethylmalonic_acid_conc_mmol_mol_crea'] <- 'Ethylmalonate'\r\n names(clean_csv)[names(clean_csv) == 'formic_acid_conc_mmol_mol_crea'] <- 'Formate'\r\n names(clean_csv)[names(clean_csv) == 'fumaric_acid_conc_mmol_mol_crea'] <- 'Fumarate'\r\n names(clean_csv)[names(clean_csv) == 'glutaric_acid_conc_mmol_mol_crea'] <- 'Glutarate'\r\n names(clean_csv)[names(clean_csv) == 'imidazole_conc_mmol_mol_crea'] <- 'Imidazole'\r\n names(clean_csv)[names(clean_csv) == 'lactic_acid_conc_mmol_mol_crea'] <- 'Lactate'\r\n names(clean_csv)[names(clean_csv) == 'maleic_acid_conc_mmol_mol_crea'] <- 'Maleic acid'\r\n names(clean_csv)[names(clean_csv) == 'methylmalonic_acid_conc_mmol_mol_crea'] <- 'Methylmalonate'\r\n names(clean_csv)[names(clean_csv) == 'propionic_acid_conc_mmol_mol_crea'] <- 'Propionate'\r\n names(clean_csv)[names(clean_csv) == 'succinic_acid_conc_mmol_mol_crea'] <- 'Succinate'\r\n names(clean_csv)[names(clean_csv) == 'tartaric_acid_conc_mmol_mol_crea'] <- 'Tartarate'\r\n names(clean_csv)[names(clean_csv) == 'trigonelline_conc_mmol_mol_crea'] <- 'Trigonelline'\r\n names(clean_csv)[names(clean_csv) == 'xanthurenic_acid_conc_mmol_mol_crea'] <- 'Xanthurenate'\r\n names(clean_csv)[names(clean_csv) == 'choline_conc_mmol_mol_crea'] <- 'Choline'\r\n names(clean_csv)[names(clean_csv) == 'd_panthenol_conc_mmol_mol_crea'] <- 'D-Panthenol'\r\n names(clean_csv)[names(clean_csv) == 'l_ascorbic_acid_conc_mmol_mol_crea'] <- 'L-Ascorbate'\r\n names(clean_csv)[names(clean_csv) == 'pantothenic_acid_conc_mmol_mol_crea'] <- 'Pantothenate'\r\n names(clean_csv)[names(clean_csv) == 'paracetamol_conc_mmol_mol_crea'] <- 'Paracetamol'\r\n names(clean_csv)[names(clean_csv) == 'paracetamol_glucuronide_conc_mmol_mol_crea'] <- 'Paracetamol-Glucuronide'\r\n names(clean_csv)[names(clean_csv) == 'x2_hydroxy_4_methylvaleric_acid_conc_mmol_mol_crea'] <- '2-Hydroxy-4-Methylvalerate'\r\n names(clean_csv)[names(clean_csv) == 'x2_hydroxyisovaleric_acid_conc_mmol_mol_crea'] <- '2-Hydroxyisovalerate'\r\n names(clean_csv)[names(clean_csv) == 'x2_methylsuccinic_acid_conc_mmol_mol_crea'] <- '2-Methylsuccinate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxy_3_methylglutaric_acid_conc_mmol_mol_crea'] <- '3-Hydroxy-3-Methylglutarate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxyisovaleric_acid_conc_mmol_mol_crea'] <- '3-Hydroxyisovalerate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxyvaleric_acid_conc_mmol_mol_crea'] <- '3-Hydroxyvalerate'\r\n names(clean_csv)[names(clean_csv) == 'x3_methylglutaconic_acid_conc_mmol_mol_crea'] <- '3-Methylglutaconate'\r\n names(clean_csv)[names(clean_csv) == 'butyric_acid_conc_mmol_mol_crea'] <- 'Butyrate'\r\n names(clean_csv)[names(clean_csv) == 'citraconic_acid_conc_mmol_mol_crea'] <- 'Citraconate'\r\n names(clean_csv)[names(clean_csv) == 'l_citramalic_acid_conc_mmol_mol_crea'] <- 'L-Citramalate'\r\n names(clean_csv)[names(clean_csv) == 'pimelic_acid_conc_mmol_mol_crea'] <- 'Pimelate'\r\n names(clean_csv)[names(clean_csv) == 'thymol_conc_mmol_mol_crea'] <- 'Thymol'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxyglutaric_acid_conc_mmol_mol_crea'] <- '3-Hydroxyglutarate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxypropionic_acid_conc_mmol_mol_crea'] <- '3-Hydroxypropionate'\r\n names(clean_csv)[names(clean_csv) == 'd_galactonic_acid_conc_mmol_mol_crea'] <- 'D-Galactonate'\r\n names(clean_csv)[names(clean_csv) == 'd_gluconic_acid_conc_mmol_mol_crea'] <- 'D-Gluconate'\r\n names(clean_csv)[names(clean_csv) == 'glycolic_acid_conc_mmol_mol_crea'] <- 'Glycolate'\r\n names(clean_csv)[names(clean_csv) == 'malic_acid_conc_mmol_mol_crea'] <- 'Malate'\r\n names(clean_csv)[names(clean_csv) == 'x2_ketobutyric_acid_conc_mmol_mol_crea'] <- '2-Ketobutyrate'\r\n names(clean_csv)[names(clean_csv) == 'x2_oxoglutaric_acid_conc_mmol_mol_crea'] <- '2-Oxoglutarate'\r\n names(clean_csv)[names(clean_csv) == 'x2_oxoisocaproic_acid_conc_mmol_mol_crea'] <- '2-Oxoisocaproate'\r\n names(clean_csv)[names(clean_csv) == 'x2_oxoisovaleric_acid_conc_mmol_mol_crea'] <- '2-Oxoisovalerate'\r\n names(clean_csv)[names(clean_csv) == 'x3_hydroxybutyric_acid_conc_mmol_mol_crea'] <- '3-Hydroxybutyrate'\r\n names(clean_csv)[names(clean_csv) == 'x3_methyl_2_oxovaleric_acid_conc_mmol_mol_crea'] <- '3-Methyl-2-Oxovalerate'\r\n names(clean_csv)[names(clean_csv) == 'x4_hydroxyphenylpyruvic_acid_conc_mmol_mol_crea'] <- '4-Hydroxyphenylpyruvate'\r\n names(clean_csv)[names(clean_csv) == 'acetoacetic_acid_conc_mmol_mol_crea'] <- 'Acetoacetate'\r\n names(clean_csv)[names(clean_csv) == 'acetoine_conc_mmol_mol_crea'] <- 'Acetoine'\r\n names(clean_csv)[names(clean_csv) == 'acetone_conc_mmol_mol_crea'] <- 'Acetone'\r\n names(clean_csv)[names(clean_csv) == 'dl_kynurenin_conc_mmol_mol_crea'] <- 'DL-Kynurenin'\r\n names(clean_csv)[names(clean_csv) == 'oxaloacetic_acid_conc_mmol_mol_crea'] <- 'Oxaloacetate'\r\n names(clean_csv)[names(clean_csv) == 'pyruvic_acid_conc_mmol_mol_crea'] <- 'Pyruvate'\r\n names(clean_csv)[names(clean_csv) == 'succinylacetone_conc_mmol_mol_crea'] <- 'Succinylacetone'\r\n names(clean_csv)[names(clean_csv) == 'x1_3_dimethyluric_acid_conc_mmol_mol_crea'] <- '1,3-Dimethylurate'\r\n names(clean_csv)[names(clean_csv) == 'x1_methyladenosine_conc_mmol_mol_crea'] <- '1-Methyladenosine'\r\n names(clean_csv)[names(clean_csv) == 'x1_methylhydantoin_conc_mmol_mol_crea'] <- '1-Methylhydantoin'\r\n names(clean_csv)[names(clean_csv) == 'x1_methylnicotinamide_conc_mmol_mol_crea'] <- '1-Methylnicotinamie'\r\n names(clean_csv)[names(clean_csv) == 'x4_pyridoxic_acid_conc_mmol_mol_crea'] <- '4-Pyridoxate'\r\n names(clean_csv)[names(clean_csv) == 'adenine_conc_mmol_mol_crea'] <- 'Adenine'\r\n names(clean_csv)[names(clean_csv) == 'adenosine_conc_mmol_mol_crea'] <- 'Adenosine'\r\n names(clean_csv)[names(clean_csv) == 'allantoin_conc_mmol_mol_crea'] <- 'Allantoin'\r\n names(clean_csv)[names(clean_csv) == 'allopurinol_conc_mmol_mol_crea'] <- 'Allopurinol'\r\n names(clean_csv)[names(clean_csv) == 'caffeine_conc_mmol_mol_crea'] <- 'Caffeine'\r\n names(clean_csv)[names(clean_csv) == 'cytosine_conc_mmol_mol_crea'] <- 'Cytosine'\r\n names(clean_csv)[names(clean_csv) == 'dihydrothymine_conc_mmol_mol_crea'] <- 'Dihydrothymine'\r\n names(clean_csv)[names(clean_csv) == 'dihydrouracil_conc_mmol_mol_crea'] <- 'Dihydrouracil'\r\n names(clean_csv)[names(clean_csv) == 'inosine_conc_mmol_mol_crea'] <- 'Inosine'\r\n names(clean_csv)[names(clean_csv) == 'neopterin_conc_mmol_mol_crea'] <- 'Neopterin'\r\n names(clean_csv)[names(clean_csv) == 'orotic_acid_conc_mmol_mol_crea'] <- 'Orotate'\r\n names(clean_csv)[names(clean_csv) == 'oxypurinol_conc_mmol_mol_crea'] <- 'Oxypurinol'\r\n names(clean_csv)[names(clean_csv) == 'quinolinic_acid_conc_mmol_mol_crea'] <- 'Quinolinate'\r\n names(clean_csv)[names(clean_csv) == 'theobromine_conc_mmol_mol_crea'] <- 'Theobromine'\r\n names(clean_csv)[names(clean_csv) == 'thymine_conc_mmol_mol_crea'] <- 'Thymine'\r\n names(clean_csv)[names(clean_csv) == 'uracil_conc_mmol_mol_crea'] <- 'Uracil'\r\n names(clean_csv)[names(clean_csv) == 'uridine_conc_mmol_mol_crea'] <- 'Uridine'\r\n names(clean_csv)[names(clean_csv) == 'd_galactose_conc_mmol_mol_crea'] <- 'D-Galactose'\r\n names(clean_csv)[names(clean_csv) == 'd_glucose_conc_mmol_mol_crea'] <- 'D-Glucose'\r\n names(clean_csv)[names(clean_csv) == 'd_lactose_conc_mmol_mol_crea'] <- 'D-Lactose'\r\n names(clean_csv)[names(clean_csv) == 'd_mannitol_conc_mmol_mol_crea'] <- 'D-Mannitol'\r\n names(clean_csv)[names(clean_csv) == 'd_mannose_conc_mmol_mol_crea'] <- 'D-Mannose'\r\n names(clean_csv)[names(clean_csv) == 'galactitol_conc_mmol_mol_crea'] <- 'Galactitol'\r\n names(clean_csv)[names(clean_csv) == 'glycerol_conc_mmol_mol_crea'] <- 'Glycerol'\r\n names(clean_csv)[names(clean_csv) == 'l_fucose_conc_mmol_mol_crea'] <- 'L-Fucose'\r\n names(clean_csv)[names(clean_csv) == 'l_threonic_acid_conc_mmol_mol_crea'] <- 'L-Threonate'\r\n names(clean_csv)[names(clean_csv) == 'd_xylose_conc_mmol_mol_crea'] <- 'D-Xylose'\r\n names(clean_csv)[names(clean_csv) == 'myo_inositol_conc_mmol_mol_crea'] <- 'Myo-Inositol'\r\n\r\n\r\n\r\n clean_csv<-clean_csv\r\n }\r\n #rest----\r\n # #Only keeps concentration columns\r\n # clean_csv=data[keeps]\r\n # clean_csv<-clean_names(clean_csv)\r\n # #cleans up all spaces and puts _ in there\r\n # newnames<-sub(\"_conc.*\",\"\",colnames(clean_csv))\r\n # #removes everything after _conc\r\n #oldnames<-colnames(clean_csv)\r\n # #gets the old names into a string\r\n #clean_csv<- clean_csv %>%\r\n # rename_with(~ newnames[which(oldnames == .x)], .cols = oldnames)\r\n # #replaces oldnames with newnames\r\n # newnames2<-sub(\"_e.*\",\"\",clean_csv [,1])\r\n # clean_csv$sample_id<-newnames2\r\n\r\n\r\n #replaces the Sample names with just their Code\r\n # write.csv(clean_csv, paste0(format(Sys.time(), \"%d-%b-%Y %H.%M.%S\"), \".csv\"),row.names=FALSE)\r\n # return(\"Bruker CSV cleaned and resaved with timestamp.\")\r\n\r\n if(names==\"No\"){\r\n clean_csv<-clean_names(data)\r\n\r\n if(\"instrument\" %in% colnames(clean_csv)){\r\n clean_csv<-subset(clean_csv, select = -c(directory,exp_no,proc_no,experiment,pulse_program,aunm,aunmp,instrument,probehead,ns,ds,p1,pld_b_1,pld_b9,rg,swh,td,si,te,phc0,phc1,o1,sf,sr,date,date_2,name,type))\r\n clean_csv<-clean_csv\r\n }\r\n if(\"is_b_i_quant_ur_ne\" %in% colnames(clean_csv)){\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ur_ne,is_quant_ur_ne_1_1_0))\r\n crea<-select(clean_csv, contains(\"creatinine_conc_\"))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_mol_crea\"))\r\n clean_csv<-clean_csv[seq(1, ncol(clean_csv),3)]\r\n clean_csv<-cbind(ID,crea,clean_csv)\r\n }\r\n if(\"is_b_i_quant_ur_e\" %in% colnames(clean_csv)){\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ur_e,is_quant_ur_e_1_1_0))\r\n crea<-select(clean_csv, contains(\"creatinine_conc_\"))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_mol_crea\"))\r\n clean_csv<-clean_csv[seq(1, ncol(clean_csv),3)]\r\n clean_csv<-cbind(ID,crea,clean_csv)\r\n }\r\n if(\"is_b_i_quant_ps\" %in% colnames(clean_csv)){\r\n ID<-subset(clean_csv, select=sample_id)\r\n ID<-sub(\"_e.*\",\"\",ID [,1])\r\n clean_csv<-subset(clean_csv, select = -c(measurement_date,reporting_date,is_b_i_quant_ps,is_quant_ps_2_0_0))\r\n clean_csv<-select(clean_csv, contains(\"_conc_mmol_l\"))\r\n clean_csv<-clean_csv[seq(1, ncol(clean_csv),3)]\r\n clean_csv<-cbind(ID,clean_csv)\r\n }\r\n\r\n }\r\n clean_csv<-clean_csv\r\n}\r\n\r\n #Waiting screen----\r\n waiting_screen <- tagList(\r\n spin_hexdots(),br(),br(),br(),br(),\r\n h4(style=\"color:black\",\"Generating Plots...\")\r\n )\r\n# UI ----\r\napp_ui <-dashboardPage(\r\n dashboardHeader(title=\"MoonNMR\"),\r\n dashboardSidebar(\r\n sidebarMenu(collapsed=FALSE,\r\n menuItem(\"Home\", tabName = \"home\", icon = icon(\"home\"),selected=T),\r\n menuItem(\"Preparation\",icon=icon(\"dolly\"),\r\n menuSubItem(\"IVDr Upload\", tabName = \"creator\", icon = icon(\"truck\")),\r\n menuSubItem(\"IVDr Manual\", tabName = \"manual\", icon = icon(\"people-carry\")),\r\n menuSubItem(\"ICON Upload\", tabName = \"iconup\", icon = icon(\"car-side\")),\r\n menuSubItem(\"ICON Manual\", tabName = \"iconman\", icon = icon(\"wrench\"))\r\n ),\r\n menuItem(\"Extraction\", icon=icon(\"bacon\"),\r\n menuSubItem(\"XML\", tabName = \"xml\", icon = icon(\"file-import\")),\r\n menuSubItem(\"Bruker File\", tabName = \"extractor\", icon = icon(\"crow\"))\r\n ),\r\n menuItem(\"Processing\", icon=icon(\"table\"),\r\n menuSubItem(\"Delete\",tabName=\"deleter\",icon=icon(\"trash\")),\r\n menuSubItem(\"Combine\",tabName=\"combiner\",icon=icon(\"handshake\"))\r\n ),\r\n menuItem(\"Analysis\",icon=icon(\"flask\"),\r\n menuSubItem(\"Normalize\", tabName = \"normalizer\", icon = icon(\"balance-scale-right\")),\r\n menuSubItem(\"Bar Plots\", tabName = \"plots\", icon = icon(\"chart-bar\")),\r\n menuSubItem(\"PCA\", tabName = \"pca\", icon = icon(\"chart-area\"))\r\n )\r\n )\r\n ),\r\n dashboardBody(\r\n\r\n tags$head(tags$style(HTML('\r\n /* logo */\r\n .skin-blue .main-header .logo {\r\n background-color: #001787;\r\n color:#c4ff00;\r\n /* logo when hovered */\r\n .skin-blue .main-header .logo:hover {\r\n background-color: #8d89ad;\r\n }\r\n }\r\n /* navbar (rest of the header) */\r\n .skin-blue .main-header .navbar {\r\n background-color: #001787;\r\n color:#c4ff00;\r\n }\r\n .skin-blue .main-header .sidebar-toffle {\r\n background-color: #001787;\r\n color:#c4ff00;\r\n }\r\n\r\n /* main sidebar */\r\n .skin-blue .main-sidebar {\r\n background-color: #001787;\r\n color:#c4ff00;\r\n }\r\n /* active selected tab in the sidebarmenu */\r\n .skin-blue .main-sidebar .sidebar .sidebar-menu .active a{\r\n background-color: #001787;\r\n color:#c4ff00;\r\n }\r\n /* other links in the sidebarmenu */\r\n .skin-blue .main-sidebar .sidebar .sidebar-menu a{\r\n background-color: #001787;\r\n color: #ffffff;\r\n }\r\n /* other links in the sidebarmenu when hovered */\r\n .skin-blue .main-sidebar .sidebar .sidebar-menu a:hover{\r\n background-color: #8d89ad;\r\n }\r\n /* toggle button when hovered */\r\n .skin-blue .main-header .navbar .sidebar-toggle{\r\n color:#c4ff00;\r\n }\r\n /* toggle button when hovered */\r\n .skin-blue .box.box-solid.box-primary>.box-header {\r\n background-color: #001787;\r\n color:#c4ff00;\r\n }\r\n\r\n '))),\r\n\r\n autoWaiter(c(\"n_histogramm\",\"n_distribution\",\"n_exampletable\",\"xmltodata\")),\r\n tabItems(\r\n #Home----\r\n tabItem(tabName=\"home\",\r\n fluidRow(\r\n column(width=4,\r\n box(width=12,\r\n h1(\"MoonNMR - Local Edition\"),\r\n #img(src=\"https://user-images.githubusercontent.com/88379260/157672281-8f3902d3-998e-48cc-a445-25dc17a42fa5.png\", height=\"20%\",width=\"20%\",align=\"left\"),\r\n h3(HTML(\"MetabolOmics OrgaNiser NMR\"),align=\"left\"),\r\n p(\"Tools for high-throughput NMR and untargeted Metabolomics\",align=\"left\"),\r\n br(),\r\n p(\"This is the local version of MoonNMR for the online version\",a(\"click here\",href=\"https://funkam.shinyapps.io/MoonShiny/\")),\r\n br(),\r\n p(\"Developed by\",a(\"Alexander Funk\",href=\"https://www.uniklinikum-dresden.de/de/das-klinikum/kliniken-polikliniken-institute/klinische-chemie-und-laboratoriumsmedizin/forschung/copy_of_EMS\")),\r\n p(\"Institute for Clinical Chemistry and Laboratory Medicine\"),\r\n p(\"University Hospital Dresden, Fetscherstr. 74, 01307 Dresden\")\r\n ),\r\n tabBox(width=12,title=\"Tools\",\r\n tabPanel(title=\"Preparation\",\r\n h4(HTML(\"IVDr Template Creator\")),\r\n p(\"Create ICON template for use with IVDr protocols and Bruker SampleJet, either by upload or by sample-by-sample creation.\"),\r\n p(\"Includes automatic filling of sample spaces in racks (1-96), automatic change of rack spaces (1-5) and interactive experiment selection.\"),\r\n p(\"Allows quick creation of large sample tables ready for submission.\"),\r\n h4(HTML(\"ICON Template Creator\")),\r\n p(\"An additinal Tool for ICON is available that allows non-IVDr template creation with an upload of a list of experiments to be performed.\"),\r\n ),\r\n tabPanel(title=\"Processing\",\r\n h4(HTML(\"Import .xml into csv\")),\r\n p(\"Upload a zipped folder containing multiple .xml from B.I.QUANT-Methods and B.I.LISA\"),\r\n p(\"Create an organised and cleaned-up datatable\"),\r\n h4(HTML(\"Clean-Up machine-CSV\")),\r\n p(\"Extract data from Bruker created files. Removes unwanted columns and uses HMBD names, when possible.\"),\r\n p(\"Due to the uneven text output by Bruker of Metabolic Profiles (;-csv) and Lipidomics Analysis (Tab-csv), it is possible to upload either one\"),\r\n h4(HTML(\"Combine tables\")),\r\n p(\"Combine two tables when a shared column (UniqueID) is present. The columns can be selected from list.\"),\r\n ),\r\n tabPanel(title=\"Analysis\",\r\n h4(HTML(\"Normalization\")),\r\n p(\"Normalize data in preparation for parametrric statistical analysis. Histogramm and Distrubtion plot interactively change upon option selection.\"),\r\n h4(HTML(\"Automated Bar Plots\")),\r\n p(\"Upload a data table and interactively select columns to plot against a group column. Creation of large amount of plots possible, but are stored in a single PDF.\"),\r\n p(\"Suggested grouped plots (e.g. Lipid-Subclasses involving Triglycerides) are also available.\")\r\n )\r\n ),\r\n # box(width=12,\r\n # title=\"Changelog\",status=\"primary\",solidHeader=TRUE,\r\n # tableOutput(\"changelog\")\r\n # )\r\n ),\r\n column(width=8,\r\n tabBox(width=12,title=\"Archive Plots\",selected=\"Samples\",\r\n tabPanel(title=\"Samples\",\r\n plotlyOutput(width=\"100%\",\"archive_2020\")),\r\n tabPanel(title=\"Projects\",\r\n plotlyOutput(width=\"100%\",\"archive_project_2020\"))\r\n ),\r\n box(width=12,title=\"Archive\",status=\"primary\",solidHeader = TRUE,\r\n DT::dataTableOutput('archive')\r\n )\r\n )\r\n )\r\n\r\n ),\r\n\r\n #IVDr Uploader----\r\n tabItem(tabName=\"creator\",\r\n h2(\"Create IVDr ICON Template from File\"),\r\n fluidRow(\r\n box(width=3,title=\"Input\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"file1_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1),\r\n fileInput(\"file1\", \"File input\", multiple=FALSE),\r\n #textInput('file1sheet','Name of Sheet (Case-Sensitive)'),\r\n radioGroupButtons(\"type\",\"Sample Type\",\r\n choices=c(\"Plasma\"=\"plasma\",\"Urine(neo)\"=\"urine_neo\",\"Urine\"=\"urine\",\"CSF\"=\"CSF\",\"MEOH\"=\"MEOH\"),\r\n selected=\"plasma\"),\r\n radioGroupButtons(\"size\",\"Sample Size\",\r\n choices=c(\"5 mm\"=\"5mm\",\"3 mm\"=\"3mm\"),\r\n selected=\"5mm\"\r\n ),\r\n pickerInput(\"experiment\", \"Select experiments\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"),multiple=TRUE,selected=c(),\r\n ),\r\n radioGroupButtons(\"rack\",\"Prefered Rack\",\r\n choices=c(\"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5),\r\n selected=\"1\"),\r\n textInput(\"path\", \"Path\",value=\"D:/data\"),\r\n selectizeInput(\"project\", \"Project\", choices = startProjects, options=list(create=TRUE)),\r\n\r\n tags$hr(),\r\n actionButton(\"archive\", \"Add to Archive\"),br(),br(),\r\n downloadButton(\"downloadData\", \"Download Template\")\r\n\r\n ),\r\n tabBox(width=8,selected=\"Template\",title=\"Output\",\r\n tabPanel(\"Template\", tableOutput(\"template\")),\r\n tabPanel(\"Raw Data\", tableOutput(\"raw\"))\r\n )\r\n )\r\n ),\r\n #IVDr Manual----\r\n tabItem(tabName=\"manual\",\r\n h2(\"Create manual (sample by sample) IVDr template for ICON\"),\r\n fluidRow(\r\n box(width=3,title=\"Input\",status=\"primary\", solidHeader=TRUE,\r\n radioGroupButtons(\"m_type\",\"Sample Type\",\r\n choices=c(\"Plasma\"=\"m_plasma\",\"Urine\"=\"m_urine\",\"Urine(neo)\"=\"m_urine_neo\",\"CSF\"=\"m_csf\",\"MEOH\"=\"m_meoh\"),\r\n selected=\"m_plasma\"),\r\n radioGroupButtons(\"m_size\",\"Sample Size\",\r\n choices=c(\"5 mm\"=\"m_5mm\",\"3 mm\"=\"m_3mm\"),\r\n selected=\"m_5mm\"),\r\n pickerInput(\"m_experiment\", \"Select experiments\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"),multiple=TRUE\r\n ),\r\n radioGroupButtons(\"m_rack\",\"Prefered Rack\",\r\n choices=c(1,2,3,4,5),\r\n selected=1),\r\n numericInput(\"m_slot\", \"Position\", value=1, min=1, max=96, step=1),\r\n selectizeInput(\"m_project\", \"Project\", choices = startProjects, options=list(create=TRUE)),\r\n textInput(\"m_samplename\",\"Sample Name\"),\r\n textInput(\"m_path\", \"Path\",value=\"D:/IVDrData/data\"),\r\n tags$hr(),\r\n actionButton(\"m_add\", \"Add Sample to Table\"),\r\n tags$hr(),\r\n downloadButton(\"m_downloadData\", \"Download Template\")\r\n ),\r\n box(width=7,title=\"Output\",status=\"primary\",solidHeader=TRUE, tableOutput(\"m_table2\")\r\n ),\r\n )\r\n\r\n ),\r\n # #ICON Uploader----\r\n tabItem(tabName=\"iconup\",\r\n h2(\"Create ICON Template from File\"),\r\n fluidRow(\r\n box(width=3,title=\"Input\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"i_file1_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1),\r\n fileInput(\"i_file1\", \"File input\", multiple=FALSE),\r\n #textInput('file1sheet','Name of Sheet (Case-Sensitive)'),\r\n textInput(\"i_type\",\"Solvent\"),\r\n radioGroupButtons(\"i_size\",\"Sample Size\",\r\n choices=c(\"5 mm\"=\"5mm\",\"3 mm\"=\"3mm\"),\r\n selected=\"5mm\"\r\n ),\r\n radioGroupButtons(\"i_file2_type_Input\",\"Choose File type for Experiment List\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1),\r\n fileInput(\"i_file2\", \"Upload Experiment List (single column file)\", multiple=FALSE),\r\n pickerInput(\"i_experiment\", \"Select experiments\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"),multiple=TRUE\r\n ),\r\n radioGroupButtons(\"i_rack\",\"Prefered Rack\",\r\n choices=c(\"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5),\r\n selected=\"1\"),\r\n textInput(\"i_path\", \"Path\",value=\"D:/data\"),\r\n selectizeInput(\"i_project\", \"Project\", choices = startProjects, options=list(create=TRUE)),\r\n\r\n tags$hr(),\r\n actionButton(\"i_archive\", \"Add to Archive\"),br(),br(),\r\n downloadButton(\"i_downloadData\", \"Download Template\")\r\n\r\n ),\r\n tabBox(width=8,selected=\"Template\",title=\"Output\",\r\n tabPanel(\"Template\", tableOutput(\"i_template\")),\r\n tabPanel(\"Raw Data\", tableOutput(\"i_raw\"))\r\n\r\n\r\n\r\n )\r\n )\r\n ),\r\n # #ICON Creator----\r\n tabItem(tabName=\"iconman\",\r\n h2(\"Create manual (sample by sample) IVDr template for ICON\"),\r\n fluidRow(\r\n box(width=3,title=\"Input\",status=\"primary\", solidHeader=TRUE,\r\n textInput(\"im_type\",\"Solvent\"),\r\n radioGroupButtons(\"im_size\",\"Sample Size\",\r\n choices=c(\"5 mm\"=\"5mm\",\"3 mm\"=\"3mm\"),\r\n selected=\"5mm\"),\r\n radioGroupButtons(\"im_file2_type_Input\",\"Choose File type for Experiment List\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1),\r\n fileInput(\"im_file2\", \"Upload Experiment List (single column file)\", multiple=FALSE),\r\n pickerInput(\"im_experiment\", \"Select experiments\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"),multiple=TRUE\r\n ),\r\n radioGroupButtons(\"im_rack\",\"Prefered Rack\",\r\n choices=c(1,2,3,4,5),\r\n selected=1),\r\n numericInput(\"im_slot\", \"Position\", value=1, min=1, max=96, step=1),\r\n selectizeInput(\"im_project\", \"Project\", choices = startProjects, options=list(create=TRUE)),\r\n textInput(\"im_samplename\",\"Sample Name\"),\r\n textInput(\"im_path\", \"Path\",value=\"D:/data\"),\r\n tags$hr(),\r\n actionButton(\"im_add\", \"Add Sample to Table\"),\r\n tags$hr(),\r\n downloadButton(\"im_downloadData\", \"Download Template\")\r\n ),\r\n box(width=7,title=\"Output\",status=\"primary\",solidHeader=TRUE, tableOutput(\"im_table2\")\r\n ),\r\n )\r\n\r\n ),\r\n #xml-er----\r\n tabItem(tabName=\"xml\",\r\n h2(\"Extract parameters directly from zipped .xml files\"),\r\n fluidRow(\r\n box(width=2,title=\"Input\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"xml_type\",\"Choose panel\",\r\n choices=c(\"Metabolites\"=\"xml_metas\",\"Lipids\"=\"xml_lipids\"),\r\n selected=\"xml_metas\"),\r\n fileInput(\"xml_file\", \"Choose zipped file\", multiple=FALSE)\r\n ),\r\n box(width = 2,title=\"Options\",status=\"primary\",solidHeader=TRUE,\r\n\r\n pickerInput(\"xml_keepers\", \"Select columns to keep\",\r\n choices=c(\"_conc\",\"_errConc\",\"_lod\",\"rawConc\",\"sigCorr\"),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=TRUE,selected=c(\"_conc\",\"_errConc\",\"_lod\",\"rawConc\",\"sigCorr\")),\r\n p(\"Only applicaple to Metabolite Panels\"),\r\n ),\r\n\r\n box(width=2,title=\"Download\",status=\"primary\",solidHeader=TRUE,\r\n downloadButton(\"xml_downloadData\",\"Download table as .csv\")\r\n ),\r\n box(width=4, background=\"black\",color=\"yellow\",\r\n p(\"Upload a .zip file with multiple .xml reports of either Metabolites (Plasma/Urine) or a Lipid Report.\",style=\"color:yellow\"),\r\n p(\"File names of the indivdiaul files are irrelevant.\",style=\"color:yellow\"),\r\n p(\"A data table is created and can be downloaded.\",style=\"color:yellow\"),\r\n p(\"Depending on the amount of xml files, this can take a few seconds.\",style=\"color:yellow\")\r\n\r\n )\r\n ),\r\n fluidRow(\r\n box(width=12, title=\"Table\",status=\"primary\",solidHeader=TRUE,\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('xmlfinal'))\r\n )\r\n )\r\n ),\r\n\r\n\r\n #Extractor----\r\n tabItem(tabName=\"extractor\",\r\n h2(\"Extract data from Bruker outputs\"),\r\n fluidRow(\r\n box(width=3,title=\"Input\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"extractor_type\",\"CSV Typ\",\r\n choices=c(\";\"=\"e_colon\",\",\"=\"e_comma\",\"Tab\"=\"e_tab\")),\r\n p(\"Metabolite: Semicolon\"),\r\n p(\"Lipoproteine: Tab\"),\r\n radioGroupButtons(\"extractor_names\", \"Use standard names?\",\r\n choices=c(\"Yes\"=\"extractor_yes\",\"No\"=\"extractor_no\")\r\n ),\r\n fileInput(\"extractor_file\", \"Pick file\", multiple=FALSE),\r\n hr(),\r\n downloadButton(\"downloadcsv\", \"Download CSV\")\r\n ),\r\n box(width=9,title=\"Output\",selected=\"Metabolite\",status=\"primary\",solidHeader=TRUE,\r\n tabPanel(\"Metabolite\",\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('final_csv'))\r\n )\r\n )\r\n ),\r\n fluidRow(\r\n box(width=3, background=\"black\",color=\"yellow\",\r\n p(\"Clean-Up the extracted text files from Bruker's IVDr Data Browser\",style=\"color:yellow\"),\r\n p(\"Names can be standardized, sample names are cleaned up.\",style=\"color:yellow\")\r\n )\r\n )\r\n ),\r\n #deleter----\r\n tabItem(tabName = \"deleter\",\r\n fluidRow(\r\n box(width=3,title=\"Upload data:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"del_file_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"del_file\", \"File input\", multiple=FALSE),\r\n ),\r\n box(width=4,title=\"Options:\",status=\"primary\",solidHeader=TRUE,\r\n p(\"Select type:\"),\r\n tabBox(width=12,selected=\"Parameter\",\r\n tabPanel(\"Parameter\",\r\n pickerInput(\"del_column\", \"Choose parameters (columns) to delete\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),multiple=TRUE\r\n )\r\n ),\r\n tabPanel(\"Sample\",\r\n pickerInput(\"del_irow\", \"Choose identifier column (e.g. SampleID)\",\r\n choices=c(\"\"),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),multiple=FALSE\r\n ),\r\n pickerInput(\"del_row\", \"Choose samples (rows) to delete\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),multiple=TRUE\r\n )\r\n \r\n ),\r\n tabPanel(\"Group\",\r\n pickerInput(\"del_igroup\", \"Choose identifier column (e.g. Group)\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),multiple=FALSE\r\n ),\r\n pickerInput(\"del_group\", \"Choose groups to delete\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),multiple=TRUE\r\n )\r\n )\r\n )\r\n ),\r\n box(width=2,title=\"Download:\",status=\"primary\",solidHeader=TRUE,\r\n downloadButton(\"del_downloader\",\"Download table\"),\r\n ),\r\n box(width=5, background=\"black\",\r\n p(\"Upload a data table and edit the parameters, samples and groups interactively.\",style=\"color:yellow\"),\r\n p(\"Multiple edits possible at the same time.\",style=\"color:yellow\")\r\n \r\n )\r\n ),\r\n \r\n fluidRow(\r\n box(width=12, title=\"Output\",status=\"primary\",solidHeader=TRUE,\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('del_table'))\r\n )\r\n )\r\n \r\n ),\r\n \r\n \r\n \r\n \r\n #Combiner-----\r\n tabItem(tabName=\"combiner\",\r\n fluidRow(\r\n box(width=2,title=\"Upload first Table:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"c_fileleft_type_Input\",\"Choose file type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"c_fileleft\", \"File input\", multiple=FALSE),\r\n pickerInput(\"c_columnleft\", \"Select column of first table\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=FALSE),\r\n ),\r\n\r\n box(width=2,title=\"Upload second Table:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"c_fileright_type_Input\",\"Choose file type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"c_fileright\", \"File input\", multiple=FALSE),\r\n pickerInput(\"c_columnright\", \"Select column of second table\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=FALSE)\r\n ),\r\n box(width=2,title=\"Download:\",status=\"primary\",solidHeader=TRUE,\r\n useWaiter(),\r\n downloadButton(\"c_downloader\",\"Download joined table\")\r\n ),\r\n box(width=3, background=\"black\",\r\n p(\"Combine two tables with an identical column, ideally a SampleID\",style=\"color:yellow\"),\r\n p(\"The columns do not need to have the same name\",style=\"color:yellow\"),\r\n p(\"The column in the output table will have the name UniqueID\",style=\"color:yellow\")\r\n )\r\n ),\r\n fluidRow(\r\n tabBox(width=12,\r\n tabPanel(\"Table 1\",\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('c_tableleft'))\r\n ),\r\n tabPanel(\"Table 2\",\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('c_tableright'))\r\n ),\r\n tabPanel(\"Table combined\",\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('c_tablecombined'))\r\n )\r\n )\r\n )\r\n ),\r\n #Normalizor----\r\n tabItem(tabName=\"normalizer\",\r\n h2(\"Normalize data\"),\r\n\r\n fluidRow(\r\n box(width=2,title=\"Input\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"n_file_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"n_file\", \"File input\", multiple=FALSE),\r\n radioGroupButtons(\"n_log\",\"Log10\",\r\n choices=c(\"Yes\"=\"n_log_yes\",\"No\"=\"n_log_no\"),\r\n selected=\"n_log_no\"\r\n ),\r\n radioGroupButtons(\"n_center\",\"MeanCenter\",\r\n choices=c(\"Yes\"=\"n_center_yes\",\"No\"=\"n_center_no\"),\r\n selected=\"n_center_no\"\r\n ),\r\n downloadButton(\"n_download\", \"Download CSV\")\r\n ),\r\n box(width=5,title=\"Histogramm\",status=\"primary\",solidHeader=TRUE,\r\n plotlyOutput(width=\"100%\",\"n_histogramm\")\r\n ),\r\n box(width=5,title=\"Distribution\",status=\"primary\",solidHeader=TRUE,\r\n div(style=\"height:400px;overflow-y:scroll\",plotlyOutput(width=\"100%\", height=\"2000px\",\"n_distribution\"))\r\n )\r\n ),\r\n fluidRow(\r\n box(width=12,title=\"Output\",status=\"primary\",solidHeader=TRUE,\r\n div(style = 'overflow-x: scroll', DT::dataTableOutput('n_exampletable'))\r\n )\r\n )\r\n ),\r\n #Plotting----\r\n tabItem(tabName=\"plots\",\r\n h2(\"Create plots of column vs a group/time\"),\r\n fluidRow(\r\n column(width=3,\r\n box(width=12,title=\"Upload data:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"p_file_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"p_file\", \"File input\", multiple=FALSE),\r\n hr(),\r\n pickerInput(\"p_group\", \"Select column for grouping\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=FALSE),\r\n pickerInput(\"p_column\", \"Choose columns to plot\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=TRUE),\r\n hr(),\r\n shinyjqui::orderInput(\"p_groupsorter\",\"Drag to re-order groups/timepoints\",\r\n items=c())\r\n )\r\n ),\r\n column(width=4,\r\n fluidRow(\r\n box(width=12,title=\"Chose plot Options:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"p_stat\",\"Statistics\",\r\n choices=c(\"None\"=\"p_none\",\"t-test\"=\"p_ttest\",\"Wilcoxon\"=\"p_wilc\",\"Anova\"=\"p_anova\",\"Kruskal-Wallis\"=\"p_krusk\"),\r\n selected=\"p_none\",individual=TRUE)\r\n )\r\n ),\r\n fluidRow(\r\n box(width=12, background=\"black\",\r\n p(\"Individual plots are shown on the right, only one at a time.\",style=\"color:yellow\"),\r\n p(\"Statistical significance is highlighted in the plots by *.\",style=\"color:yellow\"),\r\n p(\"Interactive single plot on the right does not show significance.\",style=\"color:yellow\")\r\n \r\n )\r\n ),\r\n fluidRow(\r\n box(width=12,title=\"Download:\",status=\"primary\",solidHeader=TRUE,\r\n useWaiter(),\r\n h5(\"Download a single or multiple individual plots as .PDF or a small selection of different parameters in a grouped plot (as .tiff)\"),\r\n hr(),\r\n downloadButton(\"p_downloader\",\"PDF\"),\r\n downloadButton(\"p_downloader_groups\",\"Grouped\")\r\n )\r\n )\r\n ),\r\n column(width=5,\r\n box(width=12, status=\"primary\",solidHeader=TRUE,title=\"Single plot display\",\r\n plotlyOutput(\"p_single\"),\r\n textOutput(\"p_text\"),\r\n textOutput(\"p_text2\")\r\n )\r\n )\r\n ),\r\n fluidRow(\r\n box(width=12, status=\"primary\",solidHeader=TRUE,title=\"Data Table Overview\",\r\n div(style = 'overflow-x: scroll',DT::dataTableOutput(\"p_exampletable\"))\r\n )\r\n )\r\n ),\r\n \r\n #PCA----\r\n tabItem(tabName=\"pca\",\r\n h2(\"Principal Component Analysis\"),\r\n fluidRow(\r\n box(width=3,title=\"Upload data:\",status=\"primary\",solidHeader=TRUE,\r\n radioGroupButtons(\"pca_file_type_Input\",\"Choose File type\",\r\n choices = list(\".csv/txt\" = 1, \".xlsx\" = 2),\r\n selected = 1\r\n ),\r\n fileInput(\"pca_file\", \"File input\", multiple=FALSE),\r\n pickerInput(\"pca_id\", \"Select Sample ID column\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=FALSE,selected=c()),\r\n pickerInput(\"pca_group\", \"Select Group column\",\r\n choices=c(),\r\n options=list(\"actions-box\"=TRUE, size=15,`selected-text-format` = \"count > 3\"\r\n ),\r\n multiple=FALSE,selected=NULL),\r\n radioGroupButtons(\"pca_labels\",\"Include labels in plot\",\r\n choices=c(\"Yes\"=\"pca_labels_yes\",\"No\"=\"pca_labels_no\"),\r\n selected=\"pca_labels_no\"),\r\n hr(),\r\n downloadButton(\"pca_download\",\"Download PCA table\")\r\n ),\r\n tabBox(width=9,selected=\"Data Table\",\r\n tabPanel(\"Data Table\",\r\n div(style = 'overflow-x: scroll',DT::dataTableOutput(\"pca_table\"))\r\n ),\r\n tabPanel(\"PCA\",\r\n div(style = 'overflow-x: scroll',DT::dataTableOutput(\"pca_pca\"))\r\n )\r\n )\r\n ),\r\n fluidRow(\r\n box(width=6, status=\"primary\",solidHeader=TRUE,title=\"PCA Scores\",\r\n plotlyOutput(width=\"90%\",\"pca_scores\")\r\n ),\r\n box(width=6, status=\"primary\",solidHeader=TRUE,title=\"PCA Biplot\",\r\n plotlyOutput(width=\"90%\",\"pca_biplot\")\r\n )\r\n )\r\n )\r\n )\r\n )\r\n)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Server----\r\napp_server <- function(input,output,session) {\r\n\r\n#Server Home----\r\n #create reactive archive, ->in excel tabs\r\n archive<-reactive({read.csv(\"archive.csv\")})\r\n #Output\r\n output$archive<-DT::renderDataTable(archive())\r\n #output archive plots\r\n output$archive_2020 <- renderPlotly({\r\n archive4plot<-archive()\r\n archive4plot$Date<-format(as.Date(archive4plot$Date, format=\"%m/%d/%Y\"))\r\n ggplot(archive4plot, aes(x=Type, fill=Type))+geom_bar()+theme_few()+theme(legend.position=\"none\",axis.title.y=element_blank())+labs(x=\"Sample\",y=\"Samples\")+coord_flip()})\r\n output$archive_project_2020 <- renderPlotly({\r\n archive4plot<-archive()\r\n archive4plot$Date<-format(as.Date(archive4plot$Date, format=\"%m/%d/%Y\"))\r\n ggplot(archive4plot, aes(x=Project, fill=Project))+geom_bar()+theme_few()+theme(legend.position=\"none\",axis.title.y=element_blank())+labs(y=\"Samples\")+coord_flip()})\r\n\r\n#Server Excel Importer----\r\n solvent<-reactive({switch(input$type, \"urine_neo\"=\"Urine_Neo\",\"urine\"=\"Urine\", \"plasma\"=\"Plasma\",\"CSF\"=\"CSF\",\"MEOH\"=\"MEOH\")})\r\n rack<-reactive({switch(input$rack, \"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5)})\r\n size<-reactive({switch(input$size, \"5mm\"=\"5mm\",\"3mm\"=\"3mm\")})\r\n path<-reactive(input$path)\r\n project<-reactive({input$project})\r\n\r\n\r\n raw_data<-reactive({\r\n inFile <- input$file1\r\n if (is.null(inFile)) {\r\n return(NULL) }\r\n if (input$file1_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n\r\n experiments<-reactive({input$experiment})\r\n\r\n elementpicker<-reactive({\r\n solvent<-solvent()\r\n size<-size()\r\n elementpicked<-experimentpicker(solvent,size)\r\n })\r\n\r\n #output$elementtext<-renderText({elementpicker()})\r\n\r\n #update picks\r\n experiment<-observe({\r\n req(input$file1)\r\n updatePickerInput(\r\n session,\r\n \"experiment\",\r\n choices = elementpicker()\r\n )\r\n })\r\n\r\n #Create reactive template\r\n create_template<-reactive({\r\n data_output<-excel_creator(raw_data(),solvent(),size(),rack(),experiments(),path())\r\n })\r\n #create archive addition\r\n archive_add<-reactive({\r\n Name<-raw_data()\r\n archive_current<-archive()\r\n Project<-project()\r\n Date<-Sys.Date()\r\n Type<-solvent()\r\n Size<-size()\r\n Date<-as.character(Date) #likely needs adjusting\r\n archive_updated<-cbind(Date,Name,Project,Type,Size)\r\n new_archive<-rbind(archive_current,archive_updated)\r\n })\r\n observeEvent(input$archive, {\r\n write.csv(archive(),\"archive_backup.csv\",row.names=FALSE)\r\n write.csv(archive_add(),\"archive.csv\",row.names=FALSE)\r\n showModal(modalDialog(\r\n title = \"Important message\",\r\n \"The samples have been added to the archive.\"\r\n ))\r\n })\r\n\r\n\r\n #Output\r\n output$template<-renderTable({\r\n req(input$file1)\r\n create_template()\r\n })\r\n output$raw<-renderTable({\r\n req(input$file1)\r\n raw_data()\r\n })\r\n output$changelog<-renderTable({\r\n changelog\r\n })\r\n\r\n #Buttons\r\n output$downloadData <- downloadHandler(\r\n filename = function() {\r\n paste(\"Template_\", \".xlsx\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.xlsx(create_template(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n\r\n#Server Excel Manual----\r\n #reactives\r\n m_solvent<-reactive({switch(input$m_type, \"m_urine\"=\"Urine\", \"m_plasma\"=\"Plasma\",\"m_media\"=\"Media\",\"m_urine_neo\"=\"Urine_Neo\",\"m_csf\"=\"CSF\",\"m_meoh\"=\"MEOH\")})\r\n m_rack<-reactive({switch(input$m_rack, \"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5)})\r\n m_size<-reactive({switch(input$m_size, \"m_5mm\"=\"5mm\",\"m_3mm\"=\"3mm\")})\r\n m_samplename<-reactive({input$m_samplename})\r\n m_slot<-reactive({input$m_slot})\r\n m_path<-reactive({input$m_path})\r\n m_experiments<-reactive({input$m_experiment})\r\n m_project<-reactive({input$m_project})\r\n m_archive<-reactive({read.csv(\"archive.csv\")})\r\n\r\n\r\n m_elementpicker<-reactive({\r\n solvent<-m_solvent()\r\n size<-m_size()\r\n elementpicked<-experimentpicker(solvent,size)\r\n })\r\n\r\n #output$elementtext<-renderText({elementpicker()})\r\n\r\n #update picks\r\n m_experiment<-observe({\r\n req(input$m_type)\r\n updatePickerInput(\r\n session,\r\n \"m_experiment\",\r\n choices = m_elementpicker()\r\n )\r\n })\r\n manual_archive<-reactive({\r\n Date<-as.character(Sys.Date())\r\n Name<-m_samplename()\r\n Project<-m_project()\r\n Type<-m_solvent()\r\n Size<-m_size()\r\n m_archiver<-cbind(Date,Name,Project,Type,Size)\r\n })\r\n m_updater_archive<-eventReactive(input$m_add,{\r\n m_looper_archive<<-rbind(m_looper_archive,manual_archive())\r\n })\r\n #read in manual empty template\r\n m_looper<-manual_template<-data.frame(matrix(ncol=6,nrow=0, dimnames=list(NULL, c(\"DISK\",\"NAME\",\"SOLVENT\",\"EXPERIMENT\",\"HOLDER\", \"TITLE\"))))\r\n m_looper_archive<-data.frame(matrix(ncol=5,nrow=0, dimnames=list(NULL, c(\"Date\",\"Name\",\"Project\",\"Type\",\"Size\"))))\r\n #create_manual_method\r\n manual_samples<-reactive({\r\n m_new_sample<-m_samplename()\r\n m_new_sample<-as.data.frame(m_new_sample)\r\n names(m_new_sample)[names(m_new_sample) ==\"m_new_sample\"]<-\"Name\"\r\n m_table<-excel_manual(m_new_sample,m_solvent(),m_size(),m_rack(),m_slot(),m_experiments(),m_path())\r\n })\r\n #append dataframes\r\n m_updater <- eventReactive(input$m_add,{\r\n m_slot_counter <- m_slot()+1\r\n if(m_slot_counter>96){\r\n m_slot_counter<-1\r\n updateNumericInput(session, \"m_slot\", value = m_slot_counter)\r\n m_rack_counter<-m_rack()+1\r\n if(m_rack_counter>5){\r\n m_rack_counter<-1\r\n updateRadioButtons(session, \"m_rack\", choices=c(1,2,3,4,5), selected=m_rack_counter)\r\n } else {\r\n updateRadioButtons(session, \"m_rack\", choices=c(1,2,3,4,5), selected=m_rack_counter)\r\n }\r\n } else {\r\n updateNumericInput(session, \"m_slot\", value = m_slot_counter)\r\n }\r\n write.csv(m_archive(),\"archive_backup.csv\",row.names=FALSE)\r\n write.csv(m_archive_add(),\"archive.csv\",row.names=FALSE)\r\n m_looper<<-rbind(m_looper,manual_samples())\r\n })\r\n m_archive_add<-reactive({\r\n archive_current<-archive()\r\n new_archive<-rbind(archive_current,m_updater_archive())\r\n })\r\n #Output\r\n output$m_table2<-renderTable({m_updater()})\r\n #Buttons\r\n output$m_downloadData <- downloadHandler(\r\n filename = function() {\r\n paste(\"Template_manual\", \".xlsx\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.xlsx(m_updater(), file, row.names = FALSE)\r\n }\r\n )\r\n observeEvent(input$m_archive, {\r\n write.csv(m_archive(),\"archive_backup.csv\",row.names=FALSE)\r\n write.csv(m_archive_add(),\"archive.csv\",row.names=FALSE)\r\n showModal(modalDialog(\r\n title = \"Important message\",\r\n \"The samples have been added to the archive.\"\r\n ))\r\n })\r\n\r\n#Server ICON Uploader----\r\n i_solvent<-reactive({input$i_type})\r\n i_rack<-reactive({switch(input$i_rack, \"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5)})\r\n i_size<-reactive({switch(input$i_size, \"5mm\"=\"5mm\",\"3mm\"=\"3mm\")})\r\n i_path<-reactive({input$i_path})\r\n i_experiments<-reactive({input$i_experiment})\r\n i_project<-reactive({input$project})\r\n i_archive<-reactive({read.csv(\"archive.csv\")})\r\n\r\n\r\n\r\n i_raw_data<-reactive({\r\n inFile <- input$i_file1\r\n if (is.null(inFile)) {\r\n return(NULL) }\r\n if (input$i_file1_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n\r\n i_experimentlist<-reactive({\r\n inFile <- input$i_file2\r\n if (is.null(inFile)) {\r\n return(NULL) }\r\n if (input$i_file2_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n\r\n\r\n #update picks\r\n i_experiment<-observe({\r\n req(input$i_file2)\r\n updatePickerInput(\r\n session,\r\n \"i_experiment\",\r\n choices = i_experimentlist()\r\n )\r\n })\r\n\r\n #Create reactive template\r\n i_create_template<-reactive({\r\n data_output<-excel_creator(i_raw_data(),i_solvent(),i_size(),i_rack(),i_experiments(),i_path())\r\n })\r\n #Output\r\n output$i_template<-renderTable({\r\n req(input$i_file1,input$i_file2)\r\n i_create_template()\r\n })\r\n output$i_raw<-renderTable({\r\n req(input$i_file1)\r\n i_raw_data()\r\n })\r\n\r\n #create archive addition\r\n i_archive_add<-reactive({\r\n Name<-i_raw_data()\r\n archive_current<-i_archive()\r\n Project<-i_project()\r\n Date<-Sys.Date()\r\n Type<-i_solvent()\r\n Size<-i_size()\r\n Date<-as.character(Date) #likely needs adjusting\r\n archive_updated<-cbind(Date,Name,Project,Type,Size)\r\n new_archive<-rbind(archive_current,archive_updated)\r\n })\r\n observeEvent(input$i_archive, {\r\n write.csv(i_archive(),\"archive_backup.csv\",row.names=FALSE)\r\n write.csv(i_archive_add(),\"archive.csv\",row.names=FALSE)\r\n showModal(modalDialog(\r\n title = \"Important message\",\r\n \"The samples have been added to the archive.\"\r\n ))\r\n })\r\n\r\n\r\n #Buttons\r\n output$i_downloadData <- downloadHandler(\r\n filename = function() {\r\n paste(\"Template_\", \".xlsx\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.xlsx(i_create_template(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n\r\n#Server ICON Manual----\r\n #reactives\r\n im_solvent<-reactive(input$im_type)\r\n im_rack<-reactive({switch(input$im_rack, \"1\"=1,\"2\"=2,\"3\"=3,\"4\"=4,\"5\"=5)})\r\n im_size<-reactive({switch(input$im_size, \"5mm\"=\"5mm\",\"3mm\"=\"3mm\")})\r\n im_samplename<-reactive({input$im_samplename})\r\n im_slot<-reactive({input$im_slot})\r\n im_path<-reactive({input$im_path})\r\n im_experiments<-reactive({input$im_experiment})\r\n im_project<-reactive({input$im_project})\r\n im_archive<-reactive({read.csv(\"archive.csv\")})\r\n\r\n im_experimentlist<-reactive({\r\n inFile <- input$im_file2\r\n if (is.null(inFile)) {\r\n return(NULL) }\r\n if (input$im_file2_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n\r\n #update picks\r\n im_experiment<-observe({\r\n req(input$im_file2)\r\n updatePickerInput(\r\n session,\r\n \"im_experiment\",\r\n choices = im_experimentlist()\r\n )\r\n })\r\n\r\n im_manual_archive<-reactive({\r\n Date<-as.character(Sys.Date())\r\n Name<-im_samplename()\r\n Project<-im_project()\r\n Type<-im_solvent()\r\n Size<-im_size()\r\n im_archiver<-cbind(Date,Name,Project,Type,Size)\r\n })\r\n\r\n im_updater_archive<-eventReactive(input$im_add,{\r\n im_looper_archive<<-rbind(im_looper_archive,im_manual_archive())\r\n })\r\n\r\n\r\n #read in manual empty template\r\n im_looper<-manual_template<-data.frame(matrix(ncol=6,nrow=0, dimnames=list(NULL, c(\"DISK\",\"NAME\",\"SOLVENT\",\"EXPERIMENT\",\"HOLDER\", \"TITLE\"))))\r\n im_looper_archive<-data.frame(matrix(ncol=5,nrow=0, dimnames=list(NULL, c(\"Date\",\"Name\",\"Project\",\"Type\",\"Size\"))))\r\n\r\n #create_manual_method\r\n im_manual_samples<-reactive({\r\n im_new_sample<-im_samplename()\r\n im_new_sample<-as.data.frame(im_new_sample)\r\n names(im_new_sample)[names(im_new_sample) ==\"im_new_sample\"]<-\"Name\"\r\n m_table<-excel_manual(im_new_sample,im_solvent(),im_size(),im_rack(),im_slot(),im_experiments(),im_path())\r\n })\r\n #append dataframes\r\n im_updater <- eventReactive(input$im_add,{\r\n im_slot_counter <- im_slot()+1\r\n if(im_slot_counter>96){\r\n im_slot_counter<-1\r\n updateNumericInput(session, \"im_slot\", value = im_slot_counter)\r\n im_rack_counter<-im_rack()+1\r\n if(im_rack_counter>5){\r\n im_rack_counter<-1\r\n updateRadioButtons(session, \"im_rack\", choices=c(1,2,3,4,5), selected=im_rack_counter)\r\n } else {\r\n updateRadioButtons(session, \"im_rack\", choices=c(1,2,3,4,5), selected=im_rack_counter)\r\n }\r\n } else {\r\n updateNumericInput(session, \"im_slot\", value = im_slot_counter)\r\n }\r\n write.csv(im_archive(),\"archive_backup.csv\",row.names=FALSE)\r\n write.csv(im_archive_add(),\"archive.csv\",row.names=FALSE)\r\n im_looper<<-rbind(im_looper,im_manual_samples())\r\n })\r\n\r\n im_archive_add<-reactive({\r\n im_archive_current<-im_archive()\r\n new_archive<-rbind(im_archive_current,im_updater_archive())\r\n })\r\n\r\n\r\n #Output\r\n output$im_table2<-renderTable({im_updater()})\r\n #Buttons\r\n output$im_downloadData <- downloadHandler(\r\n filename = function() {\r\n paste(\"Template_manual\", \".xlsx\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.xlsx(im_updater(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n#Server XML----\r\n raw_xmllist<-reactive({\r\n inFile <- input$xml_file\r\n if (is.null(inFile)) {\r\n return(NULL) }\r\n lapply(unzip(inFile$datapath),read_xml)\r\n })\r\n xml_keepers<-reactive(input$xml_keepers)\r\n xml_type<-reactive({switch(input$xml_type, \"xml_metas\"=\"Metabolites\",\"xml_lipids\"=\"Lipids\")})\r\n\r\n xmlextractor<-reactive({\r\n xmlextractor<-xmler(raw_xmllist(),xml_type())\r\n })\r\n xmlcleaner<-reactive({\r\n if(xml_type()==\"Metabolites\"){\r\n keepers<-xml_keepers()\r\n df1<-xmlextractor()\r\n Sample<-df1$Sample\r\n combinekeepers<-as.data.frame(lapply(keepers, FUN=function(x) {\r\n df1<-select(df1,ends_with(x))\r\n }))\r\n combinekeepers<-combinekeepers[,order(names(combinekeepers))]\r\n combined<-cbind(Sample,combinekeepers)\r\n }\r\n else {df1<-xmlextractor()}\r\n })\r\n\r\n #Output\r\n output$xmlfinal<-DT::renderDataTable({\r\n req(input$xml_file)\r\n xmlcleaner()\r\n })\r\n\r\n output$xml_downloadData <- downloadHandler(\r\n filename = function() {\r\n paste(\"Measurements_\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(xmlcleaner(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n#Server Data Exctractor Tab----\r\n e_solvent<-reactive({switch(input$extractor_type, \"e_comma\"=\"Comma\",\"e_colon\"=\"Semicolon\",\"e_tab\"=\"Tab\")})\r\n e_names<-reactive({switch(input$extractor_names, \"extractor_yes\"=\"Yes\",\"extractor_no\"=\"No\")})\r\n dirty_csv<-reactive({\r\n\r\n if(e_solvent()==\"Semicolon\"){\r\n read.csv2(input$extractor_file$datapath)\r\n } else if(e_solvent()==\"Comma\"){\r\n read.csv(input$extractor_file$datapath)\r\n } else if(e_solvent()==\"Tab\"){\r\n read.csv(input$extractor_file$datapath,sep=\"\\t\")\r\n }\r\n\r\n })\r\n #create clean_csv\r\n cleaned_csv<-reactive({\r\n clean<-clean_csv(e_names(),dirty_csv())\r\n clean<-clean\r\n })\r\n #Output\r\n output$final_csv<-DT::renderDataTable({\r\n req(input$extractor_file)\r\n cleaned_csv()\r\n })\r\n #Buttons\r\n output$downloadcsv <- downloadHandler(\r\n filename = function() {\r\n paste(\"Cleaned_\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(cleaned_csv(), file, row.names = FALSE)\r\n }\r\n )\r\n#Server Deleter----\r\n del_file <- reactive({\r\n del_inFile <- input$del_file\r\n if (is.null(del_inFile))\r\n return(NULL)\r\n if (input$del_file_type_Input == \"1\") {\r\n read.csv(del_inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(del_inFile$datapath)\r\n }\r\n })\r\n \r\n del_columns<-reactive({input$del_column})\r\n del_rows<-reactive({input$del_row})\r\n del_irows<-reactive({input$del_irow})\r\n del_groups<-reactive({input$del_group})\r\n del_igroups<-reactive({input$del_igroup})\r\n samplerows<-reactive({\r\n req(del_irows())\r\n dat<-subset(del_file(),select=names(del_file()) %in% del_irows())\r\n })\r\n grouprows<-reactive({\r\n req(del_igroups())\r\n dat<-subset(del_file(),select=names(del_file()) %in% del_igroups())\r\n dat<-unique(dat)\r\n })\r\n #update picks\r\n \r\n del_column<-observe({\r\n updatePickerInput(\r\n session,\r\n \"del_column\",\r\n choices = names(del_file())\r\n \r\n )\r\n })\r\n del_irow<-observe({\r\n updatePickerInput(\r\n session,\r\n \"del_irow\",\r\n choices = names(del_file())\r\n \r\n )\r\n })\r\n del_row<-observe({\r\n req(input$del_file)\r\n req(del_irows())\r\n \r\n updatePickerInput(\r\n session,\r\n \"del_row\",\r\n choices = samplerows()\r\n \r\n )\r\n })\r\n del_igroup<-observe({\r\n updatePickerInput(\r\n session,\r\n \"del_igroup\",\r\n choices = names(del_file())\r\n \r\n )\r\n })\r\n del_group<-observe({\r\n req(input$del_file)\r\n req(del_igroups())\r\n updatePickerInput(\r\n session,\r\n \"del_group\",\r\n choices = grouprows()\r\n )\r\n })\r\n # del_table<-reactive({\r\n # req(input$del_file)\r\n #\r\n # if(length(del_columns()!=0)){\r\n # \"%ni%\"<-Negate('%in%')\r\n # del_final<-subset(del_file(),select=names(del_file()) %ni% del_columns())\r\n # }\r\n \r\n \r\n # })\r\n del_table<-reactive({\r\n req(input$del_file)\r\n del_final<-del_file()\r\n if(sjmisc::is_empty(del_columns())==FALSE){\r\n \"%ni%\"<-Negate('%in%')\r\n del_final<-subset(del_final,select=names(del_final) %ni% del_columns())\r\n }\r\n if(sjmisc::is_empty(del_rows())==FALSE){\r\n irows<-as.name(del_irows())\r\n rows<-paste(del_rows(),collapse=\"|\")\r\n del_final<-del_final %>% filter(!str_detect(!!as.symbol(irows),rows))\r\n }\r\n if(sjmisc::is_empty(del_groups())==FALSE){\r\n igroup<-as.name(del_igroups())\r\n group<-del_groups()\r\n del_final<-del_final %>% filter(!str_detect(!!as.symbol(igroup),group))\r\n }\r\n del_final<-del_final\r\n })\r\n \r\n #output\r\n output$del_table<-DT::renderDataTable(del_table())\r\n \r\n \r\n output$del_downloader <- downloadHandler(\r\n filename = function() {\r\n paste(\"Data_\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(del_table(), file, row.names = FALSE)\r\n }\r\n )\r\n \r\n#Server Combiner----\r\n c_fileleft <- reactive({\r\n inFilel <- input$c_fileleft\r\n if (is.null(inFilel))\r\n return(NULL)\r\n if (input$c_fileleft_type_Input == \"1\") {\r\n read.csv(inFilel$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n c_fileright <- reactive({\r\n inFiler <- input$c_fileright\r\n if (is.null(inFiler))\r\n return(NULL)\r\n if (input$c_fileright_type_Input == \"1\") {\r\n read.csv(inFiler$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n\r\n c_columnleft<-reactive({input$c_columnleft})\r\n c_columnright<-reactive({input$c_columnright})\r\n\r\n\r\n\r\n #update picks\r\n c_columnlefts<-observe({\r\n updatePickerInput(\r\n session,\r\n \"c_columnleft\",\r\n choices = names(c_fileleft())\r\n )\r\n })\r\n c_columnrights<-observe({\r\n updatePickerInput(\r\n session,\r\n \"c_columnright\",\r\n choices = names(c_fileright())\r\n )\r\n })\r\n\r\n c_tablejoined<-reactive({\r\n req(input$c_fileleft)\r\n req(input$c_fileright)\r\n\r\n dataleft<-c_fileleft()\r\n dataright<-c_fileright()\r\n groupleft<-c_columnleft()\r\n groupright<-c_columnright()\r\n group<-c(\"UniqueID\")\r\n names(dataleft)[names(dataleft) == groupleft] <- group\r\n names(dataright)[names(dataright) == groupright] <- group\r\n data_merged<-merge(x=dataleft,y=dataright,by=group,all.x=TRUE)\r\n })\r\n\r\n #output\r\n output$c_tableleft<-DT::renderDataTable(c_fileleft())\r\n output$c_tableright<-DT::renderDataTable(c_fileright())\r\n output$c_tablecombined<-DT::renderDataTable(c_tablejoined())\r\n\r\n output$c_downloader <- downloadHandler(\r\n filename = function() {\r\n paste(\"Joined_csv\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(c_tablejoined(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n\r\n#Server Normalizer----\r\n n_log<-reactive({switch(input$n_log,\"n_log_yes\"=\"Yes\",\"n_log_no\"=\"No\")})\r\n n_center<-reactive({switch(input$n_center,\"n_center_yes\"=\"Yes\",\"n_center_no\"=\"No\")})\r\n\r\n n_file <- reactive({\r\n n_inFile <- input$n_file\r\n if (is.null(n_inFile))\r\n return(NULL)\r\n if (input$n_file_type_Input == \"1\") {\r\n read.csv(n_inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(n_inFile$datapath)\r\n }\r\n })\r\n w <- Waiter$new()\r\n\r\n n_normtable<-reactive({\r\n n_data_norm<-normalizor(n_file(),n_log(),n_center())\r\n\r\n })\r\n #Buttons\r\n output$n_download <- downloadHandler(\r\n filename = function() {\r\n paste(\"Norm_csv\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(n_normtable(), file, row.names = FALSE)\r\n }\r\n )\r\n\r\n n_plotters<-reactive({\r\n req(input$n_file)\r\n df1<<-as.data.frame.array(melt(n_normtable()))\r\n })\r\n\r\n output$n_exampletable<-DT::renderDataTable({\r\n req(input$n_file)\r\n n_normtable()\r\n })\r\n output$n_histogramm<-renderPlotly({\r\n req(input$n_file)\r\n ggplot(n_plotters(),aes(x=value,fill=cut(value,100)))+geom_histogram()+theme_classic()+xlab(\"\")+ylab(\"\")+theme(legend.position = \"none\")\r\n })\r\n output$n_distribution<-renderPlotly({\r\n req(input$n_file)\r\n ggplot(n_plotters(),aes(x = value, y=variable, color=variable))+geom_line(size=3)+theme_classic()+theme(legend.position = \"none\",axis.text.y = element_text(size=10))+xlab(\"\")+ylab(\"\")\r\n\r\n })\r\n\r\n#Server Plotting Tab----\r\n #input\r\n p_file <- reactive({\r\n inFile <- input$p_file\r\n if (is.null(inFile))\r\n return(NULL)\r\n if (input$p_file_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n p_groupsorters<-reactive({input$p_groupsorter})\r\n p_columns<-reactive({input$p_column})\r\n p_groups<-reactive({input$p_group})\r\n p_grouped<-reactive({switch(input$p_grouped, \"p_grouped_yes\"=\"Yes\",\"p_grouped_no\"=\"No\")})\r\n \r\n p_stat<-reactive({switch(input$p_stat,\"p_none\"=\"none\",\"p_ttest\"=\"ttest\",\"p_wilc\"=\"wilcox\",\"p_anova\"=\"anova\",\"p_krusk\"=\"kruskal\")})\r\n \r\n p_uniquegroups<-reactive({\r\n req(p_file(),p_groups())\r\n pdat<-subset(p_file(),select=names(p_file()) %in% p_groups())\r\n pdat<-unique(pdat)\r\n pdat<-pdat[[p_groups()]]\r\n })\r\n #update picks\r\n p_column<-observe({\r\n updatePickerInput(\r\n session,\r\n \"p_column\",\r\n choices = names(p_file())\r\n )\r\n })\r\n p_group<-observe({\r\n updatePickerInput(\r\n session,\r\n \"p_group\",\r\n choices = names(p_file())\r\n )\r\n })\r\n p_groupsorter<-observe({\r\n shinyjqui::updateOrderInput(\r\n session,\r\n \"p_groupsorter\",\r\n items = p_uniquegroups()\r\n )\r\n })\r\n p_filesorted<-reactive({\r\n req(p_groups(),p_file(),p_groupsorters())\r\n p_groups<-as.symbol(p_groups())\r\n p_groupsorters<-p_groupsorters()\r\n p_file2<-p_file()\r\n p_file2<-mutate(p_file2, !!p_groups := !!p_groups %>% factor(levels = p_groupsorters))\r\n })\r\n p_singleplotter<-reactive({\r\n req(p_filesorted())\r\n plotter_single(p_filesorted(),p_groups(),p_columns(),p_stat())\r\n })\r\n\r\n \r\n #outputs\r\n output$p_exampletable<-DT::renderDataTable({\r\n req(input$p_file)\r\n p_file()\r\n })\r\n\r\n output$p_single <- renderPlotly({\r\n req(input$p_file,input$p_column)\r\n p_singleplotter()\r\n })\r\n output$p_downloader_groups <- downloadHandler(\r\n filename = \"Grouped.tiff\",\r\n content = function(file) {\r\n p_plot<-plotter_grouped(p_filesorted(),p_groups(),p_columns(),p_stat())\r\n tiff(file)\r\n print(p_plot)\r\n dev.off()\r\n })\r\n output$p_downloader <- downloadHandler(\r\n filename = \"Plots_Summary.pdf\",\r\n \r\n content = function(file) {\r\n waiter_show(html=waiting_screen,color=transparent(0.9))\r\n \r\n p_data<-p_filesorted()\r\n p_plot<-plotter_v2(p_data,p_groups(),p_columns(),p_stat())\r\n pdf(file)\r\n print(p_plot)\r\n dev.off()\r\n waiter_hide()\r\n sendSweetAlert(\r\n session = session,\r\n title = \"Success!\",\r\n text = \"Plots generated!\",\r\n type = \"success\"\r\n )\r\n })\r\n\r\n#server PCA----\r\n pca_file <- reactive({\r\n inFile <- input$pca_file\r\n if (is.null(inFile))\r\n return(NULL)\r\n if (input$p_file_type_Input == \"1\") {\r\n read.csv(inFile$datapath,\r\n header = TRUE,\r\n stringsAsFactors = FALSE)\r\n } else {\r\n read.xlsx(inFile$datapath)\r\n }\r\n })\r\n pca_ids<-reactive({input$pca_id})\r\n pca_labels<-reactive({switch(input$pca_labels, \"pca_labels_yes\"=\"Yes\",\"pca_labels_no\"=\"No\")})\r\n \r\n pca_groups<-reactive({input$pca_group})\r\n #update picks\r\n pca_group<-observe({\r\n updatePickerInput(\r\n session,\r\n \"pca_group\",\r\n choices = names(pca_file())\r\n )\r\n })\r\n pca_id<-observe({\r\n updatePickerInput(\r\n session,\r\n \"pca_id\",\r\n choices = names(pca_file())\r\n )\r\n })\r\n #pccomp\r\n pca_calc<-reactive({\r\n pca_data<-pca_file()\r\n idloc<-grep(pca_ids(),colnames(pca_data))\r\n row.names(pca_data)<-pca_data[,idloc]\r\n pca_data<-pca_data[-idloc]\r\n #pca_columns<-c(pca_groups(),pca_columns())\r\n #pca_data<-select(pca_data,all_of(pca_columns()))\r\n pca_data[pca_data==0]<-NA\r\n nums<-unlist(lapply(pca_data,is.numeric))\r\n pca_obj<-prcomp(pca_data[,nums],center=TRUE,scale.=TRUE)\r\n })\r\n pca_table<-reactive({\r\n req(input$pca_file)\r\n pca<-fortify(pca_calc(),data=pca_file())\r\n dfcols<-ncol(pca_file())\r\n pca<-pca[,-(1:dfcols),drop=FALSE]\r\n pca = pca %>% `rownames<-`( NULL )\r\n })\r\n \r\n \r\n #pca_score\r\n output$pca_scores<-renderPlotly({\r\n req(input$pca_file,input$pca_id,input$pca_group)\r\n if(pca_labels()==\"Yes\"){\r\n pca_scores<-autoplot(pca_calc(), data=pca_file(),colour=pca_groups(),frame=TRUE,frame.type=\"norm\",size=2,label=TRUE)+theme_classic()+theme(legend.position=\"bottom\")\r\n } else {\r\n pca_scores<-autoplot(pca_calc(), data=pca_file(),colour=pca_groups(),frame=TRUE,frame.type=\"norm\",size=2)+theme_classic()+theme(legend.position=\"bottom\")\r\n }\r\n ggplotly(pca_scores) %>%\r\n layout(showlegend = TRUE, legend = list(font = list(size = 15)))\r\n })\r\n \r\n \r\n #pca_biplot\r\n output$pca_biplot<-renderPlotly({\r\n req(input$pca_file,input$pca_id,input$pca_group)\r\n pca_biplot<-autoplot(pca_calc(), data=pca_file(),colour=pca_groups(),loadings=TRUE,loadings.label=TRUE,loadings.label.size=3)+theme_classic()+theme(legend.position=\"none\")\r\n ggplotly(pca_biplot) %>%\r\n layout(showlegend = TRUE, legend = list(font = list(size = 15)))\r\n })\r\n output$pca_table<-DT::renderDataTable({\r\n req(input$pca_file)\r\n pca_file()\r\n })\r\n output$pca_pca<-DT::renderDataTable({\r\n req(input$pca_file)\r\n pca_table()\r\n #row.names(pca)<-NULL\r\n })\r\n output$pca_download <- downloadHandler(\r\n filename = function() {\r\n paste(\"PCA_\", \".csv\", sep = \"\")\r\n },\r\n content = function(file) {\r\n write.csv(pca_table(), file, row.names = FALSE)\r\n }\r\n )\r\n \r\n}\r\n\r\n# Run the app ----\r\n#shinyApp(ui = app_ui, server = app_server)\r\n", "meta": {"hexsha": "5d3d12b2bc8215edaec761b4b6bb3d1cad8b0318", "size": 119004, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/app/app.r", "max_stars_repo_name": "funkam/MoonNMR", "max_stars_repo_head_hexsha": "d2191e39a6fdea1b98e9ae6a6442155c3817ff2b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/app/app.r", "max_issues_repo_name": "funkam/MoonNMR", "max_issues_repo_head_hexsha": "d2191e39a6fdea1b98e9ae6a6442155c3817ff2b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/app/app.r", "max_forks_repo_name": "funkam/MoonNMR", "max_forks_repo_head_hexsha": "d2191e39a6fdea1b98e9ae6a6442155c3817ff2b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.1792782305, "max_line_length": 213, "alphanum_fraction": 0.5505277134, "num_tokens": 30689, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6187804337438502, "lm_q1q2_score": 0.33591317782509333}} {"text": "library(sylcount)\n\na = \"I am the very model of a modern major general.\"\nb = \"I have information vegetable, animal, and mineral.\"\nx = c(a, b)\ny = paste0(a, b, collapse=\" \")\n\n\n\ntest = sum(sylcount(a)[[1]])\ntruth = 16L\nstopifnot(identical(truth, test))\n\ntest = sum(sylcount(b)[[1]])\ntruth = 17L\nstopifnot(identical(truth, test))\n\n\n\ntest = sylcount(x)\ntruth = list(\n as.integer(c(1, 1, 1, 2, 2, 1, 1, 2, 2, 3)),\n as.integer(c(1, 1, 4, 4, 3, 1, 3))\n)\nstopifnot(identical(test, truth))\n\ntest = sylcount(y)[[1]]\nstopifnot(identical(test, unlist(truth)))\n\n\n\ntruth <- data.frame(\n word=c(\"I\", \"am\", \"the\", \"very\", \"model\", \"of\", \"a\", \"modern\", \"major\", \"general\", \"I\", \"have\", \"information\", \"vegetable\", \"animal\", \"and\", \"mineral\"),\n syllables = test,\n stringsAsFactors=FALSE\n)\ntest = sylcount(y, counts.only=FALSE)\nstopifnot(identical(truth, test[[1]]))\n", "meta": {"hexsha": "c305c269be1de5b44463d905d7dd0a6ae7c1209b", "size": 852, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/sylcount.r", "max_stars_repo_name": "cran/sylcount", "max_stars_repo_head_hexsha": "a9614d25918366d8314e158207f3bb47e8df4a0b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-11-25T19:08:15.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-27T01:01:50.000Z", "max_issues_repo_path": "tests/sylcount.r", "max_issues_repo_name": "cran/sylcount", "max_issues_repo_head_hexsha": "a9614d25918366d8314e158207f3bb47e8df4a0b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-04-04T05:07:40.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-07T19:22:36.000Z", "max_forks_repo_path": "tests/sylcount.r", "max_forks_repo_name": "cran/sylcount", "max_forks_repo_head_hexsha": "a9614d25918366d8314e158207f3bb47e8df4a0b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-09T06:33:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-09T06:33:45.000Z", "avg_line_length": 21.8461538462, "max_line_length": 154, "alphanum_fraction": 0.6314553991, "num_tokens": 309, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3359131778250933}} {"text": "################################################################################\r\n# observedHapFreq function\r\n#\r\n# Author: Heidi Lischer\r\n# Date: 10.2008\r\n################################################################################\r\n\r\nobservedHapFreq <- function(xmlText){\r\n # convert string data (table with names) to a numeric matrix -----------------\r\n # split string ------\r\n tagData2 <- as.character(xmlText)\r\n\r\n tagData3 <- strsplit(tagData2, \"\\n\")\r\n tagMatrix <- as.matrix(as.data.frame(tagData3))\r\n tagMatrix <- gsub(\" + \", \" \", tagMatrix) # trim white space\r\n tagMatrix <- subset(tagMatrix, tagMatrix[,1] != \"\") #trim empty lines\r\n tagMatrix <- tagMatrix[6:(nrow(tagMatrix)-1)]\r\n Data <- strsplit(tagMatrix, \" \")\r\n\r\n Row <- length(Data)\r\n\r\n # to numeric matrix --------\r\n Matrix <- as.matrix(as.data.frame(Data[1]))\r\n Matrix <- subset(Matrix, Matrix[,1] != \"\")\r\n Matrix <- as.numeric(Matrix)\r\n numericMatrix <- t(as.matrix(Matrix))\r\n\r\n for(n in 2:(Row)){\r\n nextrow <- as.matrix(as.data.frame(Data[n]))\r\n nextrow <- subset(nextrow, nextrow[,1] != \"\")\r\n nextrow <- as.numeric(nextrow)\r\n numericMatrix <- rbind(numericMatrix, t(as.matrix(nextrow)))\r\n }\r\n \r\n #get observed haplotype frequency data\r\n numericMatrix <- numericMatrix[,2]\r\n\r\n\r\n return(numericMatrix)\r\n}", "meta": {"hexsha": "39a892300baadb61c19ab51bfae9c9589a82f7cf", "size": 1309, "ext": "r", "lang": "R", "max_stars_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/observedHapFreq.r", "max_stars_repo_name": "kibet-gilbert/co1_metaanalysis", "max_stars_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-01T05:57:08.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-01T05:57:08.000Z", "max_issues_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/observedHapFreq.r", "max_issues_repo_name": "kibet-gilbert/co1_metaanalysis", "max_issues_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/tools/arlequin/arlecore_linux/Rfunctions/observedHapFreq.r", "max_forks_repo_name": "kibet-gilbert/co1_metaanalysis", "max_forks_repo_head_hexsha": "1089cc03bc4dbabab543a8dadf49130d8e399665", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-01T06:15:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-01T06:15:56.000Z", "avg_line_length": 32.725, "max_line_length": 81, "alphanum_fraction": 0.5454545455, "num_tokens": 310, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5428632831725051, "lm_q1q2_score": 0.3359131778250932}} {"text": "args<-commandArgs(TRUE)\n\nall_results <- read.table(args[1], header=TRUE, row.names=1)\ngenes <- read.table(args[2])\nresults <- all_results[,grep(sprintf(\"above_%s$\", args[3]),names(all_results))]\nmat <- as.matrix(results)\nresults$means <- rowMeans(mat)\nresults$gene <- genes[,1]\nresults$coords = rownames(results)\n\nlibrary(ggplot2)\nlibrary(plyr)\n\npdf(args[4])\nd_ply(results, .var = \"gene\", function(x) {\np <- ggplot( x, aes(y=means, x=factor(coords))) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(ylab(sprintf(\"percentage exon at least %sX\", args[3]))) + ggtitle(unique(x$gene)) + ylim(0, 100)\nprint(p)\n})\ndev.off()\n", "meta": {"hexsha": "518380f2c11e7f9b91490aca94dc1e3a78e9819b", "size": 651, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/coverage.r", "max_stars_repo_name": "CGATOxford/CGATPipelines", "max_stars_repo_head_hexsha": "a34d460b5fc64984f6da0acb18aee43c5e02d5fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49, "max_stars_repo_stars_event_min_datetime": "2015-04-13T16:49:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T10:29:14.000Z", "max_issues_repo_path": "scripts/coverage.r", "max_issues_repo_name": "CGATOxford/CGATPipelines", "max_issues_repo_head_hexsha": "a34d460b5fc64984f6da0acb18aee43c5e02d5fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 252, "max_issues_repo_issues_event_min_datetime": "2015-04-08T13:23:34.000Z", "max_issues_repo_issues_event_max_datetime": "2019-03-18T21:51:29.000Z", "max_forks_repo_path": "scripts/coverage.r", "max_forks_repo_name": "CGATOxford/CGATPipelines", "max_forks_repo_head_hexsha": "a34d460b5fc64984f6da0acb18aee43c5e02d5fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 22, "max_forks_repo_forks_event_min_datetime": "2015-05-21T00:37:52.000Z", "max_forks_repo_forks_event_max_datetime": "2019-09-25T05:04:27.000Z", "avg_line_length": 32.55, "max_line_length": 225, "alphanum_fraction": 0.6850998464, "num_tokens": 195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3358515404248872}} {"text": "#! /usr/bin/Rscript --vanilla\n\nlibrary(\"rjson\")\n\nargs = commandArgs(TRUE)\n\nvalues = fromJSON(args[2])\nprobabilities = fromJSON(args[3])\n\nres = quantile(values, probs=probabilities)\n\nprint(res)\n\n\n\n\n", "meta": {"hexsha": "c4a48b18c92fd344bbe092909132084b5e3f29c9", "size": 197, "ext": "r", "lang": "R", "max_stars_repo_path": "Risk/R/quantile.r", "max_stars_repo_name": "phoxicle/bugger", "max_stars_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-01-26T13:23:09.000Z", "max_stars_repo_stars_event_max_datetime": "2016-01-26T13:23:09.000Z", "max_issues_repo_path": "Risk/R/quantile.r", "max_issues_repo_name": "phoxicle/bugger", "max_issues_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Risk/R/quantile.r", "max_forks_repo_name": "phoxicle/bugger", "max_forks_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.5882352941, "max_line_length": 43, "alphanum_fraction": 0.7055837563, "num_tokens": 52, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.33585154042488713}} {"text": "title <- \"Fib (time)\"\n\nmasterdata <- read.table(\"master-times-Fib\", header=T)\nrecdata <- read.table(\"recursive-times-Fib\", header=T)\ncpsdata <- read.table(\"cps-times-Fib\", header=T)\norigdata <- read.table(\"orig-times-Fib\", header=T)\n\nall <- c(masterdata$usr+masterdata$sys, recdata$usr+recdata$sys, cpsdata$usr+cpsdata$sys, origdata$usr+origdata$sys)\nylim <- c(min(all), max(all))\n\nstart <- function(fr,col) {\n plot (fr$n,fr$usr+fr$sys, col=col, pch=20, main=title, xlab = \"n\", ylab =\"Time (s)\", ylim=ylim)\n lines (fr$n,fr$usr+fr$sys, col=col, pch=20)\n}\n\n\ndraw <- function(fr,col) {\n points (fr$n,fr$usr+fr$sys, col=col, pch=20)\n lines (fr$n,fr$usr+fr$sys, col=col, pch=20)\n}\n\nmastercol=\"gold\"\nreccol=\"darkolivegreen3\"\ncpscol=\"blue\"\norigcol=\"darkmagenta\"\n\n\nstart (masterdata,mastercol)\ndraw (cpsdata, cpscol)\ndraw (recdata,reccol)\ndraw (origdata, origcol)\n \nlegend(x=\"topleft\", inset=.05, legend=c(\"master\",\"recursive\", \"CPS\", \"Original CEK machine\"), col=c(mastercol, reccol, cpscol, origcol), lty=1, lw=2)\n", "meta": {"hexsha": "f834b6d8f201362676ac59f06a4d44c4bbed52e8", "size": 1029, "ext": "r", "lang": "R", "max_stars_repo_path": "notes/fomega/cek-cps-experiments/r/Fib-plot.r", "max_stars_repo_name": "AriFordsham/plutus", "max_stars_repo_head_hexsha": "f7d34336cd3d65f62b0da084a16f741dc9156413", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1299, "max_stars_repo_stars_event_min_datetime": "2018-10-02T13:41:39.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T01:10:02.000Z", "max_issues_repo_path": "notes/fomega/cek-cps-experiments/r/Fib-plot.r", "max_issues_repo_name": "AriFordsham/plutus", "max_issues_repo_head_hexsha": "f7d34336cd3d65f62b0da084a16f741dc9156413", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2493, "max_issues_repo_issues_event_min_datetime": "2018-09-28T19:28:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:31.000Z", "max_forks_repo_path": "notes/fomega/cek-cps-experiments/r/Fib-plot.r", "max_forks_repo_name": "AriFordsham/plutus", "max_forks_repo_head_hexsha": "f7d34336cd3d65f62b0da084a16f741dc9156413", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 399, "max_forks_repo_forks_event_min_datetime": "2018-10-05T09:36:10.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T11:18:25.000Z", "avg_line_length": 30.2647058824, "max_line_length": 149, "alphanum_fraction": 0.6754130224, "num_tokens": 361, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.651354857898194, "lm_q1q2_score": 0.33585153693524367}} {"text": "#' @title\n#' Plot clusters using a monochromatic low opacity gray colour.\n#'\n#' @description\n#' Using this colour scheme enables the clear visualisation of the density of profiles within each cluster.\n#'\n#' @param Tc A matrix containing time course data.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#' @param centroids A matrix containing centroids. If no matrix is provided, the centroid are then compuated, and are a simple average of all the profiles within each cluster. Alternatively, for example, centroids from FCM clustering can be provided where profiles are weighted by the membership score.\n#' @param plotNumCol Number of cluster-plots to display within a given row.\n#'\n#' @return None\n#'\n#'\n#' @importFrom graphics axis lines par plot\n#' @importFrom grDevices adjustcolor\n#' @importFrom methods is\n#'\n#'\n#' @seealso \\code{\\link[e1071]{cmeans}} for generating clusters.\n#'\n#' @export\nplotClusters <- function (Tc, clusters, centroids=NA, plotNumCol=5){\n\n\tstopifnot(is(Tc, \"matrix\"), plotNumCol > 0, (length(clusters) == nrow(Tc)))\n\n\n\tif (is.na(centroids)){\n\t\tcentroids <- calcAvgOfClusProf(Tc, clusters)\n\t}\n\n\n\tplotNumRow = ceiling(max(clusters)/plotNumCol)\n\tgraphics::par(mfrow=c(plotNumRow,plotNumCol))\n\n\n\n\tfor(clusNum in c(1:max(clusters))){\n\n\t\tprofilesInClus <- Tc[clusters == clusNum,]\n\n\n\t\tgraphics::plot(NA, xlim=c(1,ncol(Tc)), xaxt=\"n\", ylim=c(min(Tc)-0.2, max(Tc)+0.2), xlab=\"Timepoints\", ylab=\"Standardised profiles\", main = paste(\"Cluster \", toString(clusNum), \"; size=\", nrow(profilesInClus), sep=\"\"))\n\t\tgraphics::axis(side=1, at=c(1:ncol(Tc)), labels=colnames(Tc))\n\n\t\tfor (j in 1:nrow(profilesInClus)){\n\t\t\tgraphics::lines(profilesInClus[j,], col=grDevices::adjustcolor(\"gray50\", alpha.f=0.2))\n\t\t}\n\n\t\tgraphics::lines(centroids[clusNum,], col=\"black\", lwd=1.5)\n\n\t}\n\n}\n\ncalcAvgOfClusProf <- function(Tc, clusters){\n\tcentroids = matrix(ncol=ncol(Tc), nrow=max(clusters))\n\n\tfor (clusNum in c(1:max(clusters))){\n\t\tprofilesInClus <- Tc[clusters == clusNum,]\n\t\tcentroids[clusNum,] <- colMeans(profilesInClus)\n\t}\n\n\treturn(centroids)\n}\n\n\n\n#### MAIN FUNCTION 3.\n#' @title\n#' Plots of clusters with events overlaid.\n#'\n#' @description\n#' Shown overlaid on the cluster plots are events computed for the cluster centroids. The increasing and decreasing events are shown using red and blue, respectively.\n#'\n#' @param Tc A matrix containing time course data.\n#' @param clusters A vector with same length as number of profiles. The cluster labels are assumed to be numbers, starting from 1.\n#' @param mat_events A matrix containing the event information generated by running the \\code{calcEvents} function.\n#' @param plotNumCol Number of cluster-plots to display within a given row.\n#' @param centroids A matrix containing centroids. If no matrix is provided, the centroid are then compuated, and are a simple average of all the profiles within each cluster. Alternatively, for example, centroids from FCM clustering can be provided where profiles are weighted by the membership score.\n#'\n#' @return None\n#'\n#' @export\n#'\n#' @importFrom graphics axis lines par plot points\n#' @importFrom grDevices adjustcolor\n#' @importFrom methods is\n#'\n#'\n#' @seealso \\code{\\link[e1071]{cmeans}} for clustering, \\code{\\link{calcEvents}}\nplotClusters_withEvents <- function (Tc, clusters, mat_events, plotNumCol=5, centroids=NA){\n\n stopifnot((length(clusters) == nrow(Tc)), is(Tc, \"matrix\"), is(mat_events, \"matrix\"), plotNumCol > 0)\n\n\n plotNumRow = ceiling(max(clusters)/plotNumCol)\n graphics::par(mfrow=c(plotNumRow,plotNumCol))\n\n\n # allProfMemberships = as.matrix(apply(clustered$membership, 1, max)) # Getting each of the profile's best cluster num.\n\n if (is.na(centroids)){\n\t centroids = calcAvgOfClusProf(Tc, clusters)\n }\n\n for(clusNum in c(1:max(clusters))){\n\n profilesInClus <- Tc[clusters == clusNum,]\n # profMemberships <- as.matrix(allProfMemberships[clustered$cluster == clusNum,])\n\n\n graphics::plot(NA, xlim=c(1,ncol(Tc)), xaxt=\"n\", ylim=c(min(Tc)-0.2, max(Tc)+0.2), xlab=\"Timepoints\", ylab=\"Standardised profiles\", main = paste(\"Cluster \", toString(clusNum), \"; size=\", nrow(profilesInClus), sep=\"\"))\n graphics::axis(side=1, at=c(1:ncol(Tc)), labels=colnames(Tc))\n\n for (j in 1:nrow(profilesInClus)){\n\n graphics::lines(profilesInClus[j,], col=grDevices::adjustcolor(\"gray50\", alpha.f=0.2))\n }\n\n graphics::lines(centroids[clusNum,], col=\"black\", lwd=1.5)\n\n\tisYEncountered = FALSE\n\n\tfor (rowNum in 1:nrow(mat_events)){\n\t\tif (mat_events[rowNum, cols_matFifty$col_clus] == clusNum){\n\t\t\t# plot y line\n\t\t\t# if (isYEncountered == FALSE){\n\t\t\t# \tisYEncountered = TRUE\n\n\t\t\t# \tgraphics::lines(x=c(1,2,3,4,5,6,7,8,9), y=(rep(mat_fiftyPoints[rowNum, cols_matFifty$col_y], 9)), lty=\"dashed\")\n\n\t\t\t#}\n\n\t\t\tif (mat_events[rowNum, cols_matFifty$col_dir] == 1){\n\t\t\t\tcolor=\"red\"\n\t\t\t}\n\t\t\telse{\n\t\t\t\tcolor=\"#003EFF\"\n\t\t\t}\n\n\t\t\ttpStart = mat_events[rowNum, 5]\n\t\t\ttpEnd = mat_events[rowNum, 6]\n\t\t\tgraphics::lines(x=seq(tpStart, tpEnd), y=(rep(mat_events[rowNum, cols_matFifty$col_y], tpEnd-tpStart+1)), lty=\"dashed\", col=color)\n\n\n\t\t\tgraphics::points(x=mat_events[rowNum, cols_matFifty$col_x], y=mat_events[rowNum, cols_matFifty$col_y], col=color, pch=19)\n\t\t}\n\t}\n\n\n }\n\n}\n", "meta": {"hexsha": "42bc80cd2e2fd7a1671f5acb3527f388f0f34416", "size": 5274, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plotClusters.r", "max_stars_repo_name": "ODonoghueLab/Minardo-Model", "max_stars_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-11-25T03:08:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T10:14:50.000Z", "max_issues_repo_path": "R/plotClusters.r", "max_issues_repo_name": "ODonoghueLab/Minardo-Model", "max_issues_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-08T09:47:48.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-23T02:36:36.000Z", "max_forks_repo_path": "R/plotClusters.r", "max_forks_repo_name": "ODonoghueLab/Minardo-Model", "max_forks_repo_head_hexsha": "8ad697ba495f96eb5420c94f0863ab56541ddaf0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0258064516, "max_line_length": 302, "alphanum_fraction": 0.7106560485, "num_tokens": 1571, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.651354857898194, "lm_q1q2_score": 0.33585153693524367}} {"text": "rm(list=ls())\n\n# este archivo tiene los nombres de las cabeceras y los municipios de 1994. abajo uso otro que es m\u00e1s vers\u00e1til\nworkdir <- c(\"~/Dropbox/data/elecs/MXelsCalendGovt/elecReturns/datosBrutos/\")\nsetwd(workdir)\n#\nd <- read.csv(file = \"eum94dfCasilla.csv\", stringsAsFactors=FALSE)\n#\ncolnames(d)\nlibrary(plyr)\nde <- c(\"AGUASCALIENTES\", \"BAJA CALIFORNIA\", \"BAJA CALIFORNIA SUR\", \"CAMPECHE\", \"CHIAPAS\", \"CHIHUAHUA\", \"COAHUILA\", \"COLIMA\", \"DISTRITO FEDERAL\", \"DURANGO\", \"GUANAJUATO\", \"GUERRERO\", \"HIDALGO\", \"JALISCO\", \"MEXICO\", \"MICHOACAN\", \"MORELOS\", \"NAYARIT\", \"NUEVO LEON\", \"OAXACA\", \"PUEBLA\", \"QUERETARO\", \"QUINTANA ROO\", \"SAN LUIS POTOSI\", \"SINALOA\", \"SONORA\", \"TABASCO\", \"TAMAULIPAS\", \"TLAXCALA\", \"VERACRUZ\", \"YUCATAN\", \"ZACATECAS\")\na <- 1:32\nd$edon <- mapvalues (d$ENTIDAD, from = de, to = a)\nd$edon <- as.integer(as.character(d$edon))\nrm(de, a)\ncolnames(d) <- c(\"edo\", \"cabecera\", \"disn\", \"mun\", \"secn\", \"casilla\", \"pan\", \"pri\", \"pps\", \"prd\", \"pfcrn\", \"parm\", \"uno.pdm\", \"pt\", \"pvem\", \"nr\", \"nul\", \"tot\", \"status\", \"edon\")\n#\n# numbers d[is.na(d$pan)==TRUE,]\nd$pan <- as.numeric(d$pan)\nd$pri <- as.numeric(d$pri)\nd$pps <- as.numeric(d$pps)\nd$prd <- as.numeric(d$prd)\nd$pfcrn <- as.numeric(d$pfcrn)\nd$parm <- as.numeric(d$parm)\nd$uno.pdm <- as.numeric(d$uno.pdm)\nd$pt <- as.numeric(d$pt)\nd$pvem <- as.numeric(d$pvem)\nd$nr <- as.numeric(d$nr)\nd$nul <- as.numeric(d$nul)\nd$tot <- as.numeric(d$tot)\n#\n# aggregate by federal district\nd.df <- d\nd.df$pan <- ave(d.df$pan , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pri <- ave(d.df$pri , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pps <- ave(d.df$pps , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$prd <- ave(d.df$prd , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pfcrn <- ave(d.df$pfcrn , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$parm <- ave(d.df$parm , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$uno.pdm <- ave(d.df$uno.pdm, as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pt <- ave(d.df$pt , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pvem <- ave(d.df$pvem , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nr <- ave(d.df$nr , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nul <- ave(d.df$nul , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$tot <- ave(d.df$tot , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df <- d.df[duplicated(d.df$edon * 100 + d.df$disn)==FALSE,]\ndim(d.df) # debug\nd.df$mun <- d.df$secn <- d.df$casilla <- d.df$status <- NULL\n#\nhead(d.df) # debug\n\nwrite.csv(d.df, file = \"eum94dfdf.csv\")\n\nworkdir <- c(\"~/Dropbox/data/elecs/MXelsCalendGovt/elecReturns/datosBrutos/resultCasillas/casillaRes91-on/\")\nsetwd(workdir)\n\n# 1991\nd <- read.csv(file = \"dip1991.csv\", stringsAsFactors=FALSE)\ncolnames(d) <- tolower(colnames(d))\nhead(d)\n#\nd.df <- d\nd.df$pan <- ave(d.df$pan , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$parm <- ave(d.df$parm , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pdm <- ave(d.df$pdm, as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pfcrn <- ave(d.df$pfcrn , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pps <- ave(d.df$pps , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$prd <- ave(d.df$prd , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pri <- ave(d.df$pri , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pt <- ave(d.df$pt , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pem <- ave(d.df$pem , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$prt <- ave(d.df$prt , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nr <- ave(d.df$nr , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nul <- ave(d.df$nul , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$tot <- ave(d.df$tot , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df <- d.df[duplicated(d.df$edon * 100 + d.df$disn)==FALSE,]\ndim(d.df) # debug\nhead(d.df)\nd.df$munn <- d.df$seccion <- d.df$casilla <- d.df$ID_ELEC <- d.df$STATUS <- NULL\n\n# 1994\nd <- read.csv(file = \"dip1994.csv\", stringsAsFactors=FALSE)\ncolnames(d)\n#\nd.df <- d\nd.df$pan <- ave(d.df$pan , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pri <- ave(d.df$pri , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pps <- ave(d.df$pps , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$prd <- ave(d.df$prd , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pfcrn <- ave(d.df$pfcrn , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$parm <- ave(d.df$parm , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$uno.pdm <- ave(d.df$uno.pdm, as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pt <- ave(d.df$pt , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$pvem <- ave(d.df$pvem , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nr <- ave(d.df$nr , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$nul <- ave(d.df$nul , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df$tot <- ave(d.df$tot , as.factor(d.df$edon * 100 + d.df$disn), FUN=sum, na.rm=TRUE)\nd.df <- d.df[duplicated(d.df$edon * 100 + d.df$disn)==FALSE,]\ndim(d.df) # debug\nhead(d.df)\nd.df$munn <- d.df$seccion <- d.df$casilla <- d.df$ID_ELEC <- d.df$STATUS <- NULL\n", "meta": {"hexsha": "a772427e8597f548e23e72327f17596275b8ba98", "size": 5890, "ext": "r", "lang": "R", "max_stars_repo_path": "code/dfSec2df94.r", "max_stars_repo_name": "RicardoTM96/elecRetrns", "max_stars_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-02-20T01:40:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-24T18:53:36.000Z", "max_issues_repo_path": "code/dfSec2df94.r", "max_issues_repo_name": "RicardoTM96/elecRetrns", "max_issues_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-10-27T04:24:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T22:21:37.000Z", "max_forks_repo_path": "code/dfSec2df94.r", "max_forks_repo_name": "RicardoTM96/elecRetrns", "max_forks_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2018-04-04T21:36:47.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-19T02:29:37.000Z", "avg_line_length": 57.7450980392, "max_line_length": 425, "alphanum_fraction": 0.6020373514, "num_tokens": 2358, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548511303336, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3358515334456}} {"text": "bdat <- scrape(game.ids='gid_2015_06_20_pitmlb_wasmlb_1')\r\n\r\n# get the at bat and pitch data from the scraped data\r\n\r\natbat <- bdat$atbat\r\npitch <- bdat$pitch\r\n\r\n# inner join the atbat and pitch dataframes\r\n\r\nnh <- inner_join(atbat, pitch, by='num') %>%\r\n filter(inning_side.x=='top') %>%\r\n select(num,start_tfs,stand,event,inning.x,batter_name, des, tfs,start_speed,px,pz,pitch_type)\r\n\r\n# sort nh by tfs (time pitch was thrown)\r\nnh <- nh %>% arrange(tfs)\r\n\r\n# enumerate pitches by atbat\r\nnh <- nh %>% arrange(tfs)\r\n\r\ntemp <- nh %>% group_by(num) %>% summarize(num_pitches = n())\r\n\r\nnh$pitch_enum <- unlist(lapply(temp$num_pitches,seq))\r\n\r\nx <- c(-0.95, 0.95, 0.95, -0.95, -0.95)\r\nz <- c(1.6,1.6,3.5,3.5,1.6)\r\nsz <- data.frame(x,z)\r\n\r\n# creating better pitch labels\r\ntemp <- nh$pitch_type\r\ntemp[which(temp=='FF')] <- 'Fastball'\r\ntemp[which(temp=='CU')] <- 'Curveball'\r\ntemp[which(temp=='CH')] <- 'Change-up'\r\ntemp[which(temp=='SL')] <- 'Slider'\r\ntemp[which(temp=='FF')] <- 'Fastball'\r\ntemp[which(temp=='FC')] <- 'Cut fastball'\r\n\r\nnh$pitch_description <- temp\r\n\r\ndes <- nh$des\r\nevent <- nh$event\r\nindices <- which(des=='In play, out(s)')\r\ndes[indices] <- event[indices]\r\nnh$des2 <- des\r\n\r\ncolors <- c('red','blue','green','#FFFF99','purple')\r\nnames(colors) <- c('Fastball', 'Slider', 'Change-up', 'Curveball', 'Cut fastball')\r\n\r\nfor(i in unique(nh$num)){\r\n \r\n # set the atbat filter by 'num'\r\n ab <- nh %>% filter(num==i)\r\n \r\n # only one batter and inning, take the first data point\r\n batter <- ab$batter_name[1]\r\n inning <- ab$inning.x[1]\r\n \r\n pitches <- unique(ab$pitch_description)\r\n \r\n zmax <- (max(ab$start_speed)-75.4)/22\r\n zmin <- (min(ab$start_speed)-75.4)/22\r\n \r\n stand_xcoord <- ab$stand\r\n stand_xcoord[which(stand_xcoord=='R')] <- -1.5\r\n stand_xcoord[which(stand_xcoord=='L')] <- 1.5\r\n stand_xcoord <- as.numeric(stand_xcoord)\r\n ab$stand_xcoord <- stand_xcoord\r\n \r\n # plot the strikezone\r\n plot <- ggplot()+\r\n # set up strike zone square\r\n geom_path(data=sz, aes(x=x,y=z)) +\r\n # len of units on x axis same as y axis\r\n coord_equal() + \r\n xlab('feet from home plate') +\r\n ylab('feet above the ground') +\r\n # plot the pitches\r\n geom_point(data=ab, aes(x=px, y=pz,size=start_speed,color=pitch_description)) +\r\n # smallest points for slower pitches are too small, adjust scale\r\n # scale means size vs regular dot\r\n scale_size(range=c(3*zmin+2,3*zmax+2)) +\r\n # set range for different 'degrees' on color wheel with h\r\n # setting stauration by c=\r\n # set brightness/luminence with l=\r\n ### scale_color_hue(h=c(0,10),c=50, l=0)\r\n \r\n # can also set color palette\r\n ### scale_color_brewer(palette = 'Dark2')\r\n \r\n # can also set colors manually\r\n # check colors by using colors() or hexadecimals\r\n scale_color_manual(values=colors[pitches]) +\r\n \r\n # If using pitch types, must change pitch_description to factor vector\r\n # from a chr vector\r\n ### nh$pitch_description <- factor(nh$pitch_description, levels=c('Fastball', 'Cut fastball', 'Slider', 'Curveball', 'Change-up'))\r\n \r\n \r\n # adding text somehwere on the plot\r\n geom_text(data=ab, aes(label=stand, x=stand_xcoord), y=2.5, size=5) +\r\n \r\n xlim(-2,2) + \r\n ylim(0,5) +\r\n ggtitle(paste('Inning ', inning, ': ', batter,sep='')) +\r\n # add in descriptions\r\n geom_text(data=ab, aes(label=des2, x=px, y=pz), vjust=1) +\r\n geom_text(data=ab, aes(label=pitch_enum, x=px, y=pz), vjust=3)\r\n \r\n ggsave(paste('atbat',i,'.png',sep=''), plot)\r\n\r\n}", "meta": {"hexsha": "9f6ffe894213964546db6b81eeb5b560cd2e1374", "size": 3642, "ext": "r", "lang": "R", "max_stars_repo_path": "atbat_pitchfx.r", "max_stars_repo_name": "KT12/R", "max_stars_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "atbat_pitchfx.r", "max_issues_repo_name": "KT12/R", "max_issues_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "atbat_pitchfx.r", "max_forks_repo_name": "KT12/R", "max_forks_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1090909091, "max_line_length": 137, "alphanum_fraction": 0.6081823174, "num_tokens": 1083, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3356998489930982}} {"text": "setwd(\"C:/Users/prabat/Desktop/AA\")\r\nhours <- read.csv('hours.csv', as.is = T)\r\ndates <- read.csv('dates.csv', as.is = T)\r\nusers <- read.csv('output.csv', as.is = T)\r\n\r\nlibrary(ggplot2)\r\n\r\ndates$Date<-as.Date(dates$Date, format = \"%d-%m-%Y\")\r\n\r\nd <- ggplot(data=dates, aes(x = dates$Date, y = dates$Freq))\r\nd <- d + geom_area(size = 0.01, fill = \"firebrick\")\r\nd <- d + ggtitle(\"User Activity over Time\") + labs(x = \"Time\", y = \"Messages\")\r\nd <- d + theme(axis.text.x = element_text(angle = 90)) + scale_x_date(date_labels = \"%b\", date_breaks = \"1 month\")\r\nggsave(\"Dates.png\", width=15, height=9, dpi=300)\r\n\r\n\r\nu <- ggplot(data=users, aes(x = reorder(users$Name, users$Freq), y = users$Freq))\r\nu <- u + geom_bar(fill=\"firebrick\", stat = \"identity\")\r\nu <- u + labs(title = \"Most Active Users in the Group\")\r\nu <- u + geom_text(aes(label=users$Name), hjust = 1.6, color = \"white\", size = 4.6)\r\nu <- u + labs(x = NULL, y = \"Messages\") + scale_y_continuous(breaks = seq(0,1000, by = 100), minor_breaks = waiver()) + coord_flip() \r\nu <- u + theme(axis.text.y=element_blank())\r\nggsave(\"Users.png\", width=15, height=9, dpi=300)\r\n\r\n\r\nh <- ggplot(data = hours, aes(hours$Hour, hours$Freq, group = 1))\r\nh <- h + geom_line(color = \"firebrick\", size = 1.0)\r\nggsave(\"Hours.png\", width=15, height=9, dpi=300)\r\n\r\n", "meta": {"hexsha": "75cc67f5d92f4415560e7ff2b2227544ca22afc0", "size": 1297, "ext": "r", "lang": "R", "max_stars_repo_path": "Plotter.r", "max_stars_repo_name": "prab4th/WhatsApp-Chat-Analyser", "max_stars_repo_head_hexsha": "c8c9f733293e18aaf6865916fb70c64130e53d65", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-03-09T13:45:31.000Z", "max_stars_repo_stars_event_max_datetime": "2017-03-09T13:45:31.000Z", "max_issues_repo_path": "Plotter.r", "max_issues_repo_name": "prab4th/WhatsApp-Chat-Analyser", "max_issues_repo_head_hexsha": "c8c9f733293e18aaf6865916fb70c64130e53d65", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Plotter.r", "max_forks_repo_name": "prab4th/WhatsApp-Chat-Analyser", "max_forks_repo_head_hexsha": "c8c9f733293e18aaf6865916fb70c64130e53d65", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.2333333333, "max_line_length": 134, "alphanum_fraction": 0.6237471087, "num_tokens": 422, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.33569984061527874}} {"text": "#density plots of variance decomposition.\nrm(list=ls())\nsource('NEFI_functions/zero_truncated_density.r')\nsource('paths_fall2019.r')\nsource('paths.r')\n\n#set output path.\noutput.path <- 'figures/Supp._Fig._5._variance_decomposition.jpg'\n\n#load data.----\nd.ITS <- readRDS(NEON_ddirch_var.decomp_all_groups.path)\nd.16S <- readRDS(NEON_ddirch_var.decomp_16S.path)\n\n#grab individual cov, parameter and process error for ITS groups at the site-level.----\ncov.out.ITS <- list()\npar.out.ITS <- list()\npro.out.ITS <- list()\nfor(i in 1:length(d.ITS)){\n tab <- d.ITS[[i]]$site_decomp\n cov.out.ITS[[i]] <- tab[rownames(tab) == 'covariate',]\n par.out.ITS[[i]] <- tab[rownames(tab) == 'parameter',]\n pro.out.ITS[[i]] <- tab[rownames(tab) == 'process' ,]\n}\ncov.ITS <- unlist(cov.out.ITS)\npar.ITS <- unlist(par.out.ITS)\npro.ITS <- unlist(pro.out.ITS)\ncov.ITS <- cov.ITS[-grep('other',names(cov.ITS))]\npar.ITS <- par.ITS[-grep('other',names(par.ITS))]\npro.ITS <- pro.ITS[-grep('other',names(pro.ITS))]\npro.ITS <- ifelse(pro.ITS > 1, 1, pro.ITS)\n#get densities, zero bound if appropriate\ncov.d.ITS <- zero_truncated_density(cov.ITS)\npar.d.ITS <- zero_truncated_density(par.ITS)\npro.d.ITS <- zero_truncated_density(pro.ITS)\n#cov.d <- density(cov, from = 0, to = 1)\n#par.d <- density(par, from = 0, to = 1)\npro.d.ITS <- density(pro.ITS, from = 0, to = 1)\npro.d_xy.ITS <- data.frame(pro.d.ITS$x,pro.d.ITS$y)\npro.d_xy.ITS[nrow(pro.d_xy.ITS),2] <- 0\n\n#grab individual cov, parameter and process error for 16S groups at the site-level.----\ncov.out.16S <- list()\npar.out.16S <- list()\npro.out.16S <- list()\nfor(i in 1:length(d.16S)){\n tab <- d.16S[[i]]$site_decomp\n cov.out.16S[[i]] <- tab[rownames(tab) == 'covariate',]\n par.out.16S[[i]] <- tab[rownames(tab) == 'parameter',]\n pro.out.16S[[i]] <- tab[rownames(tab) == 'process' ,]\n}\ncov.16S <- unlist(cov.out.16S)\npar.16S <- unlist(par.out.16S)\npro.16S <- unlist(pro.out.16S)\ncov.16S <- cov.16S[-grep('other',names(cov.16S))]\npar.16S <- par.16S[-grep('other',names(par.16S))]\npro.16S <- pro.16S[-grep('other',names(pro.16S))]\npro.16S <- ifelse(pro.16S > 1, 1, pro.16S)\n#get densities, zero bound if appropriate\ncov.d.16S <- zero_truncated_density(cov.16S)\npar.d.16S <- zero_truncated_density(par.16S)\npro.d.16S <- zero_truncated_density(pro.16S)\n#cov.d <- density(cov, from = 0, to = 1)\n#par.d <- density(par, from = 0, to = 1)\npro.d.16S <- density(pro.16S, from = 0, to = 1)\npro.d_xy.16S <- data.frame(pro.d.16S$x,pro.d.16S$y)\npro.d_xy.16S[nrow(pro.d_xy.16S),2] <- 0\n\n#jpeg save line.----\njpeg(filename=output.path,width=10,height=5,units='in',res=300)\n\n#Global plot settings.----\npar(mfrow = c(1,2), mar = c(4.5,4,1,1))\nlimx <- c(0,1)\nlimy <- c(0, 51)\ntrans <- 0.2 #shading transparency.\no.cex <- 1.3 #outer label size.\ncols <- c('purple','cyan','yellow')\n\n#16S plot.----\nplot(cov.d.16S,xlim = limx, ylim = limy, bty = 'n', xlab = NA, ylab = NA, main = NA, yaxs='i', xaxs = 'i', las = 1, lwd = 1)\npolygon(cov.d.16S, col = adjustcolor(cols[1],trans))\npolygon(par.d.16S, col = adjustcolor(cols[2],trans))\npolygon(pro.d_xy.16S, col = adjustcolor(cols[3],trans), fillOddEven = F)\nmtext('Density', side = 2, line = 2.2, cex = o.cex)\nmtext('relative contribution to uncertainty', side = 1, line = 2.5, cex = o.cex)\nmtext('Bacteria', side = 3, line = -1, adj = 0.8, cex = o.cex)\n\n#ITS plot.----\nplot(cov.d.ITS,xlim = limx, ylim = limy, bty = 'n', xlab = NA, ylab = NA, main = NA, yaxs='i', xaxs = 'i', las = 1, lwd = 1)\npolygon(cov.d.ITS, col = adjustcolor(cols[1],trans))\npolygon(par.d.ITS, col = adjustcolor(cols[2],trans))\npolygon(pro.d_xy.ITS, col = adjustcolor(cols[3],trans), fillOddEven = F)\nmtext('Density', side = 2, line = 2.2, cex = o.cex)\nmtext('relative contribution to uncertainty', side = 1, line = 2.5, cex = o.cex)\nlegend(x = 0.7, y = 40, legend = c('covariate','parameter','process'), \n col ='black', pt.bg=adjustcolor(cols,trans), \n bty = 'n', pch = 22, pt.cex = 1.5)\nmtext('Fungi', side = 3, line = -1, adj = 0.8, cex = o.cex)\n\n#end plot.----\ndev.off()\n", "meta": {"hexsha": "8beb9845445d9a39b8e0d9d4ccc600f3adacb9f3", "size": 4016, "ext": "r", "lang": "R", "max_stars_repo_path": "figure_scripts/Supp._Fig._5._all_groups_variance_density_site.level.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "figure_scripts/Supp._Fig._5._all_groups_variance_density_site.level.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "figure_scripts/Supp._Fig._5._all_groups_variance_density_site.level.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 39.3725490196, "max_line_length": 124, "alphanum_fraction": 0.6481573705, "num_tokens": 1492, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.33569984061527874}} {"text": "###########################\r\n#### ###\r\n#### ###\r\n## ## RENEWABLES.NINJA\r\n##### WEBSITE AUTOMATOR\r\n##\r\n#\r\n# simple instructions:\r\n# change any file paths in this script from './path/to/' to the directory where you saved the R and CSV files\r\n# change the token string to match that from your user account\r\n# run through the five simple examples below\r\n#\r\n\r\n\r\n\r\n\r\n#####\r\n## ## MODEL SETUP\r\n#####\r\n\r\n\t# pre-requisites\r\n\tlibrary(curl)\r\n\tsource('./path/to/ninja_automator.r')\r\n\r\n\t# insert your API authorisation token here\r\n\ttoken = 'deadbeef0decafbeef0beeffacade0beefedbabe'\r\n\r\n\t# establish your authorisation\r\n\th = new_handle()\r\n\thandle_setheaders(h, 'Authorization'=paste('Token ', token))\r\n\r\n\r\n\r\n\r\n\r\n#####\r\n## ## DOWNLOAD RENEWABLE TIME SERIES DATA FOR A SINGLE LOCATION\r\n#####\r\n\r\n\t# EXAMPLE 1 ::: look at a very tall wind turbine on top of iain's house\r\n\t# optional args are (for example): from='2014-01-01', to='2014-12-31', dataset='merra2', capacity=1, height=60, turbine='Vestas+V80+2000' \r\n\tw = ninja_get_wind(lat=51.5, lon=0, height=30)\r\n\tplot(w$time, w$output, type='l', col='blue')\r\n\r\n\r\n\t# EXAMPLE 2 ::: or the solar panels facing south-east on stefan's house\r\n\t# optional args are (for example): from='2014-01-01', to='2014-12-31', dataset='merra2', capacity=1, system_loss=10, tracking=0, tilt=35, azim=180\r\n\ts = ninja_get_solar(47.5, 8.5, tilt=15, azim=135)\r\n\tlines(s$time, s$output, col='goldenrod')\r\n\r\n\r\n\r\n\r\n\r\n#####\r\n## ## DOWNLOAD RENEWABLE TIME SERIES DATA FOR MULTIPLE LOCATIONS\r\n#####\r\n\r\n\t# EXAMPLE 3 ::: look at wind farms in each of the UK's capitals\r\n\t# args are the same as for ninja_get_wind - either pass a single values or vectors of values\r\n\tlat = c(51.5, 56, 51.5, 54.6)\r\n\tlon = c(0, -3.2, -3.2, -5.9)\r\n\tturbine = c('Vestas+V80+2000', 'Enercon+E66+1800', 'Siemens+SWT+2.3+93', 'GE+1.5sl')\r\n\ty = ninja_aggregate_wind(lat, lon, turbine=turbine)\r\n\r\n\t# how does the hourly data look?\r\n\tdev.new(width=20)\r\n\tplot(y$time, y$outputV1, type='l', col='#0E60BA')\r\n\tlines(y$time, y$outputV2, col='#C80028')\r\n\tlines(y$time, y$outputV3, col='#2E8C39')\r\n\tlines(y$time, y$outputV4, col='#FFCF01')\r\n\r\n\t# how do they correlate?\r\n\tdev.new()\r\n\tplot(y[ , -1], pch='.')\r\n\r\n\t# what about the daily averages?\r\n\tyd = aggregate(y, by=list(as.Date(y$time)), mean)\r\n\tdev.new(width=20)\r\n\tplot(yd$time, yd$outputV1, type='l', col='#0E60BA')\r\n\tlines(yd$time, yd$outputV2, col='#C80028')\r\n\tlines(yd$time, yd$outputV3, col='#2E8C39')\r\n\tlines(yd$time, yd$outputV4, col='#FFCF01')\r\n\r\n\r\n\r\n\r\n\r\n#####\r\n## ## DOWNLOAD RENEWABLE TIME SERIES DATA FOR MULTIPLE LOCATIONS\r\n## ## USING CSV FILES FOR DATA INPUT AND OUTPUT\r\n#####\t\r\n\r\n\t# EXAMPLE 4 :::: read a set of wind farms from CSV - save their outputs to CSV\r\n\t# this is the same as example 3 - the UK capital cities\r\n\t# your csv must have a strict structure: one row per farm, colums = lat, lon, from, to, dataset, capacity, height, turbine - and optionally name (all lowercase!)\r\n\r\n\tfarms = read.csv('./path/to/renewables.ninja.wind.farms.csv', stringsAsFactors=FALSE)\r\n\r\n\tz = ninja_aggregate_wind(farms$lat, farms$lon, farms$from[1], farms$to[1], farms$dataset, farms$capacity, farms$height, farms$turbine)\r\n\r\n\twrite.csv(z, './path/to/renewables.ninja.wind.output.csv', row.names=FALSE)\r\n\r\n\r\n\r\n\t# EXAMPLE 5 :::: read a set of solar farms from CSV - save their outputs to CSV\r\n\t# this is the ten largest US cities - and uses the 'name' column to identify our farms\r\n\t# your csv must have a strict structure: one row per farm, colums = lat, lon, from, to, dataset, capacity, system_loss, tracking, tilt, azim - and optionally name (all lowercase!)\r\n\r\n\tfarms = read.csv('./path/to/renewables.ninja.solar.farms.csv', stringsAsFactors=FALSE)\r\n\r\n\tz = ninja_aggregate_solar(farms$lat, farms$lon, farms$from[1], farms$to[1], farms$dataset, farms$capacity, farms$system_loss, farms$tracking, farms$tilt, farms$azim, name=farms$name)\r\n\r\n\twrite.csv(z, './path/to/renewables.ninja.solar.output.csv', row.names=FALSE)\r\n\r\n\t# how productive are these places\r\n\tcolMeans(z[ , -1]) / farms$capacity\r\n\r\n\r\n\r\n\r\n\t# now you know the way of the ninja\r\n\t# use your power wisely\r\n\t# fight bravely\r\n\t", "meta": {"hexsha": "8e06a7c253918f9a4b3f0056e23e2575e6590d73", "size": 4219, "ext": "r", "lang": "R", "max_stars_repo_path": "R/example.r", "max_stars_repo_name": "renewables-ninja/ninja_automator", "max_stars_repo_head_hexsha": "b3f34b6503896d38ce380b54f1100f916ce2672b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2017-08-10T14:07:38.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-22T02:53:07.000Z", "max_issues_repo_path": "R/example.r", "max_issues_repo_name": "renewables-ninja/ninja_automator", "max_issues_repo_head_hexsha": "b3f34b6503896d38ce380b54f1100f916ce2672b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-09-05T12:36:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-22T10:31:08.000Z", "max_forks_repo_path": "R/example.r", "max_forks_repo_name": "renewables-ninja/ninja_automator", "max_forks_repo_head_hexsha": "b3f34b6503896d38ce380b54f1100f916ce2672b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2018-06-06T19:59:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-22T20:50:35.000Z", "avg_line_length": 33.752, "max_line_length": 184, "alphanum_fraction": 0.649917042, "num_tokens": 1264, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.33569984061527874}} {"text": "library(\"randomForest\")\n# function to widen CPP LENA data for predictive models\nCPP_aggregate <- function(data){\n CPP_agg <- aggregate(data, by=list(URSI.g=data$URSI, Session.g=data$Session), FUN=\"mean\")\n CPP_agg$URSI <- CPP_agg$URSI.g\n CPP_agg$URSI.g <- NULL\n CPP_agg$Session <- CPP_agg$Session.g\n CPP_agg$Session.g <- NULL\n CPP_agg <- sort_df(CPP_agg, vars=c(\"URSI\", \"Session\"))\n return(CPP_agg)\n}\n\n# function to collapse wide CPP LENA dataframe to one row per URSI\nCPP_collapse_wide <- function(data){\n if(length(levels(data$Session)) == 2){\n new_data <- data[0,]\n new_data$Session <- NULL\n features <- c(\"Turn_Count\", \"Child_Voc_Count\", \"Child_Voc_Duration\", \"Child_NonVoc_Duration\", \"Average_SignalLevel\", \"Peak_SignalLevel\")\n for (row in 1:nrow(data)){\n if (row %% 2 == 0)\n new_data <- rbind(new_data, data.frame(c(data[row, c(\"URSI\", \"SM_dx\")], (data[row - 1, features] - data[row, features]))))\n \n }\n data <- new_data\n } else {\n data$Session <- NULL\n data <- aggregate(data, by=list(URSI.g=data$URSI), FUN=\"mean\")\n data$URSI <- data$URSI.g\n data$URSI.g <- NULL\n }\n return(data)\n}\n\n# function to run Random Forests on CPP LENA data\n# returns a randomForest model\nCPP_random_forest <- function(data, plot_title){\n rf <- randomForest(SM_dx ~ Turn_Count + Child_Voc_Count +\n Child_Voc_Duration + Child_NonVoc_Duration + Average_SignalLevel +\n Peak_SignalLevel, data=data, importance=TRUE, ntree=2000)\n print(importance(rf))\n print(rf)\n varImpPlot(rf, color=\"#0067a0\", main = plot_title)\n return(rf)\n}\n\n# Get CPP data\nCPP_data <- read.csv(\"../data/CPP_data_all.csv\")\n\n# Build dataframe for A - non-A\nCPP_A_v_nonA <- CPP_data\nlevels(CPP_A_v_nonA$Session) <- c(\"A\", \"non-A\", \"non-A\")\n\n# Build dataframe for B - C\nCPP_B_v_C <- CPP_data[!(CPP_data$Session == \"A\"),]\nlevels(CPP_B_v_C$Session) <- droplevels(CPP_B_v_C$Session)\n\n# Widen CPP data\nCPP_A_B_C <- CPP_aggregate(CPP_data)\nCPP_A_v_nonA <- CPP_aggregate(CPP_A_v_nonA)\nCPP_B_v_C <- CPP_aggregate(CPP_B_v_C)\n\n# Collapse into one row per URSI in each wide frame\nCPP_A_B_C <- CPP_collapse_wide(CPP_data)\nCPP_A_v_nonA <- CPP_collapse_wide(CPP_A_v_nonA)\nCPP_B_v_C <- CPP_collapse_wide(CPP_B_v_C)\n\n# Random Forests\nrf_CPP_A_B_C <- CPP_random_forest(CPP_A_B_C, \"mean of all blocks\")\nrf_CPP_A_v_nonA <- CPP_random_forest(CPP_A_v_nonA, \"A - non-A\")\nrf_CPP_B_v_C <- CPP_random_forest(CPP_B_v_C, \"B - C\")\n", "meta": {"hexsha": "072b5d963690d990c954a3918badd9f1f253b4d5", "size": 2450, "ext": "r", "lang": "R", "max_stars_repo_path": "CPP/CPP_predictive.r", "max_stars_repo_name": "shnizzedy/LENA_analysis", "max_stars_repo_head_hexsha": "2b8582ea3fed2497e3eb58e9deb27cfcd268d90d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CPP/CPP_predictive.r", "max_issues_repo_name": "shnizzedy/LENA_analysis", "max_issues_repo_head_hexsha": "2b8582ea3fed2497e3eb58e9deb27cfcd268d90d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2018-06-28T18:07:23.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-06T14:16:19.000Z", "max_forks_repo_path": "CPP/CPP_predictive.r", "max_forks_repo_name": "ChildMindInstitute/LENA_BB_CPP_analysis", "max_forks_repo_head_hexsha": "2b8582ea3fed2497e3eb58e9deb27cfcd268d90d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5070422535, "max_line_length": 140, "alphanum_fraction": 0.6991836735, "num_tokens": 764, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3356998321612695}} {"text": "# -*- coding: utf-8 -*-\n\n#' Created on Fri Apr 13 15:38:28 2018\n#' R version 3.4.3 (2017-11-30)\n#' \n#' @group Group 2, DM2 2018 Semester 2\n#' @author: Martins T.\n#' @author: Mendes R.\n#' @author: Santos R.\n#'\n\n# Libs --------------------------------------------------------------------\noptions(warn=-1)\nsource(\"src/packages.r\")\ninclude_packs(c(\"dygraphs\",\"d3heatmap\",\"rockchalk\",\"forcats\",\"rJava\",\n \"xlsxjars\",\"xlsx\",\"tidyverse\",\"stringi\",\"stringr\",\"ggcorrplot\",\n \"sm\",\"lubridate\",\"magrittr\",\"ggplot2\",\"openxlsx\",\"RColorBrewer\",\n \"psych\",\"treemap\",\"data.table\",\"pROC\",\"class\",'gmodels','klaR',\n \"C50\",\"caret\",'gmodels',\"DMwR\",\"recipes\",\"epiR\",\"pubh\",\"leaps\",\n \"MASS\",\"autoimage\",\"randomForest\",\"settings\",\"factoextra\",\n \"dummies\",\"corrplot\"))\n\n\n\n# Load data ---------------------------------------------------------------\n#Load normalized data xlsx\nsource(\"src/wrangling.r\")\nnormalizedDataset <- xlsx::read.xlsx('datasets/normalizedDataset.xlsx',1, header= TRUE)\n\n#backup set\ntemp<-normalizedDataset\n\n\n#Checking correlation, dropping variables not correlated\ncorrelation <- round(cor(normalizedDataset, method = \"kendall\"), 1)\nwrite_excel_report(correlation)\n\n\ndrops <- c(\"Dependents\",\"JobPerformance\",\n \"DistanceHomeOffice\",\"JobDedication\",\"isTogether\",\"JobTypeOffice\"\n ,\"isEducAssociate\",\"isEducAreaOther\",\"isEducAreaHR\"\n ,\"isRoleHR\",\"isRoleMarketing\",\"isMale\",\"isEducMasters\",\"isMarried\",\"isSingle\"\n ,\"isEducCollege\",\"isEducAreaIT\",\"isRoleDev\",\"isRoleIT\"\n ,\"SalaryRise\",\"isEducAreaEng\",\"NumberProjectsLastYear\",\"RoleSatisfaction\" \n ,\"HierarchySatisfaction\",\"JobTypeRemote\",\"isAfterHours\",\"isRoleComercialRepr\",\n \"isEducBachelor\",\"avgSatisfaction\",\"FacilitiesSatisfaction\",\n \"isRoleDataSci\",\"isEducAreaMarketing\",\"isRoleManager\",\"LastPromotion\",\n \"TcompanyperWorking\" ,\"WorkLifeLevels\",\"NumcompaniesperWorking\",\"isChurn\")\nnormalizedDataset <- normalizedDataset[ , !(names(normalizedDataset) %in% drops)]\n\ncorrelation <- round(cor(normalizedDataset, method = \"kendall\"), 1)\n\n\npng(filename=\"presentations/correlation.png\",width=1000, height=565)\np<-corr_plot(correlation)\np\ndev.off()\np\n\n#Corr w/ target\nnormalizedDataset<-temp\n\npng(filename=\"presentations/correlationTarget.png\",width=1000, height=565)\np<-corr_target(normalizedDataset)\np\ndev.off()\np\n\n\n\n# Feature selection -------------------------------------------------------\n# Logistic Regr CV - 50 independents vars ------------------------------------\n\n###' Debug\nnormalizedDataset <- temp\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n#checking propotion of target var\nnormalizedDataset$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ntrainData$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ntestData$isChurn %>% \n table() %>% prop.table() %>% {. * 100} %>% round(0)\n\n\npng(filename=\"presentations/stratification.png\",width=300, height=250)\np<-stratification(normalizedDataset,trainData,testData,\"without SMOTE\")\np\ndev.off()\np\n\n# \ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntestData$isChurn <- ifelse(testData$isChurn==1,\"Yes\",\"No\")\n\n\n# \n# #Save test_labels\ntest_labels <- as.factor(testData$isChurn)\n\n\n#drop target from testData, not mandatory \ndrops <- c(\"isChurn\")\ntestData <- testData[ , !(names(testData) %in% drops)]\n\n\n\n \n#Setting up CV\nControlParamteres <- trainControl(method=\"cv\", \n number=10,\n #repeats=10,\n #sampling = \"up\",\n #verboseIter = FALSE,\n savePredictions = TRUE,\n classProbs = TRUE)\n\n#regLogistic\nlog_CV <- train(isChurn~.,\n data=trainData,\n method = \"glm\",\n #preProc=c(\"center\", \"scale\"),\n #metric=\"Accuracy\",\n trControl = ControlParamteres)\n\n\n\nsummary(log_CV)\n\n#The same propotion of target variable is maintained \nsummary(log_CV$finalModel$data$.outcome)\n\n\n#### Base model w/ all predictors just to have in mind what to expect \n\n# testData$pred <- predict(log_CV,testData,type = \"raw\")\n# testData$prob <-predict(log_CV, testData,type = \"prob\")[2]\n# testData$isChurn <- test_labels\n# \n# g <- roc(isChurn ~ prob$Yes, data =testData )\n# reset.par()\n# \n# p<-recordPlot()\n# plot(g, col = 4, lty = 1, \n# main = \"ROC\", asp = NA, \n# xlab = \"Specificity (%)\", \n# ylab = \"Sensitivity (%)\",\n# print.auc=TRUE, type = 'l', ps=1000)\n# legend(.40,.75, \n# legend=c(\"50 Predictors CV\"), \n# col=c(\"4\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3)\n# \n# png(filename=\"presentations/roc50PredictorsCV.png\",width=760, height=565)\n# p\n# dev.off()\n# \n# #confusionMatrix(pred, obs, positive = NULL,...), not the inverse\n# cm <- caret::confusionMatrix(testData$pred, testData$isChurn, positive = 'Yes',\n# dnn=c(\"Pred\",\"Actual\"))\n# \n# \n# png(filename=\"presentations/cm50PredictorsCV.png\",width=760, height=565)\n# draw_confusion_matrix_cv(cm,\"- Log 50 predictors\")\n# dev.off()\n# draw_confusion_matrix_cv(cm,\"- Log 50 predictors\")\n\n\n\n# Logistic Regr SMOTECV (Oversampling) - 50 independents vars ----------------------------------\n\nnormalizedDataset<-temp\n\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records after smote\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n# # ## now using SMOTE to create a more \"balanced problem\"\ntrainData$isChurn <- as.factor(trainData$isChurn)\nminorSet <- DMwR::SMOTE(isChurn ~ ., trainData, \n k = 5, \n perc.over = 300,\n perc.under=0)\n#bind new data\ntrainData<-rbind(trainData,minorSet)\n\n\n#checking propotion of target var\nnormalizedDataset$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ntrainData$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ntestData$isChurn %>% \n table() %>% prop.table() %>% {. * 100} %>% round(0)\n\npng(filename=\"presentations/stratificationSMOTE.png\",width=300, height=250)\np<-stratification(normalizedDataset,trainData,testData,\"with SMOTE\")\np\ndev.off()\np\n\n# \ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntestData$isChurn <- ifelse(testData$isChurn==1,\"Yes\",\"No\")\n\n\n# \n# #Save test_labels\ntest_labels <- as.factor(testData$isChurn)\n\n\n#drop target from testData, not mandatory \ndrops <- c(\"isChurn\")\ntestData <- testData[ , !(names(testData) %in% drops)]\n\n\n\n\n#Setting up CV\nControlParamteres <- trainControl(method=\"cv\", \n number=10,\n #repeats=10,\n #sampling = \"up\",\n #verboseIter = FALSE,\n savePredictions = TRUE,\n classProbs = TRUE)\n\n#regLogistic\nlog_CV <- train(isChurn~.,\n data=trainData,\n method = \"glm\",\n #preProc=c(\"center\", \"scale\"),\n #metric=\"Accuracy\",\n trControl = ControlParamteres)\n\n\nlog_CV\nsummary(log_CV)\n\n\n#The same propotion of target variable is maintained \nsummary(log_CV$finalModel$data$.outcome)\n\n\n\n#### Base model w/ all predictors just to have in mind what to expect \n\n# \n# testData$pred <- predict(log_CV,testData,type = \"raw\")\n# testData$prob <-predict(log_CV, testData,type = \"prob\")[2]\n# testData$isChurn <- test_labels\n# \n# \n# g2 <- roc(isChurn ~ prob$Yes, data =testData )\n# reset.par()\n# \n# \n# p<-recordPlot()\n# plot(g, col = 4, lty = 1, \n# main = \"ROC\", asp = NA, \n# xlab = \"Specificity (%)\", \n# ylab = \"Sensitivity (%)\",\n# print.auc=TRUE, type = 'l', ps=1000)\n# plot(g2, col = 3, lty = 2,\n# print.auc=TRUE,\n# print.auc.y = .45,\n# add = TRUE, ps=1000)\n# legend(.40,.75, \n# legend=c(\"50 Predictors CV\"), \n# col=c(\"4\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3)\n# legend(.40,.7, \n# legend=c(\"50 Predictors SMOTECV\"), \n# col=c(\"3\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3,lty=5)\n# \n# png(filename=\"presentations/roc50PredictorsSMOTECV.png\",width=760, height=565)\n# p\n# dev.off()\n# \n# \n# \n# #confusionMatrix(pred, obs, positive = NULL,...), not the inverse\n# cm <- caret::confusionMatrix(testData$pred, testData$isChurn, positive = 'Yes',\n# dnn=c(\"Pred\",\"Actual\"))\n# \n# png(filename=\"presentations/cm50PredictorsSMOTECV.png\",width=760, height=565)\n# draw_confusion_matrix_smote(cm,\"- Log 50 predictors\")\n# dev.off()\n# draw_confusion_matrix_smote(cm,\"- Log 50 predictors\")\n\n\n\n\n# Logistic Regr PCA SMOTECV - 3 components ---------------------------\nnumericDataset <- xlsx::read.xlsx('datasets/numericDataset.xlsx',1, header= TRUE)\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(numericDataset),\n 0.69*nrow(numericDataset))\n#1000 records\ntrainData <- numericDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- numericDataset[-trainingRowIndex, ]\n\n\n# # ## now using SMOTE to create a more \"balanced problem\"\ntrainData$isChurn <- as.factor(trainData$isChurn)\nminorSet <- DMwR::SMOTE(isChurn ~ ., trainData, \n k = 5, \n perc.over = 300,\n perc.under=0)\n#bind new data\n\ntrainData<-rbind(trainData,minorSet)\nChurn <- trainData$isChurn\n\ndrops <- c(\"isChurn\")\ntrainData <- trainData[ , !(names(trainData) %in% drops)]\n\ntrainData <- scale(trainData, center = T, scale = T)\ntrainData <- as.data.frame(trainData)\n\n#PCA doesn't help the company to understand what variables are more important\npca<- prcomp( trainData, \n center = F, \n scale. = F)\n\n# Plot of the variances (y-axis)\n# associated with the PCs (x-axis).\npng(filename=\"presentations/PCA.png\",width=450, height=250)\nfviz_eig(pca, main = \"PCA\", addlabels=TRUE, hjust = 0,\n barfill=\"#C51B7D\", barcolor =\"darkblue\",\n linecolor =9) + ylim(0, 15) + \n theme_minimal()+ labs(title = \"PCA - Variance explained\",\n x = \"Principal Components\", y = \"% of variance\")\ndev.off()\n\nfviz_eig(pca, main = \"PCA\", addlabels=TRUE, hjust = 0,\n barfill=\"#C51B7D\", barcolor =\"darkblue\",\n linecolor =9) + ylim(0, 15) + \n theme_minimal()+ labs(title = \"PCA - Variance explained\",\n x = \"Principal Components\", y = \"% of variance\")\n\n\npng(filename=\"presentations/PCA1.png\",width=300, height=300)\nfviz_contrib(pca, choice = \"var\", axes = 1, top = 11, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 1\")\ndev.off()\n\nfviz_contrib(pca, choice = \"var\", axes = 1, top = 11, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 1\")\n \n\npng(filename=\"presentations/PCA2.png\",width=300, height=300)\nfviz_contrib(pca, choice = \"var\", axes = 2, top = 8, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 2\")\ndev.off()\n\nfviz_contrib(pca, choice = \"var\", axes = 2, top = 8, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 2\")\n\npng(filename=\"presentations/PCA3.png\",width=300, height=300)\nfviz_contrib(pca, choice = \"var\", axes = 3, top = 7, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 3\")\ndev.off()\n\nfviz_contrib(pca, choice = \"var\", axes = 3, top = 7, fill=\"#C51B7D\")+\n labs(title = \"Contribution to PCA 3\")\n\n\ncomp <- pca$x[,1:3]\ncomp <- as.data.frame(comp)\n\nget_eigenvalue(pca)\n\npca$rotation[1:50,1:3]\npca_value<-pca$rotation[1:50,1:3]\npca_value<-as.data.frame(pca_value)\nwrite_excel_report(pca_value)\n\ntrainData<-comp\ntrainData$isChurn<-Churn\n\n #checking propotion of target var\nnumericDataset$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ncomp$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\ntestData$isChurn %>%\n table() %>% prop.table() %>% {. * 100} %>% round(0)\n\npng(filename=\"presentations/stratificationPCA.png\",width=300, height=250)\np<-stratification(numericDataset,trainData,testData,\"with SMOTE\")\np\ndev.off()\np\n \n\ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntestData$isChurn <- ifelse(testData$isChurn==1,\"Yes\",\"No\")\n\n# #Save test_labels\ntest_labels <- as.factor(testData$isChurn)\n\n\n#drop target from testData, not mandatory \ndrops <- c(\"isChurn\")\ntestData <- testData[ , !(names(testData) %in% drops)]\n \n \n \n# #Setting up CV\nControlParamteres <- trainControl(method=\"cv\", \n number=10,\n #repeats=10,\n #sampling = \"up\",\n #verboseIter = FALSE,\n savePredictions = TRUE,\n classProbs = TRUE)\n\n# #regLogistic\nlog_CV <- train(isChurn~.,\n data=trainData,\n method = \"glm\",\n #preProcess=c(\"pca\"),\n #metric=\"Accuracy\",\n trControl = ControlParamteres)\n \nlog_CV\nsummary(log_CV)\n\n\n### PCA Loadings are required for predict on testset\n\n# testData$pred <- predict(log_CV,testData,type = \"raw\")\n# testData$prob <-predict(log_CV, testData,type = \"prob\")[2]\n# testData$isChurn <- test_labels\n# \n# g3 <- roc(isChurn ~ prob$Yes, data =testData )\n# reset.par()\n# \n# p<-recordPlot()\n# plot(g, col = 4, lty = 1, \n# main = \"ROC\", asp = NA, \n# xlab = \"Specificity (%)\", \n# ylab = \"Sensitivity (%)\",\n# print.auc=TRUE, type = 'l', ps=1000)\n# plot(g2, col = 3, lty = 2,\n# print.auc=TRUE,\n# print.auc.y = .45,\n# add = TRUE, ps=1000)\n# plot(g3, col = \"salmon\", lty = 3,\n# print.auc=TRUE,\n# print.auc.y = .4,\n# add = TRUE, ps=1000)\n# legend(.40,.75, \n# legend=c(\"50 Predictors CV\"), \n# col=c(\"4\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3)\n# legend(.40,.7, \n# legend=c(\"50 Predictors SMOTECV\"), \n# col=c(\"3\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3,lty=5)\n# legend(.40,.65, \n# legend=c(\"3 Components SMOTECV\"), \n# col=c(\"salmon\"), \n# lwd=2, ncol = 1,bty = \"n\", cex=1.3,lty=15)\n# \n# png(filename=\"presentations/roc3ComponentsSMOTECV.png\",width=760, height=565)\n# p\n# dev.off()\n# \n# # #confusionMatrix(pred, obs, positive = NULL,...), not the inverse\n# cm <- caret::confusionMatrix(testData$pred, testData$isChurn, positive = 'Yes',\n# dnn=c(\"Pred\",\"Actual\"))\n# \n# png(filename=\"presentations/cm3ComponentsSMOTECV.png\",width=760, height=565)\n# draw_confusion_matrix_smote(cm,\"- PCA 3 comp\")\n# dev.off()\n# draw_confusion_matrix_smote(cm,\"- PCA 3 comp\")\n\n\n# Rank randomForest w/ SMOTE --------------------------------------------\nnormalizedDataset<-temp\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n# # ## now using SMOTE to create a more \"balanced problem\"\ntrainData$isChurn <- as.factor(trainData$isChurn)\nminorSet <- DMwR::SMOTE(isChurn ~ ., trainData, \n k = 5, \n perc.over = 300,\n perc.under=0)\n#bind new data\n\ntrainData<-rbind(trainData,minorSet)\n\n\n\n#Setting up CV\nControlParamteres <- trainControl(method=\"boot\", \n #number=10,\n #repeats=10,\n #sampling = \"up\",\n #verboseIter = FALSE,\n savePredictions = TRUE,\n classProbs = TRUE)\n\n#gini index\nrandomForest <- randomForest(isChurn ~ .,\n data=trainData, \n ntree=50,\n keep.forest=FALSE, \n scale=FALSE,\n #class=\"ROC\",\n importance=TRUE,\n trControl = ControlParamteres)\nrandomForest\n\n#The second measure is the total decrease in node impurities \n#from splitting on the variable, averaged over all trees\nrfImportance<-importance(randomForest)\nrfImportance\nwrite_excel_report(rfImportance)\n\n\npng(filename=\"presentations/rfAcc.png\",width=700, height=900)\nvarImpPlot(randomForest,type=1)\ndev.off()\nvarImpPlot(randomForest,type=1)\n\npng(filename=\"presentations/rfGini.png\",width=700, height=900)\nvarImpPlot(randomForest,type=2)\ndev.off()\nvarImpPlot(randomForest,type=2)\n\n\n\n# RFECV Knn w/ SMOTE ------------------------------------------------------\nnormalizedDataset<-temp\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n# # ## now using SMOTE to create a more \"balanced problem\"\ntrainData$isChurn <- as.factor(trainData$isChurn)\nminorSet <- DMwR::SMOTE(isChurn ~ ., trainData, \n k = 5, \n perc.over = 300,\n perc.under=0)\n#bind new data\n\ntrainData<-rbind(trainData,minorSet)\ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntrainData$isChurn <- as.factor(trainData$isChurn)\n\n\nrfe_controller <- rfeControl(functions=caretFuncs, \n method=\"cv\",\n rerank = FALSE,\n number=10, \n verbose = FALSE)\n\n\nControlParamteres <- trainControl(#method=\"cv\"\n #number=10,\n #repeats=10,\n #sampling = \"up\",\n #verboseIter = FALSE,\n #savePredictions = TRUE,\n classProbs = TRUE)\nknn <- rfe(isChurn~.,\n data=trainData,\n method = \"knn\",\n sizes = c(5:50),\n metric=\"Kappa\",\n rfeControl = rfe_controller,\n trControl = ControlParamteres)\n\nknn\n\nrankknn<-knn$variables\nrankknn\nwrite_excel_report(rankknn)\n\npng(filename=\"presentations/rfeKnn.png\",width=760, height=565)\np <- ggplot(knn) +\n labs(title=\"RFE Knn SMOTE CV\",\n x =\"Number of Predictors\", y = \"Kappa\")+\n geom_line(color=\"black\")+\n geom_point(color=\"blue\")+ \n xlim(2.3, 50)\np\ndev.off()\np\n\n\n# RFECV NB w/ SMOTE ------------------------------------------------------\nnormalizedDataset<-temp\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n# # ## now using SMOTE to create a more \"balanced problem\"\ntrainData$isChurn <- as.factor(trainData$isChurn)\nminorSet <- DMwR::SMOTE(isChurn ~ ., trainData, \n k = 5, \n perc.over = 300,\n perc.under=0)\n#bind new data\n\ntrainData<-rbind(trainData,minorSet)\ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntrainData$isChurn <- as.factor(trainData$isChurn)\n\n\ncontrol <- rfeControl(functions=nbFuncs,\n method=\"cv\", \n number=10)\n\n\nrfe_predictors<-trainData[,1:(ncol(trainData)-1)]\nrfe_target<- trainData[,(ncol(trainData))]\n\n# run the RFE algorithm\nresults <- rfe(isChurn~.,\n data=trainData,\n sizes=c(5:50), \n metric=\"Accuracy\", \n rfeControl=control)\n\nresults\n\nrankNB<-results$variables\nrankNB\nwrite_excel_report(rankNB)\n\npng(filename=\"presentations/rfeNB.png\",width=760, height=565)\np <- ggplot(results) +\n labs(title=\"RFE NB SMOTE\",\n x =\"Number of Predictors\", y = \"Kappa\")+\n geom_line(color=\"black\")+\n geom_point(color=\"blue\")+ \n xlim(2.3, 50)\np\ndev.off()\np\n\n\n\n\n##### Plot variables selected \nnormalizedDataset <- temp\n\ninclude <- c(\"DistanceHomeOffice\",\"isAfterHours\",\n \"RoleSatisfaction\",\"isSingle\",\n \"isMarried\",\"Age\",\n \"FacilitiesSatisfaction\",\"avgSatisfaction\",\n \"TenureWorking\",\"NumCompaniesWorked\",\n \"LastPromotion\",\"MonthlyIncome\",\"isDepartIT\",\n \"isChurn\")\n\n\nnormalizedDataset <- normalizedDataset[ , (names(normalizedDataset) %in% include)]\n\n# churn~. dont work\n# attach(normalizedDataset)\n\npairs(normalizedDataset$isChurn~normalizedDataset$DistanceHomeOffice+normalizedDataset$isAfterHours+\n normalizedDataset$RoleSatisfaction+normalizedDataset$isSingle+\n normalizedDataset$isMarried+normalizedDataset$Age+\n normalizedDataset$FacilitiesSatisfaction+normalizedDataset$avgSatisfaction,\n panel = panel.smooth, # Optional smoother\n main = \"Variables Selected\",\n diag.panel = panel.hist,\n pch = 16,\n col = brewer.pal(2, \"Pastel2\")[unclass(normalizedDataset$Churn)])\n\n\n\n", "meta": {"hexsha": "2105ce4020c5a79626aa883bd34f249110a91992", "size": 21587, "ext": "r", "lang": "R", "max_stars_repo_path": "src/featureselection.r", "max_stars_repo_name": "tmartins1996/r-binary-classification", "max_stars_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/featureselection.r", "max_issues_repo_name": "tmartins1996/r-binary-classification", "max_issues_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/featureselection.r", "max_forks_repo_name": "tmartins1996/r-binary-classification", "max_forks_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1494413408, "max_line_length": 100, "alphanum_fraction": 0.5885949877, "num_tokens": 5756, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3356998321612695}} {"text": "#!/usr/bin/env Rscript\n\n# Copyright [1999-2015] Wellcome Trust Sanger Institute and the EMBL-European Bioinformatics Institute\n# Copyright [2016-2020] EMBL-European Bioinformatics Institute\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n#How to plot the Jaccard index:\n#Rscript plotJaccardIndex.r \n\nargs = commandArgs(trailingOnly=TRUE)\n\nif (length(args)==0) {\n stop(\"Missing arguments: Rscript plotJaccardIndex.r \", call.=FALSE)\n}\n\nlibrary(ggplot2)\n\npdf(args[2])\nji_vertebrate = read.delim(args[1], sep=\"\\t\", header=FALSE)\nggplot(ji_vertebrate, aes(ji_vertebrate$V2)) + geom_density() + geom_vline(aes(xintercept=0)) + xlim(0, 1.25) + theme(legend.text=element_text(size=10)) + theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10),axis.title.x=element_text(size=10),axis.title.y=element_text(size=10))\ndev.off()\n", "meta": {"hexsha": "fe4d5b433f09574aa926bd6de83b1720680ec60e", "size": 1378, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/homology/plotJaccardIndex.r", "max_stars_repo_name": "manuelcarbajo/ensembl-compara", "max_stars_repo_head_hexsha": "0ffe653215a20e6921c5f4983ea9e4755593a491", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/homology/plotJaccardIndex.r", "max_issues_repo_name": "manuelcarbajo/ensembl-compara", "max_issues_repo_head_hexsha": "0ffe653215a20e6921c5f4983ea9e4755593a491", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/homology/plotJaccardIndex.r", "max_forks_repo_name": "manuelcarbajo/ensembl-compara", "max_forks_repo_head_hexsha": "0ffe653215a20e6921c5f4983ea9e4755593a491", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.7575757576, "max_line_length": 299, "alphanum_fraction": 0.7561683599, "num_tokens": 371, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.33565989834193494}} {"text": "rankall <- function(outcome, num = \"best\") {\n\tdata <- read.csv(\"outcome-of-care-measures.csv\", na.strings = \"Not Available\", stringsAsFactors = FALSE)\n\tdata <- data[,c(2, 7, 11, 17, 23)]\n\n\tnames(data) <- c(\"hospital\", \"state\", \"heart attack\", \"heart failure\", \"pneumonia\")\n\n\toutcomes <- c(\"heart attack\"=3, \"heart failure\"=4, \"pneumonia\"=5)\n\n\tif (!outcome %in% names(data)) {\n\t\tstop('invalid outcome')\n\t}\n\tif (num != \"best\" & num != \"worst\" & !is.numeric(num)) {\n\t\tstop('invalid rank')\n\t}\n\n\tdf <- data[, c(1, 2, outcomes[outcome])]\n\n\t#un commented this\n\tdf <- na.omit(df)\n\n\tnames(df) <- c(\"hospital\", \"state\", \"deaths\")\n\t#dfOrder <- df[order(df$state, df$deaths),]\n\t#dfOrder <- df[order(df$state, df$deaths, df$hospital),]\n\tdfSplit <- split(df, df$state)\n\n\t#dfSplit <- dfSplit[order(dfSplit$deaths, dfSplit$hospital)]\n\n\tif (num == \"best\") {\n\t\tnum <- 1\n\t} else if (num == \"worst\") {\n\t\tnum <- as.numeric(nrow(df))\n\t}\n\n\tresults <- lapply(dfSplit, function(dfSplit) dfSplit[num,])\n\tresultsUnlist <- unlist(results)\n\treturn(data.frame(hospital=resultsUnlist, state=names(results), row.names=names(resultsUnlist)))\n}\n\n\n\n", "meta": {"hexsha": "2fa7eda70ef846203e87b5fe97cf2501e74c3e25", "size": 1114, "ext": "r", "lang": "R", "max_stars_repo_path": "week4/rprog%2Fdata%2FProgAssignment3-data/rankall.r", "max_stars_repo_name": "josteinstraume/datasciencecoursera", "max_stars_repo_head_hexsha": "872db0125e1deb330bd370fe6566142e137dd4d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "week4/rprog%2Fdata%2FProgAssignment3-data/rankall.r", "max_issues_repo_name": "josteinstraume/datasciencecoursera", "max_issues_repo_head_hexsha": "872db0125e1deb330bd370fe6566142e137dd4d0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "week4/rprog%2Fdata%2FProgAssignment3-data/rankall.r", "max_forks_repo_name": "josteinstraume/datasciencecoursera", "max_forks_repo_head_hexsha": "872db0125e1deb330bd370fe6566142e137dd4d0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1707317073, "max_line_length": 105, "alphanum_fraction": 0.6382405745, "num_tokens": 358, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.33565989834193494}} {"text": "options(bitmapType='cairo')\r\nlibrary(Rsamtools)\r\nlibrary(ggplot2)\r\n\r\nfilelist<-read.table(parSampleFile1, sep=\"\\t\", header=F, stringsAsFactor=F)\r\nfileDensities<-apply(filelist, 1, function(x){\r\n res<-scanBam(x[1], index=x[1], param=ScanBamParam(what=c(\"isize\")))\r\n aa<-res[[1]]\r\n bb<-as.numeric(aa[[1]])\r\n cc<-abs(bb)\r\n cc<-cc[!is.na(cc) & cc < 1000]\r\n dens<-density(cc, from=0, to=1000, n=250)\r\n dd<-data.frame(File=x[2], InsertSize=dens$x, Density=dens$y )\r\n return(dd)\r\n})\r\nall<-do.call(rbind, fileDensities)\r\npng(file=outFile, width=2000, height=2000, res=300)\r\ng<-ggplot(all, aes(InsertSize, Density, group=File, color=File)) + geom_line() + theme_bw() + scale_x_continuous(breaks=seq(0,1000,100))\r\nprint(g)\r\ndev.off()\r\n", "meta": {"hexsha": "850cfbdfaac2cfc0f8b0daedafed3b504875ac83", "size": 733, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/Visualization/insertSize.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/Visualization/insertSize.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/Visualization/insertSize.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 34.9047619048, "max_line_length": 137, "alphanum_fraction": 0.6725784447, "num_tokens": 254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7279754489059775, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.33560889687307116}} {"text": "#\r\n#last edited at 20200712\r\n\r\n#a function similar to numbers::mGCD(),\r\n#but calculates approximate GCDs, return a numeric vector,\r\n#and may runs for a long time for its bad algorithm\r\n\r\n#get dependency\r\nif(FALSE){\r\n require(magrittr); require(tibble); require(dplyr);\r\n require(parallel)\r\n}\r\n\r\nmessage(\"\\nmGCD_approximate() is very slow. change the algorithm and use rust\\n\")\r\nwarning(\"\\nmGCD_approximate isn't stable, and isn't fully tested\\n\")\r\n\r\nmGCD_approximate <- function(x){\r\n #control the input of x-------------------------------------------------------\r\n if(!is.integer(x)){\r\n stop(\"class(x) must be integer, not numeric. please use as.integer() first.\")\r\n }\r\n if(length(x) == 0){stop(\"invalid x\");}\r\n x %<>% stats::na.omit() %>% {.[. != 0];} %>% base::abs()\r\n if(length(x) == 0){stop(\"invalid x\");}\r\n #convert x from a vector to tibble--------------------------------------------\r\n x %<>% base::table() %>% {tibble(x=as.integer(names(.)), weight=unname(.))}\r\n #get info array---------------------------------------------------------------\r\n if(nrow(x) != 1){\r\n divisor <- seq(from=1.1, to=ceiling(max(x$x) / 2), by=0.1)\r\n f_get_parameter <- function(dvs, dvdd){\r\n if(length(dvs) != 1){stop(\"invalid input\");}\r\n qtt <- dvdd %/% dvs\r\n rmd <- dvdd %% dvs\r\n ans <- rmd > (dvs / 2)\r\n qtt[ans] <- qtt[ans] + 1\r\n rmd[ans] <- dvs - rmd[ans]\r\n remove(ans)\r\n offset_dvdd <- base::signif(x=rmd / dvdd, digits=3)\r\n offset_dvs <- base::signif(x=rmd / dvs, digits=3)\r\n return(cbind(qtt, offset_dvdd, offset_dvs))\r\n }\r\n if(length(divisor) > 5 * 10^5){\r\n a_cluster <- base::ceiling(length(divisor) / 10^5) %>%\r\n base::min(., detectCores(logical=TRUE) * 2) %>%\r\n parallel::makeCluster(spec=., outfile=\"\")\r\n chunk_size <- ceiling(length(divisor) / 10^5 / length(a_cluster)) %>%\r\n {length(divisor) / length(a_cluster) / .} %>% ceiling()\r\n system.time(expr={\r\n info <- parSapply(cl=a_cluster, X=divisor, FUN=f_get_parameter, dvdd=x$x,\r\n simplify=\"array\", chunk.size=chunk_size)\r\n })\r\n stopCluster(cl=a_cluster); remove(a_cluster, chunk_size);\r\n }else{\r\n system.time(expr={\r\n info <- sapply(X=divisor, FUN=f_get_parameter, dvdd=x$x, simplify=\"array\")\r\n })\r\n }\r\n dimnames(info) <- list(x=as.character(x$x),\r\n parameter=c(\"quotient\", \"offset_dvdd\", \"offset_dvs\"),\r\n divisor=as.character(divisor))\r\n remove(divisor, f_get_parameter)\r\n }\r\n if(nrow(x) == 1) if(x$x != 1){\r\n info <- array(data=0, dim=c(1, 3, 1))\r\n info[,1,] <- 1\r\n dimnames(info) <- list(x=as.character(x$x),\r\n parameter=c(\"quotient\", \"offset_dvdd\", \"offset_dvs\"),\r\n divisor=as.character(x$x))\r\n }\r\n if(nrow(x) == 1) if(x$x == 1){\r\n info <- array(data=0, dim=c(1, 3, 0))\r\n dimnames(info) <- list(x=as.character(x$x),\r\n parameter=c(\"quotient\", \"offset_dvdd\", \"offset_dvs\"),\r\n divisor=character())\r\n }\r\n #analyse info array-----------------------------------------------------------\r\n if(dim(info)[3] > 0){\r\n info %<>% .[,\"offset_dvs\",,drop=FALSE] %>%\r\n apply(MARGIN=3, FUN=function(a){all(a < 0.4)}) %>% info[,,.,drop=FALSE]\r\n }\r\n if(dim(info)[3] > 0){\r\n info %<>% .[,\"offset_dvdd\",,drop=FALSE] %>%\r\n apply(MARGIN=3, FUN=function(a){all(a[a!=1]<0.1)}) %>% info[,,.,drop=FALSE]\r\n }\r\n if(dim(info)[3] > 0){\r\n f_ans <- function(datum, b){\r\n datum <- base::unname(datum[,\"quotient\"]) / b\r\n datum <- base::sort(datum[datum != 0])\r\n if(length(datum) == 0){stop(\"this may not be a bug, but a rare and intreasting situation\");}\r\n if(length(datum) > 1){\r\n datum <- c(continuous=base::max((datum[-1] - head(datum, n=-1)) / head(datum, n=-1)),\r\n range=(max(datum) - min(datum)) / min(datum))\r\n }else{datum <- c(continuous=0, range=0);}\r\n return(datum)\r\n }\r\n tulip <- info[,\"quotient\",,drop=FALSE] %>% apply(MARGIN=3, FUN=f_ans, b=x$x)\r\n tulip <- ((tulip[\"continuous\",] < 0.05) & (tulip[\"range\",] < 0.1))\r\n info %<>% .[,,tulip,drop=FALSE]\r\n remove(tulip, f_ans)\r\n }\r\n if(dim(info)[3] > 1){\r\n tulip <- dimnames(info)[[3]] %>% as.numeric()\r\n f_tulip <- function(i, datum, l){\r\n if(i %% 1000 == 0){cat(i, \", \");}\r\n msu <- datum[i]; spbu <- datum[(i + 1):l]; spbu <- spbu[(spbu %/% msu) > 1];\r\n return(all((spbu %% msu) > (msu * 0.1)))\r\n }\r\n i <- which(tulip > (max(tulip) / 2 * 1.11)) %>% .[1] %>% {.:1}\r\n dahlia <- rep(TRUE, times=length(tulip))\r\n cat(\"\\nthe slowest part begins\\n\")\r\n system.time(expr={\r\n dahlia[i] <- base::sapply(X=i, FUN=f_tulip, datum=tulip, l=length(tulip), simplify=\"array\")\r\n })\r\n cat(\"\\nthe slowest part ends\\n\")\r\n info %<>% .[,,dahlia,drop=FALSE]\r\n remove(tulip, f_tulip, i, dahlia)\r\n }\r\n #split info according to quotient---------------------------------------------\r\n if(dim(info)[3] > 0){\r\n tulip <- info[,\"quotient\",,drop=FALSE] %>% base::unique(MARGIN=3)\r\n dahlia <- rep(x=list(NULL), times=dim(tulip)[3]); for(i in 1:length(dahlia)){\r\n dahlia[[i]] <- tulip[,\"quotient\",i,drop=TRUE] %>% base::unname()\r\n }\r\n remove(tulip)\r\n tulip <- base::apply(X=info, MARGIN=3,\r\n FUN=function(a, b){match(list(unname(a[,\"quotient\",drop=TRUE])), b)},\r\n b=dahlia) %>% base::unname()\r\n for(i in 1:length(dahlia)){\r\n dahlia[[i]] <- info[,,tulip == i,drop=FALSE]\r\n }\r\n info <- dahlia\r\n remove(tulip, dahlia, i)\r\n }else{info <- list();}\r\n #get GCD----------------------------------------------------------------------\r\n dahlia <- tibble(GCD=rep(0, times=length(info)), is_skew=\"\", number_of_alternatives=0L)\r\n if(length(info) > 0) for(i in 1:length(info)){\r\n tulip <- info[[i]] %>% dimnames() %>% .[[3]] %>% as.numeric()\r\n tulip_mean <- info[[i]][,\"quotient\",1,drop=TRUE] %>% base::unname() %>% {. / x$x} %>%\r\n {.[. != 0]} %>% base::mean() %>% {1 / .}\r\n dahlia$GCD[i] <- base::abs(tulip - tulip_mean) %>% base::round(digits=3) %>%\r\n {which(. == min(.))} %>% tulip[.] %>% base::max()\r\n dahlia$number_of_alternatives[i] <- length(tulip)\r\n remove(tulip, tulip_mean)\r\n }; i <- 0; remove(i);\r\n if(length(info) == 0){\r\n warning(\"\\ncan't find approximate GCD. an emptye tibble is returned\\n\")\r\n }\r\n info <- dahlia; remove(dahlia);\r\n #get info$is_skew-------------------------------------------------------------\r\n f_ans <- function(datum){\r\n msu <- datum$expected; spbu <- base::range(datum$observed);\r\n datum <- (msu < spbu[1]) | (msu > spbu[2])\r\n return(datum)\r\n }\r\n if(nrow(info) > 0) for(i in 1:nrow(info)){\r\n info$is_skew[i] <- (x$x / info$GCD[i]) %>% base::split(x=., f=base::round(.)) %>%\r\n {.[names(.) != 0]} %>% {tibble(expected=as.numeric(names(.)), observed=unname(.))} %>%\r\n apply(MARGIN=1, FUN=f_ans) %>% base::table(dnn=NULL) %>% unclass() %>% c() %>%\r\n c(., `TRUE`=0, `FALSE`=0) %>% {split(x=unname(.), f=names(.))} %>% lapply(FUN=sum) %>%\r\n {c(.[[\"TRUE\"]], .[[\"TRUE\"]] + .[[\"FALSE\"]])} %>% {paste0(.[1], \"/\", .[2])}\r\n }; i <- 0; remove(i);\r\n remove(f_ans)\r\n #return results---------------------------------------------------------------\r\n info %<>% dplyr::arrange(dplyr::desc(GCD))\r\n return(info)\r\n}\r\n\r\n#here are the test codes\r\nif(FALSE){\r\n #rnorm(n=10^8, mean=5, sd=0.05) %>% range()\r\n x <- c(rnorm(n=10, mean=0, sd=0.05), rnorm(n=10, mean=10, sd=0.05),\r\n rnorm(n=10, mean=15, sd=0.05), NA, NaN) %>%\r\n {. * 10^4} %>% base::round() %>% as.integer()\r\n x <- c(rnorm(n=10, mean=0, sd=0.05), rnorm(n=10, mean=10, sd=0.05),\r\n rnorm(n=10, mean=15, sd=0.05), NA, NaN) %>%\r\n {. * 10^3} %>% base::round() %>% as.integer()\r\n x <- c(rnorm(n=10, mean=0, sd=0.05), rnorm(n=10, mean=12, sd=0.05),\r\n rnorm(n=10, mean=16, sd=0.05), NA, NaN) %>%\r\n {. * 10^2} %>% base::round() %>% as.integer()\r\n x <- c(0, 1, NA, NaN) %>% sample(size=20, replace=TRUE) %>% as.integer()\r\n x <- c(0, 3, NA, NaN) %>% sample(size=20, replace=TRUE) %>% as.integer()\r\n x <- c(1, 18) %>% sample(size=20, replace=TRUE) %>% as.integer()\r\n x <- c(16, 18) %>% sample(size=20, replace=TRUE) %>% as.integer()\r\n x <- c(17, 18) %>% sample(size=20, replace=TRUE) %>% as.integer()\r\n x <- 17:31 %>% sample(size=50, replace=TRUE) %>% sort() %>% as.integer()\r\n #\r\n hist(x, breaks=100)\r\n system.time(expr={\r\n ans <- mGCD_approximate(x=x)\r\n })\r\n remove(x, mGCD_approximate, ans)\r\n}\r\n", "meta": {"hexsha": "e0b797d5e5205b6bb64c2968bf5aab01addb3b8c", "size": 8610, "ext": "r", "lang": "R", "max_stars_repo_path": "mGCD_approximate().r", "max_stars_repo_name": "ywd5/RPhylogenyZM", "max_stars_repo_head_hexsha": "1c2ba53b793d1b20577738f0213cd5a01b2556fd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "mGCD_approximate().r", "max_issues_repo_name": "ywd5/RPhylogenyZM", "max_issues_repo_head_hexsha": "1c2ba53b793d1b20577738f0213cd5a01b2556fd", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mGCD_approximate().r", "max_forks_repo_name": "ywd5/RPhylogenyZM", "max_forks_repo_head_hexsha": "1c2ba53b793d1b20577738f0213cd5a01b2556fd", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6113989637, "max_line_length": 99, "alphanum_fraction": 0.5058072009, "num_tokens": 2818, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7341195269001831, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.33559299028610345}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\nlibrary(rJava)\n\nsetwd(\"/Users/mengmengjiang/all datas/print\")\n\nlibrary(xlsx)\n\n# reading repeart\n\n#k1<-read.xlsx(\"repert.xlsx\",sheetName=\"600\",header=TRUE)\nk1<-read.xlsx(\"repert.xlsx\",sheetName=\"600\",header=TRUE)\nk2<-read.xlsx(\"repert.xlsx\",sheetName=\"1k\",header=TRUE)\nk3<-read.xlsx(\"repert.xlsx\",sheetName=\"1.5k\",header=TRUE)\nk4<-read.xlsx(\"repert.xlsx\",sheetName=\"3k\",header=TRUE)\n\n# color setting\n\nyan<-c(\"red\",\"blue\",\"black\",\"green3\")\npcc<-c(0,1,2,5)\n\n\n# \u6bd4\u8f83\u8f6c\u6362\u4e4b\u540e\u7684\u6db2\u6ef4\u7684\u5927\u5c0f\npar(fig=c(0,1,0,1),new=F)\n\nplot(k1$no,k1$dp, col=0,xlab = expression(italic(n[\"p\"])(Number)),\n ylab = expression(italic(t[\"rp\"])(ms)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(0,300),ylim=c(0,100))\n\nmtext(\"Cost time\",3,line=-1,font=2,cex=1)\n\nlines(k1$no,k1$trp,col=yan[1],pch=pcc[1],lwd=1.5,lty=2,type=\"b\")\nlines(k2$no,k2$trp,col=yan[2],pch=pcc[2],lwd=1.5,lty=2,type=\"b\")\nlines(k3$no,k3$trp,col=yan[3],pch=pcc[3],lwd=1.5,lty=2,type=\"b\")\nlines(k4$no,k4$trp,col=yan[4],pch=pcc[4],lwd=1.5,lty=2,type=\"b\")\n\n\nleg<-c(\"600Hz\",\"1KHz\",\"1.5KHz\",\"3KHz\")\n\nlegend(\"topleft\",legend=leg,col=yan,pch=pcc,lty=2,lwd=1.5,inset=.02,\nbty=\"n\",cex=0.9)\n\n# plot 2,\u659c\u7387\u62df\u5408\n\npar(fig=c(0.35,0.99,0.1,0.5),new=TRUE)\n\nplot(k1$no,k1$dp,col=0,bty=\"n\",xlab = expression(italic(Frequency)(KHz)),\n ylab = expression(italic(slope)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\",xlim = c(1,3),ylim=c(0,1.8))\n\nz1<-lm(k1$trp~k1$no)\nz2<-lm(k2$trp~k2$no)\nz3<-lm(k3$trp~k3$no)\nz4<-lm(k4$trp~k4$no)\n\nkv1<-z1$coefficients[2]\nkv2<-z2$coefficients[2]\nkv3<-z3$coefficients[2]\nkv4<-z4$coefficients[2]\n\nkv<-c(kv2,kv3,kv4)\nx<-c(1,1.5,3)\n\nb2<-1\nb3<-1/1.5\nb4<-1/3\n\nb<-c(b2,b3,b4)\n\nlines(x,kv,col=\"red\",lwd=1.5,lty=2,pch=0,type=\"b\")\nlines(x,b,col=\"blue\",lwd=1.5,lty=2,pch=1,type=\"b\")\n\nleg2<-c(\"real\",\"fitting\")\n\nlegend(\"topright\",legend=leg2,lwd=1.5,lty=2,pch=c(0,1),col=c(\"red\",\"blue\"),\ninset=.02,bty=\"n\",cex=0.9)\n", "meta": {"hexsha": "51d96d19e9a3026dbef291847d39394d12653c59", "size": 1982, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap7/fig7-23.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap7/fig7-23.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap7/fig7-23.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.0789473684, "max_line_length": 104, "alphanum_fraction": 0.6397578204, "num_tokens": 917, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.33558003255702734}} {"text": "library(MASS)\r\n\r\nd <- fractions(4/31)\r\ndc <- 1 - d\r\n\r\ndc_dc <- dc^2\r\nd_d <- d^2\r\nd_dc <- 1-(d_d+dc_dc)\r\n\r\nd_dc_dc <- d_dc*dc\r\n\r\ng <- dc_dc + d_dc_dc\r\n \r\nd_dc_dc_g <- d_dc_dc/g\r\n\r\nprint(g)\r\nprint(d_dc_dc_g)N", "meta": {"hexsha": "e3f3d29f310b70bed6d4948abbe486e6b2119e07", "size": 207, "ext": "r", "lang": "R", "max_stars_repo_path": "code/Esercizio 11.r", "max_stars_repo_name": "mfranzil/PSUniTN", "max_stars_repo_head_hexsha": "c4baecf5b01fb7cc2cfc66f4881ae47451475dac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/Esercizio 11.r", "max_issues_repo_name": "mfranzil/PSUniTN", "max_issues_repo_head_hexsha": "c4baecf5b01fb7cc2cfc66f4881ae47451475dac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/Esercizio 11.r", "max_forks_repo_name": "mfranzil/PSUniTN", "max_forks_repo_head_hexsha": "c4baecf5b01fb7cc2cfc66f4881ae47451475dac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.1764705882, "max_line_length": 23, "alphanum_fraction": 0.5700483092, "num_tokens": 84, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.33550198216458105}} {"text": "library(tidyverse)\nlibrary(reshape2)\nlibrary(RCurl)\nlibrary(ggthemes)\n\n\ncheese_git = getURL('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-01-29/clean_cheese.csv')\ndf_cheese_wide = read_csv(cheese_git)\n\nnames(df_cheese_wide)\n\nkeep_cols = c('Year','Cheddar','American Other','Mozzarella','Italian other','Swiss','Brick','Muenster','Cream and Neufchatel','Blue','Other Dairy Cheese','Processed Cheese')\n\n\n\n#df_cheese_long = melt(df_cheese_wide[keep_cols], \n#\t\t\t\t\t\tid.vars=c(\"Year\"),\n#\t\t\t\t\t\tvalue.name = 'amount',\n#\t\t\t\t\t\t)\n# names(df_cheese_long) = c('year', 'cheese_type', 'amount')\n\n# the above is the reshape version of the dplyr below\n# the use of the -Year is a little odd at first but it is a one liner.\ndf_cheese_long = gather(df_cheese_wide[keep_cols],'cheese_type', 'amount', -Year)\n\n\nplot_cheese = ggplot(df_cheese_long, aes(x = Year, y = amount, color = cheese_type)) + \n geom_point() + \n geom_line()+\n labs(x = 'Year',\n \t\ty = 'Amount consumed (lbs)',\n \t\ttitle = 'US average yearly cheese consumption per person, by cheese type')+ \n theme_minimal() +\n theme_light() +\n theme(panel.grid.major = element_blank(),\n\t\t panel.grid.minor = element_blank(),\n\t\t )\n\nplot_cheese", "meta": {"hexsha": "6c786b84e11c8003fe6d410dca17521a18e3073e", "size": 1221, "ext": "r", "lang": "R", "max_stars_repo_path": "tidy_tues/cheese_explore.r", "max_stars_repo_name": "CNuge/RUsersGroup", "max_stars_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-26T16:52:42.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-19T21:32:38.000Z", "max_issues_repo_path": "tidy_tues/cheese_explore.r", "max_issues_repo_name": "CNuge/RUsersGroup", "max_issues_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tidy_tues/cheese_explore.r", "max_forks_repo_name": "CNuge/RUsersGroup", "max_forks_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-09-21T13:02:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-07T16:43:22.000Z", "avg_line_length": 31.3076923077, "max_line_length": 174, "alphanum_fraction": 0.7100737101, "num_tokens": 347, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3354951426279839}} {"text": "plot_4way <- function(x, y, A, B, C, D, x_range = c(-180, 180), y_range = c(-90, \r\n 90), limits = c(0.1, 0.5, 0.9), cols = c(\"FF\", \"CC\", \"99\", \r\n \"55\", \"11\"), add_legend = TRUE, smooth_image = FALSE, smooth_factor = 5, \r\n add = FALSE, normalise = TRUE, ePatternRes = 30, ePatternThick = 1/3, ...) \r\n{\r\n\t\r\n remove_nans <- function (x, y, A, B, C, D) {\r\n test = is.na(A + B + C + D) == FALSE\r\n A = A[test]\r\n B = B[test]\r\n C = C[test]\r\n D = D[test]\r\n x = x[test]\r\n y = y[test]\r\n return(list(x, y, A, B, C, D))\r\n }\r\n \r\n disagg_xyabcd <- function (x, y, A, B, C, D, smooth_factor) {\r\n `:=`(c(nn, nn, A), disagg_xyz(x, y, A, smooth_factor = smooth_factor))\r\n `:=`(c(nn, nn, B), disagg_xyz(x, y, B, smooth_factor = smooth_factor))\r\n `:=`(c(x, y, C), disagg_xyz(x, y, C, smooth_factor = smooth_factor))\r\n `:=`(c(x, y, D), disagg_xyz(x, y, D, smooth_factor = smooth_factor))\r\n return(list(x, y, A, B, C, D))\r\n }\r\n\r\n ncols = length(cols)\r\n\r\n #mag = A^2 + B^2 + C^2\r\n \r\n #A = sqrt(A^2/mag)\r\n #B = sqrt(B^2/mag)\r\n #C = sqrt(C^2/mag)\r\n #D = sqrt(D^2/mag)\r\n \r\n\tif (normalise) {\r\n\t\tmag = A + B + C\r\n\t\r\n\t\tA = A/mag\r\n\t\tB = B/mag\r\n\t\tC = C/mag\r\n\t\tD = D/mag\r\n\t\r\n\t\tA[mag == 0] = 0.33\r\n\t\tB[mag == 0] = 0.33\r\n\t\tC[mag == 0] = 0.33\r\n\t\tD[mag == 0] = 0.33\r\n } else {\r\n\t\t#Ai = (B + C)/2\r\n\t\t#Bi = (A + C)/2\r\n\t\t#Ci = (A + B)/2\r\n\t\t#A = Ai\r\n\t\t#B = Bi\r\n\t\t#C = Ci\r\n\t}\r\n\t\r\n out = rasterFromXYZ(cbind(x, y, D))\r\n\t\r\n out = addLayer(out, rasterFromXYZ(cbind(x, y, B)),\r\n rasterFromXYZ(cbind(x, y, C)),\r\n rasterFromXYZ(cbind(x, y, D)))\r\n \r\n if (smooth_image) {\r\n `:=`(c(x, y, A, B, C, D), disagg_xyabc(x, y, A, B, C, D, smooth_factor))\r\n }\r\n `:=`(c(x, y, A, B, C, D), remove_nans(x, y, A, B, C, D))\r\n \r\n Az = cut_results(A, limits)\r\n Bz = cut_results(B, limits)\r\n Cz = cut_results(C, limits)\r\n Dz = cut_results(D, limits)\r\n\t\r\n z = 1:length(Az)\r\n zcols = paste(\"#\", cols[Az], cols[Bz], cols[Cz], sep = \"\")\r\n #zcols = darken(zcols)\r\n\t#zcols = saturate(zcols, 0.1)\r\n #zcols = mapply(lighten, zcols )\r\n #zcols = mapply( darken, zcols, Dz)\r\n \r\n\t\r\n \r\n z = rasterFromXYZ(cbind(x, y, z))\r\n e = rasterFromXYZ(cbind(x, y, length(limits) + 2 - Dz))\r\n \r\n lims = (min.raster(z, na.rm = TRUE):max.raster(z, na.rm = TRUE) - 0.5)[-1]\r\n \r\n plotFun <- function(add) plot_raster_from_raster(z, cols = zcols[sort(unique(z))], \r\n limits = lims, x_range = x_range, y_range = y_range, \r\n quick = TRUE, readyCut = TRUE, \r\n add_legend = FALSE, add = add, \r\n e = e, limits_error = 0.5 + 1:length(limits), \r\n ePatternRes = ePatternRes, ePatternThick = ePatternThick, e_polygon = FALSE,\r\n ...)\r\n #plot_raster_from_raster(x, y_range = y_range,\r\n\t#\t\t\t\t\t\tcols = cols, limits = limits,\r\n\t#\t\t\t\t\t\te = e, limits_error = limits_error, \r\n\t#\t\t\t\t\t\tePatternRes = 30, ePatternThick = 0.2,\r\n\t#\t\t\t\t\t\tquick = TRUE, add_legend = FALSE)\r\n\t\r\n plotFun(add)\r\n \r\n if (add_legend) {\r\n add_raster_4way_legend(cols, limits, ...) \r\n plotFun(TRUE)\r\n }\r\n\tadd_icemask()\r\n return(out)\r\n}", "meta": {"hexsha": "65e554fbd1697337c9f55db2b387875ea846aec8", "size": 3269, "ext": "r", "lang": "R", "max_stars_repo_path": "libs/plotting/plot_4way.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/plotting/plot_4way.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libs/plotting/plot_4way.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 30.5514018692, "max_line_length": 88, "alphanum_fraction": 0.4876108902, "num_tokens": 1166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.33549513507827744}} {"text": "library(shiny)\nui <- fluidPage(\n titlePanel(\"Hello World: Shiny\"), \n sidebarLayout(\n sidebarPanel(\n sliderInput(inputId = 'bins',\n label = \"Number of bins:\",\n min = 1, \n max = 50, \n value = 30)\n ),\n mainPanel(\n plotOutput(outputId = \"distPlot\")\n )\n )\n \n)", "meta": {"hexsha": "5fa7cb32963a3847addb8e82f71150a8ec98a9cb", "size": 355, "ext": "r", "lang": "R", "max_stars_repo_path": "shiny/ui.r", "max_stars_repo_name": "joshmsds/joshmsds.github.io", "max_stars_repo_head_hexsha": "67a66ed82a81ab42f0664746cfd0a778819539dc", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shiny/ui.r", "max_issues_repo_name": "joshmsds/joshmsds.github.io", "max_issues_repo_head_hexsha": "67a66ed82a81ab42f0664746cfd0a778819539dc", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "shiny/ui.r", "max_forks_repo_name": "joshmsds/joshmsds.github.io", "max_forks_repo_head_hexsha": "67a66ed82a81ab42f0664746cfd0a778819539dc", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.8823529412, "max_line_length": 43, "alphanum_fraction": 0.4591549296, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.33549513507827744}} {"text": "################################################################################\n# Part of the R/EpiILM package\n#\n# AUTHORS:\n# Waleed Almutiry ,\n# Vineetha Warriyar. K. V. and\n# Rob Deardon \n#\n# Free software under the terms of the GNU General Public License, version 2,\n# a copy of which is available at http://www.r-project.org/Licenses/.\n################################################################################\n\nplot.epidata <- function(x, plottype, curvetype = NULL, time_id = NULL, tmin = NULL, timepoints = NULL, ...) {\n\n if (!is(x, \"epidata\")) {\n stop(\"The x must be in a class of \\\"epidata\\\"\", call. = FALSE)\n } else {\n\n n <- length(x$inftime)\n\n if (is.null(tmin)) {\n tmin <- 1\n }\n\n if (plottype == \"curve\") {\n # Error checks for input arguments\n if (any(is.null(curvetype) | !(curvetype %in% c(\"complete\",\"susceptible\",\"totalinfect\",\"newinfect\"))) == TRUE) {\n stop(\"epicurve: Specify plottype as \\\"complete\\\" , \\\"susceptible\\\",\\\"totalinfect\\\" or \\\"newinfect\\\"\", call. = FALSE)\n }\n\n # initializations\n totalinf <- rep(0)\n sus <- rep(0)\n newinf <- rep(0)\n removed <- rep(0)\n tmax <- max(x$inftime)\n timerange <- rep(tmin:tmax)\n\n # plot for Susceptible-Infectious (SI)\n if (x$type == \"SI\") {\n for (i in tmin:tmax) {\n newinf[i] <- length(x$inftime[x$inftime==i])\n xc <- subset(x$inftime, x$inftime <= i & x$inftime != 0)\n totalinf[i] <- length(xc)\n sus[i] <- n - totalinf[i]\n }\n if (tmin > 1) {\n newinf <- newinf[tmin:tmax]\n totalinf <- totalinf[tmin:tmax]\n sus <- sus[tmin:tmax]\n }\n\n\n # plot for infected and susceptible individuals\n if (all((curvetype == \"complete\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n\t\t\t\t \n plot(timerange, sus,\n xlim = c(tmin, tmax), ylim = c(1, n),\n main = \"Epidemic Curves\", ylab = \"Number of individuals \", xlab = \"Time\",\n type = \"l\", lwd = 2, cex = 1, xaxt = \"n\")\n lines(timerange, totalinf, col = \"red\", lwd = 2)\n axis(1, at = 1:tmax)\n\t\t\t\t\topar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n\t\t\t\t\tmar = c(0, 0, 0, 0), new = TRUE)\n\t\t\t\t\ton.exit(par(opar), add = TRUE)\n\t\t\t\t\tplot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n\t\t\t\t\tlegend(\"bottom\", c(\"Infected\", \"Susceptible\"), col = c(\"red\", \"black\"),\n\t\t\t\t\tlty = c(1, 1), lwd = c(2, 2), bty = \"n\", horiz = TRUE, cex = 1)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = c(1,1))\n\t\t\t\t\ton.exit(par(op), add = TRUE)\n\n } else if (all((curvetype == \"complete\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n # plot for infected and susceptible individuals with specified time points\n plot(timerange, sus,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(1,n),\n main = \"Epidemic Curves\", ylab = \"Number of individuals\", xlab = \"time\",\n type = \"l\", lwd = 2, cex = 1, xaxt = \"n\")\n lines(timerange, totalinf, col = \"red\", lwd = 2)\n axis(1, at=1:timepoints[2])\n\t\t\t\t\topar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n\t\t\t\t\tmar = c(0, 0, 0, 0), new = TRUE)\n\t\t\t\t\ton.exit(par(opar), add = TRUE)\n\t\t\t\t\tplot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n\t\t\t\t\tlegend(\"bottom\", c(\"Infected\", \"Susceptible\"), col = c(\"red\", \"black\"),\n\t\t\t\t\tlty = c(1, 1), lwd = c(2, 2), bty = \"n\", horiz = TRUE, cex = 1)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = c(1,1))\n\t\t\t\t\ton.exit(par(op), add = TRUE)\n\n } else if (all((curvetype == \"totalinfect\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, totalinf,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(totalinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of infected individuals\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"totalinfect\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, totalinf,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(1, n),\n main = \"Epidemic Curve\", ylab = \"Number of infected individuals\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n } else if (all((curvetype == \"newinfect\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, newinf,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(newinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of new infections\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"newinfect\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, newinf,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(0, (max(newinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of new infections\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n } else if (all((curvetype == \"susceptible\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(sus)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of susceptibles\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"susceptible\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(1, n),\n main = \"Epidemic Curve\", ylab = \"Number of susceptibles\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n }\n\n } else if (x$type == \"SIR\") {\n # Plot for Susceptible-Infectious-Removed (SIR)\n dat <- data.frame(x$inftime, x$remtime)\n\n for (i in tmin:tmax) {\n xcc <- subset(dat, x$inftime <= i & x$inftime != 0 & i < x$remtime)\n totalinf[i] <- length(xcc$x.inftime)\n }\n\n for (i in tmin:tmax) {\n newinf[i] <- length(x$inftime[x$inftime==i])\n }\n\n for (i in tmin:tmax) {\n xcc <- subset(dat, x$inftime<=i & x$inftime != 0)\n xc <- subset(xcc, i >= xcc$x.remtime)\n removed[i] <- length(xc$x.remtime)\n sus[i] <- n - length(xcc$x.inftime)\n }\n if (tmin>1) {\n newinf <- newinf[tmin:tmax]\n totalinf <- totalinf[tmin:tmax]\n removed <- removed[tmin:tmax]\n sus <- sus[tmin:tmax]\n }\n\n # plot for infected and susceptible individuals\n if (all((curvetype == \"complete\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(tmin, tmax), ylim = c(1, n),\n main = \"Epidemic Curve\", ylab = \"Number of individuals\", xlab = \"time\",\n type = \"l\", lwd = 2, cex = 1, xaxt = \"n\")\n lines(timerange, totalinf, col = \"red\", lwd = 2)\n lines(timerange, removed, col = \"blue\", lwd = 2)\n axis(1, at=1:tmax)\n\t\t\t\t\topar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n\t\t\t\t\tmar = c(0, 0, 0, 0), new = TRUE)\n\t\t\t\t\ton.exit(par(opar), add = TRUE)\n\t\t\t\t\tplot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n\t\t\t\t\tlegend(\"bottom\", c(\"Infected\", \"Susceptible\", \"Removed\"), col = c(\"red\", \"black\",\"blue\"),\n\t\t\t\t\tlty = c(1, 1, 1), lwd = c(2, 2, 2), bty = \"n\", horiz = TRUE, cex = 1)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = c(1,1))\n\t\t\t\t\ton.exit(par(op), add = TRUE)\n\n } else if (all((curvetype == \"complete\") & (!is.null(timepoints))) == TRUE) {\n # plot for infected and susceptible individuals with specified time points\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(1,n),\n main = \"Epidemic Curve\", ylab = \"Number of individuals\", xlab = \"time\",\n type =\"l\", lwd = 2, cex = 0.5, xaxt = \"n\")\n lines(timerange, totalinf, col = \"red\", lwd = 2)\n lines(timerange, removed, col = \"blue\", lwd = 2)\n axis(1, at=1:timepoints[2])\n\t\t\t\t\topar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n\t\t\t\t\tmar = c(0, 0, 0, 0), new = TRUE)\n\t\t\t\t\ton.exit(par(opar), add = TRUE)\n\t\t\t\t\tplot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n\t\t\t\t\tlegend(\"bottom\", c(\"Infected\", \"Susceptible\", \"Removed\"), col = c(\"red\", \"black\",\"blue\"),\n\t\t\t\t\tlty = c(1, 1, 1), lwd = c(2, 2, 2), bty = \"n\", horiz = TRUE, cex = 1)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = c(1,1))\n\t\t\t\t\ton.exit(par(op), add = TRUE)\n\n } else if (all((curvetype == \"totalinfect\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, totalinf,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(totalinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of infected individuals\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"totalinfect\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, totalinf,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(0, n),\n main = \"Epidemic Curve\", ylab = \"Number of infected individuals\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n } else if (all((curvetype == \"newinfect\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, newinf,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(newinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of new infections\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"newinfect\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, newinf,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(0, (max(newinf)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of new infections\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n } else if (all((curvetype == \"susceptible\") & (is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(min(timerange), max(timerange)), ylim = c(0, (max(sus)+1)),\n main = \"Epidemic Curve\", ylab = \"Number of susceptibles\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:max(timerange))\n\n } else if (all((curvetype == \"susceptible\") & (!is.null(timepoints))) == TRUE) {\n\n\t\t\t\t op1 <- par(no.readonly = TRUE)\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = c(1,1))\n plot(timerange, sus,\n xlim = c(timepoints[1], timepoints[2]), ylim = c(1, n),\n main = \"Epidemic Curve\", ylab = \"Number of susceptibles\", xlab = \"time\",\n type = \"b\", pch = 20, lwd = 2, xaxt = \"n\")\n axis(1, at=1:timepoints[2])\n\n }\n\n }\n } else if (plottype == \"spatial\") {\n\n\t\t\top1 <- par(no.readonly = TRUE)\n\n # plots for susceptible- infected (SI)\n if (x$type == \"SI\") {\n dat <- data.frame(x$XYcoordinates, x$inftime)\n # no specific time point (s)\n if (is.null(time_id)) {\n\t\t\t\t\tntimes <- max(x$inftime) - tmin + 1\n\t\t\t\t\tmfrow1 <- switch(min(ntimes,13), c(1,1), c(1,2), c(2,2), c(2,2), c(3,2), c(3,2), \n\t\t\t\t\tc(3,3), c(3,3), c(3,3), c(3,2), c(3,2), c(3,2), c(3,3))\n\t\t\t\t\t\n\t\t\t\t\tsepwindow <- seq(prod(mfrow1), prod(mfrow1)*ceiling(ntimes/prod(mfrow1)), \n\t\t\t\t\tby = prod(mfrow1))\n\t\t\t\t\t\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = mfrow1)\n\t\t\t\t\tu <- 1\n\t\t\t\t\t\n for (i in tmin:max(x$inftime)) {\n xcc <- subset(dat, dat$x.inftime <= i & dat$x.inftime != 0)\n xx = x$XYcoordinates[,1]\n yy = x$XYcoordinates[,2]\n plot(xx, yy,\n xlim = c(min(xx), max(xx)),\n ylim = c(min(yy), max(yy)),\n main = paste(\"time \", i), cex = 1, ...)\n points(xcc[,1], xcc[,2], pch = 16, col = \"red\")\n\t\t\t\t\t\t\n if (any(u == sepwindow) | (u == ntimes)) {\n opar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n mar = c(0, 0, 0, 0), new = TRUE)\n on.exit(par(opar), add = TRUE)\n plot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n legend(\"bottom\", c(\"Infected\", \"Susceptible\"), col = c(\"red\", \"black\"),\n\t\t\t\t\t\t\tpch = c(16, 21), bty = \"n\", horiz = TRUE, cex = 1.5)\n\t\t\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = mfrow1)\n\t\t\t\t\t\t \ton.exit(par(op), add = TRUE)\n\t\t\t\t\t\t}\n\t\t\t\t\t\tu <- u + 1\n }\n\n } else if (!is.null(time_id)) {\n\t\t\t\t\tntimes <- length(time_id)\n\t\t\t\t\tmfrow1 <- switch(min(ntimes,13), c(1,1), c(1,2), c(2,2), c(2,2), c(3,2), c(3,2), \n\t\t\t\t\tc(3,3), c(3,3), c(3,3), c(3,2), c(3,2), c(3,2), c(3,3))\n\t\t\t\t\t\n\t\t\t\t\tsepwindow <- seq(prod(mfrow1), prod(mfrow1)*ceiling(length(time_id)/prod(mfrow1)), \n\t\t\t\t\tby = prod(mfrow1))\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = mfrow1)\n\n for (i in 1:length(time_id)) {\n xcc <- subset(dat, dat$x.inftime <= time_id[i] & dat$x.inftime != 0)\n xx = x$XYcoordinates[,1]\n yy = x$XYcoordinates[,2]\n plot(xx, yy,\n xlim = c(min(xx), max(xx)),\n ylim = c(min(yy), max(yy)),\n main = paste(\"time \", time_id[i]), cex = 1, ...)\n points(xcc[,1], xcc[,2], pch = 16, col = \"red\")\n if (any(i == sepwindow) | (i == length(time_id))) {\n opar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n mar = c(0, 0, 0, 0), new = TRUE)\n on.exit(par(opar), add = TRUE)\n plot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n legend(\"bottom\", c(\"Infected\", \"Susceptible\"), col = c(\"red\", \"black\"),\n\t\t\t\t\t\t\t pch = c(16, 21), bty = \"n\", horiz = TRUE, cex = 1.5)\n\t\t\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = mfrow1)\n\t\t\t\t\t\t \ton.exit(par(op), add = TRUE)\n\t\t\t\t\t\t}\n }\n }\n # end if - SI model\n } else if (x$type == \"SIR\") {\n dat <- data.frame(x$XYcoordinates, x$inftime, x$remtime)\n if (is.null(time_id)) {\n\t\t\t\t\tntimes <- max(x$inftime) - tmin + 1\n\t\t\t\t\tmfrow1 <- switch(min(ntimes,13), c(1,1), c(1,2), c(2,2), c(2,2), c(3,2), c(3,2), \n\t\t\t\t\tc(3,3), c(3,3), c(3,3), c(3,2), c(3,2), c(3,2), c(3,3))\n\t\t\t\t\t\n\t\t\t\t\tsepwindow <- seq(prod(mfrow1), prod(mfrow1)*ceiling(ntimes/prod(mfrow1)), \n\t\t\t\t\tby = prod(mfrow1))\n\t\t\t\t\t\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = mfrow1)\n\t\t\t\t\tu <- 1\n\t\t\t\t\t\n for(i in tmin:max(x$inftime)) {\n xcc <- subset(dat, dat$x.inftime <= i & dat$x.inftime != 0)\n xx = x$XYcoordinates[,1]\n yy = x$XYcoordinates[,2]\n plot(xx, yy,\n xlim = c(min(xx), max(xx)),\n ylim = c(min(yy), max(yy)),\n pch = 21,\n main = paste(\"time \", i), cex = 1)#, ...)\n xred <- subset(xcc, i < xcc[,4])\n points(xred[,1], xred[,2], pch = 16, col = \"red\")\n xblue <- subset(xcc, i >= xcc[,4])\n points(xblue[,1], xblue[,2], pch = 16, col = \"blue\")\n\n if (any(u == sepwindow) | (u == ntimes)) {\n opar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n mar = c(0, 0, 0, 0), new = TRUE)\n on.exit(par(opar), add = TRUE)\n plot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n legend(\"bottom\", c(\"Infected\", \"Susceptible\", \"Removed\"), \n col = c(\"red\", \"black\", \"blue\"), pch = c(16, 21, 16), \n bty = \"n\", horiz = TRUE, cex = 1.5)\n\t\t\t\t\t\t op <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = mfrow1)\n\t\t\t\t\t\t \ton.exit(par(op), add = TRUE)\n\t\t\t\t\t\t}\n\t\t\t\t\t\tu <- u + 1\n }\n } else if (!is.null(time_id)) {\n\t\t\t\t\tntimes <- length(time_id)\n\t\t\t\t\tmfrow1 <- switch(min(ntimes,13), c(1,1), c(1,2), c(2,2), c(2,2), c(3,2), c(3,2), \n\t\t\t\t\tc(3,3), c(3,3), c(3,3), c(3,2), c(3,2), c(3,2), c(3,3))\n\t\t\t\t\t\n\t\t\t\t\tsepwindow <- seq(prod(mfrow1), prod(mfrow1)*ceiling(length(time_id)/prod(mfrow1)), \n\t\t\t\t\tby = prod(mfrow1))\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), \n\t\t\t\t\t\t mfrow = mfrow1)\n\n for (i in 1:length(time_id)) {\n xcc <- subset(dat, dat$x.inftime <= time_id[i] & dat$x.inftime != 0)\n xx = x$XYcoordinates[,1]\n yy = x$XYcoordinates[,2]\n plot(xx, yy,\n xlim = c(min(xx), max(xx)),\n ylim = c(min(yy), max(yy)),\n pch = 21,\n main = paste(\"time \", time_id[i]), cex = 1, ...)\n xred <- subset(xcc, time_id[i] < xcc[,4])\n points(xred[,1], xred[,2], pch = 16, col = \"red\")\n xblue <- subset(xcc, time_id[i] >= xcc[,4])\n points(xblue[,1], xblue[,2], pch = 16, col = \"blue\")\n if (any(i == sepwindow) | (i == length(time_id))) {\n opar <- par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0),\n mar = c(0, 0, 0, 0), new = TRUE)\n on.exit(par(opar), add = TRUE)\n plot(0, 0, type = 'n', bty = 'n', xaxt = 'n', yaxt = 'n')\n\t legend(-.03, 1.15, c(\"Infected\", \"Susceptible\", \"Removed\"), \n\t col = c(\"red\", \"black\", \"blue\"), pch = c(16, 21, 16), \n\t bty = \"n\", horiz = TRUE, cex = 1)\n\t\t\t\t\t\t\top <- par(mar = c(5.9, 4.0, 1.5, 0.5), omi = c(0.2, 0.05, 0.15, 0.15), mfrow = mfrow1)\n\t\t\t\t\t\t \ton.exit(par(op), add = TRUE)\n\t\t\t\t\t\t}\n }\n }\n # end if - SIR model\n }\n\n# par(old.par)\n\n\t\t\ton.exit(par(op1))\n\n # End function\n } else {\n stop(\"The plottype option should be either \\\"curve\\\" or \\\"spatial\\\"\", call. = FALSE)\n }\n }\n # End of function\n}\n", "meta": {"hexsha": "3d6d527b754f4e5d30e1ba231094933983148b7c", "size": 23196, "ext": "r", "lang": "R", "max_stars_repo_path": "R/epiplot.r", "max_stars_repo_name": "waleedalmutiry/EpiILM", "max_stars_repo_head_hexsha": "a22caac0e2f5af69fe202b2c750e237cbe6e295b", "max_stars_repo_licenses": ["Intel"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-06-30T03:50:37.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-21T21:15:25.000Z", "max_issues_repo_path": "R/epiplot.r", "max_issues_repo_name": "waleedalmutiry/EpiILM", "max_issues_repo_head_hexsha": "a22caac0e2f5af69fe202b2c750e237cbe6e295b", "max_issues_repo_licenses": ["Intel"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/epiplot.r", "max_forks_repo_name": "waleedalmutiry/EpiILM", "max_forks_repo_head_hexsha": "a22caac0e2f5af69fe202b2c750e237cbe6e295b", "max_forks_repo_licenses": ["Intel"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-02-01T20:50:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-18T17:44:20.000Z", "avg_line_length": 49.0401691332, "max_line_length": 133, "alphanum_fraction": 0.416192447, "num_tokens": 7761, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3354951275285708}} {"text": "###############################################################################\n## Purpose: this file loads joined datasets and analyzes them.\n\n## Depends:\n## O7PredictorExploration.r\n## 16SurveyExploration.r\n## 21CostDescriptive.r\n## tidyverse\n## epiR\n\n## Inputs:\n##\n\n## Outputs:\n##\n\n###############################################################################\n\n# Packages______________________________________________________________________\nlibrary(tidyverse)\nlibrary(epiR)\nlibrary(psych)\nsource(\"Functions/importing.r\")\n\n# Functions_____________________________________________________________________\ncreate_timepoints <- function(dat) {\n timepoints <- list()\n for (i in 1:4) {\n\n # Read in data to include only those where both\n # dyad members are available\n dat_for_list <- import_available_survey_data(\n data = dat,\n timepoint = i,\n survey = \"COST\",\n obs = \"dyad\"\n )\n timepoints[[i]] <- dat_for_list\n }\n return(timepoints)\n}\n\nloop_correlation <- function(list_in) {\n ### Initiate Empty Lists\n cost_cor_list <- list()\n\n ### Loop over timepoints\n for (i in 1:4) {\n dat <- list_in[[i]]\n\n # Reshape data structure\n dat_wide <- lengthen_widen(\n data = dat, start_col = \"facit_cost01\",\n end_col = \"facit_cost11\"\n )\n\n # Summarise Raw Scores\n dat_sum <- summarise_survey(data = dat_wide)\n\n # Create Correlation table\n dat_cor <- creat_corr_table(dat = dat_wide)\n\n # Join raw and correlation\n dat_raw_cor <- dat_sum %>%\n left_join(dat_cor)\n\n cost_cor_list[[i]] <- dat_raw_cor\n }\n return(cost_cor_list)\n}\n\ncalc_correlation <- function(dat, question) {\n\n ## Creates empty tibble for data\n ques_df <- tibble(\n question = NA,\n pearson_cor = NA,\n pearson_low = NA,\n pearson_high = NA,\n ccc_cor = NA,\n ccc_low = NA,\n ccc_high = NA\n )\n ## Populates question into tibble\n ques_df$question <- question\n ## Pulls data out of dataframe\n dat <- dat %>%\n filter(question == {{ question }})\n ## Run Pearson\n pear_list <- cor.test(\n x = dat$patient,\n y = dat$caregiver,\n method = \"pearson\",\n alternative = \"t\"\n )\n ## Extract Pearson\n ques_df$pearson_cor <- pear_list$estimate\n ques_df$pearson_low <- pear_list$conf.int[[1]]\n ques_df$pearson_high <- pear_list$conf.int[[2]]\n ## Run CCC\n ccc_list <- epi.ccc(\n x = dat$patient,\n y = dat$caregiver\n )\n ccc_df <- ccc_list$rho.c\n ## Extract CCC\n ques_df$ccc_cor <- ccc_df$est\n ques_df$ccc_low <- ccc_df$lower\n ques_df$ccc_high <- ccc_df$upper\n ## Return\n return(ques_df)\n}\n\ncreat_corr_table <- function(dat) {\n ## create empty df to hold output\n df <- tibble(\n question = NA,\n pearson_cor = NA,\n pearson_low = NA,\n pearson_high = NA,\n ccc_cor = NA,\n ccc_low = NA,\n ccc_high = NA\n )\n\n ## find unique question names\n vec_question_names <- unique(dat$question)\n ## Loop over unique question names\n for (i in seq_along(vec_question_names)) {\n question <- vec_question_names[i]\n\n correlation <- calc_correlation(\n dat = dat,\n question = question\n )\n\n df <- df %>%\n rbind(correlation) %>%\n filter(!is.na(ccc_cor))\n }\n ## Return\n return(df)\n}\n\nlengthen_widen <- function(data, start_col, end_col, names = \"question\",\n values = \"score\") {\n dat_long <- data %>%\n tidyr::pivot_longer(\n cols = {{ start_col }}:{{ end_col }},\n names_to = names,\n values_to = values\n )\n dat_wide <- dat_long %>%\n tidyr::pivot_wider(\n names_from = src,\n values_from = score\n )\n return(dat_wide)\n}\n\nsummarise_survey <- function(data) {\n data_out <- data %>%\n group_by(question) %>%\n summarise(\n mean_pt = mean(patient, na.rm=TRUE),\n sd_pt = sd(patient, na.rm=TRUE),\n mean_cg = mean(caregiver, na.rm=TRUE),\n sd_cg = sd(caregiver, na.rm=TRUE)\n )\n}\nloop_chronbach <- function(list_in) {\n\n\n ### Intiate empty tibble\n cost_chron_tibble <- tibble(\n tp = c(NA, NA, NA, NA),\n patient = c(NA, NA, NA, NA),\n caregiver = c(NA, NA, NA, NA)\n )\n\n ### Loop over timepoints\n for (i in 1:4) {\n\n # Store timepoint\n cost_chron_tibble$tp[i] <- i\n\n # Patient\n ## filter data\n dat <- list_in[[i]] %>%\n filter(src == \"patient\") %>%\n select(facit_cost01:facit_cost11)\n\n ## Calculate Chronbach Alpha\n chr_a <- psych::alpha(dat)$total[[1]]\n\n ## Store Value\n cost_chron_tibble$patient[i] <- chr_a\n\n # Caregiver\n ## Filter Data\n dat <- list_in[[i]] %>%\n filter(src == \"caregiver\") %>%\n select(facit_cost01:facit_cost11)\n\n ## Calculate Chronbach Alpha\n chr_a <- psych::alpha(dat)$total[[1]]\n\n ## Store Value\n cost_chron_tibble$caregiver[i] <- chr_a\n }\n return(cost_chron_tibble)\n}\n\ntidy_chronbach <- function(data) {\n tidy_data <- data %>%\n mutate(across(patient:caregiver,\n round,\n digits = 2\n )) %>%\n rename(\n \"Timepoint\" = tp,\n \"Patient\" = patient,\n \"Caregiver\" = caregiver\n )\n return(tidy_data)\n}\ntidy_correlation <- function(list_in) {\n out_list <- list()\n ### Loop over timepoints\n for (i in 1:4) {\n\n # Round\n\n dat <- list_in[[i]] %>%\n mutate(across(mean_pt:ccc_high,\n round,\n digits = 2\n ))\n\n # Merge Patient\n\n dat <- dat %>%\n mutate(patient_mean_sd = paste0(\n mean_pt,\n \" (\",\n sd_pt,\n \")\"\n )) %>%\n select(-(mean_pt:sd_pt))\n\n # Merge Caregiver\n\n dat <- dat %>%\n mutate(caregiver_mean_sd = paste0(\n mean_cg,\n \" (\",\n sd_cg,\n \")\"\n )) %>%\n select(-(mean_cg:sd_cg))\n\n # Merge Pearson\n\n dat <- dat %>%\n mutate(pearson_95_ci = paste0(\n pearson_cor,\n \" (\",\n pearson_low,\n \"-\",\n pearson_high, \")\"\n )) %>%\n select(-(pearson_cor:pearson_high))\n\n # Merge CCC\n dat <- dat %>%\n mutate(ccc_95_ci = paste0(\n ccc_cor,\n \" (\",\n ccc_low,\n \"-\",\n ccc_high, \")\"\n )) %>%\n select(-(ccc_cor:ccc_high))\n # Add timepoint\n dat <- dat %>%\n mutate(timepoint = i)\n # Store in list\n out_list[[i]] <- dat\n }\n df_out <- map_df(\n .x = out_list,\n .f = rbind\n )\n}\n# Data In_______________________________________________________________________\n## Predictors-------------------------------------------------------------------\n### Wide\npred_wide <- read_csv(file = \"IntData/dat_wide.csv\")\n\n## Cohorts----------------------------------------------------------------------\n### Cohort 3\ndat_cohort3 <- read_csv(file = \"IntData/CohortThree.csv\")\n\n## Surveys----------------------------------------------------------------------\n### Cost\ncost_full <- read_csv(\n file = \"IntData/cost_full.csv\"\n)\n\n# Data transformation___________________________________________________________\n## drop unused outcomes---------------------------------------------------------\ncost_full <- cost_full %>%\n select(-c(\n cost_lgl,\n cost_cat,\n count_na\n ))\n\n## Create Cohorts in lists------------------------------------------------------\n### Define Cohorts\ncost_cohort_one <- cost_full\ncost_cohort_two <- cost_full %>%\n filter(partid %in% pred_wide$partid)\n\ncost_cohort_three <- cost_full %>%\n filter(partid %in% dat_cohort3$partid)\n\n### Create timepoint lists\nc1_timepoints <- create_timepoints(cost_cohort_one)\nc2_timepoints <- create_timepoints(cost_cohort_two)\nc3_timepoints <- create_timepoints(cost_cohort_three)\n\n# Analysis______________________________________________________________________\n## 1: Crude correlation at each timepoint---------------------------------------\nc1_correlation <- loop_correlation(list_in = c1_timepoints)\nc2_correlation <- loop_correlation(list_in = c2_timepoints)\nc3_correlation <- loop_correlation(list_in = c3_timepoints)\n\n## 2. Chronbach's alpha---------------------------------------------------------\nc1_chronbach <- loop_chronbach(list_in = c1_timepoints)\nc2_chronbach <- loop_chronbach(list_in = c2_timepoints)\nc3_chronbach <- loop_chronbach(list_in = c3_timepoints)\n\n# Tidying_______________________________________________________________________\n## 1. Raw Correlation-----------------------------------------------------------\ncorr <- list(\n c1_tidy_corr <- tidy_correlation(list_in = c1_correlation) %>%\n mutate(Cohort = 1),\n c2_tidy_corr <- tidy_correlation(list_in = c2_correlation) %>%\n mutate(Cohort = 2),\n c3_tidy_corr <- tidy_correlation(list_in = c3_correlation) %>%\n mutate(Cohort = 3)\n)\n\ntidy_corr <- map_df(\n .x = corr,\n .f = rbind\n) %>%\n arrange(question)\n\n## 2. Chronbach-----------------------------------------------------------------\nchron <- list(\n c1_tidy_chronbach <- tidy_chronbach(c1_chronbach) %>%\n mutate(Cohort = 1),\n c2_tidy_chronbach <- tidy_chronbach(c2_chronbach) %>%\n mutate(Cohort = 2),\n c3_tidy_chronbach <- tidy_chronbach(c3_chronbach) %>%\n mutate(Cohort = 3)\n)\n\ntidy_chronbach <- map_df(\n .x = chron,\n .f = rbind\n)\n\n# Output________________________________________________________________________\n## Tidy-------------------------------------------------------------------------\npath <- \"OutData/CorrTables/\"\n\n### Correlation\nwrite_csv(\n x = tidy_corr,\n file = paste0(path, \"COST_correlation.csv\")\n)\n\n### Chronbach's\nwrite_csv(\n x = tidy_chronbach,\n file = paste0(\n path,\n \"COST_chronbach.csv\"\n )\n)\n", "meta": {"hexsha": "60063bdf355e85bb311a5746c0911e5e212cd42b", "size": 10336, "ext": "r", "lang": "R", "max_stars_repo_path": "23COSTCorrChronbach.r", "max_stars_repo_name": "RiversPharmD/AZFinTox", "max_stars_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "23COSTCorrChronbach.r", "max_issues_repo_name": "RiversPharmD/AZFinTox", "max_issues_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "23COSTCorrChronbach.r", "max_forks_repo_name": "RiversPharmD/AZFinTox", "max_forks_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2335025381, "max_line_length": 80, "alphanum_fraction": 0.5300890093, "num_tokens": 2466, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3354951275285708}} {"text": "# what weird species are where?\n# so far this script identifies the cities where Canada wide weird species occur. it also calculates the standard deviation of the SLA of Canada-wide weird species in each city and how many Canada-wide weird species a place has\n\nlibrary(purrr)\n\n# get weird species at all three spatial scales\npaths <- c(\"analysis/canada_weird.csv\", \"analysis/city_weird.csv\", \"analysis/quadrat_weird.csv\")\n\nweirdspecies <- map(paths, read.csv, header=TRUE, stringsAsFactors=FALSE)\nnames(weirdspecies) <- c(\"canada\", \"city\", \"quadrat\")\n\n\nweirdspecies$city$City <- factor(weirdspecies$city$City, levels = c(\"Vancouver\", \"Edmonton\", \"Winnipeg\", \"Toronto\", \"Montreal\", \"Halifax\"))\n\n# get all species and their SLA trait data\nspecies <- read.csv(\"data/species_and_their_traits.csv\", header=TRUE, stringsAsFactors = FALSE) %>%\n filter(TraitName == \"Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA): petiole excluded\") %>%\n mutate(TraitName = \"SLA\") # Make the trait name easier to work with\n\nspecies$City <- factor(species$City, levels = c(\"Vancouver\", \"Edmonton\", \"Winnipeg\", \"Toronto\", \"Montreal\", \"Halifax\"))\n\n# Which Canada-wide weird species are in each city?\n\nglobalweird_in_cities <- species %>%\n filter(species %in% weirdspecies$canada$species) %>%\n group_by(City)\n\nglobalweird <- ggplot(globalweird_in_cities, aes(x=value, fill=City)) +\n geom_density() +\n theme_bw() +\n ggtitle(\"Distribution of SLA\", subtitle = \"for Canada-wide weird species\") +\n facet_wrap(\"City\")\n\nggsave(\"figures/weird_slas_by_city.png\", globalweird, width=8, height=9, units=\"in\")\n\n## Which city has the highest standard deviation in SLA for weird species, and how many weird species does it have in total?\ncity_sd <- globalweird_in_cities %>%\n summarize(sd = sd(value), num=n()) %>%\n arrange(desc(sd))\n\n# Which city-wide species are in each quadrat\nquadrat_sd <- weirdspecies$city %>%\n group_by(City, quadrat) %>%\n summarize(sd=sd(value), num=n()) %>%\n arrange(desc(sd))\n\n# Top 5 weirdest quadrats\n# # Groups: City [6]\n# City quadrat sd num\n# \n# 1 Halifax 3 24.7 3\n# 2 Halifax 4 24.2 3\n# 3 Vancouver 1 23.9 83\n# 4 Montreal 1 22.8 77\n# 5 Vancouver 3 22.6 4\n\n# Plot of the \"weird\" species traits in each city and quadrat\nweirdslaquadratcity <- ggplot(weirdspecies$city, aes(x=value, fill=City)) +\n geom_density(alpha=0.5) +\n facet_grid(factor(quadrat, levels = c(\"5\", \"4\", \"3\", \"2\", \"1\")) ~ .) +\n theme_classic(base_size = 18) +\n theme(legend.position = \"bottom\", axis.text.y=element_blank() ) +\n labs(x=\"SLA\")\n\nquadrat_sd_trends <- ggplot(quadrat_sd, aes(x=quadrat, y=sd, colour=City)) +\n geom_point(aes(size=num*2)) +\n geom_line() +\n theme_classic(base_size = 25) +\n ylab(\"standard deviation of weird\") +\n scale_size(name=\"# of species\")\n\nggsave(\"figures/quadrat_trends.png\", quadrat_sd_trends, width=16, height=9, units=\"in\")\n\nggsave(\"figures/weird_slas_by_city_and_quad.png\", weirdslaquadratcity, width=8, height=9, units=\"in\")\n\n", "meta": {"hexsha": "5321aff25c1da1435548f3847396b948c7b2ffff", "size": 3105, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/whatweirdwhere.r", "max_stars_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_stars_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-07T10:18:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-11T14:44:19.000Z", "max_issues_repo_path": "scripts/whatweirdwhere.r", "max_issues_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_issues_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2020-05-11T14:56:22.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-05T12:15:24.000Z", "max_forks_repo_path": "scripts/whatweirdwhere.r", "max_forks_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_forks_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-08T14:15:36.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-08T14:15:36.000Z", "avg_line_length": 40.8552631579, "max_line_length": 227, "alphanum_fraction": 0.6869565217, "num_tokens": 921, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154239957834733, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.33538406328678555}} {"text": "require(randomForest) # required for rf\nrequire(kernlab) # required for svm\nrequire(caret) \n\n# runs cross-validation \n# if nfolds > length(y) or nfolds==-1, uses leave-one-out cross-validation\n# ...: additional parameters for train.fun\n#\n# value:\n# y: true values\n# predicted: cv predicted values\n# probabilities: cv predicted class probabilities (or NULL if unavailable)\n# confusion.matrix: confusion matrix (true x predicted)\n# nfolds: nfolds, use -1 for leave-one-out cv\n# params: list of additional parameters\n# importances: importances of features as predictors\n# regression: does both regression or classification (RMSE and r_squared will be meaningless here)\n# modelfun: valid model available in caret package\n# group.var = vector of values, to group samples by (e.g. multiple samples per subject id)\n\"cross.validation.caret\" <- function(x, y, nfolds=10, verbose=FALSE, regression=FALSE, modelfun, group.var=NULL, ...){\n if(regression==FALSE)\n \t{\n\t\tif(class(y) != 'factor') stop('y must be factor for classification\\n')\n\t\ty <- droplevels(y)\n \t}\n\n folds <- balanced.group.folds(y, nfolds, group.var)\n \n result <- list()\n result$y <- y\n result$predicted <- result$y\n # initialize class probs as an empty data frame\n result$classprob <- data.frame(matrix(vector(), 0, length(unique(result$y))+1))\n \n # K-fold cross-validation\n for(fold in sort(unique(folds))){\n if(verbose) cat(sprintf('Fold %d...\\n',fold))\n foldix <- which(folds==fold) \n newx <- x[foldix,,drop=F] # make sure df structure is kept even for 1-row newx\n\n fitControl <- trainControl(method = \"none\", classProbs = TRUE)\n #set.seed(825)\n model <- train(x=x[-foldix,], y=result$y[-foldix],\n method = modelfun, \n trControl = fitControl, \n verbose = FALSE, \n metric = \"ROC\")\n \n # class\n result$predicted[foldix] <- predict(model, newdata = newx) # class\n # probability\n result$classprob[foldix,] <- cbind(predict(model, newx, type=\"prob\"), fold=fold)\n\n }\n\tresult$nfolds <- nfolds\n result$params <- list(...)\n return(result) \n}\n\n# assign samples to folds so that classes are balanced AND groups samples are in the same fold \n# (avoid training and predicting with samples from the same group)\n# note: LOO for groups samples will never truly be LOO\n\"balanced.group.folds\" <- function(y, nfolds, group.var=NULL)\n{\n if(nfolds==-1) nfolds <- length(y) \n\n if(is.null(group.var)){\n folds <- balanced.folds(y, nfolds)\n } else {\n group <- data.frame(y, group.var, sample.id=1:length(y))\n\n # order by group, num response, then response level - then grab first sample as group rep\n # this should work fine for discordant samples (e.g. 1 tumor 1 healthy per subject)\n groupfreq <- as.data.frame(table(group[c(\"group.var\", \"y\")]))\n # Freq is important because we always pick the class level where there are the most samples for one group.var\n # e.g. 4 tongue samples and 2 tongue samples for a subject assigns it as tongue\n groupfreq_ord <- groupfreq[order(groupfreq$group.var, -groupfreq$Freq, groupfreq$y),]\n groupfreq_uniq <- groupfreq_ord[!duplicated(groupfreq_ord$group.var),]\n \n # assign folds based on group rep responses\n folds <- balanced.folds(groupfreq_uniq$y, nfolds)\n groupfreq_uniq$folds <- folds\n\n # reassign original samples to folds, so that all grouped samples remain in the same fold\n group_folds <- merge(group, groupfreq_uniq[,c(\"group.var\", \"folds\")], by=\"group.var\", all.x=T) \n \n # preserve original sample order\n folds <- group_folds[order(group_folds$sample.id),\"folds\"]\n }\n return(folds)\n}\n\n\"balanced.folds\" <- function(y, nfolds=10){\n\ty <- droplevels(as.factor(y))\n folds = rep(0, length(y))\n classes = levels(y)\n # size of each class\n Nk = table(y)\n # -1 or nfolds = len(y) means leave-one-out\n if (nfolds == -1 || nfolds == length(y)){\n invisible(1:length(y))\n }\n else{\n # Can't have more folds than there are items per class\n nfolds = min(nfolds, max(Nk))\n # Assign folds evenly within each class, then shuffle within each class\n for (k in 1:length(classes)){\n ixs <- which(y==classes[k])\n folds_k <- rep(1:nfolds, ceiling(length(ixs) / nfolds))\n folds_k <- folds_k[1:length(ixs)]\n folds_k <- sample(folds_k)\n folds[ixs] = folds_k\n }\n invisible(folds)\n }\n}\n", "meta": {"hexsha": "c45cbee535248804d0ab5bd86c21af22b25a9668", "size": 4648, "ext": "r", "lang": "R", "max_stars_repo_path": "example/lib/cross.validation.caret.r", "max_stars_repo_name": "smdabdoub/MLRepo", "max_stars_repo_head_hexsha": "d8e1116368ab641704097254441ff29677aead82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2018-07-27T18:06:28.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-23T14:49:17.000Z", "max_issues_repo_path": "example/lib/cross.validation.caret.r", "max_issues_repo_name": "smdabdoub/MLRepo", "max_issues_repo_head_hexsha": "d8e1116368ab641704097254441ff29677aead82", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-05-30T04:13:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-13T23:03:04.000Z", "max_forks_repo_path": "example/lib/cross.validation.caret.r", "max_forks_repo_name": "smdabdoub/MLRepo", "max_forks_repo_head_hexsha": "d8e1116368ab641704097254441ff29677aead82", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2019-05-20T07:50:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T23:12:02.000Z", "avg_line_length": 39.7264957265, "max_line_length": 118, "alphanum_fraction": 0.6338209983, "num_tokens": 1168, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154239836484143, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.3353840575979837}} {"text": "# Download and extract WorldClim data\nlibrary(raster)\nlibrary(dismo)\nlibrary(sf)\nsource(\"pixel-based/utils/load-sampling-data.r\")\nsource(\"pixel-based/utils/db-io.r\")\n\n# Temporary directory for storing the data before extraction\nTempDir = \"../../userdata/master-classification/climate/\"\nOutDir = \"../data/pixel-based/climate/\"\n\n# Generate bioclimatic variables manually; for raster-based processing better to download,\n# but in this case it's more efficient to generate\nDSNames = c(\"tmin\", \"tmax\", \"tavg\", \"prec\", \"srad\", \"wind\", \"vapr\")\n\nDownloadDir = file.path(TempDir, \"raw\")\nif (!file.exists(DownloadDir))\n dir.create(DownloadDir)\nif (!file.exists(OutDir))\n dir.create(OutDir)\n\nURLs = paste0(\"http://biogeo.ucdavis.edu/data/worldclim/v2.0/tif/base/wc2.0_30s_\", DSNames, \".zip\")\nFilenames = paste0(DownloadDir, \"/\", DSNames, \".zip\")\nFilenames = Filenames[!file.exists(file.path(DownloadDir, basename(Filenames)))] # Filter already downloaded\nif (length(Filenames) > 0)\n download.file(URLs, Filenames)\n\nDSDirs = file.path(TempDir, DSNames)\nDSStacks = list()\nfor (i in 1:length(DSNames))\n{\n RasterFiles = list.files(DSDirs[i], pattern=glob2rx(\"*.tif\"), full.names = TRUE)\n if (length(RasterFiles) <= 0)\n {\n unzip(Filenames[i], exdir=DSDirs[i])\n RasterFiles = list.files(DSDirs[i], pattern=glob2rx(\"*.tif\"), full.names = TRUE)\n }\n DSStacks[[i]] = stack(RasterFiles)\n}\nnames(DSStacks) = DSNames\n\n# Extract our values\nTrainingPoints = LoadGlobalTrainingData()\nValidationPoints = LoadGlobalValidationData()\nPredictionPoints = LoadGlobalRasterPoints()\nAllPoints = rbind(TrainingPoints[,c(\"x\", \"y\")], ValidationPoints[,c(\"x\", \"y\")], PredictionPoints[,c(\"x\", \"y\")])\n\nDSValues = list()\nfor (i in 1:length(DSNames))\n{\n DSValues[[i]] = extract(DSStacks[[i]], AllPoints)\n}\nnames(DSValues) = DSNames\nBioValues = dismo::biovars(DSValues$prec, DSValues$tmin, DSValues$tmax)\n\nBaseClimateData = do.call(\"cbind\", DSValues)\nAllClimateData = cbind.data.frame(X=AllPoints$x, Y=AllPoints$y, BaseClimateData, BioValues)\n\nSpatialClimate = DFtoSF(AllClimateData)\nrm(AllClimateData)\nst_write(SpatialClimate, file.path(OutDir, \"climate.gpkg\"))\n", "meta": {"hexsha": "6ce227e60dc85ebb51cd00aa4dbe09e91ee1126e", "size": 2159, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pixel-based/climate/get-worldclim.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/pixel-based/climate/get-worldclim.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pixel-based/climate/get-worldclim.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 34.8225806452, "max_line_length": 111, "alphanum_fraction": 0.7207040296, "num_tokens": 620, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3353218426814197}} {"text": "#' Generate projected seasonal (and holiday) adjustment factors\n#'\n#' Generates projected adjustment factors from the decomposition of the fractional airline model R routines\n#'\n#' @param this_est A list object generated by the estimation procedure of the fractional airline model; the regression matrix should not be extended with forecasts.\n#' @param this_decomp A list object generated by the decomposition procedure of the fractional airline model; the \\code{nfcasts} option should have been specified. \n#' @param this_xtype Character vector; Type of regressor used for including regression effects into components (\\code{'hol'} for holiday, \\code{'ao'} for point outliers, \\code{'ls'} for level change outliers).\n#' @param this_x Character vector; regression matrix. \n#' @param this_log Logical scalar; set to TRUE if the log transformation is used in the decomposition, FALSE otherwise. Default: TRUE\n#' @param nfcasts Integer scalar; number of projected adjustment factors; should match the \\code{nfcasts} argument used to generate the decomposition. \n#' @param return_series Character scalar; component returned by the routine. Default is \"combined\"; other possible choices are \"seasonal\" and \"holiday\".\n#' @param return_xts Logical scalar; return projected factors of type \\code{return_series} as an \\code{xts} time series object. Default is FALSE.\n#' If TRUE, \\code{this_x} must be an \\code{xts} time series object.\n#' @return Array of projected adjustment factors from the fractional airline decomposition\n#' @examples\n#' ic_est <-\n#' rjd3highfreq::fractionalAirlineEstimation(ic_obs, periods=c(365.25/7),\n#' x=ic_default_matrix)\n#' ic_decomp <-\n#' rjd3highfreq::fractionalAirlineDecomposition(ic_est$model$linearized, 365.25/7, \n#' stde = TRUE, nfcasts = 104)\n#' ic_xtype <- c(rep('hol', 13), rep('ao', 40))\n#' ic_proj_seasonal <- \n#' gen_air_projected_factors(ic_est, ic_decomp,\n#' this_xtype = ic_xtype,\n#' this_x = ic_holiday_matrix_fcst,\n#' this_log = FALSE, nfcasts = 104)\n#' @import stats\n#' @export\ngen_air_projected_factors <- function(this_est, this_decomp, this_xtype, this_x = NULL,\n this_log = TRUE, nfcasts = 104, return_series = \"combined\",\n return_xts = FALSE) {\n # Author: Brian C. Monsell (OEUS), Version 3.3, 3/16/2022\n test_nfcasts <- length(this_decomp$decomposition$y) - this_decomp$likelihood$nobs\n if (test_nfcasts != nfcasts) {\n stop(paste0(\"number of forecasts specified for decomposition (\",test_nfcasts,\") not the same as nfcasts (\",nfcasts,\")\"))\n }\n \n if (!(return_series == \"combined\" | return_series == \"seasonal\" | return_series == \"holiday\")) {\n stop(\"Acceptable entries for return_series are combined, seasonal, or holiday\")\n }\n \n this_max_col <- ncol(this_x) \n this_b <- this_est$model$b[1:this_max_col]\n \n # generate filters for holiday regressors\n filter_hol <- this_xtype[1:this_max_col] == \"hol\"\n \n n_hol <- sum(filter_hol)\n\n this_seasonal <- this_decomp$decomposition$s \n this_seasonal_length <- length(this_seasonal)\n \n if (this_log) {\n this_default <- rep(1, length.out = this_seasonal_length)\n } else {\n this_default <- rep(0, length.out = this_seasonal_length)\n }\n \n # generate holiday factors\n if (n_hol > 0) {\n this_hol <- as.vector(this_x[, filter_hol] %*% matrix(this_b[filter_hol], ncol = 1))\n if (this_log) {\n this_hol <- exp(this_hol)\n }\n } else {\n this_hol <- this_default\n }\n \n if (this_log) {\n this_seasonal <- exp(this_seasonal)\n this_combfac <- this_seasonal * this_hol\n } else {\n this_combfac <- this_seasonal + this_hol\n }\n \n start_factors <- this_seasonal_length - nfcasts + 1\n if (return_series == \"combined\") {\n this_proj <- this_combfac[start_factors:this_seasonal_length]\n } else {\n if (return_series == \"seasonal\") {\n this_proj <- this_seasonal[start_factors:this_seasonal_length]\n } else {\n if (return_series == \"holiday\") {\n this_proj <- this_hol[start_factors:this_seasonal_length]\n }\n }\n }\n \n # return projected factors\n if (return_xts) {\n this_index <- as.Date(zoo::index(this_x), origin = \"1970-01-01\")\n this_filter <- seq((this_decomp$likelihood$nobs+1),length(this_decomp$decomposition$y)) \n this_proj <- \n xts::xts(x = this_proj, order.by = this_index[this_filter])\n return(this_proj)\n } else {\n return(this_proj)\n }\n \n}", "meta": {"hexsha": "49e5bf0b6e78e32ad891f4320f395187fe3baf32", "size": 4797, "ext": "r", "lang": "R", "max_stars_repo_path": "R/gen_air_projected_factors.r", "max_stars_repo_name": "bcmonsell/airutilities", "max_stars_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/gen_air_projected_factors.r", "max_issues_repo_name": "bcmonsell/airutilities", "max_issues_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/gen_air_projected_factors.r", "max_forks_repo_name": "bcmonsell/airutilities", "max_forks_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.495049505, "max_line_length": 209, "alphanum_fraction": 0.6493641859, "num_tokens": 1162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410572017153, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3353044931747285}} {"text": "source(\"usa/code/plotting/format-data-plotting.r\")\nsource(\"usa/code/plotting/make-three-panel-plots.r\")\nsource(\"usa/code/plotting/make-rt-plot.r\")\n\nlibrary(ggpubr)\n\nmake_plots_all <- function(filename, SIM=FALSE, label = \"\", last_date_data,\n ext = \".png\", group = NULL){\n print(\"Making three panel plots\")\n load(filename)\n out <- rstan::extract(fit)\n \n rt_data_long <- NULL\n rt_data_wide <- NULL\n if(!SIM){\n \n all_data_out <- data.frame()\n }\n for (i in 1:length(states)){\n \n print(states[i])\n data_state_plot <- format_data(i = i, dates = dates, states = states, \n estimated_cases_raw = estimated_cases_raw, \n estimated_deaths_raw = estimated_deaths_raw, \n reported_cases = reported_cases,\n reported_deaths = reported_deaths,\n out = out)\n colnames_csv <- c(\"date\",\"state\", \"reported_cases\", \"predicted_cases\",\"cases_min\", \"cases_max\",\n \"reported_deaths\",\"estimated_deaths\",\n \"deaths_min\", \"deaths_max\",\"rt\", \"rt_min\",\"rt_max\")\n if (!SIM){\n data_state_out_temp <- data_state_plot[,colnames_csv]\n colnames(data_state_out_temp) <- c(\"date\",\"state\", \"reported_cases\", \n \"predicted_infections_mean\",\"predicted_infections_lower_CI_95\", \"predicted_infections_higher_CI_95_cumulative\",\n \"reported_deaths\", \"estimated_deaths_mean\", \"estimated_deaths_lower_CI_95\", \"estimated_deaths_higher_CI_95\",\n \"mean_time_varying_reproduction_number_R(t)\", \"time_varying_reproduction_number_R(t)_lower_CI_95\",\n \"time_varying_reproduction_number_R(t)_higher_CI_95\")\n all_data_out <- rbind(all_data_out, data_state_out_temp)\n }\n # Cuts data on last_data_date\n data_state_plot <- data_state_plot[which(data_state_plot$date <= last_date_data),]\n \n # Read in covariates\n covariates <- readRDS(\"usa/data/covariates.RDS\")\n covariates$Quarantine = NULL\n covariates$GathRecomAny = NULL\n covariates_long <- gather(covariates[which(covariates$StatePostal == states[i]), \n 2:ncol(covariates)], \n key = \"key\", value = \"value\")\n covariates_long$x <- rep(NA, length(covariates_long$key))\n covariates_long$time <- rep(\"start\", length(covariates_long$key))\n \n covariates_ended <- readRDS(\"usa/data/covariates_ended.RDS\")\n covariates_ended$Quarantine = NULL\n covariates_ended$GathRecomAny = NULL\n covariates_ended_long <- gather(covariates_ended[which(covariates_ended$StatePostal == states[i]), \n 2:ncol(covariates_ended)], \n key = \"key\", value = \"value\")\n covariates_ended_long$x <- rep(NA, length(covariates_ended_long$key))\n covariates_ended_long$time <- rep(\"ease\", length(covariates_ended_long$key))\n \n covariates_long <- rbind(covariates_long, covariates_ended_long)\n covariates_long$time <- factor(covariates_long$time, levels = c(\"start\", \"ease\"))\n \n un_dates <- unique(covariates_long$value)\n for (k in 1:length(un_dates)){\n idxs <- which(covariates_long$value == un_dates[k])\n max_val <- ceiling(max(data_state_plot$rt_max)) + 0.3\n for (k in idxs){\n covariates_long$x[k] <- max_val\n max_val <- max_val - 0.3\n }\n }\n \n date_emerg_dec <- covariates_long$value[which(covariates_long$key == \"EmergDec\" & covariates_long$time == \"start\")]\n idx_emerg_dec <- which(dates[[i]] == date_emerg_dec)\n if (length(idx_emerg_dec) == 0 || idx_emerg_dec < 3){\n print(\"Don't simulate before emerg dec\")\n rt <- NA\n rt_li <- NA\n rt_ui <- NA\n } else {\n mean_emerg_chain <- rowMeans(out$Rt_adj[,(idx_emerg_dec-3):(idx_emerg_dec+3),i])\n rt <- mean(mean_emerg_chain)\n rt_li <- quantile(mean_emerg_chain, prob=.025)\n rt_ui <- quantile(mean_emerg_chain, prob=.975)\n }\n \n idx <- which(dates[[i]] == last_date_data)\n mean_chain <- rowMeans(out$Rt_adj[,(idx-6):idx,i])\n rt_current <- mean(mean_chain)\n rt_li_current <- quantile(mean_chain, prob=.025)\n rt_ui_current <- quantile(mean_chain,prob=.975)\n \n \n # Collate rt information\n len <- length(data_state_plot$rt)\n rt_data_state_long <- data.frame(\"state\" = c(states[i], states[i]),\n \"x\" = c(\"start\", \"end\"),\n \"rt\" = c(rt, rt_current),\n \"rt_min\" = c(rt_li, rt_li_current),\n \"rt_max\" = c(rt_ui, rt_ui_current))\n rt_data_long <- rbind(rt_data_long, rt_data_state_long)\n \n \n # Make the three panel plot\n plots <- make_three_panel_plots(data_state_plot, jobid = JOBID, state = states[i], \n covariates_long = covariates_long, label = label,\n ext = ext)\n }\n if(!SIM){\n saveRDS(all_data_out, paste0(\"usa/results/\", \"three-panel-data-\",JOBID,\".RDS\"))\n }\n # Get state groupings\n groupings <- read.csv(\"usa/data/usa-regions.csv\", stringsAsFactors = FALSE)\n groupings <- select(groupings, code, region_census_sub_revised, state_name)\n names(groupings) <- c(\"state\", \"groupings\", \"state_name\")\n rt_data_long <- left_join(rt_data_long, groupings, by = \"state\")\n \n print(\"Making rt plot\")\n rt_data_long$x <- factor(rt_data_long$x, levels = c(\"start\", \"end\"))\n make_rt_point_plot(rt_data_long, JOBID = JOBID, label = label, ext = ext)\n \n}\n", "meta": {"hexsha": "97ab100b935eb5c0293eb4b1a377c4189dcb1440", "size": 5761, "ext": "r", "lang": "R", "max_stars_repo_path": "usa/code/plotting/make-plots.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "usa/code/plotting/make-plots.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "usa/code/plotting/make-plots.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 46.088, "max_line_length": 152, "alphanum_fraction": 0.6023259851, "num_tokens": 1469, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410572017153, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3353044931747285}} {"text": "#!/usr/bin/env Rscript\n\ntring <- read.table(\"../trcurve.txt\", head=TRUE)\nmcmc <-read.table(\"curvelog.txt\", head=FALSE)\n\npdf(\"curves.pdf\")\nplot(tring$r,mcmc[1e5,],col=\"light grey\", xlab=\"R [kpc]\", ylab=\"Vc (km/s)\")\nfor (n in seq(1e5,1.01e5)) {\n lines(tring$r,mcmc[n,],col=\"light grey\")\n}\n\nlines(tring$r, tring$v, lty=2, lwd=2, col=\"green\")\ndev.off() -> dumpvar\n", "meta": {"hexsha": "af148c5fe7f95164fb17cbde8560a429e3acbd91", "size": 361, "ext": "r", "lang": "R", "max_stars_repo_path": "mcmcplot.r", "max_stars_repo_name": "petehague/galaxyview", "max_stars_repo_head_hexsha": "9202a09c97d66b23213356815f3c6eaeb8958d7f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "mcmcplot.r", "max_issues_repo_name": "petehague/galaxyview", "max_issues_repo_head_hexsha": "9202a09c97d66b23213356815f3c6eaeb8958d7f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mcmcplot.r", "max_forks_repo_name": "petehague/galaxyview", "max_forks_repo_head_hexsha": "9202a09c97d66b23213356815f3c6eaeb8958d7f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.7857142857, "max_line_length": 75, "alphanum_fraction": 0.6371191136, "num_tokens": 138, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.3353044864877229}} {"text": "library(ggplot2)\nlibrary(dplyr)\nargs <- commandArgs(trailingOnly=TRUE)\n\nif (length(args) != 14) {\n print(args)\n stop(\"Usage: evaluator_bars_per_group_strategy.r common.r input.csv output.pdf granularity groupName strategy evaluator1 unit1 quantity1 indication1 evaluator2 unit2 quantity2 indication2\")\n}\n\ncommon <- args[1]\ninFile <- args[2]\noutPdf <- args[3]\n# granularity <- args[4]\ngroupName <- args[5]\nstrategy <- args[6]\ne1 <- args[7]\nu1 <- args[8]\nq1 <- args[9]\ni1 <- args[10]\ne2 <- args[11]\nu2 <- args[12]\nq2 <- args[13]\ni2 <- args[14]\n\nsource(common)\n\nres = read.csv(inFile, header=TRUE)\n\nres <- res[res$strategy == strategy,]\n\nres$origin <- paste(res$file, res$focus, res$strategy)\n\n# Select the right data\nres1 <- res[res$evaluator == e1,]\nres2 <- res[res$evaluator == e2,]\n\n# Make the output numeric\nres$output <- suppressWarnings(as.numeric(as.character(res$output)))\n\n# Replace NaN with '0'\nres$output <- replace(res$output, is.na(res$output), 0)\n\ndat <- merge(res1, res2, by = \"origin\")\ndat <- dat[!is.na(dat$output.x),]\ndat <- dat[!is.na(dat$output.y),]\n\ndat <- dat %>%\n\tgroup_by(file.x, output.x, strategy.x) %>%\n\tsummarise(output.y=mean(output.y))\n\nif (length(dat$output.y) != 0) {\n startPdf(outPdf)\n\n p <- ggplot(dat, aes(output.x, output.y, fill = strategy.x)) +\n geom_bar(stat=\"identity\", position = \"dodge\") +\n scale_fill_brewer(palette = \"Set1\") +\n labs(x = e1) + labs(y = e2) +\n geom_smooth(method='lm', formula=y~x, show.legend=FALSE, show_guide = FALSE) +\n theme(legend.position=\"none\")\n\n if (strategy == \"full-background\") {\n e2 <- paste(\"log(\", e2, \")\", sep=\"\")\n p <- p + scale_y_log10()\n }\n\n p <- p +\n xlab(paste(e1, paste(\"(\", q1, \")\", sep=\"\"))) +\n ylab(paste(e2, paste(\"(\", q2, \")\", sep=\"\"))) +\n theme(axis.title.y=element_text(angle = 0))\n print(p)\n\n} else {\n invalidDataPdf(outPdf)\n}\n\n", "meta": {"hexsha": "cfae7dda60890f59d67b21658ee620f0ee45e7b4", "size": 1855, "ext": "r", "lang": "R", "max_stars_repo_path": "easyspec-evaluate/rscripts/evaluator_bars_per_group_strategy.r", "max_stars_repo_name": "NorfairKing/easyspec", "max_stars_repo_head_hexsha": "b038b45a375cc0bed2b00c255b508bc06419c986", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 29, "max_stars_repo_stars_event_min_datetime": "2017-07-06T08:41:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-23T19:27:30.000Z", "max_issues_repo_path": "easyspec-evaluate/rscripts/evaluator_bars_per_group_strategy.r", "max_issues_repo_name": "NorfairKing/easyspec", "max_issues_repo_head_hexsha": "b038b45a375cc0bed2b00c255b508bc06419c986", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2017-09-10T19:07:51.000Z", "max_issues_repo_issues_event_max_datetime": "2017-09-17T10:58:41.000Z", "max_forks_repo_path": "easyspec-evaluate/rscripts/evaluator_bars_per_group_strategy.r", "max_forks_repo_name": "NorfairKing/easyspec", "max_forks_repo_head_hexsha": "b038b45a375cc0bed2b00c255b508bc06419c986", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-09-09T19:42:50.000Z", "max_forks_repo_forks_event_max_datetime": "2018-05-30T10:03:55.000Z", "avg_line_length": 24.4078947368, "max_line_length": 191, "alphanum_fraction": 0.6409703504, "num_tokens": 573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.33530448648772276}} {"text": "# 3. faza: Vizualizacija podatkov\n\n#Graf za turizem glede na transport za najbolj razvitih 10 dr\u017eav\ndrzave.2016 <- turizem_glede_na_transport %>% filter(leto == 2016) %>%\n top_n(10, prihodi_turistov_preko_letalskega_prometa)\n\ngraf.turisti <- ggplot(data = turizem_glede_na_transport %>% filter(COUNTRY %in% drzave.2016$COUNTRY), \n aes(x = leto, y = prihodi_turistov_preko_letalskega_prometa/100000, label = COUNTRY)) + \n geom_point(aes(x = leto, y = prihodi_turistov_preko_letalskega_prometa/100000 ), color = \"red\") +\n geom_line(aes(group = COUNTRY, x = leto, y = prihodi_turistov_preko_letalskega_prometa/100000, color=COUNTRY)) +\n labs(title =\"Prihodi turistov preko letalskega prometa za posamezno leto\") +\n xlab(\"Leto\") + ylab(\"\u0160tevilo potnikov (x 100.000)\") + theme(plot.title = element_text(hjust = 0.5)) +\n theme(axis.title = element_text(size = (9)), \n panel.background=element_rect(fill=\"#F5ECCE\"), plot.title = element_text(size = (15))) + \n scale_color_manual(name = \"Dr\u017eave\", labels = c(\"Hrva\u0161ka\", \"Ciper\", \"Francija\", \"Nem\u010dija\", \"Gr\u010dija\", \"Mad\u017earska\",\n \"Italija\", \"Poljska\", \"\u0160panija\", \"Velika Britanija\"),\n values = c(\"#0000b3\", \"#3399ff\", \"#00ff99\", \"#33cc33\", \"#008000\", \"#ff9900\", \n \"#ff3300\", \"#cc0000\", \"#993366\", \"#ff00ff\"))\n\n# Graf za primerjavo med dobro (\u0160panija) in slab\u0161e (Litva) razvito dr\u017eavo\n# Da bo graf bolj realen, bom vzela relativne podatke, zato bom \u0161tevilo turistov, ki pridejo v dr\u017eavo,\n# delila s \u0161tevilo prebivalcev tiste dr\u017eave\nprebivalci.litve1 <- prebivalstvo %>% filter(Drzava == \"Lithuania\")\nprebivalci.litve <- prebivalci.litve1[1,\"Stevilo_prebivalcev\"]\nprebivalci.spanije1 <- prebivalstvo %>% filter(Drzava == \"Spain\")\nprebivalci.spanije <- prebivalci.spanije1[1,\"Stevilo_prebivalcev\"]\nturizem.transport.span.lit <- turizem_glede_na_transport %>% filter(COUNTRY %in% c(\"Spain\", \"Lithuania\"))\nturizem.span.lit <- turizem_na_splosno %>% filter(COUNTRY %in% c(\"Spain\", \"Lithuania\"))\nzdruzena <- rbind(turizem.span.lit %>% rename(Prihodi=prihodi_turistov) %>% mutate(Kategorija=\"Vsi prihodi\"),\n turizem.transport.span.lit %>% rename(Prihodi=prihodi_turistov_preko_letalskega_prometa) %>%\n mutate(Kategorija=\"Prihodi preko letalskega prometa\"))\n\nzdruzena$Prihodi[zdruzena$COUNTRY == \"Spain\"] <- zdruzena$Prihodi[zdruzena$COUNTRY == \"Spain\"] / prebivalci.spanije\nzdruzena$Prihodi[zdruzena$COUNTRY == \"Lithuania\"] <- zdruzena$Prihodi[zdruzena$COUNTRY == \"Lithuania\"] / prebivalci.litve\n\ngraf.spa.lit <- ggplot(data = zdruzena, aes(x= leto, y = Prihodi, fill = Kategorija, group = COUNTRY)) +\n geom_col(position = \"dodge\") + ylab(\"Prihodi\") + xlab(\"Leto\") +\n labs(title =\"Primerjava \u0160panije in Litve\") + theme(plot.title = element_text(hjust = 0.5))\n\n#Grafa za pregled razvoja turizma in transporta v \u0160paniji \nustanovitve.turizem.spa <- ustanovitve_za_turizem %>% filter(COUNTRY %in% c(\"Spain\"))\nustanovitve.transport.spa <- ustanovitve_za_transport %>% filter(COUNTRY %in% c(\"Spain\"))\nzdruzena.ustanovitve.spa <- rbind(ustanovitve.turizem.spa %>% \n rename(Stevilo_ustanovljenih=stevilo_ustanovljenih_enot_za_turizem) %>% \n mutate(Kategorija=\"Ustanovitve za turizem\"), ustanovitve.transport.spa %>% \n rename(Stevilo_ustanovljenih=stevilo_ustanovljenih_enot_za_potniski_promet) %>% \n mutate(Kategorija=\"Ustanovitve za potni\u0161ki promet\"))\n\ngraf.ustanovitve.spa <- ggplot(data = zdruzena.ustanovitve.spa, aes(x=factor(leto), y=Stevilo_ustanovljenih/1000, \n fill= Kategorija, group = COUNTRY)) + geom_col(position = \"dodge\") + xlab(\"Leto\") +\n ylab(\"\u0160tevilo ustanovljenih enot (x 1000)\") + scale_fill_manual(\"Kategorija\",values = c('#0023a0', '#ffcc80')) +\n labs(title =\"Ustanavljanje v \u0160paniji\") + theme(plot.title = element_text(hjust = 0.5))\n\nizdatki.turizem.spa <- izdatki_za_turizem %>% filter(COUNTRY %in% c(\"Spain\"))\nizdatki.transport.spa <- izdatki_za_transport %>% filter(COUNTRY %in% c(\"Spain\"))\nzdruzena.izdatki.spa <- rbind(izdatki.turizem.spa %>% \n rename(Izdatki=izdatki_za_turizem_v_USD) %>% \n mutate(Kategorija=\"Izdatki za turizem\"), izdatki.transport.spa %>% \n rename(Izdatki=izdatki_za_potniski_promet_v_USD) %>% \n mutate(Kategorija=\"Izdatki za potni\u0161ki promet\"))\n\ngraf.izdatki.spa <- ggplot(data = zdruzena.izdatki.spa, aes(x=factor(leto), y=Izdatki/100000000, \n fill= Kategorija, group = COUNTRY)) + geom_col(position = \"dodge\") + xlab(\"Leto\") +\n ylab(\"Izdatki v USD (x 100.000.000)\") + scale_fill_manual(\"Kategorija\",values = c('#0023a0', '#ffcc80')) +\n labs(title =\"Izdatki v \u0160paniji\") + theme(plot.title = element_text(hjust = 0.5)) \n\n\n#Grafa za pregled razvoja turizma in transporta v Litvi\nustanovitve.turizem.lit <- ustanovitve_za_turizem %>% filter(COUNTRY %in% c(\"Lithuania\"))\nustanovitve.transport.lit <- ustanovitve_za_transport %>% filter(COUNTRY %in% c(\"Lithuania\"))\nzdruzena.ustanovitve.lit <- rbind(ustanovitve.turizem.lit %>% \n rename(Stevilo_ustanovljenih=stevilo_ustanovljenih_enot_za_turizem) %>% \n mutate(Kategorija=\"Ustanovitve za turizem\"), ustanovitve.transport.lit %>% \n rename(Stevilo_ustanovljenih=stevilo_ustanovljenih_enot_za_potniski_promet) %>% \n mutate(Kategorija=\"Ustanovitve za potni\u0161ki promet\"))\n\ngraf.ustanovitve.lit <- ggplot(data = zdruzena.ustanovitve.lit, aes(x=leto, y=Stevilo_ustanovljenih/100, \n fill= Kategorija, group = COUNTRY)) + geom_col(position = \"dodge\") + xlab(\"Leto\") +\n ylab(\"\u0160tevilo ustanovljenih enot (x 100)\") + scale_fill_manual(\"Kategorija\",values = c('#0023a0', '#ffcc80')) +\n labs(title =\"Ustanavljanje v Litvi\") + theme(plot.title = element_text(hjust = 0.5))\n\nizdatki.turizem.lit <- izdatki_za_turizem %>% filter(COUNTRY %in% c(\"Lithuania\"))\nizdatki.transport.lit <- izdatki_za_transport %>% filter(COUNTRY %in% c(\"Lithuania\"))\nzdruzena.izdatki.lit <- rbind(izdatki.turizem.lit %>% \n rename(Izdatki=izdatki_za_turizem_v_USD) %>% \n mutate(Kategorija=\"Izdatki za turizem\"), izdatki.transport.lit %>% \n rename(Izdatki=izdatki_za_potniski_promet_v_USD) %>% \n mutate(Kategorija=\"Izdatki za potni\u0161ki promet\"))\n\ngraf.izdatki.lit <- ggplot(data = zdruzena.izdatki.lit, aes(x=leto, y=Izdatki/100000000, \n fill= Kategorija, group = COUNTRY)) + geom_col(position = \"dodge\") + xlab(\"Leto\") +\n ylab(\"Izdatki v USD (x 100.000.000)\") + scale_fill_manual(\"Kategorija\",values = c('#0023a0', '#ffcc80')) +\n labs(title =\"Izdatki v Litvi\") + theme(plot.title = element_text(hjust = 0.5))\n\n#Osredoto\u010dimo se na sam letalski promet, in sicer najprej na povpre\u010dje po dr\u017eavah za vsa leta\nizracunaj.povprecje <- function() {\n letalski_promet <- letalski_promet %>% drop_na()\n povprecje.po.drzavah <- letalski_promet %>% group_by(Drzava) %>% \n summarise(Povprecje.potnikov = mean(Stevilo_potnikov)) \n return(povprecje.po.drzavah)\n}\n\npovprecje.po.drzavah <- izracunaj.povprecje()\n\n#uredimo drzave glede na povpre\u010dno \u0161tevilo potnikov skozi preu\u010devano obdobje\npovprecje.po.drzavah$Drzava <- factor(povprecje.po.drzavah$Drzava, levels = povprecje.po.drzavah$Drzava[order(povprecje.po.drzavah$Povprecje.potnikov)])\n\ngraf.povprecje.po.drzavah <- ggplot(data=povprecje.po.drzavah, aes(x=Drzava, y=Povprecje.potnikov/10000)) + \n geom_bar(stat = 'identity', position = 'dodge') + coord_flip() + labs(title =\"Povpre\u010dje potnikov po dr\u017eavah\") +\n ylab(\"Povpre\u010dje potnikov (x 10.000)\") + xlab(\"Dr\u017eava\") + theme(plot.title = element_text(hjust = 0.5))\n\n\n\n#Zanima me, kako se letalski promet giblje glede na cetrtletja in sicer konkretno za \u0160panijo\nletalski.promet.spa <- letalski_promet %>% filter(Drzava %in% c(\"Spain\"))\n\ngraf.letalski.promet.spa <- ggplot(data = letalski.promet.spa, aes(x=Cetrtletje, y=Stevilo_potnikov/100000, \n label=Drzava)) + geom_point(aes(x=Cetrtletje, y=Stevilo_potnikov/100000), color = \"red\") + geom_line() + \n labs(title = \"\u0160tevilo potnikov po \u010detrtletjih v \u0160paniji\") + theme(plot.title = element_text(hjust = 0.5)) +\n ylab(\"\u0160tevilo potnikov (x 100.000)\") + xlab(\"\u010cetrtletje\")\n\n \n\n\n\n#Uvozimo zemljevid.\n#source(\"https://raw.githubusercontent.com/jaanos/APPR-2018-19/master/lib/uvozi.zemljevid.r\")\n\nzemljevid <- uvozi.zemljevid(\"http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip\",\n \"ne_50m_admin_0_countries\", mapa = \"zemljevidi\", pot.zemljevida = \"\", encoding = \"UTF-8\") %>% \n fortify() %>% filter(CONTINENT == \"Europe\" | SOVEREIGNT %in% c(\"Cyprus\"), long < 45 & long > -45 & lat > 30 & lat < 75)\n\ncolnames(zemljevid)[11] <- 'drzava'\nzemljevid$drzava <- as.character(zemljevid$drzava)\nzemljevid$drzava[zemljevid$drzava == \"Republic of Serbia\"] <- \"Serbia\"\n\n\n#Nari\u0161imo zemljevid povpre\u010dnega \u0161tevila potnikov preko letalskega prometa po dr\u017eavah\nzemljevid.povprecje.letalskega.prometa <- ggplot() +\n geom_polygon(data = povprecje.po.drzavah %>% right_join(zemljevid, by = c(\"Drzava\" = \"drzava\")),\n aes(x = long, y = lat, group = group, fill = Povprecje.potnikov/10000), alpha = 0.8, color = \"black\")+\n scale_fill_gradient2(low = \"green\", mid = \"yellow\", high = \"red\", midpoint = 80) + \n xlab(\"\") + ylab(\"\") + ggtitle(\"Povpre\u010dno \u0161tevilo potnikov preko letalskega prometa (x 10.000)\")+\n guides(fill=guide_legend(title=\"Povpre\u010dje\")) + theme(plot.title = element_text(hjust = 0.5))\n\nzemljevid.povprecje.letalskega.prometa\n#Nari\u0161imo zemljevid povpre\u010dnega \u0161tevila prihodov turistov v dr\u017eavo\nizracunaj.povprecje1 <- function() {\n turizem_na_splosno <- turizem_na_splosno %>% drop_na()\n povprecje.turizma <- turizem_na_splosno %>% group_by(COUNTRY) %>% \n summarise(Povprecje.turistov = mean(prihodi_turistov)) \n return(povprecje.turizma)\n}\n\npovprecje.turizma <- izracunaj.povprecje1()\npovprecje.turizma$COUNTRY[povprecje.turizma$COUNTRY == \"Czech Republic\"] <- \"Czechia\"\npovprecje.turizma$COUNTRY[povprecje.turizma$COUNTRY == \"Russian Federation\"] <- \"Russia\"\n\nzemljevid.turisti <- ggplot() +\n geom_polygon(data = povprecje.turizma %>% right_join(zemljevid, by = c(\"COUNTRY\" = \"drzava\")),\n aes(x = long, y = lat, group = group, fill = Povprecje.turistov/100000), alpha = 0.8, color = \"black\")+\n scale_fill_gradient2(low = \"yellow\", mid = \"green\", high = \"blue\", midpoint = 80) + \n xlab(\"\") + ylab(\"\") + ggtitle(\"Povpre\u010dno \u0161tevilo turistov (x 100.000)\")+\n guides(fill=guide_legend(title=\"Povpre\u010dje\")) + theme(plot.title = element_text(hjust = 0.5))\n \n\n# Naredimo zemljevid, ki bo dr\u017eave razvrstil v skupine\ndrzave.2017 <- letalski_promet %>% filter(Cetrtletje == \"2017-01-01\")\ndrzave.2017 <- drzave.2017[, ! names(drzave.2017) %in% c(\"Cetrtletje\"), drop = F]\ndrzave.2017 <- drzave.2017[-c(34),]\ndrzave.2017$Drzava[drzave.2017$Drzava == \"Czechia\"] <- \"Czech Republic\"\ndrzave.norm <- drzave.2017 %>% select(-Drzava) %>% scale()\nrownames(drzave.norm) <- drzave.2017$Drzava\nk <- kmeans(na.omit(drzave.norm), 2, nstart = 100000)\nskupine <- data.frame(Drzava=drzave.2017$Drzava, skupina=factor(k$cluster))\n\nzemljevid.skupine <- ggplot() + geom_polygon(data=zemljevid %>% left_join(skupine, by=c(\"NAME_LONG\"=\"Drzava\")),\n aes(x=long, y=lat, group=group, fill=skupina)) + xlab(\"\") + ylab(\"\") + \n ggtitle(\"Dr\u017eave po skupinah\")+ guides(fill=guide_legend(title=\"Skupine\")) + \n theme(plot.title = element_text(hjust = 0.5)) \n\n# Pripravljene tabele za napredno analizo\n\nturizem <- rbind(turizem_na_splosno %>% rename(Prihodi=prihodi_turistov) %>% mutate(Kategorija=\"Vsi prihodi\"),\n turizem_glede_na_transport %>% rename(Prihodi=prihodi_turistov_preko_letalskega_prometa) %>%\n mutate(Kategorija=\"Prihodi preko letalskega prometa\"))\n\nturizem1 <- merge(turizem_na_splosno, turizem_glede_na_transport)\n\n", "meta": {"hexsha": "40e8f87a18d5fcc153a8e04cf0636f07639e9da1", "size": 12040, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "evadezelak/APPR-2018-19", "max_stars_repo_head_hexsha": "5dca20710944934f6fd98347ea711d752189131e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "evadezelak/APPR-2018-19", "max_issues_repo_head_hexsha": "5dca20710944934f6fd98347ea711d752189131e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, 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YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.33530070792946687}} {"text": "#GlitchTables.r\n# RBLandau 20190130\n\n\nfnPrintTableShockLow <- function(dfdat, sOutputFilename, sTitle, sSubtitle){\n\n# Tabulate columns by copies and sector lifetime.\ntbl.shocklow <- data.frame(with(dfdat, \n tapply(mdmlosspct, list(copies, lifem), FUN=identity)))\n\n# Re-form the data into a table for printing. \nfoo<-(with(dfdat, \n tapply(mdmlosspct, list(copies, lifem), FUN=identity)))\nfoo2<-cbind(as.numeric(levels(factor(dfdat$copies))), foo)\ntbl2<-data.frame(foo2)\ncolnames(tbl2)<-c(\"copies\",as.numeric(colnames(foo2[,2:ncol(foo2)])))\n\n# Pretty-print this to a file with explanatory headings.\nsink(sOutputFilename)\ncat(\"MIT Preservation Simulation Project\", \"\\n\\n\")\ncat(sOutputFilename, format(Sys.time(),\"%Y%m%d_%H%M%S%Z\"), \"\\n\\n\")\ncat(sTitle, \"\\n\\n\")\ncat(sSubtitle, \"\\n\\n\")\ncat(\"\\n\")\nprint(tbl2)\nsink()\n\nreturn(tbl2)\n\n}\n\n\n# f n M a k e S m a l l L o s s T a b l e \nfnMakeSmallLossTable <- function(dfIn) {\n library(reshape2)\n bar.small <- dfIn[,c(\"copies\", \"lifem\", \"mdmlosspct\")]\n bar.melted <- melt(bar.small, id=c(\"copies\", \"lifem\"))\n bar.recast <- dcast(bar.melted, copies~lifem)\n return(bar.recast)\n}\n\n\n# f n S a v e S m a l l L o s s T a b l e \nfnSaveSmallLossTable <- function(dfIn, sFilename, sHeading) {\n dfTemp <- fnMakeSmallLossTable(dfIn)\n sink(sFilename)\n cat(\"MIT Preservation Simulation Project\", \"\\n\")\n cat(sFilename, format(Sys.time(),\"%Y%m%d_%H%M%S%Z\"), \"\\n\\n\")\n cat(sHeading, \"\\n\\n\")\n cat(\" half-life-------------------->\",\"\\n\")\n print(dfTemp)\n sink()\n}\n\n\n\n\n#END\n", "meta": {"hexsha": "f79af0442099504cbfa0855b9e5ae6b765f1fe59", "size": 1570, "ext": "r", "lang": "R", "max_stars_repo_path": "oldpictures/glitches/month/GlitchTables.r", "max_stars_repo_name": "MIT-Informatics/PreservationSimulation", "max_stars_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_stars_repo_licenses": ["X11"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2016-08-24T05:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-12T16:44:48.000Z", "max_issues_repo_path": "veryoldpictures/glitch/year/GlitchTables.r", "max_issues_repo_name": "MIT-Informatics/PreservationSimulation", "max_issues_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_issues_repo_licenses": ["X11"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-20T02:55:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-20T02:55:37.000Z", "max_forks_repo_path": "veryoldpictures/glitch/year/GlitchTables.r", "max_forks_repo_name": "MIT-Informatics/PreservationSimulation", "max_forks_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_forks_repo_licenses": ["X11"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.6101694915, "max_line_length": 76, "alphanum_fraction": 0.6407643312, "num_tokens": 484, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33530069917935457}} {"text": "#!/usr/bin/env Rscript\n\nfpath <- \"benchmark/archiveit/\"\n\np00 <- read.csv(\"benchmark/archiveit/summary-ukwa-2000.csv\", header=T)\np01 <- read.csv(\"benchmark/archiveit/summary-ukwa-2001.csv\", header=T)\n\nprint(p00)\nprint(p01)\n\n#quit()\n\nf2si2<-function (number, rounding=F, sep=\" \") \n{\n lut <- c(1e-24, 1e-21, 1e-18, 1e-15, 1e-12, 1e-09, 1e-06, \n 0.001, 1, 1000, 1e+06, 1e+09, 1e+12, 1e+15, 1e+18, 1e+21, \n 1e+24)\n pre <- c(\"y\", \"z\", \"a\", \"f\", \"p\", \"n\", \"u\", \"m\", \"\", \"k\", \n \"M\", \"G\", \"T\", \"P\", \"E\", \"Z\", \"Y\")\n ix <- findInterval(number, lut)\n if (ix>0 && lut[ix]!=1) {\n if (rounding==T) {\n sistring <- paste(round(number/lut[ix]), pre[ix], sep=sep)\n }\n else {\n sistring <- paste(number/lut[ix], pre[ix], sep=sep)\n }\n }\n else {\n sistring <- as.character(number)\n }\n return(sistring)\n}\n\n\nxlb <- c(\"H1\", \"H2\", \"H3\", \"H4\", \"H5\", \"Hx\", \"P1\", \"P2\", \"P3\", \"P4\", \"P5\", \"Px\")\nkpos <- pretty(c(0, max(p01$suburi_keys, p00$suburi_keys)), n=10)\nklb <- sapply(kpos, FUN=f2si2, rounding=T, sep=\"\")\nspos <- pretty(c(0, max(p01$profile_size, p00$profile_size)), n=10)\nslb <- sapply(spos, FUN=f2si2, rounding=T, sep=\"\")\ntpos <- pretty(c(0, max(p01$profiling_time/60, p00$profiling_time/60)), n=10)\ntlb <- sapply(tpos, FUN=f2si2, rounding=T, sep=\"\")\n\n\nkeyline <- paste(fpath, \"summary-keys-lineplot.png\", sep=\"\")\npng(keyline, height=500, width=800, pointsize=18)\n\npar(mar=c(4,4,2,2)+0.1)\nplot(p01$suburi_keys, type='b', col=\"red\", xaxt=\"n\", yaxt=\"n\", ylab=\"\", xlab=\"\", ylim=c(0, max(kpos)))\nlines(p00$suburi_keys, type='b', col=\"blue\", pch=0)\naxis(1, at=c(1:12), labels=xlb)\naxis(2, at=kpos, labels=klb)\ntitle(ylab=\"Number of Sub-URI Keys\", xlab=\"Max segments (H: Host segments, P: Path segments)\")\n\ndev.off()\n\n\nsizeline <- paste(fpath, \"summary-filesize-lineplot.png\", sep=\"\")\npng(sizeline, height=500, width=800, pointsize=18)\n\npar(mar=c(4,4,2,2)+0.1)\nplot(p01$profile_size, type='b', col=\"red\", xaxt=\"n\", yaxt=\"n\", ylab=\"\", xlab=\"\", ylim=c(0, max(spos)))\nlines(p00$profile_size, type='b', col=\"blue\", pch=0)\naxis(1, at=c(1:12), labels=xlb)\naxis(2, at=spos, labels=slb)\ntitle(ylab=\"Profile size\", xlab=\"Max segments (H: Host segments, P: Path segments)\")\n\ndev.off()\n\n\ntimeline <- paste(fpath, \"summary-time-lineplot.png\", sep=\"\")\npng(timeline, height=500, width=800, pointsize=18)\n\npar(mar=c(4,4,2,2)+0.1)\nplot(p01$profiling_time/60, type='b', col=\"red\", xaxt=\"n\", yaxt=\"n\", ylab=\"\", xlab=\"\", ylim=c(0, max(tpos)))\nlines(p00$profiling_time/60, type='b', col=\"blue\", pch=0)\naxis(1, at=c(1:12), labels=xlb)\naxis(2, at=tpos, labels=tlb)\ntitle(ylab=\"Profiling time (minutes)\", xlab=\"Max segments (H: Host segments, P: Path segments)\")\n\ndev.off()\n", "meta": {"hexsha": "26a86c788204acf17235dada88a5264781f451f4", "size": 2718, "ext": "r", "lang": "R", "max_stars_repo_path": "benchmark/summarize.r", "max_stars_repo_name": "oduwsdl/archive_profiler", "max_stars_repo_head_hexsha": "339fbc9338c705c2a107702eb13dbdfc81e3e7e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2015-06-24T21:19:23.000Z", "max_stars_repo_stars_event_max_datetime": "2018-09-28T23:54:58.000Z", "max_issues_repo_path": "benchmark/summarize.r", "max_issues_repo_name": "oduwsdl/archive_profiler", "max_issues_repo_head_hexsha": "339fbc9338c705c2a107702eb13dbdfc81e3e7e1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmark/summarize.r", "max_forks_repo_name": "oduwsdl/archive_profiler", "max_forks_repo_head_hexsha": "339fbc9338c705c2a107702eb13dbdfc81e3e7e1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1463414634, "max_line_length": 108, "alphanum_fraction": 0.6070640177, "num_tokens": 1047, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33530069917935457}} {"text": "library(tibble)\nlibrary(lubridate)\n\ncontext('Test Time Filtering')\n\ninput_table <- tibble::tribble(\n ~report_month, ~sta6a, ~type, ~value,\n ymd(\"2017-03-01\"), \"foo\", \"short\", 10,\n ymd(\"2017-03-01\"), \"foo\", \"long\", 10,\n ymd(\"2017-04-01\"), \"foo\", \"short\", 10,\n ymd(\"2017-04-01\"), \"foo\", \"long\", 10,\n ymd(\"2018-01-01\"), \"foo\", \"short\", 10,\n ymd(\"2018-01-01\"), \"foo\", \"long\", 10,\n ymd(\"2018-02-01\"), \"foo\", \"short\", 10,\n ymd(\"2018-02-01\"), \"foo\", \"long\", 10,\n ymd(\"2018-03-01\"), \"foo\", \"short\", 10,\n ymd(\"2018-03-01\"), \"foo\", \"long\", 10,\n ymd(\"2018-04-01\"), \"foo\", \"short\", 10,\n ymd(\"2018-04-01\"), \"foo\", \"long\", 10\n)\n\ntest_that(\"Timepoints before most recent x are removed\", {\n result <- filter_recent_times(input_table, 4) \n expect_equal(sum(result$report_month < ymd(\"2018-01-01\")), 0)\n})\n\ntest_that(\"All timepoints after most recent x are retained\", {\n result <- filter_recent_times(input_table, 4) \n expect_equal(nrow(result), 8)\n})\n", "meta": {"hexsha": "04ea0f0a0e7feffd2acdf35cfeec11141f8c7163", "size": 1018, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_filter_recent_times.r", "max_stars_repo_name": "Landis-Lewis-Lab/goals-of-care", "max_stars_repo_head_hexsha": "6c7ac3a021ca97379cb0229845f9395a554bea3b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-02-05T19:03:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-23T21:12:47.000Z", "max_issues_repo_path": "tests/testthat/test_filter_recent_times.r", "max_issues_repo_name": "Display-Lab/goals-of-care", "max_issues_repo_head_hexsha": "6c7ac3a021ca97379cb0229845f9395a554bea3b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2017-09-26T20:46:30.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-07T19:30:14.000Z", "max_forks_repo_path": "tests/testthat/test_filter_recent_times.r", "max_forks_repo_name": "Landis-Lewis-Lab/goals-of-care", "max_forks_repo_head_hexsha": "6c7ac3a021ca97379cb0229845f9395a554bea3b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-02-05T19:05:15.000Z", "max_forks_repo_forks_event_max_datetime": "2018-02-05T19:05:15.000Z", "avg_line_length": 32.8387096774, "max_line_length": 63, "alphanum_fraction": 0.5756385069, "num_tokens": 400, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.33530069917935457}} {"text": "#' Data summary\n#'\n#' @description Compute summary statistics for data\n#'\n#' @param df data.frame\n#'\n#' @return tibble\n#' @export\n#'\n#' @examples\n#'\n#' mtcars\n#'\n#' df_summary(mtcars)\n#'\n\ndf_summary <- function(df){\n\n if(sum(stringr::str_detect(class(df),\n paste(c(\"tbl_df\",\"tbl\",\"data.frame\",\"tabyl\",\"grouped_df\",\"data.table\"),\n collapse = \"|\"))) == 0){\n stop(\"df must be a data.frame/tibble\")\n }\n\n df_num <-\n df %>%\n dplyr::select_if(is.numeric)\n\n if(ncol(df_num) == 0){\n\n print(\"0 numeric variables\")\n\n }else{\n\n df_num <-\n df_num %>%\n tidyr::gather(var,value) %>%\n dplyr::group_by(var) %>%\n dplyr::summarise(relper::num_summary(value))\n\n print(paste0(ncol(df_num),\" numeric variables\"))\n\n print(df_num)\n }\n\n df_cat <-\n df %>%\n dplyr::select_if(purrr::negate(is.numeric))\n\n if(ncol(df_cat) == 0){\n\n print(\"0 categoric variables\")\n\n }else{\n\n df_cat <-\n df_cat %>%\n tidyr::gather(var,value) %>%\n dplyr::group_by(var) %>%\n dplyr::summarise(relper::cat_summary(value))\n\n print(paste0(ncol(df_cat),\" categoric variables\"))\n\n print(df_cat)\n }\n}\n", "meta": {"hexsha": "73e410081c2cfcf20c97cbcffea9da3b3391675c", "size": 1191, "ext": "r", "lang": "R", "max_stars_repo_path": "R/df_summary.r", "max_stars_repo_name": "larissabf/relper", "max_stars_repo_head_hexsha": "fc6ed8006190fdb829ebbf8b2f24b3c8ef39c3a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-05-09T23:13:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-31T00:45:50.000Z", "max_issues_repo_path": "R/df_summary.r", "max_issues_repo_name": "larissabf/relper", "max_issues_repo_head_hexsha": "fc6ed8006190fdb829ebbf8b2f24b3c8ef39c3a4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/df_summary.r", "max_forks_repo_name": "larissabf/relper", "max_forks_repo_head_hexsha": "fc6ed8006190fdb829ebbf8b2f24b3c8ef39c3a4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-12-17T12:27:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-19T12:50:55.000Z", "avg_line_length": 17.776119403, "max_line_length": 100, "alphanum_fraction": 0.5608732158, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3353006991793545}} {"text": "#! /usr/bin/Rscript --vanilla\n\n# install.packages(\"multcomp\")\n# Download tar from http://cran.r-project.org/web/packages/nparcomp/nparcomp.pdf\n# sudo R CMD INSTALL nparcomp_2.0.tar.gz\n\nlibrary(\"rjson\")\nlibrary(\"nparcomp\")\n\nargs = commandArgs(TRUE)\n\nbugginess <- fromJSON(args[2])\nquantile = fromJSON(args[3])\n\ndf <- data.frame(bugginess, quantile)\n\nres <- npar.t.test( df$bugginess~df$quantile, data=df, alternative=\"two.sided\", method=\"logit\")\n\nprint(res$Analysis)\n\n\n\n\n", "meta": {"hexsha": "447169d7cf8068d16aaa5555a08690395861f2f0", "size": 471, "ext": "r", "lang": "R", "max_stars_repo_path": "Risk/R/npartest.r", "max_stars_repo_name": "phoxicle/bugger", "max_stars_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-01-26T13:23:09.000Z", "max_stars_repo_stars_event_max_datetime": "2016-01-26T13:23:09.000Z", "max_issues_repo_path": "Risk/R/npartest.r", "max_issues_repo_name": "phoxicle/bugger", "max_issues_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Risk/R/npartest.r", "max_forks_repo_name": "phoxicle/bugger", "max_forks_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.625, "max_line_length": 95, "alphanum_fraction": 0.7218683652, "num_tokens": 138, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6757646010190475, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.33524264874042725}} {"text": "#!/usr/bin/env Rscript\n\noptions(stringAsfactors = FALSE, useFancyQuotes = FALSE)\n# This script is used to do cv filtering based on mzMatch\n# Taking the command line arguments\nargs <- commandArgs(trailingOnly = TRUE)\n\nif(length(args)==0)stop(\"No file has been specified! Please select a file for blank filtering!\\n\")\n\nrequire(xcms)\ninputPeakML<-NA\noutput<-NA\nblank<-\"blank\"\nsample<-\"sample\"\nmethod<-\"max\"\nrest<-F\npreviousEnv<-NA\nphenoFile<-NA\nphenoDataColumn<-NA\ncvcut<-0.3\nfor(arg in args)\n{\n argCase<-strsplit(x = arg,split = \"=\")[[1]][1]\n value<-strsplit(x = arg,split = \"=\")[[1]][2]\n if(argCase==\"input\")\n {\n previousEnv=as.character(value)\n }\n if(argCase==\"qc\")\n {\n qc=as.character(value)\n }\n if(argCase==\"sample\")\n {\n sample=as.character(value)\n }\n if(argCase==\"rest\")\n {\n rest=as.logical(value)\n }\n if(argCase==\"phenoFile\")\n {\n phenoFile=as.character(value)\n }\n if(argCase==\"phenoDataColumn\")\n {\n phenoDataColumn=as.character(value)\n }\n if(argCase==\"output\")\n {\n output=as.character(value)\n }\n if(argCase==\"cvcut\")\n {\n cvcut=as.numeric(value)\n }\n}\n\nif(is.na(previousEnv) | is.na(output) | any(is.na(qc))) stop(\"All input, output and blank need to be specified!\\n\")\n\nload(file = previousEnv)\ninputXCMS<-get(varNameForNextStep)\nif(!is.na(phenoDataColumn) && !is.na(phenoFile))\n{\nfileNameMap<-read.csv(phenoFile,stringsAsFactors = F,header = T)\n\nfor(i in 1:nrow(inputXCMS@phenoData))\n{\nmassTracesXCMSSet@phenoData[i]<-fileNameMap[fileNameMap[,1]==rownames(massTracesXCMSSet@phenoData)[i],phenoDataColumn]\n}\n}\n\n CV<-function(x)\n {\n\t\tif(length(na.omit(x))<=2)return(100000)\n sd(x,na.rm = T)/mean(x,na.rm = T)\n }\nxset<-inputXCMS\nidx <- xcms:::groupidx(xset)\nremoveGR<-c()\nremovePk<-c()\n\nfor( i in seq_along(idx)){\n peak_select <- xcms::peaks(xset)[idx[[i]], ]\n peaks<-rep(NA,nrow(xset@phenoData))\nif(class(peak_select)==\"numeric\")\n{\n peaks[peak_select[\"sample\"]]<-peak_select[\"into\"]\n}else\n{\npeaks[peak_select[,\"sample\"]]<-peak_select[,\"into\"]\n}\n\n names(peaks)<-c(as.character(xset@phenoData[,1]))\n\n QCSamples<-peaks[names(peaks)==qc]\n realSamples<-NA\n if(rest)\n {\n realSamples<-peaks[names(peaks)!=qc]\n }else{\n realSamples<-peaks[names(peaks)==sample]\n }\n\n\n controlRemove<-F\n\n controlRemove<-CV(QCSamples)>=cvcut\n\n\n\n if(controlRemove)\n {\n\n removeGR<-c(removeGR,i)\n removePk<-c(removePk,idx[[i]])\n }\n}\n\nfor(i in removeGR)\n{\n xset@groupidx[[i]]<-NULL\n xset@groups<-xset@groups[-i,]\n}\n\n\npreprocessingSteps<-c(preprocessingSteps,\"cvfilter\")\n\nvarNameForNextStep<-as.character(\"xset\")\n\nsave(list = c(\"xset\",\"preprocessingSteps\",\"varNameForNextStep\"),file = output)\n", "meta": {"hexsha": "41e04168c71add35a0e985aae7fcab1120b18433", "size": 2670, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/cvfilter.r", "max_stars_repo_name": "jordeu/metaboigniter", "max_stars_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2021-02-19T12:58:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-16T18:49:31.000Z", "max_issues_repo_path": "bin/cvfilter.r", "max_issues_repo_name": "jordeu/metaboigniter", "max_issues_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 45, "max_issues_repo_issues_event_min_datetime": "2021-02-04T21:08:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T11:41:15.000Z", "max_forks_repo_path": "bin/cvfilter.r", "max_forks_repo_name": "jordeu/metaboigniter", "max_forks_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2021-02-04T09:01:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-22T07:33:05.000Z", "avg_line_length": 20.2272727273, "max_line_length": 118, "alphanum_fraction": 0.6674157303, "num_tokens": 855, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3351416291479456}} {"text": "#!/usr/bin/env Rscript\nargs = commandArgs(trailingOnly = TRUE)\n\nlibrary(tidyverse)\n\ngff_file <- args[1]\n\ndat <- ape::read.gff(gff_file) %>%\n dplyr::as_tibble() %>%\n dplyr::mutate(gene = stringr::str_remove_all(attributes, '.*gene=|;.*'),\n type = as.character(type)) %>%\n dplyr::filter(type == 'CDS') %>%\n dplyr::select(gene, start, end) %>% \n dplyr::mutate(offset = (start %% 3) - 1, \n s = dplyr::if_else(offset == 1, (start + 1) / 3, NaN ),\n s = dplyr::if_else(offset == 0, (start + 2) / 3, s),\n s = dplyr::if_else(offset == -1, (start + 3) / 3, s),\n e = round(((end - start + 1) / 3) + s - 1)) %>% \n dplyr::select(gene, start = s, end = e, offset)\n\npurrr::map(levels(factor(dat$offset)), function(off){\n dat_2 <- dplyr::filter(dat, offset == off)\n if (nrow(dat_2) == 1) {\n dplyr::bind_rows(dat_2, tibble(gene = 'debug',\n start = 10000000000,\n end = 100000000000,\n offset = dat_2$offset)) %>%\n readr::write_csv(paste0('codfreq_gff_offset_', off, '.csv'))\n } else {\n dat_2 %>%\n readr::write_csv(paste0('codfreq_gff_offset_', off, '.csv'))\n }\n})\n ", "meta": {"hexsha": "229cc4b069970d471801e797938881527238e577", "size": 1349, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/preprocess_codfreq_gff.r", "max_stars_repo_name": "MicrobialGenomics/viralrecon", "max_stars_repo_head_hexsha": "a7c316a98ae9626466fbf5a14049fb851627c50a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/preprocess_codfreq_gff.r", "max_issues_repo_name": "MicrobialGenomics/viralrecon", "max_issues_repo_head_hexsha": "a7c316a98ae9626466fbf5a14049fb851627c50a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-28T08:18:35.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-28T08:18:35.000Z", "max_forks_repo_path": "bin/preprocess_codfreq_gff.r", "max_forks_repo_name": "MicrobialGenomics/viralrecon", "max_forks_repo_head_hexsha": "a7c316a98ae9626466fbf5a14049fb851627c50a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.6764705882, "max_line_length": 76, "alphanum_fraction": 0.4751667902, "num_tokens": 409, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947425132314, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3351205140774794}} {"text": "#' Fit a local model via the local adaptive group lasso\n#' \n#' This function augments the covariates with local interactions, drops\n#' observations with zero weight, runs the adaptive grouped lasso, computes the\n#' residuals, and returns the necessary values for tuning or reporting.\n#' \n#' @param x matrix of observed covariates\n#' @param y vector of observed responses\n#' @param loc location at which to fit a model\n#' @param family exponential family distribution of the response\n#' @param varselect.method criterion to minimize in the regularization step of\n#' fitting local models - options are \\code{AIC}, \\code{AICc}, \\code{BIC},\n#' \\code{GCV}\n#' @param tuning logical indicating whether this model will be used to tune the\n#' bandwidth, in which case only the tuning criteria are returned\n#' @param kernel.weights vector of observation weights from the kernel\n#' @param prior.weights vector of prior observation weights provided by the user\n#' @param longlat \\code{TRUE} indicates that the coordinates are specified in\n#' longitude/latitude, \\code{FALSE} indicates Cartesian coordinates. Default\n#' is \\code{FALSE}.\n#' \n#' @return list of coefficients, nonzero coefficient identities, and tuning data\n#' @useDynLib lagr \n#'\nlagr.fit.inner = function(x, y, group.id, coords, loc, family, varselect.method, oracle, tuning, predict, simulation, n.lambda, lambda.min.ratio, lagr.convergence.tol, lagr.max.iter, verbose, kernel.weights=NULL, prior.weights=NULL, longlat=FALSE) {\n #Find which observations were made at the model location \n colocated = which(apply(coords, 1, function(cc) all(round(cc,5) == round(as.numeric(loc),5))))\n\n #Bail now if only one observation has weight:\n if (sum(kernel.weights)==length(colocated)) {\n return(list('tunelist'=list('df-local'=1, 'ssr-loc'=list('pearson'=Inf, 'deviance'=Inf))))\n }\n \n #Use oracular variable selection if specified\n orig.names = colnames(x)\n if (!is.null(oracle)) {\n x = matrix(x[,oracle], nrow=nrow(x), ncol=length(oracle))\n colnames(x) = oracle\n }\n\n #Establish groups for the group lasso and if there's an intercept, mark it as unpenalized\n if (0 %in% group.id)\n unpen = 0\n raw.vargroup = group.id\n \n #This is the naming system for the covariate-by-location interaction variables.\n raw.names = colnames(x)\n interact.names = vector()\n for (l in 1:length(raw.names)) {\n for (m in 1:ncol(coords)) {\n interact.names = c(interact.names, paste(raw.names[l], \":\", colnames(coords)[m], sep=\"\"))\n }\n }\n\n #Compute the covariate-by-location interactions\n q = ncol(coords)\n interacted = matrix(0, ncol=q*ncol(x), nrow=nrow(x))\n for (k in 1:ncol(x)) {\n for (ll in 1:q) {\n interacted[,q*(k-1)+ll] = x[,k, drop=FALSE]*(coords[,ll]-loc[[ll]])\n group.id = c(group.id, group.id[k])\n }\n }\n x.interacted = cbind(x, interacted)\n colnames(x.interacted) = c(raw.names, interact.names)\n\n #Combine prior weights and kernel weights\n w <- prior.weights * kernel.weights\n weighted = which(w>0)\n n.weighted = length(weighted)\n\n #Limit our attention to the observations with nonzero weight\n xxx = as.matrix(x.interacted[weighted,, drop=FALSE])\n yyy = as.matrix(y[weighted])\n colocated = which(kernel.weights[weighted]==1)\n w = w[weighted]\n sumw = sum(w)\n \n #Instantiate objects to store our output\n tunelist = list()\n\n if (is.null(oracle)) {\n #Use the adaptive group lasso to produce a local model:\n model = grouplasso(data=list(x=xxx, y=yyy), weights=w, index=group.id, family=family, maxit=lagr.max.iter, delta=2, nlam=n.lambda, min.frac=lambda.min.ratio, thresh=lagr.convergence.tol, unpenalized=unpen)\n\n vars = apply(as.matrix(model$beta), 2, function(x) {which(x!=0)})\n df = model$results$df + 1 #Add one because we must estimate the scale parameter.\n } else {\n model = glm(yyy~xxx-1, weights=w, family=family)\n vars = list(1:ncol(xxx))\n varset = vars[[1]]\n df = ncol(xxx) + 1 #Add one for the scale parameter\n \n fitted = model$fitted\n localfit = fitted[colocated]\n dispersion = summary(model)$dispersion\n k = 1\n\n #Estimating scale in penalty formula:\n loss = sumw * log(sum(w * model$residuals**2)) - log(sumw) + 1\n }\n\n if (sumw > ncol(x)) {\n if (is.null(oracle)) {\n #Extract the fitted values for each lambda:\n dispersion = model$results$dispersion\n\n #Using the grouplasso's criteria:\n loss = model$results[[varselect.method]]\n \n #Pick the lambda that minimizes the loss:\n k = which.min(loss)\n localfit = model$results$fitted[colocated,]\n df = df[k]\n if (k > 1) {\n varset = vars[[k]]\n } else {\n varset = NULL\n }\n } \n \n #Prepare some outputs for the bandwidth-finding scheme:\n tunelist[['localfit']] = localfit\n tunelist[['criterion']] = model$results[[varselect.method]]\n tunelist[['dispersion']] = dispersion\n tunelist[['n']] = sumw\n tunelist[['df']] = df\n tunelist[['df-local']] = length(colocated) * df / sumw\n \n } else {\n fitted = rep(meany, nrow(xxx))\n dispersion = 0\n loss = Inf\n loss.local = c(Inf) \n localfit = meany\n }\n \n #Get the coefficients:\n if (is.null(oracle)) {\n coefs = drop(model$beta)\n rownames(coefs) = colnames(xxx)\n\n #Use AIC weights, or not:\n if (varselect.method %in% c('wAIC','wAICc')) {\n #Big average based on a selection criterion:\n w = -model$results[[varselect.method]]\n w = matrix(w / sum(w))\n #coefs = (model$beta %*% w)[1:length(orig.names)]\n #conf.zero = drop((model$beta==0) %*% w)[1:length(orig.names)]\n coefs = drop(model$beta %*% w)\n conf.zero = drop((model$beta==0) %*% w)\n } else {\n #coefs = model$beta[1:length(orig.names),k]\n coefs = model$beta[,k]\n conf.zero = as.numeric(coefs==0)\n names(conf.zero) = names(coefs)\n }\n\n #list the covariates that weren't shrunk to zero, but don't bother listing the intercept.\n nonzero = raw.names[which(raw.vargroup %in% unique(group.id[which(conf.zero!=1)]))]\n nonzero = nonzero[nonzero != \"(Intercept)\"]\n\n #names(coefs) = raw.names\n #names(conf.zero) = colnames(raw.names) \n #names(coefs) = colnames(xxx)\n #names(conf.zero) = colnames(xxx) \n } else {\n #coefs = rep(0, length(orig.names))\n #names(coefs) = orig.names\n coefs = rep(0, ncol(xxx))\n names(coefs) = colnames(xxx)\n coefs[raw.names] = coef(model)[1:length(oracle)]\n nonzero = raw.names\n conf.zero = rep(0, length(orig.names))\n names(conf.zero) = colnames(raw.names)\n }\n \n \n if (tuning) {\n return(list(tunelist=tunelist, model=model, s=k, dispersion=dispersion, nonzero=nonzero, weightsum=sumw, loss=loss))\n } else if (predict) {\n return(list(tunelist=tunelist, coef=coefs, weightsum=sumw, s=k, dispersion=dispersion, nonzero=nonzero, conf.zero=conf.zero))\n } else if (simulation) {\n return(list(tunelist=tunelist, coef=coefs, s=k, dispersion=dispersion, fitted=localfit, nonzero=nonzero, conf.zero=conf.zero, actual=yyy[colocated], weightsum=sumw, loss=loss))\n } else {\n return(list(model=model, loss=loss, coef=coefs, nonzero=nonzero, conf.zero=conf.zero, s=k, loc=loc, df=df, loss.local=loss, dispersion=dispersion, fitted=localfit, weightsum=sumw, tunelist=tunelist))\n }\n}\n", "meta": {"hexsha": "10c4f0480ef26fdf470ac6f1776d24a97d131d2e", "size": 7827, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lagr.fit.inner.r", "max_stars_repo_name": "wrbrooks/lagr", "max_stars_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/lagr.fit.inner.r", "max_issues_repo_name": "wrbrooks/lagr", "max_issues_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lagr.fit.inner.r", "max_forks_repo_name": "wrbrooks/lagr", "max_forks_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.4126984127, "max_line_length": 249, "alphanum_fraction": 0.6174779609, "num_tokens": 2074, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33512050718409186}} {"text": "# 2. faza: Uvoz podatkov\r\n\r\nvalueForPath <- function(row, path) { \r\n row %>% html_node(xpath = path) %>% html_text()\r\n}\r\n\r\n# Tabela, ki prikazuje stevilke odseljevanja v Evropske drzave \r\n# glede na starost(mladi, zreli, stari)\r\n\r\nStarost_Evropa <- read.csv(\r\n \"podatki/starost_tujina.csv\"\r\n)\r\n\r\nStarost_Evropa <- Starost_Evropa[, c(1,7, 10,11,12,14,16)] %>%\r\n mutate(Starost = rep(c(\"Mladi\", \"Zreli\", \"Stari\"), \r\n each = length(unique(Leto)))) %>%\r\n pivot_longer(-c(\"Leto\", \"Starost\"), names_to = \"Drzava\", values_to = \"Stevilo\")\r\n\r\n# Tabela, ki prikazuje stevilke odseljevanja v Evropske drzave \r\n# v tem primeru skoraj za vsako drzavo Evrope \r\n\r\nEvropa <- read.csv(\r\n \"podatki/starost_EU.csv\",\r\n fileEncoding = \"Windows-1250\"\r\n)\r\n\r\nevropa <- Evropa %>% select(X, Skupno)\r\ncolnames(evropa) <- c(\"region\", \"SKUPNO\")\r\n\r\nmapdata = left_join(map_data(\"world\"), evropa, by=\"region\") %>%\r\n filter(!is.na(SKUPNO));\r\n\r\n\r\n# Tabela, ki prikazuje stevilke odseljevanja iz Slovenije \r\n# glede na izobrazbo, spol in starost \r\n\r\n\r\nStarost_Izobrazba_Spol <- \"podatki/starost_izobrazba_splosno.xml\" %>%\r\n read_xml(encoding = \"Windows-1250\") %>% html_nodes(xpath = \"/root/row\")\r\n\r\nStarost_Izobrazba_Spol <- tibble(\r\n Leto = as.integer(Starost_Izobrazba_Spol %>% sapply(function(row) {valueForPath(row, \"Leto\")})),\r\n Mladi = as.integer(Starost_Izobrazba_Spol %>% sapply(function(row) {valueForPath(row, \"Mladi\")})),\r\n Zreli = as.integer(Starost_Izobrazba_Spol %>% sapply(function(row) {valueForPath(row, \"Zreli\")})),\r\n Stari = as.integer(Starost_Izobrazba_Spol %>% sapply(function(row) {valueForPath(row, \"Stari\")})),\r\n) \r\n\r\nStarost_Izobrazba_Spol <- Starost_Izobrazba_Spol %>%\r\n mutate(Izobrazba = rep(c(\"OS_moski\", \"SS_moski\", \"VS_moski\", \"OS_zenske\", \"SS_zenske\", \"VS_zenske\"), \r\n each = length(unique(Leto)))) %>%\r\n pivot_longer(-c(\"Leto\", \"Izobrazba\"), names_to = \"Starost\", values_to = \"Stevilo\")\r\n\r\n\r\n# Tabela, ki prikazuje stevilke odseljevanja iz Slovenije \r\n# glede na starost in spol \r\n\r\nStarost_Spol <- \"podatki/starost_spol.xml\" %>%\r\n read_xml(encoding = \"Windows-1250\") %>% html_nodes(xpath = \"/root/row\")\r\n\r\nStarost_Spol <- tibble(\r\n Leto = as.integer(Starost_Spol %>% sapply(function(row) {valueForPath(row, \"Leto\")})),\r\n Mladi = as.integer(Starost_Spol %>% sapply(function(row) {valueForPath(row, \"Mladi\")})),\r\n Zreli = as.integer(Starost_Spol %>% sapply(function(row) {valueForPath(row, \"Zreli\")})),\r\n Stari = as.integer(Starost_Spol %>% sapply(function(row) {valueForPath(row, \"Stari\")}))\r\n) %>% \r\n mutate(Spol = rep(c(\"Moski\", \"Zenske\"), \r\n each = length(unique(Leto)))) %>%\r\n pivot_longer(-c(\"Leto\", \"Spol\"), names_to = \"Starost\", values_to = \"Stevilo\")\r\n\r\n\r\n# Tabela, ki prikazuje stevilke odseljevanja iz Slovenije\r\n# glede na aktivnost(zaposlen, nezaposlen) in spol\r\n\r\nAktivnost <- \"podatki/aktivnost_splosno.xml\" %>%\r\n read_xml(encoding = \"Windows-1250\") %>% html_nodes(xpath = \"/root/row\")\r\n\r\nAktivnost <- tibble(\r\n Leto = as.integer(Aktivnost %>% sapply(function(row) {valueForPath(row, \"Leto\")})),\r\n Zaposleni = as.integer(Aktivnost %>% sapply(function(row) {valueForPath(row, \"Zaposleni\")})),\r\n Nezaposleni = as.integer(Aktivnost %>% sapply(function(row) {valueForPath(row, \"Nezaposleni\")}))\r\n) %>%\r\n mutate(Starost = rep(c(\"Mladi\", \"Zreli\", \"Mlade\", \"Zrele\"), \r\n each = length(unique(Leto)))) %>%\r\n pivot_longer(-c(\"Leto\", \"Starost\"), names_to = \"Aktivnost\", values_to = \"Stevilo\")\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "6d86d7ec12d193b1766362471565bff365106050", "size": 3501, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "aljazkump/Analiza-odseljevanja-iz-Slovenije", "max_stars_repo_head_hexsha": "46e9f289d7a7eb3e21c93d2549d6d1b1755109cf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "uvoz/uvoz.r", "max_issues_repo_name": "aljazkump/Analiza-odseljevanja-iz-Slovenije", "max_issues_repo_head_hexsha": "46e9f289d7a7eb3e21c93d2549d6d1b1755109cf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2022-01-21T15:48:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-08T15:22:15.000Z", "max_forks_repo_path": "uvoz/uvoz.r", "max_forks_repo_name": "aljazkump/APPR-2021-22", "max_forks_repo_head_hexsha": "46e9f289d7a7eb3e21c93d2549d6d1b1755109cf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9, "max_line_length": 104, "alphanum_fraction": 0.6598114824, "num_tokens": 1212, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6548947290421275, "lm_q1q2_score": 0.33512050718409186}} {"text": "## Exerc\u00edcio 8\r\n## Prof. James Hunter\r\n## from: https://rstudio.cloud/project/1181159\r\n## 12 de maio de 2020\r\n\r\n# colocar mpg em memoria\r\n\r\nmpg <- mpg\r\n\r\n# Objetivo: displ x cty com drv\r\n\r\nggplot(data = mpg, mapping = aes(x = displ, y = cty, colour = drv)) +\r\n geom_point() \r\n", "meta": {"hexsha": "53b248993f251a3eef364152672d01a7e4133615", "size": 277, "ext": "r", "lang": "R", "max_stars_repo_path": "exercicio_8.r", "max_stars_repo_name": "jameshunterbr/Sustentare_MAD_2020", "max_stars_repo_head_hexsha": "299a7f0af7e999e59cc53c57cf22618b6eb68092", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercicio_8.r", "max_issues_repo_name": "jameshunterbr/Sustentare_MAD_2020", "max_issues_repo_head_hexsha": "299a7f0af7e999e59cc53c57cf22618b6eb68092", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exercicio_8.r", "max_forks_repo_name": "jameshunterbr/Sustentare_MAD_2020", "max_forks_repo_head_hexsha": "299a7f0af7e999e59cc53c57cf22618b6eb68092", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.7857142857, "max_line_length": 70, "alphanum_fraction": 0.6281588448, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.6261241632752915, "lm_q1q2_score": 0.3350380558937605}} {"text": "x <- function (x, y, wt = NULL, intercept = TRUE, tolerance = 1e-07, \n yname = NULL) \n{\n x <- as.matrix(x)\n y <- as.matrix(y)\n xnames <- colnames(x)\n if (is.null(xnames)) {\n if (ncol(x) == 1L) \n xnames <- \"X\"\n else xnames <- paste0(\"X\", 1L:ncol(x))\n }\n if (intercept) {\n x <- cbind(1, x)\n xnames <- c(\"Intercept\", xnames)\n }\n if (is.null(yname) && ncol(y) > 1) \n yname <- paste0(\"Y\", 1L:ncol(y))\n good <- complete.cases(x, y, wt)\n dimy <- dim(as.matrix(y))\n if (any(!good)) {\n warning(sprintf(ngettext(sum(!good), \"%d missing value deleted\", \n \"%d missing values deleted\"), sum(!good)), domain = NA)\n x <- as.matrix(x)[good, , drop = FALSE]\n y <- as.matrix(y)[good, , drop = FALSE]\n wt <- wt[good]\n }\n nrx <- NROW(x)\n ncx <- NCOL(x)\n nry <- NROW(y)\n ncy <- NCOL(y)\n nwts <- length(wt)\n if (nry != nrx) \n stop(sprintf(paste0(ngettext(nrx, \"'X' matrix has %d case (row)\", \n \"'X' matrix has %d cases (rows)\"), \", \", ngettext(nry, \n \"'Y' has %d case (row)\", \"'Y' has %d cases (rows)\")), \n nrx, nry), domain = NA)\n if (nry < ncx) \n stop(sprintf(paste0(ngettext(nry, \"only %d case\", \"only %d cases\"), \n \", \", ngettext(ncx, \"but %d variable\", \"but %d variables\")), \n nry, ncx), domain = NA)\n if (!is.null(wt)) {\n if (any(wt < 0)) \n stop(\"negative weights not allowed\")\n if (nwts != nry) \n stop(gettextf(\"number of weights = %d should equal %d (number of responses)\", \n nwts, nry), domain = NA)\n wtmult <- wt^0.5\n if (any(wt == 0)) {\n xzero <- as.matrix(x)[wt == 0, ]\n yzero <- as.matrix(y)[wt == 0, ]\n }\n x <- x * wtmult\n y <- y * wtmult\n invmult <- 1/ifelse(wt == 0, 1, wtmult)\n }\n z <- .Call(C_Cdqrls, x, y, tolerance, FALSE)\n resids <- array(NA, dim = dimy)\n dim(z$residuals) <- c(nry, ncy)\n if (!is.null(wt)) {\n if (any(wt == 0)) {\n if (ncx == 1L) \n fitted.zeros <- xzero * z$coefficients\n else fitted.zeros <- xzero %*% z$coefficients\n z$residuals[wt == 0, ] <- yzero - fitted.zeros\n }\n z$residuals <- z$residuals * invmult\n }\n resids[good, ] <- z$residuals\n if (dimy[2L] == 1 && is.null(yname)) {\n resids <- drop(resids)\n names(z$coefficients) <- xnames\n }\n else {\n colnames(resids) <- yname\n colnames(z$effects) <- yname\n dim(z$coefficients) <- c(ncx, ncy)\n dimnames(z$coefficients) <- list(xnames, yname)\n }\n z$qr <- as.matrix(z$qr)\n colnames(z$qr) <- xnames\n output <- list(coefficients = z$coefficients, residuals = resids)\n if (z$rank != ncx) {\n xnames <- xnames[z$pivot]\n dimnames(z$qr) <- list(NULL, xnames)\n warning(\"'X' matrix was collinear\")\n }\n if (!is.null(wt)) {\n weights <- rep.int(NA, dimy[1L])\n weights[good] <- wt\n output <- c(output, list(wt = weights))\n }\n rqr <- list(qt = drop(z$effects), qr = z$qr, qraux = z$qraux, \n rank = z$rank, pivot = z$pivot, tol = z$tol)\n oldClass(rqr) <- \"qr\"\n output <- c(output, list(intercept = intercept, qr = rqr))\n return(output)\n}\n", "meta": {"hexsha": "903f1c826a4d7e82fd6706b76fb488eb461b8bf2", "size": 3343, "ext": "r", "lang": "R", "max_stars_repo_path": "src/Windows/R/Editor/Test/Files/01.r", "max_stars_repo_name": "skeptycal/RTVS", "max_stars_repo_head_hexsha": "46a8f4579e4fd56219af3bb2cf3fd15b84464874", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 429, "max_stars_repo_stars_event_min_datetime": "2016-03-09T18:17:53.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-04T19:03:59.000Z", "max_issues_repo_path": "src/Windows/R/Editor/Test/Files/01.r", "max_issues_repo_name": "skeptycal/RTVS", "max_issues_repo_head_hexsha": "46a8f4579e4fd56219af3bb2cf3fd15b84464874", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2927, "max_issues_repo_issues_event_min_datetime": "2016-03-09T17:44:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-04-29T22:03:10.000Z", "max_forks_repo_path": "src/Windows/R/Editor/Test/Files/01.r", "max_forks_repo_name": "skeptycal/RTVS", "max_forks_repo_head_hexsha": "46a8f4579e4fd56219af3bb2cf3fd15b84464874", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 133, "max_forks_repo_forks_event_min_datetime": "2016-03-09T16:28:28.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-13T13:20:29.000Z", "avg_line_length": 34.112244898, "max_line_length": 90, "alphanum_fraction": 0.4944660485, "num_tokens": 1088, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3350380558937604}} {"text": "#' Plot catch curves for indices\r\n#' \r\n#' Creates catch curves to estimate Z from index catch data (converted from West Coast style indices).\r\n#' @param asap name of the variable that read in the asap.rdat file\r\n#' @param a1 list file produced by grab.aux.files function\r\n#' @param save.plots save individual plots\r\n#' @param od output directory for plots and csv files \r\n#' @param plotf type of plot to save\r\n#' @param first.age youngest age to use in catch curve, -999 finds peak age (defaults to -999)\r\n#' @export\r\n\r\nPlotCatchCurvesForIndices <- function(asap,a1,save.plots,od,plotf,first.age=-999){\r\n # first check to see if any West Coast style indices\r\n if(sum(asap$control.parms$index.age.comp.flag) == 0){\r\n return(list())\r\n }\r\n\r\n # create catch curve plots for each west coast style index\r\n usr <- par(\"usr\"); on.exit(par(usr))\r\n \r\n catch.curve.ind <- list()\r\n my.col <- rep(c(\"blue\",\"red\",\"green\",\"orange\",\"gray50\"),50)\r\n \r\n # convert the west coast style indices to catch at age matrices\r\n index.mats <- ConvertSurveyToAtAge(asap)\r\n \r\n # loop through all the indices\r\n for (ind in 1:asap$parms$nindices){\r\n if (asap$control.parms$index.age.comp.flag[ind] == 1){ # used age composition for the index\r\n title1 <- paste0(\"Index \",ind)\r\n \r\n min.age <- asap$control.parms$index.sel.start.age[ind]\r\n max.age <- asap$control.parms$index.sel.end.age[ind]\r\n ages <- seq(min.age, max.age)\r\n nages.i <- max.age - min.age + 1\r\n cohort <- seq(asap$parms$styr-nages.i-min.age, asap$parms$endyr+nages.i-min.age)\r\n \r\n # replace zeros with NA and take logs\r\n iob <- rep0log(index.mats$ob[[ind]])\r\n ipr <- rep0log(index.mats$pr[[ind]])\r\n \r\n # make cohorts\r\n iob.coh <- makecohorts(iob)\r\n ipr.coh <- makecohorts(ipr)\r\n \r\n # drop plus group\r\n if (asap$control.parms$index.sel.end.age[ind] == asap$parms$nages){\r\n iob.coh[,length(iob.coh[1,])] <- NA\r\n ipr.coh[,length(iob.coh[1,])] <- NA\r\n }\r\n \r\n first.age.label <- 1\r\n if (first.age==1) title1 <- paste0(title1,\" First Age = 1\") \r\n \r\n # determine which ages to use for each cohort (default)\r\n if (first.age == -999){\r\n iob.coh <- find_peak_age(iob.coh)\r\n ipr.coh <- find_peak_age(ipr.coh)\r\n first.age.label <- \"find_peak\"\r\n title1 <- paste0(title1,\" (Peak Age)\") \r\n \r\n }\r\n \r\n # or drop youngest ages based on user control\r\n if (first.age > min.age) {\r\n iob.coh[,1:(first.age-min.age)] <- NA\r\n ipr.coh[,1:(first.age-min.age)] <- NA\r\n title1 <- paste0(title1,\" First Age = \",first.age)\r\n first.age.label <- first.age \r\n }\r\n \r\n # compute Z by cohort\r\n z.ob <- calc_Z_cohort(iob.coh)\r\n z.pr <- calc_Z_cohort(ipr.coh)\r\n \r\n # make the plots\r\n par(mfrow=c(2,1))\r\n plot(cohort,cohort,type='n',ylim=range(c(iob.coh,ipr.coh),na.rm=T),xlab=\"\",ylab=\"Log(Index)\",main=paste0(title1,\" Observed\"))\r\n for (i in 1:length(iob.coh[,1])){\r\n lines(seq(cohort[i],cohort[i]+nages.i-1),iob.coh[i,],type='p',lty=1,pch=seq(1,nages.i),col=\"gray50\")\r\n lines(seq(cohort[i],cohort[i]+nages.i-1),iob.coh[i,],type='l',lty=1,col=my.col[i])\r\n }\r\n \r\n Hmisc::errbar(cohort,z.ob[,1],z.ob[,3],z.ob[,2],xlab=\"Year Class\",ylab=\"Z\",ylim=range(c(z.ob,z.pr),na.rm=T))\r\n \r\n if (save.plots) savePlot(paste0(od,\"catch_curve_\",title1,\"_Observed_first_age_\",first.age.label,\".\",plotf), type=plotf)\r\n \r\n plot(cohort,cohort,type='n',ylim=range(c(iob.coh,ipr.coh),na.rm=T),xlab=\"\",ylab=\"Log(Index)\",main=paste0(title1,\" Predicted\"))\r\n for (i in 1:length(iob.coh[,1])){\r\n lines(seq(cohort[i],cohort[i]+nages.i-1),ipr.coh[i,],type='p',lty=1,pch=seq(1,nages.i),col=\"gray50\")\r\n lines(seq(cohort[i],cohort[i]+nages.i-1),ipr.coh[i,],type='l',lty=1,col=my.col[i])\r\n }\r\n \r\n Hmisc::errbar(cohort,z.pr[,1],z.pr[,3],z.pr[,2],xlab=\"Year Class\",ylab=\"Z\",ylim=range(c(z.ob,z.pr),na.rm=T))\r\n \r\n if (save.plots) savePlot(paste0(od,\"catch_curve_\",title1,\"_Predicted_first_age_\",first.age.label,\".\",plotf), type=plotf)\r\n \r\n # write out .csv files for Z, one file for each fleet\r\n asap.name <- a1$asap.name\r\n \r\n colnames(z.ob) <-c(\"Z.obs\",\"low.80%\", \"high.80%\")\r\n write.csv(z.ob, file=paste0(od,\"Z.Ob.Index.\",ind,\"_\",asap.name,\".csv\"), row.names=cohort)\r\n \r\n colnames(z.pr) <-c(\"Z.pred\",\"low.80%\", \"high.80%\")\r\n write.csv(z.pr, file=paste0(od,\"Z.Pr.Index.\",ind,\"_\",asap.name,\".csv\"), row.names=cohort)\r\n }\r\n } # end loop over nindices\r\n return(catch.curve.ind)\r\n}\r\n", "meta": {"hexsha": "3ea0f48255efd414ed2b4b52d9061fa97ee30cba", "size": 4687, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_catch_curves_for_indices.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/plot_catch_curves_for_indices.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/plot_catch_curves_for_indices.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 42.6090909091, "max_line_length": 133, "alphanum_fraction": 0.5918497973, "num_tokens": 1445, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752914, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33503805589376034}} {"text": "require(tseriesChaos)\nrequire(rgl)\namin <- NULL; cosmin <- NULL; maja <- NULL; rob <- NULL; jordan <- NULL; sophia <- NULL;\n\nd <- 3; m <- 12;\n\namin$e <- read.csv('data/walks/amin.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\ncosmin$e <- read.csv('data/walks/cosmin.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\nmaja$e <- read.csv('data/walks/maja.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\njordan$e <- read.csv('data/walks/jordan.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\nrob$e <- read.csv('data/walks/rob.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\nsophia$e <- read.csv('data/walks/sophia.dat',sep=' ',colClasses=\"numeric\",header=FALSE)\n\namin$data <- t(amin$e[,1])\ncosmin$data <- t(cosmin$e[,1])\nmaja$data <- t(maja$e[,1])\njordan$data <- t(jordan$e[,1])\nrob$data <- t(rob$e[,1])\nsophia$data <- t(sophia$e[,1])\n\ny <- 2:4;\nx <- 1:4000;\n\nrgl.open()\nrgl.clear()\n#rgl.linestrips(amin$e[x,y],color=c(\"white\"), alpha=0.5)\n#rgl.linestrips(cosmin$e[x,y],color=c(\"red\"), alpha=0.5)\nrgl.linestrips(maja$e[x,y],color=c(\"blue\"), alpha=0.5)\n#rgl.linestrips(jordan$e[x,y],color=c(\"green\"), alpha=0.5)\n#rgl.linestrips(rob$e[x,y],color=c(\"yellow\"), alpha=0.5)\nrgl.linestrips(sophia$e[x,y],color=c(\"cyan\"), alpha=0.5)\n", "meta": {"hexsha": "593caaa80f4a325a25af56b98e491b5122b26101", "size": 1220, "ext": "r", "lang": "R", "max_stars_repo_path": "plotwalks.r", "max_stars_repo_name": "jwf-zz/tdetools", "max_stars_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-16T00:47:38.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-23T11:52:37.000Z", "max_issues_repo_path": "plotwalks.r", "max_issues_repo_name": "jwf-zz/tdetools", "max_issues_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plotwalks.r", "max_forks_repo_name": "jwf-zz/tdetools", "max_forks_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-02-18T06:34:54.000Z", "max_forks_repo_forks_event_max_datetime": "2019-02-18T06:34:54.000Z", "avg_line_length": 38.125, "max_line_length": 88, "alphanum_fraction": 0.6598360656, "num_tokens": 456, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3348623646504356}} {"text": "# this takes about 6 hours on a mobile i9 with 32 G of RAM\n# data saved as data/output.summary.yeast.Rda\n\n# change this to run the simulation if wanted\n# depends on TP-FP-yeast-setup.r for datasets\nrun.this = FALSE\n\n\nif(!exists('output.summaryFP.yeast')){ \n load(paste(my.path,'data/output.summaryFP.yeast.Rda', sep=''))\n}\nif(run.this == TRUE){\n\nlibrary(foreach)\nlibrary(doMC)\n\n# original tests done at 100 replicates\ntest.size <- 100\nmax.samples <- 20\nregisterDoMC(4) # this is the MC backend. \n# 4 cores seems to be the performance limit\n# beyond that there is throttling of each thread\n\noutput.summary <- list()\noutput.summaryFP.yeast <- list()\n\noutput.summaryFP.yeast <- foreach(j = 2:max.samples)%dopar%{\n print(j)\n\tn.samples = j\n\n\tsummary.stats <- matrix(data=NA, ncol=6, nrow=test.size)\n\t\n\tcolnames(summary.stats) <- c('eff.FP.5','eff.FP1','eff.FP2', 'ol.1.FP', 'diff.1.FP', 'triple.FP')\n\t\n\tfor(i in 1:test.size){\n\t\ttest.col <- c(sample(1:43,n.samples *2))\n\t\ttest.conds <- c(rep(\"a\", n.samples), rep(\"b\",n.samples))\n\t\ttest.data <- d.good[,test.col]\n\t\tx.test <- aldex.clr(test.data, test.conds, verbose=F)\n\t\tx.e.test <- aldex.effect(x.test, verbose=F)\n\t\t\n\t\ttest.diff.ol <- rownames(x.e.test)[which(x.e.test$overlap < 0.1)]\n\t\ttest.diff <- rownames(x.e.test)[which(abs(x.e.test$diff.btw) > 1)]\n\t\ttest.diff.eff <- rownames(x.e.test)[which(abs(x.e.test$effect) > 0.5)]\n\t\ttest.diff.eff1 <- rownames(x.e.test)[which(abs(x.e.test$effect) > 1)]\n\t\ttest.diff.eff2 <- rownames(x.e.test)[which(abs(x.e.test$effect) > 2)]\n\t\ttest.triple <- intersect(test.diff.eff1, intersect(test.diff.ol, test.diff))\n\n\t\tsummary.stats[i,1] <- length(test.diff.eff)\n\t\tsummary.stats[i,2] <- length(test.diff.eff1)\n\t\tsummary.stats[i,3] <- length(test.diff.eff2)\n\t\tsummary.stats[i,4] <- length(test.diff.ol)\n\t\tsummary.stats[i,5] <- length(test.diff)\n\t\tsummary.stats[i,6] <- length(test.triple)\n\n\n\t} \n output.summary[[j]] <- summary.stats\n}\nsave(output.summaryFP.yeast, file=paste(my.path,'data/output.summaryFP.yeast.Rda', sep=''))\n}\n\nif(!exists('output.summaryFP.ty')){ \n load(paste(my.path,'data/output.summaryFP.ty.Rda', sep=''))\n}\n\n# original tests done at 100 replicates\ntest.size <- 100\nmax.samples <- 20\nregisterDoMC(6) # this is the MC backend. \n# 4 cores seems to be the performance limit\n# beyond that there is throttling of each thread\n# takes less than an hour on i9 32G \n\nif(run.this == TRUE){\n\noutput.summary <- list()\noutput.summaryFP.ty <- list() # this is the young soldier cohort\n\noutput.summaryFP.ty <- foreach(j = 2:max.samples)%dopar%{\n print(j)\n\tn.samples = j\n\n\tsummary.stats <- matrix(data=NA, ncol=6, nrow=test.size)\n\t\n\tcolnames(summary.stats) <- c('eff.FP.5','eff.FP1','eff.FP2', 'ol.1.FP', 'diff.1.FP', 'triple.FP')\n\t\n\tfor(i in 1:test.size){\n\t\ttest.col <- c(sample(161:373,n.samples *2))\n\t\ttest.conds <- c(rep(\"a\", n.samples), rep(\"b\",n.samples))\n\t\ttest.data <- d[,test.col]\n\t\tx.test <- aldex.clr(test.data, test.conds, verbose=F)\n\t\tx.e.test <- aldex.effect(x.test, verbose=F)\n\t\t\n\t\ttest.diff.ol <- rownames(x.e.test)[which(x.e.test$overlap < 0.1)]\n\t\ttest.diff <- rownames(x.e.test)[which(abs(x.e.test$diff.btw) > 1)]\n\t\ttest.diff.eff <- rownames(x.e.test)[which(abs(x.e.test$effect) > 0.5)]\n\t\ttest.diff.eff1 <- rownames(x.e.test)[which(abs(x.e.test$effect) > 1)]\n\t\ttest.diff.eff2 <- rownames(x.e.test)[which(abs(x.e.test$effect) > 2)]\n\t\ttest.triple <- intersect(test.diff.eff1, intersect(test.diff.ol, test.diff))\n\n\t\tsummary.stats[i,1] <- length(test.diff.eff)\n\t\tsummary.stats[i,2] <- length(test.diff.eff1)\n\t\tsummary.stats[i,3] <- length(test.diff.eff2)\n\t\tsummary.stats[i,4] <- length(test.diff.ol)\n\t\tsummary.stats[i,5] <- length(test.diff)\n\t\tsummary.stats[i,6] <- length(test.triple)\n\n\n\t} \n output.summary[[j]] <- summary.stats\n}\nsave(output.summaryFP.ty, file=paste(my.path,'peerj/data/output.summaryFP.ty.Rda', sep=''))\n}\n\nmed.data.FP.yeast <- matrix(data=NA, nrow=(max.samples - 1), 7)\ncolnames(med.data.FP.yeast) <- c('n', colnames(output.summaryFP.yeast[[1]]))\nupper.data.FP.yeast <- matrix(data=NA, nrow=(max.samples - 1), 7)\n\nfor( i in 1:(max.samples - 1) ){\n med.data.FP.yeast[i,1] <- i+1\n med.data.FP.yeast[i,2:7] <- apply(output.summaryFP.yeast[[i]], 2, median)\n upper.data.FP.yeast[i,1] <- i+1\n upper.data.FP.yeast[i,2:7] <- apply(output.summaryFP.yeast[[i]], 2, function(x) quantile(x, probs=c(0.025, 0.5, 0.95))[3])\n}\n\n# young soldier cohort, youth cohort gives essentially same result\nmed.data.FP.ty <- matrix(data=NA, nrow=(max.samples - 1), 7)\ncolnames(med.data.FP.ty) <- c('n', colnames(output.summaryFP.ty[[1]]))\nupper.data.FP.ty <- matrix(data=NA, nrow=(max.samples - 1), 7)\n\nfor( i in 1:(max.samples - 1) ){\n med.data.FP.ty[i,1] <- i+1\n med.data.FP.ty[i,2:7] <- apply(output.summaryFP.ty[[i]], 2, median)\n upper.data.FP.ty[i,1] <- i+1\n upper.data.FP.ty[i,2:7] <- apply(output.summaryFP.ty[[i]], 2, function(x) quantile(x, probs=c(0.025, 0.5, 0.95))[3])\n}\n\n", "meta": {"hexsha": "0bf75c9574daabc980cd2bc2b89768511595b8b0", "size": 4907, "ext": "r", "lang": "R", "max_stars_repo_path": "peerj/code/FP-only-summary-parallel.r", "max_stars_repo_name": "ggloor/effect", "max_stars_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "peerj/code/FP-only-summary-parallel.r", "max_issues_repo_name": "ggloor/effect", "max_issues_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "peerj/code/FP-only-summary-parallel.r", "max_forks_repo_name": "ggloor/effect", "max_forks_repo_head_hexsha": "1d08fb3518bb012bd2612d15dc6e1cb9c3c67316", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-06-17T01:55:39.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-17T01:55:39.000Z", "avg_line_length": 35.3021582734, "max_line_length": 124, "alphanum_fraction": 0.6617077644, "num_tokens": 1624, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.33486236465043556}} {"text": "consumer_set_of_resource <- function(consumer, resource, inter_type) {\n # The arguments must represent binary interactions identifed by 1 (interaction) or 0 (non-interaction)\n consumer_list <- as.character(unique(consumer))\n cons_res <- matrix(nrow = length(consumer_list), ncol = 3, dimnames = list(c(), c('consumer', 'resource', 'non-resource')))\n cons_res[, 'consumer'] <- consumer_list\n\n for(i in 1:length(consumer_list)){\n yes.resource <- resource[names(which(inter_type[which(consumer == consumer_list[i])] == \"1\"))]\n non.resource <- resource[names(which(inter_type[which(consumer == consumer_list[i])] == \"0\"))]\n cons_res[i , 'resource'] <- paste(unique(yes.resource), collapse = ' | ')\n cons_res[i , 'non-resource'] <- paste(unique(non.resource), collapse = ' | ')\n } #i\n\nreturn(cons_res)\n\n} #function\n", "meta": {"hexsha": "e5c1f46ceedbee51074d6b3ec768e2458298fc41", "size": 828, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/consumer_set_of_resource.r", "max_stars_repo_name": "david-beauchesne/predicting_interactions", "max_stars_repo_head_hexsha": "bcddde0b04325a7c8a64467d4adcf8f13d7208c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-08-12T11:00:10.000Z", "max_stars_repo_stars_event_max_datetime": "2017-03-09T18:16:12.000Z", "max_issues_repo_path": "Script/consumer_set_of_resource.r", "max_issues_repo_name": "david-beauchesne/predicting_interactions", "max_issues_repo_head_hexsha": "bcddde0b04325a7c8a64467d4adcf8f13d7208c5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-08-12T14:42:32.000Z", "max_issues_repo_issues_event_max_datetime": "2016-08-12T15:25:21.000Z", "max_forks_repo_path": "Script/consumer_set_of_resource.r", "max_forks_repo_name": "david-beauchesne/predicting_interactions", "max_forks_repo_head_hexsha": "bcddde0b04325a7c8a64467d4adcf8f13d7208c5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2016-08-12T10:46:53.000Z", "max_forks_repo_forks_event_max_datetime": "2016-08-12T10:46:53.000Z", "avg_line_length": 48.7058823529, "max_line_length": 125, "alphanum_fraction": 0.6896135266, "num_tokens": 210, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.33486236465043556}} {"text": "#\n# Combine the results of the two classification runs with gradient boosted models\n# \n# The population and sampled classifications are in a data frame called \"results\"\n# The tasampled classifications are in a data frame called \"subsets\" since they're subsets of the single large database\n# The combined (non-subsetted) tasampled classification is in a data frame called \"combined_results\"\n#\nlibrary(mmadsenr)\n\nload(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"balancedbias-neutral-comparison-gbm-dfonly.RData\"))\nbalanced_bias_results <- data.frame(bias_results)\nrm(bias_results)\n\nload(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"bias-model-comparisons-gbm-dfonly.RData\"))\n\nbalanced_bias_results$exp_group <- 'Mixed Bias/Neutrality Comparison'\nbias_results$exp_group <- 'Pro/Anti Conformism Comparison'\n\n\nfull_bias_results <- rbind(balanced_bias_results, bias_results)\n\n\n###### Add two useful statistics #######\n\n\nfull_bias_results$fdr <- 1.0 - full_bias_results$ppv\nfull_bias_results$youdensj <- full_bias_results$sensitivity + full_bias_results$specificity - 1.0\n\n\n\n############## Complete Processing and Save Results ##########3\n\n# save objects from the environment\nimage_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"biased-gbm-merged-dfonly.RData\")\nsave(full_bias_results, file=image_file)\n\n\n", "meta": {"hexsha": "fa371023be563ee32c73f662c9c545bf6cb00e0c", "size": 1419, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-4/postsimulation/merge-biascomparison-results.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-4/postsimulation/merge-biascomparison-results.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-4/postsimulation/merge-biascomparison-results.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3421052632, "max_line_length": 139, "alphanum_fraction": 0.7836504581, "num_tokens": 335, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.33468868605387986}} {"text": "b <-\n1:5\n\nprint (\na\n)\n\n1 + 2\n-3", "meta": {"hexsha": "39ff0950201b1518041a48952d93ac3a92288efe", "size": 31, "ext": "r", "lang": "R", "max_stars_repo_path": "testData/parser/r/StatementBreakAssignment.r", "max_stars_repo_name": "DeagleGross/Rplugin", "max_stars_repo_head_hexsha": "8a2cfd87f732e658b3de07a202c058a9a9d63f11", "max_stars_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_stars_count": 171, "max_stars_repo_stars_event_min_datetime": "2015-01-25T11:23:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-14T23:19:46.000Z", "max_issues_repo_path": "testData/parser/r/StatementBreakAssignment.r", "max_issues_repo_name": "DeagleGross/Rplugin", "max_issues_repo_head_hexsha": "8a2cfd87f732e658b3de07a202c058a9a9d63f11", "max_issues_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_issues_count": 224, "max_issues_repo_issues_event_min_datetime": "2015-01-17T04:19:26.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-22T12:19:10.000Z", "max_forks_repo_path": "testData/parser/r/StatementBreakAssignment.r", "max_forks_repo_name": "DeagleGross/Rplugin", "max_forks_repo_head_hexsha": "8a2cfd87f732e658b3de07a202c058a9a9d63f11", "max_forks_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_forks_count": 39, "max_forks_repo_forks_event_min_datetime": "2015-02-10T20:30:59.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-05T10:24:32.000Z", "avg_line_length": 3.4444444444, "max_line_length": 7, "alphanum_fraction": 0.3870967742, "num_tokens": 19, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3346886718591733}} {"text": "#' Cut p-values.\n#'\n#' Cut p-values according to a significance criterium.\n#'\n#' This is a simple internal function for cutting a vector of p-values into\n#' a set of meaningful categories. The categories are taken from the original\n#' book chapter that inspired the package.\n#'\n#' @param p_values A numeric containing the p-values\n#' @return A factor containing the resulting categories.\n#' @keywords internal\ncut_pvalues = function(p_values) {\n result = cut(\n p_values,\n c(-Inf, 1e-10, 1e-5, 0.001, 0.01, 0.05, Inf),\n labels = c('< 1e-10', '< 1e-5', '< 0.001', '< 0.01', '< 0.05', 'NS'),\n include.lowest = TRUE\n )\n return(result)\n}\n\n#' Get p-value from F statistic data.\n#'\n#' Get p-value from F statistic data in the summary of an \\code{lm} object.\n#'\n#' This is an internal function that extracts the p-value from the information\n#' generated by a call to the \\code{summary} function on a \\code{lm} object. It\n#' uses the F statistic value and the degrees of freedom in order to compute the\n#' p-value.\n#'\n#' @param fstat A numeric vector of length three obtained from a linear model\n#' fit.\n#' @return The computed p-value.\n#' @keywords internal\nget_p_value = function(fstat) {\n if (is.numeric(fstat['value'])) {\n p_value = 1 - pf(fstat[['value']], fstat[['numdf']], fstat[['dendf']])\n } else {\n warning('Non-numeric F statistic. Setting p-value to 1.')\n p_value = 1\n }\n return(p_value)\n}\n\n#' Get variable names suitable for analysis.\n#'\n#' Get variable names that fulfill the conditions in order to be tested for\n#' association.\n#'\n#' This is an internal function taking the phenotype data.frame as input. It\n#' returns a character vector containing the names of those variables which can\n#' be tested for association. The current implementation avoids testing on\n#' categorical variables where all elements belong to the same level, or where\n#' every level has only one observation.\n#'\n#' @param pdata A data.frame containing the phenotypical data.\n#' @return A character vector containing the selected variable names.\n#' @keywords internal\nget_var_names = function(pdata) {\n if (!is.data.frame(pdata)) {\n stop('Input must be a data.frame.')\n }\n\n n_levels = sapply(pdata, function(xx) length(unique(xx)))\n var_names = colnames(pdata)[(n_levels > 1 & n_levels < nrow(pdata)) |\n sapply(pdata, is.numeric)]\n\n if (length(var_names) == 0) {\n stop('There are no suitable variables for testing.')\n }\n\n names(var_names) = var_names\n\n return(var_names)\n}\n\n#' Compute associations with phenotypical variables.\n#'\n#' Compute associations with phenotypical and control variables in order to\n#' find out which of them are significant, and thus possibly introducing\n#' some confounding in the data.\n#'\n#' The current implementation allows the user to use two different methods\n#' for testing association: a general linear model (lm) and a Kruskal-Wallis\n#' test. User should be careful, as the Kruskal Wallis test should not be used\n#' for continuous independent variables.\n#'\n#' An association test is performed between every component (Principal Component\n#' or Surrogate Variable) and every phenotype and control variable. P-values are\n#' then collected and stored in a \\code{tbl_df} object describing all the\n#' comparisons.\n#'\n#' This function receives variable names as a parameter.\n#'\n#' @inheritParams compute_significance_data\n#' @param var_names Variable names to be used for calculating the p_values.\n#' @return A data.frame with the results of the association tests.\n#' @importFrom dplyr %>% mutate_\n#' @importFrom tidyr gather_\n#' @importFrom stats lm na.omit pf model.matrix kruskal.test\n#' @keywords internal\ncompute_significance_data_var_names = function(values, pdata, component_names,\n var_names,\n method = c('lm', 'kruskal')) {\n method = match.arg(method)\n\n if (is.null(names(var_names))) {\n names(var_names) = var_names\n }\n\n if (method == 'kruskal') {\n is_col_numeric = vapply(pdata[, var_names], is.numeric, FALSE)\n\n if (any(is_col_numeric)) {\n stop(paste('Kruskal-Wallis method cannot be used when there are numeric',\n 'phenotype variables.'))\n }\n\n get_kruskal_p_value = function(val, pd) {\n kruskal.test(val, pd)[['p.value']]\n }\n pvalues = lapply(var_names,\n function(xx) apply(values, 2,\n get_kruskal_p_value,\n as.factor(pdata[, xx])))\n } else {\n model_fits = lapply(var_names, function(xx) lm(values ~ pdata[, xx]))\n model_summaries = lapply(model_fits, summary)\n\n # Result being a nested list depends on the number of columns of values\n if (ncol(values) == 1) {\n pvalues = lapply(\n model_summaries,\n function(xx) get_p_value(xx[['fstatistic']])\n )\n } else {\n pvalues = lapply(\n model_summaries,\n function(xx) sapply(xx, function(yy) get_p_value(yy[['fstatistic']]))\n )\n }\n }\n p_values_df = data.frame(pvalues, check.names = FALSE)\n\n significance_data = p_values_df %>%\n mutate_('PC' = ~ component_names) %>%\n gather_('Variable', 'P_Value', var_names) %>%\n mutate_('Sig' = ~ cut_pvalues(P_Value))\n\n return(significance_data)\n}\n\n#' Compute associations with phenotypical variables.\n#'\n#' Compute associations with phenotypical and control variables in order to\n#' find out which of them are significant, and thus possibly introducing\n#' some confounding in the data.\n#'\n#' The current implementation allows the user to use two different methods\n#' for testing association: a general linear model (lm) and a Kruskal-Wallis\n#' test. User should be careful, as the Kruskal Wallis test should not be used\n#' for continuous independent variables.\n#'\n#' An association test is performed between every component (Principal Component\n#' or Surrogate Variable) and every phenotype and control variable. P-values are\n#' then collected and stored in a \\code{tbl_df} object describing all the\n#' comparisons.\n#'\n#' @param values A matrix containing either principal components or surrogate\n#' variables.\n#' @param pdata A data.frame containing the phenotypical data for the samples.\n#' @param component_names A character vector containing the component names.\n#' @param method A character indicating the method used for the association\n#' tests.\n#' @return A data.frame with the results of the association tests.\n#' @importFrom dplyr %>% mutate_\n#' @importFrom tidyr gather_\n#' @keywords internal\ncompute_significance_data = function (values, pdata, component_names,\n method = c('lm', 'kruskal')) {\n var_names = get_var_names(pdata)\n\n return(compute_significance_data_var_names(values, pdata, component_names,\n var_names, method))\n}\n\n#' Get control variables.\n#'\n#' Get a matrix containing the signals for the control probes of a given\n#' RGChannelSet object.\n#'\n#' The function \\code{get_control_variables} just takes a \\code{RGChannelSet}\n#' object as input, accesses the values of its control probes, and returns\n#' them as a numeric matrix.\n#'\n#' This is an internal function. Its purpose is to provide the main analysis\n#' functions with more data to test. It is usually useful to test if control\n#' probes are introducing some confounding in the dataset, as it might be\n#' signaling that there are unknown effects in the data.\n#'\n#' @param rgset An RGChannelSet object containing the control data.\n#' @importFrom methods is\n#' @importFrom minfi getControlAddress getRed getGreen\n#' @return A matrix with the values of the control elements.\n#' @keywords internal\nget_control_variables = function(rgset) {\n if (!is(rgset, 'RGChannelSet')) {\n stop('Input must be of RGChannelSet type.')\n }\n\n bc1 = getControlAddress(rgset, controlType = c('BISULFITE CONVERSION I'))\n bc2 = getControlAddress(rgset, controlType = c('BISULFITE CONVERSION II'))\n ext = getControlAddress(rgset, controlType = c('EXTENSION'))\n tr = getControlAddress(rgset, controlType = c('TARGET REMOVAL'))\n hyb = getControlAddress(rgset, controlType = c('HYBRIDIZATION'))\n\n control_names = c(\n 'BSC-I C1 Grn',\n 'BSC-I C2 Grn',\n 'BSC-I C3 Grn',\n 'BSC-I C4 Red',\n 'BSC-I C5 Red',\n 'BSC-I C6 Red',\n 'BSC-II C1 Red',\n 'BSC-II C2 Red',\n 'BSC-II C3 Red',\n 'BSC-II C4 Red',\n 'Target Removal 1 Grn',\n 'Target Removal 2 Grn',\n 'Hyb (Low) Grn',\n 'Hyb (Medium) Grn',\n 'Hyb (High) Grn',\n 'Extension (A) Red',\n 'Extension (T) Red',\n 'Extension (C) Grn',\n 'Extension (G) Grn'\n )\n\n control_signals = rbind(\n getGreen(rgset)[bc1[1:3],],\n getRed(rgset)[bc1[7:9],],\n getRed(rgset)[bc2[1:4],],\n getGreen(rgset)[tr[1:2],],\n getGreen(rgset)[hyb[1:3],],\n getRed(rgset)[ext[1:2],],\n getGreen(rgset)[ext[3:4],]\n )\n dimnames(control_signals) = list(control_names, colnames(rgset))\n\n datac2_m = t(log2(control_signals))\n\n return(datac2_m)\n}\n", "meta": {"hexsha": "4fbe5782b00d17d7ce7a673ee53514acbb139b5c", "size": 9057, "ext": "r", "lang": "R", "max_stars_repo_path": "R/utils.r", "max_stars_repo_name": "Keyeoh/svconfound", "max_stars_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/utils.r", "max_issues_repo_name": "Keyeoh/svconfound", "max_issues_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-09-26T14:16:24.000Z", "max_issues_repo_issues_event_max_datetime": "2017-09-26T14:16:24.000Z", "max_forks_repo_path": "R/utils.r", "max_forks_repo_name": "Keyeoh/svconfound", "max_forks_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.657480315, "max_line_length": 80, "alphanum_fraction": 0.6823451474, "num_tokens": 2294, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33458382489875693}} {"text": "#calculates autofit(p,q)order/arma model,divides list into stationary & non-stationary\r\n\r\nrm(list=ls())\r\nlibrary(itsmr) #adding itsmr package\r\n\r\n\r\nfaana<-\"C:\\\\kindle_patrika\\\\jugnu_bholu\\\\\" #file path for r values \r\nmadhur<-list.files(faana)\r\n\r\nnaringi_firangi<-NULL #complete row information for Stationary model\r\nbunty_bubly<-NULL #for taking all Non-Stationary info\r\n\r\nfor (sangeet in madhur){\r\n\r\n html_page<-substr(sangeet,1,nchar(sangeet)-4) #name of file\r\n compendium<- read.table(paste(faana,sangeet,sep=\"\"),skip=1)\r\n print(paste(\"Reading-------------------------------->\",html_page))\r\n \r\n\r\n #autofit(arma)model & checking for non stationarity\r\n j<-NULL\r\n j<-tryCatch(red_money<-autofit(ts(compendium$V1)),error=function(e)e ) #autofit(arma)model (max likelihood method)\r\n \r\n if(inherits(j,\"simpleError\")){ bunty_bubly<-append(bunty_bubly,html_page);print(paste(\"Non-Stationary-------------------------->\",html_page))}\r\n else{\r\n \r\n autofit_ar_p<-length(red_money$phi) #autoregressive p value\r\n autofit_ma_q<-length(red_money$theta) #moving average q value\r\n autofit_aicc<-red_money$aicc #aicc\r\n\r\n \r\n #joining single line\r\n bumro<-cbind(html_page,autofit_ar_p,autofit_ma_q,autofit_aicc,deparse.level=0)\r\n naringi_firangi<-rbind(naringi_firangi,bumro)\r\n } #non-stationary else \r\n\r\n} #last for\r\n\r\n#only stationary model\r\nif(is.null(naringi_firangi)){write(\"Your data has no Autofit ARMA stationary models\",\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_arma_stationary.txt\") }else{\r\nwrite.table(naringi_firangi,\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_arma_stationary.txt\",col.names=c(\"PDB_file\",\"AutoF_ar_p\",\"AutoF_ma_q\",\"AutoF_aicc\"),row.names=F)\r\n } #else stationary\r\n\r\n#Non-Stationary model\r\nif(is.null(bunty_bubly)){ write(\"Your data has NO Autofit ARMA NON-stationary models\",\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_dhak_dhak_non_stat.txt\") }else{\r\nwrite.table(bunty_bubly,\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\ARMA\\\\Autofit_dhak_dhak_non_stat.txt\",col.names=\"Non-Stationary models\",row.names=F)\r\n } #else non-stationary \r\n\r\n\r\n", "meta": {"hexsha": "39bce7865e5828abba8843f41841db8ac57790d5", "size": 2170, "ext": "r", "lang": "R", "max_stars_repo_path": "I-dataset/Model/ARMA/autofit_ARMA.r", "max_stars_repo_name": "sagarnikam123/bioinfoProject", "max_stars_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "I-dataset/Model/ARMA/autofit_ARMA.r", "max_issues_repo_name": "sagarnikam123/bioinfoProject", "max_issues_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "I-dataset/Model/ARMA/autofit_ARMA.r", "max_forks_repo_name": "sagarnikam123/bioinfoProject", "max_forks_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.4, "max_line_length": 190, "alphanum_fraction": 0.7096774194, "num_tokens": 681, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.6076631698328917, "lm_q1q2_score": 0.33458381707448187}} {"text": "# Scatterplot faceted by payee, colored by account\np <- qplot(date, amount, data = ledger_data, geom = \"point\", colour = account, alpha = I(1/2)) + facet_wrap(~payee)\n\nggsave(file = \"scatter-amount-vs-date-by-account-faceted-by-payee.svg\", plot = p, width = 10, height = 10)\n", "meta": {"hexsha": "3abad3b74a3aa248efa06041fe9adda0f2fa262e", "size": 275, "ext": "r", "lang": "R", "max_stars_repo_path": "Support/lib/r/scatter-amount-vs-date-by-account-facet-payee.r", "max_stars_repo_name": "lifepillar/Ledger.tmbundle", "max_stars_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2015-11-05T08:56:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-07T00:19:12.000Z", "max_issues_repo_path": "Support/lib/r/scatter-amount-vs-date-by-account-facet-payee.r", "max_issues_repo_name": "lifepillar/Ledger.tmbundle", "max_issues_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-10-02T05:58:02.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-02T07:14:12.000Z", "max_forks_repo_path": "Support/lib/r/scatter-amount-vs-date-by-account-facet-payee.r", "max_forks_repo_name": "lifepillar/Ledger.tmbundle", "max_forks_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2016-09-08T18:30:38.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T06:21:52.000Z", "avg_line_length": 55.0, "max_line_length": 115, "alphanum_fraction": 0.6981818182, "num_tokens": 85, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3345838170744818}} {"text": "#' ---\n#' title: \"Script 3 of pipeline: read in workspace from script1, run beta diversity analysis\"\n#' author: \"Francesco Vitali\"\n#' output: html_document\n#' ---\n\n#' ```{r setup library load, include=FALSE}\n\n\nlibrary(phyloseq)\nlibrary(tidyverse)\nlibrary(data.table)\nlibrary(ggsci)\nlibrary(ggpubr)\nlibrary(microbiome)\nlibrary(patchwork)\nlibrary(metagenomeSeq)\nlibrary(vegan)\nlibrary(reshape2)\nlibrary(psych)\nlibrary(corrplot)\nlibrary(rstatix)\nlibrary(pairwiseAdonis)\n\n# red in workspace from script 1\n\nload (\"./WP3WP4_workspace\")\n\npalette_custom <- c(\"#1F78B4\", \"#E31A1C\", \"#FF7F00\", \"#33A02C\") \n\n#' knitr::opts_chunk$set(fig.width=unit(15,\"cm\"), fig.height=unit(11,\"cm\"))\n\n#' ```\n\n#' ```\n\n#' *BACTERIA*\n\n#############\n\n#' **WP3 BACTERIA**\n\n#############\n#' ```{r betadiv bact WP3, include=TRUE, echo = FALSE}\n\n# how to transform data for beta diversity analysis? \n\n\n# calculate the variation in read obtained per sample\nsdt = data.table(as(sample_data(WP3_initial_bact), \"data.frame\"),\n TotalReads = sample_sums(WP3_initial_bact), keep.rownames = TRUE)\nsummary(sdt$TotalReads)\nmax(sdt$TotalReads)/min(sdt$TotalReads)\n# variation in read depth is 7X \n\n# plot reads to find outliers\n\nsdt %>%\n filter(AnimalID != \"E149-40\") %>%\n ggboxplot(.,y = \"TotalReads\")\n\nsdt %>%\n filter(AnimalID != \"E149-40\") %>%\n summary()\n\n\n# NORMALIZATION: open problematic of which to choose\n\n# see https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-017-0237-y\n# see https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003531\n# see https://www.nature.com/articles/s41522-020-00160-w\n\n# new kid on the block! : avgdist() supported by Schloss \n# (see https://www.youtube.com/watch?v=xyufizOpc5I) also (https://www.youtube.com/watch?v=ht3AX5uZTTQ) also (https://www.youtube.com/watch?v=t5qXPIS-ECU)\n# So, the whole thing is about rarefaction or not. He support rarefaction as is the only method that is removing some relation between BC distance and \n# depth of sampling (n reads). To note, however, is that he is not doing the \"classical\" rarefaction (i.e. as in script2.r for alpha-div) using subsampling \n# and replacement, as it is not that good, as it really all depends on the seed and on which sequences get subsampled.\n# Instead, in the videos above, he use a nice combination of rarefaction and vegdist: he use rarefaction analysis followed by BC calculation multiple time, \n# so that the final distance matrix is an average od the distance matrices obtained over the different replicates and the relation with n\u00b0 seqs disappear, but \n# without the risk of normal (single) rarefaction of loosing OTUs only by chance of the random procedure.\n# So, he is using un-rarefied and un-transformed counts, but rather performs this normalization step at the level of distance calculation. \n# From the video, and also conceptually, seems very convincing.\n\n# CSS remain one of the most interesting and supported, but rarefaction is still an option. \n# Evaluate both Bray and Jaccard eventually, in one case to evaluate changes in abundance, in the other\n# changes as presence/absence\n\n# Data driven evaluation: transform with rarefaction and with CSS, calculate bray and jaccard, compare with Mantel\n\n# generate a 10% prevalece filter dataset (see https://www.nature.com/articles/s41467-022-28034-z)\n\n# calculate prevalence for each ASV\nWP3.bact.ASVprev <- prevalence(subset_samples(WP3_initial_bact, AnimalID != \"E149-40\"), detection=0, sort=TRUE, count=F)\nsummary(names(WP3.bact.ASVprev[WP3.bact.ASVprev > 0.1]))\nsummary(names(WP3.bact.ASVprev[WP3.bact.ASVprev < 0.1]))\n# create an object with only the ASV in more than 10% of samples\nWP3_initial_bact_prevalence <- prune_taxa(names(WP3.bact.ASVprev[WP3.bact.ASVprev > 0.1]), subset_samples(WP3_initial_bact, AnimalID != \"E149-40\")) \n#check\nmin(prevalence(WP3_initial_bact_prevalence, detection=0, sort=TRUE, count=F))\n\n# rarefaction on the full dataset\nWP3_raref_bact<- rarefy_even_depth(subset_samples(WP3_initial_bact, AnimalID != \"E149-40\"),\n rngseed=1234,\n sample.size=round(0.99*min(sample_sums(subset_samples(WP3_initial_bact, AnimalID != \"E149-40\")))),\n replace=F)\n\n# rarefaction on the prevalence dataset\nWP3_raref_bact.prevalence<- rarefy_even_depth(subset_samples(WP3_initial_bact_prevalence, AnimalID != \"E149-40\"),\n rngseed=1234,\n sample.size=round(0.99*min(sample_sums(subset_samples(WP3_initial_bact_prevalence, AnimalID != \"E149-40\")))),\n replace=F)\n\n# CSS scaling (not removing singletons) on the full dataset\ndata.metagenomeSeq = phyloseq_to_metagenomeSeq(subset_samples(WP3_initial_bact, AnimalID != \"E149-40\"))\np = cumNormStat(data.metagenomeSeq) #default is 0.5\ndata.cumnorm = cumNorm(data.metagenomeSeq, p=p)\n#data.cumnorm\ndata.CSS = MRcounts(data.cumnorm, norm=TRUE, log=TRUE)\nWP3_CSS_bact <- subset_samples(WP3_initial_bact, AnimalID != \"E149-40\")\notu_table(WP3_CSS_bact) <- otu_table(data.CSS, taxa_are_rows = T)\n\n# CSS scaling (not removing singletons) on the prevalence dataset\ndata.metagenomeSeq = phyloseq_to_metagenomeSeq(subset_samples(WP3_initial_bact_prevalence, AnimalID != \"E149-40\"))\np = cumNormStat(data.metagenomeSeq) #default is 0.5\ndata.cumnorm = cumNorm(data.metagenomeSeq, p=p)\n#data.cumnorm\ndata.CSS = MRcounts(data.cumnorm, norm=TRUE, log=TRUE)\nWP3_CSS_bact.prevalence <- subset_samples(WP3_initial_bact_prevalence, AnimalID != \"E149-40\")\notu_table(WP3_CSS_bact.prevalence) <- otu_table(data.CSS, taxa_are_rows = T)\n\n\n# calculate distance matrices\nbray_WP3_raref <- phyloseq::distance(WP3_raref_bact, \"bray\")\nbray_WP3_CSS <- phyloseq::distance(WP3_CSS_bact, \"bray\")\n\nbray_WP3_raref.prevalence <- phyloseq::distance(WP3_raref_bact.prevalence, \"bray\")\nbray_WP3_CSS.prevalence <- phyloseq::distance(WP3_CSS_bact.prevalence, \"bray\")\n\n# check if order of the matrix is the same\nsummary(melt(as.matrix(bray_WP3_raref))[1] == melt(as.matrix(bray_WP3_CSS))[1])\n\n# perform mantel test\nset.seed(35264)\nmantel(xdis = bray_WP3_CSS, ydis = bray_WP3_raref, permutations = 999, method = \"pearson\")\n\nset.seed(35264)\nmantel(xdis = bray_WP3_CSS, ydis = bray_WP3_CSS.prevalence, permutations = 999, method = \"pearson\")\n\nset.seed(35264)\nmantel(xdis = bray_WP3_raref, ydis = bray_WP3_raref.prevalence, permutations = 999, method = \"pearson\")\n\nmds.css <- monoMDS(bray_WP3_CSS)\nmds.raref <- monoMDS(bray_WP3_raref)\nWP3.bact.proc <- procrustes(mds.raref, mds.css)\nsummary(WP3.bact.proc)\nplot(WP3.bact.proc)\n\n# see if biological interpretation is the same or not\ndf <- as(sample_data(WP3_CSS_bact), \"data.frame\")\nperm <- how(nperm = 999)\nsetBlocks(perm) <- with(df, AnimalID)\n\nset.seed(12387)\nadonis2(bray_WP3_CSS ~ Time * Diet * Sacrif_dfference, data = df, permutations = perm)\nset.seed(12387)\nadonis2(bray_WP3_CSS.prevalence ~ Time * Diet * Sacrif_dfference, data = df, permutations = perm)\n\nset.seed(12387)\nadonis2(bray_WP3_raref ~ Time * Diet * Sacrif_dfference, data = df, permutations = perm)\nset.seed(12387)\nadonis2(bray_WP3_raref.prevalence ~ Time * Diet * Sacrif_dfference, data = df, permutations = perm)\n\n# significance and R2 only show minor changes, but the Mantel correl between the two dist matrices is not high\n# overall, higher R2 in all factors (time diet and sacrifice difference) with CSS.prevalence dataset\n\n# This set the basis of choosing the CSS transformation on the prevalence (10%) filtered dataset as this seems\n# to show higher power to discriminate over the exp factors\n\n# Perform ordination now\n\n# NMDS\nset.seed(34624)\nWP3_bact_ord <- ordinate(WP3_CSS_bact.prevalence, \"NMDS\", distance = \"bray\")\np.WP3_bact_ord <- plot_ordination(physeq = WP3_CSS_bact.prevalence, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8, aes(fill = Diet, shape = Time)) #+ geom_text(mapping = aes(label = Gender), size = 3, nudge_x = 0.015, nudge_y = -0.015)\np.WP3_bact_ord <- p.WP3_bact_ord + theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(21,22,23,24))+\n xlab(\"Dim1\") + ylab(\"Dim2\") + labs( fill = \"Diet\") + theme(legend.position=\"right\")\n\np.WP3_bact_ord\n\ndf <- as(sample_data(WP3_CSS_bact.prevalence), \"data.frame\")\nperm <- how(nperm = 999)\nsetBlocks(perm) <- with(df, AnimalID)\n\nbray_WP3_CSS.prevalence <- phyloseq::distance(WP3_CSS_bact.prevalence, \"bray\")\n\nset.seed(12387)\nadonis2(bray_WP3_CSS.prevalence ~ Time + Diet + Sacrif_dfference, data = df, permutations = perm)\n\n# NMDS with Schloss approach\n\n# starting data is the untransformed DF, will still remove the lower outlier and remove low prevalence OTUs\nphylo_schloss <- subset_samples(WP3_initial_bact_prevalence, AnimalID != \"E149-40\")\notu_table_schloss <- otu_table(phylo_schloss)@.Data\n\notu_table_schloss %>%\n t() %>%\n as.data.frame() -> otu_table_schloss\n\n# adding some metadata\nrow.names(otu_table_schloss) == row.names(sample_data(phylo_schloss))\notu_table_schloss <- cbind(sample_data(phylo_schloss)[,3:4], otu_table_schloss)\n\n# calculate depth\nnSeqs <- min(sample_sums(phylo_schloss))\n\n# calculate rarefaction of BC distance and plot MDS\nset.seed(43655826)\nraref_BC <- avgdist(x = otu_table_schloss[,-c(1,2)], sample = nSeqs, dmethod = \"bray\", iterations = 100)\nset.seed(62853)\nschloss_MDS <- metaMDS(raref_BC)\n\nscores(schloss_MDS) %>%\n as_tibble(rownames = \"Group\") %>%\n ggplot(aes(x = NMDS1, y = NMDS2)) +\n geom_point(size = 5, stroke = 0.8, aes(fill = otu_table_schloss$Diet, shape = otu_table_schloss$Time))+\n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(21,22,23,24))+\n xlab(\"Dim1\") + ylab(\"Dim2\") + labs( fill = \"Diet\") + theme(legend.position=\"right\")\n\n# PCOA\n\nWP3_CSS_bact.prevalenceT3 <- subset_samples(WP3_CSS_bact.prevalence, Time == \"T3\")\n\nWP3_bact_ord <- ordinate(WP3_CSS_bact.prevalenceT3, \"PCoA\", distance = \"bray\")\np.WP3_bact_ord <- plot_ordination(physeq = WP3_CSS_bact.prevalenceT3, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + \n geom_point(size = 3, stroke = 0.8, aes(fill = Diet), shape = 22) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n xlab(\"PCoA1 (17.4%)\") + \n ylab(\"PCoA2 (11.6%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord_1\n\np.WP3_bact_ord -> p.WP3_bact_ord_final\n\n# pairwise adonis\ndf <- as(sample_data(WP3_CSS_bact.prevalenceT3), \"data.frame\")\nd.bacteria = phyloseq::distance(WP3_CSS_bact.prevalenceT3, \"bray\")\nset.seed(12387)\npw_ad_bactT3 <- pairwise.adonis(d.bacteria, factors = df$Diet, p.adjust.m = \"BH\", perm = 999)\n\npw_ad_bactT3$R2\n\n# PCoA using the Schloss avgdist method\nWP3_bact_ord <- ordinate(phylo_schloss, \"PCoA\",distance = raref_BC)\np.WP3_bact_ord <- plot_ordination(physeq = phylo_schloss, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8, aes(fill = Diet, shape = Time)) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(21,22,23,24))+\n xlab(\"PCoA1 (21.2%)\") + \n ylab(\"PCoA2 (8.6%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord_1\n\n\n\n# plot on distance from sacrifice\np.WP3_bact_ord <- plot_ordination(physeq = WP3_CSS_bact.prevalence, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8, aes(fill = Sacrif_dfference, shape = Time)) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_viridis_c(option = \"C\") +\n scale_shape_manual(values = c(21,22,23,24))+\n xlab(\"PCoA1 (17.3%)\") + \n ylab(\"PCoA2 (11.5%)\") + \n labs(fill = \"Days from \\nfirst sacrif.\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord_2\n\np.WP3_bact_ord_1 / p.WP3_bact_ord_2 -> p_suppl_ordi\n\nggsave('./Supplementary figure 2.png', p_suppl_ordi)\n\n# Which component capture best experimental factors?\n\nplot_scree(WP3_bact_ord, title = NULL)\n\n# extract coordinates on the first 20 axis, mount a df with exp factors\ndf <- as(sample_data(WP3_CSS_bact.prevalence), \"data.frame\")\nordi.coordinates <- as.data.frame(WP3_bact_ord$vectors[,1:10])\nrow.names(df) == row.names(ordi.coordinates) # row name is in same order\nordi.coordinates <- cbind(ordi.coordinates, df[,c(3,4,9:14)]) # attach some variables\n\n# BEST AXIS FOR TIME\nordi.coordinates %>%\n wilcox_effsize(Axis.1 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.2 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.3 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.4 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.5 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.6 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.7 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.8 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.9 ~ Time)\nordi.coordinates %>%\n wilcox_effsize(Axis.10 ~ Time)\n\n\nordi.coordinates %>%\n wilcox_test(Axis.1 ~ Time)\nordi.coordinates %>%\n wilcox_test(Axis.2 ~ Time)\n\n# BEST AXIS FOR Diet\nordi.coordinates %>%\n wilcox_effsize(Axis.1 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.2 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.3 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.4 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.5 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.6 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.7 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.8 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.9 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.10 ~ Diet)\n\n\n## Other numeric variables with correlation\n\nordi.coordinates %>%\n select(-c(Diet, start_body_weight, Time)) %>%\n corr.test(scale(.),method = \"spearman\", adjust = \"none\") -> cor_test_mat # Apply corr.test function\n\ncorrplot(as.matrix(cor_test_mat$r), order=\"original\", method = \"number\",tl.cex = 0.8,number.cex = 1, type = \"lower\",\n p.mat = as.matrix(cor_test_mat$p), sig.level = 0.05)\n\n\n\n# Maybe it has more sense to see beta diversity at T3, where the diet should have had an effect\n\nWP3_CSS_bact.prevalence.T3 <- subset_samples(WP3_CSS_bact.prevalence, Time == \"T3\") %>% prune_taxa(taxa_sums(.) > 0, .)\n\n# PCOA\nWP3_bact_ord <- ordinate(WP3_CSS_bact.prevalence.T3, \"PCoA\", distance = \"bray\")\np.WP3_bact_ord <- plot_ordination(physeq = WP3_CSS_bact.prevalence.T3, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8, shape = 21, aes(fill = Diet)) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n xlab(\"PCoA1 (17.4%)\") + \n ylab(\"PCoA2 (11.6%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord.T3\n\np.WP3_bact_ord\n\n## With Schloss\n# starting data is the untransformed DF, will still remove the lower outlier and remove low prevalence OTUs\nphylo_schloss <- subset_samples(subset_samples(WP3_initial_bact_prevalence, Time == \"T3\"), AnimalID != \"E149-40\")\notu_table_schloss <- otu_table(phylo_schloss)@.Data\n\notu_table_schloss %>%\n t() %>%\n as.data.frame() -> otu_table_schloss\n\n# adding some metadata\nrow.names(otu_table_schloss) == row.names(sample_data(phylo_schloss))\notu_table_schloss <- cbind(sample_data(phylo_schloss)[,3:4], otu_table_schloss)\n\n# calculate depth\nnSeqs <- min(sample_sums(phylo_schloss))\n\n# calculate rarefaction of BC distance and plot MDS\nset.seed(43655826)\nraref_BC <- avgdist(x = otu_table_schloss[,-c(1,2)], sample = nSeqs, dmethod = \"bray\", iterations = 100)\n\n# PCoA using the Schloss avgdist method\nWP3_bact_ord <- ordinate(phylo_schloss, \"PCoA\",distance = raref_BC)\n p.WP3_bact_ord <- plot_ordination(physeq = phylo_schloss, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8, aes(fill = Diet, shape = Time)) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(21,22,23,24))+\n xlab(\"PCoA1 (24.1%)\") + \n ylab(\"PCoA2 (12.3%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord_1\n\n\n# see if biological interpretation is the same or not\nbray_WP3_CSS.prevalence.T3 <- phyloseq::distance(WP3_CSS_bact.prevalence.T3, \"bray\")\ndf <- as(sample_data(WP3_CSS_bact.prevalence.T3), \"data.frame\")\nset.seed(12387)\nadonis2(bray_WP3_CSS.prevalence.T3 ~ Diet + Sacrif_dfference + end_body_weight, data = df, permutations = 999)\n\n\n# plot on distance from sacrifice\np.WP3_bact_ord <- plot_ordination(physeq = WP3_CSS_bact.prevalence.T3, WP3_bact_ord, axes = c(1,2))\np.WP3_bact_ord <- p.WP3_bact_ord + geom_point(size = 5, stroke = 0.8,shape = 21, aes(fill = Sacrif_dfference)) \np.WP3_bact_ord <- p.WP3_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_viridis_c(option = \"C\") +\n xlab(\"PCoA1 (17.3%)\") + \n ylab(\"PCoA2 (11.5%)\") + \n labs(fill = \"Days from \\nfirst sacrif.\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP3_bact_ord.T3\n\n# Which component capture best experimental factors?\n\nplot_scree(WP3_bact_ord, title = NULL)\n\n# extract coordinates on the first 20 axis, mount a df with exp factors\ndf <- as(sample_data(WP3_CSS_bact.prevalence.T3), \"data.frame\")\nordi.coordinates <- as.data.frame(WP3_bact_ord$vectors[,1:10])\nrow.names(df) == row.names(ordi.coordinates) # row name is in same order\nordi.coordinates <- cbind(ordi.coordinates, df[,c(4,9:14)]) # attach some variables\n\n# for sacrifice difference and other numeric var, I could do a scatterplot matrix\n\nordi.coordinates %>%\n select(-c(Diet, start_body_weight)) %>%\n corr.test(scale(.),method = \"spearman\", adjust = \"none\") -> cor_test_mat # Apply corr.test function\n\ncorrplot(as.matrix(cor_test_mat$r), order=\"original\", method = \"number\",tl.cex = 0.8,number.cex = 1,\n p.mat = as.matrix(cor_test_mat$p), sig.level = 0.05)\n\n# sacrifice difference strongly correlate with axis.2 \n\n# BEST AXIS FOR Diet\n\n\nordi.coordinates %>%\n wilcox_test(Axis.1 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.2 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.3 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.4 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.5 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.6 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.7 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.8 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.9 ~ Diet)\nordi.coordinates %>%\n wilcox_test(Axis.10 ~ Diet)\n\n# axis 1:5 have a significant anova respect to diet, should calculate which has the higher effect size\n\nordi.coordinates %>%\n wilcox_effsize(Axis.1 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.2 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.3 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.4 ~ Diet)\nordi.coordinates %>%\n wilcox_effsize(Axis.5 ~ Diet)\n\n\n#############\n\n#' **WP4 BACTERIA**\n\n###########\n\n#' ```{r betadiv bact WP4, include=TRUE, echo = FALSE}\n\n# Optimization of transformation and of methods was performed only for WP3, here take the same \n\n# calculate the variation in read obtained per sample\nsdt = data.table(as(sample_data(WP4_initial_bact), \"data.frame\"),\n TotalReads = sample_sums(WP4_initial_bact), keep.rownames = TRUE)\nsummary(sdt$TotalReads)\nmax(sdt$TotalReads)/min(sdt$TotalReads)\n\n# plot reads to find outliers\n\nsdt %>%\n filter(AnimalID != \"WP4-928\") %>%\n ggboxplot(.,y = \"TotalReads\")\n\nsdt %>%\n filter(AnimalID != \"WP4-928\") %>%\n summary()\n\n\n# generate a 10% prevalece filter dataset (see https://www.nature.com/articles/s41467-022-28034-z)\n\n# calculate prevalence for each ASV\nWP4.bact.ASVprev <- prevalence(subset_samples(WP4_initial_bact, AnimalID != \"WP4-928\"), detection=0, sort=TRUE, count=F)\nsummary(names(WP4.bact.ASVprev[WP4.bact.ASVprev > 0.1]))\nsummary(names(WP4.bact.ASVprev[WP4.bact.ASVprev < 0.1]))\n# create an object with only the ASV in more than 10% of samples\nWP4_initial_bact_prevalence <- prune_taxa(names(WP4.bact.ASVprev[WP4.bact.ASVprev > 0.1]), subset_samples(WP4_initial_bact, AnimalID != \"WP4-928\")) \n#check\nmin(prevalence(WP4_initial_bact_prevalence, detection=0, sort=TRUE, count=F))\n\n# CSS scaling (not removing singletons) on the prevalence dataset\ndata.metagenomeSeq = phyloseq_to_metagenomeSeq(subset_samples(WP4_initial_bact_prevalence, AnimalID != \"WP4-928\"))\np = cumNormStat(data.metagenomeSeq) #default is 0.5\ndata.cumnorm = cumNorm(data.metagenomeSeq, p=p)\n#data.cumnorm\ndata.CSS = MRcounts(data.cumnorm, norm=TRUE, log=TRUE)\nWP4_CSS_bact.prevalence <- subset_samples(WP4_initial_bact_prevalence, AnimalID != \"WP4-928\")\notu_table(WP4_CSS_bact.prevalence) <- otu_table(data.CSS, taxa_are_rows = T)\n\n# Perform ordination now\n# PCOA\n\nWP4_bact_ord <- ordinate(WP4_CSS_bact.prevalence, \"PCoA\", distance = \"bray\")\np.WP4_bact_ord <- plot_ordination(physeq = WP4_CSS_bact.prevalence, WP4_bact_ord, axes = c(1,2))\np.WP4_bact_ord <- p.WP4_bact_ord + geom_point(size = 3, stroke = 0.8, aes(fill = Inoculum_diet, shape = Time)) \np.WP4_bact_ord <- p.WP4_bact_ord + \n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(24,21,22,23))+\n xlab(\"PCoA1 (24%)\") + \n ylab(\"PCoA2 (14.5%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) -> p.WP4_bact_ord_1\n\n## Plot with WP3\nfigure2 <- p.WP3_bact_ord_final / p.WP4_bact_ord + plot_layout(guides = \"collect\") & theme(legend.position = 'left') \n\n#ggsave('./Figure 2.png', figure2)\n\n# pairwise adonis\ndf <- as(sample_data(subset_samples(WP4_CSS_bact.prevalence, Time == \"T3\" & Type != \"Inoculum\")), \"data.frame\")\nd.bacteria = phyloseq::distance(subset_samples(WP4_CSS_bact.prevalence, Time == \"T3\" & Type != \"Inoculum\"), \"bray\")\nset.seed(12387)\npw_ad_bactWP4 <- pairwise.adonis(d.bacteria, factors = df$Diets, p.adjust.m = \"BH\", perm = 999)\n\nmat1 <- data.frame(matrix(nrow=4,ncol=4,byrow=TRUE))\n\nmat1[1,1] <- 0\nmat1[1,2] <- pw_ad_bactT3$R2[1]\nmat1[1,3] <-pw_ad_bactT3$R2[2]\nmat1[1,4] <-pw_ad_bactT3$R2[4]\nmat1[2,1] <- pw_ad_bactT3$R2[1]\nmat1[2,2] <- 0\nmat1[2,3] <-pw_ad_bactT3$R2[4]\nmat1[2,4] <-pw_ad_bactT3$R2[5]\nmat1[3,1] <-pw_ad_bactT3$R2[2] \nmat1[3,2] <- pw_ad_bactT3$R2[4]\nmat1[3,3] <- 0\nmat1[3,4] <-pw_ad_bactT3$R2[6]\nmat1[4,1] <- pw_ad_bactT3$R2[3]\nmat1[4,2] <- pw_ad_bactT3$R2[5]\nmat1[4,3] <-pw_ad_bactT3$R2[6]\nmat1[4,4] <- 0\n\ncolnames(mat1) <- c(\"MBDT\", \"PVD\", \"MBD\", \"CTR\")\nrow.names(mat1) <- c(\"MBDT\", \"PVD\", \"MBD\", \"CTR\")\n\ncorrplot(as.matrix(mat1), type = \"lower\", diag = F, method = 'number', col = \"black\")\n\n###########\n\n#' **WP3 WP4 BACTERIA**\n\n###########\n\n# here analysis of WP3 at T3 and WP4 all timepoint, to highlight the \"passage\" in the FTM\n# optionally, one could select only the ID that actually were used for the pool\n\nWP3WP4_initial_bact <- subset_samples(phyloseq_obj_initial_bact, \n AnimalID != \"E149-40\" & AnimalID != \"WP4-928\") %>% prune_taxa(taxa_sums(.) > 0, .)\n\n\n\n# select sample subset of interest (i.e. only T3 of WP3)\n\nWP3WP4_initial_bact <- subset_samples(WP3WP4_initial_bact, WP != \"WP3\" | Time != \"T0\")\n\n# obtain rarefied object for alpha diversity\n\nWP3WP4_raref_bact<- rarefy_even_depth(WP3WP4_initial_bact,\n rngseed=1234,\n sample.size=round(0.99*min(sample_sums(WP3WP4_initial_bact))),\n replace=F)\n\n# calculate the variation in read obtained per sample\nsdt = data.table(as(sample_data(WP3WP4_initial_bact), \"data.frame\"),\n TotalReads = sample_sums(WP3WP4_initial_bact), keep.rownames = TRUE)\nsummary(sdt$TotalReads)\nmax(sdt$TotalReads)/min(sdt$TotalReads)\n\n# plot reads to find outliers\n\nsdt %>%\n ggboxplot(.,y = \"TotalReads\")\n\nsdt %>%\n summary()\n\n# generate a 10% prevalece filter dataset (see https://www.nature.com/articles/s41467-022-28034-z)\n\n# calculate prevalence for each ASV\nWP34.bact.ASVprev <- prevalence(WP3WP4_initial_bact, detection=0, sort=TRUE, count=F)\nsummary(names(WP34.bact.ASVprev[WP34.bact.ASVprev > 0.1]))\nsummary(names(WP34.bact.ASVprev[WP34.bact.ASVprev < 0.1]))\n# create an object with only the ASV in more than 10% of samples\nWP34_initial_bact_prevalence <- prune_taxa(names(WP34.bact.ASVprev[WP34.bact.ASVprev > 0.1]), WP3WP4_initial_bact) \n#check\nmin(prevalence(WP34_initial_bact_prevalence, detection=0, sort=TRUE, count=F))\n\n# CSS scaling (not removing singletons) on the prevalence dataset\ndata.metagenomeSeq = phyloseq_to_metagenomeSeq(WP34_initial_bact_prevalence)\np = cumNormStat(data.metagenomeSeq) #default is 0.5\ndata.cumnorm = cumNorm(data.metagenomeSeq, p=p)\n#data.cumnorm\ndata.CSS = MRcounts(data.cumnorm, norm=TRUE, log=TRUE)\nWP34_CSS_bact.prevalence <- WP34_initial_bact_prevalence\notu_table(WP34_CSS_bact.prevalence) <- otu_table(data.CSS, taxa_are_rows = T)\n\nsample_data(WP34_CSS_bact.prevalence)$Time\n\nWP34_bact_ord <- ordinate(WP34_CSS_bact.prevalence, \"PCoA\", distance = \"bray\")\np.WP34_bact_ord <- plot_ordination(physeq = WP34_CSS_bact.prevalence, WP34_bact_ord, axes = c(1,2))\np.WP34_bact_ord <- p.WP34_bact_ord + \n geom_point(size = 3, stroke = 0.8, aes(fill = Diets, shape = Time))+\n #geom_line(aes(group = AnimalID)) + \n geom_vline(xintercept= 0, linetype=\"dotted\") +\n geom_hline(yintercept= 0, linetype=\"dotted\") +\n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(24,21,22,25))+\n xlab(\"PCoA1 (24.1%)\") + \n ylab(\"PCoA2 (11.9%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) + \n theme(legend.position=\"none\")\n\np.WP34_bact_ord\n\n# Calculate alpha-div\n# richness\nalphadiv_WP34_bact <- microbiome::richness(WP3WP4_raref_bact)\ndf <- as(sample_data(WP3WP4_raref_bact), \"data.frame\")\ndf <- cbind(df, alphadiv_WP34_bact)\n\nrow.names(alphadiv_WP34_bact$vectors) == row.names(df)\n\ndf <- cbind(df, WP34_bact_ord$vectors[,1:2])\n\nggscatter(data = df, x = \"Axis.1\", y = \"observed\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA1\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggscatter(data = df, x = \"Axis.2\", y = \"observed\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA2\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggboxplot(data = df, x = \"WP\", y = \"observed\", add = \"jitter\", \n fill = \"WP\", palette =\"grey\", rotate = F, dot.size = 4) + theme_bw() + \n stat_compare_means(label.y = 100) +\n #ggtitle(label = \"Bacteria\", subtitle = \"Richness (n\u00b0 of ASVs)\") +\n xlab(\"\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\") + \n coord_flip() + \n theme(legend.position=\"none\") -> sottoplot_b_ord\n\n\n# evenness\nalphadiv_WP34_bact <- microbiome::evenness(WP3WP4_raref_bact)\ndf <- as(sample_data(WP3WP4_raref_bact), \"data.frame\")\ndf <- cbind(df, alphadiv_WP34_bact)\n\nrow.names(alphadiv_WP34_bact$vectors) == row.names(df)\n\ndf <- cbind(df, WP34_bact_ord$vectors[,1:2])\n\nggscatter(data = df, x = \"Axis.1\", y = \"pielou\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA1\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggscatter(data = df, x = \"Axis.2\", y = \"pielou\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA2\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggboxplot(data = df, x = \"WP\", y = \"pielou\", add = \"jitter\", \n fill = \"WP\", palette =\"grey\", rotate = F, dot.size = 4) + theme_bw() + \n stat_compare_means(label.y = .3, label.x = 2) +\n #ggtitle(label = \"Bacteria\", subtitle = \"Richness (n\u00b0 of ASVs)\") +\n xlab(\"\") + \n ylab(\"Pielou's Evenness\") + \n coord_flip() + \n theme(legend.position=\"none\") -> sottoplot_b_ord_eve\n\n\n## Plot with other single \n\nfigure2 <- p.WP34_bact_ord + p.WP3_bact_ord_final/p.WP4_bact_ord + plot_layout(guides = \"collect\") & theme(legend.position = 'bottom')\n\n#ggsave('./Figure 2.png', figure2)\n\nlayout <- \"\nA\nA\nA\nB\nC\n\"\n\np.WP34_bact_ord / sottoplot_b_ord / sottoplot_b_ord_eve+ \n plot_layout(design = layout) -> panel_ord_bact\n\n#####\n\n#' *FUNGI*\n\n#' **WP3 FUNGI**\n\n#' ```{r betadiv fungi WP3, include=TRUE, echo = FALSE}\n\n\n#' **WP4 FUNGI**\n\n#' ```{r betadiv fungi WP4, include=TRUE, echo = FALSE}\n\n# how to transform data for alpha diversity analysis? \n\n#########################\n#' **WP3 WP4 FUNGI**\n######################\n\n# here analysis of WP3 at T3 and WP4 all timepoint, to highlight the \"passage\" in the FTM\n# optionally, one could select only the ID that actually were used for the pool\n\nWP3WP4_initial_fung <- subset_samples(phyloseq_obj_initial_fungi, \n AnimalID != \"E149-40\" & AnimalID != \"WP4-928\") %>% prune_taxa(taxa_sums(.) > 0, .)\n\n# select sample subset of interest (i.e. only T3 of WP3)\n\nWP3WP4_initial_fung <- subset_samples(WP3WP4_initial_fung, WP != \"WP3\" | Time != \"T0\")\n\n# obtain rarefied object for alpha diversity\n\nWP3WP4_raref_fung<- rarefy_even_depth(WP3WP4_initial_fung,\n rngseed=1234,\n sample.size=round(0.99*min(sample_sums(WP3WP4_initial_fung))),\n replace=F)\n\n\n# calculate the variation in read obtained per sample\nsdt = data.table(as(sample_data(WP3WP4_initial_fung), \"data.frame\"),\n TotalReads = sample_sums(WP3WP4_initial_fung), keep.rownames = TRUE)\nsummary(sdt$TotalReads)\nmax(sdt$TotalReads)/min(sdt$TotalReads)\n\n# plot reads to find outliers\n\nsdt %>%\n ggboxplot(.,y = \"TotalReads\")\n\nsdt %>%\n summary()\n\n# generate a 10% prevalece filter dataset (see https://www.nature.com/articles/s41467-022-28034-z)\n\n# calculate prevalence for each ASV\nWP34.fung.ASVprev <- prevalence(WP3WP4_initial_fung, detection=0, sort=TRUE, count=F)\nsummary(names(WP34.fung.ASVprev[WP34.fung.ASVprev > 0.01]))\nsummary(names(WP34.fung.ASVprev[WP34.fung.ASVprev < 0.01]))\n# create an object with only the ASV in more than 10% of samples\nWP34_initial_fung_prevalence <- prune_taxa(names(WP34.fung.ASVprev[WP34.fung.ASVprev > 0.1]), WP3WP4_initial_fung) \n#check\nmin(prevalence(WP34_initial_fung_prevalence, detection=0, sort=TRUE, count=F))\n\n# CSS scaling (not removing singletons) on the prevalence dataset\ndata.metagenomeSeq = phyloseq_to_metagenomeSeq(WP34_initial_fung_prevalence)\np = cumNormStat(data.metagenomeSeq) #default is 0.5\ndata.cumnorm = cumNorm(data.metagenomeSeq, p=p)\n#data.cumnorm\ndata.CSS = MRcounts(data.cumnorm, norm=TRUE, log=TRUE)\nWP34_CSS_fung.prevalence <- WP34_initial_fung_prevalence\notu_table(WP34_CSS_fung.prevalence) <- otu_table(data.CSS, taxa_are_rows = T)\n\nsample_data(WP34_CSS_fung.prevalence)$Time\n\n \n# Ordination and plot\n \nWP34_fung_ord <- ordinate(WP34_CSS_fung.prevalence, \"PCoA\", distance = \"bray\")\np.WP34_fung_ord <- plot_ordination(physeq = WP34_CSS_fung.prevalence, WP34_fung_ord, axes = c(1,2))\np.WP34_fung_ord <- p.WP34_fung_ord + \n geom_point(size = 3, stroke = 0.8, aes(fill = Diets, shape = Time))+\n #geom_line(aes(group = AnimalID)) + \n geom_vline(xintercept= 0, linetype=\"dotted\") +\n geom_hline(yintercept= 0, linetype=\"dotted\") +\n theme_bw() + \n theme( panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) + \n scale_fill_manual(values = palette_custom) +\n scale_shape_manual(values = c(24,21,22,25))+\n xlab(\"PCoA1 (40.9%)\") + \n ylab(\"PCoA2 (5.4%)\") + \n labs(fill = \"Diet\", shape = \"Time\") + \n theme(legend.position=\"right\") +\n guides(fill = guide_legend(override.aes = list(shape = 23))) + \n theme(legend.position=\"none\")\n\np.WP34_fung_ord\n\n\n# Calculate alpha-div\nalphadiv_WP34_fung <- microbiome::richness(WP3WP4_raref_fung)\ndf <- as(sample_data(WP3WP4_raref_fung), \"data.frame\")\ndf <- cbind(df, alphadiv_WP34_fung)\n\nrow.names(WP34_fung_ord$vectors) == row.names(df)\n\ndf <- cbind(df, WP34_fung_ord$vectors[,1:2])\n\nggscatter(data = df, x = \"Axis.1\", y = \"observed\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA1\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggscatter(data = df, x = \"Axis.2\", y = \"observed\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA2\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\")\n\nggboxplot(data = df, x = \"WP\", y = \"observed\", add = \"jitter\", \n fill = \"WP\", palette =\"grey\", rotate = F, dot.size = 4) + theme_bw() + \n stat_compare_means(label.y = 90) +\n #ggtitle(label = \"Fungi\", subtitle = \"Richness (n\u00b0 of ASVs)\") +\n xlab(\"\") + \n ylab(\"Richness (n\u00b0 of observed ASVs)\") + \n coord_flip() + \n theme(legend.position=\"none\") -> sottoplot_f_ord\n\n\n# evenness\nalphadiv_WP34_fung <- microbiome::evenness(WP3WP4_raref_fung)\ndf <- as(sample_data(WP3WP4_raref_fung), \"data.frame\")\ndf <- cbind(df, alphadiv_WP34_fung)\n\nrow.names(alphadiv_WP34_fung$vectors) == row.names(df)\n\ndf <- cbind(df, WP34_fung_ord$vectors[,1:2])\n\nggscatter(data = df, x = \"Axis.1\", y = \"pielou\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA1\") + \n ylab(\"Pielou's Evenness\")\n\nggscatter(data = df, x = \"Axis.2\", y = \"pielou\", add = \"reg.line\", add.params = list(linetype = \"dotted\")) + \n stat_cor(method = \"pearson\") + \n xlab(\"PCoA2\") + \n ylab(\"Pielou's Evenness\")\n\nggboxplot(data = df, x = \"WP\", y = \"pielou\", add = \"jitter\", \n fill = \"WP\", palette =\"grey\", rotate = F, dot.size = 4) + theme_bw() + \n stat_compare_means(label.y = .2, label.x = 2) +\n #ggtitle(label = \"Bacteria\", subtitle = \"Richness (n\u00b0 of ASVs)\") +\n xlab(\"\") + \n ylab(\"Pielou's Evenness\") + \n coord_flip() + \n theme(legend.position=\"none\") -> sottoplot_f_ord_eve\n\n\n \nlayout <- \"\nA\nA\nA\nB\nC\n\"\n\np.WP34_fung_ord / sottoplot_f_ord / sottoplot_f_ord_eve+ \n plot_layout(design = layout) -> panel_ord_fung\n\n\nggarrange(panel_ord_bact, panel_ord_fung, \n labels = c(\"A\", \"B\"),\n ncol = 2) -> figure2\n\n\nggsave('./Figure 2.png', figure2)\n\n#' ```\n\n#' ```{r write images, include=FALSE}\n#' knitr::opts_chunk$set(echo = TRUE)\n\n# use ggsave for images\n\n#' ```\n\n", "meta": {"hexsha": "5d7592c4a85fcdf9ff75792f93f37487d1565313", "size": 35478, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Script3.r", "max_stars_repo_name": "FrancescoVit/TargetMeta_pipeline", "max_stars_repo_head_hexsha": "96f97e967f76de96e4845fa00acd7c6f792571e6", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Script3.r", "max_issues_repo_name": "FrancescoVit/TargetMeta_pipeline", "max_issues_repo_head_hexsha": "96f97e967f76de96e4845fa00acd7c6f792571e6", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, 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YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3345838170744818}} {"text": "\nget_means<-function(b_data){\n assert_that(is.data.frame(b_data))\n \n means<-numeric(length(b_data[1,]))\n for(i in 1:length(means)){\n means[i]<-mean(b_data[,i])\n }\n means\n}\n\nget_sv_success_rate<-function(b_data){\n assert_that(is.data.frame(b_data))\n \n succ<-numeric(length(names(b_data))-2)\n for(i in 1:length(b_data[,1])){\n \n methods_results<-sort(b_data[i,3:length(names(b_data))])\n \n for(j in 1:length(succ)){\n if(b_data[[i,2]]==methods_results[j]){\n succ[j] <- succ[j]+1\n break\n }\n }\n }\n cum_succ <- cumsum(succ)\n succ_rate<-round(cum_succ/length(b_data[,1])*100,digits = 2)\n \n df<-data.frame(\"N-te beste Sch\\344tzung\" = 1:length(succ),\n \"# ausgew\\344hlt\"=succ,\n \"# ausgew\\344hlt (kumuliert)\"=cum_succ,\n \"Kumulierte Erfolgsrate\"=paste(succ_rate,\"%\",sep = \"\"),\n check.names = FALSE)\n \n return (df)\n}\n\nget_counts<-function(b_data){\n assert_that(is.data.frame(b_data))\n \n counts<-matrix(data = 0,nrow = 2,ncol = length(b_data[1,])-2)\n for(i in 1:length(b_data$oracle)){\n for (j in 3:length(b_data[1,])) {\n if(b_data$oracle[i]==b_data[i,j]){\n counts[1,j-2] <- counts[1,j-2]+1\n }\n if(b_data$synth_validation[i]==b_data[i,j]){\n counts[2,j-2] <- counts[2,j-2]+1\n }\n }\n }\n counts[1,] <- counts[1,]/sum(counts[1,]) \n counts[2,] <- counts[2,]/sum(counts[2,]) \n return (counts)\n}", "meta": {"hexsha": "132509da8170f5b57f2cd063a022bb60be8d1a38", "size": 1442, "ext": "r", "lang": "R", "max_stars_repo_path": "src/R/src/benchmark_analysis.r", "max_stars_repo_name": "naskoD/bachelorThesis", "max_stars_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-04T09:53:36.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-04T09:53:36.000Z", "max_issues_repo_path": "src/R/src/benchmark_analysis.r", "max_issues_repo_name": "naskoD/bachelorThesis", "max_issues_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/R/src/benchmark_analysis.r", "max_forks_repo_name": "naskoD/bachelorThesis", "max_forks_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.75, "max_line_length": 72, "alphanum_fraction": 0.5755894591, "num_tokens": 464, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6297746213017459, "lm_q1q2_score": 0.3345421819483951}} {"text": "a<-Read.size.preference()\na<-subset(a,size.model==11,select=c( Species ,size.ratio, size.var))\na$size.ratio<-round(a$size.ratio,2)\na$size.var<-round(a$size.var,2)\nnames(a)<-c('Species','mean','variance')\na\n", "meta": {"hexsha": "3f5c2fb37d38e3ab5c4eba35aa808206634e7246", "size": 206, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/size_pref_table.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/size_pref_table.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/size_pref_table.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4285714286, "max_line_length": 68, "alphanum_fraction": 0.6941747573, "num_tokens": 70, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.334542181948395}} {"text": "#\n# Copyright 2022 Erwan Mahe (github.com/erwanM974)\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nrm(list=ls())\n# ==============================================\nlibrary(ggplot2)\nlibrary(scales)\n# ==============================================\n\n# ==============================================\nread_sat_report <- function(file_path) {\n # ==============================================\n report <- read.table(file=file_path, \n header = FALSE, \n sep = \",\",\n blank.lines.skip = TRUE, \n fill = TRUE)\n \n names(report) <- as.matrix(report[1, ])\n report <- report[-1, ]\n report[] <- lapply(report, function(x) type.convert(as.character(x)))\n report\n}\n# ==============================================\n\n\ngeom_ptsize = 1\ngeom_stroke = 1\ngeom_shape = 19\n\nmake_plot_diagram <- function(report_file_str,plot_title_str) {\n report_data <- read_sat_report(report_file_str)\n \n g <- ggplot(report_data, aes(x=varisat_time, y=hibou_time, color=varisat_res)) +\n geom_point(size = geom_ptsize, stroke = geom_stroke, shape = geom_shape) + \n labs(colour = \"isSAT\", x = \"varisat time\", y = \"hibou time\") +\n ggtitle(plot_title_str) +\n theme(plot.title = element_text(margin = margin(b = -25)),\n axis.title.x = element_text(margin = margin(t = 5)),\n axis.title.y = element_text(margin = margin(r = 5)))\n g\n (g + scale_color_manual(values=c(\"False\" = \"red\", \"True\" = \"blue\")))\n}\n\nmake_plot_diagram(\"./satbenchmark_hibou/sat_membership_experiment_mahe.csv\", \n \"Custom\")\n\nmake_plot_diagram(\"./satbenchmark_hibou/sat_membership_experiment_uf20.csv\",\n \"UF20\")\n\nuf20 <- read_sat_report(\"./satbenchmark_hibou/sat_membership_experiment_uf20.csv\")\n\n\nsummary(uf20$varisat_time)\nsummary(uf20$hibou_time)\nsd(uf20$varisat_time)\nsd(uf20$hibou_time)\n\n", "meta": {"hexsha": "e2e99991b499abf4dd834a4f68f365cc9d05f33b", "size": 2370, "ext": "r", "lang": "R", "max_stars_repo_path": "compare_varisat_hibou.r", "max_stars_repo_name": "erwanM974/hibou_3sat_benchmark_experiment", "max_stars_repo_head_hexsha": "20659a6c9bd97fa69e147356efa45608f31a0eae", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "compare_varisat_hibou.r", "max_issues_repo_name": "erwanM974/hibou_3sat_benchmark_experiment", "max_issues_repo_head_hexsha": "20659a6c9bd97fa69e147356efa45608f31a0eae", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "compare_varisat_hibou.r", "max_forks_repo_name": "erwanM974/hibou_3sat_benchmark_experiment", "max_forks_repo_head_hexsha": "20659a6c9bd97fa69e147356efa45608f31a0eae", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.347826087, "max_line_length": 82, "alphanum_fraction": 0.6088607595, "num_tokens": 568, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6297746143530797, "lm_q1q2_score": 0.3345421782571985}} {"text": "centroestim <- function (data, cl, resTest, nb_markers_selection, nb_markers_max_perClass, markers_cutoff_auc, add_markers = NULL,\nmarkers_cutoff_pval_anovatest = 0.05, markers_pval_anovatest_fdr = TRUE)\n{\n cl = as.factor(as.character(cl))\n nbclasses = length(levels(cl))\n if (nb_markers_selection == \"custom\") {\n marksList = lapply(levels(cl), function(aclass) {\n FC = resTest[resTest[, paste(\"AUC\", aclass, sep = \".\")] >=\n markers_cutoff_auc, ]\n rownames(FC)[order(FC[, paste(\"logFC\", aclass, sep = \".\")],\n decreasing = T)][1:min(nrow(FC), nb_markers_max_perClass)]\n })\n names(marksList) = levels(cl)\n if (!is.null(add_markers)) {\n marksList$add = add_markers\n }\n }\n \n if (nb_markers_selection == \"optim_kappa\") {\n bornMIN <- bornMinCN(data, cl, markers_cutoff_pval_anovatest, markers_pval_anovatest_fdr)\n allmarksList = lapply(bornMIN:200, function(x) {\n marksList = lapply(levels(cl), function(aclass) {\n resTest <- resTest[order(resTest[, paste(\"AUC\",\n aclass, sep = \".\")], resTest[, paste(\"logFC\",\n aclass, sep = \".\")], decreasing = T), ]\n rownames(resTest)[1:x]\n })\n names(marksList) = levels(cl)\n return(marksList)\n })\n allmarks = lapply(allmarksList, function(x) {xtemp = as.character(unique(unlist(x))); xtemp[!is.na(xtemp)]})\n allcondnumber <- lapply(allmarks, function(marks) {\n centroid = data.frame(t(apply(data[marks, ], 1, function(x) tapply(x,\n cl, mean))))\n kappa(centroid)\n })\n sumcn <- data.frame(geneNumber=unlist(lapply(allmarks, length)), conditionNumber=unlist(allcondnumber))\n write.table(sumcn, file=\"table_with_allConditionNumber.txt\", sep=\"\\t\", row.names=FALSE)\n bestmarks <- allmarks[[which.min(allcondnumber)]]\n if (is.null(add_markers)) {\n centroid = data.frame(t(apply(data[bestmarks, ],\n 1, function(x) tapply(x, cl, mean))))\n marksList = allmarksList[[which.min(allcondnumber)]]\n }\n else {\n add_markers_present = intersect(rownames(data), add_markers)\n if (length(intersect(rownames(data), add_markers)) ==\n 0) {\n centroid = data.frame(t(apply(data[bestmarks,\n ], 1, function(x) tapply(x, cl, mean))))\n marksList = allmarksList[[which.min(allcondnumber)]]\n }\n else {\n centroid = data.frame(t(apply(data[unique(c(bestmarks,\n add_markers_present)), ], 1, function(x) tapply(x,\n cl, mean))))\n marksList = allmarksList[[which.min(allcondnumber)]]\n marksList$add = add_markers_present\n }\n }\n }\n if (sum(is.na(marksList))>0) {\n popweak = names(marksList)[which(is.na(marksList))]\n #warning(paste(\"Population\", popweak, \"has no specific markers and has been removed.\", sep=\" \"))\n names(cl) = colnames(data)\n cl = cl[-which(cl %in% popweak)]\n data = data[,names(cl)]\n cl = as.factor(as.character(cl))\n marksList = marksList[levels(cl)]\n }\n centroid = data.frame(t(apply(data[unique(unlist(marksList)),\n ], 1, function(x) tapply(x, cl, mean))))\n return(list(centroid = centroid, marksList = marksList, cl = cl, data=data))\n}\n", "meta": {"hexsha": "314b2d90b5f0f8aa1b4c3b7b5cc4380aa483581a", "size": 3472, "ext": "r", "lang": "R", "max_stars_repo_path": "R/centroestim.r", "max_stars_repo_name": "YunaBlum/WISP", "max_stars_repo_head_hexsha": "8a6bdd162ce26f9b729da8e3da7b3788fda84a9b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-08-08T08:35:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-17T14:27:57.000Z", "max_issues_repo_path": "R/centroestim.r", "max_issues_repo_name": "YunaBlum/WISP", "max_issues_repo_head_hexsha": "8a6bdd162ce26f9b729da8e3da7b3788fda84a9b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/centroestim.r", "max_forks_repo_name": "YunaBlum/WISP", "max_forks_repo_head_hexsha": "8a6bdd162ce26f9b729da8e3da7b3788fda84a9b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.2933333333, "max_line_length": 130, "alphanum_fraction": 0.577764977, "num_tokens": 873, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.668880247169804, "lm_q2_score": 0.5, "lm_q1q2_score": 0.334440123584902}} {"text": "shinyServer(\n server <- function(input, output) {\n output$tocke <- renderPlot({\n ggplot(podatki %>% filter(Draws >= input$izenaceni), aes(x = Team, y = Points)) + \n scale_fill_continuous(low = \"#69b8f6\", high = \"#142d45\") + \n geom_bar(stat =\"identity\") + \n theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +\n ggtitle(\"Katere ekipe imajo ve\u010d izena\u010denih izidov\")})\n \n names(podatki)[11] <- \"Win_Lose\"\n \n output$goli <- renderPlot({\n ggplot(podatki %>% filter(Win_Lose >= input$zmage), aes(x = Team, y = Goals_per_match), color=\"Blue\") + \n scale_fill_continuous(low = \"#69b8f6\", high = \"#142d45\") + \n geom_bar(stat =\"identity\") + \n theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +\n ggtitle(\"Goli na tekmo\")})\n }\n)", "meta": {"hexsha": "d8c162357752ff36f75542b6aeca84b2080aab14", "size": 815, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/server.r", "max_stars_repo_name": "stifler9/ANPP-2015-16", "max_stars_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/server.r", "max_issues_repo_name": "stifler9/ANPP-2015-16", "max_issues_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2015-11-28T13:07:49.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-26T13:14:41.000Z", "max_forks_repo_path": "analiza/server.r", "max_forks_repo_name": "stifler9/ANPP-2015-16", "max_forks_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.8947368421, "max_line_length": 110, "alphanum_fraction": 0.5914110429, "num_tokens": 258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3343931359885089}} {"text": "library(yaml)\nlibrary(ncdf4)\nlibrary(SPEI)\nlibrary(RColorBrewer) # nolint\n\ngetnc <- function(yml, m, lat = FALSE) {\n id <- nc_open(yml[m][[1]]$filename, readunlim = FALSE)\n if (lat){\n v <- ncvar_get(id, \"lat\")\n }else{\n v <- ncvar_get(id, yml[m][[1]]$short_name)\n }\n nc_close(id)\n return(v)\n}\n\nncwritenew <- function(yml, m, hist, wdir, bins){\n fnam <- strsplit(yml[m][[1]]$filename, \"/\")[[1]]\n pcs <- strsplit(fnam[length(fnam)], \"_\")[[1]]\n pcs[which(pcs == yml[m][[1]]$short_name)] <- \"spi\"\n onam <- paste(pcs, collapse = \"_\")\n onam <- paste0(wdir, \"/\", strsplit(onam, \".nc\"), \"_hist.nc\")\n ncid_in <- nc_open(yml[m][[1]]$filename)\n var <- ncid_in$var[[yml[m][[1]]$short_name]]\n xdim <- ncid_in$dim[[\"lon\"]]\n ydim <- ncid_in$dim[[\"lat\"]]\n hdim <- ncdim_def(\"bins\", \"level\", bins[1:(length(bins) - 1)])\n hdim2 <- ncdim_def(\"binsup\", \"level\", bins[2:length(bins)])\n var_hist <- ncvar_def(\"hist\", \"counts\", list(xdim, ydim, hdim), NA)\n idw <- nc_create(onam, var_hist)\n ncvar_put(idw, \"hist\", hist)\n nc_close(idw)\n return(onam)\n}\n\nwhfcn <- function(x, ilow, ihigh){\n return(length(which(x >= ilow & x < ihigh)))\n}\n\nargs <- commandArgs(trailingOnly = TRUE)\nparams <- read_yaml(args[1])\nmetadata <- read_yaml(params$input_files)\nmodfile <- names(metadata)\nwdir <- params$work_dir\nrundir <- params$run_dir\ndir.create(wdir, recursive = TRUE)\npdir <- params$plot_dir\ndir.create(pdir, recursive = TRUE)\nvar1_input <- read_yaml(params$input_files[1])\nnmods <- length(names(var1_input))\n\n# setup provenance file and list\nprovenance_file <- paste0(rundir, \"/\", \"diagnostic_provenance.yml\")\nprovenance <- list()\n\nhistbrks <- c(-99999, -2, -1.5, -1, 1, 1.5, 2, 99999)\nhistnams <- c(\"Extremely dry\", \"Moderately dry\", \"Dry\",\n \"Neutral\",\n \"Wet\", \"Moderately wet\", \"Extremely wet\")\nrefnam <- var1_input[1][[1]]$reference_dataset\nn <- 1\nwhile (n <= nmods){\n if (var1_input[n][[1]]$dataset == refnam) break\n n <- n + 1\n}\nnref <- n\nlat <- getnc(var1_input, nref, lat = TRUE)\nif (max(lat) > 90){\n print(paste0(\"Latitude must be [-90,90]: min=\",\n min(lat), \" max=\", max(lat)))\n stop(\"Aborting!\")\n}\nref <- getnc(var1_input, nref, lat = FALSE)\nrefmsk <- apply(ref, c(1, 2), FUN = mean, na.rm = TRUE)\nrefmsk[refmsk > 10000] <- NA\nrefmsk[!is.na(refmsk)] <- 1\n\nxprov <- list(ancestors = list(\"\"),\n authors = list(\"berg_peter\"),\n references = list(\"mckee93\"),\n projects = list(\"c3s-magic\"),\n caption = \"\",\n statistics = list(\"other\"),\n realms = list(\"atmos\"),\n themes = list(\"phys\"),\n domains = list(\"global\"))\n\nhistarr <- array(NA, c(nmods, length(histnams)))\nfor (mod in 1:nmods){\n v1 <- getnc(var1_input, mod)\n print(var1_input[mod][[1]]$cmor_table)\n d <- dim(v1)\n v1_spi <- v1 * NA\n for (i in 1:d[1]){\n wh <- which(!is.na(refmsk[i,]))\n if (length(wh) > 0){\n tmp <- v1[i,wh,]\n v1_spi[i,wh,] <- t(spi(t(tmp), 1, na.rm = TRUE,\n distribution = \"PearsonIII\")$fitted)\n }\n }\n v1_spi[is.infinite(v1_spi)] <- NA\n v1_spi[v1_spi > 10000] <- NA\n hist_spi <- array(NA, c(d[1], d[2], length(histbrks) - 1))\n for (nnh in 1:(length(histbrks) - 1)){\n hist_spi[,,nnh] <- apply(v1_spi, c(1, 2), FUN = whfcn,\n ilow = histbrks[nnh],\n ihigh = histbrks[nnh + 1])\n }\n filename <- ncwritenew(var1_input, mod, hist_spi, wdir, histbrks)\n # Set provenance for output files\n xprov$caption <- \"Histogram of SPI index per grid point.\"\n xprov$ancestors <- modfile[mod]\n provenance[[filename]] <- xprov\n # Weight against latitude\n h <- c(1:length(histnams)) * 0\n for (j in 1:d[2]){\n h <- h + hist(v1_spi[j,,], breaks = histbrks,\n plot = FALSE)$counts * cos(lat[j] * pi / 180.)\n }\n histarr[mod, ] <- h / sum(h, na.rm = TRUE)\n}\nfilehist <- paste0(params$work_dir, \"/\", \"histarr.rsav\")\nsave(histarr, file = filehist)\nplot_file <- paste0(params$plot_dir, \"/\", \"histplot.png\")\nxprov$caption <- \"Global latitude-weighted histogram of SPI index.\"\nxprov$ancestors <- list(modfile)\nxprov[[\"plot_file\"]] <- plot_file\nprovenance[[filehist]] <- xprov\nwrite_yaml(provenance, provenance_file)\n\nbhistarr <- array(NA, c(nmods - 1, 7))\nmarr <- c(1:nmods)[c(1:nmods) != nref]\ncnt <- 1\nfor (m in marr){\n bhistarr[cnt, ] <- histarr[m, ] - histarr[nref, ]\n cnt <- cnt + 1\n}\nparr <- c(nref, marr)\n\nmnam <- c(1:nmods) * NA\nfor (m in 1:nmods) mnam[m] <- var1_input[m][[1]]$dataset\n\nqual_col_pals <- brewer.pal.info[brewer.pal.info$category == \"qual\", ] # nolint\ncol_vector <- unlist(mapply(brewer.pal, qual_col_pals$maxcolors, # nolint\n rownames(qual_col_pals)))\ncols <- c(\"black\", sample(col_vector, nmods - 1))\n\npng(plot_file, width = 1000, height = 500)\n par(mfrow = c(2, 1), oma = c(3, 3, 3, 13), mar = c(2, 1, 1, 1))\n barplot(histarr[parr, ], beside = 1, names.arg = histnams,\n col = cols, xaxs = \"i\")\n box()\n mtext(\"Probability\", side = 2, line = 2.1)\n barplot(bhistarr, beside = 1, names.arg = histnams,\n col = cols[2:nmods], xaxs = \"i\")\n box()\n mtext(\"Absolute difference\", side = 2, line = 2.1)\n mtext(\"Standardized precipitation index\", outer = TRUE,\n cex = 2, font = 2)\n par(fig = c(0.8, .95, 0.1, 0.9), new = T, oma = c(0, 0, 0, 0),\n mar = c(0, 0, 0, 0))\n legend(\"topright\", mnam[parr], fill = cols)\ndev.off()\n", "meta": {"hexsha": "1f6a6e24dc2990749f033d5c30d18beece6edc19", "size": 5432, "ext": "r", "lang": "R", "max_stars_repo_path": "esmvaltool/diag_scripts/droughtindex/diag_spi.r", "max_stars_repo_name": "jeromaerts/ESMValTool", "max_stars_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "esmvaltool/diag_scripts/droughtindex/diag_spi.r", "max_issues_repo_name": "jeromaerts/ESMValTool", "max_issues_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "esmvaltool/diag_scripts/droughtindex/diag_spi.r", "max_forks_repo_name": "jeromaerts/ESMValTool", "max_forks_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.9212121212, "max_line_length": 79, "alphanum_fraction": 0.5913107511, "num_tokens": 1893, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3343931359885089}} {"text": "\n\n\n# 1. graf: vrste dohodka\n\ngraf_vrste_dohodka <- ggplot(vrste_dohodka, aes(x=factor(Leto), y=Dohodek,group=Vrsta.dohodka, colour=Vrsta.dohodka)) + \n geom_line(aes(colour=Vrsta.dohodka)) +\n geom_point(aes(colour=Vrsta.dohodka)) +\n scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +\n labs(title=\"Dohodek glede na vrsto\", x=\"Leto\", y = \"Dohodek\", colour=\"Vrsta dohodka\")\nprint(graf_vrste_dohodka)\n\n\n# 2. graf: dele\u017e vrste dohodka 2019\n\nvrste_dohodka_19 <- vrste_dohodka %>%\n filter(Leto == 2019) %>%\n select(Vrsta.dohodka, Dohodek) %>%\n mutate(Delez19 = round((Dohodek/sum(Dohodek))*100, 0))\n\ndohodek_19 <- vrste_dohodka_19 %>%\n select(Delez19) %>%\n unlist()\n\nvrste_imena <- sprintf(\"%s (%s)\", vrste_dohodka_19$Vrsta.dohodka, \n percent(round(vrste_dohodka_19$Dohodek/sum(vrste_dohodka_19$Dohodek), 2)))\n\nnames(dohodek_19) <- vrste_imena\n\ngraf_delez_vrste <- waffle(dohodek_19, rows = 6, title = \"Dele\u017e dohodka glede na vrsto 2019\")\n\nprint(graf_delez_vrste)\n\n# 3. graf: razlika po spolu, (starost = skupaj)\n\ngraf_spol <- ggplot(spol, aes(x=factor(Leto), y=Dohodek, group=Spol)) +\n geom_line(aes(color=Spol)) +\n geom_point(aes(color=Spol)) +\n scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +\n labs(x=\"Leto\", y = \"Dohodek\", fill=\"Leto\")\nprint(graf_spol)\n\n# 4. graf: razlika po spolu 2\n\nrazlika_spol <- spol %>%\n arrange(desc(Leto)) %>%\n pivot_wider(names_from = Spol, values_from = Dohodek) %>%\n mutate(Razlika = Mo\u0161ki - \u017denske)\n\ngraf_razlika_spol <- ggplot(razlika_spol, aes(x=factor(Leto), y=Razlika, group=1)) +\n geom_line() +\n geom_point() +\n scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +\n labs(x=\"Leto\", y = \"Razlika dohodka\")\n\nprint(graf_razlika_spol)\n\n# 5. graf: Razlika med spoloma - skupaj\ngraf_spol_skupaj <- ggarrange(graf_spol, graf_razlika_spol, labels=c(\"Dohodek po spolu\", \"Razlika\"),\n common.legend = TRUE, legend = \"bottom\")\nprint(graf_spol_skupaj)\n\n# 6. graf: izobrazba\n\nizobrazba_spol_2 <- izobrazba_spol %>%\n mutate(Izobrazba2=Izobrazba, Spol2=Spol)\n\n\ngraf_izobrazba_spol <- ggplot(izobrazba_spol_2, aes(x=factor(Leto), y=Dohodek)) +\n geom_line(izobrazba_spol_2 %>% select(-Izobrazba, -Spol),\n mapping=aes(group=interaction(Spol2, Izobrazba2)), color=\"grey\", size=0.5) +\n geom_line(aes(group=interaction(Spol, Izobrazba), color=Spol), size=1.2)+\n scale_color_manual(breaks=c(\"Mo\u0161ki\", \"\u017denske\"), values=c(\"#00BFC4\", \"#F8766D\"))+\n theme_bw() +\n theme(plot.title = element_text(), panel.grid = element_blank()) +\n labs(title=\"Izobrazba in spol\", x=\"Leto\", y = \"Dohodek\") +\n scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +\n facet_wrap(~Izobrazba)\n\nprint(graf_izobrazba_spol)\n\n\n#7. graf: starost\nstarost_spol_2 <- starost_spol %>%\n mutate(Starost2=Starost, Spol2=Spol)\n\n\ngraf_starost_spol <- ggplot(starost_spol_2, aes(x=factor(Leto), y=Dohodek)) +\n geom_line(starost_spol_2 %>% select(-Starost, -Spol), \n mapping=aes(group=interaction(Spol2, Starost2)), color=\"grey\", size=0.5) +\n geom_line(aes(group=interaction(Spol, Starost), color=Spol), size=1.2)+\n scale_color_manual(breaks=c(\"Mo\u0161ki\", \"\u017denske\"), values=c(\"#00BFC4\", \"#F8766D\"))+\n theme_bw() +\n theme(plot.title = element_text(), panel.grid = element_blank()) +\n labs(title=\"Starost in spol\", x=\"Leto\", y = \"Dohodek\") +\n scale_x_discrete(guide = guide_axis(check.overlap = TRUE)) +\n facet_wrap(~Starost)\n\nprint(graf_starost_spol)\n\n\n# Zemljevid statisti\u010dnih regij\n\nzemljevid_regije <- uvozi.zemljevid(\"https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_SVN_shp.zip\",\n \"gadm36_SVN_1\", encoding = \"UTF-8\")\n\nzemljevid_regije$NAME_1 <- as.factor(iconv(as.character(zemljevid_regije$NAME_1),\n \"UTF-8\"))\n\n#Spodnjeposavska = Posavska\n#Notranjsko-kra\u0161ka = Primorsko-notranjska\n\nlevels(zemljevid_regije$NAME_1)[levels(zemljevid_regije$NAME_1)==\"Spodnjeposavska\"] <- \"Posavska\"\nlevels(zemljevid_regije$NAME_1)[levels(zemljevid_regije$NAME_1)==\"Notranjsko-kra\u0161ka\"] <- \"Primorsko-notranjska\"\n\nregije_8_19 <- regije %>%\n select(Regija, Leto, Dohodek) %>%\n filter(Leto %in% c(2008, 2019)) %>%\n pivot_wider(names_from = Leto, values_from = Dohodek) %>%\n mutate(Rast = (((`2019` - `2008`) / `2008`) * 100))\n\n\nnarisi_zemljevid <- tm_shape(merge(zemljevid_regije, regije_8_19, by.x=\"NAME_1\", by.y=\"Regija\")) +\n tm_polygons(c(\"2008\", \"2019\", \"Rast\") , style=\"cont\", palette = \"YlGn\") +\n tm_layout(legend.position = c(\"right\", \"bottom\"), \n main.title = \"Zemljevid razpolo\u017eljivega dohodka gospodinjstev\") +\n tm_facets(sync = TRUE, ncol = 2)\n\nprint(narisi_zemljevid)\n\n\nnarisi_zemljevid_regije <- tm_shape(zemljevid_regije) +\n tm_polygons(col = \"white\") +\n tm_text(\"NAME_1\", size = \"AREA\")\nprint(narisi_zemljevid_regije)\n\n\n#zemljevid_skupaj <- tmap_arrange(narisi_zemljevid)\n#\n#print(zemljevid_skupaj)\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "6ad0862dfb869a47654c3226286f9102733a3050", "size": 4914, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "UrbanRupnik/APPR-2020-21", "max_stars_repo_head_hexsha": "8127c3d692f65aedc45648631410169ef3e6ead3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "UrbanRupnik/APPR-2020-21", "max_issues_repo_head_hexsha": "8127c3d692f65aedc45648631410169ef3e6ead3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-26T09:28:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-26T21:23:30.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "UrbanRupnik/APPR-2020-21", "max_forks_repo_head_hexsha": "8127c3d692f65aedc45648631410169ef3e6ead3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3289473684, "max_line_length": 120, "alphanum_fraction": 0.6912901913, "num_tokens": 1825, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.658417500561683, "lm_q1q2_score": 0.33435221843428237}} {"text": "#----------------------------#\n# LA variation analysis data #\n#----------------------------#\n\nlibrary(dplyr)\nlibrary(purrr)\n\nanalysis_dat <- readRDS(\"N:/Documents/Data & Programming/RDS files/la_analysis_dat.rds\") %>% \n filter(!la_code %in% c(\"E09000001\", \"E06000053\") & year > 2005) %>% # remove city of london & isles of scilly\n mutate(n_child_pct = n_child*100/pop_U1,\n n_child_10000 = n_child*10000/pop_U1,\n pop_U1_1000 = pop_U1/1000, \n any_ara_pct = any_ara_sum*100/n_mother,\n teen_birth_pct = teen_birth_sum*100/n_mother,\n #imd_20pc_pct = imd_20pc_sum*100/n_mother,\n imd_10pc_pct = imd_10pc_sum*100/n_mother,\n prop10_imd_pct = prop10_imd*100,\n #prop10_idaci_pct = prop10_idaci*100,\n #violence_1000 = \n #imd_quint = as.factor(case_when(decile_imd %in% c(1,2) ~ 1,\n # decile_imd %in% c(3,4) ~ 2,\n # decile_imd %in% c(5,6) ~ 3,\n # decile_imd %in% c(7,8) ~ 4,\n # decile_imd %in% c(9,10) ~ 5)),\n total_1000 = total/1000) %>% \n ungroup()%>% \n mutate(year_std = year - 2006,\n ofsted_protect = factor(case_when(protect_score == \"Inadequate\" ~ 4,\n protect_score == \"Requires improvement\" ~ 3,\n protect_score == \"Good\" ~ 2,\n protect_score == \"Outstanding\" ~ 1)),\n ofsted_prt_bad = ifelse(protect_score %in% c(\"Inadequate\", \"Requires improvement\"),1,0),\n ofsted_lscb = factor(case_when(lscb_score == \"Inadequate\" ~ 4,\n lscb_score == \"Requires improvement\" ~ 3,\n lscb_score == \"Good\" ~ 2,\n lscb_score == \"Outstanding\" ~ 1)))\n\n\n# taking out bad la data \ndat_2010 <- filter(analysis_dat, year == 2010)\n\n#dat_2010_cc <- filter(analysis_dat, year == 2010 & bad_la_linkrate == 0 & bad_bwmiss_rate == 0)\n\n#------------------------------------------#\n# adapt data for longitudinal modelling ----\n#------------------------------------------#\n\n# split out within and between LA effect ----\nmodel_dat <- analysis_dat %>% \n group_by(la_code) %>% \n dplyr::mutate_at(vars(total_1000,\n any_ara_pct,\n teen_birth_pct,\n #imd_20pc_pct,\n imd_10pc_pct,\n lowbw_pct,\n preterm_pct,\n child_lowinc,\n starts_with(\"cong_anom\")), \n list(wt = ~(. - mean(., na.rm = TRUE) ), # within-LA effect\n bt = ~mean(., na.rm = TRUE))) %>% # between-LA effect\n mutate(any_bad_linkrate = as.numeric(any(bad_la_linkrate == 1)),\n any_bad_bwrate = as.numeric(any(bad_bw_missrate == 1)),\n any_bad_gestatrate = as.numeric(any(bad_gestat_missrate == 1))) %>% \n ungroup() %>% \n left_join(dplyr::select(dat_2010, la_code, teenbirth_2010 = teen_birth_pct, lowbw_2010 = lowbw_pct,\n child_lowinc_2010 = child_lowinc, imd_10pc_2010 = imd_10pc_pct,\n ca_feud_2010 = cong_anom_feud_sum_pct, ca_hardelid_2010 = cong_anom_sum_pct, \n total_2010 = total_1000, any_ara_2010 = any_ara_pct, preterm_2010 = preterm_pct,\n early_csc_2010 = early_csc_spend_pc), by = c(\"la_code\"))\n\n\nmodel_dat_cc <- model_dat %>% \n filter(any_bad_linkrate == 0 & any_bad_bwrate == 0) %>% \n group_by(la_code) %>% \n dplyr::mutate_at(vars(total_1000,\n any_ara_pct,\n teen_birth_pct,\n #imd_20pc_pct,\n imd_10pc_pct,\n lowbw_pct,\n preterm_pct,\n child_lowinc,\n starts_with(\"cong_anom\")), \n list(wt = ~(. - mean(., na.rm = TRUE) ), # within-LA effect\n bt = ~mean(., na.rm = TRUE))) %>% # between-LA effect\n ungroup() %>% \n dplyr::select(contains(\"total_1000\"),\n contains(\"any_ara_pct\"),\n contains(\"teen_birth_pct\"),\n contains(\"pc_pct\"),\n contains(\"lowbw_pct\"),\n contains(\"preterm_pct\"),\n contains(\"child_lowinc\"),\n starts_with(\"cong_anom\"),\n contains(\"n_child\"),\n lone_parent_pct,\n violent_crime,\n prop10_imd_pct,\n pop_U1,\n la_code,\n la_name,\n region_name,\n year_std,\n ends_with(\"2010\")) \n", "meta": {"hexsha": "b90b002e8a4cd8727c147e92b597a6c1b52d7639", "size": 4849, "ext": "r", "lang": "R", "max_stars_repo_path": "Chapter 2/reformat_analysis_data_for_regression.r", "max_stars_repo_name": "RachelPearson/PhD-analyses", "max_stars_repo_head_hexsha": "ee116fa088edc7a3ce486e5262b713fc05985dd3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Chapter 2/reformat_analysis_data_for_regression.r", "max_issues_repo_name": "RachelPearson/PhD-analyses", "max_issues_repo_head_hexsha": "ee116fa088edc7a3ce486e5262b713fc05985dd3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter 2/reformat_analysis_data_for_regression.r", "max_forks_repo_name": "RachelPearson/PhD-analyses", "max_forks_repo_head_hexsha": "ee116fa088edc7a3ce486e5262b713fc05985dd3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.7452830189, "max_line_length": 112, "alphanum_fraction": 0.4877294287, "num_tokens": 1238, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3342836977684117}} {"text": "# run_analyses.r\n\n# This will run all analyses reported in the paper and send the results in\n# csv format to a subdirectory called /output.\n\n# Table 1 ------------------------------------------------------------\n\n# summary statistics\n\ntibble(\n group = c(\"total\", \"dup\", \"del\", \"noncarrier\"),\n age_mean = \n subjects$raw %>% \n map(pull, age_months) %>% \n map(mean) %>% \n map(function(x) x / 12),\n age_sd = \n subjects$raw %>% \n map(pull, age_months) %>%\n map(sd) %>%\n map(function(x) x / 12),\n iq_mean = \n subjects$raw %>% \n map(pull, diagnosis_summary.best_full_scale_iq) %>%\n map(mean),\n iq_sd = \n subjects$raw %>% \n map(pull, diagnosis_summary.best_full_scale_iq) %>%\n map(sd),\n female_n = \n subjects$raw %>% \n map(filter, gender == 1) %>% \n map(nrow),\n female_percent = \n subjects$raw %>% \n map(function(x) nrow(filter(x, gender == 1)) / nrow(x) * 100),\n asd_n = \n subjects$raw %>% \n map(filter, clinical_asd == 1) %>% \n map(nrow),\n asd_percent = \n subjects$raw %>% \n map(function(x) nrow(filter(x, clinical_asd == 1)) / nrow(x) * 100),\n ocd_n = \n subjects$raw %>% \n map(filter, ocd == 1) %>% \n map(nrow),\n ocd_percent = \n subjects$raw %>% \n map(function(x) nrow(filter(x, ocd == 1)) / nrow(x) * 100)\n) %>% \n unnest() %>% \n mutate_if(is.numeric, round, digits = 2) %>%\n write_excel_csv(here(\"r\",\"output\",\"table1\",\"summary_stats.csv\"))\n# group comparisons\n\nanova <- list()\nanova$iq <- aov(diagnosis_summary.best_full_scale_iq ~ sample_group, \n data = subjects$raw$total) \nanova$age <- aov(age_months ~ sample_group, \n data = subjects$raw$total) \n\nchi <- list()\nchi$gender <- chisq.test(subjects$raw$total$gender,\n subjects$raw$total$sample_group) \nchi$asd <- chisq.test(subjects$raw$total$clinical_asd,\n subjects$raw$total$sample_group) \nchi$ocd <- chisq.test(subjects$raw$total$ocd,\n subjects$raw$total$sample_group) \n\nanova_posthoc <- list()\nanova_posthoc$iq <- TukeyHSD(anova$iq)\nanova_posthoc$age <- TukeyHSD(anova$age) \n\nchi_posthoc <- list()\nchi_posthoc$gender <- chisq.post.hoc(\n xtabs(~ gender + sample_group, data = subjects$raw$total), \n test = \"chisq.test\", \n popsInRows = FALSE, \n control = \"bonferroni\") \nchi_posthoc$asd <- chisq.post.hoc(\n xtabs(~ clinical_asd + sample_group, data = subjects$raw$total), \n test = \"chisq.test\", \n popsInRows = FALSE, \n control = \"bonferroni\") \nchi_posthoc$ocd <- chisq.post.hoc(\n xtabs(~ ocd + sample_group, data = subjects$raw$total), \n test = \"chisq.test\", \n popsInRows = FALSE, \n control = \"bonferroni\")\n\ntibble(\n variable = c(\"iq\",\"age\"),\n test = anova %>% map(tidy) \n) %>% \n unnest() %>%\n mutate_at(vars(-variable, -term, -p.value), \n round, digits = 2) %>%\n write_excel_csv(here(\"r\",\"output\",\"table1\",\"anova.csv\"))\n\ntibble(\n variable = c(\"gender\",\"asd\",\"ocd\"),\n test = chi %>% map(tidy)\n) %>% unnest() %>%\n mutate_at(vars(-variable, -method, -p.value), \n round, digits = 2) %>% \n write_excel_csv(here(\"r\",\"output\",\"table1\",\"chi.csv\"))\n\ntibble(\n variable = c(\"iq\",\"age\"),\n test = anova_posthoc %>% map(tidy) \n) %>% \n unnest() %>%\n mutate_at(vars(-variable, -term, -comparison, -adj.p.value), \n round, digits = 2) %>% \n write_excel_csv(here(\"r\",\"output\",\"table1\",\"anova_posthoc.csv\"))\n\ntibble(\n variable = c(\"gender\",\"asd\",\"ocd\"),\n test = chi_posthoc %>% map(as.tibble)\n) %>% unnest() %>%\n write_excel_csv(here(\"r\",\"output\",\"table1\",\"chi_posthoc.csv\"))\n\n# Table 2 ------------------------------------------------------------\n\ntibble(\n group = c(\"total\", \"dup\", \"del\", \"noncarrier\"),\n ados = \n subjects$raw %>% \n map(function (x) filter(x, !is.na(ados_css_total_combined))) %>% \n map(function (x) nrow(x)),\n adir = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(adi_r.c_total))) %>% \n map(function (x) nrow(x)),\n bapq = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(bapq.total))) %>%\n map(function (x) nrow(x)),\n scq = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(scq_life.summary_score))) %>%\n map(function (x) nrow(x)),\n srs_child =\n subjects$raw %>%\n map(function (x) filter(x, !is.na(srs_parent.total))) %>%\n map(function (x) nrow(x)),\n srs_adult = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(srs_adult.total))) %>%\n map(function (x) nrow(x)),\n mullen = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(mullen.measure.measure_type))) %>% \n map(function (x) nrow(x)),\n das2_early_lower = \n subjects$raw %>%\n map(function (x) filter(\n x, !is.na(das_ii_early_years.gca_lower_standard))) %>% \n map(function (x) nrow(x)),\n das2_early_upper = \n subjects$raw %>%\n map(function (x) filter(\n x, !is.na(das_ii_early_years.gca_upper_standard))) %>% \n map(function (x) nrow(x)),\n das2_school = \n subjects$raw %>%\n map(function (x) filter(\n x,!is.na(das_ii_school_age.measure.measure_type))) %>% \n map(function (x) nrow(x)),\n wasi = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(wasi.measure.measure_type))) %>% \n map(function (x) nrow(x)),\n cbcl = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(cbcl_6_18.thought_problems_t_score))) %>% \n map(function (x) nrow(x)),\n abcl = \n subjects$raw %>%\n map(function (x) filter(x, !is.na(abcl_18_59.thought_t_score))) %>% \n map(function (x) nrow(x)),\n scl = \n subjects$raw %>%\n map(function (x) filter(x, scl_90_r.measure.eval_age_months>0)) %>% \n map(function (x) nrow(x)),\n disc =\n subjects$raw %>%\n map(function (x) filter(x, !is.na(disc_youth.measure.measure_type))) %>% \n map(function (x) nrow(x)),\n sops =\n subjects$raw %>%\n map(function (x) filter(x, !is.na(eval_age_months))) %>% \n map(function (x) nrow(x))\n) %>% \n unnest() %>%\n write_excel_csv(here(\"r\",\"output\",\"table2\",\"table2.csv\"))\n\n# Table 3 -------------------------------------------------------------\n\ntibble(\n group = c(\"total\",\"dup\",\"del\",\"noncarrier\"),\n bcl_received =\n subjects$raw %>% \n map(filter, bcl_data == 1) %>%\n map(nrow),\n bcl_positive =\n subjects$raw %>%\n map(filter, bcl_missingness == 1) %>%\n map(nrow),\n bcl_percent = \n subjects$raw %>%\n map(function (x) \n nrow(filter(x, bcl_missingness == 1)) / \n nrow(filter(x, bcl_data == 1)) * 100 ),\n scl_received =\n subjects$raw %>% \n map(filter, scl_data == 1) %>%\n map(nrow),\n scl_positive =\n subjects$raw %>%\n map(filter, scl_missingness_binary == 1) %>%\n map(nrow),\n scl_percent = \n subjects$raw %>%\n map(function (x) \n nrow(filter(x, scl_missingness_binary == 1)) / \n nrow(filter(x, scl_data == 1)) * 100 ),\n disc_received =\n subjects$raw %>% \n map(filter, disc_data == 1) %>%\n map(nrow),\n disc_positive =\n subjects$raw %>%\n map(filter, disc_missingness == 1) %>%\n map(nrow),\n disc_percent = \n subjects$raw %>%\n map(function (x) \n nrow(filter(x, disc_missingness == 1)) / \n nrow(filter(x, disc_data == 1)) * 100 ),\n sops_received =\n subjects$raw %>% \n map(filter, sops_data == 1) %>%\n map(nrow),\n sops_positive =\n subjects$raw %>%\n map(filter, sops_missingness == 1) %>%\n map(nrow),\n sops_percent = \n subjects$raw %>%\n map(function (x) \n nrow(filter(x, sops_missingness == 1)) / \n nrow(filter(x, disc_data == 1)) * 100 )\n) %>% \n unnest() %>%\n mutate_if(is.numeric, round, digits = 2) %>%\n write_excel_csv(here(\"r\",\"output\",\"table3\",\"table3.csv\"))\n\n# Table 4 -------------------------------------------------------------\n\n# number of participants with each combination\n\ntibble(\n bcl_scl_data = \n subjects$raw$total %>%\n filter(bcl_data == 1 & scl_data == 1) %>% \n nrow(),\n bcl_scl_positive =\n subjects$raw$total %>%\n filter(bcl_missingness == 1 & scl_missingness_binary == 1) %>% \n nrow(),\n\n bcl_disc_data = \n subjects$raw$total %>%\n filter(bcl_data == 1 & disc_data == 1) %>% \n nrow(),\n bcl_disc_positive =\n subjects$raw$total %>%\n filter(bcl_missingness == 1 & disc_missingness == 1) %>% \n nrow(),\n \n bcl_sops_data = \n subjects$raw$total %>%\n filter(bcl_data == 1 & sops_data == 1) %>% \n nrow(),\n bcl_sops_positive =\n subjects$raw$total %>%\n filter(bcl_missingness == 1 & sops_missingness == 1) %>% \n nrow(),\n \n scl_sops_data = \n subjects$raw$total %>%\n filter(scl_data == 1 & sops_data == 1) %>% \n nrow(),\n scl_sops_positive =\n subjects$raw$total %>%\n filter(scl_missingness_binary == 1 & sops_missingness == 1) %>% \n nrow(),\n\n disc_sops_data = \n subjects$raw$total %>%\n filter(disc_data == 1 & sops_data == 1) %>% \n nrow(),\n disc_sops_positive =\n subjects$raw$total %>%\n filter(disc_missingness == 1 & sops_missingness == 1) %>% \n nrow()\n) %>% \n write_excel_csv(here(\"r\",\"output\",\"table4\",\"pairwise_combinations.csv\"))\n\ncrosstabs <- list()\ncrosstabs$bcl_scl <- xtabs(\n ~ bcl_missingness + scl_missingness_binary, data = subjects$raw$total)\ncrosstabs$bcl_disc <- xtabs(\n ~ bcl_missingness + disc_missingness, data = subjects$raw$total)\ncrosstabs$bcl_sops <- xtabs(\n ~ bcl_missingness + sops_missingness, data = subjects$raw$total)\ncrosstabs$scl_sops <- xtabs(\n ~ scl_missingness_binary + sops_missingness, data = subjects$raw$total)\ncrosstabs$disc_sops <- xtabs(\n ~ disc_missingness + sops_missingness, data = subjects$raw$total)\n\ntibble(\n pair = c(\"BCL x SCL\",\"BCL x DISC\",\"BCL x SOPS\",\n \"SCL x SOPS\",\"Disc x SOPS\"),\n fisher = crosstabs %>%\n map(fisher.test) %>%\n map(tidy)\n) %>% \n unnest() %>% \n mutate_at(vars(-pair, -p.value, -method, -alternative), \n round, digits = 2) %>%\n write_excel_csv(here(\"r\",\"output\",\"table4\",\"relationship_strengths.csv\"))\n\nindex_measures <- subjects$raw$total %>%\n select(scl_missingness_binary, \n bcl_missingness, \n sops_missingness,\n disc_missingness)\n\nHmisc::rcorr(as.matrix(index_measures), type=c(\"spearman\"))\n\n# Correlation matrix\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n duplication_standardized = arm::rescale(\n duplication, binary.inputs = \"center\"))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n deletion_standardized = arm::rescale(\n deletion, binary.inputs = \"center\"))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n asd_standardized = arm::rescale(\n clinical_asd, binary.inputs = \"center\"))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n ocd_standardized = arm::rescale(\n ocd, binary.inputs = \"center\"))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n gender_standardized = arm::rescale(\n gender, binary.inputs = \"center\"))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n age_standardized = arm::rescale(age_months))\n\nsubjects$analysis$all <- subjects$analysis$all %>% mutate(\n iq_standardized = arm::rescale(diagnosis_summary.best_full_scale_iq))\n\nmatrix <- subjects$analysis$all %>% select(\n duplication_standardized, \n deletion_standardized,\n age_standardized,\n iq_standardized,\n asd_standardized,\n ocd_standardized,\n gender_standardized\n)\n\ncorrelations <- Hmisc::rcorr(as.matrix(matrix),type=c(\"pearson\")) \n\ncorrelations_r_rounded <- correlations$r %>% round(digits=3)\n\ncorrelations_p_rounded <- correlations$P %>% round(digits=3)\n\ncorrelations_r_rounded %>% \n as.data.frame() %>%\n write_excel_csv(here(\"r\",\"output\",\"correlation_matrix\",\"cor_r.csv\"))\n\ncorrelations_p_rounded %>% \n as.data.frame() %>%\n write_excel_csv(here(\"r\",\"output\",\"correlation_matrix\",\"cor_p.csv\"))\n\n# Table 5 ----------------------------------------\n\nmodels <- list()\n\nmodels$base$all <- \n geeglm(binary_psychosis ~ \n duplication +\n deletion +\n age_years +\n diagnosis_summary.best_full_scale_iq + \n clinical_asd + \n ocd + \n gender, \n data = subjects$analysis$all,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$base$all %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table5\",\"table5_or.csv\"))\n\n\nmodels$base$all %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table5\",\"table5_unrounded.csv\"))\n\nmodels$base$all %>%\n tidy(conf.int = TRUE, exponentiate = FALSE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table5\",\"table5_b.csv\"))\n\n# Table 6 -------------------------------------------------------------\n\n\nmodels$base$duplication_only <- \n geeglm(binary_psychosis ~ \n age_years + \n diagnosis_summary.best_full_scale_iq + \n clinical_asd + \n ocd + \n gender, \n data = subjects$analysis$duplication, \n family = binomial(link=\"logit\"), \n na.action = na.exclude, \n id = family)\n\nmodels$base$duplication_only %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"dup_or.csv\"))\n\nmodels$base$duplication_only %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"dup_b.csv\"))\n\nmodels$base$duplication_only %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"dup_unrounded.csv\"))\n\nmodels$base$deletion_only <- \n geeglm(binary_psychosis ~ \n age_years + \n diagnosis_summary.best_full_scale_iq + \n clinical_asd + \n ocd + \n gender, \n data = subjects$analysis$deletion, \n family = binomial(link=\"logit\"), \n na.action = na.exclude, \n id = family)\n\nmodels$base$deletion_only %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"del_or.csv\"))\n\nmodels$base$deletion_only %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"del_b.csv\"))\n\nmodels$base$deletion_only %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"del_unrounded.csv\"))\n\nmodels$base$noncarriers <- \n geeglm(binary_psychosis ~ \n age_years + \n diagnosis_summary.best_full_scale_iq + \n clinical_asd + \n ocd + \n gender, \n data = subjects$analysis$noncarrier, \n family = binomial(link=\"logit\"), \n na.action = na.exclude, \n id = family)\n\nmodels$base$noncarriers %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"nc_or.csv\"))\n\nmodels$base$noncarriers %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"nc_b.csv\"))\n\nmodels$base$noncarriers %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table6\",\"nc_unrounded.csv\"))\n\n# Table S2 ---------------------------------------------------------\n\nexploratory_all <- subjects$analysis$all %>% filter(\n !is.na(ados_css_total_combined))\n\nmodels$exploratory$total_css <- \n geeglm(binary_psychosis ~ \n duplication +\n deletion +\n age_years +\n diagnosis_summary.best_full_scale_iq +\n ados_css_total_combined + \n ocd + \n gender, \n data = exploratory_all,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$total_css %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_all_or.csv\"))\n\nmodels$exploratory$total_css %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_all_b.csv\"))\n\nmodels$exploratory$total_css %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_all_unrounded.csv\"))\n\nexploratory_duplication <- subjects$analysis$duplication %>% filter(\n !is.na(ados_css_total_combined))\n\nmodels$exploratory$total_css_duplication <- \n geeglm(binary_psychosis ~ \n age_years +\n diagnosis_summary.best_full_scale_iq + \n ados_css_total_combined + \n ocd + \n gender, \n data = exploratory_duplication,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$total_css_duplication %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_dup_or.csv\"))\n\nmodels$exploratory$total_css_duplication %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_dup_b.csv\"))\n\nmodels$exploratory$total_css_duplication %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_dup_unrounded.csv\"))\n\nexploratory_deletion <- subjects$analysis$deletion %>% filter(\n !is.na(ados_css_total_combined))\n\nmodels$exploratory$total_css_deletion <- \n geeglm(binary_psychosis ~ \n age_years +\n ados_css_total_combined + \n diagnosis_summary.best_full_scale_iq + \n ocd + \n gender, \n data = exploratory_deletion,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$total_css_deletion %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_del_or.csv\"))\n\nmodels$exploratory$total_css_deletion %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_del_b.csv\"))\n\nmodels$exploratory$total_css_deletion %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_del_unrounded.csv\"))\n\nexploratory_noncarrier <- subjects$analysis$noncarrier %>% filter(\n !is.na(ados_css_total_combined))\n\nmodels$exploratory$total_css_noncarrier <- \n geeglm(binary_psychosis ~ \n age_years +\n diagnosis_summary.best_full_scale_iq + \n ados_css_total_combined + \n ocd + \n gender, \n data = exploratory_noncarrier,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$total_css_noncarrier %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_nc_or.csv\"))\n\nmodels$exploratory$total_css_noncarrier %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_nc_b.csv\"))\n\nmodels$exploratory$total_css_noncarrier %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s2\",\"total_css_nc_unrounded.csv\"))\n\n# Table S3 ---------------------------------------------------------------\n\nexploratory_all_domain <- subjects$analysis$all %>% filter(\n !is.na(ados_css_rrb_derived) & !is.na(ados_css_sa_derived))\n\nmodels$exploratory$domain_css <- \n geeglm(binary_psychosis ~ \n duplication +\n deletion +\n age_years +\n diagnosis_summary.best_full_scale_iq +\n ados_css_rrb_derived +\n ados_css_sa_derived +\n ocd + \n gender, \n data = exploratory_all_domain,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$domain_css %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_all_or.csv\"))\n\nmodels$exploratory$domain_css %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_all_b.csv\"))\n\nmodels$exploratory$domain_css %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_all_unrounded.csv\"))\n\nexploratory_duplication_domain <- filter(\n subjects$analysis$duplication, \n !is.na(ados_css_rrb_derived) & !is.na(ados_css_sa_derived))\n\nmodels$exploratory$domain_css_duplication <- \n geeglm(binary_psychosis ~ \n age_years +\n diagnosis_summary.best_full_scale_iq +\n ados_css_rrb_derived +\n ados_css_sa_derived +\n ocd + \n gender, \n data = exploratory_duplication_domain,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$domain_css_duplication %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_dup_or.csv\"))\n\nmodels$exploratory$domain_css_duplication %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_dup_b.csv\"))\n\nmodels$exploratory$domain_css_duplication %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_dup_unrounded.csv\"))\n\nexploratory_deletion_domain <- subjects$analysis$deletion %>% filter(\n !is.na(ados_css_rrb_derived) & !is.na(ados_css_sa_derived))\n\nmodels$exploratory$domain_css_deletion <- \n geeglm(binary_psychosis ~ \n age_years +\n diagnosis_summary.best_full_scale_iq +\n ados_css_rrb_derived +\n ados_css_sa_derived +\n ocd + \n gender, \n data = exploratory_deletion_domain,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$domain_css_deletion %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_del_or.csv\"))\n\nmodels$exploratory$domain_css_deletion %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_del_b.csv\"))\n\nmodels$exploratory$domain_css_deletion %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_del_unrounded.csv\"))\n\nexploratory_noncarrier_domain <- filter(\n subjects$analysis$noncarrier, \n !is.na(ados_css_rrb_derived) & !is.na(ados_css_sa_derived))\n\nmodels$exploratory$domain_css_noncarrier <- \n geeglm(binary_psychosis ~ \n age_years +\n diagnosis_summary.best_full_scale_iq +\n ados_css_rrb_derived +\n ados_css_sa_derived +\n ocd + \n gender, \n data = exploratory_noncarrier_domain,\n family = binomial(link=\"logit\"), \n na.action = na.exclude,\n id = family)\n\nmodels$exploratory$domain_css_noncarrier %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_nc_or.csv\"))\n\nmodels$exploratory$domain_css_noncarrier %>%\n tidy(conf.int = TRUE) %>%\n mutate_at(vars(-term), round, digits = 3) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_nc_b.csv\"))\n\nmodels$exploratory$domain_css_noncarrier %>%\n tidy(conf.int = TRUE, exponentiate = TRUE) %>%\n write_excel_csv(here(\"r\",\"output\",\"table_s3\",\"domain_css_nc_unrounded.csv\"))", "meta": {"hexsha": "a8d3be5f2627ade6a23f583e426d74746c030e49", "size": 23981, "ext": "r", "lang": "R", "max_stars_repo_path": "r/5_run_analyses.r", "max_stars_repo_name": "amandeepjutla/2019-16p11-psychosis", "max_stars_repo_head_hexsha": "66f3f58925a4c63e40df96adcd51a7837b00012a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, 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YES\n2. YES", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3342658579782974}} {"text": "#===================================================================================================#\n\n# Expression Table\n\n #For \"Heatmap for methylation regulating TF (Fig. 2b, d)\" in Methyl850_HEP.r\n\n#===================================================================================================#\nexpMatrix <- read.table(\"iPS_HEP_diff_F5.TPM.txt\", header=TRUE, row.name=NULL, sep=\"\\t\", stringsAsFactors=FALSE)\nexpMatrix <- expMatrix[,c(1:4,14:16,5:13)] # reorder based on time point\n\n#summrize by gene\npromoterID <- expMatrix[,\"prmtrID\"] # extraction of promoter IDs\ngene_name <- sapply(strsplit(promoterID, \"@\"), function(x){x[2]}) # Extraction of gene names\nunique.gene_name <- unique(gene_name) #removing the redundancy\n## Combine all promoter tag counts of each gene\n## !!This process takes times!!\ngene_expMatrix <- NULL\ncounter <- 0 # Initialization of the counter\nfor(i in unique.gene_name){\ngene_expMatrix <- rbind(gene_expMatrix, apply(expMatrix[which(gene_name == i),-1],2,sum))\n## For loop counter\ncounter <- counter + 1\nprogress <- floor((counter/length(unique.gene_name))*100)\ncat(paste0(\" ##### Progress:\",progress, \"% #####\\r\"))\n}\nrownames(gene_expMatrix) <- unique.gene_name\nsave(gene_expMatrix, file=\"gene_expMatrix.RData\") ## aveing the data\n\n#mean and sd\nrep_group <- c(rep(1,3), rep(2,3), rep(3,3), rep(4,3), rep(5,3))\n## Mean\nmean.gene_expMatrix <- cbind(apply(gene_expMatrix[,which(rep_group==1)], 1, mean),\n apply(gene_expMatrix[,which(rep_group==2)], 1, mean),\n apply(gene_expMatrix[,which(rep_group==3)], 1, mean),\n apply(gene_expMatrix[,which(rep_group==4)], 1, mean),\n apply(gene_expMatrix[,which(rep_group==5)], 1, mean)\n)\ncolnames(mean.gene_expMatrix) <- c(\"iPS_HEP_D00\", \"iPS_HEP_D07\", \"iPS_HEP_D14\", \"iPS_HEP_D21\", \"iPS_HEP_D28\")\n## SD\nsd.gene_expMatrix <- cbind(apply(gene_expMatrix[,which(rep_group==1)], 1, sd),\n apply(gene_expMatrix[,which(rep_group==2)], 1, sd),\n apply(gene_expMatrix[,which(rep_group==3)], 1, sd),\n apply(gene_expMatrix[,which(rep_group==4)], 1, sd),\n apply(gene_expMatrix[,which(rep_group==5)], 1, sd)\n)\ncolnames(sd.gene_expMatrix) <- c(\"iPS_HEP_D00\", \"iPS_HEP_D07\", \"iPS_HEP_D14\", \"iPS_HEP_D21\", \"iPS_HEP_D28\")\n\nsave(list=ls(), file=\"out/CAGE_HEP/gene_expMatrix.RData\")\n#===================================================================================================#\n\n# Marker gene exprssion (Supplementary Figure 1c, 2c and 4c)\n\n #description: This script vidualize expression of indicated genes as a line plot.\n\n#===================================================================================================#\n#loding the CAGE expression data\nexpMatrix <- read.table(\"iPS_HEP_diff_F5.TPM.txt\", header = TRUE, row.name = NULL, sep = \"\\t\", stringsAsFactors = FALSE)\nexpMatrix <- expMatrix[,c(1:4,14:16,5:13)] # reorder based on time point\n\n#Compute mean and sd of replicates\nmean.expMatrix <- cbind(\n apply(expMatrix[,c(2:4)], 1, mean),\n apply(expMatrix[,c(5:7)], 1, mean),\n apply(expMatrix[,c(8:10)], 1, mean),\n apply(expMatrix[,c(11:13)], 1, mean),\n apply(expMatrix[, c(14:16)], 1, mean)\n)\ncolnames(mean.expMatrix) <- c(0, 7, 14, 21, 28)\nrownames(mean.expMatrix) <- expMatrix$prmtrID\n\n#compute sd of replicates\nsd.expMatrix <- cbind(\n apply(expMatrix[,c(2:4)], 1, sd),\n apply(expMatrix[,c(5:7)], 1, sd),\n apply(expMatrix[,c(8:10)], 1, sd),\n apply(expMatrix[,c(11:13)], 1, sd),\n apply(expMatrix[, c(14:16)], 1, sd)\n)\ncolnames(sd.expMatrix) <- c(0, 7, 14, 21, 28)\nrownames(sd.expMatrix) <- expMatrix$prmtrID\n\n#extraction of GOI\npsc_marker <- c(\"POU5F1\", \"NANOG\")\nde_marker <- c(\"SOX17\", \"FOXA2\")\nhep_marker <- c(\"HNF1A\", \"TBX3\", \"HNF4A\", \"ASGR1\", \"APOB\",\"SLC10A1\", \"AFP\", \"KRT18\", \"SERPINA1\" \"ALB\", \"CYP3A4\", \"FOXA2\")\nmethylation_genes <- c(\"DNMT1\", \"DNMT3A\", \"DNMT3B\", \"TET1\", \"TET2\", \"TET3\")\n\ngois <- c(psc_marker, de_marker,hep_marker, methylation_genes)\n\nfor(i in gois){\n #extraction of GOI expression data\n goi <- i\n gene_names <- sapply(strsplit(rownames(mean.expMatrix), \"@\"), function(x){ x[2] })\n\n goi.mean.expMatrix <- mean.expMatrix[gene_names %in% goi, , drop=FALSE]\n goi.sd.expMatrix <- sd.expMatrix[gene_names %in% goi, ,drop=FALSE]\n\n ##order based on promoter number\n goi.mean.expMatrix <- goi.mean.expMatrix[order(as.numeric(gsub(\"p\", \"\", sapply(strsplit(rownames(goi.mean.expMatrix), \"@\"), function(x){x[1]})))),]\n goi.sd.expMatrix <- goi.sd.expMatrix[order(as.numeric(gsub(\"p\", \"\", sapply(strsplit(rownames(goi.sd.expMatrix), \"@\"), function(x){x[1]})))),]\n\n #remove low expreeesion promoters\n cutoff <- 5\n tpm_cutoff <- which((apply(goi.mean.expMatrix, 1, max) >= cutoff))\n goi.mean.expMatrix <- goi.mean.expMatrix[tpm_cutoff, ,drop=FALSE] \n goi.sd.expMatrix <- goi.sd.expMatrix[tpm_cutoff,, drop = FALSE]\n\n if(nrow(goi.mean.expMatrix) == 0) next\n\n #line plot\n goi.mean.expMatrix %>%\n as.data.frame() %>%\n mutate(Gene = rownames(.)) %>%\n pivot_longer(cols = -Gene, values_to = \"TPM\", names_to = \"Days\") -> y\n\n goi.sd.expMatrix %>%\n as.data.frame() %>%\n mutate(Gene = rownames(.)) %>%\n pivot_longer(cols = -Gene, values_to = \"sd\", names_to = \"Days\") -> sd.y\n\n y.df <- cbind(y, sd = sd.y[,3])\n y.df$Days <- factor(y.df$Days, levels = c(0, 7, 14, 21, 28))\n\n theme <- theme(\n panel.background = element_blank(),\n panel.border = element_rect(fill = NA),\n panel.grid.major = element_line(colour = \"gray\"),\n panel.grid.minor = element_blank(),\n strip.background=element_blank(),\n axis.text.x = element_text(colour = \"black\"),\n axis.text.y = element_text(colour = \"black\"),\n axis.ticks=element_line(colour=\"black\"),\n legend.key = element_rect(fill = \"white\"),\n legend.position=\"bottom\",\n plot.margin = unit(c(1, 1, 1, 1), \"line\"))\n \n g <- ggplot(y.df, aes(x = Days, y = TPM, color = Gene))\n g <- g + geom_line(aes(group = Gene))\n g <- g + geom_errorbar(aes(ymin = TPM - sd, ymax = TPM + sd, width = 0.3))\n g <- g + theme \n g <- g + guides(color=guide_legend(\"Gene\"),fill=guide_legend(\"Gene\"))\n plot(g)\n\n ggsave(g, file = paste0(goi, \"_.expression.pdf\"), width = 9, height = 7)\n}", "meta": {"hexsha": "942440a33789503ace63e30712ffcd110a4d1c58", "size": 6249, "ext": "r", "lang": "R", "max_stars_repo_path": "src/R/CAGE_HEP.r", "max_stars_repo_name": "takahirosuzuki0626/Hep_methylation_TF", "max_stars_repo_head_hexsha": "f37f1bb2aa4f80b49edd99968301d3dece9e558a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/R/CAGE_HEP.r", "max_issues_repo_name": "takahirosuzuki0626/Hep_methylation_TF", "max_issues_repo_head_hexsha": "f37f1bb2aa4f80b49edd99968301d3dece9e558a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/R/CAGE_HEP.r", "max_forks_repo_name": "takahirosuzuki0626/Hep_methylation_TF", "max_forks_repo_head_hexsha": "f37f1bb2aa4f80b49edd99968301d3dece9e558a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-08T13:54:59.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T13:54:59.000Z", "avg_line_length": 43.0965517241, "max_line_length": 151, "alphanum_fraction": 0.6063370139, "num_tokens": 1929, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.546738151984614, "lm_q1q2_score": 0.3342658502355508}} {"text": "x=numeric(10) # allocate a numeric vector of size 10 to x\nrm(x) # remove x\n\nx=vector(\"list\",10) #allocate a list of length 10\nx=vector(\"numeric\",10) #same as x=numeric(10), space allocated to list vector above now freed\nrm(x) # remove x\n", "meta": {"hexsha": "a17d9f67c4f2536b55926ae5d40a85fbb7d1a34c", "size": 240, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Memory-allocation/R/memory-allocation.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Memory-allocation/R/memory-allocation.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Memory-allocation/R/memory-allocation.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 34.2857142857, "max_line_length": 93, "alphanum_fraction": 0.7083333333, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3342658502355507}} {"text": "suppressPackageStartupMessages(library(float))\nset.seed(1234)\n\n\nx1 = c(1, NaN, Inf, -Inf, 2, NA_real_)\ns1 = fl(x1)\n\nx2 = matrix(1:30, 10)\nx2[c(1, 3, 5, 7), 3] = NA\nx2[2, 1] = NA\ns2 = fl(x2)\n\nstopifnot(all.equal(is.na(s1), is.na(x1)))\nstopifnot(all.equal(is.na(s2), is.na(x2)))\n\n\n\nstopifnot(all.equal(dbl(na.omit(s1)), na.omit(x1), check.attributes=FALSE))\nstopifnot(all.equal(dbl(na.omit(s2)), na.omit(x2), check.attributes=FALSE))\n\nstopifnot(all.equal(dbl(na.omit(na.omit(s1))), na.omit(na.omit(x1)), check.attributes=FALSE))\nstopifnot(all.equal(dbl(na.omit(na.omit(s2))), na.omit(na.omit(x2)), check.attributes=FALSE))\n", "meta": {"hexsha": "e84dc9b439321b40f11c55d4a4391470fc1453ba", "size": 621, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/na.r", "max_stars_repo_name": "david-cortes/float", "max_stars_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2017-11-08T11:29:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-20T20:17:08.000Z", "max_issues_repo_path": "tests/na.r", "max_issues_repo_name": "david-cortes/float", "max_issues_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 37, "max_issues_repo_issues_event_min_datetime": "2017-09-02T11:14:09.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-19T15:11:19.000Z", "max_forks_repo_path": "tests/na.r", "max_forks_repo_name": "david-cortes/float", "max_forks_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2017-11-18T18:05:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-17T01:23:23.000Z", "avg_line_length": 27.0, "max_line_length": 93, "alphanum_fraction": 0.6795491143, "num_tokens": 242, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3342658502355507}} {"text": "# Project: Surveyer\n# Description: Package of Land and Engineering Surveying utilities\n# Authors: Milutin Pejovic, Milan Kilibarda, Branislav Bajat, Aleksandar Sekulic and Petar Bursac\n\n################\n# surveynet.xlsx\n################\n\n# Parameters:\n# 1. points - Excel file sheet with attributes related to points - geodetic network [Example: Data/Input/xlsx]\n# 2. observations - Excel file sheet with attributes related to observations [Example: Data/Input/xlsx]\n# 3. dest_crs - destination Coordinate Reference System - set EPSG code [default: 3857 - Web Mercator projection coordinate system]\n\nsurveynet.xlsx <- function(points = points, observations = observations, dest_crs = NA){\n # If column \"Description\" is necessary, delete it;\n j=1\n for(i in names(points)){\n if (i == \"NA.\"){\n points <- subset(points, select = -c(j))\n }\n j=j+1\n }\n\n j_1=1\n for(i in names(observations)){\n if (i == \"NA.\"){\n observations <- subset(observations, select = -c(j_1))\n }\n j_1 = j_1 + 1\n }\n\n\n # TODO Check funkcija ide ovde\n\n # Check function for point names, that can not be just numbers -> must contain letter\n points$Name <- as.character(points$Name)\n vec <- c(1:99999)\n j = 1\n for(i in points$Name){\n if(i %in% vec){\n points$Name[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n observations$from <- as.character(observations$from)\n vec <- c(1:99999)\n j = 1\n for(i in observations$from){\n if(i %in% vec){\n observations$from[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n observations$to <- as.character(observations$to)\n vec <- c(1:99999)\n j = 1\n for(i in observations$to){\n if(i %in% vec){\n observations$to[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n # Create geometry columns for points\n if (is.na(dest_crs)){\n dest_crs <- 3857\n } else{\n dest_crs = dest_crs\n }\n\n observations$x_station <- points$x[match(observations$from, points$Name)]\n observations$y_station <- points$y[match(observations$from, points$Name)]\n observations$x_obs.point <- points$x[match(observations$to, points$Name)]\n observations$y_obs.point <- points$y[match(observations$to, points$Name)]\n\n points <- points %>% as.data.frame %>% sf::st_as_sf(coords = c(\"x\",\"y\")) %>% sf::st_set_crs(dest_crs)\n\n dt <- as.data.table(observations)\n dt_1 <- dt[\n , {\n geometry <- sf::st_linestring(x = matrix(c(x_station, x_obs.point, y_station, y_obs.point), ncol = 2))\n geometry <- sf::st_sfc(geometry)\n geometry <- sf::st_sf(geometry = geometry)\n }\n , by = id\n ]\n dt_1 <- sf::st_as_sf(dt_1)\n dt_1 %<>% mutate(from = observations$from,\n to = observations$to,\n distance = observations$distance,\n direction = observations$direction,\n standard_dir = observations$standard_dir,\n standard_dist = observations$standard_dist\n )\n\n dt_1 <- dt_1 %>% sf::st_set_crs(dest_crs)\n observations <- dt_1\n\n # Creating list\n survey_net <- list(points,observations)\n\n return(survey_net)\n\n}\n\n# Parameters:\n# 1. points - updated points tabele releted to 2D net design\n# 2. observations - updated observations tabele releted to 2D net design\n# 3. dest_crs - destination Coordinate Reference System - set EPSG code [default: 3857 - Web Mercator projection coordinate system]\n# 4. raw_points - Excel file sheet with attributes related to points - geodetic network [Example: Data/Input/xlsx]\n\nsurveynet.xlsx_updated <- function(points = points, observations = observations, dest_crs = NA, raw_points = raw_points){\n\n # Check function for point names, that can not be just numbers -> must contain letter\n raw_points$Name <- as.character(raw_points$Name)\n vec <- c(1:99999)\n j = 1\n for(i in raw_points$Name){\n if(i %in% vec){\n raw_points$Name[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n # Create geometry columns for points\n if (is.na(dest_crs)){\n dest_crs <- 3857\n }else{\n dest_crs = dest_crs\n }\n\n points$x <- raw_points$x[match(points$Name, raw_points$Name)]\n points$y <- raw_points$y[match(points$Name, raw_points$Name)]\n\n observations$x_station <- points$x[match(observations$from, points$Name)]\n observations$y_station <- points$y[match(observations$from, points$Name)]\n observations$x_obs.point <- points$x[match(observations$to, points$Name)]\n observations$y_obs.point <- points$y[match(observations$to, points$Name)]\n\n points <- points %>% as.data.frame %>% sf::st_as_sf(coords = c(\"x\",\"y\")) %>% sf::st_set_crs(dest_crs)\n\n dt <- as.data.table(observations)\n dt_1 <- dt[\n , {\n geometry <- sf::st_linestring(x = matrix(c(x_station, x_obs.point, y_station, y_obs.point), ncol = 2))\n geometry <- sf::st_sfc(geometry)\n geometry <- sf::st_sf(geometry = geometry)\n }\n , by = id\n ]\n dt_1 <- sf::st_as_sf(dt_1)\n dt_1 %<>% mutate(from = observations$from,\n to = observations$to,\n distance = observations$distance,\n direction = observations$direction,\n standard_dir = observations$standard_dir,\n standard_dist = observations$standard_dist\n )\n\n dt_1 <- dt_1 %>% sf::st_set_crs(dest_crs)\n observations <- dt_1\n\n # Creating list\n survey_net <- list(points,observations)\n\n return(survey_net)\n\n}\n\n###############\n# surveynet.shp\n###############\n\n# Parameters:\n# 1. points - sf object with geometry type POINT - geodetic network [Example: Data/Input/shp]\n# 2. observations - sf object with geometry type LINESTRING [Example: Data/Input/shp]\n# 3. fix_x - list with names of points that define datum - X coordinate\n# 3. fix_y - list with names of points that define datum - Y coordinate\n# 4. st_dir - \"a priori\" standard deviation for direction observations [\"]\n# 5. st_dist - \"a priori\" standard deviation for distance observations [mm]\n# 6. dest_crs - destination Coordinate Reference System - set EPSG code [default: 3857 - Web Mercator projection coordinate system]\n# 7. points_object - list with names of points that represnt object of interests\n\nsurveynet.shp <- function(points, observations, fix_x = list(), fix_y = list(), st_dir, st_dist, dest_crs = NA, points_object = list()){\n for(i in names(points)){\n if(i == \"Naziv\"){\n points <- points %>% rename(\"Name\" = \"Naziv\")\n }\n }\n for(i in names(observations)){\n if(i == \"Name\"){\n observations <- observations %>% rename(\"id\" = \"Name\")\n }\n }\n\n # TODO Check funkcija ide ovde\n\n # Check function for point names, that can not be just numbers -> must contain letter\n points$Name <- as.character(points$Name)\n vec <- c(1:99999)\n j = 1\n for(i in points$Name){\n if(i %in% vec){\n points$Name[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n # Transformation to the destination CRS\n if (is.na(dest_crs)){\n dest_crs <- 3857\n points <- points %>% st_transform(dest_crs)\n observations <- observations %>% st_transform(dest_crs)\n }\n if (dest_crs == 3857){\n dest_crs <- 3857\n points <- points %>% st_transform(dest_crs)\n observations <- observations %>% st_transform(dest_crs)\n }\n if (st_crs(points)$epsg == \"4326\"){\n points <- points %>% st_transform(dest_crs)\n }\n if (st_crs(observations)$epsg == \"4326\"){\n observations <- observations %>% st_transform(dest_crs)\n }\n\n\n if(is.na(observations$id[[1]])){\n observations$id <- (1:length(observations$geometry))\n }\n\n # Defining datum for points\n points %<>% mutate(FIX_X = FALSE, FIX_Y = FALSE)\n\n points$FIX_X[points$Name %in% fix_x] <- TRUE\n points$FIX_Y[points$Name %in% fix_y] <- TRUE\n\n points %<>% mutate(Point_object = FALSE)\n points$Point_object[points$Name %in% points_object] <- TRUE\n\n # Observational plan - adding new columns\n observations %<>% mutate(from = NA,\n to = NA,\n distance = TRUE,\n direction = TRUE,\n standard_dir = st_dir,\n standard_dist = st_dist\n )\n\n # Observational plan - defining and ading names for first and last points for observations\n # Creating data frame from sf class object observations, with goal to extract names for first and last points\n observations_1 <- data.frame(station = NA, obs.point = NA, x_station = NA,y_station = NA, x_obs.point = NA, y_obs.point = NA)\n\n # Line string to multipoint\n ob_plan_first_point <- st_line_sample(observations,sample = 0)\n ob_plan_last_point <- st_line_sample(observations,sample = 1)\n coord_1 <- as.data.frame(st_coordinates(ob_plan_first_point))\n coord_2 <- as.data.frame(st_coordinates(ob_plan_last_point))\n\n # Multipoint to point\n pnts_1 = st_cast(ob_plan_first_point, \"POINT\")\n pnts_2 = st_cast(ob_plan_last_point, \"POINT\")\n # X- East Y - North\n observations_1 <- data.frame(station = NA, obs.point = NA, x_station = st_coordinates(pnts_1)[,1], y_station = st_coordinates(pnts_1)[,2], x_obs.point = st_coordinates(pnts_2)[,1], y_obs.point = st_coordinates(pnts_2)[,2])\n\n # Adding columns Names for stations and observation point with values from constraint exactly match coordinates\n # TODO srediti da radi i za y\n\n observations_1$station <- points$Name[match(observations_1$x_station, st_coordinates(points)[,1])]\n observations_1$obs.point <- points$Name[match(observations_1$x_obs.point, st_coordinates(points)[,1])]\n observations_1$id <- coord_1$L1\n\n observations$from <- observations_1$station[match(observations$id, observations_1$id)]\n observations$to <- observations_1$obs.point[match(observations$id, observations_1$id)]\n\n observations %<>% mutate(\n id = as.numeric(id),\n standard_dir = as.numeric(standard_dir),\n standard_dist = as.numeric(standard_dist),\n from = as.character(from),\n to = as.character(to)\n )\n\n points %<>% mutate(\n Name = as.character(Name)\n )\n # Creating list\n survey_net <- list(points,observations)\n\n return(survey_net)\n}\n\n###############\n# surveynet.kml\n###############\n\n# Parameters:\n# 1. points - sf object with geometry type POINT - geodetic network [Example: Data/Input/kml]\n# 2. observations - sf object with geometry type LINESTRING [Example: Data/Input/kml]\n# 3. fix_x - list with names of points that define datum - X coordinate\n# 3. fix_y - list with names of points that define datum - Y coordinate\n# 4. st_dir - \"a priori\" standard deviation for direction observations [\"]\n# 5. st_dist - \"a priori\" standard deviation for distance observations [mm]\n# 6. dest_crs - destination Coordinate Reference System - set EPSG code [default: 3857 - Web Mercator projection coordinate system]\n# 7. points_object - list with names of points that represnt object of interests\n\nsurveynet.kml <- function(points, observations, fix_x = list(), fix_y = list(), st_dir, st_dist, dest_crs = NA, points_object = list()){\n for(i in names(points)){\n if(i == \"Naziv\"){\n points <- points %>% rename(\"Name\" = \"Naziv\")\n }\n }\n for(i in names(observations)){\n if(i == \"Name\"){\n observations <- observations %>% rename(\"id\" = \"Name\")\n }\n }\n # If column \"Description\" is necessary, delete it;\n j=1\n for(i in names(points)){\n if (i == \"Description\"){\n points <- subset(points, select = -c(j))\n }\n j=j+1\n }\n\n j_1=1\n for(i in names(observations)){\n if (i == \"Description\"){\n observations <- subset(observations, select = -c(j_1))\n }\n j_1 = j_1 + 1\n }\n\n # TODO Check funkcija ide ovde\n\n # Check function for point names, that can not be just numbers -> must contain letter\n points$Name <- as.character(points$Name)\n vec <- c(1:99999)\n j = 1\n for(i in points$Name){\n if(i %in% vec){\n points$Name[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n\n # Transformation to the destination CRS\n if (is.na(dest_crs)){\n dest_crs <- 3857\n points <- points %>% st_transform(dest_crs)\n observations <- observations %>% st_transform(dest_crs)\n }\n if (st_crs(points)$epsg == \"4326\"){\n points <- points %>% st_transform(dest_crs)\n message(\"\\nPoints data are reprojected to the destination Coordinate Reference System!\\n\")\n message(st_crs(points))\n }\n if (st_crs(observations)$epsg == \"4326\"){\n observations <- observations %>% st_transform(dest_crs)\n message(\"\\nObservation data are reprojected to the destination Coordinate Reference System!\\n\")\n message(st_crs(observations))\n }\n\n # Handling with some columns\n for(i in names(points)){\n if(i == \"id\"){\n break\n } else {\n points$id <- (1:length(points$geometry))\n }\n }\n\n for(i in names(observations)){\n if(i == \"id\"){\n observations$id <- (1:length(observations$geometry))\n }\n }\n # Defining datum for points\n points %<>% mutate(FIX_X = FALSE, FIX_Y = FALSE)\n\n points$FIX_X[points$Name %in% fix_x] <- TRUE\n points$FIX_Y[points$Name %in% fix_y] <- TRUE\n\n points %<>% mutate(Point_object = FALSE)\n points$Point_object[points$Name %in% points_object] <- TRUE\n\n # Observational plan - adding new columns\n observations %<>% mutate(distance = TRUE,\n direction = TRUE,\n standard_dir = st_dir,\n standard_dist = st_dist,\n from = NA,\n to = NA)\n\n # Observational plan - defining and ading names for first and last points for observations\n # Creating data frame from sf class object observations, with goal to extract names for first and last points\n observations_1 <- data.frame(station = NA, obs.point = NA, x_station = NA,y_station = NA, x_obs.point = NA, y_obs.point = NA)\n\n # Line string to multipoint\n ob_plan_first_point <- st_line_sample(observations,sample = 0)\n ob_plan_last_point <- st_line_sample(observations,sample = 1)\n coord_1 <- as.data.frame(st_coordinates(ob_plan_first_point))\n coord_2 <- as.data.frame(st_coordinates(ob_plan_last_point))\n\n # Multipoint to point\n pnts_1 = st_cast(ob_plan_first_point, \"POINT\")\n pnts_2 = st_cast(ob_plan_last_point, \"POINT\")\n # X- East Y - North\n observations_1 <- data.frame(station = NA, obs.point = NA, x_station = st_coordinates(pnts_1)[,1], y_station = st_coordinates(pnts_1)[,2], x_obs.point = st_coordinates(pnts_2)[,1], y_obs.point = st_coordinates(pnts_2)[,2])\n\n # Adding columns Names for stations and observation point with values from constraint exactly match coordinates\n # TODO srediti da radi i za y\n observations_1$station <- points$Name[match(observations_1$x_station, st_coordinates(points)[,1])]\n observations_1$obs.point <- points$Name[match(observations_1$x_obs.point, st_coordinates(points)[,1])]\n observations_1$id <- coord_1$L1\n\n observations$from <- observations_1$station[match(observations$id, observations_1$id)]\n observations$to <- observations_1$obs.point[match(observations$id, observations_1$id)]\n\n observations %<>% mutate(\n id = as.numeric(id),\n standard_dir = as.numeric(standard_dir),\n standard_dist = as.numeric(standard_dist),\n from = as.character(from),\n to = as.character(to)\n )\n\n # Creating list\n survey_net <- list(points,observations)\n\n return(survey_net)\n}\n\n\n##################\n# net_spatial_view\n##################\n\n# Function for spatial data visualisation trough package ggplot2\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from surveynet.xxx function\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from surveynet.xxx function\n\nnet_spatial_view <- function(points, observations){\n\n points$fill_p <- \"red\"\n points$fill_p[points$Point_object == TRUE] <- \"DeepSkyBlue\"\n\n # Example to add little different type of observations\n # observations$distance[1:3] <- FALSE\n # observations$direction[5:7] <- FALSE\n # observations$fill_o <- \"LightGoldenRodYellow\"\n\n observations$fill_o <- ifelse(observations$distance == TRUE & observations$direction == FALSE,\"LightGoldenRodYellow\", ifelse(observations$distance == FALSE & observations$direction == TRUE, \"Khaki\",\"orange\"))\n\n net_view <- ggplot(data=observations) +\n geom_sf(size=1,stroke=1, color = observations$fill_o)+\n geom_sf(data=points, shape = 24, fill = points$fill_p, size=2.5, stroke=2) +\n geom_sf_text(data=points, aes(label=Name,hjust = 1.5, vjust =1.5))+\n xlab(\"\\nLongitude [deg]\") +\n ylab(\"Latitude [deg]\\n\") +\n ggtitle(\"Observational plan and points [geodetic network and object points]\")+\n guides(col = guide_legend())+\n theme_bw()\n return(net_view)\n\n}\n\n\n##########################################\n# net_spatial_view_web [package:: mapview]\n##########################################\n\n# Function for spatial data visualisation at web maps\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from surveynet.xxx function\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from surveynet.xxx function\n\nnet_spatial_view_web <- function(points, observations){\n Points <- st_transform(points, 4326)\n Observations <- st_transform(observations, 4326)\n\n Points$type <- \"Geodetic network\"\n Points$type[Points$Point_object == TRUE] <- \"Points at object\"\n\n Observations$type[Observations$distance == TRUE & Observations$direction == FALSE] <- \"Distance\"\n Observations$type[Observations$distance == FALSE & Observations$direction == TRUE] <- \"Direction\"\n Observations$type[Observations$distance == TRUE & Observations$direction == TRUE] <- \"Both\"\n\n web_map_1 <- mapview(Points, zcol = \"type\", col.regions = c(\"red\",\"grey\")) + mapview(Observations, zcol = \"type\")\n\n return(web_map_1)\n\n}\n\n###########\n# check_net\n###########\n\n# Function for checking input data for errors - geometrical and topological consistency, redudancy etc.\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from surveynet.xxx function\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from surveynet.xxx function\n\ncheck_net <- function(points, observations){\n\n observations_1 <- data.frame(station = NA,obs.point = NA, x_station = NA,y_station = NA,x_obs.point = NA,y_obs.point = NA)\n\n # Line string to multipoint\n pl_op_pocetne_tac <- st_line_sample(observations,sample=0)\n pl_op_krajnje_tac <- st_line_sample(observations,sample=1)\n koord_1 <- as.data.frame(st_coordinates(pl_op_pocetne_tac))\n koord_2 <- as.data.frame(st_coordinates(pl_op_krajnje_tac))\n\n # Multipoint to point\n pnts_1 = st_cast(pl_op_pocetne_tac, \"POINT\")\n pnts_2 = st_cast(pl_op_krajnje_tac, \"POINT\")\n # X- East Y - North\n observations_1 <- data.frame(station = NA,obs.point = NA, x_station = st_coordinates(pnts_1)[,1],y_station = st_coordinates(pnts_1)[,2],x_obs.point = st_coordinates(pnts_2)[,1],y_obs.point = st_coordinates(pnts_2)[,2])\n\n\n x1 <- match(observations_1$x_station, st_coordinates(points)[,1])\n x2 <- match(observations_1$x_obs.point, st_coordinates(points)[,1])\n y1 <- match(observations_1$y_station, st_coordinates(points)[,2])\n y2 <- match(observations_1$y_obs.point, st_coordinates(points)[,2])\n\n if(any(x1) == FALSE){\n message(\"\\nX koordinate tacaka i tacaka stanica u planu opazanja se ne poklapaju.\")\n } else {\n message(\"\\nX koordinate tacaka i tacaka stanica u planu opazanja se poklapaju.\")\n }\n if(any(x2) == FALSE){\n message(\"\\nX koordinate tacaka i tacaka opazanja u planu opazanja se ne poklapaju.\")\n } else {\n message(\"\\nX koordinate tacaka i tacaka opazanja u planu opazanja se poklapaju.\")\n }\n if(any(y1) == FALSE){\n message(\"\\nY koordinate tacaka i tacaka stanica u planu opazanja se ne poklapaju.\")\n } else {\n message(\"\\nY koordinate tacaka i tacaka stanica u planu opazanja se poklapaju.\")\n }\n if(any(y2) == FALSE){\n message(\"\\nY koordinate tacaka i tacaka opazanja u planu opazanja se ne poklapaju.\")\n } else {\n message(\"\\nY koordinate tacaka i tacaka opazanja u planu opazanja se poklapaju.\")\n }\n un1 <- data.frame(id = points$id)\n un1$Name <- as.data.frame(unique(points$Name))\n un2 <- as.data.frame(unique(observations$from))\n un3 <- as.data.frame(unique(observations$to))\n\n un1$station <- TRUE[match(un1$Name, un2)]\n un1$obs <- TRUE[match(un1$Name, un3)]\n\n if(any(un1$station) == FALSE){\n message(\"\\nKao stanica u planu opazanja nije iskoriscena tacka:\")\n return(un1$Name[match(un1$station, FALSE)])\n } else {\n message(\"\\nSve points su iskoriscene kao stanice u planu opazanja.\")\n }\n\n if(any(un1$obs) == FALSE){\n message(\"\\nKao opazana tacka u planu opazanja nije iskoriscena tacka:\")\n return(un1$Name[match(un1$obs, FALSE)])\n } else {\n message(\"\\nSve points su opazane u planu opazanja.\")\n }\n\n\n\n}\n\n###################\n# surveynet.mapedit\n###################\n\n# Function for interactive adding points on web maps and storage as sf [Simple Feature]\nsurveynet.mapedit_add <- function(){\n created <- mapview() %>% editMap()\n return(created$finished)\n}\n\n# Function for visualisation trough mapview package\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from function surveynet.mapedit_add\n\nsurveynet.mapedit_view <- function(points = points){\n Points <- mapview(points)\n return(Points)\n}\n\n# Function for preparing points - sf attribute table check\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from function surveynet.mapedit_add\n\nsurveynet.mapedit_points <- function(points = points){\n j=1\n for(i in names(points)){\n if (i == \"_leaflet_id\"){\n points <- subset(points, select = -c(j))\n }\n j=j+1\n }\n j=1\n for(i in names(points)){\n if (i == \"feature_type\"){\n points <- subset(points, select = -c(j))\n }\n j=j+1\n }\n\n for(i in names(points)){\n if(i == \"id\"){\n break\n } else {\n points$id <- (1:length(points$geometry))\n }\n }\n points <- points %>% rename(\"Name\" = \"id\")\n\n points$Name <- as.character(points$Name)\n vec <- c(1:99999)\n j = 1\n for(i in points$Name){\n if(i %in% vec){\n points$Name[j] <- paste(\"T\",i, sep = \"\")\n j = j +1\n\n }\n }\n return(points)\n}\n\n# Function for creating observations from points - all\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from function surveynet.mapedit_points\n\nsurveynet.mapedit_observations <- function(points = points){\n res = expand.grid(to = points$Name, from = points$Name) # combine values from two columns in all posible combinations\n res <- as.data.frame(res[!(res$to == res$from), ]) # delete rows with same values in two columns\n rownames(res) <- 1:nrow(res) # reorder index number of rows\n res <- res[ ,c(\"from\",\"to\")] # reorder columns\n return(res)\n}\n\n# Function for creating observations from points - edit and interactivly with CRUD [Create, Read, Update and Delete] functionalites edit observations\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from function surveynet.mapedit_points\n# 2. st_dir - \"a priori\" standard deviation for direction observations [\"]\n# 3. st_dist - \"a priori\" standard deviation for distance observations [mm]\n#\n\nsurveynet.mapedit_observations_edit <- function(points = points, st_dir = st_dir, st_dist = st_dist){\n res = expand.grid(to = points$Name, from = points$Name) # combine values from two columns in all posible combinations\n res <- as.data.frame(res[!(res$to == res$from), ]) # delete rows with same values in two columns\n rownames(res) <- 1:nrow(res) # reorder index number of rows\n res <- res[ ,c(\"from\",\"to\")] # reorder columns\n observations <- res\n\n points$x <- st_coordinates(points)[,1]\n points$y <- st_coordinates(points)[,2]\n\n observations$x_station <- points$x[match(observations$from, points$Name)]\n observations$y_station <- points$y[match(observations$from, points$Name)]\n observations$x_obs.point <- points$x[match(observations$to, points$Name)]\n observations$y_obs.point <- points$y[match(observations$to, points$Name)]\n\n observations$id <- 1:nrow(observations)\n\n observations %<>% mutate(distance = TRUE,\n direction = TRUE,\n standard_dir = st_dir,\n standard_dist = st_dist\n )\n return(observations)\n}\n\n# create complete sf object - points and observations\n# Parameters:\n# 1. points - sf object with geometry type POINT and related attributes as product from function surveynet.mapedit_points\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from function surveynet.mapedit_observations\n# 3. fix_x - list with names of points that define datum - X coordinate\n# 3. fix_y - list with names of points that define datum - Y coordinate\n# 4. st_dir - \"a priori\" standard deviation for direction observations [\"]\n# 5. st_dist - \"a priori\" standard deviation for distance observations [mm]\n# 6. dest_crs - destination Coordinate Reference System - set EPSG code [default: 3857 - Web Mercator projection coordinate system]\n# 7. points_object - list with names of points that represnt object of interests\n\nsurveynet.mapedit <- function(points_raw = points_raw, points = points, observations = observations, dest_crs = NA){\n\n if (is.na(dest_crs)){\n dest_crs <- 3857\n points_raw %<>% st_transform(dest_crs)\n } else{\n points_raw %<>% st_transform(dest_crs)\n }\n\n #if (st_crs(points)$epsg == 4326){\n # dest_crs <- 3857\n # points <- points %>% st_transform(dest_crs)\n # message(\"\\nPoints data are reprojected to the destination Coordinate Reference System!\\n\")\n # message(st_crs(points))\n #}\n\n #Defining datum for points\n points %<>% mutate(FIX_X = FALSE, FIX_Y = FALSE)\n\n points$FIX_X[points$Name %in% fix_x] <- TRUE\n points$FIX_Y[points$Name %in% fix_y] <- TRUE\n\n points %<>% mutate(Point_object = FALSE)\n points$Point_object[points$Name %in% points_object] <- TRUE\n\n points$FIX_X <- as.logical(points$FIX_X)\n points$FIX_Y <- as.logical(points$FIX_Y)\n points$Point_object <- as.logical(points$Point_object)\n\n observations$x_station <- points$x[match(observations$from, points$Name)]\n observations$y_station <- points$y[match(observations$from, points$Name)]\n observations$x_obs.point <- points$x[match(observations$to, points$Name)]\n observations$y_obs.point <- points$y[match(observations$to, points$Name)]\n\n observations$id <- as.numeric(1:nrow(observations))\n\n observations %<>% mutate(distance = distance,\n direction = direction,\n standard_dir = standard_dir,\n standard_dist = standard_dist\n )\n\n dt <- as.data.table(observations)\n dt_1 <- dt[\n , {\n geometry <- sf::st_linestring(x = matrix(c(x_station, x_obs.point, y_station, y_obs.point), ncol = 2))\n geometry <- sf::st_sfc(geometry)\n geometry <- sf::st_sf(geometry = geometry)\n }\n , by = id\n ]\n\n dt_1 <- sf::st_as_sf(dt_1)\n\n dt_1 %<>% mutate(from = as.character(observations$from),\n to = as.character(observations$to),\n distance = observations$distance,\n direction = observations$direction,\n standard_dir = observations$standard_dir,\n standard_dist = observations$standard_dist\n )\n\n dt_1 <- dt_1 %>% sf::st_set_crs(dest_crs)\n observations <- dt_1\n\n # Observational plan - adding new columns\n observations %<>% mutate(distance = distance,\n direction = direction,\n standard_dir = as.numeric(standard_dir),\n standard_dist = as.numeric(standard_dist)\n )\n\n\n points <- subset(points, select = -c(x,y))\n # Creating list\n survey_net_mapedit <- list(points,observations)\n\n return(survey_net_mapedit)\n}\n\n##########################################\n# adj.net_spatial_view_web [package:: mapview]\n##########################################\n\n# Function for adjusted net data visualisation at web maps\n# Parameters:\n# 1. ellipses - sf object with geometry type POLYGON and related attributes as product from design.snet function that represents error ellipses\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from design.snet function\n\nadj.net_spatial_view_web <- function(ellipses = ellipses, observations = observations){\n Ellipses <- st_transform(ellipses, 4326)\n Observations <- st_transform(observations, 4326)\n web_map_2 <- mapview(Ellipses, zcol = \"sp\") + mapview(Observations, zcol = \"rii\")\n return(web_map_2)\n}\n\n\n######################\n# adj.net_spatial_view\n######################\n\n# Function for spatial data visualisation trough package ggplot2\n# Parameters:\n# 1. ellipses - sf object with geometry type POLYGON and related attributes as product from design.snet function that represents error ellipses\n# 2. observations - sf object with geometry type LINESTRING and related attributes as product from design.snet function\n\n#adj.net_spatial_view <- function(ellipses = ellipses, observations = observations){\n# #observations$fill_o <- ifelse(observations$distance == TRUE & observations$direction == FALSE,\"LightGoldenRodYellow\", ifelse(observations$distance == FALSE & observations$direction == TRUE, \"Khaki\",\"orange\"))\n# adj.net_view <- ggplot(data=observations) +\n# geom_sf(data = observations)+\n# geom_sf(data=ellipses,aes(fill = sp))+\n# geom_sf_text(data=ellipses, aes(label=Name,hjust = 2.5, vjust =2.5))+\n# xlab(\"\\nLongitude [deg]\") +\n# ylab(\"Latitude [deg]\\n\") +\n# ggtitle(\"Adjusted observational plan - net quality\")+\n# guides(col = guide_legend())+\n# theme_bw()\n# return(adj.net_view)\n#\n#}\n\nadj_net_spatial_view <- function(adj.ellipses, adj.observations){\n adj.net_view <- ggplot() +\n geom_sf(data = adj.observations)+\n geom_sf(data=adj.ellipses, aes(fill = sp))+\n geom_sf_text(data=adj.ellipses, aes(label=Name,hjust = 2.5, vjust =2.5))+\n xlab(\"\\nLongitude [deg]\") +\n ylab(\"Latitude [deg]\\n\") +\n ggtitle(\"Adjusted observational plan - net quality\")+\n #guides(col = guide_legend())+\n theme_bw()\n return(adj.net_view)\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "f5d89318281f3952ab7fa0befb70217408082574", "size": 30232, "ext": "r", "lang": "R", "max_stars_repo_path": "R/deprecated/Shiny/old_design/input_functions.r", "max_stars_repo_name": "pejovic/Surveyor", "max_stars_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-14T22:40:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-14T22:40:36.000Z", "max_issues_repo_path": "R/deprecated/Shiny/old_design/input_functions.r", "max_issues_repo_name": "pejovic/Surveyor", "max_issues_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/deprecated/Shiny/old_design/input_functions.r", "max_forks_repo_name": "pejovic/Surveyor", "max_forks_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.7352245863, "max_line_length": 224, "alphanum_fraction": 0.6702831437, "num_tokens": 8002, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381372136563, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.33426584120485353}} {"text": "argot2gaf <- function(in_files,config){\n print(\"Reading the input file\")\n gaf_date = format(Sys.time(),\"%Y%m%d\")\n tmp_out = lapply(in_files,function(infile){\n tmp_data = fread(infile,sep = \"\\t\",header = T)\n colnames(tmp_data) = gsub(\"#\",\"\",colnames(tmp_data))\n tmp_data\n })\n argot2_data = do.call(rbind,tmp_out)\n argot2_data = argot2_data[!grep(\"#\",`SeqID`)]\n taxon_txt=paste(\"taxon:\",config$input$taxon,sep=\"\")\n \n gaf_cols=fread(config$data$go$gaf_cols,header = F)$V1\n gaf_cols\n \n print(\"Converting to GAF 2.0\")\n gaf_data = argot2_data[,.(`SeqID`,`GO ID`,`Int. Confidence`)]\n colnames(gaf_data) = c(\"db_object_id\",\"term_accession\",\"with\")\n \n \n gaf_data$with = as.numeric(gaf_data$with)\n min_score=min(gaf_data$with)\n max_score=max(gaf_data$with)\n gaf_data$with = (gaf_data$with - min_score)/(max_score - min_score)\n \n \n obo_data = check_obo_data(config$data$go$obo)\n aspect = unlist(obo_data$aspect[gaf_data$term_accession])\n gaf_data[,aspect:=aspect]\n \n #gaf_data[,db_object_id:=tmp]\n \n gaf_data[,db_object_symbol:=db_object_id]\n gaf_data[,taxon:=taxon_txt]\n gaf_data[,date:=gaf_date]\n gaf_data[,assigned_by:=\"Argot2.5\"]\n \n return(gaf_data)\n # print(\"Writing the outfile\")\n # write_gaf(gaf_data,out_file)\n}", "meta": {"hexsha": "1854ed9bcaef518496c33859c866acd36d0218b3", "size": 1337, "ext": "r", "lang": "R", "max_stars_repo_path": "code/R/argot2gaf.r", "max_stars_repo_name": "Dill-PICL/GOMAP-container", "max_stars_repo_head_hexsha": "a915e9a7da586dbf1a347f92be81e98373a77e68", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-05-02T03:21:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-04T13:05:46.000Z", "max_issues_repo_path": "code/R/argot2gaf.r", "max_issues_repo_name": "Dill-PICL/GOMAP-container", "max_issues_repo_head_hexsha": "a915e9a7da586dbf1a347f92be81e98373a77e68", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-09-12T17:00:26.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-31T18:52:28.000Z", "max_forks_repo_path": "code/R/argot2gaf.r", "max_forks_repo_name": "Dill-PICL/GOMAP-container", "max_forks_repo_head_hexsha": "a915e9a7da586dbf1a347f92be81e98373a77e68", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-02-07T22:01:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-04T01:53:47.000Z", "avg_line_length": 32.6097560976, "max_line_length": 71, "alphanum_fraction": 0.6514584892, "num_tokens": 415, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7310585903489892, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.3341937238006734}} {"text": "# Remove variables\nrm(list=ls())\n\n# Add some helper functions\nsource(\"chartr-FitRoutines.r\")\nsource(\"chartr-HelperFunctions.r\")\n\n# Which job number to run, this tells you the run number\njobnum=1\nset.seed(jobnum)\nfnam = letters[jobnum];\n\n# source the fitting code files\ndirs=\"caseStudy2\" # directory name of data files to fit\nresultDir = \"caseStudy2_Fits/\"\n\nsubjs=dir(dirs); \nnsubj=length(subjs) \nlistOfSubjects = subjs;\n\n\n\n# Setup some baseline parameters\ncontp = list(p=0) # if estcontp==FALSE, give proportion of contaminant responses\nmaxits = 750 # number of iterations of DEoptim to run\nnparticles = 400 # number of particles/chains for DEoptim\nnmc =10000 # number of MC samples for simulating the models at each iteration\nestcontp=FALSE # estimate contaminant mixture probability from data. usually set to false\n\n\nbailouttime=4 # time after which to bail out of diffusion sims, in seconds\nmaxTimeStep=as.double(length(seq(0,bailouttime,.001))) # max iterations for fitting routine, then bail out\npred=F # generate model predictions from fitting routine (only use this once you have estimtated parameters from data using DEoptim)\n\nnreps = 5;\ngub=4\n\n\n\nsubjnam=\"Subj1\"\n\nload(paste(dirs,\"/\",subjnam,sep=\"\"))\nmodel= \"DDM\"\nsaveFileName=paste(resultDir,subjnam,\"-\",model,\"-\",fnam,sep=\"\")\n\n# for simple switching in fitting routine\nqps=as.numeric(dimnames(dat$q)[[1]])\nncohs=1:dim(dat$q)[3]\n\n# make parameter vector - different scaling for Stone and Stone+UGM\n# order: drifts (7), eta, upper boundary, Ter\nnds=length(ncohs)\n\nactualParams = paramsandlims(model, nds)\nfitUGM = unname(actualParams$fitUGM)\nlowers = actualParams$lowers\nuppers = actualParams$uppers\nparnames = actualParams$parnames\n\nstepsize=ifelse(fitUGM < 0,.001,1) # time step for diffusion process: .001s (Stone), 1ms (UGM)\nstoch.s=ifelse(fitUGM < 0,.1,100) # diffusion constant\ntimecons=ifelse(fitUGM < 0,0,100) # time constant for low pass filter\nusign=ifelse(fitUGM < 0,0,1) # scaling value for linear urgency function (usign=1 is linear with time). EAM needs usign=0, timecons=0\n# cutoff for very slow RTs\n# gub = global upper bound in seconds - no RTs should be slower than this (and none were in data)\n\nprint(\"Starting optimization ...\")\nlibrary(DEoptim)\nsystem.time({\n tmp=DEoptim(\n fn=obj,\n lower=lowers,\n upper=uppers,\n dat=dat,\n nmc=nmc,\n contp=contp,\n ncohs=ncohs,\n fitUGM=fitUGM,\n gub=gub,\n pred=FALSE,\n qps=qps,\n stepsize=stepsize,\n stoch.s=stoch.s,\n timecons=timecons,\n usign=usign,\n parnames=parnames,\n maxTimeStep=maxTimeStep,\n control=DEoptim.control(itermax=maxits,NP=nparticles,trace=TRUE,\n parallelType=1,reltol=1e-6,steptol=200,\n # load objects used for fitting, for parallelType==1\n parVar=list(\"dat\",\"lowers\",\"uppers\",\"nmc\",\"contp\",\"ncohs\", \"fitUGM\",\"pred\",\n \"qps\", \"stepsize\",\"stoch.s\",\"timecons\",\"usign\",\"parnames\",\"maxTimeStep\",\"maxits\",\"nparticles\",\"gub\",\n \"diffusionC\",\"makeparamlist\",\"contaminantmixresps\",\"qmpouts\",\"getpreds\",\"obj\",\"returnListOfModels\")\n # same again, but for functions\n ))})\ncat(paste(\"\\n\",dirs,\"dataset:\",subjnam,\", model:\",model,fnam,\"\\n\\n\",sep=\" \"))\n\nout=tmp$optim$bestmem\nnames(out)=parnames\nprint(round(out,4))\n\nprint(round(tmp$optim$bestval,4))\n# re-calculate obj for best fitting parameters, to determine amount of noise\n# in the obj value for the best fit\nmcsforpreds=50000\nreobj=obj(x=tmp$optim$bestmem,dat=dat,nmc=mcsforpreds,\n contp=contp,ncohs=ncohs,fitUGM=fitUGM,gub=gub,pred=FALSE,\n qps=qps,stepsize=stepsize,stoch.s=stoch.s,timecons=timecons,usign=usign,\n parnames=parnames,maxTimeStep=maxTimeStep)\nprint(round(reobj,4))\n\n\n# Now compute it for each level of quantile. Suspicion is that you go awry for the hardest coherences and you really need to think \n# about what goes on there. Life is not easy there :)\nmcsforpreds=50000\nreobjperpoint=objPerQ(x=tmp$optim$bestmem,dat=dat,nmc=mcsforpreds,\n contp=contp,ncohs=ncohs,fitUGM=fitUGM,gub=gub,pred=FALSE,\n qps=qps,stepsize=stepsize,stoch.s=stoch.s,timecons=timecons,usign=usign,\n parnames=parnames,maxTimeStep=maxTimeStep)\nprint(round(reobjperpoint,4))\nout=list(dataset=subjnam,model=model,ndataset=fnam,pars=out,\n obj=-tmp$optim$bestval,reobj=-reobj, reobjperpoint=reobjperpoint)\n\n\nsave(out,file=saveFileName)", "meta": {"hexsha": "87057506ce0c72578f9e78874376f2ff464fbea5", "size": 4551, "ext": "r", "lang": "R", "max_stars_repo_path": "chartr-demoFit.r", "max_stars_repo_name": "mdnunez/CHaRTr", "max_stars_repo_head_hexsha": "7a315a39c6048e1a625db6f583f453f2726e58aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "chartr-demoFit.r", "max_issues_repo_name": "mdnunez/CHaRTr", "max_issues_repo_head_hexsha": "7a315a39c6048e1a625db6f583f453f2726e58aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "chartr-demoFit.r", "max_forks_repo_name": "mdnunez/CHaRTr", "max_forks_repo_head_hexsha": "7a315a39c6048e1a625db6f583f453f2726e58aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-10-01T23:11:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-02T01:04:06.000Z", "avg_line_length": 36.408, "max_line_length": 140, "alphanum_fraction": 0.6967699407, "num_tokens": 1343, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7248702880639791, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.3341773656224463}} {"text": "library(parallel)\nlibrary(dplyr)\n\n###\n\nsource(\"functions.r\")\nload(\"../data/circles.rdata\")\nload(\"../data/efficiency_params.rdata\")\nload(\"../data/lfd_fit.rdata\")\nload(\"../data/containment.rdata\")\n\n\nparams <- expand.grid(\n\t# infection characteristics\n\tprevalence = c(0.001, 0.01, 0.05),\n\tspread = c(0.8, 1, 3),\n\tcontainment = c(\"high\", \"conquest\", \"scs1\"),\n\n\t# pooling characteristic\n\tpool_size = c(2, 3, 4, 5, 10, 15, 20, 25, 30),\n\trandom_pooling = c(TRUE, FALSE),\n\n\t# costs\n\tcost_samplingkit = 3.47,\n\tcost_test = 12.46,\n\treplicates = c(1:100)\n)\n\nparams$ct_max <- efficiency_params$par[1] # maximum ct value\nparams$ct_min <- efficiency_params$par[2] # minimum ct value\nparams$ct_alpha <- efficiency_params$par[3] # Beta distribution a parameter for ct\nparams$ct_beta <- efficiency_params$par[4] # Beta distribution b parameter for ct\nparams$e_alpha <- efficiency_params$par[5] # Beta distribution a parameter for efficiency\nparams$e_beta <- efficiency_params$par[6] # Beta distribution b parameter for efficiency\nparams$e_min <- 0.9 # Minimum PCR efficiency\nparams$e_max <- 1.4 # Maximum PCR efficiency\nparams$ctthresh <- 35 # Number cycles for detection\nparams$rct <- 100000 # Log Rct fluourescence detection value - arbitrary\nparams$pcr_fp <- 0.005 # Testing false positive rate (per test)\nparams$lfd_Asym <- lfd_fit$coef[1,1]\nparams$lfd_xmid <- lfd_fit$coef[2,1]\nparams$lfd_scal <- lfd_fit$coef[3,1]\nparams$lfd_fp <- 0.0032\nparams$lfd_cost <- 5\n\nres <- mclapply(1:nrow(params), function(i) {\n\tmessage(i, \" of \", nrow(params))\n\tx <- run_simulation(ids, params[i,], containment)\n}, mc.cores=16) %>% bind_rows()\n\nsave(res, file=\"../results/sim.rdata\")\n\n\ni <- which(params$prevalence==0.05)[1]\n", "meta": {"hexsha": "141538f3565db8483b795021d8598cc52a0f7a25", "size": 1692, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/sim.r", "max_stars_repo_name": "explodecomputer/covid-uob-pooling", "max_stars_repo_head_hexsha": "79ad1440f2dd8ebf4b3d0385366a6019ca821495", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/sim.r", "max_issues_repo_name": "explodecomputer/covid-uob-pooling", "max_issues_repo_head_hexsha": "79ad1440f2dd8ebf4b3d0385366a6019ca821495", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-10-03T07:30:49.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-02T23:53:14.000Z", "max_forks_repo_path": "scripts/sim.r", "max_forks_repo_name": "explodecomputer/covid-uob-pooling", "max_forks_repo_head_hexsha": "79ad1440f2dd8ebf4b3d0385366a6019ca821495", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-12T19:25:19.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-12T19:25:19.000Z", "avg_line_length": 30.7636363636, "max_line_length": 89, "alphanum_fraction": 0.7139479905, "num_tokens": 527, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7371581626286834, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.3341256710727036}} {"text": "#' hierarch_core.means_JAGS\n#' Get site level means and sd from NEON data observed at the core scale.\n#' Also return global mean and sd for a given predictor.\n#' depends on the runjags package.\n#'\n#' @param x_mu #vector of observations to modeled.\n#' @param core_plot #NEON plot IDs associated with x_mu observations.\n#' @param n.adapt #number of adaptive iterations. default 200.\n#' @param n.burnin #number of burnin iterations. default 1000.\n#' @param n.sample #number of sample iterations. default 1000.\n#' @param n.chains #number of MCMC chains. default 3.\n#' @param parallel #run chains in parallel or not? default F, notsetup to work yet.\n#'\n#' @return\n#' @export\n#'\n#' @examples\nhierarch_core.means_JAGS <- function(x_mu, core_plot,\n n.adapt = 200, n.burnin = 1000, n.sample = 1000, n.chains = 3, parallel = F){\n \n #### specify JAGS model ####\n jags.model = \"\n model {\n #get plot level means\n for(i in 1:N.core){\n core_mu[i] ~ dnorm(plot_mu[core_plot[i]], tau.plot[core_plot[i]])\n }\n \n #get site level means.\n for(i in 1:N.plot) {\n plot_mu[i] ~ dnorm(site_mu[plot_site[i]], tau.site[plot_site[i]])\n }\n \n #get global mean and uncertainty.\n for(i in 1:N.site){\n site_mu[i] ~ dnorm(global_mu, tau.glob)\n }\n \n #Plot level priors\n for(i in 1:N.plot){\n sigma.plot[i] ~ dunif(0, 100)\n tau.plot[i] <- pow(sigma.plot[i], -2)\n }\n #Site level priors\n for(i in 1:N.site) {\n sigma.site[i] ~ dunif(0, 100)\n tau.site[i] <- pow(sigma.site[i], -2)\n }\n #Global level priors\n global_mu ~ dnorm(0,1E-3) I(0, ) #itnerval censored to be greater than zero.\n sigma.glob ~ dunif(0,100)\n tau.glob <- pow(sigma.glob, -2)\n \n }\"\n \n #### setup JAGS data object. ####\n dat <- data.frame(x_mu,core_plot)\n dat <- dat[complete.cases(dat),]\n dat <- dat[order(dat$core_plot),]\n plot_site <- substring(unique(dat$core_plot),1,4)\n plot.names <- unique((dat$core_plot))\n site.names <- unique((plot_site))\n #reorder plot.names and site.names so it matches what they are going to be used as.\n plot.order <- order(plot.names)\n site.order <- order(site.names)\n plot.names <- as.character(plot.names[plot.order])\n site.names <- as.character(site.names[site.order])\n \n \n jags.data <- list(N.core = nrow(dat), N.plot = length(plot_site), N.site = length(unique(plot_site)),\n core_mu = dat$x_mu, core_plot = droplevels(as.factor(dat$core_plot)), plot_site = droplevels(as.factor(plot_site)))\n\n #### fit JAGS model ####\n #runmode <- ifelse(parallel == T,T,F)\n mod <- runjags::run.jags(model = jags.model,\n data = jags.data,\n monitor = c('plot_mu','site_mu','global_mu'),\n adapt = n.adapt,\n burnin = n.burnin,\n sample = n.sample,\n n.chains = n.chains)\n out <- summary(mod)\n plot.table <- data.frame(out[grep('plot_mu', rownames(out)),])\n plot.table$plotID <- plot.names\n site.table <- data.frame(out[grep('site_mu', rownames(out)),])\n site.table$siteID <- site.names\n glob.table <- data.frame(t(out[grep('global_mu', rownames(out)),]))\n \n \n #### return output ####\n output.list <- list(out,plot.table,site.table,glob.table)\n names(output.list) <- c('jags.summary','plot.table','site.table','glob.table')\n return(output.list)\n\n}", "meta": {"hexsha": "7c1578b27f61a9c454888c5cb65afc0fa50378e2", "size": 3343, "ext": "r", "lang": "R", "max_stars_repo_path": "NEFI_functions/hierarch_core.means_JAGS.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "NEFI_functions/hierarch_core.means_JAGS.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "NEFI_functions/hierarch_core.means_JAGS.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 35.1894736842, "max_line_length": 135, "alphanum_fraction": 0.6263834879, "num_tokens": 959, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7185943805178138, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.33407565668281825}} {"text": "#!/usr/bin/env Rscript\n\n#Andrew Burt - a.burt@ucl.ac.uk\n\nargs <- commandArgs(trailingOnly=TRUE)\npath_to_src <- args[1]\nsource(paste(path_to_src,\"ols.r\",sep=\"\"))\nsource(paste(path_to_src,\"bootstrap.r\",sep=\"\"))\nsource(paste(path_to_src,\"crossvalidate.r\",sep=\"\"))\nsource(paste(path_to_src,\"generateplots.r\",sep=\"\"))\n\ncaldata <- read.table(args[2],col.names=c(\"study\",\"d\",\"h\",\"agb\",\"rho\"))\nruns <- as.numeric(args[3])\nncpus <- as.numeric(args[4])\nalpha <- as.numeric(args[5])\nfunc <- log(agb) ~ log(d)\nmodel <- fitOLS(caldata,func)\nbresults <- bootOLS(caldata,func,runs,ncpus)\nprint(summary(model))\nfor(i in 1:length(coefficients(model)))\n{\n\tb0_ci <- boot.ci(bresults,index=i,conf=0.95,type=\"perc\")\n\tprint(b0_ci)\n} \ncresults <- crossValidateOLS(caldata,func)\nprint(\"CROSS VALIDATION\")\nprint(\"MSA, SSPBB\")\nprint(cresults[[2]])\n", "meta": {"hexsha": "6fe939e0cf4b0f741de5f4a55afbd9493c919f1e", "size": 822, "ext": "r", "lang": "R", "max_stars_repo_path": "src/treeallom.r", "max_stars_repo_name": "apburt/nlallom", "max_stars_repo_head_hexsha": "6f9d5156b18481f5ce971ba17403afbde3458f3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-04-20T11:55:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-18T15:10:40.000Z", "max_issues_repo_path": "src/treeallom.r", "max_issues_repo_name": "apburt/nlallom", "max_issues_repo_head_hexsha": "6f9d5156b18481f5ce971ba17403afbde3458f3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/treeallom.r", "max_forks_repo_name": "apburt/nlallom", "max_forks_repo_head_hexsha": "6f9d5156b18481f5ce971ba17403afbde3458f3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-20T11:55:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-20T11:55:13.000Z", "avg_line_length": 28.3448275862, "max_line_length": 71, "alphanum_fraction": 0.700729927, "num_tokens": 253, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6477982315512488, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.33401766955797346}} {"text": "library(dplyr)\r\nlibrary(ggplot2)\r\n\r\n\r\ndirectory <- \"C:/Users/Martin Schonger/source/repos/crep_eval/log/\"\r\n\r\ntimestamp <- \"20190720-130621\"\r\nevents <- read.csv(paste(directory, timestamp, \"__events.csv\", sep = \"\")) %>% \r\n select(c(time, num_nodes_base))\r\n\r\nbreaks <- seq(0,100000,1000)\r\n\r\nevents_proc <- events %>% \r\n mutate(interval = cut(time,\r\n breaks, \r\n include.lowest = TRUE, \r\n right = TRUE)) %>%\r\n group_by(interval) %>% \r\n summarise(sum.num_nodes_base = sum(num_nodes_base))\r\n\r\nevents_proc[90:100,]\r\n\r\nbreaks_plot <- seq(0,100000,2500)\r\nploty <- ggplot(data = events_proc, aes(x = interval, y = sum.num_nodes_base)) + \r\n geom_line(group = 1) + \r\n theme_minimal()\r\nploty\r\nggsave(filename = paste(directory, timestamp, \"__events_plot.pdf\", sep = \"\"), plot = ploty)\r\n\r\nevents_for_plot <- data.frame(\"tstamp\" = 1:100, \"num_nodes_base\" = events_proc[,2])\r\n", "meta": {"hexsha": "834e01e2b47136dc05e61eb3f19a32ff34a040ef", "size": 935, "ext": "r", "lang": "R", "max_stars_repo_path": "eval/plots/experimental_scripts/events_analysis.r", "max_stars_repo_name": "martinschonger/aerdg", "max_stars_repo_head_hexsha": "d28df9fc9e2e0780f6e492e378320ed004e516ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "eval/plots/experimental_scripts/events_analysis.r", "max_issues_repo_name": "martinschonger/aerdg", "max_issues_repo_head_hexsha": "d28df9fc9e2e0780f6e492e378320ed004e516ea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "eval/plots/experimental_scripts/events_analysis.r", "max_forks_repo_name": "martinschonger/aerdg", "max_forks_repo_head_hexsha": "d28df9fc9e2e0780f6e492e378320ed004e516ea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1612903226, "max_line_length": 92, "alphanum_fraction": 0.6203208556, "num_tokens": 253, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3340176625459868}} {"text": "### Jitter plots for Case C - not separated by land use.\n\n##### Jitter Plots: ######################################################################################################\n xlimits <- c(0, 4)\n plot(x=c(1:10), y=c(1:10), xlim=xlimits, ylim=ylimits, ylab=ParamList[i], type=\"n\", xaxt=\"n\", xlab=\"\", log=\"y\", xaxs=\"i\", \n yaxs=\"i\", main=\"\\nJitter Plot\", las=2, cex.axis=0.8)\n\n palette(c(\"violet\", \"purple\", \"gray80\", \"gray60\"))\n\n if (nrow(ParamData) > 0) {\n if (nrow(ParamData) > 2) {\n jitterX <- sample(seq(from=0.05, to=3.95, length.out=nrow(ParamData)))\n } else {\n if (nrow(ParamData) == 2) {\n jitterX <- c(1.5, 2.5)\n } else {\n jitterX <- c(2)\n }\n }\n\n nonDetects <- ParamData[which(ParamData$nonDetect_Flag == 1),]\n if (nrow(nonDetects) > 0 ) {\n jitterX_ND <- jitterX[1:nrow(nonDetects)]\n segments(y0=rep(min(ylimits),nrow(nonDetects)), x0=jitterX_ND, y1=nonDetects$new_Result_Value, \n x1=jitterX_ND, col=3+nonDetects$WetSeason)\n }\n\n Detects <- ParamData[which(ParamData$nonDetect_Flag == 0),]\n if (nrow(Detects) >0 ) {\n jitterX_Det <- jitterX[nrow(nonDetects)+1:nrow(Detects)]\n points(y=Detects$new_Result_Value, x=jitterX_Det, pch=16, cex=1.2, col=1+Detects$WetSeason)\n }\n } ## end if nrow(ParamData)\n \n box()\n\n percentCensor <- 100*nrow(nonDetects)/nrow(ParamData)\n percentCensor <- round(percentCensor, 1)\n axislabels <- paste(c(\"Randomized X\"), \n c(\"\\nDet=\"), \n c(nrow(ParamData)-nrow(nonDetects)),\n c(\"\\n ND=\"), \n c(nrow(nonDetects)), \n c(\"\\n\"),\n percentCensor,\n c(\"%\"),\n sep=\"\") \n axis(side=1, at=c(2), labels=c(\"\"))\n mtext(side=1, line=3.5, at=2, text=axislabels, adj=0.5, padj=0, cex=0.6)\n\n\n\n ymax <- par(\"usr\")[4]\n ymin <- par(\"usr\")[3]\n ymin_legend <- 10^(ymax + 0.01*(ymax-ymin))\n ymax_legend <- 10^(ymax + 0.22*(ymax-ymin))\n\n legend(x=c(2.6,4.3), y=c(ymin_legend, ymax_legend), \n legend=c(\"Detect-DrySeas\", \"Detect-WetSeas\", \"NonDetect-DrySeas\", \"NonDetect-WetSeas\"),\n lty=c(\"blank\", \"blank\", \"solid\", \"solid\"), pch=c(16,16,NA, NA), pt.cex=c(1.2,1.2,1.2,1.2), \n col=c(\"violet\", \"purple\", \"gray80\", \"gray60\"), xpd=NA, cex=0.8, bty=\"o\", bg=\"white\")\n\n\n\n", "meta": {"hexsha": "0bc8a37e35ae070c848f177b0b2e695449cf39f2", "size": 2397, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/Plot_byParam_sub3C_jitterPlot.r", "max_stars_repo_name": "ethankale/ecy-wa-s8d", "max_stars_repo_head_hexsha": "8cf7036af4934bbf395a84ecfebbe44c59d890c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/Plot_byParam_sub3C_jitterPlot.r", "max_issues_repo_name": "ethankale/ecy-wa-s8d", "max_issues_repo_head_hexsha": "8cf7036af4934bbf395a84ecfebbe44c59d890c9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/Plot_byParam_sub3C_jitterPlot.r", "max_forks_repo_name": "ethankale/ecy-wa-s8d", "max_forks_repo_head_hexsha": "8cf7036af4934bbf395a84ecfebbe44c59d890c9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8769230769, "max_line_length": 124, "alphanum_fraction": 0.5214851898, "num_tokens": 815, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3340176555340001}} {"text": "\nrf <- RFclust.SGE ( dat=dat[,1:99], SGE=F, slices=1 )\n\n# rf <- RFclust.SGE ( dat=cellexalObj@data, SGE=F, slices=1 )\n#rf <- runRFclust ( rf, nforest=5)\n#groups <- createGroups( rf, c( 2,4,6,8), name=\"RFrun\" )\n\n#expect_equal( dim(groups), c(100,6))\n\n#rf <- RFclust.SGE ( dat=dat, SGE=F, slices=2, name=\"SLURM\", settings = list( A = \"lsens2017-3-2\", t=\"00:20:00\" , slurm=T) )\nrf <- runRFclust ( rf, nforest=500, ntree=500)\n\n#rf <- RFclust.SGE ( dat=dat, SGE=F, slices=2 )\n#rf <- runRFclust ( rf, nforest=5 )\ng = c( 2,4,6,8,12)\ngroups <- createGroups( rf, g )\nexpect_equal( dim(groups), c(99,length(g)+2),label='group dimensions')\n\nexpect_equal( as.vector(apply( groups[,3:length(g)+2], 2, function(x) length(unique(x)) )), g, label=\"group complexity\")\n\nrf <- runRFclust ( rf, nforest=500, ntree=500, name=\"TEST\")\ngroups2 <- createGroups( rf, g, name=\"TEST\")\n\ncomplexity = 7\n\nA = groups[,complexity]\nB = groups2[,complexity]\n\nRandG <- function( a, b , n=1000) {\n\tlapply( 1:n, function(N){\n\t\tmax(table( sample(a) ,sample(b)))\n\t})\n}\n\nt=table( A, B )\nmax = max(t)\nrandMax = unlist(RandG( A, B ))\np = length(which( randMax >= max )) / 1000\n\nexpect_true( p < 3/1000 ,label=\"group overlap is fine for random data\")\n\n\n\nif ( FALSE ){\n\trf <- RFclust.SGE ( dat=cellexalObj@data, SGE=F, slices=1 )\n\n\tstart.time <- Sys.time()\n\trf <- runRFclust ( rf, nforest=50, ntree=500, name=\"TEST\")\n\tend.time <- Sys.time()\n\n\ttime.taken <- end.time - start.time\n\n\ttime.taken\n\t#Time difference of 6.941505 mins\n\n\tgroups <- createGroups( rf, 40 , name=\"TEST\")\n\n\tstart.time <- Sys.time()\n\trf <- runRFclust ( rf, nforest=50, ntree=500)\n\tend.time <- Sys.time()\n\n\ttime.taken <- end.time - start.time\n\n\ttime.taken\n\t#3.669606 mins\n\n\tgroups2 <- createGroups( rf, 40 )\n\n\tA= groups[,3]\n\tB= groups2[,3]\n\n\tt=table( A, B )\n\tmax = max(t)\n\trandMax = unlist(RandG( A, B ))\n\tp = length(which( randMax >= max )) / 1000\n\texpect_true( p < 3/1000 ,label=\"group overlap is fine for random data\")\n}", "meta": {"hexsha": "fa3e43c6d82a8c04f140e5db5a147d1048d1ac52", "size": 1949, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test.RFclust.SGE.r", "max_stars_repo_name": "stela2502/RFclust.SGE", "max_stars_repo_head_hexsha": "aa5aa253e78930f2161e89a2e5f8177e8de4b713", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2016-02-17T11:09:10.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-27T13:08:47.000Z", "max_issues_repo_path": "tests/testthat/test.RFclust.SGE.r", "max_issues_repo_name": "stela2502/RFclust.SGE", "max_issues_repo_head_hexsha": "aa5aa253e78930f2161e89a2e5f8177e8de4b713", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test.RFclust.SGE.r", "max_forks_repo_name": "stela2502/RFclust.SGE", "max_forks_repo_head_hexsha": "aa5aa253e78930f2161e89a2e5f8177e8de4b713", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.3116883117, "max_line_length": 125, "alphanum_fraction": 0.6336582863, "num_tokens": 707, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.63341027751814, "lm_q2_score": 0.5273165233795672, "lm_q1q2_score": 0.33400770541375246}} {"text": "[[[[[\n FUNDAMENTAL DECOMPOSITION\n We prove here that \n H(y|x) <= H_C(x,y) - H(x) + c\n]]]]]\n\ndefine (all-together x*)\n\nlet c debug 100 [constant to satisfy Kraft (see lemma)]\n\nlet x debug run-utm-on debug x*\n\nlet H-of-x debug length x*\n\n[programs we've discovered that calculate pairs \n starting with x]\nlet programs nil\n\nlet (stage n)\n [generate requirements for all new programs we've\n discovered that produce (x y) pairs]\n let programs \n (add-to-set debug (halts? nil debug n) programs) \n (stage + n 1)\n\n[at stage n = 0, 1, 2, 3, ...]\n[look at all programs with <=n bits that halt within time n]\n[returns list of all of them that produce pairs (x y)]\nlet (halts? p bits-left)\n let v try n C p [C is eval read-exp if C = U]\n if = success car v (look-at cadr v)\n if = 0 bits-left nil\n append (halts? append p cons 0 nil - bits-left 1)\n (halts? append p cons 1 nil - bits-left 1)\n\n[returns (p) if C(p) = (x y), otherwise ()]\nlet (look-at v)\n if (and (is-pair v) \n = x car v ) cons p nil\n nil\n\n[logical \"and\"]\nlet (and p q)\n if p q false\n\n[is x a pair?]\nlet (is-pair? x)\n if atom x false\n if atom cdr x false\n if atom cdr cdr x true\n false\n\n[is an element in a set?]\nlet (is-in-set? element set)\n if atom set false\n if = element car set true\n (is-in-set? element cdr set)\n\n[forms set union avoiding duplicates, \n and makes requirement for each new find]\nlet (add-to-set new old)\n if atom new old \n let first-new car new\n let rest-new cdr new\n if (is-in-set? first-new old) (add-to-set rest-new old)\n (do (make-requirement first-new)\n cons first-new (add-to-set rest-new old)\n )\n \n[first argument discarded, done for side-effect only!]\nlet (do x y) y\n\n[given new p such that C(p) = (x y), \n we produce the requirement for C_x\n that there be a program for y that is |p|-H(x)+c bits long]\nlet (make-requirement p)\n display cons cadr cadr try no-time-limit C p \n cons - + c length p H-of-x\n nil\n\nlet C ' [here eval read-exp gives U]\n[test case special-purpose computer C here in place of U:] \n[C(00100001) with x-1 and y-1 0's gives pair (x xy)]\n[loop function gives number of bits up to next 1 bit]\n let (loop n)\n if = 1 read-bit n\n (loop + n 1)\n let x (loop 1)\n let y (loop 1)\n cons x cons * x y nil\n\n[HERE GOES!]\n(stage 0)\n\ndefine all-together\nvalue (lambda (x*) ((' (lambda (c) ((' (lambda (x) ((' (\n lambda (H-of-x) ((' (lambda (programs) ((' (lambda\n (stage) ((' (lambda (halts?) ((' (lambda (look-at\n ) ((' (lambda (and) ((' (lambda (is-pair?) ((' (la\n mbda (is-in-set?) ((' (lambda (add-to-set) ((' (la\n mbda (do) ((' (lambda (make-requirement) ((' (lamb\n da (C) (stage 0))) (' ((' (lambda (loop) ((' (lamb\n da (x) ((' (lambda (y) (cons x (cons (* x y) nil))\n )) (loop 1)))) (loop 1)))) (' (lambda (n) (if (= 1\n (read-bit)) n (loop (+ n 1)))))))))) (' (lambda (\n p) (display (cons (car (cdr (car (cdr (try no-time\n -limit C p))))) (cons (- (+ c (length p)) H-of-x) \n nil)))))))) (' (lambda (x y) y))))) (' (lambda (ne\n w old) (if (atom new) old ((' (lambda (first-new) \n ((' (lambda (rest-new) (if (is-in-set? first-new o\n ld) (add-to-set rest-new old) (do (make-requiremen\n t first-new) (cons first-new (add-to-set rest-new \n old)))))) (cdr new)))) (car new)))))))) (' (lambda\n (element set) (if (atom set) false (if (= element\n (car set)) true (is-in-set? element (cdr set)))))\n )))) (' (lambda (x) (if (atom x) false (if (atom (\n cdr x)) false (if (atom (cdr (cdr x))) true false)\n ))))))) (' (lambda (p q) (if p q false)))))) (' (l\n ambda (v) (if (and (is-pair v) (= x (car v))) (con\n s p nil) nil)))))) (' (lambda (p bits-left) ((' (l\n ambda (v) (if (= success (car v)) (look-at (car (c\n dr v))) (if (= 0 bits-left) nil (append (halts? (a\n ppend p (cons 0 nil)) (- bits-left 1)) (halts? (ap\n pend p (cons 1 nil)) (- bits-left 1))))))) (try n \n C p))))))) (' (lambda (n) ((' (lambda (programs) (\n stage (+ n 1)))) (add-to-set (debug (halts? nil (d\n ebug n))) programs))))))) nil))) (debug (length x*\n ))))) (debug (car (cdr (try no-time-limit (' (eval\n (read-exp))) (debug x*)))))))) (debug 100)))\n\ndefine x* 3\n\ndefine x*\nvalue 3\n\nlength bits x* \n\nexpression (length (bits x*))\nvalue 16\n\n[give all-together x*]\ntry 60 cons cons \"'\n cons all-together \n nil \n cons cons \"' \n cons bits x* \n nil \n nil \n nil\n\nexpression (try 60 (cons (cons ' (cons all-together nil)) (co\n ns (cons ' (cons (bits x*) nil)) nil)) nil)\ndebug 100\ndebug (0 0 1 1 0 0 1 1 0 0 0 0 1 0 1 0)\ndebug 3\ndebug 16\ndebug 0\ndebug ()\ndebug 1\ndebug ()\ndebug 2\ndebug ()\ndebug 3\ndebug ()\ndebug 4\ndebug ((0 0 1 1))\ndebug 5\ndebug ((0 0 1 0 1) (0 0 1 1))\ndebug 6\ndebug ((0 0 1 0 0 1) (0 0 1 0 1) (0 0 1 1))\ndebug 7\ndebug ((0 0 1 0 0 0 1) (0 0 1 0 0 1) (0 0 1 0 1) (0 0 1 \n 1))\ndebug 8\ndebug ((0 0 1 0 0 0 0 1) (0 0 1 0 0 0 1) (0 0 1 0 0 1) (\n 0 0 1 0 1) (0 0 1 1))\ndebug 9\nvalue (failure out-of-time ((3 88) (6 89) (9 90) (12 91)\n (15 92)))\n\ndefine x* 4\n\ndefine x*\nvalue 4\n\nlength bits x* \n\nexpression (length (bits x*))\nvalue 16\n\n[give all-together x*]\ntry 60 cons cons \"'\n cons all-together \n nil \n cons cons \"' \n cons bits x* \n nil \n nil \n nil\n\nexpression (try 60 (cons (cons ' (cons all-together nil)) (co\n ns (cons ' (cons (bits x*) nil)) nil)) nil)\ndebug 100\ndebug (0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0)\ndebug 4\ndebug 16\ndebug 0\ndebug ()\ndebug 1\ndebug ()\ndebug 2\ndebug ()\ndebug 3\ndebug ()\ndebug 4\ndebug ()\ndebug 5\ndebug ((0 0 0 1 1))\ndebug 6\ndebug ((0 0 0 1 0 1) (0 0 0 1 1))\ndebug 7\ndebug ((0 0 0 1 0 0 1) (0 0 0 1 0 1) (0 0 0 1 1))\ndebug 8\ndebug ((0 0 0 1 0 0 0 1) (0 0 0 1 0 0 1) (0 0 0 1 0 1) (\n 0 0 0 1 1))\ndebug 9\nvalue (failure out-of-time ((4 89) (8 90) (12 91) (16 92\n )))\n", "meta": {"hexsha": "d6a92dabce20b591051c7c510253e902fa466867", "size": 6679, "ext": "r", "lang": "R", "max_stars_repo_path": "book-examples/decomp.r", "max_stars_repo_name": "darobin/chaitin-lisp", "max_stars_repo_head_hexsha": "a06fd5647a1d69d41ec725616fa0ebcc71e55bec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-02-28T09:21:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-09T03:29:32.000Z", "max_issues_repo_path": "book-examples/decomp.r", "max_issues_repo_name": "darobin/chaitin-lisp", "max_issues_repo_head_hexsha": "a06fd5647a1d69d41ec725616fa0ebcc71e55bec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "book-examples/decomp.r", "max_forks_repo_name": "darobin/chaitin-lisp", "max_forks_repo_head_hexsha": "a06fd5647a1d69d41ec725616fa0ebcc71e55bec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2016-06-23T14:37:37.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-19T13:09:35.000Z", "avg_line_length": 29.1659388646, "max_line_length": 62, "alphanum_fraction": 0.4979787393, "num_tokens": 2263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.33400770541375235}} {"text": "\r\n################ visualization #################\r\nsetwd(.WD)\r\nsource(\"functions.r\")\r\n\r\nn <- nrow(dat); p <- ncol(dat)\r\n.gnum <- length(.ugrou)\r\n\r\ncat(\"The attributes, or chemical parameters are:\\n\")\r\nfor (i in 1:p){ cat(i,.attri[i],'\\t', fill=T)};\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\n\r\n.stay0 <- 1\r\nwhile(.stay0){\r\ncat(\"for data visualization:\\n\")\r\ncat(\"1. I want to apply to all columns\\n\")\r\ncat(\"2. I want to select a subset of all columns\\n\")\r\ncat(\"3. I want to drop a subset of all columns\\n\")\r\ncat(\"4. exit\\n\")\r\n.cho <- .scanf(1,c(1:4))\r\nif(.cho == 4) .stay0 <- 0\r\n\r\n\r\nelse{\r\n.stay1 <- 1\r\nwhile(.stay1 == 1){\r\n.chosen <- c(1:p)\r\nif(.cho != 1){\r\ncat(\"Which columns?\\n\")\r\n.colAttr <- .scanf(1,c(1:p))\r\nif(.cho == 2) .chosen <- .colAttr \r\nif(.cho == 3) .chosen <- .chosen[-.colAttr]\r\n}\r\n\r\n.stay <- 1\r\nwhile(.stay){\r\ncat(\"********************* visualization ********************\\n\")\r\ncat(\"\\n 1. scattermatrix plot\\n\")\r\ncat(\"\\n 2. boxplot with outlier detection\\n\")\r\ncat(\"\\n 3. segment plot\\n\")\r\ncat(\"\\n 4. 3D scatter plot\\n\")\r\ncat(\"\\n 5. choose different columns\\n\")\r\ncat(\"\\n 6. exit\\n\")\r\n\r\n.ans <- .scanf(1,c(1:6))\r\n\r\nif(.ans == 5) .stay <- 0\r\nif(.ans == 6) {.stay <- .stay1 <- 0}\r\n\r\nif(.ans == 1) {\r\n###################### scatterplot matrix ##################\r\n#palette(rainbow(.gnum))\r\n.par0()\r\npairs(dat[.chosen], #panel=panel.smooth,\r\n diag.panel=.panelhist, #cex.labels = 1.5, font.labels = 2,\r\n pch = 21, bg = c(2:(length(.ugrou)+1))[unclass(.ngrou)])\r\nif(.colGrou){\r\n.tick <- 1/length(.ugrou)\r\nmtext(.ugrou, side = 3, at = c(.5:(length(.ugrou)-.5))*.tick, \r\n line= 2.5, col = 2:(length(.ugrou)+1), font = rep(2,length(.ugrou)))\r\n }\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\npairs(dat[.chosen], lower.panel=panel.smooth, \r\n diag.panel=.panelhist, upper.panel=.panelcor) \r\nif(.colGrou){\r\n.tick <- 1/length(.ugrou)\r\nmtext(.ugrou, side = 3, at = c(.5:(length(.ugrou)-.5))*.tick, \r\n line= 2.5, col = 2:(length(.ugrou)+1), font = rep(2,length(.ugrou)))\r\n }\r\n.par0()\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\npalette(\"default\")\r\n}\r\n\r\n\r\n\r\nif(.ans == 2){\r\n###################### boxplot ##################\r\n.subdat <- dat[.chosen]\r\n.subattri <- .attri[.chosen]\r\n.par0()\r\npar(las=3, cex.axis=.7, font.axis=8)\r\n.tmp2 <- range(.subdat); .tick <- (.tmp2[2] - .tmp2[1])/6\r\n.tmp <- boxplot(.subdat,col='light blue',ylim=c(min(.subdat)-.tick,max(.subdat)+.tick))\r\n.y = tapply(.tmp$out, .tmp$group, .maxa); .y <- .y-(1-sign(.y))*.tick*.2\r\nwith(.tmp,\r\n text(x = unique(.tmp$group)+1/length(unique(.tmp$group)), .y, \r\n labels = round(tapply(out, group, .maxa), 2),\r\n pos = 3, cex = .8)) \r\n.par0()\r\ncat(\"Outlier detection:\\n\")\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\nif(length(.tmp$out)==0) cat(\"No outlier detected!\\n\")\r\nif(length(.tmp$out)==1) cat(\"The unique outlier detected is \", .sites[.tmp$out], \"for attribute \", .tmp$names[.tmp$group], \"\\n\") \r\nif(length(.tmp$out)>1){\r\ncat(.subattri[.tmp$group[1]],\":\",\"\\t\\t\",.sites[dat[,.tmp$group[1]]==.tmp$out[1]])\r\nfor(j in 2:length(.tmp$out)){\r\nif(.tmp$group[j]!=.tmp$group[j-1]) cat(\"\\n\",.subattri[.tmp$group[j]],\":\",\"\\t\\t\")\r\nelse cat(\"\\t\")\r\n.tmpsite <- .sites[.subdat[,.tmp$group[j]]==.tmp$out[j]]\r\ncat(.tmpsite[1],sep = '\\t\\t')\r\n}}\r\ncat(\"\\n\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\n}\r\n\r\n\r\n\r\n###################### segment plot ##################\r\nif(.ans == 3){\r\ncat(\"Segment plots\\n\")\r\npalette(rainbow(length(.chosen)))\r\n.par0()\r\nstars(dat[.chosen],labels=.sites,draw.segments=T,nrow=floor(sqrt(n))+1,\r\n len=.5, cex=.6, key.loc=c(-1,2.5))\r\n.par0()\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\npalette(\"default\") \r\n}\r\n\r\n\r\n\r\n\r\n###################### 3D visualization ##################\r\nif(.ans == 4){\r\n.substay <- \"y\"\r\nwhile(.substay == \"y\"){\r\ncat(\"3d visualization:\\n\")\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\ncat(\"The attributes are:\\n\")\r\nprint(cbind(.attri,c(1:p)))\r\ncat(\"\\n Please press Enter key to continue...\\n\")\r\nreadline()\r\ncat(\"Please choose 3 attributes: \\n\")\r\n.colAttr <- .scanf(1,c(1:p))\r\n.gnum <- 2\r\nif(.colGrou) .gnum <- length(.ugrou) \r\npalette(rainbow(.gnum))\r\nopen3d()\r\nplot3d(dat[.colAttr],col = .ngrou, size=10)\r\n.rang <- range(dat[.colAttr[3]])[2]-range(dat[.colAttr[3]])[1]\r\nif(.colGrou) text3d(rep(max(dat[.colAttr[1]]),.gnum),rep(max(dat[.colAttr[2]]),.gnum),\r\n rep(max(dat[.colAttr[3]]),.gnum)-c(1:.gnum)*.rang/20, text=paste(as.vector(.ugrou)),\r\n adj=c(1,1), color=c(1:.gnum), cex = .8)\r\ncat(\"Do you want to impose the names of sites? y/n\\n\")\r\n.sans <- .scanf(2)\r\nif(.sans == \"y\") text3d(dat[.colAttr],text = paste(.sites),col = .ngrou, cex = .7)\r\n.par0()\r\npalette(\"default\")\r\ncat(\"Do you want to try other three attributes? y/n\\n\")\r\n.substay <- .scanf(2)\r\n }\r\n }\r\n }\r\n }\r\n}\r\n}\r\n", "meta": {"hexsha": "a57216899aa99fd4c0d03993245f0ef486513b57", "size": 4942, "ext": "r", "lang": "R", "max_stars_repo_path": "visualization.r", "max_stars_repo_name": "jsanket123/PCCAT", "max_stars_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "visualization.r", "max_issues_repo_name": "jsanket123/PCCAT", "max_issues_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "visualization.r", "max_forks_repo_name": "jsanket123/PCCAT", "max_forks_repo_head_hexsha": "a8d3da687796e7c823b1ba791ec8d6fe1bff5f2a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-01-17T16:45:59.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-17T16:45:59.000Z", "avg_line_length": 30.1341463415, "max_line_length": 130, "alphanum_fraction": 0.5509915014, "num_tokens": 1632, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632979641571, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.33390862178315284}} {"text": "library(rstan)\nlibrary(loo)\nrstan_options(auto_write = TRUE);\noptions(mc.cores = parallel::detectCores());\n\nplotProfileFit <- function(fit, data, targetSco, numSamples = 10) {\n true_value = extract(fit,'true_value')$true_value;\n \n samplesToPlot = true_value[sample(1:(dim(true_value)[1]),numSamples),];\n \n #Add the raw profile\n plotData = t(rbind(subset(data, sco ==targetSco)[,\"val\"], samplesToPlot));\n defaultWidth = 1;\n lineWidths = rep.int(1, numSamples + 1);\n lineWidths[1] = defaultWidth * 2;\n matplot(plotData, lwd = lineWidths, type=\"l\") \n}\n\nplotNoChangeFit <- function(fit, data, targetSco) {\n relevant_data = subset(data, sco==targetSco);\n minTime = min(relevant_data$time)\n maxTime = max(relevant_data$time)\n \n smmry = summary(fit)$summary;\n val_mean = smmry[\"true_value\",\"mean\"];\n val_25 = smmry[\"true_value\",\"25%\"];\n val_75 = smmry[\"true_value\",\"75%\"];\n val_2_5 = smmry[\"true_value\",\"2.5%\"];\n val_97_5 = smmry[\"true_value\",\"97.5%\"];\n \n ribbon1 = data.frame(\"x\" = c(minTime,maxTime),\"ymin\" = c(val_2_5,val_2_5), \"ymax\" = c(val_97_5, val_97_5))\n ribbon2 = data.frame(\"x\" = c(minTime,maxTime),\"ymin\" = c(val_25,val_25), \"ymax\" = c(val_75, val_75))\n ribbonAes = aes(x = x, ymin = ymin, ymax = ymax)\n \n ggplot(relevant_data) +\n geom_ribbon(mapping = ribbonAes, data = ribbon1, fill = \"red\", alpha = 0.3) +\n geom_ribbon(mapping = ribbonAes, data = ribbon2, fill = \"darkred\", alpha = 0.3) +\n geom_hline(yintercept = val_mean, color = \"black\", size = 2) +\n geom_ribbon(aes(x=time, ymin = val - sd, ymax = val + sd ), alpha = 0.3, fill = \"blue\") +\n geom_ribbon(aes(x=time, ymin = val - sd, ymax = val + sd ), alpha = 0.3, fill = \"blue\") +\n geom_line(aes(x=time, y=val))\n}\n\nplotSco <- function(data, targetSco) {\n relevant_data = subset(data, sco==targetSco);\n\n ggplot(relevant_data, aes(x=time, y=val, ymin = val - sd, ymax = val + sd )) + geom_ribbon(alpha = 0.3, fill = \"blue\") + geom_line() \n}\n\nfitBySco <- function(long_data, target_sco, model='interpolate.stan', ...) {\n relevant_data = subset(long_data, sco == target_sco);\n relevant_data = relevant_data[sort.list(relevant_data$time),];\n \n #normalize time\n relevant_data$time = (relevant_data$time - min(relevant_data$time)) / (max(relevant_data$time) - min(relevant_data$time));\n \n data = list(numData = length(relevant_data$sco), y = relevant_data[,\"val\"], sigma = relevant_data[,\"sd\"], time = relevant_data[,\"time\"]);\n return(stan(file =model, data = data, ...));\n}\n\ncompareFits <- function(data, target_sco) {\n ow <- options(\"warn\");\n options(warn = 1);\n \n cat(\"No change fit\\n\");\n noChangeFit = fitBySco(data, target_sco, 'no-change.stan');\n print(plotNoChangeFit(noChangeFit, data, target_sco));\n \n cat(\"C. synth fit\\n\");\n csynthFit = fitBySco(data, target_sco, 'constant-synthesis-euler.stan', control=list(adapt_delta = 0.98), iter = 5000);\n print(plotProfileFit(csynthFit, data, target_sco));\n \n cat(\"Compare > 0 means csynth better\");\n \n options(ow);\n compare(loo(extract_log_lik(noChangeFit)), loo(extract_log_lik(csynthFit)));\n}", "meta": {"hexsha": "89e226c1ac890a3a1f773e1a8dde0cca623c391a", "size": 3072, "ext": "r", "lang": "R", "max_stars_repo_path": "stan-experiments/interpolate-funcs.r", "max_stars_repo_name": "cas-bioinf/genexpi-stan", "max_stars_repo_head_hexsha": "1164ff1c44ed967574aace8f629e5315fa70e18b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-08-31T03:06:37.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-04T18:26:05.000Z", "max_issues_repo_path": "stan-experiments/interpolate-funcs.r", "max_issues_repo_name": "cas-bioinf/genexpi-stan", "max_issues_repo_head_hexsha": "1164ff1c44ed967574aace8f629e5315fa70e18b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stan-experiments/interpolate-funcs.r", "max_forks_repo_name": "cas-bioinf/genexpi-stan", "max_forks_repo_head_hexsha": "1164ff1c44ed967574aace8f629e5315fa70e18b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8961038961, "max_line_length": 139, "alphanum_fraction": 0.6702473958, "num_tokens": 992, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3339086126849874}} {"text": "#' Plots a power spectrum and time series side-by-side, saving as a\n#' PDF.\n#'\n#' @param power.spectrum Data frame containing freq and power.\n#' @param time.series Data frame containing time and signal.\n#' @param output Name of PDF output file.\n#'\n#' @export\nplot.spectrum <- function(power.spectrum, time.series,\n output = \"spectrum_plot.pdf\") {\n pdf(output)\n\n par(mfrow = c(1, 2))\n\n plot(power.spectrum$freq, Mod(power.spectrum$power)^2,\n type = \"l\", color = \"blue\")\n plot(time.series$time, time.series$signal,\n type = \"l\", color = \"blue\")\n\n dev.off()\n}\n", "meta": {"hexsha": "f4598b22690b22502e72f1aa8026283f619c27af", "size": 612, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot.spectrum.r", "max_stars_repo_name": "dwysocki/random-noise-generation", "max_stars_repo_head_hexsha": "97bb8392faa4c25399b44b52bc471a95cb44ec7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-02-24T05:46:19.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-24T05:46:19.000Z", "max_issues_repo_path": "R/plot.spectrum.r", "max_issues_repo_name": "dwysocki/random-noise-generation", "max_issues_repo_head_hexsha": "97bb8392faa4c25399b44b52bc471a95cb44ec7d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/plot.spectrum.r", "max_forks_repo_name": "dwysocki/random-noise-generation", "max_forks_repo_head_hexsha": "97bb8392faa4c25399b44b52bc471a95cb44ec7d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8181818182, "max_line_length": 67, "alphanum_fraction": 0.6241830065, "num_tokens": 157, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3339086126849874}} {"text": "library(ggplot2)\nlibrary(reshape)\nlibrary(rtracklayer)\nlibrary(effsize)\n\nargs = commandArgs(trailingOnly=TRUE)\n\nin.bins.file <- args[1]\nin.iap.file <- args[2]\noutfile <- args[3]\n\ndf <- read.table(in.bins.file, sep='\\t', header=T)\nvalues <- df[, c('chr', 'start', 'end', 'Martire2019.ATAC.H33WT','Martire2019.ATAC.H33KO')]\n\niap.ranges <- import(in.iap.file)\nbins.grang <- makeGRangesFromDataFrame(values, keep.extra.columns = T)\niap.bins <- subsetByOverlaps(bins.grang, iap.ranges, minoverlap=2500)\niap.df <- data.frame(mcols(iap.bins))\n\ndf.melted <- melt(values, id.vars=c('chr','start','end'))\niap.melted <- melt(iap.df)\n\np <- ggplot(df.melted, aes(x=variable, y=value)) +\n geom_violin(fill='#444444') +\n geom_jitter(data=iap.melted, aes(x=variable, y=value, color=variable), alpha=0.7) +\n ylab('ATAC-seq RPGC') +\n xlab('') +\n ggtitle('ATAC-seq 5kb bins H3.3 KO vd WT at IAP') +\n theme_classic() +\n theme(legend.position='none') +\n ylim(0,10)\n\nggsave(outfile, plot=p, dpi=300)\n\n# Wilcoxon ranked sum test significance for global ATAC H33KO vs WT\nwilcox.test(values$Martire2019.ATAC.H33KO, values$Martire2019.ATAC.H33WT)\n\n# Wilcoxon ranked sum test significance for iap ATAC H33KO vs WT\nwilcox.test(iap.df$Martire2019.ATAC.H33KO, iap.df$Martire2019.ATAC.H33WT)\n\n# Effect sizes\ncohen.d(iap.df$Martire2019.ATAC.H33KO, iap.df$Martire2019.ATAC.H33WT)\ncohen.d(values$Martire2019.ATAC.H33KO, values$Martire2019.ATAC.H33WT)\n\n", "meta": {"hexsha": "b011c643142a27fc428f8382d9507808758e9726", "size": 1426, "ext": "r", "lang": "R", "max_stars_repo_path": "src/fig_3c_atac_violin_jitter.r", "max_stars_repo_name": "elsasserlab/publicchip", "max_stars_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-28T15:13:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-28T15:13:33.000Z", "max_issues_repo_path": "src/fig_3c_atac_violin_jitter.r", "max_issues_repo_name": "elsasserlab/publicchip", "max_issues_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/fig_3c_atac_violin_jitter.r", "max_forks_repo_name": "elsasserlab/publicchip", "max_forks_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.6888888889, "max_line_length": 91, "alphanum_fraction": 0.7223001403, "num_tokens": 499, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3339086050242718}} {"text": "#!/bin/Rscript\nlibrary(\"doParallel\")\nlibrary(\"R.utils\")\nlibrary(\"dplyr\")\nlibrary(\"plyr\")\nlibrary(\"ggplot2\")\nlibrary(\"dplyr\")\nlibrary(\"broom\")\nlibrary(\"ggpubr\")\nlibrary(\"Amelia\")\nlibrary(\"mlbench\")\nlibrary(\"corrplot\")\nlibrary('ANN2')\nlibrary('randomForest')\nlibrary('tree')\nlibrary('caret')\nlibrary('party')\n\ndf <- read.csv(\"./data/weatherAUS.csv\", header = TRUE, sep = \",\", fill = TRUE)\n\nn <- length(df[,1]) \nindex <- sample(1:n,n,replace=FALSE) \ndf <- df[index,]\n\ndf[1:10,]\n\ndf[, \"Date\"] <- as.Date(df[, \"Date\"])\ndf[, \"Location\"] <- as.factor(df[, \"Location\"])\ndf[, \"WindGustDir\"] <- as.factor(df[, \"WindGustDir\"])\ndf[, \"WindDir9am\"] <- as.factor(df[, \"WindDir9am\"])\ndf[, \"WindDir3pm\"] <- as.factor(df[, \"WindDir3pm\"])\ndf[, \"RainToday\"] <- as.factor(df[, \"RainToday\"])\ndf[, \"RainTomorrow\"] <- as.factor(df [, \"RainTomorrow\"])\ndf[\"Cloud9am\"][df[\"Cloud9am\"] == 9] <- 8\ndf[\"Cloud3pm\"][df[\"Cloud3pm\"] == 9] <- 8\n\n\n# # Deskriptive Analyse\n\nparameters <- names(select_if(df, is.numeric))\n\ndfmin <- subset(df, !is.na(df$RainToday) & !is.na(df$RainTomorrow))\n\nfor (x in parameters) {\n dfmin[x][is.na(dfmin[x])] <- mean(dfmin[, x], na.rm = TRUE)\n}\n\nlevels(dfmin$WindGustDir) <- c(levels(dfmin$WindGustDir), \"NOWIND\")\nlevels(dfmin$WindDir9am) <- c(levels(dfmin$WindDir9am), \"NOWIND\")\nlevels(dfmin$WindDir3pm) <- c(levels(dfmin$WindDir3pm), \"NOWIND\")\n\ndfmin$WindGustDir[is.na(dfmin[,\"WindGustDir\"])] <- \"NOWIND\"\ndfmin$WindDir9am[is.na(dfmin[,\"WindDir9am\"])] <- \"NOWIND\"\ndfmin$WindDir3pm[is.na(dfmin[,\"WindDir3pm\"])] <- \"NOWIND\"\ndfmin$WindDir3pm\n\n#Maschinelles Lernen versch. Verfahren\n\n\n### Daten splitten in Test- und Trainingsdatens\u00e4tze\n\n## 75% of the sample size\nsmp_size <- floor(0.75 * nrow(dfmin)) ## set the seed to make your partition reproducible \nset.seed(123) \ntrain_ind <- sample(seq_len(nrow(dfmin)), size = smp_size) \ntrain <- dfmin[train_ind, ] \ntest <- dfmin[-train_ind, ]\n\ntrain[train$RainTomorrow %in% c(\"NaN\", \"NA\", \"Inf\"), ]\nsummary(train)\n\n\nx_names = c(\"Date\", \n \"Location\", \n \"MinTemp\", \n \"MaxTemp\", \n \"Rainfall\", \n \"Evaporation\", \n \"Sunshine\", \n \"WindGustDir\", \n \"WindGustSpeed\", \n \"WindDir9am\", \n \"WindDir3pm\", \n \"WindSpeed9am\", \n \"WindSpeed3pm\", \n \"Humidity9am\", \n \"Humidity3pm\", \n \"Pressure9am\", \n \"Pressure3pm\", \n \"Cloud9am\", \n \"Cloud3pm\", \n \"Temp9am\", \n \"Temp3pm\", \n \"RainToday\")\ny_names = c(\"RainTomorrow\")\n\n################################ DECISION TREE ################################################\n\ntutData <- train[, !names(train) %in% c(\"Location\",\"RainToday\",\"Date\",\"WindGustDir\",\"WindDir9am\",\"WindDir3pm\")]\ndateLess <- test[, !names(test) %in% c(\"Date\")]\nnohum <- train[, !names(train) %in% c(\"Date\",\"Humidity9am\",\"Humidity3pm\",\"Pressure3pm\", \"Pressure9am\",\"Cloud9am\", \"Cloud3pm\")]\ntre <- ctree(RainTomorrow ~ ., data = nohum)\npng(\"nohum.png\", res=35, height=1000, width=32767)\nplot(tre)\ndev.off()\n\npred <- predict(tre, test)\ntPred <- table(pred, test$RainTomorrow)\nacc <- (tPred[1,1] + tPred[2,2]) / sum(tPred)\n\n\nrf.cv <- rfcv(trainx = test[, x_names], trainy = test[, y_names], cv.fold = 2, do.trace=TRUE)\nsummary(rf.cv)\nrf.cv$error.cv\n", "meta": {"hexsha": "089cd10165e097a268e31367fa254ff3ecdc1505", "size": 3301, "ext": "r", "lang": "R", "max_stars_repo_path": "dt.r", "max_stars_repo_name": "Maximilian-v-H/Hables-Rain", "max_stars_repo_head_hexsha": "5b8684a7c23925c801b3c262708d9867e7152403", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dt.r", "max_issues_repo_name": "Maximilian-v-H/Hables-Rain", "max_issues_repo_head_hexsha": "5b8684a7c23925c801b3c262708d9867e7152403", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dt.r", "max_forks_repo_name": "Maximilian-v-H/Hables-Rain", "max_forks_repo_head_hexsha": "5b8684a7c23925c801b3c262708d9867e7152403", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7043478261, "max_line_length": 126, "alphanum_fraction": 0.5961829749, "num_tokens": 1047, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7122321842389469, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.33388777195275415}} {"text": "###############################################################################################################################################################\n# Timetable Model\n# Author Mufy \n# Date 24/09/2016\n# Description -- This script models the teaching hours per academic based on their teaching load. The data for the model is extracted from the timetabling \n# spreadsheet.\n\n##############################################################################################################################################################\n\n#library(readxl)\nlibrary(xlsx)\nrequire(xlsx)\n#library(RODBC)\nlibrary(hashmap)\n\n\nhome.dr<- getwd()\n\n#Set path to the timetable\nsetwd(\"/Users/mufy/Dropbox/teaching/UEL/2015-2016/timetable\")\n\n#Change the filename if changes or updated\nfile.name <- \"CSI 2016-17-v10.xlsx\"\nsheet.name <- \"ML\"\n\ntAllocation <- read.xlsx(file.name, 4, header=FALSE, keepFormulas=FALSE, startRow = 2, endRow =25)\ntimetable <- read.xlsx(file.name, 3, header=FALSE, keepFormulas=FALSE, startRow = 2, endRow =207)\n\n# Get the current list of staff names \nstaffNames <- tAllocation[1]\n\n#get all the teaching allocated staff names \nallocatedStaffNamesOnTimetable <- timetable[12];\n\n\n#List to hold all the calculated values\n\n#TODO - Find a way to initilize\nstaffAllocationMap <- hashmap(\"\",\"\")\nstaffAllocationMap$erase(\"\")\n\nclinicTime <- 2\n\n\n\n# Function for calculating the teaching hours\ncalculateLectureHours <- function(semester, hours, npar=TRUE,print=TRUE){\n\t\n\thourCalc <- 0\n\t\n\t#cat (\"\\n calc semester: \", semester, \" : hours: \", hours, \"\\n\")\n\t\n\tif (toString(semester) == \"1 & 2\"){\n\t\t\n\t\thourCalc =\t(2* hours * 24)/43\n\t#\tcat (\"\\n hour calc: \", hourCalc, \"\\n\")\n\t\treturn (hourCalc)\n\t\t\n #Calculation for blocked mode\n }else if ((toString(semester) == \"1B\") | (toString(semester) == \"2B\")){\n \n cat (\"\\n calculating blocked mode lect hours : \", hours, \"\\n\")\n \n hourCalc =\t(2* hours * 5)/43\n return (hourCalc)\n }\n else{\n\t\thourCalc =\t(2*hours * 12)/43\n\t#\tcat (\"\\n hour calc: \", hourCalc, \"\\n\")\n\t\treturn (hourCalc)\n\t\t\n\t}\n}\n\n#Function for calculating the tutorial hours\ncalculateTutorialHours <- function(semester, hours, npar=TRUE,print=TRUE){\n\t\n\thourCalc <- 0\n\t\n\tif (semester == \"1 & 2\"){\t\t\n\t\thourCalc <-\t(1.5* hours * 24)/43\n \n\t}else if ((toString(semester) == \"1B\") | (toString(semester) == \"2B\")){\n \n cat (\"\\n calculating blocked mode tutorial hours : \", hours, \"\\n\")\n \n hourCalc <-\t(1.5* hours * 5)/43\n \n }else{\n\t\thourCalc <-\t(1.5* hours * 12)/43\n \n\t}\n\t\n\treturn (hourCalc)\n}\n\n#Function to calculate module leadership hours\n#TODO - Should take into account the module length, currently only module size\ncalculateModuleLeadership <- function(semester, moduleSize, npar=TRUE,print=TRUE){\n\t\t\n\tsmall <- 25\n\tmedium <- 75\n \n sHour <- 1\n mHour <- 1.5\n lHour <- 2\n \n \n #cat (\"\\n module size: \", moduleSize, \" semester: \", semester , \"\\n\")\n\t\n\tif (as.numeric(moduleSize) < small) {\n \n if ((semester == \"1 & 2\") | (semester == \"1B\") | (semester == \"2B\")){\n return (sHour * 1)\n }else{\n \n return (sHour)\n }\n \n\t}else if ((as.numeric(moduleSize) >= small) & (as.numeric(moduleSize) < medium)){\n \n if ((semester == \"1 & 2\") | (semester == \"1B\") | (semester == \"2B\")){\n return (mHour * 1)\n }else{\n return (mHour)\n }\n }else{\n\n if ((semester == \"1 & 2\") | (semester == \"1B\") | (semester == \"2B\")){\n return (lHour * 1)\n }else{\n return (lHour)\n }\n }\n}\n\n\n#Function for removing leading and trailing spaces from names\n#removeLeadingAndTrailingSpaces <- function (name){\n \n# nameWithoutSpaces < - gsub(\"^\\\\s+|\\\\s+$\", \"\", name)\n# return nameWithoutSpaces\n#}\n\n# Funtion to print the teaching load summary\nallocationSummary <- function (){\n \n cat (\"\\n############################### Summary ########################### \\n\")\n\n\n keys <- staffAllocationMap$keys()\n \n for (key in keys){\n \n cat (\"staff: \", key, \" ====> allocation: \", staffAllocationMap$find(key), \"\\n\")\n }\n \n cat (\"\\n################################################################### \\n\")\n \n}\n\n\n#Main body of the script\n#TODO -- Not very efficient algorithm, improve the algorith from 0(N^2) to (NLogN) \n#IMPROVEMENT -- consider using HashMAP\n\nfor (i in 1: nrow (staffNames)){\n\tprint (toString(staffNames[i,1]))\n\t\n\ttotalAllocation <- 0\n\ttotalModuleLeadership <-0\n\t\n\tfor (j in 1: nrow(allocatedStaffNamesOnTimetable)) {\n\t\tif (toString(staffNames[i,1]) == toString(allocatedStaffNamesOnTimetable[j,1])){\n\t\t\tif (toString(timetable[j:j,4]) == \"Lecture\"){\n\t\t\t\t\t\t\t\n\t\t\t\t#calculate mornalized teaching time \t\t\t\n\t\t\t\tnormalizedHours <- calculateLectureHours (toString(timetable[j:j,5]), (as.numeric(toString(timetable[j:j,9])))*24) \n\t\t\t\ttotalAllocation <- totalAllocation + normalizedHours\t\t\t\t\t\t\t\n\n\t\t\t\t# calculate module leadership \n\t\t\t\tmoduleLeadership <- calculateModuleLeadership(toString(timetable[j:j,5]), (as.numeric(toString(timetable[j:j,11]))))\t\t\t\n\t\t\t\ttotalModuleLeadership <- totalModuleLeadership+ moduleLeadership;\n\t\t\t\t\t\t\t\n\t\t\t\tcat (\"current lec allocation: \", normalizedHours, \"total allocation: \",totalAllocation, \" module code: \",toString(timetable[j:j,2]), \"module size:\", (as.numeric(toString(timetable[j:j,11]))), \" ;semester: \", toString(timetable[j:j,5]), \"; hours: \", (as.numeric(toString(timetable[j:j,9])))*24, \"module leadership: \", moduleLeadership, \"\\n\")\n\t\t\t}else{\n\n\t\t\t\tnormalizedHours <- calculateLectureHours (toString(timetable[j:j,5]), (as.numeric(toString(timetable[j:j,9])))*24) \n\t\t\t\ttotalAllocation <- totalAllocation + normalizedHours\n\n\t\t\t\tcat (\"current tut allocation: \", normalizedHours, \"total allocation: \",totalAllocation, \" module code: \",toString(timetable[j:j,2]), \"module size:\", (as.numeric(toString(timetable[j:j,11]))), \" ;semester: \", toString(timetable[j:j,5]), \"; hours: \", (as.numeric(toString(timetable[j:j,9])))*24, \"\\n\")\t\t\t\t\n\t\t\t}\n\t\t\t\n\t\t\t#Handle joint module leaders \n\t\t}else{ \n\t\t\n\t\t\tif (regexpr(\"/\", toString(allocatedStaffNamesOnTimetable[j,1]))[1]!=-1){\n\t\t\t\tnamesList <- unlist(strsplit(toString(allocatedStaffNamesOnTimetable[j,1]), \"/\"))\n\n\t\t\t\tfor (k in 1: length(namesList)){\n\t\t\t\t\n\t\t\t\t\tif (toString(staffNames[i,1]) == namesList[k]){\n\t\t\t\t\t\tif (toString(timetable[j:j,4]) == \"Lecture\"){\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t#calculate mornalized teaching time \t\t\t\n\t\t\t\t\t\t\tnormalizedHours <- calculateLectureHours (toString(timetable[j:j,5]), ((as.numeric(toString(timetable[j:j,9])))*24)/length(namesList)) \n\t\t\t\t\t\t\ttotalAllocation <- totalAllocation + normalizedHours\t\t\t\t\t\t\t\n\n\t\t\t\t\t\t\t\tcat (\"Shared current lec allocation: \", normalizedHours, \"total allocation: \",totalAllocation, \" module code: \" , toString(timetable[j:j,2]), \"module size:\", (as.numeric(toString(timetable[j:j,11]))), \" ;semester: \", toString(timetable[j:j,5]), \"; hours: \", (as.numeric(toString(timetable[j:j,9])))*24, \"\\n\")\n\n\n\t\t\t\t\t\t\t# calculate module leadership, only the first person is the module leader \n\t\t\t\t\t\t\tif (k ==1){\t\t\t\t\n\t\t\t\t\t\t\t\tmoduleLeadership <- calculateModuleLeadership(toString(timetable[j:j,5]), (as.numeric(toString(timetable[j:j,11]))))\t\t\t\n\t\t\t\t\t\t\t\ttotalModuleLeadership <- totalModuleLeadership+ moduleLeadership;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tcat (\"Shared current lec allocation, with module leadership: \", normalizedHours, \"total allocation: \",totalAllocation, \" module code: \" , toString(timetable[j:j,2]), \"module size:\", (as.numeric(toString(timetable[j:j,11]))), \" ;semester: \", toString(timetable[j:j,5]), \"; hours: \", (as.numeric(toString(timetable[j:j,9])))*24, \"module leadership: \", moduleLeadership, \"\\n\")\n\t\t\t\t\t\t\t}\t\t\t\t\t\n\t\t\t\t\t\t}else{\n\n\t\t\t\t\t\t\tnormalizedHours <- calculateLectureHours (toString(timetable[j:j,5]), ((as.numeric(toString(timetable[j:j,9])))*24)/length(namesList)) \n\t\t\t\t\t\t\ttotalAllocation <- totalAllocation + normalizedHours\n\n\t\t\t\t\t\t\tcat (\"Shared current tut allocation: \", normalizedHours, \"total allocation: \",totalAllocation, \" module code: \" ,toString(timetable[j:j,2]), \"module size:\", (as.numeric(toString(timetable[j:j,11]))), \" ;semester: \", toString(timetable[j:j,5]), \"; hours: \", (as.numeric(toString(timetable[j:j,9])))*24, \"\\n\")\t\t\t\t\n\t\t\t\t\t\t}\n # match the joint lecturer, not further entries on the list required\n\t\t\t\t\tbreak\t\n\t\t\t\t\t}\n\t\t\t\t}\t\t\t\t\n\t\t\t}\n\t\t}\n\t}\n\t\n \n\ttotalAllocation <- totalAllocation + totalModuleLeadership + clinicTime;\n totalAllocationWithoutML <-totalAllocation - totalModuleLeadership;\n \n \tcat (\" Total face-to-face time allocation for :'\", toString(staffNames[i,1]), \"' with module leadership (\",totalModuleLeadership, \") and teaching time (\",totalAllocationWithoutML,\") is: \", totalAllocation, \"\\n\")\n \n staffAllocationMap$insert(toString(staffNames[i,1]),totalAllocation)\n\n totalAllocation <- 0\n\ttotalModuleLeadership <-0\n \t\t\n}\n\nallocationSummary()\n\n#Set the homedirect to the default working directory\nsetwd(home.dr)\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "37e386f7f587a76941e4f0949ad70e96d0301470", "size": 8978, "ext": "r", "lang": "R", "max_stars_repo_path": "TModel.r", "max_stars_repo_name": "mufajjul/TimetableModel", "max_stars_repo_head_hexsha": "6ef9b3e8229f069b5ea08a835878e5966093ed2b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-07-14T08:16:29.000Z", "max_stars_repo_stars_event_max_datetime": "2017-07-14T08:16:29.000Z", "max_issues_repo_path": "TModel.r", "max_issues_repo_name": "mufajjul/TimetableModel", "max_issues_repo_head_hexsha": "6ef9b3e8229f069b5ea08a835878e5966093ed2b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2016-09-28T04:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-13T15:25:09.000Z", "max_forks_repo_path": "TModel.r", "max_forks_repo_name": "mufajjul/TimetableModel", "max_forks_repo_head_hexsha": "6ef9b3e8229f069b5ea08a835878e5966093ed2b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6640926641, "max_line_length": 381, "alphanum_fraction": 0.6084874137, "num_tokens": 2393, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.33388591893430886}} {"text": "#' Calculate the total Amazon costs associated with running Snowplow X months ahead\r\n#'\r\n#' @param uniquesPerMonth The number of unique visitors to website(s) per month (integer)\r\n#' @param eventsPerMonth The number of events per month tracked (integer)\r\n#' @param runsPerDay The number of times per day that enrichment process is run (generally 1-24) (integer)\r\n#' @param storageDatabase The type of database used to store Snowplow data. This *MUST* either be 'redshift' or 'postgres'\r\n#' @param numberOfMonths number of months ahead that the model should project costs (e.g. 12 or 36)\r\n#' @param edgeLocations The number of different locations in Amazon's Cloudfront network that each generate an independent log when hit. We believe this number is between 10000 and 100000, but are not sure. (This has an impact on S3 costs)\r\n#'\r\n#' @export\r\nsnowplowCostByMonth <- function(uniquesPerMonth, eventsPerMonth, runsPerDay, storageDatabase, numberOfMonths, edgeLocations){\r\n\t\r\n\tmonth <- seq(1, numberOfMonths, by=1)\r\n\t\r\n\t# Snowplow cost is made up of Cloudfront, S3, EMR and database (Redshift / storage) costs\r\n\tcloudfrontCost <- cfCostPerMonth(eventsPerMonth, uniquesPerMonth)\r\n\ts3Cost <- s3CostByMonth(eventsPerMonth, runsPerDay, edgeLocations, numberOfMonths)\r\n\temrCost <- emrCostPerMonth(eventsPerMonth, runsPerDay)\r\n\tdatabaseCost <- databaseCostByMonth(storageDatabase, eventsPerMonth, numberOfMonths)\r\n\r\n\t# Combine above 4 costs in a single data frame:\r\n\tsnowplowCost <- data.frame(month, cloudfrontCost, s3Cost, emrCost, databaseCost)\r\n\tsnowplowCost$totalCost <- snowplowCost$cloudfrontCost + snowplowCost$s3Cost + snowplowCost$emrCost + snowplowCost$databaseCost\r\n\r\n\t# Now return the completed data frame\r\n\tsnowplowCost\r\n}", "meta": {"hexsha": "329af36a9ec1143aaa2c9cc3ec3762c61e2bd585", "size": 1729, "ext": "r", "lang": "R", "max_stars_repo_path": "R/snowplow-costs.r", "max_stars_repo_name": "snowplow/snowplow-tco-model", "max_stars_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2015-03-26T15:32:58.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-08T13:50:12.000Z", "max_issues_repo_path": "R/snowplow-costs.r", "max_issues_repo_name": "snowplow-archive/snowplow-tco-model", "max_issues_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-07T00:44:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-24T15:13:20.000Z", "max_forks_repo_path": "R/snowplow-costs.r", "max_forks_repo_name": "snowplow-archive/snowplow-tco-model", "max_forks_repo_head_hexsha": "bef6a26f3b66d486d2c33b0589825d53a1ddc8a2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2016-01-16T23:33:14.000Z", "max_forks_repo_forks_event_max_datetime": "2017-05-02T04:55:21.000Z", "avg_line_length": 64.037037037, "max_line_length": 240, "alphanum_fraction": 0.7784846732, "num_tokens": 446, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056167854461, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.33388591275113305}} {"text": "#!/usr/bin/Rscript\n\n# helper functions\n\nnan.to.zero <- function(n) {\n if (is.nan(n)) return(0) else return(n)\n}\n\n\n# get the input VCF tabular format, assert that sites must have AC > 0\nvcf <- subset(read.table(pipe('cat /dev/stdin'), header=T), AC > 0)\n\nfilename <- commandArgs(TRUE)[1]\ntag <- commandArgs(TRUE)[2]\n\ntag.genotypes_count <- paste(tag, '.genotypes.alternate_count', sep='')\ntag.genotypes_alternate_count <- paste(tag, '.genotypes.alternate_count', sep='')\ntag.non_reference_discrepancy_count <- paste(tag, '.site.non_reference_discrepancy.count', sep='')\ntag.non_reference_discrepancy_normalizer <- paste(tag, '.site.non_reference_discrepancy.normalizer', sep='')\ntag.non_reference_sensitivity_count <- paste(tag, '.site.non_reference_sensitivity.count', sep='')\ntag.non_reference_sensitivity_normalizer <- paste(tag, '.site.non_reference_sensitivity.normalizer', sep='')\ntag.alternate_positive_discrepancy <- paste(tag, '.site.alternate_positive_discrepancy', sep='')\ntag.alternate_negative_discrepancy <- paste(tag, '.site.alternate_negative_discrepancy', sep='')\ntag.has_variant <- paste(tag, '.has_variant', sep='')\n\nvcf.numberOfSites <- length(vcf[, tag.genotypes_alternate_count])\nvcf.totalAltAlleles <- sum(vcf[, tag.genotypes_alternate_count])\nvcf.positiveDiscrepancy <- sum(vcf[, tag.alternate_positive_discrepancy]) / sum(vcf[, tag.genotypes_alternate_count])\nvcf.negativeDiscrepancy <- sum(vcf[, tag.alternate_negative_discrepancy]) / sum(vcf[, tag.genotypes_alternate_count])\nvcf.sitesTruePositive <- mean(vcf[, tag.has_variant])\n\nmin_sites <- 5 # number of sites required for \"simple plotting\"\n\n#library(ggplot2)\n#vcf2 <- data.frame(QUAL=vcf$QUAL, AC=vcf$AC, has_variant=vcf[, tag.has_variant])\n#qplot(AC, has_variant, group=AC, geom=\"boxplot\", data=subset(vcf2, AC <= 20))\n#ggsave(paste(filename, '.', tag, '.PD.vs.AC.boxplot.ac_lt_20.pdf', sep=''))\n\n\ncat('number of sites', vcf.numberOfSites, '\\n')\ncat('total alternate alleles', vcf.totalAltAlleles, '\\n')\ncat('positive discrepancy', vcf.positiveDiscrepancy, '\\n')\ncat('negative discrepancy', vcf.negativeDiscrepancy, '\\n')\n\n#x <- cbind(tapply(vcf, as.list(seq(0,max(vcf$AC))),\n# function(x) {\n# sum(x[, tag.alternate_positive_discrepancy]) / sum(x[, tag.genotypes_alternate_count])\n# }))\n\nbyac <- data.frame(ac=as.vector(seq(1,max(vcf$AC)))) #, fdr=as.vector(x))\n\n\nbyac$fdr <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(nan.to.zero(sum(s[, tag.alternate_positive_discrepancy]) / sum(s[, tag.genotypes_alternate_count])))\n })))\n\n# false detection count\nbyac$fpc <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(sum(s[, tag.alternate_positive_discrepancy]))\n })))\n\nbyac$alleles <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(sum(s[, tag.genotypes_alternate_count]))\n })))\n\nbyac$sites <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(length(s$AC))\n })))\n\n# count true positive sites\nbyac$site_tpc <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(sum(s[, tag.has_variant]))\n })))\n\n# fpc == false detection count\nbyac$site_fpc <- byac$sites - byac$site_tpc\n# site detection fpr is 1 - true positive rate\nbyac$site_fpr <- 1 - ( byac$site_tpc / byac$sites )\n\nsummary(byac)\n\n#print(byac$sites)\n#print(byac$site_tpc)\n#print(byac$site_fpc)\n#print(byac$site_fpr)\n\n#byac$site_fpr_gt0 <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n# s <- subset(byac, ac == i, select=c(site_fpr, sites))\n#if (s$sites >= min_sites) {\n# return(s$site_fpr)\n#} else {\n# return(NA)\n#}\n#})))\n\n#byac$site_fprlt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) mean(subset(byac, ac <= i, select=site_fpr)))))\nbyac$site_fprlt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(byac, ac <= i, select=c(site_fpc, sites))\n return(sum(s$site_fpc) / sum(s$sites))\n})))\n\n#byac$site_fprgt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) mean(subset(byac, ac >= i, select=site_fpr)))))\nbyac$site_fprgt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(byac, ac >= i, select=c(site_fpc, sites))\n return(sum(s$site_fpc) / sum(s$sites))\n})))\n\nbyac$cfa <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) sum(subset(byac, ac <= i, select=alleles)) / sum(byac$alleles))))\n\nbyac$cfs <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) sum(subset(byac, ac <= i, select=sites)) / length(vcf$AC))))\n\n# inappropriate collapse via averaging of fdr\n#byac$alternate_pdlt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) mean(subset(byac, ac <= i, select=fdr)))))\n\nbyac$alternate_pdr <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(byac, ac == i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\n# use this one\nbyac$alternate_pdlt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(byac, ac <= i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\n#byac$alternate_pdgt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) mean(subset(byac, ac >= i, select=fdr)))))\nbyac$alternate_pdgt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(byac, ac >= i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\nbyac$nrs <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(nan.to.zero(sum(s[, tag.non_reference_sensitivity_count]) / sum(s[, tag.non_reference_sensitivity_normalizer])))\n })))\n\nbyac$nrd <- as.vector(cbind(by(byac$ac, byac$ac,\n function(x) {\n s <- subset(vcf, AC == x)\n return(nan.to.zero(sum(s[, tag.non_reference_discrepancy_count]) / sum(s[, tag.non_reference_discrepancy_normalizer])))\n })))\n\nbyac$nrslt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) {\n s <- subset(vcf, AC <= i, select=c(tag.non_reference_sensitivity_count, tag.non_reference_sensitivity_normalizer))\n return(nan.to.zero(sum(s[, tag.non_reference_sensitivity_count]) / sum(s[, tag.non_reference_sensitivity_normalizer])))\n})))\n\nbyac$nrdlt <- as.vector(cbind(tapply(byac$ac, byac$ac, function(i) { \n s <- subset(vcf, AC <= i, select=c(tag.non_reference_discrepancy_count, tag.non_reference_discrepancy_normalizer))\n return(nan.to.zero(sum(s[, tag.non_reference_discrepancy_count]) / sum(s[, tag.non_reference_discrepancy_normalizer])))\n})))\n\nbyac_gtsites <- subset(byac, sites >= min_sites)\n\n\nif (FALSE) {\npdf(paste(filename, '.', tag, '.PD.vs.AC.smooth.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byac$cfa, ylim=c(0,1.0),\n xlab='alternate allele count (AC)', xaxt='n',\n ylab='', yaxt='n', type='l', col='red')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byac$ac),10), labels=seq(0,max(byac$ac),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(smoothed)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\n#plot(byac$alternate_pdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='green')\nplot(byac$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T)\nlines(byac$ac, predict(loess(byac$alternate_pdr ~ byac$ac, span=0.5)), col=\"green\")\npar(new=T)\nplot(byac$cfa, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='red')\npar(new=T)\nplot(byac$cfs, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='purple')\npar(new=T)\nlines(byac$ac, predict(loess(byac$site_fpr ~ byac$ac, span=0.5)), col=\"blue\")\npar(new=T)\nlines(byac$ac, predict(loess(byac$nrs ~ byac$ac, span=0.5)), col=\"magenta\")\npar(new=T)\nlines(byac$ac, predict(loess(byac$nrd ~ byac$ac, span=0.5)), col=\"brown\")\npar(new=T, cex=0.65)\nmtext(paste(\"alternate genotype PD: \", round(vcf.positiveDiscrepancy, digits=4), \", site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of alt alleles', 'cumulative fraction of sites', 'alt genotypes PD', 'site PD', 'non-ref sensitivity', 'non-ref discrepancy', 'site PD at AC'),\n fill=c('red', 'purple', 'green', 'blue', 'magenta', 'brown', 'black'))\ngarbage <- dev.off()\n}\n\n\n\npdf(paste(filename, '.', tag, '.PD.vs.AC.cumulative.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byac$cfa, ylim=c(0,1.0), \n xlab='alternate allele count (AC)', xaxt='n',\n ylab='', yaxt='n', type='l', col='red')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byac$ac),10), labels=seq(0,max(byac$ac),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(cumulative)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\n#plot(byac_gtsites$ac, byac_gtsites$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\nplot(byac$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T)\nplot(byac$alternate_pdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='green')\npar(new=T)\nplot(byac$cfa, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='red')\npar(new=T)\nplot(byac$cfs, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='purple')\npar(new=T)\nplot(byac$site_fprlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='blue')\npar(new=T)\nplot(byac$nrslt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='magenta')\npar(new=T)\nplot(byac$nrdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='brown')\npar(new=T, cex=0.65)\nmtext(paste(\"alternate genotype PD: \", round(vcf.positiveDiscrepancy, digits=4), \", site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of alt alleles', 'cumulative fraction of sites', 'alt genotypes PD', 'site PD', 'non-ref sensitivity', 'non-ref discrepancy', 'site PD at AC'),\n fill=c('red', 'purple', 'green', 'blue', 'magenta', 'brown', 'black'))\ngarbage <- dev.off()\n\n\n\npdf(paste(filename, '.', tag, '.PD.vs.AC.cumulative.simple.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byac$cfs, ylim=c(0,1.0), xlim=c(0,max(vcf$AC)),\n xlab='alternate allele count (AC)', xaxt='n',\n ylab='', yaxt='n', type='l', col='purple')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byac$ac),10), labels=seq(0,max(byac$ac),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(cumulative)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\nplot(byac$site_fprlt, ylim=c(0,1.0), xlim=c(0,max(vcf$AC)), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='blue')\npar(new=T)\nplot(byac_gtsites$ac, byac_gtsites$site_fpr, ylim=c(0,1.0), xlim=c(0,max(vcf$AC)), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T, cex=0.65)\nmtext(paste(\"site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of sites', 'cumulative site PD', paste('site PD at AC (>=', min_sites, 'sites)')),\n fill=c('purple', 'blue', 'black'))\ngarbage <- dev.off()\n\n\n\n\npdf(paste(filename, '.', tag, '.PD.vs.AC.instantaneous.ac_lt_20.pdf', sep=''))\n#par(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byac$sites, ylim=c(0,max(byac$sites)), xlim=c(0,20),\n xlab='alternate allele count (AC)', xaxt='n',\n ylab='number of sites', type='l', pch=19, col='blue')\n#axis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\n#countTicks <- round(seq(0,1,0.1) * max(byac$sites))\n#axis(2, at=countTicks, labels=countTicks)\npar(new=T)\naxis(1, at=seq(0,max(byac$ac),1), labels=seq(0,max(byac$ac),1))\ngrid(lty=5)\npar(new=T)\nplot(byac$sites, ylim=c(0,max(byac$sites)), xlim=c(0,20), type='o', pch=19, col='blue', xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(instantaneous)'))\npar(new=T)\nmtext(\"number of sites\", side=2, line=3) #, cex=0.75)\n#par(new=T)\n#plot(byac$site_fprlt, ylim=c(0,1.0), xlim=c(0,20), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='blue')\npar(new=T)\nplot(byac_gtsites$ac, byac_gtsites$site_tpc, ylim=c(0,max(byac$sites)), xlim=c(0,20), xlab='', xaxt='n', ylab='', yaxt='n', col='red', pch=19, type='o')\npar(new=T) #, cex=0.65)\nmtext(paste(\"site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T) #, cex=0.65)\n#legend('topright', c('number of sites', 'site PD count'),\n# fill=c('blue', 'red'))\ngarbage <- dev.off()\n\n# stratifying by QUAL\n\nif (FALSE) {\n\n\nx <- cbind(by(vcf, vcf$QUAL,\n function(x) {\n sum(x[, tag.alternate_positive_discrepancy]) / sum(x[, tag.genotypes_alternate_count])\n }))\n\nbyqual <- data.frame(qual=as.numeric(rownames(x)), fdr=as.vector(x))\n\n# false detection count\nbyqual$fpc <- as.vector(cbind(by(vcf, vcf$QUAL,\n function(i) { sum(i[, tag.alternate_positive_discrepancy]) } )))\n\nbyqual$alleles <- as.vector(cbind(by(vcf, vcf$QUAL,\n function(i) {\n sum(i[, tag.genotypes_alternate_count])\n })))\n\nbyqual$sites <- as.vector(cbind(by(vcf$QUAL, vcf$QUAL, function(i) length(i))))\n\n# count true positive sites\nbyqual$site_tpc <- as.vector(cbind(by(vcf[, tag.has_variant], vcf$QUAL, function(i) sum(i))))\n# fpc == false detection count\nbyqual$site_fpc <- byqual$sites - byqual$site_tpc\n# site detection fpr is 1 - true positive rate\nbyqual$site_fpr <- 1 - ( byqual$site_tpc / byqual$sites )\n\n#byqual$site_fpr_gt0 <- as.vector(cbind(tapply(byqual$ac, byqual$ac, function(i) { \n# s <- subset(byqual, ac == i, select=c(site_fpr, sites))\n#if (s$sites >= min_sites) {\n# return(s$site_fpr)\n#} else {\n# return(NA)\n#}\n#})))\n\n#byqual$site_fprlt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) mean(subset(byqual, qual <= i, select=site_fpr)))))\nbyqual$site_fprlt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(byqual, qual <= i, select=c(site_fpc, sites))\n return(sum(s$site_fpc) / sum(s$sites))\n})))\n\n#byqual$site_fprgt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) mean(subset(byqual, qual >= i, select=site_fpr)))))\nbyqual$site_fprgt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(byqual, qual >= i, select=c(site_fpc, sites))\n return(sum(s$site_fpc) / sum(s$sites))\n})))\n\nbyqual$cfa <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) sum(subset(byqual, qual <= i, select=alleles)) / sum(byqual$alleles))))\n\nbyqual$cfs <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) sum(subset(byqual, qual <= i, select=sites)) / length(vcf$QUAL))))\n\n# inappropriate collapse via averaging of fdr\n#byqual$alternate_pdlt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) mean(subset(byqual, qual <= i, select=fdr)))))\n\nbyqual$alternate_pdr <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(byqual, qual == i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\n# use this one\nbyqual$alternate_pdlt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(byqual, qual <= i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\n#byqual$alternate_pdgt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) mean(subset(byqual, qual >= i, select=fdr)))))\nbyqual$alternate_pdgt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(byqual, qual >= i, select=c(fpc, alleles))\n return(sum(s$fpc) / sum(s$alleles))\n})))\n\nnan.to.zero <- function(n) {\n if (is.nan(n)) return(0) else return(n)\n}\n\nbyqual$nrs <- as.vector(cbind(by(vcf, vcf$QUAL, function(i) {\n return(nan.to.zero(sum(i[, tag.non_reference_sensitivity_count]) / sum(i[, tag.non_reference_sensitivity_normalizer])))\n})))\n\nbyqual$nrd <- as.vector(cbind(by(vcf, vcf$QUAL, function(i) { \n return(nan.to.zero(sum(i[, tag.non_reference_discrepancy_count]) / sum(i[, tag.non_reference_discrepancy_normalizer])))\n})))\n\nbyqual$nrslt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) {\n s <- subset(vcf, QUAL <= i, select=c(tag.non_reference_sensitivity_count, tag.non_reference_sensitivity_normalizer))\n return(nan.to.zero(sum(s[, tag.non_reference_sensitivity_count]) / sum(s[, tag.non_reference_sensitivity_normalizer])))\n})))\n\nbyqual$nrdlt <- as.vector(cbind(tapply(byqual$qual, byqual$qual, function(i) { \n s <- subset(vcf, QUAL <= i, select=c(tag.non_reference_discrepancy_count, tag.non_reference_discrepancy_normalizer))\n return(nan.to.zero(sum(s[, tag.non_reference_discrepancy_count]) / sum(s[, tag.non_reference_discrepancy_normalizer])))\n})))\n\nbyqual_gt10 <- subset(byqual, sites >= min_sites)\n\n\nif (FALSE) {\npdf(paste(filename, '.', tag, '.PD.vs.QUAL.smooth.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byqual$cfa, ylim=c(0,1.0),\n xlab='QUAL', xaxt='n',\n ylab='', yaxt='n', type='l', col='red')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byqual$qual),10), labels=seq(0,max(byqual$qual),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(smoothed)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\n#plot(byqual$alternate_pdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='green')\nplot(byqual$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T)\nlines(byqual$qual, predict(loess(byqual$alternate_pdr ~ byqual$qual, span=0.5)), col=\"green\")\npar(new=T)\nplot(byqual$cfa, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='red')\npar(new=T)\nplot(byqual$cfs, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='purple')\npar(new=T)\nlines(byqual$qual, predict(loess(byqual$site_fpr ~ byqual$qual, span=0.5)), col=\"blue\")\npar(new=T)\nlines(byqual$qual, predict(loess(byqual$nrs ~ byqual$qual, span=0.5)), col=\"magenta\")\npar(new=T)\nlines(byqual$qual, predict(loess(byqual$nrd ~ byqual$qual, span=0.5)), col=\"brown\")\npar(new=T, cex=0.65)\nmtext(paste(\"alternate genotype PD: \", round(vcf.positiveDiscrepancy, digits=4), \", site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of alt alleles', 'cumulative fraction of sites', 'alt genotypes PD', 'site PD', 'non-ref sensitivity', 'non-ref discrepancy', 'site PD at QUAL'),\n fill=c('red', 'purple', 'green', 'blue', 'magenta', 'brown', 'black'))\ngarbage <- dev.off()\n}\n\n\n\npdf(paste(filename, '.', tag, '.PD.vs.QUAL.cumulative.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byqual$cfa, ylim=c(0,1.0),\n xlab='QUAL', xaxt='n',\n ylab='', yaxt='n', type='l', col='red')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byqual$qual),10), labels=seq(0,max(byqual$qual),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(cumulative)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\nplot(byqual_gt10$qual, byqual_gt10$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\n#plot(byqual$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T)\nplot(byqual$alternate_pdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='green')\npar(new=T)\nplot(byqual$cfa, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='red')\npar(new=T)\nplot(byqual$cfs, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='purple')\npar(new=T)\nplot(byqual$site_fprlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='blue')\npar(new=T)\nplot(byqual$nrslt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='magenta')\npar(new=T)\nplot(byqual$nrdlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='brown')\npar(new=T, cex=0.65)\nmtext(paste(\"alternate genotype PD: \", round(vcf.positiveDiscrepancy, digits=4), \", site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of alt alleles', 'cumulative fraction of sites', 'alt genotypes PD', 'site PD', 'non-ref sensitivity', 'non-ref discrepancy', 'site PD at QUAL (>= 10 sites)'),\n fill=c('red', 'purple', 'green', 'blue', 'magenta', 'brown', 'black'))\ngarbage <- dev.off()\n\n\n\npdf(paste(filename, '.', tag, '.PD.vs.QUAL.cumulative.simple.pdf', sep=''))\npar(cex=0.75)\npar(mar=c(5,4,4,5) + 0.1)\nplot(byqual$cfs, ylim=c(0,1.0),\n xlab='QUAL', xaxt='n',\n ylab='', yaxt='n', type='l', col='purple')\naxis(2, at=seq(0,1,0.1), labels=seq(0,1,0.1))\naxis(1, at=seq(0,max(byqual$qual),10), labels=seq(0,max(byqual$qual),10), cex=0.75)\ngrid(lty=5)\npar(new=T)\ntitle(paste(filename, 'positive discrepancy versus', tag, '(cumulative)'))\npar(new=T)\ncountTicks <- seq(0,1,0.1) * vcf.numberOfSites\naxis(4, at=seq(0,1,0.1), labels=round(countTicks))\nmtext(\"number of sites\", side=4, line=3, cex=0.75)\npar(new=T)\nplot(byqual$site_fprlt, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n', type='l', col='blue')\npar(new=T)\nplot(byqual_gt10$qual, byqual_gt10$site_fpr, ylim=c(0,1.0), xlab='', xaxt='n', ylab='', yaxt='n')\npar(new=T, cex=0.65)\nmtext(paste(\"site PD: \", round(1 - vcf.sitesTruePositive, digits=4), sep=''))\npar(new=T, cex=0.65)\nlegend('topleft', c('cumulative fraction of sites', 'site PD', 'site PD at QUAL (>= 10 sites)'),\n fill=c('purple', 'blue', 'black'))\ngarbage <- dev.off()\n\n}\n", "meta": {"hexsha": "871798737b5688ea8d5ee776c3fe8084981691d4", "size": 22061, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/freebayes/vcflib/bin/vcfplotaltdiscrepancy.r", "max_stars_repo_name": "benranco/test", "max_stars_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tools/freebayes/vcflib/bin/vcfplotaltdiscrepancy.r", "max_issues_repo_name": "benranco/test", "max_issues_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tools/freebayes/vcflib/bin/vcfplotaltdiscrepancy.r", "max_forks_repo_name": "benranco/test", "max_forks_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-01T10:32:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-01T10:32:38.000Z", "avg_line_length": 43.087890625, "max_line_length": 200, "alphanum_fraction": 0.6595349259, "num_tokens": 7778, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056040203135, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3338859065679571}} {"text": "# To run this case study, you should have R version of 3.0.2 or later \n# This is a modified version of the code available at:\n# http://bioinf.wehi.edu.au/RNAseqCaseStudy/\n# Download and extract the files\n# Running the 1.download.sh script should perform this automatically\n# Then copy this script into that directory and run it instead of code.R\n# load libraries\nlibrary(Rsubread)\nlibrary(limma)\nlibrary(edgeR)\n\n# read in target file\noptions(digits=2)\ntargets <- readTargets()\ntargets\n\n# create a design matrix\ncelltype <- factor(targets$CellType)\ndesign <- model.matrix(~celltype)\n\n# build an index for reference sequence (Chr1 in hg19)\nbuildindex(basename=\"chr1\",reference=\"hg19_chr1.fa\")\n\n# align reads\nalign(index=\"chr1\",readfile1=targets$InputFile,input_format=\"gzFASTQ\",output_format=\"BAM\",output_file=targets$OutputFile,unique=TRUE,indels=5)\n\n# count numbers of reads mapped to NCBI Refseq genes\n# the file is obtained from:\n# https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.gtf.gz\n# our version is slightly modified from the original gtf version\n# gzip -cd \"${INFILE_DIR}/GCF_000001405.25_GRCh37.p13_genomic.gtf.gz\" | \\\n# grep -P \"^#|^NC_000001.10\\t\" | sed \"s|NC_000001.10\t|chr1\t|\" > \\\n# \"${INFILE_DIR}/hg19_chr1.gtf\"\n\nfc <- featureCounts(files=targets$OutputFile,isGTFAnnotationFile=TRUE,annot.ext=\"hg19_chr1.gtf\")\nwrite.table(fc$counts, sep=\"\\t\", quote=F, \"original.tsv\")\nx <- DGEList(counts=fc$counts, genes=fc$annotation[,c(\"GeneID\",\"Length\")])\n\n# generate RPKM values if you need them\nx_rpkm <- rpkm(x,x$genes$Length)\n\n# filter out low-count genes\nisexpr <- rowSums(cpm(x) > 10) >= 2\nx <- x[isexpr,]\n\n# perform voom normalization\ny <- voom(x,design,plot=TRUE)\n\n# cluster libraries\nplotMDS(y,xlim=c(-2.5,2.5))\n\n# fit linear model and assess differential expression\nfit <- eBayes(lmFit(y,design))\ntop <- topTable(fit,coef=2,number=Inf,sort.by=\"P\")\nwrite.table(top, sep=\"\\t\", quote=F, file=\"original.tsv.top.tsv\")\n", "meta": {"hexsha": "a498fbf53b335769d5c5deda89de9fbe775c0350", "size": 2048, "ext": "r", "lang": "R", "max_stars_repo_path": "src/rnaseqcasestudy_wehi.r", "max_stars_repo_name": "tyronechen/universal_data_format", "max_stars_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/rnaseqcasestudy_wehi.r", "max_issues_repo_name": "tyronechen/universal_data_format", "max_issues_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/rnaseqcasestudy_wehi.r", "max_forks_repo_name": "tyronechen/universal_data_format", "max_forks_repo_head_hexsha": "6d2e483414dba2a3abe4d03e728e34259ee718bf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5714285714, "max_line_length": 174, "alphanum_fraction": 0.7495117188, "num_tokens": 618, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3336129207046475}} {"text": "import_package('dplyr', attach=TRUE)\nimport_package('igraph', attach=TRUE)\nb = import('ebits/base')\nio = import('ebits/io')\ntfs = import('../util/tfs')$tfs\n\nOUTFILE = commandArgs(TRUE)[1] %or% \"genenet/LIHC/snormal.RData\"\nINFILES = OUTFILE %>%\n sub(\"\\\\.RData\", \"\", .) %>%\n paste0(sprintf(\"/%i.RData\", 1:100))\n\n#' Loads a network data.frame, keeps only TF edges, and orders node1/2\nload_fun = function(fname) {\n message(fname)\n re = io$load(fname) %>%\n mutate(node1 = as.character(node1),\n node2 = as.character(node2)) %>%\n filter(node1 %in% tfs | node2 %in% tfs,\n qval < 0.01)\n\n swap = mapply(`>`, re$node1, re$node2)\n tmp = re$node1[swap]\n re$node1[swap] = re$node2[swap]\n re$node2[swap] = tmp\n\n message(\"TF edges: \", dim(re))\n re\n}\n\ngdf = lapply(INFILES, load_fun) %>%\n bind_rows()\n\nmessage(\"# of edges across all models: \", dim(gdf))\n\nmedian_pos = ceiling(length(INFILES)/2)\n\ngdf = gdf %>%\n group_by(node1, node2) %>%\n filter(length(unique(sign(pcor))) == 1) %>%\n summarize(pcor = sign(pcor[1]) * sort(abs(pcor))[median_pos],\n qval = 10^(sort(log10(qval))[median_pos])) %>%\n filter(abs(pcor) > .Machine$double.eps)\n\nmessage(\"# of edges after filtering: \", dim(gdf))\n\ng = graph_from_data_frame(gdf, directed=FALSE)\n\n# write node attribute whether gene is TF\nset_vertex_attr(g, \"tf\", index=V(g), value=V(g)$name %in% tfs)\n\ndg = decompose.graph(g)\nif (length(dg) > 1) {\n warning(\"Graph is not connected, using only first component\")\n message(\"Component size: \", paste(sapply(dg, function(x)\n length(V(x))), collapse=\", \"))\n\n g = dg[[1]]\n}\n\nsave(g, file=OUTFILE)\n", "meta": {"hexsha": "2811b9fadb8b39d0a8e641529a7747687930120f", "size": 1682, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/genenet/genenet_assemble.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "tools/genenet/genenet_assemble.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "tools/genenet/genenet_assemble.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 27.5737704918, "max_line_length": 70, "alphanum_fraction": 0.6117717004, "num_tokens": 517, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3335470215551666}} {"text": "\n\nlibrary(tidyverse)\nlibrary(tidytuesdayR)\n\nfreed_slaves <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-16/freed_slaves.csv')\n\nfreed_slaves<-mutate(freed_slaves, Free= paste(freed_slaves$Free, \"%\", sep=\"\"))\n\n\ng<-ggplot(freed_slaves, aes(Year, Slave))+\n geom_area(fill=\"black\")+\n labs(title = (\"PROPORTION OF FREEMAN AND SLAVES AMONG AMERICAN NEGROES . \\n\\n\\n PROPORTION DES N\u00c8GRES LIBRES ET DES ESCLAVES EN AM\u00c9RIQUE .\"),\n subtitle = (\"DONE BY ATLANTA UNIVERSITY .\"),\n caption=\"Data source: W.E.B. Du Bois | Visualization: Rodrigo Santos\")+\n geom_text(data=(subset(freed_slaves,freed_slaves$Year!=1870)) ,aes(y = Slave, label = Free, size = 12), vjust= -0.2, hjust=0.48, fontface=\"bold\")+\n annotate(\"text\", x = 1870, y = 91, label = \"100%\", size=4.3, fontface=\"bold\", hjust=0.35)+\n coord_cartesian(clip = \"off\")+\n theme(panel.background = element_rect(fill = \"#157645\"),\n plot.background = element_rect(fill = \"#d9cbbf\"),\n panel.grid = element_blank(),\n axis.title=element_blank(),\n axis.text.y=element_blank(),\n axis.ticks=element_blank(),\n axis.text.x=element_text(size=12, face=\"bold\", colour=\"black\"),\n panel.grid.major.x = element_line(size=0.1),\n legend.position=\"none\",\n plot.margin= margin(4, 1.2, 0.3, 1.2, \"cm\"),\n plot.title = element_text(hjust = 0.5, vjust = 37, face=\"bold\"),\n plot.subtitle = element_text(size=10,face=\"bold\", hjust = 0.5, vjust = 35))+\n scale_x_continuous(limits = c(1790,1870), expand = c(0, 0), position=\"top\", breaks = seq(1790, 1870, 10))+\n scale_y_continuous(limits=c(0, 120), expand = c(0, 0))+\n annotate(\"text\", x = 1830, y = 112, label = \"FREE - LIBRE\", size=6, fontface=\"bold\")+\n annotate(\"text\", x = 1830, y = 50, label = \"SLAVES \\nESCLAVES\", colour=\"#b2a9a1\", size=7, fontface=\"bold\")\n \n\ng\n \n\nggsave(here::here(\"week8\",\"gweek8.png\"), width=7, height=10)\n", "meta": {"hexsha": "c7996158d231e6c7d4d8950068a86b761f0d0fc6", "size": 1960, "ext": "r", "lang": "R", "max_stars_repo_path": "week8/code.r", "max_stars_repo_name": "Rodspu/tidytuesday", "max_stars_repo_head_hexsha": "5e28ba37c8a8a8ea57e7ae2452c4ecc75a3182bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "week8/code.r", "max_issues_repo_name": "Rodspu/tidytuesday", "max_issues_repo_head_hexsha": "5e28ba37c8a8a8ea57e7ae2452c4ecc75a3182bc", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "week8/code.r", "max_forks_repo_name": "Rodspu/tidytuesday", "max_forks_repo_head_hexsha": "5e28ba37c8a8a8ea57e7ae2452c4ecc75a3182bc", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.8048780488, "max_line_length": 148, "alphanum_fraction": 0.6551020408, "num_tokens": 651, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6619228691808012, "lm_q1q2_score": 0.33354701819451904}} {"text": "######\n# DOSE-RESPONSE CURVES for SKMEL2 and HCEC\n######\n\nlibrary(Cairo)\n\ncelltypes = c('HCEC','SKMEL2')\ntreatments = c('TGFB3','Wnt3A','BMP4')\njitter = c(-.01,.01)\nnames(jitter) = celltypes\n\ncols = c('black','darkred')\nnames(cols) = celltypes\n\n\nCairoSVG( 'dose', width=6, height=2 )\npar( mfrow=c(1,3) )\n\n\n\n\n########### TGFB3 DOSE-RESPONSE ############\n\n\n\nplate = 'W:/2013_06_wnt_tgfb_crosstalk/TI03/TI03_summaryStats.csv'\n\nallData = read.table( plate, sep=',', header=T, stringsAsFactors=F )\n\ntreatments = c('TGFB3')\nchannel = 2\nmarker = 'marker.2'\n\nfor( treatment in treatments ){\n \n concentrations = sort( unique(allData$concentration[allData$treatment==treatment]))\n readouts = unique(allData[allData$treatment==treatment,c('marker.2')])\n readout = readouts[readouts != '']\n feature = paste('sum_nucleus.median',channel,sep='.')\n \n logUnits = log10(concentrations)\n logUnits[is.infinite(logUnits)] = min(logUnits[!is.infinite(logUnits)])-1\n \n plot( NA,NA, xlab=paste('log10(',treatment,')',sep=''), xaxt='n',\n ylab = readout, xlim=range(logUnits), ylim=c(-.2,1.2),yaxt='n')\n axis(2,at=c(0,.5,1))\n axis(1,at=seq(min(round(logUnits)),max(round(logUnits)),by=1))\n abline( h=.5, col='gray', lty=3 )\n abline( h=1, col='gray', lty=3 )\n abline( h=0, col='gray', lty=3 )\n \n \n for( celltype in celltypes ){\n \n dataSubset = allData[allData$celltype==celltype & allData$treatment==treatment, c(feature,'concentration')]\n \n dataSubset = dataSubset[order(dataSubset$concentration),]\n \n responses = matrix(dataSubset[,feature],ncol=3,byrow=T)\n \n # Normalize to 0 - 1\n responses = responses - mean(responses[1,])\n responses = responses / mean(responses[dim(responses)[1],])\n \n means = apply(responses,1,mean)\n sds = apply(responses,1,sd)\n \n for( idx in 1:length(logUnits)){\n lines( rep(logUnits[idx],2)+jitter[celltype], means[idx] + c(-1,1)*sds[idx], col= cols[celltype] )\n }\n \n lines( logUnits, means, lwd=2, col= cols[celltype] )\n }\n legend('topleft',celltypes,text.col=cols[celltypes],bty='n')\n} \n\n\n\n\n\n\n\n########### Wnt3A and BMP4 DOSE-RESPONSE ############\n\n\n\n\nplates = paste('W:/2013_06_wnt_tgfb_crosstalk/TI01/TI01.',1:2,'_summaryStats.csv',sep='')\n\nallData = read.table( plates[1], sep=',', header=T, stringsAsFactors=F )\nallData = rbind( allData, read.table( plates[2], sep=',', header=T, stringsAsFactors=F ))\ntreatments = c('Wnt3A','BMP4')\n\n\nfor( treatment in treatments ){\n \n concentrations = sort( unique(allData$concentration[allData$treatment==treatment]))\n readouts = unique(allData[allData$treatment==treatment,c('marker.2','marker.3')])\n markers = names(readouts)[readouts != '']\n readout = readouts[readouts != '']\n channel = as.numeric(sub('marker.','',markers))\n feature = paste('sum_nucleus.median',channel,sep='.')\n \n logUnits = log10(concentrations)\n logUnits[is.infinite(logUnits)] = min(logUnits[!is.infinite(logUnits)])-1\n \n plot( NA,NA, xlab=paste('log10(',treatment,')',sep=''), xaxt='n',\n ylab = readout, xlim=range(logUnits), ylim=c(-.1,1.1),yaxt='n')\n axis(2,at=c(0,.5,1))\n axis(1,at=seq(min(round(logUnits)),max(round(logUnits)),by=1))\n abline( h=.5, col='gray', lty=3 )\n abline( h=1, col='gray', lty=3 )\n abline( h=0, col='gray', lty=3 )\n \n \n for( celltype in celltypes ){\n \n dataSubset = allData[allData$celltype==celltype & allData$treatment==treatment, c(feature,'concentration')]\n \n dataSubset = dataSubset[order(dataSubset$concentration),]\n \n responses = matrix(dataSubset[,feature],ncol=3,byrow=T)\n \n # Normalize to 0 - 1\n \n \n responses = responses - min(apply(responses,1,mean))\n responses = responses / max(apply(responses,1,mean))\n \n means = apply(responses,1,mean)\n sds = apply(responses,1,sd)\n \n for( idx in 1:length(logUnits)){\n lines( rep(logUnits[idx],2)+jitter[celltype], means[idx] + c(-1,1)*sds[idx], col= cols[celltype] )\n }\n \n lines( logUnits, means, lwd=2, col= cols[celltype] )\n }\n legend('topleft',celltypes,text.col=cols[celltypes],bty='n')\n} \n\ndev.off()\n\n", "meta": {"hexsha": "62c5d6ef821554de3fb7b9f02be9c90ad4096be4", "size": 4224, "ext": "r", "lang": "R", "max_stars_repo_path": "FIGS/insulation/dose.r", "max_stars_repo_name": "adam-coster/dissertation", "max_stars_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FIGS/insulation/dose.r", "max_issues_repo_name": "adam-coster/dissertation", "max_issues_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FIGS/insulation/dose.r", "max_forks_repo_name": "adam-coster/dissertation", "max_forks_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.3333333333, "max_line_length": 111, "alphanum_fraction": 0.6223958333, "num_tokens": 1359, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723317123102956, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3335396138347551}} {"text": "# gen_nsw\nsource(\"src/0a_setup.r\")\nsource(\"src/0c_stand_alone_gen_data.r\")\n\ni = 1\nfor(yr in 2017:2013) {\n for(mths in 12:1) {\n gen_datatable_synthetic2(N = 2e6/((1+0.04/12)^i), mk = yr*100+mths)\n }\n}", "meta": {"hexsha": "c4f502ff768265b9b4ef66a31c3c00f47791de20", "size": 204, "ext": "r", "lang": "R", "max_stars_repo_path": "src/5_synthetic_data.r", "max_stars_repo_name": "xiaodaigh/shinystress", "max_stars_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/5_synthetic_data.r", "max_issues_repo_name": "xiaodaigh/shinystress", "max_issues_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/5_synthetic_data.r", "max_forks_repo_name": "xiaodaigh/shinystress", "max_forks_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-03-19T21:41:27.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-19T21:41:27.000Z", "avg_line_length": 20.4, "max_line_length": 71, "alphanum_fraction": 0.6519607843, "num_tokens": 91, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3335122786560005}} {"text": "# attempt to make box plots\n# box_plots11.r - add shaded bars to shown medians on some plots\n# box_plots12.r - add totals to several boxplot, reduce whitespace\n\nlibrary(tidyverse)\nlibrary(cowplot)\nlibrary(RColorBrewer)\nlibrary(ggthemes)\n\n# path for output\n# out_path <- 'run - eckhardt_priors_narrow/'\nprint(out_path)\n\n# years of simulation\nnyears <- 15\nprint(paste(nyears, \"years\"))\n\n# functions\n\"%notin%\" <- function(x,y)!(\"%in%\"(x,y))\n\n# read in data\nprint(\"reading data\")\nsource('read_data9.r')\nnruns <- nrow(runlist)\n\n# read in model samples\nprint(\"organising data\")\nrows <- 1:nruns\ndatalist <- list()\nfor (i in rows) {\n\n # write header\n data_file_name <- paste(runlist$catchfile[i], '_data.dat', sep='')\n opt_file_name <- runlist$optfile[i]\n\n # assemble run options/data into vectors (?)\n arun <- runlist[i, ]\n adata <- data[grep(pattern=data_file_name, x=data$file), ]\n aarea <- tibble(area=adata$area[1])\n aoptions <- options[opt_file_name, ]\n aalloptions <- cbind(arun, aoptions, aarea) # combine into one data table\n startcalib <- aalloptions$startcalib\n endcalib <- aalloptions$endcalib\n catchname <- aalloptions$catchname\n setname <- aalloptions$setname\n setseq <- aalloptions$setseq\n \n # read samples\n temp <- read_rds(paste(out_path, setname, '_samples.rds', sep=''))\n \n # files are missing for Ot(b) and Wp(b), you can use this to create them\n if (FALSE) {\n temp[, ] <- NA\n temp$setname <- 'Ot(b)'\n write_rds(temp, paste(out_path, 'Ot(b)', '_samples.rds', sep=''))\n temp$setname <- 'Wp(b)'\n write_rds(temp, paste(out_path, 'Wp(b)', '_samples.rds', sep=''))\n }\n \n temp$setseq <- setseq\n datalist[[i]] <- temp # build a list of dataframes\n\n}\nsampledata <- bind_rows(datalist) \nsampledata$setseqf <- factor(sampledata$setseq, levels=c(1:nruns))\nsampledata$catchname <- substr(sampledata$setname, 1, 2) \nsampledata$catchnamef <- factor(sampledata$catchname)\n\n# define boxplot stats\n# https://github.com/tidyverse/ggplot2/issues/898\ncustombox1 <- function(y) { # for error bar\n data.frame(ymin=quantile(y,0.025),\n lower=quantile(y,0.25),\n middle=quantile(y,0.5),\n upper=quantile(y,0.75),\n ymax=quantile(y,0.975),\n y=y,\n width=0.5)[1, ]\n}\ncustombox2 <- function(y) { # for box\n data.frame(ymin=quantile(y,0.025),\n lower=quantile(y,0.25),\n middle=quantile(y,0.5),\n upper=quantile(y,0.75),\n ymax=quantile(y,0.975),\n y=y,\n width=0.7)[1, ]\n}\ncustombox3 <- function(y) { # for error bar\n data.frame(ymin=quantile(y,0.025),\n lower=quantile(y,0.25),\n middle=quantile(y,0.5),\n upper=quantile(y,0.75),\n ymax=quantile(y,0.975),\n y=y,\n width=0.25)[1, ]\n}\ncustombox4 <- function(y) { # for box\n data.frame(ymin=quantile(y,0.025),\n lower=quantile(y,0.25),\n middle=quantile(y,0.5),\n upper=quantile(y,0.75),\n ymax=quantile(y,0.975),\n y=y,\n width=0.3)[1, ]\n}\n\n# test inputs\nvarname <- 'medb0'\nylabel <- 'Medium b0\\n'\nybreaks <- seq(0,1,0.2)\nyref <- 0\nytrans <- \"identity\"\nmedian_fill <- NA\nmedian_colour <- NA\nmedian_alpha <- 1\n\n# handle number of runs\nif (nrow(runlist)==24){\n xlabels <- c('','Wh','',\n '','Wp','',\n '','Po','',\n '','Ot','',\n '','Ta','',\n '','Pu','',\n '','Wt','',\n '','Pi','')\n xint <- c(3.5,6.5,9.5,12.5,15.5,18.5,21.5)\n barwidth <- 2.2\n barfilter <- seq(2,nruns,3)\n}else {\n xlabels <- c('Wh',\n 'Wp',\n 'Po',\n 'Ot',\n 'Ta',\n 'Pu',\n 'Wt',\n 'Pi')\n xint <- c(1.5,2.5,3.5,4.5,5.5,6.5,7.5)\n barwidth <- 0.8\n barfilter <- 1:nruns\n}\n\n# box plot. do it using a function\nbplot <- function(sampledata, varname, ylabel, ybreaks, yref=0, ytrans=\"identity\"){\n ggplot(data=sampledata) +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_hline(yintercept=yref, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n stat_summary(mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox1, geom='errorbar') +\n # geom_boxplot(outlier.size=0.5, notch=FALSE, outlier.shape=NA) +\n # https://stackoverflow.com/questions/17479793/changing-bar-width-when-using-stat-summary-with-ggplot\n stat_summary(mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox2, geom='boxplot') + # width=? is set in custombox\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname), fun.y=median, geom='point', pch=3, size=1) +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0), trans=ytrans) +\n scale_x_discrete(labels=xlabels) \n}\n\n# testing\np1 <- bplot(sampledata, 'medb0', 'Medium b0\\n', seq(0,1,0.2), c(0,0.5))\nprint(p1)\n\n# colours\nchoose <- c(3,5,7,5,3)\ntpcol <- brewer.pal(9,\"OrRd\")[choose]\ntncol <- brewer.pal(9,\"YlGn\")[choose]\nfcol <- brewer.pal(9,\"YlOrBr\")[choose-1]\nmcol <- brewer.pal(9,\"PuBu\")[choose+1]\nscol <- brewer.pal(9,\"RdPu\")[choose+1]\n\n# box plot with medians (which make it a lot slower)\nbplot1 <- function(sampledata, varname, ylabel, ybreaks, yref=0, box_fill=\"white\",\n ytrans=\"identity\", median_fill=NA, median_colour=NA, median_alpha=1){\n # medians <- sampledata[, c(varname, \"setname\", \"setseq\", \"setseqf\")] %>%\n # rename(value=varname) %>%\n # group_by(catchnamef) %>%\n # mutate(median=median(value, na.rm=TRUE))\n if (is.na(median_fill)){\n sampledata2 <- sampledata %>%\n rename(varname=varname) %>%\n select(varname, catchnamef, setseqf, setseq) %>%\n mutate(median=NA_real_)\n } else {\n # this is very slow!!! because lots of copies of median??\n sampledata2 <- sampledata %>%\n rename(varname=varname) %>%\n select(varname, catchnamef, setseqf, setseq) %>%\n group_by(catchnamef) %>%\n mutate(median=median(varname, na.rm=TRUE)) %>%\n arrange(setseq) \n sampledata2$median[sampledata2$setseq==lag(sampledata2$setseq)] <- NA_real_ # discard dupes\n sampledata2$median[sampledata2$setseq %notin% barfilter ] <- NA_real_ # discard dupes\n }\n ggplot(data=sampledata2) +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_col(mapping=aes(x=setseq, y=median), position=\"dodge\", size=1, width=barwidth, \n alpha=median_alpha, fill=median_fill, colour=median_colour) +\n geom_hline(yintercept=yref, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n stat_summary(mapping=aes_string(group='setseqf', x='setseqf', y='varname'),\n fun.data=custombox1, geom='errorbar') +\n # geom_boxplot(outlier.size=0.5, notch=FALSE, outlier.shape=NA) +\n stat_summary(mapping=aes_string(group='setseqf', x='setseqf', y='varname'),\n fun.data=custombox2, geom='boxplot', fill=box_fill) +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname), fun.y=median, geom='point', pch=3, size=1) +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0), trans=ytrans) +\n # geom_segment(mapping=aes(x=setseq-0.5, xend=setseq+0.5, y=median, yend=median), linetype=2, size=1, colour=median_colour) +\n scale_x_discrete(labels=xlabels, expand=c(0, 0)) \n}\n\n# testing\np1 <- bplot1(sampledata, 'medb0', 'Medium b0\\n', seq(0,1,0.2), c(0,0.5), median_fill=mcol[3], median_colour=mcol[3], median_alpha=0.2)\nprint(p1)\n\n# pars\nprint(\"making pars box\")\np1 <- bplot(sampledata, 'medb0', 'Medium b0\\n', seq(0,1,0.2), c(0,0.5))\np2 <- bplot(sampledata, 'slowb0', 'Slow b0\\n', seq(0,0.005,0.001), c(0,0.01))\np3 <- bplot(sampledata, 'meda1', 'Medium a1\\n', seq(0,1,0.2), c(0.5,0.99))\np4 <- bplot(sampledata, 'slowa1', 'Slow a1\\n', seq(0.995,1,0.001), c(0.99,0.9999))\nplotbox <- plot_grid(p1, p2, p3, p4, nrow=2, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'pars', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=6, base_width=8)\n\n# Eckhardt pars\nprint(\"making eckpars box\")\np1 <- bplot(sampledata, 'medBFImax', 'Medium BFImax\\n', seq(0,1,0.2), c(0,1))\np2 <- bplot(sampledata, 'slowBFImax', 'Slow BFImax\\n', seq(0,1,0.2), c(0,1))\np3 <- bplot(sampledata, 'medrec', 'Medium k\\n', seq(0.9,1,0.02), c(0.5,1))\np4 <- bplot(sampledata, 'slowrec', 'Slow k\\n', seq(0.999,1,0.0002), c(0.99,1))\nplotbox <- plot_grid(p1, p2, p3, p4, nrow=2, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'eckpars', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=6, base_width=8)\n\n# # synthetic pars (trying to reduce unc)\n# print(\"making synth box\")\n# sampledata$synth_m1 <- log(1-sampledata$meda1)\n# sampledata$synth_s1 <- log(1-sampledata$slowa1)\n# sampledata$synth_m2 <- sampledata$medb0 * sampledata$meda1\n# sampledata$synth_s2 <- sampledata$slowb0 * sampledata$slowa1\n# p1 <- bplot(sampledata, 'synth_m1', 'Medium Synth 1\\n', seq(-6,0,1), c(0,1))\n# p2 <- bplot(sampledata, 'synth_s1', 'Slow Synth 1\\n', seq(-10,-5,1), c(0,1))\n# p3 <- bplot(sampledata, 'synth_m2', 'Medium Synth 2\\n', seq(0,1,0.1), c(0,1))\n# p4 <- bplot(sampledata, 'synth_s2', 'Slow Synth 2\\n', seq(0,0.005,0.001), c(0,1))\n# plotbox <- plot_grid(p1, p2, p3, p4, nrow=2, align=\"hv\")\n# # print(plotbox)\n# file_name <- paste(out_path, 'box_', 'synth', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=6, base_width=8)\n\n# concs\nprint(\"making conc0 box\")\np1 <- bplot1(sampledata, 'chem1fast0', expression('Fast TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'chem1med0', expression('Medium TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'chem1slow0', expression('Slow TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'chem2fast0', expression('Fast TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'chem2med0', expression('Medium TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'chem2slow0', expression('Slow TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'conc0', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n\nprint(\"making conc1 box\")\np1 <- bplot1(sampledata, 'chem1fast1', expression('Fast TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'chem1med1', expression('Medium TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'chem1slow1', expression('Slow TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'chem2fast1', expression('Fast TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'chem2med1', expression('Medium TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'chem2slow1', expression('Slow TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'conc1', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n\nprint(\"making concTP box\")\np1 <- bplot1(sampledata, 'chem1fast0', expression('Fast TP Conc0'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'chem1med0', expression('Medium TP Conc0'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'chem1slow0', expression('Slow TP Conc0'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'chem1fast1', expression('Fast TP Conc1'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'chem1med1', expression('Medium TP Conc1'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'chem1slow1', expression('Slow TP Conc1'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'concTP', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n\nprint(\"making concTN box\")\np1 <- bplot1(sampledata, 'chem2fast0', expression('Fast TN Conc0'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'chem2med0', expression('Medium TN Conc0'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'chem2slow0', expression('Slow TN Conc0'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'chem2fast1', expression('Fast TN Conc1'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'chem2med1', expression('Medium TN Conc1'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'chem2slow1', expression('Slow TN Conc1'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'concTN', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n\n# side by side two variables\nbplot2 <- function(sampledata, varname0, varname1, ylabel, ybreaks, yref=0, box_fill=\"white\",\n ytrans=\"identity\", median_fill=NA, median_colour=NA, median_alpha=1){\n if (is.na(median_fill)){\n sampledata2 <- sampledata %>%\n rename(varname0=varname0, varname1=varname1) %>%\n select(varname0, varname1, catchnamef, setseqf, setseq) %>%\n mutate(median0=NA_real_, median1=NA_real_)\n } else {\n # this is very slow!!! because lots of copies of median??\n sampledata2 <- sampledata %>%\n rename(varname0=varname0, varname1=varname1) %>%\n select(varname0, varname1, catchnamef, setseqf, setseq) %>%\n group_by(catchnamef) %>%\n mutate(median0=median(varname0, na.rm=TRUE), median1=median(varname1, na.rm=TRUE)) %>%\n arrange(setseq) \n sampledata2$median0[sampledata2$setseq==lag(sampledata2$setseq)] <- NA_real_ # discard dupes\n sampledata2$median0[sampledata2$setseq %notin% barfilter ] <- NA_real_ # discard dupes\n sampledata2$median1[sampledata2$setseq==lag(sampledata2$setseq)] <- NA_real_ # discard dupes\n sampledata2$median1[sampledata2$setseq %notin% barfilter ] <- NA_real_ # discard dupes\n }\n ggplot(data=sampledata2) +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_col(mapping=aes(x=setseq-0.25, y=median0), position=\"dodge\", size=1, width=barwidth/2, \n alpha=median_alpha, fill=median_fill, colour=median_colour) +\n geom_col(mapping=aes(x=setseq+0.25, y=median1), position=\"dodge\", size=1, width=barwidth/2, \n alpha=median_alpha, fill=median_fill, colour=median_colour) +\n geom_hline(yintercept=yref, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n stat_summary(mapping=aes(group=setseq, x=setseq-0.25, y=varname0), fun.data=custombox3, geom='errorbar') +\n stat_summary(mapping=aes(group=setseq, x=setseq-0.25, y=varname0), fun.data=custombox4, geom='boxplot', fill=box_fill) +\n stat_summary(mapping=aes(group=setseq, x=setseq+0.25, y=varname1), fun.data=custombox3, geom='errorbar') +\n stat_summary(mapping=aes(group=setseq, x=setseq+0.25, y=varname1), fun.data=custombox4, geom='boxplot', fill=box_fill) +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname), fun.y=median, geom='point', pch=3, size=1) +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0), trans=ytrans) +\n # geom_segment(mapping=aes(x=setseq-0.5, xend=setseq+0.5, y=median, yend=median), linetype=2, size=1, colour=median_colour) +\n scale_x_continuous(breaks=seq_along(xlabels), labels=xlabels, expand=c(0, 0)) \n}\n\nprint(\"making dconc box\")\np1 <- bplot2(sampledata, 'chem1fast0', 'chem1fast1', expression('Fast TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot2(sampledata, 'chem1med0', 'chem1med1', expression('Medium TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot2(sampledata, 'chem1slow0', 'chem1slow1', expression('Slow TP Conc'~(mg~L^{-1})*'\\n'), seq(0,0.8,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot2(sampledata, 'chem2fast0', 'chem2fast1', expression('Fast TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot2(sampledata, 'chem2med0', 'chem2med1', expression('Medium TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot2(sampledata, 'chem2slow0', 'chem2slow1', expression('Slow TN Conc'~(mg~L^{-1})*'\\n'), seq(0,6,1), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'dconc', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n\n# loads\nsampledata <- sampledata %>%\n mutate(\n fastflowmm = fastflow/area*86.4*365.25,\n medflowmm = medflow/area*86.4*365.25,\n slowflowmm = slowflow/area*86.4*365.25,\n totalflowmm = fastflowmm+medflowmm+slowflowmm,\n fastTPloadkg = fastTPload/area*10/nyears, # convert from t to kg/ha/y\n medTPloadkg = medTPload/area*10/nyears,\n slowTPloadkg = slowTPload/area*10/nyears,\n totalTPloadkg = fastTPloadkg+medTPloadkg+slowTPloadkg,\n fastTNloadkg = fastTNload/area*10/nyears,\n medTNloadkg = medTNload/area*10/nyears,\n slowTNloadkg = slowTNload/area*10/nyears,\n totalTNloadkg = fastTNloadkg+medTNloadkg+slowTNloadkg,\n fastTPloadpc = fastTPloadkg/(fastTPloadkg+medTPloadkg+slowTPloadkg),\n medTPloadpc = medTPloadkg/(fastTPloadkg+medTPloadkg+slowTPloadkg),\n slowTPloadpc = slowTPloadkg/(fastTPloadkg+medTPloadkg+slowTPloadkg),\n fastTNloadpc = fastTNloadkg/(fastTNloadkg+medTNloadkg+slowTNloadkg),\n medTNloadpc = medTNloadkg/(fastTNloadkg+medTNloadkg+slowTNloadkg),\n slowTNloadpc = slowTNloadkg/(fastTNloadkg+medTNloadkg+slowTNloadkg)\n )\n#\nprint(\"making totals boxes\")\ntcol <- tncol[3]\nvarname <- 'totalTPloadkg'\npt1 <- bplot1(sampledata, 'totalTPloadkg', expression('Total TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1.6,0.2), median_colour=NA, median_fill=tcol, median_alpha=0.2)\nvarname <- 'totalTNloadkg'\npt2 <- bplot1(sampledata, 'totalTNloadkg', expression('Total TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=tcol, median_alpha=0.2)\nvarname <- 'totalflowmm'\npt3 <- bplot1(sampledata, 'totalflowmm', expression('Total Flow'~(mm~y^{-1})~'\\n'), seq(0,1200,200), median_colour=NA, median_fill=tcol, median_alpha=0.2)\nplotbox <- pt3\nfile_name <- paste(out_path, 'box_', 'totalflow', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=3, base_width=4)\nplotbox <- plot_grid(pt1, pt2, nrow=2, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'totalload', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=6, base_width=4)\nplotbox <- plot_grid(pt3, pt1, pt2, nrow=3, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'totalall', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=4)\n# plotbox <- plot_grid(p1, p2, nrow=2, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'totalload', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=6, base_width=4)\n# file_name <- paste(out_path, 'box_', 'totalflow', '.png', sep=\"\")\n# save_plot(file_name, p3, base_height=3, base_width=4)\n\nprint(\"making TPload box\")\np1 <- bplot1(sampledata, 'fastTPloadkg', expression('Fast TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'medTPloadkg', expression('Medium TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'slowTPloadkg', expression('Slow TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'fastTPloadpc', 'Fast TP Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'medTPloadpc', 'Medium TP Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\nvarname <- 'slowTPloadpc'\np6 <- bplot1(sampledata, 'slowTPloadpc', 'Slow TP Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'TPload', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n# plotbox <- plot_grid(pt1, NULL, p1, p4, p2, p5, p3, p6, nrow=4, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'TPload', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=12, base_width=8)\n\nprint(\"making TNload box\")\np1 <- bplot1(sampledata, 'fastTNloadkg', expression('Fast TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'medTNloadkg', expression('Medium TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'slowTNloadkg', expression('Slow TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'fastTNloadpc', 'Fast TN Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'medTNloadpc', 'Medium TN Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'slowTNloadpc', 'Slow TN Yield Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'TNload', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n# plotbox <- plot_grid(pt2, NULL, p1, p4, p2, p5, p3, p6, nrow=4, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'TNload', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=12, base_width=8)\n\nprint(\"making BOTHload box\")\np1 <- bplot1(sampledata, 'fastTPloadkg', expression('Fast TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'medTPloadkg', expression('Medium TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'slowTPloadkg', expression('Slow TP Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,1,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\np4 <- bplot1(sampledata, 'fastTNloadkg', expression('Fast TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'medTNloadkg', expression('Medium TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'slowTNloadkg', expression('Slow TN Yield '~(kg~ha^{-1}~y^{-1})*'\\n'), seq(0,20,5), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'BOTHload', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n# plotbox <- plot_grid(pt2, NULL, p1, p4, p2, p5, p3, p6, nrow=4, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'TNload', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=12, base_width=8)\n\n# flowpc\nprint(\"making flow box\")\np4 <- bplot1(sampledata, 'fastflowpc', 'Fast Flow Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np5 <- bplot1(sampledata, 'medflowpc', 'Medium Flow Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np6 <- bplot1(sampledata, 'slowflowpc', 'Slow Flow Fraction\\n', seq(0,1,0.2), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\n# plotbox <- plot_grid(p1, p2, p3, nrow=3, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'flowpc', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=9, base_width=4)\n# flow\np1 <- bplot1(sampledata, 'fastflowmm', expression('Fast Flow'~(mm~y^{-1})~'\\n'), seq(0,1000,200), median_colour=NA, median_fill=fcol[3], median_alpha=0.2)\np2 <- bplot1(sampledata, 'medflowmm', expression('Medium Flow'~(mm~y^{-1})~'\\n'), seq(0,1000,200), median_colour=NA, median_fill=mcol[3], median_alpha=0.2)\np3 <- bplot1(sampledata, 'slowflowmm', expression('Slow Flow'~(mm~y^{-1})~'\\n'), seq(0,1000,200), median_colour=NA, median_fill=scol[3], median_alpha=0.2)\n# plotbox <- plot_grid(p1, p2, p3, nrow=3, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'flowmm', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=9, base_width=4)\nplotbox <- plot_grid(p1, p4, p2, p5, p3, p6, nrow=3, align=\"hv\")\nfile_name <- paste(out_path, 'box_', 'flow', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=8)\n# plotbox <- plot_grid(pt3, NULL, p1, p4, p2, p5, p3, p6, nrow=4, align=\"hv\")\n# file_name <- paste(out_path, 'box_', 'flow', '.png', sep=\"\")\n# save_plot(file_name, plotbox, base_height=12, base_width=8)\n\n# this version allows different ref lines for auto\nbplot2 <- function(sampledata, varname, ylabel, ybreaks, yref1, yref2){\n # medfn <- function(x){\n # med <- median(x)\n # if (med==0) med=NA\n # return(med)\n # }\n ggplot() +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_hline(yintercept=0, linetype=1, colour='black') +\n geom_path(aes(x=1:24, y=yref1), linetype=2, colour='black') +\n geom_path(aes(x=1:24, y=yref2), linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n stat_summary(data=sampledata, mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox1, geom='errorbar') +\n # geom_boxplot(outlier.size=0.5, notch=FALSE, outlier.shape=NA) +\n stat_summary(data=sampledata, mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox2, geom='boxplot') +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname), fun.y=median, geom='point', pch=3, size=1) +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0)) +\n scale_x_discrete(labels=xlabels) \n}\n# auto\nprint(\"making auto box FIXME bug!\")\nyscale <- c(-0.8,-0.6,-0.4,-0.2,0.0,0.2,0.4,0.6,0.8)\nrefline1 <- rep(-0.25303,24)\nrefline2 <- rep(0.25303,24)\nrefline1[c(4,10)] <- -0.331294\nrefline2[c(4,10)] <- 0.331294\np1 <- bplot2(sampledata, 'auto1', 'R(1) TP Calib\\n', yscale, refline1, refline2)\n# print(p1)\nrefline1 <- rep(-0.25303,24)\nrefline2 <- rep(0.25303,24)\np2 <- bplot2(sampledata, 'vauto1', 'R(1) TP Valid\\n', yscale, refline1, refline2)\np3 <- bplot2(sampledata, 'auto2', 'R(1) TN Calib\\n', yscale, refline1, refline2)\np4 <- bplot2(sampledata, 'vauto2', 'R(1) TN Valid\\n', yscale, refline1, refline2)\nplotbox <- plot_grid(p1, p2, p3, p4, nrow=2, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'auto', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=6, base_width=8)\n\n# gmrse \nprint(\"making grmse box\")\nsampledata$vgrmse1[sampledata$vgrmse1==0] <- NA\nsampledata$vgrmse2[sampledata$vgrmse2==0] <- NA\np1 <- bplot(sampledata, 'grmse1', expression('GRMSE TP Calib'~(mg~L^{-1})*'\\n'), seq(0,0.07,0.01), 0.02)\np2 <- bplot(sampledata, 'vgrmse1', expression('GRMSE TP Valid'~(mg~L^{-1})*'\\n'), seq(0,0.07,0.01), 0.02)\np3 <- bplot(sampledata, 'grmse2', expression('GRMSE TN Calib'~(mg~L^{-1})*'\\n'), seq(0,0.7,0.1), 0.2)\np4 <- bplot(sampledata, 'vgrmse2', expression('GRMSE TN Valid'~(mg~L^{-1})*'\\n'), seq(0,0.7,0.1), 0.2)\n# plotbox <- plot_grid(p1, p2, p3, p4, nrow=2, align=\"hv\")\nplotbox <- plot_grid(p1, p3, nrow=2, align=\"hv\")\n# print(plotbox)\nfile_name <- paste(out_path, 'box_', 'grmse', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=6, base_width=4)\n\n# bar plot function\nbarplot <- function(sampledata, varname, ylabel, ybreaks, yref1=NA, yref2=NA, ytrans=\"identity\"){\n ggplot() +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_bar(data=sampledata, mapping=aes_string(x='setseqf', y=varname), stat=\"identity\", fill=\"cornflowerblue\", width=0.7) +\n geom_hline(yintercept=yref1, linetype=2, colour='black') +\n geom_hline(yintercept=yref2, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n #\t stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname), fun.y=median, geom='point', pch=3, size=1) +\n scale_y_continuous(breaks=ybreaks, expand=c(0, 0), trans=ytrans) +\n coord_cartesian(ylim=c(min(ybreaks), max(ybreaks))) + # allows bars to go off the page\n scale_x_discrete(labels=xlabels) \n}\n\n# gelman r \nprint(\"making gelman box\")\nrunfile <- paste(out_path,'runrecord.tsv', sep='')\nrunrecord <- read_tsv(runfile, col_types=cols())\nif (any(!is.na(runrecord$gelmanr)) & any(!is.na(runrecord$elapsed))){\n runrecord$setseqf <- factor(runrecord$setseq, levels=c(1:nruns))\n p1 <- barplot(runrecord, 'gelmanr', 'Gelman R\\n', seq(0,1.5,0.5), 1.1, 1.0)\n temp <- ceiling(max(runrecord$elapsed, na.rm=TRUE))\n p2 <- barplot(runrecord, 'elapsed', 'Elapsed (h)\\n', seq(0,temp,0.5), 0, 0)\n plotbox <- plot_grid(p1, p2, nrow=2, align=\"v\")\n print(plotbox)\n file_name <- paste(out_path, 'box_', 'gelmanr', '.png', sep=\"\")\n save_plot(file_name, plotbox, base_height=6, base_width=4)\n} else {\n print(\"missing gelmanr or elapsed data\")\n}\n\n# redefine boxplot stats\ncustombox <- function(y) {\n data.frame(ymin=quantile(y,0.0),\n lower=quantile(y,0.25),\n middle=quantile(y,0.5),\n upper=quantile(y,0.75),\n ymax=quantile(y,1.0))\n}\n\n# annual flow\nbplot3 <- function(sampledata, varname, ylabel, ybreaks, yref, ytrans=\"identity\"){\n ggplot() +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_hline(yintercept=yref, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n stat_summary(data=sampledata, mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox1, geom='errorbar') +\n # geom_boxplot(outlier.size=0.5, notch=FALSE, outlier.shape=NA) +\n stat_summary(data=sampledata, mapping=aes_string(group='setseqf', x='setseqf', y=varname),\n fun.data=custombox2, geom='boxplot') +\n geom_point(data=sampledata, mapping=aes_string(x='setseqf', y=varname), size=1) +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname),\n # fun.y=mean, geom='point', pch=21, size=1, fill=\"white\", colour=\"black\") +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0), trans=ytrans) +\n scale_x_discrete(labels=xlabels) \n}\n\n# annual flow alternative version\nbplot4 <- function(sampledata, varname, ylabel, ybreaks, yref, ytrans=\"identity\"){\n ggplot() +\n labs(title='', y=ylabel, x='') +\n theme_cowplot(font_size=10) +\n theme(axis.ticks.x=element_blank(), \n # plot.margin=unit(c(0.2,0.2,0.2,0.2),\"cm\"),\n plot.title=element_blank()) +\n panel_border(colour='black') + \n geom_hline(yintercept=yref, linetype=2, colour='black') +\n geom_vline(xintercept=xint, colour='grey') +\n # stat_boxplot(data=sampledata, mapping=aes_string(x='setseqf', y=varname), \n # geom=\"errorbar\", width=0.35) +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname),\n # fun.data=custombox, geom='errorbar', width=0.35) +\n # geom_boxplot(outlier.size=0.5, notch=FALSE, outlier.shape=NA) +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname),\n # fun.data=custombox, geom='boxplot') +\n geom_point(data=sampledata, mapping=aes_string(x='setseqf', y=varname), size=1) +\n geom_point(data=sampledata, mapping=aes_string(x='setseqf', y=varname), size=2, pch=1, stat=\"summary\", fun.y=\"mean\") +\n # stat_summary(data=sampledata, mapping=aes_string(x='setseqf', y=varname),\n # fun.y=mean, geom='point', pch=21, size=1, fill=\"white\", colour=\"black\") +\n scale_y_continuous(breaks=ybreaks, limits=c(min(ybreaks), max(ybreaks)), expand=c(0, 0), trans=ytrans) +\n scale_x_discrete(labels=xlabels) \n}\n\n# annual flow\nprint(\"making annual_flow box\")\ndata2 <- data %>%\n filter(period!=\"None\") %>%\n group_by(setname, year, setseq) %>%\n summarize(annualmm=sum(flow2),\n meancumec=mean(flow),\n meanTP=mean(TP, na.rm=TRUE),\n meanTN=mean(TN, na.rm=TRUE))\ndata2$setseqf <- factor(data2$setseq, levels=c(1:nruns))\np1 <- bplot4(data2, 'annualmm', 'Annual Flow', seq(0,1400,200))\nprint(p1)\np2 <- bplot4(data2, 'meanTP', 'Annual Mean TP', seq(0,0.3,0.1))\nprint(p2)\np3 <- bplot4(data2, 'meanTN', 'Annual Mean TN', seq(0,3,1))\nprint(p3)\nplotbox <- plot_grid(p2, p3, p1, nrow=3, align=\"v\")\nprint(plotbox)\nfile_name <- paste(out_path, 'box_', 'annual_flow', '.png', sep=\"\")\nsave_plot(file_name, p1, base_height=3, base_width=4)\nfile_name <- paste(out_path, 'box_', 'annual_summary', '.png', sep=\"\")\nsave_plot(file_name, plotbox, base_height=9, base_width=4)\n\ndata3 <- data %>%\n filter(period!=\"None\") %>%\n group_by(shortname) %>%\n summarize(avmm=sum(flow2)/length(unique(year)),\n avcumec=mean(flow))\n\n# histograms\n# greens 2 # https://www.color-hex.com/color-palette/5016\nxpale <- \"#F0F7DA\"\nxlight <- \"#C9DF8A\"\nxmid <- \"#77AB59\"\nxdark <- \"#36802D\"\nxdata <- \"#043927\" # https://graf1x.com/shades-of-green-color-palette-html-hex-rgb-code/\nxaxis <- \"#999999\"\nxgrey <- \"grey\"\nprior_df <- vector(\"list\", length(priortab$parname))\ni <- 1\nfor (i in seq_along(priortab$parname)) {\n key <- priortab$parname[i]\n x <- seq(priortab$parmin[i], priortab$parmax[i], length.out = 101) * priortab$parscale[i]\n y <- dnorm(x, priortab$parmean[i] * priortab$parscale[i], priortab$parsd[i] * priortab$parscale[i])\n x <- case_when(\n key == \"medd1raw\" ~ x + (-0.1*log(1-0.1)) * 10,\n key == \"slowd1raw\" ~ x + (-0.1*log(1-0.99)) * 10,\n TRUE ~ x)\n prior_df[[i]] <- tibble(key = factor(key, levels = priortab$parname), x = x, y = y)\n}\nprior_df <- bind_rows(prior_df) \nmy_pretty_breaks <- function(n = 5, ...) {\n n_default <- n\n function(x, n = n_default) {\n minx <- min(x)\n maxx <- max(x)\n midx <- (minx + maxx) / 2\n x2 <- midx + (x - midx) * 0.9\n breaks <- pretty(x2, n, ...)\n names(breaks) <- attr(breaks, \"labels\")\n breaks\n }\n}\nrows <- 1:nruns\nfor (i in rows) {\n runlisti <- runlist %>% \n filter(setseq == i)\n print(paste(\"making par histogram\", runlisti$setname))\n sampledatai <- sampledata %>% \n filter(setname == runlisti$setname) %>% \n select_at(priortab$parname)\n scalelist <- setNames(priortab$parscale, priortab$parname)\n post_df <- sampledatai %>% \n pivot_longer(everything(), names_to = \"key\", values_to = \"value\") %>% \n mutate(\n key = factor(key, levels = priortab$parname),\n value = value * scalelist[key],\n value = case_when(\n key == \"medd1raw\" ~ value + (-0.1*log(1-0.5)) * 10,\n key == \"slowd1raw\" ~ value + (-0.1*log(1-0.99)) * 10,\n TRUE ~ value)\n ) \n labellist <- setNames(priortab$parlabel, priortab$parname)\n getlabels <- function(x){\n unname(labellist[x])\n }\n plot1 <- ggplot(data = prior_df) +\n labs(title = runlisti$catchname, x = \"\", y = \"\") +\n geom_line(mapping = aes(x = x, y = y), colour = xmid, size = 1) +\n geom_histogram(data = post_df, mapping = aes(x = value, y = ..density..), fill = xlight, bins = 30) +\n geom_line(mapping = aes(x = x, y = y), colour = xmid, size = 1) +\n theme_few() +\n theme(panel.spacing.x = unit(9, \"mm\"), plot.margin = unit(c(0, 5, 0, 0), \"mm\")) +\n # theme(axis.text=element_text(size=9)) +\n scale_x_continuous(expand = expand_scale(0, 0), breaks = my_pretty_breaks(n = 2, min.n = 2)) +\n scale_y_continuous(expand = expand_scale(0, 0), breaks = NULL) +\n facet_wrap(vars(key), nrow = 4, scales = \"free\", labeller = labeller(key = getlabels))\n # print(plot1)\n png(paste0(out_path, \"/\", runlisti$setname, \"_histogram.png\"),\n width = 210 * 1.5, height = 210 * 1, units = \"mm\",\n type = \"windows\", res = 300 # FIXME res = 600\n )\n print(plot1)\n dev.off()\n}\n \n", "meta": {"hexsha": "d5710e8768c0a795613febb6278c4750290bb75d", "size": 39256, "ext": "r", "lang": "R", "max_stars_repo_path": "box_plots16.r", "max_stars_repo_name": "woodwards/bach_three", "max_stars_repo_head_hexsha": "7490b5da57df4dfec13782914b03fa8d36b64467", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "box_plots16.r", "max_issues_repo_name": "woodwards/bach_three", "max_issues_repo_head_hexsha": "7490b5da57df4dfec13782914b03fa8d36b64467", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "box_plots16.r", "max_forks_repo_name": "woodwards/bach_three", "max_forks_repo_head_hexsha": "7490b5da57df4dfec13782914b03fa8d36b64467", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.2716378162, "max_line_length": 170, "alphanum_fraction": 0.656460159, "num_tokens": 13843, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3335122710777824}} {"text": "######################################################################\n# INF-0611 Recupera\u00e7\u00e3o de Informa\u00e7\u00e3o #\n# #\n# Trabalho 1 - Recupera\u00e7\u00e3o de Texto #\n######################################################################\n# Nome COMPLETO dos integrantes do grupo: #\n# - Daniele Montenegro da Silva Barros #\n# - Thiago Bruschi Martins #\n# - Rodrigo Silva Dantas #\n# #\n######################################################################\n\n######################################################################\n# Configura\u00e7\u00f5es Preliminares #\n######################################################################\n\n# Carregando as bibliotecas\nlibrary(corpus)\nlibrary(dplyr)\nlibrary(udpipe)\nlibrary(tidytext)\nlibrary(tidyverse)\n\n\n# Carregando os arquivos auxiliares\nsource(\"ranking_metrics.R\", encoding = \"UTF-8\")\nsource(\"trabalho1_base.R\", encoding = \"UTF-8\")\n\n# Configure aqui o diret\u00f3rio onde se encontram os arquivos do trabalho\n# setwd(\"~/Documents/Unicamp/Recuperac\u0327a\u0303o de Informac\u0327a\u0303o/Atividade 01/\")\n\n\n######################################################################\n#\n# Quest\u00e3o 1\n#\n######################################################################\n\n# Lendo os documentos (artigos da revista TIME)\n# sem processamento de texto (n\u00e3o mude essa linha)\ndocs <- process_data(\"time.txt\", \"XX-Text [[:alnum:]]\", \"Article_0\", \n convertcase = TRUE, remove_stopwords = FALSE)\n# Visualizando os documentos (apenas para debuging)\nhead(docs)\n\n# Lendo uma lista de consultas (n\u00e3o mude essa linha)\nqueries <- process_data(\"queries.txt\", \"XX-Find [[:alnum:]]\", \n \"Query_0\", convertcase = TRUE, \n remove_stopwords = FALSE)\n# Visualizando as consultas (apenas para debuging)\nhead(queries)\n# Exemplo de acesso aos tokens de uma consulta\nq1 <- queries[queries$doc_id == \"Query_01\",]\nq1\n\n# Lendo uma lista de vetores de ground_truth\nground_truths <- read.csv(\"relevance.csv\", header = TRUE)\n\n# Visualizando os ground_truths (apenas para debuging)\nhead(ground_truths)\n# Exemplo de acesso vetor de ground_truth da consulta 1:\nground_truths[1,]\n# Exemplo de impress\u00e3o dos ids dos documentos relevantes da consulta 1:\n# Visualizando o ranking (apenas para debuging)\nnames(ground_truths)[ground_truths[1,]==1]\n\n# Computando a matriz de termo-documento\nterm_freq <- document_term_frequencies(docs, term='word')\n\n# Computando as estat\u00edsticas da cole\u00e7\u00e3o e convertendo em data.frame\ndocs_stats <- as.data.frame(document_term_frequencies_statistics(term_freq, \n k = 1.2, \n b = 0.75))\n# Visualizando as estat\u00edsticas da cole\u00e7\u00e3o (apenas para debuging)\nhead(docs_stats)\n\n######################################################################\n#\n# Quest\u00e3o 2\n#\n######################################################################\n\n# Incluindo implementacao das metricas\nsource(\"ranking_metrics.R\")\n\n# query: Elemento da lista de consultas, use a segunda coluna desse \n# objeto para o c\u00e1lculo do ranking\n# ground_truth: Linha do data.frame de ground_truths referente a query\n# stats: data.frame contendo as estat\u00edsticas da base\n# stat_name: Nome da estat\u00edstica de interesse, como ela est\u00e1 escrita \n# no data.frame stats\n# top: Tamanho do ranking a ser usado nos c\u00e1lculos de precis\u00e3o \n# e revoca\u00e7\u00e3o\n# text: T\u00edtulo adicional do gr\u00e1fico gerado, deve ser usado para \n# identificar a quest\u00e3o e a consulta\ncomputa_resultados <- function(query, ground_truth, stats, stat_name, \n top, text) {\n # Criando ranking (fun\u00e7\u00e3o do arquivo base)\n ranking <- get_ranking_by_stats(stat_name = stat_name,\n docs_stats = stats,\n tokens_query = text_tokens(query[[1]]))\n # Visualizando o ranking (apenas para debuging)\n # head(ranking, n = 5)\n \n # Calculando a precis\u00e3o\n p <- precision(ground_truth,\n ranking$doc_id,\n top)\n\n # Calculando a revoca\u00e7\u00e3o\n r <- recall(ground_truth,\n ranking$doc_id,\n top)\n\n # Imprimindo os valores de precis\u00e3o e revoca\u00e7\u00e3o\n cat(paste(\"Consulta: \", query[1,1], \"\\nPrecis\u00e3o: \", p, \n \"\\tRevoca\u00e7\u00e3o: \", r, \"\\n\"))\n \n # Gerando o plot Precis\u00e3o + Revoca\u00e7\u00e3o (fun\u00e7\u00e3o do arquivo base)\n plot_prec_e_rev(ranking$doc_id, ground_truth, top, text) \n}\n\n# Definindo a consulta 1 \nconsulta1 <- queries[queries$doc_id == \"Query_014\", 2]\nn_consulta1 <- 14\n\n## Exemplo de uso da fun\u00e7\u00e3o computa_resultados:\n# computa_resultados(consulta1, ground_truths[n_consulta1, ],\n# docs_stats, \"nome da statistica\",\n# top = 15, \"titulo\")\n\n# Resultados para a consulta 1 e tf_idf\ncomputa_resultados(consulta1, ground_truths[n_consulta1, ],\n docs_stats, \"tf_idf\",\n top = 20, \"Ranking TF-IDF\")\n\n# Resultados para a consulta 1 e bm25\ncomputa_resultados(consulta1, ground_truths[n_consulta1, ],\n docs_stats, \"bm25\",\n top = 20, \"Ranking BM25\")\n\n\n# Definindo a consulta 2 \nconsulta2 <- queries[queries$doc_id == \"Query_020\", 2]\nn_consulta2 <- 20\n\n# Resultados para a consulta 2 e tf_idf\ncomputa_resultados(consulta2, ground_truths[n_consulta2, ],\n docs_stats, \"tf_idf\",\n top = 20, \"Ranking TF-IDF\")\n\n# Resultados para a consulta 2 e bm25\ncomputa_resultados(consulta2, ground_truths[n_consulta2, ],\n docs_stats, \"bm25\",\n top = 20, \"Ranking BM25\")\n\n\n######################################################################\n#\n# Quest\u00e3o 2 - Escreva sua an\u00e1lise abaixo\n#\n######################################################################\n# Na Query 20, quando k assume o valor de 7 o m\u00e9todo bm25 recuperou todos os objetos relevantes, \n# assim, a revoca\u00e7\u00e3o assume o valor 1, mantendo seu valor at\u00e9 k igual a 20. J\u00e1 a tf-idf a revoca\u00e7\u00e3o s\u00f3 sai do valor\n# de 0 quando k igual a 13, e quando k igual a 15 assume seu maior valor, sendo a precis\u00e3o inferior utilizando tf-id.\n# Assim, no TF-IDF da query20 n\u00e3o chega a recuperar todos os elementos relevantes nem com k igual a 20.\n\n# Enquanto na Query 14, quando k assume o valor de 5, tanto no m\u00e9todo tf-id e na BM-25, a revoca\u00e7\u00e3o \u00e9 igual 1, \n# assim, em ambos os m\u00e9todos todos os objetos relevantes foram encontrados quando k igual a 5. \n# J\u00e1 a precis\u00e3o quando k igual a 5 a precis\u00e3o \u00e9 igual a 0.8. No entanto, \n# tem uma pequena diferen\u00e7a o TF-IDF come\u00e7a acertando o primeiro elemento, assumindo um melhor desempenho no \n# \u00ednicio do processamento.\n######################################################################\n#\n# Quest\u00e3o 3\n#\n######################################################################\n# Na fun\u00e7\u00e3o process_data est\u00e1 apenas a fun\u00e7\u00e3o para remo\u00e7\u00e3o de \n# stopwords est\u00e1 implementada. Sinta-se a vontade para testar \n# outras t\u00e9cnicas de processamento de texto vista em aula.\n\n# Lendo os documentos (artigos da revista TIME) \n# com processamento de texto\ndocs_proc <- process_data(\"time.txt\", \"XX-Text [[:alnum:]]\", \n \"Article_0\", convertcase = TRUE, \n remove_stopwords = TRUE)\n# Visualizando os documentos (apenas para debuging)\n# head(docs_proc)\n\n\n# Lendo uma lista de consultas\nqueries_proc <- process_data(\"queries.txt\", \"XX-Find [[:alnum:]]\", \n \"Query_0\", convertcase = TRUE, \n remove_stopwords = TRUE)\n# Visualizando as consultas (apenas para debuging)\n# head(queries_proc)\n\n# Computando a matriz de termo-documento\nterm_freq_proc <- document_term_frequencies(docs_proc, term='word')\n\n# Computando as estat\u00edsticas da cole\u00e7\u00e3o e convertendo em data.frame\ndocs_stats_proc <- as.data.frame(document_term_frequencies_statistics(term_freq_proc, \n k = 1.2, \n b = 0.75))\n\n\n# Definindo a consulta 1 \nconsulta1_proc <- queries[queries$doc_id == \"Query_014\", 2]\nn_consulta1_proc <- 14\n# Resultados para a consulta 1 e tf_idf\ncomputa_resultados(consulta1_proc, ground_truths[n_consulta1_proc, ],\n docs_stats, \"tf_idf\",\n top = 20, \"Ranking TF-IDF (No StopWords)\")\n\n# Resultados para a consulta 1 e bm25\ncomputa_resultados(consulta1_proc, ground_truths[n_consulta1_proc, ],\n docs_stats, \"bm25\",\n top = 20, \"Ranking BM25 (No StopWords)\")\n\n\n# Definindo a consulta 2 \nconsulta2_proc <- queries[queries$doc_id == \"Query_020\", 2]\nn_consulta2_proc <- 20\n\n# Resultados para a consulta 2 e tf_idf\ncomputa_resultados(consulta2_proc, ground_truths[n_consulta2_proc, ],\n docs_stats, \"tf_idf\",\n top = 20, \"Ranking TF-IDF (No StopWords)\")\n\n# Resultados para a consulta 2 e bm25\ncomputa_resultados(consulta2_proc, ground_truths[n_consulta2_proc, ],\n docs_stats, \"bm25\",\n top = 20, \"Ranking BM25 (No StopWords)\")\n\n######################################################################\n#\n# Quest\u00e3o 3 - Escreva sua an\u00e1lise abaixo\n#\n######################################################################\n# \n# Na query 20 tanto a revoca\u00e7\u00e3o quanto a precis\u00e3o melhoram significativamente com a remo\u00e7\u00e3o das stopwords.\n# Podemos verificar isso nos gr\u00e1ficos pois os valores de revoca\u00e7\u00e3o e precis\u00e3o atingem valores mais altos para\n# K menores em reala\u00e7\u00e3o ao mesmo modelo com as stopwords. No caso da revoca\u00e7\u00e3o ela tamb\u00e9m atinge seu valor m\u00e1ximo\n# (100%) antes. No caso da precis\u00e3o, para K menores que 6 ela fica mais tempo em valores mais altos, embora no final o \n# valor seja o mesmo visto que h\u00e1 poucos elementos relevantes nesta pesquisa.\n# \n# J\u00e1 na query 14 os gr\u00e1ficos gerados foram os mesmos, ou seja, n\u00e3o houve diferen\u00e7a entre remover ou n\u00e3o as stopwords.\n# Provavelmente por ser uma query com menos palavras, e por tanto, menos stopwords.\n# \n#\n######################################################################\n#\n# Extra\n#\n# # Comando para salvar todos os plots gerados e que est\u00e3o abertos no \n# Rstudio no momemto da execu\u00e7\u00e3o. Esse comando pode ajudar a comparar \n# os gr\u00e1fico lado a lado.\n# \n# plots.dir.path <- list.files(tempdir(), pattern=\"rs-graphics\",\n# full.names = TRUE);\n# plots.png.paths <- list.files(plots.dir.path, pattern=\".png\", \n# full.names = TRUE)\n# file.copy(from=plots.png.paths, to=\"~/Desktop/\")\n######################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "c4c7d97414cc511d83323ee4a33812f6f132a9ab", "size": 11017, "ext": "r", "lang": "R", "max_stars_repo_path": "Recuperacao-de-informacao/Trabalho-1/inf0611_trabalho1.r", "max_stars_repo_name": "thiagobmartins/mdc", "max_stars_repo_head_hexsha": "e6f5375673006696b0d05ba8b8b2b0c787fd12fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Recuperacao-de-informacao/Trabalho-1/inf0611_trabalho1.r", "max_issues_repo_name": "thiagobmartins/mdc", "max_issues_repo_head_hexsha": "e6f5375673006696b0d05ba8b8b2b0c787fd12fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Recuperacao-de-informacao/Trabalho-1/inf0611_trabalho1.r", "max_forks_repo_name": "thiagobmartins/mdc", "max_forks_repo_head_hexsha": "e6f5375673006696b0d05ba8b8b2b0c787fd12fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.0942760943, "max_line_length": 119, "alphanum_fraction": 0.5680312245, "num_tokens": 2548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771035, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3335122619132849}} {"text": "#!/usr/bin/Rscript\nlibrary('tidyverse')\nlibrary('ggplot2')\nlibrary('tikzDevice')\nlibrary('xtable')\nlibrary('lemon')\nlibrary('knitr')\nlibrary('scales')\n\n# Read input data\n# For timeouts, \"States\" field is set to -1\ninput <- read_delim('results48.csv', delim=\";\", col_names=FALSE, trim_ws=TRUE)\ncolnames(input) <- c(\"Model\",\"Method\", \"Workers\", \"Time\", \"States\")\n\n# Derive Order (-rbs or -rf) from Model name and add as column\ninput_rf1 <- input %>% filter(grepl(\"-rf$\", Model)) %>% mutate(Model = str_replace(Model, \"-rf$\", \"\"))\ninput_rf2 <- input %>% filter(grepl(\"^rf-\", Method)) %>% mutate(Method = str_replace(Method, \"^rf-\", \"\"))\ninput_rf <- bind_rows(input_rf1, input_rf2)\ninput_rf$Order <- \"rf\"\ninput_rbs <- input %>% filter(!grepl(\"-rf$\", Model)) %>% filter(!grepl(\"^rf-\", Method))\ninput_rbs$Order <- \"rbs\"\ninput <- bind_rows(input_rf, input_rbs)\n\n# Rename \"ldd-par\" to \"ldd-bfs\" (because it is parallel BFS)\ninput <- input %>% mutate(Method = str_replace(Method, \"ldd-par\", \"ldd-bfs\"))\n\n# Add \"Id\" column\ninput <- input %>% mutate(Id = paste(Model, Order, Method, Workers, sep = \"-\"))\n\n# Split into and and remove timeouts for which we have times\ntimes <- input %>% filter(States != -1) %>% select(Id, Model, Order, Method, Workers, Time)\ntimeouts <- input %>% filter(!Id %in% (times %>% distinct(Id))$Id) %>% select(Id, Model, Order, Method, Workers, Time)\n\n# Compute median/mean/sd for times, and highest timeout for timeouts\ntimes <- times %>% group_by(Id, Model, Order, Method, Workers) %>% summarize(MedianTime = median(Time), MeanTime = mean(Time), sd = sd(Time)) %>% ungroup\ntimeouts <- timeouts %>% group_by(Id, Model, Order, Method, Workers) %>% summarize(Timeout = max(Time)) %>% ungroup\n\n# Compute Model-Order that are solved (or timeout) by all Method-Worker combinations\ntimes_s <- times %>% mutate(MW = paste(Method, Workers)) %>% select(MW, Model, Order, Time=MedianTime)\ntimeouts_s <- timeouts %>% mutate(MW = paste(Method, Workers)) %>% select(MW, Model, Order, Time=Timeout)\nMODone <- bind_rows(times_s, timeouts_s) %>% spread(MW, Time) %>% drop_na() %>% select(Model, Order) %>% mutate(MO = paste(Model, Order)) %>% pull(MO)\n\n# Now only keep the times/timeouts for which we have results of all Method-Worker combinations\ntimes <- times %>% mutate(MO = paste(Model, Order)) %>% filter(MO %in% MODone) %>% select(-MO)\ntimeouts <- timeouts %>% mutate(MO = paste(Model, Order)) %>% filter(MO %in% MODone) %>% select(-MO)\ntimes_s <- times %>% mutate(MW = paste(Method, Workers)) %>% select(MW, Model, Order, Time=MedianTime)\ntimeouts_s <- timeouts %>% mutate(MW = paste(Method, Workers)) %>% select(MW, Model, Order, Time=Timeout)\n\nkable(times %>% select(Model, Order, Method, Workers, Time=MeanTime) %>% spread(Workers, Time) %>% drop_na() %>% mutate(S8=`1`/`8`,S16=`1`/`16`,S24=`1`/`24`,S32=`1`/`32`,S40=`1`/`40`,S48=`1`/`48`) %>% arrange(Method), digits=1, format=\"latex\", booktabs=TRUE)\n\nkable(times %>% select(Model, Order, Method, Workers, Time=MeanTime) %>% spread(Workers, Time) %>% drop_na() %>% mutate(S8=`1`/`8`,S16=`1`/`16`,S24=`1`/`24`,S32=`1`/`32`,S40=`1`/`40`,S48=`1`/`48`) %>% arrange(Method, -S48) %>% select(Model,Order,Method,`1`,`24`,`48`,S24,S48), digits=1, format=\"latex\", booktabs=TRUE)\n\nlddsat <- times %>% filter(Method==\"ldd-sat\") %>% select(Model, Order, Method, Workers, Time=MeanTime) %>% spread(Workers, Time) %>% drop_na() %>% mutate(S1=`1`/`1`,S8=`1`/`8`,S16=`1`/`16`,S24=`1`/`24`,S32=`1`/`32`,S40=`1`/`40`,S48=`1`/`48`) %>% drop_na() %>% mutate(Id=paste(Model, Order)) %>% select(Id, `1`=S1, `8`=S8,`16`=S16,`24`=S24,`32`=S32,`40`=S40,`48`=S48) %>% gather(Workers, Speedup, -Id) %>% mutate(Workers=as.numeric(Workers))\nlddsatplot <-\n ggplot(lddsat, aes(x=Workers,y=Speedup,group=Id)) +\n geom_line() +\n scale_x_continuous(breaks=c(1,8,16,24,32,40,48)) +\n scale_y_continuous(breaks=c(0,1,5,8,10,15,20,25,30)) +\n theme_bw()\n\ntikz(\"lddsatspeedupplot.tex\", width=6, height=3, standAlone=F)\nprint(lddsatplot)\ngraphics.off()\n", "meta": {"hexsha": "3dd5842266cf1af71fd3ffc5cef78ceba16bdd57", "size": 4007, "ext": "r", "lang": "R", "max_stars_repo_path": "analyse48.r", "max_stars_repo_name": "trolando/ParallelSaturationExperiments", "max_stars_repo_head_hexsha": "ee374ed750d9d3fde1b44bf4414bef5289805090", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analyse48.r", "max_issues_repo_name": "trolando/ParallelSaturationExperiments", "max_issues_repo_head_hexsha": "ee374ed750d9d3fde1b44bf4414bef5289805090", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analyse48.r", "max_forks_repo_name": "trolando/ParallelSaturationExperiments", "max_forks_repo_head_hexsha": "ee374ed750d9d3fde1b44bf4414bef5289805090", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 62.609375, "max_line_length": 440, "alphanum_fraction": 0.6556026953, "num_tokens": 1327, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.33350089681109407}} {"text": "library(lubridate, warn.conflicts=FALSE)\n\n## script for preprocessing data ===============================================\n#\n# 1) generate cleaned data frames for all available regions\n# with all available factors and uniform naming conventions\n#\n# 2) generate aggregated data frames from 1) for availiable\n# region - sub-region combinations\n# e.g. region: \"italy\" - sub-regions: \"regions\"\n# region: \"italy\" - sub-regions: \"provinces\"\n# region: \"Deutschland\" - sub-regions: \"Bundeslaender\"\n# region: \"Deutschland\" - sub-regions: \"Lanndkreise\"\n#\n# 3) generates lookup tables for regions: regX.id - regX.name\n# with (optional) meta data: lat, long, population, area, ...\n#\n# uniform column names: (not all factors are always available)\n# time: day (counter starting at 0), yday, date\n# space: reg0.id, reg0.name, reg1.id, reg1.name, ... (so far no reg2)\n# case: tot.cases, new.cases,\n# tot.dead, new.dead,\n# tot.recovered, new.recovered,\n# tot.tested, new.tested\n# meta: age, sex, ...\n#\n## =============================================================================\n\n\n## 1) region: germany; all data ================================================\n#\n# this data.frame does NOT contain ALL combinations of factors !\n# and they are not completed either since too many combinations...\n# all combinations only in derived data frames (see below)\n#\nger <- read.csv('./rki_data/RKI_COVID19.csv')\n# subsetting and reordering: (ignore \"Datenstand\" and \"ObjectId\")\nger <- subset(ger, select=c(\"Meldedatum\", \"IdBundesland\", \"Bundesland\",\n \"IdLandkreis\", \"Landkreis\", \"AnzahlFall\",\n \"AnzahlTodesfall\", \"Altersgruppe\", \"Geschlecht\"))\n# renaming:\nnames(ger) <- c(\"date\", \"reg0.id\", \"reg0.name\", \"reg1.id\", \"reg1.name\",\n \"new.cases\", \"new.dead\", \"age\", \"sex\")\nger$date <- as_date(ger$date) # date in correct format\nger$yday <- yday(ger$date) # add yday\nger$day <- ger$yday - min(ger$yday) # add day\nger <- ger[order(ger$day),] # reorder\n# for derived data frames additonal case statistics (tot.cases, ...) are added;\n# here too many combinations of factors make this impractical !\n# reordering:\nger <- ger[, c(\"day\", \"yday\", \"date\",\n \"reg0.id\", \"reg0.name\", \"reg1.id\", \"reg1.name\",\n \"new.cases\", \"new.dead\",\n \"age\", \"sex\")]\nwrite.csv(ger, file='./clean/data_ger_all.csv')\n## 3) # create lookup table for ger all ========================================\nlookup_ger <- subset(ger, select=c(\"reg0.id\", \"reg0.name\",\n \"reg1.id\", \"reg1.name\"))\nlookup_ger <- unique(lookup_ger[order(lookup_ger$reg0.id),])\nwrite.csv(lookup_ger, file='./clean/lookup_ger_all.csv')\n", "meta": {"hexsha": "eb53bd61204c0d746d28b777d8a58e639a731d56", "size": 2763, "ext": "r", "lang": "R", "max_stars_repo_path": "data/clean.r", "max_stars_repo_name": "Stochastik-TU-Ilmenau/COVID-19", "max_stars_repo_head_hexsha": "02542a3449a1441a3f1f8301e158e9a2ba2e7019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-04-07T11:04:56.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-24T14:52:34.000Z", "max_issues_repo_path": "data/clean.r", "max_issues_repo_name": "Stochastik-TU-Ilmenau/COVID-19", "max_issues_repo_head_hexsha": "02542a3449a1441a3f1f8301e158e9a2ba2e7019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-20T08:19:57.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-20T08:49:18.000Z", "max_forks_repo_path": "data/clean.r", "max_forks_repo_name": "Stochastik-TU-Ilmenau/COVID-19", "max_forks_repo_head_hexsha": "02542a3449a1441a3f1f8301e158e9a2ba2e7019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-03-27T09:11:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-21T05:17:01.000Z", "avg_line_length": 45.2950819672, "max_line_length": 80, "alphanum_fraction": 0.5866811437, "num_tokens": 744, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.33350089681109407}} {"text": "input <- file('stdin', 'r')\n\na <- as.integer(readLines(input, n=1))\nb <- as.integer(readLines(input, n=1))\nc <- as.integer(readLines(input, n=1))\nd <- as.integer(readLines(input, n=1))\n\nresult = sprintf(\"DIFERENCA = %d\", a * b - c * d)\nwrite(result, \"\")\n", "meta": {"hexsha": "76809b63a573e6ee302d62c9e27a6086093849ce", "size": 254, "ext": "r", "lang": "R", "max_stars_repo_path": "solutions/beecrowd/1007/1007.r", "max_stars_repo_name": "deniscostadsc/playground", "max_stars_repo_head_hexsha": "11fa8e2b708571940451f005e1f55af0b6e5764a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_stars_repo_stars_event_min_datetime": "2015-01-22T04:08:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-08T22:36:47.000Z", "max_issues_repo_path": "solutions/beecrowd/1007/1007.r", "max_issues_repo_name": "deniscostadsc/playground", "max_issues_repo_head_hexsha": "11fa8e2b708571940451f005e1f55af0b6e5764a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2016-04-25T12:32:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-15T18:01:30.000Z", "max_forks_repo_path": "solutions/beecrowd/1007/1007.r", "max_forks_repo_name": "deniscostadsc/playground", "max_forks_repo_head_hexsha": "11fa8e2b708571940451f005e1f55af0b6e5764a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 25, "max_forks_repo_forks_event_min_datetime": "2015-03-02T06:21:51.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-12T20:49:21.000Z", "avg_line_length": 25.4, "max_line_length": 49, "alphanum_fraction": 0.6181102362, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.33348103128805157}} {"text": "## This replication file\r\n## makes the following tables and figures:\r\n# Tables 2 and S2-S5\r\n# Figures S1-S3\r\n## calculates numbers mentioned in Supplementary Information (SI), Main analysis section\r\n\r\nlibrary(MatchIt) # get_matches()\r\nlibrary(cobalt) # bal.tab()\r\nlibrary(lmtest) # coeftest()\r\nlibrary(sandwich) # vcovCL()\r\nlibrary(tidyverse)\r\n\r\ndata <- read.csv(\"covid.csv\")\r\ndata.all <- data[data$pref %in% c(3, 6, 7, 9, 11, 12, 13, 15,\r\n 16, 18, 21, 23, 25, 27, 31,\r\n 32, 33, 34, 35, 36, 37, 38,\r\n 41, 42, 44, 45, 46),]\r\nsurvey.dates <- c(\"0304\", \"0316\", \"0406\", \"0410\")#, \"0416\", \"0422\", \"0511\", \"0601\")\r\nperiod <- 84:180 # indicates Feb.26 to June 1 \r\n# Replace -1's with 0's\r\ncases <- NULL\r\nfor(p in 1:length(period)){\r\n data.all[, period[p]] <- as.character(data.all[, period[p]])\r\n data.all[, period[p]] <- gsub(\"-1\", \"0\", data.all[, period[p]])\r\n data.all[, period[p]] <- as.integer(data.all[, period[p]]) \r\n cases <- c(cases, data.all[, period[p]])\r\n}\r\n\r\n##################################################################\r\n# The Numbers of Treated and Control Municipalities by Survey Date\r\n\r\ntable.treat <- matrix(NA, length(survey.dates), 4)\r\ntable.health.mun <- matrix(NA, length(survey.dates), 6)\r\ntable.health.cen <- matrix(NA, length(survey.dates), 2)\r\nmatched <- rep(NA, 7)\r\n\r\n# Jan. 25 - Aug. 10\r\nperiod <- 52:250 # indicator\r\nYMD <- as.Date(\"2020-1-25\") + 1:length(period) - 1 # year-month-day\r\nsurvey.date.col <- c(91, 103, 124, 128, 134, 140, 159, 180) - 51\r\nsurvey.date.col <- survey.date.col[1:length(survey.dates)]\r\nrownames(table.treat) <- \r\n rownames(table.health.mun) <- \r\n rownames(table.health.cen) <- as.character(YMD[survey.date.col])\r\n\r\nfor(d in 1:length(survey.dates)){\r\n day <- survey.dates[d]\r\n \r\n source(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\n table.treat[d, 1:2] <- table(treat)\r\n \r\n table.out <- table(data.analysis$health.center.id, treat)\r\n mix <- as.integer(table.out[, 1] * table.out[, 2] > 0) # whether the health center has treated and control municipalities\r\n table.health.mun[d, 1:2] <- c(sum(mix * table.out[, 1]), # the number of treated municipalities in a mixed health center\r\n sum(mix * table.out[, 2])) # the number of control municipalities in a mixed health center\r\n table.health.cen[d, 1:2] <- c(nrow(table.out),\r\n sum(mix)) # whether the health center has treated and control municipalities\r\n if(d < 8){\r\n m.out <- readRDS(paste0(\"matching_results/alt_atc_\", day, \"_1000_1000.RDS\"))\r\n matched[d] <- length(unique(m.out$match.matrix))\r\n }\r\n}\r\n\r\n# Table 2 \r\ntable.treat[,3] <- apply(table.treat[,1:2],1,sum)\r\ntable.treat[,4] <- round(100*table.treat[,2]/table.treat[,3], 1)\r\ntable.treat[,1:2] <- table.treat[,2:1]\r\ncolnames(table.treat) <- c(\"Treated\", \"Control\", \"All\", \"Treated (%)\")\r\ntable.treat\r\nwrite.csv(table.treat, \"output/Fukumoto_Table2.csv\")\r\n\r\n# Table S2\r\n#(table.matched <- cbind(matched[1:7], table.treat[1:7,2]))\r\ntable.matched <- cbind(matched[1:4], table.treat[1:4,2])\r\ncolnames(table.matched) <- c(\"Treated\", \"Control\")\r\ntable.matched\r\nwrite.csv(table.matched, \"output/Fukumoto_TableS2.csv\")\r\n\r\n# Table S4\r\n#table.health.cen <- table.health.cen[-8,]\r\ncolnames(table.health.cen) <- c(\"All\", \"Mixed\")\r\ntable.health.cen\r\nwrite.csv(table.health.cen, \"output/Fukumoto_TableS4.csv\")\r\n\r\n# Table S5\r\ntable.health.mun[, 1:2] <- table.health.mun[, 2:1]\r\ntable.health.mun[, 4:5] <- table.treat[, 1:2] - table.health.mun[, 1:2]\r\ntable.health.mun[, 3] <- apply(table.health.mun[, 1:2], 1, sum)\r\ntable.health.mun[, 6] <- apply(table.health.mun[, 4:5], 1, sum)\r\n#table.health.mun <- table.health.mun[-8,]\r\ncolnames(table.health.mun) <- c(\"Treated (Mixed)\", \"Control (Mixed)\", \"All (Mixed)\", \r\n \"Treated (Non-mixed)\", \"Control (Non-mixed)\", \"All (Non-mixed)\")\r\ntable.health.mun\r\nwrite.csv(table.health.mun, \"output/Fukumoto_TableS5.csv\")\r\n\r\n##############################\r\n# Negative Binomial Regression\r\n\r\n# \"our analysis covers all of the 847 municipalities in the 27 target prefectures\r\n# from February 26 to June 1 (97 days).\" (SI, p. 12)\r\nlength(cases)\r\n847 * 97\r\n# \"the number of cases is not available for 62 municipalities in Tokyo Prefecture\r\n# from February 26 to March 31 (35 days).\" (SI, p. 12)\r\nsum(is.na(cases)) # Tokyo\r\n62 * 35\r\n# \"Thus, the total number of units is 847 * 97 - 62 * 35 = 79,989.\" (SI, p. 12)\r\ntable.cases <- table(cases)\r\nsum(table.cases)\r\nlength(cases) - sum(is.na(cases))\r\n\r\nprop.table.cases <- round(prop.table(table.cases)*100, 1)\r\n# \"the number of cases is equal to zero for 95.4% of our 79,989 municipality-day observations\r\n# and less than 10 for 99.9% of our observations.\" (SI, p. 12)\r\nprop.table.cases[1]\r\nsum(prop.table.cases[1:10])\r\n\r\nsavepdf <- function(file, width=3.46, height=3.46){ \r\n fname <- paste(\"\",file,\".pdf\",sep=\"\")\r\n pdf(fname, width=width, height=height,\r\n pointsize=7)\r\n par(mgp=c(2.2,0.45,0), tcl=-0.4, mar=c(3.3,3.6,1.1,1.1))\r\n}\r\n\r\n# Figure S2\r\nsavepdf(\"output/Fukumoto_FigS2\")\r\nhist.out <- hist(cases, freq = F, \r\n breaks = (min(cases, na.rm = T) - 1):max(cases, na.rm = T) + 0.5, \r\n xlab=\"Number of cases\", ylab=\"Proportion\", main=\"\", \r\n ylim=c(0, 0.025))\r\ndev.off()\r\n\r\nFigure.S2.source <- cbind(hist.out$mids, hist.out$density)\r\ncolnames(Figure.S2.source) <- c(\"Number of cases\", \"Proportion\")\r\nhead(Figure.S2.source)\r\nwrite.csv(Figure.S2.source, \"output/Fukumoto_FigS2_source.csv\", row.names = F)\r\n\r\n######################\r\n# Public Health Center\r\n\r\n# \"Each of the 263 public health centers is in charge of implementing public health policy, \r\n# including counting 26 COVID-19 cases, and covers 1 to 13 municipalities.\" (Main Text)\r\n(table.out.2 <- table(table(data.all$health.center.id)))\r\nsum(table.out.2)\r\n# \"66.9% of the 263 public health centers cover multiple municipalities, \r\n# which occupy 89.7% of the 847 municipalities.\" (SI, pp. 12-13)\r\ntable.out.1 <- table(data.all$health.center.id)\r\nsum(table.out.1)\r\nround((1 - table.out.2[1]/sum(table.out.2))*100, 1)\r\nround((1 - table.out.2[1]/sum(table.out.1))*100, 1)\r\n\r\n# Figure S3\r\nsavepdf(\"output/Fukumoto_FigS3\")\r\nhist.out <- hist(table.out.1, freq = F, breaks = 0:13 +0.5, \r\n xlab=\"Number of municipalities per public health center\", \r\n ylab=\"Proportion\", main=\"\")\r\ndev.off()\r\n\r\nFigure.S3.source <- cbind(hist.out$mids, hist.out$density)\r\ncolnames(Figure.S3.source) <- c(\"Number of municipalities\", \"Proportion\")\r\nFigure.S3.source\r\nwrite.csv(Figure.S3.source, \"output/Fukumoto_FigS3_source.csv\", row.names = F)\r\n\r\n#######\r\n# setup\r\n\r\nadditional <- c(\"cases_1\", \"cases_2\", \"cases_3\", \"cases_4\", \"cases_5\", \"cases_6\", \"cases_7\", \r\n \"prior.infection.per.capita\", \r\n \"lon\", \"lat\", \"prec_mean\", \"shine_mean\", \"tmean_mean\", \"electoral.time\", \"log.number\", \r\n \"shutdown.0304\", \"shutdown.0316\", \"shutdown.0406\", \"shutdown.0410\", \"shutdown.0416\", \"shutdown.0422\")\r\nbal.tab.out <- list()\r\nmains <- c(\"a March 4\", \"b March 16\", \"c April 6\", \"d April 10\",\r\n \"e April 16\", \"f April 22\", \"g May 11\", \"h April 6 (ATT)\")\r\n\r\n######\r\n# ASMD\r\n\r\n#for (d in 1:8){\r\nfor (d in c(1:4, 8)){\r\n if(d < 8){\r\n dd <- d\r\n day <- survey.dates[dd]\r\n m_path <- paste0(\"matching_results/alt_atc_\", day, \"_1000_1000.RDS\")\r\n }\r\n if(d == 8){# ATT\r\n dd <- 3\r\n day <- survey.dates[dd]\r\n m_path <- paste0(\"matching_results/alt_att_\", day, \"_1000_1000.RDS\") #\r\n } \r\n p_path <- paste0(\"preprocess/preprocess.\", day, \".R\")\r\n lm_form <- as.formula(paste0(\"gm[, part[p]] ~ shutdown.\", day)) \r\n source(paste0(\"genetic_matching/gen.\", day, \".R\"))\r\n bal.tab.out[[d]] <- bal.tab(cbind(control, pref.dummy, \r\n data.analysis[, additional[1:(dd + 14)]],\r\n data.analysis[, c(paste0(\"age.\", day),\r\n paste0(\"win_count.\", day))]),\r\n binary = \"std\", continuous = \"std\", treat = m.out$treat, \r\n weights = m.out$weights, abs = TRUE, stats = c(\"mean.diffs\"), un = TRUE)$Balance\r\n bal.tab.out[[d]] <- bal.tab.out[[d]] %>%\r\n mutate(above2 = ifelse(Diff.Un > 2, \"Yes\", \"No\"),\r\n Diff.Un.draw = ifelse(Diff.Un > 2, 2, Diff.Un),\r\n Diff.Adj.draw = ifelse(Diff.Adj > 2, 2, Diff.Adj),\r\n Diff.diff = Diff.Un - Diff.Adj,\r\n Diff.ratio = Diff.Adj / Diff.Un)\r\n}\r\n\r\nsavepdf <- function(file, width=7.28, height=3.54){ \r\n fname <- paste(\"\",file,\".pdf\",sep=\"\")\r\n pdf(fname, width=width, height=height,\r\n pointsize=7)\r\n par(mgp=c(2.2,0.45,0), tcl=-0.4, mar=c(3.3,3.6,1.1,1.1))\r\n}\r\n\r\nsavepdf(\"output/Fukumoto_FigS1\")\r\npar(mfrow=c(2,4))\r\n\r\n#for (d in 1:8){\r\nfor (d in c(1:4, 8)){\r\n plot(bal.tab.out[[d]]$Diff.Un.draw, bal.tab.out[[d]]$Diff.Adj.draw, \r\n xlim=c(0,2), ylim=c(0,2), \r\n xlab = \"ASMD before matching\", ylab=\"ASMD after matching\", \r\n main = mains[d], \r\n pch=c(1, 3)[as.factor(bal.tab.out[[d]]$above2)])\r\n abline(0, 1, lty = 2)\r\n}\r\ndev.off()\r\n\r\n# Table S3\r\ncovariate_list <- read.csv(\"covariate_list.csv\")\r\n#for (d in 1:8){\r\nfor (d in c(1:4, 8)){\r\n Figure.S1.d <- bal.tab.out[[d]][,c(\"Diff.Un\", \"Diff.Adj\")]\r\n dd <- d\r\n if(d == 8){# ATT\r\n dd <- 3\r\n }\r\n if(dd <= 3){\r\n pref.dummy.pos <- c(33:38, 40:58) # excluding Tokyo\r\n }\r\n if(dd >= 4){\r\n pref.dummy.pos <- c(33:58) # including Tokyo\r\n }\r\n rownames(Figure.S1.d) <- covariate_list[c(1:32, #control\r\n pref.dummy.pos, \r\n (58 + 1):(58 + 14 + dd), #additional[1:(dd + 14)]]\r\n 80, 81), # age, win_count\r\n 2]\r\n Figure.S1.d <- Figure.S1.d[rev(order(Figure.S1.d[, \"Diff.Un\"])),]\r\n colnames(Figure.S1.d) <- c(\"ASMD before matching\", \"ASMD after matching\")\r\n write.csv(Figure.S1.d, paste0(\"output/Fukumoto_FigS1_\", letters[d], \"_source.csv\"))\r\n if(d == 3){\r\n Figure.S1.d <- cbind(Figure.S1.d, Figure.S1.d[, 2]/Figure.S1.d[, 1])\r\n colnames(Figure.S1.d)[3] <- c(\"Ratio\")\r\n Figure.S1.d <- round(Figure.S1.d, 2)\r\n write.csv(Figure.S1.d, \"output/Fukumoto_TableS3.csv\")\r\n }\r\n}\r\n\r\n###########################\r\n# Supplementary Information\r\n# Main Analysis\r\n\r\ncol.shutdown <- 44:51\r\n\r\n# March 4\r\nd <- 1\r\n(day <- survey.dates[d])\r\nsource(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\nshutdown <- data.analysis[,col.shutdown]\r\nshutdown[is.na(shutdown)] <- 9\r\n(table.out <- table(shutdown[,1], shutdown[,2]))\r\n# \"Since 9 of the 10 (90.0\\%) control municipalities also had open schools as of the next survey date (March 16),\" (SI, p. 8) \r\ntable.out[1,1]/sum(table.out[1,])\r\n# \"718 (94.3\\%) % 718/761 treated municipalities reported that their schools continued to be closed until March 16.\" (SI, p. 8) \r\ntable.out[2,2]/sum(table.out[2,])\r\n\r\n# # April 6\r\n# d <- 3\r\n# (day <- survey.dates[d])\r\n# source(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\n# shutdown <- data.analysis[,col.shutdown]\r\n# shutdown[is.na(shutdown)] <- 9\r\n# # \"247 of the 256 (96.5\\%) treated municipalities as of April 6 closed schools on all of the following three survey dates as well (April 10, 16, and 22),\" (SI, p. 10) \r\n# all.close <- shutdown[,4]==1 & shutdown[,5]==1 & shutdown[,6]==1 \r\n# (table.out <- table(shutdown[,d], all.close))\r\n# table.out[2,2]/sum(table.out[2,])\r\n# # \"though only 79 of the 483 (16.4\\%) control municipalities opened their schools on all three dates.\" (SI, p. 10) \r\n# all.open <- shutdown[,4]==0 & shutdown[,5]==0 & shutdown[,6]==0 \r\n# (table.out <- table(shutdown[,d], all.open))\r\n# table.out[1,2]/sum(table.out[1,])\r\n# \r\n# # April 22\r\n# d <- 6\r\n# (day <- survey.dates[d])\r\n# source(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\n# shutdown <- data.analysis[,col.shutdown]\r\n# shutdown[is.na(shutdown)] <- 9\r\n# (table.out <- table(shutdown[,d], shutdown[,7]))\r\n# # \"We find that 70 of the 80 (87.5%) control municipalities and 634 (89.3%) of 710 treated municipalities as of April 22 opened and closed their schools as of the next survey date (May 11), respectively.\" (SI, p. 11)\r\n# table.out[1,1]/sum(table.out[1,])\r\n# table.out[2,2]/sum(table.out[2,])\r\n# \r\n# # May 11\r\n# d <- 7\r\n# (day <- survey.dates[d])\r\n# source(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\n# shutdown <- data.analysis[,col.shutdown]\r\n# shutdown[is.na(shutdown)] <- 9\r\n# (table.out <- table(shutdown[,d], shutdown[,8]))\r\n# # \"just 2 of the 641 (0.3%) treated municipalities as of May 11 reported closed schools as of the next survey date (June 1).\" (SI, p. 11)\r\n# table.out[2,2]/sum(table.out[2,])\r\n# # \"All of the control municipalities as of May 11 said that their schools were also open on June 1.\" (SI, p. 12)\r\n# table.out[1,1]/sum(table.out[1,])\r\n\r\n##################\r\n# Irregular values\r\n\r\n# March 16\r\n\r\nd <- 2\r\n(day <- survey.dates[d])\r\nsource(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\noutcome <- outcome/10\r\nv1 <- c(66:68)\r\nv2 <- c(117:119)\r\nm.out <- readRDS(paste0(\"matching_results/alt_atc_\", day, \"_1000_1000.RDS\"))\r\n# Create matched dataset\r\ngm <- get_matches(m.out, data = data.analysis)\r\n# estimate the ATC for every day from one week before (March 30) to three weeks after (April 27)\r\npart <- 99:127 # indicates March 9 to April 6\r\ngm[,part] <- (10^5)*gm[, part]/gm$pop.2020\r\nsummary.Match.out <- rep(NA, length(part))\r\nfor(p in 1:length(part)){\r\n fit <- lm(gm[, part[p]] ~ shutdown.0316,\r\n data = gm, weights = weights)\r\n summary.Match.out[p] <- coef(summary(fit))[2,1]\r\n}\r\nnames(summary.Match.out) <- colnames(gm[,part])\r\nsummary.Match.out[24]\r\n\r\n# \"on April 1 ... In Wadomari Town, Kagoshima Prefecture, in the control group had a case among its 6,537 residents, which is equivalent to 15.3 cases per 100,000 residents.\" (SI, p. 9)\r\nvv <- 3\r\ncolnames(outcome)[v1[vv]]\r\nuse <- which(treat == 0 & data.analysis[,v2[vv]]>0)\r\noutlier <- cbind(data.analysis[use, c(\"municipality_code\", \"pop.2020\")], \r\n data.analysis[use, v2[vv]],\r\n outcome[use, v1[vv]])\r\noutlier[2,]\r\n# \"This single municipality contributes to 103.3\\% $(=(-15.3/29)/(-0.511))$ of the ATC (-0.511).\" (SI, p. 9)\r\n(-outlier[2,4]/table(treat)[1])/summary.Match.out[24]\r\n# \"the mean of population among all of the 785 municipalities in the 26 target prefectures is 76,830.\" (SI, p. 9)\r\nmean(data.all$pop.2020[data.all$pref != 13])\r\n\r\n# # April 16\r\n# \r\n# d <- 5\r\n# (day <- survey.dates[d])\r\n# source(paste0(\"preprocess/preprocess.\", day, \".R\"))\r\n# outcome <- outcome/10\r\n# v1 <- c(89:92,98:101)\r\n# v2 <- c(140:143, 149:152)\r\n# m.out <- readRDS(paste0(\"matching_results/alt_atc_\", day, \"_1000_1000.RDS\"))\r\n# # Create matched dataset\r\n# gm <- get_matches(m.out, data = data.analysis)\r\n# # estimate the ATC for every day from one week before (March 30) to three weeks after (April 27)\r\n# part <- 130:158 # indicates April 9 to May 7\r\n# gm[,part] <- (10^5)*gm[, part]/gm$pop.2020\r\n# summary.Match.out <- rep(NA, length(part))\r\n# for(p in 1:length(part)){\r\n# fit <- lm(gm[, part[p]] ~ shutdown.0416,\r\n# data = gm, weights = weights)\r\n# summary.Match.out[p] <- coef(summary(fit))[2,1]\r\n# }\r\n# names(summary.Match.out) <- colnames(gm[,part])\r\n# summary.Match.out[c(15, 25)]\r\n# \r\n# vv <- 2\r\n# colnames(outcome)[v1[vv]]\r\n# use <- which(treat == 0 & data.analysis[,v2[vv]]>0)\r\n# outlier <- cbind(data.analysis[use, c(\"municipality_code\", \"pop.2020\")], \r\n# data.analysis[use, v2[vv]],\r\n# outcome[use, v1[vv]])\r\n# outlier[6,]\r\n# (-outlier[6,4]/table(treat)[1])/summary.Match.out[15]\r\n# \r\n# vv <- 7\r\n# colnames(outcome)[v1[vv]]\r\n# use <- which(treat == 0 & data.analysis[,v2[vv]]>0)\r\n# outlier <- cbind(data.analysis[use, c(\"municipality_code\", \"pop.2020\")], \r\n# data.analysis[use, v2[vv]],\r\n# outcome[use, v1[vv]])\r\n# outlier[1,]\r\n# (-outlier[1,4]/table(treat)[1])/summary.Match.out[25]\r\n# \r\n# # the mean of population among all of the 847 municipalities in the 27 target prefectures is 87,540.\r\n# mean(data.all$pop.2020)\r\n", "meta": {"hexsha": "0ae3213343aa21031d703c0d1c59f5cefbc92bc5", "size": 16195, "ext": "r", "lang": "R", "max_stars_repo_path": "Fukumoto2021/supplement.r", "max_stars_repo_name": "akira-endo/reanalysis_Fukumoto2021", "max_stars_repo_head_hexsha": "c0e815dd08a6009d4c962667f991988309997da0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Fukumoto2021/supplement.r", "max_issues_repo_name": "akira-endo/reanalysis_Fukumoto2021", "max_issues_repo_head_hexsha": "c0e815dd08a6009d4c962667f991988309997da0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Fukumoto2021/supplement.r", "max_forks_repo_name": "akira-endo/reanalysis_Fukumoto2021", "max_forks_repo_head_hexsha": "c0e815dd08a6009d4c962667f991988309997da0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.7934508816, "max_line_length": 219, "alphanum_fraction": 0.5953071936, "num_tokens": 5137, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5736784074525098, "lm_q1q2_score": 0.33348103128805157}} {"text": "library(ggplot2)\nlibrary(tidyverse)\nlibrary(magrittr)\n\nargs = commandArgs(trailingOnly=TRUE)\n\n# test if there is at least one argument: if not, return an error\nif (length(args) != 1) {\n stop(\"At least one argument must be supplied (input file)\", call.=FALSE)\n}\n\nprint(args[[1]])\n\ncolTypeSpec = cols(i = col_integer(),\n dist = col_double(),\n length_ = col_double(),\n dups = col_double())\n\n# \"Constants\"\nPOINTSIZE = 0.1\nglobalSize <- 5000000\n\nfilename = paste(args[[1]], sep=\"\")\nallData <- read_delim(file = filename, delim = \" \", col_types = colTypeSpec, comment=\"-\")\n\nscatterPlot <- function(data_, pointSize, title) {\n plot <- ggplot(data = data_) \n plot <- plot + geom_point(mapping = aes(x = i, y = dist),\n size = pointSize)\n #plot <- plot + facet_wrap(ByteEncoder ~ dToNRatio )\n plot <- plot + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 5), \n legend.text = element_text(size = 5),\n legend.title = element_text(size = 6))\n plot <- plot + ggtitle(title)\n return(plot)\n}\nscatterPlotDups <- function(data_, pointSize, title) {\n plot <- ggplot(data = data_) \n plot <- plot + geom_point(mapping = aes(x = i, y = dups),\n size = pointSize)\n #plot <- plot + facet_wrap(ByteEncoder ~ dToNRatio )\n plot <- plot + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 5), \n legend.text = element_text(size = 5),\n legend.title = element_text(size = 6))\n plot <- plot + ggtitle(title)\n return(plot)\n}\n\nscatterPlotLength <- function(data_, pointSize, title) {\n plot <- ggplot(data = data_) \n plot <- plot + geom_point(mapping = aes(x = i, y = length_),\n size = pointSize)\n #plot <- plot + facet_wrap(ByteEncoder ~ dToNRatio )\n plot <- plot + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 5), \n legend.text = element_text(size = 5),\n legend.title = element_text(size = 6))\n plot <- plot + ggtitle(title)\n return(plot)\n}\nsum_ <- function(data_) {\n overallSum <- sum(data_$dist)\n d <- data.frame(data_)\n curSum <- 0\n for(index in 1:(nrow(data_) - 1)) {\n d$dist[index] <- overallSum - curSum\n curSum <- curSum + data_$dist[index]\n }\n return(as.tibble(d))\n}\nintervals <- function(data_) {\n \n maxValue <- max(data_$i)\n print(maxValue)\n d <- data.frame(\"i\" = c(), \"dist\" = c())\n prevI <- 0\n for(index in c(0, 2,4,8,16,32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144)) {\n cur <- filter(data_, i >= index)\n\n curSum <- sum(cur$dist)\n curSumDups <- sum(cur$dups)\n\n d <- rbind(d, data.frame(\"i\" = c(index), \"dist\" = c(curSum - curSumDups)))\n prevI <- index\n if (index > maxValue)\n return(as.tibble(d))\n }\n return(as.tibble(d))\n}\n\n\npureDirName <- str_sub(args, start = 1, end = -2)\nprint(allData)\npdf(paste(pureDirName, \"_plots_prefixCompression.pdf\",sep=\"\"), width=10, height=5)\n\nscatterPlot(filter(allData, i < 50), POINTSIZE, \"i < 50\")\nscatterPlotDups(filter(allData, i < 50), POINTSIZE, \"i < 50\")\nscatterPlot(filter(allData, i < 200), POINTSIZE, \"i < 200\")\nscatterPlot(filter(allData, i < 2000), POINTSIZE, \"i < 2000\")\nscatterPlot(filter(allData, i < 20000), POINTSIZE, \"i < 20000\")\nsmall <- filter(allData, i < 2000)\nscatterPlot(small, POINTSIZE, \"small dist\")\nscatterPlotDups(small, POINTSIZE, \"small dups\")\nscatterPlot(intervals(small), POINTSIZE, \"intervals on sums\")\nscatterPlot(sum_(small), POINTSIZE, \"sum all dists for i > x\")\n\n\n", "meta": {"hexsha": "0a7c6afe3f36cfb46d110fe54af85320d7be1b3f", "size": 3585, "ext": "r", "lang": "R", "max_stars_repo_path": "forHLR_results/dataStats.r", "max_stars_repo_name": "bingmann/distributed-string-sorting", "max_stars_repo_head_hexsha": "238bdfd5f6139f2f14e33aed7b4d9bbffac5d295", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "forHLR_results/dataStats.r", "max_issues_repo_name": "bingmann/distributed-string-sorting", "max_issues_repo_head_hexsha": "238bdfd5f6139f2f14e33aed7b4d9bbffac5d295", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "forHLR_results/dataStats.r", "max_forks_repo_name": "bingmann/distributed-string-sorting", "max_forks_repo_head_hexsha": "238bdfd5f6139f2f14e33aed7b4d9bbffac5d295", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-10-16T06:13:28.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-16T06:13:28.000Z", "avg_line_length": 33.5046728972, "max_line_length": 115, "alphanum_fraction": 0.6150627615, "num_tokens": 1061, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.33348103128805157}} {"text": "\r\n\r\n# Goal: Display of a macroeconomic time-series, with a filled colour\r\n# bar showing a recession.\r\n\r\nyears <- 1950:2000\r\ntimeseries <- cumsum(c(100, runif(50)*5))\r\nhilo <- range(timeseries)\r\nplot(years, timeseries, type=\"l\", lwd=3)\r\n# A recession from 1960 to 1965 --\r\npolygon(x=c(1960,1960, 1965,1965),\r\n y=c(hilo, rev(hilo)),\r\n density=NA, col=\"orange\", border=NA)\r\nlines(years, timeseries, type=\"l\", lwd=3) # paint again so line comes on top\r\n\r\n# alternative method -- though not as good looking --\r\n# library(plotrix)\r\n# gradient.rect(1960, hilo[1], 1965, hilo[2],\r\n# reds=c(0,1), greens=c(0,0), blues=c(0,0),\r\n# gradient=\"y\")\r\n\r\n", "meta": {"hexsha": "6b46bc44eb523145feb69cd7cd28bcd422d7c379", "size": 685, "ext": "r", "lang": "R", "max_stars_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g5.r", "max_stars_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_stars_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g5.r", "max_issues_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_issues_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RFrontEndSolution/Tests Repo/Rscripts All (163 files)/g5.r", "max_forks_repo_name": "AlexandrosPlessias/CompilerFrontEndForRLanguage", "max_forks_repo_head_hexsha": "71e3e60476f6f83b05cc97c625265edbde086341", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1363636364, "max_line_length": 77, "alphanum_fraction": 0.6145985401, "num_tokens": 217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3334810312880515}} {"text": "#!/usr/bin/r\n\n\nsuppressMessages(library(Rcpp))\nsuppressMessages(library(inline))\n\n\nfoo <- '\n int i, j, na, nb, nab;\n double *xa, *xb, *xab;\n SEXP ab;\n\n PROTECT(a = AS_NUMERIC(a));\n PROTECT(b = AS_NUMERIC(b));\n na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;\n PROTECT(ab = NEW_NUMERIC(nab));\n xa = NUMERIC_POINTER(a); xb = NUMERIC_POINTER(b);\n xab = NUMERIC_POINTER(ab);\n for(i = 0; i < nab; i++) xab[i] = 0.0;\n for(i = 0; i < na; i++)\n for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];\n UNPROTECT(3);\n return(ab);\n'\n\nfunx <- cfunction(signature(a=\"numeric\",b=\"numeric\"), foo, Rcpp=FALSE, verbose=FALSE)\nfunx(a=1:20, b=2:11)\n", "meta": {"hexsha": "8a4083833792dbee55163fbb7b3b50e27049f84b", "size": 646, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-w64-mingw32/3.2.1/Rcpp/examples/RcppInline/RcppSimpleExample.r", "max_stars_repo_name": "Fredin/El-Habla-de-Monterrey", "max_stars_repo_head_hexsha": "dd3333663bf5f66a751033166137109f03b39ac7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max_stars_repo_stars_event_min_datetime": "2019-05-31T14:18:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T11:30:32.000Z", "max_issues_repo_path": "packrat/lib/x86_64-w64-mingw32/3.2.1/Rcpp/examples/RcppInline/RcppSimpleExample.r", "max_issues_repo_name": "Fredin/El-Habla-de-Monterrey", "max_issues_repo_head_hexsha": "dd3333663bf5f66a751033166137109f03b39ac7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-05-31T14:19:43.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-16T16:09:52.000Z", "max_forks_repo_path": "packrat/lib/x86_64-w64-mingw32/3.2.1/Rcpp/examples/RcppInline/RcppSimpleExample.r", "max_forks_repo_name": "Fredin/El-Habla-de-Monterrey", "max_forks_repo_head_hexsha": "dd3333663bf5f66a751033166137109f03b39ac7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-05-29T12:44:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-16T17:16:24.000Z", "avg_line_length": 23.0714285714, "max_line_length": 85, "alphanum_fraction": 0.5835913313, "num_tokens": 233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33343488794385295}} {"text": "Cit.HepPh.Unique<-unique(Cit.HepPh)\ncit.HepPh.dates.Unique<-unique(cit.HepPh.dates)\ncit.HepPh.dates.UniqueNode<-unique(cit.HepPh.dates$V1)\n\nnrow(Cit.HepPh)\nnrow(Cit.HepPh.Unique)\n\nnrow(cit.HepPh.dates)\nnrow(cit.HepPh.dates.Unique)\nlength(cit.HepPh.dates.UniqueNode)\n\ndateOfPaper <- function(paperNodeId){\n cit.HepPh.dates.Unique[(cit.HepPh.dates.Unique$V1==paperNodeId),]$V2\n}\ndateOfPaper(112008)\n\nlibrary(igraph)\ngg = graph.data.frame(d = Cit.HepPh.Unique, directed = T)\nis.simple(gg)", "meta": {"hexsha": "b5b8960cbf1ad49922633370e2c536a7ce87a598", "size": 486, "ext": "r", "lang": "R", "max_stars_repo_path": "restopicer-research/RCitationEvolution/code_not_used/checkUniqueAndSimple.r", "max_stars_repo_name": "RUCYuLiTeam/restopicer", "max_stars_repo_head_hexsha": "cea3e7f644a40bc8c2e2136b5d9ccfcc72ce7e25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-09-06T02:57:54.000Z", "max_stars_repo_stars_event_max_datetime": "2015-09-06T02:57:54.000Z", "max_issues_repo_path": "restopicer-research/RCitationEvolution/code_not_used/checkUniqueAndSimple.r", "max_issues_repo_name": "JoshuaZe/restopicer", "max_issues_repo_head_hexsha": "28d0833e7b950356ae6e29459991d87a53073a72", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "restopicer-research/RCitationEvolution/code_not_used/checkUniqueAndSimple.r", "max_forks_repo_name": "JoshuaZe/restopicer", "max_forks_repo_head_hexsha": "28d0833e7b950356ae6e29459991d87a53073a72", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5789473684, "max_line_length": 70, "alphanum_fraction": 0.7654320988, "num_tokens": 170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5234203638047913, "lm_q2_score": 0.6370307875894139, "lm_q1q2_score": 0.3334348865949037}} {"text": "#!/usr/bin/Rscript\n\nlibrary(DESeq2)\n\n# script arguments\nargs <- commandArgs(trailingOnly=T)\nrda_to_load <- args[1]\nrda_to_save <- args[2]\n\n# loading\nload(rda_to_load)\ndds <- data\n\n# variance stabilising transformation\nif ( nrow(dds) < 1000 ) {\n\t#vst <- DESeq2::vst( dds , nsub=nrow(dds) )\n\tvst <- DESeq2::varianceStabilizingTransformation( dds )\n} else {\n\t#vst <- DESeq2::vst(dds)\n\tvst <- DESeq2::varianceStabilizingTransformation( dds )\n}\n\n# saving\ndata <- vst\nsave(data, file=rda_to_save)\n\n", "meta": {"hexsha": "26996df712a72755e2f0d49137311ecf57b1db92", "size": 492, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/r/vst.r", "max_stars_repo_name": "nicholas-owen/BABS-RNASeq", "max_stars_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2018-12-04T15:36:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-02T19:43:56.000Z", "max_issues_repo_path": "scripts/r/vst.r", "max_issues_repo_name": "nicholas-owen/BABS-RNASeq", "max_issues_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/r/vst.r", "max_forks_repo_name": "nicholas-owen/BABS-RNASeq", "max_forks_repo_head_hexsha": "9e352966d833939e0c8dfc530b34d9de54ad742f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-02-14T12:06:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T13:46:43.000Z", "avg_line_length": 18.2222222222, "max_line_length": 56, "alphanum_fraction": 0.7052845528, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3334348807301059}} {"text": "library(ggplot2)\nlibrary(reshape2)\nlibrary(cowplot)\n\nfigWidth<-20\nfigHeight<-10\ntextFont<-10\n\n#epoch === repair tool\n#losses === tech(sbfl and profl)\n#Subs == patches (correct, plausible noncorrect)\n#model == result.txt\n\nBasedir=\"./\"\n\n\nSubs<-c(\"FL\")\n\n\nlosses<-c(\"NegFix\",\"NoneFix\",\"NoisyFix\",\"CleanFix\")\n\nmodel<-\"result\"\n\nepochs<-c(\"jGenProg\",\"GenProg-A\",\"jMutRepair\",\"kPar\",\"RSRepair-A\",\"jKali\",\"Kali-A\",\"Dynamoth\",\"ACS\",\"Cardumen\",\"Arja\",\"Simfix\",\"FixMiner\",\"AVATAR\",\"TBar\",\"PraPR\")\n\n#Metrics<-c(\"Top1\",\"Top3\",\"Top5\",\"MFR\",\"MAR\")\nMetrics<-c(\"Top1\",\"MFR\")\n\nfor(sub in Subs){\n\tplot_list = list()\n\tcount=1\n\tpdf(paste(c(\"../../Results/FinalResults/\",\"RQ2.pdf\"),collapse=\"\"), width=figWidth, height=figHeight)\n\n\t\tfor(me in Metrics){\n\t\t\t\t\n\t\t\tcombinemet=cbind(epochs)\n\t\t\tfor(loss in losses){\n\t\t\t\tdatafile=paste(Basedir,loss,\"/\",sub,\"/\",model,\".txt\",sep=\"\")\n\t\t\t\tdatamodel<-read.table(datafile,sep = \" \")\n\t\t\t\tcolnames(datamodel) <- c(\"Tool\",\"Top1\",\"Top3\",\"Top5\",\"MFR\",\"MAR\")\n\t\t\t\tcombinemet=cbind(combinemet,datamodel[,me])\n\t\t\t}\n\t\t\tcombinemet=data.frame(combinemet)\n\t\t\t#colnames(combinemet) <- c(\"epochs\",\"within-project\",\"cross-project\",\"cross-validation\")\n\t\t\tcolnames(combinemet) <- c(\"Tool\",\"NegFix\",\"NoneFix\",\"NoisyFix\",\"CleanFix\")\n\t\t\tcombinemet <- melt(combinemet, id.vars='Tool')\n\t\t\tcolnames(combinemet) <- c(\"Tool\",\"model\",\"value\")\n\t\t\tcombinemet$value <- as.numeric(as.character(combinemet$value))\n\t\t\tp<-ggplot(combinemet, aes(x=Tool, y=value, group=model, colour=model,shape=model)) +\n\t\t\t\tgeom_line(aes(linetype=model),size=1.7)+geom_point(size=2)+ ylab(me)+theme(text=element_text(size=20),legend.position=\"top\")\n\t\t\tplot_list[[count]] = p\n\t\t\tcount=count+1\n\t\t\t\t\n\t\t}\n\t\t\t\n\t\tprow<-plot_grid(plot_list[[1]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\tplot_list[[2]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[3]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[4]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[5]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#nrow = 1, align = 'h',labels = sub)\n\t\t\t\t\t\tnrow = 2, align = 'h')\n\t\tlegend_b <- get_legend(plot_list[[1]] + theme(legend.position=c(0.967,45), legend.title = element_blank(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlegend.text = element_text( size = 11)))\n\t\tp <- plot_grid( prow, legend_b, ncol = 1, rel_heights = c(1.1, .01))\n\t\tprint(p)\n\t\tdev.off()\n\t\n}\n", "meta": {"hexsha": "8c0c8aa72289c404c7c2492af3452bb45e68d047", "size": 2280, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/RQ2/rCode.r", "max_stars_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_stars_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/RQ2/rCode.r", "max_issues_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_issues_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/RQ2/rCode.r", "max_forks_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_forks_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5714285714, "max_line_length": 162, "alphanum_fraction": 0.6504385965, "num_tokens": 728, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3334348807301059}} {"text": "\n\tp = bio.snowcrab::load.environment()\n\n setwd( project.datadirectory(\"bio.snowcrab\") )\n\n det = snowcrab.db( DS =\"det.georeferenced\" )\n set = snowcrab.db( DS =\"set.complete\" )\n\n xdet = merge( det, set[,c(\"trip\",\"set\", \"t\", \"z\")], by=c(\"trip\", \"set\"), all.x=T, all.y=F )\n xdet = xdet[ which( xdet$sex %in% c(female,male) ), ]\n\n xdet$sex = as.factor( xdet$sex)\n xdet$size = \"small\"\n xdet$size[which( xdet$cw > 95) ] = \"big\"\n xdet$size[which( xdet$cw > 60 & xdet$cw < 95) ] = \"med\"\n\n xdet$size = as.factor( xdet$size )\n levels(xdet$mat) = c(\"Immature\", \"Mature\")\n levels(xdet$sex) = c(\"Male\", \"Female\")\n\n\n setup.lattice.options()\n\n histogram( ~ t | sex + mat , data = xdet,\n xlab = \"Bottom temperature (C)\", type = \"density\",\n panel = function(x, ...) {\n panel.histogram(x, col=\"white\", ...)\n panel.mathdensity( dmath=dnorm, col = \"black\", args=list(mean=mean(x),sd=sd(x)))\n } )\n\n Pr( \"png\", dname=project.datadirectory(\"bio.snowcrab\", \"output\"), fname=\"temp.hist\", trim=F , res=144)\n\n\n histogram( ~ z | sex + mat , data = xdet,\n xlab = \"Bottom temperature (C)\", type = \"density\",\n panel = function(x, ...) {\n panel.histogram(x, col=\"white\", ...)\n panel.mathdensity( dmath=dnorm, col = \"black\", args=list(mean=mean(x),sd=sd(x)))\n } )\n\n Pr( \"png\", dname=project.datadirectory(\"bio.snowcrab\", \"output\"), fname=\"temp.hist\", trim=F , res=144)\n\n\n\n histogram( ~ t | sex + size , data = xdet,\n xlab = \"depth\", type = \"density\",\n panel = function(x, ...) {\n panel.histogram(x, col=\"white\", ...)\n panel.mathdensity( dmath=dnorm, col = \"black\", args=list(mean=mean(x),sd=sd(x)))\n } )\n`\n Pr( \"png\", dname=project.datadirectory(\"bio.snowcrab\", \"output\"), fname=\"temp.hist\", trim=F , res=144)\n\n\n\n\n", "meta": {"hexsha": "63ae7f4af1d98d15d36e1205da93b21f7edd0d78", "size": 1760, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/papers/paper.alaska.habitat.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/papers/paper.alaska.habitat.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/papers/paper.alaska.habitat.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 30.8771929825, "max_line_length": 105, "alphanum_fraction": 0.5880681818, "num_tokens": 590, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33343488073010585}} {"text": "\n\n# Snow crab --- Areal unit modelling of habitat -- no reliance upon stmv fields\n\n\n# -------------------------------------------------\n# Part 1 -- construct basic parameter list defining the main characteristics of the study\n# require(aegis)\n\n year.assessment = 2021\n\n p = bio.snowcrab::snowcrab_parameters( \n project_class=\"carstm\", \n yrs=2000:year.assessment, \n areal_units_type=\"lattice\",\n selection = list(type = \"number\")\n )\n\n# ------------------------------------------------\n# Part 2 -- spatiotemporal statistical model \n\n if ( spataiotemporal_model ) {\n\n if (0) {\n # polygon structure:: create if not yet made\n # adjust based upon RAM requirements and ncores\n \n p$areal_units_constraint_nmin = 3\n p$areal_units_constraint_ntarget = 10\n\n for (au in c(\"cfanorth\", \"cfasouth\", \"cfa4x\", \"cfaall\" )) plot(polygon_managementareas( species=\"snowcrab\", au))\n xydata = snowcrab.db( p=p, DS=\"areal_units_input\", redo=TRUE )\n\n sppoly = areal_units( p=p, redo=TRUE ) # create constrained polygons with neighbourhood as an attribute\n MS = NULL\n }\n\n sppoly = areal_units( p=p ) # to reload\n plot( sppoly[, \"au_sa_km2\"] )\n\n\n # -------------------------------------------------\n M = snowcrab.db( p=p, DS=\"carstm_inputs\", redo=TRUE ) # will redo if not found\n M = NULL; gc()\n\n fit = carstm_model( \n p=p, \n data='snowcrab.db( p=p, DS=\"carstm_inputs\" )' \n num.threads=\"4:2\",\n ) # 151 configs and long optim .. 19 hrs\n # fit = carstm_model( p=p, DS=\"carstm_modelled_fit\")\n\n # extract results\n if (0) {\n # very large files .. slow \n fit = carstm_model( p=p, DS=\"carstm_modelled_fit\" ) # extract currently saved model fit\n plot(fit)\n plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )\n }\n\n\n res = carstm_model( p=p, DS=\"carstm_modelled_summary\" ) # to load currently saved results\n res$summary$dic$dic\n res$summary$dic$p.eff\n res$dyear\n\n\n plot_crs = p$aegis_proj4string_planar_km\n coastline=aegis.coastline::coastline_db( DS=\"eastcoast_gadm\", project_to=plot_crs )\n isobaths=aegis.bathymetry::isobath_db( depths=c(50, 100, 200, 400 ), project_to=plot_crs )\n managementlines = aegis.polygons::area_lines.db( DS=\"cfa.regions\", returntype=\"sf\", project_to=plot_crs )\n \n time_match = list( year=as.character(2020) )\n carstm_map( res=res, \n vn=\"predictions\", \n time_match=time_match, \n coastline=coastline,\n managementlines=managementlines,\n isobaths=isobaths,\n main=paste(\"Predicted abundance\", paste0(time_match, collapse=\"-\") ) \n )\n \n\n # map all :\n vn = \"predictions\"\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.numerical.densitites\" )\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n brks = pretty( quantile(res[[vn]], probs=c(0,0.975)) )\n\n for (y in res$year ){\n\n time_match = list( year=as.character(y) )\n fn_root = paste(\"Predicted_abundance\", paste0(time_match, collapse=\"-\"), sep=\"_\")\n fn = file.path( outputdir, paste(fn_root, \"png\", sep=\".\") )\n\n carstm_map( res=res, vn=vn, time_match=time_match, \n breaks =brks,\n coastline=coastline,\n isobaths=isobaths,\n managementlines=managementlines,\n main=paste(\"Predicted numerial abundance\", paste0(time_match, collapse=\"-\") ),\n outfilename=fn\n ) \n\n }\n \n\n\n snowcrab.db(p=p, DS=\"carstm_output_compute\" )\n \n RES = snowcrab.db(p=p, DS=\"carstm_output_timeseries\" )\n\n bio = snowcrab.db(p=p, DS=\"carstm_output_spacetime_biomass\" )\n num = snowcrab.db(p=p, DS=\"carstm_output_spacetime_number\" )\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"aggregated_biomass_timeseries\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n\n png( filename=file.path( outputdir, \"cfa_all.png\"), width=3072, height=2304, pointsize=12, res=300 )\n plot( cfaall ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfaall_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfaall_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n png( filename=file.path( outputdir, \"cfa_south.png\"), width=3072, height=2304, pointsize=12, res=300 )\n plot( cfasouth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfasouth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfasouth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n png( filename=file.path( outputdir, \"cfa_north.png\"), width=3072, height=2304, pointsize=12, res=300 )\n plot( cfanorth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfanorth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfanorth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n png( filename=file.path( outputdir, \"cfa_4x.png\"), width=3072, height=2304, pointsize=12, res=300 )\n plot( cfa4x ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfa4x_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfa4x_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n\n\n\n # map it ..mean density\n\n sppoly = areal_units( p=p ) # to reload\n\n plot_crs = p$aegis_proj4string_planar_km\n coastline=aegis.coastline::coastline_db( DS=\"eastcoast_gadm\", project_to=plot_crs )\n isobaths=aegis.bathymetry::isobath_db( depths=c(50, 100, 200, 400, 800), project_to=plot_crs )\n managementlines = aegis.polygons::area_lines.db( DS=\"cfa.regions\", returntype=\"sf\", project_to=plot_crs )\n \n vn = paste(\"biomass\", \"predicted\", sep=\".\")\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.biomass.densitites\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n brks = pretty( quantile( bio[], probs=c(0,0.975) )* 10^6 )\n\n for (i in 1:length(p$yrs) ){\n y = as.character( p$yrs[i] )\n sppoly[,vn] = bio[,y] * 10^6\n fn = file.path( outputdir , paste( \"biomass\", y, \"png\", sep=\".\") )\n\n carstm_map( sppoly=sppoly, vn=vn, \n breaks=brks, \n coastline=coastline,\n isobaths=isobaths,\n managementlines=managementlines,\n main=paste(\"Predicted biomass density\", y ), \n outfilename=fn\n )\n }\n\n plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )\n plot( fit$marginals.hyperpar$\"Phi for space\", type=\"l\") # posterior distribution of phi nonspatial dominates\n plot( fit$marginals.hyperpar$\"Precision for space\", type=\"l\")\n plot( fit$marginals.hyperpar$\"Precision for setno\", type=\"l\")\n\n\n\n (res$summary)\n \n\nTime used:\n Pre = 13.7, Running = 11277, Post = 6.19, Total = 11297 \nRandom effects:\n Name\t Model\n dyri AR1 model\n yr AR1 model\n inla.group(t, method = \"quantile\", n = 11) RW2 model\n inla.group(z, method = \"quantile\", n = 11) RW2 model\n inla.group(substrate.grainsize, method = \"quantile\", n = 11) RW2 model\n inla.group(pca1, method = \"quantile\", n = 11) RW2 model\n inla.group(pca2, method = \"quantile\", n = 11) RW2 model\n space BYM2 model\n space_time BYM2 model\n\nModel hyperparameters:\n mean sd 0.025quant 0.5quant 0.975quant\nPrecision for dyri 52.326 0.080 52.163 52.328 52.476\nRho for dyri 0.765 0.000 0.764 0.765 0.765\nPrecision for yr 55.148 0.102 54.956 55.145 55.355\nRho for yr 0.761 0.000 0.761 0.761 0.762\nPrecision for inla.group(t, method = \"quantile\", n = 11) 52.842 0.264 52.402 52.833 53.290\nPrecision for inla.group(z, method = \"quantile\", n = 11) 54.802 0.209 54.489 54.771 55.260\nPrecision for inla.group(substrate.grainsize, method = \"quantile\", n = 11) 53.714 0.159 53.488 53.689 54.061\nPrecision for inla.group(pca1, method = \"quantile\", n = 11) 54.755 0.140 54.556 54.732 55.068\nPrecision for inla.group(pca2, method = \"quantile\", n = 11) 54.875 0.227 54.529 54.850 55.319\nPrecision for space 54.589 0.049 54.488 54.588 54.700\nPhi for space 0.049 0.000 0.049 0.049 0.049\nPrecision for space_time 56.525 0.036 56.452 56.525 56.595\nPhi for space_time 0.050 0.000 0.049 0.050 0.050\nGroupRho for space_time 0.773 0.000 0.773 0.773 0.773\n mode\nPrecision for dyri 52.338\nRho for dyri 0.765\nPrecision for yr 55.134\nRho for yr 0.762\nPrecision for inla.group(t, method = \"quantile\", n = 11) 52.521\nPrecision for inla.group(z, method = \"quantile\", n = 11) 54.601\nPrecision for inla.group(substrate.grainsize, method = \"quantile\", n = 11) 53.527\nPrecision for inla.group(pca1, method = \"quantile\", n = 11) 54.601\nPrecision for inla.group(pca2, method = \"quantile\", n = 11) 54.602\nPrecision for space 54.590\nPhi for space 0.049\nPrecision for space_time 56.527\nPhi for space_time 0.049\nGroupRho for space_time 0.773\n\nExpected number of effective parameters(stdev): 480.66(15.72)\nNumber of equivalent replicates : 15.10 \n\nDeviance Information Criterion (DIC) ...............: 51941.31\nDeviance Information Criterion (DIC, saturated) ....: 94121.92\nEffective number of parameters .....................: -680.14\n\nWatanabe-Akaike information criterion (WAIC) ...: 57902.25\nEffective number of parameters .................: 3659.12\n\nMarginal log-Likelihood: -29248.16 \nPosterior marginals for the linear predictor and\n the fitted values are computed\n\n\n\n }\n\n\n ########## \n\n\n if (fishery_model) {\n\n p$fishery_model = fishery_model( DS = \"logistic_parameters\", p=p, tag=p$areal_units_type )\n p$fishery_model$stancode = stan_initialize( stan_code=fishery_model( p=p, DS=\"stan_surplus_production\" ) )\n\n str( p$fishery_model)\n\n p$fishery_model$stancode$compile()\n\n # res = fishery_model( p=p, DS=\"samples\", tag=p$areal_units_type ) # to get samples\n \n if (0) {\n \n fit = fishery_model( p=p, DS=\"fit\", tag=p$areal_units_type ) # to get samples\n \n print( fit, max_rows=30 )\n # fit$summary(\"K\", \"r\", \"q\")\n \n fit$cmdstan_diagnose()\n fit$cmdstan_summary()\n \n # (penalized) maximum likelihood estimate (MLE) \n fit_mle = p$fishery_model$stancode$optimize(data =p$fishery_model$standata, seed = 123)\n fit_mle$summary( c(\"K\", \"r\", \"q\") )\n\n u = stan_extract( as_draws_df(fit_mle$draws() ) )\n\n mcmc_hist(fit$draws(\"K\")) +\n vline_at(fit_mle$mle(), size = 1.5)\n\n # Variational Bayes \n fit_vb = p$fishery_model$stancode$variational( data =p$fishery_model$standata, seed = 123, output_samples = 4000)\n fit_vb$summary(c(\"K\", \"r\", \"q\"))\n\n u = stan_extract( as_draws_df(fit_vb$draws() ) )\n\n bayesplot_grid(\n mcmc_hist(fit$draws(\"K\"), binwidth = 0.025),\n mcmc_hist(fit_vb$draws(\"K\"), binwidth = 0.025),\n titles = c(\"Posterior distribution from MCMC\", \"Approximate posterior from VB\")\n )\n\n color_scheme_set(\"gray\")\n mcmc_dens(fit$draws(\"K\"), facet_args = list(nrow = 3, labeller = ggplot2::label_parsed ) ) + facet_text(size = 14 ) \n # mcmc_hist( fit$draws(\"K\"))\n\n res = fishery_model( \n DS=\"logistic_model\", \n p=p, \n tag=p$areal_units_type,\n fit = fit_vb\n # from here down are params for cmdstanr::sample()\n )\n \n }\n\n\n\n fit = p$fishery_model$stancode$sample( \n data=p$fishery_model$standata, \n iter_warmup = 4000,\n iter_sampling = 2000,\n seed = 123,\n chains = 3,\n parallel_chains = 3, # The maximum number of MCMC chains to run in parallel.\n max_treedepth = 18,\n adapt_delta = 0.99,\n refresh = 500\n )\n\n # save fit and get draws\n res = fishery_model( \n DS=\"logistic_model\", \n p=p, \n tag=p$areal_units_type,\n fit = fit\n # from here down are params for cmdstanr::sample()\n )\n\n\n\n color_scheme_set(\"gray\")\n mcmc_dens(res$mcmc, regex_pars=\"K\", facet_args = list(nrow = 3, labeller = ggplot2::label_parsed ) ) + facet_text(size = 14 ) \n # mcmc_hist( fit$draws(\"K\"))\n\n\n # frequency density of key parameters\n fishery_model( DS=\"plot\", vname=\"K\", res=res )\n fishery_model( DS=\"plot\", vname=\"r\", res=res )\n fishery_model( DS=\"plot\", vname=\"q\", res=res, xrange=c(0.5, 3))\n fishery_model( DS=\"plot\", vname=\"FMSY\", res=res )\n # fishery_model( DS=\"plot\", vname=\"bosd\", res=res )\n # fishery_model( DS=\"plot\", vname=\"bpsd\", res=res )\n\n # timeseries\n fishery_model( DS=\"plot\", type=\"timeseries\", vname=\"biomass\", res=res )\n fishery_model( DS=\"plot\", type=\"timeseries\", vname=\"fishingmortality\", res=res)\n\n # Summary table of mean values for inclusion in document\n biomass.summary.table()\n\n # Harvest control rules\n fishery_model( DS=\"plot\", type=\"hcr\", vname=\"default\", res=res )\n fishery_model( DS=\"plot\", type=\"hcr\", vname=\"simple\", res=res )\n\n # diagnostics\n # fishery_model( DS=\"plot\", type=\"diagnostic.errors\", res=res )\n # fishery_model( DS=\"plot\", type=\"diagnostic.phase\", res=res )\n\n\n NN = res$p$fishery_model$standata$N\n\n # K\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$K[,i] ), main=\"\")\n ( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # R\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$r[,i] ), main=\"\")\n ( qs = apply( res$mcmc$r[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # q\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$q[,i] ), main=\"\")\n ( qs = apply( res$mcmc$q[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # FMSY\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$FMSY[,i] ), main=\"\")\n ( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n\n # densities of biomass estimates for the year.assessment\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$B[,NN,i] ), main=\"\")\n ( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # densities of biomass estimates for the previous year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$B[,NN-1,i] ), main=\"\")\n ( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # densities of F in assessment year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$F[,NN,i] ), xlim=c(0.01, 0.6), main=\"\")\n ( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n ( qs = apply( res$mcmc$F[,NN,], 2, mean ) )\n\n # densities of F in previous year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$F[,NN-1,i] ), xlim=c(0.01, 0.6), main=\"\")\n ( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n ( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )\n\n # F for table ---\n summary( res$mcmc$F, median)\n }\n\n\n# end\n", "meta": {"hexsha": "8a7208afb73cb46187d80b6af91ac3b88e7588ef", "size": 17460, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/03.abundance_estimation_carstm.r", "max_stars_repo_name": "jae0/bio.snowcrab", "max_stars_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/scripts/03.abundance_estimation_carstm.r", "max_issues_repo_name": "jae0/bio.snowcrab", "max_issues_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/scripts/03.abundance_estimation_carstm.r", "max_forks_repo_name": "jae0/bio.snowcrab", "max_forks_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 39.5918367347, "max_line_length": 135, "alphanum_fraction": 0.5581328751, "num_tokens": 5459, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7549149868676284, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.33342557127109673}} {"text": "context(\"Bounds\")\n\ntest_that(\"rescale_mid returns correct results\", {\n x <- c(-1, 0, 1)\n\n expect_equal(rescale_mid(x), c(0, 0.5, 1))\n expect_equal(rescale_mid(x, mid = -1), c(0.5, 0.75, 1))\n expect_equal(rescale_mid(x, mid = 1), c(0, 0.25, 0.5))\n\n expect_equal(rescale_mid(x, mid = 1, to = c(0, 10)), c(0, 2.5, 5))\n expect_equal(rescale_mid(x, mid = 1, to = c(8, 10)), c(8, 8.5, 9))\n\n expect_equal(rescale_mid(c(1, NA, 1)), c(0.5, NA, 0.5))\n})\n\ntest_that(\"rescale_max returns correct results\", {\n expect_equal(rescale_max(0), NaN)\n expect_equal(rescale_max(1), 1)\n expect_equal(rescale_max(.3), 1)\n expect_equal(rescale_max(c(4, 5)), c(0.8, 1.0))\n expect_equal(rescale_max(c(-3, 0, -1, 2)), c(-1.5, 0, -0.5, 1))\n expect_equal(rescale_max(c(-3, 0, -1, 2)), c(-1.5, 0, -0.5, 1))\n})\n\ntest_that(\"rescale functions handle NAs consistently\", {\n expect_equal(rescale(c(2, NA, 0, -2)), c(1, NA, 0.5, 0))\n expect_equal(rescale(c(-2, NA, -2)), c(.5, NA, .5))\n\n expect_equal(rescale_mid(c(NA, 1, 2)), c(NA, 0.75, 1))\n expect_equal(rescale_mid(c(2, NA, 0, -2), mid = .5), c(0.8, NA, 0.4, 0))\n expect_equal(rescale_mid(c(-2, NA, -2)), c(.5, NA, .5))\n\n expect_equal(rescale_max(c(1, NA)), c(1, NA))\n expect_equal(rescale_max(c(2, NA, 0, -2)), c(1, NA, 0, -1))\n expect_equal(rescale_max(c(-2, NA, -2)), c(1, NA, 1))\n})\n\ntest_that(\"zero range inputs return mid range\", {\n expect_that(rescale(0), equals(0.5))\n expect_that(rescale(c(0, 0)), equals(c(0.5, 0.5)))\n})\n\ntest_that(\"scaling is possible with dates and times\", {\n dates <- as.Date(c(\"2010-01-01\", \"2010-01-03\", \"2010-01-05\", \"2010-01-07\"))\n expect_equal(rescale(dates, from = c(dates[1], dates[4])), seq(0, 1, 1 / 3))\n expect_equal(rescale_mid(dates, mid = dates[3])[3], 0.5)\n\n dates <- as.POSIXct(c(\n \"2010-01-01 01:40:40\",\n \"2010-01-01 03:40:40\",\n \"2010-01-01 05:40:40\",\n \"2010-01-01 07:40:40\"\n ))\n expect_equal(rescale(dates, from = c(dates[1], dates[4])), seq(0, 1, 1 / 3))\n expect_equal(rescale_mid(dates, mid = dates[3])[3], 0.5)\n})\n\ntest_that(\"scaling is possible with integer64 data\", {\n skip_if_not_installed(\"bit64\")\n x <- bit64::as.integer64(2^60) + c(0:3)\n expect_equal(\n rescale_mid(x, mid = bit64::as.integer64(2^60) + 1),\n c(0.25, 0.5, 0.75, 1)\n )\n})\n\ntest_that(\"scaling is possible with NULL values\", {\n expect_null(rescale(NULL))\n expect_null(rescale_mid(NULL))\n})\n\ntest_that(\"scaling is possible with logical values\", {\n expect_equal(rescale(c(FALSE, TRUE)), c(0, 1))\n expect_equal(rescale_mid(c(FALSE, TRUE), mid = 0.5), c(0, 1))\n})\n\ntest_that(\"expand_range respects mul and add values\", {\n expect_equal(expand_range(c(1,1), mul = 0, add = 0.6), c(0.4, 1.6))\n expect_equal(expand_range(c(1,1), mul = 1, add = 0.6), c(-0.6, 2.6))\n expect_equal(expand_range(c(1,9), mul = 0, add = 2), c(-1, 11))\n})\n\ntest_that(\"out of bounds functions return correct values\", {\n x <- c(-Inf, -1, 0.5, 1, 2, NA, Inf)\n\n expect_equal(oob_censor(x), c(-Inf, NA, 0.5, 1, NA, NA, Inf))\n expect_equal(oob_censor_any(x), c(NA, NA, 0.5, 1, NA, NA, NA))\n expect_equal(oob_censor(x), censor(x))\n\n expect_equal(oob_squish(x), c(-Inf, 0, 0.5, 1, 1, NA, Inf))\n expect_equal(oob_squish_any(x), c(0, 0, 0.5, 1, 1, NA, 1))\n expect_equal(oob_squish_infinite(x), c(0, -1, 0.5, 1, 2, NA, 1))\n expect_equal(oob_squish(x), squish(x))\n\n expect_equal(oob_discard(x), c(0.5, 1, NA))\n expect_equal(oob_discard(x), discard(x))\n\n expect_equal(oob_keep(x), x)\n})\n", "meta": {"hexsha": "2b9498c03cdd995e259881cf67c94436fb084895", "size": 3446, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/4.0.2/scales/tests/testthat/test-bounds.r", "max_stars_repo_name": "rafaelortegar/R-sistema-de-recomendacion", "max_stars_repo_head_hexsha": "7e306ef74020d59f8707a178818e7abb3370e80f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/4.0.2/scales/tests/testthat/test-bounds.r", "max_issues_repo_name": "rafaelortegar/R-sistema-de-recomendacion", "max_issues_repo_head_hexsha": "7e306ef74020d59f8707a178818e7abb3370e80f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/4.0.2/scales/tests/testthat/test-bounds.r", "max_forks_repo_name": "rafaelortegar/R-sistema-de-recomendacion", "max_forks_repo_head_hexsha": "7e306ef74020d59f8707a178818e7abb3370e80f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.46, "max_line_length": 78, "alphanum_fraction": 0.6259431225, "num_tokens": 1405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526660244838, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3334214526167139}} {"text": "\nfiles_dirs = dir(path = \".\", full.names = FALSE, recursive = FALSE, all.files = FALSE, no.. = FALSE)\nhome_dir = getwd()\n\n\nabb_ban = files_dirs[1]\nasc_lon = files_dirs[2]\nabb_ban_path = paste(home_dir, \"/\", files_dirs[1], \"/\", \"grd\", sep = \"\")\nasc_lon_path = paste(home_dir, \"/\", files_dirs[2], \"/\", \"grd\", sep = \"\")\nabb_ban_tiff_path\n\ntest <- function(r_dir) {\n r <- raster(r_dir)\n r[r != 1] <- NA\n setwd()\n writeRaster(r, gsub(\".grd\", \".tif\", r_dir), format=\"GTiff\")\n setwd(r_dir)\n}\n\n# First pdw to raster dir\n# loop through the files in the dir for the convertion\n# in the convertion, set the output dir to tif folder\n# test(\"~/my_rasters\")\n\nr_dir = abb_ban_path\nr = raster(r_dir)", "meta": {"hexsha": "50085059540b37ded706695319e796a423f9ca86", "size": 688, "ext": "r", "lang": "R", "max_stars_repo_path": "static/r/raster_to_tiff.r", "max_stars_repo_name": "Thru-Echoes/PEARL2.0", "max_stars_repo_head_hexsha": "dbc62f8d77c6b846ab8fef3aa3afc03adc8175d4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-09-22T03:40:50.000Z", "max_stars_repo_stars_event_max_datetime": "2017-09-22T03:40:50.000Z", "max_issues_repo_path": "static/r/raster_to_tiff.r", "max_issues_repo_name": "Thru-Echoes/BirdShader", "max_issues_repo_head_hexsha": "cc7a694956e277a772152a3eec4a29db10ec7e6c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "static/r/raster_to_tiff.r", "max_forks_repo_name": "Thru-Echoes/BirdShader", "max_forks_repo_head_hexsha": "cc7a694956e277a772152a3eec4a29db10ec7e6c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-09-20T21:04:49.000Z", "max_forks_repo_forks_event_max_datetime": "2017-09-20T21:04:49.000Z", "avg_line_length": 26.4615384615, "max_line_length": 100, "alphanum_fraction": 0.6511627907, "num_tokens": 216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.33342144406819485}} {"text": "#' 2D contours of a 3D surface\n#'\n#' ggplot2 can not draw true 3D surfaces, but you can use `geom_contour()`,\n#' `geom_contour_filled()`, and [geom_tile()] to visualise 3D surfaces in 2D. To\n#' specify a valid surface, the data must contain `x`, `y`, and `z` coordinates,\n#' and each unique combination of `x` and `y` can appear at most once.\n#' Contouring requires that the points can be rearranged so that the `z` values\n#' form a matrix, with rows corresponding to unique `x` values, and columns\n#' corresponding to unique `y` values. Missing entries are allowed, but contouring\n#' will only be done on cells of the grid with all four `z` values present. If\n#' your data is irregular, you can interpolate to a grid before visualising\n#' using the [interp::interp()] function from the `interp` package\n#' (or one of the interpolating functions from the `akima` package.)\n#'\n#' @eval rd_aesthetics(\"geom\", \"contour\")\n#' @eval rd_aesthetics(\"geom\", \"contour_filled\")\n#' @inheritParams layer\n#' @inheritParams geom_point\n#' @inheritParams geom_path\n#' @param bins Number of contour bins. Overridden by `binwidth`.\n#' @param binwidth The width of the contour bins. Overridden by `breaks`.\n#' @param breaks Numeric vector to set the contour breaks. Overrides `binwidth`\n#' and `bins`. By default, this is a vector of length ten with [pretty()]\n#' breaks.\n#' @seealso [geom_density_2d()]: 2d density contours\n#' @export\n#' @examples\n#' # Basic plot\n#' v <- ggplot(faithfuld, aes(waiting, eruptions, z = density))\n#' v + geom_contour()\n#'\n#' # Or compute from raw data\n#' ggplot(faithful, aes(waiting, eruptions)) +\n#' geom_density_2d()\n#'\n#' \\donttest{\n#' # use geom_contour_filled() for filled contours\n#' v + geom_contour_filled()\n#'\n#' # Setting bins creates evenly spaced contours in the range of the data\n#' v + geom_contour(bins = 3)\n#' v + geom_contour(bins = 5)\n#'\n#' # Setting binwidth does the same thing, parameterised by the distance\n#' # between contours\n#' v + geom_contour(binwidth = 0.01)\n#' v + geom_contour(binwidth = 0.001)\n#'\n#' # Other parameters\n#' v + geom_contour(aes(colour = after_stat(level)))\n#' v + geom_contour(colour = \"red\")\n#' v + geom_raster(aes(fill = density)) +\n#' geom_contour(colour = \"white\")\n#'\n#' # Irregular data\n#' if (requireNamespace(\"interp\")) {\n#' # Use a dataset from the interp package\n#' data(franke, package = \"interp\")\n#' origdata <- as.data.frame(interp::franke.data(1, 1, franke))\n#' grid <- with(origdata, interp::interp(x, y, z))\n#' griddf <- subset(data.frame(x = rep(grid$x, nrow(grid$z)),\n#' y = rep(grid$y, each = ncol(grid$z)),\n#' z = as.numeric(grid$z)),\n#' !is.na(z))\n#' ggplot(griddf, aes(x, y, z = z)) +\n#' geom_contour_filled() +\n#' geom_point(data = origdata)\n#' } else\n#' message(\"Irregular data requires the 'interp' package\")\n#' }\ngeom_contour <- function(mapping = NULL, data = NULL,\n stat = \"contour\", position = \"identity\",\n ...,\n bins = NULL,\n binwidth = NULL,\n breaks = NULL,\n lineend = \"butt\",\n linejoin = \"round\",\n linemitre = 10,\n na.rm = FALSE,\n show.legend = NA,\n inherit.aes = TRUE) {\n layer(\n data = data,\n mapping = mapping,\n stat = stat,\n geom = GeomContour,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n bins = bins,\n binwidth = binwidth,\n breaks = breaks,\n lineend = lineend,\n linejoin = linejoin,\n linemitre = linemitre,\n na.rm = na.rm,\n ...\n )\n )\n}\n\n#' @rdname geom_contour\n#' @export\ngeom_contour_filled <- function(mapping = NULL, data = NULL,\n stat = \"contour_filled\", position = \"identity\",\n ...,\n bins = NULL,\n binwidth = NULL,\n breaks = NULL,\n na.rm = FALSE,\n show.legend = NA,\n inherit.aes = TRUE) {\n layer(\n data = data,\n mapping = mapping,\n stat = stat,\n geom = GeomContourFilled,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n bins = bins,\n binwidth = binwidth,\n breaks = breaks,\n na.rm = na.rm,\n ...\n )\n )\n}\n\n#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\n#' @include geom-path.r\nGeomContour <- ggproto(\"GeomContour\", GeomPath,\n default_aes = aes(\n weight = 1,\n colour = \"#3366FF\",\n size = 0.5,\n linetype = 1,\n alpha = NA\n )\n)\n\n#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\n#' @include geom-polygon.r\nGeomContourFilled <- ggproto(\"GeomContourFilled\", GeomPolygon)\n\n", "meta": {"hexsha": "cc25a70da476e74f88952e30fce5b2ae15232ce1", "size": 5064, "ext": "r", "lang": "R", "max_stars_repo_path": "R/geom-contour.r", "max_stars_repo_name": "alperoglu/ggplot2", "max_stars_repo_head_hexsha": "f5e01baec86469762a707e2ca8354378d0d31ab1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3746, "max_stars_repo_stars_event_min_datetime": "2016-10-31T17:39:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T02:50:11.000Z", "max_issues_repo_path": "R/geom-contour.r", "max_issues_repo_name": "alperoglu/ggplot2", "max_issues_repo_head_hexsha": "f5e01baec86469762a707e2ca8354378d0d31ab1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3277, "max_issues_repo_issues_event_min_datetime": "2016-11-01T19:23:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:44:20.000Z", "max_forks_repo_path": "R/geom-contour.r", "max_forks_repo_name": "alperoglu/ggplot2", "max_forks_repo_head_hexsha": "f5e01baec86469762a707e2ca8354378d0d31ab1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1693, "max_forks_repo_forks_event_min_datetime": "2016-11-02T07:26:55.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T01:51:19.000Z", "avg_line_length": 32.6709677419, "max_line_length": 82, "alphanum_fraction": 0.5744470774, "num_tokens": 1313, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.5698526514141572, "lm_q1q2_score": 0.3334214358227165}} {"text": "\\name{hc_polygon-HilbertCurve-method}\n\\alias{hc_polygon,HilbertCurve-method}\n\\title{\nAdd polygons to Hilbert curve\n}\n\\description{\nAdd polygons to Hilbert curve\n}\n\\usage{\n\\S4method{hc_polygon}{HilbertCurve}(object, ir = NULL, x1 = NULL, x2 = NULL,\n gp = gpar(), end_type = c(\"expanding\", \"average\", \"shrinking\"))\n}\n\\arguments{\n\n \\item{object}{a \\code{\\link{HilbertCurve-class}} object.}\n \\item{ir}{an \\code{\\link[IRanges:IRanges-constructor]{IRanges}} object which specifies the input intervals.}\n \\item{x1}{if start positions are not integers, they can be set by \\code{x1}.}\n \\item{x2}{if end positions are not integers, they can be set by \\code{x2}.}\n \\item{gp}{graphic parameters. It should be specified by \\code{\\link[grid]{gpar}}.}\n \\item{end_type}{since two ends of a continuous interval do not necessarily completely overlap with the Hilbert curve segments, this argument controls how to determine the ends of the interval which will be presented on the curve. \\code{average}: if the end covers more than half of the segment, the whole segment is included and if the end covers less than half of the segment, the segment is removed; \\code{expanding}: segments are included as long as they are overlapped; \\code{shrinking}: segments are removed if they are not completely covered.}\n\n}\n\\details{\nDrawing polygons are quite visually similar as drawing rectangles.\nThe major differences are: 1) for rectangles, colors for the ends of the interval can change if they are not completely\ncovered by the Hilbert curve segments, and 2) polygons can have borders.\n\nNormally polygons are used to mark areas in the Hilbert curve.\n}\n\\value{\nNo value is returned.\n}\n\\author{\nZuguang Gu \n}\n\\examples{\nrequire(IRanges)\nir = IRanges(10, 40)\n\nhc = HilbertCurve(0, 100, level = 4, reference = TRUE)\nhc_segments(hc, ir)\nhc_text(hc, x1 = 10:40, labels = 10:40)\nhc_polygon(hc, ir, gp = gpar(fill = \"#FF000080\", col = 1))\n}\n", "meta": {"hexsha": "ad374b9d73938d359c9b3a6664f5c7def63f8984", "size": 1933, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/hc_polygon-HilbertCurve-method.rd", "max_stars_repo_name": "jokergoo/HilbertCurve", "max_stars_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 37, "max_stars_repo_stars_event_min_datetime": "2016-02-22T16:46:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T09:35:43.000Z", "max_issues_repo_path": "man/hc_polygon-HilbertCurve-method.rd", "max_issues_repo_name": "jokergoo/HilbertCurve", "max_issues_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T08:29:21.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T09:44:53.000Z", "max_forks_repo_path": "man/hc_polygon-HilbertCurve-method.rd", "max_forks_repo_name": "jokergoo/HilbertCurve", "max_forks_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-04-22T10:44:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T07:48:16.000Z", "avg_line_length": 42.9555555556, "max_line_length": 553, "alphanum_fraction": 0.7444386963, "num_tokens": 531, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3333221631901137}} {"text": "# For use with baseballdatawrangling2.r\r\n\r\nmurray <- bavg %>%\r\n filter(batterid=='murre001') %>%\r\n select(H,AB,RDate) %>%\r\n arrange(RDate) %>%\r\n mutate(Total_H=cumsum(H), Total_AB=cumsum(AB)) %>%\r\n mutate(AVG = round((Total_H / Total_AB),3)) %>%\r\n mutate(player='Eddie Murray')", "meta": {"hexsha": "7d4f8c2bf37f628688f96b6afface2315a05f726", "size": 283, "ext": "r", "lang": "R", "max_stars_repo_path": "murray.r", "max_stars_repo_name": "KT12/R", "max_stars_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "murray.r", "max_issues_repo_name": "KT12/R", "max_issues_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "murray.r", "max_forks_repo_name": "KT12/R", "max_forks_repo_head_hexsha": "d7aa803eab845b79f8eee5812ece31c76d665419", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4444444444, "max_line_length": 53, "alphanum_fraction": 0.6360424028, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548782017746, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3333091067309073}} {"text": "#!/usr/bin/Rscript\n\nrequire(scales)\nrequire(ggplot2)\nrequire(grid)\nrequire(png)\nrequire(gridExtra)\nrequire(reshape2)\n\n\nargs <- commandArgs(TRUE)\nonCMDLine <- !is.na(args[1])\n\nif ( onCMDLine ) {\n file <- args[1]\n sample_id <- args[2]\n gene <- args[3]\n}\n\ncnv = read.csv(file,sep=\"\\t\",header=T,check.names=FALSE)\n\n\naffected_samples = c(sample_id)\n\nstd_columns = c(\"#chr\",\"start\",\"end\")\n\naffected <- melt(subset(cnv, select=names(cnv) %in% c(std_columns,affected_samples)),id=std_columns)\n\nmelted_all <- melt(cnv,id=std_columns)\nplot.title = sprintf(\"%s\", gene)\nfile.out = sprintf(\"%s.pdf\", sample_id)\n\nq <- ggplot(melted_all,aes(x=start/1000,y=exp(value))) + geom_line(aes(group = variable),color=\"lightgrey\")\nq <- q + geom_line(data=affected,aes(x=start/1000,y=exp(value),group = variable),color=\"blue\")\nq <- q + geom_point(data=affected,aes(x=start/1000,y=exp(value),group = variable),color=\"black\") + theme_bw()\nq <- q + scale_y_continuous(\"Estimated Copy Number\", limits=c(0,4)) + scale_x_continuous(\"Position (kb)\")\nq <- q + ggtitle(plot.title)\n\npdf(file.out)\nprint(q)\ndev.off()\n", "meta": {"hexsha": "b3b1bc547a122dbd4041c204f93c2c3e9bf7560f", "size": 1084, "ext": "r", "lang": "R", "max_stars_repo_path": "cnv/plot_pdf.r", "max_stars_repo_name": "macarthur-lab/MyoSeq_reports", "max_stars_repo_head_hexsha": "e91ed57f01fcbf2e0ea982d95741a69b16c4c2fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cnv/plot_pdf.r", "max_issues_repo_name": "macarthur-lab/MyoSeq_reports", "max_issues_repo_head_hexsha": "e91ed57f01fcbf2e0ea982d95741a69b16c4c2fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cnv/plot_pdf.r", "max_forks_repo_name": "macarthur-lab/MyoSeq_reports", "max_forks_repo_head_hexsha": "e91ed57f01fcbf2e0ea982d95741a69b16c4c2fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8095238095, "max_line_length": 109, "alphanum_fraction": 0.6964944649, "num_tokens": 321, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33330909980445445}} {"text": "source('constants.r')\nsource('backtest_lib.r')\nsource('data_cleaning.r')\nsource('strategy_hmm.r')\n#source('strategy_ipr.R')\nsource('new metrics file.R')\n#install.packages(\"tseries\")\nlibrary(XLConnect)\n#library(knitr)\n#install.packages(\"regpro\")\nlibrary(\"regpro\")\nlibrary(MASS)\n#install.packages(\"forecast\")\nlibrary(\"forecast\")\n\n# notes 2015-11-20\n\n#1. ETL (cleaning, organizing section), factor in for missing data, outliers, etc. \n# -> think of the opportunistic time intervals to trade (don't decide arbritarily, decide based on\n# intelligence)\n# -> separate the strategy from the market module in the flow chart \n# -> market sends back fills and acknowledgements (assume we don't need this)\n# -> strategy will act only upon \"fill\" (may not need to do this) and \"timer\"\n# -> market reacts to order,replace,cancel and market data \n# -> change active and passive to fill and timer \n# -> highlight strategy with more detail \n\n#knitr::spin\n\n\ninit_cash = 100000\n#global_variables: position matrices, trade matrices, ourdata, order book\n\n#order format: msgtype, symbol, price, quantity, side, ordtype, orderID, time\n#execution message format: orderID, Execstatus, symbol, quantity, avg price, side, time\n#Execstatus can be the following: filled, replaced, cancelled, replacereject?, cancelreject\n\n#order book format: orderID, time, symbol, price, quantity, side, ordtype\n#trade matrix format: time, symbol, side, quantity, price, open/close, pnl\n#position matrix: time, asset, #of shares, book value, market value,\nglobal_tables = new.env()\n\nglobal_tables[[Con_GlobalVarName_LOB]]<- data.frame(matrix(0, 0, length(orderbook_spec)))\ncolnames(global_tables[[Con_GlobalVarName_LOB]]) <- orderbook_spec\n\n#the position book is a list of data frames\ninit_pos <- data.frame(matrix(0, 1, length(positionbook_spec)))\ncolnames(init_pos) <- positionbook_spec\ninit_pos[,Con_FieldName_Sym] = Con_Sym_Cash\ninit_pos[,c(Con_FieldName_Qty, Con_FieldName_BookVal, Con_FieldName_MktVal)] = init_cash\n\nglobal_tables[[Con_GlobalVarName_PositionBook]] <- list(init_pos)\nnames(global_tables[[Con_GlobalVarName_PositionBook]])[1] = 0\n\nglobal_tables[[Con_GlobalVarName_TradesBook]] <- data.frame(matrix(0, 0, length(tradesbook_spec)))\ncolnames(global_tables[[Con_GlobalVarName_TradesBook]]) <- tradesbook_spec\n\nglobal_tables[[Con_GlobalVarName_ListDates]] <- list(vector())\n\n\nimport_data(global_tables, c('BNS', 'BMO', 'AC'))\n\n#strategy_impliedpricerisk(c(\"CPD\", \"SU\", \"ABX\"), global_tables, 780, 780, 780)\n#system.time({test_results <- test_HMMM(global_tables, c('BMO'), 30, 3)})\n\ntest_results <- test_HMMM(global_tables, c('BMO'), 30, 3)\n\n#result<-sum(comparison[!is.na(comparison)]) / (nrow(comparison) * ncol(comparison))\n temp_ind_2 <- !is.na(global_tables$tradesbook[[\"PnL\"]])\n pnl_pertrade <- global_tables$tradesbook[[\"PnL\"]][temp_ind_2]\n eod_vals <- data.matrix(test_results[[\"eod_results\"]][[\"eod_value\"]])\n mu <- data.matrix(test_results[[\"mu\"]])\n A <- data.matrix(test_results[[\"A\"]])\n cov_matrix <- data.matrix(test_results[[\"cov_matrix\"]])\n accuracy <- data.matrix(test_results[[\"accuracy\"]])\n# plot(eod_vals)\n\noutput <- output(global_tables$tradesbook, global_tables$positionbook, global_tables$SPX_ask)\n\n\n", "meta": {"hexsha": "8ddc965d2a891408b7116f8e272ec52a036d056e", "size": 3192, "ext": "r", "lang": "R", "max_stars_repo_path": "main.r", "max_stars_repo_name": "yuwu10112358/APS490RBCCM", "max_stars_repo_head_hexsha": "391617f719a5099b302df34406a7c1dffc40babb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "main.r", "max_issues_repo_name": "yuwu10112358/APS490RBCCM", "max_issues_repo_head_hexsha": "391617f719a5099b302df34406a7c1dffc40babb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "main.r", "max_forks_repo_name": "yuwu10112358/APS490RBCCM", "max_forks_repo_head_hexsha": "391617f719a5099b302df34406a7c1dffc40babb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4074074074, "max_line_length": 98, "alphanum_fraction": 0.7556390977, "num_tokens": 871, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33330909980445445}} {"text": "library(shiny)\nlibrary(plotly)\n\nrenderInputs <- function(prefix) {\n wellPanel(\n fluidRow(\n column(3,\n sliderInput(paste0(prefix, \"_\", \"ngen\"), \"Number of generations:\", min = 10, max = 100, value = 100,step = 20),\n sliderInput(paste0(prefix, \"_\", \"nloci\"), \"Number of loci:\", min = 50, max = 200, value = 50, step = 25),\n sliderInput(paste0(prefix, \"_\", \"nrep\"), \"Number of repetitions:\", min = 1, max = 100, value = 50, step = 10)\n ),\n column(3,\n sliderInput(paste0(prefix, \"_\", \"nploidy\"), \"Number of ploidy:\", min = 2, max = 6, value = 2, step = 2),\n sliderInput(paste0(prefix, \"_\", \"ninit0\"), \"Number of wildtype individuals:\", min = 10, max = 100, value = 50, step = 10),\n sliderInput(paste0(prefix, \"_\", \"ninit1\"), \"Number of rescuetype individuals:\", min = 1, max = 100, value = 5)\n ),\n column(3,\n sliderInput(paste0(prefix, \"_\", \"k\"), \"Carrying capacity of the population:\", min = 10, max = 150, value = 100, step = 20),\n sliderInput(paste0(prefix, \"_\", \"r\"), \"Intrinsic growthrate of the population:\", min = 0.0, max = 0.5, value = 0.1, step = 0.05),\n sliderInput(paste0(prefix, \"_\", \"distlocal\"), \"Distance of locally adapted locus from major locus:\", min = 1, max = 10, value = 1)\n ),\n column(3,\n sliderInput(paste0(prefix, \"_\", \"scmajor\"), \"Major locus additive fitness:\", min = 0.0, max = 0.5, value = 0.05, step = 0.05),\n sliderInput(paste0(prefix, \"_\", \"sclocal\"), \"Locally adapted locus additive fitness:\", min = -0.1, max = 0.0, value = -0.1, step = 0.01),\n sliderInput(paste0(prefix, \"_\", \"rec\"), \"Recombination rate:\", min = 0.0, max = 0.5, value = 0.5, step = 0.05)\n )\n ),\n p(actionButton(paste0(prefix, \"_\", \"recalc\"),\n \"Run simulation\", icon(\"random\")\n ))\n )\n}\n\n# Define UI for application that plots random distributions\nfluidPage(theme=\"simplex.min.css\",\n tags$style(type=\"text/css\",\n \"label {font-size: 12px;}\",\n \".recalculating {opacity: 1.0;}\"\n ),\n\n # Application title\n tags$h2(\"Evolutinar rescue due to introgressive hybridization\"),\n p(\"Simulations implemented in c++\"),\n hr(),\n\n fluidRow(\n column(6,\n renderInputs(\"a\")\n ),\n column(3,\n plotOutput(\"a_PopulationPlot\")\n ),\n column(3,\n plotOutput(\"a_IntrogressionPlot\")\n )\n ),\n fluidRow(\n textOutput(\"fixation_output\")\n ),\n fluidRow(\n plotlyOutput(\"a_lociPlot\")\n )\n)", "meta": {"hexsha": "8d1a1a2ea6ddf5362fd964f569ae2e275cede7a6", "size": 2455, "ext": "r", "lang": "R", "max_stars_repo_path": "Rfiles/ShinyApp/ui.r", "max_stars_repo_name": "freekdh/Introgression", "max_stars_repo_head_hexsha": "f26c7b84efee64ec5bb4e662753e052195b7fc71", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rfiles/ShinyApp/ui.r", "max_issues_repo_name": "freekdh/Introgression", "max_issues_repo_head_hexsha": "f26c7b84efee64ec5bb4e662753e052195b7fc71", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-08-01T23:29:56.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-20T23:35:56.000Z", "max_forks_repo_path": "pkgIntrogression/R/ui.r", "max_forks_repo_name": "freekdh/Introgression", "max_forks_repo_head_hexsha": "f26c7b84efee64ec5bb4e662753e052195b7fc71", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9682539683, "max_line_length": 145, "alphanum_fraction": 0.5963340122, "num_tokens": 770, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3333090998044544}} {"text": "p <- ifelse(train$Sex == \"female\", 1,0)\n\ntrain\nView(train)\n##verificar com calma a Pclass\ntest$Pclass = as.factor(test$Pclass)\n\nnew_train <- train\nnew2_train <- new_train\nnew2_train\n\ntest$Age <- ifelse(is.na(test$Age),sample(0:80,1),test$Age)\n\nnew2_train$Embarekd <- ifelse(is.na(new2_train$Embarked),\"S\",new2_train$Embarked)\n\n\nnew2_train$Embarked <- ifelse(is.na(new2_train$Embarked),\"S\",new2_train$Embarked)\n\nView(new2_train)\n\nnew2_train$Age <- ifelse(is.na(new2_train$Age),sample(0:80,1),new2_train$Age)\nnew2_train$Sex <- as.numeric(new2_train$Sex)\nnew2_train$Survived <- as.numeric(new2_train$Survived)\nnew2_train$Embarked\n\nnew3_train <- new2_train\n\nis.na(new3_train$Embarked)\n\nnew2_train <- train\n\nView(new2_train)\n\nsummary(new_train)\nsd(new_train$age~Pclass)\nsummary(new2_train)\nnew_train <- train\n\nhist(new_train$Age)\nboxplot(new_train$Age) \nlm(new_train$Age~new_train$Survived)\n\n?numeric \nas.numeric(3.2)\nclear()\n", "meta": {"hexsha": "70ec2f5d16437366e17b79903b23519af78baef3", "size": 944, "ext": "r", "lang": "R", "max_stars_repo_path": "curacity.r", "max_stars_repo_name": "breno-sapucaia/shift-big-data-science", "max_stars_repo_head_hexsha": "e219917d41d937635f7767b74d417621c5b0da23", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-03-09T23:05:35.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-09T23:05:35.000Z", "max_issues_repo_path": "curacity.r", "max_issues_repo_name": "breno-sapucaia/shift-big-data-science", "max_issues_repo_head_hexsha": "e219917d41d937635f7767b74d417621c5b0da23", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "curacity.r", "max_forks_repo_name": "breno-sapucaia/shift-big-data-science", "max_forks_repo_head_hexsha": "e219917d41d937635f7767b74d417621c5b0da23", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.5217391304, "max_line_length": 81, "alphanum_fraction": 0.7425847458, "num_tokens": 307, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.3333090998044543}} {"text": "library(jsonlite)\nlibrary(ggplot2)\nlibrary(zoo) \nlibrary(readr)\n\nlibrary(colorspace)\nlibrary(gridExtra)\n\nlibrary(plotly)\nlibrary(htmlwidgets)\n\nlibrary(sf)\n\nlibrary(lubridate)\nlibrary(plyr)\n\nlibrary(dplyr)\n\nlibrary(tidyr)\nlibrary(lubridate)\n\nlibrary(reshape2)\nlibrary(readr)\n\n# get last_date_\nload('data/latest/last_date_.Rda')\n\n#https://www.hzjz.hr/aktualnosti/covid-19-izvjesce-hzjz-a/\nvacc_data <- read.csv(file = 'data/vaccination/cro_vaccination_per_county_2021_12_26.csv')\nvacc_date <- \"26.12.2021.\"\n\nfirst_dose_reordered <- as.numeric(vacc_data[1, 2:22])\nfully_vacc_reordered <- as.numeric(vacc_data[2, 2:22])\n\nfirst_dose_reordered\nfully_vacc_reordered\n\nhr <- st_read(dsn = \"data/Official_Croatia_Boundaries/CROATIA_HR_\u017dupanije_ADMIN1.shp\")\n\nvacc_data\n\nhr <- hr[order(hr$ZUP_IME),]\nhr$Zupanija <- hr$ZUP_IME\n\nhr$ZUP_IME\n\nhr <-cbind(hr, first_dose_reordered, fully_vacc_reordered)\n\nhr_map <- ggplot(hr, aes(text = paste(\"\u017dupanija: \", Zupanija, \"
\", \"Prva doza: \", first_dose_reordered, \"%
\",\n \"Zavr\u0161eno cijepljenje: \", fully_vacc_reordered, \"%
\",\n sep=\"\"))) +\n ggtitle(paste(\"COVID 19 u Hrvatskoj: Procijepljenost po \u017eupanijama (\", vacc_date, \")\", sep=\"\")) +\n geom_sf(aes_string(fill = 'fully_vacc_reordered')) +\n scale_fill_distiller(palette = \"RdYlGn\", direction=1, limits = c(0, 100), oob = scales::squish, name='Postotak\\nprocijepljenosti') +\n geom_sf_text(aes(label=paste(round(fully_vacc_reordered, digits= 0), \"%\", sep=\"\")), fontface=\"bold\", size=5, color=\"black\") +\n theme(legend.position = \"bottom\") +\n theme_void() +\n labs(caption = paste('Postotak procijepljenosti po \u017eupanijama (s obje doze) u odnosu na ukupno stanovni\u0161tvo \u017eupanije.\\n\\nGenerirano: ', format(Sys.time() + as.difftime(1, units=\"hours\"), '%d.%m.%Y. %H:%M:%S h'), ', izvor podataka: hzjz.hr, autor: Petar Pala\u0161ek', sep='')) +\n theme(plot.caption = element_text(hjust = 0))\n\nhr_map\n\n\nggsave(paste('img/', last_date_, '_vaccination.png', sep = ''), plot = hr_map, dpi=300, width=309.80, height=215.90, units=\"mm\")\n\nhr_map_p <- ggplotly(hr_map, tooltip = c(\"text\"))\n\nhr_map_p\n\nprint(last_date_)\n\nsaveWidget(hr_map_p, file = \"html/index_vaccination.html\", title = paste(\"COVID 19 u Hrvatskoj: Postotak procijepljenosti po \u017eupanijama (\", vacc_date, \")\", sep=\"\"))\n\n\n", "meta": {"hexsha": "dc1660531b5586dc2476ce7796c62ce8f2a4e672", "size": 2332, "ext": "r", "lang": "R", "max_stars_repo_path": "generate_vacc_map.r", "max_stars_repo_name": "ppalasek/covid_plots_croatia", "max_stars_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate_vacc_map.r", "max_issues_repo_name": "ppalasek/covid_plots_croatia", "max_issues_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate_vacc_map.r", "max_forks_repo_name": "ppalasek/covid_plots_croatia", "max_forks_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.0933333333, "max_line_length": 275, "alphanum_fraction": 0.7011149228, "num_tokens": 774, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6513548511303338, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3333090928780015}} {"text": "\n#install_keras(tensorflow=\"1.4.1-gpu\")\n\nlibrary(keras)\nuse_backend(\"tensorflow\")\n#use_backend(\"theano\")\n\n#mnist <- dataset_mnist()\nmnist <- dataset_fashion_mnist()\n\n\nx_train <- mnist$train$x\ny_train <- mnist$train$y\nx_test <- mnist$test$x\ny_test <- mnist$test$y\n\n# reshape\nx_train <- array_reshape(x_train, c(nrow(x_train), 784))\nx_test <- array_reshape(x_test, c(nrow(x_test), 784))\n# rescale\nx_train <- x_train / 255\nx_test <- x_test / 255\n\ny_train <- to_categorical(y_train, 10)\ny_test <- to_categorical(y_test, 10)\n\n\n\nmodel <- keras_model_sequential() \nmodel %>% \n layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% \n layer_dropout(rate = 0.4) %>% \n layer_dense(units = 128, activation = 'relu') %>%\n layer_dropout(rate = 0.3) %>%\n layer_dense(units = 10, activation = 'softmax')\n\nsummary(model)\n\nmodel %>% compile(\n loss = 'categorical_crossentropy',\n optimizer = optimizer_rmsprop(),\n #optimizer = optimizer_sgd(momentum=0.9, lr=0.001),\n metrics = c('accuracy')\n)\n\n\nset.seed(1234)\nhistory <- model %>% fit(\n x_train, y_train, \n epochs = 20, batch_size = 500, \n validation_split = 0.5\n)\n\nplot(history)\n\n\nmmodel <- model %>% multi_gpu_model(gpus = NULL)\n\nmmodel %>% compile(\n loss = 'categorical_crossentropy',\n optimizer = optimizer_rmsprop(),\n metrics = c('accuracy')\n)\n\n\n\n\n\nhistory <- mmodel %>% fit(\n x_train, y_train, \n epochs = 50, batch_size = 5000, \n validation_split = 0.2\n)\n\nplot(history)\n\n\nmodel %>% evaluate(x_test, y_test)\n\nmodel %>% predict_classes(x_test)\n\nrm(mnist)\n\nrm(list=ls())\n\ncifar10 <-dataset_cifar10()\nb <- dataset_cifar100()\n\nc <- dataset_fashion_mnist()\n\nstr(c)\n\nstr(cifar10)\n", "meta": {"hexsha": "7006c47055e646c8d5f2c9a9b1e07283e5dbe796", "size": 1646, "ext": "r", "lang": "R", "max_stars_repo_path": "MXNet/training/oldExp/Keras.r", "max_stars_repo_name": "agutier2/CNN-Examples", "max_stars_repo_head_hexsha": "a9e10188c0e1052ead347f62f5240ade66af623b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "MXNet/training/oldExp/Keras.r", "max_issues_repo_name": "agutier2/CNN-Examples", "max_issues_repo_head_hexsha": "a9e10188c0e1052ead347f62f5240ade66af623b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2018-01-16T15:39:43.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-31T14:40:05.000Z", "max_forks_repo_path": "MXNet/training/oldExp/Keras.r", "max_forks_repo_name": "Fubukimaru/CNN-Examples", "max_forks_repo_head_hexsha": "a9e10188c0e1052ead347f62f5240ade66af623b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.5106382979, "max_line_length": 74, "alphanum_fraction": 0.6822600243, "num_tokens": 499, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3332894268844693}} {"text": "\n################################################################################\ncontext(\"Test wrapper function (calcEF)\")\n################################################################################\n\n #calcEF==================================================================\n\n#Main\ntest_that(\"calcEF EF Baseline works for Main Engines\",\n {\n #create Benchmark\n testEFBaseline<-data.table::data.table(\n engineType=c(\"SSD\",\"MSD\",\"ST\",\"GT\",\"LNG\"),\n location=c(\"OutsideECA\",\"ECA\",\"GreatLakes\",\"ECA\",\"OutsideECA\"),\n tier=c(\"Tier 2\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\")\n )\n\n testEFBaseline[,hc:=calcEF_HC(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFBaseline[,co2:=calcEF_CO2(engineType = engineType,\n location = location,\n main_aux_boiler = \"main\")\n ]\n\n testEFBaseline[,co:=calcEF_CO(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFBaseline[,nox:=calcEF_NOx(engineType = engineType,\n location = location,\n tier = tier,\n main_aux_boiler = \"main\")\n ]\n\n testEFBaseline[,so2:=calcEF_SO2(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"main\")\n ]\n\n testEFBaseline[,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"main\"),\n ]\n\n testEFBaseline[,pm10:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize=\"pm10\",\n main_aux_boiler = \"main\")\n ]\n\n #Run Calculation\n calcEF_out<-data.table(\n calcEF(engineType = testEFBaseline$engineType,\n location = testEFBaseline$location,\n tier = testEFBaseline$tier,\n pollutants = \"ALL\",\n loadBasedBSFC = \"N\",\n output=\"EF\",\n main_aux_boiler = \"main\")\n )\n\n #Compare output against benchmark\n expect_equal(calcEF_out$hc,\n testEFBaseline$hc\n )\n\n expect_equal(calcEF_out$co2,\n testEFBaseline$co2\n )\n\n expect_equal(calcEF_out$co,\n testEFBaseline$co\n )\n\n expect_equal(calcEF_out$nox,\n testEFBaseline$nox\n )\n\n expect_equal(calcEF_out$pm2.5,\n testEFBaseline$pm2.5\n )\n\n expect_equal(calcEF_out$so2,\n testEFBaseline$so2\n )\n\n expect_equal(calcEF_out$pm10,\n testEFBaseline$pm10\n )\n\n }\n )\n\n\ntest_that(\"calcEF EF Load Based works for Main Engines\",\n {\n #create Benchmark\n testEFLoad<-data.table::data.table(\n engineType=c(\"SSD\",\"MSD\",\"ST\",\"GT\",\"LNG\"),\n location=c(\"OutsideECA\",\"GreatLakes\",\"ECA\",\"GreatLakes\",\"ECA\"),\n tier=c(\"Tier 1\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\"),\n loadFactor=c(0.284596685,0.521665062,0.766855145,0.078203076,0.708915957249701)\n )\n\n testEFLoad[,hc:=calcEF_HC(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,co2:=calcEF_CO2(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,co:=calcEF_CO(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,nox:=calcEF_NOx(engineType = engineType,\n location = location,\n tier = tier,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,so2:=calcEF_SO2(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,pm10:=calcEF_PM(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm10\",\n main_aux_boiler = \"main\")\n ]\n\n #Run Calculation\n EF_LLAF_out<-calcEF(engineType = testEFLoad$engineType,\n location = testEFLoad$location,\n tier = testEFLoad$tier,\n loadFactor = testEFLoad$loadFactor,\n loadBasedBSFC = \"Y\",\n pollutants = \"ALL\",\n output=\"EF\",\n main_aux_boiler = \"main\")\n\n #Compare output against benchmark\n expect_equal(EF_LLAF_out$hc,\n testEFLoad$hc\n )\n\n expect_equal(EF_LLAF_out$co2,\n testEFLoad$co2\n )\n\n expect_equal(EF_LLAF_out$co,\n testEFLoad$co\n )\n\n expect_equal(EF_LLAF_out$nox,\n testEFLoad$nox\n )\n\n expect_equal(EF_LLAF_out$pm2.5,\n testEFLoad$pm2.5\n )\n\n expect_equal(EF_LLAF_out$pm10,\n testEFLoad$pm10\n )\n\n expect_equal(EF_LLAF_out$so2,\n testEFLoad$so2\n )\n\n }\n )\n\ntest_that(\"calcEF EF Load range works for Main Engines\",\n {\n testEFLoad<-data.table::data.table(\n engineType=c(\"SSD\",\"MSD\",\"ST\",\"GT\",\"LNG\"),\n location=c(\"OutsideECA\",\"GreatLakes\",\"ECA\",\"GreatLakes\",\"ECA\"),\n tier=c(\"Tier 1\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\"),\n loadFactor=c(0.078203076,0.284596685,0.521665062,0.708915957249701,0.766855145),\n loadRange = \"0.0,0.5\"\n )\n\n testEFLoad[,hc:=calcEF_HC(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,#loadFactor>=0.5,\n co2:=calcEF_CO2(engineType = engineType,\n location = location,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor<0.5,\n co2:=calcEF_CO2(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,co:=calcEF_CO(engineType = engineType,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[,nox:=calcEF_NOx(engineType = engineType,\n location = location,\n tier = tier,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor>=0.5\n ,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor<0.5\n ,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor >= 0.5,\n so2:=calcEF_SO2(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor < 0.5\n ,so2:=calcEF_SO2(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor >=0.5\n ,pm10:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm10\",\n main_aux_boiler = \"main\")\n ]\n\n testEFLoad[loadFactor <0.5,\n pm10:=calcEF_PM(engineType = engineType,\n location = location,\n loadFactor = loadFactor,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm10\",\n main_aux_boiler = \"main\")\n ]\n\n #Run Calculation\n EF_LLAF_out<-calcEF(engineType = testEFLoad$engineType,\n location = testEFLoad$location,\n tier = testEFLoad$tier,\n loadFactor = testEFLoad$loadFactor,\n pollutants = \"ALL\",\n loadBasedBSFC = testEFLoad$loadRange[1],\n output=\"EF\",\n main_aux_boiler = \"main\")\n\n #Compare output against benchmark\n expect_equal(EF_LLAF_out$hc,\n testEFLoad$hc\n )\n\n expect_equal(EF_LLAF_out$co2,\n testEFLoad$co2\n )\n\n expect_equal(EF_LLAF_out$co,\n testEFLoad$co\n )\n\n expect_equal(EF_LLAF_out$nox,\n testEFLoad$nox\n )\n\n expect_equal(EF_LLAF_out$pm2.5,\n testEFLoad$pm2.5\n )\n\n expect_equal(EF_LLAF_out$pm10,\n testEFLoad$pm10\n )\n\n expect_equal(EF_LLAF_out$so2,\n testEFLoad$so2\n )\n }\n )\n\ntest_that(\"calcEF EF Load LLAF works\",\n {\n #create Benchmark\n testEFLoadLLAF<-data.table::data.table(\n engineType=c(\"SSD\", \"MSD\", \"ST\", \"GT\", \"LNG\"),\n location=c(\"OutsideECA\", \"GreatLakes\", \"ECA\", \"GreatLakes\", \"ECA\"),\n tier=c(\"Tier 1\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\"),\n loadFactor=c(0.284596685, 0.521665062, 0.766855145, 0.078203076, 0.708915957249701)\n )\n\n #The previous tests check the accuracy of the EF and LLAF components,\n #so we just need to make sure they get multiplied at the end for this test\n testEFs<-calcEF(engineType = testEFLoadLLAF$engineType,\n location = testEFLoadLLAF$location,\n tier = testEFLoadLLAF$tier,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = \"ALL\",\n output=\"EF\",\n main_aux_boiler = \"main\")\n\n testLLAFs<-calcLLAF(engineType = testEFLoadLLAF$engineType,\n location = testEFLoadLLAF$location,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = \"ALL\")\n\n #Run Calculation\n EF_LLAF_out<-calcEF(engineType = testEFLoadLLAF$engineType,\n location = testEFLoadLLAF$location,\n tier = testEFLoadLLAF$tier,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = \"ALL\",\n output=\"EF_LLAF\",\n main_aux_boiler = \"main\")\n\n #Compare output against benchmark\n expect_equal(EF_LLAF_out$hc,\n testEFs$hc*testLLAFs$hc,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$co2,\n testEFs$co2*testLLAFs$co2,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$nox,\n testEFs$nox*testLLAFs$nox,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$so2,\n testEFs$so2*testLLAFs$so2,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$pm2.5,\n testEFs$pm2.5*testLLAFs$pm2.5,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$pm10,\n testEFs$pm10*testLLAFs$pm10,\n tolerance=1e-07\n )\n\n }\n)\n\ntest_that(\"calcEF EF Load LLAF works with input LLAF table\",\n {\n #create Benchmark\n testEFLoadLLAF<-data.table::data.table(\n engineType=c(\"SSD\", \"MSD\", \"ST\", \"GT\", \"LNG\"),\n location=c(\"OutsideECA\", \"GreatLakes\", \"ECA\", \"GreatLakes\", \"ECA\"),\n tier=c(\"Tier 1\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\"),\n loadFactor=c(0.284596685, 0.521665062, 0.766855145, 0.078203076, 0.708915957249701)\n )\n\n #create LLAF table\n tbl<-data.table(load=c(1:100))\n\n tbl[,co:=load*2]\n tbl[,pm2.5:=load*2]\n tbl[,nox:=load*2]\n\n #create a temporary file!!!\n tmpName<-tempfile()\n\n fwrite(tbl, file = tmpName)\n\n\n #The previous tests check the accuracy of the EF and LLAF components,\n #so we just need to make sure they get multiplied at the end for this test\n testEFs<-calcEF(engineType = testEFLoadLLAF$engineType,\n location = testEFLoadLLAF$location,\n tier = testEFLoadLLAF$tier,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = c(\"co\",\"pm2.5\",\"nox\"),\n output=\"EF\",\n main_aux_boiler = \"main\")\n\n testLLAFs<-calcLLAF(engineType = testEFLoadLLAF$engineType,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = c(\"co\",\"pm2.5\",\"nox\"),\n inputTableLocation = tmpName)\n\n\n #Run Calculation\n EF_LLAF_out<-calcEF(engineType = testEFLoadLLAF$engineType,\n location = testEFLoadLLAF$location,\n tier = testEFLoadLLAF$tier,\n loadFactor = testEFLoadLLAF$loadFactor,\n pollutants = c(\"co\",\"pm2.5\",\"nox\"),\n output=\"EF_LLAF\",\n main_aux_boiler = \"main\",\n inputTableLocation = tmpName)\n\n file.remove(tmpName)\n\n #Compare output against benchmark\n\n expect_equal(EF_LLAF_out$nox,\n testEFs$nox*testLLAFs$nox,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$co,\n testEFs$co*testLLAFs$co,\n tolerance=1e-07\n )\n\n expect_equal(EF_LLAF_out$pm2.5,\n testEFs$pm2.5*testLLAFs$pm2.5,\n tolerance=1e-07\n )\n\n }\n)\n\n#Aux\ntest_that(\"calcEF EF Baseline works for Aux Engines\",\n {\n #create Benchmark\n testEFAux<-data.table::data.table(\n engineType=c(\"SSD\",\"MSD\",\"ST\",\"GT\",\"LNG\"),\n location=c(\"OutsideECA\",\"ECA\",\"GreatLakes\",\"ECA\",\"OutsideECA\"),\n tier=c(\"Tier 2\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\")\n )\n\n testEFAux[,hc:=calcEF_HC(engineType = engineType,\n main_aux_boiler = \"aux\")\n ]\n\n testEFAux[,co2:=calcEF_CO2(engineType = engineType,\n location = location,\n main_aux_boiler = \"aux\")\n ]\n\n testEFAux[,co:=calcEF_CO(engineType = engineType,\n main_aux_boiler = \"aux\")\n ]\n\n testEFAux[,nox:=calcEF_NOx(engineType = engineType,\n location = location,\n tier = tier,\n main_aux_boiler = \"aux\")\n ]\n\n testEFAux[,so2:=calcEF_SO2(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"aux\")\n ]\n\n testEFAux[,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"aux\"),\n ]\n\n testEFAux[,pm10:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize=\"pm10\",\n main_aux_boiler = \"aux\")\n ]\n\n #Run Calculation\n calcEF_out<-data.table(\n calcEF(engineType = testEFAux$engineType,\n location = testEFAux$location,\n tier = testEFAux$tier,\n pollutants = \"ALL\",\n loadBasedBSFC = \"N\",\n output=\"EF\",\n main_aux_boiler = \"aux\")\n )\n\n #Compare output against benchmark\n expect_equal(calcEF_out$hc,\n testEFAux$hc\n )\n\n expect_equal(calcEF_out$co2,\n testEFAux$co2\n )\n\n expect_equal(calcEF_out$co,\n testEFAux$co\n )\n\n expect_equal(calcEF_out$nox,\n testEFAux$nox\n )\n\n expect_equal(calcEF_out$pm2.5,\n testEFAux$pm2.5\n )\n\n expect_equal(calcEF_out$so2,\n testEFAux$so2\n )\n\n expect_equal(calcEF_out$pm10,\n testEFAux$pm10\n )\n\n }\n)\n\n#Boiler\ntest_that(\"calcEF EF Baseline works for Aux Engines\",\n {\n #create Benchmark\n testEFBoiler<-data.table::data.table(\n engineType=c(\"SSD\",\"MSD\",\"ST\",\"GT\",\"LNG\"),\n location=c(\"OutsideECA\",\"ECA\",\"GreatLakes\",\"ECA\",\"OutsideECA\"),\n tier=c(\"Tier 2\",\"Tier 3\",\"Tier 0\",\"Tier 0\",\"Tier 0\")\n )\n\n testEFBoiler[,hc:=calcEF_HC(engineType = engineType,\n main_aux_boiler = \"boiler\")\n ]\n\n testEFBoiler[,co2:=calcEF_CO2(engineType = engineType,\n location = location,\n main_aux_boiler = \"boiler\")\n ]\n\n testEFBoiler[,co:=calcEF_CO(engineType = engineType,\n main_aux_boiler = \"boiler\")\n ]\n\n testEFBoiler[,nox:=calcEF_NOx(engineType = engineType,\n location = location,\n tier = tier,\n main_aux_boiler = \"boiler\")\n ]\n\n testEFBoiler[,so2:=calcEF_SO2(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n main_aux_boiler = \"boiler\")\n ]\n\n testEFBoiler[,pm2.5:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize = \"pm2.5\",\n main_aux_boiler = \"boiler\"),\n ]\n\n testEFBoiler[,pm10:=calcEF_PM(engineType = engineType,\n location = location,\n ECAfuelSulfurPercentage = 0.1,\n GlobalfuelSulfurPercentage = 0.5,\n pmSize=\"pm10\",\n main_aux_boiler = \"boiler\")\n ]\n\n #Run Calculation\n calcEF_out<-data.table(\n calcEF(engineType = testEFBoiler$engineType,\n location = testEFBoiler$location,\n tier = testEFBoiler$tier,\n pollutants = \"ALL\",\n loadBasedBSFC = \"N\",\n output=\"EF\",\n main_aux_boiler = \"boiler\")\n )\n\n #Compare output against benchmark\n expect_equal(calcEF_out$hc,\n testEFBoiler$hc\n )\n\n expect_equal(calcEF_out$co2,\n testEFBoiler$co2\n )\n\n expect_equal(calcEF_out$co,\n testEFBoiler$co\n )\n\n expect_equal(calcEF_out$nox,\n testEFBoiler$nox\n )\n\n expect_equal(calcEF_out$pm2.5,\n testEFBoiler$pm2.5\n )\n\n expect_equal(calcEF_out$so2,\n testEFBoiler$so2\n )\n\n expect_equal(calcEF_out$pm10,\n testEFBoiler$pm10\n )\n\n }\n)\n", "meta": {"hexsha": "9c948b223409af6586e6e91d3313a83f3bdacb24", "size": 26811, "ext": "r", "lang": "R", "max_stars_repo_path": "ShipEF/tests/testthat/testWrapperFunction.r", "max_stars_repo_name": "USEPA/Marine_Emissions_Tools", "max_stars_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-05-13T17:14:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T18:47:39.000Z", "max_issues_repo_path": "ShipEF/tests/testthat/testWrapperFunction.r", "max_issues_repo_name": "USEPA/Marine_Emissions_Tools", "max_issues_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ShipEF/tests/testthat/testWrapperFunction.r", "max_forks_repo_name": "USEPA/Marine_Emissions_Tools", "max_forks_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-08T15:55:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-08T15:55:06.000Z", "avg_line_length": 40.4389140271, "max_line_length": 97, "alphanum_fraction": 0.3859609862, "num_tokens": 5048, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.33328942057860694}} {"text": "\nsystem(\"cat sandbox/small_run.r\")\n\nsource('mapping/epimap.r')\nsource('sandbox/limit_data.r')\n\n\nopt = Rmap_options(\n stage = \"full\",\n region_id = 1,\n first_day_modelled = \"2020-08-26\",\n last_day_modelled = \"2020-12-08\",\n spatialkernel = \"none\",\n temporalkernel = \"matern12\",\n globalkernel = \"none\",\n localkernel = \"local\",\n days_ignored=NULL,\n days_per_step = 7,\n num_steps_forecasted = 3,\n iterations=3000,\n fixed_gp_time_length_scale = -1,\n fixed_gp_space_length_scale = -1,\n clean_directory='fits/clean-1215',\n results_directory='fits/test-weekly-northeast-newrt-local'\n)\n\nenv = Rmap_setup(opt)\n#limit_data_multi(env,list(\n #list(area='Oxford',distance=.6),\n #list(area='Birmingham',distance=.6)\n# list(area='Westminster',distance=.8)\n#))\n#limit_data_by_distance(env,'Oxford',0.6)\n# timing: \n# Oxford .8 -> 64 areas, ~ 10 hours\n# Oxford .6 -> 26 areas, 2.8 hours\n# Oxford .4 -> 8 areas, ~ .5 hours (removed local effects)\n# Oxford .3 Birmingham .3 -> 21 areas, ~2.3 hours\n# Oxford .5 Birmingham .5 -> 43 areas, ~ hours\n# Oxford .6 Birmingham .6 -> 58 areas, ~ 9.2 hours\n\nprint(env$N)\nprint(env$areas[env$modelled_region[,1]==1])\n\n\nRmap_run(env)\nRmap_postprocess(env)\n\n\n\n# Rmap_merge(env,c(1,2,3))\n", "meta": {"hexsha": "3cef713a31c7064ff65c985a83b63dd6a49ff299", "size": 1251, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/small_run.r", "max_stars_repo_name": "oxcsml/Rmap", "max_stars_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-03T10:25:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-03T10:25:31.000Z", "max_issues_repo_path": "sandbox/small_run.r", "max_issues_repo_name": "oxcsml/Rmap", "max_issues_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sandbox/small_run.r", "max_forks_repo_name": "oxcsml/Rmap", "max_forks_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0576923077, "max_line_length": 62, "alphanum_fraction": 0.6738609113, "num_tokens": 394, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7745833737577157, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.3331849844805584}} {"text": "# Data Wrangling in R\n# Social Security Disability Case Study\n\n# Load the tidyverse\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(stringr)\n\n# Read in the coal dataset\nssa <- read_csv(\"http://594442.youcanlearnit.net/ssadisability.csv\")\n\n# Take a look at how this was imported\nglimpse(ssa)\n\n\n", "meta": {"hexsha": "4fde0b3d805738d3b505b695828d23588bf90345", "size": 291, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch08/08_03/ssa_3_start.r", "max_stars_repo_name": "vvpn9/Handy-Tools", "max_stars_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch08/08_03/ssa_3_start.r", "max_issues_repo_name": "vvpn9/Handy-Tools", "max_issues_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch08/08_03/ssa_3_start.r", "max_forks_repo_name": "vvpn9/Handy-Tools", "max_forks_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.1875, "max_line_length": 68, "alphanum_fraction": 0.7663230241, "num_tokens": 80, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3331832434434053}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/14_sinking-marbles-wonky/results/\")\nsource(\"rscripts/helpers.r\")\n\nload(\"data/r.RData\")\nr = read.table(\"data/sinking_marbles.csv\", sep=\",\", header=T)\nr$trial = r$slide_number_in_experiment - 2\nnrow(r)\nhead(r)\nr = r[,c(\"workerid\", \"rt\", \"effect\", \"cause\",\"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\",\"quantifier\", \"object_level\", \"response\", \"object\",\"num_objects\",\"trial\",\"enjoyment\",\"asses\",\"comments\",\"strangedescription\")]\n\n## add priors to data.frame\n# load prior dataset\nexpectations = read.table(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\", quote=\"\",sep=\"\\t\",header=T)\nrow.names(expectations) = paste(expectations$effect, expectations$object)\nhead(expectations)\nnrow(expectations)\n\nr$PriorExpectation = expectations[paste(r$effect, r$object),]$expectation\nr$Half = as.factor(ifelse(r$trial < 16, 1, 2))\nr$Quarter = as.factor(ifelse(r$trial < 8, 1, ifelse(r$trial < 16, 2, ifelse(r$trial < 24, 3, 4))))\nsummary(r)\nr$Item = as.factor(paste(r$object,r$effect))\ntable(r$Item)\n\nsave(r, file=\"data/r.RData\")\n\n##################\n\nggplot(aes(x=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=rt), data=r) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,20000))\n\nggplot(aes(x=age), data=r) +\n geom_histogram()\n\nggplot(aes(x=enjoyment), data=r) +\n geom_histogram()\n\nggplot(aes(x=asses), data=r) +\n geom_histogram()\n\nggplot(aes(x=quantifier), data=r) +\n geom_histogram()\n\nhead(r$comments)\nunique(r$comments)\n", "meta": {"hexsha": "3faf6523a594ee40d207b0e61021f90c7f35cc61", "size": 1649, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/14_sinking-marbles-wonky/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/14_sinking-marbles-wonky/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/14_sinking-marbles-wonky/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.7115384615, "max_line_length": 223, "alphanum_fraction": 0.7180109157, "num_tokens": 490, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.33318323535406213}} {"text": "GuessANumber <- function( low, high ) {\n print( sprintf(\"Guess a number between %d and %d until you get it right\", low, high ) );\n X <- low:high;\n number <- sample( X, 1 );\n repeat {\n input <- as.numeric(readline());\n if (input > number) {\n print(\"Too high, try again\"); }\n else if (input < number) {\n print(\"Too low, try again\");}\n else {\n print(\"Correct!\");\n break; }\n }\n}\n", "meta": {"hexsha": "2c615ebe643e53c87b27570bcee0d4159ecd67df", "size": 412, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Guess-the-number-With-feedback/R/guess-the-number-with-feedback.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Guess-the-number-With-feedback/R/guess-the-number-with-feedback.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Guess-the-number-With-feedback/R/guess-the-number-with-feedback.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 25.75, "max_line_length": 90, "alphanum_fraction": 0.5533980583, "num_tokens": 116, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.333183235354062}} {"text": "cm <- unit(1, \"cm\")\ncm2 <- unit(2, \"cm\")\ncm5 <- unit(5, \"cm\")\n\nnull <- unit(1, \"null\")\n", "meta": {"hexsha": "a9afbd8b578dbdc0a77eabbc90bcd7af335bbde8", "size": 87, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/gtable/tests/helper-units.r", "max_stars_repo_name": "lehoangha/GSOE9712_S115_RA", "max_stars_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/gtable/tests/helper-units.r", "max_issues_repo_name": "lehoangha/GSOE9712_S115_RA", "max_issues_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/gtable/tests/helper-units.r", "max_forks_repo_name": "lehoangha/GSOE9712_S115_RA", "max_forks_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.5, "max_line_length": 23, "alphanum_fraction": 0.4827586207, "num_tokens": 38, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.333183235354062}} {"text": "suppressMessages(library(fmlref))\n\nm = 3\nn = 2\nx = refmat(m, n)\nx$fill_linspace(1, m*n)\nxr = x$to_robj()\n\ny = refmat(m, n)\ny$fill_eye()\nyr = y$to_robj()\n\n\nsource(\"internals/common.r\")\nsource(\"internals/linalg.r\")\n", "meta": {"hexsha": "87a93f965c48db84b3a3e7f454b9150cc4d0bf9e", "size": 213, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/linalg.r", "max_stars_repo_name": "fml-fam/fmlref", "max_stars_repo_head_hexsha": "a74ae07811c5c3d54bdaaa46e97b2321b299b3ff", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/linalg.r", "max_issues_repo_name": "fml-fam/fmlref", "max_issues_repo_head_hexsha": "a74ae07811c5c3d54bdaaa46e97b2321b299b3ff", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/linalg.r", "max_forks_repo_name": "fml-fam/fmlref", "max_forks_repo_head_hexsha": "a74ae07811c5c3d54bdaaa46e97b2321b299b3ff", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.3125, "max_line_length": 33, "alphanum_fraction": 0.661971831, "num_tokens": 82, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6224593382055109, "lm_q1q2_score": 0.3330770137577294}} {"text": "#'@title Build a univariate time series of raw data from a specified table and column\n#'\n#'@description Build a univariate time series object from a specified table a column\n#'\n#'@usage \n#'\n#'@param connectionDetails An R object of type\\cr\\code{connectionDetails} created using the\n#' function \\code{createConnectionDetails} in the\n#' \\code{DatabaseConnector} package.\n#'@param dbSchema A fully qualified CDM or Results schema name.\n#'@param tableName A valid table name.\n#'@param dateColumnName A valid date column in the specified table.\n#'@param startDate A string date in the \"YYYY-MM-DD\" format\n#'@param endDate A string date in the \"YYYY-MM-DD\" format\n#'@param dataColumnName The column in the specified table to count.\n#'@param frequency A string indicating the number of observations per unit of time in the time series.\n#' Acceptable values are \"day\",\"week\",\"month\",\"quarter\", and \"year\".\n#'\n#'@return A univariate time series object\n#'\n#'@export\n\n\ngetRawTs <- function(\n\tconnectionDetails,\n\tdbSchema,\n\ttableName,\n\tdateColumnName,\n\tstartDate,\n\tendDate,\n\tdataColumnName,\n\tfrequency)\n{\n\t\n\tquery <- \"\n\t\tselect @date_column_name as start_date, sum(@data_column_name) as raw_value \n\t\t from @db_schema.@table_name\n\t\t where @date_column_name >= '@start_date' and @date_column_name <= '@end_date'\t \n\t\t group by @date_column_name \n\t\t order by @date_column_name;\"\n\t\n\tquery <- SqlRender::render(\n\t\tsql = query,\n\t\tdate_column_name = dateColumnName,\n\t\tdata_column_name = dataColumnName,\n\t\tdb_schema = dbSchema,\n\t\ttable_name = tableName,\n\t\tstart_date = startDate,\n\t\tend_date = endDate)\n\n\tquery <- SqlRender::translate(query, targetDialect = connectionDetails$dbms)\n\n\tconn <- DatabaseConnector::connect(connectionDetails)\n\t\n\tresultSetData <- DatabaseConnector::querySql(conn,query)\n\t\n if (nrow(resultSetData) == 0) stop (\"Cannot create time series from an empty data frame\")\n\t\n\tfreqString <- tolower(frequency)\n\t\n\t# Find the correct starting point for the first element in the time series\n\t# to form the c(startYear,startPos) vector given to the \"start\" argument of the time series\n\n\tstartYear <- as.integer(strftime(resultSetData$START_DATE[1], format = \"%Y\"))\n\n if (freqString == \"day\") {\n\t\tfreqNum <- 365\n\t\tstartPos <- as.integer(strftime(resultSetData$START_DATE[1], format = \"%j\"))\n\t} else if (freqString == \"week\") {\n\t\tfreqNum <- 52\n\t\tstartPos <- as.integer(strftime(resultSetData$START_DATE[1], format = \"%W\"))\n\t} else if (freqString == \"month\") {\n\t\tfreqNum <- 12\n\t\tstartPos <- as.integer(strftime(resultSetData$START_DATE[1], format = \"%m\"))\n\t} else if (freqString == \"quarter\") {\n\t\tfreqNum <- 4\n\t\tstartPos <- ceiling(as.integer(strftime(resultSetData$START_DATE[1], format = \"%m\"))/12)\n\t} else if (freqString == \"year\") {\n\t\tfreqNum <- 1\n\t\tstartPos <- 1 \n\t} else {\n\t\tstop(\"Invalid frequency string: Acceptable values are \\\"day\\\", \\\"week\\\", \\\"month\\\", \\\"quarter\\\", and \\\"year\\\"\")\n\t}\n\n # Create a vector of dense dates to capture all dates between the start and end of the time series\n lastRow <- nrow(resultSetData)\n\n denseDates <- seq.Date(\n\t from = as.Date(resultSetData$START_DATE[1],\"%Y-%m-%d\"),\n\t to = as.Date(resultSetData$START_DATE[lastRow],\"%Y-%m-%d\"),\n\t by = freqString\n\t )\n\n # Find gaps, if any, in data (e.g., dates that have no data, give that date a 0 value)\n denseDatesDf <- data.frame(START_DATE=denseDates, SUM_VALUE=rep(0,length(denseDates)))\n\n joinResults <- dplyr::left_join(denseDatesDf,resultSetData,by=c(\"START_DATE\" = \"START_DATE\"))\n\n joinResults$RAW_VALUE[which(is.na(joinResults$RAW_VALUE))] <- 0\n\n # Now that we no longer have sparse dates, keep only necessary columns and build the time series\n joinResults <- joinResults[,c(\"START_DATE\",\"RAW_VALUE\")]\n\n resultSetDataTs <- ts(\n\t data = joinResults$RAW_VALUE,\n\t start = c(startYear,startPos),\n\t frequency = freqNum\n )\n\t\n\ton.exit(DatabaseConnector::disconnect(conn))\n\n return (resultSetDataTs)\n\n}\n", "meta": {"hexsha": "f8946929adb718e83b8d64d2cc45db5e23c08ccc", "size": 4131, "ext": "r", "lang": "R", "max_stars_repo_path": "R/getRawTs.r", "max_stars_repo_name": "OHDSI/Castor", "max_stars_repo_head_hexsha": "a64faf53509b50bfedf9057f042355fe2e17974b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-02-11T18:51:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-23T16:13:55.000Z", "max_issues_repo_path": "R/getRawTs.r", "max_issues_repo_name": "OHDSI/Castor", "max_issues_repo_head_hexsha": "a64faf53509b50bfedf9057f042355fe2e17974b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-10-23T18:35:15.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-17T16:41:40.000Z", "max_forks_repo_path": "R/getRawTs.r", "max_forks_repo_name": "OHDSI/Castor", "max_forks_repo_head_hexsha": "a64faf53509b50bfedf9057f042355fe2e17974b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.9217391304, "max_line_length": 113, "alphanum_fraction": 0.67610748, "num_tokens": 1048, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33307701001008394}} {"text": "##############################################################################################\n# Fucntions usied for data Input\n##############################################################################################\n#Function used in ReadDataDump.\n#Converts time to minutes\nConvertTime <- function(x)\n{\n\tvals <- strsplit(x,\":\")[[1]]\n\tretval <- NA\n\tif(length(vals)==3)\n\t{\n\t\tretval <- as.integer(vals[1])*60+as.integer(vals[2])+as.single(vals[3])/60\n\t}\n\tif(length(vals)==2)\n\t{\n\t\tretval <- as.integer(vals[1])+as.single(vals[2])/60\n\t}\n\t\n\treturn(retval)\n\t\n}\n\n# readdatadump sam espinosa\nReadDataDump.se <- function(fname=NULL,wrdef=NULL, Wr=NULL, c.dat=NULL,img1=NULL,img2=NULL,img3=NULL,img4=NULL,rd.name=NULL,sep=\"\\t\")\n{\nrequire(png)\nrequire(zoom)\nrequire(RColorBrewer)\nrequire(MALDIquant)\n\ttmp <- read.delim(fname,fileEncoding=\"UCS-2LE\",sep=sep)\n\tall.names <- names(tmp)\n\ttime.name <- grep(\"Time\",all.names,value=T,ignore=T)[1]\n\tif(time.name != \"Time..ms.\"){warning(paste(time.name,\"assumed to be in ms\"))}\n\t\n\tid.name <- grep(\"ID\",all.names,value=T,ignore=T)[1]\n\tif(id.name != \"ID\"){warning(paste(id.name,\"assumed to be it ROI.ID\"))}\n\t\n\tratio.name <- grep(\"Ratio\",all.names,value=T,ignore=T)\n\tif(is.na(ratio.name)){stop(\"no ratio data\")}\n\telse{if(ratio.name != \"Ratio.340.380\"){warning(ratio.name,\"assumed to be Ratio data\")}}\n\t\t\n\tx.names <- unique(tmp[,id.name])\n\tx.tab <- table(tmp[,id.name])\n\tif(max(x.tab) != min(x.tab)){error(\"all ids do not have the same number of data points\")}\n\tx.row <- max(x.tab)\n\tt.dat <- matrix(tmp[,ratio.name],byrow=FALSE,nrow=x.row)\n\ttime.val <- tmp[tmp[,id.name]==x.names[1],time.name]\n\t\n\tif(length(grep(\":\",time.val[1]))==0)\n\t{\n\t\tx <- as.single(time.val)\n\t\tif(max(x) > 1000000)#in ms\n\t\t{\n\t\t\tx <- x/60000\n\t\t}\n\t\telse if(max(x) > 1500) #in seconds\n\t\t{\n\t\t\tx <- x/60\t\n\t\t}\t\t\n\t\ttime.val <- x\n\t}\n\telse{time.val <- sapply(as.character(time.val),ConvertTime)}\n\tt.dat <- cbind(time.val,t.dat) #note assumption of ms\n\tt.dat <- as.data.frame(t.dat)\n\tt.dat<- t.dat[unique(row.names(t.dat)),]\n\tnames(t.dat) <- c(\"Time\",paste(\"X.\",x.names,sep=\"\"))\n\t\n\t\n\tif(!is.null(c.dat)){\n\tc.dat<-read.delim(file=c.dat,fileEncoding=\"UCS-2LE\", sep=sep)\n\tc.dat.names<-names(c.dat)\n\t\n\tcx.name <- grep(\"Xpx\",c.dat.names,value=T,ignore=T)\n\tif(is.na(cx.name)){stop(\"no Center X data\")}\n\telse{if(cx.name != \"CentreXpx\"){warning(cx.name,\"assumed to be Center X data\")}}\n\t\n\tcy.name <- grep(\"Ypx\",c.dat.names,value=T,ignore=T)\n\tif(is.na(cy.name)){stop(\"no Center Y data\")}\n\telse{if(cy.name != \"CentreYpx\"){warning(cy.name,\"assumed to be Center Y data\")}}\n\n\tarea.name <- grep(\"Area\",c.dat.names,value=T,ignore=T)\n\tif(is.na(area.name)){stop(\"no Area data\")}\n\telse{if(area.name != \"ROIArea\"){warning(paste(area.name,\"assumed to be Area\"))}}\n\n\tmean.gfp<-grep(\"MeanGreen\",c.dat.names,value=T,ignore=T)\n\tif(length(mean.gfp)==0){warning(paste(\"no gfp.1 data from c.dat\"))}\n\telse{if(mean.gfp!=\"MeanGFP\"){warning(paste(mean.gfp, \"assumed to be GFP.1\"))}}\n\t\n\tmean.tritc<-grep(\"MeanBlue\",c.dat.names,value=T,ignore=T)\n\tif(length(mean.tritc)==0){warning(paste(\"no tritc data from c.dat\"))}\n\telse{if(mean.tritc!=\"MeanTRITC\"){warning(paste(mean.tritc, \"assumed to be TRITC\"))}}\n\t\n\tc.names <- c(area.name, cx.name, cy.name, mean.gfp, mean.tritc)\n#\to.names <- setdiff(c.dat.names,c(time.name,id.name,area.name,ratio.name,cx.name,cy.name, mean.gfp, mean.tritc))\n#\tif(length(o.names) > 0){warning(paste(o.names,\"added to c.dat\"));c.names <- c(c.names,o.names)}\n\n\tc.dat <- c.dat[,c.names]\n\tc.dat <- cbind(paste(\"X.\",x.names,sep=\"\"),c.dat)\n\tc.dat <- data.frame(c.dat)\n\tcolnames(c.dat)[1:4] <- c(\"id\",\"area\",\"center.x\", \"center.y\")\n\t\n\t# If gfp and tritc are not present then evaluate\n\t# 1st if there is only tritc, name the 6th column mean.tritc\n\t# 2nd if there is only gfp, name the 6th collumn mean.gfp\n\t# 3rd if there are both then rename both 6th and 7th collumn\n\tif(!length(mean.gfp)==0 & !length(mean.tritc)==0){\n\tif(length(mean.gfp)==0 & length(mean.tritc)==1){colnames(c.dat)[5]<-\"mean.tritc\"}\n\tif(length(mean.tritc)==0 & length(mean.gfp)==1){colnames(c.dat)[5]<-c(\"mean.gfp\")}\n\tif(length(mean.tritc)==1 & length(mean.gfp)==1){colnames(c.dat)[5:6]<-c(\"mean.gfp\",\"mean.tritc\")}\n\trow.names(c.dat) <- c.dat[,\"id\"]\n\t}}\n\telse{\n\tarea.name <- grep(\"Area\",all.names,value=T,ignore=T)[1]\n\tif(is.na(area.name)){stop(\"no ROI.Area data\")}\n\telse{if(area.name != \"ROI.Area\"){warning(paste(area.name,\"assumed to be ROI.Area\"))}}\n\t\n\tcx.name <- grep(\"Center.X\",all.names,value=T,ignore=T)\n\tif(is.na(cx.name)){stop(\"no Center X data\")}\n\telse{if(cx.name != \"Center.X\"){warning(cx.name,\"assumed to be Center X data\")}}\n\t\n\tcy.name <- grep(\"Center.Y\",all.names,value=T,ignore=T)\n\tif(is.na(cy.name)){stop(\"no Center Y data\")}\n\telse{if(cy.name != \"Center.Y\"){warning(cy.name,\"assumed to be Center Y data\")}}\n\t\n\tc.names <- c(area.name,cx.name,cy.name)\n\tc.dat <- tmp[match(x.names,tmp[,id.name]),c.names]\n\tc.dat <- cbind(paste(\"X.\",x.names,sep=\"\"),c.dat)\n\tc.dat <- data.frame(c.dat)\n\tnames(c.dat)[1:4] <- c(\"id\",\"area\",\"center.X\",\"center.Y\") \n\trow.names(c.dat) <- c.dat[,\"id\"]\n}\n\tif(!is.null(wrdef))\n\t{\n\t\twr <- ReadResponseWindowFile(wrdef)\n\t\tWr<-length(wr[,1])#complete and revise this section\n\t\tif(length(colnames(wr))<2){w.dat<-WrMultiplex(t.dat,wr,n=Wr)}\n\t\telse{w.dat <- MakeWr(t.dat,wr)}\n\t\t}\n\telse\n\t{\n\t\tWrCreate.rdd(t.dat, n=Wr)\n\t\twr <- ReadResponseWindowFile(\"wr1.csv\")\n\t\tw.dat <- MakeWr(t.dat,wr)\n\t}\n\t\n\tif(!is.null(img1)){img1<-png::readPNG(img1)}\n\tif(!is.null(img2)){img2<-png::readPNG(img2)}\n\tif(!is.null(img3)){img3<-png::readPNG(img3)}\n\tif(!is.null(img4)){img4<-png::readPNG(img4)}\n\t\n\tif(is.null(rd.name)){rd.name <- paste(\"RD\",make.names(date()),sep=\"\")}\n\t\n\tif(length(which(duplicated(row.names(t.dat))))>=1){\n\tdup<-which(duplicated(row.names(t.dat)))\n\tpaste(dup)\n\tt.dat<-t.dat[-dup,]\n\tw.dat<-w.dat[-dup,]\n\t}\n\t\t\n\ttmp.rd <- list(t.dat=t.dat,w.dat=w.dat,c.dat=c.dat, img1=img1, img2=img2, img3=img3)\n\tf.name <- paste(rd.name,\".Rdata\",sep=\"\")\n\tassign(rd.name,tmp.rd)\n\tsave(list=rd.name,file=f.name)\n\treturn(paste(nrow(tmp.rd$c.dat),\"traces read saved to \",f.name))\n\t#save as RD file\n}\n\n\n# readdatadump Lee Leavitt\n#ReadDataDump.lee <- function(fname=NULL,wrdef=NULL, Wr=NULL, c.dat=NULL,img1=NULL,img2=NULL,img3=NULL,img4=NULL,rd.name=NULL,sep=\"\\t\")\n# fancy added for cell definer\nReadDataDump.lee <- function(rd.name=NULL,img1=\"bf.f2.png\",img2=\"bf.f2.lab.png\",img3=\"bf.png\",img4=NULL, fancy=F,fname=\"Data (full).txt\",wrdef=\"wr1.csv\", Wr=NULL, c.dat=\"ROI Data.txt\" ,sep=\"\\t\")\n{\nrequire(png)\nrequire(zoom)\nrequire(RColorBrewer)\nrequire(MALDIquant)\n\ttmp <- read.delim(fname,fileEncoding=\"UCS-2LE\",sep=sep)\n\tall.names <- names(tmp)\n\ttime.name <- grep(\"Time\",all.names,value=T,ignore=T)[1]\n\tif(time.name != \"Time..ms.\"){warning(paste(time.name,\"assumed to be in ms\"))}\n\t\n\tid.name <- grep(\"ID\",all.names,value=T,ignore=T)[1]\n\tif(id.name != \"ID\"){warning(paste(id.name,\"assumed to be it ROI.ID\"))}\n\t\n\tratio.name <- grep(\"Ratio\",all.names,value=T,ignore=T)\n\tif(is.na(ratio.name)){stop(\"no ratio data\")}\n\telse{if(ratio.name != \"Ratio.340.380\"){warning(ratio.name,\"assumed to be Ratio data\")}}\n\t\t\n\tx.names <- unique(tmp[,id.name])\n\tx.tab <- table(tmp[,id.name])\n\tif(max(x.tab) != min(x.tab)){warning(\"all ids do not have the same number of data points\")}\n\tx.row <- max(x.tab)\n\tt.dat <- matrix(tmp[,ratio.name],byrow=FALSE,nrow=x.row)\n\ttime.val <- tmp[tmp[,id.name]==x.names[1],time.name]\n\t\n\tif(length(grep(\":\",time.val[1]))==0)\n\t{\n\t\tx <- as.single(time.val)\n\t\tif(max(x) > 1000000)#in ms\n\t\t{\n\t\t\tx <- x/60000\n\t\t}\n\t\telse if(max(x) > 1500) #in seconds\n\t\t{\n\t\t\tx <- x/60\t\n\t\t}\t\t\n\t\ttime.val <- x\n\t}\n\telse{time.val <- sapply(as.character(time.val),ConvertTime)}\n\tt.dat <- cbind(time.val,t.dat) #note assumption of ms\n\tt.dat <- as.data.frame(t.dat)\n\tt.dat<- t.dat[unique(row.names(t.dat)),]\n\tnames(t.dat) <- c(\"Time\",paste(\"X.\",x.names,sep=\"\"))\n\t\n\t\nif(!is.null(c.dat)){\n\tc.dat<-read.delim(file=c.dat,fileEncoding=\"UCS-2LE\", sep=sep)\n\tc.dat.names<-names(c.dat)\n\t\n\tid.name <- grep(\"id\",c.dat.names,value=T,ignore=T)\n\tif(is.na(id.name)){stop(\"no ID data\")}\n\telse{if(id.name != \"RoiID\"){warning(cx.name,\"assumed to be ID data\")}}\n\n\tcx.name <- grep(\"Xpx\",c.dat.names,value=T,ignore=T)\n\tif(is.na(cx.name)){stop(\"no Center X data\")}\n\telse{if(cx.name != \"CentreXpx\"){warning(cx.name,\"assumed to be Center X data\")}}\n\t\n\tcy.name <- grep(\"Ypx\",c.dat.names,value=T,ignore=T)\n\tif(is.na(cy.name)){stop(\"no Center Y data\")}\n\telse{if(cy.name != \"CentreYpx\"){warning(cy.name,\"assumed to be Center Y data\")}}\n\n\tperimeter.name<-grep(\"perimeter\", c.dat.names, value=T, ignore=T)\n\tif(is.na(perimeter.name)){stop(\"no Perimeter data\")}\n\telse{if(perimeter.name != \"Perimeter\"){warning(paste(perimeter.name,\"assumed to be Perimeter\"))}}\n\t\n\tarea.name <- grep(\"Area\",c.dat.names,value=T,ignore=T)\n\tif(is.na(area.name)){stop(\"no Area data\")}\n\telse{if(area.name != \"ROIArea\"){warning(paste(area.name,\"assumed to be Area\"))}}\n\n\t\n\t#mean.gfp<-grep(\"gfp.1\",c.dat.names,value=T,ignore=T)\n\tmean.gfp<-grep(\"GFP\",c.dat.names,value=T,ignore=F)\n\tif(length(mean.gfp)==0){mean.gfp<-grep(\"gfp\",c.dat.names,value=T,ignore=T);warning(paste(\"no gfp.1 data from c.dat\"))}\n\telse{if(mean.gfp!=\"MeanGFP\"){warning(paste(mean.gfp, \"assumed to be GFP.1\"))}}\n\t\n\tmean.gfp.2<-grep(\"gfp.2\",c.dat.names,value=T,ignore=T)\n\tif(length(mean.gfp.2)==0){warning(paste(\"no gfp.2 data from c.dat\"))}\n\telse{if(mean.gfp.2!=\"MeanGFP\"){warning(paste(mean.gfp.2, \"assumed to be GFP.2\"))}}\n\t\n\tmean.tritc<-grep(\"TRITC\",c.dat.names,value=T,ignore=F)\n\tif(length(mean.tritc)==0){warning(paste(\"no tritc data from c.dat\"))}\n\telse{if(mean.tritc!=\"MeanTRITC\"){warning(paste(mean.tritc, \"assumed to be TRITC\"))}}\n\t\n\tmean.dapi<-grep(\"DAPI\",c.dat.names,value=T,ignore=F)\n\tif(length(mean.dapi)==0){warning(paste(\"no dapi data from c.dat\"))}\n\telse{if(mean.dapi!=\"MeanDAPI\"){warning(paste(mean.dapi, \"assumed to be DAPI\"))}}\n\n\tc.names <- c(id.name,area.name, perimeter.name, cx.name, cy.name, mean.gfp, mean.gfp.2, mean.tritc, mean.dapi)\n#\to.names <- setdiff(c.dat.names,c(time.name,id.name,area.name,ratio.name,cx.name,cy.name, mean.gfp, mean.tritc))\n#\tif(length(o.names) > 0){warning(paste(o.names,\"added to c.dat\"));c.names <- c(c.names,o.names)}\n\t\n\tc.dat<-c.dat[c.names]#create c.dat with specified collumns from c.names\n\tc.dat <- c.dat[order(c.dat[,id.name]),] # order rows by ROIid\n\tc.dat[,id.name] <- paste(\"X.\",c.dat[,id.name],sep=\"\")#rename ROIid with a X.cell#\n\trow.names(c.dat)<-c.dat[,id.name]# assign row.names the ROIid name\n\tc.dat <- data.frame(c.dat)#convert to data frame\n\tcolnames(c.dat)[1:5] <- c(\"id\",\"area\",\"perimeter\",\"center.x\", \"center.y\")#rename collumns these names\n\tc.dat[\"circularity\"]<-((c.dat$perimeter^2)/(4*pi*c.dat$area)) # create a circularity measurement\n\n\t## If the class of the collumn is a factor, then the collumn is filled with \"N/A\"\n\t# therefore make the NULL/ remove it. If not, then perform an unecessarily complex \n\t# set of selection to rename the collumn what you want.\n\tif(class(c.dat[,mean.gfp])==\"factor\"){c.dat[,mean.gfp]<-NULL\n\t}else{\n\tcolnames(c.dat)[which(colnames(c.dat)==mean.gfp)]<-\"mean.gfp\"}\n\t\n\tif(class(c.dat[,mean.gfp.2])==\"factor\"){c.dat[,mean.gfp.2]<-NULL\n\t}else{colnames(c.dat)[which(colnames(c.dat)==mean.gfp.2)]<-\"mean.gfp.2\"}\n\t\n\tif(class(c.dat[,mean.tritc])==\"factor\"){c.dat[,mean.tritc]<-NULL\n\t}else{colnames(c.dat)[which(colnames(c.dat)==mean.tritc)]<-\"mean.tritc\"}\n\t\n\tif(class(c.dat[,mean.dapi])==\"factor\"){c.dat[,mean.dapi]<-NULL\n\t}else{colnames(c.dat)[which(colnames(c.dat)==mean.dapi)]<-\"mean.dapi\"}\n\n\t}\n\telse{\n\tarea.name <- grep(\"Area\",all.names,value=T,ignore=T)[1]\n\tif(is.na(area.name)){stop(\"no ROI.Area data\")}\n\telse{if(area.name != \"ROI.Area\"){warning(paste(area.name,\"assumed to be ROI.Area\"))}}\n\t\n\tcx.name <- grep(\"Center.X\",all.names,value=T,ignore=T)\n\tif(is.na(cx.name)){stop(\"no Center X data\")}\n\telse{if(cx.name != \"Center.X\"){warning(cx.name,\"assumed to be Center X data\")}}\n\t\n\tcy.name <- grep(\"Center.Y\",all.names,value=T,ignore=T)\n\tif(is.na(cy.name)){stop(\"no Center Y data\")}\n\telse{if(cy.name != \"Center.Y\"){warning(cy.name,\"assumed to be Center Y data\")}}\n\t\n\tc.names <- c(area.name,cx.name,cy.name)\n\tc.dat <- tmp[match(x.names,tmp[,id.name]),c.names]\n\tc.dat <- cbind(paste(\"X.\",x.names,sep=\"\"),c.dat)\n\tc.dat <- data.frame(c.dat)\n\tnames(c.dat)[1:4] <- c(\"id\",\"area\",\"center.x\",\"center.y\") \n\trow.names(c.dat) <- c.dat[,\"id\"]\n}\n\tif(!is.null(wrdef))\n\t{\n\t\twr <- ReadResponseWindowFile(wrdef)\n\t\tWr<-length(wr[,1])#complete and revise this section\n\t\tif(length(colnames(wr))<2){w.dat<-WrMultiplex(t.dat,wr,n=Wr)}\n\t\telse{w.dat <- MakeWr(t.dat,wr)}\n\t\t}\n\telse\n\t{\n\t\tWrCreate.rdd(t.dat, n=Wr)\n\t\twr <- ReadResponseWindowFile(\"wr1.csv\")\n\t\tw.dat <- MakeWr(t.dat,wr)\n\t}\n\t\n\t# Add images\n\tif(!is.null(img1)){img1<-png::readPNG(img1)}\n\tif(!is.null(img2)){img2<-png::readPNG(img2)}\n\tif(!is.null(img3)){img3<-png::readPNG(img3)}\n\tif(!is.null(img4)){img4<-png::readPNG(img4)}\n\t\n\t# Initial Data processing\n\ttmp.rd <- list(t.dat=t.dat,w.dat=w.dat,c.dat=c.dat)\n\tlevs<-setdiff(unique(as.character(w.dat[,2])),\"\")\n\tsnr.lim=4;hab.lim=.05;sm=3;ws=30;blc=\"SNIP\"\n\tpcp <- ProcConstPharm(tmp.rd,sm,ws,blc)\n\tscp <- ScoreConstPharm(tmp.rd,pcp$blc,pcp$snr,pcp$der,snr.lim,hab.lim,sm)\n\tbin <- bScore(pcp$blc,pcp$snr,snr.lim,hab.lim,levs,tmp.rd$w.dat[,\"wr1\"])\n\tbin <- bin[,levs]\n\tbin[\"drop\"] <- 0 #maybe try to generate some drop criteria from the scp file.\n\tbin<-pf.function(bin,levs)\n\n\t#Create Despiked data\n\twts <- t.dat\n\tfor(i in 1:5) #run the despike 5 times.\n\t{\n\t\twt.mn3 <- Mean3(wts)\n\t\twts <- SpikeTrim2(wts,1,-1)\n\t\tprint(sum(is.na(wts))) #this prints out the number of points removed should be close to 0 after 5 loops.\n\t\twts[is.na(wts)] <- wt.mn3[is.na(wts)]\n\t}\n\tmp <- wts\n\t\n\ttmp.rd <- list(t.dat=t.dat,w.dat=w.dat,c.dat=c.dat, bin=bin, scp=scp, snr=pcp$snr, blc=pcp$blc, der=pcp$der, mp=mp, img1=img1, img2=img2, img3=img3, img4=img4)\n\t\n\tif(fancy==TRUE){tmp.rd<-cell.creator(tmp.rd)}\t\t# Create list of binary labeled neurons}\n\telse{tmp.rd$cells<-NULL}\n\t\n\tif(is.null(rd.name)){rd.name <- paste(\"RD\",make.names(date()),sep=\"\")}\n\t\n\tif(length(which(duplicated(row.names(t.dat))))>=1){\n\tdup<-which(duplicated(row.names(t.dat)))\n\tpaste(dup)\n\tt.dat<-t.dat[-dup,]\n\tw.dat<-w.dat[-dup,]\n\t}\n\t\n\t\n\tf.name <- paste(rd.name,\".Rdata\",sep=\"\")\n\tassign(rd.name,tmp.rd)\n\tsave(list=rd.name,file=f.name)\n\treturn(paste(nrow(tmp.rd$c.dat),\"traces read saved to \",f.name))\n\t#save as RD file\n}\n\n#develope cellular binary score and place the binary label of the cells into a cell list called cells\ncell.creator<-function(dat){\t\t# Create list of binary labeled neurons\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat$wr1)),\"\")\n\t\tbin<-label.bin(dat, 50)\n\t\tdat$bin<-bin\n\t\tcells<-list()\n\t\tneuron.response<-select.list(levs, title=\"What defines Neurons?\", multiple=T)\n\t\tneurons<-cellz(dat$bin,neuron.response, 1)\n\t\tdrop<-cellz(dat$bin, \"drop\", 1)\n\t\tneurons<-setdiff(neurons,drop)\n\t\tpf<-apply(dat$bin[,c(\"mean.gfp.bin\", \"mean.tritc.bin\")],1,paste, collapse=\"\")\n\t\tdat$bin[\"lab.pf\"]<-as.factor(pf)\n\t\tlab.groups<-unique(dat$bin$lab.pf)\n\t\t\n\t\tcells<-list()\n\t\tfor(i in lab.groups){\n\t\t\tx.names<-cellz(dat$bin[neurons,], \"lab.pf\", i)\n\t\t\tcells[[i]]<-x.names\n\t\t}\n\t\t\n\t\tglia.response<-select.list(c(levs, \"none\"), title=\"What defines glia?\", multiple=T)\n\t\tif(glia.response!=\"none\"){\n\t\t\tdrop<-cellz(dat$bin, \"drop\", 1)\n\t\t\tglia<-cellz(dat$bin,glia.response, 1)\n\t\t\tglia<-setdiff(glia,drop)\n\t\t\tcells[[\"000\"]]<-setdiff(glia, neurons)\n\t\t} \n\t\telse {cells[[\"000\"]]<-setdiff(row.names(dat$c.dat), neurons)}\n\t\tdat$cells<-cells\n\t\treturn(dat)\n\t}\n\nReadResponseWindowFile <- function(fname)\n{\n dat <- read.csv(fname)\n return(dat)\n}\n\n#wr file should be\n#NEW format for wr file (three column table, treatment, at, duration)\nGetWr <- function(fname)\n{\n\twr1 <- read.csv(fname)\n\treturn(wr1)\n}\n\nMakeWr <- function(t.dat,wr1,padL=0,padR=0)\n{\n\tw.dat <- t.dat[,1:2]\n\tnames(w.dat)[2] <- \"wr1\"\n\tw.dat[\"wr1\"] <- \"\"\n\twr1[\"treatment\"] <- make.names(wr1[,\"treatment\"],unique=T)\n\tfor(i in 1:nrow(wr1))\n\t{\n\t\tx1 <- which.min(abs(wr1[i,\"at\"]-t.dat[,\"Time\"]))\n\t\tx2 <- which.min(abs((wr1[i,\"at\"]+wr1[i,\"duration\"])-t.dat[,\"Time\"]))\n\t\tw.dat[max((x1-padL),1):min((x2+padR),nrow(t.dat)),\"wr1\"] <- as.character(wr1[i,\"treatment\"])\n\t}\n\treturn(w.dat)\n}\n\n#fill forward for flevs in the window region.\nFillWR <- function(wr1,flevs)\n{\n\tu.names <- unique(wr1)\n\twr2 <- NumBlanks(wr1)\n\n\tu2.names <- unique(wr2)\n\tb.names <- grep(\"blank\",u2.names,value=T)\n\tfor(i in flevs)\n\t{\n\t\tfor(j in 1:(length(u2.names)-1))\n\t\t{\n\t\t\tif(u2.names[j]==i & is.element(u2.names[j+1],b.names) )\n\t\t\t{\n\t\t\t\twr1[wr2==u2.names[j+1]] <- i\n\t\t\t}\n\t\t}\n\t}\n\treturn(wr1)\n}\n\n#adjust the windows to maximize shift regions and peak regions\n#try to minimize the false positive rates but growing/shinking windows\n#works reasonably well, but is only counting peaks. It is not accountinf\n#for shape aspects of the trace.\nWrAdjust <- function(dat,pcp=NULL,wr=NULL,wr.levs=NULL,snrT=4,minT=10)\n{\n\tgtrfunc <- function(x,a){sum(x>a)}\n\tif(is.null(wr)){wr <- dat$w.dat[,\"wr1\"]}\n\twr.new <- wr\n\twrb <- NumBlanks(wr)\n\twi <- 1:(length(wrb))\n\tx.names <- names(dat$t.dat[,-1])\n\tif(is.element(\"bin\",names(dat)))\n\t\tif(is.element(\"drop\",names(dat$bin)))\n\t\t{\n\t\t\tx.names <- row.names(dat$bin[dat$bin[,\"drop\"]==0,])\n\t\t}\n\tif(is.null(wr.levs))\n\t{\n\t\twr.levs <- unique(wr)\n\t\twr.levs <- wr.levs[wr.levs != \"\"]\n\t}\n\tif(is.null(pcp))\n\t{\n\t\tpcp <- ProcConstPharm(dat)\n\t}\n\t#OK expand/contract each window to give best false positive ratio.\n\t#keep a min width.\n\thits <- apply(pcp$snr[,x.names],1,gtrfunc,a=snrT)\n\twrb.levs <- unique(wrb)\n\tb.levs <- grep(\"blank\",wrb.levs,value=T)\n\tfor(i in wr.levs[wr.levs != wrb.levs[length(wrb.levs)]])\n\t{\n\t\ti1 <- match(i,wrb.levs)\n\t\tif(is.element(wrb.levs[i1+1],b.levs))\n\t\t{\n\t\t\ttargs <- hits[wrb==i | wrb==wrb.levs[i1+1]]\n\t\t\ttval <- NULL\n\t\t\tendT <- length(targs)\n\t\t\tlp <- 0\n\t\t\tfor(j in minT:(endT-1))\n\t\t\t{\n\t\t\t\tlp <- lp+1\n\t\t\t\t#tval[lp] <- mean(targs[1:j])/((sum(targs[(j+1):endT])+1)/length(targs[(j+1):endT]))\n\t\t\t\ttval[lp] <- 1/((sum(targs[(j+1):endT])+1)/length(targs[(j+1):endT]))\t\t\t\t\n\t\t\t}\t\t\t\n\t\t\tiopt <- match(i,wr)+which.max(tval)+(minT-1)\n\t\t}\n\t\telse\n\t\t{iopt <- max(wi[wr==i])}\n\t\twr.new[wr==i] <- \"\"\n\t\twr.new[match(i,wr):iopt] <- i\n\t}\t\n\treturn(wr.new)\n}\n\nWrCreate.rdd<-function(t.dat, n=NULL){\n\t\n\twindow.dat<-data.frame()\n\t#dev.new(width=10,height=6) \n\tx.names<-names(t.dat)[-1]\n\tLinesSome(t.dat,m.names=x.names,lmain=\"\",subset.n=15)\n\t## Plot the total sum of all peaks\n\t#t.sum<-apply(t.dat[-1], 1, sum)\n\t#plot(t.dat[,1], t.sum, type=\"l\", lwd=2)\n\t\n\ti<-1\n\tfor(i in i:n){\n\tdose<-locator(n=2, type=\"o\", pch=15, col=\"red\")\n\tabline(v=c(dose$x[1],dose$x[2]), col=\"red\", lwd=1)\n\tdose.type<-scan(file=\"\", what=\"character\", n=1, quiet=T)\n\tduration<-dose$x[2]-dose$x[1]\n\twindow.dat[i,1]<-dose.type\n\twindow.dat[i,2]<-dose$x[1]\n\twindow.dat[i,3]<-duration\n\twindow.dat<-print(window.dat)\n\tnames(window.dat)<-c(\"treatment\", \"at\", \"duration\")\n}\ngraphics.off()\nwrite.csv(window.dat, file=\"wr1.csv\", row.names=F)}\n\n# General Read data dump for an already created RD file without window data\nWrCreate.1<-function(dat, n=14, cell=NULL){\n\twindow.dat<-data.frame()\n\tif(is.null(cell)){cell<-\"X.1\"}\n\telse(cell<-cell)\n\tt.sum<-apply(dat$t.dat[-1], 1, sum)\n\tdev.new(width=14,height=4) \n\tymax<-max(dat$t.dat[,cell])*1.05\n\tymin<-min(dat$t.dat[,cell])*.95\n\tyrange<-ymax-ymin\n\n ylim <- c(ymin,ymax)\n\txlim <- range(dat$t.dat[,1]) # use same xlim on all plots for better comparison\n\t\n\tpar(mar=c(6,4.5,3.5,11))\n\tplot(dat$t.dat[,cell]~dat$t.dat[,1], main=cell,xlim=xlim,ylim=ylim,xlab=\"\", ylab=\"\",pch=16, lwd=1, cex=.5)\n\t#axis(1, at=seq(0, length(dat$t.dat[,1]), 5),tick=TRUE ) \n\n\t\n\nfor(i in 1:n){\n\tdose<-locator(n=2, type=\"o\", pch=15, col=\"red\")\n\tabline(v=c(dose$x[1],dose$x[2]), col=\"red\", lwd=1)\n\tdose.type<-scan(file=\"\", what=\"character\", n=1, quiet=T)\n\tduration<-dose$x[2]-dose$x[1]\n\twindow.dat[i,1]<-dose.type\n\twindow.dat[i,2]<-dose$x[1]\n\twindow.dat[i,3]<-duration\n\twindow.dat<-print(window.dat)\n\tnames(window.dat)<-c(\"treatment\", \"at\", \"duration\")\n\twr1<-window.dat\n}\nt.dat<-return(MakeWr(dat$t.dat,wr1,padL=0,padR=0))\n}\n\nWrMultiplex<-function(t.dat, wr, n=NULL){\n\tw.dat<-t.dat[,1:2]\n\tnames(w.dat)[2]<-\"wr1\"\n\tw.dat[\"wr1\"]<-\"\"\n\tif(is.null(n)){n=length(wr[,1])}\n\tlibrary(cluster)\n\tpamk<-pam(w.dat[,1], k=n)\n\twr[1] <- make.names(wr[,1],unique=T)\n\tlevs<-wr[,1]\n\tw.dat[,\"wr1\"]<-levs[pamk$clustering]\n\treturn(w.dat)}\n\n##############################################################################################\n##############################################################################################\n\n\n\n##############################################################################################\n# Cornerstones of trace washing, peak detection, and binary scoring\n##############################################################################################\n\n#the first argument is the raw data\n#the second argument is the halfwindow size for smoothing (shws)\n#the third argument is the peak detection halfwindow size (phws)\n#the last argument is the baseline correction method (TopHat = blue line SNIP = red line)\n#Note that you should use the RoughReview function to determine the best values for\n#arguments 2,3 and 4.\n\n#returns a list with two dataframes: snr and blc.\n#snr has the peaks detected for all cells, blc has the baseline corrected data for all cells. \n\nSpikeTrim2 <- function(wt,ulim=NULL,dlim= NULL)\n{\n\t\n\twtd <- wt[-1,]-wt[-nrow(wt),]\n\twtd <- sweep(wtd[,-1],1,wtd[,1],'/')\n\tif(is.null(ulim) | is.null(dlim))\n\t{\n\t\tqvals <- quantile(as.vector(as.matrix(wtd)),probs=c(0,.01,.5,.99,1))\n\t}\n\tif(is.null(dlim)){dlim <- qvals[2]}\n\tif(is.null(ulim)){ulim <- qvals[4]}\t\n\twt.up <- wtd > ulim\n\twt.dn <- wtd < dlim\n\twt.ud <- wt.up[-nrow(wt.up),] + wt.dn[-1,]\n\twt.du <- wt.up[-1,] + wt.dn[-nrow(wt.dn),]\n\twt.na <- wt[2:(nrow(wt)-1),-1]\n\twt.na[wt.ud==2] <- NA\n\twt.na[wt.du==2] <- NA\t\n\tsum(is.na(wt.na))\n\twt[2:(nrow(wt)-1),-1] <- wt.na\n\n\t#impute missing using mean of flanking.\n\t#consider replicating first and last columns and doing this all as a vector\n\t\n\treturn(wt)\n}\n\n\n#each point is replaced with the mean of the two neighboring points\nMean3 <- function(wt)\n{\n\twt.mn <- (wt[-c(1,2),]+wt[-c(nrow(wt),(nrow(wt)-1)),])/2\n\twt[2:(nrow(wt)-1),] <- wt.mn\n\treturn(wt)\n}\n\n\nProcConstPharm <- function(dat,shws=2,phws=20,bl.meth=\"SNIP\")\n{\n dat1 <- dat$t.dat\n t.names <- names(dat1)[-1]#Time in first column\n dat1.snr <- dat1 #peak calls stored as SNR\n dat1.snr[,t.names] <- 0\n dat1.bc <- dat1.snr #baseline corrected data\n\n for(i in t.names)\n {\n p1 <- PeakFunc2(dat1,i,shws=shws,phws=phws,Plotit=F,bl.meth=bl.meth)\n dat1.snr[match(mass(p1$peaks),dat1[,1]),i] <- snr(p1$peaks)\n dat1.bc[i] <- intensity(p1$dat)\n }\n\tdat1.der<-dat1.bc[-1,]-dat1.bc[-nrow(dat1.bc),]\n\tdat1.der <- sweep(dat1.der[,-1],1,dat1.der[,1],'/')\n\n# dat1.crr <- allCRR(dat1,t.names,Plotit=F) #leave off advanced processing for now\n return(list(snr=dat1.snr,blc=dat1.bc, der=dat1.der))\n}\n\n#binary score for all cells for the regions of interest bScore\n#argument 1 is the baseline corrected data\n#argument 2 is the snr peak data\n#argument 3 is the threshold for significance on the peaks\n#argument 4 is the intensity above baseline theshold\n#argument 5 indicates the regions of interest. (e.g. the response windows for which the cells will be scored)\n#argument 6 indicates the response windows. \n#argument 7 indicates the cells to score (if null all cells will be scored)\n#returns the scoring for all cells subject to the above parameters.\n#as well as the sum for the snr scores and the sd for the snr scores.\nbScore <- function(blc,snr,snr.lim,blc.lim,levs,wr,c.names=NULL)\n{\n notzero <- function(x){as.integer(sum(x) > 0)}\n if(is.null(c.names)){c.names <- names(blc)[-1]}\n wr2 <- wr[is.element(wr,levs)]\n b.snr <- snr[is.element(wr,levs),c.names]\n b.blc <- blc[is.element(wr,levs),c.names]\n b.call <- b.blc\n b.call[,] <- 0\n b.call[b.snr > snr.lim & b.blc > blc.lim] <- 1\n b.score <- data.frame(tot=apply(b.snr,2,sum))\n b.score[\"sd\"] <- apply(b.snr,2,sd)\n\tfor(i in levs)\n {\n b.score[i] <- apply(b.call[wr2==i,],2,notzero)\n }\n return(b.score)\n}\n\n# Binary scoring dependent upon score const pharm talbe values\n# Best way to determine parameters is to look through trace click before hand\n# snr.min = minimun signal to noise value\n# max.min= minimun above baseline threshold\n# tot.min= area minimun to consider\n# wm.min= which max, Where within the window region does the maximun value occur\n# wm.max= where to stop looking for the maximun value\nbscore2<-function(dat, levs.1=NULL, snr.min=2.8, max.min=.03, wm.min=0, wm.max=600){\nscp<-dat$scp\nlevs<-setdiff(unique(as.character(dat$w.dat[,2])),\"\")\nif(is.null(levs.1)){levs.1<-levs}\nelse{levs.1<-levs.1}\n#dat2<-matrix(0, nrow=length(dat$c.dat[,1]), ncol=length(levs))\ndat2<-dat$bin[levs]\n#row.names(dat2)<-dat$c.dat[,1]\n#colnames(dat2)<-levs\nx.names<-dat$c.dat[,1]\nfor(j in x.names){\t\n\tfor(i in levs.1){\n\t\tsnr.name<-grep(paste(i,\".snr\", sep=\"\"), names(dat$scp), value=T)\n\t\ttot.name<-grep(paste(i,\".tot\", sep=\"\"), names(dat$scp), value=T)\n\t\tmax.name<-grep(paste(i,\".max\", sep=\"\"), names(dat$scp), value=T)\n\t\twm.name<-grep(paste(i,\".wm\", sep=\"\"), names(dat$scp), value=T)\n\t\t\n\t\tif(dat$scp[j,snr.name]>=snr.min &\n\t\t\tdat$scp[j,max.name]>=max.min &\n\t\t\tdat$scp[j,wm.name]>=wm.min &\n\t\t\tdat$scp[j,wm.name]<=wm.max)\n\t\t{dat2[j,i]<-1}\n\t\telse{dat2[j,i]<-0}\n\t\t}\n\t\t}\n\t\treturn(dat2)}\n\n# calculate a table of cell characteristics globally and \n# within specific windows\n# these specifics should include\n# mean and sd, sum of in window peaks, sum of out of window peaks\n# 1) some measure of dead cell\n# 2) yes/no peak response for each window\n# 3) peak height\n# 4) max peak SNR\n# 5) peak timing in window\n# 6)\n# variance of smoothed - raw in window\n# define and number blank windows.\nScoreConstPharm <- function(dat,blc=NULL, snr=NULL, der=NULL, snr.lim=3,blc.lim=.03,shws=2)\n{\nt.dat<-dat$t.dat\nif(is.null(blc)){blc<-dat$blc}\nelse{blc<-blc}\nif(is.null(snr)){snr<-dat$snr}\nelse{snr<-snr}\nif(is.null(der)){der<-dat$der}\nelse{der<-der}\n\n\nwr<-dat$w.dat$wr1\n\n gtfunc <- function(x,alph){sum(x > alph,na.rm=T)}\n \nlt5func <- function(x,y)\n{\n ltfunc <- function(i){summary(lm(y[i:(i+5)] ~ x[i:(i+5)]))$coefficients[2,3]}\n iseq <- 1:(length(x)-5)\n res <- sapply(iseq,ltfunc)\n return(range(res))\n}\n\n levs <- setdiff(unique(wr),\"\")\n c.names <- names(t.dat)[-1]\n res.tab <- data.frame(mean=apply(blc[,c.names],2,mean))\n res.tab[\"sd\"] <- apply(blc[,c.names],2,sd)\n res.tab[\"snr.iws\"] <- apply(snr[is.element(wr,levs),c.names],2,sum)\n res.tab[\"snr.ows\"] <- apply(snr[!is.element(wr,levs),c.names],2,sum)\n res.tab[\"snr.iwc\"] <- apply(snr[is.element(wr,levs),c.names],2,gtfunc,alph=snr.lim)\n res.tab[\"snr.owc\"] <- apply(snr[!is.element(wr,levs),c.names],2,gtfunc,alph=snr.lim)\n\n\tdat.der<-der\n\t\n for(i in c.names)\n {\n s1 <- createMassSpectrum(t.dat[,\"Time\"],t.dat[,i])\n s3 <- smoothIntensity(s1, method=\"SavitzkyGolay\", halfWindowSize=shws)\n bl.th <- estimateBaseline(s3, method=\"TopHat\")[,\"intensity\"]\n bl.snp <- estimateBaseline(s3, method=\"SNIP\")[,\"intensity\"]\n eseq <- 1:ceiling((nrow(t.dat)/2))\n lseq <- max(eseq):nrow(t.dat)\n res.tab[i,\"bl.diff\"] <- mean(bl.th-bl.snp)\n res.tab[i,\"earl.bl.diff\"] <- mean(bl.th[eseq]-bl.snp[eseq])\n res.tab[i,\"late.bl.diff\"] <- mean(bl.th[lseq]-bl.snp[lseq]) \n }\n for(i in levs)\n {\n res.tab[paste(i,\".snr\",sep=\"\")] <- apply(snr[wr==i,c.names],2,max)\n res.tab[paste(i,\".tot\",sep=\"\")] <- apply(blc[wr==i,c.names],2,sum)\n res.tab[paste(i,\".max\",sep=\"\")] <- apply(blc[wr==i,c.names],2,max)\n\t\tres.tab[paste(i,\".ph.a.r\",sep=\"\")] <-res.tab[paste(i,\".tot\",sep=\"\")]/res.tab[paste(i,\".max\",sep=\"\")]\n\n res.tab[paste(i,\".wm\",sep=\"\")] <- apply(blc[wr==i,c.names],2,which.max)\n\t\t\n\t\t## Derviative measures\n\t\t#res.tab[paste(i,\".der.tot\",sep=\"\")] <- apply(dat.der[wr==i,c.names],2,sum)\n\t\tres.tab[paste(i,\".der.tot\",sep=\"\")] <- apply(dat.der[wr==i,c.names],2,sum)\n\t\t#res.tab[paste(i,\".der.tot\",sep=\"\")] <- apply(na.omit(dat.der[wr==i,c.names]),2,function(x){sum(x[x>0])})\n res.tab[paste(i,\".der.max\",sep=\"\")] <- apply(na.omit(dat.der[wr==i,c.names]),2,max)\n\t\tres.tab[paste(i,\".der.min\",sep=\"\")] <- apply(na.omit(dat.der[wr==i,c.names]),2,min)\n res.tab[paste(i,\".der.wmax\",sep=\"\")] <- apply(na.omit(dat.der[wr==i,c.names]),2,which.max)#function(x){which.max(x[5:length(row.names(x))])})\n\t\tres.tab[paste(i,\".der.wmin\",sep=\"\")] <- apply(na.omit(dat.der[wr==i,c.names]),2,which.min)\n\n\n\t\t\n# res.tab[c(paste(i,\".dn5\",sep=\"\"),paste(i,\".up5\",sep=\"\"))] <- t(apply(t.dat[wr==i,c.names],2,lt5func,x=t.dat[wr==i,1]))\n# res.tab[paste(i,\".dn5\",sep=\"\")] <- apply(blc[wr==i,c.names],2,dn5func) \n }\n return(res.tab)\n}\n\n##############################################################################################\n##############################################################################################\n\n\n##############################################################################################\n# Response Scoring\n##############################################################################################\n#should probably break this into ScoreMulti and ReviewMulti\n#Score all RD...Rdata files in a given directory with review\n#check for an existing bin file and just review that.\n#add a \"drop\" column to the bin file\n# Needs work on drop cells\nScoreMulti <- function(dir.name=NULL,snr.lim=4,hab.lim=.05,sm=3,ws=30,review=T)\n{\n\tif(is.null(dir.name)){dir.name <- getwd()}\n\tsetwd(dir.name)\n\tf.names <- list.files(pattern=\"RD.*\\\\.Rdata$\")\n\tif(length(f.names) == 0){stop(\"no RD...Rdata files in given directory\")}\n\trd.list <- sub(\"\\\\.Rdata*\",\"\",f.names)\n\tRD.names <- rd.list #paste(rd.list,\".b\",sep=\"\")\n\tRD.f.names <- paste(RD.names,\".Rdata\",sep=\"\")\n\tsel.i <- menu(rd.list,title=\"Select Data to review\")\t\t\t\n\twhile(sel.i != 0)\n\t{\n\n\t\tj <- sel.i\n\t\tload(f.names[j])\n\t\ti <- rd.list[j]\n\t\ttmp <- get(i)\n\t\ttlevs <- c(as.character(unique(tmp$w.dat[,\"wr1\"])[-1]),\"drop\")\n\t\tif(is.null(tmp$bin))\n\t\t{\n\t\ttmp.pcp <- ProcConstPharm(tmp,sm,ws,\"TopHat\")\n\t\ttmp.scp <- ScoreConstPharm(tmp$t.dat,tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tmp$w.dat[,\"wr1\"],sm)\n\t\ttmp.bin <- bScore(tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tlevs,tmp$w.dat[,\"wr1\"])\n\t\ttmp.bin[\"drop\"] <- 0 #maybe try to generate some drop criteria from the scp file.\n\t\t}\n\t\telse\n\t\t{\n\t\t\ttmp.bin <- tmp$bin\n\t\t\ttmp.scp <- tmp$scp\n\t\t\ttmp.blc <- tmp$blc\n\t\t}\n\t\tif(review)\n\t\t{\n\t\ttmp.bin <- ScoreReview1(tmp$t.dat,tmp.bin[,tlevs],tmp$w.dat[,\"wr1\"])\n\t\ttmp.bin <- ScoreReview0(tmp$t.dat,tmp.bin[,tlevs],tmp$w.dat[,\"wr1\"])\n\t\t}\n\t\t\n\t\ttmp$bin <- tmp.bin[,tlevs]\n\t\tpf<-apply(tmp$bin[,tlevs],1,paste,collapse=\"\")\t\n\t\tpf.sum<-summary(as.factor(pf),maxsum=500)\n\t\tpf.sum<-pf.sum[order(pf.sum,decreasing=T)]\n\t\tpf.ord<-pf.sum\n\t\tpf.ord[]<-seq(1,length(pf.sum))\n\t\ttmp$c.dat[\"pf\"]<-as.factor(pf)\n\t\ttmp$c.dat[\"pf.sum\"]<-pf.sum[pf]\n\t\ttmp$c.dat[\"pf.ord\"]<-pf.ord[pf]\n\t\ttmp$c.dat<-cbind(tmp$c.dat, tmp$bin)\n\t\t\n\t\t\n\t\ttmp$scp <- tmp.scp\n\t\ttmp$snr<-tmp.pcp$snr\n\t\ttmp$blc <- tmp.pcp$blc\n\t\tassign(RD.names[j],tmp)\n\t\tsave(list=RD.names[j],file=RD.f.names[j])\n\t\tprint(paste(\"DONE REVIEWING \",RD.names[j],\" CHANGES SAVED TO FILE.\",sep=\"\"))\n\t\tsel.i <- menu(rd.list,title=\"Select Data to review\")\t\t\t\n\t}\n\treturn(RD.f.names)\t\t\n}\n\nScoreSelect <- function(t.dat,snr=NULL,m.names,wr,levs=NULL,lmain=\"\")\n{\n\tsf <- .8\n library(RColorBrewer)\n m.names <- intersect(m.names,names(t.dat))\n lwds <- 3\n if(length(m.names) == 0)\n {stop(\"no named traces exist in trace dataframe.\")}\n \n xseq <- t.dat[,1]\n cols <-brewer.pal(8,\"Dark2\")\n cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n dev.new(width=14,height=8)\n m.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n hbc <- length(m.names)*sf+min(2,max(t.dat[,m.names]))\n hb <- ceiling(hbc)\n \n plot(xseq,t.dat[,m.names[1]],ylim=c(-sf,hbc),xlab=\"Time (min)\",ylab=\"Ratio with shift\",main=lmain,type=\"n\", xaxt=\"n\")\n\taxis(1, at=seq(0, length(t.dat[,1]), 5))\n\n if(length(wr) > 0)\n {\n \tif(is.null(levs)){levs <- setdiff(unique(wr),\"\")}\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=\"lightgrey\")\n text(xseq[match(levs,wr)],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=1)\n }\n x.sel <- NULL\n xs <-rep(0,(length(m.names)+4))\n ys <- seq(1,length(m.names))*sf+t.dat[1,m.names]\n ys <- as.vector(c(ys,c(2*sf,sf,0,-sf)))\n# xs[(length(xs)-2):length(xs)] <- c(0,5,10)\n p.names <- c(m.names,\"ALL\",\"NONE\",\"FINISH\",\"DROP\")\n\tdrop.i <- length(p.names)\n done.n <- drop.i-1\n none.i <- drop.i-2\n all.i <- drop.i-3\n\n p.cols <- c(cols,c(\"black\",\"black\",\"black\",\"black\"))\n for(i in 1:length(m.names))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n if(!is.null(snr))\n {\n pp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n pp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n points(xseq[pp1],t.dat[pp1,m.names[i]]+i*sf,pch=1,col=cols[i])\n points(xseq[pp2],t.dat[pp2,m.names[i]]+i*sf,pch=0,col=cols[i])\n }\n }\n\ttext(x=xs,y=ys,labels=p.names,pos=2,cex=.7,col=p.cols)\n points(x=xs,y=ys,pch=16,col=p.cols)\n click.i <- 1 \n while(click.i < done.n)\n {\n click.i <- identify(xs,ys,n=1,plot=F)\n if(click.i < (length(m.names)+1) & click.i > 0)\n {\n i <- click.i\n if(is.element(i,x.sel))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n x.sel <- setdiff(x.sel,i)\n }\n else\n {\n\t \t lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"black\",lwd=lwds)\n #lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"white\",lwd=2,lty=2)\n x.sel <- union(x.sel,i)\n }\n }\n if(click.i == none.i)\n {\n \tx.sel <- NULL\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n\t \t}\n\t }\n if(click.i == all.i)\t\n {\n \tx.sel <- seq(1,length(m.names))\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"black\",lwd=lwds)\n\t \t}\n \t\n }\n }\n return(list(cells=m.names[x.sel],click = p.names[click.i]))\n}\n\n##review binary scoring file and toggle 1/0\n##names of binary scoring bin must be in wr\n##NO NAs\nScoreReview1 <- function(tdat,bin,wr,maxt=20)\n{\n\tsubD <- function(xdat)#trace dat with names NO TIME COL\n\t{\n\t\ts.x <- apply(xdat,2,sum)\n\t\ts.names <- names(xdat)[order(s.x)]\n\t\tsub.list <- list()\n\t\tsub.i <- seq(1,ncol(xdat),by=(maxt+1))\n\t\tif(length(sub.i) > 1)\n\t\t{\n\t\tfor(i in 1:(length(sub.i)-1))\n\t\t{\n\t\t\tsub.list[[i]] <- s.names[sub.i[i]:(sub.i[i]+maxt)]\n\t\t}\n\t\t}\n\t\ti <- length(sub.i)\n\t\tsub.list[[i]] <- s.names[sub.i[i]:(ncol(xdat))]\n\t\treturn(sub.list)\n\t}\n\t\n\tb.levs <- names(bin)[names(bin) != \"drop\"]\n\tdrop <- rep(0,nrow(bin))\n\tif(is.element(\"drop\",names(bin))){drop <- bin[,\"drop\"]}\n\tnames(drop) <- row.names(bin)\n\tfor(i in b.levs)\n\t{\n\t\tlmain <- paste(\"Scored as 1 for \",i,sep=\"\")\n\t\tb.1 <- row.names(bin)[bin[,i]==1 & drop==0]\n\t\tif(length(b.1) > 0)\n\t\t{\n\t\tif(length(b.1) < maxt){sub1 <- list(b.1)}else{sub1 <- subD(tdat[wr==i,b.1])}\n\t\tfor(x.names in sub1)\n\t\t{\n\t\t\tno.names <- NULL\n\t\t\tdropit <- TRUE\n\t\t\twhile(dropit==TRUE & (length(x.names) > 0))\n\t\t\t{\n\t\t\t\tinp <- ScoreSelect(tdat,,x.names,wr,i,lmain)\n\t\t\t\tno.names <- inp[[\"cells\"]]\n\t\t\t\tdropit <- (inp[[\"click\"]]==\"DROP\")\n\t\t\t\tif(dropit){drop[no.names] <- 1;x.names <- setdiff(x.names,no.names)}\n\t\t\t\tdev.off()\n\t\t\t}\n\t\t\tif(length(no.names) > 0)\n\t\t\t{\n\t\t\t\tbin[no.names,i] <- 0\n\t\t\t}\n\t\t}\n\t\t}\n\t}\n\tbin[\"drop\"] <- drop\n\treturn(bin)\n\t\t\n}\n\n\nScoreReview0 <- function(tdat,bin,wr,maxt=20)\n{\n\tsubD <- function(xdat)#trace dat with names NO TIME COL\n\t{\n\t\ts.x <- apply(xdat,2,sum)\n\t\ts.names <- names(xdat)[order(s.x)]\n\t\tsub.list <- list()\n\t\tsub.i <- seq(1,ncol(xdat),by=(maxt+1))\n\t\tif(length(sub.i) > 1)\n\t\t{\n\t\tfor(i in 1:(length(sub.i)-1))\n\t\t{\n\t\t\tsub.list[[i]] <- s.names[sub.i[i]:(sub.i[i]+maxt)]\n\t\t}\n\t\t}\n\t\ti <- length(sub.i)\n\t\tsub.list[[i]] <- s.names[sub.i[i]:(ncol(xdat))]\n\t\treturn(sub.list)\n\t}\n\t\n\tb.levs <- names(bin)[names(bin) != \"drop\"]\n\tdrop <- rep(0,nrow(bin))\n\tif(is.element(\"drop\",names(bin))){drop <- bin[,\"drop\"]}\n\tnames(drop) <- row.names(bin)\n\tfor(i in b.levs)\n\t{\n\t\tlmain <- paste(\"Scored as 0 for \",i,sep=\"\")\n\t\tb.1 <- row.names(bin)[bin[,i]==0 & drop==0]\n\t\tif(length(b.1) > 0)\n\t\t{\n\t\tif(length(b.1) < maxt){sub1 <- list(b.1)}else{sub1 <- subD(tdat[wr==i,b.1])}\t\t\t\n\t\tfor(x.names in sub1)\n\t\t{\n\t\t\tno.names <- NULL\n\t\t\tdropit <- TRUE\n\t\t\twhile(dropit==TRUE & (length(x.names)>0))\n\t\t\t{\n\t\t\t\tinp <- ScoreSelect(tdat,,x.names,wr,i,lmain)\n\t\t\t\tno.names <- inp[[\"cells\"]]\n\t\t\t\tdropit <- (inp[[\"click\"]]==\"DROP\")\n\t\t\t\tif(dropit){drop[no.names] <- 1;x.names <- setdiff(x.names,no.names)}\n\t\t\t\tdev.off()\n\t\t\t}\n\t\t\tif(length(no.names) > 0)\n\t\t\t{\n\t\t\t\tbin[no.names,i] <- 1\n\t\t\t}\n\t\t}\n\t\t}\n\t}\n\tbin[\"drop\"] <- drop\n\treturn(bin)\n}\n\n# Create Binary Classes of cells\n pf.function<-function(dat, levs){\n tmp<-dat\n pf<-apply(tmp[,levs],1,paste, collapse=\"\")\n pf.sum<-summary(as.factor(pf), maxsum=1500)\n pf.sum<-pf.sum[order(pf.sum, decreasing=T)]\n pf.ord<-pf.sum\n pf.ord[]<-seq(1,length(pf.sum))\n tmp[\"pf\"]<-as.factor(pf)\n tmp[\"pf.sum\"]<-pf.sum[pf]\n tmp[\"pf.ord\"]<-pf.ord[pf]\n return(tmp)\n }\n \n \n##############################################################################################\n##############################################################################################\n\n\n\n##############################################################################################\n# Drop Scoring\n##############################################################################################\n# Functions to allow for dropping of cells. Main function is DropTestMulti\n# Drops based on spikey traces, out of window peaks, and baselineshifts\n\nSpikyNorm <- function(xdat)\n{\n\t\tshapfunc <- function(x){shapiro.test(x)$p.value}\n\t\ti1 <- seq(1,nrow(xdat))\n\t\ts1 <- xdat[c(1,i1[-length(i1)]),] #shift 1 time interval forward\n\t\ts2 <- xdat[c(i1[-1],i1[length(i1)]),] #shift 1 time interval back\n\t\ts3 <- xdat-((s1+s2)/2)\n\t\ts.x <- apply(abs(s3),2,shapfunc)\n\treturn(s.x)\t\n}\n\nDropPick <- function(tdat,bin,wr,maxt=10,s.x=NULL,lmain=\"Select Cells to drop\")\n{\n\t#order traces by spikey trait.\n\t#allow drop selection until 0 selected.\n\t#spikes are defined as single point deviations from previous and next.\n\tsubD <- function(s.x)#trace dat with names NO TIME COL\n\t{\n\t\ts.names <- names(s.x)[order(s.x)]\n\t\tsub.list <- list()\n\t\tsub.i <- seq(1,length(s.x),by=(maxt+1))\n\t\tif(length(sub.i) > 1)\n\t\t{\n\t\tfor(i in 1:(length(sub.i)-1))\n\t\t{\n\t\t\tsub.list[[i]] <- s.names[sub.i[i]:(sub.i[i]+maxt)]\n\t\t}\n\t\t}\n\t\ti <- length(sub.i)\n\t\tsub.list[[i]] <- s.names[sub.i[i]:(length(s.x))]\n\t\treturn(sub.list)\n\t}\n\t\n\tb.levs <- c(\"drop\") #names(bin)[names(bin) != \"drop\"]\n\tdrop <- rep(0,nrow(bin))\n\tif(is.element(\"drop\",names(bin))){drop <- bin[,\"drop\"]}\n\tnames(drop) <- row.names(bin)\n\tfor(i in b.levs)\n\t{\n\n\t\tb.1 <- row.names(bin)[bin[,i]==0 & drop==0]\n\t\tif(is.null(s.x)){s.x <- SpikyNorm(tdat[,-1])}\n\n\t\tif(length(b.1) > 0)\n\t\t{\n\t\t\ts.x <-s.x[b.1]\n\t\tif(length(b.1) < maxt){sub1 <- list(b.1)}else{sub1 <- subD(s.x)}\n\n\t\tfor(x.names in sub1)\n\t\t{\n\t\t\tno.names <- NULL\n\t\t\tdropit <- TRUE\n\t\t\tnd <- 0\t\t\t\n\t\t\twhile(dropit==TRUE & (length(x.names)>0))\n\t\t\t{\n\n\t\t\t\tinp <- ScoreSelect(tdat,,x.names,wr,,lmain)\n\t\t\t\tno.names <- inp[[\"cells\"]]\n\t\t\t\tdropit <- (inp[[\"click\"]]==\"DROP\")\n\t\t\t\tif(dropit){drop[no.names] <- 1;x.names <- setdiff(x.names,no.names);nd=1}\n\t\t\t\tdev.off()\n\t\t\t}\n\t\t\tif(length(no.names) > 0)\n\t\t\t{\n\t\t\t\tdrop[no.names] <- 1\n\t\t\t}\n\t\t\tif(length(no.names)==0 & nd==0)\n\t\t\t{break}\n\t\t}\n\t\t}\n\t}\n\treturn(drop)\n}\n\nDropTestList <- function(tmp)\n{\n\t\t#tmp <- get(rd.name)\n\t\tx1 <- DropPick(tmp$t.dat,tmp$bin,tmp$w.dat[,\"wr1\"],lmain=\"Select spikey traces to Drop\") #defaults to spiky test\n\t\ttmp$bin[,\"drop\"] <- x1\n\t\tx1 <- DropPick(tmp$t.dat,tmp$bin,tmp$w.dat[,\"wr1\"],s.x= -apply(tmp$scp[,\"snr.owc\",drop=F],1,mean),lmain=\"Select out of window peaks to Drop\")\n\t\ttmp$bin[,\"drop\"] <- x1\t\t\n\t\tx1 <- DropPick(tmp$t.dat,tmp$bin,tmp$w.dat[,\"wr1\"],s.x= -apply(tmp$scp[,\"bl.diff\",drop=F],1,mean),lmain=\"Select Baseline Drops\")\n\t\ttmp$bin[,\"drop\"] <- x1\t\t\n\t\tif(sum(x1 > 0)) #check highest correlations with dropped cells.\n\t\t{\n\t\t\td.names <- names(x1[x1==1])\n\t\t\tct <- cor(tmp$t.dat[,-1])\n\t\t\tmn <- -apply(ct[,d.names],1,max)\n\t\t\tx1 <- DropPick(tmp$t.dat,tmp$bin,tmp$w.dat[,\"wr1\"],s.x= mn,lmain=\"Correlated with other drops\")\n\t\t\ttmp$bin[,\"drop\"] <- x1\t\t\n\t\t}\n\t\treturn(tmp)\n}\n\nDropTestMulti <- function(dir.name=NULL,snr.lim=4,hab.lim=.05,sm=3,ws=30,review=F)\n{\n\tif(is.null(dir.name)){dir.name <- getwd()}\n\tsetwd(dir.name)\n\tf.names <- list.files(pattern=\"RD.*\\\\.Rdata$\")\n\tif(length(f.names) == 0){stop(\"no RD...Rdata files in given directory\")}\n\trd.list <- sub(\"\\\\.Rdata*\",\"\",f.names)\n\tRD.names <- rd.list #paste(rd.list,\".b\",sep=\"\")\n\tRD.f.names <- paste(RD.names,\".Rdata\",sep=\"\")\n\tsel.i <- menu(rd.list,title=\"Select Data to review\")\n\twhile(sel.i != 0)\n\t{\n\t\tj <- sel.i\n\t\tload(f.names[j])\n\t\ti <- rd.list[j]\n\t\ttmp <- get(i)\n\t\ttlevs <- c(as.character(unique(tmp$w.dat[,\"wr1\"])[-1]),\"drop\")\n\t\tif(is.null(tmp$bin))\n\t\t{\n\t\ttmp.pcp <- ProcConstPharm(tmp,sm,ws,\"TopHat\")\n\t\ttmp.scp <- ScoreConstPharm(tmp$t.dat,tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tmp$w.dat[,\"wr1\"],sm)\n\t\ttmp.bin <- bScore(tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tlevs,tmp$w.dat[,\"wr1\"])\n\t\ttmp.bin[\"drop\"] <- 0 #maybe try to generate some drop criteria from the scp file.\n\t\t}\n\t\telse\n\t\t{\n\t\t\ttmp.pcp <- ProcConstPharm(tmp,sm,ws,\"TopHat\")\n\t\t\ttmp.scp <- ScoreConstPharm(tmp$t.dat,tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tmp$w.dat[,\"wr1\"],sm)\n\t\t\ttmp.bin <- tmp$bin\n\t\t\ttmp.scp <- tmp$scp\n\t\t\t#tmp.blc <- tmp$blc\n\t\t}\n\n\t\ttmp$bin <- tmp.bin[,tlevs]\n\t\ttmp$scp <- tmp.scp\n\t\t#tmp$blc <- tmp.blc\n\t\t\n\t\ttmp <- DropTestList(tmp)\n\t\tif(review)\n\t\t{\n\t\t \ttmp.bin <- ScoreReview1(tmp$t.dat,tmp.bin[,tlevs],tmp$w.dat[,\"wr1\"])\n\t\t \ttmp.bin <- ScoreReview0(tmp$t.dat,tmp.bin[,tlevs],tmp$w.dat[,\"wr1\"])\n\t \ttmp$bin <- tmp.bin[,tlevs]\n\t\t}\n\t\tpf<-apply(tmp$bin[,tlevs],1,paste,collapse=\"\")\n\t\tpf.sum<-summary(as.factor(pf),maxsum=500)\n\t\tpf.sum<-pf.sum[order(pf.sum,decreasing=T)]\n\t\tpf.ord<-pf.sum\n\t\tpf.ord[]<-seq(1,length(pf.sum))\n\t\ttmp$c.dat[\"pf\"]<-as.factor(pf)\n\t\ttmp$c.dat[\"pf.sum\"]<-pf.sum[pf]\n\t\ttmp$c.dat[\"pf.ord\"]<-pf.ord[pf]\n\t\t\n\t\t\n\t\ttmp$scp <- tmp.scp\n\t\ttmp$snr<-tmp.pcp$snr\n\t\ttmp$blc <- tmp.pcp$blc\n\t\n\t\tassign(RD.names[j],tmp)\t\t\n\t\tsave(list=RD.names[j],file=RD.f.names[j])\n\t\tprint(paste(\"DONE REVIEWING \",RD.names[j],\" CHANGES SAVED TO FILE.\",sep=\"\"))\n\t\tprint(paste(\"Dropped Cells:\", table(tmp$bin[,\"drop\"])[2]))\n\t\tsel.i <- menu(rd.list,title=\"Select Data to review\")\t\t\t\n\t}\n\treturn(RD.f.names)\t\t\n}\n\n\n##############################################################################################\n##############################################################################################\n\n##############################################################################################\n# No Scoring, only processing\n##############################################################################################\n\nTrace.prep<-function(dir.name=NULL,snr.lim=4,hab.lim=.05,sm=3,ws=30,blc=\"SNIP\")\n{\n\tif(is.null(dir.name)){dir.name <- getwd()}\n\tsetwd(dir.name)\n\tf.names <- list.files(pattern=\"RD.*\\\\.Rdata$\")\n\tif(length(f.names) == 0){stop(\"no RD...Rdata files in given directory\")}\n\trd.list <- sub(\"\\\\.Rdata*\",\"\",f.names)\n\tRD.names <- rd.list #paste(rd.list,\".b\",sep=\"\")\n\tRD.f.names <- paste(RD.names,\".Rdata\",sep=\"\")\n\tsel.i <- menu(rd.list,title=\"Select Data to review\")\n\twhile(sel.i != 0)\n\t{\n\t\tj <- sel.i\n\t\tload(f.names[j])\n\t\ti <- rd.list[j]\n\t\ttmp <- get(i)\n\t\ttlevs<-c(setdiff(unique(as.character(tmp$w.dat[,2])),\"\"),\"drop\")\n\t\t\n\t\t\n\t\ttmp.pcp <- ProcConstPharm(tmp,sm,ws,blc)\n\t\ttmp.scp <- ScoreConstPharm(tmp,tmp.pcp$blc,tmp.pcp$snr, tmp.pcp$der,snr.lim,hab.lim,sm)\n\t\ttmp.bin <- bScore(tmp.pcp$blc,tmp.pcp$snr,snr.lim,hab.lim,tlevs,tmp$w.dat[,\"wr1\"])\n\t\ttmp.bin[\"drop\"] <- 0 #maybe try to generate some drop criteria from the scp file.\n\t\t\n\t\tpf<-apply(tmp.bin[,tlevs],1,paste,collapse=\"\")\n\t\tpf.sum<-summary(as.factor(pf),maxsum=500)\n\t\tpf.sum<-pf.sum[order(pf.sum,decreasing=T)]\n\t\tpf.ord<-pf.sum\n\t\tpf.ord[]<-seq(1,length(pf.sum))\n\t\ttmp$c.dat[\"pf\"]<-as.factor(pf)\n\t\ttmp$c.dat[\"pf.sum\"]<-pf.sum[pf]\n\t\ttmp$c.dat[\"pf.ord\"]<-pf.ord[pf]\n\t\t\n\t\ttmp$bin<-tmp.bin\n\t\ttmp$scp <- tmp.scp\n\t\ttmp$snr<-tmp.pcp$snr\n\t\ttmp$blc <- tmp.pcp$blc\n\t\ttmp$der<-tmp.pcp$der\n\t\tassign(RD.names[j],tmp)\t\t\n\t\tsave(list=RD.names[j],file=RD.f.names[j])\n\t\tprint(paste(\"DONE REVIEWING \",RD.names[j],\" CHANGES SAVED TO FILE.\",sep=\"\"))\n\t\tsel.i <- menu(rd.list,title=\"Select Data to review\")\t\t\t\n\t}\n\treturn(RD.f.names)\t\n\t}\n\n\t\n#this is not complete\n#condi is the indicator for the conditional frequency table\n#this is bad\n#####add selection section of selection of experiments to include/exclude\n#####conditional expresion tables.\nSummarizeMulti <- function(dir.name=NULL,condi=1,recur=F)\n{\n\tif(is.null(dir.name)){stop(\"not a directory\")}\n\tsetwd(dir.name)\n\tf.names <- list.files(pattern=\".*RD.*\\\\.Rdata$\",recursive=recur,full.names=T)\n\tf.names <- select.list(f.names,multiple=T,title=\"Select Experiments For Analysis\")\n\tif(length(f.names) == 0){stop(\"no RD...Rdata files in given directory\")}\n\tfor(i in f.names){load(i)}\n\trd.list <- sub(\"\\\\.Rdata*\",\"\",basename(f.names))\n\tRD.names <- ls(pat=\"^RD\")\n\tRD.names <- intersect(rd.list,RD.names)\n\tif(!setequal(RD.names,rd.list)){stop(\"dataframes loaded do not match files listed in directory\")}\n\tRD.f.names <- paste(RD.names,\".Rdata\",sep=\"\")\n\t\n\ti <- rd.list[1]\n\ttmp <- get(i)\n\tif(sum(is.element(c(\"bin\",\"scp\"),names(tmp))) < 2){stop(\"Data frame has not been scored\")}\n\n\tif(names(tmp$bin)[c(1,2)]==c(\"tot\",\"sd\"))\n\t{tmp$bin <- tmp$bin[,-c(1,2)]}\n\tfreq.tab <- data.frame(mean=apply(tmp$bin[tmp$bin[,\"drop\"]==0,],2,mean))\n\tkfreq.tab <- data.frame(mean=apply(tmp$bin[tmp$bin[,\"drop\"]==0 & tmp$bin[,condi]==1,],2,mean))\n\n\tb.names <- row.names(freq.tab)[row.names(freq.tab) != \"drop\"]\n\tq.names <- paste(b.names,\".max\",sep=\"\")\n\tresp.tab <- data.frame(mean=apply(tmp$scp[tmp$bin[,\"drop\"]==0,q.names],2,mean))\n\tfor(rn in row.names(resp.tab)){resp.tab[rn,\"mean\"] <- mean(tmp$scp[tmp$bin[,\"drop\"]==0 & tmp$bin[,sub(\"\\\\.max$\",\"\",rn)]==1,rn],na.rm=T)}\n\tpf.tot <- data.frame(str = apply(tmp$bin[tmp$bin[,\"drop\"]==0,names(tmp$bin)!=\"drop\"],1,paste,collapse=\"\"))\n\tpf.tot[\"exp\"] <- i\n\tfor(j in 2:length(RD.names))\n\t{\n\t\ti <- rd.list[j]\n\t\ttmp <- get(i)\n\t\tif(names(tmp$bin)[c(1,2)]==c(\"tot\",\"sd\"))\n\t\t{tmp$bin <- tmp$bin[,-c(1,2)]}\n\t\t\n\t\tm1 <- apply(tmp$bin[tmp$bin[,\"drop\"]==0,],2,mean)\n\t\tfreq.tab[i] <- m1[row.names(freq.tab)]\n\t\tm2 <- apply(tmp$bin[tmp$bin[,\"drop\"]==0 & tmp$bin[,condi]==1,],2,mean)\n\t\tkfreq.tab[i] <- m2[row.names(kfreq.tab)]\n\t\tresp.tab[i] <- NA\n\t\tfor(rn in intersect(row.names(resp.tab),names(tmp$scp))){resp.tab[rn,i] <- mean(tmp$scp[tmp$bin[,\"drop\"]==0 & tmp$bin[,sub(\"\\\\.max$\",\"\",rn)]==1,rn],na.rm=T)}\n\t\tpf.tmp <- data.frame(str = apply(tmp$bin[tmp$bin[,\"drop\"]==0,names(tmp$bin)!=\"drop\"],1,paste,collapse=\"\"))\t\t\n\t\tpf.tmp[\"exp\"] <- i\n\t\tpf.tot <- rbind(pf.tot,pf.tmp)\n}\n\tnames(freq.tab)[1] <- rd.list[1]\n\tnames(kfreq.tab)[1] <- rd.list[1]\n\tnames(resp.tab)[1] <- rd.list[1]\n\tpf.tab <- table(pf.tot[,1],pf.tot[,2])\n\treturn(list(freq.tab=freq.tab,kfreq.tab=kfreq.tab,resp.tab=resp.tab,pf.tab=pf.tab))\n}\n\n##############################################################################################\n# Stacked traces Plotting\n##############################################################################################\nLinesSome <- function(t.dat,snr=NULL,m.names,wr=NULL,levs=NULL,lmain=\"\",pdf.name=NULL,morder=NULL,subset.n=5,sf=.25,lw=2,bcex=.6)\n{\n\tlibrary(cluster)\n\tif(length(m.names) < subset.n)\n\t{stop(\"group size lower than subset size\")}\n\tpam5 <- pam(t(t.dat[,m.names]),k=subset.n)\n\ts.names <- row.names(pam5$medoids)\n\tif(!is.null(morder))\n\t{\n\t\tnames(morder) <- m.names\n\t\tmorder <- morder[s.names]\n\t\t}\n\tpam5.tab <- table(pam5$clustering)\n\ttags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\tLinesEvery(t.dat,snr,s.names,wr,levs,lmain,pdf.name,morder,rtag=tags,sf,lw,bcex)\n\treturn(pam5$clustering)\n}\n\nLinesEvery <- function(t.dat,snr=NULL,m.names,wr,levs=NULL,lmain=\"\",pdf.name=NULL,morder=NULL,rtag=NULL,sf=.7,lw=3,bcex=1,p.ht=7,p.wd=10)\n{\n m.names <- intersect(m.names,names(t.dat))\n xseq <- t.dat[,1]\n library(RColorBrewer)\n\n if(length(m.names) > 0)\n {\n if(is.null(pdf.name))\n {dev.new(width=14,height=8)}\n else\n {if(length(grep(\"\\\\.pdf\",pdf.name))>0){pdf(pdf.name,width=p.wd,height=p.ht)}else{png(pdf.name,width=1200,height=600)}}#pdf(pdf.name,width=28,height=16)}\n if(is.null(morder))\n {\n m.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n morder <- m.pca$x[,1] * c(1,-1)[(sum(m.pca$rot[,1]) < 0)+1]\n #m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n }\n m.names <- m.names[order(morder)]\n \n hbc <- length(m.names)*sf+max(t.dat[,m.names])\n hb <- ceiling(hbc)\n #cols <- rainbow(length(m.names),start=.55)\n\t\tcols <-brewer.pal(8,\"Dark2\")\n cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n par(mar=c(4,1,4,1))\n plot(xseq,t.dat[,m.names[1]],ylim=c(0,hbc),xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",yaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+1.5))#-sf\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\tif(!is.null(wr))\n {\n \tif(!is.null(levs))\n \t{\n #levs <- setdiff(unique(wr),\"\")\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=NA,border=\"darkgrey\")\n cpx <- xseq[match(levs,wr)+round(table(wr)[levs]/2,0)]\n offs <- nchar(levs)*.5\n text(cpx,rep(c(sf/2,sf),length=length(levs)),levs,pos=1,cex=bcex)#,offset=-offs\n }\n }\n for(i in 1:length(m.names))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf, cex=.5,col=cols[i],lty=1, lwd=lw)\n\t\t\tpoints(xseq,t.dat[,m.names[i]]+i*sf,pch=15, cex=.5,col=cols[i])\n if(!is.null(snr))\n {\n pp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n pp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n # pp3 <- dat$crr[,m.names[i]] > 0\n points(xseq[pp1],t.dat[pp1,m.names[i]]+i/10,pch=1,col=cols[i])\n points(xseq[pp2],t.dat[pp2,m.names[i]]+i/10,pch=0,col=cols[i])\n # points(xseq[pp3],t.dat[pp3,m.names[i]]+i/10,pch=2,col=cols[i],cex=.5)\n } \n }\n text(rep(0,length(m.names)),seq(1,length(m.names))*sf+t.dat[1,m.names],m.names,cex=.8*bcex,col=cols,pos=2)\n if(!is.null(rtag))\n {\n \trtag <- rtag[order(morder)]\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag,cex=.8*bcex,col=cols,pos=4)\n }\n if(!is.null(pdf.name))\n {dev.off()}\n }\n \n}\n\n#Simplified LinesEvery which only needs 2 entries; RD and m.names.\nLinesEvery.2 <- function(dat,m.names, blc=FALSE, snr=NULL,lmain=\"\",cols=NULL, levs=NULL,m.order=NULL,rtag=NULL,rtag2=NULL,rtag3=NULL, plot.new=TRUE,sf=.7,lw=.9,bcex=.8,p.ht=7,p.wd=10)\n{\n\tif(blc){t.dat<-dat$blc}\n\telse{t.dat<-dat$t.dat}\n\twr<-dat$w.dat[,2]\n\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\telse{levs<-levs}\n m.names <- intersect(m.names,names(t.dat))\n xseq <- t.dat[,1]\n if(plot.new){dev.new(width=10,height=6)}\n\tlibrary(RColorBrewer)\n \n## Tool for Sorting cells based on c.dat collumn name\n\tif(length(m.names) > 0)\n { \n\t\tif(!is.null(m.order)){\t\n\t\t\ttmp<-dat$c.dat[m.names,]\n\t\t\tn.order<-tmp[order(tmp[,m.order]),]\n\t\t\tm.names <- row.names(n.order)\n\t\t}\n\t\telse{\n\t\t\tm.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n morder <- m.pca$x[,1] * c(1,-1)[(sum(m.pca$rot[,1]) < 0)+1]\n m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n\t\t\tm.names <- m.names[order(morder)]\n\t\t}\n\t\t\n\t## Tool for color labeleing\n\t\tif(is.null(cols)){\n\t\t\t#cols <- rainbow(length(m.names),start=.55)\n\t\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t} \n\t## Tool for single color labeling\n\t\telse {cols<-cols\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t}\n\t\t\n hbc <- length(m.names)*sf+max(t.dat[,m.names])\n hb <- ceiling(hbc)\n\t\t#par(xpd=TRUE)\n\t\tpar(mar=c(4,2,4,3))\n plot(xseq,t.dat[,m.names[1]],ylim=c(0,hbc),xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",yaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)))#-sf\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t axis(2, 1.4, )\n\t\ttext(rep(0,length(m.names)),seq(1,length(m.names))*sf+t.dat[1,m.names],m.names,cex=.8*bcex,col=cols,pos=2)\n\n\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n #levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n cpx <- xseq[match(levs,wr)+round(table(wr)[levs]/2,0)]\n offs <- nchar(levs)*.5\n text(dat$t.dat[match(levs,wr),\"Time\"],rep(c(sf/2,sf),length=length(levs)),levs,pos=4,offset=0,cex=bcex)#,offset=-offs}\n\t## Tool for adding line and point plot for graph\n\t\t\tfor(i in 1:length(m.names)){\n\t\t\t\tlines(xseq,t.dat[,m.names[i]]+i*sf, lty=1,col=cols[i],lwd=lw)\n\t\t\t\tpoints(xseq,t.dat[,m.names[i]]+i*sf,pch=16,col=cols[i],cex=.3)\n\n\t\t\t\tif(!is.null(snr)){\n\t\t\t\t\tpp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n\t\t\t\t\tpp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n\t\t\t\t\tpoints(xseq[pp1],t.dat[pp1,m.names[i]]+i/10,pch=1,col=cols[i])\n\t\t\t\t\tpoints(xseq[pp2],t.dat[pp2,m.names[i]]+i/10,pch=0,col=cols[i])\n\t\t\t\t} \n\t\t\t}\n\t\t}\n\t## Tool for adding cell data labeling to end of graph\n\t\t\tif(!is.null(dat$c.dat[m.names, \"area\"])){rtag<-\"area\";rtag <- round(dat$c.dat[m.names,rtag], digits=0)}\n\t\t\telse{rtag<-NULL}\n\t\t\tif(!is.null(dat$c.dat[m.names, \"CGRP\"])){rtag2<-\"CGRP\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)}\n\t\t\telse{rtag2<-NULL}\n\t\t\t#if(!is.null(dat$c.dat[m.names, \"mean.gfp\"])){rtag2<-\"mean.gfp\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)}\n\t\t\t#else{rtag2<-NULL}\n\t\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp\"])){rtag2<-\"mean.gfp\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)}\n\t\t\telse{rtag2<-NULL}\n\t\t\tif(!is.null(dat$c.dat[m.names, \"IB4\"])){rtag3<-\"IB4\";rtag3 <- round(dat$c.dat[m.names,rtag3], digits=0)}\n\t\t\telse{rtag3<-NULL}\n\t\t\tif(!is.null(dat$c.dat[m.names, \"mean.tritc\"])){rtag3<-\"mean.tritc\";rtag3 <- round(dat$c.dat[m.names,rtag3], digits=0)}\n\t\t\telse{rtag3<-NULL}\n\t\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp.2\"])){rtag4<-\"mean.gfp.2\";rtag4 <- round(dat$c.dat[m.names,rtag4], digits=0)}\n\t\t\telse{rtag4<-NULL}\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag,cex=.9*bcex,col=cols,pos=4)\n\t text(rep(max(xseq)*1.04,length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag2,cex=.9*bcex,col=\"darkgreen\",pos=4)\n\t\t\ttext(rep(max(xseq)*1.08,length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag3,cex=.9*bcex,col=\"red\",pos=4)\n\n\n\t\t \n }\n}\n\n# pic.plot=T plots images next to trace, unles more than 10 traces\n# XY.plot, shows cells in image\nLinesEvery.3 <- function(dat,m.names, img=NULL,pic.plot=TRUE, XY.plot=TRUE, blc=T, snr=NULL,lmain=\"\",cols=NULL, levs=NULL, levs.cols=\"grey90\",m.order=NULL,rtag=NULL,rtag2=NULL,rtag3=NULL, plot.new=TRUE,sf=.7,lw=.9,bcex=.6,p.ht=7,p.wd=10)\n{\n\tif(blc){t.dat<-dat$blc}\n\telse{t.dat<-dat$t.dat}\n\twr<-dat$w.dat[,2]\n\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\telse{levs<-levs}\n m.names <- intersect(m.names,names(t.dat))\n xseq <- t.dat[,1]\n\thbc <- length(m.names)*sf+max(t.dat[,m.names])\n\thb <- ceiling(hbc)\n\tlibrary(RColorBrewer)\n \n## Tool for Sorting cells based on c.dat collumn name\n\tif(length(m.names) > 0)\n {\n\n\t\tif(!is.null(m.order)){\t\n\t\t\ttmp<-dat$c.dat[m.names,]\n\t\t\tn.order<-tmp[order(tmp[,m.order]),]\n\t\t\tm.names <- row.names(n.order)\n\t\t}\n\t\telse{\n\t\t\tm.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n morder <- m.pca$x[,1] * c(1,-1)[(sum(m.pca$rot[,1]) < 0)+1]\n m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n\t\t\tm.names <- m.names[order(morder)]\n\t\t}\n### Picture Plotting!\t\t\n\tif(XY.plot==T){cell.zoom.1024(dat, cell=m.names,img=img, cols=\"white\",zoom=F, plot.new=T)}\n\t## Tool for color labeleing\n\t\tif(is.null(cols)){\n\t\t\t#cols <- rainbow(length(m.names),start=.55)\n\t\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t} \n\t## Tool for single color labeling\n\t\telse {cols<-cols\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t}\n\t\t\n\t\tif(plot.new){dev.new(width=10,height=6)}\n\t\tpar(xpd=FALSE)\n\t\tpar(mar=c(4,2,4,5))\n plot(xseq,t.dat[,m.names[1]],ylim=c(0,hbc),xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",yaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)))#-sf\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t axis(2, 1.4, )\n\t\ttext(rep(0,length(m.names)),seq(1,length(m.names))*sf+t.dat[1,m.names],m.names,cex=.8*bcex,col=cols,pos=2)\n\n\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n #levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=levs.cols,border=\"black\")\n cpx <- xseq[match(levs,wr)+round(table(wr)[levs]/2,0)]\n offs <- nchar(levs)*.5\n\t\t\tpar(xpd=TRUE)\n text(dat$t.dat[match(levs,wr),\"Time\"],rep(c((sf*.7)/5,(sf*.7)),length=length(levs)),levs,pos=4,offset=0,cex=bcex)#,offset=-offs}\n\t\t\tpar(xpd=FALSE)\n\t\t}\n\t\n\t## Tool for adding line, point and picture to the plot\n\t\tfor(i in 1:length(m.names)){\n\t\t\typos<-t.dat[,m.names[i]]+i*sf\n\t\t\tlines(xseq,ypos, lty=1,col=cols[i],lwd=lw)\n\t\t\tpoints(xseq,ypos,pch=16,col=cols[i],cex=.3)\n\t\t\tif(!is.null(snr)){\n\t\t\t\tpp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n\t\t\t\tpp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n\t\t\t\tpoints(xseq[pp1],t.dat[pp1,m.names[i]]+i/10,pch=1,col=cols[i])\n\t\t\t\tpoints(xseq[pp2],t.dat[pp2,m.names[i]]+i/10,pch=0,col=cols[i])\n\t\t\t}\n\t\t}\n\t\tpar(xpd=TRUE)\n\t\tif(!is.null(dat$c.dat[m.names, \"area\"])){rtag<-\"area\";rtag <- round(dat$c.dat[m.names,rtag], digits=0)\n\t\ttext(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],paste(rtag),cex=.9*bcex,col=cols,pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp\"])){rtag2<-\"mean.gfp\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)\n\t\ttext(rep(max(xseq)*1.04,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag2),cex=.9*bcex,col=\"springgreen3\",pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp.1\"])){rtag2<-\"mean.gfp.1\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)\n\t\ttext(rep(max(xseq)*1.04,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag2),cex=.9*bcex,col=\"springgreen3\",pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.tritc\"])){rtag3<-\"mean.tritc\";rtag3 <- round(dat$c.dat[m.names,rtag3], digits=0)\n\t\ttext(rep(max(xseq)*1.08,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag3),cex=.9*bcex,col=\"red1\",pos=4)}\n\t\t\n\tif(is.null(img)){img<-dat$img1}\n\tif(pic.plot==TRUE & length(m.names)<5){\n\t\tpic.pos<-list()\n\t\tfor(i in 1:length(m.names)){\n\t\t\typos<-t.dat[,m.names[i]]+i*sf\n\t\t\tpic.pos[[i]]<-mean(ypos)}\n\n\t\t\tfor(i in 1:length(m.names)){\t\t\n\t\t\t\tzf<-20\n\t\t\t\tx<-dat$c.dat[m.names[i],\"center.x\"]\n\t\t\t\tleft<-x-zf\n\t\t\t\tif(left<=0){left=0; right=2*zf}\n\t\t\t\tright<-x+zf\n\t\t\t\tif(right>=1024){left=1024-(2*zf);right=1024}\n\t\t\t\t\n\t\t\t\ty<-dat$c.dat[m.names[i],\"center.y\"]\n\t\t\t\ttop<-y-zf\n\t\t\t\tif(top<=0){top=0; bottom=2*zf}\n\t\t\t\tbottom<-y+zf\n\t\t\t\tif(bottom>=1024){top=1024-(2*zf);bottom=1024}\n\t\t\t\t\n\t\t\t\tpar(xpd=TRUE)\n\t\t\t\txleft<-max(dat$t.dat[,1])*1.05\n\t\t\t\txright<-max(dat$t.dat[,1])*1.13\n\t\t\t\tytop<-pic.pos[[i]]+(.06*hb)\n\t\t\t\tybottom<-pic.pos[[i]]-(.06*hb)\n\t\t\t\trasterImage(img[top:bottom,left:right,],xleft,ytop,xright,ybottom)\n\t\t\t}\n\t\t}\n\telse{multi.pic.zoom(dat, m.names,img=img, plot.new=T)}\n\t}\n#return(pic.pos)\n}\n\n# LinesEvery With all inputs into a single window, excpet XY plot\nLinesEvery.4 <- function(dat,m.names, img=NULL,pic.plot=TRUE, blc=T, snr=NULL,lmain=\"\",cols=NULL, levs=NULL, levs.cols=\"grey90\",m.order=NULL,rtag=NULL,rtag2=NULL,rtag3=NULL,plot.new=T,sf=.7,lw=.9,bcex=.6,p.ht=7,p.wd=10)\n{\n\trequire(png)\n\tif(blc){t.dat<-dat$blc}\n\telse{t.dat<-dat$t.dat}\n\twr<-dat$w.dat[,2]\n\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\telse{levs<-levs}\n m.names <- intersect(m.names,names(t.dat))\n xseq <- t.dat[,1]\n\thbc <- length(m.names)*sf+max(t.dat[,m.names])\n\thb <- ceiling(hbc)\n\tlibrary(RColorBrewer)\n \n## Tool for Sorting cells based on c.dat collumn name\n\tif(length(m.names) > 0)\n {\n\n\t\tif(!is.null(m.order)){\t\n\t\t\ttmp<-dat$c.dat[m.names,]\n\t\t\tn.order<-tmp[order(tmp[,m.order]),]\n\t\t\tm.names <- row.names(n.order)\n\t\t}\n\t\telse{\n\t\t\tm.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n morder <- m.pca$x[,1] * c(1,-1)[(sum(m.pca$rot[,1]) < 0)+1]\n m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n\t\t\tm.names <- m.names[order(morder)]\n\t\t}\n### Picture Plotting!\t\t\n\t#if(XY.plot==T){cell.zoom.1024(dat, cell=m.names,img=img, cols=\"white\",zoom=F, plot.new=T)}\n\t## Tool for color labeleing\n\t\tif(is.null(cols)){\n\t\t\t#cols <- rainbow(length(m.names),start=.55)\n\t\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t} \n\t## Tool for single color labeling\n\t\telse {cols<-cols\n\t\t\tcols <- rep(cols,ceiling(length(m.names)/length(cols)))\n\t\t\tcols <- cols[1:length(m.names)]\n\t\t}\n\t\t\n\t\tif(plot.new){\n\t\t\tif(length(m.names)>5){dev.new(width=16,height=6);layout(matrix(c(1,2), 1, 2, byrow = TRUE),widths=c(10,6), heights=c(6,6))}\n\t\t\telse(dev.new(width=10,height=6))\n\t\t}\n\t\telse{\n\t\t\tif(length(m.names)>5){layout(matrix(c(1,2), 1, 2, byrow = TRUE),widths=c(10,6), heights=c(6,6))}\n\t\t\t}\n\t\tpar(xpd=FALSE,mar=c(4,2,4,5), bty=\"l\")\n plot(xseq,t.dat[,m.names[1]],ylim=c(0,hbc),xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",yaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)))#-sf\n\t\tbob<-dev.cur()\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t axis(2, 1.4, )\n\t\ttext(rep(0,length(m.names)),seq(1,length(m.names))*sf+t.dat[1,m.names],m.names,cex=.8*bcex,col=cols,pos=2)\n\n\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n #levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=levs.cols,border=\"black\")\n cpx <- xseq[match(levs,wr)+round(table(wr)[levs]/2,0)]\n offs <- nchar(levs)*.5\n\t\t\tpar(xpd=TRUE)\n text(dat$t.dat[match(levs,wr),\"Time\"],rep(c((sf*.7)/5,(sf*.7)),length=length(levs)),levs,pos=4,offset=0,cex=bcex)#,offset=-offs}\n\t\t\tpar(xpd=FALSE)\n\t\t}\n\t\n\t## Tool for adding line, point and picture to the plot\n\t\tfor(i in 1:length(m.names)){\n\t\t\typos<-t.dat[,m.names[i]]+i*sf\n\t\t\tlines(xseq,ypos, lty=1,col=cols[i],lwd=lw)\n\t\t\tpoints(xseq,ypos,pch=16,col=cols[i],cex=.3)\n\t\t\tif(!is.null(snr)){\n\t\t\t\tpp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n\t\t\t\tpp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n\t\t\t\tpoints(xseq[pp1],t.dat[pp1,m.names[i]]+i/10,pch=1,col=cols[i])\n\t\t\t\tpoints(xseq[pp2],t.dat[pp2,m.names[i]]+i/10,pch=0,col=cols[i])\n\t\t\t}\n\t\t}\n\t\tpar(xpd=TRUE)\n\t\tif(!is.null(dat$c.dat[m.names, \"area\"])){rtag<-\"area\";rtag <- round(dat$c.dat[m.names,rtag], digits=0)\n\t\ttext(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],paste(rtag),cex=.9*bcex,col=cols,pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp\"])){rtag2<-\"mean.gfp\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)\n\t\ttext(rep(max(xseq)*1.04,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag2),cex=.9*bcex,col=\"springgreen3\",pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.gfp.1\"])){rtag2<-\"mean.gfp.1\";rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0)\n\t\ttext(rep(max(xseq)*1.04,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag2),cex=.9*bcex,col=\"springgreen3\",pos=4)}\n\n\t\tif(!is.null(dat$c.dat[m.names, \"mean.tritc\"])){rtag3<-\"mean.tritc\";rtag3 <- round(dat$c.dat[m.names,rtag3], digits=0)\n\t\ttext(rep(max(xseq)*1.08,length(m.names)),seq(1,length(m.names))*sf+(t.dat[nrow(t.dat),m.names]),paste(rtag3),cex=.9*bcex,col=\"red1\",pos=4)}\n\t\t\n\tif(is.null(img)){img<-dat$img1}\n\tif(pic.plot==TRUE & length(m.names)<=5){\n\t\tpic.pos<-list()\n\t\tfor(i in 1:length(m.names)){\n\t\t\typos<-t.dat[,m.names[i]]+i*sf\n\t\t\tpic.pos[[i]]<-mean(ypos)}\n\n\t\t\tfor(i in 1:length(m.names)){\t\t\n\t\t\t\tzf<-20\n\t\t\t\tx<-dat$c.dat[m.names[i],\"center.x\"]\n\t\t\t\tleft<-x-zf\n\t\t\t\tif(left<=0){left=0; right=2*zf}\n\t\t\t\tright<-x+zf\n\t\t\t\tif(right>=1024){left=1024-(2*zf);right=1024}\n\t\t\t\t\n\t\t\t\ty<-dat$c.dat[m.names[i],\"center.y\"]\n\t\t\t\ttop<-y-zf\n\t\t\t\tif(top<=0){top=0; bottom=2*zf}\n\t\t\t\tbottom<-y+zf\n\t\t\t\tif(bottom>=1024){top=1024-(2*zf);bottom=1024}\n\t\t\t\t\n\t\t\t\tpar(xpd=TRUE)\n\t\t\t\txleft<-max(dat$t.dat[,1])*1.05\n\t\t\t\txright<-max(dat$t.dat[,1])*1.13\n\t\t\t\tytop<-pic.pos[[i]]+(.06*hb)\n\t\t\t\tybottom<-pic.pos[[i]]-(.06*hb)\n\t\t\t\trasterImage(img[top:bottom,left:right,],xleft,ytop,xright,ybottom)\n\t\t\t}\n\t\t}\n\telse{\n\t\tpar(mar=c(0,0,0,0))\n\t\tplot(0,0,xlim=c(0,6), ylim=c(0,6), xaxs=\"i\",yaxs=\"i\", xaxt='n', yaxt='n')\n\t\ttmp.img<-multi.pic.zoom.2(dat, m.names,img=img)\n\t\tdev.set(bob) # FUCK THIS!\n\t\trasterImage(tmp.img, 0,0,6,6)\n\t}\n\t}\n#return(pic.pos)\n}\n\n\n\nLinesSome.2 <- function(dat,m.names,snr=NULL,lmain=\"\",pdf.name=NULL,morder=NULL,subset.n=5,sf=1,lw=3,bcex=1)\n{\n\tlibrary(cluster)\n\tt.dat<-dat$t.dat\n\twr<-dat$w.dat[,2]\n\tlevs<-unique(as.character(wr))[-1]\n\t\n\tif(length(m.names) < subset.n)\n\t{stop(\"group size lower than subset size\")}\n\tpam5 <- pam(t(t.dat[,m.names]),k=subset.n)\n\ts.names <- row.names(pam5$medoids)\n\tif(!is.null(morder))\n\t{\n\t\tnames(morder) <- m.names\n\t\tmorder <- morder[s.names]\n\t\t}\n\tpam5.tab <- table(pam5$clustering)\n\ttags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\tLinesEvery(t.dat,snr,s.names,wr,levs,lmain,pdf.name,morder,rtag=tags,sf,lw,bcex)\n\treturn(pam5$clustering)\n}\n\nTraceSelect <- function(dat,m.names,blc=NULL,snr=NULL,wr=NULL,levs=NULL,lmain=\"\",m.order=NULL,rtag=NULL,rtag2=NULL,rtag3=NULL)\n{\n\tif(!is.null(blc)){t.dat<-dat$blc}\n\telse{t.dat<-dat$t.dat}\n\t\n\tif(is.null(wr)){wr<-dat$w.dat[,2]}\n\tsf <- .2\n\tbcex<-1\n library(RColorBrewer)\n m.names <- intersect(m.names,names(t.dat))\n lwds <- 3\n if(length(m.names) > 0)\n {\n \n xseq <- t.dat[,1]\n cols <-brewer.pal(8,\"Dark2\")\n cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n dev.new(width=14,height=8)\n \n\tif(!is.null(m.order)){\n\t\t(tmp<-dat$c.dat[m.names,])\n\t\t(n.order<-tmp[order(tmp[,m.order]),])\n\t\t(m.names <- row.names(n.order))\n\t\t}\n\telse{\n\t\tm.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n\t\tm.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n\t\t}\n \n\t\n\thbc <- length(m.names)*sf+max(t.dat[,m.names])\n hb <- ceiling(hbc)\n \n plot(xseq,t.dat[,m.names[1]],ylim=c(-sf,hbc),xlab=\"Time (min)\",ylab=\"Ratio with shift\",main=lmain,type=\"n\", xaxt=\"n\")\n\taxis(1, at=seq(0, length(t.dat[,1]), 5))\n\n if(length(wr) > 0)\n {\n \tif(is.null(levs)){levs <- setdiff(unique(wr),\"\")}\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=\"lightgrey\")\n text(xseq[match(levs,wr)],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=1)\n }\n x.sel <- NULL\n xs <-c(rep(0,length(m.names)),c(.1,.1,.1))\n ys <- seq(1,length(m.names))*sf+t.dat[1,m.names]\n ys <- as.vector(c(ys,c(sf,0,-sf)))\n# xs[(length(xs)-2):length(xs)] <- c(0,5,10)\n p.names <- c(m.names,\"ALL\",\"NONE\",\"FINISH\")\n done.n <- length(p.names)\n none.i <- done.n-1\n all.i <- none.i-1\n p.cols <- c(cols,c(\"black\",\"black\",\"black\"))\n for(i in 1:length(m.names))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n if(!is.null(snr))\n {\n pp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n pp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n points(xseq[pp1],t.dat[pp1,m.names[i]]+i*sf,pch=1,col=cols[i])\n points(xseq[pp2],t.dat[pp2,m.names[i]]+i*sf,pch=0,col=cols[i])\n }\n }\n\ttext(x=xs,y=ys,labels=p.names,pos=2,cex=.7,col=p.cols)\n points(x=xs,y=ys,pch=16,col=p.cols)\n \t\n\t\tif(is.null(rtag)){\n\t\tif(!is.null(m.order)){\n \trtag <- dat$c.dat[m.names,m.order]\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag,cex=.8*bcex,col=cols,pos=4)\n }}\n\t\telse{\n\t\t\trtag <- round(dat$c.dat[m.names,rtag], digits=0)\n\t\t\ttext(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag,cex=.8*bcex,col=cols,pos=4)\n\t\t }\n\n\t\tif(!is.null(rtag2)){\n \t(rtag2 <- round(dat$c.dat[m.names,rtag2], digits=0))\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag2,cex=.8*bcex,col=cols,pos=3)\n }\n\t\tif(!is.null(rtag3)){\n \trtag3 <- round(dat$c.dat[m.names,rtag3], digits=0)\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag3,cex=.8*bcex,col=cols,pos=1)\n }\n\t\n\tclick.i <- 1 \n while(click.i != done.n)\n {\n click.i <- identify(xs,ys,n=1,plot=F)\n if(click.i < (length(m.names)+1) & click.i > 0)\n {\n i <- click.i\n if(is.element(i,x.sel))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n x.sel <- setdiff(x.sel,i)\n }\n else\n {\n\t \t lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"black\",lwd=lwds)\n #lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"white\",lwd=2,lty=2)\n x.sel <- union(x.sel,i)\n }\n }\n if(click.i == none.i)\n {\n \tx.sel <- NULL\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lwds)\n\t \t}\n\t }\n if(click.i == all.i)\t\n {\n \tx.sel <- seq(1,length(m.names))\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,t.dat[,m.names[i]]+i*sf,col=\"black\",lwd=lwds)\n\t \t}\n \t\n }\n }\n dev.off()\n return(m.names[x.sel])\n}}\n\nLinesStack <- function(dat,m.names,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.7, sf=.2, subset.n=5)\n{\n\tif(plot.new){dev.new(width=10,height=6)}\n\tif(length(m.names)>subset.n){\n\t\tt.dat<-dat$t.dat\n\t\twr<-dat$w.dat[,2]\n\t\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\t\telse{levs<-levs}\n\t\tm.names <- intersect(m.names,names(t.dat))\n\t\thbc <- subset.n*sf+max(t.dat[,m.names])\n\t\txseq <- t.dat[,1]\n\t\tlibrary(RColorBrewer)\n\t\tpar(mar=c(4,2,4,4))\n\t\thbc <- (subset.n*(.8*sf))+max(t.dat[,m.names])\n\t\t#ylim <- c(-.1,2.5)\n\t\tylim<-c(-.1,hbc)\n\t\tplot(xseq,t.dat[,m.names[1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+25))#-sf\n\t\taxis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n\t\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(-.05, abs(min(ylim))),length=length(levs)),levs,cex=bcex,offset=0, pos=4)#,offset=-offs}\n\t\t}\n\t\tblc<-dat$blc\n\t\t## Tool for adding line and point plot for all lines\n\t\t\t#matlines(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), lwd=.01)\n\t\t\t#matpoints(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), pch=16, cex=.03)\n\t\t\n\t\t#cols <- rainbow(length(m.names),start=.55)\n\t \n\t\tlibrary(cluster)\n\t\tblc<-dat$blc\n\t\tpam5 <- pam(t(blc[,m.names]),k=subset.n)\n\t\ts.names <- row.names(pam5$medoids)\n\t\tpam5.tab <- table(pam5$clustering)\n\t\t#tags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\t\tgroup.means<-list()\n\t\tgroup.names<-list()\n\t\tfor(i in 1:subset.n){\n\t\t\tx.names<-names(which(pam5$clustering==i, arr.ind=T))\n\t\t\tgroup.names[[i]]<-x.names\n\t\t\tgroup.means[i]<-paste(\n\t\t\tround(mean(dat$c.dat[x.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \",\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\" : \",\t\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.tritc\"]), digits=0), sep=\"\")\n\t\t\t# adding standard deviation,\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \", \n\t\t}\n\t\t\n\t\t\n\t\ttags <- paste(as.vector(pam5.tab),\":\",group.means)\n\t\tinfo<-pam5$clustering\n\t\t\n\t\t## Tool For adding color to selected Traces\n\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\tcols <- rep(cols,ceiling(length(s.names)/length(cols)))\n\t\tcols <- cols[1:length(s.names)]\n\n\t\t## Tool for adding labeling for single line within stacked traces\n\t\tfor(i in 1:length(s.names)){\n\t\t\tlines(xseq, blc[,s.names[i]]+i*sf, col=cols[i], lwd=.2)\n\t\t\tpoints(xseq, blc[,s.names[i]]+i*sf, col=cols[i], pch=16, cex=.02)\n\t\t\t\t\tmatlines(xseq, blc[,names(which(info==i, arr.ind=T))]+i*sf, col=rgb(0,0,0,50, maxColorValue=100), lwd=.01)\n\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=s.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=tags[i], col=cols[i], pos=4, cex=bcex)\n\t\t}\n\t#return(pam5$clustering)\t\n\treturn(group.names)\n\t}\n} \n\nLinesStack.2 <- function(dat,m.names,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.7, sf=.2, subset.n=5, img=NULL)\n{\n\tif(is.null(img)){img<-dat$img1}\n\tif(plot.new){dev.new(width=10,height=6)}\n\tif(length(m.names)>subset.n){\n\t\tt.dat<-dat$t.dat\n\t\twr<-dat$w.dat[,2]\n\t\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\t\telse{levs<-levs}\n\t\tm.names <- intersect(m.names,names(t.dat))\n\t\thbc <- subset.n*sf+max(t.dat[,m.names])\n\t\txseq <- t.dat[,1]\n\t\tlibrary(RColorBrewer)\n\t\tpar(mar=c(4,2,4,4))\n\t\thbc <- (subset.n*(.8*sf))+max(t.dat[,m.names])\n\t\t#ylim <- c(-.1,2.5)\n\t\tylim<-c(-.1,hbc)\n\t\tplot(xseq,t.dat[,m.names[1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+25))#-sf\n\t\taxis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n\t\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(-.05, abs(min(ylim))),length=length(levs)),levs,cex=bcex,offset=0, pos=4)#,offset=-offs}\n\t\t}\n\t\tblc<-dat$blc\n\t\t## Tool for adding line and point plot for all lines\n\t\t\t#matlines(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), lwd=.01)\n\t\t\t#matpoints(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), pch=16, cex=.03)\n\t\t\n\t\t#cols <- rainbow(length(m.names),start=.55)\n\t \n\t\tlibrary(cluster)\n\t\tblc<-dat$blc\n\t\tpam5 <- pam(t(blc[,m.names]),k=subset.n)\n\t\ts.names <- row.names(pam5$medoids)\n\t\tpam5.tab <- table(pam5$clustering)\n\t\t#tags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\t\tgroup.means<-list()\n\t\tgroup.names<-list()\n\t\tfor(i in 1:subset.n){\n\t\t\tx.names<-names(which(pam5$clustering==i, arr.ind=T))\n\t\t\tgroup.names[[i]]<-x.names\n\t\t\tgroup.means[i]<-paste(\n\t\t\tround(mean(dat$c.dat[x.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \",\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\" : \",\t\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.tritc\"]), digits=0), sep=\"\")\n\t\t\t# adding standard deviation,\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \", \n\t\t}\n\t\t\n\t\t\n\t\ttags <- paste(as.vector(pam5.tab),\":\",group.means)\n\t\tinfo<-pam5$clustering\n\t\t\n\t\t## Tool For adding color to selected Traces\n\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\tcols <- rep(cols,ceiling(length(s.names)/length(cols)))\n\t\tcols <- cols[1:length(s.names)]\n\n\t\t## Tool for adding labeling for single line within stacked traces\n\t\tfor(i in 1:length(s.names)){\n\t\t\tmatlines(xseq, blc[,names(which(info==i, arr.ind=T))]+i*sf, col=rgb(0,0,0,50, maxColorValue=100), lwd=.01)\n\t\t\tlines(xseq, blc[,s.names[i]]+i*sf, col=cols[i], lwd=.2)\n\t\t\tpoints(xseq, blc[,s.names[i]]+i*sf, col=cols[i], pch=16, cex=.02)\n\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=s.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=tags[i], col=cols[i], pos=4, cex=bcex)\n\t\t}\n\t\t\n\t\tfor(i in 1:length(s.names)){\n\t\t\tLinesEvery.4(dat,names(which(info==i, arr.ind=T)), img, pic.plot=T)\n\t\t\t#multi.pic.zoom(dat, names(which(info==i, arr.ind=T)), img, plot.new=T)\n\t\t}\n\n\t}\n\telse{LinesEvery.4(dat, m.names,img)}\n\t#return(pam5$clustering)\t\n\treturn(group.names)\n\t\n\t\n\t\n} \n \nLinesStack.2.1 <- function(dat,m.names,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.7, sf=.7, subset.n=5, img=NULL)\n{\n\tif(is.null(img)){img<-dat$img1}\n\tif(plot.new){dev.new(width=10,height=6)}\n\tif(length(m.names)>subset.n){\n\t\tt.dat<-dat$t.dat\n\t\twr<-dat$w.dat[,2]\n\t\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\t\telse{levs<-levs}\n\t\tm.names <- intersect(m.names,names(t.dat))\n\t\thbc <-max(t.dat[,m.names])*subset.n *.643\n\t\txseq <- t.dat[,1]\n\t\tlibrary(RColorBrewer)\n\t\tpar(mar=c(4,2,4,4))\n\t\t#hbc <- (subset.n*(.8*sf))+max(t.dat[,m.names])\n\t\t#ylim <- c(-.1,2.5)\n\t\tylim<-c(-.1,hbc)\n\t\tplot(xseq,t.dat[,m.names[1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+25))#-sf\n\t\taxis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\t\n\t\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n\t\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(-.05, abs(min(ylim))),length=length(levs)),levs,cex=bcex,offset=0, pos=4)#,offset=-offs}\n\t\t}\n\t\tblc<-dat$blc\n\t\t## Tool for adding line and point plot for all lines\n\t\t\t#matlines(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), lwd=.01)\n\t\t\t#matpoints(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), pch=16, cex=.03)\n\t\t\n\t\t#cols <- rainbow(length(m.names),start=.55)\n\t \n\t\tlibrary(cluster)\n\t\tblc<-dat$blc\n\t\tpam5 <- pam(t(blc[,m.names]),k=subset.n)\n\t\ts.names <- row.names(pam5$medoids)\n\t\tpam5.tab <- table(pam5$clustering)\n\t\t#tags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\t\tgroup.means<-list()\n\t\tgroup.names<-list()\n\t\tfor(i in 1:subset.n){\n\t\t\tx.names<-names(which(pam5$clustering==i, arr.ind=T))\n\t\t\tgroup.names[[i]]<-x.names\n\t\t\tgroup.means[i]<-paste(\n\t\t\tround(mean(dat$c.dat[x.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \",\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\" : \",\t\n\t\t\tround(mean(dat$c.dat[x.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.tritc\"]), digits=0), sep=\"\")\n\t\t\t# adding standard deviation,\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \", \n\t\t}\n\t\t\n\t\t\n\t\ttags <- paste(as.vector(pam5.tab),\":\",group.means)\n\t\tinfo<-pam5$clustering\n\t\t\n\t\t## Tool For adding color to selected Traces\n\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\tcols <- rep(cols,ceiling(length(s.names)/length(cols)))\n\t\tcols <- cols[1:length(s.names)]\n\n\t\t## Tool for adding labeling for single line within stacked traces\n\t\tfor(i in 1:length(s.names)){\n\t\t\tmatlines(xseq, blc[,group.names[[i]]]+i*sf, col=rgb(0,0,0,50, maxColorValue=100), lwd=.01)\n\t\t\tlines(xseq, blc[,s.names[i]]+i*sf, col=cols[i], lwd=.2)\n\t\t\tpoints(xseq, blc[,s.names[i]]+i*sf, col=cols[i], pch=16, cex=.02)\n\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=s.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=tags[i], col=cols[i], pos=4, cex=bcex)\n\t\t}\n\t\t\n\t\t# Tool for adding boxplot\n\t\tpar(xpd=T)\n\t\tfor(i in 1:length(s.names)){\n\t\t\txleft<-max(blc[,1])+xinch(.2)\n\t\t\txright<-xleft+xinch(2.2)\n\t\t\ty<-(blc[nrow(t.dat),group.names[[i]]]+i*sf)\n\t\t\tybottom<- y-yinch(.4)\n\t\t\tytop<-y+yinch(.4)\n\n\t\t\t#dev.set(dev.list()[length(dev.list())])\n\t\t\trasterImage(bpfunc.2(dat,group.names[[i]]),xleft, ybottom, xright, ytop)\n\t\t}\n\t\t\n\tcontinue<-select.list(c(\"yes\", \"no\"))\n\tif(continue==\"yes\"){\n\t\tfor(i in 1:length(s.names)){\n\t\t\tLinesEvery.4(dat,names(which(info==i, arr.ind=T)), img, pic.plot=T)\n\t\t\t#multi.pic.zoom(dat, names(which(info==i, arr.ind=T)), img, plot.new=T)\n\t\t}\n}\n\t}\n\telse{LinesEvery.4(dat, m.names,img)}\n\t#return(pam5$clustering)\t\n\treturn(group.names)\n\t\t\n} \n# Stacked Traces, \n# Input is a list of cells\n# Currently Created for the 5 cell classes of,\n# +ib4+cgrp, +IB4, +CGRP, -/-, glia\nLinesStack.3 <- function(dat,cells=NULL,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.7, sf=.9, img=NULL, sample.num=NULL)\n{\n\tif(is.null(img)){img<-dat$img1}\n\tif(is.null(sample.num)){sample.num<-10}\n\tif(is.null(cells)){cells<-dat$cells}\n\telse{\n\t\tcells.main<-dat$cells\n\t\tcells.main<-cells.main[c('000','00','01','10','11')]\n\t\tbob<-list()\n\t\tfor(i in 1:length(cells.main)){\n\t\t\tx.names<-intersect(cells,cells.main[[i]])\n\t\t\tbob[[i]]<-x.names\n\t\t\t}\n\n\t\tcells<-bob\n\t\tnames(cells)<-c('000','00','01','10','11')\n\t}\n\tcells<-cells[c('000','00','01','10','11')]\n\t\n\tif(plot.new){dev.new(width=10,height=6)}\n\t\tt.dat<-dat$t.dat\n\t\twr<-dat$w.dat[,2]\n\t\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\t\telse{levs<-levs}\n\t\t#m.names <- intersect(m.names,names(t.dat))\n\t\txseq <- t.dat[,1]\n\t\tlibrary(RColorBrewer)\n\t\tpar(mar=c(4,2,4,4), bty=\"L\")\n\t\t#hbc <- (5*(.8*sf))+max(t.dat[,Reduce(c,stack(cells)[1])])\n\t\t#hbc <- 5*sf+max(t.dat[,Reduce(c,stack(cells)[1])])\n\n\t\tylim <- c(.5,5.2)\n\t\t#ylim<-c(-.1,hbc)\n\t\tplot(xseq,t.dat[,cells[[1]][1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq), max(xseq)*1.5))#-sf\n\t\taxis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\t\n\t\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n\t\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\t\ty1s <- rep(min(ylim),length(x1s))*1.03-rep(min(ylim),length(x1s))\n\t\t\ty2s <- rep(max(ylim),length(x1s))*1.03\n\t\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(.5,.6,.7),length=length(levs)),levs,cex=.6,offset=0, pos=4)#,offset=-offs}\n\t\t}\n\t\tblc<-dat$blc\t \n\t \n\t\t## Tool for creating mean and st.dev calculation\n\t\tlibrary(cluster)\n\t\tblc<-dat$blc\n\t\tgroup.means<-list()\n\t\tgroup.names<-list()\n\t\t\n\t\tfor(i in 1:length(cells)){\n\t\t\tif(length(cells[[i]])>1){\n\t\t\t\tx.names<-cells[[i]]\n\t\t\t\tgroup.names[[i]]<-names(cells[i])\n\t\t\t\tgroup.means[i]<-paste(\n\t\t\t\tlength(cells[[i]]),\":\",\n\t\t\t\tround(mean(dat$c.dat[x.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \",\n\t\t\t\tround(mean(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\" : \",\t\n\t\t\t\tround(mean(dat$c.dat[x.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.tritc\"]), digits=0), sep=\"\")\n\t\t\t\t#adding standard deviation,\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \", \n\t\t\t}\n\t\t\telse{\n\t\t\t\tx.names<-cells[[i]]\n\t\t\t\tgroup.names[[i]]<-names(cells[i])\n\t\t\t\tgroup.means[i]<-paste(\n\t\t\t\tlength(cells[[i]]))\n\t\t\t}}\n\t\t\n\t\t\n\t\t\n\t\t## Tool For adding color to selected Traces\n\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\tcols <- rep(cols,5)\n\t\tcols <- cols[1:5]\n\t\t\n\t\tcols<-c(\"mediumpurple1\",\"goldenrod1\", \"firebrick1\", \"limegreen\", \"steelblue3\")\n\n\t\n\t\t## Tool for adding labeling for single line within stacked traces\n\t\tfor(i in 1:length(cells)){\n\t\t\tif(length(cells[[i]])>1){\n\t\t\t\tmatlines(xseq, blc[,cells[[i]]]+i*sf, col=rgb(0,0,0,80, maxColorValue=100), lwd=.3)\n\t\t\t\tlines(xseq, apply(blc[,cells[[i]]],1,mean)+i*sf, col=cols[i], lwd=1.2)\n\t\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.means[i], col=\"black\", pos=4, cex=bcex)\n\t\t\t\t}\n\t\t\telse{\n\t\t\t\tlines(xseq, blc[,cells[[i]]]+i*sf, col=rgb(0,0,0,80, maxColorValue=100), lwd=.3)\n\t\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\t}}\n\n\t\t\t\t\n\t\t## Tool for adding boxplot to plot\n\t\tfor(i in 1:length(cells)){\n\t\t\txleft<-max(blc[,1])*1.05\n\t\t\txright<-xleft+xinch(2.74)\n\t\t\ty<-(blc[nrow(t.dat),cells[[i]]]+i*sf)\n\t\t\tybottom<- y-.55\n\t\t\tytop<-ybottom+yinch(.85)\n\n\t\t\t#dev.set(dev.list()[length(dev.list())])\n\t\t\trasterImage(bpfunc.2(dat,cells[[i]]),xleft, ybottom, xright, ytop)\n\t\t}\n\t\n\t\n\tcontinue<-select.list(c(\"yes\", \"no\"))\n\tif(continue==\"yes\"){\n\tfor(i in 1:length(cells)){\n\t\t\n\t\t\tif(length(cells[[i]])<20){\n\t\t\t\tLinesEvery.4(dat,cells[[i]], img, pic.plot=T, lmain=names(cells[i]), m.order=\"area\", levs=levs, sf=.6)\n\t\t\t}\n\t\t\telse{\n\t\t\t# select the range of\n\t\t\t\tsample.num<-ceiling(sample.num/2)\n\t\t\t\tcells.n<-sort(c(ceiling(seq(1,length(cells[[i]]), length.out=5)),ceiling(seq(1,length(cells[[i]]), length.out=5))+1))\n\t\t\t\tcells.rs<-c.sort(dat$c.dat[cells[[i]],], \"area\")\n\t\t\t\tLinesEvery.4(dat, cells.rs[cells.n],img, lmain=names(cells[i]), m.order=\"area\",levs=levs, sf=.4)}\n\t\t\t#multi.pic.zoom(dat, names(which(info==i, arr.ind=T)), img, plot.new=T)\n\t\t}\n\t}\n\n\t#else{LinesEvery.4(dat, m.names,img)}\n\t#return(pam5$clustering)\t\n\treturn(group.names)\n\tprint(group.means)\n\t\n\t\n\t\n} \n \nLinesStack.4 <- function(dat,cells=NULL,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.7, sf=.9, img=NULL, sample.num=NULL)\n{\n\tif(is.null(img)){img<-dat$img1}\n\tif(is.null(sample.num)){sample.num<-10}\n\tif(is.null(cells)){cells<-dat$cells}\n\telse{\n\t\tcells.main<-dat$cells\n\t\tcells.main<-cells.main[c('000','00','01','10','11')]\n\t\tbob<-list()\n\t\tfor(i in 1:length(cells.main)){\n\t\t\tx.names<-intersect(cells,cells.main[[i]])\n\t\t\tbob[[i]]<-x.names\n\t\t\t}\n\n\t\tcells<-bob\n\t\tnames(cells)<-c('000','00','01','10','11')\n\t}\n\tcells<-cells[c('000','00','01','10','11')]\n\t\n\tif(plot.new){dev.new(width=10,height=6)}\n\t\tt.dat<-dat$t.dat\n\t\twr<-dat$w.dat[,2]\n\t\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\t\telse{levs<-levs}\n\t\t#m.names <- intersect(m.names,names(t.dat))\n\t\txseq <- t.dat[,1]\n\t\tlibrary(RColorBrewer)\n\t\tpar(mar=c(4,2,4,4), bty=\"L\")\n\t\t#hbc <- (5*(.8*sf))+max(t.dat[,Reduce(c,stack(cells)[1])])\n\t\t#hbc <- 5*sf+max(t.dat[,Reduce(c,stack(cells)[1])])\n\n\t\tylim <- c(.5,5.2)\n\t\t#ylim<-c(-.1,hbc)\n\t\tplot(xseq,t.dat[,cells[[1]][1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq), max(xseq)*1.5))#-sf\n\t\taxis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\t\n\t\t## Tool for adding window region labeling\n\t\tif(length(wr) > 0){\n\t\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\t\ty1s <- rep(min(ylim),length(x1s))*1.03-rep(min(ylim),length(x1s))\n\t\t\ty2s <- rep(max(ylim),length(x1s))*1.03\n\t\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(.5,.6,.7),length=length(levs)),levs,cex=.6,offset=0, pos=4)#,offset=-offs}\n\t\t}\n\t\tblc<-dat$blc\t \n\t \n\t\t## Tool for creating mean and st.dev calculation\n\t\tlibrary(cluster)\n\t\tblc<-dat$blc\n\t\tgroup.means<-list()\n\t\tgroup.names<-list()\n\t\t\n\t\tfor(i in 1:length(cells)){\n\t\t\tif(length(cells[[i]])>1){\n\t\t\t\tx.names<-cells[[i]]\n\t\t\t\tgroup.names[[i]]<-names(cells[i])\n\t\t\t\tgroup.means[i]<-paste(\n\t\t\t\tlength(cells[[i]]),\":\",\n\t\t\t\tround(mean(dat$c.dat[x.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \",\n\t\t\t\tround(mean(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.gfp\"]), digits=0),\" : \",\t\n\t\t\t\tround(mean(dat$c.dat[x.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[x.names, \"mean.tritc\"]), digits=0), sep=\"\")\n\t\t\t\t#adding standard deviation,\"\\u00b1\",round(sd(dat$c.dat[x.names, \"area\"]), digits=0),\" : \", \n\t\t\t}\n\t\t\telse{\n\t\t\t\tx.names<-cells[[i]]\n\t\t\t\tgroup.names[[i]]<-names(cells[i])\n\t\t\t\tgroup.means[i]<-paste(\n\t\t\t\tlength(cells[[i]]))\n\t\t\t}}\n\t\t\n\t\t\n\t\t\n\t\t## Tool For adding color to selected Traces\n\t\tcols <-brewer.pal(8,\"Dark2\")\n\t\tcols <- rep(cols,5)\n\t\tcols <- cols[1:5]\n\t\t\n\t\tcols<-c(\"mediumpurple1\",\"goldenrod1\", \"firebrick1\", \"limegreen\", \"steelblue3\")\n\n\t\n\t\t## Tool for adding labeling for single line within stacked traces\n\t\tfor(i in 1:length(cells)){\n\t\t\tif(length(cells[[i]])>1){\n\t\t\t\tmatlines(xseq, blc[,cells[[i]]]+i*sf, col=rgb(0,0,0,10, maxColorValue=100), lwd=.3)\n\t\t\t\tlines(xseq, apply(blc[,cells[[i]]],1,mean)+i*sf, col=cols[i], lwd=1.2)\n\t\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.means[i], col=\"black\", pos=4, cex=bcex)\n\t\t\t\t}\n\t\t\telse{\n\t\t\t\tlines(xseq, blc[,cells[[i]]]+i*sf, col=rgb(0,0,0,80, maxColorValue=100), lwd=.3)\n\t\t\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),cells[[i]]]+i*sf, labels=group.names[i], col=cols[i], pos=2, cex=bcex)\n\t\t\t}}\n\n\t\t\t\t\n\t\t## Tool for adding boxplot to plot\n\t\tfor(i in 1:length(cells)){\n\t\t\txleft<-max(blc[,1])*1.05\n\t\t\txright<-xleft+xinch(2.74)\n\t\t\ty<-(blc[nrow(t.dat),cells[[i]]]+i*sf)\n\t\t\tybottom<- y-.55\n\t\t\tytop<-ybottom+yinch(.85)\n\n\t\t\t#dev.set(dev.list()[length(dev.list())])\n\t\t\trasterImage(bpfunc.2(dat,cells[[i]]),xleft, ybottom, xright, ytop)\n\t\t}\n\t\n\t\n\tcontinue<-select.list(c(\"yes\", \"no\"))\n\tif(continue==\"yes\"){\n\tfor(i in 1:length(cells)){\n\t\t\n\t\t\tif(length(cells[[i]])<20){\n\t\t\t\tLinesEvery.4(dat,cells[[i]], img, pic.plot=T, lmain=names(cells[i]), m.order=\"area\", levs=levs, sf=.6)\n\t\t\t}\n\t\t\telse{\n\t\t\t# select the range of\n\t\t\t\tsample.num<-ceiling(sample.num/2)\n\t\t\t\tcells.n<-sort(c(ceiling(seq(1,length(cells[[i]]), length.out=5)),ceiling(seq(1,length(cells[[i]]), length.out=5))+1))\n\t\t\t\tcells.rs<-c.sort(dat$c.dat[cells[[i]],], \"area\")\n\t\t\t\tLinesEvery.4(dat, cells.rs[cells.n],img, lmain=names(cells[i]), m.order=\"area\",levs=levs, sf=.4)}\n\t\t\t#multi.pic.zoom(dat, names(which(info==i, arr.ind=T)), img, plot.new=T)\n\t\t}\n\t}\n\n\t#else{LinesEvery.4(dat, m.names,img)}\n\t#return(pam5$clustering)\t\n\treturn(group.names)\n\tprint(group.means)\n\t\n\t\n\t\n} \n \n\n \n \n bpfunc<-function(dat,n.names){\n\t\tif(length(n.names)>4){\n\t\t#par(width=12, height=4.5)\n\t\tpar(mfrow=c(2,3))\n\t\tpar(mar=c(2.5,2.5,2.5,2.5))\n\t\tpar(cex=.8)\n\t\tdat.names<-names(dat$c.dat)\n\t\t#lab.1<-grep(\"gfp.1\",dat.names,ignore.case=T, value=T)\n\t\t#lab.2<-grep(\"gfp.2\",dat.names, ignore.case=T, value=T)\n\t\t#lab.3<-grep(\"tritc\",dat.names, ignore.case=T, value=T)\n\t\t#lab.4<-grep(\"area\",dat.names, ignore.case=T, value=T)\n\t\t\n\t\t#if(dat$c.dat[n.names, lab.1]!=\"N/A\"){lab.1<-lab.1}\n\t\t#else{rm(lab.1)}\n\t\t#if(dat$c.dat[n.names, lab.2]!=\"N/A\"){}\n\t\t#if(dat$c.dat[n.names, lab.3]!=\"N/A\"){ }\n\t\t#if(dat$c.dat[n.names, lab.4]!=\"N/A\"){}\n\t\n\t\t\n\t\t##Color intensity 1\n\t\tboxplot(dat$c.dat[n.names,\"mean.gfp\"],main=\"GFP\",bty=\"n\",ylim=c(0,max(dat$c.dat[\"mean.gfp\"])), col=\"springgreen4\", outline=F)\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.gfp\"])), factor=10),\n\t\ty=dat$c.dat[n.names,\"mean.gfp\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\t\n\t\t#Color Intensity 2\n\t\tboxplot(dat$c.dat[n.names,\"mean.tritc\"],main=\"IB4\",ylim=c(0,max(dat$c.dat[\"mean.tritc\"])), col=\"firebrick4\", outline=F)\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.tritc\"])), factor=10),\n\t\ty=dat$c.dat[n.names,\"mean.tritc\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\n\t\t# area\n\t\tboxplot(dat$c.dat[n.names,\"area\"],main=\"Area\",ylim=c(0,max(dat$c.dat[\"area\"])), col=\"lightslateblue\", outline=F)\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"area\"])), factor=10),\n\t\ty=dat$c.dat[n.names,\"area\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\t\n\t\t##Color intensity 1 log\n\t\tboxplot(1+dat$c.dat[n.names,\"mean.gfp\"],main=\"GFP\",bty=\"n\", col=\"springgreen4\", outline=T, log=\"y\")\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.gfp\"])), factor=10),\n\t\ty=1+dat$c.dat[n.names,\"mean.gfp\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\t\n\t\t#Color Intensity 2 log\n\t\tboxplot(1+dat$c.dat[n.names,\"mean.tritc\"],main=\"IB4\", col=\"firebrick4\", outline=T, log=\"y\")\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.tritc\"])), factor=10),\n\t\ty=1+dat$c.dat[n.names,\"mean.tritc\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\t\n\t\t# area log\n \t\tboxplot(1+dat$c.dat[n.names,\"area\"],main=\"Area\", col=\"lightslateblue\", outline=T, log=\"y\")\n\t\ttext(x=jitter(rep(1, length(dat$c.dat[n.names,\"area\"])), factor=10),\n\t\ty=1+dat$c.dat[n.names,\"area\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]))\n\t\t\n\t\tdev.set(dev.list()[1])}\n\t\telse{\n\t\tpar(mfrow=c(1,3))\n\t\tpar(mar=c(2,2,2,2))\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"mean.gfp\"],main=\"GFP\",ylim=c(0,max(dat$c.dat[\"mean.gfp\"])),cex=2, col=c(\"green4\"), outline=T, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"mean.gfp\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"green4\")\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"mean.tritc\"],main=\"IB4\",ylim=c(0,max(dat$c.dat[\"mean.tritc\"])), ,cex=2,col=\"red\", outline=F, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"mean.tritc\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"red\")\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"area\"],main=\"Area\",ylim=c(0,max(dat$c.dat[\"area\"])), ,cex=2,col=\"lightslateblue\", outline=F, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"area\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"lightslateblue\")\n\t\t\n\t\tdev.set(dev.list()[5])}\n\t}\n\nbpfunc.2<-function(dat,n.names, bp.pts=T){\n\trequire(png)\n\tpng('tmp.png', width=2.74, height=.85, units=\"in\", res=200)\n\t#dev.new(width=2.74, height=1)\n\tif(length(n.names)>4){\n\n\t\t\n\t\tpar(mfrow=c(1,3),mar=c(1,3,2,0), bty=\"n\",lwd=1, lty=1, cex.axis=.8, cex=.6)\n\t\tdat.names<-names(dat$c.dat)\n\t\t#lab.1<-grep(\"gfp.1\",dat.names,ignore.case=T, value=T)\n\t\t#lab.2<-grep(\"gfp.2\",dat.names, ignore.case=T, value=T)\n\t\t#lab.3<-grep(\"tritc\",dat.names, ignore.case=T, value=T)\n\t\t#lab.4<-grep(\"area\",dat.names, ignore.case=T, value=T)\n\t\t\n\t\t#if(dat$c.dat[n.names, lab.1]!=\"N/A\"){lab.1<-lab.1}\n\t\t#else{rm(lab.1)}\n\t\t#if(dat$c.dat[n.names, lab.2]!=\"N/A\"){}\n\t\t#if(dat$c.dat[n.names, lab.3]!=\"N/A\"){ }\n\t\t#if(dat$c.dat[n.names, lab.4]!=\"N/A\"){}\n\t\n\t\t\n\t\t##Color intensity 1\n\t\tboxplot(dat$c.dat[n.names,\"mean.gfp\"],main=\"GFP\",\n\t\tylim=c(min(dat$c.dat[\"mean.gfp\"]),max(dat$c.dat[\"mean.gfp\"])), col=\"springgreen4\", outline=F,yaxt=\"n\", boxwex=.8, medlwd=.4,whisklty=1)\n\t\tif(bp.pts==T){stripchart(dat$c.dat[n.names,\"mean.gfp\"], add=T, method=\"jitter\", vertical=T, jitter=.2, pch=18, cex=.6)}\n\t\tmtext(paste(round(mean(dat$c.dat[n.names, \"mean.gfp\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[n.names, \"mean.gfp\"]), digits=0)),1, cex=.5)\n\t\t#text(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.gfp\"])), factor=10),\n\t\t#y=dat$c.dat[n.names,\"mean.gfp\"], \n\t\t#labels=as.character(dat$c.dat[n.names,\"id\"]), cex=.4)\n\t\taxis(2, at=c(round(min(dat$c.dat[\"mean.gfp\"]), digits=0),round(max(dat$c.dat[\"mean.gfp\"]), digits=0)))#,labels=x, col.axis=\"red\", las=2)\n\t\tbox(\"figure\")\n\t\t\n\t\t#Color Intensity 2\n\t\tboxplot(dat$c.dat[n.names,\"mean.tritc\"],main=\"IB4\",\n\t\tylim=c(min(dat$c.dat[\"mean.tritc\"]),max(dat$c.dat[\"mean.tritc\"])), col=\"firebrick4\", outline=F, boxwex=.8, yaxt=\"n\", medlwd=.4,whisklty=1)\n\t\tif(bp.pts==T){stripchart(dat$c.dat[n.names,\"mean.tritc\"], add=T, method=\"jitter\", vertical=T, jitter=.2, pch=18, cex=.6)}\n\t\t#text(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.tritc\"])), factor=10),\n\t\t#y=dat$c.dat[n.names,\"mean.tritc\"], \n\t\t#labels=as.character(dat$c.dat[n.names,\"id\"]), cex=.4)\n\t\tmtext(paste(round(mean(dat$c.dat[n.names, \"mean.tritc\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[n.names, \"mean.tritc\"]), digits=0)),1, cex=.5)\n\t\taxis(2, at=c(round(min(dat$c.dat[\"mean.tritc\"]), digits=0),round(max(dat$c.dat[\"mean.tritc\"]), digits=0)))#,labels=x, col.axis=\"red\", las=2)\n\t\tbox(\"figure\")\n\t\t\n\t\t# area\n\t\tboxplot(dat$c.dat[n.names,\"area\"],main=\"Area\",\n\t\tylim=c(min(dat$c.dat[\"area\"]),max(dat$c.dat[\"area\"])), col=\"firebrick4\", outline=F, boxwex=.8, yaxt=\"n\", medlwd=.4,whisklty=1)\n\t\tif(bp.pts==T){stripchart(dat$c.dat[n.names,\"area\"], add=T, method=\"jitter\", vertical=T, jitter=.2, pch=18, cex=.6)}\n\t\t#text(x=jitter(rep(1, length(dat$c.dat[n.names,\"mean.tritc\"])), factor=10),\n\t\t#y=dat$c.dat[n.names,\"mean.tritc\"], \n\t\t#labels=as.character(dat$c.dat[n.names,\"id\"]), cex=.4)\n\t\tmtext(paste(round(mean(dat$c.dat[n.names, \"area\"]), digits=0),\"\\u00b1\",round(sd(dat$c.dat[n.names, \"mean.tritc\"]), digits=0)),1, cex=.5)\n\t\taxis(2, at=c(round(min(dat$c.dat[\"area\"]), digits=0),round(max(dat$c.dat[\"area\"]), digits=0)))#,labels=x, col.axis=\"red\", las=2)\n\t\tbox(\"figure\")\n\n\t\n\n\t}\n\n\telse{\n\t\tpar(mfrow=c(1,3))\n\t\tpar(mar=c(2,2,2,2))\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"mean.gfp\"],main=\"GFP\",ylim=c(0,max(dat$c.dat[\"mean.gfp\"])),cex=2, col=c(\"green4\"), outline=T, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"mean.gfp\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"green4\")\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"mean.tritc\"],main=\"IB4\",ylim=c(0,max(dat$c.dat[\"mean.tritc\"])), ,cex=2,col=\"red\", outline=F, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"mean.tritc\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"red\")\n\t\t\n\t\tstripchart(dat$c.dat[n.names,\"area\"],main=\"Area\",ylim=c(0,max(dat$c.dat[\"area\"])), ,cex=2,col=\"lightslateblue\", outline=F, vertical=T, pch=\".\")\n\t\ttext(x=1,\n\t\ty=dat$c.dat[n.names,\"area\"], \n\t\tlabels=as.character(dat$c.dat[n.names,\"id\"]), col=\"lightslateblue\")\n\t}\n\tbox(\"figure\")\n\t\n\tdev.off()\n\ttmp.png <- png::readPNG(\"tmp.png\")\n\tdim(tmp.png)\n\tunlink(\"tmp.png\")\n\treturn(tmp.png)\t\t\t\n\n\t}\n\n\t\n\nLinesStack.select <- function(dat,m.names,lmain=\"\",levs=NULL, plot.new=TRUE,bcex=.8, sf=.2, subset.n=5)\n{\n\tt.dat<-dat$t.dat\n\twr<-dat$w.dat[,2]\n\tif(is.null(levs)){levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")}\n\telse{levs<-levs}\n m.names <- intersect(m.names,names(t.dat))\n hbc <- subset.n*sf+max(t.dat[,m.names])\n\txseq <- t.dat[,1]\n if(plot.new){dev.new(width=10,height=6)}\n\tlibrary(RColorBrewer)\n \tpar(mar=c(4,2,4,4))\n\t#ylim <- c(-.1,1.4)\n\tylim<-c(-.1,hbc)\n plot(xseq,t.dat[,m.names[1]],ylim=ylim,xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+1.5))#-sf\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t## Tool for adding window region labeling\n\tif(length(wr) > 0){\n\t\t#levs <- setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\tx1s <- tapply(xseq,as.factor(wr),min)[levs]\n\t\tx2s <- tapply(xseq,as.factor(wr),max)[levs]\n\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\trect(x1s,y1s,x2s,y2s,col=\"grey90\",border=\"black\")\n\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(c(abs(min(ylim)), abs(min(ylim*1.5))),length=length(levs)),levs,cex=bcex,offset=0, pos=4)#,offset=-offs}\n\t}\n\tblc<-dat$blc\n\t## Tool for adding line and point plot for all lines\n\t\t#matlines(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), lwd=.01)\n\t\t#matpoints(xseq, blc[,m.names], col=rgb(0,0,0,3, maxColorValue=100), pch=16, cex=.03)\n\t\n\t#cols <- rainbow(length(m.names),start=.55)\n \n\tlibrary(cluster)\n\tblc<-dat$blc\n\tpam5 <- pam(t(blc[,m.names]),k=subset.n)\n\ts.names <- row.names(pam5$medoids)\n\tpam5.tab <- table(pam5$clustering)\n\ttags <- paste(paste(\"#\",names(pam5.tab),sep=\"\"),as.vector(pam5.tab),sep=\":\")\n\tinfo<-pam5$clustering\n\t\n\t## Tool For adding color to selected Traces\n\tcols <-brewer.pal(8,\"Dark2\")\n\tcols <- rep(cols,ceiling(length(s.names)/length(cols)))\n\tcols <- cols[1:length(s.names)]\n\n\t## Tool for adding labeling for single line within stacked traces\n\tfor(i in 1:length(s.names)){\n\t\tmatlines(xseq, blc[,names(which(info==i, arr.ind=T))]+i*sf, col=rgb(0,0,0,10, maxColorValue=100), lwd=.01)\n\t\tlines(xseq, blc[,s.names[i]]+i*sf, col=cols[i], lwd=.5)\n\t\tpoints(xseq, blc[,s.names[i]]+i*sf, col=cols[i], pch=16, cex=.03)\n\t\ttext(x=min(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=s.names[i], col=cols[i], pos=2, cex=bcex)\n\t\ttext(x=max(blc[,1]), y=blc[nrow(t.dat),s.names[i]]+i*sf, labels=tags[i], col=cols[i], pos=4, cex=bcex)\n\t}\n\t\nreturn(pam5$clustering)\t\n} \n \nLines.Multi<-function(dat,n.names){\ndev.new(width=2, height=2)\npar(mar=c(0,0,0,0))\nplot(0,0, pch=NA, xlim=c(0,2), ylim=c(0,2))\npoints(x=c(1,1), y=c(1.5,1), pch=15)\ntext(x=c(1,1), y=c(1.5,1), c(\"next\", \"off\"), pos=2)\n\ndev.new()\nclick.i<-0\ni<-1\n\nwhile(click.i!=2){\n\tdev.set(dev.list()[2])\n\tLinesEvery.2(dat,n.names[i:(10+i)], m.order=\"area\", plot.new=F)\n\tdev.set(dev.list()[1])\n\tclick.i<-identify(x=c(1,1), y=c(1.5,1), n=1)\n\tif(click.i==1){i<-i+10}\n}\ngraphics.off()\n}\n\n\n#Display the analysis of a single trace \n#dat is the trace dataframe with \"Time\" in the first column and cell trace intensities in subsequent columns\n#i is the index column to be analyzed and displayed.\n#shws is the smoothing half window size\n#Plotit is a flag indicating that the results should be ploted or not.\n#wr is the response window factor \n#SNR.lim is the signal to noise ratio limit for peak detection\n#bl.meth is the method for baseline correction.\nPeakFunc2 <- function(dat,i,shws=2,phws=20,Plotit=F,wr=NULL,SNR.lim=2,bl.meth=\"TopHat\",lmain=NULL)\n{\n library(\"MALDIquant\")\n s1 <- createMassSpectrum(dat[,\"Time\"],dat[,i])\n if(shws > 1)\n s3 <- smoothIntensity(s1, method=\"SavitzkyGolay\", halfWindowSize=shws)\n else\n s3 <- s1\n if(Plotit)\n {\n bSnip <- estimateBaseline(s3, method=\"SNIP\")\n bTopHat <- estimateBaseline(s3, method=\"TopHat\")\n }\n s4 <- removeBaseline(s3, method=bl.meth)\n Baseline <- estimateBaseline(s3, method=bl.meth)\n p <- detectPeaks(s4, method=\"MAD\", halfWindowSize=phws, SNR=SNR.lim)\n if(Plotit)\n {\n xlim <- range(mass(s1)) # use same xlim on all plots for better comparison\n ylim <- c(-.1,1.4)\n# ylim <- range(intensity(s1))\n plot(s1, main=paste(lmain,i),xlim=xlim,ylim=ylim,xlab=\"Time (min)\", xaxt=\"n\")\n\t\taxis(1, at=seq(0, length(dat[,1]), 5)) \n if(length(wr) > 0)\n {\n levs <- setdiff(unique(wr),\"\")\n levs <- setdiff(levs,grep(\"blank\",levs,value=T))\n x1s <- tapply(dat[,\"Time\"],as.factor(wr),min)[levs]\n x2s <- tapply(dat[,\"Time\"],as.factor(wr),max)[levs]\n y1s <- rep(min(ylim)-.2,length(x1s))\n y2s <- rep(max(ylim)+.2,length(x1s))\n# cols <- rainbow(length(x1s))\n rect(x1s,y1s,x2s,y2s,col=\"lightgrey\")\n# points(dat[,\"Time\"],as.integer(wr==\"\")*-1,pch=15,cex=.6)\n ## for(j in levs)\n ## {\n ## x1 <- mass(s3)[min(grep(j,wr))]\n ## x2 <- mass(s3)[max(grep(j,wr))]\n ## y1 <- min(ylim)-.2\n ## y2 <- max(ylim)+.2\n ## polygon(c(x1,x2,x2,x1),c(y1,y1,y2,y2),col=\"lightgrey\",lwd=.1)\n ## }\n text(dat[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n }\n \n lines(s3,lwd=3,col=\"cyan\")\n lines(s1)\n lines(bSnip, lwd=2, col=\"red\")\n lines(bTopHat, lwd=2, col=\"blue\")\n lines(s4,lwd=2)\n }\n if((length(p) > 0)&Plotit)\n {\n points(p)\n ## label top 40 peaks\n top40 <- intensity(p) %in% sort(intensity(p), decreasing=TRUE)[1:40]\n labelPeaks(p, index=top40, underline=TRUE,labels=round(snr(p)[top40],2))\n }\n return(list(peaks=p,baseline=Baseline,dat=s4))\n}\n\nPeakFunc3 <- function(dat,n.names,shws=2,phws=20,wr=NULL,SNR.lim=2,bl.meth=\"TopHat\",lmain=NULL)\n{\n\n\txlim <- range(dat$t.dat[,1]) # use same xlim on all plots for better comparison\n\tylim <- c(-.1,1.4)\n\t# ylim <- range(intensity(s1))\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tplot(dat$t.dat[,n.names],dat$t.dat[,1], main=paste(lmain,n.names),xlim=xlim,ylim=ylim,xlab=\"\", xaxt=\"n\",pch=16, lwd=1, cex=.5)\n\taxis(1, at=seq(0, length(dat$t.dat[,1]), 5)) \n\tlines(dat$t.dat[,n.names]~dat$t.dat[,1])\n\tpoints(dat$t.dat[,n.names]~dat$t.dat[,1], pch=16, cex=.4)\n\t\n\tlines(dat$blc[,n.names]~dat$t.dat[,1], lwd=1, cex=.5)\n\tpoints(dat$blc[,n.names]~dat$t.dat[,1], pch=16, cex=.4)\n\t\n\t# Tool for labeling window regions\n\tif(is.null(wr)){\n\t\twr<-dat$w.dat[,\"wr1\"]\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\t\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\trect(x1s,y1s,x2s,y2s,col=\"grey95\")\n\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t}\n\t\n\t# Tool for labeling the binary score\n\tif(length(levs)>0){\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\tz<-t(dat$bin[n.names,levs])\n\t\tzz<-z==1\n\t\tzi<-attributes(zz)\n\t\tzzz<-which(zz, arr.ind=T)\n\t\t#levs<-zi$dimnames[[2]][zzz[,2]]\n\t\tlevs<-unique(as.character(row.names(zzz)))\n\t\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\t\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\trect(x1s,y1s,x2s,y2s,col=\"grey69\")\n\t\tlevs <- setdiff(unique(wr),\"\")\n\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t}\n\t\n\t# Tool for labeling cellular aspects, gfp.1, gfp.2, tritc, area\n\tlegend(\"topright\", xpd=TRUE, inset=c(0,-.14), legend=c(\n\t\tif(!is.null(dat$c.dat[n.names, \"CGRP\"])){paste(\"CGRP\",\"\",round(dat$c.dat[n.names,\"CGRP\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp\"])){paste(\"GFP\",\"\",round(dat$c.dat[n.names,\"mean.gfp\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.2\"])){paste(\"GFP.2\",\"\",round(dat$c.dat[n.names,\"mean.gfp.2\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"IB4\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names,\"IB4\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.tritc\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names, \"mean.tritc\"], digits=0))}, \n\t\tif(!is.null(dat$c.dat[n.names, \"area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"area\"], digits=0))})\n\t,bty=\"n\", cex=.8)\n\n\t# Tool for lableing window region information\n\tx.name<-n.names\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])), \"\")\n\tlevs.loc<-tapply(dat$t.dat[,\"Time\"],as.factor(wr),mean)[levs]\n\tmtext(c(\"snr\", \"tot\", \"max\", \"wm\"), side=1, at=-1, line=c(1.4, 2.1, 2.8, 3.5), cex=.6)\n\tfor(i in levs){\n\t\tsnr.name<-grep(paste(i,\".snr\", sep=\"\"), names(dat$scp), value=T)\n\t\ttot.name<-grep(paste(i,\".tot\", sep=\"\"), names(dat$scp), value=T)\n\t\tmax.name<-grep(paste(i,\".max\", sep=\"\"), names(dat$scp), value=T)\n\t\twm.name<-grep(paste(i,\".wm\", sep=\"\"), names(dat$scp), value=T)\n\t\tsnr.val<-round(dat$scp[x.name, snr.name], digits=1)\n\t\ttot.val<-round(dat$scp[x.name, tot.name], digits=2)\n\t\tmax.val<-round(dat$scp[x.name, max.name], digits=2)\n\t\twm.val<-round(dat$scp[x.name, wm.name], digits=1)\n\t\tmtext(snr.val, side=1, at=levs.loc[i], line=1.4, cex=.6)\n\t\tmtext(tot.val, side=1, at=levs.loc[i], line=2.1, cex=.6)\n\t\tmtext(max.val, side=1, at=levs.loc[i], line=2.8, cex=.6)\n\t\tmtext(wm.val, side=1, at=levs.loc[i], line=3.5, cex=.6)\n\t}\n}\n\nPeakFunc4 <- function(dat,n.names,Plotit.maldi=T,Plotit.der=T,lmain=NULL)\n{\n \n\tpar(mfrow=c(2,1))\n\t\nif(Plotit.der)\n{\t\n\tylim<-c(-1, 2)\n\tplot(dat$der[,n.names]~dat$t.dat[-1,1], ylim=ylim,type=\"l\",ylab=expression(paste(Delta,\" (340/380)/time\")),xlab=\"\",main=paste(\"Derivative\",n.names), xaxt=\"n\",pch=16, lwd=1, cex=.5)\t\n\t\n\t# Tool for labeling window regions\n\twr<-dat$w.dat[,\"wr1\"]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey95\")\n\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(-1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t\n\t# Tool for labeling the binary score\n\tif(length(levs)>0){\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\t\tz<-t(dat$bin[n.names,levs])\n\t\tzz<-z==1\n\t\tzi<-attributes(zz)\n\t\tzzz<-which(zz, arr.ind=T)\n\t\t#levs<-zi$dimnames[[2]][zzz[,2]]\n\t\tlevs<-unique(as.character(row.names(zzz)))\n\t\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\t\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\t\ty1s <- rep(min(ylim)-.2,length(x1s))\n\t\ty2s <- rep(max(ylim)+.2,length(x1s))\n\t\trect(x1s,y1s,x2s,y2s,col=\"grey69\")\n\t\tlevs <- setdiff(unique(wr),\"\")\n\t\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(-1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t}\n\t\n\t# Tool for lableing window region information\n\tx.name<-n.names\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])), \"\")\n\tlevs.loc<-tapply(dat$t.dat[,\"Time\"],as.factor(wr),mean)[levs]\n\tmtext(c(\"tot\", \"max\", \"min\", \"wmax\", \"wmin\"), side=1, at=-1, line=c(0.7,1.4, 2.1, 2.8, 3.5), cex=.6)\n\tfor(i in levs){\n\t\ttot.name<-grep(paste(i,\".der.tot\", sep=\"\"), names(dat$scp), value=T)\n\t\tmax.name<-grep(paste(i,\".der.max\", sep=\"\"), names(dat$scp), value=T)\n\t\tmin.name<-grep(paste(i,\".der.min\", sep=\"\"), names(dat$scp), value=T)\n\t\twmax.name<-grep(paste(i,\".der.wmax\", sep=\"\"), names(dat$scp), value=T)\n\t\twmin.name<-grep(paste(i,\".der.wmin\", sep=\"\"), names(dat$scp), value=T)\n\t\t\n\t\ttot.val<-round(dat$scp[x.name, tot.name], digits=2)\n\t\tmax.val<-round(dat$scp[x.name, max.name], digits=2)\n\t\tmin.val<-round(dat$scp[x.name, min.name], digits=2)\n\t\twmax.val<-round(dat$scp[x.name, wmax.name], digits=2)\n\t\twmin.val<-round(dat$scp[x.name, wmin.name], digits=2)\n\n\t\tmtext(tot.val, side=1, at=levs.loc[i], line=0.7, cex=.6)\n\t\tmtext(max.val, side=1, at=levs.loc[i], line=1.4, cex=.6)\n\t\tmtext(min.val, side=1, at=levs.loc[i], line=2.1, cex=.6)\n\t\tmtext(wmax.val, side=1, at=levs.loc[i], line=2.8, cex=.6)\n\t\tmtext(wmin.val, side=1, at=levs.loc[i], line=3.5, cex=.6)\n\t}\n\tlines(dat$der[,n.names]~dat$t.dat[-1,1], lwd=.01, col=\"black\")\n\tabline(h=0.5)\n\n\t#axis(1, at=seq(0, length(dat$t.dat[,1]), 5)) \n}\n\n\t\nif(Plotit.maldi)\n{\n\txlim <- range(dat$t.dat[,1]) # use same xlim on all plots for better comparison\n\tylim <- c(0,1.4)\n\t# ylim <- range(intensity(s1))\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tplot(dat$t.dat[,n.names]~dat$t.dat[,1], main=paste(lmain,n.names),xlim=xlim,ylim=ylim,xlab=\"\", ylab=\"(340/380)\", xaxt=\"n\",pch=16, lwd=1, cex=.5)\n\taxis(1, at=seq(0, length(dat$t.dat[,1]), 5)) \n\n\t\n\t# Tool for labeling window regions\n\twr<-dat$w.dat[,\"wr1\"]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey95\")\n\t#text(dat$t.dat[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t\n\t# Tool for labeling the binary score\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tz<-t(dat$bin[n.names,levs])\n\tzz<-z==1\n\tzi<-attributes(zz)\n\tzzz<-which(zz, arr.ind=T)\n\t#levs<-zi$dimnames[[2]][zzz[,2]]\n\tlevs<-unique(as.character(row.names(zzz)))\n\tx1s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat$t.dat[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey69\")\n\tlevs <- setdiff(unique(wr),\"\")\n\ttext(dat$t.dat[match(levs,wr),\"Time\"],rep(-1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t\n\t# Tool for labeling cellular aspects, gfp.1, gfp.2, tritc, area\n\tlegend(\"topright\", xpd=TRUE, inset=c(0,-.14), legend=c(\n\t\tif(!is.null(dat$c.dat[n.names, \"CGRP\"])){paste(\"CGRP\",\"\",round(dat$c.dat[n.names,\"CGRP\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp\"])){paste(\"GFP\",\"\",round(dat$c.dat[n.names,\"mean.gfp\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.1\"])){paste(\"GFP.1\",\"\",round(dat$c.dat[n.names,\"mean.gfp.1\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.2\"])){paste(\"GFP.2\",\"\",round(dat$c.dat[n.names,\"mean.gfp.2\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"IB4\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names,\"IB4\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.tritc\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names, \"mean.tritc\"], digits=0))}, \n\t\tif(!is.null(dat$c.dat[n.names, \"area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"area\"], digits=0))})\n\t,bty=\"n\", cex=.8)\n\n\t# Tool for lableing window region information\n\tx.name<-n.names\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])), \"\")\n\tlevs.loc<-tapply(dat$t.dat[,\"Time\"],as.factor(wr),mean)[levs]\n\tmtext(c(\"snr\", \"tot\", \"max\", \"wm\"), side=1, at=-1, line=c(1.4, 2.1, 2.8, 3.5), cex=.6)\n\tfor(i in levs){\n\t\tsnr.name<-grep(paste(i,\".snr\", sep=\"\"), names(dat$scp), value=T)\n\t\ttot.name<-grep(paste(i,\".tot\", sep=\"\"), names(dat$scp), value=T)\n\t\tmax.name<-grep(paste(i,\".max\", sep=\"\"), names(dat$scp), value=T)\n\t\twm.name<-grep(paste(i,\".wm\", sep=\"\"), names(dat$scp), value=T)\n\t\tsnr.val<-round(dat$scp[x.name, snr.name], digits=1)\n\t\ttot.val<-round(dat$scp[x.name, tot.name], digits=2)\n\t\tmax.val<-round(dat$scp[x.name, max.name], digits=2)\n\t\twm.val<-round(dat$scp[x.name, wm.name], digits=1)\n\t\tmtext(snr.val, side=1, at=levs.loc[i], line=1.4, cex=.6)\n\t\tmtext(tot.val, side=1, at=levs.loc[i], line=2.1, cex=.6)\n\t\tmtext(max.val, side=1, at=levs.loc[i], line=2.8, cex=.6)\n\t\tmtext(wm.val, side=1, at=levs.loc[i], line=3.5, cex=.6)\n\t}\n\t\n\tlines(dat$t.dat[,n.names]~dat$t.dat[,1])\n\tpoints(dat$t.dat[,n.names]~dat$t.dat[,1], pch=16, cex=.4)\n\t\n\tlines(dat$blc[,n.names]~dat$t.dat[,1], lwd=1, cex=.5)\n\tpoints(dat$blc[,n.names]~dat$t.dat[,1], pch=16, cex=.4)\n\t\n\t\n\t#abline(h=.5)\t\n\n}\t\n\t\n # return(list(peaks=p,baseline=Baseline,dat=s4))\n}\n\n# Fixed y axis\n# Photo addition\n# Derivative plot\n# win\nPeakFunc5 <- function(dat,n.names,select.trace=F,Plotit.trace=T,Plotit.both=F, info=T,lmain=NULL, bcex=.7)\n{\n if(Plotit.trace){ylim <- c(-.1,1.4)}\n\tif(Plotit.both){ylim <- c(-.5,1.4)}\n\tpar(xpd=FALSE)\n\tif(select.trace==TRUE){\n\t\tdat.select<-menu(names(dat))\n\t\tdat.t<-dat[[dat.select]]\n\t\t}\n\telse(dat.t<-dat$t.dat)\n\txlim <- range(dat.t[,1]) # use same xlim on all plots for better comparison\n\t\n\t# ylim <- range(intensity(s1))\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tpar(mar=c(6,4.5,3.5,11))\n\tplot(dat.t[,n.names]~dat.t[,1], main=paste(lmain,n.names),xlim=xlim,ylim=ylim,xlab=\"\", ylab=\"\",pch=16, lwd=1, cex=.5)\n\t#axis(1, at=seq(0, length(dat.t[,1]), 5),tick=TRUE ) \n\t\n\t# Tool for labeling window regions\n\twr<-dat$w.dat[,\"wr1\"]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tx1s <- tapply(dat.t[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat.t[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey95\")\n\t#text(dat.t[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t\n\t# Tool for labeling the binary score\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tz<-t(dat$bin[n.names,levs])\n\tzz<-z==1\n\tzi<-attributes(zz)\n\tzzz<-which(zz, arr.ind=T)\n\t#levs<-zi$dimnames[[2]][zzz[,2]]\n\tlevs<-unique(as.character(row.names(zzz)))\n\tx1s <- tapply(dat.t[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat.t[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey69\")\n\tlevs <- setdiff(unique(wr),\"\")\n\ttext(dat.t[match(levs,wr),\"Time\"],c(min(ylim), .1),levs,pos=4,offset=0,cex=bcex)\n\t\n\t# Tool for labeling cellular aspects, gfp.1, gfp.2, tritc, area\n\tlegend(x=max(xlim)*.95, y=2.1, xpd=TRUE, inset=c(0,-.14), legend=c(\n\t\tif(!is.null(dat$c.dat[n.names, \"CGRP\"])){paste(\"CGRP\",\"\",round(dat$c.dat[n.names,\"CGRP\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp\"])){paste(\"GFP\",\"\",round(dat$c.dat[n.names,\"mean.gfp\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.1\"])){paste(\"GFP.1\",\"\",round(dat$c.dat[n.names,\"mean.gfp.1\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.2\"])){paste(\"GFP.2\",\"\",round(dat$c.dat[n.names,\"mean.gfp.2\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.dapi\"])){paste(\"DAPI\",\"\",round(dat$c.dat[n.names,\"mean.dapi\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"IB4\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names,\"IB4\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.tritc\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names, \"mean.tritc\"], digits=0))}, \n\t\tif(!is.null(dat$c.dat[n.names, \"area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"area\"], digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"ROI.Area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"ROI.Area\"], digits=0))},\n\t\t#if(!is.null(dat$c.dat[n.names, \"perimeter\"])){paste(\"perimeter\",\"\", round(dat$c.dat[n.names, \"perimeter\"], digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"circularity\"])){paste(\"circularity\",\"\", round(dat$c.dat[n.names, \"circularity\"], digits=3))}\n\t\t)\n\t,bty=\"n\", cex=.7)\n\n\t#Adding binary scoring for labeling to plot\n\tpar(xpd=TRUE)\n\tif(!is.null(dat$bin[n.names, \"mean.gfp.bin\"])){text(y=1.9, x=max(dat.t[,1])*1.09, paste(\"mean.gfp :\",dat$bin[n.names,\"mean.gfp.bin\"]), cex=.7)}\n\tif(!is.null(dat$bin[n.names, \"mean.tritc.bin\"])){text(y=1.9, x=max(dat.t[,1])*1.19, paste(\"IB4 :\",dat$bin[n.names,\"mean.tritc.bin\"]), cex=.7)}\n\n\t\n\t# Tool for lableing window region information\n\tif(info){\n\t\tx.name<-n.names\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])), \"\")\n\t\tlevs.loc<-tapply(dat.t[,\"Time\"],as.factor(wr),mean)[levs]\n\t\tmtext(c(\"max\",\"tot\",\"snr\"), side=1, at=-max(dat.t[,1])*.05, line=c(1.4, 2.1, 2.8), cex=.6)\n\t\tfor(i in levs){\n\t\t\tmax.name<-paste(i,\".max\", sep=\"\")\n\t\t\tmax.val<-round(dat$scp[x.name, max.name], digits=3)\n\t\t\tmtext(max.val, side=1, at=levs.loc[i], line=1.4, cex=.6)\n\t\t\t\n\t\t\ttot.name<-paste(i,\".tot\", sep=\"\")\n\t\t\ttot.val<-round(dat$scp[x.name, tot.name], digits=3)\n\t\t\tmtext(tot.val, side=1, at=levs.loc[i], line=2.1, cex=.6)\n\t\t\t\n\t\t\tsnr.name<-paste(i,\".snr\", sep=\"\")\n\t\t\tsnr.val<-round(dat$scp[x.name, snr.name], digits=3)\n\t\t\tmtext(snr.val, side=1, at=levs.loc[i], line=2.8, cex=.6)\n\n\t\t}\n\t}\n\t\n\t\n\tpar(xpd=FALSE)\n\tif(Plotit.both){\n\t\tif(!is.null(dat$der)){lines(dat$der[,n.names]~dat.t[-1,1], lwd=.01, col=\"paleturquoise4\")}\n\t\tabline(h=0)\n\t\tlines(dat.t[,n.names]~dat.t[,1])\n\t\tpoints(dat.t[,n.names]~dat.t[,1], pch=16, cex=.3)\n\t}\n\t\n\tif(Plotit.trace){\n\t\tlines(dat.t[,n.names]~dat.t[,1])\n\t\tpoints(dat.t[,n.names]~dat.t[,1], pch=16, cex=.3)\n\t}\n\t\n\t## Tool for adding rasterImages to plot\n\tzf<-20\n\tx<-dat$c.dat[n.names,\"center.x\"]\n\tleft<-x-zf\n\tif(left<=0){left=0; right=2*zf}\n\tright<-x+zf\n\tif(right>=1024){left=1024-(2*zf);right=1024}\n\t\n\ty<-dat$c.dat[n.names,\"center.y\"]\n\ttop<-y-zf\n\tif(top<=0){top=0; bottom=2*zf}\n\tbottom<-y+zf\n\tif(bottom>=1024){top=1024-(2*zf);bottom=1024}\n\t\n\tpar(xpd=TRUE)\n\t\n\t\n\tif(!is.null(dat$img1)){\n\t\txleft<-max(dat.t[,1])*1.05\n\t\txright<-max(dat.t[,1])*1.13\n\t\trasterImage(dat$img1[top:bottom,left:right,],xleft,.6,xright,1.4)\n\t}\n\t\n\tif(!is.null(dat$img2)){\n\t\txleft<-max(dat.t[,1])*1.15\n\t\txright<-max(dat.t[,1])*1.23\n\t\trasterImage(dat$img2[top:bottom,left:right,],xleft,.6,xright,1.4)\n\t}\n\t\n\tif(!is.null(dat$img3)){\n\t\txleft<-max(dat.t[,1])*1.05\n\t\txright<-max(dat.t[,1])*1.13\n\t\trasterImage(dat$img3[top:bottom,left:right,],xleft,-.5,xright,.3)\n\t}\n\t\n\tif(!is.null(dat$img4)){\n\t\txleft<-max(dat.t[,1])*1.15\n\t\txright<-max(dat.t[,1])*1.23\n\t\trasterImage(dat$img4[top:bottom,left:right,],xleft,-.5,xright,.3)\n\t}\n}\t\n\n# Y axis self adjusting\tworks with trac.click.3\n#select trace added to select trace to plot\nPeakFunc6 <- function(dat,n.names,select.trace=F,Plotit.trace=T,Plotit.both=F, info=T,lmain=NULL, bcex=.7)\n{\n\tif(select.trace==TRUE){\n\t\tdat.select<-menu(names(dat))\n\t\tdat.t<-dat[[dat.select]]\n\t\t}\n\telse(dat.t<-dat$t.dat)\n\t\n\tymax<-max(dat.t[,n.names])*1.05\n\tymin<-min(dat.t[,n.names])*.95\n\tyrange<-ymax-ymin\n\n if(Plotit.trace){ylim <- c(ymin,ymax)}\n\tif(Plotit.both){ymin<- -.5;ylim <- c(ymin,ymax)}\n\tpar(xpd=FALSE)\n\txlim <- range(dat.t[,1]) # use same xlim on all plots for better comparison\n\t\n\t# ylim <- range(intensity(s1))\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tpar(mar=c(6,4.5,3.5,11))\n\tplot(dat.t[,n.names]~dat.t[,1], main=paste(lmain,n.names),xlim=xlim,ylim=ylim,xlab=\"\", ylab=\"\",pch=16, lwd=1, cex=.5)\n\t#axis(1, at=seq(0, length(dat.t[,1]), 5),tick=TRUE ) \n\t\n\t# Tool for labeling window regions\n\twr<-dat$w.dat[,\"wr1\"]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tx1s <- tapply(dat.t[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat.t[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey95\")\n\t#text(dat.t[match(levs,wr),\"Time\"],rep(-.1,length(levs)),levs,pos=4,offset=0,cex=.5)\n\t\n\t# Tool for labeling the binary score\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tz<-t(dat$bin[n.names,levs])\n\tzz<-z==1\n\tzi<-attributes(zz)\n\tzzz<-which(zz, arr.ind=T)\n\t#levs<-zi$dimnames[[2]][zzz[,2]]\n\tlevs<-unique(as.character(row.names(zzz)))\n\tx1s <- tapply(dat.t[,\"Time\"],as.factor(wr),min)[levs]\n\tx2s <- tapply(dat.t[,\"Time\"],as.factor(wr),max)[levs]\n\ty1s <- rep(min(ylim)-.2,length(x1s))\n\ty2s <- rep(max(ylim)+.2,length(x1s))\n\trect(x1s,y1s,x2s,y2s,col=\"grey69\")\n\tlevs <- setdiff(unique(wr),\"\")\n\ttext(dat.t[match(levs,wr),\"Time\"],c(ymin, ymin+(yrange*.2)),levs,pos=4,offset=0,cex=bcex)\n\t\n\t# Tool for labeling cellular aspects, gfp.1, gfp.2, tritc, area\n\tlegend(x=max(xlim)*.95, y=ymax+(.45*yrange), xpd=TRUE, inset=c(0,-.14), legend=c(\n\t\tif(!is.null(dat$c.dat[n.names, \"CGRP\"])){paste(\"CGRP\",\"\",round(dat$c.dat[n.names,\"CGRP\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp\"])){paste(\"GFP\",\"\",round(dat$c.dat[n.names,\"mean.gfp\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.1\"])){paste(\"GFP.1\",\"\",round(dat$c.dat[n.names,\"mean.gfp.1\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.gfp.2\"])){paste(\"GFP.2\",\"\",round(dat$c.dat[n.names,\"mean.gfp.2\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.dapi\"])){paste(\"DAPI\",\"\",round(dat$c.dat[n.names,\"mean.dapi\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"IB4\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names,\"IB4\"],digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"mean.tritc\"])){paste(\"IB4\",\"\",round(dat$c.dat[n.names, \"mean.tritc\"], digits=0))}, \n\t\tif(!is.null(dat$c.dat[n.names, \"area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"area\"], digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"ROI.Area\"])){paste(\"area\",\"\", round(dat$c.dat[n.names, \"ROI.Area\"], digits=0))},\n\t\t#if(!is.null(dat$c.dat[n.names, \"perimeter\"])){paste(\"perimeter\",\"\", round(dat$c.dat[n.names, \"perimeter\"], digits=0))},\n\t\tif(!is.null(dat$c.dat[n.names, \"circularity\"])){paste(\"circularity\",\"\", round(dat$c.dat[n.names, \"circularity\"], digits=3))}\n\t\t)\n\t,bty=\"n\", cex=.7)\n\t\n\t#Adding binary scoring for labeling to plot\n\tpar(xpd=TRUE)\n\tif(!is.null(dat$bin[n.names, \"mean.gfp.bin\"])){text(y=ymax+(.25*yrange), x=max(dat.t[,1])*1.09, paste(\"mean.gfp :\",dat$bin[n.names,\"mean.gfp.bin\"]), cex=.7)}\n\tif(!is.null(dat$bin[n.names, \"mean.tritc.bin\"])){text(y=ymax+(.25*yrange), x=max(dat.t[,1])*1.19, paste(\"IB4 :\",dat$bin[n.names,\"mean.tritc.bin\"]), cex=.7)}\n\n\n\t# Tool for lableing window region information\n\tif(info){\n\t\tx.name<-n.names\n\t\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])), \"\")\n\t\tlevs.loc<-tapply(dat.t[,\"Time\"],as.factor(wr),mean)[levs]\n\t\tmtext(c(\"max\",\"tot\"), side=1, at=-max(dat.t[,1])*.05, line=c(1.4, 2.1), cex=.6)\n\t\tfor(i in levs){\n\t\t\tmax.name<-paste(i,\".max\", sep=\"\")\n\t\t\tmax.val<-round(dat$scp[x.name, max.name], digits=3)\n\t\t\tmtext(max.val, side=1, at=levs.loc[i], line=1.4, cex=.6)\n\t\t\t\n\t\t\ttot.name<-paste(i,\".tot\", sep=\"\")\n\t\t\ttot.val<-round(dat$scp[x.name, tot.name], digits=3)\n\t\t\tmtext(tot.val, side=1, at=levs.loc[i], line=2.1, cex=.6)\n\t\t}\n\t}\n\t\n\n\t\n\tif(Plotit.both){\n\t\tif(!is.null(dat$der)){lines(dat$der[,n.names]~dat.t[-1,1], lwd=.01, col=\"paleturquoise4\")}\n\t\tabline(h=0)\n\t\tlines(dat.t[,n.names]~dat.t[,1])\n\t\tpoints(dat.t[,n.names]~dat.t[,1], pch=16, cex=.3)\n\t}\n\t\n\tif(Plotit.trace){\n\t\tlines(dat.t[,n.names]~dat.t[,1])\n\t\tpoints(dat.t[,n.names]~dat.t[,1], pch=16, cex=.3)\n\t}\n\t\n\t## Tool for adding rasterImages to plot\n\t\n\t###Finding the picture loaction of the cells\n\tzf<-20\n\tx<-dat$c.dat[n.names,\"center.x\"]\n\tleft<-x-zf\n\tif(left<=0){left=0; right=2*zf}\n\tright<-x+zf\n\tif(right>=1024){left=1024-(2*zf);right=1024}\n\t\n\ty<-dat$c.dat[n.names,\"center.y\"]\n\ttop<-y-zf\n\tif(top<=0){top=0; bottom=2*zf}\n\tbottom<-y+zf\n\tif(bottom>=1024){top=1024-(2*zf);bottom=1024}\n\t\n\tpar(xpd=TRUE)\n\t\n\t### Where to plot pictures\n\tymax<-max(dat.t[,n.names])*1.05\n\tymin<-min(dat.t[,n.names])*.95\n\tyrange<-ymax-ymin\n\t\n\tif(!is.null(dat$img1)){\n\t\txleft<-max(dat.t[,1])*1.05\n\t\txright<-max(dat.t[,1])*1.13\n\t\tytop<-ymax-(yrange*.05)\n\t\tybottom<-ymax-(yrange*.45)\n\t\trasterImage(dat$img1[top:bottom,left:right,],xleft,ybottom,xright,ytop)\n\t}\n\t\n\tif(!is.null(dat$img2)){\n\t\txleft<-max(dat.t[,1])*1.15\n\t\txright<-max(dat.t[,1])*1.23\n\t\tytop<-ymax-(yrange*.05)\n\t\tybottom<-ymax-(yrange*.45)\n\t\trasterImage(dat$img2[top:bottom,left:right,],xleft,ybottom,xright,ytop)\n\t}\n\t\n\tif(!is.null(dat$img3)){\n\t\txleft<-max(dat.t[,1])*1.05\n\t\txright<-max(dat.t[,1])*1.13\n\t\tytop<-ymax-(yrange*.55)\n\t\tybottom<-ymax-(yrange*.95)\n\t\trasterImage(dat$img3[top:bottom,left:right,],xleft,ybottom,xright,ytop)\n\t}\n\t\n\tif(!is.null(dat$img4)){\n\t\txleft<-max(dat.t[,1])*1.15\n\t\txright<-max(dat.t[,1])*1.23\n\t\tytop<-ymax-(yrange*.55)\n\t\tybottom<-ymax-(yrange*.95)\n\t\trasterImage(dat$img4[top:bottom,left:right,],xleft,ybottom,xright,ytop)\n\t}\n}\t\n\t\n# USe to sort based on features from bin and c.dat\nc.sort<-function(dat,char=NULL){\n\ttmp<-cbind(dat$c.dat, dat$bin)\n\tbob<-row.names(tmp[order(tmp[,char], decreasing=T),])\n\treturn(bob)\n\t}\n\n# Click through a set of selected cell and create a stack plot\n# Could use labeling improvements\nTrace.Click<-function(dat, cells=NULL)\n{\n graphics.off()\n dev.new(width=14,height=4) \n dev.new(width=12,height=8) \n if(is.null(cells)){c.names <- names(dat$t.dat[,-1])}\n\telse{c.names<-cells}\n\tlines.flag <- 0\n cell.i <- 1\n\tg.names<-NULL\n click.i <- 1\n\t#group.names<-NULL\n\tlinefunc <- function(dat,m.names,snr=NULL,lmain=\"\",cols=NULL,m.order=NULL,rtag=NULL,rtag2=NULL,rtag3=NULL, sf=.25,lw=3,bcex=1,p.ht=7,p.wd=10)\n\t{\n\tt.dat<-dat$t.dat\n\twr<-dat$w.dat[,2]\n\tlevs<-unique(as.character(dat$w.dat[,2]))[-1]\n m.names <- intersect(m.names,names(t.dat))\n xseq <- t.dat[,1]\n \n\tlibrary(RColorBrewer)\n if(length(m.names) > 0)\n { \n\t\tif(!is.null(m.order)){\t\n\t\tdat<-dat$c.dat[m.names,]\n\t\tn.order<-dat[order(dat[,m.order]),]\n\t\tm.names <- row.names(n.order)\n\t\t}\n\t\t#else{\n\t\t\t#m.pca <- prcomp(t(t.dat[,m.names]),scale=F,center=T)\n #morder <- m.pca$x[,1] * c(1,-1)[(sum(m.pca$rot[,1]) < 0)+1]\n #m.names <- m.names[order(m.pca$x[,1],decreasing=sum(m.pca$rot[,1]) < 0)]\n\t\t\t#um.names <- m.names[order(morder)]\n\t\t#}\n\t\t\n \n\t\tif(is.null(cols)){\n\t\t#cols <- rainbow(length(m.names),start=.55)\n\t\tcols <-brewer.pal(8,\"Dark2\")\n cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n\t\t} \n\t\telse { cols<-cols\n\t\t cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n\t\t}\n\t\t\n hbc <- length(m.names)*sf+max(t.dat[,m.names])\n hb <- ceiling(hbc)\n\t\tpar(mar=c(4,1,4,1))\n plot(xseq,t.dat[,m.names[1]],ylim=c(0,hbc),xlab=\"Time (min)\",main=lmain,type=\"n\", xaxt=\"n\",yaxt=\"n\",xlim=c(min(xseq)-1.5,max(xseq)+1.5))#-sf\n axis(1, at=seq(floor(min(t.dat[,1])),ceiling(max(t.dat[,1])), 1))\n\t\tif(length(wr) > 0)\n {\n \tif(!is.null(levs))\n \t{\n #levs <- setdiff(unique(wr),\"\")\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n rect(x1s,y1s,x2s,y2s,col=NA,border=\"darkgrey\")\n cpx <- xseq[match(levs,wr)+round(table(wr)[levs]/2,0)]\n offs <- nchar(levs)*.5\n text(cpx,rep(c(sf/2,sf),length=length(levs)),levs,pos=1,cex=bcex)#,offset=-offs\n }\n }\n for(i in 1:length(m.names))\n {\n lines(xseq,t.dat[,m.names[i]]+i*sf,col=cols[i],lwd=lw)\n if(!is.null(snr))\n {\n pp1 <- snr[,m.names[i]] > 0 & is.element(wr,levs)\n pp2 <- snr[,m.names[i]] > 0 & !is.element(wr,levs)\n # pp3 <- dat$crr[,m.names[i]] > 0\n points(xseq[pp1],t.dat[pp1,m.names[i]]+i/10,pch=1,col=cols[i])\n points(xseq[pp2],t.dat[pp2,m.names[i]]+i/10,pch=0,col=cols[i])\n # points(xseq[pp3],t.dat[pp3,m.names[i]]+i/10,pch=2,col=cols[i],cex=.5)\n } \n }\n text(rep(0,length(m.names)),seq(1,length(m.names))*sf+t.dat[1,m.names],m.names,cex=.8*bcex,col=cols,pos=2)\n \n\t\tif(is.null(rtag)){\n\t\tif(!is.null(m.order)){\n \trtag <- dat$c.dat[m.names,m.order]\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag,cex=.8*bcex,col=cols,pos=4)\n }}\n\t\telse{\n\t\t\trtag <- dat$c.dat[m.names,rtag]\n\t\t\ttext(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag2,cex=.8*bcex,col=cols,pos=4)\n\t\t }\n\n\t\tif(!is.null(rtag2)){\n \trtag2 <- dat$c.dat[m.names,rtag2]\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag2,cex=.8*bcex,col=\"green4\",pos=3)\n\t\t\ttext(rep(max(xseq),length(n.names)),seq(1,length(n.names))*sf+t.dat[nrow(t.dat),n.names],rtag2,cex=.8*bcex,col=\"green4\",pos=3)\n\n }\n\t\tif(!is.null(rtag3)){\n \trtag3 <- dat$c.dat[m.names,rtag3]\n\t text(rep(max(xseq),length(m.names)),seq(1,length(m.names))*sf+t.dat[nrow(t.dat),m.names],rtag3,cex=.8*bcex,col=\"Red\",pos=1)\n }\n\n\t\t} \n }\n\n\n while(click.i!=4)\n {\n cell.pick <- c.names[cell.i]\n dev.set(dev.list()[1])\n p1 <- PeakFunc2(dat,cell.pick,shws=2,phws=20,Plotit=T,wr=NULL,SNR.lim=2,bl.meth=\"SNIP\")\n p1.par<-par()\n\t\tif(lines.flag==1){dev.set(dev.list()[2]);linefunc(dat, g.names);lines.flag <- 0}\n\t\tif(lines.flag==0){dev.set(dev.list()[1])}\n #title(sub=paste(\"Group \",group.i,\" n=\",g.num,\" Cell \",cell.i,sep=\"\"))\n xs <- rep(dat$t.dat[50,\"Time\"],4)\n points(x=xs,y=c(1.2,1.1,1.0,.9),pch=16)\n text(x=xs,y=c(1.2,1.1,1.0,.9),labels=c(\"Cell +\",\"Cell -\",\"Stack\", \"off\"),pos=2,cex=.5)\n click.i <- identify(x=xs,y=c(1.2,1.1,1.0,.9),n=1,plot=F)\n \n if(click.i==1)\n {cell.i <- cell.i + 1;if(cell.i>length(c.names)){cell.i<-1}}\n if(click.i==2)\n {cell.i <- cell.i - 1;if(cell.i<1){cell.i<-length(c.names)}}\n\t\tif(click.i==3)\n {g.names<-union(g.names,c.names[cell.i]);lines.flag<-1}\n\t\tif(click.i==4){graphics.off()}\n}\nprint(g.names)}\n\n# Click Throug cells, and zoom on cell of interest\nTrace.Click.2<-function(dat, cells=NULL,img=NULL, plotit=T)\n{\n graphics.off()\n dev.new(width=14,height=4) \n dev.new(width=10,height=6) \n\tdev.new(width=8, height=8)\n if(is.null(cells)){c.names <- names(dat$t.dat[,-1])}\n\telse{c.names<-cells}\n\tlines.flag <- 0\n cell.i <- 1\n\tg.names<-NULL\n click.i <- 1\n\t#group.names<-NULL\n\n while(click.i!=5)\n {\n cell.pick <- c.names[cell.i]\n dev.set(dev.list()[1])\n p1 <- PeakFunc5(dat,cell.pick, Plotit.both=plotit)\n p1.par<-par()\n\t\tif(lines.flag==2){dev.set(dev.list()[3]);cell.veiw.1024(dat, img=img, cell=cell.i, cells=cells,cols=\"red\",plot.new=F,cell.name=T);lines.flag <- 0}\n\t\tif(lines.flag==1){dev.set(dev.list()[2]);LinesEvery.2(dat,g.names,plot.new=FALSE);lines.flag <- 0}\n\t\tif(lines.flag==0){dev.set(dev.list()[1])}\n #title(sub=paste(\"Group \",group.i,\" n=\",g.num,\" Cell \",cell.i,sep=\"\"))\n #xs <- -(rep(dat$t.dat[50,\"Time\"],5)*1.08)\n\t\txs<- rep(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095),5)\n points(x=xs,y=c(1.3,1.1,.9,.7,.5),pch=16)\n text(x=xs,y=c(1.3,1.1,.9,.7,.5),labels=c(\"Cell +\",\"Cell -\",\"Veiw\",\"Stack\",\"off\"),pos=2,cex=.5)\n \n\t\t## How many cells are you looking at\n\t\ttext(x=(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095)),y=1.6,paste(cell.i, \":\",length(c.names)))\n\t\tclick.i <- identify(x=xs,y=c(1.3,1.1,.9,.7,.5),n=1,plot=F)\n \n if(click.i==1)\n {cell.i <- cell.i + 1;if(cell.i>length(c.names)){cell.i<-1};lines.flag<-0}\n if(click.i==2)\n {cell.i <- cell.i - 1;if(cell.i<1){cell.i<-length(c.names)};lines.flag<-0}\n\t\tif(click.i==3)\n {lines.flag<-2}\n\t\tif(click.i==4)\n {g.names<-union(g.names,c.names[cell.i]);lines.flag<-1}\n\t\tif(click.i==5){graphics.off()}\n}\nprint(g.names)}\n\nTrace.Click.3<-function(dat, cells=NULL,img=NULL)\n{\n graphics.off()\n dev.new(width=14,height=4) \n dev.new(width=10,height=6) \n\tdev.new(width=8, height=8)\n if(is.null(cells)){c.names <- names(dat$t.dat[,-1])}\n\telse{c.names<-cells}\n\n\tlines.flag <- 0\n cell.i <- 1\n\tg.names<-NULL\n click.i <- 1\n\t#group.names<-NULL\n\n while(click.i!=5)\n {\n cell.pick <- c.names[cell.i]\n dev.set(dev.list()[1])\n p1 <- PeakFunc6(dat,cell.pick)\n p1.par<-par()\n\t\tif(lines.flag==2){dev.set(dev.list()[3]);cell.zoom.1024(dat, img=img, cell=cell.i,cols=\"red\",plot.new=F,cell.name=T, zoom=FALSE);lines.flag <- 0}\n\t\tif(lines.flag==1){dev.set(dev.list()[2]);LinesEvery.4(dat,g.names,plot.new=F);lines.flag <- 0}\n\t\tif(lines.flag==0){dev.set(dev.list()[1])}\n #title(sub=paste(\"Group \",group.i,\" n=\",g.num,\" Cell \",cell.i,sep=\"\"))\n #xs <- -(rep(dat$t.dat[50,\"Time\"],5)*1.08)\n\t\txs<- rep(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095),5)\n\t\tymax<-max(dat$t.dat[,cell.pick])*1.05\n\t\tymin<-min(dat$t.dat[,cell.pick])*.95\n\t\tyrange<-ymax-ymin\n\t\tyloc<-c(ymax-yrange*.1, ymax-(yrange*.2), ymax-(yrange*.3), ymax-(yrange*.4), ymax-(yrange*.5))\n\t\tpoints(x=xs,y=yloc,pch=16)\n text(x=xs,y=yloc,labels=c(\"Cell +\",\"Cell -\",\"Veiw\",\"Stack\",\"off\"),pos=2,cex=.5)\n\t\t\n\t\t## How many cells are you looking at\n\t\ttext(x=(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095)),y=(ymax+yrange*.2),paste(cell.i, \":\",length(c.names)))\n click.i <- identify(x=xs,y=yloc,n=1,plot=F)\n \n if(click.i==1)\n {cell.i <- cell.i + 1;if(cell.i>length(c.names)){cell.i<-1};lines.flag<-0}\n if(click.i==2)\n {cell.i <- cell.i - 1;if(cell.i<1){cell.i<-length(c.names)};lines.flag<-0}\n\t\tif(click.i==3)\n {lines.flag<-2}\n\t\tif(click.i==4)\n {g.names<-union(g.names,c.names[cell.i]);lines.flag<-1}\n\t\tif(click.i==5){graphics.off()}\n}\nprint(g.names)}\n\n\nbp.selector<-function(dat){\n\t## Selcet eith Area or Peak Height\n\ttype<-select.list(c(\"Peak Height\", \"Area\"), multiple=F, title=\"Parameter?\")\n\tif(type==\"Peak Height\"){type<-\".max\"}\n\telse{type<-\".tot\"}\n\n\t###Selecting Control Windows\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tlevs.mean<-sort(tapply(dat$t.dat[,\"Time\"], as.factor(dat$w.dat$wr1), mean))\n\tlevs<-setdiff(names(levs.mean),\"\")\n\tlevs.mean<-levs.mean[levs]\n\tys<-rep(1.05*(max(dat$t.dat[,\"X.1\"])), length(levs))\n\t\n\n\tdev.new(width=10, height=4)\n\tPeakFunc6(dat,row.names(dat$c.dat[1,]), lmain=\"Select Control Windows : \")\n\tpoints(levs.mean, ys, pch=16)\n\ttext(levs.mean,ys,labels=names(levs.mean),pos=c(1,3),cex=.5)\n\tcontrolwindows <- identify(x=levs.mean,y=ys,labels=\"X\",plot=T, col=\"red\")\n\tcontrolwindows<- levs[controlwindows]\n\t\n\t###Selecting Active Windows\n\tPeakFunc6(dat,row.names(dat$c.dat[1,]), lmain=\"Select Active Windows : \")\n\tpoints(levs.mean, ys, pch=16)\n\ttext(levs.mean,ys,labels=names(levs.mean),pos=c(1,3),cex=.5)\n\tactivewindows <- identify(x=levs.mean,y=ys,labels=\"X\",plot=T, col=\"red\")\n\tactivewindows<-levs[activewindows]\n\n\t# Select control windows and avtive windows to compare\n\t#controlwindows<-select.list(levs, multiple=T, title=\"Select Control Windows\")\n\t#activewindows<-select.list(levs, multiple=T, title=\"Select Active Windows\")\n\n\t#create the scp data frame names and grab their values\n\t\n\tif(length(controlwindows)>1){\n\t\tcontrolmax<-paste(controlwindows, type, sep=\"\")\n\t\tcontrolmaxmean<-rowMeans(dat$scp[,controlmax])\n\t}\n\telse{\n\t\tcontrolmax<-paste(controlwindows, type, sep=\"\")\n\t\tcontrolmaxmean<-dat$scp[,controlmax]\n\t}\n\n\tif(length(activewindows)>1){\n\t\tactivemax<-paste(activewindows, type, sep=\"\")\n\t\tactivemaxmean<-rowMeans(dat$scp[,activemax])\n\t}\n\telse{\n\t\tactivemax<-paste(activewindows, type, sep=\"\")\n\t\tactivemaxmean<-dat$scp[,activemax]\n\t}\n\n\t# Calculate percent change and select for cells\n\tmax.amp.mean<-activemaxmean/controlmaxmean\n\t\n\tgraphics.off()\n\tdev.new(width=5, height=5)\n\tboxplot(max.amp.mean, outline=F, ylim=c(0,2.5), main=paste(activewindows,\"Amplification Cutoff\"), ylab=\"Active.Max/Control.Max\")\n\tstripchart(max.amp.mean, ylim=c(0,2.5), add=T, vertical=T, method=\"jitter\", jitter=.2)\n\t\n\tloc<-locator(n=1, type=\"p\", pch=15, col=\"red\")\n\tabline(h=loc$y,col=\"red\")\n\t\n\tsaveimg<-select.list(c(\"Yes\", \"No\"), multiple=F, title=\"Save Boxplot Image?\")\n\t\n\tif(saveimg==\"Yes\"){\n\t\tdev.set(dev.list()[1])\n\t\tdev.copy(png,paste(activewindows,\"boxplot cutoff.png\"))\n\t\tdev.off()\n\t}\n\t\n\tcontinue<-select.list(c(\"Yes\", \"No\"), multiple=F, title=\"View Selected Cells?\")\n\tif(continue==\"Yes\"){\n\t\tx.names<-names(which(max.amp.mean>loc$y, arr.ind=T))\n\t\tprint(length(x.names))\n\t\t#graphics.off()\n\t\treal.cells<-Trace.Click.3(dat, x.names)\n\t\treturn(real.cells)\n\t}\n\telse{\n\t\tx.names<-names(which(max.amp.mean>loc$y, arr.ind=T))\n\t\treturn(x.names)\n\t}\n\n}\n\n\n#Repairs score from levs only\n# Uses peakfunc5\nbin.repair<-function(dat, n.names=NULL){\n\tif(is.null(n.names)){n.names<-names(dat$t.dat[,-1])}\n\tcell.i<-1\n\tcell<-n.names[cell.i]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tlevs.mean<-sort(tapply(dat$t.dat[,\"Time\"], as.factor(dat$w.dat$wr1), mean))\n\tlevs<-setdiff(names(levs.mean),\"\")\n\tlevs.mean<-levs.mean[levs]\n\txs <- c(levs.mean,rep(dat$t.dat[50,\"Time\"],4))\n\tys<-c(rep(1.4, length(levs.mean)),1.2, 1.1, 1.0, 0.9)\n\tdev.new(width=14, height=5)\n\tdev.set(dev.list()[1])\n\tPeakFunc5(dat,cell, Plotit.both=T)\n\tlinesflag<-0\n\tclick.i<-0\n\t\n\twhile(click.i!=length(levs.mean)+4){\n\t\tpoints(x=xs,y=ys,pch=16)\n\t\ttext(x=xs,y=c(rep(1.4, length(levs.mean)),1.2,1.1,1.0,0.9),labels=c(names(levs.mean),\"Cell +\",\"Cell -\",\"drop\",\"off\"),pos=2,cex=.5)\n\t\tclick.i <- identify(x=xs,y=ys,n=1,plot=T)\n\t\tcell<-n.names[cell.i]\n\t\tif(click.i<=length(levs.mean)){\n\t\t\tif(dat$bin[cell, levs[click.i]]==1){dat$bin[cell, levs[click.i]]=0;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\telse{dat$bin[cell, levs[click.i]]=1;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)\n\t\t}\n\t\t\n\t\tif(click.i==length(levs.mean)+1){cell.i <- cell.i + 1;if(cell.i>length(n.names)){cell.i<-1};linesflag<-1}\n\t\tif(click.i==length(levs.mean)+2){cell.i <- cell.i - 1;if(cell.i<1){cell.i<-length(n.names)};linesflag<-1}\n\t\tif(click.i==length(levs.mean)+3){dat$bin[cell, \"drop\"]=1;dev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)} #dat$bin[cell,levs]=0;\n\t\tif(linesflag==1){PeakFunc5(dat, n.names[cell.i], Plotit.both=T)}\n\n\t}\n\t\t\tgraphics.off()\n\t\t\treturn(dat$bin)\n\t}\n\n\t\n### Repairs GFP and TRITC score from label bin\n# uses peakfunc5\t\nbin.repair.2<-function(dat, n.names=NULL){\n\tif(is.null(n.names)){n.names<-names(dat$t.dat[,-1])}\n\tcell.i<-1\n\tcell<-n.names[cell.i]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tlevs.mean<-sort(tapply(dat$t.dat[,\"Time\"], as.factor(dat$w.dat$wr1), mean))\n\tlevs<-setdiff(names(levs.mean),\"\")\n\tlevs.mean<-levs.mean[levs]\n\t\n\trep(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095),4)\n\txs <- c(levs.mean,c(max(dat$t.dat[,1])*1.09, max(dat$t.dat[,1])*1.19),rep(max(dat$t.dat[,\"Time\"])-(max(dat$t.dat[,\"Time\"])*1.095),4))\n\tys<-c(rep(1.5, length(levs.mean)+2),1.2, 1.0, 0.8, 0.6)\n\tdev.new(width=14, height=5)\n\tdev.set(dev.list()[1])\n\tPeakFunc5(dat,cell, Plotit.both=T)\n\tlinesflag<-0\n\tclick.i<-0\n\t\n\twhile(click.i!=length(levs.mean)+2+4){\n\t\tpoints(x=xs,y=ys,pch=16)\n\t\ttext(x=xs,y=ys,labels=c(names(levs.mean),\"mean.gfp\", \"tritc\",\"Cell +\",\"Cell -\",\"drop\",\"off\"),pos=3,cex=.5)\n\t\tclick.i <- identify(x=xs,y=ys,n=1,plot=T)\n\t\tcell<-n.names[cell.i]\n\t\t\n\t\tif(click.i<=length(levs.mean)){\n\t\t\tif(dat$bin[cell, levs[click.i]]==1){dat$bin[cell, levs[click.i]]=0;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\telse{dat$bin[cell, levs[click.i]]=1;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)\n\t\t}\n\t\t\n\t\tif(click.i==length(levs.mean)+1){\n\t\t\tif(dat$bin[cell, \"mean.gfp.bin\"]==1){dat$bin[cell, \"mean.gfp.bin\"]=0;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\telse{dat$bin[cell, \"mean.gfp.bin\"]=1;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)\n\t\t}\n\n\t\tif(click.i==length(levs.mean)+2){\n\t\t\tif(dat$bin[cell, \"mean.tritc.bin\"]==1){dat$bin[cell, \"mean.tritc.bin\"]=0;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\telse{dat$bin[cell, \"mean.tritc.bin\"]=1;dat$bin[cell,\"drop\"]=0;linesflag<-0}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)\n\t\t}\n\t\t\n\t\tif(click.i==length(levs.mean)+3){cell.i <- cell.i + 1;if(cell.i>length(n.names)){cell.i<-1};linesflag<-1}\n\t\tif(click.i==length(levs.mean)+4){cell.i <- cell.i - 1;if(cell.i<1){cell.i<-length(n.names)};linesflag<-1}\n\t\tif(click.i==length(levs.mean)+5){dat$bin[cell, \"drop\"]=1;dev.set(dev.list()[1]);PeakFunc5(dat, cell, Plotit.both=T)} #dat$bin[cell,levs]=0;\n\t\tif(linesflag==1){PeakFunc5(dat, n.names[cell.i], Plotit.both=T)}\n\t\t}\n\n\t\tgraphics.off()\n\t\tneuron.response<-select.list(levs, title=\"What defines Neurons?\", multiple=T)\n\t\tneurons<-cellz(dat$bin,neuron.response, 1)\n\t\tdrop<-cellz(dat$bin, \"drop\", 1)\n\t\tneurons<-setdiff(neurons,drop)\n\t\tpf<-apply(dat$bin[,c(\"mean.gfp.bin\", \"mean.tritc.bin\")],1,paste, collapse=\"\")\n\t\tdat$bin[\"lab.pf\"]<-as.factor(pf)\n\t\tlab.groups<-unique(dat$bin$lab.pf)\n\t\t\n\t\tcells<-list()\n\t\tfor(i in lab.groups){\n\t\t\tx.names<-cellz(dat$bin[neurons,], \"lab.pf\", i)\n\t\t\tcells[[i]]<-x.names\n\t\t}\n\t\t\n\t\tglia.response<-select.list(c(levs, \"none\"), title=\"What defines glia?\", multiple=T)\n\t\tif(glia.response!=\"none\"){\n\t\t\tdrop<-cellz(dat$bin, \"drop\", 1)\n\t\t\tglia<-cellz(dat$bin,glia.response, 1)\n\t\t\tglia<-setdiff(glia,drop)\n\t\t\tcells[[\"000\"]]<-setdiff(glia, neurons)\n\t\t} \n\t\telse {cells[[\"000\"]]<-setdiff(row.names(dat$c.dat), neurons)}\n\t\tdat$cells<-cells\n\t\treturn(dat)\n\n}\n\nbin.rep.cells<-function(dat){\n\t\n\tcells<-dat$cells\n\t\n\tfor(i in 1:length(cells)){\n\t\tdat<-bin.repair.2(dat, cells[[i]])\n\t\t}\n\treturn(dat)\n}\n\n\n# Creates Binary socring for labeling\n# Input RD list, and # of cells to observe for sampling\n# Outuput bin dataframe with added intensity scoring\nlabel.bin<-function(dat, cells=10){\n\trand.names<-attributes(sample(dat$c.dat$id))$levels\n\tn.names<-rand.names[1:cells]\n\n\tcell.i<-1\n\tdev.new(width=15, height=3)\n\tyes.green<-vector()\n\tno.green<-vector()\n\tyes.red<-vector()\n\tno.red<-vector()\n\n\tfor(i in 1:length(n.names)){\n\t\t\n\t\tpar(mfrow=c(1,5))\n\t\tmulti.pic.zoom(dat, n.names[i], dat$img1, plot.new=F)\n\t\tmulti.pic.zoom(dat, n.names[i], dat$img2, plot.new=F)\n\t\tmulti.pic.zoom(dat, n.names[i], dat$img3, plot.new=F)\n\t\tmulti.pic.zoom(dat, n.names[i], dat$img4, plot.new=F)\n\n\n\t\tpar(mar=c(0,0,0,0))\n\t\txloc<-c(2,2,2,2)\n\t\tyloc<-c(3.5,2.5,1.5,0.5)\n\t\tloc<-cbind(xloc, yloc)\n\t\tplot(loc,xlim=c(0,4), pch=15, ylim=c(0,4), xaxt=\"n\", yaxt=\"n\", cex=1.5)\n\t\ttext(loc, c(\"+GFP\",\"+TRITC\", \"+GFP & +TRITC\",\"No Label\") ,pos=4, cex=1.5)\n\t\tclick.i<-identify(loc, n=1, plot=T)\t\n\t\t\n\t\tif(click.i==1){yes.green[i]<-dat$c.dat[n.names[i],\"mean.gfp\"];no.red[i]<-dat$c.dat[n.names[i],\"mean.tritc\"]}\n\t\tif(click.i==2){yes.red[i]<-dat$c.dat[n.names[i],\"mean.tritc\"];no.green[i]<-dat$c.dat[n.names[i],\"mean.gfp\"]}\n\t\tif(click.i==3){yes.red[i]<-dat$c.dat[n.names[i],\"mean.tritc\"];yes.green[i]<-dat$c.dat[n.names[i],\"mean.gfp\"]}\n\t\tif(click.i==4){no.red[i]<-dat$c.dat[n.names[i],\"mean.tritc\"];no.green[i]<-dat$c.dat[n.names[i],\"mean.gfp\"]}\n\t}\n\tgraphics.off()\n\t\n\tif(length(yes.green)>=1){yes.green<-setdiff(yes.green,c(\"NA\",NA))}\n\tif(length(no.green)>=1){no.green<-setdiff(no.green,c(\"NA\",NA))}\n\tif(length(yes.red)>=1){yes.red<-setdiff(yes.red,c(\"NA\",NA))}\n\tif(length(no.red)>=1){no.red<-setdiff(no.red,c(\"NA\",NA))}\n\n\tdat$bin[\"mean.gfp.bin\"]<-0\n\tdat$bin[\"mean.tritc.bin\"]<-0\n\n\tif(length(yes.green)>=1){green.names<-row.names(dat$c.dat)[dat$c.dat$mean.gfp>min(yes.green)]}\n\tif(length(yes.red)>=1){red.names<-row.names(dat$c.dat)[dat$c.dat$mean.tritc>min(yes.red)]}\n\n\tif(length(yes.green)>=1){dat$bin[green.names,\"mean.gfp.bin\"]<-1}\n\tif(length(yes.red)>=1){dat$bin[red.names,\"mean.tritc.bin\"]<-1}\n\t\n\tprint(paste(\"Green Cells : \",min(yes.green)))\n\tprint(paste(\"Red Cells : \",min(yes.red)))\n\tprint(paste(\"No label Green : \",max(no.green),\"No label Red\", max(no.red)))\n\t\n\tpf<-apply(dat$bin[,c(\"mean.gfp.bin\", \"mean.tritc.bin\")],1,paste, collapse=\"\")\n\tdat$bin[\"lab.pf\"]<-as.factor(pf)\n\n\treturn(dat$bin)\n}\t\n\n\n\n\n##############################################################################################\n##############################################################################################\n\n##############################################################################################\n# Cell Group Review\n##############################################################################################\n#Group summarry\n#generate pdfs with line graphs\n#table of means and frequencies for all c.dat\n#THIS MUST BE CLEANED UP 040314\nGroupSummary <- function(dat,snr,c.dat,wr,levs,groups,pref=\"Group\")\n{\n g.levs <- unique(groups)\n for(i in g.levs)\n {\n c.names <- names(groups[groups==i])\n pdf.name <- paste(pref,i,\".pdf\",sep=\"\")\n lmain <- paste(pref,i,sep=\"\")\n LinesEvery(dat,snr,c.names,wr,levs,lmain,pdf.name)\n dev.off()\n }\n res.tab <- data.frame(mean=apply(c.dat[names(groups),],2,mean))\n res.tab[\"sd\"] <- apply(c.dat[names(groups),],2,sd)\n for(i in g.levs)\n {\n c.names <- names(groups[groups==i])\n res.tab[paste(pref,i,\".mean\",sep=\"\")] <- apply(c.dat[c.names,],2,mean)\n res.tab[paste(pref,i,\".sd\",sep=\"\")] <- apply(c.dat[c.names,],2,sd)\n }\n tab.name <- paste(pref,\".table.csv\",sep=\"\")\n write.csv(res.tab,file=tab.name)\n #lines figure similar to boxplot\n ## tmp <- scale(c.dat[names(groups),],center=T,scale=T)\n ## tmp.mn <- data.frame(t(apply(tmp,2,function(x){tapply(x,as.factor(groups),mean)})))\n ## tmp.sd <- data.frame(t(apply(tmp,2,function(x){tapply(x,as.factor(groups),sd)})))\n ## tmp.se <- t(t(tmp.sd)/sqrt(summary(as.factor(groups))))\n ## ylim <- c(min(tmp.mn)-2,max(tmp.mn))\n ## miny <- min(ylim)+1\n ## dev.new()\n ## par(xaxt=\"n\",mar=c(2,4,4,2))\n ## plot(seq(1,nrow(tmp.mn)),tmp.mn[,1],ylim=ylim,xlim=c(0,(nrow(tmp.mn)+1)),type=\"n\",ylab=\"Normalized Mean +- SE\",xaxt=\"n\")\n ## cols <- rainbow(ncol(tmp.mn),start=.3)\n ## names(cols) <- names(tmp.mn)\n ## nudge <- 0\n ## ## for(i in names(tmp.mn))\n ## {\n ## xseq <- seq(1,nrow(tmp.mn))\n ## rect(nudge+seq(1,nrow(tmp.mn))-.05,tmp.mn[,i]-tmp.se[,i],nudge+seq(1,nrow(tmp.mn))+.05,tmp.mn[,i]+tmp.se[,i],col=cols[i],border=NA)\n ## points(nudge+seq(1,nrow(tmp.mn)),tmp.mn[,i],pch=16,col=cols[i],lwd=2,type=\"b\") \n ## nudge <- nudge+.1\n ## }\n ## text(rep(nrow(tmp.mn),ncol(tmp.mn)),tmp.mn[nrow(tmp.mn),],paste(pref,names(tmp.mn),sep=\"\"),cex=.8,col=cols,pos=4)\n ## text(seq(1,nrow(tmp.mn))+.25,miny,names(c.dat),srt=90,pos=3)\n c.mn <- data.frame(t(apply(c.dat,2,function(x){tapply(x,as.factor(groups),mean)})))\n c.sd <- data.frame(t(apply(c.dat,2,function(x){tapply(x,as.factor(groups),sd)})))\n c.se <- t(t(c.sd)/sqrt(summary(as.factor(groups))))\n return(list(mean=c.mn,sd=c.sd,se=c.se)) \n}\n\n\n\n\t\n# Fucntion plotting cell locations, barplots of labeled intensities, stacked traces, and\n# single traces of all scored groups.\n# Needs work on click funcitons, and recognition of NULL intensities from experiemnts\nGroupReview.2 <- function(dat,bp.plot=T,shws=2,phws=20,wr.i=2,bl.meth=\"TopHat\")\n{\n\tlibrary(cluster)\n graphics.off()\n\t#peakfunc window= dev.list()[1]\n windows(width=8,height=4, xpos=0, ypos=0) \n #linefunc window= dev.list()[2]\n\twindows(width=8,height=5, xpos=0, ypos=360) \n\t#bpfunc window= dev.list()[3]\n\twindows(width=5,height=4, xpos=800, ypos=420) \n\t#cell.locate window= dev.list[4]\n\twindows(width=12,height=12, xpos=820, ypos=0) \n\t#gui window= dev.list[5]\n\twindows(width=2,height=2, xpos=1400, ypos=620) \n# Plotting all traces ontop of each other\t\n# Could attempt something like a LinesEvery function \n# Should replace linesfunce with linesevery.2. If there are more than 15 cells\n# then i need to plot traces like tracechase. Needs window plotting.\n# shade windows according to scoring\n\n\n#Cell locate still needs to be able to move through images. \\\n# New data set will have 4-5 images\n# Also, this function needs have all click features available, including click \n# cells for peakfunc selections\n\n\t# Create a table with binary groups as rows\n\t# collumn 1=total cells in group\n\t# collumn 2=group number\n\ttotal.cell<-sort(summary(dat$c.dat[,\"pf\"]))\n\tgroup.sum<-cbind(total.cell, seq(1,length(total.cell), by=1))\n\tas.table(group.sum)\n\tcolnames(group.sum)<-c(\"c.tot\", \"g.num\")\n\t#make clust (which is the definition of clusters) be equal to the group numbers\n\t#in group.sum\n\t#clust<-group.sum[,\"g.num\"]\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,\"wr1\"])),\"\")\n\tpf<-apply(dat$bin[,levs],1,paste,collapse=\"\")\n\tpf.sum<-summary(as.factor(pf),maxsum=500)\n\tpf.sum<-pf.sum[order(pf.sum,decreasing=T)]\n\tpf.ord<-pf.sum\n\tpf.ord[]<-seq(1,length(pf.sum))\n\tdat$c.dat[\"pf\"]<-as.factor(pf)\n\tdat$c.dat[\"pf.sum\"]<-pf.sum[pf]\n\tdat$c.dat[\"pf.ord\"]<-pf.ord[pf]\n\tclust<-dat$c.dat[,\"pf.ord\"]\n\tclust.name <- unique(clust)\n\t\n\tlevs<-setdiff(unique(as.character(dat$w.dat[,2])),\"\")\n\n\tdev.set(dev.list()[5])\n\tpar(mar=c(0,0,0,0))\n\tplot(2,2, pch=NA)\n\tpoints(x=c(rep(1.75,5),rep(2.5,6)),y=c(2.5,2.25,2.0,1.75,1.5,2.5,2.25,2,1.75,1.5,1.25),pch=16)\n\ttext(x=c(rep(1.75,5),rep(2.5,6)),y=c(2.5,2.25,2.0,1.75,1.5,2.5,2.25,2,1.75,1.5,1.25),\n\tlabels=c(\"Group +\",\"Group -\",\"Cell +\",\"Cell -\",\"Done\", \"Image 1\", \"Image 2\", \"Image 3\", \"Zoom\", \"+ Pulse\", \"- Pulse\"),pos=2,cex=.8)\n\t\n\t\n\timg<-dat$img1\n\t#an intiator of the linesfunc if lines.flag=1\n\tlines.flag <- 1\n #this is a list of all cell names\n\tg.names <- names(dat$t.dat[,-1])\n #highest group #\n\tpam.k <- max(clust)\n\t#initial group and cell to start analysis\n group.i <- 1\n\tcell.i <- 1\n\tpeak.i<-1\n \n\t# define first click\n\tclick.i <- 1\n\twhile(click.i)\n {\n\t#initiate the single peak plot, but only if the group exists\n\t\tg.num <- sum(clust==group.i)\n\t\tif(g.num > 0)\n {\n\t\t#first group defined above, but can be further defined below\n group.names <- g.names[clust==group.i]\n #first cell is defined above, but can be further defined below\n\t\tcell.pick <- group.names[cell.i]\n\t\t\t\t# Intial setting for image changer\n\t\t\n #move to next plot and start peakfunc2\n\t\t#p1 <- PeakFunc2(dat,cell.pick,shws=shws,phws=phws,Plotit=T,wr=dat$w.dat[,wr.i],SNR.lim=2,bl.meth=bl.meth)\n\t\t}\n\t\t#start boxplot of color intensities\n\t\tif(lines.flag==1){\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell.pick)\n\t\t\tdev.set(dev.list()[2]);if(length(group.names)>10){LinesStack(dat, group.names, plot.new=F)}else{LinesEvery.2(dat, group.names,plot.new=F)}\n\t\t\tdev.set(dev.list()[3]);bpfunc(dat,group.names)\n\t\t\tdev.set(dev.list()[4]);cell.zoom.1024(dat,img, group.names, plot.new=F);lines.flag <- 0\n\t\t}\n\t\t\n\t\tdev.set(dev.list()[5])\n\t\n\t\tclick.i <- identify(x=c(rep(1.75,5),rep(2.5,6)),y=c(2.5,2.25,2.0,1.75,1.5,2.5,2.25,2,1.75,1.5,1.25),n=1,plot=F)\n\t\t# syntax for first click on peakfumc2. if click group+ group.i+1\n \n\t\tif(click.i==1)\n {group.i <- group.i + 1;if(group.i > pam.k){group.i <- 1};cell.i<-1;lines.flag <- 1}\n \n\t\tif(click.i==2)\n {group.i <- group.i - 1;if(group.i < 1){group.i <- pam.k};cell.i<-1;lines.flag <- 1}\n \n\t\tif(click.i==3){\n\t\t\tcell.i <- cell.i + 1\n\t\t\tif(cell.i > g.num){cell.i <- 1}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell.pick)\n\t\t}\n \n\t\tif(click.i==4){\n\t\t\tcell.i <- cell.i - 1\n\t\t\tif(cell.i < 1){cell.i <- g.num}\n\t\t\tdev.set(dev.list()[1]);PeakFunc5(dat, cell.pick)\n\t\t}\n\t\t\n\t\tif(click.i==5)\n\t\t{graphics.off();stop()}\n\t\t\n\t\tif(click.i==6){if(!is.null(dat$img1)){img<-dat$img1};lines.flag<-1}\n\t\t\n\t\tif(click.i==7){if(!is.null(dat$img2)){img<-dat$img2};lines.flag<-1}\n\t\t\n\t\tif(click.i==8){if(!is.null(dat$img3)){img<-dat$img3};dev.set(dev.list()[4]);lines.flag<-1}\n\t\t\n\t\tif(click.i==9){}#cell.pick<-group.names[cell.i];cell.locate(cell.pick, zoom=5)}\n\t\t\n\t\tif(click.i==10){peak.i<-peak.i+1;group.names<-row.names(dat$bin)[dat$bin[,levs[peak.i]]==1];lines.flag <- 1}\n\t\t\n\t\tif(click.i==11){group.names<-p.names[[peak.i-1]]; cell.i<-1 ;lines.flag<-1}\n\t}\n\tdev.off()\n}\n##############################################################################################\n##############################################################################################\n\n\t\t\n\t\t\n##############################################################################################\n# Trace Searching\n##############################################################################################\n\n#topdown parsing of all traces\nTraceChase <- function(dat,blc=NULL,levs=NULL,x.names=NULL,scale=T)\n{\n\tlibrary(cluster)\n\tif(is.null(blc)){\n\tif(is.element(\"blc\",names(dat))){blc <- dat$blc}\n\telse\n\t{tmp.pcp <- ProcConstPharm(dat);blc <- tmp.pcp$blc}}\n\tif(is.null(levs))\n\t{\n\t\tlevs <- unique(dat$w.dat[,\"wr1\"])\n\t\tlevs <- select.list(levs,multiple=T,title=\"Select Regions for clustering\")\n\t}\n\tdmat <- t(scale(blc[is.element(dat$w.dat[,\"wr1\"],levs),-1],scale=scale,center=scale))\n\ta.names <- names(blc)[-1]\n\tif(!is.null(x.names)){a.names <- intersect(x.names,names(blc))}\n\tdone=FALSE\n\twhile(!done)\n\t{\n\t\tif(length(a.names) < 21)\n\t\t{\n\t\t\tx.names <- TraceSelect(dat,a.names,dat$w.dat[,\"wr1\"],levs, \"Final Select\")\n\t\t\tdone=TRUE\n\t\t}\n\t\telse\n\t\t{\n\t\t\t#pam20 <- pam(dmat[a.names,],k=20)\n\t\t\tclmb20 <- ClimbTree(dmat[a.names,],k=20)\n\t\t\tlmain <- paste(\"Select Traces (all or none to end) n=\",length(a.names))\n\t\t\t#x.names <- SmashSelect(blc[c(\"Time\",a.names)],pam20$clustering,row.names(pam20$medoids),dat$w.dat[,\"wr1\"],levs,lmain=lmain)\t\t\t\t\n\t\t\tx.names <- SmashSelect(blc[c(\"Time\",a.names)],clmb20,names(clmb20)[match(1:length(unique(clmb20)),clmb20)],dat$w.dat[,\"wr1\"],levs,lmain=lmain)\t\t\t\t\t\t\t\n\t\t\tif(length(a.names)==length(x.names)){done = TRUE}\n\t\t\tif(length(x.names)==0){done= TRUE}\n\t\t\ta.names <- x.names\n\t\t}\n\t\n\t}\n\treturn(x.names)\t\n}\n\n#given a set of traces (or trace seqments)\n#calculate the distances and group into K groups\n#by height of tree cutting. One of the K groups will\n#be a catch-all for all small groups\nClimbTree <- function(x,k=20)\n{\n\ttabstat <- function(x){return(list(mean=mean(x),length=length(x),median=median(x),sd=sd(x),gt5c=sum(x>5)))}\n\tlibrary(cluster)\n\td1 <- dist(x)\n\th1 <- hclust(d1)\n\tq1 <- quantile(h1$height,probs=1:10/10)\n\tclust <- cutree(h1,h=q1[5])\n\tclust.tab <- table(clust)\n\tclust.tab <- clust.tab[order(clust.tab,decreasing=T)]\t\n\tnew.num <- clust.tab\n\tnew.num[] <- seq(1,length(new.num))\n\tclust[] <- new.num[as.character(clust)]\n\tclust.tab <- table(clust)\n\tif(length(clust.tab) > k)\n\t{\n\t\tin.grp <- names(clust.tab[1:(k-1)])\n\t\tout.grp <- setdiff(names(clust.tab),in.grp)\n\t\tclust[is.element(clust,out.grp)] <- k\t\n\t}\n\treturn(clust)\n\t# clust.stat <- data.frame(tabstat(clust.tab))\n\t# for(i in 2:length(q1))\n\t# {\n\t\t# clust <- cutree(h1,h=q1[i])\n\t\t# clust.tab <- table(clust)\n\t\t# clust.stat[i,] <- tabstat(clust.tab)\n\t# }\n\t# return(clust.stat)\n\t\n}\n\n#smash select plot the smashes and return the selected.\n#all data in t.dat is ploted (1st col must be time)\n#m.names are taken to be the medoids of the clusters\nSmashSelect <- function(t.dat,clust,m.names,wr,levs=NULL,lmain=\"\")\n{\t\n\trtag <- table(clust)\n\tnames(rtag) <- m.names[order(clust[m.names])]\n\tsf <- 1\n\tgcol <- rgb(10,10,10,alpha=120,max=255)\n\t#gcol <- \"grey\"\n\tx <- t.dat[,-1]\n\txm <- apply(x,2,max)\n\txn <- scale(x,center=F,scale=xm)\n\tfor(i in 1:nrow(xn)){xn[i,] <- xn[i,]+clust}\n\t\n library(RColorBrewer)\n lwds <- 2\n \n xseq <- t.dat[,1]\n cols <-brewer.pal(8,\"Dark2\")\n cols <- rep(cols,ceiling(length(m.names)/length(cols)))\n cols <- cols[1:length(m.names)]\n dev.new(width=14,height=9)\n op <- par(yaxt=\"n\",bty=\"n\",mar=c(4,0,2,1),cex=1)\n plot(xseq,xn[,m.names[1]],ylim=c((min(xn)-2),max(xn)),xlab=\"Time (min)\",ylab=\"Ratio with shift\",main=lmain,type=\"n\", xaxt=\"n\")\n\taxis(1, at=seq(0, length(t.dat[,1]), 5))\n\tapply(xn,2,lines,x=xseq,col=gcol,lwd=2)\n\thbc <- 1\n if(length(wr) > 0)\n {\n \tif(is.null(levs)){levs <- setdiff(unique(wr),\"\")}\n x1s <- tapply(xseq,as.factor(wr),min)[levs]\n x2s <- tapply(xseq,as.factor(wr),max)[levs]\n y1s <- rep(-.3,length(x1s))\n y2s <- rep(hbc+.2,length(x1s))\n #rect(x1s,y1s,x2s,y2s,col=\"lightgrey\")\n text(xseq[match(levs,wr)],rep(c(.2,-.2),length.out=length(levs)),levs,pos=4,offset=0,cex=1)\n }\n x.sel <- NULL\n xs <-c(rep(0,length(m.names)),c(.1,.1,.1))\n ys <- xn[1,m.names]\n ys <- as.vector(c(ys,c(sf*.9,0,-sf*.9)))\n# xs[(length(xs)-2):length(xs)] <- c(0,5,10)\n p.names <- c(rep(\" \",length(m.names)),\"ALL\",\"NONE\",\"FINISH\")\n done.n <- length(p.names)\n none.i <- done.n-1\n all.i <- none.i-1\n p.cols <- c(cols,c(\"black\",\"black\",\"black\"))\n for(i in 1:length(m.names))\n {\n \t #lines(xseq,xn[,m.names[i]],col=\"black\",lwd=lwds*.5)\n lines(xseq,xn[,m.names[i]],col=cols[i],lwd=lwds)\n }\n text(x=rep(max(xseq),length(m.names)),y=xn[nrow(xn),m.names],cex=.9,rtag,pos=4,col=p.cols)\n\ttext(x=xs,y=ys,labels=p.names,pos=2,cex=.7,col=p.cols)\n points(x=xs,y=ys,pch=16,col=p.cols,cex=1.5)\n click.i <- 1 \n while(click.i != done.n)\n {\n click.i <- identify(xs,ys,n=1,plot=F)\n if(click.i < (length(m.names)+1) & click.i > 0)\n {\n i <- click.i\n if(is.element(i,x.sel))\n {\n lines(xseq,xn[,m.names[i]],col=cols[i],lwd=lwds)\n x.sel <- setdiff(x.sel,i)\n }\n else\n {\n\t \t lines(xseq,xn[,m.names[i]],col=\"black\",lwd=lwds)\n x.sel <- union(x.sel,i)\n }\n }\n if(click.i == none.i)\n {\n \tx.sel <- NULL\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,xn[,m.names[i]],col=cols[i],lwd=lwds)\n\t \t}\n\t }\n if(click.i == all.i)\t\n {\n \tx.sel <- seq(1,length(m.names))\n\t \tfor(i in 1:length(m.names))\n\t\t {\n \t\t lines(xseq,xn[,m.names[i]],col=\"black\",lwd=lwds)\n\t \t}\n \t\n }\n }\n c.sel <- clust[m.names[x.sel]]\n x.ret <- names(clust[is.element(clust,c.sel)])\n dev.off()\n return(x.ret)\n}\n\n#this simply finds the traces in t.dat that are similar to targs\n#note this is \"complete\" similarity other options may be\n#\"average\" and \"best\"\nGetCloser <- function(t.dat,targs,k=20)\n{\n\tx.names <- setdiff(names(t.dat),targs)\n\tct <- cor(t.dat[,x.names],t.dat[,targs])\n\tx.max <- apply(ct,1,min)\n\ty.names <- x.names[order(x.max,decreasing=T)[1:k]]\n\treturn(y.names)\t\n}\n\n\n#this is a bit raw still\n#Given a set of traces (t.dat) and a list of targets (targs)\n#identify the 20 most similar traces using wr and the select levs.\n#allow the user to select from those to add to the master list.\nSimilarSelect <- function(t.dat,targs,wr,levs=NULL)\n{\n\tplot(t.dat[,1],t.dat[,targs[1]],type=\"n\",ylim=c(min(t.dat[-1]),(length(targs)+50)*.2))\n\tsf <- 0\n\tfor(i in targs){lines(t.dat[,1],t.dat[,i]+sf);sf<-sf+.2}\n\ta.names <- setdiff(names(t.dat)[-1],targs)\n\trjct <- rep(0,length(a.names))\n\tnames(rjct) <- a.names\n\tdone=FALSE\n\ttps <- seq(1:nrow(t.dat))\n\tif(!is.null(levs)){tps <- tps[is.element(wr,levs)]}\n\twhile(!done)\n\t{\t\n\t\tif(sum(rjct==0) < 21)\n\t\t{done=TRUE}\n\t\telse\n\t\t{\n\t\t\tx.names <- GetCloser(t.dat[tps,c(a.names[rjct==0],targs)],targs)\n\t\t\trjct[x.names] <- 1\n\t\t\ty.names <- TraceSelect(t.dat,,x.names,wr)\n\t\n\t\t\tif(length(y.names)==0){done=TRUE}\n\t\t\telse\n\t\t\t{\n\t\t\t\ttargs <- c(targs,y.names)\n\t\t\t\tfor(i in y.names){lines(t.dat[,1],t.dat[,i]+sf);sf<-sf+.2}\n\t\t\t}\n\t\t}\n\t}\n\treturn(targs)\n\t#plot targs and allow user to\n\t#paint region of interest if you can do this it makes a very good window adjust function.\n\t#find matches within t.dat\n\t#show matches in trace select allow user to choose.\n\t#merge all selected and return that list.\n\t\n\t\n}\n\n##############################################################################################\n##############################################################################################\n\n\n\n##############################################################################################\n# Interactive Image analysis\n##############################################################################################\n\n# Fucntion locates single cell or groups of cells on plot. \n# Needs more optional assignments\ncell.veiw.1024<-function(dat, img=NULL, cell=NULL, cells=NULL, cols=NULL,lmain=\"\", bcex=.5, plot.new=T, cell.name=T)\n{\nif(plot.new){dev.new()}\nrequire(png)\nrequire(zoom)\npar(mar=c(0,0,1,0))\ncells.x<-dat$c.dat[cells,\"center.x\"]\ncells.y<-dat$c.dat[cells,\"center.y\"]\ncell.x<-dat$c.dat[cell,\"center.x\"]\ncell.y<-dat$c.dat[cell,\"center.y\"]\n\nif(is.null(img)){img<-dat$img1}\nelse{img<-img}\nif(is.null(cols)){cols=\"white\"}\nelse{cols=cols}\n\nplot(0, 0, xlim=c(0,1024),ylim=c(1024,0), main=lmain,xaxs=\"i\", yaxs=\"i\", xlab=\"Pixels\", ylab=\"Pixels\")\nrasterImage(img, 0, 1024, 1024, 0)\n\n\tpoints(cell.x, cell.y, col=cols, pch=4, cex=1)\n\ttext(cell.x, cell.y, labels=cell, col=cols, pos=2, cex=1)\n\t\n\tpoints(cells.x, cells.y, col=\"white\", pch=4, cex=bcex)\n\ttext(cells.x, cells.y, labels=dat$c.dat[cells,1], col=\"white\", pch=4, pos=2, cex=bcex)\n}\n\n\n\n\ncell.zoom.1024<-function(dat, cell=NULL,img=NULL, zoom=TRUE, cols=NULL,lmain=\"\", bcex=.8, labs=T, plot.new=T, cell.name=T)\n{\nif(plot.new){dev.new()}\nrequire(png)\nrequire(zoom)\npar(mar=c(0,0,1,0))\nx<-dat$c.dat[,\"center.x\"]\ny<-dat$c.dat[,\"center.y\"]\ncell.x<-dat$c.dat[cell,\"center.x\"]\ncell.y<-dat$c.dat[cell,\"center.y\"]\n\nif(is.null(img)){img<-dat$img1}\nelse{img<-img}\nif(is.null(cols)){cols=\"white\"}\nelse{cols=cols}\n\nplot(0, 0, xlim=c(0,1024),ylim=c(1024,0), main=lmain,xaxs=\"i\", yaxs=\"i\", xlab=\"Pixels\", ylab=\"Pixels\")\nrasterImage(img, 0, 1024, 1024, 0)\n\nif(labs){\n\tif(!is.null(cell)){\n\t\tpoints(cell.x, cell.y, col=cols, pch=0, cex=2)\n\t\ttext(cell.x, cell.y, labels=cell, col=cols, pos=2, cex=bcex)\n\t}\n\telse{\n\t\tpoints(x, y, col=cols, pch=4, cex=2)\n\t\ttext(x, y, labels=dat$c.dat[,1], col=cols, pch=0, pos=2, cex=bcex)\n\t}\n}\n\nif(zoom==TRUE){\n\tcell.1<-row.names(dat$c.dat[order(dat$c.dat$center.x),])\n\tcell<-intersect(cell,cell.1)\n\tmulti.pic.zoom(dat,cell,img) \n}\n}\n\ncell.zoom.640.480<-function(dat, img=NULL, cell=NULL, zoom=NULL, cols=NULL, labs=T, plot.new=T, cell.name=T)\n{\nif(plot.new){dev.new()}\nrequire(png)\nrequire(zoom)\npar(mar=c(0,0,0,0))\nx<-dat$c.dat[,\"center.x\"]\ny<-dat$c.dat[,\"center.y\"]\ncell.x<-dat$c.dat[cell,\"center.x\"]\ncell.y<-dat$c.dat[cell,\"center.y\"]\n\nif(is.null(img)){img<-dat$img1}\nelse{img<-img}\nif(is.null(cols)){cols=\"white\"}\nelse{cols=cols}\n\nplot(0, 0, xlim=c(0,640),ylim=c(480,0), xaxs=\"i\", yaxs=\"i\", xlab=\"Pixels\", ylab=\"Pixels\")\nrasterImage(img, 0, 480, 640, 0)\n\nif(labs){\nif(!is.null(cell)){\n\tpoints(cell.x, cell.y, col=cols )\n\ttext(cell.x, cell.y, labels=cell, col=cols, pos=2, cex=.8)\n\t}\nelse{\n\tpoints(x, y, col=cols)\n\ttext(x, y, labels=dat$c.dat[,1], col=cols, pos=2, cex=.5)\n}}\n\nif(!is.null(zoom)){\nzoomplot.zoom(x=cell.x, y=cell.y, fact=zoom)\n}\nelse{zm()}\n}\n\n\n\nXYtrace.640.480 <- function(dat, img=NULL, cols=NULL, labs=T)\n{\n\tx.coor<-grep(\"\\\\.x\",names(dat$c.dat), value=T, ignore.case=T)\n\ty.coor<-grep(\"\\\\.y\",names(dat$c.dat), value=T, ignore.case=T)\n\tarea<-grep(\"area\",names(dat$c.dat), value=T, ignore.case=T)\n\t\n\tlab1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1)==0){lab1<-grep(\"gfp.1\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab1.1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1.1)==0){lab1.1<-grep(\"gfp.2\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab2<-grep(\"ib4\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab2)==0){lab2<-grep(\"tritc\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tcell.coor<-dat$c.dat[,c(x.coor, y.coor)]\n\t\t\n\t# select the names of the collumns containing coordinates\n\tlevs <- unique(dat$w.dat[,\"wr1\"])\n\tlevs<-setdiff(levs, \"\")\n\tif(labs==TRUE){\n\tif(is.null(cols)){cols=\"grey5\"} else{cols=cols}}\n\tpch=16\n\t\n\tdev.new(height=4,width=12)\n\tdev.new(width=10, height=8)\n\tdev.new(height=8,width=12)\n\tlmain<-\"XY ROI\"\n\n\tdev.set(dev.list()[2])\n\tpar(mar=c(0,0,0,0))\n\tplot(0, 0, xlim=c(0,640),ylim=c(480,0),xaxs=\"i\", yaxs=\"i\",col=cols,pch=\".\")\n\t\n\tif(is.null(img)){img<-dat$img1}\n\tif(!is.null(img)){rasterImage(img, 0, 480, 640, 0);points(cell.coor[,1],cell.coor[,2],col=cols,pch=0,cex=2.4)}\n\telse{\n\tpoints(cell.coor[,1],cell.coor[,2], col=cols, cex=dat$c.dat[,area]/200)\n\tpoints(cell.coor[,1],cell.coor[,2],col=cols, pch=4)}\n\t\n\t\n\n\ti <- identify(cell.coor[,1],cell.coor[,2],n=1,plot=F, col=NA, tolerance=0.05)\n\ti.names<-row.names(dat$c.dat)[i]\n\twhile(length(i) > 0)\n\t{\t#selected name of cell\n\t\ts.names <- row.names(dat$c.dat)[i]\n\t\tdev.set(dev.list()[1])\n\t\tPeakFunc2(dat,s.names,3,30,TRUE,,lmain=lmain)\n\t\tdev.set(dev.list()[2])\n\t\t# If a cell is selected, that has already been selected, \n\t\t# then remove that cell from the list\n\t\tif(length(intersect(i.names,s.names))==1){\n\t\ti.names<-setdiff(i.names,s.names)\n\t\tpoints(cell.coor[s.names,1],cell.coor[s.names,2],col=\"grey90\",pch=0,cex=2.4)\n\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)}\n\t\t# If it han't been selected, then add it to the list\n\t\telse{i.names<-union(i.names,s.names)\n\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)}\n\t\t\n\t\tif(length(i.names)>=2){dev.set(dev.list()[3]);LinesEvery.2(dat,m.names=i.names, plot.new=F)}\t\t\n\t\t\n\t\tdev.set(dev.list()[2])\n\t\ti <- identify(cell.coor[,1],cell.coor[,2],labels=dat$c.dat[,1],n=1,plot=T, pch=0,col=\"grey90\", tolerance=0.05)\n\t}\n\tdev.off()\n\tgraphics.off()\n\treturn(dat$c.dat[i.names,1])\n\t \n}\n\n\n# Function allows for selection and deselection of cells to build stacked traces\nXYtrace.1024 <- function(dat, cell=NULL, img=NULL, cols=NULL, labs=F)\n{\n\tx.coor<-grep(\"\\\\.x\",names(dat$c.dat), value=T, ignore.case=T)\n\ty.coor<-grep(\"\\\\.y\",names(dat$c.dat), value=T, ignore.case=T)\n\tarea<-grep(\"area\",names(dat$c.dat), value=T, ignore.case=T)\n\t\n\tlab1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1)==0){lab1<-grep(\"gfp.1\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab1.1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1.1)==0){lab1.1<-grep(\"gfp.2\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab2<-grep(\"ib4\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab2)==0){lab2<-grep(\"tritc\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tif(is.null(cell)){cell<-row.names(dat$c.dat)}\n\telse{cell<-cell}\n\tcell.coor<-dat$c.dat[cell,c(x.coor, y.coor)]\n\n\t\n\t\n\t# select the names of the collumns containing coordinates\n\tlevs <- unique(dat$w.dat[,\"wr1\"])\n\tlevs<-setdiff(levs, \"\")\n\tif(labs==TRUE){\n\tif(is.null(cols)){cols=\"orangered1\"} else{cols=cols}}\n\tpch=16\n\t\n\tdev.new(height=4,width=12)\n\tdev.new(width=8, height=8)\n\tdev.new(height=8,width=12)\n\tlmain<-\"XY ROI\"\n\n\tdev.set(dev.list()[2])\n\tpar(mar=c(0,0,0,0))\n\tplot(0, 0, xlim=c(0,1024),ylim=c(1024,0),xaxs=\"i\", yaxs=\"i\",col=cols,pch=\".\")\n\t\n\tif(is.null(img)){img<-dat$img1}\n\tif(!is.null(img)){rasterImage(img, 0, 1024, 1024, 0);points(cell.coor[,1],cell.coor[,2],col=cols,pch=0,cex=2.4)}\n\telse{\n\tpoints(cell.coor[,1],cell.coor[,2], col=cols, cex=dat$c.dat[,area]/200)\n\tpoints(cell.coor[,1],cell.coor[,2],col=cols, pch=4)}\n\t\n\t\n\n\ti <- identify(cell.coor[,1],cell.coor[,2],n=1,plot=F, col=NA, tolerance=0.05)\n\ti.names<-row.names(dat$c.dat[cell,])[i]\n\twhile(length(i) > 0)\n\t{\t#selected name of cell\n\t\ts.names <- row.names(dat$c.dat[cell,])[i]\n\t\tdev.set(dev.list()[1])\n\t\tPeakFunc6(dat,s.names, Plotit.both=F)\n\t\tdev.set(dev.list()[2])\n\t\t# If a cell is selected, that has already been selected, \n\t\t# then remove that cell from the list\n\t\tif(length(intersect(i.names,s.names))==1){\n\t\t\ti.names<-setdiff(i.names,s.names)\n\t\t\tpoints(cell.coor[s.names,1],cell.coor[s.names,2],col=\"gray70\",pch=0,cex=2.4)\n\t\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)\t\n\t\t}\n\t\t# If it han't been selected, then add it to the list\n\t\telse{i.names<-union(i.names,s.names)\n\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)}\n\t\t\n\t\tif(length(i.names)>=1){dev.set(dev.list()[3]);LinesEvery.3(dat,m.names=i.names, plot.new=F, img=img, pic.plot=T,XY.plot=F, cols=\"black\")}\t\t\t\t\n\t\tdev.set(dev.list()[2])\n\t\ti <- identify(cell.coor[,1],cell.coor[,2],labels=dat$c.dat[cell,1],n=1,plot=T, pch=0,col=\"white\", tolerance=0.05)\n\t}\n\tdev.off()\n\tgraphics.off()\n\treturn(row.names(dat$c.dat[i.names,]))\n\t \n}\n\nXYtrace.1024.2 <- function(dat, cell=NULL, img=NULL, cols=NULL, labs=F, zf=60)\n{\n\tx.coor<-grep(\"\\\\.x\",names(dat$c.dat), value=T, ignore.case=T)\n\ty.coor<-grep(\"\\\\.y\",names(dat$c.dat), value=T, ignore.case=T)\n\tarea<-grep(\"area\",names(dat$c.dat), value=T, ignore.case=T)\n\t\n\tlab1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1)==0){lab1<-grep(\"gfp.1\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab1.1<-grep(\"cgrp\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab1.1)==0){lab1.1<-grep(\"gfp.2\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tlab2<-grep(\"ib4\",names(dat$c.dat), value=T, ignore.case=T)\n\tif(length(lab2)==0){lab2<-grep(\"tritc\",names(dat$c.dat), value=T, ignore.case=T)}\n\t\n\tif(is.null(cell)){cell<-row.names(dat$c.dat)}\n\telse{cell<-cell}\n\tcell.coor<-dat$c.dat[cell,c(x.coor, y.coor)]\n\n\t\n\t\n\t# select the names of the collumns containing coordinates\n\tlevs <- unique(dat$w.dat[,\"wr1\"])\n\tlevs<-setdiff(levs, \"\")\n\tif(labs==TRUE){\n\t\tif(is.null(cols)){cols=\"orangered1\"} else{cols=cols}}\n\t\tpch=16\n\t\n\tdev.new(height=4,width=4)\n\tdev.new(width=8, height=8)\n\tdev.new(height=4,width=12)\n\tlmain<-\"XY ROI\"\n\n\tdev.set(dev.list()[2])\n\tpar(mar=c(0,0,0,0))\n\tplot(0, 0, xlim=c(0,1024),ylim=c(1024,0),xaxs=\"i\", yaxs=\"i\",col=cols,pch=\".\")\n\t\n\tif(is.null(img)){img<-dat$img1}\n\tif(!is.null(img)){rasterImage(img, 0, 1024, 1024, 0);points(cell.coor[,1],cell.coor[,2],col=cols,pch=3,cex=1)}\n\telse{\n\tpoints(cell.coor[,1],cell.coor[,2], col=cols, cex=dat$c.dat[,area]/200)\n\tpoints(cell.coor[,1],cell.coor[,2],col=cols, pch=4)}\n\t\n\t\n\n\ti <- identify(cell.coor[,1],cell.coor[,2],n=1,plot=F, col=NA, tolerance=0.05)\n\ti.names<-row.names(dat$c.dat[cell,])[i]\n\twhile(length(i) > 0)\n\t{\t#selected name of cell\n\t\ts.names <- row.names(dat$c.dat[cell,])[i]\n\t\tdev.set(dev.list()[1])\n\t\t\tmulti.pic.zoom(dat,s.names, img, plot.new=F, zf=zf)\n\t\tdev.set(dev.list()[3])\n\t\t\tPeakFunc5(dat,s.names)\n\t\tdev.set(dev.list()[2])\n\t\t# If a cell is selected, that has already been selected, \n\t\t# then remove that cell from the list\n\t\tif(length(intersect(i.names,s.names))==1){\n\t\t\ti.names<-setdiff(i.names,s.names)\n\t\t\tpoints(cell.coor[s.names,1],cell.coor[s.names,2],col=\"gray70\",pch=0,cex=2.4)\n\t\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)\t\n\t\t}\n\t\t# If it han't been selected, then add it to the list\n\t\telse{i.names<-union(i.names,s.names)\n\t\tpoints(cell.coor[i.names,1],cell.coor[i.names,2],col=\"red\",pch=0,cex=2.4)}\n\t\t\n\t\tif(length(i.names)>=1){dev.set(dev.list()[3]);LinesEvery.3(dat,m.names=i.names, plot.new=F, img=img, pic.plot=T,XY.plot=F, cols=\"black\")}\t\t\t\t\n\t\tdev.set(dev.list()[2])\n\t\ti <- identify(cell.coor[,1],cell.coor[,2],labels=dat$c.dat[cell,1],n=1,plot=T, pch=0,col=\"white\", tolerance=0.05)\n\t}\n\tdev.off()\n\tgraphics.off()\n\treturn(row.names(dat$c.dat[i.names,]))\n\t \n}\n\n\n# View Individual cell picture\nmulti.pic.zoom<-function(dat, m.names, img, labs=T,plot.new=T, zf=20){\ncol.row<-ceiling(sqrt(length(m.names)))\n\t\n\tif(plot.new){\n\t\tdev.new()\n\t\tpar(mfrow=c(col.row, col.row))\n\t\tpar(mar=c(0,0,0,0))\n\t}\n\telse{par(mar=c(0,0,0,0))}\t\n\t\tm.names<-rev(m.names)\n\tfor(i in 1:length(m.names)){\t\t\n\n\t\tx<-dat$c.dat[m.names[i],\"center.x\"]\n\t\tleft<-x-zf\n\t\tif(left<=20){left=0; right=zf}\n\t\tright<-x+zf\n\t\tif(right>=1004){left=1024-zf;right=1024}\n\t\t\t\t\t\n\t\ty<-dat$c.dat[m.names[i],\"center.y\"]\n\t\ttop<-y-zf\n\t\tif(top<=20){top=0; bottom=zf}\n\t\tbottom<-y+zf\n\t\tif(bottom>=1004){top=1024-zf;bottom=1024}\n\n\t\tpar(xpd=TRUE)\n\t\txleft<-0\n\t\txright<-20\n\t\tytop<-0\n\t\tybottom<-20\n\t\tplot(c(xright, xleft), c(ytop, ybottom), ylim=c(20,0) ,xaxs=\"i\", yaxs=\"i\", axes=F)\n\t\trasterImage(img[top:bottom,left:right,],xleft,ybottom,xright,ytop)\n\t\tpoints(x=10,y=10, type=\"p\", pch=3, cex=2,col=\"white\")\n\t\tbox(lty = 1, col = \"white\",lwd=2)\n\t\tif(labs){\n\t\t\ttext(4,1.5, labels=m.names[i], col=\"white\", cex=1.2)\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"area\"], col=\"white\")\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"ROI.Area\"], col=\"white\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp.1\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"CGRP\"], col=\"green\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"mean.tritc\"], col=\"red\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"IB4\"], col=\"red\")\n\t\t\ttext(16.5, 6.5, labels=dat$c.dat[m.names[i], \"mean.dapi\"], col=\"blue\")\n\t\t}\n\t}\n\t\t\n}\n\n# View Individual cell picture creates a png image\n# must assgin multi.pic.zoom to a variable name\n# For use in linesEvery.4\nmulti.pic.zoom.2<-function(dat, m.names, img, labs=T){\ncol.row<-ceiling(sqrt(length(m.names)))\n#png(\"tmp.png\",width=6,height=6,units=\"in\",res=72,bg=\"transparent\", type=\"cairo\")\n#dev.new()\npng('tmp.png', res=70)\t\n\t\tpar(mfrow=c(col.row, col.row))\n\t\tpar(mar=c(0,0,0,0))\n\t#else{par(mar=c(0,0,0,0))}\t\n\tm.names<-rev(m.names)\n\t\n\tfor(i in 1:length(m.names)){\t\t\n\t\tzf<-20\n\t\tx<-dat$c.dat[m.names[i],\"center.x\"]\n\t\tleft<-x-zf\n\t\tif(left<=20){left=0; right=zf}\n\t\tright<-x+zf\n\t\tif(right>=1004){left=1024-zf;right=1024}\n\t\t\t\t\t\n\t\ty<-dat$c.dat[m.names[i],\"center.y\"]\n\t\ttop<-y-zf\n\t\tif(top<=20){top=0; bottom=zf}\n\t\tbottom<-y+zf\n\t\tif(bottom>=1004){top=1024-zf;bottom=1024}\n\n\t\tpar(xpd=TRUE)\n\t\txleft<-0\n\t\txright<-20\n\t\tytop<-0\n\t\tybottom<-20\n\t\tplot(c(xright, xleft), c(ytop, ybottom), ylim=c(20,0) ,xaxs=\"i\", yaxs=\"i\", axes=F)\n\t\trasterImage(img[top:bottom,left:right,],xleft,ytop,xright,ybottom)\n\t\tpoints(x=10,y=10, type=\"p\", pch=3, cex=2,col=\"white\")\n\t\tbox(lty = 1, col = \"white\",lwd=2)\n\t\tif(labs){\n\t\t\ttext(4,1.5, labels=m.names[i], col=\"white\", cex=1.2)\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"area\"], col=\"white\")\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"ROI.Area\"], col=\"white\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp.1\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"CGRP\"], col=\"green\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"mean.tritc\"], col=\"red\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"IB4\"], col=\"red\")\n\t\t\ttext(16.5, 6.5, labels=dat$c.dat[m.names[i], \"mean.dapi\"], col=\"blue\")\n\t\t}\n\t}\n\tdev.off()png::readPNG\n\ttmp.png <- readPNG(\"tmp.png\")\n\tunlink(\"tmp.png\")\n\treturn(tmp.png)\t\t\t\n}\n\n#multipiczoom\nmulti.pic.zoom.3<-function(dat, m.names, img, labs=T,plot.new=T, zf=20){\ncol.row<-ceiling(sqrt(length(m.names)))\n\t\n\tif(plot.new){\n\t\tdev.new()\n\t\tpar(mfrow=c(col.row, col.row))\n\t\tpar(mar=c(0,0,0,0))\n\t}\n\telse{par(mar=c(0,0,0,0))}\t\n\t\tm.names<-rev(m.names)\n\tfor(i in 1:length(m.names)){\t\t\n\t\tx<-dat$c.dat[m.names[i],\"center.x\"]\n\t\tleft<-x-zf\n\t\tif(left<=20){left=0; right=zf}\n\t\tright<-x+zf\n\t\tif(right>=1004){left=1024-zf;right=1024}\n\t\t\t\t\t\n\t\ty<-dat$c.dat[m.names[i],\"center.y\"]\n\t\ttop<-y-zf\n\t\tif(top<=20){top=0; bottom=zf}\n\t\tbottom<-y+zf\n\t\tif(bottom>=1004){top=1024-zf;bottom=1024}\n\n\t\tpar(xpd=TRUE)\n\t\txleft<-0\n\t\txright<-20\n\t\tytop<-0\n\t\tybottom<-20\n\t\tplot(c(xright, xleft), c(ytop, ybottom), ylim=c(20,0) ,xaxs=\"i\", yaxs=\"i\", axes=F)\n\t\trasterImage(img[top:bottom,left:right,],xleft,ytop,xright,ybottom)\n\t\tpoints(x=10,y=10, type=\"p\", pch=3, cex=2,col=\"white\")\n\t\tbox(lty = 1, col = \"white\",lwd=2)\n\t\tif(labs){\n\t\t\ttext(4,1.5, labels=m.names[i], col=\"white\", cex=1.2)\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"area\"], col=\"white\")\n\t\t\ttext(16.5, 2, labels=dat$c.dat[m.names[i], \"ROI.Area\"], col=\"white\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp.1\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"mean.gfp\"], col=\"green\")\n\t\t\ttext(16.5, 3.5, labels=dat$c.dat[m.names[i], \"CGRP\"], col=\"green\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"mean.tritc\"], col=\"red\")\n\t\t\ttext(16.5, 5, labels=dat$c.dat[m.names[i], \"IB4\"], col=\"red\")\n\t\t\ttext(16.5, 6.5, labels=dat$c.dat[m.names[i], \"mean.dapi\"], col=\"blue\")\n\t\t}\n\t}\n\t\t\n}\n\n\nPointTrace <- function(lookat,png=F,col=rep(\"black\",nrow(lookat)),pch=16,cex=1,lmain=\"PointTrace\",x.trt=NULL,y.trt=NULL,wr=\"wr1\",t.names=NULL)\n{\n\tif(!is.null(x.trt)){lookat[\"x\"] <- lookat[,x.trt]}\n\tif(!is.null(y.trt)){lookat[\"y\"] <- lookat[,y.trt]}\t\n\tdev.new(height=4,width=14)\n\trr.dev <- dev.cur()\n\tdev.new(height=4,width=4)\n\t\n\tplot(lookat[,\"x\"],lookat[,\"y\"],col=col,pch=pch,cex=cex,main=lmain,xlab=x.trt,ylab=y.trt)\n\tret.list <- NULL\n\ti <- identify(lookat[,\"x\"],lookat[,\"y\"],n=1,plot=F)\n\tmy.dev <- dev.cur()\n\twhile(length(i) > 0)\n\t{\n\t\tx.names <- lookat[i,\"trace.id\"]\n\t\t#points(lookat[i,\"x\"],lookat[i,\"y\"],pch=8,cex=.5)\n\t\trn.i <- row.names(lookat)[i]\n\t\ttmp <- get(lookat[i,\"rd.name\"])\n\t\tlevs <- unique(tmp$w.dat[,\"wr1\"])\n\t\tlmain <- paste(i,lookat[i,\"rd.name\"])\n\t\t#LinesEvery(tmp$t.dat,,x.names,tmp$w.dat[,\"wr1\"],levs,lmain=lmain)\n\t\tdev.set(which=rr.dev)\n\t\tPeakFunc5(tmp,x.names,lmain=lookat[i,\"rd.name\"])\n\t\tif(!is.null(t.names)){mtext(paste(t.names,tmp$c.dat[x.names,t.names],collapse=\":\"))}\n\t\tif(png==TRUE)\n\t\t{\n\t\t\tf.name <- paste(lookat[i,\"rd.name\"],lookat[i,\"trace.id\"],\"png\",sep=\".\")\n\t\t\tpng(f.name,heigh=600,width=1200)\n\t\t\tPeakFunc2(tmp$t.dat,x.names,3,30,TRUE,tmp$w.dat[,wr],lmain=lookat[i,\"rd.name\"])\n\t\t\tdev.off()\n\t\t}\n\n\t\tdev.set(which=my.dev)\n\t\tif(is.element(rn.i,ret.list))\n\t\t\t{points(lookat[i,\"x\"],lookat[i,\"y\"],col=col[i],pch=pch,cex=cex);ret.list <- setdiff(ret.list,rn.i)}\t\t\n\t\telse\n\t\t\t{points(lookat[i,\"x\"],lookat[i,\"y\"],col=\"red\",pch=pch,cex=cex);ret.list <- union(rn.i,ret.list)}\t\t\n\t\ti <- identify(lookat[,\"x\"],lookat[,\"y\"],n=1,plot=F)\n\t}\n\treturn(ret.list)\n\n}\n\n\n\n##############################################################################################\n# Multi Experiment Analysis\n##############################################################################################\n#calculate means and sems for all c.names of dat\n#divided by the levels of fac.name\n#make a bargraph of these\nMeanSemGraph <- function(dat,c.names,fac.name,t.cols=NULL,ylab=NULL,main.lab=NULL,x.labs=NULL,bt=.1,lgc=\"topleft\",ylim=NULL)\n{\n\tsemfunc <- function(x)\n\t{\n\t\tn <- sum(!is.na(x))\n\t\tif(n < 3){return(NA)}\n\t\treturn(sd(x,na.rm=T)/sqrt(n))\n\t}\n\tx <- as.factor(dat[,fac.name])\n\tx.levs <- levels(x)\n\tif(1/length(x.levs) < bt){bt <- 1/length(x.levs)}\n\tsem.levs <- paste(x.levs,\"sem\",sep=\".\")\n\tx.res <- data.frame(apply(dat[x==x.levs[1],c.names,drop=F],2,mean,na.rm=T))\n\tfor(i in x.levs)\n\t{\n\t\tx.res[i] <- apply(dat[x==i,c.names,drop=F],2,mean,na.rm=T)\n\t\tx.res[paste(i,\"sem\",sep=\".\")] <- apply(dat[x==i,c.names,drop=F],2,semfunc)\n\t}\n\txlim <- c(1,length(c.names)+length(x.levs)*bt)\n\tif(is.null(ylim)){ylim <- c(-.02,max(x.res[,x.levs]+x.res[,sem.levs]*2)*1.2)}\n\t\n\tif(is.null(t.cols)){t.cols <- rainbow(length(x.levs));names(t.cols) <- x.levs}\n\tplot(x.res[,x.levs[1]],xlim=xlim,ylim=ylim,type=\"n\",xaxt=\"n\",xlab=\"\",ylab=ylab,main=main.lab)\n\tfor(i in 1:length(x.levs))\n\t{\n\t\tx1 <- seq(1,length(c.names))+(i-1)*bt\n\t\ty1 <- x.res[,x.levs[i]]\n\t\trect(x1,rep(0,length(x1)),x1+bt,y1,col=t.cols[x.levs[i]])\n\t}\n\tfor(i in 1:length(x.levs))\n\t{\n\t\tx1 <- seq(1,length(c.names))+(i-1)*bt+(bt)/2\n\t\ty1 <- x.res[,x.levs[i]] + x.res[,sem.levs[i]]*2\n\t\ty2 <- x.res[,x.levs[i]] - x.res[,sem.levs[i]]*2\t\t\n\t\tarrows(x1,y2,x1,y1,angle=90,col=\"black\",length=bt*.25,code=3)\n\t}\n\t\n\tif(is.null(x.labs)){x.labs <- row.names(x.res)}\n\ttext(seq(1,length(c.names)),rep(-.02,length(c.names)),x.labs,pos=4,cex=.8,offset=0)\n\tlegend(lgc,col=t.cols,names(t.cols),pch=15)\n\treturn(x.res[,-1])\n}\n\n\n\n\nbg.plotter<-function(gid.bin, dat, subset.n=5,multi=TRUE, pic=TRUE){\n\ttmp.names<-row.names(dat)[dat$gid.bin==gid.bin]\n\trd.names<-unique(dat$rd.name)\n\trd.list<-list()\n\t\n\tfor(i in 1:length(rd.names)){\n\t\tx.names<-row.names(dat[tmp.names,])[dat[tmp.names,\"rd.name\"]==rd.names[i]]\n\t\tx.names<-dat[x.names,\"id\"]\n\t\tx.names<-setdiff(x.names, \"NA\")\n\t\tx.name<-setdiff(x.names, NA)\n\t\trd.list[[i]]<-x.names\n\t\tnames(rd.list)[i]<-rd.names[i]\n\t}\n\t\n\tif(multi){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\tLinesStack.2(get(names(rd.list)[i]), rd.list[[i]], names(rd.list[i]), subset.n=subset.n)\n\t\t}\n\t}\n\tif(pic){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\tLinesEvery.3(get(names(rd.list)[i]), rd.list[[i]],img=get(names(rd.list)[i])$img1, lmain=names(rd.list[i]))\n\t\t}\n\t}\n\treturn(rd.list)\n}\n\npf.plotter<-function(dat,pf, subset.n=5,multi=TRUE, pic=TRUE){\n\ttmp.names<-row.names(dat)[dat$pf==pf]\n\trd.names<-unique(dat$rd.name)\n\trd.list<-list()\n\t\n\tfor(i in 1:length(rd.names)){\n\t\tx.names<-row.names(dat[tmp.names,])[dat[tmp.names,\"rd.name\"]==rd.names[i]]\n\t\tx.names<-dat[x.names,\"id\"]\n\t\t#x.names<-na.exclude(x.names)\n\t\trd.list[[i]]<-x.names\n\t\tnames(rd.list)[i]<-rd.names[i]\n\t}\n\t\n\tif(multi){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\tLinesStack.2(get(names(rd.list)[i]), rd.list[[i]], names(rd.list[i]), subset.n=subset.n)\n\t\t}\n\t}\n\tif(pic){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\tLinesEvery.3(get(names(rd.list)[i]), rd.list[[i]],img=get(names(rd.list)[i])$img3, lmain=names(rd.list[i]))\n\t\t}\n\t}\n\treturn(rd.list)\n}\n\n#Updated with Linesevery3\nlevs.plotter<-function(dat,levs,levs.no, subset.n=5,multi=F, pic=T, click=F){\n\ttmp.names<-row.names(dat)[dat[,levs]==1]\n\t\n\trd.names<-unique(dat$rd.name)\n\trd.list<-list()\n\t\n\tfor(i in 1:length(rd.names)){\n\t\tx.names<-row.names(dat[tmp.names,])[dat[tmp.names,\"rd.name\"]==rd.names[i]]\n\t\tx.names<-dat[x.names,\"id\"]\n\t\t#x.names<-na.exclude(x.names)\n\t\trd.list[[i]]<-x.names\n\t\tnames(rd.list)[i]<-rd.names[i]\n\t}\n\t\n\tif(multi){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tLinesStack(get(tmp), rd.list[[i]], names(rd.list[i]), subset.n=subset.n)\n\t\t}\n\t}\n\tif(pic){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tLinesEvery.3(get(tmp), rd.list[[i]],img=get(names(rd.list)[i])$img3, lmain=names(rd.list[i]), pic.plot=F, XY.plot=T)\n\t\t}\n\t}\n\tif(click){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tTrace.Click.3(get(tmp), rd.list[[i]],img=get(names(rd.list)[i])$img3, lmain=names(rd.list[i]), pic.plot=F, XY.plot=T)\n\t\t}\n\t}\n\n\trm(list=ls(rd.list))\n\treturn(rd.list)\n}\n\nall.plotter<-function(dat, subset.n=5,multi=F, pic=F, click=T){\n\ttmp.names<-row.names(dat)\n\t\n\trd.names<-unique(dat$rd.name)\n\trd.list<-list()\n\t\n\tfor(i in 1:length(rd.names)){\n\t\tx.names<-row.names(dat[tmp.names,])[dat[tmp.names,\"rd.name\"]==rd.names[i]]\n\t\tx.names<-dat[x.names,\"id\"]\n\t\t#x.names<-na.exclude(x.names)\n\t\trd.list[[i]]<-x.names\n\t\tnames(rd.list)[i]<-rd.names[i]\n\t}\n\t\n\tif(multi){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tLinesStack(get(tmp), rd.list[[i]], names(rd.list[i]), subset.n=subset.n)\n\t\t}\n\t}\n\tif(pic){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tLinesEvery.3(get(tmp), rd.list[[i]],img=get(names(rd.list)[i])$img3, lmain=names(rd.list[i]), pic.plot=F, XY.plot=T)\n\t\t}\n\t}\n\tselected.cells<-list()\n\tif(click){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tselected.cells[[i]]<-Trace.Click.3(get(tmp), rd.list[[i]])\n\t\t\tnames(selected.cells)[i]<-rd.names[i]\n\t\t}\n\t}\n\n\trm(list=ls(rd.list))\n\t\n\tif(multi==T | pic==T){return(rd.list)}\n\tif(click==T){return(selected.cells)}\n\t\n}\n\n\nnoci.plotter<-function(dat,type, subset.n=5,multi=F, pic=T){\n\ttmp.names<-row.names(dat)[dat$noci.type==type]\n\ttmp.names<-setdiff(tmp.names, \"NA\")\n\trd.names<-unique(dat$rd.name)\n\trd.list<-list()\n\t\n\tfor(i in 1:length(rd.names)){\n\t\tx.names<-row.names(dat[tmp.names,])[dat[tmp.names,\"rd.name\"]==rd.names[i]]\n\t\tx.names<-dat[x.names,\"id\"]\n\t\tx.names<-setdiff(x.names, c(\"NA\",NA))\n\t\t#x.names<-na.exclude(x.names)\n\t\trd.list[[i]]<-x.names\n\t\tnames(rd.list)[i]<-rd.names[i]\n\t}\n\t\n\tif(multi){\n\t\tfor(i in 1:length(rd.list)){\n\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\tLinesStack.2(get(tmp), rd.list[[i]], names(rd.list[i]), subset.n=subset.n)\n\t\trm(tmp)\n\t\t}\n\t}\n\tif(pic){\n\t\tfor(i in 1:length(rd.list)){\n\t\t\ttmp<-load(paste(names(rd.list)[i],\".rdata\",sep=\"\"))\n\t\t\tLinesEvery.3(get(tmp), rd.list[[i]],img=get(names(rd.list)[i])$img3, lmain=names(rd.list[i]))\n\t\t\trm(tmp)\n\t\t}\n\t}\n\trm(list=ls(rd.list))\n\treturn(rd.list)\n}\n\n### Function to select rows based on collumn parameters\ncellz<-function(dat,collumn=NULL, parameter){\n\tbob<-list()\n\tif(is.null(collumn)){\n\t\tcollumn<-select.list(names(dat), multiple=T)}\n\telse(collumn<-collumn)\n\tif(is.null(parameter)){\n\t\tparameter<-1}\n\telse(parameter<-parameter)\n\tfor(i in collumn){\n\t\tbob[[i]]<-row.names(dat)[dat[,i]>=parameter]\n\t}\n\tbob<-Reduce(intersect, bob)\n\treturn(bob)\n\t}\n\n# function to obtained sorted cell names based off \n# collumn names from c.dat and bin\nc.sort<-function(dat,char=NULL){\n\tchar<-select.list(names(dat))\n\tbob<-row.names(dat[order(dat[,char], decreasing=T),])\n\treturn(bob)\n\t}\n\n\n\n\n", "meta": {"hexsha": "f7e9f6f2ae6b787232451ad1840c360bdbbad3a7", "size": 186376, "ext": "r", "lang": "R", "max_stars_repo_path": "extras/Procpharm Legacy/procPharm.160607.r", "max_stars_repo_name": "leeleavitt/procPharm", "max_stars_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "extras/Procpharm Legacy/procPharm.160607.r", "max_issues_repo_name": "leeleavitt/procPharm", "max_issues_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-01-08T18:50:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T01:23:47.000Z", "max_forks_repo_path": "extras/Procpharm Legacy/procPharm.160607.r", "max_forks_repo_name": "leeleavitt/procPharm", "max_forks_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T20:45:06.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T20:45:06.000Z", "avg_line_length": 36.3873486919, "max_line_length": 237, "alphanum_fraction": 0.602835129, "num_tokens": 66279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33307701001008394}} {"text": "context(\"Matching groups\")\n\nset.seed(1410)\nnum <- matrix(sample(9, 10 * 10, replace = T), ncol = 10)\nnum_flat <- apply(num, 1, str_c, collapse = \"\")\n\nphones <- str_c(\n \"(\", num[, 1], num[ ,2], num[, 3], \") \",\n num[, 4], num[, 5], num[, 6], \" \",\n num[, 7], num[, 8], num[, 9], num[, 10])\n\ntest_that(\"special case are correct\", {\n expect_equal(str_match(NA, \"(a)\"), matrix(NA_character_))\n expect_equal(str_match(character(), \"(a)\"), matrix(character(), 0, 1))\n})\n\ntest_that(\"no matching cases returns 1 column matrix\", {\n res <- str_match(c(\"a\", \"b\"), \".\")\n\n expect_equal(nrow(res), 2)\n expect_equal(ncol(res), 1)\n\n expect_equal(res[, 1], c(\"a\", \"b\"))\n})\n\ntest_that(\"single match works when all match\", {\n matches <- str_match(phones, \"\\\\(([0-9]{3})\\\\) ([0-9]{3}) ([0-9]{4})\")\n\n expect_equal(nrow(matches), length(phones))\n expect_equal(ncol(matches), 4)\n\n expect_equal(matches[, 1], phones)\n\n matches_flat <- apply(matches[, -1], 1, str_c, collapse = \"\")\n expect_equal(matches_flat, num_flat)\n})\n\ntest_that(\"match returns NA when some inputs don't match\", {\n matches <- str_match(c(phones, \"blah\", NA),\n \"\\\\(([0-9]{3})\\\\) ([0-9]{3}) ([0-9]{4})\")\n\n expect_equal(nrow(matches), length(phones) + 2)\n expect_equal(ncol(matches), 4)\n\n expect_equal(matches[11, ], rep(NA_character_, 4))\n expect_equal(matches[12, ], rep(NA_character_, 4))\n})\n\ntest_that(\"match returns NA when optional group doesn't match\", {\n expect_equal(str_match(c(\"ab\", \"a\"), \"(a)(b)?\")[,3], c(\"b\", NA))\n})\n\ntest_that(\"match_all returns NA when option group doesn't match\",{\n expect_equal(str_match_all(\"a\", \"(a)(b)?\")[[1]][1, ], c(\"a\", \"a\", NA))\n})\n\ntest_that(\"multiple match works\", {\n phones_one <- str_c(phones, collapse = \" \")\n multi_match <- str_match_all(phones_one,\n \"\\\\(([0-9]{3})\\\\) ([0-9]{3}) ([0-9]{4})\")\n single_matches <- str_match(phones,\n \"\\\\(([0-9]{3})\\\\) ([0-9]{3}) ([0-9]{4})\")\n\n expect_equal(multi_match[[1]], single_matches)\n})\n", "meta": {"hexsha": "1c85a229c05bc5b0e685ac10e85e4e581abc845e", "size": 1949, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.3/stringr/tests/testthat/test-match.r", "max_stars_repo_name": "xinbinhuang/bitcoin-analysis", "max_stars_repo_head_hexsha": "9c496fe94100ab5e7293dc5b4328f44c2d1fda76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-06-28T21:04:57.000Z", "max_stars_repo_stars_event_max_datetime": "2017-06-28T21:04:57.000Z", "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/stringr/tests/testthat/test-match.r", "max_issues_repo_name": "lordbitin/ESWA-2017", "max_issues_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/stringr/tests/testthat/test-match.r", "max_forks_repo_name": "lordbitin/ESWA-2017", "max_forks_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5303030303, "max_line_length": 72, "alphanum_fraction": 0.6038994356, "num_tokens": 634, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.33307701001008394}} {"text": "#' Removes the intercept term from a formula if it is included\n#'\n#' Often, we prefer not to have an intercept term in a model, but user-specified\n#' formulas might have included the intercept term. In this case, we wish to\n#' update the formula but without the intercept term. This is especially true in\n#' numerous classification models, where errors and doom can occur if an\n#' intercept is included in the model.\n#' \n#' @importFrom stats terms update\n#' @param formula a model formula to remove its intercept term\n#' @param data data frame\n#' @return formula with no intercept term\n#' @examples\n#' iris_formula <- formula(Species ~ .)\n#' sparsediscrim:::no_intercept(iris_formula, data = iris)\nno_intercept <- function(formula, data) {\n # The 'terms' must be collected in case the dot (.) notation is used\n update(formula(terms(formula, data = data)), . ~ . - 1)\n}\n", "meta": {"hexsha": "569a19149bbff0596ce6c9d25dc270469b7a507e", "size": 871, "ext": "r", "lang": "R", "max_stars_repo_path": "R/helper-intercept.r", "max_stars_repo_name": "topepo/sparsediscrim", "max_stars_repo_head_hexsha": "60198a54e0ced0afa3909121eea55321dd04c56f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-11-16T08:13:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-28T21:44:00.000Z", "max_issues_repo_path": "R/helper-intercept.r", "max_issues_repo_name": "topepo/sparsediscrim", "max_issues_repo_head_hexsha": "60198a54e0ced0afa3909121eea55321dd04c56f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-05-26T12:02:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-10T03:00:06.000Z", "max_forks_repo_path": "R/helper-intercept.r", "max_forks_repo_name": "topepo/sparsediscrim", "max_forks_repo_head_hexsha": "60198a54e0ced0afa3909121eea55321dd04c56f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.55, "max_line_length": 80, "alphanum_fraction": 0.7324913892, "num_tokens": 211, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.6224593312018545, "lm_q1q2_score": 0.3330770007804223}} {"text": "# combining Delgado and Ramirez covariate data\nrm(list=ls())\nsource('NEFI_functions/crib_fun.r')\nsource(\"paths.r\")\nsource(\"paths_fall2019.r\")\nlibrary(runjags)\n\noutput.path <- delgado_ramirez_bahram_mapping.path\n\n#### prep Ramirez et al. data ####\nmap.ram <- readRDS(ramirez_clean_map.path)\nmap.ram$source <- \"Ramirez\"\nmap.ram$study_id <- map.ram$dataset\n#map.ram <- map.ram[which(map.ram$sequencing_platform != \"454\"),]\nmap.ram$pC <- map.ram$C\nmap.ram$pN <- map.ram$N\nmap.ram$cn <- map.ram$pC/map.ram$pN\nmap.ram$depth_max <- map.ram$depth_max.x\n\n\n# prep delgado mapping data\nmap.del <- readRDS(delgado_metadata_spatial.path)\nmap.del$source <- \"Delgado\"\nmap.del$study_id <- \"Delgado\"\nmap.del$depth_max <- 7.5\nmap.del$pN <- map.del$soil_n\nmap.del$NPP.del <- map.del$NPP # keep delgado's original NPP values\nmap.del$NPP <- map.del$NPP_recent\n\n\n# fill with NAs? or, only common columns?\ncommon_cols <- intersect(colnames(map.ram), colnames(map.del))\nmaster.map <- do.call(plyr::rbind.fill, list(map.ram, map.del))\nmaster.map <- master.map[,colnames(master.map) %in% c(common_cols)]\n\n# add some colors\n# study_id <- levels(as.factor(master.map$study_id))\n# pals <- distinctColorPalette(length(study_id))\n# colors <- cbind(study_id, pals)\n# master.map <- merge(master.map, colors)\n\n# subset to northern temperate latitudes\nmaster.map <- master.map[master.map$latitude < 66.5 & master.map$latitude > 23.5,]\n\n# reduce map values\nmaster.map$map <- master.map$map/1000\nmaster.map$map_sd <- master.map$map_sd/1000\n\n# save output\nsaveRDS(master.map, output.path)\n", "meta": {"hexsha": "2064bf38e7c9f66308aaab0d23173730251cd57b", "size": 1551, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/data_construction/prior_synthesis/2._combine_mapping_data.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "data_construction/3._16S/prior_synthesis/3._merge_datasets/2._combine_mapping_data.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data_construction/3._16S/prior_synthesis/3._merge_datasets/2._combine_mapping_data.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 30.4117647059, "max_line_length": 82, "alphanum_fraction": 0.7279174726, "num_tokens": 477, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.33300473388616747}} {"text": "#' @title clip_by_poly_generic\n#' @description This function takes a dataframe and a polygon, and clips\n#' the data to the extent of the polygon. The polygon can be buffered as required to select nearby \n#' data as well.\n#' @param df default is \\code{NULL}. This is the dataframe to be clipped.\n#' @param lat.field the default is \\code{\"LATITUDE\"}. the name of the field holding latitude values \n#' (in decimal degrees)\n#' @param lon.field the default is \\code{\"LONGITUDE\"}. the name of the field holding longitude \n#' values (in decimal degrees)\n#' @param clip.poly default is \\code{NULL}. This is the full path to a shapefile \n#' that the data will be clipped by (including the '.shp' extension).\n#' @param buffer.m default is \\code{NULL}. This is the distance in meters to buffer the border of \n#' \\code{clip.poly}\n#' @param return.spatial default is \\code{FALSE}. If this is TRUE, a \n#' SpatialPointsDataFrame will be returned. Otherwise it will return a df.\n#' @param env This the the environment you want this function to work in. The \n#' default value is \\code{.GlobalEnv}.\n#' @return spatialPointsDataFrame\n#' @family general_use\n#' @author Mike McMahon, \\email{Mike.McMahon@@dfo-mpo.gc.ca}\n#' @export\n#' @note If the input polygon has no projection assigned, it will be assumed to be in Geographic, \n#' WGS84. FYI, during buffering, the polygon is briefly converted to UTMZone20, and back again, \n#' since the use of distances requires projecting the data.\nclip_by_poly_generic <- function(df=NULL,\n lat.field = \"LATITUDE\", \n lon.field = \"LONGITUDE\", \n clip.poly = NULL,\n buffer.m = NULL,\n return.spatial = FALSE,\n env=.GlobalEnv){\n df=df_qc_spatial(df)\n df.sp = sp::SpatialPointsDataFrame(\n coords = df[, c(lon.field, lat.field)],\n data = df,\n proj4string = sp::CRS(SRS_string=\"EPSG:4326\")\n )\n if (class(clip.poly)==\"character\"){\n #extract the full path and name of the shapefile \n ogrPath = dirname(clip.poly)\n ogrLayer = sub('\\\\.shp$', '', basename(clip.poly))\n clip.poly_this <- rgdal::readOGR(dsn = ogrPath, layer = ogrLayer, verbose = FALSE)\n }else if(class(clip.poly)==\"SpatialPolygonsDataFrame\"){\n clip.poly_this = clip.poly\n }\n\n if (is.na(sp::proj4string(clip.poly_this))) {\n cat('\\nNo projection found for input shapefile - assuming geographic.')\n sp::proj4string(clip.poly_this) = sp::CRS(SRS_string=\"EPSG:4326\")\n } else if (sp::proj4string(clip.poly_this)!=\"EPSG:4326\") {\n clip.poly_this = suppressWarnings(sp::spTransform(clip.poly_this, sp::CRS(SRS_string=\"EPSG:4326\")))\n }\n \n if (!is.null(buffer.m)){\n #if a buffer is specified, convert poly to UTM20N, apply buffer, and convert back\n clip.poly_this = suppressWarnings(sp::spTransform(clip.poly_this, sp::CRS(SRS_string=\"EPSG:2220\")))\n clip.poly_this = rgeos::gBuffer(clip.poly_this, width=buffer.m)\n clip.poly_this = suppressWarnings(sp::spTransform(clip.poly_this, sp::CRS(SRS_string=\"EPSG:4326\")))\n }\n if (NROW(df.sp[clip.poly_this, ]) ==0) {\n stop(\"\\nNo data lies inside this polygon, aborting clip.\")\n }\n df.sp_subset <- df.sp[clip.poly_this, ] \n \n if (!return.spatial){\n df.sp_subset = df.sp_subset@data\n }\n\n return(df.sp_subset)\n\n}", "meta": {"hexsha": "7525221166ba19b940e19e82fe66fe5244d15358", "size": 3331, "ext": "r", "lang": "R", "max_stars_repo_path": "R/clip_by_poly_generic.r", "max_stars_repo_name": "Maritimes/utils", "max_stars_repo_head_hexsha": "41a3dbba9c714bd0d940ea59e0702a2414330aa6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-11-06T16:41:14.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-17T17:35:07.000Z", "max_issues_repo_path": "R/clip_by_poly_generic.r", "max_issues_repo_name": "Maritimes/utils", "max_issues_repo_head_hexsha": "41a3dbba9c714bd0d940ea59e0702a2414330aa6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-07T13:19:24.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-07T14:29:14.000Z", "max_forks_repo_path": "R/clip_by_poly_generic.r", "max_forks_repo_name": "Maritimes/utils", "max_forks_repo_head_hexsha": "41a3dbba9c714bd0d940ea59e0702a2414330aa6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.9154929577, "max_line_length": 103, "alphanum_fraction": 0.6748724107, "num_tokens": 875, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3330047258759191}} {"text": "#generate lineplots of counts of variables\n#monthly counts by year\n#total counts over whole study period\n\n#plot groups of variables on one plot\n# can't use count list because doesn't have variable names\n\n\n# find correct folders\n\nif(!require(rstudioapi)){install.packages(\"rstudioapi\")}\nlibrary(rstudioapi)\n\nprojectFolder<-dirname(rstudioapi::getSourceEditorContext()$path)\nsetwd(projectFolder)\nplotFolder_med<-paste0(projectFolder,\"/g_intermediate/populations/MEDICINES/monthly_counts\")\nplotFolder_diag<-paste0(projectFolder,\"/g_intermediate/populations/DIAGNOSES/monthly_counts\")\n\n\n\n#extract count files\n\ncount_names_diag<-list.files(plotFolder_diag, pattern=\"count\")\ncount_names_med<-list.files(plotFolder_med, pattern=\"count\")\n\ncount_files_diag<-lapply(paste0(plotFolder_diag,\"/\", count_names_diag), readRDS)\ncount_files_med<-lapply(paste0(plotFolder_med,\"/\", count_names_med), readRDS)\n\n#masking\n\n# mask<-T\n\n# group_names<-c(\"alt_med\", \"contracep\", \"etc...\")\n\n\n\n# medicines \n\nfor(i in 1:length(count_names_med)){\n main_name<-substr(count_names_med[[i]], 1,nchar(count_names_med[[i]])-11)\n \n pdf((paste0(projectFolder, \"/g_output/plots/\", main_name, \".pdf\")), width=8, height=4)\n \n var_counts<-count_files_med[[i]]$N\n if(mask==T){var_counts[(0% group_by(ENT,fecha) %>% summarise(bancomer=sum(bancomer),\n banamex=sum(banamex),\n valor=sum(valor),\n otros=sum(otros)) %>% \n filter(month(fecha)==12,year(fecha)>=2014)\n\nggplot(cnbv_muns_edo, aes(ENT, bancomer,color=ENT)) + geom_line()\nggplot(cnbv_muns_edo, aes(fecha, banamex,color=ENT)) + geom_line()\nggplot(cnbv_muns_edo, aes(fecha, valor,color=ENT)) + geom_line()\nggplot(cnbv_muns_edo, aes(fecha, otros,color=ENT)) + geom_line()\n\n\ncnbv_edo_bancomer<-cnbv_muns_edo %>% select(ENT,fecha,bancomer) %>% dcast(ENT~fecha)\nnames(cnbv_edo_bancomer)<-c(\"CVEENT\",\"bancomer_2014\",\"bancomer_2015\",\"bancomer_2016\")\n\ncnbv_edo_banamex<-cnbv_muns_edo %>% select(ENT,fecha,banamex) %>% dcast(ENT~fecha)\nnames(cnbv_edo_banamex)<-c(\"CVEENT\",\"banamex_2014\",\"banamex_2015\",\"banamex_2016\")\n\ncnbv_edo_valor<-cnbv_muns_edo %>% select(ENT,fecha,valor) %>% dcast(ENT~fecha)\nnames(cnbv_edo_valor)<-c(\"CVEENT\",\"valor_2014\",\"valor_2015\",\"valor_2016\")\n\ncnbv_edo_otros<-cnbv_muns_edo %>% select(ENT,fecha,otros) %>% dcast(ENT~fecha)\nnames(cnbv_edo_otros)<-c(\"CVEENT\",\"otros_2014\",\"otros_2015\",\"otros_2016\")\n\n#Por municipios\n\ncnbv_muns<-cnbv_muns %>% group_by(ENT,CVEMUN,fecha) %>% summarise(bancomer=sum(bancomer),\n banamex=sum(banamex),\n valor=sum(valor),\n otros=sum(otros)) %>% \n filter(month(fecha)==12,year(fecha)>=2014)\n\ncnbv_muns_bancomer<-cnbv_muns %>% select(ENT,CVEMUN,fecha,bancomer) %>% dcast(CVEMUN~fecha)\n#names(cnbv_muns_bancomer)<-c(\"CVEENT\",\"bancomer_2014\",\"bancomer_2015\",\"bancomer_2016\")\n\ncnbv_muns_banamex<-cnbv_muns %>% select(ENT,CVEMUN,fecha,banamex) %>% dcast(CVEMUN~fecha)\n#names(cnbv_muns_banamex)<-c(\"CVEENT\",\"banamex_2014\",\"banamex_2015\",\"banamex_2016\")\n\ncnbv_muns_valor<-cnbv_muns %>% select(ENT,CVEMUN,fecha,valor) %>% dcast(CVEMUN~fecha)\n#names(cnbv_muns_valor)<-c(\"CVEENT\",\"valor_2014\",\"valor_2015\",\"valor_2016\")\n\ncnbv_muns_otros<-cnbv_muns %>% select(ENT,CVEMUN,fecha,otros) %>% dcast(CVEMUN~fecha)\n#names(cnbv_muns_otros)<-c(\"CVEENT\",\"otros_2014\",\"otros_2015\",\"otros_2016\")\n\n\n", "meta": {"hexsha": "5f51bd4783a49b46d3bedb1136bddc9fd20520e8", "size": 2788, "ext": "r", "lang": "R", "max_stars_repo_path": "1_importa_cnbv_from_original.r", "max_stars_repo_name": "imco/magda", "max_stars_repo_head_hexsha": "0a8ab85557bea58efa1074cded7c44168c9fe595", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-11-05T22:56:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-18T13:24:47.000Z", "max_issues_repo_path": "1_importa_cnbv_from_original.r", "max_issues_repo_name": "imco/magda", "max_issues_repo_head_hexsha": "0a8ab85557bea58efa1074cded7c44168c9fe595", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1_importa_cnbv_from_original.r", "max_forks_repo_name": "imco/magda", "max_forks_repo_head_hexsha": "0a8ab85557bea58efa1074cded7c44168c9fe595", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-01-17T01:47:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-15T14:49:45.000Z", "avg_line_length": 48.9122807018, "max_line_length": 160, "alphanum_fraction": 0.6506456241, "num_tokens": 939, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3327867186228418}} {"text": "source(\"R/Renviron.R\")\nsource('R/load-outputs.R')\n## source('R/write_summary.R')\nsource('R/visual-latent-space-plot.R')\nsource('R/visual-trace-plot.R')\n## source('R/CIF_posm.R')\n## source('R/CIF.R')\n", "meta": {"hexsha": "de63ecd18e2ed94742fffd77bb336df9b91bae5f", "size": 199, "ext": "r", "lang": "R", "max_stars_repo_path": "R/art-analysis.r", "max_stars_repo_name": "Jonghyun-Yun/LSA", "max_stars_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/art-analysis.r", "max_issues_repo_name": "Jonghyun-Yun/LSA", "max_issues_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/art-analysis.r", "max_forks_repo_name": "Jonghyun-Yun/LSA", "max_forks_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.875, "max_line_length": 38, "alphanum_fraction": 0.6783919598, "num_tokens": 65, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3327867186228418}} {"text": "# Copyright (c) MAQ Software. All rights reserved.\n\n# Third Party Programs. This software enables you to obtain software applications from other sources. \n# Those applications are offered and distributed by third parties under their own license terms.\n# MAQ Software is not developing, distributing or licensing those applications to you, but instead, \n# as a convenience, enables you to use this software to obtain those applications directly from \n# the application providers.\n# By using the software, you acknowledge and agree that you are obtaining the applications directly\n# from the third party providers and under separate license terms, and that it is your responsibility to locate, \n# understand and comply with those license terms.\n# Microsoft grants you no license rights for third-party software or applications that is obtained using this software.\n\n#\n# WARNINGS: \n#\n# CREATION DATE: 06/12/2017\n#\n# LAST UPDATE: --/--/---\n#\n# VERSION: 3.0.0\n#\n# R VERSION TESTED: 3.4.2\n# \n# AUTHOR: MAQ Software\n\nsource('./r_files/flatten_HTML.r')\n\n############### Library Declarations ###############\n####################################################\n#setting localization and language\nSys.setlocale(\"LC_ALL\",\"English\")\n#graphics libraries\nlibraryRequireInstall(\"plotly\")\n#forecast libraries\nlibraryRequireInstall(\"forecast\")\n\n#Set seed for random number generation\nset.seed (100)\n#Remove screenshot feature from plotly utilities\ndisabledButtonsList <- list('toImage', 'sendDataToCloud')\n###############################\ntryCatch({\n\n\nfeature<-data.frame(Value)\ntSeries<-data.frame(Category)\n###############################\n#forecast settings\nforecastUnits<-10\nif(exists(\"settings_units\") && settings_units > 0)\n{\n forecastUnits<-settings_units\n}\n\ndecayRate<-0.009\nif(exists(\"settings_decay\") && settings_decay <= 1 && settings_decay >= 0)\n{\n decayRate<-settings_decay\n}\nmaxitr<-200\nif(exists(\"settings_maxitr\") && settings_maxitr > 0)\n{\n maxitr<-settings_maxitr\n}\n\nhnodes<-20\nif(exists(\"settings_size\") && settings_size >0)\n{\n hnodes<-settings_size\n}\n\nepochs<-8\nif(exists(\"settings_epochs\") && settings_epochs >0)\n{\n epochs<-settings_epochs\n}\n\nconfIntervals<- FALSE\nif(exists(\"settings_confInterval\"))\n{\n confIntervals<-settings_confInterval\n}\n\nconfLevels<- 0.80\nif(exists(\"settings_confLevel\") && settings_confLevel >= 0 && settings_confLevel <= 1)\n{\n confLevels<-settings_confLevel\n}\n\n######################################\n###########plot settings\nplotColor<-\"#FFFFFF\"\nif(exists(\"plotSettings_plotColor\"))\n{\n plotColor<-plotSettings_plotColor\n}\n\nforecastLineCol<-\"#F2C80F\"\nif(exists(\"plotSettings_fline\"))\n{\n forecastLineCol<-plotSettings_fline\n}\n\nhistoryLineCol<-\"#01B8AA\"\nif(exists(\"plotSettings_hline\"))\n{\n historyLineCol<-plotSettings_hline\n}\n\nforecastLineText<-\"Predicted\"\nif(exists(\"plotSettings_flineText\"))\n{\n forecastLineText<-plotSettings_flineText\n}\n\nhistoryLineText<-\"Observed\"\nif(exists(\"plotSettings_hlineText\"))\n{\n historyLineText<-plotSettings_hlineText\n}\n\nconfCol<-\"Gray95\"\nif(exists(\"plotSettings_confCol\"))\n{\n confCol<-plotSettings_confCol\n}\n\nConfText<-\"Confidence\"\nif(exists(\"plotSettings_confText\"))\n{\n ConfText<-plotSettings_confText\n}\n###############################\n######### x axis settings######\n\nxTitle<-names(tSeries)[1]\nif(exists(\"xaxisSettings_xTitle\") && xaxisSettings_xTitle!= '')\n{\n xTitle<-xaxisSettings_xTitle\n}\n\nxZeroline<-TRUE\nif(exists(\"xaxisSettings_xZeroline\"))\n{\n xZeroline<-xaxisSettings_xZeroline\n}\n\nxLabels<-TRUE\nif(exists(\"xaxisSettings_xLabels\"))\n{\n xLabels<-xaxisSettings_xLabels\n}\n\nxGrid<-TRUE\nif(exists(\"xaxisSettings_xGrid\"))\n{\n xGrid<-xaxisSettings_xGrid\n}\n\nxGridCol<-\"#BFC4C5\"\nif(exists(\"xaxisSettings_xGridCol\"))\n{\n xGridCol<-xaxisSettings_xGridCol\n}\n\nxGridWidth<-0.1\nif(exists(\"xaxisSettings_xGridWidth\") && xaxisSettings_xGridWidth <= 5 && xaxisSettings_xGridWidth >= 0.1)\n{\n xGridWidth<-xaxisSettings_xGridWidth\n}\n\nxAxisBaseLine<-TRUE\nif(exists(\"xaxisSettings_xAxisBaseLine\"))\n{\n xAxisBaseLine<-xaxisSettings_xAxisBaseLine\n}\n\nxAxisBaseLineCol<-\"#000000\"\nif(exists(\"xaxisSettings_xAxisBaseLineCol\"))\n{\n xAxisBaseLineCol<-xaxisSettings_xAxisBaseLineCol\n}\n\nxAxisBaseLineWidth<-4\nif(exists(\"xaxisSettings_xAxisBaseLineWidth\") && xaxisSettings_xAxisBaseLineWidth <= 11 && xaxisSettings_xAxisBaseLineWidth >= 1)\n{\n xAxisBaseLineWidth<-xaxisSettings_xAxisBaseLineWidth\n}\n\n##############################\n######y axis settings ########\n\nyTitle<-names(feature)[1]\nif(exists(\"yaxisSettings_yTitle\") && yaxisSettings_yTitle!='')\n{\n yTitle<-yaxisSettings_yTitle\n}\n\nyZeroline<-TRUE\nif(exists(\"yaxisSettings_yZeroline\"))\n{\n yZeroline<-yaxisSettings_yZeroline\n}\n\nyLabels<-TRUE\nif(exists(\"yaxisSettings_yLabels\"))\n{\n yLabels<-yaxisSettings_yLabels\n}\n\nyGrid<-TRUE\nif(exists(\"yaxisSettings_yGrid\"))\n{\n yGrid<-yaxisSettings_yGrid\n}\n\nyGridCol<-\"#BFC4C5\"\nif(exists(\"yaxisSettings_yGridCol\"))\n{\n yGridCol<-yaxisSettings_yGridCol\n}\n\nyGridWidth<-0.1\nif(exists(\"yaxisSettings_yGridWidth\") && yaxisSettings_yGridWidth <= 5 && yaxisSettings_yGridWidth >= 0.1)\n{\n yGridWidth<-yaxisSettings_yGridWidth\n}\n\nyAxisBaseLine<-TRUE\nif(exists(\"yaxisSettings_yAxisBaseLine\"))\n{\n yAxisBaseLine<-yaxisSettings_yAxisBaseLine\n}\n\nyAxisBaseLineCol<-\"#000000\"\nif(exists(\"yaxisSettings_yAxisBaseLineCol\"))\n{\n yAxisBaseLineCol<-yaxisSettings_yAxisBaseLineCol\n}\n\nyAxisBaseLineWidth<-4\nif(exists(\"yaxisSettings_yAxisBaseLineWidth\") && yaxisSettings_yAxisBaseLineWidth <= 11 && yaxisSettings_yAxisBaseLineWidth >= 1)\n{\n yAxisBaseLineWidth<-yaxisSettings_yAxisBaseLineWidth\n}\n\n#################################\n###############################\n###############################\n#prepare dataset\n\ntsStart<-tSeries[1,]\nrows<-NROW(tSeries)\ntsEnd<-tSeries[rows,]\ntsSeries<-0\nh<-forecastUnits\n##creating list of plot settings#############\n\nxAesthetics <- list(\n title = xTitle,\n zeroline = xZeroline,\n showticklabels = xLabels,\n showgrid = xGrid,\n gridcolor = toRGB(xGridCol),\n gridwidth = xGridWidth,\n showline = xAxisBaseLine,\n linecolor=toRGB(xAxisBaseLineCol),\n linewidth=xAxisBaseLineWidth\n )\n yAesthetics <- list(\n title = yTitle,\n zeroline = yZeroline,\n showticklabels = yLabels,\n showgrid = yGrid,\n gridcolor = toRGB(yGridCol),\n gridwidth = yGridWidth,\n showline = yAxisBaseLine,\n linecolor=toRGB(yAxisBaseLineCol),\n linewidth=yAxisBaseLineWidth\n )\n\n\n\n##############initiating try catch###############\n\ntryCatch({\n#form a time series\ntsSeries<-ts(data=feature, end = tsEnd, start=tsStart, frequency = 1)\n#initating procedure to coerce non date time data into series\n\npretsSeries<-data.frame(Category,Value)\n colnames(pretsSeries)<-c(\"seriesStamps\",\"dataValues\")\n x<-(pretsSeries$seriesStamps)\n\n tsSeries<-structure(list(y=c(pretsSeries$dataValues), date=c(x)))\n\n## arguments can be passed to nnet()\nfit <- nnetar(tsSeries$y, lambda=0.5)\nif(exists('settings_parameterSettings') && (settings_parameterSettings == 'Manual'))\n{\nfit <- nnetar(tsSeries$y, decay=decayRate, maxit=maxitr, repeats = epochs, size=hnodes, lambda=0.5)\n}\n\n#creating sequence\nforecastedDates <- seq((tsSeries$date[length(tsSeries$date)]),\n by=(tsSeries$date[length(tsSeries$date)] - tsSeries$date[length(tsSeries$date)-1]), len=h+1)\n\nforecastedDates<-forecastedDates[-1]\nforecastedValues<-forecast(fit,h,PI=confIntervals, level=confLevels)\n\nsegStartx<-tsSeries$date[NROW(tsSeries$date)]\nsegEndx<-forecastedDates[1]\nsegStarty<-tsSeries$y[NROW(tsSeries$y)]\nsegEndy<-forecastedValues$mean[1]\n\n\n###############################\n###############################\n# plotting for confidence intervals\nif(confIntervals==TRUE)\n{\n ribX<-c(segStartx,segEndx)\n ribYmin<-c(segStarty, forecastedValues$lower[1])\n ribYmax<-c(segStarty, forecastedValues$upper[1])\n\n ribbonFrame<-data.frame(forecastedDates,forecastedValues$lower,forecastedValues$upper)\n colnames(ribbonFrame)<-c(\"xValues\",\"yMin\",\"yMax\")\n \nplotOutput <- plot_ly() %>%\n add_lines(x = (tsSeries$date), y = tsSeries$y,\n color = I(historyLineCol), \n name = historyLineText \n ) %>% \n add_ribbons(x = ribX, ymin = ribYmin, ymax = ribYmax,color = I(confCol), name = ConfText)%>%\n add_ribbons(x = ribbonFrame$xValues, ymin = ribbonFrame$yMin, ymax = ribbonFrame$yMax, color = I(confCol), name = ConfText)%>%\n add_segments(x = segStartx, xend = segEndx, y = segStarty, yend = segEndy, showlegend = FALSE, color = I(forecastLineCol)) %>%\n add_lines(x = forecastedDates, y = forecastedValues$mean, color = I(forecastLineCol), name = forecastLineText)%>%\n layout(title = '',\n xaxis = xAesthetics, \n yaxis = yAesthetics,\n margin = list(l = 50,\n r=0,\n t=50,\n b=50),\n plot_bgcolor=plotColor,\n showlegend = FALSE\n )\n}\nelse\n{\n#plotting without confidence intervals\nplotOutput <- plot_ly() %>%\n add_lines(x = (tsSeries$date), y = tsSeries$y,\n color = I(historyLineCol), \n name = historyLineText \n ) %>% \n\n add_segments(x = segStartx, xend = segEndx, y = segStarty, yend = segEndy, showlegend = FALSE, color = I(forecastLineCol)) %>%\n add_lines(x = forecastedDates, y = forecastedValues$mean, color = I(forecastLineCol), name = forecastLineText)%>%\n layout(title = '',\n xaxis = xAesthetics, \n yaxis = yAesthetics,\n margin = list(l = 50,\n r=0,\n t=50,\n b=50),\n plot_bgcolor=plotColor,\n showlegend = FALSE\n )\n}\n\n\n###############################\nplotOutput$x$config$modeBarButtonsToRemove = disabledButtonsList\n#rendering plot to visual device\ninternalSaveWidget(config(plotOutput, collaborate = FALSE, displaylogo=FALSE), 'out.html');\nquit()\n\n},\nerror=function(e)\n{\n\n tryCatch({\n #initiating date time extraction\n pretsSeries<-data.frame(Category,Value)\n colnames(pretsSeries)<-c(\"seriesStamps\",\"dataValues\")\n x<-as.POSIXct(pretsSeries$seriesStamps)\n\n tsSeries<-structure(list(y=c(pretsSeries$dataValues), date=c(x)))\n\n\n## arguments can be passed to nnet()\nfit <- nnetar(tsSeries$y, lambda=0.5)\nif(exists('settings_parameterSettings') && (settings_parameterSettings == 'Manual'))\n{\nfit <- nnetar(tsSeries$y, decay=decayRate, maxit=maxitr, repeats = epochs, size=hnodes, lambda=0.5)\n}\n#creating date time series for forecast length\nforecastedDates <- seq(as.POSIXct(tsSeries$date[length(tsSeries$date)]),\n by=(tsSeries$date[length(tsSeries$date)] - tsSeries$date[length(tsSeries$date)-1]), len=h+1)\n\n forecastedDates<-forecastedDates[-1]\n\nforecastedValues<-forecast(fit,h,PI=confIntervals, level=confLevels)\n\nsegStartx<-tsSeries$date[NROW(tsSeries$date)]\nsegEndx<-forecastedDates[1]\nsegStarty<-tsSeries$y[NROW(tsSeries$y)]\nsegEndy<-forecastedValues$mean[1]\n\n\n###############################\n###############################\n\n#plotting with confidence intervals\nif(confIntervals==TRUE)\n{\n\n ribX<-c(segStartx,segEndx)\n ribYmin<-c(segStarty, forecastedValues$lower[1])\n ribYmax<-c(segStarty, forecastedValues$upper[1])\n\n ribbonFrame<-data.frame(forecastedDates,forecastedValues$lower,forecastedValues$upper)\n colnames(ribbonFrame)<-c(\"xValues\",\"yMin\",\"yMax\")\n\nplotOutput <- plot_ly() %>%\n add_lines(x = (tsSeries$date), y = tsSeries$y,\n color = I(historyLineCol), \n name = historyLineText \n ) %>% \n add_ribbons(x = ribX, ymin = ribYmin, ymax = ribYmax,color = I(confCol), name = ConfText)%>%\n add_ribbons(x = ribbonFrame$xValues, ymin = ribbonFrame$yMin, ymax = ribbonFrame$yMax, color = I(confCol), name = ConfText)%>%\n add_segments(x = segStartx, xend = segEndx, y = segStarty, yend = segEndy, showlegend = FALSE, color = I(forecastLineCol)) %>%\n add_lines(x = forecastedDates, y = forecastedValues$mean, color = I(forecastLineCol), name = forecastLineText)%>%\n layout(title = title,\n xaxis = xAesthetics, \n yaxis = yAesthetics,\n margin = list(l = 50,\n r=0,\n t=50,\n b=50),\n plot_bgcolor=plotColor,\n showlegend = FALSE\n )\n}\nelse\n{\n #plotting without confidence intervals\nplotOutput <- plot_ly() %>%\n add_lines(x = (tsSeries$date), y = tsSeries$y,\n color = I(historyLineCol), \n name = historyLineText \n ) %>% \n\n add_segments(x = segStartx, xend = segEndx, y = segStarty, yend = segEndy, showlegend = FALSE, color = I(forecastLineCol)) %>%\n add_lines(x = forecastedDates, y = forecastedValues$mean, color = I(forecastLineCol), name = forecastLineText)%>%\n layout(title = title,\n xaxis = xAesthetics, \n yaxis = yAesthetics,\n margin = list(l = 50,\n r=0,\n t=50,\n b=50),\n plot_bgcolor=plotColor,\n showlegend = FALSE\n )\n}\n\n###############################\n#Remove screenshot feature\ndisabledButtonsList <- list('toImage')\nplotOutput$x$config$modeBarButtonsToRemove = disabledButtonsList\n\n\n###############################\n###############################\n#rendering plot to visual device\ninternalSaveWidget(config(plotOutput, collaborate = FALSE, displaylogo=FALSE), 'out.html');\nquit()\n\n },\nerror=function(e)\n{\n #catching errors for date time instances\nxAesthetics <- list(\n title = sprintf(\"%s Please enter suitable date time\",e),\n zeroline = FALSE,\n showline = FALSE,\n showticklabels = FALSE,\n showgrid = FALSE\n )\n yAesthetics <- list(\n title = \"\",\n zeroline = FALSE,\n showline = FALSE,\n showticklabels = FALSE,\n showgrid = FALSE\n )\n plotOutput <- plot_ly() %>%\n layout(title = '',\n xaxis = xAesthetics, \n yaxis = yAesthetics)\n\n plotOutput$x$config$modeBarButtonsToRemove = disabledButtonsList\n internalSaveWidget(plotOutput, 'out.html');\n quit()\n})\n \n})\n\n},\nerror=function(e)\n{\n #catching error for invalid parameters and anything else\nxAesthetics <- list(\n title = sprintf(\"%s Please use suitable values for input data and parameter\",e),\n zeroline = FALSE,\n showline = FALSE,\n showticklabels = FALSE,\n showgrid = FALSE\n )\n yAesthetics <- list(\n title = \"\",\n zeroline = FALSE,\n showline = FALSE,\n showticklabels = FALSE,\n showgrid = FALSE\n )\n plotOutput <- plot_ly() %>%\n layout(title = '',\n xaxis = xAesthetics, \n yaxis = yAesthetics)\n \n plotOutput$x$config$modeBarButtonsToRemove = disabledButtonsList\n internalSaveWidget(plotOutput, 'out.html');\n quit()\n}\n)", "meta": {"hexsha": "22b6f017a8f559edeb7da9344466d90cd7fc0fbb", "size": 14955, "ext": "r", "lang": "R", "max_stars_repo_path": "src/CustomVisuals/Published/ForecastUsingNN/script.r", "max_stars_repo_name": 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"2021-09-16T20:31:37.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-16T20:31:37.000Z", "avg_line_length": 27.7973977695, "max_line_length": 137, "alphanum_fraction": 0.6516215313, "num_tokens": 3920, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.5, "lm_q1q2_score": 0.332705279373407}} {"text": "suppressPackageStartupMessages({\n library(data.table)\n library(sf)\n library(ggplot2)\n library(RColorBrewer)\n library(rgdal)\n})\noptions(scipen=100)\n\nsource('../../../functions.r')\nsource('../../../config.r')\n\nload('../../generated/model/model-data-image.rdata', v=F)\nload('../../../input-data/grid/grids.rdata')\n\neff.obs <- overlaps0[type == 'observed' & taxa == taxa[1],\n .(fishery_group, grid_id, year, quarter, hooks)]\n\neff.tot <- overlaps0[type == 'total' & taxa == taxa[1],\n .(fishery_group, grid_id, year, quarter, hooks)]\n\n\n## ** Important fisheries\nfgs <- eff.obs[, unique(fishery_group)]\n\neff.obs[, hooks := as.numeric(hooks)] # to avoid warning of sum(hooks) being too large for integers\neff.tot[, hooks := as.numeric(hooks)] # to avoid warning of sum(hooks) being too large for integers\n\neff.obs[, hooks := hooks / 1000]\neff.tot[, hooks := hooks / 1000]\n\n## ** Annual average\neff.tot.yravg <- eff.tot[, .(hooks = sum(hooks) / length(YEARS_PRED)), .(fishery_group, quarter, grid_id)]\n\n## *** All quarters\neff.tot <- rbind(eff.tot.yravg,\n eff.tot.yravg[, .(quarter = 0, hooks = sum(hooks)), .(fishery_group, grid_id)],\n fill = T)\n\n## * Gridded distributions\n\npal <- c(\"#E3F2FD\",\"#BBDEFB\",\"#90CAF9\",\"#64B5F6\",\"#42A5F5\",\"#2196F3\",\"#1E88E5\",\"#1976D2\",\"#1565C0\",\"#0D47A1\")\n\nefftype='tot'\nplot_eff <- function(efftype) {\n cat('\\n===', efftype, '\\n')\n eff <- get(sprintf('eff.%s', efftype))\n fgs <- unique(eff$fishery_group)\n fg=fgs[1]\n for (fg in fgs) {\n cat('\\t', fg, '\\n')\n quart=0\n for (quart in 0:4) {\n cat(sprintf('\\t\\tquarter %s\\n', ifelse(quart == 0, 'all', quart)))\n griddens <- eff[fishery_group == fg & quarter == quart, .(grid_id, hooks)]\n if (nrow(griddens)) {\n \n g <- map_grid_values(grid_values=griddens,\n sprintf(\"%s\\n(x1000 hooks)\",\n ifelse(efftype == 'tot', 'Mean annual\\ntotal effort', 'Total\\nobserved effort')),\n highlighted.lat=-30, leg.pos = c(0.91, 0.15), pal=pal, colourscale.trans='identity')\n ggsave(sprintf('../assets/map-effort_%s_%s_quarter%i.png',\n efftype, slugify(fg), quart),\n width = 7, height = 7, dpi=100)\n\n }\n }\n }\n}\n\nplot_eff('tot')\n", "meta": {"hexsha": "73d79e593c0f08784aa81df83919ba58fd846b6d", "size": 2461, "ext": "r", "lang": "R", "max_stars_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/effort-maps.r", "max_stars_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_stars_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/effort-maps.r", "max_issues_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_issues_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/effort-maps.r", "max_forks_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_forks_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.1805555556, "max_line_length": 125, "alphanum_fraction": 0.548557497, "num_tokens": 687, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.3326033191616723}} {"text": "setwd('D:\\\\Pathway_prediction\\\\Co_expression_20180123')\ndat = read.table(\"Cufflink_matrix_sample_name_20180123.txt\", sep=\"\\t\", row.names=1,head=T,stringsAsFactors=F)\ndat <- dat[,order(colnames(dat))]\n#############################################################################\n### mutation\n### 2012_TFL\nres_mutation_FPKM <- c()\nsubdat <- dat[,21:24]\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n### 2014_rin_FUL1_2\nsubdat <- dat[,323:340]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n### 2015_biosynthesis\nsubdat <- dat[,351:362]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2015_MSH1\nsubdat <- dat[,571:582]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2016_SlRBZ\nsubdat <- dat[,777:784]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2013_P450\nsubdat <- dat[,113:184]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2014_GLK\nsubdat <- dat[,198:265]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2015_AGO1\nsubdat <- dat[,341:344]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2015_ARF3\nsubdat <- dat[,347:350]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2016_CsMYBF1\nsubdat <- dat[,700:703]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2016_sulfurea\nsubdat <- dat[,785:792]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2017_ltm\nsubdat <- dat[,795:806]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2017_sldml2\nsubdat <- dat[,869:871]\ncolnames(subdat)\ncolnames(subdat)[1:2] <- substr(colnames(subdat)[1:2],1,nchar(colnames(subdat)[1:2])-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2017_ys\nsubdat <- dat[,896:901]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\n\n### 2014_leaf_dev\nsubdat <- dat[,c(266:271,290:292)]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,out)\n\t\tcolnames(res_mutation_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_mutation_FPKM),sam[i])\n\t\tres_mutation_FPKM <- cbind(res_mutation_FPKM,tem)\n\t\tcolnames(res_mutation_FPKM) <- col_name}}\n\nrownames(res_mutation_FPKM) <- rownames(dat)\nwrite.table(res_mutation_FPKM,'Results_median_FPKM_for_mutation_Sly_20180125.txt',quote=F,sep='\\t')\n\n\n#############################################################################\n### hormone\n### 2013_ABA\nres_hormone_FPKM <- c()\nsubdat <- dat[,25:31]\ncolnames(subdat)\ncolnames(subdat)[1] <- substr(colnames(subdat)[1],1,nchar(colnames(subdat)[1])-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,out)\n\t\tcolnames(res_hormone_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,tem)\n\t\tcolnames(res_hormone_FPKM) <- col_name}}\n\n\n### 2013_cytokinin\nsubdat <- dat[,59:76]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-4)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,out)\n\t\tcolnames(res_hormone_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,tem)\n\t\tcolnames(res_hormone_FPKM) <- col_name}}\n\n\n### 2013_cytokinin_auxin\nsubdat <- dat[,77:112]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-4)\ncolnames(subdat)[c(3,4,7,8,11,12,15,16,19,20,23,24,27,28,31,32,35,36)] <- substr(colnames(subdat)[c(3,4,7,8,11,12,15,16,19,20,23,24,27,28,31,32,35,36)],1,nchar(colnames(subdat)[c(3,4,7,8,11,12,15,16,19,20,23,24,27,28,31,32,35,36)])-1)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,out)\n\t\tcolnames(res_hormone_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,tem)\n\t\tcolnames(res_hormone_FPKM) <- col_name}}\n\n\n### 2015_proGRAS\nsubdat <- dat[,593:600]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,out)\n\t\tcolnames(res_hormone_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,tem)\n\t\tcolnames(res_hormone_FPKM) <- col_name}}\n\n\n### 2017_TIBA\nsubdat <- dat[,872:877]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,out)\n\t\tcolnames(res_hormone_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_hormone_FPKM),sam[i])\n\t\tres_hormone_FPKM <- cbind(res_hormone_FPKM,tem)\n\t\tcolnames(res_hormone_FPKM) <- col_name}}\n\nrownames(res_hormone_FPKM) <- rownames(dat)\nwrite.table(res_hormone_FPKM,'Results_median_FPKM_for_hormone_Sly_20180125.txt',quote=F,sep='\\t')\n\n#############################################################################\n### environmental, stress, non-stress\n### 2013_bacterial\nres_stress_FPKM <- c()\nsubdat <- dat[,32:58]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2014_pathogen\nsubdat <- dat[,296:322]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_cold\nsubdat <- dat[,448:450]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_DC3000\nsubdat <- dat[,451:462]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_fungal\nsubdat <- dat[,477:479]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2016_A2AS_heat\nsubdat <- dat[,672:679]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2016_shade_sun\nsubdat <- dat[,744:776]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2013_TYLCV\nsubdat <- dat[,185:188]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_light\nsubdat <- dat[,480:570]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_pollen_heat\nsubdat <- dat[,583:592]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_T3\nsubdat <- dat[,613:618]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2016_AvrPto_pathogen\nsubdat <- dat[,680:699]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2017_fungi\nsubdat <- dat[,793:794]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2017_pathosystem\nsubdat <- dat[,807:862]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2017_PSTVd\nsubdat <- dat[,863:868]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\n\n### 2015_circadian\nsubdat <- dat[,363:447]\ncolnames(subdat)\ncolnames(subdat) <- gsub('B', 'A', colnames(subdat))\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,out)\n\t\tcolnames(res_stress_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_stress_FPKM),sam[i])\n\t\tres_stress_FPKM <- cbind(res_stress_FPKM,tem)\n\t\tcolnames(res_stress_FPKM) <- col_name}}\n\nrownames(res_stress_FPKM) <- rownames(dat)\nwrite.table(res_stress_FPKM,'Results_median_FPKM_for_stress_Sly_20180125.txt',quote=F,sep='\\t')\n\n#############################################################################\n### developmental\n### 2015_circadian\n### see in stress\n\n### 2014_GLK\nres_develop_FPKM <- c()\nsubdat <- dat[,198:265]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2015_fruit\nsubdat <- dat[,463:476]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2012_fruit_dev\nsubdat <- dat[,1:20]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2014_leaf_dev\nsubdat <- dat[,272:295]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2015_anther\nsubdat <- dat[,345:346]\ncolnames(subdat)\n#colnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2016_early_fruit\nsubdat <- dat[,704:719]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2014_ARF\nsubdat <- dat[,189:197]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2015_root\nsubdat <- dat[,601:612]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2015_time_course\nsubdat <- dat[,619:665]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2015_trichome\nsubdat <- dat[,666:671]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2016_inflorescence\nsubdat <- dat[,732:743]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2016_fruit_ripening\nsubdat <- dat[,720:731]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\n\n### 2017_yield\nsubdat <- dat[,878:895]\ncolnames(subdat)\ncolnames(subdat) <- substr(colnames(subdat),1,nchar(colnames(subdat))-2)\ncolnames(subdat)\nsam <- unique(colnames(subdat))\nfor(i in 1:length(sam)){\n\ttem <- subdat[,colnames(subdat)==sam[i]]\n\tout <- c()\n\tif(length(tem) < 100){\n\t\tfor(j in 1:nrow(tem)){\n\t\t\tout <- c(out,median(as.numeric(tem[j,])))}\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,out)\n\t\tcolnames(res_develop_FPKM) <- col_name} else {\n\t\tcol_name <- c(colnames(res_develop_FPKM),sam[i])\n\t\tres_develop_FPKM <- cbind(res_develop_FPKM,tem)\n\t\tcolnames(res_develop_FPKM) <- col_name}}\n\nrownames(res_develop_FPKM) <- rownames(dat)\nwrite.table(res_develop_FPKM,'Results_median_FPKM_for_development_Sly_20180125.txt',quote=F,sep='\\t')\n\nres1 <- cbind(res_mutation_FPKM,res_hormone_FPKM)\nres1 <- cbind(res1,res_stress_FPKM)\nres1 <- cbind(res1,res_develop_FPKM)\nwrite.table(res1,'Results_median_FPKM_for_all_Sly_20180125.txt',quote=F,sep='\\t')\n", "meta": {"hexsha": "1ddb2d85c45dd3521feebaeef6c119a1caca32d4", "size": 33554, "ext": "r", "lang": "R", "max_stars_repo_path": "Get_median_FPKM_among_replicates.r", "max_stars_repo_name": "peipeiwang6/Pathway_prediction_in_tomato", "max_stars_repo_head_hexsha": "444c3720f6a23dd0e0eae6fa46c0532834aea3c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Get_median_FPKM_among_replicates.r", "max_issues_repo_name": "peipeiwang6/Pathway_prediction_in_tomato", "max_issues_repo_head_hexsha": "444c3720f6a23dd0e0eae6fa46c0532834aea3c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Get_median_FPKM_among_replicates.r", "max_forks_repo_name": "peipeiwang6/Pathway_prediction_in_tomato", "max_forks_repo_head_hexsha": "444c3720f6a23dd0e0eae6fa46c0532834aea3c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2547076313, "max_line_length": 234, "alphanum_fraction": 0.6969958872, "num_tokens": 11463, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757645879592641, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.3326033127338018}} {"text": "#' Parse Model\n#'\n#' @param mod formula\n#'\n#' @importFrom stats terms\n#'\n#'\nparse_model <- function(mod) {\n full_var <- all.vars(mod)\n rhs <- attr(terms(mod), \"term.labels\")\n lhs <- full_var[!(full_var %in% rhs)]\n list(lhs = lhs, rhs = rhs)\n}\n\n#' Generate expanded version of basic model\n#'\n#' @param y a string vector with outcome variables\n#' @param d a string of treatment variable\n#'\n#' @importFrom stats as.formula\n#'\n#'\nexpand_basic_mod <- function(y, d) {\n lapply(y, function(x) as.formula(paste0(x, \"~\", d)))\n}\n\n#' Generate List of Regression Models\n#'\n#' @param basemod list of baseline model.\n#' Baseline model is specified by `y ~ d`\n#' where `y` is outcome, and `d` is treatments.\n#' @param xmod list of covariate model.\n#' Covariate model is specified by one-sided formula like `~ x1 + x2`.\n#' @param include_onlyd logical.\n#' Whether to estimate a model without covariates.\n#'\n#' @importFrom fixest xpd\n#' @importFrom stats update\n#'\n#'\ngenmod <- function(basemod, xmod = NULL, include_onlyd = TRUE) {\n # check list\n basemod <- if (!is.list(basemod)) list(basemod) else basemod\n xmod <- if (!is.null(xmod) & !is.list(xmod)) list(xmod) else xmod\n # length of lists\n num_base <- length(basemod)\n num_xmod <- length(xmod)\n # generate list of models\n modlist <- vector(\"list\", num_base * (num_xmod + include_onlyd))\n for (i in seq_len(num_base)) {\n for (j in seq_len(num_xmod + include_onlyd)) {\n pos <- j + (num_xmod + include_onlyd) * (i - 1)\n if (j == 1 & include_onlyd) {\n modlist[[pos]] <- basemod[[i]]\n } else {\n mod <- update(basemod[[i]], ~ . + ..addx)\n modlist[[pos]] <- fixest::xpd(mod, ..addx = xmod[[j - include_onlyd]])\n }\n }\n }\n # output\n modlist\n}\n", "meta": {"hexsha": "1000230d3260adcce0752396a86b8d9260979bc2", "size": 1737, "ext": "r", "lang": "R", "max_stars_repo_path": "tmp/model-utils.r", "max_stars_repo_name": "KatoPachi/multiarmRCT", "max_stars_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tmp/model-utils.r", "max_issues_repo_name": "KatoPachi/multiarmRCT", "max_issues_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tmp/model-utils.r", "max_forks_repo_name": "KatoPachi/multiarmRCT", "max_forks_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.140625, "max_line_length": 78, "alphanum_fraction": 0.633851468, "num_tokens": 539, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6261241842048093, "lm_q1q2_score": 0.3326030355455911}} {"text": "# Check if RStudio is running to set the working directory to the script directory\n# https://stackoverflow.com/questions/35986037/detect-if-an-r-session-is-run-in-rstudio-at-startup\nis.na(Sys.getenv(\"RSTUDIO\", unset = NA))\nif (!is.na(Sys.getenv(\"RSTUDIO\", unset = NA))) {\n # Get current directory\n current_dir <- dirname(rstudioapi::getSourceEditorContext()$path)\n # Set working directory to current directory (script directory)\n setwd(current_dir)\n} else {\n # If sourced https://stackoverflow.com/questions/13672720/r-command-for-setting-working-directory-to-source-file-location-in-rstudio\n this.dir <- dirname(parent.frame(2)$ofile)\n setwd(this.dir)\n}\n# Include the excel helper functions\nsource(\"excelhelper.r\")\n\n# we will use ggplot2 for our charts\nlibrary(ggplot2)\n\n# Read the input files\ndiamonds<- getTable(\"sizes_barchart\")\n\n# Let's say we want to create a chart that shows how many diamonds of each size (small/medium/large) are in our data.\nggplot(diamonds, aes(x=size)) + \n geom_bar(stat=\"count\", fill=\"blue\") + \n labs(title=\"Diamond Size Distribution\", x=\"Size Category\", y=\"Number of Diamonds\")\n \n# save the chart\nsaveChart(\"sizes_barchart\")\n\n# Signal the end of the process\ndone()\n\n# free up all variables\nrm(list=ls())", "meta": {"hexsha": "7d51070cff5ac990c5423449e7a93545bedd20f8", "size": 1244, "ext": "r", "lang": "R", "max_stars_repo_path": "Examples/AnIntroToR/r/bar_chart.r", "max_stars_repo_name": "fizban99/RRunner", "max_stars_repo_head_hexsha": "7fc7e9af2179d84199ada502333599bd810b52a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2019-05-05T15:04:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-15T05:33:18.000Z", "max_issues_repo_path": "Examples/AnIntroToR/r/bar_chart.r", "max_issues_repo_name": "fizban99/RRunner", "max_issues_repo_head_hexsha": "7fc7e9af2179d84199ada502333599bd810b52a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Examples/AnIntroToR/r/bar_chart.r", "max_forks_repo_name": "fizban99/RRunner", "max_forks_repo_head_hexsha": "7fc7e9af2179d84199ada502333599bd810b52a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2019-06-06T19:30:18.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T05:51:13.000Z", "avg_line_length": 35.5428571429, "max_line_length": 134, "alphanum_fraction": 0.7427652733, "num_tokens": 320, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.33260303183960577}} {"text": "# Data Wrangling in R\n# Austin Water Quality Case Study\n\n# Load in the libraries that we'll need\nlibrary(tidyverse)\nlibrary(stringr)\nlibrary(lubridate)\n\n# Read in the dataset\nwater <- read_csv('http://594442.youcanlearnit.net/austinwater.csv')\n\n# Let's take a look at what we have\nglimpse(water)\n\n# First, let's get rid of a lot of columns that we don't need\n# I'm going to do that by building a new tibble with just\n# siteName, siteType, parameter, result and unit\n\nwater <- tibble('siteName'=water$SITE_NAME,\n 'siteType'=water$SITE_TYPE,\n 'sampleTime'=water$SAMPLE_DATE,\n 'parameterType'=water$PARAM_TYPE,\n 'parameter'=water$PARAMETER,\n 'result'=water$RESULT,\n 'unit'=water$UNIT)\n\nglimpse(water)\n\n# Now let's start finding the rows that we need.\n# First, we need pH. I might start by trying to look at all unique parameter names\n\nunique(water$parameter)\n\n# but that's way too long... what if we try searching for names that contain PH?\n\nunique(water[which(str_detect(water$parameter,'PH')),]$parameter)\n\n# still a mess... let's backtrack and look at parameter types\n\nunique(water$parameterType)\n\n# OK, what if I filter this down to look only at parameter types of Alkalinity/Hardness/pH\n# and Conventionals\n\nfiltered_water <- subset(water,(parameterType=='Alkalinity/Hardness/pH') |\n parameterType=='Conventionals')\n\n# Notice that this is much smaller in size. let's check what parameters we have now\n\nunique(filtered_water$parameter)\n\n# I want only two of these, (discuss PH and temp choices), so let's filter those\n\nfiltered_water <- subset(filtered_water, ((parameter=='PH') |\n (parameter=='WATER TEMPERATURE')))\n\nglimpse(filtered_water)\n", "meta": {"hexsha": "696c51bc6ddf9420e14034d64a90ec9a6089a987", "size": 1811, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_04/water_4_start.r", "max_stars_repo_name": "vvpn9/Handy-Tools", "max_stars_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_04/water_4_start.r", "max_issues_repo_name": "vvpn9/Handy-Tools", "max_issues_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Ex_Files_Data_Wrangling_R/Exercise Files/Ch07/07_04/water_4_start.r", "max_forks_repo_name": "vvpn9/Handy-Tools", "max_forks_repo_head_hexsha": "5b8e59e80832985c352b7f6e578462e61fcbc300", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.224137931, "max_line_length": 90, "alphanum_fraction": 0.6836002209, "num_tokens": 436, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.33260302442763506}} {"text": "library(deSolve) \r\nlibrary(ggplot2)\r\nlibrary(rstan)\r\n\r\n# ALL files should be in the same working directory. The user should set this directory under the 'path' varialbe\r\noptions(max.print=999999)\r\npath <- \"\" ###### USER INPUT HERE #####\r\nsetwd(path)\r\nload(\"results.RData\")\r\nstan_fit <- extract(fit)\r\n\r\n# !!!!!!!!! In exposure concentration, the last value will not be taken into account by the model, so it better be zero !!!!!!!!!!!!!!!!!!!\r\n##########################################\r\n# Function for creating parameter vector #\r\n##########################################\r\n\r\ncreate.params <- function(input, stan_fit, stochastic = TRUE){\r\n with( as.list(input),{\r\n \r\n # List with names of all possible compartments\r\n comps <- list(\"Lungs\" = \"Lungs\" ,\"Liver\"=\"Liver\", \"Spleen\"=\"Spleen\", \"Kidneys\"=\"Kidneys\", \"Heart\"=\"Heart\", \"Brain\"=\"Brain\",\r\n \"Uterus\"=\"Uterus\", \"Skeleton\"=\"Skeleton\", \"Skin\"=\"Skin\", \"Soft\"=\"Soft\") # List with names of all possible compartments\r\n \r\n \r\n \r\n ### Density of tissues/organs\r\n d_tissue <- 1 #g/ml\r\n d_skeleton <- 1.92 #g/ml\r\n d_adipose <- 0.940 #g/ml\r\n \r\n Q_total <- (1.54*weight^0.75)*60 # Total Cardiac Output (ml/h)\r\n \r\n Total_Blood <- 0.06*weight+0.77 # Total blood volume (ml)\r\n \r\n #Arterial blood volume\r\n Vart <- 0.15*Total_Blood #(ml)\r\n \r\n #Veins blood volume\r\n Vven <-0.64*Total_Blood #(ml)\r\n \r\n #Tissue weight fraction \r\n Tissue_fractions <- c(0.5, 3.66, 0.2, 0.73, 0.33, 0.57, 0.011, 10, 19.03, NA)/100 # % of BW. Na values refers to the volume of the rest organs(RoB)\r\n \r\n #Regional blood flow fraction\r\n Regional_flow_fractions <- c(100, 17.4, 1.22, 14.1, 4.9, 2, 1.1, 12.2, 5.8, NA)/100 # % of total cardiac output\r\n \r\n #Capillary volume fractions (fractions of tissue volume)\r\n Capillary_fractions <- c(0.36, 0.21, 0.22, 0.16, 0.26, 0.03, 0.04, 0.04, 0.02, 0.04) # fraction of tissue volume\r\n # Where NA, it is the same value as Rest of Body due to luck od data for uterus and adipose\r\n \r\n #Macrophage content (g per gram of tissue)\r\n Macrophage_fraction <- c(0.04, 0.1, 0.3, 0.02, 0.02, 0.04, 0.04, 0.04, 0.02, 0.02) \r\n Macrophage_fraction_blood <- 0.01\r\n \r\n CLE_ua <- 0\r\n CLE_muc <- 0.01181567\r\n k_ua_br <- 0\r\n CLE_hep <- 1.52e-03\r\n CLE_muc_cor <- 1\r\n k_ab0 <- 1.45\r\n k_de <- 6.7e-19\r\n k_ab0_spl <- 0.5\r\n \r\n W_tis <- rep(0,length(comps))\r\n V_tis <- rep(0,length(comps))\r\n V_cap <- rep(0,length(comps))\r\n W_macro <- rep(0,length(comps)) \r\n Q <- rep(0,length(comps))\r\n \r\n for (i in 1:(length(comps)-1)) {\r\n control <- comps[i]\r\n \r\n Tissue_fractions[i] <- ifelse(is.na(control), NA, Tissue_fractions[i])\r\n Regional_flow_fractions[i] <- ifelse(is.na(control), NA, Regional_flow_fractions[i])\r\n Capillary_fractions[i] <- ifelse(is.na(control), NA, Capillary_fractions[i])\r\n Macrophage_fraction[i] <- ifelse(is.na(control), NA, Macrophage_fraction[i])\r\n \r\n ### Calculation of tissue weights \r\n W_tis[i] <- weight*Tissue_fractions[i]\r\n W_macro[i] <- W_tis[i]*Macrophage_fraction[i]\r\n \r\n ###Calculation of tissue volumes\r\n \r\n if (i==9){\r\n V_tis[i] <- W_tis[i]/d_skeleton\r\n } else{\r\n V_tis[i] <- W_tis[i]/d_tissue \r\n }\r\n \r\n ###Calculation of capillary volumes\r\n V_cap[i] <- V_tis[i]*Capillary_fractions[i]\r\n \r\n \r\n ###Calculation of regional blood flows\r\n Q[i] <- Q_total*Regional_flow_fractions[i]\r\n }\r\n \r\n ### Calculations for \"Rest of Body\" compartment\r\n W_tis[10] <- weight - sum(W_tis[1:(length(W_tis)-1)], na.rm = TRUE)\r\n V_tis[10] <- W_tis[10]/d_adipose #(considering that the density of the rest tissues is 1 g/ml)\r\n Q[10] <- Q_total - sum(Q[2:(length(Q)-1)],na.rm = TRUE)\r\n V_cap[10] <- V_tis[10]*Capillary_fractions[10]\r\n # V_cap[9] <- Total_Blood - Vven - Vart - sum(V_cap[1:(length(V_cap)-1)], na.rm = TRUE) #this is problematic because it produces negative number\r\n W_macro[10] <- W_tis[10]*Macrophage_fraction[10]\r\n #Capillary_fractions[1] <- V_cap[1]/V_tis[1]\r\n \r\n Macro_blood <- Macrophage_fraction_blood*Total_Blood\r\n Wm_al <- W_macro[1]\r\n \r\n if(stochastic == TRUE){\r\n x_fast <- exp(rnorm(1,mean(stan_fit$theta_tr[,1]), sd(stan_fit$theta_tr[,1])))\r\n CLE_ur<- exp(rnorm(1,mean(stan_fit$theta_tr[,2]), sd(stan_fit$theta_tr[,2])))\r\n uptake <- exp(rnorm(1,mean(stan_fit$theta_tr[,3]), sd(stan_fit$theta_tr[,3])))\r\n k_alpc_tb <- exp(rnorm(1,mean(stan_fit$theta_tr[,4]), sd(stan_fit$theta_tr[,4])))\r\n k_lu_al <- exp(rnorm(1,mean(stan_fit$theta_tr[,5]), sd(stan_fit$theta_tr[,5])))\r\n k_al_lu <- exp(rnorm(1,mean(stan_fit$theta_tr[,6]), sd(stan_fit$theta_tr[,6])))\r\n uptake_al <- exp(rnorm(1,mean(stan_fit$theta_tr[,7]), sd(stan_fit$theta_tr[,7])))\r\n uptake_skel <- exp(rnorm(1,mean(stan_fit$theta_tr[,8]), sd(stan_fit$theta_tr[,8])))\r\n P_lu <- exp(rnorm(1,mean(stan_fit$theta_tr[,9]), sd(stan_fit$theta_tr[,9])))\r\n P_ki <- exp(rnorm(1,mean(stan_fit$theta_tr[,10]), sd(stan_fit$theta_tr[,10])))\r\n P_br <- exp(rnorm(1,mean(stan_fit$theta_tr[,11]), sd(stan_fit$theta_tr[,11])))\r\n P_ut <- exp(rnorm(1,mean(stan_fit$theta_tr[,12]), sd(stan_fit$theta_tr[,12])))\r\n P_skin <- exp(rnorm(1,mean(stan_fit$theta_tr[,13]), sd(stan_fit$theta_tr[,13])))\r\n P_soft_skel_ht <- exp(rnorm(1,mean(stan_fit$theta_tr[,14]), sd(stan_fit$theta_tr[,14])))\r\n P_li_spl <- exp(rnorm(1,mean(stan_fit$theta_tr[,15]), sd(stan_fit$theta_tr[,15])))\r\n \r\n }else{\r\n x_fast <- exp(mean(stan_fit$theta_tr[,1]))\r\n CLE_ur<- exp(mean(stan_fit$theta_tr[,2]))\r\n uptake <- exp(mean(stan_fit$theta_tr[,3]))\r\n k_alpc_tb <- exp(mean(stan_fit$theta_tr[,4]))\r\n k_lu_al <- exp(mean(stan_fit$theta_tr[,5]))\r\n k_al_lu <- exp(mean(stan_fit$theta_tr[,6]))\r\n uptake_al <- exp(mean(stan_fit$theta_tr[,7]))\r\n uptake_skel <- exp(mean(stan_fit$theta_tr[,8]))\r\n P_lu <- exp(mean(stan_fit$theta_tr[,9]))\r\n P_ki <- exp(mean(stan_fit$theta_tr[,10]))\r\n P_br <- exp(mean(stan_fit$theta_tr[,11]))\r\n P_ut <- exp(mean(stan_fit$theta_tr[,12]))\r\n P_skin <- exp(mean(stan_fit$theta_tr[,13]))\r\n P_soft_skel_ht <- exp(mean(stan_fit$theta_tr[,14]))\r\n P_li_spl <- exp(mean(stan_fit$theta_tr[,15]))\r\n \r\n }\r\n\r\n \r\n e1 <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = mean(stan_fit$sigma[,1]) , sd = sd(stan_fit$sigma[,1]))\r\n e2 <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = mean(stan_fit$sigma[,2]) , sd = sd(stan_fit$sigma[,2]))\r\n e3 <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = mean(stan_fit$sigma[,3]) , sd = sd(stan_fit$sigma[,3]))\r\n \r\n uptake_spl <- uptake\r\n P_li <- P_li_spl\r\n P_spl <- P_li_spl\r\n P_ht <- P_soft_skel_ht\r\n P_soft <- P_soft_skel_ht\r\n P_skel <- P_soft_skel_ht\r\n \r\n x_lu <- x_fast\r\n x_li <- x_fast\r\n x_spl <- x_fast\r\n x_ki <- x_fast\r\n x_ht <- x_fast\r\n x_br <- x_fast\r\n x_ut <- x_fast\r\n x_skel <- x_fast\r\n x_skin <- x_fast\r\n x_soft <- x_fast\r\n \r\n return(list(\"Vlu_tis\"= V_tis[1], \"Vli_tis\"= V_tis[2], \"Vspl_tis\"= V_tis[3], \"Vki_tis\"= V_tis[4],\"Vht_tis\"= V_tis[5],\r\n \"Vbr_tis\"= V_tis[6], \"Vut_tis\"= V_tis[7], \"Vskel_tis\"= V_tis[8], \"Vskin_tis\"= V_tis[9], \"Vsoft_tis\"= V_tis[10],\r\n \"Vven\"=Vven, \"Vart\"=Vart, \"V_blood\"=Total_Blood,\r\n \r\n \"Vlu_cap\"= V_cap[1], \"Vli_cap\"= V_cap[2], \"Vspl_cap\"= V_cap[3], \"Vki_cap\"= V_cap[4], \"Vht_cap\"= V_cap[5],\r\n \"Vbr_cap\"= V_cap[6], \"Vut_cap\"= V_cap[7], \"Vskel_cap\"= V_cap[8], \"Vskin_cap\"= V_cap[9], \"Vsoft_cap\"= V_cap[10],\r\n \r\n \"Q_lu\"= Q[1], \"Q_li\"= Q[2], \"Q_spl\"= Q[3], \"Q_ki\"= Q[4], \"Q_ht\"= Q[5], \"Q_br\"= Q[6], \"Q_ut\"= Q[7], \"Q_skel\"= Q[8],\r\n \"Q_skin\"= Q[9], \"Q_soft\"= Q[10], \"Q_total\"=Q_total, \r\n \r\n \"Wm_blood\" = Macro_blood, \"Wm_lu\"= W_macro[1], \"Wm_li\"= W_macro[2], \"Wm_spl\"= W_macro[3], \"Wm_ki\"= W_macro[4], \r\n \"Wm_ht\"= W_macro[5], \"Wm_br\"= W_macro[6], \"Wm_ut\"= W_macro[7], \"Wm_skel\"= W_macro[8], \"Wm_skin\"= W_macro[9],\r\n \"Wm_soft\"= W_macro[10],\r\n \r\n \"Inhaled.vol.rate\" = Inhaled.vol.rate, \"dep.ua\" = dep.ua, \"dep.tb\" = dep.tb, \"dep.al\" = dep.al, \r\n \"exposure.concentration\" = exposure.concentration, \"exposure.time\" = exposure.time,\r\n \r\n \"CLE_ua\" = CLE_ua, \"CLE_muc\" = CLE_muc,\r\n \"x_fast\" = x_fast , \"CLE_ur\" = CLE_ur, \"CLE_hep\" = CLE_hep, \r\n \"CLE_muc_cor\" = CLE_muc_cor, \"k_ab0\" = k_ab0, \"k_de\" = k_de,\r\n \"k_ab0_spl\" = k_ab0_spl, \"uptake\" = uptake,\r\n \"Wm_al\" = Wm_al, \"k_ua_br\" = k_ua_br, \"k_alpc_tb\" = k_alpc_tb, \"k_lu_al\" = k_lu_al,\r\n \"k_al_lu\" = k_al_lu, \"uptake_al\" =uptake_al, \r\n \"uptake_skel\" = uptake_skel, \"uptake_spl\" = uptake_spl,\r\n \r\n \"P_lu\"= P_lu, \"x_lu\" = x_lu, \"P_li\" = P_li, \"x_li\" = x_li, \"P_spl\" =P_spl, \r\n \"x_spl\" = x_spl, \"P_ki\" = P_ki, \"x_ki\" = x_ki, \"P_ht\" = P_ht, \"x_ht\" = x_ht,\r\n \"P_br\" = P_br, \"x_br\" = x_br, \"P_ut\" = P_ut, x_ut = x_ut,\r\n \"P_skel\" = P_skel, \"x_skel\" = x_skel, \"P_skin\" = P_skin, \"x_skin\" = x_skin,\r\n \"P_soft\" = P_soft, \"x_soft\" = x_soft,\r\n \r\n \"error1\" = e1, \"error2\" = e2, \"error3\" = e3\r\n ))\r\n \r\n }) \r\n}\r\n\r\n\r\n### store them once\r\n\r\n\r\n#################################################\r\n# Function for creating initial values for ODEs #\r\n#################################################\r\n\r\ncreate.inits <- function(parameters){\r\n with( as.list(parameters),{\r\n Mua<- 0; Mtb <- 0;Mal <- 0; Mal_pc <- 0; Mlu_cap <- 0;\r\n Mlu_tis <- 0; Mlu_pc <- 0; Mli_cap <- 0; Mli_tis <- 0;\r\n Mli_pc <- 0; Mspl_cap <- 0; Mspl_tis <- 0; Mspl_pc <- 0;\r\n Mki_cap <- 0; Mki_tis <- 0; Mki_pc <- 0; Mht_cap <- 0;\r\n Mht_tis <- 0; Mht_pc <- 0; Mbr_cap <- 0; Mbr_tis <- 0;\r\n Mbr_pc <- 0; Mut_cap <- 0; Mut_tis <- 0; Mut_pc <- 0;\r\n Mskel_cap <- 0; Mskel_tis <- 0; Mskel_pc <- 0; Mskin_cap <- 0;\r\n Mskin_tis <- 0; Mskin_pc <- 0; Msoft_cap <- 0; Msoft_tis <- 0;\r\n Msoft_pc <- 0; Art_blood <- 0; Ven_blood <- 0; Mblood_pc <- 0;\r\n Mfeces <- 0; Murine <- 0;\r\n \r\n return(c(\"Mua\" = Mua, \"Mtb\" = Mtb, \"Mal\" = Mal,\"Mal_pc\" = Mal_pc,\r\n \"Mlu_cap\" = Mlu_cap, \"Mlu_tis\" = Mlu_tis, \"Mlu_pc\" = Mlu_pc, \"Mli_cap\" = Mli_cap, \r\n \"Mli_tis\" = Mli_tis, \"Mli_pc\" = Mli_pc, \"Mspl_cap\" = Mspl_cap, \"Mspl_tis\" = Mspl_tis,\r\n \"Mspl_pc\" = Mspl_pc, \"Mki_cap\" = Mki_cap, \"Mki_tis\" = Mki_tis, \"Mki_pc\" = Mki_pc,\r\n \"Mht_cap\" = Mht_cap, \"Mht_tis\" = Mht_tis, \"Mht_pc\" = Mht_pc, \"Mbr_cap\" = Mbr_cap,\r\n \"Mbr_tis\" = Mbr_tis, \"Mbr_pc\" = Mbr_pc, \"Mut_cap\" = Mut_cap, \"Mut_tis\" = Mut_tis,\r\n \"Mut_pc\" = Mut_pc, \"Mskel_cap\" = Mskel_cap, \"Mskel_tis\" = Mskel_tis, \"Mskel_pc\" = Mskel_pc, \r\n \"Mskin_cap\" = Mskin_cap, \"Mskin_tis\" = Mskin_tis, \"Mskin_pc\" = Mskin_pc, \"Msoft_cap\" = Msoft_cap,\r\n \"Msoft_tis\" = Msoft_tis, \"Msoft_pc\" = Msoft_pc, \"Art_blood\" = Art_blood, \"Ven_blood\" = Ven_blood,\r\n \"Mblood_pc\" = Mblood_pc, \"Mfeces\" = Mfeces, \"Murine\" =Murine))\r\n }) \r\n}\r\n\r\n#################################################\r\n# Function for creating events #\r\n#################################################\r\ncreate.events<- function(parameters){\r\n with( as.list(parameters),{\r\n \r\n exposure.time <- seq(from=0, to=exposure.time , by=exposure.time /100) #inhalation time in hours (When was this applies)\r\n exposure.concentration <- rep(exposure.concentration,length(exposure.time)) # in mg/m^3\r\n \r\n \r\n lexposure <- length(exposure.concentration)\r\n ltimes <- length(exposure.time)\r\n \r\n add.tb <- rep(0,lexposure-1)\r\n add.al <- rep(0,lexposure-1)\r\n cur.time <- exposure.time[1]\r\n \r\n for (i in 1:(ltimes-1)){\r\n # Calculate the interval of exposure\r\n interval <- exposure.time[i+1] - cur.time\r\n add.tb[i] = interval * exposure.concentration[i] * (Inhaled.vol.rate/1000) * dep.tb * 1000 # deposited mass in microgram in trancheobronchial region\r\n add.al[i] = interval * exposure.concentration[i] * (Inhaled.vol.rate/1000) * dep.al * 1000 # deposited mass in microgram in alveolar region\r\n cur.time <- exposure.time[i+1]\r\n }\r\n \r\n if (lexposure == ltimes){\r\n events <- list(data = rbind(data.frame(var = \"Mtb\", time = exposure.time[2:ltimes], \r\n value = add.tb, method = c(\"add\")),\r\n data.frame(var = \"Mal\", time = exposure.time[2:ltimes], \r\n value = add.al, method = c(\"add\"))\r\n \r\n \r\n ))\r\n }else{\r\n stop(\"The user must provide t\")\r\n }\r\n \r\n \r\n return(events)\r\n }) \r\n}\r\n\r\n\r\n###################\r\n# Custom function #\r\n###################\r\n\r\ncustom.func <- function(){\r\n return()\r\n}\r\n\r\n#################\r\n# ODEs system #\r\n#################\r\n\r\n\r\node.func <- function(time, Initial.values, Parameters, custom.func){\r\n with( as.list(c(Initial.values, Parameters)),{\r\n \r\n # concentrations in tissues\r\n #C_lu <- Lu_tissue/W_lu\r\n Clu_tis <- Mlu_tis/Vlu_tis\r\n Clu_cap <- Mlu_cap/Vlu_cap\r\n Cli_tis <- Mli_tis/Vli_tis\r\n Cli_cap <- Mli_cap/Vli_cap\r\n Cspl_tis <- Mspl_tis/Vspl_tis\r\n Cspl_cap <- Mspl_cap/Vspl_cap\r\n Cki_tis <- Mki_tis/Vki_tis\r\n Cki_cap <- Mki_cap/Vki_cap\r\n Cht_tis <- Mht_tis/Vht_tis\r\n Cht_cap <- Mht_cap/Vht_cap\r\n Cbr_tis <- Mbr_tis/Vbr_tis\r\n Cbr_cap <- Mbr_cap/Vbr_cap\r\n Cut_tis <- Mut_tis/Vut_tis\r\n Cut_cap <- Mut_cap/Vut_cap\r\n Cskel_tis <- Mskel_tis/Vskel_tis\r\n Cskel_cap <- Mskel_cap/Vskel_cap\r\n Cskin_tis <- Mskin_tis/Vskin_tis\r\n Cskin_cap <- Mskin_cap/Vskin_cap\r\n Csoft_tis <- Msoft_tis/Vsoft_tis\r\n Csoft_cap <- Msoft_cap/Vsoft_cap\r\n \r\n Cart <- Art_blood/(0.19*V_blood)\r\n Cven <- Ven_blood/(0.81*V_blood)\r\n \r\n # Uptake rates by phagocytizing cells\r\n kluab <- k_ab0*(1-(Mlu_pc/(Wm_lu*uptake)))\r\n kliab <- k_ab0*(1-(Mli_pc/(Wm_li*uptake)))\r\n ksplab <- k_ab0_spl*(1-(Mspl_pc/(Wm_spl*uptake_spl)))\r\n kkiab <- k_ab0*(1-(Mki_pc/(Wm_ki*uptake)))\r\n khtab <- k_ab0*(1-(Mht_pc/(Wm_ht*uptake)))\r\n kbrab <- k_ab0*(1-(Mbr_pc/(Wm_br*uptake)))\r\n kutab <- k_ab0*(1-(Mut_pc/(Wm_ut*uptake)))\r\n kskelab <- k_ab0*(1-(Mskel_pc/(Wm_skel*uptake_skel)))\r\n kskinab <- k_ab0*(1-(Mskin_pc/(Wm_skin*uptake)))\r\n ksoftab <- k_ab0*(1-(Msoft_pc/(Wm_soft*uptake)))\r\n kbloodab <- k_ab0*(1-(Mblood_pc/(Wm_blood*uptake)))\r\n kalab <- k_ab0*(1-(Mal_pc/(Wm_al*uptake_al)))\r\n \r\n #Upper airways\r\n dMua = -(k_ua_br*Mua) - (CLE_ua * Mua) \r\n \r\n #Trancheobronchial region\r\n dMtb = (k_alpc_tb * Mal_pc) - (CLE_muc * CLE_muc_cor* Mtb) \r\n \r\n #Alveolar region\r\n dMal = (k_lu_al * Mlu_tis) - (k_al_lu * Mal) - (Mal*kalab - Mal_pc*k_de)\r\n dMal_pc = (Mal*kalab - Mal_pc*k_de) - (k_alpc_tb * Mal_pc)\r\n \r\n #Lungs\r\n dMlu_cap = (Cven*Q_lu)- (Clu_cap*Q_lu) + (x_lu*Q_lu)*Clu_tis/P_lu - (x_lu*Q_lu)*Clu_cap ; \r\n dMlu_tis = - (x_lu*Q_lu)*Clu_tis/P_lu + (x_lu*Q_lu)*Clu_cap - (Vlu_tis*Clu_tis*kluab - Mlu_pc*k_de) -\r\n (k_lu_al * Mlu_tis) + (k_al_lu * Mal) ; #Lung interstitium\r\n dMlu_pc = (Vlu_tis*Clu_tis*kluab - Mlu_pc*k_de); \r\n \r\n \r\n #Liver\r\n dMli_cap = (Cart*Q_li) + (Cspl_cap*Q_spl) - (Cli_cap*(Q_li+Q_spl)) + (x_li*Q_li)*Cli_tis/P_li - (x_li*Q_li)*Cli_cap; #capillary\r\n dMli_tis = - (x_li*Q_li)*Cli_tis/P_li + (x_li*Q_li)*Cli_cap -(Vli_tis*Cli_tis*kliab - Mli_pc*k_de) - (CLE_hep*Mli_tis) ; #tissue\r\n dMli_pc = (Vli_tis*Cli_tis*kliab - Mli_pc*k_de) ; #Phagocytized\r\n \r\n \r\n #Spleen\r\n dMspl_cap = (Cart*Q_spl) - (Cspl_cap*Q_spl) + (x_spl*Q_spl)*Cspl_tis/P_spl - (x_spl*Q_spl)*Cspl_cap ; #capillary\r\n dMspl_tis = - (x_spl*Q_spl)*Cspl_tis/P_spl + (x_spl*Q_spl)*Cspl_cap - (Vspl_tis*Cspl_tis*ksplab - Mspl_pc*k_de) ; #tissue\r\n dMspl_pc = (Vspl_tis*Cspl_tis*ksplab - Mspl_pc*k_de) ; #seq\r\n \r\n \r\n #Kidneys\r\n dMki_cap = (Cart*Q_ki) - (Cki_cap*Q_ki) + (x_ki*Q_ki)*Cki_tis/P_ki - (x_ki*Q_ki)*Cki_cap- (CLE_ur*Mki_cap) ; #capillary\r\n dMki_tis = - (x_ki*Q_ki)*Cki_tis/P_ki + (x_ki*Q_ki)*Cki_cap - (Vki_tis*Cki_tis*kkiab - Mki_pc*k_de) ; #tissue\r\n dMki_pc = (Vki_tis*Cki_tis*kkiab - Mki_pc*k_de) ; #seq\r\n \r\n #Heart\r\n dMht_cap = (Cart*Q_ht) - (Cht_cap*Q_ht) + (x_ht*Q_ht)*Cht_tis/P_ht - (x_ht*Q_ht)*Cht_cap ; #capillary\r\n dMht_tis = - (x_ht*Q_ht)*Cht_tis/P_ht + (x_ht*Q_ht)*Cht_cap - (Vht_tis*Cht_tis*khtab - Mht_pc*k_de) ; #tissue\r\n dMht_pc = (Vht_tis*Cht_tis*khtab - Mht_pc*k_de) ; #seq\r\n \r\n \r\n #Brain\r\n dMbr_cap = (Cart*Q_br) - (Cbr_cap*Q_br) + (x_br*Q_br)*Cbr_tis/P_br - (x_br*Q_br)*Cbr_cap ; #capillary\r\n dMbr_tis = - (x_br*Q_br)*Cbr_tis/P_br + (x_br*Q_br)*Cbr_cap - (Vbr_tis*Cbr_tis*kbrab - Mbr_pc*k_de) + (k_ua_br*Mua) ; #tissue\r\n dMbr_pc = (Vbr_tis*Cbr_tis*kbrab - Mbr_pc*k_de) ; #seq\r\n \r\n \r\n #Uterus\r\n dMut_cap = (Cart*Q_ut) - (Cut_cap*Q_ut) + (x_ut*Q_ut)*Cut_tis/P_ut - (x_ut*Q_ut)*Cut_cap ; #capillary\r\n dMut_tis = - (x_ut*Q_ut)*Cut_tis/P_ut + (x_ut*Q_ut)*Cut_cap - (Vut_tis*Cut_tis*kutab - Mut_pc*k_de) ; #tissue\r\n dMut_pc = (Vut_tis*Cut_tis*kutab - Mut_pc*k_de) ; #seq\r\n \r\n \r\n #Skeleton\r\n dMskel_cap = (Cart*Q_skel) - (Cskel_cap*Q_skel) + (x_skel*Q_skel)*Cskel_tis/P_skel - (x_skel*Q_skel)*Cskel_cap ; #capillary\r\n dMskel_tis = - (x_skel*Q_skel)*Cskel_tis/P_skel + (x_skel*Q_skel)*Cskel_cap - (Vskel_tis*Cskel_tis*kskelab - Mskel_pc*k_de) ; #tissue\r\n dMskel_pc = (Vskel_tis*Cskel_tis*kskelab - Mskel_pc*k_de) ; #seq\r\n \r\n #Skin\r\n dMskin_cap = (Cart*Q_skin) - (Cskin_cap*Q_skin) + (x_skin*Q_skin)*Cskin_tis/P_skin - (x_skin*Q_skin)*Cskin_cap ; #capillary\r\n dMskin_tis = - (x_skin*Q_skin)*Cskin_tis/P_skin + (x_skin*Q_skin)*Cskin_cap - (Vskin_tis*Cskin_tis*kskinab - Mskin_pc*k_de) ; #tissue\r\n dMskin_pc = (Vskin_tis*Cskin_tis*kskinab - Mskin_pc*k_de) ; #seq\r\n \r\n #Soft\r\n dMsoft_cap = (Cart*Q_soft) - (Csoft_cap*Q_soft) + (x_soft*Q_soft)*Csoft_tis/P_soft - (x_soft*Q_soft)*Csoft_cap ; #capillary\r\n dMsoft_tis = - (x_soft*Q_soft)*Csoft_tis/P_soft + (x_soft*Q_soft)*Csoft_cap - (Vsoft_tis*Csoft_tis*ksoftab - Msoft_pc*k_de) ; #tissue\r\n dMsoft_pc = (Vsoft_tis*Csoft_tis*ksoftab - Msoft_pc*k_de) ; #seq\r\n \r\n \r\n #Blood\r\n dArt_blood = (Clu_cap*Q_lu) - Cart* (Q_li + Q_spl+ Q_ki + Q_ht + Q_br + Q_ut + Q_skel + Q_skin + Q_soft) - \r\n (0.19*V_blood*Cart*kbloodab - 0.19* Mblood_pc*k_de); \r\n dVen_blood = (Cli_cap*(Q_li+Q_spl)) + (Cki_cap*Q_ki) + (Cht_cap*Q_ht) + (Cbr_cap*Q_br) + (Cut_cap*Q_ut) + (Cskel_cap*Q_skel) +\r\n (Cskin_cap*Q_skin) + (Csoft_cap*Q_soft) - (Cven*Q_lu) - (0.81*V_blood*Cven*kbloodab - 0.81* Mblood_pc*k_de)\r\n dMblood_pc <- ((0.19*V_blood*Cart + 0.81*V_blood*Cven)*kbloodab - Mblood_pc*k_de) \r\n \r\n #Feces\r\n dMfeces = (CLE_hep*Mli_tis) + (CLE_ua * Mua) + (CLE_muc* CLE_muc_cor * Mtb) ;\r\n \r\n #Urine\r\n dMurine = (CLE_ur*Mki_cap) ;\r\n \r\n Total_lungs <- Mal + Mal_pc + Mlu_cap + Mlu_tis + Mlu_pc + Mtb \r\n \r\n \r\n \r\n list(c(dMua = dMua, dMtb = dMtb, dMal = dMal,dMal_pc = dMal_pc,\r\n dMlu_cap = dMlu_cap, dMlu_tis = dMlu_tis, dMlu_pc = dMlu_pc, dMli_cap = dMli_cap, \r\n dMli_tis = dMli_tis, dMli_pc = dMli_pc, dMspl_cap = dMspl_cap, dMspl_tis = dMspl_tis,\r\n dMspl_pc = dMspl_pc, dMki_cap = dMki_cap, dMki_tis = dMki_tis, dMki_pc = dMki_pc,\r\n dMht_cap = dMht_cap, dMht_tis = dMht_tis, dMht_pc = dMht_pc, dMbr_cap = dMbr_cap,\r\n dMbr_tis = dMbr_tis, dMbr_pc = dMbr_pc, dMut_cap = dMut_cap, dMut_tis = dMut_tis,\r\n dMut_pc = dMut_pc, dMskel_cap = dMskel_cap, dMskel_tis = dMskel_tis, dMskel_pc = dMskel_pc, \r\n dMskin_cap = dMskin_cap, dMskin_tis = dMskin_tis, dMskin_pc = dMskin_pc, dMsoft_cap = dMsoft_cap,\r\n dMsoft_tis = dMsoft_tis, dMsoft_pc = dMsoft_pc, dArt_blood = dArt_blood, dVen_blood = dVen_blood,\r\n dMblood_pc = dMblood_pc, dMfeces = dMfeces, dMurine = dMurine), Total_lungs = Total_lungs)\r\n })\r\n}\r\n\r\n##############################################\r\n\r\n# User must provide a string declaring the path where the vpc_data file is stored\r\npath <- \"\"\r\ninput.data <- openxlsx::read.xlsx(paste(path,\"biodist_data.xlsx\", sep = \"\"), \r\n sheet=3,colNames = TRUE, rowNames = TRUE)\r\n\r\n# the following numbers derive from normalisation of the MPPD numbers 0.12 and 0.52\r\ntb.depo = 0.1875 \r\nal.depo = 0.8125\r\nua.depo = 0\r\n# use average input data to represent all rats\r\ninput <-list(\"exposure.concentration\" = rowMeans(input.data[3,]), \"exposure.time\" = 2,\r\n \"weight\" = 277, \"dep.ua\" = 0, \"dep.tb\" = tb.depo*rowMeans(input.data[2,]), \r\n \"dep.al\" = al.depo*rowMeans(input.data[2,]), \r\n \"Inhaled.vol.rate\" = rowMeans(input.data[10,]))\r\n\r\n# Produce one solution with mean values\r\nparams_determ <- create.params(input,stan_fit, stochastic = FALSE)\r\ninits <- create.inits(params_determ)\r\nevents <- create.events(params_determ)\r\n\r\n# Here we create a first solution to get the dimensions because the total instances will be \r\n# equal to the dimension of sample_time plus the extra events which occured at times\r\n# not provided in the sample time vector\r\nsample_time <- c(1e-08, 1e-04, 1e-03, 1e-02, 1e-01, 0.5, 1, 2, 6, 12, 26, 48, 72, 144, 256, 300, \r\n 400, 500, 600 , 700)\r\nsolution <- ode(times = sample_time, func = ode.func, y = inits, parms = params_determ, \r\n custom.func = custom.func, method=\"lsodes\", events = events)\r\nsolution <- as.data.frame(solution[solution[,1] %in% sample_time,1:40])\r\ndeterministic.df <- matrix(rep(NA, 17*dim(solution)[1]), ncol = 17)\r\ndeterministic.df[,1] <- solution[,1]\r\ndeterministic.df[,2] <- solution[,3] \r\ndeterministic.df[,3] <- solution[,4] \r\ndeterministic.df[,4] <- solution[,5] \r\ndeterministic.df[,5] <- (solution[,7] + solution[,8]) \r\ndeterministic.df[,6] <- (solution[,10] + solution[,11]) \r\ndeterministic.df[,7] <- (solution[,13] + solution[,14])\r\ndeterministic.df[,8] <- (solution[,16] + solution[,17])\r\ndeterministic.df[,9] <- (solution[,19] + solution[,20])\r\ndeterministic.df[,10] <- (solution[,22] + solution[,23])\r\ndeterministic.df[,11] <- (solution[,25] + solution[,26])\r\ndeterministic.df[,12] <- (solution[,28] + solution[,29]) \r\ndeterministic.df[,13] <- (solution[,31] + solution[,32]) \r\ndeterministic.df[,14] <- (solution[,34] + solution[,35]) \r\ndeterministic.df[,15] <- (solution[,36] + solution[,37]+ solution[,38])\r\ndeterministic.df[,16] <- solution[,39]\r\ndeterministic.df[,17] <- solution[,40]\r\n\r\ncolnames(deterministic.df) <- c( \"Time\", \"Trachea\", \"BALF\", \"BALC\", \"Lavaged lungs\", \"Liver\", \"Spleen\",\r\n \"Kidneys\", \"Heart\", \"Brain\", \"Uterus\", \"Skeleton\", \"Skin\", \"Soft tissues\",\r\n \"Blood\", \"Feces\", \"Urine\")\r\n\r\n\r\nNsim = 1000 # number of simulations\r\nNrat = 20 # number of virtual rats\r\nltime <- dim(solution)[1] # number of solution instances\r\ndata_time <- solution[,\"time\"]\r\nNcomp <- dim(solution)[2] # Number of compartments to plot\r\npred_con_tra <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_balf <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_balc <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_lav <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_li <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_spl <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_ki <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_ht <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_br <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_ut <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_skel <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_skin <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_soft <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_blood <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_feces <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\npred_con_urine <-array(rep(NA,Nrat*ltime*Nsim),dim=c(Nrat,ltime,Nsim))\r\n\r\nfailed = list()\r\n\r\nfor (sim in 1:Nsim){\r\n for (rat in 1:Nrat){\r\n print(paste(\"rat \",rat, \"Nsim \",sim))\r\n\r\n pred <- matrix(rep(NA, Ncomp*ltime), nrow = ltime)\r\n # parameter to inform about failed attempts\r\n failed_attempt <- 0\r\n # if params result in odes not solving, resample and resolve\r\n while( (dim(pred)[1] != ltime) | sum(is.na(pred)) ) {\r\n \r\n #params are selected stochastically because create.params samples from stan estimates\r\n params <- create.params(input,stan_fit)\r\n events <- create.events(params)\r\n sol <- ode(times = sample_time, func = ode.func, y = inits, parms = params, \r\n custom.func = custom.func, method=\"lsodes\", events = events, \r\n maxsteps = 50000) \r\n pred <- sol[sol[,1] %in% sample_time,]\r\n pred\r\n }\r\n \r\n \r\n for(t in 1:ltime){\r\n ###Total amount of NPs in each organ\r\n # Amount in trachea\r\n pred_con_tra[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = pred[t,3] , sd = params$error1)\r\n # Amount in Balf\r\n pred_con_balf[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = pred[t,4] , sd = params$error1/2)\r\n \r\n # Amount in Balc\r\n pred_con_balc[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = pred[t,5] , sd = params$error1/2) \r\n \r\n # Amount in lavaged lungs\r\n pred_con_lav[rat,t,sim]<- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,7] + pred[t,8]) , sd = params$error1)\r\n \r\n # Amount in liver\r\n pred_con_li[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,10] + pred[t,11]) , sd = params$error2) \r\n \r\n # Amount in spleen\r\n pred_con_spl[rat,t,sim]<- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,13] + pred[t,14]) , sd = params$error3) \r\n \r\n # Amount in kidneys\r\n pred_con_ki[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,16] + pred[t,17]) , sd = params$error2) \r\n \r\n # Amount in heart\r\n pred_con_ht[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,19] + pred[t,20]) , sd = params$error3) \r\n \r\n # Amount in brain\r\n pred_con_br[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,22] + pred[t,23]) , sd = params$error3) \r\n \r\n # Amount in uterus\r\n pred_con_ut[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,25] + pred[t,26]) , sd = params$error3) \r\n \r\n # Amount in skeleton\r\n pred_con_skel[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,28] + pred[t,29]) , sd = params$error2) \r\n \r\n # Amount in skin\r\n pred_con_skin[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,31] + pred[t,32]) , sd = params$error2) \r\n \r\n # Amount in soft tissues\r\n pred_con_soft[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,34] + pred[t,35]) , sd = params$error2) \r\n \r\n # Amount in blood\r\n pred_con_blood[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = (pred[t,36] + pred[t,37]+ pred[t,38]), sd = params$error2) \r\n \r\n # Amount in feces\r\n pred_con_feces[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = pred[t,39], sd = params$error1) \r\n \r\n # Amount in urine\r\n pred_con_urine[rat,t,sim] <- truncnorm::rtruncnorm(1, a=0, b=Inf, mean = pred[t,40], sd = params$error1) \r\n }\r\n }\r\n}\r\n\r\n\r\nbag_of_data <- list( pred_con_tra, pred_con_balf, pred_con_balc, pred_con_lav, pred_con_li, \r\n pred_con_spl, pred_con_ki, pred_con_ht, pred_con_br, pred_con_ut, pred_con_skel,\r\n pred_con_skin, pred_con_soft, pred_con_blood, pred_con_feces, pred_con_urine)\r\nnames(bag_of_data) <- c( \"Trachea\", \"BALF\", \"BALC\", \"Lavaged lungs\", \"Liver\", \"Spleen\",\r\n \"Kidneys\", \"Heart\", \"Brain\", \"Uterus\", \"Skeleton\", \"Skin\", \"Soft tissues\",\r\n \"Blood\", \"Feces\", \"Urine\")\r\ncomp_names <- names(bag_of_data) \r\n\r\n# File where data are stored\r\npath <- \"\"\r\n### load data\r\nbiodist_data <- openxlsx::read.xlsx(paste(path,\"biodist_data.xlsx\", sep = \"\"),\r\n sheet=1,colNames = TRUE,rowNames = TRUE)\r\nsd_data <- openxlsx::read.xlsx(paste(path,\"biodist_data.xlsx\", sep = \"\"),\r\n sheet=2,colNames = TRUE,rowNames = TRUE)\r\n\r\nfeces <- openxlsx::read.xlsx(paste(path,\"feces.xlsx\", sep = \"\"),\r\n sheet=2,colNames = TRUE)[,2]*input.data[1,5]\r\nurine<- openxlsx::read.xlsx(paste(path,\"urine.xlsx\", sep = \"\")\r\n , sheet=2,colNames = TRUE)[,2]*input.data[7,5]\r\n\r\nexcreta_time <- c(3.5, 7, 10.5, 14, 17.5, 21, 24.5, 27)*24\r\nbiodist_time <- c(2, 6, 26, (7*24+2), (28*24+2))\r\n\r\nexcreta.df <- as.data.frame(cbind(excreta_time, feces, urine))\r\ncolnames(excreta.df) <- c(\"Time\", \"Feces\", \"Urine\")\r\n\r\ndata.df <- data.frame(cbind(biodist_time, biodist_data))\r\ncolnames(data.df) <- c(\"Time\", colnames(biodist_data))\r\n\r\nsd.data.df <- data.frame(cbind(biodist_time, sd_data))\r\ncolnames(data.df) <- c(\"Time\", colnames(sd_data))\r\n\r\n# Set the path where the plots will be stored\r\nsetwd(path)\r\n\r\ncounter <-1\r\nfor (dat in bag_of_data) {\r\n # Get the compartment name to be plotted\r\n comp_name <- comp_names[counter]\r\n # Define the save name\r\n save_name <- paste0(comp_name, \"_vpc.png\", sep=\"\")\r\n pred_con <- dat\r\n \r\n \r\n \r\n \r\n ##########################\r\n # Calculation of 95 % CI of the 50th quantile\r\n con<-matrix(rep(NA,ltime*Nsim), nrow = ltime)\r\n ymin<- rep(NA,ltime)\r\n ymax<- rep(NA,ltime)\r\n \r\n for (i in 1:ltime){\r\n for (j in 1:Nsim){\r\n con[i,j]<-quantile(pred_con[,i,j],probs=0.5)\r\n }\r\n }\r\n \r\n for (i in 1:ltime){\r\n con[i,]<-sort(con[i,])\r\n ymin[i]<-quantile(con[i,],probs=0.025)\r\n ymax[i]<-quantile(con[i,],probs=0.975)\r\n }\r\n \r\n q50<-data.frame(time=data_time,lo=ymin,hi=ymax)\r\n ############################ \r\n # Calculation of 95 % CI of the 5th quantile\r\n \r\n con<-matrix(rep(NA,ltime*Nsim), nrow = ltime)\r\n ymin<- rep(NA,ltime)\r\n ymax<- rep(NA,ltime)\r\n \r\n for (i in 1:ltime){\r\n for (j in 1:Nsim){\r\n con[i,j]<-quantile(pred_con[,i,j],probs=0.05)\r\n }\r\n }\r\n \r\n for (i in 1:ltime){\r\n con[i,]<-sort(con[i,])\r\n ymin[i]<-quantile(con[i,],probs=0.025)\r\n ymax[i]<-quantile(con[i,],probs=0.975)\r\n }\r\n \r\n q05<-data.frame(time=data_time,lo=ymin,hi=ymax)\r\n ################################# \r\n # Calculation of 95 % CI of the 95th quantile\r\n con<-matrix(rep(NA,ltime*Nsim), nrow = ltime)\r\n ymin<- rep(NA,ltime)\r\n ymax<- rep(NA,ltime)\r\n \r\n for (i in 1:ltime){\r\n for (j in 1:Nsim){\r\n con[i,j]<-quantile(pred_con[,i,j],probs=0.95)\r\n }\r\n }\r\n \r\n for (i in 1:ltime){\r\n con[i,]<-sort(con[i,])\r\n ymin[i]<-quantile(con[i,],probs=0.025)\r\n ymax[i]<-quantile(con[i,],probs=0.975)\r\n }\r\n \r\n q95<-data.frame(time=data_time,lo=ymin,hi=ymax)\r\n ############################\r\n # Mean values of predictions from deterministic.df model\r\n use_comp <- colnames(deterministic.df)[counter+1]\r\n mean_pred = data.frame(deterministic.df[,c(\"Time\", use_comp)])\r\n colnames(mean_pred) = c(\"time\", \"value\")\r\n \r\n if(!(comp_name %in% c(\"Feces\", \"Urine\"))){\r\n use_comp <- colnames(data.df)[counter+1]\r\n df1 <- as.data.frame(cbind(data.df[,c(\"Time\",use_comp)], sd.data.df[,use_comp]))\r\n colnames(df1) <- c(\"Time\", \"Mean\", \"Sd\")\r\n my_plot <- ggplot() + \r\n geom_line(data=mean_pred, aes(x=time, y=value, linetype = \" Prediction mean\"),\r\n size=1.2)+\r\n geom_ribbon(data=q50,aes(x=time, ymin = lo, ymax = hi, fill = \"50th percentile\"),\r\n inherit.aes=FALSE, alpha = 0.2) + \r\n geom_ribbon(data=q05,aes(x=time, ymin = lo, ymax = hi, fill = \"5th percentile\"),\r\n inherit.aes=FALSE, alpha = 0.2) + \r\n geom_ribbon(data=q95,aes(x=time, ymin = lo, ymax = hi, fill=\"95th percentile\"),\r\n inherit.aes=FALSE,alpha = 0.2) + \r\n geom_point(data = df1, aes(x = Time, y=Mean),size=5)+\r\n geom_errorbar(data = df1, aes(x = Time, ymin=ifelse((Mean-Sd)>0,Mean-Sd,0), ymax=Mean+Sd),size=1)+\r\n labs(title = rlang::expr(!!comp_name), y = \"TiO2 (ug)\", x = \"Time (in hours)\") +\r\n scale_fill_manual(\"Prediction Ribbons\", values = c( \"50th percentile\" = 1, \r\n \"5th percentile\" = 2,\"95th percentile\" = 3)) + \r\n scale_linetype_manual(\"Line\", values = c(\" Prediction mean\" = 1))+\r\n scale_colour_manual(\"Point\", values = c(\"Biodistribtion data\" = 1))+\r\n theme(plot.title =element_text(hjust = 0.5, size=30, face=\"bold\"),\r\n axis.title.y =element_text(hjust = 0.5, size=20, face=\"bold\"),\r\n axis.text.y=element_text(size=18),\r\n axis.title.x =element_text(hjust = 0.5, size=20, face=\"bold\"),\r\n axis.text.x=element_text(size=18),\r\n legend.title=element_text(hjust = 0.01, size=20), \r\n legend.text=element_text(size=18))\r\n png(rlang::expr(!!save_name), width = 15, height = 10, units = 'in', res = 500)\r\n print(my_plot)\r\n } else{\r\n observed <- excreta.df[,c(\"Time\",colnames(excreta.df)[counter-13])]\r\n colnames(observed) <- c(\"Time\", \"mean\")\r\n my_plot <- ggplot(observed, aes(x=Time, y=mean, colour=\"Biodistribtion data\"))+\r\n geom_point(shape=19, size=4) + \r\n #geom_bar(aes(ymin=ifelse((mean-sd)>0,mean-sd,0), ymax=mean+sd), width=.1) +\r\n geom_line(data=mean_pred, aes(x=time, y=value, linetype = \" Prediction mean\"), \r\n size=1.2)+\r\n geom_ribbon(data=q50,aes(x=time, ymin = lo, ymax = hi, fill = \"50th percentile\"),\r\n inherit.aes=FALSE, alpha = 0.2) + \r\n geom_ribbon(data=q05,aes(x=time, ymin = lo, ymax = hi, fill = \"5th percentile\"),\r\n inherit.aes=FALSE, alpha = 0.2) + \r\n geom_ribbon(data=q95,aes(x=time, ymin = lo, ymax = hi, fill=\"95th percentile\"),\r\n inherit.aes=FALSE,alpha = 0.2) + \r\n labs(title = rlang::expr(!!comp_name), y = \"TiO2 (ug)\", x = \"Time (in hours)\") +\r\n scale_fill_manual(\"Prediction Ribbons\", values = c( \"50th percentile\" = 1, \r\n \"5th percentile\" = 2,\"95th percentile\" = 3)) +\r\n scale_linetype_manual(\"Line\", values = c(\" Prediction mean\" = 1))+\r\n scale_colour_manual(\"Point\", values = c(\"Biodistribtion data\" = 1))+\r\n theme(plot.title =element_text(hjust = 0.5, size=30, face=\"bold\"),\r\n axis.title.y =element_text(hjust = 0.5, size=20, face=\"bold\"),\r\n axis.text.y=element_text(size=18),\r\n axis.title.x =element_text(hjust = 0.5, size=20, face=\"bold\"),\r\n axis.text.x=element_text(size=18),\r\n legend.title=element_text(hjust = 0.01, size=20), \r\n legend.text=element_text(size=18))\r\n png(rlang::expr(!!save_name), width = 15, height = 10, units = 'in', res = 500)\r\n print(my_plot)\r\n \r\n }\r\n dev.off()\r\n counter <- counter +1\r\n}\r\n \r\n", "meta": {"hexsha": "0c02186207ac7e529be1b5970eb5079b16c11b6b", "size": 36149, "ext": "r", "lang": "R", "max_stars_repo_path": "VPC_plots.r", "max_stars_repo_name": "ntua-unit-of-control-and-informatics/TiO2_inhalation", "max_stars_repo_head_hexsha": "ea5a3f0ede86648571e99340492a676dc2e2c31b", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "VPC_plots.r", "max_issues_repo_name": "ntua-unit-of-control-and-informatics/TiO2_inhalation", "max_issues_repo_head_hexsha": "ea5a3f0ede86648571e99340492a676dc2e2c31b", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "VPC_plots.r", "max_forks_repo_name": "ntua-unit-of-control-and-informatics/TiO2_inhalation", "max_forks_repo_head_hexsha": "ea5a3f0ede86648571e99340492a676dc2e2c31b", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.377457405, "max_line_length": 155, "alphanum_fraction": 0.5756175828, "num_tokens": 12518, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154239836484143, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3326017341963572}} {"text": "generateDataFrame <- function(mold, m1, mneg1, key){\n matrixOld=as.matrix(mold[key])\n matrixM1=as.matrix(m1[key])\n matrixMneg1=as.matrix(mneg1[key])\n indexOld=as.matrix(mold['Index'])\n indexM1=as.matrix(m1['Index'])\n indexMneg1=as.matrix(mneg1['Index'])\n sizeMold=length(matrixOld)\n sizeM1=length(matrixM1)\n sizeMneg1=length(matrixMneg1)\n groups=c(rep(\"Original\", sizeMold), rep(\"Passo 1\", sizeM1), rep(\"Passo 2\", sizeMneg1))\n return(data.frame(series=groups, index=c(indexOld, indexM1, indexMneg1), data=c(matrixOld, matrixM1, matrixMneg1)))\n}\n\ngenerateDataFrameStats <- function(m1, mneg1, key){\n matrixM1=as.matrix(m1[key])\n matrixMneg1=as.matrix(mneg1[key])\n sizeM1=length(matrixM1)\n sizeMneg1=length(matrixMneg1)\n groups=c(rep(\"Passo 1\", sizeM1), rep(\"Passo 2\", sizeMneg1))\n return(data.frame(series=groups, data=c(matrixM1, matrixMneg1)))\n}\n\ngenerateDataFrameNew <- function(m1, mneg1, key){\n matrixM1=as.matrix(m1[key])\n matrixMneg1=as.matrix(mneg1[key])\n indexM1=as.matrix(m1['Index'])\n indexMneg1=as.matrix(mneg1['Index'])\n sizeM1=length(matrixM1)\n sizeMneg1=length(matrixMneg1)\n groups=c(rep(\"Passo 1\", sizeM1), rep(\"Passo 2\", sizeMneg1))\n return(data.frame(series=groups, index=c(indexM1, indexMneg1), data=c(matrixM1, matrixMneg1)))\n}\n\ngenerateFrameStats <- function(m1, mneg1, key){\n matrixM1=as.matrix(m1[key])\n matrixMneg1=as.matrix(mneg1[key])\n sizeM1=length(matrixM1)\n sizeMneg1=length(matrixMneg1)\n groups=c(rep(\"Passo 1\", sizeM1), rep(\"Passo 2\", sizeMneg1))\n return(data.frame(series=groups, data=c(matrixM1, matrixMneg1)))\n}\n\nlibrary(ggplot2)\n\npdf('sim 36.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_36_old, sim_36_1, sim_36_neg1, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_36_1, sim_36_neg1, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_36_1, sim_36_neg1, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_36_old, sim_36_1, sim_36_neg1, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_36_old, sim_36_1, sim_36_neg1, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 36 indiv.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_36_old_indiv, sim_36_1_indiv, sim_36_neg1_indiv, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_36_1_indiv, sim_36_neg1_indiv, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_36_1_indiv, sim_36_neg1_indiv, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_36_old_indiv, sim_36_1_indiv, sim_36_neg1_indiv, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_36_old_indiv, sim_36_1_indiv, sim_36_neg1_indiv, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 49.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_49_old, sim_49_1, sim_49_neg1, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_49_1, sim_49_neg1, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_49_1, sim_49_neg1, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_49_old, sim_49_1, sim_49_neg1, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_49_old, sim_49_1, sim_49_neg1, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 64.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_64_old, sim_64_1, sim_64_neg1, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_64_1, sim_64_neg1, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_64_1, sim_64_neg1, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_64_old, sim_64_1, sim_64_neg1, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_64_old, sim_64_1, sim_64_neg1, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 81.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_81_old, sim_81_1, sim_81_neg1, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_81_1, sim_81_neg1, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_81_1, sim_81_neg1, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_81_old, sim_81_1, sim_81_neg1, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_81_old, sim_81_1, sim_81_neg1, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 100.pdf', width=8, height=6)\nggplot(data=generateDataFrame(sim_100_old, sim_100_1, sim_100_neg1, \"Real.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_100_1, sim_100_neg1, \"Sink.Coverage\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.15, 0.2), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrameNew(sim_100_1, sim_100_neg1, \"Coverage.Delta....\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_100_old, sim_100_1, sim_100_neg1, \"Consumed.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Consumida (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.1, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\nggplot(data=generateDataFrame(sim_100_old, sim_100_1, sim_100_neg1, \"Residual.Energy\"), aes(x=index, y=data, group=series)) + scale_color_manual(values = c(\"#2FAE00\", \"#FF2400\", \"#0033FF\")) + geom_line(aes(color=series)) + theme_classic() + labs(x=\"Tempo de Vida da Rede (u.t.)\", y = \"Energia Residual (mAh)\", color=NULL) + scale_x_continuous(breaks=scales::pretty_breaks(n=10)) + scale_y_continuous(breaks=scales::pretty_breaks(n=10)) + theme(legend.position = c(0.85, 0.75), legend.background = element_rect(color = \"black\", size = 0.3, linetype = \"solid\"))\ndev.off()\n\npdf('sim 36 stats.pdf', width=8, height=6)\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_36_1_stats, sim_36_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\ndev.off()\n\npdf('sim 49 stats.pdf', width=8, height=6)\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_49_1_stats, sim_49_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\ndev.off()\n\npdf('sim 64 stats.pdf', width=8, height=6)\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_64_1_stats, sim_64_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\ndev.off()\n\npdf('sim 81 stats.pdf', width=8, height=6)\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_81_1_stats, sim_81_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\ndev.off()\n\npdf('sim 100 stats.pdf', width=8, height=6)\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Reconfigurations\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Reconfigura\u00e7\u00f5es da Rede\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Total.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Valid.Rounds\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Tempo de Vida da Rede com Cob. >= 95% (u.t.)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Real.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Sink.Coverage\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_boxplot(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\nggplot(data=generateDataFrameStats(sim_100_1_stats, sim_100_neg1_stats, \"Coverage.Delta....\"), aes(x=series, y=data)) + scale_color_manual(values = c(\"#FF2400\", \"#0033FF\")) + geom_violin(aes(color=series)) + theme_classic() + labs(x=\"Simula\u00e7\u00e3o\", y = \"Cobertura Sink - Cobertura Real (%)\", color=NULL) + theme(legend.position='NONE') + scale_y_continuous(breaks=scales::pretty_breaks(n=10))\ndev.off()\n", "meta": {"hexsha": "0fa58dc0aaa258ca3ab75809a85f43ec9f9f14fa", "size": 41444, "ext": "r", "lang": "R", "max_stars_repo_path": "commands2.r", "max_stars_repo_name": "Sinalgo/sinalgo-wsn", "max_stars_repo_head_hexsha": "a2c721a4460113431e24aff136b94625dafa593b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "commands2.r", "max_issues_repo_name": "Sinalgo/sinalgo-wsn", "max_issues_repo_head_hexsha": "a2c721a4460113431e24aff136b94625dafa593b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "commands2.r", "max_forks_repo_name": "Sinalgo/sinalgo-wsn", "max_forks_repo_head_hexsha": "a2c721a4460113431e24aff136b94625dafa593b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-28T15:58:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-28T15:58:10.000Z", "avg_line_length": 246.6904761905, "max_line_length": 574, "alphanum_fraction": 0.7282598205, "num_tokens": 12970, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.6548947425132315, "lm_q1q2_score": 0.3325633201022936}} {"text": "\r\n\r\n\r\noptions(stringsAsFactors = F)\r\n\r\nlibrary(RSQLite)\r\nlibrary(LSD)\r\n\r\n#---------------------------------------\r\n\r\n\r\n#---UTMOST---original---Whole_Blood\r\n\r\ntissue='Whole_Blood'\r\nmodel='ut_original'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c(1,4)]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='UTMOST Original',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.1),ylim = c(0,.1),main='UTMOST Original (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n#---UTMOST---original---Brain_Frontal_Cortex_BA9\r\n\r\ntissue='Brain_Frontal_Cortex_BA9'\r\nmodel='ut_original'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c(1,4)]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='UTMOST Original',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.3),ylim = c(0,.3),main='UTMOST Original (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n#---UTMOST---modified---Whole_Blood\r\n\r\ntissue='Whole_Blood'\r\nmodel='ut_modified'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='UTMOST Modified',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.1),ylim = c(0,.1),main='UTMOST Modified (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n#---UTMOST---original---Brain_Frontal_Cortex_BA9\r\n\r\ntissue='Brain_Frontal_Cortex_BA9'\r\nmodel='ut_modified'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='UTMOST Modified',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.3),ylim = c(0,.3),main='UTMOST Modified (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#---PrediXcan---Whole_Blood\r\n\r\ntissue='Whole_Blood'\r\nmodel='st'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='PrediXcan',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.1),ylim = c(0,.1),main='PrediXcan (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n#---PrediXcan---Brain_Frontal_Cortex_BA9\r\n\r\ntissue='Brain_Frontal_Cortex_BA9'\r\nmodel='st'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='PrediXcan',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.3),ylim = c(0,.3),main='PrediXcan (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#---XT-SCAN---Whole_Blood\r\n\r\ntissue='Whole_Blood'\r\nmodel='xt'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='XT-SCAN',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.1),ylim = c(0,.1),main='XT-SCAN (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (GEUVADIS)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.1,0.1,0.1,col='forestgreen',lty=2)\r\nsegments(0.1,0.1,0.1,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n#---XT-SCAN---Brain_Frontal_Cortex_BA9\r\n\r\ntissue='Brain_Frontal_Cortex_BA9'\r\nmodel='xt'\r\n\r\n#gtex\r\ncon <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #establish connections\r\ngtex = dbReadTable(con,\"extra\")\r\ndbDisconnect(con) #disconnect\r\n\r\ngtex<-gtex[,c(1,4)]\r\ncolnames(gtex)<-c('genename','r2_gtex')\r\n\r\n#rep\r\nrep<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/',model,'_',tissue,'.txt'),header = T)\r\nrep$r<-ifelse(rep$r<0,0,rep$r)\r\nrep$r2_rep<-rep$r^2\r\nrep<-rep[,c('gene','r2_rep')]\r\n\r\n#merge\r\ndf<-merge(gtex,rep,by=1)\r\n\r\n#plot\r\npng(paste0('~/../Dropbox/DansPaper/figures/tmp/',tissue,'_',model,'.png'),width = 500,height = 1200,res = 150)\r\npar(mfrow=c(2,1))\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,1),ylim = c(0,1),main='XT-SCAN',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\nheatscatter(df$r2_gtex,df$r2_rep,pch='.',xlim = c(0,.3),ylim = c(0,.3),main='XT-SCAN (zoom in)',xlab='r2 in the training set (GTEx)',ylab='r2 in the test set (PsychENCODE)',cexplot = 2)\r\nsegments(-1,-1,2,2,col='black',lty=2)\r\nsegments(-1,0.3,0.3,0.3,col='forestgreen',lty=2)\r\nsegments(0.3,0.3,0.3,-1,col='forestgreen',lty=2)\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "408ba93b24959cd619932c8164fe25d70fd6d962", "size": 11311, "ext": "r", "lang": "R", "max_stars_repo_path": "plots/S_Fig10&11_performance_external_data_heatmap.r", "max_stars_repo_name": "mjbetti/MR-JTI", "max_stars_repo_head_hexsha": "0bb96993ce15f2cb4b3e234d4de39a05b0f92d84", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2020-10-08T01:08:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:47:20.000Z", "max_issues_repo_path": "plots/S_Fig10&11_performance_external_data_heatmap.r", "max_issues_repo_name": "mjbetti/MR-JTI", "max_issues_repo_head_hexsha": "0bb96993ce15f2cb4b3e234d4de39a05b0f92d84", "max_issues_repo_licenses": ["MIT"], 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YES\n2. YES", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.33256331326150723}} {"text": "\nrunTime <- function(combs, maxDoseT, missp, Rscale, doseType = \"random\", Ne = 1.5*10^5){\n\n\n mutinf <- return_mutinf()\n druginf <- return_druginf()\n \n hours <- list()\n hours[[combs[1]]] <- 0\n hours[[combs[2]]] <- 0\n hours[[combs[3]]] <- 0\n\n #There are three dose missing types:\n #These patterns are NOT explored in this MS, but I will include them anyway\n\n #1) Totally random, with dose taking probability p\n if(doseType == \"random\"){\n missedDoses <- runif(maxDoseT, 0, 1) < missp\n\n\t#Each year, they have a missp probability of a week long treatment interruption (i.e., 336 time periods)\n\tmissedYears <- runif(10, 0, 1) < missp\n\t#Each year is 48 * 7 * 4 * 12 = 16128 time periods\n\tfor(i in 1:10){\n\t if(i == TRUE){\n\t \t #choose a time within the year\n\t\t missedDoseStart <- 16128 * (i - 1) + sample(1:16128, 1)\n\t\t missedDoses[missedDoseStart:(missedDoseStart + 336 * 2)] <- 0\n\t }\n\t}\n\t\n for(i in 2:maxDoseT){\n if(missedDoses[i] == TRUE){\n for(D in combs){ \n hours[[D]] <- append(hours[[D]], tail(hours[[D]], n = 1) + 0.5)\n }\n }else{\n for(D in combs){\n di <- druginf[[which(names(druginf) == D)]]\n dosing <- di[which(names(di) == \"dosing\")]\n hour <- tail(hours[[D]], n = 1)\n if(i %% (24/dosing) == 0){\n hour = 0\n }else{\n hour = hour + 0.5\n }\n hours[[D]] <- append(hours[[D]], hour)\n }\n }\n }\n\n }\n\n #Weekends off, weekdays on (Five on, Two off)\n if(doseType == \"FOTO\"){\n\n #Weekday index?\n #240 half hour chunks in 5 days\n #96 half hour chunks in a weekend\n missedDoses <- rep(c(rep(FALSE, 240), rep(TRUE, 96)),\n ceiling(maxDoseT/(240+96)))[1:maxDoseT]\n\n for(i in 2:maxDoseT){\n if(missedDoses[i] == TRUE){\n for(D in combs){ \n hours[[D]] <- append(hours[[D]], tail(hours[[D]], n = 1) + 0.5)\n }\n }else{\n for(D in combs){\n di <- druginf[[which(names(druginf) == D)]]\n dosing <- di[which(names(di) == \"dosing\")]\n hour <- tail(hours[[D]], n = 1)\n if(i %% (24/dosing) == 0){\n hour = 0\n }else{\n hour = hour + 0.5\n }\n hours[[D]] <- append(hours[[D]], hour)\n }\n }\n }\n\n }\n\n #The dosing approach that maximizes the time at which resistant strains have higher R than\n # sensitive strains\n if(doseType == \"resistantAdvantage\"){\n\n dosingPeriods <- foreach(D = combs, .combine = \"rbind\")%do%{\n\n di <- druginf[[which(names(druginf) == D)]]\n mi <- mutinf[[which(names(druginf) == D)]]\n\n IC_50 <- di[which(names(di) == \"IC_50\")]\n hl <- di[which(names(di) == \"hl\")]\n m <- di[which(names(di) == \"slope\")]\n C_max <- di[which(names(di) == \"c_max\")]\n dosing <- di[which(names(di) == \"dosing\")]\n\n s <- as.numeric(mi[which(names(mi) == \"s\")])\n mu <- as.numeric(mi[which(names(mi) == \"mu\")])\n rho <- as.numeric(mi[which(names(mi) == \"rho\")])\n sigma <- as.numeric((mi[which(names(mi) == \"sigma\")]))\n\n mut <- 1\n checkPeriod <- 1000\n\n #clunky\n relHours <- bind_rows(bind_cols(t = 1:checkPeriod, nhours = seq(0.5, checkPeriod/2, by = 0.5),\n mut = rep(1, checkPeriod)),\n bind_cols(t = 1:checkPeriod, nhours = seq(0.5, checkPeriod/2, by = 0.5),\n mut = rep(0, checkPeriod))) %>% \n mutate(drug = D) %>%\n mutate( R = (1 - mut * s)/(1 + (decay_instant(nhours, hl, C_max )/(IC_50 * (1 - mut*(1 - rho))))^(m * (1 + mut * (sigma)))))\n\n\n worstT <- (relHours %>% spread(mut, R) %>%\n filter(`1` <= `0`) %>% slice(n = 1))$t\n\n missedDoses <- rep(c(rep(TRUE, worstT - 1), rep(FALSE, 1)),\n ceiling(maxDoseT/worstT))[1:maxDoseT]\n\n\n for(i in 2:maxDoseT){\n\n if(missedDoses[i] == TRUE){\n hours[[D]] <- append(hours[[D]], tail(hours[[D]], n = 1) + 0.5)\n\n }else{\n hour = 0\n hours[[D]] <- append(hours[[D]], hour)\n \n }\n }\n }\n }\n\n\n Rinf <- foreach(D = combs, .combine = \"rbind\")%do%{\n\n di <- druginf[[which(names(druginf) == D)]]\n mi <- mutinf[[which(names(druginf) == D)]]\n\n IC_50 <- di[which(names(di) == \"IC_50\")]\n hl <- di[which(names(di) == \"hl\")]\n m <- di[which(names(di) == \"slope\")]\n C_max <- di[which(names(di) == \"c_max\")]\n dosing <- di[which(names(di) == \"dosing\")]\n\n s <- as.numeric(mi[which(names(mi) == \"s\")])\n mu <- as.numeric(mi[which(names(mi) == \"mu\")])\n rho <- as.numeric(mi[which(names(mi) == \"rho\")])\n sigma <- as.numeric((mi[which(names(mi) == \"sigma\")]))\n \n\n mut <- 1\n #clunky\n bind_rows(bind_cols(t = 1:maxDoseT, nhours = hours[[D]], mut = rep(1, maxDoseT)),\n bind_cols(t = 1:maxDoseT, nhours = hours[[D]], mut = rep(0, maxDoseT))) %>% \n mutate(drug = D) %>%\n mutate( R = (1 - mut * s)/(1 + (decay_instant(nhours, hl, C_max )/(IC_50 * (1 - mut*(1 - rho))))^(m * (1 + mut * (sigma)))))\n\n }\n\n toTrack <- tbl_df(expand.grid(A = c(1, 0), B = c(1, 0), C = c(1, 0)))\n names(toTrack) = combs\n druginds <- toTrack %>% mutate(ind = 1:n()) %>% gather(drug, mut, -ind)\n\n #Now, we go through and compute R at each of these timepoints for each of the resistance profiles\n allRs <- left_join(druginds, Rinf, by = c(\"drug\", \"mut\")) %>%\n group_by(ind, t) %>% summarize(R = Rscale * prod(R), .groups = 'drop')\n\n arsplit <- allRs %>% group_split(t)\n\n N <- Ne\n A_i <- 3000 #3000\n lambda <- Ne * 1 * 10/(10 - 1)\n dy <- 1\n dt <- .02\n mu <- .00001\n\n mutinf <- foreach(D = combs, .combine = 'rbind')%do%{\n\n mi <- mutinf[[which(names(mutinf) == D)]]\n s <- mi[which(names(mi) == \"s\")]\n mu <- mi[which(names(mi) == \"mu\")]\n rho <- mi[which(names(mi) == \"rho\")]\n sigma <- mi[which(names(mi) == \"sigma\")]\n\n return(c(D, mu, s, sigma, rho))\n }\n\n colnames(mutinf) <- c(\"drug\", \"mu\", \"s\", \"sigma\", \"rho\")\n mutinf <- tbl_df(mutinf)\n\n init.n <- left_join(druginds, mutinf, by = \"drug\") %>%\n mutate(mu_over_s = as.numeric(mu)/as.numeric(s) ) %>%\n group_by(ind) %>% mutate(mu_over_s = ifelse(mut == 1, mu_over_s, 1)) %>%\n summarize(lam = prod(mu_over_s), .groups = \"drop\") %>%\n mutate(i.N = round(lam*N))\n\n tmp <- init.n %>% filter(ind < 8)\n init.n <- init.n %>% mutate(i.N = ifelse(ind == 8, N - sum(tmp$i.N), i.N)) %>%\n mutate(lam = ifelse(ind == 8, 1 - sum(tmp$lam), lam))\n\n #new cells\n ys <- matrix(0, nrow = 8, ncol = maxDoseT + 1)\n ys[,1] <- init.n$i.N\n\n toMerge <- toTrack %>% mutate(ind = 1:n()) \n names(toMerge) <- c(\"m1\", \"m2\", \"m3\", \"ind\")\n\n ref <- bind_rows(\n bind_cols(ind = 0, name = \"m1\"), \n bind_cols(ind = 0, name = \"m2\"),\n bind_cols(ind = 0, name = \"m3\"))\n\n\n start_time <- Sys.time()\n i <- 1\n\n res <- init.n$lam\n while(i < maxDoseT & ! stopCondition(i, ys[,i]) ){ \n\n emergingCells <- rpois(8, A_i * dt * res)\n\n rs <- arsplit[[i]]$R\n\n yvals <- ys[,i]\n \n denom <- lambda + sum(( dy * rs * yvals))\n rates <- (yvals*dy*lambda*rs*dt)/denom\n\n newys <- rpois(8, rates)\n \n mutFrom.s1 <- rbinom(8, newys, as.numeric(mutinf$mu[1]))\n mutFrom.s2 <- rbinom(8, newys, as.numeric(mutinf$mu[2]))\n mutFrom.s3 <- rbinom(8, newys, as.numeric(mutinf$mu[3]))\n\n mutFrom <- rep(0, 8)\n mutTo <- rep(0, 8)\n\n for(ind in 1:8){\n\n if(newys[ind] > 0){\n m1 <- sample(1:newys[ind], mutFrom.s1[ind], replace = FALSE)\n m2 <- sample(1:newys[ind], mutFrom.s2[ind], replace = FALSE)\n m3 <- sample(1:newys[ind], mutFrom.s3[ind], replace = FALSE)\n\n base <- toTrack[ind,]\n \n mutsTo <- bind_rows(bind_rows(\n bind_cols(ind = m1, name = rep(\"m1\", length(m1))), \n bind_cols(ind = m2, name = rep(\"m2\", length(m2))),\n bind_cols(ind = m3, name = rep(\"m3\", length(m3)))) %>% \n mutate(i = 1), ref %>% mutate(i =0)) %>%\n spread(name, i) %>% group_by(m1, m2, m3) %>% \n summarize(n = n(), .groups = \"drop\") %>% \n filter(!(m1 == 0 & m2 == 0 & m3 == 0)) %>%\n mutate(m1 = ifelse(is.na(m1), 0, m1), \n m2 = ifelse(is.na(m2), 0, m2),\n m3 = ifelse(is.na(m3), 0, m3))\n \n #So, we actually want something different here - we want to mutate from\n #based on the index we are considering\n #Here, ind = 5, or 110. Our mutation is the same (second position), \n #so we should go to 100, instead of 000 -> 010\n \n myInd <- ind\n flips <- toMerge %>% filter(ind == myInd)\n\n mutsTo <- mutsTo %>% mutate(m1 = abs(flips$m1 - m1),\n m2 = abs(flips$m2 - m2),\n m3 = abs(flips$m3 - m3))\n \n mutFrom[ind] <- sum(mutsTo$n)\n mutTo <- mutTo + (left_join(toMerge, mutsTo, by = c(\"m1\", \"m2\", \"m3\")) %>% \n mutate(n = ifelse(is.na(n), 0, n)))$n\n \n }\n\n }\n \n ys[,i + 1] <- rbinom(8, emergingCells + newys + yvals - mutFrom + mutTo, exp(-dy * dt))\n\n i <- i + 1\n\n }\n\n end_time <- Sys.time()\n print(end_time - start_time)\n \n conc_by_drug <- Rinf\n\n R_by_geno <- left_join(druginds, Rinf, by = c(\"drug\", \"mut\")) %>%\n group_by(ind, t) %>% summarize(R = Rscale * prod(R), .groups = 'drop')\n\n return(list(f = ys, drugs = conc_by_drug, R = R_by_geno))\n}\n\nstopCondition <- function(t, ysi){\n \n #every 6 months\n if(t %% (2*24*7*4*3) == 0 ){\n\n if(sum(ysi[1:8]) >= 5000){\n return(TRUE)\n }\n return(FALSE)\n }\n return(FALSE)\n\n}\n\n\n##############################################\n################ Code to run #################\n##############################################\n\n\n#Relates to parallelization - feel free to comment\nncores <- as.numeric(Sys.getenv('SLURM_CPUS_ON_NODE'))\nregisterDoParallel(ncores)\n\ntoTrack <- tbl_df(expand.grid(A = c(1, 0), B = c(1, 0), C = c(1, 0)))\ndruginds <- toTrack %>% mutate(ind = 1:n()) %>% gather(drug, mut, -ind)\nindinf <- toTrack %>% mutate(comb = paste0(A, B, C)) %>%\n mutate(ind = 1:8) %>% select(comb, ind)\n\n#2 periods/hour, 24 hours/day, 7 days/week, 4 weeks/month, 12 months/year * 10 years\nmaxDoseT <- 2 * 24 * 7 * 4 * 12 * 10\nind <- 1\n\ndirp <- \"../dat/time_model/\"\nprint(paste0(\"making directory: \", dirp))\n\nsystem(paste0(\"mkdir \", dirp))\n\n#This refers to the random nature at which doses are missed\ntreatmentType <- \"random\"\ncombs <- c(\"3TC\", \"D4T\", \"NFV\")\n\n#These nested loops draw from our cluster set up\n#As a test to see if things are working properly, I'd recommend lowering\n#these numbers\nforeach(ind = 1:20)%dopar%{\n foreach(ind = 1:75 )%do%{\n\n R00 <- 10 \n\n Ne <- floor(10^rnorm(1, 5.2, .5))\n while(Ne < 10^4 | Ne > 10^6){\n Ne <- floor(10^rnorm(1, 5.2, .5))\n }\n rseed <- sample(1:100000, 1)\n\n #Adherence\n pv <- runif(1, 0, 1)\n\n stringToPrint <- paste(paste0(combs, collapse = \"-\"),\n treatmentType,\n pv,\n R00,\n ind, Ne, rseed, sep = \"_\")\n\n dat <- runTime(combs = combs, maxDoseT = maxDoseT, missp = pv,\n Rscale = R00, doseType = treatmentType, Ne = Ne)\n\n relInds <- c(1, which(1:ncol(dat$f) %% (2 * 24 * 7 * 4 ) == 0))\n\n write.table(dat$f[,relInds], paste0(dirp, stringToPrint, \"_f.csv\"),\n quote = FALSE, sep = \",\",\n row.names = FALSE, col.names = FALSE)\n\n }\n}\n\n\n\t\t \n\n\n\n\n\n\n\n", "meta": {"hexsha": "eb423d3603f0107b9ed1e28ec652b2eaae52853a", "size": 13025, "ext": "r", "lang": "R", "max_stars_repo_path": "code/time_runner.r", "max_stars_repo_name": "federlab/HIV-MDR-evolution", "max_stars_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/time_runner.r", "max_issues_repo_name": "federlab/HIV-MDR-evolution", "max_issues_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/time_runner.r", "max_forks_repo_name": "federlab/HIV-MDR-evolution", "max_forks_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7435233161, "max_line_length": 146, "alphanum_fraction": 0.4509021113, "num_tokens": 3830, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.33256330642072074}} {"text": "#! /usr/local/bin/RScript\n\nlibrary(data.table)\nlibrary(ggplot2)\nlibrary(ggthemes)\nlibrary(ggmap)\n\n# Set working directories\nsetwd(\"~/bu/Desktop/MCMonitor\")\nfigures.dir = \"~/bu/Desktop/MCMonitor/Plots/\"\n\n# Read data\nnodes.dd = data.table(read.delim(\"./Plots_data/hubway_plot_nodes_5.csv.gz\",\n sep=\",\"))\nedges.dd = data.table(read.delim(\"./Plots_data/hubway_plot_edges.csv.gz\",\n sep=\",\"))\n\n# Plot important stations onto map\n# p1 = qmplot(lng, lat, data=nodes.dd, maptype = \"toner-lite\", color = I(\"red\"), size=I(1), xlab=\"\", ylab=\"\", zoom=14, extent=\"panel\")\n# p1 = p1 + theme_bw()\n# p1 = p1 + theme(strip.background = element_blank(),\n# axis.text.x = element_blank(),\n# axis.text.y = element_blank(),\n# axis.ticks.x = element_blank(),\n# axis.ticks.y = element_blank(),\n# plot.margin = unit(c(0.5,0.5,0,0), \"cm\"))\n# p1\n# ggsave(paste0(figures.dir, \"hubway_stations.pdf\"), w=8, h=4, units=\"cm\")\n\np = get_map(location = c(-71.13, 42.340, -71.05, 42.365), source = \"google\", zoom = 14, maptype=\"toner-lite\")\np = ggmap(p)\np = p + geom_point(data=nodes.dd, aes(x=lng, y=lat), color=\"red\", size=I(1))\np = p + theme_bw()\np = p + theme(strip.background = element_blank(),\n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks.x = element_blank(),\n axis.ticks.y = element_blank(),\n plot.margin = unit(c(0.5,0.5,0,0), \"cm\"))\np\nggsave(paste0(figures.dir, \"hubway_stations_5.pdf\"), w=8, h=4, units=\"cm\")\n\n\n# Plot important paths onto map\n\np2 = qmplot(lng, lat, data=edges.dd, maptype = \"toner-lite\", color=node_type, size=I(1), xlab=\"\", ylab=\"\")\np2 = p2 + geom_path(data=edges.dd, aes(x=lng, y=lat, group=edge_id), color=\"black\", size=I(0.5))\np2 = p2 + labs(color=\"Node Type\")\np2 = p2 + theme_bw()\np2 = p2 + theme(strip.background = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n legend.position = c(0.10,0.15),\n legend.text = element_text(size=3),\n legend.background = element_blank(),\n legend.key = element_blank(),\n legend.key.size = unit(0.32, \"cm\"),\n legend.title = element_blank())\np2\nggsave(paste0(figures.dir, \"hubway_paths.pdf\"), w=9, h=4.5, units=\"cm\")\n", "meta": {"hexsha": "7a4f213ebffe1779e1dad668ede0c4fdac6b2da9", "size": 2514, "ext": "r", "lang": "R", "max_stars_repo_path": "src/R/plot_hubway_stations.r", "max_stars_repo_name": "chdhr-harshal/MCMonitor", "max_stars_repo_head_hexsha": "330fc1a8f8cf83620fd6b0e503707c91e97af16d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-11-04T20:35:18.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-05T09:06:43.000Z", "max_issues_repo_path": "src/R/plot_hubway_stations.r", "max_issues_repo_name": "chdhr-harshal/MCMonitor", "max_issues_repo_head_hexsha": "330fc1a8f8cf83620fd6b0e503707c91e97af16d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/R/plot_hubway_stations.r", "max_forks_repo_name": "chdhr-harshal/MCMonitor", "max_forks_repo_head_hexsha": "330fc1a8f8cf83620fd6b0e503707c91e97af16d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-05T09:10:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-05T09:10:41.000Z", "avg_line_length": 39.9047619048, "max_line_length": 134, "alphanum_fraction": 0.5791567224, "num_tokens": 717, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3325293248378098}} {"text": "# Extract the neighborhood census of a forum\n# and plot its evolution\n# author: Alberto Lumbreras\n#################################################\nlibrary(dplyr)\nlibrary(reshape2)\nlibrary(ggplot2)\nsource('R/load_participations.r')\nsource('R/count_motifs.r')\nselect <- dplyr::select # avoid confusion with MASS function\n\n###################################################\n# Load data\n###################################################\ndf.posts <- load_posts(database='reddit', forum='podemos')\ndf.threads <- plyr::count(df.posts, \"thread\")\ndf.users <- plyr::count(df.posts, 'user')\nnames(df.threads)[2] <- \"length\"\nnames(df.users)[2] <- \"posts\"\n\n######################################################\n# Extract neighborhood around every post\n######################################################\nres <- count_motifs_by_post(df.threads$thread, database='reddit')\ndf.post.motif <- res$posts.motifs\nmotifs <- res$motifs\n\n# Sort by frequency\nidx <- order(table(df.post.motif$motif), decreasing = TRUE)\nmotifs <- motifs[idx]\ndf.post.motif$motif <- match(df.post.motif$motif, idx)\n\ndf.posts <- merge(df.posts, df.post.motif, all.x=TRUE, sort=FALSE)\n\n####################################################\n# Plot neighborhoods by frequency\n####################################################\nmypalette <- c(\"black\", \"red\", \"white\")\npar(mfrow=c(3,5))\nfor(i in 1:length(motifs)){\n gmotif <- as.undirected(motifs[[i]])\n la = layout_as_tree(gmotif, mode='out', root=which.min(V(gmotif)$date))\n plot(gmotif,\n layout = la,\n vertex.color=mypalette[V(motifs[[i]])$color],\n vertex.label = \"\",\n edge.arrow.size=0.6)\n title(paste(i),sub=sum(df.post.motif$motif==i)) \n}\n\n\n################################################\n# Plot cumsum with sliding window\n# Note: lay with both times, real and sequential\n################################################\ndf.census <- arrange(df.posts, date) %>%\n acast(date~motif) %>%\n as.data.frame\n\n# chronological dates\ndf.census$date <- rownames(df.census)\n\n# sequential dates\ndf.census$date <- 1:nrow(df.census)\n\nsteps <- seq(min(as.numeric(df.census$date)),\n max(as.numeric(df.census$date)), \n by=3600)\n\ndf.census.window <- vector()\nfor(i in 1:(length(steps)-1)){\n df.census.window <- filter(df.census, date>steps[i] & date%\n select(-date) %>%\n colSums %>%\n rbind(df.census.window, .)\n #rbind(df.census.window) # TODO rbind(df.census.window, .) ?\n}\n\ndf.census.window <- as.data.frame(df.census.window)\ndf.census.window$date <- head(steps,-1)\ndf.census.window <- melt(df.census.window, id.vars=c(\"date\"))\n\ndf.census.window$date <- as.POSIXct(df.census.window$date, origin=\"1970-01-01\")\n\n# Plot absolute values to view general trend, periodicity...\nggplot(df.census.window, aes(x=date, y=value, group=variable, color=variable, fill=variable)) + \n geom_line()\n\n# Plot evolution of census (%) with total number of posts\nggplot(df.census.window, aes(x=date, y=value)) + \n geom_bar(aes(group=variable, color=variable, fill=variable), \n position = 'fill', stat='identity') + \n stat_summary(fun.y=function(x){sum(x)/200}, color = 'black', geom ='line')\n\n\n#########################################################\n# Plot relationship between length and census\n#########################################################\n\n# Compute rank of every post in its thread\n# compute cumsum per group (per thread)\n\ndf.census <- arrange(df.posts, thread, date) %>%\n dcast(thread+date~motif)\n \ndf.census.length <- vector()\nranks <- vector()\nfor(i in 1:nrow(df.census)){\n cat('\\n', i)\n ranks <- c(ranks, sum(df.census[1:i, c('thread')]==df.census$thread[i])) # store also rank of post in thread\n df.census.length <- filter(df.census[1:i,], thread==df.census$thread[i]) %>%\n select(-thread, -date) %>%\n colSums %>%\n rbind(df.census.length, .) %>%\n as.data.frame\n} \n#df.census.length <- df.census.length.bck\ndf.census.length$rank <- ranks\ndf.census.length$date <- df.census$date\ndf.census.length$thread <- df.census$thread\n\n# Select a subsample of threads to plot\n# ... at random\nsample.threads <- sample(unique(df.census.length$thread), 10)\n\n# by length\nsample.threads <- filter(df.threads, length>100) %>% select(thread) \nsample.threads <- sample.threads$thread\n\ndf.census.length.sample <- filter(df.census.length, thread %in% sample.threads)\ndf.census.length.sample <- melt(df.census.length.sample, id.vars=c('date', 'rank', 'thread'))\n\nggplot(filter(df.census.length.sample, variable==1), aes(x=rank, y=value, group=thread, color=thread)) + geom_line()\n\np <- ggplot(filter(df.census.length.sample, variable %in% c(1,2,3,4,5,6,7,8)), aes(x=rank, y=value, group=thread, color=thread)) + geom_line()\n#p + facet_grid(~ variable)\np + facet_wrap(~ variable, ncol=4, nrow=4)\n\n################################################\n# Can we predict length based on first census\n################################################\n\n# merge census matrix with thread length", "meta": {"hexsha": "11bb4e8342de0578d609d203afd2c92385b284b4", "size": 5154, "ext": "r", "lang": "R", "max_stars_repo_path": "pipeline_neighborhoods_census_evolution.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "pipeline_neighborhoods_census_evolution.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pipeline_neighborhoods_census_evolution.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.041958042, "max_line_length": 142, "alphanum_fraction": 0.5714008537, "num_tokens": 1271, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832805, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33252932378090333}} {"text": "################################################################################################\n# Dependencies\n#source(\"Code/get_dynamic_data.r\")\n\nGetLabData <- function(raw.data.name=\"train\") {\n\t\n\t# For a specified dataset in the original contest format, returns\n\t# a dataset containing only lab information. \n\t#\n\t# Args: \n\t# \traw.data.name: name of dataset\n\t#\n\t# Returns:\n\t#\tA list. The first element is a list of datasets that correspond to lab tests. The columns \n\t# in each dataset consist of subject.id, test delta and test result. \n\t#\tThe second element is a vector of column names corresponding to the test result column of \n\t#\teach dataset. \n\t#\n\t# Example usage: \n\t#\t#source(\"Code/get_lab_data.R\")\n\t#\tlab.data<-GetLabData(\"train\")\n\t\n\t# Check if RDA version of lab data has been stored\n\trda.filename = paste(\"Data/lab_\",raw.data.name,\".rda\",sep=\"\")\n\tif (file.exists(rda.filename)) {\n\t\tprint(paste(\"loading saved lab data from\",rda.filename,sep=\" \"))\n\t\tload(rda.filename)\n\t\tprint(\"finished loading\")\n\t\treturn(lab.data)\n\t}\n\n\t# Get raw data\n\t data<-GetRawData(raw.data.name)\t\n\n\t# Keep only lab data \n\t data <- data[data$form == \"Laboratory Data\", ]\t\n\n\t# Get list of all tests\t\n\t# test.names<-unique(data$value[data$field==\"Test Name\"])\n\t\n\t# Count the number of subjects there is data for for each test\n\t# NumTestSubs(test.names)\n\t\n\t# List of all tests with at least 709 subject ids (30)\n\t# test.names<-c(\"Bicarbonate\", \" HbA1c (Glycated Hemoglobin)\", \"Red Blood Cells (RBC)\",\"White Blood Cells (WBC)\",\"Creatine Kinase\",\"Chloride\",\"Triglycerides\",\"Glucose\", \"Total Cholesterol\", \"Albumin\", \"Sodium\", \"Hemoglobin\", \"Calcium\", \"Monocytes\", \"Lymphocytes\", \"Hematocrit\",\"Neutrophils\",\"Alkaline Phosphatase\",\"Gamma-glutamyltransferase\",\"Protein\",\"Eosinophils\",\"AST(SGOT)\",\"Basophils\",\"Phosphorus\",\"Creatinine\",\"Potassium\",\"Bilirubin (total)\", \"Platelets\",\"ALT(SGPT)\", \"Urine Ph\")\n\t\n\t# List of all tests with at least 709 subject ids for which there are conversions (21)\n\ttest.names<-c(\"Bicarbonate\", \"HbA1c (Glycated Hemoglobin)\",\"Red Blood Cells (RBC)\",\"Creatine Kinase\",\"Chloride\",\"Triglycerides\",\"Glucose\", \"Total Cholesterol\",\"Sodium\", \"Hemoglobin\", \"Calcium\",\"Hematocrit\",\"Alkaline Phosphatase\",\"Gamma-glutamyltransferase\",\"AST(SGOT)\",\"Phosphorus\",\"Creatinine\",\"Potassium\",\"Bilirubin (total)\",\"ALT(SGPT)\",\"Urine Ph\")\n\t\n\t# List of corresponding test names \n\tcol.names<-c(\"Bicarbonate\",\"HbA\",\"RBC\",\"Creatine.Kinase\",\"Chloride\",\"Triglycerides\",\"Glucose\",\"Cholesterol\",\"Sodium\",\"Hemoglobin\",\"Calcium\",\"Hematocrit\",\"Alk.Phos\",\"Gamma.Glutamyl\",\"AST(SGOT)\",\"Phosphorus\",\"Creatinine\",\"Potassium\",\"Bilirubin\",\"ALT(SGPT)\",\"Urine.Ph\")\n\t\n\t# Get data corresponding to each test name and put it in a list\n\tdata.list<-lapply(test.names,TestToData,data=data)\n\t\t\t\n\t# Rename list elements\n\tnames(data.list)<-test.names\n\t\n\t#lapply(data.list,TestUnitsUsed) # what units does each test show?\n\t\n\t# Get a list of conversion factors for each test in test.names \n\tconvert.list<-GetConvertList(test.names)\n\t\n\t# Get a list of new datasets with converted units and only key columns\n\tlab.data<-lapply(1:length(data.list),ConvertTestUnits,test.data=data.list,\n conversions=convert.list,col.names=col.names)\n\n\t# Name each dataset\n\tnames(lab.data)<-col.names[1:length(data.list)]\n \n # Create outlier filter if filter does not exist\n num.sd<-4 \n filter.filename<-paste(\"Data/lab_filter\",num.sd,\"sd.rda\",sep=\"_\")\n\tif (file.exists(filter.filename)) {\n\t print(paste(\"loading saved lab filter from\",filter.filename,sep=\" \"))\n\t load(filter.filename)\n\t}\n else {\n lab.filter<-lapply(lab.data,LabOutlierFilter,num.sd=num.sd)\n save(lab.filter,file=filter.filename)\n }\n \n # Discard outliers\n lab.data<-mapply(LabDiscardOutlier,test.data=lab.data,filter=lab.filter,\n SIMPLIFY=FALSE)\n\t\n\t# Save dataset to file\n\tsave(lab.data, file = rda.filename)\n\treturn(lab.data)\n}\n\nLabOutlierFilter<-function(test.data,num.sd) {\n# Creates an outlier filter for a given dataset based on standard deviations\n test.mean<-mean(test.data[,3])\n test.sd<-sd(test.data[,3])\n filter.max<-test.mean+num.sd*test.sd\n filter.min<-test.mean-num.sd*test.sd\n return(c(filter.max,filter.min))\n}\n\nLabDiscardOutlier<-function(test.data,filter) {\n# Discards outliers in dataset \n test.data<-test.data[(test.data[,3]filter[2]),]\n return(test.data)\n}\n \nTestToData<-function(test.name,data) {\n\t# Given full data, returns a dataset containing only observations corresponding to a \n\t# particular test\n\t#\n\t# Args:\n\t#\ttest.name: name of test of interest\n\t#\tdata: dataset containing only the form of interest\n\t#\n\t# Returns:\n\t#\tA dataset that contains only observations for the test specified in test.name, containing\n\t#\tthe columns subject.id, lab.delta, test.name, test.result, and test.unit\n\t\n\t# Fields\n\tfields<-c(\"Laboratory Delta\",\"Test Name\",\"Test Result\",\"Test Unit\")\n\t\n\t# Columns\n\tcolumns<-c(\"lab.delta\",\"test.name\",\"test.result\",\"test.unit\")\n\n\t# Get record ids for one test\n\ttest.record<-data$record.id[data$value==test.name]\t\n\t\n\t# Get all data that matches those record ids\n\ttest.data<-data[data$record.id %in% test.record,]\n\t\t\t\n\t# Get dynamic data\n\ttest.data<-GetDynamicData(test.data,fields,columns)\n\t\t\n\t# Coerce test result to numeric and drop NAs\n\t# Sometimes test result includes strange entries: eg, one entry of \"A\" in glucose\n\ttest.data$test.result<-as.numeric(test.data$test.result)\n\ttest.data<-test.data[!is.na(test.data$test.result),]\n\t\n\treturn(test.data)\n}\n\nConvertTestUnits<-function(i,test.data,conversions,col.names) {\n\t# Given a test dataset, returns that dataset with test.results converted to\n # consistent units\n\t#\n\t# Args:\n\t#\ti: the index of the dataset in the dataset list \n\t#\ttest.data: a list of test datasets \n\t#\tconversions: a list of conversion tables \n\t#\n\t# Returns:\n\t#\tA test dataset where test.results are converted to consistent units.\n # Columns are: subject.id, a delta column named with the test name, \n # and test.results. In addition, missing values and duplicate deltas are\n # dropped, and the dataset is sorted by subject.id and then delta.\n\n\t# Get name of test \n\tname<-col.names[i]\n\tprint(name)\n \n\tif (length(conversions[[i]])>1) {\n\t\t # Drop all test results that do not have units found in the \n\t\t # conversion list\n\t\ttest.data[[i]]<-test.data[[i]][test.data[[i]]$test.unit %in% \n\t\t names(conversions[[i]]),]\n \n\t\t# Replace old result by multiplying by conversion factor from table\n\t\ttest.data[[i]]$test.result<-\n\t\t\tunlist(conversions[[i]][test.data[[i]]$test.unit])*\n test.data[[i]]$test.result\n\t}\n\t\n\t# Drop test name and test units\n\ttest.data[[i]]<-test.data[[i]][,!(colnames(test.data[[i]]) %in%\n c(\"test.name\",\"test.unit\"))]\n\n\t# Sort by subject id and delta\n\ttest.data[[i]]<-test.data[[i]][order(test.data[[i]]$subject.id,\n test.data[[i]]$lab.delta),]\n \n\t# Remove records with missing lab.delta, test.result, or subject.id\n\ttest.data[[i]] <- test.data[[i]][!is.na(test.data[[i]]$lab.delta)&\n !is.na(test.data[[i]]$test.result)&\n !is.na(test.data[[i]]$subject.id),]\n\t\n\t# Check for duplicate deltas and delete those rows \n\t# Create combination of subject.ids and deltas\t\n\tid<-paste(as.integer(test.data[[i]]$subject.id),\n\t as.integer(test.data[[i]]$lab.delta),sep=\".\")\n\t\n\t# Check for duplicates in id\n\tdup<-duplicated(id)\n\t\n\t# Keep only nonduplicates - throws away second record with same delta\n\ttest.data[[i]]<-test.data[[i]][!dup,]\n\n\t# Rename test.result column\n\tcolnames(test.data[[i]])[colnames(test.data[[i]])==\"test.result\"]<-name\n\t\n\t# Rename delta column\n\tcolnames(test.data[[i]])[colnames(test.data[[i]])==\"lab.delta\"]<-\n\t\tpaste(name,\"delta\",sep=\".\")\n\n\treturn(test.data[[i]])\n}\n\nGetConvertList<-function(test.names) {\n\t# Given a vector of test names, returns a list of conversion tables for only\n # those tests\n\t#\n\t# Args:\n\t#\ttest.names: a vector of test names of interest\n\t#\n\t# Returns:\n\t#\tA list of conversion tables \n\n\t# Create full list of conversions\n\tconvert.list<-list(\n\t\tBicarbonate=list(\"mmol/l\"=1),\n\t\t`HbA1c (Glycated Hemoglobin)` = list(\"%\"=1, \"V/V\"=100),\n\t\t'Red Blood Cells (RBC)' = list(\"10E12/L\"=1,\"per mm3\"=1E-6,\"10E6/mm3\"=1,\"10E9/L\"=1E-3),\n\t\t#'White Blood Cells (WBC)' = list(\"10E9/L\"=1,\"per mm3\"=1E-4,\"g/L\"=,\"10E3/mm3\"=100), g/L?\n\t\t'Creatine Kinase' = list(\"IU/L\"=1,\"U/L\"=1,\"mIU/L\"=1), # the mIU/L seem like already on same order of magnitude\n\t\tChloride=list(\"mmol/l\"=1),\n\t\tTriglycerides=list(\"mmol/l\"=1,\"g/L\"=1.13),\n\t\tGlucose=list(\"mmol/l\"=1,\"g/L\"=5.55),\n\t\t'Total Cholesterol'=list(\"mmol/l\"=1,\"g/L\"=2.58),\n\t\t#Albumin=list(\"g/l\"=1,\"%\"=?), #% of what? blood serum protein?\n\t\tSodium=list(\"mmol/l\"=1),\n\t\tHemoglobin=list(\"g/l\"=1,\"mmol/L\"=1/.06206,\"mg/L\"=.001),\n\t\tCalcium=list(\"mmol/l\"=1,\"mg/L\"=.025,\"mEq/L\"=0.5),\n\t\t# Monocytes=list(\"%\"=,\"10E9/L\"=,\"per mm3\"=,\"g/L\"=), need WBC count to convert %\n\t\t# Lymphocytes=list(\"%\"=,\"10E9/L\"=,\"per mm3\"=,\"g/L\"=), need WBC count to convert %\n\t\tHematocrit=list(\"%\"=1,\"V/V\"=100,\"1\"=100),\n\t\t# Neutrophils=list(\"%\"=,\"10E9/L\"=,\"per mm3\"=,\"g/L\"=), need WBC count to convert %\n\t\t'Alkaline Phosphatase'=list(\"U/L\"=1,\"IU/L\"=1),\n\t\t'Gamma-glutamyltransferase'=list(\"U/L\"=1,\"IU/L\"=1),\n\t\t# Protein=list(\"g/L\"=1,\"g/dL\"=.1,\"G/L\"=1,\"mEq/L\"=), how to convert mEq/L?\n\t\t# Eosinophils=list(\"%\"=,\"10E9/L\"=,\"per mm3\"=,\"g/L\"=,\"10E3/mm3\"), need count to convert \n\t\t'AST(SGOT)'=list(\"u/l\"=1,\"iu/l\"=1),\n\t\t# Basophils=list(\"%\"=,\"10E9/L\"=,\"per mm3\"=,\"g/L\"=,\"10E3/mm3\"), need count to convert %\n\t\tPhosphorus=list(\"mmol/l\"=1,\"mg/L\"=.0323),\n\t\tCreatinine=list(\"umol/l\"=1,\"mg/L\"=8.84),\n\t\tPotassium=list(\"mmol/l\"=1),\n\t\t'Bilirubin (total)'=list(\"umol/l\"=1,\"mg/L\"=1.71),\n\t\t#Platelets=list(\"10E9/L\"=,\"per mm3\"=,\"g/L\"=,\"10E3/mm3\"=), g/L?\n\t\t'ALT(SGPT)'=list(\"u/l\"=1,\"iu/l\"=1),\n\t\t'Urine Ph'=list(\"pH\"=1) \n\t)\n\t# Keep only those elements that are in test.names\n\treturn(convert.list[test.names])\n}\n\nTestUnitsUsed<-function(data) {\n\tdata<-as.data.frame(data)\n\treturn(unique(data$test.unit))\n}\n\nNumTestSubs<-function(test.names) {\n\tn.test<-length(test.names)\n\t# create matrix to hold test names and how many subjects they have data for\n\t num.test.subjects<-\tdata.frame(test.names=test.names,subjects=rep(-99,length(test.names)),stringsAsFactors=FALSE)\n\t# Count the number of subjects per test\n\t for (i in 1:n.test) {\n\t\tnum.test.subjects[i,2]<-length(unique(data$subject.id[data$value==test.names[i]]))\n\t}\n\tnum.test.subjects<-num.test.subjects[order(num.test.subjects$subjects),]\t\t\n\tplot(num.test.subjects$subjects)\t\n\treturn(num.test.subjects)\n}", "meta": {"hexsha": "8da3e025a31fc4c67168924ec07d9a6628875284", "size": 10426, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/R/get_lab_data.r", "max_stars_repo_name": "ltfang/alsprize4life", "max_stars_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/R/get_lab_data.r", "max_issues_repo_name": "ltfang/alsprize4life", "max_issues_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/R/get_lab_data.r", "max_forks_repo_name": "ltfang/alsprize4life", "max_forks_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.6148148148, "max_line_length": 486, "alphanum_fraction": 0.6788797238, "num_tokens": 3235, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3325293159307168}} {"text": "# Functions to take a gene signature output from featurecounts\n# And a create 1) a DESEQ object\n# and 2) a TPM table\n\n#imports\nlibrary(DESeq2)\nlibrary(tidyverse)\n\nmake_genesig_from_file <- function (file_loc, ...) {\n # extra arguments should contain mappings for changing names\n # will likely only work for featureCounts output\n genesig_df <- read.csv(file_loc, header=T, sep=\"\\t\", comment=\"#\")\n genesig_df <- rename(genesig_df, ...)[complete.cases(gene_sig),] #only things with everything\n\n # drop unused columns\n # still hang onto length, because we can use it in TPM calculations\n # but that would need to be dropped before DESeq\n return(select(genesig_df, -Chr, -Start, -End, -Strand)) \n}\n\n# should end with a mapping of conditions that matches the order of the column names\nmake_meta_data <- function(genesig_df, ...) {\n # Gets column names from genesig_df\n # Drops geneid and length\n sample_names <- colnames(genesig_df[-(1:2)])\n\n return(data.frame(id=sample_names, ...))\n}\n\nmake_DESeq_object <- function(genesig_df, meta_data, design) {\n genesig_df <- select(genesig_df, -Length)\n\n dds <- DESeqDataSetFromMatrix(countData=genesig_df,\n colData=meta_data,\n design=design,\n tidy=T)\n return(DESeq(dds))\n}\n\nmake_results_table <- function(DESeq_object, contrast_vector) {\n # contrast should be in the form of c(column_name, perturbation, control)\n res <- results(DESeq_object, contrast=contrast_vector)\n\n return(res[order(res$padj),])\n}\n\ncalculate_tpm <- function(genesig_df) {\n counts <- genesig_df[-(1:2)]\n counts <- counts/(genesig_df$Length/1000) # normalize by kilobase length\n\n sum_columns = colSums(counts)\n for (i in (1:length(counts))) { # normalize to per million reads\n counts[i] <- (counts[i] / sum_columns[i]) * 1000000\n }\n return(cbind(genesig_df[1:2], counts))\n}", "meta": {"hexsha": "8718277e81eab9cbc0b6b9773ef9808f757dfd91", "size": 1979, "ext": "r", "lang": "R", "max_stars_repo_path": "processing_functions.r", "max_stars_repo_name": "TimNicholsonShaw/RNA-Seq-Analysis", "max_stars_repo_head_hexsha": "8c1b7afc70367dc3370ea3d1990414adac860a26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "processing_functions.r", "max_issues_repo_name": "TimNicholsonShaw/RNA-Seq-Analysis", "max_issues_repo_head_hexsha": "8c1b7afc70367dc3370ea3d1990414adac860a26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "processing_functions.r", "max_forks_repo_name": "TimNicholsonShaw/RNA-Seq-Analysis", "max_forks_repo_head_hexsha": "8c1b7afc70367dc3370ea3d1990414adac860a26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.3392857143, "max_line_length": 97, "alphanum_fraction": 0.6624557858, "num_tokens": 501, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3325293159307167}} {"text": "\nlibrary(qdapTools)\nlibrary(tidyverse)\nlibrary(dplyr)\n\n\n\n\n\n###############################################\n#### D A T A P R E P F U N C T I O N ####\n###############################################\n\n# Function to generate data files for for analysis \ndata_prep <- function(df) {\n \n # create dependent variable y\n df <- df %>% mutate(y = ifelse(hit_module == 'long_cpu_hog_Prod126' & \n hit_severity >= 2, 1, 0))\n \n # Sort by device_id and hit_date - make y an integer\n df <- arrange(df, device_id, hit_date)\n df$y <- as.numeric(df$y)\n\n \n # Reduce table to salient features \n df <- df %>% \n select(hit_date, device_id, hit_module, hit_severity, y)\n \n # Filter on Issue with memory or cpu\n df <- df %>%\n filter(str_detect(hit_module, 'memory|cpu'))\n \n # Merge all hits with same timestamp into one event\n df1 <- df #%>% \n #group_by(hit_date,device_id,hit_severity,y) %>% \n #summarise(hit_module=paste(hit_module, collapse = \":\")) \n \n glimpse(df1)\n \n xy <- mtabulate(strsplit(df1$hit_module, ':'))\n \n glimpse(xy)\n \n #Make sure all Integers are 1\n xy <- xy %>% \n mutate_if(~is.integer(.) && any(. > 0, na.rm = TRUE),\n ~ if_else(.x>0,1L,.x))\n \n #Removing labels/features with less than 500 hits\n xy <- xy[, colSums(xy) > 500]\n \n glimpse(xy)\n \n # Adding back in severity and dependent variable\n xy <- xy %>% add_column(df1$hit_severity) %>% \n add_column(df1$y)\n \n # renaming column names\n xy <- xy %>% rename(hit_severity = 'df1$hit_severity',\n y = 'df1$y')\n \n #Removing 00000 rows\n zero_rows <- xy %>% select(-hit_severity) %>% \n rowSums() > 0\n xy <- xy %>% \n filter(zero_rows)\n \n # Remove hit_severity=0\n #xy <- xy %>% filter(hit_severity!=0)\n \n xy <- xy %>%\n select(y, everything()) # %>% # move the Y variable to the \"front\"\n #rename_at(vars(2:ncol(.)), ~ paste(\"X\", 1:length(.), sep = \"\"))\n \n glimpse(xy)\n \n return(xy)\n}\n\n###################################\n#### M A I N P R O G R A M ####\n###################################\n\n# Import all hits of all devices with dependent issue \n\nrm(list=ls()) \n\nsetwd(\"D:/nils/projects/dissertation\")\ndependent_issue <- 'long_cpu_hog_prod126'\n\nfile_in <- paste('./', dependent_issue, '.csv', sep = '')\nfile_out_train <- './dataset_exp3_train.csv'\nfile_out_test <- './dataset_exp3_test.csv'\n\n# Reading CSV file in \ndf0 <- read_csv(file_in) \n\n\ncu_count <- df0 %>% count(cu_id, sort = TRUE)\ncu_count\n\n# Select train cu events #s\ndf_train <- df0 %>% filter(cu_id == 11 | \n cu_id == 4 | \n cu_id == 15 | \n cu_id == 10 | \n cu_id == 14)\n\n\n# Select test cu events #s\ndf_test <- df0 %>% filter(cu_id == 17 |\n cu_id == 1 | \n cu_id == 7 | \n cu_id == 21 |\n cu_id == 12)\n\n\ndf_test <- data_prep(df_test)\ndf_train <- data_prep(df_train)\n\n\nin_both = intersect(colnames(df_train), colnames(df_test))\ndf_train <- df_train[,in_both]\ndf_test <- df_test[,in_both]\nall_equal(df_train, df_test, ignore_row_order = TRUE)\n\nwrite_csv(df_train, file_out_train)\nwrite_csv(df_test, file_out_test)\ndf_train\ndf_test\n", "meta": {"hexsha": "a7dfe96f2828a0f5a4b645a33031c7aa75cbde6f", "size": 3293, "ext": "r", "lang": "R", "max_stars_repo_path": "code/data_prep-exp3.r", "max_stars_repo_name": "nilspeder/IM906", "max_stars_repo_head_hexsha": "07dcd37715803b0e01bf3ca6c1ee03a2d224a71a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/data_prep-exp3.r", "max_issues_repo_name": "nilspeder/IM906", "max_issues_repo_head_hexsha": "07dcd37715803b0e01bf3ca6c1ee03a2d224a71a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/data_prep-exp3.r", "max_forks_repo_name": "nilspeder/IM906", "max_forks_repo_head_hexsha": "07dcd37715803b0e01bf3ca6c1ee03a2d224a71a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.946969697, "max_line_length": 72, "alphanum_fraction": 0.5463103553, "num_tokens": 909, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3325293080805301}} {"text": "#!/usr/bin/env Rscript\n#\n# Using the methods described in the tutorial\n#\n\nlibrary(data.table)\nlibrary(WGCNA)\n\noptions(stringsAsFactors=FALSE)\n\nargv = commandArgs(trailingOnly=TRUE)\n\ngeno_file = argv[1]\nld_var_file = argv[2]\nblock_size = as.numeric(argv[3])\nn_threads = as.numeric(argv[4])\n\nenableWGCNAThreads(n_threads)\n\n# Get list of variants\nvars_to_use = scan(ld_var_file, what='character')\n\n# Read in genotype data\ngeno_data = fread(geno_file, header=TRUE)\n\n# Remove unwanted variants\ngeno_data = geno_data[geno_data$rs %in% vars_to_use, ]\n\n# Transform into WGCNA format\ngeno_mat = t(geno_data[, -(1:3)])\n\n# Check for excessive missing data and remove samples and variants\n# if necessary\ngsg = goodSamplesGenes(geno_mat, verbose=3)\ncat('No missing data issues?', gsg$allOK, '\\n')\nif(!gsg$allOK) {\n # Optionally, print the gene and sample names that were removed:\n if (sum(!gsg$goodGenes)>0)\n printFlush(paste('Removing genes:', paste(names(geno_mat)[!gsg$goodGenes], collapse=', ')))\n if (sum(!gsg$goodSamples)>0)\n printFlush(paste('Removing samples:', paste(rownames(geno_mat)[!gsg$goodSamples], collapse=', ')))\n # Remove the offending genes and samples from the data:\n geno_mat = geno_mat[gsg$goodSamples, gsg$goodGenes]\n}\n\n# Make a sample dendrogram (the tutorial uses this step to\n# check for outliers)\nsample_dendro = hclust(dist(geno_mat), method='average')\npdf(file='sample_dendrogram.pdf', width=12, height=9)\npar(cex=0.6)\npar(mar=c(0,4,2,0))\nplot(sample_dendro, main='Sample clustering to detect outliers',\n sub='', xlab='', cex.lab=1.5, cex.axis=1.5, cex.main=2)\ndev.off()\n\n# Run the threshold picking function\npowers = c(1:10, seq(12, 20, 2))\nsft = pickSoftThreshold(geno_mat, powerVector=powers,\n verbose=5, blockSize=block_size)\n\n# And make the plots\npdf('soft_threshold_plots.pdf', width=9, height=5)\npar(mfrow=c(1,2))\ncex1 = 0.9\nplot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],\n xlab='Soft Threshold (power)',\n ylab='Scale Free Topology Model Fit,signed R^2',\n type='n',\n main = paste('Scale independence'))\ntext(sft$fitIndices[,1],\n -sign(sft$fitIndices[,3])*sft$fitIndices[,2],\n labels=powers,\n cex=cex1, col='red')\nabline(h=0.90, col='red')\nplot(sft$fitIndices[,1], sft$fitIndices[,5],\n xlab='Soft Threshold (power)',\n ylab='Mean Connectivity', type='n',\n main = paste('Mean connectivity'))\ntext(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col='red')\ndev.off()\n", "meta": {"hexsha": "c5d61752b28260380fc8f86893ca1674c44e7908", "size": 2522, "ext": "r", "lang": "R", "max_stars_repo_path": "pick_soft_threshold_wgcna.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "pick_soft_threshold_wgcna.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "pick_soft_threshold_wgcna.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1358024691, "max_line_length": 106, "alphanum_fraction": 0.6982553529, "num_tokens": 769, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.33238152401711035}} {"text": "#!/usr/bin/env Rscript\n\n# Author: Jesse McNichol \n\ninvisible(library(oce))\n\nargs = commandArgs(trailingOnly = TRUE)\nfile.name = args[1]\n\nd <- read.csv(file.name)\n\ntmp <- split(d, d$latitude)\nnames(tmp) <- paste0('stn', seq_along(tmp))\n\n#Import the stations as a CTD object\nstns <- lapply(tmp, function(x) {\n o <- order(x$depth) # the depths are not always in order\n ctd <-\n as.ctd(\n x$salinity[o],\n x$temperature[o],\n swPressure(x$depth[o]),\n longitude = x$longitude,\n latitude = x$latitude\n )\n ctd <- oceSetData(ctd, 'depth', x$depth[o])\n ctd <-\n oceSetData(ctd, 'eASV_Relative_Abundance', x$Relative_Abundance[o])\n})\n\nsec <- as.section(stns) #Create a section from the CTD data\nsg <-\n sectionGrid(sec, p = seq(20, 200, 5)) #For better interpolation (though be cautious with these plots!)\n\nbasename <-\n sapply(strsplit(file.name, split = '.', fixed = TRUE), function(x)\n (x[1]))\noutname <- paste0(basename, 'eASV-plot-%02d.png')\ninvisible(png(\n outname,\n width = 7,\n height = 7,\n res = 300,\n units = 'in'\n))\n\nabun <-\n unlist(lapply(stns, function(x)\n x[['eASV_Relative_Abundance']]))\n\n#plot(sec, showstations=TRUE, ztype=\"contour\", showBottom=FALSE)\n\n###Dot plot with interpolated temperature up top, only colour proportional to the abundance\npar(mfrow = c(2, 1))\nplot(\n sg,\n which = 'temperature',\n xtype = \"latitude\",\n showstations = TRUE,\n showBottom = FALSE,\n ztype = 'image',\n zcol = oceColorsTemperature,\n cex = 2,\n pch = 16\n)\ncm <- colormap(abun, col = oceColorsViridis)\n#drawPalette(colormap=cm)\nplot(\n sec,\n which = 'eASV_Relative_Abundance',\n xtype = \"latitude\",\n showstations = TRUE,\n grid = TRUE,\n showBottom = FALSE,\n ztype = 'points',\n zcol = oceColorsViridis,\n cex = 2,\n pch = 16\n)\n\n#Change up the sizes\n#cex <- max(log10(abun))/log10(abun) #Normalized sizes\ncex <- abun / max(abun) #Normalized sizes\n\n###T-S diagram\npar(mfrow = c(1, 1))\ncm <- colormap(abun, col = oceColorsViridis)\ndrawPalette(colormap = cm)\nplotTS(\n sec,\n pch = 19,\n col = cm$zcol,\n mar = c(3.5, 3.5, 2, 4),\n cex = 3 * cex\n)\n\n###Dot plot with interpolated temperature up top, color and the size proportional to the abundance\npar(mfrow = c(2, 1))\n\nplot(\n sg,\n which = 'temperature',\n xtype = \"latitude\",\n showstations = TRUE,\n showBottom = FALSE,\n ztype = 'image',\n zcol = oceColorsTemperature,\n cex = 2,\n pch = 16\n)\n\nplot(\n sec,\n which = 'eASV_Relative_Abundance',\n showBottom = FALSE,\n ztype = 'points',\n zcol = oceColorsViridis,\n cex = 0\n) \n\n# setup the plot\npoints(sec[['distance']],\n sec[['pressure']],\n pch = 19,\n cex = 3 * cex,\n col = cm$zcol)\n\n###All interpolated (sketchy!)\npar(mfrow = c(2, 1))\n\nplot(\n sg,\n which = 'temperature',\n xtype = \"latitude\",\n showstations = TRUE,\n showBottom = FALSE,\n ztype = 'image',\n zcol = oceColorsTemperature,\n cex = 2,\n pch = 16\n)\n\nplot(\n sg,\n which = 'eASV_Relative_Abundance',\n xtype = \"latitude\",\n showstations = TRUE,\n grid = TRUE,\n showBottom = FALSE,\n ztype = 'image',\n zcol = oceColorsViridis,\n cex = 2,\n pch = 16\n)\n\ninvisible(dev.off())\n\nprint('Done.')", "meta": {"hexsha": "53ba9b31d43dec3400bd1f7a89d03adcf864b13b", "size": 3144, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/plot.r", "max_stars_repo_name": "hurwitzlab/r-oce", "max_stars_repo_head_hexsha": "decd01566d8ae1fcecdb30cf07ea73f86cad7fd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/plot.r", "max_issues_repo_name": "hurwitzlab/r-oce", "max_issues_repo_head_hexsha": "decd01566d8ae1fcecdb30cf07ea73f86cad7fd0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/plot.r", "max_forks_repo_name": "hurwitzlab/r-oce", "max_forks_repo_head_hexsha": "decd01566d8ae1fcecdb30cf07ea73f86cad7fd0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.8987341772, "max_line_length": 104, "alphanum_fraction": 0.6399491094, "num_tokens": 1041, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.33226025482661975}} {"text": "#' Machine Learning\n#' \n#' TODO\n#' \n#' \\tabular{ll}{ \n#' Package: \\tab pbdML \\cr \n#' Type: \\tab Package \\cr \n#' License: \\tab BSD 2-clause \\cr \n#' LazyLoad: \\tab yes \\cr \n#' } \n#' \n#' This package requires an MPI library (OpenMPI, MPICH2, or LAM/MPI).\n#' \n#' @importFrom pbdMPI allreduce comm.stop comm.print comm.cat comm.all\n#' @importFrom stats runif\n#' @import pbdDMAT\n#' \n#' @useDynLib pbdML R_check_badvals R_check_groupvar R_one_norm R_shrink_op\n#' \n#' @name pbdML-package\n#' @docType package\n#' @author Drew Schmidt \\email{schmidt AT math.utk.edu}, George Ostrouchov, and Wei-Chen Chen.\n#' @references Programming with Big Data in R Website: \\url{http://r-pbd.org/}\n#' @keywords Package\nNULL\n", "meta": {"hexsha": "a7ee186ff41868484ca1f72ad0535a1a466c86f5", "size": 712, "ext": "r", "lang": "R", "max_stars_repo_path": "R/pbdML-package.r", "max_stars_repo_name": "wrathematics/pbdML", "max_stars_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/pbdML-package.r", "max_issues_repo_name": "wrathematics/pbdML", "max_issues_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-09-22T22:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2015-09-22T22:45:14.000Z", "max_forks_repo_path": "R/pbdML-package.r", "max_forks_repo_name": "wrathematics/pbdML", "max_forks_repo_head_hexsha": "cac079480be8622b8ac781def5f81fe9932614bb", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.3846153846, "max_line_length": 94, "alphanum_fraction": 0.6769662921, "num_tokens": 227, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3322326462728403}} {"text": "#===============================================================\n# Penney's Game Task from Rosetta Code Wiki\n# R implementation\n#===============================================================\n\npenneysgame <- function() {\n\n #---------------------------------------------------------------\n # Who goes first?\n #---------------------------------------------------------------\n\n first <- sample(c(\"PC\", \"Human\"), 1)\n\n #---------------------------------------------------------------\n # Determine the sequences\n #---------------------------------------------------------------\n\n if (first == \"PC\") { # PC goes first\n\n pc.seq <- sample(c(\"H\", \"T\"), 3, replace = TRUE)\n cat(paste(\"\\nI choose first and will win on first seeing\", paste(pc.seq, collapse = \"\"), \"in the list of tosses.\\n\\n\"))\n human.seq <- readline(\"What sequence of three Heads/Tails will you win with: \")\n human.seq <- unlist(strsplit(human.seq, \"\"))\n\n } else if (first == \"Human\") { # Player goest first\n\n cat(paste(\"\\nYou can choose your winning sequence first.\\n\\n\"))\n human.seq <- readline(\"What sequence of three Heads/Tails will you win with: \")\n human.seq <- unlist(strsplit(human.seq, \"\")) # Split the string into characters\n pc.seq <- c(human.seq[2], human.seq[1:2]) # Append second element at the start\n pc.seq[1] <- ifelse(pc.seq[1] == \"H\", \"T\", \"H\") # Switch first element to get the optimal guess\n cat(paste(\"\\nI win on first seeing\", paste(pc.seq, collapse = \"\"), \"in the list of tosses.\\n\"))\n\n }\n\n #---------------------------------------------------------------\n # Start throwing the coin\n #---------------------------------------------------------------\n\n cat(\"\\nThrowing:\\n\")\n\n ran.seq <- NULL\n\n while(TRUE) {\n\n ran.seq <- c(ran.seq, sample(c(\"H\", \"T\"), 1)) # Add a new coin throw to the vector of throws\n\n cat(\"\\n\", paste(ran.seq, sep = \"\", collapse = \"\")) # Print the sequence thrown so far\n\n if (length(ran.seq) >= 3 && all(tail(ran.seq, 3) == pc.seq)) {\n cat(\"\\n\\nI win!\\n\")\n break\n }\n\n if (length(ran.seq) >= 3 && all(tail(ran.seq, 3) == human.seq)) {\n cat(\"\\n\\nYou win!\\n\")\n break\n }\n\n Sys.sleep(0.5) # Pause for 0.5 seconds\n\n }\n}\n", "meta": {"hexsha": "c013312fe5612771231b4f3107d8554daf21a8eb", "size": 2203, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Penneys-game/R/penneys-game.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Penneys-game/R/penneys-game.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Penneys-game/R/penneys-game.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 34.421875, "max_line_length": 123, "alphanum_fraction": 0.4543803904, "num_tokens": 536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3322326385035478}} {"text": "#Jenny Smith\n\n#May 8, 2017 \n\n#Purpose: Create a PCA, and MDS ordination plots given expression data and genes of interest. \n\n\nplotPCoA <- function(expnData,phenovector, title=\"\",colorCodes=NULL, geneList=NULL, size=3){\n #expnData is normalized counts, typically log2 scale. \n \n #factor is the name of the factor column \n suppressPackageStartupMessages(library(vegan))\n suppressPackageStartupMessages(library(ggplot2))\n suppressPackageStartupMessages(library(dplyr))\n \n #Ensure correct order of patients in both datasets\n expnData <- expnData[ ,intersect(names(phenovector), colnames(expnData))] \n phenovector <- phenovector[intersect(names(phenovector),colnames(expnData))]\n \n if (! is.null(geneList)){\n expnData <- t(expnData[geneList, ])\n }else{\n expnData <- t(expnData) #note: must remove all zero count genes or will fail on an error\n }\n \n PCoA <- capscale(expnData ~ 1, distance = \"bray\", add=TRUE)\n scores <- data.frame(scores(PCoA, display=\"sites\"), \n group=phenovector) %>%\n dplyr::arrange(desc(group))\n \n \n p <- ggplot(scores, aes(x=MDS1, MDS2)) +\n geom_point(aes(color=scores[,\"group\"]), size=size, alpha=0.75) +\n theme_numX +\n labs(title=title) \n \n if(!is.null(colorCodes)){\n p <- p + \n scale_color_manual(values=colorCodes)\n }\n #If wanted to add ellipses and convex hulls use the code below\n \n # k <- 4\n # clust <- kmeans(scores[,1:2], k)\n # groups <- as.factor(clust$cluster)\n # \n # scores <- scores %>%\n # mutate(group=groups,\n # Status=clinData[rownames(expnData),factor])\n # head(scores)\n # mds.plot <- ggscatter(scores, x=\"MDS1\", y=\"MDS2\", \n # size=3.5, \n # color=\"Status\",\n # group=\"group\",\n # palette = \"Set1\",\n # ellipse =FALSE,\n # ellipse.type = \"norm\",\n # repel = TRUE)\n # #https://stats.stackexchange.com/questions/22805/how-to-draw-neat-polygons-around-scatterplot-regions-in-ggplot2/22855\n # find_hull <- function(df) df[chull(df$MDS1, df$MDS2), ]\n # hulls <- ddply(scores,\"group\",find_hull )\n # \n # #add hulls or ellipses\n # mds.plot <- mds.plot + \n # # stat_ellipse(data = scores,\n # # mapping = aes(group=group, fill=group), geom = \"polygon\", alpha=0.15, color=\"black\", level=0.99)\n # geom_polygon(data=hulls, aes(fill=group), alpha=0.15) +\n # scale_fill_manual(values = rainbow_hcl(4)) + \n # theme(text = element_text(size=20))\n \n # aov <- aov(scores$MDS1 ~ df[,factor]) #NOTE: This is only valid for balanced experimental designs! Equal # of obs in each factor level.\n\n list <- list(PCoA,scores, p)\n names(list) <- c(\"PCoA\",\"scores\",\"plot\")\n \n return(list)\n}\n\n\n#from https://github.com/mikelove/DESeq2/blob/master/R/plots.R\n#Want to return the whole scores matrix so can examine 3d pca plots. \nplotPCA.DESeq.mod <- function(object, intgroup=\"condition\", ntop=500, returnData=FALSE, PC3=FALSE)\n{\n library(matrixStats)\n # calculate the variance for each gene\n rv <- rowVars(assay(object))\n \n # select the ntop genes by variance\n select <- order(rv, decreasing=TRUE)[seq_len(min(ntop, length(rv)))]\n \n # perform a PCA on the data in assay(x) for the selected genes\n pca <- prcomp(t(assay(object)[select,]))\n \n # the contribution to the total variance for each component\n percentVar <- pca$sdev^2 / sum( pca$sdev^2 )\n \n if (!all(intgroup %in% names(colData(object)))) {\n stop(\"the argument 'intgroup' should specify columns of colData(dds)\")\n }\n \n intgroup.df <- as.data.frame(colData(object)[, intgroup, drop=FALSE])\n \n # add the intgroup factors together to create a new grouping factor\n group <- if (length(intgroup) > 1) {\n factor(apply( intgroup.df, 1, paste, collapse=\":\"))\n } else {\n colData(object)[[intgroup]]\n }\n \n # assembly the data for the plot - first 10 PCs\n # d <- data.frame(PC1=pca$x[,1], PC2=pca$x[,2], group=group, intgroup.df, name=colnames(object))\n n <- min(10, ncol(as.data.frame(pca$x)))\n d <- data.frame(as.data.frame(pca$x)[,1:n], group=group, intgroup.df, name=colnames(object))\n \n if (returnData) {\n attr(d, \"percentVar\") <- percentVar[1:10]\n rot <- pca$rotation[,1:10] #for first 10 PCs\n dat <- list(\"scores\"=d,\"rotation\"=rot)\n return(dat)\n }\n \n if(PC3){\n ggplot(data=d, aes_string(x=\"PC1\", y=\"PC3\", color=\"group\")) + \n geom_point(size=3, alpha=0.75) + \n xlab(paste0(\"PC1: \",round(percentVar[1] * 100),\"% variance\")) +\n ylab(paste0(\"PC3: \",round(percentVar[3] * 100),\"% variance\"))\n \n }else{\n ggplot(data=d, aes_string(x=\"PC1\", y=\"PC2\", color=\"group\")) + \n geom_point(size=3, alpha=0.75) + \n xlab(paste0(\"PC1: \",round(percentVar[1] * 100),\"% variance\")) +\n ylab(paste0(\"PC2: \",round(percentVar[2] * 100),\"% variance\"))\n # coord_fixed()\n }\n}\n\n\n\n\n#Updated on 6/9/17 to use variance stabilized transformed data as input (not center scaled log2, like in princomp)\nPCA <- function(expnData,phenovector,title=\"\",round=TRUE,colorCodes=NULL,\n ntop=500,PC3=FALSE, GOI=NULL){\n \n suppressPackageStartupMessages(library(DESeq2))\n library(ggplot2)\n #expnData is the raw counts (not normalized) has patient IDs as colnames and genes as rownames. \n \n # countData <- expnData[,match(names(phenovector), colnames(expnData))]\n samples <- intersect(names(phenovector), colnames(expnData))\n countData <- expnData[,samples]\n phenovector <- phenovector[samples]\n \n countData <- round(countData, digits = 0)\n colData <- as.data.frame(phenovector)\n \n if(length(unique(phenovector)) < 2){\n dds <- DESeqDataSetFromMatrix(countData = countData,\n colData = colData,\n design = ~ 1)\n }else{\n dds <- DESeqDataSetFromMatrix(countData = countData,\n colData = colData,\n design = ~ phenovector)\n }\n \n #create deseq2 dataset and perform variance stabilized transformation for sample to sample comparisons\n dds <- dds[ rowSums(counts(dds)) > 10, ]\n varianceStab <- vst(dds, blind = TRUE)\n \n #if given a list of genes of interest\n if (! is.null(GOI)){\n GOI <- intersect(GOI, rownames(assay(varianceStab)))\n }else{\n GOI <- 1:nrow(varianceStab)\n }\n \n # Create a PCA plot \n plot.1 <- plotPCA.DESeq.mod(varianceStab[GOI,], intgroup = \"phenovector\", ntop = ntop, PC3=FALSE) + \n theme_numX + \n labs(title=title) \n \n if(PC3){\n plot.2 <- plotPCA.DESeq.mod(varianceStab[GOI,], intgroup = \"phenovector\", ntop = ntop, PC3=TRUE) + \n theme_numX + \n labs(title=title) \n }\n\n #change points to custom colors if provided \n if (!is.null(colorCodes)){\n plot.1 <- plot.1 + \n scale_color_manual(values=colorCodes)\n \n if(exists(\"plot.2\")){\n plot.2 <- plot.2 + \n scale_color_manual(values=colorCodes)\n }\n \n }\n \n #PCA data frame with the wieghts/loadings and eigen vectors\n pca.dat <- plotPCA.DESeq.mod(varianceStab[GOI,], intgroup = \"phenovector\", ntop = ntop,\n returnData=TRUE)\n \n #Final Results object\n res <- list(dds, varianceStab,pca.dat$scores,pca.dat$rotation, plot.1)\n names(res) <- c(\"dds\", \"vst\",\"pca_data\",\"pca_loadings\", \"pca_plot\")\n \n if(is.character(GOI)){\n res[[\"GOI\"]] <- GOI\n }\n \n if(PC3){\n res[[\"pca_plot2\"]] <- plot.2\n }\n \n return(res)\n}\n\n\npca_custom <- function(expnData,CDE,fillCol, colorCol, colorCode=NULL, PC3=FALSE,\n single.col.outline=FALSE, toHighlight=NULL, ellipse=FALSE){\n library(tibble)\n library(dplyr)\n #expn data is log2, normalized counts\n #CDE has patients as rownames. \n #fillCol == character string of column name for fill colors \n #colorCol == character string of column name for border colors\n #colorCode is an option named vector of colors. If specifying color and fill manually, create a list with length 2, with names c(\"fill\",\"color\")\n #single.col.outline is T/F for wether to susbet to dataframe for the border colors. \n #toHighlight is a character vector for if single.col.outline == TRUE. Gives the factor to highlight with borders from the colorColumn. \n #ellipse is T/F for an ellipse based on fillCol column\n expnData <- expnData[,intersect(rownames(CDE),colnames(expnData))]\n \n # print(dim(expnData))\n pca <- prcomp(t(expnData), scale=TRUE)\n summ <- summary(pca)\n \n scores <- as.data.frame(pca$x) %>%\n rownames_to_column(\"USI\") %>%\n inner_join(., dplyr::select(CDE,USI=matches(\"USI$\"), everything()), by=\"USI\") %>%\n dplyr::select(USI,fillCol, colorCol, everything())\n \n #Plot function for PC1 and either PC2 or anyother\n pca.plot_function <- function(scores,PC){\n \n idx <- as.numeric(gsub(\"[A-Za-z]{2}\",\"\", PC))\n \n \n pca.plot <- ggplot(scores, aes_string(x=\"PC1\", y=PC)) +\n labs(x=paste(\"PC1: \", round(summ$importance[2,1], digits=3)*100, \"% variance\"),\n y=paste(paste0(PC,\": \"), round(summ$importance[2,idx], digits=3)*100, \"% variance\")) +\n theme_numX +\n theme(legend.text = element_text(size=14),\n legend.title = element_text(size=16))\n \n if(single.col.outline){\n pca.plot <- pca.plot + \n geom_point(size=5,stroke=0.2, alpha=1,shape=21,color=\"white\",\n aes_string(fill=fillCol)) +\n geom_point(data=subset(scores, scores[,colorCol] == toHighlight),\n aes_string(x=\"PC1\", y=PC, color=colorCol),\n size=4, stroke=1, alpha=1,shape=21)\n \n }else{\n pca.plot <- pca.plot + \n geom_point(size=5, stroke=2, alpha=0.85,shape=21,\n aes_string(fill=fillCol, color=colorCol))\n }\n \n if(!is.null(colorCode)){\n \n if(is.list(colorCode)){\n pca.plot <- pca.plot + \n scale_fill_manual(values=colorCode[[\"fill\"]]) + \n scale_color_manual(values=colorCode[[\"color\"]])\n }else{\n pca.plot <- pca.plot + \n scale_fill_manual(values=colorCode) \n }\n }\n \n if(ellipse){\n pca.plot <- pca.plot +\n stat_ellipse(data=scores, type=\"norm\",\n aes_string(x=\"PC1\", y=PC, fill=fillCol),\n geom=\"polygon\", alpha=0.1)\n }\n \n return(pca.plot)\n }\n \n #PC1 and PC2 plot\n pc1.pc2 <- pca.plot_function(scores=scores,PC=\"PC2\")\n \n #PC1 and PC3 plot\n if(PC3){\n pc1.pc3 <- pca.plot_function(scores=scores, PC=\"PC3\") \n \n }\n \n\n res <- list(\"pca\"=pca,\"scores\"=scores,\"plot.1\"=pc1.pc2)\n \n if(PC3){\n res[[\"plot.2\"]] <- pc1.pc3\n }\n \n return(res)\n \n}\n\n\n\n\n\n\n", "meta": {"hexsha": "e28561fca3eb2a3a83609b2eae6c59dbf8514d45", "size": 10642, "ext": "r", "lang": "R", "max_stars_repo_path": "R/clusterAnalysis_Function.r", "max_stars_repo_name": "Meshinchi-Lab/DeGSEA", "max_stars_repo_head_hexsha": "09a95f006114b78181cf6b5c5a62df5f5ac705d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/clusterAnalysis_Function.r", "max_issues_repo_name": "Meshinchi-Lab/DeGSEA", "max_issues_repo_head_hexsha": "09a95f006114b78181cf6b5c5a62df5f5ac705d7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/clusterAnalysis_Function.r", "max_forks_repo_name": "Meshinchi-Lab/DeGSEA", "max_forks_repo_head_hexsha": "09a95f006114b78181cf6b5c5a62df5f5ac705d7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.570977918, "max_line_length": 147, "alphanum_fraction": 0.6185867318, "num_tokens": 3102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3321753021572076}} {"text": "library(foreach)\nlibrary(doSNOW)\n\nr=3\nktot=c(3,3,3,3,3,3,3,3)\nsource(\"codeKfixedLOG.r\")\nsource(\"codeKvariable.r\")\nkmax=4\n\nn=c(100,200)\nTi=c(4,6,8)\npers=10 \nsepp=c(2.5,4)\n\nex=expand.grid(n,Ti,sepp)\nnex=nrow(ex)\nfor(j in 1:nex) {\n n=ex[j,1]\n Ti=ex[j,2]\n sepp=ex[j,3]\n k=ktot[1:Ti]\nsource(\"doSimu.r\")\n}\n\n", "meta": {"hexsha": "1db841d577b21e25f790ed465bb5132c6962c791", "size": 313, "ext": "r", "lang": "R", "max_stars_repo_path": "simulationsPers10/simu4.r", "max_stars_repo_name": "afarcome/LMrectangular", "max_stars_repo_head_hexsha": "a726ff32158e975d45e11d57c3a0a5a37ece0274", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-05-15T13:47:56.000Z", "max_stars_repo_stars_event_max_datetime": "2018-05-15T13:47:56.000Z", "max_issues_repo_path": "simulationsPers10/simu4.r", "max_issues_repo_name": "afarcome/LMrectangular", "max_issues_repo_head_hexsha": "a726ff32158e975d45e11d57c3a0a5a37ece0274", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulationsPers10/simu4.r", "max_forks_repo_name": "afarcome/LMrectangular", "max_forks_repo_head_hexsha": "a726ff32158e975d45e11d57c3a0a5a37ece0274", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.52, "max_line_length": 25, "alphanum_fraction": 0.6166134185, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.33217529511315275}} {"text": "sampleFromAGroup<-function(x, y, nsize, samp_options=list(replacement=FALSE, sample_all_available=FALSE, sample_all_available_warning = TRUE)){\n# 2015-2016 WP2 FishPI\n\t\n\t# Adapted by Nuno Prista from great original work of Liz Clarke, Marine Scotland.\n\n\t# 2016-10-17 Nuno Prista: added option sample all when sampling without replacement [see comments]\n\t# 2016-10-17 Nuno Prista: added sample2 [correction of behaviour of \"sample\" when only 1 element is being sampled]\n\t# 2016-10-17 Nuno Prista: improved identification of samples in result\n\t# 2016-10-18 Nuno Prista: improved code at samp_options level [made independent from position in list - > now easier to add options]\n\t# 2016-10-18 Nuno Prista: added suppress.warnings to samp_options \n\t# 2018-05-29 Nuno Prista: adapted so that one of the group names can be NA (useful in, e.g., stratifying by maturity) \n\nnGroup<-length(nsize)\nxSamp<-NULL\nfor (i in 1:nGroup) {\n#print(i)\n\n\tif(!is.na(names(nsize)[i]))\n\t\t{\n\t\tindx<-unique(x[which(y==names(nsize)[i])]) \n\t\t} else {indx <- x [which(is.na(y))] } # nuno 20180529 [handles NAs in stratification]\n # nuno 20161017: condicao para amostrar todos os disponiveis without replacement\n if(samp_options$replacement == FALSE & samp_options$sample_all_available == TRUE & nsize[i]>length(indx)){\n nsize[i] <- length(indx)\n if(samp_options$sample_all_available_warning==TRUE) {print(paste(\"sampling all available in group\",names(nsize)[i]))}\n }\n # nuno 20161017: condicao para existencia de 1 unico elemento a amostrar (corrige comportamento de funcao sample)\n if(length(indx)>1){\n samp<-sample(indx, size=nsize[i], replace = samp_options$replacement)\n } else {samp<-sample2(indx, size=nsize[i], replace = samp_options$replacement)}\n # nuno 20161017: atribui nomes\n\tnames(samp)<-rep(names(nsize)[i], length(samp))\n if(i == 1) {\n xSamp <-samp\n } else {\n xSamp <- c(xSamp, samp)\n }\n }\n return(xSamp)\n}\n", "meta": {"hexsha": "1e926ed83a24bdb2d4310ee38330482df42a66df", "size": 1978, "ext": "r", "lang": "R", "max_stars_repo_path": "000_Funs/func_sampleFromAGroup.r", "max_stars_repo_name": "nmprista/KustMonitorOptim", "max_stars_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "000_Funs/func_sampleFromAGroup.r", "max_issues_repo_name": "nmprista/KustMonitorOptim", "max_issues_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "000_Funs/func_sampleFromAGroup.r", "max_forks_repo_name": "nmprista/KustMonitorOptim", "max_forks_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.243902439, "max_line_length": 143, "alphanum_fraction": 0.7057633974, "num_tokens": 580, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.33217529511315275}} {"text": "# Fonction R \u00e0 traduire en Python\nget_pca <- function(res.pca, element = c(\"var\", \"ind\")){\n elmt <- match.arg(element)\n if(elmt ==\"var\") get_pca_var(res.pca)\n else if(elmt == \"ind\") get_pca_ind(res.pca)\n}\n\nget_pca_ind<-function(res.pca, ...){\n \n # FactoMineR package\n if(inherits(res.pca, c('PCA'))) ind <- res.pca$ind\n \n # ade4 package\n else if(inherits(res.pca, 'pca') & inherits(res.pca, 'dudi')){ \n ind.coord <- res.pca$li\n # get the original data\n data <- res.pca$tab\n data <- t(apply(data, 1, function(x){x*res.pca$norm} ))\n data <- t(apply(data, 1, function(x){x+res.pca$cent}))\n ind <- .get_pca_ind_results(ind.coord, data, res.pca$eig,\n res.pca$cent, res.pca$norm)\n }\n \n # stats package\n else if(inherits(res.pca, 'princomp')){ \n ind.coord <- res.pca$scores\n data <- .prcomp_reconst(res.pca)\n ind <- .get_pca_ind_results(ind.coord, data, res.pca$sdev^2,\n res.pca$center, res.pca$scale)\n \n }\n else if(inherits(res.pca, 'prcomp')){\n ind.coord <- res.pca$x\n data <- .prcomp_reconst(res.pca)\n ind <- .get_pca_ind_results(ind.coord, data, res.pca$sdev^2,\n res.pca$center, res.pca$scale)\n }\n # ExPosition package\n else if (inherits(res.pca, \"expoOutput\") & inherits(res.pca$ExPosition.Data,'epPCA')){\n res <- res.pca$ExPosition.Data\n ind <- list(coord = res$fi, cos2 = res$ri, contrib = res$ci*100)\n }\n else stop(\"An object of class : \", class(res.pca), \n \" can't be handled by the function get_pca_ind()\")\n \n class(ind)<-c(\"factoextra\", \"pca_ind\")\n \n ind\n}\n\n\nget_pca_var<-function(res.pca){\n # FactoMineR package\n if(inherits(res.pca, c('PCA'))) var <- res.pca$var\n # ade4 package\n else if(inherits(res.pca, 'pca') & inherits(res.pca, 'dudi')){\n var <- .get_pca_var_results(res.pca$co)\n }\n # stats package\n else if(inherits(res.pca, 'princomp')){ \n # Correlation of variables with the principal component\n var_cor_func <- function(var.loadings, comp.sdev){var.loadings*comp.sdev}\n var.cor <- t(apply(res.pca$loadings, 1, var_cor_func, res.pca$sdev))\n var <- .get_pca_var_results(var.cor)\n }\n else if(inherits(res.pca, 'prcomp')){\n # Correlation of variables with the principal component\n var_cor_func <- function(var.loadings, comp.sdev){var.loadings*comp.sdev}\n var.cor <- t(apply(res.pca$rotation, 1, var_cor_func, res.pca$sdev))\n var <- .get_pca_var_results(var.cor)\n }\n # ExPosition package\n else if (inherits(res.pca, \"expoOutput\") & inherits(res.pca$ExPosition.Data,'epPCA')){\n res <- res.pca$ExPosition.Data\n data_matrix <- res$X\n factor_scores <- res$fi\n var.coord <- var.cor <- stats::cor(res$X, res$fi) # cor(t(data_matrix), factor_scores)\n var.coord <- replace(var.coord, is.na(var.coord), 0)\n var <- list(coord = var.coord, cor = var.coord, cos2 = res$rj, contrib = res$cj*100)\n }\n else stop(\"An object of class : \", class(res.pca), \n \" can't be handled by the function get_pca_var()\")\n class(var)<-c(\"factoextra\", \"pca_var\")\n var\n}\n\n# compute all the results for individuals : coord, cor, cos2, contrib\n# ind.coord : coordinates of variables on the principal component\n# pca.center, pca.scale : numeric vectors corresponding to the pca\n# center and scale respectively\n# data : the orignal data used during the pca analysis\n# eigenvalues : principal component eigenvalues\n.get_pca_ind_results <- function(ind.coord, data, eigenvalues, pca.center, pca.scale ){\n \n eigenvalues <- eigenvalues[1:ncol(ind.coord)]\n \n if(pca.center[1] == FALSE) pca.center <- rep(0, ncol(data))\n if(pca.scale[1] == FALSE) pca.scale <- rep(1, ncol(data))\n \n # Compute the square of the distance between an individual and the\n # center of gravity\n getdistance <- function(ind_row, center, scale){\n return(sum(((ind_row-center)/scale)^2))\n }\n d2 <- apply(data, 1,getdistance, pca.center, pca.scale)\n \n # Compute the cos2\n cos2 <- function(ind.coord, d2){return(ind.coord^2/d2)}\n ind.cos2 <- apply(ind.coord, 2, cos2, d2)\n \n # Individual contributions \n contrib <- function(ind.coord, eigenvalues, n.ind){\n 100*(1/n.ind)*(ind.coord^2/eigenvalues)\n }\n ind.contrib <- t(apply(ind.coord, 1, contrib, eigenvalues, nrow(ind.coord)))\n \n colnames(ind.coord) <- colnames(ind.cos2) <-\n colnames(ind.contrib) <- paste0(\"Dim.\", 1:ncol(ind.coord)) \n \n rnames <- rownames(ind.coord)\n if(is.null(rnames)) rnames <- as.character(1:nrow(ind.coord))\n rownames(ind.coord) <- rownames(ind.cos2) <- rownames(ind.contrib) <- rnames\n \n # Individuals coord, cos2 and contrib\n ind = list(coord = ind.coord, cos2 = ind.cos2, contrib = ind.contrib)\n ind\n}\n\n# compute all the results for variables : coord, cor, cos2, contrib\n# var.coord : coordinates of variables on the principal component\n.get_pca_var_results <- function(var.coord){\n \n var.cor <- var.coord # correlation\n var.cos2 <- var.cor^2 # variable qualities \n \n # variable contributions (in percent)\n # var.cos2*100/total Cos2 of the component\n comp.cos2 <- apply(var.cos2, 2, sum)\n contrib <- function(var.cos2, comp.cos2){var.cos2*100/comp.cos2}\n var.contrib <- t(apply(var.cos2,1, contrib, comp.cos2))\n \n colnames(var.coord) <- colnames(var.cor) <- colnames(var.cos2) <-\n colnames(var.contrib) <- paste0(\"Dim.\", 1:ncol(var.coord)) \n \n # Variable coord, cor, cos2 and contrib\n list(coord = var.coord, cor = var.cor, cos2 = var.cos2, contrib = var.contrib)\n}", "meta": {"hexsha": "d8b52d248bbd6487650eb4ba6cfdd32299835206", "size": 5490, "ext": "r", "lang": "R", "max_stars_repo_path": "vizfactor/get_pca_R.r", "max_stars_repo_name": "lucayapi/vizfactor", "max_stars_repo_head_hexsha": "a9d0505a8fbf5c05acc524bac06f68accc0db222", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizfactor/get_pca_R.r", "max_issues_repo_name": "lucayapi/vizfactor", "max_issues_repo_head_hexsha": "a9d0505a8fbf5c05acc524bac06f68accc0db222", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vizfactor/get_pca_R.r", "max_forks_repo_name": "lucayapi/vizfactor", "max_forks_repo_head_hexsha": "a9d0505a8fbf5c05acc524bac06f68accc0db222", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3469387755, "max_line_length": 90, "alphanum_fraction": 0.6546448087, "num_tokens": 1641, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.33217529511315264}} {"text": "library(tidyverse)\nlibrary(osrm)\nlibrary(sf)\noptions(osrm.server = 'http://0.0.0.0:5000/')\n\n\n#tabela RDS com localiza\u00e7\u00e3o dos munic\u00edpios (source)\nmuni <- readRDS(\"./02_ dados/01_ IBGE/dados_modificados/locus_municipios.rds\") %>% \n select(CD_GEOCODM, geometry)\n\n#tabela RDS com localiza\u00e7\u00e3o das \u00e1reas de pondera\u00e7\u00e3o (destiny)\narea <- readRDS(\"./02_ dados/03_ analise/03_01_areasPond&unidade.rds\") %>% select(Cod_Area_Pond, geometry)\nfor(i in 1:nrow(area)){\n cent <- st_centroid(area$geometry[i])\n area$geometry[i] <- cent[[1]]\n}\nrm(i)\nst_crs(area) <- st_crs(muni)\n\n#Cruzamento de informa\u00e7\u00f5es\nl_Osm <- list();\n\nfor(i in 1:279){\n i_st <- i * 20 - 19\n i_en <- ifelse(i_st + 19 > 5565, 5565, i_st + 19)\n \n srcMuni <- muni %>% slice(i_st:i_en)\n outputMuni <- osrmTable(src=srcMuni, dst=area, measure= c('duration', 'distance')) #duration in minutes; distance in meters \n cat(\"Cruzamento n\u00famero \", i, \"/279 realizado. \\n\", sep=\"\")\n l_Osm <- c(l_Osm, list(outputMuni))\n}\n\n\nsaveRDS(l_Osm, '1_tempo_municipios_areas.rds')\n", "meta": {"hexsha": "0cacde33ec984512e447ea4d401bf613eb45271d", "size": 1027, "ext": "r", "lang": "R", "max_stars_repo_path": "02_ dados/03_ analise/1_3_tempo_entre_areas_ponderacao_municipios.r", "max_stars_repo_name": "matth3us/tccENAP", "max_stars_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "02_ dados/03_ analise/1_3_tempo_entre_areas_ponderacao_municipios.r", "max_issues_repo_name": "matth3us/tccENAP", "max_issues_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2019-03-21T13:00:25.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-14T17:05:38.000Z", "max_forks_repo_path": "02_ dados/03_ analise/1_3_tempo_entre_areas_ponderacao_municipios.r", "max_forks_repo_name": "matth3us/tccENAP", "max_forks_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.3428571429, "max_line_length": 126, "alphanum_fraction": 0.6874391431, "num_tokens": 357, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.33217529511315264}} {"text": "args <- commandArgs(trailingOnly = TRUE)\ntargetExpressionFile <- toString(args[1])\nregulatorExpressionFile <- toString(args[2])\nallowedMatrixFile <- toString(args[3])\nperturbationMatrixFile <- toString(args[4])\ndifferentialExpressionMatrixFile <- toString(args[5])\nmicroarrayFlag <- as.integer(args[6])\nnonGlobalShrinkageFlag <- as.integer(args[7])\nlassoAdjMtrFileName <- toString(args[8])\ncombinedAdjMtrFileName <- toString(args[9])\noutputDirectory <- toString(args[10])\ncombinedAdjLstFileName <- toString(args[11])\nregulatorGeneNamesFileName <- toString(args[12])\ntargetGeneNamesFileName <- toString(args[13])\n\nsource(\"run_netprophet_parallel.r\")\nlibrary(Rmpi)\nmpi.bcast.Robj2slave(targetExpressionFile)\nmpi.bcast.Robj2slave(regulatorExpressionFile)\nmpi.bcast.Robj2slave(allowedMatrixFile)\nmpi.bcast.Robj2slave(perturbationMatrixFile)\nmpi.bcast.Robj2slave(microarrayFlag)\nmpi.bcast.Robj2slave(outputDirectory)\n\nmpi.remote.exec(source(\"run_netprophet_parallel_single_process.r\"))\nmpi.remote.exec(reportid())\nuniform.solution <- lars.multi.optimize.parallel()\n\nlasso_component <- uniform.solution[[1]]\nwrite.table(lasso_component,file.path(outputDirectory,lassoAdjMtrFileName),row.names=FALSE,col.names=FALSE,quote=FALSE)\n#save(solution,file=\"solution\")\n\nde_component <- as.matrix(read.table(differentialExpressionMatrixFile))\n\n## Perform model averaging to get final NetProphet Predictions\nsource(\"combine_models.r\")\n\n# if(length(args) == 13 & file.exists(regulatorGeneNamesFileName) & file.exists(targetGeneNamesFileName)){\n# source(\"make_adjacency_list.r\")\n# }\n", "meta": {"hexsha": "b38ddfa07438f508c048e5401e2c3a0cf2de941f", "size": 1566, "ext": "r", "lang": "R", "max_stars_repo_path": "SRC/NetProphet1/run_netprophet_parallel_init.r", "max_stars_repo_name": "ygidtu/NetProphet_2.0", "max_stars_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SRC/NetProphet1/run_netprophet_parallel_init.r", "max_issues_repo_name": "ygidtu/NetProphet_2.0", "max_issues_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SRC/NetProphet1/run_netprophet_parallel_init.r", "max_forks_repo_name": "ygidtu/NetProphet_2.0", "max_forks_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1951219512, "max_line_length": 119, "alphanum_fraction": 0.80651341, "num_tokens": 373, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.33217528806909774}} {"text": "#' Parse a Chase bank statement (saved in a PDF format) into a tidyverse-friendly dataframe \n#'\n#' @param file_path A file path which indicates a PDF file that contains Chase bank statement. \n#' @param report_year The year in which the bank statement was reported. The data type of this argument should be numeric (e.g., 2020). \n#' \n#' @return A dataframe with eight columns (\"Date, \"Description\", \"Amount\", \"Balance\", \"Card\", \"Venmo\", \"Withdraw\", \"Deposit\")\n#' @import dplyr\n#' @importFrom tibble enframe \n#' @importFrom magrittr \"%>%\"\n#' @importFrom tabulizer extract_tables\n#' @import stringr \n#' @import purrr\n#' @importFrom glue glue \n#' @import utils \n#' @export\n#' \n\n## ---------------------------------------------------------------------------------------------------------\n\nparse_check_statement <- function(file_path, report_year){\n\n# Import PDF file and extract table \nif (file.exists(file_path)) {\n \n check_statement <- extract_tables(file = file_path)\n \n } else {stop(\"File path is wrong.\")}\n \nif (length(check_statement) == 0) {stop(\"Something's wrong with extracting a table from the PDF file.\")}\n\n## ---------------------------------------------------------------------------------------------------------\n# Turn the list of matrices into a dataframe \ndf <- map_dfr(check_statement, data.frame)\n\n# Remove the first three rows \ndf <- df %>% filter(!str_detect(X1, \"TRANSACTION|transaction|DESCRIPTION|Beginning\"))\n\n# Create date column \ndf <- df %>% \n mutate(Date = map(X1, ~str_extract(., \"^.{1,5}\")) %>%\n map_dfr(enframe) %>%\n pull(value)\n ) \n \n# Rename columns \n\nif (ncol(df) == 4) {\n \n names(df) <- c(\"Description\", \"Amount\", \"Balance\", \"Date\")\n \n} else {\n \n # In some cases, unnecessary X2 column is created during the table extracting process. \n \n df <- df[,-2]\n \n names(df) <- c(\"Description\", \"Amount\", \"Balance\", \"Date\")\n \n}\n\n# Add columns \ndf$Card <- if_else(str_detect(df$Description, \"Card\") == TRUE, 1, 0)\ndf$Venmo <- if_else(str_detect(df$Description, \"Venmo\") == TRUE, 1, 0)\ndf$Withdraw <- if_else(str_detect(df$Amount, \"-\") == TRUE, 1, 0) \ndf$Deposit <- if_else(str_detect(df$Amount, \"-\") != TRUE, 1, 0) \n\n\n## ---------------------------------------------------------------------------------------------------------\n# Reorder columns; Date comes first \ndf <- df %>% relocate(Date)\n\n# Filter Date column by a special character \ndf$Date <- if_else(str_detect(df$Date, \"/\") == TRUE, df$Date, \"Not date\")\n\n# Filter Date column by data type \ndf$Date <- map(df$Date, ~str_extract(., \"[:alpha:]\")) %>%\n map_dfr(enframe) %>%\n mutate(value = if_else(is.na(value), df$Date, \"Not date\")) %>%\n pull(value)\n\n\n## ---------------------------------------------------------------------------------------------------------\n# Clean Description column \ndf$Description <- if_else(df$Date != \"Not date\", str_replace_all(df$Description, df$Date, \"\"), df$Description)\n\n# Filter not-date rows \ndf <- df %>% filter(Date != \"Not date\")\n\n# Remove special characters in Amount and Balance columns \ndf <- df %>%\n mutate(\n Amount = str_replace_all(Amount, \",\", \"\") %>% as.numeric(), \n Balance = str_replace_all(Balance, \",\", \"\") %>% as.numeric()\n )\n\n# Reformat Date variable \n\ndf$Date <- as.Date(glue(\"{report_year}/{df$Date}\"))\n\nreturn(df)\n\n}", "meta": {"hexsha": "e02a500dc10de01130949bb372c08458cbf0c7e5", "size": 3347, "ext": "r", "lang": "R", "max_stars_repo_path": "R/parse_check_statement.r", "max_stars_repo_name": "jaeyk/TidyChaseBankStatements", "max_stars_repo_head_hexsha": "42c8d3321f5fa72007ddf7c0c880760f12ada9ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/parse_check_statement.r", "max_issues_repo_name": "jaeyk/TidyChaseBankStatements", "max_issues_repo_head_hexsha": "42c8d3321f5fa72007ddf7c0c880760f12ada9ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/parse_check_statement.r", "max_forks_repo_name": "jaeyk/TidyChaseBankStatements", "max_forks_repo_head_hexsha": "42c8d3321f5fa72007ddf7c0c880760f12ada9ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.8137254902, "max_line_length": 136, "alphanum_fraction": 0.5616970421, "num_tokens": 804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044135, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.33209055648922714}} {"text": "\n# -----------------------------------\n# Leitura, Silvio.\n\n\n\nlibrary(stringr)\nrequire(rvest) # importa a biblioteca rvest\n\ncidades <- read_html(\"http://cidades.ibge.gov.br/download/mapa_e_municipios.php?lang=&uf=rn\") %>% html_table(fill=TRUE)\n\nbase<-data.frame(cidades[[1]][c(1,2,4)]) # Selecionando os dados de interesse de interesse\nnames(base)<-c(\"cidade\",\"cep\",\"pop2010\") # Nomeando as colunas\nrow.names(base)<-1:168\n# Enumerando as linhas\nbase$pop2010<-str_replace_all(base$pop2010,\"\\\\.\",\"\") # Retira os pontos\nbase$pop2010<-as.numeric(base$pop2010) # Converte os caracteres em num\u00e9ricos\nbase[is.na(base)] <- 0 # Substitui valores Nas por zeros\nhead(base) # lista as primeiras linhas do dataframe base\n\n\n\n\nlibrary(sp)\n\nsetwd(\"~/Documentos/Maps/\") # Define o diret\u00f3rio de trabalho\nbr <- readRDS(\"BRA_adm2.rds\") # Importa os pol\u00edgonos do arquivo no diret\u00f3rio de trabalho \nplot(br)\n\nhead(br) # as primeiras linhas mostram q existe um campo chamado $NAME_1 com os nomes dos estados\n\nrn = (br[br$NAME_1==\"Rio Grande do Norte\",]) # Filtrando apenas os munic\u00edpios do RN\nplot(rn)\nplot(rn[rn$NAME_2==\"Natal\",], add=T, col=\"red\")\n\nrn <- merge(x=rn, y=base, by.x=\"NAME_2\", by.y=\"cidade\") # Faz um merge dos dataframes\n\n# Criando os intervalos e classificando\ncol_no = as.factor(as.numeric(cut(rn$pop2010.x, breaks =\n c(0,3000,10000,100000,300000,500000,80000,1000000), labels=c(\"<3k\", \"3k-10k\", \"10k-\n100k\",\"100k-300k\", \"300k-500k\", \"500k-800k\", \">800k\"), right= FALSE)))\n# Nomeando os intervalos \u2013 ir\u00e1 aparecer na legenda do grafico\nlevels(col_no) = c(\"<3k\", \"3k-10k\", \"10k-100k\",\"100k-300k\", \"300k-500k\", \"500k-800k\",\">800k\")\n# Adicionando a informa\u00e7\u00e3o da categoria no dataframe\nrn$col_no = col_no\n\n\nlibrary(RColorBrewer)\nmyPalette = brewer.pal(7,\"Reds\")\n\nspplot(rn, \"col_no\", col=grey(.9), col.regions=myPalette, main=\"Munic\u00edpios do RN\")\n\n\n\n\nlibrary(ggmap) # importa a biblioteca ggmap para obter a geolocaliza\u00e7\u00e3o\n# Define o nome das cidade-alvos \u2013 a ser apresentada como label no mapa\nnomes = c(\"Natal\",\"Mossor\u00f3\",\"Pau dos Ferros\")\n# Define or argumentos de busca para as cidade-alvos \u2013 a ser usada pelo ggmap\nnam = c(\"Natal+Brazil+RN\",\"Mossoro+Brazil+RN\",\"Pau dos Ferros+Brazil+RN\")\n# Busca a geolocaliza\u00e7\u00e3o (Google) para cada cidade\npos = geocode(nam)\n# Define a posi\u00e7\u00e3o dos labels como sendo um pouco acima dos pontos\ntlat = pos$lat+0.05\n# -- the city name will be above the marker\n# Cria um daframe com as informa\u00e7\u00f5es (nome da cidade, longitude, latitude e posi\u00e7\u00e3o dolabel)\ncities = data.frame(nomes, pos$lon,pos$lat,tlat)\n# Nomeia as colunas de longitude e latitude\nnames(cities)[2] = \"lon\"\nnames(cities)[3] = \"lat\"\n\n# Criando os labels\ntext1 = list(\"panel.text\", cities$lon, cities$tlat, cities$nomes,col=\"black\", cex = 0.5)\n# Criando os apontamentos\nmark1 = list(\"panel.points\", cities$lon, cities$lat, col=\"blue\")\n\nspplot(rn, \"col_no\",\n sp.layout=list(text1,mark1),\n main=\"Munic\u00edpios RN\",\n col=grey(.9), col.regions=myPalette)\n\n\n\n# -----------------------------------\n# Atividade, Marcell.\n\n\n\n# TODO\n", "meta": {"hexsha": "2d5493140956566502d7b7f89090b485721efc8d", "size": 3075, "ext": "r", "lang": "R", "max_stars_repo_path": "src/ufrn_tis/scraping_with_silvio/rn_map.r", "max_stars_repo_name": "Mazuh/MISC-Algs", "max_stars_repo_head_hexsha": "7fccb3d4eb27a2511bda4b1e408ab96b0cccd5ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-04-25T19:36:22.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-08T18:22:44.000Z", "max_issues_repo_path": "src/ufrn_tis/scraping_with_silvio/rn_map.r", "max_issues_repo_name": "Mazuh/MISC-Algs", "max_issues_repo_head_hexsha": "7fccb3d4eb27a2511bda4b1e408ab96b0cccd5ae", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-04-26T10:15:26.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-26T12:19:11.000Z", "max_forks_repo_path": "src/ufrn_tis/scraping_with_silvio/rn_map.r", "max_forks_repo_name": "Mazuh/MISC-Algs", "max_forks_repo_head_hexsha": "7fccb3d4eb27a2511bda4b1e408ab96b0cccd5ae", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-04-25T23:59:48.000Z", "max_forks_repo_forks_event_max_datetime": "2017-04-25T23:59:48.000Z", "avg_line_length": 34.1666666667, "max_line_length": 119, "alphanum_fraction": 0.6887804878, "num_tokens": 987, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795672, "lm_q2_score": 0.6297745935070806, "lm_q1q2_score": 0.33209054916093395}} {"text": "library(digest)\nhexdigest <- digest(\"The quick brown fox jumped over the lazy dog's back\",\n algo=\"md5\", serialize=FALSE)\n", "meta": {"hexsha": "4c9fa775695a234b74000ed7063ba0310d9b3609", "size": 140, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/MD5/R/md5.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/MD5/R/md5.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/MD5/R/md5.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 35.0, "max_line_length": 74, "alphanum_fraction": 0.6428571429, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3320905491609339}} {"text": "processing_5p_end_positional_offset_between_identified_loci_and_small_RNA_gene<- function(fprefix='input',wdir=\".\") {\r\n\r\nstart_time <- proc.time()\r\ncat(\"Offset between loci and genes at 5' end (M3.07) start\", date(), \"\\n\")\r\n \r\n# Plots for Module 3 Analysis and visualization of SPAR output \r\n# Session: Processing characteristics \r\n\r\n# Module_3_Figure_7 (Figure 3.07) \t\r\n# Description: Normalized offset between loci and genes at the 5' end (displaying relative positions of loci on the genes) \r\n# input: input_annot.with_conservation.xls\r\n# output: 5p_end_positional_offset_between_identified_loci_and_small_RNA_gene.png\r\n# output: 5p_end_positional_offset_between_identified_loci_and_small_RNA_gene.pdf \r\n\r\n# parameters for the plot \r\ndatafile=paste(wdir, \"/\", fprefix, \"_annot.with_conservation.xls\", sep=\"\")\r\nBASENAME=\"5p_end_positional_offset_between_identified_loci_and_small_RNA_gene\"\r\nPLOTTITLE=\"Normalized offset at 5' end, between annotated loci and sncRNA gene\"\r\nXTITLE=\"Normalized_offset\"\r\nYTITLE=\"Number_of_loci\"\r\n\r\n# libraries needed \r\nsuppressPackageStartupMessages(library(ggplot2))\r\n\r\n#args<-commandArgs(TRUE)\r\n#datafile1=args[1] # input file 1\r\n#datafile2=args[2] # input file 2\r\n#wdir=args[3] # output / working directory\r\n#if (length(args)<1) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<2) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<3) { wdir=\".\" } \r\n#use current dir if no \r\n#working dir has been specified\r\n\r\n# output image file\r\npngfile= paste(wdir, \"/../figures/\", paste(BASENAME,\".png\",sep=\"\"), sep=\"\")\r\n#pdffile= paste(wdir, \"/\", paste(BASENAME,\".pdf\",sep=\"\"), sep=\"\")\r\n\r\n# Read in data\r\nD = read.table(datafile,sep='\\t',header=T,comment.ch=\"\")\r\nD_pos = subset(D,peakStrand==\"+\")\r\nD_neg = subset(D,peakStrand==\"-\")\r\n\r\n# for positive strand (peak start - annotation start) \r\nD_pos$Offset_5p = D_pos$peakChrStart-D_pos$annotChrStart + D_pos$peakMostCommon5pPosition - 1\r\nD_pos$Loci_length = D_pos$peakChrEnd-D_pos$peakChrStart \r\nD_pos$Annot_length = D_pos$annotChrEnd-D_pos$annotChrStart \r\n\r\n# for negative strand (peak end - annotation end)\r\nD_neg$Offset_5p = D_neg$annotChrEnd-D_neg$peakChrEnd + D_neg$peakMostCommon5pPosition - 1\r\nD_neg$Loci_length = D_neg$peakChrEnd-D_neg$peakChrStart \r\nD_neg$Annot_length = D_neg$annotChrEnd-D_neg$annotChrStart \r\n\r\nD_comb = rbind(D_pos,D_neg)\r\nA_final = D_comb[,c(\"annotRNAclass\",\"annotStrand\",\"Offset_5p\",\"Loci_length\",\"Annot_length\")]\r\ncolnames(A_final) = c(\"sncRNA_class\", \"Strand\", \"Offset_5p\",\"Loci_length\",\"Annot_length\")\r\n\r\nA_final = A_final[which(A_final$Loci_length<=44),]\r\nA_final[,6] = A_final[,3]/A_final[,5]\r\ncolnames(A_final)[6] = XTITLE\r\n\r\n# hist(mir_3p[,6],breaks=dim(mir_3p)[1],xlim=c(-0.2,0.2))\r\n\r\n# A histogram\r\npng(pngfile,width = 7, height = 7, units = 'in', res = 300, type=\"cairo\")\r\noptions(scipen=10000)\r\nprint(ggplot(A_final, aes(x=Normalized_offset))+ \r\ngeom_histogram(colour=\"white\", breaks=seq(round(min(A_final[,6])-0.05,1), 1, by= 0.05)+0.025,right = TRUE)+\r\nggtitle(PLOTTITLE)+theme_classic()+\r\ntheme(axis.text.x = element_text(size = 14),axis.text.y = element_text(size = 8))+\r\nfacet_grid(sncRNA_class~.,scales=\"free_y\"))\r\ndev.off()\r\n\r\ncat(\"Total time for offset between loci and genes at 5' end (M3.07) analysis:\", (proc.time() - start_time)[['elapsed']], \"seconds (\", date(), \")\\n\")\r\n}\r\n\r\n# processing_5p_end_positional_offset_between_identified_loci_and_small_RNA_gene(fprefix=fprefix,wdir=wdir)\r\n", "meta": {"hexsha": "b2b8a4d44efa24713864e00dc5f22b5f6ceb44d2", "size": 3481, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/R/module3/M3.07_processing_5p_end_positional_offset_between_identified_loci_and_small_RNA_gene.r", "max_stars_repo_name": "ConYel/spar_pipeline", "max_stars_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-12-03T10:07:54.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-03T10:07:54.000Z", "max_issues_repo_path": "scripts/R/module3/M3.07_processing_5p_end_positional_offset_between_identified_loci_and_small_RNA_gene.r", "max_issues_repo_name": "ConYel/spar_pipeline", "max_issues_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-12-09T03:48:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-08T13:35:31.000Z", "max_forks_repo_path": "scripts/R/module3/M3.07_processing_5p_end_positional_offset_between_identified_loci_and_small_RNA_gene.r", "max_forks_repo_name": "ConYel/spar_pipeline", "max_forks_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6282051282, "max_line_length": 149, "alphanum_fraction": 0.734271761, "num_tokens": 1045, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632979641571, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3318968343382971}} {"text": "\r\n# Libraries\r\nlibrary(dplyr)\r\nlibrary(RSQLite)\r\n\r\nlibrary(tidyverse)\r\nlibrary(httr)\r\nlibrary(jsonlite)\r\n\r\nlibrary(ggplot2)\r\nlibrary(data.table)\r\n", "meta": {"hexsha": "aee933a424a1be61daef283d99f0b53142623485", "size": 146, "ext": "r", "lang": "R", "max_stars_repo_path": "2020-08-31-jsa-type-v2-ch3-gender/analysis1/libraries.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2020-08-31-jsa-type-v2-ch3-gender/analysis1/libraries.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2020-08-31-jsa-type-v2-ch3-gender/analysis1/libraries.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.1666666667, "max_line_length": 20, "alphanum_fraction": 0.7328767123, "num_tokens": 36, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.33189682529494774}} {"text": "# James Rekow\r\n\r\n# when abdList[[pairs[, i][1]]] : subscript out of bounds happens, just return NA in DOCProcedure\r\n# function to prevent unnecessary termination of parent process. I should adopt this philosophy in\r\n# any helper functions that become problematic only rarely but happen to interupt slower parent\r\n# processes.\r\n\r\ntreatmentDFListListArrayCreator = function(M = 80, N = 20, iStrength = 1, univ = 1, sigmaMax = 0.1,\r\n thresholdMult = 10 ^ (-1), maxSteps = 10 ^ 4, tStep = 10 ^ (-2),\r\n intTime = 1, interSmplMult = 0.01, lambda = 10 ^ (-1),\r\n synthM = floor(M / 4), numSynthChrt = 10, numSample = 100,\r\n maxIters = 100){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS:\r\n \r\n source(\"conGraphCreator.r\")\r\n source(\"DOCNSDFListCreator.r\")\r\n \r\n # lambdaVec = c(0.05, 0.1, 0.15)\r\n # graphTypeVec = c(\"smallWorld\", \"scaleFree\", \"gnp\", \"gnm\")\r\n # meanDegreeVec = c(2, 4, 6)\r\n \r\n lambdaVec = c(0.05, 0.1, 0.15)\r\n graphTypeVec = c(\"smallWorld\", \"scaleFree\", \"gnp\", \"gnm\")\r\n meanDegreeVec = c(4, 6)\r\n \r\n x1 = 1:length(lambdaVec)\r\n x2 = 1:length(graphTypeVec)\r\n x3 = 1:length(meanDegreeVec)\r\n \r\n g = function(x, y) sapply(1:length(x), function(n) list(append(x[[n]], y[[n]])))\r\n \r\n coordArray = outer(x1, outer(x2, x3, g), g)\r\n \r\n produceTreatmentDFListList = function(coords){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS:\r\n \r\n # unlist the coordinates in the input\r\n coords = unlist(coords)\r\n \r\n # extract the parameter values corresponding to the coordinate vector\r\n lambda = lambdaVec[coords[1]]\r\n graphType = graphTypeVec[coords[2]]\r\n meanDegree = meanDegreeVec[coords[3]]\r\n \r\n # NOTE: coords in array of the from c(lambda, graphType, meanDegree)\r\n \r\n # create connectivity graph using the graph type and mean degree parameters\r\n conGraph = conGraphCreator(graphType = graphType, meanDegree = meanDegree, v = M)\r\n \r\n treatmentDFListCreator = function(x = NULL){\r\n return(\r\n DOCNSDFListCreator(conGraph = conGraph, M = M, N = N, iStrength = iStrength,\r\n univ = univ, sigmaMax = sigmaMax,\r\n thresholdMult = thresholdMult, maxSteps = maxSteps,\r\n tStep = tStep, intTime = intTime,\r\n interSmplMult = interSmplMult, lambda = lambda,\r\n synthM = synthM, numSynthChrt = numSynthChrt,\r\n numSample = numSample, maxIters = maxIters)\r\n ) # end return\r\n } # end treatmentDFListCreator function\r\n \r\n treatmentDFListList = lapply(as.list(1:10), treatmentDFListCreator)\r\n \r\n return(treatmentDFListList)\r\n \r\n } # end produceTreatmentDFListList function\r\n \r\n treatmentDFListListArray = apply(coordArray, 1:3, produceTreatmentDFListList)\r\n \r\n return(treatmentDFListListArray)\r\n \r\n} # end treatmentDFListListArrayCreator function\r\n", "meta": {"hexsha": "3465a8d1132a31228af5ec3383dabd02d967945f", "size": 3044, "ext": "r", "lang": "R", "max_stars_repo_path": "treatmentDFListListArrayCreator.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "treatmentDFListListArrayCreator.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "treatmentDFListListArrayCreator.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5316455696, "max_line_length": 108, "alphanum_fraction": 0.5880420499, "num_tokens": 817, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3318968176070757}} {"text": "LC_Diff <- function(Input_FileName) {\n\n# R Functions to process dataset from Load Cell Array\n# It calculates the difference between two timesteps -- current vs. previsou one.\n# Diff = current one - previous one\n# Negetive Diff means lossing weight; Postive Diff means gaining weight\n#\n# Author : Kuo-Hsine Chang, Ph.D. Dept. of Plant Agricuture, University of Guelph\n# Contact: changks.github.io\n# Version: 04162018\n\n\n # load library\n library(zoo)\n library(data.table)\n \n # read the csv file \n dt = fread(Input_FileName, header = T, sep = ','); \n\n # calcualte difference between two timesteps (current - previous)\n \n Diff_dt <- dt[, lapply(.SD, function(x) {x1 <- diff(x)\n NA^(x1 <= -10 | x1 >=10)*x1}), .SDcols= 2:ncol(dt)\n ][, timestamp := dt[[1]][-1]] \n \n Diff_dt[, c(ncol(Diff_dt), 1:(ncol(Diff_dt)-1)), with=FALSE] \n \n Diff_dt <- data.frame(Diff_dt)\n\n Diff_dt <- Diff_dt[c(13,1:12)]\n\n # export to a csv file \n write.table(Diff_dt, paste0(tools::file_path_sans_ext(Input_FileName),\"_Diff.csv\"), row.names=FALSE, col.names=TRUE, sep=\",\") \n}", "meta": {"hexsha": "63f38fca1623a9c575384ab343f1e7c1cf9da1b7", "size": 1124, "ext": "r", "lang": "R", "max_stars_repo_path": "LC_Diff.r", "max_stars_repo_name": "changks/Load_Cell_Array", "max_stars_repo_head_hexsha": "ed012ef6e5bb7cdc5cce5f72c3c723b83aca7d55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "LC_Diff.r", "max_issues_repo_name": "changks/Load_Cell_Array", "max_issues_repo_head_hexsha": "ed012ef6e5bb7cdc5cce5f72c3c723b83aca7d55", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LC_Diff.r", "max_forks_repo_name": "changks/Load_Cell_Array", "max_forks_repo_head_hexsha": "ed012ef6e5bb7cdc5cce5f72c3c723b83aca7d55", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0588235294, "max_line_length": 138, "alphanum_fraction": 0.640569395, "num_tokens": 327, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.33182736982495936}} {"text": "\n# Packages\nlibrary(tidyverse)\nlibrary(magrittr)\nlibrary(ggplot2)\n# library(geomnet)\nlibrary(ggnetwork)\nlibrary(sf)\nlibrary(ggmap)\nlibrary(sp)\nlibrary(rgdal)\nlibrary(leaflet)\n# library(xlsx)\nlibrary(data.table)\nlibrary(mapview)\nlibrary(mapedit)\nlibrary(matlib)\nlibrary(nngeo)\nlibrary(writexl)\nlibrary(here)\nlibrary(plotly)\nlibrary(kableExtra)\n\n\nsource(\"./R/Simulations_functions.r\")\nsource(\"./R/functions.r\")\n\nfile_path <- here::here(\"Data/Input/With_observations/Brana/Brana.xlsx\")\nbrana.snet <- read_surveynet(file = file_path)\nsnet = brana.snet\nsnet.adj = brana.snet.adj\nnet.2D = TRUE\nwebmap = TRUE\nepsg = 3857\nsp_bound =2\nrii_bound = 0.3\n# Summary\n\n# TO DO: da ne prikazuje nazive kolona, da se malo oboji i da mozda ide po tabovima...\n\n# summary.adjustment <- data.frame(Parameter = c(\"Type: \", \"Dimension: \", \"Number of iterations: \", \"Max. coordinate correction in last iteration: \", \"Datum definition: \"),\n# Value = c(\"Weighted\", \"2D\", 1, \"0.0000 m\",\n# if(all(brana.snet$points$FIX_2D == FALSE)){\n# \"Datum defined with a minimal trace of the matrix Qx\"\n# }else{\"Fixed parameters - classically defined datum\"}\n# ))\n#\n# summary.adjustment %>%\n# kable(caption = \"Adjustment settings\", digits = 4, align = \"c\", col.names = NULL) %>%\n# kable_styling(bootstrap_options = c(\"striped\", \"hover\", \"condensed\", \"responsive\"), full_width = TRUE)%>%\n# column_spec(1, bold = T, color = \"white\", background = \"#D7261E\")\n#\n#\n# summary.stations <- data.frame(Parameter = c(\"Number of (partly) known stations: \", \"Number of unknown stations: \", \"Total: \"),\n# Value = c(sum(brana.snet$points$FIX_2D == TRUE),\n# sum(brana.snet$points$FIX_2D == FALSE),\n# sum(brana.snet$points$FIX_2D == TRUE) + sum(brana.snet$points$FIX_2D == FALSE)))\n#\n#\n# summary.stations %>%\n# kable(caption = \"Stations\", digits = 4, align = \"c\") %>%\n# kable_styling(bootstrap_options = \"striped\", full_width = TRUE)\n#\n#\n# summary.observations <- data.frame(Parameter = c(\"Directions: \", \"Distances: \", \"Known coordinates: \", \"Total: \"),\n# Value = c(sum(brana.snet$observations$direction == TRUE),\n# sum(brana.snet$observations$distance == TRUE),\n# sum(brana.snet$points$FIX_2D == TRUE)*2,\n# sum(brana.snet$observations$direction == TRUE)+sum(brana.snet$observations$distance == TRUE)+(sum(brana.snet$points$FIX_2D == TRUE)*2)))\n#\n# summary.observations %>%\n# kable(caption = \"Observations\", digits = 4, align = \"c\") %>%\n# kable_styling(bootstrap_options = \"striped\", full_width = TRUE)\n#\n#\n# summary.unknowns <- data.frame(Parameter = c(\"Coordinates: \", \"Orientations: \", \"Total: \"),\n# Value = c(sum(brana.snet$points$FIX_2D == FALSE)*2,\n# length(brana.snet$observations %>% dplyr::filter(direction == TRUE) %>% .$from %>% unique()),\n# (sum(brana.snet$points$FIX_2D == FALSE)*2)+length(brana.snet$observations %>% dplyr::filter(direction == TRUE) %>% .$from %>% unique())))\n#\n# summary.unknowns %>%\n# kable(caption = \"Unknowns\", digits = 4, align = \"c\") %>%\n# kable_styling(bootstrap_options = \"striped\", full_width = TRUE)\n#\n#\n# summary.degrees <- data.frame(Parameter = \"Degrees of freedom: \", Value = summary.observations$Value[4]-summary.unknowns$Value[3])\n#\n# summary.degrees %>%\n# kable(caption = \"Degrees of freedom: \", digits = 4, align = \"c\") %>%\n# kable_styling(bootstrap_options = \"striped\", full_width = TRUE)\n\nplot_surveynet(snet = brana.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\nplot_surveynet(snet = brana.snet, webmap = TRUE, net.1D = FALSE, net.2D = TRUE)\nbrana.snet.adj <- adjust.snet(adjust = FALSE, survey.net = brana.snet, dim_type = \"2D\", sd.apriori = 1, ellipse.scale = 10, all = FALSE) # promeniti sd pravca i duzina\nplot_surveynet(snet.adj = brana.snet.adj, webmap = TRUE, net.1D = FALSE, net.2D = TRUE)\n\nfile_path <- here::here(\"Data/Input/With_observations/Makis/Makis_observations.xlsx\")\nmakis.snet <- read_surveynet(file = file_path)\n# TO DO: set_srs unutar read_surveynet\nplot_surveynet(snet = makis.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\nmakis.snet.adj <- adjust.snet(adjust = FALSE, survey.net = makis.snet, dim_type = \"2D\", sd.apriori = 3 , all = FALSE)\nplot_surveynet(snet.adj = makis.snet.adj, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\n\n\nfile_path <- here::here(\"Data/Input/With_observations/Zadatak 1/Zadatak_1.xlsx\")\nzadatak1.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = zadatak1.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\nzadatak1.snet$observations$sd_Hz <- 5\nzadatak1.snet$observations$HD[2] <- 12500\nzadatak1.snet.adj <- adjust.snet(adjust = TRUE, survey.net = zadatak1.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\nfile_path <- here::here(\"Data/Input/Without_observations/xlsx/TETO_plan opazanja1.xlsx\")\nteto.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = teto.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\nteto.snet.adj <- adjust.snet(adjust = FALSE, survey.net = teto.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\nplot_surveynet(snet.adj = teto.snet.adj, webmap = TRUE, net.1D = FALSE, net.2D = TRUE)\n\n\nadj.net_spatial_view_web(ellipses = brana.snet.adj[[1]]$ellipse.net, observations = brana.snet.adj[[2]], points = brana.snet.adj[[1]]$net.points, sp_bound = 2, rii_bound = 1)\n\n\n\nfile_path <- here::here(\"Data/Input/With_observations/Brana_Gorica/Brana_Gorica_nulta_serija.xlsx\")\ngorica0.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = gorica0.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\ngorica0.snet.adj <- adjust.snet(adjust = TRUE, survey.net = gorica0.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\nfile_path <- here::here(\"Data/Input/With_observations/Brana_Gorica/Brana_Gorica_april_2019.xlsx\")\ngorica1.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = gorica1.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\ngorica1.snet.adj <- adjust.snet(adjust = TRUE, survey.net = gorica1.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\nfile_path <- here::here(\"Data/Input/With_observations/Avala/Avala_mreza.xlsx\")\navala.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = gorica0.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\ngorica0.snet.adj <- adjust.snet(adjust = TRUE, survey.net = gorica0.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\nfile_path <- here::here(\"Data/Input/With_observations/Grdelica/Grdelica.xlsx\")\ngrdelica.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = grdelica.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\ngrdelica.snet.adj <- adjust.snet(adjust = TRUE, survey.net = grdelica.snet, dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\nfile_path <- here::here(\"Data/Input/With_observations/Grdelica/Cut2.xlsx\")\ncut2.snet <- read_surveynet(file = file_path)\nplot_surveynet(snet = cut2.snet, webmap = FALSE, net.1D = FALSE, net.2D = TRUE)\ncut2.snet.adj <- adjust.snet(adjust = TRUE, result.units = \"cm\", survey.net = cut2.snet, prob = 0.99, output = \"report\", dim_type = \"2D\", sd.apriori = 1 , all = FALSE)\n\n\n\n# 1D design and adjustment\nfile_path <- here::here(\"Data/Input/With_observations/DNS_1D/DNS_1D_nulta.xlsx\")\ndns.snet <- read_surveynet(file = file_path)\n\npoints <- dns.snet$points\nobservations <- dns.snet$observations\nnames(observations)\nobservations$id <- row_number(observations$from)\n\n\nplot_surveynet(snet = dns.snet, webmap = FALSE, net.1D = TRUE, net.2D = FALSE)\n\ndns.snet.adj <- adjust.snet(adjust = FALSE, survey.net = dns.snet, wdh_model = \"n_dh\", dim_type = \"1D\", sd.apriori = 0.2 , all = FALSE, result.units = \"mm\")\nplot_surveynet(snet.adj = dns.snet.adj, webmap = FALSE, net.1D = TRUE, net.2D = FALSE)\n\ndns.snet.adj <- adjust.snet(adjust = TRUE, survey.net = dns.snet, wdh_model = \"n_dh\", dim_type = \"1D\", sd.apriori = 0.2 , all = FALSE, result.units = \"mm\")\nplot_surveynet(snet.adj = dns.snet.adj, webmap = FALSE, net.1D = TRUE, net.2D = FALSE)\n\n\n\n\nlength(which(dns.snet$points$FIX_1D))==1\nsum(dns.snet$observations$diff_level == TRUE)\n\ndns.snet$points %<>% dplyr::mutate(FIX_1D = FALSE)\n\nif(length(which(dns.snet$points$FIX_1D))==1 || length(which(dns.snet$points$FIX_1D))==0){\n \"Free 1D geodetic network\"\n}else{\"Unfree 1D geodetic network\"}\n\nfixed_points <- survey1net$points[(survey1net$points$FIX_1D == TRUE), ]$Name %>% .[!is.na(.)]\n\n# proba 1d adjust=T\ndns.snet.adj$Observations$id <- row_number(dns.snet.adj$Observations$from)\n\nggplotly(\n ggplot()+\n geom_ribbon(data = dns.snet.adj$Observations,\n aes(x = id,\n ymin = 0,\n ymax = f) )+\n #geom_area(data = dns.snet.adj$Observations,\n # aes(x = from_to,\n # y = f, fill = \"blue\"))+\n scale_colour_gradient(low=\"orange\",\n high=\"red\", guide = FALSE)+\n xlab(\"Name\") +\n ylab(\"Residuals [mm]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"Residuals [mm]\")+\n theme_bw()+\n ylim(min(dns.snet.adj$Observations$f)-sd(dns.snet.adj$Observations$f),\n max(dns.snet.adj$Observations$f)+sd(dns.snet.adj$Observations$f)), showlegend = TRUE\n)\n\n\n", "meta": {"hexsha": "b2f1e1d3429fe348db37b3bcb3b04b8dfcf0042a", "size": 9520, "ext": "r", "lang": "R", "max_stars_repo_path": "R/deprecated/test.r", "max_stars_repo_name": "pejovic/Surveyor", "max_stars_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-14T22:40:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-14T22:40:36.000Z", "max_issues_repo_path": "R/deprecated/test.r", "max_issues_repo_name": "pejovic/Surveyor", "max_issues_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/deprecated/test.r", "max_forks_repo_name": "pejovic/Surveyor", "max_forks_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.213592233, "max_line_length": 183, "alphanum_fraction": 0.6536764706, "num_tokens": 2944, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.33182736982495936}} {"text": "\n#se trabaja con constantes para ordenar el codigo fuente\n\n\n#source(\"M:\\\\R\\\\elementary\\\\canaritos_dibujo.r\")\n\n#limpio la memoria\nrm(list=ls())\ngc()\n\n\nlibrary(\"data.table\")\nlibrary(\"rpart\")\nlibrary(\"rpart.plot\")\n\n\n#Parametros entrada\nkarchivo_entrada <- \"M:\\\\datasets\\\\201902.txt\"\nkcampos_separador <- \"\\t\"\nkcampo_id <- \"numero_de_cliente\"\nkclase_nomcampo <- \"clase_ternaria\"\nkclase_valor_positivo <- \"BAJA+2\"\nkcampos_a_borrar <- c(kcampo_id)\n\n\n#Parametros salida\nkarchivo_imagen1 <- \"M:\\\\work\\\\canaritos_010.jpg\"\nkarchivo_imagen2 <- \"M:\\\\work\\\\canaritos_011.jpg\"\n\n\n\n#cargo los datos\ndataset <- fread(karchivo_entrada)\n\n\n\n#borro las variables que no me interesan\ndataset[ , (kcampos_a_borrar) := NULL ]\n\ncolumnas_original <- colnames( dataset )\n\nmagic_canaritos <- 0.1 \ncanaritos_cantidad <- as.integer( round(ncol(dataset) * magic_canaritos) )\nfor( i in 1:canaritos_cantidad )\n{\n dataset[ , paste0( \"canarito\", i ) := runif( nrow(dataset) ) ]\n}\n\ncolumnas_canaritos <- colnames( dataset )[ which( colnames( dataset ) %like% \"canarito\" ) ]\n\n# generacion del modelo\nformula <- formula(paste(kclase_nomcampo, \"~ .\"))\n\nt0 <- Sys.time()\nmodelo <- rpart(formula, data = dataset, cp=0.0, maxdepth=8, minsplit=20, minbucket=5, xval=5)\nt1 <- Sys.time()\n\ntcorrida <- as.numeric( t1 - t0, units = \"secs\")\nprint( tcorrida)\n\n#cuento cuantas variables canarito distintas aparecen\nframe <- modelo$frame\nleaves <- frame$var == \"\"\nused <- unique(frame$var[!leaves])\ncanaritos_muertos <- sum( unlist( used ) %like% \"canarito\" )\ncat( \"canaritos_muertos: \", canaritos_muertos )\n\n\n#impresion un poco mas elaborada del arbol\njpeg(file = karchivo_imagen1, width = 30, height = 6, units = 'in', res = 300)\nprp(modelo, extra=101, digits=5, branch=1, type=4, varlen=0, faclen=0)\ndev.off()\n\nprp(modelo, extra=101, digits=5, branch=1, type=4, varlen=0, faclen=0, snip=TRUE)\n\n#------------------------------------------------------------------------------\n#Ahora hago que los canaritos sean las primeras variables del dataset\n#tengo gran curiosidad por ver si sale el mismo arbol\n\nsetcolorder( dataset, unique( c( columnas_canaritos, columnas_original ) ) )\n\n\nt0 <- Sys.time()\nmodelo <- rpart(formula, data = dataset, cp=0.0, maxdepth=8, minsplit=20, minbucket=5, xval=0)\nt1 <- Sys.time()\n\ntcorrida <- as.numeric( t1 - t0, units = \"secs\")\nprint( tcorrida)\n\n#cuento cuantas variables canarito distintas aparecen\nframe <- modelo$frame\nleaves <- frame$var == \"\"\nused <- unique(frame$var[!leaves])\ncanaritos_muertos <- sum( unlist( used ) %like% \"canarito\" )\ncat( \"canaritos_muertos: \", canaritos_muertos )\n\n#impresion un poco mas elaborada del arbol\njpeg(file = karchivo_imagen2, width = 30, height = 6, units = 'in', res = 300)\nprp(modelo, extra=101, digits=5, branch=1, type=4, varlen=0, faclen=0)\ndev.off()\n\n", "meta": {"hexsha": "39c398bae2a3def1b7490e2677d547bb930f6b49", "size": 2915, "ext": "r", "lang": "R", "max_stars_repo_path": "elementary/canaritos_dibujo_01.r", "max_stars_repo_name": "ktavo/dm-finanzas-2019", "max_stars_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "elementary/canaritos_dibujo_01.r", "max_issues_repo_name": "ktavo/dm-finanzas-2019", "max_issues_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "elementary/canaritos_dibujo_01.r", "max_forks_repo_name": "ktavo/dm-finanzas-2019", "max_forks_repo_head_hexsha": "3063bc3dbf24781acbc25efc73418bad82730511", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.5784313725, "max_line_length": 102, "alphanum_fraction": 0.6631217839, "num_tokens": 953, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.33178064807238816}} {"text": "process.jules.file <- function(file, level, varName) {\r\n\tprint(file)\r\n\tnc = try(nc_open(file))\r\n\tif (class(nc) == \"try-error\") return(NULL)\r\n\tvars = names(nc$var)\r\n\t\r\n\tif (all(vars != varName)) {\r\n\t\tnoFileWarning(c(), varName)\r\n\t\treturn(NULL)\r\n\t}\r\n\t\r\n\tgetVar <- function(var) {\r\n\t\tvar = nc$var[[which(vars == var)]]\r\n\t\tdat = ncvar_get( nc, var)\r\n\t\treturn(dat)\r\n\t}\r\n\t\r\n\tdat = getVar(varName)\r\n\tlat = getVar(\"latitude\")\r\n\tlon = getVar(\"longitude\")\r\n\ttim = getVar(\"time_bounds\")\r\n\t\r\n\tl = length(lat)\r\n\t\r\n\tmultiLayer <- function(mn, leveli = level) {\r\n\t\tmdat = dat[, leveli, mn]\r\n\t\tif (!is.null(dim(mdat)))\r\n\t\t\tmdat = apply(mdat,1 , sum)\r\n\t\treturn(mdat)\r\n\t}\r\n\t\r\n\tsingleLayer <- function(mn) dat[, mn]\r\n\t\r\n\tmonthizeData <- function(mn, FUN, ...) {\r\n\t\tmdat = FUN(mn, ...)\r\n\t\tr = rasterFromXYZ(cbind(lon, lat, mdat))\r\n\t\treturn(r)\r\n\t}\r\n\t\r\n\tif (length(dim(dat)) == 2 && ncol(dat) == 12) r = layer.apply(1:12, monthizeData, singleLayer)\r\n\t\telse if (length(dim(dat)) == 3) {\r\n\t\t\tif (varName != \"landCoverFrac\") {\r\n\t\t\t\topenWeightLayer <- function(fracLevel, varLevel) {\r\n\t\t\t\t\tfrac = process.jules.file(file, fracLevel, \"landCoverFrac\")\r\n\t\t\t\t\tr = layer.apply(1:12, monthizeData, multiLayer, varLevel)\r\n\t\t\t\t\tfrac * r / 100\r\n\t\t\t\t}\r\n\t\t\t\tri = mapply(openWeightLayer, list(1, 2, 3:5, 6:8, 9), 1:5)\r\n\t\t\t\tr = ri[[1]] + ri[[2]] + ri[[3]] + ri[[4]] + ri[[5]]\t\t\t\t\r\n\t\t\t} else r = layer.apply(1:12, monthizeData, multiLayer)\r\n\t\t} else r = sum(rasterFromXYZ(cbind(lon, lat, dat))[[level]])\r\n \t\r\n\treturn(r)\r\n}\r\n", "meta": {"hexsha": "06f171a275ae9c46f30f6a4ff8c333be14f78fd6", "size": 1491, "ext": "r", "lang": "R", "max_stars_repo_path": "libs/process_jules_file.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/process_jules_file.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libs/process_jules_file.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 27.1090909091, "max_line_length": 96, "alphanum_fraction": 0.5767940979, "num_tokens": 500, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.33178063456234447}} {"text": "library(ggplot2)\nlibrary(geofacet)\nlibrary(broom)\nlibrary(maptools)\nlibrary(rgeos)\nlibrary(rgdal)\nlibrary(colorspace)\nlibrary(sf)\nlibrary(dplyr)\nlibrary(tidyr)\n\nmake_rt_map <- function(JOBID, StanModel, last_date_data,\n ext = \".png\"){\n \n load(paste0('usa/results/', StanModel, '-', JOBID, '-stanfit.Rdata'))\n out <- rstan::extract(fit)\n \n percentages <- vector(length = length(states))\n percentage_group <- vector(length = length(states))\n for (i in 1:length(states)){\n dates_states <- dates[[i]]\n idx <- which(dates_states == last_date_data)\n rts <- rowMeans(out$Rt_adj[,(idx-6):idx,i])\n \n percentages[i] <- mean(rts < 1)\n \n if (percentages[i] < 0.20){\n percentage_group[i] <- 1\n } else if (percentages[i] < 0.40){\n percentage_group[i] <- 2\n } else if (percentages[i] < 0.60){\n percentage_group[i] <- 3\n } else if (percentages[i] < 0.80){\n percentage_group[i] <- 4\n } else {\n percentage_group[i] <- 5\n }\n } \n \n data <- data.frame(\"code\" = states,\n \"less\" = percentages,\n \"more\" = 1-percentages)\n data_long <- gather(data, \"key\" = key, \"value\" = value, -code)\n \n # Load in shape file \n shp0 <-\n readOGR(\"usa/data/cb_2018_us_state_20m/cb_2018_us_state_20m.shp\",\n encoding = \"UTF-8\",\n use_iconv = TRUE,\n stringsAsFactors = FALSE\n )\n shp0 <- spTransform(shp0, CRS=CRS(\"+init=epsg:26978\"))\n \n alaska <- shp0[shp0$STATEFP==\"02\",]\n alaska <- elide(alaska, rotate=-39)\n alaska <- elide(alaska, scale=max(apply(bbox(alaska), 1, diff)) / 4)\n alaska <- elide(alaska, shift=c(-2400000, -1000000))\n proj4string(alaska) <- proj4string(shp0)\n \n hawaii <- shp0[shp0$STATEFP==\"15\",]\n hawaii <- elide(hawaii, rotate=-35)\n hawaii <- elide(hawaii, shift=c(5200000, -1400000))\n proj4string(hawaii) <- proj4string(shp0)\n \n shp0 <- shp0[!shp0$STATEFP %in% c(\"02\", \"15\"),] # Remove old AK and HI\n shp0 <- rbind(shp0, alaska, hawaii) # Add in shifted AK and HI\n \n shp0_broom <- tidy(shp0, region = \"NAME\")\n df <- data.frame(shp0_broom)\n \n names(df) <- c(\"long\", \"lat\", \"order\", \"hole\", \"piece\", \"group\", \"state_name\")\n \n nam <- read.csv(\"usa/data/states.csv\")\n names(nam) <- c(\"state_name\", \"code\")\n df <- left_join(df, nam, by = \"state_name\")\n \n rt_percentage <- data.frame(\"code\" = states,\n \"percentage\" = percentages,\n \"percentage_group\" = factor(percentage_group))\n df <- left_join(df, rt_percentage, by = \"code\")\n \n df = df[!df$state_name %in% c(\"Puerto Rico\"),]\n p1 <- ggplot(data = df) + \n geom_polygon(data = df, \n aes(x = long, y = lat, group = group, fill = percentage_group), \n colour = \"black\") +\n #scale_fill_continuous_diverging(name = expression(\"Probability R\"[t]<1),\n # palette = \"Red-Green\", mid = 0.5, na.value = \"grey\",\n # labels = scales::percent_format(accuracy = 1),limits=c(0,1)) +\n scale_discrete_manual(\"fill\", name = expression(\"Probability R\"[t]<1), \n values = c(\"#7b3294\", \"#c2a5cf\", \"#f7f7f7\", \"#a6dba0\", \"#008837\"),\n labels = c(\"x < 20%\", expression(paste(\"20%\"<=\" x < 40%\")), \n expression(paste(\"40%\"<=\" x < 60%\")), expression(paste(\"60%\"<=\" x < 80%\")),\n expression(paste(\"x\">=\"80%\")))) + \n theme_bw() + \n theme(panel.border = element_blank(), \n panel.grid = element_blank(), \n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n axis.ticks.x = element_blank(),\n axis.text.x = element_blank(), \n axis.ticks.y = element_blank(), \n axis.text.y = element_blank(),\n axis.line = element_blank())\n print(p1)\n ggsave(paste0(\"usa/figures/\", JOBID, \"_rt_map_chloropleth\", ext), p1, width = 12)\n \n data <- data[which(data$code != \"DC\"),]\n more_fifty_percent <- length(which(data$less > 0.5))\n more_ninty_five_percent <- length(which(data$less > 0.95))\n saveRDS(c(sprintf(\"%s\", more_fifty_percent), sprintf(\"%s\", more_ninty_five_percent)), \n paste0(\"usa/results/\", JOBID, \"-chloropleth-percentage.RDS\"), version = 2)\n}\n\n\n", "meta": {"hexsha": "8c16fd8457af8a765b61fc9f8d1fd81a3bebdad7", "size": 4330, "ext": "r", "lang": "R", "max_stars_repo_path": "usa/code/plotting/make-rt-percentage-map.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "usa/code/plotting/make-rt-percentage-map.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "usa/code/plotting/make-rt-percentage-map.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 37.3275862069, "max_line_length": 112, "alphanum_fraction": 0.5678983834, "num_tokens": 1288, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.33178063456234447}} {"text": "\n#' Extract images from DynamicFire NetLogo Model saved view \n#' \n#' The images were saved with the NetLogo extension CSV each 30 steps (ticks) after 7200 steps \n#'\n#' @param fname \n#' @param plot \n#'\n#' @return\n#' @export\n#'\n#' @examples\nextract_patch_distr_nl <- function(fname,plot=FALSE){\n #\n # Extract parameters encoded in names\n #\n ss <- data.frame(str_split(tools::file_path_sans_ext(fname),\"_\",simplify=TRUE),stringsAsFactors = FALSE) %>% mutate_at(2:7,as.numeric)\n plan(multisession)\n p_df <- future_lapply( 2:length(fname), function(h){\n \n png <- read_csv(paste0(\"Data/\",fname[h-1]),col_names = c(\"i\",\"j\",\"value\"), col_types = cols()) %>% filter(value!=55 & value!=0) %>% mutate(value= value>0)\n png1 <- read_csv(paste0(\"Data/\",fname[h]),col_names = c(\"i\",\"j\",\"value\"), col_types = cols()) %>% filter(value!=55 & value!=0) %>% mutate(value= value>0)\n dif <- anti_join(png1,png, by=c(\"i\",\"j\"))\n #ggplot(dif, aes(y=i,x=j,fill=value)) +geom_raster() + theme_void()\n #ggplot(png, aes(y=i,x=j,fill=value)) +geom_raster() + theme_void() \n #ggplot(png1, aes(y=i,x=j,fill=value)) +geom_raster() + theme_void()\n if( nrow(dif)>0) {\n sm <- sparseMatrix(i=dif$i+1,j=dif$j+1,x=dif$value)\n pl <- patchdistr_sews(as.matrix(sm))\n if(plot) print(plot_distr(pl,best_only = FALSE) + ggtitle(paste(\"Days\",ss[h,5])))\n \n pl <- tibble::remove_rownames(data.frame(pl))\n patch_distr <- patchsizes(as.matrix(sm))\n pl <- pl %>% mutate(max_patch = max(patch_distr),size=as.numeric(ss[h,7])*as.numeric(ss[h,8]),tot_patch=sum(patch_distr),days = ss[h,6], \n initial_forest_density= ss[h,2], fire_probability = ss[h,3], forest_dispersal_distance = ss[h, 4],\n forest_growth= ss[h,5]\n )\n }\n }, future.seed = TRUE)\n plan(sequential)\n patch <- bind_rows(p_df)\n return(patch)\n}\n\n#' Evaluate patch distribution in a raster brick \n#'\n#' @param br raster with distribution data >0 is TRUE \n#' @param returnEWS if TRUE returns the early warnings, FALSE returns the patch distribution \n#'\n#' @return a data frame with results\n#' @export\n#'\n#' @examples\nevaluate_patch_distr <- function(br,returnEWS=TRUE){\n if( class(br)!=\"RasterLayer\")\n stop(\"Paramter br has to be a RasteLayer\")\n ## Convert to TRUE/FALSE matrix\n #\n brTF <- as.matrix(br)\n brTF <- brTF>0\n \n # Extract Date from name of the band\n #\n brName <- str_sub( str_replace_all(names(br), \"\\\\.\", \"-\"), 2)\n \n if( returnEWS ){\n patch_distr <- patchdistr_sews(brTF)\n \n patch_df <- tibble::remove_rownames(data.frame(patch_distr)) %>% mutate(date=brName) \n } else {\n patch_distr <- patchsizes(brTF)\n patch_df <- tibble(size=patch_distr) %>% mutate(date=brName) \n \n }\n return(patch_df)\n}\n\n\nconvert_to_sparse <- function(fire_bricks,region_name){\n \n future::plan(multiprocess)\n on.exit(future::plan(sequential))\n \n require(Matrix)\n p_df <- lapply( seq_along(fire_bricks), function(ii){\n \n br <- brick(paste0(\"Data/\",fire_bricks[ii]))\n df <- future_lapply(seq_len(nbands(br)), function(x){\n brName <- stringr::str_sub( stringr::str_replace_all(names(br[[x]]), \"\\\\.\", \"-\"), 2)\n mm <- as.matrix(br[[x]]>0)\n message(paste(x,\"-\", brName ,\"Suma de fuegos\", sum(mm)))\n sm <- as(mm,\"sparseMatrix\")\n \n summ <- as_tibble(summary(sm)) \n names(summ) <- c(\"i\",\"j\",\"data\")\n summ <- summ %>% mutate(t=x,date=brName) %>% dplyr::select(t,i,j,data,date)\n })\n #yy <- str_sub(names(br)[1],2,5)\n df <- do.call(rbind,df) %>% mutate(region=region_name)\n })\n p_df <- do.call(rbind,p_df)\n \n}", "meta": {"hexsha": "fbb83cca09f516b105b0964fa6c2d2f4bfe09dd6", "size": 3610, "ext": "r", "lang": "R", "max_stars_repo_path": "R/functions.r", "max_stars_repo_name": "Mario-Kart-Felix/fireNL", "max_stars_repo_head_hexsha": "2f3138019afde05bfdc0425880f478b2102b113f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/functions.r", "max_issues_repo_name": "Mario-Kart-Felix/fireNL", "max_issues_repo_head_hexsha": "2f3138019afde05bfdc0425880f478b2102b113f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/functions.r", "max_forks_repo_name": "Mario-Kart-Felix/fireNL", "max_forks_repo_head_hexsha": "2f3138019afde05bfdc0425880f478b2102b113f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-24T00:46:25.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-24T00:46:25.000Z", "avg_line_length": 35.0485436893, "max_line_length": 156, "alphanum_fraction": 0.6299168975, "num_tokens": 1081, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334525, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3316795756993032}} {"text": "windows(width=10,height=5)\r\npar(mar=c(5,7,1,1))\r\nfilled.contour(xx1,yy1,zz1,xlab=\"GC1 (min)\",ylab=\"GC2 (sec)\",col=red_blue(cstep),levels=clevels )\r\n\r\nfor (graph.i in 1:nrow(prop_coef) ){\r\n graph.name <- paste(rownames(prop_coef),\".contour\",sep=\"\")[graph.i]\r\n graph.variant <- eval(parse(text=paste(graph.name)))\r\n level.box <- replace(graph.variant, is.nan(graph.variant),0)\r\n level.range <- round(quantile(level.box,c(0.005,0.995)))\r\n level.step <- seq(level.range[1],level.range[2],1)\r\n level.step\r\n\r\n ####Log K\r\n par(new=T,mar=c(5,7,1,11),xaxs=\"i\", yaxs=\"i\", las=1,mgp=c(3, 1, 0))\r\n contour(xx1,yy1,graph.variant, yaxs=\"i\",axes=FALSE,col=rainbow(graph.i),lwd=1.2,levels=level.step,labcex = 1.1)\r\n}\r\n\r\n", "meta": {"hexsha": "842f690795ade370dc17bb5a4894b8348572a9fe", "size": 767, "ext": "r", "lang": "R", "max_stars_repo_path": "GCxGC-MS-Property-estimation_v1.1.1/Code/source_all_property_chrom.r", "max_stars_repo_name": "Yasuyuki-Zushi/GCxGC-MS-Property-estimation", "max_stars_repo_head_hexsha": "b14ef522bbcc925bf08c3e1674dc0163a0ce494f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-25T03:39:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-28T01:39:35.000Z", "max_issues_repo_path": "GCxGC-MS-Property-estimation_v1.1.1/Code/source_all_property_chrom.r", "max_issues_repo_name": "Yasuyuki-Zushi/GCxGC-MS-Property-estimation", "max_issues_repo_head_hexsha": "b14ef522bbcc925bf08c3e1674dc0163a0ce494f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GCxGC-MS-Property-estimation_v1.1.1/Code/source_all_property_chrom.r", "max_forks_repo_name": "Yasuyuki-Zushi/GCxGC-MS-Property-estimation", "max_forks_repo_head_hexsha": "b14ef522bbcc925bf08c3e1674dc0163a0ce494f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.6111111111, "max_line_length": 120, "alphanum_fraction": 0.6088657106, "num_tokens": 258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.33163168124596915}} {"text": "#######################################\n### 2010, David Ellinghaus ###\n### 2019, Florian Uellendahl-Werth ###\n#######################################\n\nrm(list=ls())\n\nfile.genome = commandArgs()[4]\ninc<-10000\nlibrary(\"stream\")\ngenome<-DSD_ReadCSV(file.genome,sep=\"\",skip=1,take=c(7,8))\nwng<-T\nlines_n<-0\npng(file=paste(file.genome, \".IBD-plot.png\", sep=\"\"), width=960, height=960)\nplot(get_points(genome,n=1,outofpoints = \"warn\"), xlim=c(0,1.0), ylim=c(0,1.0), xlab=\"ZO\", ylab=\"Z1\", pch=20, axes=F)\nwhile (wng) {\n points_tmp<-get_points(genome,n=inc,outofpoints = \"ignore\")\n points(points_tmp, xlim=c(0,1.0), ylim=c(0,1.0), pch=20)\n if(dim(points_tmp)[1]==0){wng<-F}\n lines_n<-lines_n+inc\n print(lines_n)\n flush.console()\n}\n\naxis(1, at=seq(0,1.0,0.2), tick=T)\naxis(2, at=seq(0,1.0,0.2), tick=T)\ndev.off()\n", "meta": {"hexsha": "d19f0291dcb14439036d8f39240d077f89046f61", "size": 821, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/ibd-plot-genomefile.r", "max_stars_repo_name": "jkaessens/gwas-assoc", "max_stars_repo_head_hexsha": "1053c94222701f108362e33c99155cfc148f4ca2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/ibd-plot-genomefile.r", "max_issues_repo_name": "jkaessens/gwas-assoc", "max_issues_repo_head_hexsha": "1053c94222701f108362e33c99155cfc148f4ca2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/ibd-plot-genomefile.r", "max_forks_repo_name": "jkaessens/gwas-assoc", "max_forks_repo_head_hexsha": "1053c94222701f108362e33c99155cfc148f4ca2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.3214285714, "max_line_length": 118, "alphanum_fraction": 0.580998782, "num_tokens": 298, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.33153982123410414}} {"text": "#########################################\r\n#Splinter et al. 2012 Methods\r\n#Supplemental R script\r\n#\r\n#Within code is contained that can generate spider plots.\r\n\r\n#dummy example\r\n#> source(\"spider_plots.R\")\r\n#> x1 <- seq(0,110e6, by=10e6)\r\n#> x2 <- seq(0,110e6, by=10e6)+2e6\r\n#> makeSpiderGramSingle(dom=cbind(x1,x2), chrom.len=150e6, vp.loc=130e6, col=rgb(0,0,1)) \r\n\r\n#The genes you have to add your self since these are different for every species and chromosome.\r\n\r\n#written by Elzo de Wit (2012)\r\n#mail: e.wit@hubrecht.eu\r\n#Hubrecht Institute\r\n#Utrecht, the Netherlands\r\n#########################################\r\n\r\n\r\n#draw a tubular shaped chromosome\r\ndrawChrom <- function(chrom.len = 50e6, max.wid = 100e6, hei=10, chrom.wid=1, y.loc=5){\r\n\tr <- seq(0,2*pi, len=1000)\r\n\tchrom.wid = chrom.wid/2\r\n\r\n\tdim.val <- par(\"din\")\r\n\tleft <- r[501:1000]\r\n\tright <- r[1:500]\r\n\t\r\n\tcorrection <- ((max.wid*chrom.wid)/(2*hei)) / (dim.val[1]/dim.val[2])\r\n\r\n\tx <- sin(left)*correction + correction\r\n\ty <- cos(left)*chrom.wid+y.loc\r\n\r\n\tx1 <- x\r\n\ty1 <- y\r\n\r\n\tx <- c(x, sin(right)*correction+chrom.len -correction)\r\n\ty <- c(y, cos(right)*chrom.wid+y.loc )\r\n\r\n\tx2 <- sin(right)*correction+chrom.len -correction\r\n\ty2 <- cos(right)*chrom.wid+y.loc\r\n\tpolygon(x,y, col='white')\r\n\t\r\n\tcol.seq <- seq(0.5,1,len=250)\r\n\tcol.seq <- c(col.seq,rev(col.seq))\r\n\tcols <- rgb(col.seq,col.seq,col.seq)\r\n\tsegments(x1,y1,x2,rev(y2), col = cols)\r\n\tpolygon(x,y, lwd=2)\r\n\r\n}\t\r\n\r\n\r\n#function for drawing two chromosomes\r\ndrawLocalChrom <- function(labels=c(\"Xa\",\"Xi\"), yloc1 = 3, yloc2 = 5, wid = 2, num.chrom=1, chrom.len){\r\n\tplot(c(0,chrom.len), c(yloc1-wid,yloc2+wid), type='n', axes=F, xlab=\"chromosomal location\", ylab=\"\", cex.lab=1)\r\n\r\n\tmb <- floor(chrom.len/1e6)\r\n\tmb10 <- floor(mb/10)*10\r\n\t#draw an axis\r\n\tlabel <- c(seq(0,mb10, by=10),mb)\r\n\tsegments(label*1e6, -1e9, label*1e6, 1e9, lwd=2, lty=2, col='grey90')\r\n\tmid.y <- (yloc1+yloc2)/2\r\n\tsegments(label*1e6, mid.y-0.15, label*1e6, mid.y+0.15, lwd=2)\r\n\tsegments(0, mid.y, mb*1e6, mid.y, lwd=2)\r\n\r\n\r\n\t#active X\r\n\tif(num.chrom==2){\r\n\t\tdrawChrom(chrom.len=chrom.len, max.wid=chrom.len, y.loc=yloc1)\r\n\t\ttext(-1e6,yloc1, labels[2],cex=1)\r\n\t}\t\r\n\t#inactive X \r\n\tdrawChrom(chrom.len=chrom.len, max.wid=chrom.len, y.loc=yloc2)\r\n\t\r\n\ttext(-1e6,yloc2, labels[1],cex=1)\r\n\tat <- 0:mb\r\n\taxis(1, at=at*1e6, labels=NA, cex.axis=1, lwd=1,las=1)\r\n\taxis(1, at=label*1e6, labels=label, cex.axis=1, lwd=2,las=1)\r\n\taxis(3, at=at*1e6, labels=NA, cex.axis=1, lwd=1,las=1)\r\n\taxis(3, at=label*1e6, labels=label, cex.axis=1, lwd=2)\r\n}\r\n\r\n#draw the splines showing the interactions\r\ndrawSplines.domain <- function( dom, vp.loc, y.base, y.arc, plot=F, col='black', chrom.size=166e6, relative=F){\r\n\tif(nrow(dom) == 0)\r\n\t\treturn\r\n\tfor(i in 1:nrow(dom)){\r\n\t\t#start <- min(dom[i,1], dom[i,2])\r\n\t\t#end <- max(dom[i,1], dom[i,2])\r\n\t\tstart <- dom[i,1]\r\n\t\tend <- dom[i,2]\r\n\t\txspline(c(vp.loc, (end + vp.loc)/2, end, start, (end + vp.loc)/2, vp.loc), c(y.base,y.arc,y.base,y.base,y.arc,y.base), open=F, shape=c(0,1,0,0,1,0), col=col, border=col)\r\n\t}\r\n}\r\n\r\n#dom: matrix or data.frame with two columns, start and end position of the interactions\r\n#vp.loc: the position of the viewpoint\r\n#color: color of the interaction splines\r\n#gene: data.frame containing the columns for the start, end and strand of gene\r\n#labels: what should be put on the left side of the plot\r\n#chrom.len: the length of the chromosome\r\n\r\nmakeSpiderGramSingle <- function( dom, vp.loc, color='black', gene=\"\", labels=c(\"\"), chrom.len ){\r\n\r\n\t#draw two chromosomes\r\n\tdrawLocalChrom( labels = labels, num.chrom=1, chrom.len = chrom.len )\r\n\r\n\t#and the splines\r\n\tdrawSplines.domain(dom, vp.loc=vp.loc, plot=F, relative=F, y.arc=8, y.base=5.5, col=color)\r\n\t\t\r\n\t#draw the genes\r\n\tif(! is.null(nrow(gene))){\r\n\t\tgene <- gene[gene[,2]-gene[,1] < 2e5,]\r\n\t\trect(gene[,1],5, gene[,2], ifelse(gene[,3]=='+',5.5,4.5), col='black')\r\n\t}\t\r\n\r\n}\r\n\r\n", "meta": {"hexsha": "1eb265d9c3c784689105e1a689e97c0d807eaa17", "size": 3902, "ext": "r", "lang": "R", "max_stars_repo_path": "spider_plots.r", "max_stars_repo_name": "guillermodeandajauregui/4CseqPipelines", "max_stars_repo_head_hexsha": "436054e11a4bcb1ea4fded85faf5a3b11e995ca8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "spider_plots.r", "max_issues_repo_name": "guillermodeandajauregui/4CseqPipelines", "max_issues_repo_head_hexsha": "436054e11a4bcb1ea4fded85faf5a3b11e995ca8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "spider_plots.r", "max_forks_repo_name": "guillermodeandajauregui/4CseqPipelines", "max_forks_repo_head_hexsha": "436054e11a4bcb1ea4fded85faf5a3b11e995ca8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2479338843, "max_line_length": 172, "alphanum_fraction": 0.6227575602, "num_tokens": 1396, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3315220409778917}} {"text": "guess.with.cheat <- function(n = 1000, cheat = 0.01){\r\n # Guess game: guess a number between 1 and n\r\n \r\n # generate random number between 1 and n\r\n num <- as.integer(runif(1, 1, n))\r\n # save guess history\r\n guess.log <- c()\r\n # input the number\r\n guess.input <- function(){\r\n guess.num <- suppressWarnings(\r\n as.integer(readline(\"guess an integer: \"))\r\n )\r\n # check the input \r\n while (is.na(guess.num) | guess.num < 1 | guess.num > n) {\r\n cat(\"Input Error\\n\", \r\n \"Please input an integer between 1 to\", n, \"!\\n\\n\")\r\n guess.num <- suppressWarnings(\r\n as.integer(readline(\"Guess an integer: \"))\r\n )\r\n }\r\n return(guess.num)\r\n }\r\n \r\n # Start game information\r\n cat(\"Guess Game\\n\\n\")\r\n cat(\"Guess an integer from 1 to\", n, \".\\n\\n\")\r\n \r\n # guessing...\r\n guess.num <- guess.input()\r\n # log the guess number\r\n guess.log <- c(guess.log, guess.num)\r\n \r\n while (guess.num != num) {\r\n # do not get the right number\r\n if (guess.num < num) {\r\n cat(\"Too Small !\")\r\n } else {\r\n cat(\"Too large !\")\r\n }\r\n guess.num <- guess.input()\r\n guess.log <- c(guess.log, guess.num)\r\n }\r\n \r\n # get the right number !\r\n cat(\"Bingo ! You got the number\", num, \"in\", length(guess.log), \"time(s) !\")\r\n # plot the guess log\r\n plot(guess.log, main = \"Guess log\", type = \"b\", \r\n ylim = c(1, n), ylab = \"guess number\")\r\n}\r\n\r\n", "meta": {"hexsha": "950eb681988cc1640885c1456aa21430dbcc393b", "size": 1407, "ext": "r", "lang": "R", "max_stars_repo_path": "guess with cheat.r", "max_stars_repo_name": "Yinr/r-learning", "max_stars_repo_head_hexsha": "50a4522110bc7dc13492f016c4a483683a1d1db3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "guess with cheat.r", "max_issues_repo_name": "Yinr/r-learning", "max_issues_repo_head_hexsha": "50a4522110bc7dc13492f016c4a483683a1d1db3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "guess with cheat.r", "max_forks_repo_name": "Yinr/r-learning", "max_forks_repo_head_hexsha": "50a4522110bc7dc13492f016c4a483683a1d1db3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5882352941, "max_line_length": 79, "alphanum_fraction": 0.5508173419, "num_tokens": 397, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3315220333719304}} {"text": "# 3. faza: Vizualizacija podatkov\nlibrary(jcolors)\nlibrary(dplyr)\nlibrary(ggrepel)\n#uvozimo zemljevid Evrope\n\nzemljevid <- uvozi.zemljevid(\n \"http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip\", \"ne_50m_admin_0_countries\", encoding=\"UTF-8\")\nzemljevid <- zemljevid[zemljevid$CONTINENT == \"Europe\",]\n\n\ncols1 <- c(\"#9fffe7\",\n \"#78e343\",\n \"#64bc8b\",\n \"#b7c172\",\n \"#c390b6\",\n \"#c9416d\",\n \"#cb4bab\",\n \"#de0004\",\n \"#02b0dd\",\n \"#67d564\",\n \"#025ecc\",\n \"#e7ff68\",\n \"#070043\",\n \"#ffdf41\",\n \"#f379ff\",\n \"#81a500\",\n \"#8e92ff\",\n \"#6d388a\",\n \"#32282f\",\n \"#c74d35\",\n \"#4c3560\",\n \"#086000\",\n \"#b9a192\",\n \"#ffaa44\",\n \"#004c4f\",\n \"#5d6994\",\n \"#53653b\",\n \"#ecffe2\",\n \"#4b0029\",\n \"#d0e0ff\",\n \"#7d4336\",\n \"#ff9096\",\n \"#b68247\")\n\n\n#-----------------------------------------------------------------------------------------------------------------------------------------------------\n#Grafi o skupnem priseljevanju v Slovenijo\n\nskupnopriseljevanje <- tabela1 \nskupno_leta <- skupnopriseljevanje %>% group_by(Leto) %>% summarise(Vsota=sum(Priseljeni_iz_tujine))\nskupno_leta_mz <- skupnopriseljevanje %>% group_by(Leto,Spol) %>% summarise(Stevilo_priseljenih=sum(Priseljeni_iz_tujine))\n\ngraf1 <- ggplot(data=skupno_leta, aes(x=Leto, y=Vsota)) + \n geom_point(color=rgb(0.8,0.4,0.1,0.7))+\n geom_line(color=rgb(0.8,0.4,0.1,0.7)) +\n ylab('Skupno \u0161tevilo priseljencev') + \n xlab('Leto') + \n ggtitle('\u0160tevilo priseljenih tujcev v Slovenijo skozi leta') +\n scale_x_continuous(breaks = 1*2011:2020) + \n theme(axis.text.x=element_text(angle=0))\n\n# graf 2: \u0161tevilo priseljenih po spolu__________________________-\n\ngraf2 <- skupno_leta_mz %>% ggplot(aes(x=Leto, y=Stevilo_priseljenih, col=Spol, palette=\"Pastel1\")) + \n geom_line() +\n ylab('\u0160tevilo priseljenih') + \n xlab('Leto') + \n labs(col = \"Spol\")+\n ggtitle('\u0160tevilo priseljenih ljudi v Slovenijo') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5))\n\n# graf 3: \u0161tevilo priseljenih po dr\u017eavi:\n\ngraf3 <- skupnopriseljevanje %>% ggplot(aes(x=Leto, y=Priseljeni_iz_tujine,col=Drzava, palette = \"default\")) + \n geom_point() +\n ylab('\u0160tevilo priseljenih') + \n xlab('Leto') + \n ggtitle('\u0160tevilo priseljenih ljudi v Slovenijo') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5))\ngraf3 <- graf3 + scale_color_manual(values=cols1)\n\n\n\n#\u0161e en graf \u0161tevila priseljenih po dr\u017eavi, skupno leta\npris_kolac <- ggplot(tabela1, aes(x = factor(1), y = Priseljeni_iz_tujine, fill = Drzava)) +\n xlab(\"\") + ylab(\"\") +\n geom_bar(width = 1, stat = \"identity\") + ggtitle(\"Dr\u017eave, iz katerih \\n prihajajo priseljenci\") + theme(legend.title=element_blank(), text = element_text(size=10))\n\n\npris_kolac <- pris_kolac + coord_polar(\"y\", start=0)\npris_kolac <- pris_kolac + scale_fill_manual(values=cols1)\n\n\n#_____________________________________________________________________\n#naredila graf skupaj s podatki z izseljenimi in priseljenimi, \u0161tevilo, letno\n\npriseljeniinizseljeni <- inner_join(tabela1, tabela2, by=NULL) %>% mutate(Selitveni_prirast=Priseljeni_iz_tujine-Odseljeni_v_tujino) \nskupnopi_leta <- priseljeniinizseljeni %>% group_by(Leto) %>% summarise(Priseljeni_iz_tujine=sum(Priseljeni_iz_tujine), Odseljeni_v_tujino=sum(Odseljeni_v_tujino), Selitveni_prirast=sum(Selitveni_prirast)) \nskupnopi_leta <- pivot_longer(skupnopi_leta,2:4, names_to=\"Vrsta\", values_to=\"Stevilo\") \nskupnopi_leta$Vrsta[skupnopi_leta$Vrsta == \"Odseljeni_v_tujino\"] <- \"Izseljeni\"\nskupnopi_leta$Vrsta[skupnopi_leta$Vrsta == \"Priseljeni_iz_tujine\"] <- \"Priseljeni\"\nskupnopi_leta$Vrsta[skupnopi_leta$Vrsta == \"Selitveni_prirast\"] <- \"Selitveni prirast\"\n\ngraf4 <- skupnopi_leta %>% ggplot(aes(x=Leto, y=Stevilo, col=Vrsta, palette=\"Pastel1\")) + \n geom_line() +\n geom_point()+\n ylab('\u0160tevilo ljudi') + \n xlab('Leto') + \n labs(col = \"Legenda:\")+\n ggtitle('\u0160tevilo priseljenih in izseljenih ljudi v Sloveniji') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5))\n\n# graf 5: \u0161tevilo izseljenih po spolu\nskupno_leta_mz2 <- tabela2 %>% group_by(Leto,Spol) %>% summarise(Stevilo_izseljenih=sum(Odseljeni_v_tujino))\ngraf5 <- skupno_leta_mz2 %>% ggplot(aes(x=Leto, y=Stevilo_izseljenih, col=Spol, palette=\"Pastel1\")) + \n geom_line() +\n ylab('\u0160tevilo ljudi') + \n xlab('Leto') + \n labs(col = \"Spol\")+\n ggtitle('\u0160tevilo odseljenih ljudi iz Slovenije') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5))\n\n#_______________________________________________________\n#zdru\u017eiti priseljevanje in izseljevanje po spolu - facet grid\nskupno_leta_mz_oboje <- inner_join(skupno_leta_mz,skupno_leta_mz2) %>% pivot_longer(3:4,names_to=\"Vrsta\",values_to=\"Stevilo\")\nskupno_leta_mz_oboje$Vrsta[skupno_leta_mz_oboje$Vrsta == \"Stevilo_priseljenih\"] <- \"Priseljeni\"\nskupno_leta_mz_oboje$Vrsta[skupno_leta_mz_oboje$Vrsta == \"Stevilo_izseljenih\"] <- \"Izseljeni\"\nspol_graf <- ggplot(data=skupno_leta_mz_oboje, aes(x=Leto, y=Stevilo, col=Spol)) +\n geom_line() +\n ylab('\u0160tevilo ljudi') + \n xlab('Leto') + \n labs(col = \"Spol\")+\n facet_wrap(.~Vrsta) +\n ggtitle('\u0160tevilo priseljenih in izseljenih ljudi glede na spol') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n\n#____________________________________________________\n\n#graf 6: \u0161tevilo izseljenih po dr\u017eavi\ntabelanova2 <- tabela2 %>% group_by(Leto, Drzava) %>% summarise(Odseljeni_v_tujino=sum(Odseljeni_v_tujino))\ntabelanova2 <- tabelanova2 %>% rename(\"Dr\u017eava\"=\"Drzava\")\ngraf6 <- tabelanova2 %>% ggplot(aes(x=Leto, y=Odseljeni_v_tujino, col=Dr\u017eava, palette = \"default\")) + \n geom_point() +\n ylab('\u0160tevilo izseljenih') + \n xlab('Leto') + \n ggtitle('Izseljevanje iz Slovenije') +\n scale_x_continuous(breaks = 1*2011:2020) +\n theme(axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\ngraf6 <- graf6 + scale_color_manual(values=cols1)\n\n\n#\u0161e en graf \u0161tevila izseljenih po dr\u017eavi, skupno leta\ndrzava_izs_kolac <- ggplot(tabela2, aes(x = factor(1), y = Odseljeni_v_tujino, fill = Drzava)) +\n xlab(\"\") + ylab(\"\") +\n geom_bar(width = 1, stat = \"identity\") + ggtitle(\"Dr\u017eave, kamor odhajajo Slovenci (2011-19)\") + theme(legend.title=element_blank())\ndrzava_izs_kolac <- drzava_izs_kolac + coord_polar(\"y\", start=0)\ndrzava_izs_kolac <- drzava_izs_kolac + scale_fill_manual(values=cols1)\n\n# STAROST\n#Starost izseljencev\nstarosti <- c(seq(15, 65, 5) %>% paste0(., \"-\", .+4), \"65 +\")\nstarost_graf1 <- ggplot(data=tabela3, aes(x=Leto, y=Stevilo_odseljenih, fill=factor(Starost, starosti))) +\n geom_bar(stat=\"identity\") + scale_x_continuous(breaks = 1*2011:2020) + ggtitle(\"Starost pri izselitvi iz Slovenije\") +\n xlab(\"Leto\") + ylab(\"\u0160tevilo\") + labs(fill='Starost') + theme(axis.text.x=element_text(angle=0))\n#Starost priseljencev\nstarosti2 <- c(\"0-14\", seq(15, 65, 5) %>% paste0(., \"-\", .+4), \"65 +\")\nstarost_graf2 <- ggplot(data=tabela4, aes(x=Leto, y=Stevilo_priseljenih, fill=factor(Starost, starosti2))) +\n geom_bar(stat=\"identity\") + scale_x_continuous(breaks = 1*2011:2020) + ggtitle(\"Starost pri priselitvi v Slovenijo\") +\n xlab(\"Leto\") + ylab(\"\u0160tevilo\") + labs(fill='Starost') + theme(axis.text.x=element_text(angle=0))\n\n\n# IZOBRAZBA\ntabela5nova <- tabela5 %>% rename(\"Drzava\"=\"Drzava_prihodnjega_bivalisca\")\ntabela6nova <- tabela6 %>% rename(\"Stevilo_priseljenih\"=\"Stevilo\") \ntabela_izobrazba <- inner_join(tabela5nova, tabela6nova, by=NULL) %>% \n pivot_longer(5:6, names_to=\"Vrsta\", values_to=\"Stevilo\") \ntabela_izobrazba$Vrsta[tabela_izobrazba$Vrsta == \"Stevilo_priseljenih\"] <- \"Priseljeni\"\ntabela_izobrazba$Vrsta[tabela_izobrazba$Vrsta == \"Stevilo_odseljenih\"] <- \"Izseljeni\"\n\nStopnja <- c(\"Osnovno\u0161olska ali manj\",\t\"Srednje\u0161olska\",\"Vi\u0161je\u0161olska, visoko\u0161olska\")\nizobrazba_graf <- ggplot(data=tabela_izobrazba, aes(x=Leto, y=Stevilo, fill=factor(Izobrazba, Stopnja))) +\n geom_bar(stat=\"identity\") + facet_wrap(.~Vrsta) + scale_x_continuous(breaks = 1*2011:2020) + ggtitle(\"Izobrazba ljudi, ki so se preselili in izselili\") +\n xlab(\"Leto\") + ylab(\"\u0160tevilo\") + labs(fill='Stopnja izobrazbe') + theme(axis.text.x=element_text(angle=90))\n\n\n#NAMEN PRISELITVE\n\nnamen_priselitve <- ggplot(tabela7, aes(x = factor(1), y = Stevilo, fill = Namen)) + xlab(\"\") + ylab(\"\") +\n geom_bar(width = 1, stat = \"identity\") + ggtitle(\"Namen preselitve v Slovenijo za priseljence v letih od 2011 do 2019\") + theme(legend.title=element_blank())\n\n\nnamen_priselitve <- namen_priselitve + coord_polar(\"y\", start=0)\n\n#DEJAVNOST\n\n#Priseljeni prebivalci po dejavnosti\nskupno_dejavnost <- tabela8 %>% group_by(Dejavnost) %>% summarise(Vsota=sum(Stevilo)) %>% mutate(Povpre\u010dje=round(Vsota/9,1))\nskupno_dejavnost <- skupno_dejavnost %>% filter(Dejavnost %in% c(\"Gradbeni\u0161tvo\",\"Predelovalne dejavnosti\",\"Promet in skladi\u0161\u010denje\",\"Druge raznovrstne poslovne dejavnosti\", \"Trgovina, vzdr\u017eevanje in popravila motornih vozil\", \"Gostinstvo\", \"Strokovne, znanstvene in tehni\u010dne dejavnosti\", \"Informacijske in komunikacijske dejavnosti\", \"Izobra\u017eevanje\", \"Zdravstvo in socialno varstvo\"))\ndejavnost <- skupno_dejavnost %>% ggplot(aes(x=reorder(Dejavnost,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n ylab('Povre\u010dje') + \n xlab(\"Dejavnost\")+\n ggtitle('Povpre\u010dno \u0161tevilo priseljencev glede na dejavnost v letih 2011-19') +\n theme(text = element_text(size=8), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n\n\n#Izseljeni prebivalci po dejavnosti\nskupno_dejavnost2 <- tabela9 %>% group_by(Dejavnost) %>% summarise(Vsota=sum(Stevilo)) %>% mutate(Povpre\u010dje=round(Vsota/9,1))\nskupno_dejavnost2 <- skupno_dejavnost2 %>% filter(Dejavnost %in% c(\"Gradbeni\u0161tvo\",\"Predelovalne dejavnosti\",\"Promet in skladi\u0161\u010denje\",\"Druge raznovrstne poslovne dejavnosti\", \"Trgovina, vzdr\u017eevanje in popravila motornih vozil\", \"Gostinstvo\", \"Strokovne, znanstvene in tehni\u010dne dejavnosti\", \"Informacijske in komunikacijske dejavnosti\", \"Izobra\u017eevanje\", \"Zdravstvo in socialno varstvo\", \n \"Dejavnosti javne uprave in obrambe, dejavnost obvezne socialne varnosti\",\"Kmetijstvo in lov, gozdarstvo, ribi\u0161tvo\"))\ndejavnost2 <- skupno_dejavnost2 %>% ggplot(aes(x=reorder(Dejavnost,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n ylab('Povpre\u010dje') +\n xlab(\"Dejavnost\")+\n ggtitle('Povpre\u010dno \u0161tevilo izseljencev glede na dejavnost v letih 2011-19') +\n theme(text = element_text(size=8), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n#priseljevanje po dr\u017eavah - povpre\u010dno \u0161tevilo priseljenih letno v letih 2011-19\nskupno_drzave <- tabela1 %>% group_by(Drzava) %>% summarise(Vsotadrzave=sum(Priseljeni_iz_tujine)) %>%\n mutate(Povpre\u010dje=round(Vsotadrzave/9,1))\n\ndrzave_priseljevanje <- skupno_drzave %>% ggplot(aes(x=reorder(Drzava,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n ylab('Povre\u010dje') + \n xlab(\"Dr\u017eava\")\n ggtitle('Povpre\u010dno \u0161tevilo priseljencev letno glede na dr\u017eavo iz katere prihajajo za leta 2011-19') +\n theme(text = element_text(size=5), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n#izseljevanje po dr\u017eavah - povpre\u010dno \u0161tevilo izseljenih letno v letih 2011-19\nskupno_drzave_izseljevanje <- tabela2 %>% group_by(Drzava) %>% summarise(Vsotadrzave2=sum(Odseljeni_v_tujino)) %>%\n mutate(Povpre\u010dje=round(Vsotadrzave2/9,1))\n\ndrzave_izseljevanje <- skupno_drzave_izseljevanje %>% ggplot(aes(x=reorder(Drzava,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n ylab('Povre\u010dje') + \n xlab(\"Dr\u017eava\") +\n ggtitle('Povpre\u010dno \u0161tevilo izseljencev letno glede na dr\u017eavo v katero odhajajo za leta 2011-19') +\n theme(text = element_text(size=5), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n\n#primerjava BDP in kamor se Slovenci najve\u010d izseljujejo\n#zares je vizualizacija neuporabna...\n\nizbrane <- c(\"Avstrija\", \"Nem\u010dija\", \"Italija\", \"\u0160vica\", \"Hrva\u0161ka\")\nBDPizbrane <- tabela10 %>%\n filter(Drzava %in% izbrane) %>% filter(between(Leto,2016,2019))\nizseljevanjeizbrane <- tabela2 %>% group_by(Leto, Drzava) %>%\n summarise(Odseljeni_v_tujino=sum(Odseljeni_v_tujino)) %>%\n filter(Drzava %in% izbrane) %>% filter(between(Leto,2016,2019))\nizbraneskupno <- inner_join(BDPizbrane, izseljevanjeizbrane) %>% pivot_longer(3:4, names_to=\"Vrsta\", values_to=\"Stevilo\") \n\nprimerjava <- izbraneskupno %>% ggplot(aes(x=Leto, y=Stevilo, col=Vrsta)) + geom_line() +\n ylab('Stevilo') + \n xlab('Leto') + \n facet_wrap(.~Drzava,ncol=3)+\n labs(col = \"Vrsta\")+\n ggtitle('Primerjava') +\n theme(axis.text.x=element_text(angle=90))\n\n#za priseljevanje podobno\n#isto neuporabno...\nizbrane2 <- c(\"Bosna in Hercegovina\", \"Srbija\", \"Kosovo\", \"Severna Makedonija\", \"Hrva\u0161ka\")\nBDPizbrane2 <- tabela10 %>%\n filter(Drzava %in% izbrane2) %>% filter(between(Leto,2016,2019))\npriseljevanjeizbrane <- tabela1 %>% group_by(Leto, Drzava) %>%\n summarise(Priseljeni_iz_tujine=sum(Priseljeni_iz_tujine)) %>%\n filter(Drzava %in% izbrane2) %>% filter(between(Leto,2016,2019))\nizbraneskupno2 <- inner_join(BDPizbrane2, priseljevanjeizbrane) %>% pivot_longer(3:4, names_to=\"Vrsta\", values_to=\"Stevilo\") \n\nprimerjava2 <- izbraneskupno2 %>% ggplot(aes(x=Leto, y=Stevilo, col=Vrsta)) + geom_line() +\n ylab('Stevilo') + \n xlab('Leto') + \n facet_wrap(.~Drzava,ncol=3)+\n labs(col = \"Vrsta\")+\n ggtitle('Primerjava') +\n theme(axis.text.x=element_text(angle=90))\n\n\n#ZEMLJEVIDI---------------------------------------------------------------------------\n\n#povpre\u010dne priselitve v dr\u017eavi za leta 2011-19 na 100k - ZEMLJEVID\n\nevropa_priseljevanje_leta <- tabela11 %>% group_by(Leto, Drzava) %>% summarise(Priseljeni=sum(Stevilo))\nprebpriseljevanje <- inner_join(evropa_priseljevanje_leta,tabela15)\nnovo <- prebpriseljevanje %>% mutate(Sprem=(100000/Prebivalstvo)) %>% mutate(Stevilo_pris_na100k = round(Sprem * Priseljeni,0)) \npovprecje2 <- novo %>% group_by(Drzava) %>% summarise(Povpre\u010dje=mean(Stevilo_pris_na100k)) %>% mutate(Povpre\u010dje=round(Povpre\u010dje,0))\n\nzemljevid1 <- tm_shape(merge(zemljevid,\n povprecje2,duplicateGeoms = TRUE,\n by.x=\"SOVEREIGNT\", by.y=\"Drzava\"), xlim=c(-20,32), ylim=c(32,72)) +\n tm_polygons(\"Povpre\u010dje\", title = \"Povp \u0161t. priseljenih ljudi na 100k\", breaks=c(0,500,800,1000,1500,2000,3000,4000,5000)) + \n tm_layout(bg.color = \"skyblue\") + \n tm_layout(main.title = \"Povpre\u010dno \u0161t. priseljenih ljudi \\n v dr\u017eavo 2011-19 na 100k\", main.title.size = 1, legend.title.size = 1) \n\n\n\n\n#________________________________________________________________________\n# histogram povpre\u010dnega priseljevanja na 100.000 prebivalcev\n\npovprecjepravapris <- povprecje2 %>% mutate(Drzava=slovar[Drzava]) %>% subset(Drzava!=\"Bolgarija\")\neu_priseljevanje <- povprecjepravapris %>% ggplot(aes(x=reorder(Drzava,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n xlab(\"Dr\u017eava\")+\n ylab('Povre\u010dje') + \n ggtitle('Povpre\u010dno \u0161tevilo priseljencev na 100.000 prebivalcev 2011-19') +\n theme(text = element_text(size=5), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n\n#povpre\u010dne izselitve v dr\u017eavi v letih 2011-19 na 100k - ZEMLJEVID\nevropa_izseljevanje_leta <- tabela12 %>% group_by(Leto, Drzava) %>% summarise(Izseljeni=sum(Stevilo)) %>% filter(between(Leto,2011,2018))\nprebizseljevanje <- inner_join(evropa_izseljevanje_leta,tabela15)\nnovo2 <- prebizseljevanje %>% mutate(Sprem=(100000/Prebivalstvo)) %>% mutate(Stevilo_izs_na100k = round(Sprem * Izseljeni,0)) \npovprecje3 <- novo2 %>% group_by(Drzava) %>% summarise(Povpre\u010dje=mean(Stevilo_izs_na100k)) %>% mutate(Povpre\u010dje=round(Povpre\u010dje,0))\n\nzemljevid2 <- tm_shape(merge(zemljevid,\n povprecje3,duplicateGeoms = TRUE,\n by.x=\"SOVEREIGNT\", by.y=\"Drzava\"), xlim=c(-25,32), ylim=c(32,72)) +\n tm_polygons(\"Povpre\u010dje\", title = \"Povpr. \u0161t. izseljenih na 100k\", breaks=c(0,250,500,750,1000,1500,2000,2500)) + \n tm_layout(bg.color = \"skyblue\") + \n tm_layout(main.title = \"Povpre\u010dno \u0161tevolo izseljenih \\n ljudi v dr\u017eavo 2011-19 na 100k\", main.title.size = 1, legend.title.size = 1) \n\n\n\n#________________________________________________________________________\n# histogram povpre\u010dnega izseljevanja na 100.000 prebivalcev\n\npovprecjeprava <- povprecje3 %>% mutate(Drzava=slovar[Drzava]) %>% subset(Drzava!=\"Bolgarija\")\neu_izseljevanje <- povprecjeprava %>% ggplot(aes(x=reorder(Drzava,-Povpre\u010dje), y=Povpre\u010dje,fill=Povpre\u010dje)) + \n geom_bar(position=\"dodge\", stat=\"identity\") + \n xlab(\"Dr\u017eava\")+\n ylab('Povre\u010dje') + \n ggtitle('Povpre\u010dno \u0161tevilo izseljencev na 100.000 prebivalcev 2011-19') +\n theme(text = element_text(size=5), axis.text.x=element_text(vjust=0.5, hjust=0.5, angle=90))\n\n\n#___________________________________________________________________________________\n#zemljevid izseljevanja iz slovenskih regij\nslovenija_izseljevanje_leta <- tabela13 %>% group_by(Leto, Regija) %>% summarise(Izseljeni=sum(Stevilo_odseljenih_v_tujino)) \nregijeizseljevanje <- inner_join(slovenija_izseljevanje_leta,tabela16)\nzdruzeno <- regijeizseljevanje %>% mutate(Sprem=(10000/Prebivalstvo)) %>% mutate(Stevilo_izs_na10000 = round(Sprem * Izseljeni,0)) \npovprecje_sloi <- zdruzeno %>% group_by(Regija) %>% summarise(Povpre\u010dje=mean(Stevilo_izs_na10000)) %>% mutate(Povpre\u010dje=round(Povpre\u010dje,0))\n\n\nSlovenija <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/SVN_adm_shp.zip\",\n \"SVN_adm1\", encoding = \"UTF-8\") \n\nSlovenija$NAME_1[Slovenija$NAME_1 == \"Gori\u0139\u02c7ka\"] <- \"Gori\u0161ka\"\nSlovenija$NAME_1[Slovenija$NAME_1 == \"Koro\u0139\u02c7ka\"] <- \"Koro\u0161ka\"\nSlovenija$NAME_1[Slovenija$NAME_1 == \"Notranjsko-kra\u0139\u02c7ka\"] <- \"Notranjsko-kra\u0161ka\"\nSlovenija$NAME_1[Slovenija$NAME_1 == \"Obalno-kra\u0139\u02c7ka\"] <- \"Obalno-kra\u0161ka\"\n\nSlovenija$NAME_1 <- Slovenija$NAME_1 %>%\n str_replace(\"Spodnjeposavska\", \"Posavska\") %>%\n str_replace(\"Notranjsko-kra\u0161ka\", \"Primorsko-notranjska\")\n\nzemljevid_slo1 <- tm_shape(merge(Slovenija, povprecje_sloi, by.x=\"NAME_1\", by.y=\"Regija\")) + \n tm_polygons(\"Povpre\u010dje\",palette=\"Purples\")+ \n tm_style(\"grey\") +\n tm_layout(main.title=\"Povpre\u010dno \u0161tevilo izseljenih ljudi na \\n 10.000 prebivalcev\", legend.position = c(0.75,0.1)) + tm_text(text='NAME_1', size=0.6)\n\n\n#zemljevid povpre\u010dnega priseljevanja v slovenske regije na 10.000 prebivalcev\nslovenija_priseljevanje_leta <- tabela14 %>% group_by(Leto, Regija) %>% summarise(Priseljeni=sum(Stevilo_priseljenih_iz_tujine)) \nregijepriseljevanje <- inner_join(slovenija_priseljevanje_leta,tabela16)\nzdruzeno2 <- regijepriseljevanje %>% mutate(Sprem=(10000/Prebivalstvo)) %>% mutate(Stevilo_pris_na10000 = round(Sprem * Priseljeni,0)) \npovprecje_slop <- zdruzeno2 %>% group_by(Regija) %>% summarise(Povpre\u010dje=mean(Stevilo_pris_na10000)) %>% mutate(Povpre\u010dje=round(Povpre\u010dje,0))\n\nzemljevid_slo2 <- tm_shape(merge(Slovenija, povprecje_slop, by.x=\"NAME_1\", by.y=\"Regija\")) + \n tm_polygons(\"Povpre\u010dje\",palette=\"Greens\")+ \n tm_style(\"grey\") +\n tm_layout(main.title=\"Povpre\u010dno \u0161tevilo priseljenih ljudi \\n na 10.000 prebivalcev\", legend.position = c(0.75,0.1)) + tm_text(text='NAME_1', size=0.6)\n\n#________________________________________\n\n#Opcijsko:\n#5. Zemljevid iz kje se najve\u010d preseljujejo v Slovenijo - glede na tabeli 1/2 (2 zemljevida...)\n\n#62. video glej geom text, kako dodati text na graf za nek graf\n", "meta": {"hexsha": "e465e586d817ac2476b84bffb74548287bf1bd54", "size": 19770, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "klarasirca/APPR-2020-21", "max_stars_repo_head_hexsha": "1f3e7b1b8015b238cad2da48bcd9cdf159e3b646", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "klarasirca/APPR-2020-21", "max_issues_repo_head_hexsha": "1f3e7b1b8015b238cad2da48bcd9cdf159e3b646", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-08-28T18:16:19.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-03T12:06:42.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "klarasirca/APPR-2020-21", "max_forks_repo_head_hexsha": "1f3e7b1b8015b238cad2da48bcd9cdf159e3b646", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.3053435115, "max_line_length": 385, "alphanum_fraction": 0.7089023773, "num_tokens": 7590, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.6477982315512488, "lm_q1q2_score": 0.3314891115827677}} {"text": "############################################\n# R - script to collect all moobench results\n############################################\n\n# these values are here only as documentation. The parameters are set by benchmark.sh\n#rm(list=ls(all=TRUE))\n#data_fn=\"data/\"\n#folder_fn=\"results-benchmark-binary\"\n#results_fn=paste(data_fn,folder_fn,\"/raw\",sep=\"\")\n#outtxt_fn=paste(data_fn,folder_fn,\"/results-text.txt\",sep=\"\")\n#results_fn=\"raw\"\n#outtxt_fn=\"results-text.txt\"\n\n#########\n# These are configuration parameters which are automatically prepended to this file by the benchmark.sh script.\n# Therefore, they must not be set here. The following lines only serve as documentation.\n#configs.loop=10\n#configs.recursion=c(10)\n#configs.labels=c(\"No Probe\",\"Inactive Probe\",\"Collecting Data\",\"Writing Data (ASCII)\", \"Writing Data (Bin)\")\n#results.count=2000000\n#results.skip=1000000\n\n#bars.minval=500\n#bars.maxval=600\n\n\n##########\n# Process configuration\n\n# divisor 1 = nano, 1000 = micro, 1000000 = milli seconds\ntimeUnit <- 1000 \n\n# number of Kieker writer configurations \nnumberOfWriters <- length(configs.labels)\nrecursion_depth <- configs.recursion\n\nnumberOfValues <- configs.loop*(results.count-results.skip)\nnumbers <- c(1:(numberOfValues))\nresultDimensionNames <- list(configs.labels, numbers)\n\n# result values\nresultsBIG <- array(dim=c(numberOfWriters, numberOfValues), dimnames=resultDimensionNames)\n\n##########\n# Create result\n\n## \"[ recursion , config , loop ]\"\n\nnumOfRowsToRead <- results.count-results.skip\n\nfor (writer_idx in (1:numberOfWriters)) {\n recordsPerSecond = c()\n rpsLastDuration = 0\n rpsCount = 0\n file_idx <- writer_idx - 1\n\n # loop\n for (loop_counter in (1:configs.loop)) {\n results_fn_filepath <- paste(results_fn, \"-\", loop_counter, \"-\", recursion_depth, \"-\", file_idx, \".csv\", sep=\"\")\n message(results_fn_filepath)\n results <- read.csv2(results_fn_filepath, nrows=numOfRowsToRead, skip=results.skip, quote=\"\", colClasses=c(\"NULL\",\"numeric\", \"numeric\", \"numeric\"), comment.char=\"\", col.names=c(\"thread_id\", \"duration_nsec\", \"gc\", \"t\"), header=FALSE)\n trx_idx <- c(1:numOfRowsToRead)\n resultsBIG[writer_idx,trx_idx] <- results[[\"duration_nsec\"]]\n }\n}\n\nqnorm_value <- qnorm(0.975)\n\n# print results\nprintDimensionNames <- list(c(\"mean\",\"ci95%\",\"md25%\",\"md50%\",\"md75%\",\"max\",\"min\"), c(1:numberOfWriters))\n# row number == number of computed result values, e.g., mean, min, max\nprintvalues <- matrix(nrow=7, ncol=numberOfWriters, dimnames=printDimensionNames)\n\nfor (writer_idx in (1:numberOfWriters)) {\n idx_mult <- c(1:numOfRowsToRead)\n\n valuesBIG <- resultsBIG[writer_idx,idx_mult]/timeUnit\n\n printvalues[\"mean\",writer_idx] <- mean(valuesBIG)\n printvalues[\"ci95%\",writer_idx] <- qnorm_value*sd(valuesBIG)/sqrt(length(valuesBIG))\n printvalues[c(\"md25%\",\"md50%\",\"md75%\"),writer_idx] <- quantile(valuesBIG, probs=c(0.25, 0.5, 0.75))\n printvalues[\"max\",writer_idx] <- max(valuesBIG)\n printvalues[\"min\",writer_idx] <- min(valuesBIG)\n}\nresultstext <- formatC(printvalues,format=\"f\",digits=4,width=8)\n\nprint(resultstext)\n\nwrite(paste(\"Recursion Depth: \", recursion_depth),file=outtxt_fn,append=TRUE)\nwrite(\"response time\",file=outtxt_fn,append=TRUE)\nwrite.table(resultstext,file=outtxt_fn,append=TRUE,quote=FALSE,sep=\"\\t\",col.names=FALSE)\n\nconcResult <- \"\"\nheadResult <- \"\"\n# write the first n-1 elements preceded by a comma (,)\nfor (writer_idx in (1:(numberOfWriters-1))) {\n headResult <- paste(headResult, configs.labels[writer_idx], \",\")\n concResult <- paste(concResult, printvalues[\"mean\",writer_idx], \",\")\n}\n# write the last without a comma\nheadResult <- paste(headResult, configs.labels[numberOfWriters])\nconcResult <- paste(concResult, printvalues[\"mean\", numberOfWriters])\n \nwrite(headResult,file=outcsv_fn,append=TRUE)\nwrite(concResult,file=outcsv_fn,append=TRUE)\n\n# end\n", "meta": {"hexsha": "7d3e21f069b0da249d2581c2f5272886d5ff3caa", "size": 3845, "ext": "r", "lang": "R", "max_stars_repo_path": "kieker-examples/OverheadEvaluationMicrobenchmark/MooBench/r/stats.csv.r", "max_stars_repo_name": "Zachpocalypse/kieker", "max_stars_repo_head_hexsha": "64c8a74422643362da92bb107ae94f892fa2cbf9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "kieker-examples/OverheadEvaluationMicrobenchmark/MooBench/r/stats.csv.r", "max_issues_repo_name": "Zachpocalypse/kieker", "max_issues_repo_head_hexsha": "64c8a74422643362da92bb107ae94f892fa2cbf9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "kieker-examples/OverheadEvaluationMicrobenchmark/MooBench/r/stats.csv.r", "max_forks_repo_name": "Zachpocalypse/kieker", "max_forks_repo_head_hexsha": "64c8a74422643362da92bb107ae94f892fa2cbf9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6018518519, "max_line_length": 238, "alphanum_fraction": 0.7071521456, "num_tokens": 1024, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521105, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3314891046238629}} {"text": "library(readr)\nnumlines_data <- read_table2(\"linespercell.txt\", \n col_names = FALSE)\nnumlines <- numlines_data$X1\nnumreads <- numlines/4\navg <- mean(as.numeric(numreads))\nstdev <- sd(numreads)\nnumreads_data <- data.frame(numreads, avg, stdev)\ncolnames(numreads_data) <- c(\"reads\", \"avg\", \"stdev\")\n\nlibrary(ggplot2)\n# Basic violin plot\np <- ggplot(numreads_data, aes(x=\"Cells\", y=reads)) + \n geom_violin()\np + geom_jitter(shape=16, position=position_jitter(0.2))\n\nggsave(\"reads_violin.pdf\", width=5, height=5)\n\n# Basic violin plot\nnumreads_data[\"log_reads\"] <- log(numreads_data$reads)\np <- ggplot(numreads_data, aes(x=\"Cells\", y=log_reads)) + \n geom_violin()\np + geom_jitter(shape=16, position=position_jitter(0.2))\n\nggsave(\"log_reads_violin.pdf\", width=5, height=5)\n\n", "meta": {"hexsha": "a461dfe4cce26cab5f49c14adfcfd39e0c5543b1", "size": 797, "ext": "r", "lang": "R", "max_stars_repo_path": "generate_reads_violin.r", "max_stars_repo_name": "charliewhitmore28/SPLiT-Seq_demultiplexing", "max_stars_repo_head_hexsha": "4d68824b99c897a9bd06e0715cc3e1bc51eecc55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate_reads_violin.r", "max_issues_repo_name": "charliewhitmore28/SPLiT-Seq_demultiplexing", "max_issues_repo_head_hexsha": "4d68824b99c897a9bd06e0715cc3e1bc51eecc55", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate_reads_violin.r", "max_forks_repo_name": "charliewhitmore28/SPLiT-Seq_demultiplexing", "max_forks_repo_head_hexsha": "4d68824b99c897a9bd06e0715cc3e1bc51eecc55", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5185185185, "max_line_length": 58, "alphanum_fraction": 0.7026348808, "num_tokens": 246, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33148910462386283}} {"text": "library('igraph')\n\nNFDI_edges <- read.table(header=TRUE,\n sep=\",\",\n text=\"\nfrom,to\nBERD@NFDI,KonsortSWD\nBERD@NFDI,MaRDI\nBERD@NFDI,NFDI4Memory\nBERD@NFDI,Text+\nDAPHNE4NFDI,FAIRmat\nDAPHNE4NFDI,NFDI-MatWerk\nDAPHNE4NFDI,NFDI4Cat\nDAPHNE4NFDI,NFDI4Chem\nDAPHNE4NFDI,NFDI4Health\nDAPHNE4NFDI,NFDI4Ing\nDAPHNE4NFDI,NFDI4Objects\nDAPHNE4NFDI,PUNCH4NFDI\nFAIRmat,DAPHNE4NFDI\nFAIRmat,DataPLANT\nFAIRmat,MaRDI\nFAIRmat,NFDI-MatWerk\nFAIRmat,NFDI4Cat\nFAIRmat,NFDI4Chem\nFAIRmat,NFDI4DataScience\nFAIRmat,NFDI4Ing\nFAIRmat,NFDIxCS\nFAIRmat,PUNCH4NFDI\nMaRDI,BERD@NFDI\nMaRDI,FAIRmat\nMaRDI,NFDI-MatWerk\nMaRDI,NFDI-Neuro\nMaRDI,NFDI4Cat\nMaRDI,NFDI4Chem\nMaRDI,NFDI4Ing\nMaRDI,PUNCH4NFDI\nNFDI-MatWerk,DAPHNE4NFDI\nNFDI-MatWerk,DataPLANT\nNFDI-MatWerk,FAIRmat\nNFDI-MatWerk,MaRDI\nNFDI-MatWerk,NFDI4Chem\nNFDI-MatWerk,NFDI4DataScience\nNFDI-MatWerk,NFDI4Ing\nNFDI-MatWerk,NFDIxCS\nNFDI-Neuro,DataPLANT\nNFDI-Neuro,GHGA\nNFDI-Neuro,NFDI4BioDiversity\nNFDI-Neuro,NFDI4Culture\nNFDI-Neuro,NFDI4Earth\nNFDI-Neuro,NFDI4Health\nNFDI-Neuro,NFDI4Ing\nNFDI-Neuro,NFDI4Microbiota\nNFDI4Agri,DataPLANT\nNFDI4Agri,KonsortSWD\nNFDI4Agri,NFDI4BioDiversity\nNFDI4Agri,NFDI4Earth\nNFDI4Agri,NFDI4Health\nNFDI4Agri,NFDI4Immuno\nNFDI4Agri,NFDI4Microbiota\nNFDI4DataScience,KonsortSWD\nNFDI4DataScience,MaRDI\nNFDI4DataScience,NFDI-MatWerk\nNFDI4DataScience,NFDI4BioDiversity\nNFDI4DataScience,NFDI4Cat\nNFDI4DataScience,NFDI4Chem\nNFDI4DataScience,NFDI4Culture\nNFDI4DataScience,NFDI4Health\nNFDI4DataScience,NFDI4Ing\nNFDI4DataScience,NFDI4Microbiota\nNFDI4DataScience,NFDIxCS\nNFDI4Earth,DataPLANT\nNFDI4Earth,GHGA\nNFDI4Earth,KonsortSWD\nNFDI4Earth,NFDI4Agri\nNFDI4Earth,NFDI4BioDiversity\nNFDI4Earth,NFDI4Cat\nNFDI4Earth,NFDI4Chem\nNFDI4Earth,NFDI4Culture\nNFDI4Earth,NFDI4Health\nNFDI4Earth,NFDI4Ing\nNFDI4Earth,NFDI4Objects\nNFDI4Immuno,GHGA\nNFDI4Immuno,NFDI4Agri\nNFDI4Immuno,NFDI4Health\nNFDI4Immuno,NFDI4Microbiota\nNFDI4Memory,BERD@NFDI\nNFDI4Memory,KonsortSWD\nNFDI4Memory,MaRDI\nNFDI4Memory,NFDI4Culture\nNFDI4Memory,NFDI4Objects\nNFDI4Memory,Text+\nNFDI4Microbiota,DataPLANT\nNFDI4Microbiota,GHGA\nNFDI4Microbiota,NFDI4Agri\nNFDI4Microbiota,NFDI4BioDiversity\nNFDI4Microbiota,NFDI4Chem\nNFDI4Microbiota,NFDI4DataScience\nNFDI4Microbiota,NFDI4Health\nNFDI4Microbiota,NFDI4Immuno\nNFDI4Microbiota,NFDI4Ing\nNFDI4Objects,KonsortSWD\nNFDI4Objects,NFDI4Agri\nNFDI4Objects,NFDI4BioDiversity\nNFDI4Objects,NFDI4Culture\nNFDI4Objects,NFDI4Earth\nNFDI4Objects,NFDI4Memory\nNFDI4Objects,Text+\nNFDI4SD,NFDI4Culture\nNFDI4SD,NFDI4DataScience\nNFDI4SD,NFDI4Memory\nNFDI4SD,NFDI4Objects\nNFDIxCS,FAIRmat\nNFDIxCS,MaRDI\nNFDIxCS,NFDI4Chem\nNFDIxCS,NFDI4DataScience\nNFDIxCS,NFDI4Earth\nNFDIxCS,NFDI4Ing\nPUNCH4NFDI,DAPHNE4NFDI\nPUNCH4NFDI,FAIRmat\nPUNCH4NFDI,GHGA\nPUNCH4NFDI,MaRDI\nPUNCH4NFDI,NFDI4Earth\nPUNCH4NFDI,NFDI4Ing\nPUNCH4NFDI,NFDIxCS\nText+,KonsortSWD\nText+,NFDI4BioDiversity\nText+,NFDI4Culture\nText+,NFDI4Earth\nText+,NFDI4Ing\nText+,NFDI4Memory\nText+,NFDI4Objects\n\")\n\nNFDI_network <- graph_from_data_frame(NFDI_edges,\n directed=FALSE\n )\n\nset.seed(1234)\n\nplot(NFDI_network, # loading data frame\n main = \"NFDI-Netzwerk\", # adding a title\n frame = TRUE # making a frame \n )\n\n\nset.seed(1234)\n\nplot(NFDI_network, # loading data frame\n main = \"NFDI-Netzwerk\", # adding a title\n frame = TRUE, # making a frame\n layout = layout.graphopt, #* better layout options\n )\n\n\nset.seed(1234)\n\n\nplot(NFDI_network, # loading data frame\n main = \"NFDI-Netzwerk\", # adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", #* color of nodes\n vertex.frame.color = \"#ffcc66\", #* color of the frame of nodes\n vertex.label.cex = 0.5, #* size of the description of the labels\n vertex.label.color = \"black\", #* color of the description \n edge.color = \"#808080\", #* color of edges\n edge.curved = 0.1, #* factor of \"curvity\"\n )\n\ndegree(NFDI_network) #* calculate number of edges\n\nset.seed(1234)\n\nplot(NFDI_network, # loading data frame\n main = \"NFDI-Netzwerk\", # adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", # color of nodes\n vertex.frame.color = \"#ffcc66\", # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0.1, # factor of \"curvity\"\n vertex.size = degree(NFDI_network), #* size of nodes depends on amount of edges\n )\n\nNFDI_network_directed <- graph_from_data_frame(NFDI_edges,\n directed = TRUE\n )\n\nset.seed(1234)\n\nplot(NFDI_network_directed, #<<<<<<< loading data frame\n main = \"NFDI-Netzwerk\", # adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", # color of nodes\n vertex.frame.color = \"#ffcc66\", # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0, #<<<<<<<<< factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed), #<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, #* arrow size, defaults to 1\n )\n\n\ndegree(NFDI_network_directed,\n mode = \"in\")\n\nset.seed(1234)\n\nplot(NFDI_network_directed, # loading data frame\n main = \"NFDI-Netzwerk ()\", #<<<<<<<< adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", # color of nodes\n vertex.frame.color = \"#ffcc66\", # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed,\n mode = \"in\"), #<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n )\n\ndegree(NFDI_network_directed,\n mode = \"out\")\n\nset.seed(1234)\n\nplot(NFDI_network_directed, # loading data frame\n main = \"NFDI-Netzwerk ()\", #<<<<<<<< adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", # color of nodes\n vertex.frame.color = \"#ffcc66\", # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed,\n mode = \"out\"), #<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n )\n\nNFDI_network_directed_filter <- delete_vertices(NFDI_network_directed, \n V(NFDI_network_directed)[ degree(NFDI_network_directed, mode = \"out\") == 0 ])\n\ndegree(NFDI_network_directed_filter,\n mode = \"total\")\n\nset.seed(1234)\n\nplot(NFDI_network_directed_filter, #<<<<<<<< loading data frame\n main = \"NFDI-Netzwerk ()\", #<<<<<<<< adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = \"#ffcc66\", # color of nodes\n vertex.frame.color = \"#ffcc66\", # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed_filter,\n mode = \"total\"), #<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n )\n\nNFDI_nodes <- read.table(header=TRUE,\n sep=\",\",\n text=\"\nname,group\nBERD@NFDI,3\nDAPHNE4NFDI,5\nDataPLANT,2\nFAIRmat,5\nGHGA,1\nKonsortSWD,3\nMaRDI,4\nNFDI-MatWerk,4\nNFDI-Neuro,1\nNFDI4Agri,2\nNFDI4BioDiversity,2\nNFDI4Cat,5\nNFDI4Chem,5\nNFDI4Culture,3\nNFDI4DataScience,4\nNFDI4Earth,2\nNFDI4Health,1\nNFDI4Immuno,1\nNFDI4Ing,4\nNFDI4Memory,3\nNFDI4Microbiota,2\nNFDI4Objects,3\nNFDI4SD,3\nNFDIxCS,4\nPUNCH4NFDI,5\nText+,3\n\")\n\nNFDI_network_directed <- graph_from_data_frame(d = NFDI_edges, # d = data frame =~ edges\n vertices = NFDI_nodes, #nodes\n directed = TRUE) #directed\n\nNFDI_color_code <- c(\"#f5ac9f\", # Medizin\n \"#e43516\", # Lebenswissenschaften\n \"#f9b900\", # Geisteswissenschaften\n \"#007aaf\", # Ingenieurwissenschaften\n \"#6ca11d\" # Chemie/Physik\n )\nNFDI_color_groups <- NFDI_color_code[as.numeric(as.factor(V(NFDI_network_directed)$group))]\n\nset.seed(1234)\n\nplot(NFDI_network_directed, # loading data frame\n main = \"NFDI-Netzwerk ()\", #<<<<<<<< adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.color = NFDI_color_groups, #<<<<<<<<<< color of nodes\n vertex.frame.color = NFDI_color_groups, #<<<<<<<<<< color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = \"#808080\", # color of edges\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed,\n mode = \"total\"), #<<<<<<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n )\n\n\nlegend(\"bottomright\", # x-position\n title = \"NFDI-Konferenzsystematik\", # title\n legend = c(\n \"(1) Medizin\",\n \"(2) Lebenswissenschaften\",\n \"(3) Geisteswissenschaften\",\n \"(4) Ingenieurwissenschaften\",\n \"(5) Chemie/Physik\"\n ), # the text of the legend\n col = NFDI_color_code , # colors of lines and points beside the legend text\n pch = 20, # the plotting symbols appearing in the legend\n bty = \"n\", # no frame, the type of box to be drawn around the legend (n=no frame)\n cex = .75, # character expansion factor relative to current par(\"cex\").\n pt.cex = 2 # expansion factor(s) for the points\n)\n\nset.seed(1234)\n\nNFDI_network_directed_cluster <- cluster_optimal(NFDI_network_directed)\n\n\nplot(NFDI_network_directed_cluster, #<<<<<<<<<<< clustered network data\n NFDI_network_directed, # loading data frame\n main = \"NFDI-Netzwerk ()\", # adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n #vertex.color = NFDI_color_groups, #<<<<<<<<<< color of nodes\n vertex.frame.color = NFDI_color_groups, #<<<<<<<<<< color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = NA, #<<<<<<<<<<<<<< color of edges\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed,\n mode = \"total\"), #<<<<<<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n col = NFDI_color_groups, #<<<<<<<<<<<<< color of nodes\n mark.col = \"grey\", #<<<<<<<<<< color groups\n mark.border = NA, #<<<<<<<<<< no border color\n )\n\n\nlegend(\"bottomright\", # x-position\n title = \"NFDI-Konferenzsystematik\", # title\n legend = c(\n \"(1) Medizin\",\n \"(2) Lebenswissenschaften\",\n \"(3) Geisteswissenschaften\",\n \"(4) Ingenieurwissenschaften\",\n \"(5) Chemie/Physik\"\n ), # the text of the legend\n col = NFDI_color_code , # colors of lines and points beside the legend text\n pch = 20, # the plotting symbols appearing in the legend\n bty = \"n\", # no frame, the type of box to be drawn around the legend (n=no frame)\n cex = .75, # character expansion factor relative to current par(\"cex\").\n pt.cex = 2 # expansion factor(s) for the points\n)\n\nset.seed(1234)\n\nplot(NFDI_network_directed_cluster, # clustered network data\n NFDI_network_directed, # loading data frame\n main = \"NFDI-Netzwerk ()\", # adding a title\n frame = TRUE, # making a frame \n layout = layout.graphopt, # better layout options\n vertex.frame.color = NFDI_color_groups, # color of the frame of nodes\n vertex.label.cex = 0.5, # size of the description of the labels\n vertex.label.color = \"black\", # color of the description \n # color: https://www.w3schools.com/colors/colors_picker.asp \n edge.color = c(NA, \"#bf4040\")[crossing(NFDI_network_directed_cluster, NFDI_network_directed) + 1], \n #<<<<<<<<<<<<<<<<< show only edges if they go to another group\n edge.curved = 0, # factor of \"curvity\"\n vertex.size = degree(NFDI_network_directed,\n mode = \"total\"), #<<<<<<<<<<< size of nodes depends on amount of edges\n edge.arrow.size = .5, # arrow size, defaults to 1\n col = NFDI_color_groups, # color of nodes\n mark.col = \"grey\", # color groups\n mark.border = NA, # no border color\n )\n\n\nlegend(\"bottomright\", # x-position\n title = \"NFDI-Konferenzsystematik\", # title\n legend = c(\n \"(1) Medizin\",\n \"(2) Lebenswissenschaften\",\n \"(3) Geisteswissenschaften\",\n \"(4) Ingenieurwissenschaften\",\n \"(5) Chemie/Physik\"\n ), # the text of the legend\n col = NFDI_color_code , # colors of lines and points beside the legend text\n pch = 20, # the plotting symbols appearing in the legend\n bty = \"n\", # no frame, the type of box to be drawn around the legend (n=no frame)\n cex = .75, # character expansion factor relative to current par(\"cex\").\n pt.cex = 2 # expansion factor(s) for the points\n)\n", "meta": {"hexsha": "13093d83e3a5d3ab5e6df871928f6282e0c598b8", "size": 16445, "ext": "r", "lang": "R", "max_stars_repo_path": "Notebook/das-versprechen-der-vernetzung.r", "max_stars_repo_name": "SamaMajidian11/das-versprechen-der-vernetzung", "max_stars_repo_head_hexsha": "3609467f2eeed07ef04d470c10010e772b0849ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Notebook/das-versprechen-der-vernetzung.r", "max_issues_repo_name": "SamaMajidian11/das-versprechen-der-vernetzung", "max_issues_repo_head_hexsha": "3609467f2eeed07ef04d470c10010e772b0849ae", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Notebook/das-versprechen-der-vernetzung.r", "max_forks_repo_name": "SamaMajidian11/das-versprechen-der-vernetzung", "max_forks_repo_head_hexsha": "3609467f2eeed07ef04d470c10010e772b0849ae", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8045977011, "max_line_length": 105, "alphanum_fraction": 0.6032836728, "num_tokens": 4945, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33148910462386283}} {"text": "Pairs.Explore<-function(num.plots=5,min.cor=.7,input.file,output.file,response.col=\"ResponseBinary\",cors.w.highest=FALSE,pres=TRUE,absn=TRUE,bgd=TRUE,Debug=FALSE){\n\n\n #num.plots=plots per page of display\n #min.cor=the minimum correlation to be included in determining which set of predictors to display\n #input.file=a csv assumed to have the new vistrails form with the first several rows specifiying where to find tiffs\n # and which columns to include in analysis\n #output.file=...\n #response.col=name of response column to be removed and used elsewhere\n #modifications\n #5/10/2011 altered to handle count data as well presence absence.\n #any counts higher than or equal to 1 are set to be presence though I might consider\n #adding the option to have a threshold set instead of using just presence/absence\n\n\n absn<-as.logical(absn)\n pres<-as.logical(pres)\n bgd<-as.logical(bgd)\n cors.w.highest<-as.logical(cors.w.highest)\n \n #Read input data and remove any columns to be excluded\n dat<-read.csv(input.file,skip=3,header=FALSE)\n\n hl<-readLines(input.file,1)\n hl=strsplit(hl,',')\n colnames(dat) = hl[[1]]\n\n tif.info<-readLines(input.file,3)\n tif.info<-strsplit(tif.info,',')\n include<-(as.numeric(tif.info[[2]]))\n\n #Remove coordinates, response column, site.weights\n #before exploring predictor relationship\n rm.cols <- as.vector(na.omit(c(match(\"x\",tolower(names(dat))),match(\"y\",tolower(names(dat))),\n match(\"site.weights\",tolower(names(dat))),match(tolower(response.col),tolower(names(dat))),match(\"Split\",names(dat)))))\n\n #remove testing split\n if(!is.na(match(\"Split\",names(dat)))) dat<-dat[-c(which(dat$Split==\"test\"),arr.ind=TRUE),]\n\n include[is.na(include)]<-0\n rm.cols<-unique(c(rm.cols,which(include==0,arr.ind=TRUE)))\n response<-dat[,match(tolower(response.col),tolower(names(dat)))]\n\n dat<-dat[order(response),]\n response<-response[order(response)]\n\n #for the purpose of the pairs plot, taking all counts greater than 1 and setting them equal to presence\n #this is never exported\n if(response.col==\"responseCount\") {response[response>=1]<-1\n }\n \n #remove any of pres absn or bgd that aren't desired\n temp<-c(0,1,-9999)\n temp<-temp[c(absn,pres,bgd)]\n dat<-dat[response%in%temp,]\n response<-response[response%in%temp]\n\n\n if(sum(response==-9999)>1000){\n s<-sample(which(response==-9999,arr.ind=TRUE),size=(sum(response==-9999)-1000))\n dat<-dat[-c(s),]\n response<-response[-c(s)]\n }\n\n if (response.col==\"responseCount\") {\n TrueResponse<-dat[,match(tolower(response.col),tolower(names(dat)))]\n } else TrueResponse<-response\n\n #now remove all columns except predictors\n dat<-dat[-rm.cols]\n dat[dat==-9999]<-NA\n response<-response[complete.cases(dat)]\n TrueResponse<-TrueResponse[complete.cases(dat)]\n dat<-dat[complete.cases(dat),]\n\n\n #dat<-dat[1:2000,]\n #Remove columns with only one unique value\n varr <- function(x) var(x,na.rm=TRUE)\n \n dat<-try(dat[,as.vector(apply(dat,2,varr)==0)!=1],silent=TRUE)\n if(class(dat)==\"try-error\") stop(\"mds file contains nonnumeric columns please remove and continue\")\n #record correlations for later plots\n\n cmat<-cor(dat,use=\"pairwise.complete.obs\")\n smat<-cor(dat,method=\"spearman\",use=\"pairwise.complete.obs\")\n if(dim(dat)[1]<2000){\n kmat<-cor(dat,method=\"kendall\",use=\"pairwise.complete.obs\")}\n else {s<-sample(seq(1:dim(dat)[1]),size=2000,replace=FALSE)\n kmat<-cor(dat[s,],method=\"kendall\",use=\"pairwise.complete.obs\")\n }\n \n cmat=pmax(abs(cmat),abs(smat),abs(kmat),na.rm=TRUE)\n \n High.cor<-sort(apply(abs(cmat)>min.cor,2,sum)-1,decreasing=TRUE)\n\n #take the top num.plots to put in the pairs plot or if the looking at a single\n #predictor and other predictors it's correlated with, take the top num.plots-1\n #of those with which it is correlated\n {if(cors.w.highest==FALSE){\n HighToPlot<-dat[,match(names(High.cor),names(dat))[1:min(num.plots,length(High.cor))]]\n }else{\n #take the column of the correlation matrix corresponding to the\n #predictor with the higest number of total correlations record the names\n #of the predictors that are correlated with this one predictor\n temp<-cmat[rownames(cmat)==names(High.cor[1]),]\n CorWHigh<-temp[abs(cmat[,colnames(cmat)==names(High.cor[1])])>min.cor]\n\n #record counts of total number of correlations with all predictors for those\n #predictors that are highly correlated with the Highest predictor\n High.cor<-sort(High.cor[names(CorWHigh)],decreasing=TRUE)\n HighToPlot<-dat[,match(names(High.cor),names(dat))[1:min(num.plots,length(High.cor))]]\n }}\n cor.hightoplot<-abs(cor(HighToPlot,use=\"pairwise.complete.obs\"))\n diag(cor.hightoplot)<-0\n cor.range<-c(quantile(as.vector(cor.hightoplot),probs=c(0,.5,.7,.85)),1)\n\n ## put histograms on the diagonal\n panel.hist <- function(x, ...)\n {\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(usr[1:2], 0, 1.5) )\n h <- hist(x, plot = FALSE)\n breaks <- h$breaks; nB <- length(breaks)\n y <- h$counts; y <- y/max(y)\n rect(breaks[-nB], 0, breaks[-1], y, col=\"steelblue\", ...)\n\n }\n\n\n ## put (absolute) correlations on the upper panels,\n ## with size proportional to the correlations.\n panel.cor <- function(x, y, digits=2, prefix=\"\", cor.range,cex.cor, ...)\n {\n a<-colors()\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(0, 1, 0, 1))\n r <- abs(cor(x, y,use=\"pairwise.complete.obs\"))\n spear<-abs(cor(x,y,method=\"spearman\",use=\"pairwise.complete.obs\"))\n ken<- abs(cor(x,y,method=\"kendall\",use=\"pairwise.complete.obs\"))\n all.cor<-max(r,spear,ken)\n #range.seq<-seq(from=cor.range[1],to=cor.range[2],length=20)\n if(all.cor>=cor.range[4]){\n rect(par(\"usr\")[1], par(\"usr\")[3], par(\"usr\")[2], par(\"usr\")[4], col =\n a[59])} else if(all.cor>=cor.range[3]){\n rect(par(\"usr\")[1], par(\"usr\")[3], par(\"usr\")[2], par(\"usr\")[4], col =\n a[76])} else if(all.cor>=cor.range[2]){\n rect(par(\"usr\")[1], par(\"usr\")[3], par(\"usr\")[2], par(\"usr\")[4], col =\n a[382])}\n r<-max(all.cor)\n cex.cor=3\n txt <- format(c(r, 0.123456789), digits=digits)[1]\n txt <- paste(prefix, txt, sep=\"\")\n #if(missing(cex.cor)) cex.cor <- 1.2/strwidth(txt)\n \n txt2=\"\"\n if(max(all.cor)>cor.range[2]){\n if(spear==max(all.cor) && spear!=cor(x,y,use=\"pairwise.complete.obs\")) {txt2 <- \" s\"\n } else if(ken==max(all.cor) && ken!=cor(x,y,use=\"pairwise.complete.obs\")){\n txt2 <-\" k\"\n }\n\n }\n text(0.5, 0.5, txt, cex = .7+cex.cor * (r-min(cor.range))/(max(cor.range)-min(cor.range)))\n text(.9,.1,txt2,cex=1.5) \n }\n \n# #Find a new unique file name (one in the desired directory that hasn't yet been used)\n# outfile <- paste(output.dir,\"Predictor_Correlation.pdf\",sep=\"\\\\\")\n# while(file.access(outfile)==0) outfile<-paste(output.dir,\"Predictor_Correlation.pdf\",sep=\"\\\\\")\n# \n# options(warn=-1)\n# pdf(outfile,width=11,height=9,onefile=T)\n# MyPairs(HighToPlot,cor.range=cor.range,my.labels=(as.vector(High.cor)[1:num.plots]),\n# lower.panel=panel.smooth,diag.panel=panel.hist, upper.panel=panel.cor,pch=21,bg = c(\"red\",\"steelblue\")[as.factor(response)],col.smooth = \"red\")\n# graphics.off()\n# options(warn=0)\n# \n# }\n\n \n #Find a new unique file name (one in the desired directory that hasn't yet been used)\n\n options(warn=-1)\n if(Debug==FALSE) jpeg(output.file,width=1000,height=1000,pointsize=13)\n MyPairs(cbind(TrueResponse,HighToPlot),cor.range=cor.range,my.labels=(as.vector(High.cor)[1:num.plots]),\n lower.panel=panel.smooth,diag.panel=panel.hist, upper.panel=panel.cor,pch=21,bg = c(\"green\",\"red\",\"yellow\")[factor(response,levels=c(0,1,-9999))],col.smooth = \"red\")\n\n if(Debug==FALSE) graphics.off()\n options(warn=0)\n \n }\n\nMyPairs<-function (x,my.labels,labels, panel = points, ..., lower.panel = panel,\n upper.panel = panel, diag.panel = NULL, text.panel = textPanel,\n label.pos = 0.5 + has.diag/3, cex.labels = NULL, font.labels = 1,\n row1attop = TRUE, gap = 1,Toplabs=NULL)\n{\n response<-x[,1]\n response[response==-9999]<-0\n x<-x[,2:dim(x)[2]]\n\n textPanel <- function(x = 0.5, y = 0.5, txt, cex, font) text(x,\n y, txt, cex = cex, font = font)\n localAxis <- function(side, x, y, xpd, bg, col = NULL, main,\n oma, ...) {\n if (side%%2 == 1)\n Axis(x, side = side, xpd = NA, ...)\n else Axis(y, side = side, xpd = NA, ...)\n }\n localPlot <- function(..., main, oma, font.main, cex.main) plot(...)\n localLowerPanel <- function(..., main, oma, font.main, cex.main) lower.panel(...)\n localUpperPanel <- function(..., main, oma, font.main, cex.main) upper.panel(...)\n localDiagPanel <- function(..., main, oma, font.main, cex.main) diag.panel(...)\n dots <- list(...)\n nmdots <- names(dots)\n if (!is.matrix(x)) {\n x <- as.data.frame(x)\n for (i in seq_along(names(x))) {\n if (is.factor(x[[i]]) || is.logical(x[[i]]))\n x[[i]] <- as.numeric(x[[i]])\n if (!is.numeric(unclass(x[[i]])))\n stop(\"non-numeric argument to 'pairs'\")\n }\n } else if (!is.numeric(x))\n stop(\"non-numeric argument to 'pairs'\")\n panel <- match.fun(panel)\n if ((has.lower <- !is.null(lower.panel)) && !missing(lower.panel))\n lower.panel <- match.fun(lower.panel)\n if ((has.upper <- !is.null(upper.panel)) && !missing(upper.panel))\n upper.panel <- match.fun(upper.panel)\n if ((has.diag <- !is.null(diag.panel)) && !missing(diag.panel))\n diag.panel <- match.fun(diag.panel)\n if (row1attop) {\n tmp <- lower.panel\n lower.panel <- upper.panel\n upper.panel <- tmp\n tmp <- has.lower\n has.lower <- has.upper\n has.upper <- tmp\n }\n nc <- ncol(x)\n if (nc < 2)\n stop(\"only one column in the argument to 'pairs'\")\n has.labs <- TRUE\n if (missing(labels)) {\n labels <- colnames(x)\n if (is.null(labels))\n labels <- paste(\"var\", 1L:nc)\n }\n else if (is.null(labels))\n has.labs <- FALSE\n oma <- if (\"oma\" %in% nmdots)\n dots$oma\n else NULL\n main <- if (\"main\" %in% nmdots)\n dots$main\n else NULL\n if (is.null(oma)) {\n oma <- c(4, 4, 4, 4)\n if (!is.null(main))\n oma[3L] <- 6\n }\n\n nCol<-ifelse(length(unique(response))>1,nc+1,nc)\n j.start<-ifelse(length(unique(response))>1,0,1)\n opar <- par(mfrow = c(nc, nCol), mar = rep.int(gap/2, 4), oma = oma)\n on.exit(par(opar))\n for (i in if (row1attop)\n 1L:(nc)\n else nc:1L) for (j in j.start:(nc)) {\n\n\n if(i==1){ par(mar = c(gap/2,gap/2,gap,gap/2)) #top row add extra room at top\n if(j==0){\n par(mar = c(gap/2,gap,gap,gap)) #top left corner room at top and on right\n localPlot(x[, i],response, xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)\n }else if(j==1) {par(mar = c(gap/2,gap,gap,gap/2)) #extra room on left and topfor second plot top row\n localPlot(x[, j], x[, i], xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)} else {\n par(mar = c(gap/2,gap/2,gap,gap/2)) #all other top row plots need extra room at top only\n localPlot(x[, j], x[, i], xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)\n }}else { par(mar = rep.int(gap/2, 4))\n if(j==0){ par(mar = c(gap/2,gap,gap/2,gap)) #left column needs extra room on right only\n localPlot(x[, i],response, xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)\n }else if(j==1){ par(mar = c(gap/2,gap,gap/2,gap/2)) #second column needs extra room on left so labels fit\n localPlot(x[, j], x[, i], xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)\n }else localPlot(x[, j], x[, i], xlab = \"\", ylab = \"\", axes = FALSE,\n type=\"n\",...)}\n if(j==0) {\n if(i==1) par(mar=c(gap/2,gap,gap,gap))\n else par(mar = c(gap/2,gap,gap/2,gap))\n\n if(i==1) title(main=\"Response\",line=.04,cex.main=1.5)\n\n box()\n my.lab<-paste(\"cor=\",round(max(abs(cor(x[,(i)],response,use=\"pairwise.complete.obs\")),abs(cor(x[,(i)],response,method=\"spearman\",use=\"pairwise.complete.obs\")),\n abs(cor(x[,(i)],response,method=\"kendall\",use=\"pairwise.complete.obs\"))),digits=2),sep=\"\")\n\n #panel smooth doesn't work for two reasons: one, the response is binary and it requires more than two unique values\n #second I don't think it can use weights which is necessary with an overwhelming amount of background points so that presnce points\n #can't be viewed\n #panel.smooth(as.vector(x[, (i)]), as.vector(jitter(response,factor=.1)),weights=\n # c(rep(table(response)[2]/table(response)[1],times=table(response)[1]),rep(1,times=table(response)[2])),...)\n\n if(length(unique(response))>2) panel.smooth(as.vector(x[, (i)]), as.vector(response),...)\n else my.panel.smooth(as.vector(x[, (i)]), as.vector(response),weights=\n c(rep(table(response)[2]/table(response)[1],times=table(response)[1]),rep(1,times=table(response)[2])),...)\n \n title(ylab=paste(\"cor=\",round(max(abs(cor(x[,(i)],response,use=\"pairwise.complete.obs\")),\n abs(cor(x[,(i)],response,method=\"spearman\",use=\"pairwise.complete.obs\")),abs(cor(x[,(i)],response,method=\"kendall\",use=\"pairwise.complete.obs\"))),digits=2),\n sep=\"\"),line=.02,cex.lab=1.5)\n #,y=.85,x=max(x[,(i)])-.2*diff(range(x[,(i)])),cex=1.5)\n\n } else{\n if (i == j || (i < j && has.lower) || (i > j && has.upper)) {\n box()\n if(i==1) title(main=paste(\"Total Cor=\",my.labels[j],sep=\"\"),line=.04,cex.main=1.5)\n #if (i == 1 && (!(j%%2) || !has.upper || !has.lower))\n # localAxis(1 + 2 * row1attop, x[, j], x[, i],\n # ...)\n if (i == nc)\n localAxis(3 - 2 * row1attop, x[, j], x[, i],\n ...)\n if (j == 1 && (i!=1 || !has.upper || !has.lower))\n localAxis(2, x[, j], x[, i], ...)\n #if (j == nc && (i%%2 || !has.upper || !has.lower))\n # localAxis(4, x[, j], x[, i], ...)\n mfg <- par(\"mfg\")\n if (i == j) {\n if (has.diag)\n localDiagPanel(as.vector(x[, i]),...)\n if (has.labs) {\n par(usr = c(0, 1, 0, 1))\n if (is.null(cex.labels)) {\n l.wid <- strwidth(labels, \"user\")\n cex.labels <- max(0.8, min(2, 0.9/max(l.wid)))\n }\n\n text.panel(0.5, label.pos, labels[i], cex = cex.labels,\n font = font.labels)\n }\n }\n\n else if (i < j)\n if(length(unique(x[,i])>2)){\n localLowerPanel(as.vector(x[, j]), as.vector(x[,\n i]), ...) } else {\n my.panel.smooth(as.vector(x[, j]),as.vector(x[,i]))\n }\n \n else localUpperPanel(as.vector(x[, j]), as.vector(x[,\n i]), ...)\n if (any(par(\"mfg\") != mfg))\n stop(\"the 'panel' function made a new plot\")\n }\n else par(new = FALSE)\n }}\n if (!is.null(main)) {\n font.main <- if (\"font.main\" %in% nmdots)\n dots$font.main\n else par(\"font.main\")\n cex.main <- if (\"cex.main\" %in% nmdots)\n dots$cex.main\n else par(\"cex.main\")\n mtext(main, 3, 3, TRUE, 0.5, cex = cex.main, font = font.main)\n }\n invisible(NULL)\n}\n\n\nmy.panel.smooth<-function (x, y, col = par(\"col\"), bg = NA, pch = par(\"pch\"),\n cex = 1, col.smooth = \"red\", span = 2/3, iter = 3, weights=rep(1,times=length(y)), ...)\n{\n o<-order(x)\n points(x, y, pch = pch, col = col, bg = bg, cex = cex)\n ok <- is.finite(x) & is.finite(y)\n if (any(ok) && length(unique(x))>3)\n lines(lowess(x[o],y[o],iter=0),col=\"red\")\n #lines(smooth.spline(x,w=weights,jitter(y,amount=(max(y)-min(y))/100),nknots=min(length(unique(x)),4)),col=\"red\")\n\n}\n\n\nArgs <- commandArgs(T)\n print(Args)\n #assign default values\n num.plots <- 10\n min.cor <- .7\n responseCol <- \"ResponseBinary\"\n cors.w.highest <- FALSE\n pres=TRUE\n absn=TRUE\n bgd=TRUE\n #replace the defaults with passed values\n for (arg in Args) {\n \targSplit <- strsplit(arg, \"=\")\n \targSplit[[1]][1]\n \targSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"p\") num.plots <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"m\") min.cor <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"o\") output.file <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"i\") infile <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"rc\") responseCol <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"core\") cors.w.highest <- argSplit[[1]][2]\n if(argSplit[[1]][1]==\"pres\") pres <- argSplit[[1]][2]\n if(argSplit[[1]][1]==\"absn\") absn <- argSplit[[1]][2]\n if(argSplit[[1]][1]==\"bgd\") bgd <- argSplit[[1]][2]\n }\n\n print(num.plots)\n print(min.cor)\n print(output.file)\n print (infile)\n print(responseCol)\n print(cors.w.highest)\n print(pres)\n print(absn)\n print(bgd)\n \n\t#Run the Pairs Explore function with these parameters\n Pairs.Explore(num.plots=num.plots,\n min.cor=min.cor,\n input.file=infile,\n\t\toutput.file=output.file,\n\t\tresponse.col=responseCol,\n\t\tcors.w.highest=cors.w.highest,\n\t\tpres,\n\t\tabsn,\n\t\tbgd)\n\n", "meta": {"hexsha": "92b945b6fdcae28b8b05c219597647b61ac7128a", "size": 18273, "ext": "r", "lang": "R", "max_stars_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/PairsExplore.r", "max_stars_repo_name": "celiafish/VisTrails", "max_stars_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 83, "max_stars_repo_stars_event_min_datetime": "2015-01-05T14:50:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-17T19:45:26.000Z", "max_issues_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/PairsExplore.r", "max_issues_repo_name": "celiafish/VisTrails", "max_issues_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 254, "max_issues_repo_issues_event_min_datetime": "2015-01-02T20:39:19.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T17:16:44.000Z", "max_forks_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/PairsExplore.r", "max_forks_repo_name": "celiafish/VisTrails", "max_forks_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 40, "max_forks_repo_forks_event_min_datetime": "2015-04-17T16:46:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-28T22:43:24.000Z", "avg_line_length": 41.9105504587, "max_line_length": 182, "alphanum_fraction": 0.5539867564, "num_tokens": 5260, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.33148909766495765}} {"text": "#' Plot SSB and F time series\r\n#' \r\n#' Two measures of F plotted (max F and Freport) and plotted separately below SSB time series.\r\n#' @param asap name of the variable that read in the asap.rdat file\r\n#' @param save.plots save individual plots\r\n#' @param od output directory for plots and csv files \r\n#' @param plotf type of plot to save\r\n#' @export\r\n\r\nPlotSSBFtrend<-function(asap,save.plots,od,plotf){\r\n par(mfrow=c(2,1), mar=c(4,4,2,2) )\r\n \r\n ssb <- asap$SSB\r\n fmult <- apply(asap$F.age,1, max) \r\n f.report <- asap$F.report\r\n years<- seq(asap$parms$styr, asap$parms$endyr)\r\n \r\n \r\n \r\n plot(years, ssb, type='l', lwd=2, col='blue', xlab=\"Year\", ylab=\"SSB\", \r\n ylim=c(0,1.05*max(ssb)) )\r\n \r\n plot(years, f.report, type='l', lwd=2, col='black', xlab=\"Year\", ylab=\"Fishing Mortality\", \r\n ylim=c(0,1.25*max(fmult, f.report)) )\r\n lines(years, fmult, col='red', lty=2, lwd=2)\r\n \r\n legend('topleft', horiz=T, legend=c('F.report', 'F.full'), col=c('black', 'red'),\r\n lwd=c(2,2), lty=c(1,2), cex=0.85)\r\n \r\n if (save.plots==T) savePlot(paste0(od, \"SSB.F.Timeseries.Line.\",plotf), type=plotf)\r\n return()\r\n} #end function\r\n", "meta": {"hexsha": "c4736b98d3a208e25270c8bfd33402735e991d70", "size": 1152, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_SSB_F_trends.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/plot_SSB_F_trends.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/plot_SSB_F_trends.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 34.9090909091, "max_line_length": 95, "alphanum_fraction": 0.6189236111, "num_tokens": 412, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.33129662141100197}} {"text": "library(\"dplyr\")\r\nlibrary(\"sf\")\r\nlibrary(\"assertthat\")\r\n\r\n# Ensure 01-prep-crime-data.r has been run first\r\ncrimes = readRDS(\"data/interim/crimes.rds\")\r\ncrimes = st_as_sf(crimes, coords = c(\"Longitude\", \"Latitude\"))\r\ncrimes = st_set_crs(crimes, 4326)\r\n\r\n\r\n# Open Geography Portal - last updated 2017-10-09\r\n# https://www.arcgis.com/sharing/rest/content/items/c86cdd7f86264f369789752121f0a1c4/info/metadata/metadata.xml?format=default&output=html\r\nif (!file.exists(\"data/external/boundaries.geojson\"))\r\n{\r\n url = \"https://opendata.arcgis.com/datasets/c86cdd7f86264f369789752121f0a1c4_0.geojson\"\r\n download.file(url, destfile = \"data/external/boundaries.geojson\")\r\n}\r\n\r\nboundaries = sf::read_sf(\"data/external/boundaries.geojson\")\r\nboundaries =\r\n boundaries %>%\r\n rename(\r\n id = OBJECTID,\r\n code = PFA16CD,\r\n name = PFA16NM\r\n ) %>%\r\n select(id, code, name)\r\n\r\nassert_that(all(st_is_valid(boundaries)))\r\n\r\n# NB this is lengthS not length\r\nboundaries$num_burglaries = lengths(st_intersects(boundaries, crimes))\r\nboundaries = st_transform(boundaries, crs = 27700)\r\n\r\nif (!all(st_is_valid(boundaries)))\r\n{\r\n boundaries = st_make_valid(boundaries)\r\n}\r\n\r\nassert_that(all(st_is_valid(boundaries)))\r\n\r\nboundaries = rmapshaper::ms_simplify(boundaries)\r\n\r\ndir.create(\"data/processed\", showWarnings = FALSE, recursive = TRUE)\r\nsf::st_write(boundaries, \"data/processed/boundaries.gpkg\", delete_layer = TRUE)\r\n", "meta": {"hexsha": "16c181bff7fd1a4f699f2f5f4039a5a551861eb8", "size": 1439, "ext": "r", "lang": "R", "max_stars_repo_path": "src/data/02-prep-boundaries.r", "max_stars_repo_name": "philmikejones/crime-3d-maps", "max_stars_repo_head_hexsha": "1e2dc61767d8efddfe3c07bbef3f10b8600d5b22", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/data/02-prep-boundaries.r", "max_issues_repo_name": "philmikejones/crime-3d-maps", "max_issues_repo_head_hexsha": "1e2dc61767d8efddfe3c07bbef3f10b8600d5b22", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data/02-prep-boundaries.r", "max_forks_repo_name": "philmikejones/crime-3d-maps", "max_forks_repo_head_hexsha": "1e2dc61767d8efddfe3c07bbef3f10b8600d5b22", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2826086957, "max_line_length": 139, "alphanum_fraction": 0.7171646977, "num_tokens": 416, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3312966214110019}} {"text": "\n#=========================================================\n#\n# Judas detection probability - discrete survival analysis\n#\n# Dave Ramsey (22/11/2021)\n#=========================================================\n\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(sf)\nlibrary(purrr)\nlibrary(cmdstanr)\nlibrary(posterior)\nsource(\"R/judas functions.r\")\n\n#==================================================\n# Santiago Judas goats\n#==================================================\n\n\ngoat_dyads<- read_csv(\"data/Santiago_judas_dyads.csv\")\ngoat_dyads<- goat_dyads %>% mutate(id1 = factor(as.character(id1)), id2 = factor(as.character(id2)))\ngoat_ids<- read_csv(\"data/Santiago_judas_ID.csv\")\ngoat_ids<- goat_ids %>% mutate(ID = factor(as.character(ID)))\n\nid1<- as.vector(unclass(goat_dyads$id1))\nid2<- as.vector(unclass(goat_dyads$id2))\n\ndyad<- as.vector(unclass(factor(goat_dyads$Dyad)))\nndyads<- max(dyad)\nN<- nrow(goat_dyads)\nM<- nrow(goat_ids)\nX<- model.matrix(~ distance + Sex1 + Trans1, data=goat_dyads)[,-1]\nK<- ncol(X)\nsex<- model.matrix(~Sex + Trans, goat_ids)[,-1]\n\n#---- Calculate home range scale (sigma) from Judas locations ----\n\ngoat_locs<- read_csv(\"data/santiago_judas_locs.csv\")\ngoat_locs<- goat_locs %>% mutate(ID = factor(as.character(ID)))\ngoat_locs<- goat_locs %>% group_by(ID) %>% nest() %>% arrange(ID)\n\n\ngoat_chr<- goat_locs %>% mutate(CNorm=map(data, calc_hr))\ngoat_chr<- goat_chr %>% unnest(CNorm) %>% select(-data)\n\nmaxD<- goat_chr$sigma * 2.45\n\n#---- Discrete survival analysis -----\n\nset.seed(123)\n\nmod<- cmdstan_model(\"stan/discrete_survival.stan\")\n\ndata<- list(N=N,M=M,K=K,X=X,id1=id1,id2=id2,\n time=as.integer(goat_dyads$time),\n ev=as.integer(goat_dyads$event),sex=sex,maxD=maxD)\n \n\n## MCMC settings (for illustration - increase for serious inference)\nni <- 500\nnt <- 1\nnb <- 500\nnc <- 2\n\n \ninits <- lapply(1:nc, function(i)\n list(b1= -3, beta=c(-0.5,1,1,1), sigma_id1=runif(1), sigma_id2=runif(1), q=runif(N, 0, 0.1)))\n\nout<- mod$sample(\n data=data,\n init = inits,\n iter_warmup = nb,\n iter_sampling = ni,\n chains=nc,\n parallel_chains = nc,\n refresh = 50)\n\n\nout$summary(c(\"b1\",\"beta\",\"sigma_id1\",\"sigma_id2\"))\n\nout$summary(\"pave\")\n\nout$save_object(file=\"out/goat_surv.rds\")\n\n#==================================================\n# Santa Cruz Island Judas pigs\n#==================================================\n\npig_dyads<- read_csv(\"data/Santacruz_judas_dyads.csv\")\npig_dyads<- pig_dyads %>% mutate(id1 = factor(as.character(id1)), id2 = factor(as.character(id2)))\npig_ids<- pig_dyads %>% select(ID=id1,Sex=Sex1) %>% distinct() %>% arrange(ID)\n\nid1<- as.vector(unclass(pig_dyads$id1))\nid2<- as.vector(unclass(pig_dyads$id2))\n\ndyad<- as.vector(unclass(factor(pig_dyads$Dyad)))\nndyads<- max(dyad)\nN<- nrow(pig_dyads)\nM<- nrow(pig_ids)\nX<- model.matrix(~ distance + Sex1, data=pig_dyads)[,-1]\nK<- ncol(X)\nsex<- as.matrix(ifelse(pig_ids$Sex==\"B\", 0, 1))\n\n#--- Calculate home range scale (sigma) from Judas locations\npig_locs<- read_csv(\"data/santacruz_judas_locs.csv\")\n# rescale coordinates to km\npig_locs<- pig_locs %>% mutate(ID = factor(as.character(ID)),East=East/1000, North=North/1000)\npig_locs<- pig_locs %>% group_by(ID) %>% nest() %>% arrange(ID)\n\npig_chr<- pig_locs %>% mutate(CNorm=map(data, calc_hr))\npig_chr<- pig_chr %>% unnest(CNorm) %>% select(-data)\n\nmaxD<- pig_chr$sigma * 2.45\n\n#------------------------------------------------------\n# Discrete survival analysis\n#-----------------------------------------------------------\n\nmod<- cmdstan_model(\"stan/discrete_survival.stan\")\n\ndata<- list(N=N,M=M,K=K,X=X,id1=id1,id2=id2,\n time=as.integer(pig_dyads$time),\n ev=as.integer(pig_dyads$event),sex=sex,maxD=maxD)\n\n\n## MCMC settings\nni <- 500\nnt <- 1\nnb <- 500\nnc <- 2\n\n\ninits <- lapply(1:nc, function(i)\n list(b1= -3, beta=c(-0.5,1), sigma_id1=runif(1), sigma_id2=runif(1), q=runif(N, 0, 0.1)))\n\nout<- mod$sample(\n data=data,\n init = inits,\n iter_warmup = nb,\n iter_sampling = ni,\n chains=nc,\n parallel_chains = nc,\n refresh = 50)\n\n\nout$summary(c(\"b1\",\"beta\",\"sigma_id1\",\"sigma_id2\"))\n\nout$summary(\"pave\")\n\nout$save_object(file=\"out/pig_surv.rds\")\n\n\n#---------------------------------------------------\n# Plots \n#---------------------------------------------------\n\n# Predicted probability of detection at activity centres (i.e. distance = 0)\n\nout<- readRDS(\"out/goat_surv.rds\")\n\nfit<- as_draws_rvars(out$draws(c(\"beta0\",\"beta\")))\n\nbeta0<- draws_of(fit$beta0)\nbeta<- draws_of(fit$beta)\nbeta1<- beta[,1]\nbeta2<- beta[,-1]\n\nn<- nrow(goat_ids)\nXX<- model.matrix(~Sex + Trans, goat_ids)[,-1]\n\nplam<- matrix(NA,nrow=dim(beta)[1],ncol=n)\nfor(i in 1:n){\n plam[,i]<- 1-exp(-exp(beta0[,i] + beta2 %*% XX[i,]))\n}\n\nplam<- as.data.frame(plam)\nnames(plam)<- goat_ids$ID\nplam$samp<- 1:nrow(plam)\n\nplam<- plam %>% pivot_longer(-samp, names_to = \"ID\", values_to = \"p\")\nplam<- left_join(plam, goat_ids)\nplam<- plam %>% mutate(Sex = factor(Sex, levels=c(\"Female\",\"Super\",\"Male\")),\n Trans = factor(Trans, labels = c(\"Not Translocated\",\"Translocated\")))\n\nwin.graph(10,5)\nplam %>% ggplot(aes(ID, p, color=interaction(Sex,Trans))) +\n geom_boxplot(fill=\"grey90\",outlier.color = NA) +\n labs(x=\"Judas ID\", y=\"Detection probability at centre of UD\") + \n theme_bw() +\n theme(axis.text.x = element_blank(),\n legend.position = \"bottom\",\n legend.title = element_blank())\n \n\n#--------------------------------------------------------\n# Predicted detection curves with distance from activity centre\n#--------------------------------------------------------\nfit<- as_draws_rvars(out$draws(c(\"beta0\",\"beta\")))\n\nbeta0<- E(fit$beta0)\nbeta<- E(fit$beta)\nbeta1<- beta[1]\nbeta2<- beta[-1]\n\nn<- nrow(goat_ids)\ndistance<- seq(0,20,0.1)\nXX<- model.matrix(~Sex + Trans, goat_ids)[,-1]\n\nplam<- matrix(NA,nrow=length(distance), ncol=n)\n\nfor(i in 1:n){\n plam[,i]<- 1-exp(-exp(beta0[i] + beta1*distance + as.vector(XX[i,] %*% beta2)))\n}\nplam<- as.data.frame(plam)\nnames(plam)<- goat_ids$ID\nplam<- plam %>% mutate(Distance=distance)\n \nplam<- plam %>% pivot_longer(-Distance, names_to = \"ID\", values_to = \"p\")\nplam<- left_join(plam, goat_ids)\nplam<- plam %>% mutate(Sex = factor(Sex, levels=c(\"Female\",\"Super\",\"Male\")),\n Trans = factor(Trans, labels = c(\"Not Translocated\",\"Translocated\")))\n\n\nwin.graph(8,7)\nplam %>% ggplot(aes(Distance, p, group=ID)) +\n geom_line(color=\"grey50\") +\n facet_grid(rows=vars(Sex), cols=vars(Trans)) +\n labs(x=\"Distance (km)\",y=\"Probability of association\") +\n theme_bw() +\n theme(legend.position = \"none\",\n strip.text = element_text(face=\"bold\"))\n\n #--------------------------------------------------------------------\n # Unconditional detection probability (Eqn. 2)\n #-------------------------------------------------------------------- \n\nfit<- summarise_draws(out$draws(\"pave\"), mean, ~quantile(.x, c(0.05,0.95)))\n \nplam<- goat_ids %>% mutate(pave= fit$mean, lcl = fit$`5%`, ucl=fit$`95%`)\nplam<- plam %>% mutate(Sex = factor(Sex, levels=c(\"Female\",\"Super\",\"Male\")),\n Trans = factor(Trans, labels = c(\"Not Translocated\",\"Translocated\")))\n\nwin.graph(10,5)\n plam<- plam %>% arrange(pave)\n plam %>% mutate(ID = fct_reorder(ID, pave), Sex) %>%\n ggplot(aes(x=Trans, y=pave, color=Sex)) +\n geom_pointrange(aes(ymin=lcl,ymax=ucl), position=position_dodge2(width=0.5)) +\n facet_wrap(~Sex) +\n labs(x=\"\",y=\"Probability of association\",color=\"Status\") +\n theme_bw() +\n theme(legend.position = \"none\",\n strip.text = element_text(face=\"bold\"),\n axis.title = element_text(face=\"bold\", size=12),\n axis.text.x = element_text(size=11))\n\n", "meta": {"hexsha": "848d680d6fa981ff0e9269f35b35c81ac300a6e6", "size": 7639, "ext": "r", "lang": "R", "max_stars_repo_path": "R/discrete_survival_analysis.r", "max_stars_repo_name": "dslramsey/judas_detection", "max_stars_repo_head_hexsha": "ca73d208df5c8ec952c05f49851d8e8091067c85", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/discrete_survival_analysis.r", "max_issues_repo_name": "dslramsey/judas_detection", "max_issues_repo_head_hexsha": "ca73d208df5c8ec952c05f49851d8e8091067c85", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/discrete_survival_analysis.r", "max_forks_repo_name": "dslramsey/judas_detection", "max_forks_repo_head_hexsha": "ca73d208df5c8ec952c05f49851d8e8091067c85", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6085271318, "max_line_length": 100, "alphanum_fraction": 0.5927477419, "num_tokens": 2270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7371581626286834, "lm_q2_score": 0.4493926344647597, "lm_q1q2_score": 0.3312734487209058}} {"text": "library(geobr)\nlibrary(sf)\nBrasil <- geobr::read_country(year = 2018)\nEstados <- geobr::read_state(code_state = \"all\", year=2018)\nMunicipios <- geobr::read_municipality(code_muni = \"all\", year=2018)\ngeoInfo <- list(brasil = Brasil, estados = Estados, municipios = Municipios)\n\n#teste de plotagem do mapa do Brasil\nplot(st_geometry(Brasil))\n\n", "meta": {"hexsha": "304705d507f4df61a8f53d142dd986df79be28b9", "size": 341, "ext": "r", "lang": "R", "max_stars_repo_path": "02_ dados/01_ IBGE/scripts/shapefiles Brasil 2019_08_12.r", "max_stars_repo_name": "matth3us/tccENAP", "max_stars_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "02_ dados/01_ IBGE/scripts/shapefiles Brasil 2019_08_12.r", "max_issues_repo_name": "matth3us/tccENAP", "max_issues_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2019-03-21T13:00:25.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-14T17:05:38.000Z", "max_forks_repo_path": "02_ dados/01_ IBGE/scripts/shapefiles Brasil 2019_08_12.r", "max_forks_repo_name": "matth3us/tccENAP", "max_forks_repo_head_hexsha": "bd3b607dee01ea82cef2a45b48815dd147ff5113", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.0, "max_line_length": 76, "alphanum_fraction": 0.7448680352, "num_tokens": 104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3312570992119607}} {"text": "# R script for plotting ROC curve for different tableau search methods\n# Alex Stivala\n# July 2008\n# $Id: plotsearchroc.r 1946 2008-10-04 05:14:43Z astivala $\n\n\n#\n# globals\n#\n\ncolorvec=c('deepskyblue4','brown','red','turquoise','blue','purple','green','cyan','gray20','magenta','darkolivegreen2','midnightblue','magenta3','darkseagreen','violetred3','darkslategray3')\nltyvec=c(1,2,4,5,6,1,2,1,5,6,1,2,4,5,6,1,2)\nnamevec=c('QP numeric', 'QP numeric SSE type', 'QP numeric ordering SSE type', 'QP discrete','QP discrete SSE type', 'QP discrete ordering SSE type')\n\n\n#\n# main\n#\n\nqp_numeric <- read.table('d1ubia_.tsrchn.ff.rtab',header=TRUE)\nqp_numeric_penalizessetype<-read.table('d1ubia_.tsrchn.tf.rtab',header=TRUE)\nqp_numeric_ordering_penalizessetype <- read.table('d1ubia_.tsrchn.tt.rtab', header=TRUE)\n\nqp_discrete <- read.table('d1ubia_.tsrchd.ff.rtab',header=TRUE)\nqp_discrete_penalizessetype<-read.table('d1ubia_.tsrchd.tf.rtab',header=TRUE)\nqp_discrete_ordering_penalizessetype <- read.table('d1ubia_.tsrchd.tt.rtab', header=TRUE)\n\n\n# EPS suitable for inserting into LaTeX\npostscript('searchroc.eps',onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\n\nplot(c(0,1),c(0,1),type='l',xlim=c(0,1),ylim=c(0,1),xlab=\"False Positive Rate\",ylab=\"True Positive Rate\",lty=3,main='ROC curves for query d1ubia_ against ASTRAL 95% seq id SCOP as truth')\n\nlines(qp_numeric$fpr, qp_numeric$tpr, col=colorvec[1], lty=ltyvec[1])\nlines(qp_numeric_penalizessetype$fpr, qp_numeric_penalizessetype$tpr, col=colorvec[2], lty=ltyvec[2])\nlines(qp_numeric_ordering_penalizessetype$fpr, qp_numeric_ordering_penalizessetype$tpr, col=colorvec[3], lty=ltyvec[3])\nlines(qp_discrete$fpr, qp_discrete$tpr, col=colorvec[4], lty=ltyvec[4])\nlines(qp_discrete_penalizessetype$fpr, qp_discrete_penalizessetype$tpr, col=colorvec[5], lty=ltyvec[5])\nlines(qp_discrete_ordering_penalizessetype$fpr, qp_discrete_ordering_penalizessetype$tpr, col=colorvec[6], lty=ltyvec[6])\n\n\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec)\n\n\ndev.off()\n\n", "meta": {"hexsha": "1e15339f00ee9a8ab476723989a9d706409db7b4", "size": 2048, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/plotsearchroc.r", "max_stars_repo_name": "stivalaa/cuda_satabsearch", "max_stars_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/plotsearchroc.r", "max_issues_repo_name": "stivalaa/cuda_satabsearch", "max_issues_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/plotsearchroc.r", "max_forks_repo_name": "stivalaa/cuda_satabsearch", "max_forks_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.6666666667, "max_line_length": 191, "alphanum_fraction": 0.7622070312, "num_tokens": 706, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3312570992119607}} {"text": "hummingage <- function(path,infile,segments,DiscardOutliers,EstimateNullRate,token,plot_format) {\n ##\n ## usage:\n ## hummingage(\"/home/user/data/\",\"myfile.csv\",c(NaN),TRUE,\"estimate\",\"KJmpmGgQ\",\"png,pdf\")\n ## hummingage(\"/home/user/data/\",\"myfile.csv\",c(124),FALSE,\"estimate\",\"KJmpmGgQ\",\"png\")\n ## hummingage(\"/home/user/data/\",\"myfile.csv\",c(334,856,1223),TRUE,\"estimate\",\"KJmpmGgQ\",\"pdf\")\n ## segments in cm\n\n ## path: (string): is the path to the infile\n ## infile: (string): is the data file according to a certain format\n ## segments: (integer array): is an array indicating roughly the positions of the segments, use c(NaN) for no segments\n ## DiscardOutliers: (boolean): can be TRUE or FALSE. If true, a simple algorithm is applied to identify outliers\n ## EstimateNullRate: (string or integer): possible values are\n ## \"estimate\": the x-axis offset is estimated from the data\n ## \"null\": the x-axis offset is set to 0\n ## negative or positive integer: x-axis offset is set to the inserted value, e.g. -42, 14, ... (in cm)\n ## token: (string): this is a random string, which can be choosen arbitrarily\n ## plot_format (string): this can be \"png,pdf\", \"png\" or \"pdf\" and defines what kinds of images are written to disk\n ## both, png and pdf or only png or pdf\n ##\n\n\n ## full file-path\n xfile <- paste(path,infile,sep=\"\");\n\n ## transform plot_format to array\n plot_format_tmp <- strsplit(plot_format,\",\")\n plot_format <- plot_format_tmp[[1]]\n\n \n ## load the infile and save as ageDataOrig\n ageDataOrig <- read.table(xfile, header = TRUE,sep=\",\")\n\n ## if no segment will be used set it to empty\n if (is.nan(segments)){\n segments = c()\n }\n\n ## calculate length and estimate rates\n lengthData <- length(ageDataOrig$depth)\n diffDepths <- diff(ageDataOrig$depth)\n accRatesOrig <- diff(ageDataOrig$age)/diffDepths\n\n ##--------------------------------------------------------------------------------------------##\n ## this block is only executed if DiscardOutliers == TRUE\n ## identify negative rates, i.e. outliers\n ## the difficulty is to identify the outlier\n ## i.e. which age is the outlier, because the rate is calculated from two ages\n ## thus we take the ages left and right to the two ages which make the negative rate\n ## and then we calculate which age is further away from the mean of the two left\n ## and rights\n if (DiscardOutliers==TRUE){\n ## find negative rates\n negRatesIndex <- which(accRatesOrig < 0)\n } else {\n ## set this to empty\n negRatesIndex <- integer(0)\n }\n ## make a copy of the indices of the negative rates\n negRatesIndexCopy <- negRatesIndex\n\n ## go through the negative rates and decide which age is the outlier, left or right\n m <- 1\n for (i in negRatesIndexCopy) {\n ## skip the first\n if (i==1){\n next\n }\n ## calculate the mean between the left and right age next to the two\n ## ages, which where used to calculate the rate\n TheMean <- (ageDataOrig$age[(i-1)]+ageDataOrig$age[(i+2)])/2\n ## distance of the left age to the mean\n dist1 <- abs(ageDataOrig$age[(i)]-TheMean)\n ## distance of the right age to the mean\n dist2 <- abs(ageDataOrig$age[(i+1)]-TheMean)\n ## if the right is further away then this is the outlier\n ## so increment the index\n ## otherwise it's the left and leave the index\n if (dist2>dist1){\n negRatesIndex[m] <- negRatesIndex[m]+1\n }\n m <- m+1\n }\n ##--------------------------------------------------------------------------------------------##\n\n ## remove extreme rates and exclude the first\n ## because the first must be estimated later below\n ## remove outliers from original data\n ## or copy data as they are, if there are no outliers\n LnegRatesIndex <- length(negRatesIndex)\n if (LnegRatesIndex>0){\n ## remove data\n ageData <- ageDataOrig[-negRatesIndex,]\n } else {\n ## leave data as they are\n ageData <- ageDataOrig\n }\n\n ## now, outliers have been removed calculate again length, rates etc.\n lengthData <- length(ageData$depth)\n diffDepths <- diff(ageData$depth)\n accRates <- diff(ageData$age)/diffDepths\n sigmaRates <- sqrt((ageData$error[2:lengthData]^2+ageData$error[1:(lengthData-1)]^2))/diffDepths\n\n ## convert segments from depth to point number\n ## find the position, i.e. between the two closest points\n Lsegments <- length(segments)\n if (Lsegments == 0) {\n segPos = c()\n } else {\n segPos <- rep(0,Lsegments)\n for (i in seq(1,Lsegments)){\n ## difference between depth variable and a sequence position (in cm)\n Xdiff <- ageData$depth-segments[i]\n ## find the indices smaller than the segment position\n a <- which(Xdiff<0)\n ## set the position at the last negative\n segPos[i] <- max(a)\n } \n }\n\n ## copy of segPos\n segPosPlot <- segPos\n ## add left and right boundaries to segPos\n segPos <- c(0,segPos,lengthData)\n segPosLength <- length(segPos)\n\n ##--------------------------------------------------------------------------------------------##\n ## estimate the x-axis crossing to estimate the first rate\n ## if we would take the origin 0,0 than the first rate will be wrong\n ## because there is an offset\n ##\n ## so, we fit the first segment and estimate the x-crossing\n ## cut out first segment\n xcrossing <- 0\n if (EstimateNullRate == \"estimate\"){\n N <- 3\n Lu <- length(1:N)\n u <- ageData$age[1:N]\n X <- replicate(2,rep(1,Lu))\n X[,2] <- ageData$depth[1:N]\n ## print(X)\n beta <- solve(t(X) %*% X) %*% t(X) %*% u\n ## print(beta)\n ## only if slope is positive\n if (beta[2]>0){\n xcrossing <- beta[1]/beta[2]*(-1)\n ## print(xcrossing)\n } else {\n xcrossing <- 0\n }\n }\n if (EstimateNullRate == \"null\"){\n xcrossing <- 0\n }\n if (is.numeric(EstimateNullRate) == TRUE){\n ## then this is xcrossing\n xcrossing <- EstimateNullRate\n }\n NullDiff <- ageData$depth[1]-xcrossing\n NullRate <- ageData$age[1]/NullDiff\n NullError <- ageData$error[1]/NullDiff\n ##--------------------------------------------------------------------------------------------##\n\n ## now we have the first rate and add it to the rates\n accRates <- c(NullRate,accRates)\n ## print(accRates)\n ## add also a std for the first\n sigmaRates <- c(NullError,sigmaRates)\n\n ## initialise arrays for the fits\n fittedRates <- rep(0,lengthData)\n fittedError <- rep(0,lengthData)\n\n ##--------------------------------------------------------------------------------------------##\n ## fitting the segments\n for (i in seq(2,segPosLength)) {\n ## least square\n Xsegment <- (segPos[i-1]+1):segPos[i]\n Lsegment <- length(Xsegment)\n ## create X matrix\n X <- replicate(2,rep(1,Lsegment))\n X[,2] <- ageData$depth[Xsegment]\n ## create error matrix\n g <- solve(t(X) %*% X)\n ## print(g)\n beta <- g %*% t(X) %*% accRates[Xsegment]\n ## print(beta)\n fittedRates[Xsegment] <- X %*% beta\n ## res\n fittedError[Xsegment] <- sqrt(1/(Lsegment-1) * sum((fittedRates[Xsegment]-accRates[Xsegment])^2))\n ## print(fittedError[Xsegment])\n }\n ##--------------------------------------------------------------------------------------------##\n\n \n ##--------------------------------------------------------------------------------------------##\n ## BIC \n ## BIC for Gaussian approx. is BIC=N*ln(epsilon^2)+k*ln(N)\n ## for fitting a linear regression one need k=q+2 parameters to be estimated\n ## one for the intercept, q for the slopes and one for the rmse\n ## thus in the case of fitting the mean I need k=2 (intercept, rmse)\n ## BIC[segments] <- N * log(rmse^2) + 2*segments * log(N)\n mse <- 1/lengthData * sum((fittedRates-accRates)^2)\n BIC <- lengthData * log(mse) + (2*(segPosLength-1)+1) * log(lengthData)\n ## (segPosLength-1) because above I have added 0,segPos,N to segPos\n ## thus (segPosLength-1) is exactly the number of segments\n ## write out the BIC\n write(BIC,file=paste(path,token,\"_BIC.txt\",sep=\"\"),ncolumns=1)\n ##--------------------------------------------------------------------------------------------##\n\n\n ##--------------------------------------------------------------------------------------------##\n ## apply the Bayesian approach, i.e. for the Gaussian assumption the weighted mean\n BayesFittedRates <- (fittedRates * sigmaRates^2 + accRates * fittedError^2)/(fittedError^2 + sigmaRates^2)\n ## disable Bayes\n ##BayesFittedRates <- fittedRates\n \n ## and the weighted std\n BayesSigmaRates <- sqrt((fittedError^2 * sigmaRates^2)/(fittedError^2 + sigmaRates^2))\n ## disable Bayes\n ##BayesSigmaRates <- fittedError\n\n \n ## calculate the ages from the rates\n ageFit <- cumsum(BayesFittedRates * c(NullDiff,diffDepths))\n ## print(\"sum1\")\n ## print(sum((ageFit-ageData$age)^2))\n\n ## and the errors\n ageFitError <- rep(0,lengthData)\n ageFitError[1] <- BayesSigmaRates[1]*NullDiff\n for (i in seq(1,(lengthData-1))) {\n ageFitError[i+1] <- sqrt(ageFitError[i]^2+(BayesSigmaRates[i+1]*diffDepths[i])^2)\n }\n ##--------------------------------------------------------------------------------------------##\n\n\n ##--------------------------------------------------------------------------------------------##\n ## plotting the intermediate result of fitted segments\n for (k in plot <- format){\n if (k == \"jupyter\"){\n Ascale <- 1 #4\n Bscale <- 1 #6\n Cscale <- 2 #8\n Dscale <- 4 #12\n } else {\n Ascale <- 4\n Bscale <- 6\n Cscale <- 8\n Dscale <- 12\n\n par(mar=c(14,14,4,2),mgp=c(10,4,0),bg=rgb(248/255,250/255,252/255))\n if (k == \"png\"){\n png(filename=paste(path,token,\"_intermediate.png\",sep=\"\"),width=2000,height=1400,pointsize=10)#,width=11,height=8)\n }\n if (k == \"pdf\"){\n pdf(file=paste(path,token,\"_intermediate.pdf\",sep=\"\"),width=40,height=28)#,width=11,height=8)\n }\n }\n\n\n ymin <- min(c(accRates-sigmaRates),na.rm=TRUE)\n ymax <- max(c(accRates+sigmaRates),na.rm=TRUE)\n xmin <- 0 ##min(ageDataOrig$depth)\n xmax <- max(ageDataOrig$depth)\n plot(ageData$depth,accRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"Depth [cm]\",ylab=\"Rates [ka/cm]\",cex.lab=Ascale,cex.axis=Ascale)\n if (Lsegments > 0) {\n for (i in seq(1,length(segPosPlot))) {\n par(new=TRUE)\n segPosDepth <- (ageData$depth[segPosPlot[i]]+ageData$depth[segPosPlot[i]+1])/2\n plot(c(segPosDepth,segPosDepth),c(ymin,ymax),ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(200/255,200/255,200/255),type=\"l\",lwd=Dscale,lty=2,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n }\n }\n par(new=TRUE)\n polygon(c(ageData$depth, rev(ageData$depth)), c(fittedRates-fittedError,\n rev(fittedRates+fittedError)), col=rgb(0.8,0.8,0.8),border=NA)\n par(new=TRUE)\n plot(ageData$depth,accRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(42/255,190/255,230/255),pch=19,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n par(new=TRUE)\n plot(ageData$depth,fittedRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"\",pch=19,ylab=\"\",xaxt=\"n\",yaxt=\"n\",col=\"red\",cex=Ascale)\n par(new=TRUE)\n plot(ageData$depth,fittedRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),lwd=Cscale,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",type=\"l\",col=\"red\",cex=Cscale)\n\n arrows(ageData$depth, accRates-sigmaRates, ageData$depth,\n accRates+sigmaRates, length=0.2, angle=90, code=3,col=\"black\",lwd=Bscale,cex=Cscale)\n\n legend(xmax, ymax, xjust=1,legend=c(\"data\",\"fit\",\"sequences\"),\n col=c(rgb(42/255,190/255,230/255),\"red\",rgb(200/255,200/255,200/255)), lwd=c(NaN,Cscale,Dscale), lty=c(1,1,2), pch=c(19,19,NaN), cex=c(Bscale), pt.cex=c(Cscale,Ascale,Cscale))\n if (k != \"jupyter\"){\n dev.off()\n }\n }\n ##--------------------------------------------------------------------------------------------##\n\n\n ##--------------------------------------------------------------------------------------------##\n ## plotting ages\n for (k in plot_format){\n if (k == \"jupyter\"){\n Ascale <- 1 #4\n Bscale <- 1 #6\n Cscale <- 2 #8\n Dscale <- 4 #12\n } else {\n Ascale <- 4\n Bscale <- 6\n Cscale <- 8\n Dscale <- 12\n\n par(mar=c(14,14,4,2),mgp=c(10,4,0),bg=rgb(248/255,250/255,252/255))\n if (k == \"png\"){\n png(filename=paste(path,token,\"_ages.png\",sep=\"\"),width=2000,height=1400,pointsize=10)#,width=11,height=8)\n }\n if (k == \"pdf\"){\n pdf(file=paste(path,token,\"_ages.pdf\",sep=\"\"),width=40,height=28)#,width=11,height=8)\n }\n }\n\n\n ymin <- min(c(ageData$age-ageData$error,ageDataOrig$age),na.rm=TRUE)\n ymax <- max(c(ageData$age+ageData$error,ageDataOrig$age,ageFit+ageFitError),na.rm=TRUE)\n xmin <- 0 ##min(ageDataOrig$depth)\n xmax <- max(ageDataOrig$depth)\n plot(ageData$depth,ageData$age,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"Depth [cm]\",ylab=\"Age [ka]\",cex.lab=Ascale,cex.axis=Ascale)\n if (Lsegments > 0) {\n for (i in seq(1,length(segPosPlot))) {\n par(new=TRUE)\n segPosDepth <- (ageData$depth[segPosPlot[i]]+ageData$depth[segPosPlot[i]+1])/2\n plot(c(segPosDepth,segPosDepth),c(ymin,ymax),ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(200/255,200/255,200/255),type=\"l\",lwd=Dscale,lty=2,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n }\n }\n par(new=TRUE)\n polygon(c(ageData$depth, rev(ageData$depth)), c(ageFit-ageFitError,\n rev(ageFit+ageFitError)), col=rgb(0.8,0.8,0.8),border=NA,xlim=c(xmin,xmax),)\n par(new=TRUE)\n plot(ageData$depth,ageData$age,ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(42/255,190/255,230/255),pch=19,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n par(new=TRUE)\n plot(ageDataOrig$depth[negRatesIndex],ageDataOrig$age[negRatesIndex],ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(230/255,190/255,42/255),pch=19,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n par(new=TRUE)\n plot(ageData$depth,ageFit,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"\",pch=19,ylab=\"\",xaxt=\"n\",yaxt=\"n\",col=\"red\",cex=Ascale)\n par(new=TRUE)\n plot(ageData$depth,ageFit,ylim=c(ymin,ymax),xlim=c(xmin,xmax),lwd=Cscale,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",type=\"l\",col=\"red\",cex=Cscale)\n\n arrows(ageDataOrig$depth, ageDataOrig$age-ageDataOrig$error, ageDataOrig$depth, \n ageDataOrig$age+ageDataOrig$error, length=0.2, angle=90, code=3,col=\"black\",lwd=Bscale,cex=Cscale)\n\n legend(xmax, ymin, xjust=1,yjust=0,legend=c(\"data\",\"outliers\",\"fit\",\"sequences\"),\n col=c(rgb(42/255,190/255,230/255),rgb(230/255,190/255,42/255),\"red\",rgb(200/255,200/255,200/255)), lwd=c(NaN,NaN,Cscale,Dscale), lty=c(1,1,1,2), pch=c(19,19,19,NaN), cex=c(Bscale), pt.cex=c(Cscale,Cscale,Ascale,NaN))\n\n if (k != \"jupyter\"){\n dev.off()\n }\n }\n ##--------------------------------------------------------------------------------------------##\n\n\n ##--------------------------------------------------------------------------------------------##\n ## plotting rates\n for (k in plot_format){\n if (k == \"jupyter\"){\n Ascale <- 1 #4\n Bscale <- 1 #6\n Cscale <- 2 #8\n Dscale <- 4 #12\n } else {\n Ascale <- 4\n Bscale <- 6\n Cscale <- 8\n Dscale <- 12\n\n par(mar=c(14,14,4,2),mgp=c(10,4,0),bg=rgb(248/255,250/255,252/255))\n if (k == \"png\"){\n png(filename=paste(path,token,\"_rates.png\",sep=\"\"),width=2000,height=1400,pointsize=10)#,width=11,height=8)\n }\n if (k == \"pdf\"){\n pdf(file=paste(path,token,\"_rates.pdf\",sep=\"\"),width=40,height=28)#,width=11,height=8)\n }\n }\n \n ymin <- min(c(accRates-sigmaRates),na.rm=TRUE)\n ymax <- max(c(accRates+sigmaRates),na.rm=TRUE)\n\n xmin <- 0 ##min(ageDataOrig$depth)\n xmax <- max(ageDataOrig$depth)\n plot(ageData$depth,accRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"Depth [cm]\",ylab=\"Rates [ka/cm]\",cex.lab=Ascale,cex.axis=Ascale)\n if (Lsegments > 0) {\n for (i in seq(1,length(segPosPlot))) {\n par(new=TRUE)\n segPosDepth <- (ageData$depth[segPosPlot[i]]+ageData$depth[segPosPlot[i]+1])/2\n plot(c(segPosDepth,segPosDepth),c(ymin,ymax),ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(200/255,200/255,200/255),type=\"l\",lwd=Dscale,lty=2,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n }\n }\n par(new=TRUE)\n polygon(c(ageData$depth, rev(ageData$depth)), c(BayesFittedRates-BayesSigmaRates,\n rev(BayesFittedRates+BayesSigmaRates)), col=rgb(0.8,0.8,0.8),border=NA)\n par(new=TRUE)\n plot(ageData$depth,accRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),col=rgb(42/255,190/255,230/255),pch=19,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",cex=Cscale)\n par(new=TRUE)\n plot(ageData$depth,BayesFittedRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),xlab=\"\",pch=19,ylab=\"\",xaxt=\"n\",yaxt=\"n\",col=\"red\",cex=Ascale)\n par(new=TRUE)\n plot(ageData$depth,BayesFittedRates,ylim=c(ymin,ymax),xlim=c(xmin,xmax),lwd=Cscale,xlab=\"\",ylab=\"\",xaxt=\"n\",yaxt=\"n\",type=\"l\",col=\"red\",cex=Cscale)\n\n arrows(ageData$depth, accRates-sigmaRates, ageData$depth, \n accRates+sigmaRates, length=0.2, angle=90, code=3,col=\"black\",lwd=Bscale,cex=Cscale)\n\n legend(xmax, ymax, xjust=1,legend=c(\"data\",\"fit\",\"sequences\"),\n col=c(rgb(42/255,190/255,230/255),\"red\",rgb(200/255,200/255,200/255)), lwd=c(NaN,Cscale,Dscale), lty=c(1,1,2), pch=c(19,19,NaN), cex=c(Bscale), pt.cex=c(Cscale,Ascale,Cscale))\n\n if (k != \"jupyter\"){\n dev.off()\n }\n }\n ##--------------------------------------------------------------------------------------------##\n\n\n\n ##--------------------------------------------------------------------------------------------##\n ## interpolate ages\n depthInterp <- seq(ageData$depth[1],ageData$depth[lengthData])\n lengthInterp <- length(depthInterp)\n ageFitInterp <- approx(x=ageData$depth,y=ageFit,xout=depthInterp,method=\"linear\")\n ageFitErrorInterp <- approx(x=ageData$depth,y=ageFitError,xout=depthInterp,method=\"linear\")\n\n ## interpolate rates\n ratesInterp <- approx(x=ageData$depth,y=BayesFittedRates,xout=depthInterp,method=\"linear\")\n ratesErrorInterp <- approx(x=ageData$depth,y=BayesSigmaRates,xout=depthInterp,method=\"linear\")\n\n\n ## write to disk\n out <- replicate(5,rep(0,lengthInterp))\n out[,1] <- ageFitInterp$x\n out[,2] <- ageFitInterp$y\n out[,3] <- ageFitErrorInterp$y\n out[,4] <- ratesInterp$y\n out[,5] <- ratesErrorInterp$y\n\n write(\"#depths ages sigma_ages rates sigma_rates\",file=paste(path,token,\"_interp.txt\",sep=\"\"),ncolumns=5)\n write(t(out),file=paste(path,token,\"_interp.txt\",sep=\"\"),ncolumns=5,append=TRUE)\n\n \n\n ##fitted data\n out <- replicate(5,rep(0,lengthData))\n out[,1] <- ageData$depth\n out[,2] <- ageFit\n out[,3] <- ageFitError\n out[,4] <- BayesFittedRates\n out[,5] <- BayesSigmaRates\n\n \n write(\"#depths ages sigma_ages rates sigma_rates\",file=paste(path,token,\"_fit.txt\",sep=\"\"),ncolumns=5)\n write(t(out),file=paste(path,token,\"_fit.txt\",sep=\"\"),ncolumns=5,append=TRUE)\n ##--------------------------------------------------------------------------------------------##\n\n}\n", "meta": {"hexsha": "a5c683923b99fab9ec7f7286cb75db765a5f258a", "size": 20529, "ext": "r", "lang": "R", "max_stars_repo_path": "hummingage.r", "max_stars_repo_name": "hummingbird-dev/hummingage", "max_stars_repo_head_hexsha": "61c49913e677932b14ae83c66cb1516984109cba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-17T11:30:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T11:30:20.000Z", "max_issues_repo_path": "hummingage.r", "max_issues_repo_name": "hummingbird-dev/hummingage", "max_issues_repo_head_hexsha": "61c49913e677932b14ae83c66cb1516984109cba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "hummingage.r", "max_forks_repo_name": "hummingbird-dev/hummingage", "max_forks_repo_head_hexsha": "61c49913e677932b14ae83c66cb1516984109cba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9212253829, "max_line_length": 231, "alphanum_fraction": 0.5402114083, "num_tokens": 5861, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3311966576574422}} {"text": "rm(list = ls()) # This clears everything from memory.\n\nlibrary(dplyr)\nlibrary(ggplot2)\n\nsetwd(\"~/Dropbox/BCI_Turnover\")\nload(\"BCI_turnover20150611.RData\")\nsource(\"~/Dropbox/MS/TurnoverBCI/TurnoverBCImain/source.R\")\n# prepare data set\nab.data <- as.data.frame(sapply(D20m,function(x)apply(x,2,sum)))\nab.data$sp <- rownames(ab.data)\ntrait.temp <- data.frame(sp=rownames(trait),\n moist=trait$Moist,\n slope=trait$sp.slope.mean,\n slope.sd = trait$sp.slope.sd,\n convex=trait$sp.convex.mean,\n convex.sd=trait$sp.convex.sd,\n WSG=trait$WSG,\n slope10=trait$slope_size_10,\n slope20=trait$slope_size_20,\n slope30=trait$slope_size_30,\n slope40=trait$slope_size_40,\n slope50=trait$slope_size_50,\n slope60=trait$slope_size_60,\n slope70=trait$slope_size_70,\n slope80=trait$slope_size_80,\n slope90=trait$slope_size_90,\n slope100=trait$slope_size_100,\n convex10=trait$convex_size_10,\n convex20=trait$convex_size_20,\n convex30=trait$convex_size_30,\n convex40=trait$convex_size_40,\n convex50=trait$convex_size_50,\n convex60=trait$convex_size_60,\n convex70=trait$convex_size_70,\n convex80=trait$convex_size_80,\n convex90=trait$convex_size_90,\n convex100=trait$convex_size_100)\n\nab.t.data <- merge(ab.data,trait.temp,by=\"sp\")\nrownames(ab.t.data) <- ab.t.data$sp\nab.t.data2 <- na.omit(ab.t.data)\n\n#this may be useful way to detect species.\n#using the product of delta abundance and deviaiton from mean (or median) trait for each species\nWSGab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n delta_ab2 = ab.t.data$census_2010 - ab.t.data$census_1982,\n delta_ab3 = ab.t.data$census_2010/ab.t.data$census_1982,\n WSG = ab.t.data$WSG,\n WSG_delta =ab.t.data$WSG - mean(ab.t.data$WSG,na.rm=T))\nWSGab$index <- as.numeric(scale(WSGab$delta_ab)) * as.numeric(scale(WSGab$WSG_delta))\n\nWSGab <- WSGab[order(WSGab$index),]\n\n\n\n# moistab\nmoistab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n moist = ab.t.data2$moist,\n moist_delta =ab.t.data2$moist - mean(ab.t.data2$moist))\nmoistab$index <- as.numeric(scale(moistab$delta_ab)) * as.numeric(scale(moistab$moist_delta))\n\n# moistab$index2 <- moistab$delta_ab * moistab$moist_delta\n\nmoistab <- moistab[order(moistab$index),]\n\n# convex100ab\nconvexab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n convex = ab.t.data2$convex,\n convex_delta =ab.t.data2$convex - mean(ab.t.data2$convex))\nconvexab$index <- as.numeric(scale(convexab$delta_ab)) * as.numeric(scale(convexab$convex_delta))\n\n# convexab$index2 <- convexab$delta_ab * convexab$_delta\n\nconvexab <- convexab[order(convexab$index),]\n\n# slope100ab\nslopeab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n slope = ab.t.data2$slope,\n slope_delta =ab.t.data2$slope - mean(ab.t.data2$slope))\nslopeab$index <- as.numeric(scale(slopeab$delta_ab)) * as.numeric(scale(slopeab$slope_delta))\n\nslopeab <- slopeab[order(slopeab$index),]\n\n\n\n#using the product of delta abundance and deviaiton from mean (or median) trait for each species\nWSGab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n WSG = ab.t.data$WSG,\n WSG_delta =ab.t.data$WSG - mean(WSG100[[1]])) %>%\n mutate(WSG_delta2 = WSG - mean(WSG, na.rm = T))\n\nWSGab$index <- WSGab$delta_ab * WSGab$WSG_delta*100\n\nWSGab <- WSGab[order(WSGab$index),]\n\n# moistab\nmoistab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n moist = ab.t.data$moist,\n moist_delta = ab.t.data$moist - mean(Moist100[[1]]))\nmoistab$index <- moistab$delta_ab * moistab$moist_delta*100\n\nmoistab <- moistab[order(moistab$index),]\n\n# convex100ab\nconvexab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n convex = ab.t.data$convex,\n convex_delta =ab.t.data$convex - mean(convex100[[1]]))\nconvexab$index <- convexab$delta_ab * convexab$convex_delta*100\n\nconvexab <- convexab[order(convexab$index),]\n# convexab <- convexab[order(convexab$convex),]\n\n# slopeab\nslopeab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n slope = ab.t.data$slope,\n slope_delta =ab.t.data$slope - mean(slope100[[1]]))\nslopeab$index <- slopeab$delta_ab * slopeab$slope_delta*100\n\nslopeab <- slopeab[order(slopeab$index),]\n\n\n# sp list for appendix ====================================================\n\ntaxa <- read.csv(\"~/Dropbox/MS/TurnoverBCI/nomenclature_R_20120305_Rready-2.csv\")\n\nsp_list <- WSGab %>%\n mutate(Wood_density = round(index, 4)) %>%\n mutate(Abundance_change = round(delta_ab, 4)) %>%\n dplyr::select(sp, Abundance_change, Wood_density, -index) %>%\n full_join(., moistab, by = \"sp\") %>%\n mutate(Moisture = round(index, 4)) %>%\n dplyr::select(-index) %>%\n full_join(., convexab, by = \"sp\") %>%\n mutate(Convexity = round(index, 4)) %>%\n dplyr::select(-index) %>%\n full_join(., slopeab, by = \"sp\") %>%\n mutate(Slope = round(index, 4), sp6 = sp) %>%\n left_join(., taxa, by = \"sp6\") %>%\n dplyr::select(sp, family, genus, species,\n Abundance_change, Wood_density, Moisture, Convexity, Slope) %>%\n arrange(sp) %>%\n mutate(species = paste(genus, species)) %>%\n dplyr::select(-genus)\n\nwrite.csv(sp_list, \"/Users/mattocci/Dropbox/MS/TurnoverBCI/sp_list.csv\")\n\n\n\n#\n# par(mfrow = c(2,2))\n# moge <- sp_list$Wood_density %>% sort(decreasing = T)\n# wsg_posi <- moge[moge>0]\n# plot(1:length(wsg_posi), 100 *cumsum(wsg_posi)/sum(moge,na.rm=T), type = \"b\",\n# ylab = \"cumulative contributoin index of wood density (%)\", xlab = \"Number of species\", ylim = c(0, max(100 *cumsum(wsg_posi)/sum(moge,na.rm=T))))\n# abline(h = 100, lty = 2)\n#\n# moge <- sp_list$Moisture %>% sort(decreasing = T)\n# moist_posi <- moge[moge>0]\n# plot(1:length(moist_posi), 100 *cumsum(moist_posi)/sum(moge,na.rm=T), type = \"b\", ylab = \"cumulative contributoin index of moisture (%)\", xlab = \"Number of species\", ylim = c(0, max(100 *cumsum(moist_posi)/sum(moge,na.rm=T))))\n# abline(h = 100, lty = 2)\n#\n#\n# moge <- sp_list$Convexity %>% sort(decreasing = T)\n# convex_posi <- moge[moge>0]\n# plot(1:length(convex_posi), 100 *cumsum(convex_posi)/sum(moge,na.rm=T), type = \"b\", ylab = \"cumulative contributoin index of convexity (%)\", xlab = \"Number of species\",ylim = c(0, max(100 *cumsum(convex_posi)/sum(moge,na.rm=T))))\n# abline(h = 100, lty = 2)\n#\n#\n# moge <- sp_list$Slope %>% sort(decreasing = F)\n# slope_posi <- moge[moge<0]\n# plot(1:length(slope_posi), 100 *cumsum(slope_posi)/sum(moge,na.rm=T), type = \"b\", ylab = \"cumulative contributoin index of slope (%)\", xlab = \"Number of species\", ylim = c(0, max(100 *cumsum(slope_posi)/sum(moge,na.rm=T))))\n# abline(h = 100, lty = 2)\n# par(mfrow = c(1,1))\n\n\n## don't use %\nfig_dat <- sp_list %>%\n arrange(desc(Wood_density)) %>%\n mutate(wsg_sp = 1:nrow(sp_list)) %>%\n mutate(wsg_posi = cumsum(Wood_density) /sum(Wood_density, na.rm = T) * 100) %>%\n mutate(wsg_posi = cumsum(Wood_density)) %>%\n\n arrange(desc(Moisture)) %>%\n mutate(moist_sp = 1:nrow(sp_list)) %>%\n mutate(moist_posi = cumsum(Moisture) /sum(Moisture, na.rm = T) * 100) %>%\n mutate(moist_posi = cumsum(Moisture)) %>%\n\n arrange(desc(Convexity)) %>%\n mutate(convex_sp = 1:nrow(sp_list)) %>%\n mutate(convex_posi = cumsum(Convexity) /sum(Convexity, na.rm = T) * 100) %>%\n mutate(convex_posi = cumsum(Convexity)) %>%\n\n arrange(Slope) %>%\n mutate(slope_sp = 1:nrow(sp_list)) %>%\n mutate(slope_nega = cumsum(Slope) /sum(Slope, na.rm = T) * 100) %>%\n mutate(slope_nega = cumsum(Slope))\n\ntemp1 <- fig_dat %>%\n tidyr::gather(\"trait\", \"val\", c(wsg_posi, moist_posi, convex_posi, slope_nega))\n\ntemp2 <- fig_dat %>%\n tidyr::gather(\"trait2\", \"n_sp\", c(wsg_sp, moist_sp, convex_sp, slope_sp))\n\nfig_dat2 <- temp1 %>%\n mutate(n_sp = temp2$n_sp) %>%\n mutate(trait = factor(trait, levels = c(\"wsg_posi\", \"moist_posi\", \"convex_posi\", \"slope_nega\"))) %>%\n mutate(trait2 = factor(trait, labels = c(\"Wood density\", \"Moisture\", \"Convexity\", \"Slope\")))\n\n\n\nfig_dat3 <- fig_dat2 %>%\n filter(trait != \"wsg_posi\" | Wood_density > 0) %>%\n filter(trait != \"moist_posi\" | Moisture > 0) %>%\n filter(trait != \"convex_posi\" | Convexity > 0) %>%\n filter(trait != \"slope_nega\" | Slope < 0)\n\ndummy1 <- data_frame(x = 1,\n trait2 = \"Wood density\",\n val = c(0, max(fig_dat3 %>% filter(trait == \"wsg_posi\") %>% .$val)))\ndummy2 <- data_frame(x = 1,\n trait2 = \"Moisture\",\n val = c(0, max(fig_dat3 %>% filter(trait == \"moist_posi\") %>% .$val)))\ndummy3 <- data_frame(x = 1,\n trait2 = \"Convexity\",\n val = c(0, max(fig_dat3 %>% filter(trait == \"convex_posi\") %>% .$val)))\ndummy4 <- data_frame(x = 1,\n trait2 = \"Slope\",\n val = c(0, max(fig_dat3 %>% filter(trait == \"slope_nega\") %>% .$val)))\n\ndummy <- bind_rows(dummy1, dummy2, dummy3, dummy4) %>%\n mutate(n_sp =1) %>%\n mutate(trait2 = factor(trait2, levels = c(\"Wood density\", \"Moisture\", \"Convexity\", \"Slope\"))) %>%\n mutate(census_1982 = 1) %>%\n mutate(inc = \"Increased\")\n\ndummy_line <- data_frame(x = 1,\n trait2 = c(\"Wood density\", \"Moisture\", \"Convexity\", \"Slope\"),\n n_sp = 1,\n h = c(sum(fig_dat$Wood_density, na.rm = T),\n sum(fig_dat$Moisture, na.rm = T),\n sum(fig_dat$Convexity, na.rm = T),\n sum(fig_dat$Slope, na.rm = T))) %>%\n mutate(trait2 = factor(trait2, levels = c(\"Wood density\", \"Moisture\", \"Convexity\", \"Slope\"))) %>%\n mutate(census_1982 = 1) %>%\n mutate(inc = \"Increased\")\n\nfig_dat4 <- left_join(fig_dat3, ab.data, by = \"sp\") %>%\n mutate(inc = ifelse(census_2010 - census_1982 > 0, \"Increased\", \"Decreased\"))\n\n##ver colour\npostscript(\"~/Dropbox/MS/TurnoverBCI/fig/fig4_abs.eps\", width = 6, height = 6.5)\n\n ggplot(fig_dat4, aes(x = n_sp, y= val, colour = inc)) +\n geom_line(col = \"black\") +\n geom_point() +\n facet_wrap(~ trait2, scale = \"free\") +\n theme_bw() +\n geom_blank(data = dummy) +\n geom_hline(data = dummy_line, aes(yintercept = h), lty = 2) +\n # geom_hline(yintercept = 100, lty = 2) +\n ylab(\"Cumulative contributoin index\") +\n xlab(\"Number of species\") +\n geom_text(data = fig_dat4 %>% filter(n_sp <6),\n aes(label = species), hjust= -0.2, vjust = 1,\n fontface=\"italic\", size = 3) +\n geom_segment(data = fig_dat4 %>% filter(n_sp <6),\n mapping = aes(x = n_sp + 10, y = val - 0.05, xend = n_sp + 2, yend= val),\n arrow=arrow(length = unit(0.05, \"inches\")), size=0.25) +\n theme(legend.position = \"bottom\") +\n scale_colour_manual(values = c(\"red3\", \"royalblue2\")) +\n guides(colour = guide_legend(title = \"Abundance\"))\n\ndev.off()\n\n###\n# WSG\n# Mean :0.5586\n\nyy<-dnorm(seq(-0.2,0.8, length=100), mean = 0.399, sd = 0.128)\nxx <- seq(-0.2,0.8, length=100)\nplot(yy ~ xx , type = \"l\")\n\nyy<-dnorm(seq(-0.2,0.8, length=100), mean = 0.399 + 0.003, sd = 0.128)\nxx <- seq(-0.2,0.8, length=100)\npoints(yy ~ xx , type = \"l\", col = \"blue\")\n\n##ver white\npostscript(\"~/Dropbox/MS/TurnoverBCI/fig/fig4_abs.eps\", width = 6, height = 6.5)\n\n ggplot(fig_dat4, aes(x = n_sp, y= val)) +\n geom_line(col = \"black\") +\n geom_point() +\n facet_wrap(~ trait2, scale = \"free\") +\n theme_bw() +\n geom_blank(data = dummy) +\n geom_hline(data = dummy_line, aes(yintercept = h), lty = 2) +\n # geom_hline(yintercept = 100, lty = 2) +\n ylab(\"Cumulative contributoin index\") +\n xlab(\"Number of species\") +\n geom_text(data = fig_dat4 %>% filter(n_sp <6),\n aes(label = species), hjust= -0.2, vjust = 1,\n fontface=\"italic\", size = 3) +\n geom_segment(data = fig_dat4 %>% filter(n_sp <6),\n mapping = aes(x = n_sp + 10, y = val - 0.05, xend = n_sp + 2, yend= val),\n arrow=arrow(length = unit(0.05, \"inches\")), size=0.25)\n\ndev.off()\n", "meta": {"hexsha": "991b6793d9a4a3a5de44c732b3f21967b680cd33", "size": 12776, "ext": "r", "lang": "R", "max_stars_repo_path": "old/fig_cum.r", "max_stars_repo_name": "mattocci27/TurnoverBCImain", "max_stars_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "old/fig_cum.r", "max_issues_repo_name": "mattocci27/TurnoverBCImain", "max_issues_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "old/fig_cum.r", "max_forks_repo_name": "mattocci27/TurnoverBCImain", "max_forks_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3028391167, "max_line_length": 231, "alphanum_fraction": 0.6303224796, "num_tokens": 4279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056167854461, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.33119665152406835}} {"text": "#Script to resample and mosaic mapbiomas data\n\nrm(list=ls())\n\nlibrary(raster)\n\ninpath <- \"Data/MapBiomas/\"\noutpath <- \"Data/Unclassified/\"\n\n#set years for analysis\nstartyr <- 2000\nendyr <- 2018\nstepyr <- 1\nyrs <- seq(startyr,endyr,stepyr)\nyrs <- yrs - 1984 #because 1985 is in band 1 of the MapBiomas tif files\nlabelyr <- paste0(startyr,\"-\",endyr,\"-\",stepyr)\n\n#set biomes for analysis\nbiomes <- list(\"PAMPA\", \"PANTANAL\",\"MATAATLANTICA\",\"CAATINGA\",\"CERRADO\",\"AMAZONIA\") \n\nprint(paste0(\"Start: \",Sys.time()))\n\n#loop by biome to aggregate to 1km\nfor(b in biomes){\n\n print(b)\n sraw <- stack(paste0(inpath,b,\".tif\")) #read 30m file to a stack\n sraw <- sraw[[yrs]] #select only required years\n \n #resample to 5km in two steps\n map1km <- aggregate(sraw, fact=33, fun=modal, expand=TRUE, \n filename=paste0(inpath,b,\"_\",labelyr,\"_1km.tif\", overwrite=TRUE))\n \n #write to file as multi-layer tif\n writeRaster(map1km, paste0(inpath,b,\"_\",labelyr,\"_1km.tif\"), format=\"GTiff\", bylayer=FALSE)\n}\n\n#reset years as the 1km tif layers may differ from the 30m tif \nyrs <- seq(startyr,endyr,stepyr)\nyrs <- yrs - startyr + 1 #+1 because raster reads multi-layer files with 1 index file\n\n#loop by year to create Brazil mosaic and aggregate to 5km\nfor(y in yrs){\n \n year = y+startyr-1\n print(year)\n \n #for each biome read the 1km tif layer for this year\n P <- raster(paste0(inpath,\"PAMPA\",\"_\",labelyr,\"_1km.tif\"),band=y)\n M <- raster(paste0(inpath,\"MATAATLANTICA\",\"_\",labelyr,\"_1km.tif\"),band=y)\n C <- raster(paste0(inpath,\"CAATINGA\",\"_\",labelyr,\"_1km.tif\"),band=y)\n O <- raster(paste0(inpath,\"CERRADO\",\"_\",labelyr,\"_1km.tif\"),band=y)\n A <- raster(paste0(inpath,\"AMAZONIA\",\"_\",labelyr,\"_1km.tif\"),band=y)\n L <- raster(paste0(inpath,\"PANTANAL\",\"_\",labelyr,\"_1km.tif\"),band=y)\n \n #specify (in MapBiomas data) to NoData\n P <- reclassify(P, cbind(0, NA))\n M <- reclassify(M, cbind(0, NA))\n C <- reclassify(C, cbind(0, NA))\n O <- reclassify(O, cbind(0, NA))\n A <- reclassify(A, cbind(0, NA))\n L <- reclassify(L, cbind(0, NA))\n \n #1km base used to resample biomes to standard origin\n base1km <- raster(ncols=4505,\n nrows=4455,\n xmn=-75.00007,\n ymn=-34.01103,\n ymx=6.053681,\n xmx=-34.48571,\n crs=crs(P),\n resolution=0.008993,\n vals=NULL)\n \n #resample biomes to standard origin for mosaic below\n Pbase <- resample(P,base1km,method=\"ngb\")\n Mbase <- resample(M,base1km,method=\"ngb\")\n Cbase <- resample(C,base1km,method=\"ngb\")\n Obase <- resample(O,base1km,method=\"ngb\")\n Abase <- resample(A,base1km,method=\"ngb\")\n Lbase <- resample(L,base1km,method=\"ngb\")\n \n #mosaic the 1km map for all of Brazil\n mos1km <- mosaic(Pbase,Mbase,Cbase,Obase,Abase,Lbase, fun=max)\n \n #aggregate the 1km map to 5km\n mos5km <- aggregate(mos1km, fact=5, fun=modal, na.rm=T, expand=TRUE)\n \n #5km base for use to ensure identical origin to 1km\n base5km <- raster(ncols=901,\n nrows=891,\n xmn=-75.00007,\n ymn=-34.01103,\n ymx=6.053681,\n xmx=-34.48571,\n crs=crs(P),\n resolution=0.044966,\n vals=NULL)\n \n #resample to ensure identical origin to 1km\n mos5km_res <- resample(mos5km,base5km,method=\"ngb\")\n \n #write Brazil 5km map for this year to file\n writeRaster(mos5km_res,paste0(outpath,\"Brazil_\",year,\"_5km.asc\"),format=\"ascii\", overwrite=T)\n\n}\n\nprint(paste0(\"End: \",Sys.time()))\n\n\n", "meta": {"hexsha": "443ea6b22ac3a75f9a7f7d0ece51c396483ea081", "size": 3620, "ext": "r", "lang": "R", "max_stars_repo_path": "ResampleMosaic.r", "max_stars_repo_name": "jamesdamillington/BrazilInputMaps", "max_stars_repo_head_hexsha": "8bb5807008a566e7db27aeff577d50a8ebe1db82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-04T17:04:56.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-04T17:04:56.000Z", "max_issues_repo_path": "ResampleMosaic.r", "max_issues_repo_name": "jamesdamillington/BrazilInputMaps", "max_issues_repo_head_hexsha": "8bb5807008a566e7db27aeff577d50a8ebe1db82", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ResampleMosaic.r", "max_forks_repo_name": "jamesdamillington/BrazilInputMaps", "max_forks_repo_head_hexsha": "8bb5807008a566e7db27aeff577d50a8ebe1db82", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6126126126, "max_line_length": 95, "alphanum_fraction": 0.6187845304, "num_tokens": 1182, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3311966515240683}} {"text": "########################################################\r##### Author: Diego Valle Jones\r##### Website: www.diegovalle.net\r##### Date Created: Sat Feb 27 22:38:37 2010\r########################################################\r#The counties with the highest homicide rates for women and men\r#data source: Estad\u00edsticas Vitales INEGI\r\rremoveCommas <- function(hom, col2cvt) {\r hom[,col2cvt] <- lapply(hom[,col2cvt],\r function(x){as.numeric(gsub(\",\", \"\", x))})\r hom\r}\r\rcleanHom <- function(hom) {\r names(hom)[1:4] <- c(\"Code\",\"County\",\"Year.of.Murder\",\"Sex\")\r hom$County <- iconv(hom$County, \"windows-1252\", \"utf-8\")\r hom$Code <- iconv(hom$Code, \"windows-1252\", \"utf-8\")\r hom <- hom[-grep(\"=CONCATENAR\", hom$Code),]\r hom <- hom[-grep(\"Total\", hom$County),]\r hom <- hom[-grep(\"No especificado\", hom$County),]\r hom <- hom[-grep(\"Total\", hom$Year.of.Murder),]\r hom <- hom[-grep(\"No especificado\", hom$Year.of.Murder),]\r hom <- hom[-grep(\"Total\", hom$Sex),]\r hom$X.4 <- NULL\r hom$Year.of.Murder <- as.numeric(as.numeric(gsub('[[:alpha:]]', '',\r hom$Year.of.Murder)))\r hom <- subset(hom, Year.of.Murder >= 1990)\r col2cvt <- 5:ncol(hom)\r hom <- removeCommas(hom, col2cvt)\r hom[is.na(hom)] <- 0\r hom$tot <- apply(hom[ , col2cvt], 1, sum)\r hom$CLAVE <- as.numeric(gsub(\" \", \"\", hom$Code))\r hom\r}\r\rcleanPop <- function(filename, sex) {\r pop <- read.csv(filename)\r pop <- na.omit(pop)\r col2cvt <- 3:ncol(pop)\r pop[,col2cvt] <- lapply(pop[ ,col2cvt],\r function(x){as.numeric(gsub(\" \", \"\", x))})\r pop$Sex <- c(sex)\r names(pop)[1:2] <- c(\"CLAVE\", \"Mun\")\r popm <- melt(pop, id = c(\"CLAVE\", \"Mun\", \"Sex\"))\r popm$Mun <- iconv(popm$Mun, \"windows-1252\", \"utf-8\")\r popm$variable <- as.numeric(substring(popm$variable, 2))\r popm\r}\r\r#Some counties in Oaxaca have changed recently so we have to merge\r#them by name instead of code\rjoinChangedMun <- function(hom, popm, hom.popm) {\r changed <- setdiff(hom$CLAVE, popm$CLAVE)\r hom.ch <- subset(hom, CLAVE %in% changed)\r hom.popm.ch <- merge(hom.ch, popm, by.x = c(\"County\",\r \"Year.of.Murder\", \"Sex\"),\r by.y = c(\"Mun\", \"variable\", \"Sex\"))\r hom.popm.ch$CLAVE.x <- NULL\r hom.popm.ch$Mun <- hom.popm.ch$County\r names(hom.popm.ch)[25] <- \"CLAVE\"\r hom.popm <- rbind(hom.popm, hom.popm.ch)\r hom.popm$rate <- (hom.popm$tot / hom.popm$value) *\r 100000\r hom.popm\r}\r\rjoinHomPop <- function(hom,popm){\r hom.popm <- merge(hom, popm, by.x =c(\"CLAVE\", \"Year.of.Murder\",\r \"Sex\"),\r by.y = c(\"CLAVE\", \"variable\", \"Sex\"),\r all.y = TRUE)\r hom.popm[is.na(hom.popm$tot), ]$tot <- 0\r hom.popm <- joinChangedMun(hom, popm, hom.popm)\r}\r\rgetMeans <- function(hom.plot) {\r ddply(hom.plot, .(CLAVE), function(df) mean(df$rate))\r}\r\rgetMun <- function(df, mostviol, size){\r hom.plot <- subset(df, value > size)\r means <- getMeans(hom.plot)\r if(mostviol) {\r means <- means[order(-means$V1), ]\r }\r else {\r means <- means[order(means$V1), ]\r }\r high.murder <- unique(means$CLAVE[1:25])\r high.murder\r}\r\rplotRate <- function(df, title=\"\", mostviol = TRUE){\r size <- 50000\r hom.plot <- subset(df, CLAVE %in% getMun(df, mostviol, size))\r means <- getMeans(hom.plot)\r hom.plot <- merge(means, hom.plot, by = \"CLAVE\")\r hom.plot <- ddply(hom.plot, .(CLAVE), transform,\r max = max(rate))\r ifelse(mostviol, hom.plot$Mun <- with(hom.plot, reorder(factor(Mun),\r -max)),\r hom.plot$Mun <- with(hom.plot, reorder(factor(Mun),\r max)))\r if(9015 %in% hom.plot$CLAVE){\r hom.plot$Mun <- factor(hom.plot$Mun, levels =\r c(levels(hom.plot$Mun),\r \"Benito Juarez (Cancun)\", \"Cuauht\u00e9moc DF\"))\r\r #hom.plot[hom.plot$CLAVE == 23005, ]$Mun = \"Benito Juarez (Cancun)\"\r hom.plot[hom.plot$CLAVE == 9015, ]$Mun = \"Cuauht\u00e9moc DF\"\r }\r ggplot(hom.plot, aes(Year.of.Murder, rate)) +\r geom_line() + geom_point(aes(size = tot)) +\r facet_wrap(~ Mun) +\r theme_bw() +\r geom_hline(aes(yintercept = V1), linetype = 2, color=\"gray70\") +\r scale_x_continuous(breaks = c(2005,2008)) +\r # labels = c(\"05\",\"06\",\"07\", \"08\")) +\r opts(title = title) +\r ylab(\"Homicide rate\") + xlab(\"\") +\r opts(axis.text.x=theme_text(angle=60, hjust=1.2 )) +\r scale_size(\"Number of\\nHomicides\")\r}\r\rhom <- read.csv(bzfile(\"states/data/homicide-mun-2008.csv.bz2\"), skip=4)\rhom <- cleanHom(hom)\r\r#for men\rpopm <- cleanPop(\"most-violent-counties/data/poblacionh.csv.bz2\", \"Hombre\")\rhom.popm <- joinHomPop(hom, popm)\r#for women\rpopf <- cleanPop(\"most-violent-counties/data/poblacionm.csv.bz2\", \"Mujer\")\rhom.popf <- joinHomPop(hom, popf)\r\r\rprint(plotRate(hom.popm, \"Most violent municipalities for men (with more than 50,000 men)\", TRUE))\rdev.print(png, file=\"most-violent-counties/output/Most violent municipalities for men.png\",\r width=800, height=600)\r\rprint(plotRate(hom.popf, \"Most violent municipalities for women (with more than 50,000 women)\", TRUE))\rdev.print(png, file=\"most-violent-counties/output/Most violent municipalities for women.png\",\r width=800, height=600)\r\rprint(plotRate(hom.popm, \"Least violent municipalities for men (with more than 50,000 men)\", FALSE))\rdev.print(png, file=\"most-violent-counties/output/Least violent municipalities for men.png\",\r width=800, height=600)\r\rprint(plotRate(hom.popf, \"Least violent municipalities for women (with more than 50,000 women)\", FALSE))\rdev.print(png,file=\"most-violent-counties/output/Least violent municipalities for women.png\",\r width=800, height=600)\r\rjuar <- subset(hom, CLAVE == \"8037\" & Sex == \"Mujer\")\rggplot(juar, aes(Year.of.Murder, tot)) +\r geom_line()\r#According to the movie \"on the edge\" there were 437 femicides\r#(I assume from 1993 to 2006, when the movie was released)\r#http://political.detritus.net/juarez/\rsum(subset(juar, Year.of.Murder > 2006 & Year.of.Murder < 2008)$tot)\r#108 - 2008\r#19 - 2007\r", "meta": {"hexsha": "51b7ece8cd1aeed595dbd532b2a1d315051094c3", "size": 6262, "ext": "r", "lang": "R", "max_stars_repo_path": "most-violent-counties/most-violent.r", "max_stars_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_stars_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2015-05-14T01:06:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-16T12:52:10.000Z", "max_issues_repo_path": "most-violent-counties/most-violent.r", "max_issues_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_issues_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "most-violent-counties/most-violent.r", "max_forks_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_forks_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2015-02-05T15:09:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-10T02:19:40.000Z", "avg_line_length": 6262.0, "max_line_length": 6262, "alphanum_fraction": 0.5855956563, "num_tokens": 2062, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727231, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3311781064336618}} {"text": "library(ngram)\n\nx <- \"a b a c a b b\"\nng <- ngram(x)\n\ntest <- babble(ng, seed=1234)\ntruth <- \"b a c a b a c a b b c a b a c a b b b b c a b b a c a b b b a c a b a c a b a c a b b b b b b b a c a b a c a b b a b b b b a b b b a c a b a c a b a c a b b b a c a b b b b b b b b c a b a c a b b a b a c a b b b a c a b b a c a b a c a b b a c a b a c a b a c a b a c a b a c a b b a b a c a b a c \"\n\nstopifnot(all.equal(test, truth))\n", "meta": {"hexsha": "090ab0bb67a5ea915ea1f35e6c0cf9b2691b41f1", "size": 430, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/dontrun/babble.r", "max_stars_repo_name": "russey/ngram", "max_stars_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2015-03-10T17:47:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:26:08.000Z", "max_issues_repo_path": "tests/dontrun/babble.r", "max_issues_repo_name": "russey/ngram", "max_issues_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2015-06-23T14:59:00.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-22T17:14:00.000Z", "max_forks_repo_path": "tests/dontrun/babble.r", "max_forks_repo_name": "russey/ngram", "max_forks_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 22, "max_forks_repo_forks_event_min_datetime": "2015-02-09T14:21:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T00:58:48.000Z", "avg_line_length": 43.0, "max_line_length": 311, "alphanum_fraction": 0.5325581395, "num_tokens": 196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5851011542032313, "lm_q1q2_score": 0.33117809824366123}} {"text": "#' @title pie.draw\n#' @description couldn't accurately describe\n#' @param \\code{x} NULL\n#' @param \\code{y} NULL\n#' @param \\code{z} NULL\n#' @param \\code{radius} NULL\n#' @param \\code{scale} NULL\n#' @param \\code{labels} NULL\n#' @param \\code{silent} NULL\n#' @family plotting\n#' @note \n#' useage\n#' data(landings)\n#' data(coast)\n#' xlim <- c(-12,-5)\n#' ylim <- c(50,56)\n#' xyz <- make.xyz(landings$Lon,landings$Lat,landings$LiveWeight,landings$Species)\n#' col <- rainbow(5)\n#' basemap(xlim, ylim, main = \"Species composition of gadoid landings\")\n#' draw.shape(coast, col=\"cornsilk\")\n#' draw.pie(xyz$x, xyz$y, xyz$z, radius = 0.3, col=col)\n#' legend.pie(-13.25,54.8,labels=c(\"cod\",\"had\",\"hke\",\"pok\",\"whg\"), radius=0.3, bty=\"n\", col=col,\n#' cex=0.8, label.dist=1.3)\n#' legend.z <- round(max(rowSums(xyz$z,na.rm=TRUE))/10^6,0)\n#' legend.bubble(-13.25,55.5,z=legend.z,round=1,maxradius=0.3,bty=\"n\",txt.cex=0.6)\n#' text(-12.25,56,\"landings (kt)\",cex=0.8) \n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\npie.draw <- function (x, y, z, radius, scale = T, labels = NA, silent = TRUE, \n ...) \n{\n nx <- length(x)\n nz <- dim(z)[2]\n if (length(y) != nx) \n stop(\"x and y should be vectors of the same length\")\n if (length(dim(z)) != 2) \n stop(\"z should be a 2-dimensional array\")\n if (dim(z)[1] != nx) \n stop(\"the number of rows in of z should match as the length of x and y\")\n if (sum(z, na.rm = T) == 0) \n stop(\"z has no data\")\n maxsumz <- max(rowSums(z), na.rm = T)\n #pm <- setProgressMsg(1, nx)\n for (i in 1:nx) {\n xi <- x[i]\n yi <- y[i]\n zi <- z[i, ]\n zi <- ifelse(is.na(zi), 0, zi)\n if (length(radius) > 1) \n radiusi <- radius[i]\n else radiusi = radius\n if (scale & length(radius) == 1) \n radiusi <- radius * sqrt(sum(zi, na.rm = T))/sqrt(maxsumz)\n if (sum(zi) > 0) \n pie.add(zi, xi, yi, labels, radius = radiusi, ...)\n if (!silent) \n iw=11\n #pm <- progressMsg(pm, i)\n }\n}", "meta": {"hexsha": "8a43426f9c741b66adf612e9d32fd72a7091515b", "size": 1972, "ext": "r", "lang": "R", "max_stars_repo_path": "R/pie.draw.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/pie.draw.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/pie.draw.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.8666666667, "max_line_length": 96, "alphanum_fraction": 0.5796146045, "num_tokens": 723, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3311780982436612}} {"text": "cat(sapply(strsplit(readLines(tail(commandArgs(), n=1)), \" \"), function(s) {\n paste(apply(t(matrix(s, sqrt(length(s)))), 2, rev), collapse=\" \")\n}), sep=\"\\n\")\n", "meta": {"hexsha": "d82876b452253dc6f1fc470d3802bd8e6eb76c2f", "size": 159, "ext": "r", "lang": "R", "max_stars_repo_path": "easy/matrix_rotation.r", "max_stars_repo_name": "IlkhamGaysin/ce-challenges", "max_stars_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-06-24T17:09:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-03T11:44:54.000Z", "max_issues_repo_path": "easy/matrix_rotation.r", "max_issues_repo_name": "IlkhamGaysin/ce-challenges", "max_issues_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "easy/matrix_rotation.r", "max_forks_repo_name": "IlkhamGaysin/ce-challenges", "max_forks_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.75, "max_line_length": 76, "alphanum_fraction": 0.6100628931, "num_tokens": 47, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3311780982436612}} {"text": "library(ggplot2)\naed <- read.table(\"all_aed_scores.txt\",header=F,sep=\" \")\nnames(aed) <- c(\"sample\",\"AED\")\n\nggplot(aed, aes(x = AED, color=sample)) +\n stat_ecdf(aes(x = AED), geom=\"line\",lwd=1.4) +\n xlim(0,1.0) +\n theme_bw() +\n xlab(\"AED\") +\n ylab(\"Cumulative fraction of gene annotations\") +\n scale_color_discrete(limits=c(\"makerre\",\"makerst\",\"sunflower\",\"tair10\"),\n labels=c(\"MAKER Arab. update\",\"MAKER Arab. de novo\",\n \"MAKER sunflower\",\"TAIR10 annotations\")) +\n theme(axis.text.x = element_text(size = 12),\n axis.text.y = element_text(size = 12),\n axis.title.x = element_text(size = 14),\n axis.title.y = element_text(size = 12),\n legend.title = element_blank())", "meta": {"hexsha": "e2f6ca84dcb146ee2667babb931471bcd4eeadf1", "size": 744, "ext": "r", "lang": "R", "max_stars_repo_path": "gene_annotation/r_scripts/all_aed.r", "max_stars_repo_name": "sestaton/sesbio", "max_stars_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2015-01-14T17:25:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-09T01:15:18.000Z", "max_issues_repo_path": "gene_annotation/r_scripts/all_aed.r", "max_issues_repo_name": "sestaton/sesbio", "max_issues_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gene_annotation/r_scripts/all_aed.r", "max_forks_repo_name": "sestaton/sesbio", "max_forks_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-10-09T02:56:51.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-02T12:07:39.000Z", "avg_line_length": 41.3333333333, "max_line_length": 74, "alphanum_fraction": 0.6061827957, "num_tokens": 217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7122321842389469, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3311178616353871}} {"text": "## DEPDENCY #######################################################################################\n\nsource(\"https://raw.githubusercontent.com/oltkkol/vmod/master/basic_text.r\", encoding=\"UTF-8\")\nsource(\"https://raw.githubusercontent.com/oltkkol/vmod/master/basic_ml.r\", encoding=\"UTF-8\")\n\n####################################################################################################\n## EXMAPLE 0\n## Simple examle with Bag Of Words\n####################################################################################################\n\n# 1. Read files & make Bag-Of-Words\nadamFiles\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/Adam\", \"Adam\")\nhenryFiles\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/HenryVegetarian\", \"HenryVegetarian\")\n\nadamTokens\t\t<- TokenizeTexts(adamFiles)\nhenryTokens\t\t<- TokenizeTexts(henryFiles)\nallTokens\t\t<- append(adamTokens, henryTokens)\n\nallBOW\t\t\t<- MakeBOWModel(allTokens)\n\n# 2. Prepare datasets\nallBOW\t\t\t<- FirstColNameWordsToColumn(allBOW)\ndatasets\t\t<- PrepareTrainAndTest(allBOW, \"CLASS\", 1/2, scaleBy=\"none\", convertToFactors=TRUE)\n\n# 3. Train Naive Bayes\nmodelNB\t\t\t<- naiveBayes(datasets$Train$X, datasets$Train$Y)\nEvaluateModelAndPlot(modelNB, datasets$Train, datasets$Test)\n\n# 4. See inside Naive Bayes\nInspectNaiveBayes(modelNB, \"Adam\")\nInspectNaiveBayes(modelNB, \"HenryVegetarian\")\n\n####################################################################################################\n##\tEXAMPLE 1\n##\tBag Of Words vs language vs Authorship attribution\n##\tNaive approach \n####################################################################################################\n\n# 1. Read files & make Bag-Of-Words\nasimovFiles\t\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/Asimov\", \"ASIMOV\")\nfoglarFiles\t\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/Foglar\", \"FOGLAR\")\n\nasimovFileTokensAll\t<- TokenizeTexts(asimovFiles)\nfoglarFileTokensAll\t<- TokenizeTexts(foglarFiles)\n\nnumberOfTokens\t\t<- 50\nasimovFileTokens\t<- LimitTokensInTexts(asimovFileTokensAll, count=numberOfTokens, takeRandom=TRUE)\nfoglarFileTokens\t<- LimitTokensInTexts(foglarFileTokensAll, count=numberOfTokens, takeRandom=TRUE)\n\nallTokens\t\t<- append(asimovFileTokens, foglarFileTokens)\nallBOW\t\t\t<- MakeBOWModel(allTokens)\nallBOW.Target <- FirstColNameWordsToColumn(allBOW, \"AuthorTarget\")\n\nsprintf(\"Original BOW has %d words\", ncol(allBOW))\n\n# 2. Train Naive Bayes with binary features (YES/NO; see convertToFactors=TRUE)\ndatasets\t<- PrepareTrainAndTest(allBOW.Target, \"AuthorTarget\", 2/3, scaleBy=\"binarize\", convertToFactors=TRUE)\nmodelBayes\t<- naiveBayes(datasets$Train$X, datasets$Train$Y)\n\nEvaluateModelAndPlot(modelBayes, datasets$Train, datasets$Test)\n\n# 3. Train SVM with binary features (numeric 1/0; see convertToFactors=FALSE)\ndatasets\t<- PrepareTrainAndTest(allBOW.Target, \"AuthorTarget\", 2/3, scaleBy=\"binarize\", convertToFactors=FALSE)\nmodelSVM <- svm(datasets$Train$X, datasets$Train$Y) \n\nEvaluateModelAndPlot(modelSVM, datasets$Train, datasets$Test)\n\n####################################################################################################\n##\tEXAMPLE 2\n##\tBag Of Words vs language vs Authorship attribution\n##\tGood approach\n####################################################################################################\n\n# 1. Read files & Tokenize them (+ limit to random X words)\nasimovFiles\t\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/Asimov\", \"ASIMOV\")\nfoglarFiles\t\t\t<- GetFilesContentsFromFolder(\"D:/VMOD/DATASETY/Foglar\", \"FOGLAR\")\n\nasimovFileTokensAll\t<- TokenizeTexts(asimovFiles)\nfoglarFileTokensAll\t<- TokenizeTexts(foglarFiles)\n\nnumberOfTokens\t\t<- 50\nasimovFileTokensAll\t<- LimitTokensInTexts(asimovFileTokensAll, count=numberOfTokens, takeRandom=TRUE)\nfoglarFileTokensAll\t<- LimitTokensInTexts(foglarFileTokensAll, count=numberOfTokens, takeRandom=TRUE)\n\n# 2. Bag of Words: per texts\nallBOW\t\t\t\t<- MakeBOWModel( append( asimovFileTokensAll, foglarFileTokensAll) )\n\n# 3. Bag Of Words: per author\nasimovCorpora \t<- MergeTokenizedTexts(asimovFileTokensAll)\nfoglarCorpora \t<- MergeTokenizedTexts(foglarFileTokensAll)\n\nbowAsimovVsFoglar <- MakeBOWModel( list(Asimov = asimovCorpora, Foglar = foglarCorpora) )\n\n# - calculate per author TF-IDF to identify author specific words:\nweights <- CalculateTFIDFOnBOW(bowAsimovVsFoglar, omitZeroWeightTerms=TRUE)\nkeepingWords\t\t<- names(weights)\nnewAllBOW\t\t\t<- KeepOnlyGivenColumns(allBOW, keepingWords)\n\nsprintf(\"All BOW has: %d words. New authors specific BOW has: %d words\", ncol(bowAsimovVsFoglar), ncol(newAllBOW))\n\n# 5. Prepare datasets & Train\nnewAllBOW.Target\t<- FirstColNameWordsToColumn(newAllBOW, \"AuthorTarget\")\n\n# - see: binarize, factors=TRUE\ndatasets\t<- PrepareTrainAndTest(newAllBOW.Target, \"AuthorTarget\", 2/3, scaleBy=\"binarize\", convertToFactors=TRUE)\nmodelNB\t\t<- naiveBayes(datasets$Train$X, datasets$Train$Y)\nEvaluateModelAndPlot(modelNB, datasets$Train, datasets$Test)\n\nInspectNaiveBayes(modelNB, \"FOGLAR\", 20)\nInspectNaiveBayes(modelNB, \"ASIMOV\", 20)\n\n# - see: binarize, factors=FALSE\ndatasets\t<- PrepareTrainAndTest(newAllBOW.Target, \"AuthorTarget\", 2/3, scaleBy=\"binarize\", convertToFactors=FALSE)\nmodelSVM\t<- svm(datasets$Train$X, datasets$Train$Y, kernel='linear')\nEvaluateModelAndPlot(modelSVM, datasets$Train, datasets$Test)\n\n####################################################################################################\n##\tEXAMPLE 3\n##\tBag Of Words & Naive Bays: Sentiment Analysis\n####################################################################################################\n\ngoodFiles\t\t<- GetFilesContentsFromFolder(\"C:/DATA/Sentiment/GOOD\", \"GOOD\") # 1000 files with positive reviews\nbadFiles\t\t<- GetFilesContentsFromFolder(\"C:/DATA/Sentiment/BAD\", \"BAD\") # 1000 files with negative reviews\n\ngoodTokens\t\t<- TokenizeTexts(goodFiles)\nbadTokens\t\t<- TokenizeTexts(badFiles)\n\n# BOW per texts\nallBOW\t\t\t<- MakeBOWModel( append( goodTokens, badTokens) )\n\n# BOW per sentiment\ngoodCorpora \t<- MergeTokenizedTexts(goodTokens)\nbadCorpora \t<- MergeTokenizedTexts(badTokens)\nbowGoodVsBad\t<- MakeBOWModel( list(Goods = goodCorpora, Bads = badCorpora) )\n\n# Remove unsignificant words\nweights <- CalculateTFIDFOnBOW(bowGoodVsBad, omitZeroWeightTerms=TRUE)\nnewAllBOW\t\t<- KeepOnlyGivenColumns(allBOW, names(weights))\n\n# Prepare dataset & Train & Eval\nnewAllBOW.Target\t<- FirstColNameWordsToColumn(newAllBOW, \"Sentiment\")\n\ndatasets\t<- PrepareTrainAndTest(newAllBOW.Target, \"Sentiment\", 2/3, scaleBy=\"binarize\", convertToFactors=TRUE)\nmodelNB\t\t<- naiveBayes(datasets$Train$X, datasets$Train$Y)\n\nEvaluateModelAndPlot(modelNB, datasets$Train, datasets$Test)\nInspectNaiveBayes(modelNB, \"GOOD\", 20)\nInspectNaiveBayes(modelNB, \"BAD\", 20)", "meta": {"hexsha": "f795d531ecd212ec0effb9d29b61cbd7cd1f4197", "size": 6710, "ext": "r", "lang": "R", "max_stars_repo_path": "basic_nlp.r", "max_stars_repo_name": "oltkkol/vmod", "max_stars_repo_head_hexsha": "4973b1eadc68de9a14c724b1a1eee2c94f833e36", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "basic_nlp.r", "max_issues_repo_name": "oltkkol/vmod", "max_issues_repo_head_hexsha": "4973b1eadc68de9a14c724b1a1eee2c94f833e36", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "basic_nlp.r", "max_forks_repo_name": "oltkkol/vmod", "max_forks_repo_head_hexsha": "4973b1eadc68de9a14c724b1a1eee2c94f833e36", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.3378378378, "max_line_length": 114, "alphanum_fraction": 0.6725782414, "num_tokens": 1882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.33110844528482436}} {"text": "## get.2pl.params takes ranges of parameters and appends the \n## appropriate variable names. \nget.2pl.params <- function( theta.range, a.range, \n b.range, sigma.range) {\n ## Append the variable names to the ranges\n if( !is.null(theta.range) ) { \n theta.range <- paste('theta.abl', theta.range) \n }\n if( !is.null(a.range ) ) { \n a.range <- paste('a.disc' , a.range ) \n }\n if( !is.null(b.range ) ) { \n b.range <- paste('b.diff' , b.range ) \n }\n if( !is.null(sigma.range ) ) { \n sigma.range <- 'sigma.theta' \n }\n \n return( c(theta.range, a.range, b.range, sigma.range ))\n}", "meta": {"hexsha": "bc560b633f45b14f09f627b91e24624e223c3709", "size": 777, "ext": "r", "lang": "R", "max_stars_repo_path": "2PL/functions/get_2pl_params.r", "max_stars_repo_name": "jevanluo/BayesianIRT", "max_stars_repo_head_hexsha": "788b34e951968222493b7ad2c65f3086ddff84e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-11-23T03:08:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T21:45:25.000Z", "max_issues_repo_path": "2PL/functions/get_2pl_params.r", "max_issues_repo_name": "jevanluo/BayesianIRT", "max_issues_repo_head_hexsha": "788b34e951968222493b7ad2c65f3086ddff84e5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2PL/functions/get_2pl_params.r", "max_forks_repo_name": "jevanluo/BayesianIRT", "max_forks_repo_head_hexsha": "788b34e951968222493b7ad2c65f3086ddff84e5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.85, "max_line_length": 65, "alphanum_fraction": 0.4594594595, "num_tokens": 189, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.33110843776122734}} {"text": "#' Generate the fixed , single-node diffusion node rankings, starting from a given perturbed variable.\n#'\n#' This function calculates the node rankings starting from a given perturbed variable in a subset of variables in the background knowledge graph.\n#' @param n - The index (out of a vector of node names) of the node ranking you want to calculate.\n#' @param G - A list of probabilities with list names being the node names of the background graph.\n#' @param S - A character vector of node names in the subset you want the network walker to find.\n#' @param num.misses - The number of \"misses\" the network walker will tolerate before switching to fixed length codes for remaining nodes to be found.\n#' @param verbose - If TRUE, print statements will execute as progress is made. Default is FALSE.\n#' @return current_node_set - A character vector of node names in the order they were drawn by the probability diffusion algorithm.\n#' @keywords probability diffusion\n#' @keywords network walker\n#' @export singleNode.getNodeRanksN\n#' @examples\n#' # Get node rankings for graph\n#' ranks = list()\n#' for (n in 1:length(G)) {\n#' print(sprintf(\"Generating node rankings starting with node %s\", names(G)[n]))\n#' ranks[[n]] = singleNode.getNodeRanksN(n, G)\n#' }\n#' names(ranks) = names(G)\nsingleNode.getNodeRanksN = function(n, G, S=NULL, num.misses=NULL, verbose=FALSE) {\n if (!is.null(num.misses)) {\n if (is.null(S)) {\n print(\"You must supply a subset of nodes as parameter S if you supply num.misses.\")\n return(0)\n }\n }\n all_nodes = names(G)\n if (verbose) {\n print(sprintf(\"Calculating node rankings %d of %d.\", n, length(all_nodes)))\n }\n current_node_set = NULL\n stopIterating=FALSE\n startNode = all_nodes[n]\n currentGraph = G\n numMisses = 0\n current_node_set = c(current_node_set, startNode)\n while (stopIterating==FALSE) {\n # Clear probabilities\n currentGraph[1:length(currentGraph)] = 0 #set probabilities of all nodes to 0\n #determine base p0 probability\n baseP = p0/(length(currentGraph)-length(current_node_set))\n #set probabilities of unseen nodes to baseP\n currentGraph[!(names(currentGraph) %in% current_node_set)] = baseP\n # Sanity check. p0_event should add up to exactly p0 (global variable)\n p0_event = sum(unlist(currentGraph[!(names(currentGraph) %in% current_node_set)]))\n currentGraph = graph.diffuseP1(p1, startNode, currentGraph, current_node_set, 1, verbose=FALSE)\n # Sanity check. p1_event should add up to exactly p1 (global variable)\n p1_event = sum(unlist(currentGraph[!(names(currentGraph) %in% current_node_set)]))\n if (abs(p1_event-1)>thresholdDiff) {\n extra.prob.to.diffuse = 1-p1_event\n currentGraph[names(current_node_set)] = 0\n currentGraph[!(names(currentGraph) %in% names(current_node_set))] = unlist(currentGraph[!(names(currentGraph) %in% names(current_node_set))]) + extra.prob.to.diffuse/sum(!(names(currentGraph) %in% names(current_node_set)))\n }\n #Set startNode to a node that is the max probability in the new currentGraph\n maxProb = names(which.max(currentGraph))\n # Break ties: When there are ties, choose the first of the winners.\n startNode = names(currentGraph[maxProb[1]])\n if (!is.null(num.misses)) {\n if (startNode %in% S) {\n numMisses = 0\n } else {\n numMisses = numMisses + 1\n }\n current_node_set = c(current_node_set, startNode)\n if (numMisses>num.misses || length(c(startNode,current_node_set))>=(length(G))) {\n stopIterating = TRUE\n }\n } else {\n # Keep drawing until you've drawn all nodes.\n current_node_set = c(current_node_set, startNode)\n if (length(current_node_set)>=(length(G))) {\n stopIterating = TRUE\n }\n }\n\n }\n return(current_node_set)\n}\n", "meta": {"hexsha": "1de928ba53ddd7f8031adc8555930e1a0037464f", "size": 3787, "ext": "r", "lang": "R", "max_stars_repo_path": "R/singleNode.getNodeRanksN.r", "max_stars_repo_name": "Xiqi-Li/CTD", "max_stars_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/singleNode.getNodeRanksN.r", "max_issues_repo_name": "Xiqi-Li/CTD", "max_issues_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/singleNode.getNodeRanksN.r", "max_forks_repo_name": "Xiqi-Li/CTD", "max_forks_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.3375, "max_line_length": 228, "alphanum_fraction": 0.7045154476, "num_tokens": 966, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3311084377612273}} {"text": "# allocativ 3001.2021.0002\n\n## Libraries\n### Hadley Wickham\nlibrary(tidyverse) # All of the libraries above in one line of code\nlibrary(ggplot2) # ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.\nlibrary(dplyr) # dplyr provides a grammar of data manipulation, providing a consistent set of verbs that solve the most common data manipulation challenges.\nlibrary(tidyr) # tidyr provides a set of functions that help you get to tidy data. Tidy data is data with a consistent form: in brief, every variable goes in a column, and every column is a variable.\nlibrary(readr) # readr provides a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.\nlibrary(purrr) # purrr enhances R\u2019s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. Once you master the basic concepts, purrr allows you to replace many for loops with code that is easier to write and more expressive.\nlibrary(tibble) # tibble is a modern re-imagining of the data frame, keeping what time has proven to be effective, and throwing out what it has not. Tibbles are data.frames that are lazy and surly: they do less and complain more forcing you to confront problems earlier, typically leading to cleaner, more expressive code.\nlibrary(stringr) # stringr provides a cohesive set of functions designed to make working with strings as easy as possible. It is built on top of stringi, which uses the ICU C library to provide fast, correct implementations of common string manipulations.\nlibrary(forcats) # forcats provides a suite of useful tools that solve common problems with factors. R uses factors to handle categorical variables, variables that have a fixed and known set of possible values.\nlibrary(reshape2) # Long/wide conversions from Hadley Wickham\n### DBMS\nlibrary(RSQLite) # SQLite databases in R\nlibrary(sqldf) # Use SQL commands on R dataframes\nlibrary(RPostgreSQL) # Postgre SQL databases in R\n### Statistics\nlibrary(lme4) # Linear mixed effect modeling in R\nlibrary(arm) # Visualizations of linear mixed effect modeling using 'lme4' in R\nlibrary(lmtest) # Linear model tests including Breusch Pagan\nlibrary(lmerTest) # Linear mixed effect model tests allowing for Saittherwaier DOF and signficiance tests\nlibrary(DescTools) # Descriptive statistics including Jarque-Bera, Andrerson-Darling, Durbin-Watson, Cronbach's Alpha\nlibrary(ineq) # Gini coefficient and Lorenz curve\nlibrary(MASS) # Stepwise inclusion model with linear and logistic options and Box-Cox transformations\nlibrary(bestNormalize) # nromalization autmation including boxcox\nlibrary(performance) # Inter class correlation coefficient foir HLM\n### Machine Learning\nlibrary(randomForest) # Popular random forest package for R\nlibrary(randomForestExplainer) # Complimentary to randfomForest package with tools for analysis\n### Spatial\nlibrary(rpostgis) # PostGIS with Postgres in R\nlibrary(sp) # S4 classes for spatial data in R\nlibrary(ggmap) # General use mapping library with ggplot API\nlibrary(GWmodel) # Geographic weighted regression in R\nlibrary(rgdal)\nlibrary(rgeos) \nlibrary(maptools) \nlibrary(ggsn)\nlibrary(spdep) \nlibrary(DCluster) \nlibrary(gstat) \nlibrary(tigris) \nlibrary(raster) \nlibrary(dismo) \nlibrary(smacpod)\nlibrary(rsatscan)\nlibrary(spatstat)\n### Visualization\nlibrary(RColorBrewer) # Creates nice looking color palettes especially for thematic maps in R\n\n## Variables\nday = Sys.Date() # Save dimple date as string\nstamp = date() # Save Date and timestamp\ndirectory = paste(local, subject, day, sep = \"\") # Set wd to project repository using variables\n\n## Directories\nsetwd(directory) # Set working directory\n\n## Database\ncon_1 = dbConnect(RSQLite::SQLite(), '_data/public.db') # create a connection to the postgres database\n", "meta": {"hexsha": "e54b50d7f4f5ea4bc31731209babd3d539e68d35", "size": 4092, "ext": "r", "lang": "R", "max_stars_repo_path": "FINAL/_setup.r", "max_stars_repo_name": "andrewcistola/PHC6194_student", "max_stars_repo_head_hexsha": "02b83f0f634ed476a702ec633a7ccaf67608c4f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-07-13T17:49:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-13T17:49:58.000Z", "max_issues_repo_path": "FINAL/_setup.r", "max_issues_repo_name": "andrewcistola/PHC6194_student", "max_issues_repo_head_hexsha": "02b83f0f634ed476a702ec633a7ccaf67608c4f4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FINAL/_setup.r", "max_forks_repo_name": "andrewcistola/PHC6194_student", "max_forks_repo_head_hexsha": "02b83f0f634ed476a702ec633a7ccaf67608c4f4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 64.9523809524, "max_line_length": 322, "alphanum_fraction": 0.7949657869, "num_tokens": 952, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3311084377612273}} {"text": "\n\n# Snow crab --- Areal unit modelling of habitat -- no reliance upon stmv fields\n\n\n# -------------------------------------------------\n# Part 1 -- construct basic parameter list defining the main characteristics of the study\n# require(aegis)\n\n year.assessment = 2021\n require(bio.snowcrab) # loadfunctions(\"bio.snowcrab\") \n\n # choose one: \n if (0) {\n # default: best DIC 31457.19, WAIC 32405.81\n # P(y) = Pois(y|y>0) ; p = exp(theta) / ( 1 + exp(theta) )\n family=\"poisson\" \n carstm_model_label = \"tesselation\" \n \n # estimates suggest 30% prob of 0 overdispersion: DIC: 36196.11, WAIC 36850.04\n # P(y) = p 1_{y=0} + (1-p) Pois(y|y>0) ; pois is of positive valued only \n family=\"zeroinflatedpoisson0\" \n carstm_model_label = \"tesselation_zip0\" \n \n # unstable ... will not complete due to singularities ... might be overparamterized\n # P(y) = p 1_{y=0} + (1-p) Pois(y) ; pois is of all values\n family=\"zeroinflatedpoisson1\" \n carstm_model_label = \"tesselation_zip1\"\n \n }\n\n\n p = snowcrab_parameters(\n project_class=\"carstm\",\n yrs=2000:year.assessment,\n areal_units_type=\"tesselation\",\n# areal_units_constraint_ntarget = 20,\n# areal_units_constraint_nmin = 5,\n family=family,\n carstm_model_label = carstm_model_label,\n selection = list(type = \"number\")\n )\n\n\n\n# ------------------------------------------------\n# Part 2 -- spatiotemporal statistical model\n\n if ( spatiotemporal_model ) {\n\n if (0) {\n # polygon structure:: create if not yet made\n # adjust based upon RAM requirements and ncores\n # create if not yet made\n for (au in c(\"cfanorth\", \"cfasouth\", \"cfa4x\", \"cfaall\" )) plot(polygon_managementareas( species=\"snowcrab\", au))\n xydata = snowcrab.db( p=p, DS=\"areal_units_input\", redo=TRUE )\n\n sppoly = areal_units( p=p, hull_alpha=15, redo=TRUE, verbose=TRUE ) # create constrained polygons with neighbourhood as an attribute\n plot( sppoly[, \"npts\"] )\n\n MS = NULL\n\n # p$carstm_model_label = \"tesselation_overdispersed\" # default is the name of areal_units_type\n # p$family = \"zeroinflatedpoisson0\" # \"binomial\", # \"nbinomial\", \"betabinomial\", \"zeroinflatedbinomial0\" , \"zeroinflatednbinomial0\"\n # p$carstm_model_inla_control_familiy = NULL\n\n }\n\n sppoly = areal_units( p=p ) # to reload\n\n\n # -------------------------------------------------\n M = snowcrab.db( p=p, DS=\"carstm_inputs\", redo=TRUE ) # will redo if not found\n M = NULL; gc()\n\n fit = carstm_model( \n p=p, \n data='snowcrab.db( p=p, DS=\"carstm_inputs\" )', \n posterior_simulations_to_retain=\"predictions\" ,\n scale_offsets = TRUE, # required to stabilize as offsets are so small, required for : inla.mode = \"experimental\" \n # redo_fit = FALSE, # only to redo sims and extractions \n # toget=\"predictions\", # this updates a specific subset of calc\n control.inla = list( strategy='adaptive' ), # strategy='laplace', \"adaptive\" int.strategy=\"eb\" \n num.threads=\"4:2\"\n )\n\n if (0) {\n # control.compute=list(smtp=\"default\", dic=TRUE, waic=TRUE, cpo=FALSE, config=TRUE, return.marginals.predictor=TRUE)\n # control.fixed=list(prec=1,prec.intercept=1) \n # 151 configs and long optim .. 19 hrs\n # fit = carstm_model( p=p, DS=\"carstm_modelled_fit\")\n \n # extract results\n # very large files .. slow\n fit = carstm_model( p=p, DS=\"carstm_modelled_fit\" ) # extract currently saved model fit\n fit$summary$dic$dic\n fit$summary$dic$p.eff\n\n plot(fit)\n plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )\n\n }\n\n\n res = carstm_model( p=p, DS=\"carstm_modelled_summary\" ) # to load currently saved results\n\n\n map_centre = c( (p$lon0+p$lon1)/2 - 0.5, (p$lat0+p$lat1)/2 -0.8 )\n map_zoom = 5\n\n plot_crs = p$aegis_proj4string_planar_km\n\n \n require(tmap)\n \n additional_features = \n tm_shape( aegis.polygons::area_lines.db( DS=\"cfa.regions\", returntype=\"sf\", project_to=plot_crs ), projection=plot_crs ) + \n tm_lines( col=\"slategray\", alpha=0.75, lwd=2) + \n tm_shape( aegis.bathymetry::isobath_db( depths=c( seq(0, 400, by=50), 1000), project_to=plot_crs ), projection=plot_crs ) +\n tm_lines( col=\"slategray\", alpha=0.5, lwd=0.5) +\n tm_shape( aegis.coastline::coastline_db( DS=\"eastcoast_gadm\", project_to=plot_crs ), projection=plot_crs ) +\n tm_polygons( col=\"lightgray\", alpha=0.5 , border.alpha =0.5)\n\n (additional_features)\n\n vn=c( \"random\", \"space\", \"combined\" )\n vn=c( \"random\", \"spacetime\", \"combined\" )\n vn=\"predictions\" # numerical density (km^-2)\n\n tmatch=\"2015\"\n\n # densities\n carstm_map( res=res, vn=vn, tmatch=tmatch, \n palette=\"-RdYlBu\",\n plot_elements=c( \"compass\", \"scale_bar\", \"legend\" ),\n additional_features=additional_features,\n tmap_zoom= c(map_centre, map_zoom),\n title =paste( vn, paste0(tmatch, collapse=\"-\"), \"no/km^2\" )\n )\n\n\n # map all :\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.numerical.densitites\" )\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n vn=\"predictions\"\n toplot = carstm_results_unpack( res, vn )\n brks = pretty( quantile(toplot[,,\"mean\"], probs=c(0,0.975), na.rm=TRUE ) )\n\n \n for (y in res$time ){\n tmatch = as.character(y)\n fn_root = paste(\"Predicted_numerical_abundance\", paste0(tmatch, collapse=\"-\"), sep=\"_\")\n fn = file.path( outputdir, paste(fn_root, \"png\", sep=\".\") )\n\n o = carstm_map( res=res, vn=vn, tmatch=tmatch,\n breaks =brks,\n palette=\"-RdYlBu\",\n plot_elements=c( \"compass\", \"scale_bar\", \"legend\" ),\n additional_features=additional_features,\n title=paste(\"Predicted numerical density (no./km^2) \", paste0(tmatch, collapse=\"-\") ),\n map_mode=\"plot\",\n scale=0.75,\n outformat=\"tmap\",\n outfilename=fn\n )\n\n }\n\n\n fit = meanweights_by_arealunit_modelled( p=p, redo=TRUE, returntype=\"carstm_modelled_fit\" ) ## used in carstm_output_compute\n\n if (0) {\n fit = meanweights_by_arealunit_modelled( p=p, returntype=\"carstm_modelled_fit\" ) \n \n res = meanweights_by_arealunit_modelled( p=p, returntype=\"carstm_modelled_summary\" ) ## used in carstm_output_compute\n\n map_centre = c( (p$lon0+p$lon1)/2 - 0.5, (p$lat0+p$lat1)/2 -0.8 )\n map_zoom = 5\n\n plot_crs = p$aegis_proj4string_planar_km\n\n\n additional_features = \n tm_shape( aegis.polygons::area_lines.db( DS=\"cfa.regions\", returntype=\"sf\", project_to=plot_crs ), projection=plot_crs ) + \n tm_lines( col=\"slategray\", alpha=0.75, lwd=2) + \n tm_shape( aegis.bathymetry::isobath_db( depths=c( seq(50, 400, by=50), 500), project_to=plot_crs ), projection=plot_crs ) +\n tm_lines( col=\"slategray\", alpha=0.5, lwd=0.5) +\n tm_shape( aegis.coastline::coastline_db( DS=\"eastcoast_gadm\", project_to=plot_crs ), projection=plot_crs ) +\n tm_polygons( col=\"lightgray\", alpha=0.5 , border.alpha =0.5)\n\n (additional_features)\n\n vn=\"predictions\"\n tmatch = \"2020\"\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.mean.weight\" )\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n fn_root = paste(\"Predicted_mean_size\", paste0(tmatch, collapse=\"-\"), sep=\"_\")\n\n fn = file.path( outputdir, paste(fn_root, \"png\", sep=\".\") )\n \n carstm_map( res=res, vn=vn, tmatch=tmatch, \n palette=\"-RdYlBu\",\n plot_elements=c( \"compass\", \"scale_bar\", \"legend\" ),\n additional_features=additional_features,\n tmap_zoom= c(map_centre, map_zoom),\n title =paste(\"Predicted mean weight of individual (kg)\", paste0(tmatch, collapse=\"-\") )\n )\n\n \n }\n\n snowcrab.db(p=p, DS=\"carstm_output_compute\" )\n\n RES = snowcrab.db(p=p, DS=\"carstm_output_timeseries\" )\n\n bio = snowcrab.db(p=p, DS=\"carstm_output_spacetime_biomass\" )\n num = snowcrab.db(p=p, DS=\"carstm_output_spacetime_number\" )\n\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"aggregated_biomass_timeseries\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n\n ( fn = file.path( outputdir, \"cfa_all.png\") )\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfaall ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfaall_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfaall_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n ( fn = file.path( outputdir, \"cfa_south.png\") )\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfasouth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfasouth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfasouth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n ( fn = file.path( outputdir, \"cfa_north.png\") )\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfanorth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfanorth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfanorth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n ( fn = file.path( outputdir, \"cfa_4x.png\") )\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfa4x ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Biomass index (kt)\", xlab=\"\")\n lines( cfa4x_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfa4x_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n\n # map it ..mean density\n\n sppoly = areal_units( p=p ) # to reload\n\n\n\n vn = paste(\"biomass\", \"predicted\", sep=\".\")\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.biomass.densitites\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n B = apply( bio, c(1,2), mean ) \n \n brks = pretty( quantile( B[], probs=c(0,0.975) )* 10^6 )\n \n\n for (i in 1:length(p$yrs) ){\n y = as.character( p$yrs[i] )\n sppoly[,vn] = B[,y]* 10^6\n fn = file.path( outputdir , paste( \"biomass\", y, \"png\", sep=\".\") )\n\n carstm_map( sppoly=sppoly, vn=vn,\n breaks=brks,\n additional_features=additional_features,\n title=paste(\"Predicted biomass density\", y ),\n outfilename=fn\n )\n }\n\n plot( fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )\n plot( fit$marginals.hyperpar$\"Phi for space_time\", type=\"l\") # posterior distribution of phi nonspatial dominates\n plot( fit$marginals.hyperpar$\"Precision for space_time\", type=\"l\")\n plot( fit$marginals.hyperpar$\"Precision for setno\", type=\"l\")\n\n\n (res$summary)\n \n\n }\n\n\n ##########\n\n\n if (fishery_model) {\n\n # you need a stan installation on your system as well (outside of R), and the R-interface \"cmdstanr\":\n # install.packages(\"cmdstanr\", repos = c(\"https://mc-stan.org/r-packages/\", getOption(\"repos\")))\n \n require(cmdstanr)\n\n p$fishery_model = fishery_model( DS = \"logistic_parameters\", p=p, tag=p$areal_units_type )\n p$fishery_model$stancode = stan_initialize( stan_code=fishery_model( p=p, DS=\"stan_surplus_production\" ) )\n str( p$fishery_model)\n p$fishery_model$stancode$compile()\n to_look = c(\"K\", \"r\", \"q\", \"qc\", \"logtheta\")\n# to_look = c(\"K\", \"r\", \"q\", \"qc\" )\n\n if (0) {\n # testing other samplers and optimizsers ... faster , good for debugging\n\n # (penalized) maximum likelihood estimate (MLE)\n fit_mle = p$fishery_model$stancode$optimize(data =p$fishery_model$standata, seed = 123)\n fit_mle$summary( to_look )\n u = stan_extract( as_draws_df(fit_mle$draws() ) )\n\n mcmc_hist(fit$draws(\"K\")) + vline_at(fit_mle$mle(), size = 1.5)\n\n # Variational Bayes\n fit_vb = p$fishery_model$stancode$variational( data =p$fishery_model$standata, seed = 123, output_samples = 4000)\n fit_vb$summary(to_look)\n fit_vb$cmdstan_diagnose()\n fit_vb$cmdstan_summary()\n\n\n u = stan_extract( as_draws_df(fit_vb$draws() ) )\n\n bayesplot_grid(\n mcmc_hist(fit$draws(\"K\"), binwidth = 0.025),\n mcmc_hist(fit_vb$draws(\"K\"), binwidth = 0.025),\n titles = c(\"Posterior distribution from MCMC\", \"Approximate posterior from VB\")\n )\n\n color_scheme_set(\"gray\")\n mcmc_dens(fit$draws(\"K\"), facet_args = list(nrow = 3, labeller = ggplot2::label_parsed ) ) + facet_text(size = 14 )\n # mcmc_hist( fit$draws(\"K\"))\n\n # obtain mcmc samples from vb solution\n res_vb = fishery_model(\n DS=\"logistic_model\",\n p=p,\n tag=p$areal_units_type,\n fit = fit_vb\n )\n\n names(res_vb$mcmc)\n\n }\n\n\n\n fit = p$fishery_model$stancode$sample(\n data=p$fishery_model$standata,\n iter_warmup = 15000,\n iter_sampling = 10000,\n seed = 123,\n chains = 3,\n parallel_chains = 3, # The maximum number of MCMC chains to run in parallel.\n max_treedepth = 16,\n adapt_delta = 0.99,\n refresh = 1000\n )\n\n if (0) {\n fit = fishery_model( p=p, DS=\"fit\", tag=p$areal_units_type ) # to load samples (results)\n fit$summary(c(\"K\", \"r\", \"q\", \"qc\"))\n print( fit, max_rows=30 )\n fit$cmdstan_diagnose()\n fit$cmdstan_summary()\n\n\n }\n\n fit$summary(to_look)\n\n # save fit and get draws\n res = fishery_model( p=p, DS=\"logistic_model\", tag=p$areal_units_type, fit=fit ) # from here down are params for cmdstanr::sample()\n\n # frequency density of key parameters\n fishery_model( DS=\"plot\", vname=\"K\", res=res )\n fishery_model( DS=\"plot\", vname=\"r\", res=res )\n fishery_model( DS=\"plot\", vname=\"q\", res=res, xrange=c(0.5, 2.5))\n fishery_model( DS=\"plot\", vname=\"FMSY\", res=res )\n # fishery_model( DS=\"plot\", vname=\"bosd\", res=res )\n # fishery_model( DS=\"plot\", vname=\"bpsd\", res=res )\n\n # timeseries\n fishery_model( DS=\"plot\", type=\"timeseries\", vname=\"biomass\", res=res )\n fishery_model( DS=\"plot\", type=\"timeseries\", vname=\"fishingmortality\", res=res)\n\n # Summary table of mean values for inclusion in document\n biomass.summary.table(x)\n\n # Harvest control rules\n fishery_model( DS=\"plot\", type=\"hcr\", vname=\"default\", res=res )\n fishery_model( DS=\"plot\", type=\"hcr\", vname=\"simple\", res=res )\n\n # diagnostics\n # fishery_model( DS=\"plot\", type=\"diagnostic.errors\", res=res )\n # fishery_model( DS=\"plot\", type=\"diagnostic.phase\", res=res )\n\n\n NN = res$p$fishery_model$standata$N\n\n # bosd\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$bosd[,i] ), main=\"\")\n ( qs = apply( res$mcmc$bosd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n\n # bpsd\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$bpsd[,i] ), main=\"\")\n ( qs = apply( res$mcmc$bpsd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # rem_sd\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$rem_sd[,i] ), main=\"\")\n ( qs = apply( res$mcmc$rem_sd[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n\n # qc\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$qc[,i] ), main=\"\")\n ( qs = apply( res$mcmc$qc[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # b0\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$b0[,i] ), main=\"\")\n ( qs = apply( res$mcmc$b0[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n\n # K\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$K[,i] ), main=\"\")\n ( qs = apply( res$mcmc$K[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # R\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$r[,i] ), main=\"\")\n ( qs = apply( res$mcmc$r[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # q\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$q[,i] ), main=\"\")\n ( qs = apply( res$mcmc$q[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # FMSY\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$FMSY[,i] ), main=\"\")\n ( qs = apply( res$mcmc$FMSY[,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n\n # densities of biomass estimates for the year.assessment\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density(res$mcmc$B[,NN,i] ), main=\"\")\n ( qs = apply( res$mcmc$B[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # densities of biomass estimates for the previous year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$B[,NN-1,i] ), main=\"\")\n ( qs = apply( res$mcmc$B[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n\n # densities of F in assessment year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$F[,NN,i] ), xlim=c(0.01, 0.6), main=\"\")\n ( qs = apply( res$mcmc$F[,NN,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n ( qs = apply( res$mcmc$F[,NN,], 2, mean ) )\n\n # densities of F in previous year\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n for (i in 1:3) plot(density( res$mcmc$F[,NN-1,i] ), xlim=c(0.01, 0.6), main=\"\")\n ( qs = apply( res$mcmc$F[,NN-1,], 2, quantile, probs=c(0.025, 0.5, 0.975) ) )\n ( qs = apply( res$mcmc$F[,NN-1,], 2, mean ) )\n\n # F for table ---\n summary( res$mcmc$F, median)\n }\n\n# end\n", "meta": {"hexsha": "91e35c3a9b5b3dfc3d6d8a734172b8aece98f55b", "size": 19310, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/03.abundance_estimation_carstm_tesselation.r", "max_stars_repo_name": "jae0/bio.snowcrab", "max_stars_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/scripts/03.abundance_estimation_carstm_tesselation.r", "max_issues_repo_name": "jae0/bio.snowcrab", "max_issues_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, 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YES\n2. NO\n\n", "lm_q1_score": 0.7431680086124811, "lm_q2_score": 0.44552953503957266, "lm_q1q2_score": 0.3311032973334038}} {"text": "# Test script localy\r\nwdpaid <- '11.3_53.6_15.5_55.9'\r\n#wdpaid <- 'minlon_minlat_maxlon_maxlat'\r\nwdpaidsplit <- unlist(strsplit(wdpaid, \"[_]\"))\r\nxmin <- as.numeric(wdpaidsplit[1])\r\nymin <- as.numeric(wdpaidsplit[2])\r\nxmax <- as.numeric(wdpaidsplit[3])\r\nymax <- as.numeric(wdpaidsplit[4])\r\ntemp_path<- \"c:/temp\"\r\n\r\nlibrary(knitr)\r\nlibrary(kableExtra)\r\n\r\nlibrary(rgdal)\r\nlibrary(downloader)\r\nlibrary(ggplot2)\r\nlibrary(mapdata)\r\nlibrary(geojsonio)\r\nlibrary(ggmap)\r\nlibrary(ggrepel)\r\n\r\n\r\nlibrary(\"rgdal\")\r\nlibrary(\"rasterVis\")\r\nlibrary(\"downloader\")\r\nlibrary(\"ggplot2\")\r\n\r\n\r\nlibrary(\"XML\")\r\nlibrary(\"RCurl\")\r\nlibrary(\"bitops\")\r\nlibrary(\"lattice\")\r\nlibrary(\"latticeExtra\")\r\nlibrary(\"RColorBrewer\")\r\nlibrary(\"mapdata\")\r\nlibrary(\"maps\")\r\nlibrary(\"maptools\")\r\nlibrary(\"wq\")\r\nlibrary(\"xtable\")\r\nlibrary(\"zoo\")\r\nlibrary(\"jsonlite\")\r\nlibrary(ncdf4)\r\n\r\nrequire(xtable)\r\n\r\nlibrary(lattice)\r\n\r\n\r\n# Script for Wekeo environment\r\nsr=SpatialPolygons(list(Polygons(list(Polygon(cbind(c(xmin, xmin, xmax, xmax),c(ymax, ymin, ymin, ymax)))),\"1\")))\r\nmpa=SpatialPolygonsDataFrame(sr, data.frame(cbind(1:1), row.names=c(\"1\")))\r\nproj4string(mpa)<-CRS(\"+proj=longlat +datum=WGS84\")\r\n\r\nbbox<-paste(xmin,ymin,xmax,ymax,sep=\",\")\r\n\r\n\r\n#FUNCTION GET VESSELDENSITY\r\n#=================================================\r\n\r\ngetvesseldensityLite<-function (name = \"emodnet:2017_01_st_All\", resolution = \"30 arcsec / 900m\", xmin = 15, xmax = 20.5, ymin = 30, ymax = 32.5) \r\n{\r\nbbox <- paste(xmin, ymin, xmax, ymax, sep = \",\") \r\ncon <- paste(ogc_url,\"/wcs?service=wcs&version=1.0.0&request=getcoverage&coverage=\",name,\"&crs=EPSG:4326&BBOX=\", bbox, \"&format=image/tiff&interpolation=nearest&resx=0.00833333&resy=0.00833333\", sep = \"\") \r\n#con <- paste(\"http://77.246.172.208/geoserver/emodnet/wcs?service=wcs&version=1.0.0&request=getcoverage&coverage=\",name,\"&crs=EPSG:3035&BBOX=\", bbox, \"&format=image/tiff&interpolation=nearest&resx=0.00833333&resy=0.00833333\", sep = \"\") \r\nnomfich <- paste(name, \"img.tiff\", sep = \"_\")\r\nnomfich <- tempfile(nomfich)\r\ndownload(con, nomfich, quiet = TRUE, mode = \"wb\")\r\nimg <- raster(nomfich)\r\nimg[img == 0] <- NA\r\n#img[img < 0] <- 0\r\n#img[img > 100] <- 0\r\nnames(img) <- paste(name)\r\nreturn(img)\r\n}\r\n\r\n\r\ngetvesseldensity_byvesseltype<-function (name = \"emodnet:2017_01_st_All\", vessel_type= \"st_09\" ,resolution = \"30 arcsec / 900m\", xmin = 15, xmax = 20.5, ymin = 30, ymax = 32.5) \r\n{\r\n##################### getvesseldensityLite2017\r\nfor (month in c('01','02','03','04','05','06','07','08','09','10','11','12')){\r\n\tprint(paste(\"The month is\", month))\r\n\timg<-getvesseldensityLite(name = paste(\"emodnet:2017_\",month,\"_\",vessel_type, sep=\"\"), resolution = \"30 arcsec\", xmin, xmax, ymin, ymax)\r\n\tnames(img) <- paste(\"2017-\",month, sep=\"\")\r\n\tif (month == '01') {imgs <- img\r\n} else {\r\n \timgs <- stack(imgs, img)\r\n\t}\r\n}\r\nmpa_vesselAll2017<-imgs\r\n##################### getvesseldensityLite2018\r\nfor (month in c('01','02','03','04','05','06','07','08','09','10','11','12')){\r\n\tprint(paste(\"The month is\", month))\r\n\timg<-getvesseldensityLite(name = paste(\"emodnet:2018_\",month,\"_\",vessel_type, sep=\"\"), resolution = \"30 arcsec\", xmin, xmax, ymin, ymax)\r\n\tnames(img) <- paste(\"2018-\",month, sep=\"\")\r\n\tif (month == '01') {imgs <- img\r\n} else {\r\n \timgs <- stack(imgs, img)\r\n\t}\r\n}\r\nmpa_vesselAll2018<-imgs\r\n##################### getvesseldensityLite2019\r\nfor (month in c('01','02','03','04','05','06','07','08','09','10','11','12')){\r\n\tprint(paste(\"The month is\", month))\r\n\timg<-getvesseldensityLite(name = paste(\"emodnet:2019_\",month,\"_\",vessel_type, sep=\"\"), resolution = \"30 arcsec\", xmin, xmax, ymin, ymax)\r\n\tnames(img) <- paste(\"2019-\",month, sep=\"\")\r\n\tif (month == '01') {imgs <- img\r\n} else {\r\n \timgs <- stack(imgs, img)\r\n\t}\r\n}\r\nmpa_vesselAll2019<-imgs\r\n\r\nmpa<-stack(mpa_vesselAll2017,mpa_vesselAll2018,mpa_vesselAll2019)\r\n\r\nreturn(mpa)\r\n}\r\n\r\n\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Cargo) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_09\"\r\n\r\nmpa_cargo<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (dredging or underwater ops) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_03\"\r\n\r\nmpa_dredging<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (High Speed Craft) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_06\"\r\n\r\nmpa_speedcraft<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Fishing) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_01\"\r\n\r\nmpa_fishing<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Military and Law Enforcement) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_11\"\r\n\r\nmpa_military<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Passenger) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_08\"\r\n\r\nmpa_passenger<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Pleasure Craft) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_05\"\r\n\r\nmpa_pleasure<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Sailing) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_04\"\r\n\r\nmpa_sailing<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Service) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_02\"\r\n\r\nmpa_service<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Tanker) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_10\"\r\n\r\nmpa_tanker<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Tug and Towing) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_07\"\r\n\r\nmpa_tug<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n#####################\r\nlayer_title<-\"Vessel density (Unknown) 2017-2019\"\r\nogc_url <- \"https://ows.emodnet-humanactivities.eu\"\r\nvessel_type <- \"st_12\"\r\n\r\nmpa_unknown<-getvesseldensity_byvesseltype(name, vessel_type, resolution = \"30 arcsec/900m\", xmin, xmax, ymin, ymax)\r\n\r\n", "meta": {"hexsha": "90a4bbb14e4e76d74023683d88c6d9925a430a4c", "size": 7201, "ext": "r", "lang": "R", "max_stars_repo_path": "OH_2020/Script/proto_pascal/vesseldensity_alltypes_2017-2019.r", "max_stars_repo_name": "ldbk/SeineMSP", "max_stars_repo_head_hexsha": "1e85bd87d9f8aea5937b2b8d5773e137df9417b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-04-06T13:25:34.000Z", 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"1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33105963094473895}} {"text": "make_three_panel_plots <- function(data_country, covariates_long, \n jobid, country, label=label){\n \n data_cases_95 <- data.frame(data_country$date, data_country$cases_min, \n data_country$cases_max)\n names(data_cases_95) <- c(\"date\", \"cases_min\", \"cases_max\")\n data_cases_95$key <- rep(\"nintyfive\", length(data_cases_95$date))\n data_cases_50 <- data.frame(data_country$date, data_country$cases_min2, \n data_country$cases_max2)\n names(data_cases_50) <- c(\"date\", \"cases_min\", \"cases_max\")\n data_cases_50$key <- rep(\"fifty\", length(data_cases_50$date))\n data_cases <- rbind(data_cases_95, data_cases_50)\n levels(data_cases$key) <- c(\"ninetyfive\", \"fifty\")\n \n p1 <- ggplot(data_country) +\n geom_bar(data = data_country, aes(x = date, y = reported_cases), \n fill = \"coral4\", stat='identity', alpha=0.5) + \n geom_ribbon(data = data_cases, \n aes(x = date, ymin = cases_min, ymax = cases_max, fill = key)) +\n xlab(\"\") +\n ylab(\"Daily number of infections\") +\n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) + \n scale_y_continuous(labels = comma, expand=c(0,0.1)) +\n scale_fill_manual(name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(alpha(\"deepskyblue4\", 0.55), \n alpha(\"deepskyblue4\", 0.45))) + \n theme_bw() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\") + \n guides(fill=guide_legend(ncol=1)) + \n ggtitle(country) \n \n data_deaths_95 <- data.frame(data_country$date, data_country$deaths_min, \n data_country$deaths_max)\n names(data_deaths_95) <- c(\"date\", \"death_min\", \"death_max\")\n data_deaths_95$key <- rep(\"nintyfive\", length(data_deaths_95$date))\n data_deaths_50 <- data.frame(data_country$date, data_country$deaths_min2, \n data_country$deaths_max2)\n names(data_deaths_50) <- c(\"date\", \"death_min\", \"death_max\")\n data_deaths_50$key <- rep(\"fifty\", length(data_deaths_50$date))\n data_deaths <- rbind(data_deaths_95, data_deaths_50)\n levels(data_deaths$key) <- c(\"ninetyfive\", \"fifty\")\n \n p2 <- ggplot(data_country, aes(x = date)) +\n geom_bar(data = data_country, aes(y = reported_deaths, fill = \"reported\"),\n fill = \"coral4\", stat='identity', alpha=0.5) +\n geom_ribbon(\n data = data_deaths,\n aes(ymin = death_min, ymax = death_max, fill = key)) +\n xlab(\"\") +\n ylab(\"Daily number of deaths\") +\n scale_y_continuous(labels = comma, expand=c(0,0.1)) + \n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) +\n scale_fill_manual(name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(alpha(\"deepskyblue4\", 0.55), \n alpha(\"deepskyblue4\", 0.45))) + \n theme_bw() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\") + \n guides(fill=guide_legend(ncol=1))\n \n # Plotting interventions\n data_rt_95 <- data.frame(data_country$date, \n data_country$rt_min, data_country$rt_max)\n names(data_rt_95) <- c(\"date\", \"rt_min\", \"rt_max\")\n data_rt_95$key <- rep(\"nintyfive\", length(data_rt_95$date))\n data_rt_50 <- data.frame(data_country$date, data_country$rt_min2, \n data_country$rt_max2)\n names(data_rt_50) <- c(\"date\", \"rt_min\", \"rt_max\")\n data_rt_50$key <- rep(\"fifty\", length(data_rt_50$date))\n data_rt <- rbind(data_rt_95, data_rt_50)\n levels(data_rt$key) <- c(\"ninetyfive\", \"fifth\")\n \n plot_labels <- c(\"Lockdown\", \"Public events banned\", \"School and universities closed\", \"Self isolate if ill\", \"Social distancing encouraged\")\n \n p3 <- ggplot(data_country) +\n geom_ribbon(data = data_rt, aes(x = date, ymin = rt_min, ymax = rt_max, \n group = key,\n fill = key)) +\n geom_hline(yintercept = 1, color = 'black', size = 1) + \n geom_segment(data = covariates_long,\n aes(x = value, y = 0, xend = value, yend = max(x, na.rm=TRUE)), \n linetype = \"dashed\", colour = \"grey\", alpha = 0.75) +\n geom_point(data = covariates_long, aes(x = value, \n y = x, \n group = key, \n shape = key, \n col = key), size = 2) +\n xlab(\"\") +\n ylab(expression(R[t])) +\n scale_fill_manual(name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(alpha(\"seagreen\", 0.75), alpha(\"seagreen\", 0.5))) + \n scale_shape_discrete(name = \"Interventions\", labels = plot_labels) + \n scale_colour_discrete(name = \"Interventions\", labels = plot_labels) + \n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\"), \n limits = c(data_country$date[1], \n data_country$date[length(data_country$date)])) + \n scale_y_continuous(expand=c(0,0.1)) + \n theme_bw() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n theme(legend.position=\"right\")\n \n p <- plot_grid(p1, p2, p3, ncol = 3, rel_widths = c(1, 1, 2))\n save_plot(filename = paste0(\"Italy//figures/\", country, \"-three-panel-\", label, \"-\", jobid, \".png\"), \n p, base_width = 14)\n\n return (p)\n}", "meta": {"hexsha": "29cc71b380120f41f178fcfafce618f48d8a027b", "size": 5470, "ext": "r", "lang": "R", "max_stars_repo_path": "Italy/code/plotting/make-three-panel-plots.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "Italy/code/plotting/make-three-panel-plots.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "Italy/code/plotting/make-three-panel-plots.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 50.6481481481, "max_line_length": 143, "alphanum_fraction": 0.5769652651, "num_tokens": 1498, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33105963094473895}} {"text": "#Title: estimate_normalization_factor\r\n#Auther: Naoto Imamachi\r\n#ver: 1.0.0\r\n#Date: 2015-10-07\r\n\r\n###Estimate_normalization_factor_function###\r\nBridgeRNormalizationFactorsHK <- function(InputFile, group, hour, InforColumn = 4, InforHKGenes = 2, HKGenes = c(\"GAPDH\",\"PGK1\",\"PPIA\",\"ENO1\",\"ATP5B\",\"ALDOA\"), OutputFile = \"BridgeR_3B_Normalizaion_factor_from_House_keeping_genes.txt\"){\r\n ###Calc_normalization_factor###\r\n group_number <- length(group)\r\n time_points <- length(hour)\r\n input_file <- fread(InputFile, header=T)\r\n \r\n ###Output_file_infor###nfname\r\n hour_label <- NULL\r\n for(x in hour){\r\n label <- x\r\n if(x < 10){\r\n label <- paste(\"0\",x,sep=\"\")\r\n }\r\n hour_label <- append(hour_label, paste(\"T\", label, \"_0\", sep=\"\"))\r\n }\r\n cat('Sample',hour_label, sep=\"\\t\", file=OutputFile)\r\n cat(\"\\n\", file=OutputFile, append=T)\r\n \r\n for(a in 1:group_number){\r\n ###Search_House-keeping_genes###\r\n HKgenes_infor <- input_file[[InforHKGenes]]\r\n HKgenes_infor_index <- NULL\r\n for(x in 1:length(HKGenes)){\r\n HKgenes_infor_index <- append(HKgenes_infor_index, which(HKgenes_infor == HKGenes[x]))\r\n }\r\n \r\n ###Information&exp_data column###\r\n infor_st <- 1 + (a - 1)*(time_points + InforColumn)\r\n infor_ed <- (InforColumn)*a + (a - 1)*time_points\r\n exp_st <- infor_ed + 1\r\n exp_ed <- infor_ed + time_points\r\n\r\n HKGenes_raw_data <- NULL\r\n for(x in 1:length(HKgenes_infor_index)){\r\n if(x == 1){\r\n HKGenes_raw_data <- input_file[HKgenes_infor_index[x],exp_st:exp_ed,with=F]\r\n }else{\r\n HKGenes_raw_data <- rbind(HKGenes_raw_data,input_file[HKgenes_infor_index[x],exp_st:exp_ed,with=F])\r\n }\r\n }\r\n \r\n nf <- NULL\r\n for(x in 1:length(HKgenes_infor_index)){\r\n if(x == 1){\r\n nf <- as.numeric(as.vector(as.matrix(HKGenes_raw_data[x,])))\r\n }else{\r\n nf <- as.numeric(HKGenes_raw_data[x,]) * nf\r\n }\r\n }\r\n \r\n nf <- nf^(1/length(HKGenes))\r\n\r\n cat(group[a],nf, sep=\"\\t\", file=OutputFile, append=T)\r\n cat(\"\\n\", file=OutputFile, append=T)\r\n\r\n }\r\n}\r\n", "meta": {"hexsha": "35727d999abe07b832bb490b067141961d40da43", "size": 2287, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Z3_estimate_normalization_factor_House_keeping_genes.r", "max_stars_repo_name": "ChristophRau/BridgeR", "max_stars_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-10T15:03:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-10T15:03:45.000Z", "max_issues_repo_path": "R/Z3_estimate_normalization_factor_House_keeping_genes.r", "max_issues_repo_name": "ChristophRau/BridgeR", "max_issues_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Z3_estimate_normalization_factor_House_keeping_genes.r", "max_forks_repo_name": "ChristophRau/BridgeR", "max_forks_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2016-05-26T15:49:18.000Z", "max_forks_repo_forks_event_max_datetime": "2016-05-26T15:49:18.000Z", "avg_line_length": 35.734375, "max_line_length": 237, "alphanum_fraction": 0.5675557499, "num_tokens": 642, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.33105962283241486}} {"text": "#' Class ngram\n#' \n#' An n-gram is an ordered sequence of n \"words\" taken from a body of \"text\".\n#' The terms \"words\" and \"text\" can easily be interpreted literally, or with a\n#' more loose interpretation.\n#' \n#' For example, consider the sequence \"A B A C A B B\". If we examine the\n#' 2-grams (or bigrams) of this sequence, they are\n#' \n#' A B, B A, A C, C A, A B, B B\n#' \n#' or without repetition:\n#' \n#' A B, B A, A C, C A, B B\n#' \n#' That is, we take the input string and group the \"words\" 2 at a time (because\n#' \\code{n=2}). Notice that the number of n-grams and the number of words are\n#' not obviously related; counting repetition, the number of n-grams is equal\n#' to\n#' \n#' \\code{nwords - n + 1}\n#' \n#' Bounds ignoring repetition are highly dependent on the input. A correct but\n#' useless bound is\n#' \n#' \\code{\\#ngrams = nwords - (\\#repeats - 1) - (n - 1)}\n#' \n#' An \\code{ngram} object is an S4 class container that stores some basic\n#' summary information (e.g., n), and several external pointers. For\n#' information on how to construct an \\code{ngram} object, see\n#' \\code{\\link{ngram}}.\n#' \n#' @slot str_ptr\n#' A pointer to a copy of the original input string.\n#' @slot strlen\n#' The length of the string.\n#' @slot n\n#' The eponymous 'n' as in 'n-gram'.\n#' @slot ngl_ptr\n#' A pointer to the processed list of n-grams.\n#' @slot ngsize\n#' The length of the ngram list, or in other words, the number of\n#' unique n-grams in the input string.\n#' @slot sl_ptr\n#' A pointer to the list of words from the input string.\n#' \n#' @name ngram-class\n#' @seealso \\code{\\link{ngram}}\n#' @keywords Tokenization\nsetClass(\"ngram\", \n representation(\n str_ptr = \"externalptr\",\n strlen = \"integer\",\n n = \"integer\",\n ngl_ptr = \"externalptr\",\n ngsize = \"integer\",\n sl_ptr = \"externalptr\"\n )\n)\n", "meta": {"hexsha": "d1d871ffc9bd1117f1353457068d1bf6385ba61b", "size": 1810, "ext": "r", "lang": "R", "max_stars_repo_path": "R/00-classes.r", "max_stars_repo_name": "russey/ngram", "max_stars_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 74, "max_stars_repo_stars_event_min_datetime": "2015-03-10T17:47:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T23:26:08.000Z", "max_issues_repo_path": "R/00-classes.r", "max_issues_repo_name": "russey/ngram", "max_issues_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2015-06-23T14:59:00.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-22T17:14:00.000Z", "max_forks_repo_path": "R/00-classes.r", "max_forks_repo_name": "russey/ngram", "max_forks_repo_head_hexsha": "2650adbec2968f55fff41d42629fbbfdba5e330d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 22, "max_forks_repo_forks_event_min_datetime": "2015-02-09T14:21:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T00:58:48.000Z", "avg_line_length": 30.1666666667, "max_line_length": 79, "alphanum_fraction": 0.6552486188, "num_tokens": 542, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621764862150634, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3310596223053028}} {"text": "#' Output genes cell modules footprint matrix with metadata on the cell modules\n#'\n#' @param mc_id meta cell object id\n#' @param gstat_id gstat object id\n#' @param T_gene_tot minimal number of umis to include gene (to reduce table size)\n#' @param T_fold threshold for maximal fold change over metacells\n#' @param metadata_fields metadata field names to be used as factors and breakdown over mcs\n#'\n#' @return Nothing. But save a table in the figure directory that can be loaded to excel\n#'\n#' @export\n#'\nmcell_mc_export_tab = function(mc_id, gstat_id = NULL, mat_id = NULL,\n\t\t\t\t\t\t\t\t\t\t\tT_gene_tot = 50,\n\t\t\t\t\t\t\t\t\t\t\tT_fold = 1, # should we also export genes with depletion (i.e. below certain threshold)?\n\t\t\t\t\t\t\t\t\t\t\tmetadata_fields = c(\"batch_set_id\"))\n{\n\tmc = scdb_mc(mc_id)\n\tgstat = scdb_gstat(gstat_id)\n\tscmat = scdb_mat(mat_id)\n\tif(is.null(mc)) {\n\t\tstop(\"MC-ERR non existing mc_id \", mc_id, \" when trying to export fp table\")\n\t}\n\n\tif(is.null(gstat)) {\n\t\tstop(\"MC-ERR non existing gstat id \", gstat_id, \" when trying to export fp table\")\n\t}\n\n\tif(is.null(scmat)) {\n\t\tstop(\"MC-ERR non existing mat id \", mat_id, \" when trying to export fp table\")\n\t}\n\n\t# add #cells per clust, mean #umis (~ cell size) and assigned group (if exist)\n\tif (nrow(mc@color_key) > 0) {\n\t\tcol2group = as.character(mc@color_key$group)\n\t\tnames(col2group) = as.character(mc@color_key$color)\n\n\t\tgroups = col2group[mc@colors]\n\t}\n\telse {\n\t\tgroups = rep(NA, max(mc@mc))\n\t}\n\n\tout_df = rbind(tapply(colSums(scmat@mat[, names(mc@mc)]), mc@mc, mean), table(mc@mc), groups, seq_along(groups))\n\tout_df = cbind(rep(\"\", nrow(out_df)), out_df)\n\trownames(out_df) = c('mean_umis', 'n_cells', 'group', 'mc_id')\n\n\t# add required breakdown to features\n\tif (!is.null(metadata_fields)) {\n\t\tfor (s in metadata_fields) {\n\t\t new_df = table(scmat@cell_metadata[names(mc@mc), s], mc@mc)\n\t\t new_df = cbind(rep(s, nrow(new_df)), new_df)\n\t\t\tout_df = rbind(new_df, out_df)\n\t\t}\n\t}\n\n\tfp_max = apply(mc@mc_fp, 1, max)\n\tfp_tot = gstat[intersect(rownames(mc@mc_fp), rownames(gstat)), \"tot\"]\n\n\t# genes to export\n\tf = fp_max > T_fold & fp_tot > T_gene_tot\n\n\t# actual clust_fp\n\tout_df = rbind(out_df, cbind(rep(\"\", sum(f)), round(log2(mc@mc_fp[f,]), 2)))\n\n\tout_df = cbind(out_df[, 1], rownames(out_df), out_df[, -1])\n\ttab_clust_fp_fn = sprintf(\"%s/%s.log2_mc_fp.txt\", .scfigs_base, mc_id)\n\twrite.table(out_df, tab_clust_fp_fn, sep = \"\\t\", quote = F, row.names = F, col.names = F)\n}\n", "meta": {"hexsha": "e13c164bfce1627563179b626427b3ed8f3ff451", "size": 2422, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mc_export_tab.r", "max_stars_repo_name": "echomsky/metacell", "max_stars_repo_head_hexsha": "39b91cf2cda6192994035ffe62c90b83142647aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-14T14:08:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-14T14:08:10.000Z", "max_issues_repo_path": "R/mc_export_tab.r", "max_issues_repo_name": "breme86/metacell", "max_issues_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mc_export_tab.r", "max_forks_repo_name": "breme86/metacell", "max_forks_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6, "max_line_length": 113, "alphanum_fraction": 0.6804293972, "num_tokens": 742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3309614379249971}} {"text": "#!/usr/bin/Rscript --vanilla\n# Author: Miguel Morard and modified by Peris\n\nlibrary(ggplot2)\nlibrary(gridExtra)\n\nargs <- commandArgs(TRUE)\ninputName <- args[1] #PATH of the converted file\nchromosomeInfo <- args[2] #\"pairwiseDivFile_chromosome.txt\"\n\nf = read.table(inputName,sep= \"\\t\", header = T) #Lee la tabla 000.vcf.qaf.tab\n\nchromosome_properties = read.csv(chromosomeInfo,header=TRUE,sep='\\t')\nallchr <- c(as.character(chromosome_properties$NAME))\nchrlen <- list()\nfor (i in 1:length(chromosome_properties[,1])){\n chrlen[[i]] <- subset(chromosome_properties, chromosome_properties$NAME == allchr[i])\n}\n\nlista_p = list() #lista para almacenar los ggplots\nsubs = list() #lista para almacenar los datos de cada cromosoma\nfor (i in 1:length(allchr)){\n subs[[i]] <- subset(f, f$CHR == allchr[i])\n}\n\ncolor_species <- c(\"#ff0000\",\"#cc6600\",\"#008000\",\"#00ffff\",\"#0070c0\",\"#7030a0\",\"#ff00ff\")\npdf(paste0(inputName,\"_distr.pdf\"),height=6,width=14)\n\nprint(ggplot(f, aes(x=AFA)) +\n geom_freqpoly(aes(y=..count..),binwidth = 0.03) +\n geom_histogram(binwidth = 0.03) +\n theme_classic() +\n scale_colour_manual(values = mycolor) +\n scale_linetype_manual(values = myshapes) +\n #ggtitle(\"Total counts\") +\n #scale_x_continuous(limits = c(0.0,1.5)) + \n theme(axis.line.x = element_line(colour = \"black\"),axis.line.y = element_line(colour = \"black\")))\n\nlista_p = list()\nchromCHECK <- c(1,17,33,49,65,81,97)\nnames_plots <- c()\ncount <- 1\nfor(i in 1:length(chrlen)){\n if(i %in% chromCHECK){\n color2plot <- color_species[count]\n lista_p[[i]] <- ggplot(subs[[i]], aes_string(x=\"POS\",y=\"AFA\")) +\n theme(panel.background = element_rect(fill = \"#FFFFFF\"),plot.title = element_text(hjust = 0.5, colour =\"#429221\",size = 11),axis.text.x=element_blank(),axis.ticks.x=element_blank(),axis.title.y = element_blank(),axis.text.y = element_text(face = \"bold\", size = 16)) +\n geom_point(colour = color2plot, size=1) +\n scale_y_continuous(limits = c(0,1),breaks = c(0,0.5,1)) +\n scale_x_continuous(limits = c(0,chrlen[[i]]$LEN)) +\n xlab(unique(as.character(subs[[i]]$CHR)))\n count <- count + 1\n }else{\n lista_p[[i]] <- ggplot(subs[[i]], mapping = aes_string(x=\"POS\",y=\"AFA\")) +\n theme(panel.background = element_rect(fill = \"#FFFFFF\"),plot.title = element_text(hjust = 0.5, colour =\"#429221\",size = 11),axis.text.x=element_blank(),axis.ticks.x=element_blank(),axis.title.y = element_blank(),axis.text.y = element_blank()) + \n geom_point(colour = color2plot, size=1) +\n scale_y_continuous(limits = c(0,1)) +\n scale_x_continuous(limits = c(0,chrlen[[i]]$LEN)) +\n xlab(unique(as.character(subs[[i]]$CHR)))\n }\n names_plots <- c(lista_p, paste0(\"p\",i))\n}\nprint(do.call(grid.arrange,c(lista_p, ncol=16)))\n#Show a allele frequency distribution (f)\n\ndev.off()\n\n", "meta": {"hexsha": "2c39f74029fc9b401f6e209963307cb61b19470c", "size": 2784, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/snp_distrib_v2.r", "max_stars_repo_name": "PerisD/Sac2.0", "max_stars_repo_head_hexsha": "274aeca4f6298b2d1d816e5640bf3f67bb5dd729", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/snp_distrib_v2.r", "max_issues_repo_name": "PerisD/Sac2.0", "max_issues_repo_head_hexsha": "274aeca4f6298b2d1d816e5640bf3f67bb5dd729", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/snp_distrib_v2.r", "max_forks_repo_name": "PerisD/Sac2.0", "max_forks_repo_head_hexsha": "274aeca4f6298b2d1d816e5640bf3f67bb5dd729", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9411764706, "max_line_length": 271, "alphanum_fraction": 0.6788793103, "num_tokens": 857, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3309614379249971}} {"text": "###VISUALISE MODEL OUTPUTS###\nlibrary(tidyverse)\nlibrary(ggpubr)\n\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder_inputs = file.path(folder, '..', 'results')\n\n#get peru cost data\ndata_per = read.csv(file.path(folder_inputs, 'PER', 'cost_item_results.csv'))\ndata_per$country = \"Peru (PER)\"\n\nper_deciles = select(data_per, modeling_region, pop_density_km2)\nper_deciles = unique(per_deciles)\nper_deciles$pop_d_decile <- cut(per_deciles$pop_density_km2, \n breaks = quantile(per_deciles$pop_density_km2, probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\nper_deciles$modeling_region = NULL\ndata_per <- merge(data_per,per_deciles,by=\"pop_density_km2\")\n\n#get indonesia cost data\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder_inputs = file.path(folder, '..', 'results')\n\ndata_idn = read.csv(file.path(folder_inputs, 'IDN', 'cost_item_results.csv'))\ndata_idn$country = ifelse(grepl(\"Papua\", data_idn$names), \n \"Papua (IDN)\", \"Kalimantan (IDN)\")\n\npapua = data_idn[data_idn$country == 'Papua (IDN)',]\nkalimantan = data_idn[data_idn$country == 'Kalimantan (IDN)',]\n\n###papua\npapua_deciles = select(papua, modeling_region, pop_density_km2)\npapua_deciles = unique(papua_deciles)\npapua_deciles$pop_d_decile <- cut(papua_deciles$pop_density_km2, \n breaks = quantile(papua_deciles$pop_density_km2, \n probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\npapua_deciles$modeling_region = NULL\npapua <- merge(papua,papua_deciles,by=\"pop_density_km2\")\n\n###kalimantan\nkalimantan_deciles = select(kalimantan, modeling_region, pop_density_km2)\nkalimantan_deciles = unique(kalimantan_deciles)\nkalimantan_deciles$pop_d_decile <- cut(kalimantan_deciles$pop_density_km2, \n breaks = quantile(kalimantan_deciles$pop_density_km2, \n probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\nkalimantan_deciles$modeling_region = NULL\nkalimantan <- merge(data_idn,kalimantan_deciles,by=\"pop_density_km2\")\n\n###combine\ndata_idn = rbind(papua, kalimantan)\ndata = rbind(data_per, data_idn)\nremove(data_per, data_idn, papua, kalimantan, per_deciles, papua_deciles, kalimantan_deciles)\n\ndata$strategy = factor(data$strategy,\n levels=c('clos', 'nlos'),\n labels=c('CLOS-Only', 'Hybrid CLOS-NLOS'))\n\ndata$pop_d_decile = as.factor(as.character(data$pop_d_decile))\ndata$pop_d_decile = factor(data$pop_d_decile,\n levels=c(1,2,3,4,5,6,7,8,9,10),\n labels=c(1,2,3,4,5,6,7,8,9,10))\n\ndata$cost_usd = data$cost_usd / 1e6\n\ndata <- data[!(data$asset_type == \"radio_installation\"),]\n\ndata$asset_type = factor(data$asset_type, levels=c(\n 'two_60cm_antennas_usd', 'two_90cm_antennas_usd', \n 'cost_freq_for_link_usd', #or radio_costs_usd\n 'tower_10_m_usd', \n 'site_survey_and_acquisition', 'power_system'),\n labels=c('Antennas', 'Antennas', 'Radios', \n 'Tower/Transport',\n 'Site Survey/Acquisition', \n 'Power System'))\n\ndata$strategy = factor(data$strategy, levels=c(\n 'CLOS-Only', 'Hybrid CLOS-NLOS'),\n labels=c('CLOS-Only', 'Hybrid CLOS-NLOS'))\n\ntotals <- data %>%\n select(country, strategy, pop_d_decile, cost_usd) %>%\n group_by(country, strategy, pop_d_decile) %>%\n summarize(total = round(sum(cost_usd)))\n\npop_costs = ggplot(data, aes(x=pop_d_decile, y=cost_usd, fill=asset_type)) +\n geom_bar(stat=\"identity\") +\n theme(legend.position = 'bottom') +\n scale_y_continuous(expand = c(0, 0), #breaks = seq(0,60,100),\n limits = c(0, 110)) +\n labs(colour=NULL,\n title = \"(A) Aggregate Cost by Population Density Deciles\",\n subtitle = \"Deciles labelled from the highest density to the lowest (1-10)\",\n x = 'Population Density Deciles', y = \"Cost (Millions $USD)\", \n fill='') + #Cost\\nType\n theme(panel.spacing = unit(0.6, \"lines\"), \n axis.text.x = element_text(angle = 45)) +\n expand_limits(y=0) +\n guides() +\n geom_text(\n aes(pop_d_decile, total + 5, label = total, fill = NULL), #total + 225\n size=2.5, data = totals) +\n facet_grid(strategy~country, scales = \"free\")\n\n##################\n##########ID Range\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder_inputs = file.path(folder, '..', 'results')\n\n#get peru cost data\ndata_per = read.csv(file.path(folder_inputs, 'PER', 'cost_item_results.csv'))\ndata_per$country = \"Peru (PER)\"\n\nper_deciles = select(data_per, modeling_region, id_range_m)\nper_deciles = unique(per_deciles)\nper_deciles$id_decile <- cut(per_deciles$id_range_m, \n breaks = quantile(per_deciles$id_range_m, probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\nper_deciles$modeling_region = NULL\ndata_per <- merge(data_per,per_deciles,by=\"id_range_m\")\n\n#get indonesia cost data\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder_inputs = file.path(folder, '..', 'results')\n\ndata_idn = read.csv(file.path(folder_inputs, 'IDN', 'cost_item_results.csv'))\ndata_idn$country = ifelse(grepl(\"Papua\", data_idn$names), \n \"Papua (IDN)\", \"Kalimantan (IDN)\")\n\npapua = data_idn[data_idn$country == 'Papua (IDN)',]\nkalimantan = data_idn[data_idn$country == 'Kalimantan (IDN)',]\n\n###papua\npapua_deciles = select(papua, modeling_region, id_range_m)\npapua_deciles = unique(papua_deciles)\npapua_deciles$id_decile <- cut(papua_deciles$id_range_m, \n breaks = quantile(papua_deciles$id_range_m, \n probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\npapua_deciles$modeling_region = NULL\npapua <- merge(papua,papua_deciles,by=\"id_range_m\")\n\n###kalimantan\nkalimantan_deciles = select(kalimantan, modeling_region, id_range_m)\nkalimantan_deciles = unique(kalimantan_deciles)\nkalimantan_deciles$id_decile <- cut(kalimantan_deciles$id_range_m, \n breaks = quantile(kalimantan_deciles$id_range_m, \n probs = seq(0, 1, 0.1)), \n labels = 10:1, include.lowest = TRUE)\nkalimantan_deciles$modeling_region = NULL\nkalimantan <- merge(data_idn,kalimantan_deciles,by=\"id_range_m\")\n\n###combine\ndata_idn = rbind(papua, kalimantan)\ndata = rbind(data_per, data_idn)\nremove(data_per, data_idn, papua, kalimantan, per_deciles, papua_deciles, kalimantan_deciles)\n\ndata$strategy = factor(data$strategy,\n levels=c('clos', 'nlos'),\n labels=c('CLOS-Only', 'Hybrid CLOS-NLOS'))\n\ndata$id_decile = as.factor(as.character(data$id_decile))\ndata$id_decile = factor(data$id_decile,\n levels=c(10,9,8,7,6,5,4,3,2,1),\n labels=c(1,2,3,4,5,6,7,8,9,10))#\n\ndata$cost_usd = data$cost_usd / 1e6\n\ndata <- data[!(data$asset_type == \"radio_installation\"),]\n\ndata$asset_type = factor(data$asset_type, levels=c(\n 'two_60cm_antennas_usd', 'two_90cm_antennas_usd', \n 'cost_freq_for_link_usd', #or radio_costs_usd\n 'tower_10_m_usd', \n 'site_survey_and_acquisition', 'power_system'),\n labels=c('Antennas', 'Antennas', 'Radios', \n 'Tower/Transport',\n 'Site Survey/Acquisition', \n 'Power System'))\n\ndata$strategy = factor(data$strategy, levels=c(\n 'CLOS-Only', 'Hybrid CLOS-NLOS'),\n labels=c('CLOS-Only', 'Hybrid CLOS-NLOS'))\n\ntotals <- data %>%\n select(country, strategy, id_decile, cost_usd) %>%\n group_by(country, strategy, id_decile) %>%\n summarize(total = round(sum(cost_usd)))\n\nterrain_costs = ggplot(data, aes(x=id_decile, y=cost_usd, fill=asset_type)) +\n geom_bar(stat=\"identity\") +\n theme(legend.position = 'bottom') +\n scale_y_continuous(expand = c(0, 0), #breaks = seq(0,60,100),\n limits = c(0, 110)) +\n labs(colour=NULL,\n title = \"(B) Aggregate Cost by Terrain Irregularity Decile\",\n subtitle = \"Deciles labelled from the lowest terrain iregularity to the highest (1-10)\",\n x = 'Terrain Irregularity Deciles', y = \"Cost (Millions $USD)\", \n fill='') + \n theme(panel.spacing = unit(0.6, \"lines\"), \n axis.text.x = element_text(angle = 45)) +\n expand_limits(y=0) +\n guides() +\n geom_text(\n aes(id_decile, total + 5, label = total, fill = NULL), #total + 225\n size=2.5, data = totals) +\n facet_grid(strategy~country, scales = \"free\")\n\ncombined <- ggarrange(pop_costs, terrain_costs, \n ncol = 1, nrow = 2,\n common.legend = TRUE, legend=\"bottom\")\n\npath = file.path(folder, 'figures', 'cost_panel_plot.png')\nggsave(path, units=\"in\", width=7, height=9, dpi=300)\nprint(combined)\ndev.off()\n\n", "meta": {"hexsha": "e0a02a1b52c2d6076f919fabada3430875a110d3", "size": 9014, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/costs.r", "max_stars_repo_name": "edwardoughton/e3nb", "max_stars_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-10T21:45:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T21:45:07.000Z", "max_issues_repo_path": "vis/costs.r", "max_issues_repo_name": "edwardoughton/e3nb", "max_issues_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/costs.r", "max_forks_repo_name": "edwardoughton/e3nb", "max_forks_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-13T16:27:41.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-13T16:27:41.000Z", "avg_line_length": 40.6036036036, "max_line_length": 99, "alphanum_fraction": 0.6452185489, "num_tokens": 2583, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.661922862511608, "lm_q2_score": 0.5, "lm_q1q2_score": 0.330961431255804}} {"text": "#=============================================================\n# c:/1work/Python/djcode/fn_portal/utils/rwt_tlen_coefs.r\n# Created: 16 Sep 2016 14:13:34\n#\n# DESCRIPTION:\n#\n# A. Cottrill\n#=============================================================\n\n# LIBRARIES:\nlibrary(RODBC)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(plyr)\n\n#=============================================================\n\nDBASE <- 'C:/1work/ScrapBook/Biomass_per_net.mdb'\nDBConnection <- odbcConnectAccess(DBASE, uid = \"\", pwd = \"\")\nDF <- sqlFetch(DBConnection, 'get_tlen_rwt_data', colnames=FALSE,\n rownames=FALSE, stringsAsFactors=FALSE)\nnames(DF) <- toupper(names(DF))\nhead(DF)\nstr(DF)\nnrow(DF)\nodbcClose(DBConnection)\n\n\nwt_length<- function(x){\n # a helper function used by plyr to fit a von-bert curve to the\n # data in x (x is a dataframe that must contain the variables AGE\n # and FLEN.\n x <- subset(x, !is.na(x$TLEN) & !is.na(x$RWT))\n\n x$logrwt = log(x$RWT)\n x$logtlen = log(x$TLEN)\n\n fit <- try( lm(logrwt ~ logtlen, data=x), silent=TRUE)\n if(inherits(fit, 'try-error')){\n return(data.frame(alpha=NA, beta=NA))\n } else {\n coefs = coef(fit)\n return(data.frame(alpha=coefs[2], beta=coefs[1]))\n }\n\n }\n\n\nwtlength_coefs <- ddply(DF, 'SPC', wt_length)\n\n(nrow(wtlength_coefs))\n\nwtlength_coefs <- subset(wtlength_coefs, !is.na(wtlength_coefs$alpha)\n & wtlength_coefs$alpha>0)\nwtlength_coefs$SPC <- sprintf('%03d', wtlength_coefs$SPC)\n\n\nDBConnection <- odbcConnectAccess(DBASE, uid = \"\", pwd = \"\")\nsqlSave(DBConnection, wtlength_coefs, safer = FALSE, fast = TRUE,\n rownames=FALSE)\nodbcClose(DBConnection)\n", "meta": {"hexsha": "14381a3e321a565bb6a5aa48239eb8a7d292e4a4", "size": 1641, "ext": "r", "lang": "R", "max_stars_repo_path": "utils/rwt_tlen_coefs.r", "max_stars_repo_name": "AdamCottrill/FishNetPortal", "max_stars_repo_head_hexsha": "4e58e05f52346ac1ab46698a03d4229c74828406", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "utils/rwt_tlen_coefs.r", "max_issues_repo_name": "AdamCottrill/FishNetPortal", "max_issues_repo_head_hexsha": "4e58e05f52346ac1ab46698a03d4229c74828406", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utils/rwt_tlen_coefs.r", "max_forks_repo_name": "AdamCottrill/FishNetPortal", "max_forks_repo_head_hexsha": "4e58e05f52346ac1ab46698a03d4229c74828406", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.4677419355, "max_line_length": 69, "alphanum_fraction": 0.6002437538, "num_tokens": 488, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3309510876326517}} {"text": "#script to create moisture capital map from GCM output\n\n#requires climate data from cru_ts4.zip (via https://crudata.uea.ac.uk/cru/data/hrg/)\n\nrm(list = ls())\nlibrary(raster)\nlibrary(tidyverse)\nlibrary(ncdf4)\n\n\n#read munis.r as latlong\n#unzip(zipfile=\"Data/sim10_BRmunis_latlon_5km.zip\",exdir=\"Data\") #unzip\nmunis.r <- raster(\"Data/sim10_BRmunis_latlon_5km.asc\")\nlatlong <- \"+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs \"\ncrs(munis.r) <- latlong\n\n#soil is used to calculate plant available water\n#read soil texture and set bucket size (see email from Daniel Victoria 2017-11-21)\n#unzip(zipfile=\"Data/soilT_2018-05-01.zip\",exdir=\"Data\") #unzip\nsoil<-raster(\"Data/soilT_2018-05-01.asc\") \n\n#PAW is Plant Available Water\nPAW<-soil\nPAW[PAW==1]<-0\nPAW[PAW==2]<-0\nPAW[PAW==3]<-75\nPAW[PAW==4]<-55\nPAW[PAW==5]<-35\n#plot(PAW)\n\n\n#this function January to December (NH agricultural year)\nnc2raster <- function(ncyear, ncvar)\n{\n #this is hacked version of cruts2raster function in library(cruts) see https://rdrr.io/cran/cruts/src/R/import-export.R\n #returns a raster stack of 12 layers for a single year from cru_ts data (e.g. http://data.ceda.ac.uk//badc/cru/data/cru_ts/cru_ts_4.01/data/)\n #needed becase cruts2raster returns same data (year) regardless of timeRange provided \n \n #ncname is a character string of the ncdf file (e.g.\"cru_ts4.01.2001.2010.pre.dat.nc\")\n #ncyear is an integer indicating which year from the ncdf file is to be returned (so for above file ncyear <- 1 would return data for 2001, ncyear <- 4 would give 2004 etc)\n #ncvar is a character string indicating which variable from the nc file to access (e.g. \"pre\", \"tmp\")\n \n ncname <- fn_fromYearVar(ncyear,ncvar)\n nc <- nc_open(ncname)\n pre_array <- ncvar_get(nc,ncvar)\n lon <- ncvar_get(nc,\"lon\")\n lat <- ncvar_get(nc,\"lat\")\n \n M <- length(lon)\n N <- length(lat)\n dx <- diff(lon[1:2])\n dy <- diff(lat[1:2])\n \n tr <- y_fromYear(ncyear)\n startmonth <- (tr * 12) - 11\n \n s1 <- raster(t(pre_array[,,startmonth][,N:1]), xmn=lon[1]-dx/2, xmx=lon[M]+dx/2, ymn=lat[1]-dy/2, ymx=lat[N]+dy/2, crs=CRS(\"+init=epsg:4326\"))\n \n startmonth <- startmonth + 1\n endmonth <- startmonth + 10 \n \n for(mon in startmonth:endmonth)\n {\n s1 <- stack(s1, raster(t(pre_array[,,mon][,N:1]), xmn=lon[1]-dx/2, xmx=lon[M]+dx/2, ymn=lat[1]-dy/2, ymx=lat[N]+dy/2, crs=CRS(\"+init=epsg:4326\")))\n }\n \n names(s1) <- c(\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\")\n \n return(s1)\n}\n\n\n\n#this function July to June (SH agricultural year)\nnc2rasterSH <- function(ncyear, ncvar)\n{\n #this is hacked version of cruts2raster function in library(cruts) see https://rdrr.io/cran/cruts/src/R/import-export.R\n #returns a raster stack of 12 layers for a single year from cru_ts data (e.g. http://data.ceda.ac.uk//badc/cru/data/cru_ts/cru_ts_4.01/data/)\n #needed becase cruts2raster returns same data (year) regardless of timeRange provided \n \n #ncname is a character string of the ncdf file (e.g.\"cru_ts4.01.2001.2010.pre.dat.nc\")\n #ncyear is an integer indicating which year from the ncdf file is to be returned (so for above file ncyear <- 1 would return data for 2001, ncyear <- 4 would give 2004 etc)\n #ncvar is a character string indicating which variable from the nc file to access (e.g. \"pre\", \"tmp\")\n \n\n #for SH neeed to get two years of data! \n #agricultural year is July ncyear-1 (yrA) to June ncyear (yrB)\n \n #as long as ncyear is not the first year in a ncdf file (e.g. not 2001) we can use just a single ncdf file\n #otherwise we need to get data two ncdf file (this is handled below by the if statement)\n \n #so first get the ncdf file in which yrB is located\n \n ncnameB <- fn_fromYearVar(ncyear,ncvar)\n ncB <- nc_open(ncnameB) #get data for yrB\n\n #get these parameters here (if we need to get for another ncdf file we will below)\n pre_arrayB <- ncvar_get(ncB,ncvar)\n lonB <- ncvar_get(ncB,\"lon\")\n latB <- ncvar_get(ncB,\"lat\")\n \n MB <- length(lonB)\n NB <- length(latB)\n dxB <- diff(lonB[1:2])\n dyB <- diff(latB[1:2])\n \n #need to split the remaining stacking into two loops, one for yrB, one for yrA\n #startmonth becomes July of yrA\n #endmonth of yrA is December \n \n #startmonth of yrB is January\n #endmonth of yrB is June\n \n #startmonth <- (ncyear * 12) - 11\n \n tr <- y_fromYear(ncyear)\n \n #works as long as ncyear (yrB) is not the first in the ncdf file\n if(tr != 1) {\n \n startmonth <- (tr * 12) - 17 \n \n s1 <- raster(t(pre_arrayB[,,startmonth][,NB:1]), xmn=lonB[1]-dxB/2, xmx=lonB[MB]+dxB/2, ymn=latB[1]-dyB/2, ymx=latB[NB]+dyB/2, crs=CRS(\"+init=epsg:4326\"))\n \n startmonth <- startmonth + 1\n endmonth <- startmonth + 10 \n \n for(mon in startmonth:endmonth)\n {\n s1 <- stack(s1, raster(t(pre_arrayB[,,mon][,NB:1]), xmn=lonB[1]-dxB/2, xmx=lonB[MB]+dxB/2, ymn=latB[1]-dyB/2, ymx=latB[NB]+dyB/2, crs=CRS(\"+init=epsg:4326\")))\n }\n }\n \n #otherwise data for yrA is in an entirely different ncdf file\n else { \n \n ncnameA <- fn_fromYearVar(ncyear-1, ncvar)\n print(paste0(\"reading file \",ncnameA))\n\n ncA <- nc_open(ncnameA) #get data for this year\n\n pre_arrayA <- ncvar_get(ncA,ncvar)\n lonA <- ncvar_get(ncA,\"lon\")\n latA <- ncvar_get(ncA,\"lat\")\n \n MA <- length(lonA)\n N.A <- length(latA)\n dxA <- diff(lonA[1:2])\n dyA <- diff(latA[1:2])\n\n #from earliest nc file get last 6 months\n trA <- y_fromYear(ncyear-1)\n startmonthA <- (trA * 12) - 5 \n \n s1 <- raster(t(pre_arrayA[,,startmonthA][,N.A:1]), xmn=lonA[1]-dxA/2, xmx=lonA[MA]+dxA/2, ymn=latA[1]-dyA/2, ymx=latA[N.A]+dyA/2, crs=CRS(\"+init=epsg:4326\"))\n \n startmonthA <- startmonthA + 1\n endmonthA <- startmonthA + 4 \n \n for(mon in startmonthA:endmonthA)\n {\n s1 <- stack(s1, raster(t(pre_arrayA[,,mon][,N.A:1]), xmn=lonA[1]-dxA/2, xmx=lonA[MA]+dxA/2, ymn=latA[1]-dyA/2, ymx=latA[N.A]+dyA/2, crs=CRS(\"+init=epsg:4326\")))\n }\n\n #from later nc file get first 6 months\n startmonth <- 1 \n endmonth <- 6 \n \n for(mon in startmonth:endmonth)\n {\n s1 <- stack(s1, raster(t(pre_arrayB[,,mon][,NB:1]), xmn=lonB[1]-dxB/2, xmx=lonB[MB]+dxB/2, ymn=latB[1]-dyB/2, ymx=latB[NB]+dyB/2, crs=CRS(\"+init=epsg:4326\")))\n }\n \n }\n \n names(s1) <- c(paste0(\"Jul\",ncyear-1),paste0(\"Aug\",ncyear-1),paste0(\"Sep\",ncyear-1),paste0(\"Oct\",ncyear-1),paste0(\"Nov\",ncyear-1),paste0(\"Dec\",ncyear-1),paste0(\"Jan\",ncyear),paste0(\"Feb\",ncyear),paste0(\"Mar\",ncyear),paste0(\"Apr\",ncyear),paste0(\"May\",ncyear),paste0(\"Jun\",ncyear))\n \n return(s1)\n}\n\n\n\n#function to return final digit from a four-digit integer (for use in nc2raster)\ny_fromYear <- function(year)\n{\n y <- year %% 10\n if(y == 0) { y <- 10 }\n return(y)\n}\n\n\n#function to set climate file name (for use in nc2raster) from a year integer and variable (pre, tmn, tmx)\nfn_fromYearVar <- function(year, var)\n{\n yr = \"Data/cruts/cru_ts4.03.1991.2000.\"\n if(year > 2000 & year <= 2010) { yr = \"Data/cruts/cru_ts4.03.2001.2010.\"}\n if(year > 2010 & year <= 2018) { yr = \"Data/cruts/cru_ts4.03.2011.2018.\"}\n \n return(paste0(yr,var,\".dat.nc\"))\n}\n\n\n#extent object to use in pdf map plots\nBRA.ext <- extent(-62.39713, -35.43949, -33.89756, -4.06125)\n\n\ncalcMoistureMaps <- function(munis.r, PAW, year, BRA.e, hemi, season, GS)\n{\n #generate timeRange and filenames for this year\n tr <- y_fromYear(year)\n prefn <- fn_fromYearVar(year,\"pre\") #precipitation,\tmillimetres per month see: https://crudata.uea.ac.uk/cru/data/hrg/#info \n tmnfn <- fn_fromYearVar(year,\"tmn\") #monthly average daily minimum temperature,\tdegrees Celsiusunits are\n tmxfn <- fn_fromYearVar(year,\"tmx\") #monthly average daily maximum temperature,\tdegrees Celsius\n \n #month labels for plots\n monlab <- c(paste0(\"Jan\",year),paste0(\"Feb\",year),paste0(\"Mar\",year),paste0(\"Apr\",year),paste0(\"May\",year),paste0(\"Jun\",year),paste0(\"Jul\",year),paste0(\"Aug\",year),paste0(\"Sep\",year),paste0(\"Oct\",year),paste0(\"Nov\",year),paste0(\"Dec\",year))\n if(hemi == \"S\") monlab <- c(paste0(\"Jul\",year-1),paste0(\"Aug\",year-1),paste0(\"Sep\",year-1),paste0(\"Oct\",year-1),paste0(\"Nov\",year-1),paste0(\"Dec\",year-1),paste0(\"Jan\",year),paste0(\"Feb\",year),paste0(\"Mar\",year),paste0(\"Apr\",year),paste0(\"May\",year),paste0(\"Jun\",year))\n \n #create season filelabel\n season_label <- paste0(season, collapse=\"\")\n if(hemi == \"S\" && season_label == \"JulAugSepOctNovDecJanFebMarAprMayJun\") season_label <- \"All\"\n if(hemi == \"N\" && season_label == \"JanFebMarAprMayJunJulAugSepOctNovDec\") season_label <- \"All\"\n \n \n #for testing\n #print(tr)\n #print(prefn)\n #print(tmnfn)\n #print(tmxfn)\n \n #read climate files\n \n #northern hemisphere\n if(hemi == \"N\") {\n pre <- nc2raster(ncyear=year,ncvar=\"pre\")\n tmn <- nc2raster(ncyear=year,ncvar=\"tmn\")\n tmx <- nc2raster(ncyear=year,ncvar=\"tmx\")\n }\n \n #souther hemisphere\n if(hemi == \"S\") {\n pre <- nc2rasterSH(ncyear=year,ncvar=\"pre\")\n tmn <- nc2rasterSH(ncyear=year,ncvar=\"tmn\")\n tmx <- nc2rasterSH(ncyear=year,ncvar=\"tmx\")\n }\n \n #pdf(paste0(\"Data/pre\",year,\".pdf\"))\n #plot(pre, ext = BRA.e)\n #dev.off()\n \n #project and crop bricks to extent we want for Brazil\n pre.b <- projectRaster(pre, munis.r)\n pre.b <- mask(x=pre.b, mask=munis.r)\n \n tmn.b <- projectRaster(tmn, munis.r)\n tmn.b <- mask(x=tmn.b, mask=munis.r)\n \n tmx.b <- projectRaster(tmx, munis.r)\n tmx.b <- mask(x=tmx.b, mask=munis.r)\n \n #caclulate average temperature by month (brick)\n avtemp.b <- 0.36*(3*tmx.b-tmn.b)\n \n #annual temp raster layer\n Ta<-mean(avtemp.b)\n \n #total pptn raster layer\n Pa<-sum(pre.b)\n \n #set params needed to calculate PET\n Days <- list(31,28,31,30,31,30,31,31,30,31,30,31) #list of days in the month \n Idex <- 12*((0.2*Ta)^1.514)#1st regional thermal index\n Adex <- 0.49239+1.7912*10^-2*Idex-7.71*10^-5*Idex^2+6.75*10^-7*Idex^3#2nd regional thermal index\n Ndex <- 12##NEED REAL VALUE\n \n \n #initialize PET with mean temperatures\n PET.b <- avtemp.b \n \n #function to calculate Potential Evapotranspiration (PET)\n calcPET <- function(PET, D, I, a, N)\n {\n #if mean temperature <= 0\n PET[PET<=0]<-0\n \n #if mean temperature > 26.5\n PET[PET>26.5]<-(-415.85+32.24*PET[PET>26.5]-0.43*(PET[PET>26.5]^2))*(N/12)*(D/30)\n \n #else\n PET[PET>0&PET<=26.5]<-0.0444*((10*(PET[PET>0&PET<=26.5]/I[PET[]>0&PET[]<=26.5]))^a[PET[]>0&PET[]<=26.5])*N*D\n \n return(PET)\n }\n \n \n #map2 to loop over raster brick (layer per month) and Days list (from purrr, see http://r4ds.had.co.nz/iteration.html)\n #brick needs to passed as a list (see https://geocompr.robinlovelace.net/location.html)\n PET.b <- \n map2(as.list(PET.b), Days, calcPET, I = Idex, a = Adex, N = Ndex) %>% \n stack() #remember to re-stack the list after function\n \n names(PET.b) <- monlab\n \n #see Victoria et al. 2007 DOI: 10.1175/EI198.1 Table 2 for equations\n #initialise water storage variables \n \n Stoi <- PAW #Stoii is month i-1 storage\n Stoii <- Stoi #Stoi is month i storage (in first month use same values)\n\n allmeanStoi <- vector(\"double\", 12) #vector to hold meanStoi for each month\n \n #for creating empty rasters and bricks\n nullRaster <- munis.r\n nullRaster[!is.na(nullRaster)] <- 0 #set anywhere that is not 'no data' in munis.r to 0\n\n DEF.b <- stack(replicate(12, nullRaster)) #empty brick to save all month's DEF\n ET.b <- stack(replicate(12, nullRaster)) #empty brick to save all month's ET\n \n DEF <- nullRaster #empty layer for temp useage in loop\n ET <- nullRaster #empty layer for temp useage in loop\n \n #par(mfrow=c(1,1))\n \n #see loopProofs (need to use loop, cannot use map)\n for(i in 1:12)\n {\n #hold current values of Stoi to set Stoii for next month below (this is why we can't use map)\n tempStoi <- Stoi\n \n P <- pre.b[[i]] #get this month's precipitation (for clarity in equations below)\n PET <- PET.b[[i]] #get this month's PET (for clarity in equations below)\n \n #if pptn < PET set storage\n Stoi[P= PET set storage\n Stoi[P>=PET] <- Stoii[P>=PET] + (P[P>=PET] - PET[P>=PET])\n \n #update Stoii ready for next month\n Stoii<-tempStoi\n \n #where Sto > PAW\n Stoi[Stoi[]>PAW[]] <- PAW[Stoi[]>PAW[]]\n \n #save mean Stoi value for this month\n allmeanStoi[i] <- cellStats(Stoi, \"mean\")\n \n #calculate delta storage\n trSto <- Stoi - Stoii\n\n #reset ET for this loop\n ET <- nullRaster\n \n #where pptn < PET\n ET[P= PET\n ET[P>=PET] <- PET[P>=PET]\n \n #reset DEF for this loop \n DEF <- nullRaster\n \n #where pptn < PET\n DEF[P= PET\n DEF[P>=PET]<-0\n \n #copy DEF to DEF brick\n DEF.b[[i]] <- DEF\n ET.b[[i]] <- ET\n \n }\n \n names(DEF.b) <- monlab #apply month-year names to the layes (for plotting and access below)\n names(ET.b) <- monlab\n\n si_yr <- lapply(season, paste0, year) #add the year to month names to match monlab format\n season_indices <- ifelse(monlab %in% si_yr,1,2) #create index of months to use in stackApply below (1 is in season, 2 is not)\n \n #calculate Dryness Index\n #avDEF<-mean(DEF.b)#mean annual DEF\n avDEF <- stackApply(DEF.b, season_indices, mean) #mean DEF (for specified months), #creates a stack of two layers (season months and non-season months)\n #avPET<-mean(PET.b)#mean annual PET\n avPET <- stackApply(PET.b, season_indices, mean) #mean PET (for specified months), #creates a stack of two layers (season months and non-season months)\n\n avDi <- (100*avDEF) / avPET #creates a stack of two layers (season months and non-season months)\n Di <- (100*DEF.b) / PET.b\n \n \n #pptn and temp by season if needed\n avPptn <- stackApply(pre.b, season_indices, mean)\n avTemp <- stackApply(avtemp.b, season_indices, mean)\n \n \n #Number of months with water deficit - helper function\n countWD <- function(vect, na.rm=T) { return(sum(vect > 5)) }\n \n #Number of months in the season - helper function\n countMonths <- function(vect, na.rm=T) { return(length(vect)) }\n\n #calculate various ways of defining water deficif months\n DEFmonths <- stackApply(DEF.b,season_indices, countWD) #creates a stack of two layers (season months and non-season months)\n allmonths <- stackApply(DEF.b,season_indices, countMonths) #creates a stack of two layers (season months and non-season months)\n DEFmonths_prop <- DEFmonths / allmonths #proportion, #creates a stack of two layers (season months and non-season months)\n \n #Stoidiffc <- allmeanStoi[12] - allmeanStoi[1] #this does not seem to be used elsewhere...\n #Stoidiffc\n \n #write data to files\n if(writeClimRast)\n {\n writeRaster(avDEF[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/MeanDEF_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(avPET[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/MeanPET_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(avTemp[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/MeanTemp_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(avPptn[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/MeanPrecip_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(avDi[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/MeanDI_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(DEFmonths[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/CountDEFmonths_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n writeRaster(DEFmonths_prop[[\"index_1\"]], paste0(outputDir,\"/\",className,\"/PropDEFmonths_\",season_label,\"_\",year,hemi,\".asc\"), format = 'ascii', overwrite=T)\n }\n \n #write pdfs\n if(writeClimPdf)\n {\n\n pdf(paste0(outputDir,\"/\",className,\"/DEF_\",year,hemi,\".pdf\"))\n plot(DEF.b, ext = BRA.e)\n dev.off()\n\n pdf(paste0(outputDir,\"/\",className,\"/PET_\",year,hemi,\".pdf\"))\n plot(PET.b, ext = BRA.e)\n dev.off()\n\n pdf(paste0(outputDir,\"/\",className,\"/ET_\",year,hemi,\".pdf\"))\n plot(ET.b, ext = BRA.e)\n dev.off()\n\n pdf(paste0(outputDir,\"/\",className,\"/PPTN_\",year,hemi,\".pdf\"))\n plot(pre.b, ext = BRA.e)\n dev.off()\n\n pdf(paste0(outputDir,\"/\",className,\"/DI_\",year,hemi,\".pdf\"))\n plot(Di, ext = BRA.e)\n dev.off()\n \n pdf(paste0(outputDir,\"/\",className,\"/ClimateVariables_\",season_label,\"_\",year,hemi,\".pdf\"))\n #pdf(paste0(outputDir,\"/\",className,\"/meanDEF_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(avDEF[[\"index_1\"]], ext = BRA.e, main=paste(\"meanDEF\",season_label,year,hemi, sep=\" \")) #need to use \"index_1\" to get to months labelled 1 in season_indices\n #dev.off()\n\n #pdf(paste0(outputDir,\"/\",className,\"/meanPET_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(avPET[[\"index_1\"]], ext = BRA.e, main=paste(\"meanPET\",season_label,year,hemi, sep=\" \")) #need to use \"index_1\" to get to months labelled 1 in season_indices\n #dev.off()\n\n #pdf(paste0(outputDir,\"/\",className,\"/meanDI_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(avDi[[\"index_1\"]], ext = BRA.e, main=paste(\"meanDI\",season_label,year,hemi, sep=\" \")) #need to use \"index_1\" to get to months labelled 1 in season_indices\n #dev.off()\n \n #pdf(paste0(outputDir,\"/\",className,\"/DEFmonths_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(DEFmonths[[\"index_1\"]], ext = BRA.e, main=paste(\"count DEFmonths\",season_label,year,hemi, sep=\" \"))\n #dev.off()\n \n #pdf(paste0(outputDir,\"/\",className,\"/DEFmonths_prop_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(DEFmonths_prop[[\"index_1\"]], ext = BRA.e, main=paste(\"prop DEFmonths\",season_label,year,hemi, sep=\" \"))\n dev.off()\n \n }\n \n #create the Moisture Capital Map\n MoistureCap <- (75 - avDi[[\"index_1\"]]) / 75\n MoistureCap[MoistureCap[]<0]<-0\n \n\n if(GS) {\n GSCap <- MoistureCap\n GSCap[munis.r %/% 100000 == 42]<-0 #set SC state to 0\n GSCap[munis.r %/% 100000 == 43]<-0 #set RS state to 0\n \n pdf(paste0(outputDir,\"/\",className,\"/GSCap_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(GSCap, ext = BRA.e, main=paste(\"GSCap\",season_label,year,hemi, sep=\" \")) #need to use \"index_1\" to get to months labelled 1 in season_indices\n dev.off()\n \n writeRaster(GSCap, paste0(outputDir,\"/\",className,\"/GSCap_\",season_label,\"_\",hemi,\"_\",year,\".asc\"), format = 'ascii', overwrite=T)\n \n }\n \n if(!GS){\n \n writeRaster(MoistureCap, paste0(outputDir,\"/\",className,\"/MoistureCap_\",season_label,\"_\",hemi,\"_\",year,\".asc\"), format = 'ascii', overwrite=T)\n \n pdf(paste0(outputDir,\"/\",className,\"/MoistureCap_\",season_label,\"_\",year,hemi,\".pdf\"))\n plot(MoistureCap, ext = BRA.e, main=paste(\"MoistureCap\",season_label,year,hemi, sep=\" \")) #need to use \"index_1\" to get to months labelled 1 in season_indices\n dev.off()\n }\n\n rm(pre,tmn,tmx,pre.b,tmn.b,tmx.b,PET.b,DEF.b,ET.b)\n \n}\n\n\n\n\noutputDir <- \"Data\"\nclassName <- \"Moisture\"\n\n#crop_season <- c(\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\")\n#soy_season <- c(\"Oct\",\"Nov\",\"Dec\",\"Jan\",\"Feb\",\"Mar\")\nmz1_season <- c(\"Oct\",\"Nov\",\"Dec\",\"Jan\",\"Feb\",\"Mar\")\nmz2_season <- c(\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\")\ncrops <- list(mz1_season, mz2_season)\n\n\n#create the output directory for this classification if it does not exist\nif(!dir.exists(paste0(outputDir,\"/\",className))) { dir.create(paste0(outputDir,\"/\",className)) }\n\n\n#yr <-2000\nfor(yr in 2001:2018)\n{\n writeClimRast <- F\n writeClimPdf <- F\n calcMoistureMaps(munis.r, PAW, yr, BRA.ext, \"S\", mz1_season, GS = F)\n print(paste0(yr,\" done\"))\n}\n\n\n", "meta": {"hexsha": "8ab1ff1b769d4c3aec45054d47a7e3f3758b8171", "size": 19585, "ext": "r", "lang": "R", "max_stars_repo_path": "moistureMap.r", "max_stars_repo_name": "jamesdamillington/CRAFTYInput", "max_stars_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "moistureMap.r", "max_issues_repo_name": "jamesdamillington/CRAFTYInput", "max_issues_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "moistureMap.r", "max_forks_repo_name": "jamesdamillington/CRAFTYInput", "max_forks_repo_head_hexsha": "0086066dc6f2c015786c9835d6c2b857d74a7b26", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.1631878558, "max_line_length": 281, "alphanum_fraction": 0.6544294103, "num_tokens": 6693, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3309510876326516}} {"text": "######\n# Generates predicted (counterfactual) time series of EB estimates for\n# greylisted countries had they NOT been greylisted\n# Uses GBM and public data for predictions\n# Includes estimated std errors for OPE estimates of value of greylisting\n######\n\nlibrary(gbm)\nlibrary(ggplot2)\nlibrary(reshape2)\n\n\n##########################\n# read in files + preprocess\n##########################\n\n# read in pre-processed public data file\nX <- read.csv(\"../OtherData/Xall.csv\", header = TRUE)\n\n# remove row numbers (1st column)\nX <- X[,-1]\n\n# rename features & convert date to correct type\nnames(X) <- c(\"country\", \"date\", paste(\"cases_minus\", 1:20, sep=\"\"), \"cases_zero\", \n paste(\"cases_plus\", 1:19, sep=\"\"), paste(\"deaths_minus\", 1:20, sep=\"\"),\n \"deaths_zero\", paste(\"deaths_plus\", 1:19, sep=\"\"), paste(\"tests_minus\", 1:20, sep=\"\"),\n \"tests_zero\", paste(\"tests_plus\", 1:19, sep=\"\"))\nX$date <- as.Date(X$date, format = \"%Y-%m-%d\")\n\n# identify countries as white/black\n# note that all greylisted countries were white\nctry_white_list <- read.csv(\"https://raw.githubusercontent.com/ovelixasterix/greek_project/master/countries_allowed.csv\", header = FALSE)\nX$white <- as.integer(as.character(X$country) %in% ctry_white_list[,1])\n\n# identify when countries were greylisted\ngrey_list_se <- read.csv(\"https://raw.githubusercontent.com/ovelixasterix/greek_project/master/grey_list_start_end.csv\")\ngrey_list_se$start_date <- as.Date(grey_list_se$start_date, format = \"%Y-%m-%d\")\ngrey_list_se$end_date <- as.Date(grey_list_se$end_date, format = \"%Y-%m-%d\")\ngrey_list_se[is.na(grey_list_se$end_date),]$end_date <- as.Date(\"2022-01-01\")\nX$grey <- FALSE\nfor(i in 1:nrow(X)){\n ind <- which(grey_list_se$country == as.character(X$country)[i])\n if(length(ind) > 0){\n X$grey[i] <- as.logical(X$date[i] >= grey_list_se[ind,]$start_date &\n X$date[i] <= grey_list_se[ind,]$end_date)\n }\n}\n\n# merge with OPE outputs file of private EB predictions\ntmp <- read.csv(\"../OPE_outputs/hist_eb_timeseries_TRUE_Window_3_MinTest_30_SmoothPrior_TRUE_2001_0.9.csv\")\ntmp <- tmp[c(\"country\", \"eb_type\", \"date\", \"eb_prev\")]\ntmp$date <- as.Date(tmp$date, format = \"%Y-%m-%d\")\nfor(i in 1:nrow(grey_list_se)){\n # remove non-* version of EB type for greylisted period\n ind <- which(tmp$date >= grey_list_se[i,]$start_date & tmp$date <= grey_list_se[i,]$end_date\n & as.character(tmp$country) == grey_list_se[i,]$country\n & as.character(tmp$eb_type) == grey_list_se[i,]$country)\n if(length(ind) > 0) tmp <- tmp[-ind,]\n \n # remove * version of EB type for non-greylisted period\n ind <- which((tmp$date < grey_list_se[i,]$start_date | tmp$date > grey_list_se[i,]$end_date)\n & as.character(tmp$country) == grey_list_se[i,]$country\n & as.character(tmp$eb_type) == paste(grey_list_se[i,]$country, \"*\", sep=\"\"))\n if(length(ind) > 0) tmp <- tmp[-ind,]\n}\n\n# merge with public data file\nX2 <- merge(X, tmp, by=c(\"country\", \"date\"))\n\nX2$country <- as.factor(X2$country)\nX2$eb_type <- NULL\n\n##########################\n# predict greylist counterfactual EB prevalence\n##########################\n\n# test set, convert date to a numeric since some start date\nX_grey <- X2[X2$grey,]\nX_grey$grey <- NULL\nX_grey$date <- as.integer(X_grey$date - min(X2$date))\n\n# train + validation set on non-grey\ntmp <- X2[!X2$grey,]\ntmp$grey <- NULL\ntmp$date <- as.integer(tmp$date - min(X2$date))\n\n# randomly split into train and validation set\nn <- round(nrow(tmp)*.7)\nind <- sample(1:nrow(tmp))[1:n]\ntrainX <- tmp[ind, -which(names(tmp) %in% c(\"eb_prev\"))]\ntrainy <- tmp[ind, \"eb_prev\"]\nvalX <- tmp[-ind, -which(names(tmp) %in% c(\"eb_prev\"))]\nvaly <- tmp[-ind, \"eb_prev\"]\n\n# fit GBM model on train and predict on validation set\nfit <- gbm(trainy ~ ., data = trainX, distribution = \"gaussian\", n.trees = 500, \n bag.fraction = .75, cv.folds = 5, interaction.depth = 9)\npred_val <- predict(fit, newdata = valX)\nsummary(fit)\n\n# get standard deviation of residuals on validation set\nresiduals <- pred_val-valy\nhist(residuals)\nsd_val <- sd(residuals)\n\n# retrain on entire dataset (train + val)\nfit <- gbm(c(trainy, valy) ~ ., data = rbind(trainX, valX), distribution = \"gaussian\", n.trees = 500, \n bag.fraction = .75, cv.folds = 5, interaction.depth = 9)\n\n# test set predictions\npred_grey <- predict(fit, newdata = X_grey[,-which(names(X_grey) %in% c(\"eb_prev\"))])\nX_grey <- cbind(X_grey, \"pred_prev\" = pred_grey, \"sd_prev\" = sd_val)\n\n# predict on past data\ngrey <- unique(as.character(X_grey$country))\ndat <- X2[(as.character(X2$country) %in% grey) & !X2$grey,]\ndat$date <- as.integer(dat$date - min(X2$date))\ndat$pred_prev <- predict(fit, newdata = dat[,-which(names(dat) %in% c(\"grey\", \"eb_prev\"))])\ndat$sd_prev <- sd_val\n\n# combine with test set\ndat <- dat[,c(\"country\", \"date\", \"eb_prev\", \"pred_prev\", \"sd_prev\")]\ndat <- rbind(dat, X_grey[,c(\"country\", \"date\", \"eb_prev\", \"pred_prev\", \"sd_prev\")])\ndat$date <- as.Date(dat$date + min(X2$date))\n\n# make sure predicted prevalence is non-negative\ndat$pred_prev <- pmax(dat$pred_prev, 0)\n\n# make sure counterfactual prevalence is above observed prevalence\n# i.e., requiring negative-PCR pretest cannot increase prevalence\ndat$pred_prev <- pmax(dat$pred_prev, dat$eb_prev)\n\nwrite.csv(dat, \"../OPE_outputs/grey_eb_preds.csv\")\n\n", "meta": {"hexsha": "b72f627f565e62f942e28e0cba4557aa9448a4e5", "size": 5346, "ext": "r", "lang": "R", "max_stars_repo_path": "src/grey_ebpred.r", "max_stars_repo_name": "vgupta1/EVA_CounterfactualAnalysis", "max_stars_repo_head_hexsha": "39be643fe7a16c34b00a71fcd37a8af5292174ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-09-22T23:02:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-13T01:52:53.000Z", "max_issues_repo_path": "src/grey_ebpred.r", "max_issues_repo_name": "vgupta1/EVA_CounterfactualAnalysis", "max_issues_repo_head_hexsha": "39be643fe7a16c34b00a71fcd37a8af5292174ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/grey_ebpred.r", "max_forks_repo_name": "vgupta1/EVA_CounterfactualAnalysis", "max_forks_repo_head_hexsha": "39be643fe7a16c34b00a71fcd37a8af5292174ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-23T10:22:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-23T10:22:29.000Z", "avg_line_length": 39.6, "max_line_length": 137, "alphanum_fraction": 0.6647961092, "num_tokens": 1548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623216, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3309510876326515}} {"text": "## First install the amcat-r package from github\n#install.packages('devtools')\n#library(devtools)\n#install_github(\"amcat/amcat-r\") \n\nlibrary(amcatr)\nsource('lda.r') # from amcat-r-tools folder\n\nconn = amcat.connect('http://preview.amcat.nl') # AmCAT vraagt om je inloggegevens\n\nproject_id = 403\narticleset_id = 10277 # 1083 english articles about the Rwandan genocide in The Guardian, NYT and The Toronto Star\ntokens = amcat.gettokens(conn, project_id, articleset_id, page_size=10, module=\"corenlp_lemmatize\")\n\ndtm = amcat.dtm.create(tokens$aid, tokens$lemma, tokens$freq)\n\ntermstats = amcat.term.statistics(dtm)\ntermstats = termstats[termstats$termfreq > 2 & termstats$nonalpha == F,]\nvoca = as.character(termstats[order(termstats$tfidf, decreasing=T),][1:8000,'term'])\ndtm = dtm[,voca]\n\nm = amcat.lda.fit(dtm)\ntop.topic.words(m$topics)\n\n## visualize topics\nmeta = amcat.getarticlemeta(conn, articleset_id)\nm = amcat.lda.addMeta(m, meta)\n\n## visualize topics\npar(mar=c(5,3,3,3))\namcat.plot.lda.wordcloud(m, topic_nr=1)\namcat.plot.lda.time(m, topic_nr=1, date_interval='week') # default time variable is m$meta$date \namcat.plot.lda.category(m, 1) # default category variable is m$meta$medium\namcat.plot.lda.topic(m, 1)\n\n# print topic plots for all topics\namcat.plot.lda(m, date_interval='day', path='/tmp/clouds/') ## set path to existing directory\n\n## in progress\n## Plot model fit for different values of K (takes a long time as it calculats a model for every value in k_values)\n#source('lda_ktests.r')\n#fit_scores = amcat.lda.find.best.K(dtm, k_values=seq(2,100,10))\n", "meta": {"hexsha": "091b44cf160edab9eb2992f2ce24300efcd14257", "size": 1570, "ext": "r", "lang": "R", "max_stars_repo_path": "demo_lda.r", "max_stars_repo_name": "amcat/amcat-r-tools", "max_stars_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-02-18T04:45:08.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-18T04:45:08.000Z", "max_issues_repo_path": "demo_lda.r", "max_issues_repo_name": "amcat/amcat-r-tools", "max_issues_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo_lda.r", "max_forks_repo_name": "amcat/amcat-r-tools", "max_forks_repo_head_hexsha": "2556c9616824be0a4e93efd606531886473385e6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.511627907, "max_line_length": 115, "alphanum_fraction": 0.7484076433, "num_tokens": 468, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3309136856050476}} {"text": "# train an rf classifier for every timepoint clustering\nrf.generate.classifiers <- function(otus, clusterings, percentage=0.8, kernel='linear'){\n \n # execute analysis per timepoint\n rfs <- lapply(clusterings, function(clustering){\n \n # filter otus with timepoint\n otus.of.timepoint <- otus.all.records.for.timepoints(otus, clustering$timepoint)\n \n # normalize otus\n otus.normalized <- otus.normalize(otus.of.timepoint, 0)\n \n # train a classifier for every timepoint \n rf.classifier <- rf.train.classifier(otus.normalized, clustering$best.clustering_by_ch, percentage, kernel) \n \n result <- list('timepoint' = clustering$timepoint, 'rf' = rf.classifier)\n \n return(result)\n })\n \n return(rfs)\n}\n\n\nrf.train.classifier <- function(data, classification, percentage, kernel){\n \n # if a timepoing has (n) clusters this list contains (n) classfication values \n unique.classification.values <- unique(classification)\n \n # initiate training and validation data sets\n training.data.set <- array(dim=c(0,ncol(data)))\n training.classification.set <- array(dim=c(0,ncol(data)))\n validation.data.set <- array(dim=c(0,ncol(data)))\n validation.classification.set <- array(dim=c(0,ncol(data)))\n \n training.stats <- ''\n training.stats <- paste(training.stats, nrow(data), sep = '')\n training.stats <- paste(training.stats, ':', sep = '')\n \n # for every classification value create separate training and validation sets\n # Merge them all togenter and then train the random forests\n for(classification.value in unique.classification.values){\n \n classification.value.index <- as.vector(which(classification == classification.value))\n \n nrOfTrainingSamples <- round(percentage * length(classification.value.index), 0L)\n \n training.stats <- paste(training.stats, '[', sep = '')\n training.stats <- paste(training.stats, classification.value, sep = '')\n training.stats <- paste(training.stats, ':', sep = '')\n \n if (nrOfTrainingSamples == 1){\n training.indeces <- classification.value.index\n validation.indeces <- classification.value.index\n }\n else{\n training.indeces <- sample(classification.value.index, nrOfTrainingSamples, replace = F)\n validation.indeces <- setdiff(classification.value.index, training.indeces) \n }\n \n training.stats <- paste(training.stats, toString(length(training.indeces)), sep = '')\n training.stats <- paste(training.stats, '-', sep = '')\n training.stats <- paste(training.stats, toString(length(validation.indeces)), sep = '')\n \n training.data.set.for.specific.value <- data[training.indeces,]\n training.classification.set.for.specific.value <- classification[training.indeces]\n \n validation.data.set.for.specific.value <- data[validation.indeces,]\n validation.classification.set.for.specific.value <- classification[validation.indeces] \n \n training.data.set <- rbind(training.data.set, training.data.set.for.specific.value)\n training.classification.set <- c(training.classification.set, training.classification.set.for.specific.value)\n \n validation.data.set <- rbind(validation.data.set, validation.data.set.for.specific.value)\n validation.classification.set <- c(validation.classification.set, validation.classification.set.for.specific.value) \n \n training.stats <- paste(training.stats, ']', sep = '')\n }\n \n # train random forest\n classifier <- randomForest(x=training.data.set, \n y=as.factor(training.classification.set), \n ntree = 1, mtry = ncol(data), importance = T)\n \n # validate training\n prediction.on.validation.set <- rf.predict(classifier, validation.data.set)\n\n confusion.matrix <- table(prediction.on.validation.set, validation.classification.set)\n \n result <- list('classifier' = classifier,\n 'training.stats' = training.stats,\n 'confusion.matrix'= confusion.matrix)\n \n return(result)\n}\n\nrf.predict <- function(classifier, data){\n prediction <- predict(classifier, data, type='class')\n return(prediction)\n}", "meta": {"hexsha": "4aca1b0f8d2fa73fa2566c1266cd163e81feb3ae", "size": 4107, "ext": "r", "lang": "R", "max_stars_repo_path": "down-stream-analysis/random.forest.r", "max_stars_repo_name": "tzouvanas/microbiome", "max_stars_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "down-stream-analysis/random.forest.r", "max_issues_repo_name": "tzouvanas/microbiome", "max_issues_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "down-stream-analysis/random.forest.r", "max_forks_repo_name": "tzouvanas/microbiome", "max_forks_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.07, "max_line_length": 121, "alphanum_fraction": 0.6941806672, "num_tokens": 898, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3309136856050475}} {"text": "library(ggplot2)\nlibrary(extrafont)\nargs=commandArgs(trailingOnly = TRUE)\n\n\noutbase=\"STEMBits\"\ntstring=gsub(\" .*$\",\"\",Sys.time(),perl=TRUE)\n\npwmtab= args[1]\nditab=args[2]\n\npwm=read.table(pwmtab,sep = \"\\t\")\ndi=read.table(ditab,sep = \"\\t\")\ndiname=di$V1\nndiname=gsub(\".di.pfm\",\"_short.pfm\",diname)\n#ndipre=cbind(di,ndiname)\ndi$V1=ndiname\n\noveralloutname=paste(outbase,\"_overall_\",tstring,\".pdf\",sep=\"\")\nperbaseoutname=paste(outbase,\"_perbase_\",tstring,\".pdf\",sep=\"\")\ndistem=subset(di,V3==\"STEM\")\npwmstem=subset(pwm,V3==\"STEM\")\n\n\n#dimean=aggregate(distem$V2,by=list(distem$V1),FUN=mean)\n#pwmmean=aggregate(pwmstem$V2,by=list(pwmstem$V1),FUN=mean)\n#colnames(dimean)=c(\"motif\",\"Di\")\n#colnames(pwmmean)=c(\"motif\",\"Mono\")\ncolnames(distem)=c(\"motif\",\"Di\",\"Tag\")\ncolnames(pwmstem)=c(\"motif\",\"Mono\",\"Tag\")\nallstem=merge(distem,pwmstem,by=\"motif\")\nq=ggplot(allstem,aes(Mono,Di/2)) +geom_point(col=\"royalblue\",alpha=0.5,size=2)+scale_fill_brewer(palette=\"Pastel1\")+scale_y_continuous(\"Di\")+scale_x_continuous(\"Mono\")+ggtitle(\"Perbase IC of stem in Mono & Di matrix\")+\n theme(panel.background=element_blank(),axis.line.x=element_line(size=1,color=\"grey\",linetype=\"solid\"),axis.line.y=element_line(size=1,color=\"grey\",linetype=\"solid\"),legend.position=c(0.5,0.95),legend.direction=\"horizontal\",legend.title=element_blank(),legend.background=element_blank(),plot.title=element_text(size=14,hjust = 0.5),axis.text=element_text(size=12),axis.title=element_text(size=14),legend.text=element_text(size=12),text = element_text(family = \"Arial Narrow\"))\n\nggsave(filename = perbaseoutname,width = 10,height =10,units = \"cm\",plot = q,useDingbats=FALSE)\n\n# the overall\ndnn=distem$motif\nmnn=pwmstem$motif\ndistem$motif=gsub(\"pfm.*\",\"pfm\",dnn)\npwmstem$motif=gsub(\"pfm.*\",\"pfm\",mnn)\ndisum=aggregate(distem$Di,by=list(distem$motif),FUN=sum)\npwmsum=aggregate(pwmstem$Mono,by=list(pwmstem$motif),FUN=sum)\ncolnames(disum)=c(\"motif\",\"Di\")\ncolnames(pwmsum)=c(\"motif\",\"Mono\")\nallsum=merge(disum,pwmsum,by=\"motif\")\np=ggplot(allsum,aes(Mono,Di/2)) +geom_point(col=\"blue\",alpha=0.5,size=2)+scale_x_continuous(limits=c(0,8))+scale_y_continuous(\"Di\",limits=c(0,8))+ggtitle(\"Overall IC of stem in Mono & Di matrix\")+\n theme(panel.background=element_blank(),axis.line.x=element_line(size=1,color=\"grey\",linetype=\"solid\"),axis.line.y=element_line(size=1,color=\"grey\",linetype=\"solid\"),legend.position=c(0.5,0.95),legend.direction=\"horizontal\",legend.title=element_blank(),legend.background=element_blank(),plot.title=element_text(size=14,hjust = 0.5),axis.text=element_text(size=12),axis.title=element_text(size=14),legend.text=element_text(size=12),text = element_text(family = \"Arial Narrow\"))\nggsave(filename = overalloutname,width = 10,height =10,units = \"cm\",plot = p,useDingbats=FALSE)\n\noverallIncreaseBits=mean(allsum$Di -allsum$Mono)\nwrite.table(overallIncreaseBits,file=paste(\"OverallIncreasedBits\",tstring,\".txt\",sep = \"\"),col.names = F,row.names = F,quote = F)\n\n\n", "meta": {"hexsha": "66d62dba13ef893d20ac1de2d71e04b46146a53b", "size": 2932, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts-HTR/5.InformationContent/StemIC_corrPlot.r", "max_stars_repo_name": "zhjilin/rmap", "max_stars_repo_head_hexsha": "44dd7e336303181c53733a3fa30cdd274c08fa5d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-27T06:12:41.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-06T22:14:40.000Z", "max_issues_repo_path": "Scripts-HTR/5.InformationContent/StemIC_corrPlot.r", "max_issues_repo_name": "zhjilin/rmap", "max_issues_repo_head_hexsha": "44dd7e336303181c53733a3fa30cdd274c08fa5d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts-HTR/5.InformationContent/StemIC_corrPlot.r", "max_forks_repo_name": "zhjilin/rmap", "max_forks_repo_head_hexsha": "44dd7e336303181c53733a3fa30cdd274c08fa5d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.3090909091, "max_line_length": 477, "alphanum_fraction": 0.7482946794, "num_tokens": 967, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6723316860482763, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.33091367914212033}} {"text": "#' Compute relative distances between intervals.\n#'\n#' @param x [tbl_interval()]\n#' @param y [tbl_interval()]\n#' @param detail report relative distances for each `x` interval.\n#'\n#' @family interval statistics\n#'\n#' @return\n#' If `detail = FALSE`, a [tbl_interval()] that summarizes\n#' calculated `.reldist` values with the following columns:\n#'\n#' - `.reldist` relative distance metric\n#' - `.counts` number of metric observations\n#' - `.total` total observations\n#' - `.freq` frequency of observation\n#'\n#' If `detail = TRUE`, the `.reldist` column reports the relative\n#' distance for each input `x` interval.\n#'\n#' @template stats\n#'\n#' @seealso \\url{http://bedtools.readthedocs.io/en/latest/content/tools/reldist.html}\n#'\n#' @examples\n#' genome <- read_genome(valr_example('hg19.chrom.sizes.gz'))\n#'\n#' x <- bed_random(genome, seed = 1010486)\n#' y <- bed_random(genome, seed = 9203911)\n#'\n#' bed_reldist(x, y)\n#'\n#' bed_reldist(x, y, detail = TRUE)\n#'\n#' @export\nbed_reldist <- function(x, y, detail = FALSE) {\n if (!is.tbl_interval(x)) x <- as.tbl_interval(x)\n if (!is.tbl_interval(y)) y <- as.tbl_interval(y)\n\n x <- group_by(x, chrom, add = TRUE)\n y <- group_by(y, chrom, add = TRUE)\n\n if (utils::packageVersion(\"dplyr\") < \"0.7.99.9000\"){\n x <- update_groups(x)\n y <- update_groups(y)\n }\n\n grp_indexes <- shared_group_indexes(x, y)\n\n res <- dist_impl(x, y,\n grp_indexes$x,\n grp_indexes$y,\n distcalc = \"reldist\")\n\n if (detail) return(res)\n\n res[[\".reldist\"]] <- floor(res[[\".reldist\"]] * 100) / 100\n nr <- nrow(res)\n res <- group_by(res, .reldist)\n res <- summarize(\n res,\n .counts = n(),\n .total = nr,\n .freq = .counts / .total\n )\n res\n}\n", "meta": {"hexsha": "958130ba590fe67a895778c00224667fb69f2433", "size": 1740, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bed_reldist.r", "max_stars_repo_name": "kriemo/valr", "max_stars_repo_head_hexsha": "6355681e84b1aece2fc3800da4c15ed29c8f754c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/bed_reldist.r", "max_issues_repo_name": "kriemo/valr", "max_issues_repo_head_hexsha": "6355681e84b1aece2fc3800da4c15ed29c8f754c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/bed_reldist.r", "max_forks_repo_name": "kriemo/valr", "max_forks_repo_head_hexsha": "6355681e84b1aece2fc3800da4c15ed29c8f754c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5882352941, "max_line_length": 85, "alphanum_fraction": 0.6166666667, "num_tokens": 513, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.3309017694292729}} {"text": "res <- read.table('../original_results/section_v_e_anytime_problem7_results.csv', header=TRUE, sep=',')\n\nmin_obj = min(min(res$qos, res$freecycles))\nmax_obj = max(max(res$qos, res$freecycles))\n\nxlimits = c(min(res$timeout), max(res$timeout))\nylimits = c(min_obj, max_obj)\n\npdf('problem7.pdf')\nfs = 1.5\nfs_leg = 1.25\n\nplot(res$timeout[res$method==\"minizinc\"], res$qos[res$method==\"minizinc\"], \n\txlim=xlimits,ylim=ylimits, type='b', pch='+', xlab='Time (s)', ylab='Normalised Objective Value',\n\tcex.lab=fs, cex.axis=fs, cex.main=fs, cex.sub=fs)\n#, main='Problem 7: Anytime Algorithms')\nlines(res$timeout[res$method==\"minizinc\"], res$freecycles[res$method==\"minizinc\"], pch='+', type='b', lty=2)\n\nlines(res$timeout[res$method==\"local\"], res$qos[res$method==\"local\"], type='b', pch='o')\nlines(res$timeout[res$method==\"local\"], res$freecycles[res$method==\"local\"], type='b', pch='o', lty=2)\n\nx_pos = max(res$timeout) * 0.6\ny_pos = max_obj * 0.75\n\nlegend(legend=c('Minizinc QoS', 'MiniZinc Util', 'Local Search QoS', 'Local Search Util'), \n\tx=x_pos, y=y_pos, lty=c(1,2,1,2), pch=c('+','+','o','o'), cex=fs_leg)\n\ndev.off()", "meta": {"hexsha": "92e5d71e5b3646466286a920550a1df1cd8282d6", "size": 1115, "ext": "r", "lang": "R", "max_stars_repo_path": "plot/plot_7.r", "max_stars_repo_name": "ipab-rad/task_var_alloc", "max_stars_repo_head_hexsha": "30ffecae48735f7b5a4fbf0bf122af098d0f2795", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-17T10:46:29.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-17T10:46:29.000Z", "max_issues_repo_path": "plot/plot_7.r", "max_issues_repo_name": "ipab-rad/task_alloc", "max_issues_repo_head_hexsha": "30ffecae48735f7b5a4fbf0bf122af098d0f2795", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot/plot_7.r", "max_forks_repo_name": "ipab-rad/task_alloc", "max_forks_repo_head_hexsha": "30ffecae48735f7b5a4fbf0bf122af098d0f2795", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8214285714, "max_line_length": 108, "alphanum_fraction": 0.6753363229, "num_tokens": 388, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.33090176139532157}} {"text": "#----------------------------------------------------------------------------#\n#' @title ticks number control\n#' @description ticks number control for ggplot\n#' @param n\tnumber of ticks\n#' @examples \"scale_x_continuous(break = n.ticks(20))\"\n#' @export\nn_ticks = function (n) {function(limits) pretty(limits, n)}\n", "meta": {"hexsha": "b19f77b7fa467e572c09855e1c0fd7575c0ec050", "size": 312, "ext": "r", "lang": "R", "max_stars_repo_path": "R/n_ticks.r", "max_stars_repo_name": "RWE-Lab/publishr", "max_stars_repo_head_hexsha": "7f1b2c9836713c1f7d5f2633020509a753902ff2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/n_ticks.r", "max_issues_repo_name": "RWE-Lab/publishr", "max_issues_repo_head_hexsha": "7f1b2c9836713c1f7d5f2633020509a753902ff2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/n_ticks.r", "max_forks_repo_name": "RWE-Lab/publishr", "max_forks_repo_head_hexsha": "7f1b2c9836713c1f7d5f2633020509a753902ff2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.0, "max_line_length": 78, "alphanum_fraction": 0.5384615385, "num_tokens": 65, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.33090176139532157}} {"text": "#'---\n#'title: \"2.1 MRI, fMRI and MEG\"\n#'author: \"Denis A. Engemann\"\n#'date: \"8/6/2019\"\n#'output:\n#' html_document:\n#' code_folding:\n#' hide\n#' md_document:\n#' variant:\n#' markdown_github\n#'---\n\n# seed!\nset.seed(42)\n\n#+ config\nlibrary(ggplot2)\nlibrary(ggbeeswarm)\nlibrary(ggrepel)\n\n# imports `color_cats` and `get_label_from_marker`\nsource('./utils.r')\nsource('./config.r')\n\nPREDICTIONS <- './data/age_stacked_predictions_megglobal.csv'\nPREDICTIONS2 <- './data/age_stacked_predictions__na_coded.csv'\nPREDICTIONS3 <- './data/age_stacked_predictions_meglocal.csv'\n\nDUMMY <- './data/age_stacked_dummy.csv'\nSCORES <- './data/age_stacked_scores_megglobal.csv'\n\ndata_dummy <- read.csv(DUMMY)\ndata_dummy$MAE <- -data_dummy$MAE\n \ndata_pred_wide <- read.csv(PREDICTIONS)\ndata_scores_wide <- read.csv(SCORES)\n\n# for chance-level predictiong and differences between methods\n\n# fix column names and add dummy\n\nnames(data_scores_wide) <- fix_column_names(names(data_scores_wide))\ndata_scores_wide$chance <- data_dummy$MAE\n\n#'Preprocess data.\n\n#'r data\nstacked_keys <- c(\n \"MEG_handcrafted\",\n \"MEG_powers\",\n \"MEG_powers_cross_powers\",\n \"MEG_powers_cross_powers_handrafted\",\n \"MEG_cat_powers_cross_powers_correlation\",\n \"MEG_cat_powers_cross_powers_correlation_handcrafted\",\n \"MEG_cross_powers_correlation\",\n \"MEG_powers_cross_powers_correlation\",\n \"MEG_all\",\n \"ALL\",\n \"ALL_no_fMRI\",\n \"MRI\",\n \"ALL_MRI\",\n \"fMRI\",\n \"chance\"\n)\n\n#' Let's first first get an overview on all models\n\nbase_line_sel <- c(\n \"ALL\", \"ALL_no_fMRI\", \"ALL_MRI\", \"MRI\", \"fMRI\", \"MEG_all\", \"chance\")\n\ndata_all_scores <- data_scores_wide[,base_line_sel]\nnames(data_all_scores) <- c('Multimodal', 'MRI & MEG', 'MRI & MRI', \"MRI\", 'fMRI', \"MEG\", \"Chance\")\n\n#' Now let's investigate difference from chance baseline\n\nsummarize_table <- function(data){\n data <- rbind(\n apply(data, FUN = mean, 2),\n apply(data, FUN = sd, 2),\n apply(data, FUN = function(x) quantile(x, c(0.025, 0.975)), 2)\n )\n\n rownames(data) <- c(\"M\", \"SD\", rownames(data)[c(3, 4)])\n data <- round(t(data), 3)\n data <- data.frame(model = rownames(data), data)\n rownames(data) <- NULL\n return(data)\n}\n\ndata_all_summary <- summarize_table(data_all_scores)\n\nknitr:::kable(data_all_summary)\n\nwrite.csv(\n data.frame(\n stat = rownames(data_all_summary), data_all_summary),\n './viz_intermediate_files/all_scores_summary.csv'\n)\n\ndata_dummy_diff <- data_all_scores - data_all_scores$Chance\n\ndata_dummy_diff_summary <- summarize_table(data_dummy_diff)\n\ndata_dummy_diff_summary$Pr <- t(t(apply(data_dummy_diff, FUN = function(x) sum(x < 0), 2)))\n\nknitr:::kable(data_dummy_diff_summary)\n\nwrite.csv(\n data.frame(\n stat = rownames(data_dummy_diff_summary), data_dummy_diff_summary),\n './viz_intermediate_files/all_scores_dummy_diff_summary.csv'\n)\n\n\ndata_mri_diff <- data_all_scores - data_all_scores$MRI\n\ndata_mri_diff_summary <- summarize_table(data_mri_diff)\n\ndata_mri_diff_summary$Pr <- t(t(apply(data_mri_diff, FUN = function(x) sum(x < 0), 2)))\n\nknitr:::kable(data_mri_diff_summary)\n\nwrite.csv(\n data.frame(\n stat = rownames(data_mri_diff_summary), data_mri_diff_summary),\n './viz_intermediate_files/all_scores_mri_diff_summary.csv'\n)\n\n\n# XXX idea, rank statistics\nmodel_ranking <- do.call(\n rbind, \n lapply(seq_along(data_all_scores[,1]),\n function(ii) {\n out <- data.frame(\n rank = rank(data_all_scores[ii,]),\n model = names(data_all_scores),\n fold = ii)\n rownames(out) <- NULL\n return(out)\n }))\n\nmodel_ranking$model <- factor(\n model_ranking$model,\n levels = rev(c(\"Chance\", \"fMRI\", \"MEG\", \"MRI\", \"MRI & MEG\",\n \"MRI & MRI\", \"Multimodal\")),\n labels = rev(c(\n \"Chance\", \"fMRI\", \"MEG\", \"MRI\", \"MRI, MEG\", \"MRI, fMRI\",\n \"MRI, fMRI, MEG\")))\n\ncolors_multimodal <- setNames(\n with(color_cats, c(black, orange, `blueish green`, blue, violet, vermillon, gray)),\n c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG', 'Chance'))\n\nfig_rank_box <- ggplot(data = model_ranking,\n mapping = aes(x = rank, y = reorder(model, rank, mean), color = model, fill = model)) +\ngeom_boxplot(show.legend = F, alpha = 0.3) +\ngeom_jitter(show.legend = F, size=1.5, width = 0.12, alpha=0.5) +\nscale_x_continuous(breaks = 1:7) +\nscale_color_manual(values = colors_multimodal,\n labels = names(colors_multimodal),\n breaks = names(colors_multimodal)) +\nscale_fill_manual(values = colors_multimodal,\n labels = names(colors_multimodal),\n breaks = names(colors_multimodal)) +\nlabs(x = 'Ranking across CV testing-splits', y = 'Stacking Models')\nprint(fig_rank_box)\n\n\nfname <- \"./figures/elements_fig2_stacking_mri_meg_supp_rank_box.\"\nggsave(paste0(fname, \"pdf\"),\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"),\n width = save_width, height = save_height, dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n##\n# explore rank stats\nrankmat <- data.frame(\n do.call(\n cbind,\n with(model_ranking,\n by(model_ranking, model, function(x) x$rank))))\nrankmat$n <- 1\nrank_ds <- destat(rankmat)\nrownames(rank_ds$pair) <- names(rankmat)[-ncol(rankmat)]\ncolnames(rank_ds$pair) <- names(rankmat)[-ncol(rankmat)]\nrownames(rank_ds$mar) <- names(rankmat)[-ncol(rankmat)]\ncolnames(rank_ds$mar) <- names(rankmat)[-ncol(rankmat)]\n\nprint(rank_ds$pair)\nprint(rank_ds$mar)\n\nget_mat <- function(mat) {\n mat <- data.frame(mat)\n colnames(mat) <- levels(model_ranking$model)\n rownames(mat) <- levels(model_ranking$model)\n\n mat_long <- do.call(rbind, lapply(colnames(mat), function(x) {\n data.frame(count = mat[, x], x = x, y = rownames(mat))\n }))\n mat_long$x <- factor(mat_long$x, levels = colnames(mat))\n mat_long$y <- factor(mat_long$y, levels = colnames(mat))\n mat_long\n}\n\nfig_rank_mat_pair <- ggplot(data = get_mat(rank_ds$pair),\n mapping = aes(x = x, y = y, fill = count, label = count)) +\n geom_tile() + \n scale_fill_viridis_c(begin = 0.01, end = 0.99) +\n theme(axis.text.x = element_text(angle = 90, hjust = 1)) +\n geom_text(color = 'white', size = 8) +\n labs(x = element_blank(), y = element_blank())\nprint(fig_rank_mat_pair)\n\nfname <- \"./figures/elements_fig2_stacking_mri_meg_supp_rank_mat_pair.\"\nggsave(paste0(fname, \"pdf\"),\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"),\n width = save_width, height = save_height, dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n\n#' Now we can investigate the differences between models in depth.\nreshape_sel <- names(data_scores_wide)[!names(data_scores_wide) %in% c(\n 'repeat_', 'repeat_idx', 'age')]\n\ndata_scores <- reshape(data = data_scores_wide,\n direction = 'long',\n varying = reshape_sel,\n v.names = 'MAE',\n timevar = 'marker',\n times = reshape_sel)\n\ndata_scores['modality'] <- 'MEG'\ndata_scores[grepl('MEG_', data_scores$marker),]['modality'] <- 'MEG'\ndata_scores[grepl('Connectivity_Matrix', \n data_scores$marker),]['modality'] <- 'fMRI'\ndata_scores[grepl('ALL', data_scores$marker),]['modality'] <- 'Multimodal'\ndata_scores[grepl('chance', data_scores$marker),]['modality'] <- 'Chance'\n\ndata_scores$marker <- sub(\"stacked_\", \"\", data_scores$marker)\ndata_scores$prediction <- factor(ifelse(\n data_scores$marker %in% stacked_keys, 'stacked', 'linear'))\ndata_scores$marker <- factor(data_scores$marker)\n\n#'Select stacked data.\n\n#+r stacked_data\ndata_stacked <- subset(data_scores, prediction == 'stacked')\n\nstacked_selection <- c(\n \"ALL\",\n \"ALL_MRI\",\n \"ALL_no_fMRI\",\n \"MRI\",\n \"chance\"\n)\n\ndata_stacked_sel <- within(\n data_stacked[data_stacked$marker %in% stacked_selection,],\n {\n family <- rep('Multimodal', length(marker))\n family[marker == 'ALL_MRI'] <- 'MRI & fMRI'\n family[marker == 'ALL_no_fMRI'] <- 'MRI & MEG'\n family[marker == 'MRI'] <- 'MRI'\n family[marker == 'chance'] <- 'Chance'\n family <- factor(family)\n }\n)\n\n#'Plot error distibution.\n#+ fig2a\ncolors <- setNames(\n with(color_cats, c(black, orange, `blueish green`, blue, gray)),\n c('Multimodal', 'MRI & fMRI', 'MRI & MEG', 'MRI', \"Chance\"))\n\nsort_idx <- order(aggregate(MAE ~ marker,\n data = data_stacked_sel, FUN = mean)$MAE)\ndata_stacked_sel$cv_idx <- rep(rep(1:10, times = 10), length(colors))\n\n\nset_na_chance <- function(data){\n data$is_chance <- F\n\n data <- rbind(\n data,\n within(data, {\n is_chance <- T\n MAE[family != \"Chance\"] <- NA})\n )\n\n data[\n (data$is_chance == F &\n data$family == \"Chance\"),]$MAE <- NA\n return(data)\n}\n\nsort_idx <- order(\n aggregate(MAE~marker, data = data_stacked_sel, FUN = mean)$MAE)\n\ndata_stacked_sel$marker <- factor(\n data_stacked_sel$marker,\n levels(factor(data_stacked_sel$marker))[sort_idx])\n\ndata_stacked_sel <- subset(data_stacked_sel, family != \"Chance\")\n\nmri_mean <- aggregate(MAE ~ family, data_stacked_sel, mean)[1, 2]\n\n\n#'Now let us look at the difference from baseline.\n\n#+ fig2b\n# The power of R ... do multi-line assignment expressions inside data frame\n# environment and return updated data.frame\n\ndata_diff <- within(subset(data_stacked_sel, family != \"Chance\"),\n {\n MAE_diff <- c(MAE[family == 'Multimodal'] - MAE[family == 'MRI'],\n MAE[family == 'MRI & MEG'] - MAE[family == 'MRI'],\n mri_mean - MAE[family == 'MRI'],\n MAE[family == 'MRI & fMRI'] - MAE[family == 'MRI'])\n family <- factor(gsub(\"Multimodal\", \"Fully multimodal\", family))\n }\n)\n\nlegend_name <- \"Improvement over anatomical MRI\"\ncolors_fig2b <- setNames(\n with(color_cats,\n c(black, orange, `blueish green`, blue)),\n c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI'))\n\nsort_idx <- order(aggregate(MAE_diff ~ family, data_diff, mean)$MAE_diff)\n\nlevels(data_diff$family) <- c(\"MRI, fMRI, MEG\", \"MRI\", \"MRI, fMRI\", \"MRI, MEG\")\n\n\nfig2b <- ggplot(\n data = data_diff,\n mapping = aes(y = MAE_diff,\n x = reorder(family, MAE_diff, function(x) mean(x)),\n color = family, fill = family)) +\n coord_flip(ylim = c(-2.9, 1.7)) +\n stat_summary(geom = \"boxplot\", fun.data = my_quantiles,\n alpha = 0.5, size = 0.7, width = 0.8) +\n stat_summary(geom = \"errorbar\", fun.data = my_quantiles,\n alpha = 0.5, size = 0.7, width = 0.5) +\n stat_summary(geom = 'text',\n mapping = aes(label = sprintf(\"%1.1f\",\n ..y.. +\n mri_mean)),\n fun.y= mean, size = 3.2, show.legend = FALSE,\n position = position_nudge(x=-0.49)) +\n geom_beeswarm(alpha=0.3, show.legend = F, size = 3) +\n geom_hline(yintercept = 0, linetype = 'dashed') +\n guides(color = guide_legend(nrow = 3, title.position = \"top\")) +\n ylab(\"MAE difference (years)\") +\n xlab(\"Multimodal stacking\") +\n scale_color_manual(values = colors_fig2b,\n labels = names(colors_fig2b),\n breaks = names(colors_fig2b),\n name = legend_name) +\n scale_fill_manual(values = colors_fig2b,\n labels = names(colors_fig2b),\n breaks = names(colors_fig2b),\n name = legend_name) +\n guides(color = guide_legend(nrow = 1, title.position = 'top'),\n fill = guide_legend(nrow = 1)) +\n scale_y_continuous(breaks = seq(-3, 1.5, 0.5)) +\n theme(axis.text.y = element_blank(),\n legend.position = 'top',\n legend.justification = 'left',\n legend.text.align = 0)\nprint(fig2b)\n\nfname <- \"./figures/elements_fig2_stacking_mri_meg_diff.\"\nggsave(paste0(fname, \"pdf\"),\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"), width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n#'Let's investigate the interaction between the MEG and MRI.\n#'For this we will have to extract the raw prediction errors.\n#'\n#'We will first prepare a subset of data that allows us to\n#'compare MEG and fRMI.\n\n#+ data_wide\nstacked_selection2 <- c(stacked_selection, \"fMRI\", \"MEG_all\")\ndata_pred_stacked_sel <- preprocess_prediction_data(\n df_wide = data_pred_wide, stack_sel = stacked_selection2, drop_na = T)\n\n#'Now we can package the data for plotting.\n\n#+ data_diff\ndata_pred_stacked_comp <- rbind(\n data.frame(\n MAE_meg = subset(data_pred_stacked_sel, family == 'MEG')$MAE,\n MAE_fmri = subset(data_pred_stacked_sel, family == 'fMRI')$MAE,\n combined = F),\n data.frame(\n MAE_meg = subset(data_pred_stacked_sel, family == 'MRI & MEG')$MAE,\n MAE_fmri = subset(data_pred_stacked_sel, family == 'MRI & fMRI')$MAE,\n combined = T)\n)\n\ndata_pred_stacked_comp <- cbind(\n data_pred_stacked_comp,\n rbind(subset(data_pred_stacked_sel, family == 'MEG', select = -c(MAE)),\n subset(data_pred_stacked_sel, family == 'MEG', select = -c(MAE))))\n\ndata_pred_stacked_comp$combined <- factor(\n ifelse(data_pred_stacked_comp$combined, \"MRI[anat.]~added\", \"no~MRI[anat.]\"),\n levels = c(\"no~MRI[anat.]\", \"MRI[anat.]~added\"))\n\n#'Now we can plot it.\ndata_corr_agg <- aggregate(\n cbind(MAE_fmri, MAE_meg, age) ~ X*combined,\n data = data_pred_stacked_comp, FUN = mean)\n\n\ncor1 <- with(subset(data_corr_agg, combined != \"MRI[anat.]~added\"),\n cor.test(MAE_meg, MAE_fmri, method = \"spearman\"))\ncor2 <- with(subset(data_corr_agg, combined == \"MRI[anat.]~added\"),\n cor.test(MAE_meg, MAE_fmri, method = \"spearman\"))\n\ncor.details <- data.frame(\n p.value = c(sprintf(\"%e\", cor1$p.value),\n ifelse(cor2$p.value == 0, \"2.2e-16\", sprintf(\"%e\", cor2$p.value))),\n r2 = round(c(cor1$estimate, cor2$estimate) ^ 2, 3),\n rho = round(c(cor1$estimate, cor2$estimate), 3),\n mri = c(F, T)\n)\n\nwrite.csv(cor.details, './viz_intermediate_files/correlation_meg_mri.csv')\n\n# annotation <- data.frame(\n# x = c(2,4.5),\n# y = c(20,25),\n# label = c(\"label 1\", \"label 2\")\n# )\n\n#+ fig2c\nfig2c <- ggplot(\n data = data_corr_agg,\n mapping = aes(x = MAE_fmri, y = MAE_meg, size = age, color = age)) +\n geom_point(alpha = 0.8) +\n scale_size_continuous(range = c(0.01, 3),\n trans = 'sqrt') +\n ylab(expression(MAE[MEG] ~ (years))) +\n xlab(expression(MAE[fMRI] ~ (years))) +\n facet_wrap(~combined, labeller = label_parsed) +\n scale_color_viridis_c() +\n theme(legend.position = 'top',\n legend.justification = 'left',\n legend.text.align = 0) +\n guides(\n color = guide_legend(title.position = \"left\"),\n size = guide_legend(title.position = \"left\")) +\n coord_fixed(ylim = c(0, 30.5), xlim = c(0, 30.5)) +\n labs(color = \"age\", shape = \"age\")\nprint(fig2c)\n\nfname <- \"./figures/elements_fig2_supplement_mri_meg_scatter.\"\nggsave(paste0(fname, \"pdf\"), plot = fig2c,\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"), plot = fig2c,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n#'One can see that a) the MEG and MRI errors are not\n#'strongly related b) comabining each of them with MRI\n#'Makes the error somewhat more similar, especially in old\n#'people, yet leaves them rather uncorrelated.\n#'\n\n#'Let us now plot the error by age group and modality.\n\n#+ error_by_age\n# make qualitative ordered age group\n\ncolors_fig2e <- setNames(\n with(color_cats, c(black, orange, `blueish green`, blue, violet, vermillon)),\n c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG'))\n\ndata_pred_stacked_sel$age_group <- cut(data_pred_stacked_sel$age, breaks = seq(15, 90, 5))\ndata_pred_stacked_sel$family <- factor(\n data_pred_stacked_sel$family,\n levels = c(\"MRI\", \"fMRI\", \"MEG\", \"MRI & fMRI\", \"MRI & MEG\", \"Multimodal\"),\n labels = c(\"MRI\", \"fMRI\", \"MEG\", \"MRI, fMRI\", \"MRI, MEG\", \"MRI, fMRI, MEG\"))\n\n\nnames(data_pred_stacked_sel) <- c(\"subject\", names(data_pred_stacked_sel)[-1])\n\ndata_pred_stacked_sel_agg <- aggregate(\n cbind(MAE, age) ~ subject*family, data = data_pred_stacked_sel, FUN = mean)\n\ndata_pred_stacked_sel_agg$age_group <- cut_number(data_pred_stacked_sel_agg$age, 7)\n\n(anomod <- summary(aov(log(MAE) ~ age_group * family,\n data = data_pred_stacked_sel_agg)))\n\nwriteLines(capture.output(anomod),\n \"./viz_intermediate_files/anova_stacking_error.txt\")\n\n\nfig2d <- ggplot(data = data_pred_stacked_sel,\n mapping = aes(x = age, y = MAE\n # size = MAE, \n # color = family, \n # fill = family\n )) +\n facet_wrap(.~family, scales = 'free_x') +\n\n # geom_jitter(shape=21, fill='white', show.legend = F, alpha=0.5) +\n\n geom_hex(mapping = aes(x = age, y = MAE),\n show.legend = T, size = 0.1, bins = 15) +\n stat_smooth(size = 1.2, show.legend = F,\n method = loess, \n color='red',\n method.args = list(degree = 2),\n fill = NA, level = .9999) +\n scale_fill_continuous(type = \"viridis\") +\n theme(strip.text.y = element_text(angle = 0),\n panel.spacing.x = unit(0.05, 'in')) +\n xlab(\"Age (years)\") +\n ylab(\"MAE (years)\")\n # facet_wrap(~family, ncol = 3)\nprint(fig2d)\n\nfname <- \"./figures/elements_fig2_error_by_age_group.\"\nggsave(paste0(fname, \"pdf\"), plot = fig2d,\n width = save_width, height = save_height, useDingbats = F)\nggsave(paste0(fname, \"png\"), plot = fig2d,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n#' We see, what we knew before that, stacking helps improve the error\n#' However, we see that this is probably due to at least two mechanisms.\n#' 1) extreme error is unifomely reduced. 2) Error in young and old groups\n#' is mitigated. However, we also learn that the best model still shows\n#' considerable brain age bias, with young and old subjects systematically\n#' suffering from more error.\n\n#' Time to investigate the 2D dependence. To select the right variables,\n\n#' We can see from the printed outputs that MEG power always makes it under\n#' the top markers. Thus, it makes sense to focus on power in a 2D dependence\n#' analysis.\n\nOUT_DEPENDENCE_2D <- './data/age_stacked_dependence_model-full-2d.csv'\ndata_dependence2d <- read.csv(OUT_DEPENDENCE_2D)\ndata_dependence2d$marker <- fix_marker_names(data_dependence2d$marker)\ndata_dependence2d$var_x <- fix_marker_names(data_dependence2d$var_x)\ndata_dependence2d$var_y <- fix_marker_names(data_dependence2d$var_y)\ndata_dependence2d$model <- gsub(\" \", \"_\", data_dependence2d$model)\n\npdp2dmap <- list(\n \"ALL\" = list(\n cases = c(\n \"Connectivity_Matrix,_MODL_256_tan--power_diag\",\n \"Cortical_Thickness--Connectivity_Matrix,_MODL_256_tan\",\n \"Cortical_Thickness--power_diag\",\n \"Cortical_Thickness--Subcortical_Volumes\",\n \"Subcortical_Volumes--Connectivity_Matrix,_MODL_256_tan\",\n \"Subcortical_Volumes--power_diag\"),\n labels = c(\n \"fMRI-P[cat]\",\n \"CrtT-fMRI\",\n \"CrtT-P[cat]\",\n \"CrtT-SbcV\",\n \"SbcV-fMRI\",\n \"SbcV-P[cat]\")\n )\n)\n\nprint(nrow(data_dependence2d))\nprint(unique(data_dependence2d$model))\nmodels <- c(\"ALL\")\nfor (i_model in seq_along(models)){\n this_model <- models[[i_model]]\n print(this_model)\n this_data <- subset(data_dependence2d,\n model == this_model & model_type == 'rf_msqrt')\n\n # make nicer marker labels for titles.\n this_data$marker_label <- this_data$marker\n for (i_case in seq_along(pdp2dmap[[this_model]][['cases']])) {\n this_data$marker_label <- gsub(\n pdp2dmap[[this_model]][['cases']][i_case],\n pdp2dmap[[this_model]][['labels']][i_case],\n this_data$marker_label)\n }\n\n marker_split <- strsplit(this_data$marker_label, '-')\n this_data$marker_label_x <- sapply(marker_split, `[[`, 1)\n this_data$marker_label_y <- sapply(marker_split, `[[`, 2)\n\n if(this_model == \"ALL_MRI\"){\n this_breaks <- seq(30, 80, 4)\n }else{\n this_breaks <- seq(46, 62, 2)\n }\n\n fig2e <- ggplot(data = this_data,\n mapping = aes(x = x, y = y, z = pred)) +\n geom_raster(aes(fill = pred), show.legend = T) +\n stat_contour(breaks = this_breaks,\n color = \"white\", bins = 7, show.legend = F) +\n scale_fill_viridis_c(breaks = this_breaks,\n name = expression(hat(y)),\n guide = guide_colorbar(barheight = 10)) +\n theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank()) +\n facet_wrap(marker_label_x ~ marker_label_y,\n scales = \"free\",\n labeller = function(x) label_parsed(x, multi_line = F)) +\n xlab(\"Input age 1\") +\n ylab(\"Input age 2\")\n \n fname <- paste0(\"./figures/elements_fig2e_meg_pdp_2d\",\n \"_\", this_model, \".\")\n ggsave(paste0(fname, \"pdf\"), plot = fig2e,\n width = save_width, height = save_height, useDingbats = T)\n embedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\n ggsave(paste0(fname, \"png\"), plot = fig2e,\n width = save_width, height = save_height,\n dpi = 300)\n}\n\n#' ## Session info\n\n#+ session_info\nprint(sessionInfo())\n", "meta": {"hexsha": "b11d5bfeaa71ba894c7a8b90bb12c77915d45326", "size": 21774, "ext": "r", "lang": "R", "max_stars_repo_path": "figure_mri_fmri_meg.r", "max_stars_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_stars_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-11-11T21:26:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-18T17:18:45.000Z", "max_issues_repo_path": "figure_mri_fmri_meg.r", "max_issues_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_issues_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-03-14T07:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-14T07:56:17.000Z", "max_forks_repo_path": "figure_mri_fmri_meg.r", "max_forks_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_forks_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-06-10T08:34:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T01:37:08.000Z", "avg_line_length": 33.8105590062, "max_line_length": 99, "alphanum_fraction": 0.6453109213, "num_tokens": 6277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.33090176139532157}} {"text": "# GetShockData_compare_cop5.r\n# RBLandau 20171230\n# where to run?\n# setwd(\"C:/cygwin64/home/landau/working/PreservationSimulation/shocks/compare/cop5\")\n# \n# For copies=5, what are the failure rates at shock impacts 50, 80, 100%?\n# \n\n\nsource(\"./DataUtil.r\")\nsource(\"./PlotUtil.r\")\n\n\n# G E T D A T A \nif (exists(\"results\")) {rm(results)}\nresults <- fndfGetGiantData(\"./\")\n# Get fewer columns to work with, easier to see.\ndat.shockallcopies <- as.data.frame(fndfGetShockData(results))\ndat.shockall <- dat.shockallcopies[dat.shockallcopies$copies>=3\n & dat.shockallcopies$copies<=5,]\n\n# S U B S E T S \n\nif (nrow(dat.shockall) > 0)\n{\n source(\"./ShockLowPlots.r\")\n \n dat.shock5.3 <- dat.shockall[dat.shockall$copies==5 \n & dat.shockall$shockspan==3,\n ]\n gp <- fnPlotShock4(trows=dat.shock5.3, nCopies=5)\n plot(gp)\n fnPlotMakeFile(gp, \"Shock_compare_cop5_freq2yr_dur1yr_span3.png\")\n\n\n\n}\n\n\n\n# Unwind any remaining sink()s to close output files. \nwhile (sink.number() > 0) {sink()}\n\n\n#END\n", "meta": {"hexsha": "20dc1213f9a8db9d099d8cb0c33f96e33c51e223", "size": 1109, "ext": "r", "lang": "R", "max_stars_repo_path": "veryoldpictures/shocks/compare/cop5/GetShockData_compare_cop5.r", "max_stars_repo_name": "MIT-Informatics/PreservationSimulation", "max_stars_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_stars_repo_licenses": ["X11"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2016-08-24T05:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-12T16:44:48.000Z", "max_issues_repo_path": "veryoldpictures/shocks/compare/cop5/GetShockData_compare_cop5.r", "max_issues_repo_name": "MIT-Informatics/PreservationSimulation", "max_issues_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_issues_repo_licenses": ["X11"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-20T02:55:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-20T02:55:37.000Z", "max_forks_repo_path": "veryoldpictures/shocks/compare/cop5/GetShockData_compare_cop5.r", "max_forks_repo_name": "MIT-Informatics/PreservationSimulation", "max_forks_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_forks_repo_licenses": ["X11"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.1086956522, "max_line_length": 85, "alphanum_fraction": 0.6239855726, "num_tokens": 339, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.33090175336137007}} {"text": "# By Yea-Hung Chen\n\n# clear work space\nrm(list=ls())\n\n# set random seed\nset.seed(2018) \n\n# define time point of interest for survival analysis\nTIME<-3\n\n# load libraries\nlibrary(survival)\nif_else<-dplyr::if_else\n\n# load functions\nsource('impute dates.r')\nsource('define survival variables.r')\nsource('community-level estimates.r')\nsource('Preprocess_Functions.R')\nsource('Stage2_Functions.R')\nsource('Adapt_Functions.R')\nsource('s2.r')\n\n# pri: load data\nload('prepared data.RData')\n\n# pri: define risk period\nii$start<-ii$tb_risk_start\nii$end<-ii$tb_risk_end\n\n# pri: impute missing dates\nii<-impute_dates(c('ht.end','di.end','do.end','om.end'))\n\n# pri: define survival variables\novar<-c('ht','di')\ncvar<-c('do','om')\nii<-define_survival_variables(ii)\n\n# pri: define population\nii<-subset(ii,!data_flag)\nii<-subset(ii,!tb_censor_0)\nii<-subset(ii,!(dead_0|move_0))\nii<-subset(ii,adult_0)\nii<-subset(ii,resident_0)\nii<-subset(ii,!tb_0)\nii<-subset(ii,hiv_0|is.na(hiv_0))\nii<-subset(ii,stable_0)\n\n# pri: initiate community-level data frame\ncc<-c('com','community_name','pair','intervention',\n 'tb_incidence_0','hiv_prev_0')\ncc<-unique(ii[,cc])\ncc<-cc[order(cc$com),]\nrow.names(cc)<-NULL \n\n# pri: outcome variables\ncc<-oo('pri',method='survival')\ncc<-oo('pri_cd4_1',method='survival',dd=subset(ii,hiv_0&(cd4_0<=500)))\n\n# pri: pre-process data\ncc<-preprocess(cc,YHC=TRUE)\n\n# pri: stage-ii analysis\ns2('pri',CLUST.ADJ=c('U','tb_incidence_0','hiv_prev_0'),SURVIVAL=TRUE)\ns2('pri_cd4_1',CLUST.ADJ=c('U','tb_incidence_0','hiv_prev_0'),SURVIVAL=TRUE)\n\n# annual: load data\nload('prepared data.RData')\n\n# annual: define risk periods\nii$start<-ii$tb_risk_start\nii$end<-ii$tb_risk_end\nsource('risk periods for annual rates.r')\n\n# annual: impute missing dates\nii<-impute_dates(c('tb.end','da.end','om.end'))\n\n# annual: define survival variables\novar<-c('tb')\ncvar<-c('da','om')\nii<-define_survival_variables(ii)\n\n# annual: define population\nii<-subset(ii,!data_flag)\nii<-subset(ii,!tb_censor_0)\nii<-subset(ii,!(dead_0|move_0))\nii<-subset(ii,adult_0)\nii<-subset(ii,resident_0)\nii<-subset(ii,!tb_0)\n\n# annual: initiate community-level data frame\ncc<-c('com','community_name','pair','intervention',\n 'tb_incidence_0','hiv_prev_0')\ncc<-unique(ii[,cc])\ncc<-cc[order(cc$com),]\nrow.names(cc)<-NULL \n\n# annual: outcome variables\noo('neg',method='annual',dd=subset(ii,!hiv_0))\noo('pos',method='annual',dd=subset(ii,hiv_0))\n\n# annual: pre-process data\ncc<-preprocess(cc,YHC=TRUE)\n\n# annual: stage-ii analysis\nOUTCOME<-paste(c('neg','pos'),rep(1:3,each=2),sep='')\ninvisible(lapply(OUTCOME,s2,CLUST.ADJ=c('U','tb_incidence_0','hiv_prev_0'),\n SURVIVAL=FALSE))\n\n# annual, crude: load data\nload('prepared data.RData')\n\n# annual, crude: define risk periods\nii$start<-ii$tb_risk_start\nii$end<-ii$tb_risk_end\nsource('risk periods for annual rates.r')\n\n# annual, crude: impute missing dates\nii<-impute_dates(c('tb.end','da.end','om.end'))\n\n# annual, crude: define survival variables\novar<-c('tb')\ncvar<-c('da','om')\nii<-define_survival_variables(ii)\n\n# annual, crude: define population\nii<-subset(ii,!data_flag)\nii<-subset(ii,!tb_censor_0)\nii<-subset(ii,!(dead_0|move_0))\nii<-subset(ii,adult_0)\nii<-subset(ii,resident_0)\nii<-subset(ii,!tb_0)\n\n# annual, crude: rates\nnegtxt<-yy(TRUE,'intervention',subset(ii,!hiv_0),'y')\nnegcon<-yy(FALSE,'intervention',subset(ii,!hiv_0),'y')\npostxt<-yy(TRUE,'intervention',subset(ii,hiv_0),'y')\nposcon<-yy(FALSE,'intervention',subset(ii,hiv_0),'y')\n\n# output estimates\ntt<-rbind(negtxt,negcon,postxt,poscon)\ntt$hiv<-c('neg','neg','pos','pos')\ntt<-tt[,c('hiv','intervention','y1','y2','y3')]\nwrite.csv(tt,'tuberculosis crude incidence rates.csv',row.names=FALSE)\ntt<-rbind(pri,pri_cd4_1,neg1,neg2,neg3,pos1,pos2,pos3)\nwrite.csv(tt,'tuberculosis intervention effects.csv',row.names=FALSE)", "meta": {"hexsha": "8581b6bfa224d94714aa5d8efe7c64a08f7218a2", "size": 3810, "ext": "r", "lang": "R", "max_stars_repo_path": "tuberculosis analysis.r", "max_stars_repo_name": "LauraBalzer/SEARCH_Analysis_Adults", "max_stars_repo_head_hexsha": "39bdf64eef898e71ef0b25489f91d126fdb94545", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tuberculosis analysis.r", "max_issues_repo_name": "LauraBalzer/SEARCH_Analysis_Adults", "max_issues_repo_head_hexsha": "39bdf64eef898e71ef0b25489f91d126fdb94545", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tuberculosis analysis.r", "max_forks_repo_name": "LauraBalzer/SEARCH_Analysis_Adults", "max_forks_repo_head_hexsha": "39bdf64eef898e71ef0b25489f91d126fdb94545", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.9183673469, "max_line_length": 76, "alphanum_fraction": 0.7152230971, "num_tokens": 1250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.3308654054631334}} {"text": "# Performs preprocessing of categorical (multiple) fields, namely:\n# 1) Reassigning values as specified in data coding file\n# 2) Generating binary variable for each category in field, restricting to correct set of participants as specified\n# in CAT_MULT_INDICATOR_FIELDS field of variable info file (either NO_NAN, ALL or a field ID)\n# 3) Checking derived variable has at least catmultcutoff cases in each group\n# 4) Calling binaryLogisticRegression function for this derived binary variable\n\n\ntestCategoricalMultiple <- function(varName, varType, thisdata, varlogfile)\n{\n data_to_add_mat <- matrix(nrow=nrow(thisdata), ncol=0)\n data_to_add_names <- c()\n\n cat(\"CAT-MULTIPLE || \", file=varlogfile, append=TRUE)\n\n pheno <- thisdata[,phenoStartIdx:ncol(thisdata), drop=FALSE]\n pheno <- reassignValue(pheno, varName, varlogfile)\n\n # Get unique values from all columns of this variable\n # uniqueValues <- unique(na.omit(c(pheno)))\n numRows <- nrow(pheno)\n numCols <- ncol(pheno)\n\n uniqueValues = unique(na.omit(pheno[,1]));\n numCols = ncol(pheno);\n numRows = nrow(pheno);\n if (numCols>1) {\n for (num in 2:numCols) {\n u = unique(na.omit(pheno[,num]))\n uniqueValues = union(uniqueValues,u);\n }\n }\n\n print(uniqueValues)\n\n # For each value create a binary variable and test this\n if (length(uniqueValues) > 1) {\n for (variableVal in uniqueValues) {\n # Numeric negative values we assume are missing.\n if (is.numeric(variableVal) & variableVal < 0) {\n cat(\"SKIP_val:\", variableVal,\" < 0 || \", sep=\"\",\n file=varlogfile, append=TRUE)\n next\n }\n\n # Make variable for this value - make sure that this is just the sample (the rows).\n idxForVar <- which(pheno == variableVal, arr.ind=TRUE)[,1]\n\n cat(\"CAT-MUL-BINARY-VAR \", variableVal, \" || \", sep=\"\", file=varlogfile, append=TRUE)\n incrementCounter(\"catMul.binary\")\n\n # Make zero vector and set 1s for those with this variable value\n varBinary <- rep.int(FALSE, numRows)\n varBinary[idxForVar] <- TRUE\n varBinaryFactor <- factor(varBinary)\n\n # Data for this new binary variable\n newthisdata <- cbind.data.frame(thisdata[,1:numPreceedingCols], varBinaryFactor)\n newthisdata_to_save <- cbind.data.frame(thisdata[,1:numPreceedingCols], varBinary)\n \n # One of 3 ways to decide which examples are negative\n idxsToRemove <- restrictSample(varName, pheno, variableVal, varlogfile)\n \n # Create an ids to save vector, and plug these in NAs at the right positions. \n \tif (!is.null(idxsToRemove)) {\n newthisdata_to_save <- newthisdata\n newthisdata_to_save[idxsToRemove,] <- NA\n newthisdata <- newthisdata[-idxsToRemove,]\n }\n\n facLevels <- levels(newthisdata_to_save[,phenoStartIdx])\n idxTrue <- length(which(newthisdata_to_save[,phenoStartIdx] == TRUE))\n idxFalse <- length(which(newthisdata_to_save[,phenoStartIdx] == FALSE))\n \n if (idxTrue < opt$catmultcutoff || idxFalse < opt$catmultcutoff) {\n cat(\"CAT-MULT-SKIP-\", opt$catmultcutoff, \" (\", idxTrue, \" vs \", idxFalse, \") || \",\n sep=\"\", file=varlogfile, append=TRUE)\n incrementCounter(paste(\"catMul.\", opt$catmultcutoff, sep=\"\"))\n } else {\n incrementCounter(paste(\"catMul.over\", opt$catmultcutoff, sep=\"\"))\n \t# Binary so logistic regression\n data_to_add <- binaryLogisticRegression(paste(varName, variableVal, sep=\"_\"),\n varType, newthisdata_to_save, varlogfile)\n data_to_add_mat <- cbind(data_to_add_mat, as.logical(data_to_add[[1]]))\n data_to_add_names <- c(data_to_add_names, data_to_add[[2]])\n \t}\n }\n }\n return(list(data_to_add_mat, data_to_add_names))\n}\n\n# Restricts sample based on value in CAT_MULT_INDICATOR_FIELDS column of variable info file,\n# either NO_NAN, ALL or a field ID.\n# Returns idx's that should be removed from the sample.\nrestrictSample <- function(varName, pheno, variableVal, varlogfile)\n{\n # Get definition for sample for this variable either NO_NAN, ALL or a variable ID\n varIndicator <- vl$phenoInfo$CAT_MULT_INDICATOR_FIELDS[which(vl$phenoInfo$FieldID == varName)]\n return(restrictSample2(varName, pheno, varIndicator, variableVal, varlogfile))\n}\n\nrestrictSample2 <- function(varName,pheno, varIndicator, variableVal, varlogfile)\n{\n if (varIndicator==\"NO_NAN\") { \n # Remove NAs (remove all people with no value for this variable)\n\n # Row indexes with NA in all columns of this cat mult field\t\t\n ind <- apply(pheno, 1, function(x) all(is.na(x)))\n naIdxs <- which(ind == TRUE)\n cat(\"NO_NAN Remove NA participants\", length(naIdxs), \"|| \",\n file=varlogfile, append=TRUE)\n } else if (varIndicator == \"ALL\") {\n # Use all people (no missing assumed) so return empty vector\n # e.g. hospital data and death registry\n naIdxs <- c()\n cat(\"ALL || \", file=varlogfile, append=TRUE)\n } else if (varIndicator != \"\") {\n # Remove people who have no value for indicator variable\n print(varIndicator)\n indName <- grep(paste0(\"x\", varIndicator,\"_0_\"), names(data), value=TRUE)\n print(indName)\n cat(\"Indicator name \", indName, \" || \", sep=\"\", file=varlogfile, append=TRUE)\n indicatorVar <- data[,indName, drop=FALSE]\n\n # Remove participants with all NAs in this related field\n where_change <- which(sapply(indicatorVar, is.nan) | indicatorVar == \"NaN\" | indicatorVar == \"\", arr.ind=TRUE)\n \n if(nrow(where_change) > 0) {\n indicatorVar[which(sapply(indicatorVar, is.nan) | indicatorVar == \"NaN\" | indicatorVar == \"\", arr.ind=TRUE)] <- NA\n }\n\n naIdxs <- which(apply(indicatorVar, 1, allNAs))\n\n cat(\"Remove indicator var NAs:\", length(naIdxs), \"|| \", file=varlogfile, append=TRUE)\n\n if (is.numeric(as.matrix(indicatorVar))) {\n # Remove participants with value <0 in this related field - assumed missing indicators\n lessZero <- which(indicatorVar < 0, arr.ind=TRUE)[,1]\n naIdxs <- union(naIdxs, lessZero)\n cat(\"Remove indicator var <0:\", length(lessZero), \"|| \", file=varlogfile, append=TRUE)\n }\n } else {\n stop(\"Categorical multiples variables need a value for CAT_MULT_INDICATOR_FIELDS\", call.=FALSE)\n }\n\n # Remove people with pheno < 0 if they aren't a positive example for this \n # variable indicator because we can't know if they are a negative example or not.\n if (is.numeric(as.matrix(pheno))) {\n\n idxForVar <- which(pheno == variableVal, arr.ind = TRUE)[,1]\n idxMissing <- which(pheno < 0, arr.ind = TRUE)[,1]\n\n # All people with < 0 value and not variableVal\n naMissing <- setdiff(idxMissing, idxForVar)\n # Add these people with unknowns to set to remove from sample\n naIdxs <- union(naIdxs, naMissing)\n\n cat(\"Removed\", length(naMissing) , \"examples !=\",\n variableVal, \"but with missing value (<0) || \",\n file=varlogfile, append=TRUE)\n } else {\n cat(\"Not numeric || \", file=varlogfile, append=TRUE)\n }\n return(naIdxs)\n}\n", "meta": {"hexsha": "de22954e0f4c3bd7667acae151aa3413c2bea71f", "size": 7515, "ext": "r", "lang": "R", "max_stars_repo_path": "WAS/testCatMultiple.r", "max_stars_repo_name": "EvaAusChina/PHESANT", "max_stars_repo_head_hexsha": "5e114342f22c447b663ac496b79dc7633fc6cd51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-12-21T13:46:04.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-21T13:46:04.000Z", "max_issues_repo_path": "WAS/testCatMultiple.r", "max_issues_repo_name": "EvaAusChina/PHESANT", "max_issues_repo_head_hexsha": "5e114342f22c447b663ac496b79dc7633fc6cd51", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "WAS/testCatMultiple.r", "max_forks_repo_name": "EvaAusChina/PHESANT", "max_forks_repo_head_hexsha": "5e114342f22c447b663ac496b79dc7633fc6cd51", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.2710843373, "max_line_length": 126, "alphanum_fraction": 0.629407851, "num_tokens": 1867, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.3308654054631334}} {"text": "library(shiny)\nlibrary(ggplot2)\nlibrary(ggvis)\n\n# Data processing libraries\nlibrary(data.table)\nlibrary(reshape2)\nlibrary(dplyr)\n\nlibrary(mapproj)\nlibrary(maps)\n\n# Load data\ndt <- fread('data/gdp.csv')\nworld_map <- map_data(\"world\")\n\n\n\n\nshinyServer(\n function(input,output){\n \n output$gdpMap <- renderPlot({\n year = input$year\n world_map$gdp_percapita <- nchar(dt[ , year]) + sample(nrow(world_map))\n \n # Prepare map based on GDP data\n gg <- ggplot(world_map,aes(map_id=region)) \n gg <- gg + geom_map(data=world_map, map=world_map, aes(map_id=region, x=long, y=lat, fill=gdp_percapita))\n gg <- gg + scale_fill_gradient(low = \"red\", high = \"green\", guide = \"colourbar\")\n gg <- gg + coord_equal()\n \n print(\n gg\n \n )\n }) \n }\n)", "meta": {"hexsha": "d12fa834426ba2c60cf66406b8e36ffe1c32ba31", "size": 804, "ext": "r", "lang": "R", "max_stars_repo_path": "server.r", "max_stars_repo_name": "tvalentius/DevelopingDataProducts-Shiny", "max_stars_repo_head_hexsha": "eda433dd977dba2b546f45c6d130eb61b152a64e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-05-29T06:07:39.000Z", "max_stars_repo_stars_event_max_datetime": "2016-05-29T06:07:39.000Z", "max_issues_repo_path": "server.r", "max_issues_repo_name": "tvalentius/DevelopingDataProducts-Shiny", "max_issues_repo_head_hexsha": "eda433dd977dba2b546f45c6d130eb61b152a64e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "server.r", "max_forks_repo_name": "tvalentius/DevelopingDataProducts-Shiny", "max_forks_repo_head_hexsha": "eda433dd977dba2b546f45c6d130eb61b152a64e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6153846154, "max_line_length": 111, "alphanum_fraction": 0.6231343284, "num_tokens": 226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370112, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.33086540546313337}} {"text": "library(pacman)\np_load(tidyverse, leaflet, dplyr, sf)\n\n# Reading file with municipal border (1:2 mil)\nmunicipalities_mil <- st_read(\"KOMMUNE.shp\") %>% dplyr::select(geometry, KOMNAVN) %>% st_transform(crs = st_crs(4326)) %>% st_zm()\n\n# Using st_union to create one polygon of each municipality\nunited_municipalities_mil <- municipalities_mil %>% \n group_by(KOMNAVN) %>%\n summarise(geometry = sf::st_union(geometry)) %>%\n ungroup()\n\n# Writing shapefile\nst_write(united_municipalities_mil, \"municipal_mil_united.shp\")\nwrite.csv(united_municipalities_mil$KOMNAVN, \"kommun.csv\")", "meta": {"hexsha": "84d9ef0bb2996d1efef54fa6229987fc1800c822", "size": 584, "ext": "r", "lang": "R", "max_stars_repo_path": "preprocessing/municipal_borders.r", "max_stars_repo_name": "frillecode/DigForWhat", "max_stars_repo_head_hexsha": "48dc411392c26de87daca5c3c83c49363078a0f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "preprocessing/municipal_borders.r", "max_issues_repo_name": "frillecode/DigForWhat", "max_issues_repo_head_hexsha": "48dc411392c26de87daca5c3c83c49363078a0f7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "preprocessing/municipal_borders.r", "max_forks_repo_name": "frillecode/DigForWhat", "max_forks_repo_head_hexsha": "48dc411392c26de87daca5c3c83c49363078a0f7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-06-09T14:21:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-09T14:21:46.000Z", "avg_line_length": 38.9333333333, "max_line_length": 131, "alphanum_fraction": 0.75, "num_tokens": 186, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704796847395, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.3307045727092253}} {"text": "library(minfi)\n# specify directory\nminfi_baseDir = paste0(\"/Users/nrigby/Desktop/idats_standard/batch_1052641/\")\n# read samplesheet\nminfi_targets = read.metharray.sheet(minfi_baseDir)\n# read IDAT's into RGChannelSet\nrgSet <- read.metharray.exp(targets = minfi_targets1,verbose = TRUE)\n# preprocessRaw\nmSet.raw = preprocessRaw(rgSet)\n# make files\nmeth.raw = getMeth(mSet.raw)\nwrite.csv(file='~/Desktop/idats_standard/minfi_raw_meth.csv',x=meth.raw1,row.names=TRUE,col.names=TRUE)\nunmeth.raw = getUnmeth(mSet.raw)\nwrite.csv(file='~/Desktop/idats_standard/minfi_raw_unmeth.csv',x=unmeth.raw1,row.names=TRUE,col.names=TRUE)\nbetas.raw = getBeta(mSet.raw)\nwrite.csv(file='~/Desktop/idats_standard/minfi_raw_betas.csv',x=betas.raw1,row.names=TRUE,col.names=TRUE)\n# preprocessNoob\nmSet.noob = preprocessNoob(rgSet)\n# make files\nmeth.noob = getMeth(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_meth.csv',x=meth.noob,row.names=TRUE,col.names=TRUE)\nunmeth.noob = getUnmeth(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_unmeth.csv',x=unmeth.noob,row.names=TRUE,col.names=TRUE)\nbetas.noob = getBeta(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_betas.csv',x=betas.noob,row.names=TRUE,col.names=TRUE)\n", "meta": {"hexsha": "d3f2bf68b986f65d4b4a4411b1baeee83f47cda7", "size": 1239, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/processing/test_compare_to_minfi.r", "max_stars_repo_name": "holonomicjl/methylprep", "max_stars_repo_head_hexsha": "6a503263ae4399a25e13238a6e4890d891f4dec9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-08T22:10:55.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-08T22:10:55.000Z", "max_issues_repo_path": "tests/processing/test_compare_to_minfi.r", "max_issues_repo_name": "holonomicjl/methylprep", "max_issues_repo_head_hexsha": "6a503263ae4399a25e13238a6e4890d891f4dec9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/processing/test_compare_to_minfi.r", "max_forks_repo_name": "holonomicjl/methylprep", "max_forks_repo_head_hexsha": "6a503263ae4399a25e13238a6e4890d891f4dec9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.6538461538, "max_line_length": 108, "alphanum_fraction": 0.7917675545, "num_tokens": 371, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3307045718820767}} {"text": "b: a -> b (5)\nc: b -> c (6)\ne: c -> e (3)\nd: d -> c (2)\n", "meta": {"hexsha": "c55ac7594d16f8adabfb58af8d32c018f206a23a", "size": 56, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/02.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/02.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/02.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.2, "max_line_length": 13, "alphanum_fraction": 0.2857142857, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.33070456392715736}} {"text": "library (dplyr)\nlibrary(tidyr)\nlibrary(ggplot2)\ntable <- read.csv(\"tabula-Expenditure Budget Volume 2 (Part I)_0.csv\", header = TRUE,na=\"\")\ntable_1 <- read.csv(\"tabula-Expenditure Budget Volume 2 (Part II)_0 (1).csv\", header = FALSE, na = \"\")\n\nnames(table)[1] <- \"Heads\"\nnames(table_1)[1] <- \"Heads\"\nnames(table)[5] <- \"Budget2020\"\nnames(table_1)[5] <- \"Budget2020\"\n#table <- na.omit(table)\n\n\ntable$Heads <- as.character(table$Heads)\ntable_1$Heads <- as.character(table_1$Heads)\n#table_office <- table %>% filter(Heads == \"Office Expenses\")\n#table[,grep(\"Office Expenses\", names(df), value=TRUE)]\ntableoffice <- table[grep(\"Office Expenses\", table$Heads),]\ntableoffice$Budget2020 <- as.numeric(as.character(tableoffice$Budget2020))\n#tableoffice$Heads <- sapply(strsplit(tableoffice$Heads,\" \"),1)\ntableoffice$Heads <- substr(tableoffice$Heads,1,4)\n#write.csv(tableoffice,\"C:/Users/Preethi's Laptop/Documents/R/datascience_coursera_main/HP expenditure/HP-expenditure/tableoffice.csv\", row.names = FALSE)\ntableoffice<- tableoffice %>% group_by(Heads) %>% summarise_at(\"Budget2020\",sum, na.rm = TRUE)\n\ntable_1_office <- table_1[grep(\"Office Expenses\", table_1$Heads),]\ntable_1_office$Budget2020 <- as.numeric(as.character(table_1_office$Budget2020))\ntable_1_office$Heads <- sapply(strsplit(table_1_office$Heads,\" \"), `[`, 1)\ntable_1_office <- table_1_office %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nOffice_table <- rbind(tableoffice, table_1_office)\nOffice_table <- Office_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nOffice_table_sum<- sum(Office_table$Budget2020)\n\n\noffice_plot <- Office_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\")+ \n ggtitle (\"Budget Estimates for Office Expenses for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(office_plot)\nggsave(\"office_plot.png\")\n\n\ntable_vehicle <- table[grep(\"Vehicle\", table$Heads),]\ntable_vehicle$Budget2020 <- as.numeric(as.character(table_vehicle$Budget2020))\ntable_vehicle$Heads <- sapply(strsplit(table_vehicle$Heads,\" \"), `[`, 1)\ntable_vehicle<- table_vehicle %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\ntable_1_vehicle <- table_1[grep(\"Vehicle\", table_1$Heads),]\ntable_1_vehicle$Budget2020 <- as.numeric(as.character(table_1_vehicle$Budget2020))\ntable_1_vehicle$Heads <- sapply(strsplit(table_1_vehicle$Heads,\" \"), `[`, 1)\ntable_1_vehicle<- table_1_vehicle %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nvehicle_table <- rbind(table_vehicle, table_1_vehicle)\nvehicle_table <- vehicle_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nvehicle_table_sum<- sum(vehicle_table$Budget2020)\n\n\nvehicle_plot <- vehicle_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n theme(axis.text.x = element_text(angle = 90))+ \n ggtitle (\"Budget Estimates for Vehicles for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(vehicle_plot)\nggsave(\"vehicle_plot.png\")\n\ntable_electricity <- table[grep(\"Electricity\", table$Heads),]\ntable_electricity$Budget2020 <- as.numeric(as.character(table_electricity$Budget2020))\ntable_electricity <- table_electricity[- grep(\"Electricity Charges Total\", table_electricity$Heads),]\ntable_electricity <- table_electricity[!is.na(table_electricity$Budget2020), ]\ntable_electricity$Heads <- sapply(strsplit(table_electricity$Heads,\" \"), `[`, 1)\ntable_electricity<- table_electricity %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\n\ntable_1_electricity <- table_1[grep(\"Electricity\", table_1$Heads),]\ntable_1_electricity$Budget2020 <- as.numeric(as.character(table_1_electricity$Budget2020))\ntable_1_electricity$Heads <- sapply(strsplit(table_1_electricity$Heads,\" \"), `[`, 1)\ntable_1_electricity<- table_1_electricity %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nelectricity_table <- rbind(table_electricity, table_1_electricity)\nelectricity_table <- electricity_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nelectricity_table_sum<- sum(electricity_table$Budget2020)\n\n\nelec_plot <- electricity_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n theme(axis.text.x = element_text(angle = 90))+\n ggtitle (\"Budget Estimates for Electricity for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(elec_plot)\nggsave(\"elec_plot.png\")\n\n#table_enter <- table[grep(\"Entertainment\", table$Heads),]\n#table_enter$Budget2020 <- as.numeric(as.character(table_enter$Budget2020))\n#table_enter<- table_enter[- grep(\"Total\", table_enter$Heads),]\n#table_enter <- table_enter[!is.na(table_enter$Budget2020), ]\n#table_enter$Heads <- sapply(strsplit(table_enter$Heads,\" \"), `[`, 1)\n#table_enter<- table_enter %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n#table_1_enter <- table_1[grep(\"Entertainment\", table_1$Heads),]\n#table_1_enter$Budget2020 <- as.numeric(table_1_enter$Budget2020)\n#table_1_enter$Heads <- sapply(strsplit(table_1_enter$Heads,\" \"), `[`, 1)\n#table_1_enter <- table_1_enter %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n#entertainment_table <- rbind(table_enter, table_1_enter)\n#entertainment_table <- entertainment_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n#entertainment_table_sum<- sum(entertainment_table$Budget2020)\n#ent_plot <- entertainment_table %>% arrange(desc(Budget2020)) %>%\n # slice(1:5) %>%\n# ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n # theme(axis.text.x = element_text(angle = 90))\n#print(ent_plot)\n#ggsave(\"ent_plot.png\")\n\n\n#tablesuppl <- table[grep(\"Supplies and Materials\", table$Heads),]\n#tablesuppl$Budget2020 <- as.numeric(tablesuppl$Budget2020)\n#tablesuppl$Heads <- sapply(strsplit(tablesuppl$Heads,\" \"), `[`, 1)\n#tablesuppl <- tablesuppl %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\n#table_1_suppl <- table_1[grep(\"Supplies and Materials\", table_1$Heads),]\n#table_1_suppl$Budget2020 <- as.numeric(table_1_suppl$Budget2020)\n#table_1_suppl$Heads <- sapply(strsplit(table_1_suppl$Heads,\" \"), `[`, 1)\n#table_1_suppl <- table_1_suppl %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\n#supply_table <- rbind(tablesuppl, table_1_suppl)\n#supply_table <- supply_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n#supply_table_sum<- sum(supply_table$Budget2020)\n\n#keywords <- c(\"Advertisement\", \"Advertising\")\n#table_ad <- filter(table, grepl(paste(keywords, collapse=\"|\"), Heads))\ntable_ad <- table[grep(\"Advertis.*\", table$Heads),]\ntable_ad$Budget2020 <- as.numeric(as.character(table_ad$Budget2020))\n#table_ad$Heads <- substr(table_ad$Heads,1,4)\n#table_ad$Heads <- sapply(strsplit(table_ad$Heads,\" \"), `[`, 1)\n\ntable_ad <- table_ad[- grep(\"Total\", table_ad$Heads),]\ntable_ad <- table_ad %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\ntable_1_ad <- table_1[grep(\"Advertis.*\", table_1$Heads),]\ntable_1_ad$Budget2020 <- as.numeric(as.character(table_1_ad$Budget2020))\n#table_1_ad$Heads <- sapply(strsplit(table_1_ad$Heads,\" \"), `[`, 1)\n#table_1_ad <- table_1_ad[- grep(\"Total\", table_ad$Heads),]\ntable_1_ad <- table_1_ad %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nad_table <- rbind(table_ad, table_1_ad)\nad_table <- ad_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nad_table_sum<- sum(ad_table$Budget2020)\n\nad_plot <- ad_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n theme(axis.text.x = element_text(angle = 90))+\n ggtitle (\"Budget Estimates for advertising for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(ad_plot)\nggsave(\"ad_plot.png\")\n\n\ntable_wage <- table[grep(\"Wages\", table$Heads),]\ntable_wage$Budget2020 <- as.numeric(as.character(table_wage$Budget2020))\ntable_wage <- table_wage[- grep(\"Wages Total\", table_wage$Heads),]\ntable_wage <- table_wage[!is.na(table_wage$Budget2020), ]\ntable_wage$Heads <- sapply(strsplit(table_wage$Heads,\" \"), `[`, 1)\ntable_wage<- table_wage %>% group_by(Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\ntable_1_wage <- table_1[grep(\"Wages\", table_1$Heads),]\ntable_1_wage$Budget2020 <- as.numeric(as.character(table_1_wage$Budget2020))\n#table_1_wage <- table_1_wage[- grep(\"Wages Total\", table_1_wage$Heads),]\ntable_1_wage <- table_1_wage[!is.na(table_1_wage$Budget2020), ]\ntable_1_wage$Heads <- sapply(strsplit(table_1_wage$Heads,\" \"), `[`, 1)\ntable_1_wage <- table_1_wage %>% group_by(Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nwage_table <- rbind(table_wage,table_1_wage)\nwage_table <- wage_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nwage_table_sum<- sum(wage_table$Budget2020)\n\nwage_plot <- wage_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n theme(axis.text.x = element_text(angle = 90))+\n ggtitle (\"Budget Estimates for wages for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(wage_plot)\nggsave(\"wage_plot.png\")\n\n\ntable_salaries <- table[grep(\"Salaries\", table$Heads),]\ntable_salaries$Budget2020 <- as.numeric(as.character(table_salaries$Budget2020))\ntable_salaries <- table_salaries[- grep(\"Salaries Total\", table_salaries$Heads),]\ntable_salaries <- table_salaries[!is.na(table_salaries$Budget2020), ]\ntable_salaries$Heads <- sapply(strsplit(table_salaries$Heads,\" \"), `[`, 1)\ntable_salaries<- table_salaries %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\ntable_1_salaries <- table_1[grep(\"Salaries\", table_1$Heads),]\ntable_1_salaries$Budget2020 <- as.numeric(as.character(table_1_salaries$Budget2020))\ntable_1_salaries<- table_1_salaries[- grep(\"Salaries Total\", table_1_salaries$Heads),]\ntable_1_salaries<- table_1_salaries[!is.na(table_1_salaries$Budget2020), ]\ntable_1_salaries$Heads <- sapply(strsplit(table_1_salaries$Heads,\" \"), `[`, 1)\ntable_1_salaries <- table_1_salaries %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\n\nsalary_table <- rbind(table_salaries, table_1_salaries)\nsalary_table <- salary_table %>% group_by (Heads) %>% summarise_at(\"Budget2020\", sum, na.rm = T)\nsalary_table_sum<- sum(salary_table$Budget2020)\n\nsalary_plot <- salary_table %>% arrange(desc(Budget2020)) %>%\n slice(1:5) %>%\n ggplot(., aes(x=Heads,y=Budget2020)) + geom_bar(fill = \"#21405d\", stat = \"identity\") + \n theme(axis.text.x = element_text(angle = 90))+\n ggtitle (\"Budget Estimates for salaries for the year 2020-21\")+\n ylab (\"Budget Estimates in rs lakhs\")\n\nprint(salary_plot)\nggsave(\"salary_plot.png\")\n\n", "meta": {"hexsha": "197bfe923596713635e06757d2259ab7a7e3d5a9", "size": 10974, "ext": "r", "lang": "R", "max_stars_repo_path": "Expenditure_tripura.r", "max_stars_repo_name": "preethical/HP-expenditure", "max_stars_repo_head_hexsha": "1e8b3f14ae8a5ec9934a2e3e99d43b202afac992", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, 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YES\n2. YES", "lm_q1_score": 0.5544704649604272, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3307045639271573}} {"text": "pdf_file<-\"pdf/scatterplots_symbols.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=7.5,height=12)\n\npar(omi=c(0.5,0.5,0,0),mai=c(0.5,1.25,0,0.25),family=\"Lato Light\",las=1)\nlibrary(maptools)\n\n# Import data and prepare chart\n\nlibrary(gdata)\nmyData<-read.xls(\"myData/Intra-StateWarData_v4.1.xlsx\", encoding=\"latin1\")\nmySelection<-subset(myData, myData$StartYear1>=1995 & myData$SideADeaths > 0 & myData$SideADeaths < 2000 & myData$SideBDeaths > 0 & myData$SideBDeaths < 4000)\nattach(mySelection)\n\nmyColour<-\"darkred\"\nmyN<-nrow(mySelection)\nh<-rep(0, myN)\nv<-rep(0, myN)\nmyOffset<-cbind(h, v)\n\n# mySelection[, c(\"WarName\", \"StartYear1\", \"SideADeaths\", \"SideBDeaths\")]\nmyOffset[1, \"h\"]<--400\nmyOffset[5, \"h\"]<-232\nmyOffset[4, \"h\"]<--275\nmyOffset[2, \"h\"]<-270; myOffset[2, \"v\"]<-100\nmyOffset[13, \"h\"]<--275\nmyOffset[12, \"h\"]<--300\n\nmyX<-as.numeric(SideADeaths)\nmyY<-as.numeric(SideBDeaths)\n\n# Define chart and other elements\n\nplot(myX, myY, typ=\"n\", xlab=\"\", ylab=\"\", axes=F, xlim=c(0, 2000), ylim=c(0, 4000))\naxis(1,col=par(\"bg\"),col.ticks=\"grey81\",lwd.ticks=0.5,tck=-0.025)\naxis(2,col=par(\"bg\"),col.ticks=\"grey81\",lwd.ticks=0.5,tck=-0.025)\ntext(myX+130+myOffset[, \"h\"], myY-180+myOffset[, \"v\"], paste(WarName, StartYear1, sep=\" \"), cex=0.8, xpd=T, col=\"grey\")\n\nmtext(side=1, \"Side A Deaths (Authorities)\", adj=0.5, line=3)\nmtext(side=2, \"Side B Deaths (Rebels)\", las=0, adj=0.5, line=4)\n\n# Titling\n\nmtext(\"Deaths by Intra-state Wars\",3,adj=1,line=-3,cex=2.1,family=\"Lato Black\")\nmtext(\"1997-2007\",3,adj=1,line=-5,cex=1.4,font=3)\nmtext(\"Source: correlatesofwar.org\",1,line=1,adj=0,cex=0.95,outer=T,font=3)\n\n# Other elements of chart\n\npar(family=\"Datendesign\")\ntext(myX, myY, \"b\", col=myColour, cex=5, xpd=T)\ndev.off()\n", "meta": {"hexsha": "109eba52e188c1f83a152fec0560b4e212594643", "size": 1709, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/scatterplots_symbols.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/scatterplots_symbols.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/scatterplots_symbols.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.8653846154, "max_line_length": 158, "alphanum_fraction": 0.6846108836, "num_tokens": 693, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3306562312783846}} {"text": "## Setup R error handling to go to stderr\noptions( show.error.messages=F,\n error = function () { cat( geterrmessage(), file=stderr() ); q( \"no\", 1, F ) } )\nwarnings()\nlibrary(optparse)\nlibrary(ggplot2)\nlibrary(reshape2)\n\noption_list <- list(\n make_option(c(\"-i\", \"--input\"), type=\"character\", help=\"Path to dataframe\"),\n make_option(c(\"-t\", \"--title\"), type=\"character\", help=\"Main Title\"),\n make_option(\"--xlab\", type = \"character\", help=\"X-axis legend\"),\n make_option(\"--ylab\", type = \"character\", help=\"Y-axis legend\"),\n make_option(\"--sample\", type = \"character\", help=\"a space separated of sample labels\"),\n make_option(\"--method\", type = \"character\", help=\"bedtools or pysam\"),\n make_option(c(\"-o\", \"--output\"), type = \"character\", help=\"path to the pdf plot\")\n )\n \nparser <- OptionParser(usage = \"%prog [options] file\", option_list = option_list)\nargs = parse_args(parser)\nsamples = substr(args$sample, 2, nchar(args$sample)-2)\nsamples = strsplit(samples, \", \")\n \n# data frames implementation\n\nTable <- read.delim(args$input, header=F)\nheaders = c(\"chromosome\", \"start\", \"end\", \"id\")\nfor (i in seq(1, length(Table)-4)) {\n headers <- c(headers, samples[[1]][i])\ncolnames(Table) <- headers\n}\n\n## function\nif (args$method == 'bedtools') {\n cumul <- function(x,y) sum(Table[,y]/(Table$end-Table$start) > x)/length(Table$chromosome)\n } else {\n cumul <- function(x,y) sum(Table[,y] > x)/length(Table$chromosome)\n }\nscaleFUN <- function(x) sprintf(\"%.3f\", x)\n\n## end of function\n## let's do a dataframe before plotting\nif (args$method == 'bedtools') {\n maxdepth <- trunc(max(Table[,5:length(Table)]/(Table$end-Table$start))) + 20\n } else {\n maxdepth <- trunc(max(Table[,5:length(Table)])) + 20\n }\n\ngraphpoints <- data.frame(1:maxdepth)\ni <- 5\nfor (colonne in colnames(Table)[5:length(colnames(Table))]) {\n graphpoints <- cbind(graphpoints, mapply(cumul, 1:maxdepth, rep(i, maxdepth)))\n i <- i + 1\n }\ncolnames(graphpoints) <- c(\"Depth\", colnames(Table)[5:length(Table)])\nmaxfrac = max(graphpoints[,2:length(graphpoints)])\n\ngraphpoints <- melt(graphpoints, id.vars=\"Depth\", variable.name=\"Samples\", value.name=\"sample_value\")\n\n## GRAPHS\n\npdf(file=args$output)\nggplot(data=graphpoints, aes(x=Depth, y=sample_value, colour=Samples)) +\n geom_line(size=1) +\n scale_x_continuous(trans='log2', breaks = 2^(seq(0,log(maxdepth, 2)))) +\n scale_y_continuous(breaks = seq(0, maxfrac, by=maxfrac/10), labels=scaleFUN) +\n labs(x=args$xlab, y=args$ylab, title=args$title) +\n theme(legend.position=\"top\", legend.title=element_blank(), legend.text=element_text(colour=\"blue\", size=7))\n \n \n## facet_wrap(~Samples, ncol=2)\n\ndevname=dev.off()\n\n", "meta": {"hexsha": "0aa0724c825f1784b17a07a8b6e4f34da1b16e73", "size": 2730, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/probecoverage/probecoverage.r", "max_stars_repo_name": "lgpdv/tools-artbio", "max_stars_repo_head_hexsha": "af48e9f6df2717ffd3731a974be1ec36e4eff779", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tools/probecoverage/probecoverage.r", "max_issues_repo_name": "lgpdv/tools-artbio", "max_issues_repo_head_hexsha": "af48e9f6df2717ffd3731a974be1ec36e4eff779", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tools/probecoverage/probecoverage.r", "max_forks_repo_name": "lgpdv/tools-artbio", "max_forks_repo_head_hexsha": "af48e9f6df2717ffd3731a974be1ec36e4eff779", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4, "max_line_length": 113, "alphanum_fraction": 0.6542124542, "num_tokens": 764, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3306562312783846}} {"text": "x <- 1+1", "meta": {"hexsha": "fafbc5e8c0fb2fe29595556fe3a220b1b4681ba0", "size": 8, "ext": "r", "lang": "R", "max_stars_repo_path": "somefunction.r", "max_stars_repo_name": "J-R-Johnson/training_test", "max_stars_repo_head_hexsha": "260e09b71b10cad059f21de3cb4ce4157b6fbe07", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "somefunction.r", "max_issues_repo_name": "J-R-Johnson/training_test", "max_issues_repo_head_hexsha": "260e09b71b10cad059f21de3cb4ce4157b6fbe07", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "somefunction.r", "max_forks_repo_name": "J-R-Johnson/training_test", "max_forks_repo_head_hexsha": "260e09b71b10cad059f21de3cb4ce4157b6fbe07", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 8.0, "max_line_length": 8, "alphanum_fraction": 0.375, "num_tokens": 6, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.33065623127838456}} {"text": "pdf_file<-\"pdf/timeseries_quarterly_lines.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=14,height=7)\n\npar(omi=c(0.65,0.75,0.95,0.75),mai=c(0.9,0,0.25,0.02),fg=rgb(64,64,64,maxColorValue=255),bg=\"azure2\",family=\"Lato Light\",las=1) \n\t\n# Read data and prepare chart\n\ngdp<-read.xls(\"myData/GDP_germany_quarter.xlsx\",sheet=1)\ngdp<-subset(gdp,gdp$year > 2007)\nx<-ts(rev(gdp$jeworiginal),start=2008,frequency=4)\n\n# Create chart and other elements\n\nplot(x,type=\"n\",axes=F,xlim=c(2008,2012),ylim=c(560,670),xlab=\"\",ylab=\"\")\nabline(v=c(2008:2012),col=\"white\",lty=1,lwd=1)\nlines(x,lwd=8,type=\"b\",col=rgb(0,0,139,80,maxColorValue=255))\npoints(x,pch=19,cex=3,col=rgb(139,0,0,maxColorValue=255))\nfaktor<-rep(0.985,length(x))\nfor (i in 1:length(x)) \n{\nif (i>1 & ix[i-1] & x[i]>x[i+1]) { faktor[i]<-1.015 } }\ntext((2008+i*0.25)-0.25,faktor[i]*x[i],x[i],col=rgb(64,64,64,maxColorValue=255),cex=1.1)\n}\naxis(1,at=c(2008:2012),tck=0)\naxis(2,col=NA,col.ticks=rgb(24,24,24,maxColorValue=255),lwd.ticks=0.5,cex.axis=1.0,tck=-0.025)\n\n# Titling\n\nmtext(\"Gross Domestic Product in Germany 2000 - 2011\",3,line=2.3,adj=0,cex=2,family=\"Lato Black\",outer=T)\nmtext(\"Original values in current prices, Bill. EUR, quarterly values\",3,line=0,adj=0,cex=1.75,font=3,outer=T)\nmtext(\"Source: destatis.de\",1,line=1,adj=1,cex=1.25,font=3,outer=T)\ndev.off()", "meta": {"hexsha": "54f62da80cebc6fe24b024774d2758b0dd006aa3", "size": 1335, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/timeseries_quarterly_lines.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/timeseries_quarterly_lines.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/timeseries_quarterly_lines.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.71875, "max_line_length": 128, "alphanum_fraction": 0.6943820225, "num_tokens": 568, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.33065623127838456}} {"text": "library(dplyr)\nlibrary(tibble)\nlibrary(tidyr)\nlibrary(caret)\nlibrary(gtools)\nlibrary(FinCal)\nlibrary(doParallel)\n\n\n\n\n#setwd(\"~/Documents/nstringjul18\")\nload(file=\"allns_data.rdata\")\nload(\"modelgbm.rdata\")\nload(\"models.rdata\")\nload(\"vardat.rdata\")\nload(file=\"markerchoiceinfo.rdata\")\n\ncl <- makePSOCKcluster(7)\nregisterDoParallel(cl)\n\n\nmd <- as.data.frame(subset(t(ctall.norm), sampleinfo$sex == \"F\" &\n sampleinfo$prep== \"Luke\"&\n sampleinfo$exp %in% c(\"Size-age\", \"cohorts\", \"OvY\") &\n sampleinfo$codeset == \"phaw_1\" &\n sampleinfo$type %in% c(\"O\", \"Y\", \"M\")\n))\n\nmd <- subset(md, rownames(md)%in% qualsum$sample[qualsum$good])\n\nmd <- md[, lmdf2$in61 ==T & lmdf2$pvalovy < 0.05]\n\nmd <- md[, colnames(md) %in% c(minf$name[minf$in61==T], \"predage\")]\n\nmd$sample <- rownames(md)\n\nmd <- left_join(md, select(sampleinfo, sample, predage), by=\"sample\") %>%\n filter(!(is.na(predage))) %>%\n column_to_rownames(var=\"sample\")\n\n\ntrainchoice <- sample(1:nrow(md))[1:floor(4*(nrow(md)/5))]\n\n\ntrainchoice <- rownames(md) %in% rownames(currbest$trainingData)\ntrdat <- md[trainchoice,]\ntedat <- md[-trainchoice,]\n\n\ngbmagemodelp5 <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*200, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\nmd <- as.data.frame(subset(t(ctall.norm), sampleinfo$sex == \"F\" &\n sampleinfo$prep== \"Luke\"&\n sampleinfo$exp %in% c(\"Size-age\", \"cohorts\", \"OvY\") &\n sampleinfo$codeset == \"phaw_1\" &\n sampleinfo$type %in% c(\"O\", \"Y\", \"M\")\n))\n\nmd <- subset(md, rownames(md)%in% qualsum$sample[qualsum$good])\n\nmd <- md[, lmdf2$in61 ==T & lmdf2$pvalovy < 0.04]\n\nmd <- md[, colnames(md) %in% c(minf$name[minf$in61==T], \"predage\")]\n\nmd$sample <- rownames(md)\n\nmd <- left_join(md, select(sampleinfo, sample, predage), by=\"sample\") %>%\n filter(!(is.na(predage))) %>%\n column_to_rownames(var=\"sample\")\n\n\ntrainchoice <- sample(1:nrow(md))[1:floor(4*(nrow(md)/5))]\n\n\ntrainchoice <- rownames(md) %in% rownames(currbest$trainingData)\ntrdat <- md[trainchoice,]\ntedat <- md[-trainchoice,]\n\n\ngbmagemodelp4 <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*200, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\n\nmd <- as.data.frame(subset(t(ctall.norm), sampleinfo$sex == \"F\" &\n sampleinfo$prep== \"Luke\"&\n sampleinfo$exp %in% c(\"Size-age\", \"cohorts\", \"OvY\") &\n sampleinfo$codeset == \"phaw_1\" &\n sampleinfo$type %in% c(\"O\", \"Y\", \"M\")\n))\n\nmd <- subset(md, rownames(md)%in% qualsum$sample[qualsum$good])\n\nmd <- md[, lmdf2$in61 ==T & lmdf2$pvalovy < 0.03]\n\nmd <- md[, colnames(md) %in% c(minf$name[minf$in61==T], \"predage\")]\n\nmd$sample <- rownames(md)\n\nmd <- left_join(md, select(sampleinfo, sample, predage), by=\"sample\") %>%\n filter(!(is.na(predage))) %>%\n column_to_rownames(var=\"sample\")\n\n\ntrainchoice <- sample(1:nrow(md))[1:floor(4*(nrow(md)/5))]\n\n\ntrainchoice <- rownames(md) %in% rownames(currbest$trainingData)\ntrdat <- md[trainchoice,]\ntedat <- md[-trainchoice,]\n\n\ngbmagemodelp3 <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*200, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\nmd <- as.data.frame(subset(t(ctall.norm), sampleinfo$sex == \"F\" &\n sampleinfo$prep== \"Luke\"&\n sampleinfo$exp %in% c(\"Size-age\", \"cohorts\", \"OvY\") &\n sampleinfo$codeset == \"phaw_1\" &\n sampleinfo$type %in% c(\"O\", \"Y\", \"M\")\n))\n\nmd <- subset(md, rownames(md)%in% qualsum$sample[qualsum$good])\n\nmd <- md[, lmdf2$in61 ==T & lmdf2$pvalovy < 0.02]\n\nmd <- md[, colnames(md) %in% c(minf$name[minf$in61==T], \"predage\")]\n\nmd$sample <- rownames(md)\n\nmd <- left_join(md, select(sampleinfo, sample, predage), by=\"sample\") %>%\n filter(!(is.na(predage))) %>%\n column_to_rownames(var=\"sample\")\n\n\ntrainchoice <- sample(1:nrow(md))[1:floor(4*(nrow(md)/5))]\n\n\ntrainchoice <- rownames(md) %in% rownames(currbest$trainingData)\ntrdat <- md[trainchoice,]\ntedat <- md[-trainchoice,]\n\n\ngbmagemodelp2 <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*200, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\nmd <- as.data.frame(subset(t(ctall.norm), sampleinfo$sex == \"F\" &\n sampleinfo$prep== \"Luke\"&\n sampleinfo$exp %in% c(\"Size-age\", \"cohorts\", \"OvY\") &\n sampleinfo$codeset == \"phaw_1\" &\n sampleinfo$type %in% c(\"O\", \"Y\", \"M\")\n))\n\nmd <- subset(md, rownames(md)%in% qualsum$sample[qualsum$good])\n\nmd <- md[, lmdf2$in61 ==T & lmdf2$pvalovy < 0.01]\n\nmd <- md[, colnames(md) %in% c(minf$name[minf$in61==T], \"predage\")]\n\nmd$sample <- rownames(md)\n\nmd <- left_join(md, select(sampleinfo, sample, predage), by=\"sample\") %>%\n filter(!(is.na(predage))) %>%\n column_to_rownames(var=\"sample\")\n\n\ntrainchoice <- sample(1:nrow(md))[1:floor(4*(nrow(md)/5))]\n\n\ntrainchoice <- rownames(md) %in% rownames(currbest$trainingData)\ntrdat <- md[trainchoice,]\ntedat <- md[-trainchoice,]\n\n\ngbmagemodelp1 <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*200, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\ngbmagemodelp1b <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*500, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 10, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\ngbmagemodelp1c <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*500, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 20, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\ngbmagemodelp1d <- train(\n predage~.,\n tuneLength = 5,\n data = trdat, \n method = \"gbm\",\n tuneGrid=expand.grid(n.trees = (0:50)*500, \n interaction.depth = c(1,2,3,4,5,6,7,8,9,10), \n shrinkage = c(0.0001,.001, 0.01, 0.1, 0.2, 0.3) ,\n n.minobsinnode = c(2,3,4,5,6,7,8,9,10)),\n trControl = trainControl(method = 'cv', \n number = 40, \n summaryFunction=defaultSummary, \n verboseIter = T)\n)\n\nstopCluster(cl)\n\nsave(gbmagemodelp5, gbmagemodelp4, gbmagemodelp3, gbmagemodelp2, gbmagemodelp1, gbmagemodelp1b, gbmagemodelp1c, gbmagemodelp1d, file=\"gbmbig2.rdata\")", "meta": {"hexsha": "e5b1e16541ceab459fd4d44c56db20f40c63bd56", "size": 9166, "ext": "r", "lang": "R", "max_stars_repo_path": "junk/gbmclust.r", "max_stars_repo_name": "luke-hayden/nstringfull", "max_stars_repo_head_hexsha": "bb0d260939697a715a964fdf1ab94c8c9d5c5c32", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-09-28T15:31:31.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T22:45:12.000Z", "max_issues_repo_path": "junk/gbmclust.r", "max_issues_repo_name": "luke-hayden/nstringfull", "max_issues_repo_head_hexsha": "bb0d260939697a715a964fdf1ab94c8c9d5c5c32", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "junk/gbmclust.r", "max_forks_repo_name": "luke-hayden/nstringfull", "max_forks_repo_head_hexsha": "bb0d260939697a715a964fdf1ab94c8c9d5c5c32", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.048951049, "max_line_length": 149, "alphanum_fraction": 0.5211651756, "num_tokens": 2920, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.33062512299989755}} {"text": "\nsuppressMessages(library(edgeR))\noptions(scipen=999)\n\nx <- read.delim('/btl/foundry/users/alex/20190228_novel_chassis/run_DGE/edgeR_matfile.csv', sep=',', stringsAsFactors=TRUE)\n\nx$strain <- factor(x$strain)\n\nx$Arabinose <- factor(x$Arabinose)\n\nx$IPTG <- factor(x$IPTG)\n\nx$Temp <- factor(x$Temp)\n\nx$Timepoint <- factor(x$Timepoint)\n\ndrops <- c('strain', 'Arabinose', 'IPTG', 'Temp', 'Timepoint')\ncounts <- x[, !(names(x) %in% drops)]\nt_cts <- t(counts)\nt_cts[is.na(t_cts)] <- 0\n\n#colnames(t_cts) <- x$filename\n\ngroup <- factor(c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, 31, 32, 32, 32, 32, 33, 33, 33, 33, 34, 34, 34, 34, 35, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 37, 38, 38, 38, 38, 39, 39, 39, 39, 40, 40, 40, 40, 41, 41, 41, 41, 42, 42, 42, 42, 43, 43, 43, 43, 44, 44, 44, 44, 45, 45, 45, 45, 46, 46, 46, 46, 47, 47, 47, 47, 48, 48, 48, 48, 49, 49, 49, 49, 50, 50, 50, 50, 51, 51, 51, 51, 52, 52, 52, 52, 53, 53, 53, 53, 54, 54, 54, 54, 55, 55, 55, 55, 56, 56, 56, 56, 57, 57, 57, 57, 58, 58, 58, 58, 59, 59, 59, 59, 60, 60, 60, 60, 61, 61, 61, 61, 62, 62, 62, 62, 63, 63, 63, 63, 64, 64, 64, 64, 65, 65, 65, 65, 66, 66, 66, 66, 67, 67, 67, 67, 68, 68, 68, 68, 69, 69, 69, 69, 70, 70, 70, 70, 71, 71, 71, 71, 72, 72, 72, 72, 73, 73, 73, 73, 74, 74, 74, 74, 75, 75, 75, 75, 76, 76, 76, 76, 77, 77, 77, 77, 78, 78, 78, 78, 79, 79, 79, 79, 80, 80, 80, 80, 81, 81, 81, 81, 82, 82, 82, 82, 83, 83, 83, 83, 84, 84, 84, 84, 85, 85, 85, 85, 86, 86, 86, 86, 87, 87, 87, 87, 88, 88, 88, 88))\n\ny <- DGEList(counts=t_cts, group=group)\ny <- calcNormFactors(y)\ndesign <- model.matrix(~0 + group)\nkeep <- rowSums(cpm(y[, c(225, 226, 227, 228, 245, 246, 247, 248)]) >1) >= 8\ny <- y[keep, ,]\ny <- estimateDisp(y, design)\nfit <- glmQLFit(y, design)\nqlf <- glmQLFTest(fit, contrast=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0))\ntab <- topTags(qlf, n=Inf)\nwrite.table(tab, file=\"Strain4MG1655GenomicNANDCircuit_False_True_30_18-vs-Strain3MG1655GenomicIcaRGate_False_True_30_18.txt\")", "meta": {"hexsha": "7916ea9a544ec9b8a36102c82f4e091df2a3174c", "size": 2518, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/notebook_example/scripts/dge_102.r", "max_stars_repo_name": "SD2E/omics_tools", "max_stars_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-17T17:39:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-28T07:56:48.000Z", "max_issues_repo_path": "examples/notebook_example/scripts/dge_102.r", "max_issues_repo_name": "SD2E/omics_tools", "max_issues_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/notebook_example/scripts/dge_102.r", "max_forks_repo_name": "SD2E/omics_tools", "max_forks_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 71.9428571429, "max_line_length": 1390, "alphanum_fraction": 0.5687053217, "num_tokens": 1585, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.754914997895581, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.3305195259124336}} {"text": "#' Produce a DEM with basins filled in\n#'\n#' @param dem A [raster::raster]; a digital elevation model.\n#' @param file The file name of the raster to be returned, see `details`.\n#' @details This is a wrapper for [r.fill.dir](https://grass.osgeo.org/grass76/manuals/r.fill.dir.html)\n#'\n#' It is recommended to specify the `file` parameter (including the extension to specify\n#' file format; e.g., .tif, .grd). If not specified, a temp file will be created and will be\n#' lost at the end of the R session.\n#' @return A filled DEM raster\n#' @examples\n#' \\donttest{\n#' data(kamp_dem)\n#' kamp_fill = fill_dem(kamp_dem)\n#' }\n#' @export\nfill_dem = function(dem, file = NA) {\n\tinp_name = \"dem\"\n\tout_name = \"dem_filled\"\n\tdir_name = \"flow_direction\"\n\tproblem_name= \"problems\"\n\n\t## launch grass and copy data\n\t.start_grass(dem, inp_name)\n\n\t## perform computation\n\trgrass7::execGRASS(\"r.fill.dir\", flags=c(\"overwrite\", \"quiet\"), input=inp_name,\n\t\t\t\toutput = out_name, direction = dir_name, areas = problem_name)\n\t# make sure to add the names of created rasters to the list of layers\n\tws_env$rasters = c(ws_env$rasters, out_name, dir_name, problem_name)\n\n\t## gather data\n\tres = .read_rasters(out_name, file)\n\n\t## clean up files\n\t.clean_grass()\n\n\tres\n}\n", "meta": {"hexsha": "02e8bc71f01b9d504a7fb4426c7d2a58d7190788", "size": 1246, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dem.r", "max_stars_repo_name": "frawalther/watershed", "max_stars_repo_head_hexsha": "2d47c4c59c0b159e26273fea9ae245ba5747e3d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-13T10:18:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-13T10:18:05.000Z", "max_issues_repo_path": "R/dem.r", "max_issues_repo_name": "frawalther/watershed", "max_issues_repo_head_hexsha": "2d47c4c59c0b159e26273fea9ae245ba5747e3d7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-04-14T11:09:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-14T11:09:18.000Z", "max_forks_repo_path": "R/dem.r", "max_forks_repo_name": "frawalther/watershed", "max_forks_repo_head_hexsha": "2d47c4c59c0b159e26273fea9ae245ba5747e3d7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-14T09:45:37.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T09:45:37.000Z", "avg_line_length": 31.15, "max_line_length": 103, "alphanum_fraction": 0.702247191, "num_tokens": 349, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33046813919244383}} {"text": "library(ggplot2)\nlibrary(cowplot)\nlibrary(randomForest)\n \n## NOTE: The data used in this demo comes from the UCI machine learning\n## repository.\n## http://archive.ics.uci.edu/ml/index.php\n## Specifically, this is the heart disease data set.\n## http://archive.ics.uci.edu/ml/datasets/Heart+Disease\n##\n## SOURCE: https://statquest.org/2018/02/26/statquest-random-forests-in-r/ \nurl <- \"http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"\n \ndata <- read.csv(url, header=FALSE)\n \n#####################################\n##\n## Reformat the data so that it is\n## 1) Easy to use (add nice column names)\n## 2) Interpreted correctly by randomForest..\n##\n#####################################\nhead(data) # you see data, but no column names\n \ncolnames(data) <- c(\n \"age\",\n \"sex\",# 0 = female, 1 = male\n \"cp\", # chest pain\n # 1 = typical angina,\n # 2 = atypical angina,\n # 3 = non-anginal pain,\n # 4 = asymptomatic\n \"trestbps\", # resting blood pressure (in mm Hg)\n \"chol\", # serum cholestoral in mg/dl\n \"fbs\", # fasting blood sugar greater than 120 mg/dl, 1 = TRUE, 0 = FALSE\n \"restecg\", # resting electrocardiographic results\n # 1 = normal\n # 2 = having ST-T wave abnormality\n # 3 = showing probable or definite left ventricular hypertrophy\n \"thalach\", # maximum heart rate achieved\n \"exang\", # exercise induced angina, 1 = yes, 0 = no\n \"oldpeak\", # ST depression induced by exercise relative to rest\n \"slope\", # the slope of the peak exercise ST segment\n # 1 = upsloping\n # 2 = flat\n # 3 = downsloping\n \"ca\", # number of major vessels (0-3) colored by fluoroscopy\n \"thal\", # this is short of thalium heart scan\n # 3 = normal (no cold spots)\n # 6 = fixed defect (cold spots during rest and exercise)\n # 7 = reversible defect (when cold spots only appear during exercise)\n \"hd\" # (the predicted attribute) - diagnosis of heart disease\n # 0 if less than or equal to 50% diameter narrowing\n # 1 if greater than 50% diameter narrowing\n )\n \nhead(data) # now we have data and column names\n \nstr(data) # this shows that we need to tell R which columns contain factors\n # it also shows us that there are some missing values. There are \"?\"s\n # in the dataset.\n \n## First, replace \"?\"s with NAs.\ndata[data == \"?\"] <- NA\n \n## Now add factors for variables that are factors and clean up the factors\n## that had missing data...\ndata[data$sex == 0,]$sex <- \"F\"\ndata[data$sex == 1,]$sex <- \"M\"\ndata$sex <- as.factor(data$sex)\n \ndata$cp <- as.factor(data$cp)\ndata$fbs <- as.factor(data$fbs)\ndata$restecg <- as.factor(data$restecg)\ndata$exang <- as.factor(data$exang)\ndata$slope <- as.factor(data$slope)\n \ndata$ca <- as.integer(data$ca) # since this column had \"?\"s in it (which\n # we have since converted to NAs) R thinks that\n # the levels for the factor are strings, but\n # we know they are integers, so we'll first\n # convert the strings to integiers...\ndata$ca <- as.factor(data$ca) # ...then convert the integers to factor levels\n \ndata$thal <- as.integer(data$thal) # \"thal\" also had \"?\"s in it.\ndata$thal <- as.factor(data$thal)\n \n## This next line replaces 0 and 1 with \"Healthy\" and \"Unhealthy\"\ndata$hd <- ifelse(test=data$hd == 0, yes=\"Healthy\", no=\"Unhealthy\")\ndata$hd <- as.factor(data$hd) # Now convert to a factor\n \nstr(data) ## this shows that the correct columns are factors and we've replaced\n ## \"?\"s with NAs because \"?\" no longer appears in the list of factors\n ## for \"ca\" and \"thal\"\n \n#####################################\n##\n## Now we are ready to build a random forest.\n##\n#####################################\nset.seed(42)\n \n## NOTE: For most machine learning methods, you need to divide the data\n## manually into a \"training\" set and a \"test\" set. This allows you to train\n## the method using the training data, and then test it on data it was not\n## originally trained on.\n##\n## In contrast, Random Forests split the data into \"training\" and \"test\" sets\n## for you. This is because Random Forests use bootstrapped\n## data, and thus, not every sample is used to build every tree. The\n## \"training\" dataset is the bootstrapped data and the \"test\" dataset is\n## the remaining samples. The remaining samples are called\n## the \"Out-Of-Bag\" (OOB) data.\n \n## impute any missing values in the training set using proximities\ndata.imputed <- rfImpute(hd ~ ., data = data, iter=6)\n## NOTE: iter = the number of iterations to run. Breiman says 4 to 6 iterations\n## is usually good enough. With this dataset, when we set iter=6, OOB-error\n## bounces around between 17% and 18%. When we set iter=20,\n# set.seed(42)\n# data.imputed <- rfImpute(hd ~ ., data = data, iter=20)\n## we get values a little better and a little worse, so doing more\n## iterations doesn't improve the situation.\n##\n## NOTE: If you really want to micromanage how rfImpute(),\n## you can change the number of trees it makes (the default is 300) and the\n## number of variables that it will consider at each step.\n \n## Now we are ready to build a random forest.\n \n## NOTE: If the thing we're trying to predict (in this case it is\n## whether or not someone has heart disease) is a continuous number\n## (i.e. \"weight\" or \"height\"), then by default, randomForest() will set\n## \"mtry\", the number of variables to consider at each step,\n## to the total number of variables divided by 3 (rounded down), or to 1\n## (if the division results in a value less than 1).\n## If the thing we're trying to predict is a \"factor\" (i.e. either \"yes/no\"\n## or \"ranked\"), then randomForest() will set mtry to\n## the square root of the number of variables (rounded down to the next\n## integer value).\n \n## In this example, \"hd\", the thing we are trying to predict, is a factor and\n## there are 13 variables. So by default, randomForest() will set\n## mtry = sqrt(13) = 3.6 rounded down = 3\n## Also, by default random forest generates 500 trees (NOTE: rfImpute() only\n## generates 300 tress by default)\nmodel <- randomForest(hd ~ ., data=data.imputed, proximity=TRUE)\n \n## RandomForest returns all kinds of things\nmodel # gives us an overview of the call, along with...\n # 1) The OOB error rate for the forest with ntree trees.\n # In this case ntree=500 by default\n # 2) The confusion matrix for the forest with ntree trees.\n # The confusion matrix is laid out like this:\n#\n# Healthy Unhealthy\n# --------------------------------------------------------------\n# Healthy | Number of healthy people | Number of healthy people |\n# | correctly called \"healthy\" | incorectly called \"unhealthy\" |\n# | by the forest. | by the forest |\n# --------------------------------------------------------------\n# Unhealthy| Number of unhealthy people | Number of unhealthy peole |\n# | incorrectly called | correctly called \"unhealthy\" |\n# | \"healthy\" by the forest | by the forest |\n# --------------------------------------------------------------\n \n## Now check to see if the random forest is actually big enough...\n## Up to a point, the more trees in the forest, the better. You can tell when\n## you've made enough when the OOB no longer improves.\noob.error.data <- data.frame(\n Trees=rep(1:nrow(model$err.rate), times=3),\n Type=rep(c(\"OOB\", \"Healthy\", \"Unhealthy\"), each=nrow(model$err.rate)),\n Error=c(model$err.rate[,\"OOB\"],\n model$err.rate[,\"Healthy\"],\n model$err.rate[,\"Unhealthy\"]))\n \nggplot(data=oob.error.data, aes(x=Trees, y=Error)) +\n geom_line(aes(color=Type))\n# ggsave(\"oob_error_rate_500_trees.pdf\")\n \n## Blue line = The error rate specifically for calling \"Unheathly\" patients that\n## are OOB.\n##\n## Green line = The overall OOB error rate.\n##\n## Red line = The error rate specifically for calling \"Healthy\" patients\n## that are OOB.\n \n## NOTE: After building a random forest with 500 tress, the graph does not make\n## it clear that the OOB-error has settled on a value or, if we added more\n## trees, it would continue to decrease.\n## So we do the whole thing again, but this time add more trees.\n \nmodel <- randomForest(hd ~ ., data=data.imputed, ntree=1000, proximity=TRUE)\nmodel\n \noob.error.data <- data.frame(\n Trees=rep(1:nrow(model$err.rate), times=3),\n Type=rep(c(\"OOB\", \"Healthy\", \"Unhealthy\"), each=nrow(model$err.rate)),\n Error=c(model$err.rate[,\"OOB\"],\n model$err.rate[,\"Healthy\"],\n model$err.rate[,\"Unhealthy\"]))\n \nggplot(data=oob.error.data, aes(x=Trees, y=Error)) +\n geom_line(aes(color=Type))\n# ggsave(\"oob_error_rate_1000_trees.pdf\")\n \n## After building a random forest with 1,000 trees, we get the same OOB-error\n## 16.5% and we can see convergence in the graph. So we could have gotten\n## away with only 500 trees, but we wouldn't have been sure that number\n## was enough.\n \n## If we want to compare this random forest to others with different values for\n## mtry (to control how many variables are considered at each step)...\noob.values <- vector(length=10)\nfor(i in 1:10) {\n temp.model <- randomForest(hd ~ ., data=data.imputed, mtry=i, ntree=1000)\n oob.values[i] <- temp.model$err.rate[nrow(temp.model$err.rate),1]\n}\noob.values\n## [1] 0.1716172 0.1716172 0.1617162 0.1848185 0.1749175 0.1947195 0.1815182\n## [8] 0.2013201 0.1881188 0.1947195\n## The lowest value is when mtry=3, so the default setting was the best.\n \n## Now let's create an MDS-plot to show how the samples are related to each\n## other.\n##\n## Start by converting the proximity matrix into a distance matrix.\ndistance.matrix <- dist(1-model$proximity)\n \nmds.stuff <- cmdscale(distance.matrix, eig=TRUE, x.ret=TRUE)\n \n## calculate the percentage of variation that each MDS axis accounts for...\nmds.var.per <- round(mds.stuff$eig/sum(mds.stuff$eig)*100, 1)\n \n## now make a fancy looking plot that shows the MDS axes and the variation:\nmds.values <- mds.stuff$points\nmds.data <- data.frame(Sample=rownames(mds.values),\n X=mds.values[,1],\n Y=mds.values[,2],\n Status=data.imputed$hd)\n \nggplot(data=mds.data, aes(x=X, y=Y, label=Sample)) +\n geom_text(aes(color=Status)) +\n theme_bw() +\n xlab(paste(\"MDS1 - \", mds.var.per[1], \"%\", sep=\"\")) +\n ylab(paste(\"MDS2 - \", mds.var.per[2], \"%\", sep=\"\")) +\n ggtitle(\"MDS plot using (1 - Random Forest Proximities)\")\n", "meta": {"hexsha": "c9b7719b9b08a7a2e8da83ee0fc4544322d4d8be", "size": 10520, "ext": "r", "lang": "R", "max_stars_repo_path": "RandomForest/RandForest.r", "max_stars_repo_name": "isix/R", "max_stars_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RandomForest/RandForest.r", "max_issues_repo_name": "isix/R", "max_issues_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RandomForest/RandForest.r", "max_forks_repo_name": "isix/R", "max_forks_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.2489959839, "max_line_length": 104, "alphanum_fraction": 0.6475285171, "num_tokens": 2849, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.33046813131717984}} {"text": "#' Match a specific date\n#'\n#' Create an indicator variables for a specific date (year, month, day) for a weekly time series as defined in \\code{tis}.\n#'\n#' @param x Numeric tis object.\n#' @param date_string Character string; Year, month and day of the date (Example - January 1, 1990 is '19900101').\n#' @return An indicator variable where the week contains the date entered = 1, 0 otherwise.\n#' @examples\n#' pandemic_start <- match_date(ic_week, '20200317')\n#' @import stats\n#' @export\nmatch_date <- function(x, date_string) {\n # Author: Brian C. Monsell (OEUS), Version 1.4, 3/23/2021\n \n # generate a vector of strings of dates associated with weekly observations.\n ymd_string <- as.character(tis::ymd(tis::ti(x)))\n \n # initialize indicator variable with 0\n dummy <- array(0, dim = length(x))\n \n # set observation matching the date to 1\n dummy[ymd_string == date_string] <- 1\n \n # return indicator variable\n return(dummy)\n}\n", "meta": {"hexsha": "24b4be9c4bddb88c61e9b70c81069ea471b0cc90", "size": 965, "ext": "r", "lang": "R", "max_stars_repo_path": "R/match_date.r", "max_stars_repo_name": "bcmonsell/airutilities", "max_stars_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/match_date.r", "max_issues_repo_name": "bcmonsell/airutilities", "max_issues_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/match_date.r", "max_forks_repo_name": "bcmonsell/airutilities", "max_forks_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.7407407407, "max_line_length": 122, "alphanum_fraction": 0.6849740933, "num_tokens": 257, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.33046813131717984}} {"text": "#' FLOPs Benchmark Plotter\n#' \n#' @param groupby\n#' Should operations be grouped by \"color\" or by \"shape\"? The\n#' latter is useful when black/white plots are needed.\n#' \n#' @examples\n#' \\dontrun{\n#' library(pbdPAPI)\n#' data <- list(rnorm(1e4), rnorm(2e4), rnorm(3e4))\n#' x <- flopsbench(exp, sqrt, sum, data=data)\n#' \n#' library(hpcvis)\n#' papiplot(x)\n#' papiplot(x, groupby=\"shape\")\n#' }\n#' \n#' @rdname papiplot\n#' @method papiplot flopsbench\n#' @export\npapiplot.flopsbench <- function(x, ..., title, opnames, groupby=\"color\")\n{\n groupby <- match.arg(tolower(groupby), c(\"color\", \"shape\"))\n \n if (!missing(opnames))\n {\n if (length(opnames) != length(x))\n stop(\"argument 'opnames' is of incorrect length\")\n \n names(x) <- opnames\n }\n \n df <- lapply(x, as.data.frame)\n nm <- names(df)\n df <- lapply(1:length(df), function(i) cbind(df[[i]], nm[i]))\n df <- do.call(rbind, df)\n names(df)[length(df)] <- \"Operation\"\n df$operation <- factor(df$Operation)\n\n g <- ggplot(df, aes_string(\"n\", \"mflops\", group=\"Operation\")) + \n theme_bw() + \n ylab(\"Megaflops\")\n \n if (groupby == \"color\")\n g <- g + geom_line(aes(color=Operation)) + geom_point(aes(color=Operation))\n else if (groupby == \"shape\")\n g <- g + geom_point(aes(shape=Operation))\n \n if (missing(title))\n g <- g + ggtitle(\"Floating Point Operations Benchmark\")\n else if (!is.null(title))\n g <- g + ggtitle(title)\n \n g\n}\n", "meta": {"hexsha": "966687c2099fbbdd92925d783482cdce87ce52a7", "size": 1419, "ext": "r", "lang": "R", "max_stars_repo_path": "R/flopsbench.r", "max_stars_repo_name": "RBigData/scribe", "max_stars_repo_head_hexsha": "af5d15fb108a5d590823a064b008d128c4e916e5", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/flopsbench.r", "max_issues_repo_name": "RBigData/scribe", "max_issues_repo_head_hexsha": "af5d15fb108a5d590823a064b008d128c4e916e5", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/flopsbench.r", "max_forks_repo_name": "RBigData/scribe", "max_forks_repo_head_hexsha": "af5d15fb108a5d590823a064b008d128c4e916e5", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2016-04-10T17:14:19.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-28T16:16:15.000Z", "avg_line_length": 25.3392857143, "max_line_length": 79, "alphanum_fraction": 0.6152219873, "num_tokens": 444, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073507867328, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.33046812246529733}} {"text": "\n#' @export\nTestResult <- function(test_name, H0_description, test_statistic, distribution, ...){\n crit_values <- critical_values(distribution=distribution, ...)\n \n test_result <- structure(list(\"test\" = test_name,\n \"H0\"= H0_description,\n \"test_statistic\" = test_statistic,\n \"critical_values\" = crit_values),\n class=\"TestResult\")\n return(test_result)\n}\n\n#' @export\nprint.TestResult <- function(test_result){\n cat(paste0(\"\\033[1m\", test_result$test, \"\\033[0m\\n\")) # Bold font for test name\n cat(\"Null Hypothesis (H0): \", test_result$H0, \"\\n\\n\")\n cat(\"Test Statistic = \", test_result$test_statistic, \"\\n\\n\")\n cat(capture.output(test_result$critical_values), sep=\"\\n\")\n}", "meta": {"hexsha": "c5396382f16670292369a9b314b1d99567b4dc66", "size": 817, "ext": "r", "lang": "R", "max_stars_repo_path": "R/test_result.r", "max_stars_repo_name": "kevinkevin556/econometrics", "max_stars_repo_head_hexsha": "735a25f3eea03d9d1d1d27d90e7c1ef311604c84", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/test_result.r", "max_issues_repo_name": "kevinkevin556/econometrics", "max_issues_repo_head_hexsha": "735a25f3eea03d9d1d1d27d90e7c1ef311604c84", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/test_result.r", "max_forks_repo_name": "kevinkevin556/econometrics", "max_forks_repo_head_hexsha": "735a25f3eea03d9d1d1d27d90e7c1ef311604c84", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.85, "max_line_length": 85, "alphanum_fraction": 0.5862913097, "num_tokens": 191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199306096343, "lm_q2_score": 0.6406358617010351, "lm_q1q2_score": 0.330324618556331}} {"text": "#install.packages('rJava', type='mac.binary')\n#install.packages('xlsx', type='mac.binary')\n{\n library(\"xlsx\")\n \n #\u0423\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0434\u0438\u0440\u0435\u043a\u0442\u043e\u0440\u0438\u044e\n setwd(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/analytics\")\n \n #\u0426\u0438\u043a\u043b \u043f\u043e \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u0442\u043e\u0432\u0430\u0440\u0443. \u041d\u0430\u0434\u0435\u0435\u043c\u0441\u044f \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u0432\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0430\u0445 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u0435 \u0442\u043e\u0432\u0430\u0440\u044b\n goods.table <- read.table(file = 'store1_price.txt', head = TRUE)\n goods <- goods.table[, 1]\n \n ############ \u0420\u0430\u0431\u043e\u0442\u0430\u0435\u043c \u0442\u043e\u043b\u044c\u043a\u043e \u0441 \u0442\u0430\u0431\u043b\u0438\u0446\u0435\u0439 .csv \u0438 .xlsx #########################\n \n for (prod in goods) {\n #\u0418\u043d\u0434\u0435\u043a\u0441 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430\n element_index <- which(goods.table == prod)\n #\u0426\u0435\u043d\u0430 \u043f\u0440\u043e\u0434\u0430\u0436\u0438\n product_price <- goods.table[element_index, 3]\n #\u0426\u0435\u043d\u0430 \u043f\u043e\u0441\u0442\u0430\u0432\u043a\u0438\n supply_price <- goods.table[element_index, 2]\n #\u0426\u0435\u043d\u0430 \u0443\u0442\u0438\u043b\u0438\u0437\u0430\u0446\u0438\u0438\n util_price <- goods.table[element_index, 4]\n \n #\u041d\u0430\u0437\u0432\u0430\u043d\u0438\u044f \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u0432\n shop_names <- c()\n #\u0412\u044b\u0440\u0443\u0447\u043a\u0430\n shop_revenues <- c()\n #\u041f\u0440\u0438\u0431\u044b\u043b\u044c\n shop_profits <- c()\n #\u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f\n shop_sales <- c()\n #\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n shop_writeoffs <- c()\n #\u0420\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0430\u0436\n shop_sr <- c()\n #\u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\n shop_sales_max <- c()\n #\u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\n shop_sales_maxdays <- c()\n #\u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\n shop_sales_min <- c()\n #\u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\n shop_sales_mindays <- c()\n #\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043c\u0430\u043a\u0441\n shop_writeoff_max <- c()\n #\u0414\u0435\u043d\u044c \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043c\u0430\u043a\u0441\n shop_writeoff_maxdays <- c()\n \n \n #\u0426\u0438\u043a\u043b \u043f\u043e \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0443\n for (i in 1:10) {\n in1 <-\n read.table(file = paste0('store', as.character(i), '_in.txt'),\n head = TRUE)\n \n out1 <-\n read.table(file = paste0('store', as.character(i), '_out.txt'),\n head = TRUE)\n \n \n # \u041d\u0430\u0437\u0432\u0430\u043d\u0438\u0435 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\n shop_names <-\n append(shop_names, paste0('shop', as.character(i)))\n \n # \u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n buf_writeoff <- sum(in1[, prod]) - sum(out1[, prod])\n shop_writeoffs <- append(shop_writeoffs, buf_writeoff)\n \n # \u0412\u044b\u0440\u0443\u0447\u043a\u0430\n buf_shoprevenue <- product_price * sum(out1[, prod])\n shop_revenues <- append(shop_revenues, buf_shoprevenue)\n \n # \u0417\u0430\u0442\u0440\u0430\u0442\u044b\n buf_cost <-\n (sum(in1[, prod]) * supply_price) + (buf_writeoff * util_price)\n \n # \u041f\u0440\u0438\u0431\u044b\u043b\u044c\n shop_profits <-\n append(shop_profits, buf_shoprevenue - buf_cost)\n \n # \u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f\n shop_sales <- append(shop_sales, sum(out1[, prod]))\n \n # \u0420\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0430\u0436\n shop_sr <- append(shop_sr, sd(out1[, prod]))\n \n # \u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\n shop_sales_max <- append(shop_sales_max, max(out1[, prod]))\n \n # \u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\n shop_sales_maxdays <-\n append(shop_sales_maxdays, out1[which.max(out1[, prod]), 1])\n \n # \u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\n shop_sales_min <- append(shop_sales_min, min(out1[, prod]))\n \n # \u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\n shop_sales_mindays <-\n append(shop_sales_mindays, out1[which.min(out1[, prod]), 1])\n \n # \u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043c\u0430\u043a\u0441\n shop_writeoff_max <-\n append(shop_writeoff_max, max(c(in1[, prod] - out1[, prod])))\n \n # \u0414\u0435\u043d\u044c \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043c\u0430\u043a\u0441\n shop_writeoff_maxdays <-\n append(shop_writeoff_maxdays, in1[which.max(c(in1[, prod] - out1[, prod])), 1])\n }\n \n #\u0412\u044b\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c \u0438\u0442\u043e\u0433 \u0438 \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0434\u043b\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0438, \u043f\u0440\u0438\u0431\u044b\u043b\u0438, \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438, \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0438, \u0440\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u0438\n shop_names <- c(shop_names, c(\"\u0418\u0442\u043e\u0433\", \"\u0421\u0440\u0435\u0434\u043d\u0435\u0435\"))\n shop_revenues <-\n c(shop_revenues, c(sum(shop_revenues), mean(shop_revenues)))\n shop_profits <-\n c(shop_profits, c(sum(shop_profits), mean(shop_profits)))\n shop_sales <-\n c(shop_sales, c(sum(shop_sales), mean(shop_sales)))\n shop_writeoffs <-\n c(shop_writeoffs, c(sum(shop_writeoffs), mean(shop_writeoffs)))\n shop_sr <- c(shop_sr, c(sum(shop_sr), mean(shop_sr)))\n shop_sales_max <- c(shop_sales_max, c(\"\", \"\"))\n shop_sales_maxdays <- c(shop_sales_maxdays, c(\"\", \"\"))\n shop_sales_min <- c(shop_sales_min, c(\"\", \"\"))\n shop_sales_mindays <- c(shop_sales_mindays, c(\"\", \"\"))\n shop_writeoff_max <- c(shop_writeoff_max, c(\"\", \"\"))\n shop_writeoff_maxdays <- c(shop_writeoff_maxdays, c(\"\", \"\"))\n \n #\u0424\u043e\u0440\u043c\u0438\u0440\u0443\u0435\u043c \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\n table <-\n data.frame(\n shop_names,\n shop_revenues,\n shop_profits,\n shop_sales ,\n shop_writeoffs,\n shop_sr,\n shop_sales_max,\n shop_sales_maxdays,\n shop_sales_min,\n shop_sales_mindays,\n shop_writeoff_max,\n shop_writeoff_maxdays\n )\n \n #\u041f\u0440\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u043c \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0438\n col_headings <-\n c(\n \"\u041c\u0430\u0433\u0430\u0437\u0438\u043d\" ,\n \"\u0412\u044b\u0440\u0443\u0447\u043a\u0430, \u0440\u0443\u0431\" ,\n \"\u041f\u0440\u0438\u0431\u044b\u043b\u044c\",\n \"\u0420\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f\" ,\n \"\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435, \u043a\u043e\u043d\u0442.\",\n \"\u0420\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0434\u0430\u0436\" ,\n \"\u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\",\n \"\u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0430\u043a\u0441\",\n \"\u041f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\",\n \"\u0414\u0435\u043d\u044c \u043f\u0440\u043e\u0434\u0430\u0436\u0438 \u043c\u0438\u043d\" ,\n \"\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043c\u0430\u043a\u0441\",\n \"\u0414\u0435\u043d\u044c \u043c\u0430\u043a\u0441 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f\"\n )\n names(table) <- col_headings\n \n # \u0417\u0430\u043f\u0438\u0441\u044c \u0432 .csv\n write.table(\n table,\n file = paste0(\n \"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/\u0442\u0430\u0431\u043b\u0438\u0446\u0430_\",\n prod,\n \".csv\"\n ),\n col.names = TRUE,\n row.names = FALSE,\n sep = ';',\n dec = ',',\n fileEncoding = 'UTF-8'\n )\n \n # \u0417\u0430\u043f\u0438\u0441\u044c \u0432 .xlsx (\u044d\u0442\u043e \u0447\u0442\u043e\u0431 \u043d\u0430 \u043c\u0430\u043a\u0435 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u043b\u043e\u0441\u044c \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e)\n write.xlsx(\n table,\n file = paste0(\n \"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/\u0442\u0430\u0431\u043b\u0438\u0446\u0430_\",\n prod,\n \".xlsx\"\n ),\n sheetName = \"DATA\",\n col.names = TRUE,\n row.names = FALSE,\n append = FALSE\n )\n }\n \n ######################## \u0424\u043e\u0440\u043c\u0438\u0440\u0443\u0435\u043c \u0433\u0440\u0430\u0444\u0438\u043a\u0438 ###################################\n \n # \u0412\u0435\u043a\u0442\u043e\u0440 \u0432\u0441\u0435\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0446\u0432\u0435\u0442\u043e\u0432 \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u0442\u043e\u0432\u0430\u0440\u043e\u0432\n plot_colors <- c(\"red3\",\"forestgreen\", \"steelblue\", \"darkgreen\",\"darkolivegreen3\", \"darkorange1\",\"firebrick1\",\"gold1\", \"lightcoral\",\"mediumvioletred\",\"navyblue\", \"tan1\",\"turquoise1\",\"chocolate1\",\"blue\",\"black\",\"brown\", \"darkseagreen\" )\n # \u0412\u0435\u043a\u0442\u043e\u0440 \u0432\u0441\u0435\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u043a\u043e\u0432 \u0434\u043b\u044f \u0442\u043e\u0432\u0430\u0440\u043e\u0432\n plot_pchs <- seq(15,25)\n \n #\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430 \u0441\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u0432 \u0438 \u0441\u043e \u0432\u0441\u0435\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\n super_summ_shoprevenue <- rep(0,7)\n #\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c \u0441\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u0432 \u0438 \u0441\u043e \u0432\u0441\u0435\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\n super_summ_shopprofits <- rep(0,7)\n #\u041e\u0431\u0449\u0435\u0435 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0441\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u043e\u0432 \u0438 \u0441\u043e \u0432\u0441\u0435\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\n super_summ_writeoffs <- rep(0,7)\n \n #\u0414\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c \u0441 \u043f\u0440\u0438\u0431\u044b\u043b\u044c\u044e\n super_df_shopprofits <- data.frame(buf=rep(0,7))\n #\u0414\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c \u0441 \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\u044e\n super_df_profitability <- data.frame(buf=rep(0,7))\n \n #\u0421\u043f\u0438\u0441\u043e\u043a \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u043e\u0432 \u0432\u0441\u0435\u0433\u043e \u0442\u043e\u0432\u0430\u0440\u0430 \u043f\u043e \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c. \u041d\u0443\u0436\u0435\u043d \u0434\u043b\u044f \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0438 \u043f\u0440\u043e\u0434\u0430\u0436 \u0432\u0441\u0435\u0445 \u0442\u043e\u0432\u0430\u0440\u043e\u0432 \u043f\u043e \u0432\u0441\u0435\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\n goods_list <- list()\n #\u0426\u0438\u043a\u043b \u043f\u043e \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043c\u0430\u0433\u0430\u0437\u0443\n for (i in 1:10) {\n in1 <- read.table(file = paste0('store',as.character(i),'_in.txt'), head = TRUE)\n out1 <- read.table(file = paste0('store',as.character(i),'_out.txt'), head = TRUE)\n price1 <- read.table(file = paste0('store',as.character(i),'_price.txt'), head = TRUE)\n \n #\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430 \u0441\u043e \u0432\u0441\u0435\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\n summ_shopprofits <- rep(0,7)\n summ_shoprevenue <- rep(0,7)\n summ_writeoffs <- rep(0,7)\n \n \n #\u0414\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u044b \u043f\u043e \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0443 \u0434\u043b\u044f \u0432\u044b\u0432\u043e\u0434\u0430 \u0435\u0434\u0438\u043d\u044b\u0445 \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0432 \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430\u043c\u0438\n #\u043e\u0431\u044a\u0435\u043c \u043f\u0440\u043e\u0434\u0430\u0436\n df_salesvolume <- data.frame(buf=rep(0,7))\n #\u0432\u044b\u0440\u0443\u0447\u043a\u0430\n df_shoprevenue <- data.frame(buf=rep(0,7))\n #\u043f\u0440\u0438\u0431\u044b\u043b\u044c\n df_shopprofits <- data.frame(buf=rep(0,7))\n #\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n df_writeoffs <- data.frame(buf=rep(0,7))\n #\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\n df_profitability <- data.frame(buf=rep(0,7))\n \n #\u0426\u0438\u043a\u043b \u043f\u043e \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0443 \u0432 \u043a\u0430\u0436\u0434\u043e\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435\n for (prod in goods){\n \n element_index <- which(goods.table == prod)\n #\u0426\u0435\u043d\u0430 \u043f\u0440\u043e\u0434\u0430\u0436\u0438\n product_price <- goods.table[element_index, 3]\n #\u0426\u0435\u043d\u0430 \u043f\u043e\u0441\u0442\u0430\u0432\u043a\u0438\n supply_price <- goods.table[element_index, 2]\n #\u0426\u0435\u043d\u0430 \u0443\u0442\u0438\u043b\u0438\u0437\u0430\u0446\u0438\u0438\n util_price <- goods.table[element_index, 4]\n \n # \u0412\u044b\u0440\u0443\u0447\u043a\u0430\n buf_shoprevenue <- product_price * out1[, prod]\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\" (\",prod,\").png\"),width=600, height=450)\n plot(buf_shoprevenue, main=paste0('\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u043f\u043e \u0434\u043d\u044f\u043c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),' (',prod,')'), xlab='\u0414\u0435\u043d\u044c', ylab=paste0(\"\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0443 '\",prod,\"', \u0440\u0443\u0431.\"),type='o')\n dev.off()\n \n # \u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n buf_writeoff <- in1[, prod] - out1[, prod]\n xrange <- range(seq(1,7))\n yrange <- range(buf_writeoff)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\" (\",prod,\").png\"),width=600, height=450)\n plot(xrange,\n yrange,\n main=paste0('\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 ',prod,' \u0432 ',as.character(i),' \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435'), \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435, \u0448\u0442.\",\n type = \"n\"\n )\n points(seq(1,7), buf_writeoff, pch=19, col=\"red\")\n lines(seq(1,7), buf_writeoff, pch=19, col=\"black\")\n dev.off()\n \n # \u0417\u0430\u0442\u0440\u0430\u0442\u044b\n buf_cost <- (in1[, prod] * supply_price) + (buf_writeoff * util_price)\n # \u041f\u0440\u0438\u0431\u044b\u043b\u044c\n shop_profits <- buf_shoprevenue - buf_cost\n \n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\" (\",prod,\").png\"),width=600, height=450)\n plot(shop_profits, main=paste0('\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u0432 ',as.character(i),' \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 (',prod,')'), xlab='\u0414\u0435\u043d\u044c', ylab='\u041f\u0440\u0438\u0431\u044b\u043b\u044c, .\u0440\u0443\u0431.',type='S')\n dev.off()\n \n #\u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432\u043e \u0444\u0440\u0435\u0439\u043c\u044b \u0434\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u0433\u0440\u0430\u0444\u0438\u043a\u043e\u0432 \u043d\u0438\u0436\u0435\n #\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436\n df_salesvolume <- data.frame(df_salesvolume, out1[, prod])\n #\u0432\u044b\u0440\u0443\u0447\u043a\u0430\n df_shoprevenue <- data.frame(df_shoprevenue, buf_shoprevenue)\n #\u043f\u0440\u0438\u0431\u044b\u043b\u044c\n df_shopprofits <- data.frame(df_shopprofits, shop_profits)\n #\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435\n df_writeoffs <- data.frame(df_writeoffs, buf_writeoff)\n #\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c\n df_profitability <- data.frame(df_profitability, floor((shop_profits/buf_shoprevenue) * 100))\n \n \n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0441\u0443\u043c\u043c\u0435 \u0432\u044b\u0440\u0443\u0447\u043a\u0438\n summ_shoprevenue <- summ_shoprevenue + buf_shoprevenue\n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0441\u0443\u043c\u043c\u0435 \u043f\u0440\u0438\u0431\u044b\u043b\u0438\n summ_shopprofits <- summ_shopprofits + shop_profits\n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0441\u0443\u043c\u043c\u0435 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0439\n summ_writeoffs <- summ_writeoffs + buf_writeoff\n \n }\n \n ############################################\u0413\u0440\u0430\u0444\u0438\u043a\u0438 \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u0442\u043e\u0432\u0430\u0440\u0430\u043c\u0438 \u043d\u0430 \u043e\u0434\u043d\u043e\u043c \u0433\u0440\u0430\u0444\u0438\u043a\u0435##########################\n \n #\u0413\u0440\u0430\u0444\u0438\u043a \u043e\u0431\u044a\u0451\u043c\u0430 \u043f\u0440\u043e\u0434\u0430\u0436 \u0442\u043e\u0432\u0430\u0440\u0430\u0432 \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 \u043f\u043e \u0434\u043d\u044f\u043c\n df_salesvolume <- subset(df_salesvolume, select = -c(buf))\n names(df_salesvolume) <- goods\n #\u0417\u0430\u043a\u0438\u0434\u044b\u0432\u0430\u0435\u043c \u0432 list\n goods_list[[paste0(\"shop\",as.character(i))]] <- df_salesvolume\n xrange <- range(seq(1,7))\n yrange <- range(df_salesvolume)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n graph <- plot(xrange,\n yrange,\n main=paste0('\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),\" \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0430\u043c\"), \n xlab=\"\u0414\u0435\u043d\u044c \u043d\u0435\u0434\u0435\u043b\u0438\", \n ylab=\"\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043f\u0440\u043e\u0434\u0430\u043d\u043d\u043e\u0433\u043e \u0442\u043e\u0432\u0430\u0440\u0430, \u0448\u0442\",\n type = \"n\",\n )\n for (j in 1:length(goods)){\n points(seq(1,7),df_salesvolume[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n lines(seq(1,7), df_salesvolume[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n }\n legend(\"topright\", legend=goods,col=plot_colors, pch=plot_pchs)\n dev.off()\n \n #\u0413\u0440\u0430\u0444\u0438\u043a \u0432\u044b\u0440\u0443\u0447\u043a\u0438 \u043e\u0442 \u0442\u043e\u0432\u0430\u0440\u0430\u0432 \u043f\u043e \u0434\u043d\u044f\u043c\n df_shoprevenue <- subset(df_shoprevenue, select = -c(buf))\n names(df_shoprevenue) <- goods\n xrange <- range(seq(1,7))\n yrange <- range(df_shoprevenue)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n graph <- plot(xrange,\n yrange,\n main=paste0('\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),\" \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0430\u043c\"), \n xlab=\"\u0414\u0435\u043d\u044c \u043d\u0435\u0434\u0435\u043b\u0438\", \n ylab=\"\u0412\u044b\u0440\u0443\u0447\u043a\u0430, \u0440\u0443\u0431\",\n type = \"n\",\n )\n for (j in 1:length(goods)){\n points(seq(1,7),df_shoprevenue[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n lines(seq(1,7), df_shoprevenue[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n }\n legend(\"topright\", legend=goods,col=plot_colors, pch=plot_pchs)\n dev.off()\n\n # \u0413\u0440\u0430\u0444\u0438\u043a \u043f\u0440\u0438\u0431\u044b\u043b\u0438 \u043e\u0442 \u0442\u043e\u0432\u0430\u0440\u0430\u0432 \u043f\u043e \u0434\u043d\u044f\u043c\n df_shopprofits <- subset(df_shopprofits, select = -c(buf))\n names(df_shopprofits) <- goods\n xrange <- range(seq(1,7))\n yrange <- range(df_shopprofits)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n graph <- plot(xrange,\n yrange,\n main=paste0('\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),\" \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0430\u043c\"), \n xlab=\"\u0414\u0435\u043d\u044c \u043d\u0435\u0434\u0435\u043b\u0438\", \n ylab=\"\u041f\u0440\u0438\u0431\u044b\u043b\u044c, \u0440\u0443\u0431\",\n type = \"n\",\n )\n for (j in 1:length(goods)){\n points(seq(1,7),df_shopprofits[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n lines(seq(1,7), df_shopprofits[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n }\n legend(\"topright\", legend=goods,col=plot_colors, pch=plot_pchs)\n dev.off()\n\n # \u0413\u0440\u0430\u0444\u0438\u043a \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u0442\u043e\u0432\u0430\u0440\u0430\u0432 \u043f\u043e \u0434\u043d\u044f\u043c\n df_writeoffs <- subset(df_writeoffs, select = -c(buf))\n names(df_writeoffs) <- goods\n xrange <- range(seq(1,7))\n yrange <- range(df_writeoffs)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n graph <- plot(xrange,\n yrange,\n main=paste0('\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),\" \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0430\u043c\"), \n xlab=\"\u0414\u0435\u043d\u044c \u043d\u0435\u0434\u0435\u043b\u0438\", \n ylab=\"\u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u043f\u0438\u0441\u0430\u043d\u043d\u043e\u0433\u043e \u0442\u043e\u0432\u0430\u0440\u0430, \u0448\u0442\",\n type = \"n\",\n )\n for (j in 1:length(goods)){\n points(seq(1,7),df_writeoffs[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n lines(seq(1,7), df_writeoffs[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n }\n legend(\"topright\", legend=goods,col=plot_colors, pch=plot_pchs)\n dev.off()\n\n # \u0413\u0440\u0430\u0444\u0438\u043a \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0442\u043e\u0432\u0430\u0440\u0430\u0432 \u043f\u043e \u0434\u043d\u044f\u043c\n df_profitability <- subset(df_profitability, select = -c(buf))\n names(df_profitability) <- goods\n xrange <- range(seq(1,7))\n yrange <- range(df_profitability)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n graph <- plot(xrange,\n yrange,\n main=paste0('\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i),\" \u043f\u043e \u0442\u043e\u0432\u0430\u0440\u0430\u043c\"), \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c, %\",\n type = \"n\"\n )\n for (j in 1:length(goods)){\n points(seq(1,7),df_profitability[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n lines(seq(1,7), df_profitability[, goods[j]], pch=plot_pchs[j], col=plot_colors[j])\n }\n legend(\"topright\", legend=goods,col=plot_colors, pch=plot_pchs)\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u043e\u0431\u0449\u0438\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u0432\u044b\u0440\u0443\u0447\u043a\u0438 \u043f\u043e \u0434\u043d\u044f\u043c\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n plot(summ_shoprevenue, main=paste0('\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u043f\u043e \u0434\u043d\u044f\u043c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i)), xlab='\u0414\u0435\u043d\u044c', ylab=paste0(\"\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430, \u0440\u0443\u0431.\"),type='o')\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u043e\u0431\u0449\u0438\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u043f\u0440\u0438\u0431\u044b\u043b\u0438 \u043f\u043e \u0434\u043d\u044f\u043c\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n plot(summ_shopprofits, main=paste0('\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435 ',as.character(i)), xlab='\u0414\u0435\u043d\u044c', ylab='\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c, \u0440\u0443\u0431.',type='S')\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u043e\u0431\u0449\u0438\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0439 \u043f\u043e \u0434\u043d\u044f\u043c\n xrange <- range(seq(1,7))\n yrange <- range(summ_writeoffs)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041e\u0431\u0449\u0435\u0435 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n plot(xrange,yrange,main=paste0('\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043f\u043e \u0434\u043d\u044f\u043c \u0432 ',as.character(i),' \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435'), xlab=\"\u0414\u0435\u043d\u044c\", ylab=\"\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435, \u0448\u0442.\", type = \"n\")\n points(seq(1,7), summ_writeoffs, pch=19, col=\"red\")\n lines(seq(1,7), summ_writeoffs, pch=19, col=\"black\")\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u0434\u043b\u044f \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\n summ_profitability <- floor((summ_shopprofits/summ_shoprevenue) * 100)\n xrange <- range(seq(1,7))\n yrange <- range(summ_profitability)\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/shop\",as.character(i),\"/\u041e\u0431\u0449\u0430\u044f \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043c\u0430\u0433\u0430\u0437\u0438\u043d \",as.character(i),\".png\"),width=600, height=450)\n plot(xrange,\n yrange,\n main=paste(\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u0432\",as.character(i),\"\u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435\"), \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c, %\",\n type = \"n\"\n )\n lines(seq(1,7), summ_profitability, pch=20, col=\"red3\",lwd = 3, lty = 2)\n dev.off()\n \n \n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0432\u044b\u0440\u0443\u0447\u043a\u0443 \u043a \u043e\u0431\u0449\u0435\u0439 \u0432\u044b\u0440\u0443\u0447\u043a\u0435\n super_summ_shoprevenue <- super_summ_shoprevenue + summ_shoprevenue\n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043f\u0440\u0438\u0431\u044b\u043b\u044c \u043a \u043e\u0431\u0449\u0435\u0439 \u043f\u0440\u0438\u0431\u044b\u043b\u0438\n super_summ_shopprofits <- super_summ_shopprofits + summ_shopprofits\n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f \u043a \u043e\u0431\u0449\u0438\u043c \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u044f\u043c\n super_summ_writeoffs <- super_summ_writeoffs + summ_writeoffs\n \n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u0443 \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438\n super_df_profitability <- data.frame(super_df_profitability, summ_profitability)\n #\u041f\u0440\u0438\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u043a \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u0443 \u043f\u0440\u0438\u0431\u044b\u043b\u0438\n super_df_shopprofits <- data.frame(super_df_shopprofits, summ_shopprofits)\n \n }\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u043e\u0431\u0449\u0435\u0439 \u0432\u044b\u0440\u0443\u0447\u043a\u0438\n super_summ_shoprevenue1 <- super_summ_shoprevenue / 1000\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430.png\", width=600, height=450)\n plot(super_summ_shoprevenue1, main='\u0412\u044b\u0440\u0443\u0447\u043a\u0430 \u0432\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u0445 \u043f\u043e \u0434\u043d\u044f\u043c', xlab='\u0414\u0435\u043d\u044c', ylab=\"\u041e\u0431\u0449\u0430\u044f \u0432\u044b\u0440\u0443\u0447\u043a\u0430, \u0442\u044b\u0441 \u0440\u0443\u0431.\",type='o')\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u043e\u0431\u0449\u0435\u0439 \u043f\u0440\u0438\u0431\u044b\u043b\u0438\n super_summ_shopprofits1 <- super_summ_shopprofits / 1000\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c.png\", width=600, height=450)\n plot(super_summ_shopprofits1, main='\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u0432\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u0445 \u043f\u043e \u0434\u043d\u044f\u043c', xlab='\u0414\u0435\u043d\u044c', ylab='\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c, \u0442\u044b\u0441 \u0440\u0443\u0431.',type='S')\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u043e\u0431\u0449\u0438\u0445 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0439\n xrange <- range(seq(1,7))\n yrange <- range(super_summ_writeoffs)\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0435\u0435 \u0441\u043f\u0438\u0441\u0430\u043d\u0438\u0435.png\",width=600, height=450)\n plot(xrange,yrange,main='\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0432\u043e \u0432\u0441\u0435\u0445 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u0445 \u043f\u043e \u0434\u043d\u044f\u043c', xlab=\"\u0414\u0435\u043d\u044c\", ylab=\"\u0421\u043f\u0438\u0441\u0430\u043d\u0438\u0435, \u0448\u0442.\", type = \"n\")\n points(seq(1,7), super_summ_writeoffs, pch=19, col=\"red\")\n lines(seq(1,7), super_summ_writeoffs, pch=19, col=\"black\")\n dev.off()\n \n #\u0421\u0442\u0440\u043e\u0438\u043c \u0433\u0440\u0430\u0444\u0438\u043a \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438\n super_summ_profitability <- floor((super_summ_shopprofits/super_summ_shoprevenue) * 100)\n xrange <- range(seq(1,7))\n yrange <- range(super_summ_profitability)\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0430\u044f \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c.png\",width=600, height=450)\n plot(xrange,\n yrange,\n main=\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u043e\u0431\u0449\u0430\u044f\", \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c, %\",\n type = \"n\"\n )\n lines(seq(1,7), super_summ_profitability, pch=20, col=\"red3\",lwd = 3, lty = 2)\n dev.off()\n \n ########################\u0421\u0442\u0440\u043e\u0438\u043c \u0441\u043b\u043e\u0436\u043d\u044b\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438##########################\n #\u0412\u044b\u043a\u0438\u0434\u044b\u0432\u0430\u0435\u043c \u043d\u0443\u043b\u0435\u0432\u043e\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446\n super_df_profitability <- subset(super_df_profitability, select = -c(buf))\n #\u041f\u0440\u0438\u0441\u0432\u0430\u0438\u0432\u0430\u0435\u043c \u0438\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0449\u0435\u043d\u0438\u044f \u043f\u043e \u043d\u0438\u043c\n names(super_df_profitability) <- paste0(\"shop\",as.character(seq(1,10)))\n xrange <- range(seq(1,7))\n yrange <- range(super_df_profitability)\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0430\u044f \u0440\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e.png\",width=716, height=630)\n plot(xrange,\n yrange,\n main='\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u0445', \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u0420\u0435\u043d\u0442\u0430\u0431\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c, %\",\n type = \"n\"\n )\n for (i in 1:length(super_df_profitability)){\n points(seq(1,7), super_df_profitability[,paste0(\"shop\",as.character(i))], pch=19, col=plot_colors[i])\n }\n legend(\"bottomleft\", legend=paste(\"\u041c\u0430\u0433\u0430\u0437\u0438\u043d\", seq(1,10)),col=plot_colors,pch=c(19))\n dev.off()\n \n ########################\u0421\u0442\u0440\u043e\u0438\u043c \u0441\u043b\u043e\u0436\u043d\u044b\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u043f\u0440\u0438\u0431\u044b\u043b\u0438##########################\n #\u0412\u044b\u043a\u0438\u0434\u044b\u0432\u0430\u0435\u043c \u043d\u0443\u043b\u0435\u0432\u043e\u0439 \u0441\u0442\u043e\u043b\u0431\u0435\u0446\n super_df_shopprofits <- subset(super_df_shopprofits, select = -c(buf))\n #\u041f\u0440\u0438\u0441\u0432\u0430\u0438\u0432\u0430\u0435\u043c \u0438\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0449\u0435\u043d\u0438\u044f \u043f\u043e \u043d\u0438\u043c\n names(super_df_shopprofits) <- paste0(\"shop\",as.character(seq(1,10)))\n super_df_shopprofits <- super_df_shopprofits / 1000\n xrange <- range(seq(1,7))\n yrange <- range(super_df_shopprofits)\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u0449\u0430\u044f \u043f\u0440\u0438\u0431\u044b\u043b\u044c \u043f\u043e\u0434\u0440\u043e\u0431\u043d\u043e.png\",width=716, height=630)\n plot(xrange,\n yrange,\n main='\u041f\u0440\u0438\u0431\u044b\u043b\u044c \u043f\u043e \u0434\u043d\u044f\u043c \u0432 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u0445', \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u041f\u0440\u0438\u0431\u044b\u043b\u044c, \u0442\u044b\u0441 \u0440\u0443\u0431\",\n type = \"n\"\n )\n for (i in 1:length(super_df_shopprofits)){\n points(seq(1,7), super_df_shopprofits[,paste0(\"shop\",as.character(i))], pch=18, col=plot_colors[i], cex=2.0)\n }\n legend(\"bottomleft\", legend=paste(\"\u041c\u0430\u0433\u0430\u0437\u0438\u043d\", seq(1,10)),col=plot_colors,pch=c(18))\n dev.off()\n \n ############################\u0413\u0440\u0430\u0444\u0438\u043a \u0434\u0438\u043d\u0430\u043c\u0438\u043a\u0438 \u043f\u0440\u043e\u0434\u0430\u0436 \u0442\u043e\u0432\u0430\u0440\u043e\u0432 \u043f\u043e \u0432\u0441\u0435\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c#####################\n #\u041a\u0430\u0436\u0434\u044b\u0438\u0306 \u043c\u0430\u0433\u0430\u0437\u0438\u043d \u0432\u044b\u0434\u0435\u043b\u044f\u0442\u044c \u0441\u0432\u043e\u0438\u043c \u0446\u0432\u0435\u0442\u043e\u043c\n #\u041a\u0430\u0436\u0434\u044b\u0438\u0306 \u0442\u043e\u0432\u0430\u0440 \u0432\u044b\u0434\u0435\u043b\u044f\u0442\u044c \u0441\u0432\u043e\u0438\u043c \u0437\u043d\u0430\u0447\u043a\u043e\u043c.\n \n xrange <- range(seq(1,7))\n yrange <- range(goods_list)\n png(file=\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u0414\u0438\u043d\u0430\u043c\u0438\u043a\u0430 \u043f\u0440\u043e\u0434\u0430\u0436 \u0442\u043e\u0432\u0430\u0440\u043e\u0432.png\",width=800, height=1000)\n plot(xrange,\n yrange,\n main='\u0414\u0438\u043d\u0430\u043c\u0438\u043a\u0430 \u043f\u0440\u043e\u0434\u0430\u0436 \u0442\u043e\u0432\u0430\u0440\u043e\u0432 \u043f\u043e \u0432\u0441\u0435\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c', \n xlab=\"\u0414\u0435\u043d\u044c\", \n ylab=\"\u041f\u0440\u043e\u0434\u0430\u0436\u0430, \u0448\u0442.\",\n type = \"n\"\n )\n for (i in 1:length(goods_list)){\n for (j in 1:length(goods_list[[paste0(\"shop\",as.character(i))]])){\n points(seq(1,7), goods_list[[paste0(\"shop\",as.character(i))]][j][, 1], pch=plot_pchs[j], col=plot_colors[i])\n }\n }\n legend(\"bottomright\", legend=paste(\"\u041c\u0430\u0433\u0430\u0437\u0438\u043d\", seq(1,10)),col=plot_colors,pch=19)\n legend(\"topright\", legend=goods,col=\"black\",pch=plot_pchs)\n dev.off()\n \n #\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u0438\u0442\u044c \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u0443, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u043e\u0438\u0306 \u0431\u0443\u0434\u0435\u0442 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u044b \u043e\u0431\u044a\u0435\u043c\u044b\n #\u043f\u0440\u043e\u0434\u0430\u0436 \u043e\u0434\u043d\u043e\u0433\u043e \u0442\u043e\u0432\u0430\u0440\u0430 \u0441\u0440\u0430\u0437\u0443 \u043f\u043e \u0432\u0441\u0435\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c. \u041a\u0430\u0436\u0434\u044b\u0438\u0306 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\n #\u0432\u044b\u0434\u0435\u043b\u044f\u0442\u044c \u0441\u0432\u043e\u0438\u043c \u0446\u0432\u0435\u0442\u043e\u043c.\n \n for (good in goods){\n \n #\u0417\u043d\u0430\u0447\u0435\u043d\u0438\u044f \n good_values <- c()\n for (i in 1:length(goods_list)){\n \n value <- goods_list[[paste0(\"shop\",as.character(i))]][, good]\n #\u0417\u0430\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u0441\u0443\u043c\u043c\u0443 \u0442\u043e\u0432\u0430\u0440\u0430 \u0437\u0430 \u043d\u0435\u0434\u0435\u043b\u044e\n good_values <- append(good_values, sum(value))\n \n }\n png(file=paste0(\"/Users/georgiydemo/Projects/FA/Course II/R/Diksi/result/graph/\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \",good,\".png\"),width=650, height=500)\n barplot(height = good_values, names=seq(1,10),main=paste0('\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436 \u0442\u043e\u0432\u0430\u0440\u0430 ', good, \" \u043f\u043e \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\u043c\"), col=\"#69b3a2\",xlab=\"\u2116 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0430\", ylab=\"\u041e\u0431\u044a\u0451\u043c \u043f\u0440\u043e\u0434\u0430\u0436, \u0448\u0442\")\n dev.off()\n }\n\n print(\"\u0417\u0430\u0432\u0435\u0440\u0448\u0435\u043d\u0438\u0435 \u0440\u0430\u0431\u043e\u0442\u044b \u0441\u043a\u0440\u0438\u043f\u0442\u0430\")\n \n}", "meta": {"hexsha": "f3a6e57c84eaa8dcea2c181534ebc47ec74db8f2", "size": 23006, "ext": "r", "lang": "R", "max_stars_repo_path": "Course II/R/Diksi/code/table.r", "max_stars_repo_name": "GeorgiyDemo/FA", "max_stars_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_stars_repo_licenses": ["WTFPL"], "max_stars_count": 27, "max_stars_repo_stars_event_min_datetime": "2019-08-18T20:54:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T02:39:45.000Z", "max_issues_repo_path": "Course II/R/Diksi/code/table.r", "max_issues_repo_name": "GeorgiyDemo/FA", "max_issues_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_issues_repo_licenses": ["WTFPL"], "max_issues_count": 217, "max_issues_repo_issues_event_min_datetime": "2019-09-22T14:43:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T13:49:18.000Z", "max_forks_repo_path": "Course II/R/Diksi/code/table.r", "max_forks_repo_name": "GeorgiyDemo/FA", "max_forks_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_forks_repo_licenses": ["WTFPL"], "max_forks_count": 42, "max_forks_repo_forks_event_min_datetime": "2019-09-18T11:36:28.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-19T18:43:00.000Z", "avg_line_length": 39.6655172414, "max_line_length": 237, "alphanum_fraction": 0.6337042511, "num_tokens": 7510, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.33032460548169035}} {"text": "\n\nggplot(aes(x=condition, y=diff), data=d) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=type)) + stat_summary(fun.y=mean, geom=\"line\", aes(color=type, group=type)) + ylab('Annotated time - aligned time (s)') + scale_color_hue(name = 'Timepoint', labels = c('Onset of vowel', 'Vowel-consonant boundary', 'Consonant end')) + xlab('Alignment style')\nggsave('overall.pdf',width = 6, height = 4, units = 'in', dpi =300)\n\nggplot(aes(x=prec_consonant, y=diff), data=subset(d, type == 'vowel_begin')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition)) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=condition)) + ylab('Annotated time - aligned time (s)') +ggtitle('Vowel onset data only') + xlab('Previous consonant') + scale_color_hue(name = 'Alignment style')\nggsave('vowelonset.pdf',width = 6, height = 4, units = 'in', dpi =300)\n\nggplot(aes(x=foll_consonant, y=diff), data=subset(d, type != 'vowel_begin')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition, shape = condition, group = interaction(condition,type))) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=interaction(condition,type), linetype = type)) + ylab('Annotated time - aligned time (s)') + facet_wrap(~type) +ggtitle('Consonant boundary data only')+ scale_linetype_discrete(name = 'Timepoint', labels = c('Vowel-consonant boundary', 'Consonant end')) + scale_color_discrete(name = 'Alignment style') + scale_shape_discrete(name = 'Alignment style') + xlab('Following consonant')\nggsave('consonantboundary.pdf',width = 6, height = 4, units = 'in', dpi =300)\n\nggplot(aes(x=vowel, y=diff), data=subset(d, type != 'cons_end')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition, shape = condition, group = interaction(condition,type))) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=interaction(condition,type), linetype = type)) + ylab('Annotated time - aligned time (s)') + facet_wrap(~type)+ scale_linetype_discrete(name = 'Timepoint', labels = c('Vowel-consonant boundary', 'Consonant end')) + scale_color_discrete(name = 'Alignment style') + scale_shape_discrete(name = 'Alignment style') + xlab('Vowel')\n\n#MAGNITUDE\n\nggplot(aes(x=condition, y=diff_mag), data=d) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=type)) + stat_summary(fun.y=mean, geom=\"line\", aes(color=type, group=type)) + ylab('Annotated time - aligned time (s)') + scale_color_hue(name = 'Timepoint', labels = c('Onset of vowel', 'Vowel-consonant boundary', 'Consonant end')) + xlab('Alignment style')\n\nggplot(aes(x=prec_consonant, y=diff_mag), data=subset(d, type == 'vowel_begin')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition)) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=condition)) + ylab('Annotated time - aligned time (s)') +ggtitle('Vowel onset data only') + xlab('Previous consonant') + scale_color_hue(name = 'Alignment style') + facet_wrap(~condition, scales='free')\n\nggplot(aes(x=foll_consonant, y=diff_mag), data=subset(d, type != 'vowel_begin')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition, shape = condition, group = interaction(condition,type))) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=interaction(condition,type), linetype = type)) + ylab('Annotated time - aligned time (s)') + facet_wrap(~type) +ggtitle('Consonant boundary data only')+ scale_linetype_discrete(name = 'Timepoint', labels = c('Vowel-consonant boundary', 'Consonant end')) + scale_color_discrete(name = 'Alignment style') + scale_shape_discrete(name = 'Alignment style') + xlab('Following consonant')\n\nggplot(aes(x=vowel, y=diff_mag), data=subset(d, type != 'cons_end')) + stat_summary(fun.data=\"mean_cl_boot\", aes(color=condition, shape = condition, group = interaction(condition,type))) + stat_summary(fun.y=mean, geom=\"line\", aes(color=condition, group=interaction(condition,type), linetype = type)) + ylab('Annotated time - aligned time (s)') + facet_wrap(~type) +ggtitle('Consonant boundary data only')+ scale_linetype_discrete(name = 'Timepoint', labels = c('Vowel-consonant boundary', 'Consonant end')) + scale_color_discrete(name = 'Alignment style') + scale_shape_discrete(name = 'Alignment style') + xlab('Vowel')\n\n", "meta": {"hexsha": "d2f4c0f2adb18230c6d7734eb55d77cc59158d82", "size": 4216, "ext": "r", "lang": "R", "max_stars_repo_path": "comp.r", "max_stars_repo_name": "mmcauliffe/memcauliffe-blog-scripts", "max_stars_repo_head_hexsha": "fd4fd1816df1191c1d3456df793aad624a6e0471", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "comp.r", "max_issues_repo_name": "mmcauliffe/memcauliffe-blog-scripts", "max_issues_repo_head_hexsha": "fd4fd1816df1191c1d3456df793aad624a6e0471", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "comp.r", "max_forks_repo_name": "mmcauliffe/memcauliffe-blog-scripts", "max_forks_repo_head_hexsha": "fd4fd1816df1191c1d3456df793aad624a6e0471", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 175.6666666667, "max_line_length": 647, "alphanum_fraction": 0.7298387097, "num_tokens": 1184, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.33032460548169035}} {"text": "# identify clusters of linked loci from ngsLD\n# run after nsdLD_bypop.sh\n# currently only set up for GATK no dam loci\n\n#parameters\nminr2 <- 0.5 # consider pairs of loci with r2 > minr2 to be linked\n\n# functions\nrequire(data.table)\n\n# read in list of loci\ngatk <- fread('data_2020.05.07/GATK_filtered_SNP_no_dam2.tab')\nsetnames(gatk, c('CHROM', 'POS', 'REF', 'ALT'))\n\n# read in inversion coords\ninv <- fread('data/inversions.csv')\n\n# read in LD data (can do this for gatk loci only at this time). files from ngsLD_bypop.sh\nldCan40 <- fread('analysis/ld.Can_40.gatk.nodam.gz')\nldCan14 <- fread('analysis/ld.Can_14.gatk.nodam.gz')\nld07 <- fread('analysis/ld.Lof_07.gatk.nodam.gz')\nld11 <- fread('analysis/ld.Lof_11.gatk.nodam.gz')\nld14 <- fread('analysis/ld.Lof_14.gatk.nodam.gz')\n\n# add column names\nnms <- c('pos1nm', 'pos2nm', 'dist', 'r2', 'D', 'Dprime', 'r2em')\nsetnames(ldCan14, nms)\nsetnames(ldCan40, nms)\nsetnames(ld14, nms)\nsetnames(ld11, nms)\nsetnames(ld07, nms)\n\n# make a chromosome column\nldCan40[, chr := vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 1)]\nldCan14[, chr := vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 1)]\nld07[, chr := vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 1)]\nld11[, chr := vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 1)]\nld14[, chr := vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 1)]\n\n# remove unplaced\nldCan40 <- ldCan40[!(chr == 'Unplaced'), ]\nldCan14 <- ldCan14[!(chr == 'Unplaced'), ]\nld07 <- ld07[!(chr == 'Unplaced'), ]\nld11 <- ld11[!(chr == 'Unplaced'), ]\nld14 <- ld14[!(chr == 'Unplaced'), ]\n\n# make position columns\nldCan40[, pos1 := as.numeric(vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nldCan40[, pos2 := as.numeric(vapply(strsplit(pos2nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nldCan14[, pos1 := as.numeric(vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nldCan14[, pos2 := as.numeric(vapply(strsplit(pos2nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld07[, pos1 := as.numeric(vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld07[, pos2 := as.numeric(vapply(strsplit(pos2nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld11[, pos1 := as.numeric(vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld11[, pos2 := as.numeric(vapply(strsplit(pos2nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld14[, pos1 := as.numeric(vapply(strsplit(pos1nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\nld14[, pos2 := as.numeric(vapply(strsplit(pos2nm, \":\", fixed = TRUE), \"[\", \"\", 2))]\n \n\n# cluster loci into linkage blocks\n# average r2 within a population\nldCan <- merge(ldCan40[ ,.(chr, pos1, pos2, r21 = r2)], ldCan14[ ,.(chr, pos1, pos2, r22 = r2)], all = TRUE) \nldCan[, r2 := rowMeans(cbind(r21, r22), na.rm = TRUE)]\n\nldLof <- merge(ld07[ ,.(chr, pos1, pos2, r21 = r2)], ld11[ ,.(chr, pos1, pos2, r22 = r2)], all = TRUE) \nldLof <- merge(ldLof, ld14[ ,.(chr, pos1, pos2, r23 = r2)], all = TRUE) \nldLof[, r2 := rowMeans(cbind(r21, r22, r23), na.rm = TRUE)]\n\nrm(ldCan40, ldCan14, ld07, ld11, ld14)\n\n# trim to locus pairs that are linked to cluster these\nldCan <- ldCan[r2 > minr2,]\nldLof <- ldLof[r2 > minr2,]\n\n# order by r2\nsetorder(ldCan, chr, pos1, pos2)\nsetorder(ldLof, chr, pos1, pos2)\n\n# find the most linked loci\nldcanpos1 <- ldCan[, .(numr21 = .N), by = .(chr, pos = pos1)] # number of linked loci for each locus (when listed in pos1)\nldcanpos2 <- ldCan[, .(numr22 = .N), by = .(chr, pos = pos2)] # for pos2\nldcanpos <- merge(ldcanpos1, ldcanpos2, all = TRUE) # merge\nldcanpos[, num := rowSums(cbind(numr21, numr22), na.rm = TRUE)] # total number of linked loci\nsetorder(ldcanpos, -num, chr, pos) # order decreasing by # linked loci\n\nldlofpos1 <- ldLof[, .(numr21 = .N), by = .(chr, pos = pos1)] # number of linked loci for each locus (when listed in pos1)\nldlofpos2 <- ldLof[, .(numr22 = .N), by = .(chr, pos = pos2)] # for pos2\nldlofpos <- merge(ldlofpos1, ldlofpos2, all = TRUE) # merge\nldlofpos[, num := rowSums(cbind(numr21, numr22), na.rm = TRUE)] # total number of linked loci\nsetorder(ldlofpos, -num, chr, pos) # decreasing by # linked loci\n\n# label clusters in pairwise LD matrix\nclustID <- 1 # for Can\nldCan[, cluster := NA_real_] # column for the cluster IDs in the pairwise LD dataset\nnrow(ldcanpos)\nfor(i in 1:nrow(ldcanpos)){ # for each locus that is linked, working from most to least\n if(i %% 1000 == 0) cat(paste0(i, ' '))\n inds <- ldCan[, which(chr == ldcanpos$chr[i] & (pos1 == ldcanpos$pos[i] | pos2 == ldcanpos$pos[i]))] # get indices to pairwise ld that include this locus\n \n if(ldCan[inds, all(is.na(cluster))]){ # if no cluster IDs used yet for this locus\n ldCan[inds, cluster := clustID] # label with the next cluster ID\n clustID <- clustID + 1 # increment the cluster ID\n } else { # if a cluster ID (or more) has already been used\n thisclustIDs <- ldCan[inds, ][!is.na(cluster), unique(cluster)] # get the ID(s) already used\n if(length(thisclustIDs) == 1) ldCan[inds, cluster := thisclustIDs] # if only one, use it\n if(length(thisclustIDs) > 1){ # if the locus links two or more clusters\n thisclustID <- min(thisclustIDs) # pick the lowest cluster ID\n ldCan[cluster %in% thisclustIDs, cluster := thisclustID] # label all clusters with one ID \n ldCan[inds, cluster := thisclustID] # label this locus\n }\n }\n}\n\nclustID <- 1 # for Lof\nldLof[, cluster := NA_real_] # column for the cluster IDs in the pairwise LD dataset\nnrow(ldlofpos)\nfor(i in 1:nrow(ldlofpos)){ # for each locus that is linked, working from most to least\n if(i %% 1000 == 0) cat(paste0(i, ' '))\n inds <- ldLof[, which(chr == ldlofpos$chr[i] & (pos1 == ldlofpos$pos[i] | pos2 == ldlofpos$pos[i]))] # get indices to pairwise ld that include this locus\n \n if(ldLof[inds, all(is.na(cluster))]){ # if no cluster IDs used yet for this locus\n ldLof[inds, cluster := clustID] # label with the next cluster ID\n clustID <- clustID + 1 # increment the cluster ID\n } else { # if a cluster ID (or more) has already been used\n thisclustIDs <- ldLof[inds, ][!is.na(cluster), unique(cluster)] # get the ID(s) already used\n if(length(thisclustIDs) == 1) ldLof[inds, cluster := thisclustIDs] # if only one cluster ID, use it\n if(length(thisclustIDs) > 1){ # if the locus links two or more clusters\n thisclustID <- min(thisclustIDs) # pick the lowest cluster ID\n ldLof[cluster %in% thisclustIDs, cluster := thisclustID] # label all clusters with one ID \n ldLof[inds, cluster := thisclustID] # label this locus\n }\n }\n}\n\n# label clusters in locus list\ngatk[, cluster_can := NA_real_] # column for the CAN cluster IDs\nclusts <- ldCan[, sort(unique(cluster), decreasing = TRUE)] # start from least linked locus cluster\nfor(i in 1:length(clusts)){\n inds <- gatk[, which(CHROM == ldCan[cluster == clusts[i], unique(chr)] & POS %in% ldCan[cluster == clusts[i], c(pos1, pos2)])]\n inds <- min(inds):max(inds) # make sure all loci from min to max position get labeled as part of this cluster\n gatk[inds, cluster_can := clusts[i]]\n}\n\ngatk[, cluster_lof := NA_real_] # column for the CAN cluster IDs\nclusts <- ldLof[, sort(unique(cluster), decreasing = TRUE)] # start from least linked locus cluster\nfor(i in 1:length(clusts)){\n inds <- gatk[, which(CHROM == ldLof[cluster == clusts[i], unique(chr)] & POS %in% ldLof[cluster == clusts[i], c(pos1, pos2)])]\n inds <- min(inds):max(inds) # make sure all loci from min to max position get labeled as part of this cluster\n gatk[inds, cluster_lof := clusts[i]]\n}\n\n# mark the inversions\nfor(i in 1:nrow(inv)){\n gatk[CHROM == inv$CHROM[i] & POS >= inv$POSstart[i] & POS <= inv$POSend[i], cluster_can := -i] # mark as cluster -i in CAN\n gatk[CHROM == inv$CHROM[i] & POS >= inv$POSstart[i] & POS <= inv$POSend[i], cluster_lof := -i] # in LOF\n}\n\n\n# write out\nwrite.csv(gatk, gzfile('analysis/ld.blocks.gatk.nodam.csv.gz'))\n", "meta": {"hexsha": "022f8fe1327beb8befd27c94f4b740621fca3eb2", "size": 7784, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/ngsLD_find_blocks.r", "max_stars_repo_name": "pinskylab/codEvol", "max_stars_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/ngsLD_find_blocks.r", "max_issues_repo_name": "pinskylab/codEvol", "max_issues_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-04-11T11:14:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-21T19:57:31.000Z", "max_forks_repo_path": "scripts/ngsLD_find_blocks.r", "max_forks_repo_name": "pinskylab/codEvol", "max_forks_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.347826087, "max_line_length": 155, "alphanum_fraction": 0.6545477903, "num_tokens": 2637, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3303246054816903}} {"text": "getBatch <- function(test, vClass, vPerc, testSize){\r\n \r\n dts <- NULL\r\n \r\n vsizes <- trunc(testSize*vPerc)\r\n resto <- testSize - sum(vsizes)\r\n vsizes[length(vsizes)] <- vsizes[length(vsizes)]+resto\r\n indices <- NULL\r\n \r\n for(i in 1:length(vClass)){\r\n aux <- which(test$class==vClass[i])\r\n \r\n ind <- sample(aux, min(vsizes[i],length(aux)) )\r\n dts <- rbind(dts, test[ind,])\r\n indices <- c(indices, ind)\r\n \r\n }\r\n return(list(dts, indices))\r\n}\r\n", "meta": {"hexsha": "12cf6e6fd7d44e70a266b32530e3a79942b66555", "size": 471, "ext": "r", "lang": "R", "max_stars_repo_path": "DySyn_synthetic/functions/getBatch.r", "max_stars_repo_name": "andregustavom/icdm21_paper", "max_stars_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DySyn_synthetic/functions/getBatch.r", "max_issues_repo_name": "andregustavom/icdm21_paper", "max_issues_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "DySyn_synthetic/functions/getBatch.r", "max_forks_repo_name": "andregustavom/icdm21_paper", "max_forks_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.55, "max_line_length": 57, "alphanum_fraction": 0.5817409766, "num_tokens": 140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.6406358411176238, "lm_q1q2_score": 0.3303245984062125}} {"text": "##' Plot figure 4\n##'\n##' @return plot\n##' @importFrom dplyr %>% filter mutate\n##' @importFrom lubridate year\n##' @importFrom ggplot2 ggplot aes geom_abline geom_point scale_color_manual scale_x_continuous scale_y_continuous expand_limits geom_hline theme\n##' @importFrom ggrepel geom_label_repel\n##' @importFrom cowplot theme_cowplot\n##' @author Sebastian Funk\n##' @export\n##' @param m a prediction model of class \\code{glm}, such as returned by \\code{\\link{prediction_model}}\nfigure5 <- function(m)\n{\n df <- gather_2015_data() %>%\n mutate(incidence=cases/population) %>%\n filter(!is.na(mean_coverage))\n\n df$hat <- predict(m, type=\"response\")\n\n df <- df %>%\n mutate(size=if_else(hat > 2000, \"real_big\", \"small\"),\n size=if_else(cases > 2000, \"model_big\", size),\n size=factor(size, levels=c(\"model_big\", \"real_big\", \"small\")),\n label_zs=if_else(size == \"small\", \"\", health_zone))\n\n p <- ggplot(df, aes(x=hat, cases, label=label_zs))+\n geom_abline(slope=1, intercept=0) +\n geom_label_repel(label.padding = 0.25, label.size=NA, segment.color=\"darkgrey\") +\n geom_point(aes(color=size)) +\n scale_color_manual(values=c(\"black\", \"red\", \"darkgrey\")) +\n scale_x_continuous(\"Predicted number of cases in 2015\") +\n scale_y_continuous(\"Number of cases in 2015\") +\n expand_limits(y=-1000) +\n geom_hline(yintercept=0, linetype=\"dashed\")+\n theme_cowplot() +\n theme(legend.position=\"none\")\n\n return(p)\n}\n", "meta": {"hexsha": "c3748d1d02c1a0d2a871269244f13aedbea51db8", "size": 1536, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure5.r", "max_stars_repo_name": "sbfnk/measles.katanga", "max_stars_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-01T13:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-01T13:58:45.000Z", "max_issues_repo_path": "R/figure5.r", "max_issues_repo_name": "sbfnk/measles.katanga", "max_issues_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure5.r", "max_forks_repo_name": "sbfnk/measles.katanga", "max_forks_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4, "max_line_length": 145, "alphanum_fraction": 0.646484375, "num_tokens": 425, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.33016562433992086}} {"text": "#' @title\n#' Prepare for plotting the bootstrapped network level metric results of one or\n#' multiple networks.\n#'\n#' @description\n#' Takes a list of network interactions (each interaction being repeated as\n#' many times as it was observed), and bootstraps the network level metric\n#' (`index`) for each network. Runs `boot_networklevel_n()` for a list of\n#' network interactions and prepares the data for plotting with `ggplot`. The\n#' output list can be passed to \\code{\\link[bootstrapnet]{gg_networklevel}}.\n#' See examples below.\n#'\n#' @param lst\n#' A list of one or multiple data frames of interactions from which to build\n#' and sample web matrices. Each interaction (row in the data frame) must be\n#' repeated as many times as it was observed. E.g. if the interaction species_1\n#' x species_2 was observed 5 times, then repeat that row 5 times within the\n#' data frame.\n#'\n#' @param col_lower\n#' Quoted column name in `data` for lower trophic level species (plants).\n#'\n#' @param col_higher\n#' Quoted column name in `data` for higher trophic level species (insects).\n#'\n#' @param index\n#' The name of the network level metric. Passed to\n#' \\code{\\link[bipartite]{networklevel}}. See `?bipartite::networklevel` for\n#' details.\n#'\n#' @param level\n#' For which level should the level-specific indices be computed: 'both'\n#' (default), 'lower' or 'higher'? Passed to\n#' \\code{\\link[bipartite]{networklevel}}. See `?bipartite::networklevel` for\n#' details.\n#'\n#' @param start\n#' Integer. The sample size (number of interactions) to start the bootstrap\n#' with. If the start sample size is small (e.g. 5 or 10), then first\n#' iterations might results in NaN-s and warning messages are displayed.\n#' Consider to set `start` to maybe 10\\\\% of your total unique interactions.\n#'\n#' @param step\n#' Integer. Sample size (number of interactions) used to increase gradually the\n#' sampled network until all interactions are sampled. If `step` is too small\n#' (e.g. 1) then the computation time is very long depending on your total\n#' number of interactions from which samples are taken. Consider to set `step`\n#' to maybe 5-10\\\\% of your total unique interactions.\n#'\n#' @param n_boot\n#' Number of desired bootstraps (50 or 100 can be enough).\n#'\n#' @param n_cpu\n#' Number of CPU-s to use for parallel processing.\n#'\n#' @param probs\n#' A numeric vector of two probabilities in `[0, 1]`. Passed to\n#' \\code{\\link[matrixStats]{rowQuantiles}} and used for building the lower and\n#' upper bounds of the confidence intervals. Defaults to `c(0.025, 0.975)`,\n#' which corresponds to a 95\\\\% confidence interval.\n#'\n#' @param ...\n#' Other arguments passed to \\code{\\link[bipartite]{networklevel}} like\n#' `logbase`, etc.\n#'\n#' @return\n#' A list of one (when `level = 'lower'` or `level = 'higher'`) or two\n#' sub-lists (when `level = 'both'`). The list can be passed to\n#' \\code{\\link[bootstrapnet]{gg_networklevel}}. Each sub-list, contains two\n#' data frames: `stats_df` and `lines_df`, which can be used by the\n#' `ggplot2::geom_line()` function. See the return section of\n#' \\code{\\link[bootstrapnet]{get_stats_single}} for more details about\n#' `stats_df` and `lines_df` data frames.\n#'\n#' @examples\n#'\n#' library(bootstrapnet)\n#' library(bipartite)\n#' library(magrittr)\n#' data(Safariland)\n#'\n#' set.seed(321)\n#' Safariland_1 <- Safariland[, sort(sample.int(ncol(Safariland), 10))]\n#' set.seed(123)\n#' Safariland_2 <- Safariland[, sort(sample.int(ncol(Safariland), 10))]\n#'\n#' lst <- list(s1 = Safariland_1, s2 = Safariland_2) %>%\n#' lapply(web_matrix_to_df) %>%\n#' boot_networklevel(col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"nestedness\",\n#' level = \"both\",\n#' start = 10,\n#' step = 10,\n#' n_boot = 10,\n#' n_cpu = 2)\n#' gg_networklevel(lst)\n#'\n#' @export\n#'\n#' @md\nboot_networklevel <- function(lst,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n n_boot,\n n_cpu,\n probs = c(0.025, 0.975),\n ...) {\n\n webs_stats <- vector(mode = \"list\", length = length(lst))\n names(webs_stats) <- names(lst)\n\n for (i in 1:length(webs_stats)){\n webs_stats[[i]] <- lst[[i]] %>%\n boot_networklevel_n(col_lower = col_lower,\n col_higher = col_higher,\n index = index,\n level = level,\n start = start,\n step = step,\n n_boot = n_boot,\n n_cpu = n_cpu,\n ...) %>%\n lapply(FUN = get_stats_single, probs = probs)\n }\n\n webs_stats %>%\n get_stats_multi() %>%\n return()\n}\n\n\n#' @title\n#' Bootstrap network level metric multiple times.\n#'\n#' @description\n#' Bootstrap a single network of interactions n times in parallel and collects\n#' the network level metrics. Starts with a small sample size of interactions\n#' (e.g. `start = 30`), builds the corresponding web matrix/network, computes\n#' its metric (e.g. `index = \"nestedness\"`) using\n#' \\code{\\link[bipartite]{networklevel}}, then adds new interactions (e.g.\n#' `step = 20`) until all interactions are sampled. The last sample is actually\n#' the entire network. Repeats n times (as given in `n_boot`) these steps in\n#' parallel on multiple CPUs.\n#'\n#' @param data\n#' Data frame of interactions from which to build and sample web matrices. Each\n#' interaction (row in the data frame) must be repeated as many times as it was\n#' observed. E.g. if the interaction species_1 x species_2 was observed 5\n#' times, then repeat that row 5 times within the data frame. See examples\n#' below.\n#'\n#' @param col_lower\n#' Quoted column name in `data` for lower trophic level species (plants).\n#'\n#' @param col_higher\n#' Quoted column name in `data` for higher trophic level species (insects).\n#'\n#' @param index\n#' Passed to \\code{\\link[bipartite]{networklevel}}. See\n#' `?bipartite::networklevel` for details.\n#'\n#' @param level\n#' Passed to \\code{\\link[bipartite]{networklevel}}. See\n#' `?bipartite::networklevel` for details. For which level should the\n#' level-specific indices be computed: 'both' (default), 'lower' or 'higher'?\n#'\n#' @param start\n#' Integer. The sample size (number of interactions) to start the bootstrap\n#' with. If the start sample size is small (e.g. 5 or 10), then first\n#' iterations might results in NaN-s and warning messages are displayed.\n#' Consider to set `start` to maybe 10\\\\% of your total unique interactions.\n#'\n#' @param step\n#' Integer. Sample size (number of interactions) used to increase gradually the\n#' sampled network until all interactions are sampled. If `step` is too small\n#' (e.g. 1) then the computation time is very long depending on your total\n#' number of interactions from which samples are taken. Consider to set `step`\n#' to maybe 5-10\\\\% of your total unique interactions.\n#'\n#' @param n_boot\n#' Number of desired bootstraps (50 or 100 can be enough).\n#'\n#' @param n_cpu\n#' Number of CPU-s to use for parallel processing.\n#'\n#' @param ...\n#' Other arguments passed to \\code{\\link[bipartite]{networklevel}} like\n#' `logbase`, etc.\n#'\n#' @return\n#' Returns a list of a single matrix or a list of two matrices depending on the\n#' provided `index` metric and `level` value. For example if\n#' `index='nestedness'` and `level='both'`, then it returns a list of a single\n#' matrix. But in the case of `index='niche overlap'`, it returns a list of two\n#' matrices, first matrix (`niche.overlap.HL`) corresponding to the higher level\n#' (e.g. insects), and the second (`niche.overlap.LL`) for the lower level (e.g.\n#' plants). The number of rows of a matrix indicates how many iterations took\n#' place. This is decided internally based on the given values to `start`,\n#' `step` and the total number of rows (interactions) in `data`.The row names\n#' give the sample size at each iteration. The last iteration (last row name) is\n#' always the entire network (total number of interactions in `data`). The\n#' number of columns corresponds to `n_boot` (number of bootstraps).\n#'\n#' @examples\n#'\n#' library(bootstrapnet)\n#' library(magrittr)\n#' library(bipartite)\n#' data(Safariland)\n#'\n#' Safariland %>%\n#' web_matrix_to_df() %>%\n#' boot_networklevel_n(col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"niche overlap\",\n#' level = \"both\",\n#' start = 100,\n#' step = 100,\n#' n_boot = 10,\n#' n_cpu = 2)\n#'\n#' @import data.table\n#'\n#' @importFrom foreach foreach %dopar% registerDoSEQ\n#' @importFrom parallel splitIndices makeCluster stopCluster\n#' @importFrom doParallel registerDoParallel\n#' @importFrom iterators iter\n#'\n#' @export\n#'\n#' @md\nboot_networklevel_n <- function(data,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n n_boot,\n n_cpu,\n ...){\n test_data(data, col_lower, col_higher)\n cls_data <- class(data)\n if (! \"data.table\" %in% cls_data) data.table::setDT(data)\n\n test_index_networklevel(index)\n\n test_level_value(level)\n\n test_data_species_names(data, col_lower, col_higher)\n\n # Get sample sizes. Row names of the resulting bootstrap matrices will carry\n # information about the sample size at each iteration/bootstrap step. This is\n # run also before the parallel processing initiation because it can throw\n # error messages if the start and step values are not adequate. No need to\n # initiate parallel processing for something that will fail.\n iter_spl_size <- sample_indices(data = data, start = start, step = step, seed = 42) %>%\n sapply(length)\n\n { # Start parallel processing\n chunks <- parallel::splitIndices(n_boot, n_cpu)\n cl <- parallel::makeCluster(n_cpu)\n doParallel::registerDoParallel(cl)\n i <- NULL # to avoid 'Undefined global functions or variables: i' in R CMD check\n\n boot_lst <-\n foreach::foreach(i = iterators::iter(chunks),\n .errorhandling = 'pass',\n .packages = c(\"magrittr\",\n \"data.table\",\n \"bipartite\"),\n .export = c(\"boot_networklevel_once\",\n \"split_in_chunks\",\n \"sample_indices\")) %dopar% {\n lapply(i, FUN = function(x) # note the lapply!\n boot_networklevel_once(data = data,\n col_lower = col_lower,\n col_higher = col_higher,\n index = index,\n level = level,\n start = start,\n step = step,\n seed = x,\n ...)\n )\n }\n parallel::stopCluster(cl)\n remove(cl)\n foreach::registerDoSEQ()\n } # End of parallel processing\n\n boot_lst <- unlist(boot_lst, recursive = FALSE)\n\n metric_names <- names(boot_lst[[1]])\n n <- length(metric_names)\n\n if (n == 1) {\n results <- get_list_of_matrices(boot_lst, metric_names, n, iter_spl_size)\n # Convert data back to data frame if applicable\n if (! \"data.table\" %in% cls_data) data.table::setDF(data)\n return(results)\n\n } else if (n == 2) {\n results <- get_list_of_matrices(boot_lst, metric_names, n, iter_spl_size)\n if (! \"data.table\" %in% cls_data) data.table::setDF(data)\n return(results)\n }\n}\n\n\n#' @title\n#' Bootstrap network level metric once.\n#'\n#' @description\n#' You will rarely use this function alone. It was designed to be executed in\n#' parallel by \\code{\\link[bootstrapnet]{boot_networklevel_n}}. See more\n#' details there.\n#'\n#' @param data\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param col_lower\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param col_higher\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param index\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param level\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param start\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param step\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @param seed\n#' Set seed to get reproducible random results. Passed to `set.seed()`.\n#'\n#' @param ...\n#' See \\code{\\link[bootstrapnet]{boot_networklevel_n}}.\n#'\n#' @return\n#' Returns a data frame of one or two columns depending on the provided `index`\n#' metric and `level` value. For example if `index='nestedness'` and\n#' `level='both'`, then it returns a one column data frame. But in case of\n#' `index='niche overlap'`, it returns a two columns data frame, first column\n#' ('niche.overlap.HL') corresponding to the higher level (e.g. insects), and\n#' the second column ('niche.overlap.LL') for the lower level (e.g. plants).\n#' The number of rows in the returned data frame corresponds to the number of\n#' splits given by `bootstrapnet:::sample_indices` (is the length/number of\n#' elements of the list returned by this function). The last value(s) in the\n#' data frame correspond to the results of\n#' \\code{\\link[bipartite]{networklevel}} on the entire network - see in\n#' examples.\n#'\n#' @references\n#' This function is a wrapper of \\code{\\link[bipartite]{networklevel}}.\n#'\n#' @import data.table\n#'\n#' @importFrom magrittr %>%\n#' @importFrom bipartite networklevel\n#'\n#' @examples\n#'\n#' library(bootstrapnet)\n#' library(bipartite)\n#' library(data.table)\n#'\n#' data(Safariland)\n#'\n#' df <- web_matrix_to_df(Safariland)\n#' setDT(df) # df must be a data.table and not data.frame\n#'\n#' # Example with \"nestedness\":\n#'\n#' boot_networklevel_once(data = df,\n#' col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"nestedness\",\n#' level = \"both\",\n#' start = 100,\n#' step = 100,\n#' seed = 2020-11-5)\n#' # Returns a one column data frame. The last value must equal the result of:\n#' set.seed(42) # this is the same seed used in the boot_networklevel_once() function\n#' bipartite::networklevel(table(df$lower, df$higher),\n#' index = \"nestedness\")\n#' # which is also the equivalent of:\n#' Safariland_sorted <- Safariland[order(rownames(Safariland)), order(colnames(Safariland))]\n#' set.seed(42)\n#' bipartite::networklevel(Safariland_sorted, index = \"nestedness\")\n#'\n#'\n#' # Example with \"niche overlap\":\n#'\n#' boot_networklevel_once(data = df,\n#' col_lower = \"lower\", # column name for plants\n#' col_higher = \"higher\", # column name for insects\n#' index = \"niche overlap\",\n#' level = \"both\",\n#' start = 100,\n#' step = 100,\n#' seed = 2020-11-5)\n#' # Returns a two column data frame\n#'\n#' @export\n#'\n#' @md\nboot_networklevel_once <- function(data,\n col_lower,\n col_higher,\n index,\n level,\n start,\n step,\n seed,\n ...){\n\n # List of sampled row indices from data to be used for bootstrapping. Each row\n # represents a plant-pollinator interaction. Each vector of sampled indices is\n # used to build a web matrix/network from data.\n ids_lst <- sample_indices(data = data, start = start, step = step, seed = seed)\n\n # Allocate memory for the objects that will grow during iterations/bootstrapping.\n metric_lst <- vector(mode = \"list\", length = length(ids_lst))\n\n # Sample interactions with the indices build above and compute the network\n # level metric until all interactions are sampled. In case of errors (most\n # probably related to small sample size of a network at first iterations),\n # then `try()` doesn't break the bootstrapping process.\n for (i in 1:length(ids_lst)){\n metric_lst[[i]] <- try({\n # Some metrics, like nestedness have random processes in their\n # computation, so set seed here also for reproducibility. That is, if one\n # wants to run the same random processes again, should get identical\n # results.\n web <- data[ids_lst[[i]], table(get(col_lower), get(col_higher))]\n set.seed(42)\n bipartite::networklevel(web = web, index = index, level = level, ...)\n })\n }\n\n # Prepare results as data frame.\n df <- metric_lst %>%\n lapply(rbind) %>%\n lapply(as.data.frame) %>%\n data.table::rbindlist()\n setDF(df)\n\n return(df)\n}\n", "meta": {"hexsha": "ac016a2f6c92b8c88f35024868d3d83083c8a9ff", "size": 18019, "ext": "r", "lang": "R", "max_stars_repo_path": "R/boot_networklevel.r", "max_stars_repo_name": "valentinitnelav/bootstrapnet", "max_stars_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-12-13T14:09:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-25T13:53:11.000Z", "max_issues_repo_path": "R/boot_networklevel.r", "max_issues_repo_name": "valentinitnelav/bootstrapnet", "max_issues_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 34, "max_issues_repo_issues_event_min_datetime": "2019-11-06T00:07:03.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T16:57:00.000Z", "max_forks_repo_path": "R/boot_networklevel.r", "max_forks_repo_name": "valentinitnelav/bootstrapnet", "max_forks_repo_head_hexsha": "aeeb283db0467f660232ec14038f9f6a0b447fff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-12-03T15:41:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-03T15:41:38.000Z", "avg_line_length": 39.0021645022, "max_line_length": 92, "alphanum_fraction": 0.5964259948, "num_tokens": 4370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.33016561698226715}} {"text": "\n# armar plot ranking anual\n\n#levantar_datos \nlibrary(\"tidyverse\")\nbase::load(here::here(\"data\",\"temporal_acumulado_data.Rdata\"))\n\nget_tabla_valores_metrica_seleccionada <- function(data_df,cant_n,metrica_s,fundadores_df){\n # fundadores_df=fundadores\n # data_df=temporal_acumulado_nodos_grafos\n # cant_n=5;\n # metrica_s='degree';\n metrica_sym <-rlang::sym(metrica_s) \n # armar para cada periodo segun parametros en top N\n resu_final <- data_df %>% select(-es_fundador) %>% \n nest(data = c(autor, fuerza_colaboracion, degree, betweeness, eigen_centrality, \n closeness, page_rank, count_triangles)) %>% \n mutate(top_n_periodo=purrr::map(data,\n cant=cant_n,\n metrica=metrica_s,\n .f=function(d,metrica,cant){\n metrica_sym <-rlang::sym(metrica_s)\n ranking <- seq(from=1,to = cant,by = 1)\n \n ret <- d %>% arrange(desc(!!metrica_sym)) %>% \n nest(-!!metrica_sym) %>% # fix empates\n arrange(desc(!!metrica_sym)) %>% head(cant) %>% \n mutate(posicion=ranking) %>% \n mutate(valor_seleccion= !!metrica_sym) %>%\n unnest()%>% \n select(autor,valor_seleccion,posicion)\n \n ret\n })) %>% \n unnest(top_n_periodo) %>% \n select(-data) %>% \n left_join(fundadores_df,by=\"autor\") %>% \n group_by(periodo,valor_seleccion,posicion,es_fundador) %>% \n summarise(autores_posicion=paste(autor,collapse = \";\"),cant_autores=n()) %>% \n nest(data = c(periodo, valor_seleccion, es_fundador, autores_posicion, cant_autores) ) %>% #nest(-posicion)\n mutate(mantiene_data=purrr::map(data,.f=function(dat){\n resu = dat %>% \n mutate(mantiene = purrr::map2(.x=autores_posicion,.y=lag(autores_posicion,1),.f=function(auts,anterior){\n # que pasa si vienen varios, o si van varios. es un muchos v muchos.\n autores_originales <- str_split(auts,\";\",simplify = TRUE) \n autores_lag <- str_split(anterior,\";\",simplify = TRUE) # ojo si es el 1ro viene vacio.\n \n #si alguno de los originales estaba en lag, entonces true.\n resultado <- any(autores_originales %in% autores_lag )\n resultado\n })) %>% unnest(mantiene)\n resu\n \n })) %>% \n select(-data) %>% \n unnest(mantiene_data) %>% select(-cant_autores,-autores_posicion) %>% \n rename(valor=valor_seleccion) %>% \n mutate(metrica=metrica_s) %>% \n mutate(es_fundador=if_else(is.na(es_fundador),FALSE,es_fundador)) %>% \n pivot_longer(cols = c(\"mantiene\",\"es_fundador\",\"valor\"),names_to=\"variable\",values_to=\"valores\") %>% \n #ungroup() %>% \n select(periodo,metrica,posicion,variable,valores) %>% \n pivot_wider(names_from = periodo,values_from=valores) %>% \n arrange(posicion)\n resu_final\n}\n\n\ntemporal_acumulado_nodos_grafos <- temporal_acumulado_nodos_grafos %>% \n mutate(es_fundador=(periodo==1996)) %>% as_tibble()\n\nfundadores <- temporal_acumulado_nodos_grafos %>% filter(es_fundador) %>% distinct(autor,es_fundador)\n\n# definicion parametros\n\n#metrica_s='betweeness';\n#metrica_s='betweeness';\n\n# tomar caracter dinamico\ncant_n=5;\n\nmetrica_s='betweeness';\nresumen_btw <- get_tabla_valores_metrica_seleccionada(temporal_acumulado_nodos_grafos,cant_n=5,metrica_s=metrica_s,fundadores_df=fundadores)\nreadr::write_tsv(x = resumen_btw,here::here(\"tmp\",\"resumen_betweeness.csv\"))\n\n\n# aca hay algo que se repiten filas, sin embargo los valores repetidos son iguales. es probable que haya mas de 1 autor y por eso repite.\n# toca validar para que quede limpito de una.\nmetrica_s='degree';\nresumen_degree <- get_tabla_valores_metrica_seleccionada(temporal_acumulado_nodos_grafos,cant_n=5,metrica_s=metrica_s,fundadores_df=fundadores) %>% \n unnest(cols = c(`1996`, `1999`, `2001`, `2002`, `2003`, `2004`, `2005`, `2006`, \n `2007`, `2008`, `2009`, `2010`, `2011`, `2012`, `2013`, `2014`, \n `2015`, `2016`))\nreadr::write_tsv(x = resumen_degree,here::here(\"tmp\",\"resumen_degree.csv\"))\n\nmetrica_s='fuerza_colaboracion';\nresumen_fuerza_colab <- get_tabla_valores_metrica_seleccionada(temporal_acumulado_nodos_grafos,cant_n=5,metrica_s=metrica_s,fundadores_df=fundadores)\nreadr::write_tsv(x = resumen_fuerza_colab,here::here(\"tmp\",\"resumen_fuerza_colaboracion.csv\"))\n\n", "meta": {"hexsha": "4c0758c33d5895ed1e02908c0c08b34d1aafbb3b", "size": 5069, "ext": "r", "lang": "R", "max_stars_repo_path": "test/test_prueba_concepto_cuadro_mantienen_ranking.r", "max_stars_repo_name": "jas1/raab_coaut_tesis", "max_stars_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-01-26T03:25:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T03:25:05.000Z", "max_issues_repo_path": "test/test_prueba_concepto_cuadro_mantienen_ranking.r", "max_issues_repo_name": "jas1/raab_coaut_tesis", "max_issues_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-12-05T19:22:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-05T19:22:29.000Z", "max_forks_repo_path": "test/test_prueba_concepto_cuadro_mantienen_ranking.r", "max_forks_repo_name": "jas1/raab_coaut_tesis", "max_forks_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.69, "max_line_length": 149, "alphanum_fraction": 0.5760505031, "num_tokens": 1322, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3301060722590912}} {"text": "#!/usr/bin/env Rscript\nargs <- commandArgs(TRUE)\nintable = paste(\"\", args[1], sep=\"\")\noutpdf = paste(\"\", args[2], sep=\"\")\n\nif(intable == 'NA' || outpdf == 'NA') {\n message(\"Usage: plot_trans_reads_counts.r [input table] [output pdf]\")\n \n} else {\n\n dat = read.delim(intable, header=T)\n dat[is.na(dat[,4]),4]=0\n\n x.reads = cumsum(dat[,2])\n x.reads = x.reads/max(x.reads)\n x.genes = cumsum(dat[,3])\n x.genes = x.genes/max(x.genes)\n x.unmap = cumsum(dat[,4])\n x.unmap = x.unmap/max(x.unmap)\n x.tot = cumsum(dat[,2] + dat[,4])\n x.tot = x.tot/max(x.tot)\n\n pdf(outpdf)\n plot(1:length(x.reads), x.reads, type='l', col=\"blue\", xlab=\"cell barcodes\", ylab=\"cumulative fraction\", ylim=c(0,1))\n lines(1:length(x.genes), x.genes, type='l', col=\"green\")\n lines(1:length(x.unmap), x.unmap, type='l', col=\"gray\")\n lines(1:length(x.tot), x.tot, type='l', col=\"black\")\n legend(\"bottomright\", inset=0.01, lty=1, cex=0.8, col=c(\"blue\",\"green\",\"gray\",\"black\"), legend=c(\"genic_reads\",\"genes\",\"intergenic_reads\",\"total_reads\"))\n\n plot(1:length(x.reads), x.reads, type='l', col=\"blue\", xlab=\"cell barcodes\", ylab=\"cumulative fraction\", xlim=c(1,50000), ylim=c(0,1))\n lines(1:length(x.genes), x.genes, type='l', col=\"green\")\n lines(1:length(x.unmap), x.unmap, type='l', col=\"gray\")\n lines(1:length(x.tot), x.tot, type='l', col=\"black\")\n legend(\"topleft\", inset=0.01, lty=1, cex=0.8, col=c(\"blue\",\"green\",\"gray\",\"black\"), legend=c(\"genic_reads\",\"genes\",\"intergenic_reads\",\"total_reads\"))\n\n plot(1:length(x.reads), x.reads, type='l', col=\"blue\", xlab=\"cell barcodes\", ylab=\"cumulative fraction\", xlim=c(1,5000), ylim=c(0,1))\n lines(1:length(x.genes), x.genes, type='l', col=\"green\")\n lines(1:length(x.unmap), x.unmap, type='l', col=\"gray\")\n lines(1:length(x.tot), x.tot, type='l', col=\"black\")\n legend(\"topleft\", inset=0.01, lty=1, cex=0.8, col=c(\"blue\",\"green\",\"gray\",\"black\"), legend=c(\"genic_reads\",\"genes\",\"intergenic_reads\",\"total_reads\"))\n\n plot(1:length(x.reads), x.reads, type='l', col=\"blue\", xlab=\"cell barcodes\", ylab=\"cumulative fraction\", xlim=c(1,500), ylim=c(0,1))\n lines(1:length(x.genes), x.genes, type='l', col=\"green\")\n lines(1:length(x.unmap), x.unmap, type='l', col=\"gray\")\n lines(1:length(x.tot), x.tot, type='l', col=\"black\")\n legend(\"topleft\", inset=0.01, lty=1, cex=0.8, col=c(\"blue\",\"green\",\"gray\",\"black\"), legend=c(\"genic_reads\",\"genes\",\"intergenic_reads\",\"total_reads\"))\n dev.off()\n}\n", "meta": {"hexsha": "069aed969cc25e4189c5483b2a9b89a02d14a1a8", "size": 2488, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_trans_reads_counts.r", "max_stars_repo_name": "alecw/Dropseq", "max_stars_repo_head_hexsha": "a7fbf35ef3130c8b1d85648d47cd90ecab281e9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-10-25T18:30:12.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-18T16:21:47.000Z", "max_issues_repo_path": "plot_trans_reads_counts.r", "max_issues_repo_name": "alecw/Dropseq", "max_issues_repo_head_hexsha": "a7fbf35ef3130c8b1d85648d47cd90ecab281e9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-01-17T04:31:35.000Z", "max_issues_repo_issues_event_max_datetime": "2019-01-17T04:31:35.000Z", "max_forks_repo_path": "plot_trans_reads_counts.r", "max_forks_repo_name": "alecw/Dropseq", "max_forks_repo_head_hexsha": "a7fbf35ef3130c8b1d85648d47cd90ecab281e9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-01-17T14:19:22.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-17T14:19:22.000Z", "avg_line_length": 50.7755102041, "max_line_length": 157, "alphanum_fraction": 0.6173633441, "num_tokens": 853, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.33010607225909117}} {"text": "#xdata <- peakShape_XCMS3(my_data,cor.val=0.9, useNoise = setNoise)\n\n#and then proceed with retention time correction and correspondence/grouping\n# Decreasing cor.val will allow more non-gaussian peaks through the filter\n\n#original file version from the Google Groups for xcms from Tony Larson\n#KL corrected this version 8/23/2011 for XCMS < 3\n#KL updating to using XCMS3, 11/5/2018\n\n#original peakShape function to remove non-gaussian peaks from an xcmsSet\n#code originally had cor.val = 0.9; 0.5 is too low (not doing enough pruning)\n#object is updated to be an XCMSnExp class\n\npeakShape_XCMS3 <- function(object, cor.val=0.9, useNoise = setNoise)\n{\nrequire(xcms)\n\n#files <- object@filepaths #old code\nfiles <- fileNames(object)\n \n#peakmat <- object@peaks #old code\npeakmat <- chromPeaks(object) #extract everything\n\npeakmat.new <- matrix(-1,1,ncol(peakmat)) \ncolnames(peakmat.new) <- colnames(peakmat)\n\nfor(f in 1:length(files))\n {\n #xraw <- xcmsRaw(files[f], profstep=0) #old code\n raw_data <- readMSData(files[f],msLevel = 1, mode = \"onDisk\") #use 'onDisk' to make the next step faster\n sub.peakmat <- peakmat[which(peakmat[,\"sample\"]==f),,drop=F]\n corr <- numeric()\n for (p in 1:nrow(sub.peakmat))\n {\n #old code\n #tempEIC <-\n #as.integer(rawEIC(xraw,mzrange=c(sub.peakmat[p,\"mzmin\"]-0.001,sub.peakmat[p,\"mzmax\"]+0.001))$intensity)\n #minrt.scan <- which.min(abs(xraw@scantime-sub.peakmat[p,\"rtmin\"]))[1]\n #maxrt.scan <- which.min(abs(xraw@scantime-sub.peakmat[p,\"rtmax\"]))[1]\n #eics <- tempEIC[minrt.scan:maxrt.scan]\n \n mzRange = c(sub.peakmat[p,\"mzmin\"]-0.001,sub.peakmat[p,\"mzmax\"]+0.001)\n subsetOnMZ <- filterMz(raw_data, mz = mzRange)\n \n #now set the Rt range...use sub.peakmat min and max RT\n rtRange = c(sub.peakmat[p,\"rtmin\"],sub.peakmat[p,\"rtmax\"])\n subsetOnMZandRT <- filterRt(subsetOnMZ, rt = rtRange)\n \n eics <- intensity(subsetOnMZandRT) #get the intensity values\n eics[sapply(eics, function(x) length(x)==0)] <- 0 #if empty in a scan, convert to 0\n eics <- as.integer(unlist(eics)) #use as.double for Lumos\n\n #filter out features that are less than the noise level I have already set...\n setThreshold <- which(eics < useNoise)\n eics <- eics[-setThreshold]\n rm(setThreshold)\n\n #remove any NA (easier bc downstream leaving it in causes issues)\n eics <- eics[!is.na(eics)]\n \n getIdx <- which(eics == min(eics))[1] #if multiple values, just need the first match\n #set min to 0 and normalise\n eics <- eics-eics[getIdx]\n \n if(max(eics,na.rm=TRUE)>0)\n {\n eics <- eics/max(eics, na.rm=TRUE)\n }\n #fit gauss and let failures to fit through as corr=1\n fit <- try(nls(y ~ SSgauss(x, mu, sigma, h), \n data.frame(x = 1:length(eics), y = eics)),silent=T)\n \n if(class(fit) == \"try-error\")\n {\n corr[p] <- 1\n } else {\n #calculate correlation of eics against gaussian fit\n if (length(which(!is.na(eics - fitted(fit)))) > 4 &&\n length(!is.na(unique(eics)))>4 && \n length(!is.na(unique(fitted(fit))))>4)\n {\n cor <- NULL\n options(show.error.messages = FALSE)\n cor <- try(cor.test(eics,fitted(fit),method=\"pearson\",use=\"complete\"))\n options(show.error.messages = TRUE)\n if (!is.null(cor))\n {\n if(cor$p.value <= 0.05) {\n corr[p] <- cor$estimate \n } else {\n corr[p] <- 0 }\n } \n else corr[p] <- 0\n } else corr[p] <- 0\n }\n } #this ends to the 'p' loop (going through one mzRT feature at a time)\n \n filt.peakmat <- sub.peakmat[which(corr >= cor.val),]\n peakmat.new <- rbind(peakmat.new, filt.peakmat)\n n.rmpeaks <- nrow(sub.peakmat)-nrow(filt.peakmat)\n cat(\"Peakshape evaluation: sample \", \n basename(files[f]),\"\n \",n.rmpeaks,\"/\",nrow(sub.peakmat),\" peaks removed\",\"\\n\")\n \n if (.Platform$OS.type == \"windows\") flush.console()\n \n \n } #this ends the 'f' loop (going through one file at a time())\n\npeakmat.new <- peakmat.new[-1,] #all but the first row that is all -1\nobject.new <- object #copy to a new object\n\n#object.new@peaks <- peakmat.new #old code\nchromPeaks(object.new) <- peakmat.new #this will return an answer, but losing information\n\n#add this line to retain the history information\nobject.new@.processHistory <- object@.processHistory\n\nreturn(object.new) \n} #this ends the function itself", "meta": {"hexsha": "1e01f3c7592f9b05cf97a9b3f8f6d7fa6fa21889", "size": 5510, "ext": "r", "lang": "R", "max_stars_repo_path": "peakShape_XCMS3.r", "max_stars_repo_name": "WHOIGit/advancingMS", "max_stars_repo_head_hexsha": "a303f41f53a2b6fc1d492f222d6d4a54b86b857d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "peakShape_XCMS3.r", "max_issues_repo_name": "WHOIGit/advancingMS", "max_issues_repo_head_hexsha": "a303f41f53a2b6fc1d492f222d6d4a54b86b857d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "peakShape_XCMS3.r", "max_forks_repo_name": "WHOIGit/advancingMS", "max_forks_repo_head_hexsha": "a303f41f53a2b6fc1d492f222d6d4a54b86b857d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.9166666667, "max_line_length": 124, "alphanum_fraction": 0.5161524501, "num_tokens": 1341, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.33010607225909117}} {"text": "\\name{to_igraph}\n\\alias{to_igraph}\n%- Also NEED an '\\alias' for EACH other topic documented here.\n\\title{\n Convert a PAFit_net object to an igraph object\n}\n\\description{\n This function converts a \\code{PAFit_net} object to an \\code{igraph} object (of package \\pkg{igraph}).\n}\n\\usage{\nto_igraph(net_object)\n}\n%- maybe also 'usage' for other objects documented here.\n\\arguments{\n\\item{net_object}{\nAn object of class \\code{PAFit_net}.\n}\n}\n\n\\value{\nThe function returns an \\code{igraph} object.\n}\n\n\\author{\nThong Pham \\email{thongpham@thongpham.net}\n}\n\n\n\\examples{\nlibrary(\"PAFit\")\n# a network from Bianconi-Barabasi model\nnet <- generate_BB(N = 50 , m = 10 , s = 10)\nigraph_graph <- to_igraph(net)\n}\n", "meta": {"hexsha": "8cbf7cf6c185eb81259f8ccbe7b4a6e26298207a", "size": 708, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/to_igraph.rd", "max_stars_repo_name": "eddelbuettel/PAFit", "max_stars_repo_head_hexsha": "4e362700bccb0de76778f31312b113cce85f6b4b", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "man/to_igraph.rd", "max_issues_repo_name": "eddelbuettel/PAFit", "max_issues_repo_head_hexsha": "4e362700bccb0de76778f31312b113cce85f6b4b", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/to_igraph.rd", "max_forks_repo_name": "eddelbuettel/PAFit", "max_forks_repo_head_hexsha": "4e362700bccb0de76778f31312b113cce85f6b4b", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.2285714286, "max_line_length": 104, "alphanum_fraction": 0.7146892655, "num_tokens": 208, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6548947357776795, "lm_q1q2_score": 0.3300054984053845}} {"text": "add <- 0\nst <- proc.time()\n\nfor(index in 0:100000001)\n{\n add <- add + index;\n}\n\ned <- proc.time() - st\n\nmessage(\"==========================\")\nmessage(\"R \ud14c\uc2a4\ud2b8\")\nmessage(add)\nprint(ed)", "meta": {"hexsha": "f6c7a92eaef9e20ef58ff5bf5fb6fdb542bbfd4f", "size": 184, "ext": "r", "lang": "R", "max_stars_repo_path": "R/r.r", "max_stars_repo_name": "bluetsys/language-benchmarking", "max_stars_repo_head_hexsha": "ebbfed16f2e56384d70a27d2836efdf6328b7a9c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/r.r", "max_issues_repo_name": "bluetsys/language-benchmarking", "max_issues_repo_head_hexsha": "ebbfed16f2e56384d70a27d2836efdf6328b7a9c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/r.r", "max_forks_repo_name": "bluetsys/language-benchmarking", "max_forks_repo_head_hexsha": "ebbfed16f2e56384d70a27d2836efdf6328b7a9c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.1428571429, "max_line_length": 37, "alphanum_fraction": 0.5, "num_tokens": 54, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.32996463380631563}} {"text": "#!/usr/bin/env bash Rscript --vanilla\n\n## --------------------------------------------------------------------------------------------------------------------\n## Script: \n## Author: Mike Freund (mfreundc@gmail.com)\n## Input: \n## - ...\n## Output:\n## - ...\n## Notes:\n## - TODO: should probably add handling of vertices with no bold\n## --------------------------------------------------------------------------------------------------------------------\n\n\n## setup ----\n\n\n## packages and sourced variables\n\nsuppressMessages(library(here))\nsuppressMessages(library(dplyr))\nlibrary(tidyr)\nsuppressMessages(library(data.table))\nsuppressMessages(library(gifti))\nlibrary(abind)\nlibrary(rhdf5)\nlibrary(foreach)\nsuppressMessages(library(doParallel))\nsuppressMessages(library(CovTools))\nlibrary(pracma)\nsource(here(\"src\", \"stroop-rsa-pc.R\"))\nlibrary(profvis)\n\n## set variables\n\ntask <- \"Stroop\"\n\nif (interactive()) { \n glmname <- \"lsall_1rpm\"\n roiset <- \"Schaefer2018Network\"\n prewh <- \"obspc50\" ## obsresamp, obsall, obsresampbias, obsresamppc50, obsbias, obspc50\n subjlist <- \"mcmi\"\n subjects <- fread(here(paste0(\"out/subjlist_\", subjlist, \".txt\")))[[1]]\n waves <- \"wave1\"\n sessions <- \"proactive\"\n n_cores <- 10\n ii <- 2#321 ## Vis: 331, SomMot: 341\n overwrite <- FALSE\n n_resamples <- 1E2\n} else {\n source(here(\"src\", \"parse_args.r\"))\n print(args)\n}\n\nstopifnot(prewh %in% expected$prewh)\n\natlas <- read_atlas(roiset)\nrois <- names(atlas$roi)\n\nif (prewh %in% c(\"obsresamp\", \"obsall\")) {\n ttype_subset <- \"all\"\n} else if (prewh %in% c(\"obsresampbias\", \"obsbias\")) {\n ttype_subset <- \"bias\"\n} else if (prewh %in% c(\"obsresamppc50\", \"obspc50\")) {\n ttype_subset <- \"pc50\"\n} else {\n stop(\"unexpected input for prewh argument\")\n}\n\n\n## execute ----\n\ninput <- construct_filenames_h5(\n prefix = \"coefs\", subjects = subjects, waves = waves, sessions = sessions, rois = rois, runs = runs, \n glmname = glmname, prewh = \"none\"\n)\n\n## option to skip if dset already exists (not overwrite):\nif (!overwrite) {\n dset_name_new <- gsub(\"prewh-none\", paste0(\"prewh-\", prewh), input$dset_name)\n file_ls <- mclapply(input$file_name, h5ls, mc.cores = 20)\n dset_exists <- mapply(function(x, y) x %in% y$name, x = dset_name_new, y = file_ls)\n input <- input[!dset_exists, ]\n}\n\n\ncl <- makeCluster(n_cores, type = \"FORK\", outfile = \"\")\nregisterDoParallel(cl)\nres <- foreach(ii = seq_along(input$file_name), .inorder = FALSE) %dopar% {\n\n input_val <- input[ii, ]\n subject <- input_val[, subj]\n session <- input_val[, session]\n wave <- input_val[, wave]\n run <- input_val[, run]\n roi <- input_val[, roi]\n\n B <- read_dset(input_val$file_name, input_val$dset_name)\n \n ## remove trial-wise variance due to conditions from each voxel\n X <- indicator_matrix(colnames(B)) ## colnames of B gives the condition\n resids <- resid(.lm.fit(X, t(B)))\n \n ## estimate covariance matrix of residuals and invert\n resids <- resids[rownames(resids) %in% ttypes[[ttype_subset]], ] ## extract specified trialtypes\n if (grepl(\"resamp\", prewh)) {\n S <- resample_apply_combine(\n x = t(resids), \n resample_idx = get_resampled_idx(\n conditions = rownames(resids), \n n_resamples, \n expected_min = expected_min[[paste0(session, \"_\", ttype_subset)]]\n ),\n apply_fun = function(.x) CovEst.2010OAS(t(.x))$S,\n combine_fun = \"iterative_add\",\n outdim = c(ncol(resids), ncol(resids))\n )\n } else {\n S <- CovEst.2010OAS(resids)\n }\n \n W <- crossprod(sqrtm(S$S)$Binv, B) ## apply sqrt of inverse\n\n out <- write_dset(\n mat = W,\n dset_prefix = \"coefs\", \n subject = subject, \n session = session, \n wave = wave, \n run = run, \n roiset = roiset, \n roi = roi,\n glmname = glmname,\n prewh = prewh, \n write_colnames = TRUE\n )\n\n c(out, rho = S$rho) ## returns metadata\n\n\n}\nstopCluster(cl)\n\ndata_info <- as.data.table(do.call(rbind, res))\nfn <- construct_filename_datainfo(\n prefix = \"coefs\", subjlist = subjlist, glmname = glmname, roiset = roiset, prewh = prewh\n )\nfwrite(data_info, fn)", "meta": {"hexsha": "51ef02e283af69d308bd896c22a189b2a6625bb0", "size": 4277, "ext": "r", "lang": "R", "max_stars_repo_path": "src/4_rsa/prewhiten_coefs.r", "max_stars_repo_name": "mcfreund/stroop-rsa-pc", "max_stars_repo_head_hexsha": "c330c3ea40b390ab524bc476bebb42f18f1c14b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/4_rsa/prewhiten_coefs.r", "max_issues_repo_name": "mcfreund/stroop-rsa-pc", "max_issues_repo_head_hexsha": "c330c3ea40b390ab524bc476bebb42f18f1c14b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-01-03T23:13:21.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T23:13:21.000Z", "max_forks_repo_path": "src/4_rsa/prewhiten_coefs.r", "max_forks_repo_name": "mcfreund/stroop-rsa-pc", "max_forks_repo_head_hexsha": "c330c3ea40b390ab524bc476bebb42f18f1c14b9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8986486486, "max_line_length": 119, "alphanum_fraction": 0.5840542436, "num_tokens": 1170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.32996463380631563}} {"text": "###### multiple response LARS (single combined)\n##TODO: Need to make # of folds a parameter exposed to used\nlibrary(lars)\n\nlars.multi.optimize <- function(tdata,rdata,pert,prior,allowed)\n{\n\tcat(as.character(Sys.time()),\"\\n\")\n\tlars.paths.cv <- lars.multi.cv(tdata,rdata,pert,prior,allowed,10)\n\t\n\tlars.paths <- lars.multi(tdata,rdata,pert,prior,allowed)\n\tB <- lars.multi.path.step(lars.paths,lars.paths.cv[[2]])\n\n\tB.adj <- matrix(0,nrow=dim(rdata)[1],ncol=dim(tdata)[1])\n\n\tfor (i in seq(dim(tdata)[1]))\n\t{\n\t\t#Scale B.adj according to prior\n\t\tB.adj[,i] <- B[[i]] * prior[,i]\n\t}\n\trval <- list()\n\trval[[1]] <- B.adj\n\trval[[2]] <- lars.paths\n\trval[[3]] <- lars.paths.cv\n\tcat(as.character(Sys.time()),\"\\n\")\n\trval\n}\n\nlars.multi.cv <- function(tdata,rdata,pert,prior,allowed,fold)\n{\t\n\tall.folds <- cv.folds(dim(tdata)[2], fold)\n\tlars.cv.paths <- list()\n\tcMax <- 0;\n\tfor (f in 1:fold)\n\t{\n\t\tcat(\"Building path for cv\",f,\"of\",fold,\"\\n\")\n\t\tomit <- all.folds[[f]]\n\t\tlars.cv.paths[[f]] <- lars.multi(tdata[,-omit], rdata[,-omit], pert[,-omit],prior,allowed)\n\t\tif (lars.cv.paths[[f]]$R[1,1]>cMax)\n\t\t{\n\t\t\tcMax <- lars.cv.paths[[f]]$R[1,1]\n\t\t}\n\t\t\n\t}\n\n\tcSteps <- c()\n\tmse <- c()\n\tbnorm <- c()\n\t\n\tconverged <- FALSE\n\tP <- c(cMax/10,0)\n\tFP <- c(lars.multi.cv.eval(P[1],fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed),lars.multi.cv.eval(P[2],fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed))\n\n\twhile (!converged)\n\t{\n\t\tImin <- which.min(FP)\n\t\tImax <- which.max(FP)\n\t\tif (Imin==Imax)\n\t\t\tconverged=TRUE\n\n\t\tcat(\"P:\",P,\"F(P):\",FP,\"Max/Min\",Imax,Imin,\"\\n\")\n\t\tPhat <- P[Imin]\n\t\tPref <- 2*Phat - P[Imax]\n\n\t\tif (Pref>cMax)\n\t\t\tFPref <- max(FP) + (Pref-cMax)^2\n\t\telse if (Pref<0)\n\t\t\tFPref <- max(FP) + Pref^2\n\t\telse\n\t\t\tFPref <- lars.multi.cv.eval(Pref,fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed)\n\t\t\n\t\t\tif (FPref < FP[Imin])\t\t\n\t\t\t{\n\t\t\t\t#Attempt expansion\n\t\t\t\tPexp <- 2*Pref - Phat\n\t\t\t\tFPexp <- lars.multi.cv.eval(Pexp,fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed)\n\t\t\t\tif (FPexp < FPref) #Expand\n\t\t\t\t{\n\t\t\t\t\tcat(\"Expand\\n\")\n\t\t\t\t\tP[Imax] <- Pexp\n\t\t\t\t\tFP[Imax] <- FPexp\n\t\t\t\t}\n\t\t\t\telse #Reflect\n\t\t\t\t{\n\t\t\t\t\tcat(\"Reflect\\n\")\n\t\t\t\t\tP[Imax] <- Pref\n\t\t\t\t\tFP[Imax] <- FPref\n\t\t\t\t}\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\t#Contract\n\t\t\t\tif (FP[Imax] < FPref)\n\t\t\t\t{\n\t\t\t\t\tcat(\"Contract (1)\\n\")\n\t\t\t\t\tP[Imax] <- (P[Imax] + Phat)/2\n\t\t\t\t\tFP[Imax] <- lars.multi.cv.eval(P[Imax],fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed)\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tcat(\"Contract (2)\\n\")\n\t\t\t\t\tP[Imax] <- (Pref + Phat)/2\n\t\t\t\t\tFP[Imax] <- lars.multi.cv.eval(P[Imax],fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed)\n\t\t\t\t}\n\t\t\t}\n\t}\n\trval <- list()\n\trval[[1]] <- lars.cv.paths\n\trval[[2]] <- P[which.min(FP)]\n\trval[[3]] <- min(FP)\n\trval[[4]] <- all.folds\n\trval\n}\n\nlars.multi.cv.eval <- function(cVal,fold,lars.cv.paths,all.folds,tdata,rdata,pert,prior,allowed,targets=NULL)\n{\n\t\tif (is.null(targets))\n\t\t\ttargets <- seq(dim(tdata)[1])\n\t\tmse <- rep(0,times=fold)\n\t\tfor (f in 1:fold)\n\t\t{\n\t\t\tB <- lars.multi.path.step(lars.cv.paths[[f]],cVal)\n\t\t\tfor (i in targets)\n\t\t\t{\n\t\t\t\tomit <- all.folds[[f]]\n\t\t\t\tomit <- setdiff(omit,which(pert[i,]!=0))\n\t\t\t\t\n\t\t\t\ttestx <- rdata[,omit]\n\t\t\t\ttesty <- tdata[i,omit]\n\t\t\t\ttestx[which(allowed[,i]==0),] <- 0\n\t\t\t\ttestx <- testx * prior[,i]\n\n\t\t\t\ttestp <- scale(t(testx) , lars.cv.paths[[f]][[i]]$meanx, FALSE) %*% matrix(B[[i]]) + lars.cv.paths[[f]][[i]]$mu\n\t\t\t\ttestr <- apply((testp - testy)^2,2,mean)\n\t\t\t\tmse[f] <- mse[f] + testr\n\t\t\t}\n\t\t\tmse[f] <- mse[f]/length(targets)\n\t\t}\n\t\ttotalmse <- sum(mse) / fold\n\t\ttotalmse\n}\n\n\nlars.multi <- function(tdata,rdata,pert,prior,allowed)\n{\n\tlars.paths <- list()\n\ttargets <- seq(dim(tdata)[1])\n\t\n\n\tcat(\"Building path for target: \")\n\tfor (i in targets)\n\t{\n\t\tcat(i,\"\")\n\t\tmindices <- which(pert[i,]==0)\n\n\t\tx <- rdata[,mindices] * prior[,i] \n\t\tx[which(allowed[,i]==0),]<-0; \n\n\t\t##### YK patch 05-23-2017\n\t\t# results.lars <- lars(t(x),tdata[i,mindices],trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,use.Gram=FALSE);\n\t\ty <- tdata[i,mindices];\n\t\tresults.lars.flag <- TRUE;\n\t\tsample.indx <- length(y); \n\t\twhile (results.lars.flag) {\n\t\t\tresults.lars <- try(lars(t(x[,1:sample.indx]),y[1:sample.indx],trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,use.Gram=FALSE));\n\t\t\tresults.lars.flag <- class(results.lars) =='try-error';\n\t\t\tif (results.lars.flag) { cat(' *no soln* '); }\n\t\t\tsample.indx <- sample.indx-1; \n\t\t}\n\t\t#####\n\t\tlars.paths[[i]] <- list(beta=results.lars$beta, mu=results.lars$mu, meanx=results.lars$meanx, C.Max=c(results.lars$C.Max,0))\n\t}\n\tcat(\"\\n\")\n\n\tcat(\"Building combined C.Max ranking\\n\")\n\tall.C.lars <- c()\n\tfor (i in targets)\n\t{\n\t\tall.C.lars <- rbind(all.C.lars, cbind(lars.paths[[i]]$C.Max,rep(i,times=length(lars.paths[[i]]$C.Max)),1:length(lars.paths[[i]]$C.Max)))\n\t}\n\tR <- all.C.lars[sort.list(all.C.lars[,1],decreasing=TRUE),]\n\tlars.paths$R <- R\n\tlars.paths$targets <- targets\n\tlars.paths\n}\n\nlars.multi.path.step <- function(lars.paths,stepC)\n{\n\tk <- 1\n\tif (length(which(lars.paths$R[,1]>stepC))>0)\n\t{\n\t\tk<-max(which(lars.paths$R[,1]>stepC))\n\t}\n\n\tB <- list()\n\tA <- c()\n\tAi <- unique(lars.paths$R[1:k,2])\n\tfor (i in Ai)\n\t{\t\n\t\tA <- rbind(A,c(i,lars.paths$R[max(which(lars.paths$R[1:k,2]==i)),3]))\n\t}\n\n\tfor (i in lars.paths$targets)\n\t{\n\t\tindx <- which(A[,1]==i)\n\t\tif (length(indx)==0)\n\t\t{\n\t\t\tB[[i]] <- rep(0,times=dim(lars.paths[[i]]$beta)[2])\n\t\t}\n\t\telse\n\t\t{\n\t\t\tplen <- dim(lars.paths[[i]]$beta)[1]\n\t\t\tif (A[indx,2]==plen)\n\t\t\t{\n\t\t\t\tB[[i]] <- lars.paths[[i]]$beta[plen,]\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif ( lars.paths$R[k,1] < lars.paths[[i]]$C.Max[A[indx,2]+1] )\n\t\t\t\t{\n\t\t\t\t\tif (A[indx,2] < plen)\n\t\t\t\t\t\tA[indx,2] <- A[indx,2] + 1\n\t\t\t\t}\n\t\t\t\tgamma <- (lars.paths[[i]]$C.Max[A[indx,2]] - lars.paths$R[k,1]) / (lars.paths[[i]]$C.Max[A[indx,2]] - lars.paths[[i]]$C.Max[A[indx,2]+1])\n\t\t\t\tu <- (lars.paths[[i]]$beta[A[indx,2]+1,] - lars.paths[[i]]$beta[A[indx,2],])\n\t\t\t\tB[[i]] <- lars.paths[[i]]$beta[A[indx,2],] + u * gamma\n\t\t\t}\n\t\t}\n\t}\n\tB\n}\n\n###\n\nlars.multi.cv.singlefold <- function(tdata,rdata,pert,prior,allowed,fold,seed,f)\n{\n\tset.seed(seed)\n\tall.folds <- cv.folds(dim(tdata)[2], fold)\n\tcat(\"Building path for cv\",f,\"of\",fold,\"\\n\")\n\tomit <- all.folds[[f]]\n\tlars.cv.paths <- lars.multi(tdata[,-omit], rdata[,-omit], pert[,-omit],prior,allowed)\n\tlars.cv.paths\n}\n\nlars.single.cv <- function(tdata,rdata,pert,prior,allowed,fold,seed)\n{\n\ttargets <- seq(dim(tdata)[1])\n\tlars.paths.cv <- list()\n\tlars.paths <- list()\n\tB <- matrix(0,nrow=dim(rdata)[1],ncol=dim(tdata))\n\n\tminFrac <- c()\n cat(\"Building path for target: \")\n for (i in targets)\n {\n cat(i,\"\")\n mindices <- which(pert[i,]==0)\n x <- rdata[,mindices] * prior[,i]\n x[which(allowed[,i]==0),]<-0;\n set.seed(seed)\n lars.paths.cv[[i]] <- cv.lars(t(x),tdata[i,mindices],K=fold,plot.it=FALSE,trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,se=FALSE);\n\t\tminFrac[i] <- lars.paths.cv[[i]]$index[which.min(lars.paths.cv[[i]]$cv)]\n\t\tlars.paths[[i]] <- lars(t(x),tdata[i,mindices],trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE);\n\t\tB[,i] <- predict(lars.paths[[i]],s=minFrac[i],type=\"coefficients\",mode=\"fraction\")$coefficients\n }\t\n\tcat(\"\\n\")\n\tB\n}\n\n## Parallel functionality\nlars.multi.cv.eval.parallel <- function(cVal)\n{\n\tmse <- 0;\n\tB <- lars.multi.path.step(cv.obj,cVal)\n\tfor (i in targets)\n\t{\n\t\tomit <- all.folds[[f]]\n\t\tomit <- setdiff(omit,which(pert[i,]!=0))\n\t\t\n\t\ttestx <- rdata[,omit]\n\t\ttesty <- tdata[i,omit]\n\t\ttestx[which(allowed[,i]==0),] <- 0\n\t\ttestx <- testx * prior[,i]\n\n\t\ttestp <- scale(t(testx) , cv.obj[[i]]$meanx, FALSE) %*% matrix(B[[i]]) + cv.obj[[i]]$mu\n\t\ttestr <- apply((testp - testy)^2,2,mean)\n\t\tmse <- mse + testr\n\t}\n\tmse <- mse/length(targets)\n\tmse\n}\n\nlars.multi.cv.cmax.parallel <- function()\n{\n\tcat(cv.obj$R[1,1],\"\\n\")\n\tcv.obj$R[1,1]\n}\n\nlars.multi.cv.parallel <- function()\n{\t\n\tcMax <- max(as.numeric(mpi.remote.exec(lars.multi.cv.cmax.parallel())))\n\tcat(\"cMax: \", cMax, \"\\n\")\n\tconverged <- FALSE\n\tP <- c(cMax/10,0)\n\t\n\tFP <- c(mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,P[1]))),mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,P[2]))))\n\t\n\twhile (!converged)\n\t{\n\t\tImin <- which.min(FP)\n\t\tImax <- which.max(FP)\n\t\tif (Imin==Imax)\n\t\t\tconverged=TRUE\n\t\n\t\tcat(\"P:\",P,\"F(P):\",FP,\"Max/Min\",Imax,Imin,\"\\n\")\n\t\tPhat <- P[Imin]\n\t\tPref <- 2*Phat - P[Imax]\n\t\n\t\tif (Pref>cMax)\n\t\t\tFPref <- max(FP) + (Pref-cMax)^2\n\t\telse if (Pref<0)\n\t\t\tFPref <- max(FP) + Pref^2\n\t\telse\n\t\t\tFPref <- mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,Pref)))\n\t\t\n\t\t\tif (FPref < FP[Imin])\t\t\n\t\t\t{\n\t\t\t\t#Attempt expansion\n\t\t\t\tPexp <- 2*Pref - Phat\n\t\t\t\tFPexp <- mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,Pexp)))\n\t\t\t\tif (FPexp < FPref) #Expand\n\t\t\t\t{\n\t\t\t\t\tcat(\"Expand\\n\")\n\t\t\t\t\tP[Imax] <- Pexp\n\t\t\t\t\tFP[Imax] <- FPexp\n\t\t\t\t}\n\t\t\t\telse #Reflect\n\t\t\t\t{\n\t\t\t\t\tcat(\"Reflect\\n\")\n\t\t\t\t\tP[Imax] <- Pref\n\t\t\t\t\tFP[Imax] <- FPref\n\t\t\t\t}\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\t#Contract\n\t\t\t\tif (FP[Imax] < FPref)\n\t\t\t\t{\n\t\t\t\t\tcat(\"Contract (1)\\n\")\n\t\t\t\t\tP[Imax] <- (P[Imax] + Phat)/2\n\t\t\t\t\tFP[Imax] <- mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,P[Imax])))\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tcat(\"Contract (2)\\n\")\n\t\t\t\t\tP[Imax] <- (Pref + Phat)/2\n\t\t\t\t\tFP[Imax] <- mean(as.numeric(mpi.remote.exec(cmd=lars.multi.cv.eval.parallel,P[Imax])))\n\t\t\t\t}\n\t\t\t}\n\t}\n\t\n\tcat(P[which.min(FP)],min(FP),\"\\n\")\n\n\tP[which.min(FP)]\n}\n\nlars.multi.optimize.parallel <- function()\n{\n\tcat(as.character(Sys.time()),\"\\n\")\n\tc.cvmin <- lars.multi.cv.parallel()\n\t\n\tlars.paths <- lars.multi(tdata,rdata,pert,prior,allowed)\n\tB <- lars.multi.path.step(lars.paths,c.cvmin)\n\t\n\tB.adj <- matrix(0,nrow=dim(rdata)[1],ncol=dim(tdata)[1])\n\t\n\tfor (i in seq(dim(tdata)[1]))\n\t{\n\t\t#Scale B.adj according to prior\n\t\tB.adj[,i] <- B[[i]] * prior[,i] \n\t}\n\trval <- list()\n\trval[[1]] <- B.adj\n\trval[[2]] <- lars.paths\n\tcat(as.character(Sys.time()),\"\\n\")\n\trval\n}\n\nlars.local <- function(tdata,rdata,pert,prior,allowed,skip_reg,skip_gen)\n{\n cat(as.character(Sys.time()),\"\\n\")\n B.adj <- matrix(0,nrow=dim(rdata)[1],ncol=dim(tdata)[1])\n\t\n cat(\"Working on Gene:\")\n\tfor(i in 1:dim(tdata)[1]) {\n\t\tif (skip_gen[i] == 0) {\n\t\t \tcat(i,\",\")\n\t\t\tmindices <- which(pert[i,]==0)\n\t\t\tx <- rdata[,mindices] * prior[,i]\n\t\t\tx[which(allowed[,i]==0),]<-0;\n\t\t\tnindices <- which(skip_reg==0)\n\t\t\tx <- x[nindices,]\n\t\t\tlars.paths.cv <- cv.lars(t(x),tdata[i,mindices],K=3,trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,se=FALSE, plot.it=FALSE,use.Gram=FALSE);\n\t\t\tminFrac <- lars.paths.cv$index[which.min(lars.paths.cv$cv)]\n\t\t\tlars.paths <- lars(t(x),tdata[i,mindices],trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,use.Gram=FALSE);\n\t\t\t# lars.paths <- lars(t(x),tdata[i,mindices],trace=FALSE,max.steps=600,type=\"lasso\",normalize=FALSE,intercept=TRUE,use.Gram=TRUE);\n\t\t\ttempVec <- predict(lars.paths,s=minFrac,type=\"coefficients\",mode=\"fraction\")$coefficients\n\t\t\t# tempVec <- lars.paths$beta\n\t\t\t# print(tempVec)\n\t\t\tnindices <- which(skip_reg==1)\n tempCol <- c(tempVec, rep(0,length(nindices)))\n tempIndices <- c(seq_along(tempVec), nindices+.5)\n B.adj[,i] <- tempCol[order(tempIndices)]\n #Scale B.adj according to prior\t\t\n\t\t\tB.adj[,i] <- B.adj[,i] * prior[,i]\n\t\t}\n\t}\n\tcat(\"\\n\")\n\n\trval <- list()\n rval[[1]] <- B.adj\n rval\n}\n\n", "meta": {"hexsha": "2806311c0e981cfd3399787c4565927697567faa", "size": 11237, "ext": "r", "lang": "R", "max_stars_repo_path": "SRC/NetProphet1/global.lars.regulators.r", "max_stars_repo_name": "ygidtu/NetProphet_2.0", "max_stars_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SRC/NetProphet1/global.lars.regulators.r", "max_issues_repo_name": "ygidtu/NetProphet_2.0", "max_issues_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SRC/NetProphet1/global.lars.regulators.r", "max_forks_repo_name": "ygidtu/NetProphet_2.0", "max_forks_repo_head_hexsha": "1ca665d15c6f06a732443bbbe251c58dd221e063", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5023584906, "max_line_length": 179, "alphanum_fraction": 0.5982913589, "num_tokens": 4123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.734119526900183, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.3299079082056962}} {"text": "###### 19. COMPARE MAFS ACROSS STRATEGIES (COMPLETE ONLY) ########\n#### DO STEPS 1-4, IRRESPECTIVE OF TREE, THEN PROCEED\niden <- which(rowSums(prPAM_g2[,-c(1:2)]) > 5)\nssp<-coordinates(prPAM_g2[iden,1:2])\nf <- clean_coordinates(as.data.frame(ssp), lon = \"LON\",lat = \"LAT\",\n tests=\"seas\",species=NULL,value=\"flagged\",seas_scale = 110)\n\n#### NULL 2 #####\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SA <- MDT_crown\nMAFstem_SA <- MDT_stem\nMAFfuse_SA <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_crown_NULL2.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SA <- (MAFcrown_SA - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_stem_NULL2.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SA <- (MAFstem_SA - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_fuse_non_NULL2.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SA <- (MAFfuse_SA - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SB <- MDT_crown\nMAFstem_SB <- MDT_stem\nMAFfuse_SB <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_crown_NULL2.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SB <- (MAFcrown_SB - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_stem_NULL2.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SB <- (MAFstem_SB - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_fuse_non_NULL2.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SB <- (MAFfuse_SB - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SD <- MDT_crown\nMAFstem_SD <- MDT_stem\nMAFfuse_SD <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_crown_NULL2.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SD <- (MAFcrown_SD - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_stem_NULL2.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SD <- (MAFstem_SD - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_fuse_non_NULL2.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SD <- (MAFfuse_SD - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\npdf(\"~/Desktop/NATURE_rev/2.MAF_null2_comparison_strategies.pdf\",useDingbats = F)\nplot(MAFcrown_SB[f],MAFcrown_SA[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SB[f],MAFcrown_SA[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\nplot(MAFcrown_SB[f],MAFcrown_SD[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SB[f],MAFcrown_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\nplot(MAFcrown_SA[f],MAFcrown_SD[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SA[f],MAFcrown_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\n\nplot(MAFstem_SB[f],MAFstem_SA[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(MAFstem_SB[f],MAFstem_SA[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\nplot(MAFstem_SB[f],MAFstem_SD[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFstem_SB[f],MAFstem_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\nplot(MAFstem_SA[f],MAFstem_SD[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFstem_SA[f],MAFstem_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\n\nplot(SES_crownMAF_SB[f],SES_crownMAF_SA[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SB[f],SES_crownMAF_SA[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 2)\")\nplot(SES_crownMAF_SB[f],SES_crownMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SB[f],SES_crownMAF_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 2)\")\nplot(SES_crownMAF_SA[f],SES_crownMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SA[f],SES_crownMAF_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 2)\")\n\nplot(SES_stemMAF_SB[f],SES_stemMAF_SA[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SB[f],SES_stemMAF_SA[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 2)\")\nplot(SES_stemMAF_SB[f],SES_stemMAF_SD[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SB[f],SES_stemMAF_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 2)\")\nplot(SES_stemMAF_SA[f],SES_stemMAF_SD[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SA[f],SES_stemMAF_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 2)\")\n\nplot(SES_fuseMAF_SB[f],SES_fuseMAF_SA[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SB[f],SES_fuseMAF_SA[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 2)\")\nplot(SES_fuseMAF_SB[f],SES_fuseMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SB[f],SES_fuseMAF_SD[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 2)\")\nplot(SES_fuseMAF_SA[f],SES_fuseMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SA[f],SES_fuseMAF_SD[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 2)\")\n\ndev.off()\n###########\n\n##### NULL 1 ######\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SA <- MDT_crown\nMAFstem_SA <- MDT_stem\nMAFfuse_SA <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_crown_NULL1.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SA <- (MAFcrown_SA - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_stem_NULL1.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SA <- (MAFstem_SA - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SAcomplete_v3/ MDT_fuse_non_NULL1.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SA <- (MAFfuse_SA - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SB <- MDT_crown\nMAFstem_SB <- MDT_stem\nMAFfuse_SB <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_crown_NULL1.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SB <- (MAFcrown_SB - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_stem_NULL1.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SB <- (MAFstem_SB - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SBcomplete_v3/ MDT_fuse_non_NULL1.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SB <- (MAFfuse_SB - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/WeightedMAFS.RData\")\nMAFcrown_SD <- MDT_crown\nMAFstem_SD <- MDT_stem\nMAFfuse_SD <- MDT_fuse_non\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_crown_NULL1.Rdata\")\nnull_crown <- MDT_crown_NULL2\nSES_crownMAF_SD <- (MAFcrown_SD - apply(null_crown[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_crown[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_stem_NULL1.Rdata\")\nnull_stem <- MDT_stem_NULL2\nSES_stemMAF_SD <- (MAFstem_SD - apply(null_stem[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_stem[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nload(\"~/Desktop/NATURE_rev/RES_SDcomplete_v3/ MDT_fuse_non_NULL1.Rdata\")\nnull_fuse <- MDT_fuse_non_NULL2\nSES_fuseMAF_SD <- (MAFfuse_SD - apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(null_fuse[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\npdf(\"~/Desktop/NATURE_rev/2.MAF_null1_comparison_strategies.pdf\",useDingbats = F)\nplot(MAFcrown_SB[f],MAFcrown_SA[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SB[f],MAFcrown_SA[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\nplot(MAFcrown_SB[f],MAFcrown_SD[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SB[f],MAFcrown_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\nplot(MAFcrown_SA[f],MAFcrown_SD[f],xlim=c(30,120),ylim=c(30,120),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFcrown_SA[f],MAFcrown_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"MAF crown\")\n\nplot(MAFstem_SB[f],MAFstem_SA[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(MAFstem_SB[f],MAFstem_SA[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\nplot(MAFstem_SB[f],MAFstem_SD[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFstem_SB[f],MAFstem_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\nplot(MAFstem_SA[f],MAFstem_SD[f],xlim=c(60,180),ylim=c(60,180),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(MAFstem_SA[f],MAFstem_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"MAF stem\")\n\nplot(SES_crownMAF_SB[f],SES_crownMAF_SA[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SB[f],SES_crownMAF_SA[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 1)\")\nplot(SES_crownMAF_SB[f],SES_crownMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SB[f],SES_crownMAF_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 1)\")\nplot(SES_crownMAF_SA[f],SES_crownMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_crownMAF_SA[f],SES_crownMAF_SD[f],pch=21,bg=\"blue\",cex=0.8)\ntitle(\"SES MAF crown (NULL 1)\")\n\nplot(SES_stemMAF_SB[f],SES_stemMAF_SA[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SB[f],SES_stemMAF_SA[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 1)\")\nplot(SES_stemMAF_SB[f],SES_stemMAF_SD[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SB[f],SES_stemMAF_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 1)\")\nplot(SES_stemMAF_SA[f],SES_stemMAF_SD[f],xlim=c(-10,5),ylim=c(-10,5),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_stemMAF_SA[f],SES_stemMAF_SD[f],pch=21,bg=\"red\",cex=0.8)\ntitle(\"SES MAF stem (NULL 1)\")\n\nplot(SES_fuseMAF_SB[f],SES_fuseMAF_SA[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Constrained Calibration (CC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SB[f],SES_fuseMAF_SA[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 1)\")\nplot(SES_fuseMAF_SB[f],SES_fuseMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Relaxed Calibration (RC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SB[f],SES_fuseMAF_SD[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 1)\")\nplot(SES_fuseMAF_SA[f],SES_fuseMAF_SD[f],xlim=c(-5,10),ylim=c(-5,10),type=\"n\",xlab=\"Constrained Calibration (CC)\",ylab=\"Unconstrained Calibration (UC)\")\nabline(a=0,b=1)\npoints(SES_fuseMAF_SA[f],SES_fuseMAF_SD[f],pch=21,bg=\"green\",cex=0.8)\ntitle(\"SES fuse (NULL 1)\")\n\ndev.off()\n", "meta": {"hexsha": "2ca28f86b9f848a3792e34795c172f0e87467de0", "size": 13468, "ext": "r", "lang": "R", "max_stars_repo_path": "code/CompareMAFStrategies.r", "max_stars_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_stars_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-14T03:29:42.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-14T03:29:42.000Z", "max_issues_repo_path": "code/CompareMAFStrategies.r", "max_issues_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_issues_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/CompareMAFStrategies.r", "max_forks_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_forks_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-07-06T15:43:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-06T15:43:48.000Z", "avg_line_length": 58.5565217391, "max_line_length": 154, "alphanum_fraction": 0.7304722305, "num_tokens": 5285, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.734119526900183, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.3299079082056962}} {"text": "#load environment\nlibrary(reshape2)\nlibrary(plyr)\noptions(\"scipen\" = 10)\n\n# functions\nseq_order_fun <- function(x) {\n seq_along(x)\n}\nto_numeric <- function(data, feature){\n data[[feature]] <- as.factor(data[[feature]])\n data[[feature]]<- mapvalues(data[[feature]], from=levels(data[[feature]]), \n to = 1:length(levels(data[[feature]])))\n return(data)\n} \n\ndynamic_dt_prep <- function(data, seq_length, nm=nms){\n length_dst <- as.data.frame(table(data$sessionId))\n sequences_long <- subset(length_dst, Freq>=seq_length)$Var1\n dt_long <- subset(data, sessionId %in% sequences_long)\n dt_longg <- dt_long[,-c('accountId',nm[-1],'gcName'),with=F]\n dt_long <- ddply(dt_longg, .(sessionId), mutate, seq_order = seq_order_fun(sessionId))\n dt_long_frst <- subset(dt_long, seq_order<=seq_length)\n dt_long_frst <- as.data.table(dt_long_frst)\n for(i in c(\"gcCause\", \"gcAction\")){\n dt_long_frst <- to_numeric(dt_long_frst, i)\n }\n suppressWarnings(for (i in seq_along(dt_long_frst)) set(dt_long_frst, i=which(dt_long_frst[[i]]==-1), j=i, value=0))\n dt_wide <- dcast.data.table(dt_long_frst, sessionId ~ seq_order, value.var=colnames(dt_long_frst)[-c(1,2,21)], fill=NaN)\n return(dt_wide)\n}\n#---------------------------------------------------------------------------------#\n\n# gcoverhead calculation\ndt$duration <- as.numeric(dt$duration)\ndt$sessionId <- as.factor(dt$sessionId)\ndt$timestamp <- as.numeric(dt$timestamp)\ngc_overhead <- ddply(dt, .(sessionId), summarize, gc_overhead=sum(duration)/(max(timestamp)-min(timestamp)))\ngc_overhead$gc_overhead <- ifelse(is.finite(gc_overhead$gc_overhead)!=T, 0, gc_overhead$gc_overhead)\ngc_overhead <- subset(gc_overhead, gc_overhead<=1)\n\n# 0 1 \n# 70597 9299 \n\n# labels\ngc_overhead$label <- ifelse(gc_overhead$gc_overhead>=0.0065,1,0)\ngc_overhead$index <- c(1:nrow(gc_overhead))\ntable(gc_overhead$label)\n\n# static\nsds <- aggregate(data=dt_sample, .~sessionId, sd)\ncolSums(sds[,-c(1)])\nnms <- as.character(c(\"sessionId, javaVersion, cpuCoreCount, maxAvailableMemory, PSPermGenmaxbefore, PSOldGenmaxbefore, PSPermGenmaxafter, PSOldGenmaxafter\"))\nnms <- strsplit(nms, split = \", \")[[1]]\n\ndt_static <- dt[,nms, with=F]\ndt_static_agg <- dt_static[!duplicated(dt_static$sessionId), ]\ndt_static <- merge(dt_static_agg, gc_overhead[,c(1,4)], by=\"sessionId\")\ndt_static$javaVersion <- ifelse(dt_static$javaVersion==1.7,1,2)\ndt_static$PSPermGenmaxbefore <- ifelse(dt_static$PSPermGenmaxbefore==-1,0,dt_static$PSPermGenmaxbefore)\ndt_static$PSPermGenmaxafter <- ifelse(dt_static$PSPermGenmaxafter==-1,0,dt_static$PSPermGenmaxafter)\ndt_static_final <- dt_static[,-c(1,9),with=F]\n\n# ToDO:\n# labels based on gc_overhead for each session\n# for each session data: all numeric?\n# static and dynamic separate\n# how many events to take?\n# each session - one line, list all features for one timestamp, then for another timestamp, etc\ndt_wide <- dynamic_dt_prep(dt, seq_length=10)\ndt_wide <- merge(dt_wide, gc_overhead[,c(1,4)], by=\"sessionId\")\ndt_dynamic <- dt_wide[order(dt_wide$index, decreasing=F),]\n\n# writing results\n# check consistency\ndim(as.data.frame(gc_overhead$label))\ndim(dt_static_final)\ndim(dt_dynamic)\n\nwrite.table(as.data.frame(gc_overhead$label), \"/Users/annaleontjeva/Desktop/My_files/Generative-Models-in-Classification/Data/labels_piper.txt\",\n col.names=F, row.names=F, sep=',',quote=F)\n\nwrite.table(dt_static_final, \"/Users/annaleontjeva/Desktop/My_files/Generative-Models-in-Classification/Data/static_piper.txt\",\n col.names=F, row.names=F, sep=',',quote=F)\n\nwrite.table(dt_wide_filtered, \"/Users/annaleontjeva/Desktop/My_files/Generative-Models-in-Classification/Data/dynamic_piper.txt\",\n col.names=F, row.names=F, sep=',',quote=F)\n\n#------------------------#\n# Plots and figures\n# Gc overhead\nggplot(gc_overhead, aes(x=gc_overhead)) + geom_histogram() + theme_bw(base_size=24)\nggplot(gc_overhead, aes(x=log(gc_overhead+0.000001, base=10))) + geom_density() + theme_bw(base_size=24)\n\ntmp <- subset(length_dst, Freq>=10)\ngc_overhead_tmp <- subset(gc_overhead, sessionId %in% tmp$Var1)\ntable(gc_overhead_tmp$label)\n\nggplot(gc_overhead_tmp, aes(x=log(gc_overhead+0.000001, base=10))) + geom_density() + theme_bw(base_size=24)\n\n# 0 1 \n# 34 8451\n\n# dynamic dataset\nlength_dst <- as.data.frame(table(dt$sessionId))\nggplot(length_dst, aes(x=log(Freq, base=10)))+geom_density()+theme_bw(base_size=24)\nggplot(gc_overhead_tmp, aes(x=gc_overhead)) + geom_histogram() + theme_bw(base_size=24)\n", "meta": {"hexsha": "4e92a52c9d212ad9a90ff12c325932a078b8f0d7", "size": 4500, "ext": "r", "lang": "R", "max_stars_repo_path": "DataNexus/prepare_piper.r", "max_stars_repo_name": "annitrolla/Generative-Models-in-Classification", "max_stars_repo_head_hexsha": "eca360a2fb7cf5548e8987c2524654e2340a7209", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2019-12-31T01:38:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T14:06:49.000Z", "max_issues_repo_path": "DataNexus/prepare_piper.r", "max_issues_repo_name": "annitrolla/Generative-Models-in-Classification", "max_issues_repo_head_hexsha": "eca360a2fb7cf5548e8987c2524654e2340a7209", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "DataNexus/prepare_piper.r", "max_forks_repo_name": "annitrolla/Generative-Models-in-Classification", "max_forks_repo_head_hexsha": "eca360a2fb7cf5548e8987c2524654e2340a7209", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-03-07T11:23:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-16T01:49:11.000Z", "avg_line_length": 41.6666666667, "max_line_length": 158, "alphanum_fraction": 0.7193333333, "num_tokens": 1299, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32987803115272485}} {"text": "'\n\nThe file \"figure1_mortality.r\" creates a figure, plotting\nexpropriation risk against settler mortality respectively.\nIt accounts for campaign/laborer indicators and distinguishes between\ncountries with original data and countries with conjectured mortality data.\n\n'\n\n\nrm(list=ls())\n\nargs = commandArgs(trailingOnly=TRUE)\n\nlibrary(foreign)\n\n\ndata <- read.csv(args[1])\n\n\n## Rebuilding of Figures 2A and 2B\n\ndatacamp <- data[grep(1,data$campaign),] #mortality from campaign\n\ndata2 <- data[grep(0,data$campaign),]\n\ndatabar <- data2[grep(0,data2$slave),] #mortality from barrack\ndatalab <- data2[grep(1,data2$slave),] #mortality from laborer\n\n## Plot that data first against risk\ndatacamphome <- datacamp[grep(1,datacamp$source0),]\ndatacampcon <- datacamp[grep(0,datacamp$source0),]\n\ndatabarhome <- databar[grep(1,databar$source0),]\ndatabarcon <- databar[grep(0,databar$source0),]\n\ndatalabhome <- datalab[grep(1,datalab$source0),]\ndatalabcon <- datalab[grep(0,datalab$source0),]\n\npng(filename=args[2])\nplot(\n datacamphome$logmort0, datacamphome$risk, pch=15,\n xlab=\"Logarithm of settler mortality\", ylab=\"Expropriation risk\",\n ylim=c(3,10), xlim=c(2,8), bty=\"L\"\n)\nlines(datacampcon$logmort0, datacampcon$risk, pch=22, type=\"p\")\n\nlines(databarhome$logmort0, databarhome$risk, pch=16, type=\"p\")\nlines(databarcon$logmort0, databarcon$risk, pch=21, type=\"p\")\n\nlines(datalabhome$logmort0, datalabhome$risk, pch=17, type=\"p\")\nlines(datalabcon$logmort0, datalabcon$risk, pch=2, type=\"p\")\n\ndev.off()\n", "meta": {"hexsha": "1f68445449295b06be574f32479b26d0af18466f", "size": 1502, "ext": "r", "lang": "R", "max_stars_repo_path": "docs/bld/example/r/r_example/src/final/figure_mortality.r", "max_stars_repo_name": "hmgaudecker/econ-project-templates", "max_stars_repo_head_hexsha": "0bf8c4701c112a96e2f2a2efe05be5ff39040111", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 67, "max_stars_repo_stars_event_min_datetime": "2015-01-19T17:41:02.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-17T19:12:49.000Z", "max_issues_repo_path": "docs/bld/example/r/r_example/src/final/figure_mortality.r", "max_issues_repo_name": "hmgaudecker/econ-project-templates", "max_issues_repo_head_hexsha": "0bf8c4701c112a96e2f2a2efe05be5ff39040111", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 64, "max_issues_repo_issues_event_min_datetime": "2015-02-23T15:18:24.000Z", "max_issues_repo_issues_event_max_datetime": "2019-11-20T19:58:59.000Z", "max_forks_repo_path": "{{cookiecutter.project_slug}}/src_r/final/figure_mortality.r", "max_forks_repo_name": "hmgaudecker/econ-project-templates", "max_forks_repo_head_hexsha": "0bf8c4701c112a96e2f2a2efe05be5ff39040111", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 51, "max_forks_repo_forks_event_min_datetime": "2015-01-15T16:10:33.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-28T21:14:03.000Z", "avg_line_length": 27.3090909091, "max_line_length": 75, "alphanum_fraction": 0.7463382157, "num_tokens": 483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3298780311527248}} {"text": "#' @export\nbib_invert = function(x)\n{\n storage.mode(x) = \"double\"\n .Call(\"bib_invert_\", x)\n}\n", "meta": {"hexsha": "41f0d330b76ac6236ff4ca42f8637441d84b62af", "size": 95, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/Rpkg/R/lapack.r", "max_stars_repo_name": "wrathematics/bib", "max_stars_repo_head_hexsha": "f4790504b89e203739ece6f84c1de1a616e64e6e", "max_stars_repo_licenses": ["BSD-1-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-11-09T19:44:12.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-26T16:32:04.000Z", "max_issues_repo_path": "tests/Rpkg/R/lapack.r", "max_issues_repo_name": "wrathematics/bib", "max_issues_repo_head_hexsha": "f4790504b89e203739ece6f84c1de1a616e64e6e", "max_issues_repo_licenses": ["BSD-1-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/Rpkg/R/lapack.r", "max_forks_repo_name": "wrathematics/bib", "max_forks_repo_head_hexsha": "f4790504b89e203739ece6f84c1de1a616e64e6e", "max_forks_repo_licenses": ["BSD-1-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.5714285714, "max_line_length": 28, "alphanum_fraction": 0.6210526316, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32987802343849515}} {"text": "#' Plots a given data.frame as a boxplot with optional subsets\n#'\n#' @param df data.frame holding at least the columns specified by x, y, and label\n#' @param x Column to be plotted on the horizontal axis\n#' @param y Column to be plotted on the vertical axis\n#' @param label Column of label to be used for each sample; indicates subsets\n#' @param quantiles If `x=\"group\"` quantiles will eb used to assign `up`, `down`, and `null`\n#' @param drop Whether to drop unused factor levels in `label`\nbox = function(df, x=\"group\", y=\"y\", label=\"label\", quantiles=c(0.2,0.8), drop=TRUE) {\n df$group = st$map.quantiles(score, quantiles, c('down', 'null', 'up'))\n\n ggplot(df, aes_string(x=\"group\", y=\"y\")) +\n geom_point(aes_string(fill=label), position=position_jitter(width=.25),\n pch=21, size=3, colour=\"black\") +\n geom_boxplot(fill=\"grey\", outlier.shape = NA) +\n scale_fill_discrete(drop=drop) +\n scale_colour_discrete(drop=drop)\n}\n", "meta": {"hexsha": "7bdbbfaece86cfed45ca884095e37c91bfb0d169", "size": 1006, "ext": "r", "lang": "R", "max_stars_repo_path": "plot/box.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "plot/box.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "plot/box.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 52.9473684211, "max_line_length": 93, "alphanum_fraction": 0.6520874751, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32987802343849515}} {"text": "# Version info: R 2.14.1, Biobase 2.15.3, GEOquery 2.23.2, limma 3.10.1\n\n\n################################################################\n# Differential expression analysis with limma\nlibrary(Biobase)\nlibrary(GEOquery)\nlibrary(limma)\n\n# load series and platform data from GEO\n\ngset <- getGEO(\"GSE15573\", GSEMatrix =TRUE)\nif (length(gset) > 1) idx <- grep(\"GPL6102\", attr(gset, \"names\")) else idx <- 1\ngset <- gset[[idx]]\n\n# make proper column names to match toptable \nfvarLabels(gset) <- make.names(fvarLabels(gset))\n\n# group names for all samples\nsml <- c(\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G0\",\"G0\",\"G1\",\"G0\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\");\n\n# log2 transform\nex <- exprs(gset)\nqx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))\nLogC <- (qx[5] > 100) ||\n (qx[6]-qx[1] > 50 && qx[2] > 0) ||\n (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)\nif (LogC) { ex[which(ex <= 0)] <- NaN\n exprs(gset) <- log2(ex) }\n\n# set up the data and proceed with analysis\nfl <- as.factor(sml)\ngset$description <- fl\ndesign <- model.matrix(~ description + 0, gset)\ncolnames(design) <- levels(fl)\nfit <- lmFit(gset, design)\ncont.matrix <- makeContrasts(G1-G0, levels=design)\nfit2 <- contrasts.fit(fit, cont.matrix)\nfit2 <- eBayes(fit2, 0.01)\ntT <- topTable(fit2, adjust=\"fdr\", sort.by=\"B\", number=250)\n\n# load NCBI platform annotation\ngpl <- annotation(gset)\nplatf <- getGEO(gpl, AnnotGPL=TRUE)\nncbifd <- data.frame(attr(dataTable(platf), \"table\"))\n\n# replace original platform annotation\ntT <- tT[setdiff(colnames(tT), setdiff(fvarLabels(gset), \"ID\"))]\ntT <- merge(tT, ncbifd, by=\"ID\")\ntT <- tT[order(tT$P.Value), ] # restore correct order\n\ntT <- subset(tT, select=c(\"ID\",\"adj.P.Val\",\"P.Value\",\"t\",\"B\",\"logFC\",\"Gene.symbol\",\"Gene.title\"))\nwrite.table(tT, file=stdout(), row.names=F, sep=\"\\t\")\n\n################################################################\n# Boxplot for selected GEO samples\nlibrary(Biobase)\nlibrary(GEOquery)\n\n# load series and platform data from GEO\n\ngset <- getGEO(\"GSE15573\", GSEMatrix =TRUE)\nif (length(gset) > 1) idx <- grep(\"GPL6102\", attr(gset, \"names\")) else idx <- 1\ngset <- gset[[idx]]\n\n# group names for all samples in a series\nsml <- c(\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G0\",\"G0\",\"G1\",\"G0\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\",\"G1\",\"G0\",\"G0\",\"G1\",\"G1\",\"G0\",\"G1\",\"G0\",\"G1\")\n\n# order samples by group\nex <- exprs(gset)[ , order(sml)]\nsml <- sml[order(sml)]\nfl <- as.factor(sml)\nlabels <- c(\"control\",\"case\")\n\n# set parameters and draw the plot\npalette(c(\"#dfeaf4\",\"#f4dfdf\", \"#AABBCC\"))\ndev.new(width=4+dim(gset)[[2]]/5, height=6)\npar(mar=c(2+round(max(nchar(sampleNames(gset)))/2),4,2,1))\ntitle <- paste (\"GSE15573\", '/', annotation(gset), \" selected samples\", sep ='')\nboxplot(ex, boxwex=0.6, notch=T, main=title, outline=FALSE, las=2, col=fl)\nlegend(\"topleft\", labels, fill=palette(), bty=\"n\")\n\n", "meta": {"hexsha": "8e307e0b93d6a1b9d9c07632c6ecda6e4913e831", "size": 2967, "ext": "r", "lang": "R", "max_stars_repo_path": "Paper-Scripts/Eric/GEO.replication.RA.r", "max_stars_repo_name": "theMechanic23/PrediXcan", "max_stars_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 92, "max_stars_repo_stars_event_min_datetime": "2015-05-05T16:37:07.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-05T12:09:16.000Z", "max_issues_repo_path": "Paper-Scripts/Eric/GEO.replication.RA.r", "max_issues_repo_name": "theMechanic23/PrediXcan", "max_issues_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2016-04-15T13:22:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-08T19:58:34.000Z", "max_forks_repo_path": "Paper-Scripts/Eric/GEO.replication.RA.r", "max_forks_repo_name": "theMechanic23/PrediXcan", "max_forks_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 66, "max_forks_repo_forks_event_min_datetime": "2015-04-24T16:56:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-30T14:37:49.000Z", "avg_line_length": 35.7469879518, "max_line_length": 175, "alphanum_fraction": 0.5958881025, "num_tokens": 1087, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7154239836484143, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.3298224607843789}} {"text": "subFunc <- function(data.train=NULL, data.valid=NULL, data.eval=NULL,\n method=NULL, meta=NULL, base=NULL, \n yname.numeric=NULL, yname.nominal=NULL, xnames=NULL, xname=NULL, model_control=NULL) {\n if (is.null(method)) {\n if (!is.null(meta)) { method <- paste(meta, base, sep=\"+\") } else { method <- base }\n } else {\n base <- method\n }\n\n pred.train <- pred.valid <- NULL\n \n if (grepl(\"UP\", method)) {\n tryCatch({\n data.train[, xname]\n\n }, error = function(err){\n print(err)\n print(xname)\n browser()\n }\n )\n pred.train <- 10000/(data.train[, xname]+1) # why 10000?\n pred.valid <- 10000/(data.valid[, xname]+1)\n } else if (grepl(\"EALR\",method)) {\n formula <- as.formula(paste(yname.numeric, paste(xnames, collapse=\"+\"), sep=\"~\"))\n # browser()\n model <- glm(formula=formula, data=data.train, family=gaussian)\n try(model <- step(model, k=log(nrow(data.train)), trace=FALSE), silent=TRUE)\n \n if (length(attr(model$terms, \"term.labels\"))>=2) {\n err <- try(model.vif <- max(car::vif(model)), silent=TRUE)\n if (class(err)!=\"try-error\") {\n if (!is.na(model.vif)) {\n if (model.vif > 10) { cat(\"VIF of EALR model is larger than 10.\\n\") }\n }\n }\n }\n \n pred.train <- predict(model, new=data.train)\n tryCatch({\n pred.valid <- predict(model, new=data.valid)\n }, error = function(err){\n print(err)\n browser()\n }\n )\n\n \n step.xnames <- attr(model$terms, \"term.labels\")\n for (aname in step.xnames) {\n data.train[, aname] <- (data.train[, aname]-min(data.train[, aname]))/(max(data.train[, aname])-min(data.train[, aname]))\n data.valid[, aname] <- (data.valid[, aname]-min(data.valid[, aname]))/(max(data.valid[, aname])-min(data.valid[, aname]))\n }\n } else if (!is.null(meta)) {\n data.train[, yname.nominal] <- as.factor(data.train[, yname.nominal])\n formula <- as.formula(paste(yname.nominal, paste(xnames, collapse=\"+\"), sep=\"~\"))\n ########################################\n ### Ensemble methods\n ########################################\n WC <- getWekaClassifier(meta)\n BC <- getWekaClassifier(base)\n model <- WC(formula=formula, data=data.train, control=RWeka::Weka_control(W=BC))\n \n if (is.null(yname.numeric)) {\n pred.train <- predict(model, new=data.train, type=\"probability\")[, \"1\"]\n pred.valid <- predict(model, new=data.valid, type=\"probability\")[, \"1\"]\n } else {\n pred.train <- predict(model, new=data.train)\n pred.valid <- predict(model, new=data.valid)\n }\n } else {\n data.train[, yname.nominal] <- as.factor(data.train[, yname.nominal])\n formula <- as.formula(paste(yname.nominal, paste(xnames, collapse=\"+\"), sep=\"~\"))\n \n WC <- getWekaClassifier(base)\n model <- NULL\n if (method==\"IBk\") {\n model <- WC(formula=formula, data=data.train, control=RWeka::Weka_control(K=8))\n } else {\n model <- WC(formula=formula, data=data.train, control=model_control)\n }\n \n if (is.null(yname.numeric)) {\n pred.train <- predict(model, new=data.train, type=\"probability\")[, \"1\"]\n pred.valid <- predict(model, new=data.valid, type=\"probability\")[, \"1\"]\n # pred.train <- predict(model, new=data.train)\n # pred.valid <- predict(model, new=data.valid)\n # levels(pred.valid) <- c(0,1)\n } else {\n pred.train <- predict(model, new=data.train)\n pred.valid <- predict(model, new=data.valid)\n }\n }\n \n pred.train <- as.vector(pred.train)\n pred.valid <- as.vector(pred.valid)\n train.dt <- valid.dt <- NULL\n if (grepl(\"UP\", method)) { # why \"UP\" method use different datasets than others.\n train.dt <- data.frame(NUM=data.eval$fbug.old, REL=data.eval$fbug.old, LOC=data.eval$churn.fit.old, PRE=pred.train)\n valid.dt <- data.frame(NUM=data.eval$ebug.old, REL=data.eval$ebug.old, LOC=data.eval$churn.est.old, PRE=pred.valid)\n } else {\n train.dt <- data.frame(NUM=data.eval$fbug, REL=data.eval$fbug, LOC=data.eval$churn.fit, PRE=pred.train)\n valid.dt <- data.frame(NUM=data.eval$ebug, REL=data.eval$ebug, LOC=data.eval$churn.est, PRE=pred.valid)\n }\n \n sorted <- FALSE\n worstcase <- TRUE ### compute the worst performance for unsupervised models\n bestcase <- FALSE\n LOCUP <- FALSE\n allpercentcutoff <- TRUE\n sub.Popt <- sub.ACC <-sub.Prec.F1<- NULL\n if (grepl(\"UP\", method)) {\n sub.Popt <- ComputePopt(sorted=sorted, data=valid.dt, worstcase=worstcase, bestcase=bestcase, LOCUP=LOCUP)\n sub.ACC <- ComputeACC(sorted=sorted, data=valid.dt, worstcase=worstcase, bestcase=bestcase, LOCUP=LOCUP)\n sub.Prec.F1 <- ComputePrecF1(sorted=sorted, data=valid.dt, worstcase=worstcase, bestcase=bestcase, LOCUP=LOCUP)\n } else {\n\n sub.Popt <- ComputePopt(sorted=sorted, data=valid.dt)\n sub.ACC <- ComputeACC(sorted=sorted, data=valid.dt)\n sub.Prec.F1 <- ComputePrecF1(sorted=sorted, data=valid.dt)\n }\n \n sub.Prec <- sub.Prec.F1$Prec\n sub.F1 <- sub.Prec.F1$F1\n if(grepl(\"_Sup\",method)){\n method <- \"OneR.UP\"\n }\n names(sub.Popt) <- paste(method, \"Popt\", sep=\".\")\n names(sub.ACC) <- paste(method, \"ACC\", sep=\".\")\n names(sub.Prec) <-paste(method,\"Prec\", sep=\".\")\n names(sub.F1) <-paste(method,\"F1\", sep=\".\")\n return (list(Popt= sub.Popt, ACC=sub.ACC, Prec=sub.Prec, F1=sub.F1))\n \n}", "meta": {"hexsha": "6510c3cc879bc7a24ba8759870e1c25817efcc1f", "size": 5355, "ext": "r", "lang": "R", "max_stars_repo_path": "jit/script_r/evaluate.r", "max_stars_repo_name": "bharlow058/OneWayMethod", "max_stars_repo_head_hexsha": "d1423345698fffe4bf78631a3027615c2c781cd2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-29T14:09:14.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-29T14:09:14.000Z", "max_issues_repo_path": "jit/script_r/evaluate.r", "max_issues_repo_name": "bharlow058/OneWayMethod", "max_issues_repo_head_hexsha": "d1423345698fffe4bf78631a3027615c2c781cd2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "jit/script_r/evaluate.r", "max_forks_repo_name": "bharlow058/OneWayMethod", "max_forks_repo_head_hexsha": "d1423345698fffe4bf78631a3027615c2c781cd2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-08-29T15:11:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-29T15:11:43.000Z", "avg_line_length": 39.9626865672, "max_line_length": 127, "alphanum_fraction": 0.6042950514, "num_tokens": 1513, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7401743620390163, "lm_q2_score": 0.44552953503957277, "lm_q1q2_score": 0.32976953936745534}} {"text": "library(tidyr)\r\nlibrary(ggplot2)\r\nlibrary(shiny)\r\nlibrary(shinydashboard)\r\nlibrary(heatmaply)\r\nlibrary(GGally)\r\nsource(\"cleandata.r\")\r\nsource(\"graphics.r\")\r\n\r\n\r\ntitle = tags$span(tags$img(src=\"favicon.png\",\r\n height = '50',\r\n width = '50'),\r\n 'Easy Viz')\r\n\r\n\r\n\r\nheader <- dashboardHeader(title = title, dropdownMenuOutput(outputId = \"menu\"))\r\n\r\n\r\nsidebar <- dashboardSidebar(\r\n sidebarMenu(\r\n fileInput(inputId = \"file\", \"Upload\", multiple = TRUE),\r\n menuItem(\"Data\", tabName = \"data\", icon = icon(\"database\")),\r\n menuItem(\"Scatter Matrix\", tabName = \"scat\", icon = icon(\"cloud\")),\r\n menuItem(\"Heatmap\", tabName = \"heat\", icon = icon(\"chess-board\")),\r\n menuItem(\"Density\", tabName = \"dens\", icon = icon(\"chart-line\")),\r\n menuItem(\"Network\", tabName = \"net\", icon = icon(\"sitemap\")),\r\n menuItem(\"Built By\", tabName = \"bb\", icon = icon(\"tools\"))\r\n \r\n \r\n\r\n )\r\n)\r\n\r\n\r\n\r\nbody <- dashboardBody(\r\n fluidRow(\r\n tabItems(\r\n \r\n tabItem(tabName = \"data\",\r\n fluidRow(infoBoxOutput(outputId = \"IBdata\", width = 10)),\r\n fluidRow(box(tableOutput(outputId = \"tabledata\"), width = 10))),\r\n tabItem(tabName = \"scat\",\r\n fluidRow(infoBoxOutput(outputId = \"IBscat\", width = 10)),\r\n fluidRow(box(plotOutput(outputId = \"scatt\"), width = 10))),\r\n tabItem(tabName = \"heat\",\r\n fluidRow(infoBoxOutput(outputId = \"IBheat\", width = 10)),\r\n fluidRow(box(plotOutput(outputId = \"heatm2\"), width = 10))),\r\n tabItem(tabName = \"dens\",\r\n fluidRow(infoBoxOutput(outputId = \"IBdens\", width = 10)),\r\n fluidRow(box(plotOutput(outputId = \"densi\"), width = 10))),\r\n tabItem(tabName = \"net\",\r\n fluidRow(infoBoxOutput(outputId = \"IBnet\", width = 10)),\r\n fluidRow(box(plotOutput(outputId = \"netw\"), width = 10))),\r\n tabItem(tabName = \"bb\",\r\n fluidRow(infoBoxOutput(outputId = \"IBbb\", width = 10)))\r\n \r\n )\r\n )\r\n )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nui <- dashboardPage(header, sidebar, body, skin = \"black\")\r\n\r\nserver <- function(input, output) {\r\n \r\n output$tabledata <- renderTable({\r\n file_to_read = input$file\r\n if(is.null(file_to_read)){\r\n return()\r\n }\r\n c = read.csv(file_to_read$datapath)\r\n cleanData(c)\r\n \r\n })\r\n \r\n \r\n output$scatt <- renderPlot({\r\n file_to_read = input$file\r\n if(is.null(file_to_read)){\r\n return()\r\n }\r\n df = read.csv(file_to_read$datapath)\r\n scattermatrix(df)\r\n })\r\n \r\n \r\n\r\n \r\n \r\n output$heatm2 <- renderPlot({\r\n file_to_read = input$file\r\n if(is.null(file_to_read)){\r\n return()\r\n }\r\n df = read.csv(file_to_read$datapath)\r\n df = cleanData(df)\r\n heatgraph(df)\r\n })\r\n \r\n \r\n \r\n \r\n output$densi <- renderPlot({\r\n file_to_read = input$file\r\n if(is.null(file_to_read)){\r\n return()\r\n }\r\n df = read.csv(file_to_read$datapath)\r\n densgraph(df)\r\n })\r\n \r\n \r\n output$netw <- renderPlot({\r\n file_to_read = input$file\r\n if(is.null(file_to_read)){\r\n return()\r\n }\r\n df = read.csv(file_to_read$datapath)\r\n net(df)\r\n })\r\n\r\n \r\n output$IBdata <- renderInfoBox({\r\n infoBox(title = \"Data InfoBox\",\r\n value = \"Processing\",\r\n subtitle = \"Your cleaned Data. Keep all the numeric variables.\",\r\n icon = shiny::icon(\"database\"),\r\n fill = TRUE)\r\n })\r\n \r\n \r\n\r\n output$IBheat <- renderInfoBox({\r\n infoBox(title = \"Heatmap InfoBox\",\r\n value = \"Correlation\",\r\n subtitle = \"Heatmap plot. Clearest cases shows the positive correlation between variables. Darkest cases shows negative correlation.\",\r\n icon = shiny::icon(\"chess-board\"),\r\n fill = TRUE)\r\n })\r\n \r\n \r\n output$IBscat <- renderInfoBox({\r\n infoBox(title = \"Scatter InfoBox\",\r\n value = \"Distribution\",\r\n subtitle = \"Scatter Matrix plot. Visualise scatter plots of all your features.\",\r\n icon = shiny::icon(\"cloud\"),\r\n fill = TRUE)\r\n })\r\n \r\n \r\n output$IBdens <- renderInfoBox({\r\n infoBox(title = \"Histogram InfoBox\",\r\n value = \"Distribution\",\r\n subtitle = \"Density plot. All your densities are plots one by one.\",\r\n icon = shiny::icon(\"chart-line\"),\r\n fill = TRUE)\r\n })\r\n \r\n \r\n \r\n output$IBnet <- renderInfoBox({\r\n infoBox(title = \"Network InfoBox\",\r\n value = \"Connexion\",\r\n subtitle = \"Visualise the network and connexions of your variables in your data\",\r\n icon = shiny::icon(\"sitemap\"),\r\n color = 'orange',\r\n fill = TRUE)\r\n })\r\n \r\n output$IBbb <- renderInfoBox({\r\n infoBox(title = \"Built By\",\r\n value = \"Issam Merikhi - 2021 - All right reserved\",\r\n subtitle = \"UDS - University of Strasbourg\",\r\n icon = shiny::icon(\"tools\"),\r\n color = 'green',\r\n href = \"https://github.com/IssamMerikhi\",\r\n fill = TRUE)\r\n })\r\n \r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n}\r\n\r\nshinyApp(ui, server)\r\n\r\n\r\n", "meta": {"hexsha": "34df3c470b073c584400c14fc518567af723c01e", "size": 5122, "ext": "r", "lang": "R", "max_stars_repo_path": "app.r", "max_stars_repo_name": "IssamMerikhi/EasyViz", "max_stars_repo_head_hexsha": "239eece4f0bc27a05b7668e3a157f2d4d1db8b0a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "app.r", "max_issues_repo_name": "IssamMerikhi/EasyViz", "max_issues_repo_head_hexsha": "239eece4f0bc27a05b7668e3a157f2d4d1db8b0a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "app.r", "max_forks_repo_name": "IssamMerikhi/EasyViz", "max_forks_repo_head_hexsha": "239eece4f0bc27a05b7668e3a157f2d4d1db8b0a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.9853658537, "max_line_length": 147, "alphanum_fraction": 0.5450995705, "num_tokens": 1237, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166195971441, "lm_q2_score": 0.6442251064863697, "lm_q1q2_score": 0.32966069375081525}} {"text": "#---------\n#Title\n#---------\n#Respiratory control and thermal sensibility in benthic \n#life stages of porcelain crab (Petrolisthes laevigatus).\n#-----------------------------------------------\n\n#---------\n#Cleaning working space\nrm(list=ls()) \n#Creator: 20160817 FPL\n#Modifications: 20170408 CG\ntoday<-format(Sys.Date(),\"%Y%m%d\")\n#--------------------------------------------\n#From Linux\n# get working directory\nsetwd(\"D:/Dropbox/FL+WB/PELA/data\")\n# Crist\u00f3bal\n#setwd(\"~/Insync/Laboratorio.Poblaciones.Marinas/Publicaciones/Plasticidad pela/analisis/datos\")\ngetwd() #verifico directorio\n#----------------------------------------------\n\n#-----------------------------------------------\n#Libraries\n#-----------------------------------------------\nlibrary(plotrix);library(ggplot2);library(nlme);\nlibrary(car);library(multcomp)\n#-----------------------------------------------\n\n#------------------------------------------------\n#reading my data\n#------------------------------------------------\npela<-read.csv(\"data.pela.csv\", header=TRUE, \n sep=\",\",dec=\".\", strip.white=TRUE)\nnames(pela)\nsummary(pela)\nstr(pela)\n#------------------------------------------------\n\n#---------------------------------------------------------------------------\n#Units for each explicative variable\n#---------------------------------------------------------------------------\n#dry_weight_g: individual dry weight,expressed in grams\n#mo2: metabolic rate, expressed as mg O2 por hour per individual\n#mo2.1: metabolic rate, expressed as mg O2 per hour per gram dry weight\n#mo2.2: metabolic rate, expressed as mmol O2 per hour per gram dry weight\n#mo2.3: metabolic rate, expressed as umol O2 per hour per gram dry weight\n#mo2.4: metabolic rate, expressed as umol O2 por hour per individual\n#---------------------------------------------------------------------------\n\n#--------------------------------------------------------\n#Transformation of variables wrongly assigned to numeric\n#--------------------------------------------------------\npela$stage=as.factor(pela$stage)\npela$oxygen_mg=as.numeric(pela$oxygen_mg)\npela$saturation=as.numeric(pela$saturation)\npela$mo2=as.numeric(pela$mo2)\npela$temperature=as.factor(pela$temperature)\n#--------------------------------------------------------\n\n#--------------------------------------------------------\n# Mean and standard deviation \n#--------------------------------------------------------\nmean1<-aggregate(cbind(mo2,mo2.1, mo2.2,mo2.3,mo2.4)~stage+oxygen_mg+saturation+kpa+temperature,data=pela, mean)\nds1<-aggregate(cbind(mo2,mo2.1, mo2.2,mo2.3,mo2.4)~stage+oxygen_mg+saturation+kpa+temperature,data=pela, sd)\ndatos<-merge(mean1,ds1,by=c(\"stage\",\"oxygen_mg\",\"saturation\",\"kpa\",\"temperature\"))\nnames(datos)[6:15]<-c(\"mo2\",\"mo2.1\",\"mo2.2\",\"mo2.3\",\"mo2.4\",\"mo2.sd\",\"mo2.1.sd\",\n \"mo2.2.sd\",\"mo2.3.sd\",\"mo2.4.sd\")\n#--------------------------------------------------------\n\n#--------------------------------------------------------\n# subset by stage\n#--------------------------------------------------------\nunique(pela$stage)\nembrio<-subset(datos,datos$stage==\"Embryo\")\nadult<-subset(datos,datos$stage==\"Adult\")\njuvenil<-subset(datos,datos$stage==\"Juvenile\")\nmegalopa<-subset(datos,datos$stage==\"Megalopae\")\n#--------------------------------------------------------\n\n#--------------------------------------------------------\n# Calculation of Q10 =(MR2/MR1)^(10/(T2-T1))\n#--------------------------------------------------------\n# Eggs\n#--------------------------------------------------------\nembq10<-with(embrio,data.frame(t6.12= (mo2.4[temperature==12]/mo2.4[temperature==6])^(10/(12-6)),\n t6.18= (mo2.4[temperature==18]/mo2.4[temperature==6])^(10/(18-6)),\n t12.18=(mo2.4[temperature==18]/mo2.4[temperature==12])^(10/(18-12)),\n kpa6=kpa[temperature==6],\n kpa12=kpa[temperature==12],\n kpa18=kpa[temperature==18])\n)\nembq10$kpa.mean<- rowMeans(embq10[,c(4,5,6)], na.rm=TRUE)\n\n#--------------------------------------------------------\n#Megalopae\n#--------------------------------------------------------\nmegaq10<-with(megalopa,data.frame(t6.12= (mo2.4[temperature==12]/mo2.4[temperature==6])^(10/(12-6)),\n t6.18= (mo2.4[temperature==18]/mo2.4[temperature==6])^(10/(18-6)),\n t12.18=(mo2.4[temperature==18]/mo2.4[temperature==12])^(10/(18-12)),\n kpa6=kpa[temperature==6],\n kpa12=kpa[temperature==12],\n kpa18=kpa[temperature==18])\n)\nmegaq10$kpa.mean<- rowMeans(megaq10[,c(4,5,6)], na.rm=T)\n\n#--------------------------------------------------------\n#Juveniles\n#--------------------------------------------------------\njuvq10<-with(juvenil,data.frame(t6.12= (mo2.4[temperature==12]/mo2.4[temperature==6])^(10/(12-6)),\n t6.18= (mo2.4[temperature==18]/mo2.4[temperature==6])^(10/(18-6)),\n t12.18=(mo2.4[temperature==18]/mo2.4[temperature==12])^(10/(18-12)),\n kpa6=kpa[temperature==6],\n kpa12=kpa[temperature==12],\n kpa18=kpa[temperature==18])\n)\njuvq10$kpa.mean<- rowMeans(juvq10[,c(4,5,6)], na.rm=TRUE)\n\n#--------------------------------------------------------\n# Adults at each oxygen level\n#--------------------------------------------------------\naduq10<-with(adult,data.frame(t6.12= (mo2.4[temperature==12]/mo2.4[temperature==6])^(10/(12-6)),\n t6.18= (mo2.4[temperature==18]/mo2.4[temperature==6])^(10/(18-6)),\n t12.18=(mo2.4[temperature==18]/mo2.4[temperature==12])^(10/(18-12)), \n kpa6=kpa[temperature==6],\n kpa12=kpa[temperature==12],\n kpa18=kpa[temperature==18])\n)\naduq10$kpa.mean<- rowMeans(aduq10[,c(4,5,6)], na.rm=T)\n\n#---------------------------------------------------------------------------------------------\n# Q10 by stages, oxygen (kpa.mean) and delta temperature (DTemp)\n#---------------------------------------------------------------------------------------------\nQ10<-data.frame(stage= rep(c(\"Embryo\", \"Megalopae\", \"Juvenile\", \"Adult\"),1,each=15),\n DTemp=rep(c(names(embq10)[1:3]),4,each=5), \n kpa.mean= rep(c(2.36, 4.72, 9.4, 14.2, 21.2),12,each=1),\n Q10= c(embq10[1:5,1], embq10[1:5,2], embq10[1:5,3],\n megaq10[1:5,1], megaq10[1:5,2] ,megaq10[1:5,3],\n juvq10[1:5,1], juvq10[1:5,2], juvq10[1:5,3],\n aduq10[1:5,1], aduq10[1:5,2], aduq10[1:5,3])\n)\n\nQ10$stage<-as.factor(Q10$stage)\nQ10$DTemp<-as.factor(Q10$DTemp)\n#Q10$kpa.mean<-as.factor(Q10$kpa.mean)\n#Full model\nq10<-subset(Q10, !round(Q10,1)==20.4)\nlcu<-with(q10,powerTransform(Q10~kpa.mean*DTemp,family=\"bcPower\")$lambda)\nq10=cbind(q10,bcPower(q10$Q10,lcu))\nnames(q10)[5]<-\"bc\"\n# modelos\n###############\nlm1.1<-lm(bc~as.factor(kpa.mean)*stage, data=q10)\nsummary(lm1.1)\nAnova(lm1.1)\nAIC(lm1.1)\n################\nlm1<-lm(bc~kpa.mean*stage, data=q10)\nsummary(lm1)\nAnova(lm1)\nAIC(lm1)\n################\nlm.r<-lm(bc~kpa.mean*stage*DTemp, data=q10)\nsummary(lm.r)\nAIC(lm1.1,lm1,lm.r)\nAnova(lm.r)\n# para respuesta a revisor\nres1<-residuals(lm.r, type=\"pearson\")\nqqPlot(res1); shapiro.test(res1)\nleveneTest(res1~stage*DTemp,data=q10)\n# Los datos presentan distribuci\u00f3n normal, el unico ruido lo provoca el outlier Q10=20 en adulto\n# observo dentro de etapas\nlm.e<-lm(bc~kpa.mean*DTemp, data=subset(q10,stage==\"Embryo\")); \nlm.m<-lm(bc~kpa.mean*DTemp, data=subset(q10,stage==\"Megalopae\")); \nlm.j<-lm(bc~kpa.mean*DTemp, data=subset(q10,stage==\"Juvenile\"));\nlm.a<-lm(bc~kpa.mean*DTemp, data=subset(q10,stage==\"Adult\")); \n# anova embryos\nres.e<-residuals(lm.e, type=\"pearson\")\nqqPlot(res.e); shapiro.test(res.e)\nleveneTest(res.e~DTemp,data=subset(q10,stage==\"Embryo\"))\nAnova(lm.e)\n# anova megalopae\nres.m<-residuals(lm.m, type=\"pearson\")\nqqPlot(res.m); shapiro.test(res.m)\nleveneTest(res.m~DTemp,data=subset(q10,stage==\"Megalopae\"))\nAnova(lm.m)\n# anova juvenile\nres.j<-residuals(lm.j, type=\"pearson\")\nqqPlot(res.j); shapiro.test(res.j)\nleveneTest(res.j~DTemp,data=subset(q10,stage==\"Juvenile\"))\nAnova(lm.j)\n# anova adultos\nres.a<-residuals(lm.a, type=\"pearson\")\nqqPlot(res.a); shapiro.test(res.a)\nleveneTest(res.a~DTemp,data=subset(q10,stage==\"Adult\"))\nAnova(lm.a)\nlibrary(lsmeans)\nleastsquare = lsmeans(lm.a,pairwise ~ DTemp:kpa.mean)\nleastsquare$contrasts\ncld(leastsquare)\n\n# observamos medias y medianas (no sensibles a outliers) por variables\n# tensi\u00f3n de oxigeno\naggregate(Q10~kpa.mean,data=Q10,mean)\naggregate(Q10~kpa.mean,data=Q10,median)\n# etapa\naggregate(Q10~stage,data=Q10,mean)\naggregate(Q10~stage,data=Q10,median)\n#--------------------------------------------------------\n#Graphics\n#--------------------------------------------------------\n\n################################\n#END OF SCRIPT\n################################\npng(filename=\"Figure 4.png\",width=5.8,height=5.5,units=\"in\",res=600)\n\npar(mfrow=c(2,2), tcl=-0.3, family=\"Arial\", oma=c(2,2.2,0,0),frame.plot=FALSE)\nomi=c(0.1,0.1,0,0)\n# Embryos\npar(mai=c(0.2,0.2,0.2,0.2))\n# embryos\nplot(t6.12~kpa.mean,data=embq10, pch=21,cex=1.4, xlab=\"\",ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",xaxt=\"n\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=embq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=embq10,pch=21,bg=\"gray\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nlegend(\"topleft\",\"Eggs\",bty=\"n\")\n#Megalopae\nplot(t6.12~kpa.mean,data=megaq10, pch=21, cex=1.4, xlab=\"\",ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",xaxt=\"n\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=megaq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=megaq10, pch=21, bg=\"gray\",cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nlegend(\"topleft\",\"Megalopae\",bty=\"n\")\n#Juveniles\nplot(t6.12~kpa.mean,data=juvq10, pch=21, cex=1.4, ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=juvq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=juvq10, pch=21, bg=\"gray\",cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nlegend(\"topleft\",\"Juveniles\",bty=\"n\")\n#Adults\n# showing a X axis break \naduq10$t6.12.2<- ifelse(aduq10$t6.12< 8, aduq10$t6.12+10, aduq10$t6.12) #+1\nyat<-pretty(aduq10$t6.12.2)\nylab <- ifelse(yat< 19, yat-10, yat) #-17 Y -1\nplot(t6.12.2~kpa.mean,data=aduq10, cex=1.4,yaxt=\"n\", ylim=c(10,24),xlim=c(0,25), yaxs=\"i\",xaxs=\"i\",ylab=\"\", xlab=\"\",frame.plot=FALSE)\naxis(2, las=1, at=yat, labels=ylab)\naxis.break(axis=2,breakpos=19,bgcol=\"white\",breakcol=\"black\", # 17\n style=\"slash\",brw=0.02)\npoints(t6.18+10~kpa.mean,data=aduq10,cex=1.4,pch=21,bg=\"black\",xaxt=\"n\", ylim=c(20,30))\npoints(t12.18+10~kpa.mean,data=aduq10,cex=1.4,pch=21,bg=\"gray\",xaxt=\"n\", ylim=c(20,30))\naxis(1, labels=F)\nmtext(\"Oxygen tension (kPa)\", side=1, outer=T, at=0.5,cex=1,line=1)\nmtext(expression(paste(Q)[10]), side=2, outer=T, at=0.5,cex=1,line = 1)\nlegend(\"topleft\",\"Adults\",bty=\"n\")\ndev.off()\n#------------------------------------\n# plot version 2\n#-----------------------------------\nsetwd(\"~/Insync/Laboratorio.Poblaciones.Marinas/Publicaciones/Plasticidad pela/analisis/Submission\")\n#png(filename=\"3.1.Thermal quotient V2 .png\",width=7,height=7,units=\"in\",res=600)\n\n# panel 1 (Embriones)\npar(mar=c(3,2,2,1), oma=c(2,2,1,2))\npar(fig=c(0,5,6.6,9.9)/10)\nplot(t6.12~kpa.mean,data=embq10, pch=21,cex=1.4, xlab=\"\",ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",xaxt=\"n\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=embq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=embq10,pch=21,bg=\"gray\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nmtext(expression(paste( italic(\"(a)\"))), side=2, line=1,at=6, las=2)\nmtext(expression(paste(Q)[10]), side=2, las=2, line=2)\n# panel 2 (Megalopas)\npar(fig=c(0,5,3.3,6.6)/10)\npar(new=T)\nplot(t6.12~kpa.mean,data=megaq10, pch=21, cex=1.4, xlab=\"\",ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",xaxt=\"n\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=megaq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=megaq10, pch=21, bg=\"gray\",cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nmtext(expression(paste( italic(\"(b)\"))), side=2, line=1,at=6, las=2)\nmtext(expression(paste(Q)[10]), side=2, las=2, line=2)\n# panel 3 (juveniles)\npar(fig=c(0,5,0,3.3)/10)\npar(new=T)\nplot(t6.12~kpa.mean,data=juvq10, pch=21, cex=1.4, ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,5),yaxs=\"i\",xaxs=\"i\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=juvq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=juvq10, pch=21, bg=\"gray\",cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nmtext(expression(paste( italic(\"(c)\"))), side=2, line=1,at=6, las=2)\nmtext(expression(paste(Q)[10]), side=2, las=2, line=2)\nmtext(\"Oxygen tension (kPa)\", side=1, line=2, las=1)\n# panel 4 (Adultos)\npar(fig=c(5,10,0,9.9)/10)\npar(new=T)\nplot(t6.12~kpa.mean,data=aduq10, pch=21, cex=1.4, ylab=expression(paste(Q)[10]),\n xlim=c(0,25), ylim=c(0,25),yaxs=\"i\",xaxs=\"i\",las=1,frame.plot=FALSE)\npoints(t6.18~kpa.mean,data=aduq10, pch=21,bg=\"black\", cex=1.4, ylim=c(0,5), xaxt=\"n\")\npoints(t12.18~kpa.mean,data=aduq10, pch=21, bg=\"gray\",cex=1.4, ylim=c(0,5), xaxt=\"n\")\naxis(1, labels=F)\nmtext(expression(paste( italic(\"(d)\"))), side=2, line=1,at=26, las=2)\nmtext(expression(paste(Q)[10]), side=2, line=1.5, las=2)\nmtext(\"Oxygen tension (kPa)\", side=1, line=2, las=1)\n#dev.off()\n", "meta": {"hexsha": "76481e649f28bbc7df03402c376b2618e6d0b37f", "size": 13987, "ext": "r", "lang": "R", "max_stars_repo_path": "Estimation of Q10.r", "max_stars_repo_name": "felixpleiva/respiration_life_stages_crabs", "max_stars_repo_head_hexsha": "a3438a37c360c4fd248f0d5d3115942c3ff28ed6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Estimation of Q10.r", "max_issues_repo_name": "felixpleiva/respiration_life_stages_crabs", "max_issues_repo_head_hexsha": "a3438a37c360c4fd248f0d5d3115942c3ff28ed6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Estimation of Q10.r", "max_forks_repo_name": "felixpleiva/respiration_life_stages_crabs", "max_forks_repo_head_hexsha": "a3438a37c360c4fd248f0d5d3115942c3ff28ed6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.4123376623, "max_line_length": 133, "alphanum_fraction": 0.5415028241, "num_tokens": 4763, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3296368374847078}} {"text": "library(ggplot2)\nlibrary(viridis)\n\ndf <- read.csv(\"importances.csv\")\n\n#Attempt to aggregate feature importances\n#library(reshape2)\n#mdata <- aggregate(formula = . ~ feature_pos, data = df, FUN = sum)\n#Todo, alter size of circle based on # of features.... maybe rbind to prior df?\n#mdata2 <- aggregate(formula = . ~ feature_pos, data = df, FUN = length)\n\n\n#With dplyr\nlibrary(dplyr)\ndCount <- df %>% \n count(feature_pos) \n\ndImp <- df %>%\n group_by(feature_pos) %>%\n summarise(Imp=sum(importance)) %>%\n select(feature_pos,Imp)\nmerged <- merge(dCount, dImp, by = \"feature_pos\")\n\n#Set identity row to 0, convert to factor, resort array, convert identity back to 1\nidentityRow <- df %>%\n dplyr::filter(feature_pos %in% c(\"identity\"))\nmerged <- merged[-c(which(merged$feature_pos == \"identity\")),]\n\n#Convert to numeric and sort\nmerged$feature_pos <- as.numeric(as.character(merged$feature_pos))\n\n#Bubbles\ntiff(\"test.tiff\", units=\"mm\", width=178, height=133.5, res=300, compression='lzw')\nggplot(merged, aes(x=feature_pos, y=Imp, size=n, fill=Imp)) +\n geom_point(alpha=0.7, shape = 21) +\n geom_text(aes(label=ifelse(Imp>0.008, feature_pos,'')),hjust=0.5,vjust=-1, size=4) +\n # ggtitle(\"Cumulative GINI feature importance\") +\n labs(x = \"Amino acid position\", y = \"Importance\") +\n scale_fill_viridis(option=\"magma\") + \n scale_size_continuous(range = c(2,10)) +\n scale_x_continuous(minor_breaks = seq(0, 350, 25), breaks = seq(0, 350, 50)) +\n theme_minimal() \n\n\n# insert ggplot code\ndev.off()\n\n#Backmap to protein\n#install.packages(\"colourvalues\")\nlibrary(colourvalues)\nmerged$col <- colour_values(merged$Imp, palette = \"magma\", include_alpha = FALSE)\nwrite.csv(merged,\"colors.csv\")\n\n#PRINT SELE and COLOR statements\nfor (row in 1:nrow(merged)) {\n print(paste(\"select resi\", merged$feature_pos,\";\"))\n}\n\n", "meta": {"hexsha": "ca5d5bdb1038029324275a72e1a004621ed9132a", "size": 1808, "ext": "r", "lang": "R", "max_stars_repo_path": "paper_figures/importance_figure/features.r", "max_stars_repo_name": "flu-crew/antigenic-prediction", "max_stars_repo_head_hexsha": "1d1a51d1bb39f7b83fd121ec2a4caea3e1d05a88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-16T02:54:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-16T02:54:34.000Z", "max_issues_repo_path": "paper_figures/importance_figure/features.r", "max_issues_repo_name": "flu-crew/antigenic-prediction", "max_issues_repo_head_hexsha": "1d1a51d1bb39f7b83fd121ec2a4caea3e1d05a88", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "paper_figures/importance_figure/features.r", "max_forks_repo_name": "flu-crew/antigenic-prediction", "max_forks_repo_head_hexsha": "1d1a51d1bb39f7b83fd121ec2a4caea3e1d05a88", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-01-01T00:36:32.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-13T18:16:19.000Z", "avg_line_length": 30.6440677966, "max_line_length": 86, "alphanum_fraction": 0.701880531, "num_tokens": 548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.4804786780479071, "lm_q1q2_score": 0.32958408735001865}} {"text": "Ghuber <- function (u, k = 30, deriv = 0)\n{\n if (!deriv)\n {\n return(pmin(1, k/abs(u)))\n } else {\n return(abs(u) <= k)\n }\n}\n\n#' Univariate LDSC\n#'\n#' Imported here to help estimate sample overlap between studies\n#'\n#' @param Z summary Z-statistics for M variants\n#' @param r2 average reference LD scores for M variants\n#' @param N GWAS sample size for each variant (could be different across variants)\n#' @param W variant weight\n#'\n#' @keywords internal\n#' @return model fit\nldsc_h2_internal <- function(Z, r2, N, W=NULL)\n{\n if (is.null(W))\n {\n W <- rep(1, length(Z))\n }\n tau <- (mean(Z^2) - 1) / mean(N * r2)\n Wv <- 1 / (1 + tau * N * r2)^2\n id <- which(Z^2 > 30)\n if (length(id) > 0)\n {\n Wv[id] <- sqrt(Wv[id])\n }\n mod <- MASS::rlm(I(Z^2) ~ I(N * r2), weight = W * Wv,\n psi = Ghuber, k = 30)\n return(summary(mod))\n}\n\n\n#' Bivariate LDSC\n#'\n#' Imported here to help estimate sample overlap between studies\n#'\n#' @param Zs Mx2 matrix of summary Z-statistics for M variants from two GWAS\n#' @param r2 average reference LD scores for M variants\n#' @param N1 sample size for the 1st GWAS\n#' @param N2 sample size for the 2nd GWAS\n#' @param Nc overlapped sample size between the two GWAS\n#' @param W variant weight\n#' @param h1 hsq for trait 1\n#' @param h2 hsq for trait 2\n#'\n#' @return List of models\n#' @references\n#' Bulik-Sullivan,B.K. et al. (2015) An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236\u20131241.\n#'\n#' Guo,B. and Wu,B. (2018) Principal component based adaptive association test of multiple traits using GWAS summary statistics. bioRxiv 269597; doi: 10.1101/269597\n#'\n#' Gua,B. and Wu,B. (2019) Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach. Bioinformatics. 2019 Jul 1;35(13):2251-2257. doi: 10.1093/bioinformatics/bty961. \n#'\n#' https://github.com/baolinwu/MTAR \n#' @keywords internal\nldsc_rg_internal <- function(Zs, r2, h1, h2, N1, N2, Nc=0, W=NULL)\n{\n if(is.null(W))\n {\n W = rep(1,length(r2))\n }\n\n Y <- Zs[,1] * Zs[,2]\n\n X <- (sqrt(N1) * sqrt(N2) + sqrt(Nc/N1 * N2)) * r2\n N1r2 <- N1 * r2\n N2r2 <- N2 * r2\n r0 <- 0\n\n ## 1st round\n if(any(Nc > 0))\n {\n rcf <- as.vector(MASS::rlm(Y ~ X, psi = Ghuber)$coef)\n r0 <- rcf[1]\n gv <- rcf[-1]\n } else {\n gv <- as.vector(MASS::rlm(Y ~ X-1, psi = Ghuber)$coef)\n }\n\n ## 2nd round\n Wv <- 1 / ((h1 * N1r2 + 1) * (h2 * N2r2 + 1) + (X * gv + r0)^2)\n id <- which(abs(Zs[,1] * Zs[,2]) > 30)\n if(length(id) > 0)\n {\n Wv[id] <- sqrt(Wv[id])\n }\n\n if(any(Nc > 0))\n {\n rcf <- MASS::rlm(Y ~ X, weight = W * Wv, psi = Ghuber, k = 30)\n } else {\n rcf <- MASS::rlm(Y ~ X - 1, weight = W * Wv, psi = Ghuber, k = 30)\n }\n return(summary(rcf))\n}\n\n\n#' Univariate LDSC\n#'\n#' Imported here to help estimate sample overlap between studies\n#'\n#' @param id ID to analyse\n#' @param ancestry ancestry of traits 1 and 2 (AFR, AMR, EAS, EUR, SAS) or 'infer' (default) in which case it will try to guess based on allele frequencies\n#' @param snpinfo Output from ieugwasr::afl2_list(\"hapmap3\"), or NULL for it to be done automatically\n#' @param splitsize How many SNPs to extract at one time. Default=20000\n#'\n#' @export\n#' @return model fit\n#' @references\n#' Bulik-Sullivan,B.K. et al. (2015) An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236\u20131241.\n#'\n#' Guo,B. and Wu,B. (2018) Principal component based adaptive association test of multiple traits using GWAS summary statistics. bioRxiv 269597; doi: 10.1101/269597\n#'\n#' Gua,B. and Wu,B. (2019) Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach. Bioinformatics. 2019 Jul 1;35(13):2251-2257. doi: 10.1093/bioinformatics/bty961. \n#'\n#' https://github.com/baolinwu/MTAR \nldsc_h2 <- function(id, ancestry=\"infer\", snpinfo = NULL, splitsize=20000)\n{\n if(is.null(snpinfo))\n {\n snpinfo <- ieugwasr::afl2_list(\"hapmap3\")\n }\n\n snpinfo <- snpinfo %>%\n dplyr::filter(complete.cases(.))\n\n d <- extract_split(snpinfo$rsid, id, splitsize) %>%\n ieugwasr::fill_n() %>%\n dplyr::mutate(z = beta / se) %>%\n dplyr::select(rsid, z = z, n = n, eaf) %>%\n dplyr::filter(complete.cases(.))\n\n stopifnot(nrow(d) > 0)\n\n if(ancestry == \"infer\")\n {\n ancestry <- ieugwasr::infer_ancestry(d, snpinfo)$pop[1]\n }\n\n d <- snpinfo %>% \n dplyr::select(rsid, l2=paste0(\"L2.\", ancestry)) %>%\n dplyr::inner_join(., d, by=\"rsid\") %>%\n dplyr::filter(complete.cases(.))\n\n return(ldsc_h2_internal(d$z, d$l2, d$n))\n}\n\n#' Bivariate LDSC\n#'\n#' Imported here to help estimate sample overlap between studies\n#'\n#' @param id1 ID 1 to analyse\n#' @param id2 ID 2 to analyse\n#' @param ancestry ancestry of traits 1 and 2 (AFR, AMR, EAS, EUR, SAS) or 'infer' (default) in which case it will try to guess based on allele frequencies\n#' @param snpinfo Output from ieugwasr::afl2_list(\"hapmap3\"), or NULL for it to be done automatically\n#' @param splitsize How many SNPs to extract at one time. Default=20000\n#'\n#' @export\n#' @return model fit\nldsc_rg <- function(id1, id2, ancestry=\"infer\", snpinfo = NULL, splitsize=20000)\n{\n if(is.null(snpinfo))\n {\n snpinfo <- ieugwasr::afl2_list(\"hapmap3\")\n }\n\n x <- extract_split(snpinfo$rsid, c(id1, id2), splitsize)\n d1 <- subset(x, id == id1) %>%\n ieugwasr::fill_n() %>%\n dplyr::mutate(z = beta / se) %>%\n dplyr::select(rsid, z1 = z, n1 = n, eaf) %>%\n dplyr::filter(complete.cases(.))\n\n stopifnot(nrow(d1) > 0)\n\n d2 <- subset(x, id == id2) %>%\n ieugwasr::fill_n() %>%\n dplyr::mutate(z = beta / se) %>%\n dplyr::select(rsid, z2 = z, n2 = n, eaf) %>%\n dplyr::filter(complete.cases(.))\n\n stopifnot(nrow(d2) > 0)\n\n if(ancestry == \"infer\")\n {\n ancestry1 <- ieugwasr::infer_ancestry(d1, snpinfo)\n ancestry2 <- ieugwasr::infer_ancestry(d2, snpinfo)\n if(ancestry1$pop[1] != ancestry2$pop[1])\n {\n stop(\"d1 ancestry is \", ancestry1$pop[1], \" and d2 ancestry is \", ancestry2$pop[1])\n }\n ancestry <- ancestry1$pop[1]\n }\n\n d1 <- snpinfo %>% \n dplyr::select(rsid, l2=paste0(\"L2.\", ancestry)) %>%\n dplyr::inner_join(., d1, by=\"rsid\")\n\n d2 <- snpinfo %>% \n dplyr::select(rsid, l2=paste0(\"L2.\", ancestry)) %>%\n dplyr::inner_join(., d2, by=\"rsid\")\n\n h1 <- ldsc_h2_internal(d1$z1, d1$l2, d1$n1, d1$l2)\n h2 <- ldsc_h2_internal(d2$z2, d2$l2, d2$n2, d1$l2)\n\n dat <- dplyr::inner_join(d1, d2, by=\"rsid\") %>%\n dplyr::mutate(\n l2 = l2.x,\n n1 = as.numeric(n1),\n n2 = as.numeric(n2),\n rhs = l2 * sqrt(n1 * n2)\n )\n\n gcov <- dat %>%\n {\n ldsc_rg_internal(\n Zs = cbind(.$z1, .$z2),\n r2 = .$l2,\n h1 = h1$coefficients[2,1] * nrow(d1),\n h2 = h2$coefficients[2,1] * nrow(d2),\n N1 = .$n1,\n N2 = .$n2,\n W = .$l2\n )\n }\n return(list(\n gcov = gcov,\n h1=h1,\n h2=h2,\n rg = (gcov$coefficients[1,1] * nrow(dat)) / sqrt(h1$coefficients[2,1] * nrow(d1) * h2$coefficients[2,1] * nrow(d2))\n ))\n}\n\n\nextract_split <- function(snplist, id, splitsize=20000)\n{\n nsplit <- round(length(snplist)/splitsize)\n split(snplist, 1:nsplit) %>%\n pbapply::pblapply(., function(x)\n {\n ieugwasr::associations(x, id, proxies=FALSE)\n }) %>% dplyr::bind_rows()\n}\n", "meta": {"hexsha": "1a5d4798ae9e398d044ab09f0e3af88a0fac3252", "size": 7743, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ldsc.r", "max_stars_repo_name": "simonmfr/TwoSampleMR", "max_stars_repo_head_hexsha": "c4cff8bac98114b20c927da6e9f03e48318fa143", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 142, "max_stars_repo_stars_event_min_datetime": "2016-02-10T16:58:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T11:46:12.000Z", "max_issues_repo_path": "R/ldsc.r", "max_issues_repo_name": "simonmfr/TwoSampleMR", "max_issues_repo_head_hexsha": "c4cff8bac98114b20c927da6e9f03e48318fa143", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 296, "max_issues_repo_issues_event_min_datetime": "2016-03-15T20:28:29.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T09:02:30.000Z", "max_forks_repo_path": "R/ldsc.r", "max_forks_repo_name": "simonmfr/TwoSampleMR", "max_forks_repo_head_hexsha": "c4cff8bac98114b20c927da6e9f03e48318fa143", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 113, "max_forks_repo_forks_event_min_datetime": "2016-02-10T16:58:12.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-21T03:02:04.000Z", "avg_line_length": 30.7261904762, "max_line_length": 229, "alphanum_fraction": 0.581299238, "num_tokens": 2636, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6859494550081926, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.32958408735001865}} {"text": "# to run on saga\n\nrequire(data.table)\n\n\n#accept arguments\nargs<-commandArgs(TRUE)\n\n\n\ngt <- fread(args[1]) # takes 45 sec or so\nad <- fread(args[2]) # takes 45 sec or so\n\ndim(gt)\ndim(ad)\n\nsetnames(gt, 3:ncol(gt), paste(names(gt)[3:ncol(gt)], '_gt', sep=''))\n\nsetkey(gt, CHROM, POS)\nsetkey(ad, CHROM, POS)\nad2 <- merge(ad, gt)\n\n# mask uncalled or homozygote individuals (set to NA)\nindivs <- setdiff(names(ad), c('CHROM', 'POS'))\nfor(i in indivs){\n\tad2[get(paste(i, '_gt', sep='')) %in% c('0/0', './.', '1/1'), eval(i):=NA]\n}\n\n\t# check that it worked\n#\tad2[1:10, .(BM_026_gt, BM_026)]\n#\tad2[BM_026_gt=='0/1', .(BM_026_gt, BM_026)]\n#\n#\tad2[1:10, .(BM_027_gt, BM_027)]\n#\tad2[BM_027_gt=='0/1', .(BM_027_gt, BM_027)]\n\n# remove gt columns (to clean up)\nfor(i in indivs){\n\tad2[, eval(paste(i, '_gt', sep='')):=NULL]\n}\n\n# extract ref and alt alleles for het individuals (slow: takes 30? min or so)\nfor(i in indivs){\n\tcat(paste(i, ' ', sep=''))\n\tad2[, eval(paste(i, '_ref', sep='')) := as.numeric(sapply(strsplit(get(i), split=','), '[', 1))]\n\tad2[, eval(paste(i, '_alt', sep='')) := as.numeric(sapply(strsplit(get(i), split=','), '[', 2))]\n}\n\n# sum ref and alt alleles across het individuals\nad2[, sumRef := rowSums(.SD, na.rm=TRUE), .SDcol = grep('_ref', names(ad2))]\nad2[, sumAlt := rowSums(.SD, na.rm=TRUE), .SDcol = grep('_alt', names(ad2))]\n\n\n# binomial test for allele balance == 0.5\nad2[,r:=1:.N] # row number\nad2[sumAlt>0 & sumRef>0 & !is.na(sumAlt) & !is.na(sumRef),binomp := binom.test(sumRef, sumRef+sumAlt, p=0.5, alternative='two.sided')$p.value, by=r]\n\n\t# ad2[,.(sumAlt, sumRef, binomp)]\n\n# calc FDR\nad2[,binompFDR := p.adjust(binomp, method='fdr')]\n\n# write out\n#write.table(ad2[,.(CHROM, POS, sumRef, sumAlt)], file='Allele_balance/Allele_balance.binomp.tsv', row.names=FALSE, sep='/t')\n\nx=paste0(c(args[3]),c(\".tsv\"))\n\n\nwrite.table(ad2[,.(CHROM, POS, sumRef, sumAlt, binomp, binompFDR)], file=x, row.names=FALSE, sep='\\t', quote=FALSE)\n", "meta": {"hexsha": "272998456a61eedb73ec7bad7cd93d1057aa51e4", "size": 1944, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/filter_allele_balance.r", "max_stars_repo_name": "pinskylab/codEvol", "max_stars_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/filter_allele_balance.r", "max_issues_repo_name": "pinskylab/codEvol", "max_issues_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-04-11T11:14:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-21T19:57:31.000Z", "max_forks_repo_path": "scripts/filter_allele_balance.r", "max_forks_repo_name": "pinskylab/codEvol", "max_forks_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.1739130435, "max_line_length": 148, "alphanum_fraction": 0.6301440329, "num_tokens": 712, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.32952462503647323}} {"text": "#\n# Example R program\n#\n\nlibrary(gmp)\n\n# Integers are differents\nurand.bigz()\nurand.bigz()\nurand.bigz()\n# Integers are the same\nurand.bigz(seed=\"234234234324323\")\nurand.bigz(seed=\"234234234324323\")\n# Vector\nurand.bigz(nb=50,size=30)\n", "meta": {"hexsha": "96cbaf0ca224ef666cf460aede033db94c9c6e91", "size": 233, "ext": "r", "lang": "R", "max_stars_repo_path": "test/gmp/prog.r", "max_stars_repo_name": "tranlm/heroku-r-test", "max_stars_repo_head_hexsha": "12b3df941d037c45e6095d8ae2b2cbf269c7f277", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-12-02T14:31:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-31T16:52:24.000Z", "max_issues_repo_path": "test/gmp/prog.r", "max_issues_repo_name": "tranlm/heroku-r-test", "max_issues_repo_head_hexsha": "12b3df941d037c45e6095d8ae2b2cbf269c7f277", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2019-06-26T13:18:52.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-03T19:25:00.000Z", "max_forks_repo_path": "test/gmp/prog.r", "max_forks_repo_name": "tranlm/heroku-r-test", "max_forks_repo_head_hexsha": "12b3df941d037c45e6095d8ae2b2cbf269c7f277", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-06-02T13:47:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-18T19:07:23.000Z", "avg_line_length": 14.5625, "max_line_length": 34, "alphanum_fraction": 0.7381974249, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3295246250364732}} {"text": "context(\"Joins\")\n\n# Univariate keys --------------------------------------------------------------\n\na <- data.frame(x = c(1, 1, 2, 3), y = 1:4)\nb <- data.frame(x = c(1, 2, 2, 4), z = 1:4)\n\ntest_that(\"univariate inner join has all columns, repeated matching rows\", {\n j <- inner_join(a, b, \"x\")\n\n expect_equal(names(j), c(\"x\", \"y\", \"z\"))\n expect_equal(j$y, c(1, 2, 3, 3))\n expect_equal(j$z, c(1, 1, 2, 3))\n})\n\ntest_that(\"univariate left join has all columns, all rows\", {\n j1 <- left_join(a, b, \"x\")\n j2 <- left_join(b, a, \"x\")\n\n expect_equal(names(j1), c(\"x\", \"y\", \"z\"))\n expect_equal(names(j2), c(\"x\", \"z\", \"y\"))\n\n expect_equal(j1$z, c(1, 1, 2, 3, NA))\n expect_equal(j2$y, c(1, 2, 3, 3, NA))\n})\n\ntest_that(\"univariate semi join has x columns, matching rows\", {\n j1 <- semi_join(a, b, \"x\")\n j2 <- semi_join(b, a, \"x\")\n\n expect_equal(names(j1), c(\"x\", \"y\"))\n expect_equal(names(j2), c(\"x\", \"z\"))\n\n expect_equal(j1$y, 1:3)\n expect_equal(j2$z, 1:3)\n})\n\ntest_that(\"univariate anti join has x columns, missing rows\", {\n j1 <- anti_join(a, b, \"x\")\n j2 <- anti_join(b, a, \"x\")\n\n expect_equal(names(j1), c(\"x\", \"y\"))\n expect_equal(names(j2), c(\"x\", \"z\"))\n\n expect_equal(j1$y, 4)\n expect_equal(j2$z, 4)\n})\n\ntest_that(\"univariate right join has all columns, all rows\", {\n j1 <- right_join(a, b, \"x\")\n j2 <- right_join(b, a, \"x\")\n\n expect_equal(names(j1), c(\"x\", \"y\", \"z\"))\n expect_equal(names(j2), c(\"x\", \"z\", \"y\"))\n\n expect_equal(j1$x, c(1, 1, 2, 2, 4))\n expect_equal(j1$y, c(1, 2, 3, 3, NA))\n expect_equal(j1$z, c(1, 1, 2, 3, 4))\n\n expect_equal(j2$x, c(1, 1, 2, 2, 3))\n expect_equal(j2$y, c(1, 2, 3, 3, 4))\n expect_equal(j2$z, c(1, 1, 2, 3, NA))\n})\n\n# Bivariate keys ---------------------------------------------------------------\n\nc <- data.frame(\n x = c(1, 1, 2, 3),\n y = c(1, 1, 2, 3),\n a = 1:4\n)\nd <- data.frame(\n x = c(1, 2, 2, 4),\n y = c(1, 2, 2, 4),\n b = 1:4\n)\n\ntest_that(\"bivariate inner join has all columns, repeated matching rows\", {\n j <- inner_join(c, d, c(\"x\", \"y\"))\n\n expect_equal(names(j), c(\"x\", \"y\", \"a\", \"b\"))\n expect_equal(j$a, c(1, 2, 3, 3))\n expect_equal(j$b, c(1, 1, 2, 3))\n})\n\ntest_that(\"bivariate left join has all columns, all rows\", {\n j1 <- left_join(c, d, c(\"x\", \"y\"))\n j2 <- left_join(d, c, c(\"x\", \"y\"))\n\n expect_equal(names(j1), c(\"x\", \"y\", \"a\", \"b\"))\n expect_equal(names(j2), c(\"x\", \"y\", \"b\", \"a\"))\n\n expect_equal(j1$b, c(1, 1, 2, 3, NA))\n expect_equal(j2$a, c(1, 2, 3, 3, NA))\n})\n\ntest_that(\"bivariate semi join has x columns, matching rows\", {\n j1 <- semi_join(c, d, c(\"x\", \"y\"))\n j2 <- semi_join(d, c, c(\"x\", \"y\"))\n\n expect_equal(names(j1), c(\"x\", \"y\", \"a\"))\n expect_equal(names(j2), c(\"x\", \"y\", \"b\"))\n\n expect_equal(j1$a, 1:3)\n expect_equal(j2$b, 1:3)\n})\n\ntest_that(\"bivariate anti join has x columns, missing rows\", {\n j1 <- anti_join(c, d, c(\"x\", \"y\"))\n j2 <- anti_join(d, c, c(\"x\", \"y\"))\n\n expect_equal(names(j1), c(\"x\", \"y\", \"a\"))\n expect_equal(names(j2), c(\"x\", \"y\", \"b\"))\n\n expect_equal(j1$a, 4)\n expect_equal(j2$b, 4)\n})\n\n\n# Duplicate column names --------------------------------------------------\n\ne <- data.frame(x = c(1, 1, 2, 3), z = 1:4)\nf <- data.frame(x = c(1, 2, 2, 4), z = 1:4)\n\ntest_that(\"univariate inner join has all columns, repeated matching rows\", {\n j <- inner_join(e, f, \"x\")\n\n expect_equal(names(j), c(\"x\", \"z.x\", \"z.y\"))\n expect_equal(j$z.x, c(1, 2, 3, 3))\n expect_equal(j$z.y, c(1, 1, 2, 3))\n})\n\ntest_that(\"univariate left join has all columns, all rows\", {\n j1 <- left_join(e, f, \"x\")\n j2 <- left_join(f, e, \"x\")\n\n expect_equal(names(j1), c(\"x\", \"z.x\", \"z.y\"))\n expect_equal(names(j2), c(\"x\", \"z.x\", \"z.y\"))\n\n expect_equal(j1$z.y, c(1, 1, 2, 3, NA))\n expect_equal(j2$z.y, c(1, 2, 3, 3, NA))\n})\n\ntest_that(\"can control suffixes with suffix argument\", {\n j1 <- inner_join(e, f, \"x\", suffix = c(\"1\", \"2\"))\n j2 <- left_join(e, f, \"x\", suffix = c(\"1\", \"2\"))\n j3 <- right_join(e, f, \"x\", suffix = c(\"1\", \"2\"))\n j4 <- full_join(e, f, \"x\", suffix = c(\"1\", \"2\"))\n\n expect_named(j1, c(\"x\", \"z1\", \"z2\"))\n expect_named(j2, c(\"x\", \"z1\", \"z2\"))\n expect_named(j3, c(\"x\", \"z1\", \"z2\"))\n expect_named(j4, c(\"x\", \"z1\", \"z2\"))\n})\n\ntest_that(\"can handle empty string in suffix argument, left side (#2228, #2182, #2007)\", {\n j1 <- inner_join(e, f, \"x\", suffix = c(\"\", \"2\"))\n j2 <- left_join(e, f, \"x\", suffix = c(\"\", \"2\"))\n j3 <- right_join(e, f, \"x\", suffix = c(\"\", \"2\"))\n j4 <- full_join(e, f, \"x\", suffix = c(\"\", \"2\"))\n\n expect_named(j1, c(\"x\", \"z\", \"z2\"))\n expect_named(j2, c(\"x\", \"z\", \"z2\"))\n expect_named(j3, c(\"x\", \"z\", \"z2\"))\n expect_named(j4, c(\"x\", \"z\", \"z2\"))\n})\n\ntest_that(\"can handle empty string in suffix argument, right side (#2228, #2182, #2007)\", {\n j1 <- inner_join(e, f, \"x\", suffix = c(\"1\", \"\"))\n j2 <- left_join(e, f, \"x\", suffix = c(\"1\", \"\"))\n j3 <- right_join(e, f, \"x\", suffix = c(\"1\", \"\"))\n j4 <- full_join(e, f, \"x\", suffix = c(\"1\", \"\"))\n\n expect_named(j1, c(\"x\", \"z1\", \"z\"))\n expect_named(j2, c(\"x\", \"z1\", \"z\"))\n expect_named(j3, c(\"x\", \"z1\", \"z\"))\n expect_named(j4, c(\"x\", \"z1\", \"z\"))\n})\n\ntest_that(\"disallow empty string in both sides of suffix argument (#2228)\", {\n expect_error(\n inner_join(e, f, \"x\", suffix = c(\"\", \"\")),\n \"`suffix` can't be empty string for both `x` and `y` suffixes\",\n fixed = TRUE\n )\n expect_error(\n left_join(e, f, \"x\", suffix = c(\"\", \"\")),\n \"`suffix` can't be empty string for both `x` and `y` suffixes\",\n fixed = TRUE\n )\n expect_error(\n right_join(e, f, \"x\", suffix = c(\"\", \"\")),\n \"`suffix` can't be empty string for both `x` and `y` suffixes\",\n fixed = TRUE\n )\n expect_error(\n full_join(e, f, \"x\", suffix = c(\"\", \"\")),\n \"`suffix` can't be empty string for both `x` and `y` suffixes\",\n fixed = TRUE\n )\n})\n\ntest_that(\"disallow NA in any side of suffix argument\", {\n expect_error(\n inner_join(e, f, \"x\", suffix = c(\".x\", NA)),\n \"`suffix` can't be NA\",\n fixed = TRUE\n )\n expect_error(\n left_join(e, f, \"x\", suffix = c(NA, \".y\")),\n \"`suffix` can't be NA\",\n fixed = TRUE\n )\n expect_error(\n right_join(e, f, \"x\", suffix = c(NA_character_, NA)),\n \"`suffix` can't be NA\",\n fixed = TRUE\n )\n expect_error(\n full_join(e, f, \"x\", suffix = c(\"x\", NA)),\n \"`suffix` can't be NA\",\n fixed = TRUE\n )\n})\n\ntest_that(\"doesn't add suffix to by columns in x (#3307)\", {\n j1 <- inner_join(e, f, by = c(\"x\" = \"z\"))\n j2 <- left_join(e, f, by = c(\"x\" = \"z\"))\n j3 <- right_join(e, f, by = c(\"x\" = \"z\"))\n j4 <- full_join(e, f, by = c(\"x\" = \"z\"))\n\n expect_named(j1, c(\"x\", \"z\", \"x.y\"))\n expect_named(j2, c(\"x\", \"z\", \"x.y\"))\n expect_named(j3, c(\"x\", \"z\", \"x.y\"))\n expect_named(j4, c(\"x\", \"z\", \"x.y\"))\n})\n\ng <- data.frame(A = 1, A.x = 2)\nh <- data.frame(B = 3, A.x = 4, A = 5)\n\ntest_that(\"can handle 'by' columns with suffix (#3266)\", {\n j1 <- inner_join(g, h, \"A.x\")\n j2 <- left_join(g, h, \"A.x\")\n j3 <- right_join(g, h, \"A.x\")\n j4 <- full_join(g, h, \"A.x\")\n\n expect_named(j1, c(\"A.x.x\", \"A.x\", \"B\", \"A.y\"))\n expect_named(j2, c(\"A.x.x\", \"A.x\", \"B\", \"A.y\"))\n expect_named(j3, c(\"A.x.x\", \"A.x\", \"B\", \"A.y\"))\n expect_named(j4, c(\"A.x.x\", \"A.x\", \"B\", \"A.y\"))\n})\n\ntest_that(\"can handle 'by' columns with suffix, reverse (#3266)\", {\n j1 <- inner_join(h, g, \"A.x\")\n j2 <- left_join(h, g, \"A.x\")\n j3 <- right_join(h, g, \"A.x\")\n j4 <- full_join(h, g, \"A.x\")\n\n expect_named(j1, c(\"B\", \"A.x\", \"A.x.x\", \"A.y\"))\n expect_named(j2, c(\"B\", \"A.x\", \"A.x.x\", \"A.y\"))\n expect_named(j3, c(\"B\", \"A.x\", \"A.x.x\", \"A.y\"))\n expect_named(j4, c(\"B\", \"A.x\", \"A.x.x\", \"A.y\"))\n})\n\ntest_that(\"check suffix input\", {\n expect_error(\n inner_join(e, f, \"x\", suffix = letters[1:3]),\n \"`suffix` must be a character vector of length 2, not character of length 3\",\n fixed = TRUE\n )\n expect_error(\n inner_join(e, f, \"x\", suffix = letters[1]),\n \"`suffix` must be a character vector of length 2, not string of length 1\",\n fixed = TRUE\n )\n expect_error(\n inner_join(e, f, \"x\", suffix = 1:2),\n \"`suffix` must be a character vector of length 2, not integer of length 2\",\n fixed = TRUE\n )\n})\n\n\n# Misc --------------------------------------------------------------------\n\ntest_that(\"inner_join does not segfault on NA in factors (#306)\", {\n a <- data.frame(x = c(\"p\", \"q\", NA), y = c(1, 2, 3), stringsAsFactors = TRUE)\n b <- data.frame(x = c(\"p\", \"q\", \"r\"), z = c(4, 5, 6), stringsAsFactors = TRUE)\n expect_warning(res <- inner_join(a, b, \"x\"), \"joining factors with different levels\")\n expect_equal(nrow(res), 2L)\n})\n\ntest_that(\"joins don't reorder columns #328\", {\n a <- data.frame(a = 1:3)\n b <- data.frame(a = 1:3, b = 1, c = 2, d = 3, e = 4, f = 5)\n res <- left_join(a, b, \"a\")\n expect_equal(names(res), names(b))\n})\n\ntest_that(\"join handles type promotions #123\", {\n df <- data.frame(\n V1 = c(rep(\"a\", 5), rep(\"b\", 5)),\n V2 = rep(c(1:5), 2),\n V3 = c(101:110),\n stringsAsFactors = FALSE\n )\n\n match <- data.frame(\n V1 = c(\"a\", \"b\"),\n V2 = c(3.0, 4.0),\n stringsAsFactors = FALSE\n )\n res <- semi_join(df, match, c(\"V1\", \"V2\"))\n expect_equal(res$V2, 3:4)\n expect_equal(res$V3, c(103L, 109L))\n})\n\ntest_that(\"indices don't get mixed up when nrow(x) > nrow(y). #365\", {\n a <- data.frame(V1 = c(0, 1, 2), V2 = c(\"a\", \"b\", \"c\"), stringsAsFactors = FALSE)\n b <- data.frame(V1 = c(0, 1), V3 = c(\"n\", \"m\"), stringsAsFactors = FALSE)\n res <- inner_join(a, b, by = \"V1\")\n expect_equal(res$V1, c(0, 1))\n expect_equal(res$V2, c(\"a\", \"b\"))\n expect_equal(res$V3, c(\"n\", \"m\"))\n})\n\ntest_that(\"join functions error on column not found #371\", {\n expect_error(\n left_join(data.frame(x = 1:5), data.frame(y = 1:5), by = \"x\"),\n \"`by` can't contain join column `x` which is missing from RHS\",\n fixed = TRUE\n )\n expect_error(\n left_join(data.frame(x = 1:5), data.frame(y = 1:5), by = \"y\"),\n \"`by` can't contain join column `y` which is missing from LHS\",\n fixed = TRUE\n )\n expect_error(\n left_join(data.frame(x = 1:5), data.frame(y = 1:5)),\n \"`by` required, because the data sources have no common variables\",\n fixed = TRUE\n )\n\n expect_error(\n left_join(data.frame(x = 1:5), data.frame(y = 1:5), by = 1:3),\n \"`by` must be a (named) character vector, list, or NULL for natural joins (not recommended in production code), not integer\",\n fixed = TRUE\n )\n})\n\ntest_that(\"inner_join is symmetric (even when joining on character & factor)\", {\n foo <- data_frame(id = factor(c(\"a\", \"b\")), var1 = \"foo\")\n bar <- data_frame(id = c(\"a\", \"b\"), var2 = \"bar\")\n\n expect_warning(tmp1 <- inner_join(foo, bar, by = \"id\"), \"joining factor and character\")\n expect_warning(tmp2 <- inner_join(bar, foo, by = \"id\"), \"joining character vector and factor\")\n\n expect_is(tmp1$id, \"character\")\n expect_is(tmp2$id, \"character\")\n\n expect_equal(names(tmp1), c(\"id\", \"var1\", \"var2\"))\n expect_equal(names(tmp2), c(\"id\", \"var2\", \"var1\"))\n\n expect_equal(tmp1, tmp2)\n})\n\ntest_that(\"inner_join is symmetric, even when type of join var is different (#450)\", {\n foo <- tbl_df(data.frame(id = 1:10, var1 = \"foo\"))\n bar <- tbl_df(data.frame(id = as.numeric(rep(1:10, 5)), var2 = \"bar\"))\n\n tmp1 <- inner_join(foo, bar, by = \"id\")\n tmp2 <- inner_join(bar, foo, by = \"id\")\n\n expect_equal(names(tmp1), c(\"id\", \"var1\", \"var2\"))\n expect_equal(names(tmp2), c(\"id\", \"var2\", \"var1\"))\n\n expect_equal(tmp1, tmp2)\n})\n\ntest_that(\"left_join by different variable names (#617)\", {\n x <- data_frame(x1 = c(1, 3, 2))\n y <- data_frame(y1 = c(1, 2, 3), y2 = c(\"foo\", \"foo\", \"bar\"))\n res <- left_join(x, y, by = c(\"x1\" = \"y1\"))\n expect_equal(names(res), c(\"x1\", \"y2\"))\n expect_equal(res$x1, c(1, 3, 2))\n expect_equal(res$y2, c(\"foo\", \"bar\", \"foo\"))\n})\n\ntest_that(\"joins support complex vectors\", {\n a <- data.frame(x = c(1, 1, 2, 3) * 1i, y = 1:4)\n b <- data.frame(x = c(1, 2, 2, 4) * 1i, z = 1:4)\n j <- inner_join(a, b, \"x\")\n\n expect_equal(names(j), c(\"x\", \"y\", \"z\"))\n expect_equal(j$y, c(1, 2, 3, 3))\n expect_equal(j$z, c(1, 1, 2, 3))\n})\n\ntest_that(\"joins suffix variable names (#655)\", {\n a <- data.frame(x = 1:10, y = 2:11)\n b <- data.frame(z = 5:14, x = 3:12) # x from this gets suffixed by .y\n res <- left_join(a, b, by = c(\"x\" = \"z\"))\n expect_equal(names(res), c(\"x\", \"y\", \"x.y\"))\n\n a <- data.frame(x = 1:10, z = 2:11)\n b <- data.frame(z = 5:14, x = 3:12) # x from this gets suffixed by .y\n res <- left_join(a, b, by = c(\"x\" = \"z\"))\n expect_equal(names(res), c(\"x\", \"z\", \"x.y\"))\n})\n\ntest_that(\"right_join gets the column in the right order #96\", {\n a <- data.frame(x = 1:10, y = 2:11)\n b <- data.frame(x = 5:14, z = 3:12)\n res <- right_join(a, b)\n expect_equal(names(res), c(\"x\", \"y\", \"z\"))\n\n a <- data.frame(x = 1:10, y = 2:11)\n b <- data.frame(z = 5:14, a = 3:12)\n res <- right_join(a, b, by = c(\"x\" = \"z\"))\n expect_equal(names(res), c(\"x\", \"y\", \"a\"))\n})\n\ntest_that(\"full_join #96\", {\n a <- data.frame(x = 1:3, y = 2:4)\n b <- data.frame(x = 3:5, z = 3:5)\n res <- full_join(a, b, \"x\")\n expect_equal(res$x, 1:5)\n expect_equal(res$y[1:3], 2:4)\n expect_true(all(is.na(res$y[4:5])))\n\n expect_true(all(is.na(res$z[1:2])))\n expect_equal(res$z[3:5], 3:5)\n})\n\ntest_that(\"JoinStringFactorVisitor and JoinFactorStringVisitor handle NA #688\", {\n x <- data.frame(Greek = c(\"Alpha\", \"Beta\", NA), numbers = 1:3)\n y <- data.frame(\n Greek = c(\"Alpha\", \"Beta\", \"Gamma\"),\n Letters = c(\"C\", \"B\", \"C\"),\n stringsAsFactors = F\n )\n\n expect_warning(\n res <- left_join(x, y, by = \"Greek\"),\n \"Column `Greek` joining factor and character vector, coercing into character vector\",\n fixed = TRUE\n )\n expect_true(is.na(res$Greek[3]))\n expect_true(is.na(res$Letters[3]))\n expect_equal(res$numbers, 1:3)\n\n expect_warning(\n res <- left_join(y, x, by = \"Greek\"),\n \"Column `Greek` joining character vector and factor, coercing into character vector\",\n fixed = TRUE\n )\n expect_equal(res$Greek, y$Greek)\n expect_equal(res$Letters, y$Letters)\n expect_equal(res$numbers[1:2], 1:2)\n expect_true(is.na(res$numbers[3]))\n})\n\n\ntest_that(\"JoinFactorFactorVisitor_SameLevels preserve levels order (#675)\", {\n input <- data.frame(g1 = factor(c(\"A\", \"B\", \"C\"), levels = c(\"B\", \"A\", \"C\")))\n output <- data.frame(\n g1 = factor(c(\"A\", \"B\", \"C\"), levels = c(\"B\", \"A\", \"C\")),\n g2 = factor(c(\"A\", \"B\", \"C\"), levels = c(\"B\", \"A\", \"C\"))\n )\n\n res <- inner_join(group_by(input, g1), group_by(output, g1))\n expect_equal(levels(res$g1), levels(input$g1))\n expect_equal(levels(res$g2), levels(output$g2))\n})\n\ntest_that(\"inner_join does not reorder (#684)\", {\n test <- data_frame(Greek = c(\"Alpha\", \"Beta\", \"Gamma\"), Letters = LETTERS[1:3])\n lookup <- data_frame(Letters = c(\"C\", \"B\", \"C\"))\n res <- inner_join(lookup, test)\n expect_equal(res$Letters, c(\"C\", \"B\", \"C\"))\n})\n\ntest_that(\"joins coerce factors with different levels to character (#684)\", {\n d1 <- data_frame(a = factor(c(\"a\", \"b\", \"c\")))\n d2 <- data_frame(a = factor(c(\"a\", \"e\")))\n expect_warning(res <- inner_join(d1, d2))\n expect_is(res$a, \"character\")\n\n # different orders\n d2 <- d1\n attr(d2$a, \"levels\") <- c(\"c\", \"b\", \"a\")\n expect_warning(res <- inner_join(d1, d2))\n expect_is(res$a, \"character\")\n})\n\ntest_that(\"joins between factor and character coerces to character with a warning (#684)\", {\n d1 <- data_frame(a = factor(c(\"a\", \"b\", \"c\")))\n d2 <- data_frame(a = c(\"a\", \"e\"))\n expect_warning(res <- inner_join(d1, d2))\n expect_is(res$a, \"character\")\n\n expect_warning(res <- inner_join(d2, d1))\n expect_is(res$a, \"character\")\n})\n\ntest_that(\"group column names reflect renamed duplicate columns (#2330)\", {\n d1 <- data_frame(x = 1:5, y = 1:5) %>% group_by(x, y)\n d2 <- data_frame(x = 1:5, y = 1:5)\n res <- inner_join(d1, d2, by = \"x\")\n expect_groups(d1, c(\"x\", \"y\"))\n expect_groups(res, c(\"x\", \"y.x\"))\n})\n\ntest_that(\"group column names are null when joined data frames are not grouped (#2330)\", {\n d1 <- data_frame(x = 1:5, y = 1:5)\n d2 <- data_frame(x = 1:5, y = 1:5)\n res <- inner_join(d1, d2, by = \"x\")\n expect_no_groups(res)\n})\n\n# Guessing variables in x and y ------------------------------------------------\n\ntest_that(\"unnamed vars are the same in both tables\", {\n by1 <- common_by_from_vector(c(\"x\", \"y\", \"z\"))\n expect_equal(by1$x, c(\"x\", \"y\", \"z\"))\n expect_equal(by1$y, c(\"x\", \"y\", \"z\"))\n\n by2 <- common_by_from_vector(c(\"x\" = \"a\", \"y\", \"z\"))\n expect_equal(by2$x, c(\"x\", \"y\", \"z\"))\n expect_equal(by2$y, c(\"a\", \"y\", \"z\"))\n})\n\ntest_that(\"join columns are not moved to the left (#802)\", {\n df1 <- data.frame(x = 1, y = 1:5)\n df2 <- data.frame(y = 1:5, z = 2)\n\n out <- left_join(df1, df2)\n expect_equal(names(out), c(\"x\", \"y\", \"z\"))\n})\n\ntest_that(\"join can handle multiple encodings (#769)\", {\n text <- c(\"\\xC9lise\", \"Pierre\", \"Fran\\xE7ois\")\n Encoding(text) <- \"latin1\"\n x <- data_frame(name = text, score = c(5, 7, 6))\n y <- data_frame(name = text, attendance = c(8, 10, 9))\n res <- left_join(x, y, by = \"name\")\n expect_equal(nrow(res), 3L)\n expect_equal(res$name, x$name)\n\n x <- data_frame(name = factor(text), score = c(5, 7, 6))\n y <- data_frame(name = text, attendance = c(8, 10, 9))\n res <- suppressWarnings(left_join(x, y, by = \"name\"))\n expect_equal(nrow(res), 3L)\n expect_equal(res$name, y$name)\n\n x <- data_frame(name = text, score = c(5, 7, 6))\n y <- data_frame(name = factor(text), attendance = c(8, 10, 9))\n res <- suppressWarnings(left_join(x, y, by = \"name\"))\n expect_equal(nrow(res), 3L)\n expect_equal(res$name, x$name)\n\n x <- data_frame(name = factor(text), score = c(5, 7, 6))\n y <- data_frame(name = factor(text), attendance = c(8, 10, 9))\n res <- suppressWarnings(left_join(x, y, by = \"name\"))\n expect_equal(nrow(res), 3L)\n expect_equal(res$name, x$name)\n})\n\ntest_that(\"join creates correctly named results (#855)\", {\n x <- data.frame(q = c(\"a\", \"b\", \"c\"), r = c(\"d\", \"e\", \"f\"), s = c(\"1\", \"2\", \"3\"))\n y <- data.frame(q = c(\"a\", \"b\", \"c\"), r = c(\"d\", \"e\", \"f\"), t = c(\"xxx\", \"xxx\", \"xxx\"))\n res <- left_join(x, y, by = c(\"r\", \"q\"))\n expect_equal(names(res), c(\"q\", \"r\", \"s\", \"t\"))\n expect_equal(res$q, x$q)\n expect_equal(res$r, x$r)\n})\n\ntest_that(\"inner join gives same result as merge by default (#1281)\", {\n set.seed(75)\n x <- data.frame(\n cat1 = sample(c(\"A\", \"B\", NA), 5, 1),\n cat2 = sample(c(1, 2, NA), 5, 1), v = rpois(5, 3),\n stringsAsFactors = FALSE\n )\n y <- data.frame(\n cat1 = sample(c(\"A\", \"B\", NA), 5, 1),\n cat2 = sample(c(1, 2, NA), 5, 1), v = rpois(5, 3),\n stringsAsFactors = FALSE\n )\n ij <- inner_join(x, y, by = c(\"cat1\", \"cat2\"))\n me <- merge(x, y, by = c(\"cat1\", \"cat2\"))\n expect_true(equal_data_frame(ij, me))\n})\n\ntest_that(\"join handles matrices #1230\", {\n df1 <- data_frame(x = 1:10, text = letters[1:10])\n df2 <- data_frame(x = 1:5, text = \"\")\n df2$text <- matrix(LETTERS[1:10], nrow = 5)\n\n res <- left_join(df1, df2, by = c(\"x\" = \"x\")) %>% filter(x > 5)\n text.y <- res$text.y\n expect_true(is.matrix(text.y))\n expect_equal(dim(text.y), c(5, 2))\n expect_true(all(is.na(text.y)))\n})\n\ntest_that(\"ordering of strings is not confused by R's collate order (#1315)\", {\n a <- data.frame(character = c(\"\\u0663\"), set = c(\"arabic_the_language\"), stringsAsFactors = F)\n b <- data.frame(character = c(\"3\"), set = c(\"arabic_the_numeral_set\"), stringsAsFactors = F)\n res <- b %>% inner_join(a, by = c(\"character\"))\n expect_equal(nrow(res), 0L)\n res <- a %>% inner_join(b, by = c(\"character\"))\n expect_equal(nrow(res), 0L)\n})\n\ntest_that(\"joins handle tzone differences (#819)\", {\n date1 <- structure(-1735660800, tzone = \"America/Chicago\", class = c(\"POSIXct\", \"POSIXt\"))\n date2 <- structure(-1735660800, tzone = \"UTC\", class = c(\"POSIXct\", \"POSIXt\"))\n\n df1 <- data.frame(date = date1)\n df2 <- data.frame(date = date2)\n\n expect_equal(attr(left_join(df1, df1)$date, \"tzone\"), \"America/Chicago\")\n})\n\ntest_that(\"joins matches NA in character vector by default (#892, #2033)\", {\n x <- data.frame(\n id = c(NA_character_, NA_character_),\n stringsAsFactors = F\n )\n\n y <- expand.grid(\n id = c(NA_character_, NA_character_),\n LETTER = LETTERS[1:2],\n stringsAsFactors = F\n )\n\n res <- left_join(x, y, by = \"id\")\n expect_true(all(is.na(res$id)))\n expect_equal(res$LETTER, rep(rep(c(\"A\", \"B\"), each = 2), 2))\n})\n\ntest_that(\"joins avoid name repetition (#1460)\", {\n d1 <- data.frame(id = 1:5, foo = rnorm(5))\n d2 <- data.frame(id = 1:5, foo = rnorm(5))\n d3 <- data.frame(id = 1:5, foo = rnorm(5))\n d <- d1 %>%\n left_join(d1, by = \"id\") %>%\n left_join(d2, by = \"id\") %>%\n left_join(d3, by = \"id\")\n expect_equal(names(d), c(\"id\", \"foo.x\", \"foo.y\", \"foo.x.x\", \"foo.y.y\"))\n})\n\ntest_that(\"join functions are protected against empty by (#1496)\", {\n x <- data.frame()\n y <- data.frame(a = 1)\n expect_error(\n left_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n expect_error(\n right_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n expect_error(\n semi_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n expect_error(\n full_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n expect_error(\n anti_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n expect_error(\n inner_join(x, y, by = names(x)),\n \"`by` must specify variables to join by\",\n fixed = TRUE\n )\n})\n\ntest_that(\"joins takes care of duplicates in by (#1192)\", {\n data2 <- data_frame(a = 1:3)\n data1 <- data_frame(a = 1:3, c = 3:5)\n\n res1 <- left_join(data1, data2, by = c(\"a\", \"a\"))\n res2 <- left_join(data1, data2, by = c(\"a\" = \"a\"))\n expect_equal(res1, res2)\n})\n\n# Joined columns result in correct type ----------------------------------------\n\ntest_that(\"result of joining POSIXct is POSIXct (#1578)\", {\n data1 <- data_frame(\n t = seq(as.POSIXct(\"2015-12-01\", tz = \"UTC\"), length.out = 2, by = \"days\"),\n x = 1:2\n )\n data2 <- inner_join(data1, data1, by = \"t\")\n res1 <- class(data2$t)\n expected <- c(\"POSIXct\", \"POSIXt\")\n expect_identical(res1, expected)\n})\n\ntest_that(\"joins allows extra attributes if they are identical (#1636)\", {\n tbl_left <- data_frame(\n i = rep(c(1, 2, 3), each = 2),\n x1 = letters[1:6]\n )\n tbl_right <- data_frame(\n i = c(1, 2, 3),\n x2 = letters[1:3]\n )\n\n attr(tbl_left$i, \"label\") <- \"iterator\"\n attr(tbl_right$i, \"label\") <- \"iterator\"\n\n res <- left_join(tbl_left, tbl_right, by = \"i\")\n expect_equal(attr(res$i, \"label\"), \"iterator\")\n\n attr(tbl_left$i, \"foo\") <- \"bar\"\n attributes(tbl_right$i) <- NULL\n attr(tbl_right$i, \"foo\") <- \"bar\"\n attr(tbl_right$i, \"label\") <- \"iterator\"\n\n res <- left_join(tbl_left, tbl_right, by = \"i\")\n expect_equal(attr(res$i, \"label\"), \"iterator\")\n expect_equal(attr(res$i, \"foo\"), \"bar\")\n})\n\ntest_that(\"joins work with factors of different levels (#1712)\", {\n d1 <- iris[, c(\"Species\", \"Sepal.Length\")]\n d2 <- iris[, c(\"Species\", \"Sepal.Width\")]\n d2$Species <- factor(as.character(d2$Species), levels = rev(levels(d1$Species)))\n expect_warning(res1 <- left_join(d1, d2, by = \"Species\"))\n\n d1$Species <- as.character(d1$Species)\n d2$Species <- as.character(d2$Species)\n res2 <- left_join(d1, d2, by = \"Species\")\n expect_equal(res1, res2)\n})\n\ntest_that(\"anti and semi joins give correct result when by variable is a factor (#1571)\", {\n big <- data.frame(letter = rep(c(\"a\", \"b\"), each = 2), number = 1:2)\n small <- data.frame(letter = \"b\")\n expect_warning(\n aj_result <- anti_join(big, small, by = \"letter\"),\n \"Column `letter` joining factors with different levels, coercing to character vector\",\n fixed = TRUE\n )\n expect_equal(aj_result$number, 1:2)\n expect_equal(aj_result$letter, factor(c(\"a\", \"a\"), levels = c(\"a\", \"b\")))\n\n expect_warning(\n sj_result <- semi_join(big, small, by = \"letter\"),\n \"Column `letter` joining factors with different levels, coercing to character vector\",\n fixed = TRUE\n )\n expect_equal(sj_result$number, 1:2)\n expect_equal(sj_result$letter, factor(c(\"b\", \"b\"), levels = c(\"a\", \"b\")))\n})\n\ntest_that(\"inner join not crashing (#1559)\", {\n df3 <- data_frame(\n id = c(102, 102, 102, 121),\n name = c(\"qwer\", \"qwer\", \"qwer\", \"asdf\"),\n k = factor(c(\"one\", \"two\", \"total\", \"one\"), levels = c(\"one\", \"two\", \"total\")),\n total = factor(c(\"tot\", \"tot\", \"tot\", \"tot\"), levels = c(\"tot\", \"plan\", \"fact\")),\n v = c(NA_real_, NA_real_, NA_real_, NA_real_),\n btm = c(25654.957609, 29375.7547216667, 55030.7123306667, 10469.3523273333),\n top = c(22238.368946, 30341.516924, 52579.88587, 9541.893144)\n )\n df4 <- data_frame(\n id = c(102, 102, 102, 121),\n name = c(\"qwer\", \"qwer\", \"qwer\", \"asdf\"),\n k = factor(c(\"one\", \"two\", \"total\", \"one\"), levels = c(\"one\", \"two\", \"total\")),\n type = factor(c(\"fact\", \"fact\", \"fact\", \"fact\"), levels = c(\"tot\", \"plan\", \"fact\")),\n perc = c(0.15363485835208, -0.0318297270618471, 0.0466114830816894, 0.0971986553754823)\n )\n # all we want here is to test that this does not crash\n expect_message(res <- replicate(100, df3 %>% inner_join(df4)))\n for (i in 2:100) expect_equal(res[, 1], res[, i])\n})\n\n\n# Encoding ----------------------------------------------------------------\n\ntest_that(\"join handles mix of encodings in data (#1885, #2118, #2271)\", {\n with_non_utf8_encoding({\n special <- get_native_lang_string()\n\n for (factor1 in c(FALSE, TRUE)) {\n for (factor2 in c(FALSE, TRUE)) {\n for (encoder1 in c(enc2native, enc2utf8)) {\n for (encoder2 in c(enc2native, enc2utf8)) {\n df1 <- data.frame(x = encoder1(special), y = 1, stringsAsFactors = factor1)\n df1 <- tbl_df(df1)\n df2 <- data.frame(x = encoder2(special), z = 2, stringsAsFactors = factor2)\n df2 <- tbl_df(df2)\n df <- data.frame(x = special, y = 1, z = 2, stringsAsFactors = factor1 && factor2)\n df <- tbl_df(df)\n\n info <- paste(\n factor1,\n factor2,\n Encoding(as.character(df1$x)),\n Encoding(as.character(df2$x))\n )\n\n if (factor1 != factor2) {\n warning_msg <- \"coercing\"\n } else {\n warning_msg <- NA\n }\n\n expect_warning_msg <- function(code, msg = warning_msg) {\n expect_warning(\n code, msg,\n info = paste(deparse(substitute(code)[[2]][[1]]), info)\n )\n }\n\n expect_equal_df <- function(code, df_ = df) {\n code <- substitute(code)\n eval(bquote(\n expect_equal(\n .(code), df_,\n info = paste(deparse(code[[1]]), info)\n )\n ))\n }\n\n expect_warning_msg(expect_equal_df(inner_join(df1, df2, by = \"x\")))\n expect_warning_msg(expect_equal_df(left_join(df1, df2, by = \"x\")))\n expect_warning_msg(expect_equal_df(right_join(df1, df2, by = \"x\")))\n expect_warning_msg(expect_equal_df(full_join(df1, df2, by = \"x\")))\n expect_warning_msg(\n expect_equal_df(\n semi_join(df1, df2, by = \"x\"),\n data.frame(x = special, y = 1, stringsAsFactors = factor1)\n )\n )\n expect_warning_msg(\n expect_equal_df(\n anti_join(df1, df2, by = \"x\"),\n data.frame(x = special, y = 1, stringsAsFactors = factor1)[0, ]\n )\n )\n }\n }\n }\n }\n })\n})\n\ntest_that(\"left_join handles mix of encodings in column names (#1571)\", {\n with_non_utf8_encoding({\n special <- get_native_lang_string()\n\n df1 <- data_frame(x = 1:6, foo = 1:6)\n names(df1)[1] <- special\n\n df2 <- data_frame(x = 1:6, baz = 1:6)\n names(df2)[1] <- enc2native(special)\n\n expect_message(res <- left_join(df1, df2), special, fixed = TRUE)\n expect_equal(names(res), c(special, \"foo\", \"baz\"))\n expect_equal(res$foo, 1:6)\n expect_equal(res$baz, 1:6)\n expect_equal(res[[special]], 1:6)\n })\n})\n\n# Misc --------------------------------------------------------------------\n\ntest_that(\"NAs match in joins only with na_matches = 'na' (#2033)\", {\n df1 <- data_frame(a = NA)\n df2 <- data_frame(a = NA, b = 1:3)\n for (na_matches in c(\"na\", \"never\")) {\n accept_na_match <- (na_matches == \"na\")\n expect_equal(inner_join(df1, df2, na_matches = na_matches) %>% nrow(), 0 + 3 * accept_na_match)\n expect_equal(left_join(df1, df2, na_matches = na_matches) %>% nrow(), 1 + 2 * accept_na_match)\n expect_equal(right_join(df2, df1, na_matches = na_matches) %>% nrow(), 1 + 2 * accept_na_match)\n expect_equal(full_join(df1, df2, na_matches = na_matches) %>% nrow(), 4 - accept_na_match)\n expect_equal(anti_join(df1, df2, na_matches = na_matches) %>% nrow(), 1 - accept_na_match)\n expect_equal(semi_join(df1, df2, na_matches = na_matches) %>% nrow(), 0 + accept_na_match)\n }\n})\n\ntest_that(\"joins regroups (#1597, #3566)\", {\n df1 <- data_frame(a = 1:3) %>% group_by(a)\n df2 <- data_frame(a = rep(1:4, 2)) %>% group_by(a)\n\n expect_grouped <- function(df) {\n expect_true(is_grouped_df(df))\n }\n\n expect_grouped(inner_join(df1, df2))\n expect_grouped(left_join(df1, df2))\n expect_grouped(right_join(df2, df1))\n expect_grouped(full_join(df1, df2))\n expect_grouped(anti_join(df1, df2))\n expect_grouped(semi_join(df1, df2))\n})\n\n\ntest_that(\"join accepts tz attributes (#2643)\", {\n # It's the same time:\n df1 <- data_frame(a = as.POSIXct(\"2009-01-01 10:00:00\", tz = \"Europe/London\"))\n df2 <- data_frame(a = as.POSIXct(\"2009-01-01 11:00:00\", tz = \"Europe/Paris\"))\n result <- inner_join(df1, df2, by = \"a\")\n expect_equal(nrow(result), 1)\n})\n\ntest_that(\"join takes LHS with warning if attributes inconsistent\", {\n df1 <- tibble(a = 1:2, b = 2:1)\n df2 <- tibble(\n a = structure(1:2, foo = \"bar\"),\n c = 2:1\n )\n\n expect_warning(\n out1 <- left_join(df1, df2, by = \"a\"),\n \"Column `a` has different attributes on LHS and RHS of join\"\n )\n expect_warning(out2 <- left_join(df2, df1, by = \"a\"))\n expect_warning(\n out3 <- left_join(df1, df2, by = c(\"b\" = \"a\")),\n \"Column `b`/`a` has different attributes on LHS and RHS of join\"\n )\n\n expect_equal(attr(out1$a, \"foo\"), NULL)\n expect_equal(attr(out2$a, \"foo\"), \"bar\")\n})\n\ntest_that(\"common_by() message\", {\n df <- tibble(!!!set_names(letters, letters))\n\n expect_message(\n left_join(df, df %>% select(1)),\n 'Joining, by = \"a\"',\n fixed = TRUE\n )\n\n expect_message(\n left_join(df, df %>% select(1:3)),\n 'Joining, by = c(\"a\", \"b\", \"c\")',\n fixed = TRUE\n )\n\n expect_message(\n left_join(df, df),\n paste0(\"Joining, by = c(\", paste0('\"', letters, '\"', collapse = \", \"), \")\"),\n fixed = TRUE\n )\n})\n\ntest_that(\"semi- and anti-joins preserve order (#2964)\", {\n expect_identical(\n data_frame(a = 3:1) %>% semi_join(data_frame(a = 1:3)),\n data_frame(a = 3:1)\n )\n expect_identical(\n data_frame(a = 3:1) %>% anti_join(data_frame(a = 4:6)),\n data_frame(a = 3:1)\n )\n})\n\ntest_that(\"join handles raw vectors\", {\n df1 <- data_frame(r = as.raw(1:4), x = 1:4)\n df2 <- data_frame(r = as.raw(3:6), y = 3:6)\n\n expect_identical(\n left_join(df1, df2, by = \"r\"),\n data_frame(r = as.raw(1:4), x = 1:4, y = c(NA, NA, 3:4))\n )\n\n expect_identical(\n right_join(df1, df2, by = \"r\"),\n data_frame(r = as.raw(3:6), x = c(3:4, NA, NA), y = c(3:6))\n )\n\n expect_identical(\n full_join(df1, df2, by = \"r\"),\n data_frame(r = as.raw(1:6), x = c(1:4, NA, NA), y = c(NA, NA, 3:6))\n )\n\n expect_identical(\n inner_join(df1, df2, by = \"r\"),\n data_frame(r = as.raw(3:4), x = c(3:4), y = c(3:4))\n )\n})\n\ntest_that(\"nest_join works (#3570)\",{\n df1 <- tibble(x = c(1, 2), y = c(2, 3))\n df2 <- tibble(x = c(1, 1), z = c(2, 3))\n res <- nest_join(df1, df2, by = \"x\")\n expect_equal(names(res), c(names(df1), \"df2\"))\n expect_identical(res$df2[[1]], select(df2, z))\n expect_identical(res$df2[[2]], tibble(z = double()))\n})\n\ntest_that(\"nest_join handles multiple matches in x (#3642)\", {\n df1 <- tibble(x = c(1, 1))\n df2 <- tibble(x = 1, y = 1:2)\n\n tbls <- df1 %>%\n nest_join(df2) %>%\n pull()\n\n expect_identical(tbls[[1]], tbls[[2]])\n})\n\ntest_that(\"joins reject data frames with duplicate columns (#3243)\", {\n df1 <- data.frame(x1 = 1:3, x2 = 1:3, y = 1:3)\n names(df1)[1:2] <- \"x\"\n df2 <- data.frame(x = 2:4, y = 2:4)\n\n expect_error(\n left_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n left_join(df2, df1, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n right_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n right_join(df2, df1, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n inner_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n inner_join(df2, df1, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n full_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n full_join(df2, df1, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n semi_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n # FIXME: Compatibility, should throw an error eventually\n expect_warning(\n expect_equal(\n semi_join(df2, df1, by = c(\"x\", \"y\")),\n data.frame(x = 2:3, y = 2:3)\n ),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n expect_error(\n anti_join(df1, df2, by = c(\"x\", \"y\")),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n\n # FIXME: Compatibility, should throw an error eventually\n expect_warning(\n expect_equal(\n anti_join(df2, df1, by = c(\"x\", \"y\")),\n data.frame(x = 4L, y = 4L)\n ),\n \"Column `x` must have a unique name\",\n fixed = TRUE\n )\n})\n\ntest_that(\"joins reject data frames with NA columns (#3417)\", {\n df_a <- tibble(B = c(\"a\", \"b\", \"c\"), AA = 1:3)\n df_b <- tibble(AA = 2:4, C = c(\"aa\", \"bb\", \"cc\"))\n\n df_aa <- df_a\n names(df_aa) <- c(NA, \"AA\")\n df_ba <- df_b\n names(df_ba) <- c(\"AA\", NA)\n\n expect_error(\n left_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n left_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n left_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n\n expect_error(\n right_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n right_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n right_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n\n expect_error(\n inner_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n inner_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n inner_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n\n expect_error(\n full_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n full_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_error(\n full_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n\n expect_warning(\n semi_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_warning(\n semi_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_warning(\n semi_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n\n expect_warning(\n anti_join(df_aa, df_b),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_warning(\n anti_join(df_aa, df_ba),\n \"Column `1` cannot have NA as name\",\n fixed = TRUE\n )\n expect_warning(\n anti_join(df_a, df_ba),\n \"Column `2` cannot have NA as name\",\n fixed = TRUE\n )\n})\n", "meta": {"hexsha": "ff053f8c4c63badd485f7719772afc6fd72ef9d0", "size": 36261, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-joins.r", "max_stars_repo_name": "woodywonge/dplyr", "max_stars_repo_head_hexsha": "b15e0d23bb09539b78434cc12ac07f0c9fd6c973", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-10-29T15:46:22.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-29T15:46:22.000Z", "max_issues_repo_path": "tests/testthat/test-joins.r", "max_issues_repo_name": "woodywonge/dplyr", "max_issues_repo_head_hexsha": "b15e0d23bb09539b78434cc12ac07f0c9fd6c973", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test-joins.r", "max_forks_repo_name": "woodywonge/dplyr", "max_forks_repo_head_hexsha": "b15e0d23bb09539b78434cc12ac07f0c9fd6c973", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9430222956, "max_line_length": 129, "alphanum_fraction": 0.5708061002, "num_tokens": 12396, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3295246250364732}} {"text": "library(bigrquery)\nlibrary(zipcode)\nlibrary(tidyverse)\nlibrary(ggplot2)\nlibrary(rgdal)\nlibrary(rgeos)\nlibrary(maptools)\nlibrary(viridis)\nlibrary(lubridate)\nlibrary(RColorBrewer)\nlibrary(reshape2)\nlibrary(tidycensus)\nlibrary(sp)\nlibrary(sf)\nlibrary(tigris)\nlibrary(rmapshaper)\n\n# us_leg_dist_projection<- CRS(\"+proj=longlat +ellps=GRS80 +datum=NAD83 +no_defs\")\n#https://www.nceas.ucsb.edu/scicomp/usecases/point-in-polygon\n\n# Install your census api key for tidycensus\n#census_api_key(\"xxxxxxxxxxxxxxxxxxxxxxx\", install=TRUE)\n\n#code to get all of the geom files for a country (or large region defined by totalpop_sf) in one sf object\nus <- unique(fips_codes$state)[1:51]\n\ntotalpop_sf <- reduce(\n map(us, function(x) {\n # get_acs does not yet support congr distr geometry\n # get_acs(geography = \"congressional districts\", variables = \"B01003_001\", \n get_acs(geography = \"tract\", variables = \"B01003_001\", \n state = x, geometry = TRUE)\n }), \n rbind\n)\n\n# Import shapefile as and sf dataframe\ncd_115<- st_read(dsn=\"examples/geofiles/us_legislative_districts\", layer=\"cb_2017_us_cd115_500k\", quiet=TRUE)\n\n# Filter to the state we're interested in\nMN_cd_115 <- filter(cd_115, STATEFP == \"27\")\n\n# Convert to an simple features dataframe\ncoordinates(MN_2018_download) <- c(\"client_lon\", \"client_lat\")\nMN2018_down_sf <- st_as_sf(MN_2018_download)\n\ncoordinates(MN_2018_upload) <- c(\"client_lon\", \"client_lat\")\nMN2018_up_sf <- st_as_sf(MN_2018_upload)\n\n# Set the layer CRSs to be the same\nst_crs(MN2018_down_sf) <- st_crs(MN_cd_115)\nst_crs(MN2018_up_sf) <- st_crs(MN_cd_115)\n\n# do spatial join to add congressional district to speed test rows\nMN_2018_down_cd115 <- bind_cols( \n MN_2018_download@data,\n MN_cd_115[as.numeric(st_within(MN2018_down_sf, MN_cd_115)),]\n)\nMN_2018_up_cd115 <- bind_cols( \n MN_2018_upload@data,\n MN_cd_115[as.numeric(st_within(MN2018_up_sf, MN_cd_115)),]\n)\n\n# First summary of median download speed to reduce sample bias\n# This first step produces download speed medians by district, day, and client_ip, reducing potential sample bias from \n# multiple tests from the same ip addresses over time\nMN_2018_DL_IP_day_district <- MN_2018_down_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip) %>%\n summarize(median_dl = median(download_speed_Mbps))\n\nMN_2018_UL_IP_day_district <- MN_2018_up_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip) %>%\n summarize(median_ul = median(upload_speed_Mbps))\n\n# Rename the 'day' column generated by the transform of log_time in the previous DF\nnames(MN_2018_DL_IP_day_district)[2] = \"day\"\nnames(MN_2018_UL_IP_day_district)[2] = \"day\"\n\n# Second and final summary of median download speed by district \n# This second summarization groups the previously calculated medians by district, day, and client_ip to simply medians by district\nMN_2018_download_median_cd115 <- MN_2018_DL_IP_day_district %>%\n group_by(CD115FP) %>%\n summarize(median_download_Mbps = median(median_dl))\n\nMN_2018_upload_median_cd115 <- MN_2018_UL_IP_day_district %>%\n group_by(CD115FP) %>%\n summarize(median_upload_Mbps = median(median_ul))\n\n# Rejoin the geometry field so we can map median speeds by district\nMN_2018_DL_median_ip_day_district <- left_join(MN_2018_download_median_cd115, MN_cd_115, by=c(\"CD115FP\"))\nMN_2018_UL_median_ip_day_district <- left_join(MN_2018_upload_median_cd115, MN_cd_115, by=c(\"CD115FP\"))\n\n# Do the mapping with ggplot\nMN_2018_median_down_cd115_plot <- MN_2018_DL_median_ip_day_district %>%\n ggplot(aes(fill=median_download_Mbps),color=median_download_Mbps) + \n guides(fill=guide_legend(title=\"Median Download Speed (Mbps)\")) + \n theme_bw() +\n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"), \n axis.text = element_blank(), axis.ticks = element_blank() ) + \n geom_sf() +\n geom_text(centroids.MNdist, label=id, x = \"lon\", y = \"lat\")\n\nMN_2018_median_up_cd115_plot <- MN_2018_UL_median_ip_day_district %>%\n ggplot(aes(fill=median_upload_Mbps),color=median_upload_Mbps) + geom_sf()\n\ndistricts <- readOGR(dsn=\"examples/geofiles/us_legislative_districts\", layer=\"cb_2017_us_cd115_500k\",stringsAsFactors = FALSE)\nMN_districts <- districts[districts$STATEFP==\"27\",]\nidList <- MN_districts@data$CD115FP\ncentroids.districts <- as.data.frame(coordinates(MN_districts))\ncentroids.MNdist <- as.data.frame(coordinates(MN_districts), MN_districts@data$CD115FP)\n\ntest.df <- data.frame(id=idList, centroids.districts)\n\ngeom_col(mapping = NULL, data = NULL, position = \"stack\", ...,\n width = NULL, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)\n", "meta": {"hexsha": "4bb77e279f1a57b6021714d62b6c02ad18055afb", "size": 4766, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wip/tidycensus-example.r", "max_stars_repo_name": "m-lab/data-support", "max_stars_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-08-07T14:41:13.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-14T17:57:33.000Z", "max_issues_repo_path": "R/wip/tidycensus-example.r", "max_issues_repo_name": "m-lab/data-support", "max_issues_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 15, "max_issues_repo_issues_event_min_datetime": "2020-04-13T18:48:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-22T16:24:10.000Z", "max_forks_repo_path": "R/wip/tidycensus-example.r", "max_forks_repo_name": "m-lab/data-support", "max_forks_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-08-14T16:10:22.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-08T01:17:35.000Z", "avg_line_length": 40.735042735, "max_line_length": 132, "alphanum_fraction": 0.7652119178, "num_tokens": 1378, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.3295246250364731}} {"text": "# Copyright Syncfusion Inc. 2001 - 2016. All rights reserved.\n# Use of this code is subject to the terms of our license.\n# A copy of the current license can be obtained at any time by e-mailing\n# licensing@syncfusion.com. Any infringement will be prosecuted under\n# applicable laws. \n\n\n# If you are not familiar with R you can obtain a quick introduction by downloading\n# R Succinctly for free from Syncfusion - http://www.syncfusion.com/resources/techportal/ebooks/rsuccinctly\n# R Succinctly is also included with this installation and is available here\n# Installed Drive :\\Program Files (x86)\\Syncfusion\\Essential Studio\\XX.X.X.XX\\Infrastructure\\EBooks\\R_Succintly.pdf OF R Succinctly\n# Uncomment below lines to install necessary packages if not installed already\n#install.packages(\"gbm\")\n#install.packages(\"devtools\")\n#install.packages(\"rJava\")\n# install.packages(\"caret\")\n#install.packages(\"githubinstall\")\n#Use 'r2pmml' package version v0.3.0 or below since, the function 'tdist' has been deprecated in later versions. \n#library(githubinstall)\n#gh_install_packages(\"jpmml/r2pmml\", ref=\"0.3.0\")\n\n# Load below packages\nlibrary(caret) # This package is specifically loaded for Cars dataset shipped within it.\nlibrary(devtools)\nlibrary(gbm)\nlibrary(rJava)\nlibrary(r2pmml)\n\n# Here we directly load the cars dataset installed with the \"caret\" package.\ndata(cars)\n\n# rename column names in cars dataset from caret package\ncarsOriginal <- setNames(cars, c(\"Price\", \"Mileage\", \"Cylinder\", \"Doors\", \"Cruise\", \"Sound\", \"Leather\", \"Buick\", \"Cadillac\", \"Chevy\", \"Pontiac\", \"Saab\", \"Saturn\", \n\"Convertible\", \"Coupe\", \"Hatchback\", \"Sedan\", \"Wagon\"))\n\n# Omit rows with missing values\ncarsOriginal = na.omit(carsOriginal)\n\n# Code below demonstrates loading the same dataset from a CSV file shipped with our installer.\n# Please check installed samples (Data) location to set actual working directory \n# Uncomment below lines and comment out the code to read data from CSV file.\n# setwd(\"C:/actual_data_location\")\n# cars= read.csv(\"Cars.csv\")\n\n# Divide dataset for training and test\ntrainData<-carsOriginal[1:643,]\ntestData<-carsOriginal[644:804,]\n\n# Applying the GBM - tdist function to predict cens\ncars_GBM = gbm(Price~., data=trainData,distribution=\"tdist\")\n\n# Display the predicted results\n# Predict \"Price\" column probability for test data set\ncarsTestProbabilities =predict.gbm(cars_GBM, newdata=testData, n.trees=100,type=\"response\")\n# Display predicted probabilities\ncarsTestProbabilities\n\n# PMML generated will be of version v4.2 since, older version(v0.3.0) 'r2pmml' has been loaded. \nr2pmml(cars_GBM,\"Cars.pmml\")\n\n# The code below is used for evaluation purpose. \n# The model is applied for original cars data set and predicted results are saved in \"ROutput.csv\"\n# \"ROutput.csv\" file used for comparing the R results with PMML Evaluation engine results\n\n# Applying General Regression model to entire dataset and save the results in a CSV file\ncarsEntireProbabilities = predict.gbm(cars_GBM,carsOriginal,n.trees=100,type=\"response\")\n\n# Save predicted value in a data frame\nresult = data.frame(carsEntireProbabilities)\nnames(result) = c(\"Predicted_RetailPrice\")\n\n# Write the results in a CSV file\nwrite.csv(result,\"ROutput.csv\",quote=F)\n\n", "meta": {"hexsha": "3686552640e1eb833c2bce0b2397227bff9cbbcd", "size": 3235, "ext": "r", "lang": "R", "max_stars_repo_path": "common/Analytics/Gradient Boosting Model/CarsTdist/Model/Cars.r", "max_stars_repo_name": "aTiKhan/winforms-demos", "max_stars_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 103, "max_stars_repo_stars_event_min_datetime": "2018-11-08T07:10:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T06:20:59.000Z", "max_issues_repo_path": "common/Analytics/Gradient Boosting Model/CarsTdist/Model/Cars.r", "max_issues_repo_name": "aTiKhan/winforms-demos", "max_issues_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-11-12T20:02:11.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-15T06:45:35.000Z", "max_forks_repo_path": "common/Analytics/Gradient Boosting Model/CarsTdist/Model/Cars.r", "max_forks_repo_name": "aTiKhan/winforms-demos", "max_forks_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 95, "max_forks_repo_forks_event_min_datetime": "2018-10-23T08:37:10.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T07:56:59.000Z", "avg_line_length": 43.1333333333, "max_line_length": 163, "alphanum_fraction": 0.772797527, "num_tokens": 834, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514778, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.329339511669453}} {"text": "## directory for outputs (plots, csv files, etc.)\noutput.dir <- \"output-20210811\"\n\n## consider at most the top 1000 associations reported by each study\ntop.n <- 1000 ## p_rank in sql query <= 1000\n\nlibrary(Cairo)\nlibrary(readr)\nlibrary(randomcoloR)\n\n## package for saving the outputs of computations\n## install: devtools::install_github(\"perishky/eval.save\")\nlibrary(eval.save)\neval.save.dir(\".eval\")\n\n## load Hannah's phenotype groups\nsource(\"https://raw.githubusercontent.com/hannah-e/collapse_EWAS_catalog_phenotypes/main/functional_analysis_regroup_EWAS_catalogue_phenotypes.R\")\n## out: EWAS_catalog\ngroups <- unique(EWAS_catalog[,c(\"StudyID\",\"phenotype\")])\ncolnames(groups) <- c(\"study\",\"group\")\n\n## read number of overlapping associations per study\noverlaps <- read.table(file.path(output.dir, \"overlaps.txt\"), sep=\"\\t\", stringsAsFactors=F, header=T)\n\noverlaps$n1 <- as.integer(overlaps$n1)\noverlaps$n2 <- as.integer(overlaps$n2)\n\noverlaps$n1 <- pmin(top.n, overlaps$n1)\noverlaps$n2 <- pmin(top.n, overlaps$n2)\n\n## calculate overlap significance using fisher's exact test\n## for each pair of studies\nn <- 485000\noverlaps$ff <- n - overlaps$n1 - overlaps$n2 + overlaps$overlap\noverlaps$tt <- overlaps$overlap\noverlaps$tf <- overlaps$n1 - overlaps$overlap\noverlaps$ft <- overlaps$n2 - overlaps$overlap\noverlaps$p <- eval.save({\n apply(overlaps[,c(\"ff\",\"ft\",\"tf\",\"tt\")], 1, function(vals) {\n tryCatch(\n fisher.test(matrix(vals, ncol=2), alternative=\"greater\")$p.value\n , error=function(e) NA)\n })\n}, \"overlaps-p\") ## 20 minutes\n\n## add symmetric comparisons\noverlaps.r <- overlaps\noverlaps.r$study1 <- overlaps.r$study2\noverlaps.r$n1 <- overlaps.r$n2\noverlaps.r$study2 <- overlaps$study1\noverlaps.r$n2 <- overlaps$n1\noverlaps.r$p <- overlaps$p\noverlaps <- rbind(overlaps, overlaps.r)\n\n## create 'overlap.p', matrix of fisher exact test p-values\nstudies <- unique(c(overlaps$study1, overlaps$study2))\noverlap.p <- matrix(NA, ncol=length(studies), nrow=length(studies),\n dimnames=list(studies, studies))\nidx <- cbind(r=match(overlaps$study1, rownames(overlap.p)),\n c=match(overlaps$study2, colnames(overlap.p)))\noverlap.p[idx] <- overlaps$p\n\n## calculate log of overlap p-values\nlog.p <- -log(overlap.p,10)\nlog.p[which(log.p > 50)] <- 50\n\n## Bonferroni adjusted -log(p-value) threshold\nthreshold <- -log(0.05/length(log.p)*2, 10)\nthreshold\n## [1] 7.664473\n\n## plot heatmap of log.p\nsource(\"heatmap-function.r\")\n\nCairoPNG(file.path(output.dir, \"heatmap.png\"),\n width=16384, height=16384)\nplot.new()\ngrid.clip()\n\ncols <- heatmap.color.scheme(\n low.breaks=seq(0,threshold,length.out=50),\n high.breaks=seq(threshold,max(log.p,na.rm=T),length.out=50))\n\nall.groups <- sort(unique(groups[,\"group\"]))\ngroup.cols <- data.frame(\n group=all.groups,\n col=rainbow(length(all.groups)))\nstudy.cols <- data.frame(\n study=colnames(log.p),\n group=groups$group[match(colnames(log.p), groups$study)])\nstudy.cols$col <- group.cols$col[match(study.cols$group,group.cols$group)]\nclinical <- matrix(study.cols$col, nrow=1)\nrownames(clinical) <- \"phenotype group\"\n\nh.out <- heatmap.simple(log.p,\n color.scheme=cols,\n key.min=0,\n\t\t\tkey.max=max(log.p, na.rm=T),\n\t\t\tna.color=\"gray\",\n\t\t\tscale=\"none\",\n clinical=clinical,\n title=\"EWAS catalog clustering\") ## 5 minutes\n\ndev.off() \n\n## replace missing p-values with p=1\nlog.p[is.na(log.p)] <- 0\n\n\n\n\n\n## use networks to identify clusters of studies (louvain clustering)\n## VD Blondel, J-L\n## Guillaume, R Lambiotte and E Lefebvre: Fast unfolding of community\n## hierarchies in large networks, \nweights <- log.p\nweights[which(weights < -log(1e-4,10))] <- 0 \n\nlibrary(igraph)\ngraph <- graph_from_adjacency_matrix(weights,\n mode=\"undirected\",\n weighted=T,\n diag=F)\nclusters <- cluster_louvain(graph)\n\nlength(clusters)\n## [1] 699\n\n## break the graph unto subgraphs corresponding to clusters\nsubgraphs <- lapply(1:length(clusters), function(i)\n induced_subgraph(graph, which(membership(clusters)==i)))\n\n## compare edge weights (i.e. log p-values)\n## between nodes/studies in the same\n## cluster versus elsewhere\nsubgraphs.weights <- lapply(subgraphs, function(g) E(g)$weight)\nsubgraphs.weights <- unlist(subgraphs.weights)\nquantile(E(graph)$weight)\nquantile(subgraphs.weights)\n## > quantile(E(graph)$weight)\n## 0% 25% 50% 75% 100% \n## 4.000195 5.083681 7.675126 14.748110 50.000000 \n## > quantile(subgraphs.weights)\n## 0% 25% 50% 75% 100% \n## 4.000510 5.208620 9.235719 19.081418 50.000000 \n\n\n\n## show sizes of clusters and median -log(p-value) per cluster\nret <- t(sapply(subgraphs, function(g) {\n c(n=length(V(g)),\n median.weight = median(sapply(V(g), function(v) {\n median(incident(g,v)$weight, na.rm=T)\n })))\n}))\nret <- ret[order(ret[,\"n\"],decreasing=T),]\nret[ret[,\"n\"] > 4,]\n## n median.weight\n## [1,] 328 6.608221\n## [2,] 305 8.307027\n## [3,] 192 11.945131\n## [4,] 143 8.381339\n## [5,] 92 16.468290\n## [6,] 79 34.567194\n## [7,] 36 6.513009\n## [8,] 19 5.083682\n## [9,] 12 21.595919\n## [10,] 11 9.766727\n## [11,] 10 26.784681\n## [12,] 10 47.299716\n## [13,] 9 5.384712\n## [14,] 9 12.937390\n## [15,] 8 5.573377\n## [16,] 8 4.796666\n## [17,] 7 9.514150\n## [18,] 6 14.759602\n## [19,] 5 5.384712\n## [20,] 5 5.685741\n## [21,] 5 50.000000\n## [22,] 5 17.197919\n\n\n## median number of neighbors ('deg') within each cluster\nret <- cbind(n=sapply(subgraphs, vcount),\n deg=sapply(subgraphs, function(g) median(degree(g))))\nret <- ret[order(ret[,\"n\"],decreasing=T),]\nret[ret[,\"n\"] > 4,]\n## n deg\n## [1,] 328 90.0\n## [2,] 305 6.0\n## [3,] 192 81.0\n## [4,] 143 54.0\n## [5,] 92 59.0\n## [6,] 79 66.0\n## [7,] 36 3.5\n## [8,] 19 2.0\n## [9,] 12 9.0\n## [10,] 11 8.0\n## [11,] 10 3.5\n## [12,] 10 7.0\n## [13,] 9 3.0\n## [14,] 9 5.0\n## [15,] 8 2.0\n## [16,] 8 4.0\n## [17,] 7 2.0\n## [18,] 6 1.0\n## [19,] 5 3.0\n## [20,] 5 4.0\n## [21,] 5 4.0\n## [22,] 5 2.0\n\nclusters <- do.call(rbind, lapply(1:length(subgraphs), function(i) {\n data.frame(cluster=i,\n cluster.size=length(V(subgraphs[[i]])),\n study=names(V(subgraphs[[i]])))\n}))\nclusters$group <- groups$group[match(clusters$study, groups$study)]\n\nwrite.csv(clusters, file=file.path(output.dir, \"louvain-clusters.csv\"), row.names=F)\n\n", "meta": {"hexsha": "a378b1eef2af94a66c92b4dd46a5f1dc729098d5", "size": 6762, "ext": "r", "lang": "R", "max_stars_repo_path": "paper/clusters.r", "max_stars_repo_name": "MRCIEU/ewascatalog", "max_stars_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-05T09:39:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-05T09:39:48.000Z", "max_issues_repo_path": "paper/clusters.r", "max_issues_repo_name": "MRCIEU/ewascatalog", "max_issues_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "paper/clusters.r", "max_forks_repo_name": "MRCIEU/ewascatalog", "max_forks_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3228699552, "max_line_length": 146, "alphanum_fraction": 0.6143152913, "num_tokens": 2239, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.32921492837244704}} {"text": "\n#################################################################################\n## File Name: v3_Analysis_TimeSeries.R ##\n## Date: 27 APr 2016 ##\n## Author: Gento Kato ##\n## Project: Foreign Image News Project ##\n## Purpose: Conduct Time Series Analysis ##\n#################################################################################\n\n## For Jupyter Notebook (Ignore if Using Other Software) ##\nlibrary(IRdisplay)\n\ndisplay_html(\n'\n
\n \n
'\n)\n\n## Suppress Warning\n#options(warn=-1)\n#options(warn=0) # put it back\n\n#################\n## Preparation ##\n#################\n\n## Clear Workspace\nrm(list=ls())\n\n## Library Required Packages\nlibrary(rprojroot); library(doBy); library(descr)\n\n##################Prepare Packages##################\nlibrary(vars)#;detach(\"package:vars\", unload=TRUE)\nlibrary(tseries);library(urca);\nlibrary(tsDyn)#;detach(\"package:tsDyn\", unload=TRUE)\n#library(MSBVAR);detach(\"package:MSBVAR\", unload=TRUE)\n#library(FIAR);\n####################################################\nlibrary(ggplot2)\n\n## Set Working Directory (Automatically or Manually) ##\nprojdir <- find_root(has_file(\"README.md\")); projdir; setwd(projdir) #In Atom\n#setwd(dirname(rstudioapi::getActiveDocumentContext()$path)); setwd(\"../\") #In RStudio\n#setwd(\"C:/GoogleDrive/Projects/Agenda-Setting Persuasion Framing/Foreign_Image_News_Project/codes\")\n\n\n#######################\n## Load Monthly Data ##\n#######################\n\nload(\"data/v3_Data5_MonthlySubset.Rdata\")\n\nls()\n\n\n#############################\n## Agenda Setting Analysis ##\n#############################\n\n## Data #######################################\n\nstv <- min(which(!is.na(usmonth$imprel)))\nedv <- max(which(!is.na(usmonth$imprel)))\n\n## Create Data ##\nattach(usmonth[stv:edv,])\nusimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(usmonth[stv:edv,])\nattach(chnmonth[stv:edv,])\nchnimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(chnmonth[stv:edv,])\nattach(skormonth[stv:edv,])\nskorimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(skormonth[stv:edv,])\nattach(nkormonth[stv:edv,])\nnkorimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(nkormonth[stv:edv,])\nattach(rusmonth[stv:edv,])\nrusimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(rusmonth[stv:edv,])\nattach(euromonth[stv:edv,])\neuroimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(euromonth[stv:edv,])\nattach(mneastmonth[stv:edv,])\nmneastimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(mneastmonth[stv:edv,])\nattach(taiwanmonth[stv:edv,])\ntaiwanimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(taiwanmonth[stv:edv,])\nattach(seasiamonth[stv:edv,])\nseasiaimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(seasiamonth[stv:edv,])\nattach(msamericamonth[stv:edv,])\nmsamericaimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(msamericamonth[stv:edv,])\nattach(oceaniamonth[stv:edv,])\noceaniaimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(oceaniamonth[stv:edv,])\nattach(africamonth[stv:edv,])\nafricaimp<-data.frame(trade_gdp,statecount_w_both_per,imprel)\ndetach(africamonth[stv:edv,])\n\n\n## ADF Test (Non-Stationary Variable Exists for All Except for US)\n# US\nsummary(ur.df(usimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usimp$trade_gdp,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# China\nsummary(ur.df(chnimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(chnimp$imprel),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Stationary\n# S.Korea\nsummary(ur.df(skorimp$imprel,type=\"drift\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(skorimp$imprel),type=\"none\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (5%)\nsummary(ur.df(diff(skorimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# N.Korea\nsummary(ur.df(nkorimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(nkorimp$imprel),type=\"drift\",selectlags=\"AIC\")) # Stationary \nsummary(ur.df(nkorimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(nkorimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## None-Stationary (5%)\nsummary(ur.df(diff(nkorimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# Russia\nsummary(ur.df(rusimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(rusimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(rusimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (5%)\nsummary(ur.df(diff(rusimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# Europe\nsummary(ur.df(euroimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(euroimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(euroimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (5%)\nsummary(ur.df(diff(euroimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary \n# Middle-Near East\nsummary(ur.df(mneastimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(mneastimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(mneastimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(mneastimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# Taiwan\nsummary(ur.df(taiwanimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(taiwanimp$imprel),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(taiwanimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(taiwanimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(taiwanimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# South-East Asia\nsummary(ur.df(seasiaimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(seasiaimp$imprel),type=\"none\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(seasiaimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(seasiaimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Stationary\n# Middle-South America\nsummary(ur.df(msamericaimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(msamericaimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(msamericaimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(msamericaimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# Oceania\nsummary(ur.df(oceaniaimp$imprel,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(oceaniaimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(oceaniaimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (1%)\nsummary(ur.df(diff(oceaniaimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# Africa\nsummary(ur.df(africaimp$imprel,type=\"trend\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(africaimp$statecount_w_both_per,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(africaimp$trade_gdp,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (5%)\nsummary(ur.df(diff(africaimp$trade_gdp),type=\"drift\",selectlags=\"AIC\")) ## Stationry\n\n\n# Optimal Lag Based on AIC\nusIp <- VARselect(usimp,lag.max=12, type=\"both\")$selection[1]; usIp # 3\nchnIp <- VARselect(chnimp,lag.max=12, type=\"both\")$selection[1]; chnIp # 12\nskorIp <- VARselect(skorimp,lag.max=12, type=\"both\")$selection[1]; skorIp # 4\nnkorIp <- VARselect(nkorimp,lag.max=12, type=\"both\")$selection[1]; nkorIp # 4\nrusIp <- VARselect(rusimp,lag.max=12, type=\"both\")$selection[1]; rusIp # 6\neuroIp <- VARselect(euroimp,lag.max=12, type=\"both\")$selection[1]; euroIp # 7\nmneastIp <- VARselect(mneastimp,lag.max=12, type=\"both\")$selection[1]; mneastIp # 3\ntaiwanIp <- VARselect(taiwanimp,lag.max=12, type=\"both\")$selection[1]; taiwanIp # 5\nseasiaIp <- VARselect(seasiaimp,lag.max=12, type=\"both\")$selection[1]; seasiaIp # 7\nmsamericaIp <- VARselect(msamericaimp,lag.max=12, type=\"both\")$selection[1]; msamericaIp # 3\noceaniaIp <- VARselect(oceaniaimp,lag.max=12, type=\"both\")$selection[1]; oceaniaIp # 7\nafricaIp <- VARselect(africaimp,lag.max=12, type=\"both\")$selection[1]; africaIp # 5\n\n\n# VECM with Cointegration Trace Test (Cointegration Exists for All *This is Used)\nusimp.vecm <- ca.jo(usimp, ecdet=\"const\", type='trace', K=usIp, spec=\"transitory\"); summary(usimp.vecm) # 2\nchnimp.vecm <- ca.jo(chnimp, ecdet=\"const\", type='trace', K=chnIp, spec=\"transitory\"); summary(chnimp.vecm) # 2\nskorimp.vecm <- ca.jo(skorimp, ecdet=\"const\", type='trace', K=skorIp, spec=\"transitory\"); summary(skorimp.vecm) # 1\nnkorimp.vecm <- ca.jo(nkorimp, ecdet=\"const\", type='trace', K=nkorIp, spec=\"transitory\"); summary(nkorimp.vecm) # 2\nrusimp.vecm <- ca.jo(rusimp, ecdet=\"const\", type='trace', K=rusIp, spec=\"transitory\"); summary(rusimp.vecm) # 1\neuroimp.vecm <- ca.jo(euroimp, ecdet=\"const\", type='trace', K=euroIp, spec=\"transitory\"); summary(euroimp.vecm) # 1\nmneastimp.vecm <- ca.jo(mneastimp, ecdet=\"const\", type='trace', K=mneastIp, spec=\"transitory\"); summary(mneastimp.vecm) # 2\ntaiwanimp.vecm <- ca.jo(taiwanimp, ecdet=\"const\", type='trace', K=taiwanIp, spec=\"transitory\"); summary(taiwanimp.vecm) #1\nseasiaimp.vecm <- ca.jo(seasiaimp, ecdet=\"const\", type='trace', K=seasiaIp, spec=\"transitory\"); summary(seasiaimp.vecm) # 1\nmsamericaimp.vecm <- ca.jo(msamericaimp, ecdet=\"const\", type='trace', K=msamericaIp, spec=\"transitory\"); summary(msamericaimp.vecm) # 2\noceaniaimp.vecm <- ca.jo(oceaniaimp, ecdet=\"const\", type='trace', K=oceaniaIp, spec=\"transitory\"); summary(oceaniaimp.vecm) # 1\nafricaimp.vecm <- ca.jo(africaimp, ecdet=\"const\", type='trace', K=africaIp, spec=\"transitory\"); summary(africaimp.vecm) # 1\n\n\n# Cointegration Maximal Eigenvalue Test (Result Identical: not used for the final analysis)\n# summary(ca.jo(usimp, ecdet=\"const\", type='eigen', K=usIp, spec=\"transitory\")) # 2\n# summary(ca.jo(chnimp, ecdet=\"const\", type='eigen', K=chnIp, spec=\"transitory\")) # 2\n# summary(ca.jo(skorimp, ecdet=\"const\", type='eigen', K=skorIp, spec=\"transitory\")) # 1\n# summary(ca.jo(nkorimp, ecdet=\"const\", type='eigen', K=nkorIp, spec=\"transitory\")) # 2\n# summary(ca.jo(rusimp, ecdet=\"const\", type='eigen', K=rusIp, spec=\"transitory\")) # 1\n# summary(ca.jo(euroimp, ecdet=\"const\", type='eigen', K=euroIp, spec=\"transitory\")) # 1\n# summary(ca.jo(mneastimp, ecdet=\"const\", type='eigen', K=mneastIp, spec=\"transitory\")) # 2\n# summary(ca.jo(taiwanimp, ecdet=\"const\", type='eigen', K=taiwanIp, spec=\"transitory\")) # 1\n# summary(ca.jo(seasiaimp, ecdet=\"const\", type='eigen', K=seasiaIp, spec=\"transitory\")) # 1\n# summary(ca.jo(msamericaimp, ecdet=\"const\", type='eigen', K=msamericaIp, spec=\"transitory\")) # 2\n# summary(ca.jo(oceaniaimp, ecdet=\"const\", type='eigen', K=oceaniaIp, spec=\"transitory\")) # 1\n# summary(ca.jo(africaimp, ecdet=\"const\", type='eigen', K=africaIp, spec=\"transitory\")) # 1\n\n\n## Restrictions for Short Run Matrix\nSR <- matrix(NA,nrow=3,ncol=3); SR[1,2:3] <- 0; SR[2,3] <- 0\n## NO Restrictions for Long Run Matrix\nLR <- matrix(NA,nrow=3,ncol=3)\n\n## Structural VECM Result\nusimp.vecm.res <- SVEC(usimp.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE,boot=FALSE,runs=500)\nchnimp.vecm.res <- SVEC(chnimp.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE,boot=FALSE,runs=500)\nskorimp.vecm.res <- SVEC(skorimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\nnkorimp.vecm.res <- SVEC(nkorimp.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE,boot=FALSE,runs=500)\nrusimp.vecm.res <- SVEC(rusimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\neuroimp.vecm.res <- SVEC(euroimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\nmneastimp.vecm.res <- SVEC(mneastimp.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE,boot=FALSE,runs=500)\ntaiwanimp.vecm.res <- SVEC(taiwanimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\nseasiaimp.vecm.res <- SVEC(seasiaimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\nmsamericaimp.vecm.res <- SVEC(msamericaimp.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE,boot=FALSE,runs=500)\noceaniaimp.vecm.res <- SVEC(oceaniaimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\nafricaimp.vecm.res <- SVEC(africaimp.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE,boot=FALSE,runs=500)\n\n\n## IRF\nusirf<-irf(usimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nchnirf<-irf(chnimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nskorirf<-irf(skorimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nnkorirf<-irf(nkorimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nrusirf<-irf(rusimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\neuroirf<-irf(euroimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nmneastirf<-irf(mneastimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\ntaiwanirf<-irf(taiwanimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nseasiairf<-irf(seasiaimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nmsamericairf<-irf(msamericaimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\noceaniairf<-irf(oceaniaimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nafricairf<-irf(africaimp.vecm.res,n.ahead=12,ci=0.95,runs=1000)\n\n# plot(usirf) # small\n# plot(chnirf) # none\n# plot(skorirf) # good short term\n# plot(nkorirf) # good and persistent\n# plot(rusirf) # good short term\n# plot(euroirf) # small short term\n# plot(mneastirf) # small short term\n# plot(taiwanirf) # small short term\n# plot(seasiairf) # no effect\n# plot(msamericairf) # small\n# plot(oceaniairf) # none\n# plot(africairf) # none\n\n\n################################\n## Framing and Agenda-Setting ##\n################################\n\nattach(usmonth[stv:edv,])\nusimp_frame<-data.frame(trade_gdp,defense_w_both_per2,econ_w_both_per2,imprel)\ndetach(usmonth[stv:edv,])\nattach(chnmonth[stv:edv,])\nchnimp_frame<-data.frame(trade_gdp,defense_w_both_per2,econ_w_both_per2,imprel)\ndetach(chnmonth[stv:edv,])\nattach(skormonth[stv:edv,])\nskorimp_frame<-data.frame(trade_gdp,defense_w_both_per2,econ_w_both_per2,imprel)\ndetach(skormonth[stv:edv,])\nattach(nkormonth[stv:edv,])\nnkorimp_frame<-data.frame(trade_gdp,defense_w_both_per2,econ_w_both_per2,imprel)\ndetach(nkormonth[stv:edv,])\n\n\n## ADF Test (All new Variables are stationary)\n# US\nsummary(ur.df(usimp_frame$econ_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usimp_frame$defense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# China\nsummary(ur.df(chnimp_frame$econ_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnimp_frame$defense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# S.Korea\nsummary(ur.df(skorimp_frame$econ_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorimp_frame$defense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# N.Korea\nsummary(ur.df(nkorimp_frame$econ_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(nkorimp_frame$defense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n\n\n# Optimal Lag Based on AIC\nusIFp <- VARselect(usimp_frame,lag.max=12, type=\"both\")$selection[1]; usIFp; usIFp=2 # 1\nchnIFp <- VARselect(chnimp_frame,lag.max=12, type=\"both\")$selection[1]; chnIFp # 2\nskorIFp <- VARselect(skorimp_frame,lag.max=12, type=\"both\")$selection[1]; skorIFp # 2\nnkorIFp <- VARselect(nkorimp_frame,lag.max=12, type=\"both\")$selection[1]; nkorIFp # 4\n\n\n# VECM with Cointegration Trace Test (Cointegration Exists for All *This is Used)\nusimpF.vecm <- ca.jo(usimp_frame, ecdet=\"const\", type='trace', K=usIFp, spec=\"transitory\"); summary(usimpF.vecm) # 3\nchnimpF.vecm <- ca.jo(chnimp_frame, ecdet=\"const\", type='trace', K=chnIFp, spec=\"transitory\"); summary(chnimpF.vecm) # 2\nskorimpF.vecm <- ca.jo(skorimp_frame, ecdet=\"const\", type='trace', K=skorIFp, spec=\"transitory\"); summary(skorimpF.vecm) # 2\nnkorimpF.vecm <- ca.jo(nkorimp_frame, ecdet=\"const\", type='trace', K=nkorIFp, spec=\"transitory\"); summary(nkorimpF.vecm) # 2\n\n\n## Restrictions for Short Run Matrix\nSR <- matrix(NA,nrow=4,ncol=4); SR[1,2:4]<-0; SR[2,3:4]<-0; SR[3,4]<-0; #SR[3,2]<-0\n## NO Restrictions for Long Run Matrix\nLR <- matrix(NA,nrow=4,ncol=4)\n\n## VECM Result\nusimpF.vecm.res <- SVEC(usimpF.vecm,LR=LR,SR=SR,r=3,lrtest=FALSE)\nchnimpF.vecm.res <- SVEC(chnimpF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\nskorimpF.vecm.res <- SVEC(skorimpF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\nnkorimpF.vecm.res <- SVEC(nkorimpF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\n\n\n## IRF\nusirf_frame<-irf(usimpF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nchnirf_frame<-irf(chnimpF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nskorirf_frame<-irf(skorimpF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nnkorirf_frame<-irf(nkorimpF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\n\n# plot(usirf_frame)\n# plot(chnirf_frame)\n# plot(skorirf_frame)\n# plot(nkorirf_frame)\n\n\n###############################\n## Persuasion (Like/Dislike) ##\n###############################\n\nattach(usmonth)\nusldl<-data.frame(surplus_gdp,statetone_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(usmonth)\nattach(chnmonth)\nchnldl<-data.frame(surplus_gdp,statetone_w_both_per2,ldlstate)[-nrow(chnmonth),]\ndetach(chnmonth)\nattach(skormonth)\nskorldl<-data.frame(surplus_gdp,statetone_w_both_per2,ldlstate)[-nrow(skormonth),]\ndetach(skormonth)\nattach(nkormonth)\nnkorldl<-data.frame(surplus_gdp,statetone_w_both_per2,ldlstate)[-nrow(nkormonth),]\ndetach(nkormonth)\n\n\n## ADF Test\n# US\nsummary(ur.df(usldl$ldlstate,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(usldl$ldlstate),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usldl$statetone_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usldl$surplus_gdp,type=\"trend\",selectlags=\"AIC\")) ## Stationary\n# China\nsummary(ur.df(chnldl$ldlstate,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(chnldl$ldlstate),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnldl$statetone_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnldl$surplus_gdp,type=\"trend\",selectlags=\"AIC\")) ## Stationary\n# S.Korea\nsummary(ur.df(skorldl$ldlstate,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (10%)\nsummary(ur.df(diff(skorldl$ldlstate),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorldl$statetone_w_both_per2,type=\"none\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorldl$surplus_gdp,type=\"trend\",selectlags=\"AIC\")) ## Stationary\n# N.Korea\nsummary(ur.df(nkorldl$ldlstate,type=\"trend\",selectlags=\"AIC\")) ## Non-Stationary (5%)\nsummary(ur.df(diff(nkorldl$ldlstate),type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(nkorldl$statetone_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(nkorldl$surplus_gdp,type=\"none\",selectlags=\"AIC\")) ## Stationary\n\n\n# Optimal Lag Based on AIC\nusTp <- VARselect(usldl,lag.max=12, type=\"both\")$selection[1]; usTp # 12\nchnTp <- VARselect(chnldl,lag.max=12, type=\"both\")$selection[1]; chnTp # 12\nskorTp <- VARselect(skorldl,lag.max=12, type=\"both\")$selection[1]; skorTp # 12\nnkorTp <- VARselect(nkorldl,lag.max=12, type=\"both\")$selection[1]; nkorTp # 4\n\n\n# VECM with Cointegration Trace Test (Cointegration Exists for All *This is Used)\nusldl.vecm <- ca.jo(usldl, ecdet=\"const\", type='trace', K=usTp, spec=\"transitory\"); summary(usldl.vecm) # 1\nchnldl.vecm <- ca.jo(chnldl, ecdet=\"const\", type='trace', K=chnTp, spec=\"transitory\"); summary(chnldl.vecm) # 1\nskorldl.vecm <- ca.jo(skorldl, ecdet=\"const\", type='trace', K=skorTp, spec=\"transitory\"); summary(skorldl.vecm) # 1\nnkorldl.vecm <- ca.jo(nkorldl, ecdet=\"const\", type='trace', K=nkorTp, spec=\"transitory\"); summary(nkorldl.vecm) # 2\n\n\n## Restrictions for Short Run Matrix\nSR <- matrix(NA,nrow=3,ncol=3); SR[1,2:3] <- 0; SR[2,3] <- 0\n## NO Restrictions for Long Run Matrix\nLR <- matrix(NA,nrow=3,ncol=3)\n\n## Structural VECM Result\nusldl.vecm.res <- SVEC(usldl.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE)\nchnldl.vecm.res <- SVEC(chnldl.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE) #,boot=TRUE,runs=500\nskorldl.vecm.res <- SVEC(skorldl.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE) #,boot=TRUE,runs=500\nnkorldl.vecm.res <- SVEC(nkorldl.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\n\n\n## IRF\nusirf_ldl<-irf(usldl.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nchnirf_ldl<-irf(chnldl.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nskorirf_ldl<-irf(skorldl.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nnkorirf_ldl<-irf(nkorldl.vecm.res,n.ahead=12,ci=0.95,runs=1000)\n\n# plot(usirf_ldl)\n# plot(chnirf_ldl)\n# plot(skorirf_ldl)\n# plot(nkorirf_ldl)\n\n\n#############################\n## Persuasion (ldl, frame) ##\n#############################\n\nattach(usmonth)\nusldl_frame<-data.frame(surplus_gdp,tonedefense_w_both_per2,toneecon_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(usmonth)\nattach(chnmonth)\nchnldl_frame<-data.frame(surplus_gdp,tonedefense_w_both_per2,toneecon_w_both_per2,ldlstate)[-nrow(chnmonth),]\ndetach(chnmonth)\nattach(skormonth)\nskorldl_frame<-data.frame(surplus_gdp,tonedefense_w_both_per2,toneecon_w_both_per2,ldlstate)[-nrow(skormonth),]\ndetach(skormonth)\nattach(nkormonth)\nnkorldl_frame<-data.frame(surplus_gdp,tonedefense_w_both_per2,toneecon_w_both_per2,ldlstate)[-nrow(nkormonth),]\ndetach(nkormonth)\n\n\n## ADF Test (All new Variables are stationary)\n# US\nsummary(ur.df(usldl_frame$toneecon_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(usldl_frame$tonedefense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# China\nsummary(ur.df(chnldl_frame$toneecon_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(chnldl_frame$tonedefense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# S.Korea\nsummary(ur.df(skorldl_frame$toneecon_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(skorldl_frame$tonedefense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n# N.Korea\nsummary(ur.df(nkorldl_frame$toneecon_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\nsummary(ur.df(nkorldl_frame$tonedefense_w_both_per2,type=\"drift\",selectlags=\"AIC\")) ## Stationary\n\n\n# Optimal Lag Based on AIC\nusTFp <- VARselect(usldl_frame,lag.max=12, type=\"both\")$selection[1]; usTFp # 12\nchnTFp <- VARselect(chnldl_frame,lag.max=12, type=\"both\")$selection[1]; chnTFp # 3\nskorTFp <- VARselect(skorldl_frame,lag.max=12, type=\"both\")$selection[1]; skorTFp; skorTFp=2 # 1\nnkorTFp <- VARselect(nkorldl_frame,lag.max=12, type=\"both\")$selection[1]; nkorTFp # 4\n\n\n# VECM with Cointegration Trace Test (Cointegration Exists for All *This is Used)\nusldlF.vecm <- ca.jo(usldl_frame, ecdet=\"const\", type='trace', K=usTFp, spec=\"transitory\"); summary(usldlF.vecm) # 2\nchnldlF.vecm <- ca.jo(chnldl_frame, ecdet=\"const\", type='trace', K=chnTFp, spec=\"transitory\"); summary(chnldlF.vecm) # 1\nskorldlF.vecm <- ca.jo(skorldl_frame, ecdet=\"const\", type='trace', K=skorTFp, spec=\"transitory\"); summary(skorldlF.vecm) # 2\nnkorldlF.vecm <- ca.jo(nkorldl_frame, ecdet=\"const\", type='trace', K=nkorTFp, spec=\"transitory\"); summary(nkorldlF.vecm) # 3\n\n\n## Restrictions for Short Run Matrix\nSR <- matrix(NA,nrow=4,ncol=4); SR[1,2:4]<-0; SR[2,3:4]<-0; SR[3,4]<-0; #SR[3,2]<-0\n## NO Restrictions for Long Run Matrix\nLR <- matrix(NA,nrow=4,ncol=4)\n\n## VECM Result\nusldlF.vecm.res <- SVEC(usldlF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE)\nchnldlF.vecm.res <- SVEC(chnldlF.vecm,LR=LR,SR=SR,r=1,lrtest=FALSE) #,boot=TRUE,runs=500\nskorldlF.vecm.res <- SVEC(skorldlF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\nnkorldlF.vecm.res <- SVEC(nkorldlF.vecm,LR=LR,SR=SR,r=3,lrtest=FALSE) #,boot=TRUE,runs=500\n\n\n## IRF\nusirf_ldl_frame<-irf(usldlF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nchnirf_ldl_frame<-irf(chnldlF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nskorirf_ldl_frame<-irf(skorldlF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nnkorirf_ldl_frame<-irf(nkorldlF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\n\n# plot(usirf_ldl_frame) # small\n# plot(chnirf_ldl_frame) # none\n# plot(skorirf_ldl_frame) # good short term\n# plot(nkorirf_ldl_frame) # good and persistent\n\n\n###########################################\n## Persuasion (ldl, frame by frequency ) ##\n###########################################\n\n# Create Datasets\nattach(usmonth)\nusldlI_frame<-data.frame(surplus_gdp,defense_w_both_per2,econ_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(usmonth)\nattach(chnmonth)\nchnldlI_frame<-data.frame(surplus_gdp,defense_w_both_per2,econ_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(chnmonth)\nattach(skormonth)\nskorldlI_frame<-data.frame(surplus_gdp,defense_w_both_per2,econ_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(skormonth)\nattach(nkormonth)\nnkorldlI_frame<-data.frame(surplus_gdp,defense_w_both_per2,econ_w_both_per2,ldlstate)[-nrow(usmonth),]\ndetach(nkormonth)\n\n\n# Optimal Lag Based on AIC\nusTIFp <- VARselect(usldlI_frame,lag.max=12, type=\"both\")$selection[1]; usTIFp # 12\nchnTIFp <- VARselect(chnldlI_frame,lag.max=12, type=\"both\")$selection[1]; chnTIFp # 12\nskorTIFp <- VARselect(skorldlI_frame,lag.max=12, type=\"both\")$selection[1]; skorTIFp; skorTIFp=2 # 1 (Changed to 2)\nnkorTIFp <- VARselect(nkorldlI_frame,lag.max=12, type=\"both\")$selection[1]; nkorTIFp # 3\n\n\n# VECM with Cointegration Trace Test (Cointegration Exists for All *This is Used)\nusldlIF.vecm <- ca.jo(usldlI_frame, ecdet=\"const\", type='trace', K=usTIFp, spec=\"transitory\"); summary(usldlIF.vecm) # 2\nchnldlIF.vecm <- ca.jo(chnldlI_frame, ecdet=\"const\", type='trace', K=chnTIFp, spec=\"transitory\"); summary(chnldlIF.vecm) # 2\nskorldlIF.vecm <- ca.jo(skorldlI_frame, ecdet=\"const\", type='trace', K=skorTIFp, spec=\"transitory\"); summary(skorldlIF.vecm) # 2\nnkorldlIF.vecm <- ca.jo(nkorldlI_frame, ecdet=\"const\", type='trace', K=nkorTIFp, spec=\"transitory\"); summary(nkorldlIF.vecm) # 3\n\n\n## Restrictions for Short Run Matrix\nSR <- matrix(NA,nrow=4,ncol=4); SR[1,2:4]<-0; SR[2,3:4]<-0; SR[3,4]<-0; #SR[3,2]<-0\n## NO Restrictions for Long Run Matrix\nLR <- matrix(NA,nrow=4,ncol=4)\n\n## SVECM Result\nusldlIF.vecm.res <- SVEC(usldlIF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE)\nchnldlIF.vecm.res <- SVEC(chnldlIF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\nskorldlIF.vecm.res <- SVEC(skorldlIF.vecm,LR=LR,SR=SR,r=2,lrtest=FALSE) #,boot=TRUE,runs=500\nnkorldlIF.vecm.res <- SVEC(nkorldlIF.vecm,LR=LR,SR=SR,r=3,lrtest=FALSE) #,boot=TRUE,runs=500\n\n\nusirf_ldlI_frame<-irf(usldlIF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nchnirf_ldlI_frame<-irf(chnldlIF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nskorirf_ldlI_frame<-irf(skorldlIF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\nnkorirf_ldlI_frame<-irf(nkorldlIF.vecm.res,n.ahead=12,ci=0.95,runs=1000)\n\n# plot(usirf_ldlI_frame) # small\n# plot(chnirf_ldlI_frame) # none\n# plot(skorirf_ldlI_frame) # good short term\n# plot(nkorirf_ldlI_frame) # good and persistent\n\n\n######################################\n## Save Result (separate from data) ##\n######################################\n\n## Remove Data from Work Space ##\nrm(usmonth, chnmonth, skormonth, nkormonth, rusmonth, euromonth, mneastmonth, #indiamonth,\n taiwanmonth, seasiamonth, msamericamonth, oceaniamonth, africamonth)\n\n## Save the Remaining Analytical Result ##\nsave.image(\"./outputs/v3_Analysis_TimeSeries.RData\")\n#load(\"./outputs/v3_Analysis_TimeSeries.RData\")\n\n", "meta": {"hexsha": "4eb05bc0fd8b66b99eeafff04511c045c97f6195", "size": 27885, "ext": "r", "lang": "R", "max_stars_repo_path": "codes/v3_Analysis_TimeSeries.r", "max_stars_repo_name": "gentok/Foreign_Image_News_Project", "max_stars_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "codes/v3_Analysis_TimeSeries.r", "max_issues_repo_name": "gentok/Foreign_Image_News_Project", "max_issues_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "codes/v3_Analysis_TimeSeries.r", "max_forks_repo_name": "gentok/Foreign_Image_News_Project", "max_forks_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.4414893617, "max_line_length": 135, "alphanum_fraction": 0.7165859781, "num_tokens": 10047, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.668880247169804, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.32921492187497076}} {"text": "## amp_rarecurve function from https://github.com/MadsAlbertsen/ampvis2/blob/master/R/amp_rarecurve.R ###\r\n\r\n#' Calculate rarefaction curve for each sample.\r\n#'\r\n#' Calculate rarefaction curve for each sample using the vegan rarecurve function directly from a phyloseq object.\r\n#'\r\n#' @usage amp_rarecurve(data)\r\n#'\r\n#' @param data (required) A phyloseq object.\r\n#' @param step Step size for sample sizes in rarefaction curves (default: 100).\r\n#' @param ylim vector of y-axis limits.\r\n#' @param xlim vector of x-axis limits.\r\n#' @param label Label rarefaction curves (default: F).\r\n#' @param color Color lines by metadata.\r\n#' @param color.vector Vector with colors e.g. c(\"red\",\"white\") (default: NULL).\r\n#' @param legend Add a legend to the plot if color is used (default: T).\r\n#' @param legend.position Position of the legend (default: \"topleft\").\r\n#' \r\n#' @export\r\n#' @import phyloseq\r\n#' @import vegan\r\n#' \r\n#' @author Mads Albertsen \\email{MadsAlbertsen85@@gmail.com}\r\n\r\namp_rarecurve <- function(data, step = 100, ylim = NULL, xlim = NULL, label = F, color = NULL, legend = T, color.vector = NULL, legend.position = \"topleft\"){\r\n \r\n abund = otu_table(data)@.Data %>% as.data.frame()\r\n \r\n if (!is.null(color)) {\r\n gg_color_hue <- function(n) {\r\n hues = seq(15, 375, length=n+1)\r\n hcl(h=hues, l=65, c=100)[1:n]\r\n }\r\n group_vector<-sample_data(data)[,color]@.Data %>% as.data.frame()\r\n names(group_vector)<-\"color_variable\"\r\n group_vector<-as.character(group_vector$color_variable)\r\n groups<-unique(group_vector)\r\n n = length(groups)\r\n cols = gg_color_hue(n)\r\n if (!is.null(color.vector)){ cols <- color.vector}\r\n \r\n col_vector<-rep(\"black\",length(group_vector))\r\n for (i in 1:length(group_vector)){\r\n col_vector[i]<-cols[match(group_vector[i],groups)]\r\n }\r\n } else {\r\n col_vector = \"black\"\r\n }\r\n \r\n if (is.null(ylim) & is.null(xlim)){\r\n rarecurve(t(abund), step = step, label = label, col = col_vector)\r\n }\r\n if (!is.null(ylim) & !is.null(xlim)){\r\n rarecurve(t(abund), step = step, ylim = ylim, xlim = xlim, label = label, col = col_vector)\r\n }\r\n if (!is.null(ylim) & is.null(xlim)){\r\n rarecurve(t(abund), step = step, ylim = ylim, label = label, col = col_vector)\r\n }\r\n if (is.null(ylim) & !is.null(xlim)){\r\n rarecurve(t(abund), step = step, xlim = xlim, label = label, col = col_vector)\r\n }\r\n \r\n if (!is.null(color) & legend == T){\r\n legend(legend.position,legend = groups,fill = cols, bty = \"n\")\r\n }\r\n}", "meta": {"hexsha": "3caba46db2eaab656d8fb429d52c1ff3cf01409d", "size": 2500, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/amp_rarecurve.r", "max_stars_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_stars_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/amp_rarecurve.r", "max_issues_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_issues_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/amp_rarecurve.r", "max_forks_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_forks_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8787878788, "max_line_length": 158, "alphanum_fraction": 0.6416, "num_tokens": 693, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3292087502808415}} {"text": "# 4. faza: Analiza podatkov\n#Uporabljeni grafi\n\nggplot(podatki) + aes(tournamentRegionName=tournamentRegionName, teamName=teamName, x=rating, y=goal, color=tournamentRegionName) + geom_point(stat=\"identity\") +\n labs(x=\"Rating\", y=\"Dani goli v sezoni\", colour=\"dr\u017eava\") + geom_smooth(aes(group=tournamentRegionName), method=\"lm\", se=FALSE)\n\nggplot(podatki) + aes(tournamentRegionName=tournamentRegionName, teamName=teamName, x=shotsPerGame, y=goalPerGame, color=tournamentRegionName) + geom_point(stat=\"identity\") +\n labs(x=\"Streli na tekmo\", y=\"Dani goli na tekmo\", colour=\"dr\u017eava\")\n\nggplot(podatki) + aes(tournamentRegionName=tournamentRegionName, teamName=teamName, x=rating, y=passSuccess*100, color=tournamentRegionName) + geom_point(stat=\"identity\") +\n labs(x=\"Rating\", y=\"Natan\u010dnost podaj v %\", colour=\"dr\u017eava\") + geom_smooth(aes(group=tournamentRegionName), method=\"lm\", se=FALSE)\n\nggplot(podatki) + aes(x=teamName, y=possession*100, color=tournamentRegionName) + geom_bar(stat=\"identity\") +\n scale_y_continuous(limits=c(40,66), oob=rescale_none) + labs(x=\"Ekipa\", y=\"Povpre\u010dna posest v %\", colour=\"dr\u017eava\")\n\nggplot(podatki) + aes(x=teamName, y=goal, color=tournamentRegionName) + geom_bar(stat=\"identity\") +\n labs(x=\"Ekipa\", y=\"\u0160tevilo danih golov v sezoni\", colour=\"dr\u017eava\")\n\nggplot(podatki) + aes(x=teamName, y=rating, color=tournamentRegionName) + geom_bar(stat=\"identity\") + scale_y_continuous(limits=c(6,7.4), oob=rescale_none) +\n labs(x=\"Ekipa\", y=\"Povpre\u010dni rating\", colour=\"dr\u017eava\")\n\n#----------------------------\n \ntm_shape(merge(zemljevid1,\n podatki %>% group_by(tournamentRegionName) %>% summarise(goalPerGame=(sum(goal)/(sum(apps)/2))),\n by.x=\"SOVEREIGNT\", by.y=\"tournamentRegionName\"), xlim=c(-15, 38), ylim=c(30, 75)) +\n tm_polygons(\"goalPerGame\", title=\"Goli na tekmo\") + ggtitle(\"Goli na tekmo po ligah\")\n\nggplot(podatki, aes(x=tournamentRegionName, teamName=teamName, group_by(tournamentRegionName))) +\n geom_col(aes(y=yellowCard, fill=\"rumeni kartoni\")) +\n geom_col(aes(y=redCard, fill=\"rde\u010di kartoni\")) + xlab(\"\") + ylab(\"Vsi kartoni po ligah\") +\n ggtitle(\"\u0160tevilo rumenih in rde\u010dih kartonov\") + labs(fill=\"\")\n\nggplot(podatki) + aes(x=tournamentName, teamName=teamName, y=goal) + labs(x=\"Liga\", y=\"Goli\") + \n geom_bar(stat=\"identity\", fill = \"#FF6666\")", "meta": {"hexsha": "3119fabb4aee029b595816d38600887cf19e0b14", "size": 2319, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "Shafko/APPR-2020-21", "max_stars_repo_head_hexsha": "dc77b8fd16b7729d37e454d57795f2183ee2a6aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "Shafko/APPR-2020-21", "max_issues_repo_head_hexsha": "dc77b8fd16b7729d37e454d57795f2183ee2a6aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-26T14:24:09.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-07T16:46:10.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "Shafko/APPR-2020-21", "max_forks_repo_head_hexsha": "dc77b8fd16b7729d37e454d57795f2183ee2a6aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 66.2571428571, "max_line_length": 174, "alphanum_fraction": 0.717119448, "num_tokens": 759, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3291325525050998}} {"text": "#written and kindly provided by Roger Mundry\nboot.glmm.pred<-function(model.res, excl.warnings=F, nboots=1000, para=F, resol=1000, level=0.95, use=NULL, circ.var.name=NULL, circ.var=NULL, use.u=F, \n\tn.cores=c(\"all-1\", \"all\"), save.path=NULL, load.lib=T, lib.loc=.libPaths(), set.all.effects.2.zero=F){\n\tif(load.lib){library(lme4, lib.loc=lib.loc)}\n\tn.cores=n.cores[1]\n\tkeepWarnings<-function(expr){\n\t\tlocalWarnings <- list()\n\t\tvalue <- withCallingHandlers(expr,\n\t\t\twarning = function(w) {\n\t\t\t\tlocalWarnings[[length(localWarnings)+1]] <<- w\n\t\t\t\tinvokeRestart(\"muffleWarning\")\n\t\t\t}\n\t\t)\n\t\tlist(value=value, warnings=localWarnings)\n\t}\n\t##define function extracting all estimated coefficients (fixed and random effects) and also the model summary wrt random effects:\n\textract.all<-function(mres){\n\t\t##extract random effects model summary:\n\t\tvc.mat=as.data.frame(summary(mres)$varcor)##... and prepare/extract variance covariance matrix from the model handed over\n\t\txx=lapply(summary(mres)$varcor, function(x){attr(x, \"stddev\")})##append residual variance\n\t\t##create vector with names of the terms in the model\n\t\txnames=c(names(fixef(mres)), paste(rep(names(xx), unlist(lapply(xx, length))), unlist(lapply(xx, names)), sep=\"@\"))\n\t\tif(class(mres)[1]==\"lmerMod\"){xnames=c(xnames, \"Residual\")}##append \"Residual\" to xnames in case of Gaussian model\n\t\tif(class(mres)[1]==\"lmerMod\"){res.sd=vc.mat[vc.mat$grp==\"Residual\", \"sdcor\"]}##extract residual sd i case of Gaussian model\n\t\t#if(vc.mat$grp[nrow(vc.mat)]==\"Residual\"){vc.mat=vc.mat[-nrow(vc.mat), ]}##and drop residuals-row from vc.mat in case of Gaussisn model\n\t\t##deal with names in vc.mat which aren't exactly the name of the resp. random effect\n\t\tr.icpt.names=names(ranef(mres))##extract names of random intercepts...\n\t\tnot.r.icpt.names=setdiff(vc.mat$grp, r.icpt.names)##... and names of the random effects having random slopes\n\t\tnot.r.icpt.names=unlist(lapply(strsplit(not.r.icpt.names, split=\".\", fixed=T), function(x){paste(x[1:(length(x)-1)], collapse=\".\")}))\n\t\tvc.mat$grp[!vc.mat$grp%in%r.icpt.names]=not.r.icpt.names\n\t\tif(vc.mat$grp[nrow(vc.mat)]==\"Residual\"){vc.mat$var1[nrow(vc.mat)]=\"\"}\n\t\txnames=paste(vc.mat$grp, vc.mat$var1, sep=\"@\")\n\t\tre.summary=unlist(vc.mat$sdcor)\n\t\tnames(re.summary)=xnames\n\t\tranef(mres)\n\t\t##extract random effects model summary: done\n\t\t##extract random effects details:\n\t\tre.detail=ranef(mres)\n\t\txnames=paste(\n\t\t\trep(x=names(re.detail), times=unlist(lapply(re.detail, function(x){nrow(x)*ncol(x)}))),\n\t\t\t\tunlist(lapply(re.detail, function(x){rep(colnames(x), each=nrow(x))})), \n\t\t\t\tunlist(lapply(re.detail, function(x){rep(rownames(x), times=ncol(x))})),\n\t\t\t\tsep=\"@\")\n\t\tre.detail=unlist(lapply(re.detail, function(x){\n\t\t\treturn(unlist(c(x)))\n\t\t}))\n\t\t#browser()\n\t\t#deal with negative binomial model to extract theta:\n\t\txx=as.character(summary(mres)$call)\n\t\tif(any(grepl(x=xx, pattern=\"negative.binomial\"))){\n\t\t\txx=xx[grepl(x=xx, pattern=\"negative.binomial\")]\n\t\t\txx=gsub(x=xx, pattern=\"negative.binomial(theta = \", replacement=\"\", fixed=T)\n\t\t\txx=gsub(x=xx, pattern=\")\", replacement=\"\", fixed=T)\n\t\t\tre.detail=c(re.detail, as.numeric(xx))\n\t\t\txnames=c(xnames, \"theta\")\n\t\t}\n\t\tnames(re.detail)=xnames\n\t\tns=c(length(fixef(mres)), length(re.summary), length(re.detail))\n\t\tnames(ns)=c(\"n.fixef\", \"n.re.summary\", \"n.re.detail\")\n\t\treturn(c(fixef(mres), re.summary, re.detail, ns))\n\t}\t\n\tif(excl.warnings){\n\t\tboot.fun<-function(x, model.res., keepWarnings., use.u., save.path.){\n\t\t\txdone=F\n\t\t\twhile(!xdone){\n\t\t\t\ti.res=keepWarnings(bootMer(x=model.res., FUN=extract.all, nsim=1, use.u=use.u.)$t)\n\t\t\t\tif(length(unlist(i.res$warnings)$message)==0){\n\t\t\t\t\txdone=T\n\t\t\t\t}\n\t\t\t}\n\t\t\test.effects=i.res$value\n\t\t\ti.warnings=NULL\n\t\t\tif(length(save.path.)>0){save(file=paste(c(save.path., \"/b_\", x, \".RData\"), collapse=\"\"), list=c(\"est.effects\", \"i.warnings\"))}\n\t\t\treturn(i.res$value)\n\t\t}\n\t}else{\n\t\tboot.fun<-function(y, model.res., keepWarnings., use.u., save.path.){\n\t\t\t#keepWarnings.(bootMer(x=model.res., FUN=fixef, nsim=1)$t)\n\t\t\ti.res=keepWarnings(bootMer(x=model.res., FUN=extract.all, nsim=1, use.u=use.u.))\n\t\t\tif(length(save.path.)>0){\n\t\t\t\test.effects=i.res$value$t\n\t\t\t\ti.warnings=i.res$warnings\n\t\t\t\tsave(file=paste(c(save.path., \"/b_\", y, \".RData\"), collapse=\"\"), list=c(\"est.effects\", \"i.warnings\"))\n\t\t\t}\n\t\t\treturn(list(ests=i.res$value$t, warns=unlist(i.res$warnings)))\n\t\t}\n\t}\n\tif(para){\n\t\ton.exit(expr = parLapply(cl=cl, X=1:length(cl), fun=function(x){rm(list=ls())}), add = FALSE)\n\t\ton.exit(expr = stopCluster(cl), add = T)\n\t\tlibrary(parallel)\n\t\tcl <- makeCluster(getOption(\"cl.cores\", detectCores()))\n\t\tif(n.cores!=\"all\"){\n\t\t\tif(n.cores==\"all-1\"){n.cores=length(cl)-1}\n\t\t\tif(n.cores0){\n\t\t#extract fixed effects terms from the model:\n\t\txcall=as.character(model.res@call)[2]\n\t\tmodel.terms=attr(terms(as.formula(xcall)), \"term.labels\")\n\t\tREs=names(ranef(model.res))\n\t\t#for(i in 1:length(REs)){\n\t\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"|\", fixed=T)]\n\t\t#}\n\t\tmodel=paste(model.terms, collapse=\"+\")\n\t\t#build model wrt to the fixed effects:\n\t\tmodel.terms=unique(unlist(strsplit(x=model.terms, split=\":\", fixed=T)))\n\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"I(\", fixed=T)]\n\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"^2)\", fixed=T)]\n\t\t#exclude interactions and squared terms from model.terms:\n\t\t#create new data to be used to determine fitted values:\n\t\tii.data=model.res@frame\n\t\t\n\t\tif(length(circ.var.name)==1){\n\t\t\tset.circ.var.to.zero=sum(circ.var.name%in%use)==0\n\t\t}else{\n\t\t\tset.circ.var.to.zero=F\n\t\t}\n\t\t\n\t\tif(length(use)==0){use=model.terms}\n\t\tnew.data=vector(\"list\", length(model.terms))\n\t\tusel=model.terms%in%use\n\t\t#if(length(use)>0)\n\t\tfor(i in 1:length(model.terms)){\n\t\t\tif(is.factor(ii.data[, model.terms[i]])){\n\t\t\t\tnew.data[[i]]=levels(ii.data[, model.terms[i]])\n\t\t\t}else if(!is.factor(ii.data[, model.terms[i]]) & usel[i] & ifelse(length(circ.var.name)==0, T, !grepl(x=model.terms[i], pattern=circ.var.name))){\n\t\t\t\tnew.data[[i]]=seq(from=min(ii.data[, model.terms[i]]), to=max(ii.data[, model.terms[i]]), length.out=resol)\n\t\t\t}else if(!is.factor(ii.data[, model.terms[i]]) & ifelse(length(circ.var.name)==0, T, !grepl(x=model.terms[i], pattern=circ.var.name))){\n\t\t\t\tnew.data[[i]]=mean(ii.data[, model.terms[i]])\n\t\t\t}\n\t\t}\n\t\tnames(new.data)=model.terms\n\t\tif(length(circ.var.name)==1){\n\t\t\tnew.data=new.data[!(model.terms%in%paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\"))]\n\t\t\tif(sum(grepl(pattern=circ.var.name, x=use))>0){\n\t\t\t\tnew.data=c(new.data, list(seq(min(circ.var, na.rm=T), max(circ.var, na.rm=T), length.out=resol)))\n\t\t\t\tnames(new.data)[length(new.data)]=circ.var.name\n\t\t\t}else{\n\t\t\t\tnew.data=c(new.data, list(0))\n\t\t\t}\n\t\t\tmodel.terms=model.terms[!(model.terms%in%paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\"))]\n\t\t}\n\t\txnames=names(new.data)\n\t\t#browser()\n\t\tnew.data=data.frame(expand.grid(new.data))\n\t\tnames(new.data)=xnames\n\t\t#names(new.data)[1:length(model.terms)]=model.terms\n\t\t#browser()\n\t\tif(length(circ.var.name)==1){\n\t\t\tnames(new.data)[ncol(new.data)]=circ.var.name\n\t\t}\n\t\t#create predictors matrix:\n# \t\tif(length(circ.var.name)>0 & length(intersect(circ.var.name, names(new.data)))>0){\n# \t\t\tnew.data=cbind(new.data, sin(new.data[, circ.var.name]), cos(new.data[, circ.var.name]))\n# \t\t\tnames(new.data)[(ncol(new.data)-1):ncol(new.data)]=paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\")\n# \t\t\tnew.data=new.data[, !names(new.data)==circ.var.name]\n# \t\t}\n# \t\tbrowser()\n\t\tr=runif(nrow(new.data))\n\t\tif(set.all.effects.2.zero){\n\t\t\tfor(iterm in setdiff(colnames(new.data), c(\"(Intercept)\", use))){\n\t\t\t\tnew.data[, iterm]=0\n\t\t\t}\n\t\t}\n\t\tm.mat=model.matrix(object=as.formula(paste(c(\"r\", model), collapse=\"~\")), data=new.data)\n\t\tif(set.circ.var.to.zero){\n\t\t\tm.mat[,paste(c(\"sin(\", circ.var.name, \")\"), collapse=\"\")]=0\n\t\t\tm.mat[,paste(c(\"cos(\", circ.var.name, \")\"), collapse=\"\")]=0\n\t\t}\n\t\t#m.mat=t(m.mat)\n\t\t#get the CIs for the fitted values:\n\t\tci=lapply(all.res, function(x){\n\t\t\t#return(apply(m.mat[names(fixef(model.res)), , drop=F]*as.vector(x[, names(fixef(model.res))]), 2, sum))\n\t\t\treturn(m.mat[, names(fixef(model.res)), drop=F]%*%as.vector(x[, names(fixef(model.res))]))\n\t\t})\n\t\tci=matrix(unlist(ci), ncol=nboots, byrow=F)\n\t\tci=t(apply(ci, 1, quantile, prob=c((1-level)/2, 1-(1-level)/2), na.rm=T))\n\t\tcolnames(ci)=c(\"lower.cl\", \"upper.cl\")\n\t\tfv=m.mat[, names(fixef(model.res))]%*%fixef(model.res)\n\t\tif(class(model.res)[[1]]!=\"lmerMod\"){\n\t\t\tif(model.res@resp$family$family==\"binomial\"){\n\t\t\t\tci=exp(ci)/(1+exp(ci))\n\t\t\t\tfv=exp(fv)/(1+exp(fv))\n\t\t\t}else if(model.res@resp$family$family==\"poisson\" | (model.res@resp$family$family==\"Gamma\" & model.res@resp$family$link==\"log\") | substr(x=model.res@resp$family$family, start=1, stop=17)==\"Negative Binomial\"){\n\t\t\t\tci=exp(ci)\n\t\t\t\tfv=exp(fv)\n\t\t\t}\n\t\t}\n\t\tresult=data.frame(new.data, fitted=fv, ci)\n\t}else{\n\t\tresult=NULL\n\t}\n\tall.boots=matrix(unlist(all.res), nrow=nboots, byrow=T)\n\tcolnames(all.boots)=colnames(all.res[[1]])\n\tci.est=apply(all.boots, 2, quantile, prob=c((1-level)/2, 1-(1-level)/2), na.rm=T)\n\tif(length(fixef(model.res))>1){\n\t\tci.est=data.frame(orig=fixef(model.res), t(ci.est)[1:length(fixef(model.res)), ])\n\t}else{\n\t\tci.est=data.frame(orig=fixef(model.res), t(t(ci.est)[1:length(fixef(model.res)), ]))\n\t}\n\treturn(list(ci.predicted=result, ci.estimates=ci.est, all.warns=all.warns, all.boots=all.boots))\n}\n###########################################################################################################\n###########################################################################################################\nboot.glmm.pred.list<-function(model.res, excl.warnings=F, nboots=1000, para=F, resol=100, level=0.95, use.list=NULL, circ.var.name=NULL, circ.var=NULL, use.u=F, \n\tn.cores=c(\"all-1\", \"all\"), save.path=NULL, load.lib=T, lib.loc=.libPaths(), set.all.effects.2.zero=F){\n\tif(load.lib){library(lme4, lib.loc=lib.loc)}\n\tn.cores=n.cores[1]\n\tkeepWarnings<-function(expr){\n\t\tlocalWarnings <- list()\n\t\tvalue <- withCallingHandlers(expr,\n\t\t\twarning = function(w) {\n\t\t\t\tlocalWarnings[[length(localWarnings)+1]] <<- w\n\t\t\t\tinvokeRestart(\"muffleWarning\")\n\t\t\t}\n\t\t)\n\t\tlist(value=value, warnings=localWarnings)\n\t}\n\t##define function extracting all estimated coefficients (fixed and random effects) and also the model summary wrt random effects:\n\textract.all<-function(mres){\n\t\t##extract random effects model summary:\n\t\tvc.mat=as.data.frame(summary(mres)$varcor)##... and prepare/extract variance covariance matrix from the model handed over\n\t\txx=lapply(summary(mres)$varcor, function(x){attr(x, \"stddev\")})##append residual variance\n\t\t##create vector with names of the terms in the model\n\t\txnames=c(names(fixef(mres)), paste(rep(names(xx), unlist(lapply(xx, length))), unlist(lapply(xx, names)), sep=\"@\"))\n\t\tif(class(mres)[1]==\"lmerMod\"){xnames=c(xnames, \"Residual\")}##append \"Residual\" to xnames in case of Gaussian model\n\t\tif(class(mres)[1]==\"lmerMod\"){res.sd=vc.mat[vc.mat$grp==\"Residual\", \"sdcor\"]}##extract residual sd i case of Gaussian model\n\t\t#if(vc.mat$grp[nrow(vc.mat)]==\"Residual\"){vc.mat=vc.mat[-nrow(vc.mat), ]}##and drop residuals-row from vc.mat in case of Gaussisn model\n\t\t##deal with names in vc.mat which aren't exactly the name of the resp. random effect\n\t\tr.icpt.names=names(ranef(mres))##extract names of random intercepts...\n\t\tnot.r.icpt.names=setdiff(vc.mat$grp, r.icpt.names)##... and names of the random effects having random slopes\n\t\tnot.r.icpt.names=unlist(lapply(strsplit(not.r.icpt.names, split=\".\", fixed=T), function(x){paste(x[1:(length(x)-1)], collapse=\".\")}))\n\t\tvc.mat$grp[!vc.mat$grp%in%r.icpt.names]=not.r.icpt.names\n\t\tif(vc.mat$grp[nrow(vc.mat)]==\"Residual\"){vc.mat$var1[nrow(vc.mat)]=\"\"}\n\t\txnames=paste(vc.mat$grp, vc.mat$var1, sep=\"@\")\n\t\tre.summary=unlist(vc.mat$sdcor)\n\t\tnames(re.summary)=xnames\n\t\tranef(mres)\n\t\t##extract random effects model summary: done\n\t\t##extract random effects details:\n\t\tre.detail=ranef(mres)\n\t\txnames=paste(\n\t\t\trep(x=names(re.detail), times=unlist(lapply(re.detail, function(x){nrow(x)*ncol(x)}))),\n\t\t\t\tunlist(lapply(re.detail, function(x){rep(colnames(x), each=nrow(x))})), \n\t\t\t\tunlist(lapply(re.detail, function(x){rep(rownames(x), times=ncol(x))})),\n\t\t\t\tsep=\"@\")\n\t\tre.detail=unlist(lapply(re.detail, function(x){\n\t\t\treturn(unlist(c(x)))\n\t\t}))\n\t\t#browser()\n\t\t#deal with negative binomial model to extract theta:\n\t\txx=as.character(summary(mres)$call)\n\t\tif(any(grepl(x=xx, pattern=\"negative.binomial\"))){\n\t\t\txx=xx[grepl(x=xx, pattern=\"negative.binomial\")]\n\t\t\txx=gsub(x=xx, pattern=\"negative.binomial(theta = \", replacement=\"\", fixed=T)\n\t\t\txx=gsub(x=xx, pattern=\")\", replacement=\"\", fixed=T)\n\t\t\tre.detail=c(re.detail, as.numeric(xx))\n\t\t\txnames=c(xnames, \"theta\")\n\t\t}\n\t\tnames(re.detail)=xnames\n\t\tns=c(length(fixef(mres)), length(re.summary), length(re.detail))\n\t\tnames(ns)=c(\"n.fixef\", \"n.re.summary\", \"n.re.detail\")\n\t\treturn(c(fixef(mres), re.summary, re.detail, ns))\n\t}\t\n\tif(excl.warnings){\n\t\tboot.fun<-function(x, model.res., keepWarnings., use.u., save.path.){\n\t\t\txdone=F\n\t\t\twhile(!xdone){\n\t\t\t\ti.res=keepWarnings(bootMer(x=model.res., FUN=extract.all, nsim=1, use.u=use.u.)$t)\n\t\t\t\tif(length(unlist(i.res$warnings)$message)==0){\n\t\t\t\t\txdone=T\n\t\t\t\t}\n\t\t\t}\n\t\t\test.effects=i.res$value\n\t\t\ti.warnings=NULL\n\t\t\tif(length(save.path.)>0){save(file=paste(c(save.path., \"/b_\", x, \".RData\"), collapse=\"\"), list=c(\"est.effects\", \"i.warnings\"))}\n\t\t\treturn(i.res$value)\n\t\t}\n\t}else{\n\t\tboot.fun<-function(y, model.res., keepWarnings., use.u., save.path.){\n\t\t\t#keepWarnings.(bootMer(x=model.res., FUN=fixef, nsim=1)$t)\n\t\t\ti.res=keepWarnings(bootMer(x=model.res., FUN=extract.all, nsim=1, use.u=use.u.))\n\t\t\tif(length(save.path.)>0){\n\t\t\t\test.effects=i.res$value$t\n\t\t\t\ti.warnings=i.res$warnings\n\t\t\t\tsave(file=paste(c(save.path., \"/b_\", y, \".RData\"), collapse=\"\"), list=c(\"est.effects\", \"i.warnings\"))\n\t\t\t}\n\t\t\treturn(list(ests=i.res$value$t, warns=unlist(i.res$warnings)))\n\t\t}\n\t}\n\tif(para){\n\t\ton.exit(expr = parLapply(cl=cl, X=1:length(cl), fun=function(x){rm(list=ls())}), add = FALSE)\n\t\ton.exit(expr = stopCluster(cl), add = T)\n\t\tlibrary(parallel)\n\t\tcl <- makeCluster(getOption(\"cl.cores\", detectCores()))\n\t\tif(n.cores!=\"all\"){\n\t\t\tif(n.cores!=\"all-1\"){n.cores=length(cl)-1}\n\t\t\tif(n.cores0){\n\t\t#extract fixed effects terms from the model:\n\t\txcall=as.character(model.res@call)[2]\n\t\tmodel.terms=attr(terms(as.formula(xcall)), \"term.labels\")\n\t\tREs=names(ranef(model.res))\n\t\t#for(i in 1:length(REs)){\n\t\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"|\", fixed=T)]\n\t\t#}\n\t\tmodel=paste(model.terms, collapse=\"+\")\n\t\t#build model wrt to the fixed effects:\n\t\tmodel.terms=unique(unlist(strsplit(x=model.terms, split=\":\", fixed=T)))\n\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"I(\", fixed=T)]\n\t\tmodel.terms=model.terms[!grepl(x=model.terms, pattern=\"^2)\", fixed=T)]\n\t\t#exclude interactions and squared terms from model.terms:\n\t\t#create new data to be used to determine fitted values:\n\t\tii.data=model.res@frame\n\t\tall.fitted=lapply(use.list, function(use){\n\t\t\tif(length(circ.var.name)==1){\n\t\t\t\tset.circ.var.to.zero=sum(circ.var.name%in%use)==0\n\t\t\t}else{\n\t\t\t\tset.circ.var.to.zero=F\n\t\t\t}\n\t\t\t\n\t\t\tif(length(use)==0){use=model.terms}\n\t\t\tnew.data=vector(\"list\", length(model.terms))\n\t\t\tusel=model.terms%in%use\n\t\t\t#if(length(use)>0)\n\t\t\tfor(i in 1:length(model.terms)){\n\t\t\t\tif(is.factor(ii.data[, model.terms[i]])){\n\t\t\t\t\tnew.data[[i]]=levels(ii.data[, model.terms[i]])\n\t\t\t\t}else if(!is.factor(ii.data[, model.terms[i]]) & usel[i] & ifelse(length(circ.var.name)==0, T, !grepl(x=model.terms[i], pattern=circ.var.name))){\n\t\t\t\t\tnew.data[[i]]=seq(from=min(ii.data[, model.terms[i]]), to=max(ii.data[, model.terms[i]]), length.out=resol)\n\t\t\t\t}else if(!is.factor(ii.data[, model.terms[i]]) & ifelse(length(circ.var.name)==0, T, !grepl(x=model.terms[i], pattern=circ.var.name))){\n\t\t\t\t\tnew.data[[i]]=mean(ii.data[, model.terms[i]])\n\t\t\t\t}\n\t\t\t}\n\t\t\tnames(new.data)=model.terms\n\t\t\tif(length(circ.var.name)==1){\n\t\t\t\tnew.data=new.data[!(model.terms%in%paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\"))]\n\t\t\t\tif(sum(grepl(pattern=circ.var.name, x=use))>0){\n\t\t\t\t\tnew.data=c(new.data, list(seq(min(circ.var, na.rm=T), max(circ.var, na.rm=T), length.out=resol)))\n\t\t\t\t\tnames(new.data)[length(new.data)]=circ.var.name\n\t\t\t\t}else{\n\t\t\t\t\tnew.data=c(new.data, list(0))\n\t\t\t\t}\n\t\t\t\tmodel.terms=model.terms[!(model.terms%in%paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\"))]\n\t\t\t}\n\t\t\txnames=names(new.data)\n\t\t\t#browser()\n\t\t\tnew.data=data.frame(expand.grid(new.data))\n\t\t\tnames(new.data)=xnames\n\t\t\t#names(new.data)[1:length(model.terms)]=model.terms\n\t\t\t#browser()\n\t\t\tif(length(circ.var.name)==1){\n\t\t\t\tnames(new.data)[ncol(new.data)]=circ.var.name\n\t\t\t}\n\t\t\t#create predictors matrix:\n\t# \t\tif(length(circ.var.name)>0 & length(intersect(circ.var.name, names(new.data)))>0){\n\t# \t\t\tnew.data=cbind(new.data, sin(new.data[, circ.var.name]), cos(new.data[, circ.var.name]))\n\t# \t\t\tnames(new.data)[(ncol(new.data)-1):ncol(new.data)]=paste(c(\"sin(\", \"cos(\"), circ.var.name, \")\", sep=\"\")\n\t# \t\t\tnew.data=new.data[, !names(new.data)==circ.var.name]\n\t# \t\t}\n\t# \t\tbrowser()\n\t\t\tr=runif(nrow(new.data))\n\t\t\tif(set.all.effects.2.zero){\n\t\t\t\tfor(iterm in setdiff(colnames(new.data), c(\"(Intercept)\", use))){\n\t\t\t\t\tnew.data[, iterm]=0\n\t\t\t\t}\n\t\t\t}\n\t\t\tm.mat=model.matrix(object=as.formula(paste(c(\"r\", model), collapse=\"~\")), data=new.data)\n\t\t\tif(set.circ.var.to.zero){\n\t\t\t\tm.mat[,paste(c(\"sin(\", circ.var.name, \")\"), collapse=\"\")]=0\n\t\t\t\tm.mat[,paste(c(\"cos(\", circ.var.name, \")\"), collapse=\"\")]=0\n\t\t\t}\n\t\t\t#m.mat=t(m.mat)\n\t\t\t#get the CIs for the fitted values:\n\t\t\tci=lapply(all.res, function(x){\n\t\t\t\t#return(apply(m.mat[names(fixef(model.res)), , drop=F]*as.vector(x[, names(fixef(model.res))]), 2, sum))\n\t\t\t\treturn(m.mat[, names(fixef(model.res)), drop=F]%*%as.vector(x[, names(fixef(model.res))]))\n\t\t\t})\n\t\t\tci=matrix(unlist(ci), ncol=nboots, byrow=F)\n\t\t\tci=t(apply(ci, 1, quantile, prob=c((1-level)/2, 1-(1-level)/2), na.rm=T))\n\t\t\tcolnames(ci)=c(\"lower.cl\", \"upper.cl\")\n\t\t\tfv=m.mat[, names(fixef(model.res))]%*%fixef(model.res)\n\t\t\tif(class(model.res)[[1]]!=\"lmerMod\"){\n\t\t\t\tif(model.res@resp$family$family==\"binomial\"){\n\t\t\t\t\tci=exp(ci)/(1+exp(ci))\n\t\t\t\t\tfv=exp(fv)/(1+exp(fv))\n\t\t\t\t}else if(model.res@resp$family$family==\"poisson\" | substr(x=model.res@resp$family$family, start=1, stop=17)==\"Negative Binomial\"){\n\t\t\t\t\tci=exp(ci)\n\t\t\t\t\tfv=exp(fv)\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn(data.frame(new.data, fitted=fv, ci))\n\t\t})\n\t\tresult=all.fitted\n\t\tnames(result)=unlist(lapply(use.list, paste, collapse=\"@\"))\n\t}else{\n\t\tresult=NULL\n\t}\n\t\n\tall.boots=matrix(unlist(all.res), nrow=nboots, byrow=T)\n\tcolnames(all.boots)=colnames(all.res[[1]])\n\tci.est=apply(all.boots, 2, quantile, prob=c((1-level)/2, 1-(1-level)/2), na.rm=T)\n\tif(length(fixef(model.res))>1){\n\t\tci.est=data.frame(orig=fixef(model.res), t(ci.est)[1:length(fixef(model.res)), ])\n\t}else{\n\t\tci.est=data.frame(orig=fixef(model.res), t(t(ci.est)[1:length(fixef(model.res)), ]))\n\t}\n\treturn(list(ci.predicted=result, ci.estimates=ci.est, all.warns=all.warns, all.boots=all.boots))\n}\n\n\n###########################################################################################################\n###########################################################################################################\nboot.glmm<-function(model.res, excl.warnings=F, nboots=1000, para=F, use.u=F, n.cores=c(\"all-1\", \"all\"), save.path=NULL){\n\tn.cores=n.cores[1]\n\tkeepWarnings<-function(expr) {\n\t\tlocalWarnings <- list()\n\t\tvalue <- withCallingHandlers(expr,\n\t\t\twarning = function(w) {\n\t\t\t\tlocalWarnings[[length(localWarnings)+1]] <<- w\n\t\t\t\tinvokeRestart(\"muffleWarning\")\n\t\t\t}\n\t\t)\n\t\tlist(value=value, warnings=localWarnings)\n\t}\n\tif(excl.warnings){\n\t\tboot.fun<-function(x, model.res., keepWarnings., use.u., save.path.){\n\t\t\txdone=F\n\t\t\twhile(!xdone){\n\t\t\t\ti.res=keepWarnings.(bootMer(x=model.res., FUN=fixef, nsim=1, use.u=use.u.)$t)\n\t\t\t\tif(length(unlist(i.res$warnings)$message)==0){\n\t\t\t\t\txdone=T\n\t\t\t\t}\n\t\t\t}\n\t\t\tif(length(save.path.)>0){\n\t\t\t\ti.res=i.res$value\n\t\t\t\tsave(file=paste(c(save.path., \"/b_\", x, \".RData\"), collapse=\"\"), list=\"i.res\")\n\t\t\t}\n\t\t\treturn(i.res$value)\n\t\t}\n\t}else{\n\t\tboot.fun<-function(x, model.res., keepWarnings., use.u., save.path.){\n\t\t\ti.res=bootMer(x=model.res, FUN=fixef, nsim=1, use.u=use.u.)$t\n\t\t\tif(length(save.path.)>0){\n\t\t\t\tsave(file=paste(c(save.path., \"/b_\", x, \".RData\"), collapse=\"\"), list=\"i.res\")\n\t\t\t}\n\t\t\treturn(i.res)\n\t\t}\n\t}\n\tif(para){\n\t\trequire(parallel)\n cl <- makeCluster(getOption(\"cl.cores\", detectCores()))\n\t\tif(n.cores!=\"all\"){\n\t\t\tif(n.cores!=\"all-1\"){n.cores=length(cl)-1}\n\t\t\tif(n.cores thresh])/-log10(thresh))\n out[x > thresh] <- rgb(rgbs[,1], rgbs[,2], rgbs[,3], maxColorValue = 256)\n return(out)\n}\n\n# draw a vertical colorbar at xposmin to xposmax, from yposmin to yposmax\n# cols in hex, x are values to go with each col\ncolor.bar <- function(cols, x, axis = TRUE, cex = 1, nticks=11, \n xposmin, xposmax, yposmin, yposmax, title = '', titley = yposmax) {\n scale = (length(cols)-1)/(yposmax - yposmin)\n \n for (i in 1:(length(cols)-1)) {\n y = (i-1)/scale + yposmin\n rect(xposmin,y,xposmax,y+1/scale, col=cols[i], border=NA)\n }\n \n if(axis){\n line(c(xposmax, xposmax), c(yposmin, yposmax))\n line(c(xposmax, xposmax + (xposmax-xposmin)/2), c(yposmin, yposmin))\n line(c(xposmax, xposmax + (xposmax-xposmin)/2), c(yposmax, yposmax))\n at = seq(yposmin, yposmax, length.out = nticks)\n labs <- signif(x[round(seq(1, length(x), length.out = nticks))], 2)\n text(rep(xposmax + (xposmax-xposmin)/2, nticks), y = at, labels = labs, adj = 0, cex = cex)\n }\n \n text(xposmin, titley, title, adj = 0.5, cex = cex)\n}\n\n\n##########\n## Prep \n##########\n\n# 1907-2011-2014\nfst <- fread('analysis/gatk.lof07-11-14.weir.fst', header=TRUE)\ndat <- readRDS(file=paste('analysis/wfs_nullmodel_pos&pvals_07-11-14.rds', sep='')) # has p-values\nnrow(dat)\ndat <- merge(fst, dat[, .(CHROM, POS, p)], by = c('CHROM', 'POS'))\nnrow(dat)\n\n# Canada\nfstCan <- fread('analysis/gatk.can.weir.fst', header=TRUE)\ndatCan <- readRDS(file=paste('analysis/wfs_nullmodel_pos&pvals_Can.rds', sep='')) # has p-values\nnrow(datCan)\ndatCan <- merge(fstCan, datCan[, .(CHROM, POS, p)], by = c('CHROM', 'POS'))\nnrow(datCan)\n\n# trim to unlinked nodam2 loci\nunlCan <- fread('analysis/ld.unlinked.Can.gatk.nodam.csv.gz')\ndatCan <- merge(datCan, unlCan, by = c('CHROM', 'POS'))\n\nunlLof <- fread('analysis/ld.unlinked.Lof.gatk.nodam.csv.gz')\ndat <- merge(dat, unlLof, by = c('CHROM', 'POS'))\n\n\n# only consider Lof loci for which 2011-2014 are more similar than to 1907\nfreqLof07 <- fread('data_31_01_20/Lof_07_freq.mafs.gz')\nfreqLof11 <- fread('data_31_01_20/Lof_11_freq.mafs.gz')\nfreqLof14 <- fread('data_31_01_20/Lof_14_freq.mafs.gz')\n\nnrow(dat)\ndat <- merge(dat, freqLof07[, .(CHROM = chromo, POS = position, freq07 = knownEM)], by = c('CHROM', 'POS'))\ndat <- merge(dat, freqLof11[, .(CHROM = chromo, POS = position, freq11 = knownEM)], by = c('CHROM', 'POS'))\ndat <- merge(dat, freqLof14[, .(CHROM = chromo, POS = position, freq14 = knownEM)], by = c('CHROM', 'POS'))\nnrow(dat)\ndat[, sum(p < 0.05)]\ndat[, sum(p < 0.005)]\n\ndat <- dat[abs(freq11 - freq14) < abs(freq14 - freq07) & abs(freq11 - freq14) < abs(freq11 - freq07), ]\nnrow(dat)\ndat[, sum(p < 0.05)]\ndat[, sum(p < 0.005)]\n\ndat[, c('freq07', 'freq11', 'freq14') := NULL]\n\n# combine\ndat[, pop := 'Lof']\ndatCan[, pop := 'Can']\ndat <- rbind(dat, datCan)\n\n# remove unplaced\ndat <- dat[CHROM != 'Unplaced', ]\n\n# add genome position\nchrmax <- fread('data/lg_length.csv')\nchrmax[, start := c(0,cumsum(chrmax$len)[1:(nrow(chrmax)-1)])]\n\ndat <- merge(dat, chrmax[, .(CHROM = chr, start)], by = c('CHROM'))\ndat[, POSgen := POS + start]\ndat[,start := NULL]\n\n\n\n# calculate FDR\ndat[, p.adj := p.adjust(p), by = pop]\n\n# write out\nwrite.csv(dat[, .(CHROM, POS, pop, p, p.adj)], file = gzfile('analysis/wfs_nullmodel_padj.csv.gz'), row.names = FALSE)\n\n\n##################\n## basic analysis\n##################\ndat[,min(p, na.rm=TRUE), by = pop]\ndat[,min(p.adj, na.rm=TRUE), by = pop]\n\n# lowest FDR-correct p-values\ndat[pop == 'Can' & p.adj < 0.21, .(CHROM, POS, WEIR_AND_COCKERHAM_FST, nloci, p, p.adj)]\ndat[pop == 'Lof' & p.adj < 0.14, .(CHROM, POS, WEIR_AND_COCKERHAM_FST, nloci, p, p.adj)]\n\n#####################\n## plots\n#####################\ncolout <- '#b2182b' # red, part of RdGy colorbrewer, for outlier loci\n\n\n# add a vector for color by LG\nlgs <- dat[, sort(unique(CHROM))]\ndat[,lgcol := lgcolsramp(p.adj, lg = 1, thresh = 0.1)]\ndat[CHROM %in% lgs[seq(2, length(lgs),by=2)], lgcol := lgcolsramp(p.adj, lg = 2, thresh = 0.1)]\n\n# order\nsetorder(dat, pop, -p.adj)\n\n# histogram of p-values\n# png(width=5, height=4, filename='figures/wfs_nullmodel_hist_pvals.png', units='in', res=300)\nggplot(dat, aes(p, group = pop)) +\n geom_histogram() +\n facet_grid(~pop)\n\n\tdev.off()\n\n# histogram of FDR-adjusted p-values\n# png(width=5, height=4, filename='figures/wfs_nullmodel_hist_padjvals.png', units='in', res=300)\n\tggplot(dat, aes(p.adj, group = pop)) +\n\t geom_histogram() +\n\t facet_grid(~pop)\n\t\n\tdev.off()\n\n\n\n# Per locus FST vs. genome position, colored by -log10(p)\n# for plotting a colorbar\ncbar <- data.frame(logx = seq(0, 1.30103, length.out=50))\ncbar$x <- 10^(-cbar$logx)\ncbar$col1 <- lgcolsramp(cbar$x, lg = 1, thresh = 0.1)\ncbar$col2 <- lgcolsramp(cbar$x, lg = 2, thresh = 0.1)\n\t\n\t\npng(filename = paste0('figures/wfs_nullmodel_fst_vs_position_by_p.png'), width = 20, height = 12, units = \"in\", res = 150)\npar(mfrow = c(2,1))\ndat[pop == 'Can' & !is.na(WEIR_AND_COCKERHAM_FST), plot(POSgen/1e6, WEIR_AND_COCKERHAM_FST, xlab = 'Position (Mb)', ylab = \"FST\",\n main = 'Canada', col = lgcol, cex = 0.5)]\n\ndat[pop == 'Lof' & !is.na(WEIR_AND_COCKERHAM_FST), plot(POSgen/1e6, WEIR_AND_COCKERHAM_FST, xlab = 'Position (Mb)', ylab = \"FST\",\n main = 'Lof', col = lgcol, cex = 0.5)]\n\ncolor.bar(cbar$col1, cbar$x, axis = FALSE, xposmin =640, xposmax = 645, \n yposmin = 0.2, yposmax = 0.5)\ncolor.bar(cbar$col2, cbar$x, cex = 0.5, axis = TRUE, nticks = 5, \n xposmin =645, xposmax = 650, yposmin = 0.2, yposmax = 0.5, title = 'FDR p-value', titley = 0.55)\n\ndev.off()\n\t\n\n", "meta": 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YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.3291325449539595}} {"text": "\nstom<-Read.stomach.data()\n\na<-aggregate(list(Pred.avail=stom$Prey.avail.part),\n list(Year=stom$Year,Quarter=stom$Quarter,Predator=stom$Predator,Predator.length.class=stom$Predator.length.class),sum)\nstom<-merge(stom,a)\nstom$stomcon.hat.part<-stom$Prey.avail.part/stom$Pred.avail\nstom$Residual.part<-stom$stom.input-stom$stomcon.hat.part\nstom<-transform(stom,year.range=ifelse(Year<=1981,'1977-81','1982-93'),year=paste(\"Y\",Year,sep=''))\n\nwrite.csv(stom, file = file.path(data.path,'ASCII_stom_data.csv'),row.names = FALSE)\nstomAll<-stom # all stomach input irrespectiv of actually use\nstom<-subset(stom,stom.used.all==1) # used stomachs specified for input\n\n\ntr<-trellis.par.get( \"background\")\n# old value tr$col=\"#909090\"\ntr$col=\"white\"\ntrellis.par.set(\"background\", tr)\n\n################################################\n\n\n####################################\n# observation by predator-prey size ratio\n## prog size 1b\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\n\n\nstom3<-subset(stom, (Size.model==1 | Size.model==11) & Prey.no>0 & Predator %in% c('H. porpoise','Grey seal'))\n\nstom3<-subset(stom, (Size.model==1 | Size.model==11) & Prey.no>0 )\n\n\n# just checking\na<-aggregate(list(stom=stom3$stomcon,stom.input=stom3$stom.input,stom.hat=stom3$stomcon.hat,stom.hat.part=stom3$stomcon.hat.part),\n list(Year=stom3$Year,Quarter=stom3$Quarter,Predator=stom3$Predator,Pred_l=stom3$Predator.length),sum)\n\n# use the individual data points (not the liklihood data) when size mode is 11\nstom3[stom3$Size.model==11,]$stomcon<- stom3[stom3$Size.model==11,]$stom.input\nstom3[stom3$Size.model==11,]$stomcon.hat<-stom3[stom3$Size.model==11,]$stomcon.hat.part\n\n\n# to get rit of \"Other\"\nwrite.csv(stom3, file = file.path(data.path,'junk.csv'),row.names = FALSE)\nstom3<-read.csv(file = file.path(data.path,'junk.csv'))\n\n# start with a high fac for calc of mean and var\nfac<-50\na<-aggregate(list(stom=stom3$stomcon*5000,stom.hat=stom3$stomcon.hat*5000)\n ,list(Predator=stom3$Predator,PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum)\n\n# by(a,list(a$Predator),function(x) weighted.mean(x$PredPrey, x$stom))\n\nbb<-data.frame(size=rep(a$PredPrey,trunc(a$stom)),Predator=rep(a$Predator,trunc(a$stom)))\nk1<-aggregate(list(mean=bb$size),list(Predator=bb$Predator),mean)\nk2<-aggregate(list(var=bb$size),list(Predator=bb$Predator),var)\nstat<-merge(k1,k2)\n\nbb<-data.frame(size=rep(a$PredPrey,trunc(a$stom.hat)),Predator=rep(a$Predator,trunc(a$stom.hat)))\nk1<-aggregate(list(mean.hat=bb$size),list(Predator=bb$Predator),mean)\nk2<-aggregate(list(var.hat=bb$size),list(Predator=bb$Predator),var)\nstat.hat<-merge(k1,k2)\n\nstat<-merge(stat,stat.hat)\nstat\n\n# decrease fac for a nice plot\nfac<-2\na<-aggregate(list(stom=stom3$stomcon, stomNo=stom3$stomcon/stom3$Prey.weight,stom.hat=stom3$stomcon.hat),list(Predator=stom3$Predator,\n PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum)\nb<-aggregate(list(sumstom=a$stom, sumstomNo=a$stomNo, sumstom.hat=a$stom.hat),list(Predator=a$Predator),sum)\na<-merge(a,b)\na<-merge(a,stat)\na$stom<-a$stom/a$sumstom\na$stomNo<-a$stomNo/a$sumstomNo\na$stom.hat<-a$stom.hat/a$sumstom.hat\n\n## observed\na$headt<-paste(a$Predator,\", mean=\",formatC(a$mean,digits=2,format='f'),\" var=\",formatC(a$var,digits=2,format='f'),sep='')\n\ncleanup()\ntrellis.device(device = \"windows\",\n color = T, width=9, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\ncleanup()\ntrellis.device(device = \"windows\",\n color = T, width=18, height=22,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|headt, data=a,\n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.6, lines=2),\n scales = list(x = list(rot = 90, cex=0.5), y= list(alternating = 1,cex=0.5)),\n layout = c(3,4) ,col='grey')\n\n\nb1<-a\nb1$type<-paste(a$headt,'\\nObserved',sep='');\n\n## predicted\na$headt<-paste(a$Predator,\" mean=\",formatC(a$mean.hat,digits=2,format='f'),\" var=\",formatC(a$var.hat,digits=2,format='f'),sep='')\n\ntrellis.device(device = \"windows\",\n color = T, width=18, height=22,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n\n barchart(stom.hat~as.factor(PredPrey)|headt, data=a,\n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.6, lines=2),\n scales = list(x = list(rot = 90, cex=0.5), y= list(alternating = 1,cex=0.5)),\n layout = c(3,4) ,col='grey')\n\n\n\n\nb2<-a\n\nb2$type<-paste(a$headt,'\\nPredicted',sep='');\n\n# both on the same plot\nb2$stom<-b2$stom.hat\naa<-rbind(b1,b2)\n\ntrellis.device(device = \"windows\",\n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|type, data=aa,\n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 3) ,col='grey')\n\n\n#####################\n# same as above, but with contributions from prey species\n\nfac<-2\na<-aggregate(list(stom=stom3$stomcon, stomNo=stom3$stomcon/stom3$Prey.weight,stom.hat=stom3$stomcon.hat),list(Predator=stom3$Predator,\n Prey=stom3$Prey,\n PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum)\nb<-aggregate(list(sumstom=a$stom, sumstomNo=a$stomNo, sumstom.hat=a$stom.hat),list(Predator=a$Predator),sum)\na<-merge(a,b)\na<-merge(a,stat)\na$stom<-a$stom/a$sumstom\na$stomNo<-a$stomNo/a$sumstomNo\na$stom.hat<-a$stom.hat/a$sumstom.hat\n\n## observed\na$headt<-paste(a$Predator,\", mean=\",formatC(a$mean,digits=2,format='f'),\" var=\",formatC(a$var,digits=2,format='f'),sep='')\n\n#cleanup()\ntrellis.device(device = \"windows\",\n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE )\n\nb<- tapply(stom3$stomcon,list(stom3$Prey),sum)\ndim(b)\n\ncol<-1:dim(b)\nlab<-dimnames(b)[[1]]\n\n\n barchart(stom~as.factor(PredPrey)|headt, data=a,\n groups = Prey, stack = TRUE, col=1:dim(b),\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.75, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:dim(b)),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 2) )\n\n\nb1<-a\nb1$type<-paste(a$headt,'\\nObserved',sep='');\n\n## predicted\na$headt<-paste(a$Predator,\" mean=\",formatC(a$mean.hat,digits=2,format='f'),\" var=\",formatC(a$var.hat,digits=2,format='f'),sep='')\ntrellis.device(device = \"windows\",\n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE )\n\n\n barchart(stom.hat~as.factor(PredPrey)|headt, data=a,\n groups = Prey, stack = TRUE, col=1:dim(b),\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.75, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:dim(b)),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 2) )\n\nb2<-a\n\nb2$type<-paste(a$headt,'\\nPredicted',sep='');\n\n# both on the same plot\nb2$stom<-b2$stom.hat\naa<-rbind(b1,b2)\n\ntrellis.device(device = \"windows\",\n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|type, data=aa,\n groups = Prey, stack = TRUE, col=1:7, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.75, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:7),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 3) )\n", "meta": {"hexsha": "7150ee7ec470fae3c09bc252aec732fadd4c7ce2", "size": 8484, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_size_preference_wgsam.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_size_preference_wgsam.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_size_preference_wgsam.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.3891402715, "max_line_length": 134, "alphanum_fraction": 0.6531117397, "num_tokens": 2935, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32913254495395944}} {"text": "#' Calculate GC3 value of a codon sequence\r\n#'\r\n#' Outputs an array of numbers, representing the proportion of codons with G/C bases in the third position\r\n#'\r\n#' @param x a list of KZsqns objects.\r\n#' @return a number array of GC3s.\r\n#'\r\n#' @examples\r\n#' data('CodonTable0')\r\n#' x = vector('list', 5) # Creating an empty list of length 5\r\n#' for(i in 1:5){\r\n#' x[[i]] = CodonTable0[sample(1:64, 10*i, TRUE),1]\r\n#' attr(x[[i]], 'class') = 'KZsqns'\r\n#' }\r\n#'\r\n#' cat(gc3(x))\r\n#' @export\r\n\r\ngc3 <- function(x){\r\n if(!is.list(x)){\r\n cat(\"Just one perhap very long sequence?\\n\")\r\n x = list(x)\r\n }\r\n ans = matrix(0, length(x), 1)\r\n colnames(ans) <- 'GC3'\r\n rownames(ans) <- paste0('s_', 1:length(x))\r\n temp = n3_freq(x)\r\n ans[,1] = rowSums(temp[,c('G3', 'C3'), drop=F])\r\n return(ans)\r\n}\r\n", "meta": {"hexsha": "c14613fcf1b2f83fec2dd9937ede9167b68cc715", "size": 801, "ext": "r", "lang": "R", "max_stars_repo_path": "R/gc3.r", "max_stars_repo_name": "HVoltBb/kondonz", "max_stars_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/gc3.r", "max_issues_repo_name": "HVoltBb/kondonz", "max_issues_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-01-07T00:23:48.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-07T18:19:25.000Z", "max_forks_repo_path": "R/gc3.r", "max_forks_repo_name": "HVoltBb/kodonz", "max_forks_repo_head_hexsha": "5fd777eca9f07a983c485be76de981a52efa42f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8387096774, "max_line_length": 107, "alphanum_fraction": 0.5792759051, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.32908726503052177}} {"text": "fn.flow = file.path(PROJECT_DIR, \"data\", \"CHI\", \"flow\", \"bp_flow_data.txt\")\nflow = fread(fn.flow) %>% tibble::column_to_rownames(\"Sample\") %>% \n data.matrix()\n\nflow.poB = flow[,-(1:5)] / flow[,\"B_cells\"] * 100\n\nflow.info = sub(\"^\\\\d+: (.+)\\\\.fcs\",\"\\\\1\",rownames(flow.poB)) %>% \n gsub(\"pre \",\"-\",.) %>% data.frame(sample=.) %>% \n separate(sample, c(\"subject\",\"time\"), sep=\"_\", remove = F) %>% \n mutate(time = as.integer(time)) %>% \n mutate(time.point = paste0(ifelse(time<0,\"pre\",\"day\"),abs(time))) %>% \n mutate(sample = paste(subject, time.point, sep=\"_\"))\n\nrownames(flow.poB) = flow.info$sample\n\nfn.flag = file.path(PROJECT_DIR, \"data\", \"CHI\", \"flow\", \"bp_flow_sample_flagged.txt\")\nflow.flag = fread(fn.flag)\n\nflow.info = flow.info %>% left_join(flow.flag, by=\"sample\")\n\nfn.flow.poB = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_percent_of_B.txt\")\nfn.flow.si = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_sample_info.txt\")\n\nflow.poB %>% as.data.frame() %>% tibble::rownames_to_column(\"sample\") %>% \n fwrite(fn.flow.poB, sep=\"\\t\", quote=T)\n\nfwrite(flow.info, fn.flow.si, sep=\"\\t\", quote=T)\n", "meta": {"hexsha": "0cb5482739d66374c5ee8d8e6adf9cd104da9c97", "size": 1167, "ext": "r", "lang": "R", "max_stars_repo_path": "R/chi_dataprep/chi_flow_procents.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/chi_dataprep/chi_flow_procents.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/chi_dataprep/chi_flow_procents.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 41.6785714286, "max_line_length": 86, "alphanum_fraction": 0.6092544987, "num_tokens": 350, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3290701375239353}} {"text": "library(ggplot2)\nlibrary(ggthemes)\n\nsource('colors.r')\n\n# pdf('../pdfs/peak_pos_histogram.pdf',width=7.46,height=4.1983)\n\ndata = read.csv(file='../cleaned_data.csv', sep=',', header = TRUE)\n\nreset <- par(mar=rep(0,4))\n\nplot <- ggplot(data, aes(x=peak_pos)) +\n geom_histogram(data = data, aes(x=peak_pos), color='white', fill = '#fcde05', binwidth = 1) +\n theme_wsj(color='white') +\n # coord_flip() +\n scale_y_continuous(breaks = seq(0, 900, by = 200)) +\n scale_x_continuous(breaks = seq(0, 100, by = 10)) +\n guides(alpha = FALSE) +\n theme(plot.margin=grid::unit(c(0,0,0,0), 'mm'))\n\npar(reset)\nprint(plot)\npar(reset)\n# dev.off()\n", "meta": {"hexsha": "b3f795e46894f8062a2001b86a73230a185a5021", "size": 677, "ext": "r", "lang": "R", "max_stars_repo_path": "completed/peak_pos_histogram.r", "max_stars_repo_name": "ruddfawcett/spotify-data", "max_stars_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "completed/peak_pos_histogram.r", "max_issues_repo_name": "ruddfawcett/spotify-data", "max_issues_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "completed/peak_pos_histogram.r", "max_forks_repo_name": "ruddfawcett/spotify-data", "max_forks_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.08, "max_line_length": 101, "alphanum_fraction": 0.611521418, "num_tokens": 213, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3290701375239353}} {"text": "library(scales)\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(data.table)\nlibrary(RColorBrewer)\n\nargs <- commandArgs(trailingOnly = TRUE)\n\nfile1 <- args[1]\nfile2 <- args[2]\nfile3 <- args[3]\ncall <- args[4]\ndate1 <- args[5]\ndate2 <- args[6]\n\n# file1 <- 'wsprspots-2016-04-SWLJO20.csv'\n# file2 <- 'wsprspots-2016-04-PI4THT.csv'\n# file3 <- 'wsprspots-2016-04-snr-diff-SWLJO20.png'\n# call <- 'SWLJO20'\n# date1 <- '2016-04-01'\n# date2 <- '2016-12-31'\n\ncolNames <- c('id', 'epoch', 'rcall',\n 'rgrid', 'snr', 'freq', 'call',\n 'grid', 'power', 'drift', 'distance',\n 'azimuth', 'band', 'version', 'code')\ncolClasses <- c('numeric', 'numeric', 'character',\n 'character', 'numeric', 'numeric', 'character',\n 'character', 'numeric', 'numeric', 'numeric',\n 'numeric', 'numeric', 'character', 'numeric')\n\nspots1 <- as.data.table(read.table(file1, header = F, sep = ',', col.names = colNames, colClasses = colClasses))\nspots2 <- as.data.table(read.table(file2, header = F, sep = ',', col.names = colNames, colClasses = colClasses))\n\nspots1[, time := as.POSIXct(epoch, origin = '1970-01-01')]\nspots2[, time := as.POSIXct(epoch, origin = '1970-01-01')]\n\nfreqs <- c(0, 1, 3, 5, 7, 10, 14, 18, 21, 24, 28, 50)\nbands <- c('MF', '160m', '80m', '60m', '40m', '30m', '20m', '17m', '15m', '12m', '10m', '6m')\n\nproc1 <- function(x){\n x[\n ,\n list(\n diff = mean(as.numeric(snr.x) - as.numeric(snr.y))\n ),\n list(\n mday = mday(time),\n band\n )\n ]\n}\n\nspots <- merge(spots1, spots2, by = c('time', 'band', 'call'))\n\ndiff <- proc1(spots)\n# diff[, time := as.POSIXct(paste0('2016/04/', mday, ' ', hour, ':00:00'))]\ndiff[, time := as.POSIXct(paste0('2016/04/', mday, ' 12:00:00'))]\n\nsubset <- diff[time >= as.POSIXct(paste0(date1, ' 00:00:00')) & time <= as.POSIXct(paste0(date2, ' 23:59:59'))]\n\np <- lapply(2:7, function(i)\n ggplot(data = subset[band == freqs[i]], aes(x = time, y = diff)) +\n geom_point() +\n scale_x_datetime(breaks = date_breaks('1 day'), minor_breaks = date_breaks('1 hour')) +\n theme(axis.text.x = element_text(angle = 45)) +\n labs(title = bands[i]) + labs(x = '', y = 'average SNR difference') +\n theme(legend.position = 'none') + coord_cartesian(ylim = c(-20.0, 10.0))\n)\n\npng(file3, width = 1200, height = 600, res = 90)\ngrid.arrange(arrangeGrob(grobs = p, nrow = 2))\ndev.off()\n", "meta": {"hexsha": "997d7f28eeddedccd3d91bea36ae2902d87d6768", "size": 2318, "ext": "r", "lang": "R", "max_stars_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-diff.r", "max_stars_repo_name": "KI7MT/wsprana-utils", "max_stars_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-03-15T14:58:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-12T08:42:42.000Z", "max_issues_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-diff.r", "max_issues_repo_name": "KI7MT/wsprana-utils", "max_issues_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-diff.r", "max_forks_repo_name": "KI7MT/wsprana-utils", "max_forks_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-12T08:42:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-12T08:42:45.000Z", "avg_line_length": 31.3243243243, "max_line_length": 112, "alphanum_fraction": 0.6052631579, "num_tokens": 834, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3290701375239353}} {"text": "\nlibrary(Seurat)\nlibrary(CHETAH)\n\noptions_table<-read.table(parSampleFile1, sep=\"\\t\", header=F, stringsAsFactors = F)\nmyoptions<-split(options_table$V1, options_table$V2)\n\nspecies=myoptions$species\nprefix=myoptions$prefix\n\n#load reference\nload(parFile2)\n\nfinalList<-readRDS(parFile1)\nobj<-finalList$obj\n\n#keep umap only\nobj@reductions$pca=NULL\nobj@reductions$tsne=NULL\n\nprefix=paste0(outFile, \".CHETAH\")\n\nobj.sce = as.SingleCellExperiment(obj)\nobj.sce<-CHETAHclassifier(input=obj.sce, ref_cells = reference)\n\nct=data.frame(cell=c(1:length(colnames(obj.sce))), \"seurat_cluster\"=obj$seurat_clusters, \"celltype_CHETAH\"=obj.sce$celltype_CHETAH)\nrow.names(ct)<-colnames(obj)\nwrite.csv(ct, paste0(prefix,\".csv\"))\nsaveRDS(obj.sce, file=paste0(prefix, \".rds\"))\n\npng(paste0(prefix, \".png\"), width=6000, height=2500, res=300)\ng<-PlotCHETAH(obj.sce)\nprint(g)\ndev.off()\n\n", "meta": {"hexsha": "65231da6c68b6ee877c264471c9bf866b555baaa", "size": 859, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/scRNA/CHETAH.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/scRNA/CHETAH.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/scRNA/CHETAH.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 23.8611111111, "max_line_length": 131, "alphanum_fraction": 0.7648428405, "num_tokens": 272, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3290701375239352}} {"text": "pdf_file<-\"pdf/histograms_overlay.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=11,height=7)\n\nsource(\"scripts/inc_datadesign_dbconnect.r\")\npar(omi=c(0.75,0.2,0.75,0.2),mai=c(0.25,1.25,0.25,0.25),family=\"Lato Light\",las=1)\n\n# Import data and prepare chart\n\nsql<-\"select * from v_women_men\"\nmyDataset<-dbGetQuery(con,sql)\nattach(myDataset)\nmyCol1<-rgb(191,239,255,180,maxColorValue=255)\nmyCol2<-rgb(255,0,210,80,maxColorValue=255)\nbrandenburg<-subset(myDataset,bundesland == 'Brandenburg')\n\n# Create chart\n\nhist(brandenburg$wm,col=myCol1,xlim=c(0.9,1.2),border=F,main='',xlab=\"Ratio\",ylab=\"Number of Counties\",axes=F)\naxis(1,col=par(\"bg\"),col.ticks=\"grey81\",lwd.ticks=0.5,tck=-0.025)\naxis(2,col=par(\"bg\"),col.ticks=\"grey81\",lwd.ticks=0.5,tck=-0.025)\nrp<-subset(myDataset,bundesland == 'Rheinland-Pfalz')\nhist(rp$wm,col=myCol2,xlim=c(0.9,1.2),border=F,add=T,main='')\nlegend(\"right\",c(\"Brandenburg\",\"Rhineland-Palatinate\"),border=F,pch=15,col=c(myCol1,myCol2),bty=\"n\",cex=1.25,xpd=T,ncol=1)\n\n# Titling\n\nmtext(\"Distribution of Women-Men-Ratio\",3,line=1.8,adj=0,family=\"Lato Black\",cex=1.5,outer=T)\nmtext(\"Brandenburg and Rhineland-Palatinate\",3,line=-0.2,adj=0,font=3,cex=1.2,outer=T)\nmtext(\"Source: Bundeswahlleiter\",1,line=2,adj=1.0,font=3,cex=1.2,outer=T)\ndev.off()\n", "meta": {"hexsha": "9bb7dddb41993f6543518f7b2ace61390966063d", "size": 1261, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/histograms_overlay.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/histograms_overlay.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/histograms_overlay.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.6774193548, "max_line_length": 122, "alphanum_fraction": 0.7287866772, "num_tokens": 498, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.3290283323825388}} {"text": "#-h- outnum\t\t\t 381 local 12/01/80 15:54:32\n# outnum - output decimal number\n include defs\n\n subroutine outnum (n)\n integer n\n\n character chars (MAXCHARS)\n\n integer i, m\n\n m = iabs (n)\n i = 0\n repeat {\n i = i + 1\n chars (i) = mod (m, 10) + DIG0\n m = m / 10\n } until (m == 0 | i >= MAXCHARS)\n if (n < 0)\n call outch (MINUS)\n for ( ; i > 0; i = i - 1)\n call outch (chars (i))\n return\n end\n", "meta": {"hexsha": "5286971e1883f005130253e7583c8a2a634eeabc", "size": 447, "ext": "r", "lang": "R", "max_stars_repo_path": "iraf.v2161/unix/boot/spp/rpp/rpprat/outnum.r", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-12-01T15:19:09.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-02T16:48:42.000Z", "max_issues_repo_path": "unix/boot/spp/rpp/rpprat/outnum.r", "max_issues_repo_name": "kirxkirx/iraf", "max_issues_repo_head_hexsha": "fcd7569b4e0ddbea29f7dbe534a25759e0c31883", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-11-30T13:48:50.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-02T19:40:25.000Z", "max_forks_repo_path": "unix/boot/spp/rpp/rpprat/outnum.r", "max_forks_repo_name": "kirxkirx/iraf", "max_forks_repo_head_hexsha": "fcd7569b4e0ddbea29f7dbe534a25759e0c31883", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.88, "max_line_length": 48, "alphanum_fraction": 0.4966442953, "num_tokens": 177, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5660185351961016, "lm_q1q2_score": 0.32902832387146086}} {"text": "#' Categorizing Computation as CPU or RAM Bound\n#' \n#' This attmpts to declare a computation as compute or memory bound. Some\n#' simplifying assumptions are made. See the details section for more\n#' information.\n#' \n#' To make our determination, we measure the total number of floating point\n#' instructions and the total number of L2 cache accesses. If the number of\n#' floating point instructions is greater, then we say the computation is\n#' compute bound, and otherwise we say the computation is memory bound.\n#' \n#' @param expr \n#' A valid R expression to be profiled.\n#' @param gcFirst \n#' logical; determines if garbage collection should be called\n#' before profiling.\n#' @param burnin \n#' logical; determines if the function should first be evaluated\n#' with an empty expression.\n#' \n#' @return \n#' The results of the requested PAPI events are returned, in a named\n#' list, with values stored in double precision.\n#' \n#' @keywords programming\n#' \n#' @examples\n#' \\dontrun{\n#' library(pbdPAPI)\n#' \n#' system.idle(1+1, events=\"float\")\n#' }\n#' \n#' @export\nsystem.cpuormem <- function(expr, gcFirst=TRUE, burnin=TRUE)\n{\n if (missing(expr))\n expr <- NULL\n \n events <- c(\"PAPI_FP_INS\", \"PAPI_L2_TCA\")\n papi.avail.lookup(events=events)\n \n ret <- system.event(expr=expr, events=events, gcFirst=gcFirst)\n \n if (ret[[1L]] >= ret[[2L]])\n ret <- c(ret, \"compute bound\")\n else \n ret <- c(ret, \"memory bound\")\n \n names(ret)[3L] <- \"Program Characterization\"\n \n return( ret )\n}\n\n\n", "meta": {"hexsha": "680631662c3df64fd4df252c9388ff59284e6fab", "size": 1498, "ext": "r", "lang": "R", "max_stars_repo_path": "R/characterize.r", "max_stars_repo_name": "wrathematics/pbdPAPI", "max_stars_repo_head_hexsha": "cb3fad3bccd54b7aeeef9e687b52d938613a356e", "max_stars_repo_licenses": ["Intel", "BSD-3-Clause"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2015-02-14T17:00:51.000Z", "max_stars_repo_stars_event_max_datetime": "2016-02-01T20:13:43.000Z", "max_issues_repo_path": "R/characterize.r", "max_issues_repo_name": "QuantScientist3/pbdPAPI", "max_issues_repo_head_hexsha": "708bee501de20eb82829e03b92b24b6352044f49", "max_issues_repo_licenses": ["Intel", "BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/characterize.r", "max_forks_repo_name": "QuantScientist3/pbdPAPI", "max_forks_repo_head_hexsha": "708bee501de20eb82829e03b92b24b6352044f49", "max_forks_repo_licenses": ["Intel", "BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-09-05T05:21:14.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-28T16:17:37.000Z", "avg_line_length": 26.75, "max_line_length": 76, "alphanum_fraction": 0.691588785, "num_tokens": 393, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3290283238714608}} {"text": "\nlibrary(\"testthat\");\n#install.packages(\"testthat\")\n\nsource(\"../testCatMultiple.r\");\nsource(\"../replaceNaN.r\")\n\n# categorical multiple phenotype with two arrays\npheno_0_0 <- c(1,2,-1,NA,1,2,-1,NA);\npheno_0_1 <- c(NA,-1,NA,NA,NA,-1,NA,NA)\n\nother_0_0 <- c(1,1,1,1,NA,NA,-1,-1)\n\ndata = cbind.data.frame(pheno_0_0,pheno_0_1,other_0_0)\ncolnames(data)[1] <- \"pheno_0_0\"\ncolnames(data)[2] <- \"pheno_0_1\"\ncolnames(data)[3] <- \"xother_0_0\"\n\n####\n# include ALL - pheno with NK values aren't included as -ve examples\nidxNA = restrictSample2('test1',data[,1:2], \"ALL\", 1)\nexpect_equal(sort(idxNA), c(2,3,6,7))\n\n# here examples 2 and 6 aren't included because they correspond to the -ve class\nidxNA = restrictSample2('test1',data[,1:2], \"ALL\", 2) \nexpect_equal(sort(idxNA), c(3,7))\n\n####\n# include NO_NAN\nidxNA = restrictSample2('test1',data[,1:2], \"NO_NAN\", 1)\ncat(\"\\n\")\nexpect_equal(sort(idxNA), c(2,3,4,6,7,8))\n\n# here examples 2 and 6\taren't included\tbecause\tthey correspond\tto the -ve class\nidxNA = restrictSample2('test1',data[,1:2], \"NO_NAN\", 2)\ncat(\"\\n\")\nexpect_equal(sort(idxNA), c(3,4,7,8))\n\n####\n# include only those with (non missing) value for other field\nidxNA = restrictSample2('test1',data[,1:2], \"other\", 1)\ncat(\"\\n\")\nexpect_equal(sort(idxNA), c(2,3,5,6,7,8))\n\n# \nidxNA = restrictSample2('test1',data[,1:2], \"other\", 2)\ncat(\"\\n\")\nexpect_equal(sort(idxNA), c(3,5,6,7,8))\n\n\n## if cat mult is not numeric then can't have missing values\npheno_0_0 <- c(\"A\",\"B\",\"C\",NA,\"A\",\"B\",\"C\",NA);\npheno_0_1 <- c(NA,\"C\",NA,NA,NA,\"C\",NA,NA)\nother_0_0 <- c(\"A\",\"A\",\"A\",\"A\",NA,NA,\"C\",\"C\")\ndata = cbind.data.frame(pheno_0_0,pheno_0_1,other_0_0)\ncolnames(data)[1] <- \"pheno_0_0\"\ncolnames(data)[2] <- \"pheno_0_1\"\ncolnames(data)[3] <- \"xother_0_0\"\n\n\nidxNA = restrictSample2('test1',data[,1:2], \"ALL\", \"A\")\nexpect_equal(idxNA, NULL)\n\nidxNA = restrictSample2('test1',data[,1:2], \"NO_NAN\", \"A\")\nexpect_equal(sort(idxNA), c(4,8))\n\nidxNA = restrictSample2('test1',data[,1:2], \"other\", \"A\")\nexpect_equal(sort(idxNA), c(5,6))\n\n\n", "meta": {"hexsha": "1db0fb7318541de47a1727668d328a8198fceb48", "size": 2000, "ext": "r", "lang": "R", "max_stars_repo_path": "WAS/unittests/test_testCatMultiple.r", "max_stars_repo_name": "carbocation/PHESANT", "max_stars_repo_head_hexsha": "9f7609e905ceecc0aa0e8173b425e0e81fd0c0c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2017-02-27T00:47:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-14T15:42:01.000Z", "max_issues_repo_path": "WAS/unittests/test_testCatMultiple.r", "max_issues_repo_name": "carbocation/PHESANT", "max_issues_repo_head_hexsha": "9f7609e905ceecc0aa0e8173b425e0e81fd0c0c0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2017-05-07T13:39:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-25T12:23:55.000Z", "max_forks_repo_path": "WAS/unittests/test_testCatMultiple.r", "max_forks_repo_name": "carbocation/PHESANT", "max_forks_repo_head_hexsha": "9f7609e905ceecc0aa0e8173b425e0e81fd0c0c0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 47, "max_forks_repo_forks_event_min_datetime": "2017-02-27T12:25:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-02T22:03:16.000Z", "avg_line_length": 28.1690140845, "max_line_length": 80, "alphanum_fraction": 0.6645, "num_tokens": 778, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3290283238714608}} {"text": "\\name{comb_name}\n\\alias{comb_name}\n\\title{\nNames of the Combination sets\n}\n\\description{\nNames of the Combination sets\n}\n\\usage{\ncomb_name(m)\n}\n\\arguments{\n\n \\item{m}{A combination matrix returned by \\code{\\link{make_comb_mat}}.}\n\n}\n\\details{\nThe name of the combination sets are formatted as a string\nof binary bits. E.g. for three sets of \"a\", \"b\", \"c\", the combination\nset with name \"101\" corresponds to select set a, not select set b\nand select set c. The definition of \"select\" depends on the value of\n\\code{mode} from \\code{\\link{make_comb_mat}}.\n}\n\\value{\nA vector of names of the combination sets.\n}\n\\examples{\nset.seed(123)\nlt = list(a = sample(letters, 10),\n b = sample(letters, 15),\n c = sample(letters, 20))\nm = make_comb_mat(lt)\ncomb_name(m)\n}\n", "meta": {"hexsha": "c49d5ebebd7b7e74f55222eb149ec3e67e74e698", "size": 776, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/comb_name.rd", "max_stars_repo_name": "zhongmicai/complexHeatmap", "max_stars_repo_head_hexsha": "02ad1d0a5097d21f748c4bab5f97d1505cdd8642", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-07-30T13:07:46.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-30T13:07:46.000Z", "max_issues_repo_path": "man/comb_name.rd", "max_issues_repo_name": "songyang1992/ComplexHeatmap", "max_issues_repo_head_hexsha": "38cd0ae5391aedd5c1e4733e61de51490019a8bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/comb_name.rd", "max_forks_repo_name": "songyang1992/ComplexHeatmap", "max_forks_repo_head_hexsha": "38cd0ae5391aedd5c1e4733e61de51490019a8bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.1714285714, "max_line_length": 73, "alphanum_fraction": 0.7010309278, "num_tokens": 221, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3290283238714608}} {"text": "library(data.table)\nlibrary(ggplot2)\nlibrary(colorspace)\n\nsource('../../../functions.r')\nsource('../../../labs.r')\n\nspplist <- fread('../../acap-species-list.csv')\n\nMAX_RISKRATIO <- 10\n\n\n## * Summary table\n\nriskratios <- fread('../../generated/risk-ratios-summary.csv')\n\nsetorder(riskratios, -rr_p_ov_1, -rr_med)\n\nriskratios <- riskratios[, .(cname = upper1st(cname),\n pst_mean = format(round(pst_mean, 1), big.mark = ' '), pst_ci,\n apf_mean = format(round(apf_mean, 1), big.mark = ' '), apf_ci,\n rr_med = round(rr_med, 2), rr_ci,\n rr_p_ov_1 = round(rr_p_ov_1, 2))]\n\nfwrite(riskratios, '../assets/risk-ratios-summary.csv')\n\n\n## * Violin plot\n\nrisksamples <- readRDS('../../generated/risk-ratios.rds')\n\nget_dens <- function(x) {\n d <- density(x, from = quantile(x, 0.025), to = pmin(quantile(x, 0.975), MAX_RISKRATIO))\t\n return(data.table(x = as.numeric(d$x), y = as.numeric(d$y)))\t\n}\n\ndens <- risksamples[, get_dens(riskratio), .(cname, species_code)]\ndens[, y := y / max(y), .(cname)]\n\ndd <- dens[, rbind(.SD, .SD[.N:1, .(x = x, y = -y)], .SD[1]), .(cname, species_code)]\n\ndd[spplist, spgroup := labs[i.vulgroup], on = c('species_code' = 'code.sra')]\n\nstats <- risksamples[, .(rr_med = median(riskratio), p_rr_ov = mean(riskratio > 1)), .(cname, species_code)]\n\n## ** Order taxa\nspp <- stats[order(-p_rr_ov, -rr_med), cname]\ndd[, lab := factor(as.character(cname), levels = spp)]\n \n## ** Median line\ndd[stats, med := i.rr_med, on = c('cname')]\nmed_lims <- dd[, .SD[which.min(abs(med - x))], .(cname, species_code, spgroup)]\n\noutname <- '../assets/plot-risk-ratios.png'\n\ng <- ggplot(dd, aes(x=x, y=y, fill=spgroup)) +\n geom_polygon(alpha = 0.6) +\n geom_segment(data = med_lims, aes(x = med, xend = med, y = y, yend = -y, colour=spgroup)) +\n scale_fill_discrete_qualitative(palette = \"Harmonic\", name = NULL) +\n scale_colour_discrete_qualitative(palette = \"Harmonic\", name = NULL) +\n scale_x_continuous(expand = c(0,0), limits = c(0, 1.02*MAX_RISKRATIO)) +\n facet_wrap(~ lab, scales = 'free_y', ncol = 1, strip.position = 'left') +\n labs(x = 'Risk ratio', y = NULL) +\n theme_minimal() +\n theme(panel.grid.major.y = element_blank(),\n panel.grid.minor.y = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks.y = element_blank(),\n strip.text.y =element_text(angle=180, hjust = 1),\n legend.position = c(0.8, 0.2),\n legend.background = element_rect(fill='#EEEEEE', colour=NA),\n legend.margin = margin(0,2,2,2, 'mm'),\n panel.spacing.y=unit(0, 'mm'))\n\nggsave(outname, g, width = 8, height = 6)\n\ncat('Plot saved as `', outname, '`\\n')\n", "meta": {"hexsha": "7c8eb566ecea686056a4dc2cd35180660bc2e0a7", "size": 2699, "ext": "r", "lang": "R", "max_stars_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/risk-ratios.r", "max_stars_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_stars_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/risk-ratios.r", "max_issues_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_issues_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12-genus-tracking-interaction-fleet/report/asset-making/risk-ratios.r", "max_forks_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_forks_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6025641026, "max_line_length": 108, "alphanum_fraction": 0.6105965172, "num_tokens": 842, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.32898063554570944}} {"text": "#!/usr/bin/env Rscript\n\n\n\n\nrequire(plyr)\nrequire(ggplot2)\nrequire(pracma)\nrequire(grid)\n\nargv <- commandArgs(trailingOnly = TRUE)\n\nprefix <- gsub(\"\\\\s\",\"\", argv[1])\nprint(prefix)\ntruthset <- argv[2]\nprint(truthset)\nresults <- argv[3]\nprint(results)\nxmin <- as.numeric(argv[4])\nxmax <- as.numeric(argv[5])\nymin <- as.numeric(argv[6])\nymax <- as.numeric(argv[7])\n\nroc <- read.delim(results)\n\nbests <- ddply(roc, .(set), function(x) { data.frame(best_snps=with(x, min(false_negative_snps + false_positive_snps)), best_snp_threshold=min(subset(x, (false_negative_snps + false_positive_snps) == with(x, min(false_negative_snps + false_positive_snps)))$threshold ), best_indels=with(x, min(false_negative_indels + false_positive_indels)), best_indel_threshold=min(subset(x, (false_negative_indels + false_positive_indels) == with(x, min(false_negative_indels + false_positive_indels)))$threshold )) })\n\nwrite.table(bests, paste(prefix, \".bests.tsv\", sep=\"\"), row.names=FALSE, quote=FALSE, sep=\"\\t\")\n\n#abs(trapz(c(1, roc$complexfpr), c(1, roc$complextpr)))\n\ntrue_snps <- with(subset(roc, set==truthset), max(num_snps))\ntrue_indels <- with(subset(roc, set==truthset), max(num_indels))\n\n# get ROC AUC\nauc <- ddply(roc, .(set),\n function(x) {\n data.frame(\n snp_auc=ifelse(true_snps>0,\n with(x,\n abs(trapz(c(1,\n false_positive_snps/(false_positive_snps+ max(false_negative_snps + num_snps - false_positive_snps))),\n c(max(1- false_negative_snps/true_snps),\n 1- false_negative_snps/true_snps)))),\n 0),\n indel_auc=ifelse(true_indels>0,\n with(x,\n abs(trapz(c(1,\n false_positive_indels/(false_positive_indels+ max(false_negative_indels + num_indels - false_positive_indels))),\n c(max(1- false_negative_indels/true_indels),\n 1- false_negative_indels/true_indels)))),\n 0)\n )\n }\n )\n\nwrite.table(auc, paste(prefix, \".auc.tsv\", sep=\"\"), row.names=FALSE, quote=FALSE, sep=\"\\t\")\n\n\nrocsnps <- ddply(roc, .(set),\n function(x) {\n data.frame(\n FPR=\n with(x,\n c(1,\n false_positive_snps/(false_positive_snps+ max(false_negative_snps + num_snps - false_positive_snps)))),\n TPR=\n with(x,\n c(max(1- false_negative_snps/true_snps),\n 1- false_negative_snps/true_snps)),\n type=as.factor(\"snps\")\n )\n }\n )\n\nrocindels <- ddply(roc, .(set),\n function(x) {\n data.frame(\n FPR=\n with(x,\n c(1,\n false_positive_indels/(false_positive_indels+ max(false_negative_indels + num_indels - false_positive_indels)))),\n TPR=\n with(x,\n c(max(1- false_negative_indels/true_indels),\n 1- false_negative_indels/true_indels)),\n type=as.factor(\"indels\")\n )\n }\n )\n\n\nif (FALSE) {\nif (true_snps>0) {\n ggplot(subset(roc, set != truthset),\n aes(false_positive_snps/(false_positive_snps+with(subset(roc, set==set), max(false_negative_snps + num_snps - false_positive_snps))),\n 1- false_negative_snps/with(subset(roc, set==set), max(false_negative_snps + num_snps - false_positive_snps)),\n group=set,\n color=set)) + scale_x_continuous(\"false positive rate\") + scale_y_continuous(\"true positive rate\") + geom_path() + theme_bw()\n + coord_cartesian(xlim=c(xmin,xmax), ylim=c(ymin,ymax))\n ggsave(paste(prefix, \".snps.png\", sep=\"\"), height=6, width=9)\n}\n\nif (true_indels>0) {\n ggplot(subset(roc, set != truthset),\n aes(false_positive_indels/(false_positive_indels+with(subset(roc, set==set), max(false_negative_indels + num_indels - false_positive_indels))),\n 1- false_negative_indels/with(subset(roc, set==set), max(false_negative_indels + num_indels - false_positive_indels)),\n group=set,\n color=set)) + scale_x_continuous(\"false positive rate\") + scale_y_continuous(\"true positive rate\") + geom_path() + theme_bw()\n + coord_cartesian(xlim=c(xmin,xmax), ylim=c(ymin,ymax))\n ggsave(paste(prefix, \".indels.png\", sep=\"\"), height=6, width=9)\n}\n}\n\n\n# new versions\nif (true_snps>0) {\n ggplot(subset(rocsnps, set != truthset),\n aes(FPR,\n TPR,\n group=set,\n color=set)) + scale_x_continuous(\"false positive rate\") + scale_y_continuous(\"true positive rate\") + geom_path() + theme_bw() + coord_cartesian(xlim=c(xmin,xmax), ylim=c(ymin,ymax))\n ggsave(paste(prefix, \".snps.png\", sep=\"\"), height=6, width=9)\n}\n\nif (true_indels>0) {\n ggplot(subset(rocindels, set != truthset),\n aes(FPR,\n TPR,\n group=set,\n color=set)) + scale_x_continuous(\"false positive rate\") + scale_y_continuous(\"true positive rate\") + geom_path() + theme_bw() + coord_cartesian(xlim=c(xmin,xmax), ylim=c(ymin,ymax))\n ggsave(paste(prefix, \".indels.png\", sep=\"\"), height=6, width=9)\n}\n\nif (true_indels>0 && true_snps>0) {\n\n(\n ggplot(subset(rbind(rocsnps,rocindels), set != truthset),\n aes(FPR,\n TPR,\n group=set,\n color=set))\n + scale_x_continuous(\"false positive rate\")\n + scale_y_continuous(\"true positive rate\")\n + geom_path()\n + theme_bw()\n + coord_cartesian(xlim=c(xmin,xmax), ylim=c(ymin,ymax))\n + facet_grid(type ~ .)\n + theme(panel.margin = unit(1, \"lines\")) \n)\n ggsave(paste(prefix, \".both.png\", sep=\"\"), height=5, width=5)\n\n}\n", "meta": {"hexsha": "ca1f86d87432e46242b2928f59a81c443fdc8672", "size": 6005, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/vcflib/scripts/plot_roc.r", "max_stars_repo_name": "benranco/test", "max_stars_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-09-04T08:07:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-04T08:07:46.000Z", "max_issues_repo_path": "tools/vcflib/scripts/plot_roc.r", "max_issues_repo_name": "benranco/test", "max_issues_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-10-08T23:44:16.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-10T08:04:09.000Z", "max_forks_repo_path": "tools/vcflib/scripts/plot_roc.r", "max_forks_repo_name": "benranco/test", "max_forks_repo_head_hexsha": "7cf9740108844da30dcc506e733015fd5dd76a05", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-12-19T09:10:28.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-08T17:07:44.000Z", "avg_line_length": 38.9935064935, "max_line_length": 505, "alphanum_fraction": 0.5776852623, "num_tokens": 1526, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.32898063554570944}} {"text": "## options for fringe analysis routines, wavefront fitting and wavefront display\n\npsfit_options <- function(colors=topo.colors(256), refine=TRUE, puw_alg = \"qual\", fringescale=1,\n wt=NULL, bgsub=TRUE,\n maxiter=20, ptol=1.e-4, trace=1, nzcs = 2,\n zc0=6:7,\n satarget=c(0,0), astig.bath=c(0,0),\n maxorder=14, uselm=FALSE, isoseq=FALSE, sgs=1,\n nthreads=parallel::detectCores()/2,\n plots=TRUE, crop=FALSE) {\n list(colors=colors, refine=refine, puw_alg=puw_alg, fringescale=fringescale,\n wt=wt, bgsub=bgsub,\n maxiter=maxiter, ptol=ptol, trace=trace, nzcs=nzcs,\n zc0=zc0, satarget=satarget, astig.bath=astig.bath,\n maxorder=maxorder, uselm=uselm, isoseq=isoseq, sgs=sgs,\n nthreads=nthreads,\n plots=plots, crop=crop)\n}\n\npsifit <- function(images, phases, cp=NULL, satarget=NULL, psialg =\"ls\", options=psfit_options()) {\n dims <- dim(images)\n nr <- dims[1]\n nc <- dims[2]\n nf <- dims[3]\n im.mat <- matrix(images, ncol=nf)\n if (!is.null(satarget)) {\n options$satarget <- satarget\n }\n refine <- options$refine\n if (length(phases) < nf) {\n phases <- wrap((0:(nf-1)) * phases[1])\n }\n if (!is.null(cp)) {\n prt <- pupil.rhotheta(nr, nc, cp)\n mask <- as.vector(prt$rho)\n im.mat <- im.mat[!is.na(mask),]\n refine <- FALSE\n } else {\n mask <- numeric(nr*nc)\n }\n switch(psialg,\n ls = {\n if (is.null(options$wt)) {\n wt <- rep(1, nf)\n } else {\n wt <- options$wt\n };\n psfit <- lspsiC(im.mat, phases, wt);\n extras <- NULL\n },\n aia = {\n if (is.null(options$ptol)) {\n ptol <- 0.001\n } else {\n ptol <- options$ptol\n };\n if (is.null(options$maxiter)) {\n maxiter <- 20\n } else {\n maxiter <- options$maxiter\n };\n if (is.null(options$trace)) {\n trace <- 1\n } else {\n trace <- options$trace\n };\n psfit <- aiapsiC(im.mat, phases, ptol, maxiter, trace);\n phases <- psfit$phases;\n },\n pc1 = {\n if (is.null(options$bgsub)) {\n bgsub <- TRUE\n } else {\n bgsub <- options$bgsub\n };\n group_diag <- \"v\";\n psfit <- pcapsi(im.mat, bgsub, group_diag);\n phases <- psfit$phases\n },\n pc2 = {\n if (is.null(options$bgsub)) {\n bgsub <- TRUE\n } else {\n bgsub <- options$bgsub\n };\n group_diag <- \"u\";\n psfit <- pcapsi(im.mat, bgsub, group_diag);\n phases <- psfit$phases\n },\n gpc = ,\n gpcthentilt = {\n if (is.null(options$ptol)) {\n ptol <- 0.001\n } else {\n ptol <- options$ptol\n };\n if (is.null(options$maxiter)) {\n maxiter <- 20\n } else {\n maxiter <- options$maxiter\n };\n if (is.null(options$trace)) {\n trace <- 1\n } else {\n trace <- options$trace\n };\n psfit <- gpcapsiC(im.mat, ptol, maxiter, trace);\n phases <- psfit$phases\n },\n tilt = {\n if (is.null(cp)) stop(\"This algorithm must have measured interferogram outline\");\n if (is.null(options$ptol)) {\n ptol <- 0.001\n } else {\n ptol <- options$ptol\n };\n if (is.null(options$maxiter)) {\n maxiter <- 20\n } else {\n maxiter <- options$maxiter\n };\n if (is.null(options$trace)) {\n trace <- 1\n } else {\n trace <- options$trace\n };\n if (is.null(options$nzcs)) {\n nzcs <- 2\n } else {\n nzcs <- min(options$nzcs, 8)\n };\n rho <- prt$rho;\n theta <- prt$theta;\n rho <- rho[!is.na(rho)];\n theta <- theta[!is.na(theta)];\n coords <- zpmC(rho, theta, maxorder=4);\n coords <- coords[, 2:(nzcs+1)]\n psfit <- tiltpsiC(im.mat, phases, coords,\n maxiter=maxiter, ptol=ptol, trace=trace);\n phases <- psfit$phases;\n },\n { ## if no match to ls\n if (is.null(options$wt)) {\n wt <- rep(1, nf)\n } else {\n wt <- options$wt\n };\n psfit <- lspsiC(im.mat, phases, wt);\n extras <- NULL\n }\n )\n phi <- matrix(NA, nr, nc)\n mod <- matrix(0, nr, nc)\n phi[!is.na(mask)] <- psfit$phi\n mod[!is.na(mask)] <- psfit$mod\n if (is.null(cp)) {\n phi <- matrix(psfit$phi, ncol=nc)\n mod <- matrix(psfit$mod, ncol=nc)\n cp <- circle.pars(mod, plot=options$plots)\n prt <- pupil.rhotheta(nr, nc, cp)\n }\n if (refine || psialg==\"gpcthentilt\") {\n mask <- as.vector(prt$rho)\n im.mat <- matrix(images, ncol=nf)\n im.mat <- im.mat[!is.na(mask),]\n switch(psialg,\n ls = {\n mask <- numeric(nr*nc)\n },\n aia = {\n psfit <- aiapsiC(im.mat, phases, ptol, maxiter, trace);\n phases <- psfit$phases;\n },\n pc1 = {\n psfit <- pcapsi(im.mat, bgsub, group_diag);\n phases <- psfit$phases\n },\n pc2 = {\n psfit <- pcapsi(im.mat, bgsub, group_diag);\n phases <- psfit$phases\n },\n gpc = {\n psfit <- gpcapsiC(im.mat, ptol, maxiter, trace);\n phases <- psfit$phases\n },\n gpcthentilt = ,\n tilt = {\n if (is.null(options$ptol)) {\n ptol <- 0.001\n } else {\n ptol <- options$ptol\n };\n if (is.null(options$maxiter)) {\n maxiter <- 20\n } else {\n maxiter <- options$maxiter\n };\n if (is.null(options$trace)) {\n trace <- 1\n } else {\n trace <- options$trace\n };\n if (is.null(options$nzcs)) {\n nzcs <- 2\n } else {\n nzcs <- min(options$nzcs, 8)\n };\n rho <- prt$rho;\n theta <- prt$theta;\n rho <- rho[!is.na(rho)];\n theta <- theta[!is.na(theta)];\n coords <- zpmC(rho, theta, maxorder=4);\n coords <- coords[, 2:(nzcs+1)]\n psfit <- tiltpsiC(im.mat, phases, coords,\n maxiter=maxiter, ptol=ptol, trace=trace);\n phases <- psfit$phases;\n }\n )\n phi <- matrix(NA, nr, nc)\n mod <- matrix(0, nr, nc)\n phi[!is.na(mask)] <- psfit$phi\n mod[!is.na(mask)] <- psfit$mod\n }\n phi[is.na(prt$rho)] <- NA\n class(phi) <- \"pupil\"\n cp.orig <- cp\n if (options$crop) {\n phi <- crop(phi, cp)\n mod <- crop(mod, cp)$im\n cp <- phi$cp\n phi <- phi$im\n nr <- nrow(phi)\n nc <- ncol(phi)\n }\n wf.raw <- switch(options$puw_alg,\n qual = qpuw(phi, mod),\n brcut = brcutpuw(phi),\n lp = lppuw::netflowpuw(phi, mod)\n )\n wf.raw <- options$fringescale * wf.raw\n class(wf.raw) <- \"pupil\"\n wfnets <- wf_net(wf.raw, cp, options)\n if(length(psfit) > 3) extras <- psfit[4:length(psfit)]\n outs <- list(phi=phi, mod=mod, phases=wrap(as.vector(phases)), \n cp=cp, cp.orig=cp.orig,\n wf.net=wfnets$wf.net, wf.smooth=wfnets$wf.smooth,wf.residual=wfnets$wf.residual,\n fit=wfnets$fit, zcoef.net=wfnets$zcoef.net, extras=extras)\n class(outs) <- append(class(outs), \"wf_fitted\")\n outs\n}\n\n", "meta": {"hexsha": "a75b61e6a351cb859ceb99f399ef9a0f09e127d9", "size": 7019, "ext": "r", "lang": "R", "max_stars_repo_path": "R/psifit.r", "max_stars_repo_name": "gmke/zernike", "max_stars_repo_head_hexsha": "0880b0ae43cbb051afb54aa5decc246252467a8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-05-15T09:28:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-16T17:28:29.000Z", "max_issues_repo_path": "R/psifit.r", "max_issues_repo_name": "gmke/zernike", "max_issues_repo_head_hexsha": "0880b0ae43cbb051afb54aa5decc246252467a8d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/psifit.r", "max_forks_repo_name": "gmke/zernike", "max_forks_repo_head_hexsha": "0880b0ae43cbb051afb54aa5decc246252467a8d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-01-17T13:30:49.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-17T13:30:49.000Z", "avg_line_length": 27.8531746032, "max_line_length": 99, "alphanum_fraction": 0.5086194615, "num_tokens": 2198, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3289680006597456}} {"text": "#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\nStatBindot <- ggproto(\"StatBindot\", Stat,\n required_aes = \"x\",\n non_missing_aes = \"weight\",\n default_aes = aes(y = after_stat(count)),\n\n setup_params = function(data, params) {\n if (is.null(params$binwidth)) {\n cli::cli_inform(\"Bin width defaults to 1/30 of the range of the data. Pick better value with {.arg binwidth}.\")\n }\n params\n },\n\n compute_layer = function(self, data, params, layout) {\n data <- remove_missing(data, params$na.rm, name = snake_class(self))\n ggproto_parent(Stat, self)$compute_layer(data, params, layout)\n },\n\n compute_panel = function(self, data, scales, na.rm = FALSE, binwidth = NULL,\n binaxis = \"x\", method = \"dotdensity\",\n binpositions = \"bygroup\", origin = NULL,\n width = 0.9, drop = FALSE,\n right = TRUE) {\n\n # If using dotdensity and binning over all, we need to find the bin centers\n # for all data before it's split into groups.\n if (method == \"dotdensity\" && binpositions == \"all\") {\n if (binaxis == \"x\") {\n newdata <- densitybin(x = data$x, weight = data$weight, binwidth = binwidth,\n method = method)\n\n data <- data[order(data$x), ]\n newdata <- newdata[order(newdata$x), ]\n\n } else if (binaxis == \"y\") {\n newdata <- densitybin(x = data$y, weight = data$weight, binwidth = binwidth,\n method = method)\n\n data <- data[order(data$y), ]\n newdata <- newdata[order(newdata$x), ]\n }\n\n data$bin <- newdata$bin\n data$binwidth <- newdata$binwidth\n data$weight <- newdata$weight\n data$bincenter <- newdata$bincenter\n\n }\n\n ggproto_parent(Stat, self)$compute_panel(data, scales, binwidth = binwidth,\n binaxis = binaxis, method = method, binpositions = binpositions,\n origin = origin, width = width, drop = drop,\n right = right)\n },\n\n compute_group = function(self, data, scales, binwidth = NULL, binaxis = \"x\",\n method = \"dotdensity\", binpositions = \"bygroup\",\n origin = NULL, width = 0.9, drop = FALSE,\n right = TRUE) {\n # Check that weights are whole numbers (for dots, weights must be whole)\n if (!is.null(data$weight) && !(is_integerish(data$weight) && all(data$weight >= 0))) {\n cli::cli_abort(\"Weights must be nonnegative integers.\")\n }\n\n if (binaxis == \"x\") {\n range <- scales$x$dimension()\n values <- data$x\n } else if (binaxis == \"y\") {\n range <- scales$y$dimension()\n values <- data$y\n # The middle of each group, on the stack axis\n midline <- mean(range(data$x))\n }\n\n if (method == \"histodot\") {\n closed <- if (right) \"right\" else \"left\"\n if (!is.null(binwidth)) {\n bins <- bin_breaks_width(range, binwidth, boundary = origin, closed = closed)\n } else {\n bins <- bin_breaks_bins(range, 30, boundary = origin, closed = closed)\n }\n\n data <- bin_vector(values, bins, weight = data$weight, pad = FALSE)\n\n # Change \"width\" column to \"binwidth\" for consistency\n names(data)[names(data) == \"width\"] <- \"binwidth\"\n names(data)[names(data) == \"x\"] <- \"bincenter\"\n\n } else if (method == \"dotdensity\") {\n\n # If bin centers are found by group instead of by all, find the bin centers\n # (If binpositions==\"all\", then we'll already have bin centers.)\n if (binpositions == \"bygroup\")\n data <- densitybin(x = values, weight = data$weight, binwidth = binwidth,\n method = method, range = range)\n\n # Collapse each bin and get a count\n data <- dapply(data, \"bincenter\", function(x) {\n new_data_frame(list(\n binwidth = .subset2(x, \"binwidth\")[1],\n count = sum(.subset2(x, \"weight\"))\n ))\n })\n\n if (sum(data$count, na.rm = TRUE) != 0) {\n data$count[is.na(data$count)] <- 0\n data$ncount <- data$count / max(abs(data$count), na.rm = TRUE)\n if (drop) data <- subset(data, count > 0)\n }\n }\n\n if (binaxis == \"x\") {\n names(data)[names(data) == \"bincenter\"] <- \"x\"\n # For x binning, the width of the geoms is same as the width of the bin\n data$width <- data$binwidth\n } else if (binaxis == \"y\") {\n names(data)[names(data) == \"bincenter\"] <- \"y\"\n # For y binning, set the x midline. This is needed for continuous x axis\n data$x <- midline\n }\n return(data)\n }\n)\n\n\n# This does density binning, but does not collapse each bin with a count.\n# It returns a data frame with the original data (x), weights, bin #, and the bin centers.\ndensitybin <- function(x, weight = NULL, binwidth = NULL, method = method, range = NULL) {\n\n if (length(stats::na.omit(x)) == 0) return(new_data_frame())\n if (is.null(weight)) weight <- rep(1, length(x))\n weight[is.na(weight)] <- 0\n\n if (is.null(range)) range <- range(x, na.rm = TRUE, finite = TRUE)\n if (is.null(binwidth)) binwidth <- diff(range) / 30\n\n # Sort weight and x, by x\n weight <- weight[order(x)]\n x <- x[order(x)]\n\n cbin <- 0 # Current bin ID\n bin <- rep.int(NA, length(x)) # The bin ID for each observation\n binend <- -Inf # End position of current bin (scan left to right)\n\n # Scan list and put dots in bins\n for (i in 1:length(x)) {\n # If past end of bin, start a new bin at this point\n if (x[i] >= binend) {\n binend <- x[i] + binwidth\n cbin <- cbin + 1\n }\n\n bin[i] <- cbin\n }\n\n results <- new_data_frame(list(\n x = x,\n bin = bin,\n binwidth = binwidth,\n weight = weight\n ), n = length(x))\n results <- dapply(results, \"bin\", function(df) {\n df$bincenter = (min(df$x) + max(df$x)) / 2\n return(df)\n })\n\n return(results)\n}\n", "meta": {"hexsha": "edd11ae94957197fde6ce86ff85f443a645886de", "size": 6030, "ext": "r", "lang": "R", "max_stars_repo_path": "R/stat-bindot.r", "max_stars_repo_name": "sthagen/tidyverse-ggplot2", "max_stars_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/stat-bindot.r", "max_issues_repo_name": "sthagen/tidyverse-ggplot2", "max_issues_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/stat-bindot.r", "max_forks_repo_name": "sthagen/tidyverse-ggplot2", "max_forks_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0581395349, "max_line_length": 117, "alphanum_fraction": 0.5605306799, "num_tokens": 1608, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982315512488, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.32895962764081144}} {"text": "library(readxl)\nlibrary(ComplexHeatmap)\nlibrary(circlize)\n\n# Flow data ---------------------------------------------------------------\n\nfn.flow = \"data/Pregnancy_flow_cytometry.xlsx\"\ndf.flow = read_xlsx(fn.flow, sheet = \"frequencies\") %>% \n dplyr::rename(Visit = TimePoint)\ninfo.flow = df.flow[,1:2] %>% \n mutate(sample = paste(Subject, Visit, sep=\"_\"))\ndat.flow = df.flow[,-(1:2)] %>% \n data.matrix()\nrownames(dat.flow) = info.flow$sample\nids = colnames(dat.flow)[-1]\nids = round(as.numeric(ids), digits = 1) %>% as.character()\ncolnames(dat.flow)[-1] = ids\ncolnames(df.flow)[-(1:3)] = ids\n\nann.flow = read_xlsx(fn.flow, sheet = \"all gates\") %>% \n dplyr::select(ID = `Pop Code`, Name = `Subset name`)\nia = match(colnames(dat.flow), ann.flow$ID)\nann.flow = ann.flow[ia,]\nann.flow[is.na(ann.flow$ID),] = c(\"LYM\",\"Lymphocytes\")\n\n\ndir.create(\"data_generated\", showWarnings = F)\nsave(info.flow, dat.flow, ann.flow, file=\"data_generated/Pregnancy_flow_cytometry.RData\")\n\n\n# Compare cell population frequencies between visits ----------------------\n\nfc31 = c()\nfc41 = c()\nfc43 = c()\nwt31 = list()\nwt41 = list()\nwt43 = list()\nfor (ci in colnames(dat.flow)) {\n fc31[ci] = mean(dat.flow[info.flow$Visit==3,ci], na.rm=T) / mean(dat.flow[info.flow$Visit==1,ci], na.rm=T)\n fc41[ci] = mean(dat.flow[info.flow$Visit==4,ci], na.rm=T) / mean(dat.flow[info.flow$Visit==1,ci], na.rm=T)\n fc43[ci] = mean(dat.flow[info.flow$Visit==4,ci], na.rm=T) / mean(dat.flow[info.flow$Visit==3,ci], na.rm=T)\n wt31[[ci]] = wilcox.test(dat.flow[info.flow$Visit==1,ci], dat.flow[info.flow$Visit==3,ci], paired=T, exact = F)\n wt41[[ci]] = wilcox.test(dat.flow[info.flow$Visit==1 & info.flow$Subject!=8,ci], dat.flow[info.flow$Visit==4,ci], paired=T, exact = F)\n wt43[[ci]] = wilcox.test(dat.flow[info.flow$Visit==3 & info.flow$Subject!=8,ci], dat.flow[info.flow$Visit==4,ci], paired=T, exact = F)\n}\n\nwt31.p = map_dbl(wt31, function(x) x$p.value)\nwt31.padj = p.adjust(wt31.p, method = \"BH\")\ni.31 = names(wt31)[wt31.padj < 0.05]\n\nwt41.p = map_dbl(wt41, function(x) x$p.value)\nwt41.padj = p.adjust(wt41.p, method = \"BH\")\ni.41 = names(wt41)[wt41.padj < 0.05]\n\nwt43.p = map_dbl(wt43, function(x) x$p.value)\nwt43.padj = p.adjust(wt43.p, method = \"BH\")\ni.43 = names(wt43)[wt43.padj < 0.05]\n\ni.flow = unique(c(i.31, i.41, i.43))\ni.flow = i.flow[order(as.numeric(i.flow), na.last = T)]\n\n# save test results to file\n\nF.df.all = data.frame(ID = colnames(dat.flow), FC.3.v.1 = fc31, FC.4.v.3 = fc43, FC.4.v.1 = fc41,\n wP.3.v.1 = wt31.p, wP.4.v.3 = wt43.p, wP.4.v.1 = wt41.p,\n wQ.3.v.1 = wt31.padj, wQ.4.v.3 = wt43.padj, wQ.4.v.1 = wt41.padj)\nF.df.all = left_join(F.df.all, ann.flow, by=\"ID\")\nfwrite(F.df.all, \"results/flow_visits_comparison_all.txt\", sep=\"\\t\")\n\nF.df = F.df.all[match(i.flow, F.df.all$ID),]\nfwrite(F.df, \"results/flow_visits_comparison.txt\", sep=\"\\t\")\n\nF.df.V31 = data.frame(ID = colnames(dat.flow), FC = fc31, wP = wt31.p, wQ = wt31.padj) %>% \n dplyr::filter(wQ <= 0.05) %>% \n arrange(wP)\nfwrite(F.df.V31, \"results/flow_visits.3.v.1_comparison.txt\", sep=\"\\t\")\n\n\n# combine the data and filter by fold change\n\nDF = data.frame(ID = i.flow, V31 = fc31[i.flow], V43 = fc43[i.flow], V41 = fc41[i.flow])\nX = DF %>% inner_join(ann.flow, by=\"ID\") %>% \n mutate(Name = glue::glue(\"{Name} [{ID}]\")) %>% \n dplyr::select(-ID) %>% \n tibble::column_to_rownames(\"Name\") %>% \n data.matrix()\n\nX.log2 = log2(X)\nX.max = apply(X.log2, 1, function(x)max(abs(x),na.rm=T)) \nfc.th = 1.2\ni.sel = X.max > log2(fc.th)\niord = order(X.max[i.sel], decreasing = T)\nXX = X.log2[i.sel, ]#[i.ord,]\nrange(XX)\n\nP.df = data.frame(ID = i.flow, V31 = wt31.padj[i.flow], V43 = wt43.padj[i.flow], V41 = wt41.padj[i.flow])\nP = P.df %>% inner_join(ann.flow %>% dplyr::select(ID, Name), by=\"ID\") %>%\n mutate(Name = glue::glue(\"{Name} [{ID}]\")) %>% \n tibble::column_to_rownames(\"Name\") %>% \n dplyr::select(-ID) %>% \n data.matrix()\nPP = P[i.sel, ]\n\n\n# additional annotation of selected gates (with BH-adjusted Wilcoxon p-value < 0.05 and FC > 1.2)\n\nann.flow.selected = read_xlsx(fn.flow, sheet = \"selected gates\") %>% \n mutate(Name2 = glue::glue(\"{Name} [{ID}]\"))\nro = match(ann.flow.selected$ID, i.flow[i.sel])\nsplit.vec = ann.flow.selected$group %>% fct_inorder()\n\n# prepare data for heatmap\n\nmax.val = max(abs(X.log2))\nfnt.sz = 12\n\ncolnames(XX) = c(\"V3 / V1\", \"V4 / V3\", \"V4 / V1\")\nXX = XX[ro,]\nPP = PP[ro,]\n\nrownames(XX) = ann.flow.selected$Name2\nhm = Heatmap(XX, name = \"Fold Change, log2\", cluster_columns = F, cluster_rows = F,\n split = split.vec, \n gap = unit(2,\"mm\"),\n row_names_max_width = max_text_width( rownames(XX), gp = gpar(fontsize = 12) ),\n column_names_side = \"top\",\n col = colorRamp2(c(-1.4,0,1.4), c(\"blue\", \"white\", \"red\")),\n cell_fun = function(j, i, x, y, width, height, fill) {\n if(PP[i, j] < 0.001) {\n grid.text(\"***\", x, y, gp = gpar(fontsize = fnt.sz))\n } else if(PP[i, j] < 0.01) {\n grid.text(\"**\", x, y, gp = gpar(fontsize = fnt.sz))\n } else if(PP[i, j] < 0.05) {\n grid.text(\"*\", x, y, gp = gpar(fontsize = fnt.sz))\n }\n },\n heatmap_legend_param = list(color_bar = \"continuous\", \n legend_width = unit(3,\"cm\"),\n legend_direction = \"horizontal\"))\n\nfn.hm = glue::glue(\"figures/Flow_FC_heatmap_fc.th.{fc.th}\")\npng(paste0(fn.hm, \".png\"), w=450, h=700)\ndraw(hm, heatmap_legend_side = \"bottom\")\ndev.off()\npdf(paste0(fn.hm, \".pdf\"), w=6, h=10)\ndraw(hm, heatmap_legend_side = \"bottom\")\ndev.off()\n\n\n# plot individual samples -------------------------------------------------\n\npop.use = i.flow[i.sel]\npop.name = paste(ann.flow.selected$subset, ann.flow.selected$ID, sep=\", population \")[match(pop.use, ann.flow.selected$ID)]\nDF2 = df.flow %>%\n dplyr::select(Subject, Visit, one_of(pop.use)) %>%\n gather(\"ID\",\"freq\", -Subject, -Visit) %>%\n arrange(Visit, Subject) %>%\n mutate(Sample = paste(Subject, Visit, sep=\"_\") %>% fct_inorder()) %>% \n mutate(Subject = fct_inorder(as.character(Subject))) %>%\n mutate(ID = factor(ID, levels = pop.use)) %>% \n group_by(ID, Subject) %>% \n mutate(FC = freq / freq[Visit==1]) %>% \n ungroup()\n\ndir.create(\"figures/flow_profiles\", showWarnings = F)\n\nfor(k in seq_along(pop.use)) {\n cat(pop.use[k],\" \")\n ggplot(DF2 %>% dplyr::filter(ID==pop.use[k]), aes(Visit, FC, group=Subject, col=Subject)) +\n geom_path(show.legend = F) +\n geom_hline(yintercept = 1, col=\"black\") +\n # scale_y_continuous(trans = \"log2\") +\n xlab(\"Study visit\") +\n ylab(\"Fold change in percent\\nof parent population\") +\n ggtitle(pop.name[k]) +\n theme_bw()\n ggsave(glue::glue(\"figures/flow_profiles/flow_profile_{pop.use[k]}.png\"), w=4,h=3)\n}\n\n", "meta": {"hexsha": "7622ff015bf147f4df69d3948cc3b06e6aeb6bc8", "size": 6773, "ext": "r", "lang": "R", "max_stars_repo_path": "R/flow_analysis.r", "max_stars_repo_name": "kotliary/pregnancy", "max_stars_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-03T12:52:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-03T12:52:51.000Z", "max_issues_repo_path": "R/flow_analysis.r", "max_issues_repo_name": "kotliary/pregnancy", "max_issues_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/flow_analysis.r", "max_forks_repo_name": "kotliary/pregnancy", "max_forks_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.4198895028, "max_line_length": 136, "alphanum_fraction": 0.6022442049, "num_tokens": 2265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521105, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3289596207350076}} {"text": "#THIS SCRIPT GENERATES THE PLOT FOR THE DIAGONAL K=1 IN THE CONFIGURATION TOTAL_TRIPLE_IN_WINDOW/EW PLOTTING THE FOUR BASELINES IN THE SAME FIGURE IN 4 DIFFERENT PLOTS\n#IT USES LOG SCALING\n\nlog_plot_dash <- function(x,y,sdx,sdy, col, pch){\n \n lx <- log(x)\n ly <- log(y)\n \n par(new=TRUE)\n points(x = lx, y = ly, col=col, type = \"p\", pch=pch)\n arrows(lx, log(y-sdy),lx,log(y+sdy),, length=0.05, angle=90, code=3, col=\"grey55\")\n arrows(log(x), ly,log(x+sdx),ly, length=0.05, angle=90, code=3, col=\"gray55\")\n if(log(sdx) 0.05: N.S. \\np < 0.05: * \\np < 0.01: ** \\np < 0.001: ***\") +\n coord_cartesian(ylim=c(0, 2)) +\n theme_classic() +\n theme(plot.title = element_text(face=\"bold\", size=13, hjust = 0.5)) +\n theme(plot.subtitle = element_text(face = \"italic\", size = 10, hjust = 0.5)) +\n theme(plot.caption = element_text(face = \"italic\", size = 8, hjust = 0.0)) +\n theme(axis.title = element_text(size = 12)) +\n theme(axis.text.x = element_text(size = 14, color = \"Black\")) +\n theme(axis.text.y = element_text(size = 14, color = \"Black\"))\nrm(m1)", "meta": {"hexsha": "82732b643987ea219fd3197ed0788370097b4a55", "size": 992, "ext": "r", "lang": "R", "max_stars_repo_path": "R/solutions/ex11.r", "max_stars_repo_name": "TU-Coding-Outreach-Group/intro-to-coding-2021", "max_stars_repo_head_hexsha": "505d2f348127f1acb08ea4ee4f2ee40b8e9fe727", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-12-13T16:29:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T14:44:19.000Z", "max_issues_repo_path": "R/solutions/ex11.r", "max_issues_repo_name": "TU-Coding-Outreach-Group/intro-to-coding-2021", "max_issues_repo_head_hexsha": "505d2f348127f1acb08ea4ee4f2ee40b8e9fe727", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2021-12-15T16:43:48.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T20:52:36.000Z", "max_forks_repo_path": "R/solutions/ex11.r", "max_forks_repo_name": "TU-Coding-Outreach-Group/intro-to-coding-2021", "max_forks_repo_head_hexsha": "505d2f348127f1acb08ea4ee4f2ee40b8e9fe727", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 62.0, "max_line_length": 92, "alphanum_fraction": 0.5625, "num_tokens": 277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.32879269837837494}} {"text": "\\name{hc_rect-dispatch}\n\\alias{hc_rect}\n\\title{\nMethod dispatch page for hc_rect\n}\n\\description{\nMethod dispatch page for \\code{hc_rect}.\n}\n\\section{Dispatch}{\n\\code{hc_rect} can be dispatched on following classes:\n\n\\itemize{\n\\item \\code{\\link{hc_rect,HilbertCurve-method}}, \\code{\\link{HilbertCurve-class}} class method\n\\item \\code{\\link{hc_rect,GenomicHilbertCurve-method}}, \\code{\\link{GenomicHilbertCurve-class}} class method\n}\n}\n\\examples{\n# no example\nNULL\n\n\n}\n", "meta": {"hexsha": "9ab029fb8f679be6931d3b8f8c7b34510a28f8b2", "size": 467, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/hc_rect-dispatch.rd", "max_stars_repo_name": "jokergoo/HilbertCurve", "max_stars_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 37, "max_stars_repo_stars_event_min_datetime": "2016-02-22T16:46:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T09:35:43.000Z", "max_issues_repo_path": "man/hc_rect-dispatch.rd", "max_issues_repo_name": "jokergoo/HilbertCurve", "max_issues_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T08:29:21.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T09:44:53.000Z", "max_forks_repo_path": "man/hc_rect-dispatch.rd", "max_forks_repo_name": "jokergoo/HilbertCurve", "max_forks_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-04-22T10:44:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T07:48:16.000Z", "avg_line_length": 20.3043478261, "max_line_length": 108, "alphanum_fraction": 0.7580299786, "num_tokens": 136, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.32879269837837494}} {"text": "source(\"R/functions/get_score.r\")\n\nfn.si = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_sample_info_2_sle_lowDA.txt\")\ninfo = fread(fn.si, data.table=F)\n\nfn.ge = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_ge_matrix_gene_sle_lowDA.txt\")\ndat = fread(fn.ge, data.table = F) %>% \n tibble::remove_rownames() %>% tibble::column_to_rownames(\"gene\") %>% \n data.matrix()\n\n\nfn.sig = file.path(PROJECT_DIR, \"generated_data\", \"signatures\", \"CD38_ge_sig.txt\")\ncd38.genes = fread(fn.sig, header = F) %>% unlist(use.names=F)\n\ngi = toupper(rownames(dat)) %in% toupper(cd38.genes)\nsum(gi)\n\ndf.cd38.score = cbind(\n dplyr::select(info, SUBJECT, VISIT, CUMULATIVE_TIME, SAMPLE_NAME), \n data.frame(CD38_score=get_score(dat[gi,]))\n )\n\nfn.sig = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_lowDA_cd38_ge_sig_score.txt\")\nfwrite(df.cd38.score, fn.sig, sep=\"\\t\", quote=F)\n\ndf.cd38.score.subj = df.cd38.score %>%\n dplyr::select(SUBJECT, CD38_score) %>% \n mutate(SUBJECT = factor(SUBJECT,\n levels=unique(SUBJECT[order(as.numeric(sub(\"SLE-\",\"\",SUBJECT)))]))) %>% \n group_by(SUBJECT) %>%\n dplyr::summarise(CD38_score_mean=mean(CD38_score, na.rm=T)) %>%\n ungroup()\n\nfn.sig.subj = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_lowDA_cd38_ge_sig_score_subjects.txt\")\nfwrite(df.cd38.score.subj, fn.sig.subj, sep=\"\\t\", quote=F)\n", "meta": {"hexsha": "06b5a278f49c394318a799c2865eaabbe5155a57", "size": 1406, "ext": "r", "lang": "R", "max_stars_repo_path": "R/sle_signature_analysis/sle_lowDA_cd38_score.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/sle_signature_analysis/sle_lowDA_cd38_score.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/sle_signature_analysis/sle_lowDA_cd38_score.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 39.0555555556, "max_line_length": 105, "alphanum_fraction": 0.6699857752, "num_tokens": 439, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3287926903216}} {"text": "ts_dat1 <- read.csv(\"~/Pending/ss3sim-ms/final_results_ts-run1.csv\")\nts_dat2 <- read.csv(\"~/Pending/ss3sim-ms/final_results_ts.csv\")\nts_dat <- rbind(ts_dat1, ts_dat2)\n#ts_dat <- subset(ts_dat, replicate %in% 1:50)\nscalar_dat1 <- read.csv(\"~/Pending/ss3sim-ms/final_results_scalar-run1.csv\")\nscalar_dat2 <- read.csv(\"~/Pending/ss3sim-ms/final_results_scalar.csv\")\nscalar_dat1$StartTime <- NULL\nscalar_dat2$StartTime <- NULL\nscalar_dat1$EndTime <- NULL\nscalar_dat2$EndTime <- NULL\nscalar_dat <- rbind(scalar_dat1, scalar_dat2)\n#scalar_dat <- subset(scalar_dat, replicate %in% 1:50)\n#save(ts_dat, file = \"ts_dat.rda\")\n#save(scalar_dat, file = \"scalar_dat.rda\")\n\n\n\n#load(\"../../vignettes/ts_dat.rda\")\n#load(\"../../vignettes/scalar_dat.rda\")\nts_dat <- subset(ts_dat, D %in% c(\"D1\", \"D2\"))\nscalar_dat <- subset(scalar_dat, D %in% c(\"D1\", \"D2\"))\n\nf_merge <- subset(ts_dat, year == max(ts_dat$year))[,c(\"scenario\", \"replicate\", \"F_om\", \"F_em\")]\nscalar_dat <- join(scalar_dat, f_merge)\n\n##\n\nscalar_dat <- transform(scalar_dat,\n steep = (SR_BH_steep_om - SR_BH_steep_em)/SR_BH_steep_om,\n logR0 = (SR_LN_R0_om - SR_LN_R0_em)/SR_LN_R0_om,\n depletion = (depletion_om - depletion_em)/depletion_om,\n SSB_MSY = (SSB_MSY_em - SSB_MSY_om)/SSB_MSY_om,\n SR_sigmaR = (SR_sigmaR_em - SR_sigmaR_om)/SR_sigmaR_om,\n NatM =\n (NatM_p_1_Fem_GP_1_em - NatM_p_1_Fem_GP_1_om)/\n NatM_p_1_Fem_GP_1_om,\n Fmort = (F_em - F_om) / F_om)\n\n# add a tiny bit of noise for violin plotting:\nN <- length(scalar_dat[scalar_dat$E == \"E0\", \"NatM\"])\n#scalar_dat[scalar_dat$E == \"E0\", \"NatM\"] <- rnorm(N, 0, 0.0001)\nscalar_dat[scalar_dat$E == \"E0\", \"NatM\"] <- NA\n\n\nts_dat <- transform(ts_dat,\n SpawnBio = (SpawnBio_em - SpawnBio_om)/SpawnBio_om,\n Recruit_0 = (Recruit_0_em - Recruit_0_om)/Recruit_0_om)\n\n#ts_dat <- merge(ts_dat, scalar_dat[,c(\"scenario\", \"replicate\",\n #\"max_grad\")])\n\nscalar_dat_det <- subset(scalar_dat, E %in% c(\"E100\", \"E101\"))\nscalar_dat_sto <- subset(scalar_dat, E %in% c(\"E0\", \"E1\"))\nts_dat_det <- subset(ts_dat, E %in% c(\"E100\", \"E101\"))\nts_dat_sto <- subset(ts_dat, E %in% c(\"E0\", \"E1\"))\nts_dat_sto <- droplevels(ts_dat_sto)\nscalar_dat_sto <- droplevels(scalar_dat_sto)\n\nlibrary(plyr)\nquant_dat <- ddply(ts_dat_sto, c(\"D\", \"E\", \"year\"), summarize,\n q05 = quantile(SpawnBio, probs = 0.05),\n q25 = quantile(SpawnBio, probs = 0.25),\n q50 = quantile(SpawnBio, probs = 0.50),\n q75 = quantile(SpawnBio, probs = 0.75),\n q95 = quantile(SpawnBio, probs = 0.95)\n)\n\ncols <- RColorBrewer::brewer.pal(8, \"Blues\")\nlevels(quant_dat$D) <- c(\"atop(sigma[survey]==0.1,\n Increased~survey~effort)\", \"atop(sigma[survey]==0.4,\n Decreased~survey~effort)\")\nlevels(quant_dat$E) <- c(\"Fixed~italic('M')[historical]\",\n \"Estimated~italic('M')\")\n\nlibrary(ggplot2)\np <- ggplot(quant_dat) +\n geom_ribbon(aes(year, ymax = q05, ymin = q95), fill = cols[4]) +\n geom_ribbon(aes(year, ymax = q25, ymin = q75), fill = cols[6]) +\n geom_line(aes(year, q50), colour = cols[8]) +\n facet_grid(D~E, labeller = label_parsed) +\n theme_bw() +\n theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n strip.background = element_rect(fill = NA, linetype = 0),\n axis.text = element_text(colour = \"grey50\"),\n axis.title = element_text(colour = \"grey30\"),\n axis.ticks = element_line(colour = \"grey50\"),\n strip.text = element_text(colour = \"grey30\")\n #plot.margin = grid::unit(c(.1,.1,.1,.1), \"cm\"),\n #panel.margin = grid::unit(0, \"cm\")\n ) +\n geom_hline(yintercept = 0, linetype = 3, colour = \"#FFFFFF95\") +\n ylab(\"Relative error in SSB\") + xlab(\"Year\")\n\nggsave(p, file = \"spawnb-re-ts.pdf\", width = 6, height = 4.25)\n\nlevels(scalar_dat_sto$D) <- c(\"atop(sigma[survey]==0.1,\n Increased~survey~effort)\", \"atop(sigma[survey]==0.4,\n Decreased~survey~effort)\")\nlevels(scalar_dat_sto$E) <- c(\"Fixed~italic('M')[historical]\",\n \"Estimated~italic('M')\")\n\np <- ggplot(scalar_dat_sto, aes(x = 0, y = depletion),\n labeller = label_parsed) +\n geom_violin(fill = cols[4], colour = cols[6]) +\n geom_jitter(position = position_jitter(height = 0, width = 0.05), alpha = 0.6, colour = cols[8], pch = 21, size = 1.5) +\n facet_grid(D~E, labeller = label_parsed) +\n geom_hline(yintercept = 0, linetype = 3) +\n theme_bw() +\n theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n strip.background = element_rect(fill = NA, linetype = 0),\n axis.text = element_text(colour = \"grey50\"),\n axis.title = element_text(colour = \"grey30\"),\n axis.ticks = element_line(colour = \"grey50\"),\n strip.text = element_text(colour = \"grey30\"),\n axis.ticks.x = element_blank(),\n axis.text.x = element_blank()\n ) +\n ylab(\"Relative error in depletion\") + xlab(\"\")\n\n\nggsave(p, file = \"depletion-re-scalar.pdf\", width = 6, height = 4.25)\n\nscalar_dat_sto <- subset(scalar_dat, E %in% c(\"E0\", \"E1\"))\nscalar_dat_long <- reshape2::melt(scalar_dat_sto[,c(\"scenario\", \"D\",\n \"E\", \"replicate\", \"max_grad\", \"depletion\", \"NatM\", \"SSB_MSY\", \"Fmort\")],\n id.vars = c(\"scenario\", \"D\", \"E\", \"replicate\", \"max_grad\"))\nscalar_dat_long <- plyr::rename(scalar_dat_long,\n c(\"value\" = \"relative_error\"))\n\nscalar_dat_long <- droplevels(scalar_dat_long)\nlevels(scalar_dat_long$D) <- c(\"High\", \"Low\")\nlevels(quant_dat$E) <- c(\"Fixed~italic('M')[historical]\", \"Estimated~italic('M')\")\n#levels(scalar_dat_long$variable) <- c(\"Depletion\", \"italic('M')\")\nlevels(scalar_dat_long$variable) <- c(\"Depletion\", \"M\", \"SSB[MSY]\", \"F\")\n\n\n\n# p <- ggplot(scalar_dat_long, aes(x = D, y = relative_error),\n# labeller = label_parsed) +\n# geom_violin(fill = cols[4], colour = cols[6]) +\n# facet_grid(variable~E, labeller = label_parsed, scales = \"free_y\") +\n# geom_jitter(position = position_jitter(height = 0, width = 0.05), alpha = 0.6, colour = cols[8], pch = 21, size = 1.5) +\n# geom_hline(yintercept = 0, linetype = 3) +\n# theme_bw() +\n# theme(panel.grid.major = element_blank(),\n# panel.grid.minor = element_blank(),\n# strip.background = element_rect(fill = NA, linetype = 0),\n# axis.text = element_text(colour = \"grey50\"),\n# axis.title = element_text(colour = \"grey30\"),\n# axis.ticks = element_line(colour = \"grey50\"),\n# strip.text = element_text(colour = \"grey30\")\n# ) +\n# ylab(\"Relative error\") + xlab(\"Survey effort\")\n\n#ggsave(p, file = \"depletion-re-scalar.pdf\", width = 4, height = 4.25)\n\nlevels(scalar_dat_long$D) <- c(\"atop(sigma[survey]==0.1,\n Increased~survey~effort)\", \"atop(sigma[survey]==0.4,\n Decreased~survey~effort)\")\nlevels(scalar_dat_long$E) <- c(\"Fixed~italic('M')[historical]\", \"Estimated~italic('M')\")\n\np <- ggplot(scalar_dat_long, aes(x = variable, y = relative_error)) +\n geom_boxplot(aes(fill = variable), colour = \"grey50\", outlier.colour = \"grey50\", notch = TRUE, outlier.size = 1.5) +\n facet_grid(D~E, labeller = label_parsed)+\n geom_hline(yintercept = 0, linetype = 3) +\n theme_bw() +\n theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n strip.background = element_rect(fill = NA, linetype = 0),\n axis.text = element_text(colour = \"grey50\"),\n axis.title = element_text(colour = \"grey30\"),\n axis.ticks = element_line(colour = \"grey50\"),\n strip.text = element_text(colour = \"grey30\"),\n legend.position = \"none\"\n ) +\n\n ylab(\"Relative error\") + xlab(\"\")\n #\n\nggsave(p, file = \"scalar-boxplots.pdf\", width = 6, height = 4.25)\n\n#levels(scalar_dat_long$scenario) <- c(\"High survey effort, Fixed M\", \"High survey effort, Estimate M\", \"Low survey effort, Fixed M\", \"Low survey effort, Estimated M\" )\n#levels(scalar_dat_long$scenario) <- c(\"s_surv = 0.1, Fixed M\", \"s_surv = 0.1, Estimate M\", \"s_surv = 0.4, Fixed M\", \"s_surv = 0.4, Estimated M\" )\n\n#### put the same variable in the same panel:\np2 <- ggplot(scalar_dat_long, aes(x = scenario, y = relative_error)) +\n #geom_boxplot(aes(fill = variable), colour = \"grey50\", outlier.colour = \"grey50\", notch = TRUE, outlier.size = 1.5) +\n geom_violin(fill = cols[4], colour = cols[7]) +\n geom_jitter(position = position_jitter(height = 0, width = 0.05), alpha = 0.4, colour = cols[7], pch = 20, size = 1) +\n facet_grid(~variable, labeller = label_parsed, scales = \"fixed\")+\n geom_hline(yintercept = 0, linetype = 3) +\n theme_bw() +\n theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n strip.background = element_rect(fill = NA, linetype = 0),\n axis.text = element_text(colour = \"grey50\"),\n axis.text.x = element_text(angle = 90, hjust = 1),\n axis.title = element_text(colour = \"grey30\"),\n axis.ticks = element_line(colour = \"grey50\"),\n strip.text = element_text(colour = \"grey30\"),\n legend.position = \"none\"\n ) +\n\n ylab(\"Relative error\") + xlab(\"\")\n print(p)\n #\n\n#ggsave(p, file = \"scalar-boxplots.pdf\", width = 6, height = 4.25)\n\nlibrary(gridExtra)\np3 <- grid.arrange(p1, p2, ncol = 1, heights = c(1, 1))\nprint(p3)\n", "meta": {"hexsha": "3beedc085252c852ded05446a1fa641dd68a96ab", "size": 8850, "ext": "r", "lang": "R", "max_stars_repo_path": "make-eg-fig-ggplot.r", "max_stars_repo_name": "ss3sim/ss3sim_andersonetal", "max_stars_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "make-eg-fig-ggplot.r", "max_issues_repo_name": "ss3sim/ss3sim_andersonetal", "max_issues_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "make-eg-fig-ggplot.r", "max_forks_repo_name": "ss3sim/ss3sim_andersonetal", "max_forks_repo_head_hexsha": "a20ab92b98f0aa0a2ed4fed6ff93d4b764471f4c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.1627906977, "max_line_length": 168, "alphanum_fraction": 0.6649717514, "num_tokens": 2922, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040617, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.32870197393392786}} {"text": "#!/usr/bin/env Rscript\n\nsource(\"/tmp/class-libs.R\")\n\nclass_name = \"2022 Spring Stat 20\"\n\nclass_libs = c(\n \"fivethirtyeight\", \"0.6.2\",\n \"gapminder\", \"0.3.0\",\n \"googlesheets4\", \"1.0.0\",\n \"infer\", \"1.0.0\",\n \"janitor\", \"2.1.0\",\n \"openintro\", \"2.2.0\",\n \"pagedown\", \"0.16\",\n \"palmerpenguins\", \"0.1.0\",\n \"patchwork\", \"1.1.1\",\n \"patchwork\", \"1.1.1\",\n \"showtext\", \"0.9-4\",\n \"swirl\", \"2.4.5\",\n \"tidycensus\", \"1.1\",\n \"tidymodels\", \"0.1.4\",\n \"tigris\", \"1.5\",\n \"unvotes\", \"0.3.0\",\n \"xaringanthemer\", \"0.4.1\"\n)\n\nclass_libs_install_version(class_name, class_libs)\n\ndevtools::install_github(\"mdbeckman/dcData\", ref=\"56888a6\")\ndevtools::install_github(\"hadley/emo@3f03b11\")\ndevtools::install_github(\"andrewpbray/boxofdata@8afd934\")\n\nfile.symlink(\"/opt/shared/stat20/stat20data\", \"/usr/local/lib/R/site-library/stat20data\")\n\nprint(paste(\"Done installing packages for\",class_name))\n", "meta": {"hexsha": "8a0a890c099027ee79c217a74be59da3f8f5bc10", "size": 914, "ext": "r", "lang": "R", "max_stars_repo_path": "deployments/stat20/image/r-packages/2022-spring-stat-20.r", "max_stars_repo_name": "sean-morris/datahub", "max_stars_repo_head_hexsha": "6e62a50b8e6610e24c424bcd3704e09bed5adb64", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "deployments/stat20/image/r-packages/2022-spring-stat-20.r", "max_issues_repo_name": "sean-morris/datahub", "max_issues_repo_head_hexsha": "6e62a50b8e6610e24c424bcd3704e09bed5adb64", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "deployments/stat20/image/r-packages/2022-spring-stat-20.r", "max_forks_repo_name": "sean-morris/datahub", "max_forks_repo_head_hexsha": "6e62a50b8e6610e24c424bcd3704e09bed5adb64", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.3888888889, "max_line_length": 89, "alphanum_fraction": 0.6269146608, "num_tokens": 350, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6187804337438502, "lm_q1q2_score": 0.32870196646501176}} {"text": "writePlotToFile <- function(x, y, xLabel, yLabel, pathOutputFile){\n\tpng(filename= pathOutputFile);\n\tplot(x, y, xlab=xLabel, ylab=yLabel);\n\t# This line draws a vertical,red line at position 5 on the x-axis\n\t# abline(v=5, col=\"red\")\n}\n\nconvertMsToMin <- function(ms){\n\treturn(ms/60000)\n}\n\nargs <- commandArgs(trailingOnly = TRUE)\nargLength <- length(args)\nif(argLength != 2){\n \tcat(\"Usage: script \\n\")\n\tquit()\n}\n\npathInputFile = args[1];\npathOutputFile = args[2];\n\noperations <- read.csv(file=pathInputFile,head=TRUE,sep=\",\")\ntimepointsInMin <- convertMsToMin(operations$ms)\nwritePlotToFile(timepointsInMin, operations$latency, \"Minuten\", \"Latency (\u00b5s)\", pathOutputFile)", "meta": {"hexsha": "dc9629f26b4ced7b21d0633ba49418fee11ba1f3", "size": 692, "ext": "r", "lang": "R", "max_stars_repo_path": "front_end/plot/plot_latency.r", "max_stars_repo_name": "arnaudsjs/YCSB-1", "max_stars_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "front_end/plot/plot_latency.r", "max_issues_repo_name": "arnaudsjs/YCSB-1", "max_issues_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "front_end/plot/plot_latency.r", "max_forks_repo_name": "arnaudsjs/YCSB-1", "max_forks_repo_head_hexsha": "dc557d209244df72d68c9cb0a048d54e7bd72637", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8333333333, "max_line_length": 95, "alphanum_fraction": 0.7268786127, "num_tokens": 207, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704796847395, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.32861613396965667}} {"text": "# random.org package tryout\nsetwd(\"~/3-Personal/blog/posts\")\nrm(list=ls())\nlibrary(random)\nlibrary(ggplot2)\nlibrary(scales)\n?random\n\nload(\"rndorg.Rdata\")\n\n# pull down the whole quotum\nwhile (quotaCheck() == TRUE){\n rndorg <- rbind(rndorg,randomNumbers(n=10000, min = 0, max = 9))\n}\n\nsave(rndorg, file = \"rndorg.Rdata\")\n\nrndorgvec <- as.vector(rndorg)\nrndorgraw <- as.raw(rndorg)\nrndorggzip <- memCompress(rndorgraw, type = \"gzip\")\nrndorgbzip <- memCompress(rndorgraw, type = \"bzip2\")\nrndorgxz <- memCompress(rndorgraw, type = \"xz\")\n\nrunifvec <- as.integer(ceiling(runif(length(rndorgvec), min = -1, max = 9)))\nrunifraw <- as.raw(runifvec)\nrunifgzip <- memCompress(runifraw, type = \"gzip\")\nrunifbzip <- memCompress(runifraw, type = \"bzip2\")\nrunifxz <- memCompress(runifraw, type = \"xz\")\n\n\ndf <- data.frame(Source=rep(c(\"Random.org\", \"stats::runif\"), each=3),\n value=c(length(rndorggzip),\n length(rndorgbzip),\n length(rndorgxz),\n length(runifgzip),\n length(runifbzip),\n length(runifxz)),\n type=rep(c(\"gzip\",\"bzip\",\"xz\"),2))\n\n\np <- ggplot(data=df, mapping=aes(x=type, y=value, fill=Source))+ \n geom_bar(stat = \"identity\", position = \"dodge\") + \n scale_y_continuous(limits=c(min(df$value)-1000,max(df$value)+1000), oob = rescale_none) + \n labs(y = \"Compressed size\", x = \"Compression type\", title = paste(\"Compressing\", length(runifvec), \"integers\"))\n", "meta": {"hexsha": "0de5da1eb2a066859c0b82e721085519ae36f3f9", "size": 1503, "ext": "r", "lang": "R", "max_stars_repo_path": "posts/rndorg_download.r", "max_stars_repo_name": "vankesteren/blog", "max_stars_repo_head_hexsha": "7eac26b06f4cb73a4685d2e15a39d5c359fda3f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "posts/rndorg_download.r", "max_issues_repo_name": "vankesteren/blog", "max_issues_repo_head_hexsha": "7eac26b06f4cb73a4685d2e15a39d5c359fda3f3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "posts/rndorg_download.r", "max_forks_repo_name": "vankesteren/blog", "max_forks_repo_head_hexsha": "7eac26b06f4cb73a4685d2e15a39d5c359fda3f3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4, "max_line_length": 113, "alphanum_fraction": 0.624085163, "num_tokens": 447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32861613322150723}} {"text": "#' Matrix of SNP 'beta' values\n#'\n#' @param qc.objects List of objects obtained from \\code{\\link{meffil.qc}()}\n#' or \\code{\\link{meffil.create.qc.object}()}.\n#' \n#' @export \nmeffil.snp.betas <- function(qc.objects) {\n stopifnot(sapply(qc.objects, is.qc.object))\n sapply(qc.objects, function(object) object$snp.betas)\n}\n\n#' Obtain the list of identifiers for the SNPs on the microarray.\n#'\n#' @param featureset Name from \\code{\\link{meffil.list.featuresets}()} (Default: \"450k\").\n#' @export\nmeffil.snp.names <- function(featureset=\"450k\") {\n features <- meffil.featureset(featureset)\n features$name[which(features$target == \"snp\")]\n}\n\nextract.snp.betas <- function(rg, probes, verbose=F) {\n msg(verbose=verbose)\n stopifnot(is.rg(rg))\n \n probes.M.R <- probes[which(probes$target == \"M-snp\" & probes$dye == \"R\"),]\n probes.M.G <- probes[which(probes$target == \"M-snp\" & probes$dye == \"G\"),]\n probes.U.R <- probes[which(probes$target == \"U-snp\" & probes$dye == \"R\"),]\n probes.U.G <- probes[which(probes$target == \"U-snp\" & probes$dye == \"G\"),]\n\n stopifnot(all(c(probes.M.R$address, probes.U.R$address) %in% rownames(rg$R)))\n stopifnot(all(c(probes.M.G$address, probes.U.G$address) %in% rownames(rg$G)))\n \n M <- c(rg$R[probes.M.R$address,\"Mean\"],\n rg$G[probes.M.G$address,\"Mean\"])\n U <- c(rg$R[probes.U.R$address,\"Mean\"],\n rg$G[probes.U.G$address,\"Mean\"])\n\n names(M) <- c(probes.M.R$name, probes.M.G$name)\n names(U) <- c(probes.U.R$name, probes.U.G$name)\n\n get.beta(M,U[names(M)])\n}\n\n", "meta": {"hexsha": "14bb2aabc58edf8134c295fcc7511bc0705f48dc", "size": 1555, "ext": "r", "lang": "R", "max_stars_repo_path": "R/snp-betas.r", "max_stars_repo_name": "RichardJActon/meffil", "max_stars_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": 33, "max_stars_repo_stars_event_min_datetime": "2015-04-21T18:35:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-21T10:48:31.000Z", "max_issues_repo_path": "R/snp-betas.r", "max_issues_repo_name": "RichardJActon/meffil", "max_issues_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2015-02-17T11:13:33.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-28T21:48:56.000Z", "max_forks_repo_path": "R/snp-betas.r", "max_forks_repo_name": "RichardJActon/meffil", "max_forks_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-11-17T22:40:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T16:10:42.000Z", "avg_line_length": 35.3409090909, "max_line_length": 89, "alphanum_fraction": 0.6276527331, "num_tokens": 495, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5544704649604274, "lm_q1q2_score": 0.3286161252430487}} {"text": "#====================================================================\r\n# Sarah Middleton\r\n# \r\n# graph_gc.r\r\n#====================================================================\r\n\r\n#get command line args\r\nargs<-commandArgs(TRUE) \r\ninFile <- args[1]\r\n\r\n# graph gc content\r\nlibrary('ggplot2')\r\ntab <- read.table(inFile, header=T)\r\n\r\npng('gc_content_dist.png')\r\nggplot(tab, aes(x=GC)) + geom_density() + ggtitle(\"Distribution of GC content across sequences\") \r\ndev.off()\r\n\r\n", "meta": {"hexsha": "89428a1f7c0bb30b6e2037a3eebc0c9cdf8fb4c7", "size": 471, "ext": "r", "lang": "R", "max_stars_repo_path": "lab7/graph_gc.r", "max_stars_repo_name": "sarahmid/programming-bootcamp", "max_stars_repo_head_hexsha": "6dc6ab0ecfac662eb9676956ab0ae799953e88ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-06T03:29:24.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-06T03:29:24.000Z", "max_issues_repo_path": "lab7/graph_gc.r", "max_issues_repo_name": "sarahmid/programming-bootcamp", "max_issues_repo_head_hexsha": "6dc6ab0ecfac662eb9676956ab0ae799953e88ae", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lab7/graph_gc.r", "max_forks_repo_name": "sarahmid/programming-bootcamp", "max_forks_repo_head_hexsha": "6dc6ab0ecfac662eb9676956ab0ae799953e88ae", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.7894736842, "max_line_length": 98, "alphanum_fraction": 0.4734607219, "num_tokens": 92, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5544704649604274, "lm_q1q2_score": 0.3286161252430486}} {"text": "\n\nmy.plotcorr <- function (corr, outline = FALSE, col = \"grey\", upper.panel = c(\"ellipse\", \"number\", \"none\"), lower.panel = c(\"ellipse\", \"number\", \"none\"), diag = c(\"none\", \"ellipse\", \"number\"), digits = 2, bty = \"n\", axes = FALSE, xlab = \"\", ylab = \"\", asp = 1, cex.lab = par(\"cex.lab\"), cex = 0.75 * par(\"cex\"), mar = 0.1 + c(2, 2, 4, 2), ...)\n{\n# this is a modified version of the plotcorr function from the ellipse package\n# this prints numbers and ellipses on the same plot but upper.panel and lower.panel changes what is displayed\n# diag now specifies what to put in the diagonal (numbers, ellipses, nothing)\n# digits specifies the number of digits after the . to round to\n# unlike the original, this function will always print x_i by x_i correlation rather than being able to drop it\n# modified by Esteban Buz\n pdf(\"teste.pdf\")\n if (!require('ellipse', quietly = TRUE, character = TRUE)) {\n stop(\"Need the ellipse library\")\n }\n savepar <- par(pty = \"s\", mar = mar)\n on.exit(par(savepar))\n if (is.null(corr))\n return(invisible())\n if ((!is.matrix(corr)) || (round(min(corr, na.rm = TRUE), 6) < -1) || (round(max(corr, na.rm = TRUE), 6) > 1))\n stop(\"Need a correlation matrix\")\n plot.new()\n par(new = TRUE)\n rowdim <- dim(corr)[1]\n coldim <- dim(corr)[2]\n rowlabs <- c(TeX('$l_1$'),TeX('$l_2$'),TeX('$l_3$'),TeX('$l_4$'), TeX('$l_5$')) \n collabs <- c(TeX('$\\\\alpha_1$'), TeX('$\\\\alpha_2$'),TeX('$\\\\alpha_3$'),TeX('$\\\\alpha_4$'),TeX('$\\\\alpha_5$')) \n if (is.null(rowlabs))\n rowlabs <- 1:rowdim\n if (is.null(collabs))\n collabs <- 1:coldim\n # rowlabs <- as.character(rowlabs)\n # print(rowlabs)\n # collabs <- as.character(collabs)\n col <- rep(col, length = length(corr))\n dim(col) <- dim(corr)\n upper.panel <- match.arg(upper.panel)\n lower.panel <- match.arg(lower.panel)\n diag <- match.arg(diag)\n cols <- 1:coldim\n rows <- 1:rowdim\n maxdim <- max(length(rows), length(cols))\n plt <- par(\"plt\")\n xlabwidth <- max(strwidth(rowlabs[rows], units = \"figure\", cex = cex.lab))/(plt[2] - plt[1])\n xlabwidth <- xlabwidth * maxdim/(1 - xlabwidth)\n ylabwidth <- max(strwidth(collabs[cols], units = \"figure\", cex = cex.lab))/(plt[4] - plt[3])\n ylabwidth <- ylabwidth * maxdim/(1 - ylabwidth)\n plot(c(-xlabwidth - 0.5, maxdim + 0.5), c(0.5, maxdim + 1 + ylabwidth), type = \"n\", bty = bty, axes = axes, xlab = \"\", ylab = \"\", asp = asp, cex.lab = cex.lab, ...)\n text(rep(0, length(rows)), length(rows):1, labels = rowlabs[rows], adj = 1, cex = cex.lab)\n text(cols, rep(length(rows) + 1, length(cols)), labels = collabs[cols], srt = 90, adj = 0, cex = cex.lab)\n mtext(xlab, 1, 0)\n mtext(ylab, 2, 0)\n mat <- diag(c(1, 1))\n plotcorrInternal <- function() {\n if (i == j){ #diag behavior\n if (diag == 'none'){\n return()\n } else if (diag == 'number'){\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n } else if (diag == 'ellipse') {\n mat[1, 2] <- corr[i, j]\n mat[2, 1] <- mat[1, 2]\n ell <- ellipse(mat, t = 0.43)\n ell[, 1] <- ell[, 1] + j\n ell[, 2] <- ell[, 2] + length(rows) + 1 - i\n polygon(ell, col = col[i, j])\n if (outline)\n lines(ell)\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n }\n } else if (i >= j){ #lower half of plot\n if (lower.panel == 'ellipse') { #check if ellipses should go here\n mat[1, 2] <- corr[i, j]\n mat[2, 1] <- mat[1, 2]\n ell <- ellipse(mat, t = 0.43)\n ell[, 1] <- ell[, 1] + j\n ell[, 2] <- ell[, 2] + length(rows) + 1 - i\n polygon(ell, col = col[i, j])\n if (outline)\n lines(ell)\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n } else if (lower.panel == 'number') { #check if ellipses should go here\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n } else {\n return()\n }\n } else { #upper half of plot\n if (upper.panel == 'ellipse') { #check if ellipses should go here\n mat[1, 2] <- corr[i, j]\n mat[2, 1] <- mat[1, 2]\n ell <- ellipse(mat, t = 0.43)\n ell[, 1] <- ell[, 1] + j\n ell[, 2] <- ell[, 2] + length(rows) + 1 - i\n polygon(ell, col = col[i, j])\n if (outline)\n lines(ell)\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n } else if (upper.panel == 'number') { #check if ellipses should go here\n text(j+0.13, length(rows) + 1 - i, round(corr[i, j], digits=digits), adj = 1, cex = cex)\n } else {\n return()\n }\n }\n }\n for (i in 1:dim(corr)[1]) {\n for (j in 1:dim(corr)[2]) {\n plotcorrInternal()\n }\n }\n invisible()\n dev.off()\n}\n", "meta": {"hexsha": "1217e32dd39b45d1dd2694171383960b63e008ea", "size": 4826, "ext": "r", "lang": "R", "max_stars_repo_path": "plots/my.plotcorr.r", "max_stars_repo_name": "carolmb/viewing-profiles-of-scientific-articles", "max_stars_repo_head_hexsha": "6a9248ea0dc458488b7f1556f358692529d2faf6", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plots/my.plotcorr.r", "max_issues_repo_name": "carolmb/viewing-profiles-of-scientific-articles", "max_issues_repo_head_hexsha": "6a9248ea0dc458488b7f1556f358692529d2faf6", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plots/my.plotcorr.r", "max_forks_repo_name": "carolmb/viewing-profiles-of-scientific-articles", "max_forks_repo_head_hexsha": "6a9248ea0dc458488b7f1556f358692529d2faf6", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.0892857143, "max_line_length": 343, "alphanum_fraction": 0.5536676337, "num_tokens": 1684, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3286161252430486}} {"text": "library(plotly)\r\ndfree <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')\r\nhead(dfree)\r\n\r\n# geo styling\r\ng <- list(\r\n scope = 'usa',\r\n projection = list(type = 'albers usa'),\r\n showland = TRUE,\r\n landcolor = toRGB(\"gray95\"),\r\n subunitcolor = toRGB(\"gray85\"),\r\n countrycolor = toRGB(\"gray85\"),\r\n countrywidth = 0.5,\r\n subunitwidth = 0.5\r\n)\r\n\r\np <- plot_geo(dfree, lat = ~lat, lon = ~long)\r\np\r\n\r\np <- add_markers(p,\r\n text = ~paste(airport, city, state, paste(\"Arrivals:\", cnt), sep = \"
\"),\r\n color = ~cnt, symbol = I(\"square\"), size = I(8), hoverinfo = \"text\"\r\n ) \r\np\r\n\r\n%>%\r\n colorbar(title = \"Incoming flights
February 2011\") %>%\r\n layout(\r\n title = 'Most trafficked US airports
(Hover for airport)', geo = g\r\n )\r\np\r\n\r\n\r\n# Create a shareable link to your chart\r\n# Set up API credentials: https://plot.ly/r/getting-started\r\nchart_link = api_create(p, filename=\"maps-traffic\")\r\nchart_link\r\n\r\n\r\n\r\nfile.path <-\"https://raw.githubusercontent.com/GitAMoveOn/DDP/gh-pages/nfl_draft.csv\"\r\ndraft <- read.csv(file.path)\r\n\r\nnames(draft)\r\nCollege\r\n\r\nz<-getwd()\r\nz\r\ndir()\r\nsetwd(paste(z,\"/9. Developing Data Products/\",sep=\"\"))\r\ngetwd()\r\ndir()\r\nsetwd(\"./Week 4\")\r\ngetwd()\r\ndir()\r\nsetwd(\"./Project/app\")\r\ndir()\r\nsetwd('./data')\r\ndir()\r\ncolleges <- read.csv('college_scorecard.csv')\r\nhead(colleges)\r\n\r\ninstall.packages('fuzzyjoin')\r\nlibrary(fuzzyjoin)\r\n\r\nhead(draft)\r\nnames(draft)\r\n#College.Univ\r\nnames(colleges)\r\n#INSTNM\r\n?fuzzy_left_join\r\nzz <- stringdist_left_join(draft,colleges,c(\"College.Univ\"=\"INSTNM\"))\r\nhead(zz)\r\nzz[zz$College.Univ=='Baylor',]\r\n\r\n\r\nlibrary(dplyr)\r\ndd <- draft %>%\r\n group_by(College.Univ) %>%\r\n select(College.Univ)\r\ndd<-unique(draft$College.Univ)\r\nclass(dd)\r\nnfl.colleges<-as.data.frame(dd)\r\nnfl.colleges\r\nstr(nfl.colleges)\r\nzz <- stringdist_left_join(nfl.colleges,colleges,c(\"dd\"=\"INSTNM\"),method='lv',distance_col=\"SCORE\")\r\n\r\nhead(zz[zz['dd']=='Washington',c('dd','INSTNM','SCORE')])\r\n\r\nlibrary(plotly)\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)\r\n\r\nfile.path <-\"https://raw.githubusercontent.com/GitAMoveOn/DDP/gh-pages/nfl_draft.csv\"\r\ndraft <- read.csv(file.path)\r\n\r\ndraft <- draft %>%\r\n filter(Year<=2012) %>%\r\n filter(Year>=1994)\r\n\r\navg.4.AV.round <- draft %>%\r\n group_by(Rnd) %>%\r\n summarise(avg.F4.AV = mean(First4AV)) %>%\r\n arrange(Rnd)\r\n# avg.4.AV.round\r\n\r\nct.4.AV.round <- draft %>%\r\n group_by(Rnd) %>%\r\n count(Rnd) %>%\r\n arrange(Rnd)\r\n# ct.4.AV.round\r\n\r\ntbl.round <- inner_join(avg.4.AV.round,ct.4.AV.round,by=\"Rnd\")\r\n# tbl.round\r\n\r\navg.4.AV.age <- draft %>%\r\n group_by(Age) %>%\r\n summarise(avg.F4.AV = mean(First4AV)) %>%\r\n arrange(Age)\r\n# avg.4.AV.age\r\n\r\nct.4.AV.age <- draft %>%\r\n group_by(Age) %>%\r\n count(Age) %>%\r\n arrange(Age)\r\n# ct.4.AV.age\r\n\r\ntbl.age<- inner_join(avg.4.AV.age,ct.4.AV.age,by=\"Age\")\r\n# tbl.age\r\n\r\navg.4.AV.pos <- draft %>%\r\n group_by(Pos) %>%\r\n summarise(avg.F4.AV = mean(First4AV)) %>%\r\n arrange(Pos)\r\n# avg.4.AV.pos\r\n\r\nct.4.AV.pos <- draft %>%\r\n group_by(Pos) %>%\r\n count(Pos) %>%\r\n arrange(Pos)\r\n# ct.4.AV.pos\r\n\r\ntbl.pos<- inner_join(avg.4.AV.pos,ct.4.AV.pos,by=\"Pos\")\r\n# tbl.pos\r\n\r\nxx <- list(title = \"Draft Round\")\r\nyy <- list(title = \"AV over First 4 Years\")\r\n\r\np1 <- plot_ly(tbl.round\r\n , x = ~Rnd\r\n , y = ~avg.F4.AV\r\n , color = ~n\r\n , type = \"bar\"\r\n , colors = brewer.pal(1, \"Blues\")\r\n , name = 'Draft Round' ) %>%\r\n layout(xaxis = xx, yaxis = yy) \r\n# p1\r\np2 <- plot_ly(tbl.age\r\n , x = ~Age\r\n , y = ~avg.F4.AV\r\n , type = \"bar\"\r\n , color = ~n\r\n , colors = brewer.pal(1, \"Greens\")\r\n , name = 'Age' ) %>%\r\n layout(xaxis = xx, yaxis = yy) \r\n# p2\r\np3 <- plot_ly(tbl.pos\r\n , x = ~Pos\r\n , y = ~avg.F4.AV\r\n , type = \"bar\"\r\n # , color = ~n\r\n , colors = brewer.pal(1, \"Reds\")\r\n , name = 'Position' ) %>%\r\n layout(xaxis = xx, yaxis = yy) \r\n# p3\r\nps <-subplot(p1,p2,p3, shareX = FALSE, shareY = TRUE, nrows = 2) %>%\r\n layout(title = \"4 Yr AV by Features\")\r\nps\r\n?subplot\r\n\r\nfile.path <-\"https://raw.githubusercontent.com/GitAMoveOn/DDP/gh-pages/seahawks_results.csv\"\r\nsea.res <- read.csv(file.path)\r\n\r\ncolnames(sea.res)[1]<-\"Round\"\r\nsea.res\r\nlibrary(DT)\r\n\r\ndt.sea <- datatable(sea.res)\r\ndt.sea\r\n", "meta": {"hexsha": "52de0a1871bd19ac905f8eaa64406554af3fb04b", "size": 4388, "ext": "r", "lang": "R", "max_stars_repo_path": "slides/adhoc.draft.map.plotly.r", "max_stars_repo_name": "MangrobanGit/nfl_draft_av", "max_stars_repo_head_hexsha": "b146f1a0121cb83c6353a28a94c4fa1a08ae24fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "slides/adhoc.draft.map.plotly.r", "max_issues_repo_name": "MangrobanGit/nfl_draft_av", "max_issues_repo_head_hexsha": "b146f1a0121cb83c6353a28a94c4fa1a08ae24fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "slides/adhoc.draft.map.plotly.r", "max_forks_repo_name": "MangrobanGit/nfl_draft_av", "max_forks_repo_head_hexsha": "b146f1a0121cb83c6353a28a94c4fa1a08ae24fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.0947368421, "max_line_length": 115, "alphanum_fraction": 0.5836371923, "num_tokens": 1402, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3286161252430486}} {"text": "# filenames for input data\nnewCW<-'Total_catch_in_numbers_and_mean_weight_2011.csv'\n\nfirst.year.out<-1991\nlast.year.out<-2011\nn.season<-4\n\n\n####################################\n\n\n# WECA and WEST\n\na<-read.table(file=file.path(data.path,'Input',newCW),header=T,sep=',')\na[is.na(a$mw0),'mw0']<-0\nb<-subset(a,select=c(year,quarter, mw0,mw1,mw2,mw3,mw4))\nb\nout<-file.path(data.path,\"weca.in\")\nunlink(out)\nfor (y in(first.year.out:last.year.out)) {\n cat(paste(\"# year:\",y,\"\\n\"), file=out,append=TRUE)\n for (s in(1:n.season)){ \n aa<-subset(b,year==y & quarter==s)\n cat(format(c(aa$mw0,aa$mw1, aa$mw2, aa$mw3, aa$mw4) ,format='fg',width=10,justify='right',digits=2,nsmall=5),file=out,append=T)\n cat(\"\\n\",file=out,append=T)\n }\n}\n\nfile.copy(out, file.path(data.path,\"west.in\"), overwrite = TRUE)\n\n# CANUM\n\na$n0<-round(a$n0,0)\na$n1<-round(a$n1,0)\na$n2<-round(a$n2,0)\na$n3<-round(a$n3,0)\na$n4<-round(a$n4,0)\n\nb<-subset(a,select=c(year,quarter, n0,n1,n2,n3,n4))\n\n\nout<-file.path(data.path,\"canum.in\")\nunlink(out)\nfor (y in(first.year.out:last.year.out)) {\n cat(paste(\"# year:\",y,\"\\n\"), file=out,append=TRUE)\n for (s in(1:n.season)){ \n aa<-subset(b,year==y & quarter==s)\n cat(format(c(aa$n0,aa$n1, aa$n2, aa$n3, aa$n4) ,format='fg',width=12,justify='right'),file=out,append=T)\n cat(\"\\n\",file=out,append=T)\n }\n}\n\n################################\n# natural mortality\na<-read.table(file=file.path(data.path,'Input','m.csv'),header=T,sep=',')\nm<-tapply(a$m,list(a$Quarter,a$Age),sum)\nm[is.na(m)]<-0\nm\nm<-round(cbind(m,m[,'3']),2)\n\nout<-file.path(data.path,'natmor.in')\ncat(\"####################################\\n\",\"### Sprat Natural Mortality\\n\",file=out,append=F)\n\nfor (y in (first.year.out:(last.year.out+1))) {\n cat(\"# \",y,'\\n', file=out,append=T)\n write.table(m,file=out,append=T,col.names=F,row.names=F)\n}\n\n\n################################\n# maturity\na<-read.table(file=file.path(data.path,'Input','maturity.csv'),header=T,sep=',')\nm<-round(tapply(a$m,list(a$quarter,a$age),sum),2)\nm[is.na(m)]<-0\nm\nm<-matrix(rep(m,each=4),nrow=4)\n\nout<-file.path(data.path,'propmat.in')\ncat(\"####################################\\n\",\"### Sprat Proportion mature\\n\",file=out,append=F)\n\nfor (y in (first.year.out:(last.year.out+1))) {\n cat(\"# \",y,'\\n', file=out,append=T)\n write.table(m,file=out,append=T,col.names=F,row.names=F)\n}\n\n\n##################################\n# CPUE indeces\n\na<-read.table(file=file.path(data.path,'Input','ibtsq1_q3.csv'),header=T,sep=',')\nhead(a)\na$survey<-'IBTS'\n\nb<-read.table(file=file.path(data.path,'Input','heras.csv'),header=T,sep=',')\nb$survey<-'Heras'\n\nab<-rbind(a,b)\nab<-subset(ab,year>=1991)\n\na<-tapply(ab$n,list(ab$survey,ab$year,ab$quarter,ab$age),sum)\nftable(round(a,1))\n\na['IBTS','1995','3','0'] <- -a['IBTS','1995','3','0']\nftable(round(a,1))\n\n\nout<-file.path(data.path,'fleet_catch.in')\n\ncat(\"####################################\\n\",\"### Sprat\\n\",file=out,append=F)\n\ncat(\"####################################\\n\",\"### IBTS Q1\\n\",file=out,append=T)\ncat(\"# effort Age1 Age2 Age3 Age4\\n\",file=out,append=T)\nx<-cbind(1E-4,round(a['IBTS',as.character(seq(first.year.out,last.year.out+1)),'1',c('1','2','3','4')],2))\nwrite.table(x,file=out,append=T,col.names=F,row.names=F)\n\ncat(\"####################################\\n\",\"### IBTS Q3\\n\",file=out,append=T)\ncat(\"# effort Age1 Age2 Age3\\n\",file=out,append=T)\nx<-cbind(1E-4,round(a['IBTS',as.character(seq(first.year.out,last.year.out)),'3',c('1','2','3')],2))\nwrite.table(x,file=out,append=T,col.names=F,row.names=F)\n\ncat(\"####################################\\n\",\"### Heras Q2\\n\",file=out,append=T)\ncat(\"# effort Age1 Age2 Age3 \\n\",file=out,append=T)\nx<-cbind(1E-3,a['Heras',as.character(seq(2003,last.year.out)),'2',c('1','2','3')])\nwrite.table(x,file=out,append=T,col.names=F,row.names=F)\n\n\n", "meta": {"hexsha": "c8a516309601d57dcbee3cd0ccf75b7eae2d5864", "size": 3803, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/sprat_read_new_data_format.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/sprat_read_new_data_format.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/sprat_read_new_data_format.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9448818898, "max_line_length": 132, "alphanum_fraction": 0.5813831186, "num_tokens": 1255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7310585786300049, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.32853234059860054}} {"text": "REBOL [\n\tTitle: \"Generates Red float! tests\"\n\tAuthor: \"Peter W A Wood\"\n\tFile: \t %make-float-auto-test.r\n\tVersion: 0.1.0\n\tTabs:\t 4\n\tRights: \"Copyright (C) 2011-2015 Peter W A Wood. All rights reserved.\"\n\tLicense: \"BSD-3 - https://github.com/red/red/blob/origin/BSD-3-License.txt\"\n]\n\n;; initialisations\ntests: copy \"\" ;; string to hold generated tests\ntest-number: 0 ;; number of the generated test\nmake-dir %auto-tests/\nfile-out: %auto-tests/float-auto-test.red\n\n;; create a block of values to be used in the binary ops tests\ntest-values: [\n\t0.0\n\t-2147483648.0\n\t2147483647.0\n\t-1.0\n\t3.0\n\t-7.0\n\t5.0\n\t123456.7890\n\t1.222090944E+33\n\t9.99999E-45\n\t7.7E18\n]\n\ntol: 1e-4\n\n;; create blocks of operators to be applied\ntest-binary-ops: [\n\t+\n\t-\n\t*\n\t/\n; //\t\t\t\t\t\t\t\t\t;; not implemented fully yet\n; \"%\"\t\t\t\t\t\t\t\t\t;; not implemented fully yet\n]\n\ntest-comparison-ops: [\n\t=\n\t<>\n\t<\n\t>\n\t>=\n\t<=\n]\n\ntest-comparison-values: [\n\t-1E-13\n\t0.0\n\t+1E-13\n]\n\n;; create test file with header\nappend tests \"Red [^(0A)\"\nappend tests { Title: \"Red auto-generated float! tests\"^(0A)}\nappend tests {\tAuthor: \"Peter W A Wood\"^(0A)}\nappend tests { File: \t %float-auto-test.red^(0A)}\nappend tests { License: \"BSD-3 - https://github.com/dockimbel/Red/blob/origin/BSD-3-License.txt\"^(0A)}\nappend tests \"]^(0A)^(0A)\"\nappend tests \"^(0A)^(0A)comment {\"\nappend tests \" This file is generated by make-float-auto-test.r^(0A)\"\nappend tests \" Do not edit this file directly.^(0A)\"\nappend tests \"}^(0A)^(0A)\"\nappend tests join \";make-length:\"\n\t[length? read %make-float-auto-test.r \"^(0A)^(0A)\"]\nappend tests \"#include %../../../../quick-test/quick-test.red^(0A)^(0A)\"\nappend tests {~~~start-file~~~ \"Auto-generated tests for float\"^(0A)^(0A)}\nappend tests {===start-group=== \"Auto-generated tests for float\"^(0A)^(0A)}\n\nwrite file-out tests\ntests: copy \"\"\n\n;; binary operator tests - in global context\nforeach op test-binary-ops [\n\tforeach operand1 test-values [\n\t\tforeach operand2 test-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif attempt [\n\t\t\t\teither op = \"%\" [\n\t\t\t\t\texpected: to decimal! do reduce [remainder operand1 operand2]\n\t\t\t\t][\n\t\t\t\t\texpected: to decimal! do reduce [operand1 op operand2]\n\t\t\t\t]\n\t\t\t][\n\t\t\t\t;; test with literal values\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests \" --assertf~= \"\n\t\t\t\tappend tests reform [expected \" (\" operand1 op operand2 \") \" tol \"^(0A)\"]\n\n\t\t\t\t;; test with variables\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests join \" i: \" [operand1 \"^(0A)\"]\n\t\t\t\tappend tests join \" j: \" [operand2 \"^(0A)\"]\n\t\t\t\tappend tests rejoin [\" k: i \" op \" j^(0A)\"]\n\t\t\t\tappend tests \" --assertf~= \"\n\t\t\t\tappend tests reform [expected \" k \" tol \"^(0A)\"]\n\n\t\t\t\t;; write tests to file\n\t\t\t\twrite/append file-out tests\n\t\t\t\ttests: copy \"\"\n\t\t\t]\n\t\t\trecycle\n\t\t]\n\t]\n]\n\n;; binary operator tests - inside a function\n\n;; write function spec\ntests: {\nfloat-auto-test-func: func [\n\t/local\n\ti [float!]\n\tj [float!]\n\tk [float!]\n][\n}\n\nwrite/append file-out tests\ntests: copy \"\"\n\nforeach op test-binary-ops [\n\tforeach operand1 test-values [\n\t\tforeach operand2 test-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif attempt [expected: do reduce [operand1 op operand2]][\n\n\t\t\t\texpected: to decimal! expected\n\n\t\t\t\t;; test with variables inside the function\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests join \" i: \" [operand1 \"^(0A)\"]\n\t\t\t\tappend tests join \" j: \" [operand2 \"^(0A)\"]\n\t\t\t\tappend tests rejoin [\" k: i \" op \" j^(0A)\"]\n\t\t\t\tappend tests \" --assertf~= \"\n\t\t\t\tappend tests reform [expected \" k \" tol \"^(0A)\"]\n\n\t\t\t\t;; write tests to file\n\t\t\t\twrite/append file-out tests\n\t\t\t\ttests: copy \"\"\n\t\t\t]\n\t\t\trecycle\n\t\t]\n\t]\n]\n\n;; write closing bracket and function call\nappend tests \" ]^(0a)\"\nappend tests \"float-auto-test-func^(0a)\"\nwrite/append file-out tests\ntests: copy \"\"\n\n\n;; comparison tests\nforeach op test-comparison-ops [\n\tforeach operand1 test-values [\n\t\tforeach oper2 test-comparison-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif all [\n\t\t\t\tattempt [operand2: operand1 + oper2]\n\t\t\t\tnone <> attempt [expected: do reduce [operand1 op operand2]]\n\t\t\t][\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests \" --assert \"\n\t\t\t\tappend tests reform [expected \" = (\" operand1 op operand2 \")^(0A)\"]\n\n\t\t\t\t;; write tests to file\n\t\t\t\twrite/append file-out tests\n\t\t\t\ttests: copy \"\"\n\t\t\t]\n\t\t]\n\t]\n]\n\n\n;; write file epilog\nappend tests \"^(0A)===end-group===^(0A)^(0A)\"\nappend tests {~~~end-file~~~^(0A)^(0A)}\n\nwrite/append file-out tests\n\nprint [\"Number of assertions generated\" test-number]", "meta": {"hexsha": "16b4661a2e0b555a45a892716aa156df342cda1d", "size": 4865, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/source/units/make-float-auto-test.r", "max_stars_repo_name": "0xflotus/red", "max_stars_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_stars_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_stars_count": 5234, "max_stars_repo_stars_event_min_datetime": "2015-01-01T12:59:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:28:22.000Z", "max_issues_repo_path": "tests/source/units/make-float-auto-test.r", "max_issues_repo_name": "0xflotus/red", "max_issues_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_issues_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_issues_count": 3406, "max_issues_repo_issues_event_min_datetime": "2015-01-02T08:53:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T17:47:35.000Z", "max_forks_repo_path": "tests/source/units/make-float-auto-test.r", "max_forks_repo_name": "0xflotus/red", "max_forks_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_forks_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_forks_count": 509, "max_forks_repo_forks_event_min_datetime": "2015-01-27T21:26:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T10:10:32.000Z", "avg_line_length": 25.3385416667, "max_line_length": 103, "alphanum_fraction": 0.6261048304, "num_tokens": 1493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199306096344, "lm_q2_score": 0.6370307875894139, "lm_q1q2_score": 0.32846577049305437}} {"text": "#' @title percentDifference\n#' @description unknown\n#' @family abysmally documented\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\npercentDifference <- function(x) {\n\t \n\tif(is.vector(x)) {\n\t\tn <- length(x)\n\t x <- x[order(x)]\n\t v <- (x[2:n]-x[1:n-1])/((x[2:n]+x[1:n-1])/2)*100\n \t\n\t} else {\n\t\tv <- (x[,1] - x[,2]) / ((x[,1]+x[,2])/2) * 100\n}\n \treturn(v)\n\n}\n", "meta": {"hexsha": "e8433b8310d1c85de0d56651f8667bd360ddd88a", "size": 401, "ext": "r", "lang": "R", "max_stars_repo_path": "R/percentDifference.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/percentDifference.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/percentDifference.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.1052631579, "max_line_length": 57, "alphanum_fraction": 0.5037406484, "num_tokens": 146, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.32846576456294085}} {"text": "# color compensation / multi-channel deconvolution\n\n\n# function: get cross-over constant matrix k\nget_k <- function(\n db_conn, # MySQL database connection\n dcv_exp_info, # deconvolution experiment ids, i.e. the data in these experiments are for constructing deconvolution matrix. a named vector (by channel) where each element is the experiment id using the target dye of each channel\n well_proc='dim3' # options: 'mean', 'dim3'.\n ) { \n \n dei_names <- names(dcv_exp_info)\n channel_names <- dei_names[2:length(dei_names)]\n channels <- sapply(channel_names, function(channel_name) strsplit(channel_name, '_')[[1]][2])\n num_channels <- length(channels)\n names(channel_names) <- channels\n \n dcv_list <- get_full_calib_data(db_conn, dcv_exp_info)\n \n water_data <- dcv_list[['water']]\n well_nums <- colnames(water_data)\n num_wells <- length(well_nums)\n \n k_list_bydy <- lapply(channel_names, function(channel_name) dcv_list[[channel_name]] - water_data)\n names(k_list_bydy) <- channels\n \n dye_names <- paste('dye_', channels)\n \n k_inv_array <- array(\n 0, \n dim=c(num_channels, num_channels, num_wells), \n dimnames=list(channels, dye_names, well_nums)\n )\n \n k_singular <- ''\n \n if (well_proc == 'mean') {\n k <- as.matrix(do.call(cbind, lapply(channels, function(channel) {\n k_data_1dye <- rowMeans(k_list_bydy[[channel]])\n k_data_1dye / sum(k_data_1dye)\n })))\n colnames(k) <- dye_names\n k_inv <- tryCatch(solve(k), error=err_e)\n if ('error' %in% class(k_inv)) {\n k_singular <- c('Well mean K matrix is singular. ')\n } else {\n for (well_num in well_nums) {\n k_inv_array[,,well_num] <- k_inv\n }\n }\n \n } else if (well_proc == 'dim3') {\n k <- k_inv_array\n k_singular_vec <- c()\n for (well_num in well_nums) {\n k_mtx <- as.matrix(do.call(cbind, lapply(channels, function(channel) {\n k_data_1dye <- k_list_bydy[[channel]][,well_num]\n k_data_1dye / sum(k_data_1dye)\n })))\n k[,,well_num] <- k_mtx\n k_inv <- tryCatch(solve(k_mtx), error=err_e)\n if ('error' %in% class(k_inv)) {\n k_singular_vec <- c(k_singular_vec, well_num)\n } else {\n k_inv_array[,,well_num] <- k_inv\n }\n }\n if (length(k_singular_vec) > 0) {\n k_singular <- sprintf(\n 'Well-specific K matrix is singular for the following well(s): %s. ',\n paste(k_singular_vec, collapse=', ')\n )\n }\n }\n \n return(list('k'=k, 'k_inv_array'=k_inv_array, 'k_singular'=k_singular))\n }\n\n\n# multi-channel deconvolution\ndeconv <- function(\n array2dcv, # dim1 must be channel, dim3 must be well; dim2 is cycle for amplification and temperature point for melting curve\n db_conn,\n calib_info\n ) {\n \n a2d_dim1 <- dim(array2dcv)[1]\n a2d_dim2 <- dim(array2dcv)[2]\n a2d_dim3 <- dim(array2dcv)[3]\n a2d_dimnames <- dimnames(array2dcv)\n \n # if data only has 1 cycle (amplification) or 1 temperature point (melt curve)\n if (is.na(a2d_dim3)) array2dcv <- array(c(array2dcv), \n dim=c(a2d_dim1, 1, a2d_dim2), \n dimnames=list(a2d_dimnames[[1]], '1', a2d_dimnames[[2]]))\n \n if (class(calib_info) == \"numeric\" || \n sum(duplicated(sapply(calib_info, function(calib_ele) calib_ele[['step_id']]))) > 0\n ) {\n k_list_temp <- k_list\n } else {\n k_list_temp <- get_k(db_conn, calib_info, 'dim3')\n }\n \n k_inv_array = k_list_temp[['k_inv_array']]\n \n dcvd_by_dim2_well <- lapply(1:a2d_dim2, \n function(dim2_i) do.call(cbind, lapply(1:a2d_dim3, \n function(well_i) k_inv_array[,,well_i] %*% array2dcv[,dim2_i,well_i]))) # dim2 is cycle_num for amp and temperature for melt curve\n \n dcvd_array <- array(NA, dim(array2dcv))\n for (dim2_i in 1:dim(array2dcv)[2]) dcvd_array[,dim2_i,] <- dcvd_by_dim2_well[[dim2_i]]\n dimnames(dcvd_array) <- dimnames(array2dcv)\n \n # scale by channel to adjust for different fluorescence excitation strengths among dyes\n for (channel in dimnames(dcvd_array)[1]) {\n dcvd_array[channel,,] <- dcvd_array[channel,,] * scaling_factors_deconv[channel] }\n \n return(list('dcvd_array'=dcvd_array, 'k_list_temp'=k_list_temp))\n }\n\n", "meta": {"hexsha": "f4fd701c9f1d5894db55c32a619a94d50117e269", "size": 4545, "ext": "r", "lang": "R", "max_stars_repo_path": "bioinformatics/deconv.r", "max_stars_repo_name": "MakerButt/chaipcr", "max_stars_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-25T19:58:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-25T19:58:26.000Z", "max_issues_repo_path": "bioinformatics/deconv.r", "max_issues_repo_name": "MakerButt/chaipcr", "max_issues_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bioinformatics/deconv.r", "max_forks_repo_name": "MakerButt/chaipcr", "max_forks_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5619834711, "max_line_length": 232, "alphanum_fraction": 0.602640264, "num_tokens": 1254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7371581626286833, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.328425733446583}} {"text": "#' Calculate Progeny Scores with Permutations\n#'\n#'This function generate a series of scatter plot with marginal distribution\n#'(in the form of an arrangeGrob object), for each progeny pathway and\n#'sample/contrast. Each scatter plot has progeny weights as x-axis and the gene\n#'level stat used to compute progeny score as the y-axis. The marginal\n#'distribution of the gene level stats is displayed on the right of the plot\n#'to give visual support of the significance of each gene contributing to\n#'the progeny pathway score. The green and red colors represent the positive\n#'and negative contribution of genes to the progeny pathway, respectively.\n#'For each gene contribution, 4 cases are possible, as the combinations of\n#'the sign of the gene level stat and the sign of the gene level weight.\n#'Positive weight will lead to a positive(green)/negative(red) gene contribution\n#'if the gene level stat is positive/negative. Negative weight will lead to\n#'a negative(red)/positive(green) gene contribution if the gene level stat\n#'is positive/negative.\n#'\n#'@param df an n*m data frame, where n is the number of omic features (genes). \n#'m isn't really important, as long as at least one column corresponds to a \n#'sample or contrast statistic. One of the columns should correspond to the gene \n#'symbols.\n#'@param weight_matrix A progeny coefficient matrix. the first column should be\n#'the identifiers of the omic features, and should be coherent with \n#'the identifiers provided in df.\n#'@param dfID an integer corresponding to the column number of \n#'the gene identifiers of df.\n#'@param weightID an integer corresponding to the column number of the gene \n#'identifiers of the weight matrix.\n#'@param statName The name of the stat used, to be displayed on the plot\n#'@param verbose Logical indicating whether we want to have the messages \n#'indicating the different computed weights. \n#'@importFrom stats ecdf\n#'@import ggplot2\n#'@import ggrepel\n#'@import gridExtra\n#'@return The function returns a list of list of arrangeGrob objects.\n#'The first level list elements correspond to samples/contrasts. \n#'The second level correspond to pathways.\n#'The plots can be saved in a pdf format using the saveProgenyPlots function.\n#'@examples\n#' # use example gene expression matrix\n#' \n#' gene_expression <- read.csv(system.file(\"extdata\", \n#' \"human_input.csv\", package = \"progeny\"))\n#'\n#' # getting a model matrix with 100 top significant genes and converting to df\n#' weight_matrix <- getModel(\"Human\", top=100)\n#' weight_matrix <- data.frame(names = row.names(weight_matrix), \n#' row.names = NULL, weight_matrix)\n#' \n#' #use progenyScatter function\n#' plots <- progenyScatter(gene_expression, weight_matrix)\n#'@export\nprogenyScatter <- function(df,weight_matrix,dfID = 1, weightID = 1, \n statName = \"gene stats\", verbose = FALSE) {\n \n weight <- color <- ID <- NULL \n plot_list_contrasts <- list(0)\n for (i in 2:length(df[1,])) {\n plot_list_pathways <- list(0)\n for (j in 2:length(weight_matrix[1,])) {\n sub_df <- df[,c(dfID,i)]\n pathway_weights <- weight_matrix[,c(weightID,j)]\n names(sub_df) <- c(\"ID\",\"stat\")\n minstat <- min(sub_df$stat)\n maxstat <- max(sub_df$stat)\n \n histo <- ggplot(sub_df, aes(x = stat, fill = \"blue\")) + \n geom_density() + coord_flip() + \n scale_fill_manual(values = c(\"#00c5ff\")) + \n xlim(minstat, maxstat) + theme_minimal() + \n theme(legend.position = \"none\", axis.text.x = element_blank(),\n axis.ticks.x = element_blank(), axis.title.y = element_blank(),\n axis.text.y = element_blank(), axis.ticks.y = element_blank(),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank())\n \n names(pathway_weights) <- c(\"ID\",\"weight\")\n pathway_weights <- pathway_weights[pathway_weights$weight != 0,]\n percentile <- ecdf(sub_df$stat)\n sub_df <- merge(sub_df,pathway_weights,by = \"ID\")\n sub_df$color <- \"3\"\n sub_df[(sub_df$weight > 0 & sub_df$stat > 0),\"color\"] <- \"1\"\n sub_df[(sub_df$weight > 0 & sub_df$stat < 0),\"color\"] <- \"2\"\n sub_df[(sub_df$weight < 0 & sub_df$stat > 0),\"color\"] <- \"2\"\n sub_df[(sub_df$weight < 0 & sub_df$stat < 0),\"color\"] <- \"1\"\n sub_df[(percentile(sub_df$stat) < .95 & \n percentile(sub_df$stat) > .05),1] <- NA\n \n if (verbose){\n message(paste(\"weights of \",names(weight_matrix)[j], sep = \"\"))\n }\n \n title <- paste(\"weights of \",names(weight_matrix)[j], sep = \"\")\n \n scatterplot <- ggplot(sub_df, aes(x = weight, y = stat, \n color = color)) + geom_point() +\n scale_colour_manual(values = c(\"red\",\"royalblue3\",\"grey\")) +\n geom_label_repel(aes(label = ID)) +\n ylim(minstat, maxstat) + theme_minimal() +\n theme(legend.position = \"none\") +\n geom_vline(xintercept = 0, linetype = 'dotted') +\n geom_hline(yintercept = 0, linetype = 'dotted') +\n labs(x = title, y = statName)\n \n lay <- t(as.matrix(c(1,1,1,1,2)))\n gg <- arrangeGrob(scatterplot, histo, nrow = 1, ncol = 2, \n layout_matrix = lay)\n plot_list_pathways[[j-1]] <- gg\n }\n names(plot_list_pathways) <- names(weight_matrix[,-weightID])\n plot_list_contrasts[[i-1]] <- plot_list_pathways\n }\n return(plot_list_contrasts)\n}\n\n#'Function to save Progeny plots\n#'\n#'This function is designed to save the plots (in pdf format) of a nested \n#'(2 level) list of arrangeGrob objects, such as the one returned by \n#'the progenyScatter function.\n#'\n#'@param plots a list of list of arrangeGrob object (such as the one returned \n#'by the progenyScatter function.).The first level list elements correspond \n#'to samples/contrasts. The second level corresponds to pathways.\n#'The plots can be saved in a pdf format using the saveProgenyPlots function.\n#'@param contrast_names a vector of the same length as the first level of \n#'the plot list corresponding to the names of each sample/contrast\n#'@param dirpath the path to the directory where the plots should be saved\n#'@import ggplot2\n#'@examples\n#' #create plots using progneyScatter function\n#' gene_expression <- read.csv(system.file(\"extdata\", \n#' \"human_input.csv\", package = \"progeny\"))\n#' \n#' # getting a weight_matrix\n#' weight_matrix <- getModel(\"Human\", top=100)\n#' weight_matrix <- data.frame(names = row.names(weight_matrix), \n#' row.names = NULL, weight_matrix) \n#' plots <- progenyScatter(gene_expression, weight_matrix)\n#'\n#' #create a list with contrast names\n#' contrast_names <- names(gene_expression[2:ncol(gene_expression)])\n#'\n#' #assign a path to store your plots\n#' dirpath <- \"./progeny_plots/\"\n#' \n#' # save it\n#' # saveProgenyPlots(plots, contrast_names, dirpath)\n#' @return This function produces the pdf files of plots taken from the \n#' progenyScatter function\n#'@export\nsaveProgenyPlots <- function(plots, contrast_names, dirpath) {\n \n i <- 1\n for (condition in plots) {\n dirname <- paste(dirpath,contrast_names[i], sep = \"\")\n dir.create(dirname, recursive = TRUE, showWarnings = FALSE)\n j <- 1\n for (pathway in condition) {\n filename <- paste(dirname,names(condition)[j],sep = \"/\")\n filename <- paste(filename,\".pdf\",sep = \"\")\n ggsave(filename, pathway,device = \"pdf\", dpi = 300)\n j <- j+1\n }\n i <- i+1\n }\n}\n\n#'Returns the Progeny Model\n#'\n#'This function is designed for getting a model matrix with top significant\n#'genes for each pathway\n#'\n#'@param organism \"Human\" or \"Mouse\" taken from the main function's argument. \n#'Default to \"Human\"\n#'@param top Desired top number of genes for each pathway according to their\n#'significance(p.value). Default to 100\n#'@examples #getting a model matrix according to the desired top n significant \n#'model <- getModel(\"Human\", top=100)\n#'@return This function returns model matrix according to the top n significant\n#'@importFrom dplyr group_by top_n ungroup select \n#'@importFrom tidyr spread %>%\n#'@export\ngetModel <- function(organism = \"Human\", top= 100) {\n \n pathway <- p.value <- weight <- NULL\n\n if (organism == \"Human\") {\n full_model <- progeny::model_human_full\n } else if (organism == \"Mouse\") {\n full_model <- progeny::model_mouse_full\n } else {\n stop(\"Wrong organism name. Please specify 'Human' or 'Mouse'.\")\n }\n \n if (!(is.numeric(top)) || top < 1){\n stop(\"perm should be an integer value\")\n }\n\n model <- full_model %>%\n dplyr::group_by(pathway) %>%\n dplyr::top_n(top, wt = -p.value) %>%\n dplyr::ungroup(pathway) %>%\n dplyr::select(-p.value) %>%\n tidyr::spread(pathway, weight, fill=0) %>%\n data.frame(row.names = 1, check.names = FALSE, stringsAsFactors = FALSE)\n \n return(model)\n}\n", "meta": {"hexsha": "53d9126e8997230ff93e8c11d3cbb91a8c78775b", "size": 9125, "ext": "r", "lang": "R", "max_stars_repo_path": "R/progenySuppFunc.r", "max_stars_repo_name": "roramirezf/progeny", "max_stars_repo_head_hexsha": "27e3a59116568565eca2feeaf545ccac1aa24018", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 41, "max_stars_repo_stars_event_min_datetime": "2017-11-19T18:07:44.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T13:39:31.000Z", "max_issues_repo_path": "R/progenySuppFunc.r", "max_issues_repo_name": "roramirezf/progeny", "max_issues_repo_head_hexsha": "27e3a59116568565eca2feeaf545ccac1aa24018", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 36, "max_issues_repo_issues_event_min_datetime": "2018-02-03T11:43:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T08:58:32.000Z", "max_forks_repo_path": "R/progenySuppFunc.r", "max_forks_repo_name": "roramirezf/progeny", "max_forks_repo_head_hexsha": "27e3a59116568565eca2feeaf545ccac1aa24018", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2017-10-23T06:54:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T16:41:06.000Z", "avg_line_length": 43.2464454976, "max_line_length": 81, "alphanum_fraction": 0.6524931507, "num_tokens": 2293, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228625116081, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.32837584767773664}} {"text": "#' Evaluate the bandwidth selection criterion for a given bandwidth\n#' \n#' @param formula symbolic representation of the model\n#' @param data data frame containing observations of all the terms represented in the formula\n#' @param weights vector of prior observation weights (due to, e.g., overdispersion). Not related to the kernel weights.\n#' @param family exponential family distribution of the response\n#' @param bw bandwidth for the kernel\n#' @param kernel kernel function for generating the local observation weights\n#' @param coords matrix of locations, with each row giving the location at which the corresponding row of data was observed\n#' @param longlat \\code{TRUE} indicates that the coordinates are specified in longitude/latitude, \\code{FALSE} indicates Cartesian coordinates. Default is \\code{FALSE}.\n#' @param varselect.method criterion to minimize in the regularization step of fitting local models - options are \\code{AIC}, \\code{AICc}, \\code{BIC}, \\code{GCV}\n#' @param tol.loc tolerance for the tuning of an adaptive bandwidth (e.g. \\code{knn} or \\code{nen})\n#' @param bw.type type of bandwidth - options are \\code{dist} for distance (the default), \\code{knn} for nearest neighbors (bandwidth a proportion of \\code{n}), and \\code{nen} for nearest effective neighbors (bandwidth a proportion of the sum of squared residuals from a global model)\n#' @param bwselect.method criterion to minimize when tuning bandwidth - options are \\code{AICc}, \\code{BICg}, and \\code{GCV}\n#' @param verbose print detailed information about our progress?\n#' \n#' @return value of the \\code{bwselect.method} criterion for the given bandwidth\n#' \nlagr.tune.bw = function(x, y, weights, coords, dist, family, bw, kernel, env, oracle, varselect.method, tol.loc, bw.type, bwselect.method, min.dist, max.dist, lambda.min.ratio, n.lambda, lagr.convergence.tol, lagr.max.iter, verbose) { \n #Fit the model with the given bandwidth:\n cat(paste('Bandwidth: ', round(bw, 3), '; ', sep=\"\"))\n\n # Tell lagr.dispatch whether to select bandwidth via the jacknife\n if (bwselect.method=='jacknife') {\n jacknife = TRUE\n } else {\n jacknife = FALSE\n }\n\n\n vcr.model = lagr.dispatch(\n x=x,\n y=y,\n coords=coords,\n fit.loc=NULL,\n D=dist,\n family=family,\n prior.weights=weights,\n tuning=TRUE,\n predict=FALSE,\n simulation=FALSE,\n oracle=oracle,\n varselect.method=varselect.method,\n verbose=verbose,\n bw=bw,\n bw.type=bw.type,\n kernel=kernel,\n min.dist=min.dist,\n max.dist=max.dist,\n tol.loc=tol.loc,,\n lambda.min.ratio=lambda.min.ratio,\n n.lambda=n.lambda, \n lagr.convergence.tol=lagr.convergence.tol,\n lagr.max.iter=lagr.max.iter\n )\n \n res = mget('trace', env=env, ifnotfound=list(matrix(NA, nrow=0, ncol=3)))\n res$trace = as.data.frame(rbind(res$trace, c(bw, vcr.model[[bwselect.method]], vcr.model$df)))\n colnames(res$trace) = c(\"bw\", \"loss\", \"df\")\n res$trace = res$trace[order(res$trace$bw),]\n assign('trace', res$trace, env=env)\n \n cat(paste('df: ', round(vcr.model$df,4), '; Loss: ', signif(vcr.model[[bwselect.method]], 5), '\\n', sep=''))\n return(vcr.model[[bwselect.method]])\n}\n", "meta": {"hexsha": "e0a23bcad65c347070b90aa9cb6d02ec6a3e9af2", "size": 3283, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lagr.tune.bw.r", "max_stars_repo_name": "wrbrooks/lagr", "max_stars_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/lagr.tune.bw.r", "max_issues_repo_name": "wrbrooks/lagr", "max_issues_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lagr.tune.bw.r", "max_forks_repo_name": "wrbrooks/lagr", "max_forks_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.7424242424, "max_line_length": 284, "alphanum_fraction": 0.6807797746, "num_tokens": 843, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3282884048402795}} {"text": "library(flume)\nlibrary(Matrix)\nlibrary(units)\n\ndata(\"flume_networks\")\nnet = flume_networks$kamp\n\nset.seed(1703)\n\noptions(mc.cores = 4)\n\n# generate x species with same niche breadth, niche height but random location\nnsp = 50\n\n# use niches_uniform to calculate niche location for all species at once\nnopts = list(location = runif(nsp, 0.1, 0.9), \n\tbreadth = 0.1, scale_e = 1.25e-7, scale_c = 6e-6, r_use = 0.05)\n\n# number of different dispersal abilities\nn_disp = 6\nalpha = seq(0.05, 1, length.out = n_disp) \n\n# passive dispersal is kept constant at the moment\nbeta = 0.1\n\n# starting prevalence for species in the network\nprev = 0.2\n\n# starting/boundary state of the network\nstart_r = matrix(0.5, nrow = length(net), ncol = 1)\n## headwaters are most extreme\nstart_r[c(10:12, 16, 20:24), ] = 0.9\nstart_r[c(42:43, 46, 47:52), ] = 0.1\n\nstart_r[c(9, 13:15, 17:19), ] = 0.7\nstart_r[c(38:41, 44:45), ] = 0.3\n\n\n## gamma diversity scenarios\n## how many species are possible in each scenario\nn_gamma = 6\ng_div = c(2, 10, 20, 30, 40, 50)\n\n\n## how many times to repeat each particular species richness (with new random species)\n## and dispersal combo\nn_reps = 40\n\n# generate random species lists for each gamma\nsps = lapply(g_div[-n_gamma], \\(gam) t(sapply(1:n_reps, \\(n) sample(nsp, gam, replace = FALSE))))\nnames(sps) = paste(\"g\", g_div[-n_gamma], sep=\"\")\nsps$g50 = matrix(1:nsp, nrow = 1)\nsaveRDS(sps, \"splists.rds\")\n\n## how many times to repeat each particular combination\n## multithreaded, so up to mc.cores is free\nn_iter = 2 * getOption(\"mc.cores\")\n\nnt = 60 # number of time steps of one simulation\n\n# total number of simulations, and time\n# n_sims = n_reps * n_gamma * n_disp * ceiling((n_iter / getOption(\"mc.cores\")))\n# set_units(n_sims * set_units(19.223, \"seconds\"), \"hours\")\n\n\n# loop across all the scenarios\nfor(d_alpha in alpha) {\n\tcat(\"alpha = \", d_alpha, \" (\", which(alpha == d_alpha), \" of \", length(alpha), \")\\n\", sep=\"\")\n\tdopts = list(alpha = d_alpha, beta = beta)\n\tmcom = metacommunity(nsp = nsp, nr = 1, niches = niches_custom, niche_args = nopts, \n\t\t\tdispersal = dispersal_custom, dispersal_args = dopts)\n\n\tfor(j in 1:n_gamma) {\n\t\tgam = g_div[j]\n\t\tsplist = sps[[j]]\n\t\tcat(\" gamma diversity = \", gam, \" (\", j, \" of \", n_gamma, \")\\n\", sep=\"\")\n\t\tfor(i in 1:nrow(splist)) {\n\t\t\tspecies_ids = splist[i,]\n\t\t\tcat(\" species \", paste(species_ids, collapse = \" \"), \" (\", i, \" of \", \n\t\t\t\tnrow(splist), \")\\n\", sep=\"\")\n\t\t\tfbase = paste0(\"fit_alpha-\", round(d_alpha, 2), \"_sp-\", \n\t\t\t\tpaste(species_ids, collapse='.'), \".rds\")\n\t\t\tfname = file.path(\"ex3\", \"res\", fbase)\n\t\t\tstart_sp = matrix(0, nrow = length(net), ncol = nsp)\n\t\t\tfor(k in species_ids)\n\t\t\t\tstart_sp[,k] = rbinom(length(net), 1, prev)\n\t\t\tmod = flume(mcom, net, start_sp, start_r)\n\t\t\tsuppressMessages(mod <- run_simulation(mod, nt, reps = n_iter))\n\t\t\tsaveRDS(mod, fname)\n\t\t}\n\t}\n}", "meta": {"hexsha": "cb6542972fd7e1565ebc19c72ef1e2245bf34e77", "size": 2848, "ext": "r", "lang": "R", "max_stars_repo_path": "flume_examples/ex2/R/ex2.r", "max_stars_repo_name": "mtalluto/FLUFLUX_model", "max_stars_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "flume_examples/ex2/R/ex2.r", "max_issues_repo_name": "mtalluto/FLUFLUX_model", "max_issues_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "flume_examples/ex2/R/ex2.r", "max_forks_repo_name": "mtalluto/FLUFLUX_model", "max_forks_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.9565217391, "max_line_length": 97, "alphanum_fraction": 0.6601123596, "num_tokens": 958, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7431680086124811, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.3282372480776689}} {"text": "# Best fixed AR(1) model for drift detection\n# Rest is taken over from analysis v23 and v26\n\nsource('./../../gwloggeR/R/fourier.R')\n\nlogger.names <- grep('barometer/', gwloggeR.data::enumerate(), value = TRUE)\n\nlogger.names <- setdiff(logger.names, 'barometer/BAOL016X_W1666.csv')\nlogger.names <- setdiff(logger.names, 'barometer/BAOL050X_56819.csv') # high freq manu in range of 80 cmH2O\n\nround_timestamp <- function(ts, scalefactor.sec = 3600*12) {\n as.POSIXct(round(as.numeric(ts)/scalefactor.sec) * scalefactor.sec, origin = '1970-01-01', tz = 'UTC')\n}\n\nread.baro <- function(logger.name) {\n df <- gwloggeR.data::read(logger.name)$df\n if (nrow(df) == 0L) return(df)\n df <- df[!is.na(TIMESTAMP_UTC), ]\n df <- df[!is.na(PRESSURE_VALUE), ]\n df <- df[!duplicated(TIMESTAMP_UTC), ]\n df <- df[, .('PRESSURE_VALUE' = mean(PRESSURE_VALUE)),\n by = .('TIMESTAMP_UTC' = round_timestamp(TIMESTAMP_UTC))]\n\n # Meta data\n df[, 'FILE' := basename(logger.name)]\n df[, 'N' := .N]\n\n data.table::setkey(df, TIMESTAMP_UTC)\n data.table::setattr(df, 'logger.name', logger.name)\n\n df\n}\n\n# from analysis 04\ncompare <- function(df1, df2) {\n if (nrow(df1) == 0L || nrow(df2) == 0L) return(data.table::data.table())\n\n diff.df <- df1[J(df2), .('PRESSURE_DIFF' = x.PRESSURE_VALUE - i.PRESSURE_VALUE, TIMESTAMP_UTC)][!is.na(PRESSURE_DIFF), ]\n\n data.table::setkey(diff.df, TIMESTAMP_UTC)\n\n diff.df\n}\n\nref.compare <- function(logger.name) {\n compare(read.baro(logger.name), read.baro('KNMI_20200312_hourly'))\n}\n\ncoef.pvals <- function(M) {\n (1-pnorm(abs(M$coef[colnames(M$var.coef)])/sqrt(diag(M$var.coef))))*2\n}\n\nplt.comp <- function(TIMESTAMP_UTC, Y, M = NULL, significant = NULL, front_layer = NULL,\n xlim = range(TIMESTAMP_UTC), ylim = quantile(Y, c(0.005, 0.995))) {\n\n force(TIMESTAMP_UTC); force(Y); force(M)\n force(ylim); force(xlim)\n\n p <- ggplot2::ggplot(mapping = ggplot2::aes(x = TIMESTAMP_UTC, y = Y)) +\n front_layer +\n ggplot2::geom_line(col = 'red', alpha = 0.9)\n\n if (!is.null(M))\n p <- p + ggplot2::geom_line(mapping = ggplot2::aes(y = fitted(M)), col = 'black', size = 1.5)\n\n if (!is.null(significant)) {\n p <- p + ggplot2::annotate(\n \"label\", x = as.POSIXct(-Inf, origin = '1970-01-01'), y = Inf,\n size = 4, col = if (significant) 'green' else 'red',\n hjust = 0, vjust = 1, fill = 'grey', label.size = NA,\n label = if (significant) 'SIGNIFINCANT' else 'NOT SIGNIFICANT'\n )\n }\n\n if (!is.null(M[['btrend']]) && coef.pvals(M)['btrend'] < 0.0001)\n p <- p + ggplot2::geom_vline(xintercept = TIMESTAMP_UTC[which.max(M$btrend > 0)],\n col = 'blue', linetype = 'dotted', size = 1.2)\n\n p <- p +\n ggplot2::coord_cartesian(ylim = ylim, xlim = xlim) +\n ggplot2::ylab('PRESSURE_DIFF') +\n ggplot2::theme_light() +\n ggplot2::theme(axis.text.y = ggplot2::element_text(angle = 90, hjust = 0.5),\n axis.title.y = ggplot2::element_blank(),\n axis.title.x = ggplot2::element_blank())\n\n p\n}\n\nreport <- function(logger.name) {\n require(forecast)\n\n environment(plt.comp) <- environment() # fitted(Arima) then can find the source data.\n\n print(logger.name)\n\n df.diff <- ref.compare(logger.name)\n\n if (nrow(df.diff) < 10L) return(invisible(FALSE))\n\n M <- arima(x = df.diff$PRESSURE_DIFF, order = c(0, 0, 0))\n df.diff[, M_pred := fitted(M)]\n\n M.AR90 <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), transform.pars = FALSE, fixed = c(0.90, NA))\n df.diff[, M.AR90.pred := fitted(M.AR90)]\n\n M.AR60 <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), transform.pars = FALSE, fixed = c(0.60, NA))\n df.diff[, M.AR60.pred := fitted(M.AR60)]\n\n trend <- c(0, cumsum(diff(as.numeric(df.diff$TIMESTAMP_UTC))/3600/24))\n breakpoints <- seq(from = 1, to = nrow(df.diff) - 1, by = 10)\n\n M.ARD90 <- M.AR90\n for (bp in breakpoints) {\n btrend <- c(rep(0, bp - 1), trend[bp:length(trend)] - trend[bp])\n .M <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = btrend, transform.pars = FALSE, fixed = c(0.90, NA, NA))\n .M[['btrend']] <- btrend\n if (logLik(.M) > logLik(M.ARD90)) M.ARD90 <- .M\n }\n df.diff[, M.ARD90.pred := fitted(M.ARD90)]\n\n M.ARD60 <- M.AR60\n for (bp in breakpoints) {\n btrend <- c(rep(0, bp - 1), trend[bp:length(trend)] - trend[bp])\n .M <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = btrend, transform.pars = FALSE, fixed = c(0.60, NA, NA))\n .M[['btrend']] <- btrend\n if (logLik(.M) > logLik(M.ARD60)) M.ARD60 <- .M\n }\n df.diff[, M.ARD60.pred := fitted(M.ARD60)]\n\n\n sbasis <- fbasis(timestamps = df.diff$TIMESTAMP_UTC, frequencies = 1/(365.25*3600*24))\n\n M.ARS90 <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = sbasis, transform.pars = FALSE, fixed = c(0.90, NA, NA, NA))\n\n M.ARDS90 <- M.ARS90\n for (bp in breakpoints) {\n btrend <- c(rep(0, bp - 1), trend[bp:length(trend)] - trend[bp])\n .M <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = cbind(sbasis, btrend), transform.pars = FALSE, fixed = c(0.90, NA, NA, NA, NA))\n .M[['btrend']] <- btrend\n if (logLik(.M) > logLik(M.ARDS90)) M.ARDS90 <- .M\n }\n\n M.ARS60 <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = sbasis, transform.pars = FALSE, fixed = c(0.60, NA, NA, NA))\n\n M.ARDS60 <- M.ARS60\n for (bp in breakpoints) {\n btrend <- c(rep(0, bp - 1), trend[bp:length(trend)] - trend[bp])\n .M <- arima(x = df.diff$PRESSURE_DIFF, order = c(1, 0, 0), xreg = cbind(sbasis, btrend), transform.pars = FALSE, fixed = c(0.60, NA, NA, NA, NA))\n .M[['btrend']] <- btrend\n if (logLik(.M) > logLik(M.ARDS60)) M.ARDS60 <- .M\n }\n\n M.AUTO <- forecast::auto.arima(df.diff$PRESSURE_DIFF, trace = TRUE,\n stationary = TRUE, ic = 'aicc',\n xreg = data.matrix(cbind(sbasis, 'btrend' = M.ARDS90[['btrend']])))\n M.AUTO[['btrend']] <- M.ARDS90[['btrend']]\n\n df.diff[, TIMESTAMP_UTC_DIFF_HOURS := c(NA, round(diff(as.numeric(TIMESTAMP_UTC))/3600))]\n\n p.comp <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF)\n\n p.M <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M)\n\n p.M.AR60 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.AR60)\n p.M.AR90 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.AR90)\n\n p.M.ARS60 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARS60)\n p.M.ARS90 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARS90)\n\n p.M.ARD60 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARD60)\n p.M.ARD90 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARD90)\n\n p.M.ARDS60 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARDS60)\n p.M.ARDS90 <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.ARDS90)\n\n p.M.AUTO <- plt.comp(df.diff$TIMESTAMP_UTC, df.diff$PRESSURE_DIFF, M.AUTO)\n\n p.ts.diff <- ggplot2::ggplot(data = df.diff[-1, ], mapping = ggplot2::aes(\n x = TIMESTAMP_UTC,\n y = TIMESTAMP_UTC_DIFF_HOURS)) +\n ggplot2::scale_y_continuous(\n trans = scales::trans_new(name = 'log12',\n transform = function(x) logb(x = x, base = 12),\n inverse = function(x) 12^x)) +\n ggplot2::geom_point(pch = '-', size = 8) +\n ggplot2::ylab('PRESSURE_DIFF') +\n ggplot2::theme_light() +\n ggplot2::theme(axis.text.y = ggplot2::element_text(angle = 90, hjust = 0.5),\n axis.title.y = ggplot2::element_blank(),\n axis.title.x = ggplot2::element_blank())\n\n # File export ----------------------------------------------------------------\n\n filename <- sprintf('./drifts/analysis_28/%s.png', tools::file_path_sans_ext(basename(logger.name)))\n dir.create(dirname(filename), showWarnings = FALSE, recursive = TRUE)\n\n layout_matrix <- rbind(c(1, 2),\n c(3, 4),\n c(5, 6))\n\n grob.title <- grid::textGrob(sprintf('%s (#%s differences with KNMI data from %s to %s) -> %s | Fixed AR(1) = 0.9 and 0.6',\n basename(logger.name), nrow(df.diff),\n min(df.diff$TIMESTAMP_UTC), max(df.diff$TIMESTAMP_UTC),\n forecast:::arima.string(M.AUTO)),\n x = 0.05, hjust = 0)\n\n p.empty <- ggplot2::ggplot() + ggplot2::theme_void()\n\n local({\n png(filename, width = 1280, height = 720)\n on.exit(dev.off())\n gridExtra::grid.arrange(p.M.AUTO, p.ts.diff,\n p.M.ARD90, p.M.ARDS90,\n p.M.ARD60, p.M.ARDS60,\n layout_matrix = layout_matrix,\n top = grob.title)\n })\n\n invisible(TRUE)\n\n}\n\n# report('BAOL009X_78680') # drifter\n# report('BAOL008X_72528') # x-y model AR component significantly down to 0.46\n# report('BAOL538X_B_002A2') # strong undetected (?) drift\n# report('BAOL031X_B5554') # shift at the end\ninvisible(lapply(logger.names, report))\n", "meta": {"hexsha": "075248e14431d57b22fb373f7bef3d9d0d1bf42f", "size": 9022, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r/drifts/analysis_28.r", "max_stars_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_stars_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-16T10:47:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-16T10:47:56.000Z", "max_issues_repo_path": "src/r/drifts/analysis_28.r", "max_issues_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_issues_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 61, "max_issues_repo_issues_event_min_datetime": "2019-05-17T21:14:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-26T13:47:40.000Z", "max_forks_repo_path": "src/r/drifts/analysis_28.r", "max_forks_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_forks_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-07-30T10:39:48.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-16T10:48:04.000Z", "avg_line_length": 39.3973799127, "max_line_length": 149, "alphanum_fraction": 0.5987585901, "num_tokens": 2926, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5273165233795672, "lm_q1q2_score": 0.32823309047453253}} {"text": "\nlibrary(\"testthat\");\n#install.packages(\"testthat\")\n\nsource(\"../testCatSingle.r\");\n\n# reordering values of a categorical variable\nx1 = reorderOrderedCategory(c(7,3,5,1,8,10,1,5,3,7), \"7|3|5|1\");\nexpect_equal(x1, c(1,2,3,4,NA,NA,4,3,2,1));\n\n\n\n\n\n\n\n", "meta": {"hexsha": "1f31f01871c026407bfaa890c3be56de2e42322b", "size": 246, "ext": "r", "lang": "R", "max_stars_repo_path": "WAS/unittests/test_testCatSingle.r", "max_stars_repo_name": "lganel/PHESANT", "max_stars_repo_head_hexsha": "0f94a3683986b18ca90e20bff0d8bf723bd80211", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2017-02-27T00:47:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-14T15:42:01.000Z", "max_issues_repo_path": "WAS/unittests/test_testCatSingle.r", "max_issues_repo_name": "EvaAusChina/PHESANT", "max_issues_repo_head_hexsha": "5e114342f22c447b663ac496b79dc7633fc6cd51", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2017-05-07T13:39:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-25T12:23:55.000Z", "max_forks_repo_path": "WAS/unittests/test_testCatSingle.r", "max_forks_repo_name": "EvaAusChina/PHESANT", "max_forks_repo_head_hexsha": "5e114342f22c447b663ac496b79dc7633fc6cd51", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 47, "max_forks_repo_forks_event_min_datetime": "2017-02-27T12:25:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-02T22:03:16.000Z", "avg_line_length": 14.4705882353, "max_line_length": 64, "alphanum_fraction": 0.662601626, "num_tokens": 95, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.651354857898194, "lm_q1q2_score": 0.32822173209899236}} {"text": "technical_ui <- tabItem(tabName = \"Technical\",\n\th2(\"Technical\"),\n\ttabsetPanel(id=\"tabSelected\",\n\t\ttabPanel(\"MARS\"), #multiple adaptive regression splines? \n\t\ttabPanel(\"LM\"), #linear model\n\t\ttabPanel(\"MACD\") #provides thresholds for buying and selling based on stretched points?\n\t)\n)\n", "meta": {"hexsha": "6fbbfe89e7b5ad228bb39bcfa3e805994dc4b155", "size": 283, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/Stonkalysis/ui/technical.r", "max_stars_repo_name": "jjlynch2/Stonkalysis", "max_stars_repo_head_hexsha": "0e3b7e82774d0442637078253bdf9eb7caf7298b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/Stonkalysis/ui/technical.r", "max_issues_repo_name": "jjlynch2/Stonkalysis", "max_issues_repo_head_hexsha": "0e3b7e82774d0442637078253bdf9eb7caf7298b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2021-02-28T02:12:22.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-16T16:31:47.000Z", "max_forks_repo_path": "inst/Stonkalysis/ui/technical.r", "max_forks_repo_name": "jjlynch2/VALAPP", "max_forks_repo_head_hexsha": "0e3b7e82774d0442637078253bdf9eb7caf7298b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4444444444, "max_line_length": 89, "alphanum_fraction": 0.7314487633, "num_tokens": 72, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6513548511303338, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3282217286886258}} {"text": "\n#------------------------------\n# library\n#------------------------------\n\nlibrary(magrittr)\nlibrary(dplyr)\n\n#------------------------------\n# main\n#------------------------------\n\n# setting\ndir_path <- file.path(\"..\", \"submission\")\nprj_name <- \"submission_pre\"\n\n# stack filenames\nprint(\"==> Stack filenames\")\nfilename_regex <- \".+_train(.+)_valid(.+)_seed(.+).csv\"\n\nresult <- list.files(file.path(\"..\", \"submission\", prj_name)) %>%\n\tdata.frame(\n\t\tfilenames=.,\n\t\ttrain=gsub(filename_regex, \"\\\\1\", .) %>% as.numeric,\n\t\tvalid=gsub(filename_regex, \"\\\\2\", .) %>% as.numeric,\n\t\tseed=gsub(filename_regex, \"\\\\3\", .) %>% as.integer\n\t\t) %>%\n\tarrange(desc(valid)) %T>% \n\tprint\n\n# number of input\nnum_model <- nrow(result)\n\n# averaging\nprint(\"==> Load data\")\ndata_all <- NULL\nfor(i in 1:num_model){\n\ttarget_file <- as.character(result$filename[i])\n\tcat(\"filename: \", target_file, \"\\n\")\n\n\tdatai <- read.csv(file.path(dir_path, prj_name, target_file))\n\tprint(table(datai$Response))\n\n\tcolnames(datai) <- c(\"Id\", result$seed[i])\n\n\tif(i==1){\n\t\tdata_all <- datai\n\t}else{\n\t\tdata_all <- merge(data_all, datai, by=\"Id\")\n\t}\n}\n\n# ensemble\nprint(\"==> Averaging\")\nthreshold <- ceiling(num_model / 2)\nensemble <- as.integer(rowSums(data_all[,-1]) >= threshold)\ncat(\"ensemble result\\n\")\nprint(table(ensemble))\n\nsubmission <- data.frame(Id=data_all$Id, Response=ensemble)\n\n# display scores\ntrain_scores <- result$train[1:num_model]\nvalid_scores <- result$valid[1:num_model]\ncat(\"train: \", sprintf(\"%.4f +- %.4f\", mean(train_scores), sd(train_scores)), \"\\n\")\ncat(\"valid: \", sprintf(\"%.4f +- %.4f\", mean(valid_scores), sd(valid_scores)), \"\\n\")\n\n# save output\nmeantrain <- mean(train_scores)\nmeanvalid <- mean(valid_scores)\nst <- function(score) return(sprintf(\"%.3f\", score))\nfilename <- paste0(\"submission_ensemble\", num_model, \"_mtrain\", st(meantrain), \"_mvalid\", st(meanvalid), \".csv\")\nwrite.table(submission, file.path(dir_path, filename), row.names = FALSE, sep=\",\", quote = FALSE)\n", "meta": {"hexsha": "f554e82c27d107f2a5a7ffe741a6a5c12db7ce42", "size": 1959, "ext": "r", "lang": "R", "max_stars_repo_path": "source/03_averaging.r", "max_stars_repo_name": "toshi-k/kaggle-bosch-production-line-performance", "max_stars_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:30:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T16:47:31.000Z", "max_issues_repo_path": "source/03_averaging.r", "max_issues_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_issues_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-05-19T00:19:14.000Z", "max_issues_repo_issues_event_max_datetime": "2017-05-19T16:20:59.000Z", "max_forks_repo_path": "source/03_averaging.r", "max_forks_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_forks_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2016-12-10T23:02:50.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-15T12:48:28.000Z", "avg_line_length": 26.472972973, "max_line_length": 112, "alphanum_fraction": 0.6263399694, "num_tokens": 525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3281458822045251}} {"text": "# Model prediction Accuracy calculating function.\r\n#The accuracy calculation function work based on +- one log bacterial count of predicted values from the actual values.\r\n\r\n# x = is predicted values using the trained model\r\n# y = is the actual test data \r\nPercentage<-function(x, y){\r\n SVMLM_TVC<-c()\r\n for ( i in 1:length(x)){\r\n if((x[i])<=((y[i])+1) & x[i]>=((y[i])-1)){\r\n G<-i\r\n SVMLM_TVC<-c(SVMLM_TVC, G)\r\n \r\n }\r\n else{\r\n \r\n }\r\n \r\n }\r\n percentage_SVMLM_TVC<- length(SVMLM_TVC)/length(y)*100\r\n}", "meta": {"hexsha": "79d197c3ec6406f905354eed0c87640bc9c1a6e0", "size": 537, "ext": "r", "lang": "R", "max_stars_repo_path": "MachineLearning/MSI/Percentage_Accuracy.r", "max_stars_repo_name": "saha-19/Food-Spoilage-API", "max_stars_repo_head_hexsha": "eb8dc55d03313c94d65013428f7e2b5084e414f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "MachineLearning/MSI/Percentage_Accuracy.r", "max_issues_repo_name": "saha-19/Food-Spoilage-API", "max_issues_repo_head_hexsha": "eb8dc55d03313c94d65013428f7e2b5084e414f3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MachineLearning/MSI/Percentage_Accuracy.r", "max_forks_repo_name": "saha-19/Food-Spoilage-API", "max_forks_repo_head_hexsha": "eb8dc55d03313c94d65013428f7e2b5084e414f3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-14T16:25:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-14T16:25:57.000Z", "avg_line_length": 26.85, "max_line_length": 120, "alphanum_fraction": 0.6033519553, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32814587438460197}} {"text": "# Extract values from reference products\nlibrary(terra)\nsource(\"pixel-based/utils/load-sampling-data.r\")\n\nInDir = \"../data/pixel-based/reference-products\"\nOutDir = \"../data/pixel-based/reference-products\"\n\nif (!dir.exists(OutDir))\n dir.create(OutDir)\n\n# Points to extract\n#TrainingPoints = LoadGlobalTrainingData()\nValidationPoints = LoadGlobalValidationData()\n#PredictionPoints = LoadGlobalRasterPoints()\nAllPoints = ValidationPoints[,c(\"x\", \"y\")]#rbind(TrainingPoints[,c(\"x\", \"y\")], ValidationPoints[,c(\"x\", \"y\")], PredictionPoints[,c(\"x\", \"y\")])\n\n# Put all data sources into a list\nMetricNames = c(\"treecover\", \"gsw\", \"ghsl\", \"impervious\")\nMetricFiles = c(\n file.path(InDir, \"MeasuresTreecover.vrt\"),\n file.path(InDir, \"GSW2015.vrt\"),\n file.path(InDir, \"GHSLBuiltup2014.vrt\"),\n file.path(InDir, \"QinghuaImpervious2015.vrt\"))\nnames(MetricFiles) = MetricNames\n\nfor (f in 1:length(MetricFiles))\n{\n MetricRast = rast(MetricFiles[f])\n \n ChunkSize = 5000\n Iterator = seq(1, nrow(AllPoints), ChunkSize)\n pb = txtProgressBar(min = 1, max = nrow(AllPoints), style = 3)\n for (i in Iterator)\n {\n ChunkEnd = min(i+ChunkSize-1, nrow(AllPoints))\n DataChunk = AllPoints[i:ChunkEnd,]\n \n DSValues = cbind(extract(MetricRast, vect(DataChunk)), DataChunk)\n names(DSValues)[1] = MetricNames[f]\n st_write(DSValues, file.path(OutDir, paste0(MetricNames[f], \".gpkg\")), append=TRUE)\n rm(DSValues)\n rm(DataChunk)\n gc()\n \n setTxtProgressBar(pb, ChunkEnd)\n }\n \n close(pb)\n}\n", "meta": {"hexsha": "b6006bf5571dde69ad121cf1d0ae542c1663b291", "size": 1572, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pixel-based/post-classification/ingest-reference-data.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/pixel-based/post-classification/ingest-reference-data.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pixel-based/post-classification/ingest-reference-data.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 31.44, "max_line_length": 142, "alphanum_fraction": 0.6647582697, "num_tokens": 453, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32814587438460197}} {"text": "nextState <- function(x, y, dir, inp) {\n cmd <- substring(inp, 1, 1)\n val <- strtoi(substring(inp, 2, nchar(inp)))\n\n result <- switch(cmd,\n \"N\" = move(x, y, \"N\", val, dir),\n \"S\" = move(x, y, \"S\", val, dir),\n \"E\" = move(x, y, \"E\", val, dir),\n \"W\" = move(x, y, \"W\", val, dir),\n \"F\" = move(x, y, dir, val, dir),\n \"R\" = c(x, y, rotate(val, dir)),\n \"L\" = c(x, y, rotate(-val, dir)))\n\n result\n}\n\nmove <- function(x, y, moveDir, val, dir) {\n switch(moveDir,\n \"N\" = c(x, y + val, dir),\n \"S\" = c(x, y - val, dir),\n \"E\" = c(x + val, y, dir),\n \"W\" = c(x - val, y, dir))\n}\n\n\nfromDirection <- function(dir) {\n switch(dir,\n \"N\" = 0,\n \"E\" = 90,\n \"S\" = 180,\n \"W\" = 270)\n}\n\ntoDirection <- function(deg) {\n switch((deg %/% 90) + 1,\n \"N\",\n \"E\",\n \"S\",\n \"W\")\n}\n\nrotate <- function(rotation, dir) {\n toDirection((360 + fromDirection(dir) + rotation) %% 360)\n}\n\nfileName <- \"input.txt\"\nx <- 0\ny <- 0\ndir <- \"E\"\n\ninp <- file(fileName, \"r\")\n\nfor (line in readLines(inp)) {\n s <- nextState(x, y, dir, line)\n x <- strtoi(s[1])\n y <- strtoi(s[2])\n dir <- s[3]\n}\n\nclose(inp)\n\nprint(abs(x) + abs(y))\n", "meta": {"hexsha": "ead1df948cdbaf9cdeba11cb09d8224b500975f6", "size": 1239, "ext": "r", "lang": "R", "max_stars_repo_path": "12/first.r", "max_stars_repo_name": "madetara/advent2020", "max_stars_repo_head_hexsha": "39492ef746baa8e49de880cb2604b5b67a5792ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12/first.r", "max_issues_repo_name": "madetara/advent2020", "max_issues_repo_head_hexsha": "39492ef746baa8e49de880cb2604b5b67a5792ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12/first.r", "max_forks_repo_name": "madetara/advent2020", "max_forks_repo_head_hexsha": "39492ef746baa8e49de880cb2604b5b67a5792ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6666666667, "max_line_length": 61, "alphanum_fraction": 0.4447134786, "num_tokens": 437, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32814587438460197}} {"text": "########################################################################################\n## This file is a part of YAP package of scripts. https://github.com/shpakoo/YAP\n## Distributed under the MIT license: http://www.opensource.org/licenses/mit-license.php\n## Copyright (c) 2011-2013 Sebastian Szpakowski\n########################################################################################\n\nlibrary(ggplot2)\nlibrary(grid)\nlibrary(scales)\n\nmultiplot <- function(..., plotlist=NULL, cols=1, layout=NULL)\n{\n\trequire(grid)\n\t\n\t# Make a list from the ... arguments and plotlist\n\tplots = c(list(...), plotlist)\n\t\n\tnumPlots = length(plots)\n\tprint (numPlots)\n\t\n\t# If layout is NULL, then use 'cols' to determine layout\n\tif (is.null(layout)) \n\t{\n\t\tlayout = grid.layout(ceiling(numPlots/cols), cols)\n\t}\n\t\n\t# create a panel that will be used to map ids later\n\t\n\tpanel = matrix(1:(layout$nrow * layout$ncol), nrow = layout$nrow, ncol = layout$ncol, byrow=TRUE )\n\t\n\tif (numPlots==1) \n\t{\n\t\tprint(plots[[1]])\n\t} \n\telse \n\t{\n\t\t# Set up the page\n\t\tgrid.newpage()\n\t\tpushViewport(viewport(layout = layout))\n\t\t\n\t\tfor (i in 1:numPlots) \n\t\t{\n\t\t\tmatching <- as.data.frame(which(panel == i, arr.ind = TRUE))\n\t\t\t\n\t\t\tprint ( plots[[i]], vp = viewport(\tlayout.pos.row = matching$row, \n\t\t\t\t\t\t\tlayout.pos.col = matching$col)\n\t\t\t)\n\t\t}\n\t}\n}\n\n\nmakePlot = function(filename = \"read.groups.tab.txt\", minreads = 0, mingroups = 0, nlevels = 500, mode=\"OTU\", xlim=c(0,0), ylim=c(0,0))\n{\n\tif (mode==\"OTU\")\n\t{\n\t\tx = read.table(filename, as.is=T, header=F, sep=\"\\t\")\n\t\tx=x[-1,]\n\t\tnames(x) = c(\"reads\", \"groups\")\n\t\t\n\t\tylab = \"number of samples showing an OTU\"\n\t\txlab = \"OTU size (i.e. number of reads in OTU, log10 scale)\"\n\t\t\n\t\thistlab=\"OTU size\"\n\t\t\n\t\tgroups = unique(x$groups)\n\t\tlabs = groups\n\t\t\n\t\talldata = x\n\t\tsubsetdata = x[x$reads>minreads & x$groups>mingroups,]\n\t\t\n\t\tv = NA\n\t\th = NA\n\t\t\n\t\tforhist = subsetdata$reads\n\t\tforhist[forhist>100]=100\n\t\t\n\t}\n\telse if ( mode==\"coverage\")\n\t{\n\t\tx = read.table(filename, as.is=T, header=F, sep=\"\", skip=34)\n\t\t#print (dim(x))\n\t\t#x=x[-1,]\n\t\t\n\t\t### Contig Sites Reads Coverage\n\t\tnames(x) = c(\"x1\", \"reads\", \"x2\", \"groups\")\n\t\t\n\t\tylab = \"coverage\"\n\t\txlab = \"Contig Size (log10 scale)\"\n\t\t\n\t\thistlab=\"Contig Coverage\"\n\t\t\t\t\n\t\tx$groups = log10(x$groups)\n\t\t\n\t\t#groups = round(seq(0,ceiling(max(x$groups)), length.out=20),2)\n\t\tgroups = log10(c(1,10,100,1000,10000))\n\t\tlabs = round(10^groups,0)\n\t\t\n\t\talldata = x\n\t\tsubsetdata = x[x$reads>minreads & x$groups>mingroups,]\n\t\t\n\t\tforhist = 10^subsetdata$groups\n\t\t\n\t\th = mean(subsetdata$reads)\n\t\tv = sum(subsetdata$x2) / sum(subsetdata$reads) * 100\n\t\t\n\t\txlim = log10(c(100,100000))\n\t\tylim = log10 (c(0.1,10000))\n\t\t\n\t\tforhist[forhist>100]=100\n\t\tprint (v)\n\t\t\n\t}\n\telse\n\t{\n\t\tprint (\"unknown plotting mode.\")\n\t\treturn (0)\n\t}\n\t\n\tif (nrow(subsetdata)>2 && nrow(alldata)>2)\n\t{\n\t\talldata = alldata[is.finite(alldata$groups),]\n\t\tsubsetdata = subsetdata[is.finite(subsetdata$groups),]\n\t\t\n\t\t### setup canvas using all data\n\t\n\t\tnf <- layout(matrix(c(1,2,3,0), 2, 2, byrow=TRUE), respect=FALSE, width=c(0.8,0.2))\n\t\t\n\t\tif (xlim[1]==xlim[2])\n\t\t{\n\t\t\tylim = c(min(alldata$groups)-0.5,max(alldata$groups)+0.5)\n\t\t\tplot (log10(alldata$reads), alldata$groups, type= \"n\", ylab = ylab, xlab = xlab, ylim=ylim, axes=FALSE, main=filename)\n\t\n\t\t}\n\t\telse\n\t\t{\n\t\t\tplot (log10(alldata$reads), alldata$groups, type= \"n\", ylab = ylab, xlab = xlab, ylim=ylim, xlim=xlim, axes=FALSE, main=filename)\n\t\t}\n\t\t\n\t\t\n\t\t\n\t\taxis(2, at=groups, lab=labs, las=2)\n\t\tA = c(1:9)\n\t\tB =seq(0, log10(max(alldata$reads)), by=1)\n\t\tC =expand.grid(A, B)\n\t\tC$at = C[,1] * 10^(C[,2])\n\t\tC$lab = ifelse (C[,1]%%10==1, 10^C[,2], \"\")\n\t\t\n\t\taxis(1, at=log10(C$at), lab=rep(\"\", nrow(C)) , col.ticks=\"gray\", lwd.ticks=2 )\n\t\tC2 = C[C$lab!=\"\", ]\n\t\taxis(1, at=log10(C2$at), lab=C2$lab , col.ticks=\"black\", lwd.ticks= 4 )\n\t\n\t\t### use only subset data from now on:\n\t\tsubsetdata$reads = log10(subsetdata$reads)\n\t\t\t\n\t\t# contour\n\t\trequire(MASS)\n try( {\n\t\txykde = MASS::kde2d(subsetdata$reads, subsetdata$groups, lims = c( min(subsetdata$reads)-5, max(subsetdata$reads)+5, min(subsetdata$groups)-5, max(subsetdata$groups)+5) )\n\t\t\n\t\tif (sum(is.nan(xykde$z))>0)\n\t\t{\n\t\t\tcat(\"have to nudge groups...\\n\")\n\t\t\tb = subsetdata$groups + sample(c(-0.1, 0.1), length(subsetdata$groups), replace=T)\n\t\t\txykde = MASS::kde2d(subsetdata$reads, b, lims = c( min(subsetdata$reads)-5, max(subsetdata$reads)+5, min(subsetdata$groups)-5, max(subsetdata$groups)+5) )\n\t\t}\n\t\t\n\t\tzlim = range(xykde$z, finite = TRUE)\n\t\tlev = seq(zlim[1], zlim[2], le = nlevels)\n\t\tcol = rev(heat.colors(length(lev)))\n\t\tcontour(xykde, add = TRUE, levels = lev,\n\t\t\tdrawlabels = FALSE, col=col ) \n\t\t\t\t\n\t\t# trendline\n\t\ttrendline = lowess((subsetdata$reads), subsetdata$groups, f=0.1)\n\t\tpoints(trendline , type=\"l\", lwd=5, col=\"gray30\")\n\t\t\n\t\t# points\n\t\tpoints(subsetdata$reads, subsetdata$group, pch=19, col=\"gray60\", cex=0.4) \n\t\t\n\t\t# cutoff lines\tif (mingroups>0)\n\t\t{\n\t\t\tabline(h=mingroups, col=\"red\", lty=2, lwd=0.5)\n\t\t}\n\t\tif (minreads>0)\n\t\t{\n\t\t\tabline(v=log10(minreads), col=\"red\", lty=2, lwd=0.5)\n\t\t}\n\t\tbox(lwd=5)\n\t\t\n\t\t# legend\n\t\tplot (0,100, xlim=c(0,1), ylim = c(min(lev), max(lev)), xlab=\"\", ylab=\"\", axes=F, main=\"\")\n\t\trect(0, lev[-length(lev)], 1, lev[-1L], col = col, border=NA) \n\t\n\t\t#print (lev)\n\t\t\n\t\tat = unique(round(seq(0, max(lev), length.out=10),2))\n\t\tlab = paste(round((1-(at/ceiling(max(at)))) * 100,0) , \"%\", sep=\"\")\n\t\taxis(4, las=1, cex.axis=0.5)\n\t\taxis(2, las=1, at = at, lab=lab)\n\t\tbox()\n\t\t\t\n\t\t\n\t\t# histogram\n\t\thist ((forhist), col=\"gray\", br=seq(0.5,100.5, by=1), axes =FALSE, xlab = histlab, main=\"\")\n\t\taxis(2)\n\t\tat = c(1:10, seq(20,100, by=10) )\n\t\tlabs = paste(at)\n\t\tlabs [2:9]=\"\"\n\t\t\n\t\tlabs[length(labs)] = paste(labs[length(labs)], \"+\", sep=\"\")\n\t\taxis(1, at = at, lab=labs, las=3)\n\t\t\n\t\tif (! is.na(v))\n\t\t{\t\n\t\t\t#abline(v= v, lwd=5, col=\"green\")\n\t\t\t#abline(h= h, lwd=5, col=\"blue\")\n\t\t\tlegend(\"top\", c(\n\t\t\t\t\t\t\t\tpaste( \"Average coverage: \", round(v,2), sep=\" \"),\n\t\t\t\t\t\t\t\tpaste( \"Average length: \", round(h,2), sep=\" \")\n\t\t\t\t\t\t\t\t), \n\t\t\t\t\t\t\t\t\n\t\t\tfill=c(\"green\", \"blue\"))\n\t\t}\n\t\n\t\t#print ( nrow(x[x$groups<=1 & x$reads<=0.1,]) )\n\t\t#print ( nrow(x))\n } ) \n\t}\n\n\t \n}\n\n\ngetData = function(files, mode=\"clccoverage\")\n{\n\tglobal = data.frame()\n\tfor (f in files)\n\t{\n\t\t\n\t\tprint (f)\n\t\tif (mode ==\"clccoverage\")\n\t\t{\n\t\t\ttmp = read.table(f, as.is=T, header=F, sep=\"\", skip=34)\n\t\t\tnames(tmp) <- c(\"Contig\", \"Sites\", \"Reads\", \"Coverage\")\n\t\t\t\n\t\t}\n\t\telse if (mode == \"otu\")\n\t\t{\n\t\t\ttmp = read.table(f, as.is=T, header=F, sep=\"\\t\")\n\t\t\tnames(tmp) <- c(\"Reads\", \"Groups\")\n\t\t\t\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn (global)\n\t\t}\n\t\t\n\t\ta = strsplit( f, \"\\\\.\")[[1]][2]\n\t\tb = strsplit( a,\"-\")[[1]][1]\n\t\tcat(f, \"->\", a, \"->\", b, \"\\n\")\n\t\ttmp$File = rep(b, nrow(tmp))\n\t\t\n\t\tif (nrow(global)==0)\n\t\t{\n\t\t\tglobal=tmp\n\t\t}\n\t\telse\n\t\t{\n\t\t\tglobal=rbind(tmp,global) \n\t\t}\n\t\t\t\n\t}\n\treturn (global)\n}\n\nmakeGGplotOTU = function(global)\n{\n\tprint (\"not there yet\")\n}\n\t\nmakeGGplotCOVERAGE = function(global)\n{\n\t\n\t### how do files compare w/r to contig sizes\n\tplots = list()\n\t\n\tfor (S in c(\"Reads\", \"Sites\", \"Coverage\"))\n\t{\n\t\tx1 = (ggplot(global, aes_string(y=S, x=\"File\", color=\"File\"))\n\t\t\t\t\t+ geom_boxplot() \n\t\t\t\t\t+ scale_y_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t\t+ coord_flip()\n\t\t\t\t\t+ theme(axis.ticks = element_blank(), axis.text.y = element_blank())\n\t\t\t\t\t\n\t\t\t\t\t)\n\t\t\n\t\tx2 = (ggplot(global, aes_string(x=S, y=\"..count..\", fill=\"File\", group=\"File\" )) \n\t\t\t\t\t+ stat_bin(aes(y=..count..), geom=\"area\", position=\"stack\", weight=2)\n\t\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t\t)\n\t\t\n\t\tx3 = (ggplot(global, aes_string(x=S, y=\"..count..\", fill=\"File\", group=\"File\" )) \n\t\t\t\t\t+ stat_bin(aes(y=..count..), geom=\"area\", position=\"dodge\", alpha=0.5, weight=2)\n\t\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t\t)\n\t\t\n\t\tx4 = (ggplot(global, aes_string(x=S, y=\"..count..\", fill=\"File\", group=\"File\" )) \n\t\t\t\t\t+ geom_bar(position=\"fill\")\n\t\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t\t)\n\t\n\t\t\n\t \tplots = append(list(x1, x2, x3, x4), plots)\n\t\t\n\t}\n\ty1 = (ggplot(global, aes(y=Reads / Sites, x=File, color=File))\n\t\t\t\t+ geom_boxplot() \n\t\t\t\t+ scale_y_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t+ coord_flip()\n\t\t\t\t+ theme(axis.ticks = element_blank(), axis.text.y = element_blank())\n\t\t\t\t\n\t\t\t\t)\n\ty2 = (ggplot(global, aes(x= Reads/Sites , y=..count.., fill=File, group=File )) \n\t\t\t\t+ stat_bin(aes(y=..count..), geom=\"area\", position=\"stack\", weight=2)\n\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t)\n\t\n\ty3 = (ggplot(global, aes(x= Reads/Sites , y=..count.., fill=File, group=File )) \n\t\t\t\t+ stat_bin(aes(y=..count..), geom=\"area\", position=\"dodge\", alpha=0.5, weight=2)\n\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t)\n\t\n\ty4 = (ggplot(global, aes(x= Reads/Sites , y=..count.., fill=File, group=File )) \n\t\t\t\t+ geom_bar(position=\"fill\")\n\t\t\t\t+ scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n\t\t\t\t)\n\t\t\n\tplots = append(plots, list(y1, y2, y3, y4), )\n\t\n\t\n\t\n\t#return (plots)\n\t\n#\tB = (ggplot(global, aes(File, Sites, color=File)) \n#\t\t\t\t+ geom_boxplot() \n#\t\t\t\t+ scale_y_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n#\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n#\t\t\t\t+ coord_flip()\n#\t\t\t\t+ theme(axis.ticks = element_blank(), axis.text.y = element_blank())\n#\t\t\t\t\n#\t\t\t\t)\n#\tC = (ggplot(global, aes(File, Coverage, color=File)) \n#\t\t\t\t+ geom_boxplot() \n#\t\t\t\t+ scale_y_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n#\t\t\t\t\t\tlabels = trans_format(\"log10\", math_format(10^.x)))\n#\t\t\t\t+ coord_flip()\n#\t\t\t\t+ theme(axis.ticks = element_blank(), axis.text.y = element_blank())\n#\t\t\t\t\n#\t\t\t\t)\n\t\n\t\n\tpng(\"AllAssemblyStats_GG.png\", width=20, height = 20, units=\"in\", res=250)\n\tmultiplot(plotlist=plots, cols=4)\n\tdev.off()\n\tinvisible(plots)\n\n}\n\nmakeBatchOTU = function(filename) \n{\n\tpdf(paste(filename,\"otusizes.pdf\", sep=\".\"), paper=\"special\", width=11, height=10)\n\tmakePlot(filename = filename, minreads=0, mingroups=0)\n\tmakePlot(filename = filename, minreads=1, mingroups=0)\n\tmakePlot(filename = filename, minreads=1, mingroups=1)\n\tmakePlot(filename = filename, minreads=5, mingroups=1)\n\tdev.off()\n}\n\nmakeBatchCoverage = function(filename)\n{\n\tpdf(paste(filename,\"coveragestats.pdf\", sep=\".\"), paper=\"special\", width=11, height=10)\n\tmakePlot(filename = filename, minreads=0, mingroups=0, mode=\"coverage\", nlevels=50)\n\tmakePlot(filename = filename, minreads=0, mingroups=log10(10), mode=\"coverage\", nlevels=50)\n\tmakePlot(filename = filename, minreads=1000, mingroups=log10(10), mode=\"coverage\", nlevels=50)\n\tdev.off()\n}\n\n\n###################################################################\n##### OTU stats\nfiles = dir( pattern=glob2rx(\"*.otustats\"))\nfor (f in files)\n{\n\tprint (f)\n\tmakeBatchOTU (f)\n}\n\nif (length(files)>0)\n{\n\tinputdata = getData(files, mode=\"otu\") \n\tmakeGGplotOTU (inputdata)\n}\n\n##### COVERAGE stats\nfiles = dir( pattern=glob2rx(\"*.clcassemblystats\"))\nfor (f in files)\n{\n\tprint (f)\n\tmakeBatchCoverage (f)\n}\n\nif (length(files)>0)\n{\n\tinputdata = getData(files, mode=\"clccoverage\") \n\tmakeGGplotCOVERAGE (inputdata)\n}\n", "meta": {"hexsha": "90e511f6b71389c7fc4b3d03c8b8ff2fc4fe2528", "size": 11587, "ext": "r", "lang": "R", "max_stars_repo_path": "OtuReadPlots.r", "max_stars_repo_name": "andreyto/YAP", "max_stars_repo_head_hexsha": "5e897b7bbc8d3dc7a7d1d5ac6485ad474f2d51c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "OtuReadPlots.r", "max_issues_repo_name": "andreyto/YAP", "max_issues_repo_head_hexsha": "5e897b7bbc8d3dc7a7d1d5ac6485ad474f2d51c0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "OtuReadPlots.r", "max_forks_repo_name": "andreyto/YAP", "max_forks_repo_head_hexsha": "5e897b7bbc8d3dc7a7d1d5ac6485ad474f2d51c0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1995305164, "max_line_length": 172, "alphanum_fraction": 0.5960990766, "num_tokens": 4048, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32814587438460197}} {"text": "base_datos <- read.csv(\"../base_data/dataTableAnaliticEmotionText.csv\")\nbase_datos[1,]\n\n# fecha <- '2018-06-27T19:40:00.000Z'\n\n# formatoFecha = function(fecha){\n# substr(fecha,12,13)\n# }\n\n# cleanBase_datos = replace(base_datos$fecha,,substr(base_datos$fecha,12,13))\n\n# cleanBase_datos[1:2,]\n# cleanBase_datos = base_datos[base_datos$fecha <- substr(base_datos$fecha,12,13),]\n# eliminar nulos \nbase_datos <- base_datos[!is.na(base_datos$puntajeEmocion),]\n#unir valores por hora\nemotionHourMean = aggregate(base_datos$puntajeEmocion, by=list(fecha=base_datos$fecha), FUN=mean)\n\ntitulo = 'grafica_emotion_hour'\npng(file = paste(\"../public/\",titulo, \".png\")) \nplot(puntajeEmocion ~ fecha,data =base_datos)\nabline(lm(base_datos$fecha ~ base_datos$puntajeEmocion ))\ncorrelacion <- cor(base_datos$puntajeEmocion,base_datos$fecha) \nlegend(\"bottomleft\",col=c(correlacion),legend =c(correlacion), lwd=3, bty = \"n\")\n\ndev.off()\n\ntitulo = 'grafica_emotion_hour_mean'\npng(file = paste(\"../public/\",titulo, \".png\")) \nplot(emotionHourMean )\ndev.off()\n\ntitulo = 'bandera_emotion_hour'\npng(file = paste(\"../public/\",titulo, \".png\")) \nboxplot(puntajeEmocion ~ fecha,data =base_datos)\ndev.off()\n\ntitulo = 'histograma_emotion_hour_mean'\npng(file = paste(\"../public/\",titulo, \".png\")) \nhist(emotionHourMean$x )\ndev.off()", "meta": {"hexsha": "41a3bf5385c54c57884223e19ef39b97f9184db4", "size": 1322, "ext": "r", "lang": "R", "max_stars_repo_path": "logic/analiticHour.r", "max_stars_repo_name": "AndresManuelArias/sentiment_chat", "max_stars_repo_head_hexsha": "8429f22780eed55833343241285b45aaf6d7b8af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "logic/analiticHour.r", "max_issues_repo_name": "AndresManuelArias/sentiment_chat", "max_issues_repo_head_hexsha": "8429f22780eed55833343241285b45aaf6d7b8af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "logic/analiticHour.r", "max_forks_repo_name": "AndresManuelArias/sentiment_chat", "max_forks_repo_head_hexsha": "8429f22780eed55833343241285b45aaf6d7b8af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.243902439, "max_line_length": 97, "alphanum_fraction": 0.7246596067, "num_tokens": 391, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883592602049, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3281458743846019}} {"text": "# ruth's unifrac method\n\n\nlibrary(phangorn)\nlibrary(ape)\nlibrary(zCompositions)\n\ngm_mean = function(x, na.rm=TRUE){\n exp(mean(log(x), na.rm=na.rm) )\n}\n\n# gm_vector = function(node_count, other_counts, geometric_mean) {\n# gm_vec <- array(0,length(node_count))\n# # a count of -1 means that the OTU has been amalgamated to other OTUs in the node_count\n# zero_means <- list()\n# counter = 1\n# for (i in 1:length(node_count)) {\n# # CHECK IF THIS IS CORRECT - indexing for other_counts\n# gm_vec[i] <- gm_mean(c(node_count[i],other_counts[i,][which(other_counts[i,]!=-1)]))\n# if (gm_vec[i] == 0) {\n# gm_vec[i] = geometric_mean[i]\n# }\n# }\n# return(gm_vec)\n# }\n\ngm_from_props = function(rownum, colnums) {\n otuPropsPerNode.adjustedZeros <- get(\"otuPropsPerNode.adjustedZeros\",envir = .GlobalEnv)\n return(log2(otuPropsPerNode.adjustedZeros[rownum,colnums[1]]) - mean(log2(otuPropsPerNode.adjustedZeros[rownum,colnums])))\n}\n\nmake_otu_props_positive = function() {\n otuPropsPerNode <- get(\"otuPropsPerNode\",envir = .GlobalEnv)\n otuPropsPerNode.adjustedZeros <- get(\"otuPropsPerNode.adjustedZeros\",envir = .GlobalEnv)\n otuPropsPerNode <- abs(otuPropsPerNode)\n otuPropsPerNode.adjustedZeros <- abs(otuPropsPerNode.adjustedZeros)\n assign(\"otuPropsPerNode\", otuPropsPerNode, envir = .GlobalEnv)\n assign(\"otuPropsPerNode.adjustedZeros\", otuPropsPerNode.adjustedZeros, envir = .GlobalEnv)\n}\n\nget_children = function(root) {\n unifrac.tree <- get(\"unifrac.tree\",envir=.GlobalEnv)\n children <- unifrac.tree$edge[which(unifrac.tree$edge[,1]==root),2]\n return(children)\n}\n\nget_subtree = function() {\n otuPropsPerNode.adjustedZeros <- get(\"otuPropsPerNode.adjustedZeros\",envir = .GlobalEnv)\n return(colnames(otuPropsPerNode.adjustedZeros)[which(otuPropsPerNode.adjustedZeros[1,] < 0)])\n}\n\nbuild_weights = function(root) {\n # make all weight values positive\n make_otu_props_positive()\n # if root is leaf, then weight has already been calculated and we can skip this\n if (root > length(unifrac.tree$tip.label)) {\n \n children <- get_children(root)\n \n # construct values for left child\n build_weights(children[1])\n leftChildSubtree <- get_subtree()\n \n # construct values for right child\n build_weights(children[2])\n rightChildSubtree <- get_subtree()\n \n otuPropsPerNode <- get(\"otuPropsPerNode\",envir = .GlobalEnv)\n otuPropsPerNode.adjustedZeros <- get(\"otuPropsPerNode.adjustedZeros\",envir = .GlobalEnv)\n weightsPerNode <- get(\"weightsPerNode\",envir = .GlobalEnv)\n unifrac.tree <- get(\"unifrac.tree\",envir=.GlobalEnv)\n unifrac.treeLeaves <- get(\"unifrac.treeLeaves\",envir=.GlobalEnv)\n \n # the negative values in leftChildProportions are for all the nodes in the subtree of the left child\n # the negative values in rightChildProportions are for all the nodes in the subtree of the right child\n \n leafColumns <- colnames(otuPropsPerNode)[which(colnames(otuPropsPerNode) %in% unifrac.treeLeaves)]\n \n # make both left and right child subtrees negative\n otuPropsPerNode[,leftChildSubtree] <- (-1)*abs(otuPropsPerNode[,leftChildSubtree])\n otuPropsPerNode.adjustedZeros[,leftChildSubtree] <- (-1)*abs(otuPropsPerNode.adjustedZeros[,leftChildSubtree])\n \n # get columns for use in weight calculation (leaves not in right or left subtree plus root)\n columnsForWeightCalculation <- leafColumns\n columnsForWeightCalculation <- columnsForWeightCalculation[which(!(columnsForWeightCalculation %in% leftChildSubtree))]\n columnsForWeightCalculation <- columnsForWeightCalculation[which(!(columnsForWeightCalculation %in% rightChildSubtree))]\n columnsForWeightCalculation <- c(root, columnsForWeightCalculation)\n \n # calculate proportion of current node\n otuPropsPerNode[,root] <- abs(otuPropsPerNode[,children[1]]) + abs(otuPropsPerNode[,children[2]])\n otuPropsPerNode.adjustedZeros[,root] <- abs(otuPropsPerNode.adjustedZeros[,children[1]]) + abs(otuPropsPerNode.adjustedZeros[,children[2]])\n assign(\"otuPropsPerNode\", otuPropsPerNode, envir = .GlobalEnv)\n assign(\"otuPropsPerNode.adjustedZeros\", otuPropsPerNode.adjustedZeros, envir = .GlobalEnv)\n \n # calculate weight of current node\n weightsPerNode[,root] <- sapply(c(1:nrow(otuPropsPerNode.adjustedZeros)),function(x) { gm_from_props(x, columnsForWeightCalculation)})\n }\n # make root node values negative\n otuPropsPerNode[,root] <- (-1)*abs(otuPropsPerNode[,root])\n otuPropsPerNode.adjustedZeros[,root] <- (-1)*abs(otuPropsPerNode.adjustedZeros[,root])\n \n assign(\"otuPropsPerNode\", otuPropsPerNode, envir = .GlobalEnv)\n assign(\"otuPropsPerNode.adjustedZeros\", otuPropsPerNode.adjustedZeros, envir = .GlobalEnv)\n assign(\"weightsPerNode\", weightsPerNode, envir = .GlobalEnv)\n}\n\ncalculateDistanceMatrix <- function(weights, method, otuTable, verbose, pruneTree, normalize) {\n unifrac.tree <- get(\"unifrac.tree\",envir=.GlobalEnv)\n otuPropsPerNode <- get(\"otuPropsPerNode\",envir=.GlobalEnv)\n \n nSamples <- nrow(otuTable)\n distanceMatrix <- matrix(NA,nrow=nSamples,ncol=nSamples)\n rownames(distanceMatrix) <- rownames(otuTable)\n colnames(distanceMatrix) <- rownames(otuTable)\n \n branchLengths <- unifrac.tree$edge.length\n \n weightColnames <- as.numeric(colnames(weights))\n \n weights <- weights[,match(unifrac.tree$edge[,2], weightColnames)]\n \n\tfor (i in 1:nSamples) {\n\t\tfor (j in i:nSamples) {\n\n\t\t\t\tif (method == \"weighted\" || method == \"information\" || method == \"ratio\" || method == \"ratio_no_log\") {\n\t\t\t\t\t# the formula is sum of (proportional branch lengths * | proportional abundance for sample A - proportional abundance for sample B| )\n\t\t\t\t\tif (pruneTree==TRUE){\n\t\t\t\t\t\tincludeBranchLengths <- which( (otuPropsPerNode[i,] > 0) | (otuPropsPerNode[j,] > 0) )\n\t\t\t\t\t\tif (normalize==TRUE && (method != \"ratio\" || method != \"ratio_no_log\")) {\n\t\t\t\t\t\t\tdistance <- sum( branchLengths[includeBranchLengths] * abs(weights[i,includeBranchLengths] - weights[j,includeBranchLengths]) )/sum( branchLengths[includeBranchLengths]* (weights[i,includeBranchLengths] + weights[j,includeBranchLengths]) )\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tdistance <- sum( branchLengths[includeBranchLengths] * abs(weights[i,includeBranchLengths] - weights[j,includeBranchLengths]) )/sum( branchLengths[includeBranchLengths])\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tdistance <- sum( branchLengths * abs(weights[i,] - weights[j,]) )/sum(branchLengths)\n\t\t\t\t\t\tif (normalize==TRUE) {\n\t\t\t\t\t\t\tdistance <- sum( branchLengths * abs(weights[i,] - weights[j,]) )/sum(branchLengths * (weights[i,] + weights[j,]))\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\telse {\n\t\t\t\tif (method!=\"unweighted\") {\n\t\t\t\t\twarning(paste(\"Invalid method\",method,\", using unweighted Unifrac instead\"))\n\t\t\t\t}\n\t\t\t\t# the formula is sum of (branch lengths * (1 if one sample has counts and not the other, 0 otherwise) )\n\t\t\t\t#\ti call the (1 if one sample has counts and not the other, 0 otherwise) xorBranchLength\n\t\t\t\txorBranchLength <- as.numeric(xor( weights[i,] > 0, weights[j,] > 0))\n\t\t\t\tif (pruneTree==TRUE) {\n\t\t\t\t\tincludeBranchLengths <- which( (weights[i,] > 0) | (weights[j,] > 0) )\n\t\t\t\t\tdistance <- sum( branchLengths[includeBranchLengths] * xorBranchLength[includeBranchLengths])/sum(branchLengths[includeBranchLengths])\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tdistance <- sum( branchLengths * xorBranchLength)/sum(branchLengths)\n\t\t\t\t}\n\n\t\t\t}\n\t\t\tdistanceMatrix[i,j] <- distance\n\t\t\tdistanceMatrix[j,i] <- distance\n\n\t\t}\n\t}\n\n\tif(verbose) {\tprint(\"done\")\t}\n\n\treturn(distanceMatrix)\n\n}\n\n#valid methods are unweighted, weighted, information, and ratio. Any other method will result in a warning and the unweighted analysis\n#pruneTree option prunes the tree for each comparison to exclude branch lenxgths not present in both samples\n#normalize divides the value at each node by sum of weights to guarantee output between 0 and 1 (breaks the triangle inequality)\n\n#otuTable must have samples as rows, OTUs as columns\n#tree must be phylo tree object from ape package (can use read.tree method to read in a newick tree to this format)\n\ngetDistanceMatrix <- function(otuTable,tree,method=\"weighted\",verbose=FALSE,pruneTree=FALSE,normalize=TRUE) {\n\n\tif (length(which(is.na(otuTable))) > 0) {\n\t\tstop(\"OTU count table has NA\")\n\t}\n\n\tif (!is.rooted(tree)) {\n\t\ttree <- midpoint(tree)\n\t\tif(verbose) { print(\"Rooting tree by midpoint\") }\n\t}\n\n\tif (attributes(tree)$order!=\"postorder\") {\n\t\ttree <- reorder(tree,order=\"postorder\")\n\t\tif (verbose) { print(\"Reordering tree as postorder for distance calculation algorithm\") }\n\t}\n \n #make globally available copy of tree\n assign(\"unifrac.tree\", tree, envir = .GlobalEnv)\n unifrac.tree <- get(\"unifrac.tree\", envir = .GlobalEnv)\n \n #make globally available copy of leaves\n assign(\"unifrac.treeLeaves\", c(1:length(tree$tip.label)), envir = .GlobalEnv)\n unifrac.treeLeaves <- get(\"unifrac.treeLeaves\", envir = .GlobalEnv)\n \n\t# get proportions\n\treadsPerSample <- apply(otuTable,1,sum)\n\totu.prop <- otuTable/readsPerSample\n\totu.prop <- as.matrix(otu.prop)\n\trownames(otu.prop) <- rownames(otuTable)\n\tcolnames(otu.prop) <- colnames(otuTable)\n\tif(verbose) {\tprint(\"calculated proportional abundance\")\t}\n\n\t# add priors to zeros based on bayesian approach\n\totuTable.adjustedZeros <- cmultRepl(otuTable, method=\"CZM\", output=\"counts\")\n # make any negative numbers as close to zero as possible - this is probably due to a precision error.\n otuTable.adjustedZeros <- apply(otuTable.adjustedZeros,2,function(x) { x[which(x < 0)] <- .Machine$double.eps; return(x) })\n readsPerSample <- apply(otuTable.adjustedZeros,1,sum)\n otu.prop.adjustedZeros <- otuTable.adjustedZeros/readsPerSample\n otu.prop.adjustedZeros <- as.matrix(otu.prop.adjustedZeros)\n\trownames(otu.prop.adjustedZeros) <- rownames(otuTable)\n\tcolnames(otu.prop.adjustedZeros) <- colnames(otuTable)\n \n\t##get cumulative proportional abundance for the nodes (nodes are ordered same as in the phylo tree representation)\n\n otuPropsPerNode <- matrix(NA, ncol=length(tree$edge.length)+1, nrow=length(rownames(otuTable)))\n otuPropsPerNode.adjustedZeros <- matrix(NA, ncol=length(tree$edge.length)+1, nrow=length(rownames(otuTable)))\n weightsPerNode <- matrix(NA, ncol=length(tree$edge.length)+1, nrow=length(rownames(otuTable)))\n \n\t#each row is a sample\n rownames(otuPropsPerNode) <- rownames(otuTable)\n rownames(otuPropsPerNode.adjustedZeros) <- rownames(otuTable)\n rownames(weightsPerNode) <- rownames(otuTable)\n #each column is a node in the tree\n colnames(otuPropsPerNode) <- c(1:(length(tree$edge.length)+1))\n colnames(otuPropsPerNode.adjustedZeros) <- c(1:(length(tree$edge.length)+1))\n colnames(weightsPerNode) <- c(1:(length(tree$edge.length)+1))\n \n leafNodes <- c(1:length(tree$tip.label))\n leafOrder <- match(tree$tip.label,colnames(otu.prop))\n \n otuPropsPerNode[,leafNodes] <- otu.prop[,leafOrder]\n otuPropsPerNode.adjustedZeros[,leafNodes] <- otu.prop.adjustedZeros[,leafOrder]\n weightsPerNode[,leafNodes] <- log2(otu.prop.adjustedZeros[,leafOrder])\n weightsPerNode[,leafNodes] <- t(apply(weightsPerNode[,leafNodes],1,function(x) { return(x - mean(x))}))\n \n # the tree is in postorder, so the last edges belong to the root\n root <- tree$edge[nrow(tree$edge),1]\n\n if(verbose) {\tprint(\"calculating weights...\")\t}\n \n assign(\"otuPropsPerNode\", otuPropsPerNode, envir = .GlobalEnv)\n assign(\"otuPropsPerNode.adjustedZeros\", otuPropsPerNode.adjustedZeros, envir = .GlobalEnv)\n assign(\"weightsPerNode\", weightsPerNode, envir = .GlobalEnv)\n \n build_weights(root)\n \n otuPropsPerNode <- get(\"otuPropsPerNode\",envir = .GlobalEnv)\n otuPropsPerNode.adjustedZeros <- get(\"otuPropsPerNode.adjustedZeros\",envir = .GlobalEnv)\n weightsPerNode <- get(\"weightsPerNode\",envir = .GlobalEnv)\n \n # build_weights makes everything negative, make everything positive again\n otuPropsPerNode[,c(1:ncol(otuPropsPerNode))] <- abs(otuPropsPerNode[,c(1:ncol(otuPropsPerNode))])\n otuPropsPerNode.adjustedZeros[,c(1:ncol(otuPropsPerNode.adjustedZeros))] <- abs(otuPropsPerNode.adjustedZeros[,c(1:ncol(otuPropsPerNode.adjustedZeros))])\n \n assign(\"otuPropsPerNode\", otuPropsPerNode, envir = .GlobalEnv)\n assign(\"otuPropsPerNode.adjustedZeros\", otuPropsPerNode.adjustedZeros, envir = .GlobalEnv)\n assign(\"weightsPerNode\", weightsPerNode, envir = .GlobalEnv)\n \n if(verbose) {\tprint(\"calculating pairwise distances...\")\t}\n \n #convert table according to weight\n if (method == \"all\") {\n returnList <- list()\n # unweighted\n print(\"calculating unweighted distance matrix\")\n weights <- otuPropsPerNode\n returnList[[\"unweighted\"]] <- calculateDistanceMatrix(weights, \"unweighted\", otuTable, verbose, pruneTree, normalize)\n # weighted\n print(\"calculating weighted distance matrix\")\n weights <- otuPropsPerNode\n returnList[[\"weighted\"]] <- calculateDistanceMatrix(weights, \"weighted\", otuTable, verbose, pruneTree, normalize)\n # information\n print(\"calculating information distance matrix\")\n weights <- otuPropsPerNode.adjustedZeros*log2(otuPropsPerNode.adjustedZeros)\n returnList[[\"information\"]] <- calculateDistanceMatrix(weights, \"information\", otuTable, verbose, pruneTree, normalize)\n # ratio\n print(\"calculating ratio distance matrix\")\n weights <- weightsPerNode\n returnList[[\"ratio\"]] <- calculateDistanceMatrix(weights, \"ratio\", otuTable, verbose, pruneTree, normalize)\n # ratio no log\n print(\"calculating no log ratio distance matrix\")\n weights <- weightsPerNode\n weights <- 2^weights\n returnList[[\"ratio_no_log\"]] <- calculateDistanceMatrix(weights, \"ratio_no_log\", otuTable, verbose, pruneTree, normalize)\n return(returnList)\n } else if (method==\"information\") {\n weights <- otuPropsPerNode.adjustedZeros*log2(otuPropsPerNode.adjustedZeros)\n return(calculateDistanceMatrix(weights, method, otuTable, verbose, pruneTree, normalize))\n } else if (method == \"ratio_no_log\") {\n weights <- weightsPerNode\n weights <- 2^weights\n return(calculateDistanceMatrix(weights, method, otuTable, verbose, pruneTree, normalize))\n } else if (method == \"ratio\") {\n weights <- weightsPerNode\n return(calculateDistanceMatrix(weights, method, otuTable, verbose, pruneTree, normalize))\n }\n else {\n weights <- otuPropsPerNode\n return(calculateDistanceMatrix(weights, method, otuTable, verbose, pruneTree, normalize))\n }\n}\n", "meta": {"hexsha": "422bd39985a22defd26e6c32de3817a20b737c58", "size": 14289, "ext": "r", "lang": "R", "max_stars_repo_path": "UniFrac.r", "max_stars_repo_name": "ruthgrace/ruth_unifrac_workshop", "max_stars_repo_head_hexsha": "2597b438dcbc5676bfbb2d6029432d7936cc4b90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-02-29T13:48:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-06T16:12:25.000Z", "max_issues_repo_path": "UniFrac.r", "max_issues_repo_name": "ruthgrace/ruth_unifrac_workshop", "max_issues_repo_head_hexsha": "2597b438dcbc5676bfbb2d6029432d7936cc4b90", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-01-24T20:50:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-01T15:12:18.000Z", "max_forks_repo_path": "UniFrac.r", "max_forks_repo_name": "ruthgrace/ruth_unifrac_workshop", "max_forks_repo_head_hexsha": "2597b438dcbc5676bfbb2d6029432d7936cc4b90", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-06T16:12:29.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-04T17:53:07.000Z", "avg_line_length": 46.0935483871, "max_line_length": 246, "alphanum_fraction": 0.7287423893, "num_tokens": 4110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.749087201911703, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.3279679798826485}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/11_sinking-marbles-normal/results/\")\nsource(\"rscripts/helpers.r\")\n\nload(\"data/r.RData\")\nr1 = read.table(\"data/sinking_marbles.tsv\", sep=\"\\t\", header=T, quote=\"\")\nr2 = read.csv(\"data/sinking_marbles.csv\", header=T)\nr2$workerid = as.numeric(as.character(r2$workerid))+60\n\nr = rbind(r1,r2)\nr$trial = r$slide_number_in_experiment - 2\nr = r[,c(\"workerid\", \"rt\", \"effect\", \"cause\",\"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\",\"quantifier\", \"object_level\", \"response\", \"object\",\"num_objects\",\"trial\",\"enjoyment\",\"asses\",\"comments\")]\nr$Item = as.factor(paste(r$effect,r$object))\n\n## add priors to data.frame\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/24_sinking-marbles-prior-fourstep/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\n# prior expectation for each item\ngathered_probs <- priorprobs %>%\n gather(State,Probability,X0:X15)\ngathered_probs$State = as.numeric(as.character(gsub(\"X\",\"\",gathered_probs$State)))\nprior_exps <- gathered_probs %>%\n group_by(Item) %>%\n summarise(exp.val=sum(State*Probability))\nprior_exps = as.data.frame(prior_exps)\nrow.names(prior_exps) = prior_exps$Item\n\nr$PriorExpectation = prior_exps[as.character(r$Item),]$exp.val\nsummary(r)\n\nsave(r, file=\"data/r.RData\")\n\n##################\n\nggplot(aes(x=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=rt), data=r) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,20000))\n\nggplot(aes(x=age), data=r) +\n geom_histogram()\n\nggplot(aes(x=enjoyment), data=r) +\n geom_histogram()\n\nggplot(aes(x=asses), data=r) +\n geom_histogram()\n\nggplot(aes(x=quantifier), data=r) +\n geom_histogram()\n\nhead(r$comments)\nunique(r$comments)\n\n\nggplot(r, aes(x=PriorExpectation, fill=response)) +\n geom_histogram() +\n facet_grid(workerid~quantifier)\nggsave(\"graphs/subject_variability.pdf\")\n", "meta": {"hexsha": "09a8f49f03f474ff6f176c088a7368a2c8521650", "size": 1982, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/11_sinking-marbles-normal/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/11_sinking-marbles-normal/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/11_sinking-marbles-normal/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9677419355, "max_line_length": 222, "alphanum_fraction": 0.7300706357, "num_tokens": 574, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3279621317857262}} {"text": "\n\n#' Outlier adjustment estimation\n#' \n#' How much of the heterogeneity due to the outlier can be explained by alternative pathways?\n#' \n#' @param tryxscan Output from \\code{tryx.scan}\n#' @param id_remove List of IDs to exclude from the adjustment analysis. It is possible that in the outlier search a candidate trait will come up which is essentially just a surrogate for the outcome trait (e.g. if you are analysing coronary heart disease as the outcome then a variable related to heart disease medication might come up as a candidate trait). Adjusting for a trait which is essentially the same as the outcome will erroneously nullify the result, so visually inspect the candidate trait list and remove those that are inappropriate.\n#'\n#' @export\n#' @return data frame of adjusted effect estimates and heterogeneity stats\ntryx.adjustment <- function(tryxscan, id_remove=NULL)\n{\n\t# for each outlier find the candidate MR analyses\n\t# if only exposure then ignore\n\t# if only outcome then re-estimate the snp-outcome association\n\t# if exposure and outcome then re-estimate the snp-exposure and snp-outcome association\n\t# outlier\n\t# candidate\n\t# what \n\t# old.beta.exposure\n\t# adj.beta.exposure\n\t# old.beta.outcome\n\t# adj.beta.outcome\n\t# candidate.beta.outcome\n\t# candidate.se.outcome\n\t# candidate.beta.exposure\n\t# candidate.se.exposure\n\t# old.deviation\n\t# new.deviation\n\n\tif(!any(tryxscan$search$sig))\n\t{\n\t\treturn(tibble())\n\t}\n\n\tl <- list()\n\tsig <- subset(tryxscan$search, sig & !id.outcome %in% id_remove)\n\tsige <- subset(tryxscan$candidate_exposure_mr, sig & !id.exposure %in% id_remove)\n\tsigo <- subset(tryxscan$candidate_outcome_mr, sig & !id.exposure %in% id_remove)\n\n\tdat <- tryxscan$dat\n\tdat$qi <- cochrans_q(dat$beta.outcome / dat$beta.exposure, dat$se.outcome / abs(dat$beta.exposure))\n\tdat$Q <- sum(dat$qi)\n\tfor(i in 1:nrow(sig))\n\t{\n\t\ta <- subset(dat, SNP == sig$SNP[i], select=c(SNP, beta.exposure, beta.outcome, se.exposure, se.outcome, qi, Q))\n\t\tif(sig$id.outcome[i] %in% sigo$id.exposure)\n\t\t{\n\t\t\ta$candidate <- sig$outcome[i]\n\t\t\ta$i <- i\n\t\t\tif(sig$id.outcome[i] %in% sige$id.exposure) {\n\t\t\t\tmessage(\"x<-p->y: \", a$SNP, \"\\t- \", sig$outcome[i])\n\t\t\t\ta$what <- \"p->x; p->y\"\n\t\t\t\ta$candidate.beta.exposure <- sige$b[sige$id.exposure == sig$id.outcome[i]]\n\t\t\t\ta$candidate.se.exposure <- sige$se[sige$id.exposure == sig$id.outcome[i]]\n\t\t\t\ta$candidate.beta.outcome <- sigo$b[sigo$id.exposure == sig$id.outcome[i]]\n\t\t\t\ta$candidate.se.outcome <- sigo$se[sigo$id.exposure == sig$id.outcome[i]]\n\t\t\t\tb <- bootstrap_path(a$beta.exposure, a$se.exposure, sig$beta.outcome[i], sig$se.outcome[i], sige$b[sige$id.exposure == sig$id.outcome[i]], sige$se[sige$id.exposure == sig$id.outcome[i]])\n\t\t\t\ta$adj.beta.exposure <- b[1]\n\t\t\t\ta$adj.se.exposure <- b[2]\n\t\t\t\tb <- bootstrap_path(a$beta.outcome, a$se.outcome, sig$beta.outcome[i], sig$se.outcome[i], sigo$b[sigo$id.exposure == sig$id.outcome[i]], sigo$se[sigo$id.exposure == sig$id.outcome[i]])\n\t\t\t\ta$adj.beta.outcome <- b[1]\n\t\t\t\ta$adj.se.outcome <- b[2]\n\t\t\t} else {\n\t\t\t\tmessage(\" p->y: \", a$SNP, \"\\t- \", sig$outcome[i])\n\t\t\t\ta$what <- \"p->y\"\n\t\t\t\ta$candidate.beta.exposure <- NA\n\t\t\t\ta$candidate.se.exposure <- NA\n\t\t\t\ta$candidate.beta.outcome <- sigo$b[sigo$id.exposure == sig$id.outcome[i]]\n\t\t\t\ta$candidate.se.outcome <- sigo$se[sigo$id.exposure == sig$id.outcome[i]]\n\t\t\t\tb <- bootstrap_path(a$beta.outcome, a$se.outcome, sig$beta.outcome[i], sig$se.outcome[i], sigo$b[sigo$id.exposure == sig$id.outcome[i]], sigo$se[sigo$id.exposure == sig$id.outcome[i]])\n\t\t\t\ta$adj.beta.exposure <- a$beta.exposure\n\t\t\t\ta$adj.se.exposure <- a$se.exposure\n\t\t\t\ta$adj.beta.outcome <- b[1]\n\t\t\t\ta$adj.se.outcome <- b[2]\n\t\t\t}\n\t\t\ttemp <- dat\n\t\t\ttemp$beta.exposure[temp$SNP == a$SNP] <- a$adj.beta.exposure\n\t\t\ttemp$se.exposure[temp$SNP == a$SNP] <- a$adj.se.exposure\n\t\t\ttemp$beta.outcome[temp$SNP == a$SNP] <- a$adj.beta.outcome\n\t\t\ttemp$se.outcome[temp$SNP == a$SNP] <- a$adj.se.outcome\n\t\t\ttemp$qi <- cochrans_q(temp$beta.outcome / temp$beta.exposure, temp$se.outcome / abs(temp$beta.exposure))\n\t\t\ta$adj.qi <- temp$qi[temp$SNP == a$SNP]\n\t\t\ta$adj.Q <- sum(temp$qi)\n\n\t\t\tl[[i]] <- a\n\t\t}\n\n\t}\n\tl <- bind_rows(l)\n\tl$d <- (l$adj.qi / l$adj.Q) / (l$qi / l$Q)\n\treturn(l)\n}\n\n\ntryx.adjustment.mv <- function(tryxscan, lasso=TRUE, id_remove=NULL, proxies=FALSE)\n{\n\t# for each outlier find the candidate MR analyses\n\t# if only exposure then ignore\n\t# if only outcome then re-estimate the snp-outcome association\n\t# if exposure and outcome then re-estimate the snp-exposure and snp-outcome association\n\t# outlier\n\t# candidate\n\t# what \n\t# old.beta.exposure\n\t# adj.beta.exposure\n\t# old.beta.outcome\n\t# adj.beta.outcome\n\t# candidate.beta.outcome\n\t# candidate.se.outcome\n\t# candidate.beta.exposure\n\t# candidate.se.exposure\n\t# old.deviation\n\t# new.deviation\n\n\tif(!any(tryxscan$search$sig))\n\t{\n\t\treturn(tibble())\n\t}\n\n\tl <- list()\n\tsig <- subset(tryxscan$search, sig & !id.outcome %in% id_remove)\n\tsige <- subset(tryxscan$candidate_exposure_mr, sig & !id.exposure %in% id_remove)\n\tsigo <- subset(tryxscan$candidate_outcome_mr, sig & !id.exposure %in% id_remove)\n\n\tid.exposure <- tryxscan$dat$id.exposure[1]\n\tid.outcome <- tryxscan$dat$id.outcome[1]\n\n#\tfor each outlier SNP find its effects on \n\n\tsigo1 <- subset(sig, id.outcome %in% sigo$id.exposure) %>% group_by(SNP) %>% mutate(snpcount=n()) %>% arrange(desc(snpcount), SNP)\n\tsnplist <- unique(sigo1$SNP)\n\tmvo <- list()\n\tdat <- tryxscan$dat\n\tdat$qi <- cochrans_q(dat$beta.outcome / dat$beta.exposure, dat$se.outcome / abs(dat$beta.exposure))\n\tdat$Q <- sum(dat$qi)\n\tdat$orig.beta.outcome <- dat$beta.outcome\n\tdat$orig.se.outcome <- dat$se.outcome\n\tdat$orig.qi <- dat$qi\n\tdat$outlier <- FALSE\n\tdat$outlier[dat$SNP %in% tryxscan$outliers] <- TRUE\n\tfor(i in 1:length(snplist))\n\t{\n\t\tmessage(\"Estimating joint effects of the following trait(s) associated with \", snplist[i])\n\t\ttemp <- subset(sigo1, SNP %in% snplist[i])\n\t\tcandidates <- unique(temp$outcome)\n\t\tmessage(paste(candidates, collapse=\"\\n\"))\n\t\tif(lasso & length(candidates) == 1)\n\t\t{\n\t\t\tmessage(\"Only one candidate trait for SNP \", snplist[i], \" so performing standard MVMR instead of LASSO\")\n\t\t}\n\t\tmvexp <- suppressMessages(mv_extract_exposures(c(id.exposure, unique(temp$id.outcome)), find_proxies=proxies))\n\t\tmvout <- suppressMessages(extract_outcome_data(mvexp$SNP, id.outcome))\n\t\tmvdat <- suppressMessages(mv_harmonise_data(mvexp, mvout))\n\t\tif(lasso & length(candidates) > 1)\n\t\t{\n\t\t\tmessage(\"Performing shrinkage\")\t\t\n\t\t\tb <- glmnet::cv.glmnet(x=mvdat$exposure_beta, y=mvdat$outcome_beta, weight=1/mvdat$outcome_se^2, intercept=0)\n\t\t\tc <- coef(b, s = \"lambda.min\")\n\t\t\tkeeplist <- unique(c(rownames(c)[!c[,1] == 0], tryxscan$dat$id.exposure[1]))\n\t\t\tmessage(\"After shrinkage keeping:\")\n\t\t\tmessage(paste(keeplist, collapse=\"\\n\"))\n\t\t\tmvexp2 <- subset(mvexp, id.exposure %in% keeplist)\n\t\t\tremsnp <- group_by(mvexp2, SNP) %>% summarise(mp = min(pval.exposure)) %>% filter(mp > 5e-8) %$% as.character(SNP)\n\t\t\tmvexp2 <- subset(mvexp2, !SNP %in% remsnp)\n\t\t\tmvout2 <- subset(mvout, SNP %in% mvexp2$SNP)\n\t\t\tmvdat2 <- suppressMessages(mv_harmonise_data(mvexp2, mvout2))\n\t\t\tmvo[[i]] <- mv_multiple(mvdat2)$result\n\t\t} else {\n\t\t\tmvo[[i]] <- mv_multiple(mvdat)$result\n\t\t}\n\t\tmvo[[i]] <- subset(mvo[[i]], !exposure %in% tryxscan$dat$exposure[1])\n\t\ttemp2 <- with(temp, tibble(SNP=SNP, exposure=outcome, snpeff=beta.outcome, snpeff.se=se.outcome, snpeff.pval=pval.outcome))\n\t\tmvo[[i]] <- merge(mvo[[i]], temp2, by=\"exposure\")\n\t\tboo <- with(subset(dat, SNP == snplist[i]), \n\t\t\tbootstrap_path(\n\t\t\t\tbeta.outcome,\n\t\t\t\tse.outcome,\n\t\t\t\tmvo[[i]]$snpeff,\n\t\t\t\tmvo[[i]]$snpeff.se,\n\t\t\t\tmvo[[i]]$b,\n\t\t\t\tmvo[[i]]$se\n\t\t\t))\n\t\tdat$beta.outcome[dat$SNP == snplist[i]] <- boo[1]\n\t\tdat$se.outcome[dat$SNP == snplist[i]] <- boo[2]\n\t}\n\tmvo <- bind_rows(mvo)\n\n\tdat$qi[dat$mr_keep] <- cochrans_q(dat$beta.outcome[dat$mr_keep] / dat$beta.exposure[dat$mr_keep], dat$se.outcome[dat$mr_keep] / abs(dat$beta.exposure[dat$mr_keep]))\n\treturn(list(mvo=mvo, dat=dat))\n}\n\n\n\n\n\n\n\n\n#' Analyse tryx results\n#' \n#' This returns various heterogeneity statistics, IVW estimates for raw, \n#' adjusted and outlier removed datasets, and summary of peripheral \n#' traits detected etc.\n#' \n#' @param tryxscan Output from \\code{tryx.scan}\n#' @param plot Whether to plot or not. Default is TRUE\n#' @param id_remove List of IDs to exclude from the adjustment analysis. It is possible that in the outlier search a candidate trait will come up which is essentially just a surrogate for the outcome trait (e.g. if you are analysing coronary heart disease as the outcome then a variable related to heart disease medication might come up as a candidate trait). Adjusting for a trait which is essentially the same as the outcome will erroneously nullify the result, so visually inspect the candidate trait list and remove those that are inappropriate.\n#' @param duplicate_outliers_method Sometimes more than one trait will associate with a particular outlier. TRUE = only keep the trait that has the biggest influence on heterogeneity\n#' \n#' @export\n#' @return List of \n#' - adj_full: data frame of SNP adjustments for all candidate traits\n#' - adj: The results from adj_full selected to adjust the exposure-outcome model\n#' - Q: Heterogeneity stats\n#' - estimates: Adjusted and unadjested exposure-outcome effects\n#' - plot: Radial plot showing the comparison of different methods and the changes in SNP effects ater adjustment\ntryx.analyse <- function(tryxscan, plot=TRUE, id_remove=NULL, filter_duplicate_outliers=TRUE)\n{\n\n\tanalysis <- list()\n\tadj_full <- tryx.adjustment(tryxscan, id_remove)\n\tif(nrow(adj_full) == 0)\n\t{\n\t\treturn(NULL)\n\t}\n\tanalysis$adj_full <- adj_full\n\tif(filter_duplicate_outliers)\n\t{\n\t\tadj <- adj_full %>% arrange(d) %>% filter(!duplicated(SNP))\n\t} else {\n\t\tadj <- adj_full\n\t}\n\tanalysis$adj <- adj\n\n\t# Detection\n\t# if(\"simulation\" %in% names(tryxscan))\n\t# {\n\t# \tdetection <- list()\n\t# \tdetection$nu1_correct <- sum(adj$SNP %in% 1:tryxscan$simulation$nu1 & adj$what != \"p->y\")\n\t# \tdetection$nu1_incorrect <- sum(adj$SNP %in% 1:tryxscan$simulation$nu1 & adj$what == \"p->y\")\n\t# \tdetection$nu2_correct <- sum(adj$SNP %in% (1:tryxscan$simulation$nu2 + tryxscan$simulation$nu1) & adj$what == \"p->y\")\n\t# \tdetection$nu2_incorrect <- sum(adj$SNP %in% (1:tryxscan$simulation$nu2 + tryxscan$simulation$nu1) & adj$what != \"p->y\")\n\t# \tdetection$no_outlier_flag <- tryxscan$simulation$no_outlier_flag\n\t# \tanalysis$detection <- detection\n\t# }\n\n\tcpg <- require(ggrepel)\n\tif(!cpg)\n\t{\n\t\tstop(\"Please install the ggrepel package\\ninstall.packages('ggrepel')\")\n\t}\n\n\tdat <- subset(tryxscan$dat, mr_keep, select=c(SNP, beta.exposure, beta.outcome, se.exposure, se.outcome))\n\tdat$ratio <- dat$beta.outcome / dat$beta.exposure\n\tdat$weights <- sqrt(dat$beta.exposure^2 / dat$se.outcome^2)\n\tdat$ratiow <- dat$ratio * dat$weights\n\tdat$what <- \"Unadjusted\"\n\tdat$candidate <- \"NA\"\n\tdat$qi <- cochrans_q(dat$beta.outcome / dat$beta.exposure, dat$se.outcome / abs(dat$beta.exposure))\n\n\tanalysis$Q$full_Q <- adj$Q[1]\n\tanalysis$Q$num_reduced <- sum(adj$adj.qi < adj$qi)\n\tanalysis$Q$mean_d <- mean(adj$d)\n\n\n\ttemp <- subset(adj, select=c(SNP, adj.beta.exposure, adj.beta.outcome, adj.se.exposure, adj.se.outcome, candidate))\n\ttemp$qi <- cochrans_q(temp$adj.beta.outcome / temp$adj.beta.exposure, temp$adj.se.outcome / abs(temp$adj.beta.exposure))\n\tind <- grepl(\"\\\\|\", temp$candidate)\n\tif(any(ind))\n\t{\n\t\ttemp$candidate[ind] <- sapply(strsplit(temp$candidate[ind], split=\" \\\\|\"), function(x) x[[1]])\n\t}\n\tnames(temp) <- gsub(\"adj.\", \"\", names(temp))\n\ttemp$what <- \"Adjusted\"\n\ttemp$weights <- sqrt(temp$beta.exposure^2 / temp$se.outcome^2)\n\ttemp$ratio <- temp$beta.outcome / temp$beta.exposure\n\ttemp$ratiow <- temp$ratio * temp$weights\n\n\tdat_adj <- rbind(temp, dat) %>% filter(!duplicated(SNP))\n\tdat_adj$qi <- cochrans_q(dat_adj$beta.outcome / dat_adj$beta.exposure, dat_adj$se.outcome / abs(dat_adj$beta.exposure))\n\tanalysis$Q$adj_Q <- sum(dat_adj$qi)\n\n\tdat_rem <- subset(dat, !SNP %in% temp$SNP)\n\tdat_rem2 <- subset(dat, !SNP %in% tryxscan$outliers)\n\t# adjust everything that's possible remove remaining outliers\n\tdat_rem3 <- rbind(temp, dat_rem2) %>% filter(!duplicated(SNP))\n\n\n\n\testimates <- tibble()\n\n\t\n\t# Raw IVW\n\ttt <- dat\n\tmod <- summary(lm(ratiow ~ -1 + weights, data=tt))\n\testimates <- bind_rows(estimates, \n\t\ttibble(\n\t\t\test=\"Raw\",\n\t\t\tb=coefficients(mod)[1,1], \n\t\t\tse = coefficients(mod)[1,2],\n\t\t\tpval=coefficients(mod)[1,4],\n\t\t\tnsnp = nrow(tt),\n\t\t\tQ = sum(tt$qi),\n\t\t\tint = 0\n\t\t)\n\t)\n\n\t# Outliers removed (all)\n\ttt <- subset(dat, !SNP %in% tryxscan$outliers)\n\tmod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))\n\tif(class(mod) != \"try-error\")\n\t{\n\t\testimates <- bind_rows(estimates, \n\t\t\ttibble(\n\t\t\t\test=\"Outliers removed (all)\",\n\t\t\t\tb=coefficients(mod)[1,1], \n\t\t\t\tse = coefficients(mod)[1,2],\n\t\t\t\tpval=coefficients(mod)[1,4],\n\t\t\t\tnsnp = nrow(tt),\n\t\t\t\tQ = sum(tt$qi),\n\t\t\t\tint = 0\n\t\t\t)\n\t\t)\n\t}\n\n\t# Outliers removed (candidates)\n\ttt <- subset(dat, !SNP %in% temp$SNP)\n\tmod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))\n\tif(class(mod) != \"try-error\")\n\t{\n\t\testimates <- bind_rows(estimates, \n\t\t\ttibble(\n\t\t\t\test=\"Outliers removed (candidates)\",\n\t\t\t\tb=coefficients(mod)[1,1], \n\t\t\t\tse = coefficients(mod)[1,2],\n\t\t\t\tpval=coefficients(mod)[1,4],\n\t\t\t\tnsnp = nrow(tt),\n\t\t\t\tQ = sum(tt$qi),\n\t\t\t\tint = 0\n\t\t\t)\n\t\t)\n\t}\n\n\t# Outliers adjusted\n\ttt <- rbind(temp, dat) %>% filter(!duplicated(SNP))\n\ttt$qi <- cochrans_q(tt$beta.outcome / tt$beta.exposure, tt$se.outcome / abs(tt$beta.exposure))\n\tanalysis$Q$adj_Q <- sum(tt$qi)\n\tmod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))\n\tif(class(mod) != \"try-error\")\n\t{\n\t\testimates <- bind_rows(estimates, \n\t\t\ttibble(\n\t\t\t\test=\"Outliers adjusted\",\n\t\t\t\tb=coefficients(mod)[1,1], \n\t\t\t\tse = coefficients(mod)[1,2],\n\t\t\t\tpval=coefficients(mod)[1,4],\n\t\t\t\tnsnp = nrow(tt),\n\t\t\t\tQ = sum(tt$qi),\n\t\t\t\tint = 0\n\t\t\t)\n\t\t)\n\t}\n\n\tif(\"mvres\" %in% names(tryxscan))\n\t{\n\t\testimates <- bind_rows(estimates, \n\t\t\ttibble(\n\t\t\t\test=\"Multivariable MR\",\n\t\t\t\tb=tryxscan$mvres$result$b[1], \n\t\t\t\tse = tryxscan$mvres$result$se[1],\n\t\t\t\tpval = tryxscan$mvres$result$pval[1],\n\t\t\t\tnsnp = tryxscan$mvres$result$nsnp[1],\n\t\t\t\tQ = NA,\n\t\t\t\tint = 0\n\t\t\t)\n\t\t)\n\t}\n\n\tif(\"true_outliers\" %in% names(tryxscan))\n\t{\n\t\ttt <- subset(dat, !SNP %in% tryxscan$true_outliers)\n\t\tmod <- try(summary(lm(ratiow ~ -1 + weights, data=tt)))\n\t\tif(class(mod) != \"try-error\")\n\t\t{\n\t\t\testimates <- bind_rows(estimates,\n\t\t\t\ttibble(\n\t\t\t\t\test=c(\"Oracle\"),\n\t\t\t\t\tb=c(coefficients(mod)[1,1]), \n\t\t\t\t\tse=c(coefficients(mod)[1,2]), \n\t\t\t\t\tpval=c(coefficients(mod)[1,4]),\n\t\t\t\t\tnsnp=c(nrow(tt)),\n\t\t\t\t\tQ = c(sum(tt$qi)),\n\t\t\t\t\tint=0\n\t\t\t\t)\n\t\t\t)\n\t\t}\n\t}\n\n\n\testimates <- bind_rows(estimates)\n\testimates$Isq <- pmax(0, (estimates$Q - estimates$nsnp - 1) / estimates$Q) \n\n\tanalysis$estimates <- estimates\n\n\n\t# est3 <- summary(lm(ratiow ~ -1 + weights, data=dat_adj))\n\t# est4 <- try(summary(lm(ratiow ~ -1 + weights, data=dat_rem2)))\n\t# est5 <- try(summary(lm(ratiow ~ -1 + weights, data=dat_rem3)))\n\t# if(class(est4) == \"try-error\")\n\t# {\n\t# \test4 <- list(coefficients = matrix(NA, 2,4))\n\t# }\n\n\t# estimates <- data.frame(\n\t# \test=c(\"Raw\", \"Outliers removed (candidates)\", \"Outliers removed (all)\", \"Outliers adjusted\", \"Mixed\", \"Multivariable MR\"),\n\t# \tb=c(coefficients(est2)[1,1], coefficients(est1)[1,1], coefficients(est4)[1,1], coefficients(est3)[1,1], coefficients(est5)[1,1], tryxscan$mvres$result$b[1]), \n\t# \tse=c(coefficients(est2)[1,2], coefficients(est1)[1,2], coefficients(est4)[1,2], coefficients(est3)[1,2], coefficients(est5)[1,2], tryxscan$mvres$result$se[1]), \n\t# \tpval=c(coefficients(est2)[1,4], coefficients(est1)[1,4], coefficients(est4)[1,4], coefficients(est3)[1,4], coefficients(est5)[1,4], tryxscan$mvres$result$pval[1]),\n\t# \tnsnp=c(nrow(dat), nrow(dat_rem), nrow(dat_rem2), nrow(dat_adj), nrow(dat_rem3), tryxscan$mvres$result$nsnp[1]),\n\t# \tQ = c(sum(dat$qi), sum(dat_rem$qi), sum(dat_rem2$qi), sum(dat_adj$qi), sum(dat_rem3$qi), NA),\n\t# \tint=0\n\t# )\n\t# estimates$Isq <- pmax(0, (estimates$Q - estimates$nsnp - 1) / estimates$Q) \n\n\t# estimates$Isq <- pmax(0, (estimates$Q - estimates$nsnp - 1) / estimates$Q) \n\n\t# analysis$estimates <- estimates\n\n\tif(plot)\n\t{\t\n\t\ttemp2 <- merge(dat, temp, by=\"SNP\")\n\t\tlabs <- rbind(\n\t\t\tdata.frame(label=temp2$SNP, x=temp2$weights.x, y=temp2$weights.x * temp2$ratio.x),\n\t\t\tdata.frame(label=temp$candidate, x=temp$weights, y=temp$weights * temp$ratio)\n\t\t)\n\t\tp <- ggplot(rbind(dat, temp), aes(y=ratiow, x=weights)) +\n\t\tgeom_abline(data=estimates, aes(slope=b, intercept=0, colour=est)) +\n\t\tgeom_label_repel(data=labs, aes(x=x, y=y, label=label), size=2, segment.color = \"grey10\") +\n\t\tgeom_point() +\n\t\tgeom_segment(data=temp2, colour=\"grey50\", aes(x=weights.x, xend=weights.y, y=ratiow.x, yend=ratiow.y), arrow = arrow(length = unit(0.02, \"npc\"))) +\n\t\tlabs(colour=\"\") +\n\t\txlim(c(0, max(dat$weights))) +\n\t\tylim(c(min(0, dat$ratiow, temp$ratiow), max(dat$ratiow, temp$ratiow))) +\n\t\tscale_colour_brewer(type=\"qual\")\n\t\tanalysis$plot <- p\n\t}\n\treturn(analysis)\n}\n\n\n\n#' Analyse tryx results\n#' \n#' This returns various heterogeneity statistics, IVW estimates for raw, \n#' adjusted and outlier removed datasets, and summary of peripheral \n#' traits detected etc.\n#' \n#' @param tryxscan Output from \\code{tryx.scan}\n#' @param plot Whether to plot or not. Default is TRUE\n#' @param filter_duplicate_outliers Whether to only allow each putative outlier to be adjusted by a single trait (in order of largest divergence). Default is TRUE.\n#' \n#' @export\n#' @return List of \n#' - adj_full: data frame of SNP adjustments for all candidate traits\n#' - adj: The results from adj_full selected to adjust the exposure-outcome model\n#' - Q: Heterogeneity stats\n#' - estimates: Adjusted and unadjested exposure-outcome effects\n#' - plot: Radial plot showing the comparison of different methods and the changes in SNP effects ater adjustment\n\n\n\n#' Adjust and analyse the tryx results\n#'\n#' Similar to tryx.analyse, but when there are multiple traits associated with a single variant then we use a LASSO-based multivariable approach \n#'\n#' @param tryxscan Output from \\code{tryx.scan}\n#' @param lasso Whether to shrink the estimates of each trait within SNP. Default=TRUE.\n#' @param plot Whether to plot or not. Default is TRUE\n#' @param id_remove List of IDs to exclude from the adjustment analysis. It is possible that in the outlier search a candidate trait will come up which is essentially just a surrogate for the outcome trait (e.g. if you are analysing coronary heart disease as the outcome then a variable related to heart disease medication might come up as a candidate trait). Adjusting for a trait which is essentially the same as the outcome will erroneously nullify the result, so visually inspect the candidate trait list and remove those that are inappropriate.\n#' @param proxies Look for proxies in the MVMR methods. Default = FALSE.\n#'\n#' @export\n#' @return List of \n#' - adj_full: data frame of SNP adjustments for all candidate traits\n#' - adj: The results from adj_full selected to adjust the exposure-outcome model\n#' - Q: Heterogeneity stats\n#' - estimates: Adjusted and unadjested exposure-outcome effects\n#' - plot: Radial plot showing the comparison of different methods and the changes in SNP effects ater adjustment\ntryx.analyse.mv <- function(tryxscan, lasso=TRUE, plot=TRUE, id_remove=NULL, proxies=FALSE)\n{\n\tadj <- tryx.adjustment.mv(tryxscan, lasso=lasso, id_remove=id_remove, proxies=proxies)\n\tdat <- subset(adj$dat, mr_keep)\n\tdat$orig.ratio <- dat$orig.beta.outcome / dat$beta.exposure\n\tdat$orig.weights <- sqrt(dat$beta.exposure^2 / dat$orig.se.outcome^2)\n\tdat$orig.ratiow <- dat$orig.ratio * dat$orig.weights\n\tdat$ratio <- dat$beta.outcome / dat$beta.exposure\n\tdat$weights <- sqrt(dat$beta.exposure^2 / dat$se.outcome^2)\n\tdat$ratiow <- dat$ratio * dat$weights\n\n\test_raw <- summary(lm(orig.ratiow ~ -1 + orig.weights, data=dat))\n\test_adj <- summary(lm(ratiow ~ -1 + weights, data=dat))\n\test_out1 <- summary(lm(orig.ratiow ~ -1 + orig.weights, data=subset(dat, !SNP %in% tryxscan$outliers)))\n\test_out2 <- summary(lm(orig.ratiow ~ -1 + orig.weights, data=subset(dat, !SNP %in% adj$mvo$SNP)))\n\n\testimates <- data.frame(\n\t\test=c(\"Raw\", \"Outliers removed (all)\", \"Outliers removed (candidates)\", \"Outliers adjusted\"),\n\t\tb=c(coefficients(est_raw)[1,1], coefficients(est_out2)[1,1], coefficients(est_out1)[1,1], coefficients(est_adj)[1,1]), \n\t\tse=c(coefficients(est_raw)[1,2], coefficients(est_out2)[1,2], coefficients(est_out1)[1,2], coefficients(est_adj)[1,2]), \n\t\tpval=c(coefficients(est_raw)[1,4], coefficients(est_out2)[1,4], coefficients(est_out1)[1,4], coefficients(est_adj)[1,4]),\n\t\tnsnp=c(nrow(dat), nrow(subset(dat, !SNP %in% tryxscan$outliers)), nrow(subset(dat, !SNP %in% adj$mvo$SNP)), nrow(dat)),\n\t\tQ = c(sum(dat$orig.qi), sum(subset(dat, !SNP %in% tryxscan$outliers)$qi), sum(subset(dat, !SNP %in% adj$mvo$SNP)$qi), sum(dat$qi)),\n\t\tint=0\n\t)\n\testimates$Isq <- pmax(0, (estimates$Q - estimates$nsnp - 1) / estimates$Q) \n\n\tanalysis <- list(\n\t\testimates=estimates,\n\t\tmvo=adj$mvo,\n\t\tdat=adj$dat\n\t)\n\n\tif(plot)\n\t{\t\n\t\tdato <- subset(dat, SNP %in% tryxscan$outliers)\n\t\tdatadj <- subset(dat, SNP %in% adj$mvo$SNP)\n\t\tdatadj$x <- datadj$orig.weights\n\t\tdatadj$xend <- datadj$weights\n\t\tdatadj$y <- datadj$orig.ratiow\n\t\tdatadj$yend <- datadj$ratiow\n\n\t\tmvog <- tidyr::separate(adj$mvo, exposure, sep=\"\\\\|\", c(\"exposure\", \"temp\", \"id\"))\n\t\tmvog$exposure <- gsub(\" $\", \"\", mvog$exposure)\n\t\tmvog <- group_by(mvog, SNP) %>% summarise(label = paste(exposure, collapse=\"\\n\"))\n\t\tdatadj <- merge(datadj, mvog, by=\"SNP\")\n\n\t\tlabs <- rbind(\n\t\t\ttibble(label=dato$SNP, x=dato$orig.weights, y=dato$orig.ratiow, col=\"grey50\"),\n\t\t\ttibble(label=datadj$label, x=datadj$weights, y=datadj$ratiow, col=\"grey100\")\n\t\t)\n\n\n\t\tp <- ggplot(dat, aes(y=orig.ratiow, x=orig.weights)) +\n\t\tgeom_abline(data=estimates, aes(slope=b, intercept=0, linetype=est)) +\n\t\tgeom_label_repel(data=labs, aes(label=label, x=x, y=y, colour=col), size=2, segment.color = \"grey50\", show.legend = FALSE) +\n\t\tgeom_point(data=dato, size=4) +\n\t\t# geom_label_repel(data=labs, aes(label=label, x=weights, y=ratiow), size=2, segment.color = \"grey50\") +\n\t\tgeom_point(data=datadj, aes(x=weights, y=ratiow)) +\n\t\tgeom_point(aes(colour=SNP %in% dato$SNP), show.legend=FALSE) +\n\t\tgeom_segment(data=datadj, colour=\"grey50\", aes(x=x, xend=xend, y=y, yend=yend), arrow = arrow(length = unit(0.01, \"npc\"))) +\n\t\tlabs(colour=NULL, linetype=\"Estimate\", x=\"w\", y=\"beta * w\")\n\t\t# xlim(c(0, max(dat$weights))) +\n\t\t# ylim(c(min(0, dat$ratiow, temp$ratiow), max(dat$ratiow, temp$ratiow)))\n\t\tanalysis$plot <- p\n\t}\n\treturn(analysis)\n\n}\n\n#' Cochran's Q statistic\n#' \n#' @param b vector of effecti \n#' @param se vector of standard errors\n#' \n#' @return q values\n#' \n#' @export\ncochrans_q <- function(b, se)\n{\n\txw <- sum(b / se^2) / sum(1/se^2)\n\tqi <- (1/se^2) * (b - xw)^2\n\treturn(qi)\n}\n\nbootstrap_path1 <- function(gx, gx.se, gp, gp.se, px, px.se, nboot=1000)\n{\n\tres <- rnorm(nboot, gx, gx.se) - rnorm(nboot, gp, gp.se) * rnorm(nboot, px, px.se)\n\tpe <- gx - gp * px\n\treturn(c(pe, sd(res)))\n}\n\nbootstrap_path <- function(gx, gx.se, gp, gp.se, px, px.se, nboot=1000)\n{\n\tnalt <- length(gp)\n\taltpath <- tibble(\n\t\tp = rnorm(nboot * nalt, gp, gp.se) * rnorm(nboot * nalt, px, px.se),\n\t\tb = rep(1:nboot, each=nalt)\n\t)\n\taltpath <- group_by(altpath, b) %>%\n\t\tsummarise(p = sum(p))\n\tres <- rnorm(nboot, gx, gx.se) - altpath$p\n\tpe <- gx - sum(gp * px)\n\treturn(c(pe, sd(res)))\n}\n\n\nradialmr <- function(dat, outlier=NULL)\n{\n\tlibrary(ggplot2)\n\tbeta.exposure <- dat$beta.exposure\n\tbeta.outcome <- dat$beta.outcome\n\tse.outcome <- dat$se.outcome\n\tw <- sqrt(beta.exposure^2 / se.outcome^2)\n\tratio <- beta.outcome / beta.exposure\n\tratiow <- ratio*w\n\tdat <- data.frame(w=w, ratio=ratio, ratiow=ratiow)\n\tif(is.null(outlier))\n\t{\n\t\tdat2 <- dat\n\t} else {\n\t\tdat2 <- dat[-c(outlier), ]\n\t}\n\tmod <- lm(ratiow ~ -1 + w, dat2)$coefficients[1]\n\tggplot(dat, aes(x=w, y=ratiow)) +\n\tgeom_point() +\n\tgeom_abline(slope=mod, intercept=0)\n}\n", "meta": {"hexsha": "6a87484a3dd8ffe1e0d1298a5aeaf1a0dfaa1bd0", "size": 23846, "ext": "r", "lang": "R", "max_stars_repo_path": "R/adjustment.r", "max_stars_repo_name": "explodecomputer/tryx", "max_stars_repo_head_hexsha": "f86a10402dd12b79a510dbff1764dcddf09707c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2019-03-14T14:33:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T15:46:39.000Z", "max_issues_repo_path": "R/adjustment.r", "max_issues_repo_name": "explodecomputer/tryx", "max_issues_repo_head_hexsha": "f86a10402dd12b79a510dbff1764dcddf09707c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 20, "max_issues_repo_issues_event_min_datetime": "2018-02-17T23:54:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-30T07:42:06.000Z", "max_forks_repo_path": "R/adjustment.r", "max_forks_repo_name": "explodecomputer/tryx", "max_forks_repo_head_hexsha": "f86a10402dd12b79a510dbff1764dcddf09707c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-05-13T13:29:03.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-13T13:29:03.000Z", "avg_line_length": 38.5234248788, "max_line_length": 549, "alphanum_fraction": 0.6815398809, "num_tokens": 7735, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.682573734412324, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3279621255806566}} {"text": "context(\"Test reportingRateModel and associated functions\")\n\n# Create data\nn <- 3000 #size of dataset\nnyr <- 10 # number of years in data\nnSamples <- 30 # set number of dates\nnSites <- 15 # set number of sites\nset.seed(1985)\n\n# Create somes dates\nfirst <- as.POSIXct(strptime(\"2010/01/01\", \"%Y/%m/%d\")) \nlast <- as.POSIXct(strptime(paste(2010+(nyr-1),\"/12/31\", sep=''), \"%Y/%m/%d\")) \ndt <- last-first \nrDates <- first + (runif(nSamples)*dt)\n\n# taxa are set as random letters\ntaxa <- sample(letters, size = n, TRUE)\n\n# three sites are visited randomly\nsite <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)\n\n# the date of visit is selected at random from those created earlier\ntime_period <- sample(rDates, size = n, TRUE)\n\n# create a numeric vector of time periods to test for\nnum_period <- as.numeric(format(time_period,'%Y'))\n\n# create additional dates with month and dat = 01 in to test if numeric and date format time_periods give equivalent results\ndates <- as.POSIXct(strptime(paste0(num_period, \"/01/01\"), \"%Y/%m/%d\")) \n\n# combine this to a dataframe (adding a final row of 'bad' data)\ndf <- data.frame(taxa = c(taxa,'bad'),\n site = c(site,'A1'),\n time_period = c(time_period, as.POSIXct(strptime(\"1200/01/01\", \"%Y/%m/%d\"))),\n num_period = c(num_period, 1200),\n comparison_dates = c(dates, as.POSIXct(strptime(\"1200/01/01\", \"%Y/%m/%d\"))))\n\n######################\ntest_that(\"Test errors and warnings\", {\n \n # outside errorChecks this is the only catch present in this set of functions\n\n # A couple in errorChecks for this function\n # warning when family is overridden by listlength\n expect_warning(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n list_length = TRUE,\n family = 'Binomial'),\n \"When list_length is TRUE family will default to Bernoulli\")\n expect_error(suppressWarnings(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n list_length = 1)),\n \"list_length must be logical\")\n expect_error(suppressWarnings(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n overdispersion = 1)),\n \"overdispersion must be logical\")\n expect_error(suppressWarnings(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n verbose = 1)),\n \"verbose must be logical\")\n expect_error(suppressWarnings(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n site_effect = 1)),\n \"site_effect must be logical\")\n expect_warning(RR_out <- reportingRateModel(df$taxa,\n df$site,\n df$time_period,\n species_to_include = c('tom','a','b','c')),\n \"The following species in species_to_include are not in your data: tom\")\n})\n\ntest_that(\"Test formulaBuilder\", {\n\n \n family <- 'binomial'\n list_length <- FALSE\n site_effect <- FALSE\n overdispersion <- FALSE\n \n # Here we test thay the formula builder is prodicing the right responses under a range of conditions\n model_formula <- formulaBuilder(family,list_length,site_effect,overdispersion)\n expect_identical(model_formula, \"cbind(successes, failures) ~ year\")\n model_formula <- formulaBuilder('Bernoulli',list_length,site_effect,overdispersion)\n expect_identical(model_formula, \"taxa ~ year\")\n model_formula <- formulaBuilder(family,TRUE,site_effect,overdispersion)\n expect_identical(model_formula, \"taxa ~ year + log(listLength)\")\n model_formula <- formulaBuilder(family,TRUE,TRUE,overdispersion)\n expect_identical(model_formula, \"taxa ~ year + log(listLength) + (1|site)\")\n model_formula <- formulaBuilder(family,TRUE,TRUE,TRUE)\n expect_identical(model_formula, \"taxa ~ year + log(listLength) + (1|site) + (1|obs)\")\n model_formula <- formulaBuilder(family,list_length,TRUE,TRUE)\n expect_identical(model_formula, \"cbind(successes, failures) ~ year + (1|site) + (1|obs)\")\n \n})\n\ntest_that(\"Check outputs are in the correct form\", {\n \n expect_warning(RR_out <- reportingRateModel(df$taxa, df$site, df$time_period, species_to_include = c('a','b','c')),\n \"353 out of 3001 observations will be removed as duplicates\")\n atts <- attributes(RR_out) \n expect_equal(atts$intercept_year, 2014)\n expect_equal(atts$min_year, -814)\n expect_equal(atts$max_year, 5)\n expect_equal(atts$nVisits, 450)\n expect_equal(atts$model_formula, \"cbind(successes, failures) ~ year\")\n expect_is(RR_out, \"data.frame\")\n expect_identical(c('a','b','c'), sort(as.character(RR_out$species_name)))\n ## check outputs are equal when time_periods are numeric or in date format\n expect_equal(reportingRateModel(df$taxa, df$site, df$num_period, species_to_include = c('a','b','c')), \n reportingRateModel(df$taxa, df$site, df$num_period, species_to_include = c('a','b','c')))\n\n})\n\n", "meta": {"hexsha": "cc47cd2c0c05617b710b99173dc77714a0233dfd", "size": 5559, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/testreportingRateModel.r", "max_stars_repo_name": "robboyd/sparta", "max_stars_repo_head_hexsha": "8898a78c3e4914829e5bbb72fc7f20302a5e4997", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/testreportingRateModel.r", "max_issues_repo_name": "robboyd/sparta", "max_issues_repo_head_hexsha": "8898a78c3e4914829e5bbb72fc7f20302a5e4997", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/testreportingRateModel.r", "max_forks_repo_name": "robboyd/sparta", "max_forks_repo_head_hexsha": "8898a78c3e4914829e5bbb72fc7f20302a5e4997", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.325, "max_line_length": 124, "alphanum_fraction": 0.5972297176, "num_tokens": 1297, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3278524180688652}} {"text": "if(!require(foreach))\n{\n\tprint(\"You are missing the package 'foreach', we will now try to install it...\")\n\tinstall.packages(\"foreach\")\n\t#library(foreach)\n}\n\nif(!require(xtable))\n{\n\tprint(\"You are missing the package 'xtable', we will now try to install it...\")\n\tinstall.packages(\"xtable\")\n\t#library(xtable)\n}\n\nif(!require(igraph))\n{\n\tprint(\"You are missing the package 'igraph', we will now try to install it...\")\n\tinstall.packages(\"igraph\")\n\t#library(igraph)\n}\n\nlibrary(coin)\nlibrary(multcomp)\nlibrary(colorspace)\nlibrary(foreach)\nlibrary(xtable)\nlibrary(igraph)\n\nlabels <- strsplit(row.names(output$PostHoc.Test), \" - \")\nmatrix <- matrix(nrow = length(methods), ncol = length(methods), dimnames=list(methods, methods))\nadjMatrix <- matrix #adjacency matrix\n\nalpha <- 0.05\n\nraw_ranks <- c()\nforeach(p = 1:length(problems)) %do% {\n indexes <- seq((p-1)*(length(methods)-1) + p, p*(length(methods)-1) + p)\n p_ranks <- rank(Data$Table[indexes])\n raw_ranks <- rbind(raw_ranks, p_ranks)\n}\n# ranks consists of raw ranks now\n#print(ranks)\n\n# aggregate ranks\nranks = c()\nforeach(m = 1:length(methods)) %do% {\n ranks[m] <- mean(raw_ranks[,m])\n}\nprint(paste(\"Ranks: \", ranks))\n\nforeach(i = 1:length(output$PostHoc.Test)) %do% {\n\tl1 <- unlist(labels[i])[1]\n\tl2 <- unlist(labels[i])[2]\n\t#print(paste(\"label1: \", l1, \" label2: \", l2))\n\t\n\tl1Idx <- match(l1, methods)[1]\n\tl2Idx <- match(l2, methods)[1]\n\t#avg1 <- sum(Data$Table[seq(l1Idx, length(Data$Table), length(methods))]) / length(problems)\n\t#avg2 <- sum(Data$Table[seq(l2Idx, length(Data$Table), length(methods))]) / length(problems)\n\t\n\t#print(paste(\"avg1: \", avg1, \" avg2: \", avg2))\n\t\n\tif (ranks[l1Idx] < ranks[l2Idx]) {\n\t\tif (output$PostHoc.Test[i] < alpha) {\n\t\t\tmatrix[l1, l2] <- sprintf(\"\\\\textbf{%.3f}\", output$PostHoc.Test[i])\n\t\t\tadjMatrix[l1, l2] <- 1\n\t\t}\n\t\telse\n\t\t{\n\t\t\tmatrix[l1, l2] <- sprintf(\"%.3f\", output$PostHoc.Test[i])\n\t\t}\n\t} else {\n\t\tif (output$PostHoc.Test[i] < alpha) {\n\t\t\tmatrix[l2, l1] <- sprintf(\"\\\\textbf{%.3f}\", output$PostHoc.Test[i])\n\t\t\tadjMatrix[l2, l1] <- 1\n\t\t}\n\t\telse\n\t\t{\n\t\t\tmatrix[l2, l1] <- sprintf(\"%.3f\", output$PostHoc.Test[i])\n\t\t}\n\t}\n}\n\n#print(matrix)\nprint(xtable(matrix, digits = 3), type=\"latex\", sanitize.text.function = function(x){x})\n\ngraph <- graph.adjacency(adjMatrix)\nV(graph)$label <- methods\nV(graph)$label.cex <- 2 #font size\nV(graph)$color <- \"Lightgray\"\n\n#reset margin\npar(mar=c(0,0,0,0))\nplot(graph, layout=layout.circle, vertex.size=75, edge.color=\"Black\") \n\n", "meta": {"hexsha": "9df68c61dd770365e49449957be4f55b32f07b4e", "size": 2466, "ext": "r", "lang": "R", "max_stars_repo_path": "friedmanPostAnalysis.r", "max_stars_repo_name": "krml19/cma-es-modeling", "max_stars_repo_head_hexsha": "4c10c5862c6449ab969433572b6025601469c1f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "friedmanPostAnalysis.r", "max_issues_repo_name": "krml19/cma-es-modeling", "max_issues_repo_head_hexsha": "4c10c5862c6449ab969433572b6025601469c1f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "friedmanPostAnalysis.r", "max_forks_repo_name": "krml19/cma-es-modeling", "max_forks_repo_head_hexsha": "4c10c5862c6449ab969433572b6025601469c1f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6875, "max_line_length": 97, "alphanum_fraction": 0.6524736415, "num_tokens": 797, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5428632831725051, "lm_q1q2_score": 0.32785241806886517}} {"text": "library(dplyr)\nlibrary(ggplot2)\nlibrary(bootstrap)\nlibrary(lme4)\n\ntheme_set(theme_bw(18))\nsetwd(\"/Users/elisakreiss/Documents/stanford/study/overinformativeness/experiments/10_distributional_learning/results\")\nsource(\"rscripts/helpers.r\")\n\nd = read.table(file=\"data/norming.csv\",sep=\",\", header=T)#, quote=\"\")\nhead(d)\nnrow(d)\nsummary(d)\ntotalnrow = nrow(d)\nd$Trial = d$slide_number_in_experiment - 1\nlength(unique(d$workerid))\n\ntyp = read.table(file=\"../../11_color_norming/results/data/meantypicalities.csv\",sep=\",\",header=T)\nrow.names(typ) = paste(typ$Color,typ$Item)\nhead(typ)\n\n# look at turker comments\nunique(d$comments)\n\nggplot(d, aes(rt)) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,15000))\n\nggplot(d, aes(log(rt))) +\n geom_histogram() \n\nsummary(d$Answer.time_in_minutes)\nggplot(d, aes(Answer.time_in_minutes)) +\n geom_histogram()\n\nggplot(d, aes(gender)) +\n stat_count()\n\nggplot(d, aes(asses)) +\n stat_count()\n\nggplot(d, aes(age)) +\n geom_histogram()\ntable(d$age)\n\nggplot(d, aes(education)) +\n stat_count()\n\nggplot(d, aes(language)) +\n stat_count()\n\nggplot(d, aes(enjoyment)) +\n stat_count()\n\n# process typicality data\ntypicality = droplevels(d[!is.na(d$position_in_exposure),])\ntypicality <- typicality[,colSums(is.na(typicality)) .5, \"typical\", \"atypical\"))\n\nsummary(production)\nsummary(typicality)\ntypicality$response = as.numeric(as.character(typicality$response))\ntable(typicality$item,typicality$proportion,typicality$binaryTypicality)\n\nagr = typicality %>%\n group_by(item,color,proportion,binaryTypicality) %>%\n summarise(MeanTypicality = mean(response),ci.low=ci.low(response),ci.high=ci.high(response))\nagr = as.data.frame(agr)\nagr$YMin = agr$MeanTypicality - agr$ci.low\nagr$YMax = agr$MeanTypicality + agr$ci.high\n\nggplot(agr, aes(x=proportion,y=MeanTypicality,color=color)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_wrap(~item)\nggsave(\"graphs/png/meantypicality_byitem.png\")\n\nggplot(agr, aes(x=proportion,y=MeanTypicality,color=binaryTypicality)) +\n geom_point() +\n geom_smooth(method=\"lm\") \nggsave(\"graphs/png/meantypicality.png\")\n\ntable(typicality$item,typicality$binaryTypicality,typicality$proportion)\n\n# process production data\nproduction = droplevels(d[is.na(d$position_in_exposure),])\nproduction = production[production$target_item != \"orange\",]\nproduction$NormedTypicality = typ[paste(production$target_color,production$target_item),]$Typicality\nproduction$binaryTypicality = as.factor(ifelse(production$NormedTypicality > .5, \"typical\", \"atypical\"))\nproduction <- production[,colSums(is.na(production))%\n group_by(Proportion,condition,binaryTypicality) %>%\n summarise(PropColorMentioned=mean(ColorMentioned),ci.low=ci.low(ColorMentioned),ci.high=ci.high(ColorMentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$PropColorMentioned - agr$ci.low\nagr$YMax = agr$PropColorMentioned + agr$ci.high\n\nggplot(agr, aes(x=Proportion,y=PropColorMentioned,color=condition)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_wrap(~binaryTypicality)\nggsave(\"graphs/png/excl_orange_distribution_effect_production.png\",height=3.5)\n\n# condition on whether or not item was mentioned\ntable(production$Proportion,production$condition,production$binaryTypicality)\nagr = production %>%\n group_by(Proportion,condition,binaryTypicality,ItemMentioned) %>%\n summarise(PropColorMentioned=mean(ColorMentioned),ci.low=ci.low(ColorMentioned),ci.high=ci.high(ColorMentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$PropColorMentioned - agr$ci.low\nagr$YMax = agr$PropColorMentioned + agr$ci.high\n\nggplot(agr, aes(x=Proportion,y=PropColorMentioned,color=condition)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_grid(ItemMentioned~binaryTypicality)\nggsave(\"graphs/png/excl_orange_distribution_effect_production_byitemmention.png\",height=6.5)\n\nagr = production %>%\n group_by(Proportion,condition,binaryTypicality,workerid) %>%\n summarise(PropColorMentioned=mean(ColorMentioned),ci.low=ci.low(ColorMentioned),ci.high=ci.high(ColorMentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$PropColorMentioned - agr$ci.low\nagr$YMax = agr$PropColorMentioned + agr$ci.high\n\nggplot(agr, aes(x=Proportion,y=PropColorMentioned,color=condition)) +\n geom_jitter(width = 10,height=0) +\n geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_grid(workerid~binaryTypicality)\nggsave(\"graphs/png/excl_orange_distribution_effect_production_bysubject.png\",height=25)\n\nagr = production %>%\n group_by(Proportion,condition,binaryTypicality,target_item) %>%\n summarise(PropColorMentioned=mean(ColorMentioned),ci.low=ci.low(ColorMentioned),ci.high=ci.high(ColorMentioned))\nagr = as.data.frame(agr)\nagr$YMin = agr$PropColorMentioned - agr$ci.low\nagr$YMax = agr$PropColorMentioned + agr$ci.high\n\nggplot(agr, aes(x=Proportion,y=PropColorMentioned,color=condition)) +\n geom_jitter(width = 10,height=0) +\n geom_errorbar(aes(ymin=YMin,ymax=YMax),width=.25) +\n facet_grid(target_item~binaryTypicality)\nggsave(\"graphs/png/excl_orange_distribution_effect_production_byitem.png\",height=10)\n\n\ncentered = cbind(production, myCenter(production[,c(\"binaryTypicality\",\"Proportion\",\"condition\")]))\nm = glmer(ColorMentioned ~ cProportion*cbinaryTypicality*ccondition + (1|workerid) , data=centered, family=\"binomial\")\nsummary(m)\n\noverinf = droplevels(production[production$condition == \"overinformative\",])\ncentered = cbind(overinf, myCenter(overinf[,c(\"binaryTypicality\",\"Proportion\",\"condition\")]))\nm = glmer(ColorMentioned ~ cProportion*cbinaryTypicality + (1|workerid) , data=centered, family=\"binomial\")\nsummary(m)\n\nm.simple = glmer(ColorMentioned ~ cProportion*binaryTypicality - cProportion + (1|workerid) , data=centered, family=\"binomial\")\nsummary(m.simple)\n\nm = glm(ColorMentioned ~ Proportion*cbinaryTypicality , data=centered, family=\"binomial\")\nsummary(m)\n\nm = glm(ColorMentioned ~ Proportion*binaryTypicality - Proportion, data=centered, family=\"binomial\")\nsummary(m)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "1f7c7ac10eb17b9e46dd18473d04ed94373fe0de", "size": 8117, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/10_distributional_learning/results/rscripts/norming.r", "max_stars_repo_name": "thegricean/overinformativeness", "max_stars_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-10-27T18:41:57.000Z", "max_stars_repo_stars_event_max_datetime": "2016-10-27T18:41:57.000Z", "max_issues_repo_path": "experiments/10_distributional_learning/results/rscripts/norming.r", "max_issues_repo_name": "thegricean/overinformativeness", "max_issues_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2015-11-30T21:44:31.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-21T01:26:05.000Z", "max_forks_repo_path": "experiments/10_distributional_learning/results/rscripts/norming.r", "max_forks_repo_name": "thegricean/overinformativeness", "max_forks_repo_head_hexsha": "d20b66148c13af473b57cc4d1736191a49660349", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-11-25T09:53:20.000Z", "max_forks_repo_forks_event_max_datetime": "2017-03-17T21:51:18.000Z", "avg_line_length": 38.4691943128, "max_line_length": 176, "alphanum_fraction": 0.7814463472, "num_tokens": 2362, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32785241032908846}} {"text": "#!/usr/bin/env Rscript\n\ncargs = commandArgs(trailingOnly=TRUE)\n\ninfile = cargs[1]\noutfile = cargs[2]\n\nx = read.table(infile)\nx[, 3] = x[sample(1:nrow(x), nrow(x), FALSE), 3]\n\nwrite.table(x, outfile, sep='\\t', col.names=FALSE, row.names=FALSE,\n quote=FALSE)\n", "meta": {"hexsha": "a9db8493c09e04acc252cb2237f71642d376eaf1", "size": 268, "ext": "r", "lang": "R", "max_stars_repo_path": "shuffle_plink_phenotypes_file.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shuffle_plink_phenotypes_file.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "shuffle_plink_phenotypes_file.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6153846154, "max_line_length": 67, "alphanum_fraction": 0.6455223881, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32782401151233864}} {"text": "#' Get Patient Report\n#' @param PatientID - The patient identifier string associated with the patient's profile. \n#' @param all_data - The entire data matrix loaded based on the diagnosis selected in the dropdown menu input$diagClass.\n#' @return patientReport - a data table with the metabolites and z-scores associated with the selected patient ID.\n#' @export getPatientReport\n#'\n#' @examples\n#' data(Miller2015_Heparin)\n#' # Input is supplied by R shiny app, but you can hard code parameters as a list object, too, to test functionality.\n#' input = list()\n#' input$ptIDs = colnames(Miller2015_Heparin)[4]\n#' input$diagClass = \"paa\"\n#' rpt = getPatientReport(input, Miller2015_Heparin)\n#' head(rpt$patientReport)\ngetPatientReport = function(input, all_data) {\n print(input$diagClass)\n print(input$ptIDs)\n \n # MetaboliteName Zscore\n all_data = data.matrix(all_data)\n tmp.zscore = rownames(all_data)\n if (length(input$ptIDs)>1) {\n zscore.data = apply(all_data[ , which(colnames(all_data) %in% input$ptIDs)], 1, function(i) mean(na.omit(i)))\n } else {\n zscore.data = all_data[ , which(colnames(all_data)==input$ptIDs)]\n }\n names(zscore.data) = tmp.zscore\n print(head(zscore.data))\n \n ind = which(is.na(zscore.data))\n if (length(ind)>0) {\n zscore.data = zscore.data[-ind]\n }\n \n data = data.frame(Metabolite=character(), Zscore=numeric(), stringsAsFactors = FALSE)\n for (row in 1:length(zscore.data)) {\n data[row, \"Metabolite\"] = names(zscore.data)[row]\n data[row, \"Zscore\"] = round(zscore.data[names(zscore.data)[row]], 2)\n }\n \n # Remove mets that were NA in zscore \n ind0 = which(is.na(data[,\"Zscore\"]))\n if (length(ind0)>0) {\n data = data[-ind0,]\n }\n print(dim(data))\n \n # Order by abs(Zscore)\n class(data[,\"Zscore\"]) = \"numeric\"\n data = data[order(abs(data[,\"Zscore\"]), decreasing = TRUE), ]\n names(data) = c(\"Metabolite\", \"Z-score\")\n \n return(list(patientReport=data))\n}", "meta": {"hexsha": "18ca742faa662d763a9259128fb69437b2d3c602", "size": 1921, "ext": "r", "lang": "R", "max_stars_repo_path": "R/getPatientReport.r", "max_stars_repo_name": "NCBI-Hackathons/Metabolomics-Data-Portal", "max_stars_repo_head_hexsha": "9c9696f9220addfc7f2c2cf8055b4f3ac9ed390a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2019-02-23T15:00:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-30T08:46:31.000Z", "max_issues_repo_path": "R/getPatientReport.r", "max_issues_repo_name": "NCBI-Hackathons/Metabolomics-Data-Portal", "max_issues_repo_head_hexsha": "9c9696f9220addfc7f2c2cf8055b4f3ac9ed390a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2019-02-21T15:51:34.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-22T21:53:00.000Z", "max_forks_repo_path": "R/getPatientReport.r", "max_forks_repo_name": "NCBI-Hackathons/Metabolomics-Data-Portal", "max_forks_repo_head_hexsha": "9c9696f9220addfc7f2c2cf8055b4f3ac9ed390a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-02-13T04:33:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-17T19:58:31.000Z", "avg_line_length": 35.5740740741, "max_line_length": 120, "alphanum_fraction": 0.6887038001, "num_tokens": 555, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6406358685621719, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3278240115123386}} {"text": "#' @title gaphunter\n#' @name gaphunter\n#' @param object a matrix or object of class: \"GenomicRatioSet\", \"GenomicMethylSet\", \"MethylSet\", or\"RatioSet\"\n#' @param threshold (default = 0.05)\n#' @param keepOutliers (default = FALSE)\n#' @param outCutoff (default = 0.01)\n#' @param verbose (default = TRUE)\n#' gaphunter() function from:\n#' https://epigeneticsandchromatin.biomedcentral.com/articles/10.1186/s13072-016-0107-z\n#' copied with minor modification from https://raw.githubusercontent.com/kasperdanielhansen/minfi/master/R/gaphunter.R (2018-03-23)\n#' @export\ngaphunter <- function(object, threshold = 0.05, keepOutliers = FALSE,\n\t\t\t\t\t outCutoff = 0.01, verbose = TRUE) {\n\tif ((threshold <= 0) || (threshold >= 1))\n\t\tstop(\"[gaphunter] 'threshold' must be between 0 and 1.\") \n\tif ((outCutoff <= 0) || (outCutoff >= 0.5))\n\t\tstop(\"[gaphunter] 'outCutoff' must be between 0 and 0.5.\") \n\tif (is(object, \"GenomicRatioSet\") || is(object, \"GenomicMethylSet\") ||\n\t\tis(object, \"MethylSet\") || is(object,\"RatioSet\")) {\n\t\tif(verbose)\n\t\t\tmessage(\"[gaphunter] Calculating beta matrix.\")\n\t\tBeta <- getBeta(object)\n\t} else {\n\t\tif(is(object,\"matrix\")) {\n\t\t\ttest <- matrixStats::rowRanges(object)\n\t\t\tif(sum(test[,1] < 0,na.rm = TRUE) == 0 && sum(test[,2] > 1,na.rm = TRUE) == 0) {\n\t\t\t\tBeta <- object } else { stop(\"[gaphunter] Matrix must be of Beta values with range from 0 to 1\") }\n\t\t} else {\n\t\t\tstop(\"[gaphunter] Object must be one of (Genomic)RatioSet, (Genomic)MethylSet, or matrix\")\n\t\t}\n\t}\n\tnacheck <- rowSums(is.na(Beta))\n\tif (sum(nacheck > 0) > 0) {\n\t\tif(verbose)\n\t\t\tmessage(\"[gaphunter] Removing probes containing missing beta values.\")\n\t\tBeta <- Beta[which(nacheck == 0),]\n\t}\n\t\n\tif (verbose) {\n\t\tmessage(\"[gaphunter] Using \",prettyNum(nrow(Beta),big.mark = \",\",scientific = FALSE),\n\t\t\t\t\" probes and \",prettyNum(ncol(Beta),big.mark = \",\",scientific = FALSE),\" samples.\")\n\t\tmessage(\"[gaphunter] Searching for gap signals.\")\n\t}\n\t\n\tsorting <- t(apply(Beta, 1, sort.int, method = \"quick\", index.return = TRUE))\n\tsortedindices <- lapply(sorting, function(n) { return(n$ix) })\n\tsortedbeta <- do.call(\"rbind\", lapply(sorting, function(n) {return(n$x)} ))\n\trownames(sortedbeta) <- rownames(Beta) \n\tdiffs <- matrixStats::rowDiffs(sortedbeta) \n\tgapind <- rowSums(diffs > threshold)\n\tsortedindices <- lapply(which(gapind > 0), function(h) { return(sortedindices[[h]]) })\n\tsortedbeta <- sortedbeta[which(gapind > 0),]\n\tdiffs <- diffs[which(gapind > 0),]\n\tgapind <- gapind[which(gapind > 0)]\n\t\n\tbreakpoints <- apply(diffs,1,function(x) { return(which(x > threshold)) })\n\t\n\treturngroups <- function(x,y) {\n\t\ttemplate <- rep(1, ncol(sortedbeta))\n\t\tcount <- 2\n\t\tfor (j in x) {\n\t\t\ttemplate[y[(j+1):length(y)]] <- count\n\t\t\tcount <- count+1\n\t\t}\n\t\treturn(template)\n\t}\n\t\n\tgroupanno <- t(mapply(returngroups, breakpoints, sortedindices))\n\tmaxGroups <- max(groupanno)\n\tgapanno <- matrix(0, nrow = nrow(groupanno), ncol = maxGroups + 1)\n\tgapanno[,1] <- matrixStats::rowMaxs(groupanno)\n\tfor(ii in 1:maxGroups) {\n\t\tgapanno[, ii + 1] <- matrixStats::rowCounts(groupanno, value = ii)\n\t}\n\t## gapanno <- apply(groupanno, 1, function(k) {\n\t##\t tempgap <- rep(0, max(gapind) + 2)\n\t##\t kl <- length(unique(k))\n\t##\t tempgap[1] <- kl \n\t##\t tempgap[1 + 1:kl] <- table(k)\n\t##\t return(tempgap)\n\t## })\n\t## gapanno <- t(gapanno)\n\trownames(gapanno) <- rownames(sortedbeta)\n\trownames(groupanno) <- rownames(sortedbeta)\n\t\n\tif(verbose)\n\t\tmessage(\"[gaphunter] Found \",\n\t\t\t\tprettyNum(nrow(gapanno),big.mark = \",\",scientific = FALSE), \" gap signals.\")\n\t\n\tif (keepOutliers == FALSE) {\n\t\tif (verbose)\n\t\t\tmessage(\"[gaphunter] Filtering out gap signals driven by outliers.\")\n\t\tmarkme <- unlist(lapply(1:nrow(gapanno), function(blah) {\n\t\t\tanalyze <- gapanno[blah, -1]\n\t\t\tmaxgroup <- which(analyze == max(analyze,na.rm=TRUE))\n\t\t\tif (length(maxgroup) == 1) {\n\t\t\t\tif(sum(analyze[-maxgroup],na.rm=TRUE) < outCutoff*ncol(Beta)) {\n\t\t\t\t\treturn(blah)\n\t\t\t\t}\n\t\t\t}\n\t\t}))\n\t\tif (length(markme) > 0) {\n\t\t\tgapanno <- gapanno[-markme,]\n\t\t\tgroupanno <- groupanno[-markme,]\n\t\t}\n\t\tif (verbose)\n\t\t\tmessage(\"[gaphunter] Removed \",prettyNum(length(markme),big.mark = \",\",scientific = FALSE),\n\t\t\t\t\t\" gap signals driven by outliers from results.\")\n\t}\n\tgapanno <- data.frame(gapanno)\n\t\n\tcolnames(gapanno) <- c(\"Groups\", paste0(\"Group\",1:(ncol(gapanno)-1)))\n\t\n\talgorithm <- list(\"threshold\" = threshold, \"outCutoff\" = outCutoff, \"keepOutliers\" = keepOutliers)\n\t\n\treturn(list(\"proberesults\" = gapanno, \"sampleresults\" = groupanno, \"algorithm\" = algorithm))\n}\n", "meta": {"hexsha": "290cd9799d1f20ed13d6fc3e365fcd36c2882b7e", "size": 4473, "ext": "r", "lang": "R", "max_stars_repo_path": "R/gaphunter.r", "max_stars_repo_name": "RichardJActon/meffil_duplicate", "max_stars_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/gaphunter.r", "max_issues_repo_name": "RichardJActon/meffil_duplicate", "max_issues_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/gaphunter.r", "max_forks_repo_name": "RichardJActon/meffil_duplicate", "max_forks_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.906779661, "max_line_length": 131, "alphanum_fraction": 0.6561591773, "num_tokens": 1449, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32782400449042315}} {"text": "#!/usr/bin/env Rscript\n\nargs = commandArgs(trailingOnly = TRUE)\n#load required packages\nlibrary(\"IRanges\")\nlibrary(\"GenomicRanges\")\nlibrary(\"rtracklayer\")\nlibrary(\"Rsamtools\")\nlibrary(\"GenomicAlignments\")\nlibrary(\"GenomicFeatures\")\n\n#USAGE:\n#Rscript box_plot.r \\\n#<1. project directory> \\\n#<2. bam1, WT> \\\n#<3. bam1 name> \\\n#<4. bam2, Test> \\\n#<5. bam2 name> \\\n#<6. peaks1> \\\n#<7. peaks1 name> \\\n#<8. peaks2> \\\n#<9. peaks2 name> \\\n#<10. file out name>\n\ndir <- args[1]\nsetwd(dir)\n\n#read bams\nbam1.name <- args[3]\nbam1.bam <- readGAlignments(file.path(args[2]))\nbam1.gr <- granges(bam1.bam)\nbam1 <- bam1.gr\n#find library size\nlibrary_bam1 <- NROW(bam1)\n\nbam2.name <- args[5]\nbam2.bam <- readGAlignments(file.path(args[4]))\nbam2.gr <- granges(bam2.bam)\nbam2 <- bam2.gr\n#find library size\nlibrary_bam2 <- NROW(bam2)\n\n#read peaks\npeaks1.name <- args[7]\npeaks1 <- import(file.path(args[6]), format = \"BED\")\n\npeaks2.name <- args[9]\npeaks2 <- import(file.path(args[8]), format = \"BED\")\n\noutprefix <- args[10]\n\n#Calculate RPKM, = numReads / ( geneLength/1000 * totalNumReads/1,000,000 )\nrpkm_bam1_peaks1 <- countOverlaps(peaks1, bam1) / (width(peaks1)/1000 * library_bam1/1000000)\nrpkm_bam2_peaks1 <- countOverlaps(peaks1, bam2) / (width(peaks1)/1000 * library_bam2/1000000)\nrpkm_peaks1<- data.frame(rpkm_bam1_peaks1,rpkm_bam2_peaks1)\n\n#write pdf\nboxplot_file_peak1 <- paste(outprefix,peaks1.name,\"box_plot.pdf\", sep = \"_\")\npdf(file=boxplot_file_peak1)\npar(pty = \"s\")\nboxplot(rpkm_peaks1,col=(c(\"blue\",\"red\")), main=peaks1.name, ylab=\"RPKM\",outline=F, notch=T, \n names=c(bam1.name,bam2.name),\n boxwex = 0.4,cex.axis=1,lwd=4,lty=1,ylim=c(0,12))\ndev.off()\n\nout_cox_peaks1.name <- paste(outprefix,peaks1.name,\"wilcox_test.txt\", sep = \"_\")\nwilcox_peaks1 <- wilcox.test(rpkm_bam1_peaks1,rpkm_bam2_peaks1,conf.int=T)\nwrite.table(wilcox_peaks1$p.value, file=file.path(dir, out_cox_peaks1.name), sep=\"\\t\", quote=F, row.names=F, col.names=F)\n\n\n#Peak2\nrpkm_bam1_peaks2 <- countOverlaps(peaks2, bam1) / (width(peaks1)/1000 * library_bam1/1000000)\nrpkm_bam2_peaks2 <- countOverlaps(peaks2, bam2) / (width(peaks1)/1000 * library_bam2/1000000)\nrpkm_peaks2<- data.frame(rpkm_bam1_peaks2,rpkm_bam2_peaks2)\n\n#write pdf\nboxplot_file_peak2 <- paste(outprefix,peaks2.name, \"box_plot.pdf\", sep = \"_\")\npdf(file=boxplot_file_peak2)\npar(pty = \"s\")\nboxplot(rpkm_peaks2,col=(c(\"blue\",\"red\")), main=peaks2.name, ylab=\"RPKM\",outline=F, notch=T, \n names=c(bam1.name,bam2.name),\n boxwex = 0.4,cex.axis=1,lwd=4,lty=1,ylim=c(0,12))\ndev.off()\n\nout_cox_peaks2.name <- paste(outprefix,peaks2.name,\"wilcox_test.txt\", sep = \"_\")\nwilcox_peaks2 <- wilcox.test(rpkm_bam1_peaks2,rpkm_bam2_peaks2,conf.int=T)\nwrite.table(wilcox_peaks2$p.value, file=file.path(dir, out_cox_peaks2.name), sep=\"\\t\", quote=F, row.names=F, col.names=F)\n", "meta": {"hexsha": "93adfe735625e0be8e16e7a9b28a7d7420789f5c", "size": 2808, "ext": "r", "lang": "R", "max_stars_repo_path": "figures/rpkm_boxplot.r", "max_stars_repo_name": "utsw-medical-center-banaszynski-lab/Teng_ATRX_Gquadruplex", "max_stars_repo_head_hexsha": "8bfb7d08a0def75d211034cc4fc0042c4a08bcd5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "figures/rpkm_boxplot.r", "max_issues_repo_name": "utsw-medical-center-banaszynski-lab/Teng_ATRX_Gquadruplex", "max_issues_repo_head_hexsha": "8bfb7d08a0def75d211034cc4fc0042c4a08bcd5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "figures/rpkm_boxplot.r", "max_forks_repo_name": "utsw-medical-center-banaszynski-lab/Teng_ATRX_Gquadruplex", "max_forks_repo_head_hexsha": "8bfb7d08a0def75d211034cc4fc0042c4a08bcd5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9090909091, "max_line_length": 121, "alphanum_fraction": 0.7122507123, "num_tokens": 1016, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3277261353223074}} {"text": "# #-------------------------SVM analysis\n\n## the code perform a svm analysis with 2 groups,\n## inputs: Multi_datainput_m2, Multi_datainput_m (chosen via Projects_metadata$confound_by)\n## 2 types of strategy depending on testvalidation, which depends on sample size.\n\n\n##---testset validation if enough data\nif (testvalidation){\n #---- Determine the trainset and testset of data, more complex if there is a confounding factor.\n \n if (!is.na(Projects_metadata$confound_by)) {\n Input = Multi_datainput_m2 #take the data with confounding variable\n \n L = levels(Input$groupingvar)\n L2 = levels(Input$confoundvar)\n Glass = Input %>% filter (groupingvar == L[1])\n Glass2 = Input %>% filter (groupingvar == L[2])\n # message if group of different size\n if (nrow (Glass) != nrow (Glass2))\n print(\"the groups do not have the same size !\")\n \n # split each randomly over confoundvar\n \n Glass_1 = Glass %>% filter (confoundvar == L2[1])\n Glass_2 = Glass %>% filter (confoundvar == L2[2])\n Glass2_1 = Glass2 %>% filter (confoundvar == L2[1])\n Glass2_2 = Glass2 %>% filter (confoundvar == L2[2])\n \n # prepare datasets\n indexl = min(nrow(Glass), nrow(Glass2))\n index <- 1:indexl\n trainlength = max(4, trunc(length(index) / 3)) #max fct deprecated, this run only if there is enough data\n indexl2 = min(nrow(Glass_1),\n nrow(Glass_2),\n nrow(Glass2_1),\n nrow(Glass2_2))\n index <- 1:indexl2\n trainindex <- sample(index, trainlength)\n \n \n trainset <-\n rbind(Glass_1[trainindex, ], Glass_2[trainindex, ], Glass2_1[trainindex, ], Glass2_2[trainindex, ])\n testset <-\n rbind(Glass_1[-trainindex, ], Glass_2[-trainindex, ], Glass2_1[-trainindex, ], Glass2_2[-trainindex, ])\n trainset <- trainset %>% select (-confoundvar)\n testset <- testset %>% select (-confoundvar)\n \n } else { # if no counfounding variable, it is faster!\n Input = Multi_datainput_m\n L = levels(Input$groupingvar)\n Glass = Input %>% filter (groupingvar == L[1])\n Glass2 = Input %>% filter (groupingvar == L[2])\n if (nrow (Glass) != nrow (Glass2))\n print(\"the groups do not have the same size !\")\n indexl = min(nrow(Glass), nrow(Glass2))\n \n # split each randomly\n index <- 1:indexl\n trainindex <- sample(index, max(8, 2 * trunc(length(index) / 3)))\n trainset <- rbind(Glass[trainindex,], Glass2[trainindex,])\n testset <- rbind(Glass[-trainindex,], Glass2[-trainindex,])\n }\n ## we now have trainset and testset!!\n ######--------------------------------run SVM analysis\n \n ##---- getting rid of variables with no variability (all 0)\n temp = trainset %>% select (-groupingvar)\n cfreq <- colSums(temp)\n E1 = names(temp[, cfreq == 0])\n temp = testset %>% select (-groupingvar)\n cfreq <- colSums(temp)\n E2 = names(temp[, cfreq == 0])\n trainset = trainset [,!names(trainset) %in% c(E1, E2)]\n testset = testset [,!names(testset) %in% c(E1, E2)]\n Input = Input[,!names(Input) %in% c(E1, E2)]\n \n ##--- tuning: choose best kernel and parameters (error rate minimal), all data (accuracy on trainset maximal, use all if identical), this takes time!\n bestk= tune.svm2(trainset,groupingvar)\n best.parameters = bestk[[2]]\n ##--- train model with best parameters on trainset data\n svm.model <- svm(groupingvar ~ ., data = trainset, cost = best.parameters$cost, gamma = best.parameters$gamma, kernel = bestk[[1]])\n \n #-- run model on test data\n svm.pred <- predict(svm.model, testset %>% select(-groupingvar))\n #-- compare prediction with real data\n SVMprediction_res = table(pred = svm.pred, true = testset$groupingvar)\n #SVMprediction = as.data.frame(SVMprediction_res)\n \n #-- Accuracy of grouping and plot\n temp = classAgreement (SVMprediction_res)\n Accuracyreal = temp$kappa\n kernel = bestk[[1]]\n\n}\n\n#### 2-out cross-validation if sample size too low. \nif (!testvalidation){\n ## set data to use for the svm\n if (!is.na(Projects_metadata$confound_by)) {\n out_sel = Multi_datainput_m2[order(Multi_datainput_m2$groupingvar, Multi_datainput_m2$confoundvar),]\n out_sel =out_sel %>% select (-confoundvar)\n }else {\n out_sel = Multi_datainput_m[order(Multi_datainput_m$groupingvar),]\n }\n \n # ## make same number of animal in each group, deprecated as svm is not running if this is the case.\n # out_sel_ori= out_sel\n # GP= out_sel$groupingvar\n # L =levels(GP)\n # out_sel1= out_sel [GP == L[1],]\n # out_sel2= out_sel [GP == L[2],]\n # NG = min (nrow (out_sel1),nrow (out_sel2))\n # out_sel =rbind (out_sel1[1:NG,],out_sel2[1:NG,])\n \n ## get rid of columns with NAs\n all_na <- function(x) any(!is.na(x))\n out_sel = out_sel %>% select_if(all_na)\n \n ## do the svm, with a radial kernel\n kernel =\"radial\"\n source (\"Rcode/2_out_svm.r\")\n Accuracyreal = temp$kappa\n\n\n}\n\nAccuracy = paste0(\n ncol(out_sel) - 1,\n \" variables: Accuracy of the prediction with \",\n kernel,\n \" kernel (Kappa index: 0 denotes chance level, maximum is 1):\",\n Accuracyreal\n)\n", "meta": {"hexsha": "dfe5c5f03bcdaf30de370dab5c3987b7370cdc80", "size": 5040, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/Rcode/multidimensional_analysis_svm.r", "max_stars_repo_name": "jcolomb/HCS_analysis", "max_stars_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-09-27T08:57:12.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-22T08:44:06.000Z", "max_issues_repo_path": "analysis/Rcode/multidimensional_analysis_svm.r", "max_issues_repo_name": "jcolomb/HCS_analysis", "max_issues_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 36, "max_issues_repo_issues_event_min_datetime": "2017-09-27T10:42:45.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-01T08:47:42.000Z", "max_forks_repo_path": "analysis/Rcode/multidimensional_analysis_svm.r", "max_forks_repo_name": "jcolomb/HCS_analysis", "max_forks_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-12-10T12:45:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-19T14:28:05.000Z", "avg_line_length": 37.3333333333, "max_line_length": 151, "alphanum_fraction": 0.6496031746, "num_tokens": 1502, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7549149758396752, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.3276191070173019}} {"text": "require(tidyverse)\nrequire(lubridate)\nsource(\"~/projects/maize.expression/src/me.fun.r\")\ndirw= '~/projects/wgc/Rmd'\n\ndates = ymd('2018-11-3') + 0:6\nta = tibble(Date = dates, Freq = c(5,5,0,1,0,1,1)) %>%\n mutate(DateLabel = sprintf(\"%s %s\", month(Date, label=T), mday(Date)))\n\np = ggplot(ta) +\n geom_bar(aes(x=Date, y=Freq, fill=as.character(Date)), stat='identity', width=.8) +\n scale_x_discrete(breaks = ta$Date, labels = ta$DateLabel) +\n scale_y_continuous(name = 'Frequency of Angry') +\n scale_fill_npg() +\n otheme(legend.pos = 'none', \n xtext=T, xtitle=T, ytext=T, ytitle=T, xgrid=F, ygrid=T,\n xtick = T, ytick = T)\nfo = file.path(dirw, 'july1.pdf')\nggsave(p, file = fo, width = 5, height = 4)\n\ntl = tibble(Date = rep(dates, times=c(5,5,1,1,1,1,1)),\n level = c(2,3,2,1,2, 2,3,1,4,4, 0, 4, 0, 3, 1))\ntls = tl %>% group_by(Date) %>% summarise(level = mean(level)) %>%\n mutate(DateLabel = sprintf(\"%s %s\", month(Date, label=T), mday(Date)))\nirs_note = c(\n\"Not angry\",\n'Feel OK but a little bit angry',\n'Feel not OK and a little bit angry',\n'Feel somewhat angry and irritate insights',\n'Feel very angry but can control actions',\n'Feel extremely angry and cannot control actions'\n)\nirs = tibble(level=0:5, note=irs_note) %>%\n mutate(lab = sprintf(\"%d - %s\", level, note))\np = ggplot(tls) +\n geom_point(aes(x=Date, y=level)) +\n geom_line(aes(x=Date, y=level)) +\n geom_point(data = tibble(x=ymd('2018-11-3'),y=0,level=0:5), aes(x=x, y=y, color=level), alpha = 0) +\n scale_x_date(breaks = tls$Date, labels = tls$DateLabel) +\n scale_color_viridis(name = \"Individual Rating Scales (IRS)\", breaks = irs$level, labels = irs$lab) +\n scale_y_continuous(name = 'Level of Angry', limits = c(0,5)) +\n otheme(legend.pos = 'right',, legend.dir = 'v',\n xtext=T, xtitle=T, ytext=T, ytitle=T, xgrid=F, ygrid=T,\n xtick = T, ytick = T) +\n theme(legend.title.align = 0,\n legend.title = element_text(size = 9),\n legend.key.size = unit(1, 'lines'), \n legend.text = element_text(size = 8))\nfo = file.path(dirw, 'july2.pdf')\nggsave(p, file = fo, width = 9, height = 5)\n\n", "meta": {"hexsha": "b74734415537f2624af36bbccbe2ae8e68e4f6e6", "size": 2143, "ext": "r", "lang": "R", "max_stars_repo_path": "Rmd/july.r", "max_stars_repo_name": "orionzhou/wgc", "max_stars_repo_head_hexsha": "c64c5900fb7d1721cf3ee9cba42e2af3361fdd3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rmd/july.r", "max_issues_repo_name": "orionzhou/wgc", "max_issues_repo_head_hexsha": "c64c5900fb7d1721cf3ee9cba42e2af3361fdd3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rmd/july.r", "max_forks_repo_name": "orionzhou/wgc", "max_forks_repo_head_hexsha": "c64c5900fb7d1721cf3ee9cba42e2af3361fdd3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-12-09T18:58:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-31T14:50:28.000Z", "avg_line_length": 41.2115384615, "max_line_length": 104, "alphanum_fraction": 0.6271581895, "num_tokens": 737, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.32752025236981913}} {"text": "library(plyr) # install.packages(\"plyr\")\n\nmin_confidence = 0.5\n\n# INPUTS\noptions(scipen=999) # disable scientific notation\ndata <- read.csv(\"../output.csv\", sep=\",\")\n\ntmp.go_projects <- data[data$language == \"go\" & data$confidence >= min_confidence,]\ntmp.c_projects <- data[data$language == \"c\" & data$confidence >= min_confidence,]\n\n# Convert project names\ntmp.go_projects[] <- lapply(tmp.go_projects, as.character)\ntmp.c_projects[] <- lapply(tmp.c_projects, as.character)\n\n# Go\ntmp.rules_equal_count = count(tmp.go_projects[tmp.go_projects$lhs_file==tmp.go_projects$rhs_file,], \"project_name\")\ntmp.rules_diff_count = count(tmp.go_projects[tmp.go_projects$lhs_file!=tmp.go_projects$rhs_file,], \"project_name\")\n\npar(mar=c(3,5,2,1)) # margin\nboxplot(tmp.rules_equal_count$freq, tmp.rules_diff_count$freq , log = \"y\", at = c(1,2), names = c(\"Same file\", \"Different file\"), ylab=\"Number of rules\")\nquantile(tmp.rules_equal_count$freq)\nquantile(tmp.rules_diff_count$freq)\n\n# C\ntmp.rules_equal_count = count(tmp.c_projects[tmp.c_projects$lhs_file==tmp.c_projects$rhs_file,], \"project_name\")\ntmp.rules_diff_count = count(tmp.c_projects[tmp.c_projects$lhs_file!=tmp.c_projects$rhs_file,], \"project_name\")\n\npar(mar=c(3,5,2,1)) # margin\nboxplot(tmp.rules_equal_count$freq, tmp.rules_diff_count$freq , log = \"y\", at = c(1,2), names = c(\"Same file\", \"Different file\"), ylab=\"Number of rules\")\nquantile(tmp.rules_equal_count$freq)\nquantile(tmp.rules_diff_count$freq)\n\n\nrm(list = ls(pattern = \"tmp.*\"))", "meta": {"hexsha": "88d8907b069419c1c7558572a26d3b80750d7d8c", "size": 1489, "ext": "r", "lang": "R", "max_stars_repo_path": "results/scripts_r/3_same_file.r", "max_stars_repo_name": "rodrigo-brito/co-change-analysis", "max_stars_repo_head_hexsha": "298bb5437371ab29fb94a9e2f9012d3a5cf033f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-04-15T22:27:52.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-15T22:27:52.000Z", "max_issues_repo_path": "results/scripts_r/3_same_file.r", "max_issues_repo_name": "rodrigo-brito/co-change-analysis", "max_issues_repo_head_hexsha": "298bb5437371ab29fb94a9e2f9012d3a5cf033f7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-05-09T01:55:12.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-09T02:14:41.000Z", "max_forks_repo_path": "results/scripts_r/3_same_file.r", "max_forks_repo_name": "rodrigo-brito/co-change-analysis", "max_forks_repo_head_hexsha": "298bb5437371ab29fb94a9e2f9012d3a5cf033f7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-05-09T01:41:29.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-12T18:59:45.000Z", "avg_line_length": 42.5428571429, "max_line_length": 153, "alphanum_fraction": 0.7441235729, "num_tokens": 417, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.3275202523698191}} {"text": "# Collect arguments\nargs <- commandArgs(trailingOnly = TRUE)\n \n## Default setting when no arguments passed\nif(length(args) < 1) {\n args <- c(\"--help\")\n}\n \n## Help section\nif(\"--help\" %in% args) {\n cat(\"\n The R Script\n \n Arguments:\n --nobad.wg filename - character, name of whole genome hg19.50k.k50.nobad.varbin.data file\n --nobad.short filename - character, name of whole genome hg19.50k.k50.nobad.varbin.short.txt\n --help - print this text\n \n Example:\n Rscript copynumber.r --nobad.wg=sample1.hg19.50k.k50.nobad.varbin.data.txt --nobad.short=sample1.hg19.50k.k50.nobad.varbin.short.txt \\n\\n\")\n \n q(save=\"no\")\n}\n \n## Parse arguments (we expect the form --arg=value)\nparseArgs <- function(x) strsplit(sub(\"^--\", \"\", x), \"=\")\n\nargsDF <- as.data.frame(do.call(\"rbind\", parseArgs(args)))\nargsL <- as.list(as.character(argsDF$V2))\nnames(argsL) <- argsDF$V1\nargsL\n\n\n## Arg1 default\nif(! file.exists(argsL$nobad.wg)) {\n stop(\"`nobad.wg` file not found\")\n q(save = \"no\")\n}\n\n## Arg2 default\nif(! file.exists(argsL$nobad.short)) {\n stop(\"`nobad.short` file not found\")\n q(save = \"no\")\n}\n\nsource(\"cbsLib.R\")\n\ncellID <- gsub(\"(.*).hg19.*.txt\", \"\\\\1\", argsL$nobad.wg)\n\ndf <- read.table(argsL$nobad.wg, header = TRUE)\ndfs <- read.table(argsL$nobad.short, header = TRUE)\n\nstarts <- c()\nends <- c()\nprevEnd <- 0\nlen <- nrow(dfs)\nfor (j in 1:len) {\n\tthisStart = prevEnd + 1\n\tthisEnd = thisStart + dfs$num.mark[j] - 1\n\tstarts <- c(starts, thisStart)\n\tends <- c(ends, thisEnd)\n\tprevEnd = thisEnd\n}\n\namat <- matrix(data = 0, nrow = 1500000, ncol=1)\ncounter <- 1\nfor (j in 1:(len-1)) {\n\tfor (k in (j+1):len) {\n\t\tN <- round((starts[j] - ends[j] + 1) * (starts[k] - ends[k] + 1)/1000)\n\t\tD <- abs(2^dfs$seg.mean[j] - 2^dfs$seg.mean[k])\n\t\tcat(N, \"\\t\")\n\t\tif (N > 0) {\n\t\t\tamat[(counter:(counter+N-1)), 1] <- rep.int(D, N)\n\t\t\tcounter <- counter+N\n\t\t}\n\t}\n}\na3 <- amat[(1:counter),1]\na3.95 <- sort(a3)[round(.95*counter)]\na3d <- density(a3[which(a3 < a3.95)], n = 1000)\ncn0 <- a3d$x[which(peaks(as.vector(a3d$y), span =59))][1]\ncn1 <- a3d$x[which(peaks(as.vector(a3d$y), span = 59))][2]\n\ndf$cn.ratio <- df$lowratio / cn1\ndf$cn.seg <- df$seg.mean.LOWESS / cn1\ndf$copy.number <- round(df$cn.seg)\n\nwrite.table(df, sep = \"\\t\", file = paste(cellID, \".hg19.50k.k50.varbin.data.copynumber.txt\", sep = \"\"), quote = FALSE, row.names = FALSE)\n\npng(paste(cellID, \".wg.cn.density.png\", sep = \"\"), height = 148, width = 298, units = \"mm\", res = 350)\npar(mar = c(5.1,4.1,4.1,4.1))\nplot(a3d, main = paste(cellID, \"seg.mean difference density\"))\ndev.off()\n\npng(paste(cellID, \".wg.cn.png\", sep=\"\"), height = 148, width = 298, units = \"mm\", res = 350)\npar(mar = c(5.1,4.1,4.1,4.1))\nplot(x = df$abspos, y = df$cn.ratio, main=paste(\"CN profile: \", cellID, sep=\"\"), xlab=\"Bin\", ylab=\"Ratio\", col=\"#CCCCCC\")\nlines(x = df$abspos, y = df$cn.ratio, col=\"#CCCCCC\")\npoints(x = df$abspos, y = df$cn.seg, col=\"#0000DD\")\nlines(x = df$abspos, y = df$cn.seg, col=\"#0000DD\")\npoints(x = df$abspos, y = df$copy.number, col=\"#DD0000\")\nlines(x = df$abspos, y = df$copy.number, col=\"#DD0000\")\ndev.off()\n\nfor (a in 1:24) {\n\tpng(paste(cellID, \".chr\", a, \".cn.png\", sep=\"\"), height = 148, width = 298, units = \"mm\", res = 350)\n\tpar(mar = c(5.1,4.1,4.1,4.1))\n\tplot(df$cn.ratio[df$chrom == a], main=paste(cellID, \" chr\", a, sep = \"\"), xlab = \"Bin\", ylab = \"Ratio\", col = \"#CCCCCC\")\n\tlines(df$cn.ratio[df$chrom == a], col = \"#CCCCCC\")\n\tpoints(df$cn.seg[df$chrom == a], col = \"#0000DD\")\n\tlines(df$cn.seg[df$chrom == a], col = \"#0000DD\")\n\tpoints(df$copy.number[df$chrom == a], col = \"#DD0000\")\n\tlines(df$copy.number[df$chrom == a], col = \"#DD0000\")\n\tdev.off()\n}\n\n", "meta": {"hexsha": "ace9c33768109ed983e54bb45d15cb8c3cadb9cd", "size": 3662, "ext": "r", "lang": "R", "max_stars_repo_path": "src/copynumber.r", "max_stars_repo_name": "RodrigoGM/single_cell_cnv", "max_stars_repo_head_hexsha": "19a42bb279d3f22d0326d89b993026c43d2ec552", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/copynumber.r", "max_issues_repo_name": "RodrigoGM/single_cell_cnv", "max_issues_repo_head_hexsha": "19a42bb279d3f22d0326d89b993026c43d2ec552", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-01-05T17:04:39.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-05T17:04:39.000Z", "max_forks_repo_path": "src/copynumber.r", "max_forks_repo_name": "RodrigoGM/single_cell_cnv", "max_forks_repo_head_hexsha": "19a42bb279d3f22d0326d89b993026c43d2ec552", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2991452991, "max_line_length": 145, "alphanum_fraction": 0.6051338067, "num_tokens": 1332, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226291, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.327520244710726}} {"text": "library(ggplot2)\nlibrary(rjson)\nlibrary(dplyr)\nlibrary(\"RColorBrewer\")\n\n# New 100k thresholds\nthresholds <- fromJSON(file = \"201906_thresholds_grch38_v4.json\")\ndat <- read.delim(\"201908019_coverage_based_sex_thresholds.txt\",\n na.strings=\"None\", header=FALSE, stringsAsFactors=FALSE)\ncolnames(dat) <- c(\"study\", \"sample\", \"deliveryId\", \"infKaryotype\", \"avg.X.auto\", \"avg.Y.auto\")\n\n\n# Selected samples (file)\n# selected_samples <- scan(\"~/gel/cajondesastre/coverage_based_sex/robust_cbsc_samples.txt\", what = \"character\")\n# Selected samples (manual entry)\nselected_samples <- c(\"LP3001659-DNA_E03\")\n\n# identify test samples\nmycolours = c(\"\")\ndat$selected <- \"notSelected\"\ndat[dat$sample %in% selected_samples, \"selected\"] <- \"selected\"\n\n# Plot\ndat$cbskaryotype <- \"\"\nfil_xx <- which(dat$med.X.auto >= thresholds$xx.xmin & dat$med.X.auto <= thresholds$xx.xmax & dat$med.Y.auto >= thresholds$xx.ymin & dat$med.Y.auto <= thresholds$xx.ymax)\nfil_xy <- which(dat$med.X.auto >= thresholds$xy.xmin & dat$med.X.auto <= thresholds$xy.xmax & dat$med.Y.auto >= thresholds$xy.ymin & dat$med.Y.auto <= thresholds$xy.ymax)\nfil_xo <- which(dat$med.X.auto >= thresholds$xo_clearcut.xmin & dat$med.X.auto <= thresholds$xo_clearcut.xmax & dat$med.Y.auto >= thresholds$xo_clearcut.ymin & dat$med.Y.auto <= thresholds$xo_clearcut.ymax)\nfil_xxy <- which(dat$med.X.auto >= thresholds$xxy.xmin & dat$med.X.auto <= thresholds$xxy.xmax & dat$med.Y.auto >= thresholds$xxy.ymin & dat$med.Y.auto <= thresholds$xxy.ymax)\nfil_xxx <- which(dat$med.X.auto >= thresholds$xxx.xmin & dat$med.X.auto <= thresholds$xxx.xmax & dat$med.Y.auto >= thresholds$xxx.ymin & dat$med.Y.auto <= thresholds$xxx.ymax)\nfil_xyy <- which(dat$med.X.auto >= thresholds$xyy.xmin & dat$med.X.auto <= thresholds$xyy.xmax & dat$med.Y.auto >= thresholds$xyy.ymin & dat$med.Y.auto <= thresholds$xyy.ymax)\nfil_xxxy <- which(dat$med.X.auto >= thresholds$xxxy.xmin & dat$med.X.auto <= thresholds$xxxy.xmax & dat$med.Y.auto >= thresholds$xxxy.ymin & dat$med.Y.auto <= thresholds$xxxy.ymax)\nfil_xyyy <- which(dat$med.X.auto >= thresholds$xyyy.xmin & dat$med.X.auto <= thresholds$xyyy.xmax & dat$med.Y.auto >= thresholds$xyyy.ymin & dat$med.Y.auto <= thresholds$xyyy.ymax)\n\ndat[fil_xx, \"cbskaryotype\"] <- \"XX\"\ndat[fil_xy, \"cbskaryotype\"] <- \"XY\"\ndat[fil_xo, \"cbskaryotype\"] <- \"XO\"\ndat[fil_xxx, \"cbskaryotype\"] <- \"XXX\"\ndat[fil_xxy, \"cbskaryotype\"] <- \"XXY\"\ndat[fil_xyy, \"cbskaryotype\"] <- \"XYY\"\ndat[fil_xxxy, \"cbskaryotype\"] <- \"XXXY\"\ndat[fil_xyyy, \"cbskaryotype\"] <- \"XYYY\"\n\np <- ggplot(data=dat, mapping = aes(x = avg.X.auto, y=avg.Y.auto, colour=selected)) +\n geom_point(alpha=0.7) +\n xlab(\"Median X / median autosomal coverage\") +\n ylab(\"Median Y / median autosomal coverage\") +\n scale_color_manual(values = c(\"#999999\", \"#FF0000\")) +\n ggtitle(label = \"Dragen CBS using median coverage per chromosome\") +\n theme_bw(16)\n\np <- p +\n geom_rect(mapping = aes(xmin = thresholds$xy.xmin, xmax = thresholds$xy.xmax, ymin = thresholds$xy.ymin, ymax = thresholds$xy.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xx.xmin, xmax = thresholds$xx.xmax, ymin = thresholds$xx.ymin, ymax = thresholds$xx.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xo_clearcut.xmin, xmax = thresholds$xo_clearcut.xmax, ymin = thresholds$xo_clearcut.ymin, ymax = thresholds$xo_clearcut.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xyy.xmin, xmax = thresholds$xyy.xmax, ymin = thresholds$xyy.ymin, ymax = thresholds$xyy.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xxy.xmin, xmax = thresholds$xxy.xmax, ymin = thresholds$xxy.ymin, ymax = thresholds$xxy.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xxx.xmin, xmax = thresholds$xxx.xmax, ymin = thresholds$xxx.ymin, ymax = thresholds$xxx.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xxxy.xmin, xmax = thresholds$xxxy.xmax, ymin = thresholds$xxxy.ymin, ymax = thresholds$xxxy.ymax), alpha = 0, size = 0.2, color=\"#107aa7\") +\n geom_rect(mapping = aes(xmin = thresholds$xyyy.xmin, xmax = thresholds$xyyy.xmax, ymin = thresholds$xyyy.ymin, ymax = thresholds$xyyy.ymax), alpha = 0, size = 0.2, color=\"#107aa7\")\np", "meta": {"hexsha": "b9479a46931910c04438159a7ecb7ddc925e2f24", "size": 4395, "ext": "r", "lang": "R", "max_stars_repo_path": "opencga-analysis/src/main/R/sample-qc/plot_coverage_base_sex.r", "max_stars_repo_name": "mwhamgenomics/opencga", "max_stars_repo_head_hexsha": "a6b521f441fbefa35f6fbaadd6dd97e33bb33e7b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 146, "max_stars_repo_stars_event_min_datetime": "2015-03-05T19:14:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T03:46:48.000Z", "max_issues_repo_path": "opencga-analysis/src/main/R/sample-qc/plot_coverage_base_sex.r", "max_issues_repo_name": "mwhamgenomics/opencga", "max_issues_repo_head_hexsha": "a6b521f441fbefa35f6fbaadd6dd97e33bb33e7b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1623, "max_issues_repo_issues_event_min_datetime": "2015-01-27T00:30:36.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T14:42:33.000Z", "max_forks_repo_path": "opencga-analysis/src/main/R/sample-qc/plot_coverage_base_sex.r", "max_forks_repo_name": "mwhamgenomics/opencga", "max_forks_repo_head_hexsha": "a6b521f441fbefa35f6fbaadd6dd97e33bb33e7b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 93, "max_forks_repo_forks_event_min_datetime": "2015-01-28T17:13:01.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-09T20:46:47.000Z", "avg_line_length": 73.25, "max_line_length": 214, "alphanum_fraction": 0.6996587031, "num_tokens": 1391, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3274473577855117}} {"text": "#NEON core-scale cross-validation.\n#This has a massive amount of burnin, so takes a while to run.\n#There was a problem in MAP values in prior that resulted in no convergence and wack values. Need to try again. All paths should work!\n#Fit MULTINOMIAL dirlichet models to all groups of fungi from 50% of NEON core-scale observations.\n#Not going to apply hierarchy, because it would not be a fair comparison to the Tedersoo model.\n#Missing data are allowed.\n#clear environment\nrm(list = ls())\nlibrary(data.table)\nlibrary(doParallel)\nsource('paths.r')\nsource('NEFI_functions/ddirch_site.level_JAGS.r')\nsource('NEFI_functions/crib_fun.r')\nsource('NEFI_functions/tic_toc.r')\n\n#detect and register cores.----\nn.cores <- detectCores()\nregisterDoParallel(cores=n.cores)\n\n#set output path.----\n output.path <- plot.CV_NEON_dmulti.ddirch_all.path\ncalval_data.path <- plot.CV_NEON_cal.val_data.path\n\n#set cal-val split.----\ncal.val_split <- 0.7 #70% calibration, 30% validation.\n\n#load NEON plot-scale data.----\n#NOTE: MAP MEANS AND SDS MUST BE DIVIDED BY 1000.\n#WE SHOULD REALLY MOVE THIS TO DATA PRE-PROCESSING.\ndat <- readRDS(hierarch_filled.path)\ny <- readRDS(NEON_all.phylo.levels_plot.site_obs_fastq_1k_rare.path)\n\n\n#get core-level covariate means and sd.----\ncore_mu <- dat$core.plot.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n\n#merge together.\nplot_mu$siteID <- NULL\ncore.preds <- merge(core_mu , plot_mu)\ncore.preds <- merge(core.preds, site_mu)\ncore.preds$relEM <- NULL\nnames(core.preds)[names(core.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get core-level SD.\ncore_sd <- dat$core.plot.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nplot_sd$siteID <- NULL\ncore.sd <- merge(core_sd , plot_sd)\ncore.sd <- merge(core.sd, site_sd)\ncore.sd$relEM <- NULL\nnames(core.sd)[names(core.sd)==\"b.relEM\"] <- \"relEM\"\n#IMPORTANT: Reduce magnitude of MAP!\n#log transform map means and standard deviations, magnitudes in 100s-1000s break JAGS code.\ncore.preds$map <- core.preds$map / 1000\ncore.sd $map <- core.sd $map / 1000\n\n#convert relEM back to 0-100 scale.\ncore.preds$relEM <- boot::inv.logit(core.preds$relEM)\n\n#Split into calibration / validation data sets.----\nset.seed(420)\n#ID <- rownames(y$phylum$plot.fit$mean)\n#cal.ID <- sample(ID, round(length(ID)/ 2))\n#val.ID <- ID[!(ID %in% cal.ID)]\n#Subset by plot and site.\nplotID <- rownames(y$phylum$plot.fit$mean)\nsiteID <- substr(plotID,1, 4)\nplots <- data.frame(plotID, siteID)\nsites <- unique(plots$siteID)\ncal <- list()\nval <- list()\nfor(i in 1:length(sites)){\n sub <- plots[plots$siteID == sites[i],]\n cal_sub <- sub[sub$plotID %in% sample(sub$plotID, round(nrow(sub) * cal.val_split)),]\n val_sub <- sub[!(sub$plotID %in% cal_sub$plotID),]\n cal[[i]] <- cal_sub\n val[[i]] <- val_sub\n}\ncal <- do.call(rbind, cal)\nval <- do.call(rbind, val)\ncal.ID <- as.character(cal$plotID)\nval.ID <- as.character(val$plotID)\n\n#loop through y values. Wasn't an easy way to loop through levels of list.\ny.cal <- list()\ny.val <- list()\nfor(i in 1:length(y)){\n lev <- y[[i]]$plot.fit\n lev.cal <- list()\n lev.val <- list()\n lev.cal$mean <- lev$mean[rownames(lev$mean) %in% cal.ID,]\n lev.val$mean <- lev$mean[rownames(lev$mean) %in% val.ID,]\n lev.cal$lo95 <- lev$lo95[rownames(lev$lo95) %in% cal.ID,]\n lev.val$lo95 <- lev$lo95[rownames(lev$lo95) %in% val.ID,]\n lev.cal$hi95 <- lev$hi95[rownames(lev$hi95) %in% cal.ID,]\n lev.val$hi95 <- lev$hi95[rownames(lev$hi95) %in% val.ID,]\n #return to larger list.\n y.cal[[i]] <- lev.cal\n y.val[[i]] <- lev.val\n}\nnames(y.cal) <- names(y)\nnames(y.val) <- names(y)\n\n#Drop groups that just aren't observed frequently enough (>30% of samples) to fit a decent model (mostly zeros).\nfreq.filter <- list()\nfor(i in 1:length(y.cal)){\n check <- y.cal[[i]]$mean\n abundance.check <- list()\n for(j in 1:ncol(check)){\n z <- check[,j]\n abundance.check[[j]] <- sum(z > 0.01)/ length(z)\n }\n abundance.check <- unlist(abundance.check)\n names(abundance.check) <- colnames(check)\n abundance.check <- abundance.check[abundance.check >= 0.3]\n freq.filter[[i]] <- abundance.check\n}\nnames(freq.filter) <- names(y.cal)\n\n#update your lists.\nfor(i in 1:length(y.cal)){\n z.cal <- y.cal[[i]]$mean\n z.val <- y.val[[i]]$mean\n z.cal <- z.cal[,colnames(z.cal) %in% names(freq.filter[[i]])]\n z.val <- z.val[,colnames(z.val) %in% names(freq.filter[[i]])]\n z.cal[,1] <- 1 - rowSums(z.cal[,2:ncol(z.cal)])\n z.val[,1] <- 1 - rowSums(z.val[,2:ncol(z.val)])\n if(mean(rowSums(z.cal)) != 1){cat('Warning: rowSums of calibration data do not sum to 1.')}\n if(mean(rowSums(z.val)) != 1){cat('Warning: rowSums of calibration data do not sum to 1.')}\n y.cal[[i]]$mean <- z.cal\n y.val[[i]]$mean <- z.val\n}\n\n#split x means and sd's.\nx_mu.cal <- core.preds[core.preds$plotID %in% cal.ID,]\nx_mu.val <- core.preds[core.preds$plotID %in% val.ID,]\nx_sd.cal <- core.sd [core.sd $plotID %in% cal.ID,]\nx_sd.val <- core.sd [core.sd $plotID %in% val.ID,]\n\n#match the order.\nx_mu.cal <- x_mu.cal[order(match(x_mu.cal$plotID, rownames(y.cal$phylum$mean))),]\nx_sd.cal <- x_sd.cal[order(match(x_sd.cal$plotID, rownames(y.cal$phylum$mean))),]\nx_mu.val <- x_mu.val[order(match(x_mu.val$plotID, rownames(y.val$phylum$mean))),]\nx_sd.val <- x_sd.val[order(match(x_sd.val$plotID, rownames(y.val$phylum$mean))),]\n\n#subset to predictors of interest, drop in intercept.----\nrownames(x_mu.cal) <- rownames(y.cal$phylum$abundances)\nintercept <- rep(1, nrow(x_mu.cal))\nx_mu.cal <- cbind(intercept, x_mu.cal)\nx_mu.cal <- x_mu.cal[,c('intercept','pH','pC','cn','relEM','map','mat','NPP','forest','conifer')]\n\n\n#save calibration/valiation data sets.----\ndat.cal <- list(y.cal, x_mu.cal, x_sd.cal)\ndat.val <- list(y.val, x_mu.val, x_sd.val)\nnames(dat.cal) <- c('y.cal','x_mu.cal','x_sd.cal')\nnames(dat.val) <- c('y.val','x_mu.val','x_sd.val')\ndat.out <- list(dat.cal, dat.val)\nnames(dat.out) <- c('cal','val')\nsaveRDS(dat.out, calval_data.path)\n\n#fit model using function in parallel loop.-----\n#for running production fit on remote.\ncat('Begin model fitting loop...\\n')\ntic()\noutput.list<-\n foreach(i = 1:length(y)) %dopar% {\n y.group <- y.cal[[i]]$mean\n fit <- site.level_dirlichet_jags(y=y.group,x_mu=x_mu.cal, #x_sd=x_sd.cal,\n adapt = 200, burnin = 30000, sample = 6000, \n #adapt = 200, burnin = 200, sample = 200)#, #testing\n parallel = T, parallel_method = 'parallel') #setting parallel rather than rjparallel. \n return(fit) #allows nested loop to work.\n }\ncat('Model fitting loop complete! ')\ntoc()\n\n\n#name the items in the list\nnames(output.list) <- names(y.cal)\n\n#save output.----\ncat('Saving fit...\\n')\nsaveRDS(output.list, output.path)\ncat('Script complete. \\n')\n", "meta": {"hexsha": "c153da3f59f976f63a678f4931ea622ea2e66e73", "size": 6836, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/3._NEON_all.groups_ddirch_plot_CV_fit.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/3._NEON_all.groups_ddirch_plot_CV_fit.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/3._NEON_all.groups_ddirch_plot_CV_fit.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 35.9789473684, "max_line_length": 134, "alphanum_fraction": 0.6612053833, "num_tokens": 2123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419958239132, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.32740042663936264}} {"text": "library(methylKit) #load files\nfile.list=list(\"pig_rrbs_METcall.CGmap.gz.Mkit\")\nmyobj=methRead(file.list,sample.id=list(\"s1\"),assembly=\"gg\",treatment=c(0))\nsave.image()\n#produce methylation histogram\npdf('Sample1_histogram.pdf')\ngetMethylationStats(myobj[[1]],plot=T,both.strands=F)\npdf('Sample1_cov.pdf')\ngetCoverageStats(myobj[[1]],plot=T,both.strands=F)\ndev.off()\n", "meta": {"hexsha": "4ec196fbe15468551bf531d91ec9155d3761bd3e", "size": 367, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/methylkit_rscript.r", "max_stars_repo_name": "FAANG/proj-gs-meth", "max_stars_repo_head_hexsha": "c31ad17c0bd0d8f7f52273cc882cd7a921f84f37", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/methylkit_rscript.r", "max_issues_repo_name": "FAANG/proj-gs-meth", "max_issues_repo_head_hexsha": "c31ad17c0bd0d8f7f52273cc882cd7a921f84f37", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-07T12:43:40.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-07T14:50:19.000Z", "max_forks_repo_path": "bin/methylkit_rscript.r", "max_forks_repo_name": "FAANG/proj-gs-meth", "max_forks_repo_head_hexsha": "c31ad17c0bd0d8f7f52273cc882cd7a921f84f37", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3636363636, "max_line_length": 75, "alphanum_fraction": 0.7711171662, "num_tokens": 109, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6757645879592642, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.32732690806711}} {"text": "library(pbdPAPI)\n\nsystem.cache(1+1, events=\"l1.all\")\n", "meta": {"hexsha": "eafe9f59777cd93d3f702662f855eba6f5ffd428", "size": 53, "ext": "r", "lang": "R", "max_stars_repo_path": "demo/cache.r", "max_stars_repo_name": "wrathematics/pbdPAPI", "max_stars_repo_head_hexsha": "cb3fad3bccd54b7aeeef9e687b52d938613a356e", "max_stars_repo_licenses": ["Intel", "BSD-3-Clause"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2015-02-14T17:00:51.000Z", "max_stars_repo_stars_event_max_datetime": "2016-02-01T20:13:43.000Z", "max_issues_repo_path": "demo/cache.r", "max_issues_repo_name": "QuantScientist3/pbdPAPI", "max_issues_repo_head_hexsha": "708bee501de20eb82829e03b92b24b6352044f49", "max_issues_repo_licenses": ["Intel", "BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/cache.r", "max_forks_repo_name": "QuantScientist3/pbdPAPI", "max_forks_repo_head_hexsha": "708bee501de20eb82829e03b92b24b6352044f49", "max_forks_repo_licenses": ["Intel", "BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-09-05T05:21:14.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-28T16:17:37.000Z", "avg_line_length": 13.25, "max_line_length": 34, "alphanum_fraction": 0.7169811321, "num_tokens": 19, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.629774621301746, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.32718134379056185}} {"text": "#' @export\n#'\n#' @title Plot M posterior distribution\n#'\n#' @description Plot method for the M estimates\n#' (from \\code{evoab}) posterior distribution.\n#'\n#' @param obj An object of class \"Mest\". Usually output by the\n#' routine \\code{estimateM.EoA}.\n#'\n#' @param plot.like Logical for whether to plot the likelihood for M as\n#' well as the posterior.\n#'\n#' @param plot.prior Logical for whether to plot the prior for M as well\n#' as the posterior.\n#'\n#' @details \\code{plot.like} and \\code{plot.prior} are additive. Set both\n#' to TRUE and the prior, likelihood, and posterior will all three be plotted.\n#' If neither \\code{plot.like} nor \\code{plot.prior} are TRUE, the posterior\n#' distribution is plotted.\n#'\n#' @author Trent McDonald\n#'\n#' @seealso \\code{\\link{estimateM.EoA}}\n#'\n#'\nplot.Mest <- function(obj, plot.like=FALSE, plot.prior=FALSE){\n\n x <- obj$M.margin$M\n fx <- obj$M.margin$pdf\n max.fx <- max(fx)\n ml <- obj$M.est$M.lo\n mh <- obj$M.est$M.hi\n M <- obj$M.est$M\n conf.level <- obj$M.est$ci.level*100\n\n old.par <- par()\n par(mar=c(5.1,4.1,1,1))\n\n rng.x <- range(x)\n\n if(plot.like & plot.prior){\n rng.fx <- range(fx, obj$M.margin$like.pdf, obj$M.margin$prior.pdf)\n } else if( !plot.like & plot.prior){\n rng.fx <- range(fx, obj$M.margin$prior.pdf)\n } else if( plot.like & !plot.prior){\n rng.fx <- range(fx, obj$M.margin$like.pdf)\n } else if( !plot.like & !plot.prior){\n rng.fx <- range(fx)\n }\n max.fx <- rng.fx[2]\n\n\n plot(rng.x, rng.fx, type=\"n\", lwd=2,\n xlab=\"Mortalities (M)\", ylab=\"\",\n yaxt=\"n\", ylim=c(0,max.fx*1.7), bty=\"n\")\n axis(2, at=pretty(c(0,max.fx)))\n mtext(side=2, text=\"Prob. Density (pdf)\", at=max.fx/2, line=2.5)\n\n ci.poly.x <- c(x,rev(x))\n ci.poly.y <- c(fx,rep(0,length(fx)))\n midInterval <- (ml <= ci.poly.x) & (ci.poly.x <= mh)\n ci.poly.x <- ci.poly.x[midInterval]\n ci.poly.y <- ci.poly.y[midInterval]\n\n doTheLegend <- FALSE\n legEntries <- c(\"Posterior\")\n legCols <- c(\"black\")\n if(plot.like){\n lines(x, obj$M.margin$like.pdf, lwd=2, col=\"blue\")\n doTheLegend <- TRUE\n legEntries <- c(\"Likelihood\",legEntries)\n legCols <- c(\"blue\", legCols)\n }\n if(plot.prior){\n lines(x, obj$M.margin$prior.pdf, lwd=2, col=\"red\")\n doTheLegend <- TRUE\n legEntries <- c(\"Prior\",legEntries)\n legCols <- c(\"red\", legCols)\n }\n if(!plot.prior & !plot.like){\n polygon(ci.poly.x, ci.poly.y, col=\"cornsilk\", border=NA)\n }\n lines(x, fx, lwd=2 )\n\n\n oneLineHgt <- par(\"cxy\")[2]\n segments(ml,max.fx+4*oneLineHgt,mh,max.fx+4*oneLineHgt,col='grey60',lwd=13)\n\n points(ml,max.fx+4*oneLineHgt,bg=\"red\",pch=21,cex=1.8,xpd=T)\n points(mh,max.fx+4*oneLineHgt,bg=\"red\",pch=21,cex=1.8,xpd=T)\n points(M,max.fx+4*oneLineHgt,bg='turquoise',pch=21,cex=2.2,xpd=T)\n\n segments(ml,max.fx,ml,0,lty=3,lwd=2,col='darkred')\n segments(mh,max.fx,mh,0,lty=3,lwd=2,col='darkred')\n\n text(M,max.fx+5.5*oneLineHgt,paste(\"Estimate:\\n median=\",format(M,big.mark=\",\")),\n cex=.8,font=2,family=\"sans\",col='darkslategray')\n\n text(ml,max.fx+2*oneLineHgt,paste0(\"LOWER\\n\",conf.level,\"% Conf\\n\",format(ml,big.mark=\",\")),\n cex=.7,col=\"darkred\",font=2,family=\"sans\",xpd=NA)\n\n text(mh,max.fx+2*oneLineHgt,paste0(\"UPPER\\n\",conf.level,\"% Conf\\n\",format(mh,big.mark=\",\")),\n cex=.7,col=\"darkred\",font=2,family=\"sans\",xpd=NA)\n\n if(doTheLegend){\n legend(\"topright\",legend=legEntries, lty=1, col=legCols, lwd=2,\n inset=c(0,1-(max.fx/(max.fx+6*oneLineHgt))), cex=0.75)\n }\n\n\n}\n", "meta": {"hexsha": "1bfff4b8d7221f37e04356ab5ec04587e25a3bbc", "size": 3458, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot.Mest.r", "max_stars_repo_name": "tmcd82070/EoAR", "max_stars_repo_head_hexsha": "30bdd48e88046332fdb1c97d55fb9a6a1a983e06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/plot.Mest.r", "max_issues_repo_name": "tmcd82070/EoAR", "max_issues_repo_head_hexsha": "30bdd48e88046332fdb1c97d55fb9a6a1a983e06", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/plot.Mest.r", "max_forks_repo_name": "tmcd82070/EoAR", "max_forks_repo_head_hexsha": "30bdd48e88046332fdb1c97d55fb9a6a1a983e06", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6017699115, "max_line_length": 94, "alphanum_fraction": 0.6304222094, "num_tokens": 1225, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3271813437905617}} {"text": "# Define x\nx = c(1, 2, 1, 4, 1, 3)\n\n# What is going on here?\nx[c(1, 2, 4, 6)][-2][c(3, 2, 1)][-3]\n\n# Find an easier way to do it\nx[___]", "meta": {"hexsha": "4e68ebd43aba846ffa56e9e7882de4ceef57a63a", "size": 135, "ext": "r", "lang": "R", "max_stars_repo_path": "exercises/exc_01_02.r", "max_stars_repo_name": "SMAC-Group/course_intro_ds", "max_stars_repo_head_hexsha": "ddbd74fc54a6989e96edddee92ea4bf4cf451786", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercises/exc_01_02.r", "max_issues_repo_name": "SMAC-Group/course_intro_ds", "max_issues_repo_head_hexsha": "ddbd74fc54a6989e96edddee92ea4bf4cf451786", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-03-19T12:43:41.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-19T12:44:16.000Z", "max_forks_repo_path": "exercises/exc_01_02.r", "max_forks_repo_name": "SMAC-Group/course_intro_ds", "max_forks_repo_head_hexsha": "ddbd74fc54a6989e96edddee92ea4bf4cf451786", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.875, "max_line_length": 36, "alphanum_fraction": 0.5111111111, "num_tokens": 75, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6297746143530797, "lm_q1q2_score": 0.32718134018058154}} {"text": "library(sentimentr)\nlibrary(stringr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidytext)\nlibrary(tidyr)\nlibrary(wordcloud)\n\ndata <- readRDS(\"./deathrow.rds\")\n\n## ---- executions-per-year\nexecutions_per_year <- data %>%\n group_by(year = format(date, \"%Y\")) %>%\n count() %>%\n ungroup() %>%\n mutate_at(\"year\", as.numeric)\n\nggplot(executions_per_year, aes(x = year, y = n)) +\n geom_bar(stat = \"identity\") +\n labs(title = \"Executions per year\", x = \"Year\", y = \"Executions\")\n\n## ---- average-age-by-year\nyear_avg <- data %>%\n group_by(year = format(date, \"%Y\")) %>%\n summarise(mean_age = mean(age)) %>%\n mutate_at(\"year\", as.numeric)\n\nggplot(year_avg, aes(x = year, y = mean_age)) +\n geom_bar(stat = \"identity\") +\n labs(title = \"Average age at execution by year\", x = \"Year\", y = \"Age\")\n\n## ---- average-age-by-race\nage_by_race <- aggregate(age ~ race, data, mean)\n\nggplot(age_by_race, aes(x = race, y = age)) +\n geom_bar(stat = \"identity\", width = 0.5) +\n labs(title = \"Average age at execution by race\", x = \"Year\", y = \"Race\")\n\n## ---- race-distribution-by-year\nrace_by_year <- data %>%\n group_by(year = format(date, \"%Y\")) %>%\n count(race) %>%\n ungroup() %>%\n mutate_at(\"year\", as.numeric) %>%\n pivot_wider(names_from = race,\n values_from = n,\n values_fill = list(n = 0)) %>%\n select(-c(\"Other\")) # Sample too small\n\nrace_by_year_long <- race_by_year %>%\n pivot_longer(-year, names_to = \"race\", values_to = \"count\")\n\nggplot(race_by_year_long, aes(x = year, y = count, fill = race)) +\n geom_bar(stat = \"identity\") +\n labs(title = \"Race distribution by year\", x = \"Year\", y = \"Race\") +\n theme_bw() +\n scale_fill_grey()\n\n## ---- frequent-words\nwords <- data %>%\n select(\"statement\") %>%\n unnest_tokens(word, statement) %>%\n anti_join(stop_words) %>%\n count(word, sort = TRUE) %>%\n slice(1:100)\n\nwordcloud(words$word, words$n, random.order = FALSE, scale = c(10, 1))\n\n## ---- frequent-phrases\ntrigrams <- data %>%\n select(\"statement\") %>%\n unnest_tokens(trigram, statement, token = \"ngrams\", n = 3) %>%\n count(trigram, sort = TRUE) %>%\n slice(1:50)\n\nwordcloud(trigrams$trigram, trigrams$n, random.order = FALSE, scale = c(10, 1))\n\n## ---- sentiment-by-age\nsentiment_by_age <- data %>%\n mutate(cuts = cut(age, seq(20, 80, 10))) %>%\n mutate(sentiment = sentiment_by(statement)$ave_sentiment) %>%\n group_by(cuts) %>%\n select(cuts, sentiment)\n\nggplot(sentiment_by_age, aes(x = cuts, y = sentiment)) +\n geom_boxplot() +\n labs(title = \"Sentiment by age\", x = \"Age group\", y = \"Sentiment\")\n\n## ---- sentiment-by-race\nsentiment_by_race <- data %>%\n mutate(sentiment = sentiment_by(statement)$ave_sentiment) %>%\n group_by(race) %>%\n filter(race != \"Other\") # Sample too small\n\nggplot(sentiment_by_race, aes(x = race, y = sentiment)) +\n geom_boxplot() +\n labs(title = \"Sentiment by race\", x = \"Race\", y = \"Sentiment\")\n", "meta": {"hexsha": "1e2956d19266117b633c752055b866a533dcbee4", "size": 2871, "ext": "r", "lang": "R", "max_stars_repo_path": "analyse.r", "max_stars_repo_name": "infinitelytight/death-row", "max_stars_repo_head_hexsha": "3ec5a64af12c88750ee602c170e04db91db955d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analyse.r", "max_issues_repo_name": "infinitelytight/death-row", "max_issues_repo_head_hexsha": "3ec5a64af12c88750ee602c170e04db91db955d2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analyse.r", "max_forks_repo_name": "infinitelytight/death-row", "max_forks_repo_head_hexsha": "3ec5a64af12c88750ee602c170e04db91db955d2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.2959183673, "max_line_length": 79, "alphanum_fraction": 0.6335771508, "num_tokens": 857, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3271813365706012}} {"text": "# \n# \n# \n# \n# \n# # extract the important count information\n# get_values <- function(taxon, strat, shelly) {\n# # which occurrences correspond to focal group\n# focus <- taxon[taxon$group == shelly, ]\n# \n# # these occurrences are in collections\n# # collections are from units\n# # geologic units with fossils from focal\n# unitfocus <- cltn2unit[cltn2unit[, 1] %in% focus$collection_no, 2] \n# stratfocus <- strat[strat$unit_id %in% unitfocus, ]\n# \n# # assign a bin\n# stratfocus$bin <- c()\n# for(ii in seq(nrow(brks))) {\n# mm <- stratfocus$m_age <= brks[ii, 1] & stratfocus$m_age > brks[ii, 2]\n# stratfocus$bin[mm] <- ii\n# }\n# \n# oc <- stratfocus %>%\n# group_by(unit_id) %>%\n# dplyr::transmute(collections = sum(pbdb_collections))\n# stratfocus <- left_join(stratfocus, oc, by = 'unit_id')\n# \n# unitdiv <- llply(split(focus, focus$unit), \n# function(x) length(unique(x$genus)))\n# mm <- match(stratfocus$unit_id, names(unitdiv))\n# stratfocus$diversity <- unlist(unitdiv)[mm]\n# \n# stratfocus\n# }\n# \n# # export focus to walk on\n# export_standata <- function(x, name, type = c('diversity', 'occurrence')) {\n# litmat <- strict.lithology(x)\n# bpod <- x[match(rownames(litmat), x$unit_id), ]\n# \n# standata <- list()\n# if(type == 'diversity') {\n# standata$N <- length(bpod$diversity)\n# standata$y <- bpod$diversity\n# } else if(type == 'occurrence') {\n# standata$N <- length(bpod$collections)\n# standata$y <- bpod$collections\n# }\n# \n# standata$t <- bpod$bin\n# standata$T <- max(bpod$bin)\n# \n# # make X\n# K <- 4 # all plus intercept; initial\n# # initially just intercept\n# X <- matrix(1, nrow = standata$N, ncol = K)\n# X[, 2] <- arm::rescale(log1p(bpod$max_thick))\n# X[, 3] <- arm::rescale(log1p(bpod$col_area))\n# \n# nt <- pmap_dbl(list(bln = bpod$b_plng,\n# bpl = bpod$b_plat,\n# tln = bpod$t_plng,\n# tpl = bpod$t_plat),\n# .f = function(bln, bpl, tln, tpl) \n# geomean(rbind(c(bln, bpl), c(tln, tpl)))[2])\n# X[, 4] <- arm::rescale(nt)\n# \n# X <- cbind(X, ilr(litmat))\n# K <- ncol(X)\n# standata$X <- X\n# standata$K <- K\n# \n# if(name %in% c('Anthozoa', 'Bivalvia')) { \n# standata$prior_intercept_location <- 1\n# standata$prior_intercept_scale <- 2\n# standata$prior_phi_scale <- 3\n# } else {\n# standata$prior_intercept_location <- 2\n# standata$prior_intercept_scale <- 2\n# #standata$prior_phi_scale <- 5\n# }\n# \n# if(type == 'occurrence') {\n# standata$prior_intercept_location <- 3\n# standata$prior_intercept_scale <- 3\n# #standata$prior_phi_scale <- 50\n# }\n# \n# \n# temp.name <- paste0('../data/data_dump/diversity_data_', \n# name, '_', type, '.data.R')\n# with(standata, {stan_rdump(list = alply(names(standata), 1),\n# file = temp.name)})\n# temp.name <- paste0('../data/data_dump/diversity_image_', \n# name, '_', type, '.rdata')\n# save(standata, file = temp.name)\n# }\n", "meta": {"hexsha": "bfea1f09d4947446cec037e0f3b7e89ae4b6a415", "size": 3105, "ext": "r", "lang": "R", "max_stars_repo_path": "R/helper01_prepare_foo.r", "max_stars_repo_name": "psmits/not_fossil", "max_stars_repo_head_hexsha": "1fa6639757da9521731f36f0617ca44b3e7a3ec3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-05-20T19:44:52.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-20T19:44:52.000Z", "max_issues_repo_path": "R/helper01_prepare_foo.r", "max_issues_repo_name": "psmits/notfossil", "max_issues_repo_head_hexsha": "1fa6639757da9521731f36f0617ca44b3e7a3ec3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/helper01_prepare_foo.r", "max_forks_repo_name": "psmits/notfossil", "max_forks_repo_head_hexsha": "1fa6639757da9521731f36f0617ca44b3e7a3ec3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3636363636, "max_line_length": 77, "alphanum_fraction": 0.5735909823, "num_tokens": 1020, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.32718132935064054}} {"text": "library(jsonlite)\nlibrary(gtrendsR)\nlibrary(plyr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(scales)\nlibrary(abind)\nlibrary(data.table)\nlibrary(tidyr)\n\n\n#So the only real difference between this code and that of the fixgop.r file is that there were sources such as Wilson Perkins Allen polling that did not poll for the Democratic party. At the same time, there were alot less candidates for the Democratic party (as is the case much of the time for incumbant parties)\n\n\n\nincludeNA <- function(argDF) {\n returnDF <- data.frame(stringsAsFactors=FALSE)\n argDF <- as.data.frame(argDF, stringsAsFactors=FALSE)\n\n dateSeq <- seq(as.Date(\"2015-01-01\"), as.Date(\"2015-12-31\"), by=\"days\")\n count = 1\n for(i in seq_along(dateSeq)){\n if(any(dateSeq[i] %in% argDF$dates)==TRUE){\n returnDF <- rbind(returnDF, argDF[match(dateSeq[i], argDF$dates),])\n colnames(returnDF) <- colnames(argDF) \n }else{\n naList <- data.frame(dateSeq[i], NA, NA, NA, NA, NA, stringsAsFactors=FALSE)\n colnames(naList) <- colnames(argDF)\n returnDF <- rbind(returnDF, naList)\n count <- count + 1}}\n rownames(returnDF) <- as.character(seq(as.Date(\"2015-01-01\"), as.Date(\"2015-12-31\"), by=\"days\"))\n return(returnDF[1:nrow(returnDF), 2:ncol(returnDF)])\n}\n\nfixday <- function(argDF) {\n modDF<- data.frame(Start.Date=argDF$start, End.Date=argDF$end, Trump=argDF$Trump, Bernie=argDF$Bernie, Hillary=argDF$Hillary, Carson=argDF$Carson, Rand=argDF$Rand, stringsAsFactors=FALSE)\n modDF[1:2] <- lapply(modDF[1:2], as.Date)\n returnDF <- setDT(modDF)[, list(dates=seq(Start.Date, End.Date, by = '1 day'),\n Trump=Trump, Bernie=Bernie, Hillary=Hillary, Carson=Carson, Rand=Rand),\n by = 1:nrow(modDF)][, nrow:= NULL][]\n return(returnDF[order(dates),])\n}\n\n\n\n\n#Google Trends Package Set Up\nusr <- \"cs125fp@gmail.com\" \npsw <- \"Abcd@1234\" \ngconnect(usr, psw) \npres_trend <- gtrends(c(\"Trump\", \"Bernie\", \"Hillary\", \"Carson\", \"Rand\"))\n\n\npres_trendDF <- pres_trend[[3]][574:nrow(pres_trend[[3]]),]\npres_trendDF <- fixday(pres_trendDF)\npres_trendDF <- includeNA(pres_trendDF)\nsave(pres_trendDF, file=\"/Users/andrewlee/Documents/School/CS125/Final Project/pres_trendDF.RData\")", "meta": {"hexsha": "0ca0c330236985e701b11bc8d757f2d7792dede8", "size": 2248, "ext": "r", "lang": "R", "max_stars_repo_path": "fixgtrends.r", "max_stars_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_stars_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fixgtrends.r", "max_issues_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_issues_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fixgtrends.r", "max_forks_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_forks_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4385964912, "max_line_length": 315, "alphanum_fraction": 0.6797153025, "num_tokens": 673, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6297745935070806, "lm_q1q2_score": 0.32718132935064054}} {"text": "# __ _____ _ __ _ \n# <(o )___ | _ |___ ___ ___| |_ ___ ___ | | ___| |_ ___ \n# ( ._> / | __| _| -_|_ -| _| -_|_ -| | |__| .'| . |_ -|\n# `---' |__| |_| |___|___|_| |___|___| |_____|__,|___|___|\n#==============================================================================\n# Google Trends and R\n#==============================================================================\n# Title : GoogleTrendsTS.r\n# Description : Analyzing Google Trends Data in R.\n# Author : Isaias V. Prestes \n# Date : 20180412\n# Version : 0.0.1\n# Usage : Run in R 3.4\n# Notes : based on \n# Jake Hoare (DisplayR)\n# https://datascienceplus.com/analyzing-google-trends-data-in-r/\n# gtrendsR documentation\n# https://github.com/PMassicotte/gtrendsR\n# Peer Christensen https://rpubs.com/PChristensen/307008\n#\n# R version : 3.4\n#==============================================================================\n\n#==============================================================================\n# PACKAGE INSTALATION\n#==============================================================================\nif(!require(\"devtools\")) install.packages(\"devtools\")\nif(!require(\"gtrendsR\")) install.packages(\"gtrendsR\") # google data\n# devtools::install_github('PMassicotte/gtrendsR', ref = 'new-api')\nif(!require(\"ggplot2\")) install.packages(\"ggplot2\")\nif(!require(\"forecast\")) install.packages(\"forecast\") # time series\nif(!require(\"maps\")) install.packages(\"maps\") # map plots\nif(!require(\"reshape2\")) install.packages(\"reshape2\")\nif(!require(\"xxxxx\")) install.packages(\"xxxxx\")\nif(!require(\"xxxxx\")) install.packages(\"xxxxx\")\n\n#==============================================================================\n# LIBRARY DEPENDENCE\n#==============================================================================\nlibrary(devtools)\nlibrary(gtrendsR)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(forecast)\nlibrary(xxxxx)\nlibrary(xxxxx)\nlibrary(xxxxx)\nlibrary(xxxxx)\n\n# Things that I like...\nSys.setenv(TZ = \"UTC\")\ngoogle.trends = gtrends(c(\"Fortran\",\"Sudoku\"), gprop = \"web\", time = \"all\")\nplot(google.trends)\ngoogle.trends = gtrends(c(\"Fortran\",\"Sudoku\"), gprop = \"web\", time = \"today+5-y\")\nplot(google.trends)\ngoogle.trends = gtrends(c(\"Fortran\"), gprop = \"web\", time = \"today+5-y\")\nwindows() ; plot(google.trends)\ngoogle.trends = gtrends(c(\"Sudoku\"), gprop = \"web\", time = \"today+5-y\")\nwindows() ; plot(google.trends)\n\n#==============================================================================\n# Fire in the hole!\n#==============================================================================\n\n# Search Parameters -----------------------------------------------------------\nsearch_terms <- c(\"Machine Learning\", \"Big Data\", \"R programming language\", \"Statitics\")\n# Countries codes https://en.wikipedia.org/wiki/ISO_3166-2\n# detaild version http://www.unece.org/cefact/codesfortrade/codes_index.html\nsearch_country <- c(\"BR\") # default is worldwide\n# Time interval - Time span between two dates (ex.: \"2010-01-01 2010-04-03\")\nsearch_time <- \"2004-01-01 2018-01-04\" \n# Options available are \"news\",\"images\", \"froogle\", \"youtube\"\nsearch_type <- c(\"web\") \n# A character denoting the category, defaults to \u201c0\u201d.\nsearch_category <- 0\n# ISO language code\nsearch_language <- \"pt-BR\"\n\ndata_scientist <- \ngtrends(search_terms, geo = search_country, time = search_time,\n gprop = search_type, category = search_category, hl = search_language)\n\n## Playing with time format\nsearch_terms <- c(\"Python\", \"Machine Learning\")\ngtrends(search_terms, time = \"now 1-H\") # Last hour\ngtrends(search_terms, time = \"now 4-H\") # Last four hours\ngtrends(search_terms, time = \"now 1-d\") # Last day\ngtrends(search_terms, time = \"now 7-d\") # Last seven days\ngtrends(search_terms, time = \"today 1-m\") # last 30 days\ngtrends(search_terms, time = \"today 3-m\") # last 90 days\ngtrends(search_terms, time = \"today 12-m\") # last 12 months\ngtrends(search_terms, time = \"today+5-y\") # last 5 years (default)\ngtrends(search_terms, time = \"all\") # Since the beginning of Google Trends (2004)\n\ndata_scientist$interest_over_time%>%\n as_tibble()%>%\n mutate(hits=as.numeric(hits))%>%\nggplot(aes(x=date,y=hits,colour=keyword))+\n geom_line()+theme_bw()+\n labs(y=\"Popularidade\",x=\"Ano\",colour=\"Termo de pesquisa:\", title=\"S\u00e9rie temporal da popularidade dos termos de pesquisas\")\n\n# Gtrends function actually returns a list of data frames.\nstr(hurricanes) \n \n \n \n \n \n \n#==============================================================================\n# Time series analysis\n#==============================================================================\n\n#Decompondo a s\u00e9rie para o termo Estat\u00edstica\nts_Estatistica=data_scientist$interest_over_time%>%\n as_tibble()%>%\n mutate(hits=as.numeric(hits))%>%\n filter(keyword==\"Estat\u00edstica\")%>%\n select(hits)%>%\n na.omit()%>%\n ts(freq=12)\n\nts_dec_Estatistica=decompose(ts_Estatistica)\n\n#Decompondo a s\u00e9rie para o termo Bigdata\nts_BigData=data_scientist$interest_over_time%>%\n as_tibble()%>%\n mutate(hits=as.numeric(hits))%>%\n filter(keyword==\"Big Data\")%>%\n select(hits)%>%\n na.omit()%>%\n ts(freq=12)\nts_dec_BigData=decompose(ts_BigData)\n\ng1=ts_dec_Estatistica %>% autoplot+ theme_bw()\ng2=ts_dec_BigData %>% autoplot+theme_bw()\ngridExtra::grid.arrange(g1,g2,ncol=2)\n\n# Trend test (Wald-Wolfowitz)\nrandtests::runs.test(ts_Estatistica)\nrandtests::runs.test(ts_BigData)\n\n# SAZONALIDADE\n\ndata=data.frame(cbind(serie=as.numeric(ts_Estatistica),mes_ano=rep(seq(1,12),11)))\nkruskal.test(data=data,serie~mes_ano) \ndata=data.frame(cbind(serie=as.numeric(ts_BigData),mes_ano=rep(seq(1,12),11)))\nkruskal.test(data=data,serie~mes_ano) \n\n# M\u00c9TODO DE AMORTECIMENTO EXPONENCIAL DE HOLT-WINTERS (DADOS COM SAZONALIDADE)\n\najuste_com_sazonalidade_Estatistica<-HoltWinters(ts_Estatistica)\nplot(ajuste_com_sazonalidade_Estatistica)\n\najuste_com_sazonalidade_BigData<-HoltWinters(ts_BigData)\nplot(ajuste_com_sazonalidade_BigData)\n\nsuppressMessages(library(forecast))\n\n#Para estatistica\nprevisao_com_sazonalidade_Estatistica<-forecast(ajuste_com_sazonalidade_Estatistica,h = 12)\nplot(previsao_com_sazonalidade_Estatistica)\n\n#Para BigData\nprevisao_com_sazonalidade_BigData<-forecast(ajuste_com_sazonalidade_BigData,h = 12)\nplot(previsao_com_sazonalidade_BigData)\n\n\n\n\n#==============================================================================\n# Map plot\n#==============================================================================\nFortran = gtrends(c(\"Fortran\"), gprop = \"web\",time=\"2017-08-18 2017-08-25\", geo = c(\"US\"))\nFortran = Fortran$interest_by_region\nFortran$region = sapply(Fortran$location,tolower)\nstatesMap = map_data(\"state\")\nFortranMerged = merge(statesMap,Fortran,by=\"region\")\nFortranPlot=ggplot() +\n geom_polygon(data=FortranMerged,aes(x=long,y=lat,group=group,fill=hits),colour=\"white\") +\n scale_fill_continuous(low=\"thistle2\",high=\"darkred\",guide=\"colorbar\",trans=\"log10\") +\n theme_bw() +\n labs(title=\"Google search interest for Fortran in each state\")\nFortranPlot\n\n\nFortran = gtrends(c(\"Fortran\"), gprop = \"web\", time=\"2018-03-03 2018-04-03\",geo = c(\"US\"))\nFortran = Fortran$interest_by_region\nstatesMap = map_data(\"state\")\nFortran$region = sapply(Fortran$location,tolower)\nFortranMerged = merge(statesMap ,Fortran,by=\"region\")\n\nregionLabels <- aggregate(cbind(long, lat) ~ region, data=FortranMerged, \n FUN=function(x) mean(range(x)))\n\nFortranPlot=ggplot() +\n geom_polygon(data=FortranMerged,aes(x=long,y=lat,group=group,fill=hits),colour=\"white\") +\n scale_fill_continuous(low=\"thistle1\",high=\"darkblue\",guide=\"colorbar\",trans=\"log10\") +\n geom_text(data=regionLabels, aes(long, lat, label = region), size=2) +\n theme_bw() +\n coord_fixed(1.3) +\n labs(title=\"Google search interest for Fortran language programming in each state\\nfrom the week prior to landfall in the US\") \nFortranPlot\n\n ", "meta": {"hexsha": "addc5bd6a28e89361be775b06e661e1b965618e6", "size": 7994, "ext": "r", "lang": "R", "max_stars_repo_path": "Data_Science/GoogleTrends.r", "max_stars_repo_name": "isix/R", "max_stars_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Data_Science/GoogleTrends.r", "max_issues_repo_name": "isix/R", "max_issues_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Data_Science/GoogleTrends.r", "max_forks_repo_name": "isix/R", "max_forks_repo_head_hexsha": "806e2a22e5abd93dc7933d3b9e8c3368562e1eaa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9951219512, "max_line_length": 129, "alphanum_fraction": 0.6132099074, "num_tokens": 2172, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.3271495422657466}} {"text": "yahoo.statistics.scrape <- function(symbol) {\n \n ## https://stackoverflow.com/questions/40245464/web-scraping-of-key-stats-in-yahoo-finance-with-r\n url <- paste('https://finance.yahoo.com/quote/', symbol,\n '/key-statistics?p=', symbol, sep='')\n webpage <- readLines(url)\n html <- XML::htmlTreeParse(webpage, useInternalNodes = TRUE, asText = TRUE)\n tableNodes <- XML::getNodeSet(html, \"//table\")\n\n valuation <- XML::readHTMLTable(tableNodes[[1]])\n dates <- names(valuation)[3:ncol(valuation)]\n names(valuation) <- c('measure', 'current', dates)\n peg <- valuation[valuation$measure == 'PEG Ratio (5 yr expected)',]\n \n price <- XML::readHTMLTable(tableNodes[[2]])\n share_statistics <- XML::readHTMLTable(tableNodes[[3]])\n dividends_splits <- XML::readHTMLTable(tableNodes[[4]])\n fiscal_year <- XML::readHTMLTable(tableNodes[[5]])\n profitability <- XML::readHTMLTable(tableNodes[[6]])\n effectiveness <- XML::readHTMLTable(tableNodes[[7]])\n income_statement <- XML::readHTMLTable(tableNodes[[8]])\n balance_sheet <- XML::readHTMLTable(tableNodes[[9]])\n cash_flow <- XML::readHTMLTable(tableNodes[[10]])\n\n return(list(valuation = valuation,\n price = price,\n share_statistics = share_statistics,\n dividends_splits = dividends_splits,\n fiscal_year = fiscal_year,\n profitability = profitability,\n effectiveness = effectiveness,\n income_statement = income_statement,\n balance_sheet = balance_sheet,\n cash_flow = cash_flow,\n peg = peg))\n \n}\n## out <- yahoo.statistics.scrape('AAPL')\n## peg <- out$valuation[out$valuation$measure == 'PEG Ratio (5 yr expected) 1',]\n\n## ## this works but is a paid service; AAPL symbol is provided as a free example\n## api.token <- \"OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX\"\n## symbol <- \"AAPL.US\"\n## ticker.link <- paste(\"http://nonsecure.eodhistoricaldata.com/api/eod/\", symbol,\n## \"?api_token=\", api.token, \"&period=m&order=d\", sep=\"\")\n## data <- read.csv(url(ticker.link))\n## head(data)\n\n## ## this also works but not sure what it is doing\n## url <- \"http://finance.yahoo.com/webservice/v1/symbols/AAPL/quote?format=json&view=detail\"\n## url <- \"https://yfapi.net/v6/finance/quote?region=US&lang=en&symbols=AAPL%2CBTC-USD%2CEURUSD%3DX\"\n## req <- curl::curl_fetch_memory(url)\n## str(req)\n## curl::parse_headers(req$headers)\n", "meta": {"hexsha": "26a307fb4f329c0d0ed9d2f6ffdc2fe0d762cc69", "size": 2614, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/yahoo.statistics.scrape.r", "max_stars_repo_name": "dhjelmar/Finance", "max_stars_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/yahoo.statistics.scrape.r", "max_issues_repo_name": "dhjelmar/Finance", "max_issues_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/yahoo.statistics.scrape.r", "max_forks_repo_name": "dhjelmar/Finance", "max_forks_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.5272727273, "max_line_length": 101, "alphanum_fraction": 0.6143840857, "num_tokens": 630, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.3271495422657466}} {"text": "portfolio.calc.print <- function(df) {\n ## df = output from portfolio.calc\n df <- df$perf\n ## change twrc, std, twrc.ann, and std.ann to character and apply % sign\n df[2:5] <- sapply(df[2:5], function(x) scales::percent(x, accuracy=0.01))\n df$alpha <- round(df$alpha , 3)\n df$beta <- round(df$beta , 3)\n df$weight <- round(df$weight, 2)\n df$value <- scales::dollar(df$value)\n df\n}\n", "meta": {"hexsha": "6c998fd76c4ef57c7d84243260977cede87ffa73", "size": 415, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/portfolio.calc.print.r", "max_stars_repo_name": "dhjelmar/Finance", "max_stars_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/portfolio.calc.print.r", "max_issues_repo_name": "dhjelmar/Finance", "max_issues_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/portfolio.calc.print.r", "max_forks_repo_name": "dhjelmar/Finance", "max_forks_repo_head_hexsha": "dc0241fab29472150c77137159c31f5f8bf42349", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5833333333, "max_line_length": 79, "alphanum_fraction": 0.6, "num_tokens": 137, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32714954077824554}} {"text": "library(tidyverse)\nlibrary(WDI)\nlibrary(data.table)\nlibrary(countrycode)\nlibrary(zoo)\nlibrary(scales)\nlibrary(ggforce)\nlibrary(viridisLite)\nlibrary(rnaturalearth)\nlibrary(rnaturalearthdata)\nlibrary(sf)\nlibrary(cowplot)\n# library(RcppRoll)\n# install.packages('roll')\nlibrary(roll)\nlibrary(gganimate)\nlibrary(gifski)\nlibrary(readxl)\nlibrary(sf)\nlibrary(RColorBrewer)\nlibrary(gridExtra)\nlibrary(fpc)\nlibrary(dbscan)\nlibrary(factoextra)\nlibrary(ggrepel)\nlibrary(lmtest)\nlibrary(xgboost)\nlibrary(igraph)\n\nout_location = \"~/Public_Policy/Projects/COVID-19 Mismanagement/output\"\n\nlarge_text_theme = theme(\n plot.title = element_text(size = 24),\n plot.subtitle = element_text(size = 18, face = 'italic'),\n plot.caption = element_text(size = 13, face = 'italic', hjust = 0),\n axis.text = element_text(size = 16),\n axis.title = element_text(size = 18)\n) \n\n#### use the imf projections for growth \n\nsetwd(\"~/Public_Policy/Projects/COVID-19\")\nnordic_countries = c('Sweden', 'Finland', 'Norway', 'Denmark')\ntop_europe = c('Spain', 'United Kingdom', 'Italy', 'France', 'Germany', 'Belgium')\n\n##### get map data #####\nworld <- ne_countries(scale = \"medium\", returnclass = \"sf\") %>% \n mutate(\n name = recode(name, \n `Dem. Rep. Korea` = 'South Korea', \n `Czech Rep.` = 'Czech Republic', \n `Slovakia` = 'Slovak Republic',\n `Bosnia and Herz.` = 'Bosnia and Herzegovina',\n `Macedonia` = 'North Macedonia')\n )\n\n\neurope = filter(world, continent == 'Europe') \n\neurope_cropped <- st_crop(europe, xmin = -24, xmax = 45,\n ymin = 30, ymax = 73)\n\npolitical_freedom_index = read_csv('https://object.cato.org/sites/cato.org/files/human-freedom-index-files/human-freedom-index-2019.csv')\n\n# a = st_intersects(europe_cropped, europe_cropped)\n# sweden_intersects = a[which(europe_cropped$name == 'Sweden')] %>% unlist()\n# \n# europe_cropped$name[sweden_intersects]\n\n\n# countrycode(sourcevar = 'South Korea', destination = 'iso3c', origin = 'un.name.en')\n\n##### Population data #####\nwdi_indicators = c(\n 'SP.POP.TOTL', # population\n 'NE.TRD.GNFS.ZS', # trade / GDP \n 'SP.POP.65UP.TO.ZS', # age 65+ % of population\n 'NY.GDP.PCAP.CD', # per capita gdp \n 'SI.POV.GINI', # gini index\n 'SH.XPD.CHEX.GD.ZS', # health exp % gdp\n 'ST.INT.ARVL', # tourist arrivals\n 'SP.URB.TOTL.IN.ZS' # Urban population %\n)\n\n\nWDI_data_long = map(wdi_indicators, function(x){\n tryCatch({\n download = WDI(indicator = x, start = 1965, end = 2020, extra = T) %>%\n mutate(indicator = x)\n names(download)[names(download) == x] = 'value'\n return(download)\n }, error = function(e){\n print(e)\n cat('error with ', x, '\\n')\n return(NULL)\n })\n \n})\n\nwdi_data_stacked = bind_rows(WDI_data_long) \nwdi_data_wide = pivot_wider(wdi_data_stacked, id_cols = c('country', 'year', 'income', 'region'), values_from = 'value', names_from = 'indicator') %>%\n arrange(country, year)\n\n\n##### OECD Data trust in government #####\nsetwd(\"~/Public_Policy/Projects/COVID-19 Mismanagement/data/OECD\")\n\ntrust_in_government = fread('DP_LIVE_30102020213545055.csv') %>%\n mutate(\n country = countrycode(LOCATION, origin = 'iso3c', destination = 'country.name'),\n trust_in_government_pct = Value / 100\n ) %>%\n rename(\n year = TIME\n ) %>%\n group_by(country) %>%\n summarize(\n mean_trust_in_gov = mean(trust_in_government_pct), \n last_trust_in_gov = trust_in_government_pct[year == max(year)]\n ) %>%\n ungroup()\n# summarize_if(trust_in_government, is.character, unique)\n\n\nlatest_country_pop = filter(wdi_data_wide) %>%\n group_by(country, income, region) %>%\n summarize(\n latest_pop_year = max(year[!is.na(SP.POP.TOTL)], na.rm = T),\n population = SP.POP.TOTL[year == latest_pop_year],\n gini_index = tail(SI.POV.GINI[!is.na(SI.POV.GINI)], 1),\n international_tourism = tail(ST.INT.ARVL[!is.na(ST.INT.ARVL)], 1),\n trade_pct_gdp = tail(NE.TRD.GNFS.ZS[!is.na(NE.TRD.GNFS.ZS)], 1) / 100,\n gdp_per_capita_us = tail(NY.GDP.PCAP.CD[!is.na(NY.GDP.PCAP.CD)], 1),\n pop_pct_65_over = tail(SP.POP.65UP.TO.ZS[!is.na(SP.POP.65UP.TO.ZS)], 1) / 100,\n health_exp_gdp = tail(SH.XPD.CHEX.GD.ZS[!is.na(SH.XPD.CHEX.GD.ZS)], 1) / 100,\n urban_pop_pct = tail(SP.URB.TOTL.IN.ZS[!is.na(SP.URB.TOTL.IN.ZS)], 1) / 100\n ) %>%\n rename(\n year = latest_pop_year\n ) %>%\n left_join(world, by = c('country'= 'name')) %>%\n left_join(trust_in_government) %>%\n mutate(\n country = recode(country, `Korea, Rep.` = 'South Korea', `Russian Federation` = 'Russia')\n )\n\n##### stringency and mobility data #####\n\n\n\nsetwd(\"~/Public_Policy/Projects/COVID-19\")\n\noxford_stringency_index = read.csv(\"https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv\") %>%\n rename(\n country = CountryName\n ) %>%\n mutate(\n entity_name = ifelse(is.na(RegionName) | RegionName == \"\", country, RegionName),\n stringency_geo_type = ifelse(entity_name == country, 'country', 'region'),\n date = as.Date(Date %>% as.character(), format = '%Y%m%d')\n ) \n\n\nmobility_dataset_df = tibble(\n dsn = list.files('data', pattern = 'applemobilitytrends', full.names = T),\n date = str_extract(dsn, '[0-9]{4}-[0-9]{2}-[0-9]{2}') %>% as.Date()\n) %>% \n arrange(date) %>% \n tail(1)\n\napple_mobility_dat = read_csv(mobility_dataset_df$dsn) %>%\n pivot_longer(cols = matches('^([0-9+]{4})'), names_to = 'date') %>%\n mutate(\n date = as.Date(date),\n week_day = lubridate::wday(date),\n weekend_ind = ifelse(week_day %in% c(7, 1), 'Weekend', \"Week Day\"),\n entity_name = recode(region, \n `UK` = 'United Kingdom',\n `San Francisco - Bay Area` = 'San Francisco', \n `Republic of Korea` = 'South Korea')\n ) \n\ncountry_mobility_data = filter(apple_mobility_dat, geo_type == 'country/region', transportation_type == 'walking')\n\ncountry_stringency = filter(oxford_stringency_index, stringency_geo_type == 'country') %>%\n left_join(country_mobility_data, by = c('country' = 'entity_name', 'date' = 'date')) %>%\n rename(\n mobility = value\n )\n\n\n##### Covid data #####\n\njohns_hopkins_cases = read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv') %>%\n pivot_longer(cols = matches('^([0-9])'), names_to = 'date', values_to = 'cases')\n\njohns_hopkins_deaths = read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv') %>%\n pivot_longer(cols = matches('^([0-9])'), names_to = 'date', values_to = 'deaths')\n\njh_joined = left_join(johns_hopkins_cases, johns_hopkins_deaths, by = c('Province/State', 'Country/Region', 'date')) %>%\n select(-contains('lat'), -contains('long')) %>%\n mutate(\n date_upd = as.Date(date, format = '%m/%d/%y')\n ) \n\n\nnames(jh_joined) = names(jh_joined) %>% tolower() %>% str_replace('[\\\\/]', '_')\n\njh_with_pop = mutate(jh_joined, is_country = is.na(province_state)) %>%\n rename(country = country_region) %>%\n arrange(province_state, country, date_upd) %>%\n mutate(\n country = recode(country, \n `Russian Federation` = 'Russia',\n US = 'United States', \n `Korea, South` = 'South Korea', Czechia = 'Czech Republic', Slovakia = 'Slovak Republic')\n ) %>%\n left_join(\n latest_country_pop\n ) \nhead(jh_with_pop)\n\ndeaths_by_country_province = group_by(jh_with_pop, country, province_state) %>%\n summarize(\n max_cases = max(cases),\n max_deaths = max(deaths),\n last_date = max(date_upd)\n )\n\ncovid_deaths_by_country_date = group_by(jh_with_pop, country, province_state, date = date_upd) %>%\n summarize(\n cumulative_cases = max(cases),\n cumulative_deaths = sum(deaths)\n ) %>%\n group_by(country, date) %>%\n summarize(\n cumulative_cases = sum(cumulative_cases),\n cumulative_deaths = sum(cumulative_deaths)\n ) %>%\n left_join(country_stringency) %>%\n left_join(\n select(latest_country_pop %>% ungroup(), country, population)\n ) %>%\n arrange(country, date) %>%\n mutate(\n Stringency_z = (StringencyIndex - mean(StringencyIndex, na.rm = T)) / sd(StringencyIndex, na.rm = T),\n Stringency_median_over_iqr = (StringencyIndex - median(StringencyIndex, na.rm = T)) / IQR(StringencyIndex ,na.rm = T)\n ) %>%\n data.table() \n\n##### final covid daily data #####\noptions(na.action = na.exclude)\ncovid_deaths_by_country_date_diffs = covid_deaths_by_country_date[, {\n # us = filter(covid_deaths_by_country_date, country == 'Norway')\n # attach(us)\n # detach(us)\n # \n new_deaths = c(NA, diff(cumulative_deaths))\n death_50_date = date[cumulative_deaths >= 50][1]\n days_since_death_50_date = as.numeric(date - death_50_date)\n mortality_rate = cumulative_deaths / population \n \n new_cases = c(NA, diff(cumulative_cases))\n roll_7_new_cases = roll_mean(new_cases, 7)\n \n roll_7_new_deaths = roll_mean(new_deaths, 7)\n roll_14_new_deaths = roll_mean(new_deaths, 14)\n roll_7_new_deaths_per_100k = (roll_7_new_deaths / population) * 1e5\n peak_daily_deaths = roll_7_new_deaths_per_100k >= quantile(roll_7_new_deaths_per_100k, probs = 0.75, na.rm = T)\n peak_daily_deaths_lead_14 = lead(peak_daily_deaths, 14)\n \n smoothed_roll_7_new_deaths_per_100k = rep(NA, length(date)) %>% as.numeric()\n tryCatch({\n smoothed_roll_7_new_deaths_per_100k = loess(roll_7_new_deaths_per_100k ~ as.integer(date), span = 0.2) %>% predict() \n }, error = function(e){\n \n cat(\"\\nerror with smoothed_roll_7_new_deaths_per_100k for\",.BY[[1]],\"\\n\\n\")\n })\n roll_7_new_cases_per_100k = (roll_7_new_cases / population) * 1e5\n rolling_regression_new_cases = roll_lm(as.integer(date), roll_7_new_cases_per_100k, width = 7)\n rolling_regression_new_cases_7 = rolling_regression_new_cases$coefficients[,2]\n \n roll_7_new_cases_per_100k_q3_month = roll_quantile(roll_7_new_cases_per_100k, 30, p = 0.75)\n \n \n rolling_regression_smoothed = roll_lm(as.integer(date), smoothed_roll_7_new_deaths_per_100k, width = 5)\n \n rolling_regression = roll_lm(as.integer(date), roll_7_new_deaths_per_100k, width = 7)\n rolling_regression_14 = roll_lm(as.integer(date), roll_7_new_deaths_per_100k, width = 14)\n \n regression_coefs_7_smoothed = rolling_regression_smoothed$coefficients[,2]\n quarter_sd = sd(regression_coefs_7_smoothed, na.rm = T) / 3\n \n inflection_point = between(regression_coefs_7_smoothed, 0 - quarter_sd, 0 + quarter_sd)\n n_pos_slopes = roll_sum(regression_coefs_7_smoothed > 0, 7)\n n_neg_slopes = roll_sum(regression_coefs_7_smoothed < 0, 7)\n \n inflection_point_desc = ifelse(inflection_point & n_pos_slopes >= 4, 'peak', ifelse(inflection_point & n_neg_slopes >= 4, 'floor', 'normal')) %>% as.character()\n inflection_point_desc[inflection_point_desc == 'normal' & n_pos_slopes>= 4] = 'increasing'\n inflection_point_desc[inflection_point_desc == 'normal' & n_neg_slopes >= 4] = 'decreasing'\n \n floor_peak_cycles = data.frame(\n inflection_point_desc,\n date,\n roll_7_new_deaths_per_100k\n ) %>%\n filter(inflection_point_desc %in% c('floor', 'peak') & ifelse(inflection_point_desc == 'peak', roll_7_new_deaths_per_100k > 0, T))\n \n # capture window start and end times\n \n if (nrow(floor_peak_cycles) > 0) {\n window_catcher = list()\n last_desc = NA\n window_it = 0\n window_df = NA\n for (it in 1:nrow(floor_peak_cycles)) {\n # it = 1\n this_desc = floor_peak_cycles$inflection_point_desc[it]\n \n if (is.na(last_desc) | this_desc != last_desc) {\n if (window_it > 0) {\n window_df$end_date = floor_peak_cycles$date[it - 1]\n window_catcher[[length(window_catcher) + 1]] = window_df\n }\n window_it = window_it + 1\n window_df = data.frame(\n window_start = floor_peak_cycles$date[it],\n this_desc,\n window_it\n ) \n } \n last_desc = this_desc\n }\n windows_stacked = bind_rows(window_catcher)\n first_floor_to_floor = filter(windows_stacked, this_desc == 'floor') \n if (nrow(first_floor_to_floor) >= 2) {\n first_floor_to_floor_sub = head(first_floor_to_floor, 2)\n in_first_cycle = between(date, first_floor_to_floor_sub$end_date[1], first_floor_to_floor_sub$end_date[2]) \n } else {\n in_first_cycle = rep(NA, length(date))\n }\n } else {\n in_first_cycle = rep(NA, length(date))\n }\n \n \n regression_coefs = rolling_regression$coefficients[,2]\n regression_coefs_14 = rolling_regression_14$coefficients[,2]\n \n roll_7_new_deaths_avg = roll_7_new_deaths / roll_14_new_deaths\n roll_7_new_deaths_avg_roll = roll_mean(roll_7_new_deaths_avg, 7)\n \n phases = sign(roll_7_new_deaths_avg_roll - 1)\n \n \n \n roll_7_new_deaths_rollsum_7 = roll_sum(roll_7_new_deaths_avg > 1, 7)\n avg_equalized = between(roll_7_new_deaths_avg_roll, 0.95, 1.05)\n in_equal_band = c(rep(NA, 13), roll_sum(avg_equalized, 14))\n avg_equalized - lag(avg_equalized)\n \n in_death_peak = roll_7_new_deaths_rollsum_7 == 7\n peak_changes = in_death_peak - lag(in_death_peak, 1)\n peak_changes_filled = ifelse(peak_changes == 0, roll_7_new_deaths_rollsum_7, peak_changes)\n new_deaths_pct_of_max = new_deaths / max(new_deaths, na.rm = T)\n # peak_date = date[new_deaths == max(new_deaths, na.rm = T) & phase_descs == 'pos after na' ] %>% min(na.rm = T)\n \n # loop over roll_7_new_deaths_rollsum_7. A peak window is when this exceeds 7 for a while and then comes back down \n new_cases_per_100k = (new_cases / population ) * 1e5\n pct_change_new_cases_per_100k = (new_cases_per_100k - lag(new_cases_per_100k)) / lag(new_cases_per_100k)\n \n \n pct_change_roll_7_new_cases_per_100k = (roll_7_new_cases_per_100k - lag(roll_7_new_cases_per_100k)) / lag(roll_7_new_cases_per_100k)\n \n list(\n days_since_death_50_date = days_since_death_50_date, \n date = date, \n in_first_cycle = in_first_cycle,\n new_cases_elevated = roll_7_new_cases_per_100k > roll_7_new_cases_per_100k_q3_month,\n rolling_regression_new_cases_7 = rolling_regression_new_cases_7,\n peak_daily_deaths_lead_14 = lead(peak_daily_deaths, 14),\n inflection_point_desc = inflection_point_desc,\n n_pos_slopes = n_pos_slopes, \n n_neg_slopes = n_neg_slopes,\n \n roll_7_new_cases_per_100k = roll_7_new_cases_per_100k,\n pct_change_roll_7_new_cases_per_100k = pct_change_roll_7_new_cases_per_100k,\n # phase_descs = phase_descs,\n new_deaths_pct_of_max = new_deaths_pct_of_max,\n # initial_peak = phase_descs == 'pos after na',\n new_deaths = new_deaths,\n mortality_rate = mortality_rate,\n new_deaths_per_100k = (new_deaths / population) * 1e5,\n \n roll_7_new_deaths = roll_7_new_deaths,\n roll_14_new_deaths = roll_14_new_deaths, \n new_cases_per_100k = new_cases_per_100k,\n pct_change_new_cases_per_100k = pct_change_new_cases_per_100k, \n \n regression_coefs_new_deaths7_100k = regression_coefs,\n regression_coefs_new_deaths14_100k = regression_coefs_14, \n regression_coefs_new_deaths7_100k_smoothed = regression_coefs_7_smoothed,\n \n smoothed_roll_7_new_deaths_per_100k = smoothed_roll_7_new_deaths_per_100k,\n \n roll_7_new_deaths_per_100k = roll_7_new_deaths_per_100k,\n inflection_point = inflection_point,\n roll_14_new_deaths_per_100k = (roll_14_new_deaths / population) * 1e5,\n \n mortality_per_100k = mortality_rate * 1e5,\n \n new_deaths_pct_14_avg = new_deaths / roll_14_new_deaths,\n new_deaths_pct_7_avg = new_deaths / roll_7_new_deaths,\n roll_7_new_deaths_avg_roll = roll_7_new_deaths_avg_roll, \n roll_7_new_deaths_avg = roll_7_new_deaths_avg,\n roll_7_new_deaths_rollsum_7 = roll_7_new_deaths_rollsum_7,\n new_cases = new_cases, \n roll_7_new_cases = roll_7_new_cases,\n \n roll_7_new_deaths_pct_max = roll_7_new_deaths / max(roll_7_new_deaths, na.rm = T),\n roll_7_new_deaths_pct = cume_dist(roll_7_new_deaths),\n StringencyIndex_pct = cume_dist(StringencyIndex),\n cumulative_cases = cumulative_cases, \n cumulative_deaths = cumulative_deaths,\n ContainmentHealthIndex = ContainmentHealthIndex , \n EconomicSupportIndex = EconomicSupportIndex ,\n GovernmentResponseIndex = GovernmentResponseIndex , \n StringencyIndex = StringencyIndex,\n StringencyIndex_diff = StringencyIndex - lag(StringencyIndex),\n StringencyIndex_pct_change = (StringencyIndex - lag(StringencyIndex)) / lag(StringencyIndex),\n one_week_stringency = roll_mean(StringencyIndex, 7),\n two_week_stringency = roll_mean(StringencyIndex, 14),\n Stringency_z = Stringency_z,\n Stringency_median_over_iqr = Stringency_median_over_iqr,\n StringencyIndex_percentile = cume_dist(StringencyIndex),\n change_StringencyIndex = c(NA, diff(StringencyIndex, 1)),\n mobility = mobility,\n mobility_roll_7 = roll_mean(mobility, 7),\n daily_cumulative_deaths_percent_of_total = cumulative_cases / max(cumulative_cases)\n )\n \n}, by = list(country)] %>%\n left_join(latest_country_pop %>% select(-population)) \n\n# avg_stringency_by_date = group_by(covid_deaths_by_country_date_diffs, date) %>%\n# summarize(\n# mean_stringency_by_date = mean(StringencyIndex, na.rm = T),\n# mean_mobility_by_date = mean(mobility, na.rm = T),\n# smoothed_mobility_mean_by_date = mean(mobility_roll_7, na.rm = T),\n# max_stringency_by_date = max(StringencyIndex, na.rm = T),\n# median_stringency_by_date = median(StringencyIndex, na.rm = T)\n# )\n# covid_deaths_by_country_date_diffs = left_join(covid_deaths_by_country_date_diffs, avg_stringency_by_date)\n\n\nfilter(covid_deaths_by_country_date_diffs, country == 'Italy') %>%\n ggplot(aes(date, rolling_regression_new_cases_7)) +\n geom_bar(stat = 'identity')\n\n\nfilter(covid_deaths_by_country_date_diffs, country == 'Sweden') %>%\n ggplot(aes(date, roll_7_new_deaths_per_100k)) +\n geom_bar(stat = 'identity', aes(fill = in_first_cycle))\n\ncovid_deaths_by_country_date_diffs\n\n# \n# stringency_model = lm(StringencyIndex ~ lag(StringencyIndex, 1) + mean_stringency_by_date + \n# roll_7_new_deaths_per_100k + roll_7_new_cases_per_100k, data = covid_deaths_by_country_date_diffs)\n\n\nboost_sub = select(covid_deaths_by_country_date_diffs, country, date, \n StringencyIndex, roll_7_new_cases_per_100k, roll_7_new_deaths_per_100k, mortality_per_100k, rolling_regression_new_cases_7) %>% na.omit()\n\nmodel_predictions_by_country = unique(boost_sub$country) %>%\n map(function(the_country){\n \n not_country_sub = filter(boost_sub, country != the_country)\n stats_by_date = group_by(not_country_sub, date) %>% \n summarize(\n q20_stringency_date = quantile(StringencyIndex, probs = 0.2, na.rm = T),\n mean_stringency_date = mean(StringencyIndex, probs = 0.5, na.rm = T),\n q80_stringency_date = quantile(StringencyIndex, probs = 0.8, na.rm = T)\n )\n not_country_sub = left_join(not_country_sub, stats_by_date)\n \n country_sub = filter(boost_sub, country == the_country) %>% left_join(stats_by_date)\n \n the_formula = as.formula('StringencyIndex ~ mean_stringency_date + q20_stringency_date + q80_stringency_date + roll_7_new_cases_per_100k + roll_7_new_deaths_per_100k + date')\n stringency_model = lm(the_formula, data = not_country_sub)\n \n model_mat = model.matrix(the_formula, data = not_country_sub) \n boosted_stringency_model = xgboost(data = model_mat, label = not_country_sub$StringencyIndex, nrounds = 100,verbose = 0) \n \n country_model_mat = model.matrix(the_formula, data = country_sub) \n country_sub$boosted_stringency = predict(boosted_stringency_model, newdata = country_model_mat)\n country_sub$linear_stringency = predict(stringency_model, newdata = country_sub)\n \n ensemble_prediction = lm(StringencyIndex ~ linear_stringency + boosted_stringency, data = country_sub)\n country_sub$ensemble_stringency_prediction = predict(ensemble_prediction)\n \n return(country_sub)\n }) %>%\n bind_rows()\n\n\nstringency_model_correlations = group_by(model_predictions_by_country, country) %>%\n summarize(\n stringency_correlation = cor(ensemble_stringency_prediction, StringencyIndex)\n )\n\ncovid_deaths_by_country_date_diffs = \n left_join(\n covid_deaths_by_country_date_diffs,\n model_predictions_by_country %>% select(country, date, boosted_stringency, linear_stringency, ensemble_stringency_prediction)\n ) %>%\n mutate(\n ensemble_stringency_prediction_error = ensemble_stringency_prediction - StringencyIndex\n )\n\n\n\n# \n# daily_stats = group_by(covid_deaths_by_country_date_diffs, date) %>%\n# summarize(\n# mean_roll_7_new_deaths_per_100k = mean(roll_7_new_deaths_per_100k, na.rm = T),\n# mean_stringency = mean(ContainmentHealthIndex, na.rm = T),\n# median_stringency = median(ContainmentHealthIndex, na.rm = T),\n# mean_roll_7_new_deaths_pct = mean(roll_7_new_deaths_pct, na.rm = T),\n# countries_near_peak = n_distinct(country[roll_7_new_deaths_pct > 0.75]),\n# mean_stringency_new_deaths_20 = mean(ContainmentHealthIndex[roll_7_new_deaths_pct >= 0.2], na.rm = T),\n# mean_stringency_new_deaths_40 = mean(ContainmentHealthIndex[roll_7_new_deaths_pct >= 0.4], na.rm = T),\n# mean_stringency_new_deaths_60 = mean(ContainmentHealthIndex[roll_7_new_deaths_pct >= 0.6], na.rm = T),\n# mean_stringency_new_deaths_80 = mean(ContainmentHealthIndex[roll_7_new_deaths_pct >= 0.8], na.rm = T)\n# )\n\n# match high periods with stringency, stringency being measured across countries\n\n# covid_deaths_by_country_date_diffs = \n# left_join(covid_deaths_by_country_date_diffs, daily_stats) %>%\n# mutate(\n# containment_mean_diff = ContainmentHealthIndex - mean(ContainmentHealthIndex, na.rm = T),\n# new_deaths_pct_mean = new_deaths_per_100k / mean(new_deaths_per_100k, na.rm = T),\n# roll_7_new_deaths_pct_mean = roll_7_new_deaths_per_100k / mean(roll_7_new_deaths_per_100k, na.rm = T),\n# roll_7_new_deaths_per_100k_pct_mean = roll_7_new_deaths_per_100k / mean_roll_7_new_deaths_per_100k\n# )\n\n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States')), aes(date)) +\n geom_line(aes(y = roll_14_new_deaths_per_100k), colour = 'blue') +\n geom_line(aes(y = roll_7_new_deaths_per_100k), colour = 'red') +\n geom_point(aes(y = roll_7_new_deaths_avg_roll, colour = roll_7_new_deaths_rollsum_7 == 7)) +\n geom_line(aes(y = roll_7_new_deaths_avg_roll), colour = 'black') \n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Brazil')), aes(date)) +\n facet_wrap(~country) +\n # geom_line(aes(y = roll_2_new_deaths), colour = 'red') +\n # geom_line(aes(y = roll_3_new_deaths)) +\n geom_line(aes(y = roll_7_new_deaths), colour = 'red') +\n geom_line(aes(y = roll_14_new_deaths))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Brazil', 'Italy', 'Sweden')), aes(date)) +\n facet_wrap(~country) +\n geom_line(aes(y = roll_7_new_deaths_per_100k), colour = 'black') +\n geom_point(aes(y = roll_7_new_deaths_per_100k, colour = roll_7_new_deaths_pct_change > 0)) \n\n\n\n\ncovid_deaths_by_country_date_diffs %>% filter(country %in% c('Italy'), month(date) == 10) %>% \n select(date, roll_7_new_deaths_avg_roll)\n\n\nfilter(covid_deaths_by_country_date_diffs, roll_7_new_deaths_rollsum_7 == 7, country == 'Sweden')\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Sweden')), aes(date)) +\n geom_step(aes(y = roll_7_new_deaths_rollsum_7)) +\n geom_point(aes(y = roll_7_new_deaths_rollsum_7, colour = roll_7_new_deaths_rollsum_7 == 7))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States')), aes(date)) +\n geom_step(aes(y = roll_7_new_deaths_rollsum_7)) +\n geom_point(aes(y = roll_7_new_deaths_rollsum_7, colour = roll_7_new_deaths_rollsum_7 == 7))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Brazil')), aes(date)) +\n geom_step(aes(y = roll_7_new_deaths_rollsum_7)) +\n geom_point(aes(y = roll_7_new_deaths_rollsum_7, colour = roll_7_new_deaths_rollsum_7 == 7))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Sweden', 'Italy')), aes(date)) +\n geom_step(aes(y = roll_7_new_deaths_rollsum_7)) +\n facet_wrap(~country) +\n geom_point(aes(y = roll_7_new_deaths_rollsum_7, fill = StringencyIndex, size = StringencyIndex), pch = 21) +\n scale_fill_viridis_c(option = 'A')\n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('Sweden', 'Italy'), roll_7_new_deaths_rollsum_7 == 7), aes(date)) +\n geom_point(aes(y = roll_7_new_deaths_per_100k, colour = country)) +\n geom_hline(aes(yintercept = 1))\n\n\n#### analyze peak covid vs. stringency during the peak ####\nggplot(covid_deaths_by_country_date_diffs %>% \n filter(country %in% c('Sweden', 'Italy', 'United States', 'Brazil', 'United Kingdom', 'Spain', 'Peru'), \n roll_7_new_deaths_rollsum_7 == 7), aes(date)) +\n geom_point(aes(y = roll_7_new_deaths_per_100k, colour = country)) +\n geom_hline(aes(yintercept = 1))\n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date)) +\n geom_line(aes(y = roll_7_new_deaths_per_100k, colour = country)) +\n geom_hline(aes(yintercept = 1))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date)) +\n geom_line(aes(y = roll_7_new_deaths_pct_max, colour = country)) +\n geom_hline(aes(yintercept = 1))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date)) +\n geom_line(aes(y = ContainmentHealthIndex, colour = country)) \n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date)) +\n geom_line(aes(y = roll_7_new_deaths_pct_mean, colour = country)) +\n geom_hline(aes(yintercept = 1))\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(Stringency_z, roll_7_new_deaths_pct_mean)) +\n geom_point()\n\n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States')), aes(date)) +\n geom_line(aes(y = roll_14_new_deaths, colour = country)) \n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date, colour = country)) +\n geom_line(aes(y = roll_7_new_deaths_avg)) \n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country %in% c('United States', 'Sweden', 'Italy')), aes(date, colour = country)) +\n geom_line(aes(y = roll_7_new_deaths_per_100k_pct_mean)) \n\ngroup_by(covid_deaths_by_country_date_diffs, country) %>%\n summarize(\n median_roll_7_new_deaths_per_100k_pct_mean = median(roll_7_new_deaths_per_100k_pct_mean, na.rm = T)\n ) %>%\n arrange(\n -median_roll_7_new_deaths_per_100k_pct_mean\n ) %>%\n View()\n\n\n\n\nggplot(daily_stringency_stats, aes(date)) +\n geom_line(aes(y = countries_near_peak))\n\nggplot(covid_deaths_by_country_date_diffs, aes(date)) +\n geom_line(aes(y = countries_near_peak))\n\nggplot(daily_stringency_stats, aes(date)) +\n geom_line(aes(y = mean_roll_7_new_deaths_pct))\n\n\n\n\nfilter(covid_deaths_by_country_date_diffs, country %in% nordic_countries) %>%\n ggplot(aes(date, stringency_pct_new_deaths_interaction, colour = country)) +\n geom_point()\n\n\ncovid_deaths_by_country_date_diffs_dt = data.table(arrange(covid_deaths_by_country_date_diffs, region, country, date))\n\n\nstats_by_region = covid_deaths_by_country_date_diffs_dt[!is.na(date),{\n the_countries = country\n \n total_pop = sum(filter(latest_country_pop, country %in% the_countries)$population, na.rm = T)\n \n country_df = data.frame(country, date, new_deaths) %>%\n group_by(date) %>%\n summarize(\n total_new_deaths = sum(new_deaths, na.rm = T)\n ) %>%\n ungroup() %>%\n mutate(\n total_new_deaths_100k = (total_new_deaths / total_pop) * 1e5,\n total_new_death_100k_roll_7 = c(rep(NA, 6), roll_mean(total_new_deaths_100k, 7))\n )\n \n \n country_df\n \n}, by = list(region)] %>%\n filter(!is.na(region))\n\n\n##### IMF Data #####\nsetwd(\"~/Public_Policy/Projects/COVID-19 Mismanagement/data\")\nimf_real_gdp_projections = read_excel(\"IMF october projections data.xlsx\", 'all countries projections')\nneg_character = imf_real_gdp_projections$`2013` %>% str_extract('^([^0-9]{1})') %>% unique() %>% na.omit() %>% as.character()\n\nimf_real_gdp_projections = imf_real_gdp_projections %>% \n mutate_all(function(x){\n y = str_replace(x, neg_character, '-')\n y_num = as.numeric(y)\n na_y = sum(is.na(y))\n na_y_num = sum(is.na(y_num))\n if (na_y_num <= na_y) {\n return(y_num)\n } else {\n return(y)\n }\n }) %>%\n mutate(\n entity = recode(entity, `Korea` = 'South Korea')\n ) \n\n\n\n\n##### OECD Data #####\nsetwd(\"~/Public_Policy/Projects/COVID-19 Mismanagement/data/OECD\")\n\n## MEASURE -- PC_CHGPY -- SAME PERIOD PRIOR YEAR\n## PC_CHGPP \nquarterly_gdp = fread('quarterly_gdp.csv') %>%\n filter(FREQUENCY == 'Q', SUBJECT == 'TOT', MEASURE == 'PC_CHGPP') %>%\n mutate(\n Value_Pct = Value / 100,\n quarter = str_extract(TIME, 'Q[0-9]{1}') %>% str_remove('Q') %>% as.numeric(),\n year = str_extract(TIME, '[0-9]{4}') %>% as.numeric(),\n year_qtr = as.yearqtr(paste(year, quarter, sep = '-'), format = '%Y-%q'),\n country = countrycode(LOCATION, origin = 'iso3c', destination = 'country.name')\n )\n\nmonthly_unemployment_rate = fread('unemployment_rate.csv') %>%\n filter(SUBJECT == 'TOT', FREQUENCY == 'M') %>%\n mutate(\n Value_Pct = Value / 100,\n year = str_extract(TIME, '[0-9]{4}') %>% as.numeric(),\n month = str_extract(TIME, '\\\\-[0-9]{2}') %>% str_remove('-') %>% as.numeric(),\n month_date = as.Date(paste(year, month, '01', sep = '-')),\n country = countrycode(LOCATION, origin = 'iso3c', destination = 'country.name')\n ) %>%\n filter(!is.na(country)) %>%\n arrange(country, month_date)\n\n# find best as-of month\nlast_month_by_country = group_by(monthly_unemployment_rate, country) %>%\n summarize(\n last_month = max(month_date)\n )\n\nmost_common_months_latest_data =\n last_month_by_country %>%\n group_by(last_month) %>%\n summarize(obs = n()) %>%\n arrange(-obs)\n\nselected_ur_countries = filter(last_month_by_country, last_month >= most_common_months_latest_data$last_month[1])\n\nmonthly_2020_unemployment_rate_dt = filter(monthly_unemployment_rate,\n year == 2020,\n month_date <= most_common_months_latest_data$last_month[1],\n country %in% selected_ur_countries$country\n) %>%\n data.table()\n\nmonthly_2020_unemployment_rate_dt_indexes = monthly_2020_unemployment_rate_dt[, {\n starting_value = Value_Pct[1]\n list(\n val_index = Value_Pct / starting_value,\n Value_Pct = Value_Pct,\n month_date = month_date,\n is_last_date = month_date == max(month_date)\n )\n \n}, by = list(country)]\n\ncovid_ur_indexes = monthly_2020_unemployment_rate_dt[, {\n starting_value = Value_Pct[1]\n ending_value = tail(Value_Pct, 1)\n \n avg_value = mean(Value_Pct)\n val_index = Value_Pct / starting_value\n mean_index = mean(val_index)\n \n list(\n obs = length(month_date),\n starting_value = starting_value,\n ending_value = ending_value,\n period_index = ending_value / starting_value,\n mean_index = mean_index,\n avg_value = avg_value,\n starting_month = month_date[1],\n ending_month = tail(month_date, 1)\n )\n \n}, by = list(country)] %>%\n arrange(-mean_index) %>%\n mutate(\n country_ur_factor = factor(country, levels = rev(country))\n )\n\n## for each country, calculate UR index since December 2019\n\n\n\n# https://fiscaldata.treasury.gov/datasets/monthly-statement-public-debt/summary-of-treasury-securities-outstanding\n\n##### analysis data -- combine everything, compute stats by country #####\n\ncovid_stats_by_country = \n covid_deaths_by_country_date_diffs %>%\n arrange(country, date) %>%\n group_by(country, income) %>%\n summarize(\n first_cycle_time = as.numeric(max(date[in_first_cycle == T], na.rm = T) - min(date[in_first_cycle == T], na.rm = T)),\n first_cycle_deaths = sum(new_deaths[in_first_cycle], na.rm = T),\n mean_stringency_during_first_cycle_increase = mean(StringencyIndex[in_first_cycle & n_pos_slopes >= 4], na.rm = T),\n mean_stringency_error_during_first_cycle_increase = mean(ensemble_stringency_prediction_error[in_first_cycle & n_pos_slopes >= 4], na.rm = T),\n mean_stringency_error_during_first_cycle = mean(ensemble_stringency_prediction_error[in_first_cycle], na.rm = T),\n mean_stringency_errors = mean(ensemble_stringency_prediction_error, na.rm = T),\n max_stringency_during_peak = max(StringencyIndex[peak_daily_deaths_lead_14], na.rm = T),\n mean_stringency_errors_during_peak = mean(ensemble_stringency_prediction_error[peak_daily_deaths_lead_14], na.rm = T),\n max_stringency_errors_during_peak = max(ensemble_stringency_prediction_error[peak_daily_deaths_lead_14], na.rm = T),\n median_stringency_errors = median(ensemble_stringency_prediction_error, na.rm = T),\n max_stringency_errors = max(ensemble_stringency_prediction_error, na.rm = T),\n min_stringency_errors = min(ensemble_stringency_prediction_error, na.rm = T),\n case_1k_date = min(date[cumulative_cases >= 1000], na.rm = T),\n death_100_date = min(date[cumulative_deaths >= 100], na.rm = T),\n peak_deaths_date = min(date[roll_7_new_deaths_per_100k == max(roll_7_new_deaths_per_100k , na.rm = T)], na.rm = T),\n as_of_date = max(date, na.rm = T),\n total_deaths = max(cumulative_deaths, na.rm = T),\n mean_mobility = mean(mobility, na.rm = T),\n mean_mobility_during_first_cycle = mean(mobility[in_first_cycle], na.rm = T),\n mean_mobility_during_first_cycle_increase = mean(mobility[in_first_cycle & n_pos_slopes >= 4], na.rm = T),\n median_mobility = median(mobility, na.rm = T),\n median_mobility_during_peak = median(mobility[peak_daily_deaths_lead_14], na.rm = T),\n max_stringency = max(StringencyIndex, na.rm = T),\n median_stringency = median(StringencyIndex, na.rm = T),\n median_ContainmentHealthIndex = median(ContainmentHealthIndex, na.rm = T),\n max_ContainmentHealthIndex = max(ContainmentHealthIndex, na.rm = T),\n mean_stringency = mean(StringencyIndex, na.rm = T),\n mean_new_deaths_pct_of_max = mean(new_deaths_pct_of_max, na.rm = T),\n mean_new_deaths_per_100k = mean(new_deaths_per_100k, na.rm = T),\n mortality_per_100k = max(mortality_per_100k)\n ) %>%\n ungroup() %>%\n arrange(mortality_per_100k) %>%\n mutate(\n country_ranked_mortality = factor(country, levels = rev(country)),\n country_factor = factor(country, levels = rev(country)),\n case_1k_minus_min = as.numeric(case_1k_date - min(case_1k_date, na.rm = T)),\n death_100_minus_min = as.numeric(death_100_date - min(death_100_date, na.rm = T)),\n peak_date_minus_min = as.numeric(peak_deaths_date - min(peak_deaths_date, na.rm = T))\n ) %>%\n left_join(\n imf_real_gdp_projections, by = c('country' = \"entity\")\n ) %>%\n left_join(\n latest_country_pop\n ) %>%\n rename(\n projection_2020 = `2020`\n ) %>%\n # filter(population > 5e6, projection_2020 >= -15) %>%\n mutate(\n first_cycle_mortality_100k = (first_cycle_deaths / population) * 1e5,\n three_year_avg_growth = (`2019` + `2018` + `2017` ) / 3,\n two_year_avg_growth = (`2019` + `2018`) / 2,\n last_year_growth = `2019`,\n diff_projection_avg = (1 - ((1 + three_year_avg_growth / 100) / (1 + projection_2020/100))) * 100,\n diff_projection_avg_simple = projection_2020 - three_year_avg_growth,\n international_tourism_pop = international_tourism / population\n ) %>%\n arrange(-diff_projection_avg) %>%\n mutate(\n country_ranked_gdp = factor(country, levels = rev(country)),\n ) %>%\n arrange(-gdp_per_capita_us)\n<<<<<<< HEAD\ncovid_stats_by_country$\n\ncovid_stats_by_country$diff_projection_avg / 100\n### output data ###\ncovid_stats_by_country$geometry = NULL\n\nwrite.csv(covid_stats_by_country, 'covid_stats_by_country.csv', row.names = F)\n# write.csv(covid_deaths_by_country_date_diffs, 'covid_deaths_by_country_date.csv', row.names = F)\n\n=======\n>>>>>>> 45dc18cdb0eeda0ffaa3308922c6adce67096026\n\nfilter(covid_stats_by_country, income == 'High income') %>%\n ggplot(aes(mean_mobility_during_first_cycle, first_cycle_mortality_100k)) +\n geom_point()\n\nfilter(covid_stats_by_country, income == 'High income') %>%\n ggplot(aes(mean_mobility_during_first_cycle_increase, first_cycle_mortality_100k)) +\n geom_point()\n\n\n\nfilter(covid_stats_by_country, income == 'High income') %>%\n ggplot(aes(mean_stringency_during_first_cycle_increase, mean_mobility_during_first_cycle_increase)) +\n geom_point(aes(size = first_cycle_mortality_100k), pch = 21) +\n scale_size(range = c(2, 12)) +\n stat_smooth(method = 'lm')\n\n\n\n\nfilter(covid_stats_by_country, country %in% c('Spain', 'Italy', 'United States', 'Sweden', 'Denmark', 'Germany', 'Belgium', 'France', 'United Kingdom')) %>%\n select(\n country,\n first_cycle_time,\n first_cycle_deaths,\n population,\n mean_stringency_during_first_cycle_increase\n ) %>%\n mutate(\n (first_cycle_deaths / population) * 1e5\n ) %>%\n select(-population) %>%\n arrange(-first_cycle_time)\n\neurope_stats = filter(covid_stats_by_country, continent == 'Europe', population >= 5e6) %>%\n select(\n country,\n first_cycle_time,\n first_cycle_deaths,\n population,\n mean_mobility_during_first_cycle,\n mean_mobility_during_first_cycle_increase, \n mean_stringency_error_during_first_cycle_increase,\n mean_stringency_during_first_cycle_increase\n ) %>%\n mutate(\n mortality_100k = (first_cycle_deaths / population) * 1e5\n ) %>%\n select(-population) %>%\n arrange(-mean_stringency_error_during_first_cycle_increase) \n\n\neurope_stats %>%\n ggplot(aes(mean_stringency_error_during_first_cycle_increase, mortality_100k)) +\n geom_text(aes(label = country))\n\neurope_stats %>%\n ggplot(aes(mean_stringency_during_first_cycle_increase, mean_mobility_during_first_cycle_increase)) +\n geom_text(aes(label = country, size= mortality_100k)) +\n stat_smooth(method = 'lm')\n\neurope_stats %>%\n ggplot(aes(mean_mobility_during_first_cycle_increase, mortality_100k)) +\n geom_text(aes(label = country)) +\n stat_smooth(method = 'lm')\n\n\n\nnames(covid_stats_by_country)\ncovid_deaths_by_country_date_diffs$roll_7_new_deaths_per_100k\n\nfilter(covid_deaths_by_country_date_diffs, country %in% c('Russia','United States', 'Spain', 'Germany', 'Italy', 'Brazil', 'Peru', 'Mexico', 'Canada', 'Iceland','South Africa', nordic_countries)) %>%\n ggplot(aes(date, roll_7_new_deaths_per_100k, fill = in_first_cycle)) +\n facet_wrap(~country) +\n geom_bar(stat = 'identity')\n\nggplot(covid_stats_by_country, aes(max_stringency, mortality_per_100k)) +\n geom_point()\n\ngroup_by(covid_stats_by_country, income) %>%\n summarize(\n median_cycle_time = median(first_cycle_time, na.rm = T),\n median_first_cycle_deaths = median((first_cycle_deaths / population) * 1e5, na.rm = T)\n )\nggplot(covid_stats_by_country, aes(first_cycle_time)) +\n facet_wrap(~income) +\n stat_density() +\n geom_vline(data= covid_stats_by_country %>% filter(country %in% nordic_countries), aes(xintercept = first_cycle_time, colour = country)) +\n geom_text(data= covid_stats_by_country %>% filter(country %in% nordic_countries), aes(x = first_cycle_time, y = 0, label = country, colour = country), angle = 90, vjust = 1)\n\nggplot(covid_stats_by_country %>% filter(income == 'High income'), aes(first_cycle_time, log((first_cycle_deaths / population) * 1e5))) +\n geom_text(aes(label = country)) + \n facet_wrap(~income) +\n stat_smooth(method = 'lm') \n\nggplot(covid_stats_by_country, aes(first_cycle_time, log((first_cycle_deaths / population) * 1e5))) +\n geom_text(aes(label = country)) + \n stat_smooth(method = 'lm') \n\n\nggplot(covid_stats_by_country, aes(first_cycle_time, mean_stringency_during_first_cycle_increase)) +\n geom_text(aes(label = country)) + \n stat_smooth(method = 'lm') \n\n\nggplot(covid_stats_by_country, aes(mean_stringency_during_first_cycle_increase, log((first_cycle_deaths / population) * 1e5))) +\n geom_text(aes(label = country)) + \n stat_smooth(method = 'lm') \n\nggplot(covid_stats_by_country, aes(mean_stringency_error_during_first_cycle_increase, log((first_cycle_deaths / population) * 1e5))) +\n geom_text(aes(label = country)) +\n facet_wrap(~income) +\n stat_smooth(method = 'lm', se = F) \n\n\n\ncovid_stats_by_country$mean_stringency_errors_during_peak\n\nggplot(covid_stats_by_country, aes(mean_stringency_errors_during_peak, mortality_per_100k)) +\n geom_point() +\n stat_smooth()\n\nggplot(covid_stats_by_country, aes(max_stringency_errors_during_peak, mortality_per_100k)) +\n geom_point() +\n stat_smooth()\n\n\nfilter(covid_stats_by_country, country %in% nordic_countries) %>%\n select(country, mortality_per_100k, max_stringency, mean_stringency_errors, max_stringency_errors, mean_stringency_errors_during_peak)\n\n\nggplot(covid_stats_by_country, aes(median_roll_7_new_deaths_per_100k_pct_mean, mortality_per_100k)) +\n geom_text(aes(label = country, colour = region)) +\n stat_smooth()\n\n\nggplot(covid_stats_by_country, aes(mean_stringency_errors, mortality_per_100k)) +\n geom_point() +\n stat_smooth(method = 'lm')\nggplot(covid_deaths_by_country_date_diffs %>% filter(country == 'Sweden'), aes(date, stringency_errors)) +\n geom_point()\n\nggplot(covid_deaths_by_country_date_diffs, aes(stringency_errors, roll_7_new_deaths_per_100k)) + \n geom_point()\n\n##### test stringency errors against mortality outcomes #####\n\ncountry_list = unique(covid_deaths_by_country_date_diffs$country)\nresult_list = country_list %>% \n map(function(the_country){\n the_sub = filter(covid_deaths_by_country_date_diffs, country == the_country)\n tryCatch({\n the_model = lm(new_deaths_per_100k ~ lag(ensemble_stringency_prediction_error, 14), data = the_sub)\n return(coefficients(the_model)[2])\n }, error = function(e){\n cat('error for ', the_country, '\\n')\n return(NA)\n })\n \n })\nnames(result_list) = country_list\nstringency_analysis_result_df = data.frame(\n country = country_list,\n coefficients = unlist(result_list)\n) %>%\n arrange(\n -abs(coefficients)\n )\n\n\nfilter(covid_deaths_by_country_date_diffs, country %in% c('Italy', nordic_countries, 'Sweden', 'United States')) %>%\n ggplot(aes(date, y = smoothed_roll_7_new_deaths_per_100k)) +\n facet_wrap(~country) +\n geom_line() +\n geom_point(aes(colour = inflection_point_desc))\n\nfilter(covid_deaths_by_country_date_diffs, country %in% c('Italy', nordic_countries, 'Sweden', 'United States')) %>%\n ggplot(aes(date, y = smoothed_roll_7_new_deaths_per_100k)) +\n facet_wrap(~country) +\n geom_line() +\n geom_point(aes(colour = n_neg_slopes > 4))\n\nmin(covid_deaths_by_country_date_diffs$ensemble_stringency_prediction, na.rm = T)\nfilter(covid_deaths_by_country_date_diffs, country %in% c('Italy', nordic_countries, 'Sweden', 'United States')) %>%\n ggplot(aes(date, y = regression_coefs_new_deaths7_100k_smoothed)) +\n facet_wrap(~country) +\n geom_point(aes(colour = inflection_point_desc))\ncovid_deaths_by_country_date_diffs$ensemble_stringency_prediction\n\nggplot(covid_deaths_by_country_date_diffs, aes(ensemble_stringency_prediction, StringencyIndex)) +\n geom_point(alpha = 0.3)\n\n# grangertest(new_deaths_per_100k ~ new_cases_per_100k, order = 14, data = covid_deaths_by_country_date_diffs)\n# \n# a = lm(new_deaths_per_100k ~ lag(stringency_errors, 14), data = spain)\n\nggplot(covid_deaths_by_country_date_diffs, aes())\n\nthe_sub = covid_deaths_by_country_date_diffs %>% \n filter(country %in% c('Belgium','Norway','Germany', 'Sweden', \n 'South Korea', 'United States', 'Spain', 'Italy', 'Denmark', 'Finland', 'France', 'United Kingdom'))\n\n\nggplot(the_sub, aes(date)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_step(aes(y = ensemble_stringency_prediction), colour = '#e41a1c', size = 1) +\n geom_step(aes(y = StringencyIndex), colour = '#377eb8', size = 1)\n\nfilter(the_sub, n_pos_slopes >= 4) %>%\n group_by(country) %>%\n summarize(\n sum(lead(new_deaths, 14), na.rm = T)\n )\nthe_sub$new_deaths\n\nggplot(aes(date, roll_7_new_cases_per_100k)) +\n facet_wrap(~country) +\n scale_fill_viridis_c(option = 'C') +\n geom_bar(stat = 'identity', aes(fill = ensemble_stringency_prediction_error))\n\nggplot(the_sub, aes(date, roll_7_new_deaths_per_100k)) +\n facet_wrap(~country) +\n theme_minimal() +\n geom_line() +\n geom_point(aes(colour = ensemble_stringency_prediction_error)) +\n scale_colour_viridis_c(option = 'C')\n\nggplot(the_sub, aes(lag(ensemble_stringency_prediction_error, 14), roll_7_new_deaths_per_100k)) +\n facet_wrap(~country, scales = 'free') +\n theme_minimal() +\n geom_point(aes(colour = lag(ensemble_stringency_prediction_error, 14) > 0)) +\n stat_smooth(method = 'lm', se = F)\n\n\n\n\nthe_sub$new_cases_elevated\nggplot(the_sub, aes(date, rolling_regression_new_cases_7)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_bar(aes(fill = new_cases_elevated), stat = 'identity') \n\nggplot(the_sub, aes(date, ensemble_stringency_prediction_error)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_bar(aes(fill = new_cases_elevated), stat = 'identity') \n\n\nggplot(the_sub, aes(date, roll_7_new_cases_per_100k)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_bar(aes(fill = new_cases_elevated), stat = 'identity') \n\n\nggplot(the_sub, aes(rolling_regression_new_cases_7, lead(roll_7_new_deaths_per_100k, 14))) +\n geom_point\nthe_sub$roll_7_new_deaths_per_100k\n\nggplot(the_sub, aes(date)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_step(aes(y = linear_stringency), colour = '#e41a1c', size = 1) +\n geom_step(aes(y = StringencyIndex), colour = '#377eb8', size = 1)\n\nggplot(the_sub, aes(date)) +\n facet_wrap(~country) +\n theme_minimal() +\n # geom_ribbon(data = filter(the_sub, predicted_stringency > StringencyIndex), aes(ymin = StringencyIndex, ymax = predicted_stringency), fill = 'red', alpha = 0.4) +\n # geom_ribbon(data = filter(the_sub, predicted_stringency <= StringencyIndex), aes(ymin = predicted_stringency, ymax = StringencyIndex), fill = 'blue', alpha = 0.4) \n geom_step(aes(y = boosted_stringency), colour = '#e41a1c', size = 1) +\n geom_step(aes(y = StringencyIndex), colour = '#377eb8', size = 1)\n\n\n\n\n\ngeom_step(aes(y = predicted_stringency), colour = 'orange', size = 1) +\n geom_step(aes(y = StringencyIndex), colour = 'steelblue', size = 1)\n?geom_area\n\nfilter(covid_deaths_by_country_date_diffs, country %in% head(stringency_analysis_result_df, 12)$country) %>%\n ggplot(aes(lag(ensemble_stringency_prediction_error, 14), new_deaths_per_100k)) +\n geom_point() +\n facet_wrap(~country, scales = 'free') +\n stat_smooth(method = 'lm', se = F)\n\nfilter(covid_deaths_by_country_date_diffs, country %in% the_sub$country) %>%\n ggplot(aes(lag(ensemble_stringency_prediction_error, 14), new_deaths_per_100k)) +\n geom_point() +\n facet_wrap(~country, scales = 'free') +\n stat_smooth(method = 'lm', se = F)\n\nfilter(covid_deaths_by_country_date_diffs, country %in% (arrange(stringency_analysis_result_df, -coefficients) %>% pull(country) %>% head(12))) %>%\n ggplot(aes(lag(ensemble_stringency_prediction_error, 14), new_deaths_per_100k)) +\n geom_point() +\n facet_wrap(~country, scales = 'free') +\n stat_smooth(method = 'lm', se = F)\n\nfilter(covid_deaths_by_country_date_diffs, country %in% (arrange(stringency_analysis_result_df, -coefficients) %>% pull(country) %>% head(12))) %>%\n ggplot(aes(date, roll_7_new_deaths_per_100k)) +\n facet_wrap(~country) +\n geom_line()\n\n\n\n\nstringency_analysis_result_df$coefficients\n\n\n\nplot(stringency_model)\nccf(us$stringency_errors, us$new_deaths_per_100k)\na = ccf(us$stringency_errors, us$new_deaths_per_100k)\na\n\ndata.frame(a$lag, a$acf)\ncovid_deaths_by_country_date_diffs$roll_7_new_deaths_per_100k\n\nggplot(covid_deaths_by_country_date_diffs %>% \n filter(country %in% c('Sweden', 'Germany', 'Spain', 'United Kingdom', 'United States', 'Italy', 'Brazil', 'Denmark', 'Norway')), aes(date, stringency_errors)) +\n geom_bar(aes(fill = stringency_errors), stat = 'identity') +\n facet_wrap(~country) +\n scale_fill_viridis_c(option = 'C') +\n # scale_fill_gradient2(midpoint = 0, low = 'blue', high = 'red') +\n theme_dark()\n\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country == 'Japan'), aes(date, stringency_errors)) +\n geom_point()\n\nggplot(covid_deaths_by_country_date_diffs %>% filter(country == 'Japan'), aes(date, new_deaths_per_100k)) +\n geom_point()\ncovid_deaths_by_country_date_diffs$new_cases_per_100k\n\n\ncovid_deaths_by_country_date_diffs %>% filter(country == 'Japan') %>% pull(stringency_errors) %>% mean(na.rm = T)\ncovid_deaths_by_country_date_diffs %>% filter(country == 'Sweden') %>% pull(stringency_errors) %>% mean(na.rm = T)\ncovid_deaths_by_country_date_diffs %>% filter(country == 'Germany') %>% pull(stringency_errors) %>% mean(na.rm = T)\ncovid_deaths_by_country_date_diffs %>% filter(country == 'Denmark') %>% pull(stringency_errors) %>% mean(na.rm = T)\ncovid_deaths_by_country_date_diffs %>% filter(country == 'United States') %>% pull(stringency_errors) %>% mean(na.rm = T)\narrange(covid_stats_by_country, -mean_stringency_errors) %>% select(country, mortality_per_100k)\n\n\n# ggplot(covid_stats_by_country, aes(death_100_minus_min, mortality_per_100k)) + \n# geom_point(aes(size = mortality_per_100k)) +\n# stat_smooth()\n\nggplot(covid_stats_by_country %>% filter(continent == 'Europe', death_100_minus_min <= 90), \n aes(case_1k_minus_min, mortality_per_100k)) + \n # geom_point(aes(size = mortality_per_100k), pch = 21) +\n geom_text(aes(label = country)) +\n stat_smooth(se = F)\n\n\ncovid_stats_by_country$geometry = NULL\nmin_mortality_not_zero = covid_stats_by_country$mortality_per_100k[covid_stats_by_country$mortality_per_100k > 0] %>% min()\ncovid_stats_by_country$mortality_per_100k_log = ifelse(covid_stats_by_country$mortality_per_100k == 0, min_mortality_not_zero, covid_stats_by_country$mortality_per_100k)\n\neurope_map_data = left_join(europe_cropped, covid_stats_by_country, by = c('name' = 'country'))\n\nselected_european_countries = c('Sweden', 'Denmark', 'Finland', 'Norway', 'Germany', 'Italy', 'Spain', 'France', 'United Kingdom', 'Ireland')\nggplot(europe_map_data) +\n geom_sf(aes(fill = mortality_per_100k)) +\n scale_fill_viridis_c(name = 'Deaths Per\\n100k Pop.',option = 'A') +\n theme_map() +\n # theme_dark() +\n # theme_minimal() +\n geom_sf_label(data = filter(europe_map_data, name %in% selected_european_countries), aes(label = paste0(name, '\\n', comma(mortality_per_100k, accuracy = 0.1))), size = 3.5) +\n labs(\n x = '', y = '', \n title = 'COVID-19 Mortality Rates in Europe',\n subtitle = sprintf('Data through %s', max(covid_stats_by_country$as_of_date, na.rm=T) %>% format('%b %d, %Y')),\n caption = 'Chart: Taylor G. White\\nData: Johns Hopkins CSSE, World Bank'\n ) +\n theme(\n # axis.text = element_blank(),\n plot.subtitle = element_text(face = 'italic'),\n plot.caption = element_text(hjust = 0, face = 'italic')\n )\n\nggsave('europe_mortality_rate_map.png', height = 9, width = 12, units = 'in', dpi = 600)\n\n\n\nggplot(covid_stats_by_country , aes(gini_index, last_trust_in_gov)) +\n theme_bw() +\n # geom_point(aes(size = mortality_per_100k_log)) +\n geom_text_repel(aes(label = country)) +\n large_text_theme +\n labs(x = 'GINI Index (Inequality)', y = 'Trust in National Government', title = 'Income Inequality vs. Trust in Government') +\n scale_y_continuous(labels = percent) +\n geom_quantile(quantiles = 0.5, size = 1) \n\n# stat_smooth(method = 'lm')\nggsave('inequality_vs_trust.png', height = 8, width = 10, units = 'in')\n# \n# ggplot(covid_stats_by_country, aes(log(international_tourism), log(mortality_per_100k_log))) +\n# geom_point(aes(colour = region)) +\n# stat_smooth(span = 1)\n# \n# ggplot(covid_stats_by_country, aes(international_tourism_pop, log(mortality_per_100k_log))) +\n# geom_point(aes(colour = region)) +\n# stat_smooth(span = 1)\n# \n# \n# ggplot(covid_stats_by_country, aes(international_tourism_pop, log(mortality_per_100k_log))) +\n# facet_wrap(~continent, scales = 'free_x') +\n# geom_point(aes(size = gdp_per_capita_us)) +\n# stat_smooth(span = 1)\n# \n# ggplot(covid_stats_by_country, aes(gini_index, log(mortality_per_100k_log))) +\n# facet_wrap(~continent) +\n# geom_point() +\n# stat_smooth(span = 1)\n# \n# ggplot(covid_stats_by_country, aes(gini_index, log(mortality_per_100k_log))) +\n# facet_wrap(~continent) +\n# geom_point() +\n# stat_smooth(span = 1)\n# \n# \n# ggplot(covid_stats_by_country, aes(gini_index, log(mortality_per_100k_log))) +\n# geom_point(aes(size = gdp_per_capita_us)) +\n# stat_smooth(span = 1)\n# \n# ggplot(covid_stats_by_country, aes(gini_index, gdp_per_capita_us)) +\n# geom_point() +\n# stat_smooth(span = 1)\n# \n# ggplot(covid_stats_by_country, aes(health_exp_gdp, log(mortality_per_100k_log))) +\n# geom_point() +\n# stat_smooth(span = 1)\n# \n# \n# ggplot(covid_stats_by_country, aes(health_exp_gdp, pop_pct_65_over)) +\n# geom_point(aes(size = gdp_per_capita_us, colour = log(mortality_per_100k_log))) +\n# scale_color_viridis_c(option = 'A') +\n# stat_smooth(method = 'lm')\n\n\n\n##### us comparator rank plots #####\nsetwd(out_location)\n\n# us_comparator_countries = filter(covid_stats_by_country, str_detect(economy, 'G7') | str_detect(economy, 'Emerging') | income == 'High income', !is.na(projection_2020))\ncovid_deaths_by_country_date_diffs$new_cases_elevated\ncovid_deaths_by_country_date_diffs$rolling_regression_new_cases_7\nfilter(covid_deaths_by_country_date_diffs, country %in% c('Sweden', 'United States', 'Spain', 'Italy', 'Germany')) %>%\n ggplot(aes(date, ensemble_stringency_prediction_error)) +\n facet_wrap(~country) +\n geom_bar(aes(fill = rolling_regression_new_cases_7 > 0), stat = 'identity')\n\nus_comparator_countries = filter(covid_stats_by_country, population > 5e6, projection_2020 >= -15) %>% head(60) \n\nfilter(us_comparator_countries, country == 'United States')\n\nmortality_model = lm(log(mortality_per_100k) ~ log(international_tourism) + max_stringency + gdp_per_capita_us + urban_pop_pct + mean_stringency_errors_during_peak + pop_pct_65_over, data = us_comparator_countries)\nmortality_model = lm(log(mortality_per_100k) ~ log(international_tourism) + max_stringency + gdp_per_capita_us + urban_pop_pct + mean_stringency_errors_during_peak + pop_pct_65_over, data = us_comparator_countries)\nsummary(mortality_model)\n\ngrowth_sd = sd(us_comparator_countries$projection_2020)\ngrowth_mean = mean(us_comparator_countries$projection_2020)\nmortality_iqr = IQR(us_comparator_countries$mortality_per_100k)\nmortality_median = median(us_comparator_countries$mortality_per_100k)\nus_comparator_countries$diff_projection_avg %>% median()\n\n\n# stacked_dat = bind_rows(\n# covid_stats_by_country, \n# mutate(covid_stats_by_country, region = 'Overall')\n# ) %>%\n# filter(region %in% c('Overall', 'North America', 'Europe & Central Asia', 'Latin America & Carribean', 'Middle East & North Africa')) %>%\n# mutate(\n# region_upd = recode(region, `North America` = \"Europe and North America\", \n# `Europe & Central Asia` = \"Europe and North America\", \n# `East Asia & Pacific` = 'East Asia, South Asia, and Pacific',\n# ``) %>%\n# factor(\n# levels = c('East Asia & Pacific', '')\n# )\n# )\n\n\nggplot(us_comparator_countries, aes(country_ranked_gdp)) +\n geom_hline(aes(yintercept = 0)) +\n geom_linerange(aes(ymin = projection_2020, ymax = three_year_avg_growth), size = 0.75) +\n geom_point(aes(y = projection_2020), colour = 'firebrick', size = 3) +\n geom_point(aes(y = three_year_avg_growth), colour = 'steelblue', size = 3) + \n theme_bw() +\n \n labs()\n\n\nregion_order = c('East Asia & Pacific', 'Europe & Central Asia', 'North America', '')\n\n#### growth projections vs. mortality \n# anim = ggplot(stacked_dat, aes(mortality_per_100k_log, diff_projection_avg/100)) +\n# geom_point(aes(colour = region)) +\n# transition_states(region,\n# transition_length = 2,\n# state_length = 4) + \n# enter_fade() +\n# exit_shrink() +\n# # scale_color_brewer(palette = 'Set1') +\n# scale_color_hue(name = 'Region') +\n# stat_smooth(method = 'lm') +\n# theme_bw() +\n# theme(legend.position = 'bottom') +\n# labs(\n# x = 'COVID Mortality Per 100k, Log Scale', y = 'COVID Economic Impact\\n2020 Real GDP Growth Projection vs. Three-Year Average'\n# ) + \n# scale_y_continuous(labels = percent, breaks = seq(-.20, 0.00, by = .05), limits = seq()) +\n# scale_x_continuous(trans = 'log10', labels = trans_format(\"log10\", math_format(10^.x)))\n# \n# # anim\n# ggsave('economic_impact_vs_mortality.png', height = 9, width = 12, units = 'in', dpi = 400)\n# \n\n\n\n## simple calculations ###\nmedian_econ_impact = median(us_comparator_countries$diff_projection_avg, na.rm = T)\nmedian_mortality = median(us_comparator_countries$mortality_per_100k, na.rm = T)\n\nus_data = filter(us_comparator_countries, country == 'United States')\nus_calcs = us_data %>% select(mortality_per_100k, diff_projection_avg) %>% summarise_all(median)\n\nus_calcs$mortality_per_100k / median_mortality\nus_calcs$diff_projection_avg / median_econ_impact\n\nsuperior_countries = filter(us_comparator_countries, mortality_per_100k <= us_calcs$mortality_per_100k & diff_projection_avg >= us_calcs$diff_projection_avg, country != 'United States')\nlength(superior_countries$country) / nrow(us_comparator_countries)\n\n\n# lives that could have been saved at median mortality\nus_data$total_deaths - ((us_data$population / 1e5) * median_mortality)\n\n##### compare mortality and economic outcomes #####\n\nmortality_rank_plot = ggplot(us_comparator_countries, aes(country_ranked_mortality, mortality_per_100k, fill = mortality_per_100k)) +\n geom_bar(stat = 'identity', fill = 'steelblue') +\n geom_bar(data = filter(us_comparator_countries, country == 'United States'), fill = 'firebrick', stat = 'identity') +\n scale_y_continuous(labels = comma) +\n coord_flip() +\n theme_bw() +\n labs(\n title = 'COVID Mortality Rate',\n subtitle = 'High income countries, minimum 5M population.',\n caption = '\\nChart: Taylor G. White\\nData: IMF October Economic Outlook, Johns Hopkins CSSE\\nVertical lines show median values.',\n x = '', y = '\\nCOVID-19 Mortality Rate\\n(Deaths / 100k Population)') +\n geom_hline(aes(yintercept = median_mortality), colour = 'darkslategray', size = 0.75) +\n theme(\n plot.title = element_text(size = 28),\n plot.subtitle = element_text(size = 18, face = 'italic'),\n axis.title = element_text(size = 22),\n plot.caption = element_text(size = 14, face = 'italic', hjust = 0),\n axis.text = element_text(size = 20),\n legend.text = element_text(size = 14),\n legend.title = element_text(size = 16),\n legend.position = 'right', \n panel.grid.minor = element_blank()\n ) \n\n\ngdp_rank_plot = ggplot(us_comparator_countries, aes(country_ranked_gdp, diff_projection_avg/100, fill = diff_projection_avg/100)) +\n geom_bar(stat = 'identity', fill = 'steelblue') +\n geom_bar(data = filter(us_comparator_countries, country == 'United States'), fill = 'firebrick', stat = 'identity') +\n coord_flip() +\n theme_bw() +\n scale_y_continuous(labels = percent) +\n labs(\n title = 'COVID Economic Impact',\n subtitle = 'High income countries, minimum 5M population.',\n caption = '\\n\\n\\n',\n x = '', y = '\\nReal GDP Growth\\nProjected Difference from Three Year Average') +\n geom_hline(aes(yintercept = median_econ_impact/100), colour = 'darkslategray', size = 0.75) +\n # geom_segment(aes(x = 5, xend = 20, y = .95 * min(diff_projection_avg/100) , yend = .95 * min(diff_projection_avg/100)), size = 1) + \n theme(\n plot.title = element_text(size = 28),\n plot.subtitle = element_text(size = 18, face = 'italic'),\n axis.title = element_text(size = 22),\n plot.caption = element_text(size = 14, face = 'italic', hjust = 0),\n axis.text = element_text(size = 20),\n legend.text = element_text(size = 14),\n legend.title = element_text(size = 16),\n legend.position = 'right', \n panel.grid.minor = element_blank()\n ) \n\n\ncombined_plot = plot_grid(mortality_rank_plot, gdp_rank_plot)\nsave_plot('mortality_growth_comparison_oecd.png', base_height = 15, base_width = 20, \n units = 'in', dpi = 600, plot = combined_plot)\n\n\n##### map european mortality rates ##### \n\n# date_seq = seq.Date(min(europe_map_data_daily$date, na.rm = T), max(europe_map_data_daily$date, na.rm = T), by = 7)\n\n# europe_map_data_daily = left_join(europe_cropped, covid_deaths_by_country_date_diffs, by = c('name' = 'country')) \n\n# animated_mortality_map = \n# ggplot(europe_map_data_daily) +\n# geom_sf(aes(fill = mortality_per_100k)) +\n# transition_time(date, range = as.Date(c('2020-02-01', '2020-08-01'))) +\n# scale_fill_viridis_c(name = 'Deaths Per\\n100k Pop.',option = 'A') +\n# theme_map() +\n# # theme_dark() +\n# # theme_minimal() +\n# # geom_sf_label(data = filter(europe_map_data, name %in% selected_countries), aes(label = paste0(name, '\\n', comma(mortality_per_100k, accuracy = 0.1))), size = 2.5) +\n# labs(\n# x = '', y = '', \n# title = 'COVID-19 Mortality Rates in Selected European Countries',\n# subtitle = sprintf('Data through {frame_time}'),\n# caption = 'Chart: Taylor G. White\\nData: Johns Hopkins CSSE, World Bank'\n# ) +\n# theme(\n# # axis.text = element_blank(),\n# plot.subtitle = element_text(face = 'italic'),\n# plot.caption = element_text(hjust = 0, face = 'italic')\n# )\n# \n# \n# # ?transition_reveal\n# \n# animate(animated_mortality_map, \n# nframes = 450,\n# renderer = gifski_renderer(\"europe_mortality_map.gif\"),\n# height = 8, width = 8, units = 'in', type = 'cairo-png', res = 200)\n\n##### analysis --- covid response and effectiveness #####\n\n\n# covid_deaths_by_country_date_diffs$country_factor = factor(covid_deaths_by_country_date_diffs$country, levels = covid_stats_by_country$country)\n# \n# filter(covid_deaths_by_country_date_diffs, country %in% head(covid_stats_by_country, 9)$country) %>%\n# ggplot(aes(date, roll_7_new_deaths_per_100k, fill = StringencyIndex)) +\n# geom_bar(stat = 'identity') +\n# facet_wrap(~country_factor, nrow=3) +\n# theme_bw() +\n# scale_y_continuous(limits = c(0, 3)) +\n# scale_fill_viridis_c(option = 'A', name = 'Stringency\\nIndex') + \n# theme(\n# strip.text = element_text(face = 'bold', size = 15),\n# axis.title = element_text(size = 14),\n# axis.text = element_text(size = 12),\n# plot.title = element_text(size = 16),\n# plot.caption = element_text(size = 11, face = 'italic', hjust = 0),\n# plot.subtitle = element_text(size = 11, face = 'italic'),\n# strip.background = element_rect(fill = 'white'),\n# panel.grid = element_blank(),\n# panel.background = element_rect(fill = 'black'),\n# legend.text = element_text(size = 11),\n# legend.title = element_text(size = 12)\n# ) +\n# labs(\n# x = '', y = '7 Day Average of Daily Mortality\\nPer 100,000 Population\\n',\n# # subtitle = 'The stringency index shows the \"strictness\" or degree of government response to the COVID pandemic. A higher value means a more significant response, not necessarily a better response.',\n# title = 'COVID Daily Mortality vs. Stringency of Government Response\\nTop OECD Countries by Mortality Rate, Minimum 5M Population',\n# caption = 'Chart: Taylor G. White\\nData: Johns Hopkins CSSE, Oxford OxCGRT'\n# )\n# ggsave('daily_mortality_vs_stringency.png', height = 10, width = 14, units = 'in', dpi = 600)\n# \n\n\n\n\n##### Stats by Region #####\n\nstacked_daily_stats_selected_regions = bind_rows(\n us_daily_stats = filter(covid_deaths_by_country_date_diffs, country %in% c('United States', 'Canada')) %>% select(region = country, roll_7_new_deaths_per_100k, date),\n stats_by_region %>% filter(region %in% c('Europe & Central Asia', 'Latin America & Caribbean')) %>% select(region, roll_7_new_deaths_per_100k = total_new_death_100k_roll_7, date)\n)\n\npeak_mortality_dates = group_by(stacked_daily_stats_selected_regions, region) %>%\n summarize(\n mean_mortality = mean(roll_7_new_deaths_per_100k, na.rm = T),\n peak_mortality = max(roll_7_new_deaths_per_100k, na.rm = T),\n peak_mortality_date = min(date[roll_7_new_deaths_per_100k == peak_mortality], na.rm = T)\n ) %>%\n arrange(-mean_mortality)\n\nstacked_daily_stats_selected_regions$region_factor = factor(stacked_daily_stats_selected_regions$region, levels = peak_mortality_dates$region)\n\nggplot(stacked_daily_stats_selected_regions, aes(date, roll_7_new_deaths_per_100k, colour = region_factor)) +\n geom_line(size = 1) + \n labs(\n y = '7 Day Average of Daily Mortality\\nPer 100k Population', \n x = '',\n caption = sprintf('Chart: Taylor G. White\\nData: Johns Hopkins CSSE\\nData through %s', max(stacked_daily_stats_selected_regions$date, na.rm = T) %>% format('%b %d')),\n title = 'COVID-19 Daily Mortality, by Region'\n ) +\n theme_bw() +\n # geom_point(data = peak_mortality_dates, aes(peak_mortality_date, peak_mortality, colour = region), size = 3.5, pch = 18) + \n theme(\n plot.title = element_text(size = 22),\n plot.subtitle = element_text(size = 14, face = 'italic'),\n plot.caption = element_text(hjust = 0, face = 'italic', size = 11),\n axis.text = element_text(size = 14),\n axis.title = element_text(size = 15),\n legend.text = element_text(size = 15),\n legend.title = element_text(size = 16),\n legend.position = 'bottom'\n ) +\n scale_color_brewer(name = '', palette = 'Set1') +\n # scale_colour_hue() +\n scale_x_date(date_breaks = '1 month', date_labels = '%b', limits = c(as.Date('2020-03-01'), max(stats_by_region$date))) +\n guides(colour = guide_legend(override.aes = list(size = 2.5)))\nggsave('average_daily_mortality_by_region.png', height= 10, width = 12, units = 'in', dpi = 600)\n\n\n\n##### growth models #####\n\nregion_growth_stats_by_country = lapply(unique(covid_stats_by_country$country), function(the_country){\n the_region = filter(covid_stats_by_country, country == the_country)$region\n stats_for_region_excl_country = filter(covid_stats_by_country, region == the_region, country != the_country)\n mean_proj_for_region = mean(stats_for_region_excl_country$projection_2020, na.rm = T)\n covid_deaths = sum(stats_for_region_excl_country$total_deaths, na.rm = T)\n population = sum(stats_for_region_excl_country$population, na.rm = T)\n region_deaths_per_100k = (covid_deaths / population) * 1e5\n data.frame(\n country = the_country, \n mean_proj_for_region = mean_proj_for_region,\n region_deaths_per_100k = region_deaths_per_100k\n )\n}) %>%\n bind_rows()\n\ncovid_stats_by_country = left_join(covid_stats_by_country, region_growth_stats_by_country) %>%\n mutate(\n \n )\n\noptions(na.action = na.exclude)\n# \n# simple_mod = lm(projection_2020 ~ log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# \n# simple_mod_region_deaths = lm(projection_2020 ~ log(mortality_per_100k_log) + log(region_deaths_per_100k) + last_year_growth, data = covid_stats_by_country)\n# simple_mod_region_proj = lm(projection_2020 ~ log(mortality_per_100k_log) + last_year_growth + mean_proj_for_region, data = covid_stats_by_country)\n# simple_mod_region = lm(projection_2020 ~ log(mortality_per_100k_log) + last_year_growth + region, data = covid_stats_by_country)\n# simple_mod_region_income = lm(projection_2020 ~ log(mortality_per_100k_log) + last_year_growth + region + income, data = covid_stats_by_country)\n# max_health_index = lm(projection_2020 ~ max_ContainmentHealthIndex + pop_pct_65_over + income + trade_pct_gdp + log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# max_stringency = lm(projection_2020 ~ max_stringency + pop_pct_65_over + income + trade_pct_gdp + log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# max_stringency_region_proj = lm(projection_2020 ~ max_stringency + pop_pct_65_over + income + trade_pct_gdp * mean_proj_for_region + log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# median_health_index = lm(projection_2020 ~ median_ContainmentHealthIndex + pop_pct_65_over + income + trade_pct_gdp + log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# median_stringency = lm(projection_2020 ~ median_stringency + pop_pct_65_over + income + trade_pct_gdp + log(mortality_per_100k_log) + last_year_growth, data = covid_stats_by_country)\n# \n\n\n##### Sweden analysis ##### \nnordics = filter(covid_deaths_by_country_date_diffs, country %in% nordic_countries)\nggplot(nordics, aes(date, roll_7_new_deaths_per_100k, fill = StringencyIndex)) +\n geom_bar(stat = 'identity') +\n geom_line(aes(), show.legend = F, size = 0.75) +\n facet_wrap(~country) +\n scale_fill_viridis_c(option = 'A', name = 'Stringency Index') + \n theme_bw() +\n labs(\n x = '', y = '7 Day Average of New Deaths\\nPer 100k Population'\n )\n\nggplot(nordics, aes(date, roll_7_new_deaths_per_100k, colour = country)) +\n geom_line(size = 0.75) +\n scale_color_brewer(palette = 'Set1', name = '') +\n theme_bw() +\n labs(\n x = '', y = '7 Day Average of New Deaths\\nPer 100k Population', \n title = 'COVID Daily Mortality'\n ) +\n scale_x_date(date_breaks = '1 month', date_labels = '%b') +\n theme(\n legend.position = 'bottom'\n ) +\n guides(\n colour = guide_legend(override.aes = list(size = 2.5))\n )\n\nggplot(nordics, aes(date, avg_7_mobility, colour = country)) +\n geom_line(size = 0.75) +\n # geom_point(aes(size = roll_7_new_deaths_per_100k)) +\n scale_color_brewer(palette = 'Set1', name = '') +\n theme_bw() +\n labs(\n x = '', y = '7 Day Average Mobility (Walking)', \n title = 'Mobility Analysis'\n ) +\n scale_x_date(date_breaks = '1 month', date_labels = '%b') +\n theme(\n legend.position = 'bottom'\n ) +\n guides(\n colour = guide_legend(override.aes = list(size = 2.5))\n )\n\n\nggplot(nordics, aes(date, roll_7_new_cases_per_100k, colour = country)) +\n geom_line(size = 0.75) +\n geom_point(aes(size = roll_7_new_deaths_per_100k)) +\n scale_color_brewer(palette = 'Set1', name = '') +\n theme_bw() +\n labs(\n x = '', y = '7 Day Average New Cases\\nPer 100k', \n title = 'Mobility Analysis'\n ) +\n scale_x_date(date_breaks = '1 month', date_labels = '%b') +\n theme(\n legend.position = 'bottom'\n ) +\n guides(\n colour = guide_legend(override.aes = list(size = 2.5))\n )\nnordics$roll_7_new_deaths_per_100k\n\n# sub = select(nordics %>% filter(country == 'United States'), roll_7_new_deaths_per_100k, roll_7_new_cases_per_100k) %>% na.omit()\n# ccf(sub$roll_7_new_cases_per_100k, sub$roll_7_new_deaths_per_100k)\n# sub$roll_7_new_deaths_per_100k\n# head(sub)\n# tail(sub)\n# ccf(x-variable name, y-variable name)\n\ncountries_with_covid_phases = map(unique(us_comparator_countries$country), function(the_country){\n # the_country = 'Sweden'\n country_sub = filter(covid_deaths_by_country_date_diffs, country == the_country) %>% \n select(country, roll_7_new_deaths_per_100k, date) %>% \n mutate(\n date_num = as.numeric(date - min(date, na.rm = T)) %>% log()\n ) %>% \n na.omit()\n max_mortality = max(country_sub$roll_7_new_deaths_per_100k)\n \n # country_sub$days_to_max_sq = as.numeric(with(country_sub, date - date[roll_7_new_deaths_per_100k == max_mortality]))^2\n \n the_dat = country_sub %>% \n select(date_num, roll_7_new_deaths_per_100k) %>% as.data.frame() %>% scale() %>% as.data.frame()\n \n \n # Ward Hierarchical Clustering\n \n # data(\"multishapes\")\n # df <- multishapes[, 1:2]\n # km.res <- kmeans(df, 5, nstart = 25)\n # fviz_cluster(km.res, df, frame = FALSE, geom = \"point\")\n # db <- fpc::dbscan(df, eps = 0.15, MinPts = 5)\n # plot(db, df, main = \"DBSCAN\", frame = FALSE)\n \n \n # http://www.sthda.com/english/wiki/wiki.php?id_contents=7940\n \n \n n_groups = 3\n \n d <- dist(the_dat, method = \"euclidean\") # distance matrix\n fit <- hclust(d, method=\"ward.D\")\n # plot(fit) # display dendogram\n groups <- cutree(fit, k=n_groups) # cut tree into 5 clusters\n # draw dendogram with red borders around the 5 clusters\n # rect.hclust(fit, k=3, border=\"red\")\n sweden_roll_7$groups = groups\n # ggplot(sweden_roll_7, aes(date, roll_7_new_deaths_per_100k, colour = factor(groups))) +\n # geom_point()\n \n km.res <- kmeans(the_dat, n_groups, nstart = 25)\n \n \n \n # dbscan::kNNdistplot(the_dat, k = 5)\n db = fpc::dbscan(the_dat, 0.4, MinPts = 5)\n \n the_dat$kmeans_cluster = km.res$cluster\n the_dat$hclust_cluster = groups\n the_dat$db_cluster = db$cluster\n \n fin_hclust = hclust(dist(the_dat, method = \"euclidean\"), method=\"ward.D\")\n fin_groups <- cutree(fin_hclust, k=n_groups)\n country_sub = mutate(country_sub,\n fin_groups = fin_groups,\n z_score = (roll_7_new_deaths_per_100k - mean(roll_7_new_deaths_per_100k)) / sd(roll_7_new_deaths_per_100k)\n )\n return(country_sub)\n}) %>%\n bind_rows()\n\nstats_by_phase = group_by(countries_with_covid_phases, country, fin_groups) %>%\n summarize(\n phase_time = as.numeric(max(date) - min(date)),\n mean_mortality = mean(roll_7_new_deaths_per_100k)\n ) %>%\n arrange(\n -mean_mortality\n )\n\ncountry_group_stats = group_by(stats_by_phase, country) %>%\n summarize(\n highest_mortality = max(mean_mortality),\n highest_mortality_phase = fin_groups[mean_mortality == highest_mortality],\n highest_mortality_phase_time = phase_time[mean_mortality == highest_mortality]\n ) %>% \n ungroup() %>%\n arrange(-highest_mortality)\n\n\nphase_2_peak_countries = filter(country_group_stats, highest_mortality_phase == 2)\nggplot(phase_2_peak_countries %>% head(12), aes(country, highest_mortality_phase_time)) +\n geom_bar(stat = 'identity') +\n coord_flip()\n\n# ggplot(stats_by_phase, aes(time, mean_mortality, shape = factor(fin_groups), colour = country)) + \n# geom_point()\n\nselected_countries = countries_with_covid_phases %>% \n filter(country %in% head(phase_2_peak_countries, 12)$country) \n\nggplot(selected_countries, aes(date, roll_7_new_deaths_per_100k)) +\n geom_point(aes(colour = factor(fin_groups))) + \n facet_wrap(~country, scales = 'free_y') \n\n\n##### nordics analysis #####\nnordics_daily_stats = filter(covid_deaths_by_country_date_diffs, country %in% nordic_countries) \n\nnordics_daily_stats$urban_pop_pct\n# output helper data \nnames(us_comparator_countries)\nus_comparator_countries$total_deaths\nnordic_table_data = filter(us_comparator_countries, country %in% nordic_countries) %>% \n select(country, three_year_avg_growth, projection_2020, diff_projection_avg, mortality_per_100k, \n population, median_stringency, max_stringency, gdp_per_capita_us, trade_pct_gdp, \n total_deaths, \n urban_pop_pct, \n pop_pct_65_over, international_tourism, case_1k_date) %>% as.data.frame() %>%\n arrange(-diff_projection_avg)\n\n\n\neconomic_table = select(nordic_table_data, \n `IMF Projected Growth, 2020` = projection_2020,\n `3-Year Avg. Growth` = three_year_avg_growth,\n `Diff. from Avg. Growth`= diff_projection_avg,\n `GDP Per Capita` = gdp_per_capita_us,\n `Annual Tourist\\nArrivals (M)` = international_tourism,\n `Trade/GDP` = trade_pct_gdp\n) %>%\n mutate(\n `3-Year Avg. Growth` = percent(`3-Year Avg. Growth`/100, accuracy = 0.1),\n `GDP Per Capita` = dollar(`GDP Per Capita`, accuracy = 1),\n `Diff. from Avg. Growth` = percent(`Diff. from Avg. Growth`/100, accuracy = 0.1),\n `IMF Projected Growth, 2020` = percent(`IMF Projected Growth, 2020`/ 100, accuracy = 0.1),\n `Annual Tourist\\nArrivals (M)` = round(`Annual Tourist\\nArrivals (M)`/1e6, 1),\n `Trade/GDP` = percent(`Trade/GDP`, accuracy = 1)\n )\n\nrow.names(economic_table) = nordic_table_data$country\n\ndemographics_table = select(nordic_table_data, \n \n `Population (M)` = population,\n `COVID Deaths` = total_deaths,\n `Mortality Per 100k` = mortality_per_100k, \n `Max. Stringency` = max_stringency,\n `Date Cases > 1k` = case_1k_date,\n `Urban Pop.` = urban_pop_pct,\n `Pop. Age 65+` = pop_pct_65_over\n \n) %>%\n mutate(\n `Population (M)` = round(`Population (M)`/1e6, 1),\n `COVID Deaths`= comma(`COVID Deaths`),\n `Mortality Per 100k` = round(`Mortality Per 100k`, 1),\n `Urban Pop.` = percent(`Urban Pop.`, accuracy = 0.1),\n `Pop. Age 65+` = percent(`Pop. Age 65+`, accuracy = 0.1),\n `Date Cases > 1k` = format(`Date Cases > 1k`, '%b-%d'),\n `Max. Stringency` = round(`Max. Stringency`, 1)\n )\n\nrow.names(demographics_table) = nordic_table_data$country\n\nlabel_data$peak_deaths_date\npeak_deaths_data = filter(covid_stats_by_country, country %in% nordic_countries)\nlabel_data = inner_join(nordics_daily_stats, peak_deaths_data, by = c('country', 'date' = 'peak_deaths_date'))\n\nmortality_plot = ggplot(nordics_daily_stats, aes(date, roll_7_new_deaths_per_100k, colour = country)) +\n theme_bw() +\n \n geom_line(size = 1, show.legend = F) +\n geom_label_repel(data = label_data, aes(date, roll_7_new_deaths_per_100k, label = country), \n show.legend = F, size = 4.5) +\n labs(\n y = '7 Day Average of Daily Mortality\\nPer 100k Population', \n x = '',\n # caption = 'Chart: Taylor G. White\\nData: Johns Hopkins CSSE',\n title = \"Comparing COVID Outcomes Across Nordic Countries\"\n ) +\n theme(\n plot.title = element_text(size = 28),\n plot.subtitle = element_text(hjust = 0, face = 'italic', size = 18),\n plot.caption = element_text(hjust = 0, face = 'italic', size = 13),\n axis.text = element_text(size = 16),\n axis.title = element_text(size = 18),\n legend.text = element_text(size = 17),\n legend.title = element_text(size = 17)\n ) +\n scale_colour_brewer(palette = 'Set1') +\n scale_x_date(date_breaks = '1 month', date_labels = '%b', limits = c(as.Date('2020-03-01'), max(stats_by_region$date) + 10)) \n\nggsave('nodic_mortality_comparison.png', height = 9, width = 12, units = 'in', dpi = 600, plot = mortality_plot)\n\neconomic_table_plot = ggplot(data.frame(x = 0:10, y = seq(0, 5, by = 0.5)), aes(x, y)) +\n geom_blank() +\n # theme_nothing() +\n annotation_custom(tableGrob(economic_table), xmin = 0, xmax = 10, ymin = 0.5, ymax = 2) +\n coord_cartesian(ylim = c(1, 1.5), clip = \"on\") +\n theme(\n plot.subtitle = element_text(size = 24),\n plot.caption = element_text(size = 14, face = 'italic', hjust = 0.5)\n ) \n\ntt2 = ttheme_default(\n core=list(bg_params = list(fill = c('white', 'lightgray')))\n)\n\ncombined_tables_plot = ggplot(data.frame(x = 0:10, y = 0:10), aes(x, y)) +\n geom_blank() +\n theme_nothing() +\n annotate('text', x=0, y = 10, label = 'Demographics and COVID Outcomes', size = 5, fontface = 'italic', hjust = 0) +\n annotate('text', x=0, y = 5, label = 'Economic Characteristics and Outcomes', size = 5, fontface = 'italic', hjust = 0) +\n annotation_custom(tableGrob(demographics_table, theme = tt2), xmin = 0, xmax = 10, ymin = 5.5, ymax = 9.5) +\n annotation_custom(tableGrob(economic_table, theme = tt2), xmin = 0, xmax = 10, ymin = 0, ymax = 4.5) +\n theme(\n plot.subtitle = element_text(size = 24),\n plot.caption = element_text(size = 12, face = 'italic', hjust = 0.5)\n ) +\n labs(\n caption = 'Chart: Taylor G. White\\nData: Johns Hopkins CSSE, IMF October Outlook, World Bank, Oxford Stringency Index'\n )\n\n\ncombined_mortality_comparison_plot = plot_grid(\n plotlist = list(mortality_plot, combined_tables_plot),\n nrow = 2,\n rel_heights = c(1, 0.85)\n)\n\n\nsave_plot('nordic_mortality_comparison_with_tables.png', base_height = 9, base_width = 12, dpi = 600, plot = combined_mortality_comparison_plot)\n\n\n\nnordic_table_data %>%\n write.csv('comparison_stats_nordics.csv', row.names = F)\n\n##### case correlations ####\n\nwide_high_income_cases = filter(covid_deaths_by_country_date_diffs, income == 'High income') %>%\n pivot_wider(\n names_from = 'country',\n id_cols = c('date'),\n values_from = c('roll_7_new_cases_per_100k')\n ) %>% na.omit()\n\ncor(wide_high_income_cases %>% select(-date) %>% as.matrix()) %>% write.csv('high_income_countries_case_correlation.csv')\n# \n# plot(wide_high_income_cases$Denmark, wide_high_income_cases$Germany)\n# plot(wide_high_income_cases$Denmark, wide_high_income_cases$Sweden)\n# ccf(wide_high_income_cases$Denmark, wide_high_income_cases$Sweden)\n# ccf(wide_high_income_cases$Sweden, wide_high_income_cases$Denmark)\nnames(wide_high_income_cases) = str_replace_all(names(wide_high_income_cases), ' ', '_')\n\nhigh_income_countries = names(wide_high_income_cases %>% select(-date))\ncountry_combinations = combn(high_income_countries, 2) %>% t()\n\n# for each country, run a granger causality test against all other countries. Record which countries are granger caused\n\ngranger_test_results = map(high_income_countries, function(the_country){\n the_country = 'United_Kingdom'\n other_countries = setdiff(high_income_countries, the_country)\n \n inner_results = map_dbl(other_countries, function(other_country){\n # other_country = other_countries[1]\n test_formula = paste(other_country, the_country, sep = ' ~ ') %>% as.formula()\n grangertest(test_formula, order = 7, data = wide_high_income_cases)$`Pr(>F)`[2] \n })\n tibble(\n y_country = other_countries, \n x_country = the_country, \n p_x_predicts_y = inner_results\n )\n \n}) %>%\n bind_rows()\n\ngranger_test_result_summary = group_by(granger_test_results, x_country) %>%\n summarize(\n n_countries_predicted = sum(p_x_predicts_y < 0.05)\n ) %>%\n arrange(-n_countries_predicted) %>%\n mutate(\n pct_predicted = n_countries_predicted / length(high_income_countries),\n x_country = str_replace_all(x_country, '_', ' ')\n )\n\n\nshell('explorer .')\n# a = ccf(wide_high_income_cases$Germany, wide_high_income_cases$Austria)\n# ccf(wide_high_income_cases$Austria, wide_high_income_cases$Germany)\n# ccf(wide_high_income_cases$Netherlands, wide_high_income_cases$France)\n# ccf(wide_high_income_cases$Netherlands, wide_high_income_cases$Austria)\n# ccf(wide_high_income_cases$Netherlands, wide_high_income_cases$Germany)\n# ccf(wide_high_income_cases$Netherlands, wide_high_income_cases$Slovak_Republic)\n# ccf(wide_high_income_cases$Netherlands, wide_high_income_cases$Slovenia)\n\n\neurope_cropped_granger = left_join(europe_cropped, granger_test_result_summary, by = c('name' = 'x_country'))\nggplot(europe_cropped_granger) +\n geom_sf(aes(fill = pct_predicted)) +\n scale_fill_viridis_c(option = 'C')\n\n", "meta": {"hexsha": "10b1939c4bbe715d1d70421ef8222832ea974c31", "size": 84205, "ext": "r", "lang": "R", "max_stars_repo_path": "Projects/COVID-19 Mismanagement/scripts/analyze_covid_economy_clean.r", "max_stars_repo_name": "vishalbelsare/Public_Policy", "max_stars_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-03-09T01:39:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-08T19:11:44.000Z", "max_issues_repo_path": "Projects/COVID-19 Mismanagement/scripts/analyze_covid_economy_clean.r", "max_issues_repo_name": "vishalbelsare/Public_Policy", "max_issues_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-06-03T20:11:43.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-07T00:03:58.000Z", "max_forks_repo_path": "Projects/COVID-19 Mismanagement/scripts/analyze_covid_economy_clean.r", "max_forks_repo_name": "vishalbelsare/Public_Policy", "max_forks_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-04T22:48:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-09T14:00:22.000Z", "avg_line_length": 41.6650173182, "max_line_length": 214, "alphanum_fraction": 0.7163113829, "num_tokens": 24193, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32714953320033724}} {"text": "# Generate changepoint2.csv file, using moving t-test (stars)\n\nlibrary(changepoint)\nlibrary(dplyr)\nlibrary(zoo)\nlibrary(bcp)\nlibrary(cpm)\n\nsource(\"stars.r\")\n\n## Wide regime, mortality on, count dead (normal)\nFD_wide=read.csv(\"E:/nm_scardata/e3_range_wide/FD_timeseries_TRUE.csv\")\n\n## narrow regime, mortality on, count dead (normal)\nFD_narrow=read.csv(\"E:/nm_scardata/e3_range_narrow/FD_timeseries_TRUE.csv\")\n\n\n#Upper 95% CI\nu_95=function(x){\n quantile(x,.95,na.rm=TRUE)\n}\n\nl_95=function(x){\n quantile(x,.05,na.rm=TRUE)\n}\n\n### Create a time-series, from a variable, of mean, sd\ncomb_ts=function(data,var,splityear){\n n_data=subset(data,select=c(\"ID\",\"year\",var),SPLIT_YEAR==splityear)\n names(n_data)=c(\"ID\",\"year\",\"var\")\n as.data.frame(n_data)\n}\n\n\n\n\ncd_ts=function(data1,inpf,tit,sy){\n ids=unique(data1$ID)\n for(i in seq_along(ids)){\n this_d = subset(data1,ID==ids[i])\n #plot(this_d$var ~ this_d$year)\n input = this_d$var\n names(input)=1:length(input)\n cd = stars(input,L=29)\n theseq = which.min(cd$starsResult[,3])\n #if(cd$changeDetected==TRUE){\n tdf=data.frame(file=inpf,sampler=tit,splityear=sy,changedetection=theseq)\n #}else{\n # tdf=data.frame(file=inpf,sampler=tit,splityear=sy,changedetection=-Inf)\n #}\n write.table(tdf,file=\"changepoint.csv\",append=TRUE,row.names=FALSE,col.names=FALSE,sep=\",\",eol=\"\\n\")\n }\n}\n\n\n\n## Do a given regime for FD\ndo_FD_chart=function(data,file,splityear,typeofd){\n base_ts=comb_ts(data,\"FD_RealSeries\",splityear)\n \n \n # Targeted \n this_ts=comb_ts(data,\"FD_T_10\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_10\",splityear)\n \n this_ts=comb_ts(data,\"FD_T_25\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_25\",splityear)\n \n this_ts=comb_ts(data,\"FD_T_50\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_50\",splityear)\n \n this_ts=comb_ts(data,\"FD_T_100\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_100\",splityear)\n \n this_ts=comb_ts(data,\"FD_T_250\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_250\",splityear)\n \n this_ts=comb_ts(data,\"FD_T_500\",splityear)\n cd_ts(this_ts,typeofd,\"FD_T_500\",splityear)\n \n this_ts=comb_ts(data,\"FD_CensusSeries\",splityear)\n cd_ts(this_ts,typeofd,\"FD_CensusSeries\",splityear)\n \n # Random \n this_ts=comb_ts(data,\"FD_R_10\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_10\",splityear)\n \n this_ts=comb_ts(data,\"FD_R_25\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_25\",splityear)\n \n this_ts=comb_ts(data,\"FD_R_50\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_50\",splityear)\n \n this_ts=comb_ts(data,\"FD_R_100\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_100\",splityear)\n \n this_ts=comb_ts(data,\"FD_R_250\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_250\",splityear)\n \n this_ts=comb_ts(data,\"FD_R_500\",splityear)\n cd_ts(this_ts,typeofd,\"FD_R_500\",splityear)\n \n}\n\nsplit_list=unique(FD_wide$SPLIT_YEAR) \ntemplate_frame= data.frame(file=\"Test\",sampler=\"Test\",splityear=0,changedetection=0)\nwrite.csv(template_frame,file=\"changepoint.csv\",row.names=FALSE)\n\n\n\n\nfor(j in seq_along(split_list)){\n \n this_split = split_list[j]\n print(this_split)\n # Mortality on, include dead\n do_FD_chart(FD_wide,file=paste0(\"figures/exp3b_FD_wide_\",this_split,\".pdf\"),this_split,\"Wide\")\n do_FD_chart(FD_narrow,file=paste0(\"figures/exp3b_FD_narrow_\",this_split,\".pdf\"),this_split,\"Narrow\")\n}\n\n", "meta": {"hexsha": "9cca99fe90171e2b09a27e25d8e5a96af94c04ab", "size": 3266, "ext": "r", "lang": "R", "max_stars_repo_path": "changepoint.r", "max_stars_repo_name": "ozjimbob/FireScar", "max_stars_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-03T05:25:44.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-03T05:47:55.000Z", "max_issues_repo_path": "changepoint.r", "max_issues_repo_name": "ozjimbob/FireScar", "max_issues_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "changepoint.r", "max_forks_repo_name": "ozjimbob/FireScar", "max_forks_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.9917355372, "max_line_length": 104, "alphanum_fraction": 0.7323943662, "num_tokens": 1098, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32714953320033724}} {"text": "library(GenomicRanges)\nlibrary(matrixStats)\n\nget_load <- function(filename) {\n message(\"Loading \", filename, \" ... \", appendLF=FALSE)\n o <- updateObject(get(load(filename)))\n message(\"OK\")\n o\n}\n\ncalc_total_signal_for_coverage_object <- function(cov.object) {\n sum(as.numeric(sapply(cov.object, function(x) sum(as.numeric(x), na.rm=TRUE))), na.rm=TRUE)\n}\n\ncheck_gene_list_for_proper_columns <- function(gene_list) {\n if(sum(c(\"chr\", \"start\", \"end\", \"strand\") %in% names(gene_list)) != 4) {\n stop(\"gene_list is missing required columns (chr, start, end, strand)\")\n }\n uniq_strand_values <- unique(gene_list$strand)\n if(!identical(uniq_strand_values, uniq_strand_values[uniq_strand_values %in% c(-1, 1)])) {\n stop(\"gene_list$strand must be 1 or -1. Values found: \", paste(sort(unique(gene_list$strand)), sep=\" \"))\n } \n}\n\nget_metagene_scaled <- function(sample.cov, gene_list, width, smooth=NULL, sample=NULL) {\n sample_object_name <- deparse(substitute(sample.cov))\n\n # update the provided object in case it was built with a previous version of IRanges\n sample.cov <- updateObject(sample.cov)\n \n message(\"Scaled metagene call for \", nrow(gene_list), \" genes (\", sample_object_name, \")\")\n \n check_gene_list_for_proper_columns(gene_list)\n \n # extract reads for each gene and scale to width\n reads <- matrix(nrow=nrow(gene_list), ncol=width)\n for(i in 1:nrow(gene_list)) {\n if(gene_list$strand[i] == 1) {\n read_start <- gene_list$start[i]\n read_stop <- gene_list$end[i]\n } else if (gene_list$strand[i] == -1) {\n read_start <- gene_list$end[i]\n read_stop <- gene_list$start[i]\n }\n \n chr_max <- length(sample.cov[[as.character(gene_list$chr[i])]])\n if(read_start <= 0 | read_stop <= 0 | read_start > chr_max | read_stop > chr_max) {\n message(\"Coordinates exceed chromosome limits: index \", i, \", \", gene_list$chr[i], \", skipping.\")\n next\n }\n this_gene <- as.numeric(sample.cov[[as.character(gene_list$chr[i])]][read_start:read_stop])\n if(length(which(is.na(this_gene))) > 0) {\n stop(\"NA values found: \", gene_list$chr[i], \" \", read_start, \"-\", read_stop)\n }\n if(!is.null(smooth)) {\n #message(\"Smoothing: \", i, \" (\", length(this_gene), \" values) \", appendLF=F)\n this_gene <- as.numeric(runmean(Rle(this_gene), smooth, \"constant\"))\n #message(\"done.\")\n }\n this_gene.scaled <- approx(this_gene, n=width)$y\n reads[i, ] <- this_gene.scaled\n }\n results <- data.frame(position=1:width, avg_reads=colMeans(reads))\n if(!is.null(sample)) { \n results$sample <- sample\n }\n results\n}\n\npad_with_nas <- function(x, target_length, reverse=FALSE) {\n if(reverse == TRUE) x <- rev(x)\n if(length(x) < target_length) {\n c(as.numeric(x), rep(NA, times=target_length - length(x)))\n } else {\n as.numeric(x)\n }\n}\n\nto_granges <- function(genelist) {\n GRanges(ranges = IRanges(start=genelist$metagene_start, end=genelist$metagene_stop), \n seqnames = genelist$chr)\n}\n\nget_read_matrix <- function(genelist, sample.cov, pad_length, reverse=FALSE) {\n reads.gr <- to_granges(genelist)\n reads.rl <- as(reads.gr, \"IntegerRangesList\")\n reads.view <- RleViewsList(rleList=sample.cov[names(reads.rl)], rangesList = reads.rl)\n reads <- viewApply(reads.view, function(x) pad_with_nas(x, pad_length, reverse=reverse))\n reads.m <- matrix(unlist(sapply(reads, sapply, as.numeric)), nrow=nrow(genelist), byrow=TRUE)\n reads.m\n}\n \nget_metagene_reads <- function(sample.cov, gene_list, \n before_tss=1000, after_tss=1000, \n smooth=NULL, sample=NULL, \n normalization_target=NULL,\n confidence_intervals=FALSE,\n return_read_matrix=FALSE) {\n\n sample_object_name <- deparse(substitute(sample.cov))\n\n # update the provided object in case it was built with a previous version of IRanges\n sample.cov <- updateObject(sample.cov)\n\n # total signal in sample\n sample.ts <- calc_total_signal_for_coverage_object(sample.cov)\n \n # normalization factor\n if(is.null(normalization_target)) {\n normalization.factor <- 1\n } else {\n normalization.factor <- normalization_target / sample.ts\n }\n\n message(\"Metagene call for \", nrow(gene_list), \" genes (\", sample_object_name, \")\")\n\n check_gene_list_for_proper_columns(gene_list)\n\n chr_lengths <- sapply(sample.cov, length)\n \n genes.p <- subset(gene_list, strand == 1)\n genes.n <- subset(gene_list, strand == -1)\n \n reads.p <- NULL\n reads.n <- NULL\n \n if(nrow(genes.p) > 0) {\n genes.p <- transform(genes.p, metagene_start = start - before_tss,\n metagene_stop = start + after_tss)\n original_count <- nrow(genes.p)\n genes.p <- subset(genes.p, metagene_start > 0 & metagene_stop <= chr_lengths[as.character(chr)])\n if(nrow(genes.p) < original_count) {\n message(\"Removing \", original_count - nrow(genes.p), \" gene(s) on positive strand due to chromosome boundary\")\n }\n reads.p <- get_read_matrix(genes.p, sample.cov, pad_length=before_tss + after_tss + 1, reverse=FALSE)\n }\n\n if(nrow(genes.n) > 0) {\n genes.n <- transform(genes.n, metagene_start = end - after_tss,\n metagene_stop = end + before_tss)\n original_count <- nrow(genes.n)\n genes.n <- subset(genes.n, metagene_start > 0 & metagene_stop <= chr_lengths[as.character(chr)])\n if(nrow(genes.n) < original_count) {\n message(\"Removing \", original_count - nrow(genes.n), \" gene(s) on negative strand due to chromosome boundary\")\n }\n reads.n <- get_read_matrix(genes.n, sample.cov, pad_length=before_tss + after_tss + 1, reverse=TRUE)\n }\n \n reads <- rbind(reads.p, reads.n)\n \n results <- data.frame(tss_distance=(-1*before_tss):after_tss, avg_reads=NA)\n results$avg_reads <- colMeans(reads, na.rm=T) * normalization.factor\n\n if(confidence_intervals) {\n errors <- qnorm(0.975) * colSds(reads, na.rm=T) / sqrt(nrow(reads))\n results$std_dev <- colSds(reads, na.rm=T)\n results <- transform(results, ci_upper = avg_reads + errors,\n ci_lower = avg_reads - errors)\n if(!is.null(smooth)) {\n results$ci_upper_smooth <- as.numeric(runmean(Rle(results$ci_upper), smooth, \"constant\"))\n results$ci_lower_smooth <- as.numeric(runmean(Rle(results$ci_lower), smooth, \"constant\"))\n }\n }\n \n if(!is.null(smooth)) results$smooth <- as.numeric(runmean(Rle(results$avg_reads), smooth, \"constant\"))\n if(!is.null(sample)) results$sample <- sample\n if(return_read_matrix) {\n list(results=results, read_matrix=reads)\n } else {\n results\n }\n}\n\nget_metagene_enrichment <- function(data_lst, gene_list, before_tss=200, after_tss=1500, \n smooth=NULL, sample=NULL, confidence_intervals=FALSE,\n return_read_matrix=FALSE) {\nip.cov<-data_lst[[\"ip\"]]\nbg.cov<-data_lst[[\"wce\"]]\n\n ip.cov <- updateObject(ip.cov)\n bg.cov <- updateObject(bg.cov)\n\n ip.sum <- calc_total_signal_for_coverage_object(ip.cov)\n bg.sum <- calc_total_signal_for_coverage_object(bg.cov)\n\n\n check_gene_list_for_proper_columns(gene_list)\n message(\"Metagene call\")\n \n chr_lengths <- sapply(ip.cov, length)\n \n genes.p <- subset(gene_list, strand == 1)\n genes.n <- subset(gene_list, strand == -1)\n\n reads.ip.p <- NULL\n reads.bg.p <- NULL\n message(\"Metagene call\")\n \n if(nrow(genes.p) > 0) {\n genes.p <- transform(genes.p, metagene_start = start - before_tss,\n metagene_stop = start + after_tss)\n original_count <- nrow(genes.p)\n genes.p <- subset(genes.p, metagene_start > 0 & metagene_stop <= chr_lengths[as.character(chr)])\n if(nrow(genes.p) < original_count) {\n message(\"Removing \", original_count - nrow(genes.p), \" gene(s) on positive strand due to chromosome boundary\")\n }\n reads.ip.p <- get_read_matrix(genes.p, ip.cov, pad_length=before_tss + after_tss + 1, reverse=FALSE)\n reads.bg.p <- get_read_matrix(genes.p, bg.cov, pad_length=before_tss + after_tss + 1, reverse=FALSE)\n }\n\n reads.ip.n <- NULL\n reads.bg.n <- NULL\n\n if(nrow(genes.n) > 0) {\n genes.n <- transform(genes.n, metagene_start = end - after_tss,\n metagene_stop = end + before_tss)\n original_count <- nrow(genes.n)\n genes.n <- subset(genes.n, metagene_start > 0 & metagene_stop <= chr_lengths[as.character(chr)])\n if(nrow(genes.n) < original_count) {\n message(\"Removing \", original_count - nrow(genes.n), \" gene(s) on negative strand due to chromosome boundary\")\n }\n reads.ip.n <- get_read_matrix(genes.n, ip.cov, pad_length=before_tss + after_tss + 1, reverse=TRUE)\n reads.bg.n <- get_read_matrix(genes.n, bg.cov, pad_length=before_tss + after_tss + 1, reverse=TRUE)\n }\n \n reads.ip <- rbind(reads.ip.p, reads.ip.n)\n reads.bg <- rbind(reads.bg.p, reads.bg.n)\n\n reads.e <- (reads.ip / ip.sum) / (reads.bg / bg.sum)\n reads.e[!is.finite(reads.e)] <- NA\n\n results <- data.frame(tss_distance=(-1*before_tss):after_tss, avg_reads_ip=NA, avg_reads_bg=NA)\n\n results$avg_reads_ip <- colMeans(reads.ip, na.rm=T) / ip.sum\n results$avg_reads_bg <- colMeans(reads.bg, na.rm=T) / bg.sum\n\n results$enrichment <- results$avg_reads_ip / results$avg_reads_bg\n #results$enrichment <- colMeans(reads.e, na.rm=T)\n \n if(confidence_intervals) {\n errors <- qnorm(0.975) * colSds(reads.e, na.rm=T) / sqrt(nrow(reads.e))\n results <- transform(results, ci_upper = enrichment + errors,\n ci_lower = enrichment - errors)\n if(!is.null(smooth)) results$ci_upper_smooth <- as.numeric(runmean(Rle(results$ci_upper), smooth, \"constant\"))\n if(!is.null(smooth)) results$ci_lower_smooth <- as.numeric(runmean(Rle(results$ci_lower), smooth, \"constant\"))\n }\n\n if(!is.null(smooth)) results$smooth <- as.numeric(runmean(Rle(results$enrichment), smooth, \"constant\"))\n if(!is.null(sample)) results$sample <- sample\n\n if(return_read_matrix) {\n list(results=results, read_matrix=reads.e)\n } else {\n results\n }\n}\n\n\n", "meta": {"hexsha": "c7a524521f17040af0c81f373e2b7572dfb9f489", "size": 10123, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/shared/metagene_common.r", "max_stars_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_stars_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/shared/metagene_common.r", "max_issues_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_issues_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/shared/metagene_common.r", "max_forks_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_forks_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.0849420849, "max_line_length": 116, "alphanum_fraction": 0.6556356811, "num_tokens": 2808, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.32714953320033724}} {"text": "# /* !/usr/bin/Rscript */\n\n#' ## Data munging\n#' Pre-process and explore datasets in preparation for feature creation.\n#'\n#'
\n#'
survival
\n#'
Survival (0 = No; 1 = Yes)
\n#'
pclass
\n#'
Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
\n#'
name
\n#'
Name (Title-Firstnames-Surname)
\n#'
sex
\n#'
Sex (Male/Female)\n#'
age
\n#'
Age (in years, float)\n#'
sibsp
\n#'
Number of Siblings/Spouses aboard
\n#'
parch
\n#'
Number of Parents/Children aboard
\n#'
ticket
\n#'
Ticket Number (alphanumeric)
\n#'
fare
\n#'
Passenger Fare (in US$)
\n#'
cabin
\n#'
Cabin (alphanumeric)
\n#'
embarked
\n#'
Port of Embarkation (C=Cherbourg; Q=Queenstown; S=Southampton)
\n#'
\n#'\n#' #### Family Relation Details\n#' With respect to the family relation variables (i.e. *Sibsp* and\n#' *Parch*) some relations were ignored. The following are the\n#' definitions used for sibsp and parch (aboard Titanic):\n#' - Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger\n#' - Spouse: Husband or Wife of Passenger (Mistresses and Fiances\n#' ignored)\n#' - Parent: Mother or Father of Passenger\n#' - Child: Son, Daughter, Stepson, or Stepdaughter of Passenger\n#'\n#' Other family relatives excluded from this study include cousins,\n#' nephews/nieces, aunts/uncles, and in-laws. Some children travelled\n#' only with a nanny, therefore `parch=0` for them. As well, some\n#' travelled with very close friends or neighbors in a village, however,\n#' the definitions do not support such relations.\n\n# /*\nwriteLines(\"\\n-------------\")\nwriteLines(\"Combining datasets, cleaning & imputing for exploration\")\n# */\n\n#' ### Combine raw datasets\n#' The data provided by Kaggle is pre-split into \"Training\" and \"Test\" sets,\n#' the former possessing the \"ground truth\" (or real outcome), the latter\n#' requiring prediction.\n#'\n#' In order to obtain the clearest possible picture of the date, as well as\n#' clean, impute missing data, and create new features, it is necessary to\n#' combine the two sets into one. The two sets will be re-created based on\n#' record ID before the modelling phase of the analysis.\n#'\n#' First a `Survived` column is added to the \"Test\" set (since it was missing\n#' and is required to match the columns in the \"Training\" set), and then the\n#' two combined:\ntest$Survived <- NA\ncombi <- rbind(raw, test)\n\n#' ### Inspection\n#' First assert the viability of creating a predictor for the response\n#' variable by examining the proportions of each level. Prevailing wisdom\n#' holds that if any proportion < 15% (0.15) then it might be classifed\n#' \"rare\", in which case it is much more difficult to model.\nprop.table(table(combi$Survived))\n\n#' Next use str to obtain a quick overview of the combined dataset dimensions\n#' and features (auto-created onRead).\nstr(combi)\n\n#' Looks like *Name* and *Cabin* should be of type `character`, not `factor`:\ncombi$Name <- as.character(combi$Name)\ncombi$Cabin <- as.character(combi$Cabin)\n\n#' Determine missing values:\n#' - is.na tests for any missing vals\n#' - missmap in Amelia package visualizes this\nsum(is.na(combi))\n\nmissmap(combi,\n main=\"Titanic Training Data - Missings Map\",\n col=c(\"yellow\", \"black\"),\n legend=FALSE\n)\n\n#' *Age*, *Cabin*, *Fare* and *Embarkation* have missing data points. Impute\n#' *Embarkation* data using most common value. The others require the\n#' creation of new synthetic features for optimal imputation.\nsummary(combi$Embarked)\ncombi$Embarked[which(combi$Embarked == \"\")] <- \"S\"\n\n#' Before engineering new features, convert and re-level `numeric` classes to\n#' factors, and rename *Survived* => *Fate* (as \"Survived\" is one of the\n#' levels). This aids understanding of confusion matrices, visualizations,\n#' and summary output.\ncombi$Class <- as.factor(combi$Pclass)\nlevels(combi$Class) <- c(\"Upper\", \"Middle\", \"Lower\")\ncombi$Fate <- as.factor(combi$Survived)\nlevels(combi$Fate) <- c(\"Perished\", \"Survived\")\n\n# /*\nwriteLines(\"\\n-------------\")\nwriteLines(\"Munging DONE\")\n# */\n\n", "meta": {"hexsha": "f98635b7e8f5c98a958473ca940be179ed00edec", "size": 4110, "ext": "r", "lang": "R", "max_stars_repo_path": "src/clean.r", "max_stars_repo_name": "andybeeching/kaggle-titanic", "max_stars_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-07-13T14:20:50.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-13T14:20:50.000Z", "max_issues_repo_path": "src/clean.r", "max_issues_repo_name": "andybeeching/kaggle-titanic", "max_issues_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/clean.r", "max_forks_repo_name": "andybeeching/kaggle-titanic", "max_forks_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0526315789, "max_line_length": 77, "alphanum_fraction": 0.6941605839, "num_tokens": 1172, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.32714953320033724}} {"text": "#'\n#' @importFrom R6 R6Class\n#'\n#'\nRCTtoolbox <- R6::R6Class(\"RCTtoolbox\",\n public = list(\n data = NULL,\n initialize = function(formula_yd = NULL,\n formula_x = NULL,\n yvec = NULL,\n xvec = NULL,\n dvec = NULL,\n dvec_levels = NULL,\n dvec_labels = NULL,\n data = NULL) {\n private$formula.yd <- formula_yd\n private$formula.x <- formula_x\n private$yvec <- yvec\n private$xvec <- xvec\n private$dvec <- dvec\n private$dvec.levels <- dvec_levels\n private$dvec.labels <- dvec_labels\n self$data <- data\n },\n print = function(...) {\n cat(\"-- Activate Information ---------------------\\n\")\n cat(\"Create new toolbox (Class: RCTtoolbox)\\n\")\n cat(\"- Outcomes:\", private$yvec, \"\\n\")\n cat(\"- Treatment:\", private$dvec, \"\\n\")\n cat(\" - Level:\", private$dvec.levels)\n cat(\" (Control arm:\", private$dvec.levels[1], \")\\n\")\n cat(\" - Label:\", private$dvec.labels, \"\\n\")\n cat(\"- Covariates: \", private$xvec, \"\\n\")\n cat(\"-- Fields and Methods -----------------------\\n\")\n cat(\"- data: Store data\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- ttest(): Run t-test/permutation test\\n\")\n cat(\"- power(): Run power analysis\\n\")\n cat(\"- balance(): Run balance test\\n\")\n cat(\"- lm(): Estimate linear model\\n\")\n cat(\"- chi2test(): Run chi-square/fisher exact test\\n\")\n },\n ttest = function(...) {\n RCTtoolbox.ttest$new(\n private$formula.yd,\n self$data,\n private$dvec.levels,\n private$dvec.labels,\n ...\n )\n },\n power = function(...) {\n RCTtoolbox.power.analysis$new(\n private$dvec,\n self$data,\n private$dvec.levels,\n private$dvec.labels,\n ...\n )\n },\n balance = function(...) {\n RCTtoolbox.balance.test$new(\n private$xvec,\n private$dvec,\n self$data,\n private$dvec.levels,\n private$dvec.labels,\n ...\n )\n },\n lm = function(...) {\n RCTtoolbox.lm$new(\n private$formula.yd,\n private$formula.x,\n self$data,\n private$dvec,\n private$dvec.levels,\n private$dvec.labels,\n ...\n )\n },\n chi2test = function(...) {\n RCTtoolbox.chi2test$new(\n private$formula.yd,\n self$data,\n private$dvec.levels,\n private$dvec.labels,\n ...\n )\n }\n ),\n private = list(\n formula.yd = NULL,\n formula.x = NULL,\n yvec = NULL,\n xvec = NULL,\n dvec = NULL,\n dvec.levels = NULL,\n dvec.labels = NULL\n )\n)\n\nRCTtoolbox.ttest <- R6::R6Class(\"RCTtoolbox.ttest\",\n public = list(\n result = NULL,\n initialize = function(baseline, data, levels, labels, ...) {\n self$result <- ttest_multi_mod_arm(\n baseline, data, levels, labels, ...\n )\n },\n print = function(...) {\n cat(\"-- Activate Information ----------------\\n\")\n cat(\"Run t-test (Class: RCTtoolbox.ttest) \\n\")\n cat(\"-- Fields and Methods ------------------\\n\")\n cat(\"- result: Store estimated result\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- plot(): Visualization\\n\")\n cat(\"- summary(): Print result in console\\n\")\n },\n plot = function(...) rctplot(self, ...),\n summary = function(...) summary(self, ...)\n )\n)\n\nRCTtoolbox.power.analysis <- R6::R6Class(\"RCTtoolbox.power.analysis\",\n public = list(\n result = NULL,\n initialize = function(treat, data, levels, labels, ...) {\n self$result <- power_calculation(\n treat, data, levels, labels, ...\n )\n },\n print = function(...) {\n cat(\"-- Activate Information ------------------------------\\n\")\n cat(\"Run Power Analysis (Class: RCTtoolbox.power.analysis) \\n\")\n cat(\"-- Fields and Methods --------------------------------\\n\")\n cat(\"- result: Store estimated result\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- summary(): Print result in console\\n\")\n cat(\"- table(): Create output table\\n\")\n },\n summary = function(...) summary(self, ...),\n table = function(...) rcttable(self, ...)\n )\n)\n\nRCTtoolbox.balance.test <- R6::R6Class(\"RCTtoolbox.balance.test\",\n public = list(\n result = NULL,\n initialize = function(x, d, data, levels, labels, ...) {\n self$result <- balance_test_multi_var(\n x, d, data, levels, labels, ...\n )\n },\n print = function(...) {\n cat(\"-- Activate Information ------------------------------\\n\")\n cat(\"Run Balance Test (Class: RCTtoolbox.balance.test) \\n\")\n cat(\"-- Fields and Methods --------------------------------\\n\")\n cat(\"- result: Store estimated result\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- table(): Create output table\\n\")\n },\n table = function(...) rcttable(self, ...)\n )\n)\n\nRCTtoolbox.lm <- R6::R6Class(\"RCTtoolbox.lm\",\n public = list(\n result = NULL,\n initialize = function(yd, x, data, dvec, levels, labels, ...) {\n self$result <- rct_lm(\n yd, x, data, levels, labels, ...\n )\n private$dvar <- dvec\n },\n print = function(...) {\n cat(\"-- Activate Information ------------------------------\\n\")\n cat(\"Estimate Linear Model (Class: RCTtoolbox.lm) \\n\")\n cat(\"-- Fields and Methods --------------------------------\\n\")\n cat(\"- result: Store estimated result\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- summary(): Print result in console\\n\")\n cat(\"- table(): Create output table\\n\")\n },\n summary = function(...) summary(self, ...),\n table = function(...) rcttable(self, private$dvar, ...)\n ),\n private = list(dvar = NULL)\n)\n\nRCTtoolbox.chi2test <- R6::R6Class(\"RCTtoolbox.chi2test\",\n public = list(\n result = NULL,\n initialize = function(yd, data, levels, labels, ...) {\n self$result <- chi2test_multi_mod(\n yd, data, levels, labels, ...\n )\n private$labels <- labels\n },\n print = function(...) {\n cat(\"-- Activate Information ------------------------------\\n\")\n cat(\"Estimate Linear Model (Class: RCTtoolbox.chi2test) \\n\")\n cat(\"-- Fields and Methods --------------------------------\\n\")\n cat(\"- result: Store estimated result\\n\")\n cat(\"- print(): Show this message\\n\")\n cat(\"- table(): Create output table\\n\")\n },\n table = function(...) rcttable(self, private$labels, ...)\n ),\n private = list(labels = NULL)\n)", "meta": {"hexsha": "c7b32e84c57e7c6e359edbc1eba54860e13d411f", "size": 6567, "ext": "r", "lang": "R", "max_stars_repo_path": "R/R6-class.r", "max_stars_repo_name": "KatoPachi/multiarmRCT", "max_stars_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/R6-class.r", "max_issues_repo_name": "KatoPachi/multiarmRCT", "max_issues_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/R6-class.r", "max_forks_repo_name": "KatoPachi/multiarmRCT", "max_forks_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1232227488, "max_line_length": 69, "alphanum_fraction": 0.5133241967, "num_tokens": 1560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.3271495256224288}} {"text": "\n\n\nGetshiftsperrotation<- function() {\n g<- seq(from = 50, to = 480, by = 12)\n h<- seq(from = 61, to = 481, by = 12)\n h[36]<- h[36]-1\n rotation<- c()\n localizations<- c()\n VP_Data<- getreachesformodel(variation_localization)\n \n for (i in 1:length(g)) {\n \n localizations[i]<- mean(VP_Data$meanreaches[g[i]:h[i]], na.rm = TRUE)\n rotation[i]<- variation_reaches$distortion[g[i]]\n }\n \n return(variation_prop<- data.frame(rotation, localizations))\n}\n\n\n\nGetreachesperrotation<- function() {\n g<- seq(from = 50, to = 480, by = 12)\n h<- seq(from = 61, to = 481, by = 12)\n h[36]<- h[36]-1\n rotation<- c()\n stuff<- c()\n VR_Data<- getreachesformodel(variation_reaches)\n \n for (i in 1:length(g)) {\n \n stuff[i]<- mean(VR_Data$meanreaches[g[i]:h[i]], na.rm = TRUE)\n rotation[i]<- variation_reaches$distortion[g[i]]\n }\n \n return(variation_reach<- data.frame(rotation, stuff))\n}\n\n\nplotvariation<- function (){\n vprop<- Getshiftsperrotation()\n vreac<- Getreachesperrotation()\n localizations<-vprop$localizations\n Variation_means<- cbind(vreac,localizations)\n \n \n g<- seq(from = 50, to = 480, by = 12)\n h<- seq(from = 61, to = 481, by = 12)\n h[36]<- h[36]-2\n \n z<-c(0,50)\n for (i in 1:36) {\n \n z<- c(z, g[i], h[i]+1)\n }\n \n \n sizes<- c(0,0)\n \n for (i in 1:36){\n \n sizes<- c(sizes,Variation_means$rotation[i],Variation_means$rotation[i]) \n sizes[sizes == 360] <- NA\n }\n g<- seq(from = 50, to = 480, by = 12)\n g<- c(1,g,480)\n for (i in 1:length(sizes)){\n \n \n if (is.na(sizes[i])){\n sizes[i]<- 0\n }\n \n }\n plot(NULL, col = 'white', axes = F,cex.lab = 1.5,\n cex.main = 1.5, xlab = \"Trial\",\n ylab = \"Hand Location [\u00b0]\", ylim = c(-30, 30), xlim = c(1,480))\n \n lines(x = z[1:25], y = sizes[1:25], type = 'l')\n lines(x = z[25:26], y = c(0,0), lty = 2)\n lines(x = z[26:33], y = sizes[26:33], type = 'l')\n lines(x = z[33:36], y = c(0,0,0,0), lty = 2)\n lines(x = z[36:51], y = sizes[36:51], type = 'l')\n lines(x = z[51:52], y = c(0,0), lty = 2)\n lines(x = z[52:61], y = sizes[52:61], type = 'l')\n lines(x = z[61:62], y = c(0,0), lty = 2)\n lines(x = z[62:71], y = sizes[62:71], type = 'l')\n lines(x = z[71:72], y = c(0,0), lty = 2)\n lines(x = z[73:74], y = sizes[73:74], type = 'l')\n \n legend(\n -5,\n 30,\n legend = c(\n 'Reaches',\n 'Localizations'),\n col = c('blue', 'red'),\n lty = c(1),\n lwd = c(2),\n bty = 'n', \n cex = 1.2\n )\n axis(2, at = c(-30, -15, 0, 15, 30), cex.axis = 1.5,\n las = 2)\n axis(1, at = g, cex.axis = .75, las = 2)\n reachdata<- getreachesformodel(variation_reaches)\n lines(reachdata$meanreaches*-1, type = 'l', col = 'Blue')\n locdata<- getreachesformodel(variation_localization)\n lines(locdata$meanreaches, type = 'l', col = 'red')\n dataCIs <- trialCI(data = variation_localization)\n dataCIs <- dataCIs\n x <- c(c(1:480), rev(c(1:480)))\n y <- c(dataCIs[, 1], rev(dataCIs[, 2]))\n polygon(x, y, col = rgb(1,0,0,.2), border = NA)\n \n dataCIs <- trialCI(data = variation_reaches)\n dataCIs <- dataCIs*-1\n x <- c(c(1:480), rev(c(1:480)))\n y <- c(dataCIs[, 1], rev(dataCIs[, 2]))\n polygon(x, y, col = rgb(0,0,1,.2), border = NA)\n \n}\n\n\n\n\n", "meta": {"hexsha": "7afb76e3ed6f2514eef029d5854269f4d2d8586c", "size": 3202, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/variationmeans.r", "max_stars_repo_name": "JennR1990/Demographics", "max_stars_repo_head_hexsha": "1696819abf2a60f61cebbaeba98cf2a02aa43ae4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/variationmeans.r", "max_issues_repo_name": "JennR1990/Demographics", "max_issues_repo_head_hexsha": "1696819abf2a60f61cebbaeba98cf2a02aa43ae4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/variationmeans.r", "max_forks_repo_name": "JennR1990/Demographics", "max_forks_repo_head_hexsha": "1696819abf2a60f61cebbaeba98cf2a02aa43ae4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.015625, "max_line_length": 77, "alphanum_fraction": 0.5505933791, "num_tokens": 1258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324418, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32714952562242877}} {"text": "a <- sum(x)/length(x)\n", "meta": {"hexsha": "59099693601250b57be219e0f69c6fde03cdae6f", "size": 22, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 11.0, "max_line_length": 21, "alphanum_fraction": 0.5454545455, "num_tokens": 8, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792046, "lm_q2_score": 0.6442251064863697, "lm_q1q2_score": 0.32714515234031244}} {"text": "remove(list=ls())\nlibrary(plyr)\n\n\n#assign and display starting values and call parameter-estimation function\n# encoding-factor ; encoding-exponent ; preptime\n\n## three params:\nstartParms <- c(0.004, 0.004, 0.0130)\ntransvect <- c(1, 100, 10)\nlower <- c(0.002, 0.003, 0.0110)\nupper <- c(0.005, 0.005, 0.0135)\n\n## two params:\n#startParms <- c(0.003, 0.0125)\n#transvect <- c(1, 10)\n#lower <- c(0.001, 0.0100)\n#upper <- c(0.006, 0.0135)\n\n\nwrite.table(rbind(c(0,NA,startParms*transvect)), \"output/estimation-r.txt\", col.names=c(\"cycle\", \"score\", \"enc.fact\", \"enc.exp\", \"preptime\"))\n\n\nbestvalues <- list()\nbestscore <- 1000\nrun <- 1\nl <- 0\n#################################\ngetpred <-function(parms) {\n\tl <<- l+1\n\tprint(paste(\"Cycle: \",l))\n\tparms <- parms * transvect\n\tprint(parms)\n\tparms1 <- parms\n#\tparms <- c(parms[1], 0.4, parms[2]) # insert non-estimated parameter\n result <- run.paramlist(parms)\n\trscore <- ifelse(result!=\"NA\", analyze(), NA)\n\twrite.table(rbind(c(l,rscore,parms1)), \"output/estimation-r.txt\", append=T, col.names=FALSE)\n\tif(rscore listener\",sep=\"\")\n system(cm)\n # wait for lisp to send result\n result <- system(\"cat listener\", intern=T)\n if(result == \"STOP\") {run <<- 0; stop(\"Aborted from LISP side\")}\n as.numeric(result)\n}\n\nanalyze <- function() { \n source(\"1-analyze.R\")\n source(\"2-f-statistics.R\")\n load(\"data/fstat.RData\")\n\n ## penalize range difference and variance\n scores <- as.vector(1*c(fstat1.rmsd,fstat2.rmsd) + 1*(1 - c(fstat1.cor,fstat2.cor)) + 0.01*fstat.rangediff)\n pscore <- mean(scores, na.rm=T) #+ var(scores, na.rm=T) + 0.1*fstat.zeros\n# print(pscore)\n return(pscore)\n# return(mean(c(fstat1.rmsd,fstat2.rmsd),na.rm=T))\n}\n\n\n#################################\n\n#xout <- optim(startParms, getpred, gr=NULL, method=\"Nelder-Mead\", control=list(maxit=100,trace=1)) #to be equivalent to fminsearch\n\n#xout <- optim(startParms, getpred, gr=NULL, method=\"Nelder-Mead\", control=list(maxit=100,trace=1,reltol=2e-3)) #to be equivalent to fminsearch\n\nxout <- optim(startParms, getpred, gr=NULL, method=\"L-BFGS-B\", lower=lower, upper=upper, control=list(maxit=300,trace=2,factr=1e3))\n\n#xout <- optim(startParms, getpred, gr=NULL, method=\"SANN\", lower=lower, upper=upper, control=list(maxit=100, trace=2, factr=1e3, reltol=0.01, abstol=0.1))\n\n\n# send stop command to lisp\nif(run==1) system(\"echo STOP > listener\")\n\nprint(bestvalues)\nprint(bestscore)\nwrite.table(rbind(c(0,bestscore,bestvalues)), \"output/estimation-r.txt\", append=T, col.names=FALSE)\n", "meta": {"hexsha": "62995fbfd5e808ec2e8bc91b5058d6d9e507870f", "size": 2743, "ext": "r", "lang": "R", "max_stars_repo_path": "chapters4to6_simulations/chapter_5/data/model-src/estimate.r", "max_stars_repo_name": "vasishth/RetrievalModels", "max_stars_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-05T15:49:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-05T15:49:49.000Z", "max_issues_repo_path": "chapters4to6_simulations/chapter_5/data/model-src/estimate.r", "max_issues_repo_name": "vasishth/RetrievalModels", "max_issues_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-06-08T10:53:53.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-08T11:14:40.000Z", "max_forks_repo_path": "chapters4to6_simulations/chapter_5/data/model-src/estimate.r", "max_forks_repo_name": "vasishth/RetrievalModels", "max_forks_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1704545455, "max_line_length": 155, "alphanum_fraction": 0.6598614655, "num_tokens": 906, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3271451523403124}} {"text": "add.alpha <- function(col, alpha=1){\n if(missing(col))\n stop(\"Please provide a vector of colours.\")\n apply(sapply(col, col2rgb)/255, 2, \n function(x) \n rgb(x[1], x[2], x[3], alpha=alpha)) \n}\n\nallMetric <- function(ls, legend = 1) {\n simreps = ncol(ls[[1]][[1]]$covout)\n \n cols <- c(\"darkgreen\",\"red\", \"purple\", \"darkorange\", \"cyan\", \"darkgrey\", \"black\")\n frac <- log(ls[[1]][[1]]$r * 150000, base = 2)\n xlim <- c(min(frac), max(frac))\n mets <- 4\n \n ### Power\n \n plot(1, type=\"n\", xaxt = 'n', ylab = \"Power\", xlab = \"Compounds Tested\",\n ylim = c(0,1), xlim = xlim)\n ticks <- c(1, 2, log(10, base = 2), seq(6, 8, by = 2), log(1500, base = 2), log(15000, base = 2))\n axis(1, at = ticks, labels = 2^ticks)\n \n for(i in 1:mets) {\n covprobs <- apply(ls[[1]][[i]]$covout, MARGIN=1, mean)\n \n # col.i <- ifelse(j == 2 & i == 4, cols[i+1], cols[i])\n lines(frac, covprobs, type=\"l\", col = cols[i], lty = 1, lwd = 1.25)\n se <- sqrt(covprobs*(1-covprobs)/simreps)\n ucl <- covprobs + 1.96 * se\n lcl <- covprobs - 1.96 * se\n polygon(c(frac, rev(frac)), c(ucl, rev(lcl)),\n col = add.alpha(cols[i], .25), border = NA)\n }\n \n \n if(legend){\n legend(x= 0.5, y = 1.05, legend= c(\"EmpProc\", \"CorrBinom\", \"JZ Ind\", \"McNemar\"),\n col=cols[1:4], lty=rep(1, 5), cex=.75, bty = \"n\", lwd = 2)\n }\n}", "meta": {"hexsha": "e503e4798b97e1d87a46c1bb0c0e2a134dfc29f0", "size": 1361, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation_files/power_figures/bibeta/compare_power.r", "max_stars_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_stars_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation_files/power_figures/bibeta/compare_power.r", "max_issues_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_issues_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation_files/power_figures/bibeta/compare_power.r", "max_forks_repo_name": "jrash/Enrichment-Inference-Supplemental-Materials", "max_forks_repo_head_hexsha": "0f0191cfcc5bf4a80cc39a593e912a59e5970cbb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1951219512, "max_line_length": 99, "alphanum_fraction": 0.5275532697, "num_tokens": 543, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3271451523403124}} {"text": "library(rstan)\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(gdata)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(EnvStats)\nlibrary(optparse)\nlibrary(stringr)\nlibrary(bayesplot)\nlibrary(matrixStats)\nlibrary(scales)\nlibrary(gridExtra)\nlibrary(ggpubr)\nlibrary(cowplot)\nlibrary(ggplot2)\nlibrary(abind)\n\nsource('nature/utils/process-covariates.r')\n\n# Commandline options and parsing\nparser <- OptionParser()\nparser <- add_option(parser, c(\"-D\", \"--debug\"), action=\"store_true\",\n help=\"Perform a debug run of the model\")\nparser <- add_option(parser, c(\"-F\", \"--full\"), action=\"store_true\",\n help=\"Perform a full run of the model\")\ncmdoptions <- parse_args(parser, args = commandArgs(trailingOnly = TRUE), positional_arguments = TRUE)\n\n# Default run parameters for the model\nif(is.null(cmdoptions$options$debug)) {\n DEBUG = Sys.getenv(\"DEBUG\") == \"TRUE\"\n} else {\n DEBUG = cmdoptions$options$debug\n}\n\n# Sys.setenv(FULL = \"TRUE\")\nif(is.null(cmdoptions$options$full)) {\n FULL = Sys.getenv(\"FULL\") == \"TRUE\"\n} else {\n FULL = cmdoptions$options$full\n}\n\nif(DEBUG && FULL) {\n stop(\"Setting both debug and full run modes at once is invalid\")\n}\n\nif(length(cmdoptions$args) == 0) {\n StanModel = 'base-nature'\n} else {\n StanModel = cmdoptions$args[1]\n}\n\nprint(sprintf(\"Running %s\",StanModel))\nif(DEBUG) {\n print(\"Running in DEBUG mode\")\n} else if (FULL) {\n print(\"Running in FULL mode\")\n}\n\ncat(sprintf(\"Running:\\nStanModel = %s\\nDebug: %s\\n\",\n StanModel,DEBUG))\n\n# Read which countires to use\ncountries <- readRDS('nature/data/regions.rds')\n# Read deaths data for regions\nd <- readRDS('nature/data/COVID-19-up-to-date.rds')\n# Read IFR and pop by country\nifr.by.country <- readRDS('nature/data/popt-ifr.rds')\n\n# Read interventions\ninterventions <- readRDS('nature/data/interventions.rds')\n\nforecast <- 0 # increase to get correct number of days to simulate\n# Maximum number of days to simulate\nN2 <- (max(d$DateRep) - min(d$DateRep) + 1 + forecast)[[1]]\n\nprocessed_data <- process_covariates(countries = countries, interventions = interventions, \n d = d , ifr.by.country = ifr.by.country, N2 = N2)\n\nstan_data = processed_data$stan_data\ndates = processed_data$dates\ndeaths_by_country = processed_data$deaths_by_country\nreported_cases = processed_data$reported_cases\noptions(mc.cores = parallel::detectCores())\nrstan_options(auto_write = TRUE)\nm = stan_model(paste0('nature/stan-models/',StanModel,'.stan'))\n\nif(DEBUG) {\n fit = sampling(m,data=stan_data,iter=40,warmup=20,chains=2)\n} else if (FULL) {\n fit = sampling(m,data=stan_data,iter=1800,warmup=1000,chains=5,thin=1,control = list(adapt_delta = 0.99, max_treedepth = 20))\n} else { \n fit = sampling(m,data=stan_data,iter=600,warmup=300,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 10))\n} \n\nout = rstan::extract(fit)\nprediction = out$prediction\nestimated.deaths = out$E_deaths\nestimated.deaths.cf = out$E_deaths0\n\nJOBID = Sys.getenv(\"PBS_JOBID\")\nif(JOBID == \"\")\n JOBID = as.character(abs(round(rnorm(1) * 1000000)))\nprint(sprintf(\"Jobid = %s\",JOBID))\n\ncountries <- countries$Regions\nsave(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.deaths,estimated.deaths.cf,stan_data, file=paste0('nature/results/',StanModel,'-',JOBID,'-stanfit.Rdata'))\n\n\nlibrary(bayesplot)\nfilename <- paste0(StanModel,'-',JOBID)\n\nprint('Generating mu, rt plots')\nmu = (as.matrix(out$mu))\ncolnames(mu) = countries\ng = (mcmc_intervals(mu,prob = .9))\nggsave(sprintf(\"nature/figures/%s-mu.png\",filename),g,width=4,height=6)\ntmp = lapply(1:length(countries), function(i) (out$Rt_adj[,stan_data$N[i],i]))\nRt_adj = do.call(cbind,tmp)\ncolnames(Rt_adj) = countries\ng = (mcmc_intervals(Rt_adj,prob = .9))\nggsave(sprintf(\"nature/figures/%s-final-rt.png\",filename),g,width=4,height=6)\n\nprint(\"Generate 3-panel plots\")\nsource('nature/utils/plot-3-panel.r')\nmake_three_panel_plot(filename)\n\nprint('Covars plots')\nsource('nature/utils/covariate-size-effects.r')\nplot_covars(filename)\n\nprint('Making table')\nsource('nature/utils/make-table.r')\nmake_table(filename)\n", "meta": {"hexsha": "dda9b1237cc88f542ba7b2dfac18f3b6142be03a", "size": 4101, "ext": "r", "lang": "R", "max_stars_repo_path": "base-nature.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "base-nature.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "base-nature.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 30.3777777778, "max_line_length": 185, "alphanum_fraction": 0.7173860034, "num_tokens": 1156, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3271451523403124}} {"text": "\nsource(\"./Data/column_constants.r\")\n\nAddMissingColumns <- function(country_data) {\n missing_columns <- setdiff(COLUMNS, names(country_data))\n country_data[missing_columns] <- NA\n return(country_data)\n}\n\nGenerateDiffColumn <- function(infection_data, dimension_name, base_column_name) {\n col_names <- colnames(infection_data)\n\n total_name <- paste(\"total_\", base_column_name, sep=\"\")\n daily_name <- paste(\"daily_\", base_column_name, sep=\"\")\n\n if(total_name %in% col_names) {\n if(!(daily_name %in% col_names)) {\n infection_data[[daily_name]] <- ave(infection_data[[total_name]], infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n }\n }\n\n return(infection_data)\n}\n\n", "meta": {"hexsha": "e15853d9974b51d4dbe3eb1484ee5c808f42b5bb", "size": 732, "ext": "r", "lang": "R", "max_stars_repo_path": "ETL/CountrySources/util.r", "max_stars_repo_name": "sbikun/CoronaGraphR", "max_stars_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-16T11:35:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-20T16:37:24.000Z", "max_issues_repo_path": "ETL/CountrySources/util.r", "max_issues_repo_name": "sbikun/CoronaGraphR", "max_issues_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ETL/CountrySources/util.r", "max_forks_repo_name": "sbikun/CoronaGraphR", "max_forks_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.28, "max_line_length": 145, "alphanum_fraction": 0.6871584699, "num_tokens": 170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6442250928250375, "lm_q1q2_score": 0.32714514540292583}} {"text": "#' Function to detect year transitions and calculate cumulative age\n#' of model results\n#' \n#' Takes the result of iterative growth modeling and\n#' transforms data from Julian Day (0 - 365) to cumulative\n#' day of the shell age by detecting where transitions\n#' from one year to the next occur and adding full years\n#' (365 days) to simulations in later years.\n#' @param resultarray Array containing the full results of\n#' the optimized growth model\n#' @param threshold Artificial threshold value used to recognize\n#' peaks in occurrences of year transitions (default = 5)\n#' @param plotyearmarkers Should the location of identified year\n#' transitions be plotted? \\code{TRUE/FALSE}\n#' @return A new version of the resultarray with Julian Day model \n#' estimates replaced by estimates of cumulative age of the record\n#' in days.\n#' @references package dependencies: zoo 1.8.7\n#' @importFrom graphics plot\n#' @examples\n#' testarray <- array(NA, dim = c(20, 16, 9)) # Create empty array\n#' # with correct third dimension\n#' windowfill <- seq(50, 500, 50) # Create dummy simulation data \n#' # (ages) to copy through the array\n#' for(i in 6:length(testarray[1, , 1])){\n#' testarray[, i, 3] <- c(windowfill, rep(NA, length(testarray[, 1, 3]) -\n#' length(windowfill)))\n#' windowfill <- c(NA, (windowfill + 51) %% 365)\n#' }\n#' testarray[, 1, 3] <- seq(1, length(testarray[, 1, 3]), 1) # Add\n#' # dummy /code{D} column.\n#' testarray2 <- cumdy(testarray, 3, FALSE) # Apply function on array\n#' @export\ncumdy <- function(resultarray, # Align Day of year results from modeling in different windows to a common time axis\n threshold = 5, # Threshold for separating peaks in year changes for marking the transitions between years\n plotyearmarkers = TRUE\n ){ \n \n dat <- resultarray[, 1:5, 3]\n Yearends <- rbind(rep(NA, length(resultarray[1, -(1:length(dat[1, ])), 3])), diff(resultarray[, -(1:length(dat[1, ])), 3]) < 0) # Matrix of positions in individual windows where end of year (day 365) is recorded\n Tyearmarkers <- cbind(c(0, dat[-nrow(dat), 1]) + c(0, diff(dat[, 1])), rowSums(Yearends, na.rm = TRUE)) # Aggregate of end of year (Day = 365) positions across windows against depth of record\n Tyearmarkers <- cbind(Tyearmarkers, c(rep(0, floor(threshold / 2)), zoo::rollmean(Tyearmarkers[, 2], threshold, align = \"center\"), rep(0, threshold - floor(threshold / 2) - 1))) # Add moving average of threshold value, pad with zeroes to match column length\n if(!all(Tyearmarkers[, 3] < (3 / threshold))){Tyearmarkers[which(Tyearmarkers[, 3] < (3 / threshold)), 3] <- 0} # Remove small numbers consisting of averages of 1 or 2 yearends in windows except in cases with very low resolution\n\n pks <- which(diff(sign(diff(Tyearmarkers[, 3], na.pad = FALSE)), na.pad = FALSE) < 0) + 2 # Find peaks in the number of yearmarkers by taking the second derivative\n Tyearmarkers <- cbind(Tyearmarkers, rep(0, length(Tyearmarkers[, 3]))) # Add column to store yearmarkers based on age modeling\n for(i in 1:(length(which(diff(pks) > threshold)) + 1)){ # Loop through the instances where peaks are far enough apart to be taken as separate (as judged through the threshold)\n Tyearmarkers[mean(pks[(c(0, which(diff(pks) > threshold), length(pks))[i] + 1) : c(0, which(diff(pks) > threshold), length(pks))[i + 1]]), 4] <- 1 # Combine peaks that cluster together and add markers to the mean positions where windows record the end of the year\n }\n Tyearends <- cbind(seq(1, sum(Tyearmarkers[, 4]), 1), Tyearmarkers[which(Tyearmarkers[, 4] == 1), 1]) # Find numbers and positions of mean yearmarkers in record based on age modeling\n\n if(plotyearmarkers == TRUE){\n dev.new()\n plot(Tyearmarkers[, c(1, 3)], type = \"l\") # Plot aggregate of yearmarkers\n points(Tyearmarkers[which(Tyearmarkers[, 4] == 1), c(1, 3)], col = \"red\") # Plot location of yearmarkers\n }\n\n Yearends[which(Yearends == TRUE)] <- apply(abs(outer(Tyearends[, 2], Tyearmarkers[which(Yearends == TRUE) %% nrow(Yearends), 1], \"-\")), 2, which.min) # Find distance values for year ends in all windows and replace the positions of the year ends with the number of years along the records based on the nearest peak in yearends found earlier\n\n for(col in 1:ncol(Yearends)){ # Loop through columns and create matrix of the cumulative year in which the datapoints of all models are set.\n Yearends[min(which(!is.na(Yearends[, col]))) - 1, col] <- 0 # Replace the last NA before the modeled values start with a zero to compensate for the missing first value due to diff() function above (first line of function)\n if(length(Yearends[which(Yearends[, col] > 0), col]) > 0){ # Check if there are year ends in the column\n X <- Yearends[which(Yearends[, col] > 0), col] # Find values associated with the year ends\n row <- which(Yearends[, col] %in% X) # Find rows in which these year end values are contained\n row0 <- which(Yearends[, col] == 0) # Find rownumbers of zeroes in column\n if(length(row) > 1){ # Check if multiple year ends are present on the column\n rowcomp <- outer(row0, row, \"-\") # Match rownumbers of all zeroes in the column with the rownumbers of all year end values\n rowcomp[rowcomp < 0] <- NA # Remove all instances where zeroes precede year ends\n Yearends[row0, col] <- as.numeric(apply(rowcomp, 1, which.min)) # Replace zeroes with closest year end directly above\n Yearends[row0[which(is.na(Yearends[row0, col]))], col] <- X[1] - 1 # Replace all zeroes above the year end with the number of the previous year (year end - 1)\n }else{ # Alternatively, if there is only one year end:\n Yearends[row0[which(row0 > row)], col] <- Yearends[row, col] # Replace all zeroes below the year end with the number belonging to the year end\n Yearends[row0[which(row0 < row)], col] <- Yearends[row, col] - 1 # Replace all zeroes above the year end with the number of the previous year (year end - 1)\n }\n }else if(col > 1){\n Yearends[which(!is.na(Yearends[, col])), col] <- rep(as.numeric(names(sort(table(Yearends[, (col - 1)]), decreasing = TRUE)[1])), length(which(!is.na(Yearends[, col])))) # If no yearends are present, replace all zeroes with the most common year in the previous column\n }\n }\n\n result <- resultarray[, , 3] + cbind(matrix(0, ncol = length(dat[1, ]), nrow = length(Yearends[,1])), Yearends) * 365 # Create matrix of cumulative days in model centered on position of highest summer value (default t_maxtemp = day 182.5) and based on length of day (default T_per = G_per = 365)\n return(result)\n}", "meta": {"hexsha": "1bc804cd30049b77f55bc58771b84b08de22de1c", "size": 6733, "ext": "r", "lang": "R", "max_stars_repo_path": "R/CumDY.r", "max_stars_repo_name": "nhoeche/ShellChron.jl", "max_stars_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/CumDY.r", "max_issues_repo_name": "nhoeche/ShellChron.jl", "max_issues_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/CumDY.r", "max_forks_repo_name": "nhoeche/ShellChron.jl", "max_forks_repo_head_hexsha": "935bcde7e015581a18eee324b7a5ff2ab7f1970f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 82.1097560976, "max_line_length": 344, "alphanum_fraction": 0.6785979504, "num_tokens": 1856, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154239836484143, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32704657102468343}} {"text": "#install.packages(\"bio3d\")\n#downloading the file with protein ids in the Home folder\n#f=download.file(\"ftp://ftp.wwpdb.org/pub/pdb/derived_data/index/compound.idx\",destfile=\"idx_file.idx\");\n\n\n#-------------------------------import libraries---------------------------\n\nlibrary(\"bio3d\");\n\n#------------------read from idx file with ids and move data into matrix-----------------------\nfileName <- 'idx_file.idx'\nfff= readChar(fileName, file.info(fileName)$size);\nfff=strsplit(fff, \"\\n\");\nl=length(fff[[1]]);\nccc=fff[[1]][5:l];\nremove(l);\nfff=ccc;\nremove(ccc);\nfff=strsplit(fff, \"\\t\");\noutput <- matrix(unlist(fff), ncol = 2, byrow = TRUE);\nremove(fff);\nremove(fileName);\n#remove(f);\n\n#============================ask from user the Query========1N21========5L5V=====2QPS====1U98====1U99=====5NQ3\n\n#Q=readline(prompt = \"Give the pdb_id of the query \\n\");\n\n#-----------------------download from database or load if there is already downloaded----------------\nquery_id = Q;\nQ=read.pdb(Q, maxlines = -1, multi = FALSE, rm.insert = FALSE,rm.alt = TRUE, ATOM.only = FALSE, hex = FALSE, verbose = TRUE);\n\n\n\n\n#***********************************start of algorithm**************************\n\n\n\n\n\n#=======================computation of Query=======================================\n#--------------------we need only Calpha coordinates-----------------------------------------\n\n\n\nt = length((which(Q$calpha==TRUE))*3);\ncord = rep(NaN,t);\ncounter=1;\n\nfor (i in 1:length(Q$calpha)) {\n if(Q$calpha[i]==TRUE){\n cord[counter] = Q$xyz[[(i-1)*3+1]];\n cord[counter+1] = Q$xyz[[(i-1)*3+2]];\n cord[counter+2] = Q$xyz[[(i-1)*3+3]];\n counter=counter+3;\n }\n}\n\ncoordinates_Q = c(xyz=c(\"xyz\"),size=0,name=query_id);\ncoordinates_Q$xyz = cord;\ncoordinates_Q$size = length((which(Q$calpha==TRUE)))*3;\n\n\n#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-finding centroids G_right, G_left-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n\nhalf_v = length(coordinates_Q$xyz)-(length(coordinates_Q$xyz))%%2;\nhalf_v = (half_v - half_v%%3)/2;\n\nsumx = 0;\nsumy = 0;\nsumz = 0;\ni=1;\nwhile(i=tr){\n for (j in 1:(length(pdb_file$calpha))) {\n if(pdb_file$calpha[j]==TRUE){\n cord[counter] = pdb_file$xyz[[(j-1)*3+1]];\n cord[counter+1] = pdb_file$xyz[[(j-1)*3+2]];\n cord[counter+2] = pdb_file$xyz[[(j-1)*3+3]];\n counter=counter+3;\n }\n }\n coordinates_P$xyz = c(coordinates_P$xyz,cord);\n coordinates_P$size = c(coordinates_P$size,length(cord));\n coordinates_P$name = c(coordinates_P$name,ids[i])\n }else{\n #-remove from list------\n ids = ids[(which(ids!=ids[i]))]; \n }\n }else{\n #----------------if pdb doesn't exist remove from list----------------------\n print(ids[i]); print(\"doesn't exist or cannot be read properly due to C++ error remove from list\");\n #ids = ids[(which(ids!=ids[i]))]; \n }\n }#--------------------end of for--------------\n \n \n coordinates_P$size = coordinates_P$size[which(coordinates_P$size!=\"NaN\")];\n coordinates_P$xyz = coordinates_P$xyz[which(coordinates_P$xyz!=\"NaN\")];\n coordinates_P$name = coordinates_P$name[which(coordinates_P$name!=\"NaN\")];\n \n #---------------remove false data------------------------------\n if(length((which(coordinates_P$size!=0)))){\n ids = ids[(which(coordinates_P$size!=0))]; \n coordinates_P$size = coordinates_P$size[(which(coordinates_P$size!=0))]; \n \n }\n \n coordinates_P$size = strtoi(coordinates_P$size);\n \n \n \n \n \n \n \n \n b = -1;\n \n D_values = -1;\n #si = sum(coordinates_P$size)/3+length(coordinates_P$size) - length(coordinates_P$size)*coordinates_Q$size/3;\n F2 = list(value=rep(0, 2),name=coordinates_P$name,pos=1:2,pos_in_protein=1:2);\n \n #=======================calculating from 100 substructures of P=======================================\n number_of_proteins = number_of_proteins+length(coordinates_P$size);\n number_of_substructures = number_of_substructures/3 + sum(coordinates_P$size)/3-sum(coordinates_Q$size)/3+length(coordinates_P$size)/3;\n start_time <- Sys.time();\n counter3=1;\n counter4=0;\n si=0;\n for (i in 1:(length(coordinates_P$size))) {\n start=TRUE;\n j=1;\n \n \n \n while(j<=(coordinates_P$size[i]-(length(coordinates_Q$xyz))+1)) {\n \n if(start==TRUE){\n \n #finding centroids G_right, G_left\n \n half_v = length(coordinates_Q$xyz)-(length(coordinates_Q$xyz))%%2;\n half_v = (half_v - half_v%%3)/2;\n \n sumx = 0;\n sumy = 0;\n sumz = 0;\n e=1;\n while(e<=half_v) {\n sumx = sumx + coordinates_P$xyz[e+(counter4+j)-1];\n sumy = sumy + coordinates_P$xyz[e+(counter4+j)+1-1];\n sumz = sumz + coordinates_P$xyz[e+(counter4+j)+2-1];\n e=e+3;\n }\n G_right = c(sumx,sumy,sumz) /half_v;\n \n sumx = 0;\n sumy = 0;\n sumz = 0;\n e=half_v+1;\n while(e<=length(coordinates_Q$xyz)) {\n sumx = sumx + coordinates_P$xyz[e+(counter4+j)-1];\n sumy = sumy + coordinates_P$xyz[e+(counter4+j)+1-1];\n sumz = sumz + coordinates_P$xyz[e+(counter4+j)+2-1];\n e=e+3;\n }\n G_left = c(sumx,sumy,sumz) /(length(coordinates_Q$xyz)-half_v);\n \n start=FALSE;\n }else{\n #finding centroids G_right, G_left\n \n tt= length(coordinates_Q$xyz) - (length(coordinates_Q$xyz))%%2;\n tt = tt/2;\n G_left = G_left - (1/tt)*( c(coordinates_P$xyz[(counter4+j)],coordinates_P$xyz[(counter4+j)+1],coordinates_P$xyz[(counter4+j)+2]) );\n G_right = G_right - (1/tt)*(c(coordinates_P$xyz[tt+(counter4+j)],coordinates_P$xyz[tt+(counter4+j)+1],coordinates_P$xyz[tt+(counter4+j)+2]));\n }\n \n \n \n #----------------------compute F for even or odd n---------------------------------\n if((length(Q$xyz)/3)%%2==0 ){\n F2$value[si+(j-1)/3 +1] = sqrt((G_right[1]-G_left[1])^2+ (G_right[2]-G_left[2])^2+(G_right[3]-G_left[3])^2 )/2;\n }else{\n F2$value[si+(j-1)/3 +1] = (sqrt( ((coordinates_Q$size/3)/2-1)/((coordinates_Q$size/3)/2) ))*(sqrt((G_right[1]-G_left[1])^2+ (G_right[2]-G_left[2])^2+(G_right[3]-G_left[3])^2 )/2);\n }\n F2$pos_in_protein[si+(j-1)/3 +1] = sum(coordinates_P$size[1:i])-coordinates_P$size[i] +j;\n F2$pos[si+(j-1)/3 +1] = si+(j-1)/3+1;\n F2$name[si+(j-1)/3 +1] = coordinates_P$name[i];\n \n #----------------------compute D---------------------------\n D = abs(F1 - F2$value[si+(j-1)/3 +1]);\t\n \n \n if((1/(2*sqrt(2))*D)<1){\n r =rmsd(coordinates_Q$xyz,coordinates_P$xyz[(counter4+j):(length(coordinates_Q$xyz)+(counter4+j) -1)]);\n if(r<1){\n #rmsd_var = c(rmsd_var$value,r);\n #rmsd_var$value = c(rmsd_var$value,r);\n #rmsd_var$name = c(rmsd_var$name,coordinates_P$name[i]);\n #rmsd_var$start = c(rmsd_var$start,j);\n \n }\n }\n \n \n \n \n counter_iter = counter_iter +1;\n counter3=counter3+1;\n j=j+3;\n \n \n }#end of whilej\n si = si+sum(coordinates_P$size[i])/3-sum(coordinates_Q$size)/3+1;\n counter4=counter4+coordinates_P$size[i];\n \n }#end of fori\n \n \n \n \n \n if(iter!=counter1){\n counter1 = counter1 +1;\n }else{\n packet = length(output)%%packet;\n }\n \n \n}#-----------end of while iter----------\n\n\nF2 = list(value=F2$value,name=F2$name,pos=F2$pos,pos_in_protein=F2$pos_in_protein);\n\n\n\n\n#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@------------pre-processing---------------@@@@\nN=sum(coordinates_P$size)/3;\nm =coordinates_Q$size/3;\n\n\n#sorting\nfor (l in 1:(length(F2$value)-1)) {\n for (k in 2:(length(F2$value))) {\n if (F2$value[k-1] > F2$value[k]){\n v=F2$value[k-1];\n F2$value[k-1] = F2$value[k];\n F2$value[k] = v;\n v=F2$name[k-1];\n F2$name[k-1] = F2$name[k];\n F2$name[k] = v;\n v=F2$pos_in_protein[k-1];\n F2$pos_in_protein[k-1] = F2$pos_in_protein[k];\n F2$pos_in_protein[k] = v;\n v=F2$pos[k-1];\n F2$pos[k-1] = F2$pos[k];\n F2$pos[k] = v;\n }\n }\n}\n\n \n#which(F2$value==F2$value[(which(F2$name==\"1N21\"))])\n\n#binary_search\nleft=1;\nright=length(F2$value);\nconst=(1/(2*sqrt(2)));\nm = (left+right-(left+right)%%2)/2\nflag=TRUE;\nwhile((left<=right)&&(flag==TRUE)){\n m = (left+right-(left+right)%%2)/2\n \n \n if(abs(F1-F2$value[right]) colnames(gr_Create) <- c(\"Rank\", \"Average\")\n> gr_Create <- gr_Create[order(gr_Create$Average), ]\n> gr_Create$Rank <- factor(gr_Create$Rank, levels = gr_Create$Rank)\n> ggplot(gr_Create, aes(x=Rank, y=Average))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"Rank Vs Average Reports Created\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n\n## Bar Chart for view\n\n> view <- c(1,3)\n> gr_View <- aggCreate[, view]\n> colnames(gr_View) <- c(\"Rank\", \"Average\")\n> gr_View <- gr_View[order(gr_View$Average), ]\n> gr_View$Rank <- factor(gr_View$Rank, levels = gr_View$Rank)\n> ggplot(gr_View, aes(x=Rank, y=Average))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"Rank Vs Average Reports Viewed\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n## Bar Chart for recency of views, by rank\n\n> gr_Recent <- aggRecent2[ , view]\n\n> gr_Recent\n Group.1 daysSince\n1 2LT 200.16667 \n2 CAPT 157.33333 \n3 CDR 248.16667 \n4 CIV 148.94131 \n5 CPL 60.16667 \n6 LCDR 90.16667 \n7 LCOL 184.36667 \n8 LT(N) 97.16667 \n9 MAJ 110.71212 \n10 MCPL 115.50000 \n11 MWO 150.83333 \n12 WO 61.16667 \n> colnames(gr_Recent) <- c(\"Rank\", \"Days\")\n> gr_Recent <- gr_Recent[order(gr_Recent$Days),]\n> gr_Recent$Rank <- factor(gr_Recent$Rank, levels = gr_Recent$Rank)\n> ggplot(gr_Recent, aes(x=Rank, y=Days))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"Rank Vs Average Days Since Last Used\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n> gr_Recent$Days <- as.numeric(gr_Recent$Days)\n> ggplot(gr_Recent, aes(x=Rank, y=Days))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"Rank Vs Average Days Since Last Used\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n## Bar Chart for create, by L1\n\ngr_CreateL1 <- aggCreateL1[ , 1:2]\n> colnames(gr_CreateL1) <- c(\"L1\", \"Average\")\n> gr_CreateL1 <- gr_CreateL1[order(gr_CreateL1$Average), ]\n> gr_CreateL1$L1 <- factor(gr_CreateL1$L1, levels = gr_CreateL1$L1)\n> ggplot(gr_CreateL1, aes(x=L1, y=Average))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"L1 Vs Average Reports Created\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n## Bar Chart for view, by L1\n\n> view <- c(1,3)\n> gr_ViewL1 <- aggCreateL1[, view]\n> colnames(gr_ViewL1) <- c(\"L1\", \"Average\")\n> gr_ViewL1 <- gr_ViewL1[order(gr_ViewL1$Average), ]\n> gr_ViewL1$L1 <- factor(gr_ViewL1$L1, levels = gr_ViewL1$L1)\n> ggplot(gr_ViewL1, aes(x=L1, y=Average))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"L1 Vs Average Reports Viewed\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n\n## Bar Chart for recency of views, by L1\n\ngr_Recent2L1 <- aggRecent2L1[ , view]\n> colnames(gr_Recent2L1) <- c(\"L1\", \"Days\")\n> gr_Recent2L1 <- gr_Recent2L1[order(gr_Recent2L1$Days),]\n> gr_Recent2L1$L1 <- factor(gr_Recent2L1$L1, levels = gr_Recent2L1$L1)\n> ggplot(gr_Recent2L1, aes(x=L1, y=Days))+ geom_bar(stat = \"identity\", width = .5, fill = \"tomato3\")+ \nlabs(title = \"Ordered Bar Chart\", subtitle = \"L1 Vs Average Days Since Last Used\", caption = \"source: DND\")+ \ntheme(axis.text.x = element_text(angle = 65, vjust = 0.6))\n", "meta": {"hexsha": "148279534c63d178fbd91dbd2d16f9eb29b89669", "size": 3750, "ext": "r", "lang": "R", "max_stars_repo_path": "data-analysis/activity-training-and-use-graph.r", "max_stars_repo_name": 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"max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.1034482759, "max_line_length": 111, "alphanum_fraction": 0.6730666667, "num_tokens": 1261, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3269121615458638}} {"text": "# Initialize the speed variable\nspeed<-64\n\n# Code the while loop\nwhile(speed>=30 ) {\n print(paste(\"Slow down!\",speed))\n speed<-speed-7\n}\n\n# Print out the speed variable\nspeed\n", "meta": {"hexsha": "94b551de92b22548b3a6926a10df01858e2fd413", "size": 177, "ext": "r", "lang": "R", "max_stars_repo_path": "Coursera_courses/Data_science/speed.r", "max_stars_repo_name": "Navuchodonosor/octo-turtle", "max_stars_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-11-22T21:41:48.000Z", "max_stars_repo_stars_event_max_datetime": "2016-11-22T21:41:48.000Z", "max_issues_repo_path": "Coursera_courses/Data_science/speed.r", "max_issues_repo_name": "YuriyOrlov/octo-turtle", "max_issues_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Coursera_courses/Data_science/speed.r", "max_forks_repo_name": "YuriyOrlov/octo-turtle", "max_forks_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.75, "max_line_length": 34, "alphanum_fraction": 0.7005649718, "num_tokens": 49, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3268730806850108}} {"text": "source('./r_files/flatten_HTML.r')\n\n############### Library Declarations ###############\nlibraryRequireInstall(\"ggplot2\");\nlibraryRequireInstall(\"plotly\");\nlibraryRequireInstall(\"dplyr\");\nlibraryRequireInstall(\"reshape2\");\n####################################################\n\n################### Actual code ####################\n \n# Reformatting the data so it can be used as a plotly surface plot\ndata_z <- acast(Values, Frequency~Timestamp, value.var='Magnitude')\n \n# Plotting the surface plot\ng <- plot_ly(z = data_z, type='surface') %>%\n\t layout(\n\t\tscene = list(\n\t\t xaxis = list(title = \"Timestamp\"),\n\t\t yaxis = list(title = \"Frequency\"),\n\t\t zaxis = list(title = \"Magnitude\")\n\t\t))\n\n############# Create and save widget ###############\ninternalSaveWidget(g, 'out.html');\n####################################################\n", "meta": {"hexsha": "b64c57081cda471688bab90349852ebb321aa195", "size": 832, "ext": "r", "lang": "R", "max_stars_repo_path": "templates/PSG-VibrationDeviceReport-Chart/script.r", "max_stars_repo_name": "doverpublic/dds-launchpad-iiot-ref-dev", "max_stars_repo_head_hexsha": "0aae2ab9832e2cc52bbc89aed1898ae90ede6181", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "templates/PSG-VibrationDeviceReport-Chart/script.r", "max_issues_repo_name": "doverpublic/dds-launchpad-iiot-ref-dev", "max_issues_repo_head_hexsha": "0aae2ab9832e2cc52bbc89aed1898ae90ede6181", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "templates/PSG-VibrationDeviceReport-Chart/script.r", "max_forks_repo_name": "doverpublic/dds-launchpad-iiot-ref-dev", "max_forks_repo_head_hexsha": "0aae2ab9832e2cc52bbc89aed1898ae90ede6181", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8148148148, "max_line_length": 67, "alphanum_fraction": 0.5360576923, "num_tokens": 173, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3268730806850108}} {"text": "rm(list = ls())\ngc()\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(ggthemes)\nlibrary(ggfortify)\n#library(plotly)\nlibrary(knitr)\nlibrary(factoextra)\nlibrary(cluster)\nlibrary(viridis)\nlibrary(treemapify)\nlibrary(jsonlite)\nlibrary(scales)\nlibrary(LaplacesDemon)\nlibrary(RColorBrewer)\nlibrary(gganimate)\n\n\ndata <- read_rds(\"data/data_cadena.rds\")\n#renombro las variables\ndata <- data %>% \n rename(reporter = Reporter, partner = Partner, year = Year, flow = TradeFlowName, value = Valor.en.1000.USD)\n\nagregados <- c(\"Mercosur\", \"Sudamerica\",\"RDM_Mercosur\", \"World\")\ndata_filt <- data %>% \n filter(!partner %in% agregados)\n\n\n\n##### Videos treemaps por cadena y subcadena y uso, expo e impo #######\ngrow_treemap_cadsubcad_gif <- function(data = data_filt, reportante = \"Argentina\"){\n \n cadenas <- unique(data$Cadena)\n #Tomo toda la escala crom\u00e1tica, y eligo por k-means los colores m\u00e1s distintivos posibles\n colores <- c(\"#455930\",\n \"#c057c5\",\n \"#63b649\",\n \"#6148b9\",\n \"#c49d35\",\n \"#7a8fd0\",\n \"#d1503b\",\n \"#5ea7a3\",\n \"#c7407f\",\n \"#80a25e\",\n \"#56426d\",\n \"#bc8960\",\n \"#c787a5\",\n \"#793933\")\n names(colores) <- cadenas\n \n \n data_filt <- data %>% \n filter(reporter==reportante) \n \n if (reportante == \"Sudamerica\") {\n data_filt <- data\n }\n if (nrow(data_filt)==0) {\n print(paste(\"No hay data para \",reportante,\", \",nano))\n \n }else{\n \n graf <- data_filt %>% \n group_by(Cadena,Subcadena, flow, year) %>% \n summarise(value = sum(value)) %>%\n ggplot(., aes(area = value, fill = Cadena, label = Subcadena, subgroup = Cadena, frame = year)) + \n geom_treemap( fixed = TRUE, alpha = 1)+\n #geom_treemap_subgroup_border(fixed = TRUE)+\n geom_treemap_subgroup_text(place = \"bottomright\", grow = T, alpha = 0.5, colour =\n \"black\", fontface = \"italic\", min.size = 0, fixed = TRUE) +\n geom_treemap_text(colour = \"white\", place = \"topleft\", reflow = T, fixed = TRUE)+\n # geom_treemap_text(colour = \"white\", place = \"left\",\n # grow = F)+\n facet_grid(.~flow)+\n labs(title= paste0('Treemap ', reportante, \".A\u00f1o \"))+\n scale_fill_manual(values = colores)+\n theme_tufte()+\n theme(legend.position = \"None\",\n title = element_text(size = 18),\n strip.text = element_text(size=18))\n \n \n \n gganimate(graf,filename = paste0(\"graficos/treemap_byproduct_\",reportante,\".mp4\"), \n ani.width = 1600, ani.height = 900, interval = 0.3) \n \n }\n}\ngrow_treemap_caduse_gif <- function(data = data_filt, reportante = \"Argentina\"){\n \n cadenas <- unique(data$Cadena)\n #Tomo toda la escala crom\u00e1tica, y eligo por k-means los colores m\u00e1s distintivos posibles\n colores <- c(\"#455930\",\n \"#c057c5\",\n \"#63b649\",\n \"#6148b9\",\n \"#c49d35\",\n \"#7a8fd0\",\n \"#d1503b\",\n \"#5ea7a3\",\n \"#c7407f\",\n \"#80a25e\",\n \"#56426d\",\n \"#bc8960\",\n \"#c787a5\",\n \"#793933\")\n names(colores) <- cadenas\n \n \n data_filt <- data %>% \n filter(reporter==reportante) \n \n if (reportante == \"Sudamerica\") {\n data_filt <- data\n }\n if (nrow(data_filt)==0) {\n print(paste(\"No hay data para \",reportante,\", \",nano))\n \n }else{\n \n graf <- data_filt %>% \n group_by(Cadena,Flores,Cadena,flow, year) %>% \n summarise(value = sum(value)) %>%\n ggplot(., aes(area = value, fill = Cadena, label = Flores, subgroup = Cadena, frame = year)) + \n geom_treemap( fixed = TRUE, alpha = 1)+\n #geom_treemap_subgroup_border(fixed = TRUE)+\n geom_treemap_subgroup_text(place = \"bottomright\", grow = T, alpha = 0.5, colour =\n \"black\", fontface = \"italic\", min.size = 0, fixed = TRUE) +\n geom_treemap_text(colour = \"white\", place = \"topleft\", reflow = T, fixed = TRUE)+\n # geom_treemap_text(colour = \"white\", place = \"left\",\n # grow = F)+\n facet_grid(.~flow)+\n labs(title= paste0('Treemap ', reportante, \".A\u00f1o \"))+\n scale_fill_manual(values = colores)+\n theme_tufte()+\n theme(legend.position = \"None\",\n title = element_text(size = 18),\n strip.text = element_text(size=18))\n \n \n gganimate(graf,filename = paste0(\"graficos/treemap_byproduc_tUse_\",reportante,\".mp4\"), \n ani.width = 1600, ani.height = 900, interval = 0.3) \n \n }\n}\n######treemap con y sin RDM ########\ngrow_treemap_cadsubcad_rdm_gif <- function(data = data_filt, reportante = \"Argentina\", flow_filter = \"Export\"){\n \n cadenas <- unique(data$Cadena)\n #Tomo toda la escala crom\u00e1tica, y eligo por k-means los colores m\u00e1s distintivos posibles\n colores <- c(\"#455930\",\n \"#c057c5\",\n \"#63b649\",\n \"#6148b9\",\n \"#c49d35\",\n \"#7a8fd0\",\n \"#d1503b\",\n \"#5ea7a3\",\n \"#c7407f\",\n \"#80a25e\",\n \"#56426d\",\n \"#bc8960\",\n \"#c787a5\",\n \"#793933\")\n names(colores) <- cadenas\n \n \n data_filt <- data %>% \n filter(reporter==reportante, flow== flow_filter) \n \n if (reportante == \"Sudamerica\") {\n data_filt <- data %>% \n filter(flow== flow_filter) \n }\n if (nrow(data_filt)==0) {\n print(paste(\"No hay data para \",reportante,\", \",nano))\n \n }else{\n \n graf <- data_filt %>% \n mutate(RDM = case_when(partner==\"RDM_Sudamerica\"~\"Resto del mundo\",\n TRUE ~ \"Sudam\u00e9rica\")) %>% \n group_by(Cadena,Subcadena, year,RDM) %>% \n summarise(value = sum(value)) %>%\n ggplot(., aes(area = value, fill = Cadena, label = Subcadena, subgroup = Cadena, frame = year)) + \n geom_treemap( fixed = TRUE, alpha = 1)+\n #geom_treemap_subgroup_border(fixed = TRUE)+\n geom_treemap_subgroup_text(place = \"bottomright\", grow = T, alpha = 0.5, colour =\n \"black\", fontface = \"italic\", min.size = 0, fixed = TRUE) +\n geom_treemap_text(colour = \"white\", place = \"topleft\", reflow = T, fixed = TRUE)+\n # geom_treemap_text(colour = \"white\", place = \"left\",\n # grow = F)+\n facet_grid(.~RDM)+\n labs(title= paste0('Treemap ',flow_filter,\", \", reportante, \".A\u00f1o \"))+\n scale_fill_manual(values = colores)+\n theme_tufte()+\n theme(legend.position = \"None\",\n title = element_text(size = 18),\n strip.text = element_text(size=18))\n \n \n gganimate(graf,filename = paste0(\"graficos/treemap_byproduct_RDM_\",reportante,\"_\", flow_filter,\".mp4\"), \n ani.width = 1600, ani.height = 900, interval = 0.3) \n \n }\n}\n\nfor (pais in c(paste(unique(data_filt$reporter)),\"Sudamerica\")) {\n grow_treemap_cadsubcad_gif(data = data_filt, reportante = pais)\n}\n\nfor (pais in c(paste(unique(data_filt$reporter)),\"Sudamerica\")) {\n grow_treemap_caduse_gif(data = data_filt, reportante = pais)\n}\n\nfor (pais in c(paste(unique(data_filt$reporter)),\"Sudamerica\")) {\n for (flow_filt in c(\"Export\", \"Import\")) {\n grow_treemap_cadsubcad_rdm_gif(data = data_filt, reportante = pais,flow_filter = flow_filt)\n }\n}\n\n# for (pais in c(paste(unique(data_filt$reporter)),\"Sudamerica\")) {\n# for (flow_filt in c(\"Import\")) {\n# grow_treemap_cadsubcad_rdm_gif(data = data_filt, reportante = pais,flow_filter = flow_filt)\n# }\n# }\n\n", "meta": {"hexsha": "8c713dc9c6c7bfaafa0a32bfefc9d51cd851740a", "size": 7671, "ext": "r", "lang": "R", "max_stars_repo_path": "desagregado/EDA/treemaps.r", "max_stars_repo_name": "DiegoKoz/master_thesis", "max_stars_repo_head_hexsha": "92891e5ad9d3fb4f3fa214e189af8d7fac26356d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "desagregado/EDA/treemaps.r", "max_issues_repo_name": "DiegoKoz/master_thesis", "max_issues_repo_head_hexsha": "92891e5ad9d3fb4f3fa214e189af8d7fac26356d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "desagregado/EDA/treemaps.r", "max_forks_repo_name": "DiegoKoz/master_thesis", "max_forks_repo_head_hexsha": "92891e5ad9d3fb4f3fa214e189af8d7fac26356d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4978165939, "max_line_length": 111, "alphanum_fraction": 0.5642028419, "num_tokens": 2201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185205547239, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.32687307222968315}} {"text": "#######DESCRIPTION###############################\n#processes stage topology table from GIS analysis\n#stage stations upstream of a given station\n#################################################\n##load libraries\nlibrary(dplyr)\nlibrary(data.table)\nlibrary(stringr)\nlibrary(tidyr)\nlibrary(readr)\n\n##user inputs\ndir_ref = '/Volumes/gonggong/flood forecasting/data/reference tables/'\nfile_name = 'Upstream_Gages_Stage_Station_Wsheds.txt'\n\n##read in raw data\nstage_meta = fread(paste0(dir_ref, 'cwc_honumber_match_edit_thm_v3.csv')) \nstage_meta = stage_meta %>% dplyr::select(sta_name, sta_id, ho_number) %>% dplyr::mutate(ho_number = as.numeric(ho_number))\nstage_meta_us = stage_meta %>% dplyr::rename(sta_name_us = sta_name, sta_id_us = sta_id, ho_number_us = ho_number)\n\nstage_topology_raw = readLines(file.path(dir_ref, file_name))\n\n##process data\nstage_topology = NULL\nfor(i in 2:length(stage_topology_raw)){\n\tstr_temp = unlist(strsplit(stage_topology_raw[i], split = ','))\n\tstr_temp = str_replace_all(str_temp, '[[:punct:]]', '')\n\tstr_temp = str_trim(str_temp)\n\tnele = length(str_temp)\n\tstage_topology_temp = data.table(sta_name = str_temp[2], ho_number = str_temp[1], ho_number_us = str_temp[3:nele])\n\tstage_topology = bind_rows(stage_topology, stage_topology_temp)\n\t# gauge_order = rbind_list(gauge_order, gauge_order_tmp)\n}\n\nstage_topology = stage_topology %>% dplyr::mutate(ho_number = as.numeric(ho_number), ho_number_us = as.numeric(str_replace_all(ho_number_us, 'u', ''))) %>% dplyr::filter(!is.na(ho_number_us))\nstage_topology = stage_topology %>% left_join(stage_meta) \nstage_topology = stage_topology %>% left_join(stage_meta_us) \n\n##save processed tables\nsaveRDS(stage_topology, paste0(dir_ref, 'stage_topology_tbl.rda'))\nwrite.csv(stage_topology, paste0(dir_ref, 'stage_topology_tbl.csv'), row.names = F)\n", "meta": {"hexsha": "02b34d6efe683910e518a6e5f56a733a7a1a0db8", "size": 1818, "ext": "r", "lang": "R", "max_stars_repo_path": "stage_topology_processing.r", "max_stars_repo_name": "dpbroman/floodforecasting", "max_stars_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-22T22:15:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-26T06:52:11.000Z", "max_issues_repo_path": "stage_topology_processing.r", "max_issues_repo_name": "dpbroman/floodforecasting", "max_issues_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stage_topology_processing.r", "max_forks_repo_name": "dpbroman/floodforecasting", "max_forks_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.2857142857, "max_line_length": 191, "alphanum_fraction": 0.7376237624, "num_tokens": 472, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.32679494560810607}} {"text": "p = read.table(\"D:/_tmp/20150330/sim3/RocCfb.txt\", header = T, sep =\",\")\nplot(p$FA * 50000, p$TP, type = 'b')\n\n###\nrealchps = read.table(\"D:/_tmp/data/dataAchps.out\", header=F)$V1\nmean(diff(realchps[1:12]))\nsd(diff(realchps[1:12]))\nmax (diff(realchps[1:12]))\n#\nsig = read.table(\"D:/_tmp/data/dataA.out\")$V1\ntruechps = read.table(\"D:/_tmp/data/dataAchps.out\")$V1\ndetected = read.table(\"D:/_tmp/20150330/exp2cfb/true-chps-cfb.txt\")$V1\n\n# PLOT DETECTED TRUE CFB CHANGES\nplot(sig, cex = 0.1)\nplot(sig, xlim = c(20000, 30000), cex = 0.1)\nabline(v = detected, col = \"red\")\nabline(v = truechps, col = \"blue\")\n#\nplot(sig, xlim = c(11300, 11450), cex = 0.1)\nabline(v = detected, col = \"red\")\nabline(v = truechps, col = \"blue\")\n# END\n\n#pcfb = read.table(\"D:/_tmp/20150330/exp2cfb/ProbsCfb.txt\")$V1\n#plot(pcfb)\n\n#\n", "meta": {"hexsha": "895ebf595961b9b42bc213916b2fbe44e8603857", "size": 803, "ext": "r", "lang": "R", "max_stars_repo_path": "v.1.0_clojure/src/r-code/5RCO.r", "max_stars_repo_name": "av-maslov/Recurrency", "max_stars_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "v.1.0_clojure/src/r-code/5RCO.r", "max_issues_repo_name": "av-maslov/Recurrency", "max_issues_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "v.1.0_clojure/src/r-code/5RCO.r", "max_forks_repo_name": "av-maslov/Recurrency", "max_forks_repo_head_hexsha": "620a34badc66247e348a47b76828ed5a562246c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.6896551724, "max_line_length": 72, "alphanum_fraction": 0.6550435866, "num_tokens": 330, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.3267949374525201}} {"text": "library(tidyverse)\n\n# Create class method to split sample data into traing and test data\n# All columns except last are assumed to be independant variables (predictor)\n# Last column is assumed to be dependant (response)\n# Therefore Input Data must be in the format of an Analytics Base Table\n\nsplit_sample <- function(df){\n # Using Sample_frac from dplyr lib takes a percentage of source to use as training data\n # anti_join then takes remaining rows to be used for test data\n train <- sample_frac(df, 0.7)\n test <- anti_join(df, train)\n \n sampleclass <- setClass(\n \"Split_sample\", \n slots = c(\n xtrain=\"tbl_df\", \n ytrain=\"tbl_df\", \n xtest=\"tbl_df\", \n ytest=\"tbl_df\"\n )\n )\n \n sampleData <- sampleclass(\n xtrain = train[,-ncol(train)],\n ytrain = train[,ncol(train)],\n xtest = test[,-ncol(test)],\n ytest = test[,ncol(test)]\n )\n \n return(sampleData)\n}\n\n", "meta": {"hexsha": "c6b1f1695d410445342f73aff12826f6f9f32ebd", "size": 904, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/parse_input.r", "max_stars_repo_name": "Fergal-Stapleton/perceptron", "max_stars_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/parse_input.r", "max_issues_repo_name": "Fergal-Stapleton/perceptron", "max_issues_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/parse_input.r", "max_forks_repo_name": "Fergal-Stapleton/perceptron", "max_forks_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5882352941, "max_line_length": 89, "alphanum_fraction": 0.6725663717, "num_tokens": 239, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.32679493745252}} {"text": "#' Impute missing values as lowest observed value.\n#'\n#' @param data - Normalized, imputed data. Data matrix with observations as rows, features as columns.\n#' @param ref - Reference samples normalized, imputed data.\n#' @return imputed.data - Z-transformed data.\n#' @export data.imputeData\ndata.imputeData = function(data, ref) {\n data = data[which(rownames(data) %in% rownames(ref)),]\n ref = ref[which(rownames(ref) %in% rownames(data)),]\n data = data[sort(rownames(data)),]\n ref = ref[sort(rownames(ref)),]\n \n imputed.data = data\n for (met in 1:nrow(ref)) {\n rowData = ref[met,]\n if (any(is.na(rowData))) {\n rowData = as.numeric(rowData[-which(is.na(rowData))])\n } else {\n rowData = as.numeric(rowData)\n }\n # Impute using uniform random variable, where a = 0.99*observed minimum, and b = observed minimum\n min_row = min(rowData)\n if (min_row<0) {\n min_row = -1*min_row\n imputed.data[met, is.na(data[met,])] = tryCatch(-1*runif(sum(is.na(data[met,])), min = 0.99*min_row, max= min_row), \n error = function(e) e, warning=function(w) print(sprintf(\"%s: met%d\", w, met)))\n } else {\n imputed.data[met, is.na(data[met,])] = tryCatch(runif(sum(is.na(data[met,])), min = 0.99*min(rowData), max= min(rowData)), \n error = function(e) e, warning=function(w) print(sprintf(\"%s: met%d\", w, met)))\n }\n }\n return(imputed.data)\n}\n", "meta": {"hexsha": "b5876d2a836f89cca5950574533f3b64b13b6b62", "size": 1483, "ext": "r", "lang": "R", "max_stars_repo_path": "R/data.imputeData.r", "max_stars_repo_name": "Xiqi-Li/CTD", "max_stars_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/data.imputeData.r", "max_issues_repo_name": "Xiqi-Li/CTD", "max_issues_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/data.imputeData.r", "max_forks_repo_name": "Xiqi-Li/CTD", "max_forks_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.6176470588, "max_line_length": 133, "alphanum_fraction": 0.6001348618, "num_tokens": 424, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.32679493745252}} {"text": "# 3. faza: Vizualizacija podatkov\n\n# Uvozimo zemljevid.\n#zemljevid <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/OB.zip\", \"OB\",\n# pot.zemljevida=\"OB\", encoding=\"Windows-1250\")\n#levels(zemljevid$OB_UIME) <- levels(zemljevid$OB_UIME) %>%\n# { gsub(\"Slovenskih\", \"Slov.\", .) } %>% { gsub(\"-\", \" - \", .) }\n#zemljevid$OB_UIME <- factor(zemljevid$OB_UIME, levels=levels(obcine$obcina))\n#zemljevid <- fortify(zemljevid)\n\n# Izra\u010dunamo povpre\u010dno velikost dru\u017eine\n#povprecja <- druzine %>% group_by(obcina) %>%\n#summarise(povprecje=sum(velikost.druzine * stevilo.druzin) / sum(stevilo.druzin))\n\n\npovprecje.regije <- povprecja.kmetijskih.kultur %>% group_by(regija) %>% summarise(povprecje = mean(povprecje, na.rm = TRUE))\n\n\nlibrary(tmap)\nsource(\"https://raw.githubusercontent.com/TrafelaT18/APPR-2019-20/master/lib/uvozi.zemljevid.r\")\nobcine <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/OB.zip\", \"OB\",\n pot.zemljevida=\"OB\", encoding=\"Windows-1250\")", "meta": {"hexsha": "ea20b2913fa08b9dc16ff8ef7685b563f825eac6", "size": 992, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "TrafelaT18/APPR-2019-20", "max_stars_repo_head_hexsha": "97e0db12a981ed8c3c1e786ef753543ffe6f519a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "TrafelaT18/APPR-2019-20", "max_issues_repo_head_hexsha": "97e0db12a981ed8c3c1e786ef753543ffe6f519a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-12-14T15:18:07.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-03T20:53:55.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "TrafelaT18/APPR-2019-20", "max_forks_repo_head_hexsha": "97e0db12a981ed8c3c1e786ef753543ffe6f519a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.0909090909, "max_line_length": 125, "alphanum_fraction": 0.6723790323, "num_tokens": 377, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.32679493745252}} {"text": "\nlibrary(data.table)\nlibrary(curl)\nlibrary(jsonlite)\n\nh <- new_handle()\nhandle_setheaders(h, \"Accepts\" = \"application/json\",\n \"LOL_API_KEY\" = apikey)\n\nchampions <- curl_fetch_memory(\n \"http://ddragon.leagueoflegends.com/cdn/11.23.1/data/en_US/champion.json\",\n handle = h\n)\nchampions <- fromJSON(rawToChar(champions$content), flatten = T)\nchampions <- champions$data\n\nchamp_stats <- c()\nfor (i in 1:length(champions)){\n champ_stats <- unique(c(champ_stats, names(champions[[i]]$stats)))\n}\n\nchampions_base_stats <- data.table()\nfor (i in 1:length(champions)){\n len <- length(champions[[i]]$stats)\n champions_base_stats <- rbind(\n champions_base_stats,\n data.table(\n champion = rep(names(champions[i]), len),\n statistic = names(champions[[i]]$stats),\n value = unlist(champions[[i]]$stats)\n )\n )\n}\n\n\nchampions_base_stats[,value_std := (value - min(value)) / (max(value) - min(value)), by = .(statistic)]\nchampions_base_stats[is.na(value_std), value_std := 0] \n", "meta": {"hexsha": "556768ff45956a0411eeda8235603a936ae1f983", "size": 1003, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/Riot_API/champions_base_stats.r", "max_stars_repo_name": "ANearchou/RDataScience", "max_stars_repo_head_hexsha": "158553607e5128a128ee00f06c8692ceda708401", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/Riot_API/champions_base_stats.r", "max_issues_repo_name": "ANearchou/RDataScience", "max_issues_repo_head_hexsha": "158553607e5128a128ee00f06c8692ceda708401", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/Riot_API/champions_base_stats.r", "max_forks_repo_name": "ANearchou/RDataScience", "max_forks_repo_head_hexsha": "158553607e5128a128ee00f06c8692ceda708401", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.3947368421, "max_line_length": 103, "alphanum_fraction": 0.6789631107, "num_tokens": 276, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3267404417947565}} {"text": "source(\"functions_setup/mlab-setup.r\")\n\n#####################\n# Joining with Congressional Districts \n#\t\tSince the US Census API does not yet support geometries for Congressional \n#\t\tDistricts, instead, we'll use an imported shapefile to obtain the geometries.\n# \n# Import shapefile as an sf dataframe\ncd_115<- st_read(dsn=\"geofiles/us_legislative_districts\", \n\tlayer=\"cb_2017_us_cd115_500k\", quiet=TRUE)\n\n# Filter to the state we're interested in\nMN_cd_115 <- filter(cd_115, STATEFP == \"27\")\n\n#####################\n# Get and prepare M-Lab Download speed data\n#####################\nwith_config(config(http_version = 2), { \n\tMN_2018_download_initial <- pull_mm_ndt(\"2018-01-01\", \"2018-07-31\", sql_download, \n\t\t\"'Minnesota'\",\"MN\") })\n\n## A little data cleaning to filter data any rows that might not be in the correct country\nMN_2018_download <- filter(MN_2018_download_initial, country_code == \"US\")\n\n## Convert to a simple features dataframe\ncoordinates(MN_2018_download) <- c(\"client_lon\", \"client_lat\")\nMN2018_down_sf <- st_as_sf(MN_2018_download)\n\n## Set the layer CRSs to be the same\nst_crs(MN2018_down_sf) <- st_crs(MN_cd_115)\n\n## Do spatial join to add congressional district to speed test rows\nMN_2018_down_cd115 <- bind_cols( \n MN_2018_download@data,\n MN_cd_115[as.numeric(st_within(MN2018_down_sf, MN_cd_115)),]\n)\n\n#####################\n# Compute Download medians by district\n#####################\n# First summary of median download speed to reduce sample bias\n# This first step produces download speed medians by district, day, and client_ip, reducing potential sample bias from \n# multiple tests from the same ip addresses over time\nMN_2018_DL_IP_day_district <- MN_2018_down_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip) %>%\n summarize(median_dl = median(download_speed_Mbps))\n\n## Rename the 'day' column generated by the transform of log_time in the previous DF\nnames(MN_2018_DL_IP_day_district)[2] = \"day\"\n\n## Second and final summary of median download speed by district \n## This second summarization groups the previously calculated medians by district, day, and client_ip to simply medians by district\nMN_2018_download_median_cd115 <- MN_2018_DL_IP_day_district %>%\n group_by(CD115FP) %>%\n summarize(median_download_Mbps = median(median_dl))\n\n## Prepare for mapping\nMN_districts <- readOGR(dsn=\"examples/geofiles/MN_legis_dists\", layer=\"MN_cd115\",\n\tstringsAsFactors = FALSE)\ncentroids.MN_districts <- as.data.frame(coordinates(MN_districts))\nMN_2018_DL_median_ip_day_district_withCentroids <- data.frame(MN_2018_download_median_cd115, centroids.MN_districts)\n\n## Rejoin the geometry field so we can map median speeds by district\nMN_2018_DL_median_ip_day_district_map_centers <- left_join(MN_2018_download_median_cd115, \n MN_2018_DL_median_ip_day_district_withCentroids, by=c(\"CD115FP\"))\nMN_2018_DL_median_ip_day_district_map_final <- left_join(MN_2018_DL_median_ip_day_district_map_centers, \n MN_cd_115, by=c(\"CD115FP\"))\nnames(MN_2018_DL_median_ip_day_district_map_final)[4] = \"longitude\"\nnames(MN_2018_DL_median_ip_day_district_map_final)[5] = \"latitude\"\nnames(MN_2018_DL_median_ip_day_district_map_final)[2] = \"median_download_mbps\"\n\n#####################\n# Create some visualizations\n#####################\n\n## Make a map with the ggplot package\nMN_2018_median_down_cd115_plot <- MN_2018_DL_median_ip_day_district_map_final %>%\n ggplot(aes(fill=median_download_mbps),color=median_download_mbps) + \n guides(fill=guide_legend(title=\"Median Download Speed (Mbps)\")) + \n theme_bw() +\n geom_sf() +\n geom_label_repel(aes(x=longitude, y=latitude, label=CD115FP), color=\"white\", fill=\"darkgrey\") +\n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"), \n axis.text = element_blank(), axis.ticks = element_blank() )\n\n## Make a bar chart by district\nggplot(MN_2018_DL_median_ip_day_district_map_final,aes(CD115FP, median_download_mbps, fill=median_download_mbps)) +\n guides(fill=guide_legend(title=\"Median Download Speed (Mbps)\")) + \n theme_bw() +\n geom_col() + \n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"))+\n ggtitle(\"M-Lab Median Download Speeds by Congressional District, 2018 Q1-2\") +\n xlab(\"Minnesota Congressional District\") + ylab(\"Median Download Speed (Mbps)\") +\n geom_text(aes(label=round(median_download_mbps,2)), vjust=1.5, color=\"white\")\n\n#####################\n# Compute Download Speed by District and City\n#####################\nMN_2018_DL_IP_day_district_city <- MN_2018_down_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip,city) %>%\n summarize(median_dl = median(download_speed_Mbps))\n\nnames(MN_2018_DL_IP_day_district_city)[2] = \"day\"\n\nMN_2018_download_median_cd115_city <- MN_2018_DL_IP_day_district_city %>%\n group_by(CD115FP,city) %>%\n summarize(median_download_Mbps = median(median_dl))\n\nMN_3_DL_city <- filter(MN_2018_download_median_cd115_city, CD115FP == \"03\")\n\n## Create a bar chart by city\nggplot(MN_3_DL_city,aes(reorder(city, -median_download_Mbps), median_download_Mbps, fill=median_download_Mbps)) +\n guides(fill=guide_legend(title=\"Median Download Speed (Mbps)\")) + \n theme_bw() +\n geom_col() + \n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"))+\n ggtitle(\"M-Lab Median Download Speeds by City, MN 3, 2018 Q1-2\") +\n xlab(\"City\") + ylab(\"Median Download Speed (Mbps)\") +\n geom_text(aes(label=round(median_download_Mbps,2)), hjust=1, color=\"white\") +\n coord_flip()\n\n#####################\n# Get and prepare M-Lab Upload speed data\n#####################\nwith_config(config(http_version = 2), { \n MN_2018_upload_initial <- pull_mm_ndt(\"2018-01-01\", \"2018-07-31\", sql_upload, \n \t\"'Minnesota'\",\"MN\")})\n\nMN_2018_upload <- filter(MN_2018_upload_initial, country_code == \"US\")\ncoordinates(MN_2018_upload) <- c(\"client_lon\", \"client_lat\")\n\nMN2018_up_sf <- st_as_sf(MN_2018_upload)\n\nst_crs(MN2018_up_sf) <- st_crs(MN_cd_115)\n\nMN_2018_up_cd115 <- bind_cols( \n MN_2018_upload@data,\n MN_cd_115[as.numeric(st_within(MN2018_up_sf, MN_cd_115)),]\n)\n\nMN_2018_UL_IP_day_district <- MN_2018_up_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip) %>%\n summarize(median_ul = median(upload_speed_Mbps))\n\nnames(MN_2018_UL_IP_day_district)[2] = \"day\"\n\nMN_2018_upload_median_cd115 <- MN_2018_UL_IP_day_district %>%\n group_by(CD115FP) %>%\n summarize(median_upload_Mbps = median(median_ul))\n\nMN_2018_UL_median_ip_day_district <- left_join(MN_2018_upload_median_cd115, MN_cd_115, by=c(\"CD115FP\"))\n\nMN_districts <- readOGR(dsn=\"examples/geofiles/MN_legis_dists\", layer=\"MN_cd115\",stringsAsFactors = FALSE)\ncentroids.MN_districts <- as.data.frame(coordinates(MN_districts))\n\nMN_2018_UL_median_ip_day_district_withCentroids <- data.frame(MN_2018_upload_median_cd115, centroids.MN_districts)\n\nMN_2018_UL_median_ip_day_district_map_centers <- left_join(MN_2018_upload_median_cd115, \n MN_2018_UL_median_ip_day_district_withCentroids, by=c(\"CD115FP\"))\nMN_2018_UL_median_ip_day_district_map_final <- left_join(MN_2018_UL_median_ip_day_district_map_centers, \n MN_cd_115, by=c(\"CD115FP\"))\nnames(MN_2018_UL_median_ip_day_district_map_final)[4] = \"longitude\"\nnames(MN_2018_UL_median_ip_day_district_map_final)[5] = \"latitude\"\nnames(MN_2018_UL_median_ip_day_district_map_final)[2] = \"median_upload_mbps\"\n\n\nMN_2018_median_up_cd115_plot <- MN_2018_UL_median_ip_day_district_map_final %>%\n ggplot(aes(fill=median_upload_mbps),color=median_upload_mbps) + \n guides(fill=guide_legend(title=\"Median Upload Speed (Mbps)\")) + \n theme_bw() +\n geom_sf() +\n geom_label_repel(aes(x=longitude, y=latitude, label=CD115FP), color=\"white\", fill=\"darkgrey\") +\n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"), \n axis.text = element_blank(), axis.ticks = element_blank() )\n\nggplot(MN_2018_UL_median_ip_day_district_map_final,aes(CD115FP, median_upload_mbps, fill=median_upload_mbps)) +\n guides(fill=guide_legend(title=\"Median Upload Speed (Mbps)\")) + \n theme_bw() +\n geom_col() + \n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"))+\n ggtitle(\"M-Lab Median Upload Speeds by Congressional District, 2018 Q1-2\") +\n xlab(\"Minnesota Congressional District\") + ylab(\"Median Upload Speed (Mbps)\") #+\n\n#####################\n# Compute Upload Speed by District and City\n#####################\nMN_2018_UL_IP_day_district_city <- MN_2018_up_cd115 %>%\n group_by(CD115FP,format(as.Date(log_time), \"%Y-%m-%d\"),client_ip,city) %>%\n summarize(median_ul = median(upload_speed_Mbps))\n\nnames(MN_2018_UL_IP_day_district_city)[2] = \"day\"\n\nMN_2018_upload_median_cd115_city <- MN_2018_UL_IP_day_district_city %>%\n group_by(CD115FP,city) %>%\n summarize(median_upload_Mbps = median(median_ul))\n\nMN_3_UL_city <- filter(MN_2018_upload_median_cd115_city, CD115FP == \"03\")\n\nggplot(MN_3_UL_city,aes(reorder(city, -median_upload_Mbps), median_upload_Mbps, fill=median_upload_Mbps)) +\n guides(fill=guide_legend(title=\"Median Upload Speed (Mbps)\")) + \n theme_bw() +\n geom_col() + \n theme(panel.background = element_rect(fill= NA), panel.grid = element_blank(), panel.border = element_blank(),\n panel.grid.major = element_line(colour = \"white\"), panel.grid.minor = element_line(colour = \"white\"))+\n ggtitle(\"M-Lab Median Upload Speeds by City, MN 3, 2018 Q1-2\") +\n xlab(\"City\") + ylab(\"Median Upload Speed (Mbps)\") +\n geom_text(aes(label=round(median_upload_Mbps,6)), hjust=1.2, color=\"white\") +\n coord_flip()\n", "meta": {"hexsha": "448823a43614c3da788cca24c7f0be600e691bf2", "size": 10411, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wip/mlab-data-with-us-congressional-districts.r", "max_stars_repo_name": "m-lab/data-support", "max_stars_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-08-07T14:41:13.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-14T17:57:33.000Z", "max_issues_repo_path": "R/wip/mlab-data-with-us-congressional-districts.r", "max_issues_repo_name": "m-lab/data-support", "max_issues_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 15, "max_issues_repo_issues_event_min_datetime": "2020-04-13T18:48:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-22T16:24:10.000Z", "max_forks_repo_path": "R/wip/mlab-data-with-us-congressional-districts.r", "max_forks_repo_name": "m-lab/data-support", "max_forks_repo_head_hexsha": "88b3cae0a7d50ba2d67c1c375a095d0e5feef92d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-08-14T16:10:22.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-08T01:17:35.000Z", "avg_line_length": 48.6495327103, "max_line_length": 133, "alphanum_fraction": 0.7299971184, "num_tokens": 2825, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3267404417947565}} {"text": "#Usage:\n#Rscript cell_classified_trajectory.r dimension_number\nlibrary(\"monocle\")\nlibrary(\"reshape\")\nlibrary('ggplot2')\nargs <- commandArgs(trailingOnly=TRUE)\nif(length(args)==0)\n{\n\tprint(\"Usage: Rscript cell_classified_trajectory.r dimension_number\")\n}\ndimension_number <- args[1]\nHSMM <- readRDS(\"classified_cells.rds\")\nHSMM <- reduceDimension(HSMM, max_components=as.numeric(dimension_number))\nHSMM <- orderCells(HSMM, reverse=FALSE)\nprint(plot_cell_trajectory(HSMM, color_by=\"CellType\"))\nggsave(\"cell_classified_trajectory_plot.png\")\nggsave(\"cell_classified_trajectory_plot.tiff\", width = 7, height = 7,dpi=600)\ndev.off() ", "meta": {"hexsha": "0cd36a47f303b9ddea6a97698b7fbfb12035a089", "size": 626, "ext": "r", "lang": "R", "max_stars_repo_path": "RScripts/cell_classified_trajectory.r", "max_stars_repo_name": "nikhil/RAS", "max_stars_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RScripts/cell_classified_trajectory.r", "max_issues_repo_name": "nikhil/RAS", "max_issues_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RScripts/cell_classified_trajectory.r", "max_forks_repo_name": "nikhil/RAS", "max_forks_repo_head_hexsha": "1ed1f70872b700bb422128a537ab4ddb4fbeb97f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7777777778, "max_line_length": 77, "alphanum_fraction": 0.7907348243, "num_tokens": 168, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3267404342984972}} {"text": "library(scales)\nlibrary(OECD)\nlibrary(glue)\nlibrary(ggrepel)\nlibrary(lubridate)\nlibrary(tidyverse)\n\npays <- c(\"ITA\", \"GRC\", \"SVK\", \"ESP\", \"POL\", \"KOR\", \"PRT\", \"HUN\", \"IRL\", \"AUT\", \"CAN\", \"BEL\", \"NZL\", \"GBR\", \"FRA\", \"DEU\", \"FIN\", \"USA\", \"NLD\", \"DNK\", \"SWE\", \"NOR\")\n\ndatasets <- OECD::get_datasets() |> filter(str_detect(id, \"SNA_TABLE11\"))\n\nquery_cofog <- \".TLYCG+D1CG+B1_GE.T+010+020+030+040+050+060+070+080+090+100.GS13+S1.C\"\nquery_d1 <- \".P1A+P2A+B1GA+D1A+D11A+B2G_B3GA+K1A+B2N_B3NA+D29_D39A+B1_GE.VTOT+VO+VP+VQ.C\"\n\nd1_oecd <- OECD::get_dataset(dataset=\"SNA_TABLE6A\", query_d1) |> \n transmute(value=as.numeric(ObsValue),\n measure = factor(MEASURE),\n year=as.numeric(Time),\n country = factor(LOCATION),\n branche = factor(ACTIVITY),\n transac = factor(TRANSACT), \n unit = factor(UNIT)) |> \n filter(unit!=\"IDX\") |> \n group_by(country, year) |> \n summarize(\n rd1 = sum(value[branche%in%c(\"VO\", \"VP\", \"VQ\")&transac==\"D1A\"]),\n d1tot = value[branche==\"VTOT\"&transac==\"D1A\"],\n pib = value[branche==\"VTOT\"&transac==\"B1_GE\"],\n educ = value[branche==\"VP\"&transac==\"D1A\"]/d1tot,\n admin = value[branche==\"VO\"&transac==\"D1A\"]/d1tot,\n sante = value[branche==\"VQ\"&transac==\"D1A\"]/d1tot,\n d1_nm = rd1/pib, \n unit = unique(unit)) |> \n filter(!near(rd1,0))\n\ncofog_oecd <- OECD::get_dataset(dataset=\"SNA_TABLE11\", query_cofog) |> \n transmute(value=as.numeric(ObsValue),\n year=as.numeric(Time),\n country = factor(LOCATION),\n cofog = factor(ACTIVITY),\n transac = factor(TRANSACT), \n unit = factor(UNIT)) |> \n group_by(cofog, year, country) |>\n summarize(d1s13_td = value[transac==\"D1CG\"]/value[transac==\"TLYCG\"],\n d1s13_pib = value[transac==\"D1CG\"]/value[transac==\"B1_GE\"],\n d1s13 = value[transac==\"D1CG\"]) |> \n ungroup() |>\n arrange(country, cofog, -year) |> \n mutate(country = fct_reorder(country, d1s13_pib, .fun = first)) \n\ndata <- cofog_oecd |> filter(cofog==\"T\") |> \n left_join(d1_oecd, by=c(\"year\", \"country\")) |> \n drop_na(d1_nm) |> \n group_by(country) |> \n summarize(year_max = max(year),\n d1_pib = d1s13_pib[year==year_max],\n d1_nm = d1_nm[year==year_max],\n d1_d1tot = d1s13[year==year_max]/d1tot[year==year_max], \n d1_educ = educ[year==year_max],\n d1_sante = sante[year==year_max],\n d1_admin = admin[year==year_max],\n d1_vnm = d1_educ+d1_admin+d1_sante,\n delta_d1tot = d1s13[year==year_max]/d1tot[year==year_max]-d1s13[year==year_max-5]/d1tot[year==year_max-5]) |> \n ungroup() |>\n mutate(\n country_a = str_c(country,case_when(\n year_max==2020 ~ \"\",\n year_max==2019 ~ \"*\",\n year_max <=2018 ~ \"**\")),\n country2 = fct_reorder(str_c(country, ' (', year_max, ')'), d1_nm),\n country_a = fct_reorder(country_a, d1_vnm)) |> \n filter(country%in%pays) \n \n \nggcofog <- ggplot(data |> select(country_a, d1_educ, d1_sante, d1_admin) |> pivot_longer(cols=-country_a), aes(x=country_a)) +\n geom_col(aes(y=value, fill = name), width = 0.5, alpha=0.75)+\n scale_fill_discrete(type = RColorBrewer::brewer.pal(3, \"Blues\"), labels=c(\"Administration\", \"Education\", \"Sant\u00e9\")) +\n theme_minimal(base_family=\"Nunito\", base_size = unit(8, \"pt\"))+\n scale_y_continuous(labels = label_percent(1))+\n xlab(\"\")+ylab(\"\")+labs(fill=\"Part de la masse \\ndu secteur dans la masse \\nsalariale totale\", size=3)+\n geom_segment(data = data,\n mapping=aes(x=country_a, xend=country_a, y=d1_d1tot-delta_d1tot, yend=d1_d1tot), \n col=\"orange\", arrow=arrow(type=\"closed\", length=unit(2.5,\"pt\")))+\n geom_point(data = data, mapping=aes(x=country_a, y=d1_d1tot), col=\"darkorange\")+\n geom_point(data = data, mapping=aes(x=country_a, y=d1_d1tot-delta_d1tot), col=\"orange\" |> colorspace::lighten())+\n annotate(\"text\", x=\"SVK*\", y=0.32, color= \"orange\", hjust = 0, size=2,\n label=\"Part de la masse salariale des employ\u00e9s publics \\ndans la masse salariale totale\\n(dernier point connu et 5 ans avant)\")+\n annotate(\"curve\", curvature=0, x=\"SVK*\", xend=\"HUN*\", yend=0.25, y=0.32, arrow = arrow(length = unit(2, \"mm\")), color= \"orange\")+\n theme(legend.position = \"bottom\")+\n labs(caption=\"donn\u00e9es pour 2020, *2019, **2018\\n Sources : OCDE Comptes Nationaux table 11 et table 6a\")\n\nggsave(\"fonctionnaires en masse salariale.svg\", plot=ggcofog, width = 16, height=14, unit=\"cm\")\n \ndata.table::fwrite(data |> select(country_a, d1_educ, d1_sante, d1_admin, d1_d1tot, delta_d1tot), \"secteur public data.csv\")\n", "meta": {"hexsha": "95674bf160ee870ecd39c4690483da2ce53a0903", "size": 4580, "ext": "r", "lang": "R", "max_stars_repo_path": "AE_21_12/COFOG.r", "max_stars_repo_name": "OFCE/RR2021", "max_stars_repo_head_hexsha": "94eeb4e91ebbd613507a83b9ab1a8ab68edde15f", "max_stars_repo_licenses": ["CECILL-B"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "AE_21_12/COFOG.r", "max_issues_repo_name": "OFCE/RR2021", "max_issues_repo_head_hexsha": "94eeb4e91ebbd613507a83b9ab1a8ab68edde15f", "max_issues_repo_licenses": ["CECILL-B"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AE_21_12/COFOG.r", "max_forks_repo_name": "OFCE/RR2021", "max_forks_repo_head_hexsha": "94eeb4e91ebbd613507a83b9ab1a8ab68edde15f", "max_forks_repo_licenses": ["CECILL-B"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.2105263158, "max_line_length": 163, "alphanum_fraction": 0.6323144105, "num_tokens": 1583, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.32674043429849714}} {"text": "#' Extract the information from the simulation data frame to analyse the dose response effects\n#'\n#' @param allsim dataset with all simulations values\n#' @param dataset dataset with all variables\n#' @param dr a vector with dose response values\n#' @return a data frame with dose response values\n#' @export\n\ndose_resp_ind <- function(allsim, dataset, dr = seq(0, 1, 0.1)) {\n dataset <- data.frame(dataset)\n N <- dim(dataset)[1]\n sim <- dim(allsim)[2]\n size <- length(dr)\n m2 <- NA\n sum_dr <- data.frame(matrix(NA, size, 3))\n names(sum_dr) <- c(\"Quantile\", \"Mean\", \"SE\")\n pos <- 1\n for (g in 1:size) {\n newpos <- pos + N - 1\n m3 <- as.matrix(allsim[pos:newpos, ])\n for (f in 1:sim) {\n m1 <- mean(as.matrix(allsim[pos:newpos, f]))\n m2 <- c(m2, m1)\n }\n m2 <- m2[-1]\n se <- stats::sd(m2)\n sum_dr[g, 2] <- mean(m3)\n sum_dr[g, 3] <- se\n sum_dr[g, 1] <- paste0(\"DR_\", dr[g])\n pos <- newpos + 1\n }\n return(sum_dr)\n}\n", "meta": {"hexsha": "de35fe86c4ff2ff489d746f078568f458400b3fe", "size": 1029, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dose_resp_ind.r", "max_stars_repo_name": "itamuria/expose", "max_stars_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/dose_resp_ind.r", "max_issues_repo_name": "itamuria/expose", "max_issues_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-13T02:01:15.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-13T02:01:15.000Z", "max_forks_repo_path": "R/dose_resp_ind.r", "max_forks_repo_name": "itamuria/expose", "max_forks_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2647058824, "max_line_length": 94, "alphanum_fraction": 0.5597667638, "num_tokens": 317, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.32674042680223786}} {"text": "library(data.table)\nN = 2e9/8\nK = 100\n\nDT <- data.table(\n id3 = sample(sprintf(\"id%010d\",1:(N/K)), N, TRUE), \n v1 = sample(5, N, TRUE) \n)\n\nsystem.time(DT[, sum(v1),keyby = id3])\nsystem.time(DT[, sum(v1),keyby = id3])\n\nsystem.time(setkey(DT, id3))\nsystem.time(DT[, sum(v1),id3])\nsystem.time(DT[, sum(v1),id3])\n", "meta": {"hexsha": "d40205eab869fcc573b768034bd8ffedd5374a67", "size": 356, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r/cut_down_tests3.r", "max_stars_repo_name": "JuliaTagBot/DataBench.jl", "max_stars_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-01T20:03:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-01T20:07:41.000Z", "max_issues_repo_path": "src/r/cut_down_tests3.r", "max_issues_repo_name": "JuliaTagBot/DataBench.jl", "max_issues_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/r/cut_down_tests3.r", "max_forks_repo_name": "JuliaTagBot/DataBench.jl", "max_forks_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:58:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T17:21:58.000Z", "avg_line_length": 22.25, "max_line_length": 72, "alphanum_fraction": 0.5365168539, "num_tokens": 124, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727231, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.32667777688715643}} {"text": "getwd()\nworkingDir <- \"/home/luke/Documents/E_FEM_clean/E_FEM\"\nsetwd(workingDir)\n\nrequire(haven)\nrequire(dplyr)\n\npar(mfrow=c(2,2))\n\nbaseline <- read_dta('output/ELSA_Baseline/ELSA_Baseline_summary.dta')\n\ncohort <- read_dta('output/ELSA_cohort/ELSA_cohort_summary.dta')\n\n\n### COMMIT INTERVENTION\ncommit3 <- read_dta('output/COMMIT_cSmoken3/COMMIT_cSmoken3_summary.dta')\n## Visualise the 3% intervention\n#plot(cohort$year, cohort$n_smoken_all, type='l', col='red')\n#lines(commit3$year, commit3$n_smoken_all, col='green')\n\nplot(commit3$year, commit3$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(commit3$year, commit3$n_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$n_lunge_all, col='red')\n\nplot(commit3$year, commit3$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(commit3$year, commit3$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\ntmp <- commit3$m_endpop_all / commit3$m_endpop_all[1]\ntmp2 <- cohort$m_endpop_all / cohort$m_endpop_all[1]\n\nplot(commit3$year, tmp, type='l', col='green')\nlines(cohort$year, tmp2, col='red')\n\n## Now work with the PSmoke_stopMult intervention\nsmoke_stop <- read_dta('output/Smoke_Stop_Cohort200/Smoke_Stop_Cohort200_summary.dta')\n## Visualise\n#plot(smoke_stop$year, smoke_stop$n_smoken_all, type='l', col='green')\n#lines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(cohort$year, cohort$p_smoken_all, type='l', col='red')\nlines(smoke_stop$year, smoke_stop$p_smoken_all, col='green')\n\nplot(cohort$year, cohort$p_lunge_all, type='l', col='red')\nlines(smoke_stop$year, smoke_stop$p_lunge_all, col='green')\n\nplot(smoke_stop$year, smoke_stop$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$n_smoke_stop_all, type='l', col='green')\nlines(cohort$year, cohort$n_smoke_stop_all, col='red')\n\n\n###########\nSmokeStopInt <- read_dta('output/SmokeStopIntervention/SmokeStopIntervention_summary.dta')\n\n#plot(SmokeStopInt$year, SmokeStopInt$n_smoken_all, type='l', col='green')\n#lines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\n\n#################################################\nsmoke_stop_init <- read_dta('output/Smoke_Stop_Cohort_Init/Smoke_Stop_Cohort_Init_summary.dta')\n\n#plot(smoke_stop_init$year, smoke_stop_init$n_smoken_all, type='l', col='green')\n#lines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(cohort$year, cohort$p_smoken_all, col='red', type='l')\nlines(smoke_stop_init$year, smoke_stop_init$p_smoken_all, col='green')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n", "meta": {"hexsha": "dcdce5e1997bfa26146a5fc0fa345b6fdbebe07f", "size": 3730, "ext": "r", "lang": "R", "max_stars_repo_path": "FEM_R/publth_int_vis.r", "max_stars_repo_name": "ld-archer/E_FEM", "max_stars_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-11-22T10:59:33.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-10T10:32:02.000Z", "max_issues_repo_path": "FEM_R/pubHlth_int_vis.r", "max_issues_repo_name": "ld-archer/E_FEM", "max_issues_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 39, "max_issues_repo_issues_event_min_datetime": "2019-11-22T10:39:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T15:32:18.000Z", "max_forks_repo_path": "FEM_R/publth_int_vis.r", "max_forks_repo_name": "ld-archer/E_FEM", "max_forks_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3, "max_line_length": 95, "alphanum_fraction": 0.7528150134, "num_tokens": 1160, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727232, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.32667777688715643}} {"text": "# Initialize i\ni<-1\n\n# Code the while loop\nwhile (i<=10) {\n if (i%%8==0){\n\tprint (i*3)\n break\n\t}\n else {\n print (i*3)\n i<-i+1\n\t}\n }", "meta": {"hexsha": "44fdc5e31dcac01b5c47e9e6789f789e5c21b7ec", "size": 146, "ext": "r", "lang": "R", "max_stars_repo_path": "Coursera_courses/Data_science/probe.r", "max_stars_repo_name": "Navuchodonosor/octo-turtle", "max_stars_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-11-22T21:41:48.000Z", "max_stars_repo_stars_event_max_datetime": "2016-11-22T21:41:48.000Z", "max_issues_repo_path": "Coursera_courses/Data_science/probe.r", "max_issues_repo_name": "YuriyOrlov/octo-turtle", "max_issues_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Coursera_courses/Data_science/probe.r", "max_forks_repo_name": "YuriyOrlov/octo-turtle", "max_forks_repo_head_hexsha": "09d5f497dfa55b4624093e399cdee09ba9a4db86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.4285714286, "max_line_length": 21, "alphanum_fraction": 0.4657534247, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.32667776880844845}} {"text": "################################################################################\n\nGetSlopeData <- function(raw.data.name=\"train\",datasets=c(\"alsfrs\",\"fvc\",\"svc\",\"vital\",\"lab\")) {\n\t# Calculates the slope data for alsfrs, fvc, svc, vital, and lab data \n\t#\n\t# Args:\n\t#\traw.data.name: name of the raw dataset \n\t#\tdatasets: a vector with the names of datasets to get slopes for \n\t#\n\t# Returns: \n\t#\ta list of slope datasets \n\t#\n\t# Example usage:\n\t# \tslope.list <- GetSlopeData(\"train\")\n \n # Initialize slope data list \n slope.data.list<-list()\n \n \tif (\"alsfrs\" %in% datasets) {\n \t\t\n \t\t# Check to see if slope data has been created, if so, load\n\t\trda.filename = paste(\"Data/slope_alsfrs_\",raw.data.name,\".rda\",sep=\"\")\n \t\t\n \t\tif (file.exists(rda.filename)) {\n \t\tprint(paste(\"loading saved alsfrs slope from\",rda.filename,sep=\" \"))\n \t\tload(rda.filename)\n \t\tprint(\"finished loading\")\n \t\t}\n\n\t\telse {\n \t\t\t# Get ALSFRS data\n\t \t\talsfrs.data<-GetAlsfrsData(raw.data.name)\n \n\t \t\t# Create vector of alsfrs measures to get slopes for \n\t \t\tals.measures<-c(\"alsfrs.score\",\"speech\",\"salivation\",\"swallowing\",\n \t\t\t\"handwriting\",\"cutting\",\"dressing\",\"turning\",\"walking\",\n \t\t\t\"climbing.stairs\")\n \n\t \t\talsfrs.slopes<-lapply(als.measures,CalcSlopeData,dataset=alsfrs.data)\n \n\t \t\t# Name datasets in list\n\t \t\tnames(alsfrs.slopes)<-paste(\"slope\",als.measures,sep=\".\")\n\t \t\t\n\t \t\t# Save to file\n\t \t\tsave(alsfrs.slopes,file=rda.filename)\n\t }\n\t # Add to list of slope datasets\n\t slope.data.list<-c(slope.data.list,alsfrs.slopes)\n }\n \n if (\"fvc\" %in% datasets) {\n \t\n\t# Check to see if slope data has been created, if so, load\n\trda.filename = paste(\"Data/slope_fvc_\",raw.data.name,\".rda\",sep=\"\")\n \t\t\n \tif (file.exists(rda.filename)) {\n \t\tprint(paste(\"loading saved fvc slope from\",rda.filename,sep=\" \"))\n \t\tload(rda.filename)\n \t\tprint(\"finished loading\")\n \t}\n \telse {\n\t\t# Get FVC slopes\n\t \tfvc.slopes<-CalcSlopeData(GetFvcData(raw.data.name),\"fvc.liters\")\n\t \t\n\t \t# Save to file\n\t \tsave(fvc.slopes,file=rda.filename)\n\t } \n\t # Add to list of slope datasets\n\t slope.data.list<-c(slope.data.list,list(slope.fvc.liters=fvc.slopes))\n }\n \n if (\"svc\" %in% datasets) { \t\n \t\n \t# Check to see if slope data has been created, if so, load\n\trda.filename = paste(\"Data/slope_svc_\",raw.data.name,\".rda\",sep=\"\")\n\n \t if (file.exists(rda.filename)) {\n \t\tprint(paste(\"loading saved svc slope from\",rda.filename,sep=\" \"))\n \t\tload(rda.filename)\n \t\tprint(\"finished loading\")\n \t}\n\telse {\n \t\t# Get SVC slopes\n\t \tsvc.slopes<-CalcSlopeData(GetSvcData(raw.data.name),\"svc.liters\")\n\t \t\n\t \t# Save to file\n\t \tsave(svc.slopes,file=rda.filename)\n\t}\n\t\n\t# Add to list of slope datasets\n\tslope.data.list<-c(slope.data.list,list(slope.svc.liters=svc.slopes))\n }\n \n if (\"vital\" %in% datasets) {\n \t\n \t# Check to see if slope data has been created, if so, load\n\trda.filename = paste(\"Data/slope_vital_\",raw.data.name,\".rda\",sep=\"\")\n\n \tif (file.exists(rda.filename)) {\n \t\tprint(paste(\"loading saved vital slope from\",rda.filename,sep=\" \"))\n \t\tload(rda.filename)\n \t\tprint(\"finished loading\")\n \t}\n\telse {\n \t\t# Get vital slopes\n\t \tvital.data<-GetVitalData(raw.data.name)\n\t \tvital.names<-names(vital.data)\n\t\tvital.slopes<-mapply(CalcSlopeData,vital.data,vital.names,SIMPLIFY=FALSE)\n \n\t \t# Rename datasets in vital list\n\t \tnames(vital.slopes)<-paste(\"slope\",vital.names,sep=\".\")\n\t \t\n\t \t# Save to file\n\t \tsave(vital.slopes,file=rda.filename)\n\t }\n\t \n\t # Add to list of slope datasets\n\t slope.data.list<-c(slope.data.list,vital.slopes)\n }\n \n if (\"lab\" %in% datasets) {\n \t\n \t# Check to see if slope data has been created, if so, load\n\trda.filename = paste(\"Data/slope_lab_\",raw.data.name,\".rda\",sep=\"\")\n\n \tif (file.exists(rda.filename)) {\n \t\tprint(paste(\"loading saved lab slope from\",rda.filename,sep=\" \"))\n \t\tload(rda.filename)\n \t\tprint(\"finished loading\")\n \t}\n \telse {\n\t\t# Get lab slopes\n\t \tlab.data<-GetLabData(raw.data.name)\n\t\tlab.names<-names(lab.data)\n\t \tlab.slopes<-mapply(CalcSlopeData,lab.data,lab.names,SIMPLIFY=FALSE)\n\t\n\t \t# Rename datasets in lab list\n\t \tnames(lab.slopes)<-paste(\"slope\",lab.names,sep=\".\")\n\t \t\n\t \t# Save to file\n\t \tsave(lab.slopes,file=rda.filename)\n\t }\n\t # Add to list of slope datasets\n\tslope.data.list<-c(slope.data.list,lab.slopes)\n\t}\n\t\n return(slope.data.list)\n}\n\nCalcSlopeData<-function(dataset, measure) {\n# Given a dataset, returns a new dataset where the new measure is the slope of \n# the old measure. If dataset is empty, returns an empty dataset with the \n# correct slope column names\n#\n# Args:\n# dataset: data frame generated from Get*Dataa\n# measure: the name of the measurement column\n#\n# Returns: \n# a data frame with columns consisting of subject ids, mid point deltas, and\n# slopes of the measure in the dataset\n#\n# Example usage:\n# slope.data<-CalcSlopeData(GetFvcData(\"train\"),\"fvc.liters\")\n\n # Get length of dataset\n nrow<-dim(dataset)[1]\n \n # Get delta column number\n delta.col<-grep(\"delta\",colnames(dataset))\n \n # Get value column number\n value.col<-match(measure,colnames(dataset))\n \n # Calculate slopes\n slope<-diff(dataset[,value.col])/\n DaysToMonths(diff(dataset[,delta.col]))\n \n # Calculate midpoint deltas\n delta.mid<-dataset[,delta.col][2:nrow]-diff(dataset[,delta.col]/2)\n \n # Get lagged differences in subjects to indicate change in subject.id\n sub.diff<-diff(dataset$subject.id)\n \n # Form dataset containing subject ids, midpoint deltas, slopes, and sub diff\n slope.data<-data.frame(subject.id=dataset$subject.id[-1],delta.mid,slope,\n sub.diff)\n \n # Drop rows where subject changes and drop sub diff column \n slope.data<-slope.data[slope.data$sub.diff==0,1:3]\n \n # Rename columns\n colnames(slope.data)<-c(\"subject.id\",paste(\"slope\",measure,\"delta\",sep=\".\"),\n paste(\"slope\",measure,sep=\".\"))\n \n return(slope.data)\n \n}", "meta": {"hexsha": "116732d181e9224623b81056a78df2aca3509a4a", "size": 5883, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/R/get_slope_data.r", "max_stars_repo_name": "ltfang/alsprize4life", "max_stars_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/R/get_slope_data.r", "max_issues_repo_name": "ltfang/alsprize4life", "max_issues_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/R/get_slope_data.r", "max_forks_repo_name": "ltfang/alsprize4life", "max_forks_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7121212121, "max_line_length": 96, "alphanum_fraction": 0.6530681625, "num_tokens": 1714, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.32667776880844845}} {"text": "# Libraries\n\nlibrary(htmlwidgets)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(dygraphs)\nlibrary(htmltools)\nlibrary(widgetframe)\nlibrary(icesSAG)\nlibrary(plotly)\n\nlibrary(shiny)\nlibrary(shinythemes)\nlibrary(glue)\nlibrary(sf)\nlibrary(leaflet)\nlibrary(fisheryO)\nlibrary(DT)\nlibrary(tidyverse)\nlibrary(icesVocab)\nlibrary(tm)\nlibrary(shinyWidgets)\nlibrary(shinyjs)\nlibrary(reshape2)\nlibrary(scales)\nlibrary(ggradar)\nlibrary(icesFO)\nlibrary(icesTAF)\n\n# required if using most recent version of sf\nsf::sf_use_s2(FALSE)\n\n################################\n# sources\nsource(\"Shiny/utilities_load_shapefiles.r\")\nsource(\"Shiny/utilities_shiny_formatting.r\")\nsource(\"Shiny/utilities_plotting.r\")\nsource(\"Shiny/utilities_mapping.r\")\nsource(\"Shiny/utilities_sag_data.r\")\nsource(\"Shiny/utilities_shiny_Input.r\")\nsource(\"Shiny/utilities_SID_data.r\")\nsource(\"Shiny/utilities_catch_scenarios.r\")\n\n## If this code is run for the first time and the SAG data in not present on the local machine\n## the following line will download the last 5 years of SAG data (summary and ref points).\n## This process will take several minutes but, once the data is in the local folder, \n## the app will run much faster. \nif (!file.exists(\"SAG_ 2021/SAG_summary.csv\")) {\n source(\"Shiny/update_SAG_data.r\")\n}\n\n# ui and server\n# source(\"Shiny/ui_05052021.r\")\n# source(\"Shiny/server_18052021.r\")\n\n### run app\n# shinyApp(server = server, ui = ui)\n\n\n### runApp function (Colin way of running the app which shows the png images in folder www)\nrunApp(\"temp\")\n", "meta": {"hexsha": "044695f530312b6b28acac9f6e10275436d5507f", "size": 1512, "ext": "r", "lang": "R", "max_stars_repo_path": "Shiny/advice_shiny_app_stable_version.r", "max_stars_repo_name": "lucalamoni/online-advice", "max_stars_repo_head_hexsha": "6252dfce306bfd09e5a8d7072943515bbab850b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Shiny/advice_shiny_app_stable_version.r", "max_issues_repo_name": "lucalamoni/online-advice", "max_issues_repo_head_hexsha": "6252dfce306bfd09e5a8d7072943515bbab850b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Shiny/advice_shiny_app_stable_version.r", "max_forks_repo_name": "lucalamoni/online-advice", "max_forks_repo_head_hexsha": "6252dfce306bfd09e5a8d7072943515bbab850b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.3870967742, "max_line_length": 94, "alphanum_fraction": 0.7566137566, "num_tokens": 398, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369905, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3266777602084071}} {"text": "\n a <- read.table(\"allNN_nolabel.txt\",sep=\"\\t\",header=F)\n b <- read.table(\"label.txt\",sep=\"\\t\",header=F)\n b1 <- as.factor(b)\n\n for (i in 1:nrow(a)){\n\n a1 <- as.numeric(a[i,])\n\n c <- data.frame(a1,b1)\n\n score_aov<-aov(a1~b1,data = c)\n\n x <- anova(score_aov)\n\n x1 <- x$ 'Pr(>F)'\n\n x2 <- as.matrix(x1)\n\n x3 <- x2[1,]\n\n write.table(x3,\"gene_p.txt\",sep = \"\\t\", append=T ,quote=F, col.names=F, row.names=F)\n\n}\n\n\n\n\n\n", "meta": {"hexsha": "274ffd7a8efaf248b4ae1852ad0de98b6f9f7ede", "size": 409, "ext": "r", "lang": "R", "max_stars_repo_path": "source/genep.r", "max_stars_repo_name": "Crystal-JJ/maize", "max_stars_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-03-06T04:40:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-06T04:40:19.000Z", "max_issues_repo_path": "source/genep.r", "max_issues_repo_name": "Crystal-JJ/maize", "max_issues_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/genep.r", "max_forks_repo_name": "Crystal-JJ/maize", "max_forks_repo_head_hexsha": "6f772fd0b3f060027a4f6f1bdca9f0d96c2970a8", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.6333333333, "max_line_length": 85, "alphanum_fraction": 0.564792176, "num_tokens": 152, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.32663686589461144}} {"text": "\nbottom.contact.gating.depth = function( Z, good, bcp ) {\n \n ## Preliminary gating: simple range limits (gating) of depths to remove real extremes\n # eliminate records with NA for depth \n i = which(!is.finite(Z))\n if (length(i) > 0) good[i] = FALSE\n\n\n ##--------------------------------\n # eliminiate shallow records due to operation at sea level\n i = which(Z< bcp$depth.min )\n if (length(i) > 0) good[i] = FALSE\n\n\n\n zm = modes( Z[good] )\n zsd = zm$sd\n ir = which( Z < (zm$lb2 +bcp$depth.range[1]) | Z > (zm$ub2 + bcp$depth.range[2]) ) # a bit more inclusive as these are crude gates at an early stage\n if (length(ir) > 0) good[ir] =FALSE\n depth.bottom = zm$mode\n\n ##--------------------------------\n # eliminiate records that are shallower than a given percentage of the bottom depth\n i = which( Z< ( bcp$depthproportion * depth.bottom ) )\n if (length(i) > 0) good[i] = FALSE\n # points( Z[good] ~ iz[good] , pch=20, col=\"green\" )\n\n ##--------------------------------\n ## record filter\n depth.diff = Z - depth.bottom \n i = which( depth.diff < bcp$depth.range[1] | depth.diff> bcp$depth.range[2])\n if (length(i) > 0) good[i] = FALSE\n # points( Z[good] ~ iz[good] , pch=20, col=\"cyan\" )\n\n \n return(good)\n\n}\n\n\n", "meta": {"hexsha": "7fcbd3c910f0a6bbcf0b77b1a7b3ea51fa712f7c", "size": 1242, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bottom.contact.gating.depth.r", "max_stars_repo_name": "PEDsnowcrab/netmensuration", "max_stars_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/bottom.contact.gating.depth.r", "max_issues_repo_name": "PEDsnowcrab/netmensuration", "max_issues_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/bottom.contact.gating.depth.r", "max_forks_repo_name": "PEDsnowcrab/netmensuration", "max_forks_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-05-04T14:40:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T12:56:48.000Z", "avg_line_length": 29.5714285714, "max_line_length": 152, "alphanum_fraction": 0.5748792271, "num_tokens": 393, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3266368592443166}} {"text": "#iv fastplm boot\r\nivfastplm.boot <- function(seed, \r\n y = NULL, \r\n x = NULL, \r\n z = NULL,\r\n ind = NULL, \r\n sfe.index = NULL, \r\n cfe.index = NULL, \r\n cluster = NULL,\r\n robust = FALSE,\r\n parallel = FALSE,\r\n wild = FALSE, \r\n jackknife = FALSE,\r\n refinement = FALSE, \r\n pos = NULL,\r\n test.value = NULL,\r\n nboots = 200, \r\n bootcluster = NULL, #2-way clusters in wild (refinement)\r\n core.num = 1){\r\n p <- dim(x)[2]\r\n if(is.null(z)){\r\n stop(\"No exogenous variables.\\n\")\r\n }\r\n\r\n if(is.null(colnames(x))){\r\n colnames(x) <- paste0(\"x.\",c(1:dim(x)[2]))\r\n }\r\n\r\n if(is.null(colnames(z))){\r\n colnames(z) <- paste0(\"z.\",c(1:dim(z)[2]))\r\n }\r\n\r\n if(is.null(colnames(y))){\r\n colnames(y) <- 'y'\r\n }\r\n\r\n if (jackknife == 1) {\r\n wild <- 0\r\n }\r\n\r\n # get \r\n model.iv <- ivfastplm.core(y = y, x = x, z = z, \r\n ind = ind, se = 1, \r\n robust = robust,\r\n sfe.index = sfe.index, \r\n cfe.index = cfe.index, \r\n cl = cluster, \r\n core.num = core.num, \r\n need.fe = TRUE,\r\n iv.test = FALSE)\r\n\r\n dy <- model.iv$demeaned$dy\r\n dx <- model.iv$demeaned$dx\r\n dz <- model.iv$demeaned$dz\r\n\r\n y.fit <- fitted.y <- model.iv$fitted.values\r\n res <- residual.y <- model.iv$residuals\r\n\r\n iv.name <- model.iv$names\r\n en.var.x <- iv.name$en.var.x\r\n ex.var.x <- iv.name$ex.var.x\r\n include.iv <- iv.name$include.iv\r\n exclude.iv <- iv.name$exclude.iv\r\n \r\n iv.matrix <- model.iv$matrix\r\n inv.z <- iv.matrix$inv.z # inv(z'z)\r\n inv.xPzx <- iv.matrix$inv.xPzx\r\n\r\n #save df.original & df.cl for wild bootstrap(only)\r\n df.use <- df.original <- model.iv$df.original\r\n df.cl.use <- df.cl <- model.iv$df.cl\r\n\r\n ## function to get two-sided p-values\r\n get.pvalue <- function(vec,to_test=0) {\r\n if (NaN%in%vec|NA%in%vec) {\r\n nan.pos <- is.nan(vec)\r\n na.pos <- is.na(vec)\r\n pos <- c(which(nan.pos),which(na.pos))\r\n vec.a <- vec[-pos]\r\n a <- sum(vec.a >= to_test)/(length(vec)-sum(nan.pos|na.pos)) * 2\r\n b <- sum(vec.a <= to_test)/(length(vec)-sum(nan.pos|na.pos)) * 2 \r\n } else {\r\n a <- sum(vec >= to_test)/length(vec) * 2\r\n b <- sum(vec <= to_test)/length(vec) * 2 \r\n }\r\n return(min(as.numeric(min(a, b)),1))\r\n }\r\n\r\n if(parallel==TRUE){\r\n core.num <- 1\r\n requireNamespace(\"doParallel\")\r\n\t\t## require(iterators)\r\n\t\tmaxcores <- detectCores()\r\n\t\tcores <- min(maxcores, 4)\r\n\t\tpcl<-makeCluster(cores) \r\n\t\tdoParallel::registerDoParallel(pcl)\r\n\t cat(\"Parallel computing with\", cores,\"cores...\\n\")\r\n use.fun <- c(\"SEQ\",\"name.fe.model\",\"ivfastplm.core\",\"get.dof\",\"iv.cluster.se\",\"fastplm.core\")\r\n }\r\n\r\n if(wild==TRUE){\r\n raw.cluster <- level.cluster <- level.count <- NULL\r\n if (!is.null(cluster)) {\r\n if(dim(cluster)[2]==1){\r\n ## one-way cluster \r\n raw.cluster <- as.numeric(as.factor(cluster))\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }\r\n if(dim(cluster)[2]==2){\r\n if(is.null(bootcluster)){\r\n level.cluster.1 <- unique(as.numeric(as.factor(cluster[,1])))\r\n level.cluster.2 <- unique(as.numeric(as.factor(cluster[,2])))\r\n if(length(level.cluster.1)<=length(level.cluster.2)){\r\n raw.cluster <- as.numeric(as.factor(cluster[,1]))\r\n bootcluster <- colnames(cluster)[1]\r\n }else{\r\n raw.cluster <- as.numeric(as.factor(cluster[,2]))\r\n bootcluster <- colnames(cluster)[2]\r\n }\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }else{\r\n raw.cluster <- as.numeric(as.factor(cluster[,bootcluster]))\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }\r\n cat(paste0(\"Mutiple Clustered Wild Bootstrap: (Boot)Clustered at \",bootcluster,\" level.\\n\"))\r\n }\r\n }\r\n\r\n if(length(en.var.x)>0){\r\n en.var.x.matrix <- matrix(dx[,en.var.x],ncol=length(en.var.x))\r\n sub.lm <- simpleols(Y=en.var.x.matrix,X=dz)\r\n en.var.x.coef <- sub.lm[['coef']]\r\n colnames(en.var.x.coef) <- en.var.x\r\n en.var.x.hat <- sub.lm[['Y_hat']]\r\n colnames(en.var.x.hat) <- en.var.x\r\n res.en.x <- en.var.x.res <- sub.lm[[\"u_hat\"]]\r\n en.var.x.fitted <- matrix(x[,en.var.x],ncol=length(en.var.x)) - en.var.x.res\r\n colnames(en.var.x.fitted) <- colnames(en.var.x.res) <- colnames(en.var.x.hat) <- en.var.x \r\n }\r\n ####\r\n\r\n if (refinement == 0) {\r\n ## wild bootstrap\r\n cat(\"Wild Bootstrap without Percentile-t Refinement.\\n\")\r\n boot.coef <- matrix(NA, p, nboots)\r\n \r\n one.boot <- function(num = NULL) { \r\n if (!is.null(cluster)) {\r\n ## wild boot by cluster \r\n res.p <- rep(0,dim(cluster)[1])\r\n for (j in 1:length(level.cluster)) {\r\n temp.level <- sample(c(-1,1), 1)\r\n res.p[which(raw.cluster==level.cluster[j])] <- temp.level\r\n }\r\n res.p <- as.matrix(res.p)\r\n } else {\r\n res.p <- as.matrix(sample(c(-1, 1), dim(y)[1], replace = TRUE))\r\n }\r\n\r\n y.boot <- y.fit + res.p * res \r\n \r\n if(length(en.var.x)>0){\r\n x.boot.en <- matrix(en.var.x.fitted + sweep(en.var.x.res, MARGIN=1, res.p, `*`),ncol=length(en.var.x)) \r\n colnames(x.boot.en) <- en.var.x\r\n }else{\r\n x.boot.en <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n }\r\n \r\n if(length(ex.var.x)>0){\r\n x.boot.ex <- matrix(x[,ex.var.x],ncol=length(ex.var.x))\r\n colnames(x.boot.ex) <- ex.var.x\r\n }else{\r\n x.boot.ex <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n }\r\n\r\n x.boot <- cbind(x.boot.en,x.boot.ex)\r\n x.boot <- x.boot[,colnames(x)]\r\n #only need coefficients, don't need se\r\n boot.model.iv <- try(ivfastplm.core(y = y.boot, x = x.boot,z=z, ind = ind, \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num,\r\n df.use = df.use, df.cl.use = df.cl.use, need.fe = FALSE, iv.test = FALSE\r\n ))\r\n\r\n if ('try-error' %in% class(boot.model.iv)) {\r\n return(rep(NA, p))\r\n } else {\r\n return(c(boot.model.iv$coefficients))\r\n }\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n\r\n P_x <- as.matrix(apply(boot.coef, 1, get.pvalue))\r\n stderror <- as.matrix(apply(boot.coef, 1, sd, na.rm = TRUE))\r\n\r\n CI <- t(apply(boot.coef, 1, quantile, c(0.025, 0.975), na.rm = TRUE))\r\n ## rewrite uncertainty estimates\r\n est.coefficients <- cbind(model.iv$coefficients, stderror,model.iv$coefficients/stderror, P_x, CI)\r\n colnames(est.coefficients) <- c(\"Coef\", \"Std. Error\",\"Z Value\", \"P Value\", \"CI_lower\", \"CI_upper\")\r\n rownames(est.coefficients) <- colnames(dx)\r\n model.iv$est.coefficients <- est.coefficients\r\n\r\n vcov <- as.matrix(cov(t(boot.coef),use=\"na.or.complete\"))\r\n colnames(vcov) <- colnames(dx)\r\n rownames(vcov) <- colnames(dx)\r\n model.iv$vcov <- vcov\r\n }\r\n\r\n if(refinement==1){\r\n if(is.null(pos)==TRUE){\r\n cat(\"Unrestricted Wild Bootstrap with Percentile-t Refinement.\\n\")\r\n boot.coef <- matrix(NA, p, nboots)\r\n boot.wald <- rep(NA, nboots)\r\n one.boot <- function(num = NULL) {\r\n if (!is.null(cluster)) {\r\n ## wild boot by cluster \r\n res.p <- rep(0,dim(cluster)[1])\r\n for (j in 1:length(level.cluster)) {\r\n temp.level <- sample(c(-1,1), 1)\r\n res.p[which(raw.cluster==level.cluster[j])] <- temp.level\r\n }\r\n res.p <- as.matrix(res.p)\r\n } else {\r\n res.p <- as.matrix(sample(c(-1, 1), dim(y)[1], replace = TRUE))\r\n }\r\n y.boot <- y.fit + res.p * res \r\n \r\n if(length(en.var.x)>0){\r\n x.boot.en <- matrix(en.var.x.fitted + sweep(en.var.x.res, MARGIN=1, res.p, `*`),ncol=length(en.var.x)) \r\n colnames(x.boot.en) <- en.var.x\r\n }else{\r\n x.boot.en <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n }\r\n \r\n if(length(ex.var.x)>0){\r\n x.boot.ex <- matrix(x[,ex.var.x],ncol=length(ex.var.x))\r\n colnames(x.boot.ex) <- ex.var.x\r\n }else{\r\n x.boot.ex <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n } \r\n x.boot <- cbind(x.boot.en,x.boot.ex)\r\n x.boot <- x.boot[,colnames(x)]\r\n\r\n boot.model <- try(ivfastplm.core(y = y.boot, x = x.boot, z = z, ind = ind, robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = cluster, core.num = core.num,\r\n df.use = df.use,\r\n df.cl.use = df.cl.use))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(NA)\r\n } else {\r\n boot.use <- (boot.model$est.coefficients[, \"Coef\"]-model.iv$est.coefficients[, \"Coef\"])/boot.model$est.coefficients[, \"Std. Error\"]\r\n return(c(boot.use))\r\n }\r\n }\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n wald.refinement <- matrix(NA,nrow=0,ncol=6)\r\n null.totest <- model.iv$est.coefficients[,'t value']\r\n for(k in 1:length(null.totest)){\r\n p.refine <- get.pvalue(boot.coef[k,],to_test=null.totest[k])\r\n CI.refine <- quantile(boot.coef[k,], probs = c(0.025,0.975), na.rm = TRUE)*model.iv$est.coefficients[k,'Std. Error'] + model.iv$est.coefficients[k,'Coef']\r\n refine.sub <- c(model.iv$est.coefficients[k,'Coef'],\r\n model.iv$est.coefficients[k,'Std. Error'],\r\n model.iv$est.coefficients[k,'t value'],\r\n p.refine,\r\n CI.refine[1],\r\n CI.refine[2])\r\n wald.refinement <- rbind(wald.refinement,refine.sub)\r\n }\r\n colnames(wald.refinement) <- c(\"Coef\",\"Std Error\",\"t value\",\r\n \"Refined P value\",\"Refined CI_lower\",\r\n \"Refined CI_upper\")\r\n rownames(wald.refinement) <- colnames(dx)\r\n model.iv$refinement <- list(wald.refinement = wald.refinement, boot.wald = boot.coef)\r\n }\r\n\r\n if(is.null(pos)==FALSE){\r\n\r\n if(is.null(test.value)==TRUE){\r\n test.value <- 0\r\n }\r\n\r\n if(pos %in% en.var.x){\r\n cat(\"Restricted Wild Efficient Residual Bootstrap with Percentile-t Refinement.\\n\")\r\n if(length(en.var.x)==1){\r\n dy.restricted <- dy - test.value*dx[,en.var.x]\r\n dx1 <- matrix(dx[,ex.var.x],ncol=length(ex.var.x))\r\n inv.dx1 <- solve(t(dx1)%*%dx1)\r\n reg1.coef <- inv.dx1%*%t(dx1)%*%dy.restricted\r\n u.hat1 <- dy.restricted - dx1%*%reg1.coef\r\n \r\n dy2 <- dx[,en.var.x]\r\n dx2 <- cbind(dz,u.hat1)\r\n inv.dx2 <- solve(t(dx2)%*%dx2)\r\n reg2.coef <- inv.dx2%*%t(dx2)%*%dy2\r\n u.hat2 <- dy2 - dx2%*%reg2.coef + reg2.coef[dim(dz)[2]+1,1]*u.hat1\r\n\r\n one.boot <- function(num = NULL) {\r\n if (!is.null(cluster)) {\r\n ## wild boot by cluster \r\n res.p <- rep(0,dim(cluster)[1])\r\n for (j in 1:length(level.cluster)) {\r\n temp.level <- sample(c(-1,1), 1)\r\n res.p[which(raw.cluster==level.cluster[j])] <- temp.level\r\n }\r\n res.p <- as.matrix(res.p)\r\n } else {\r\n res.p <- as.matrix(sample(c(-1, 1), dim(y)[1], replace = TRUE))\r\n }\r\n\r\n y.boot <- y-u.hat1+res.p * u.hat1\r\n en.x.boot <- x[,en.var.x] - u.hat2 + res.p * u.hat2\r\n\r\n if(length(ex.var.x)>0){\r\n x.boot.ex <- matrix(x[,ex.var.x],ncol=length(ex.var.x))\r\n colnames(x.boot.ex) <- ex.var.x\r\n }else{\r\n x.boot.ex <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n } \r\n\r\n x.boot <- cbind(en.x.boot,x.boot.ex)\r\n colnames(x.boot) <- c(en.var.x,ex.var.x) \r\n x.boot <- x.boot[,colnames(x)] \r\n\r\n boot.model <- try(ivfastplm.core(y = y.boot, x = x.boot, z = z, ind = ind, robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = cluster, core.num = core.num,\r\n df.use = df.use,\r\n df.cl.use = df.cl.use))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(NA)\r\n } else {\r\n boot.use <- (boot.model$est.coefficients[pos, \"Coef\"]-test.value)/boot.model$est.coefficients[pos, \"Std. Error\"]\r\n return(c(boot.use))\r\n }\r\n }\r\n }\r\n\r\n if(length(en.var.x)>1){\r\n dy.restricted <- dy - test.value*dx[,pos]\r\n en.dx <- dx[,en.var.x]\r\n pos.index <- which(colnames(en.dx)==pos)\r\n en.dx.other <- matrix(en.dx[,-pos.index],ncol=dim(en.dx)[2]-1)\r\n \r\n #a simple 2sls\r\n dx1 <- cbind(en.dx.other,dx[,ex.var.x])\r\n inv.dx1 <- solve(t(dx1)%*%dx1)\r\n dz1 <- dz\r\n inv.dz1 <- inv.z\r\n Pdz1 <- dz1%*%inv.dz1%*%t(dz1)\r\n inv.dxPzdx1 <- solve(t(dx1)%*%Pdz1%*%dx1)\r\n reg1.coef <- matrix(inv.dxPzdx1%*%t(dx1)%*%Pdz1%*%dy.restricted,ncol=1)\r\n u.hat1 <- matrix(dy.restricted-dx1%*%reg1.coef,ncol=1)\r\n\r\n dy2 <- dx[,en.var.x]\r\n dx2 <- cbind(dz,u.hat1)\r\n inv.dx2 <- solve(t(dx2)%*%dx2)\r\n reg2.coef <- inv.dx2%*%t(dx2)%*%dy2\r\n\r\n reg2.coef.slice <- matrix(reg2.coef[(dim(dz)[2]+1),],ncol=dim(reg2.coef)[2])\r\n u.hat2 <- dy2 - dx2%*%reg2.coef + kronecker(reg2.coef.slice,u.hat1)\r\n\r\n one.boot <- function(num = NULL) {\r\n if (!is.null(cluster)) {\r\n ## wild boot by cluster \r\n res.p <- rep(0,dim(cluster)[1])\r\n for (j in 1:length(level.cluster)) {\r\n temp.level <- sample(c(-1,1), 1)\r\n res.p[which(raw.cluster==level.cluster[j])] <- temp.level\r\n }\r\n res.p <- as.matrix(res.p)\r\n } else {\r\n res.p <- as.matrix(sample(c(-1, 1), dim(y)[1], replace = TRUE))\r\n }\r\n\r\n y.boot <- y-u.hat1+res.p * u.hat1\r\n en.x.boot <- matrix(x[,en.var.x] - u.hat2 + sweep(u.hat2, MARGIN=1, res.p, `*`),ncol=length(en.var.x))\r\n if(length(ex.var.x)>0){\r\n x.boot.ex <- matrix(x[,ex.var.x],ncol=length(ex.var.x))\r\n colnames(x.boot.ex) <- ex.var.x\r\n }else{\r\n x.boot.ex <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n } \r\n\r\n x.boot <- cbind(en.x.boot,x.boot.ex)\r\n colnames(x.boot) <- c(en.var.x,ex.var.x) \r\n x.boot <- x.boot[,colnames(x)] \r\n\r\n boot.model <- try(ivfastplm.core(y = y.boot, x = x.boot, z = z, ind = ind, robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = cluster, core.num = core.num,\r\n df.use = df.use,\r\n df.cl.use = df.cl.use))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(NA)\r\n } else {\r\n boot.use <- (boot.model$est.coefficients[pos, \"Coef\"]-test.value)/boot.model$est.coefficients[pos, \"Std. Error\"]\r\n return(c(boot.use))\r\n }\r\n }\r\n }\r\n\r\n boot.wald <- rep(NA, nboots)\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n boot.wald <- c(unlist(boot.out)) \r\n } else {\r\n for (i in 1:nboots) {\r\n boot.wald[i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n\r\n p.refine <- get.pvalue(boot.wald,to_test=(model.iv$est.coefficients[pos, \"Coef\"]-test.value)/model.iv$est.coefficients[pos, \"Std. Error\"])\r\n CI.refine <- quantile(boot.wald, probs = c(0.025,0.975), na.rm = TRUE)*model.iv$est.coefficients[pos,'Std. Error'] + model.iv$est.coefficients[pos, \"Coef\"]\r\n\r\n refine.sub <- c(model.iv$est.coefficients[pos,'Coef'],\r\n model.iv$est.coefficients[pos,'Std. Error'],\r\n (model.iv$est.coefficients[pos, \"Coef\"]-test.value)/model.iv$est.coefficients[pos, \"Std. Error\"],\r\n p.refine,\r\n #paste0(pos,\"=\",test.value),\r\n CI.refine[1],\r\n CI.refine[2])\r\n \r\n refine.sub <- matrix(refine.sub,nrow=1)\r\n #refine.sub <- as.data.frame(refine.sub)\r\n rownames(refine.sub) <- pos\r\n colnames(refine.sub) <- c(\"Coef\",\"Std Error\",\"t value(H0)\",\r\n \"Refined P value\",\"Refined CI_lower\",\"Refined CI_upper\")\r\n model.iv$refinement <- list(wald.refinement = refine.sub, boot.wald = boot.wald)\r\n }\r\n\r\n if(pos %in% ex.var.x){\r\n # wild restricted residual bootstrap\r\n cat(\"Wild Restricted Bootstrap with Percentile-t Refinement.\\n\")\r\n boot.wald <- rep(NA, nboots)\r\n\r\n dy.restricted <- dy - test.value*dx[,pos]\r\n pos.index <- which(colnames(dx)==pos)\r\n dx.other <- matrix(dx[,-pos.index],ncol=dim(dx)[2]-1)\r\n\r\n #a simple 2sls\r\n dx1 <- dx.other\r\n inv.dx1 <- solve(t(dx1)%*%dx1)\r\n dz1 <- dz\r\n inv.dz1 <- inv.z\r\n Pdz1 <- dz1%*%inv.dz1%*%t(dz1)\r\n inv.dxPzdx1 <- solve(t(dx1)%*%Pdz1%*%dx1)\r\n reg1.coef <- matrix(inv.dxPzdx1%*%t(dx1)%*%Pdz1%*%dy.restricted,ncol=1)\r\n u.hat1 <- matrix(dy.restricted-dx1%*%reg1.coef,ncol=1)\r\n\r\n boot.wald <- rep(NA, nboots)\r\n one.boot <- function(num = NULL) {\r\n if (!is.null(cluster)) {\r\n ## wild boot by cluster \r\n res.p <- rep(0,dim(cluster)[1])\r\n for (j in 1:length(level.cluster)) {\r\n temp.level <- sample(c(-1,1), 1)\r\n res.p[which(raw.cluster==level.cluster[j])] <- temp.level\r\n }\r\n res.p <- as.matrix(res.p)\r\n } else {\r\n res.p <- as.matrix(sample(c(-1, 1), dim(y)[1], replace = TRUE))\r\n }\r\n y.boot <- y - u.hat1 + res.p * u.hat1\r\n x.boot.en <- matrix(en.var.x.fitted + sweep(en.var.x.res, MARGIN=1, res.p, `*`),ncol=length(en.var.x))\r\n colnames(x.boot.en) <- en.var.x\r\n\r\n if(length(ex.var.x)>0){\r\n x.boot.ex <- matrix(x[,ex.var.x],ncol=length(ex.var.x))\r\n colnames(x.boot.ex) <- ex.var.x\r\n }else{\r\n x.boot.ex <- matrix(NA,ncol=0,nrow=dim(dy)[1])\r\n } \r\n\r\n x.boot <- cbind(x.boot.en,x.boot.ex)\r\n colnames(x.boot) <- c(en.var.x,ex.var.x) \r\n x.boot <- x.boot[,colnames(x)] \r\n\r\n boot.model <- try(ivfastplm.core(y = y.boot, x = x.boot, z = z, ind = ind, robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = cluster, core.num = core.num,\r\n df.use = df.use,\r\n df.cl.use = df.cl.use))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(NA)\r\n } else {\r\n boot.use <- (boot.model$est.coefficients[pos, \"Coef\"]-test.value)/boot.model$est.coefficients[pos, \"Std. Error\"]\r\n return(c(boot.use))\r\n }\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.wald[j] <- c(unlist(boot.out[[j]])) \r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.wald[i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n \r\n p.refine <- get.pvalue(boot.wald,to_test=(model.iv$est.coefficients[pos, \"Coef\"]-test.value)/model.iv$est.coefficients[pos, \"Std. Error\"])\r\n CI.refine <- quantile(boot.wald, probs = c(0.025,0.975), na.rm = TRUE)*model.iv$est.coefficients[pos,'Std. Error'] + model.iv$est.coefficients[pos, \"Coef\"]\r\n\r\n refine.sub <- c(model.iv$est.coefficients[pos,'Coef'],\r\n model.iv$est.coefficients[pos,'Std. Error'],\r\n (model.iv$est.coefficients[pos, \"Coef\"]-test.value)/model.iv$est.coefficients[pos, \"Std. Error\"],\r\n p.refine,\r\n #paste0(pos,\"=\",test.value),\r\n CI.refine[1],\r\n CI.refine[2])\r\n \r\n refine.sub <- matrix(refine.sub,nrow=1)\r\n #refine.sub <- as.data.frame(refine.sub)\r\n rownames(refine.sub) <- pos\r\n colnames(refine.sub) <- c(\"Coef\",\"Std Error\",\"t value(H0)\",\r\n \"Refined P value\",\"Refined CI_lower\",\"Refined CI_upper\")\r\n model.iv$refinement <- list(wald.refinement = refine.sub, boot.wald = boot.wald)\r\n }\r\n }\r\n }\r\n }\r\n else{\r\n \r\n if (jackknife ==1 || is.null(cluster)) { \r\n jack.pos <- 1\r\n if (jackknife == 1) {\r\n nboots <- length(unique(ind[,1]))\r\n jack.pos <- as.numeric(as.factor(ind[,1]))\r\n }\r\n boot.coef <- matrix(NA, p, nboots)\r\n \r\n if(refinement==0){\r\n if (jackknife == 1){\r\n cat(\"Jackknife without Percentile-t Refinement.\\n\")\r\n }else{\r\n cat(\"Pairs Bootstrap without Percentile-t Refinement.\\n\")\r\n }\r\n\r\n one.boot <- function(num = NULL) {\r\n if (is.null(num)) {\r\n boot.id <- sample(1:dim(y)[1], dim(y)[1], replace = TRUE)\r\n } else {\r\n boot.id <- (1:dim(y)[1])[which(jack.pos != num)]\r\n }\r\n \r\n boot.model <- try(ivfastplm.core(y = as.matrix(y[boot.id,]), \r\n x = as.matrix(x[boot.id,]),\r\n z = as.matrix(z[boot.id,]), \r\n ind = as.matrix(ind[boot.id,]), \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num))\r\n \r\n if ('try-error' %in% class(boot.model)) {\r\n return(rep(NA, p))\r\n } else {\r\n return(c(boot.model$coefficients))\r\n }\r\n }\r\n\r\n boot.seq <- NULL\r\n if (jackknife == 1) {\r\n boot.seq <- unique(jack.pos)\r\n }\r\n \r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot(boot.seq[j]))\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot(boot.seq[i])\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n\r\n if (jackknife == 0) {\r\n P_x <- as.matrix(apply(boot.coef, 1, get.pvalue))\r\n stderror <- as.matrix(apply(boot.coef, 1, sd, na.rm = TRUE))\r\n CI <- t(apply(boot.coef, 1, quantile, c(0.025, 0.975), na.rm = TRUE))\r\n ## rewrite uncertainty estimates\r\n est.coefficients <- cbind(model.iv$coefficients, stderror,model.iv$coefficients/stderror, P_x, CI)\r\n vcov <- as.matrix(cov(t(boot.coef),use=\"na.or.complete\"))\r\n } else {\r\n beta.j <- jackknifed(model.iv$coefficients, boot.coef, 0.05)\r\n est.coefficients <- cbind(model.iv$coefficients, beta.j$se,model.iv$coefficients/beta.j$se, beta.j$P, beta.j$CI.l, beta.j$CI.u)\r\n vcov <- beta.j$Yvcov\r\n }\r\n colnames(est.coefficients) <- c(\"Coef\", \"Std. Error\",\"Z Value\", \"P Value\", \"CI_lower\", \"CI_upper\")\r\n rownames(est.coefficients) <- colnames(dx)\r\n model.iv$est.coefficients <- est.coefficients\r\n\r\n colnames(vcov) <- colnames(dx)\r\n rownames(vcov) <- colnames(dx)\r\n model.iv$vcov <- vcov\r\n }\r\n\r\n if(refinement==1){\r\n if (jackknife == 1){\r\n stop(\"Can't refine a Jackknife P-Value,\\n\")\r\n #cat(\"Jackknife with Percentile-t Refinement.\\n\")\r\n }else{\r\n cat(\"Pairs Bootstrap with Percentile-t Refinement.\\n\")\r\n }\r\n\r\n one.boot <- function(num = NULL) {\r\n boot.id <- sample(1:dim(y)[1], dim(y)[1], replace = TRUE)\r\n boot.model <- try(ivfastplm.core(y = as.matrix(y[boot.id,]), \r\n x = as.matrix(x[boot.id,]), \r\n z = as.matrix(z[boot.id,]),\r\n ind = as.matrix(ind[boot.id,]), \r\n robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = NULL, core.num = core.num))\r\n \r\n if ('try-error' %in% class(boot.model)) {\r\n return(rep(NA, p))\r\n } else {\r\n boot.use <- (boot.model$est.coefficients[, \"Coef\"]-model.iv$est.coefficients[, \"Coef\"])/boot.model$est.coefficients[, \"Std. Error\"]\r\n return(c(boot.use))\r\n }\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n \r\n wald.refinement <- matrix(NA,nrow=0,ncol=6)\r\n null.totest <- model.iv$est.coefficients[,'t value']\r\n for(k in 1:length(null.totest)){\r\n p.refine <- get.pvalue(boot.coef[k,],to_test=null.totest[k])\r\n CI.refine <- quantile(boot.coef[k,], probs = c(0.025,0.975), na.rm = TRUE)*model.iv$est.coefficients[k,'Std. Error'] + model.iv$est.coefficients[k,'Coef']\r\n refine.sub <- c(model.iv$est.coefficients[k,'Coef'],\r\n model.iv$est.coefficients[k,'Std. Error'],\r\n model.iv$est.coefficients[k,'t value'],\r\n p.refine,\r\n CI.refine[1],\r\n CI.refine[2])\r\n wald.refinement <- rbind(wald.refinement,refine.sub)\r\n }\r\n colnames(wald.refinement) <- c(\"Coef\",\"Std Error\",\"t value\",\r\n \"Refined P value\",\"Refined CI_lower\",\r\n \"Refined CI_upper\")\r\n rownames(wald.refinement) <- colnames(dx)\r\n model.iv$refinement <- list(wald.refinement = wald.refinement, boot.wald = boot.coef)\r\n }\r\n }\r\n else{ #clustered\r\n if (refinement == 0) {\r\n cat(\"Pairs Bootstrap without Percentile-t Refinement.\\n\")\r\n boot.coef <- matrix(NA, p, nboots)\r\n if (dim(cluster)[2] == 1) {\r\n ## one-way cluster \r\n raw.cluster <- as.numeric(as.factor(cluster))\r\n level.cluster <- unique(raw.cluster)\r\n ## level.count <- table(raw.cluster)\r\n split.id <- split(1:dim(y)[1], raw.cluster)\r\n one.boot <- function(num = NULL) {\r\n level.id <- sample(level.cluster, length(level.cluster), replace = TRUE)\r\n boot.id <- c()\r\n for (j in 1:length(level.id)) {\r\n boot.id <- c(boot.id, split.id[[level.id[j]]])\r\n }\r\n boot.model <- try(ivfastplm.core(y = as.matrix(y[boot.id,]), \r\n x = as.matrix(x[boot.id,]), \r\n z = as.matrix(z[boot.id,]),\r\n ind = as.matrix(ind[boot.id,]), \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(rep(NA, p))\r\n } else {\r\n return(c(boot.model$coefficients))\r\n }\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n\r\n P_x <- as.matrix(apply(boot.coef, 1, get.pvalue))\r\n stderror <- as.matrix(apply(boot.coef, 1, sd, na.rm = TRUE))\r\n CI <- t(apply(boot.coef, 1, quantile, c(0.025, 0.975), na.rm = TRUE))\r\n ## rewrite uncertainty estimates\r\n est.coefficients <- cbind(model.iv$coefficients, stderror,model.iv$coefficients/stderror, P_x, CI)\r\n colnames(est.coefficients) <- c(\"Coef\", \"Std. Error\",\"Z Value\", \"P Value\", \"CI_lower\", \"CI_upper\")\r\n rownames(est.coefficients) <- colnames(dx)\r\n model.iv$est.coefficients <- est.coefficients\r\n\r\n vcov <- as.matrix(cov(t(boot.coef),use=\"na.or.complete\"))\r\n colnames(vcov) <- colnames(dx)\r\n rownames(vcov) <- colnames(dx)\r\n model.iv$vcov <- vcov\r\n }\r\n else if (dim(cluster)[2] == 2) {\r\n ## two-way cluster\r\n boot.coef1 <- boot.coef2 <- boot.coef3 <- matrix(NA, p, nboots)\r\n ## level 1\r\n raw.cluster1 <- as.numeric(as.factor(cluster[,1]))\r\n level.cluster1 <- unique(raw.cluster1)\r\n ## level 2\r\n raw.cluster2 <- as.numeric(as.factor(cluster[,2]))\r\n level.cluster2 <- unique(raw.cluster2)\r\n ## intersection\r\n raw.cluster3 <- as.numeric(as.factor(paste(cluster[,1], \"-:-\", cluster[,2], sep = \"\")))\r\n level.cluster3 <- unique(raw.cluster3)\r\n\r\n split.id1 <- split(1:dim(y)[1], raw.cluster1)\r\n split.id2 <- split(1:dim(y)[1], raw.cluster2)\r\n split.id3 <- split(1:dim(y)[1], raw.cluster3)\r\n\r\n one.boot <- function(num = NULL) {\r\n ## level 1\r\n level.id1 <- sample(level.cluster1, length(level.cluster1), replace = TRUE)\r\n boot.id1 <- c()\r\n for (j in 1:length(level.id1)) {\r\n boot.id1 <- c(boot.id1, split.id1[[level.id1[j]]])\r\n }\r\n\r\n ## level 2\r\n level.id2 <- sample(level.cluster2, length(level.cluster2), replace = TRUE)\r\n boot.id2 <- c()\r\n for (j in 1:length(level.id2)) {\r\n boot.id2 <- c(boot.id2, split.id2[[level.id2[j]]])\r\n }\r\n\r\n ## intersection\r\n level.id3 <- sample(level.cluster3, length(level.cluster3), replace = TRUE)\r\n boot.id3 <- c()\r\n for (j in 1:length(level.id3)) {\r\n boot.id3 <- c(boot.id3, split.id3[[level.id3[j]]])\r\n }\r\n\r\n ## level 1\r\n boot.model1 <- try(ivfastplm.core(y = as.matrix(y[boot.id1,]), \r\n x = as.matrix(x[boot.id1,]),\r\n z = as.matrix(z[boot.id1,]), \r\n ind = as.matrix(ind[boot.id1,]), \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num))\r\n if ('try-error' %in% class(boot.model1)) {\r\n oneboot.coef1 <- rep(NA, p)\r\n } else {\r\n oneboot.coef1 <- c(boot.model1$coefficients)\r\n }\r\n\r\n ## level 2\r\n boot.model2 <- try(ivfastplm.core (y = as.matrix(y[boot.id2,]), \r\n x = as.matrix(x[boot.id2,]), \r\n z = as.matrix(z[boot.id2,]),\r\n ind = as.matrix(ind[boot.id2,]), \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num))\r\n if ('try-error' %in% class(boot.model2)) {\r\n oneboot.coef2 <- rep(NA, p)\r\n } else {\r\n oneboot.coef2 <- c(boot.model2$coefficients)\r\n }\r\n\r\n ## intersection\r\n boot.model3 <- try(ivfastplm.core (y = as.matrix(y[boot.id3,]), \r\n x = as.matrix(x[boot.id3,]), \r\n z = as.matrix(z[boot.id3,]),\r\n ind = as.matrix(ind[boot.id3,]), \r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 0, cl = NULL, core.num = core.num))\r\n if ('try-error' %in% class(boot.model3)) {\r\n oneboot.coef3 <- rep(NA, p)\r\n } else {\r\n oneboot.coef3 <- c(boot.model3$coefficients)\r\n }\r\n\r\n return(list(oneboot.coef1 = oneboot.coef1, \r\n oneboot.coef2 = oneboot.coef2,\r\n oneboot.coef3 = oneboot.coef3))\r\n\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef1[, j] <- c(boot.out[[j]]$oneboot.coef1)\r\n boot.coef2[, j] <- c(boot.out[[j]]$oneboot.coef2)\r\n boot.coef3[, j] <- c(boot.out[[j]]$oneboot.coef3)\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.sub <- one.boot()\r\n boot.coef1[, i] <- c(boot.sub$oneboot.coef1)\r\n boot.coef2[, i] <- c(boot.sub$oneboot.coef2)\r\n boot.coef3[, i] <- c(boot.sub$oneboot.coef3)\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n \r\n stderror1 <- as.matrix(apply(boot.coef1, 1, sd, na.rm = TRUE))\r\n stderror2 <- as.matrix(apply(boot.coef2, 1, sd, na.rm = TRUE))\r\n stderror3 <- as.matrix(apply(boot.coef3, 1, sd, na.rm = TRUE))\r\n stderror <- stderror1 + stderror2 - stderror3\r\n\r\n vcov1 <- as.matrix(cov(t(boot.coef1),use=\"na.or.complete\"))\r\n vcov2 <- as.matrix(cov(t(boot.coef2),use=\"na.or.complete\"))\r\n vcov3 <- as.matrix(cov(t(boot.coef3),use=\"na.or.complete\"))\r\n vcov <- vcov1 + vcov2 - vcov3\r\n colnames(vcov) <- colnames(dx)\r\n rownames(vcov) <- colnames(dx)\r\n model.iv$vcov <- vcov\r\n\r\n CI <- cbind(c(model.iv$coefficients) - qnorm(0.975)*c(stderror), c(model.iv$coefficients) + qnorm(0.975)*c(stderror))\r\n Zx <- c(model.iv$coefficients)/c(stderror)\r\n P_z <- 2 * min(1 - pnorm(Zx), pnorm(Zx))\r\n ## rewrite uncertainty estimates\r\n est.coefficients <- cbind(model.iv$coefficients, stderror, Zx, P_z, CI)\r\n colnames(est.coefficients) <- c(\"Coef\", \"Std. Error\", \"Z value\", \"Pr(>|Z|)\", \"CI_lower\", \"CI_upper\")\r\n rownames(est.coefficients) <- colnames(dx)\r\n model.iv$est.coefficients <- est.coefficients\r\n }\r\n }\r\n\r\n if(refinement==1){\r\n cat(\"Pairs Bootstrap with Percentile-t Refinement.\\n\")\r\n ## bootstrap refinement \r\n boot.coef <- matrix(NA, p, nboots)\r\n\r\n if(dim(cluster)[2]==1){\r\n ## one-way cluster \r\n raw.cluster <- as.numeric(as.factor(cluster))\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }\r\n if(dim(cluster)[2]==2){\r\n stop(\"For pairs bootstrap with refinement, please only specify one cluster variable.\\n\")\r\n if(is.null(bootcluster)){\r\n level.cluster.1 <- unique(as.numeric(as.factor(cluster[,1])))\r\n level.cluster.2 <- unique(as.numeric(as.factor(cluster[,2])))\r\n if(length(level.cluster.1)<=length(level.cluster.2)){\r\n raw.cluster <- as.numeric(as.factor(cluster[,1]))\r\n bootcluster <- colnames(cluster)[1]\r\n }else{\r\n raw.cluster <- as.numeric(as.factor(cluster[,2]))\r\n bootcluster <- colnames(cluster)[2]\r\n }\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }else{\r\n raw.cluster <- as.numeric(as.factor(cluster[,bootcluster]))\r\n level.cluster <- unique(raw.cluster)\r\n level.count <- table(raw.cluster)\r\n }\r\n cat(paste0(\"Mutiple Clustered Pairs Bootstrap with Refinement: (Boot)Clustered at \",bootcluster,\" level.\\n\"))\r\n }\r\n\r\n split.id <- split(1:dim(y)[1], raw.cluster)\r\n\r\n one.boot <- function(num = NULL) {\r\n level.id <- sample(level.cluster, length(level.cluster), replace = TRUE)\r\n boot.id <- c()\r\n boot.cluster <- c()\r\n for (j in 1:length(level.id)) {\r\n boot.id <- c(boot.id, split.id[[level.id[j]]])\r\n boot.cluster <- c(boot.cluster, rep(j, level.count[level.id[j]]))\r\n }\r\n boot.cluster <- as.matrix(boot.cluster)\r\n\r\n boot.model <- try(ivfastplm.core (y = as.matrix(y[boot.id,]), \r\n x = as.matrix(x[boot.id,]), \r\n z = as.matrix(z[boot.id,]),\r\n ind = as.matrix(ind[boot.id,]), robust = robust,\r\n sfe.index = sfe.index, cfe.index = cfe.index, \r\n se = 1, cl = boot.cluster, core.num = core.num))\r\n if ('try-error' %in% class(boot.model)) {\r\n return(rep(NA, p))\r\n } else {\r\n return(c(boot.model$coefficients - model.iv$coefficients)/c(boot.model$est.coefficients[, \"Std. Error\"]))\r\n }\r\n }\r\n\r\n if (parallel == TRUE) {\r\n boot.out <- foreach(j = 1:nboots, \r\n .inorder = FALSE,\r\n .export = use.fun,\r\n .packages = c(\"fastplm\")\r\n ) %dopar% {\r\n return(one.boot())\r\n }\r\n for (j in 1:nboots) { \r\n boot.coef[, j] <- c(boot.out[[j]])\r\n }\r\n } else {\r\n for (i in 1:nboots) {\r\n boot.coef[, i] <- one.boot()\r\n if (i%%50==0) cat(i) else cat(\".\")\r\n }\r\n }\r\n \r\n wald.refinement <- matrix(NA,nrow=0,ncol=6)\r\n null.totest <- model.iv$est.coefficients[,'t value']\r\n for(k in 1:length(null.totest)){\r\n p.refine <- get.pvalue(boot.coef[k,],to_test=null.totest[k])\r\n CI.refine <- quantile(boot.coef[k,], probs = c(0.025,0.975), na.rm = TRUE)*model.iv$est.coefficients[k,'Std. Error'] + model.iv$est.coefficients[k,'Coef']\r\n refine.sub <- c(model.iv$est.coefficients[k,'Coef'],\r\n model.iv$est.coefficients[k,'Std. Error'],\r\n model.iv$est.coefficients[k,'t value'],\r\n p.refine,\r\n CI.refine[1],\r\n CI.refine[2])\r\n wald.refinement <- rbind(wald.refinement,refine.sub)\r\n }\r\n colnames(wald.refinement) <- c(\"Coef\",\"Std Error\",\"t value\",\r\n \"Refined P value\",\"Refined CI_lower\",\r\n \"Refined CI_upper\")\r\n rownames(wald.refinement) <- colnames(dx)\r\n model.iv$refinement <- list(wald.refinement = wald.refinement, boot.wald = boot.coef)\r\n } \r\n }\r\n }\r\n if(parallel==TRUE){\r\n suppressWarnings(stopCluster(pcl))\r\n }\r\n\r\n \r\n\r\n\r\n\r\n\r\n return(model.iv)\r\n \r\n}", "meta": {"hexsha": "210511ea28e2a5e24171b46db561384b267f8e40", "size": 52831, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ivfastplm_boot.r", "max_stars_repo_name": "xuyiqing/fastplm", "max_stars_repo_head_hexsha": "f80a68cfa890774f83713637ce969ace4bb0c9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2019-09-02T17:33:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-06T12:54:00.000Z", "max_issues_repo_path": "R/ivfastplm_boot.r", "max_issues_repo_name": "xuyiqing/fastplm", "max_issues_repo_head_hexsha": "f80a68cfa890774f83713637ce969ace4bb0c9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-01-07T17:07:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-20T05:58:47.000Z", "max_forks_repo_path": "R/ivfastplm_boot.r", "max_forks_repo_name": "xuyiqing/fastplm", "max_forks_repo_head_hexsha": "f80a68cfa890774f83713637ce969ace4bb0c9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2019-09-02T00:58:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-22T00:55:38.000Z", "avg_line_length": 51.7950980392, "max_line_length": 176, "alphanum_fraction": 0.3678332797, "num_tokens": 10901, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.32660313102624683}} {"text": "####################################################################\n#\n# LD-Calculation Chicken Data\n#\n####################################################################\n\n### NH (Thuy) wrote this script for Chicken and sent it to TB (Tim) on May 17, \n### 2017, to modify for maize data\n\nwqsGeno <- read.csv(\"/home/beissinger/Documents/WQS/DATA/genoWQS_randImp.csv\",stringsAsFactors=F,header=T)\nwqsMap <- read.csv(\"/home/beissinger/Documents/WQS/DATA/wqsMap_randImp.csv\",stringsAsFactors=FALSE, header=TRUE)\nall(order(wqsMap[,2],wqsMap[,3])==1:nrow(wqsMap))\n#[1] TRUE --> Check whether SNPs are already ordered\n\n### Remove chromosome 0\nremMap <- which(wqsMap$Chromosome == 0)\nremGeno <- remMap+1\nwqsGeno<-wqsGeno[,-remGeno]\nwqsMap<-wqsMap[-remMap,]\n\n### Subset genotypic data by cycle\nC0Gen <- wqsGeno[1:5,]\nC1Gen <- wqsGeno[6:16,]\nC2Gen <- wqsGeno[17:179,]\nC3Gen <- wqsGeno[180:267,]\nC4Gen <- wqsGeno[268:437,]\nC5Gen <- wqsGeno[438:648,]\n\n\n### function to calculate correlation pairwise --> more efficient than cor:\ncor1 <- function(x1,x2){\n x1 <- as.matrix(x1)\n x2 <- as.matrix(x2) \n sx1 <- t(x1)-colMeans(x1) \n sx2 <- t(x2)-colMeans(x2)\n cc <- (rowSums(sx1*sx2))/(sqrt(rowSums(sx1^2)*rowSums(sx2^2)))\n return(cc)\n}\n\n### package for parallelization\nlibrary(\"parallel\") \n\n### Max distant between SNPs in #SNPs to calculate\nmdiff <- 2000 \n \n### Analyze all 10 chromosomes:\nalld1 <- matrix(0,nrow=10,ncol=mdiff)\n\n## Set up for loop\namap <- wqsMap ## assign wqsMap to \"amap\", as needed for loop below.\n#a <- C5Gen[,2:ncol(C5Gen)] ## This creates \"a\", a genotype matrix that matches map (no individual names)\na <- wqsGeno[,2:ncol(wqsGeno)] ## This creates \"a\", a genotype matrix that matches map (no individual names)\nrownames(a) <- C5Gen$x ## Assign individual names as taxa\n\nfor(rr in 10:1){ ## loop starts with smallest chromosome\n print(paste(\"Chromosome\",rr))\n af <- a[,amap[,2]==rr,drop=FALSE]\n if(ncol(af)==0) next\n print(dim(af))\n ind <- 1:ncol(af)\n\n fun <- function(i){ # 1:mdiff; function to calculate r^2 for all SNPs with distance i to each other\n if(i<=ncol(af)){ld1 <- mean(cor1(af[,head(ind,-i)],af[,tail(ind,-i)])^2,na.rm=TRUE)}\n else{ld1 <- 0}\n return(ld1)\n }\n \n ## calculation of r^2 ; parallelization using 4 cores\n ld1 <- unlist(mclapply(1:2000, FUN=fun, mc.preschedule = TRUE, mc.cores = 4))\n\n alld1[rr,] <- ld1\n}\n\n#save(alld1,file=\"Res_maize_Chrwise.RData\")\n\n\n\n##########################################################################\n#\n# Plotting r^2 results\n#\n##########################################################################\n\ntag <- \"Maize\" # Brown or white layer\n\n## loading data\n#a <- readRDS(paste(\"zMat_\",tag, \".RDS\",sep=\"\"))\n#amap <- read.table(paste(\"markerkarte_\",tag,\".txt\",sep=\"\"),stringsAsFactors=FALSE, header=TRUE)\nlaeng <- unlist(lapply(by(amap[,3],amap[,2],diff),FUN=median)) ## median distance between snps, chromosomewise \n\n## load r^2 results\n#load(file=paste(\"Res_\",tag,\"_Chrwise.RData\",sep=\"\"))\n#alld1 <- alld1[-c(29,30),] ## chromosome 29 and 30 had no SNPs data\n\n### Make pdf\njpeg(\"maizeLD_03.jpg\",height=800,width=1000)\n#pdf(\"maizeLD_03.pdf\",height=8.5,width=11)\n\n## function to get the minimal distance with r^2 < 0.05\nf <- function(x){return(min(which(x<0.03)))}\ndd <- apply(alld1,1,f)\n\nltt <- rep(1:5,each=8) ## to set the line types\n\n## set screens for split.screen\nsc <- rbind(c(0,0.8,0,1),c(0.75,1,0,1),c(0.4,0.75,0.5,1))\nsplit.screen(sc)\n\n## Plot LD-curves\nscreen(1)\nplot(alld1[1,],ylab=\"r^2\", xlab=\"Distance in #SNPs\", type=\"l\", xlim=c(0,500),ylim=c(0,0.5),col=\"white\",bty=\"n\",main=paste(\"Maize\",ncol(a),\"SNPs and\",nrow(a),\"Individuals\" ))\nfor(i in 1:nrow(alld1)){\n lines(alld1[i,], lwd=2, col=i,lty=ltt[i])\n}\nabline(h=0.03,col=\"red\",lwd=2)\n\n## Plot Legends\nscreen(2)\npar(mai=c(0,0,0,0))\nplot(1:1000,1:1000, col=\"white\",axes=FALSE,xlab=\"\", ylab=\"\",bty=\"n\")\nlegend(1,900, legend=c(\"Chr: #SNPs : Thres.(LD<0.03)\",paste(c(1:10),tag,dd,sep=\" : \")),col=0:nrow(alld1),lty=c(0,ltt[1:nrow(alld1)]),lwd=2,bty=\"n\",cex=0.9)\n\n## Plot mean distance versus minima? distance\nscreen(3)\nx <- as.numeric(laeng)\ny <- dd\nfit <- lm(y~x)$coefficients\nplot(x, y,type=\"p\",xlab=\"Mean Distance (bp) between SNPs\", ylab=\"Threshold\",pch=18,col=1:nrow(alld1))\nabline(fit,col=\"red\",lwd=2)\n\nclose.screen(all=T)\ndev.off()\n\n\n\n", "meta": {"hexsha": "a3f0d6c91d564b3b0b03030a97845478afd4ae36", "size": 4431, "ext": "r", "lang": "R", "max_stars_repo_path": "Maize/ld_calc_maize_03.r", "max_stars_repo_name": "timbeissinger/ComplexSelection", "max_stars_repo_head_hexsha": "08813427fe64d0e8d0757ba9873e1ee3a78aa4d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2017-12-22T08:47:48.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-26T17:16:07.000Z", "max_issues_repo_path": "Maize/ld_calc_maize_03.r", "max_issues_repo_name": "timbeissinger/ComplexSelection", "max_issues_repo_head_hexsha": "08813427fe64d0e8d0757ba9873e1ee3a78aa4d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Maize/ld_calc_maize_03.r", "max_forks_repo_name": "timbeissinger/ComplexSelection", "max_forks_repo_head_hexsha": "08813427fe64d0e8d0757ba9873e1ee3a78aa4d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2018-03-15T22:11:08.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-27T10:09:04.000Z", "avg_line_length": 32.5808823529, "max_line_length": 173, "alphanum_fraction": 0.6009930038, "num_tokens": 1508, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710085, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.326603124580318}} {"text": "\r\n# Set up data filters:\r\n# Since there are so many possibilities there will be no attempt to parametrize all\r\n# filtering. It is the responsibility of the user to construct any extra filtering.\r\n# Each filter will be passed a 3d matrix\r\n# 1st dim is time; 2nd is variable; 3rd is site.\r\n# A condensed example:\r\n# dimnames(site.data) =\r\n# $MAIAC.time\r\n# NULL\r\n# $data.names\r\n# [1] \"year\" \"month\" \r\n# [3] \"day\" \"DOY\"\r\n# $site\r\n# [1] \"EPA_Stockton\" \"EPA_Manteca\" \"EPA_Modesto\"\r\n# The value returned should have the same structure.\r\n\r\n# Remove extreme PM2.5 values:\r\nfilter.PM2.5 = function(dat) {\r\n PM2.5.min = 10\r\n PM2.5.max = 55\r\n dat.2 = dat[,'PM2.5',]\r\n dat.2[dat.2PM2.5.max] = NA\r\n dat[,'PM2.5',] = dat.2\r\n dat\r\n}\r\n# Delete some sites\r\n# What was the criterion for this?\r\nfilter.sites = function(dat) {\r\n Omit.sites = c(\r\n \"EPA_Roseville\", \"EPA_Folsom\", \"EPA_Sacramento\", \"EPA_Sacramento-DelPasoManor\", \"EPA_Sacramento-TStreet\",\r\n \"EPA_Davis\", \"EPA_San.Andreas\",\r\n \"DRAGON_Madera\", \"DRAGON_Parlier\", \"DRAGON_Visalia\", \"EPA_Coarsegold\", \"EPA_Corcoran\", \"EPA_Santa.Rosa.Rancheria\", \"DRAGON_Shafter\")\r\n keep.sites = setdiff(dimnames(dat)[[3]],Omit.sites)\r\n dat = dat[,,keep.sites]\r\n dat\r\n}\r\nfilter.site.sample = function(dat) {\r\n w.samp = sample(1:(dim(dat)[3]),.1*(dim(dat)[3]))\r\n w.samp = sample(1:(dim(dat)[3]),2)\r\n dat[,,w.samp,drop=F]\r\n}\r\n# Restrict to afternoon hours\r\nfilter.localhour = function(dat) {\r\n lon.mean = mean(dat[1,'lon',])\r\n local.hour = dat[,'hour',1] + lon.mean / 15\r\n w.aft = which(local.hour >= 12 & local.hour <= 18)\r\n dat[w.aft,,]\r\n}\r\n# Remove nonpositive PBL heights\r\n# Remove zero also since using AOT/ML\r\nfilter.RAP_PBL_hgt = function(dat) {\r\n dat.2 = dat[,'PBL_hgt.R',]\r\n dat.2[dat.2<=0] = NA\r\n dat[,'PBL_hgt.R',] = dat.2\r\n dat\r\n}\r\n# Smaller number of days\r\n# Use only days points by another regression\r\n# Works on the data frame instead of the 3d array\r\nfilter.df.DOY = function(df) {\r\n d.r = readRDS('~esswein/science/region/daq/CA2013/StatModel/SJV.PM2.5.Regress.results/Regress.PM2.5.CWV.Bc.rds')\r\n rows.used = as.integer(rownames(model.frame(d.r$fit)))\r\n index.used = which(as.integer(rownames(df)) %in% rows.used)\r\n df = df[index.used,]\r\n df\r\n}\r\n", "meta": {"hexsha": "0adcd0f65f8f18d23e7d54e37a3b20307f2fc579", "size": 2351, "ext": "r", "lang": "R", "max_stars_repo_path": "PM2.5.Analysis.filters.r", "max_stars_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_stars_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "PM2.5.Analysis.filters.r", "max_issues_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_issues_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "PM2.5.Analysis.filters.r", "max_forks_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_forks_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0724637681, "max_line_length": 138, "alphanum_fraction": 0.626967248, "num_tokens": 802, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.63341027751814, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.32659895391199534}} {"text": "library(tidyverse)\nlibrary(dslabs)\ndata(murders)\n\nggplot(data = murders)\n\nmurders %>% ggplot\n\np <- ggplot(data = murders)\n\nmurders %>% ggplot() +\n geom_point(aes(x = population/10^6, y = total))\n\n# add points layer to predefined ggplot object\np <- ggplot(data = murders)\np + geom_point(aes(population/10^6, total))\n\n# add text layer to scatterplot\np + geom_point(aes(population/10^6, total)) +\n geom_text(aes(population/10^6, total, label = abb))\n\n#Code: Example of aes behavior\n\np_test <- p + geom_text(aes(population/10^6, total, label = abb))\n\n# change the size of the points\np + geom_point(aes(population/10^6, total), size = 3) +\n geom_text(aes(population/10^6, total, label = abb))\n\n# move text labels slightly to the right\np + geom_point(aes(population/10^6, total), size = 3) +\n geom_text(aes(population/10^6, total, label = abb), nudge_x = 1)\n\n# simplify code by adding global aesthetic\np <- murders %>% ggplot(aes(population/10^6, total, label = abb))\np + geom_point(size = 3) +\n geom_text(nudge_x = 1.5)\n\n# local aesthetics override global aesthetics\np + geom_point(size = 3) +\n geom_text(aes(x = 10, y = 800, label = \"Hello there!\"))\n", "meta": {"hexsha": "9a879cd4ede12fd4115b7690839c373cf9b290c0", "size": 1164, "ext": "r", "lang": "R", "max_stars_repo_path": "ggplotsBasics.r", "max_stars_repo_name": "adarshkhare1/DataVisualization", "max_stars_repo_head_hexsha": "5bfb183d2073ac20a2e489f4a3f861338c6e88e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ggplotsBasics.r", "max_issues_repo_name": "adarshkhare1/DataVisualization", "max_issues_repo_head_hexsha": "5bfb183d2073ac20a2e489f4a3f861338c6e88e8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ggplotsBasics.r", "max_forks_repo_name": "adarshkhare1/DataVisualization", "max_forks_repo_head_hexsha": "5bfb183d2073ac20a2e489f4a3f861338c6e88e8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7142857143, "max_line_length": 68, "alphanum_fraction": 0.6881443299, "num_tokens": 359, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.6334102636778401, "lm_q1q2_score": 0.32659894677566104}} {"text": "# R --slave --args epi tombo prefix < join.r\n\nargs<-commandArgs(TRUE)\nepi\t<- args[1]\ntombo\t<- args[2]\nprefix <- args[3]\n\na<-read.table(epi, sep=\"\\t\", header=FALSE)\nb<-read.table(tombo, sep=\"\\t\", header=FALSE)\n\na$V2<-\"epinano\"\nb$V2<-\"tombo\"\n\nc<-merge(a,b, all=T, by.x=\"V1\", by.y=\"V1\", sort=FALSE)\ncolnames(c)<-c(\"positions\", \"epinano\", \"tombo\")\n\nc[c==\"epinano\"]<-1\nc[c==\"tombo\"]<-1\nc[is.na(c)]<-0\n\nwrite.table(c, file = \"RNA_modifications.txt\", sep=\"\\t\", row.names = FALSE)\n\nlibrary(VennDiagram)\nlibrary(RColorBrewer)\nmyCol <- c(\"#B3E2CD\",\"#FDCDAC\")\n\nvenn.diagram(\n x = list(a$V1, b$V1),\n category.names = c(\"Epinano\", \"Tombo\"),\n filename = 'venn_diagram.png',\n imagetype = \"png\",\n output=TRUE,\n height = 1024, \n width = 1024 , \n resolution = 300,\n compression = \"lzw\",\n main.pos = c(0.5,0.7),\n \n\t# Circles\n\tlwd = 2,\n\tlty = 'blank',\n\tfill = myCol,\n\tmargin = 0.6,\n main = prefix,\n main.fontfamily = \"sans\",\n \n\t# Numbers\n\tcex = .6,\n\tfontface = \"bold\",\n\tfontfamily = \"sans\",\n \n # Set names\n cat.cex = 0.6,\n cat.fontface = \"bold\",\n cat.default.pos = \"outer\",\n cat.pos = c(-135, 135),\n cat.fontfamily = \"sans\"\n)\n\n\n", "meta": {"hexsha": "2a8643bad36cdcb2a42da31677bbee76db0e0993", "size": 1172, "ext": "r", "lang": "R", "max_stars_repo_path": "NanoMod/bin/join.r", "max_stars_repo_name": "vares-gui/master_of_pores", "max_stars_repo_head_hexsha": "5899c20f4a4500c7328aee02dc2dba8ddd698c2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 80, "max_stars_repo_stars_event_min_datetime": "2019-09-20T08:37:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T23:01:35.000Z", "max_issues_repo_path": "NanoMod/bin/join.r", "max_issues_repo_name": "vares-gui/master_of_pores", "max_issues_repo_head_hexsha": "5899c20f4a4500c7328aee02dc2dba8ddd698c2a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 129, "max_issues_repo_issues_event_min_datetime": "2019-08-05T11:21:59.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T03:44:54.000Z", "max_forks_repo_path": "NanoMod/bin/join.r", "max_forks_repo_name": "vares-gui/master_of_pores", "max_forks_repo_head_hexsha": "5899c20f4a4500c7328aee02dc2dba8ddd698c2a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2019-10-28T06:59:23.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T04:18:28.000Z", "avg_line_length": 19.2131147541, "max_line_length": 75, "alphanum_fraction": 0.5784982935, "num_tokens": 428, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32652163014698166}} {"text": "# vim: shiftwidth=2 tabstop=2\n##------------------------------------------------------------------------------\n#' binR\n#' \n#' Bin a dataset using a formula and a breaks function\n#' \n#' Discretize a dataset using a formula.\n#' \n#' @author Vilhelm von Ehrenheim\n#' @import foreach\n#' @import rpart\n#' @export\n##------------------------------------------------------------------------------\nbinR <- function(fx, data, algorithm=c(\"quantile\", \"rpart\", \"manual\"), ...) {\n # Extract additional arguments\n dots <- list(...)\n\n # Match algorithm and run the respective function w additional args\n out <- switch(match.arg(algorithm, c(\"quantile\", \"rpart\", \"manual\")),\n quantile=do.call(binR_quantile, c(list(fx), list(data), dots)),\n rpart=do.call(binR_rpart, c(list(fx), list(data), dots)),\n manual=do.call(binR_manual, c(list(fx), list(data), dots)))\n out\n}\n", "meta": {"hexsha": "c0692024d5d5fefda61d58c813fcf5681d5dea9f", "size": 900, "ext": "r", "lang": "R", "max_stars_repo_path": "R/binR.r", "max_stars_repo_name": "while/binR", "max_stars_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/binR.r", "max_issues_repo_name": "while/binR", "max_issues_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/binR.r", "max_forks_repo_name": "while/binR", "max_forks_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0, "max_line_length": 80, "alphanum_fraction": 0.52, "num_tokens": 210, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32652163014698166}} {"text": "#install.packages(\"magrittr\") #uncomment if library function says not installed :)\nlibrary(\"magrittr\")\n\n\ndf1 <- read.table(\"R:/R_WD/Thesis/JustDeserts/WorldValuesSurvey/ESSGSSCombinedDataset1.txt\", sep = \"|\", header = TRUE, stringsAsFactors = FALSE)\nstr(df1)\ndf1$income %>% range()\n#supposed to be ordinal from [1, 12]\n#so oops didn't finish cleaning\n\ndf1$paytaxes <- df1$paytaxes %>% as.ordered()\ndf1$sex <- df1$sex %>% as.factor()\n# #just an example of how as.ordered() works\n# #uncomment and run if you want to see how it works :)git lol I guess that's where my git command went XD\n# x <- c(1,2,1,2,4,3,2,1,2,3,2,3,2,1,1,1,1,1)\n# x <- as.ordered(x)\n# str(x)\n\n(incomelevels <- df1$income %>% as.ordered() %>% levels())\n#the GSS coded income:\n# 13 <- refused\n# 98 <- Don't Know\n# 99 <- no answer\n# 0 <- NA \n#The ESS coded hinctnt:\n# 77 <- Refusal\n# 88 <- Don't know\n# 99 <- No answer\n# So the ESS wasn't completely cleaned. Darnit! lol\n\ndf1$income <- ifelse(df1$income > 12, NA, df1$income)\n(incomelevels <- df1$income %>% as.ordered() %>% levels())\n\ndf2 <- df1[!is.na(df1$income ),]\n#This looks messy so I'll explain\n# is.na(df1$income) returns a boolean vector the same length as df1$income\n# true if na, false if any other value\n# the ! operator gives a logical not, so it flips true -> false, false -> true\n# [ a, b] is indexing the datastructure\n# for a dataframe, a is the row, b is the column\n# since the dataframe stores variables as columns, the vector of is.na(df1$income)\n# indicates if each row in that column is na\n# I don't want NA so I told R to only keep the non na values\nstr(df1) #2339 obs\nstr(df2) #2095 obs\ndf2$income %>% range() # 1 12\n\n\ndf2 %>% write.table(file = \"R:/R_WD/Thesis/JustDeserts/WorldValuesSurvey/ESSGSSCombinedDataset2.txt\", sep = \"|\", col.names = TRUE, row.names = FALSE)\n# ls()\n# rm(list = ls())\n# #I ran this but I commented it in case anyone else trys to run my code :)\n\n\n\n\n\n\n\ndf3 <- read.table(file = \"R:/R_WD/Thesis/JustDeserts/WorldValuesSurvey/ESSGSSCombinedDataset2.txt\", sep = \"|\", header = TRUE )\n\ndf3 %>% str()\ndf3$gssOrEss %>% hist\n\ndf3$gssOrEss2 <- -1 * df3$gssOrEss\ndf3$gssOrEss2 <- df3$gssOrEss2 + 2\nhist(df3$gssOrEss2) #now that it's been swapped, need to reassign to gssOrEss\ndf3$gssOrEss <- df3$gssOrEss2\nhist(df3$gssOrEss)\ndf3$gssOrEss2 <- NULL #removes variable from df3\n#df3$gssOrEss <- df3$gssOrEss - 1 #Realized I needed to swap the ordering and make it a 0/1 dummy\n#df3$gssOrEss %>% str()\n\n\ndf3$paytaxes <- df3$paytaxes %>% as.ordered()\n#paytaxes is needed to fit the model using polr\n#the rest seem to be a good idea but I'll see if it makes sense later\n\ndf3$sex <- df3$sex %>% as.factor()\ndf3$gssOrEss <- df3$gssOrEss %>% as.factor()\ndf3$income <- df3$income %>% as.ordered()\ndf3$year <- as.factor(df3$year)\ndf3 %>% str()\n\n\n\n\n\n\n\nlibrary(\"MASS\")\nform <- \"paytaxes ~ gssOrEss + year + income + age + sex\"\nfm1 <- polr(form, data = df3, method = \"logistic\")\n# dv paytaxes, independant varaible hypothesis testing: gssOrEss\n# Gss is all USA respondants\n# Ess was subsetted to only have italian respondants\n# decided not t use weights. \nsummary(fm1)\n\n\n# what happens if I fit the model with weights\n# not sure it makes sense to use these weights this way. *shrug*\nfm2 <- polr( form, data = df3, weights = weight, method = \"logistic\")\nsummary(fm2)\n", "meta": {"hexsha": "ed603ae1aa8339cb14e894970c736fa74e35fa8a", "size": 3318, "ext": "r", "lang": "R", "max_stars_repo_path": "essGssCleaningAndAnalysis.r", "max_stars_repo_name": "DU-ds/MiscRScripts", "max_stars_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "essGssCleaningAndAnalysis.r", "max_issues_repo_name": "DU-ds/MiscRScripts", "max_issues_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "essGssCleaningAndAnalysis.r", "max_forks_repo_name": "DU-ds/MiscRScripts", "max_forks_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.6, "max_line_length": 149, "alphanum_fraction": 0.689270645, "num_tokens": 1136, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32652163014698166}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of model predictions and empirical data\"\n#' author: \"Judith Degen\"\n#' date: \"November 28, 2014\"\n#' ---\n\nlibrary(ggplot2)\ntheme_set(theme_bw(18))\n#setwd(\"~/Dropbox/thegricean_sinking-marbles/models/complex_prior/results/\")\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/complex_prior/smoothed_unbinned15/results/\")\nsource(\"rscripts/helpers.r\")\n\n#' get model predictions\nload(\"data/mp.RData\")\nmp = read.table(\"data/parsed_nullutterance_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nnrow(mp)\nhead(mp)\nsummary(mp)\n\n# get prior expectations\npriorexpectations = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorexpectations) = paste(priorexpectations$effect,priorexpectations$object)\nhead(priorexpectations)\nmp$PriorExpectation = priorexpectations[as.character(mp$Item),]$expectation\n\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nhead(priorprobs)\nrow.names(priorprobs) = paste(priorprobs$effect,priorprobs$object)\nmpriorprobs = melt(priorprobs, id.vars=c(\"effect\", \"object\"))\nhead(mpriorprobs)\nrow.names(mpriorprobs) = paste(mpriorprobs$effect,mpriorprobs$object,mpriorprobs$variable)\nmp$PriorProbability = mpriorprobs[paste(as.character(mp$Item),\" X\",mp$State,sep=\"\"),]$value\nmp$AllPriorProbability = priorprobs[paste(as.character(mp$Item)),]$X15\nhead(mp)\n\n# get empirical state posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/3_sinking-marbles-nullutterance/results/data/r.RData\")\nhead(r)\nr$Item = as.factor(paste(r$effect,r$object))\n# because posteriors come in 4 bins, make Bin variable for model prediction dataset:\nmp$Proportion = as.factor(ifelse(mp$State == 0, \"0\", ifelse(mp$State == 15, \"100\", ifelse(mp$State < 8, \"1-50\", \"51-99\"))))\n\nagr = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=mean)\n#agr$CILow = aggregate(normresponse ~ Item + quantifier + Proportion,data=r, FUN=ci.low)$normresponse\n#agr$CIHigh = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=ci.high)$normresponse\n#agr$YMin = agr$normresponse - agr$CILow\n#agr$YMax = agr$normresponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Proportion, agr$Quantifier)\nmp$PosteriorProbability_empirical = agr[paste(mp$Item,mp$Proportion,\"some\"),]$normresponse\n\nsummary(mp)\n#plot empirical against predicted distributions for \"some\"\nsome = ddply(mp, .(Item, QUD, Alternatives, SpeakerOptimality, PriorExpectation, Proportion, PosteriorProbability_empirical), summarise, PosteriorProbability_predicted=sum(PosteriorProbability), PriorProbability_smoothed=sum(PriorProbability))\n#some= subset(some, Quantifier == \"some\")\nnrow(some)\nhead(some)\nmsome = melt(some, measure.vars=c(\"PosteriorProbability_empirical\",\"PosteriorProbability_predicted\",\"PriorProbability_smoothed\"))\nmsome$ptype = as.factor(ifelse(msome$variable == \"PosteriorProbability_empirical\", \"posterior (empirical)\",ifelse(msome$variable == \"PosteriorProbability_predicted\",\"posterior (model)\", \"prior\")))\nhead(msome)\nnrow(msome)\nsummary(msome)\n\ntoplot = droplevels(subset(msome, QUD == \"how-many\" & SpeakerOptimality == 2 & Alternatives == \"0_basic\"))#\"0_basic1_lownum2_extra4_twowords5_threewords\"))\nnrow(toplot)\ntoplot$Probability = as.factor(ifelse(toplot$ptype == \"prior\",\"prior\",\"posterior\"))\ntoplot$Prop = factor(toplot$Proportion, levels=c(\"1-50\",\"51-99\",\"100\"))\nggplot(toplot, aes(x=Prop, y=value,color=ptype, group=ptype, size=Probability)) +\n geom_point() +\n geom_line() +\n scale_size_discrete(range=c(1,2)) +\n scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_wrap(~Item)\nggsave(\"graphs/model-empirical-howmany-2-basic-null.pdf\",width=35,height=30)\n\n#plot empirical against predicted allstate-prbabilities for \"some\"\nallstate = droplevels(subset(mp, State == 15))\ncors = ddply(allstate, .(Alternatives, QUD, SpeakerOptimality), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n# .55 correlation despite being shitty model\n\nggplot(allstate, aes(x=PosteriorProbability, y=PosteriorProbability_empirical,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-nullutterance-allstateprobs.pdf\",width=30,height=10)\n\n#maybe COGSCI plot basis? plot predicted allstate-prbabilities for \"some\" as a function of prior allstate-probabilities\n\nggplot(allstate, aes(x=PriorProbability, y=PosteriorProbability,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-nullutterance-allstateprobs.pdf\",width=30,height=10)\n\n\n\n#plot empirical against predicted expectations for \"some\"\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nsummary(r)\nr$Item = as.factor(paste(r$effect, r$object))\nagr = aggregate(ProportionResponse ~ Item + quantifier, data=r, FUN=mean)\n#agr$CILow = aggregate(ProportionResponse ~ Item + quantifier,data=r, FUN=ci.low)$ProportionResponse\n#agr$CIHigh = aggregate(ProportionResponse ~ Item + quantifier,data=r,FUN=ci.high)$ProportionResponse\n#agr$YMin = agr$ProportionResponse - agr$CILow\n#agr$YMax = agr$ProportionResponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Quantifier)\nmp$PosteriorExpectation_empirical = agr[paste(mp$Item,\"some\"),]$ProportionResponse\nmp$PriorExpectation_smoothed = mp$PriorExpectation/15\n\npexpectations = ddply(mp, .(Item, QUD, Alternatives, SpeakerOptimality,PriorExpectation_smoothed, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability)/15)\nhead(pexpectations)\nsummary(pexpectations)\nsome = pexpectations#droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ncors = ddply(some, .(Alternatives, QUD, SpeakerOptimality), summarise, r=cor(PosteriorExpectation_predicted, PosteriorExpectation_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n\nggplot(some, aes(x=PosteriorExpectation_predicted, y=PosteriorExpectation_empirical,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-nullutterance-expectations.pdf\",width=30,height=10)\n\n\n# plot posterior expectation against prior expectation\nggplot(some, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n# geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-nullutterance-expectations.pdf\",width=30,height=10)\n\n# plot posterior expectation against prior expectation for basic model with speaker optimality =2 and qud = how-many and alternatives = basic\ntoplot = droplevels(subset(some, QUD==\"how-many\" & Alternatives == \"0_basic\" & SpeakerOptimality == 2))\nggplot(toplot, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted)) +\n geom_point(color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth(color=\"#00B0F6\") +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,1), name=\"Model predicted posterior expectation\")\n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) \nggsave(\"graphs/model-nullutterance-expectations.pdf\",width=5.5,height=4.5)#,width=30,height=10)\nggsave(\"~/cogsci/conferences_talks/_2015/2_cogsci_pasadena/wonky_marbles/paper/pics/model-nullutterance-expectations.pdf\",width=5.5,height=4.5)\nsave(toplot, file=\"data/toplot-nullutterance-expectations.RData\")\n\n\nsave(mp, file=\"data/mp-nullutterance.RData\")\n\n\n\n", "meta": {"hexsha": "b965dc00cffdc49b83a2ec4e73f3aff695987578", "size": 9314, "ext": "r", "lang": "R", "max_stars_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions-nullutterance.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions-nullutterance.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions-nullutterance.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.7444444444, "max_line_length": 243, "alphanum_fraction": 0.7684131415, "num_tokens": 2696, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678319, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3265216301469816}} {"text": "#user interface for London Shiny\nlibrary(plotly)\nlibrary(leaflet)\nlibrary(sp)\nlibrary(maptools)\nlibrary(RColorBrewer)\nlibrary(maptools)\nlibrary(rgdal)\nlibrary(classInt)\nlibrary(shiny)\n\nsource(\"dataloader.r\")\n\nshinyUI(fluidPage(\n \n #in this new page, first plot me a nice big map\n leafletOutput(\"map1\", width = \"100%\", height = 1000),\n \n #then, create me a nice floaty panel which has all of the options in in, plus \n #a histogram of the variables selected\n absolutePanel(id = \"controls\", class = \"panel panel-default\", fixed = TRUE,\n draggable = TRUE, top = 60, left = \"auto\", right = 20, bottom = \"auto\",\n width = 330, height = \"auto\",\n #gimme a title\n h2(\"Social Statistics for Wards in London\"),\n #gimme a drop down to select variables from the ward \n #boundaries dataset\n selectInput(\"variable\", \"Variable\",\n names(wardBoundaries@data)[11:79]),\n #gimme some colour options from colourbrewer\n selectInput(\"colourbrewerpalette\", \"Color Scheme\",\n rownames(subset(brewer.pal.info, category %in% c(\"seq\", \"div\")))\n ),\n selectInput(\"classIntStyle\", \"Interval Style\",\n c(\"Jenks Natural Breaks\" = \"jenks\",\n \"Quantile\" = \"quantile\",\n \"Equal Interval\" = \"equal\",\n \"Pretty\" = \"pretty\")),\n #plot me a nice plotly histogram\n #plotlyOutput(\"plot1\")\n plotOutput(\"plot1\")\n )\n))\n\n#length(names(wardBoundaries@data))\n", "meta": {"hexsha": "6e08937582539269cfc269419738174002906dbd", "size": 1691, "ext": "r", "lang": "R", "max_stars_repo_path": "prac5_data/ui.r", "max_stars_repo_name": "bettinagruen/CASA0005repo", "max_stars_repo_head_hexsha": "b7f603aa6109c95f5bae4f32027cab93d30cd86b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "prac5_data/ui.r", "max_issues_repo_name": "bettinagruen/CASA0005repo", "max_issues_repo_head_hexsha": "b7f603aa6109c95f5bae4f32027cab93d30cd86b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "prac5_data/ui.r", "max_forks_repo_name": "bettinagruen/CASA0005repo", "max_forks_repo_head_hexsha": "b7f603aa6109c95f5bae4f32027cab93d30cd86b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7608695652, "max_line_length": 92, "alphanum_fraction": 0.552927262, "num_tokens": 389, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3265216221458573}} {"text": "#### HARMONIZING WITH THE COV LOOPS FOR AGGREGATE AND REPLACE\r\n\r\n\r\n\r\n\r\n# Load libraries #\r\n\r\nlibrary(\"foreign\")\r\nlibrary(\"dplyr\")\r\nlibrary(\"xlsx\")\r\nlibrary(\"ggplot2\")\r\nlibrary(\"broom\")\r\nlibrary(\"MatchIt\")\r\nlibrary(\"ggrepel\")\r\nlibrary(\"calibrate\")\r\nlibrary(\"plm\")\r\nlibrary(\"gridExtra\")\r\nlibrary(\"tidyr\")\r\nlibrary(\"foreach\")\r\n\r\n# Code Structure\r\n\r\n# 1) Data Construction for Matching\r\n# 2) Matching\r\n# 3) Plot Matching Results - Covariate Balance pre and post matching\r\n# 4) Extract Matched Pairs from matching output\r\n# 5) Data construction for DiD regression using matched pairs\r\n# 6) DiD Regression \r\n# 7) Quantifying Avoided Deforestation\r\n# 8) Fixed Effects Panel Regression\r\n\r\n\r\n# --------------------- 1) Data Construction for Matching -----------------------------------------#\r\n\r\nsetwd(\"c:/Users/desbures/Documents/Sebastien/11_Autres/Katie/Ambatovy/\")\r\n\r\n\r\n# Function to read in input data for each offset, remove unnecessary columns and add column to indicate treatment status\r\n# and offset of origin. \r\n\r\n\r\n tidy_data <-function(path, name, number, label){\r\n name = read.dbf(path)\r\n name = subset(name, select= -c(1, 16))\r\n name$treated = number # Number = 1 for pixels from an offset and 0 for control pixels. \r\n name$offset = label\r\n return(name)\r\n }\r\n \r\n \r\n TTF <- tidy_data(\"Input_data/Final_covariates/Sample_TTF3.dbf\", TTF, 1, \"TTF\") \r\n ANK <- tidy_data(\"Input_data/Final_covariates/Sample_ANK3.dbf\", ANK, 1, \"ANK\")\r\n CFAM <- tidy_data(\"Input_data/Final_covariates/Sample_CFAM3.dbf\", CFAM, 1, \"CFAM\")\r\n CZ <- tidy_data(\"Input_data/Final_covariates/Sample_CZ3.dbf\", CZ, 1, \"CZ\")\r\n \r\n # TTF = Torotorofotsy\r\n # ANK = Ankerana\r\n # CFAM = Corridor Forestier Analamay-Mantadia\r\n # CZ = Conservation Zone\r\n \r\n \r\n # Load Control separately because columns are different # \r\n \r\n Control = read.dbf(\"Input_data/Final_covariates/Final_control.dbf\")\r\n Control$treated = 0\r\n Control$offset = \"Cont\"\r\n Control <- subset(Control, select = -c(4)) # Remove unwanted fire variable\r\n Control <- Control[,c(2,3,1,4:16)] # Re-order columns to match offset dataframes\r\n \r\n\r\n\r\n# Rename columns #\r\ncols <- c(\"X\",\"Y\",\"Tree_loss\", \"Pop_density\", \"Dist_sett\", \"Slope\", \"Elevation\", \"Aspect\", \"Annual_Rain\",\r\n \"Dist_track\", \"Dist_road\", \"Dist_river\", \"Dist_edge\", \"Dist_defor\", \"treated\", \"offset\")\r\n\r\nnames(Control) <- paste0(cols)\r\nnames(ANK) <- paste0(cols)\r\nnames(CZ) <- paste0(cols)\r\nnames(CFAM) <- paste0(cols)\r\nnames(TTF) <- paste0(cols)\r\n\r\n# Merge each offset with the control dataset and create a column with row ID.\r\n# These offset + control datasets will the input for matching #\r\n\r\nANKCONT <- rbind(ANK, Control)\r\nANKCONT$ID <- seq(nrow(ANKCONT))\r\nCZCONT <- rbind(CZ, Control)\r\nCZCONT$ID <- seq(nrow(CZCONT))\r\nCFAMCONT <- rbind(CFAM, Control)\r\nCFAMCONT$ID <- seq(nrow(CFAMCONT))\r\nTTFCONT <- rbind(TTF, Control)\r\nTTFCONT$ID <- seq(nrow(TTFCONT))\r\n\r\n\r\n\r\n\r\n\r\n#----------------------------2) Robustness: addition of covariates -----------------------------------------#\r\n\r\n\r\n# 1 - Load the functions in the separete script\r\n\r\n# 2 - We evaluate the function\r\nthe_cov_loop(ANKCONT,11)\r\nwrite.csv(synthesis_loop_cov,\"YOURPATH/loopAnk_cov.csv\")\r\n\r\n\r\nthe_cov_loop(CFAMCONT,13)\r\nwrite.csv(synthesis_loop_cov,\"YOURPATH/loopCFAM_cov.csv\")\r\n\r\nthe_cov_loop(CZCONT,9)\r\nwrite.csv(synthesis_loop_cov,\"YOURPATH/loopCZ_cov.csv\")\r\n\r\nthe_cov_loop(TTFCONT,14)\r\nwrite.csv(synthesis_loop_cov,\"YOURPATH/loopTTF_cov.csv\")\r\n\r\n\r\n\r\n\r\n#----------------------------3) Robustness: different parameters -----------------------------------------#\r\n\r\nmemory.limit(100000)\r\nthe_parameters_loop(ANKCONT,11,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\n write.csv(loop_parameters,\"YOURPATH/loopANK_parameters.csv\")\r\n\r\nthe_parameters_loop(CFAMCONT,13,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\nwrite.csv(loop_parameters,\"YOURPATH/loopCFAM_parameters.csv\")\r\n\r\nthe_parameters_loop(CZCONT,9,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\n write.csv(loop_parameters,\"YOURPATH/loopCZ_parameters.csv\")\r\n\r\nthe_parameters_loop(TTFCONT,14,\r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n caliper_value = c(0.25,0.5,1), \r\n ratio=c(1,5,10))\r\nwrite.csv(loop_parameters,\"YOURPATH/loopTTF_parameters.csv\")\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n#----------------------------4) Robustness: different parameters and different covariates -----------------------------------------#\r\n\r\nthe_cov_parameters_loop(ANKCONT,11,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\nwrite.csv(synthesis_loop_cov_param,\"YOURPATH/loopANK_cov_parameters.csv\")\r\n\r\nthe_cov_parameters_loop(CFAMCONT,13,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\nwrite.csv(synthesis_loop_cov_param,\"YOURPATH/loopCFAM_cov_parameters.csv\")\r\n\r\nthe_cov_parameters_loop(CZCONT,9,\r\n caliper_value = c(0.25,0.5,1), \r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n ratio=c(1,5,10))\r\nwrite.csv(synthesis_loop_cov_param,\"YOURPATH/loopCZ_cov_parameters.csv\")\r\n\r\nthe_cov_parameters_loop(TTFCONT,14,\r\n replacement = c(T,F), \r\n distance = c(\"mahalanobis\",\"glm\",\"randomforest\"),\r\n caliper_value = c(0.25,0.5,1), \r\n ratio=c(1,5,10))\r\nwrite.csv(synthesis_loop_cov_param,\"YOURPATH/loopTTF_cov_parameters.csv\")\r\n\r\n \r\n\r\n\r\n#----------------------------5) Robustness: dropping years -----------------------------------------#\r\nthe_year_loop(ANKCONT,11)\r\ndid_res_ank <- did_res \r\n \r\nthe_year_loop(CZCONT,9)\r\ndid_res_cz <- did_res \r\n\r\nthe_year_loop(TTFCONT,14)\r\ndid_res_ttf <- did_res \r\n\r\n\r\n \r\n \r\n#-------------------------- Graphs \r\n\r\n\r\nlabels <- list(\"Balance:\" = c(\"<10% obs. unmatched\",\">90% obs. unmatched\", \"Mean bal. achieved\", \"Balanced for all cov.\"),\r\n \"Parallel trend:\" = c(\"Achieved\"), \r\n \"Additional covariates:\" = c(\"Population density\", \"Dist. nearest settlement\", \"Annual rainfall\", \"Dist. nearest track\", \"Dist. nearest river\"),\r\n \"Model parameters:\" = c(\"0.25SD caliper\",\"0.5SD caliper\", \"1SD caliper\", \"With replacement\"),\r\n \"Distance:\" = c(\"Mahalanobis\", \"Standard PSM\", \"Random forest PSM\"),\r\n \"Nb. nearest neighbors:\" = c(\"1\", \"5\", \"10\"))\r\n\r\n\r\n## ANK\r\nANK_cov <- read.csv(\"YOURPATH/loopANK_cov.csv\")\r\nANK_param <- read.csv(\"YOURPATH/loopANK_parameters.csv\")\r\nANK_covparam <- read.csv(\"YOURPATH/loopANK_cov_parameters.csv\")\r\nANK <- rbind(ANK_cov[,-1],ANK_param[,-1],ANK_covparam[,-1]) \r\nANK$less_10pc_unmatched <- ifelse(ANK$unmatched<2862*0.1,T,F)\r\nANK$more_90pc_unmatched <- ifelse(ANK$unmatched>2862*0.9,T,F)\r\nANK$bal__mean_achieved <- ifelse(ANK$mean_std_diff<0.25,T,F)\r\nANK$bal_achieved_all_cov <- ifelse(ANK$max_std_diff<0.25,T,F)\r\nANK <- ANK[,-c(3:5)]\r\nANK <- ANK[,c(1,2,19:22,3:18)]\r\n\r\nrows_invalid_models = which( \r\n ANK$more_90pc_unmatched==T | \r\n ANK$bal__mean_achieved==F |\r\n ANK$parralel_trend==F) \r\n\r\nrow_main_model = which(ANK$cal1==T & ANK$replacement==F & ANK$maha==T & ANK$ratio1==T & ANK$Pop_density==F & ANK$Dist_sett==F & ANK$Annual_Rain==F &ANK$Dist_track==F & ANK$Dist_river==F) \r\n\r\n\r\n\r\ntiff(file=\"YOURPATH/ANK_rob4.tiff\",\r\n height = 550, width = 950, pointsize=0.3)\r\n\r\nschart(ANK, labels, \r\n order = \"increasing\",\r\n highlight = rows_invalid_models,\r\n highlight2 = row_main_model,\r\n col.est = c(\"#E69F00\",\"#fcd67e\",\"black\"),\r\n bg.dot=c(\"grey60\",\"grey95\",\"grey95\",\"grey60\"),\r\n ylab = \"Raw treatment effect\",\r\n fonts=c(2,3))\r\n#text(x=row_main_model,y=-1.5,\"*\",pos=3,cex=1.5)\r\n\r\ndev.off()\r\n\r\n\r\n## CFAM\r\nCFAM_cov <- read.csv(\"YOURPATH/loopCFAM_cov.csv\")\r\nCFAM_param <- read.csv(\"YOURPATH/loopCFAM_parameters.csv\")\r\nCFAM_covparam <- read.csv(\"YOURPATH/loopCFAM_cov_parameters.csv\")\r\nCFAM <- rbind(CFAM_cov[,-c(1)],CFAM_param[,-c(1)],CFAM_covparam[,-c(1)]) \r\nCFAM$less_10pc_unmatched <- ifelse(CFAM$unmatched<2626*0.1,T,F)\r\nCFAM$more_90pc_unmatched <- ifelse(CFAM$unmatched>2626*0.9,T,F)\r\nCFAM$bal__mean_achieved <- ifelse(CFAM$mean_std_diff<0.25,T,F)\r\nCFAM$bal_achieved_all_cov <- ifelse(CFAM$max_std_diff<0.25,T,F)\r\nCFAM <- CFAM[,-c(3:5)]\r\nCFAM <- CFAM[,c(1,2,19:22,3:18)]\r\n\r\nrows_invalid_models = which( \r\n CFAM$more_90pc_unmatched==T | \r\n CFAM$bal__mean_achieved==F |\r\n CFAM$parralel_trend==F) \r\n\r\nrow_main_model = which(CFAM$cal1==T & CFAM$replacement==F & CFAM$maha==T & CFAM$ratio1==T & CFAM$Pop_density==F & CFAM$Dist_sett==F & CFAM$Annual_Rain==F &CFAM$Dist_track==F & CFAM$Dist_river==F) \r\n\r\n\r\n\r\n\r\ntiff(file=\"YOURPATH/CFAM_rob4.tiff\",\r\n height = 600, width = 950, pointsize=0.3)\r\n\r\nschart(CFAM, labels,\r\n order = \"increasing\",\r\n highlight = rows_invalid_models,\r\n highlight2 = row_main_model,\r\n col.est = c(\"chartreuse3\",\"#b7ff6e\",\"black\"),\r\n bg.dot=c(\"grey60\",\"grey95\",\"grey95\",\"grey60\"),\r\n ylab = \"Raw treatment effect\",\r\n fonts=c(2,3))\r\ndev.off()\r\n\r\n\r\n## CZ\r\nCZ_cov <- read.csv(\"YOURPATH/loopCZ_cov.csv\")\r\nCZ_param <- read.csv(\"YOURPATH/loopCZ_parameters.csv\")\r\nCZ_covparam <- read.csv(\"YOURPATH/loopCZ_cov_parameters.csv\")\r\n CZ <- rbind(CZ_cov[,-c(1)],CZ_param[,-c(1)],CZ_covparam[,-c(1)]) \r\n CZ$less_10pc_unmatched <- ifelse(CZ$unmatched<1340*0.1,T,F)\r\n CZ$more_90pc_unmatched <- ifelse(CZ$unmatched>1340*0.9,T,F)\r\n CZ$bal__mean_achieved <- ifelse(CZ$mean_std_diff<0.25,T,F)\r\n CZ$bal_achieved_all_cov <- ifelse(CZ$max_std_diff<0.25,T,F)\r\n CZ <- CZ[,-c(3:5)]\r\n CZ <- CZ[,c(1,2,19:22,3:18)]\r\n \r\n rows_invalid_models = which( \r\n CZ$more_90pc_unmatched==T | \r\n CZ$bal__mean_achieved==F |\r\n CZ$parralel_trend==F) \r\n \r\n row_main_model = which(CZ$cal1==T & CZ$replacement==F & CZ$maha==T & CZ$ratio1==T & CZ$Pop_density==F & CZ$Dist_sett==F & CZ$Annual_Rain==F &CZ$Dist_track==F & CZ$Dist_river==F) \r\n \r\n\r\ntiff(file=\"YOURPATH/CZ_rob4.tiff\",\r\n height = 550, width = 950, pointsize=0.3)\r\n\r\nschart(CZ, labels, \r\n order = \"increasing\",\r\n highlight = rows_invalid_models,\r\n highlight2 = row_main_model,\r\n col.est = c(\"#F0E442\",\"#fffab3\",\"black\"),\r\n bg.dot=c(\"grey60\",\"grey95\",\"grey95\",\"grey60\"),\r\n ylab = \"Raw treatment effect\",\r\n fonts=c(2,3))\r\ndev.off()\r\n\r\n\r\n \r\n## TTF\r\nTTF_cov <- read.csv(\"YOURPATH/loopTTF_cov.csv\")\r\nTTF_param <- read.csv(\"YOURPATH/loopTTF_parameters.csv\")\r\nTTF_covparam <- read.csv(\"YOURPATH/loopTTF_cov_parameters.csv\")\r\nTTF <- rbind(TTF_cov[,-c(1)],TTF_param[,-c(1)],TTF_covparam[,-c(1)]) \r\nTTF$less_10pc_unmatched <- ifelse(TTF$unmatched<1170*0.1,T,F)\r\nTTF$more_90pc_unmatched <- ifelse(TTF$unmatched>1170*0.9,T,F)\r\nTTF$bal__mean_achieved <- ifelse(TTF$mean_std_diff<0.25,T,F)\r\nTTF$bal_achieved_all_cov <- ifelse(TTF$max_std_diff<0.25,T,F)\r\nTTF <- TTF[,-c(3:5)]\r\nTTF <- TTF[,c(1,2,19:22,3:18)]\r\n\r\nrows_invalid_models = which( \r\n TTF$more_90pc_unmatched==T | \r\n TTF$bal__mean_achieved==F |\r\n TTF$parralel_trend==F) \r\n\r\nrow_main_model = which(TTF$cal1==T & TTF$replacement==F & TTF$maha==T & TTF$ratio1==T & TTF$Pop_density==F & TTF$Dist_sett==F & TTF$Annual_Rain==F &TTF$Dist_track==F & TTF$Dist_river==F) \r\n\r\n\r\ntiff(file=\"YOURPATH/TTF_rob4.tiff\",\r\n height = 600, width = 950, pointsize=0.3)\r\n\r\nschart(TTF, labels, \r\n order = \"increasing\",\r\n highlight = rows_invalid_models,\r\n highlight2 = row_main_model,\r\n col.est = c(\"#56B4E9\",\"#bfe8ff\",\"black\"),\r\n bg.dot=c(\"grey60\",\"grey95\",\"grey95\",\"grey60\"),\r\n ylab = \"Raw treatment effect\",\r\n fonts=c(2,3))\r\ndev.off() \r\n\r\n\r\n\r\n\r\n\r\n# --------- Graphs for years\r\n\r\n### Ank\r\nplot <- ggplot(did_res_ank, aes(x=Year, y=coef)) +\r\n geom_errorbar(aes(ymin = coef + 1.96*se, ymax = coef - 1.96*se, col=Drop)) +\r\n scale_color_manual(values=c(\"#E69F00\",\"#fcd67e\")) +\r\n geom_point() +\r\n labs(title = \"Ankerana\", x = \"Year dropped\", y = \"Coefficient\") +\r\n ylim(-4.5,0) +\r\n geom_hline(yintercept = 0, linetype=2) +\r\n guides(color=F) +\r\n #scale_x_continuous(breaks = 2000:2019) +\r\n theme_classic()\r\nplot\r\n\r\nggsave(plot, \r\n filename = \"ANK_years.png\")\r\n\r\n\r\n### CZ\r\nplot <- ggplot(did_res_cz, aes(x=Year, y=coef)) +\r\n geom_errorbar(aes(ymin = coef + 1.96*se, ymax = coef - 1.96*se, col=Drop)) +\r\n scale_color_manual(values=c(\"#F0E442\",\"#fffab3\")) +\r\n geom_point() +\r\n labs(title = \"Conservation Zone\", x = \"Year dropped\", y = \"Coefficient\") +\r\n ylim(-2.5,0) +\r\n geom_hline(yintercept = 0, linetype=2) +\r\n guides(color=F) +\r\n #scale_x_continuous(breaks = 2000:2019) +\r\n theme_classic()\r\n\r\n\r\nggsave(plot, \r\n filename = \"CZ_years.png\")\r\n\r\n\r\n### TTF\r\nplot <- ggplot(did_res_ttf, aes(x=Year, y=coef)) +\r\n geom_errorbar(aes(ymin = coef + 1.96*se, ymax = coef - 1.96*se, col=Drop)) +\r\n scale_color_manual(values=c(\"#56B4E9\",\"#bfe8ff\")) +\r\n geom_point() +\r\n labs(title = \"Torotorofotsy\", x = \"Year dropped\", y = \"Coefficient\") +\r\n geom_hline(yintercept = 0, linetype=2) +\r\n guides(color=F) +\r\n #scale_x_continuous(breaks = 2000:2019) +\r\n theme_classic()\r\n\r\n\r\nggsave(plot, \r\n filename = \"TTF_years.png\")\r\n\r\n\r\n", "meta": {"hexsha": "39d7f674ef9b8ae6f7d5762f1acb5d8f0e87b8d7", "size": 14228, "ext": "r", "lang": "R", "max_stars_repo_path": "Offset_Effectiveness_slim_loop_func4.r", "max_stars_repo_name": "katie-devs/Biodiversity_offset_effectiveness", "max_stars_repo_head_hexsha": "c2c0dac4461669129ecc452f2dd4172848209d9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Offset_Effectiveness_slim_loop_func4.r", "max_issues_repo_name": "katie-devs/Biodiversity_offset_effectiveness", "max_issues_repo_head_hexsha": "c2c0dac4461669129ecc452f2dd4172848209d9e", 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YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.32642951928381064}} {"text": "## ============================================================================================\n##\n## analyseData\n##\n## ============================================================================================\n\nanalyseData <- function(path, startStopTimes, n, caseStr, data, followUpTime,\n data0=c(), followUpTime0=c(), weights=c(), weights0=c()) {\n\n print(paste(path, n))\n set.seed(100)\n\n funcList <- computeFuncList(caseStr)\n commonNullDist <- funcList$commonNullDist\n sampleData <- funcList$sampleData\n computeSortedStat <- funcList$computeSortedStat\n computeNofExtreme <- funcList$computeNofExtreme\n xlab <- funcList$xlab\n ylab <- funcList$ylab\n \n nofPairsVec <- c()\n nullDistList <- list()\n nullDist <- c()\n nofPeriods <- length(startStopTimes)-n+1\n\n nofPairs0vec <- rep(0, nofPeriods)\n nofPairs1vec <- rep(0, nofPeriods)\n pVals <- matrix(NA, nrow=nofPeriods, ncol=nrow(data))\n for (p in 1:nofPeriods) {\n print(paste(\" \", p, \"of\", nofPeriods))\n curData <- data[, followUpTime>=startStopTimes[p] &\n followUpTime<=startStopTimes[p+n-1]] \n curWeights <- c()\n if (length(weights)>0)\n curWeights <- weights[, followUpTime>=startStopTimes[p] &\n followUpTime<=startStopTimes[p+n-1]]\n nofPairs <- nofPairs1vec[p] <- ncol(curData)\n nofPairs0 <- 0\n compStat <- TRUE\n if (caseStr==\"Weight\") {\n if ((sum(followUpTime0>=startStopTimes[p] &\n followUpTime0<=startStopTimes[p+n-1])<3) | nofPairs<3) {\n compStat <- FALSE\n nofPairs0vec[p] <- sum(followUpTime0>=startStopTimes[p] &\n followUpTime0<=startStopTimes[p+n-1])\n } else {\n curData <- cbind(curData, data0[, followUpTime0>=startStopTimes[p] &\n followUpTime0<=startStopTimes[p+n-1]])\n if (length(weights)>0)\n curWeights <- cbind(curWeights, weights0[, followUpTime0>=startStopTimes[p] &\n followUpTime0<=startStopTimes[p+n-1]])\n nofPairs0 <- nofPairs0vec[p] <- ncol(curData)-nofPairs\n }\n }\n\n if (!compStat) {\n pVals[p, ] <- 1\n } else {\n expSd <- computeSortedStat(curData, nofPairs, nofPairs0, curWeights)\n if (!commonNullDist || length(nofPairsVec)==0 || !(nofPairsVec %in% nofPairs)) {\n ## Compute null distribution\n runif(1)\n mc.reset.stream()\n workerFunc <- function(s) {\n curDlist <- sampleData(data, curData, nofPairs, weights, curWeights)\n curD <- curDlist$data\n curW <- curDlist$weights\n res <- computeSortedStat(curD, nofPairs, nofPairs0, curW)\n rm(curDlist, curD, curW)\n res\n }\n \n res <- mclapply(values, workerFunc, mc.cores = numWorkers)\n nullDist <- matrix(NA, nrow=nSim, ncol=nrow(curData))\n for (s in 1:nSim)\n nullDist[s, ] <- res[[s]]\n \n if (commonNullDist) {\n nofPairsVec <- c(nofPairsVec, nofPairs)\n nullDistList[[length(nofPairsVec)]] <- nullDist\n }\n } else { ## commonNullDist && (nofPairs %in% nofPairsVec)\n nullDist <- nullDistList[[which(nofPairsVec %in% nofPairs)]]\n }\n \n nofExtreme <- computeNofExtreme(expSd, nullDist)\n pVals[p, ] <- nofExtreme/nSim\n }\n }\n\n write.table(pVals, paste(path, \"pVals\", n, \".txt\", sep=\"\"), col.names=F, row.names=F, quote=F)\n\n pdf(paste(path, \"plotPvals\", n, \".pdf\", sep=\"\"))\n for (p in 1:nofPeriods) {\n main <- \"\"\n if (caseStr==\"Weight\")\n main <- paste(\"Nof with spread:\", nofPairs1vec[p], \" Nof without spread:\", nofPairs0vec[p]) \n plot(pVals[p,], type=\"l\", ylim=c(0,1), log=\"x\", main=main)\n abline(h=0.05, col=2)\n }\n dev.off()\n \n pdf(paste(path, \"pVals\", n, \"AllGenes.pdf\", sep=\"\"))\n ind <- 1:ncol(pVals)\n mm <- 4000\n nn <- 100 \n xVals <- 6:(length(pVals[1,ind])-5) \n plot(xVals, runmed(pVals[1,ind],11)[xVals], type=\"l\", ylim=c(0,1), ylab=\"p-value\",\n xlab=xlab)\n for (p in 1:nofPeriods) \n lines(xVals, runmed(pVals[p,ind],11)[xVals])\n for (p in (nofPeriods-5):nofPeriods) {\n pV <- runmed(pVals[p,ind],11)[xVals]\n lines(pV,col=2)\n number <- nofPeriods-p+1\n points(xVals[mm+nn*number], pV[mm+nn*number], pch=paste(number))\n }\n for (p in 1:6) {\n pV <- runmed(pVals[p,ind],11)[xVals]\n lines(pV,col=4)\n number <- p\n points(xVals[mm+nn*number], pV[mm+nn*number], pch=paste(number))\n }\n abline(h=0.05, col=3)\n dev.off()\n \n pdf(paste(path, \"pVals\", n, \"2000Genes.pdf\", sep=\"\"))\n ind <- 1:2000\n mm <- 300\n nn <- 30 \n xVals <- 6:(length(pVals[1,ind])-5) \n plot(xVals, runmed(pVals[1,ind],11)[xVals], type=\"l\", ylim=c(0,1), ylab=\"p-value\",\n xlab=xlab) \n for (p in 1:nofPeriods) \n lines(xVals, runmed(pVals[p,ind],11)[xVals])\n for (p in (nofPeriods-5):nofPeriods) {\n pV <- runmed(pVals[p,ind],11)[xVals]\n lines(pV,col=2)\n number <- nofPeriods-p+1\n points(xVals[mm+nn*number], pV[mm+nn*number], pch=paste(number))\n }\n for (p in 1:6) {\n pV <- runmed(pVals[p,ind],11)[xVals]\n lines(pV,col=4)\n number <- p\n points(xVals[mm+nn*number], pV[mm+nn*number], pch=paste(number))\n }\n abline(h=0.05, col=3)\n dev.off()\n\n ## Make plots of p-values over time\n \n ## smooth pVals - median-filter over genes - window l\n pValsSmooth <- pVals\n for (p in 1:nofPeriods)\n pValsSmooth[p,] <- runmed(pVals[p,],99)\n \n if (length(followUpTime0)>length(followUpTime)) {\n meanTimeForPeriod <- rep(NA, nofPeriods)\n for (p in 1:nofPeriods)\n meanTimeForPeriod[p] <- (followUpTime0[p+n-1]+followUpTime0[p])/2\n } else {\n meanTimeForPeriod <- rep(NA, nofPeriods)\n for (p in 1:nofPeriods)\n meanTimeForPeriod[p] <- (followUpTime[p+n-1]+followUpTime[p])/2\n }\n \n days <- min(dataList$followUpTime):max(dataList$followUpTime)\n periodForDay <- rep(NA, length(days))\n periodForDay[days < mean(meanTimeForPeriod[1:2])] <- 1\n periodForDay[days > mean(meanTimeForPeriod[(length(meanTimeForPeriod)-1):\n (length(meanTimeForPeriod))])] <- nofPeriods\n for (p in 2:(nofPeriods-1))\n periodForDay[days >= mean(meanTimeForPeriod[(p-1):p]) &\n days <= mean(meanTimeForPeriod[(p):(p+1)])] <- p\n \n library(caTools)\n \n for (winLen in c(1, 30, 90, 365)) {\n pdf(paste(path, \"pValsTime\", caseStr, winLen, \".pdf\", sep=\"\"), height=6, width=7) \n \n pValVec <- (pValsSmooth[,50])[periodForDay]\n pValVec <- caTools::runmean(pValVec, winLen)\n plot(days, pValVec, type=\"l\", xlim=rev(range(dataList$followUpTime)), ylim =c(0,1),\n ylab=\"p-value\", xlab=\"Follow up time (days)\", cex=1.0, cex.axis=1.0, cex.lab=1.0)\n text(days[1], pValVec[1], labels=paste(50), cex=0.6)\n col <- 1\n for (r in c(200, 500, 1000, 2000)) {\n col <- col+1\n pValVec <- (pValsSmooth[,r])[periodForDay]\n pValVec <- caTools::runmean(pValVec, winLen)\n lines(days, pValVec, col=col)\n text(days[1], pValVec[1], labels=paste(r), col=col, cex=0.6)\n }\n for (y in 0:7)\n abline(v=y*365)\n abline(h=0.05, lty=2)\n \n dev.off()\n }\n}\n", "meta": {"hexsha": "89439ee9c5dfc729259ff8441d80ac965f0d7f63", "size": 7165, "ext": "r", "lang": "R", "max_stars_repo_path": "analyseData.r", "max_stars_repo_name": "theresenoest/Local_in_time_statistics", "max_stars_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2017-12-07T11:16:23.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-22T19:17:48.000Z", "max_issues_repo_path": "analyseData.r", "max_issues_repo_name": "theresenoest/Local_in_time_statistics", "max_issues_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analyseData.r", "max_forks_repo_name": "theresenoest/Local_in_time_statistics", "max_forks_repo_head_hexsha": "88e0437b2f480ed26de1ddde3e1a08b667ff9070", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.4702970297, "max_line_length": 100, "alphanum_fraction": 0.5741800419, "num_tokens": 2357, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3264295192838106}} {"text": "library(dplyr)\nlibrary(lubridate)\nsource(\"Subnational_Analysis/code/plotting/format_data_plotting.R\")\n\nmake_scenario_comparison_plots_mobility <- function(JOBID, StanModel, len_forecast, last_date_data, \n baseline, mobility_increase = 20){\n print(paste0(\"Making scenario comparision plots for \", mobility_increase , \"%\"))\n load(paste0('Subnational_Analysis/results/sim-constant-mob-', StanModel, '-', len_forecast, '-0-', JOBID, '-stanfit.Rdata'))\n out <- rstan::extract(fit)\n \n mob_data <- NULL\n for (i in 1:length(countries)){\n data_state_plot <- format_data(i = i, dates = dates, countries = countries, \n estimated_cases_raw = estimated_cases_raw, \n estimated_deaths_raw = estimated_deaths_raw, \n reported_cases = reported_cases,\n reported_deaths = reported_deaths,\n out = out, forecast = 0, SIM = TRUE)\n # Cuts data on last_data_date\n data_state_plot <- data_state_plot[which(data_state_plot$date <= last_date_data),]\n \n subset_data <- select(data_state_plot, country, date, reported_deaths, estimated_deaths, \n deaths_min, deaths_max)\n subset_data$key <- rep(\"Constant mobility\", length(subset_data$country))\n mob_data <- rbind(mob_data, subset_data)\n } \n \n if (baseline == TRUE){\n load(paste0('Subnational_Analysis/results/sim-increase-mob-baseline-', StanModel, '-', len_forecast, '-', mobility_increase, '-', JOBID, '-stanfit.Rdata'))\n } else {\n load(paste0('Subnational_Analysis/results/sim-increase-mob-current-', StanModel, '-', len_forecast, '-', mobility_increase, '-', JOBID, '-stanfit.Rdata'))\n }\n out <- rstan::extract(fit)\n \n for (i in 1:length(countries)){\n data_state_plot <- format_data(i = i, dates = dates, countries = countries, \n estimated_cases_raw = estimated_cases_raw, \n estimated_deaths_raw = estimated_deaths_raw, \n reported_cases = reported_cases,\n reported_deaths = reported_deaths,\n out = out, forecast = 0, SIM = TRUE)\n # Cuts data on last_data_date\n data_state_plot <- data_state_plot[which(data_state_plot$date <= last_date_data),]\n subset_data <- select(data_state_plot, country, date, reported_deaths, estimated_deaths, \n deaths_min, deaths_max)\n subset_data$key <- rep(\"Increased mobility\", length(subset_data$country))\n mob_data <- rbind(mob_data, subset_data)\n } \n \n data_half <- mob_data[which(mob_data$key == \"Increased mobility\"),]\n mob_data$key <- factor(mob_data$key)\n data_half$key <- factor(data_half$key)\n \n nametrans <- read.csv(\"Subnational_Analysis/Italy/province_name_translation.csv\")\n \n mob_data <- mob_data %>% filter(!(country %in% c(\"Lombardia\",\"Marche\",\"Veneto\",\"Toscana\",\"Piemonte\",\"Emilia-Romagna\",\"Liguria\"))) %>%\n droplevels()\n \n mob_data$country<-recode(mob_data$country, Lombardia=\"Lombardy\",Piemonte=\"Piedmont\",Toscana=\"Tuscany\",P.A._Bolzano=\"Bolzano\",\n P.A._Trento=\"Trento\",Puglia=\"Apulia\",Sardegna=\"Sardinia\",Sicilia=\"Sicily\",Valle_dAosta=\"Aosta\")\n \n data_half <- data_half %>% filter(!(country %in% c(\"Lombardia\",\"Marche\",\"Veneto\",\"Toscana\",\"Piemonte\",\"Emilia-Romagna\",\"Liguria\"))) %>%\n droplevels()\n\n data_half$country<-recode(data_half$country, Lombardia=\"Lombardy\",Piemonte=\"Piedmont\",Toscana=\"Tuscany\",P.A._Bolzano=\"Bolzano\",\n P.A._Trento=\"Trento\",Puglia=\"Apulia\",Sardegna=\"Sardinia\",Sicilia=\"Sicily\",Valle_dAosta=\"Aosta\")\n \n last_date_data<-mob_data$date[nrow(mob_data)]\n \n p <- ggplot(mob_data) +\n geom_bar(data = data_half, aes(x = date, y = reported_deaths), stat='identity') +\n geom_ribbon(aes(x = date, ymin = deaths_min, ymax = deaths_max, group = key, fill = key), alpha = 0.5) +\n #geom_line(aes(date,deaths_max),color=\"black\",size=0.2)+\n #geom_line(aes(date,deaths_min),color=\"black\",size=0.2)+\n #geom_line(aes(date,estimated_deaths),color=\"black\",size=0.3)+\n #geom_ribbon(aes(x = date, ymin = deaths_min, ymax = deaths_max, fill = \"ICL\"), alpha = 0.5) +\n scale_fill_manual(name = \"\", values = c(\"pink\", \"skyblue\")) + \n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\"), limits = c(as.Date(\"2020-03-02\"), last_date_data)) + \n facet_wrap(~country, scales = \"free\") + \n xlab(\"\") + ylab(\"Daily number of deaths\") +\n theme_minimal() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1,size = 14), axis.title = element_text( size = 14 ),axis.text = element_text( size = 14 ),\n legend.position = \"right\",strip.text = element_text(size = 14),legend.text=element_text(size=14))\n \n ggsave(paste0(\"Subnational_Analysis/figures/scenarios_increase_baseline-\", len_forecast, '-', mobility_increase, '-', JOBID, \"top_7.pdf\"), p, height = 15, width = 20)\n \n if (baseline == TRUE){\n ggsave(paste0(\"Subnational_Analysis/figures/scenarios_increase_baseline-\", len_forecast, '-', mobility_increase, '-', JOBID, \".pdf\"), p, height = 15, width = 20)\n } else {\n ggsave(paste0(\"Subnational_Analysis/figures/scenarios_increase_current-\", len_forecast, '-', mobility_increase, '-', JOBID, \".pdf\"), p, height = 15, width = 20)\n }\n}\n", "meta": {"hexsha": "2eb81f7f880d82f7ab1836b56849cbf382bae240", "size": 5505, "ext": "r", "lang": "R", "max_stars_repo_path": "Italy/code/plotting/make-scenario-plots.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "Italy/code/plotting/make-scenario-plots.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "Italy/code/plotting/make-scenario-plots.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 59.1935483871, "max_line_length": 168, "alphanum_fraction": 0.6363306085, "num_tokens": 1455, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3264295124311206}} {"text": "#' ssvm: Simple SVM\n#' \n#' @description\n#' A simple two-class linear SVM implementation using\n#' the Pegasos algorithm.\n#' \n#' @useDynLib ssvm, R_svm_pegasos_fit, R_svm_pegasos_pred,\n#' R_svm_recode_response\n#' \n#' @docType package\n#' @name ssvm-package\n#' @author Drew Schmidt\n#' @keywords package\nNULL\n", "meta": {"hexsha": "20034f438a96037f7431c9f15cd2eaa2649d016a", "size": 306, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ssvm-pkg.r", "max_stars_repo_name": "hiendn/PEGASOS", "max_stars_repo_head_hexsha": "54849403fcb1663c9864d5a38444781ad5e54ea1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-10-08T18:25:54.000Z", "max_stars_repo_stars_event_max_datetime": "2016-10-21T20:22:59.000Z", "max_issues_repo_path": "R/ssvm-pkg.r", "max_issues_repo_name": "hiendn/PEGASOS", "max_issues_repo_head_hexsha": "54849403fcb1663c9864d5a38444781ad5e54ea1", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ssvm-pkg.r", "max_forks_repo_name": "hiendn/PEGASOS", "max_forks_repo_head_hexsha": "54849403fcb1663c9864d5a38444781ad5e54ea1", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.4, "max_line_length": 58, "alphanum_fraction": 0.7189542484, "num_tokens": 95, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5273165233795672, "lm_q1q2_score": 0.32629314705710766}} {"text": "library(tidyverse)\n\n# read in data\ncounty <- read.csv(\"county.csv\")\n\n# calculate supervisors per 100,000 population\ncounty$pc.supervisor <- county$supervisors/(county$pop17/100000)\n\n# create annotation\n\nannot <- read.table(text=\n \"county|number|just|text\n 15.5|0.625|0|Broward & Orange County, FL are the median county
with 0.45 supervisors per 100,000 residents.\n 40.5|0.375|1|Some of the largest U.S. counties have the
fewest supervisors per 100,000 residents.\",\n sep=\"|\", header=TRUE, stringsAsFactors=FALSE)\n# 2.75|1.375|0|Both Nashville & Louisville are consolidated,
city-county governments.\nannot$text <- gsub(\"
\", \"\\n\", annot$text)\n\n# plot\np <- ggplot(data=county,\n aes(x=reorder(county, -pc.supervisor), y=pc.supervisor, width=0.9))+\n geom_bar(stat=\"identity\", fill=\"#617A89\")+\n scale_y_continuous(breaks=seq(0, 1.75, by=0.25))+\n # read in annotations\n geom_label(data=annot, aes(x=county, y=number, label=text, hjust=just),\n family=\"Open Sans Condensed Light\", lineheight=0.95,\n size=3.5, label.size=0, color=\"#2b2b2b\")+\n # Theming\n labs(\n title=\"Boards of supervisors tend to be small relative to population\",\n subtitle=\"County supervisors/commissioners per 100,000 residents, 40 largest U.S. counties, 2017\",\n caption=\"Author: Chris Goodman (@cbgoodman), Data: U.S. Census Bureau & Author's calculations.\",\n y=NULL,\n x=NULL) +\n theme_minimal(base_family=\"Open Sans Condensed Light\") +\n # light, dotted major y-grid lines only\n theme(panel.grid=element_line())+\n theme(panel.grid.major.y=element_line(color=\"#2b2b2b\", linetype=\"dotted\", size=0.15))+\n theme(panel.grid.major.x=element_blank())+\n theme(panel.grid.minor.x=element_blank())+\n theme(panel.grid.minor.y=element_blank())+\n # light x-axis line only\n theme(axis.line=element_line())+\n theme(axis.line.y=element_blank())+\n theme(axis.line.x=element_blank())+\n # tick styling\n theme(axis.ticks=element_line())+\n theme(axis.ticks.x=element_blank())+\n theme(axis.ticks.y=element_blank())+\n theme(axis.ticks.length=unit(5, \"pt\"))+\n # x-axis labels\n theme(axis.text.x=element_text(size=10, angle=90, hjust=0.95,vjust=0.2))+\n # breathing room for the plot\n theme(plot.margin=unit(rep(0.5, 4), \"cm\"))+\n # move the y-axis tick labels over a bit\n #theme(axis.text.y=element_text(margin=margin(r=-5)))+\n # make the plot title bold and modify the bottom margin a bit\n theme(plot.title=element_text(family=\"Open Sans Condensed Bold\", margin=margin(b=15)))+\n # make the subtitle italic\n theme(plot.subtitle=element_text(family=\"Open Sans Condensed Light Italic\"))+\n theme(plot.caption=element_text(size=8, hjust=0, margin=margin(t=15)))\n\nggsave(plot=p, \"supervisors.png\", width=10, height=6, units=\"in\", dpi=\"retina\")\n", "meta": {"hexsha": "71439cb4e7e875af005c4da90f5135d8331d11ac", "size": 2744, "ext": "r", "lang": "R", "max_stars_repo_path": "supervisors.r", "max_stars_repo_name": "cbgoodman/county-elect", "max_stars_repo_head_hexsha": "c6b6cb2ec809fafcb0b1c4f4f7db693c0c8e618e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "supervisors.r", "max_issues_repo_name": "cbgoodman/county-elect", "max_issues_repo_head_hexsha": "c6b6cb2ec809fafcb0b1c4f4f7db693c0c8e618e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "supervisors.r", "max_forks_repo_name": "cbgoodman/county-elect", "max_forks_repo_head_hexsha": "c6b6cb2ec809fafcb0b1c4f4f7db693c0c8e618e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-08-25T12:14:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-25T12:14:50.000Z", "avg_line_length": 42.875, "max_line_length": 112, "alphanum_fraction": 0.7153790087, "num_tokens": 821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438502, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.32629314705710766}} {"text": "library(RColorBrewer)\nlibrary(rgdal)\nlibrary(raster)\nlibrary(sp)\nlibrary(ggplot2)\nproj_latlon<<-\"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0\"\n#source(\"/home/sinan/workspace/visotmed/R/getRegularGrid.r\")\n#source(\"/home/sinan/workspace/visotmed/R/getProjRaster.r\")\n#source(\"/home/sinan/workspace/visotmed/R/mapProjection.r\")\n\nsource(\"/home/sinan/workspace/visotmed/R/mapConvert.r\")\nsource(\"/home/sinan/workspace/visotmed/R/mapConvert.nc.r\") \n\nsource(\"/home/sinan/workspace/visotmed/R/mapProjection.r\")\nsource(\"/home/sinan/workspace/visotmed/R/addPolygons.r\")\nsource(\"/home/sinan/workspace/visotmed/R/saveMap.r\") \nsource(\"/home/sinan/workspace/visotmed/R/graticules.r\")", "meta": {"hexsha": "c95ee3dd421e21ca1b11f33d2e93f6a0544fbb4c", "size": 686, "ext": "r", "lang": "R", "max_stars_repo_path": "R/header.r", "max_stars_repo_name": "sinanshi/visotmed", "max_stars_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T02:17:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T10:32:36.000Z", "max_issues_repo_path": "R/header.r", "max_issues_repo_name": "sinanshi/visotmed", "max_issues_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/header.r", "max_forks_repo_name": "sinanshi/visotmed", "max_forks_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3529411765, "max_line_length": 79, "alphanum_fraction": 0.7798833819, "num_tokens": 205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3262931470571076}} {"text": "\n# Load packages\nlibrary(tidyverse)\nlibrary(gggibbous)\nlibrary(ggridges)\nlibrary(showtext)\nlibrary(ggimage)\nlibrary(magick)\nlibrary(patchwork)\nlibrary(ggtext)\n\n# Import tidytuesday data - Week 44\nultra_rankings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/ultra_rankings.csv')\nrace <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-26/race.csv')\n\n# Add goofle fonts for the plots\nfont_add_google(\"Roboto Condensed\", \"roboto condensed\")\nfont_add_google(\"Bitter\", \"Bitter\")\nfont_add_google(\"Playfair Display\", \"Playfair Display\")\nshowtext_opts(dpi = 320)\nshowtext_auto(enable = TRUE)\n\n# Clean Ultra Marathons races\n# Calculate average elevation_gain per event and country (can vary a little bit from year to year)\nrace_clean <- race %>% \n mutate(year = format(date, format=\"%Y\"),\n event = str_to_upper(event)) %>% # everything to upper case\n filter(distance > 100) %>% \n group_by(event, country) %>% \n summarise(avg_elevgain = mean(elevation_gain, na.rm = TRUE),\n avg_elevloss= mean(elevation_loss, na.rm = TRUE),\n avg_distance = mean(distance, na.rm = TRUE)) %>% \n ungroup()\n\n\n# Take only countries with at least 10 Ultra Marathons referenced in the data\nrace_clean_country <- race_clean %>% \n group_by(country) %>% \n mutate(n = n()) %>% \n ungroup() %>% \n filter(n > 10) %>% \n mutate(country = fct_reorder(country, n)) %>% \n arrange(country, n, desc(avg_elevgain)) %>% \n mutate(country_num = as.numeric(fct_rev(country))) \n\n# Pull country order for display\ncountry_order <- race_clean_country %>%\n group_by(country) %>% \n filter(avg_elevgain == max(avg_elevgain)) %>%\n ungroup() %>% \n arrange(n) %>%\n pull(country)\n\nn <- race_clean_country %>%\n group_by(country) %>%\n filter(avg_elevgain == max(avg_elevgain)) %>%\n ungroup() %>% \n arrange(n) %>%\n pull(n)\n\n# Color countries\ncolor_country <- c()\ncolor_country[country_order] <- colorRampPalette(c(\"#fafaf4\", \"#00429d\", \"#2d5f47\"))(8)\n\n# Label country\nlabel_country <- tibble(label = rev(country_order),\n x = c(4000,4700,8350,5900,5970,4300,6080,6500),\n y = c(9,25,13,32,36.2,44.5,39.7,42.3),\n idy = seq(1:8),\n n = rev(n),\n nred = n /100)\n\n# Axis ticks of 1st plot\naxis_ticks <- tibble(x = -500,\n xend = c(850, 1600, 2250, 3100),\n y = seq(10,40,10),\n yend = seq(10,40,10))\n\n# Area plot (cumulative by country) of number of events per D+\narea_plot <- ggplot(race_clean_country, \n aes(x = avg_elevgain)) + \n geom_area(aes(fill = country), \n stat = \"bin\", bins = 10, alpha=0.7) +\n scale_fill_manual(values = color_country) +\n scale_color_manual(values = color_country) +\n scale_x_continuous(breaks = seq(0,12000,2500), \n labels = glue::glue(\"{seq(0,12000,2500)} m\"),\n limits = c(-500,12000)) +\n geom_segment(data = label_country, aes(x = 8000, xend = 12500, y = 32, yend = 32), color = \"black\", size = 0.5) +\n geom_segment(data = axis_ticks, aes(x= x, xend = xend, y =y, yend = yend), linetype = \"13\", color = \"white\") +\n geom_text(data = axis_ticks, aes(x = 700, y = y, label = glue::glue(\"{y} Ultra Marathons\")), hjust = 1, nudge_y = 1.2, color = \"white\", family = \"roboto condensed\", face = \"italic\", size = 2.5) +\n geom_text(data = label_country,\n aes(x = x, y = y, label = label), hjust = 0.5, color = \"grey90\", size = 4, family = \"Playfair Display\") +\n guides(fill = \"none\") +\n labs(x = \"Elevation gain (D+) in Ultra Marathons\") +\n theme_light() +\n theme(plot.background = element_rect(fill = \"grey20\", color = NA),\n panel.border = element_rect(fill = NA, \n colour = NA),\n panel.background = element_rect(fill = \"grey20\", \n colour = NA),\n panel.grid = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks.y = element_blank(),\n axis.title.y = element_blank(),\n axis.line.x = element_line(color = \"grey88\", size = 3),\n axis.ticks.x = element_blank(),\n axis.text.x = element_text(color = \"grey88\", vjust=-1, size = 12, family = \"roboto condensed\"),\n axis.title.x = element_text(color = \"grey88\", vjust=-2, size = 14, family = \"Playfair Display\"),\n plot.margin = margin(10,5,15,40))\n\n# Customize legend \nlegend <- ggplot(label_country,\n aes(x = 1,y = idy)) +\n geom_moon(aes(ratio = 0.7, size = n), \n stat = \"identity\",\n fill = colorspace::lighten(rev(color_country)), \n color = rev(color_country),\n stroke = .3,\n right = FALSE) +\n geom_moon(aes(ratio = 0.3, size = n), \n stat = \"identity\",\n fill = rev(color_country), \n color = rev(color_country),\n stroke = .3) +\n geom_text(aes(label = n), hjust = 0.9, color = colorspace::darken(rev(color_country),0.8), family = \"Bitter\", fontface = \"italic\", size = 4) +\n geom_text(aes(label = label), color = colorspace::lighten(\"#c6c6a9\",0.4), hjust = .5, nudge_y = 0.5, family = \"Playfair Display\", fontface = \"bold\", size = 5) +\n scale_size_continuous(range = c(9, 24)) +\n scale_y_continuous(limits = c(.4,9)) +\n guides(size = \"none\") +\n coord_cartesian(expand = FALSE, clip = \"off\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"grey20\", color = NA))\n\n# density ridges by country of races per D+ \ndensity_ridges <- ggplot(race_clean_country, \n aes(x = avg_elevgain, y = country_num, fill = country, color = country)) +\n geom_density_ridges(\n jittered_points = FALSE, quantile_lines = FALSE, scale = 1.1, alpha = 0.7,\n vline_size = 0.5, vline_color = \"grey88\",\n point_size = 0.4, point_alpha = 1,\n position = position_raincloud(adjust_vlines = FALSE)\n ) +\n scale_fill_cyclical(values = colorspace::lighten(color_country,0.2)) +\n scale_color_cyclical(values = color_country) +\n geom_boxplot(\n aes(y = country_num - .15), \n width = .15, \n outlier.shape = NA\n ) +\n geom_point(\n aes(y = country_num - .3, group = country, fill = after_scale(colorspace::darken(color, .2))), \n shape = \"|\",\n size = 2.5,\n alpha = .33\n ) +\n scale_x_continuous(breaks = seq(0,12000, 2500),\n labels = glue::glue(\"{seq(0,12000,2500)} m\"),\n limits = c(0,12000)) +\n labs(x = \"Elevation gain (D+) in Ultra Marathons\") +\n coord_cartesian(expand = TRUE, clip = \"on\") +\n theme_light() +\n theme(plot.background = element_rect(fill = \"grey20\", color = NA),\n panel.border = element_rect(fill = NA, \n colour = NA),\n panel.background = element_rect(fill = \"grey20\", \n colour = NA),\n panel.grid = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks.y = element_blank(),\n axis.title.y = element_blank(),\n axis.line.x = element_line(color = \"grey88\", size = 3),\n axis.ticks.x = element_blank(),\n axis.text.x = element_text(color = \"grey88\", vjust=-1, size = 12, family = \"roboto condensed\"),\n axis.title.x = element_text(color = \"grey88\", vjust=-2, size = 14, family = \"Playfair Display\"),\n plot.margin = margin(10,5,15,5)\n )\n\n# Import image and transform it with transparent background \nimage <- image_read(here::here(\"running-man-color3.jpg\")) \nimage <- image_fill(image, \"transparent\", fuzz = 40) %>% \n image_scale(\"400\") \nimage_write(image, path = \"running-man-color3.png\", format = \"png\")\nimage_runner <- here::here(\"running-man-color3.png\")\n\n# Create image plot\nimage_runner_p <- ggplot() +\n geom_image(aes(x = 0, y = 0, image = image_runner), size = 1) +\n scale_x_continuous(limits = c(-10,10)) +\n scale_y_continuous(limits = c(-10,10)) +\n coord_fixed(ratio = 1) +\n theme_void()+\n theme(panel.background = element_rect(fill = \"transparent\", color = NA),\n plot.background = element_rect(fill = \"transparent\", color = NA), \n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank())\n\n# Assembling elements to create final plot\nfinal <- \n area_plot + inset_element(image_runner_p,0.001, 0.835, 0.9, 1, align_to = 'full') +\n legend + density_ridges + \n plot_layout(widths = c(1,0.25,1)) + \n plot_annotation(\n caption = \"Visualization: Guillaume Abgrall \u2022 Data: Benjamin Nowak from International Trail Running Association (ITRA)\",\n title = \"Ultra Marathons : Where elevation gain (D+) make runs harder\",\n subtitle = \"
The distance isn\u2019t the only problem, as competitors are asked to wrestle with mountains where elevation gain (D+) make the runs even harder !
\n Since they\u2019re events that allow you to experience the world in a unique way, I've chosen to show countries where you can find races with the best D+ *(at least 10 ultra marathons in each country)*

\n While **United Stated** has the most ultra marathon races referenced **(92)**, **France** is by far the first grantor of highest D+ in their Ultra Marathons !\",\n theme=theme(\n plot.title = element_text(color = \"grey88\", size = 21, family = \"Playfair Display\", hjust = .5),\n plot.subtitle = element_markdown(color = \"grey88\", size = 10, family = \"Bitter\", hjust = .5),\n plot.caption = element_markdown(color = \"grey88\", size = 6, family = \"Playfair Display\", hjust = .985),\n plot.background = element_rect(fill = \"grey20\", color = NA),\n plot.margin = margin(10,25,20,25))) \n\n# Save data\nragg::agg_png(here::here(paste0(\"ultra_marathons\", \".png\")), res = 320, width = 16, height = 10, units = \"in\")\nfinal\ndev.off()\n\n\n", "meta": {"hexsha": "e8bf912056f35b8a88e0597fada2e9d9e3f6ed3a", "size": 10280, "ext": "r", "lang": "R", "max_stars_repo_path": "2021/2021-Week44/ultra_marathons.r", "max_stars_repo_name": "guigui351/tidytuesday", "max_stars_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2021-11-15T08:28:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-18T03:19:45.000Z", "max_issues_repo_path": "2021/2021-Week44/ultra_marathons.r", "max_issues_repo_name": "jvilltolentino/tidytuesday", "max_issues_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2021/2021-Week44/ultra_marathons.r", "max_forks_repo_name": "jvilltolentino/tidytuesday", "max_forks_repo_head_hexsha": "2910a86ec70074ef35f742935e09eab2fe0667d8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-11-15T12:00:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-15T17:28:01.000Z", "avg_line_length": 46.3063063063, "max_line_length": 405, "alphanum_fraction": 0.6258754864, "num_tokens": 2932, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3262931396429256}} {"text": "\nPlotNMDS = function(NMDS1, # results of the NMDS you want to plot\n data, # Environmental data (not species matrix)\n textp = T, # do you want to plot text for the species centroids?\n group, # Group you want to put hulls around\n group2= NA, # Group you want to put spiders around\n taxa = NA) { # Vector of relative percentages of a given taxa from your species matrix, used to scale the points.\n require(vegan)\n #set up colors\n cols = c(\"red\", \"blue\", \"green\", \"purple\", \"yellow\", \n \"pink1\", \"brown\", \"orangered\", \"black\", \"cyan\")\n #set up sapes\n shapes = c(1,2,3,4,5,6)\n #set up group\n names(data)[(which(names(data)== group))] = \"group\"\n #number of groups\n data = droplevels(data)\n m = length(levels(data$group))\n \n #set parameters so the legend is outside the plot area\n par(xpd=NA,oma=c(3,0,0,0)) \n \n # plot the sample points\n plot(NMDS1, type=\"n\", shrink = T, cex=1, xlim=c(-1, 1))\n if (is.na(taxa[1])) points(NMDS1, pch=as.numeric(data$group), cex=1, col=cols[as.numeric(data$group)]) else {\n # scale the points by relative abundance of a certain taxa\n scalef = max(taxa) - min(taxa)\n points(NMDS1, pch=as.numeric(data$group), cex=.1 + taxa/mean(taxa), col=\"blue\")\n }\n \n # plot text for species, if desired\n if(textp) text(NMDS1, dis=\"species\", cex=.8) \n \n # draw a hull around each group\n for (i in 1:m) {\n \n with(data, ordiellipse(NMDS1, kind = \"sd\", conf = 0.95, groups = group, \n show = levels(group)[i], col=cols[i], lwd=2, lty = 1:length(group)))\n }\n \n #second group with spiders\n if (is.na(group2)==F) {\n names(data)[(which(names(data)== group2))] = \"group2\"\n for (j in 1:length(levels(data$group2))) {\n with(data, ordispider(NMDS1, group = group2, show = levels(group2)[j], col=cols[j + length(levels(group))], lwd=1))\n }\n legend(par(\"usr\")[1],par(\"usr\")[3], legend = c(levels(data$group),levels(data$group2)),\n col = cols[1:(length(c(levels(data$group), levels(data$group2))))], \n lwd=c(rep(2, length(levels(data$group))), rep(1, length(levels(data$group2)))))\n } else legend(par(\"usr\")[1],par(\"usr\")[3], legend = levels(data$group),\n col = cols[1:length(levels(data$group))], \n lty = 1:length(levels(data$group)),\n pch = shapes[1:length(levels(data$group))], lwd=2, bty = \"o\")\n# legend(par(\"usr\")[1]+1,par(\"usr\")[3], legend = levels(data$group), \n # pch = shapes[1:length(levels(data$group))], col = \"blue\", bty = \"n\")\n \n}\n\n\nPlotNMDS2 = function(NMDS1, # results of the NMDS you want to plot\n data, # Environmental data (not species matrix)\n textp = T, # do you want to plot text for the species centroids?\n lines, #variables to use for ordisurf\n group #group to change color/shape\n ) { \n require(vegan)\n #set up colors\n cols = c(\"red\", \"blue\", \"green\", \"purple\", \"yellow\", \n \"pink1\", \"brown\", \"orangered\", \"black\", \"cyan\")\n #set up sapes\n shapes = c(1,2,3,4,5,6)\n #set up group\n names(data)[(which(names(data)== group))] = \"group\"\n names(data)[(which(names(data)== lines))] = \"lines\"\n #number of groups\n data = droplevels(data)\n \n #set parameters so the legend is outside the plot area\n par(xpd=NA,oma=c(3,0,0,0)) \n \n # plot the sample points\n plot(NMDS1, type=\"n\", shrink = T, cex=1, xlim=c(-1, 1))\n points(NMDS1, pch=as.numeric(data$group), cex=1, col=cols[as.numeric(data$group)]) \n \n # plot text for species, if desired\n if(textp) text(NMDS1, dis=\"species\", cex=.8) \n \n # \n with(data, ordisurf(NMDS1~lines, data = data, add=T, col = \"black\"))\n \n legend(par(\"usr\")[1],par(\"usr\")[3], legend = levels(data$group),\n col = cols[1:length(levels(data$group))], \n lty = 1:length(levels(data$group)),\n pch = shapes[1:length(levels(data$group))], lwd=2, bty = \"o\")\n # legend(par(\"usr\")[1]+1,par(\"usr\")[3], legend = levels(data$group), \n # pch = shapes[1:length(levels(data$group))], col = \"blue\", bty = \"n\")\n \n}", "meta": {"hexsha": "04e5f1288837c63a44f00396586d8f4849b7e4c6", "size": 4179, "ext": "r", "lang": "R", "max_stars_repo_path": "WY2017_WY2019/zoop/plotNMDS.r", "max_stars_repo_name": "AEU-DISE/ltreport", "max_stars_repo_head_hexsha": "9ca3a3da55acb7aa46cab15acf83f3cff64db40c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "WY2017_WY2019/zoop/plotNMDS.r", "max_issues_repo_name": "AEU-DISE/ltreport", "max_issues_repo_head_hexsha": "9ca3a3da55acb7aa46cab15acf83f3cff64db40c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "WY2017_WY2019/zoop/plotNMDS.r", "max_forks_repo_name": "AEU-DISE/ltreport", "max_forks_repo_head_hexsha": "9ca3a3da55acb7aa46cab15acf83f3cff64db40c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.6428571429, "max_line_length": 133, "alphanum_fraction": 0.5831538646, "num_tokens": 1297, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3262931396429256}} {"text": "#' # **** action **** ----\n#' #' household_incomes_and_house_prices_fit_model_and_find_outliers (action)\n#' #' @param data Data\n#' #' @param argset Argset\n#' #' @param schema DB Schema\n#' #' @export\n#' household_incomes_and_house_prices_fit_model_and_find_outliers_action <- function(data, argset, schema) {\n#' # tm_run_task(\"household_incomes_and_house_prices_fit_model_and_find_outliers\")\n#'\n#' if(plnr::is_run_directly()){\n#' # sc::tm_get_plans_argsets_as_dt(\"household_incomes_and_house_prices_fit_model_and_find_outliers\")\n#'\n#' index_plan <- 1\n#' index_analysis <- 1\n#'\n#' data <- sc::tm_get_data(\"household_incomes_and_house_prices_fit_model_and_find_outliers\", index_plan = index_plan)\n#' argset <- sc::tm_get_argset(\"household_incomes_and_house_prices_fit_model_and_find_outliers\", index_plan = index_plan, index_analysis = index_analysis)\n#' schema <- sc::tm_get_schema(\"household_incomes_and_house_prices_fit_model_and_find_outliers\")\n#' }\n#'\n#' # code goes here\n#' # special case that runs before everything\n#' if(argset$first_analysis == TRUE){\n#'\n#' }\n#'\n#' d <- merge(\n#' data$income,\n#' data$price,\n#' by = c(\"location_code\", \"calyear\")\n#' )\n#'\n#' d_pred <- d |> {\\(x)\n#' lme4::lmer(\n#' new_house_price_per_m2_nok ~\n#' household_income_median_all_households_nok +\n#' (1|location_code),\n#' data = x\n#' )}() |>\n#' merTools::predictInterval(d, level = 0.95) |>\n#' {\\(x) cbind(d, x)}() |>\n#' data.table()\n#'\n#' setnames(\n#' d_pred,\n#' c(\n#' \"fit\",\n#' \"upr\",\n#' \"lwr\"\n#' ),\n#' c(\n#' \"new_house_price_per_m2_baseline_nok\",\n#' \"new_house_price_per_m2_nok_predinterval_q97x5\",\n#' \"new_house_price_per_m2_nok_predinterval_q02x5\"\n#' )\n#' )\n#'\n#' d_pred[, new_house_price_per_m2_nok_status := \"normal\"]\n#' d_pred[new_house_price_per_m2_baseline_nok > new_house_price_per_m2_nok_predinterval_q97x5, new_house_price_per_m2_nok_status := \"high\"]\n#'\n#' xtabs(~d_pred$new_house_price_per_m2_nok_status)\n#'\n#' # put data in db table\n#' sc::fill_in_missing_v8(d_pred, border = config$border)\n#' schema$anon_example_house_prices_outliers_after_adjusting_for_income$drop_all_rows_and_then_insert_data(d_pred)\n#'\n#' # check that it uploaded\n#' nrow(d_pred)\n#' schema$anon_example_house_prices_outliers_after_adjusting_for_income$tbl() |> dplyr::summarize(n()) |> dplyr::collect()\n#'\n#' # special case that runs after everything\n#' # copy to anon_web?\n#' if(argset$last_analysis == TRUE){\n#' # sc::copy_into_new_table_where(\n#' # table_from = \"anon_X\",\n#' # table_to = \"anon_webkht\"\n#' # )\n#' }\n#' }\n#'\n#' # **** data_selector **** ----\n#' #' household_incomes_and_house_prices_fit_model_and_find_outliers (data selector)\n#' #' @param argset Argset\n#' #' @param schema DB Schema\n#' #' @export\n#' household_incomes_and_house_prices_fit_model_and_find_outliers_data_selector = function(argset, schema){\n#' if(plnr::is_run_directly()){\n#' # sc::tm_get_plans_argsets_as_dt(\"household_incomes_and_house_prices_fit_model_and_find_outliers\")\n#'\n#' index_plan <- 1\n#'\n#' argset <- sc::tm_get_argset(\"household_incomes_and_house_prices_fit_model_and_find_outliers\", index_plan = index_plan)\n#' schema <- sc::tm_get_schema(\"household_incomes_and_house_prices_fit_model_and_find_outliers\")\n#' }\n#'\n#' # The database schemas can be accessed here\n#' # schema$anon_example_income$print_dplyr_select()\n#' d_income <- schema$anon_example_income$tbl() %>%\n#' sc::mandatory_db_filter(\n#' granularity_time = \"calyear\",\n#' granularity_time_not = NULL,\n#' granularity_geo = \"county\",\n#' granularity_geo_not = NULL,\n#' country_iso3 = NULL,\n#' location_code = NULL,\n#' age = \"total\",\n#' age_not = NULL,\n#' sex = \"total\",\n#' sex_not = NULL\n#' ) %>%\n#' dplyr::filter(calyear %in% 2000:2019) %>%\n#' dplyr::select(\n#' granularity_time,\n#' granularity_geo,\n#' # country_iso3,\n#' location_code,\n#' # border,\n#' age,\n#' sex,\n#' #\n#' # date,\n#' #\n#' # isoyear,\n#' # isoweek,\n#' # isoyearweek,\n#' # season,\n#' # seasonweek,\n#'\n#' calyear,\n#' # calmonth,\n#' # calyearmonth,\n#'\n#' household_income_median_all_households_nok\n#' # household_income_median_singles_nok,\n#' # household_income_median_couples_without_children_nok,\n#' # household_income_median_couples_with_children_nok,\n#' # household_income_median_single_with_children_nok\n#' ) %>%\n#' dplyr::collect() %>%\n#' as.data.table() %>%\n#' setorder(\n#' location_code,\n#' calyear\n#' )\n#'\n#' # schema$anon_example_house_prices$print_dplyr_select()\n#' d_price <- schema$anon_example_house_prices$tbl() %>%\n#' sc::mandatory_db_filter(\n#' granularity_time = \"calyear\",\n#' granularity_time_not = NULL,\n#' granularity_geo = \"county\",\n#' granularity_geo_not = NULL,\n#' country_iso3 = NULL,\n#' location_code = NULL,\n#' age = \"total\",\n#' age_not = NULL,\n#' sex = \"total\",\n#' sex_not = NULL\n#' ) %>%\n#' dplyr::filter(calyear %in% 2000:2019) %>%\n#' dplyr::select(\n#' # granularity_time,\n#' # granularity_geo,\n#' # country_iso3,\n#' location_code,\n#' # border,\n#' # age,\n#' # sex,\n#' #\n#' # date,\n#' #\n#' # isoyear,\n#' # isoweek,\n#' # isoyearweek,\n#' # season,\n#' # seasonweek,\n#'\n#' calyear,\n#' # calmonth,\n#' # calyearmonth,\n#'\n#' new_house_price_per_m2_nok\n#' # used_house_price_per_m2_nok\n#' ) %>%\n#' dplyr::collect() %>%\n#' as.data.table() %>%\n#' setorder(\n#' location_code,\n#' calyear\n#' )\n#'\n#' # The variable returned must be a named list\n#' retval <- list(\n#' \"income\" = d_income,\n#' \"price\" = d_price\n#' )\n#' retval\n#' }\n#'\n#' # **** functions **** ----\n#'\n#'\n#'\n#'\n", "meta": {"hexsha": "2661213bfc66ddcfbc215e9d4fe18ffc4b91d4c3", "size": 6106, "ext": "r", "lang": "R", "max_stars_repo_path": "R/household_incomes_and_house_prices_fit_model_and_find_outliers.r", "max_stars_repo_name": "sykdomspulsen-org/sc-tutorial-start", "max_stars_repo_head_hexsha": "10e65b7386081e183572ec08c8c8ff4d30629a85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/household_incomes_and_house_prices_fit_model_and_find_outliers.r", "max_issues_repo_name": "sykdomspulsen-org/sc-tutorial-start", "max_issues_repo_head_hexsha": "10e65b7386081e183572ec08c8c8ff4d30629a85", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/household_incomes_and_house_prices_fit_model_and_find_outliers.r", "max_forks_repo_name": "sykdomspulsen-org/sc-tutorial-start", "max_forks_repo_head_hexsha": "10e65b7386081e183572ec08c8c8ff4d30629a85", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7853658537, "max_line_length": 158, "alphanum_fraction": 0.6118571896, "num_tokens": 1717, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3262931396429256}} {"text": "require(PBSadmb)\r\nsource(\"/Users/Jim/Dropbox/r_common/adfunctions.r\")\r\nsetwd(\"/Users/Jim/_mymods/AMAK/examples/chub/\")\r\n\r\nmod1 <- readList(\"arc/mod1_R.rep\")\r\nmod2 <- readList(\"arc/mod2_R.rep\")\r\nmod3 <- readList(\"arc/mod3_R.rep\")\r\nlength(mod3$Like_Comp)\r\nrbind(mod2$Like_Comp,mod3$Like_Comp)\r\n# show projections\r\nplt_proj(mod1,fy=1970,ly=2020)\r\nplt_proj(mod2)\r\n\r\n# show fit to catch biomass\r\nCatchFit(mod1) \r\nCatchFit\r\np.catch.fit(mod1,f=1,ylab=\"Catch biomass (t)\" ,ylim=c(0,1700000))\r\np.catch.fit(mod1,f=2,ylab=\"Catch biomass (t)\" ,ylim=c(0,1700000))\r\np.catch.fit(mod1,f=3,ylab=\"Catch biomass (t)\" ,ylim=c(0,1700000))\r\n\r\nsel.age.mountain(mod2, f=1, xvec=NULL, yvec=NULL, zscale=3, fy=1977,ly=2014)\r\n,nshades=50, xaxs=\"i\", yaxs=\"i\", xlab=\"\", ylab=\"\", las=1, new=TRUE, addbox=FALSE, cex.xax=1, cex.yax=1, main=\"\", ...)\r\np.catch.fit(mod2,f=1,ylab=\"Catch biomass (t)\" ,ylim=c(0,500000))\r\np.catch.fit(mod2,f=2,ylab=\"Catch biomass (t)\" ,ylim=c(0,500000))\r\n\r\n\r\n# example of writing multiple plots to pdf file:\r\npdf(\"agefits.pdf\",width=9, height=7)\r\n AgeFits(mod1,f=1)\r\n AgeFits(mod2,f=1)\r\ndev.off()\r\n\r\n# another example of writing multiple plots to pdf file:\r\npdf(\"indices.pdf\",width=9, height=7)\r\n Indices(mod1,\"Model 1\",fy=1970,ly=2015)\r\n Indices(mod2,\"Model 2\",fy=1970,ly=2015)\r\n Indices(mod3,\"Model 3\",fy=1970,ly=2015)\r\ndev.off()\r\n\r\npdf(\"selectivity.pdf\",width=9, height=7)\r\nMntns(mod1,\"Model 1\")\r\nMntns(mod2 ,\"Model 2\")\r\ndev.off()\r\n\r\nMntns_srv(mod1,\"Model 1\")\r\ndetach()\r\n# Stock recruitment curve\r\nstyr=1950\r\n p.stock.rec(mod1)\r\n p.stock.rec(mod2,main=\"Model 2\")\r\n# recruitment hist w/ errors\r\n vpa_r <- read.table(\"clipboard\")\r\n rec_age=0\r\n p.rec.hist(mod1,ylab=\"Age 0 recruitment\",fy=1950,ly=2014,main=\"model 1\")\r\n p.rec.hist(mod2,ylab=\"Age 0 recruitment\",fy=1950,ly=2014,main=\"model 2\")\r\n base=mod1$Stock_Rec[26:64,4]\r\nlength(base) \r\n wchina=mod2$Stock_Rec[20:65,4]\r\n vpa_r=(t(vpa_r))\r\n plot(base,vpa_r,xlab=\"Base\",ylab=\"VPA\",pch=19)\r\n plot(mod1$Stock_Rec[20:65,4],mod2$Stock_Rec[20:65,4],ylab=\"Base\",xlab=\"Including Chinese catches\",pch=19)\r\n lines(supsmu(wchina,base))\r\n ?supsmu\r\n\r\n\r\n# Fishing mortality \r\n p.full.f(mod1)\r\n p.full.f(mod2)\r\n\r\n# sample sizes\r\n p.eff.n(mod1,typ=\"F\")\r\n p.eff.n(mod2,typ=\"F\",main=\"Model 2\")\r\n\r\n# spawning biomass and last year's estimates \r\n p.biom.pol(mod1,main=\"Model 1\")\r\n p.biom.pol(mod2,main=\"Model 2\",fy=1950,ly=2014)\r\n p.biom.pol(mod3,main=\"Model 3\")\r\n lines(mod1a$TotBiom[,1],mod1a$TotBiom[,2],lty=3,lw=3)\r\n\r\n# Survey fit\r\n p.sur.stk(mod1,S=1)\r\ndetach()\r\n# Rec\r\n p.biom.stk(mod1,typ=\"R\")\r\n\r\n# Numbers at age\r\n p.bub.age(mod1c,siz=3000)\r\n\r\n\r\n# show spawning biomass relative to population with no fishing\r\nspwn_ratio(mod1) ", "meta": {"hexsha": "353f4c2728ffd44793c9cf02477ece1a72e034cf", "size": 2681, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/chub/R/am.r", "max_stars_repo_name": "NMFS-toolbox/AMAK", "max_stars_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-12T17:39:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-12T17:39:20.000Z", "max_issues_repo_path": "examples/chub/R/am.r", "max_issues_repo_name": "afsc-assessments/AMAK", "max_issues_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/chub/R/am.r", "max_forks_repo_name": "afsc-assessments/AMAK", "max_forks_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-05-21T18:18:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-12T04:18:42.000Z", "avg_line_length": 29.4615384615, "max_line_length": 118, "alphanum_fraction": 0.6754942186, "num_tokens": 985, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6959583376458153, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.32625874521074066}} {"text": "# James Rekow\r\n\r\ncreateMeanDissDF = function(MVec = NULL, N = 20, numReplicates = 2){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS:\r\n \r\n source(\"abdListCreator.r\")\r\n source(\"DOCProcedure.r\")\r\n \r\n if(is.null(MVec)){\r\n MVec = seq(from = 40, to = 200, by = 40)\r\n } # end if\r\n \r\n MVec = c(MVec, MVec)\r\n numM = length(MVec) / 2\r\n \r\n univVec = c(rep(1, numM), rep(0, numM))\r\n \r\n inputDF = data.frame(M = MVec, univ = univVec)\r\n \r\n testReplicate = function(inputRow){\r\n \r\n M = inputRow[1]\r\n univ = inputRow[2]\r\n \r\n abdList = abdListCreator(M = M, N = N, univ = univ)\r\n \r\n ## testing\r\n \r\n createSampledMeanDiss = function(zz = NULL){\r\n \r\n keepIx = sample(N, 20)\r\n \r\n speciesSelector = function(abdVec) abdVec[keepIx]\r\n \r\n speciesSubsetAbdList = lapply(abdList, speciesSelector)\r\n \r\n speciesSubsetDoc = DOCProcedure(abdList = speciesSubsetAbdList)\r\n \r\n speciesSubsetMeanDiss = mean(speciesSubsetDoc$y)\r\n \r\n return(speciesSubsetMeanDiss)\r\n \r\n } # end createSampledMeanDiss function\r\n \r\n sampledMeanDissVec = sapply(1:10, createSampledMeanDiss)\r\n sampledMeanDiss = mean(sampledMeanDissVec)\r\n \r\n return(sampledMeanDiss)\r\n \r\n ##\r\n \r\n doc = DOCProcedure(abdList = abdList)\r\n meanDiss = mean(doc$y)\r\n \r\n return(meanDiss)\r\n \r\n } # end testReplicate function\r\n \r\n testDFReplicate = function(n){\r\n \r\n replicateIDVec = rep(n, 2 * numM)\r\n \r\n meanDiss = apply(inputDF, 1, testReplicate)\r\n \r\n outputDF = cbind(inputDF, replicateIDVec, meanDiss)\r\n \r\n return(outputDF)\r\n \r\n } # end testDFReplicate function\r\n \r\n testResultsDFList = lapply(as.list(1:numReplicates), testDFReplicate)\r\n # testResultsDF = Reduce(rbind, testResultsDFList)\r\n testResultsDF = do.call(rbind, testResultsDFList)\r\n testResultsDF$univ = as.factor(testResultsDF$univ)\r\n \r\n return(testResultsDF)\r\n \r\n} # end createMeanDissDF function\r\n", "meta": {"hexsha": "6721b374d9d4ff29c074aa279426ca789b3db74e", "size": 1973, "ext": "r", "lang": "R", "max_stars_repo_path": "createMeanDissDF.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "createMeanDissDF.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "createMeanDissDF.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0609756098, "max_line_length": 72, "alphanum_fraction": 0.6082108464, "num_tokens": 595, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32609275617109024}} {"text": "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Preliminary data exploration plots\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nrm(list=ls()) # clears workspace\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nlibrary(ggplot2)\nlibrary(plyr)\nsource('R/FracFeed-Functions.r')\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Run to update data files\n# source('R/FracFeed-DataPrep-GoogleImport.r')\n# Or load from saved file\nload('Data/FracFeed_Data.Rdata') # load saved 'DB'\ndat<-fdat\n# Load the factor levels (to maintain orders)\nload('Data/FracFeed_FactorLevels.Rdata')\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nalpha<-0.3 # alpha level for colours\n\n##################\n# Data exploration\n##################\n\npdf('Output/Plots/FracFeed-DataSummaries.pdf',height=11,width=8)\n\top<-par(mfrow=c(4,2),tcl=-0.3,mgp=c(2,0.4,0),cex=0.7,yaxs='i',cex.lab=1.3)\n\n\t# Count of surveys by name\n\ttab<-sort(table(dat$Name))\n\tbarplot(tab,las=2,ylab='Surveys',log='y')\n\tlegend('topleft',legend=paste('n = ',sum(tab)),bty='n')\n\n\t# Count of citations by name\n\ttab<-apply(table(dat$Name,dat$Citation),1,function(x){sum(x>0)})\n\tbarplot(sort(tab),las=2,ylab='Citations')\n\tlegend('topleft',legend=paste('n = ',sum(tab)),bty='n')\n\n\t# Count of Taxon groups\n\ttaxCnt<-tab<-sort(table(dat$Taxon.group))\n\tbarplot(tab,las=2,ylab='Frequency',log='y')\n\n\t# Count of Taxon groups by species\n\ttab<-table(dat$Consumer.identity,dat$Taxon.group)\n\ttab<-tab[,order(apply(tab,2,sum))]\n\tpredCnt<-apply(tab,2,function(x){sum(x>0)})\n\tb<-barplot(tab,las=2,ylab='Frequency',ylim=c(0,1.1*max(taxCnt)))\n\ttext(b,taxCnt+20,paste0('(',predCnt,')'),cex=0.7)\n\tlegend('topleft',legend=paste0('(',sum(predCnt),' predator species)'),bty='n')\n\n\t# Count of ecosystems\n\ttab<-table(factor(dat$Ecosystem,levels=EcosystemLevels),useNA='ifany')\n\tbarplot(tab,las=2,ylab='Frequency',log='y')\n\n\t# Count of SpaceTime.replicate types\n\ttab<-rev(table(factor(dat$SpaceTime.replicate, levels=rev(SpaceTimeLevels)),useNA='ifany'))\n\tbarplot(tab,las=2,ylab='Frequency')\n\n\t# Count of Spatial averaging scales\n\ttab<-rev(table(factor(dat$Space.averaging, levels=rev(SpaceAvgLevels)),useNA='ifany'))\n\tbarplot(tab,las=2,ylab='Frequency')\n\n\t# Count of Temporal averaging scales\n\ttab<-table(factor(dat$Time.averaging, levels=TimeAvgLevels),useNA='ifany')\n\tbarplot(tab,las=2,ylab='Frequency')\n\n\t# Histogram of citation year\n\tcite.yrs<-as.numeric(substring(sapply(strsplit(dat$Citation, \"_\"), \"[\", 2),1,4))\n\trng<-range(cite.yrs,na.rm=TRUE)\n\tyrs<-seq(rng[1]-1,rng[2]+2,1)+0.5\n\th<-hist(cite.yrs,plot=FALSE,breaks=yrs)\n\tplot(h,axes=FALSE,xlab='Citation year',col=adjustcolor('grey',alpha),main='')\n\taxis(2);axis(1,at=h$mids[h$mids%%10==0],labels=h$mids[h$mids%%10==0])\n\n\t# Histogram of sampling year\n\trng<-range(dat$Year,na.rm=TRUE)\n\tyrs<-seq(rng[1]-1,rng[2]+2,1)+0.5\n\th<-hist(dat$Year,plot=FALSE,breaks=yrs)\n\tplot(h,axes=FALSE,xlab='Sample year',col=adjustcolor('grey',alpha),main='')\n\taxis(2);axis(1,at=h$mids[h$mids%%10==0],labels=h$mids[h$mids%%10==0])\n\n\t# Histogram of latitude\n\th<-hist(dat$Latitude,breaks=seq(-90,90,5),plot=FALSE)\n\tplot(h,axes=FALSE,xlab='Latitude',col=adjustcolor('grey',alpha),main='')\n\tlabs<-c('90S',abs(seq(-80,80,10)),'90N')\n\taxis(2);axis(1,at=seq(-90,90,10),labels=labs)\n\n\t# Histogram of sampling month - Should split by Northern/Southern hemisphere - see below \n\tmnths<-seq(0.5,12.5,1)\n\th<-hist(dat$Month,plot=FALSE,breaks=mnths)\n\tplot(h,axes=FALSE,xlab='Month',col=adjustcolor('grey',alpha),main='')\n\taxis(2);axis(1,at=h$mids,labels=h$mids)\n\n\t# Histogram of sample sizes per survey\n\th<-hist(log10(dat$Total.stomachs.count),plot=FALSE,breaks=20)\n\tplot(h,axes=FALSE,xlab='Individuals sampled',col=adjustcolor('grey',alpha),main='')\n\taxis(2);axis(1,at=h$breaks,labels=10^(h$breaks))\n\n\t# Histogram of diet richness per survey\n\tbks<-seq(0,log(max(dat$Diet.richness.minimum,na.rm=TRUE)+1),0.5)\n\th<-hist(log10(dat$Diet.richness.minimum),plot=FALSE,breaks=bks)\n\tplot(h,axes=FALSE,xlab='Diet richness',col=adjustcolor('grey',alpha),main='')\n\tax1.at<-pretty(h$breaks)\n\taxis(2);axis(1,at=ax1.at,labels=ax1.at)\n\n\t# Histogram of Percent feeding\n\th<-hist(dat$Percent.feeding,plot=FALSE,breaks=50)\n\tplot(h,axes=FALSE,xlab='Percent feeding',col=adjustcolor('grey',alpha),main='')\n\taxis(2);axis(1)\n\n\t# Compare given and calculated Percent Feeding\n\top2<-par(pty='s',xaxs='i')\n\tplot(dat$Percent.feeding,\n\t dat$Percent.feeding.given, \n\t xlab='Calculated % feeding',\n\t ylab='Given % feeding',\n\t pch=21,bg=adjustcolor('grey',alpha))\n\tpar(op2)\n\n\tpar(yaxs='r')\n\t\n # Diet richness vs. Individuals surveys\n\tplot(dat$Diet.richness.minimum~dat$Total.stomachs.count,\n\t log='x',\n\t pch=21,\n\t bg=adjustcolor('grey',alpha), \n\t xlab='Individuals sampled',\n\t ylab='Minimmum diet richness')\n\n\t# Percent feeding vs. Individuals surveyed\n\tplot(dat$Percent.feeding~dat$Total.stomachs.count,\n\t log='x',\n\t pch=21,\n\t bg=adjustcolor('grey',alpha), \n\t xlab='Individuals sampled',\n\t ylab='Percent feeding')\n\n\t# Percent feeding vs. Diet richness\n\tplot(dat$Percent.feeding~dat$Diet.richness.min,\n\t log='x',\n\t pch=21,\n\t bg=adjustcolor('grey',alpha), \n\t xlab='Minimum diet richness',\n\t ylab='Percent feeding')\n\n\t# Percent feeding vs. Latitude\n \tplot(dat$Percent.feeding~dat$Latitude,\n \t pch=21,\n \t bg=adjustcolor('grey',alpha), \n \t xlab='Latitude',\n \t ylab='Percent feeding')\n\n\t# Percent feeding vs. Year\n \tplot(dat$Percent.feeding~dat$Year,\n \t pch=21,\n \t bg=adjustcolor('grey',alpha), \n \t xlab='Year',\n \t ylab='Percent feeding')\n\n\t# Percent feeding vs. Hour\n \tplot(dat$Percent.feeding~dat$Hour,\n \t pch=21,\n \t bg=adjustcolor('grey',alpha), \n \t xlab='Hour',\n \t ylab='Percent feeding',\n \t axes=FALSE);\n \taxis(2);\n \taxis(1,seq(0,24,2));\n \tbox(lwd=1)\n\n\t# Percent feeding vs. Month split by hemisphere\n\tdatN<-subset(dat,Latitude>0)\n\tdatS<-subset(dat,Latitude<0)\n \tplot(datN$Percent.feeding~jitter(datN$Month),\n \t pch=21,\n \t bg=adjustcolor('grey',alpha), \n \t xlab='Month',\n \t ylab='Percent feeding',\n \t axes=FALSE,main=\"Northern\");\n \taxis(2);\n \taxis(1,at=1:12);\n \tbox(lwd=1)\n \tplot(datS$Percent.feeding~jitter(datS$Month),\n \t pch=21,\n \t bg=adjustcolor('grey',alpha),\n \t xlab='Month',\n \t ylab='Percent feeding',\n \t axes=FALSE,\n \t main=\"Southern\");\n \taxis(2);\n \taxis(1,at=1:12);\n \tbox(lwd=1)\n \t\n \t# Daylength vs. Latitude\n \tplot(dat$Latitude,dat$DL,\n \t xlab='Latitude',\n \t ylab='Day length',\n \t pch=21,\n \t bg=adjustcolor('grey',alpha))\n\n \t# Percent feeding vs. Daylength\n \tplot(dat$DL,dat$Percent.feeding,\n \t xlab='Day length',\n \t ylab='Percent feeding',\n \t pch=21,\n \t bg=adjustcolor('grey',alpha))\n \t\n \t# Daylength vs. Days since winter solstice\n \tplot(dat$tWS,dat$DL,\n \t xlab='Days post winter solstice',\n \t ylab='Day length',\n \t pch=21,\n \t bg=adjustcolor('grey',alpha))\t\n\n# Percent feeding vs. Days since winter solstice\n \tplot(dat$tWS,dat$Percent.feeding,\n \t xlab='Days since winter solstice',\n \t ylab='Percent feeding',pch=21,\n \t bg=adjustcolor('grey',alpha))\n \tfit <- mgcv::gam(Percent.feeding ~ s(tWS), data = dat)\n \tnewx1<-data.frame(tWS=seq(0,365,1))\n \tpredvals<-predict(fit,newdata=newx1,type = \"response\")\n \tplot(fit,residuals=TRUE,seWithMean = TRUE,shade=TRUE,rug=FALSE,all.terms=TRUE,pch=21)\n\n\n# Evaluate potential methodological biases over time\n \tdecades <- seq(1920,2020,10)\n \tlab.decades <- decades[-length(decades)]\n \t# By Feeding Data Type\n p1 <- ggplot(dat) +\n \t aes(x=cut(Year, \n \t breaks=decades, \n \t include.lowest=TRUE,\n \t right=FALSE,\n \t labels=lab.decades), \n \t fill=factor(Feeding.data.type)) +\n \t geom_bar(position = 'fill') + \n \t scale_y_continuous(limits = c(0,1), breaks = c(0,1)) +\n \t facet_grid(rows = vars(Taxon.group)) +\n \t labs(x = \"\", y = \"Proportion\", fill = \"Observation type\") +\n theme(strip.text.y = element_text(angle = 0))\n \t\n \t# By Rate-limiting-step\n p2 <- ggplot(dat) +\n aes(x=cut(Year, \n breaks=decades, \n include.lowest=TRUE,\n right=FALSE,\n labels=lab.decades), \n fill=factor(Rate.limiting.step)) +\n geom_bar(position = 'fill') + \n scale_y_continuous(limits = c(0,1), breaks = c(0,1)) +\n facet_grid(rows = vars(Taxon.group)) +\n labs(x = \"\", y = \"Proportion\", fill = \"Rate\\nlimiting\\nstep?\") +\n theme(strip.text.y = element_text(angle = 0))\n \n \t# By Survey type\n p3 <- ggplot(dat) +\n aes(x=cut(Year, \n breaks=decades, \n include.lowest=TRUE,\n right=FALSE,\n labels=lab.decades), \n fill=factor(SpaceTime.replicate)) +\n geom_bar(position = 'fill') + \n scale_y_continuous(limits = c(0,1), breaks = c(0,1)) +\n facet_grid(rows = vars(Taxon.group)) +\n labs(x = \"\", y = \"Proportion\", fill = \"Survey type\") +\n theme(strip.text.y = element_text(angle = 0))\n \t\n multiplot(p1, p2, p3, rows=3)\n \t\n \n \t# Percent feeding vs. Time-averaging\n\tp1 <- ggplot(dat, aes(factor(Time.averaging, levels=TimeAvgLevels), Percent.feeding)) + geom_violin(scale = \"width\") + labs(x='')\n\n\t# Percent feeding vs. Spatial-averaging\n\tp2 <- ggplot(dat, aes(factor(Space.averaging,levels=SpaceAvgLevels), Percent.feeding)) + geom_violin(scale = \"width\") + labs(x='')\n\n\t# Percent feeding vs. Taxon group\n\tPercFeed.Taxon<-ddply(dat,.(Taxon.group),summarize,Mean=mean(Percent.feeding), SD=sd(Percent.feeding))\n\tPercFeed.Taxon<-PercFeed.Taxon[order(PercFeed.Taxon$Mean),]\n\tTaxonGroupLevels<-PercFeed.Taxon$Taxon.group\n\tp3 <- ggplot(dat, aes(factor(Taxon.group,levels=TaxonGroupLevels), Percent.feeding)) + geom_violin(scale = \"width\") + labs(x='')\n\n\t# Percent feeding vs. Ecosystem\n\tPercFeed.Ecosystem<-ddply(dat,.(Ecosystem),summarize,Mean=mean(Percent.feeding), SD=sd(Percent.feeding))\n\tPercFeed.Ecosystem<-PercFeed.Ecosystem[order(PercFeed.Ecosystem$Mean),]\n\tEcosystemLevels<-PercFeed.Ecosystem$Ecosystem\n\tp4 <- ggplot(dat, aes(factor(Ecosystem,levels=EcosystemLevels), Percent.feeding)) + geom_violin(scale = \"width\") + labs(x='')\n\n\tmultiplot(p1, p2, p3, p4, rows=4)\n\n\n\t# Diet richness vs. Individuals surveys\n\tp1 <- ggplot(dat) + geom_point(aes(x=Total.stomachs.count,y=Diet.richness.minimum,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank()) + scale_x_log10()\n\n\t# Percent feeding vs. Individuals surveyed\n\tp2 <- ggplot(dat) + geom_point(aes(x=Total.stomachs.count,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank()) + scale_x_log10()\n\n\t# Percent feeding vs. Diet richness\n\tp3 <- ggplot(dat) + geom_point(aes(x=Diet.richness.minimum,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank()) + scale_x_log10()\n\n\t# Percent feeding vs. Latitude\n\tp4 <- ggplot(dat) + geom_point(aes(x=Latitude,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\n\t# Percent feeding vs. Year\n \tp5 <- ggplot(dat) + geom_point(aes(x=Year,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\n\t# Percent feeding vs. Hour\n\tp6 <- ggplot(dat) + geom_point(aes(x=Hour,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\n\t# Percent feeding vs. Month split by hemisphere\n\tdatN<-subset(dat,Latitude>0)\n\tdatS<-subset(dat,Latitude<0)\n\tp7 <- ggplot(datN) + geom_point(aes(x=Month,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\tp8 <- ggplot(datS) + geom_point(aes(x=Month,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\t\n\t# Percent feedings vs. Daylength\n\tp9 <- ggplot(dat) + geom_point(aes(x=DL,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\t\n\t# Percent feeding vs. Days since winter solstice\n\tp10 <- ggplot(dat) + geom_point(aes(x=tWS,y=Percent.feeding,color=Taxon.group),alpha=alpha) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())\n\n\tmultiplot(p1,p2,p3)\n\tmultiplot(p4,p5,p6)\n multiplot(p7,p8,p9)\n multiplot(p10,p10,p10)\t\n\n\ndev.off()\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n", "meta": {"hexsha": "d884474d5757b8565738cbbbd5cd0b1e64690670", "size": 13559, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/FracFeed-Explore-Plots.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/FracFeed-Explore-Plots.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/FracFeed-Explore-Plots.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.6461988304, "max_line_length": 221, "alphanum_fraction": 0.6168596504, "num_tokens": 4011, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3260927561710902}} {"text": "fn.poB = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_percent_of_B_filtered.txt\")\nflow.poB = fread(fn.poB) %>% \n select(sample, matches(\"Gate\")) %>% \n dplyr::rename(CD38high = Gate3, \n CD38high.CD10pos = Gate2,\n CD38high.CD10neg = Gate1,\n CD38pos = Gate4)\n\nfn.info = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_sample_info_filtered.txt\")\nflow.info = fread(fn.info)\n\n\n# get originally gated flow data\nflow.old = data.frame()\nfor (tp in c(\"day0\", \"pre7\",\"day70\")) {\n fn.old = file.path(PROJECT_DIR, \"data\", \"CHI/flow/original_gates\", sprintf(\"%s.log10.txt\", tp))\n df.tmp = read.table(fn.old, sep=\"\\t\", header=T, row.names=1, stringsAsFactors=F)\n df.tmp = as.data.frame(t(df.tmp)) %>% \n dplyr::select(ID87) %>% \n tibble::rownames_to_column(\"subject\") %>% \n mutate(subject=sub(\"X\",\"\",subject), time=tp)\n flow.old = rbind(flow.old, df.tmp)\n}\nflow.old = flow.old %>% \n mutate(sample = paste(subject, time, sep=\"_\")) %>% \n dplyr::select(-time, -subject)\n\nflow = inner_join(flow.poB, flow.info, by=\"sample\") %>% \n dplyr::filter(time %in% c(0, -7, 70)) %>%\n mutate(subject = as.character(subject)) %>% \n inner_join(flow.old, by=\"sample\")\n\n\npops = names(flow)[c(rev(2:5),11)]\npops.name = c(\n \"CD20+CD38+ of total B cells\",\n \"CD20+CD38++ of total B cells\",\n \"CD20+CD38++CD10+ of total B cells\\n(transitional-like)\",\n \"CD20+CD38++CD10- of total B cells\\n(memory-like)\",\n \"CD20-CD38++CD27++ of total B cells\\n(plasmablasts)\"\n)\npops.group = (1:3)[c(2,2,2,2,3)] %>% factor()\n\ndf.stab = data.frame(Population=factor(pops, levels=rev(pops)), group=pops.group, ISV=NA)\nfor (i in seq_along(pops)) {\n form = as.formula(sprintf(\"%s ~ subject\", pops[i]))\n fit = aov(form, data=flow)\n ss = summary(fit)[[1]][\"Sum Sq\"][[1]]\n ssn = ss / sum(ss)\n df.stab$ISV[i] = ssn[1]\n}\n\nggplot(df.stab, aes(Population, ISV, fill=group)) + geom_bar(stat=\"identity\") +\n scale_x_discrete(labels=rev(pops.name), position=\"top\") +\n xlab(\"\") +\n ylab(\"ISV\") +\n coord_flip() +\n theme_bw() + \n theme(legend.position=\"none\", axis.ticks.y=element_blank())\n\nfn.fig = file.path(PROJECT_DIR, \"figure_generation\", sprintf(\"CHI_flow_selected_gates_ISV\"))\nggsave(paste0(fn.fig, \".png\"), w=6,h=3)\nggsave(paste0(fn.fig, \".pdf\"), w=6,h=3)\n", "meta": {"hexsha": "f1b0071e5f4913d57321a02dbafcd9f1992e1e55", "size": 2280, "ext": "r", "lang": "R", "max_stars_repo_path": "R/chi_flow_analysis/selected_gates_stability.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/chi_flow_analysis/selected_gates_stability.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/chi_flow_analysis/selected_gates_stability.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 35.625, "max_line_length": 97, "alphanum_fraction": 0.6385964912, "num_tokens": 765, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32597924229594544}} {"text": "compare <- function(a, b)\n{\n cat(paste(a, \"is of type\", class(a), \"and\", b, \"is of type\", class(b), \"\\n\"))\n\n printer <- function(a, b, msg) cat(paste(a, msg, b, \"\\n\"))\n\n op <- c(`<`, `<=`, `>`, `>=`, `==`, `!=`)\n msgs <- c(\n \"is strictly less than\",\n \"is less than or equal to\",\n \"is strictly greater than\",\n \"is greater than or equal to\",\n \"is equal to\",\n \"is not equal to\"\n )\n\n sapply(1:length(msgs), function(i) if(op[[i]](a, b)) printer(a, b, msgs[i]))\n\n invisible()\n}\n", "meta": {"hexsha": "3882fb1d2a69a3405522ac560fb94a8f615248e7", "size": 498, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/String-comparison/R/string-comparison-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/String-comparison/R/string-comparison-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/String-comparison/R/string-comparison-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 23.7142857143, "max_line_length": 79, "alphanum_fraction": 0.5180722892, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5117166195971441, "lm_q2_score": 0.6370307875894138, "lm_q1q2_score": 0.3259792412045612}} {"text": "library(ggplot2)\nlibrary(VGAM)\nlibrary(monocle3)\n\n# Args\nargs = commandArgs(trailingOnly=TRUE)\nroot_dir = args[1]\ncluster_backend = args[2]\n\ndir.create(root_dir)\n\n# Add more filepaths here based on how many datasets we want to analyze\n# TODO: Update this to read from files\nfilepaths = c(\n '/home/lexent/Downloads/cyclic_1.rds',\n '/home/lexent/Downloads/cyclic_5.rds',\n '/home/lexent/Downloads/cyclic_8.rds'\n)\n\ndatasets = c(\n 'dyntoy_cyclic_1',\n 'dyntoy_cyclic_5',\n 'dyntoy_cyclic_8'\n)\n\nresults = data.frame()\n\n\nfor(i in 1:length(filepaths)){\n dataset = readRDS(filepaths[i])\n start_cell_ids = dataset$prior_information$start_id\n expression_matrix = dataset$counts\n cell_metadata = dataset$cell_ids\n gene_annotation = dataset$prior_information$features_id\n \n cell_m = matrix(unlist(cell_metadata), ncol = 1, byrow = TRUE)\n row.names(cell_m) = cell_m\n \n gene_m = matrix(unlist(gene_annotation), ncol = 1, byrow = TRUE)\n row.names(gene_m) = gene_m\n \n cds = new_cell_data_set(\n t(expression_matrix),\n cell_metadata = cell_m,\n gene_metadata = gene_m\n )\n# Preprocess\n cds <- preprocess_cds(cds,norm_method='log', pseudo_count=1)\n \n# Dimensionality reduction\n cds <- reduce_dimension(cds)\n \n# Cluster generation\n cds <- cluster_cells(cds, cluster_method=cluster_backend, verbose=TRUE, random_seed=0)\n \n# Learn trajectory\n cds <- learn_graph(cds)\n \n# Plot graph\n plot_cells(cds,\n color_cells_by = \"cluster\",\n label_groups_by_cluster=TRUE,\n label_leaves=FALSE,\n label_branch_points=FALSE\n )\n ggsave(paste(datasets[i], '.png', sep=\"\"), path=root_dir)\n \n# Pseudotime computation\n cds = order_cells(cds, root_cells=start_cell_ids)\n plot_cells(cds,\n color_cells_by = \"pseudotime\",\n label_cell_groups=FALSE,\n label_leaves=FALSE,\n label_branch_points=FALSE,\n graph_label_size=1.5\n )\n ggsave(paste(datasets[i], '_pseudotime.png', sep=\"\"), path=root_dir)\n \n# Metric computation (KT, SR etc.)\n monocle3_pseudotime = cds@principal_graph_aux[['UMAP']]$pseudotime\n gt_pseudotime = dataset$prior_information$timecourse_continuous\n gt_pseudotime[which(!is.finite(gt_pseudotime))] = 0\n monocle3_pseudotime[which(!is.finite(monocle3_pseudotime))] = 0\n \n gt_df = data.frame(gt_pseudotime, row.names=names(gt_pseudotime))\n m_df = data.frame(monocle3_pseudotime, row.names=names(monocle3_pseudotime))\n reorder_idx = match(rownames(m_df),rownames(gt_df))\n gt_df = data.frame(gt_df[reorder_idx,], row.names=names(monocle3_pseudotime))\n\n kt = cor.test(x=as.vector(t(gt_df)), y=as.vector(t(m_df)), method = 'kendall')\n sr = cor.test(x=as.vector(t(gt_df)), y=as.vector(t(m_df)), method = 'spearman')\n results[datasets[i], 'KT'] = kt$estimate\n results[datasets[i], 'SR'] = sr$estimate\n}\n\nprint(results)\n\nwrite.csv(results, paste(root_dir, 'results.csv', sep=\"\"))\n", "meta": {"hexsha": "22485b8711013fd2c5b7076c2a22e07b4900bdeb", "size": 2983, "ext": "r", "lang": "R", "max_stars_repo_path": "margaret/utils/R/monocle3.r", "max_stars_repo_name": "kpandey008/Margaret", "max_stars_repo_head_hexsha": "402da11c9c9c6730553916cbcd5293c040556a08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-12-01T15:45:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-07T17:17:37.000Z", "max_issues_repo_path": "margaret/utils/R/monocle3.r", "max_issues_repo_name": "kpandey008/Margaret", "max_issues_repo_head_hexsha": "402da11c9c9c6730553916cbcd5293c040556a08", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "margaret/utils/R/monocle3.r", "max_forks_repo_name": "kpandey008/Margaret", "max_forks_repo_head_hexsha": "402da11c9c9c6730553916cbcd5293c040556a08", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-12-16T06:25:51.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T23:55:53.000Z", "avg_line_length": 30.1313131313, "max_line_length": 90, "alphanum_fraction": 0.6872276232, "num_tokens": 841, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3259792352434987}} {"text": "library(phyclust, quietly = TRUE)\nlibrary(parallel)\n\n### Load data\ndata.path <- paste(.libPaths()[1], \"/phyclust/data/pony524.phy\", sep = \"\")\npony.524 <- read.phylip(data.path)\nX <- pony.524$org\nK0 <- 1\nKa <- 2\n\n### Find MLEs\nret.K0 <- find.best(X, K0)\nret.Ka <- find.best(X, Ka)\nLRT <- -2 * (ret.Ka$logL - ret.K0$logL)\n\n### The user defined function\nFUN <- function(jid){\n X.b <- bootstrap.seq.data(ret.K0)$org\n\n ret.K0 <- phyclust(X.b, K0)\n repeat{\n ret.Ka <- phyclust(X.b, Ka)\n if(ret.Ka$logL > ret.K0$logL){\n break\n }\n }\n\n LRT.b <- -2 * (ret.Ka$logL - ret.K0$logL)\n LRT.b\n}\n\n### Task pull and summary\nret <- mclapply(1:100, FUN)\nLRT.B <- unlist(ret) \ncat(\"K0: \", K0, \"\\n\",\n \"Ka: \", Ka, \"\\n\",\n \"logL K0: \", ret.K0$logL, \"\\n\",\n \"logL Ka: \", ret.Ka$logL, \"\\n\",\n \"LRT: \", LRT, \"\\n\",\n \"p-value: \", mean(LRT > LRT.B), \"\\n\", sep = \"\")\n", "meta": {"hexsha": "7944e66a24ad8d3af95e9b4693d82ce4eb7a82b2", "size": 867, "ext": "r", "lang": "R", "max_stars_repo_path": "tutorials/NIMBioS2014/presentations/phyclust.r", "max_stars_repo_name": "RBigData/website", "max_stars_repo_head_hexsha": "1783bbc523405ce66463c7eac98e162a3953bc59", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tutorials/NIMBioS2014/presentations/phyclust.r", "max_issues_repo_name": "RBigData/website", "max_issues_repo_head_hexsha": "1783bbc523405ce66463c7eac98e162a3953bc59", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tutorials/NIMBioS2014/presentations/phyclust.r", "max_forks_repo_name": "RBigData/website", "max_forks_repo_head_hexsha": "1783bbc523405ce66463c7eac98e162a3953bc59", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.1463414634, "max_line_length": 74, "alphanum_fraction": 0.5490196078, "num_tokens": 336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3259792352434987}} {"text": "#!/usr/bin/env Rscript\nargs = commandArgs(trailingOnly=TRUE)\nif (length(args)==0) {\n stop(\"At least one argument must be supplied (input file).n\", call.=FALSE)\n}\noutputfile<-\"Alleles_largedist\"\nlargedist1a<- read.table (args[1], header = TRUE)\nattach (largedist1a)\nlargedist2a <- as.dist(largedist1a) #this gives a triangular matrix dist 2 is triangular matrix\nlargehca <- hclust(largedist2a, \"average\") #UPGMA\n\npdf (file =paste(outputfile,\"_tree.pdf\", sep=\"\"), width =10, height = 5, pointsize =6)\nplot(largehca, cex=0.8)\ndev.off()\n", "meta": {"hexsha": "b5ae7f12bc5132d03b1efa9bfbfc1b76c38c75e3", "size": 535, "ext": "r", "lang": "R", "max_stars_repo_path": "POPGEN/allele_tree.r", "max_stars_repo_name": "Hammarn/Scripts", "max_stars_repo_head_hexsha": "eb9fb51b614d29aea425168aa16c58410d975f46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "POPGEN/allele_tree.r", "max_issues_repo_name": "Hammarn/Scripts", "max_issues_repo_head_hexsha": "eb9fb51b614d29aea425168aa16c58410d975f46", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "POPGEN/allele_tree.r", "max_forks_repo_name": "Hammarn/Scripts", "max_forks_repo_head_hexsha": "eb9fb51b614d29aea425168aa16c58410d975f46", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6666666667, "max_line_length": 96, "alphanum_fraction": 0.7252336449, "num_tokens": 175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32597923524349864}} {"text": "library(tidyverse)\n\n# read in data\ncanada <- read.csv(\"canadiancitycouncil.csv\")\n\n# calculate councilors per 100,000 population\ncanada$pc.council <- canada$council/(canada$pop16/100000)\n\nannot <- read.table(text=\n \"city|number|just|text\n 33.5|2.7|0|Current number
of councilors - 44.\n 41.5|2.6|1|Proposed number
of councilors - 25.\n 16.5|5.4|0|Richmond Hill is the median city with
4.03 councilors per 100,000 residents.\",\n sep=\"|\", header=TRUE, stringsAsFactors=FALSE)\nannot$text <- gsub(\"
\", \"\\n\", annot$text)\n\n# plot\nc <- ggplot(data=canada,\n aes(x=reorder(city, -pc.council), y=pc.council, width=0.9, fill=highlight))+\n geom_bar(stat=\"identity\")+\n scale_fill_manual(values = c(\"yes\"=\"#E69F00\", \"no\"=\"#999999\" ), guide = FALSE )+\n scale_y_continuous(breaks=seq(0, 12, by=2))+\n # read in annotations\n geom_label(data=annot, aes(x=city, y=number, label=text, hjust=just),\n family=\"Open Sans Condensed Light\", lineheight=0.95,\n size=3, label.size=0, color=\"#2b2b2b\", inherit.aes = FALSE)+\n # Theming\n labs(\n title=\"Doug Ford's proposal will result in fewest city councilors per capita among the largest Canadian cities\",\n subtitle=\"City councilors per 100,000 residents, 40 largest Canadian cities, 2016\",\n caption=\"Author: Chris Goodman (@cbgoodman), Data: Statistics Canada & Author's calculations.\",\n y=NULL,\n x=NULL) +\n theme_minimal(base_family=\"Open Sans Condensed Light\") +\n # light, dotted major y-grid lines only\n theme(panel.grid=element_line())+\n theme(panel.grid.major.y=element_line(color=\"#2b2b2b\", linetype=\"dotted\", size=0.15))+\n theme(panel.grid.major.x=element_blank())+\n theme(panel.grid.minor.x=element_blank())+\n theme(panel.grid.minor.y=element_blank())+\n # light x-axis line only\n theme(axis.line=element_line())+\n theme(axis.line.y=element_blank())+\n theme(axis.line.x=element_blank())+\n # tick styling\n theme(axis.ticks=element_line())+\n theme(axis.ticks.x=element_blank())+\n theme(axis.ticks.y=element_blank())+\n theme(axis.ticks.length=unit(5, \"pt\"))+\n # x-axis labels\n theme(axis.text.x=element_text(size=10, angle=90, hjust=0.95,vjust=0.2))+\n # breathing room for the plot\n theme(plot.margin=unit(rep(0.5, 4), \"cm\"))+\n # move the y-axis tick labels over a bit\n #theme(axis.text.y=element_text(margin=margin(r=-5)))+\n # make the plot title bold and modify the bottom margin a bit\n theme(plot.title=element_text(family=\"Open Sans Condensed Bold\", margin=margin(b=15)))+\n # make the subtitle italic\n theme(plot.subtitle=element_text(family=\"Open Sans Condensed Light Italic\"))+\n theme(plot.caption=element_text(size=8, hjust=0, margin=margin(t=15)))\n\nggsave(plot=c, \"canadiancouncilors.png\", width=10, height=6, units=\"in\", dpi=\"retina\")\n", "meta": {"hexsha": "4a32791d75bbcd4cee42b9cb56f86e5fe7608852", "size": 2737, "ext": "r", "lang": "R", "max_stars_repo_path": "canadiancitycouncil.r", "max_stars_repo_name": "cbgoodman/citycouncil", "max_stars_repo_head_hexsha": "39bbc5771ecd73d287a579fa9cc49840764942a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "canadiancitycouncil.r", "max_issues_repo_name": "cbgoodman/citycouncil", "max_issues_repo_head_hexsha": "39bbc5771ecd73d287a579fa9cc49840764942a1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "canadiancitycouncil.r", "max_forks_repo_name": "cbgoodman/citycouncil", "max_forks_repo_head_hexsha": "39bbc5771ecd73d287a579fa9cc49840764942a1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.4444444444, "max_line_length": 116, "alphanum_fraction": 0.7109974425, "num_tokens": 834, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32597923524349864}} {"text": "source('~/project/main.r')\nlibrary('highcharter')\n\nraw_tmp <-arrange(filter(row_refine,\u8eca\u7a2e=='C03'))\n\nraw_tmp <- select(raw_tmp,\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b\u610f\u7fa9,\u6b7b,\u53d7\u50b7)\nraw_tmp <- raw_tmp %>% drop_na(\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b\u610f\u7fa9)\nraw_tmp$count <- 1\nraw_tmp[is.na(raw_tmp)] <- 0\nraw_tmp_summary <- (raw_tmp %>% group_by(\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b\u610f\u7fa9) %>%\n summarise_all(sum)\n)\n\nraw_tmp_summary$\u6b7b\u6bd4\u7387 <- raw_tmp_summary$\u6b7b / sum(raw_tmp_summary$count) * 100\nraw_tmp_summary$\u53d7\u50b7\u6bd4\u7387 <- raw_tmp_summary$\u53d7\u50b7 / sum(raw_tmp_summary$count) * 100\nraw_tmp_summary$\u610f\u5916\u6bd4\u7387 <- raw_tmp_summary$count / sum(raw_tmp_summary$count) * 100\n\n# highchart() %>%\n# hc_chart(type =\"column\", options3d = list(enabled = TRUE, beta = 15, alpha = 15)) %>%\n# hc_xAxis(categories = raw_tmp_summary$\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b\u610f\u7fa9) %>%\n# hc_add_series(data = raw_tmp_summary$\u6b7b\u6bd4\u7387, name = \"\u6b7b/\u7e3d\u610f\u5916\u6bd4\u7387\") %>%\n# hc_add_series(data = raw_tmp_summary$\u53d7\u50b7\u6bd4\u7387, name = \"\u53d7\u50b7/\u7e3d\u610f\u5916\u6bd4\u7387\") %>%\n# hc_add_series(data = raw_tmp_summary$\u610f\u5916\u6bd4\u7387, name = \"\u610f\u5916/\u7e3d\u610f\u5916\u6bd4\u7387\")\n\nraw_tmp_summary %>% hchart(\n \"pie\", hcaes(x = \u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b\u610f\u7fa9, y = count),\n name = \"Fruit consumption\"\n)\n\nraw_tmp <-arrange(filter(row_refine,\u7576\u4e8b\u8005\u884c\u52d5\u72c0\u614b=='9'))\n\nraw_tmp <- select(raw_tmp,\u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b\u610f\u7fa9,\u6b7b,\u53d7\u50b7)\nraw_tmp <- raw_tmp %>% drop_na(\u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b\u610f\u7fa9)\nraw_tmp$count <- 1\nraw_tmp[is.na(raw_tmp)] <- 0\nraw_tmp_summary <- (raw_tmp %>% group_by(\u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b\u610f\u7fa9) %>%\n summarise_all(sum)\n)\n\nraw_tmp_summary %>% hchart(\n \"pie\", hcaes(x = \u4e8b\u6545\u985e\u578b\u53ca\u578b\u614b\u610f\u7fa9, y = count),\n name = \"Fruit consumption\"\n)\n", "meta": {"hexsha": "4b87c7400fca70e055bf995cc06e41648791160b", "size": 1385, "ext": "r", "lang": "R", "max_stars_repo_path": "bigdata-and-r/data/project/bike-insight/action.r", "max_stars_repo_name": "vinnson/nkust", "max_stars_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bigdata-and-r/data/project/bike-insight/action.r", "max_issues_repo_name": "vinnson/nkust", "max_issues_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bigdata-and-r/data/project/bike-insight/action.r", "max_forks_repo_name": "vinnson/nkust", "max_forks_repo_head_hexsha": "5fab7bbe980acf1168cd41d8c4e76574b199a0b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4772727273, "max_line_length": 89, "alphanum_fraction": 0.6996389892, "num_tokens": 623, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32597923524349864}} {"text": "cat(sapply(strsplit(readLines(tail(commandArgs(), n=1)), \" | \", fixed=TRUE), function(s) {\n c <- sapply(strsplit(s[1], \" \")[[1]], as.integer)\n for (i in 2:length(s)) {\n d <- sapply(strsplit(s[i], \" \")[[1]], as.integer)\n for (j in 1:length(d)) {\n if (d[j] > c[j]) {\n c[j] <- d[j]\n }\n }\n }\n paste(c, collapse=\" \")\n}), sep=\"\\n\")\n", "meta": {"hexsha": "0832f6f882438911472618cad03c29f44fb9724b", "size": 356, "ext": "r", "lang": "R", "max_stars_repo_path": "easy/find_the_highest_score.r", "max_stars_repo_name": "IlkhamGaysin/ce-challenges", "max_stars_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-06-24T17:09:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-03T11:44:54.000Z", "max_issues_repo_path": "easy/find_the_highest_score.r", "max_issues_repo_name": "IlkhamGaysin/ce-challenges", "max_issues_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "easy/find_the_highest_score.r", "max_forks_repo_name": "IlkhamGaysin/ce-challenges", "max_forks_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.3846153846, "max_line_length": 90, "alphanum_fraction": 0.4803370787, "num_tokens": 123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6370307944803831, "lm_q1q2_score": 0.32597923524349864}} {"text": "plot.width = {img_width}\nplot.height = {img_height}\nplot.range = {date_range}\nplot.typeids = c(29668,34133,34132,40519)\nplot.locationid = {locationid}\nplot.title = '{plot_title}'\nplot.path = '{plot_path}'\n\ndate.min <- Sys.Date() - plot.range\nplot.typeids.str <- paste(plot.typeids, collapse=',')\n## Event List ##\nevent.query <- paste0(\n 'SELECT datetime, eventName ',\n 'FROM event_list ',\n 'WHERE eventGroup in (\\'patch\\') ',\n 'AND datetime > \\'', date.min, '\\' '\n)\nevent <- sqlQuery(emd, event.query)\nevent$datetime <- as.POSIXlt(event$datetime, tz='GMT')\ndo_lines <- TRUE\nif(nrow(event)==0){{do_lines <- FALSE}}\n\nec.query <- paste0(\n 'SELECT price_date AS `date`, price_time AS `hour`, locationid, typeid, ',\n 'SUM(IF(buy_sell=1, price_best, NULL)) AS `SellOrder`, ',\n 'SUM(IF(buy_sell=0, price_best, NULL)) AS `BuyOrder` ',\n 'FROM snapshot_evecentral ',\n 'WHERE locationid=', plot.locationid, ' ',\n 'AND typeid IN (', plot.typeids.str, ') ',\n 'AND price_date > \\'', date.min, '\\' ',\n 'GROUP BY price_date, price_time, typeid'\n)\nec <- sqlQuery(emd, ec.query)\nodbcClose(emd)\nec$date <- as.Date(ec$date)\nec$typeid <- as.factor(ec$typeid)\nec <- subset(ec, SellOrder > 0)\nec$datetime <- paste(ec$date, ec$hour, sep=' ')\nec$datetime <- as.POSIXlt(ec$datetime, tz='GMT')\n\n## CREST Lookups ##\nCREST_BASE = 'https://crest-tq.eveonline.com/'\nsolarsystem.addr <- paste0(CREST_BASE, 'solarsystems/', plot.locationid, '/')\nsolarsystem.json <- fromJSON(readLines(solarsystem.addr))\nsolarsystem.name <- solarsystem.json$name\n\nec$locationName <- solarsystem.name\n\ntype_list <- unique(ec$typeid)\nec$typeName <- NA\nfor(type.index in 1:length(type_list)){{\n type.name <- ''\n type.id <- type_list[type.index]\n type.addr <- paste0(CREST_BASE, 'inventory/types/', type.id, '/')\n type.json <- fromJSON(readLines(type.addr))\n type.name <- type.json$name\n ec$typeName[ec$typeid==type.id] <- type.name\n}}\n\n## Plot Theme ##\ntheme_dark <- function( ... ) {{\n theme(\n text = element_text(color=\"gray90\"),\n title = element_text(size=rel(2),hjust=0.05,vjust=3.5),\n axis.title.x = element_text(size=rel(1),hjust=0.5, vjust=0),\n axis.title.y = element_text(size=rel(1),hjust=0.5, vjust=1.5),\n plot.margin = unit(c(2,1,1,1), \"cm\"),\n plot.background=element_rect(fill=\"gray8\",color=\"gray8\"),\n panel.background=element_rect(fill=\"gray10\",color=\"gray10\"),\n panel.grid.major = element_line(colour=\"gray17\"),\n panel.grid.minor = element_line(colour=\"gray12\"),\n axis.line = element_line(color = \"gray50\"),\n plot.title = element_text(color=\"gray80\"),\n axis.title = element_text(color=\"gray70\"),\n axis.text = element_text(color=\"gray50\",size=rel(1.1)),\n legend.key = element_rect(fill=\"gray8\",color=\"gray8\"),\n legend.background = element_rect(fill=\"gray8\"),\n legend.title = element_text(size=rel(0.6)),\n legend.text = element_text(size=rel(1.1)),\n strip.background = element_rect(fill=\"gray1\"),\n strip.text = element_text(size=rel(1.2))\n ) + theme(...)\n}}\n\nec <- subset(ec, SellOrder > 0)\nplot.data <- ec\nplot.data$SellOrder[plot.data$typeid==34132] <- 3.5 * plot.data$SellOrder[plot.data$typeid==34132]\nplot.data$BuyOrder[ plot.data$typeid==34132] <- 3.5 * plot.data$BuyOrder[ plot.data$typeid==34132]\nplot.data$SellOrder[plot.data$typeid==40519] <- 35/8* plot.data$SellOrder[plot.data$typeid==40519]\nplot.data$BuyOrder[ plot.data$typeid==40519] <- 35/8* plot.data$BuyOrder[ plot.data$typeid==40519]\n\nprice.max_scale <- max(plot.data$SellOrder, na.rm=TRUE)\nprice.min_scale <- min(plot.data$BuyOrder, na.rm=TRUE)\n\n## Build Plot ##\nalpha_group <- c(1.0,0.3,0.3,0.8)\nplot <- ggplot(\n plot.data,\n aes(\n x=datetime,\n ymin=BuyOrder,\n ymax=SellOrder,\n fill=typeName,\n alpha=typeName)\n)\nplot <- plot + geom_ribbon()\nplot <- plot + theme_dark()\nplot <- plot + scale_alpha_manual(values=alpha_group)\nplot <- plot + scale_y_continuous(\n limit=c(price.min_scale, NA),\n labels=function(x)sprintf('%.1fB', x/1e9),\n position='right'\n)\nif(do_lines){{\n plot <- plot + geom_vline(\n xintercept=as.numeric(event$datetime),\n linetype=2,\n color='white'\n )\n plot <- plot + geom_text(\n aes(\n x=datetime,\n y=Inf,\n label=eventName),\n color='white',\n angle=-90,\n vjust=1.2,\n hjust=0,\n data=event,\n inherit.aes=FALSE\n )\n}}\nplot <- plot + labs(\n title=plot.title,\n color='PriceKey',\n x='date',\n y='price'\n)\n\n## Print Plot To File ##\npng(\n plot.path,\n width=plot.width,\n height=plot.height\n)\nprint(plot)\ndev.off()\n", "meta": {"hexsha": "0ec9e24ecd5d71af381ddef441ae5e999de18b1f", "size": 4645, "ext": "r", "lang": "R", "max_stars_repo_path": "ProsperSlides/R_templates/RMT_plot.r", "max_stars_repo_name": "EVEprosper/ProsperSlides", "max_stars_repo_head_hexsha": "85adf047d1c3719016af448a7031d1d703651fee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-05-06T01:09:28.000Z", "max_stars_repo_stars_event_max_datetime": "2018-05-06T01:09:28.000Z", "max_issues_repo_path": "ProsperSlides/R_templates/RMT_plot.r", "max_issues_repo_name": "EVEprosper/ProsperSlides", "max_issues_repo_head_hexsha": "85adf047d1c3719016af448a7031d1d703651fee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2017-01-09T21:20:11.000Z", "max_issues_repo_issues_event_max_datetime": "2017-01-10T19:15:42.000Z", "max_forks_repo_path": "ProsperSlides/R_templates/RMT_plot.r", "max_forks_repo_name": "EVEprosper/ProsperSlides", "max_forks_repo_head_hexsha": "85adf047d1c3719016af448a7031d1d703651fee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-20T23:14:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-20T23:14:16.000Z", "avg_line_length": 31.3851351351, "max_line_length": 98, "alphanum_fraction": 0.6447793326, "num_tokens": 1347, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3258233753961364}} {"text": "N <- 100\n\n\ninsSort <- function(x){\n return(8*x^2)\n}\n\nmerSort<- function(x){\n return(log(x) * 64 * x)\n}\n\nmer <- c()\nins <- c()\n\nfor(i in 1:N){\n mer <- c(mer, merSort(i))\n ins <- c(ins, insSort(i))\n}\n\n\nmerSortBetter <- mer < ins\n\ninsSortBetter <- ins < mer\n\nequals <- mer == ins\n\nsummary(merSortBetter)\nsummary(insSortBetter)\n\nhead(merSortBetter, n=50)", "meta": {"hexsha": "4a9a7cacb7425dc86d3c3977f20b96936cd0b4af", "size": 362, "ext": "r", "lang": "R", "max_stars_repo_path": "CompareMergSortInsertionSort.r", "max_stars_repo_name": "DU-ds/MiscRScripts", "max_stars_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CompareMergSortInsertionSort.r", "max_issues_repo_name": "DU-ds/MiscRScripts", "max_issues_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CompareMergSortInsertionSort.r", "max_forks_repo_name": "DU-ds/MiscRScripts", "max_forks_repo_head_hexsha": "012fb6ecb60414f8952e3884271dba7add9f4d33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.0666666667, "max_line_length": 29, "alphanum_fraction": 0.591160221, "num_tokens": 118, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6893056040203135, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3258233693622692}} {"text": "suppressPackageStartupMessages({\n source(\"../../../.Rprofile\", chdir = TRUE)\n library(ggplot2)\n library(Seurat)\n library(DoubletFinder)\n})\n\ninputdata.10x <- Read10X_h5(\"pbmc_granulocyte_sorted_10k_filtered_feature_bc_matrix.h5\")\nmeta.data <- read.csv(\"wnn_meta_data.csv\", row.names = 1)\n\nrna.counts <- inputdata.10x$`Gene Expression`[, rownames(meta.data)]\nrna.so <- CreateSeuratObject(counts = rna.counts, assay = \"RNA\", meta.data = meta.data)\n\nrna.so <- NormalizeData(rna.so)\n\nrna.so <- FindVariableFeatures(rna.so, selection.method = \"vst\", nfeatures = 2000)\n\nrna.so <- ScaleData(rna.so)\n\nrna.so <- RunPCA(rna.so, npcs = 30, verbose=FALSE)\n\nrna.so <- RunUMAP(rna.so, dims = 1:30)\n\nsweep.res.list <- paramSweep_v3(rna.so, PCs = 1:30, sct = FALSE)\n\nsweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)\n\nbcmvn <- find.pK(sweep.stats)\n\noptions(repr.plot.width=12, repr.plot.height=6)\nggplot(data=bcmvn, mapping=aes(x=pK, y=BCmetric, group=1)) + geom_point() + geom_line()\n\nannotations <- rna.so@meta.data$celltype\nhomotypic.prop <- modelHomotypic(annotations)\nnExp_poi <- round(0.075*nrow(rna.so@meta.data))\n# nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop))\n\nrna.so <- doubletFinder_v3(rna.so, PCs = 1:30, pN = 0.25, pK = 0.005, nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)\n\nclassification_key <- paste0(\"DF.classifications_0.25_0.005_\", nExp_poi)\nwrite.csv(data.frame(\n doubletfinder = rna.so@meta.data[, classification_key],\n row.names = rownames(rna.so@meta.data)\n), \"doubletfinder_inference.csv\", quote = FALSE)\n", "meta": {"hexsha": "a480311c793ab5313ea7712ba96aa5dea34abd55", "size": 1546, "ext": "r", "lang": "R", "max_stars_repo_path": "data/download/10x-Multiome-Pbmc10k/doubletfinder.r", "max_stars_repo_name": "gao-lab/GLUE", "max_stars_repo_head_hexsha": "e84cb6483971dcb1e2485080f812899baaf31b5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 41, "max_stars_repo_stars_event_min_datetime": "2021-08-23T07:29:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-12T00:29:52.000Z", "max_issues_repo_path": "data/download/10x-Multiome-Pbmc10k/doubletfinder.r", "max_issues_repo_name": "gao-lab/GLUE", "max_issues_repo_head_hexsha": "e84cb6483971dcb1e2485080f812899baaf31b5b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2021-11-25T21:25:50.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T02:22:57.000Z", "max_forks_repo_path": "data/download/10x-Multiome-Pbmc10k/doubletfinder.r", "max_forks_repo_name": "gao-lab/GLUE", "max_forks_repo_head_hexsha": "e84cb6483971dcb1e2485080f812899baaf31b5b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2021-10-05T07:24:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T22:46:16.000Z", "avg_line_length": 34.3555555556, "max_line_length": 119, "alphanum_fraction": 0.713454075, "num_tokens": 492, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056040203135, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3258233693622692}} {"text": "########################################################\n##### Author: Diego Valle Jones\n##### Website: www.diegovalle.net\n##### Date Created: Tue May 25 09:58:24 2010\n########################################################\n#Pretty plots of marijuana and opium eradication, cultivation and\n#cocaine prices\n\ndrugPlot <- function(df, filename, ylab, title) {\n print(ggplot(df, aes(years, area, group = type,\n color = type)) +\n geom_line() +\n geom_rect(xmin = 2006, xmax = 2009,\n ymin=0, ymax=Inf, alpha = .02, fill = \"red\",\n color= \"#efefef\") +\n annotate(\"text\", x = 2007.5, y = 28000, label = \"Drug War\") +\n ylab(ylab) + xlab(\"\") +\n opts(title = title) +\n scale_y_continuous(formatter = \"comma\", limits = c(0, max(df$area, na.rm = TRUE))))\n filename <- paste(\"drugs/output/\", filename)\n dev.print(png, filename, width=640, height=480)\n}\n\n#http://www.state.gov/p/inl/rls/nrcrpt/2010/vol1/137197.htm\n#Eradication (ha)\nmj <- c(14135, 18663, 23316, 30162, 30857, 30852, 36585, 30775, 28699)\nopium <- c(11471, 13189, 11410, 16890, 21609, 15926, 20034, 19158, 19115)\ndrugs <- data.frame(area = c(mj, opium),\n years = rep(2009:2001, 2),\n type = rep(c(\"marijuana\", \"poppy\"), each = 9))\ndrugPlot(drugs, \"cannabis-poppy-eradication.png\", \"Eradication (ha)\",\n \"The amount of cannabis and opium poppy\\neradicated has decreased\")\n\n#Harvestable / Net Cultivation (ha)\nmj <- c(12000, 8900, NA, 8600, 5600, 5800, 7500, 7900, 4100)\npoppy <- c(15000, 6900, NA, 5100, 3300, 3500, 4800, 2700, 4400)\ndrugs <- data.frame(area = c(mj, poppy),\n years = rep(2009:2001, 2),\n type = rep(c(\"marijuana\", \"poppy\"), each = 9))\ndrugPlot(drugs, \"cannabis-poppy-cultivation.png\",\n \"Net Cultivation (ha)\",\n \"The amount of cannabis and opium poppy\\ncultivated has increased\")\n\n########################################################\n#Cocaine Prices\n########################################################\n#WORLD DRUG REPORT 2009\n#http://www.unodc.org/documents/wdr/WDR_2009/WDR2009_eng_web.pdf page 220\n#Prices adjusted for purity and inflation\ncok.prc <- c(421,343,263,251,232,275,217,208,189,193,224,227,158,166,147,140,134,162,216)\np <- qplot(1990:2008, cok.prc, geom=\"line\") +\n geom_rect(xmin = 2006, xmax = 2009,\n ymin=0, ymax=Inf, alpha = .02, fill = \"red\") +\n annotate(\"text\", x = 2007.5, y = 370, label = \"Drug War\") +\n opts(title = \"There has been an increase in the price of\\ncocaine adjusted for purity and inflation\\nsince the start of the drug war\") +\n ylab(\"Street price - US$/gram\") + xlab(\"year\") +\n ylim(c(0, max(cok.prc)))\nprint(p)\ndev.print(png, \"drugs/output/coke-price.png\", width=640, height=480)\n", "meta": {"hexsha": "0dcf609fd48eaca807a3690dadb4bead8d4024d6", "size": 2809, "ext": "r", "lang": "R", "max_stars_repo_path": "drugs/eradication.r", "max_stars_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_stars_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2015-05-14T01:06:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-16T12:52:10.000Z", "max_issues_repo_path": "drugs/eradication.r", "max_issues_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_issues_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "drugs/eradication.r", "max_forks_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_forks_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2015-02-05T15:09:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-10T02:19:40.000Z", "avg_line_length": 46.8166666667, "max_line_length": 140, "alphanum_fraction": 0.5731577074, "num_tokens": 890, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32582022322991394}} {"text": "# 4. faza: Napredna analiza podatkov\nsetwd(\"/Users/blazpovh/Documents/R_projektna_naloga/APPR-2021-22-Blaz-Povh/\")\nTabela_porabe1 <- read.csv(\"~/Documents/R_projektna_naloga/APPR-2021-22-Blaz-Povh/podatki/zdruzeni_podatki/Tabela_porabe.csv\", sep=\"\")\n\ndnevna_poraba <-rowSums(Tabela_porabe1[ ,3:98],is.na(Tabela_porabe1) == FALSE)\nlength(dnevna_poraba)\nporabniki <- labels(table(Tabela_porabe1$porabnik))[[1]]\n\npovpr_poraba=c()\nfor (i in 1:length(porabniki)){\n print(i)\n poraba=dnevna_poraba[Tabela_porabe1$porabnik==porabniki[i]]\n povpr_poraba[i]=mean(poraba)\n}\nfrekvenca <- c(1:51)\ndata_povpr_poraba <- data.frame(povpr_poraba,frekvenca)\n\njpeg(\"slike/prikaz_skupne_povprecne_porabe_za_razlicne_porabnike.jpg\")\nchol <- data_povpr_poraba\nqplot(chol$povpr_poraba, \n geom = \"histogram\",\n binwidth= 40,\n main = \"Povpre\u010dna poraba el. energije za razli\u010dne porabnike\",\n xlab = \"Povpre\u010dna poraba\",\n ylab = \"Frekvenca\",\n ylim = c(0,10))\ndev.off()\njpeg(\"slike/prikaz_skupne_povprecne_porabe_za_razlicne_meritve.jpg\")\nTabela_delovnih_dni <- filter(Tabela_porabe1, Prosti_dan == \"FALSE\")\nmeritev3 <- colMeans(Tabela_delovnih_dni[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np3 <-plot(meritev3, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za razli\u010dne tipe dni\",xaxt='n')\nmeritev1 <- colMeans(Tabela_porabe1[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np1 <-points(meritev1, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za razl. meritve\",xaxt='n',col=\"blue\")\nTabela_prostih_dni <- filter(Tabela_porabe1, Prosti_dan == \"TRUE\")\nmeritev2 <- colMeans(Tabela_prostih_dni[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np2 <-points(meritev2, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za proste dni\",xaxt='n',col=\"red\")\nTabela_delovnih_dni <- filter(Tabela_porabe1, Prosti_dan == \"FALSE\")\nmeritev3 <- colMeans(Tabela_delovnih_dni[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np3 <-points(meritev3, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za delavne dni\",xaxt='n',col=\"green\")\nxlabels=c(\"3h\",\"6h\",\"9h\",\"12h\",\"15h\",\"18h\",\"21h\",\"24h\")\naxis(side=1, at=c(12,24,36,48,60,72,84,96),labels=xlabels)\nlegend(title = \"Legenda\", legend = c(\"vsi dnevi\",\"prosti dnevi\",\"delovni dnevi\"),\"topright\",col = c(\"blue\",\"red\",\"green\"),lty = 1)\ndev.off()\n\njpeg(\"slike/prikaz_skupne_povprecne_porabe_za_proste_dni.jpg\")\nTabela_prostih_dni <- filter(Tabela_porabe1, Prosti_dan == \"TRUE\")\nmeritev2 <- colMeans(Tabela_prostih_dni[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np2 <-plot(meritev2, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za proste dni\",xaxt='n')\nxlabels=c(\"3h\",\"6h\",\"9h\",\"12h\",\"15h\",\"18h\",\"21h\",\"24h\")\naxis(side=1, at=c(12,24,36,48,60,72,84,96),labels=xlabels)\ndev.off()\n\njpeg(\"slike/prikaz_skupne_povprecne_porabe_za_delovne_dni.jpg\")\nTabela_delovnih_dni <- filter(Tabela_porabe1, Prosti_dan == \"FALSE\")\nmeritev3 <- colMeans(Tabela_delovnih_dni[ , 3:98 ],is.na(Tabela_porabe1)== FALSE)\np3 <-plot(meritev3, type = \"o\",pch = 20, xlab=\"\u010das\",ylab = \"povpre\u010dna poraba\",main = \"Graf povpre\u010dne poraba el. energije za delavne dni\",xaxt='n')\nxlabels=c(\"3h\",\"6h\",\"9h\",\"12h\",\"15h\",\"18h\",\"21h\",\"24h\")\naxis(side=1, at=c(12,24,36,48,60,72,84,96),labels=xlabels)\ndev.off()\n\njpeg(\"slike/prikaz_skupne_povprecne_porabe_za_razlicne_dneve.jpg\")\npar(mar=c(5,6,4,1)+.0001)\nplot(c(0,97),c(0.5,4),main=\"Povpre\u010dna poraba za dneve v tednu\",type = \"n\",pch=20, xlab = \"\u010das\", ylab = \"povpre\u010dna poraba\",xaxt='n')\npovpr_poraba_ponedeljek <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Monday\")[ ,3:98])\ngraf_ponedeljek <- points(povpr_poraba_ponedeljek,type = \"o\",pch=20,xaxt='n',col=\"black\")\npovpr_poraba_torek <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Tuesday\")[ ,3:98])\ngraf_torek <- points(povpr_poraba_torek, type = \"o\",pch=20,xaxt='n',col=\"red\")\npovpr_poraba_sreda <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Wednesday\")[ ,3:98])\ngraf_sreda <- points(povpr_poraba_sreda, type = \"o\",pch=20,xaxt='n',col=\"blue\")\npovpr_poraba_cetrtek <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Thursday\")[ ,3:98])\ngraf_cetrtek <- points(povpr_poraba_cetrtek, type = \"o\",pch=20,xaxt='n',col=\"green\")\npovpr_poraba_petek <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Friday\")[ ,3:98])\ngraf_petek <- points(povpr_poraba_petek, type = \"o\",pch=20,xaxt='n',col=\"orange\")\npovpr_poraba_sobota <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Saturday\")[ ,3:98])\ngraf_sobota <- points(povpr_poraba_sobota, type = \"o\",pch=20,xaxt='n',col=\"yellow\")\npovpr_poraba_nedelja <- colMeans(filter(Tabela_porabe1, Ime_dneva ==\"Sunday\")[ ,3:98])\ngraf_nedelja <- points(povpr_poraba_nedelja, type = \"o\",pch=20,xaxt='n',col=\"purple\")\nxlabels=c(\"3h\",\"6h\",\"9h\",\"12h\",\"15h\",\"18h\",\"21h\",\"24h\")\naxis(side=1, at=c(12,24,36,48,60,72,84,96),labels=xlabels)\nlegend(title = \"Legenda\", legend = c(\"ponedeljek\",\"torek\",\"sreda\",\"cetrtek\",\"petek\",\"sobota\",\"nedelja\"),\"topright\",col = c(\"black\",\"red\",\"blue\",\"green\",\"orange\",\"yellow\",\"purple\"),lty=1)\ndev.off()\n\n\n# *********************************************** \n# od tu naprej imam analizo za vremenske podatke\n# **********************************************\ndata_vreme2_tidy <- read.table(\"podatki/zdruzeni_podatki/data_vreme2_tidy.txt\",sep=\" \")\ndatumi <- (data_vreme2_tidy$datum)\npadavine_po_dnevih <- data.frame(rowSums(data_vreme2_tidy[ ,4:51], is.na(data_vreme2_tidy) == FALSE))\ncolnames(padavine_po_dnevih) <- c('Kolicina padavin za dan')\nrownames(padavine_po_dnevih) <-c(datumi)\nwrite.table(padavine_po_dnevih,\"podatki/zdruzeni_podatki/padavine_po_dnevih.txt\",sep=\" \")\n\ntemperature_po_dnevih <- data.frame(rowMeans(data_vreme2_tidy[ ,52:99], is.na(data_vreme2_tidy) == FALSE))\ncolnames(temperature_po_dnevih) <- c('Skupna temperatura za dan')\nrownames(temperature_po_dnevih) <-c(datumi)\nwrite.table(temperature_po_dnevih,\"podatki/zdruzeni_podatki/temperature_po_dnevih.txt\",sep=\" \")\n\npadavine_po_urah <- data.frame(colSums(data_vreme2_tidy[ ,4:51]))\ncolnames(padavine_po_urah) <- c('Povprecna kol padavina za to uro')\nwrite.table(padavine_po_urah,\"podatki/zdruzeni_podatki/padavine_po_urah.txt\",sep=\" \")\n\ntemperature_po_urah <- data.frame(colMeans(data_vreme2_tidy[ ,52:99], is.na(data_vreme2_tidy) == FALSE))\ncolnames(temperature_po_urah) <- c('Povprecna temperatura za vsako casovno obdobje')\nwrite.table(temperature_po_urah,\"podatki/zdruzeni_podatki/temperature_po_urah.txt\",sep=\" \")\n\n\n# *************************************************\n# Korelacije\n# *************************************************\npovpr_poraba_za_vsak_dan <-rowMeans(Tabela_porabe1[ ,3:98])\npovpr_poraba_matrika <- t(matrix(povpr_poraba_za_vsak_dan,198,51))\npovpr_poraba_za_vsak_dan1 <- as.vector(colSums(povpr_poraba_matrika))\npovpr_poraba_za_vsak_dan1<-povpr_poraba_za_vsak_dan1[-198]\npovprecna_T_za_vsak_dan <- temperature_po_dnevih$`Skupna temperatura za dan`\nskupne_padavine_za_vsak_dan <- padavine_po_dnevih$`Kolicina padavin za dan`\nr12=cor(povpr_poraba_za_vsak_dan1,povprecna_T_za_vsak_dan[1:197]) #brisem zato ker za porabo na koncu 0\nr13=cor(povpr_poraba_za_vsak_dan1,skupne_padavine_za_vsak_dan[1:197])\n\n\n# ***************************************************\n# Napovedni model s \u010dasovnimi vrstami\n# ***************************************************\npovpr_poraba_za_vsak_dan_ts <- ts(povpr_poraba_za_vsak_dan1, frequency = 365, start = c(2021,1))\narima_model <- auto.arima(povpr_poraba_za_vsak_dan_ts)\nsummary(arima_model)\nholt_model <- holt(povpr_poraba_za_vsak_dan_ts, h=1)\nsummary(holt_model)\nse_model <- ses(povpr_poraba_za_vsak_dan_ts, h = 1)\nsummary(se_model)\nnaive_mod <- naive(povpr_poraba_za_vsak_dan_ts, h = 1)\nsummary(naive_mod, \"NA\"= 0)\n\n\njpeg(\"slike/prikaz_natancnosti_razlicnih_napovednih_modelov.jpg\")\nmar.default <- c(5,2,4,2) + 0.1\npar(mar = mar.default + c(0, 4, 0, 0),family=\"serif\") #axis(side, at=, labels=, pos=, lty=, col=, las=, tck=, ...)\ntitle=\"Povpre\u010dna poraba in njene napovedi\"\nplot(c(2021,2021.6), c(0,170),yaxt='n',xaxt='n',main=title, type = \"n\", pch=19,cex.lab=1.2, cex.axis=1.3, cex.main=1.5,xlab = \"\u010cas\",ylab = \"Povpre\u010dna poraba\") # setting up coord. system\nxlabels = c(2021,2021.1, 2021.2, 2021.3, 2021.4, 2021.5, 2021.6)\nylabels = c(1:10)*17\naxis(side=1,at=xlabels)\naxis(side=2,at=ylabels)\nlines(arima_model$fitted, type = \"l\", col = \"blue\",lwd=1,cex = 1,pch=16)\nlines(holt_model$fitted, type = \"l\", col = \"green\",lwd=1,cex = 1,pch=16)\nlines(se_model$fitted, type = \"l\", col = \"red\",lwd=1,cex = 1,pch=16)\nlines(naive_mod$fitted, type = \"l\", col = \"purple\",lwd=1,cex = 1,pch=16)\nlines(povpr_poraba_za_vsak_dan_ts, type = \"l\", col = \"orange\",lwd=2,cex = 1,pch=16)\nlegend(\"top\", legend=(c(\"Dejanska poraba\",\"Arima model\", \"Holt model\", \"Se model\", \"Naive mod\")), pch=c(16,16),cex=c(1.2,1.2),col=c(\"orange\",\"blue\", \"green\", \"red\", \"purple\"),y.intersp=0.8)\ndev.off()\n# ***************************************************\n# Grupiranje rezultatov\n# ***************************************************\npovpr_poraba_za_vsak_dan <-rowMeans(Tabela_porabe1[ ,3:98])\npovpr_poraba_matrika <- t(matrix(povpr_poraba_za_vsak_dan,198,51))\nset.seed(123)\n\n# *************************************************************\n# Ugotavljanje optimalnega \u0161tevila skupin s silhuetnim diagramom\n# *************************************************************\nobrisi = function(podatki, hc = TRUE, od = 2, do = NULL) {\n n = nrow(podatki)\n if (is.null(do)) {\n do = n - 1\n }\n \n razdalje = dist(podatki)\n \n k.obrisi = tibble()\n for (k in od:do) {\n if (hc) {\n o.k = hclust(razdalje) %>%\n cutree(k) %>%\n silhouette(razdalje)\n } else {\n set.seed(42) # zato, da so rezultati ponovljivi\n o.k = kmeans(podatki, k)$cluster %>%\n silhouette(razdalje)\n }\n k.obrisi = k.obrisi %>% bind_rows(\n tibble(\n k = rep(k, n),\n obrisi = o.k[, \"sil_width\"]\n )\n )\n }\n k.obrisi$k = as.ordered(k.obrisi$k)\n \n k.obrisi\n}\n\nobrisi.povprecje = function(k.obrisi) {\n k.obrisi.povprecje = k.obrisi %>%\n group_by(k) %>%\n summarize(obrisi = mean(obrisi))\n}\n\nobrisi.k = function(k.obrisi) {\n obrisi.povprecje(k.obrisi) %>%\n filter(obrisi == max(obrisi)) %>%\n summarize(k = min(k)) %>%\n unlist() %>%\n as.character() %>%\n as.integer()\n}\n\nr.hc = povpr_poraba_matrika[c(1:9,11:43,45:51),] %>% obrisi(hc = TRUE, od=2, do=20)\nr.km = povpr_poraba_matrika[c(1:9,11:43,45:51),] %>% obrisi(hc = FALSE,od=2, do=20)\n\n\n\ndiagram.obrisi = function(k.obrisi) {\n ggplot() +\n geom_boxplot(\n data = k.obrisi,\n mapping = aes(x = k, y = obrisi)\n ) +\n geom_point(\n data = obrisi.povprecje(k.obrisi),\n mapping = aes(x = k, y = obrisi),\n color = \"red\"\n ) +\n geom_line(\n data = obrisi.povprecje(k.obrisi),\n mapping = aes(x = as.integer(k), y = obrisi),\n color = \"red\"\n ) +\n geom_point(\n data = obrisi.povprecje(k.obrisi) %>%\n filter(obrisi == max(obrisi)) %>%\n filter(k == min(k)),\n mapping = aes(x = k, y = obrisi),\n color = \"blue\"\n ) +\n xlab(\"\u0161tevilo skupin (k)\") +\n ylab(\"obrisi (povpre\u010dje obrisov)\") +\n ggtitle(paste(\"Maksimalno povpre\u010dje obrisov pri k =\", obrisi.k(k.obrisi))) +\n theme_classic()\n}\njpeg(\"slike/hirarhicni_model.jpg\")\ndiagram.obrisi(r.hc)\ndev.off()\n\njpeg(\"slike/model_k_tih_voditeljev.jpg\")\ndiagram.obrisi(r.km)\ndev.off()\n# ***************************************************\n# Generiranje skupin\n# ***************************************************\nklas <- kmeans(povpr_poraba_matrika,2)\nklasifikacija <- klas$cluster\npovpr_poraba_matrika1 <- cbind(povpr_poraba_matrika, klasifikacija)\n\nM=max(max(povpr_poraba_matrika1[,1:198]))\nm=min(min(povpr_poraba_matrika1[,1:198]))\n\nTabela_razred1 <- povpr_poraba_matrika1[povpr_poraba_matrika1[,199] == \"1\",1:198]\nTabela_razred2 <- povpr_poraba_matrika1[povpr_poraba_matrika1[,199] == \"2\",1:198]\n\n\njpeg(\"slike/prikaz_povprecne_porabe_za_razlicne_razrede.jpg\")\nmar.default <- c(5,2,4,2) + 0.1\npar(mar = mar.default + c(0, 4, 0, 0),family=\"serif\") #axis(side, at=, labels=, pos=, lty=, col=, las=, tck=, ...)\ntitle=\"Povpre\u010dna poraba za razli\u010dne razrede porabnikov\"\nplot(c(0,199), c(0,100),yaxt='n',xaxt='n',main=title, type = \"n\", pch=19,cex.lab=1.2, cex.axis=1.3, cex.main=1.5,xlab = \"Dnevi\",ylab = \"Povpre\u010dna poraba\") # setting up coord. system\nxlabels=c(1:19)*10\naxis(side=1,at=xlabels,labels=xlabels)\naxis(side=2,at=c(0:6)*20,labels=as.factor(c(0:6)*20))\nlines(Tabela_razred1, type = \"l\", col = \"black\",lwd=2,cex = 1,pch=16)\nlines(colSums(Tabela_razred2), type = \"l\", col = \"red\",lwd=2,cex = 1,pch=16)\nlegend(\"top\", legend=(c(\"Sk1\",\"Sk2\")), pch=c(16,16),cex=c(1,1),col=c(\"black\",\"red\"),y.intersp=1.5,horiz=T)\ndev.off()\n\n\n", "meta": {"hexsha": "d25ab287190e5190d2c99c499bddfe2e82674854", "size": 12805, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "Blazpovh/APPR-2021-22", "max_stars_repo_head_hexsha": "ba8161ec0636ba4ef31ebf36e8bab2f236f8859c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "Blazpovh/APPR-2021-22", "max_issues_repo_head_hexsha": "ba8161ec0636ba4ef31ebf36e8bab2f236f8859c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-04T12:41:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-07T19:45:52.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "Blazpovh/APPR-2021-22", "max_forks_repo_head_hexsha": "ba8161ec0636ba4ef31ebf36e8bab2f236f8859c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.5636363636, "max_line_length": 189, "alphanum_fraction": 0.6662241312, "num_tokens": 4875, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5428632831725051, "lm_q1q2_score": 0.3258202232299139}} {"text": "#nens landings pre may 15\n\np = bio.snowcrab::load.environment()\n\na = logbook.db('logbook')\n\na = a[which(a$cfa=='cfanorth'),]\n\nb = aggregate(landings~yr,data=a,FUN=sum)\n\ny=2005:2015\n\nx=data.frame(yr=NA,tot.l=NA,ear.l=NA)\nfor(i in 1:length(y)){\n\tr = a[which(a$yr==y[i]),]\n\tdd = paste(y[i],'05','16',sep=\"-\")\n\tr = r[which(r$date.fished0) {g = aggregate(landings~yr,data=r,FUN=sum)[2]}\n\tx[i,] = c(y[i],b[which(b$yr==y[i]),'landings'],g)\n}\n\ntable.view(x)\n", "meta": {"hexsha": "8108b80fcf8cfb75af8aef7a48a22ecb3dbddf07", "size": 474, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/requests/nens.landings.premay15.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/requests/nens.landings.premay15.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/requests/nens.landings.premay15.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 19.75, "max_line_length": 61, "alphanum_fraction": 0.6075949367, "num_tokens": 188, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3258078876680581}} {"text": "\nnox<-3; noy<-3;\npaper<-F # graphics on paper=file (TRUE) or on screen (FALSE)\n\n\n\nrun.ID<-'retro-N' # file id used for paper output\ncleanup()\n\nfirst.year.on.plot<-1975\nlast.year.on.plot<-2012\n\ndoGrid<-T\n#incl.sp<-c(\"Cod\") # species to be included. Name(s) or \"all\"\nincl.sp<-\"all\"\n\nfirst.pch<-1 # first pch symbol\nfirst.color<-1 # first color\n\npalette(\"default\") # good for clolorfull plots\n#palette(gray(seq(0,.9,len=6))) # gray scale for papers, use len =500 to get black only\n\n\nif (F) { # sometimes the dir and labels are defined outside this script !\n\n dirs<-c(\"baltic-2012-keyRun-WGSAM2012\",\"baltic-2012-keyRun-updated\")\n labels<-c(\"Key run 2012\",\"Updated\")\n \n \n dirs<-c(\"bal-1-area-run-01-final\",\"baltic-2012-keyRun-updated\",\"bal-4-areas-run-01-final\",\"baltic-2012-keyRun-4-areas\")\n dirs<-c(\"bal-1-area-final - afterMeeting\",\"baltic-2012-keyRun-updated\",\"bal-4-areas-run-01-final\",\"baltic-2012-keyRun-4-areas\")\n labels<-c(\"WKmultbal\",\"Key run 2012 updated\",\"4 areas 2011\",\"4 areas 2012\")\n \n \n dirs<-c(\"baltic-2012-keyRun-updated\",\"baltic-2012-keyRun-4-areas\")\n labels<-c(\"Key run 2012 updated\",\"4 areas 2012\")\n \n \n dirs<-c(\"NS_63-10-Sep-2014\",\"NS_74-13-Oct-2014\")\n labels<-c(\"Old\",\"New\")\n \n\n}\n\ndirs<-c(\"Baltic-2012-keyRun-results\",\"Baltic-2019-keyRun\")\nlabels<-c(\"2012 key run\",\"2019 key run\")\n\nfor (dir in dirs) {\n if ( file.access(file.path(root,dir,\"sms.dat\"), mode = 0)!=0) stop(paste('Directory',dir,'does not exist'))\n} \n\nInit.function() # get SMS.contol object including sp.names\n\n\nfor (dir in dirs) {\n a<-Read.summary.data(dir=file.path(root,dir),read.init.function=F)\n a<-subset(a,(Year>=first.year.on.plot & Year<=last.year.on.plot & Age<=8),\n select=c(Species, Year,Quarter,Age,C.obs))\n M2<-data.frame(scen=labels[which(dirs==dir)],vari='C.obs',a)\n names(M2)<-c(\"scenario\",\"Variable\",\"Species\",\"Year\",\"Quarter\",\"Age\",\"Value\")\n if (dir==dirs[1]) all<-rbind(M2) else all<-rbind(all,M2)\n}\n#all<-droplevels(subset(all,Species=='Sprat'))\nvalues<-tapply(all$Value,list(all$Year,all$scenario,all$Species,all$Variable,all$Age),sum)\nvalues<-values/1000\ny<-as.numeric(dimnames(values)[[1]])\n\n\nif (paper) dev<-\"wmf\" else dev<-\"screen\"\nif (incl.sp==\"all\") sp.plot<-sp.names else sp.plot<-incl.sp\n\nlen.dir<-length(dirs)\n\n\n plotvar<-function(sp=sp,vari='M2',ylab='') {\n if (sp %in% dimnames(values)[[3]]) {\n v<-values[,,sp,vari,]\n #print(v)\n maxval<-max(v,na.rm=T)\n if (maxval>0) {\n if ((gi %% (nox*noy))==0 | gi==0) {\n newplot(dev,nox,noy,filename=paste(\"com_\",run.ID,'_',sp,sep=''),Portrait=F);\n\n par(mar=c(0,0,0,0))\n # make legends\n if (paper) lwds<-2\n else lwds<-2\n plot(10,10,axes=FALSE,xlab=' ',ylab=' ',xlim=c(0,1),ylim=c(0,1))\n legend(\"center\",legend=labels,col=first.color:(first.color+len.dir-1),\n pch=first.pch:(first.pch+len.dir-1),cex=2,title=sp)\n gi<<-gi+1\n\n par(mar=c(3,3,3,1)) # c(bottom, left, top, right)\n }\n \n gi<<-gi+1\n\n ages<-dimnames(v)[[3]][1:min(nox*noy-1,dim(v)[[3]])]\n for (ag in ages) {\n maxval<-max(v[,,ag],na.rm=T)\n minval<-min(0,v[,,ag],na.rm=T)\n if (maxval>0) {\n plot(y,v[,labels[1],ag],main=paste(\"age\",ag),xlab=\"\",ylab=ylab,type='b',lwd=lwds,ylim=c(minval,maxval),\n col=first.color,pch=first.pch)\n if (doGrid) grid()\n for (i in (2:len.dir)) {\n if (paper) lwds<-1\n else lwds<-2;\n lines(y,v[,labels[i],ag],col=first.color+i-1,pch=first.pch+i-1,type='b',lwd=lwds)\n }\n }\n }\n }\n }\n }\n \n for (sp in (sp.plot)) {\n gi<-0\n plotvar(sp=sp,vari=\"C.obs\",ylab=\"C\")\n}\n\nif (paper) cleanup();\n", "meta": {"hexsha": "ae22f5e7d78a1a1ca3ed8f71a9e7d6627051a194", "size": 3766, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/compare_runs_c.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/compare_runs_c.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/compare_runs_c.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6178861789, "max_line_length": 129, "alphanum_fraction": 0.592936803, "num_tokens": 1287, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3258078876680581}} {"text": "## 17022020 CKR\n#Figure 2E\nlibrary(vegan)\nlibrary(ggsci)\nlibrary(tidyr)\nlibrary(reshape2)\nlibrary(dplyr)\nlibrary(ggpubr)\nlibrary(stringr)\nlibrary(matrixStats)\n\n#Canada\nCaz_Canada=read.table(\"../Data/Suppl4_CA.txt\",sep=\"\\t\",head=T,row.names=1)\nCaz_Canada=select(Caz_Canada,contains(\"Number\"))%>%\n mutate(label=rownames(Caz_Canada))\ncolnames(Caz_Canada)[1]=\"Number\"\n#Canada PTR\nCanadaPTR=read.table(\"../Data/Suppl5_CA.txt\",sep=\"\\t\",head=T,row.names=1)#read in PTR\nCanadaPTR=CanadaPTR[1:(nrow(CanadaPTR)-1),] #remove last row\nCanadaPTR[is.na(CanadaPTR)]=0 #change NA to 0\nCanadaPTR=CanadaPTR[rowSums(CanadaPTR)!=0,]\n#filter for common species\nCanadaPTR=CanadaPTR[rowSums(CanadaPTR==0)<(ncol(CanadaPTR)/2),] #less than 50% of subjects do not have PTR for the species\nCanadaPTR[CanadaPTR<1]=1 #set PTR < 1 to 1 for non-removed species\nCanadaPTR=summarise_all(CanadaPTR,median)\nCanadaPTR=melt(CanadaPTR)\nCanada=merge(CanadaPTR,Caz_Canada,by.x=\"variable\",by.y=\"label\")\n\n#plot \nggplot(Canada, aes(x=Number,y=value)) +\n geom_point() +\n geom_smooth(method = \"lm\")+\n theme_classic()+border()\ncor.test(Canada$value,Canada$Number,method=\"spearman\")\n\n#Singapore\nCaz_Singapore=read.table(\"../Data/Suppl4_SG_Final.txt\",sep=\"\\t\",head=T,check.names=F,row.names=1)\nCaz_Singapore=select(Caz_Singapore,contains(\"Number\"))%>%\n mutate(label=rownames(Caz_Singapore))\ncolnames(Caz_Singapore)[1]=\"Number\"\n\nSingPTR=read.table(\"../Data/Suppl5_SG_Final.txt\",sep=\"\\t\",head=T,row.names=1,check.names=F) #read in PTR\nSingPTR=SingPTR[1:(nrow(SingPTR)-1),] #remove last row\nSingPTR[is.na(SingPTR)]=0 #change NA to 0\nSingPTR=SingPTR[rowSums(SingPTR)!=0,] #remove row with no abundances \n\n#less than 50% of subjects do not have PTR for the species\nSingPTR=SingPTR[(rowSums(SingPTR!=0)>(ncol(SingPTR)/2)),]\nSingPTR[SingPTR<1]=1\nSingPTR=summarise_all(SingPTR,median)\nSingPTR=melt(SingPTR)\nSingapore=merge(SingPTR,Caz_Singapore,by.x=\"variable\",by.y=\"label\")\n\n#plot \nggplot(Singapore, aes(x=Number,y=value)) +\n geom_point() +\n geom_smooth(method = \"lm\")+\n theme_classic()+border()\ncor.test(Singapore$value,Singapore$Number,method=\"spearman\")\n", "meta": {"hexsha": "6687689da80ef25faa521e45d288a7e8c0e53a69", "size": 2114, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/2E.r", "max_stars_repo_name": "CSB5/Recovery_Determinants_Study", "max_stars_repo_head_hexsha": "a8651a834947f7eca8f176711bf7b3b976b4dced", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-07-30T11:47:59.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-08T18:40:46.000Z", "max_issues_repo_path": "Scripts/2E.r", "max_issues_repo_name": "CSB5/Recovery_Determinants_Study", "max_issues_repo_head_hexsha": "a8651a834947f7eca8f176711bf7b3b976b4dced", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/2E.r", "max_forks_repo_name": "CSB5/Recovery_Determinants_Study", "max_forks_repo_head_hexsha": "a8651a834947f7eca8f176711bf7b3b976b4dced", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-07-24T16:12:55.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-28T02:07:29.000Z", "avg_line_length": 35.2333333333, "max_line_length": 122, "alphanum_fraction": 0.752128666, "num_tokens": 719, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3258078803363456}} {"text": "#impoting dataset\r\ndataset = read.csv('50_Startups.csv')", "meta": {"hexsha": "80300c226c4215686ace6dd4af7d99e4c9702fba", "size": 56, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Regression/Multiple_Linear_Regression/multiple_linear_regression.r", "max_stars_repo_name": "luther001/ML", "max_stars_repo_head_hexsha": "d83696e9f9bdc3d2f58de1754f69dab658e029be", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Regression/Multiple_Linear_Regression/multiple_linear_regression.r", "max_issues_repo_name": "luther001/ML", "max_issues_repo_head_hexsha": "d83696e9f9bdc3d2f58de1754f69dab658e029be", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Regression/Multiple_Linear_Regression/multiple_linear_regression.r", "max_forks_repo_name": "luther001/ML", "max_forks_repo_head_hexsha": "d83696e9f9bdc3d2f58de1754f69dab658e029be", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0, "max_line_length": 37, "alphanum_fraction": 0.7678571429, "num_tokens": 15, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.6224593241981982, "lm_q1q2_score": 0.3258078766704893}} {"text": "#!/usr/bin/Rscript\npdf(\"optimise_cost_progress_scatter_plot.pdf\")\ncandidates <- read.table(\"optimise_cost.tsv\",header = TRUE)\nplot(candidates$generation,candidates$fitness,col=rgb(0,100,0,50,maxColorValue=255),pch=16)\ndev.off()\n", "meta": {"hexsha": "96393e259b0a8a3a2d5f6f9da1f0a130eb587d23", "size": 228, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/visualise_optimise_cost_progress.r", "max_stars_repo_name": "decc/decc_2050_optimizer", "max_stars_repo_head_hexsha": "368b520624bae60a523556405efc8c4103de415d", "max_stars_repo_licenses": ["MIT", "Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/visualise_optimise_cost_progress.r", "max_issues_repo_name": "decc/decc_2050_optimizer", "max_issues_repo_head_hexsha": "368b520624bae60a523556405efc8c4103de415d", "max_issues_repo_licenses": ["MIT", "Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/visualise_optimise_cost_progress.r", "max_forks_repo_name": "decc/decc_2050_optimizer", "max_forks_repo_head_hexsha": "368b520624bae60a523556405efc8c4103de415d", "max_forks_repo_licenses": ["MIT", "Unlicense"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-10T21:29:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-10T21:29:57.000Z", "avg_line_length": 38.0, "max_line_length": 91, "alphanum_fraction": 0.7894736842, "num_tokens": 64, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6224593171945417, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.32580787300463304}} {"text": "require(ncdf4)\n\n###########\n## Read in warming (all calculations)\n##########\n\n# read in monthly warming rcp26\ninfile <- nc_open('data/cmip5/d20161206_1632_19653.nc') # deltas for each month, 2081-2100 minus 1986-2005\n\tlstwarm26 <- ncvar_get(infile, 'diff') # lst warming x x 12 (last is months)\n\tdim(lstwarm26)\n\tdimnames(lstwarm26) <- list(lon=1:144, lat=1:72, mo3=1:12)\n\tdimnames(lstwarm26)[[1]] <- infile$var[['diff']]$dim[[1]]$vals # add lon\n\tdimnames(lstwarm26)[[2]] <- infile$var[['diff']]$dim[[2]]$vals # add lat\n\tnc_close(infile)\n\tlstwarm26 <- lstwarm26[,dim(lstwarm26)[2]:1,]\n\ninfile <- nc_open('data/cmip5/d20161206_1630_11885.nc') # deltas for each month, 2081-2100 minus 1986-2005\n\tsstwarm26 <- ncvar_get(infile, 'diff') # sst warming 288 x 144 x 12 (last is months)\n\tdim(sstwarm26)\n\tdimnames(sstwarm26) <- list(lon=1:288, lat=1:144, mo3=1:12) # dummy dimnames for lat and lon\n\tdimnames(sstwarm26)[[1]] <- infile$var[['diff']]$dim[[1]]$vals # add lon\n\tdimnames(sstwarm26)[[2]] <- infile$var[['diff']]$dim[[2]]$vals # add lat\n\tnc_close(infile)\n\tsstwarm26 <- sstwarm26[,dim(sstwarm26)[2]:1,] # rearrange\n\n\n# read in monthly warming rcp85\ninfile <- nc_open('data/cmip5/d20161127_1456_12434.nc') # deltas for each month, 2081-2100 minus 1986-2005\n\tlstwarm85 <- ncvar_get(infile, 'diff') # lst warming x x 12 (last is months)\n\tdim(lstwarm85)\n\tdimnames(lstwarm85) <- list(lon=1:144, lat=1:72, mo3=1:12)\n\tdimnames(lstwarm85)[[1]] <- infile$var[['diff']]$dim[[1]]$vals # add lon\n\tdimnames(lstwarm85)[[2]] <- infile$var[['diff']]$dim[[2]]$vals # add lat\n\tnc_close(infile)\n\tlstwarm85 <- lstwarm85[,dim(lstwarm85)[2]:1,]\n\ninfile <- nc_open('data/cmip5/d20161127_1011_30742.nc') # deltas for each month, 2081-2100 minus 1986-2005\n\tsstwarm85 <- ncvar_get(infile, 'diff') # sst warming 288 x 144 x 12 (last is months)\n\tdim(sstwarm85)\n\tdimnames(sstwarm85) <- list(lon=1:288, lat=1:144, mo3=1:12) # dummy dimnames for lat and lon\n\tdimnames(sstwarm85)[[1]] <- infile$var[['diff']]$dim[[1]]$vals # add lon\n\tdimnames(sstwarm85)[[2]] <- infile$var[['diff']]$dim[[2]]$vals # add lat\n\tnc_close(infile)\n\tsstwarm85 <- sstwarm85[,dim(sstwarm85)[2]:1,] # rearrange\n\n\n################\n## For annual mean\n################\n\n# read in climatologies for annual mean\nload('temp/lstclimatology.rdata') # lstclim\nload('temp/sstclimatology.rdata') # sstclim\n\n# calculate warming \n\tlstwarm26ann <- t(apply(lstwarm26, MARGIN=c(1,2), FUN=mean, na.rm=TRUE))\n\tlstwarm85ann <- t(apply(lstwarm85, MARGIN=c(1,2), FUN=mean, na.rm=TRUE))\n\tsstwarm26ann <- t(apply(sstwarm26, MARGIN=c(1,2), FUN=mean, na.rm=TRUE))\n\tsstwarm85ann <- t(apply(sstwarm85, MARGIN=c(1,2), FUN=mean, na.rm=TRUE))\n\n# interpolate warming to 0.5x0.5\n\t# create new grid\n\tngrd <- expand.grid(lat=as.numeric(rownames(lstclim)), lon=as.numeric(colnames(lstclim)))\n\tngrd.sp <- ngrd[,c('lon', 'lat')]\n\tcoordinates(ngrd.sp) <- ~lon + lat\n\tproj4string(ngrd.sp) <- '+proj=longlat +datum=WGS84'\n\t\t\n\t# interpolate: raster query approach with points\n\trast <- raster(lstwarm26ann, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tlstwarm26ann05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\t\t\t# par(mfrow=c(1,2))\n\t\t\t# image(lstwarm26ann)\n\t\t\t# image(lstwarm26ann05)\n\n\trast <- raster(lstwarm85ann, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tlstwarm85ann05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- raster(sstwarm26ann, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm26ann05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- raster(sstwarm85ann, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm85ann05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\n# calculate future temperatures\n\tdim(lstclim)\n\tdim(lstwarm26ann05) # match: good\n\t\n\tpar(mfrow=c(1,2)) # make sure they are aligned\n\timage(lstclim)\n\timage(lstwarm26ann05)\n\n\t\n\tlstfut26ann <- lstclim + lstwarm26ann05\n\tsstfut26ann <- sstclim + sstwarm26ann05\n\n\tlstfut85ann <- lstclim + lstwarm85ann05\n\tsstfut85ann <- sstclim + sstwarm85ann05\n\n\n# plot the present temperature maps\n\tpar(mfrow=c(2,1))\n\timage(t(lstclimn)[,nrow(lstclimn):1])\n\timage(t(sstclimn)[,nrow(sstclimn):1])\n\n\n# plot the future temperature maps\n\tpar(mfrow=c(2,1))\n\timage(t(lstfut85ann)[,nrow(lstfut85ann):1])\n\timage(t(sstfut85ann)[,nrow(sstfut85ann):1])\n\t\n\n# write out\nsave(lstfut26ann, file='temp/lstfut26ann.rdata')\nsave(sstfut26ann, file='temp/sstfut26ann.rdata')\nsave(lstfut85ann, file='temp/lstfut85ann.rdata')\nsave(sstfut85ann, file='temp/sstfut85ann.rdata')\n\n\n#####################\n## For mean summer\n#####################\n\n# read in climatologies for summer mean\nload('temp/lstclimatology_warm3.rdata') # lstclimwarm3\nload('temp/sstclimatology_warm3.rdata') # sstclimwarm3\n\n# calculate warming \n\t# lst\n\tlstwarm26sumN <- apply(lstwarm26[,,6:8], MARGIN=c(1,2), FUN=mean) # delta for northern hemisphere summer\n\tlstwarm26sumS <- apply(lstwarm26[,,c(1,2,12)], MARGIN=c(1,2), FUN=mean) # delta for southern hemisphere summer\n\tlstwarm26sum <- t(cbind(lstwarm26sumN[,as.character(seq(88.75,1.25,by=-2.5))], lstwarm26sumS[,as.character(seq(-1.25,-88.75,by=-2.5))]))\n\n\tlstwarm85sumN <- apply(lstwarm85[,,6:8], MARGIN=c(1,2), FUN=mean) # delta for northern hemisphere summer\n\tlstwarm85sumS <- apply(lstwarm85[,,c(1,2,12)], MARGIN=c(1,2), FUN=mean) # delta for southern hemisphere summer\n\tlstwarm85sum <- t(cbind(lstwarm85sumN[,as.character(seq(88.75,1.25,by=-2.5))], lstwarm85sumS[,as.character(seq(-1.25,-88.75,by=-2.5))]))\n\n\n\t# sst\n\tsstwarm26sumN <- apply(sstwarm26[,,6:8], MARGIN=c(1,2), FUN=mean) # delta for northern hemisphere summer\n\tsstwarm26sumS <- apply(sstwarm26[,,c(1,2,12)], MARGIN=c(1,2), FUN=mean) # delta for southern hemisphere summer\n\tsstwarm26sum <- t(cbind(sstwarm26sumN[,as.character(seq(89.375,0.625,by=-1.25))], sstwarm26sumS[,as.character(seq(-0.625,-89.375,by=-1.25))]))\n\n\tsstwarm85sumN <- apply(sstwarm85[,,6:8], MARGIN=c(1,2), FUN=mean) # delta for northern hemisphere summer\n\tsstwarm85sumS <- apply(sstwarm85[,,c(1,2,12)], MARGIN=c(1,2), FUN=mean) # delta for southern hemisphere summer\n\tsstwarm85sum <- t(cbind(sstwarm85sumN[,as.character(seq(89.375,0.625,by=-1.25))], sstwarm85sumS[,as.character(seq(-0.625,-89.375,by=-1.25))]))\n\n\t# clean up\n\trm(sstwarm26sumN, sstwarm26sumS, lstwarm26sumN, lstwarm26sumS, sstwarm85sumN, sstwarm85sumS, lstwarm85sumN, lstwarm85sumS)\n\n# interpolate warming to 0.5x0.5\n\t# create new grid\n\tngrd <- expand.grid(lat=as.numeric(rownames(lstclimwarm3)), lon=as.numeric(colnames(lstclimwarm3)))\n\tngrd.sp <- ngrd[,c('lon', 'lat')]\n\tcoordinates(ngrd.sp) <- ~lon + lat\n\tproj4string(ngrd.sp) <- '+proj=longlat +datum=WGS84'\n\t\t\n\t# interpolate: raster query approach with points\n\trast <- raster(lstwarm26sum, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tlstwarm26sum05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\t\t\t# par(mfrow=c(1,2))\n\t\t\t# image(lstwarm26ann)\n\t\t\t# image(lstwarm26ann05)\n\n\trast <- raster(lstwarm85sum, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tlstwarm85sum05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- raster(sstwarm26sum, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm26sum05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- raster(sstwarm85sum, xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm85sum05 <- as.matrix(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\n# calculate future temperatures\n\tdim(lstclimwarm3)\n\tdim(lstwarm26sum05)\n\t\n\tpar(mfrow=c(1,2)) # make sure they are aligned\n\timage(lstclimwarm3)\n\timage(lstwarm26sum05)\n\n\tlstfut26sum <- lstclimwarm3 + lstwarm26sum05 # for summer mean\n\tsstfut26sum <- sstclimwarm3 + sstwarm26sum05\n\n\tlstfut85sum <- lstclimwarm3 + lstwarm85sum05 # for summer mean\n\tsstfut85sum <- sstclimwarm3 + sstwarm85sum05\n\n\n# plot the present temperature maps\n\tpar(mfrow=c(2,1))\n\timage(t(lstclimwarm3)[,nrow(lstclimwarm3):1])\n\timage(t(sstclimwarm3)[,nrow(sstclimwarm3):1])\n\n\n# plot the future temperature maps\n\tpar(mfrow=c(2,1))\n\timage(t(lstfut85sum)[,nrow(lstfut85sum):1], main='rcp85')\n\timage(t(sstfut85sum)[,nrow(sstfut85sum):1], main='rcp85')\n\n\n# write out\nsave(lstfut26sum, file='temp/lstfut26sum.rdata')\nsave(sstfut26sum, file='temp/sstfut26sum.rdata')\nsave(lstfut85sum, file='temp/lstfut85sum.rdata')\nsave(sstfut85sum, file='temp/sstfut85sum.rdata')\n\n\n#####################\n## For warmest month\n#####################\n\n# read in climatologies for all months\nload('temp/sstclimatology_bymonth.rdata') # sstclimbymo\nload('temp/lstclimatology_bymonth.rdata') # lstclimbymo\n\n\n# interpolate warming for each month to 0.5x0.5\n\t# create new grid\n\tngrd <- expand.grid(lat=as.numeric(rownames(lstclim)), lon=as.numeric(colnames(lstclim)))\n\tngrd.sp <- ngrd[,c('lon', 'lat')]\n\tcoordinates(ngrd.sp) <- ~lon + lat\n\tproj4string(ngrd.sp) <- '+proj=longlat +datum=WGS84'\n\t\t\n\t# interpolate: raster query approach with points\n\t# have to exchange axes for lstwarm26\n\trast <- brick(aperm(lstwarm26, c(2,1,3)), xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\trast2 <- rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss))\n\t\tlstwarm26.05 <- as.array(rast2)\n\t\t\n\t\t\t# par(mfrow=c(1,2))\n\t\t\t# image(lstwarm26[,,1])\n\t\t\t# image(lstwarm26.05[,,1]) # should be rotated\n\n\trast <- brick(aperm(lstwarm85, c(2,1,3)), xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tlstwarm85.05 <- as.array(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- brick(aperm(sstwarm26, c(2,1,3)), xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm26.05 <- as.array(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\trast <- brick(aperm(sstwarm85, c(2,1,3)), xmn=1.25, xmx=358.75, ymn=-88.75, ymx=88.75, crs='+proj=longlat +datum=WGS84') # make a raster object\n\t\tnewvalss <- extract(rast, ngrd.sp, method='bilinear')\n\t\tsstwarm85.05 <- as.array(rasterFromXYZ(cbind(ngrd[,c('lon', 'lat')], newvalss)))\n\n\n# check alignment\n\tdim(lstclimbymo)\n\tdim(sstclimbymo)\n\tdim(lstwarm26.05)\n\tdim(lstwarm85.05)\n\tdim(sstwarm26.05)\n\tdim(sstwarm85.05)\n\t\n\tpar(mfrow=c(1,2)) # check for alignment\n\timage(lstclimbymo[,,2])\n\timage(lstwarm26.05[,,2])\n\n\tpar(mfrow=c(1,2)) # check for alignment\n\timage(lstclimbymo[,,2])\n\timage(lstwarm85.05[,,2])\n\n\tpar(mfrow=c(1,2)) # check for alignment\n\timage(sstclimbymo[,,2])\n\timage(sstwarm26.05[,,2])\n\n\tpar(mfrow=c(1,2)) # check for alignment\n\timage(sstclimbymo[,,2])\n\timage(sstwarm85.05[,,2])\n\n\n# calculate future temperatures for each month\n\tlstfut26bymo <- lstclimbymo + lstwarm26.05\n\tsstfut26bymo <- sstclimbymo + sstwarm26.05\n\n\tlstfut85bymo <- lstclimbymo + lstwarm85.05\n\tsstfut85bymo <- sstclimbymo + sstwarm85.05\n\n# plot the present and future temperature maps\n\t# January\n\tzliml = range(c(lstclimbymon[,,1], lstfut85bymo[,,1]), na.rm=TRUE)\n\tzlimo = range(c(sstclimbymon[,,1], sstfut85bymo[,,1]), na.rm=TRUE)\n\tpar(mfrow=c(3,2))\n\timage(lstclimbymo[,,1], zlim=zliml)\n\timage(sstclimbymo[,,1], zlim=zlimo)\n\timage(lstwarm85.05[,,1])\n\timage(sstwarm85.05[,,1])\n\timage(lstfut85bymo[,,1], main='rcp85 jan', zlim=zliml)\n\timage(sstfut85bymo[,,1], main='rcp85 jan', zlim=zlimo)\n\n\t# July\n\tzliml = range(c(lstclimbymo[,,7], lstfut85bymo[,,7]), na.rm=TRUE)\n\tzlimo = range(c(sstclimbymo[,,7], sstfut85bymo[,,7]), na.rm=TRUE)\n\tpar(mfrow=c(3,2))\n\timage(lstclimbymo[,,7], zlim=zliml)\n\timage(sstclimbymo[,,7], zlim=zlimo)\n\timage(lstwarm85.05[,,7])\n\timage(sstwarm85.05[,,7])\n\timage(lstfut85bymo[,,7], main='rcp85 jul', zlim=zliml)\n\timage(sstfut85bymo[,,7], main='rcp85 jul', zlim=zlimo)\n\n# calculate the warmest month\nlstfut26warmestmo <- apply(lstfut26bymo, MARGIN=c(1,2), FUN=max)\nsstfut26warmestmo <- apply(sstfut26bymo, MARGIN=c(1,2), FUN=max)\nlstfut85warmestmo <- apply(lstfut85bymo, MARGIN=c(1,2), FUN=max)\nsstfut85warmestmo <- apply(sstfut85bymo, MARGIN=c(1,2), FUN=max)\n\n\n# plot the future temperature maps\n\tpar(mfrow=c(2,1))\n\timage(t(lstfut85warmestmo)[,nrow(lstfut85warmestmo):1], main='rcp85')\n\timage(t(sstfut85warmestmo)[,nrow(sstfut85warmestmo):1], main='rcp85')\n\n\n# write out\nsave(lstfut26warmestmo, file='temp/lstfut26warmestmo.rdata')\nsave(sstfut26warmestmo, file='temp/sstfut26warmestmo.rdata')\nsave(lstfut85warmestmo, file='temp/lstfut85warmestmo.rdata')\nsave(sstfut85warmestmo, file='temp/sstfut85warmestmo.rdata')\n", "meta": {"hexsha": "4f1b17f281c68b5cdbb249e03bcf8c090c2b96ce", "size": 13431, "ext": "r", "lang": "R", "max_stars_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/future_temperatures.r", "max_stars_repo_name": "HuckleyLab/phyto-mhw", "max_stars_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-13T02:37:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-27T04:41:09.000Z", "max_issues_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/future_temperatures.r", "max_issues_repo_name": "HuckleyLab/phyto-mhw", "max_issues_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2020-07-19T10:54:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-17T19:53:09.000Z", "max_forks_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/future_temperatures.r", "max_forks_repo_name": "HuckleyLab/phyto-mhw", "max_forks_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.4548192771, "max_line_length": 144, "alphanum_fraction": 0.6984587894, "num_tokens": 5170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.3257905862555563}} {"text": "require(tidyverse)\nrequire(foreach)\nrequire(cowplot)\nrequire(RColorBrewer)\nrequire(seqinr)\nlibrary(ape)\n\n\nsource('initial_processing.r')\nsource('call_mutations.r')\n\nsource('determine_first_mutation.r')\nsource('process_kaplan_meier_curves.r')\nsource('CalculatingTargetSize_MutRate.R')\nsource('test_first_mutation.r')\nsource('plot_figure_2.r')\n\n\n", "meta": {"hexsha": "d9af8cec7b791066c7b308b093f53816a656b18e", "size": 344, "ext": "r", "lang": "R", "max_stars_repo_path": "code/mutation-ordering-WHO.r", "max_stars_repo_name": "federlab/HIV-MDR-evolution", "max_stars_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/mutation-ordering-WHO.r", "max_issues_repo_name": "federlab/HIV-MDR-evolution", "max_issues_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/mutation-ordering-WHO.r", "max_forks_repo_name": "federlab/HIV-MDR-evolution", "max_forks_repo_head_hexsha": "10665ab24f0193d620bae03d413692f063b9a165", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.1052631579, "max_line_length": 41, "alphanum_fraction": 0.8110465116, "num_tokens": 91, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228758499941, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3257905862555562}} {"text": "## Setup R error handling to go to stderr\noptions(show.error.messages = F,\n error = function() {\n cat(geterrmessage(), file = stderr())\n q(\"no\", 1, F)\n }\n)\nwarnings()\n\nlibrary(RColorBrewer)\nlibrary(lattice)\nlibrary(latticeExtra)\nlibrary(grid)\nlibrary(gridExtra)\nlibrary(optparse)\n\noption_list <- list(\n make_option(\"--h_dataframe\", type = \"character\",\n help = \"path to h-signature dataframe\"),\n make_option(\"--z_dataframe\", type = \"character\",\n help = \"path to z-signature dataframe\"),\n make_option(\"--plot_method\", type = \"character\",\n help = \"How data should be plotted (global or lattice)\"),\n make_option(\"--pdf\", type = \"character\", help = \"path to the pdf file with plots\"),\n make_option(\"--title\", type = \"character\", help = \"Graph Title\")\n )\nparser <- OptionParser(usage = \"%prog [options] file\", option_list = option_list)\nargs <- parse_args(parser)\n \n# data frames implementation\nh_dataframe <- read.delim(args$h_dataframe, header = F)\ncolnames(h_dataframe) <- c(\"chrom\", \"overlap\", \"sig\", \"z-score\")\nh_dataframe$sig <- h_dataframe$sig * 100 # to get probs in %\nz_dataframe <- read.delim(args$z_dataframe, header = F)\ncolnames(z_dataframe) <- c(\"chrom\", \"overlap\", \"sig\", \"z-score\")\n\n# functions\n globalgraph <- function() {\n pdf(args$pdf)\n par(mfrow = c(2, 2), oma = c(0, 0, 3, 0))\n plot(z_dataframe[z_dataframe$chrom == \"all_chromosomes\", c(2, 3)],\n type = \"h\", main = \"Numbers of pairs\", cex.main = 1, xlab = \"overlap (nt)\",\n ylab = \"Numbers of pairs\", col = \"darkslateblue\", lwd = 4)\n\n plot(z_dataframe[z_dataframe$chrom == \"all_chromosomes\", c(2, 4)],\n type = \"l\", main = \"Number of pairs Z-scores\", cex.main = 1, xlab = \"overlap (nt)\",\n ylab = \"z-score\", pch = 19, cex = 0.2, col = \"darkslateblue\", lwd = 2)\n\n plot(h_dataframe[h_dataframe$chrom == \"all_chromosomes\", c(2, 3)],\n type = \"l\", main = \"Overlap probabilities\", cex.main = 1,\n xlab = \"overlap (nt)\",\n ylab = \"Probability [%]\", ylim = c(0, 50), pch = 19,\n col = \"darkslateblue\", lwd = 2)\n\n plot(h_dataframe[h_dataframe$chrom == \"all_chromosomes\", c(2, 4)],\n type = \"l\", main = \"Overlap Probability Z-scores\", cex.main = 1,\n xlab = \"overlap (nt)\", ylab = \"z-score\", pch = 19, cex = 0.2,\n col = \"darkslateblue\", lwd = 2)\n mtext(args$title, outer = TRUE, cex = 1)\n dev.off()\n }\n\n treillisgraph <- function(df, ...) {\n pdf(args$pdf, paper = \"special\", height = 11.69, width = 6)\n p <- xyplot(sig ~ overlap | factor(method, levels = unique(method)) + chrom,\n data = df,\n type = \"l\",\n col = \"darkblue\",\n cex = 0.5,\n scales = list(y = list(tick.number = 4, relation = \"free\", cex = 0.6,\n rot = 0),\n x = list(cex = 0.6, alternating = FALSE)),\n xlab = \"Overlap\",\n ylab = \"signature (Nbr of pairs / Overlap prob.)\",\n main = args$title,\n par.strip.text = list(cex = .5),\n pch = 19, lwd = 2,\n as.table = TRUE,\n layout = c(2, 12),\n newpage = T,\n ...)\n plot(p)\n dev.off()\n }\n\n# main\n\nif (args$plot_method == \"global\") {\n globalgraph()\n}\n\nif (args$plot_method == \"lattice\") {\n # rearrange dataframes\n h_sig <- h_dataframe[, c(1, 2, 3)]\n h_sig <- cbind(rep(\"Overlap Prob (%)\", length(h_sig[, 1])), h_sig)\n colnames(h_sig) <- c(\"method\", \"chrom\", \"overlap\", \"sig\")\n z_pairs <- z_dataframe[, c(1, 2, 3)]\n z_pairs <- cbind(rep(\"Nbr of pairs\", length(z_pairs[, 1])), z_pairs)\n colnames(z_pairs) <- c(\"method\", \"chrom\", \"overlap\", \"sig\")\n lattice_df <- rbind(z_pairs, h_sig)\n par_settings_treillis <- list(strip.background = list(\n col = c(\"lightblue\", \"lightgreen\")))\n treillisgraph(lattice_df, par.settings = par_settings_treillis)\n}\n", "meta": {"hexsha": "90f515306a4d3ebc229f1cf192f254cdd16d0756", "size": 4197, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/small_rna_signatures/signature.r", "max_stars_repo_name": "ARTbio/tools-artbio", "max_stars_repo_head_hexsha": "4a36bccf4c745cc7f1f13189140252721ff5e61d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2015-09-13T13:29:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-24T09:01:40.000Z", "max_issues_repo_path": "tools/small_rna_signatures/signature.r", "max_issues_repo_name": "ARTbio/tools-artbio", "max_issues_repo_head_hexsha": "4a36bccf4c745cc7f1f13189140252721ff5e61d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 210, "max_issues_repo_issues_event_min_datetime": "2015-08-31T14:04:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-23T08:52:03.000Z", "max_forks_repo_path": "tools/small_rna_signatures/signature.r", "max_forks_repo_name": "ARTbio/tools-artbio", "max_forks_repo_head_hexsha": "4a36bccf4c745cc7f1f13189140252721ff5e61d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 16, "max_forks_repo_forks_event_min_datetime": "2015-08-31T13:15:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-24T09:03:22.000Z", "avg_line_length": 39.9714285714, "max_line_length": 96, "alphanum_fraction": 0.5372885394, "num_tokens": 1148, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548782017745, "lm_q2_score": 0.5, "lm_q1q2_score": 0.32567743910088726}} {"text": "#RevHelper Model\n\n#loading the library\nlibrary(randomForest)\nlibrary(caTools)\nlibrary(e1071)\nlibrary(caret)\n\n#load the model data\ndata=read.table(file=\"path/to/modeldata-training-and-testing.txt\", header=TRUE)\n#show a summary if needed\n#summary(data)\n\n#replacing the NA values\ncleandata=na.omit(data)\nsummary(cleandata)\n\n\n#initialize the performance measures\nsumAcc=0\nsumPosPrec=0\nsumPosRec=0\nsumNegPrec=0\nsumNegRec=0\nsumImp=0\n\n\nfor(i in 200:209){\n\n#setting seed points to ensure different randomization in each run\nset.seed(i)\n\n#shuffling the data\nsdata=cleandata[sample(nrow(cleandata), nrow(cleandata)), ] \n\n#divide the dataset into training and test set\nn=nrow(sdata)\ntrainIndex = sample(1:n, size = round(0.65*n), replace=FALSE)\ntrain=sdata[trainIndex,]\ntest=sdata[-trainIndex,]\n\n#summary of train and test data if needed.\n#summary(train)\n#summary(test)\n\n\n\n#developing the RevHelper-RF fit\nfit <- randomForest(as.factor(className) ~\n \n #all features considered \n #., \n \n #textual features only\n # ReadingEase+ReadingEase.NL. +StopwordRatio+StopKeyRatio+\t\n # QuestionRatio+ CodeElementRatio+ConceptualSimilarity\n \n #developer experience based features only \n #AuthorCommitsFile+ CommittedTwice + TotalAuthoredCommits+ ReviewingTwice +ReviewedCommitsFile +\tTotalReviewedCommits\n #+ ReviewedPRs +ExtLibSimilarity\n \n #optimal set of features\n ConceptualSimilarity\n +\n ReviewedCommitsFile + AuthorCommitsFile +ExtLibSimilarity +\n TotalReviewedCommits+ ReviewedPRs+StopwordRatio, \n \n data=train, \n importance=TRUE, \n ntree=2000)\n\n\nimp=importance(fit)\nsumImp =sumImp + imp\n\n\n#prediction model\nPrediction <- predict(fit, test)\n\n\n#calculate accuracy\ncm=confusionMatrix(data=Prediction,\n reference=test$class,\n positive='u')\n\n\n#show the confusion metrics if needed.\n#print(cm)\n\n#accumulating performance data from each run\nsumAcc = sumAcc+ cm$overall['Accuracy']\nsumPosPrec=sumPosPrec+ cm$byClass['Pos Pred Value']\nsumPosRec=sumPosRec + cm$byClass['Sensitivity']\nsumNegPrec=sumNegPrec + cm$byClass['Neg Pred Value']\nsumNegRec=sumNegRec + cm$byClass['Specificity']\n\n#showing performance from each run\nprint(c(cm$byClass['Pos Pred Value'],cm$byClass['Sensitivity'],cm$byClass['Neg Pred Value'],cm$byClass['Specificity'],cm$overall['Accuracy']) ) \n\n}\n\n#showing average performance for 10 runs\nprint(sumPosPrec/10)\nprint(sumPosRec/10)\nprint(sumNegPrec/10)\nprint(sumNegRec/10)\nprint(sumAcc/10)\n\n#showing average feature importance\nmyImp=sumImp/10\nprint(myImp)\n\n\n\n\n", "meta": {"hexsha": "2f2cc0b288872379cb03922c5547507069af1201", "size": 2899, "ext": "r", "lang": "R", "max_stars_repo_path": "prediction model/revhelper-model-rf.r", "max_stars_repo_name": "masud-technope/RevHelper-MSR2017", "max_stars_repo_head_hexsha": "1811a14c74f1c16ed35de5ea392a1fa0c0d44d77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-06-26T02:31:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-09T19:18:03.000Z", "max_issues_repo_path": "prediction model/revhelper-model-rf.r", "max_issues_repo_name": "ilya-palachev/RevHelper-MSR2017", "max_issues_repo_head_hexsha": "1811a14c74f1c16ed35de5ea392a1fa0c0d44d77", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "prediction model/revhelper-model-rf.r", "max_forks_repo_name": "ilya-palachev/RevHelper-MSR2017", "max_forks_repo_head_hexsha": "1811a14c74f1c16ed35de5ea392a1fa0c0d44d77", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-06-26T03:10:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-17T18:11:14.000Z", "avg_line_length": 24.9913793103, "max_line_length": 144, "alphanum_fraction": 0.6574680924, "num_tokens": 705, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.5, "lm_q1q2_score": 0.32567743233302715}} {"text": "library(dplyr)\nlibrary(RColorBrewer)\nsource(\"style.r\")\n\nargs = commandArgs(trailingOnly = TRUE)\ndata = read.csv(args[1])\n\ndata$time = data$time/60\ndata = data %>%\n\tgroup_by(alpha, app_id) %>%\n\tmutate(\n\t\t thp = cumsum(tasks_fulfilled)/time,\n\t\t req = cumsum(tasks_requested)/time) %>%\n\tsummarize(norm = last(thp/req))\ndata = data.frame(data)\ndata$app_id = factor(\n\t\t\t\t\t data$app_id,\n\t\t\t\t\t levels = c(3, 4, 5, 1, 2),\n\t\t\t\t\t labels = c(\"iPerf\", \"Air Quality\", \"Roof\", \"Traffic\", \"Parking\"))\ndata$alpha = factor(data$alpha, levels = c(100, 1, 0, -1), labels = c(\"Mobius\\n(Max-Min)\", \"Mobius\\n(Prop. Fair)\", \"Max\\nThroughput\", \"Dedicated\\nDrones\"))\ndata = subset(data, app_id != -1)\n\ncolors = brewer.pal(n = 5, name = 'Dark2')\ncolors = c(colors[3], colors[4], colors[5], colors[1], colors[2])\n\np = ggplot(data, aes(x = alpha, y = 100 * norm, fill = app_id)) +\n\tgeom_col(position = \"dodge\", color = \"black\") +\n\tscale_fill_manual(values = colors) +\n\n\t# formatting\n\ttheme(\n\t\t legend.position = \"top\", legend.box = \"vertical\", legend.margin = margin(),\n\t\t legend.box.margin=margin(-4,-4,-7,-7),\n\t\t legend.text = element_text(size = 10),\n\t\t axis.title.x = element_blank()\n\t) +\n\tylim(c(0, NA)) +\n\tylab(\"Tasks\\nCompleted (%)\") +\n\tlabs(fill = \"\", shape = \"\")\nggsave(args[2], p, width = 5, height = 1.5, units = 'in')\n\n", "meta": {"hexsha": "704fc439889a6dc9a24d00828aed58d1425ec74d", "size": 1311, "ext": "r", "lang": "R", "max_stars_repo_path": "completion.r", "max_stars_repo_name": "mobius-scheduler/evaluation", "max_stars_repo_head_hexsha": "2f24eb564130d4108d5f5f358a9948c3909f5ceb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "completion.r", "max_issues_repo_name": "mobius-scheduler/evaluation", "max_issues_repo_head_hexsha": "2f24eb564130d4108d5f5f358a9948c3909f5ceb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "completion.r", "max_forks_repo_name": "mobius-scheduler/evaluation", "max_forks_repo_head_hexsha": "2f24eb564130d4108d5f5f358a9948c3909f5ceb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2142857143, "max_line_length": 155, "alphanum_fraction": 0.6254767353, "num_tokens": 426, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3256774323330271}} {"text": "# Processes the family age file and a family species richness file to merge them into one table. \n# The species richness estimates per family and subfamily were obtained from the Angiosperm Phylogeny Website. \n# The code plots mean family ages against species richness.\n# Continues with R objects generated in previous steps.\n# Uses additional files.\nrichness_file <- \"SPP.RICHNESS.csv\" #### File in data folder\n\nresB<-read.table(paste(ruta_write,\"2.Ages_complete.csv\",sep=\"\"),sep=\",\",header=T)\nrich<-read.table(richness_file,heade=T,sep=\",\")\nrich_ord<-rich[which(rich$Family==\"\"),]\nresB$Order_richness<-rich_ord[match(resB$Order,rich_ord$Order),\"Spp_richness\"]\nrich_fams<-rich[which(rich$Subfamily==\"\"),]\nrich_fams<-rich_fams[which(rich_fams$Family!=\"\"),]\nresB$Family_richness<-rich_fams[match(resB$Family,rich_fams$Family),\"Spp_richness\"]\nrich_subfams<-rich[which(rich$Subfamily!=\"\"),]\nresB$Subfamily_richness<-rich_subfams[match(resB$Subfamily,rich_subfams$Subfamily),\"Spp_richness\"]\n### Families with suspect crown nodes\nnon<-which(rownames(resB) %in% c( \"Aphloiaceae\",\"Brunelliaceae\",\"Cynomoriaceae\", \n \"Daphniphyllaceae\", \"Mayacaceae\",\n \"Mitrastemonaceae\",\"Oncothecaceae\"))\nresB[non,\"CG_age_Acal\"]<-NA; resB[non,\"CG_minHPD\"]<-NA; resB[non,\"CG_maxHPD\"]<-NA\nresB$Total_Richness <- rep(NA,dim(resB)[1])\nfor (i in 1:dim(resB)[1]){ \n if(!is.na(resB$Subfamily_richness[i])){resB$Total_Richness[i] <- resB$Subfamily_richness[i];next}\n if(!is.na(resB$Family_richness[i])){resB$Total_Richness[i] <- resB$Family_richness[i];next}\n resB$Total_Richness[i] <- resB$Order_richness[i]}\nout.group <- which(resB$Order%in%c(\"Pinales\",\"Gnetales\",\"Ginkgoales\",\"Cycadales\"))\nresB <- resB[-out.group,]\norders <- 1:64\nfams <- 65:500\nsubfams <- 501:dim(resB)[1]\nresB$Fuse <- resB$SG_age_Acal - resB$CG_age_Acal\nresB_fams <- resB[fams,]\n\nsum(resB[fams,\"Total_Richness\"],na.rm = T)\npar(mfrow = c(1, 1), pty = \"s\")\npdf(paste(ruta_write,\"1.Temporal_distirbution_ages.pdf\",sep=\"\"))\nages <- resB_fams[which(!is.na(resB_fams[,c(\"CG_age_Acal\")])),c(\"CG_age_Acal\",\"SG_age_Acal\")]\nages <- ages[order((ages$SG_age_Acal-ages$CG_age_Acal)),]\nplot(ages$CG_age_Acal,cex=0.3,pch=19,ylim=c(0,150),type=\"n\",xaxt=\"n\",xlab=\"Families ranked by fuse period\",\n ylab=\"Million years\")\nfor (i in 1:length(ages$CG_age_Acal)){ \n segments(x0 = i,x1 = i, y0 = 0, y1= ages$SG_age_Acal[i]-ages$CG_age_Acal[i],\n col=adjustcolor(\"black\",alpha.f = 0.7),lwd=2)\n}\ntitle(\"Fuse period (stem to crown age)\")\nabline(h=mean(ages$SG_age_Acal-ages$CG_age_Acal),col=\"red\",lwd=3,lty=3)\ncat(mean(ages$SG_age_Acal-ages$CG_age_Acal),\"\\n\")\nhist(ages$SG_age_Acal-ages$CG_age_Acal,xlim=c(0,220),xlab=\"Fuse period (stem to crown age)\",ylab=\"No. of families\",main=\"\",breaks=c(seq(0,220,10)))\nages[which(ages$SG_age_Acal-ages$CG_age_Acal > 120),]\nages[which(ages$SG_age_Acal-ages$CG_age_Acal < 10),]\n\nfull_SG <- c()\nfor (i in 1:nrow(resB_fams)){\n cat(i,\"\\r\")\n if(is.na(resB_fams$SG_age_Acal[i])) next\n full_SG <- c(full_SG,seq(round(resB_fams$SG_maxHPD[i],1),round(resB_fams$SG_minHPD[i],0.5)))\n}\ncat(\"DONE ------\")\nfull_CG <- c()\nfor (i in 1:nrow(resB_fams)){\n cat(i,\"\\r\")\n if(is.na(resB_fams$CG_age_Acal[i])) next\n full_CG <- c(full_CG,seq(round(resB_fams$CG_maxHPD[i],1),round(resB_fams$CG_minHPD[i],0.5)))\n}\ncat(\"DONE ------\")\n\nhist(na.omit(resB_fams$SG_age_Acal),breaks=seq(0,250,10),freq=F,ylim=c(0,0.018),border=\"red\",col=NULL,main=\"Family ages (95HPD)\",xlab=\"million years ago\")\nhist(na.omit(resB_fams$CG_age_Acal),breaks=seq(0,250,10),freq=F,add=T,border=\"blue\",col=NULL)\nlines(density(full_SG,bw=5),col=\"red\")\nlines(density(full_CG,bw=5),col=\"blue\")\ndev.off()\n\nperiods <- data.frame(\"Stem (number)\"=rep(NA,4),\"Stem (percent)\"=rep(NA,4),\"Crown (number)\"=rep(NA,4),\"Crown (percent)\"=rep(NA,4),\n row.names = c(\"Cenozoic\",\"Cretaceous\",\"Jurassic\",\"Prior to first fossil\"))\nperiods[1,3] <- length(which(resB_fams$CG_age_Acal < 66))\nperiods[1,4] <- round((length(which(resB_fams$CG_age_Acal < 66)) / length(which(!is.na(resB_fams$CG_age_Acal)))) * 100,1)\nperiods[2,3] <- length(which(resB_fams$CG_age_Acal >= 66 & resB_fams$CG_age_Acal < 145))\nperiods[2,4] <- round((length(which(resB_fams$CG_age_Acal >= 66 & resB_fams$CG_age_Acal < 145)) / length(which(!is.na(resB_fams$CG_age_Acal)))) * 100,1)\nperiods[3,3] <- length(which(resB_fams$CG_age_Acal >= 145))\nperiods[3,4] <- round((length(which(resB_fams$CG_age_Acal >= 145)) / length(which(!is.na(resB_fams$CG_age_Acal)))) * 100,1)\nperiods[4,3] <- length(which(resB_fams$CG_age_Acal >= 136))\nperiods[4,4] <- round((length(which(resB_fams$CG_age_Acal >= 136)) / length(which(!is.na(resB_fams$CG_age_Acal)))) * 100,1)\nperiods[1,1] <- length(which(resB_fams$SG_age_Acal < 66))\nperiods[1,2] <- round((length(which(resB_fams$SG_age_Acal < 66)) / length(which(!is.na(resB_fams$SG_age_Acal)))) * 100,1)\nperiods[2,1] <- length(which(resB_fams$SG_age_Acal >= 66 & resB_fams$SG_age_Acal < 145))\nperiods[2,2] <- round((length(which(resB_fams$SG_age_Acal >= 66 & resB_fams$SG_age_Acal < 145)) / length(which(!is.na(resB_fams$SG_age_Acal)))) * 100,1)\nperiods[3,1] <- length(which(resB_fams$SG_age_Acal >= 145))\nperiods[3,2] <- round((length(which(resB_fams$SG_age_Acal >= 145)) / length(which(!is.na(resB_fams$SG_age_Acal)))) * 100,1)\nperiods[4,1] <- length(which(resB_fams$SG_age_Acal >= 136))\nperiods[4,2] <- round((length(which(resB_fams$SG_age_Acal >= 136)) / length(which(!is.na(resB_fams$SG_age_Acal)))) * 100,1)\nsink(paste(ruta_write,\"3.Numbers_by_period.txt\",sep=\"\"))\nperiods\nsink()\n\npdf(paste(ruta_write,\"6.ARCs.pdf\",sep=\"\"),useDingbats = F)\npar(mfrow = c(1, 1), pty = \"s\")\nData<-data.frame(Order=1:dim(resB_fams)[1],z=log(resB_fams$Total_Richness))\nData<-Data[order(Data$z),]\nk=20\nbreaks <- getJenksBreaks(Data$z,k)\ncols_breaks <- (colorRampPalette(c(\"yellow\",\"gold\",\"tomato2\",\"red4\"))(k))\nData$col <- cols_breaks[k]\nfor (i in k:1){Data$col[which(Data$z <= breaks[i])] <- cols_breaks[i]}\norderedcolors<-Data[order(Data$Order),\"col\"]\nplot(resB_fams$Fuse,log(resB_fams$Total_Richness),pch=21,cex=log(resB_fams$Total_Richness)*0.5,xlab=\"Fuse period (My)\"\n ,ylab=\"Species Richness (log)\",bg=orderedcolors)\nabline(lm(log(resB_fams$Total_Richness)~resB_fams$Fuse),lwd=3,lty=3,col=\"black\")\nsumm_lm <- summary(lm(log(resB_fams$Total_Richness)~resB_fams$Fuse))\nlegend(\"bottomright\",inset=0.03,legend=paste(\"b = \",round(summ_lm$coefficients[2,1],2),\";\",\"F = \",round(summ_lm$fstatistic[1],2),\";\",\n \"p-val = \",round(summ_lm$coefficients[2,4],2),\";\",\"R2* = \",round(summ_lm$adj.r.squared,3),sep=\"\"))\ndev.off()\n", "meta": {"hexsha": "1ee5a00c2555a4d300a4123556d6ba57b7c1beca", "size": 6617, "ext": "r", "lang": "R", "max_stars_repo_path": "code/final_analyses/2.AgeRichness.r", "max_stars_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_stars_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-14T03:29:42.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-14T03:29:42.000Z", "max_issues_repo_path": "code/final_analyses/2.AgeRichness.r", "max_issues_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_issues_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/final_analyses/2.AgeRichness.r", "max_forks_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_forks_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-07-06T15:43:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-06T15:43:48.000Z", "avg_line_length": 58.5575221239, "max_line_length": 154, "alphanum_fraction": 0.7012241197, "num_tokens": 2450, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3256774323330271}} {"text": "library(ggplot2)\nlibrary(scales)\nlibrary(ggpubr)\nlibrary(bayesplot)\nlibrary(matrixStats)\nlibrary(cowplot)\nlibrary(svglite)\nargs <- commandArgs(trailingOnly = TRUE)\nfilename <- args[1]\n\nload(paste0(\"results/\", filename))\n\nalpha = data.frame(as.matrix(out$alpha))\nplot_labels <- c(\"School Closure\",\n \"Self Isolation\",\n \"Public Events\",\n \"First Intervention\",\n \"Lockdown\", 'Social distancing')\ncolnames(alpha) = plot_labels\nfirst.intervention = alpha[,c(1,2,3,5,6)] + alpha[,4]\ndata1 = mcmc_intervals_data(first.intervention,prob=.95,transformation=function(x) 1-exp(-x),point_est=\"mean\")\ndata1$type = \"First Intervention\"\n\ndata2 = mcmc_intervals_data(alpha[,c(1,2,3,5,6)], prob = .95,transformation=function(x) 1-exp(-x),point_est=\"mean\")\ndata2$type = \"Later Intervention\"\n# data = rbind(rbind(data2[6,],data1),data2[1:5,])\ndata = rbind(data1,data2[1:5,])\ncolQuantiles(data.matrix(first.intervention),probs=c(.025,.975))\n\n#data$type[1] = \"First Intervention\"\n\nlevels(data$parameter) = gsub(\"t(\", \"\", levels(data$parameter), fixed=TRUE)\nlevels(data$parameter) = gsub(\")\", \"\", levels(data$parameter), fixed=TRUE)\ndata$parameter = (as.character(data$parameter))\n\nno_point_est <- all(data$point_est == \"none\")\nx_lim <- range(c(data$ll, data$hh))\nx_range <- diff(x_lim)\nx_lim[1] <- x_lim[1] - 0.05 * x_range\nx_lim[2] <- x_lim[2] + 0.05 * x_range\nlayer_vertical_line <- if (0 > x_lim[1] && 0 < x_lim[2]) {\n vline_0(color = \"gray90\", size = 0.5)\n} else {\n geom_blank(\n mapping = NULL, data = NULL,\n show.legend = FALSE, inherit.aes = FALSE)\n}\nargs_outer <- list(mapping = aes_(x = ~ll, xend = ~hh, y = ~parameter, \n yend = ~parameter)) #, color = bayesplot::get_color(\"mid\"))\nargs_inner <- list(mapping = aes_(x = ~l, xend = ~h, y = ~parameter, \n yend = ~parameter), size = 2, show.legend = FALSE)\nargs_point <- list(mapping = aes_(x = ~m, y = ~parameter), \n data = data, size = 4, shape = 21)\n\nargs_point$color <- \"blue\" #get_color(\"dark_highlight\")\n\npoint_func <- geom_point\nlayer_outer <- do.call(geom_segment, args_outer)\nlayer_inner <- do.call(geom_segment, args_inner)\nlayer_point <- do.call(point_func, args_point)\n\ndata$parameter = factor(as.character(data$parameter),levels=plot_labels[order(plot_labels)[6:1]])\n# data = data[order(-data$m),]\np = ggplot(data) +theme_pubr() + geom_point(aes(x=m,y=parameter,colour=type),position = position_dodge(-.5)) + \n geom_linerange(aes(xmin=ll,xmax=hh,y=parameter,colour=type),\n position = position_dodge(-.5)) + \n scale_x_continuous(breaks=seq(0,1,.25),labels = c(\"0%\\n(no effect on transmissibility)\",\n \"25%\",\"50%\",\"75%\",\"100%\\n(ends transmissibility)\"),\n expand=c(0.005,0.005),expression(paste(\"Relative % reduction in \",R[t]))) +\n scale_colour_manual(name = \"\", #labels = c(\"50%\", \"95%\"),\n values = c((\"coral4\"), (\"seagreen\"))) + \n \n geom_vline(xintercept=1,colour=\"darkgray\") +\n scale_y_discrete(\"Governmental intervention\\n\") +\n #geom_vline(xintercept=0,colour=\"darkgray\") + \n theme(plot.margin = margin(0, 2, 0, .5, \"cm\"))\n#+ guides(fill=guide_legend(nrow=2))\np \nggsave(filename = \"results/covars-alpha-reduction.png\",\n p,height=4,width=8)\ndir.create(\"web/figures/desktop/\", showWarnings = FALSE, recursive = TRUE)\ndir.create(\"web/figures/mobile/\", showWarnings = FALSE, recursive = TRUE)\nsave_plot(filename = paste0(\"web/figures/desktop/\", \"covars-alpha-reduction.svg\"), \n p, base_height = 4, base_asp = 1.618 * 2 * 8/12)\nsave_plot(filename = paste0(\"web/figures/mobile/\", \"covars-alpha-reduction.svg\"), \n p, base_height = 4, base_asp = 1.1)", "meta": {"hexsha": "567796f2ad0dc4635f324c091d2e985cfe69a17c", "size": 3792, "ext": "r", "lang": "R", "max_stars_repo_path": "covariate-size-effects.r", "max_stars_repo_name": "Azure/covid19model", "max_stars_repo_head_hexsha": "73f1474a076c7aabde7d66c4bd480b89afb4f702", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-01T14:02:31.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-01T14:02:31.000Z", "max_issues_repo_path": "covariate-size-effects.r", "max_issues_repo_name": "covid19sc/ImperialCollegeCovid19SC", "max_issues_repo_head_hexsha": "d68351445947e49c79b5efe228a09b84c5cd1499", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-10T06:13:00.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-10T06:13:00.000Z", "max_forks_repo_path": "covariate-size-effects.r", "max_forks_repo_name": "covid19sc/ImperialCollegeCovid19SC", "max_forks_repo_head_hexsha": "d68351445947e49c79b5efe228a09b84c5cd1499", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-04-08T01:18:17.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-03T17:20:42.000Z", "avg_line_length": 44.0930232558, "max_line_length": 116, "alphanum_fraction": 0.641350211, "num_tokens": 1107, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548511303336, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3256774255651668}} {"text": "#' fits_index\n#'\n#' Calculate Fighter Index of Thermal Stress FITS.\n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Relative humidity in percentage.\n#' @param wind numeric Windspeed in meters per second.\n#' @param pair numeric Air pressure in hPa.\n#' @return fits index\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @references Nunneley SH, Stribley F, 1979, Fighter index of thermal stress FITS guidance for hot-weather aircraft operations.Aviat Space Environ Med 50, 639 42.\n#' @export\n#'\n\nfits_index=function(t,rh,wind=0.2,pair=1010) {\n \n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"pair\", as.array(pair))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=fits_index(t[i],rh[i],wind[0],pair[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n\n", "meta": {"hexsha": "85e36d5329e62f59ef6c03d431fdc777bdb468e1", "size": 1127, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fits_index.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/fits_index.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/fits_index.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 38.8620689655, "max_line_length": 164, "alphanum_fraction": 0.5767524401, "num_tokens": 293, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3256640851105507}} {"text": "'\nThe file \"analysis.r\" firstly checks whether the manipulations manipulate altruistic motivation vs. obeying social norm motivation in our experiment.\nSecondly, this file computes the interactive effect of consumers gift-giving motivations and their perception of whether indulgence is moral or immoral on consumers self-indulgence.\nFinally, we test the mediating role of happiness in the relatonship between gift-giving motivations and self-indulgence.\n\n'\n\nrm(list=ls())\noptions(digits=3)\n\n\nsource(\"project_paths.r\")\nlibrary(foreign, lib=PATH_OUT_LIBRARY_R)\nlibrary(mediation, lib=PATH_OUT_LIBRARY_R)\nlibrary(sandwich, lib=PATH_OUT_LIBRARY_R)\n\ndelete_outlier <- read.table(\n file=paste(PATH_OUT_DATA, \"delete_outlier.txt\", sep = \"/\"),\n header = TRUE\n)\n\nsink(paste(PATH_OUT_ANALYSIS,\"analysis_results.txt\",sep=\"/\"))\n####################MANIPULATION CHECK############################\n#####CHECK THE MANIPULATION OF ALTRUISTIC MOTIVATION#####\naltruism_check<-aov(altruism_ave~Condition,data=delete_outlier)\nsummary(altruism_check)\nprint(model.tables(altruism_check,\"means\"),digits=3)\n\n#####CHECK THE MANIPULATION OF SOCIAL NORM MOTIVATION#####\nsn_check<-aov(sn_ave~Condition,data=delete_outlier)\nsummary(sn_check)\nprint(model.tables(sn_check,\"means\"),digits=3)\n\n#################REGRESSION##################\ncenter_appropriateness<-delete_outlier$appropriateness-mean(delete_outlier$appropriateness)\ndelete_outlier<-data.frame(delete_outlier,center_appropriateness)\n#####CHECK THE INTERACTION EFFECT OF GIFT-GIVING MOTIVATION AND PERCEPTION OF MORALITY ON SELF-INDULGENCE#####\ninter_effect<-lm(brand_ave~Altruism*center_appropriateness,data=delete_outlier)\nsummary(inter_effect)\ninter_effect$coefficients\n#####CHECK THE INFLUENCE OF PERCEPTION OF MORALITY ON SELF-INDULGENCE FOR PARTICIPANTS WITH AN ALTRUISTIC MOTIVATION#####\ndelete_outlier_al<-delete_outlier[delete_outlier$Condition==1,]\nrate_al<-lm(brand_ave~center_appropriateness,data=delete_outlier_al)\nsummary(rate_al)\nrate_al$coefficients\n#####CHECK THE INFLUENCE OF PERCEPTION OF MORALILTY ON SELF-INDULGENCE FOR PARTICIPANTS WITH A SOCIAL NORM MOTIVATION#####\ndelete_outlier_sn<-delete_outlier[delete_outlier$Condition==3,]\nrate_sn<-lm(brand_ave~center_appropriateness,data=delete_outlier_sn)\nsummary(rate_sn)\n\n\n#########MEDIATION, CHECK WHETHER HAPPINESS IS THE POTETIAL MEDIATOR#############\nmodel.m<-lm(happiness~Altruism,data=delete_outlier)\nmodel.y<-lm(brand_ave~Altruism+happiness,data=delete_outlier)\nm<-mediate(model.m,model.y,sims=10000,treat=\"Altruism\",mediator=\"happiness\")\nsummary(m)\nsink()\n\n\n\n", "meta": {"hexsha": "ae9b317f7c53c9a6983fd6f807a79cbad6c16157", "size": 2574, "ext": "r", "lang": "R", "max_stars_repo_path": "src/analysis/analysis.r", "max_stars_repo_name": "TTSHR/econ-project-R-Ning", "max_stars_repo_head_hexsha": "ced36462ff33e5b1af43052fe21ad7e9dffc02a0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/analysis/analysis.r", "max_issues_repo_name": "TTSHR/econ-project-R-Ning", "max_issues_repo_head_hexsha": "ced36462ff33e5b1af43052fe21ad7e9dffc02a0", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/analysis/analysis.r", "max_forks_repo_name": "TTSHR/econ-project-R-Ning", "max_forks_repo_head_hexsha": "ced36462ff33e5b1af43052fe21ad7e9dffc02a0", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.1967213115, "max_line_length": 181, "alphanum_fraction": 0.7808857809, "num_tokens": 652, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3256640851105507}} {"text": "#' Perform SVA confounder analysis\n#'\n#' Perform SVA confounder analysis and association tests between the Surrogate\n#' Variables of the given input values and the phenotype data.\n#'\n#' This function performs a series of association tests between the Surrogate\n#' Variables of the input values and the provided phenotype data.\n#'\n#' If a RGChannelSet is given, it can also compute the association with the\n#' control probes. This implementation uses Linear models.\n#'\n#' @param values A matrix containing the data to analyze. Rows represent\n#' variables and columns samples.\n#' @param pdata A data.frame containing the phenotype information for the\n#' samples.\n#' @param main_formula A formula describing the main model of interest.\n#' @param null_formula A formula describing the null model of interest.\n#' @param vfilter The number of most variable rows to use in order to\n#' estimate the number of surrogate variables.\n#' @param rgset If not NULL, compute association with control probes data\n#' contained in the provided RGChannelSet.\n#' @return A list containing the number of Surrogate Variables (num_sv), the\n#' results of the confounding analysis tests (significance), and the Surrogate\n#' Variables obtained by SVA (surrogates).\n#' @importFrom sva num.sv sva\n#' @export\nsva_analysis = function(values, pdata, main_formula, null_formula = NULL,\n vfilter = NULL, rgset = NULL) {\n\n if (!is.null(vfilter)) {\n vfilter = min(vfilter, nrow(values))\n }\n\n mod = model.matrix(main_formula, data = pdata)\n mod0 = NULL\n\n if (!is.null(null_formula)) {\n mod0 = model.matrix(null_formula, data = pdata)\n }\n\n num_sv = num.sv(values, mod = mod, vfilter = vfilter)\n\n if (num_sv == 0) {\n stop(paste(\n 'Number of estimated surrogate variables is zero.',\n 'Check model and/or dataset.'\n ))\n }\n\n svs = sva(values, mod = mod, mod0 = mod0, n.sv = num_sv)\n\n sv_names = paste0('SV-', 1:(svs[['n.sv']]))\n sv_names = factor(sv_names, levels = sv_names)\n\n if (num_sv == 1) {\n message(\n paste('If there is only a surrogate variable, the output from sva()',\n 'is not ok, as it does not use the drop=FALSE parameter.',\n 'Converting the surrogate variable back to a matrix.')\n )\n svs[['sv']] = matrix(svs[['sv']], ncol = 1)\n }\n\n significance_data = compute_significance_data(svs[['sv']], pdata, sv_names)\n\n sig_data_rgset = NULL\n\n if (!is.null(rgset)) {\n data_control_values = get_control_variables(rgset)\n var_names = colnames(data_control_values)\n names(var_names) = var_names\n sig_data_rgset = compute_significance_data_var_names(\n svs[['sv']],\n data_control_values,\n sv_names,\n var_names\n )\n significance_data = rbind(significance_data, sig_data_rgset)\n\n significance_data[['Variable']] = factor(\n significance_data[['Variable']],\n levels = unique(significance_data[['Variable']])\n )\n }\n\n colnames(svs[['sv']]) = gsub('-', '_', sv_names)\n\n result = list(\n num_sv = num_sv,\n mod0 = mod0,\n mod = mod,\n significance = significance_data,\n significance_control = sig_data_rgset,\n surrogates = svs[['sv']]\n )\n\n class(result) = append(class(result), 'SVAAnalysis')\n return(result)\n}\n", "meta": {"hexsha": "59dee26733e674b213cf65406a6a6cbfbe3cab28", "size": 3244, "ext": "r", "lang": "R", "max_stars_repo_path": "R/sva_analysis.r", "max_stars_repo_name": "Keyeoh/svconfound", "max_stars_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/sva_analysis.r", "max_issues_repo_name": "Keyeoh/svconfound", "max_issues_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-09-26T14:16:24.000Z", "max_issues_repo_issues_event_max_datetime": "2017-09-26T14:16:24.000Z", "max_forks_repo_path": "R/sva_analysis.r", "max_forks_repo_name": "Keyeoh/svconfound", "max_forks_repo_head_hexsha": "b98321493685345e9406dce3fe510209566dd0af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.44, "max_line_length": 80, "alphanum_fraction": 0.6840320592, "num_tokens": 804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.32566408511055067}} {"text": "library(a5udes)\nlibrary(dplyr)\nlibrary(sf)\nlibrary(tidygraph)\nlibrary(ggraph)\nlibrary(igraph)\nlibrary(progress)\nlibrary(furrr)\n\nCE=osmdata::getbb(place_name='Cear\u00e1', format_out = \"sf_polygon\") %>% slice(1)\n\nres_geom=allocate_reservoir_to_river(river_geometry)\nreservoir_geometry=build_reservoir_topology(res_geom)\nsave(reservoir_geometry,file='data/reservoir_geometry.RData')reservoir_tidygraph\n\n\nreservoir_graph = reservoir_geometry %>%\n st_set_geometry(NULL) %>%\n dplyr::select(id_jrc,res_down) %>%\n filter(res_down>0) %>%\n rename(to=id_jrc,from=res_down) %>%\n as_tbl_graph\nsave(reservoir_graph,file='data/reservoir_graph.RData')\n\n# reservoir_tidygraph %>% morph(to_local_neighborhood,2,order=1) %>%\n# crystallise %>% .$graph %>% .[[1]] %>%\n# ggraph(.)+geom_node_label(aes(label=name))+geom_edge_bend0()\n#\n# reservoir_tidygraph %>% activate(nodes) %>% as_tibble %>% nrow\n\n#\n# res_geom_df=reservoir_geometry %>%\n# st_set_geometry(NULL) %>%\n# dplyr::select(id_jrc,area_max,SUB_AREA,`distance to river`,`nearest river`,`UP_CELLS`) %>%\n# mutate(name=as.character(id_jrc))\n#\n# res_tidy=reservoir_tidygraph %>%\n# left_join(res_geom_df)\n#\n# ## not a tree! forest!! need to loop over trees first\n#\n#\n#\n# g=res_tidy %>%\n# mutate(rank=map_dfs_back_int(node_is_root(),unreachable=TRUE,.f=function(node,rank,...) {\n# return(rank)\n# }))\n#\n# g\n#\n#\n# neigh = g %>% activate(nodes) %>%\n# arrange(desc(rank)) %>%\n# mutate(neighborhood = local_members(order = 1,mode='out',mindist=1)) %>%\n# as_tibble\n#\n# neigh %>% head\n#\n# contrib=vector(mode='list',length=nrow(neigh))\n#\n# # for(i in seq(1,nrow(neigh))) {\n#\n# if(neigh$`distance to river`[i]>0){\n# contrib[[i]]=neigh$area_max[i]*0.3664\n# } else {\n# riv_l=filter(river_geometry,ARCID==neigh$`nearest river`[i])\n#\n#\n# }\n#\n#\n# # }\n# riv_l=res_geom[1,] %>% pull()\n# res_geom[1,]\n", "meta": {"hexsha": "1233ddff3fae1cf81b901a6145d1af66ebeb42dd", "size": 1860, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/import_reservoirs.r", "max_stars_repo_name": "jmigueldelgado/a5udes", "max_stars_repo_head_hexsha": "970d597c9b5456088e9dc745496b5416a7090a89", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/import_reservoirs.r", "max_issues_repo_name": "jmigueldelgado/a5udes", "max_issues_repo_head_hexsha": "970d597c9b5456088e9dc745496b5416a7090a89", "max_issues_repo_licenses": ["NASA-1.3"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-06-10T11:46:59.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T09:51:02.000Z", "max_forks_repo_path": "scripts/import_reservoirs.r", "max_forks_repo_name": "jmigueldelgado/a5udes", "max_forks_repo_head_hexsha": "970d597c9b5456088e9dc745496b5416a7090a89", "max_forks_repo_licenses": ["NASA-1.3"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.8, "max_line_length": 94, "alphanum_fraction": 0.685483871, "num_tokens": 583, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6723316860482762, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.325664078750151}} {"text": "library(ggplot2)\n\nha <- read.table(\"all_lines_family_stats_6-30_ha_over2pc.tsv\",comment.char=\"\",header=F,sep=\"\\t\")\nnames(ha) <- c(\"ID\",\"Line\",\"Oil\",\"Superfamily\",\"Family\",\"PA\")\n\n## superfamily summary\nggplot(ha1, aes(x=reorder(ID, PA, sum), y=PA, fill=Superfamily)) + \n\t geom_bar(stat=\"summary\", fun.y=sum,color=\"black\") + \n\t theme_bw() + \n\t theme(axis.text.x=element_blank(), \n\t axis.ticks.x=element_blank(), \n\t axis.title.x=element_text(size=16),\n\t axis.title.y=element_text(size=16),\n\t axis.text.y=element_text(size=14)) + \n\t ylab(\"Genome abundance [%]\") + \n\t xlab(\"Line\")\n\n## family summary\nggplot(ha1, aes(x=reorder(ID, PA, sum), y=PA, fill=Family)) + \n\t geom_bar(stat=\"identity\",color=\"black\") + \n\t theme_bw() + \n\t theme(axis.text.x=element_blank(), \n\t axis.ticks.x=element_blank(), \n\t axis.title.x=element_text(size=16),\n\t axis.title.y=element_text(size=16),\n\t axis.text.y=element_text(size=14)) + \n\t ylab(\"Genome abundance [%]\") + \n\t xlab(\"Line\")\n\n", "meta": {"hexsha": "d706736893a8eb704addc6fc7216390f60d4b03f", "size": 1010, "ext": "r", "lang": "R", "max_stars_repo_path": "transposon_annotation/r_scripts/barplot-lines.r", "max_stars_repo_name": "sestaton/sesbio", "max_stars_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2015-01-14T17:25:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-09T01:15:18.000Z", "max_issues_repo_path": "transposon_annotation/r_scripts/barplot-lines.r", "max_issues_repo_name": "sestaton/sesbio", "max_issues_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "transposon_annotation/r_scripts/barplot-lines.r", "max_forks_repo_name": "sestaton/sesbio", "max_forks_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-10-09T02:56:51.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-02T12:07:39.000Z", "avg_line_length": 33.6666666667, "max_line_length": 96, "alphanum_fraction": 0.6346534653, "num_tokens": 303, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7122321964553657, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32558748790644115}} {"text": "library(tidyverse)\n\nlut_zms <- read_csv(\"../datos/util/zonas_metropolitanas_2015.csv\")\nzm_pobs <- lut_zms %>%\n group_by(CVE_ZM) %>%\n summarise(pob = sum(POB_2015),\n NOM_ZM = unique(NOM_ZM)) %>%\n ungroup()\nzm_pobs\n\n\n\nselected_zms <- lut_zms %>%\n filter(CVE_ZM != \"09.01\") %>%\n group_by(CVE_ZM, CVE_ENT) %>%\n summarise(pob = sum(POB_2015),\n NOM_ENT = unique(NOM_ENT),\n CVE_ENT = unique(CVE_ENT),\n NOM_ZM = unique(NOM_ZM),\n CVE_ZM = unique(CVE_ZM)) %>%\n ungroup() %>%\n split(.$CVE_ZM) %>%\n map_dfr(function(d){\n d %>%\n mutate(pob_prop = pob / sum(pob)) %>%\n arrange(desc(pob)) %>%\n head(1)\n }) %>%\n select(-pob) %>%\n left_join(zm_pobs, by = c(\"CVE_ZM\", \"NOM_ZM\")) %>%\n # print(n = 100) %>%\n split(.$CVE_ENT) %>%\n map_dfr(function(d){\n d %>%\n arrange(desc(pob)) %>%\n head(1)\n }) %>%\n # print(n = 100) %>%\n bind_rows(tibble(CVE_ZM = \"09.01\",\n CVE_ENT = \"09\",\n NOM_ENT = \"Ciudad de M\u00e9xico\",\n NOM_ZM = \"Valle de M\u00e9xico\")) %>%\n select(-pob_prop, -pob) %>%\n print(n = 100)\n\n\nlut_zms %>%\n select(CVE_ZM, NOM_ZM, CVE_MUN, NOM_MUN) %>%\n inner_join(selected_zms, by = c(\"CVE_ZM\", \"NOM_ZM\")) %>%\n write_csv(\"selected_zms.csv\")\n \n \n", "meta": {"hexsha": "7d16858e425dfb47fc67ead583f5af5568d1769a", "size": 1281, "ext": "r", "lang": "R", "max_stars_repo_path": "selecting_zms.r", "max_stars_repo_name": "coronamex/covid-model", "max_stars_repo_head_hexsha": "0473be0a1653dafb4b311ecc046be17a42347969", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "selecting_zms.r", "max_issues_repo_name": "coronamex/covid-model", "max_issues_repo_head_hexsha": "0473be0a1653dafb4b311ecc046be17a42347969", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "selecting_zms.r", "max_forks_repo_name": "coronamex/covid-model", "max_forks_repo_head_hexsha": "0473be0a1653dafb4b311ecc046be17a42347969", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.1698113208, "max_line_length": 66, "alphanum_fraction": 0.5378610461, "num_tokens": 442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832804, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3255091325018933}} {"text": "##### PLOT HEATMAP OF TWEETS GATHERED #######\n\nlibrary(ggplot2)\nlibrary(maps)\nlibrary(fiftystater)\nlibrary(maps)\n\n## Read the dataset collected and stored from reverse.r\ndata <- read.csv(\"../data_collected/statesFrequency\")\ndata<- subset(data, select = -c(X)) #removing column named X\nsetwd(\"../Scripts\")\nalpha = sort(table(data), decreasing = TRUE)\ndata(\"fifty_states\")\n\nchecker <- data.frame(state.name)\ncolnames(checker) <- c(\"state\")\nrownames <- rownames(alpha)\nrownames(alpha) <- NULL\nalpha <- data.frame(cbind(rownames,alpha))\ncolnames(alpha) <- c(\"state\", \"count\")\n\npointer = 1\n## Code to filter and keep only the 50 - us states(48 continental + Hawaii + Alaska)\n## Discards all non-us locations\nfor (i in 1:nrow(alpha))\n{\n for (j in 1:nrow(checker))\n {\n if (as.character(alpha$state[i])==as.character(checker$state[j]))\n {\n if (pointer == 1)\n {\n temp = alpha[i,]\n pointer = 2\n next\n \n }\n if (pointer == 2)\n {\n temp = rbind(temp,alpha[i,])\n }\n }\n }\n}\n\n\n## Plotting HEATMAP\ntemp$count<-as.numeric(levels(temp$count))[temp$count]\ntemp$state <- unlist(as.list(tolower(temp$state)))\n\np <- ggplot(temp, aes(map_id = state)) + \n geom_map(aes(fill = count), map = fifty_states, color = \"black\") + \n expand_limits(x = fifty_states$long, y = fifty_states$lat) +\n coord_map() +\n scale_fill_gradient2(low=\"green\", mid=\"yellow\", high=\"red3\", midpoint = mean(temp$count), guides(fill = \"Number of Tweets\"), limits=c(0,max(temp$count)+100), breaks=c(0,50,100,150,200,250,300,350,400,450,500,550,600,650,700,750,800,850,900,950,1000)) +\n guides(fill = guide_colorbar(barwidth = 1.5, barheight = 25)) +\n scale_x_continuous(breaks = NULL) + \n scale_y_continuous(breaks = NULL) +\n labs(x = \"\", y = \"\") +\n ggtitle(\"2017-18 Influenza Season Week 4 ending Jan 27, 2018\") +\n theme(legend.position = \"right\",\n plot.title = element_text(family = \"Helvetica Neue\", color=\"#666666\", face=\"bold\", size=12, hjust=0))\n\nprint(p + ggtitle(\"HeatMap of Tweets on Influenza in USA\"))\n\n# add border boxes to AK/HI\np + fifty_states_inset_boxes()\n\n", "meta": {"hexsha": "f7dd86112f5a31d5e9b08907c8a581f88b21f8a3", "size": 2112, "ext": "r", "lang": "R", "max_stars_repo_path": "Lab1Part3/Scripts/plotHeatMapOfTweets.r", "max_stars_repo_name": "Muthu2093/Twitter-client-for-Data-Collection-and-Exploratory-Data-Analysis-", "max_stars_repo_head_hexsha": "67f7cfdae786f08beedae846f24b1e2007462f2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Lab1Part3/Scripts/plotHeatMapOfTweets.r", "max_issues_repo_name": "Muthu2093/Twitter-client-for-Data-Collection-and-Exploratory-Data-Analysis-", "max_issues_repo_head_hexsha": "67f7cfdae786f08beedae846f24b1e2007462f2f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Lab1Part3/Scripts/plotHeatMapOfTweets.r", "max_forks_repo_name": "Muthu2093/Twitter-client-for-Data-Collection-and-Exploratory-Data-Analysis-", "max_forks_repo_head_hexsha": "67f7cfdae786f08beedae846f24b1e2007462f2f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6086956522, "max_line_length": 254, "alphanum_fraction": 0.6548295455, "num_tokens": 621, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.325509124817436}} {"text": "\n# These features are motivated by faron's idea.\n# Road-2-0.4+\n# https://www.kaggle.com/mmueller/bosch-production-line-performance/road-2-0-4/code\n\n#------------------------------\n# library\n#------------------------------\n\nlibrary(data.table)\nlibrary(magrittr)\nlibrary(dtplyr)\nlibrary(dplyr)\n\n#------------------------------\n# main\n#------------------------------\n\ndata_dir <- file.path(\"..\", \"..\", \"dataset\")\ninput_dir <- file.path(\"..\", \"..\", \"input\")\n\ntic <- proc.time()\n\n# load data --------------------\n\ntrain_numeric_pach <- file.path(data_dir, \"train_numeric.csv\") \ntrain_id <- fread(train_numeric_pach, data.table=FALSE, select=\"Id\") %>% use_series(Id)\n\ntest_numeric_path <- file.path(data_dir, \"test_numeric.csv\")\ntest_id <- fread(test_numeric_path, data.table=FALSE, select=\"Id\") %>% use_series(Id)\n\ntrain_date_path <- file.path(input_dir, \"train_date_features.csv\")\ntrain_date <- fread(train_date_path, data.table=FALSE, select=c(\"Id\", \"date_min\"))\n\ntest_date_path <- file.path(input_dir, \"test_date_features.csv\")\ntest_date <- fread(test_date_path, data.table=FALSE, select=c(\"Id\", \"date_min\"))\n\n# joint data --------------------\n\njoint_date <- rbind(train_date, test_date)\n\njoint_id <- data.frame(\"Id\" = c(train_id, test_id))\njoint_id <- left_join(joint_id, joint_date, by=\"Id\")\n\n# generate faron features --------------------\n\njoint_id$faron_feature_01 <- c(1, diff(joint_id$Id))\njoint_id$faron_feature_02 <- c(rev(diff(rev(joint_id$Id))), -1)\n\njoint_id <- joint_id %>% arrange(date_min, Id)\n\njoint_id$faron_feature_03 <- c(1, diff(joint_id$Id))\njoint_id$faron_feature_04 <- c(rev(diff(rev(joint_id$Id))), -1)\n\njoint_id$date_min <- NULL\n\n# save result --------------------\n\ntrain <- data.frame(\"Id\"=train_id) %>% left_join(joint_id, by=\"Id\")\nwrite.table(train, file.path(input_dir, \"train_id_features.csv\"), row.names=FALSE, quote=FALSE, sep=\",\")\n\ntest <- data.frame(\"Id\"=test_id) %>% left_join(joint_id, by=\"Id\")\nwrite.table(test, file.path(input_dir, \"test_id_features.csv\"), row.names=FALSE, quote=FALSE, sep=\",\")\n\ntrain$faron_feature_02[nrow(train)] <- -1\ntest$faron_feature_01[1] <- 1\n\n# display computational time --------------------\n\ncat(\"==> Display computational time\\n\")\ncalc <- proc.time() - tic\nprint(calc)\n", "meta": {"hexsha": "6824030aeebafce1b7b637520d6e48da744d1c33", "size": 2234, "ext": "r", "lang": "R", "max_stars_repo_path": "source/01_preprocess/preprocess_faron.r", "max_stars_repo_name": "toshi-k/kaggle-bosch-production-line-performance", "max_stars_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:30:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T16:47:31.000Z", "max_issues_repo_path": "source/01_preprocess/preprocess_faron.r", "max_issues_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_issues_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-05-19T00:19:14.000Z", "max_issues_repo_issues_event_max_datetime": "2017-05-19T16:20:59.000Z", "max_forks_repo_path": "source/01_preprocess/preprocess_faron.r", "max_forks_repo_name": "dyln/kaggle-bosch-production-line-performance", "max_forks_repo_head_hexsha": "b663b7397da0162bc09f85fb2eff580fa1140446", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2016-12-10T23:02:50.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-15T12:48:28.000Z", "avg_line_length": 30.602739726, "max_line_length": 104, "alphanum_fraction": 0.6432408236, "num_tokens": 590, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.325509124817436}} {"text": "'\n\u0421\u043e\u0437\u0434\u0430\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u0435\u043a\u0442\u043e\u0440\u043e\u0432 \u0440\u0430\u0437\u043d\u044b\u0445 \u0442\u0438\u043f\u043e\u0432 \u0438 \u0434\u043b\u0438\u043d\u044b \u0438 \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c\n\u0441 \u043d\u0438\u043c\u0438 \u0442\u0440\u0438 \u0444\u043e\u0440\u043c\u0443\u043b\u044b \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438\u0306.\n\u041e\u0431\u044a\u044f\u0441\u043d\u0438\u0442\u044c \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u043d\u044b\u0438\u0306 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442, \u0434\u043e\u0431\u0430\u0432\u0438\u0432 \u0432 \u0441\u043a\u0440\u0438\u043f\u0442 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0438\n'\n\nvector1 <- c(\"string1\", \"string2\", \"string3\")\nvector2 <- c(1,26,4,4,76,43)\nvector3 <- c(TRUE, FALSE, TRUE)\nvector4 <- c(3i, 53.4i, 4i)\n\nresult1 <- vector2 & vector3\nresult1 <- vector2 && vector3\nresult3 <- vector2 | vector4\nresult4 <- vector3 || vector4\n\n\n#\u0411\u0443\u0434\u0435\u0442 \u043e\u0448\u0438\u0431\u043a\u0430\nresult2 <- vector1 & vector2", "meta": {"hexsha": "d7cdf231f8902e2641a29600fa35a0350665c4c9", "size": 482, "ext": "r", "lang": "R", "max_stars_repo_path": "Course II/R/pract/pract4/task5.r", "max_stars_repo_name": "GeorgiyDemo/FA", "max_stars_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_stars_repo_licenses": ["WTFPL"], "max_stars_count": 27, "max_stars_repo_stars_event_min_datetime": "2019-08-18T20:54:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T02:39:45.000Z", "max_issues_repo_path": "Course II/R/pract/pract4/task5.r", "max_issues_repo_name": "GeorgiyDemo/FA", "max_issues_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_issues_repo_licenses": ["WTFPL"], "max_issues_count": 217, "max_issues_repo_issues_event_min_datetime": "2019-09-22T14:43:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T13:49:18.000Z", "max_forks_repo_path": "Course II/R/pract/pract4/task5.r", "max_forks_repo_name": "GeorgiyDemo/FA", "max_forks_repo_head_hexsha": "641a29d088904302f5f2164c9b3e1f1c813849ec", "max_forks_repo_licenses": ["WTFPL"], "max_forks_count": 42, "max_forks_repo_forks_event_min_datetime": "2019-09-18T11:36:28.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-19T18:43:00.000Z", "avg_line_length": 25.3684210526, "max_line_length": 61, "alphanum_fraction": 0.7261410788, "num_tokens": 187, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.32550912481743594}} {"text": "not_all_na <- function(x) any(!is.na(x))\n\nuvozi.hdi.csv <- function(tabela, stolpci, skip, max) {\n uvoz <- read_csv(tabela,\n locale = locale(encoding=\"utf-8\"),\n col_names = stolpci,\n skip = skip,\n n_max = max,\n na = c(\"\", \" \", \"-\", \"..\"))\n uvoz <- uvoz %>% select_if(not_all_na) %>%\n select(-\"HDI Rank (2018)\") %>%\n pivot_longer(!Drzava, names_to=\"Leto\", values_to=\"Stevilo\")\n \n uvoz$Leto <- as.numeric(uvoz$Leto)\n uvoz <- uvoz %>% filter(uvoz$Leto >= 1998)\n \n uvoz$Drzava <- str_replace(uvoz$Drzava, \" *\\\\(.*?\\\\) *\", \"\")\n return(uvoz)\n}\n\n## 1. tabela (HDI)\nst.hdi <- c(\"HDI Rank (2018)\", \"Drzava\", 1990:2018)\nHDI_drzave_leta <- uvozi.hdi.csv(\"podatki/HDI_countries_years.csv\", st.hdi, 2, 195)\n\n# Funkcija za uvoz csv (Indeksi za leto 2018)\nuvozi.csv <- function(tabela, stolpci, skip, max) {\n uvoz <- read_csv(tabela,\n locale = locale(encoding=\"utf-8\"),\n col_names = stolpci,\n skip = skip,\n n_max = max,\n na = c(\"\", \" \", \"-\", \"..\"))\n uvoz <- uvoz %>% select_if(not_all_na) %>%\n select(-\"HDI Rank (2018)\") %>%\n pivot_longer(!Drzava, names_to=\"Leto\", values_to=\"Indeks\")\n \n uvoz$Leto <- as.numeric(uvoz$Leto)\n uvoz <- uvoz %>% \n filter(uvoz$Leto == 2018) %>%\n select(-\"Leto\")\n \n uvoz$Drzava <- str_replace(uvoz$Drzava, \" *\\\\(.*?\\\\) *\", \"\")\n return(uvoz)\n}\n\n## 2. tabela (Education Index)\nst.ei <- c(\"HDI Rank (2018)\",\"Drzava\",\"1990\",\"\",\"1991\",\"\",\"1992\",\"\",\"1993\",\"\",\"1994\",\"\",\"1995\",\"\",\"1996\",\"\",\"1997\",\"\",\"1998\",\"\",\"1999\",\"\",\"2000\",\"\",\"2001\",\"\",\"2002\",\"\",\"2003\",\"\",\"2004\",\"\",\"2005\",\"\",\"2006\",\"\",\"2007\",\"\",\"2008\",\"\",\"2009\",\"\",\"2010\",\"\",\"2011\",\"\",\"2012\",\"\",\"2013\",\"\",\"2014\",\"\",\"2015\",\"\",\"2016\",\"\",\"2017\",\"\",\"2018\",\"\")\nizobrazba <- uvozi.csv(\"podatki/education_index.csv\", st.ei, 2, 189) %>% \n rename(\"Indeks_izobrazbe\" = \"Indeks\")\n\n## 3. tabela (Life Expectancy Index)\nst.lei <- c(\"HDI Rank (2018)\",\"Drzava\",\"1990\",\"\",\"1991\",\"\",\"1992\",\"\",\"1993\",\"\",\"1994\",\"\",\"1995\",\"\",\"1996\",\"\",\"1997\",\"\",\"1998\",\"\",\"1999\",\"\",\"2000\",\"\",\"2001\",\"\",\"2002\",\"\",\"2003\",\"\",\"2004\",\"\",\"2005\",\"\",\"2006\",\"\",\"2007\",\"\",\"2008\",\"\",\"2009\",\"\",\"2010\",\"\",\"2011\",\"\",\"2012\",\"\",\"2013\",\"\",\"2014\",\"\",\"2015\",\"\",\"2016\",\"\",\"2017\",\"\",\"2018\",\"\")\nzivljenje <- uvozi.csv(\"podatki/life_expectancy_index.csv\", st.lei, 2, 189) %>%\n rename(\"Indeks_zivljenja\" = \"Indeks\")\n\n## 4. tabela (Income Index)\nst.ii <- c(\"HDI Rank (2018)\",\"Drzava\",\"1990\",\"\",\"1991\",\"\",\"1992\",\"\",\"1993\",\"\",\"1994\",\"\",\"1995\",\"\",\"1996\",\"\",\"1997\",\"\",\"1998\",\"\",\"1999\",\"\",\"2000\",\"\",\"2001\",\"\",\"2002\",\"\",\"2003\",\"\",\"2004\",\"\",\"2005\",\"\",\"2006\",\"\",\"2007\",\"\",\"2008\",\"\",\"2009\",\"\",\"2010\",\"\",\"2011\",\"\",\"2012\",\"\",\"2013\",\"\",\"2014\",\"\",\"2015\",\"\",\"2016\",\"\",\"2017\",\"\",\"2018\",\"\")\nprihodek <- uvozi.csv(\"podatki/income_index.csv\", st.ii, 2, 191) %>%\n rename(\"Indeks_prihodka\" = \"Indeks\")\n\n## 5. tabela (Coefficient of human inequality)\nst.ineq <- c(\"HDI Rank (2018)\",\"Drzava\",\"2010\",\"\",\"2011\",\"\",\"2012\",\"\",\"2013\",\"\",\"2014\",\"\",\"2015\",\"\",\"2016\",\"\",\"2017\",\"\",\"2018\",\"\")\nneenakost <- uvozi.csv(\"podatki/coefficient_of_human_inequality.csv\", st.ineq, 2, 189) %>%\n rename(\"Koeficient\" = \"Indeks\")\n\n### nov stolpec - Indeks neenakosti\nneenakost$\"Indeks_neenakosti\" <- round(1 - ((neenakost$Koeficient - min(neenakost$Koeficient, na.rm = TRUE))/(max(neenakost$Koeficient, na.rm = TRUE) - min(neenakost$Koeficient, na.rm = TRUE))), digits = 3)\n\nneenakost[neenakost$Drzava == \"Comoros\",]$Indeks_neenakosti <- 0.01\n\n# Funkcija za uvoz datoteke 'co2 emissions'\nuvozi.co2.csv <- function(tabela, stolpci, skip, max) {\n uvoz <- read_csv(tabela,\n locale = locale(encoding=\"utf-8\"),\n col_names = stolpci,\n skip = skip,\n n_max = max,\n na = c(\"\", \" \", \"-\", \"..\"))\n uvoz <- uvoz %>% select(-\"Code\")\n \n uvoz$Leto <- as.numeric(uvoz$Leto)\n uvoz <- uvoz %>% \n filter(uvoz$Leto == 2018) %>%\n select(-\"Leto\")\n return(uvoz)\n}\n\n## 6. tabela (CO2 Emissions)\nst.co2 <- c(\"Drzava\", \"Code\", \"Leto\", \"CO2_izpust_per_capita\")\nizpusti <- uvozi.co2.csv(\"podatki/co2_emissions_per_capita.csv\", st.co2, 1, Inf) \n\n### nov stolpec - Ekolo\u0161ki indeks\nizpusti$\"Ekoloski_indeks\" <- round(1 - ((izpusti$\"CO2_izpust_per_capita\" - min(izpusti$\"CO2_izpust_per_capita\", na.rm = TRUE))/(max(izpusti$\"CO2_izpust_per_capita\", na.rm = TRUE) - min(izpusti$\"CO2_izpust_per_capita\", na.rm = TRUE))), digits = 3)\n\nizpusti[izpusti$Drzava == \"Qatar\",]$\"Ekoloski_indeks\" <- 0.01\n\n# Funkcija za uvoz datoteke WHO-COVID-19-global-data\nuvozi.who.csv <- function(tabela, stolpci, skip, max) {\n uvoz <- read_csv(tabela,\n locale = locale(encoding=\"utf-8\"),\n col_names = stolpci,\n skip = skip,\n n_max = max,\n na = c(\"\", \" \", \"-\", \"..\"))\n uvoz <- uvoz %>% \n select(\"Drzava\", \"Stevilo_primerov_na_milijon\")\n uvoz$Drzava <- str_replace(uvoz$Drzava, \" *\\\\(.*?\\\\) *\", \"\")\n return(uvoz)\n}\n\n## 7. tabela (COVID data)\nst.cov <- c(\"Drzava\",\"WHO_Region\" ,\"Stevilo_primerov\" ,\"Stevilo_primerov_na_milijon\" ,\"Cases_in_last_7_days\",\"Cases_in_last_24_hours\",\"Deaths-cumulative_total\",\"Deaths_per_1_million_population\",\"Deaths_in_last_7_days\",\"Deaths_in_last_24_hours\",\"Transmission_Classification\")\ncovid <- uvozi.who.csv(\"podatki/WHO-COVID-19-global-data.csv\", st.cov, 2, Inf) %>% drop_na()\n\ncovid$\"Indeks_COVID\" <- round((1 - ((covid$\"Stevilo_primerov_na_milijon\" - min(covid$\"Stevilo_primerov_na_milijon\", na.rm = TRUE)) / (max(covid$\"Stevilo_primerov_na_milijon\", na.rm = TRUE) - min(covid$\"Stevilo_primerov_na_milijon\", na.rm = TRUE)))), digits = 5)\n\ncovid[covid$Drzava == \"Andorra\",]$\"Indeks_COVID\" <- 0.01\n\n### Nekatera imena moramo ro\u010dno popraviti, da se bodo tabele ujemale\nizpusti[izpusti$Drzava == \"Brunei\",]$Drzava <- \"Brunei Darussalam\"\nizpusti[izpusti$Drzava == \"Cape Verde\",]$Drzava <- \"Cabo Verde\"\nizpusti[izpusti$Drzava == \"Cote d'Ivoire\",]$Drzava <- \"C\u00f4te d'Ivoire\"\nizpusti[izpusti$Drzava == \"Czech Republic\",]$Drzava <- \"Czechia\"\nizpusti[izpusti$Drzava == \"Russia\",]$Drzava <- \"Russian Federation\"\nizpusti[izpusti$Drzava == \"Syria\",]$Drzava <- \"Syrian Arab Republic\"\nizpusti[izpusti$Drzava == \"Vietnam\",]$Drzava <- \"Viet Nam\"\ncovid[covid$Drzava == \"Kosovo[1]\",]$Drzava <- \"Kosovo\"\ncovid[covid$Drzava == \"occupied Palestinian territory, including east Jerusalem\",]$Drzava <- \"Palestine, State of\"\nizpusti[izpusti$Drzava == \"Palestine\",]$Drzava <- \"Palestine, State of\"\ncovid[covid$Drzava == \"Republic of Korea\",]$Drzava <- \"Korea\"\nizpusti[izpusti$Drzava == \"South Korea\",]$Drzava <- \"Korea\"\ncovid[covid$Drzava == \"Republic of Moldova\",]$Drzava <- \"Moldova\"\ncovid[covid$Drzava == \"The United Kingdom\",]$Drzava <- \"United Kingdom\"\ncovid[covid$Drzava == \"United Republic of Tanzania\",]$Drzava <- \"Tanzania\"\ncovid[covid$Drzava == \"United States of America\",]$Drzava <- \"United States\"\nizpusti[izpusti$Drzava == \"Timor\",]$Drzava <- \"Timor-Leste\"\n\n## 8. tabela (Zdru\u017eimo tabele z indeksi izobrazbe, \u017eivljenja, prihodka, neenakosti, izpustov in COVIDa)\nnov.hdi <- left_join(izobrazba, zivljenje, by='Drzava') %>%\n left_join(., prihodek, by='Drzava') %>%\n left_join(., neenakost, by=\"Drzava\") %>%\n left_join(., izpusti, by=\"Drzava\") %>%\n left_join(., covid, by=\"Drzava\") %>%\n select(\"Drzava\", \"Indeks_izobrazbe\", \"Indeks_zivljenja\", \"Indeks_prihodka\", \"Indeks_neenakosti\", \"Ekoloski_indeks\", \"Indeks_COVID\")\n\n### nova stolpca za star in nov HDI\nnov.hdi$\"Stari_HDI\" <- round((nov.hdi$`Indeks_izobrazbe` * nov.hdi$`Indeks_zivljenja` * nov.hdi$`Indeks_prihodka`) ** (1/3), digits = 3)\nnov.hdi$\"Novi_HDI\" <- round((nov.hdi$`Indeks_izobrazbe` * nov.hdi$`Indeks_zivljenja` * nov.hdi$`Indeks_prihodka` * nov.hdi$`Indeks_neenakosti` * nov.hdi$`Ekoloski_indeks` * nov.hdi$`Indeks_COVID`) ** (1/6), digits = 3)\n\n### Popravimo, kjer je za isto dr\u017eavo ve\u010d vrstic \n### Filtriramo glede na novi HDI, kar sicer povzro\u010di izbris dr\u017eav, ki tega podatka nimajo, vendar bomo v nadaljevanju tako ali tako analizirali le tiste dr\u017eave, ki imajo vse podatke\nnov.hdi <- nov.hdi %>% \n group_by(Drzava) %>% \n filter(Novi_HDI == max(Novi_HDI)) %>% \n distinct\n\n## 9. tabela (Tabelo nov.hdi pre\u010distimo in spravimo v tidy data)\nnov.hdi.tidy <- nov.hdi %>% pivot_longer(c(-Drzava), names_to=\"Indeks\", values_to=\"Vrednost\")\n\n### Sem potrebovala pri COVID tabeli, ko nisem uporabila stolpca '\u0161tevilo primerov na milijon prebivalcev', je pa to koda za uvoz iz html-ja\n### # Uvoz iz HTML (Prebivalstvo)\n### uvozi.html <- function() {\n### url <- \"https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population\"\n### stran <- read_html(url) %>% html_table(fill = TRUE)\n### uvoz <- stran[[1]]\n### uvoz <- uvoz %>%\n### rename(\"Drzava\" = \"Country(or dependent territory)\") %>%\n### rename(\"Prebivalstvo\" = \"Population\") %>%\n### select(\"Drzava\", \"Prebivalstvo\")\n### \n### uvoz$Drzava <- str_replace(uvoz$Drzava, \" *\\\\(.*?\\\\) *\", \"\")\n### uvoz$Drzava <- str_replace(uvoz$Drzava, \" *\\\\[.*?\\\\] *\", \"\")\n### \n### uvoz$Prebivalstvo <- as.numeric(gsub(\",\", \"\", uvoz$Prebivalstvo))\n### return(uvoz)\n### }\n### \n### ## tabela (Prebivalstvo po dr\u017eavah)\n### prebivalstvo <- uvozi.html()\n", "meta": {"hexsha": "663ea5b7b1a0540992c1b4f7fccea56de19fba4a", "size": 9269, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz1.r", "max_stars_repo_name": "CebuljIza/APPR-2020-21", "max_stars_repo_head_hexsha": "e1675d125bff771490f843215ea552f04da72a4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-02T21:03:25.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-02T21:03:25.000Z", "max_issues_repo_path": "uvoz/uvoz1.r", "max_issues_repo_name": "CebuljIza/APPR-2020-21", "max_issues_repo_head_hexsha": "e1675d125bff771490f843215ea552f04da72a4f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-11-05T07:44:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-15T12:54:22.000Z", "max_forks_repo_path": "uvoz/uvoz1.r", "max_forks_repo_name": "CebuljIza/APPR-2020-21", "max_forks_repo_head_hexsha": "e1675d125bff771490f843215ea552f04da72a4f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.4944444444, "max_line_length": 329, "alphanum_fraction": 0.6094508577, "num_tokens": 3397, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.32550912481743594}} {"text": "#' Identify intervals in a genome not covered by a query.\n#'\n#' @param x [ivl_df]\n#' @param genome [ivl_df]\n#'\n#' @family single set operations\n#'\n#' @return [ivl_df]\n#'\n#' @examples\n#' x <- tibble::tribble(\n#' ~chrom, ~start, ~end,\n#' 'chr1', 0, 10,\n#' 'chr1', 75, 100\n#' )\n#'\n#' genome <- tibble::tribble(\n#' ~chrom, ~size,\n#' 'chr1', 200\n#' )\n#'\n#' bed_glyph(bed_complement(x, genome))\n#'\n#' genome <- tibble::tribble(\n#' ~chrom, ~size,\n#' 'chr1', 500,\n#' 'chr2', 600,\n#' 'chr3', 800\n#' )\n#'\n#' x <- tibble::tribble(\n#' ~chrom, ~start, ~end,\n#' 'chr1', 100, 300,\n#' 'chr1', 200, 400,\n#' 'chr2', 0, 100,\n#' 'chr2', 200, 400,\n#' 'chr3', 500, 600\n#' )\n#'\n#' # intervals not covered by x\n#' bed_complement(x, genome)\n#'\n#' @export\nbed_complement <- function(x, genome) {\n x <- check_interval(x)\n genome <- check_genome(genome)\n\n res <- bed_merge(x)\n\n # non-overlapping chroms\n chroms_no_overlaps <- anti_join(genome, res, by = \"chrom\")\n chroms_no_overlaps <- mutate(chroms_no_overlaps, start = 0)\n chroms_no_overlaps <- select(chroms_no_overlaps, chrom, start, end = size)\n\n # remove rows from x that are not in genome\n res <- semi_join(res, genome, by = \"chrom\")\n\n res <- group_by(res, chrom)\n\n res <- complement_impl(res, genome)\n\n res <- bind_rows(res, as_tibble(chroms_no_overlaps))\n res <- bed_sort(res)\n\n res\n}\n", "meta": {"hexsha": "e80bce35ef1e574314d5d7ca85d6d057112c619c", "size": 1400, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bed_complement.r", "max_stars_repo_name": "jimhester/valr", "max_stars_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 72, "max_stars_repo_stars_event_min_datetime": "2017-02-22T15:22:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T06:39:42.000Z", "max_issues_repo_path": "R/bed_complement.r", "max_issues_repo_name": "jimhester/valr", "max_issues_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 178, "max_issues_repo_issues_event_min_datetime": "2016-12-06T15:42:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T00:10:30.000Z", "max_forks_repo_path": "R/bed_complement.r", "max_forks_repo_name": "jimhester/valr", "max_forks_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 32, "max_forks_repo_forks_event_min_datetime": "2017-03-06T23:01:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-27T12:32:30.000Z", "avg_line_length": 20.8955223881, "max_line_length": 76, "alphanum_fraction": 0.5835714286, "num_tokens": 488, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.32550911587285486}} {"text": "#writes_separate potentials files(of selected size)\r\nrm(list=ls())\r\n\r\nblack_pearl<-list.files(\"K:\\\\Project_Data_Sagar\\\\my_truth\\\\recruit_truth\\\\\")\r\naltis<-as.numeric(substr(black_pearl,10,12)) #only numbers indicating no of AA residues in each files\r\n\r\n\r\n#>100\r\nlength(which(altis>110 & altis<120)) #---> 1110\r\nlength(which(altis>120 & altis<130)) #907\r\nlength(which(altis>130 & altis<140)) #728\r\nlength(which(altis>90 & altis<100)) #1546\r\n.\r\nlength(which(altis>=100 & altis<=110)) #2199\r\nlength(which(altis>=110 & altis<=120)) # 1313\r\nlength(which(altis>=100 & altis<=120)) # 3422 ---->\r\n\r\nlength(which(altis>=110 & altis<=115)) #756\r\nlength(which(altis>=115 & altis<=120)) #728\r\n\r\n\r\nlength(which(altis>=400 & altis<=500)) #533\r\nlength(which(altis>=500 & altis<=600)) #153\r\nlength(which(altis>=200 & altis<=300)) #2759 ------>\r\nlength(which(altis>=300 & altis<=400)) #1342\r\n\r\nlength(which(altis>100 & altis<200)) #9001\r\n\r\nshekarya<-black_pearl[which(altis>=100 & altis<=110)] #taking requied\r\n\r\n#writing only requied files\r\nfor(atireki in shekarya){\r\nmetadata<-substr(atireki,1,8) #file name\r\nfile.copy(paste(\"K:\\\\Project_Data_Sagar\\\\my_truth\\\\recruit_truth\\\\\",atireki,sep=\"\"), paste(\"K:\\\\Project_Data_Sagar\\\\my_truth\\\\sangram_clipso\\\\\",metadata,\".txt\",sep=\"\") )\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "7b45c6bcf22894263e7eaec92245290a40e2b373", "size": 1316, "ext": "r", "lang": "R", "max_stars_repo_path": "II-dataset/Potential cal/sorting_pot_files.r", "max_stars_repo_name": "sagarnikam123/bioinfoProject", "max_stars_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "II-dataset/Potential cal/sorting_pot_files.r", "max_issues_repo_name": "sagarnikam123/bioinfoProject", "max_issues_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "II-dataset/Potential cal/sorting_pot_files.r", "max_forks_repo_name": "sagarnikam123/bioinfoProject", "max_forks_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.32, "max_line_length": 170, "alphanum_fraction": 0.6633738602, "num_tokens": 440, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7634837743174787, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.3254896185511962}} {"text": "library(shiny)\nlibrary(cv2r)\nlibrary(ggplot2)\nlibrary(bioacoustics)\nlibrary(data.table)\n\nif ( cv2_available() ) {\n ui <- fluidPage(fluidRow(\n actionButton(inputId = \"snap\", label = \"Take\"),\n actionButton(inputId = \"snap2\", label = \"Take2\"),\n actionButton(inputId = \"plot_audio\", label = \"Audio\")),\n fluidRow(\n column(3,inputCv2Cam(\"capture\",\n auto_send_audio = T, audio = T)),\n column(3,cv2Output(outputId = \"zoom\")),\n column(3,cv2Output(outputId = \"border\"))\n ),\n fluidRow(column(12, plotOutput(outputId = \"envelop\", height = 200))),\n fluidRow(column(12, plotOutput(outputId = \"fullplot_veryhigh\", height = 200))),\n fluidRow(column(12, plotOutput(outputId = \"fullplot_high\", height = 200))),\n fluidRow(column(12, plotOutput(outputId = \"fullplot_low\", height = 200)))\n )\n \n server <- function(input, output, session) {\n \n full_data <- numeric(0)\n \n observeEvent(input$capture_audio, {\n \n l_data_sel <- input$capture_audio\n \n cat(length(full_data), \"\\n\")\n full_data <<- c(full_data, l_data_sel)\n })\n \n observeEvent(input$plot_audio, {\n l_data_sel <- full_data\n full_data <<- numeric(0)\n \n .GlobalEnv$l_data_sel <- l_data_sel\n #plot.Wav(tuneR::Wave(left = l_data_sel, right = l_data_sel, bit=16, samp.rate=44100))\n \n l_plot <- data.table(\n time = seq_along(l_data_sel)/44100,\n amplitude = l_data_sel,\n envelope = abs(l_data_sel)\n )\n \n output$envelop <- renderPlot({\n ggplot(l_plot[sample.int(nrow(l_plot), 1000),] ) +\n geom_line(aes(x=time, y=envelope )) + \n theme_minimal() + stat_smooth(aes(x=time, y=envelope ))\n })\n \n output$fullplot_veryhigh <- renderPlot({\n spectro(tuneR::Wave(left = l_data_sel, bit=16, samp.rate=44100),\n flim = c(3000, 10000),\n FFT_size = 512 )\n })\n \n output$fullplot_high <- renderPlot({\n spectro(tuneR::Wave(left = l_data_sel, bit=16, samp.rate=44100),\n flim = c(1000, 3000),\n FFT_size = 1024 )\n })\n \n output$fullplot_low <- renderPlot({\n spectro(tuneR::Wave(left = l_data_sel, bit=16, samp.rate=44100),\n flim = c(0, 1000),\n FFT_size = 2048 )\n })\n \n })\n \n observeEvent(input$snap, {\n inputCv2CamSnap(session, \"capture\")\n })\n\n observeEvent(input$snap2, {\n inputCv2CamSnap(session, \"capture\", top = 100, left = 100, width = 200, height = 100)\n })\n \n \n output$zoom <- renderCv2({\n if ( !is.null(input$capture)) input$capture[5:200,5:200]\n })\n \n output$border <- renderCv2({\n imshow(mat = cv2r$Canny(input$capture, 10L, 50L) )\n })\n }\n \n if (interactive()) {\n shinyApp(ui, server)\n }\n \n}\n\n", "meta": {"hexsha": "3207b865a7a0e98e4ec4aa3b74709737deb413d8", "size": 3271, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/examples/sampleApp.r", "max_stars_repo_name": "battmanux/cv2r", "max_stars_repo_head_hexsha": "3caa5bf15fcb7d06dd94d01350e39f9d25163295", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-05-28T11:50:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-09T16:04:41.000Z", "max_issues_repo_path": "inst/examples/sampleApp.r", "max_issues_repo_name": "battmanux/cv2r", "max_issues_repo_head_hexsha": "3caa5bf15fcb7d06dd94d01350e39f9d25163295", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-11-11T19:27:47.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-11T19:27:47.000Z", "max_forks_repo_path": "inst/examples/sampleApp.r", "max_forks_repo_name": "battmanux/cv2r", "max_forks_repo_head_hexsha": "3caa5bf15fcb7d06dd94d01350e39f9d25163295", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0404040404, "max_line_length": 99, "alphanum_fraction": 0.5029043106, "num_tokens": 807, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.32532248777035044}} {"text": "#Get summary a set of .rds files in a directory\n#Get list of all .rds files\nfilenames = list.files(pattern=\"*\\\\.rds$\")\n#Load them all into a list of dataframes\ndata = lapply(filenames,readRDS)\n#Collapse dataframes into one dataframe\ndata = do.call(\"rbind\",data)\nprint(data)\nprint(summary(data))\n", "meta": {"hexsha": "9861668eb528885753e74c29dfca30a18b3d2f55", "size": 295, "ext": "r", "lang": "R", "max_stars_repo_path": "languages/R/linear_algebra/summary_rds.r", "max_stars_repo_name": "mikecroucher/HPC_Examples", "max_stars_repo_head_hexsha": "dd0a01888e4f4f3cd6f2c6ae3273e421fe2cb1a1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "languages/R/linear_algebra/summary_rds.r", "max_issues_repo_name": "mikecroucher/HPC_Examples", "max_issues_repo_head_hexsha": "dd0a01888e4f4f3cd6f2c6ae3273e421fe2cb1a1", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-07-05T14:58:26.000Z", "max_issues_repo_issues_event_max_datetime": "2017-07-05T14:58:26.000Z", "max_forks_repo_path": "languages/R/linear_algebra/summary_rds.r", "max_forks_repo_name": "mikecroucher/HPC_Examples", "max_forks_repo_head_hexsha": "dd0a01888e4f4f3cd6f2c6ae3273e421fe2cb1a1", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-09-09T16:27:56.000Z", "max_forks_repo_forks_event_max_datetime": "2020-09-09T16:27:56.000Z", "avg_line_length": 29.5, "max_line_length": 47, "alphanum_fraction": 0.7457627119, "num_tokens": 83, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.640635854839898, "lm_q1q2_score": 0.32532248777035044}} {"text": "#!/usr/bin/env Rscript\n#\n# Extract means and HPD intervals for PGE, PVE, and n_gamma. Works\n# on just one file.\n#\n\nlibrary(coda)\nlibrary(data.table)\n\nargv = commandArgs(trailingOnly=TRUE)\n\nthin = as.numeric(argv[2])\ninfile = argv[1]\ninfile_log = argv[3]\n\nmake_vec = function() numeric(1)\nstats = data.frame('run'=make_vec(),\n 'pve_95_l'=make_vec(),\n 'pve_95_h'=make_vec(),\n 'pve_mean'=make_vec(),\n 'pge_95_l'=make_vec(),\n 'pge_95_h'=make_vec(),\n 'pge_mean'=make_vec(),\n 'ng_95_l'=make_vec(),\n 'ng_95_h'=make_vec(),\n 'ng_mean'=make_vec(),\n 'lmm_pve'=make_vec())\n\nif(grepl('\\\\.gz$', infile)) {\n x = fread(paste('zcat', infile))\n} else {\n x = fread(infile)\n}\n\nmcmc_data = as.mcmc(x, start=1, end=thin*nrow(x),\n thin=thin)\nhpd_95 = HPDinterval(mcmc_data, 0.95)\nmeans = colMeans(x)\n\nstats[1, 'pve_95_l'] = hpd_95['pve', 'lower']\nstats[1, 'pve_95_h'] = hpd_95['pve', 'upper']\nstats[1, 'pge_95_l'] = hpd_95['pge', 'lower']\nstats[1, 'pge_95_h'] = hpd_95['pge', 'upper']\nstats[1, 'ng_95_l'] = hpd_95['n_gamma', 'lower']\nstats[1, 'ng_95_h'] = hpd_95['n_gamma', 'upper']\nstats[1, 'pve_mean'] = means['pve']\nstats[1, 'pge_mean'] = means['pge']\nstats[1, 'ng_mean'] = means['n_gamma']\nstats[1, 'run'] = 1\n\nl = scan(infile_log, what='character', sep='\\n', quiet=TRUE)\nstats[1, 'lmm_pve'] = as.numeric(gsub('.+= ', '', l[grepl('pve', l)][1]))\n\n\nwrite.table(stats, file=stdout(), sep='\\t', col.names=TRUE,\n row.names=FALSE, quote=FALSE)\n", "meta": {"hexsha": "bca222ae8c9d0457245fa6cb42eb63fb6e4c479c", "size": 1636, "ext": "r", "lang": "R", "max_stars_repo_path": "extract_bslmm_stats.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "extract_bslmm_stats.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "extract_bslmm_stats.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.701754386, "max_line_length": 73, "alphanum_fraction": 0.5617359413, "num_tokens": 544, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.32532248080201676}} {"text": "suppressPackageStartupMessages(library(optparse, warn.conflicts=F, quietly=T))\n\noption_list <- list(\n make_option(c(\"-t\", \"--treatment\"), \n type=\"character\",\n default=NA,\n help=\"Treatment BigWig file\"),\n make_option(c(\"-c\", \"--control\"), \n type=\"character\",\n default=NA,\n help=\"Control BigWig file\"),\n make_option(c(\"-o\", \"--output\"),\n type=\"character\",\n default=NA,\n help=\"Name of output BigWig file\"),\n make_option(c(\"-w\", \"--window\"),\n type=\"integer\",\n default=101,\n help=\"Sliding window size\"),\n make_option(c(\"-l\", \"--transform\"),\n type=\"character\",\n default=\"log2\",\n help=\"Transform to apply to enrichment values (log2 or linear)\"),\n make_option(c(\"--min\"),\n type=\"integer\",\n default=-2,\n help=\"Floor for enrichment values (after transform)\"),\n make_option(c(\"--max\"),\n type=\"integer\",\n default=NA,\n help=\"Ceiling for enrichment values (after transform)\"),\n make_option(c(\"-f\", \"--filter\"),\n type=\"character\",\n default=NA,\n help=\"Expression to filter out chromosomes\")\n )\n\n# OPTION PROCESSING\n\nopt <- parse_args(OptionParser(option_list=option_list))\n\nif(is.na(opt$treatment)) {\n message(\"No treatment file specified.\")\n q(status=1)\n}\n\nif(is.na(opt$control)) {\n message(\"No control file specified.\")\n q(status=1)\n}\n\nif(is.na(opt$output)) {\n message(\"No output name specified.\")\n q(status=1)\n}\n\nif(! opt$transform %in% c(\"log2\", \"linear\")) {\n message(\"Transform type must be either 'log2' or 'linear'\")\n q(status=1)\n}\n\nsuppressPackageStartupMessages(library(GenomicRanges, warn.conflicts=F))\nsuppressPackageStartupMessages(library(rtracklayer, warn.conflicts=F))\n\n# COMMON FUNCTIONS\n\ntotal_signal_rlelist <- function(cvg) {\n stopifnot(is(cvg, \"RleList\"))\n sum(as.numeric(sum(cvg)))\n}\n\nfilter_chrs <- function(cov, match_expression=\"H|M|U|_\") {\n exclude.chrs <- grep(match_expression, names(cov))\n if(length(exclude.chrs) > 0) cov <- cov[-exclude.chrs]\n cov\n}\n\nsingle_value_rlelist <- function(value, lengths) {\n RleList(lapply(lengths, function(i) Rle(rep(value, times=i))))\n}\n\nmessage(\"Loading: \", opt$treatment)\nip.cov <- import(opt$treatment, as=\"RleList\")\nmessage(\"Loading: \", opt$control)\nbg.cov <- import(opt$control, as=\"RleList\")\n\ncommon.chrs <- intersect(names(ip.cov), names(bg.cov))\nip.cov <- ip.cov[common.chrs]\nbg.cov <- bg.cov[common.chrs]\n\nif(!is.na(opt$filter)) {\n ip.cov <- filter_chrs(ip.cov, opt$filter)\n bg.cov <- filter_chrs(bg.cov, opt$filter)\n}\n\n# START\n\nmessage(\"Processing treatment...\")\nip.cov <- runsum(ip.cov, opt$window, endrule=\"constant\")\nip.ts <- total_signal_rlelist(ip.cov)\n\nmessage(\"Processing control...\")\nbg.cov <- runsum(bg.cov, opt$window, endrule=\"constant\")\nbg.ts <- total_signal_rlelist(bg.cov)\n\nmessage(\"Calculating enrichments...\")\n\ntransform_function <- get(ifelse(opt$transform == \"log2\", \"log2\", \"identity\"))\ne.cov <- transform_function((ip.cov / ip.ts) / (bg.cov / bg.ts))\n\nif(!is.na(opt$max)) {\n message(\"Ceiling: \", opt$max)\n e.cov <- pmin(e.cov, single_value_rlelist(opt$max, elementLengths(e.cov)))\n}\n\nif(!is.na(opt$min)) {\n message(\"Floor: \", opt$min)\n e.cov <- pmax(e.cov, single_value_rlelist(opt$min, elementLengths(e.cov)))\n}\n\nmessage(\"Removing non-finite values...\")\ne.gr <- as(e.cov, \"GRanges\")\ne.gr <- e.gr[is.finite(e.gr$score)]\ne.gr$score <- round(e.gr$score, 2)\ne.cov <- coverage(e.gr, weight=\"score\")\n\nmessage(\"Saving: \", opt$output)\nexport(e.cov, opt$output, format=\"BigWig\")\n", "meta": {"hexsha": "4c8f3373700cb8a5af19db61ce7d8626adeabffe", "size": 3689, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/enrichment_track.r", "max_stars_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_stars_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/enrichment_track.r", "max_issues_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_issues_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/enrichment_track.r", "max_forks_repo_name": "zeitlingerlab/Ramalingam_Lola_2020", "max_forks_repo_head_hexsha": "f300af0d2afab9fc3b7f137fd04b621d643454c7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.5968992248, "max_line_length": 79, "alphanum_fraction": 0.6275413391, "num_tokens": 985, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.32528486025981473}} {"text": "#UPDATED August 5, 2021\r\n\r\nnumber_of_threads <- as.numeric(readLines(\"THREADS\"))\r\n\r\nalleles = list()\r\nfrequencies = list()\r\n\r\nfor (j in 1:nloci) {\r\n\tlocicolumns = grepl(paste(locinames[j],\"\",sep=\"\"),colnames(data))\r\n\traw_alleles = c(as.matrix(data[,locicolumns]))\r\n\traw_alleles[raw_alleles == \"NA\"] = NA\r\n\traw_alleles[raw_alleles == 0] = NA\r\n\talleles[[j]] = unique(raw_alleles[!is.na(raw_alleles)])\r\n\tfrequencies[[j]] = sapply(alleles[[j]], function(x) sum(raw_alleles == x,na.rm=TRUE))\r\n\tfrequencies[[j]] = frequencies[[j]] / sum(frequencies[[j]])\r\n}\r\n\r\n\r\nobserveddatamatrix = list()\r\nfor (j in 1:nloci) {\r\n\tlocus = locinames[j]\r\n\tlocicolumns = grepl(paste(locus,\"\",sep=\"\"),colnames(data))\r\n\toldalleles = as.vector(data[,locicolumns])\r\n\toldalleles [oldalleles == \"NA\"] = NA\r\n\toldalleles [oldalleles == 0] = NA\r\n\tif (length(dim(oldalleles)[2]) == 0) {\r\n\t\toldalleles = matrix(oldalleles,length(oldalleles),1)\r\n\t}\r\n\tobserveddatamatrix[[j]] = oldalleles \r\n}\r\n\r\nm <<- rep(1,nloci)\r\n\r\nH_nu = sapply(1:nloci, function (j) -sum(frequencies[[j]] * logb(frequencies[[j]],2)))\r\n\r\nsub_per_locus = function(isolate1,isolate2,j) {\r\n\t\tv1 = observeddatamatrix[[j]][isolate1,]\r\n\t\tv1 = v1[!is.na(v1)]\r\n\t\tp1 = frequencies[[j]][match(v1,alleles[[j]])]\r\n\t\tv2 = observeddatamatrix[[j]][isolate2,]\r\n\t\tv2 = v2[!is.na(v2)]\r\n\t\tp2 = frequencies[[j]][match(v2,alleles[[j]])]\r\n\r\n\r\n\t\tif (ploidy[j] > 1) {\r\n\t\t\tx = length(unique(v1)) + length(unique(v2))\r\n\t\t\tn = min(length(unique(v1)),length(unique(v2)))\r\n\t\t\tw = x * (n > 1) + 4 * (n == 1) * (x == 2) + (1+x) * (n== 1) * (x > 2)\r\n\t\t\tjj = 2 * (m[j] == 1) + m[j] * (m[j] > 1)\r\n\t\t\ty = length(intersect(v1,v2))\r\n\t\t\tz = 3 * (((2*(n == 1) + 1 * (y == 1) * (x > 2)))==3) + 2 * (y *(n>1)*(jj>=y)+jj*(n > 1)* (y > jj) + jj * (n==1) * (x==2) * (y==1))\r\n\t\t\tdelta_nu_raw = w * (y == 0) + 2 * jj * (y > 0) + sum(sapply(jj:(2*jj), function (ii) -ii*(z == ii)))\r\n\t\t\r\n\t\t\tshared_alleles = intersect(v1 , v2)\r\n\t\t\tif (length(shared_alleles) > 0) {\r\n\t\t\t\ttemp_shared = sapply(1:nids, function (x) sum(shared_alleles %in% as.matrix(observeddatamatrix[[j]][x,])))\r\n\t\t\t\tnotmissing = sapply(1:nids, function (x) sum(!is.na(observeddatamatrix[[j]][x,])))\r\n\t\t\t\r\n\t\t\t\tP_nu = (sum(temp_shared == length(shared_alleles)) / sum(notmissing != 0))^2\r\n\t\t\t} else {\r\n\t\t\t\tP_nu = 1\r\n\t\t\t}\r\n\t\t\tk = 1 * (y == 0) + P_nu * (y > 0)\r\n\t\t\tdelta_nu = H_nu[j] * ( delta_nu_raw * (delta_nu_raw > 0) + P_nu * (delta_nu_raw == 0))*k\r\n\t\t\tdelta = delta_nu\r\n\t\t} else {\r\n\t\t\tdelta_ex_raw = 0\r\n\t\t\tx = length(unique(v1)) + length(unique(v2))\r\n\t\t\ty = length(intersect(v1,v2))\r\n\t\t\tdelta_ex_raw = 2*x*(y == 0)\r\n\t\t\tshared_alleles = intersect(v1 , v2)\r\n\t\t\tif (length(shared_alleles) > 0) {\r\n\t\t\t\ttemp_shared = sapply(1:nids, function (x) sum(shared_alleles %in% as.matrix(observeddatamatrix[[j]][x,])))\r\n\t\t\t\tnotmissing = sapply(1:nids, function (x) sum(!is.na(observeddatamatrix[[j]][x,])))\r\n\t\t\t\tP_ex = (sum(temp_shared == length(shared_alleles)) / sum(notmissing != 0))^2\r\n\t\t\t} else {\r\n\t\t\t\tP_ex = 1\r\n\t\t\t}\r\n\t\t\tk = 1 * (y == 0) + P_ex * (y > 0)\r\n\t\t\tdelta_ex = H_nu[j] * ( delta_ex_raw * (delta_ex_raw > 0) + P_ex * (delta_ex_raw == 0))*k\r\n\t\t\tdelta = delta_ex\r\n\t\t}\r\n\t\tif (sum(!is.na(v1)) == 0 | sum(!is.na(v2)) == 0) { delta = NA }\r\n\t\tdelta\r\n}\r\n\r\npairwisedistance_heuristic = function(isolate1,isolate2){\r\n\tprint(((isolate2-1)*nids+isolate1)/ (nids*nids))\r\n\tdelta = sapply(1:nloci, function (x) sub_per_locus(isolate1,isolate2,x))\r\n\tc(delta,sum(delta))\t\r\n}\r\n\r\nallpossiblepairs = expand.grid(1:nids,1:nids)\r\nallpossiblepairs = unique(allpossiblepairs[allpossiblepairs[,1] <= allpossiblepairs[,2],])\r\npairwisedistancevector = do.call(cbind,mclapply(1:dim(allpossiblepairs)[1], function (x) pairwisedistance_heuristic(allpossiblepairs[x,1],allpossiblepairs[x,2]),mc.cores=number_of_threads))\r\n\r\npairwisedistancematrix_components = list()\r\nfor (j in 1:(nloci+1)) { \r\n\tpairwisedistancematrix_temp = matrix(NA,nids,nids)\r\n\tsapply(1:dim(allpossiblepairs)[1], function (x) pairwisedistancematrix_temp[allpossiblepairs[x,1],allpossiblepairs[x,2]] <<- pairwisedistancevector[j,x])\r\n\tsapply(1:dim(allpossiblepairs)[1], function (x) pairwisedistancematrix_temp[allpossiblepairs[x,2],allpossiblepairs[x,1]] <<- pairwisedistancevector[j,x])\r\n\tpairwisedistancematrix_components[[j]] = pairwisedistancematrix_temp\r\n}\r\n\r\n\r\n#### impute missing values\r\npairwisedistancematrix_components_imputed = pairwisedistancematrix_components\r\nwhichna = which(rowSums(is.na(pairwisedistancematrix_components[[nloci+1]])) == nids)\r\n\r\nimputemissing = function(isolate1) {\r\n\tmissingloci = which(sapply(1:nloci, function (j) sum(!is.na(observeddatamatrix[[j]][isolate1,]))) == 0)\r\n\tnonmissingloci = (1:nloci)[-missingloci]\r\n\tmatchingsamples = which(rowSums(rbind(sapply(nonmissingloci, function (j) sapply(1:nids, function (x) (setequal(observeddatamatrix[[j]][x,],observeddatamatrix[[j]][isolate1,]))))))==length(nonmissingloci))\r\n\tmatchingsamples = setdiff( matchingsamples , whichna)\r\n\tfor (j in missingloci ) {\r\n\t\tif (length(matchingsamples) > 0) {\r\n\t\t\tsapply(1:nids, function (x) pairwisedistancematrix_components_imputed[[j]][isolate1,x] <<- mean(pairwisedistancematrix_components[[j]][x,matchingsamples],na.rm=TRUE))\r\n\t\t\tsapply(1:nids, function (x) pairwisedistancematrix_components_imputed[[j]][x,isolate1] <<- mean(pairwisedistancematrix_components[[j]][x,matchingsamples],na.rm=TRUE))\r\n\t\t\tpairwisedistancematrix_components_imputed[[j]][isolate1,isolate1] <<- mean(diag(pairwisedistancematrix_components_imputed[[j]])[matchingsamples],na.rm=TRUE)\r\n\t\t} else {\r\n\t\t\tpairwisedistancematrix_components_imputed[[j]][isolate1,] <<-mean(pairwisedistancematrix_components[[j]],na.rm=TRUE)\r\n\t\t\tpairwisedistancematrix_components_imputed[[j]][,isolate1] <<-mean(pairwisedistancematrix_components[[j]],na.rm=TRUE)\r\n\t\t\tpairwisedistancematrix_components_imputed[[j]][isolate1,isolate1] <<-mean(diag(pairwisedistancematrix_components_imputed[[j]]),na.rm=TRUE)\r\n\t\t}\r\n\t}\r\n}\r\nsapply(whichna, imputemissing)\r\n\r\ntemppairwisedistancematrix = matrix(0,nids,nids)\r\nfor (j in 1:(nloci)) { \r\n\ttemppairwisedistancematrix = temppairwisedistancematrix + pairwisedistancematrix_components_imputed[[j]]\r\n}\r\nwhichna2 = which(rowSums(is.na(temppairwisedistancematrix )) != 0)\r\n\r\npairwisedistancematrix_components_imputed_secondpass = pairwisedistancematrix_components_imputed\r\n\r\nimputemissing_secondpass = function(isolate1) {\r\n\tmissingloci = which(sapply(1:nloci, function (j) sum(!is.na(observeddatamatrix[[j]][isolate1,]))) == 0)\r\n\tnonmissingloci = (1:nloci)[-missingloci]\r\n\tmatchingsamples = which(rowSums(rbind(sapply(nonmissingloci, function (j) sapply(1:nids, function (x) (setequal(observeddatamatrix[[j]][x,],observeddatamatrix[[j]][isolate1,]))))))==length(nonmissingloci))\r\n\tmatchingsamples = setdiff( matchingsamples , whichna)\r\n\tfor (j in missingloci ) {\r\n\t\tif (length(matchingsamples) > 0) {\r\n\t\t\tsapply(1:nids, function (x) pairwisedistancematrix_components_imputed_secondpass[[j]][isolate1,x] <<- mean(pairwisedistancematrix_components_imputed[[j]][x,matchingsamples],na.rm=TRUE))\r\n\t\t\tsapply(1:nids, function (x) pairwisedistancematrix_components_imputed_secondpass[[j]][x,isolate1] <<- mean(pairwisedistancematrix_components_imputed[[j]][x,matchingsamples],na.rm=TRUE))\r\n\t\t\tpairwisedistancematrix_components_imputed_secondpass[[j]][isolate1,isolate1] <<- mean(diag(pairwisedistancematrix_components_imputed[[j]])[matchingsamples],na.rm=TRUE)\r\n\t\t} else {\r\n\t\t\tpairwisedistancematrix_components_imputed_secondpass[[j]][isolate1,] <<-mean(pairwisedistancematrix_components_imputed[[j]],na.rm=TRUE)\r\n\t\t\tpairwisedistancematrix_components_imputed_secondpass[[j]][,isolate1] <<-mean(pairwisedistancematrix_components_imputed[[j]],na.rm=TRUE)\r\n\t\t\tpairwisedistancematrix_components_imputed_secondpass[[j]][isolate1,isolate1] <<-mean(diag(pairwisedistancematrix_components_imputed[[j]]),na.rm=TRUE)\r\n\t\t}\r\n\t}\r\n}\r\n\r\nsapply(whichna2, imputemissing_secondpass)\r\n\r\n\r\n\r\n# calculate final\r\nfinalpairwisedistancematrix = matrix(0,nids,nids)\r\nfor (j in 1:(nloci)) { \r\n\tfinalpairwisedistancematrix = finalpairwisedistancematrix + pairwisedistancematrix_components_imputed_secondpass[[j]]\r\n}\r\n\r\n\r\ncolnames(pairwisedistancematrix) = ids \r\nrownames(pairwisedistancematrix) = ids\r\n\r\nHeuristic_pairwisedistancematrix = finalpairwisedistancematrix \r\n\r\n\r\n", "meta": {"hexsha": "ef67c1cef4ceb11a80c590f093aa89fadbffd198", "size": 8237, "ext": "r", "lang": "R", "max_stars_repo_path": "Complete_Cyclospora_typing_workflow_MacOS_High_Sierra_ALPHA_TEST/EUKARYOTYPING/euk_heuristic_fulldataset.r", "max_stars_repo_name": "Joel-Barratt/CDC-Complete-Cyclospora-typing-workflow-ALPHA-TEST", "max_stars_repo_head_hexsha": "bb50a804b834925d1bec4687cdf16f9f524d14e2", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Complete_Cyclospora_typing_workflow_MacOS_High_Sierra_ALPHA_TEST/EUKARYOTYPING/euk_heuristic_fulldataset.r", "max_issues_repo_name": "Joel-Barratt/CDC-Complete-Cyclospora-typing-workflow-ALPHA-TEST", "max_issues_repo_head_hexsha": "bb50a804b834925d1bec4687cdf16f9f524d14e2", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, 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YES\n2. YES", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32515961491573514}} {"text": "setwd(\"D:/Dropbox/Data analyze/Rdocuments/datas\")\r\nlibrary(xlsx)\r\n library(RColorBrewer)\r\n \r\n ##\u8bfb\u53d6t_for\u7684\u5927\u5c0f\r\n k1<-read.xlsx(\"t_for.xls\", sheetName = \"0.2-2\", header = TRUE)\r\nk2<-read.xlsx(\"t_for.xls\", sheetName = \"0.3\", header = TRUE)\r\nk3<-read.xlsx(\"t_for.xls\", sheetName = \"0.4\", header = TRUE)\r\nk4<-read.xlsx(\"t_for.xls\", sheetName = \"0.5\", header = TRUE)\r\n\r\n\r\nv<-4.66e-13 #\u5f2f\u6708\u9762\u4f53\u79ef\u5927\u5c0f\r\n\r\nq<-c(2.5e-14, 4.5e-13, 9e-13, 3e-12) #\u6d41\u91cf\u4ece1.5\u5230180\r\n\r\nk<-c(0.2,0.3,0.4,0.5) #\u5360\u7a7a\u6bd4\r\n\r\npchc<-c(19,22,23,24)\r\n\r\nmycolors<-c(\"red\",\"blue\", \"darkgreen\", \"yellow3\")\r\n\r\nh<-0.3e-3 #\u5f2f\u6708\u9762\u53d8\u5f62\u5927\u5c0f\r\n\r\nduty<-1-k\r\n\r\n#par(mfrow=c(2,2), mar=c(4,2,2,2), oma=c(2,4,2,2))\r\n\r\n\r\n#######\u6d41\u91cf1####\r\nplot(k1[,1], (duty[1]*q[1])/(k1[,1]*k1[,2]^2), log=\"x\", lwd=2, xlab=expression(log(italic(f[\"v\"])) (Hz)), ylab=expression(italic(F)), main=\"External forces k=0.2 & Q=180nl/min\", col=mycolors[1], pch=pchc[1], ylim=c(0,5e-14))\r\n\r\nlines(lowess(k1[,1], (duty[1]*q[1])/(k1[,1]*k1[,2]^2)), col=mycolors[1], lwd=2.5,lty=2)\r\n\r\nfor(i in 1:3){\r\n\r\npoints(lowess(k1[,1], (duty[1]*q[i+1])/(k1[,1]*k1[,i+2]^2)),pch=pchc[i+1], lwd=2, col=mycolors[i+1])\r\nlines(lowess(k1[,1], (duty[1]*q[i+1])/(k1[,1]*k1[,i+2]^2)), lwd=2.5, col=mycolors[i+1], lty=2)\r\n}\r\n\r\nlegend(\"topright\",c(\"1.5nl/min\", \"27nl/min\",\"54nl/min\",\"180nl/min\"), inset=0.02, col=mycolors, pch=pchc, lwd=2, bty=\"n\")\r\n\r\nabline(h= (duty[1]*q[1])/(k1[11,1]*k1[11,2]^2), col=\"red\", lwd=2,lty=3)\r\nabline(h= (duty[1]*q[2])/(k1[11,1]*k1[11,3]^2), col=\"blue\", lwd=2, lty=3)\r\nabline(h= (duty[1]*q[3])/(k1[11,1]*k1[11,4]^2), col=\"darkgreen\", lwd=2,lty=3)\r\nabline(h= (duty[1]*q[4])/(k1[11,1]*k1[11,5]^2), col=\"yellow3\", lty=3,lwd=2)\r\ntext(20, 1.5e-14,(duty[1]*q[1])/(k1[11,1]*k1[11,2]^2), col=\"red\", font=2, cex=1)\r\ntext(20, 1.8e-14,(duty[1]*q[2])/(k1[11,1]*k1[11,3]^2), ,font=2, col=\"blue\", cex=1)\r\ntext(20, 2.1e-14,(duty[1]*q[3])/(k1[11,1]*k1[11,4]^2), ,font=2, col=\"darkgreen\", cex=1)\r\ntext(20, 2.4e-14,(duty[1]*q[4])/(k1[11,1]*k1[11,5]^2), ,font=2,col=\"yellow3\", cex=1)", "meta": {"hexsha": "023a266bf6ee986179399476919905c181001711", "size": 1965, "ext": "r", "lang": "R", "max_stars_repo_path": "volume-chap3/force_2.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "volume-chap3/force_2.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "volume-chap3/force_2.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.1020408163, "max_line_length": 226, "alphanum_fraction": 0.5699745547, "num_tokens": 1009, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3251596149157351}} {"text": "#' Plot deep learning SDM map\n#'\n#' A function that enables the plotting of deep learning predictions on a map.\n#' @param raster_data A raster dataset containing the occurrence data.\n#' @param keras_model A trained deep learning model.\n#' @param custom_fun A custom predict function.\n#' @param map_type A logical indicating if the map should be static or interactive.\n#' @return An interactive leaflet map, showing the species distribution.\n#' @examples\n#' \\dontrun{\n#' # download benchmarking data\n#' benchmarking_data <- get_benchmarking_data(\"Lynx lynx\",\n#' limit = 1500)\n#'\n#' # transform benchmarking data into a format suitable for deep learning\n#' # if you have previously used a partitioning method you should specify it here\n#' benchmarking_data_dl <- prepare_dl_data(input_data = benchmarking_data$df_data,\n#' partitioning_type = \"default\")\n#'\n#' # perform sanity check on the transformed dataset\n#' # for the training set\n#' head(benchmarking_data_dl$train_tbl)\n#' table(benchmarking_data_dl$y_train_vec)\n#'\n#' # for the test set\n#' head(benchmarking_data_dl$test_tbl)\n#' table(benchmarking_data_dl$y_test_vec)\n#'\n#' # train neural network\n#' keras_results <- train_dl(benchmarking_data_dl)\n#'\n#' # this function is needed for plotting\n#' temp_fun <- function(model, input_data) {\n#' input_data <- tibble::as_tibble(input_data)\n#' data <- recipes::bake(benchmarking_data_dl$rec_obj, new_data = input_data)\n#' v <- keras::predict_proba(object = model, x = as.matrix(data))\n#' as.vector(v)\n#' }\n#'\n#' # plot SDM map of neural network predictions\n#' # change the map_type argument if you want a dynamic leaflet map\n#' plot_dl_map(benchmarking_data$raster_data$climate_variables,\n#' keras_results$model,\n#' custom_fun = temp_fun,\n#' map_type = \"static\")\n#'}\n#'@export\nplot_dl_map <- function(raster_data, keras_model, custom_fun, map_type = \"static\") {\n pr <- dismo::predict(raster_data$climate_variables, keras_model, fun = custom_fun)\n if (map_type == \"static\") {\n raster::plot(pr, main = \"DL SDM Map\")\n } else if (map_type == \"interactive\") {\n pal <- leaflet::colorNumeric(c(\"#ffdbe2\", \"#fff56b\", \"#58ff32\"), raster::values(pr), na.color = \"transparent\")\n leaflet::leaflet(data = raster_data$coords_presence) %>%\n leaflet::addTiles() %>%\n leaflet::addRasterImage(pr, colors = pal, opacity = 0.5) %>%\n leaflet::addLegend(title = \"Habitat Suitability\", pal = pal, values = raster::values(pr), opacity = 1)\n }\n}\n", "meta": {"hexsha": "657bd7841d3b896deaa4001e79cabac890a1bfa1", "size": 2608, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_dl_map.r", "max_stars_repo_name": "boyanangelov/sdmbench", "max_stars_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2018-06-25T19:55:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-06T08:36:48.000Z", "max_issues_repo_path": "R/plot_dl_map.r", "max_issues_repo_name": "boyanangelov/sdmbench", "max_issues_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2018-08-01T01:31:09.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-12T16:02:07.000Z", "max_forks_repo_path": "R/plot_dl_map.r", "max_forks_repo_name": "boyanangelov/sdmbench", "max_forks_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-10-12T06:07:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-02T17:52:53.000Z", "avg_line_length": 43.4666666667, "max_line_length": 118, "alphanum_fraction": 0.6786809816, "num_tokens": 639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3251596073118461}} {"text": "setwd('C:\\\\kaggle\\\\competitions\\\\hackathon')\r\n\r\nlibrary(reshape)\r\nlibrary(plyr)\r\n\r\n#Read the data:\r\nTrainingData = read.csv(\"./upload/TrainingData.csv\")\r\nSubmissionZeros = read.csv(\"./upload/SubmissionZerosExceptNAs.csv\")\r\n\r\nnames(TrainingData)\r\n\r\n#Convenient to have a way to refer to target variables:\r\ntarget_names = names(SubmissionZeros)[6:44]\r\ntarget_names\r\n\r\n#Average over hours\r\ntarget_means_by_hour_of_day = ddply(TrainingData, \"hour\", function(df) colMeans(df[,target_names], na.rm = TRUE))\r\nnames(SubmissionZeros)\r\n\r\n#Create slim submission skeleton for merging onto\r\nsubmission_hour_means = SubmissionZeros[,1:5]\r\n\r\n#Create full set of submissions via merge\r\nsubmission_hour_means = merge(submission_hour_means, target_means_by_hour_of_day)\r\n\r\n#function for putting -1000000's wherever they appear in the sample submission (I'm sorry this is necessary)\r\nreplaceNAs = function(submission, sample) {\r\n submission = submission[,names(sample)]\r\n stopifnot(mean(names(submission) == names(sample))==1)\r\n stopifnot(dim(submission) == dim(sample))\r\n submission = submission[order(submission$rowID),]\r\n submission[sample == -1000000] = -1000000\r\n submission\r\n}\r\n\r\n\r\nsubmission_hour_means_NAs = replaceNAs(submission_hour_means, SubmissionZeros)\r\nwrite.csv(submission_hour_means_NAs, \"./upload/submission_hour_means.csv\", row.names = FALSE)\r\n\r\n#Average by hour and chunk:\r\ntarget_means_by_hour_and_chunkID = ddply(TrainingData, c(\"chunkID\", \"hour\"), function(df) colMeans(df[,target_names], na.rm = TRUE))\r\nnames(SubmissionZeros)\r\nsubmission_hour_chunk_means = SubmissionZeros[,1:5]\r\n\r\n#The first time I did this I didn't use \"all.x=TRUE\" and my data frame was too short. Good thing I have error checking in \"replaceNAs\"!\r\nsubmission_hour_chunk_means = merge(submission_hour_chunk_means, target_means_by_hour_and_chunkID, all.x=TRUE)\r\nsubmission_hour_chunk_means_NAs = replaceNAs(submission_hour_chunk_means, SubmissionZeros)\r\n\r\n#A few hour/chunk combinations seem to have given us NA's (always missing in training data).\r\n#So we'll replace anywhere we have NA's with whatever we would have predicted in the last submission:\r\nsubmission_hour_chunk_means_NAs[is.na(submission_hour_chunk_means_NAs)] = submission_hour_means_NAs[is.na(submission_hour_chunk_means_NAs)]\r\nsummary(submission_hour_chunk_means_NAs)\r\nwrite.csv(submission_hour_chunk_means_NAs, \"./upload/submission_hour_chunk_means.csv\", row.names = FALSE)\r\n", "meta": {"hexsha": "52054a9539c380b11ba984edc7793eb038489d53", "size": 2425, "ext": "r", "lang": "R", "max_stars_repo_path": "AI/MachineLearning/Code/WeatherForcasting/AirQualityPrediction/sample_code.r", "max_stars_repo_name": "Louie-in-yr-AREA/HiTechOnlineCourse", "max_stars_repo_head_hexsha": "24a2be43ab2dab47af587ca6c71eb1c7b2ecc849", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_stars_repo_stars_event_min_datetime": "2019-11-25T15:12:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-25T19:12:24.000Z", "max_issues_repo_path": "AI/MachineLearning/Code/WeatherForcasting/AirQualityPrediction/sample_code.r", "max_issues_repo_name": "Louie-in-yr-AREA/HiTechOnlineCourse", "max_issues_repo_head_hexsha": "24a2be43ab2dab47af587ca6c71eb1c7b2ecc849", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AI/MachineLearning/Code/WeatherForcasting/AirQualityPrediction/sample_code.r", "max_forks_repo_name": "Louie-in-yr-AREA/HiTechOnlineCourse", "max_forks_repo_head_hexsha": "24a2be43ab2dab47af587ca6c71eb1c7b2ecc849", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 23, "max_forks_repo_forks_event_min_datetime": "2019-06-24T10:02:13.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-17T10:39:27.000Z", "avg_line_length": 44.9074074074, "max_line_length": 140, "alphanum_fraction": 0.7868041237, "num_tokens": 580, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32515960731184607}} {"text": "library(qvalue)\r\nfor(i in c(1:22,\"X\")){\r\n d = read.table(paste(\"eqtl.nopermute.chr\",i,sep=\"\"), head=F, sep=\" \", stringsAsFactors=F)\r\n d$qvalue = qvalue(d$V12)$qvalues\r\n write.table(d[which(!is.na(d$qvalue)),], paste(\"eqtl.nopermute.chr\",i,\".qvalue\",sep=\"\"), quote=F, row.names=F, col.names=T, sep=\"\\t\")\r\n}\r\n", "meta": {"hexsha": "36f3456c2f88cf7f87d878283cfe290c47f3c017", "size": 316, "ext": "r", "lang": "R", "max_stars_repo_path": "robust eQTL/FDR.r", "max_stars_repo_name": "liusihan/Comparison_EAS_EUR", "max_stars_repo_head_hexsha": "ca29b738e3eb862253b76fdc19d0e006675ffa6a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-09-14T01:20:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T02:52:12.000Z", "max_issues_repo_path": "robust eQTL/FDR.r", "max_issues_repo_name": "zhoujiaqi704/population-compare-pipeline", "max_issues_repo_head_hexsha": "e8564bf3010bde6eb45ffd818bcafb23eb0b785c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "robust eQTL/FDR.r", "max_forks_repo_name": "zhoujiaqi704/population-compare-pipeline", "max_forks_repo_head_hexsha": "e8564bf3010bde6eb45ffd818bcafb23eb0b785c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-07-28T01:58:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T02:14:36.000Z", "avg_line_length": 45.1428571429, "max_line_length": 138, "alphanum_fraction": 0.6139240506, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32515960731184607}} {"text": "#!/usr/bin/env Rscript\n\n#===========================================================\n# R script for summarizing the results of generalized FitHiC\n# Here, two different plots are generated:\n# 1) distribution of contact count vs interaction distance for two different sets of interactions (separated by Q value)\n# 2) distribution of contact count (as boxplot) for two different sets of interactions (separated by Q value)\n\n# Q threshold is set as 0.01\n\n#Author: Sourya Bhattacharyya\n#Vijay-Ay lab, LJI\n\n# usage: Rscript result_summary.r $inpfile\n#===========================================================\n\nsuppressMessages(library(ggplot2))\nsuppressMessages(library(data.table))\n\noptions(scipen = 10)\noptions(datatable.fread.datatable=FALSE)\n\nargs <- commandArgs(TRUE)\n\n# the 1st file is the file without any q-value based filtering\nunfilt.file <- args[1] \ninpdir <- dirname(unfilt.file)\n\n# column no having the absolute contact count\ncontactcol <- as.integer(args[2])\n\n# threshold of Q value which is used for filtering the contacts\nQTHR <- as.double(args[3])\n\noutdir <- paste0(inpdir,'/Results')\nsystem(paste('mkdir -p', outdir))\n\nboxplotCCQvalfile <- paste0(outdir,'/','CC_Qval.png')\nplotfile <- paste0(outdir,'/','CC_IntDist.png')\n\nif (1) { #((file.exists(boxplotCCQvalfile) == FALSE) | (file.exists(plotfile) == FALSE)) {\n\n\t# load the interaction matrix of the unfiltered file\n\t# Note: this unfiltered interaction file has header information\n\tunfilt.data <- data.table::fread(unfilt.file, header=T, sep=\"\\t\", stringsAsFactors=F)\n\n\t# absolute genomic distance data\n\tif (0) {\n\t\t## old code - when genomic distance field was not specified in the output interactions\n\t\tgen.dist <- abs(unfilt.data[,5] - unfilt.data[,2])\n\t} else {\n\t\t## new code - genomic distance field is specified\n\t\tgen.dist <- unfilt.data$Dist\n\t}\n\n\t# indices corresponding to significant and insignificant interactions\n\t# get the genomic distance and contact count for the significant interactions (Q value < QTHR)\n\tidx.qthr.pass <- which(unfilt.data[,ncol(unfilt.data)] < QTHR)\n\t# get the genomic distance and contact count for the nonsignificant interactions (Q value >= QTHR)\n\tidx.qthr.fail <- which(unfilt.data[,ncol(unfilt.data)] >= QTHR)\n\n\t# we only process and analyze if we have at least 2 success and 2 failure candidates\n\tif ((length(idx.qthr.pass) > 1) && (length(idx.qthr.fail) > 1)) {\n\t\t\n\t\tcontactcountcol <- unfilt.data[,contactcol]\n\n\t\tCC.qthr.pass <- contactcountcol[idx.qthr.pass]\n\t\tgen.dist.qthr.pass <- gen.dist[idx.qthr.pass]\n\t\t\n\t\tCC.qthr.fail <- contactcountcol[idx.qthr.fail]\n\t\tgen.dist.qthr.fail <- gen.dist[idx.qthr.fail]\n\n\t\t#===========================================\n\t\t# plot the distributions of contact count for two different sets of Q values\n\t\t# The first set is the set of significant interactions (Q val < 0.01)\n\t\t# The second set is the set of significant interactions (Q val >= 0.01)\n\t\tif (file.exists(boxplotCCQvalfile) == FALSE) {\n\t\t\ta <- data.frame(group = paste0(\"CC_Qval<\", QTHR), value = CC.qthr.pass)\n\t\t\tb <- data.frame(group = paste0(\"CC_Qval>=\", QTHR), value = CC.qthr.fail)\n\t\t\tccQVal <- rbind(a, b)\n\t\t\t#pdf(boxplotCCQvalfile, width=10, height=6)\n\t\t\tcurrplot <- ggplot(ccQVal, aes(x=group, y=value, fill=group)) + geom_boxplot()\n\t\t\tcurrplot + ggtitle(\"Box plot for contact counts - significant vs non-significant interactions\")\n\t\t\tggsave(boxplotCCQvalfile, width=10, height=6)\n\t\t\t#dev.off()\t\t\n\t\t}\n\t\t#===========================================\n\n\t\t# first bind the genomic distance and the contact count information\n\t\t# for both Q value sets\n\t\tqthr.pass_GenDist_CC <- cbind(gen.dist.qthr.pass, CC.qthr.pass)\n\t\tqthr.fail_GenDist_CC <- cbind(gen.dist.qthr.fail, CC.qthr.fail)\n\n\t\t# now sort the data frames according to the genomic distance\n\t\t# (first column ascending order)\n\t\t# also remove the duplicate entries\n\t\tqthr.pass_GenDist_CC_uniq <- unique(qthr.pass_GenDist_CC[ order(qthr.pass_GenDist_CC[,1]), ])\n\t\tqthr.fail_GenDist_CC_uniq <- unique(qthr.fail_GenDist_CC[ order(qthr.fail_GenDist_CC[,1]), ])\n\n\t\t# now plot all the elements of the significant (pass) interactions\n\t\t# and selectively plot the elements of the non-significant (fail) interactions\n\t\tnelem <- length(qthr.pass_GenDist_CC_uniq[,1])\n\n\t\tqval.fail.meanCC <- c()\n\t\tAvgIntDist.fail <- c()\n\t\tnum.qval.fail <- length(qthr.fail_GenDist_CC_uniq[,1])\n\t\tnentry <- floor(num.qval.fail / nelem)\n\t\tfor (i in (1:nelem)) {\n\t\t\tsi <- (i - 1) * nentry + 1\n\t\t\tif (i < nelem) {\n\t\t\t\tei <- si + nentry - 1\t\t\n\t\t\t} else {\n\t\t\t\tei <- num.qval.fail\n\t\t\t}\n\t\t\tqval.fail.meanCC[i] <- mean(qthr.fail_GenDist_CC_uniq[si:ei,2])\n\t\t\tAvgIntDist.fail[i] <- mean(qthr.fail_GenDist_CC_uniq[si:ei,1])\n\t\t}\n\n\t\ta <- data.frame(group = \"Qval<0.01\", x = qthr.pass_GenDist_CC_uniq[,1], y = qthr.pass_GenDist_CC_uniq[,2])\n\t\tb <- data.frame(group = \"Qval>=0.01\", x = AvgIntDist.fail, y = qval.fail.meanCC)\n\t\tcurr_plot <- ggplot(rbind(a,b), aes(x=x, y=y, fill=group, colour=group)) + geom_point(size=0.01) + xlab('Average interaction distance') + ylab('Contact count')\n\t\tcurr_plot + ggtitle(\"Interaction distance vs Contact count - both Qvalue classes\")\n\t\tggsave(plotfile, width=12, height=8)\n\n\t#=========================================== \t\t\n\t} else {\n\t\tcat(sprintf(\"\\n Either significant or insignificant interactions have count 0 - quit !!\"))\n\t}\n}\n\n", "meta": {"hexsha": "e82a566b9921b7833789b95b51a46e13c7ec09c4", "size": 5284, "ext": "r", "lang": "R", "max_stars_repo_path": "Analysis/result_summary.r", "max_stars_repo_name": "torchij/FitHiChIP", "max_stars_repo_head_hexsha": "02cf7f07b17a8d5ae418eb4e5e00d681e5f9a7da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Analysis/result_summary.r", "max_issues_repo_name": "torchij/FitHiChIP", "max_issues_repo_head_hexsha": "02cf7f07b17a8d5ae418eb4e5e00d681e5f9a7da", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Analysis/result_summary.r", "max_forks_repo_name": "torchij/FitHiChIP", "max_forks_repo_head_hexsha": "02cf7f07b17a8d5ae418eb4e5e00d681e5f9a7da", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3358778626, "max_line_length": 161, "alphanum_fraction": 0.6778955337, "num_tokens": 1477, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32515959970795694}} {"text": "# Estimates mean age of families, mean phylogenetic fuse of families, and phylogenetic species variability (PSV) across three super biome categories: arid, temperate, tropical.\n# The super biome categorization follows the grouping of the original biomes of Olson et al. 2001 described in the Supplementary Methods.\n# Continues with R objects generated in previous steps (loads objects).\n# Uses additional files.\n\nload(\"~/Documents/BIOMES/super_biomeRaster.Rdata\") # file in data folder\nload(\"~/Documents/NATURE/DISTS.july19/prPAM_FAM_grid.r\") # file in data folder\n\nload(paste(ruta_write,\"WeightedMAFS.RData\",sep=\"\"))\ncat(\"Maximum number of species per grid-cell:\",max(rowSums(prPAM_g[,-c(1:2)])),\"species\",\"\\n\")\nwhich(rowSums(prPAM_g[,-c(1:2)]) == 0) -> iden_inf\nprPAM_g2<- prPAM_g[-iden_inf,]\niden <- which(rowSums(prPAM_g2[,-c(1:2)]) > 5)\npointos <- SpatialPoints(prPAM_g2[iden,1:2])\nproj4string(pointos) <- \"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0\"\nbiome_points<- cbind(prPAM_g2[iden,1:2],raster::extract(super_biomes,pointos))\ncolnames(biome_points)[3]<-\"BIOME\"\nwhich(is.na(biome_points$BIOME)) -> iden_bim\nbiome_points <- biome_points[-which(is.na(biome_points$BIOME)),]\npsv <- psv(samp = prPAM_g2[iden,-c(1:2)],tree = tre.pruned)\nPSV <- psv$PSVs\nStem <- MDT_stem\nCrown <- MDT_crown\nFuse <- MDT_fuse_non\nload(paste(ruta_write,\" MDT_stem_NULL2.Rdata\",sep=\"\"))\nload(paste(ruta_write,\" MDT_crown_NULL2.Rdata\",sep=\"\"))\nload(paste(ruta_write,\" MDT_fuse_non_NULL2.Rdata\",sep=\"\"))\nload(paste(ruta_write,\" MDT_stem_NULL1.Rdata\",sep=\"\"))\nload(paste(ruta_write,\" MDT_crown_NULL1.Rdata\",sep=\"\"))\nload(paste(ruta_write,\" MDT_fuse_non_NULL1.Rdata\",sep=\"\"))\nSES_stem_N1 <- (MDT_stem - apply(MDT_stem_NULL1[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_stem_NULL1[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_crown_N1 <- (MDT_crown - apply(MDT_crown_NULL1[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_crown_NULL1[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_fuse_N1 <- (MDT_fuse_non - apply(MDT_fuse_non_NULL1[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_fuse_non_NULL1[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_stem_N2 <- (MDT_stem - apply(MDT_stem_NULL2[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_stem_NULL2[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_crown_N2 <- (MDT_crown - apply(MDT_crown_NULL2[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_crown_NULL2[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_fuse_N2 <- (MDT_fuse_non - apply(MDT_fuse_non_NULL2[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(MDT_fuse_non_NULL2[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\n\nload(paste(ruta_write,\"PSV_random_rich.Rdata\",sep=\"\"))\nload(paste(ruta_write,\"PSV_random_freq.Rdata\",sep=\"\"))\nSES_PSV_N1 <- (PSV - apply(PSV_random_rich[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(PSV_random_rich[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSES_PSV_N2 <- (PSV - apply(PSV_random_freq[,-c(1:2)],MARGIN=1,FUN=mean,na.rm=T))/(apply(PSV_random_freq[,-c(1:2)],MARGIN=1,FUN=sd,na.rm=T))\nSR <- rowSums(prPAM_g2[iden,-c(1:2)]); print(min(SR))\ndata<-data.frame(LAT=biome_points$LAT,LON=biome_points$LON,Stem=Stem[-iden_bim],Crown=Crown[-iden_bim],\n FUSE=Fuse[-iden_bim],\n PSV=PSV[-iden_bim],\n SES_crown_1 = SES_crown_N1[-iden_bim],\n SES_stem_1 = SES_stem_N1[-iden_bim],\n SES_PSV_1 = SES_PSV_N1[-iden_bim],\n SES_fuse_1 = SES_fuse_N1[-iden_bim],\n SES_crown_2 = SES_crown_N2[-iden_bim],\n SES_stem_2 = SES_stem_N2[-iden_bim],\n SES_PSV_2 = SES_PSV_N2[-iden_bim],\n SES_fuse_2 = SES_fuse_N2[-iden_bim],\n Super_biome = as.factor(biome_points$BIOME),\n SR=SR[-iden_bim])\nunique(data$Super_biome)\nlevels(data$Super_biome) <- c(\"Temperate\",\"Temperate\",\"Tropical\",\"Arid\"); levels(data$Super_biome)\nssp<-coordinates(data[,2:1])\niden <- clean_coordinates(as.data.frame(ssp), lon = \"LON\",lat = \"LAT\",\n tests=\"seas\",species=NULL,value=\"flagged\",seas_scale=110)\n\nstem <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=Stem,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'Mean family stem age') +\n geom_hline(yintercept = mean(data[iden,\"Stem\"])) +\n geom_hline(yintercept = mean(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"Stem\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"Stem\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(Stem ~ Super_biome,data=data[iden,]))\n\ncrown <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=Crown,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'Mean family Crown age') +\n geom_hline(yintercept = mean(data[iden,\"Crown\"])) +\n geom_hline(yintercept = mean(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"Crown\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"Crown\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(Crown ~ Super_biome,data=data[iden,]))\n\n\nfuse <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=FUSE,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'Mean Fuse of Families') +\n geom_hline(yintercept = mean(data[iden,\"FUSE\"])) +\n geom_hline(yintercept = mean(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"FUSE\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"FUSE\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(FUSE ~ Super_biome,data=data[iden,]))\n\npsv <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=PSV,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'PSV') +\n geom_hline(yintercept = mean(data[iden,\"PSV\"],na.rm=T)) +\n geom_hline(yintercept = mean(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Arid\")],na.rm=T),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Temperate\")],na.rm=T),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Tropical\")],na.rm=T),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"PSV\"],method=\"Venter\",na.rm=T),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\",na.rm=T),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\",na.rm=T),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"PSV\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\",na.rm=T),col=\"blue\",lty=3)\nsummary(aov(PSV ~ Super_biome,data=data[iden,]))\n\nses.psv1 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_PSV_1,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_PSV_1') +\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_1\"],na.rm=T)) +\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Arid\")],na.rm=T),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Temperate\")],na.rm=T),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Tropical\")],na.rm=T),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_1\"],method=\"Venter\",na.rm=T),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\",na.rm=T),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\",na.rm=T),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_1\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\",na.rm=T),col=\"blue\",lty=3)\nsummary(aov(SES_PSV_1 ~ Super_biome,data=data[iden,]))\n\nses.psv2 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_PSV_2,fill=Super_biome)) +\n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_PSV_2') +\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_2\"],na.rm=T)) +\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Arid\")],na.rm=T),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Temperate\")],na.rm=T),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Tropical\")],na.rm=T),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_2\"],method=\"Venter\",na.rm=T),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\",na.rm=T),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\",na.rm=T),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_PSV_2\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\",na.rm=T),col=\"blue\",lty=3)\nsummary(aov(SES_PSV_2 ~ Super_biome,data=data[iden,]))\n\nses.stem1 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_stem_1,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_stem_1') +\n geom_hline(yintercept = mean(data[iden,\"SES_stem_1\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_1\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_1\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_stem_1 ~ Super_biome,data=data[iden,]))\n\nses.stem2 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_stem_2,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_stem_2') +\n geom_hline(yintercept = mean(data[iden,\"SES_stem_2\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_2\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_stem_2\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_stem_2 ~ Super_biome,data=data[iden,]))\n\nses.crown1 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_crown_1,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_crown_1') +\n geom_hline(yintercept = mean(data[iden,\"SES_crown_1\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_1\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_1\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_crown ~ Super_biome,data=data[iden,]))\n\nses.crown2 <-data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_crown_2,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_crown_2') +\n geom_hline(yintercept = mean(data[iden,\"SES_crown_2\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_2\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_crown_2\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_crown_2 ~ Super_biome,data=data[iden,]))\n\nses.fuse1 <- data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_fuse_1,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_fuse_1') +\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_1\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_1\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_1\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_fuse_1 ~ Super_biome,data=data[iden,]))\n\nses.fuse2 <-data[iden,] %>%\n mutate(Super_biome = fct_reorder(Super_biome, Stem)) %>%\n ggplot(aes(x=Super_biome, y=SES_fuse_2,fill=Super_biome)) + \n geom_violin(alpha=0.6) +\n theme(legend.position=\"none\") +\n labs(title = 'SES_fuse_2') +\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_2\"])) +\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Arid\")]),col=\"red\")+\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Temperate\")]),col=\"green\")+\n geom_hline(yintercept = mean(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Tropical\")]),col=\"blue\")+\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_2\"],method=\"Venter\"),lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Arid\")],method=\"Venter\"),col=\"red\",lty=3) +\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Temperate\")],method=\"Venter\"),col=\"green\",lty=3)+\n geom_hline(yintercept = mlv(data[iden,\"SES_fuse_2\"][which(data$Super_biome[iden]==\"Tropical\")],method=\"Venter\"),col=\"blue\",lty=3)\nsummary(aov(SES_fuse_2 ~ Super_biome,data=data[iden,]))\n\npdf(paste(ruta_write,\"8.BiomeMAF_SBcomplete_20SR.pdf\",sep=\"\"))\ngridExtra::grid.arrange(stem, crown, fuse, psv, nrow = 2)\ngridExtra::grid.arrange(ses.stem1, ses.stem2, ses.crown1, ses.crown2, nrow = 2)\ngridExtra::grid.arrange(ses.psv1, ses.psv2, ses.fuse1, ses.fuse2, nrow = 2)\ndev.off()\n\nsink(paste(ruta_write,\"Superbiome_tests.txt\"))\ncat(\"STEM ----------------\",\"\\n\")\nsummary(aov(Stem ~ Super_biome,data=data[iden,]))\ncat(\"CROWN ----------------\",\"\\n\")\nsummary(aov(Crown ~ Super_biome,data=data[iden,]))\ncat(\"PSV ----------------\",\"\\n\")\nsummary(aov(PSV ~ Super_biome,data=data[iden,]))\ncat(\"SES CROWN ----------------\",\"\\n\")\nsummary(aov(SES_crown ~ Super_biome,data=data[iden,]))\ncat(\"SES STEM ----------------\",\"\\n\")\nsummary(aov(SES_stem ~ Super_biome,data=data[iden,]))\ncat(\"SES PSV ----------------\",\"\\n\")\nsummary(aov(SES_PSV ~ Super_biome,data=data[iden,]))\nsink()\n", "meta": {"hexsha": "da44cab9d511be53f236c395c31368dbb21e8f42", "size": 18865, "ext": "r", "lang": "R", "max_stars_repo_path": "code/final_analyses/7.StatisticsBiome.r", "max_stars_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_stars_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-14T03:29:42.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-14T03:29:42.000Z", "max_issues_repo_path": "code/final_analyses/7.StatisticsBiome.r", "max_issues_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_issues_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/final_analyses/7.StatisticsBiome.r", "max_forks_repo_name": "spiritu-santi/angiosperm-time-tree-2.0", "max_forks_repo_head_hexsha": "d1a8a14edadd15834ccba052991886853f4c852a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-07-06T15:43:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-06T15:43:48.000Z", "avg_line_length": 68.6, "max_line_length": 176, "alphanum_fraction": 0.6999734959, "num_tokens": 6668, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6992544085240401, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.32508447331259555}} {"text": "#!/usr/bin/env Rscript\nlengths <- as.integer(readLines(\"stdin\"))\nargs<-commandArgs(TRUE)\noutput = args[1]\noutput = paste(output,'.pdf',sep='')\npdf(output,width=5,height=5)\nhist(lengths, seq(0,10000,by=100))\ndev.off()", "meta": {"hexsha": "0402eb0a40a32bb4b5d375c4edf2684e61675bf4", "size": 216, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/plotHistogram.r", "max_stars_repo_name": "odingsy/NGStoolkit", "max_stars_repo_head_hexsha": "68d73810351550b9ba75f9184f26bc8e55708fcc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-05-05T06:24:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-04T22:24:13.000Z", "max_issues_repo_path": "bin/plotHistogram.r", "max_issues_repo_name": "odingsy/NGStoolkit", "max_issues_repo_head_hexsha": "68d73810351550b9ba75f9184f26bc8e55708fcc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/plotHistogram.r", "max_forks_repo_name": "odingsy/NGStoolkit", "max_forks_repo_head_hexsha": "68d73810351550b9ba75f9184f26bc8e55708fcc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-12-27T22:02:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-28T20:28:26.000Z", "avg_line_length": 27.0, "max_line_length": 41, "alphanum_fraction": 0.7037037037, "num_tokens": 66, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.665410572017153, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.324908933370279}} {"text": "# svmtest\n\n## Check data: can the SVM be performed\nsvmMessage = \"\"\nif (length(unique(Multi_datainput_m$groupingvar)) > 3) {\n NO_svm = TRUE\n svmMessage = paste0(svmMessage, \"SVM can only be done if there no more than 3 groups.\")\n}\n\nif (min(summary(\n Multi_datainput_m$groupingvar)) < 10 ) {\n NO_svm = TRUE\n svmMessage = paste0(svmMessage, \" At least one group has a sample size lower than 10\")\n}\n\n# choose the type of validation depending on the sample size (>15 in each group ?)\ntestvalidation = ifelse((min(summary(\n Multi_datainput_m$groupingvar\n)) > 15), TRUE, FALSE)\nValidation_type = ifelse(testvalidation, \"independent test dataset\", \"2-out\")\n\n# check groups size difference.\n\nif (Validation_type == \"2-out\" ){\n if (max(summary(Multi_datainput_m$groupingvar)) - min(summary(Multi_datainput_m$groupingvar)) != 0){\n NO_svm = TRUE\n svmMessage = paste0(svmMessage, \"in 2-out validation, we need the same number of animals per group, please select animals manually.\")\n }\n}\n \n\n\n print(svmMessage)\n Accuracyreal = NA # set Accuracyreal for the case no SVM can be performed\n ", "meta": {"hexsha": "98981905932ba7df2163e3118af7ed2bcbfc8781", "size": 1092, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/Rcode/svmtest.r", "max_stars_repo_name": "jcolomb/HCS_analysis", "max_stars_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-09-27T08:57:12.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-22T08:44:06.000Z", "max_issues_repo_path": "analysis/Rcode/svmtest.r", "max_issues_repo_name": "jcolomb/HCS_analysis", "max_issues_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 36, "max_issues_repo_issues_event_min_datetime": "2017-09-27T10:42:45.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-01T08:47:42.000Z", "max_forks_repo_path": "analysis/Rcode/svmtest.r", "max_forks_repo_name": "jcolomb/HCS_analysis", "max_forks_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-12-10T12:45:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-19T14:28:05.000Z", "avg_line_length": 31.2, "max_line_length": 137, "alphanum_fraction": 0.7216117216, "num_tokens": 304, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3248892273478976}} {"text": "#' Match a specific week\n#'\n#' Create an indicator variable for a specific week.\n#'\n#' @param x_week Numeric tis object.\n#' @param week_number Numeric scalar; Week of the year to match.\n#' @return An indicator variable where the observation that matches the week entered = 1, 0 otherwise.\n#' @examples\n#' xmas_w53 <- match_week(ic_week, 53)\n#' @import stats\n#' @export\nmatch_week <- function(x_week, week_number) {\n # Author: Brian C. Monsell (OEUS), Version 1.2, 3/23/2021\n \n # generate a vector of strings of dates associated with weekly observations.\n date_index <- tis::ymd(tis::ti(x_week))\n \n # set up filter with observations that match the number of the week\n week_filter <- date_index[x_week == week_number]\n \n # initialize indicator variable with 0\n dummy <- array(0, dim = length(x_week))\n \n # set observation matching the filter to 1\n dummy[date_index %in% week_filter] <- 1\n \n # return indicator variable\n return(dummy)\n}\n", "meta": {"hexsha": "b0d53f3649b90fb243db0cd731ade59de093b1db", "size": 982, "ext": "r", "lang": "R", "max_stars_repo_path": "R/match_week.r", "max_stars_repo_name": "bcmonsell/airutilities", "max_stars_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/match_week.r", "max_issues_repo_name": "bcmonsell/airutilities", "max_issues_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/match_week.r", "max_forks_repo_name": "bcmonsell/airutilities", "max_forks_repo_head_hexsha": "278d52b6accf576fea1f21801564664e15d5f2b0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.7333333333, "max_line_length": 102, "alphanum_fraction": 0.6904276986, "num_tokens": 250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.32477184400040326}} {"text": "#Author: Josh Roll \r\n#Date: 11/12/2020\r\n#Subject: Analyze FARS data to understand ped fatal injuries by RACE\r\n#Description: This script uses data from US Census and NHTSA FARS to calculate the rate of injury per 100,000 using age-adjusted methods to determine existing disparities for \r\n#ODOT Research Project SPR 841\r\n#License: Apache 2.0\r\n#Version notes:\r\n#Version 4 - Now calcualting rates using person years instead of single years\r\n\r\n#Load libraries\r\n#------------------------------\r\n\t#Establish library path\r\n\tlibrary(lubridate)\r\n\tlibrary(tigris)\r\n\tlibrary(dplyr)\r\n\tlibrary(readxl)\r\n\tlibrary(rgdal)\r\n\tlibrary(sf)\r\n\tlibrary(stringr)\r\n\tlibrary(leaflet)\r\n\tlibrary(htmlwidgets)\r\n\tlibrary(tidyr)\r\n\tlibrary(ggplot2)\r\n\tlibrary(MASS)\r\n\tlibrary(mfx)\r\n\tlibrary(pscl)\r\n\tlibrary(corrplot)\r\n\tlibrary(readxl)\r\n\tlibrary(Metrics)\r\n\tlibrary(epitools)\r\n\tlibrary(data.table)\r\n\tlibrary(acs)\r\n\tlibrary(scales)\r\n\tlibrary(gridExtra)\r\n\t\r\n\r\n\r\n#Set environmanetal \r\n#-----------------\r\n options( scipen = 10 )\r\n\r\n#Define custom scripts functions\r\n#------------------------------\r\n\t#Function that simplifies loading .RData objects\r\n\tassignLoad <- function(filename){\r\n load(filename)\r\n get(ls()[ls() != \"filename\"])\r\n }\t\r\n\t\t\r\n\t#Function to create bin labels\r\n\tlabel_Bins <- function(Breaks.){\r\n\t\tLabels_ <- list()\r\n\t\tfor(i in 1:(length(Breaks.)-1)){\r\n\t\t\tLabels_[[i]] <- paste(Breaks.[[i]], Breaks.[[i + 1]], sep=\"-\")\r\n\t\t}\r\n\t\t#Return result\r\n\t\tunlist(Labels_)\r\n\t}\r\n\r\n#Load Data\r\n#---------------------------------------------\r\n\t########################\r\n\t#State Level population data by age/Race (5 year ACS)\r\n\tState_Census.. <- assignLoad(file = \"/Data/State_Age_Sex_2009_2018_5yr.RData\")\r\n\t\t\r\n\t#Load US FARS Person table\r\n\t#############################\r\n\tLoad_Person.. <- assignLoad(file = \"//wpdotfill09/R_VMP3_USERS/tdb069/Data/Crash/Data/FARS/Data/Processed/Person_2009-2018.RData\")\r\n\t#Prepare FARS data - Aggregate data to create a composite BIPOC race category\r\n\t#Create data set classifying participants as BIPOC\r\n\t#Make a copy\r\n\tPerson_Bipoc.. <- Load_Person..\r\n\t#Mutate disaggregate BIPOC races into composite BIPOC category\r\n\tPerson_Bipoc.. <- filter(Person_Bipoc.. %>% mutate(Race_desc = ifelse(Race_desc%in%c(\"AIAN\",\"Combined Other Asian Or Pacific Islander, Includes Data\",\"Black\",\"Asian\",\"NHPI\",\"Latino\",\"Guamanian\",\"Hawaiian (Includes Part-Hawaiian)\",\r\n\t\t\"Other Indian (Includes South and Central America, Since\"), \"BIPOC\",\r\n\t\tifelse(Race_desc == \"White\", \"White\",\r\n\t\tifelse(Race_desc%in%c(\"Other\", \"Not A Fatality (Not Applicable)\",\"Unknown\"), \"Other or Unknown\",NA)))), Race_desc ==\"BIPOC\")\r\n\t#Combine\r\n\tLoad_Person.. <- rbind(Load_Person.. , Person_Bipoc..)\r\n\t\r\n\t#Define state of interest\r\n\t############################\r\n\t#FARS Data\r\n\tFARS_Select_State <- 41\r\n\t#Census population data state\r\n\tPopulation_Select_State <- \"Oregon\"\r\n\t\r\n\t#init a dataframe to store results\r\n\tMaster_Rate.. <- data.frame()\r\n\t#Loop through all periods of data\r\n\tfor(yr in c(2013,2018)){\r\n\t\t#Define Study Period\r\n\t\tStudy_Year <- yr\r\n\t\t#FARS_Year. <- 2014:2018\r\n\t\tFARS_Period. <- c((yr -4):yr)\t\t\r\n\t\t#Make a copy \r\n\t\tPerson.. <- Load_Person..\r\n\t\t#Select State data that are fatals (interested in another state define above)\r\n\t\tSelect_State_Person.. <- filter(Person.., State == FARS_Select_State & Inj_sev==4)\r\n\t\tUS_Person.. <- filter(Person.., Inj_sev==4)\r\n\t\t#Prepare Oregon FARS\r\n\t\t################\r\n\t\tOregon_Fatal_Summary.. <- filter(Select_State_Person.., Year%in%FARS_Period.) %>% group_by(Race_desc,Mode,Age_Cohort) %>% summarise(Count = length(St_case))\r\n\t\t#Look just at pedestrians\r\n\t\tSelect_State_Fatal.. <- filter(Oregon_Fatal_Summary..)\r\n\t\t#Create statewide fora all populations\r\n\t\tSelect_State_Fatal.. <- rbind(Select_State_Fatal..,cbind(Select_State_Fatal.. %>% group_by(Mode, Age_Cohort) %>% summarise(Count = sum(Count,na.rm=T)), Race_desc = \"Oregon\")[,colnames(Select_State_Fatal..)])\r\n\t\t#Prepare US FARS\r\n\t\tUS_Fatal_Summary.. <- filter(US_Person.., Year%in%FARS_Period.) %>% group_by(Race_desc,Mode,Age_Cohort) %>% summarise(Count = length(St_case))\r\n\t\tUS_Fatal.. <- filter(US_Fatal_Summary..)\r\n\t\t#Create nationwide fora all populations\r\n\t\tUS_Fatal.. <- rbind(US_Fatal..,cbind(US_Fatal.. %>% group_by(Mode, Age_Cohort) %>% summarise(Count = sum(Count,na.rm=T)), Race_desc = \"US\")[,colnames(Select_State_Fatal..)])\r\n\t\t#Alter study year for population \r\n\t\tif(yr%in%2013){Study_Year <- 2009:2013}\r\n\t\tif(yr%in%2018){Study_Year <- 2014:2018}\r\n\t\t\r\n\t\t#Summarise Oregon Population\r\n\t\t##################### \r\n\t\t#Pivote data\r\n\t\tPopulation_Summary.. <-\tfilter(State_Census.., State == Population_Select_State & Year%in%Study_Year) %>%\r\n\t\t\tpivot_longer(!c(State,GEOID,NAME,Year), names_to = \"Age_Cohort\", values_to = \"Population\")\r\n\t\t#Create new column for race\r\n\t\tPopulation_Summary.. <- mutate(Population_Summary.., \r\n\t\t\t#Age cohort\r\n\t\t\tAge_Cohort_Update = ifelse(grepl(\"Age_Under5\", Age_Cohort, ignore.case = T), \"Age_Under5\", \r\n\t\t\t\tifelse(grepl(\"Age_5_9\", Age_Cohort, ignore.case = T), \"Age_5_9\",\r\n\t\t\t\tifelse(grepl(\"Age_10_14\", Age_Cohort, ignore.case = T),\"Age_10_14\",\r\n\t\t\t\tifelse(grepl(\"Age_15_17\", Age_Cohort, ignore.case = T),\"Age_15_17\",\r\n\t\t\t\tifelse(grepl(\"Age_18_19\", Age_Cohort, ignore.case = T),\"Age_18_19\",\r\n\t\t\t\tifelse(grepl(\"Age_20_24\", Age_Cohort, ignore.case = T),\"Age_20_24\",\r\n\t\t\t\tifelse(grepl(\"Age_25_29\", Age_Cohort, ignore.case = T),\"Age_25_29\",\r\n\t\t\t\tifelse(grepl(\"Age_30_34\", Age_Cohort, ignore.case = T),\"Age_30_34\",\r\n\t\t\t\tifelse(grepl(\"Age_35_44\", Age_Cohort, ignore.case = T),\"Age_35_44\",\r\n\t\t\t\tifelse(grepl(\"Age_45_54\", Age_Cohort, ignore.case = T),\"Age_45_54\",\r\n\t\t\t\tifelse(grepl(\"Age_55_64\", Age_Cohort, ignore.case = T),\"Age_55_64\",\r\n\t\t\t\tifelse(grepl(\"Age_65_74\", Age_Cohort, ignore.case = T),\"Age_65_74\",\r\n\t\t\t\tifelse(grepl(\"Age_75_84\", Age_Cohort, ignore.case = T),\"Age_75_84\",\r\n\t\t\t\tifelse(grepl(\"Age_Over_84\", Age_Cohort, ignore.case = T),\"Age_Over_84\",\"Other\")))))))))))))),\r\n\t\t\t#Race\r\n\t\t\tRace_desc = ifelse(grepl(\"_W\", Age_Cohort, ignore.case = F), \"White\", \r\n\t\t\t\tifelse(grepl(\"_B\", Age_Cohort, ignore.case = F), \"Black\",\r\n\t\t\t\tifelse(grepl(\"_NHPI\", Age_Cohort, ignore.case = F),\"NHPI\",\r\n\t\t\t\tifelse(grepl(\"_AIAN\", Age_Cohort, ignore.case = F),\"AIAN\",\r\n\t\t\t\tifelse(grepl(\"_A\", Age_Cohort, ignore.case = F),\"Asian\",\t\t\t\t\t\t\r\n\t\t\t\tifelse(grepl(\"_L\", Age_Cohort, ignore.case = F),\"Latino\",\"Other\")))))),\r\n\t\t\t#Sex\r\n\t\t\tSex = ifelse(grepl(\"_Male\", Age_Cohort, ignore.case = T), \"Male\", \r\n\t\t\t ifelse(grepl(\"_Female\", Age_Cohort, ignore.case = T), \"Female\",\"Both\"\r\n\t\t)))\r\n\t\t#Do again for aggregate BIPOC Category\r\n\t\tPopulation_Summary_Bipoc.. <-\tfilter(State_Census.., State == Population_Select_State & Year%in% Study_Year) %>%\r\n\t\t\tpivot_longer(!c(State,GEOID,NAME,Year), names_to = \"Age_Cohort\", values_to = \"Population\")\r\n\t\t#Create new column for race\r\n\t\tPopulation_Summary_Bipoc.. <- mutate(Population_Summary_Bipoc.., \r\n\t\t\t#Age cohort\r\n\t\t\tAge_Cohort_Update = ifelse(grepl(\"Age_Under5\", Age_Cohort, ignore.case = T), \"Age_Under5\", \r\n\t\t\t\tifelse(grepl(\"Age_5_9\", Age_Cohort, ignore.case = T), \"Age_5_9\",\r\n\t\t\t\tifelse(grepl(\"Age_10_14\", Age_Cohort, ignore.case = T),\"Age_10_14\",\r\n\t\t\t\tifelse(grepl(\"Age_15_17\", Age_Cohort, ignore.case = T),\"Age_15_17\",\r\n\t\t\t\tifelse(grepl(\"Age_18_19\", Age_Cohort, ignore.case = T),\"Age_18_19\",\r\n\t\t\t\tifelse(grepl(\"Age_20_24\", Age_Cohort, ignore.case = T),\"Age_20_24\",\r\n\t\t\t\tifelse(grepl(\"Age_25_29\", Age_Cohort, ignore.case = T),\"Age_25_29\",\r\n\t\t\t\tifelse(grepl(\"Age_30_34\", Age_Cohort, ignore.case = T),\"Age_30_34\",\r\n\t\t\t\tifelse(grepl(\"Age_35_44\", Age_Cohort, ignore.case = T),\"Age_35_44\",\r\n\t\t\t\tifelse(grepl(\"Age_45_54\", Age_Cohort, ignore.case = T),\"Age_45_54\",\r\n\t\t\t\tifelse(grepl(\"Age_55_64\", Age_Cohort, ignore.case = T),\"Age_55_64\",\r\n\t\t\t\tifelse(grepl(\"Age_65_74\", Age_Cohort, ignore.case = T),\"Age_65_74\",\r\n\t\t\t\tifelse(grepl(\"Age_75_84\", Age_Cohort, ignore.case = T),\"Age_75_84\",\r\n\t\t\t\tifelse(grepl(\"Age_Over_84\", Age_Cohort, ignore.case = T),\"Age_Over_84\",\"Other\")))))))))))))),\r\n\t\t\t#Race\r\n\t\t\tRace_desc = ifelse(grepl(\"_W\", Age_Cohort, ignore.case = F), \"White\", \r\n\t\t\t\tifelse(grepl(\"_B\", Age_Cohort, ignore.case = F), \"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_NHPI\", Age_Cohort, ignore.case = F),\"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_AIAN\", Age_Cohort, ignore.case = F),\"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_A\", Age_Cohort, ignore.case = F),\"BIPOC\",\t\t\t\t\t\t\r\n\t\t\t\tifelse(grepl(\"_L\", Age_Cohort, ignore.case = F),\"BIPOC\",\"Other\")))))),\r\n\t\t\t#Sex\r\n\t\t\tSex = ifelse(grepl(\"_Male\", Age_Cohort, ignore.case = T), \"Male\", \r\n\t\t\t ifelse(grepl(\"_Female\", Age_Cohort, ignore.case = T), \"Female\",\"Both\"\r\n\t\t)))\r\n\t\t\r\n\t\t#Summarize\r\n\t\tPopulation_Summary_Bipoc.. <- Population_Summary_Bipoc.. %>% group_by( Race_desc, State, Age_Cohort_Update, Sex) %>% summarise(Population = sum(Population) )\r\n\t\t#Sum all years\r\n\t\tPopulation_Summary.. <- Population_Summary.. %>% group_by( Race_desc, State, Age_Cohort_Update, Sex) %>% summarise(Population = sum(Population) )\r\n\t\t#Combine\r\n\t\tPopulation_Summary.. <- rbind(Population_Summary..[,colnames(Population_Summary_Bipoc..)], filter(Population_Summary_Bipoc.., Race_desc !=\"White\"))\r\n\t\t\r\n\t\t#Prepare standard population data (US)\r\n\t\t##########################################\r\n\t\t#Pivot data\r\n\t\tUS_Population_Summary.. <-\t filter(State_Census.., Year%in%Study_Year) %>%\r\n\t\t\tpivot_longer(!c(State,GEOID,NAME,Year), names_to = \"Age_Cohort\", values_to = \"Population\") %>%\r\n\t\t\t#Update names\r\n\t\t\tmutate(\r\n\t\t\t#Age cohort\r\n\t\t\tAge_Cohort_Update = ifelse(grepl(\"Age_Under5\", Age_Cohort, ignore.case = T), \"Age_Under5\", \r\n\t\t\t\tifelse(grepl(\"Age_5_9\", Age_Cohort, ignore.case = T), \"Age_5_9\",\r\n\t\t\t\tifelse(grepl(\"Age_10_14\", Age_Cohort, ignore.case = T),\"Age_10_14\",\r\n\t\t\t\tifelse(grepl(\"Age_15_17\", Age_Cohort, ignore.case = T),\"Age_15_17\",\r\n\t\t\t\tifelse(grepl(\"Age_18_19\", Age_Cohort, ignore.case = T),\"Age_18_19\",\r\n\t\t\t\tifelse(grepl(\"Age_20_24\", Age_Cohort, ignore.case = T),\"Age_20_24\",\r\n\t\t\t\tifelse(grepl(\"Age_25_29\", Age_Cohort, ignore.case = T),\"Age_25_29\",\r\n\t\t\t\tifelse(grepl(\"Age_30_34\", Age_Cohort, ignore.case = T),\"Age_30_34\",\r\n\t\t\t\tifelse(grepl(\"Age_35_44\", Age_Cohort, ignore.case = T),\"Age_35_44\",\r\n\t\t\t\tifelse(grepl(\"Age_45_54\", Age_Cohort, ignore.case = T),\"Age_45_54\",\r\n\t\t\t\tifelse(grepl(\"Age_55_64\", Age_Cohort, ignore.case = T),\"Age_55_64\",\r\n\t\t\t\tifelse(grepl(\"Age_65_74\", Age_Cohort, ignore.case = T),\"Age_65_74\",\r\n\t\t\t\tifelse(grepl(\"Age_75_84\", Age_Cohort, ignore.case = T),\"Age_75_84\",\r\n\t\t\t\tifelse(grepl(\"Age_Over_84\", Age_Cohort, ignore.case = T),\"Age_Over_84\",\"Other\")))))))))))))),\r\n\t\t\t#Race\r\n\t\t\tRace_desc = ifelse(grepl(\"_W\", Age_Cohort, ignore.case = F), \"White\", \r\n\t\t\t\tifelse(grepl(\"_B\", Age_Cohort, ignore.case = F), \"Black\",\r\n\t\t\t\tifelse(grepl(\"_NHPI\", Age_Cohort, ignore.case = F),\"NHPI\",\r\n\t\t\t\tifelse(grepl(\"_AIAN\", Age_Cohort, ignore.case = F),\"AIAN\",\r\n\t\t\t\tifelse(grepl(\"_A\", Age_Cohort, ignore.case = F),\"Asian\",\t\t\t\t\t\t\r\n\t\t\t\tifelse(grepl(\"_L\", Age_Cohort, ignore.case = F),\"Latino\",\"Other\")))))),\r\n\t\t\t#Sex\r\n\t\t\tSex = ifelse(grepl(\"_Male\", Age_Cohort, ignore.case = T), \"Male\", \r\n\t\t\t ifelse(grepl(\"_Female\", Age_Cohort, ignore.case = T), \"Female\",\"Both\"\r\n\t\t\t))) %>%\r\n\t\t\t#Sum all states for national figures\r\n\t\t\tgroup_by(Age_Cohort_Update, Race_desc, Sex) %>% summarise(Population = sum(Population,na.rm=T) ) %>%\r\n\t\t\tmutate(Age_Cohort = Age_Cohort_Update) \r\n\t\t\t\r\n\t\t#Prepare standard population data (US) - Do for aggregate BIPOC----\r\n\t\t##########################################\r\n\t\t#Pivot data\r\n\t\tUS_Population_Summary_Bipoc.. <-\t filter(State_Census.., Year%in%Study_Year) %>%\r\n\t\t\tpivot_longer(!c(State,GEOID,NAME,Year), names_to = \"Age_Cohort\", values_to = \"Population\") %>%\r\n\t\t\t#Update names\r\n\t\t\tmutate(\r\n\t\t\t#Age cohort\r\n\t\t\tAge_Cohort_Update = ifelse(grepl(\"Age_Under5\", Age_Cohort, ignore.case = T), \"Age_Under5\", \r\n\t\t\t\tifelse(grepl(\"Age_5_9\", Age_Cohort, ignore.case = T), \"Age_5_9\",\r\n\t\t\t\tifelse(grepl(\"Age_10_14\", Age_Cohort, ignore.case = T),\"Age_10_14\",\r\n\t\t\t\tifelse(grepl(\"Age_15_17\", Age_Cohort, ignore.case = T),\"Age_15_17\",\r\n\t\t\t\tifelse(grepl(\"Age_18_19\", Age_Cohort, ignore.case = T),\"Age_18_19\",\r\n\t\t\t\tifelse(grepl(\"Age_20_24\", Age_Cohort, ignore.case = T),\"Age_20_24\",\r\n\t\t\t\tifelse(grepl(\"Age_25_29\", Age_Cohort, ignore.case = T),\"Age_25_29\",\r\n\t\t\t\tifelse(grepl(\"Age_30_34\", Age_Cohort, ignore.case = T),\"Age_30_34\",\r\n\t\t\t\tifelse(grepl(\"Age_35_44\", Age_Cohort, ignore.case = T),\"Age_35_44\",\r\n\t\t\t\tifelse(grepl(\"Age_45_54\", Age_Cohort, ignore.case = T),\"Age_45_54\",\r\n\t\t\t\tifelse(grepl(\"Age_55_64\", Age_Cohort, ignore.case = T),\"Age_55_64\",\r\n\t\t\t\tifelse(grepl(\"Age_65_74\", Age_Cohort, ignore.case = T),\"Age_65_74\",\r\n\t\t\t\tifelse(grepl(\"Age_75_84\", Age_Cohort, ignore.case = T),\"Age_75_84\",\r\n\t\t\t\tifelse(grepl(\"Age_Over_84\", Age_Cohort, ignore.case = T),\"Age_Over_84\",\"Other\")))))))))))))),\r\n\t\t\t#Race\r\n\t\t\tRace_desc = ifelse(grepl(\"_W\", Age_Cohort, ignore.case = F), \"White\", \r\n\t\t\t\tifelse(grepl(\"_B\", Age_Cohort, ignore.case = F), \"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_NHPI\", Age_Cohort, ignore.case = F),\"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_AIAN\", Age_Cohort, ignore.case = F),\"BIPOC\",\r\n\t\t\t\tifelse(grepl(\"_A\", Age_Cohort, ignore.case = F),\"BIPOC\",\t\t\t\t\t\t\r\n\t\t\t\tifelse(grepl(\"_L\", Age_Cohort, ignore.case = F),\"BIPOC\",\"Other\")))))),\r\n\t\t\t#Sex\r\n\t\t\tSex = ifelse(grepl(\"_Male\", Age_Cohort, ignore.case = T), \"Male\", \r\n\t\t\t ifelse(grepl(\"_Female\", Age_Cohort, ignore.case = T), \"Female\",\"Both\"\r\n\t\t\t))) %>%\r\n\t\t\t#Sum all states for national figures\r\n\t\t\tgroup_by(Age_Cohort_Update,Race_desc,Sex) %>% summarise(Population = sum(Population,na.rm=T)) %>%\r\n\t\t\tmutate(Age_Cohort = Age_Cohort_Update) \t\t\r\n\t\t#Sumamrise\r\n\t\tUS_Population_Summary_Bipoc.. <- US_Population_Summary_Bipoc.. %>% group_by(Age_Cohort_Update, Race_desc, Sex) %>% summarise(Population = sum(Population) )\r\n\t\t#Sum all years\r\n\t\t#US_Population_Summary.. <- US_Population_Summary.. %>% group_by( Race_desc,Age_Cohort_Update, Sex) %>% summarise(Population = sum(Population) / length(Study_Year))\r\n\t\t#Combine \r\n\t\tUS_Population_Summary.. <- rbind(US_Population_Summary..[,colnames(US_Population_Summary_Bipoc..)], filter(US_Population_Summary_Bipoc.., Race_desc !=\"White\")) %>% \r\n\t\t\tmutate(Age_Cohort = Age_Cohort_Update)\r\n\t\t\r\n\t\t#Join population and crash data Oregon\r\n\t\t##############################\r\n\t\t#Remove and replace Age cohort column\r\n\t\tPopulation_Summary.. <- dplyr::select(mutate(Population_Summary.., Age_Cohort = Age_Cohort_Update), -c(Age_Cohort_Update))\r\n\t\t#Create oregon average\r\n\t\tPopulation_Summary.. <- rbind(Population_Summary..,cbind(filter(Population_Summary.., Race_desc !=\"BIPOC\") %>% group_by(State, Age_Cohort, Sex) %>% \r\n\t\t\tsummarise(Population = sum(Population) ),Race_desc = \"Oregon\")[,c(\"Race_desc\", \"State\" , \"Sex\", \"Population\", \"Age_Cohort\")])\r\n\t\t#Join with fatal \r\n\t\tPopulation_Summary.. <- left_join(filter(Population_Summary..,Sex ==\"Both\"), Select_State_Fatal.., by = c(\"Age_Cohort\", \"Race_desc\"))\r\n\t\t#Calcuate rate\r\n\t\tPopulation_Summary.. <- mutate(Population_Summary.., Rate = Count / (Population /100000))\r\n\t\t#Create oregon population summary, create weights and add to data\r\n\t\tPopulation_Summary.. <- left_join(Population_Summary..,\r\n\t\t\t#Calculate weights\r\n\t\t\tmutate(cbind(filter(US_Population_Summary..) %>% group_by(Age_Cohort) %>% summarise(US_Population = sum(Population)),\r\n\t\t\t\tTotal_Population = US_Population_Summary.. %>% group_by() %>% \r\n\t\t\t\tsummarise(Total_US_Population = sum(Population))),US_Wgt = US_Population / Total_US_Population)[,c(\"Age_Cohort\",\"US_Population\",\"US_Wgt\")],\r\n\t\t\t\tby = \"Age_Cohort\")\r\n\t\t#Create population adjusted rates\r\n\t\tPopulation_Summary.. <- mutate(Population_Summary.., Adj_Rate = Rate * US_Wgt)\r\n\t\t#Remove age cohorts with zero traffic deaths\r\n\t\tPopulation_Summary.. <- Population_Summary..[!(is.na(Population_Summary..$Count)),]\r\n\t\t#Sum the weighted rates \r\n\t\tOR_AgeAdj_Rate.. <- cbind(mutate(Population_Summary.. %>% group_by(Race_desc,Mode) %>% summarise(Rate = sum(Adj_Rate), Count = sum(Count),SE = sum(Adj_Rate) / sum(Count),\r\n\t\t\tUB = (sum(Adj_Rate) / sum(Count)) * 1.96, LB = (sum(Adj_Rate) / sum(Count)) * -1.96, Population = sum(Population)), Rate_LB = Rate + LB, Rate_UB = Rate + UB),Geography = \"Oregon\")\r\n\t\t\r\n\t\t#Do national rate calculation\r\n\t\t###########################\r\n\t\t#US_Population_Summary.. <- ungroup(US_Population_Summary..) \r\n\t\tUS_Population_Summary.. <- dplyr::select(mutate(ungroup(US_Population_Summary..) , Age_Cohort = Age_Cohort_Update), -c(Age_Cohort_Update)) \r\n\t\t#US_Population_Summary.. <- mutate(US_Population_Summary.., Age_Cohort = Age_Cohort_Update)\r\n\t\t\r\n\t\t#Create US average\r\n\t\tUS_Population_Summary.. <- rbind(US_Population_Summary..,cbind(US_Population_Summary.. %>% group_by( Age_Cohort, Sex) %>% \r\n\t\t\tsummarise(Population = sum(Population)),Race_desc = \"US\")[,colnames(US_Population_Summary..)])\r\n\t\t#Join with fatal \r\n\t\tUS_Population_Summary.. <- left_join(filter(US_Population_Summary..,Sex ==\"Both\"), US_Fatal.., by = c(\"Age_Cohort\", \"Race_desc\"))\r\n\t\t#Calcuate rate\r\n\t\tUS_Population_Summary.. <- mutate(US_Population_Summary.., Rate = Count / (Population /100000))\r\n\t\t#Create US population summary, create weights and add to data\r\n\t\tUS_Population_Summary.. <- left_join(US_Population_Summary..,\r\n\t\t\t#Calculate weights\r\n\t\t\tmutate(cbind(US_Population_Summary.. %>% group_by(Age_Cohort) %>% summarise(US_Population = sum(Population)),\r\n\t\t\t\tTotal_Population = US_Population_Summary.. %>% group_by() %>% \r\n\t\t\t\tsummarise(Total_US_Population = sum(Population))),US_Wgt = US_Population / Total_US_Population)[,c(\"Age_Cohort\",\"US_Population\",\"US_Wgt\")],\r\n\t\t\t\tby = \"Age_Cohort\")\r\n\t\t#Create population adjusted rates\r\n\t\tUS_Population_Summary.. <- mutate(US_Population_Summary.., Adj_Rate = Rate * US_Wgt)\r\n\t\t#Remove age cohorts with zero traffic deaths\r\n\t\tUS_Population_Summary.. <- US_Population_Summary..[!(is.na(US_Population_Summary..$Count)),]\r\n\t\t#Sum the weighted rates \r\n\t\tUS_AgeAdj_Rate.. <- cbind(mutate(US_Population_Summary.. %>% group_by(Race_desc, Mode) %>% summarise(Rate = sum(Adj_Rate), Count = sum(Count),SE = sum(Adj_Rate) / sum(Count),\r\n\t\t\tUB = (sum(Adj_Rate) / sum(Count)) * 1.96,LB = (sum(Adj_Rate) / sum(Count)) * -1.96, Population = sum(Population)), Rate_LB = Rate + LB, Rate_UB = Rate + UB),Geography = \"US\")\r\n\t\t\t\t\r\n\t\t#Store rate\r\n\t\t#################\r\n\t\tMaster_Rate.. <- rbind(Master_Rate..,cbind(rbind(OR_AgeAdj_Rate..,US_AgeAdj_Rate..),Year = paste(FARS_Period.[1],\"-\",FARS_Period.[5],sep=\"\")))\r\n\t\t\r\n\t}\r\n\t\t\t\r\n\t#Change names to better descriptions for charting\r\n\t#################################\r\n\tMaster_Rate.. <- mutate(Master_Rate.., Race_desc = ifelse(Race_desc == \"Oregon\", \"Oregon Average\",\r\n\t\tifelse(Race_desc == \"US\", \"US Average\",\r\n\t\t#Chnage to more descr. race categories\r\n\t\tifelse(Race_desc == \"AIAN\", \"American Indian &\\n Alaskan Native\",\r\n\t\tifelse(Race_desc == \"NHPI\", \"Native Hawaiian &\\n Pacific Islander\",\r\n\t\tifelse(Race_desc == \"Latino\", \"Latinx\",\r\n\t\tifelse(!(Race_desc%in%c(\"AIAN\",\"Oregon\",\"NHPI\")), Race_desc,NA)))))),\r\n\t\tRace_desc = factor(Race_desc,c(\"American Indian &\\n Alaskan Native\",\"Asian\",\"Black\",\"Latinx\",\"Native Hawaiian &\\n Pacific Islander\",\"White\",\"BIPOC\",\"Oregon Average\",\"US Average\")))\r\n\t\r\n\t#Store data\r\n\tsave(Master_Rate.., file = \"/Data/Injury_Rates.RData\")\r\n\t\r\n\t#Explore results\r\n\tas.data.frame(filter(Master_Rate.. , Mode ==\"Pedestrian\" & Geography ==\"Oregon\")) [,c(\"Race_desc\",\"Mode\",\"Count\",\"Population\",\"Rate\",\"Rate_LB\",\"Rate_UB\",\"SE\",\"UB\",\"LB\",\"Geography\",\"Year\")]\r\n\t\r\n\t#Chart the results\r\n\t#-----------------------\r\n\t#Chart most current period of Fatal Pedestrian Injury Rates\r\n\t###########################\r\n\tdat <- \tmutate(filter(Master_Rate.., Year == \"2014-2018\" & Geography == \"Oregon\" & Mode == \"Pedestrian\" ))\r\n\t#dat <- P2\r\n\tdat$Rate_LB[dat$Rate_LB <0] <- 0\r\n\tColors. <- colorRampPalette(c(\"skyblue\", \"lightgreen\"))(nrow(dat))\r\n\tPlot1 <- ggplot(dat, aes(x = Race_desc, y = Rate)) + \r\n\t\t#coord_flip() +\r\n\t\tgeom_bar(stat=\"identity\", width=.5, position = \"dodge\",aes(fill = Race_desc)) +\r\n\t\tgeom_errorbar(aes(ymin=Rate_LB, ymax=Rate_UB), width=.2, position=position_dodge(.9)) +\r\n\t\t#scale_y_continuous(labels=percent) +\r\n\t\tgeom_text(aes(x = Race_desc, y = 1, label = round(Rate,1)),fontface = \"bold\", size = 8, position = position_dodge(width = .5)) +\r\n\t\tlabs(y = \"Fatal Pedestrian Injuries per 100,000 Person-Years\" , x = \"Census/NHTSA Race Categories\", caption =\"Source: FARS & Census/n*Age-adjusted Rates\") +\r\n\t\tggtitle(\"Pedestrian /n Traffic Injury Death Rates* by Race/nOregon/n2014-2018\") + \r\n\t\tscale_fill_manual( values=Colors. ) +\r\n\t\ttheme(text = element_text(size = 16)) +\r\n\t\tfacet_wrap(~Mode, nrow = 1,scales = \"free\") +\r\n\t\t#Center plot\r\n\t\ttheme(plot.title = element_text(hjust = 0.5)) + \r\n\t\ttheme(legend.position = \"none\") +\r\n\t\ttheme(legend.text=element_text(size=16),legend.title=element_text(size=16)) +\r\n\t\ttheme(axis.text.y=element_text(size=20),axis.title.y=element_text(size=16)) +\r\n\t\ttheme(axis.text.x=element_text(size=16),axis.title.x=element_text(size=16)) +\r\n\t\ttheme(axis.title.x=element_text(size=16)) +\r\n\t\ttheme(strip.text.x = element_text(size = 14)) \t\r\n\r\n\t#Chart second period of Fatal Pedestrian Injury Rates\r\n\tdat <- \tmutate(filter(Master_Rate.., Year == \"2009-2013\" & Geography == \"Oregon\" & Mode == \"Pedestrian\" ))\r\n\tdat$Rate_LB[dat$Rate_LB <0] <- 0\r\n\tColors. <- colorRampPalette(c(\"skyblue\", \"lightgreen\"))(nrow(dat))\r\n\tPlot2 <- ggplot(dat, aes(x = Race_desc, y = Rate)) + \r\n\t\t#coord_flip() +\r\n\t\tgeom_bar(stat=\"identity\", width=.5, position = \"dodge\",aes(fill = Race_desc)) +\r\n\t\tgeom_errorbar(aes(ymin=Rate_LB, ymax=Rate_UB), width=.2, position=position_dodge(.9)) +\r\n\t\t#scale_y_continuous(labels=percent) +\r\n\t\tgeom_text(aes(x = Race_desc, y = 1, label = round(Rate,1)),fontface = \"bold\", size = 8, position = position_dodge(width = .5)) +\r\n\t\tlabs(y = \"Fatal Pedestrian Injuries per 100,000 Person-Years\" , x = \"Census/NHTSA Race Categories\", caption =\"Source: FARS & Census/n*Age-adjusted Rates\") +\r\n\t\tggtitle(\"Pedestrian /n Traffic Injury Death Rates* by Race/nOregon/n2009-2013\") + \r\n\t\tscale_fill_manual( values=Colors. ) +\r\n\t\ttheme(text = element_text(size = 16)) +\r\n\t\tfacet_wrap(~Mode, nrow = 1,scales = \"free\") +\r\n\t\t#Center plot\r\n\t\ttheme(plot.title = element_text(hjust = 0.5)) + \r\n\t\ttheme(legend.position = \"none\") +\r\n\t\ttheme(legend.text=element_text(size=16),legend.title=element_text(size=16)) +\r\n\t\ttheme(axis.text.y=element_text(size=20),axis.title.y=element_text(size=16)) +\r\n\t\ttheme(axis.text.x=element_text(size=16),axis.title.x=element_text(size=16)) +\r\n\t\ttheme(axis.title.x=element_text(size=16)) +\r\n\t\ttheme(strip.text.x = element_text(size = 14)) \t\r\n\t\r\n\t#Arrange data into a single plot\r\n\tgrid.arrange(Plot1, Plot2)\r\n\t\r\n\t#Line chart show Oregon - Shows pedestrian but select different user type for other modes\r\n\t#####################################\r\n\t#Select a user type to show\r\n\tuser_type <- \"Pedestrian\"\r\n\tdat <- \tfilter(Master_Rate.., Mode%in%user_type & Year%in%c(\"2009-2013\",\"2014-2018\") & Race_desc != \"Native Hawaiian &\\n Pacific Islander\")\r\n\tdat$Rate_LB[dat$Rate_LB <0] <- 0\r\n\tdat2 <- mutate(filter(dat, Race_desc%in%c(\"Oregon Average\",\"US Average\")), Geography = paste(Geography, \" Average\"))\r\n\tdat <- filter(dat, !(Race_desc%in%c(\"Oregon Average\",\"US Average\")))\r\n\tfor(i in 1:length(unique(dat$Race_desc))){dat2 <- rbind(dat2,mutate(dat2,Race_desc = unique(dat$Race_desc)[i]))}\r\n\tdat2 <- filter(dat2, !(Race_desc%in%c(\"Oregon Average\",\"US Average\")))\r\n\tuser_type\r\n\t#dat2$Type <- \"Average\"\r\n\tdat <- rbind(dat, dat2)\r\n\tdat <- filter(dat, !(Geography%in%c(\"US\",\"US Average\")))\r\n\tdat$Geography[dat$Geography%in%\"Oregon\"] <- \"Rate for Race\"\r\n\tdat$Geography[dat$Geography%in%\"US\"] <- \"US Rate for Race\"\r\n\tdat$Legend <- dat$Geography\r\n\tdat$Label <- round(dat$Rate,1)\r\n\tdat$Label[!(dat$Legend%in%\"Rate for Race\")] <- \"\"\r\n\tColors. <- colorRampPalette(c(\"blue\", \"darkgreen\"))(3)\r\n\tggplot(dat, aes(x = Year, y = Rate, group = Legend)) + \r\n\t\tgeom_line(aes(x = Year, y = Rate, color = Legend), size = .75) +\r\n\t\tgeom_point(aes(x = Year, y = Rate, color = Legend), size = 4.5) +\r\n\t\tgeom_ribbon(aes(ymin = Rate_LB, ymax = Rate_UB, fill = Legend),alpha=0.3) +\r\n\t\tgeom_text(aes(x = Year, y = Rate + 1, label = Label), fontface = \"bold\", size = 4) +\r\n\t\tfacet_wrap(~Race_desc) + \r\n\t\tscale_y_continuous( breaks=pretty_breaks(7), expand = c(0.15,0)) +\r\n\t\t# geom_text(aes(x = Year, y = Rate, label = round(Rate,1)),fontface = \"bold\", size = 4, position = position_dodge(width = .25)) +\r\n\t\tlabs(y = \"Deaths per 100,000 Person-Years\" , x = \"Analysis Period\", caption =\"*Age-adjusted Rates/nNHPI not shown due to small cell size\") +\r\n\t\tggtitle(paste(user_type,\"\\n Traffic Injury Death Rates* by Race/nOregon/nChange Over Time\",sep=\"\")) + \r\n\t\tscale_color_manual( values=Colors. ) +\r\n\t\ttheme(text = element_text(size = 16)) +\r\n\t\t#facet_wrap(~Year, nrow = 1) +\r\n\t\t#Center plot\r\n\t\ttheme(plot.title = element_text(hjust = 0.5)) + \r\n\t\tguides(color=guide_legend(title=\"Legend\")) +\t \r\n\t\ttheme(legend.text=element_text(size=16),legend.title=element_text(size=16)) +\r\n\t\ttheme(axis.text.y=element_text(size=20),axis.title.y=element_text(size=16)) +\r\n\t\ttheme(axis.text.x=element_text(size=16),axis.title.x=element_text(size=16)) +\r\n\t\ttheme(axis.title.x=element_text(size=16)) +\r\n\t\ttheme(strip.text.x = element_text(size = 14)) \r\n\r\n\t#Cycle through select modes and construct charts for those modes\r\n\t#####################################\r\n\tfor(user_type in c(\"Bicyclist\",\"Motor Vehicle\",\"Pedestrian\")){\r\n\t\t#Show change over time\r\n\t\t####################################\r\n\t\tdat <- \tmutate(filter(Master_Rate.., Geography == \"Oregon\" & Mode == user_type))\r\n\t\tdat$Rate_LB[dat$Rate_LB <0] <- 0\r\n\t\t\r\n\t\tColors. <- colorRampPalette(c(\"skyblue\", \"lightgreen\"))(3)\r\n\t\tPlot3 <- ggplot(dat, aes(x = Race_desc, y = Rate, group = Year)) + \r\n\t\t\t#coord_flip() +\r\n\t\t\tgeom_bar(stat=\"identity\", width=.5, position = \"dodge\",aes(fill = Year)) +\r\n\t\t\tgeom_errorbar(aes(ymin=Rate_LB, ymax=Rate_UB), width=.2, position = position_dodge(width = 0.5))+\r\n\t\t\tgeom_text(aes(x = Race_desc, y = Rate, label = round(Rate,1)),fontface = \"bold\", size = 5, position = position_dodge(width = .5)) +\r\n\t\t\tlabs(y = \"Deaths per 100,000 Person-Years\" , x = \"Census/NHTSA Race Categories\", caption =\"*Age-adjusted Rates\") +\r\n\t\t\tggtitle(paste(user_type,\"\\n Traffic Injury Death Rates* by Race/nOregon/nChange Over Time\",sep=\"\")) + \r\n\t\t\tscale_fill_manual( values=Colors. ) +\r\n\t\t\ttheme(text = element_text(size = 16)) +\r\n\t\t\t#facet_wrap(~Year, nrow = 1) +\r\n\t\t\t#Center plot\r\n\t\t\ttheme(plot.title = element_text(hjust = 0.5)) + \r\n\t\t\t#theme(legend.position = \"none\") +\r\n\t\t\ttheme(legend.text=element_text(size=12),legend.title=element_text(size=16)) +\r\n\t\t\ttheme(axis.text.y=element_text(size=20),axis.title.y=element_text(size=16)) +\r\n\t\t\ttheme(axis.text.x=element_text(size=16),axis.title.x=element_text(size=16)) +\r\n\t\t\ttheme(axis.title.x=element_text(size=16)) +\r\n\t\t\ttheme(strip.text.x = element_text(size = 14)) \r\n\t\t\r\n\t\t\r\n\t\t#Line chart show Oregon\r\n\t\t#####################################\r\n\t\tdat <- \tfilter(Master_Rate.., Mode%in% user_type)\r\n\t\tdat$Rate_LB[dat$Rate_LB <0] <- 0\r\n\t\tdat2 <- mutate(filter(dat, Race_desc%in%c(\"Oregon Average\",\"US Average\")), Geography = paste(Geography, \" Average\"))\r\n\t\tdat <- filter(dat, !(Race_desc%in%c(\"Oregon Average\",\"US Average\")))\r\n\t\tfor(i in 1:length(unique(dat$Race_desc))){dat2 <- rbind(dat2,mutate(dat2,Race_desc = unique(dat$Race_desc)[i]))}\r\n\t\tdat2 <- filter(dat2, !(Race_desc%in%c(\"Oregon Average\",\"US Average\")))\r\n\t\t\r\n\t\t#dat2$Type <- \"Average\"\r\n\t\tdat <- rbind(dat, dat2)\r\n\t\tdat <- filter(dat, !(Geography%in%c(\"US\",\"US Average\")))\r\n\t\tdat$Geography[dat$Geography%in%\"Oregon\"] <- \"Rate for Race\"\r\n\t\tdat$Geography[dat$Geography%in%\"US\"] <- \"US Rate for Race\"\r\n\t\tdat$Legend <- dat$Geography\r\n\t\tColors. <- colorRampPalette(c(\"blue\", \"darkgreen\"))(3)\r\n\t\tPlot4 <- ggplot(dat, aes(x = Year, y = Rate, group = Legend)) + \r\n\t\t #coord_flip() +\r\n\t\t #geom_bar(stat=\"identity\", width=.5, position = \"dodge\",aes(fill = Year)) +\r\n\t\t geom_line(aes(x = Year, y = Rate, color = Legend), size = .75) +\r\n\t\t geom_point(aes(x = Year, y = Rate, color = Legend), size = 4.5) +\r\n\t\t geom_ribbon(aes(ymin = Rate_LB, ymax = Rate_UB, fill = Legend),alpha=0.3) +\r\n\r\n\t\t facet_wrap(~Race_desc) + \r\n\t\t scale_y_continuous( breaks=pretty_breaks(7), expand = c(0.15,0)) +\r\n\t\t # geom_text(aes(x = Year, y = Rate, label = round(Rate,1)),fontface = \"bold\", size = 4, position = position_dodge(width = .25)) +\r\n\t\t labs(y = \"Deaths per 100,000\" , x = \"Analysis Period\", caption =\"*Age-adjusted Rates\") +\r\n\t\t ggtitle(paste(user_type,\"\\n Traffic Injury Death Rates* by Race/nOregon/nChange Over Time\",sep=\"\")) + \r\n\t\t scale_color_manual( values=Colors. ) +\r\n\t\t theme(text = element_text(size = 16)) +\r\n\t\t #facet_wrap(~Year, nrow = 1) +\r\n\t\t #Center plot\r\n\t\t theme(plot.title = element_text(hjust = 0.5)) + \r\n\t\t guides(color=guide_legend(title=\"Legend\")) +\t \r\n\t\t theme(legend.text=element_text(size=16),legend.title=element_text(size=16)) +\r\n\t\t theme(axis.text.y=element_text(size=20),axis.title.y=element_text(size=16)) +\r\n\t\t theme(axis.text.x=element_text(size=16),axis.title.x=element_text(size=16)) +\r\n\t\t theme(axis.title.x=element_text(size=16)) +\r\n\t\t theme(strip.text.x = element_text(size = 14)) \r\n\t\t\r\n\t\t\t \r\n\t\t#Write out pdf\r\n\t\tpdf(file = paste(\"Reports/\",user_type,\"_FARS_by_Race.pdf\",sep=\"\"), height = 11, width = 18)\r\n\t\tprint(Plot3)\r\n\t\tprint(Plot4)\r\n\t\t#print(Plot5)\r\n\t\tdev.off()\r\n}\r\n\t\r\n\t", "meta": {"hexsha": "204e4f767950ac2bbfd727654b0c5affb1985af4", "size": 29155, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/analyze_FARS_race.r", "max_stars_repo_name": "JoshRoll/Pedestrian-Fatal_Injury_Rate", "max_stars_repo_head_hexsha": "a359b46309278e4e50ff89cb45b53543183371fd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-17T05:08:47.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T05:08:47.000Z", "max_issues_repo_path": "Scripts/analyze_FARS_race.r", "max_issues_repo_name": "JoshRoll/Pedestrian-Fatal_Injury_Rate", "max_issues_repo_head_hexsha": "a359b46309278e4e50ff89cb45b53543183371fd", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": 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YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3247320443237448}} {"text": "\n decompose.into.sets = function( x, data.series=\"seabird\" ) {\n \n # determine an empirical threshold for fishing vs not fishing \n # this algorithm uses empircal CDF's and quantiles: most of the time is spent near the surface not fishing\n # set a threshold of the 0.05 quantile as the cutoff.. and make sure there is no drift either too\n \n\n ndata = length(x)\n X = data.frame( dataorder=1:ndata, X=x, fishing=1, id=NA )\n\n # based upon empirical observation from 2012 data, near-surface values should be ~ 0.014 decibar\n # detect any drift in the initial pressure values\n if (data.series==\"seabird\") {\n # error checking values to detect drift in pressure sensors at surface\n expectedValue=0.014 \n PrThreshold = 0.05 # median value ~ 0.055 decibars\n fishing.threshold = 10 # 5 decibars will remove most non-fishing events\n lscale = 60 # ==60 / 5 *5, the number of pings expected for a 5 min tow at 5 pings per second == 60/5 *5)\n # for the seabird there is a ping every 5 sec ; 2min minimum = 2*60/5 = 24 pings required \n }\n\n threshold = quantile( X$X, probs=PrThreshold ) \n if (abs (threshold - expectedValue ) > (2 * sd( X$X[ X$X <= threshold] )) ) {\n print( \"----------\")\n print( \"A significant deviation was detected in the data stream for the data file, above\" )\n print( \"Assuming it was drift, but the data should be checked.\" )\n print( paste( \"observed:\", threshold) ) \n print( \"expected: ~ 0.1 decibar\" )\n print( paste( \"mean: \", mean( x[x<=threshold] ) ) )\n print( paste( \"sd: \", sd( x[x<=threshold] ) ) )\n print( \"----------\")\n \n if (abs(threshold) > 1) {\n print( \"In fact, the deviation was > 1 decibar\" )\n print( \"Stopping now as there may be something wrong with the data\" )\n stop()\n }\n \n X$X = X$X - threshold \n \n }\n\n \n # flag to indicate is fishing is occurring via bottom contact\n not.fishing = which( X$X < fishing.threshold) # 5 decibars is a safe limit given the survey\n if (length(not.fishing) > 0) X$fishing[ not.fishing ] = 0\n \n # by taking the sum of fishing with fishing offset by 1, we can \n # get the boundaries where fishing starts and stop by looking for 1's\n X$fishing.boundary = X$fishing + c(0, X$fishing[1:(ndata-1)] ) \n intervals = which( X$fishing.boundary == 1 ) # these are the boundaries between fishing and non-fishing events\n\n \n count = 0\n for (o in 1:(length((intervals))-1)) { \n i0 = intervals[o]\n i1 = intervals[o+1]\n if ( (i1 - i0) < lscale*0.75 ) next() # accept only if the number of pings > 75% of expected number to be safe \n # number of pings is sufficient ... now check if fishing or not\n if (mean( X$X[i0:i1], na.rm=T) < 2*fishing.threshold ) next()\n count = count + 1\n X$id [ i0:i1 ] = count\n }\n return( X$id[ order(X$dataorder) ])\n }\n\n\n", "meta": {"hexsha": "a1d5e34361b2414e329666d5eb6dd91c935a02ae", "size": 2929, "ext": "r", "lang": "R", "max_stars_repo_path": "R/decompose.into.sets.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/decompose.into.sets.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/decompose.into.sets.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 42.4492753623, "max_line_length": 118, "alphanum_fraction": 0.6193240014, "num_tokens": 856, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7549149758396752, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.3247246752651819}} {"text": "# function to display estimation results 2015-3-11 Thong Pham\nprint.PAFit_result <- function(x,...) {\n if (length(x$oneshot) > 0) {\n if (x$oneshot == TRUE) \n cat(\"Containing the result of the PAFit_oneshot method. \\n\")\n } else {\n cat(\"\\nContaining the estimation results by the PAFit method. \\n\")\n if (x$only_PA == TRUE) {\n cat(\"Mode: Only the attachment function was estimated. \\n\") \n } else if (x$only_f == TRUE) {\n cat(\"Mode: Only node fitnesses were estimated. \\n\")\n }\n else {\n cat(\"Mode: Both the attachment function and node fitness were estimated. \\n\")\n }\n \n }\n #cat(\" Form of the PA function:\",x$mode_f,\"\\n\");\n if (x$only_f == FALSE) {\n if (length(x$auto_lambda) > 0) { \n if (x$auto_lambda == TRUE) {\n cat(\"Selected r parameter:\", x$ratio,\"\\n\"); \n } else cat(\"Lambda used:\", x$lambda,\"\\n\");\n }\n }\n \n if (x$only_PA == FALSE)\n cat(\"Selected s parameter: \",x$shape,\"\\n\", sep = \"\")\n cat(\"Estimated attachment exponent:\",x$alpha,\"\\n\");\n if (x$ci[1] == \"N\") {\n cat(\"No possible confidence interval for the estimated attachment exponent.\\n\");\n } else if (length(x$mode_f) > 0) {\n if (x$mode_f != \"Log_linear\") {\n cat(\"Attachment exponent \",\"\\u00B1\", \" 2 s.d.\", \": (\", x$ci[1], \",\", \n x$ci[2],\")\\n\",sep = \"\");\n }\n } else {\n cat(\"Attachment exponent \",\"\\u00B1\", \" 2 s.d.\", \": (\", x$ci[1], \",\", \n x$ci[2],\")\\n\",sep = \"\"); \n\n }\n}", "meta": {"hexsha": "e939e76aa2a66c6dcd3d7605a7eab5bfce0df456", "size": 1481, "ext": "r", "lang": "R", "max_stars_repo_path": "R/print.pafit_result.r", "max_stars_repo_name": "thongphamthe/PAFit", "max_stars_repo_head_hexsha": "e1c3b32e0f886eb0b90a8bcbed0c3719bd28ef44", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2017-08-22T14:24:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T18:38:48.000Z", "max_issues_repo_path": "R/print.pafit_result.r", "max_issues_repo_name": "thongphamthe/PAFit", "max_issues_repo_head_hexsha": "e1c3b32e0f886eb0b90a8bcbed0c3719bd28ef44", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-02-22T16:33:00.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-25T01:01:52.000Z", "max_forks_repo_path": "R/print.pafit_result.r", "max_forks_repo_name": "thongphamthe/PAFit", "max_forks_repo_head_hexsha": "e1c3b32e0f886eb0b90a8bcbed0c3719bd28ef44", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-05-12T04:40:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-05T12:09:33.000Z", "avg_line_length": 35.2619047619, "max_line_length": 84, "alphanum_fraction": 0.5422012154, "num_tokens": 460, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3247243299943589}} {"text": "library(tidyverse)\nlibrary(igraph)\n\npartes_tesis <- tibble::tribble(\n ~codigo, ~x, ~y, ~nodo_desc, ~tarea, ~origen, ~detalle,\n \"h1\", 1, 5, \"La red agregada total (desde 1996 a 2016) es de mundo peque\u00f1o puesto que es una caracter\u00edstica presente en la mayor\u00eda de las redes de coautor\u00edas cient\u00edficas.\", \"hipotesis\", \"Plan tesis\", \"mundo pequenio y libre escala\",\n \"h2\", 1, 10, \"La cantidad de autores aumenta en el tiempo debido al crecimiento de la disciplina en el pa\u00eds, lo que se refleja en un aumento en la cantidad de nodos en la red.\", \"hipotesis\", \"Plan tesis\", \"cuidado con crecimiento de disciplina\",\n \"h3\", 1, 15, \"Los autores fundadores se refuerzan en el tiempo, lo cual se ver\u00e1 reflejado en mayores medidas de grado, intermediaci\u00f3n y fuerza de colaboraci\u00f3n.\", \"hipotesis\", \"Plan tesis\", \"verificar en diferentes T las metricas.\",\n \"h4\", 1, 20, \"Los componentes m\u00e1s grandes absorben a los m\u00e1s peque\u00f1os a lo largo del tiempo, dado que autores perif\u00e9ricos pasan a trabajar en conjunto con autores principales.\", \"hipotesis\", \"Plan tesis\", \"si se abosrben componentes en tiempo, los links que se generan.\",\n \"t1\", 3, 1, \"Desambiguaci\u00f3n de nombres de autor, se considera un proceso necesario para el armado de la base de datos. Esto puede verse reflejado en el trabajo de Morel et al. (2009) [ 9 ] y una fundamentaci\u00f3n m\u00e1s extensa de por qu\u00e9 realizar esta tarea se observa en el trabajo de Tang (2010) [ 10 ]\", \"tarea\", \"kumar\", \"--\",\n \"t2\", 3, 3, \"Visualizaci\u00f3n de la red. Se plantea tener diferentes puntos de vista que pueden ayudar con el an\u00e1lisis de la red complementario a las m\u00e9tricas.\", \"tarea\", \"kumar\", \"--\",\n \"t3\", 3, 5, \"An\u00e1lisis de mundo peque\u00f1o y red de libre escala (Small World & scale free) para la red agregada total y para cada instante temporal, para poder comparar la red obtenida con simulaciones basadas en sus particularidades.\", NA, \"kumar\", \"--\",\n \"t4\", 3, 7, \"El tama\u00f1o del componente gigante y su evoluci\u00f3n en el tiempo. Este an\u00e1lisis brinda la posibilidad de ver qu\u00e9 tan cohesiva o fragmentada es la red.\", \"tarea\", \"kumar\", \"--\",\n \"t5\", 3, 9, \"El componente gigante podr\u00eda significar la principal actividad de investigaci\u00f3n; mientras que los otros componentes pueden ser agrupamientos especializados o sub-comunidades. (Fatt et al., 2010) [ 8 ]\", \"tarea\", \"kumar\", \"--\",\n \"t6\", 3, 11, \"B\u00fasqueda de Comunidades. Este an\u00e1lisis permite evaluar la ubicaci\u00f3n e interacciones entre los distintos grupos de investigaci\u00f3n dentro de la red y buscarles un sentido a las comunidades dentro del contexto de la disciplina.\", \"tarea\", \"koseglu\", \"--\",\n \"t7\", 3, 13, \"M\u00e9tricas generales a fin de analizar por per\u00edodos de agregaci\u00f3n y la red completa. De las m\u00e9tricas que se tendr\u00e1n en cuenta se destacan: (a) art\u00edculos con m\u00e1s de un autor, (b) autores que participan en art\u00edculos con m\u00e1s de un autor, (c) \u00edndice de colaboraci\u00f3n, siendo este \u00faltimo una relaci\u00f3n entre las medidas (b) / (c)\", \"tarea\", \"koseglu\", \"--\",\n \"t8\", 3, 15, \"Patrones de autor\u00eda, es decir, cu\u00e1ntos art\u00edculos por cantidad de autores existen en los diferentes periodos, dejando expuesto cuantos participan individualmente, en colaboraci\u00f3n de a 2 autores, o m\u00e1s. Este an\u00e1lisis es \u00fatil para evaluar si existen tendencias de mayor trabajo en equipo a lo largo del tiempo.\", \"tarea\", \"koseglu\", \"--\",\n \"t9\", 3, 17, \"Comparaci\u00f3n con otras redes de coautor\u00eda realizadas en otros trabajos utilizando m\u00e9tricas que est\u00e1n presentes en todos ellos por ejemplo: (a) art\u00edculos por autor, (b) coeficiente de clustering (transitividad), (c) tama\u00f1o del componente principal, (d) componente principal representado en porcentaje, y (e) distancia media.\", \"tarea\", \"koseglu\", \"--\",\n \"s1\", 5, 1, \"Capitulo 1: introduccion\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"Establecer contexto, plantear problema, plantear plan de solucion, pie a capitulo 2\",\n \"s2\", 5, 3, \"Capitulo 2: generacion Datos\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"intro de capitulo, materiales y metodos, resultados de procesos, resumen, pie al 3\",\n \"s3\", 5, 5, \"Capitulo 3: generacion herramienta\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"intro del capitulo, materiales y metodos, resumen del capitulo, pie al 4\",\n \"s4\", 5, 7, \"Capitulo 4: analisis de red de coautoria\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"intro del capitulo contestar hipotesis. Contestar objetivos. Resumen capitulo pie al 5\",\n \"s5\", 5, 9, \"Capitulo 5: discucion & cierre\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"Intro, discucion, cierre, trabajos futuros. Agradecimientos.\",\n \"s6\", 5, 11, \"ANEXO: software y librer\u00edas utilizadas\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"creditos al software\",\n \"s7\", 5, 13, \"ANEXO: Detalle estandarizaci\u00f3n autores\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"detalle de como se realizo la estandarizacion de autores\",\n \"s8\", 5, 15, \"ANEXO: Detalle consideraciones PDF\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"detalle de los pdf\\\\\",\n \"s9\", 5, 17, \"ANEXO: Detalle procesamiento bibTEX\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"detalle de los bibtex\",\n \"s10\", 5, 19, \"ANEXO: Detalle de algoritmos para b\u00fasqueda de comunidades\", \"seccion tesis\", \"Escritura \u2013 tesis\", \"detalle de algoritmos para la busqueda de comunidades\",\n \"a1\", 7, 1, \"cosideraciones: generales\", \"seccion app\", \"desarrollo app\", \"consideraciones\",\n \"a2\", 7, 3, \"consideraciones: EDA\", \"seccion app\", \"desarrollo app\", \"analisis exploratorio\",\n \"a3\", 7, 5, \"analisis estatico: visualizacion\", \"seccion app\", \"desarrollo app\", \"visualizacion\",\n \"a4\", 7, 7, \"analisis estatico: articulos asociados\", \"seccion app\", \"desarrollo app\", \"datos crudos\",\n \"a5\", 7, 9, \"analisis estatico: estructura red\", \"seccion app\", \"desarrollo app\", \"estructura grafo\",\n \"a6\", 7, 11, \"analisis estatico: comparacion modelos\", \"seccion app\", \"desarrollo app\", \"modelos\",\n \"a7\", 7, 13, \"analisis estatico: comunidades\", \"seccion app\", \"desarrollo app\", \"comunidades\",\n \"a8\", 7, 15, \"analisis temporal: visualizacion dinamica\", \"seccion app\", \"desarrollo app\", \"temporal evolucion\",\n \"a9\", 7, 17, \"analisis temporal: metricas red por anio\", \"seccion app\", \"desarrollo app\", \"metricas anuales\",\n \"a10\", 7, 19, \"analisis temporal: metricas red acumuladas\", \"seccion app\", \"desarrollo app\", \"metricas acumuladas\",\n \"a11\", 7, 21, \"analisis temporal: metricas nodos top N tiempo\", \"seccion app\", \"desarrollo app\", \"metricas top n\",\n \"a12\", 7, 23, \"analisis temporal: metricas nodos top N tiempo acumuladas\", \"seccion app\", \"desarrollo app\", \"metricas top n totales\"\n )\n\n\ngrafo_vacio <- make_empty_graph(n = 0, directed = FALSE) \n\nhipotesis <- partes_tesis %>% filter(str_detect(codigo,pattern = 'h')) %>% pull(codigo)\ntareas <- partes_tesis %>% filter(str_detect(codigo,pattern = 't')) %>% pull(codigo)\nsecciones <- partes_tesis %>% filter(str_detect(codigo,pattern = 's')) %>% pull(codigo)\n\ngrafo_resultado <- grafo_vacio +\n igraph::vertices(hipotesis,color='red') +\n igraph::vertices(tareas,color='blue') +\n igraph::vertices(secciones,color='green')\n\ndegree(grafo_resultado)\n\nvisNetwork::visIgraph(grafo_resultado)\n", "meta": {"hexsha": "3f9c8484061070f3bd0c85c20ce7bfd442d94a7a", "size": 18323, "ext": "r", "lang": "R", "max_stars_repo_path": "helpers/helper_ordenar_informacion_tesis.r", "max_stars_repo_name": "jas1/raab_coaut_tesis", "max_stars_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-01-26T03:25:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T03:25:05.000Z", "max_issues_repo_path": "helpers/helper_ordenar_informacion_tesis.r", "max_issues_repo_name": "jas1/raab_coaut_tesis", "max_issues_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-12-05T19:22:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-05T19:22:29.000Z", "max_forks_repo_path": "helpers/helper_ordenar_informacion_tesis.r", "max_forks_repo_name": "jas1/raab_coaut_tesis", "max_forks_repo_head_hexsha": "7ce2f11f9d2cc3e69e653c35699568e28d611492", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 315.9137931034, "max_line_length": 490, "alphanum_fraction": 0.277574633, "num_tokens": 2488, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3247243299943589}} {"text": "#' extract_pH.r\n#' Supplied with latitude and longtiude, this returns pH values at 250m resolution from the SoilGrids data product.\n#' http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169748\n#' https://soilgrids.org/\n#' \n#' Depends on the following packages:\n#' raster,\n#'\n#' @param latitude #a vector of latitude\n#' @param longitude #a vector of longitude\n#' @param folder #path to folder with SoilGrids raster. Defaults to scc1, built in check to work on pecan2.\n#'\n#' @return #returns a vector of pH values.\n#' @export\n#'\n#' @examples\n#' 4 points from very different places as (longitude, latitude).\n#' points <- structure(c(-102.644235, -58.015319, 50.508187, -148.2747566, 38.816171, -7.719157, 21.754477, 64.766184), .Dim = c(4L,2L))\n#' test.out <- extract_pH(points[,2], points[,1])\nextract_pH <- function(latitude, longitude,folder = '/project/talbot-lab-data/spatial_raster_data/SoilGrids_uncertainty'){\n #default directory path is scc1. Check if you are pecan2. Can be modified to work on more local machines this way.\n host <- system('hostname', intern=T)\n if(host == 'pecan2'){folder <- '/fs/data3/caverill/SoilGrids_pH/'}\n \n #specify raster path\n raster.path <- paste0(folder,'PHIHOX_M_sl2_250m.tif')\n \n #get lat-long as an object.\n points <- cbind(longitude, latitude)\n \n #load SoilGrids 1km pH raster.\n dat <- raster::raster(raster.path)\n \n #extract pH values.\n output <- raster::extract(dat, points)\n \n #divide by ten because to transform to true pH.\n output <- output/10\n \n #return output\n return(output)\n}\n", "meta": {"hexsha": "6a5aec86bb0f5f23bdb75f47f637e6fec2378cc6", "size": 1579, "ext": "r", "lang": "R", "max_stars_repo_path": "NEFI_functions/extract_pH.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "NEFI_functions/extract_pH.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "NEFI_functions/extract_pH.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 36.7209302326, "max_line_length": 136, "alphanum_fraction": 0.7023432552, "num_tokens": 472, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.32471262025934383}} {"text": "\nlibrary(data.table)\nlibrary(ggplot2)\nlibrary(corrplot) \n\n# install.packages(\"corrplot\")\n# install.packages(\"data.table\")\n\nsetwd(\"D:/data\")\ngetwd()\n\n\n\n\n# test code\n\n\nfilename <- \"1_result.csv_stat.csv\"\n\nd = read.csv(filename) \n\ndt <- data.table(d)\n\n\n\nplot(dt)\n\n", "meta": {"hexsha": "f2f8703f571cafdf4b7e29ce8feb5f1700377d1a", "size": 261, "ext": "r", "lang": "R", "max_stars_repo_path": "data-table.r", "max_stars_repo_name": "FiaDot/R-usage", "max_stars_repo_head_hexsha": "3ad5975499ba463fcdd40f8ab2e7bb1670b5ce33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data-table.r", "max_issues_repo_name": "FiaDot/R-usage", "max_issues_repo_head_hexsha": "3ad5975499ba463fcdd40f8ab2e7bb1670b5ce33", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data-table.r", "max_forks_repo_name": "FiaDot/R-usage", "max_forks_repo_head_hexsha": "3ad5975499ba463fcdd40f8ab2e7bb1670b5ce33", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 9.3214285714, "max_line_length": 35, "alphanum_fraction": 0.6743295019, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32471261185990175}} {"text": "png(\"temperature_distribution.png\")\ndata <- read.table(\"output_sorted\")\nplot(data, xlab=\"Temperature\", ylab=\"Number of readings\")\ndev.off()", "meta": {"hexsha": "e3a8cd60cc5f6d67608bfcc3de521a859573d91a", "size": 139, "ext": "r", "lang": "R", "max_stars_repo_path": "tomwhite-hadoop-book/ch09-mr-features/src/main/r/temperature_distribution.r", "max_stars_repo_name": "booknu/study-hadoop-book", "max_stars_repo_head_hexsha": "68c9f00d224289c470ba03533c571492979d850f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tomwhite-hadoop-book/ch09-mr-features/src/main/r/temperature_distribution.r", "max_issues_repo_name": "booknu/study-hadoop-book", "max_issues_repo_head_hexsha": "68c9f00d224289c470ba03533c571492979d850f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-08-02T17:05:27.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T22:28:27.000Z", "max_forks_repo_path": "tomwhite-hadoop-book/ch09-mr-features/src/main/r/temperature_distribution.r", "max_forks_repo_name": "booknu/study-hadoop-book", "max_forks_repo_head_hexsha": "68c9f00d224289c470ba03533c571492979d850f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.75, "max_line_length": 57, "alphanum_fraction": 0.7553956835, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961013, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3247126118599017}} {"text": "library(TMB)\n\n#don't uncomment this, just paste it into R if you have used the debugger flags above.\n#gdbsource(\"ss_mat_paper_v4.r\")\n\n#this allows dbs debugger to be used\ndyn.unload(\"ss_mat_paper_v4.so\")\ncompile(\"ss_mat_paper_v4.cpp\",\"-O0 -g\")\ndyn.load(\"ss_mat_paper_v4.so\")\n\nmat.dat <- read.csv('../../cod_gb_19702014s_0yr_mvavg.csv', header = TRUE)\nmat.dat = mat.dat[which(mat.dat$yc %in% 1963:2014),]\nmat.dat = mat.dat[which(mat.dat$AGE > 0),]\nfall.temp.dat = read.csv(\"../../1963-2014-fall-temp-for-state-space.csv\", header = TRUE)\ntemp = aggregate(as.integer(!(mat.dat$MATURITY == 'I')), by = list(yc = mat.dat$yc, age = mat.dat$AGE), FUN = sum)\ntemp = cbind.data.frame(temp, aggregate(as.integer(!(mat.dat$MATURITY == 'I')), by = list(yc = mat.dat$yc, age = mat.dat$AGE), FUN = length)[[3]])\nnames(temp)[3:4] = c(\"Y\",\"N\")\nx = list(Y = temp$Y)\nx$N = temp$N\nx$age_obs = temp$age\nx$year_obs = temp$age + temp$yc - 1962\nx$cy_obs = temp$yc - 1962\nx$Ecov_maxages_k = rep(0,length(x$Y))\nx$Ecov_maxages_a50 = rep(0,length(x$Y))\nx$Ecov_obs = cbind(fall.temp.dat$anom_bt)\nx$use_Ecov_obs = cbind(match(as.integer(!is.na(x$Ecov_obs)),c(0,1)) - 1)\nx$Ecov_obs[which(is.na(x$Ecov_obs))] = -999\nx$Ecov_obs_sigma = cbind(fall.temp.dat$sd1_bt)\nx$Ecov_obs_sigma[which(is.na(x$Ecov_obs_sigma))] = -999\nx$fit_k = 0\nx$fit_a50 = 0\nx$binomial = 1\n\ny = list(beta_k = 0)\ny$beta_a50 = 0\ny$beta_Ecov_k = rep(0,max(1,x$Ecov_maxages_k+1))\ny$beta_Ecov_a50 = rep(0,max(1,x$Ecov_maxages_a50+1))\ny$Ecov_mu = 0\ny$Ecov_AR_pars = rep(0, 2)\ny$Ecov_re = cbind(c(x$Ecov_obs))\ny$Ecov_re[which(x$use_Ecov_obs == 0)] = 0\ny$beta_phi = 0\ny$k_AR_pars = rep(0,2)\ny$k_re = rep(0, length(y$Ecov_re))\ny$a50_AR_pars = rep(0,2)\ny$a50_re = rep(0, length(y$Ecov_re))\n\ngbcod.mat.rev = list(dat = x, par = y)\n\ngbcod.mat.rev$map = list(\n beta_Ecov_k = factor(rep(NA, length(gbcod.mat.rev$par$beta_Ecov_k))),\n beta_Ecov_a50 = factor(rep(NA, length(gbcod.mat.rev$par$beta_Ecov_a50))),\n beta_phi = factor(rep(NA, length(gbcod.mat.rev$par$beta_phi))),\n k_AR_pars = factor(rep(NA, length(gbcod.mat.rev$par$k_AR_pars))),\n k_re = factor(rep(NA, length(gbcod.mat.rev$par$k_re))),\n a50_AR_pars = factor(rep(NA, length(gbcod.mat.rev$par$a50_AR_pars))),\n a50_re = factor(rep(NA, length(gbcod.mat.rev$par$a50_re)))\n )\n\ntemp = gbcod.mat.rev\n#no temperature effects, constant a50, k\nx = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod0 = x\nmod0$rep = mod0$report()\nmod0$sdrep = sdreport(x)\n\n#fall temperature effects at age 0 on k\ntemp = gbcod.mat.rev\ntemp$map = temp$map[which(names(temp$map) != \"beta_Ecov_k\")]\nmod1 = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nmod1$opt = nlminb(mod1$par,mod1$fn,mod1$gr)\nmod1$rep = mod1$report()\nmod1$sdrep = sdreport(mod1)\n\n#fall temperature effects at age 0 on a50\ntemp = gbcod.mat.rev\ntemp$map = temp$map[which(names(temp$map) != \"beta_Ecov_a50\")]\nmod2 = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nmod2$opt = nlminb(mod2$par,mod2$fn,mod2$gr)\nmod2$rep = mod2$report()\nmod2$sdrep = sdreport(mod2)\n\n#fall temperature effects at age 0 on k and a50\ntemp = gbcod.mat.rev\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\")))]\nmod3 = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nmod3$opt = nlminb(mod3$par,mod3$fn,mod3$gr)\nmod3$rep = mod3$report()\nmod3$sdrep = sdreport(mod3)\n\n#fall temperature effects at age 0 on a50 and 0,1 on k\ngbcod.mat.rev.4 = gbcod.mat.rev\ngbcod.mat.rev.4$dat$Ecov_maxages_k = rep(1,length(gbcod.mat.rev.4$dat$Y))\ngbcod.mat.rev.4$dat$Ecov_maxages_k[gbcod.mat.rev.4$dat$age_obs==1] = 0\ngbcod.mat.rev.4$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.4$dat$Ecov_maxages_k+1))\ngbcod.mat.rev.4$map = gbcod.mat.rev.4$map[which(!(names(gbcod.mat.rev.4$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\")))]\nmod4 = MakeADFun(gbcod.mat.rev.4$dat,gbcod.mat.rev.4$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.4$map)\nmod4$opt = nlminb(mod4$par,mod4$fn,mod4$gr)\nmod4$rep = mod4$report()\nmod4$sdrep = sdreport(mod4)\n\n#fall temperature effects at age 0,1 on a50 and 0 on k\n#fall temperature effects at age 0 and 1 on k\ngbcod.mat.rev.5 = gbcod.mat.rev.4\ngbcod.mat.rev.5$dat$Ecov_maxages_k = rep(0,length(gbcod.mat.rev.5$dat$Y))\ngbcod.mat.rev.5$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.5$dat$Ecov_maxages_k+1))\ngbcod.mat.rev.5$dat$Ecov_maxages_a50 = rep(1,length(gbcod.mat.rev.5$dat$Y))\ngbcod.mat.rev.5$dat$Ecov_maxages_a50[gbcod.mat.rev.5$dat$age_obs==1] = 0\ngbcod.mat.rev.5$par$beta_Ecov_a50 = rep(0,max(1,gbcod.mat.rev.5$dat$Ecov_maxages_a50+1))\n#gbcod.mat.rev.5$map = gbcod.mat.rev.5$map[which(!(names(gbcod.mat.rev.5$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\")))]\nmod5 = MakeADFun(gbcod.mat.rev.5$dat,gbcod.mat.rev.5$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.5$map)\nmod5$opt = nlminb(mod5$par,mod5$fn,mod5$gr)\nmod5$rep = mod5$report()\nmod5$sdrep = sdreport(mod5)\n\n#fall temperature effects at ages 0,1 on a50 and k\ngbcod.mat.rev.6 = gbcod.mat.rev.5\ngbcod.mat.rev.6$dat$Ecov_maxages_k = rep(1,length(gbcod.mat.rev.6$dat$Y))\ngbcod.mat.rev.6$dat$Ecov_maxages_k[gbcod.mat.rev.6$dat$age_obs==1] = 0\ngbcod.mat.rev.6$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.6$dat$Ecov_maxages_k+1))\n#gbcod.mat.rev.6$map = gbcod.mat.rev.6$map[which(!(names(gbcod.mat.rev.6$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\")))]\nmod6 = MakeADFun(gbcod.mat.rev.6$dat,gbcod.mat.rev.6$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.6$map)\nmod6$opt = nlminb(mod6$par,mod6$fn,mod6$gr)\nmod6$rep = mod6$report()\nmod6$sdrep = sdreport(mod6)\n\n#fall temperature effects at ages 0,1 on a50 and 0,1,2 on k\ngbcod.mat.rev.7 = gbcod.mat.rev.6\ngbcod.mat.rev.7$dat$Ecov_maxages_k = rep(2,length(gbcod.mat.rev.7$dat$Y))\ngbcod.mat.rev.7$dat$Ecov_maxages_k[gbcod.mat.rev.7$dat$age_obs==1] = 0\ngbcod.mat.rev.7$dat$Ecov_maxages_k[gbcod.mat.rev.7$dat$age_obs==2] = 1\ngbcod.mat.rev.7$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.7$dat$Ecov_maxages_k+1))\n#gbcod.mat.rev.7$map = gbcod.mat.rev.7$map[which(!(names(gbcod.mat.rev.7$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\")))]\nmod7 = MakeADFun(gbcod.mat.rev.7$dat,gbcod.mat.rev.7$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.7$map)\nmod7$opt = nlminb(mod7$par,mod7$fn,mod7$gr)\nmod7$rep = mod7$report()\nmod7$sdrep = sdreport(mod7)\n\n#fall temperature effects at ages 0,1,2 on a50 and 0,1 on k\ngbcod.mat.rev.8 = gbcod.mat.rev.7\ngbcod.mat.rev.8$dat$Ecov_maxages_k = rep(1,length(gbcod.mat.rev.8$dat$Y))\ngbcod.mat.rev.8$dat$Ecov_maxages_k[gbcod.mat.rev.8$dat$age_obs==1] = 0\ngbcod.mat.rev.8$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.8$dat$Ecov_maxages_k+1))\ngbcod.mat.rev.8$dat$Ecov_maxages_a50 = rep(2,length(gbcod.mat.rev.8$dat$Y))\ngbcod.mat.rev.8$dat$Ecov_maxages_a50[gbcod.mat.rev.8$dat$age_obs==1] = 0\ngbcod.mat.rev.8$dat$Ecov_maxages_a50[gbcod.mat.rev.8$dat$age_obs==2] = 1\ngbcod.mat.rev.8$par$beta_Ecov_a50 = rep(0,max(1,gbcod.mat.rev.8$dat$Ecov_maxages_a50+1))\nmod8 = MakeADFun(gbcod.mat.rev.8$dat,gbcod.mat.rev.8$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.8$map)\nmod8$opt = nlminb(mod8$par,mod8$fn,mod8$gr)\nmod8$rep = mod8$report()\nmod8$sdrep = sdreport(mod8)\n\n#fall temperature effects at ages 0,1,2 on a50 and 0,1,2 on k\ngbcod.mat.rev.9 = gbcod.mat.rev.8\ngbcod.mat.rev.9$dat$Ecov_maxages_k = rep(2,length(gbcod.mat.rev.9$dat$Y))\ngbcod.mat.rev.9$dat$Ecov_maxages_k[gbcod.mat.rev.9$dat$age_obs==1] = 0\ngbcod.mat.rev.9$dat$Ecov_maxages_k[gbcod.mat.rev.9$dat$age_obs==2] = 1\ngbcod.mat.rev.9$par$beta_Ecov_k = rep(0,max(1,gbcod.mat.rev.9$dat$Ecov_maxages_k+1))\nmod9 = MakeADFun(gbcod.mat.rev.9$dat,gbcod.mat.rev.9$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = gbcod.mat.rev.9$map)\nmod9$opt = nlminb(mod9$par,mod9$fn,mod9$gr)\nmod9$rep = mod9$report()\nmod9$sdrep = sdreport(mod9)\n\n#betabinomial models\ndyn.unload(\"ss_mat_paper_v4.so\")\ncompile(\"ss_mat_paper_v4.cpp\",\"-O0 -g\")\ndyn.load(\"ss_mat_paper_v4.so\")\n\ntemp = gbcod.mat.rev\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(names(temp$map) != \"beta_phi\")]\nx = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod0.bb = x\nmod0.bb$rep = mod0.bb$report()\nmod0.bb$sdrep = sdreport(x)\n\nx = mod0.bb$simulate(complete=TRUE)\ntemp = list(mod0.bb$rep$mat, exp(mod0.bb$opt$par['beta_phi']))\ntemp = rbeta(length(temp[[1]]), temp[[1]]*temp[[2]], (1-temp[[1]])*temp[[2]])\ntemp = rbinom(length(temp), size = mod0.bb$env$data$N, prob = temp)\nx = mod0.bb$env$data\nx$Y = as.numeric(temp) \nx = MakeADFun(x,mod0.bb$env$parList(),random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = mod0.bb$env$map, silent = TRUE)\nnlminb(x$par,x$fn,x$gr)$par\nmod0.bb$opt$par\n\ntemp = gbcod.mat.rev\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(names(temp$map) != \"beta_Ecov_k\")]\ntemp$map = temp$map[which(names(temp$map) != \"beta_phi\")]\nx = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod1.bb = x\nmod1.bb$rep = mod1.bb$report()\nmod1.bb$sdrep = sdreport(x)\n\ntemp = gbcod.mat.rev\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(names(temp$map) != \"beta_Ecov_a50\")]\ntemp$map = temp$map[which(names(temp$map) != \"beta_phi\")]\nx = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod2.bb = x\nmod2.bb$rep = mod2.bb$report()\nmod2.bb$sdrep = sdreport(x)\n\ntemp = gbcod.mat.rev\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_Ecov_k\",\"beta_Ecov_a50\",\"beta_phi\")))]\nx = MakeADFun(temp$dat,temp$par,random=c(\"Ecov_re\"),DLL=\"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod3.bb = x\nmod3.bb$rep = mod3.bb$report()\nmod3.bb$sdrep = sdreport(x)\n\n#binomial models with AR1 in k or a50\n\n#AR1 in k at age 0\ntemp = gbcod.mat.rev\n#temp$dat$Ecov_maxages_k = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$par$beta_Ecov_k = rep(0, max(temp$dat$age_obs))\ntemp$dat$fit_k = 1\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\")))]\n#temp$map$beta_Ecov_k = factor(rep(NA,length(temp$par$beta_Ecov_k)))\nmod0.k.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.k.AR$opt = nlminb(mod0.k.AR$par,mod0.k.AR$fn,mod0.k.AR$gr)\n#801.72\n\n#AR1 in a50 at age 0\ntemp = gbcod.mat.rev\n#temp$dat$Ecov_maxages_a50 = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$par$beta_Ecov_a50 = rep(0, max(temp$dat$age_obs))\ntemp$dat$fit_a50 = 1\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"a50_AR_pars\",\"a50_re\")))]\n#temp$map$beta_Ecov_a50 = factor(rep(NA,length(temp$par$beta_Ecov_a50)))\nmod0.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.a50.AR$opt = nlminb(mod0.a50.AR$par,mod0.a50.AR$fn,mod0.a50.AR$gr)\n#665.97\n\n#AR1 in k at age 0:1 and a50 age 0\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_k = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par$beta_Ecov_k = rep(0,max(1,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod1.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod1.k.a50.AR$opt = nlminb(mod1.k.a50.AR$par,mod1.k.a50.AR$fn,mod1.k.a50.AR$gr)\n#669.85\n\n#AR1 in a50 at age 0:1 and k age 0\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par$beta_Ecov_a50 = rep(0,max(1,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod2.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod2.k.a50.AR$opt = nlminb(mod2.k.a50.AR$par,mod2.k.a50.AR$fn,mod2.k.a50.AR$gr)\n#665.02\n\n#AR1 in k at age 0:1\n#AR1 in a50 at age 0:1\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$Ecov_maxages_k = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par$beta_Ecov_a50 = rep(0,max(1,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(1,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod3.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod3.k.a50.AR$opt = nlminb(mod3.k.a50.AR$par,mod3.k.a50.AR$fn,mod3.k.a50.AR$gr)\n#662.09\n\n#AR1 in k at age 0:2\n#AR1 in a50 at age 0:2\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(2,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==2] = 1\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$Ecov_maxages_k = rep(2,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==2] = 1\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par$beta_Ecov_a50 = rep(0,max(2,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(2,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod4.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.k.a50.AR$opt = nlminb(mod4.k.a50.AR$par,mod4.k.a50.AR$fn,mod4.k.a50.AR$gr)\nmod4.k.a50.AR$rep = mod4.k.a50.AR$report()\nx = mod4.k.a50.AR\nmod4.k.a50.AR$sdrep = sdreport(x)\n#626.39\n\ntemp = list(dat = mod4.k.a50.AR$env$data, par = mod4.k.a50.AR$env$parList(), map = mod4.k.a50.AR$env$map)\ntemp$map$beta_Ecov_k = factor(c(1,NA,NA))\n#temp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.k.a50.AR.Ecov.1 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.k.a50.AR.Ecov.1$opt = nlminb(mod4.k.a50.AR.Ecov.1$par,mod4.k.a50.AR.Ecov.1$fn,mod4.k.a50.AR.Ecov.1$gr)\n#625.93\n\ntemp = list(dat = mod4.k.a50.AR$env$data, par = mod4.k.a50.AR$env$parList(), map = mod4.k.a50.AR$env$map)\n#temp$map$beta_Ecov_k = factor(c(1,NA,NA))\ntemp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.k.a50.AR.Ecov.2 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.k.a50.AR.Ecov.2$opt = nlminb(mod4.k.a50.AR.Ecov.2$par,mod4.k.a50.AR.Ecov.2$fn,mod4.k.a50.AR.Ecov.2$gr)\n#626.03\n\ntemp = list(dat = mod4.k.a50.AR$env$data, par = mod4.k.a50.AR$env$parList(), map = mod4.k.a50.AR$env$map)\ntemp$map$beta_Ecov_k = factor(c(1,NA,NA))\ntemp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.k.a50.AR.Ecov.3 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.k.a50.AR.Ecov.3$opt = nlminb(mod4.k.a50.AR.Ecov.3$par,mod4.k.a50.AR.Ecov.3$fn,mod4.k.a50.AR.Ecov.3$gr)\n#625.75\n\n\n#AR1 in k at age 0:3 \n#AR1 in a50 at age 0:3\nmaxage = 3\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\n#610.08\n\n#AR1 in k at age 0:4 \n#AR1 in a50 at age 0:4\nmaxage = 4\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par = mod4.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\n#602.73\n\n#AR1 in k at age 0:5 \n#AR1 in a50 at age 0:5\nmaxage = 5\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par = mod4.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\n#601.68\n\n#AR1 in k at age 0:6 \n#AR1 in a50 at age 0:6\nmaxage = 6\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par = mod4.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\n#601.02\n\n#AR1 in k at age 0:7 \n#AR1 in a50 at age 0:7\nmaxage = 7\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par = mod4.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod5.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod5.k.a50.AR$opt = nlminb(mod5.k.a50.AR$par,mod5.k.a50.AR$fn,mod5.k.a50.AR$gr)\n#600.67\n\n#AR1 in k at age 0:8 \n#AR1 in a50 at age 0:7\ntemp = gbcod.mat.rev\ntemp$par = mod4.k.a50.AR$env$parList()\nmaxage = 7\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 8\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod6.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod6.k.a50.AR$opt = nlminb(mod6.k.a50.AR$par,mod6.k.a50.AR$fn,mod6.k.a50.AR$gr)\n#600.69\n\n#AR1 in k at age 0:7 \n#AR1 in a50 at age 0:8\ntemp = gbcod.mat.rev\ntemp$par = mod4.k.a50.AR$env$parList()\nmaxage = 8\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 7\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod7.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod7.k.a50.AR$opt = nlminb(mod7.k.a50.AR$par,mod7.k.a50.AR$fn,mod7.k.a50.AR$gr)\nmod7.k.a50.AR$rep = mod7.k.a50.AR$report()\nx = mod7.k.a50.AR\nmod7.k.a50.AR$sdrep = sdreport(x)\n#600.66\n\n#AR1 in k at age 0:8 \n#AR1 in a50 at age 0:8\nmaxage = 8\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$par = mod4.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\nmod8.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod8.k.a50.AR$opt = nlminb(mod8.k.a50.AR$par,mod8.k.a50.AR$fn,mod8.k.a50.AR$gr)\n#600.68\n\n#betabinomial models with AR1 in k or a50\n\n#AR1 in k at age 0\ntemp = gbcod.mat.rev\n#temp$dat$Ecov_maxages_k = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$par = mod0.k.AR$env$parList()\n#temp$par$beta_Ecov_k = rep(0, max(temp$dat$age_obs))\ntemp$dat$fit_k = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\")))]\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\n#temp$map$beta_Ecov_k = factor(rep(NA,length(temp$par$beta_Ecov_k)))\nmod0.bb.k.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.bb.k.AR$opt = nlminb(mod0.bb.k.AR$par,mod0.bb.k.AR$fn,mod0.bb.k.AR$gr)\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\ntemp$par = mod0.bb.k.AR$env$parList()\nmod0.bb.k.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.bb.k.AR$opt = nlminb(mod0.bb.k.AR$par,mod0.bb.k.AR$fn,mod0.bb.k.AR$gr)\n\n#AR1 in a50 at age 0\ntemp = gbcod.mat.rev\n#temp$dat$Ecov_maxages_a50 = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$par = mod0.a50.AR$env$parList()\n#temp$par$beta_Ecov_a50 = rep(0, max(temp$dat$age_obs))\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"a50_AR_pars\",\"a50_re\")))]\n#temp$map$beta_Ecov_a50 = factor(rep(NA,length(temp$par$beta_Ecov_a50)))\nmod0.bb.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.bb.a50.AR$opt = nlminb(mod0.bb.a50.AR$par,mod0.bb.a50.AR$fn,mod0.bb.a50.AR$gr)\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\ntemp$par = mod0.bb.a50.AR$env$parList()\nmod0.bb.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.bb.a50.AR$opt = nlminb(mod0.bb.a50.AR$par,mod0.bb.a50.AR$fn,mod0.bb.a50.AR$gr)\n\n#AR1 in k and a50 at age 0\ntemp = gbcod.mat.rev\n#temp$dat$Ecov_maxages_k = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$dat$Ecov_maxages_a50 = rep(temp$dat$age_obs-1,length(temp$dat$Y))\n#temp$par = mod0.bb.k.AR$env$parList()\n#temp$par$beta_Ecov_k = rep(0, max(temp$dat$age_obs))\n#temp$par$beta_Ecov_a50 = rep(0, max(temp$dat$age_obs))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\n#temp$map$beta_Ecov_k = factor(rep(NA,length(temp$par$beta_Ecov_k)))\n#temp$map$beta_Ecov_a50 = factor(rep(NA,length(temp$par$beta_Ecov_a50)))\nmod0.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod0.bb.k.a50.AR$opt = nlminb(mod0.bb.k.a50.AR$par,mod0.bb.k.a50.AR$fn,mod0.bb.k.a50.AR$gr)\n#temp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\n#temp$par = mod0.bb.k.a50.AR$env$parList()\n#mod0.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\n#mod0.bb.k.a50.AR$opt = nlminb(mod0.bb.k.a50.AR$par,mod0.bb.k.a50.AR$fn,mod0.bb.k.a50.AR$gr)\nmod0.bb.k.a50.AR$rep = mod0.bb.k.a50.AR$report()\nx = mod0.bb.k.a50.AR\nmod0.bb.k.a50.AR$sdrep = sdreport(x)\n#571.70\n\n#AR1 in k at age 0:1 and a50 age 0\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_k = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_k = rep(0,max(1,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\")))]\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"beta_phi\")))]\nmod1.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod1.bb.k.a50.AR$opt = nlminb(mod1.bb.k.a50.AR$par,mod1.bb.k.a50.AR$fn,mod1.bb.k.a50.AR$gr)\nmod1.bb.k.a50.AR$rep = mod1.bb.k.a50.AR$report()\nx = mod1.bb.k.a50.AR\nmod1.bb.k.a50.AR$sdrep = sdreport(x)\n#556.56\n\n#AR1 in a50 at age 0:1 and k age 0\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(1,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod2.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod2.bb.k.a50.AR$opt = nlminb(mod2.bb.k.a50.AR$par,mod2.bb.k.a50.AR$fn,mod2.bb.k.a50.AR$gr)\nmod2.bb.k.a50.AR$rep = mod2.bb.k.a50.AR$report()\nx = mod2.bb.k.a50.AR\nmod2.bb.k.a50.AR$sdrep = sdreport(x)\n#554.73\n\n#AR1 in k at age 0:1\n#AR1 in a50 at age 0:1\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$Ecov_maxages_k = rep(1,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(1,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(1,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod3.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod3.bb.k.a50.AR$opt = nlminb(mod3.bb.k.a50.AR$par,mod3.bb.k.a50.AR$fn,mod3.bb.k.a50.AR$gr)\nmod3.bb.k.a50.AR$rep = mod3.bb.k.a50.AR$report()\nx = mod3.bb.k.a50.AR\nmod3.bb.k.a50.AR$sdrep = sdreport(x)\n#554.69\n\n#AR1 in k at age 0:1\n#AR1 in a50 at age 0:2\nmaxage = 2\ntemp = gbcod.mat.rev\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nmod4.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.bb.k.a50.AR$opt = nlminb(mod4.bb.k.a50.AR$par,mod4.bb.k.a50.AR$fn,mod4.bb.k.a50.AR$gr)\nmod4.bb.k.a50.AR$rep = mod4.bb.k.a50.AR$report()\nx = mod4.bb.k.a50.AR\nmod4.bb.k.a50.AR$sdrep = sdreport(x)\n#545.03\nmod4.bb.k.a50.AR$parList = mod4.bb.k.a50.AR$env$parList()\nsave(mod4.bb.k.a50.AR, file = \"mod4.bb.k.a50.AR.RData\")\n\ntemp = list(dat = mod4.bb.k.a50.AR$env$data, par = mod4.bb.k.a50.AR$env$parList(), map = mod4.bb.k.a50.AR$env$map)\ntemp$map$beta_Ecov_k = factor(c(1,NA))\n#temp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.bb.k.a50.AR.Ecov.1 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.bb.k.a50.AR.Ecov.1$opt = nlminb(mod4.bb.k.a50.AR.Ecov.1$par,mod4.bb.k.a50.AR.Ecov.1$fn,mod4.bb.k.a50.AR.Ecov.1$gr)\n#544.97\n\ntemp = list(dat = mod4.bb.k.a50.AR$env$data, par = mod4.bb.k.a50.AR$env$parList(), map = mod4.bb.k.a50.AR$env$map)\n#temp$map$beta_Ecov_k = factor(c(1,NA))\ntemp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.bb.k.a50.AR.Ecov.2 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.bb.k.a50.AR.Ecov.2$opt = nlminb(mod4.bb.k.a50.AR.Ecov.2$par,mod4.bb.k.a50.AR.Ecov.2$fn,mod4.bb.k.a50.AR.Ecov.2$gr)\n#544.97\n\ntemp = list(dat = mod4.bb.k.a50.AR$env$data, par = mod4.bb.k.a50.AR$env$parList(), map = mod4.bb.k.a50.AR$env$map)\ntemp$map$beta_Ecov_k = factor(c(1,NA))\ntemp$map$beta_Ecov_a50 = factor(c(1,NA,NA))\nmod4.bb.k.a50.AR.Ecov.3 = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod4.bb.k.a50.AR.Ecov.3$opt = nlminb(mod4.bb.k.a50.AR.Ecov.3$par,mod4.bb.k.a50.AR.Ecov.3$fn,mod4.bb.k.a50.AR.Ecov.3$gr)\n#544.93\n\n\n#AR1 in k at age 0:1\n#AR1 in a50 at age 0:2\nmaxage = 1\ntemp = gbcod.mat.rev\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 2\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod5.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod5.bb.k.a50.AR$opt = nlminb(mod5.bb.k.a50.AR$par,mod5.bb.k.a50.AR$fn,mod5.bb.k.a50.AR$gr)\nmod5.bb.k.a50.AR$rep = mod5.bb.k.a50.AR$report()\nx = mod5.bb.k.a50.AR\nmod5.bb.k.a50.AR$sdrep = sdreport(x)\n#554.52\n\n#AR1 in k at age 0:2\n#AR1 in a50 at age 0:2\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(2,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==2] = 1\ntemp$dat$Ecov_maxages_a50[temp$dat$age_obs==1] = 0\ntemp$dat$Ecov_maxages_k = rep(2,length(temp$dat$Y))\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==2] = 1\ntemp$dat$Ecov_maxages_k[temp$dat$age_obs==1] = 0\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(2,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(2,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod6.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod6.bb.k.a50.AR$opt = nlminb(mod6.bb.k.a50.AR$par,mod6.bb.k.a50.AR$fn,mod6.bb.k.a50.AR$gr)\nmod6.bb.k.a50.AR$rep = mod6.bb.k.a50.AR$report()\nx = mod6.bb.k.a50.AR\nmod6.bb.k.a50.AR$sdrep = sdreport(x)\n#545.01\n\n#AR1 in k at age 0:2\n#AR1 in a50 at age 0:3\nmaxage = 3\ntemp = gbcod.mat.rev\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 2\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nx = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nx$opt = nlminb(x$par,x$fn,x$gr)\nx$opt$obj\n#548.49\n\n#AR1 in k at age 0:3 \n#AR1 in a50 at age 0:2\nmaxage = 2\ntemp = gbcod.mat.rev\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 3\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod7.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod7.bb.k.a50.AR$opt = nlminb(mod7.bb.k.a50.AR$par,mod7.bb.k.a50.AR$fn,mod7.bb.k.a50.AR$gr)\nmod7.bb.k.a50.AR$rep = mod7.bb.k.a50.AR$report()\nx = mod7.bb.k.a50.AR\nmod7.bb.k.a50.AR$sdrep = sdreport(x)\n#544.97\nmod7.bb.k.a50.AR$parList = mod7.bb.k.a50.AR$env$parList()\nsave(mod7.bb.k.a50.AR, file = \"mod7.bb.k.a50.AR.RData\")\n\nx = summary(mod7.bb.k.a50.AR$sdrep)\nx[rownames(x) %in% \"a50_re\",1]\nx = x[rownames(x) %in% c(\"Ecov_y\",\"k_re\",\"a50_re\",\"beta_a50\",\"beta_k\",\"beta_Ecov_k\", \"beta_Ecov_a50\"),1]\nind = sum(names(x) == \"Ecov_y\")\nind = c(1:(sum(names(x) == \"Ecov_y\")-2),ind)\nx = x[-which(names(x) == \"Ecov_y\")[ind]]\nx = x[-which(names(x) == \"k_re\")[ind]]\nx = x[-which(names(x) == \"a50_re\")[ind]]\nx\nk = exp(x[\"beta_k\"] + x[\"k_re\"])\na50 = exp(x[\"beta_a50\"] + x[\"a50_re\"])\n1/(1 + exp(-k*(1-a50)))\n\nx = summary(mod7.bb.k.a50.AR$sdrep)\nx = x[rownames(x) %in% \"a50_re\",1]\n\nexp(temp[\"beta_Linf\"])*(1-exp(-k1*(1-temp[\"t0\"])))\n\n\n#AR1 in k at age 0:3 \n#AR1 in a50 at age 0:3\nmaxage = 3\ntemp = gbcod.mat.rev\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nfor(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod8.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod8.bb.k.a50.AR$opt = nlminb(mod8.bb.k.a50.AR$par,mod8.bb.k.a50.AR$fn,mod8.bb.k.a50.AR$gr)\nmod8.bb.k.a50.AR$opt$obj\n#546.14\n#not as good\n\n#AR1 in k at age 0:4 \n#AR1 in a50 at age 0:2\nmaxage = 2\ntemp = gbcod.mat.rev\ntemp$par = mod0.bb.k.a50.AR$env$parList()\ntemp$dat$Ecov_maxages_a50 = rep(maxage,length(temp$dat$Y))\nif(maxage>0) for(i in 1:maxage) temp$dat$Ecov_maxages_a50[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_a50 = rep(0,max(maxage,temp$dat$Ecov_maxages_a50+1))\ntemp$map$beta_Ecov_a50 = factor(rep(NA, length(temp$par$beta_Ecov_a50)))\nmaxage = 4\ntemp$dat$Ecov_maxages_k = rep(maxage,length(temp$dat$Y))\nif(maxage>0) for(i in 1:maxage) temp$dat$Ecov_maxages_k[temp$dat$age_obs==i] = i-1\ntemp$par$beta_Ecov_k = rep(0,max(maxage,temp$dat$Ecov_maxages_k+1))\ntemp$map$beta_Ecov_k = factor(rep(NA, length(temp$par$beta_Ecov_k)))\ntemp$dat$fit_k = 1\ntemp$dat$fit_a50 = 1\ntemp$dat$binomial = 0\ntemp$map = temp$map[which(!(names(temp$map) %in% c(\"k_AR_pars\",\"k_re\",\"a50_AR_pars\",\"a50_re\",\"beta_phi\")))]\nmod9.bb.k.a50.AR = MakeADFun(temp$dat, temp$par, random = c(\"Ecov_re\",\"k_re\",\"a50_re\"), DLL = \"ss_mat_paper_v4\", map = temp$map)\nmod9.bb.k.a50.AR$opt = nlminb(mod9.bb.k.a50.AR$par,mod9.bb.k.a50.AR$fn,mod9.bb.k.a50.AR$gr)\nmod9.bb.k.a50.AR$opt$obj\n#545.06\n#not as good\n\n\ntemp = list(mod0$opt,mod1$opt,mod2$opt,mod3$opt,mod4$opt,mod5$opt,mod6$opt,mod7$opt,mod8$opt,mod9$opt)\ntemp = sapply(temp, function(x) 2 * x$obj + 2 * length(x$par))\ntemp - min(temp)\n#temp = list(mod0.bb$opt,mod1.bb$opt,mod2.bb$opt,mod3.bb$opt,mod4.bb$opt,mod5.bb$opt,mod6.bb$opt,mod7.bb$opt,mod8.bb$opt,mod9.bb$opt)\ntemp = list(mod0.bb$opt,mod1.bb$opt,mod2.bb$opt,mod3.bb$opt)\ntemp = sapply(temp, function(x) 2 * x$obj + 2 * length(x$par))\ntemp - min(temp)\n#temp = list(mod0.bb$opt,mod1.bb$opt,mod2.bb$opt,mod3.bb$opt,mod4.bb$opt,mod5.bb$opt,mod6.bb$opt,mod7.bb$opt,mod8.bb$opt,mod9.bb$opt,\ntemp = list(mod0$opt,mod1$opt,mod2$opt,mod3$opt,mod4$opt,mod5$opt,mod6$opt,mod7$opt,mod8$opt,mod9$opt,\n mod0.bb$opt,mod1.bb$opt,mod2.bb$opt,mod3.bb$opt)\naic = sapply(temp, function(x) 2 * x$obj + 2 * length(x$par))\ndelta.aic = aic - min(aic)\naic.wts = exp(-0.5*delta.aic)/sum(exp(-0.5*delta.aic))\n\ntemp = list(\n mod0$opt,mod7$opt,mod4.k.a50.AR$opt,mod4.k.a50.AR.Ecov.1$opt,mod4.k.a50.AR.Ecov.2$opt,mod4.k.a50.AR.Ecov.3$opt,\n mod0.bb$opt,mod1.bb$opt,mod2.bb$opt,mod3.bb$opt,mod4.bb.k.a50.AR$opt,\n mod4.bb.k.a50.AR.Ecov.1$opt,mod4.bb.k.a50.AR.Ecov.2$opt,mod4.bb.k.a50.AR.Ecov.3$opt)\nsapply(temp, function(x) length(x$par))\naic = sapply(temp, function(x) 2 * x$obj + 2 * length(x$par))\naic - min(aic)\n\n\nlibrary(plotrix)\ntcol <- col2rgb('black')\ntcol <- paste(rgb(tcol[1,],tcol[2,], tcol[3,], maxColorValue = 255), \"55\", sep = '')\n\n\nmat.dat.table = table(mat.dat$YEAR, mat.dat$AGE)\ntemp = apply(mat.dat.table[,-(1:8)], 1, sum)\nmat.dat.table = cbind(mat.dat.table[,1:8], \"$>$ 8\" = temp)\nmat.dat.table = cbind(mat.dat.table, Weight = sapply(unique(mat.dat$YEAR), function(x) sum(mat.dat$YEAR == x & mat.dat$INDWT>0)))\n#mat.dat.table = cbind(mat.dat.table, Length = sapply(unique(mat.dat$YEAR), function(x) sum(mat.dat$YEAR == x & mat.dat$LENGTH>0)))\nmat.dat.table = cbind(mat.dat.table, Total = sapply(unique(mat.dat$YEAR), function(x) sum(mat.dat$YEAR == x)))\nmat.dat.table = rbind(mat.dat.table, Total = apply(mat.dat.table, 2, sum))\n\nlibrary(Hmisc)\nx = latex(mat.dat.table, file = '~/work/cod/ss_maturity/tex/mat_data_table_v2.tex', \n rowlabel = 'Year', cgroup = c(\"Age\", \"\", \"\"), n.cgroup = c(9,1,1), table.env = FALSE, rowlabel.just = \"c\", cgroupTexCmd = NULL)#, collabel.just)\n\nx = cbind(gbcod.mat$dat$Ecov_obs, gbcod.mat$dat$Ecov_obs_sigma)\nx[which(x < -99)] = NA\ncolnames(x) = c(\"Anomaly\", \"Standard Error\")\nrownames(x) = 1963:2014\ntemp = latex(x, file = '~/work/cod/ss_maturity/tex/bottom_temperature_anomalies.tex', \n rowlabel = 'Year', table.env = FALSE, rowlabel.just = \"c\")#, collabel.just)\ntemp = latex(x[1963:2000-1962,], file = '~/work/cod/ss_maturity/tex/bottom_temperature_anomalies_1.tex', \n rowlabel = 'Year', table.env = FALSE, rowlabel.just = \"c\")#, collabel.just)\ntemp = latex(x[2001:2014-1962,], file = '~/work/cod/ss_maturity/tex/bottom_temperature_anomalies_2.tex', \n rowlabel = 'Year', table.env = FALSE, rowlabel.just = \"c\")#, collabel.just)\n\npar(mfrow = c(1,1), mar = c(1,4,1,1), oma = c(4,1,0,0))\ntemp = summary(mod0.sdrep)\ntemp = temp[which(rownames(temp) == \"Ecov_y\"),]\ntemp = cbind(temp[,1], temp[,1] + qnorm(0.975)*cbind(-temp[,2],temp[,2]))\nplot(1963:2014, temp[,1], type = 'n', axes = FALSE, ylim = range(temp), xlab = \"\", ylab = \"\")\ngrid(col = gray(0.7), lwd = 1)\nlines(1963:2014, temp[,1], lwd = 2)\npolygon(c(1963:2014,2014:1963), c(temp[,2],rev(temp[,3])), col = tcol, border = \"transparent\", lty = 2)\nx = gbcod.mat$dat\ntemp <- which(x$use_Ecov_obs == 1)\nplotCI((1963:2014)[temp], x$Ecov_obs[temp], li = (x$Ecov_obs - qnorm(0.975)*x$Ecov_obs_sigma)[temp], ui = (x$Ecov_obs + qnorm(0.975)*x$Ecov_obs_sigma)[temp], \n add = TRUE, lwd = 2)\naxis(1, labels = FALSE, lwd = 2)\naxis(2, lwd = 2, cex.axis = 1.5)\nbox(lwd = 2)\nmtext(side = 2, outer = FALSE, line = 3, \"Bottom temperature anomaly\", cex = 1.5)\n\n###########################################################\n#plots comparing maturity predictions and emperical estimates\n\ny = summary(mod7.bb.k.a50.AR$sdrep)\ny = y[rownames(y) == \"logit_pmat\",]\nz = matrix(1/(1+exp(-y[,1])), ncol = 3)\nplot((1963:2014)[-(1:3)],z[-(1:3),3], ylim = c(0,1), xlab = '', ylab = '', type = 'l')\nlines((1963:2014)[-1],z[-1,1], col = 'red')\nplot((1963:2014)[-(1:2)],z[-(1:2),2], ylim = c(0,1), xlab = '', ylab = '', type = 'n')\n\ntemp.fn = function(whichage = 2, model = mod7.bb.k.a50.AR)\n{\n library(plotrix)\n y = summary(model$sdrep)\n #y = summary(sdreport(x))\n y = y[rownames(y) == \"logit_pmat\",]\n y = cbind(y[,1], y[,1] +qnorm(0.975)*cbind(-y[,2],y[,2]))\n z = cbind(matrix(1/(1+exp(-y[,1])), ncol = 3)[,whichage], matrix(1/(1+exp(-y[,2])), ncol = 3)[,whichage],matrix(1/(1+exp(-y[,3])), ncol = 3)[,whichage])\n years = 1963:2014\n years.dat = 1970:2014\n ind = which(years %in% years.dat)#[-(1:whichage)]\n plot(years.dat,z[ind,2], ylim = c(0,1), xlab = '', ylab = '', type = 'n', axes = FALSE, xlim = range(years.dat))\n grid(col = gray(0.7), lwd = 1)\n lines(years.dat ,z[ind,1], col = 'black', lwd = 2)\n polygon(c(years.dat,rev(years.dat)), c(z[ind,2],rev(z[ind,3])), col = tcol, border = \"transparent\", lty = 2)\n\n temp = cbind.data.frame(mat = gbcod.mat.rev$dat$Y, n = gbcod.mat.rev$dat$N, age = gbcod.mat.rev$dat$age_obs, \n cohort = gbcod.mat.rev$dat$cy_obs + 1962, year = gbcod.mat.rev$dat$year_obs + 1962)\n x = t(sapply(1970:2014, function(y)\n {\n #x = summary(glm(mat ~ 1, family = binomial, data = temp, subset = age == 3 & year == 1975))$coef[1:2]\n x = try(summary(glm(cbind(mat,n-mat) ~ 1, family = binomial, data = temp, subset = age == whichage & year == y))$coef[1:2])\n if(!is.character(x))\n {\n x = c(1/(1 + exp(-x[1])), 1/(1 + exp(-(x[1] + c(-1,1)*qnorm(0.975)*x[2]))))\n# print(x)\n plotCI(x = y, y = x[1], li = x[2], ui = x[3], add = TRUE, slty = 2, sfrac = 0, lwd = 2)\n return(x)\n }\n else return(rep(NA,3))\n }))\n}\n\npar(mfcol = c(2,3), mar = c(1,1,1,1), oma = c(4,4,0,0))\ntemp.fn(3, mod4.k.a50.AR)\ntemp.fn(2, mod4.k.a50.AR)\ntemp.fn(3, mod7.k.a50.AR)\ntemp.fn(2, mod7.k.a50.AR)\ntemp.fn(3, model = mod4.bb.k.a50.AR)\ntemp.fn(2, model = mod4.bb.k.a50.AR)\n\ncairo_pdf('~/work/cod/ss_maturity/tex/maturity_at_age.pdf', family = \"Times\", height = 10, width = 7)\n#png(filename = '~/work/cod/ss_maturity/tex/maturity_at_age.png', width = 7*144, height = 10*144, res = 144, pointsize = 12, family = \"Times\")#,\npar(mfrow = c(2,1), mar = c(1,1,1,1), oma = c(4,4,0,0))\ntemp.fn(3, model = mod4.bb.k.a50.AR)\naxis(2, lwd = 2, cex.axis = 1.5)\naxis(1, labels = FALSE, cex.axis = 1.5)\nbox(lwd =2)\ntemp.fn(2, model = mod4.bb.k.a50.AR)\naxis(2, lwd = 2, cex.axis = 1.5)\naxis(1, lwd = 2, cex.axis = 1.5)\nbox(lwd =2)\n#temp.fn(1)\n#axis(2, lwd = 2, cex.axis = 1.5)\n#axis(1, lwd = 2, cex.axis = 1.5)\n#box(lwd =2)\nmtext(side = 1, line = 2, \"Year\", cex = 2, outer = TRUE)\nmtext(side = 2, line = 2, \"Proportion mature\", cex = 2, outer = TRUE)\ndev.off()\n\n\n", "meta": {"hexsha": "6088b9ba05a7b40fd45b1cc96939d9807899715b", "size": 46721, "ext": "r", "lang": "R", "max_stars_repo_path": "maturity_examples/ss_mat_paper_v4.r", "max_stars_repo_name": "NOAA-FIMS/TMB_training", "max_stars_repo_head_hexsha": "a146639e566d21852baa430a3598133ec6fdbd08", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "maturity_examples/ss_mat_paper_v4.r", "max_issues_repo_name": "NOAA-FIMS/TMB_training", "max_issues_repo_head_hexsha": "a146639e566d21852baa430a3598133ec6fdbd08", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3246904821692033}} {"text": "#' A box and whiskers plot (in the style of Tukey)\n#'\n#' The boxplot compactly displays the distribution of a continuous variable.\n#' It visualises five summary statistics (the median, two hinges\n#' and two whiskers), and all \"outlying\" points individually.\n#'\n#' @eval rd_orientation()\n#'\n#' @section Summary statistics:\n#' The lower and upper hinges correspond to the first and third quartiles\n#' (the 25th and 75th percentiles). This differs slightly from the method used\n#' by the [boxplot()] function, and may be apparent with small samples.\n#' See [boxplot.stats()] for for more information on how hinge\n#' positions are calculated for [boxplot()].\n#'\n#' The upper whisker extends from the hinge to the largest value no further than\n#' 1.5 * IQR from the hinge (where IQR is the inter-quartile range, or distance\n#' between the first and third quartiles). The lower whisker extends from the\n#' hinge to the smallest value at most 1.5 * IQR of the hinge. Data beyond the\n#' end of the whiskers are called \"outlying\" points and are plotted\n#' individually.\n#'\n#' In a notched box plot, the notches extend `1.58 * IQR / sqrt(n)`.\n#' This gives a roughly 95% confidence interval for comparing medians.\n#' See McGill et al. (1978) for more details.\n#'\n#' @eval rd_aesthetics(\"geom\", \"boxplot\")\n#'\n#' @seealso [geom_quantile()] for continuous `x`,\n#' [geom_violin()] for a richer display of the distribution, and\n#' [geom_jitter()] for a useful technique for small data.\n#' @inheritParams layer\n#' @inheritParams geom_bar\n#' @param geom,stat Use to override the default connection between\n#' `geom_boxplot()` and `stat_boxplot()`.\n#' @param outlier.colour,outlier.color,outlier.fill,outlier.shape,outlier.size,outlier.stroke,outlier.alpha\n#' Default aesthetics for outliers. Set to `NULL` to inherit from the\n#' aesthetics used for the box.\n#'\n#' In the unlikely event you specify both US and UK spellings of colour, the\n#' US spelling will take precedence.\n#'\n#' Sometimes it can be useful to hide the outliers, for example when overlaying\n#' the raw data points on top of the boxplot. Hiding the outliers can be achieved\n#' by setting `outlier.shape = NA`. Importantly, this does not remove the outliers,\n#' it only hides them, so the range calculated for the y-axis will be the\n#' same with outliers shown and outliers hidden.\n#'\n#' @param notch If `FALSE` (default) make a standard box plot. If\n#' `TRUE`, make a notched box plot. Notches are used to compare groups;\n#' if the notches of two boxes do not overlap, this suggests that the medians\n#' are significantly different.\n#' @param notchwidth For a notched box plot, width of the notch relative to\n#' the body (defaults to `notchwidth = 0.5`).\n#' @param varwidth If `FALSE` (default) make a standard box plot. If\n#' `TRUE`, boxes are drawn with widths proportional to the\n#' square-roots of the number of observations in the groups (possibly\n#' weighted, using the `weight` aesthetic).\n#' @export\n#' @references McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of\n#' box plots. The American Statistician 32, 12-16.\n#' @examples\n#' p <- ggplot(mpg, aes(class, hwy))\n#' p + geom_boxplot()\n#' # Orientation follows the discrete axis\n#' ggplot(mpg, aes(hwy, class)) + geom_boxplot()\n#'\n#' p + geom_boxplot(notch = TRUE)\n#' p + geom_boxplot(varwidth = TRUE)\n#' p + geom_boxplot(fill = \"white\", colour = \"#3366FF\")\n#' # By default, outlier points match the colour of the box. Use\n#' # outlier.colour to override\n#' p + geom_boxplot(outlier.colour = \"red\", outlier.shape = 1)\n#' # Remove outliers when overlaying boxplot with original data points\n#' p + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.2)\n#'\n#' # Boxplots are automatically dodged when any aesthetic is a factor\n#' p + geom_boxplot(aes(colour = drv))\n#'\n#' # You can also use boxplots with continuous x, as long as you supply\n#' # a grouping variable. cut_width is particularly useful\n#' ggplot(diamonds, aes(carat, price)) +\n#' geom_boxplot()\n#' ggplot(diamonds, aes(carat, price)) +\n#' geom_boxplot(aes(group = cut_width(carat, 0.25)))\n#' # Adjust the transparency of outliers using outlier.alpha\n#' ggplot(diamonds, aes(carat, price)) +\n#' geom_boxplot(aes(group = cut_width(carat, 0.25)), outlier.alpha = 0.1)\n#'\n#' \\donttest{\n#' # It's possible to draw a boxplot with your own computations if you\n#' # use stat = \"identity\":\n#' y <- rnorm(100)\n#' df <- data.frame(\n#' x = 1,\n#' y0 = min(y),\n#' y25 = quantile(y, 0.25),\n#' y50 = median(y),\n#' y75 = quantile(y, 0.75),\n#' y100 = max(y)\n#' )\n#' ggplot(df, aes(x)) +\n#' geom_boxplot(\n#' aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100),\n#' stat = \"identity\"\n#' )\n#' }\ngeom_boxplot <- function(mapping = NULL, data = NULL,\n stat = \"boxplot\", position = \"dodge2\",\n ...,\n outlier.colour = NULL,\n outlier.color = NULL,\n outlier.fill = NULL,\n outlier.shape = 19,\n outlier.size = 1.5,\n outlier.stroke = 0.5,\n outlier.alpha = NULL,\n notch = FALSE,\n notchwidth = 0.5,\n varwidth = FALSE,\n na.rm = FALSE,\n orientation = NA,\n show.legend = NA,\n inherit.aes = TRUE) {\n\n # varwidth = TRUE is not compatible with preserve = \"total\"\n if (is.character(position)) {\n if (varwidth == TRUE) position <- position_dodge2(preserve = \"single\")\n } else {\n if (identical(position$preserve, \"total\") & varwidth == TRUE) {\n warn(\"Can't preserve total widths when varwidth = TRUE.\")\n position$preserve <- \"single\"\n }\n }\n\n layer(\n data = data,\n mapping = mapping,\n stat = stat,\n geom = GeomBoxplot,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n outlier.colour = outlier.color %||% outlier.colour,\n outlier.fill = outlier.fill,\n outlier.shape = outlier.shape,\n outlier.size = outlier.size,\n outlier.stroke = outlier.stroke,\n outlier.alpha = outlier.alpha,\n notch = notch,\n notchwidth = notchwidth,\n varwidth = varwidth,\n na.rm = na.rm,\n orientation = orientation,\n ...\n )\n )\n}\n\n#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\nGeomBoxplot <- ggproto(\"GeomBoxplot\", Geom,\n\n # need to declare `width` here in case this geom is used with a stat that\n # doesn't have a `width` parameter (e.g., `stat_identity`).\n extra_params = c(\"na.rm\", \"width\", \"orientation\"),\n\n setup_params = function(data, params) {\n params$flipped_aes <- has_flipped_aes(data, params)\n params\n },\n\n setup_data = function(data, params) {\n data$flipped_aes <- params$flipped_aes\n data <- flip_data(data, params$flipped_aes)\n data$width <- data$width %||%\n params$width %||% (resolution(data$x, FALSE) * 0.9)\n\n if (!is.null(data$outliers)) {\n suppressWarnings({\n out_min <- vapply(data$outliers, min, numeric(1))\n out_max <- vapply(data$outliers, max, numeric(1))\n })\n\n data$ymin_final <- pmin(out_min, data$ymin)\n data$ymax_final <- pmax(out_max, data$ymax)\n }\n\n # if `varwidth` not requested or not available, don't use it\n if (is.null(params) || is.null(params$varwidth) || !params$varwidth || is.null(data$relvarwidth)) {\n data$xmin <- data$x - data$width / 2\n data$xmax <- data$x + data$width / 2\n } else {\n # make `relvarwidth` relative to the size of the largest group\n data$relvarwidth <- data$relvarwidth / max(data$relvarwidth)\n data$xmin <- data$x - data$relvarwidth * data$width / 2\n data$xmax <- data$x + data$relvarwidth * data$width / 2\n }\n data$width <- NULL\n if (!is.null(data$relvarwidth)) data$relvarwidth <- NULL\n\n flip_data(data, params$flipped_aes)\n },\n\n draw_group = function(data, panel_params, coord, fatten = 2,\n outlier.colour = NULL, outlier.fill = NULL,\n outlier.shape = 19,\n outlier.size = 1.5, outlier.stroke = 0.5,\n outlier.alpha = NULL,\n notch = FALSE, notchwidth = 0.5, varwidth = FALSE, flipped_aes = FALSE) {\n data <- flip_data(data, flipped_aes)\n # this may occur when using geom_boxplot(stat = \"identity\")\n if (nrow(data) != 1) {\n abort(\"Can't draw more than one boxplot per group. Did you forget aes(group = ...)?\")\n }\n\n common <- list(\n colour = data$colour,\n size = data$size,\n linetype = data$linetype,\n fill = alpha(data$fill, data$alpha),\n group = data$group\n )\n\n whiskers <- new_data_frame(c(\n list(\n x = c(data$x, data$x),\n xend = c(data$x, data$x),\n y = c(data$upper, data$lower),\n yend = c(data$ymax, data$ymin),\n alpha = c(NA_real_, NA_real_)\n ),\n common\n ), n = 2)\n whiskers <- flip_data(whiskers, flipped_aes)\n\n box <- new_data_frame(c(\n list(\n xmin = data$xmin,\n xmax = data$xmax,\n ymin = data$lower,\n y = data$middle,\n ymax = data$upper,\n ynotchlower = ifelse(notch, data$notchlower, NA),\n ynotchupper = ifelse(notch, data$notchupper, NA),\n notchwidth = notchwidth,\n alpha = data$alpha\n ),\n common\n ))\n box <- flip_data(box, flipped_aes)\n\n if (!is.null(data$outliers) && length(data$outliers[[1]] >= 1)) {\n outliers <- new_data_frame(list(\n y = data$outliers[[1]],\n x = data$x[1],\n colour = outlier.colour %||% data$colour[1],\n fill = outlier.fill %||% data$fill[1],\n shape = outlier.shape %||% data$shape[1],\n size = outlier.size %||% data$size[1],\n stroke = outlier.stroke %||% data$stroke[1],\n fill = NA,\n alpha = outlier.alpha %||% data$alpha[1]\n ), n = length(data$outliers[[1]]))\n outliers <- flip_data(outliers, flipped_aes)\n\n outliers_grob <- GeomPoint$draw_panel(outliers, panel_params, coord)\n } else {\n outliers_grob <- NULL\n }\n\n ggname(\"geom_boxplot\", grobTree(\n outliers_grob,\n GeomSegment$draw_panel(whiskers, panel_params, coord),\n GeomCrossbar$draw_panel(box, fatten = fatten, panel_params, coord, flipped_aes = flipped_aes)\n ))\n },\n\n draw_key = draw_key_boxplot,\n\n default_aes = aes(weight = 1, colour = \"grey20\", fill = \"white\", size = 0.5,\n alpha = NA, shape = 19, linetype = \"solid\"),\n\n required_aes = c(\"x|y\", \"lower|xlower\", \"upper|xupper\", \"middle|xmiddle\", \"ymin|xmin\", \"ymax|xmax\")\n)\n", "meta": {"hexsha": "4a2d44ce886faa3e931ce338a7aa5842716abcc3", "size": 10797, "ext": "r", "lang": "R", "max_stars_repo_path": "R/geom-boxplot.r", "max_stars_repo_name": "netique/ggplot2", "max_stars_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3746, "max_stars_repo_stars_event_min_datetime": "2016-10-31T17:39:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T02:50:11.000Z", "max_issues_repo_path": "R/geom-boxplot.r", "max_issues_repo_name": "netique/ggplot2", "max_issues_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3277, "max_issues_repo_issues_event_min_datetime": "2016-11-01T19:23:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:44:20.000Z", "max_forks_repo_path": "R/geom-boxplot.r", "max_forks_repo_name": "netique/ggplot2", "max_forks_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1693, "max_forks_repo_forks_event_min_datetime": "2016-11-02T07:26:55.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T01:51:19.000Z", "avg_line_length": 37.3598615917, "max_line_length": 107, "alphanum_fraction": 0.6199870334, "num_tokens": 2895, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.32465433185377307}} {"text": "\ntest_that(\"define_means\",{\n result <- define_means(\"variable_of_interest_name\")\n expect_equal(result, 'variable_of_interest_name_level ~ 0*1')\n})\n\n\ntest_that(\"define_means - with include_slopes true\",{\n result <- define_means(\"variable_of_interest_name\", TRUE)\n expect_equal(result, 'variable_of_interest_name_level ~ 0*1\\nvariable_of_interest_name_slope ~ 1')\n})\n\ntest_that(\"define_means - with include_slopes true and constrain_group_means_to_be_equal is true\",{\n result <- define_means(\"variable_of_interest_name\", TRUE, TRUE)\n expect_equal(result, 'variable_of_interest_name_level ~ 0*1\\nvariable_of_interest_name_slope ~ c(SM,SM)*1')\n})\n", "meta": {"hexsha": "1c5f325c24e56ba0d9521bdd033c18d07fd0f86d", "size": 649, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-means.r", "max_stars_repo_name": "epf02013/r2sem", "max_stars_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test-means.r", "max_issues_repo_name": "epf02013/r2sem", "max_issues_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test-means.r", "max_forks_repo_name": "epf02013/r2sem", "max_forks_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1764705882, "max_line_length": 109, "alphanum_fraction": 0.7919876733, "num_tokens": 161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3246543236779258}} {"text": "#Read refence points from file reference_points.in \n\nRead.reference.points<-function(dir=data.path,read.init.function=TRUE){\n if (read.init.function) Init.function()\n \n a<-scan(file.path(dir,\"reference_points.in\"),comment.char = \"#\",quiet = TRUE)\n b<-matrix(a,nrow=nsp,ncol=4,byrow=TRUE)\n colnames(b)<-c(\"Flim\",\"Fpa\",\"Blim\",\"Bpa\") \n rownames(b)<-sp.names[1:nsp]\n b\n}\n\n\nRead.reference.points.OP<-function(dir=data.path,read.init.function=TRUE){\n if (read.init.function) Init.function()\n \n a<-scan(file.path(dir,\"OP_reference_points.in\"),comment.char = \"#\",quiet = TRUE)\n b<-matrix(a,ncol=4,byrow=TRUE)\n colnames(b)<-c(\"Flim\",\"Fpa\",\"Blim\",\"Bpa\") \n rownames(b)<-sp.names[first.VPA:nsp]\n b\n}\n", "meta": {"hexsha": "688b436caeedbc3ac6393f8569d6e01b7303cdb2", "size": 734, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/function/read_reference_points.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/function/read_reference_points.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/function/read_reference_points.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9130434783, "max_line_length": 84, "alphanum_fraction": 0.6539509537, "num_tokens": 220, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3246543236779258}} {"text": "#!/usr/bin/env Rscript\n\nsource(\"/tmp/class-libs.R\")\n\nclass_name = \"2021 Spring Stat 20\"\nclass_libs = c(\n \"swirl\", \"2.4.5\"\n)\nclass_libs_install_version(class_name, class_libs)\n\ndevtools::install_github(\"mdbeckman/dcData\", ref=\"56888a6\")\n", "meta": {"hexsha": "e940928fdc716355f97bf8ec5d36adf8b8ddc5c8", "size": 237, "ext": "r", "lang": "R", "max_stars_repo_path": "deployments/datahub/images/default/r-packages/2021-spring-stat-20.r", "max_stars_repo_name": "ryanlovett/datahub", "max_stars_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 104, "max_stars_repo_stars_event_min_datetime": "2017-08-14T18:29:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T16:38:28.000Z", "max_issues_repo_path": "deployments/datahub/images/default/r-packages/2021-spring-stat-20.r", "max_issues_repo_name": "ryanlovett/datahub", "max_issues_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 793, "max_issues_repo_issues_event_min_datetime": "2017-08-16T21:49:14.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T01:14:41.000Z", "max_forks_repo_path": "deployments/datahub/images/default/r-packages/2021-spring-stat-20.r", "max_forks_repo_name": "ryanlovett/datahub", "max_forks_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 129, "max_forks_repo_forks_event_min_datetime": "2017-08-10T01:26:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-03T01:24:19.000Z", "avg_line_length": 19.75, "max_line_length": 59, "alphanum_fraction": 0.7299578059, "num_tokens": 77, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3246543236779258}} {"text": "#\u7814\u7a76f_eject\u5c04\u51fa\u6b21\u6570\u548c\u9891\u7387\r\n#\u5206\u4e3asingle\u5355\u6b21\u5c04\u51fa\uff0c\u5373\u5355\u8109\u51b2\u5185\u5c04\u51fa\u7684\u6b21\u6570\r\n#\u8fd8\u6709eject\uff0c\u603b\u4f53\u5c04\u51fa\u9891\u7387\r\n\r\n#\u8bbe\u5b9a\u5de5\u4f5c\u76ee\u5f55\r\nsetwd(\"D:/data\")\r\nlibrary(xlsx)\r\n\r\n#\u8bfb\u53d6\u6570\u636e\r\n#k2 <- read.xlsx(\"f_eject.xls\", sheetName = \"single_k4\", header = TRUE)\r\n#k4 <- read.xlsx(\"f_eject.xls\", sheetName = \"single_k4\", header = TRUE)\r\nk4 <- read.xlsx(\"f_eject.xls\", sheetName = \"single_k4\", header = TRUE)\r\n#k5 <- read.xlsx(\"f_eject.xls\", sheetName = \"single_k5\", header = TRUE)\r\n\r\n#\u4fdd\u5b58\u6570\u636e\r\n#pdf(\"f_k0.4.pdf\")\r\n\r\n#\u753b\u56fe\r\n#\u8bbe\u5b9a\u7a7a\u95f4\u4f4d\u7f6e\r\npar(fig=c(0,1,0,1), new = F)\r\n\r\n##################################\u753b\u56fe--\u5360\u7a7a\u6bd4k\u4e3a0.2#######################\r\nplot(k4$fv, k4$X1.5, xaxs = \"i\", col = 0, xlab = \"Voltage frequency (Hz)\", ylab = \"Frequency in cycle (Hz)\", main = \"Eject frequency in duty = 0.4\", cex.lab = 1.2, cex.main = 1.5, xlim = c(0, 2050), ylim = c(0, 172))\r\n\r\n#model1 = loess(k4$X1.5 ~ k4$fv, span = 0.8 , degree = 2)\r\nlines(k4$fv, k4$X1.5, col=\"#458B74\", pch=21, lwd=2.5, type=\"b\", lty=2)\r\n\r\n#model2 = loess(k4$X27 ~ k4$fv, span = 0.8, degree = 2)\r\nlines(k4$fv, k4$X27, col=\"#FF00FF\", pch=22, lwd=2.5, type=\"b\", lty=2)\r\n\r\n#model3 = loess(k4$X54 ~ k4$fv, span = 0.8, degree = 2)\r\nlines(k4$fv, k4$X54, col=\"#EE0000\", pch=23, lwd=2.5, type=\"b\", lty=2)\r\n\r\n#model4 = loess(k4$X180 ~ k4$fv, span = 0.8, degree =2)\r\nlines(k4$fv, k4$X180, col=\"#27408B\", pch=24, lwd=2.5, type=\"b\", lty=2)\r\n\r\n#\u533a\u57df\u5212\u7ebf\r\n\r\n\r\nlegend(\"topleft\", c(\"1.5nl/min\", \"27nl/min\", \"54nl/min\", \"180nl/min\"), col = c(\"#458B74\", \"#FF00FF\", \"#EE0000\", \"#27408B\"), pch = c(21, 22, 23, 24), lwd = 2, lty = 2, cex = 1.2, inset = .03, bty = \"n\")\r\n\r\n###############\u753b\u5c0f\u56fe########\r\npar(fig = c(0.22,0.98,0.2,0.98), new = TRUE)\r\n\r\nplot(k4$fv, k4$X1.5, xaxs = \"i\", col = 0, xlab = \"0 ~ 250 Hz\", ylab = \"f_eject in cycle (Hz)\", main = \"\", cex.lab = 1.2, cex.main = 1.5, xlim = c(0, 250), ylim = c(0, 172))\r\n\r\n#model1 = loess(k4$X1.5 ~ k4$fv, span = 0.8 , degree = 2)\r\nlines(k4$fv, k4$X1.5, col=\"#458B74\", lwd=2.5, type=\"l\", lty=3)\r\n\r\n#model2 = loess(k4$X27 ~ k4$fv, span = 0.8, degree = 2)\r\nlines(k4$fv, k4$X27, col=\"#FF00FF\", lwd=2.5, type=\"l\", lty=3)\r\n\r\n#model3 = loess(k4$X54 ~ k4$fv, span = 0.8, degree = 2)\r\nlines(k4$fv, k4$X54, col=\"#EE0000\", lwd=2.5, type=\"l\", lty=3)\r\n\r\n#model4 = loess(k4$X180 ~ k4$fv, span = 0.8, degree =2)\r\nlines(k4$fv, k4$X180, col=\"#27408B\", lwd=2.5, type=\"l\", lty=3)\r\n\r\n#legend(\"topright\", c(\"1.5nl/min\", \"27nl/min\", \"54nl/min\", \"180nl/min\"), col = c(\"#458B74\", \"#FF00FF\", \"#EE0000\", \"#27408B\"), pch = c(21, 22, 23, 24), lwd = 2, lty = 2, cex = 1.2, bty = \"n\", inset = .03)\r\n\r\n#\u753b\u5c0f\u5c0f\u56fe\r\npar(fig = c(0.35,0.95,0.4,0.95), new = TRUE)\r\n\r\nplot(k4$fv, k4$X1.5, xaxs = \"i\", col = 0, xlab = \"0 ~ 75 Hz\", ylab = \"f_eject in cycle (Hz)\", main = \"\", cex.lab = 1.2, cex.main = 1.5, xlim = c(0, 75), ylim = c(0, 172))\r\n\r\n#text(260, 0.85, \"\u5c04\u51fa\u65f6\u95f4\u5728[0.3ms, 0.8ms]\u5185\", font = 2, col = \"blue\", cex = 1.3)\r\n\r\n#model1 = loess(k4$X1.5 ~ k4$fv, span = 1.6 , degree = 2)\r\nlines(k4$fv, k4$X1.5, col=\"#458B74\", lwd=2, type=\"l\")\r\n\r\n#model2 = loess(k4$X27 ~ k4$fv, span = 1.6, degree = 2)\r\nlines(k4$fv, k4$X27, col=\"#FF00FF\", lwd=2, type=\"l\")\r\n\r\n#model3 = loess(k4$X54 ~ k4$fv, span = 1.6, degree = 2)\r\nlines(k4$fv, k4$X54, col=\"#EE0000\", lwd=2, type=\"l\")\r\n\r\n#model4 = loess(k4$X180 ~ k4$fv, span = 1.6, degree =2)\r\nlines(k4$fv, k4$X180, col=\"#27408B\", lwd=2, type=\"l\")\r\n\r\nabline(h=40, lty = 3, col = \"red\", lwd = 2)\r\nabline(h=1, lty = 3, col = \"red\", lwd = 2)\r\n\r\n#legend(\"topright\", c(\"1.5nl/min\", \"27nl/min\", \"54nl/min\", \"180nl/min\"), col = c(\"#458B74\", \"#FF00FF\", \"#EE0000\", \"#27408B\"), pch = c(21, 22, 23, 24), lwd = 2, lty = 2, cex = 1.2, bty = \"n\", inset = .03)\r\n\r\n#dev.off()\r\n", "meta": {"hexsha": "b9589deee7d5b40827cb3a4dca8bcc60d0c08d7f", "size": 3545, "ext": "r", "lang": "R", "max_stars_repo_path": "Liquids/fe emission/f_k4.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Liquids/fe emission/f_k4.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Liquids/fe emission/f_k4.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.2209302326, "max_line_length": 217, "alphanum_fraction": 0.5503526093, "num_tokens": 1732, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3246543236779258}} {"text": "#' Constructor function to create a likert class.\n#'\n#' This function will provide various summary statistics about a set of likert\n#' items. The resulting object will have the following items:\n#' \\itemize{\n#' \\item results this data frame will contain a column 'Item','Group' (if a \n#' grouping variable was specified,and a column for each level of the\n#' items (e.g. agree,disagree,etc.). The value within each cell corresponds\n#' to the percentage of responses for that level and group.\n#' \\item items a copy of the original items data frame.\n#' \\item grouping a copy of the original grouping vector.\n#' \\item nlevels the number of levels used in the calculations.\n#' \\item summary this data frame provides additional summary information. It\n#' will contain 'Item' and 'Group' columns similiar to the results data\n#' frame as well as a column 'low' corresponding to the sum of levels below\n#' neutral,a column 'high' corresponding to the sum of levels above\n#' neutral,and columns 'mean' and 'sd' corresponding to the mean and\n#' standard deviation,respectively,of the results. The numeric values\n#' are determined by as.numeric which will use the values of the factors.\n#' }\n#'\n#' @export\n#' @param items data frame containing the likert based items. The variables\n#' in the data frame should be factors.\n#' @param grouping (optional) should the results be summarized by the given\n#' grouping variable.\n#' @param nlevels number of possible levels. Only necessary if there are missing levels.\n#' @return a likert class with the following elements: results,items,grouping,\n#' nlevels,and summary.\nlikert<-function(items,grouping=NULL,nlevels=length(levels(items[,1]))) {\n\tlowrange <- 1 : ceiling(nlevels / 2 - nlevels %% 2)\n\thighrange <- ceiling(nlevels / 2 + 1 ) : nlevels\n\n\tresults<-data.frame()\n\tif(!is.null(grouping)) {\n\t\tresults <- data.frame(\n\t\t\tGroup=rep(unique(grouping),each=nlevels),\n\t\t\tResponse=rep(1:nlevels,length(unique(grouping)))\n\t\t\t)\n\t\tfor(i in 1:ncol(items)) {\n\t\t\tt <- as.data.frame(table(grouping, items[, i]))\n\t\t\tt <- cast(t, Var2 ~ grouping, value='Freq', add.missing=T)\n\t\t\tt <- apply(t, 2, FUN=function(x) { x / sum(x) * 100 } )\n\t\t\tt <- melt(t)\n\t\t\tresults <- cbind(results, t[, 3])\n\t\t}\n\n\t\tnames(results)[3:ncol(results)] <- names(items)\n\n\t\tresults2 <- data.frame(Group=rep(unique(results$Group), each=ncol(items)),\n Item=rep(names(items),length(unique(results$Group))),\n low=rep(NA,ncol(items) * length(unique(results$Group))),\n high=rep(NA,ncol(items) * length(unique(results$Group))),\n mean=rep(NA,ncol(items) * length(unique(results$Group))),\n sd=rep(NA,ncol(items) * length(unique(results$Group))) )\n\t\tfor(g in unique(results$Group)) {\n\t\t\tresults2[which(results2$Group == g),]$low <- apply(\n\t\t\t\tresults[results$Response %in% lowrange & \n\t\t\t\t\tresults$Group == g,3:ncol(results)],2,sum)\n\t\t\tresults2[which(results2$Group == g),]$high <- apply(\n\t\t\t\tresults[results$Response %in% highrange & \n\t\t\t\t\tresults$Group == g,3:ncol(results)],2,sum)\n\t\t\tfor(i in names(items)) {\n\t\t\t\tresults2[which(results2$Group == g & results2$Item == i),'mean'] <-\n\t\t\t\t\tmean(as.numeric(items[which(grouping == g),i]),na.rm=T)\n\t\t\t\tresults2[which(results2$Group == g & results2$Item == i),'sd'] <-\n\t\t\t\t\tsd(as.numeric(items[which(grouping == g),i]),na.rm=T)\n\t\t\t}\n\t\t}\n\n\t\tresults$Response <- factor(results$Response, levels=1:nlevels,\n labels=levels(items[,i]))\n\t\tresults <- melt(results, id=c('Group', 'Response'))\n\t\tresults <- cast(results, Group + variable ~ Response)\n\t\tresults <- as.data.frame(results)\n\t\tnames(results)[2] <- 'Item'\n\t} else {\n\t\tresults <- data.frame(Response=1:nlevels)\n\t\tmeans <- numeric()\n\t\tsds <- numeric()\n\t\tfor(i in 1:ncol(items)) {\n\t\t\tt <- table(items[, i])\n\t\t\tt <- (t / sum(t) * 100)\n\t\t\tmeans[i] <- mean(as.numeric(items[, i]), na.rm=T)\n\t\t\tsds[i] <- sd(as.numeric(items[, i]), na.rm=T)\n\t\t\tresults <- cbind(results, as.data.frame(t)[2])\n\t\t\tnames(results)[ncol(results)] <- names(items)[i]\n\t\t}\t\t\n\t\tresults <- as.data.frame(t(results))\n\t\tnames(results) <- as.character(unique(items[!is.na(items[, 1]), 1]))\n\t\tresults <- results[2:nrow(results),]\n\t\tresults$Item <- row.names(results)\n\t\trow.names(results) <- 1:nrow(results)\n\t\tresults2 <- data.frame(Item=results$Item,\n low=apply(results[,lowrange],1,sum),\n high=apply(results[,highrange],1,sum),\n mean=means, sd=sds)\n\t\t#results=melt(results,id.vars='Item')\n\t}\n\n\tr <- list(results=results, items=items, grouping=grouping, nlevels=nlevels,\n summary=results2)\n\tclass(r) <- 'likert'\n\treturn(r)\n}\n\n#' Prints results table.\n#'\n#' @param x the likert class to print.\n#' @export\n#' @method print likert\n#' @S3method print likert\nprint.likert<-function(x,...) {\n\treturn(x$results)\n}\n\n#' Prints summary table.\n#'\n#' @param x the likert class to summarize.\n#' @export\n#' @method summary likert\n#' @S3method summary likert\nsummary.likert<-function(x,...) {\n\treturn(x$summary)\n}\n\n#' Plots a set of likert items. \n#'\n#' @param likert the likert items to plot\n#' @param low.colour colour corresponding to the lowest value likert items\n#' @param high.colour colour corresponding to the highest value likert items\n#' @param neutral.colour colour for middle values. Only used when there are an odd\n#' number of levels.\n#' @param text.size size or text labels\n#' @param type whether to plot a bar or heat map graphic\n#' @export\n#' @method plot likert\n#' @S3method plot likert\nplot.likert<-function(likert,low.colour='blue',high.colour='red',\n\t\t\tneutral.colour='white',text.colour='white',text.size=2,\n\t\t\ttype=c('bar','heat'),...)\n{\n\tif(type[1] == 'bar') {\n\t\tplot.likert.bar(likert,low.colour=low.colour,high.colour=high.colour,\n\t\t\t\t\t\tneutral.colour=neutral.colour,text.size=text.size,...)\n\t} else {\n\t\tplot.likert.heat(likert,low.colour=low.colour,high.colour=high.colour,\n\t\t\t\t\t\ttext.size=text.size,...)\n\t}\n}\n\n", "meta": {"hexsha": "7d63b3bb810b5c9085f020ac8e02ef31010bb01d", "size": 6170, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/functions/lickert.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/functions/lickert.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/functions/lickert.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.5921052632, "max_line_length": 88, "alphanum_fraction": 0.6421393841, "num_tokens": 1626, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3246543155020784}} {"text": "## compare DNAm values from ONT to Array values\n\nfilterRD<-function(data, threshold){\n\treturn(data[which(data$called_sites/data$num_motifs_in_group > threshold),])\n}\n\nsetwd(\"DNAmethylation/\")\n\nfiles<-list.files(\"Nanopolish/\", pattern = \"_methylation_frequency.tsv\", recursive=TRUE)\n\nont.nano<-lapply(paste0(\"Nanopolish/\",files), read.table, header = TRUE)\n\n## only consider CpGs with at least 10 reads.\nont.nano.filt<-lapply(ont.nano, filterRD, 10)\n\n## convert to GRanges\nsam1<-GRanges(seqnames = ont.nano.filt[[1]]$chromosome, strand = \"*\", ranges = IRanges(start = ont.nano.filt[[1]]$start, end = ont.nano.filt[[1]]$end), DNAm = ont.nano.filt[[1]]$methylated_frequency, Reads = ont.nano.filt[[1]]$called_sites, Coverage = ont.nano.filt[[1]]$called_sites/ont.nano.filt[[1]]$num_motifs_in_group, nCpG = ont.nano.filt[[1]]$num_motifs_in_group)\n\nsam2<-GRanges(seqnames = ont.nano.filt[[2]]$chromosome, strand = \"*\", ranges = IRanges(start = ont.nano.filt[[2]]$start, end = ont.nano.filt[[2]]$end), DNAm = ont.nano.filt[[2]]$methylated_frequency, Reads = ont.nano.filt[[2]]$called_sites, Coverage = ont.nano.filt[[2]]$called_sites/ont.nano.filt[[2]]$num_motifs_in_group, nCpG = ont.nano.filt[[2]]$num_motifs_in_group)\n\n## focus in on target regions\n\nallGuides<-read.csv(\"../Resources/SmokingEWAS/GuideRNAsFINAL.csv\")\n\ntargetRegions<-cbind(aggregate(allGuides$hg38, by = list(allGuides$Chr), min), aggregate(allGuides$hg38, by = list(allGuides$Chr), max)$x)\n\t\ntargetGRanges<-GRanges(seqnames = paste0(\"chr\", targetRegions[,1]), strand = \"*\", ranges = IRanges(start = targetRegions[,2], end = targetRegions[,3]))\n\ninTargets1<-subsetByOverlaps(sam1, targetGRanges)\ninTargets2<-subsetByOverlaps(sam2, targetGRanges)\n\n### how many overlapping sites ?\nsharedSites<-findOverlaps(inTargets1, inTargets2)\nallCpGs<-union(inTargets1, inTargets2)\n\n<<<<<<< HEAD\n\n=======\n>>>>>>> 638f31e2662b1d1498cd237a8809634e886c7425\naggregate(inTargets1$nCpG, by = list(as.character(seqnames(inTargets1))), sum)\naggregate(inTargets2$nCpG, by = list(as.character(seqnames(inTargets2))), sum)\naggregate(inTargets1$nCpG[queryHits(sharedSites)], by = list(as.character(seqnames(inTargets1[queryHits(sharedSites)]))), sum)\n\n\n## first genome-wide correlations and error metrics\nload(\"../Resources/SmokingEWAS/ArrayData.rda\")\n\nprobeAnno<-read.table(\"/gpfs/mrc0/projects/Research_Project-MRC190311/References/EPICArray/EPIC.anno.GRCh38.tsv\", header = TRUE, fill = TRUE)\nprobeAnno<-probeAnno[match(rownames(smokebetas), probeAnno$probeID),]\narrayData<-GRanges(seqnames = probeAnno$chrm, strand = \"*\", ranges = IRanges(start = probeAnno$start, end = probeAnno$end), smokebetas)\n\n## count number of sites on EPIC array within these regions\nepicOverlap<-subsetByOverlaps(arrayData, targetGRanges)\ntable(seqnames(epicOverlap))\n\n## compare the spacing profile\n<<<<<<< HEAD\nintraDist<-NULL\nfor(i in 1:length(targetGRanges)){\n\tsubCpGs<-allCpGs[which(as.character(seqnames(allCpGs)) == as.character(seqnames(targetGRanges)[i])),]\n\tintraDist<-c(intraDist, width(gaps(allCpGs)))\n\t## Need to add in distances between CpGs called in a single region\n\t## calculate average distance\t\n}\n\n\n\n\n=======\nfor(i in 1:length(targetGRanges)){\n\tsubCpGs<-allCpGs[which(as.character(seqnames(allCpGs)) == as.character(seqnames(targetGRanges)[i]),]\n\twidth(gaps(allCpGs))\n\t\n}\n\n\n>>>>>>> 638f31e2662b1d1498cd237a8809634e886c7425\noverlapArray1<-findOverlaps(sam1, arrayData)\noverlapArray2<-findOverlaps(sam2, arrayData)\n\n## need to double check which sample is which.\npdf(\"Plots/ScatterplotDNAmArrayvsNanopolishGenomewide.pdf\", width = 10, height = 5)\npar(mfrow = c(1,2))\nplot(sam1$DNAm[queryHits(overlapArray1)], arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)], pch = 16, xlab = \"ONT\", ylab = \"EPIC array\")\nmtext(side = 3, line = 0, adj = 1, paste0(\"cor = \", signif(cor(sam1$DNAm[queryHits(overlapArray1)], arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)]),3), \"; n = \", length(overlapArray1), \" sites\"))\n\nplot(sam2$DNAm[queryHits(overlapArray2)], arrayData$Smoker[subjectHits(overlapArray2)], pch = 16, xlab = \"ONT\", ylab = \"EPIC array\")\nmtext(side = 3, line = 0, adj = 1, paste0(\"cor = \", signif(cor(sam2$DNAm[queryHits(overlapArray2)], arrayData$Smoker[subjectHits(overlapArray2)]),3), \"; n = \", length(overlapArray2), \" sites\"))\ndev.off()\n\n## cor as a function of read depth\nthresholds<-seq(5,100,1)\nerrorMetrics<-matrix(data = NA, nrow = length(thresholds), ncol = 7)\nerrorMetrics[,1]<-thresholds\nrowNum<-1\nfor(rdThres in thresholds){\n\tkeep<-which(sam2$Coverage[queryHits(overlapArray2)] > rdThres)\n\tr1<-cor(sam2$DNAm[queryHits(overlapArray2)[keep]], arrayData$Smoker[subjectHits(overlapArray2)[keep]])\n\trmse1<-sqrt(median((sam2$DNAm[queryHits(overlapArray2)[keep]] - arrayData$Smoker[subjectHits(overlapArray2)[keep]])^2))\n\tncpg1<-length(keep)\n\t\n\tkeep<-which(sam1$Coverage[queryHits(overlapArray1)] > rdThres)\n\tr2<-cor(sam1$DNAm[queryHits(overlapArray1)[keep]], arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)[keep]])\n\trmse2<-sqrt(median((sam1$DNAm[queryHits(overlapArray1)[keep]] - arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)[keep]])^2))\n\tncpg2<-length(keep)\n\t\n\terrorMetrics[rowNum,2:7]<-c(r1, rmse1, ncpg1, r2, rmse2, ncpg2)\n\trowNum<-rowNum+1\n}\n\npdf(\"Plots/LineGraphArrayONTComparisionAgainstReadDepth.pdf\", width = 10, height = 5)\npar(mfrow = c(1,2))\nplot(errorMetrics[,1], errorMetrics[,2], xlab = \"RD Threshold\", ylab = \"r\", type = \"l\", lwd = 2, ylim = c(0.8,1), xlim = c(0,45))\nlines(errorMetrics[,1], errorMetrics[,5], lwd = 2)\n\nplot(errorMetrics[,1], errorMetrics[,3], xlab = \"RD Threshold\", ylab = \"RMSE\", type = \"l\", lwd = 2, ylim = c(0,0.18), xlim = c(0,45))\nlines(errorMetrics[,1], errorMetrics[,6], lwd = 2)\ndev.off()\n\n## as a function of DNAm level\n\nerrorMetrics.dnam<-matrix(data = NA, ncol = 7, nrow = 10)\nerrorMetrics.dnam[,1]<-seq(0.05, 0.95, 0.1)\n\ndnamFilter<-cut(arrayData$Smoker[subjectHits(overlapArray1)], breaks = seq(0,1,0.1))\nrowNum<-1\nfor(each in levels(dnamFilter)){\n\tkeep<-which(dnamFilter == each)\n\tr1<-cor(sam2$DNAm[queryHits(overlapArray2)[keep]], arrayData$Smoker[subjectHits(overlapArray2)[keep]])\n\trmse1<-sqrt(median((sam2$DNAm[queryHits(overlapArray2)[keep]] - arrayData$Smoker[subjectHits(overlapArray2)[keep]])^2))\n\tncpg1<-length(keep)\n\terrorMetrics.dnam[rowNum,2:4]<-c(r1, rmse1,ncpg1)\n\t\n\trowNum<-rowNum+1\n}\n\ndnamFilter<-cut(arrayData$\"Non-Smoker\"[subjectHits(overlapArray2)], breaks = seq(0,1,0.1))\nrowNum<-1\nfor(each in levels(dnamFilter)){\t\n\tkeep<-which(dnamFilter == each)\n\tr2<-cor(sam1$DNAm[queryHits(overlapArray1)[keep]], arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)[keep]])\n\trmse2<-sqrt(median((sam1$DNAm[queryHits(overlapArray1)[keep]] - arrayData$\"Non-Smoker\"[subjectHits(overlapArray1)[keep]])^2))\n\tncpg2<-length(keep)\n\t\n\terrorMetrics.dnam[rowNum,5:7]<-c(r2, rmse2,ncpg2)\n\t\trowNum<-rowNum+1\n}\n\n\npdf(\"Plots/LineGraphArrayONTComparisionAgainstDNAmLevel.pdf\", width = 10, height = 5)\npar(mfrow = c(1,2))\nplot(errorMetrics.dnam[,1], errorMetrics.dnam[,2], xlab = \"DNAm mean\", ylab = \"r\", type = \"l\", lwd = 2, ylim = c(0.8,1))\nlines(errorMetrics.dnam[,1], errorMetrics.dnam[,5], lwd = 2)\n\nplot(errorMetrics.dnam[,1], errorMetrics.dnam[,3], xlab = \"DNAm mean\", ylab = \"RMSE\", type = \"l\", lwd = 2, ylim = c(0,0.18))\nlines(errorMetrics.dnam[,1], errorMetrics.dnam[,6], lwd = 2)\ndev.off()\n\n\n\n", "meta": {"hexsha": "396010783e11513f1ec95b9ca0c46652c1beb74c", "size": 7287, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/CompareArrayONT.r", "max_stars_repo_name": "ejh243/ONTMethCalling", "max_stars_repo_head_hexsha": "aa49682e0cc55913491d7f563e35062c54ab23bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/CompareArrayONT.r", "max_issues_repo_name": "ejh243/ONTMethCalling", "max_issues_repo_head_hexsha": "aa49682e0cc55913491d7f563e35062c54ab23bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/CompareArrayONT.r", "max_forks_repo_name": "ejh243/ONTMethCalling", "max_forks_repo_head_hexsha": "aa49682e0cc55913491d7f563e35062c54ab23bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.2608695652, "max_line_length": 370, "alphanum_fraction": 0.727048168, "num_tokens": 2431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863698, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.32462900636823566}} {"text": "#Comparing R2 and R2 1:1 for Mike.\nrm(list=ls())\nsource('paths.r')\nsource('NEFI_functions/zero_truncated_density.r')\n\n#set output path.----\noutput.path <- 'test.png'\n\n#Load calibration data.----\nd <- readRDS(NEON_dmilti.ddirch_analysis_summary.path)\n\n#get calibration/validation values by group.----\ncal.mu.a <- d$calibration$cal.stat.predictable.sum$rsq\nval.mu.a <- d$validation$site.stat.predictable.sim$rsq\ncal.mu.b <- d$calibration$cal.stat.predictable.sum$rsq.1\nval.mu.b <- d$validation$site.stat.predictable.sim$rsq.1\nnames(cal.mu.a) <- rownames(d$calibration$cal.stat.predictable.sum)\nnames(cal.mu.b) <- rownames(d$calibration$cal.stat.predictable.sum)\n\n#png save line.----\npng(filename=output.path,width=9,height=6,units='in',res=300)\n\n#global plot settings.----\npar(mfrow = c(1,2), mar = c(5,4,2,1), oma = c(1,1,1,1))\nlimx <- c(0,1)\nlimy <- c(0, 5.1)\ntrans <- 0.2 #shading transparency.\no.cex <- 1.2 #outer label size.\ncols <- c('purple','cyan','yellow')\n \n\n#Calibration and Validation rsq ~ function/phylo scale.----\nx <- 1:length(cal.mu.a)\nlimy <- c(0,max(cal.mu.a)*1.1)\nplot(cal.mu.a ~ x, cex = 2.5, ylim = limy, pch = 16, ylab = NA, xlab = NA, bty='l', xaxt = 'n', yaxs='i', las = 1, lwd = 0)\n#arrows(x, lev.mu - lev.se, x1 = x, y1 = lev.mu + lev.se, length=0.00, angle=90, code=3, col = 'black')\nlines(x, cal.mu.a, lty = 2)\nlines(x, val.mu.a, lty = 2)\npoints(val.mu.a ~ x, cex = 2.5, pch = 16, col = 'gray')\nmtext(expression(paste(\"Site-Level R\"^\"2\")), side = 2, line = 2.5, cex = o.cex)\naxis(1, labels = F)\ntext(x=x+0.05, y = limy[1] - limy[2]*0.1, labels= names(cal.mu.a), srt=45, adj=1, xpd=TRUE, cex = 1.1)\n#legend\nlegend(x = 1, y = 0.1, legend = c('calibration','validation'), col =c('black','gray'), bty = 'n', pch = 16, pt.cex = 2.5, cex = 1.2)\nmtext('(a)', side = 3, adj = 0.95, line = -2)\n\n#Calibration and Validation RMSE ~ function/phylo scale.----\nx <- 1:length(cal.mu.b)\nlimy <- c(min(val.mu.b)*1.1,max(cal.mu.b)*8)\nplot(cal.mu.b ~ x, cex = 2.5, ylim = limy, pch = 16, ylab = NA, xlab = NA, bty='l', xaxt = 'n', yaxs='i', las = 1, lwd = 0)\n#arrows(x, lev.mu - lev.se, x1 = x, y1 = lev.mu + lev.se, length=0.00, angle=90, code=3, col = 'black')\nlines(x, cal.mu.b, lty = 2)\nlines(x, val.mu.b, lty = 2)\npoints(val.mu.b ~ x, cex = 2.5, pch = 16, col = 'gray')\nmtext(expression(paste(\"Site-Level R\"^\"2\",\" 1:1\")), side = 2, line = 2.5, cex = o.cex)\naxis(1, labels = F)\ntext(x=x+0.05, y = limy[1] - abs(limy[1])*0.1, labels= names(cal.mu.a), srt=45, adj=1, xpd=TRUE, cex = 1.1)\nmtext('(b)', side = 3, adj = 0.95, line = -2)\n\n#end plot.----\ndev.off()\n", "meta": {"hexsha": "381c26ed77e77bcc59a99a468d8274cbdb60f44b", "size": 2570, "ext": "r", "lang": "R", "max_stars_repo_path": "to_retire/xx_r2_vs_r21.1_for_mike.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "to_retire/xx_r2_vs_r21.1_for_mike.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "to_retire/xx_r2_vs_r21.1_for_mike.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 41.4516129032, "max_line_length": 132, "alphanum_fraction": 0.6210116732, "num_tokens": 1025, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3246290063682356}} {"text": "#!/usr/bin/env Rscript\n\n#===========================================================\n# R script for finding the significant contact count distribution for individual peaks\n\n#Author: Sourya Bhattacharyya\n#Vijay-Ay lab, LJI\n\n# usage: Rscript ContactCountDistr.r $PeakFile $InteractionFile $PlotFile $OutText\n#===========================================================\n\nlibrary(data.table)\n\noptions(scipen = 999)\noptions(datatable.fread.datatable=FALSE)\n\nargs <- commandArgs(TRUE)\n\nPeakFile <- args[1]\nInteractionFile <- args[2]\nPlotFile <- args[3]\nOutText <- args[4]\n\n# peaks in the input peak file (PD = PeakData)\n# PD <- read.table(PeakFile, header=FALSE)\nPD <- data.table::fread(PeakFile, header=FALSE)\n\n# peaks from the interaction file (PI = peaks from interactions)\n# PI <- read.table(InteractionFile, header=FALSE)\nPI <- data.table::fread(InteractionFile, header=FALSE)\n\n# significant contact count (filtered according to Q value) for individual peaks\ncount <- c()\n\nfor (i in (1:nrow(PD))) {\n\tchr1 <- as.character(PD[i,1])\n\ts1 <- as.integer(PD[i,2])\n\te1 <- as.integer(PD[i,3])\n\n\t# cat(sprintf(\"\\n\\n ===>>> checking chr1: %s s1: %s e1: %s \\n \", chr1, s1, e1))\n\n\t# overlap between peaks in the peak file and in the interaction file\n\t# comparison with respect to three columns\n\t# note: use of '&' operator\n\tc <- 0\n\n\t# set of indices matching with chr1\n\tidx <- which(PI[,1] == chr1)\n\n\t# cat(sprintf(\"\\n idx: %s \\n length of idx: %s \\n \", idx, length(idx)))\n\n\tif (length(idx) > 0) {\n\t\tfor (j_idx in (1:length(idx))) {\n\t\t\tj <- idx[j_idx]\n\n\t\t\tchr2 <- as.character(PI[j,1])\n\t\t\ts2 <- as.integer(PI[j,2])\n\t\t\te2 <- as.integer(PI[j,3])\n\n\t\t\t# cat(sprintf(\"\\n --- individual chr1: %s s1: %s e1: %s chr2: %s s2: %s e2: %s \\n \", chr1, s1, e1, chr2, s2, e2)) \t\t\n\n\t\t\tif ((!is.na(s1)) & (!is.na(e1)) & (!is.na(s2)) & (!is.na(e2))) {\n\t\t\t\tif ((s2 <= s1) & (e2 >= e1)) {\n\t\t\t\t\tc <- c + 1\n\t\t\t\t} else if ((s2 >= s1) & (e2 <= e1)) {\n\t\t\t\t\tc <- c + 1\n\t\t\t\t} else if ((s2 <= s1) & (e2 >= s1)) {\n\t\t\t\t\tc <- c + 1\n\t\t\t\t} else if ((s2 <= e1) & (e2 >= e1)) {\n\t\t\t\t\tc <- c + 1\n\t\t\t\t}\t\t\t\n\t\t\t}\n\t\t}\t\t\n\t}\n\tcount[i] <- c\n}\n\n# dump the original peaks vs significant contact count in a text file\nwrite.table(cbind(PD[,1:3], count), OutText, row.names = FALSE, col.names = FALSE, sep = \"\\t\", quote=FALSE, append=FALSE)\n\n# first sort the count vector and then draw the frequency distribution\npdf(PlotFile, width=14, height=10)\nplot(sort(count, decreasing=TRUE), type = \"o\", cex=0.5, col=\"red\", xlab=\"Peak counter\", ylab=\"Contact count (filtered)\")\ntitle(sub('\\\\.pdf$', '', basename(PlotFile)))\ndev.off()\n\n\n", "meta": {"hexsha": "23087f36afb0c40624e8cbda7a06494793d3af2d", "size": 2567, "ext": "r", "lang": "R", "max_stars_repo_path": "Analysis/ContactCountDistr.r", "max_stars_repo_name": "gahanleeo/FitHiChIP", "max_stars_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 21, "max_stars_repo_stars_event_min_datetime": "2017-11-07T09:39:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T05:35:24.000Z", "max_issues_repo_path": "Analysis/ContactCountDistr.r", "max_issues_repo_name": "gahanleeo/FitHiChIP", "max_issues_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 66, "max_issues_repo_issues_event_min_datetime": "2018-03-28T02:36:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T10:44:28.000Z", "max_forks_repo_path": "Analysis/ContactCountDistr.r", "max_forks_repo_name": "gahanleeo/FitHiChIP", "max_forks_repo_head_hexsha": "f34fe9eff5d65ad3dfaaecc43a6c245221b840ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2017-12-08T22:02:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T06:49:25.000Z", "avg_line_length": 29.1704545455, "max_line_length": 122, "alphanum_fraction": 0.5909622127, "num_tokens": 830, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.32455721549704386}} {"text": "# Merge EU DEM with GLSDEM, preferring EU DEM\nlibrary(raster)\n\nGLSDir = \"../../userdata/dem/glsdem\"\nEUDir = \"../../userdata/dem/eeadem\"\nMergedDir = \"../../userdata/dem/merged\"\nfiles = c(\"pv-height.tif\", \"pv-aspect.tif\", \"pv-tpi.tif\", \"pv-slope.tif\")\nGLSFiles = paste0(GLSDir, \"/\", files)\nEUFiles = paste0(EUDir, \"/\", files)\nMergedFiles = paste0(MergedDir, \"/\", files)\n\nfor (i in 1:length(files))\n{\n if (!file.exists(MergedFiles[i]))\n raster::merge(raster(EUFiles[i]), raster(GLSFiles[i]), filename=MergedFiles[i])\n}\n", "meta": {"hexsha": "96c2feb5f8aa6a329f3b8d261bc303aed80227d4", "size": 526, "ext": "r", "lang": "R", "max_stars_repo_path": "src/raster-based/elevation/dem-merge.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/raster-based/elevation/dem-merge.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/raster-based/elevation/dem-merge.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 30.9411764706, "max_line_length": 87, "alphanum_fraction": 0.6577946768, "num_tokens": 161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3245572073973032}} {"text": "# Converts a time to the previous time in whole minutes. Eg: 15:41:40 goes to 15:41:00.\nwholemin <- function(x) trunc(x, units=\"minutes\")", "meta": {"hexsha": "8dc0cbe9236a420959544e704053ee796070d250", "size": 137, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/wholemin.r", "max_stars_repo_name": "robintw/VisAOT", "max_stars_repo_head_hexsha": "03cb5013008cc59ae1a18ce024924382a01fda62", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-12-21T19:29:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-03T08:45:30.000Z", "max_issues_repo_path": "lib/wholemin.r", "max_issues_repo_name": "robintw/VisAOT", "max_issues_repo_head_hexsha": "03cb5013008cc59ae1a18ce024924382a01fda62", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/wholemin.r", "max_forks_repo_name": "robintw/VisAOT", "max_forks_repo_head_hexsha": "03cb5013008cc59ae1a18ce024924382a01fda62", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2016-06-09T15:30:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-31T02:08:48.000Z", "avg_line_length": 68.5, "max_line_length": 87, "alphanum_fraction": 0.7226277372, "num_tokens": 42, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3245572073973032}} {"text": "fs <- dir(\"Results\")\nfsfull <- dir(\"Results\",full.names = T)\n\nlibrary(data.table)\nlibrary(magrittr)\nlibrary(tidyr)\n\nhehe <- lapply(split(fsfull, substr(fs,1,1) == \"r\"), function(fss) {\n lapply(fss, fread) %>% rbindlist\n})\n\nhehe$`FALSE`$test <- paste0(\"test\",2:11)\n\ncomp <- merge(hehe$`FALSE`,hehe$`TRUE`)\n\ncomp_for_plotting <- comp[,.(Julia = mean(julia_timings), R = mean(elapsed)), test] %>% \n gather(key = jr, value = timings, -test)\n\nlibrary(ggplot2)\n\ncomp_for_plotting %>% ggplot + \n geom_bar(aes(fill = jr,x = test, y = timings), \n stat = \"identity\", position = \"dodge\")\n\n\n\nlibrary(data.table)\nsystem.time(lapply(\n file.path(dir(\"../../data/InitialTrainingSet_rev1/\",full.names = T),\"ASDI/asdifpwaypoint.csv\"),\n function(f) {\n fst::write.fst(fread(f),paste0(f,\".fst\"), compress=100)\n gc()\n }\n))\n\nlibrary(data.table)\nlibrary(future)\nplan(multiprocess)\n\nsystem.time(future_lapply(\n file.path(dir(\"../../data/InitialTrainingSet_rev1/\",full.names = T),\"ASDI/asdifpwaypoint.csv\"),\n function(f) {\n fst::write.fst(fread(f),paste0(f,\".fst\"), compress=100)\n gc()\n }\n))\n\nlibrary(magrittr)\nsystem.time(a <- future_lapply(\n file.path(dir(\"../../data/InitialTrainingSet_rev1/\",full.names = T),\"ASDI/asdifpwaypoint.csv.fst\"),\n fst::read.fst, as.data.table = T)\n %>% rbindlist)\n\n\nsystem.time(a <- future_lapply(\n file.path(dir(\"../../data/InitialTrainingSet_rev1/\",full.names = T),\"ASDI/asdifpwaypoint.csv\"),\n fread)\n%>% rbindlist)\n", "meta": {"hexsha": "ebcd668e2ec611203801f5a14deeaf83ae0e9278", "size": 1458, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r/99_compare_results.r", "max_stars_repo_name": "JuliaTagBot/DataBench.jl", "max_stars_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-01T20:03:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-01T20:07:41.000Z", "max_issues_repo_path": "src/r/99_compare_results.r", "max_issues_repo_name": "JuliaTagBot/DataBench.jl", "max_issues_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/r/99_compare_results.r", "max_forks_repo_name": "JuliaTagBot/DataBench.jl", "max_forks_repo_head_hexsha": "9227c8b4eb836b3009fd41facf2adaa2c4461b67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-02-08T10:58:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-01T17:21:58.000Z", "avg_line_length": 24.7118644068, "max_line_length": 101, "alphanum_fraction": 0.6598079561, "num_tokens": 447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.32455719929756244}} {"text": "#!/usr/bin/env Rscript\n#\n# indicator_species.r - R slave for indicator species analysis with R package indicspecies\n#\n# Version 1.0.0 (July, 16, 2016)\n#\n# Copyright (c) 2016-- Lela Andrews\n#\n# This software is provided 'as-is', without any express or implied\n# warranty. In no event will the authors be held liable for any damages\n# arising from the use of this software.\n#\n# Permission is granted to anyone to use this software for any purpose,\n# including commercial applications, and to alter it and redistribute it\n# freely, subject to the following restrictions:\n#\n# 1. The origin of this software must not be misrepresented; you must not\n# claim that you wrote the original software. If you use this software\n# in a product, an acknowledgment in the product documentation would be\n# appreciated but is not required.\n# 2. Altered source versions must be plainly marked as such, and must not be\n# misrepresented as being the original software.\n# 3. This notice may not be removed or altered from any source distribution.\n#\n\n## Recieve input files from bash\nargs <- commandArgs(TRUE)\nmapfile=(args[1])\nbiomfile=(args[2])\nfactor=(args[3])\noutdir=(args[4])\nperms0=(args[5])\nperms <- as.integer(perms0)\n\n## Load libraries\nlibrary(indicspecies)\noptions(width=300)\n\n## Read in data\nmap <- read.csv(mapfile, sep=\"\\t\", header=TRUE)\nbiom <- read.csv(biomfile, sep=\"\\t\", header=TRUE)\nf1 <- map[,factor]\n\n## Run indval and print to screen\ninvalperms <- paste(\"\\n********************************\\nIndicator value analysis summary\\n(\", perms, \" permutations, uncorrected for multiple testing):\", sep=\"\")\nwriteLines(invalperms)\nindval = multipatt(biom, f1, control=how(nperm=perms))\nsummary(indval)\n\n## Run coverage and print to screen\nwriteLines(\"\\n********************************\\nCoverage (IndVal):\")\ncoverage(biom, indval)\n\n## Run phi and print to screen\nphiperms <- paste(\"\\n********************************\\nPearson's phi coefficient of association summary\\n(\", perms, \" permutations, uncorrected for multiple testing):\", sep=\"\")\nwriteLines(phiperms)\nphi = multipatt(biom, f1, func=\"r.g\", control=how(nperm=perms))\nsummary(phi)\n\n## Run coverage and print to screen\nwriteLines(\"\\n********************************\\nCoverage (Phi):\")\ncoverage(biom, phi)\n\n## Read all indval results to variable\nindval.all <- indval$sign\n\n## Extract p-values to separate vector\nindval.all.pvals <- indval.all[,\"p.value\"]\n\n## Correct p-values (FDR) and bind the result to original output\nfdr.p.value <- p.adjust(indval.all.pvals, method=\"fdr\")\nindval.all.fdr <- cbind(indval.all, fdr.p.value)\n\n## Omit NA values and print only those with p <= 0.05\nattach(indval.all.fdr)\nindval.all.fdr.nona.sort <- indval.all.fdr[order(fdr.p.value, p.value, na.last=NA),]\nindvalfdrperms <- paste(\"\\n********************************\\nIndVal results with FDR corrections\\n(only valid p-values shown, \", perms, \" permutations):\\n\", sep=\"\")\nwriteLines(indvalfdrperms)\nindval.all.fdr.nona.sort\ndetach(indval.all.fdr)\n\n## Repeat FDR correction and NA omission for Phi output\nphi.all <- phi$sign\nphi.all.pvals <- phi.all[,\"p.value\"]\nfdr.p.value <- p.adjust(phi.all.pvals, method=\"fdr\")\nphi.all.fdr <- cbind(phi.all, fdr.p.value)\nattach(phi.all.fdr)\nphi.all.fdr.nona.sort <- phi.all.fdr[order(fdr.p.value, p.value, na.last=NA),]\nphivalfdrperms <- paste(\"\\n********************************\\nPhi results with FDR corrections\\n(only valid p-values shown, \", perms, \" permutations):\\n\", sep=\"\")\nwriteLines(phivalfdrperms)\nphi.all.fdr.nona.sort\n\n## Blank line at end of file\nwriteLines(\"\")\n## End\nq()\n", "meta": {"hexsha": "233e02fcfb7cd99e26b30e89ad7fb939d6c7a6bb", "size": 3575, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/indicator_species.r", "max_stars_repo_name": "lvandrews/amptools", "max_stars_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2016-02-01T20:25:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T16:52:49.000Z", "max_issues_repo_path": "scripts/indicator_species.r", "max_issues_repo_name": "lvandrews/amptools", "max_issues_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_issues_repo_licenses": ["Zlib"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2016-06-29T18:01:54.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-18T18:47:09.000Z", "max_forks_repo_path": "scripts/indicator_species.r", "max_forks_repo_name": "lvandrews/amptools", "max_forks_repo_head_hexsha": "c4d69d0afc8c91ae7a6bc4d9b530b2e6e9b9f91d", "max_forks_repo_licenses": ["Zlib"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-05-02T18:37:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-13T22:22:27.000Z", "avg_line_length": 36.8556701031, "max_line_length": 176, "alphanum_fraction": 0.6875524476, "num_tokens": 937, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727231, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32442131704285415}} {"text": "REBOL [\r\n\tSystem: \"REBOL [R3] Language Interpreter and Run-time Environment\"\r\n\tTitle: \"REBOL Graphics - SHAPE commands\"\r\n\tRights: {\r\n\t\tCopyright 2012 REBOL Technologies\r\n\t\tREBOL is a trademark of REBOL Technologies\r\n\t}\r\n\tLicense: {\r\n\t\tLicensed under the Apache License, Version 2.0.\r\n\t\tSee: http://www.apache.org/licenses/LICENSE-2.0\r\n\t}\r\n\tName: shape\r\n\tType: extension\r\n\tExports: none\r\n\tNote: \"Run make-host-ext.r to convert\"\r\n]\r\n\r\n;don't change order of already defined words unless you know what you are doing\r\n\r\nwords: [\r\n\t;arc\r\n\tnegative\r\n\tpositive\r\n\tsmall\r\n\tlarge\r\n]\r\n\r\n;temp hack - will be removed later\r\ninit-words: command [\r\n\twords [block!]\r\n]\r\n\r\ninit-words words\r\n\r\n;please alphabetize the order of commands so it easier to lookup things\r\n\r\narc: command [\r\n\t\"Draws an elliptical arc from the current point.\"\r\n\tend-point [pair!]\r\n\tradius [pair!]\r\n\tangle [number!] \r\n\t'sweep-flag [word!] \"The arc will be drawn in POSITIVE or NEGATIVE angle direction\"\r\n\t'arc-flag [word!] \"User SMALL or LARGE arc sweep\"\r\n]\r\n\r\narc': command [\r\n\t\"Draws an elliptical arc from the current point.(uses relative coordinates)\"\r\n\tend-point [pair!]\r\n\tradius [pair!]\r\n\tangle [number!] \r\n\t'sweep-flag [word!] \"The arc will be drawn in POSITIVE or NEGATIVE angle direction\"\r\n\t'arc-flag [word!] \"User SMALL or LARGE arc sweep\"\r\n]\r\n\r\nclose: command [\r\n\t\"Closes previously defined set of lines in the SHAPE block.\"\r\n]\r\n\r\ncurv: command [\r\n\t\"Draws a cubic Bezier curve or polybezier using two points.\"\r\n\tpoints [block!] \"Block of point pairs (2nd control point, end point)\"\r\n]\r\n\r\ncurv': command [\r\n\t\"Draws a cubic Bezier curve or polybezier using two points.(uses relative coordinates)\"\r\n\tpoints [block!] \"Block of point pairs (2nd control point, end point)\"\r\n]\r\n\r\ncurve: command [\r\n\t\"Draws a cubic Bezier curve or polybezier using three points.\"\r\n\tpoints [block!] \"Block of point triplets (1st control point, 2nd control point, end point)\"\r\n]\r\n\r\ncurve': command [\r\n\t\"Draws a cubic Bezier curve or polybezier using three points.(uses relative coordinates)\"\r\n\tpoints [block!] \"Block of point triplets (1st control point, 2nd control point, end point)\"\r\n]\r\n\r\nhline: command [\r\n\t\"Draws a horizontal line from the current point.\"\r\n\tend-x [number!]\r\n]\r\n\r\nhline': command [\r\n\t\"Draws a horizontal line from the current point.(uses relative coordinates)\"\r\n\tend-x [number!]\r\n]\r\n\r\nline: command [\r\n\t\"Draws a line from the current point through the given points.\"\r\n\tpoints [pair! block!]\r\n]\r\n\r\nline': command [\r\n\t\"Draws a line from the current point through the given points.(uses relative coordinates)\"\r\n\tpoints [pair! block!]\r\n]\r\n\r\nmove: command [\r\n\t\"Set's the starting point for a new path without drawing anything.\"\r\n\tpoint [pair!]\r\n]\r\n\r\nmove': command [\r\n\t\"Set's the starting point for a new path without drawing anything.(uses relative coordinates)\"\r\n\tpoint [pair!]\r\n]\r\n\r\nqcurv: command [\r\n\t\"Draws a quadratic Bezier curve from the current point to end point.\"\r\n\tend-point [pair!]\r\n]\r\n\r\nqcurv': command [\r\n\t\"Draws a quadratic Bezier curve from the current point to end point.(uses relative coordinates)\"\r\n\tend-point [pair!]\r\n]\r\n\r\nqcurve: command [\r\n\t\"Draws a quadratic Bezier curve using two points.\"\r\n\tpoints [block!] \"Block of point pairs (control point, end point)\"\r\n]\r\n\r\nqcurve': command [\r\n\t\"Draws a quadratic Bezier curve using two points.(uses relative coordinates)\"\r\n\tpoints [block!] \"Block of point pairs (control point, end point)\"\r\n]\r\n\r\nvline: command [\r\n\t\"Draws a vertical line from the current point.\"\r\n\tend-y [number!]\r\n]\r\n\r\nvline': command [\r\n\t\"Draws a vertical line from the current point.(uses relative coordinates)\"\r\n\tend-y [number!]\r\n]\r\n", "meta": {"hexsha": "ffe6cd2451ec5ba892e28a40d092cc2b9d1b9d24", "size": 3628, "ext": "r", "lang": "R", "max_stars_repo_path": "src/boot/shape.r", "max_stars_repo_name": "gchiu/r3", "max_stars_repo_head_hexsha": "0b5c11d4bd15a610bc9db5a5e18176f751153174", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/boot/shape.r", "max_issues_repo_name": "gchiu/r3", "max_issues_repo_head_hexsha": "0b5c11d4bd15a610bc9db5a5e18176f751153174", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/boot/shape.r", "max_forks_repo_name": "gchiu/r3", "max_forks_repo_head_hexsha": "0b5c11d4bd15a610bc9db5a5e18176f751153174", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2898550725, "max_line_length": 98, "alphanum_fraction": 0.6987320838, "num_tokens": 923, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32442130901994826}} {"text": "#' @name afrilandcover\n#' @aliases afrilandcover\n#' @title landcover raster for Africa, categorical, 20km resolution\n#'\n#' @description a \\code{raster} object storing the majority landcover in 2019 for all 20km squares in Africa.\n#' Categorical, 20km resolution from [MODIS](https://lpdaac.usgs.gov/products/mcd12c1v006/).\n#' Cell values are numeric, landcover type names are stored in Raster Attribute Table (RAT) that can be accessed via `levels(afrilandcover)`\n#' See data-raw/afrilearndata-creation.R for how the data object is created.\n#'\n#' @format Formal class 'raster';\n#'\n#' Geographical coordinates WGS84 datum (CRS EPSG 4326)\n#'\n#' @seealso\n#' Friedl, M., D. Sulla-Menashe. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD12C1.006. Accessed 2021-06-07.#'\n#' @source \\url{https://lpdaac.usgs.gov/products/mcd12c1v006/}\n#' @docType data\n#' @keywords datasets sf\n#' @examples\n#' if (requireNamespace(\"raster\", quietly = TRUE)) {\n#' library(raster)\n#' data(afrilandcover)\n#' # or\n#' filename <- system.file(\"extdata\",\"afrilandcover.grd\", package=\"afrilearndata\", mustWork=TRUE)\n#' afrilandcover <- raster::raster(filename)\n#'\n#' plot(afrilandcover)\n#' }\n#'\n#' # interactive plotting with mapview\n#' if (requireNamespace(\"mapview\", quietly = TRUE) &\n#' requireNamespace(\"rgdal\", quietly = TRUE)) {\n#' library(mapview)\n#' mapview(afrilandcover,\n#' att=\"landcover\",\n#' col.regions=levels(afrilandcover)[[1]]$colour)\n#' }\n#'\n#'\n\"afrilandcover\"\n", "meta": {"hexsha": "9b482e84e1a2204b2ce323fda52c9d2e94abe28e", "size": 1602, "ext": "r", "lang": "R", "max_stars_repo_path": "R/afrilandcover.r", "max_stars_repo_name": "afrimapr/afrilearndata", "max_stars_repo_head_hexsha": "c275fafef4ef03eca5c1b2d7d762010ec0437f86", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-11-27T11:55:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T09:27:29.000Z", "max_issues_repo_path": "R/afrilandcover.r", "max_issues_repo_name": "afrimapr/afrilearndata", "max_issues_repo_head_hexsha": "c275fafef4ef03eca5c1b2d7d762010ec0437f86", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2020-11-16T16:50:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-09T16:01:48.000Z", "max_forks_repo_path": "R/afrilandcover.r", "max_forks_repo_name": "afrimapr/afrilearndata", "max_forks_repo_head_hexsha": "c275fafef4ef03eca5c1b2d7d762010ec0437f86", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2020-12-19T22:17:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-01T20:14:31.000Z", "avg_line_length": 39.0731707317, "max_line_length": 228, "alphanum_fraction": 0.7066167291, "num_tokens": 484, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.32442130901994826}} {"text": "\\docType{data}\n\\name{mtautos}\n\\alias{mtautos}\n\\title{Pruebas de ruta de autom\u00f3viles de Motor Trend}\n\\format{Un data.frame con 32 filas y 12 columnas\n\\describe{\n\\item{millas}{millas por gal\u00f3n de Estados Unidos}\n\\item{cilindros}{n\u00famero de cilindros}\n\\item{cilindrada}{suma del volumen \u00fatil de todos los cilindros del motor en pulgadas c\u00fabicas}\n\\item{caballos}{caballos de fuerza brutos}\n\\item{eje}{relaci\u00f3n del eje de transmisi\u00f3n trasero}\n\\item{peso}{peso (1000 libras)}\n\\item{velocidad}{tiempo en recorrer 1/4 de milla}\n\\item{forma}{forma del motor (en V o en l\u00ednea)}\n\\item{transmision}{tipo de transmisi\u00f3n (0 = autom\u00e1tico, 1 = manual)}\n\\item{cambios}{n\u00famero de cambios de la caja de cambios}\n\\item{carburadores}{n\u00famero de carburadores}\n}}\n\\usage{mtautos}\n\\description{Los datos fueron extra\u00eddos de la revista Motor Trend de Estados Unidos de 1974, y tiene datos de consumo de combustible y 10 aspectos de dise\u00f1o y rendimiento de autom\u00f3viles para 32 autom\u00f3viles (modelos de 1973-1974).}\n\\keyword{datasets}\n", "meta": {"hexsha": "9b852fc47f43187d773422c53f2882b9313d9e4a", "size": 1005, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/mtautos.rd", "max_stars_repo_name": "RubenMaier/datos", "max_stars_repo_head_hexsha": "c0606f7df0701d944cf0fe762cd29e821e53e75c", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-10-15T08:12:23.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-15T08:12:23.000Z", "max_issues_repo_path": "man/mtautos.rd", "max_issues_repo_name": "RubenMaier/datos", "max_issues_repo_head_hexsha": "c0606f7df0701d944cf0fe762cd29e821e53e75c", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/mtautos.rd", "max_forks_repo_name": "RubenMaier/datos", "max_forks_repo_head_hexsha": "c0606f7df0701d944cf0fe762cd29e821e53e75c", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.6818181818, "max_line_length": 230, "alphanum_fraction": 0.7751243781, "num_tokens": 328, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.32442130901994826}} {"text": "#ggplot2\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\ninstall.packages(\"ggplot2\")\n\nlibrary(ggplot2)\n\n#data.frame\u306e\u8981\u7d20\u304c\u6570\u5b57\u306e\u5834\u5408\u306f\u30b0\u30e9\u30d5\u304c\u4f5c\u6210\u3067\u304d\u306a\u3044\u306e\u3067\n#\u305d\u308c\u305e\u308ca, b, c\u306b\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\n\nsekkaku <- data.frame(\"a\" <- tsuchinoko, \"b\" <- sabaiteiku, \n\"c\"<- Awajishima, \"x\" <- xx)\n\n#ggplot\u3067\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\nggplot(sekkaku, aes(x)) +\n geom_line(aes(y = a, colour = \"3.4\")) +\n geom_line(aes(y = b, colour = \"3.5\")) +\n geom_line(aes(y = c, colour = \"3.55\"))\n", "meta": {"hexsha": "a41d3a401ca6c1e3a5f79b0155eb478953e5af51", "size": 378, "ext": "r", "lang": "R", "max_stars_repo_path": "Question2-ggplot2.r", "max_stars_repo_name": "K-Rintaro/LogisticRepo", "max_stars_repo_head_hexsha": "af044a43e6f7f38d7e747515a2e62ae398a1de2b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-24T06:47:55.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-24T06:47:55.000Z", "max_issues_repo_path": "Question2-ggplot2.r", "max_issues_repo_name": "K-Rintaro/LogisticRepo", "max_issues_repo_head_hexsha": "af044a43e6f7f38d7e747515a2e62ae398a1de2b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Question2-ggplot2.r", "max_forks_repo_name": "K-Rintaro/LogisticRepo", "max_forks_repo_head_hexsha": "af044a43e6f7f38d7e747515a2e62ae398a1de2b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.2352941176, "max_line_length": 60, "alphanum_fraction": 0.6534391534, "num_tokens": 178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.32442130901994826}} {"text": "\nlibrary(jpeg)\nlibrary(png)\n#a=\"/home/xiaoyh/inmic/inmic/static/image/phylo_tree.jpg\"\n#a=\"/home/xiaoyh/source/graphlan.png\"\na=\"1.jpg\"\n x <- readJPEG(a)\n dimx <- dim(x)\n n <- dimx[1]*dimx[2]\n r <- x[1:n]\n g <- x[(n+1):(2*n)]\n b <- x[(2*n+1):(3*n)]\n ps <- 10; ps <- dimx[1]*(ps-1) + ps # \u80cc\u666f\u53d6\u503c\uff0cps\u4e3a\u5de6\u4e0a\u5230\u53f3\u4e0b\u89d2\u7684\u50cf\u7d20\uff0c5\u3002\u6309\u60c5\u51b5\u4fee\u6539 \n tv <- 0.1 # tv\u4e3a\u5bb9\u5dee\u8303\u56f4\uff0c0-1\u53d6\u503c\uff0c\u8d8a\u5c0f\u8d8a\u7cbe\u786e \n sel <- abs(r-r[ps])1) >= 6\ny <- y[keep, ,]\ny <- estimateDisp(y, design)\nfit <- glmQLFit(y, design)\nqlf <- glmQLFTest(fit, contrast=c(0,0,0,0,0,0,1,0,0,0,0,0,-1,0,0))\ntab <- topTags(qlf, n=Inf)\nwrite.table(tab, file=\"Bacillussubtilis168Marburg_False_False_0_False_False-vs-Bacillussubtilis168Marburg_False_False_18_True_False.txt\")", "meta": {"hexsha": "e96a35ba880c5fd973b1ea06db606543a089b392", "size": 1185, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/scaled_example/scripts/dge_12.r", "max_stars_repo_name": "SD2E/omics_tools", "max_stars_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-17T17:39:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-28T07:56:48.000Z", "max_issues_repo_path": "examples/scaled_example/scripts/dge_12.r", "max_issues_repo_name": "SD2E/omics_tools", "max_issues_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/scaled_example/scripts/dge_12.r", "max_forks_repo_name": "SD2E/omics_tools", "max_forks_repo_head_hexsha": "c1f4e3d84b5e5050605285bf2d16f40905e3e582", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.027027027, "max_line_length": 180, "alphanum_fraction": 0.6506329114, "num_tokens": 534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7217431943271999, "lm_q2_score": 0.4493926344647597, "lm_q1q2_score": 0.3243460755057114}} {"text": "library(tidyverse)\nlibrary(ppcor)\nlibrary(ggpubr)\nlibrary(broom)\nlibrary(svglite)\n\n#### import and format tract-tracing connectivity matrix\ntt_path <- '/home/celine/ferret-mri-annex/scratch/work_celine/compDiffTT/data/Bizley2009/retrogradeTT_raw_dzero_normFLN_sym.csv' \ntt_data <- read.csv(tt_path, header = FALSE, sep = '')\nmask <- upper.tri(tt_data,diag = FALSE)\ntt_data_flat <- tt_data[mask]\n\n\n#### import euclidean distance between parcels\neucldist_path = '/home/celine/ferret-mri-annex/scratch/work_celine/compDiffTT/data/Bizley2009/euclideanDistance_selected.csv'\neucldist = read.csv(eucldist_path, header = FALSE, sep = '')\neucldist_flat <- eucldist[mask]\n\n#### import and format diffusion data (connectivity matrices and average streamline lengths)\ni <- 0\nnbfib <- '1M'\npathConn <- '/home/celine/ferret-mri-annex/scratch/work_celine/compDiffTT/data/connectomes/F01_Adult/selected/processed/'\npathLen <- '/home/celine/ferret-mri-annex/scratch/work_celine/compDiffTT/data/connectomes/F01_Adult/length/selected/'\nsavepath <- '/home/celine/ferret-mri-annex/scratch/work_celine/compDiffTT/figures/'\n\n\ndti_det_path <- paste(pathConn,'connectomeBizley_whole_dti_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ndti_det_data <- read.csv(dti_det_path, header = FALSE, sep = ' ')\ndti_det_data_flat <- dti_det_data[mask]\ndti_det_length_path <- paste(pathLen,'connectomeBizley_whole_dti_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ndti_det_length_data <- read.csv(dti_det_length_path, header = FALSE, sep = ' ')\ndti_det_length_data_flat <- dti_det_length_data[mask]\n\ndti_prob_path <- paste(pathConn,'connectomeBizley_whole_dti_Prob_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ndti_prob_data <- read.csv(dti_prob_path, header = FALSE, sep = ' ')\ndti_prob_data_flat <- dti_prob_data[mask]\ndti_prob_length_path <- paste(pathLen,'connectomeBizley_whole_dti_Prob_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ndti_prob_length_data <- read.csv(dti_prob_length_path, header = FALSE, sep = ' ')\ndti_prob_length_data_flat <- dti_prob_length_data[mask]\n\ncsd_det_path <- paste(pathConn,'connectomeBizley_whole_csd_SD_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ncsd_det_data <- read.csv(csd_det_path, header = FALSE, sep = ' ')\ncsd_det_data_flat <- csd_det_data[mask]\ncsd_det_length_path <- paste(pathLen,'connectomeBizley_whole_csd_SD_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ncsd_det_length_data <- read.csv(csd_det_length_path, header = FALSE, sep = ' ')\ncsd_det_length_data_flat <- csd_det_length_data[mask]\n\ncsd_prob_path <- paste(pathConn,'connectomeBizley_whole_csd_iFOD2_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ncsd_prob_data <- read.csv(csd_prob_path, header = FALSE, sep = ' ')\ncsd_prob_data_flat <- csd_prob_data[mask]\ncsd_prob_length_path <- paste(pathLen,'connectomeBizley_whole_csd_iFOD2_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ncsd_prob_length_data <- read.csv(csd_prob_length_path, header = FALSE, sep = ' ')\ncsd_prob_length_data_flat <- csd_prob_length_data[mask]\n\ndholl_det_path <- paste(pathConn,'connectomeBizley_whole_msmt_dhollander_SD_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ndholl_det_data <- read.csv(dholl_det_path, header = FALSE, sep = ' ')\ndholl_det_data_flat <- dholl_det_data[mask]\ndholl_det_length_path <- paste(pathLen,'connectomeBizley_whole_msmt_dhollander_SD_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ndholl_det_length_data <- read.csv(dholl_det_length_path, header = FALSE, sep = ' ')\ndholl_det_length_data_flat <- dholl_det_length_data[mask]\n\ndholl_prob_path <- paste(pathConn,'connectomeBizley_whole_msmt_dhollander_',nbfib,'_',i,'_weights_selected_dzero_normFS_sym.csv',sep = '' )\ndholl_prob_data <- read.csv(dholl_prob_path, header = FALSE, sep = ' ')\ndholl_prob_data_flat <- dholl_prob_data[mask]\ndholl_prob_length_path <- paste(pathLen,'connectomeBizley_whole_msmt_dhollander_',nbfib,'_',i,'_lengths_selected.csv',sep = '' )\ndholl_prob_length_data <- read.csv(dholl_prob_length_path, header = FALSE, sep = ' ')\ndholl_prob_length_data_flat <- dholl_prob_length_data[mask]\n \n#### compute correlations SPEARMAN\n\ncor.test(x = tt_data_flat,y = dti_det_data_flat,method = 'spearman')\ncor.test(x = tt_data_flat,y = dti_prob_data_flat,method = 'spearman')\n\ncor.test(x = tt_data_flat,y = csd_det_data_flat,method = 'spearman')\ncor.test(x = tt_data_flat,y = csd_prob_data_flat,method = 'spearman')\n\ncor.test(x = tt_data_flat,y = dholl_det_data_flat,method = 'spearman')\ncor.test(x = tt_data_flat,y = dholl_prob_data_flat,method = 'spearman')\n\n#### compute bootstrapped confidence intervals for spearman correlations\nlibrary(DescTools)\n\nspearman <- function(x,y) cor(x,y,method = \"spearman\",use = \"p\")\n\nDescTools::BootCI(tt_data_flat, dti_det_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\nDescTools::BootCI(tt_data_flat, dti_prob_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\n\nDescTools::BootCI(tt_data_flat, csd_det_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\nDescTools::BootCI(tt_data_flat, csd_prob_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\n\nDescTools::BootCI(tt_data_flat, dholl_det_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\nDescTools::BootCI(tt_data_flat, dholl_prob_data_flat,FUN = spearman, R = 10000, conf.level = 0.95)\n\n \n#### compute correlations PEARSON (to be computed with the connectivity matrices incremented by one before normalisation)\n \ncor.test(x = log10(tt_data_flat),y = log10(dti_det_data_flat),method = 'pearson')\ncor.test(x = log10(tt_data_flat),y = log10(dti_prob_data_flat),method = 'pearson')\n\ncor.test(x = log10(tt_data_flat),y = log10(csd_det_data_flat),method = 'pearson')\ncor.test(x = log10(tt_data_flat),y = log10(csd_prob_data_flat),method = 'pearson')\n\ncor.test(x = log10(tt_data_flat),y = log10(dholl_det_data_flat),method = 'pearson')\ncor.test(x = log10(tt_data_flat),y = log10(dholl_prob_data_flat),method = 'pearson')\n \n\n#### PLOT correlation tt/diff ####\n\nmax_len = max(cbind(t(dti_det_length_data_flat),t(dti_prob_length_data_flat),t(csd_det_length_data_flat),t(csd_prob_length_data_flat),t(dholl_det_length_data_flat),t(dholl_prob_length_data_flat)))\n\n(cdti_det <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(dti_det_data_flat))) +\n geom_point(aes(fill=dti_det_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='royalblue1',fill='royalblue1',alpha=0.2) + \n scale_fill_gradient(guide = FALSE,low = 'white',high = 'black') +\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(cdti_prob <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(dti_prob_data_flat))) +\n geom_point(aes(fill=dti_prob_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='royalblue1',fill='royalblue1',alpha=0.2) + \n scale_fill_gradient(guide = FALSE,low = 'white',high = 'black') +\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(ccsd_det <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(csd_det_data_flat))) +\n geom_point(aes(fill=csd_det_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='darkorange',fill='darkorange',alpha=0.2) + \n scale_fill_gradient(guide = FALSE,low = 'white',high = 'black')+\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(ccsd_prob <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(csd_prob_data_flat))) +\n geom_point(aes(fill=csd_prob_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='darkorange',fill='darkorange',alpha=0.2) + \n scale_fill_gradient(guide = FALSE,low = 'white',high = 'black')+\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(cdholl_det <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(dholl_det_data_flat))) +\n geom_point(aes(fill=dholl_det_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='green4',fill='green4',alpha=0.2) + #se=TRUE\n scale_fill_gradient(guide=FALSE,low = 'white',high = 'black')+#guide='colorbar'\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(cdholl_prob <- ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(dholl_prob_data_flat))) +\n geom_point(aes(fill=dholl_prob_length_data_flat/max_len),shape=21,size=2) + \n geom_smooth(method = 'lm',se = TRUE,color='green4',fill='green4',alpha=0.2) + #se=TRUE\n scale_fill_gradient(guide=FALSE,low = 'white',high = 'black')+#guide='colorbar'\n scale_x_continuous(limits = c(-1,length(tt_data_flat)+1.5)) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)+1.5))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(ggarrange(cdti_det,ccsd_det,cdholl_det,cdti_prob,ccsd_prob,cdholl_prob,ncol=3,nrow = 2))\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FSFLN.svg'), device = svg(),width = 18,height = 10,units = 'cm')\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FSFLN.png'), device = svg(),width = 18,height = 10,units = 'cm')\n\n(ggarrange(cdti_det,ccsd_det,cdholl_det,ncol=3,nrow = 1))\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FS-DET.svg'), device = svg(),width = 18,height = 5,units = 'cm')\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FS-DET.png'), device = svg(),width = 18,height = 5,units = 'cm')\n\n(ggarrange(cdti_prob,ccsd_prob,cdholl_prob,ncol=3,nrow = 1))\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FS-PROB.svg'), device = svg(),width = 18,height = 5,units = 'cm')\nggsave(paste(savepath,'spearmancorr_det_prob_sym_plot_FS-PROB.png'), device = svg(),width = 18,height = 5,units = 'cm')\n\n\n##save colorbar\n(ggplot(data = NULL, aes(x=rank(tt_data_flat),y = rank(dti_det_data_flat))) +\n geom_point(aes(fill=dti_det_length_data_flat/max(dti_det_length_data_flat)),shape=21,size=3) + \n geom_smooth(method = 'lm',se = TRUE,color='royalblue1',fill='royalblue1',alpha=0.2) + #se=TRUE\n scale_fill_gradient(guide='colorbar',low = 'white',high = 'black')+#guide='colorbar'\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n guides(fill=guide_colorbar(barwidth = 2.5,barheight = 15))+\n theme(legend.text = element_text(size = 15),\n legend.title = element_blank(),\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\nggsave(paste(savepath,'spearmancorr_colorbar.svg'),dpi=300)\n\n\n#### partial corr, distance regressed ####\n\nttlm <- lm(log10(tt_data_flat)~eucldist_flat,na.action=na.exclude)\nttresid <- resid(ttlm)\n\ndti_det_data_na <- dti_det_data_flat\ndti_det_data_na[dti_det_data_na==0] <- NA\ndti_det_dist_data_na <- eucldist_flat\ndti_det_dist_data_na[is.na(dti_det_data_na)] <- NA\ndtilm_d <- lm(log10(dti_det_data_na)~dti_det_dist_data_na,na.action=na.exclude)\ndtiresid_d <- resid(dtilm_d)\n\ndti_prob_data_na <- dti_prob_data_flat\ndti_prob_data_na[dti_prob_data_na==0] <- NA\ndti_prob_dist_data_na <- eucldist_flat\ndti_prob_dist_data_na[is.na(dti_prob_data_na)] <- NA\ndtilm_p <- lm(log10(dti_prob_data_na)~dti_prob_dist_data_na,na.action=na.exclude)\ndtiresid_p <- resid(dtilm_p)\n\n\ncsd_det_data_na <- csd_det_data_flat\ncsd_det_data_na[csd_det_data_na==0] <- NA\ncsd_det_dist_data_na <- eucldist_flat\ncsd_det_dist_data_na[is.na(csd_det_data_na)] <- NA\ncsdlm_d <- lm(log10(csd_det_data_na)~csd_det_dist_data_na,na.action=na.exclude)\ncsdresid_d <- resid(csdlm_d)\n\ncsd_prob_data_na <- csd_prob_data_flat\ncsd_prob_data_na[csd_prob_data_na==0] <- NA\ncsd_prob_dist_data_na <- eucldist_flat\ncsd_prob_dist_data_na[is.na(csd_prob_data_na)] <- NA\ncsdlm_p <- lm(log10(csd_prob_data_na)~csd_prob_dist_data_na,na.action=na.exclude)\ncsdresid_p <- resid(csdlm_p)\n\n\ndholl_det_data_na <- dholl_det_data_flat\ndholl_det_data_na[dholl_det_data_na==0] <- NA\ndholl_det_dist_data_na <- eucldist_flat\ndholl_det_dist_data_na[is.na(dholl_det_data_na)] <- NA\ndholllm_d <- lm(log10(dholl_det_data_na)~dholl_det_dist_data_na,na.action=na.exclude)\ndhollresid_d <- resid(dholllm_d)\n\ndholl_prob_data_na <- dholl_prob_data_flat\ndholl_prob_data_na[dholl_prob_data_na==0] <- NA\ndholl_prob_dist_data_na <- eucldist_flat\ndholl_prob_dist_data_na[is.na(dholl_prob_data_na)] <- NA\ndholllm_p <- lm(log10(dholl_prob_data_na)~dholl_prob_dist_data_na,na.action=na.exclude)\ndhollresid_p <- resid(dholllm_p)\n\n\ndtiresid_d[is.na(dtiresid_d)] <- 0\ndtiresid_p[is.na(dtiresid_p)] <- 0\ncsdresid_d[is.na(csdresid_d)] <- 0\ncsdresid_p[is.na(csdresid_p)] <- 0\ndhollresid_d[is.na(dhollresid_d)] <- 0\ndhollresid_p[is.na(dhollresid_p)] <- 0\n\n#### SPEARMAN semi-partial correlations\n\ncor.test(ttresid,dtiresid_d,method = 'spearman')\ncor.test(ttresid,dtiresid_p,method = 'spearman')\ncor.test(ttresid,csdresid_d,method = 'spearman')\ncor.test(ttresid,csdresid_p,method = 'spearman')\ncor.test(ttresid,dhollresid_d,method = 'spearman')\ncor.test(ttresid,dhollresid_p,method = 'spearman')\n\n#### PEARSON semi-partial correlations\n\ncor.test(ttresid,(dtiresid_d),method = 'pearson')\ncor.test(ttresid,(dtiresid_p),method = 'pearson')\ncor.test(ttresid,(csdresid_d),method = 'pearson')\ncor.test(ttresid,(csdresid_p),method = 'pearson')\ncor.test(ttresid,(dhollresid_d),method = 'pearson')\ncor.test(ttresid,(dhollresid_p),method = 'pearson')\n\n#### plot partial correlations\n\n(rdti_d <- ggplot(mapping = aes(x=rank(ttresid),y=rank(dtiresid_d))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='royalblue1',fill='royalblue1',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(rdti_p <- ggplot(mapping = aes(x=rank(ttresid),y=rank(dtiresid_p))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='royalblue1',fill='royalblue1',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(rcsd_d <- ggplot(mapping = aes(x=rank(ttresid),y=rank(csdresid_d))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='darkorange',fill='darkorange',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(rcsd_p <- ggplot(mapping = aes(x=rank(ttresid),y=rank(csdresid_p))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='darkorange',fill='darkorange',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(rdholl_d <- ggplot(mapping = aes(x=rank(ttresid),y=rank(dhollresid_d))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='green4',fill='green4',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(rdholl_p <- ggplot(mapping = aes(x=rank(ttresid),y=rank(dhollresid_p))) + \n geom_point()+geom_smooth(method = 'lm', se = TRUE,color='green4',fill='green4',alpha=0.2)+\n scale_x_continuous(limits = c(-1,length(tt_data_flat))) +\n scale_y_continuous(limits = c(-1,length(tt_data_flat)))+\n theme_set(theme_gray(base_size = 11))+\n theme(\n axis.title.x=element_blank(),\n axis.title.y=element_blank())+\n coord_fixed())\n\n(ggarrange(rdti_d,rcsd_d,rdholl_d,rdti_p,rcsd_p,rdholl_p,nrow = 2,ncol = 3))\nggsave(paste(savepath,'spearmancorr_residuals_partcorr_det_prob_sym_plot.svg'),dpi=300,width = 18,height = 10,units = 'cm')\nggsave(paste(savepath,'spearmancorr_residuals_partcorr_det_prob_sym_plot.png'),dpi=300,width = 18,height = 10,units = 'cm')\n", "meta": {"hexsha": "34ab660ccd80445e3abe72fb7183eb320c34d8b7", "size": 17473, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/dataanalysis/compute&plotCorrelations.r", 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YES\n2. NO", "lm_q1_score": 0.7745833841649233, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.324315668988122}} {"text": "# This file creates introductory data used by all following code\n# In particular it creates grids of points at which to generate simulations\n# set the directory locations in lines 5-13 then run whole file.\n\n# base directory to save all output\nbase_dir_on_comp <- \"C:/Users/christina/\"\nbase_dir_sim <- base_dir_on_comp + \"multinomial_simulations/\"\n# subdirectories to save csv output and generated images\nfile_save_dir <- paste0(base_dir_sim, \"csv_files/\")\nimg_save_dir <- paste0(base_dir_sim, \"image_files/\")\n# code currently in multinomial branch of MRSea so need to download package from github to base dir on comp \n# then switch to multinomial branch\ndevtools::load_all(paste0(base_dir_on_comp, \"MRsea\"))\n\nlibrary(knitr)\nlibrary(tidyverse)\nlibrary(kableExtra)\nlibrary(viridis)\nlibrary(hexbin)\nlibrary(car)\nlibrary(mvtnorm)\nlibrary(sf)\nlibrary(Rfast)\n\nchangeSciNot <- function(n) {\n output <- format(n, scientific = TRUE) #Transforms the number into scientific notation even if small\n output <- sub(\"e\", \"*10^\", output) #Replace e with 10^\n output <- sub(\"\\\\+0?\", \"\", output) #Remove + symbol and leading zeros on expoent, if > 1\n output <- sub(\"-0?\", \"-\", output) #Leaves - symbol but removes leading zeros on expoent, if < 1\n output <- sub(\"1\\\\*\", \"\", output) # Removes 1*10 but leaves anything else raised to 10 \n parse(text=output)\n}\n\n\ncbbPalette <- c(\"#004949\",\"#009292\",\"#ff6db6\",\"#ffb6db\",\"#490092\",\"#006ddb\",\"#b66dff\",\"#6db6ff\",\"#b6dbff\",\"#920000\",\"#924900\",\"#db6d00\",\"#24ff24\",\"#ffff6d\", \"#000000\")\n\n\n\nif (file.exists(paste0(file_save_dir, \"location_grid.csv\"))) { \n grid <- read.csv(paste0(file_save_dir, \"location_grid.csv\"))\n} else { \n n <- seq(0.005, 0.995, length.out=100)\n xx <- rep(n, times=100)\n yy <- rep(n, each=100)\n grid <- cbind(xx,yy)\n grid <- as.data.frame(grid)\n write.csv(grid, paste0(file_save_dir, \"location_grid.csv\"), row.names=F)\n}\n\npng(filename=paste0(img_save_dir, \"Grid.png\"))\npar(pty=\"s\")\nplot(grid$xx,grid$yy, type='p', pch=4, cex=.2, main='Base grid', asp=1, xlab=\"X coordinate\", ylab=\"Y coordinate\")\ndev.off()\n\nlinestr_create <- function(pt1, pt2){\n linestr <- st_linestring(rbind(st_coordinates(pt1), st_coordinates(pt2)))\n return(linestr)\n}\n\ndist_exclude_vec <- function(vec, pt, ez) {\n distpt <- st_distance(vec, pt)\n linechk <- st_sfc(lapply(vec, linestr_create, pt2=pt))\n intchk <- as.vector(st_intersects(ez, linechk, sparse=FALSE))\n distpt <- ifelse(intchk, Inf, distpt)\n return(distpt)\n}\n\nif (file.exists(paste0(file_save_dir,\"location_grid_island.csv\"))) { \n grid_i <- read.csv(paste0(file_save_dir, \"location_grid_island.csv\"))\n} else { \n nnn <- nrow(grid)\n pointz <- cbind(grid$xx, grid$yy)\n pt1 <- st_point(pointz[1,])\n pt2 <- st_point(pointz[2,])\n ptlist <- list(pt1, pt2)\n for (pt in 3:nnn){\n pt1 <- st_point(pointz[pt,])\n ptlist[[pt]] <- pt1\n }\n sfpointz <- st_sfc(ptlist)\n #create exclusion zone and remove points in exclusion zone\n #island_in <- st_polygon(list(rbind(c(0.1, 0.3), c(0.15, 0.3), c(0.8, 0.55), \n # c(0.75, 0.75), c(0.1, 0.4), c(0.1, 0.3))))\n island_in <- st_polygon(list(rbind(c(0.0, 0.3), c(0.15, 0.3), c(0.8, 0.55), \n c(0.75, 0.75), c(0.0, 0.4), c(0.0, 0.3))))\n pointz_keep <- st_disjoint(island_in, sfpointz)\n sfpointz <- sfpointz[unlist(pointz_keep)]\n nnn <- length(sfpointz)\n # create distmat\n distmat1 <- dist_exclude_vec(sfpointz[2:nnn], pt=sfpointz[1], ez=island_in)\n distmat1 <- c(0, distmat1)\n # need to do the first two so that there is a row for the distmat[ii,] line to work \n distcol <- dist_exclude_vec(sfpointz[3:nnn], sfpointz[2], island_in)\n distcol <- c(distmat1[2], 0, distcol)\n distmat1 <- cbind(distmat1, distcol)\n for (ii in 3:(nnn-1)) {\n print(ii)\n distcol <- dist_exclude_vec(sfpointz[(ii + 1):nnn], sfpointz[ii], island_in)\n distcol <- c(distmat1[ii,], 0, distcol)\n distmat1 <- cbind(distmat1, distcol)\n }\n distmat1 <- cbind(distmat1, c(distmat1[nnn,],0))\n write.csv(distmat1, paste0(file_save_dir, \"island_distmat1.csv\"), row.names=F)\n positionMat <- st_coordinates(sfpointz)\n xxxx <- positionMat[, 1]\n yyyy <- positionMat[, 2]\n GeoDistMat <- floyd(as.matrix(distmat1))\n write.csv(GeoDistMat, paste0(file_save_dir, \"island_geodistmat.csv\"), row.names=FALSE)\n grid_i <- as.data.frame(cbind(xxxx, yyyy))\n colnames(grid_i) <- c(\"xx\", \"yy\")\n write.csv(grid_i, paste0(file_save_dir, \"location_grid_island.csv\"), row.names=FALSE)\n}\n\npng(filename=paste0(img_save_dir, \"GridIsland.png\"))\npar(pty=\"s\")\nplot(grid_i$xx,grid_i$yy, type='p', pch=4, cex=.2, main='Points with exclusion zone', asp=1, xlab=\"X coordinate\", ylab=\"Y coordinate\")\ndev.off()\n\nnsim <- 100\nnnn <- dim(grid)[1]\n\nset.seed(3004)\nstore_state <- .Random.seed\nstore_state <- as.data.frame(store_state)\ncolnames(store_state) <- \"Start\"\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows1.csv\"))) {\n dataset_rows1 <- read.csv(paste0(file_save_dir, \"dataset_rows1.csv\"))\n} else {\n nsamp1 <- 100\n dataset_rows1 <- matrix(rep(NA, nsim*nsamp1), nrow=nsim)\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen\")\n # data generation\n for (ii in 1:nsim) {\n dataset_rows1[ii,] <- sample(nnn, nsamp1)\n }\n write.csv(dataset_rows1, paste0(file_save_dir,\"dataset_rows1.csv\"), row.names=F)\n}\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows2.csv\"))) {\n dataset_rows2 <- read.csv(paste0(file_save_dir, \"dataset_rows2.csv\"))\n} else {\n nsamp2 <- 500\n dataset_rows2 <- matrix(rep(NA, nsim*nsamp2), nrow=nsim)\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen2\")\n # data generation\n for (ii in 1:nsim) {\n dataset_rows2[ii,] <- sample(nnn, nsamp2)\n }\n write.csv(dataset_rows2, paste0(file_save_dir,\"dataset_rows2.csv\"), row.names=F)\n}\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows3.csv\"))) {\n dataset_rows3 <- read.csv(paste0(file_save_dir, \"dataset_rows3.csv\"))\n} else {\n nsamp3 <- 5000\n dataset_rows3 <- matrix(rep(NA, nsim*nsamp3), nrow=nsim)\n # data generation\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen3\")\n for (ii in 1:nsim) {\n dataset_rows3[ii,] <- sample(nnn, nsamp3)\n }\n write.csv(dataset_rows3, paste0(file_save_dir,\"dataset_rows3.csv\"), row.names=F)\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- \"postdatasetgen\"\n write.csv(store_state, paste0(file_save_dir,\"store_state.csv\"), row.names=F)\n}\n \nnnn_i <- dim(grid_i)[1]\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows1_island.csv\"))) {\n dataset_rows1 <- read.csv(paste0(file_save_dir, \"dataset_rows1_island.csv\"))\n} else {\n nsamp1 <- 100\n dataset_rows1 <- matrix(rep(NA, nsim*nsamp1), nrow=nsim)\n # data generation\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen1island\")\n for (ii in 1:nsim) {\n dataset_rows1[ii,] <- sample(nnn_i, nsamp1)\n }\n write.csv(dataset_rows1, paste0(file_save_dir,\"dataset_rows1_island.csv\"), row.names=F)\n}\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows2_island.csv\"))) {\n dataset_rows2 <- read.csv(paste0(file_save_dir, \"dataset_rows2_island.csv\"))\n} else {\n nsamp2 <- 500\n dataset_rows2 <- matrix(rep(NA, nsim*nsamp2), nrow=nsim)\n # data generation\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen2island\")\n for (ii in 1:nsim) {\n dataset_rows2[ii,] <- sample(nnn_i, nsamp2)\n }\n write.csv(dataset_rows2, paste0(file_save_dir,\"dataset_rows2_island.csv\"), row.names=F)\n}\n\nif (file.exists(paste0(file_save_dir, \"dataset_rows3_island.csv\"))) {\n dataset_rows3 <- read.csv(paste0(file_save_dir, \"dataset_rows3_island.csv\"))\n} else {\n nsamp3 <- 5000\n dataset_rows3 <- matrix(rep(NA, nsim*nsamp3), nrow=nsim)\n # data generation\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- paste0(\"predatasetgen3island\")\n for (ii in 1:nsim) {\n dataset_rows3[ii,] <- sample(nnn_i, nsamp3)\n }\n write.csv(dataset_rows3, paste0(file_save_dir,\"dataset_rows3_island.csv\"), row.names=F)\n store_state <- cbind(store_state, .Random.seed)\n colnames(store_state)[ncol(store_state)] <- \"postdatasetgenisland\"\n write.csv(store_state, paste0(file_save_dir,\"store_state.csv\"), row.names=F)\n}\n\n\n", "meta": {"hexsha": "54f76c88f8444654b99aa257a1f2fedf424dac20", "size": 8453, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation_code/part1_intro.r", "max_stars_repo_name": "CMFell/phd_stat_sim", "max_stars_repo_head_hexsha": "d324241f89c0b2ab2d69dc995838c6cb085080ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation_code/part1_intro.r", "max_issues_repo_name": "CMFell/phd_stat_sim", "max_issues_repo_head_hexsha": "d324241f89c0b2ab2d69dc995838c6cb085080ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation_code/part1_intro.r", "max_forks_repo_name": "CMFell/phd_stat_sim", "max_forks_repo_head_hexsha": "d324241f89c0b2ab2d69dc995838c6cb085080ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4227272727, "max_line_length": 167, "alphanum_fraction": 0.6978587484, "num_tokens": 2680, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.32424669288441366}} {"text": "otus.all.records.for.sample <- function(X, sample_id){\n row_names <- row.names(X)\n indeces <- which(startsWith(row_names, paste(\"X\", sample_id, sep = '')))\n return(X[indeces, ])\n}\n\notus.all.records.for.timepoints <- function(otus, timepoint){\n row_names <- row.names(otus)\n indeces <- which(substr(samples, start = 5, stop = 7) %in% timepoint)\n return(otus[indeces, ])\n}\n\notus.normalize <- function(otus, otu_threashold){\n \n # calculate otu percentages per sample\n otus_sums_by_sample <- rowSums(otus)\n otus_percentages <- otus / otus_sums_by_sample\n \n # filter otus. if an otu is below threashold for all samples, exclude it\n otus_to_keep <- colSums(otus_percentages > otu_threashold) > 0\n otus_percentages_filtered <- otus_percentages[, otus_to_keep]\n \n # normalize otus\n otus_norm <- min(rowSums(otus)) * otus_percentages_filtered\n #mode(otus_norm) <- \"integer\"\n \n return(otus_norm)\n}\n\notus.load <- function(data.folder, otus.filename, otus.tree.filename){\n \n # read otus file\n otus <- t(read.delim2(paste(data.folder, otus.filename, sep = ''), header=T, sep=\"\\t\", row.names = 1))\n \n # remove taxonomy row from otu table\n otus.contains.taxonomy.row <- tail(rownames(otus), n=1) == \"taxonomy\"\n if (otus.contains.taxonomy.row) {otus <- otus[1:nrow(otus)-1,]}\n \n # make all strings integers\n mode(otus) <- \"integer\"\n \n # order otus based on rownames (aka sample names)\n otus <- otus[ order(row.names(otus)), ]\n \n # read the tree\n otus.tree <- read.tree(paste(data.folder, otus.tree.filename, sep = ''))\n\n result <- list('otus' = otus, 'otus.tree' = otus.tree)\n return(result)\n}", "meta": {"hexsha": "52935d450e550adc8e40f63eb080a26699db4746", "size": 1615, "ext": "r", "lang": "R", "max_stars_repo_path": "down-stream-analysis/otus.r", "max_stars_repo_name": "tzouvanas/microbiome", "max_stars_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "down-stream-analysis/otus.r", "max_issues_repo_name": "tzouvanas/microbiome", "max_issues_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "down-stream-analysis/otus.r", "max_forks_repo_name": "tzouvanas/microbiome", "max_forks_repo_head_hexsha": "988b50f70cb481e849165ba5a77a99e4fbf265b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3, "max_line_length": 104, "alphanum_fraction": 0.6866873065, "num_tokens": 507, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3242466849390346}} {"text": "#!/usr/bin/env Rscript\n\n#########################################\n# Author: [Ivan Juric](https://github.com/ijuric)\n# File: MAPS_regression_and_peak_caller.r\n# Source: https://github.com/ijuric/MAPS/blob/master/bin/MAPS/MAPS_regression_and_peak_caller.r\n# Source+commit: https://github.com/ijuric/MAPS/blob/e6d5fdee7241b8f9a466cc778be6d0769b984e81/bin/MAPS/MAPS_regression_and_peak_caller.r\n# Data: 11/08/2021, commit: e6d5fde\n# modified by Jianhong:\n# ## 1. set the Rscript environment.\n# ## 2. export the COUNT_CUTOFF, RATIO_CUTOFF and FDR parameters\n# ## 3. Automatic detect the chromosome names\n# ## 4. Handle the error if the input count table is empty\n# ## 5. Prefilter the data before fit to vglm to handle the NA error by replace the loglikelihood function.\n# ## 6. Handle the error if output is empty\n# ## 7. Handle the error if AND or XOR table is empty\n# ## 8. clean unused code\n# ## 9. fix the indent space\n#########################################\n\n## run example:\n## Rscript MAPS_regression_and_peak_caller.r /home/jurici/work/PLACseq/MAPS_pipe/results/mESC_test/ MY_115.5k 5000 1 None pospoisson NA\n##\n## arguments:\n## INFDIR - dir with reg files\n## SET - dataset name\n## RESOLUTION - resolution (for example 5000 or 10000)\n## COUNT_CUTOFF - count cutoff, default 12\n## RATIO_CUTOFF - ratio cutoff, default 2.0\n## FDR - -log10(fdr) cutoff, default 2\n## FILTER - file containing bins that need to be filtered out. Format: two columns \"chrom\", \"bin\". \"chrom\" contains 'chr1','chr2',.. \"bin\" is bin label\n## regresison_type - pospoisson for positive poisson regression, negbinom for negative binomial. default is pospoisson\n\nlibrary(VGAM)\nlibrary(MASS)\noptions(warn=-1)\n\n### constants\nchroms = NULL\nruns = c(1)\nRESOLUTION = NULL\n\nCOUNT_CUTOFF = 12\nRATIO_CUTOFF = 2.0\nGAP = 15000\nFDR = 2\n\nREG_TYPE = 'pospoisson'\n###\n\nargs <- commandArgs(trailingOnly=TRUE)\nfltr = data.frame(chr='chrNONE',bin=-1)\n\nif (length(args) < 3 || length(args) > 8) {\n print('Wrong number of arguments. Stopping.')\n print('Arguments needed (in this order): INFDIR, SET, RESOLUTION, COUNT_CUTOFF, RATIO_CUTOFF, FDR, FILTER, regression_type.')\n print('FILTER is optional argument. Omitt it if no filtering required.')\n print(paste('Number of arguments entered:',length(args)))\n print('Arguments entered:')\n print(args)\n quit()\n} else {\n print(args)\n INFDIR = args[1]\n SET = args[2]\n RESOLUTION = as.integer(args[3])\n SET <- paste0(SET, \".\", ceiling(RESOLUTION/1e3), \"k\")\n chroms <- dir(INFDIR, \"reg_raw.*\")\n chroms <- unique(sub(\"reg_raw\\\\.(.*?)\\\\..*$\", \"\\\\1\", chroms))\n if(length(args)>3){\n COUNT_CUTOFF = as.numeric(args[4])\n RATIO_CUTOFF = as.numeric(args[5])\n FDR = as.numeric(args[6])\n print('filter used (if any):')\n if (length(args) > 6) {\n if (args[7] != 'None') {\n FILTER = args[7]\n fltr = read.table(FILTER,header=T)\n print(fltr)\n } else {\n print('None')\n }\n ## this done so that the script is compatible with previous run_pipeline scripts\n if (length(args) > 7) {\n if (args[8] != 'pospoisson' && args[8] != 'negbinom') {\n print(paste('wrong regression choice. Your choice:', args[8], '. Avaiable choices: pospoisson or negbinom'),sep = ' ')\n quit()\n }\n REG_TYPE = args[8]\n }\n } else {\n print('None')\n }\n }\n}\n\n## loading data\nmm_combined_and = data.frame()\nmm_combined_xor = data.frame()\noutf_names = c()\nfor (i in chroms) {\n for (j in c('.and','.xor')) {\n print(paste('loading chromosome ',i,' ',j,sep=''))\n inf_name = paste(INFDIR,'reg_raw.',i,'.',SET,sep='')\n if(file.exists(paste(inf_name,j,sep=''))){\n outf_names = c(outf_names, paste(inf_name,j,'.MAPS2_',REG_TYPE,sep = ''))\n mm = read.table(paste(inf_name,j,sep=''),header=T)\n mm$chr = rep(i, nrow(mm))\n mm = subset( mm, dist > 1) # removing adjacent bins\n mm = subset(mm, !(mm$chr %in% fltr$chr & (mm$bin1_mid %in% fltr$bin | mm$bin2_mid %in% fltr$bin ))) ## filtering out bad bins\n if (j == '.and') {\n mm_combined_and = rbind(mm_combined_and, mm)\n } else if (j == '.xor') {\n mm_combined_xor = rbind(mm_combined_xor, mm)\n }\n }\n }\n}\n\ndataset_length_and = length(mm_combined_and$bin1_mid)\ndataset_length_xor = length(mm_combined_xor$bin1_mid)\ndataset_length = dataset_length_and + dataset_length_xor\n\n## doing statistics and resampling\nloglikelihood <- function (mu, y, w, residuals = FALSE, eta, extra = NULL, summation = TRUE) {\n lambda <- eta2theta(eta, \"loglink\", earg = list(bvalue = NULL, inverse = FALSE, deriv = 0, short = TRUE,\n tag = FALSE))\n if (residuals) {\n stop(\"loglikelihood residuals not implemented yet\")\n }\n else {\n ll.elts <- c(w) * dgaitpois(y, lambda, truncate = 0,\n log = TRUE)\n if (summation) {\n sum(ll.elts[!is.infinite(ll.elts)])\n }\n else {\n ll.elts\n }\n }\n}\npospoisson_regression <- function(mm, dataset_length) {\n family <- pospoisson()\n family@loglikelihood <- loglikelihood\n # fit <- vglm(count ~ logl + loggc + logm + logdist + logShortCount, family = pospoisson(), data = mm)\n fit <- vglm(count ~ loggc + logm + logdist + logShortCount, family = family, data = mm)\n mm$expected = fitted(fit)\n mm$p_val = ppois(mm$count, mm$expected, lower.tail = FALSE, log.p = FALSE) / ppois(0, mm$expected, lower.tail = FALSE, log.p = FALSE)\n m1 = mm[ mm$p_val > 1/length(mm$p_val),]\n # fit <- vglm(count ~ logl + loggc + logm + logdist + logShortCount, family = pospoisson(), data = m1)\n fit <- vglm(count ~ loggc + logm + logdist + logShortCount, family = family, data = m1)\n coeff<-round(coef(fit),10)\n # mm$expected2 <- round(exp(coeff[1] + coeff[2]*mm$logl + coeff[3]*mm$loggc + coeff[4]*mm$logm + coeff[5]*mm$logdist + coeff[6]*mm$logShortCount), 10)\n mm$expected2 <- round(exp(coeff[1] + coeff[2]*mm$loggc + coeff[3]*mm$logm + coeff[4]*mm$logdist + coeff[5]*mm$logShortCount), 10)\n mm$expected2 <- mm$expected2 /(1-exp(-mm$expected2))\n mm$ratio2 <- mm$count / mm$expected2\n mm$p_val_reg2 = ppois(mm$count, mm$expected2, lower.tail = FALSE, log.p = FALSE) / ppois(0, mm$expected2, lower.tail = FALSE, log.p = FALSE)\n mm$p_bonferroni = mm$p_val_reg2 * dataset_length\n mm$fdr <- p.adjust(mm$p_val_reg2, method='fdr')\n return(mm)\n}\n\nnegbinom_regression <- function(mm, dataset_length) {\n #fit <- glm.nb(count ~ logl + loggc + logm + logdist + logShortCount, data = mm)\n fit <- glm.nb(count ~ loggc + logm + logdist + logShortCount, data = mm)\n mm$expected = fitted(fit)\n sze = fit$theta ##size parameter\n mm$p_val = pnbinom(mm$count, mu = mm$expected, size = sze, lower.tail = FALSE)\n m1 = mm[ mm$p_val > ( 1 / length(mm$p_val)),]\n ## second regression\n # fit <- glm.nb(count ~ logl + loggc + logm + logdist + logShortCount, data = m1)\n fit <- glm.nb(count ~ loggc + logm + logdist + logShortCount, data = m1)\n coeff<-round(fit$coefficients,10)\n sze = fit$theta\n #mm$expected2 <- round(exp(coeff[1] + coeff[2]*mm$logl + coeff[3]*mm$loggc + coeff[4]*mm$logm + coeff[5]*mm$logdist + coeff[6]*mm$logShortCount), 10) ## mu parameter\n mm$expected2 <- round(exp(coeff[1] + coeff[2]*mm$loggc + coeff[3]*mm$logm + coeff[4]*mm$logdist + coeff[5]*mm$logShortCount), 10) ## mu parameter\n mm$ratio2 <- mm$count / mm$expected2\n mm$p_val_reg2 = pnbinom(mm$count, mu = mm$expected2, size = sze, lower.tail = FALSE)\n mm$p_bonferroni = mm$p_val_reg2 * dataset_length\n mm$fdr <- p.adjust(mm$p_val_reg2, method='fdr')\n return(mm)\n}\n\ndo_summaries <- function(peaks_and,peaks_xor, peaks, fraction, r) {\n ## no peaks with this fdr\n if (ncol(peaks_and) == 0) {\n peaks_and = data.frame('count' = NA, 'dist'=NA, 'p_val_reg2'=NA, 'fdr'=NA)\n }\n if (ncol(peaks_xor) == 0) {\n peaks_xor = data.frame('count' = NA, 'dist'=NA, 'p_val_reg2'=NA, 'fdr'=NA)\n }\n if (ncol(peaks_and) == 0 & ncol(peaks_xor) == 0) {\n peaks = data.frame('count' = NA, 'dist'=NA, 'p_val_reg2'=NA, 'fdr'=NA)\n }\n summary_one_fdr_val = data.frame('run' = r, 'log10_fdr_cutoff' = fdr_cutoff, 'singleton_fraction' = fraction,\n'AND_size'=length(peaks_and$count), 'AND_mean_dist'=mean(peaks_and$dist)*RESOLUTION,'AND_median_dist'=median(peaks_and$dist)*RESOLUTION,\n'AND_min_count'=min(peaks_and$count), 'AND_max_pval'=max(peaks_and$p_val_reg2), 'AND_max_fdr'=max(peaks_and$fdr),\n'XOR_size'=length(peaks_xor$count), 'XOR_mean_dist'=mean(peaks_xor$dist)*RESOLUTION,'XOR_median_dist'=median(peaks_xor$dist)*RESOLUTION,\n'XOR_min_count'=min(peaks_xor$count), 'XOR_max_pval'=max(peaks_xor$p_val_reg2), 'XOR_max_fdr'=max(peaks_xor$fdr),\n'size'=length(peaks$count), 'mean_dist'=mean(peaks$dist)*RESOLUTION,'median_dist'=median(peaks$dist)*RESOLUTION,\n'min_count'=min(peaks$count), 'max_pval'=max(peaks$p_val_reg2), 'max_fdr'=max(peaks$fdr)\n )\n return(summary_one_fdr_val)\n}\n\nlabel_peaks <- function(df) {\n chroms = unique(df$chr)\n print('chromosomes with potential interactions:')\n print(chroms)\n final = data.frame()\n for (CHR in chroms) {\n y = df[df$chr == CHR,]\n y$p_val_reg2[ y$p_val_reg2 == 0 ] = 1111111\n y$p_val_reg2[ y$p_val_reg2 == 1111111 ] = min(y$p_val_reg2)\n for(i in 1:nrow(y)) {\n z <- y[ abs(y$bin1_mid - y$bin1_mid[i])<=GAP & abs(y$bin2_mid - y$bin2_mid[i])<=GAP,]\n y$CountNei[i] <- nrow(z)\n }\n u <- y[ y$CountNei == 1 ,] # singletons\n v <- y[ y$CountNei >= 2 ,] # peak cluster: sharp peak + broad peak\n out <- NULL\n if(nrow(u)>0) {\n u$label <- 0 # for singletons, assign cluster label 0\n # for singletons, cluster size = 1\n u$NegLog10P <- -log10(u$p_val_reg2 )\n u$ClusterSize <- 1\n out <- rbind(out, u)\n }\n if(nrow(v)>0) {\n v$label <- seq(1,nrow(v),1) # for peak cluster, assign label 1, 2, ..., N\n # for all bin pairs within the neighborhood\n # assign the same cluster label, using the minimal label\n for(i in 1:nrow(v)) {\n w <- v[ abs(v$bin1_mid - v$bin1_mid[i])<=GAP & abs(v$bin2_mid - v$bin2_mid[i])<=GAP ,]\n w.min <- min(w$label)\n w.label <- sort(unique(w$label))\n for(j in 2:length(w.label)) {\n v$label[ v$label == w.label[j] ] <- w.min\n }\n }\n # step 4: assign consecutive label number\n v.rec <- sort( unique( v$label ) )\n v.rec <- cbind(v.rec, seq(1, length(v.rec), 1))\n for(i in 1:nrow(v)) {\n v$label[i] <- v.rec[ v.rec[,1]==v$label[i] ,2]\n }\n # step 5: calculate cumulative NegLog10P and the cluster size\n v$NegLog10P <- 0\n v$ClusterSize <- 0\n for(i in 1:nrow(v.rec)) {\n # find all bin pairs within the same cluster i\n vtmp <- v[ v$label == i ,]\n v$NegLog10P[ v$label == i ] <- sum( -log10( vtmp$p_val_reg2 ) )\n v$ClusterSize[ v$label == i ] <- nrow(vtmp)\n }\n out <- rbind(out, v)\n }\n final<-rbind(final, out)\n }\n print(dim(final))\n return(final)\n}\n\nclassify_peaks <- function(final) {\n\n # only keep unique peak clusters, not bin pairs\n x <- unique( final[ final$label != 0, c('chr', 'label', 'NegLog10P', 'ClusterSize')] )\n if(nrow(x)==0){\n final$ClusterType <- 'Singleton'\n return(final)\n }\n\n # sort rows by cumulative -log10 P-value\n x <- x[ order(x$NegLog10P) ,]\n y<-sort(x$NegLog10P)\n z<-cbind( seq(1,length(y),1), y )\n\n # keep a record of z before normalization\n z0 <- z\n\n z[,1]<-z[,1]/max(z[,1])\n z[,2]<-z[,2]/max(z[,2])\n\n u<-z\n u[,1] <- 1/sqrt(2)*z[,1] + 1/sqrt(2)*z[,2]\n u[,2] <- -1/sqrt(2)*z[,1] + 1/sqrt(2)*z[,2]\n\n v<-cbind(u, seq(1,nrow(u),1) )\n RefPoint <- v[ v[,2]==min(v[,2]) , 3] # 1423\n RefValue <- z0[RefPoint,2]\n\n # define peak cluster type\n final$ClusterType <- '0'\n final$ClusterType[ final$label==0 ] <- 'Singleton'\n final$ClusterType[ final$label>=1 & final$NegLog10P=1 & final$NegLog10P>=RefValue ] <- 'BroadPeak'\n #table(final$ClusterType)\n return(final)\n}\n\n\nmx_combined_and = data.frame()\nmx_combined_xor = data.frame()\nsummary_all_runs = data.frame()\n\nsingletons_names = paste(chroms,'_0',sep='')\nfor (r in runs) {\n## in case you want to do resampling\n## you'd put resampling code here\n name_counter = 1\n for (i in chroms) {\n ## regression\n tryCatch({\n print(paste('run',r,': regression on chromosome',i))\n print(outf_names[name_counter])\n mm = subset(mm_combined_and, chr == i)\n if(nrow(mm)>6){\n if (REG_TYPE == 'pospoisson') {\n mm = pospoisson_regression(mm, dataset_length)\n } else if (REG_TYPE == 'negbinom') {\n mm = negbinom_regression(mm, dataset_length)\n }\n mx_combined_and = rbind(mx_combined_and, mm)\n }\n write.table(mm,outf_names[name_counter],row.names = TRUE,col.names = TRUE,quote=FALSE)\n name_counter = name_counter + 1\n print(outf_names[name_counter])\n mm = subset(mm_combined_xor, chr == i)\n if(nrow(mm)>6){\n if (REG_TYPE == 'pospoisson') {\n mm = pospoisson_regression(mm, dataset_length)\n } else if (REG_TYPE == 'negbinom') {\n mm = negbinom_regression(mm, dataset_length)\n }\n mx_combined_xor = rbind(mx_combined_xor, mm)\n }\n write.table(mm,outf_names[name_counter],row.names = TRUE,col.names = TRUE,quote=FALSE)\n }, error=function(.e){\n message(.e)\n })\n name_counter = name_counter + 1\n ## finding min FDR so I can set appropriate lower boundary for FDR\n }\n ## save QC file\n qc_out = paste(INFDIR,SET,'.maps.qc',sep = '')\n qc_label = c('AND_set','XOR_set')\n qc_val = c(sum(mm_combined_and$count), sum(mm_combined_xor$count))\n qc_name = c('number of sequencing pairs in AND set', 'number of sequencing pairs in XOR set')\n df_qc = data.frame()\n\n summary_one_run = data.frame()\n singletons = data.frame()\n for (fdr_cutoff in FDR) {\n peaks_and = if(nrow(mx_combined_and)>0) subset(mx_combined_and, count >= COUNT_CUTOFF & ratio2 >= RATIO_CUTOFF & -log10(fdr) > fdr_cutoff) else data.frame()\n peaks_xor = if(nrow(mx_combined_xor)>0) subset(mx_combined_xor, count >= COUNT_CUTOFF & ratio2 >= RATIO_CUTOFF & -log10(fdr) > fdr_cutoff) else data.frame()\n print(\"finding peaks\")\n peaks = rbind(peaks_and, peaks_xor)\n if (dim(peaks)[1] == 0) {\n print(paste('ERROR MAPS_regression_and_peak_caller.r: 0 bin pairs with count >= ',COUNT_CUTOFF,' observed/expected ratio >= ',RATIO_CUTOFF,' and -log10(fdr) > ',fdr_cutoff,sep=''))\n quit()\n }\n peaks = label_peaks(peaks)\n peaks$lab = paste(peaks$chr, peaks$label,sep='_')\n peak_types = classify_peaks(peaks)\n outf_name = paste(INFDIR,SET, '.',fdr_cutoff,'.peaks',sep='')\n write.table(peak_types,outf_name, row.names = FALSE, col.names = TRUE, quote=FALSE)\n print(\"finding singletons\")\n peak_classes = table(peaks$lab)\n n_singletons = sum(peak_classes[names(peak_classes) %in% singletons_names])\n n_singleton_chroms = length(peak_classes[names(peak_classes) %in% singletons_names])\n\n fraction= n_singletons / (length(peak_classes) + n_singletons - n_singleton_chroms)\n fraction = n_singletons / (length(peak_classes) + n_singletons - n_singleton_chroms)\n\n singletons_one_run = data.frame('fdr'=fdr_cutoff, 'fraction'=fraction)\n singletons = rbind(singletons, singletons_one_run)\n print(paste(fdr_cutoff,':',singletons))\n summary_one_run = rbind(summary_one_run, do_summaries(peaks_and, peaks_xor, peaks, fraction, r))\n }\n ## find singletons, sharp peaks, broad peaks\n summary_all_runs = rbind(summary_all_runs, summary_one_run)\n}\nsummary_outf_name = paste(INFDIR,'summary.',SET,'.txt',sep='')\nwrite.table(summary_all_runs, summary_outf_name, row.names = FALSE, col.names = TRUE, quote=FALSE)\n", "meta": {"hexsha": "d63018913ff9e14a5138234c08205dcecebdde6e", "size": 16824, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/MAPS_regression_and_peak_caller.r", "max_stars_repo_name": "jianhong/nf-core-hicar", "max_stars_repo_head_hexsha": "27158ef8e8a4b28363b52959bdf4ab634f3256fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-29T15:59:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-01T02:49:42.000Z", "max_issues_repo_path": "bin/MAPS_regression_and_peak_caller.r", "max_issues_repo_name": "nf-core/nf-core-hicar", "max_issues_repo_head_hexsha": "343641e56d3a3edf30dbf72eac604ac59b5e1b3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 49, "max_issues_repo_issues_event_min_datetime": "2021-11-02T21:05:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T10:45:36.000Z", "max_forks_repo_path": "bin/MAPS_regression_and_peak_caller.r", "max_forks_repo_name": "nf-core/nf-core-hicar", "max_forks_repo_head_hexsha": "343641e56d3a3edf30dbf72eac604ac59b5e1b3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-08-12T15:11:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-22T08:37:05.000Z", "avg_line_length": 43.6987012987, "max_line_length": 192, "alphanum_fraction": 0.6005111745, "num_tokens": 4944, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6859494550081926, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3242369731791636}} {"text": "\n#Function to read and plot survey residuals\n# parameter start.year: first year on X-axis, default=0 (defined from data)\n# parameter end.year: end year on X-axis, default=0 (defined from data)\n# \n# use over.all.max to set the maximum size of the reference buble. A value of 0 scales bubles individually \n\n\nover.all.max<-6 # use over.all.max different from 0 to set bublesize \nover.all.max<-2.5\n\nuse.ref.dot<-TRUE\n\n\nplot.catch.residuals2<-function(dev,nox=1,noy=1,Portrait=T,start.year=0,end.year=0,reverse.colors=F,standardize=F,use.ref.dot=TRUE,add.title=TRUE,over.all.max=1.5,my.species=NA) {\n\nfile<-file.path(data.path,'catch_survey_residuals.out')\nres<-read.table(file,comment.char = \"#\",header=T)\nres<-subset(res,data=='catch')\nif (standardize) res$residual<- res$stand.residual\nres[res$residual==-99.9,'residual']<-NA\nif (reverse.colors) res$residual<- -res$residual\nquarters<-unique(res$Quarter)\nmax.buble<-max(abs(res$residual),na.rm=TRUE)\n\nInit.function() # get SMS.contol object including sp.names\n\nnsp<-nsp-first.VPA+1\n\nnox.noy<-nox*noy\nplot.no<-0\n\nyears<-rep(0,2)\nages<-rep(0,2)\nif (is.na(my.species)) my.species<-1:nsp\n\nfor (sp in 1:nsp) if (sp %in% my.species) {\n aa<-subset(res,Species.n==first.VPA+sp-1)\n sp.name<-sp.names[sp+first.VPA-1]\n \n quarters<-unique(aa$Quarter)\n \n for (q in quarters) {\n\n print(paste(sp.name,\" quarter:\",q))\n\n nyr<-years[2]-years[1]+1\n nag<-ages[2]-ages[1]+1\n plot.no<-plot.no+1\n\n if (plot.no%%nox.noy==0 || q==1){\n newplot(dev,nox,noy,Portrait=Portrait,filename=paste(\"catch\",sp,q,plot.no))\n par(mar=c(3,4,3,2))\n if (dev==\"wmf\") par(mar=c(2,4,2,2))\n plot.no<-0\n }\n\n bb<-subset(aa,Quarter==q)\n tmp<-tapply(bb$residual,list(age=bb$Age,year=bb$Year),sum,na.rm=T)\n tmp[tmp==-99.99]<-0\n \n xpos <- as.numeric(dimnames(tmp)[[2]]) # years\n ypos <- as.numeric(dimnames(tmp)[[1]]) #ages\n title<- paste(sp.name,\" Q:\",q,sep=\"\")\n\n if (length(ypos)==1) {\n \n tmp2<-tmp\n tmp2[]<-0\n if (ypos[1]>=1) {\n tmp<-rbind(tmp2,tmp,tmp2)\n ypos <- (ypos-1):(ypos+1)\n }\n else if (ypos[1]==0) {\n tmp<-rbind(tmp,tmp2)\n ypos <- ypos:(ypos+1)\n } \n }\n #print(tmp)\n if (over.all.max>0) residplot(tmp,xpos,ypos,main=title,refdot=use.ref.dot,start.year=start.year,end.year=end.year,maxn=over.all.max)\n else residplot(tmp,xpos,ypos,main=title,refdot=use.ref.dot,start.year=start.year,end.year=end.year)\n\n }\n}\n\ncat(\"Max buble size=\",max.buble,'\\n')\ncat(\"log(Survey observed CPUE) - log(expected CPUE). 'Red' positive, 'White' negative\\n\")\nif (dev !='screen') cleanup()\n}\n", "meta": {"hexsha": "44c1c7f33453f9a586406ceafa5ed2a1570b95ca", "size": 2648, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/function/make_catch_residuals_bubles2.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/function/make_catch_residuals_bubles2.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/function/make_catch_residuals_bubles2.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4222222222, "max_line_length": 179, "alphanum_fraction": 0.6389728097, "num_tokens": 862, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.63341027751814, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.32412655659630574}} {"text": "library(scales)\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(data.table)\nlibrary(RColorBrewer)\n\nargs <- commandArgs(trailingOnly = TRUE)\n\nfile1 <- args[1]\nfile2 <- args[2]\ncall <- args[3]\ndate1 <- args[4]\ndate2 <- args[5]\n\n# file1 <- 'wsprspots-2016-04-SWLJO20.csv'\n# file2 <- 'wsprspots-2016-04-snr-norm-SWLJO20.png'\n# call <- 'SWLJO20'\n# date1 <- '2016-04-01'\n# date2 <- '2016-12-31'\n\ncolNames <- c('id', 'epoch', 'rcall',\n 'rgrid', 'snr', 'freq', 'call',\n 'grid', 'power', 'drift', 'distance',\n 'azimuth', 'band', 'version', 'code')\ncolClasses <- c('numeric', 'numeric', 'character',\n 'character', 'numeric', 'numeric', 'character',\n 'character', 'numeric', 'numeric', 'numeric',\n 'numeric', 'numeric', 'character', 'numeric')\n\nspots <- as.data.table(read.table(file1, header = F, sep = ',', col.names = colNames, colClasses = colClasses))\n\nspots[, time := as.POSIXct(epoch, origin = '1970-01-01')]\n\nfreqs <- c(0, 1, 3, 5, 7, 10, 14, 18, 21, 24, 28, 50)\nbands <- c('MF', '160m', '80m', '60m', '40m', '30m', '20m', '17m', '15m', '12m', '10m', '6m')\n\nproc1 <- function(x){\n x[\n ,\n list(\n norm = mean(snr/power)\n ),\n list(\n mday = mday(time),\n hour = hour(time),\n band\n )\n ]\n}\n\nnorm <- proc1(spots)\nnorm[, time := as.POSIXct(paste0('2016/04/', mday, ' ', hour, ':00:00'))]\n\nsubset <- norm[time >= as.POSIXct(paste0(date1, ' 00:00:00')) & time <= as.POSIXct(paste0(date2, ' 23:59:59'))]\n\np <- lapply(2:7, function(i)\n ggplot(data = subset[band == freqs[i]], aes(x = time, y = norm)) +\n geom_point() +\n scale_x_datetime(breaks = date_breaks('1 day'), minor_breaks = date_breaks('1 hour')) +\n theme(axis.text.x = element_text(angle = 45)) +\n labs(title = bands[i]) + labs(x = '', y = 'SNR over power') +\n theme(legend.position = 'none') + coord_cartesian(ylim = c(-2.0, 1.0))\n)\n\npng(file2, width = 1200, height = 600, res = 90)\ngrid.arrange(arrangeGrob(grobs = p, nrow = 2))\ndev.off()\n", "meta": {"hexsha": "c5e690a56615f0536865022ef7eb53ebb138db20", "size": 1940, "ext": "r", "lang": "R", "max_stars_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-norm.r", "max_stars_repo_name": "KI7MT/wsprana-utils", "max_stars_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-03-15T14:58:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-12T08:42:42.000Z", "max_issues_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-norm.r", "max_issues_repo_name": "KI7MT/wsprana-utils", "max_issues_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "wsprana/wsprana/resources/rscripts/wsprspots-snr-norm.r", "max_forks_repo_name": "KI7MT/wsprana-utils", "max_forks_repo_head_hexsha": "5a06db3335e8fa6e9b6584334b4a95d7f2e36ba0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-12T08:42:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-12T08:42:45.000Z", "avg_line_length": 28.5294117647, "max_line_length": 111, "alphanum_fraction": 0.5953608247, "num_tokens": 697, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102498375401, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.3241265424316831}} {"text": "# wahpenayo at gmail dot com\n# 2018-01-21\n#-----------------------------------------------------------------\nif (file.exists('e:/porta/projects/taigabench')) {\n setwd('e:/porta/projects/taigabench')\n} else {\n setwd('c:/porta/projects/taigabench')\n}\nsource('src/scripts/r/functions.r')\nreadr.show_progress <- FALSE\n#-----------------------------------------------------------------\ndataset <- 'ontime'\nproblem <- 'l2'\nresponse <- 'arrdelay'\ndataf <- ontime.data\ndtest <- dataf(test.file(dataset=dataset))\n#suffixes <- c('32768','131072','524288','2097152','8388608','33554432')\nsuffix <- '2097152'\nmincounts <- c(7,15,31,63,127,255,511,1023,2047,4095)\n#mincounts <- c(1023,511,255)\n#-----------------------------------------------------------------\nsweep.mincount(\n dataset=dataset,\n problem=problem,\n dataf=dataf,\n dtest=dtest,\n response=response, \n suffix=suffix,\n #trainf=l2.randomForestSRC,\n trainf=l2.h2o.randomForest,\n prefix='h2o',\n mincounts=mincounts)\n#-----------------------------------------------------------------\n", "meta": {"hexsha": "469149f02eee13940c34c87b8efe61c7966a2ec6", "size": 1039, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/r/l2/ontime/mincount.r", "max_stars_repo_name": "wahpenayo/taigabench", "max_stars_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/r/l2/ontime/mincount.r", "max_issues_repo_name": "wahpenayo/taigabench", "max_issues_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/r/l2/ontime/mincount.r", "max_forks_repo_name": "wahpenayo/taigabench", "max_forks_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.5588235294, "max_line_length": 72, "alphanum_fraction": 0.5235803657, "num_tokens": 274, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.63341024983754, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3241265424316831}} {"text": "#core-level forecast to NEON.\n#once data have been aggregated at core-plot-site level, we need to fill in missing values and uncertainties hierarchically.\n#this product is then shipped to the forecast, which draws missing data.\n#1. hierarchically execute your site-level forecast, it should basically be the same, but at the core level.\n#2. No need to update yet, Just get hierarchical means.\n#3. You in principle can use this as your plot and site level forecast? Once you figure out hierarchical ddirch means.\n#4. The relEM problem- you solve this in forecast because missing data happens outside of JAGS.\n#5. Once you get this done, hope ddirch aggregation is solved. If not, ask Mike. THen we can validate.\n#6. Replication *should* be quick on the bacteria side. Clustered, binned taxonomically.\n#7. Fit prior, then send prior through core-level forecast, aggregate plot and site level using something.\n#8. NOTE: CPER, STER and WOOD are plains. You need to tell the filling that there are no ectomycorrhizal trees here.\n#clearn environment, loads paths.\nrm(list=ls())\nsource('paths.r')\nsource('NEFI_functions/precision_matrix_match.r')\n\n#set output path.\noutput.path <- hierarch_filled.path\n\n#specify sites with zero ectomycorrhizal trees (plains sites)\nno.ecm <- c('CPER','STER','WOOD')\n\n#load prior model fit- model fit at site level.\nmod <- readRDS(ted_ITS.prior_fg_JAGSfit)\npreds <- mod$all.preds$species_parameter_output$other$predictor\nif('relEM' %in% preds){preds <- c(as.character(preds),'b.relEM')}\n#add plot, site and sampleID to preds.\nkeys <- c('sampleID','plotID','siteID')\ncheck <- c(as.character(preds),keys)\ncheck_sd <-c(paste0(as.character(preds), '_sd'), keys)\n\n#load NEON data.\ncore_obs <- readRDS( core_obs.path)\ncore_core <- readRDS(core_core.path)\ncore_plot <- readRDS(core_plot.path)\ncore_site <- readRDS(core_site.path)\ncore_glob <- readRDS(core_glob.path)\nplot_plot <- readRDS(plot_plot.path)\nplot_site <- readRDS(plot_site.path)\nplot_glob <- readRDS(plot_glob.path)\nsite_site <- readRDS(site_site.path)\nsite_glob <- readRDS(site_glob.path)\n\n#cores, plots and sites that need to be present.\nneeded <- list()\nneeded[[1]] <- as.character(unique(core_obs$geneticSampleID))\nneeded[[2]] <- as.character(unique(core_obs$plotID))\nneeded[[3]] <- as.character(unique(core_obs$siteID))\nnames(needed) <- c('geneticSampleID','plotID','siteID')\n\n#all y-cores present in core_core geneticSampleIDs?\nsum(unique(core_obs$geneticSampleID) %in% core_core$geneticSampleID) == length(unique(core_obs$geneticSampleID))\nnrow(core_obs) == nrow(core_core)\n#all plots in core_core present in core_plot?\nsum(unique(core_core$plotID) %in% core_plot$plotID) == length(unique(core_core$plotID))\n#all sites in core_core present in core_site?\nsum(unique(core_core$siteID) %in% core_site$siteID) == length(unique(core_core$siteID))\n#all y-plots present in plot_plot plotIDs? FALSE\nsum(unique(core_obs$plotID) %in% plot_plot$plotID) == length(unique(core_obs$plotID))\n#all sites in plot_plot present in plot_site?\nsum(unique(plot_plot$siteID) %in% plot_site$siteID) == length(unique(plot_plot$siteID))\n#all y-sites present in site_site siteIDs?\nsum(unique(core_obs$siteID) %in% site_site$siteID) == length(unique(core_obs$siteID))\n\n\n\n#Get core-plot-site indexing variables.\nsites <- site_site$siteID\nplots <- plot_plot$plotID\ncores <- core_core$sampleID\nplot.site <- substring(plots,1,4)\ncore.plot <- substring(cores,1,8)\ncore.site <- substring(cores,1,4)\n\n#subset based on predictors actually in the model.\ncore_site_mu <- core_site[,colnames(core_site) %in% (check )]\ncore_site_sd <- core_site[,colnames(core_site) %in% (check_sd)]\ncore_site_sd <- precision_matrix_match(core_site_mu,core_site_sd)\ncore_plot_mu <- core_plot[,colnames(core_plot) %in% (check )]\ncore_plot_sd <- core_plot[,colnames(core_plot) %in% (check_sd)]\ncore_plot_sd <- precision_matrix_match(core_plot_mu,core_plot_sd)\ncore_core_mu <- core_core[,colnames(core_core) %in% (check )]\ncore_core_sd <- core_core[,colnames(core_core) %in% (check_sd)]\ncore_core_sd <- precision_matrix_match(core_core_mu,core_core_sd)\nplot_site_mu <- plot_site[,colnames(plot_site) %in% (check )]\nplot_site_sd <- plot_site[,colnames(plot_site) %in% (check_sd)]\nplot_site_sd <- precision_matrix_match(plot_site_mu,plot_site_sd)\nplot_plot_mu <- plot_plot[,colnames(plot_plot) %in% (check )]\nplot_plot_sd <- plot_plot[,colnames(plot_plot) %in% (check_sd)]\nplot_plot_sd <- precision_matrix_match(plot_plot_mu,plot_plot_sd)\nsite_site_mu <- site_site[,colnames(site_site) %in% (check )]\nsite_site_sd <- site_site[,colnames(site_site) %in% (check_sd)]\nsite_site_sd <- precision_matrix_match(site_site_mu,site_site_sd)\n\n#Step 1. Missing data model. Assign missing means and sd hierarchically as necessary\n#core_site\nfor(j in 1:ncol(core_site_mu)){\n name <- colnames(core_site_mu)[j]\n if(name %in% preds){\n core_site_mu[is.na(core_site_mu[,j]),j] <- core_glob[core_glob$pred == name,'Mean']\n core_site_sd[is.na(core_site_sd[,j]),j] <- core_glob[core_glob$pred == name,'SD' ]\n }\n}\n#core_plot\nfor(j in 1:ncol(core_plot_mu)){\n name <- colnames(core_plot_mu)[j]\n if(name %in% preds){\n for(i in 1:nrow(core_plot_mu)){\n c.site <- core_plot_mu$siteID[i]\n if(is.na(core_plot_mu[i,j])){\n core_plot_mu[i,j] <- core_site_mu[core_site_mu$siteID == c.site,name]\n core_plot_sd[i,j] <- core_site_sd[core_site_sd$siteID == c.site,name]\n }\n }\n }\n}\n#core_core\nfor(j in 1:ncol(core_core_mu)){\n name <- colnames(core_core_mu)[j]\n if(name %in% preds){\n for(i in 1:nrow(core_core_mu)){\n c.plot <- core_core_mu$plotID[i]\n if(is.na(core_core_mu[i,j])){\n core_core_mu[i,j] <- core_plot_mu[core_plot_mu$plotID == c.plot,name]\n core_core_sd[i,j] <- core_plot_sd[core_plot_sd$plotID == c.plot,name]\n }\n }\n }\n}\n#plot_site\nfor(j in 1:ncol(plot_site_mu)){\n name <- colnames(plot_site_mu)[j]\n if(name %in% preds){\n plot_site_mu[is.na(plot_site_mu[,j]),j] <- plot_glob[plot_glob$pred == name,'Mean']\n plot_site_sd[is.na(plot_site_sd[,j]),j] <- plot_glob[plot_glob$pred == name,'SD' ]\n }\n if(name == 'b.relEM'){\n plot_site_mu[plot_site_mu$siteID %in% no.ecm,j] <- -10\n plot_site_sd[plot_site_sd$siteID %in% no.ecm,j] <- 0.01\n }\n}\n#plot_plot\nfor(j in 1:ncol(plot_plot_mu)){\n name <- colnames(plot_plot_mu)[j]\n if(name %in% preds){\n for(i in 1:nrow(plot_plot_mu)){\n c.site <- plot_plot_mu$siteID[i]\n if(is.na(plot_plot_mu[i,j]) & name %in% colnames(plot_site_mu)){\n plot_plot_mu[i,j] <- plot_site_mu[plot_site_mu$siteID == c.site,name]\n plot_plot_sd[i,j] <- plot_site_sd[plot_site_sd$siteID == c.site,name]\n }\n }\n }\n if(name == 'b.relEM'){\n plot_plot_mu[plot_plot_mu$siteID %in% no.ecm, j] < -10\n plot_plot_sd[plot_plot_sd$siteID %in% no.ecm, j] <- 0.01\n }\n}\n\n\n#site_site\nsite_site_mu <- site_site_mu[site_site_mu$siteID %in% core_obs$site,]\nsite_site_sd <- site_site_sd[site_site_sd$siteID %in% core_obs$site,]\n\noutput.list <- list(core_obs,\n core_core_mu,core_core_sd,\n core_plot_mu,core_plot_sd,\n core_site_mu,core_site_sd,\n plot_plot_mu,plot_plot_sd,\n plot_site_mu,plot_site_sd,\n site_site_mu,site_site_sd)\nnames(output.list) <- c('core.obs',\n 'core.core.mu','core.core.sd',\n 'core.plot.mu','core.plot.sd',\n 'core.site.mu','core.site.sd',\n 'plot.plot.mu','plot.plot.sd',\n 'plot.site.mu','plot.site.sd',\n 'site.site.mu','site.site.sd')\n#save output.\nsaveRDS(output.list, output.path)\n", "meta": {"hexsha": "7b6d2f96c4b252fafc14e5f5bcf94be3c03f971b", "size": 7702, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/data_construction/NEON_ITS/2._covariate_aggregation/hierarch_fiill_missing_values.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ITS/data_construction/NEON_ITS/2._covariate_aggregation/hierarch_fiill_missing_values.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "ITS/data_construction/NEON_ITS/2._covariate_aggregation/hierarch_fiill_missing_values.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 42.5524861878, "max_line_length": 124, "alphanum_fraction": 0.7066995586, "num_tokens": 2178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7090191460821871, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32411868460784493}} {"text": "setwd(\"/home/sugon/code/R/ClassicalGroupmean/files/\")\nlibrary(ANTsR)\nlibrary(fslr)\nlibrary(parallelDist)\n\navg_1<-0\nchosen_num<-93\niter <<- 10\nsum<-0\nbatch_size<-20\nSys.setenv(ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS = 64)\n\nflowSigma_start <- 3.5\n# totalSigma_start <- 5\nflowSigma_end <- 1\n# totalSigma_end <- 0\n\nfor(i in seq(batch_size))\n{\n if(i != chosen_num)\n {\n print(paste0(\"Linear reg \", i))\n img <- flirt(infile = paste(\"/run/media/sugon/4t/T1Img/temp/ext\", i, sep = \"\"), reffile = paste(\"/run/media/sugon/4t/T1Img/temp/ext\", chosen_num, sep = \"\"), dof = 12, outfile = paste(\"0-\", i, sep = \"\"), retimg = T)\n sum<-sum+img@.Data\n }\n else\n {\n img <- antsImageRead(paste0(\"/run/media/sugon/4t/T1Img/temp/ext\", chosen_num, \".nii.gz\"))\n antsImageWrite(img, paste0(\"0-\", i, \".nii.gz\"))\n }\n}\nimg <- antsImageRead(\"0-1.nii.gz\")\nantsImageWrite(as.antsImage(sum/batch_size, direction = antsGetDirection(img)), \"avg0.nii.gz\")\n\n\n\nfor(i in seq(batch_size))\n{\n img <- antsImageRead(paste0(\"/run/media/sugon/4t/T1Img/temp/ext\", i, \".nii.gz\"))\n sum<-sum+img[]\n}\na <- 1/11081*3-6\n\nflowSigma_current <- c(a, 3*a, 6*a, 10*a, 15*a, 21*a, 28*a, 36*a, 45*a, 55*a)\n\n\n sum <- 0\n for(j in seq(iter))\n {\n sum <- 0\n for(i in seq(batch_size))\n {\n flowSigma_current <- -2/81*iter^2+4/81*iter+237/81\n # flowSigma_current <- 2/(iter-1)\n # flowSigma_current <- 17-(iter-1)^2\n # flowSigma_current <- getParam(ceiling(), iter, flowSigma_end, flowSigma_start)\n # totalSigma_current <- getParam(%%2, iter, totalSigma_end, totalSigma_start)\n mov <- antsImageRead(paste(\"/run/media/sugon/4t/T1Img/temp/ext\", i, \".nii.gz\", sep = \"\"))\n # mov <- antsImageRead(paste0(j-1, \"-\", i, \".nii.gz\"))\n fix <- antsImageRead(paste0(\"avg\", j-1, \".nii.gz\"))\n print(paste0(\"Nonlinear reg \", j, \" - \", i))\n img <- antsRegistration(moving = mov, fixed = fix, typeofTransform = \"SyNOnly\", flowSigma = flowSigma_current)\n # img <- antsRegistration(moving = mov, fixed = fix, typeofTransform = \"SyNOnly\", flowSigma = flowSigma_current, totalSigma = totalSigma_current)\n antsImageWrite(img$warpedmovout, paste(j, \"-\", i, \".nii.gz\", sep = \"\"))\n sum<-sum+img$warpedmovout[]\n }\n avg_0<-sum/batch_size\n print(paste(\"Difference = \", sum(avg_0 - avg_1)), sep = \"\")\n antsImageWrite(as.antsImage(avg_0, direction = antsGetDirection(img$warpedmovout)), paste0(\"avg\", j, \".nii.gz\"))\n avg_1<-avg_0\n }\n", "meta": {"hexsha": "e7ee09be20855cd918d2eb212d44c850c27ed5bb", "size": 2446, "ext": "r", "lang": "R", "max_stars_repo_path": "group_reg.r", "max_stars_repo_name": "chewnglow/ClassicalGroupmean", "max_stars_repo_head_hexsha": "5e01b1fb88b443d6d7b224912ef918c2033bb333", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "group_reg.r", "max_issues_repo_name": "chewnglow/ClassicalGroupmean", "max_issues_repo_head_hexsha": "5e01b1fb88b443d6d7b224912ef918c2033bb333", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "group_reg.r", "max_forks_repo_name": "chewnglow/ClassicalGroupmean", "max_forks_repo_head_hexsha": "5e01b1fb88b443d6d7b224912ef918c2033bb333", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9722222222, "max_line_length": 218, "alphanum_fraction": 0.640637776, "num_tokens": 817, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7090191214879991, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32411867336493855}} {"text": "#MODE <- '24hrs'\nlibrary(doParallel)\nlibrary(foreach)\nregisterDoParallel(cores=32)\n#list.mode.results <- foreach(MODE=c('24hrs', '48hrs', '72hrs', '24hrs~72hrs', '7days', '30days')) %dopar% {\nlist.mode.results <- foreach(MODE=c('48hrs', '7days', 'Bounceback')) %dopar% {\n#for(MODE in c('24hrs', '48hrs', '72hrs', '24hrs~72hrs', '7days', '30days', 'bounceback')) {\nPrefix <- './FOLDS_PROBS/IsReadmitted_'\n\nlibrary(glmnet)\nlibrary(xgboost)\nlibrary(pROC)\nsource('./xgboostWrapper.R')\nsource('./catToBin.R')\n\nlist.predglm <- list()\nlist.predxgb <- list()\nlist.rocglm <- list()\nlist.rocxgb <- list()\nlist.xgbparam <- list()\nlist.glm <- list()\nlist.xgb <- list()\nlist.featurenames <- list()\nlist.labels <- list()\n\nlist.train.in <- list()\nlist.test.in <- list()\nlist.train.out <- list()\nlist.test.out <- list()\n\nfor(f in 1:5) {\n cat(paste('fold:', f, '\\n'))\n train.data <- read.csv(paste(Prefix, MODE, '/fold_', f, '/training.csv', sep=''))\n test.data <- read.csv(paste(Prefix, MODE, '/fold_', f, '/testing.csv', sep=''))\n \n cols.remove <- which(grepl('HADM_ID', colnames(train.data)) |\n grepl('ICUSTAY_ID', colnames(train.data)) |\n grepl('INTIME', colnames(train.data)) |\n grepl('OUTTIME', colnames(train.data)) |\n grepl('SUBJECT_ID', colnames(train.data)) |\n grepl('INSURANCE', colnames(train.data)) |\n grepl('RELIGION', colnames(train.data)) |\n grepl('LANGUAGE', colnames(train.data)) |\n grepl('MARITAL_STATUS', colnames(train.data)))\n \n train.data <- train.data[,-cols.remove]\n test.data <- test.data[,-cols.remove]\n cols.out <- which(grepl('IsReadmitted_', colnames(train.data)))\n \n train.out <- train.data[,cols.out]\n test.out <- test.data[,cols.out]\n \n train.in <- train.data[,-cols.out]\n test.in <- test.data[,-cols.out]\n \n first.care <- catToBin(train.in$FIRST_CAREUNIT)\n colnames(first.care) <- paste('FIRST_CARE_', colnames(first.care), sep='')\n first.care.test <- catToBin(test.in$FIRST_CAREUNIT)\n colnames(first.care.test) <- paste('FIRST_CARE_', colnames(first.care.test), sep='') \n \n train.in <- cbind(train.in, first.care)\n test.in <- cbind(test.in, first.care.test) \n \n train.in$FIRST_CAREUNIT <- NULL\n test.in$FIRST_CAREUNIT <- NULL\n \n adm.type <- catToBin(train.in$ADMISSION_TYPE)\n colnames(adm.type) <- paste('ADMISSION_TYPE_', colnames(adm.type), sep='')\n adm.type.test <- catToBin(test.in$ADMISSION_TYPE)\n colnames(adm.type.test) <- paste('ADMISSION_TYPE', colnames(adm.type.test), sep='') \n \n train.in <- cbind(train.in, adm.type)\n test.in <- cbind(test.in, adm.type.test) \n \n train.in$ADMISSION_TYPE <- NULL\n test.in$ADMISSION_TYPE <- NULL\n \n ethn <- catToBin(train.in$ETHNICITY)\n colnames(ethn) <- paste('ETHNICITY_', colnames(ethn), sep='')\n ethn.test <- catToBin(test.in$ETHNICITY)\n colnames(ethn.test) <- paste('ETHNICITY_', colnames(ethn.test), sep='') \n \n #Rebuild Ethnicity into groups\n # ethn.nativeamerican <- rowSums(ethn[,c(1,2)])\n # ethn.asian <- rowSums(ethn[,c(3,4,5,6,7,8,9,10,11,12)])\n # ethn.black <- rowSums(ethn[,c(13,14,15,16,17)])\n # ethn.hispanic <- rowSums(ethn[,seq(18,27,1)])\n # ethn.white <- rowSums(ethn[,c(33,34,37,38,39,40,41)])\n # ethn.other <- rowSums(ethn[,c(28,29,30,31,32,35,36)])\n # \n # ethn.nativeamerican[which(ethn.nativeamerican > 1)] <- 1\n # ethn.asian[which(ethn.asian > 1)] <- 1\n # ethn.black[which(ethn.black > 1)] <- 1\n # ethn.hispanic[which(ethn.hispanic > 1)] <- 1\n # ethn.white[which(ethn.white > 1)] <- 1\n # ethn.other[which(ethn.other > 1)] <- 1\n # \n # ethn.nativeamerican.test <- rowSums(ethn.test[,c(1,2)])\n # ethn.asian.test<- rowSums(ethn.test[,c(3,4,5,6,7,8,9,10,11,12)])\n # ethn.black.test <- rowSums(ethn.test[,c(13,14,15,16,17)])\n # ethn.hispanic.test <- rowSums(ethn.test[,seq(18,27,1)])\n # ethn.white.test <- rowSums(ethn.test[,c(33,34,37,38,39,40,41)])\n # ethn.other.test <- rowSums(ethn.test[,c(28,29,30,31,32,35,36)])\n # \n # ethn.nativeamerican.test[which(ethn.nativeamerican.test > 1)] <- 1\n # ethn.asian.test[which(ethn.asian.test > 1)] <- 1\n # ethn.black.test[which(ethn.black.test > 1)] <- 1\n # ethn.hispanic.test[which(ethn.hispanic.test > 1)] <- 1\n # ethn.white.test[which(ethn.white.test > 1)] <- 1\n # ethn.other.test[which(ethn.other.test > 1)] <- 1\n # \n # train.in <- cbind(train.in, ethn.nativeamerican, ethn.asian, ethn.black, ethn.hispanic, ethn.white, ethn.other)\n # test.in <- cbind(test.in, ethn.nativeamerican.test, ethn.asian.test, ethn.black.test, ethn.hispanic.test, ethn.white.test, ethn.other.test)\n # \n # \n train.in$ETHNICITY <- NULL\n test.in$ETHNICITY <- NULL\n \n train.in <- data.matrix(train.in)\n test.in <- data.matrix(test.in)\n \n col.means <- colMeans(train.in ,na.rm=TRUE)\n for(j in 1:dim(train.in)[2]) {\n if(any(is.na(train.in[,j]))) {\n train.in[,j][which(is.na(train.in[,j]))] <- col.means[j]\n }\n }\n \n for(j in 1:dim(test.in)[2]) {\n if(any(is.na(test.in[,j]))) {\n test.in[,j][which(is.na(test.in[,j]))] <- col.means[j]\n }\n }\n \n if(MODE == '24hrs') {\n train.labels <- train.out$IsReadmitted_24hrs\n test.labels <- test.out$IsReadmitted_24hrs\n } else if(MODE== '48hrs') {\n train.labels <- train.out$IsReadmitted_48hrs\n test.labels <- test.out$IsReadmitted_48hrs\n } else if(MODE == '72hrs') {\n train.labels <- train.out$IsReadmitted_72hrs\n test.labels <- test.out$IsReadmitted_72hrs\n } else if(MODE == '24hrs~72hrs') {\n train.labels <- train.out$IsReadmitted_24hrs.72hrs\n test.labels <- test.out$IsReadmitted_24hrs.72hrs\n } else if(MODE == '7days') {\n train.labels <- train.out$IsReadmitted_7days\n test.labels <- test.out$IsReadmitted_7days\n } else if(MODE == '30days') {\n train.labels <- train.out$IsReadmitted_30days\n test.labels <- test.out$IsReadmitted_30days\n } else {\n train.labels <- train.out$IsReadmitted_Bounceback\n test.labels <- test.out$IsReadmitted_Bounceback\n }\n \n \n w.samples <- rep(1, dim(train.in)[1])\n w.samples[which(train.labels == 1)] <- round((length(train.labels) - sum(train.labels))/sum(train.labels)) \n #round((length(train.labels)-sum(train.labels))/sum(train.labels))\n library(doParallel)\n library(foreach)\n registerDoParallel(cores=32)\n source('fMeasure.R')\n cat(paste('fold:', f, ' GLM\\n'))\n glm.model <- cv.glmnet(x=train.in,y=as.factor(train.labels), family='binomial',\n type.measure='auc', parallel=TRUE, weights=w.samples)\n glm.pred <- predict(glm.model, test.in, type='response')\n glm.roc <- roc(as.numeric(test.labels), as.numeric(glm.pred))\n glm.f <- allROC_par(as.numeric(glm.pred), as.numeric(test.labels))[[1]]\n cat(paste('fold:', f, ' GLM ROC:', glm.roc$auc, '\\n'))\n \n xgb.params <- xgcv_par(train.in, train.labels, seq(1,6,1), c(0.1), c(50,100,150, 200), FALSE, nfolds = 5, ncores=16, nthread=32, linux=TRUE)\n \n xgb.model <- xgboost(train.in, as.numeric(train.labels), verbose=0, nrounds=xgb.params[3], \n eta=0.1, max.depth=xgb.params[2], objective='binary:logistic',nthread=32,\n save_period=NULL, save_name=NULL)\n xgb.pred <- predict(xgb.model, test.in)\n xgb.roc <- roc(as.numeric(test.labels), as.numeric(xgb.pred))\n xgb.f <- allROC_par(as.numeric(xgb.pred), as.numeric(test.labels))[[1]]\n cat(paste('fold:', f, ' XGB:', xgb.roc$auc, '\\n'))\n \n list.featurenames[[f]] <- colnames(train.in)\n list.predglm[[f]] <- glm.pred\n list.predxgb[[f]] <- xgb.pred\n list.rocglm[[f]] <- glm.roc\n list.rocxgb[[f]] <- xgb.roc\n list.xgbparam[[f]] <- xgb.params\n list.glm[[f]] <- glm.model\n list.xgb[[f]] <- xgb.model\n list.labels[[f]] <- list(train.labels, test.labels)\n \n list.train.in[[f]] <- train.in\n list.test.in[[f]] <- test.in\n list.train.out[[f]] <- train.out\n list.test.out[[f]] <- test.out\n}\n\nsave('list.predglm',\n 'list.predxgb',\n 'list.rocglm',\n 'list.rocxgb',\n 'list.xgbparam',\n 'list.glm',\n 'list.xgb',\n 'list.featurenames',\n 'list.labels',\n \n 'list.train.in',\n 'list.test.in',\n 'list.train.out',\n 'list.test.out',\n\n file=paste('./results/aucs_FEB2018_', MODE,'_data.RData', sep=''))\n\n}", "meta": {"hexsha": "cfe967ebc83c7ccc46efcc8e66a6886f9bb74146", "size": 8255, "ext": "r", "lang": "R", "max_stars_repo_path": "models2/readmission_pred.r", "max_stars_repo_name": "ExaScience/ICU72hReadmissionMIMICIII", "max_stars_repo_head_hexsha": "8ebf984e30f938f32eef44ae0a924279d2aa5e35", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13, "max_stars_repo_stars_event_min_datetime": "2018-02-16T23:11:43.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-05T18:16:58.000Z", "max_issues_repo_path": "models2/readmission_pred.r", "max_issues_repo_name": "ExaScience/ICU72hReadmissionMIMICIII", "max_issues_repo_head_hexsha": "8ebf984e30f938f32eef44ae0a924279d2aa5e35", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-10-14T05:54:37.000Z", "max_issues_repo_issues_event_max_datetime": "2018-10-16T15:13:56.000Z", "max_forks_repo_path": "models2/readmission_pred.r", "max_forks_repo_name": "ExaScience/ICU72hReadmissionMIMICIII", "max_forks_repo_head_hexsha": "8ebf984e30f938f32eef44ae0a924279d2aa5e35", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-03-01T17:34:01.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-23T22:52:40.000Z", "avg_line_length": 38.0414746544, "max_line_length": 143, "alphanum_fraction": 0.6250757117, "num_tokens": 2662, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3240652887253384}} {"text": "# movies is already pre-loaded\n\n# Create a boxplot of the runtime variable\nboxplot(movies$runtime)\n\n# Subset the dateframe and plot it entirely\nplot(data.frame(movies$rating,movies$votes,movies$runtime))\n\n# Create a pie chart of the table of counts of the genres\npie(table(movies$genre))", "meta": {"hexsha": "204a0e12dc3ca1bb91f88c0b823ac2f63546a9ec", "size": 287, "ext": "r", "lang": "R", "max_stars_repo_path": "R/7.1/diff_plots.r", "max_stars_repo_name": "applecool/DataScience", "max_stars_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/7.1/diff_plots.r", "max_issues_repo_name": "applecool/DataScience", "max_issues_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/7.1/diff_plots.r", "max_forks_repo_name": "applecool/DataScience", "max_forks_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7, "max_line_length": 59, "alphanum_fraction": 0.7839721254, "num_tokens": 71, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3240334416018925}} {"text": "# Calculate codon usage and other statistics for gene sets.\n# For the terminology used in this module, please see\n#\n# Konrad Rudolph, \u201cInvestigating the link between tRNA and mRNA abundance in mammals\u201d,\n# 2015 (PhD thesis). Chapter 1.6 \u201cQuantifying codon usage and anticodon abundance\u201d\n#\n\n.bios = modules::import_package('Biostrings')\nmodules::import_package('dplyr', attach = TRUE)\ntidyr = modules::import_package('tidyr')\n\n#' The genetic code\ngenetic_code = data.frame(AA = .bios$GENETIC_CODE) %>%\n add_rownames('Codon') %>%\n tbl_df()\n\n#' Vector of stop codons\nstop_codons = filter(genetic_code, AA == '*')$Codon\n\n#' Calculate codon usage for gene set\n#'\n#' @param genes the DNA sequences (see \\link{Details})\n#' @return Tidy table of per-gene codon usage.\n#' @details \\code{genes} is either a named character vector or a\n#' \\code{\\link{data.frame}} of \\code{Gene}\u2013\\code{Sequence} pairs, or a\n#' \\code{\\link[Biostrings]{DNAStringSet}}.\ncu = function (genes) UseMethod('cu')\n\ncu.default = function (genes) {\n stopifnot(is.character(genes))\n cu(.bios$DNAStringSet(genes))\n}\n\ncu.data.frame = function (genes)\n cu(.bios$DNAStringSet(setNames(genes$Sequence, genes$Gene)))\n\ncu.DNAStringSet = function (genes)\n .bios$trinucleotideFrequency(genes, 3) %>%\n as.data.frame() %>%\n mutate(Gene = names(genes)) %>%\n tidyr$gather(Codon, CU, -Gene) %>%\n mutate(Codon = as.character(Codon)) %>%\n filter(! Codon %in% stop_codons) %>%\n tbl_df() %>%\n `class<-`(c('codon_usage$cu', class(.)))\n\n#' Calculate relative codon usage for gene set\n#'\n#' @param x gene set (see \\link{Details})\n#' @return Tidy table of per-gene relative codon usage.\n#' @details The accepted input corresponds to either the output of\n#' \\code{\\link{cu}}, or any input that function accepts.\nrcu = function (x, column = Gene)\n rcu_(x, deparse(substitute(column)))\n\nrcu_ = function (x, column = 'Gene') UseMethod('rcu_')\n\n`rcu_.codon_usage$cu` = function (x, column = 'Gene')\n inner_join(x, genetic_code, by = 'Codon') %>%\n group_by_(.dots = c(column, 'AA')) %>%\n mutate(RCU = CU / sum(CU)) %>%\n mutate(RCU = ifelse(is.nan(RCU), 0, RCU)) %>%\n ungroup() %>%\n `class<-`(c('codon_usage$rcu', class(.)))\n\nrcu_.default = function (x, column = 'Gene')\n rcu_(cu(x), column)\n\n#' Calculate adaptation between codons and tRNA\n#'\n#' Calculate the correlation between codon usage and anticodon abundance as a\n#' measure of goodness of adaptation of the anticodon supply to the codon\n#' demand.\n#'\n#' @param cu codon usage as given by \\code{cu}\n#' @param aa anticodon abundance, with \\code{Codon} column\n#' @param cds coding sequences\n#' @note \\code{raa} is equivalent to \\code{rcu} for the abundance of anticodons.\n#' The function expects this to be given with reverse complemented anticodons,\n#' so that the format of the data is equivalent for \\code{cu} and \\code{aa}.\nadaptation = function (cu, aa, cds, method = adaptation_no_wobble)\n method(cu, aa, cds)\n\n#' \\code{adaptation_no_wobble} computes a simple codon\u2013anticodon correlation,\n#' ignoring wobble base pairing.\n#' @rdname adaptation\nadaptation_no_wobble = function (cu, aa, cds)\n cu %>%\n group_by(Codon, add = TRUE) %>%\n summarize(CU = sum(CU)) %>%\n inner_join(aa, by = 'Codon') %>%\n mutate(CU = CU / sum(CU),\n AA = AA / sum(AA)) %>%\n summarize(Cor = cor(CU, AA, method = 'spearman')) %>%\n .$Cor\n\nwobble_pairing = import('./wobble_pairing')\n\n#' \\code{adaptation_wobble} computes a codon\u2013anticodon correlation while\n#' accounting for wobble base pairing.\nadaptation_wobble = function (cu, aa, cds)\n cu %>% do(Cor = wobble_pairing$adaptation(., aa)) %>% .$Cor %>% unlist()\n\n# Calculate outside function for speed \u2014 `adaptation` is called very frequently.\ncoding_codons = setdiff(genetic_code$Codon, stop_codons)\ntai = import('./tai')\n\n#' \\code{adaptation_tai} computes the tRNA adaptation index.\n#' @param s tAI s-values\n#' @rdname adaptation\nadaptation_tai = function (cu, aa, cds, s = tai$naive_s) {\n lengths = setNames(cds$Length, cds$Gene)[unique(cu$Gene)]\n cu = tidyr$spread(cu, Codon, CU) %>% select(one_of(coding_codons))\n aa = setNames(aa$AA, aa$Codon)\n tai$tai(cu, tai$w(aa, s), lengths)\n}\n\n#' Normalize codon usage\n#'\n#' Normalizing codon usage ensures that the codon usage sums to 1, in other\n#' words, calculate \\code{n[i] = x[i] / sum(x)}.\n#' @param x codon usage in tidy data format\n#' @return The normalized codon usage in tidy data format.\nnorm = function (x) UseMethod('norm')\n\n`norm.codon_usage$cu` = function (x)\n mutate(x, CU = CU / sum(CU)) %>%\n `class<-`(c('codon_usage$cu', class(.)))\n\n`norm.codon_usage$rcu` = function (x) {\n warning('Normalizing RCU makes no sense, skipped.')\n x\n}\n\n#' Reverse complement a set of sequences\n#'\n#' @param seq a character vector\n#' @return A character vector of the reverse complemented sequences.\nrevcomp = function (seq)\n as.character(.bios$reverseComplement(.bios$DNAStringSet(seq)))\n", "meta": {"hexsha": "a738d2b2bcedccb02810afa87c3f6830f5c30285", "size": 4975, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/codon_usage.r", "max_stars_repo_name": "klmr/codons", "max_stars_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-12-19T00:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2015-12-19T00:54:46.000Z", "max_issues_repo_path": "scripts/codon_usage.r", "max_issues_repo_name": "klmr/codons", "max_issues_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-03-06T14:47:12.000Z", "max_issues_repo_issues_event_max_datetime": "2016-03-06T14:47:12.000Z", "max_forks_repo_path": "scripts/codon_usage.r", "max_forks_repo_name": "klmr/codons", "max_forks_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2836879433, "max_line_length": 88, "alphanum_fraction": 0.6822110553, "num_tokens": 1458, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3240334416018925}} {"text": "data <- read.csv('../data/MXvideos_cc50_202101.csv')\n\n# Eliminar columnas con ID vacio y categoryId\ndata <- data[!is.na(data$video_id),]\ndata <- data[!is.na(data$category_id),]\ndata <- data[!is.na(data$views),]\n\nempty_data <- is.na(data)\nsummary(empty_data)\n# to clean = likes, dislikes, comment_count\ndata$likes.mean <- ifelse(is.na(data$likes), mean(data$likes, na.rm = TRUE), data$likes)\ndata$dislikes.mean <- ifelse(is.na(data$dislikes), mean(data$dislikes, na.rm = TRUE), data$dislikes)\ndata$comment_count.mean <- ifelse(is.na(data$comment_count), mean(data$comment_count, na.rm = TRUE), data$comment_count)\nsummary(is.na(data))\n\nView(data)\n\nwrite.csv(data,\"../data/cleaned_MXvideos_cc50_202101.csv\", row.names = FALSE)", "meta": {"hexsha": "569cffeb17c0671d91c339d7cfcce6a6f0949b1b", "size": 724, "ext": "r", "lang": "R", "max_stars_repo_path": "pre-processing/data_clean.r", "max_stars_repo_name": "Cesarmosqueira/EB-2021-1-CC51", "max_stars_repo_head_hexsha": "58901207e830a627cf1f17ebea9d781854eae76b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "pre-processing/data_clean.r", "max_issues_repo_name": "Cesarmosqueira/EB-2021-1-CC51", "max_issues_repo_head_hexsha": "58901207e830a627cf1f17ebea9d781854eae76b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pre-processing/data_clean.r", "max_forks_repo_name": "Cesarmosqueira/EB-2021-1-CC51", "max_forks_repo_head_hexsha": "58901207e830a627cf1f17ebea9d781854eae76b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2222222222, "max_line_length": 120, "alphanum_fraction": 0.729281768, "num_tokens": 216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3240334416018925}} {"text": "##\n## Analysis code for Thomas et al. (2015): Saami reindeer herders cooperate with social group members and genetic kin\n##\n## This modifies the `standardize` function in the package `arm` to work with GEE models.\n##\n## Author: Matthew Gwynfryn Thomas\n##\n## {------- email --------}\n## {-- twitter --}\n## mgt@matthewgthomas.co.uk\n## {------ web -------}\n##\n##\n## Copyright (c) 2015 Matthew Gwynfryn Thomas\n## \n## This program is free software; you can redistribute it and/or modify\n## it under the terms of the GNU General Public License as published by\n## the Free Software Foundation; either version 2 of the License, or\n## (at your option) any later version.\n##\n## This program is distributed in the hope that it will be useful,\n## but WITHOUT ANY WARRANTY; without even the implied warranty of\n## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n## GNU General Public License for more details.\n##\n## You should have received a copy of the GNU General Public License along\n## with this program; if not, write to the Free Software Foundation, Inc.,\n## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n##\nstandardize.default <- function(call, unchanged=NULL,\n standardize.y=FALSE, binary.inputs=\"center\"){\n form <- call$formula\n varnames <- all.vars (form)\n n.vars <- length (varnames)\n #\n # Decide which variables will be unchanged\n #\n transform <- rep (\"leave.alone\", n.vars)\n if (standardize.y) {\n transform[1] <- \"full\"\n }\n for (i in 2:n.vars){\n v <- varnames[i]\n if (is.null(call$data)) {\n thedata <- get(v)\n }\n else {\n thedata <- get(as.character(call$data))[[v]]\n }\n if (is.na(match(v,unchanged))){\n num.categories <- length (unique(thedata[!is.na(thedata)]))\n if (num.categories==2){\n transform[i] <- binary.inputs\n }\n else if (num.categories>2 & is.numeric(thedata)){\n transform[i] <- \"full\"\n }\n }\n }\n #\n # New variable names:\n # prefix with \"c.\" if centered or \"z.\" if centered and scaled\n #\n varnames.new <- ifelse (transform==\"leave.alone\", varnames,\n ifelse (transform==\"full\", paste (\"z\", varnames, sep=\".\"),\n paste (\"c\", varnames, sep=\".\")))\n transformed.variables <- (1:n.vars)[transform!=\"leave.alone\"]\n \n \n #Define the new variables\n if (is.null(call$data)) {\n for (i in transformed.variables) {\n assign(varnames.new[i], rescale(get(varnames[i]), binary.inputs))\n }\n }\n else {\n newvars <- NULL\n for (i in transformed.variables) {\n assign(varnames.new[i], rescale(get(as.character(call$data))[[varnames[i]]], \n binary.inputs))\n newvars <- cbind(newvars, get(varnames.new[i]))\n }\n assign(as.character(call$data), cbind(get(as.character(call$data)), newvars))\n }\n \n # Now call the regression with the new variables\n \n call.new <- call\n L <- sapply (as.list (varnames.new), as.name)\n names(L) <- varnames\n call.new$formula <- do.call (substitute, list (form, L))\n formula <- as.character (call.new$formula)\n if (length(formula)!=3) stop (\"formula does not have three components\")\n formula <- paste (formula[2],formula[1],formula[3])\n formula <- gsub (\"factor(z.\", \"factor(\", formula, fixed=TRUE)\n formula <- gsub (\"factor(c.\", \"factor(\", formula, fixed=TRUE)\n call.new$formula <- as.formula (formula) \n return (eval (call.new))\n}\n\nstandardize.gee = function(object, unchanged=NULL, \n standardize.y=FALSE, binary.inputs=\"center\")\n {\n call <- object$call\n out <- standardize.default(call=call, unchanged=unchanged, \n standardize.y=standardize.y, binary.inputs=binary.inputs)\n return(out)\n }\n", "meta": {"hexsha": "5080261e33e41e64d61e6935820da8e15ce6444a", "size": 3834, "ext": "r", "lang": "R", "max_stars_repo_path": "data functions/standardize-gee.r", "max_stars_repo_name": "matthewgthomas/mosuo-kinship", "max_stars_repo_head_hexsha": "14996bc45585d46300dfb30b176dcce4775d4747", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data functions/standardize-gee.r", "max_issues_repo_name": "matthewgthomas/mosuo-kinship", "max_issues_repo_head_hexsha": "14996bc45585d46300dfb30b176dcce4775d4747", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data functions/standardize-gee.r", "max_forks_repo_name": "matthewgthomas/mosuo-kinship", "max_forks_repo_head_hexsha": "14996bc45585d46300dfb30b176dcce4775d4747", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.1743119266, "max_line_length": 117, "alphanum_fraction": 0.6124152321, "num_tokens": 950, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32403344160189246}} {"text": "#loading data\n\nDIFSLIKA <- vector(\"list\", 1000)\n\nfor (i in 1:1000) {\n str_path = paste0(\"Desktop/1000/input\", i, \"/DIFSLIKA.TXT\")\n temp_data_frame <- read.table(str_path, quote = \"\\\"\", comment.char = \"\")\n DIFSLIKA[[i]] <- temp_data_frame\n}\n\n#loading input parameters\n\nINPUT <- array(, dim = c(1000, 2))\n\nfor (i in 1:1000) {\n str_path = paste0(\"Desktop/1000/input\", i, \"/INPUT.TXT\")\n res <- readLines(str_path)\n INPUT[i, 1] <- as.double(res[1])\n INPUT[i, 2] <- as.double(res[2])\n}\n\n# finding max luminance\n\nmax_lum <- max(DIFSLIKA[[1]]$V3)\n\nfor (i in 2:1000) {\n max_cur <- max(DIFSLIKA[[i]]$V3)\n if (max_cur > max_lum)\n max_lum <- max_cur\n}\n\n# normalizing\n\nfor (i in 1:1000) {\n DIFSLIKA[[i]]$V3 <- DIFSLIKA[[i]]$V3 / max_lum\n}\n\n# transforming indexes\n\nfor (i in 1:1000) {\n DIFSLIKA[[i]]$V1 <- (DIFSLIKA[[i]]$V1 + 10000) / 100 + 1\n DIFSLIKA[[i]]$V2 <- (DIFSLIKA[[i]]$V2 + 10000) / 100 + 1\n}\n\n\n# creating matrices \n\nIMG_MAT <- array(, dim = c(1000, 201, 201))\n\nfor (i in 1:1000) {\n for (j in 1:nrow(DIFSLIKA[[i]])) {\n IMG_MAT[i, DIFSLIKA[[i]][j, 1], DIFSLIKA[[i]][j, 2]] <- DIFSLIKA[[i]][j, 3]\n }\n}\n\n# writing matrices to files\n\nfor (i in 1:1000) {\n str_path <- paste0(\"Desktop/1000_imgs/SLIKA\", i, \".txt\")\n write.table(IMG_MAT[i, ,], file = str_path, row.names = FALSE, col.names = FALSE)\n}", "meta": {"hexsha": "c16de16c789653e6a69b688fbe29bf4efc6a0d62", "size": 1310, "ext": "r", "lang": "R", "max_stars_repo_path": "utils/transforming_data/transforming.r", "max_stars_repo_name": "amnesia15/diffraction-image-rcg", "max_stars_repo_head_hexsha": "97b6aae7a9ff1c0222773a631870d709fc9fe995", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-05-04T00:36:34.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-04T00:36:34.000Z", "max_issues_repo_path": "utils/transforming_data/transforming.r", "max_issues_repo_name": "amnesia15/diffraction-image-rcg", "max_issues_repo_head_hexsha": "97b6aae7a9ff1c0222773a631870d709fc9fe995", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2020-02-23T10:27:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-11T02:44:54.000Z", "max_forks_repo_path": "utils/transforming_data/transforming.r", "max_forks_repo_name": "amnesia15/diffraction-image-rcg", "max_forks_repo_head_hexsha": "97b6aae7a9ff1c0222773a631870d709fc9fe995", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.4754098361, "max_line_length": 83, "alphanum_fraction": 0.6061068702, "num_tokens": 543, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647394, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3240334337346966}} {"text": "library(stm)\n\n#setwd() #set working directory to your home directory\n\n### Load Data\n\ndf <- read.csv(\"\")\ndf <- df[ which (df$code!=5),]\nnamesdf)\n)\n####################################\n######### Pre-processing ###########\n####################################\n\n\ntemp<-textProcessor(documents=df$text,metadata=df)\nmeta<-temp$meta\nvocab<-temp$vocab\ndocs<-temp$documents\nout <- prepDocuments(docs, vocab, meta)\ndocs<-out$documents\nvocab<-out$vocab\nmeta <-out$meta\nmeta$text <- as.character(meta$text)\n\n#head(meta)\n\nset.seed(02138)\n\n#plotRemoved(out$documents,lower.thresh=seq(1,200,by=100))\n\n##################################\n######### Choose Model ###########\n##################################\n\n\n### Model search across numbers of topics\n\nstorage <- manyTopics(docs,vocab,K=c(10,20,30,40,50,60),prevalence=~year,data=meta,runs=10,max.em.its=15)\n\nmean(storage$exclusivity[[3]])\n\n\nmod.10 <- storage$out[[1]]\nmod.20 <- storage$out[[2]] # most coherent\nmod.30 <- storage$out[[3]] \nmod.40 <- storage$out[[4]] # most exclusive \nmod.50 <- storage$out[[5]]\nmod.60 <- storage$out[[6]]\n\nmodel <- mod.60\n# Labels\nlabelTopics(model)\n\nsave.image(\"stm_allmodels.RData\")\n\n\nlabels <- labelTopics(model, n=20)\n\nwrite.csv(labels$prob, file=\"stm60_terms.csv\")\nwrite.csv(labels$frex, file=\"stm60_terms_frex.csv\")\n\nfindThoughts(model,texts=meta$text,n=5,topics=47)$docs[[1]]\n\nmeta$ID <- seq.int(nrow(df))\ntheta <- data.frame(model$theta)\ntheta$ID <- seq.int(nrow(theta))\n\ndf_all <- merge(meta, theta)\ndf_all$X1\n\nwrite.csv(df_all, file=\"stm60_theta.csv\")\n", "meta": {"hexsha": "cd07c5354450fd143c5abfa14e211db483fc463a", "size": 1540, "ext": "r", "lang": "R", "max_stars_repo_path": "03-UnsupervisedMachineLearning/03-gen_stm.r", "max_stars_repo_name": "lknelson/future-of-coding", "max_stars_repo_head_hexsha": "ac3620502d255c224623f2cc560f6fb780c5a91d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-10-30T06:18:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-27T22:40:53.000Z", "max_issues_repo_path": "03-UnsupervisedMachineLearning/03-gen_stm.r", "max_issues_repo_name": "lknelson/future-of-coding", "max_issues_repo_head_hexsha": "ac3620502d255c224623f2cc560f6fb780c5a91d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "03-UnsupervisedMachineLearning/03-gen_stm.r", "max_forks_repo_name": "lknelson/future-of-coding", "max_forks_repo_head_hexsha": "ac3620502d255c224623f2cc560f6fb780c5a91d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-13T20:45:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-16T23:46:33.000Z", "avg_line_length": 21.095890411, "max_line_length": 105, "alphanum_fraction": 0.6207792208, "num_tokens": 422, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3239922832841208}} {"text": "#' Calculate cluster similarity between clusters from different single cell samples.\n#'\n#' `cluster_fold_similarity()` returns a dataframe containing the best top similarities between all possible pairs of single cell samples.\n#'\n#' This function will calculate a similarity coeficient using the fold changes of shared genes among clusters of different samples/batches. The similarity coeficient\n#' is calculated using the dotproduct of every pairwise combination of Fold Changes between a source cluster i of sample n and all the target clusters in sample j.\n#'\n#' @param sce_list List. A list of single cell experiments. At least 2 single cell experiments are needed. The objects are expected to be subsets of the original data, \n#' containing the selected features (e.g. 2000 most variable genes).\n#' @param sample_names Character Vector. Specify the sample names, if not a number corresponding with its position on (sce_list).\n#' @param top_n Numeric. Specifies the number of target clusters with best similarity to report for each cluster comparison (default 1).\n#' @param top_n_genes Numeric. Number of top genes that explains the clusters similarity to report for each cluster comparison (default 1).\n#' \n#' @return The function returns a \\linkS4class{DataFrame} containing the best top similarities between all possible pairs of single cell samples. Column values are:\n#' \\tabular{ll}{\n#' \\code{similarity_value} \\tab The top similarity value calculated between dataset_l:cluster_l and dataset_r. \\cr\n#' \\tab \\cr\n#' \\code{sem} \\tab Standar Error of the Mean (SEM) of the mean of the values of the coeficient calculated for all genes. \\cr\n#' \\tab \\cr\n#' \\code{dataset_l} \\tab Sample left, the sample source which is being compared. \\cr\n#' \\tab \\cr\n#' \\code{cluster_l} \\tab Cluster left, the cluster source which is being compared. \\cr\n#' \\tab \\cr\n#' \\code{dataset_r} \\tab Sample right, the sample target which is being compared with dataset_l. \\cr\n#' \\tab \\cr\n#' \\code{cluster_r} \\tab Cluster right, the cluster target which is being compared with cluster_l. \\cr\n#' \\tab \\cr\n#' \\code{top_gene_conserved} \\tab The gene that showed most similar between cluster_l & cluster_r. \\cr\n#' }\n#' \n#' @export\ncluster_fold_similarity <- function(sce_list = NULL,\n sample_names = NULL,\n top_n = 1,\n top_n_genes = 1){\n if(is.null(sce_list) | (length(sce_list)<2)){\n stop(\"At least two Single Cell Experiments are needed for cluster comparison.\")\n }\n is_seurat <- FALSE\n is_sce <- FALSE\n # Function starts by loading dependencies\n if(class(sce_list[[1]]) == \"Seurat\" ){\n if (!requireNamespace(\"Seurat\", quietly = TRUE)) {\n stop(\"Package \\\"Seurat\\\" needed for this function to work. Please install it.\",\n call. = FALSE)}\n is_seurat <- TRUE\n is_sce <- FALSE\n }\n if(class(sce_list[[1]]) == \"SingleCellExperiment\" ){\n if (!requireNamespace(\"SingleCellExperiment\", quietly = TRUE)) {\n stop(\"Package \\\"SingleCellExperiment\\\" needed for this function to work. Please install it.\",\n call. = FALSE)}\n is_seurat <- FALSE\n is_sce <- TRUE\n }\n if (!is_seurat & !is_sce){\n stop(\"One or more objects in the input list is neither of class Seurat nor SingleDataExperiment.\")\n }\n summary_results <- data.frame(similarity_value=integer(),\n sem=integer(),\n dataset_l=integer(), \n cluster_l=integer(),\n dataset_r=integer(),\n cluster_r=integer(),\n top_gene_conserved=character(),\n stringsAsFactors=FALSE)\n # Select common genes in all samples \n features <- Reduce(intersect,lapply(sce_list,function(x){rownames(x)}))\n if(length(features) == 0){\n stop(\"No common genes between datasets. Please, select a subset of common variable genes among all datasets.\")\n }\n if(length(features) < 100){\n warning(paste(\"The number of common genes among datasets is: \",length(features),\". More than 100 common genes among all datasets is recomended.\",sep = \"\"))\n }\n # Control filtered level factors using droplevels\n for (i in length(sce_list)){\n if(is_sce){\n if(is.null(SingleCellExperiment::colLabels(sce_list[[i]]))){\n stop(paste(\"No clusters specified on colLabels on sample\",i))\n }\n SingleCellExperiment::colLabels(sce_list[[i]]) <- droplevels(SingleCellExperiment::colLabels(sce_list[[i]]))\n }\n if(is_seurat){\n Seurat::Idents(sce_list[[i]]) <- droplevels(Seurat::Idents(sce_list[[i]]))\n }\n }\n # Save the cluster name identification given by the user\n if(is_sce){cluster_names <- lapply(sce_list,function(x)levels(SingleCellExperiment::colLabels(x)))}\n if(is_seurat){cluster_names <- lapply(sce_list,function(x)levels(Seurat::Idents(x)))}\n # Calculate cluster FoldChange pairwise values:\n markers_sce_list <- list()\n # markers_sce_list <- lapply(sce_list,function(x){findMarkers(x,pval.type=\"all\")})\n message(\"Computing fold changes.\")\n if(is_sce){\n markers_sce_list <- lapply(sce_list,function(x){\n pairwise_cluster_fold_change(x = as.matrix(SingleCellExperiment::logcounts(x)),clusters = SingleCellExperiment::colLabels(x))\n })\n }\n if(is_seurat){\n markers_sce_list <- lapply(sce_list,function(x){\n pairwise_cluster_fold_change(x = as.matrix(Seurat::GetAssayData(x, slot = \"data\")),clusters = Seurat::Idents(x))\n })\n }\n # Main loop - samples\n for (i in 1:(length(markers_sce_list))){\n # Pick a sample in ascending order\n y <- markers_sce_list[[i]]\n for (j in seq_along(y)){\n # We choose a root cluster and compare it with clusters of samples i+1+...+n\n root <- y[[j]]\n # for (k in seq(from=i+1,to=length(markers_sce_list),by = 1)){\n for (k in seq(from=1,to=length(markers_sce_list),by = 1)){\n if(k == i)next() # If root and target samples are the same, we go for the next sample and skip this loop\n sce_comparative <- markers_sce_list[[k]]\n results <- data.frame(similarity_value=integer(),\n sem=integer(),\n dataset_l=integer(), \n cluster_l=integer(),\n dataset_r=integer(),\n cluster_r=integer(),\n top_gene_conserved=character(),\n stringsAsFactors=FALSE)\n for(n in seq_along(sce_comparative)){\n # The sample for comparing will be the sample_i+1\n comparative <- sce_comparative[[n]]\n # Comparative = single cluster from sample_i+1\n message(paste(\"Comparing [ cluster\",cluster_names[[i]][j],\"] from dataset:\",i,\"with [ cluster:\",cluster_names[[k]][n] ,\"] from dataset:\",k))\n # Create a matrix A and B to compute the dotproduct of all possible combinations of cluster comparison FoldChanges for each gene\n mat <- foldchange_composition(root[features,],comparative[features,])\n mat_colmean <- colMeans(mat)\n top_genes <- head(colnames(mat)[order(mat_colmean,decreasing = T)],n=top_n_genes)\n n_negative <- sum(mat_colmean < 0)\n n_positive <- sum(mat_colmean > 0)\n sem <- round(sd(mat_colmean) / sqrt(n_negative + n_positive), digits = 4)\n weight <- round( sqrt(abs(n_negative - n_positive) / (n_negative + n_positive)), digits = 4) # Weight based on the number of concordant-discordant FCs\n # We divide the similarity by the SEM value -> then we scale it using sqrt and conserve the sign\n # similarity_weighted <- sqrt(abs(sum(mat_colmean)) / sem) * sign(sum(mat_colmean))\n similarity_weighted <- sqrt(abs(sum(mat_colmean))) * weight * sign(sum(mat_colmean))\n # Save results\n for (g in top_genes){\n results[nrow(results)+1,] <- list(\n similarity_weighted, # Similarity_value\n sem, # Standar Error of the Mean\n i, # dataset_l\n cluster_names[[i]][j], # Cluster_l (left; source of comparison) -> j corresponding to the loop\n k, # dataset_r\n cluster_names[[k]][n], # Cluster_r (right; target of comparison) -> n corresponding to the internal loop\n g) # Top gene conserved\n }\n }\n results <- data.table::rbindlist(by(results,results$cluster_l,function(x){head(x[order(x[,\"similarity_value\"],decreasing = T),], n=top_n*top_n_genes)}))\n summary_results <- rbind.data.frame(summary_results,results)\n }\n }\n }\n message(\"Ploting graph using the similarity values of clusters.\")\n plot_clusters_graph(similarity.table = summary_results)\n message(\"Returning similarity table.\")\n return(summary_results)\n}\n", "meta": {"hexsha": "9d660c7764d4343cdb31bed5852d807a36514aaa", "size": 8971, "ext": "r", "lang": "R", "max_stars_repo_path": "R/cluster_fold_similarity.r", "max_stars_repo_name": "OscarGVelasco/ClusterFoldSimilarity", "max_stars_repo_head_hexsha": "d95e4e3750bad7c70cc7cf974f2a732f1af367e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-30T11:01:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T11:01:12.000Z", "max_issues_repo_path": "R/cluster_fold_similarity.r", "max_issues_repo_name": "OscarGVelasco/ClusterFoldSimilarity", "max_issues_repo_head_hexsha": "d95e4e3750bad7c70cc7cf974f2a732f1af367e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/cluster_fold_similarity.r", "max_forks_repo_name": "OscarGVelasco/ClusterFoldSimilarity", "max_forks_repo_head_hexsha": "d95e4e3750bad7c70cc7cf974f2a732f1af367e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 55.7204968944, "max_line_length": 168, "alphanum_fraction": 0.647530933, "num_tokens": 2088, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.5, "lm_q1q2_score": 0.32389911577562447}} {"text": "# importing libraries\nlibrary(plyr)\nlibrary(foreign)\nlibrary(RWeka)\nlibrary(dplyr)\nlibrary(caret)\nlibrary(xgboost)\n\n# reading datasets\nesl = read.arff(\"esl.arff\")\nera = read.arff(\"era.arff\")\nlev = read.arff(\"lev.arff\")\nswd = read.arff(\"swd.arff\")\n\n# function to create dataset train and test partitions\ncreatePartitions = function(dataset){\n set.seed (20)\n # let's change class attribute name to \"class\"\n colnames(dataset)[length(dataset)] = \"class\"\n # we use 66% for train an the rest for test\n trainIndex = sample(1:nrow(dataset), (66*nrow(dataset))/100)\n # returning train and test\n list(as.data.frame(dataset[trainIndex,]),as.data.frame(dataset[-trainIndex,]))\n}\n\n\n# function to create n-1 models to ordinal classification\nordinalDatasets = function(dataset)\n{\n # extracting the original class attribute\n # (assuming that class attribute is in the end)\n originalClassAttribute = dataset[,dim(dataset)[2]]\n \n # inspecting which classes contains class attribute of the dataset \n classes = sort(unique(originalClassAttribute))\n \n # we select the first one as current class\n currentClasses = classes[1]\n \n # let's remove it from classes\n classes = classes[-1]\n \n # we only need to make numberOfClasses-1 models, so last \"classes\" \n # item won't be considered.\n # we create a list containing each class combination attribute.\n newClassAttributes = originalClassAttribute\n for(class in classes){\n newClassAttributes = cbind(newClassAttributes,Newclass = ifelse(originalClassAttribute%in%currentClasses, 0, 1))\n currentClasses = c(currentClasses,class)\n }\n \n # we remove the original class attribute\n newClassAttributes = newClassAttributes[,-1]\n \n # let's create the models\n models = apply(newClassAttributes, 2, function(class){\n # we assign the new class attribute\n dataset[,dim(dataset)[2]] = class\n # xgboost only accepts matrix or xgb.DMatrix\n dataset = xgb.DMatrix(as.matrix(dataset[,-dim(dataset)[2]]), label=dataset[,dim(dataset)[2]])\n # let's call de model\n xgboost(data = dataset, monotone_constraints=1,nrounds = 2)\n })\n}\n\n# function to predict an instance (or set of instances) class\nmonotonePrediction = function(models, newInstances,classes){\n \n # firstly we get the probabilities of each instance\n probabilities = lapply(models, function(model){as.numeric(predict(model,as.matrix(newInstances[,-ncol(newInstances)])) > 0.5)})\n #probabilities = lapply(models, function(model){predict(model,as.matrix(newInstances[,-ncol(newInstances)]))})\n print(probabilities)\n \n # we transform the list into data.frame\n probs = do.call(\"cbind\",probabilities)\n \n # let's predict\n indexOfPredictedClasses = apply(probs, 1, function(p){\n sum(p)+1\n })\n print(indexOfPredictedClasses)\n classes[indexOfPredictedClasses]\n}\n\n\n####################################################################################################\n####################################################################################################\n\n# firstly we partition datasets\n# Partitiors[[1]] will contain train partition\n# Partitiors[[2]] will contain test partition\neslPartitions = createPartitions(esl)\neraPartitions = createPartitions(era)\nlevPartitions = createPartitions(lev)\nswdPartitions = createPartitions(swd)\n\n\n# let's create the models with the train datasets\neslModels = ordinalDatasets(eslPartitions[[1]])\neraModels = ordinalDatasets(eraPartitions[[1]])\nlevModels = ordinalDatasets(levPartitions[[1]])\nswdModels = ordinalDatasets(swdPartitions[[1]])\n\n# we predict using test dataset without class attribute\neslPredictedResults = monotonePrediction(eslModels, eslPartitions[[2]], sort(unique(esl$out1)))\neraPredictedResults = monotonePrediction(eraModels, eraPartitions[[2]], sort(unique(era$out1)))\nlevPredictedResults = monotonePrediction(levModels, levPartitions[[2]], sort(unique(lev$Out1)))\nswdPredictedResults = monotonePrediction(swdModels, swdPartitions[[2]], sort(unique(swd$Out1)))\n\n# now we get the accuracy results\neslAccuracy <- sum(eslPredictedResults == eslPartitions[[2]]$class)/length(eslPredictedResults)\neraAccuracy <- sum(eraPredictedResults == eraPartitions[[2]]$class)/length(eraPredictedResults)\nlevAccuracy <- sum(levPredictedResults == levPartitions[[2]]$class)/length(levPredictedResults)\nswdAccuracy <- sum(swdPredictedResults == swdPartitions[[2]]$class)/length(swdPredictedResults)\n\n# let's see how this model fits the datasets\neslAccuracy\neraAccuracy\nlevAccuracy\nswdAccuracy\n\n# as we can see, accuracy values using ordinal classification method is not really good, so \n# it may be because datasets are note made to apply this classification technique", "meta": {"hexsha": "60319db05ca2e21a2e660d8f44251b88b29ca438", "size": 4668, "ext": "r", "lang": "R", "max_stars_repo_path": "monoxgboost.r", "max_stars_repo_name": "CarlosSequi/DataMining-OrdinalMonotonicClassification", "max_stars_repo_head_hexsha": "4a4c5055b37540f5394779b89746d21964b5e727", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-01-17T10:49:28.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-17T10:49:28.000Z", "max_issues_repo_path": "monoxgboost.r", "max_issues_repo_name": "CarlosSequi/DataMining-OrdinalMonotonicClassification", "max_issues_repo_head_hexsha": "4a4c5055b37540f5394779b89746d21964b5e727", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "monoxgboost.r", "max_forks_repo_name": "CarlosSequi/DataMining-OrdinalMonotonicClassification", "max_forks_repo_head_hexsha": "4a4c5055b37540f5394779b89746d21964b5e727", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.9512195122, "max_line_length": 129, "alphanum_fraction": 0.7249357326, "num_tokens": 1152, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.32388227054517593}} {"text": "# GetAuditVsNotData.r\nsource(\"../common/DataUtil.r\")\nlibrary(ggplot2)\nsource(\"../common/PlotUtil.r\")\n\n# Params\nsPlotFile <- \"auditvsnot.png\"\nfnGroupBy <- function(dfIn) {group_by(dfIn, copies, lifem, auditfrequency)}\nfnSubset <- function(dfIn) {subset(dfIn, copies==5 & lifem<=1000)}\nsTitleLine <- ( \"Auditing dramatically decreases \"\n %+% \"permanent document losses \"\n %+% \"over a wide range \"\n %+% \"\\n\"\n %+% \"\\n(Copies=5, \"\n %+% \"annual total auditing vs no auditing, \"\n %+% \"duration = 10 years)\"\n )\nsLegendLabel <- \"Audited? Y/N\"\nlLegendItemLabels <- c(\"Not audited\", \"Yes, audited\")\nsXLabel <- (\"1MB sector half-life (megahours)\"\n %+% \" (lower error rate =====>)\")\nsYLabel <- (\"permanent document losses (%)\")\n\n\n# G E T D A T A \n# Get the data into the right form for these plots.\nalldat.df <- fndfGetGiantDataRaw(\"\")\nnewdat <- alldat.df %>% fnGroupBy() %>% \nsummarize(mdmlosspct=round(midmean(lost/docstotal)*100.0, 2), n=n()) %>%\nfnSubset()\ntrows <- newdat\n\n\n# P L O T D A T A \n\ngp <- ggplot(data=trows\n , aes(x=lifem,y=safe(mdmlosspct), color=factor(auditfrequency))\n ) \ngp <- gp + labs(color=sLegendLabel)\n\ngp <- fnPlotLogScales(gp, x=\"YES\", y=\"YES\"\n ,xbreaks=c(2,5,10,100,1000)\n ,ybreaks=c(0.01,0.10,1.00)\n )\ngp <- gp + geom_line(\n size=3\n , show.legend=TRUE\n )\ngp <- gp + geom_point(data=trows\n , size=6\n , show.legend=TRUE\n , color=\"black\"\n ) \n\ngp <- gp + theme(legend.position=c(0.8,0.7))\ngp <- gp + theme(legend.background=element_rect(fill=\"lightgray\", \n size=0.5, linetype=\"solid\"))\ngp <- gp + theme(legend.key.size=unit(0.3, \"in\"))\ngp <- gp + theme(legend.key.width=unit(0.6, \"in\"))\ngp <- gp + theme(legend.text=element_text(size=16))\ngp <- gp + theme(legend.title=element_text(size=14))\ngp <- gp + scale_color_discrete(labels=lLegendItemLabels)\n\ngp <- fnPlotTitles(gp\n , titleline=sTitleLine\n , xlabel=sXLabel\n , ylabel=sYLabel\n ) \n# Label the percentage lines out on the right side.\nxlabelposition <- log10(800)\ngp <- fnPlotPercentLine(gp, xloc=xlabelposition)\ngp <- fnPlotMilleLine(gp, xloc=xlabelposition)\ngp <- fnPlotSubMilleLine(gp, xloc=xlabelposition)\n\nplot(gp)\nfnPlotMakeFile(gp, sPlotFile)\n\n# Unwind any remaining sink()s to close output files. \nwhile (sink.number() > 0) {sink()}\n", "meta": {"hexsha": "f75c45c755f5772034a2d30dc747aad842866241", "size": 2607, "ext": "r", "lang": "R", "max_stars_repo_path": "pictures/AUDITVSNOT/GetAuditVsNotData.r", "max_stars_repo_name": "MIT-Informatics/PreservationSimulation", "max_stars_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_stars_repo_licenses": ["X11"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2016-08-24T05:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-12T16:44:48.000Z", "max_issues_repo_path": "pictures/AUDITVSNOT/GetAuditVsNotData.r", "max_issues_repo_name": "MIT-Informatics/PreservationSimulation", "max_issues_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_issues_repo_licenses": ["X11"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-20T02:55:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-20T02:55:37.000Z", "max_forks_repo_path": "pictures/AUDITVSNOT/GetAuditVsNotData.r", "max_forks_repo_name": "MIT-Informatics/PreservationSimulation", "max_forks_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_forks_repo_licenses": ["X11"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5875, "max_line_length": 75, "alphanum_fraction": 0.5738396624, "num_tokens": 742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.32388227054517593}} {"text": "x1 = c(.09, .19, .2, .25, .3, .4)\ny1 = c(.09, .43, .3, .1, .4, .25)\nx2 = c(.68, .7, .82, .66, .91, 1.02)\ny2 = c(.6, 1, .9, .7, 1, .85)\n\nplot(x1, y1, xlim=range(c(x1,x2)), ylim=range(c(y1,y2)), col=\"blue\", xlab=\"x\", ylab=\"y\", xaxt=\"n\", yaxt=\"n\")\npoints(x2, y2, pch=4, col=\"red\")\n\nset.seed(1)\nrand_indices = sample(1:12)\nx = c(x1,x2)\ny = c(y1,y2)\nxr1 = vector(length=6)\nxr2 = vector(length=6) \nyr1 = vector(length=6) \nyr2 = vector(length=6) \nfor (i in 1:6) {\n\txr1[i] <- x[rand_indices[i]]\n\tyr1[i] <- y[rand_indices[i]]\n}\nfor (i in 1:6) {\n\txr2[i] <- x[rand_indices[i+6]]\n\tyr2[i] <- y[rand_indices[i+6]]\n}\n\nquartz()\nplot(xr1, yr1, xlim=range(c(xr1,xr2)), ylim=range(c(yr1,yr2)), col=\"blue\", xlab=\"x\", ylab=\"y\", xaxt=\"n\", yaxt=\"n\")\npoints(xr2, yr2, pch=4, col=\"red\")\n", "meta": {"hexsha": "b45a2287cbb438e0cfd09b382cf2136425f58ad0", "size": 762, "ext": "r", "lang": "R", "max_stars_repo_path": "R_Code/Making_Figures/permtest_expl.r", "max_stars_repo_name": "Halmoni100/miRNA_dataPlus", "max_stars_repo_head_hexsha": "47f145fbfc9e33a18ae395bfa89e2904e5b7e693", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R_Code/Making_Figures/permtest_expl.r", "max_issues_repo_name": "Halmoni100/miRNA_dataPlus", "max_issues_repo_head_hexsha": "47f145fbfc9e33a18ae395bfa89e2904e5b7e693", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R_Code/Making_Figures/permtest_expl.r", "max_forks_repo_name": "Halmoni100/miRNA_dataPlus", "max_forks_repo_head_hexsha": "47f145fbfc9e33a18ae395bfa89e2904e5b7e693", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.275862069, "max_line_length": 114, "alphanum_fraction": 0.5564304462, "num_tokens": 368, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.32388227054517593}} {"text": "#We've now added interactivity to the plot. The user can (a) specify the Variable (Var) scenario they wish to plot the PCA of, and (b) adjust the text's cex values. The server.r script now includes code to add colors that correspond to each selected variable's parameter values (param), as well as a legend.\n\nlibrary(shiny)\t#First load shiny library\nload(\"../pcas.RDATA\")\t#Load data\n\n#Define a server for the Shiny app\nshinyServer(function(input, output) {\n\n\t#Create a reactive Shiny plot to send to the ui.r called \"pcaplot\"\n\toutput$pcaplot <- renderPlot({\n\t\t#Match user's variable input selection by subsetting for when foo$Var==input$var\n\t\tfbar<-droplevels(foo[which(foo$Var==input$var),])\n\t\t\n\t\t#Generate colors to correspond to the different Variables' parameters. E.g., for the variable \"colless\", different colors for \"loIc\",\"midIc\", and \"hiIc\"\n\t\tcols<-c(\"#1f77b4\",\"#ff7f0e\",\"#2ca02c\")[1:length(levels(fbar$param))]\n\t\t\n\t\t#Render the scatterplot of PCA\n\t\tplot(PC2 ~ PC1, data=fbar, type=\"n\", xlab=\"PC1\", ylab=\"PC2\")\n\n\t\t#For each of the different parameter values (different colors), plot the text names of the metrics\n\t\tfor(i in levels(fbar$param)){\n\t\t\tfoo2<-subset(fbar,param==i)\n\t\t\ttext(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)], cex=input$cexSlider)\n\t\t}\n\t\t#Add a legend for the different parameter values (with corresponding colors) of the selected Variable\n\t\tlegend(\"topright\", fill=cols, legend=levels(fbar$param))\n })\n})\n\n#TASKS:\n#1. Depending on the variable that people choose (input$var), we now want to display the chosen variable's parameter (param) values as a check box group. Even though logically we would begin with the checkboxGroupInput function on the ui.R script to display check box options, first remember what the capabilities of the user side (ui.R) are. ui.R cannot evaluate/process information, but only displays output values calculated on the server side (server.R). In other words, the ui.R only takes in the values a user specifies and then sends this information to be evaluated/processed on the server side. \n#\tSo to create a checkboxgroup of param values that will change based on what the user selects for a variable (input$var), our first step is to evaluate what the user selects as a variable. This evaluation can only take place on the server side. We therefore need to create an object on the server side (output$paramchkbxgrp) that will evaluate the user's Variable selection (input$var), which will then decide which check box options to display. This decision of what to display is then fed back to the ui.R with the object uiOutput. \n\n#\t1.a. Depending on what Variable is selected by the user (input$var), generate a different set of check box options to display (with the function checkboxGroupInput). For instance, if \"colless\" is selected as the variable, display the parameters values that correspond to \"colless\", e.g., loIc, midIc and hiIc. This switch function takes the given value (input$var) and then evaluates it. When input$var==\"colless\", then it evaluates the checkboxGroupInput function with the choices of c(\"Low\" = \"loIc\", \"Mid\" = \"midIc\", \"High\" = \"hiIc\"). \n#Use the following script as a framework for the paramchkbxgrp object:\n\n\t#\toutput$paramchkbxgrp <- renderUI({\n\t\t\t# if (is.null(input$var))\n\t\t\t # return()\n\t\t## Depending on input$var, we'll generate a different\n\t\t## UI component and send it to the client.\n\t\t\t# switch(input$var,\n\t\t\t# \"colless\" = checkboxGroupInput(inputId = \"PARAMS\", label = \"\",\n\t\t\t\t# choices = c(\n\t\t\t\t\t# \"Low\" = \"loIc\",\n\t\t\t\t\t# \"Mid\" = \"midIc\",\n\t\t\t\t\t# \"High\" = \"hiIc\"\n\t\t\t\t\t# ),\n\t\t\t\t# selected = c(\"loIc\",\"midIc\",\"hiIc\")),\t\n\t\t\t##Fill in the ellipses with the corresponding parameter values \n\t\t\t# \"numsp\" = checkboxGroupInput(...),\n\t\t\t# \"spatial\" = checkboxGroupInput(...),\n\t\t\t# )\n\t\t # })\n\t\t\t\n\t# })\n#2. When certain checkboxes are unselected, display the text as grey with some transparency. To do this, within the renderPlot function, adjust the cols object such that the unselected are black with high transparency (display as grey). To do this for the cols object, subset for when input$PARAMS is not in levels(fbar$PARAMS):\n#\tcols[which(!(levels(fbar$param) %in% input$PARAMS))]\n#Then assign all of these instances as black rgb(0,0,0, maxColorValue=255) with very high transparency rgb(0,0,0, alpha =25, maxColorValue=255):\n#\tcols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)\n\n# #HINTS\n# 1.a \tDrop-down menu to select Factor\t\n\t# output$paramchkbxgrp <- renderUI({\n\t\t# if (is.null(input$var))\n\t\t # return()\n\n\t\t# # Depending on input$var, we'll generate a different\n\t\t# # UI component and send it to the client.\n\t\t# switch(input$var,\n\t\t # \"colless\" = checkboxGroupInput(\"PARAMS\", \"\",\n\t\t\t# choices = c(\"Low\" = \"loIc\",\n\t\t\t\t\t\t# \"Mid\" = \"midIc\",\n\t\t\t\t\t\t# \"High\" = \"hiIc\"\n\t\t\t\t\t\t# ),\n\t\t\t# selected = c(\"loIc\",\"midIc\",\"hiIc\")),\n\t\t # \"numsp\" = checkboxGroupInput(\"PARAMS\", \"\",\n\t\t\t# choices = c(\"16 Species\" = 16,\n\t\t\t\t\t\t# \"64 Species\" = 64,\n\t\t\t\t\t\t# \"256 Species\" = 256),\n\t\t\t# selected = c(16,64,256)),\n\t\t # \"spatial\" = checkboxGroupInput(\"PARAMS\", \"\",\n\t\t\t# choices = c(\"True\" = \"TRUE\",\n\t\t\t\t\t\t# \"False\" = \"FALSE\"),\n\t\t\t# selected = c(\"TRUE\",\"FALSE\")),\n\t\t# )\n\t # })\n\n\n# 2. \n# output$pcaplot <- renderPlot({\n\t\t# #Match user's variable input selection by subsetting for when foo$Var==input$var\n\t\t# fbar<-droplevels(foo[which(foo$Var==input$var),])\n\t\t\n\t\t# #Generate colors to correspond to the different Variables' parameters. E.g., for the variable \"colless\", different colors for \"loIc\",\"midIc\", and \"hiIc\"\n\t\t# cols<-c(\"#1f77b4\",\"#ff7f0e\",\"#2ca02c\")[1:length(levels(fbar$param))]\n\t\t\n\t\t# #***********************ADD THIS TO EXISTING SCRIPT *************************\n\t\t# #If the param is not selected, set the color value to grey with transparency\n\t\t# cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)\n\t\t# #****************************************************************************\n\t\t\n\t\t# #Render the scatterplot of PCA\n\t\t# plot(PC2 ~ PC1, data=fbar, type=\"n\", xlab=\"PC1\", ylab=\"PC2\")\n\n\t\t# #For each of the different parameter values (different colors), plot the text names of the metrics\n\t\t# for(i in levels(fbar$param)){\n\t\t\t# foo2<-subset(fbar,param==i)\n\t\t\t# text(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)], cex=input$cexSlider)\n\t\t# }\n\t\t\n\t\t# #Add a legend for the different parameter values (with corresponding colors) of the selected Variable\n\t\t# legend(\"topright\", fill=cols, legend=levels(fbar$param))\n\t\t\n # })\n\n\n", "meta": {"hexsha": "b5acb047110d7a42e5b03a95d1152df503c33b4c", "size": 6574, "ext": "r", "lang": "R", "max_stars_repo_path": "lessons/r/shiny/3/server.r", "max_stars_repo_name": "vmzhang/studyGroup", "max_stars_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 105, "max_stars_repo_stars_event_min_datetime": "2015-06-22T15:23:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T12:20:09.000Z", "max_issues_repo_path": "lessons/r/shiny/3/server.r", "max_issues_repo_name": "vmzhang/studyGroup", "max_issues_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 314, "max_issues_repo_issues_event_min_datetime": "2015-06-18T22:10:34.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T16:47:52.000Z", "max_forks_repo_path": "lessons/r/shiny/3/server.r", "max_forks_repo_name": "vmzhang/studyGroup", "max_forks_repo_head_hexsha": "d49ddc32bdd7ac91d73cb8890154e1965d1dcfd0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 142, "max_forks_repo_forks_event_min_datetime": "2015-06-18T22:11:53.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-03T16:14:43.000Z", "avg_line_length": 55.7118644068, "max_line_length": 604, "alphanum_fraction": 0.6837541831, "num_tokens": 1900, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.32388227054517593}} {"text": "#!/usr/bin/Rscript --slave\n\n#usage: chmod +x bibliobypubmed.r \n#usage: ./bibliobypubmed.r \"your name\"\n\nargv<-commandArgs(TRUE)\nquery<-toString(argv[1])\n\n\n#query <- \"your name\"\n\n#install require R packages if necessary and load them\nif(!require(RISmed)){\n install.packages(\"RISmed\")\n library(RISmed)\n}\n\nif(!require(ggplot2)){\n install.packages(\"ggplot2\")\n library(ggplot2)\n}\n\nif(!require(dplyr)){\n install.packages(\"dplyr\")\n library(dplyr)\n}\n\n\n#library(RISmed)\n#library(ggplot2)\n\n#get the time machine\ntime<-toString(Sys.time())\n\ncat(\"The Query concerned: \", query, \"\\n\")\n\n#perform the pubmed query with the input name\nsearch <- EUtilsSummary(query, type=\"esearch\",db = \"pubmed\", retmax=30000)\nnb <- QueryCount(search)\n\nnb<-toString(nb)\ncat (\"Number of articles found on Pubmed: \",nb , \"\\n\")\n\nrecords <- EUtilsGet(search)\nyears <- YearPubmed(records)\npubs_count <- as.data.frame(table(years))\n\n#print the query time on the screen\ncat(time, \"\\n\")\nstr(pubs_count)\nwrite.table(pubs_count,file=\"bibliobyyear.txt\")\npdf('bibliobyyear.pdf')\n \n#build the graph barplot with ggplot2\nggplot(pubs_count,aes(years, Freq,fill=years)) + geom_bar (stat=\"identity\") +\nxlab(\"Year\") +\nylab(\"PUBMED articles by year\")+\ncoord_flip() +\nggtitle(query, time)+\ntheme_classic()+\nlabs(caption = nb )+\ntheme(legend.position=\"none\")+\ntheme(\n plot.title = element_text(color = \"red\", size = 20, face = \"bold\"),\n plot.subtitle = element_text(color = \"blue\", size = 18),\n plot.caption = element_text(color = \"darkgreen\", face = \"italic\", size = 16)\n)\ndev.off()\n\n#journal analysis\njournal <- MedlineTA(records)\njournal_count <- as.data.frame(table(journal))\njournal_count_order <- journal_count[order(-journal_count[,2]),]\nstr(journal_count_order)\nwrite.table(journal_count_order, file=\"bibliobyjournal.txt\")\n\n#attribute levels to reorder journals with their frequencies\njournal_count$journal <- factor(journal_count$journal, levels = journal_count$journal[order(journal_count$Freq)])\n\n#make journal graph\npdf('bibliobyjournal.pdf')\nggplot(journal_count,aes(journal, Freq, fill=journal)) + geom_bar (stat=\"identity\") +\nxlab(\"Journals\") +\nylab(\"Counts\")+\ncoord_flip() +\nggtitle(query, time)+\ntheme_classic()+\nlabs(caption = nb )+\ntheme(axis.text.x = element_text(angle = 90, hjust = 1))+\ntheme(legend.position=\"none\")+\ntheme(\n plot.title = element_text(color = \"red\", size = 20, face = \"bold\"),\n plot.subtitle = element_text(color = \"blue\", size = 18),\n plot.caption = element_text(color = \"darkgreen\", face = \"italic\", size = 16)\n)\ndev.off()\n\n\n\n\n", "meta": {"hexsha": "8afa9cdc4c2b10c89716cb18e957cfabb3408502", "size": 2537, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/bibliobypubmed.r", "max_stars_repo_name": "cdesterke/bibliography", "max_stars_repo_head_hexsha": "603ac5d5fefbc8324cb7c6bcb9e5594cb33008fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/bibliobypubmed.r", "max_issues_repo_name": "cdesterke/bibliography", "max_issues_repo_head_hexsha": "603ac5d5fefbc8324cb7c6bcb9e5594cb33008fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/bibliobypubmed.r", "max_forks_repo_name": "cdesterke/bibliography", "max_forks_repo_head_hexsha": "603ac5d5fefbc8324cb7c6bcb9e5594cb33008fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.1188118812, "max_line_length": 113, "alphanum_fraction": 0.7126527395, "num_tokens": 712, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.61878043374385, "lm_q1q2_score": 0.3238822705451759}} {"text": "## Need to generate a heatmap that has all 4 celltypes\n## at the 1, 2,and 18hr marks. Need to generate one\n## heatmap per timepoint and readout. The readouts are\n## Smad23, and CTNNB1.\n\ntimepoints = c('1hr','2hr','18hr')\ndatafiles = c('data/TI04.60_summaryStats.csv',\n 'data/TI04.120_summaryStats.csv',\n 'data/TI16.18_summaryStats.csv')\nreps = 1\nreadouts = c('Smad23','CTNNB1')\ncolors = matrix(c(rgb(0,(0:10)*.1,0),\n rgb((0:10)*.1,0,0)),ncol=2)\ncolnames(colors)=readouts\nchannels = 2:3\ncelltypes = c('HCEC','SKMEL2','MALME3M','IEC6')\ntreatments = c('ctrl+ctrl','TGFB3+ctrl',\n 'ctrl+Wnt3A','TGFB3+Wnt3A')\nsetToOne = c('TGFB3+ctrl','ctrl+Wnt3A')\nfeature = 'mean_nucleus'\nminHeat = -.2\nmaxHeat = 1.5\n\nfor( rIdx in 1:length(readouts) ){\n heatmap = matrix(0,nrow=reps*length(treatments),\n ncol=length(timepoints)*length(celltypes))\n \n for( tIdx in 1:length(timepoints)){\n data = read.table(datafiles[tIdx],sep=',',header=T,\n stringsAsFactors=F )\n for( cIdx in 1:length(celltypes) ){\n readout = readouts[ rIdx]\n channel = channels[ rIdx]\n marker = paste('marker',channel,sep='.')\n celltype = celltypes[cIdx]\n \n subdata = data[data$celltype==celltype &\n data$marker.2==readouts[1] &\n data$marker.3==readouts[2] &\n data$treatment %in% treatments,]\n \n # Convert to ctrl-based Z-scores\n thisFeature = paste(feature,'median',channel,sep='.')\n ctrls = subdata[subdata$treatment == 'ctrl+ctrl',\n thisFeature,]\n subdata[,thisFeature] = subdata[,thisFeature]-mean(ctrls)\n subdata[,thisFeature] = subdata[,thisFeature]/\n mean(subdata[subdata$treatment==setToOne[rIdx],thisFeature])\n \n # Get it into the correct matrix coords\n for( treatIdx in 1:length(treatments)){\n treatment = treatments[treatIdx]\n rows = (treatIdx-1)*reps+1:reps\n cols = (tIdx-1)*length(celltypes)+ cIdx\n heatmap[rows,cols] = mean(subdata[subdata$treatment==treatment,thisFeature])\n \n }\n }\n }\n tempMap = heatmap\n tempMap[tempMap < minHeat] = minHeat\n tempMap[tempMap > maxHeat] = maxHeat\n heatmap(tempMap,Rowv=NA,Colv=NA,col=colors[,readout],scale='none',\n labRow = rep(treatments,each=reps),\n labCol = paste(rep(celltypes,length(timepoints)),\n rep(timepoints,each=length(celltypes))))\n}", "meta": {"hexsha": "2bd9189d43aa533f2e6bfb837087940d9053b055", "size": 2548, "ext": "r", "lang": "R", "max_stars_repo_path": "FIGS/insulation/data/wntTgfbInsulation.r", "max_stars_repo_name": "adam-coster/dissertation", "max_stars_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FIGS/insulation/data/wntTgfbInsulation.r", "max_issues_repo_name": "adam-coster/dissertation", "max_issues_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FIGS/insulation/data/wntTgfbInsulation.r", "max_forks_repo_name": "adam-coster/dissertation", "max_forks_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.0298507463, "max_line_length": 84, "alphanum_fraction": 0.5985086342, "num_tokens": 753, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300449389326, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.3238781386878833}} {"text": "library(speedyseq)\nlibrary(phyloseq2ML)\nlibrary(futile.logger)\nflog.threshold(TRACE)\n\ndata(TNT_communities)\n\n# modify phyloseq object\nTNT_communities2 <- add_unique_lineages(TNT_communities)\ntestps <- standardize_phyloseq_headers(\n phyloseq_object = TNT_communities2, taxa_prefix = \"ASV\", use_sequences = FALSE)\n\n# translate ASVs to genus\nlevels_tax_dictionary <- c(\"Family\", \"Genus\")\ntaxa_vector_list <- create_taxonomy_lookup(testps, levels_tax_dictionary)\ntranslate_ID(ID = c(\"ASV02\", \"ASV17\"), tax_rank = \"Genus\", taxa_vector_list)\n\n# phyloseq objects as list\nsecond_phyloseq_object <- subset_samples(testps, Technical_replicate == 1)\nsubset_list <- list(\n vignette_V4_surface = testps,\n vignette_V4_replicate2 = second_phyloseq_object\n)\n\n# define subsetting parameters\nselected_taxa_1 <- setNames(c(\"To_Genus\", \"To_Family\"), c(\"Genus\", \"Family\"))\nsubset_list_rel <- to_relative_abundance(subset_list)\nsubset_list_tax <- create_community_table_subsets(\n subset_list = subset_list_rel, \n thresholds = c(5, 1.5),\n taxa_prefix = \"ASV\",\n num_samples = 1,\n tax_ranks = selected_taxa_1)\nsubset_list_df <- otu_table_to_df(subset_list = subset_list_tax)\n# add sample data columns to the count table\n#names(sample_data(testps))\ndesired_sample_data <- c(\"TOC\", \"P_percent\", \"Munition_near\", \"Run\")\nsubset_list_extra <- add_sample_data(phyloseq_object = testps, \n community_tables = subset_list_df, sample_data_names = desired_sample_data)\n# get response variables\ndesired_response_vars <- c(\"UXO_sum\")\nresponse_variables <- extract_response_variable(\n response_variables = desired_response_vars, phyloseq_object = testps)\n# cut response numeric values into 3 classes\nresponses_multi <- categorize_response_variable(\n ML_mode = \"classification\", \n response_data = response_variables, \n my_breaks = c(-Inf, 10, 20, Inf), \n class_labels = c(\"below_10\", \"below_20\", \"above_20\"))\n\n# or for two classes\nresponses_binary <- categorize_response_variable(\n ML_mode = \"classification\", \n response_data = response_variables, \n my_breaks = c(-Inf, 20, Inf),\n class_labels = c(\"below_20\", \"above_20\"))\n\nresponses_regression <- categorize_response_variable(\n ML_mode = \"regression\", \n response_data = response_variables)\n\n# merge the input tables with the response variables\nmerged_input_binary <- merge_input_response(subset_list_extra, responses_binary)\nmerged_input_multi <- merge_input_response(subset_list_extra, responses_multi)\nmerged_input_regression <- merge_input_response(subset_list_extra, responses_regression)\n\n###### for keras\n\n# dummify input tables for keras ANN\nkeras_dummy_binary <- dummify_input_tables(merged_input_binary)\nkeras_dummy_multi <- dummify_input_tables(merged_input_multi)\nkeras_dummy_regression <- dummify_input_tables(merged_input_regression)\nsplitted_keras_binary <- split_data(keras_dummy_binary, c(0.6, 0.8))\nsplitted_keras_multi <- split_data(keras_dummy_multi, c(0.6, 0.8))\nsplitted_keras_regression <- split_data(keras_dummy_regression, c(0.6, 0.8))\n\nkeras_dummy_multi[[1]]\n\n# augmentation\naugmented_keras_binary <- augment(splitted_keras_binary, 2, 0.5)\naugmented_keras_multi <- augment(splitted_keras_multi, 2, 0.5)\naugmented_keras_regression <- augment(splitted_keras_regression, 2, 0.5)\n\n# scaling\nscaled_keras_binary <- scaling(augmented_keras_binary)\nscaled_keras_multi <- scaling(augmented_keras_multi)\nscaled_keras_regression <- scaling(augmented_keras_regression)\n\nscaled_keras_multi[[1]][[\"train_set\"]]\n\n# keras format\nready_keras_binary <- inputtables_to_keras(scaled_keras_binary)\nready_keras_multi <- inputtables_to_keras(scaled_keras_multi)\nready_keras_regression <- inputtables_to_keras(scaled_keras_regression)\nstr(ready_keras_binary, max = 2)\n\nready_keras_multi[[1]][[\"trainset_labels\"]]\n\n###### for ranger\n\n# split merged list into training and test parts\nsplitted_input_binary <- split_data(merged_input_binary, c(0.6, 0.8))\nsplitted_input_multi <- split_data(merged_input_multi, c(0.6, 0.8))\nsplitted_input_regression <- split_data(merged_input_regression, c(0.6, 0.8))\n\n\n# augmentation\naugmented_input_binary <- augment(splitted_input_binary, 1, 0.5)\naugmented_input_multi <- augment(splitted_input_multi, 1, 0.5)\naugmented_regression <- augment(splitted_input_regression, 1, 0.5)\n\n####### ranger classification\n# set up a parameter data.frame\nparameter_df <- extract_parameters(augmented_input_multi)\n\nhyper_grid <- expand.grid(\n ML_object = names(augmented_input_multi),\n Number_of_trees = c(151),\n Mtry_factor = c(1),\n Importance_mode = c(\"none\"),\n Cycle = 1:5,\n step = \"training\")\n\nmaster_grid <- merge(parameter_df, hyper_grid, by = \"ML_object\")\n# string arguments needs to be passed as character, not factor level \nmaster_grid$Target <- as.character(master_grid$Target)\n\ntest_grid <- head(master_grid, 2)\n\n#master_grid$results <- purrr::pmap(cbind(master_grid, .row = rownames(master_grid)), \n# ranger_classification, the_list = augmented_input_multi, master_grid = master_grid)\n#results_df <- as.data.frame(tidyr::unnest(master_grid, results))\n\n#### ranger regression\nparameter_regress <- extract_parameters(augmented_regression)\n\nhyper_grid_regress <- expand.grid(\n ML_object = names(augmented_regression),\n Number_of_trees = c(151),\n Mtry_factor = c(1),\n Importance_mode = c(\"none\"),\n Cycle = 1:5,\n step = \"prediction\")\n\nmaster_grid_regress <- merge(parameter_regress, hyper_grid_regress, by = \"ML_object\")\nmaster_grid_regress$Target <- as.character(master_grid_regress$Target)\ntest_grid_regress <- head(master_grid_regress, 1)\n\n# running ranger\n#master_grid_regress$results <- purrr::pmap(cbind(master_grid_regress, .row = rownames(master_grid_regress)), \n# ranger_regression, the_list = augmented_regression, master_grid = master_grid_regress)\n#results_regress <- as.data.frame(tidyr::unnest(master_grid_regress, results))\n\n#test_grid_regress$results <- purrr::pmap(cbind(test_grid_regress, .row = rownames(test_grid_regress)), \n# ranger_regression, the_list = augmented_regression, master_grid = test_grid_regress)\n#results_regress_test <- as.data.frame(tidyr::unnest(test_grid_regress, results))\n\n\n####### for keras multi\n# set up a parameter data.frame\nparameter_keras_multi <- extract_parameters(ready_keras_multi)\n\nhyper_keras_multi <- expand.grid(\n ML_object = names(ready_keras_multi),\n Epochs = 5, \n Batch_size = 2, \n k_fold = 1, \n current_k_fold = 1,\n Early_callback = \"accuracy\", #prediction: \"accuracy\", training: \"val_loss\"\n Layer1_units = 20,\n Layer2_units = 8,\n Dropout_layer1 = 0.2,\n Dropout_layer2 = 0.0,\n Dense_activation_function = \"relu\",\n Output_activation_function = \"softmax\", # sigmoid for binary\n Optimizer_function = \"rmsprop\",\n Loss_function = \"categorical_crossentropy\", # binary_crossentropy for binary\n Metric = \"accuracy\",\n Cycle = 1:3,\n step = \"prediction\",\n Classification = \"multiclass\",\n Delay = 2)\n\nmaster_keras_multi <- merge(parameter_keras_multi, hyper_keras_multi, by = \"ML_object\")\n# order by current_k_fold \nmaster_keras_multi <- master_keras_multi[order(\n master_keras_multi$ML_object, \n master_keras_multi$Cycle, \n master_keras_multi$current_k_fold), ]\nrownames(master_keras_multi) <- NULL\ntest_keras_multi_prediction <- head(master_keras_multi, 2)\n\n#test_keras_multi_prediction$results <- purrr::pmap(cbind(test_keras_multi_prediction, .row = rownames(test_keras_multi_prediction)), \n# keras_classification, the_list = ready_keras_multi, master_grid = test_keras_multi_prediction)\n#keras_df_multi_prediction <- as.data.frame(tidyr::unnest(test_keras_multi_prediction, results))\n\n####### for keras binary\n# set up a parameter data.frame\nparameter_keras_binary <- extract_parameters(ready_keras_binary)\n\nhyper_keras_binary <- expand.grid(\n ML_object = names(ready_keras_binary),\n Epochs = 5, \n Batch_size = 2, \n k_fold = 4, \n current_k_fold = 1:4,\n Early_callback = \"val_loss\", #prediction: \"accuracy\", training: \"val_loss\"\n Layer1_units = 20,\n Layer2_units = 8,\n Dropout_layer1 = 0.2,\n Dropout_layer2 = 0.0,\n Dense_activation_function = \"relu\",\n Output_activation_function = \"softmax\", # sigmoid for binary\n Optimizer_function = \"rmsprop\",\n Loss_function = \"categorical_crossentropy\", # binary_crossentropy for binary\n Metric = \"accuracy\",\n Cycle = 1:3,\n step = \"training\",\n Classification = \"binary\",\n Delay = 2)\n\nmaster_keras_binary <- merge(parameter_keras_binary, hyper_keras_binary, by = \"ML_object\")\nmaster_keras_binary <- master_keras_binary[order(\n master_keras_binary$ML_object, \n master_keras_binary$Cycle, \n master_keras_binary$current_k_fold), ]\nrownames(master_keras_binary) <- NULL\ntest_keras_binary_training <- head(master_keras_binary, 2)\n\n#test_keras_binary_training$results <- purrr::pmap(cbind(test_keras_binary_training, .row = rownames(test_keras_binary_training)), \n# keras_classification, the_list = ready_keras_binary, master_grid = test_keras_binary_training)\n#keras_df_binary_training <- as.data.frame(tidyr::unnest(test_keras_binary_training, results))\n\n####### for keras regression\n# set up a parameter data.frame\nparameter_keras_regression <- extract_parameters(ready_keras_regression)\n\nhyper_keras_regression_training <- expand.grid(\n ML_object = names(ready_keras_regression),\n Epochs = 5, \n Batch_size = 2, \n k_fold = 4, \n current_k_fold = 1:4,\n Early_callback = \"mae\",\n Layer1_units = 20,\n Layer2_units = 8,\n Dropout_layer1 = 0.2,\n Dropout_layer2 = 0.0,\n Dense_activation_function = \"relu\",\n Optimizer_function = \"rmsprop\",\n Loss_function = \"mse\",\n Metric = \"mae\",\n Cycle = 1:3,\n step = \"training\",\n Delay = 2)\n\nmaster_keras_regression_training <- merge(parameter_keras_regression, hyper_keras_regression_training, by = \"ML_object\")\nmaster_keras_regression_training <- master_keras_regression_training[order(\n master_keras_regression_training$ML_object, \n master_keras_regression_training$Cycle, \n master_keras_regression_training$current_k_fold), ]\nrownames(master_keras_regression_training) <- NULL\ntest_keras_regression_training <- head(master_keras_regression_training, 2)\n\n#test_keras_regression_training$results <- purrr::pmap(cbind(test_keras_regression_training, .row = rownames(test_keras_regression_training)), \n# keras_regression, the_list = ready_keras_regression, master_grid = test_keras_regression_training)\n#keras_df_regression_training <- as.data.frame(tidyr::unnest(test_keras_regression_training, results))\n\n#### regression prediction\nhyper_keras_regression_prediction <- expand.grid(\n ML_object = names(ready_keras_regression),\n Epochs = 5, \n Batch_size = 2, \n k_fold = 1, \n current_k_fold = 1,\n Early_callback = \"mae\",\n Layer1_units = 20,\n Layer2_units = 8,\n Dropout_layer1 = 0.2,\n Dropout_layer2 = 0.0,\n Dense_activation_function = \"relu\",\n Optimizer_function = \"rmsprop\",\n Loss_function = \"mse\",\n Metric = \"mae\",\n Cycle = 1:3,\n step = \"prediction\",\n Delay = 2)\n\nmaster_keras_regression_prediction <- merge(parameter_keras_regression, hyper_keras_regression_prediction, by = \"ML_object\")\nmaster_keras_regression_prediction <- master_keras_regression_prediction[order(\n master_keras_regression_prediction$ML_object, \n master_keras_regression_prediction$Cycle, \n master_keras_regression_prediction$current_k_fold), ]\nrownames(master_keras_regression_prediction) <- NULL\ntest_keras_regression_prediction <- head(master_keras_regression_prediction, 2)\n\n#test_keras_regression_prediction$results <- purrr::pmap(cbind(test_keras_regression_prediction, .row = rownames(test_keras_regression_prediction)), \n# keras_regression, the_list = ready_keras_regression, master_grid = test_keras_regression_prediction)\n#keras_df_regression_prediction <- as.data.frame(tidyr::unnest(test_keras_regression_prediction, results))\n", "meta": {"hexsha": "b9560c236828368bfa75aa7bd36a62a35347c713", "size": 11696, "ext": "r", "lang": "R", "max_stars_repo_path": "example_workflow/example_workflow.r", "max_stars_repo_name": "RJ333/phyloseq2ML", "max_stars_repo_head_hexsha": "391ce779d9cb816ca109ab6f89bd7246c27c784c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-05T14:20:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-05T14:20:05.000Z", "max_issues_repo_path": "example_workflow/example_workflow.r", "max_issues_repo_name": "RJ333/phyloseq2ML", "max_issues_repo_head_hexsha": "391ce779d9cb816ca109ab6f89bd7246c27c784c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "example_workflow/example_workflow.r", "max_forks_repo_name": "RJ333/phyloseq2ML", "max_forks_repo_head_hexsha": "391ce779d9cb816ca109ab6f89bd7246c27c784c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9866666667, "max_line_length": 149, "alphanum_fraction": 0.7858242134, "num_tokens": 3027, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3237816559741216}} {"text": "#PRI SEMINARSKI NALOGI SEM UPORABIL NASLEDNJE KNJIZNICE:\n\n\nlibrary(knitr)\nlibrary(rvest)\nlibrary(gsubfn)\nlibrary(tidyr)\nlibrary(shiny)\nlibrary(readr)\nlibrary(dplyr)\nlibrary(tmap)\nlibrary(ggplot2)\nlibrary(grid)\nlibrary(rworldmap)\nlibrary(cowplot)\nlibrary(googleway)\nlibrary(ggrepel)\nlibrary(ggspatial)\nlibrary(rnaturalearth)\nlibrary(rnaturalearthdata)\nlibrary(RColorBrewer)\nlibrary(mgcv)\nlibrary(tidyverse)\nlibrary(shiny)\nlibrary(leaflet)\n", "meta": {"hexsha": "fcf035032ea74a1f449e54edcd1cbcb19b1a9497", "size": 438, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/libraries.r", "max_stars_repo_name": "BulaRebula/APPR-2019-20", "max_stars_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-03-01T11:20:57.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-01T11:20:57.000Z", "max_issues_repo_path": "lib/libraries.r", "max_issues_repo_name": "BulaRebula/APPR-2019-20", "max_issues_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2019-12-15T15:58:06.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-23T17:59:16.000Z", "max_forks_repo_path": "lib/libraries.r", "max_forks_repo_name": "BulaRebula/APPR-2019-20", "max_forks_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.8461538462, "max_line_length": 56, "alphanum_fraction": 0.8242009132, "num_tokens": 135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.3237816559741216}} {"text": "context(\"Alpha\")\n\nhex <- function(x) {\n rgb <- col2rgb(x, TRUE) / 255\n rgb(rgb[1, ], rgb[2, ], rgb[3, ], rgb[4, ])\n}\n\ntest_that(\"missing alpha preserves existing\", {\n cols <- col2rgb(rep(\"red\", 5), TRUE) / 255\n cols[4, ] <- seq(0, 1, length.out = ncol(cols))\n\n reds <- rgb(cols[1, ], cols[2, ], cols[3, ], cols[4, ])\n\n expect_equal(reds, alpha(reds, NA))\n expect_equal(reds, alpha(reds, rep(NA, 5)))\n})\n\ntest_that(\"alpha values recycled to match colour\", {\n cols <- hex(c(\"red\", \"green\", \"blue\", \"pink\"))\n\n expect_equal(cols, alpha(cols, NA))\n expect_equal(cols, alpha(cols, 1))\n})\n\ntest_that(\"col values recycled to match alpha\", {\n alphas <- round(seq(0, 255, length.out = 3))\n reds <- alpha(\"red\", alphas / 255)\n reds_alpha <- col2rgb(reds, TRUE)[4, ]\n\n expect_equal(alphas, reds_alpha)\n})\n", "meta": {"hexsha": "112ed01667fe41b923f161c42fd9453a6d023e74", "size": 807, "ext": "r", "lang": "R", "max_stars_repo_path": "spiro-shiny-1/packrat/lib/x86_64-pc-linux-gnu/3.5.2/scales/tests/testthat/test-alpha.r", "max_stars_repo_name": "dskard/indy-use-r-201903", "max_stars_repo_head_hexsha": "a9fd61f1d6f6995d682d01473ec47330a31b61ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "spiro-shiny-1/packrat/lib/x86_64-pc-linux-gnu/3.5.2/scales/tests/testthat/test-alpha.r", "max_issues_repo_name": "dskard/indy-use-r-201903", "max_issues_repo_head_hexsha": "a9fd61f1d6f6995d682d01473ec47330a31b61ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "spiro-shiny-1/packrat/lib/x86_64-pc-linux-gnu/3.5.2/scales/tests/testthat/test-alpha.r", "max_forks_repo_name": "dskard/indy-use-r-201903", "max_forks_repo_head_hexsha": "a9fd61f1d6f6995d682d01473ec47330a31b61ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.21875, "max_line_length": 57, "alphanum_fraction": 0.6133828996, "num_tokens": 271, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32378165597412156}} {"text": "## Cord blood reference data generated by Kristina Gervin and Robert Lyle.\n\n## Kristina Gervin \n## Robert Lyle \n \n\ncreate.gervin.lyle.reference <- function(data.dir, verbose=T) {\n number.pcs <- 8\n \n samplesheet <- meffil.read.samplesheet(data.dir, \"csv$\")\n \n ds <- meffil.normalize.dataset(samplesheet,\n just.beta=F,\n qc.file=\"gervin-and-lyle-qc-report.html\",\n author=\"Kristina Gervin and Robert Lyle\",\n study=\"Purified cord blood cell type methylation\",\n number.pcs=number.pcs,\n norm.file=\"gervin-and-lyle-normalization-report.html\",\n chip=\"450k\",\n featureset=\"common\",\n verbose=verbose)\n\n ## pc.plot <- meffil.plot.pc.fit(ds$norm.objects)\n ## suggests that number.pcs should be 8\n \n samplesheet <- samplesheet[match(colnames(ds$M), samplesheet$Sample_Name),]\n samplesheet$cell.type <- toupper(samplesheet$cellType)\n samplesheet$cell.type[which(samplesheet$cell.type == \"CD19\")] <- \"Bcell\"\n samplesheet$cell.type[which(samplesheet$cell.type == \"GRAN\")] <- \"Gran\"\n samplesheet$cell.type[which(samplesheet$cell.type == \"CD4\")] <- \"CD4T\"\n samplesheet$cell.type[which(samplesheet$cell.type == \"CD8\")] <- \"CD8T\"\n cell.types <- c(\"CD14\", \"Bcell\", \"CD4T\", \"CD8T\", \"NK\",\"Gran\")\n selected <- samplesheet$cell.type %in% cell.types\n meffil.add.cell.type.reference(\"gervin and lyle cord blood\",\n ds$M[,selected], ds$U[,selected],\n cell.types=samplesheet$cell.type[selected],\n chip=\"450k\",\n featureset=\"common\",\n verbose=verbose)\n}\n\n", "meta": {"hexsha": "72b9837ccc918942d30a07827b0e717bd8610507", "size": 2011, "ext": "r", "lang": "R", "max_stars_repo_path": "data-raw/gervin-lyle-reference.r", "max_stars_repo_name": "RichardJActon/meffil", "max_stars_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": 33, "max_stars_repo_stars_event_min_datetime": "2015-04-21T18:35:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-21T10:48:31.000Z", "max_issues_repo_path": "data-raw/gervin-lyle-reference.r", "max_issues_repo_name": "RichardJActon/meffil", "max_issues_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2015-02-17T11:13:33.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-28T21:48:56.000Z", "max_forks_repo_path": "data-raw/gervin-lyle-reference.r", "max_forks_repo_name": "RichardJActon/meffil", "max_forks_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-11-17T22:40:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T16:10:42.000Z", "avg_line_length": 47.880952381, "max_line_length": 89, "alphanum_fraction": 0.529090005, "num_tokens": 455, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32378165597412156}} {"text": "ns = \"Neuroscience\"\nno = \"Neurology\"\npb = \"Psychological and Brain Sciences\"\nbme = \"Biomedical Engineering\"\nbio = \"Biology\"\ncs = \"Computer Science\"\nece = \"Electrical Engineering\"\nmb = \"Molecular Biology\"\npc = \"Pharmacology\"\nro = \"Radiology\"\ncbe = \"Chemical and Biomolecular Engineering\"\nams = \"Applied Mathematics and Statistics\"\nphy = \"Physics\"\napl = \"Applied Physics Laboratory\"\nbs = \"Biostatistics\"\nmse = \"Materials Science & Engineering\"\ndepts <- c(ns, bme, bs, no, bs, phy, ns, bme, ns, ns, ns, cbe, cs, ams, bme, ns, bio, ro,ns, ns, ns, bme, pb, bme, mse, ns, ams, pc, bme, ns, bs, bme, ams, pb, ns, apl, no, bme, ece, pb, cs, cs, bme, ams, ro, mb, cbe)\n\nlibrary(\"igraph\")\ngg = read.graph('jhu_neuro.graphml', format=c(\"graphml\"))\nV(gg)$dept <- depts\n\nV(gg)$color=V(gg)$dept\nV(gg)$color=gsub(ns, \"indianred2\", V(gg)$color)\nV(gg)$color=gsub(no, \"lightslateblue\", V(gg)$color)\nV(gg)$color=gsub(pb, \"royalblue\", V(gg)$color)\nV(gg)$color=gsub(bme, \"powderblue\", V(gg)$color)\nV(gg)$color=gsub(mb, \"orchid\", V(gg)$color)\nV(gg)$color=gsub(bio, \"seagreen\", V(gg)$color)\nV(gg)$color=gsub(cs, \"aquamarine\", V(gg)$color)\nV(gg)$color=gsub(ece, \"pink2\", V(gg)$color)\nV(gg)$color=gsub(pc, \"lightsteelblue\", V(gg)$color)\nV(gg)$color=gsub(ro, \"lightpink\", V(gg)$color)\nV(gg)$color=gsub(cbe, \"khaki1\", V(gg)$color)\nV(gg)$color=gsub(ams, \"lawngreen\", V(gg)$color)\nV(gg)$color=gsub(apl, \"magenta3\", V(gg)$color)\nV(gg)$color=gsub(phy, \"lightseagreen\", V(gg)$color)\nV(gg)$color=gsub(bs, \"orange2\", V(gg)$color)\nV(gg)$color=gsub(mse, \"wheat1\", V(gg)$color)\n\ntkplot(gg, edge.width=ceiling(log(E(gg)$weight)), layout=layout.fruchterman.reingold)\npng(filename=\"/Users/gkiar/git/neuro-pub-graph/graphs/org_network.png\", height=800, width=600)\n", "meta": {"hexsha": "76b3490017805a6c76478e6cd170004df3de0583", "size": 1723, "ext": "r", "lang": "R", "max_stars_repo_path": "graphs/plotgraph.r", "max_stars_repo_name": "alexbaden/neuro-pub-graph", "max_stars_repo_head_hexsha": "32c8ddac07545cb8ca4b810545e7a9f8ed45d068", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-01-08T01:22:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-30T08:23:17.000Z", "max_issues_repo_path": "graphs/plotgraph.r", "max_issues_repo_name": "alexbaden/neuro-pub-graph", "max_issues_repo_head_hexsha": "32c8ddac07545cb8ca4b810545e7a9f8ed45d068", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graphs/plotgraph.r", "max_forks_repo_name": "alexbaden/neuro-pub-graph", "max_forks_repo_head_hexsha": "32c8ddac07545cb8ca4b810545e7a9f8ed45d068", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-04-07T14:47:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-30T08:23:39.000Z", "avg_line_length": 40.0697674419, "max_line_length": 217, "alphanum_fraction": 0.680789321, "num_tokens": 655, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32378165597412156}} {"text": "# Packages\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(tidygraph)\nlibrary(ggraph)\nlibrary(stringr)\nlibrary(reshape2)\n\n# Mac Uni Data commented out\n# macq_units.df <- read.csv(\"macquarie-unsw.csv\", na.strings = \"null\", stringsAsFactors = F)\n# \n# named_edges <- macq_units.df %>%\n# na.omit() %>%\n# mutate(source = str_extract(unit.codes, \"[A-Z]{3,4}[0-9]{3,4}\")) %>%\n# select(source, destination=prereqs)\n\n# UNSW Data\nunsw_courses.df <- read.csv(\"unsw-courses.csv\", na.strings = \"null\",\n stringsAsFactors = F)\n\n# DF of prereq links and faculty wrangled from raw data\nnamed_edges <- unsw_courses.df %>%\n na.omit() %>%\n mutate(destination = str_extract(courses, \"[A-Z]{4}\\\\d{4}(?!\\\\d)\")) %>%\n mutate(prereqs = str_extract_all(conditions, \"[A-Z]{4}\\\\d{4}(?!\\\\d)\")) %>%\n unnest(prereqs) %>%\n distinct() %>%\n select(source = prereqs, destination, faculties)\n\n# igraph DF of nodes (id, label and faculty)\n# Also filters by subject code or faculty\nnodes <- melt(named_edges, id.vars = 'faculties') %>%\n select(label = value, faculties) %>%\n unique() %>%\n filter(str_detect(faculties, \"Law\")) %>%\n mutate(id = 1:n()) %>%\n select(id, label, faculties)\n\n# igraph DF of edges by node id\nedges <- named_edges %>%\n inner_join(nodes, by = c(\"source\" = \"label\")) %>%\n rename(from = id) %>%\n inner_join(nodes, by = c(\"destination\" = \"label\")) %>%\n rename(to = id) %>%\n select(from, to)\n\n# Tidygraph routes data from nodes and edges\nroutes <- tbl_graph(nodes = nodes, edges = edges, directed = T)\n\n# Plot the routes data\nggraph(routes,\n # Uses first number of subj code as layer number.\n # layers = as.numeric(str_extract(nodes$label, \"\\\\d{1}\")),\n layout = \"sugiyama\",\n hgap = 4,\n maxiter = 10000) +\n geom_edge_diagonal(alpha = 0.1,\n colour = 'white') +\n geom_node_point(size = 1.5, aes(colour = faculties)) +\n theme_graph() +\n theme(\n panel.background = element_rect(fill = \"black\"),\n plot.background = element_rect(fill = \"black\"),\n legend.text = element_text(colour = \"white\"),\n legend.title = element_text(colour = \"white\")\n ) +\n scale_colour_brewer(palette = \"Set1\")\n\n", "meta": {"hexsha": "94e054017f53ba950660011840708684a890a829", "size": 2235, "ext": "r", "lang": "R", "max_stars_repo_path": "src.r", "max_stars_repo_name": "PubeSeal/Prerequisite-Network-Visualisation", "max_stars_repo_head_hexsha": "1935b178b00a1ccd0fd4fdfe9f549cdd398e87c5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src.r", "max_issues_repo_name": "PubeSeal/Prerequisite-Network-Visualisation", "max_issues_repo_head_hexsha": "1935b178b00a1ccd0fd4fdfe9f549cdd398e87c5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src.r", "max_forks_repo_name": "PubeSeal/Prerequisite-Network-Visualisation", "max_forks_repo_head_hexsha": "1935b178b00a1ccd0fd4fdfe9f549cdd398e87c5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3913043478, "max_line_length": 92, "alphanum_fraction": 0.6134228188, "num_tokens": 625, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32378165597412156}} {"text": "library(\"dplyr\")\nfind_start_node <- function(edges) {\n start <- sort(edges$Parent)[1] # reasonable guess\n if(is.factor(start)) start <- levels(start)[start]\n repeat {\n if(move_up(edges, start) == start) break\n start <- move_up(edges, start)\n }\n return(start)\n}\nmove_up <- function(edges, identity) {\n if(!(identity %in% edges$Identity) & !(identity %in% edges$Parent)) stop(\"Invalid identity.\")\n parent <- filter_(edges, ~Identity == identity)$Parent\n if(length(parent) == 0) return(identity) # if it is the initial genotype then don't move\n if(is.factor(parent)) parent <- levels(parent)[parent]\n return(parent)\n}\nmove_right <- function(edges, identity) {\n\n if(!(identity %in% edges$Identity) & !(identity %in% edges$Parent)) stop(\"Invalid identity.\")\n parent <- filter_(edges, ~Identity == identity)$Parent\n if(length(parent) == 0) return(identity) # if it is the initial genotype then don't move\n siblings <- sort(filter_(edges, ~Parent == parent)$Identity)\n siblings <- siblings[which(siblings == identity) + 1]\n if(length(siblings) == 0) return(identity) # if it is the initial genotype then don't move\n if(is.na(siblings)) return(identity) # if it is the initial genotype then don't move\n if(is.factor(siblings)) siblings <- levels(siblings)[siblings]\n return(siblings)\n}\nmove_down <- function(edges, parent) {\n\n if(!(parent %in% edges$Identity) & !(parent %in% edges$Parent)) stop(\"Invalid parent.\")\n daughters <- filter_(edges, ~Parent == parent)$Identity\n if(length(daughters) == 0) return(parent) # if it is not a parent then don't move\n if(is.factor(daughters)) daughters <- levels(daughters)[daughters]\n return(sort(daughters)[1])\n}\n\npath_vector <- function(edges) {\n n <- 1\n path <- find_start_node(edges)\n debugvaluepath = path[n]\n upped <- FALSE\n repeat {\n if(!upped) repeat { # a downwards move should never follow an upwards move\n n <- n + 1\n path[n] <- move_down(edges, path[n - 1])\n upped <- FALSE\n if(path[n] == path[n - 1]) break\n }\n if(move_right(edges, path[n]) != path[n]) {\n n <- n + 1\n path[n] <- move_right(edges, path[n - 1])\n upped <- FALSE\n } else if(move_up(edges, path[n]) != path[n]) {\n n <- n + 1\n path[n] <- move_up(edges, path[n - 1])\n upped <- TRUE\n }\n if(path[n] == path[1]) break\n if(n > 2 * dim(edges)[1] + 2) stop(\"Error: stuck in a loop\")\n if(max(table(path) > 2)) stop(\"Error: adjacency matrix seems to include loops.\")\n }\n if(length(path) != 2 * dim(edges)[1] + 2) stop(\"Error: adjacency matrix seems to be bipartite.\")\n return(path)\n}\n\nargs = commandArgs(trailingOnly=TRUE)\n\ntable_edges <- read.table(args[1], sep = \"\\t\", header = TRUE)\n\nresult <- path_vector(table_edges)\nwrite(result, file = args[2])\n\n", "meta": {"hexsha": "1db915950b63fbbc741adb9d06dc38b9d5c03d57", "size": 2761, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/data/scripts_ggmuller/rscript.adjacencymatrix.pathvector.r", "max_stars_repo_name": "andreashirley/Lolipop", "max_stars_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-04-18T15:43:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-19T18:43:23.000Z", "max_issues_repo_path": "tests/data/scripts_ggmuller/rscript.adjacencymatrix.pathvector.r", "max_issues_repo_name": "andreashirley/Lolipop", "max_issues_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-05-04T16:09:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-13T03:52:56.000Z", "max_forks_repo_path": "tests/data/scripts_ggmuller/rscript.adjacencymatrix.pathvector.r", "max_forks_repo_name": "cdeitrick/muller_diagrams", "max_forks_repo_head_hexsha": "5b87b00a2c7ccbeeb3876bddb32e54aedf6bdf6d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-03-23T17:12:56.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-24T22:22:12.000Z", "avg_line_length": 36.8133333333, "max_line_length": 98, "alphanum_fraction": 0.6497645781, "num_tokens": 767, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6370308082623217, "lm_q1q2_score": 0.32349180234697805}} {"text": "library(progeny)\nlibrary(dplyr)\nlibrary(tibble)\nlibrary(ggplot2)\n\nmessage(\"STARTING PROGENY PROCESS\")\n\nmessage(\"Reading input arguments:\")\nargs = commandArgs(trailingOnly = TRUE)\nprogeny_file = args[1]\norganism = args[2] # \"Human\"\nzscores = as.logical(args[3]) # T\ntop = as.numeric(args[4]) # 100\n\nmessage(progeny_file)\nmessage(organism)\nmessage(zscores)\nmessage(top)\n\nmessage(\"Creating output file\")\n\nfile_csv = paste0(\"progeny_scores_\", organism, \"_\", top, \".csv\")\nmessage(file_csv)\n\nmessage(\"Reading input file\")\n\nprogeny_data <- as.matrix(read.csv(progeny_file, row.names = 1))\n\nmessage(\"Running Progeny\") \n\nPathwayActivity_counts <- progeny::progeny(progeny_data, \n scale = TRUE, \n organism = organism, \n top = top, \n perm = 10000, \n z_scores = zscores)\n\nPathwayActivity_counts <- as.data.frame(t(PathwayActivity_counts))\n\nmessage(\"Writing output file\")\n\nwrite.csv(PathwayActivity_counts, file_csv, quote = F)\n\nmessage(\"FINISHING PROGENY PROCESS\")\n", "meta": {"hexsha": "f20db0bf5da4b56c2f50bd2becd573990897ba07", "size": 1163, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/run_progeny.r", "max_stars_repo_name": "anspiess/RNAseq_statistical_analysis", "max_stars_repo_head_hexsha": "b75422e4c508bf0dd95e0667af775422e3d0e910", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/run_progeny.r", "max_issues_repo_name": "anspiess/RNAseq_statistical_analysis", "max_issues_repo_head_hexsha": "b75422e4c508bf0dd95e0667af775422e3d0e910", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/run_progeny.r", "max_forks_repo_name": "anspiess/RNAseq_statistical_analysis", "max_forks_repo_head_hexsha": "b75422e4c508bf0dd95e0667af775422e3d0e910", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-12-01T09:00:52.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-01T09:00:52.000Z", "avg_line_length": 25.8444444444, "max_line_length": 66, "alphanum_fraction": 0.6096302666, "num_tokens": 266, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.32349179534834616}} {"text": "#!/usr/bin/env Rscript\n\n#sink(file(\"/dev/null\", \"w\"), type = \"message\");\n\nlibrary(affy)\nlibrary(GEOquery);\nlibrary(pathprint);\n\n# Figure out the relative path to the galaxy-pathprint.r library.\nscript.args <- commandArgs(trailingOnly = FALSE);\nscript.name <- sub(\"--file=\", \"\", script.args[grep(\"--file=\", script.args)])\nscript.base <- dirname(script.name)\nlibrary.path <- file.path(script.base, \"galaxy-pathprint.r\");\nsource(library.path)\n\ndata(GEO.metadata.matrix);\n\nusage <- function() {\n sink(stderr(), type = \"message\");\n stop(\"Usage: fingerprint.r [ARGS]\", call. = FALSE)\n}\n\n## Get the command line arguments.\nargs <- commandArgs(trailingOnly = TRUE)\n\ntype <- ifelse(! is.na(args[1]), args[1], usage())\n\nif (type == \"geo\") {\n geoID <- ifelse(! is.na(args[2]), args[2], usage());\n output <- ifelse(! is.na(args[3]), args[3], usage());\n\n fingerprint <- generateFingerprint(geoID);\n} else if (type == \"cel\") {\n input <- ifelse(! is.na(args[2]), args[2], usage());\n output <- ifelse(! is.na(args[3]), args[3], usage());\n\n fingerprint <- loadFingerprintFromCELFile(input);\n} else if (type == \"expr\") {\n input <- ifelse(! is.na(args[2]), args[2], usage());\n platform <- ifelse(! is.na(args[3]), args[3], usage());\n output <- ifelse(! is.na(args[4]), args[4], usage());\n\n fingerprint <- loadFingerprintFromExprsFile(input, platform);\n} else {\n usage();\n};\n\nsaveFingerprint(fingerprint, output);\n\nquit(\"no\", 0)\n", "meta": {"hexsha": "383ada1c4e7828422178752568e2c53c23ff1646", "size": 1471, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/scde_pathprint/fingerprint.r", "max_stars_repo_name": "hidelab/galaxy-central-hpc", "max_stars_repo_head_hexsha": "75539db90abe90377db95718f83cafa7cfa43301", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tools/scde_pathprint/fingerprint.r", "max_issues_repo_name": "hidelab/galaxy-central-hpc", "max_issues_repo_head_hexsha": "75539db90abe90377db95718f83cafa7cfa43301", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tools/scde_pathprint/fingerprint.r", "max_forks_repo_name": "hidelab/galaxy-central-hpc", "max_forks_repo_head_hexsha": "75539db90abe90377db95718f83cafa7cfa43301", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8431372549, "max_line_length": 77, "alphanum_fraction": 0.6206662135, "num_tokens": 405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3234917953483461}} {"text": "library(lattice)\n\nwin_metrics <- read.csv('metrics_win_clf.csv')\n\ncolors <- palette(rainbow(nrow(win_metrics))) \n\nwin_table <- data.matrix(win_metrics, rownames.force=NA)\n\nwin_table <- win_table[, -1]\n\nbarplot(win_table, main=\"Winner Metrics\", xlab=\"Metric\", col=colors, beside=TRUE)\n\npar(xpd=TRUE)\nlegend(17, 0.67, win_metrics$Algorithm, lty=c(1,1), lwd=c(2.5, 2.5), col=colors)", "meta": {"hexsha": "4b44081bc2ced354a95b6214bf1682c1a167f3ef", "size": 379, "ext": "r", "lang": "R", "max_stars_repo_path": "r-scripts/wins_metrics.r", "max_stars_repo_name": "omazhary/dm-oscars", "max_stars_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-25T09:39:48.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-25T09:39:48.000Z", "max_issues_repo_path": "r-scripts/wins_metrics.r", "max_issues_repo_name": "mahon94/oscars-prediction", "max_issues_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r-scripts/wins_metrics.r", "max_forks_repo_name": "mahon94/oscars-prediction", "max_forks_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0714285714, "max_line_length": 81, "alphanum_fraction": 0.7255936675, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3234914548933221}} {"text": "#' wiki_graph\n#'\n#' data.frame with three variables (v1, v2 and w) that contains the edges of the\n#' graph (from v1 to v2) with the weight of the edge (w)\n#'\n#' @format A data frame with 18 rows and 3 variables:\n#' \\describe{\n#' \\item{v1}{first edge}\n#' \\item{v2}{second edge}\n#' \\item{w}{weight between edges}\n#' }\n#' @source \\url{https://en.wikipedia.org/wiki/Graph}\n\"wiki_graph\"\n", "meta": {"hexsha": "dbe9eef23fb08acc736aa0fc93193abc371c0554", "size": 388, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wiki_graph.r", "max_stars_repo_name": "ahmedNwayyir/myFirstRPackage", "max_stars_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/wiki_graph.r", "max_issues_repo_name": "ahmedNwayyir/myFirstRPackage", "max_issues_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/wiki_graph.r", "max_forks_repo_name": "ahmedNwayyir/myFirstRPackage", "max_forks_repo_head_hexsha": "49165b3ba22e593fbef7cd900113312fbb14ce4f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7142857143, "max_line_length": 80, "alphanum_fraction": 0.6597938144, "num_tokens": 122, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3234914548933221}} {"text": "pdf_file<-\"pdf/networks_undirected_network.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=8,height=7)\n\npar(mai=c(0.25,0.25,0.25,0.5),omi=c(0.25,0.25,0.25,0.25), family=\"Lato Light\",las=1)\n\nlibrary(igraph)\nlibrary(sqldf)\nlibrary(gdata)\n\n# Import data and prepare chart\n\nX2013_2014 <- read.csv(\"myData/2013_2014.txt\",sep=\"\\t\", head=FALSE)\nX2014_2015 <- read.csv(\"myData/2014_2015.txt\",sep=\"\\t\", head=FALSE)\nX2015_2016 <- read.csv(\"myData/2015_2016.txt\",sep=\"\\t\", head=FALSE)\nlinks<-rbind(X2013_2014, X2014_2015, X2015_2016)\n\nteams<-as.data.frame(unique(c(links$V1, links$V2)))\nteams<-sqldf(\"select team, count(*) games from (select V1 team from links union all select V2 team from links) a group by team\")\nteams$col<-\"grey55\"\nteams$col[c(11, 12, 13, 14, 24, 51)]<-\"#f768a1\"\n\nmySeed <- as.POSIXlt(Sys.time())\nmySeed <- 1000*(mySeed$hour*3600 + mySeed$min*60 + mySeed$sec)\nmySeed\n\nset.seed(56313585)\nnet2 <- graph_from_data_frame(d=links, directed=F, vertices=teams)\nnet2simp<-simplify(net2, edge.attr.comb=list(weight=\"sum\",\"ignore\"))\n\n# Crete chart\n\nplot(net2simp, vertex.shape=\"none\", vertex.label=V(net2simp)$media, vertex.label.font=2, vertex.label.color=teams$col, vertex.label.cex=0.7*sqrt(teams$games/23), edge.color=\"grey80\", vertex.label.family=ifelse(teams$col==\"grey95\", \"Avenir Next Condensed Ultra Light\", \"Avenir Next Condensed Demi Bold\"))\n\n# Titling\n\nmtext(\"Champions League - Matches\", line=-1.5, adj=0, cex=2, family=\"Lato Black\", col=\"grey40\", outer=T)\nmtext(\"Base: all matches 2013-2016\", line=-2.75, adj=0, cex=0.9, family=\"Lato Bold\", col=\"grey40\", outer=T)\nmtext(\"Source: http://www.weltfussball.de/alle_spiele/champions-league-2015-2016/\", side=1, line=-1, adj=1, cex=0.9, font=3, outer=T)\n\ndev.off()\n", "meta": {"hexsha": "2f335630b69762e1daa352d1cca9cb96b49752d3", "size": 1718, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/networks_undirected_network.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/networks_undirected_network.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/networks_undirected_network.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.9024390244, "max_line_length": 303, "alphanum_fraction": 0.7200232829, "num_tokens": 622, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3234914471843166}} {"text": "pdf_file<-\"pdf/maps_nrw_symbols.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=8,height=6.5)\n\npar(mai=c(0.5,0.0,0.0,0.5),omi=c(0,0.5,0.75,0),family=\"Lato Light\") \nlibrary(sp); library(plotrix); library(gdata)\n\n# Import data and prepare chart\n\nakwnrw<-read.xls(\"myData/akwnrw.xlsx\",head=T, encoding=\"latin1\")\nmyX<-akwnrw$long; myY<-akwnrw$lat\ndm<-rep(0.2,length(myX))\nmyNpp<-function(myX,myY,dm,... ){\nfloating.pie(myX,myY,c(1,1,1,1,1,1),radius=dm,startpos=45,border=F,col=c(\"yellow\",\"black\",\"yellow\",\"black\",\"yellow\",\"black\"))\npoints(myX,myY,pch=19,cex=2,col=\"yellow\")\npoints(myX,myY,pch=19,cex=1,col=\"black\")\n}\nload(\"myData/gadm2/DEU_adm1.RData\")\nmyNrw<-gadm[gadm$NAME_1==\"Nordrhein-Westfalen\",]\n\n# Create chart\n\nplot(myNrw,border=\"black\",axes=F,lwd=0.5)\nn<-length(myX)\nfor (i in 1:n) myNpp(myX[i],myY[i],dm[i])\ntext(akwnrw$namlong,akwnrw$namlat-0.2,akwnrw$name,xpd=T)\n\n# Titling\n\nmtext(\"Nuclear Country North Rhine-Westphalia\",3,line=1,adj=0,cex=2,family=\"Lato Black\",outer=T)\nmtext(\"important companies of the nuclear industry are located in NRW\",3,line=-0.5,adj=0,cex=1.25,font=3,outer=T)\nmtext(\"Source: www.bund-nrw.de\",1,line=-1,adj=1.0,cex=0.95,font=3)\ndev.off()", "meta": {"hexsha": "13b09ff3625ad2f3921b0eaf76bc8b6596c5762e", "size": 1165, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/maps_nrw_symbols.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/maps_nrw_symbols.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/maps_nrw_symbols.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.40625, "max_line_length": 125, "alphanum_fraction": 0.7107296137, "num_tokens": 471, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.32349144718431655}} {"text": "## stan model specific utilities to wrangle data and provide initial values to Stan\n\n## normalize galaxy and stellar library fluxes by mean over all good data points\n\nprep_data_avg <- function(gdat, dz, nnfits, which.spax) {\n i <- which.spax\n z <- gdat$meta$z+dz[i]\n flux <- gdat$flux[i, ]\n ivar <- gdat$ivar[i, ]\n lambda.rest <- gdat$lambda/(1+z)\n logl <- log10(lambda.rest)\n T.gyr <- 10^(ages-9)\n dT <- diff(c(0, T.gyr))\n \n lib.ssp$lambda <- airtovac(lib.ssp$lambda)\n lib.st <- regrid(lambda.rest, lib.ssp)\n x.st <- blur.lib(lib.st, nnfits$vdisp.st[i])\n n.st <- ncol(x.st)\n nz <- length(Z)\n nt <- n.st/nz\n ind.young <- (0:(nz-1))*nt+1\n allok <- complete.cases(flux, ivar, x.st)\n nl <- length(which(allok))\n norm_g <- mean(pmax(flux[allok], 0))\n norm_st <- colMeans(x.st[allok,])\n x.st <- scale(x.st[allok,], center=FALSE, scale=norm_st)\n \n x.em <- make_emlib(emlines, nnfits$vdisp.em[i], logl, allok)\n in.em <- x.em$in_em\n x.em <- x.em$x_em\n n.em <- ncol(x.em)\n norm_em <- 1\n \n list(nt=nt, nz=nz, nl=nl, n_em=n.em,\n ind_young=ind.young,\n lambda=lambda.rest[allok],\n gflux=flux[allok]/norm_g,\n g_std=1/sqrt(ivar[allok])/norm_g,\n norm_g=norm_g,\n norm_st=norm_st,\n norm_em=norm_em,\n sp_st=x.st,\n dT=rep(dT, nz),\n sp_em=x.em[allok,],\n in_em=in.em\n )\n}\n\n## normalize by mean over wavelength interval (5375, 5625)\n\nprep_data_mod <- function (gdat, dz, nnfits, which.spax) {\n i <- which.spax\n z <- gdat$meta$z + dz[i]\n flux <- gdat$flux[i, ]\n ivar <- gdat$ivar[i, ]\n lambda.rest <- gdat$lambda/(1 + z)\n logl <- log10(lambda.rest)\n T.gyr <- 10^(ages - 9)\n dT <- diff(c(0, T.gyr))\n lib.ssp$lambda <- airtovac(lib.ssp$lambda)\n lib.st <- regrid(lambda.rest, lib.ssp)\n x.st <- blur.lib(lib.st, nnfits$vdisp.st[i])\n n.st <- ncol(x.st)\n nz <- length(Z)\n nt <- n.st/nz\n ind.young <- (0:(nz - 1)) * nt + 1\n allok <- complete.cases(flux, ivar, x.st)\n lambda <- lambda.rest[allok]\n gflux <- flux[allok]\n x.st <- x.st[allok,]\n wl_ind <- findInterval(c(5375,5625), lambda)\n nl <- length(which(allok))\n norm_g <- mean(pmax(gflux[wl_ind[1]:wl_ind[2]], 0))\n norm_st <- colMeans(x.st[wl_ind[1]:wl_ind[2], ])\n x.st <- scale(x.st, center = FALSE, scale = norm_st)\n x.em <- make_emlib(emlines, nnfits$vdisp.em[i], logl, allok)\n in.em <- x.em$in_em\n x.em <- x.em$x_em\n n.em <- ncol(x.em)\n norm_em <- 1\n list(nt = nt, nz = nz, nl = nl, n_em = n.em, ind_young = ind.young, \n lambda = lambda, gflux = gflux/norm_g, \n g_std = 1/sqrt(ivar[allok])/norm_g, norm_g = norm_g, \n norm_st = norm_st, norm_em = norm_em, sp_st = x.st, \n dT = rep(dT, nz), sp_em = x.em[allok, ], in_em = in.em)\n}\n\n## initialize Stan's optimizer to estimate MAP solution\n\ninit_opt_mod <- function(spm_data, nnfits, which.spax, jv) {\n n_st <- ncol(spm_data$sp_st)\n n_em <- spm_data$n_em\n norm_st <- spm_data$norm_st\n norm_em <- spm_data$norm_em\n norm_g <- spm_data$norm_g\n b <- nnfits$nnfits[which.spax,]\n b_st_s <- b[1:n_st] * norm_st/norm_g + runif(n_st, min=jv/10, max=jv)\n a <- sum(b_st_s)\n b_st_s <- b_st_s/a\n b_em <- b[(n_st+1):(n_st+n_em)] * norm_em/norm_g + runif(n_em, min=jv/10, max=jv)\n tauv <- nnfits$tauv[which.spax]\n if (tauv == 0) tauv=runif(1, min=jv/10, max=jv)\n delta <- rnorm(1, 0, jv)\n list(a=a, b_st_s=b_st_s, b_em=b_em, tauv=tauv, delta=delta)\n}\n\n## initialize Stan's sampler\n\ninit_sampler_mod <- function(X, stan_opt, jv) {\n a <- as.numeric(stan_opt$a + rnorm(1, sd=jv))\n b_st_s <- stan_opt$b_st_s + runif(length(stan_opt$b_st_s), min=jv/10, max=jv)\n b_st_s <- b_st_s/sum(b_st_s)\n b_em <- stan_opt$b_em + runif(length(stan_opt$b_em), min=jv/10, max=jv)\n tauv <- as.numeric(stan_opt$tauv + runif(1, min=jv/10, max=jv))\n delta <- as.numeric(stan_opt$delta + rnorm(1, sd=jv))\n list(a=a, b_st_s=b_st_s, b_em=b_em, tauv=tauv, delta=delta)\n}\n\n## Stan model specific data to track in stanfit_batch\n\ninit_tracked_mod <- function(nsim, n_st, n_em, nr) {\n assign(\"b_st\", array(NA, dim=c(nsim, n_st, nr)), parent.frame())\n assign(\"b_em\", array(NA, dim=c(nsim, n_em, nr)), parent.frame())\n assign(\"a\", matrix(NA, nrow=nsim, ncol=nr), parent.frame())\n assign(\"tauv\", matrix(NA, nrow=nsim, ncol=nr), parent.frame())\n assign(\"delta\", matrix(NA, nrow=nsim, ncol=nr), parent.frame())\n assign(\"in_em\", matrix(NA, nrow=n_em, ncol=nr), parent.frame())\n assign(\"ll\", matrix(NA, nrow=nsim, ncol=nr), parent.frame())\n assign(\"walltime\", rep(NA, nr), parent.frame())\n assign(\"divergences\", rep(NA, nr), parent.frame())\n assign(\"max_treedepth\", rep(NA, nr), parent.frame())\n assign(\"norm_g\", rep(NA, nr), parent.frame())\n assign(\"norm_st\", matrix(NA, nrow=n_st, ncol=nr), parent.frame())\n assign(\"norm_em\", rep(NA, nr), parent.frame())\n}\n\nupdate_tracked_mod <- function(i, sfit, fpart) {\n post <- rstan::extract(sfit$stanfit)\n env <- parent.frame()\n env$b_st[,,i] <- post$b_st\n env$b_em[,sfit$in_em,i] <- post$b_em\n env$a[,i] <- post$a\n env$tauv[,i] <- post$tauv\n env$delta[,i] <- post$delta\n env$in_em[sfit$in_em,i] <- sfit$in_em\n env$ll[,i] <- post$ll\n env$walltime[i] <- max(rowSums(rstan::get_elapsed_time(sfit$stanfit)))\n sp <- rstan::get_sampler_params(sfit$stanfit, inc_warmup=FALSE)\n env$divergences[i] <- sum(sapply(sp, function(x) sum(x[, \"divergent__\"])))\n env$max_treedepth[i] <- max(sapply(sp, function(x) max(x[, \"treedepth__\"])))\n env$norm_g[i] <- sfit$norm_g\n env$norm_st[,i] <- sfit$norm_st\n env$norm_em[i] <- sfit$norm_em\n save(b_st, b_em, tauv, delta, a, in_em, ll,\n walltime, divergences, max_treedepth,\n norm_g, norm_st, norm_em, envir=env, file = fpart)\n}\n\nreturn_tracked_mod <- function() {\n env <- parent.frame()\n retval <- list(b_st= env$b_st, b_em= env$b_em, a= env$a, tauv= env$tauv, delta= env$delta, in_em= env$in_em, ll= env$ll,\n walltime= env$walltime, divergences= env$divergences, max_treedepth= env$max_treedepth,\n norm_g= env$norm_g, norm_st= env$norm_st, norm_em= env$norm_em)\n retval\n}\n\n## replace a single stan run in batch fits\n\nreplace_sfit_mod <- function(sfit.all, sfit.one, which.spax) {\n post <- rstan::extract(sfit.one$stanfit)\n sp <- rstan::get_sampler_params(sfit.one$stanfit, inc_warmup=FALSE)\n sfit.all$b_st[,,which.spax] <- post$b_st\n sfit.all$b_em[,sfit.one$in.em,which.spax] <- post$b_em\n sfit.all$a[,which.spax] <- post$a\n sfit.all$tauv[,which.spax] <- post$tauv\n sfit.all$delta[,which.spax] <- post$delta\n sfit.all$in_em[sfit.one$in.em,which.spax] <- sfit.one$in.em\n sfit.all$ll[,which.spax] <- post$ll\n sfit.all$walltime[which.spax] <- max(rowSums(rstan::get_elapsed_time(sfit.one$stanfit)))\n sfit.all$divergences[which.spax] <- sum(sapply(sp, function(x) sum(x[, \"divergent__\"])))\n sfit.all$max_treedepth[which.spax] <- max(sapply(sp, function(x) max(x[, \"treedepth__\"])))\n sfit.all$norm_g[which.spax] <- sfit.one$norm_g\n sfit.all$norm_st[,which.spax] <- sfit.one$norm_st\n sfit.all$norm_em[which.spax] <- sfit.one$norm_em\n sfit.all\n}\n\n\n", "meta": {"hexsha": "a3265b78c15471a144cc26d4af6c8e8f3bfe2a79", "size": 6966, "ext": "r", "lang": "R", "max_stars_repo_path": "spmutils/R/spm_prep_data.r", "max_stars_repo_name": "mlpeck/spmutils", "max_stars_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "spmutils/R/spm_prep_data.r", "max_issues_repo_name": "mlpeck/spmutils", "max_issues_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "spmutils/R/spm_prep_data.r", "max_forks_repo_name": "mlpeck/spmutils", "max_forks_repo_head_hexsha": "ebd2a6a634f1f34c4bb0399136f8164092fa5e67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8571428571, "max_line_length": 122, "alphanum_fraction": 0.6468561585, "num_tokens": 2566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.752012562644147, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.3234762099246249}} {"text": "REBOL [\n\tTitle: \"Generates Red/System float! tests\"\n\tAuthor: \"Peter W A Wood\"\n\tFile: \t %make-float32-auto-test.r\n\tVersion: 0.1.0\n\tRights: \"Copyright (C) 2012-2015 Peter W A Wood. All rights reserved.\"\n\tLicense: \"BSD-3 - https://github.com/red/red/blob/origin/BSD-3-License.txt\"\n]\n\n;; initialisations \ntests: copy \"\"\t\t\t\t\t\t\t;; string to hold generated tests\ntest-number: 0\t\t\t\t\t\t\t;; number of the generated test\nmake-dir %auto-tests/\nfile-out: %auto-tests/float32-auto-test.reds\n\n;; create a block of values to be used in the binary ops tests\ntest-values: [\n 0.0 \n -2147483.0 \n 2147483.0\n -1.0\n 3.0\n -7.0\n 5.0\n 456.7890\n 123456.7\n 1.222944E+22\n 9.99999E-7\n 7.7E18\n]\n\ntol: 1e-6 \n\n;; create blocks of operators to be applied\ntest-binary-ops: [\n\t+\n\t-\n\t*\n\t/\n]\n\ntest-comparison-ops: [\n\t=\n\t<>\n\t<\n\t>\n\t>=\n\t<=\n]\n\ntest-comparison-values: [\t\t\t;; these are relative not absolute\n\t-1E-6\n\t0.0\n\t+1E-6\n]\n \n\n;; create test file with header\nappend tests \"Red/System [^(0A)\"\nappend tests { Title: \"Red/System auto-generated float! tests\"^(0A)}\nappend tests {\tAuthor: \"Peter W A Wood\"^(0A)}\nappend tests { File: \t %floa32-auto-test.reds^(0A)}\nappend tests { License: \"BSD-3 - https://github.com/dockimbel/Red/blob/origin/BSD-3-License.txt\"^(0A)}\nappend tests \"]^(0A)^(0A)\"\nappend tests \"^(0A)^(0A)comment {\"\nappend tests \" This file is generated by make-float32-auto-test.r^(0A)\"\nappend tests \" Do not edit this file directly.^(0A)\"\nappend tests \"}^(0A)^(0A)\"\nappend tests join \";make-length:\" \n [length? read %make-float32-auto-test.r \"^(0A)^(0A)\"]\nappend tests \"#include %../../../../../quick-test/quick-test.reds^(0A)^(0A)\"\nappend tests {~~~start-file~~~ \"Auto-generated tests for float32\"^(0A)^(0A)}\nappend tests {===start-group=== \"Auto-generated tests for float32\"^(0A)^(0A)}\n\nwrite file-out tests\ntests: copy \"\"\n\n;; binary operator tests - in global context\nforeach op test-binary-ops [\n\tforeach operand1 test-values [\n\t\tforeach operand2 test-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif all [\n\t\t\t\tattempt [expected: to decimal! do reduce [operand1 op operand2]]\n\t\t\t\texpected < 3.3E38\n\t\t\t\texpected > 0.2E-37\n\t\t\t][\n\t\t\t \n\t\t\t\t;; test with literal values\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests \" --assertf32~= \"\n\t\t\t\tappend tests reform [\n\t\t\t\t\t\"as float32! \" expected \" ((\" \"as float32! \" operand1 \")\"\n\t\t\t\t\top \"( as float32! \" operand2 \") ) \" \"as float32! \" tol \"^(0A)\" \n\t\t\t ]\n\t\t\t \n\t\t\t ;; test with variables\n\t\t\t test-number: test-number + 1\n\t\t\t append tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t append tests join \" i: \" [\"as float32! \" operand1 \"^(0A)\"]\n\t\t\t append tests join \" j: \" [\"as float32! \" operand2 \"^(0A)\"]\n\t\t\t append tests rejoin [\" k: i \" op \" j^(0A)\"]\n\t\t\t append tests \" --assertf32~= \"\n\t\t\t append tests reform [\"as float32! \" expected \" k \" \"as float32! \" tol \"^(0A)\"]\n\t\t\t ;; write tests to file\n\t\t\t write/append file-out tests\n\t\t\t tests: copy \"\"\n\t\t\t]\n\t\t\trecycle\n\t\t]\n\t]\n]\n\n;; binary operator tests - inside a function\n\n;; write function spec\ntests: {\nfloat-auto-test-func: func [\n\t/local\n\t\ti [float32!]\n\t\tj [float32!]\n\t\tk [float32!]\n][\n}\n\nwrite/append file-out tests\ntests: copy \"\"\n\nforeach op test-binary-ops [\n\tforeach operand1 test-values [\n\t\tforeach operand2 test-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif all [\n\t\t\t\tattempt [expected: to decimal! do reduce [operand1 op operand2]]\n\t\t\t\texpected < 3.3E38\n\t\t\t\texpected > 0.2E-37\n\t\t\t][\n\t\t\t\t\n\t\t\t\t;; test with variables inside the function\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests join \" i: \" [\"as float32! \" operand1 \"^(0A)\"]\n\t\t\t\tappend tests join \" j: \" [\"as float32! \" operand2 \"^(0A)\"]\n\t\t\t\tappend tests rejoin [\" k: i \" op \" j^(0A)\"]\n\t\t\t\tappend tests \" --assertf32~= \"\n\t\t\t\tappend tests reform [\"as float32! \" expected \" k \" \"as float32! \" tol \"^(0A)\"]\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t;; write tests to file\n\t\t\t\twrite/append file-out tests\n\t\t\t\ttests: copy \"\" \n\t\t\t]\n\t\t\trecycle\n\t\t]\n\t]\n]\n\n;; write closing bracket and function call\nappend tests \" ]^(0a)\"\nappend tests \"float-auto-test-func^(0a)\"\nwrite/append file-out tests\ntests: copy \"\"\n\n\n;; comparison tests\nforeach op test-comparison-ops [\n\tforeach operand1 test-values [\n\t\tforeach oper2 test-comparison-values [\n\t\t\t;; only write a test if REBOL produces a result\n\t\t\tif all [\n\t\t\t\tattempt [operand2: operand1 + (operand1 * oper2)] \n\t\t\t\toper2 < 3.3E+38\n\t\t\t\toper2 > 0.2E-37\n\t\t\t\tnone <> attempt [expected: do reduce [operand1 op operand2]]\n\t\t\t][\n\t\t\t\ttest-number: test-number + 1\n\t\t\t\tappend tests join { --test-- \"float-auto-} [test-number {\"^(0A)}]\n\t\t\t\tappend tests \" --assert \"\n\t\t\t\tappend tests reform [\n\t\t\t\t expected \" = (\" \"(as float32! \" operand1 \")\" op \n\t\t\t\t \"(as float32! \" operand2 \") )^(0A)\"\n\t\t\t\t]\n\t\t\t\t\n\t\t\t\t;; write tests to file\n\t\t\t\twrite/append file-out tests\n\t\t\t\ttests: copy \"\"\n\t\t\t]\n\t\t]\n\t]\n]\n\n;; write file epilog\nappend tests \"^(0A)===end-group===^(0A)^(0A)\"\nappend tests {~~~end-file~~~^(0A)^(0A)}\n\nwrite/append file-out tests\n \nprint [\"Number of assertions generated\" test-number]\n", "meta": {"hexsha": "b9be032d11d6b0719d27d92a424f220e4147e62e", "size": 5341, "ext": "r", "lang": "R", "max_stars_repo_path": "system/tests/source/units/make-float32-auto-test.r", "max_stars_repo_name": "0xflotus/red", "max_stars_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_stars_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_stars_count": 5234, "max_stars_repo_stars_event_min_datetime": "2015-01-01T12:59:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:28:22.000Z", "max_issues_repo_path": "system/tests/source/units/make-float32-auto-test.r", "max_issues_repo_name": "0xflotus/red", "max_issues_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_issues_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_issues_count": 3406, "max_issues_repo_issues_event_min_datetime": "2015-01-02T08:53:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T17:47:35.000Z", "max_forks_repo_path": "system/tests/source/units/make-float32-auto-test.r", "max_forks_repo_name": "0xflotus/red", "max_forks_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_forks_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_forks_count": 509, "max_forks_repo_forks_event_min_datetime": "2015-01-27T21:26:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T10:10:32.000Z", "avg_line_length": 26.705, "max_line_length": 103, "alphanum_fraction": 0.5991387381, "num_tokens": 1660, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.3233808946074032}} {"text": "\ncompute.proportions = function( x, var=\"egg\" ) {\n \n x$dummy = 1\n ncrab=tapply( x$dummy, x$timevalue, FUN=length ) # number of primi crab on each day\n ncrab = as.data.frame( ncrab )\n ncrab$timevalue = as.numeric( rownames( ncrab))\n sub1=which( x[,var]==1 ) \n ncrab1=tapply( x$dummy[sub1], x$timevalue[sub1], FUN=length )\n ncrab1= as.data.frame (ncrab1)\n ncrab1$timevalue = as.numeric (rownames(ncrab1))\n prop = merge( ncrab, ncrab1, by=c(\"timevalue\"), all.x=T, all.y=F) # combine total number of crab with var==1\n prop[is.na(prop)]=0 # turn na's to zero\n prop$fraction=NA\n prop$fraction= prop$ncrab1/prop$ncrab \n return ( prop)\n\n} \n\n\n", "meta": {"hexsha": "6309fc498b1b213b267a882e513c7aa3c85ceac0", "size": 666, "ext": "r", "lang": "R", "max_stars_repo_path": "R/compute.proportions.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/compute.proportions.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/compute.proportions.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 31.7142857143, "max_line_length": 110, "alphanum_fraction": 0.6516516517, "num_tokens": 240, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228625116081, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.323205942731106}} {"text": "testData <- data.frame(Strata = paste(\"size \",1:5), numberTotalBoxes = c(1:3,4,4), numberSampledBoxes = 1)\n\n# kg/fish\ntestData$totalWeightBoxes <- testData$numberTotalBoxes*10 \n\ntestData$sampledWeightBoxes <- c(10, 10, 10, 10, 10)\n\ntestData$fishSampled<-testData$sampledWeight*1/c(7, 5.5, 3, 1.5, 1)", "meta": {"hexsha": "30b1ca54d4df92e85a4fe0517a5a3de3c01c08e1", "size": 299, "ext": "r", "lang": "R", "max_stars_repo_path": "Subgroup2/personal/Nuno/size_categories.r", "max_stars_repo_name": "AnaRibeiroSantos/WKRATIO", "max_stars_repo_head_hexsha": "bc84a8d70f1712cc74fdda3f0b94d7275f11289f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Subgroup2/personal/Nuno/size_categories.r", "max_issues_repo_name": "AnaRibeiroSantos/WKRATIO", "max_issues_repo_head_hexsha": "bc84a8d70f1712cc74fdda3f0b94d7275f11289f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-06-04T10:03:28.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-04T10:03:28.000Z", "max_forks_repo_path": "Subgroup2/personal/Nuno/size_categories.r", "max_forks_repo_name": "AnaRibeiroSantos/WKRATIO", "max_forks_repo_head_hexsha": "bc84a8d70f1712cc74fdda3f0b94d7275f11289f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-06-03T08:38:44.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-04T09:56:50.000Z", "avg_line_length": 37.375, "max_line_length": 106, "alphanum_fraction": 0.7224080268, "num_tokens": 116, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318479832805, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.323162982853419}} {"text": "library(cummeRbund)\n\nargs = commandArgs(trailingOnly=TRUE)\n\nsetwd(args[1])\n\ncuff = readCufflinks(args[2])\n\nsamples = strsplit(args[3], \",\", fixed = TRUE)[[1]]\nsamples = gsub(\"-\",\"_\",samples)\nsamples = gsub(\"\\\\.\", \"_\", samples)\nfor (i in 1:length(samples)) {samples[i] = paste(\"X\",samples[i],sep=\"\")}\n\nwrite.table(replicates(cuff), \"replicates.csv\", sep=\",\", quote=F)\nwrite.table(annotation(genes(cuff)), \"annotations.csv\", sep=\",\", quote=F)\nwrite.table(fpkm(genes(cuff)), \"fpkm-genes.csv\", sep=\",\", quote=F)\nwrite.table(repFpkm(genes(cuff)), \"repFpkm-genes.csv\", sep=\",\", quote=F)\nwrite.table(count(genes(cuff)), \"count-genes.csv\", sep=\",\", quote=F)\nwrite.table(fpkm(isoforms(cuff)), \"repFpkm-genes.csv\", sep=\",\", quote=F)\nwrite.table(diffData(genes(cuff)), \"diffData-genes.csv\", sep=\",\", quote=F)\n##write.table(, \"repFpkm-genes.csv\", sep=\",\", quote=F)\n##write.table(, \"repFpkm-genes.csv\", sep=\",\", quote=F)\n\n\ncsDensity(genes(cuff))\ncsDendro(genes(cuff))\ndispersionPlot(genes(cuff))\nfpkmSCVPlot(genes(cuff))\nfpkmSCVPlot(isoforms(cuff))\ncsBoxplot(genes(cuff))\n\ncsScatterMatrix(genes(cuff))\ncsVolcanoMatrix(genes(cuff))\nsigMatrix(cuff,level='genes',alpha=0.05)\nPCAplot(genes(cuff),\"PC1\",\"PC2\")\ncsDistHeat(genes(cuff))\n\n\ngene_diff_data=diffData(genes(cuff))\nsig_gene_data = subset(gene_diff_data,significant=='yes')\nsig_genes = getGenes(cuff,sig_gene_data$gene_id)\n\nwrite.table(sig_gene_data,'sig-diff-genes.csv',sep=',',quote=F)\n\n\npdf(file=\"expression-barplot.pdf\")\nlclsigs = getSig(cuff,alpha=0.05,level='genes')[1:10]\nexpressionBarplot(getGenes(cuff,lclsigs),logMode=T,showErrorbars=T)\ndev.off()\n\n\npdf(file=\"expression-heatmap.pdf\")\nlclsigs = getSig(cuff,alpha=0.05,level='genes')[1:10]\ncsHeatmap(getGenes(cuff,lclsigs),cluster='both')\ndev.off()\n", "meta": {"hexsha": "c361c06df939e345497e99dba4eac663d50d9ad0", "size": 1746, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/cummerbundrep.r", "max_stars_repo_name": "jsa-aerial/aerobio", "max_stars_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-01-23T16:08:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-17T19:40:12.000Z", "max_issues_repo_path": "Scripts/cummerbundrep.r", "max_issues_repo_name": "jsa-aerial/aerobio", "max_issues_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2017-06-08T19:17:52.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-04T21:26:30.000Z", "max_forks_repo_path": "Scripts/cummerbundrep.r", "max_forks_repo_name": "jsa-aerial/aerobio", "max_forks_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-03T02:18:02.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-03T02:18:02.000Z", "avg_line_length": 31.1785714286, "max_line_length": 74, "alphanum_fraction": 0.7130584192, "num_tokens": 558, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32316297522434834}} {"text": "library(openair)\r\n\r\ntimep <- function(data, pollutants, window) {\r\n timestamp <- as.POSIXct(window$center, tz=\"GMT\")\r\n\r\n chart <- timePlot(data,\r\n cols = \"hue\",\r\n group = FALSE,\r\n key.columns = 5,\r\n lwd = 1.5,\r\n pollutant = pollutants,\r\n windflow = list(col = \"grey50\", lwd = 1, scale = 0.05, length=0.05),\r\n ref.x = (list(v = timestamp, col = \"grey70\", lty = 2, lwd = 1.5)),\r\n y.relation = \"free\")\r\n\r\n chart$plot\r\n}\r\n\r\ncorrp <- function(data, pollutants) {\r\n chart <- corPlot(data, cols = \"default\", pollutant = pollutants)\r\n chart$plot\r\n}\r\n\r\nprosep <- function(data, pollutant) {\r\n data$dayHour <- format(data$date, \"%Y-%m-%d %H:00\")\r\n\r\n chart <- pollutionRose(data,\r\n angle=22.5,\r\n annotate = FALSE,\r\n cols = \"default\",\r\n grid.line = 25,\r\n key.position = \"right\",\r\n layout = c(5, 5),\r\n paddle = FALSE,\r\n pollutant = pollutant,\r\n type = \"dayHour\")\r\n chart$plot\r\n}\r\n\r\nwindp <- function(data) {\r\n data$dayHour <- format(data$date, \"%Y-%m-%d %H:00\")\r\n\r\n chart <- windRose(data,\r\n angle=22.5,\r\n breaks = c(0, 3, 6, 9, 12),\r\n cols = \"default\",\r\n grid.line = 25,\r\n key.footer = \"(km/h)\",\r\n key.position = \"right\",\r\n layout = c(5, 5),\r\n paddle = FALSE,\r\n type = \"dayHour\")\r\n\r\n chart$plot\r\n}\r\n", "meta": {"hexsha": "9053478f9bcd5cf99213c95f54fdfd68299d14c2", "size": 1815, "ext": "r", "lang": "R", "max_stars_repo_path": "plots.r", "max_stars_repo_name": "GaganKapoor/Air-Monitoring", "max_stars_repo_head_hexsha": "a33f4830b519bcf6707d462e197766a4b12825f7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plots.r", "max_issues_repo_name": "GaganKapoor/Air-Monitoring", "max_issues_repo_head_hexsha": "a33f4830b519bcf6707d462e197766a4b12825f7", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plots.r", "max_forks_repo_name": "GaganKapoor/Air-Monitoring", "max_forks_repo_head_hexsha": "a33f4830b519bcf6707d462e197766a4b12825f7", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4107142857, "max_line_length": 91, "alphanum_fraction": 0.3878787879, "num_tokens": 398, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.32316297522434834}} {"text": "library(cn.mops)\n\nBAMFiles <- list.files(pattern=\"sort.bam$\")\n\n# Setting the window length is important because the default will be extremely large.\nbamDataRanges <- getReadCountsFromBAM(BAMFiles, refSeqName = c(\"Ha4\", \"Ha16\"), mode = \"paired\", WL = 100)\nres <- cn.mops(bamDataRanges, normType = \"mean\")\nres <- calcIntegerCopyNumbers(res)\n\nsegm <- as.data.frame(segmentation(res))\nCNVs <- as.data.frame(cnvs(res))\nCNVRegions <- as.data.frame(cnvr(res))\n\nwrite.table(segm,file=\"segmentation.tsv\",sep=\"\\t\",quote=FALSE)\nwrite.table(CNVs,file=\"cnvs.tsv\",sep=\"\\t\",quote=FALSE)\t\nwrite.table(CNVRegions,file=\"cnvr.tsv\",quote=FALSE)\n\n# Look in the cnvs file to get the CNV IDs. The code below prints only the first called region\nplot(res, which=1)\n\n# By default this prints all chromosomes in the same plot.\nsegplot(res)", "meta": {"hexsha": "b68645c29575e14f71c12a76e85ed8fd3cc4b721", "size": 812, "ext": "r", "lang": "R", "max_stars_repo_path": "gene_annotation/r_scripts/cnmops.r", "max_stars_repo_name": "sestaton/sesbio", "max_stars_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 15, "max_stars_repo_stars_event_min_datetime": "2015-01-14T17:25:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-09T01:15:18.000Z", "max_issues_repo_path": "gene_annotation/r_scripts/cnmops.r", "max_issues_repo_name": "sestaton/sesbio", "max_issues_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gene_annotation/r_scripts/cnmops.r", "max_forks_repo_name": "sestaton/sesbio", "max_forks_repo_head_hexsha": "a50c08d47db810669f257e6fce0b05a1bd3db24c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-10-09T02:56:51.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-02T12:07:39.000Z", "avg_line_length": 36.9090909091, "max_line_length": 105, "alphanum_fraction": 0.7376847291, "num_tokens": 224, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.689305616785446, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.32314001202983866}} {"text": "# Methods to find the best alpha, beta, tau parameters in the \n# likelihood function of the EM\n##############################################################\nlibrary(doParallel)\nlibrary(foreach)\n\nopt.grid <- function(Q, params0, ...){\n source('likelihood.r')\n \n # Grid search\n cl <- makeCluster(detectCores()-3);\n registerDoParallel(cl)\n df.results <- foreach(alpha = seq(0.01, 0.1, by=0.01), .combine = rbind, .packages=c('foreach'), .export=c(\"likelihood.post\")) %dopar% {\n foreach(beta = seq(0.01, 0.14, by=0.01), .combine = rbind) %do% {\n foreach(tau = seq(0.01, 0.1, by=0.01), .combine = rbind) %do% {\n like <- Q(alpha, beta, tau, ...)\n data.frame(alpha = alpha, beta = beta, tau = tau, like = like)\n }\n }\n }\n stopCluster(cl)\n df.results$like <- df.results$like - max(df.results$like)\n df.results#[which.max(df.results$like),]\n}\n\n#pis_k <- c(0.5,0.5)\nif(FALSE){\n df.results <- opt.grid(Qopt, params0=c(1,2,3), \n resp_k = responsabilities_k,\n df.trees = df.trees)\n}\n\n########################################################\n########################################################\nif(FALSE){\noutfun <- function(x, optimValues, state){\n cat('\\n', x)\n}\nopt <- optimset(OutputFcn=outfun, MaxFunEvals=10)\nopt.fminnd <- function(Q){\n \n fminbnd(fun = cost.function, x0=c(alphas[k],betas[k],taus[k]), \n xmin=c(0.09,0.09,0.09), xmax=c(10,200,0.99), optimset(OutputFcn=outfun.bar))\n xopt <- neldermead.get(sol, \"xopt\")\n}\n}", "meta": {"hexsha": "ee73c70e43e79e82f47e921cfa0b556af3c153c7", "size": 1603, "ext": "r", "lang": "R", "max_stars_repo_path": "R/optimization.r", "max_stars_repo_name": "alumbreras/dynamic-thread-stochastic-model", "max_stars_repo_head_hexsha": "96b7943754bcdba688391244f4fc8251a60a36ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/optimization.r", "max_issues_repo_name": "alumbreras/dynamic-thread-stochastic-model", "max_issues_repo_head_hexsha": "96b7943754bcdba688391244f4fc8251a60a36ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/optimization.r", "max_forks_repo_name": "alumbreras/dynamic-thread-stochastic-model", "max_forks_repo_head_hexsha": "96b7943754bcdba688391244f4fc8251a60a36ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.847826087, "max_line_length": 138, "alphanum_fraction": 0.505926388, "num_tokens": 440, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7577943712746406, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.32306410318532897}} {"text": "library(lattice)\n\nnom_metrics <- read.csv('metrics_nom_clf.csv')\n\ncolors <- palette(rainbow(nrow(nom_metrics))) \n\nnom_table <- data.matrix(nom_metrics, rownames.force=NA)\n\nnom_table <- nom_table[, -1]\n\nbarplot(nom_table, main=\"Nominee Metrics\", xlab=\"Metric\", col=colors, beside=TRUE)\n\npar(xpd=TRUE)\nlegend(17, 1.07, nom_metrics$Algorithm, lty=c(1,1), lwd=c(2.5, 2.5), col=colors)", "meta": {"hexsha": "61219f9f3d8563cf2a16b97327b582f069f372ba", "size": 380, "ext": "r", "lang": "R", "max_stars_repo_path": "r-scripts/nominations_metrics.r", "max_stars_repo_name": "mahon94/oscars-prediction", "max_stars_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-11-15T18:22:40.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-15T18:22:40.000Z", "max_issues_repo_path": "r-scripts/nominations_metrics.r", "max_issues_repo_name": "mahon94/oscars-prediction", "max_issues_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r-scripts/nominations_metrics.r", "max_forks_repo_name": "mahon94/oscars-prediction", "max_forks_repo_head_hexsha": "a603498f2953f066c8fe43716c81f238296b4920", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1428571429, "max_line_length": 82, "alphanum_fraction": 0.7263157895, "num_tokens": 123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3228420954946003}} {"text": "library(SparkR)\n# sparkR.session()\nsparkR.session(master = \"local[*]\", sparkConfig = list(spark.driver.memory = \"2g\"))\n\n\nsparkDF <- read.df(\"../data/flight-data/csv/2015-summary.csv\",\n source = \"csv\", header=\"true\", inferSchema = \"true\")\ntake(sparkDF, 5)\n\n# COMMAND ----------\n\ncollect(orderBy(sparkDF, \"count\"), 20)\n\n\n# COMMAND ----------\n\nlibrary(magrittr)\nsparkDF %>%\n orderBy(desc(sparkDF$count)) %>%\n groupBy(\"ORIGIN_COUNTRY_NAME\") %>%\n count() %>%\n limit(10) %>%\n collect()\n\n\n# COMMAND ----------\n\n", "meta": {"hexsha": "b751703e2845b8794f10d2e5157459ae00d8427c", "size": 517, "ext": "r", "lang": "R", "max_stars_repo_path": "code/A_Gentle_Introduction_to_Spark-Chapter_3_A_Tour_of_Sparks_Toolset.r", "max_stars_repo_name": "kalona/Spark-The-Definitive-Guide", "max_stars_repo_head_hexsha": "0b495c4710b2030aa59d5a7f4053ee0a8345d0d8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-01-02T14:24:29.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-02T15:54:47.000Z", "max_issues_repo_path": "code/A_Gentle_Introduction_to_Spark-Chapter_3_A_Tour_of_Sparks_Toolset.r", "max_issues_repo_name": "kalona/Spark-The-Definitive-Guide", "max_issues_repo_head_hexsha": "0b495c4710b2030aa59d5a7f4053ee0a8345d0d8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/A_Gentle_Introduction_to_Spark-Chapter_3_A_Tour_of_Sparks_Toolset.r", "max_forks_repo_name": "kalona/Spark-The-Definitive-Guide", "max_forks_repo_head_hexsha": "0b495c4710b2030aa59d5a7f4053ee0a8345d0d8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.4642857143, "max_line_length": 83, "alphanum_fraction": 0.6054158607, "num_tokens": 145, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.32284209549460025}} {"text": "options(scipen=999)\n\n# read in window summary\nx <- read.table(\"wbnu_window_summary.txt\", header=T, stringsAsFactors=F)\n\n# define window size\nwindow_size <- x$end[1] - x$start[1] + 1\n\n# define number of windows for line plots\nnum_windows <- 10\n\n# set up row numbers for line plots\nline_rows <- seq(from=1,to=nrow(x), by=1)[seq(from=1,to=nrow(x), by=1) %% num_windows == 0]\n\n# what are the unique chromosomes and their bounding areas for plotting?\ntotal_windows <- nrow(x)\nscaffold <- unique(x$scaffold)\nchr_polygons <- list()\n# make the plotting polygons\nfor(a in 1:length(scaffold)) {\n\ta1 <- as.numeric(rownames(x))[x[,1] == scaffold[a]]\n\ta2 <- a1[length(a1)]\n\ta1 <- a1[1]\n\tchr_polygons[[a]] <- rbind(c(a1, 0), c(a2, 0), c(a2, 0.5), c(a1, 0.5), c(a1, 0))\n}\n\n########################################################################\n########################################################################\n########################################################################\n########################################################################\n# set up plotting dimensions\npar(mfrow=c(2,1))\npar(mar=c(1,5,1,0))\n\n########################################################################\n########################################################################\n########################################################################\n########################################################################\n# plot CDS\nplot(c(-1,-1), ylim=c(0,0.2), xlim=c(1, total_windows), xaxt=\"n\", col=\"white\", bty=\"n\", cex.axis=1.1, cex.lab=1.3, ylab=\"CDS Content\")\nodd <- 0\nfor(a in 1:length(chr_polygons)) {\n\tif(odd == 1) {\n\t\tpolygon(chr_polygons[[a]], col=\"snow2\", border=\"white\")\n\t\todd <- 0\t\n\t} else {\n\t\todd <- 1\n\t}\n}\n# plot\npoints(as.numeric(rownames(x)), x$cds, pch=19, cex=0.1, col=\"gray85\")\n\n# plot sliding mean line plots\nx_windows <- as.numeric(rownames(x))\nline_x_axis <- line_rows\nline_y_axis <- list()\nline_scaffold <- list()\nfor(b in line_rows) {\n\tline_y_axis[[b]] <- mean(na.omit(x$cds[x_windows %in% (b - (floor(num_windows / 2))):(b + (floor(num_windows / 2))) & x$scaffold == x$scaffold[as.numeric(rownames(x)) == b]][1:num_windows]))\n\tline_scaffold[[b]] <- x$scaffold[x_windows == b] \n}\nline_plotting <- data.frame(line_x_axis=as.numeric(line_x_axis), line_y_axis=as.numeric(unlist(line_y_axis)), line_scaffold=as.character(unlist(line_scaffold)))\n# plot each scaffold at a time (so lines don't connect between scaffolds)\nfor(a in 1:length(unique(scaffold))) {\n\ta_rep <- line_plotting[line_plotting[,3] == unique(scaffold)[a],]\n\tlines(a_rep[,1:2], lwd=0.8, col=\"gray55\")\n}\n########################################################################\n########################################################################\n########################################################################\n########################################################################\n# plot repeats\nplot(c(-1,-1), ylim=c(0,0.4), xlim=c(1, total_windows), xaxt=\"n\", col=\"white\", bty=\"n\", cex.axis=1.1, cex.lab=1.3, ylab=\"Repeat Content\")\nodd <- 0\nfor(a in 1:length(chr_polygons)) {\n\tif(odd == 1) {\n\t\tpolygon(chr_polygons[[a]], col=\"snow2\", border=\"white\")\n\t\todd <- 0\t\n\t} else {\n\t\todd <- 1\n\t}\n}\n# plot\npoints(as.numeric(rownames(x)), x$repeats, pch=19, cex=0.1, col=\"gray85\")\n\n# plot sliding mean line plots\nx_windows <- as.numeric(rownames(x))\nline_x_axis <- line_rows\nline_y_axis <- list()\nline_scaffold <- list()\nfor(b in line_rows) {\n\tline_y_axis[[b]] <- mean(na.omit(x$repeats[x_windows %in% (b - (floor(num_windows / 2))):(b + (floor(num_windows / 2))) & x$scaffold == x$scaffold[as.numeric(rownames(x)) == b]][1:num_windows]))\n\tline_scaffold[[b]] <- x$scaffold[x_windows == b] \n}\nline_plotting <- data.frame(line_x_axis=as.numeric(line_x_axis), line_y_axis=as.numeric(unlist(line_y_axis)), line_scaffold=as.character(unlist(line_scaffold)))\n# plot each scaffold at a time (so lines don't connect between scaffolds)\nfor(a in 1:length(unique(scaffold))) {\n\ta_rep <- line_plotting[line_plotting[,3] == unique(scaffold)[a],]\n\tlines(a_rep[,1:2], lwd=0.8, col=\"gray55\")\n}\n\n\n\n\n\n\n\n", "meta": {"hexsha": "784dda23a4aa8c4bd29f9e8a77c3bd97b43e7124", "size": 4068, "ext": "r", "lang": "R", "max_stars_repo_path": "11_plot_window_TE_CDS.r", "max_stars_repo_name": "jdmanthey/wbnu_annotation", "max_stars_repo_head_hexsha": "26e5b565874bd9ebdc5d035b9e21e59f25c0e654", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "11_plot_window_TE_CDS.r", "max_issues_repo_name": "jdmanthey/wbnu_annotation", "max_issues_repo_head_hexsha": "26e5b565874bd9ebdc5d035b9e21e59f25c0e654", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "11_plot_window_TE_CDS.r", "max_forks_repo_name": "jdmanthey/wbnu_annotation", "max_forks_repo_head_hexsha": "26e5b565874bd9ebdc5d035b9e21e59f25c0e654", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.6666666667, "max_line_length": 195, "alphanum_fraction": 0.5277777778, "num_tokens": 1110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283033, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3228420954946002}} {"text": "library(igraphdata)\nlibrary(igraph)\nlibrary(yenpathy)\ndata(\"UKfaculty\")\nk_shortest_paths(UKfaculty, 1, 2, 1000)\n\n# Run this from console to check for memory leaks\n# make sure valgrind is installed for this to work\n# R -d \"valgrind --leak-check=full\" -e \"source('tests/find_memory_leaks.r')\"\n\n", "meta": {"hexsha": "36bc3c82eed09f5c4d4160f8f9703e5a82fa25ee", "size": 292, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/find_memory_leaks.r", "max_stars_repo_name": "tilltnet/yenpathy", "max_stars_repo_head_hexsha": "2488df7138b023907a0cf40cf730fc67b7315a49", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/find_memory_leaks.r", "max_issues_repo_name": "tilltnet/yenpathy", "max_issues_repo_head_hexsha": "2488df7138b023907a0cf40cf730fc67b7315a49", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/find_memory_leaks.r", "max_forks_repo_name": "tilltnet/yenpathy", "max_forks_repo_head_hexsha": "2488df7138b023907a0cf40cf730fc67b7315a49", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5454545455, "max_line_length": 76, "alphanum_fraction": 0.7568493151, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.32282036723073554}} {"text": "#!/usr/bin/env Rscript\n\nargs=commandArgs(trailingOnly=TRUE)\n#if (length(args)<15) {\n# stop(\"Must supply inputs\\n\",call.=FALSE)\n#}\n\nlibrary(GSEABase)\nlibrary(GSVA)\n\n\nmethod=args[1]\nkcdf=args[2]\nabs.ranking=as.logical(toupper(args[3]))\nmin.sz=as.numeric(args[4])\nmax.sz=args[5]\nif(max.sz=='None') {\n max.sz = Inf\n} else {\n max.sz = as.numeric(max.sz)\n}\nparallel.sz=as.numeric(args[6])\nparallel.type=args[7]\nmx.diff=as.logical(toupper(args[8]))\ntau=args[9]\nssgsea.norm=as.logical(toupper(args[10]))\nverbose=as.logical(toupper(args[11]))\ntempdir=args[12]\n\n\ngeneSets = getGmt(file.path(tempdir,'gs.gmt'))\nmat = as.matrix(read.csv(file.path(tempdir,'expr.csv'),header=TRUE,row.names=1,check.names=FALSE))\n\noutput = NA\nif(tau != 'None') {\n if(tau=='NA') { tau=NA}\n else { tau = as.numeric(tau)}\n output = gsva(mat,geneSets,\n method=method,\n kcdf=kcdf,\n abs.ranking=abs.ranking,\n min.sz=min.sz,\n max.sz=max.sz,\n parallel.sz=parallel.sz,\n parallel.type=parallel.type,\n mx.diff=mx.diff,\n tau=tau,\n ssgsea.norm=ssgsea.norm,\n verbose=verbose\n )\n} else {\n output = gsva(mat,geneSets,\n method=method,\n kcdf=kcdf,\n abs.ranking=abs.ranking,\n min.sz=min.sz,\n max.sz=max.sz,\n parallel.sz=parallel.sz,\n parallel.type=parallel.type,\n mx.diff=mx.diff,\n ssgsea.norm=ssgsea.norm,\n verbose=verbose\n )\n}\nofile = file.path(tempdir,\"pathways.csv\")\nprint(ofile)\nwrite.csv(output,ofile)\n", "meta": {"hexsha": "192b623c39d82a13087b6cf2e26fa09be50a76cb", "size": 1530, "ext": "r", "lang": "R", "max_stars_repo_path": "GSVA/gsva.r", "max_stars_repo_name": "jason-weirather/GSVA", "max_stars_repo_head_hexsha": "179bee2c4ba6fe7790b879b704713e3e4263b710", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2018-11-05T03:21:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-12T16:23:54.000Z", "max_issues_repo_path": "GSVA/gsva.r", "max_issues_repo_name": "jason-weirather/GSVA", "max_issues_repo_head_hexsha": "179bee2c4ba6fe7790b879b704713e3e4263b710", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2018-09-15T20:51:02.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-28T21:19:03.000Z", "max_forks_repo_path": "GSVA/gsva.r", "max_forks_repo_name": "jason-weirather/GSVA", "max_forks_repo_head_hexsha": "179bee2c4ba6fe7790b879b704713e3e4263b710", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2018-11-05T03:49:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-07T19:55:56.000Z", "avg_line_length": 22.5, "max_line_length": 98, "alphanum_fraction": 0.6254901961, "num_tokens": 451, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3228203672307354}} {"text": "#' Generates and evolve community under given parameters.\n#'\n#' @param arena_size Size of square landscape.\n#' @param arena_roughness Level of spatial autocorrelation of landscape.\n#' @param phylo_b Birth rate of phylogenetic tree.\n#' @param phylo_d Death rate of phylogenetic tree.\n#' @param phylo_n_taxa A number of species in the tree.\n#' @param trait_model A model, under which trait should be generated (\"BM\" or \"OU\").\n#' @param trait_sigma A standard-deviation of the random component for each branch in trait simulation.\n#' @param trait_alpha A strength of the selective constraint for each branch in OU model.\n#' @param com_range A number, range in which modeling species interact.\n#' @param betaEnv A number, coefficient for environmental filtering.\n#' @param betaComp A number, coefficient for limiting similarity.\n#' @param com_nu A number, rate of speciation.\n#' @param com_sigma A number, niche width.\n#' @param sim_n_iter A number of iterations of community simulation.\n#' @param be_verbose Boolean, returns messages during the execution.\n#' @param sim_verb_iter A number of iterations in community simulation after which the message is returned.\n#'\n#' @return A list, community data.\n#' @export\n#'\n#' @examples\n#' create_com(arena_size = 258, arena_roughness = 0.75,\n#' phylo_b = 0.1, phylo_d = 0, phylo_n_taxa = 500,\n#' trait_model = \"BM\", trait_sigma = 0.1, trait_alpha = 0.1,\n#' com_range = 3, betaEnv = 0, betaComp = 0,\n#' com_nu = 0.001, com_sigma = 10,\n#' sim_n_iter = 2000, be_verbose = T, sim_verb_iter = 500)\n\ncreate_com <- function(arena_size = 258, arena_roughness = 0.75,\n phylo_b = 0.1, phylo_d = 0, phylo_n_taxa = 500,\n trait_model = \"BM\", trait_sigma = 0.1, trait_alpha = 0.1,\n com_range = 3, betaEnv = 0, betaComp = 0,\n com_nu = 0.001, com_sigma = 10,\n sim_n_iter = 2000, be_verbose = T, sim_verb_iter = 500){\n\n mat <- sim_arena(size = arena_size, roughness = arena_roughness)\n tree_n_traits <- sim_tree_n_traits(b = phylo_b, d = phylo_d, n_taxa = phylo_n_taxa,\n model = trait_model, sigma = trait_sigma, alpha = trait_alpha)\n\n com <- createCom(mat, tree_n_traits$trait, range = com_range,\n betaEnv = betaEnv, betaComp = betaComp,\n nu = com_nu, sigma = com_sigma)\n\n if (be_verbose){\n print('Community is generated. Initiating community simulation.')\n }\n com <- evolve_com(com, n_iter = sim_n_iter, be_verbose = be_verbose, verb_iter = sim_verb_iter)\n com <- append(com, list(tree_n_traits$phylo))\n names(com)[16] <- 'phylo'\n\n if (be_verbose){\n print('Community is simulated.')\n }\n\n return(com)\n\n}\n", "meta": {"hexsha": "ecd6d3f2fe5de4cf93122f3869125f1048d82c2f", "size": 2727, "ext": "r", "lang": "R", "max_stars_repo_path": "R/create_com.r", "max_stars_repo_name": "arrirh/sim.assembly", "max_stars_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/create_com.r", "max_issues_repo_name": "arrirh/sim.assembly", "max_issues_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/create_com.r", "max_forks_repo_name": "arrirh/sim.assembly", "max_forks_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.45, "max_line_length": 107, "alphanum_fraction": 0.6776677668, "num_tokens": 730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3228203603159969}} {"text": "set.seed(1)\nM = 512\nN = 6\np = matrix(rnorm(M * N), N, M)\nwrite.table(p,\n file = 'test01.512',\n row.names = FALSE,\n col.names = TRUE,\n quote = FALSE)\n", "meta": {"hexsha": "2cf26976553511f4c572653a535879dfe3c3a421", "size": 153, "ext": "r", "lang": "R", "max_stars_repo_path": "test/data0A/test01.r", "max_stars_repo_name": "wnfldchen/agent", "max_stars_repo_head_hexsha": "af915c89b89738159f331531aaceb610dc9e380c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test/data0A/test01.r", "max_issues_repo_name": "wnfldchen/agent", "max_issues_repo_head_hexsha": "af915c89b89738159f331531aaceb610dc9e380c", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/data0A/test01.r", "max_forks_repo_name": "wnfldchen/agent", "max_forks_repo_head_hexsha": "af915c89b89738159f331531aaceb610dc9e380c", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 15.3, "max_line_length": 30, "alphanum_fraction": 0.5816993464, "num_tokens": 58, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3227963792178653}} {"text": "#+-----------------------------------------------------------------+\n#| AV1_modelos_linguagem_programacao.r |\n#| Copyright 2020, Carlos Bezerra Vilela. |\n#| https://github.com/carlosvilela/exemplosR |\n#+-----------------------------------------------------------------+\n\n# definindo a estrutura dos dados\nsetClass(\n \"vetorN\",\n slots = list(\n tamanhoN = \"numeric\",\n vetor = \"numeric\",\n valorMaximo = \"numeric\",\n valorDesvioPadrao = \"numeric\",\n somatorio = \"numeric\"\n )\n)\n\n# Criando uma constante que definir\u00e1 o range dos poss\u00edveis valores do vetor\nrangeVetor = 1:100\n\n# obtendo o tamanho do vetor\ntamanhoVetor = 10\n\n# Gerando o vetor\ngerarVetor<- sample(rangeVetor, tamanhoVetor, replace=TRUE, prob=NULL)\n\n# obtendo o numero m\u00e1ximo que foi registrado no vetor\nnumeroMaximo = as.integer(max(gerarVetor))\n\n# Calculando o desvio padr\u00e3o\ndesvioPadrao = sd(gerarVetor)\n\n# Calculando o somat\u00f3rio\nsomaVetor = sum(gerarVetor)\n\n# registrando e estruturando os dados\nvetor <- new(\"vetorN\",\n tamanhoN = tamanhoVetor,\n vetor = gerarVetor,\n valorMaximo = numeroMaximo,\n valorDesvioPadrao = desvioPadrao,\n somatorio = somaVetor\n)\n\n# exibindo resultado\nprint(str(vetor))\n\nprint(vetor@tamanhoN)\nprint(vetor@vetor)\nprint(vetor@valorMaximo)\nprint(vetor@valorDesvioPadrao)\nprint(vetor@somatorio)\n", "meta": {"hexsha": "3fb8e17cf5c0d5eae36c6a446112cc6edb08938b", "size": 1464, "ext": "r", "lang": "R", "max_stars_repo_path": "AV1_modelos_linguagem_programacao.r", "max_stars_repo_name": "carlosvilela/exemplosR", "max_stars_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "AV1_modelos_linguagem_programacao.r", "max_issues_repo_name": "carlosvilela/exemplosR", "max_issues_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AV1_modelos_linguagem_programacao.r", "max_forks_repo_name": "carlosvilela/exemplosR", "max_forks_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1111111111, "max_line_length": 75, "alphanum_fraction": 0.5901639344, "num_tokens": 399, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.607663184043154, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.3227963792178653}} {"text": "#' @include translate-sql-helpers.r\n#' @include sql-escape.r\nNULL\n\n\nsql_if <- function(cond, if_true, if_false = NULL) {\n build_sql(\n \"CASE WHEN (\", cond, \")\",\n \" THEN (\", if_true, \")\",\n if (!is.null(if_false)) build_sql(\" ELSE (\", if_false, \")\"),\n \" END\"\n )\n}\n\n#' @export\n#' @rdname sql_variant\n#' @format NULL\nbase_scalar <- sql_translator(\n `+` = sql_infix(\"+\"),\n `*` = sql_infix(\"*\"),\n `/` = sql_infix(\"/\"),\n `%%` = sql_infix(\"%\"),\n `^` = sql_prefix(\"power\", 2),\n `-` = function(x, y = NULL) {\n if (is.null(y)) {\n if (is.numeric(x)) {\n -x\n } else {\n build_sql(sql(\"-\"), x)\n }\n } else {\n build_sql(x, sql(\" - \"), y)\n }\n },\n\n `!=` = sql_infix(\"!=\"),\n `==` = sql_infix(\"=\"),\n `<` = sql_infix(\"<\"),\n `<=` = sql_infix(\"<=\"),\n `>` = sql_infix(\">\"),\n `>=` = sql_infix(\">=\"),\n\n `!` = sql_prefix(\"not\"),\n `&` = sql_infix(\"and\"),\n `&&` = sql_infix(\"and\"),\n `|` = sql_infix(\"or\"),\n `||` = sql_infix(\"or\"),\n xor = function(x, y) {\n sql(sprintf(\"%1$s OR %2$s AND NOT (%1$s AND %2$s)\", escape(x), escape(y)))\n },\n\n abs = sql_prefix(\"abs\", 1),\n acos = sql_prefix(\"acos\", 1),\n acosh = sql_prefix(\"acosh\", 1),\n asin = sql_prefix(\"asin\", 1),\n asinh = sql_prefix(\"asinh\", 1),\n atan = sql_prefix(\"atan\", 1),\n atan2 = sql_prefix(\"atan2\", 2),\n atanh = sql_prefix(\"atanh\", 1),\n ceil = sql_prefix(\"ceil\", 1),\n ceiling = sql_prefix(\"ceil\", 1),\n cos = sql_prefix(\"cos\", 1),\n cosh = sql_prefix(\"cosh\", 1),\n cot = sql_prefix(\"cot\", 1),\n coth = sql_prefix(\"coth\", 1),\n exp = sql_prefix(\"exp\", 1),\n floor = sql_prefix(\"floor\", 1),\n log = function(x, base = exp(1)) {\n build_sql(sql(\"log\"), list(x, base))\n },\n log10 = sql_prefix(\"log10\", 1),\n round = sql_prefix(\"round\", 2),\n sign = sql_prefix(\"sign\", 1),\n sin = sql_prefix(\"sin\", 1),\n sinh = sql_prefix(\"sinh\", 1),\n sqrt = sql_prefix(\"sqrt\", 1),\n tan = sql_prefix(\"tan\", 1),\n\n tolower = sql_prefix(\"lower\", 1),\n toupper = sql_prefix(\"upper\", 1),\n nchar = sql_prefix(\"length\", 1),\n\n `if` = sql_if,\n if_else = sql_if,\n ifelse = sql_if,\n\n sql = function(...) sql(...),\n `(` = function(x) {\n build_sql(\"(\", x, \")\")\n },\n `{` = function(x) {\n build_sql(\"(\", x, \")\")\n },\n desc = function(x) {\n build_sql(x, sql(\" DESC\"))\n },\n\n is.null = function(x) {\n build_sql(\"(\", x, \") IS NULL\")\n },\n is.na = function(x) {\n build_sql(\"(\", x, \") IS NULL\")\n },\n na_if = sql_prefix(\"NULL_IF\", 2),\n\n as.numeric = function(x) build_sql(\"CAST(\", x, \" AS NUMERIC)\"),\n as.integer = function(x) build_sql(\"CAST(\", x, \" AS INTEGER)\"),\n as.character = function(x) build_sql(\"CAST(\", x, \" AS TEXT)\"),\n\n c = function(...) escape(c(...)),\n `:` = function(from, to) escape(from:to),\n\n between = function(x, left, right) {\n build_sql(x, \" BETWEEN \", left, \" AND \", right)\n },\n\n pmin = sql_prefix(\"min\"),\n pmax = sql_prefix(\"max\"),\n\n `__dplyr_colwise_fun` = function(...) {\n stop(\"colwise verbs only accept bare functions with local sources\",\n call. = FALSE)\n }\n)\n\nbase_symbols <- sql_translator(\n pi = sql(\"PI()\"),\n `*` = sql(\"*\"),\n `NULL` = sql(\"NULL\")\n)\n\n#' @export\n#' @rdname sql_variant\n#' @format NULL\nbase_agg <- sql_translator(\n # SQL-92 aggregates\n # http://db.apache.org/derby/docs/10.7/ref/rrefsqlj33923.html\n n = sql_prefix(\"count\"),\n mean = sql_prefix(\"avg\", 1),\n var = sql_prefix(\"variance\", 1),\n sum = sql_prefix(\"sum\", 1),\n min = sql_prefix(\"min\", 1),\n max = sql_prefix(\"max\", 1),\n n_distinct = function(x) {\n build_sql(\"COUNT(DISTINCT \", x, \")\")\n }\n)\n\n#' @export\n#' @rdname sql_variant\n#' @format NULL\nbase_win <- sql_translator(\n # rank functions have a single order argument that overrides the default\n row_number = win_rank(\"row_number\"),\n min_rank = win_rank(\"rank\"),\n rank = win_rank(\"rank\"),\n dense_rank = win_rank(\"dense_rank\"),\n percent_rank = win_rank(\"percent_rank\"),\n cume_dist = win_rank(\"cume_dist\"),\n ntile = function(order_by, n) {\n over(\n build_sql(\"NTILE\", list(as.integer(n))),\n partition_group(),\n order_by %||% partition_order()\n )\n },\n\n # Recycled aggregate fuctions take single argument, don't need order and\n # include entire partition in frame.\n mean = win_recycled(\"avg\"),\n sum = win_recycled(\"sum\"),\n min = win_recycled(\"min\"),\n max = win_recycled(\"max\"),\n n = function() {\n over(sql(\"COUNT(*)\"), partition_group())\n },\n\n # Cumulative function are like recycled aggregates except that R names\n # have cum prefix, order_by is inherited and frame goes from -Inf to 0.\n cummean = win_cumulative(\"mean\"),\n cumsum = win_cumulative(\"sum\"),\n cummin = win_cumulative(\"min\"),\n cummax = win_cumulative(\"max\"),\n\n # Finally there are a few miscellaenous functions that don't follow any\n # particular pattern\n nth = function(x, order = NULL) {\n over(build_sql(\"NTH_VALUE\", list(x)), partition_group(), order %||% partition$order())\n },\n first = function(x, order = NULL) {\n over(build_sql(\"FIRST_VALUE\", list(x)), partition_group(), order %||% partition_order())\n },\n last = function(x, order = NULL) {\n over(build_sql(\"LAST_VALUE\", list(x)), partition_group(), order %||% partition_order())\n },\n\n lead = function(x, n = 1L, default = NA, order = NULL) {\n over(\n build_sql(\"LEAD\", list(x, n, default)),\n partition_group(),\n order %||% partition_order()\n )\n },\n lag = function(x, n = 1L, default = NA, order = NULL) {\n over(\n build_sql(\"LAG\", list(x, n, default)),\n partition_group(),\n order %||% partition_order()\n )\n },\n\n order_by = function(order_by, expr) {\n old <- set_partition(partition_group(), order_by)\n on.exit(set_partition(old))\n\n expr\n }\n)\n\n#' @export\n#' @rdname sql_variant\n#' @format NULL\nbase_no_win <- sql_translator(\n row_number = win_absent(\"row_number\"),\n min_rank = win_absent(\"rank\"),\n rank = win_absent(\"rank\"),\n dense_rank = win_absent(\"dense_rank\"),\n percent_rank = win_absent(\"percent_rank\"),\n cume_dist = win_absent(\"cume_dist\"),\n ntile = win_absent(\"ntile\"),\n mean = win_absent(\"avg\"),\n sum = win_absent(\"sum\"),\n min = win_absent(\"min\"),\n max = win_absent(\"max\"),\n n = win_absent(\"n\"),\n cummean = win_absent(\"mean\"),\n cumsum = win_absent(\"sum\"),\n cummin = win_absent(\"min\"),\n cummax = win_absent(\"max\"),\n nth = win_absent(\"nth_value\"),\n first = win_absent(\"first_value\"),\n last = win_absent(\"last_value\"),\n lead = win_absent(\"lead\"),\n lag = win_absent(\"lag\"),\n order_by = win_absent(\"order_by\")\n)\n", "meta": {"hexsha": "ccfb0a396589536d3ef2f54ddb182113aa6f6af0", "size": 6818, "ext": "r", "lang": "R", "max_stars_repo_path": "R/translate-sql-base.r", "max_stars_repo_name": "sctyner/dplyr050", "max_stars_repo_head_hexsha": "2cbd6a865be61043b1076785db6d9e076ee4b1d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-03-20T02:46:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-03-20T02:46:38.000Z", "max_issues_repo_path": "R/translate-sql-base.r", "max_issues_repo_name": "sctyner/dplyr050", "max_issues_repo_head_hexsha": "2cbd6a865be61043b1076785db6d9e076ee4b1d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/translate-sql-base.r", "max_forks_repo_name": "sctyner/dplyr050", "max_forks_repo_head_hexsha": "2cbd6a865be61043b1076785db6d9e076ee4b1d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.6032388664, "max_line_length": 92, "alphanum_fraction": 0.5604282781, "num_tokens": 2082, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3227963792178653}} {"text": "#' @export\nstr.float32 = function(object, digits.d=strO$digits.d, ...)\n{\n strO = utils::strOptions()\n MAXLEN = 10L\n len = length(object)\n printlen = min(len, MAXLEN)\n \n # this is hideous, but I don't think there's any way to play the same game with S4\n s = DATA(object)[1:printlen, drop=TRUE]\n vals = dbl(float32(s))\n \n cat(\"Formal class 'float32' [package \\\"float\\\"] with 1 slot\\n\")\n \n if (isavec(object))\n cat(paste0(\"..@ Data: int [1:\", len, \"] \"))\n else\n cat(paste0(\"..@ Data: int [1:\", nrow(object), \", 1:\", ncol(object), \"] \"))\n \n cat(format(vals, digits=digits.d))\n if (length(vals) < length(object))\n cat(\" ...\")\n \n cat(\"\\n\")\n \n invisible()\n}\n", "meta": {"hexsha": "552cf01dcd31368730fabb691f519dc5c1addfaf", "size": 680, "ext": "r", "lang": "R", "max_stars_repo_path": "R/str.r", "max_stars_repo_name": "david-cortes/float", "max_stars_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2017-11-08T11:29:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-20T20:17:08.000Z", "max_issues_repo_path": "R/str.r", "max_issues_repo_name": "david-cortes/float", "max_issues_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 37, "max_issues_repo_issues_event_min_datetime": "2017-09-02T11:14:09.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-19T15:11:19.000Z", "max_forks_repo_path": "R/str.r", "max_forks_repo_name": "david-cortes/float", "max_forks_repo_head_hexsha": "df58b4040a352f006c299233c2c920e11b0dcae3", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2017-11-18T18:05:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-17T01:23:23.000Z", "avg_line_length": 24.2857142857, "max_line_length": 84, "alphanum_fraction": 0.5882352941, "num_tokens": 217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3227963716692408}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\nlibrary(rJava)\n\nsetwd(\"/Users/mengmengjiang/all datas/print\")\n\nlibrary(xlsx)\n\n# reading ux and sy\n\nk1<-read.xlsx(\"doty.xlsx\",sheetName=\"600\",header=TRUE)\nk2<-read.xlsx(\"doty.xlsx\",sheetName=\"1khz\",header=TRUE)\nk3<-read.xlsx(\"doty.xlsx\",sheetName=\"2khz\",header=TRUE)\n\n# par\n\n#par(mfrow = c(2,1), mar = c(2,2.4,2,2), oma = c(1,1,1,1))\n#layout(matrix(c(1,2), 2, 1,byrow = TRUE))\n\n# errorbar\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\nif(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\nstop(\"vectors must be same length\")\narrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n# color setting\n\nyan<-c(\"red\",\"blue\",\"black\")\npcc<-c(0,1,2)\n\n# plot\n\nplot(k1$uy,k1$syeva, col=0,xlab = expression(italic(U[\"y\"]) (um)),\n ylab = expression(italic(S[\"y\"]) (um)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(50,150),ylim=c(-20,70))\n\n mtext(\"Line space\",3,line=0.2,font=2,cex=1.2)\n\n lines(k1$uy,k1$syeva,lwd=1.5,lty=2,col=yan[1],pch=pcc[1],type=\"b\")\n lines(k2$uy,k2$syeva,lwd=1.5,lty=2,col=yan[2],pch=pcc[2],type=\"b\")\n lines(k3$uy,k3$syeva,lwd=1.5,lty=2,col=yan[3],pch=pcc[3],type=\"b\")\n\n error.bar(k1$uy,k1$syeva,k1$systd/2,col=yan[1])\n error.bar(k2$uy,k2$syeva,k2$systd/2,col=yan[2])\n error.bar(k3$uy,k3$syeva,k3$systd/2,col=yan[3])\n\n leg<-c(\"600Hz\",\"1KHz\",\"2KHz\")\n\n legend(\"topleft\",legend=leg,col=yan,pch=pcc,lwd=1.5,lty=2,inset=.02,bty=\"n\",cex=0.8)\n", "meta": {"hexsha": "1c0c67f409a44287c6662a52fc3eaf1047fdf9e2", "size": 1686, "ext": "r", "lang": "R", "max_stars_repo_path": "print-chap7/Y/fig5_sy.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "print-chap7/Y/fig5_sy.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "print-chap7/Y/fig5_sy.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.72, "max_line_length": 104, "alphanum_fraction": 0.5984578885, "num_tokens": 650, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.3227963716692408}} {"text": "#version of analyseProduction script but using service calculated from cells output file\n#used with data from run that do not use STELLA (no FromMaestro files created)\n\n\nrm(list=ls())\n\nlibrary(tidyverse)\nlibrary(ggplot2)\nlibrary(readxl)\nlibrary(viridis)\n\n#set for the run in CRAFTY (althrough runID difficult to control)\nscenario <- \"scenario_observed_2001-2035_2020-02-13\"\n#demand <- \"Demand_2020-02-06b\"\nrunID <- \"0-0\"\nsim_yrs <- seq(2001, 2035, 1) #consolidate these years\n\n#output can be printed to pdf by setting following variable appropriately (TRUE/FALSE)\npdfprint <- TRUE\n\ndata_dir <- \"C:/Users/k1076631/craftyworkspace/CRAFTY_TemplateCoBRA/output/Brazil/Unknown/\"\n#data_dir <- \"C:/Users/k1076631/Google Drive/Shared/Crafty Telecoupling/CRAFTY_testing/CRAFTYOutput/Data/\"\n\noutput_name <- paste0(data_dir,scenario,\"/\",runID,\"/\",scenario,\"-\",runID,\"_ProductionAnalysis_NoSTELLA.pdf\")\n\n\n\n#year, service, Measure, value \nmod_dat <- data.frame(\n year = integer(),\n service = character(),\n Measure = integer(),\n value = numeric()\n \n )\ntbl_df(mod_dat)\n\nfor(i in seq_along(sim_yrs)){\n \n #i <- 11\n #Load model output data\n output <- read.csv(paste0(data_dir,scenario,\"/\",runID,\"/\",scenario,\"-\",runID,\"-Cell-\",sim_yrs[i],\".csv\"))\n \n Soy <- output %>%\n filter(`Service.Soy` > 0) %>%\n summarise(mn = mean(`Service.Soy`), sm = sum(`Service.Soy`), mm = min(`Service.Soy`), mx = max(`Service.Soy`), sd = sd(`Service.Soy`))\n\n Maize <- output %>%\n filter(`Service.Maize` > 0) %>%\n summarise(mn = mean(`Service.Maize`), sm = sum(`Service.Maize`), mm = min(`Service.Maize`), mx = max(`Service.Maize`), sd = sd(`Service.Maize`))\n\n Agri <- output %>%\n mutate(`Service.Agri` = `Service.Soy` + `Service.Maize`) %>%\n filter(`Service.Agri` > 0) %>%\n summarise(mn = mean(`Service.Agri`), sm = sum(`Service.Agri`), mm = min(`Service.Agri`), mx = max(`Service.Agri`), sd = sd(`Service.Agri`))\n \n Nature <- output %>%\n filter(`Service.Nature` > 0) %>%\n summarise(mn = mean(`Service.Nature`), sm = sum(`Service.Nature`), mm = min(`Service.Nature`), mx = max(`Service.Nature`), sd = sd(`Service.Nature`))\n\n OAgri <- output %>%\n filter(`Service.Other.Agriculture` > 0) %>%\n summarise(mn = mean(`Service.Other.Agriculture`), sm = sum(`Service.Other.Agriculture`), mm = min(`Service.Other.Agriculture`), mx = max(`Service.Other.Agriculture`), sd = sd(`Service.Other.Agriculture`))\n\n Other <- output %>%\n filter(`Service.Other` > 0) %>%\n summarise(mn = mean(`Service.Other`), sm = sum(`Service.Other`), mm = min(`Service.Other`), mx = max(`Service.Other`), sd = sd(`Service.Other`))\n\n Meat <- output %>%\n filter(`Service.Pasture` > 0) %>%\n summarise(mn = mean(`Service.Pasture`), sm = sum(`Service.Pasture`), mm = min(`Service.Pasture`), mx = max(`Service.Pasture`), sd = sd(`Service.Pasture`))\n\n \n #lserv <- c(\"Soy\", \"Maize\", \"Pasture\", \"Agri\")\n lserv <- c(\"Soy\", \"Maize\", \"Meat\")\n \n for(j in lserv) {\n\n \n mod_dat <- mod_dat %>%\n add_row(year = sim_yrs[i], service = j, Measure = \"Mean\", value = round(UQ(as.name(j))$mn,3)) %>%\n add_row(year = sim_yrs[i], service = j, Measure = \"Sum\", value = round(UQ(as.name(j))$sm,3)) %>%\n add_row(year = sim_yrs[i], service = j, Measure = \"Min\", value = round(UQ(as.name(j))$mm,3)) %>%\n add_row(year = sim_yrs[i], service = j, Measure = \"Max\", value = round(UQ(as.name(j))$mx,3)) %>%\n add_row(year = sim_yrs[i], service = j, Measure = \"SD\", value = round(UQ(as.name(j))$sd,3)) \n \n }\n}\n\n\n\n\n\n\nmod_serv <- mod_dat %>%\n filter(Measure == \"Sum\") %>%\n #filter(service != \"Other\", service != \"OAgri\") %>%\n rename(Commodity = service) %>%\n mutate(Measure = \"Production\") %>%\n mutate(value_gg = if_else(Commodity == \"Soy\", value * 25,\n if_else(Commodity == \"Maize\", value * 37.5, value * 0.275) \n )) %>%\n mutate(value_gg = round(value_gg,1)) %>%\n dplyr::select(-value) %>%\n mutate(Source = \"Mod\") %>%\n dplyr::select(year, value_gg, Commodity, Source, Measure)\n\n\n\n\n\nMeat_prod_Astates_Data <- read_excel(paste0(data_dir,\"Cattle_Meat_production_Kg_2000_2018_all_states.xlsx\"), sheet = \"Plan1\", skip = 1) #data for all states Astates\nmaize_prod_Amunis_Data <- read_excel(paste0(data_dir,\"maize_brazil.xlsx\"), sheet = \"Production (tons)\", skip = 1, na = c(\"\", \"-\", \"...\"))\nsoy_prod_Amunis_Data <- read_excel(paste0(data_dir,\"soybean_brazil.xlsx\"), sheet = \"Production (Tons)\", skip = 1, na = c(\"\", \"-\", \"...\"))\n\n\nFstate_vals <- c(17, 29, 31, 35, 41, 42, 43, 50, 51, 52)\nFstate_abbrev <- c(\"TO\", \"BA\", \"MG\", \"SP\", \"PR\", \"SC\", \"RS\", \"MS\", \"MT\", \"GO\")\n\nAstate_codes <- Meat_prod_Astates_Data %>%\n dplyr::select(NM_UF_SIGLA, CD_GCUF) %>%\n rename(state = NM_UF_SIGLA, stateid = CD_GCUF) %>%\n filter(!is.na(state)) #safer way to remove text line at bottom of state column\n \n\n##Meat\nMeat_prod_Astates <- Meat_prod_Astates_Data %>%\n rename(state = NM_UF_SIGLA) %>%\n dplyr::select(-NM_UF, -CD_GCUF) %>% #drop columns\n filter(!is.na(state)) %>% #safer way to remove text line at bottom of state column\n mutate_at(vars(\"2001\":\"2018\"), as.numeric) \n\nMeat_prod_Astates_long <- Meat_prod_Astates %>%\n gather(key = year, value = Meat_kg, -state) %>%\n mutate_at(vars(year), as.integer) %>%\n mutate(Meat = Meat_kg * 0.000001) %>% #convert from kg to gg\n dplyr::select(-Meat_kg)\n\n\n#MAIZE\n#has the same data strucutre (with some differences in unit conversions - could write function to cover both?) \nmaize_prod_Amunis <- maize_prod_Amunis_Data %>%\n rename(muniID = `IBGE CODE`) %>%\n filter(!is.na(muniID)) %>% #safer way to remove text line in muniID\n mutate(state = substr(muniID, 1, 2)) %>% #extract the muniID\n mutate_at(vars(\"2001\":\"2018\"), as.numeric) %>% #convert values to numeric\n dplyr::select(-Municipality) #drop unwanted columns\n\nmaize_prod_Astates <- maize_prod_Amunis %>%\n group_by(state) %>%\n summarise_all(sum, na.rm=T) %>% #summarise munis to states\n mutate(state=replace(state, 1:length(Astate_codes$stateid), Astate_codes$state)) #re-label stated ids with state abbrevs\n\nmaize_prod_Astates_long <- maize_prod_Astates %>%\n gather(key = year, value = maize_kg, -state, -muniID) %>%\n mutate_at(vars(year), as.integer) %>%\n mutate(Maize = maize_kg * 0.001) %>% #convert from tons to gg\n dplyr::select(-maize_kg, -muniID)\n\n##SOY\nsoy_prod_Amunis <- soy_prod_Amunis_Data %>%\n rename(muniID = `IBGE CODE`) %>%\n filter(!is.na(muniID)) %>% #safer way to remove text line in muniID\n mutate(state = substr(muniID, 1, 2)) %>% #extract the muniID\n mutate_at(vars(\"2001\":\"2018\"), as.numeric) %>% #convert values to numeric\n dplyr::select(-Municipality) #drop unwanted columns\n\nsoy_prod_Astates <- soy_prod_Amunis %>%\n group_by(state) %>%\n summarise_all(sum, na.rm=T) %>% #summarise munis to states\n mutate(state=replace(state, 1:length(Astate_codes$stateid), Astate_codes$state)) #re-label stated ids with state abbrevs\n\nsoy_prod_Astates_long <- soy_prod_Astates %>%\n gather(key = year, value = soy_kg, -state, -muniID) %>%\n mutate_at(vars(year), as.integer) %>%\n mutate(Soy = soy_kg * 0.001) %>% #convert from tons to gg\n dplyr::select(-soy_kg, -muniID)\n\nprod_state_year <- left_join(Meat_prod_Astates_long, maize_prod_Astates_long, by = c(\"year\", \"state\"))\n\nprod_state_year <- left_join(prod_state_year, soy_prod_Astates_long, by = c(\"year\", \"state\"))\n\n#add focal states indicator\nprod_state_year <- prod_state_year %>%\n mutate(simulated = state %in% Fstate_abbrev) \n\npsy_long <- prod_state_year %>%\n gather(key = Commodity, value = gg, -state, -year, -simulated)\n\npsimy_long <- psy_long %>%\n group_by(simulated, year, Commodity) %>%\n summarise(value_gg = sum(gg, na.rm=T)) %>%\n filter(simulated == TRUE) %>%\n mutate(Source = \"Obs\", Measure = \"Production\") %>%\n ungroup() %>%\n dplyr::select(-simulated)\n\n\n\n\n######\n#old observed\n####### \n\n\n#read Demand_Empirical.csv here and join to mod_serv...\n\n# mod_demand_dat <- read_csv(paste0(\"Data/\",scenario,\"/Demand_Empirical.csv\"))\n# \n# mod_demand<- mod_demand_dat %>%\n# dplyr::select(Year, Soy, Maize, Pasture) %>%\n# gather(key = Commodity, value = value, -Year) %>%\n# mutate(value_gg = if_else(Commodity == \"Soy\", value * 30,\n# if_else(Commodity == \"Maize\", value * 20, value * 1.9)\n# )) %>%\n# mutate(Source = \"Mod\", Measure = \"Demand\") %>%\n# rename(year = Year) %>%\n# dplyr::select(year, Measure, value_gg, Commodity, Source)\n\n\n\n odata <- read_csv(paste0(data_dir,\"Production_Export_Internal.csv\"))\n \n obs_data <- odata %>%\n filter(Year > 2000) %>%\n dplyr::select(-ends_with(\"export_China_gg\")) %>%\n rename(year = Year)\n# \n# Dairy_long <- obs_data %>%\n# dplyr::select(year, starts_with(\"Dairy\")) %>%\n# gather(key = Measure, value = value_gg, -year) %>%\n# mutate(Commodity = \"Dairy\") %>%\n# mutate(Measure = \n# if_else(grepl(\"Production\", Measure), \"Production\", \n# if_else(grepl(\"Export\", Measure), \"Export\", \"Internal\")\n# ) \n# )\n# \nMaize_long <- obs_data %>%\n dplyr::select(year, starts_with(\"Maize\")) %>%\n gather(key = Measure, value = value_gg, -year) %>%\n mutate(Commodity = \"Maize\") %>%\n mutate(Measure =\n if_else(grepl(\"Production\", Measure), \"Production\",\n if_else(grepl(\"Export\", Measure), \"Export\", \"Internal\")\n )\n )\n\nMeat_long <- obs_data %>%\n dplyr::select(year, starts_with(\"Meat\")) %>%\n gather(key = Measure, value = value_gg, -year) %>%\n mutate(Commodity = \"Meat\") %>%\n mutate(Measure =\n if_else(grepl(\"Production\", Measure), \"Production\",\n if_else(grepl(\"Export\", Measure), \"Export\", \"Internal\")\n )\n )\n\nSoy_long <- obs_data %>%\n dplyr::select(year, starts_with(\"Soy\")) %>%\n gather(key = Measure, value = value_gg, -year) %>%\n mutate(Commodity = \"Soy\") %>%\n mutate(Measure =\n if_else(grepl(\"Production\", Measure), \"Production\",\n if_else(grepl(\"Export\", Measure), \"Export\", \"Internal\")\n )\n )\n \nobs_long <- bind_rows(Maize_long, Soy_long, Meat_long) %>%\n mutate(Source = \"Obs\")\n \nall_dat <- bind_rows(mod_serv, psimy_long) %>%\n mutate(Source = factor(Source), Measure = factor(Measure), Commodity = factor(Commodity))\n \n\n \nsummary(all_dat)\n\n######\n\nall_dat <- bind_rows(mod_serv, psimy_long) %>%\n mutate(Source = factor(Source), Commodity = factor(Commodity))\n \n\n \nsummary(all_dat)\n\n\n\n######\n#output\n######\n\n\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#0072B2\", \"#D55E00\", \"#CC79A7\", \"#F0E442\")\n\n\nif(pdfprint) {\n pdf(file = output_name)\n}\n\nc <- mod_dat %>%\n filter(Measure == \"Sum\") %>%\n ggplot(aes(x = year, y = value, color = service)) + \n geom_line(size=1) +\n scale_y_continuous(name = \"CRAFTY units\", labels = scales::comma) +\n ggtitle(\"Sum Service\")\nprint(c)\n\n# a <- all_dat %>%\n# filter(Commodity == \"Soy\") %>%\n# ggplot(aes(x=year, y=value_gg, color=Measure, linetype=Source)) +\n# geom_line() +\n# scale_colour_manual(values=cbPalette) +\n# ylab(\"Value (gg)\") +\n# xlab(\"Year\") +\n# ggtitle(\"Soy\")\n# print(a)\n\n\nagriplot <- all_dat %>%\n #filter(Commodity != \"Meat\") %>%\n ggplot(aes(x=year, y=value_gg, color=Commodity, linetype=Source)) +\n geom_line(size=1) +\n scale_color_viridis_d(begin=0, end=0.75) +\n scale_y_continuous(name = \"Production (Gg)\", labels = scales::comma) +\n #scale_colour_manual(values=cbPalette) +\n #ylab(\"Production (gg)\") +\n xlab(\"Year\") +\n guides(color = guide_legend(order = 1), \n linetype = guide_legend(order = 2))\n\n #ggtitle(\"Soy\")\nprint(agriplot)\n\nagriplot <- all_dat %>%\n filter(Commodity != \"Meat\") %>%\n ggplot(aes(x=year, y=value_gg, color=Commodity, linetype=Source)) +\n geom_line(size=1) +\n #scale_colour_manual(values=cbPalette) +\n ylab(\"Value (Gg)\") +\n xlab(\"Year\") \n #ggtitle(\"Soy\")\nprint(agriplot)\n \n \n# a <- all_dat %>%\n# filter(Commodity == \"Maize\") %>%\n# ggplot(aes(x=year, y=value_gg, color=Measure, linetype=Source)) +\n# geom_line() +\n# scale_colour_manual(values=cbPalette) +\n# ylab(\"Value (gg)\") +\n# xlab(\"Year\") +\n# ggtitle(\"Maize\")\n# print(a)\n \nMeatplot <- all_dat %>% \n filter(Commodity == \"Meat\") %>%\n ggplot(aes(x=year, y=value_gg, linetype=Source)) +\n geom_line(size=1) +\n scale_colour_manual(values=cbPalette) +\n scale_y_continuous(limits = c(0, 10000), labels = scales::comma) +\n ylab(\"Value (Gg)\") +\n xlab(\"Year\") +\n ggtitle(\"Meat\")\nprint(Meatplot)\n \nif(pdfprint) {\n dev.off()\n}\n\n", "meta": {"hexsha": "cd7abe5aed29af18125cdbabe558af3974a0b7a9", "size": 12333, "ext": "r", "lang": "R", "max_stars_repo_path": "8a_analyseProduction_solo.r", "max_stars_repo_name": "jamesdamillington/CRAFTYOutput", "max_stars_repo_head_hexsha": "b75b931d1830761dfad32e686a58210cfc18ee08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "8a_analyseProduction_solo.r", "max_issues_repo_name": "jamesdamillington/CRAFTYOutput", "max_issues_repo_head_hexsha": "b75b931d1830761dfad32e686a58210cfc18ee08", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "8a_analyseProduction_solo.r", "max_forks_repo_name": "jamesdamillington/CRAFTYOutput", "max_forks_repo_head_hexsha": "b75b931d1830761dfad32e686a58210cfc18ee08", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1532258065, "max_line_length": 208, "alphanum_fraction": 0.6442876835, "num_tokens": 3954, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.32279637166924074}} {"text": "#' Banded Matrix Constructors\n#' \n#' Constructors for banded matrices. WARNING: for advanced users only.\n#' Use the caster function for conversions.\n#' \n#' @details\n#' See url in the references for an explanation of the storage.\n#' \n#' @param dim\n#' The matrix dimension.\n#' @param Data\n#' The compact band matrix representation.\n#' @param kl,ku\n#' The lower and upper bandwidths, respectively.\n#' \n#' @references \\url{http://www.netlib.org/lapack/lug/node124.html}\n#' @seealso \\code{\\link{as.banded}} and \\code{\\link{as.matrix}}\n#' @name constructors\n#' @rdname constructors\nNULL\n\n#' @rdname constructors\n#' @export\nzeromat <- function(dim)\n{\n new(\"ZeroMat\", dim=dim)\n}\n\n#' @rdname constructors\n#' @export\ndiagmat <- function(Data, dim)\n{\n new(\"DiagMat\", Data=Data, dim=dim)\n}\n\n#' @param triangle\n#' \"u\" or \"l\" for upper or lower, as in \\code{as.symmetric()}.\n#' @rdname constructors\n#' @export\nsymmat <- function(Data, dim, triangle)\n{\n new(\"SymMat\", Data=Data, dim=dim, triangle=triangle)\n}\n\n#' @rdname constructors\n#' @export\ngenbandmat <- function(Data, dim, kl, ku)\n{\n new(\"GenBandMat\", Data=Data, dim=dim, kl=kl, ku=ku)\n}\n", "meta": {"hexsha": "cf609b8f1cbb824f7b4a855ec9a723a5d0ffcdfe", "size": 1133, "ext": "r", "lang": "R", "max_stars_repo_path": "R/constructors.r", "max_stars_repo_name": "wrathematics/band", "max_stars_repo_head_hexsha": "0133e65eab999245c8076bd4ba94c230704f5a1d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-06-02T21:58:35.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-05T21:43:55.000Z", "max_issues_repo_path": "R/constructors.r", "max_issues_repo_name": "wrathematics/band", "max_issues_repo_head_hexsha": "0133e65eab999245c8076bd4ba94c230704f5a1d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/constructors.r", "max_forks_repo_name": "wrathematics/band", "max_forks_repo_head_hexsha": "0133e65eab999245c8076bd4ba94c230704f5a1d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.2156862745, "max_line_length": 70, "alphanum_fraction": 0.6831421006, "num_tokens": 325, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7371581626286834, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.32274516616268606}} {"text": "#-------------------------------------------------------------------------------\r\n# Script to run the assessment of Jack Mackerel and look at outputs\r\n#-------------------------------------------------------------------------------\r\n\r\nsetwd(\"N:/Projecten/SouthPacific/2011/Assessment2/\")\r\n\r\nrm(list=ls())\r\nmemory.size(4000)\r\n\r\n # Set libraries & source code\r\nlibrary(lattice)\r\nrequire(PBSadmb)\r\nlibrary(RColorBrewer)\r\nsource(\"R/ADMB2R.r\")\r\n\r\n # Define population characteristics\r\npopSettings <- scan(\"mod1_Proj.dat\",what='numeric', quiet=TRUE,sep=\"\\n\",comment.char=\"#\",allowEscapes=T)\r\npopSettingsMods <- scan(\"Mod_Schedules_ForNiels.txt\",what='numeric',quiet=TRUE,sep=\"\\n\",comment.char=\"#\",allowEscapes=T)\r\nrecrYrs <- 5\r\n\r\nages <- 2:12\r\nnatmort <- na.omit(an(unlist(strsplit(popSettings[13],\" \"))))\r\nstockwt <- na.omit(an(unlist(strsplit(popSettings[15],\" \"))))\r\nmat <- na.omit(an(unlist(strsplit(popSettings[14],\" \"))))/2\r\nspwn <- na.omit(an(popSettings[10]))/12\r\nlandwt <- na.omit(an(unlist(strsplit(popSettings[16],\" \"))))\r\nfpattern <- na.omit(an(unlist(strsplit(popSettings[17],\" \"))))\r\n\r\nfpattern <- matrix(c(c(0.047435075,0.210153162,0.460193999,0.722022158,0.813072429,0.771646581,0.947260551,1,0.928219578,0.928219578,0.928219578),\r\n c(0.063793696,0.283687898,0.57459911,0.907039853,1,0.896024784,0.935015955,0.994236322,0.915103449,0.915103449,0.915103449),\r\n c(0.046168417,0.208166215,0.458854538,0.732492445,0.848439077,0.806886618,0.947484642,1,0.953838298,0.953838298,0.953838298)),\r\n nrow=3,ncol=length(ages),dimnames=list(model=1:3,ages=ages),byrow=T)\r\nN2011 <- matrix(c(c(12488800,4555270,3626240,1026310,398459,215514,290787,166396, 70561, 55213.4, 60371.1),\r\n c(15662100,5933790,5588200,1760890,755634,396340,520966,331232, 163681, 136339, 158328),\r\n c(8632520, 3152440,2445910,706608, 272997,136430,167424,93548.2,40340.2, 32978.3, 36713.5)),\r\n nrow=3,ncol=length(ages),dimnames=list(model=1:3,ages=ages),byrow=T)\r\nrecr <- matrix(c(c(2786050,2423700,3501710,7428160,6043840),\r\n c(3440060,3499960,5000230,10419100,7721940),\r\n c(2588660,2159060,2891570,5532510,4275660)),\r\n nrow=3,ncol=5,dimnames=list(model=1:3,years=2006:2010),byrow=T)\r\n # Get projection settings\r\nprojSettings <- scan(\"mod1.prj\",what='numeric', quiet=TRUE,sep=\"\\n\",comment.char=\"#\",allowEscapes=T)\r\nprojYrs <- an(projSettings[7])\r\nsimNum <- an(projSettings[8])\r\nscnNum <- 5\r\nImYCatch <- an(projSettings[13])\r\n\r\n\r\n\r\n # Store variables\r\nSSB <- array(NA,dim=c(1, projYrs,simNum,scnNum,3),dimnames=list(ages=1, years=1:projYrs,iter=1:simNum,scenario=1:scnNum,model=1:3))\r\nF <- array(NA,dim=c(length(ages), projYrs,simNum,scnNum,3),dimnames=list(ages=ages,years=1:projYrs,iter=1:simNum,scenario=1:scnNum,model=1:3))\r\nFmult <- array(NA,dim=c(1, projYrs,simNum,scnNum,3),dimnames=list(ages=1, years=1:projYrs,iter=1:simNum,scenario=1:scnNum,model=1:3))\r\nyield <- array(NA,dim=c(length(ages), projYrs,simNum,scnNum,3),dimnames=list(ages=ages,years=1:projYrs,iter=1:simNum,scenario=1:scnNum,model=1:3))\r\nN <- array(NA,dim=c(length(ages), projYrs,simNum,scnNum,3),dimnames=list(ages=ages,years=1:projYrs,iter=1:simNum,scenario=1:scnNum,model=1:3))\r\n\r\n # Random structures\r\nfor(iMod in 1:3){\r\n meanR <- mean(recr[iMod,])\r\n HmeanR <- 1 / mean( 1 / recr[iMod,])\r\n gamm <- meanR / HmeanR\r\n gi_beta <- meanR\r\n delta <- 1 / (gamm - 1)\r\n cvrec <- sqrt(1 / delta)\r\n psi <- rnorm(projYrs*simNum*scnNum,0,1)^2\r\n omega <- gi_beta * (1 + (psi -sqrt(4 * delta * psi + psi^2)) / (2 * delta))\r\n zeta <- gi_beta * (1 + (psi +sqrt(4 * delta * psi + psi^2)) / (2 * delta))\r\n gtheta <- gi_beta / (gi_beta + omega)\r\n\r\n runifs <- runif(projYrs*simNum*scnNum,0,1)\r\n N[1,,,,iMod][which(runifs <= gtheta)] <- omega[which(runifs <= gtheta)]\r\n N[1,,,,iMod][which(runifs > gtheta)] <- zeta[which(runifs > gtheta)]\r\n}\r\nfor(iMod in 1:3) N[,1,,,iMod] <- rep(N2011[iMod,],simNum*scnNum)\r\n\r\n # Functions needed for projection calculation\r\ncalcCatch <- function(x,f,m,n,fwt,tac){\r\n catch <- sum(f*x / (f*x + m) * n * (1 - exp(-(f*x+m))) *fwt)\r\n return(sqrt((tac-catch)^2))}\r\n\r\n# Simulate projections\r\nfor(iYr in 1:(projYrs-1)){\r\n for(iScn in 1:scnNum){\r\n if(iScn == 1) target <- 520000\r\n if(iScn == 2) target <- 390000\r\n if(iScn == 3) target <- 260000\r\n if(iScn == 4) target <- 130000\r\n if(iScn == 5) target <- 5000\r\n if(iYr == 1) target <- ImYCatch\r\n for(iTer in 1:simNum){\r\n for(iMod in 1:3){\r\n Fmult[,iYr,iTer,iScn,iMod] <- optim(1,fn=calcCatch,lower=0,upper=3,method=\"L-BFGS-B\",f=fpattern[iMod,],m=natmort,n=N[,iYr,iTer,iScn,iMod],fwt=landwt,tac=target)$par\r\n }\r\n }\r\n }\r\n for(iMod in 1:3){\r\n F[,iYr,,,iMod] <- outer(fpattern[iMod,],Fmult[,iYr,,,iMod],\"*\")\r\n }\r\n survivors <- N[,iYr,,,] * exp(-sweep(F[,iYr,,,],1,natmort,\"+\"))\r\n N[2:length(ages),iYr+1,,,] <- survivors[-dim(survivors)[1],,,]\r\n # Plusgroup\r\n N[length(ages),iYr+1,,,] <- N[length(ages),iYr+1,,,] + survivors[dim(survivors)[1],,,]\r\n SSB[,iYr,,,] <- apply(sweep(N[,iYr,,,],1,stockwt * mat,\"*\") * exp(-sweep(F[,iYr,,,]*spwn,1,natmort*spwn,\"+\")),2:4,sum)\r\n}\r\nsave.image(\"projections.RData\")\r\n\r\n\r\n\r\nSSBmed <- apply(SSB, c(2,4:5),median,na.rm=T)\r\nFmean <- apply(F, c(2:5), mean,na.rm=T)\r\nFmed <- apply(Fmean,c(1,3:4),median,na.rm=T)\r\n\r\nprojRes <- as.data.frame(cbind(year=rep(2011:2024,5*3),F=Fmed,SSB=SSBmed,scenario=rep(rep(1:5,each=14),3),model=rep(1:3,each=14*5)))\r\n\r\n\r\n#- Figures\r\n # Legend to figures\r\n ikey <- simpleKey(text=c(\"99%\",\"75%\",\"50%\",\"25%\",\"0%\"),\r\n points=T,lines=T,columns = 3)\r\n ikey$points$pch <- c(3,8,15,16,17)\r\n ikey$points$col <- c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\")\r\n ikey$lines$lwd <- 2\r\n ikey$lines$col <- c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\")\r\n\r\nFmedrange <- c(0,0.4)#c(0,range(Fmed,na.rm=T)[2])\r\nxyplot(F~year,data=subset(projRes,year %in% 2011:2021 & model == 1),\r\n groups=scenario,xlab=\"Years\",ylab=\"Fishing mortality\",main=\"Model 1\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,Fmedrange))})\r\nxyplot(F~year,data=subset(projRes,year %in% 2011:2021 & model == 2),\r\n groups=scenario,xlab=\"Years\",ylab=\"Fishing mortality\",main=\"Model 2\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,Fmedrange))})\r\nxyplot(F~year,data=subset(projRes,year %in% 2011:2021 & model == 3),\r\n groups=scenario,xlab=\"Years\",ylab=\"Fishing mortality\",main=\"Model 3\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,Fmedrange))})\r\nsavePlot(\"./Results/Model1_F.png\",type=\"png\")\r\n\r\nSSBmedrange <- c(0,8000000)#c(0,range(SSBmed,na.rm=T)[2])\r\nxyplot(SSB~year,data=subset(projRes,year %in% 2011:2021 & model == 1),\r\n groups=scenario,xlab=\"Years\",ylab=\"Spawning Stock Biomass\",main=\"Model 1\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,SSBmedrange))})\r\nxyplot(SSB~year,data=subset(projRes,year %in% 2011:2021 & model == 2),\r\n groups=scenario,xlab=\"Years\",ylab=\"Spawning Stock Biomass\",main=\"Model 2\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,SSBmedrange))})\r\nxyplot(SSB~year,data=subset(projRes,year %in% 2011:2021 & model == 3),\r\n groups=scenario,xlab=\"Years\",ylab=\"Spawning Stock Biomass\",main=\"Model 3\",\r\n type=\"b\",col=c(\"darkblue\",\"red\",\"green\",\"purple\",\"cyan\"),\r\n pch=c(3,8,15,16,17),\r\n key=ikey,prepanel=function(...) {list(ylim=c(0,SSBmedrange))})\r\n\r\nsavePlot(\"./Results/Model1_B.png\",type=\"png\")", "meta": {"hexsha": "8a8083afbefb2b0b228a84038549005a1946db61", "size": 8533, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/atka/R/JJM_projections.r", "max_stars_repo_name": "NMFS-toolbox/AMAK", "max_stars_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-12T17:39:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-12T17:39:20.000Z", "max_issues_repo_path": "examples/chub/R/JJM_projections.r", "max_issues_repo_name": "afsc-assessments/AMAK", "max_issues_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/chub/R/JJM_projections.r", "max_forks_repo_name": "afsc-assessments/AMAK", "max_forks_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2015-05-21T18:18:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-04-12T04:18:42.000Z", "avg_line_length": 53.33125, "max_line_length": 173, "alphanum_fraction": 0.5777569436, "num_tokens": 2982, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7634837635542925, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.32257542870706835}} {"text": "library(readr)\nlibrary(dplyr)\nmammals <- read_csv(\"data-raw/mammals.csv\")\nmammals_sub <- mammals %>%\n mutate(offspring.year = litter_size * litters_inyear) %>%\n mutate(log.oy = log(offspring.year)) %>%\n mutate(log.wm = log(weaning_month)) %>%\n mutate(log.ml = log(max_life_month)) %>%\n mutate(log.AFR = log(AFR_mo)) %>%\n filter(order == 'Artiodactyla' | order == 'Carnivora' | order == 'Cetacea' | order == 'Insectivora' | order == 'Lagomorpha' | order == 'Primates' | order == 'Rodentia' )\n\n\ndevtools::use_data(mammals, mammals_sub, overwrite = TRUE)\n\n", "meta": {"hexsha": "d90c0244847460a70be5c43cf11aef111482065b", "size": 571, "ext": "r", "lang": "R", "max_stars_repo_path": "data-raw/saving.r", "max_stars_repo_name": "UofTCoders/eeb430-2017-python", "max_stars_repo_head_hexsha": "d2f8c12ca284aef871eec0d510ef8e0a38e4ca64", "max_stars_repo_licenses": ["CC-BY-4.0", "MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2017-10-17T20:00:15.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-17T20:00:24.000Z", "max_issues_repo_path": "data-raw/saving.r", "max_issues_repo_name": "UofTCoders/eeb430-2017-python", "max_issues_repo_head_hexsha": "d2f8c12ca284aef871eec0d510ef8e0a38e4ca64", "max_issues_repo_licenses": ["CC-BY-4.0", "MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data-raw/saving.r", "max_forks_repo_name": "UofTCoders/eeb430-2017-python", "max_forks_repo_head_hexsha": "d2f8c12ca284aef871eec0d510ef8e0a38e4ca64", "max_forks_repo_licenses": ["CC-BY-4.0", "MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.0666666667, "max_line_length": 173, "alphanum_fraction": 0.6549912434, "num_tokens": 178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526660244838, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32254717130077154}} {"text": "# clean\nrm(list=ls())\n\n# load libs\nlibrary(doParallel)\nlibrary(raster)\n\n# open cluster\ncl <- makeCluster(35)\nregisterDoParallel(cl)\n\n# load raster ref\ngrid_ref <- readRDS(\"./data/futClimGrid/rasterClim/res1000/rcp85-MIROC_ESM_CHEM-1985-2000.rda\")\n\n######### WINDOW 1985-2000\n\n# list files\nprobs2000_files <- list.files(\"./data/futStatesGrid/probs\",full.names=TRUE,pattern=\"1985-2000\")\n# prep stack res\nTempGrids2000 <- stack()\n\nforeach(i=1:length(probs2000_files),.packages=c('raster','rgdal'))%do%{\n\n # read file and compute temperate prob\n probsGrid <- read.csv(probs2000_files[i])\n probsGrid$ProbTemp <- probsGrid$T + probsGrid$M\n\n # set coordinates from grid_ref\n probsGrid <- cbind(probsGrid,coordinates(grid_ref))\n\n # Turn df into raster\n df_probsTemp <- probsGrid[,c(8,7,6)]\n coordinates(df_probsTemp) <- ~ x + y\n gridded(df_probsTemp) <- TRUE\n rs_probsTemp <- raster(df_probsTemp)\n\n TempGrids2000 <- addLayer(TempGrids2000,rs_probsTemp)\n\n}\n\nmetadata <- unlist(lapply(strsplit(probs2000_files,\"[./-]\"),function(x) x[7]))\nprojection(TempGrids2000) <- projection(grid_ref)\nnames(TempGrids2000) <- metadata\n\nsaveRDS(TempGrids2000,\"./res/2000_tempProbSolved.rda\")\n\n######### WINDOW 2000-2015\n\n# list files\nprobs2015_files <- list.files(\"./data/futStatesGrid/probs\",full.names=TRUE,pattern=\"2000-2015\")\n# prep stack res\nTempGrids2015 <- stack()\n\nforeach(i=1:length(probs2015_files),.packages=c('raster','rgdal'))%do%{\n\n # read file and compute temperate prob\n probsGrid <- read.csv(probs2015_files[i])\n probsGrid$ProbTemp <- probsGrid$T + probsGrid$M\n\n # set coordinates from grid_ref\n probsGrid <- cbind(probsGrid,coordinates(grid_ref))\n\n # Turn df into raster\n df_probsTemp <- probsGrid[,c(8,7,6)]\n coordinates(df_probsTemp) <- ~ x + y\n gridded(df_probsTemp) <- TRUE\n rs_probsTemp <- raster(df_probsTemp)\n\n TempGrids2015 <- addLayer(TempGrids2015,rs_probsTemp)\n\n}\n\nmetadata <- unlist(lapply(strsplit(probs2015_files,\"[./-]\"),function(x) x[7]))\nprojection(TempGrids2015) <- projection(grid_ref)\nnames(TempGrids2015) <- metadata\n\nsaveRDS(TempGrids2015,\"./res/2015_tempProbSolved.rda\")\n\n######### WINDOW 2045-2030\n\n# list files\nprobs2045_files <- list.files(\"./data/futStatesGrid/probs\",full.names=TRUE,pattern=\"2030-2045\")\n# prep stack res\nTempGrids2045 <- stack()\n\nforeach(i=1:length(probs2045_files),.packages=c('raster','rgdal'))%do%{\n\n # read file and compute temperate prob\n probsGrid <- read.csv(probs2045_files[i])\n probsGrid$ProbTemp <- probsGrid$T + probsGrid$M\n\n # set coordinates from grid_ref\n probsGrid <- cbind(probsGrid,coordinates(grid_ref))\n\n # Turn df into raster\n df_probsTemp <- probsGrid[,c(8,7,6)]\n coordinates(df_probsTemp) <- ~ x + y\n gridded(df_probsTemp) <- TRUE\n rs_probsTemp <- raster(df_probsTemp)\n\n TempGrids2045 <- addLayer(TempGrids2045,rs_probsTemp)\n\n}\n\nmetadata <- unlist(lapply(strsplit(probs2045_files,\"[./-]\"),function(x) x[7]))\nprojection(TempGrids2045) <- projection(grid_ref)\nnames(TempGrids2045) <- metadata\n\nsaveRDS(TempGrids2045,\"./res/2045_tempProbSolved.rda\")\n\n\n######### WINDOW 2080-2095\n\n# list files\nprobs2095_files <- list.files(\"./data/futStatesGrid/probs\",full.names=TRUE,pattern=\"2080-2095\")\n# prep stack res\nTempGrids2095 <- stack()\n\nforeach(i=1:length(probs2095_files),.packages=c('raster','rgdal'))%do%{\n\n # read file and compute temperate prob\n probsGrid <- read.csv(probs2095_files[i])\n probsGrid$ProbTemp <- probsGrid$T + probsGrid$M\n\n # set coordinates from grid_ref\n probsGrid <- cbind(probsGrid,coordinates(grid_ref))\n\n # Turn df into raster\n df_probsTemp <- probsGrid[,c(8,7,6)]\n coordinates(df_probsTemp) <- ~ x + y\n gridded(df_probsTemp) <- TRUE\n rs_probsTemp <- raster(df_probsTemp)\n\n TempGrids2095 <- addLayer(TempGrids2095,rs_probsTemp)\n\n}\n\nmetadata <- unlist(lapply(strsplit(probs2095_files,\"[./-]\"),function(x) x[7]))\nprojection(TempGrids2095) <- projection(grid_ref)\nnames(TempGrids2095) <- metadata\n\nsaveRDS(TempGrids2095,\"./res/2095_tempProbSolved.rda\")\n", "meta": {"hexsha": "0726bc3a6f44b20bf2219c9b9182b007c8f1800f", "size": 3972, "ext": "r", "lang": "R", "max_stars_repo_path": "4_mapStatesSolved.r", "max_stars_repo_name": "QUICC-FOR/STModel-Band", "max_stars_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "4_mapStatesSolved.r", "max_issues_repo_name": "QUICC-FOR/STModel-Band", "max_issues_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "4_mapStatesSolved.r", "max_forks_repo_name": "QUICC-FOR/STModel-Band", "max_forks_repo_head_hexsha": "6acf24b116d7dd4fba19b0ea0eb1a971375aed28", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7762237762, "max_line_length": 95, "alphanum_fraction": 0.7298590131, "num_tokens": 1237, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3225471630310558}} {"text": "# 3. faza: Vizualizacija podatkov\n\n#starej\u0161i manj izgubljenih \u017eog\nizgubljene_36 <- per.36.stats[,c(3, 24)] \nizgubljene_36$TOV <- round(izgubljene_36$TOV)\n\nizgubljene_annual <- annual.totals[,c(3, 4, 19)]\nizgubljene_annual$TOV <- round(izgubljene_annual$TOV/izgubljene_annual$G)\nizgubljene_annual <- izgubljene_annual[,c(1, 3)]\n\nizgubljene_36 <- ddply(izgubljene_36, .(Age, TOV), nrow)\nizgubljene_annual <- ddply(izgubljene_annual, .(Age, TOV), nrow)\n\ngraf_izgubljene_36 <- ggplot(izgubljene_36 %>% filter(TOV < 20), aes(x = Age, y = TOV, fill=V1, color=V1, size = V1)) + \n geom_point() + ggtitle(\"Na 36 minut\") +\n labs(fill = \"\u0160tevilo ko\u0161arkarjev\") +\n scale_color_gradient(low=\"green\", high=\"purple\") +\n scale_fill_gradient(low=\"green\",high=\"purple\") + \n guides(color = FALSE, size = FALSE)\n\ngraf_izgubljene_annual <- ggplot(izgubljene_annual, aes(x = Age, y = TOV, color = V1, fill=V1, size = V1)) + geom_point() +\n ggtitle(\"Na tekmo\")+ \n scale_color_gradient(low=\"green\", high=\"purple\") +\n scale_fill_gradient(low=\"green\",high=\"purple\") + \n guides(color = FALSE, size = FALSE, fill=FALSE)\n\n\ngraf_izgubljene <- ggarrange(graf_izgubljene_36, graf_izgubljene_annual,\n nrow = 1, common.legend = TRUE, legend=\"bottom\")\n\n\n\n#starej\u0161i manj skokov\nskoki_36 <- per.36.stats[,c(3, 20)]\nskoki_36$TRB <- round(skoki_36$TRB)\n\nskok_annual <- annual.totals[,c(3, 4, 16)]\nskok_annual$TRB <- round(skok_annual$TRB/skok_annual$G)\nskok_annual <- skok_annual[,c(1, 3)]\n\nskoki_36 <- ddply(skoki_36, .(Age, TRB), nrow)\nskok_annual <- ddply(skok_annual, .(Age, TRB), nrow)\n\ngraf_skoki_36 <- ggplot(skoki_36 %>% filter(TRB < 30), aes(x=Age, y=TRB)) + geom_point(aes(size = V1,fill=V1 ,color=V1)) + \n ggtitle(\"Na 36 minut\") + labs(fill = \"\u0160tevilo ko\u0161arkarjev\") + \n scale_color_gradient(low=\"green\", high=\"purple\") +\n scale_fill_continuous(low=\"green\",high=\"purple\", breaks = c(50, 150, 250)) + \n guides(color = FALSE, size = FALSE)\n\ngraf_skoki_annual <- ggplot(skok_annual, aes(x=Age, y=TRB)) + geom_point(aes(size = V1, fill=V1 ,color=V1)) + \n ggtitle(\"Na tekmo\") + \n scale_color_gradient(low=\"green\", high=\"purple\") +\n scale_fill_continuous(low=\"green\",high=\"purple\") + \n guides(color = FALSE, size = FALSE, fill=FALSE)\n\ngraf_skoki <- ggarrange(graf_skoki_36, graf_skoki_annual, nrow = 1, \n common.legend = TRUE, legend=\"bottom\")\n\n\n\n\n#vi\u0161ji igralci ve\u010d skokov, blokad\ngraf_visina_skoki <- ggplot(per.36.stats %>% filter(TRB < 30), aes(x = Height, y=TRB)) + \n geom_point() + geom_smooth(method = 'loess', color=\"green\") +\n ggtitle(\"Skoki\")\n\ngraf_visina_blokade <- ggplot(per.36.stats %>% filter(BLK < 30) , aes(x = Height, y = BLK)) +\n geom_point() + geom_smooth(method = 'loess', color=\"green\") +\n ggtitle(\"Blokade\")\n\n\ngraf_visina <- ggarrange(graf_visina_skoki, graf_visina_blokade, nrow =1)\n\n\n\n#ni\u017eji ve\u010d asistenc\nnizji_ast_36 <- aggregate(AST~Height, per.36.stats[,c(28, 21)], mean)\n\nnizji_ast_annual <- annual.totals %>%\n select(AST, Height, G)\nnizji_ast_annual_ast <- aggregate(AST~Height, nizji_ast_annual[,c(1, 2)], sum)\nnizji_ast_annual_game <- aggregate(G~Height, nizji_ast_annual[,c(2, 3)], sum)\nnizji_ast_annual <- left_join(nizji_ast_annual_ast, nizji_ast_annual_game)\nnizji_ast_annual$AST <- round(nizji_ast_annual$AST/nizji_ast_annual$G, 2)\n\ngraf_nizji_ast_36 <- ggplot(nizji_ast_36, aes(x=Height, y=AST)) + \n geom_line() + \n ggtitle(\"Na 36 minut\")\n\ngraf_nizji_ast_annual <- ggplot(nizji_ast_annual, aes(x=Height, y=AST)) + \n geom_line() + ggtitle(\"Na tekmo\")\n\n\ngraf_nizji_ast <- ggarrange(graf_nizji_ast_36, graf_nizji_ast_annual, nrow = 1)\n\n\n\n#ni\u017eji bolj\u0161i odtotki prostih metov in metov iz igre\nnizji_ft_fg_36 <- per.36.stats[,c(28, 7, 8, 16, 17)]\nnizji_ft_fg_annual <- annual.totals[,c(25, 24, 13, 21, 7)]\n\n\ngraf_nizji_ft_36 <- ggplot(nizji_ft_fg_36 %>% filter(FTA >= 2), aes(x=Height, y =FT_procent)) +\n geom_point() + ggtitle(\"Uspe\u0161nost pri FT na 36 min\") +\n geom_smooth(color=\"purple\")\n\ngraf_nizji_ft_annual <- ggplot(nizji_ft_fg_annual %>% filter(FTA >= 2), aes(x=Height, y =FT_procent)) +\n geom_point() + ggtitle(\"Uspe\u0161nost pri FT na tekmo\") +\n geom_smooth(color=\"purple\")\n\ngraf_nizji_fg_36 <- ggplot(nizji_ft_fg_36 %>% filter(FGA >= 2), aes(x=Height, y =FG_procent)) +\n geom_point() + ggtitle(\"Uspe\u0161nost pri FG na 36 min\") +\n geom_smooth(color=\"green\")\n\ngraf_nizji_fg_annual <- ggplot(nizji_ft_fg_annual %>% filter(FGA >= 2), aes(x=Height, y =FG_procent)) +\n geom_point() + ggtitle(\"Uspe\u0161nost pri FG na tekmo\") +\n geom_smooth(color=\"green\")\n\n\n#zemljevid\nzemljevid <- map_data(\"world\")\nannual.totals$Birth_State <- gsub(\"District of Columbia\", \"Maryland\", annual.totals$Birth_State)\nigralci_drzave <- annual.totals[!duplicated(annual.totals[,c('Player')]),][,c(1, 28)]\ndrzave <- data.frame(table(igralci_drzave$Birth_State))\nvsota_ameriskih <- 0\nfor (state in drzave$Var1) {\n if (state %in% state.name) {\n vsota_ameriskih <- vsota_ameriskih + drzave$Freq[drzave$Var1 == state]\n }\n}\n\n'%ni%' <- Negate('%in%')\nUSA <- data.frame(\"USA\", vsota_ameriskih)\nnames(USA) <- c(\"Var1\", \"Freq\")\ndrzave <- rbind(drzave, USA)\ndrzave <- drzave %>% filter(Var1 %ni% state.name)\nnames(drzave)[1] <- \"region\"\nzemljevid <- full_join(drzave, zemljevid, by= \"region\")\n\n\n \n\nmap_svet <- ggplot(zemljevid, aes(x = long, y = lat, group=group)) +\n geom_polygon(data = zemljevid %>% filter(region != \"USA\"), aes(fill=Freq)) +\n scale_fill_gradientn(colors=c(\"blue\", \"yellow\", \"green\")) +\n geom_polygon(data = zemljevid %>% filter(region == \"USA\"), fill = \"red\")+\n theme_void()+ theme(legend.position=\"bottom\") +labs(fill=\"\u0160tevilo ko\u0161arkarjev\")\n\n\n#zemljevid - ZDA\n\namerika_states <- data.frame(table(igralci_drzave$Birth_State)) %>%\n filter(Var1 %in% state.name)\nnames(amerika_states)[1] <- \"region\"\namerika_states$region <- tolower(amerika_states$region)\namerika_states <- full_join(amerika_states, map_data(\"state\"), by=\"region\")\n\nzemljevid_zda <- ggplot(amerika_states, aes(x = long, y = lat, group = group))+\n geom_polygon(aes(fill = Freq)) + theme_void() +\n scale_fill_gradientn(colors=c(\"blue\", \"yellow\", \"green\")) +\n labs(fill=\"\u0160tevilo ko\u0161arkarjev\")\n\n\n#AST TOV ratio\ndrzave_ast_tov <- annual.totals[c(\"Birth_State\", \"TOV\", \"AST\")] %>% \n group_by(Birth_State) %>% summarise_each(funs(sum))\nvsota_ast <- 0\nvsota_tov <- 0\nfor (state in drzave_ast_tov$Birth_State) {\n if (state %in% state.name) {\n vsota_ast <- vsota_ast + drzave_ast_tov$AST[drzave_ast_tov$Birth_State == state]\n vsota_tov <- vsota_tov + drzave_ast_tov$TOV[drzave_ast_tov$Birth_State == state]\n }\n}\n\nUSA_ast_tov <- data.frame(\"USA\", vsota_tov, vsota_ast)\nnames(USA_ast_tov) <- c(\"Birth_State\", \"TOV\", \"AST\")\ndrzave_ast_tov <- rbind(drzave_ast_tov, USA_ast_tov)\ndrzave_ast_tov <- drzave_ast_tov %>% filter(Birth_State %ni% state.name)\nnames(drzave_ast_tov)[1] <- \"region\"\ndrzave_ast_tov$ast_tov <- round(drzave_ast_tov$AST/drzave_ast_tov$TOV, 2)\n\nzemljevid_ast_tov <- full_join(drzave_ast_tov, zemljevid, by=\"region\")\n\nmap_ast_tov <- ggplot(zemljevid_ast_tov, aes(x = long, y = lat, group = group))+\n geom_polygon(aes(fill = ast_tov)) + theme_void() +\n scale_fill_gradientn(colors=c(\"blue\", \"yellow\", \"green\")) + \n labs(fill =\"AST/TOV\")\n\n\n\n", "meta": {"hexsha": "ca2de2311a3224101475d56346bd6136ca19564d", "size": 7191, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "Babnik21/APPR-2019-20", "max_stars_repo_head_hexsha": 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"alphanum_fraction": 0.7000417188, "num_tokens": 2628, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038302, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32254715476134}} {"text": "library(tikzDevice)\n library(raster)\nlibrary(rgdal)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(\"grid\") \nload(\"../data/clim.R\")\n# map<-cru_raster_10min_window$val[,,2]\n# Longitude<<-cru_raster_10min_window$lon\n# Latitude<<-cru_raster_10min_window$lat\n# x<-vector()\n# y<-vector()\n# z<-vector()\n# k<-1\n# for(i in 1:length(Longitude)){\n# \t for(j in 1: length(Latitude)){\n# \t\t z[k]<-map[i,j]\n# \t\t x[k]<-Longitude[i]\n# \t\t y[k]<-Latitude[j]\n# \t\t k<-k+1\n# \t\t }\n# \t }\n# \tmap<-data.frame(lon=x,lat=y,val=z)\n# \tmap<-map[which(!is.na(map$val)),]\n\t \n# read shapefile\nwmap <- readOGR(dsn=\"ne_110m_land\", layer=\"ne_110m_land\")\n# convert to dataframe\nwmap_df <- fortify(wmap)\n\n# # create a blank ggplot theme\ntheme_opts <- list(theme(\n\t\t\tpanel.grid.minor = element_blank(),\n panel.grid.major = element_blank(),\n panel.background = element_rect(color = \"black\",fill=NA, size = 0.4, linetype = \"solid\"),\n #panel.border=element_rect(color = \"black\",fill=NA, size = 0.4, linetype = \"solid\"),\n plot.background = element_blank(),\n #plot.background=element_blank(),\n #panel.border = element_blank(),\n axis.line = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks = element_blank(),\n panel.margin = unit(5,\"lines\"),\n #axis.title.x = element_blank(),\n #axis.title.y = element_blank(),\n plot.title = element_text()))\n# \n# # plot map\na<-ggplot(wmap_df, aes(long,lat, group=group)) + geom_polygon() + labs(title=\"World map (longlat)\") + \n coord_equal() + theme_opts\n\nggsave(\"map1.png\", width=12.5, height=8.25, dpi=72) \n\n\nwmap_robin <- spTransform(wmap, CRS(\"+proj=robin\"))\nwmap_df_robin <- fortify(wmap_robin)\nggplot(wmap_df_robin, aes(long,lat, group=group)) + \n geom_polygon() + \n labs(title=\"World map (robinson)\") + \n coord_equal() +\n theme_opts\n\nggsave(\"map2.png\", width=12.5, height=8.25, dpi=72)\n\nggplot(wmap_df_robin, aes(long,lat, group=group, fill=hole)) +\n geom_polygon() + \n labs(title=\"World map (robin)\") +\n coord_equal() + \n theme_opts\nggsave(\"map3.png\", width=12.5, height=8.25, dpi=72) \n\n\n# \n# ggplot(wmap_df_robin, aes(long,lat, group=group, fill=hole)) + \n# geom_polygon() + \n# labs(title=\"World map (Robinson)\") + \n# coord_equal() + \n# theme_opts +\n# scale_fill_manual(values=c(\"#262626\", \"#e6e8ed\"), guide=\"none\") # change colors & remove legend\n# \n# ggsave(\"map4.png\", width=12.5, height=8.25, dpi=72) \n# # \n# # \n# # \n# # \n# # \n# # \n# # \ngrat <- readOGR(\"ne_110m_graticules_all\", layer=\"ne_110m_graticules_15\") \ngrat_df <- fortify(grat)\n\nbbox <- readOGR(\"ne_110m_graticules_all\", layer=\"ne_110m_wgs84_bounding_box\") \nbbox_df<- fortify(bbox)\n# \n# a<-ggplot(bbox_df, aes(long,lat, group=group)) + \n# geom_polygon(fill=\"white\") +\n# geom_polygon(data=wmap_df, aes(long,lat, group=group, fill=hole)) + \n# geom_path(data=grat_df, aes(long, lat, group=group, fill=NULL), linetype=\"dashed\", color=\"grey50\") +\n# labs(title=\"World map + graticule (longlat)\") + \n# coord_equal() +\n# theme_opts +\n# scale_fill_manual(values=c(\"black\", \"white\"), guide=\"none\") # change colors & remove legend\n# \n# #ggsave(\"map5.png\", width=12.5, height=8.25, dpi=72) \n# # \n# # graticule (Robin)\ngrat_robin <- spTransform(grat, CRS(\"+proj=robin\")) # reproject graticule\ngrat_df_robin <- fortify(grat_robin)\nbbox_robin <- spTransform(bbox, CRS(\"+proj=robin\")) # reproject bounding box\nbbox_robin_df <- fortify(bbox_robin)\n# \n# ggplot(bbox_robin_df, aes(long,lat, group=group)) + \n# geom_polygon(fill=\"white\") +\n# geom_polygon(data=wmap_df_robin, aes(long,lat, group=group, fill=hole)) + \n# geom_path(data=grat_df_robin, aes(long, lat, group=group, fill=NULL), linetype=\"dashed\", color=\"grey50\") +\n# labs(title=\"World map (Robinson)\") + \n# coord_equal() + \n# theme_opts +\n# scale_fill_manual(values=c(\"black\", \"white\"), guide=\"none\") # change colors & remove legend\n# \n# ggsave(\"map6.png\", width=12.5, height=8.25, dpi=72) \n# \n# \n# \n# \n# # add country borders\n# countries <- readOGR(\"ne_110m_admin_0_countries\", layer=\"ne_110m_admin_0_countries\") \n# countries_robin <- spTransform(countries, CRS(\"+proj=robin\"))\n# countries_robin_df <- fortify(countries_robin)\n# \n# ggplot(bbox_robin_df, aes(long,lat, group=group)) + \n# geom_polygon(fill=\"white\") +\n# theme_opts +\n# geom_polygon(data=countries_robin_df, aes(long,lat, group=group, fill=hole)) + \n# geom_path(data=countries_robin_df, aes(long,lat, group=group, fill=hole), color=\"white\", size=0.3) +\n# geom_path(data=grat_df_robin, aes(long, lat, group=group, fill=NULL), linetype=\"dashed\", color=\"grey50\") +\n# labs(title=\"World map (Robinson)\") + \n# coord_equal() + xlim(-2e6,5e6)\t + ylim(2e6,7e6)+\n# scale_fill_manual(values=c(\"black\", \"white\"), guide=\"none\") # change colors & remove legend\n# \n myPalette <- colorRampPalette(rev(brewer.pal(11, \"Spectral\")), space=\"Lab\")\n# # \n# # zp1 <- ggplot(longData,\n# # aes(x = Var2, y = Var1, fill = value))\n# # zp1 <- zp1 + geom_tile()\n# # zp1 <- zp1 + scale_fill_gradientn(colours = myPalette(100))\n\n#===============\n#scale bottom (a)\n#===============\n# plot.title = \"General Circulation Models Climate Data Output\"\n# plot.subtitle = 'created by GCM runs'\n# \n# a<-ggplot(map,aes(lon,lat,fill=val))+\n# geom_raster(hjust = 0, vjust = 0)+\n# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),\n# \t\t guide= guide_colorbar(title=expression(Surface~Temperature~degree~C), title.position=\"top\",\n# \t\t\t barwidth = 25, barheight = 1,nbin=100, \n# \t\t\t draw.ulim = FALSE, draw.llim = FALSE ))+\n# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+\n# #coord_cartesian()+coord_map()+\n# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype=\"dashed\", color=\"grey50\")+\n# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+\n# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+\n# theme_opts+theme(legend.position=\"bottom\", legend.background = element_rect(color = \"black\", \n# fill = \"grey90\", size = 0.4, linetype = \"solid\"))+\n# coord_equal()+\n# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), \"\")))) +\n# labs( x = \"\", y=\"\")\n# # #labs(title=\"Mean Surface Temperature \",x=\"GCM\")#+\n# # #scale_x_discrete(grat@data$display)\n# # #expression(Depth[mm])\n# # ggsave(\"map10.png\",width=12.5,height=6,dpi=72)\n# #===============\n# #scale right (b)\n# #===============\n# b<-ggplot(map,aes(lon,lat,fill=val))+\n# geom_raster(hjust = 0, vjust = 0)+\n# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),\n# \t\t guide= guide_colorbar(title=expression(degree~C), title.position=\"top\",\n# \t\t\t barwidth = 1, barheight = 15,#nbin=100, \n# \t\t\t draw.ulim = FALSE, draw.llim = FALSE ))+\n# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+\n# #coord_cartesian()+coord_map()+\n# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype=\"dashed\", color=\"grey50\")+\n# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+\n# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+\n# theme_opts+theme(legend.position=\"right\", legend.background = element_rect(color = \"black\", \n# fill = \"white\", size = 0.4, linetype = \"solid\"))+\n# coord_equal()+\n# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), \"\")))) +\n# labs( x = \"\", y=\"\")\n# #labs(title=\"Mean Surface Temperature \",x=\"GCM\")#+\n# #scale_x_discrete(grat@data$display)\n# #expression(Depth[mm])\n# # ggsave(\"map10.png\",width=12.5,height=6,dpi=72)\n# #===============\n# #scale inside (c)\n# #===============\n# plot.title = \"General Circulation Models Climate Data Output\"\n# plot.subtitle = 'created by GCM runs'\n# \n# c<-ggplot(map,aes(lon,lat,fill=val))+\n# geom_raster(hjust = 0, vjust = 0)+\n# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),\n# \t\t guide= guide_colorbar(title=expression(degree~C), title.position=\"top\",\n# \t\t\t # barwidth = 25, barheight = 1,nbin=100, \n# \t\t\t draw.ulim = FALSE, draw.llim = FALSE ))+\n# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+\n# #coord_cartesian()+coord_map()+\n# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype=\"dashed\", color=\"grey50\")+\n# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+\n# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+\n# theme_opts+theme(legend.position=c(0.9,0.3), legend.background = element_rect(color = \"black\", \n# fill = \"grey90\", size = 0.4, linetype = \"solid\"))+\n# coord_equal()+\n# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), \"\")))) +\n# labs( x = \"\", y=\"\")\n\n#===============\n#scale inside (d)\n#===============\n# plot.title = \"GCM Surface Temperature\"\n# plot.subtitle = 'created by GCM runs'\n# # plot.title = \"\"\n# # plot.subtitle = ''\n# # mr<-fortify(map_robin)\n# d<-ggplot(map,aes(long,lat,fill=group))+\n# geom_tile(hjust = 0, vjust = 0)+\n# scale_fill_gradientn(colours= myPalette(10),breaks=c(15,5,0,-5,-15),\n# \t\t guide= guide_legend(title=expression(degree~C), title.position=\"top\",\n# \t\t\t # barwidth = 25, barheight = 1,nbin=100, \n# \t\t\t draw.ulim = FALSE, draw.llim = FALSE ))+\n# \n# #scale_color_manual(values=myPalette(1000))+\n# # scale_fill_brewer(palette=\"Spectral\")+\n# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+\n# #coord_cartesian()+coord_map()+\n# #geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype=\"dashed\", color=\"grey50\")+\n# #geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+\n# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+\n# theme_opts+theme(legend.position=c(0.9,0.3), legend.background = element_rect(color = \"black\", \n# fill = \"grey90\", size = 0.4, linetype = \"solid\"))+\n# coord_equal()+\n# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), \"\")))) +\n# labs( x = \"\", y=\"\")\n# ggsave(\"map10.png\",width=12.5,height=6,dpi=72)\n \n \n# names(map)<-c(\"long\",\"lat\",\"val\")\n# grid<-expand.grid(lon=unique(map$lon),lat=unique(map$lat))\n# val<-array(NA,dim(grid)[1] )\n# for(i in 1:dim(map)[1]){\n# \tval[i]<-map$val[which(grid$lon==map$lon[i]&grid$lat==map$lat[i])]\n# }\t\n## coordinates (map)= ~long+lat\n projection(map)<-CRS(\"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0\")\n# k<-spTransform(map,CRS(\"+proj=robin\"))\n# \n# cor<-as.data.frame(k@coords)\n# pp<-data.frame(\"long\"=cor$x,\"lat\"=cor$y,\"val\"=k@data)\n# \n# ggplot(pp,aes(long,lat,fill=z))+geom_tile()#+scale_fill_gradientn(colours= myPalette(10))\n# \n\ncoordinates (map)= ~long+lat\nprojection(map)<-CRS(\"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0\")\npp_robin<-spTransform(pp,CRS(\"+proj=robin\"))\na<-data.frame(pp_robin@coords,pp_robin@data)\n\n\nmap_pj_c$lat<-round(map_pj$lat/10000)\n\n\n# s100 <- matrix(c(267573.9, 2633781, 213.29545, 262224.4, 2633781, 69.78261, 263742.7, 2633781, 51.21951, 259328.4, 2633781, 301.98413, 264109.8, 2633781, 141.72414, 255094.8, 2633781, 88.90244), ncol=3, byrow=TRUE)\n# colnames(s100) <- c('X', 'Y', 'Z')\n# \n# library(raster)\n# # set up an 'empty' raster, here via an extent object derived from your data\n# e <- extent(s100[,1:2])\n# e <- e + 1000 # add this as all y's are the same\n# \n# r <- raster(e, ncol=10, nrow=2)\n# # or r <- raster(xmn=, xmx=, ...\n# \n# # you need to provide a function 'fun' for when there are multiple points per cell\n# x <- rasterize(s100[, 1:2], r, s100[,3], fun=mean)\n# plot(x)", "meta": {"hexsha": "e5adde53303b1cf7ec2a6deb8b613e1304a3805c", "size": 11642, "ext": "r", "lang": "R", "max_stars_repo_path": "test/test_gg.r", "max_stars_repo_name": "sinanshi/visotmed", "max_stars_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T02:17:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T10:32:36.000Z", "max_issues_repo_path": "test/test_gg.r", "max_issues_repo_name": "sinanshi/visotmed", "max_issues_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_gg.r", "max_forks_repo_name": "sinanshi/visotmed", "max_forks_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2837370242, "max_line_length": 218, "alphanum_fraction": 0.6399244116, "num_tokens": 3824, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3225085197353891}} {"text": "library(testthat)\nlibrary(htnorm)\n\ntest_check(\"htnorm\")\n", "meta": {"hexsha": "35b689b5952b61d773da45ca4386085f8711e660", "size": 56, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat.r", "max_stars_repo_name": "zoj613/htnorm", "max_stars_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-12-06T22:29:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-20T06:10:16.000Z", "max_issues_repo_path": "tests/testthat.r", "max_issues_repo_name": "zoj613/htnorm", "max_issues_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-07-20T18:43:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-23T07:48:11.000Z", "max_forks_repo_path": "tests/testthat.r", "max_forks_repo_name": "zoj613/htnorm", "max_forks_repo_head_hexsha": "da31350449c437bbffe2d30664c1d7a5f23e554c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-02-01T16:15:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-23T20:29:13.000Z", "avg_line_length": 11.2, "max_line_length": 20, "alphanum_fraction": 0.7857142857, "num_tokens": 14, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.32243125148680374}} {"text": "#The image import has the wrong names, tritc is actually cy5\r\n\r\n# Do this for multiple folders. So i am going to select.list ll the folders\r\n#in my current directory that hass all my experiments listed.\r\n\r\n#first select the folder that contains ALL experiments\r\nmain.dir<-\"E:/Data/Lee Leavitt\"\r\n# Change Directory manually to you folder Ex: \"E:/Data/Mario/\"\r\nsetwd(main.dir)\r\n# select the folder for each experimetn from the list\r\nexp.dir<-select.list(list.dirs(), multiple=T)\r\n\r\n#Create names for the experiments you will assess today.\r\n#rd.names<-paste(\"RD.\",sub(\"rotocol \",\"\",sub(\"/\",\".\",sub(\"./\",\"\",exp.dir))), sep=\"\")\r\nrd.names<-paste(\"RD.\",sub(\"/\",\".\",sub(\"./\",\"\",exp.dir)), sep=\"\")\r\n\r\n\r\n#select your um for pixel conversion\r\narea.conversion<-1.625\r\n\r\n#Input the names of the files that CP created\r\ncell.data.name<-\"celldatacells_filtered.txt\"\r\nvideo.data.name<-\"video_dataROI.txt\"\r\n\r\n#Names of the files you want loaded into the RD file\r\nimg1.1<-\"bf.gfp.tritc.start.png\"\r\nimg2.1<-\"gfp.tritc.dapi.start.ci.ltl.rs.png\"\r\nimg3.1<-\"tritc.start.ci.ltl.rs.png\"\r\nimg4.1<-\"tritc.dapi.end.ci.ltl.rs.png\"\r\nimg5.1<-\"gfp.dapi.end.ci.ltl.rs.png\"\r\nimg6.1<-\"fura2.png\"\r\nimg7.1<-\"bf.end.lab.png\"\r\nimg8.1<-\"dapi.end.lab.png\"\r\n\r\nsetwd(main.dir)\r\n\r\nfor(i in 1:length(rd.names)){\r\nsetwd(exp.dir[i])\r\n\r\n\r\n# read in cell data. This is a big data frame, but we only need a \r\n# few collumns for now.\r\nc.dat<-read.delim(cell.data.name, header=T, sep=\"\\t\")\r\n\r\n#These are the collumns needed for analysis\r\nc.dat.names<-c(\r\n\t\"ObjectNumber\", \r\n\t\"AreaShape_Area\", \r\n\t\"AreaShape_Center_X\", \r\n\t\"AreaShape_Center_Y\", \r\n\t\"AreaShape_FormFactor\", \r\n\t\"AreaShape_Perimeter\",\r\n\t\"Intensity_MeanIntensity_CGRP_start_ci\",\r\n\t\"Intensity_MeanIntensity_CGRP_end_ci\",\r\n\t\"Intensity_MeanIntensity_IB4_start_ci\",\r\n\t\"Intensity_MeanIntensity_IB4_end_ci\",\r\n\t\"Intensity_MeanIntensity_DAPI_ci\",\r\n\t\"Intensity_MeanIntensity_BF_start\",\r\n\t\"Location_Center_X\",\r\n\t\"Location_Center_Y\")\r\n\r\n\tc.dat.1<-c.dat[,c.dat.names] \r\n\r\n\t# i like to rename the collumns for my procpharm\r\nnames(c.dat.1)<-c(\r\n\t\"id\",\r\n\t\"area\",\r\n\t\"center.x.simplified\", \r\n\t\"center.y.simplified\",\r\n\t\"circularity\",\r\n\t\"perimeter\",\r\n\t\"mean.gfp.start\",\r\n\t\"mean.gfp.end\",\r\n\t\"mean.cy5.start\",\r\n\t\"mean.cy5.end\",\r\n\t\"mean.dapi\",\r\n\t\"mean.bf\",\r\n\t\"center.x\",\r\n\t\"center.y\")\r\nc.dat<-cbind(c.dat.1, c.dat)\t\r\n\r\n\r\n# Now read in video Data\r\nrequire(data.table)\r\nf2.img<-fread(video.data.name)\r\n#cell names\r\n\r\n##Mean Trace\r\nt.340<-xtabs(Intensity_MeanIntensity_f2_340~ImageNumber+ObjectNumber, f2.img)\r\nt.380<-xtabs(Intensity_MeanIntensity_f2_380~ImageNumber+ObjectNumber, f2.img)\r\nt.dat<-t.340/t.380\r\n\r\n# add time info from file exported on nis veiwer\r\ntime.info<-read.delim(\"time.info.txt\", sep=\"\\t\", fileEncoding=\"UCS-2LE\")\r\nif(length(which(is.na(time.info[3]), arr.ind=T)[,1])>=1){\r\n\ttime.info<-time.info[-which(is.na(time.info[3]), arr.ind=T)[,1],] \r\n}else{time.info<-time.info}#remove any nas\r\ntime.min<-round(time.info[\"Time..s.\"]/60, digits=3)\r\n\r\n# Create row.names\r\ncell.names<-paste(\"X.\",c.dat$id,sep=\"\")\r\nc.dat[,\"id\"]<-cell.names\r\nrow.names(c.dat)<-cell.names\r\n\r\nt.340<-cbind(time.min[,1], t.340)\r\nt.340<- t.340[unique(row.names(t.340)),]#incase of duplicated rownames\r\ncolnames(t.340)<- c(\"Time\",cell.names)\r\nrow.names(t.340)<-time.min[,1]\r\nt.340<-as.data.frame(t.340)\r\n\r\nt.380<-cbind(time.min[,1], t.380)\r\nt.380<- t.380[unique(row.names(t.380)),]#incase of duplicated rownames\r\ncolnames(t.380)<- c(\"Time\",cell.names)\r\nrow.names(t.380)<-time.min[,1]\r\nt.380<-as.data.frame(t.380)\r\n\r\nt.dat<-cbind(time.min[,1], t.dat)\r\nt.dat<- t.dat[unique(row.names(t.dat)),]#incase of duplicated rownames\r\ncolnames(t.dat) <- c(\"Time\",cell.names)\r\nrow.names(t.dat)<-time.min[,1]\r\nt.dat<-as.data.frame(t.dat)\r\n\r\n##LowerQuartile Trace\r\n#tlq.340<-xtabs(Intensity_LowerQuartileIntensity_f2_340~ImageNumber+ObjectNumber, f2.img)\r\n#tlq.380<-xtabs(Intensity_LowerQuartileIntensity_f2_380~ImageNumber+ObjectNumber, f2.img)\r\n#tlq.dat<-tlq.340/tlq.380\r\n\r\n#tlq.340<-cbind(time.min[,1], tlq.340)\r\n#tlq.340<- tlq.340[unique(row.names(tlq.340)),]#incase of duplicated rownames\r\n#colnames(tlq.340)<- c(\"Time\",cell.names)\r\n#row.names(tlq.340)<-time.min[,1]\r\n#tlq.340<-as.data.frame(tlq.340)\r\n\r\n#tlq.380<-cbind(time.min[,1], tlq.380)\r\n#tlq.380<- tlq.380[unique(row.names(tlq.380)),]#incase of duplicated rownames\r\n#colnames(tlq.380)<- c(\"Time\",cell.names)\r\n#row.names(tlq.380)<-time.min[,1]\r\n#tlq.380<-as.data.frame(tlq.380)\r\n\r\n#tlq.dat<-cbind(time.min[,1], tlq.dat)\r\n#tlq.dat<- tlq.dat[unique(row.names(tlq.dat)),]#incase of duplicated rownames\r\n#colnames(tlq.dat) <- c(\"Time\",cell.names)\r\n#row.names(tlq.dat)<-time.min[,1]\r\n#tlq.dat<-as.data.frame(tlq.dat)\r\n\r\n##upperQuartile Trace\r\n#tuq.340<-xtabs(Intensity_UpperQuartileIntensity_f2_340~ImageNumber+ObjectNumber, f2.img)\r\n#tuq.380<-xtabs(Intensity_UpperQuartileIntensity_f2_380~ImageNumber+ObjectNumber, f2.img)\r\n#tuq.dat<-tuq.340/tuq.380\r\n\r\n#tuq.340<-cbind(time.min[,1], tuq.340)\r\n#tuq.340<- tuq.340[unique(row.names(tuq.340)),]#incase of duplicated rownames\r\n#colnames(tuq.340)<- c(\"Time\",cell.names)\r\n#row.names(tuq.340)<-time.min[,1]\r\n#tuq.340<-as.data.frame(tuq.340)\r\n\r\n#tuq.380<-cbind(time.min[,1], tuq.380)\r\n#tuq.380<- tuq.380[unique(row.names(tuq.380)),]#incase of duplicated rownames\r\n#colnames(tuq.380)<- c(\"Time\",cell.names)\r\n#row.names(tuq.380)<-time.min[,1]\r\n#tuq.380<-as.data.frame(tuq.380)\r\n\r\n#tuq.dat<-cbind(time.min[,1], tuq.dat)\r\n#tuq.dat<- tuq.dat[unique(row.names(tuq.dat)),]#incase of duplicated rownames\r\n#colnames(tuq.dat) <- c(\"Time\",cell.names)\r\n#row.names(tuq.dat)<-time.min[,1]\r\n#tuq.dat<-as.data.frame(tuq.dat)\r\n\r\n##edge Trace\r\n#te.340<-xtabs(Intensity_MeanIntensityEdge_f2_340~ImageNumber+ObjectNumber, f2.img)\r\n#te.380<-xtabs(Intensity_MeanIntensityEdge_f2_380~ImageNumber+ObjectNumber, f2.img)\r\n#te.dat<-te.340/te.380\r\n\r\n#te.340<-cbind(time.min[,1], te.340)\r\n#te.340<- te.340[unique(row.names(te.340)),]#incase of duplicated rownames\r\n#colnames(te.340)<- c(\"Time\",cell.names)\r\n#row.names(te.340)<-time.min[,1]\r\n#te.340<-as.data.frame(te.340)\r\n\r\n#te.380<-cbind(time.min[,1], te.380)\r\n#te.380<- te.380[unique(row.names(te.380)),]#incase of duplicated rownames\r\n#colnames(te.380)<- c(\"Time\",cell.names)\r\n#row.names(te.380)<-time.min[,1]\r\n#te.380<-as.data.frame(te.380)\r\n\r\n#te.dat<-cbind(time.min[,1], te.dat)\r\n#te.dat<- te.dat[unique(row.names(te.dat)),]#incase of duplicated rownames\r\n#colnames(te.dat) <- c(\"Time\",cell.names)\r\n#row.names(te.dat)<-time.min[,1]\r\n#te.dat<-as.data.frame(te.dat)\r\n\r\n##MaxIntenistyEdge Trace\r\n#tme.340<-xtabs(Intensity_MaxIntensityEdge_f2_340~ImageNumber+ObjectNumber, f2.img)\r\n#tme.380<-xtabs(Intensity_MaxIntensityEdge_f2_380~ImageNumber+ObjectNumber, f2.img)\r\n#tme.dat<-tme.340/tme.380\r\n\r\n#tme.340<-cbind(time.min[,1], tme.340)\r\n#tme.340<- tme.340[unique(row.names(tme.340)),]#incase of duplicated rownames\r\n#colnames(tme.340)<- c(\"Time\",cell.names)\r\n#row.names(tme.340)<-time.min[,1]\r\n#tme.340<-as.data.frame(tme.340)\r\n\r\n#tme.380<-cbind(time.min[,1], tme.380)\r\n#tme.380<- tme.380[unique(row.names(tme.380)),]#incase of duplicated rownames\r\n#colnames(tme.380)<- c(\"Time\",cell.names)\r\n#row.names(tme.380)<-time.min[,1]\r\n#tme.380<-as.data.frame(tme.380)\r\n\r\n#tme.dat<-cbind(time.min[,1], tme.dat)\r\n#tme.dat<- tme.dat[unique(row.names(tme.dat)),]#incase of duplicated rownames\r\n#colnames(tme.dat) <- c(\"Time\",cell.names)\r\n#row.names(tme.dat)<-time.min[,1]\r\n#tme.dat<-as.data.frame(tme.dat)\r\n\r\n\r\n## Proof my dataframes are already sorting in the correct way.\r\n#first create a dataframe from the video file x and y location data\r\n#t.loc.x<-matrix(f2.img[,\"Location_Center_X\"],byrow=FALSE, nrow=max(cell.names))[,1]\r\n#t.loc.y<-matrix(f2.img[,\"Location_Center_Y\"],byrow=FALSE, nrow=max(cell.names))[,1]\r\n#t.loc<-cbind(t.loc.x, t.loc.y)\r\n#t.loc<-round(t.loc, digits=3) #Then round dataframe to three digits\r\n\r\n## Create datatrame from c.dat(c created above)\r\n#c.xy<-c.dat[,c(\"center.x\", \"center.y\")]\r\n#c.xy<-round(c.xy, digits=3) # also round to three digits\r\n#setdiff(t.loc[,2], c.xy[,2]) #There should be no difference between the two data frames\r\n\r\n#setequal(t.loc[,1], c.xy[,1])#There should be no difference between the two data frame\r\n#setequal(t.loc[,2], c.xy[,2])#There should be no difference between the two data frame\r\n\r\n#example\r\n#a<-seq(from=1,to=10,by=10)\r\n#b<-seq(from=1,to=10,by=10)\r\n\r\n############ wr1 import\r\nwrdef<-\"wr1.csv\"\r\nif(!is.null(wrdef))\r\n\t{\r\n\t\twr <- ReadResponseWindowFile(wrdef)\r\n\t\tWr<-length(wr[,1])#complete and revise this section\r\n\t\tif(length(colnames(wr))<2){w.dat<-WrMultiplex(t.dat,wr,n=Wr)}\r\n\t\telse{w.dat <- MakeWr(t.dat,wr)}\r\n\t\t}\r\n\r\n# Initial and simple Data processing\r\ntmp.rd <- list(t.dat=t.dat,w.dat=w.dat,c.dat=c.dat)\r\nlevs<-setdiff(unique(as.character(w.dat[,2])),\"\")\r\n\tsnr.lim=5;hab.lim=.05;sm=2;ws=3;blc=\"SNIP\"\r\npcp <- ProcConstPharm(tmp.rd,sm,ws,blc)\r\nscp <- ScoreConstPharm(tmp.rd,pcp$blc,pcp$snr,pcp$der,snr.lim,hab.lim,sm)\r\nbin <- bScore(pcp$blc,pcp$snr,snr.lim,hab.lim,levs,tmp.rd$w.dat[,\"wr1\"])\r\nbin <- bin[,levs]\r\nbin[\"drop\"] <- 0 #maybe try to generate some drop criteria from the scp file.\r\nbin<-pf.function(bin,levs)\r\n\r\n# Add images\r\nrequire(png)\r\nimg1<-tryCatch(png::readPNG(img1.1), error=function(e)NULL)\r\nimg2<-tryCatch(png::readPNG(img2.1), error=function(e)NULL)\r\nimg3<-tryCatch(png::readPNG(img3.1), error=function(e)NULL)\r\nimg4<-tryCatch(png::readPNG(img4.1), error=function(e)NULL)\r\nimg5<-tryCatch(png::readPNG(img5.1), error=function(e)NULL)\r\nimg6<-tryCatch(png::readPNG(img6.1), error=function(e)NULL)\r\nimg7<-tryCatch(png::readPNG(img7.1), error=function(e)NULL)\r\nimg8<-tryCatch(png::readPNG(img8.1), error=function(e)NULL)\r\n#png::readPNG(\"bf.gfp.tritc.dapi.png\")\r\n\r\n#img.t<-png::readPNG(\"tritc.png\")\r\n#img.b<-png::readPNG(\"bf.png\")\r\n#img.bl<-png::readPNG(\"bf.lab.png\")\r\n#img.f<-png::readPNG(\"fura2.png\")\r\n\r\ntmp.rd <- list(t.dat=t.dat,t.340=t.340,t.380=t.380, \r\n#tlq.dat=tlq.dat, tlq.340=tlq.340,tlq.380=tlq.380,\r\n#tuq.dat=tuq.dat,tuq.340=tuq.340,tuq.380=tuq.380,\r\n#te.dat=te.dat,te.340=te.340,te.380=te.380,\r\n#tme.dat=tme.dat,tme.340=tme.340,tme.380=tme.380,\r\nw.dat=w.dat,c.dat=c.dat, bin=bin, scp=scp, snr=pcp$snr, blc=pcp$blc, der=pcp$der, \r\nimg1=img1,img2=img2,img3=img3,img4=img4,img5=img5,img6=img6,img7=img7, img8=img8)\r\n#img.gtd=img.gtd, img.g=img.g, img.t=img.t, img.b=img.b,img.bl=img.bl, img.f=img.f)\r\n\r\ntmp.rd$c.dat[,\"area\"]<-tmp.rd$c.dat$area*area.conversion\r\n\r\n## Check for duplicated rows\t\r\nif(length(which(duplicated(row.names(t.dat))))>=1){\r\ndup<-which(duplicated(row.names(t.dat)))\r\npaste(dup)\r\nt.dat<-t.dat[-dup,]\r\nw.dat<-w.dat[-dup,]\r\n}\r\n\r\n##DESPIKE\r\nwts <- tmp.rd$t.dat\r\nfor(j in 1:5) #run the despike 5 times.\r\n{\r\n\twt.mn3 <- Mean3(wts)\r\n\twts <- SpikeTrim2(wts,1,-1)\r\n\tprint(sum(is.na(wts))) #this prints out the number of points removed should be close to 0 after 5 loops.\r\n\twts[is.na(wts)] <- wt.mn3[is.na(wts)]\r\n}\r\ntmp.rd$mp <- wts\r\n\r\n#170127\r\n# Take the despiked data, subtract the minimum value from the trace, then divide by the maximun value\r\n# to create traces that are all on the same 0 to 1 scale\r\ntmp.dat<-tmp.rd$mp\r\n\r\nfor(k in 1:length(colnames(tmp.rd$mp))){\r\n\r\n\ttmp.dat[,k]<-tmp.rd$mp[,k]-min(tmp.rd$mp[,k])\r\n\ttmp.dat[,k]<-tmp.dat[,k]/max(tmp.dat[,k])\r\n\r\n}\r\ntmp.dat[,1]<-tmp.rd$t.dat[,1]\r\n\r\ntmp.rd$mp.1<-tmp.dat\r\n\r\n\r\nrd.name<-rd.names[i]\r\nf.name <- paste(rd.name,\".Rdata\",sep=\"\")\r\nassign(rd.name,tmp.rd)\r\nsave(list=rd.name,file=f.name)\r\n\r\nrm(f2.img)\r\nrm(rd.name)\r\nrm(tmp.rd)\r\nrm(c.dat)\r\nrm(cell.names)\r\nsetwd(main.dir)\r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "626973db1e95d8b83e706a70bd77bc35c74625cc", "size": 11437, "ext": "r", "lang": "R", "max_stars_repo_path": "extras/Cell Profiler Import/171002.full.cp.import.gfp.cy5.dapi.r", "max_stars_repo_name": "leeleavitt/procPharm", "max_stars_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "extras/Cell Profiler Import/171002.full.cp.import.gfp.cy5.dapi.r", "max_issues_repo_name": "leeleavitt/procPharm", "max_issues_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-01-08T18:50:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T01:23:47.000Z", "max_forks_repo_path": "extras/Cell Profiler Import/171002.full.cp.import.gfp.cy5.dapi.r", "max_forks_repo_name": "leeleavitt/procPharm", "max_forks_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T20:45:06.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T20:45:06.000Z", "avg_line_length": 32.6771428571, "max_line_length": 106, "alphanum_fraction": 0.6821718982, "num_tokens": 3927, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.32243125148680374}} {"text": "\n# Information about code:\n# This code corresponds to exploratory data analyses for my MSc thesis.\n# They are pertaining to EDA for the authors \n# (relevant for Chapter 3, the section on Determinants of taxonomic resources flow).\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n\n\n# Set up\nsource('2019-06-19-jsa-type/init/init.R')\nsource('2019-06-19-jsa-type/subset.r')\n\n# Libraries\nlibrary(networkD3)\nlibrary(ggplot2)\n\n# Read lookup\nlu <- get_lp_statoid()\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - resource flow\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nprint(paste0(Sys.time(), \" --- resource flow\"))\n\n# Summarising where there is flow\nspp <- get_df1(write=F)\nspp1 <- spp[,c(\"idx\", \"type.country.n\", \"full.name.of.describer\")]\nspp2 <- get_df2(write=F)[status==\"Synonym\"][,c(\"idx\", \"type.country.n\", \"full.name.of.describer\")]\n\nspp_s <- rbind(spp1, spp2)\nspp_s <- data.table(spp_s %>% separate_rows(full.name.of.describer, sep=\"; \"))\ndes <- get_des(write=F)\ndes <- des[, c(\"full.name.of.describer.n\", \"residence.country.describer.n\")]\ndes <- data.table(des %>% separate_rows(residence.country.describer.n, sep=\"; \"))\ndes <- des[, id := seq_len(.N), by = full.name.of.describer.n][\n order(full.name.of.describer.n, id),][!duplicated(full.name.of.describer.n)]\nspp_s <- merge(spp_s, des, by.x=\"full.name.of.describer\", by.y=\"full.name.of.describer.n\",\n all.x=T, all.y=F)\nspp_s <- spp_s[, c(\"type.country.n\", \"residence.country.describer.n\", \"idx\")]\nspp_s <- unique(spp_s)\n\nt <- table(spp_s$type.country.n, spp_s$residence.country.describer.n)\nt <- data.table(t)\ndim(t); t <- t[N!=0]; dim(t)\nnames(t) <- c(\"type.country\", \"residence.country\", \"N\")\ndim(t); t <- t[!(type.country == \"\" | residence.country==\"[unknown]\")]; dim(t)\nto_merge1 <- lookup.cty[, c(\"DL\", \"centroid_lat\", \"centroid_lon\")]\nto_merge2 <- lookup.cty[, c(\"DL\", \"centroid_lat\", \"centroid_lon\")]\nt <- merge(t, to_merge1, by.x=\"type.country\", by.y=\"DL\", all.x=T, all.y=F)\nt <- merge(t, to_merge2, by.x=\"residence.country\", by.y=\"DL\", all.x=T, all.y=F,\n suffixes=c(\"_type.country\", \"_residence.country\"))\nt[is.na(centroid_lat_type.country)]\nt[is.na(centroid_lat_residence.country)]$residence.country\nnames(t) <- c(\"ori\", \"des\", \"N\", \"dY\", \"dX\", \"oY\", \"oX\")\nt$Geom <- paste0(\"LINESTRING (\", as.character(t$oX), \n \" \", as.character(t$oY), \", \", \n as.character(t$dX), \" \", as.character(t$dY), \")\")\nt$no_flow <- t$ori == t$des\n\nwrite.csv(t,\n paste0(dir_data_ch3_flow, \"2019-09-22-flow-map-type-loc-des-country.csv\"), \n na='', row.names=F, fileEncoding=\"UTF-8\")\n\nwrite.csv(t,\n paste0(dir_shiny, \"eda1.1_shiny/data/2019-09-22-flow-map-type-loc-des-country.csv\"), \n na='', row.names=F, fileEncoding=\"UTF-8\")\n", "meta": {"hexsha": "66900351292775626735168cb23527b3cc2deb72", "size": 2813, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch3-flow/analysis1/prep.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch3-flow/analysis1/prep.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch3-flow/analysis1/prep.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.9850746269, "max_line_length": 98, "alphanum_fraction": 0.607536438, "num_tokens": 863, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3224312514868036}} {"text": "##' Significance Plot with D3 (vertical)\n##'\n##' Makes an errorbar plot using the D3.js library with a hover-over\n##' effect that highlights all cagtegories that are significantly differnet\n##' than the one being hovered over.\n##'\n##' @aliases sigd3.default sigd3.mcmc.list sigd3.mcmc\n##'\n##' @param object A model that has a categorical predictor. The function\n##' uses both \\code{ggpredict} from the \\pkg{ggeffects} package and\n##' \\code{factorplot} from the \\pkg{factorplot} package (with the\n##' variable given by the \\code{factor.var=term} argument), so the object\n##' must be amenable to these manipulations. Alternatively, it could be\n##' an mcmc object in which case all columns will be plotted.\n##' @param term The term from the model to be plotted.\n##' @param fname Name of the file to which the html code will be written.\n##' @param height Height of the plot in pixels\n##' @param width Width of the plot in pixels\n##' @param return_iFrame Logical indicating whether an \\code{iframe}\n##' should be returned to hold the plot. If \\code{FALSE} only the html\n##' file is produced and the function prpduces no other visible output.\n##' @param ylab Label to put on y-axis.\n##' @param level Confidence level at which to do the test (or in the mcmc case, credibility level for the credible intervals).\n##' @param axfont Font size for axis tick mark labels (in px).\n##' @param labfont Font size for y-axis label (in px).\n##' @param labfam Font-family for y-axis label (must be one of \"serif\" or \"sans-serif\").\n##' @param colors Vector of three colors the first for the selected items, the second for the un-selected items and the third for when no points are hovered over.\n##' @param ptSize Vector of two point sizes - the first for selected observations and the second for unselected ones.\n##' @param lineSize Vector of line widths - the first for selected observations and the second for unselected ones.\n##' @param names An optional vector of names for the parameters.\n##' @param order How should parameters be organized by size-ascending, size-descending or natural (i.e., the order in the data).\n##'\n##' @importFrom ggeffects ggpredict\n##' @importFrom factorplot factorplot\n##' @importFrom dplyr mutate rename select bind_cols arrange\n##' @importFrom magrittr %>%\n##' @importFrom jsonlite toJSON\n##' @importFrom htmltools tags\n##' @importFrom stats quantile\n##' @importFrom utils combn\n##' @importFrom rlang .data\n##'\n##' @return Either an html file or an iframe linked to the html file.\n##' @export\n\n\nsigd3 <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n UseMethod(\"sigd3\")\n }\n\n\n\n##' @method sigd3 default\n##' @export\nsigd3.default <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n\n labfam <- match.arg(labfam)\n order <- match.arg(order)\n g <- ggpredict(object, terms = term)\n g <- g %>% mutate(obs = 1:nrow(g)) %>%\n rename(\"y\" = \"predicted\", \"ylow\" = \"conf.low\", \"yup\" = \"conf.high\") %>%\n select(\"x\", \"y\", \"ylow\", \"yup\", \"obs\")\n\n if (!is.null(names)) {\n if (length(names) == nrow(g)) {\n g <- mutate(x = names)\n } else{\n stop(\n paste0(\"mismatch between vector lengths names: (\", length(names),\n \") and parameters: (\", nrow(g), \").\\n\")\n )\n }\n }\n\n fp <- factorplot(object, factor.var = term, pval = 1 - level)\n mat <- matrix(0, nrow = nrow(fp$pval) + 1, ncol = ncol(fp$pval) + 1)\n mat[upper.tri(mat, diag = FALSE)] <-\n fp$pval[upper.tri(fp$pval, diag = TRUE)]\n mat <- t(mat)\n mat[upper.tri(mat, diag = FALSE)] <-\n fp$pval[upper.tri(fp$pval, diag = TRUE)]\n mat <- ifelse(mat < .05, 1, 0)\n diag(mat) <- 0\n\n mat <- as.data.frame(mat)\n names(mat) <- paste0(\"g\", 1:ncol(mat))\n g <- g %>% bind_cols(mat)\n\n if (order == \"size-ascending\") {\n g <- g %>% arrange(.data$y)\n } else if (order == \"size-descending\") {\n g <- g %>% arrange(-.data$y)\n }\n\n h <- '\n\n\n\n \n\n sigd3.default\n \n \n \n\n\n\n\n \n \n\n')\n\n cat(h, \"\\n\", sep = \"\", file = fname, append = FALSE)\n cat(\"let data = \", toJSON(g), \";\\n\", sep = \"\", file = fname, append = TRUE)\n cat(b, sep = \"\", file = fname, append = TRUE)\n\n tags$iframe(src = fname, height = height * 1.05, width = width * 1.05, style = \"border: none\")\n\n r2d3(data=g, options = list(axfont = 12, colors = c(\"#B80000\", \"#000000\", \"#000000\"), ptSize = c(7,5), lineSize = c(3,1.5), labfont = 12, ylab = \"Effect of Region\", labfam = c(\"sans-serif\", \"serif\")), script = \"sigd3.js\", height=\"500\", width=\"750\")\n }\n\n\n\n##' @method sigd3 mcmc.list\n##' @export\nsigd3.mcmc.list <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n object <- do.call(rbind, object)\n sigd3.mcmc(\n object,\n term = term,\n height = height,\n width = width,\n return_iFrame = return_iFrame,\n level = level,\n ylab = ylab,\n axfont = axfont,\n labfont = labfont,\n labfam = labfam,\n colors = colors,\n ptSize = ptSize,\n lineSize = lineSize,\n names = names,\n order = order\n )\n }\n\n\n\n\n##' @method sigd3 mcmc\n##' @export\nsigd3.mcmc <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n labfam <- match.arg(labfam)\n order <- match.arg(order)\n\n if (!is.null(names)) {\n if (length(names) == ncol(object)) {\n colnames(object) = names\n } else {\n stop(\n paste0(\n \"mismatch between vector lengths names: )\", length(names),\n \") and object: (\", ncol(object), \").\\n\"\n )\n )\n }\n }\n\n g <- data.frame(\n x = colnames(object),\n y = colMeans(object),\n ylow = apply(object, 2, quantile, (1 - level) / 2),\n yup = apply(object, 2, quantile, 1 - (1 - level) / 2),\n obs = 1:ncol(object)\n )\n\n combs <- combn(nrow(g), 2)\n m1 <- object[, combs[1, ]]\n m2 <- object[, combs[2, ]]\n diffs <- m2 - m1\n g0 <- apply(diffs, 2, function(x)\n mean(x > 0))\n\n mat <- matrix(0, nrow = ncol(object), ncol = ncol(object))\n mat[cbind(combs[1, ], combs[2, ])] <- g0\n mat <- t(mat)\n mat[cbind(combs[1, ], combs[2, ])] <- g0\n mat <- ifelse(mat > level | mat < (1 - level), 1, 0)\n diag(mat) <- 0\n mat <- as.data.frame(mat)\n names(mat) <- paste0(\"g\", 1:ncol(mat))\n g <- g %>% bind_cols(mat)\n\n if (order == \"size-ascending\") {\n g <- g %>% arrange(.data$y)\n } else if (order == \"size-descending\") {\n g <- g %>% arrange(-.data$y)\n }\n\n h <- '\n\n\n\n \n\n sigd3.default\n \n \n \n\n\n\n\n \n \n\n'\n )\n\n cat(h, \"\\n\", sep = \"\", file = fname, append = FALSE)\n cat(\"var data = \", toJSON(g), \";\\n\", sep = \"\", file = fname, append = TRUE)\n cat(b, sep = \"\", file = fname, append = TRUE)\n\n tags$iframe(src = fname, height = height * 1.05, width = width * 1.05, style = \"border: none\")\n\n r2d3(data=g, options = list(axfont = 12, colors = c(\"#B80000\", \"#000000\", \"#000000\"), ptSize = c(7,5), lineSize = c(3,1.5), labfont = 12, ylab = \"Effect of Region\", labfam = c(\"sans-serif\", \"serif\")), script = \"sigd3.js\", height=\"500\", width=\"750\")\n }\n\n##' Significance Plot with D3 (horizontal)\n##'\n##' Makes an errorbar plot using the D3.js library with a hover-over\n##' effect that highlights all cagtegories that are significantly differnet\n##' than the one being hovered over. The orientation of this plot is\n##' horizontal rather than vertical\n##'\n##' @aliases sigd3h.default sigd3h.mcmc.list sigd3h.mcmc\n##'\n##' @param object A model that has a categorical predictor. The function\n##' uses both \\code{ggpredict} from the \\pkg{ggeffects} package and\n##' \\code{factorplot} from the \\pkg{factorplot} package (with the\n##' variable given by the \\code{factor.var=term} argument), so the object\n##' must be amenable to these manipulations. Alternatively, it could be\n##' an mcmc object in which case all columns will be plotted.\n##' @param term The term from the model to be plotted.\n##' @param fname Name of the file to which the html code will be written.\n##' @param height Height of the plot in pixels\n##' @param width Width of the plot in pixels\n##' @param return_iFrame Logical indicating whether an \\code{iframe}\n##' should be returned to hold the plot. If \\code{FALSE} only the html\n##' file is produced and the function prpduces no other visible output.\n##' @param ylab Label to put on y-axis.\n##' @param level Confidence level at which to do the test (or in the mcmc case, credibility level for the credible intervals).\n##' @param axfont Font size for axis tick mark labels (in px).\n##' @param labfont Font size for y-axis label (in px).\n##' @param lmexpand Factor by which the left-margin should be expanded to accommodate tick mark labels.\n##' @param labfam Font-family for y-axis label (must be one of \"serif\" or \"sans-serif\").\n##' @param colors Vector of three colors the first for the selected items, the second for the un-selected items and the third for when no points are hovered over.\n##' @param ptSize Vector of two point sizes - the first for selected observations and the second for unselected ones.\n##' @param lineSize Vector of line widths - the first for selected observations and the second for unselected ones.\n##' @param names An optional vector of names for the parameters.\n##' @param order How should parameters be organized by size-ascending, size-descending or natural (i.e., the order in the data).\n\n##'\n##' @importFrom ggeffects ggpredict\n##' @importFrom factorplot factorplot\n##' @importFrom dplyr mutate rename select bind_cols\n##' @importFrom magrittr %>%\n##' @importFrom jsonlite toJSON\n##' @importFrom htmltools tags\n##' @importFrom stats quantile\n##' @importFrom utils combn\n##'\n##' @return Either an html file or an iframe linked to the html file.\n##' @export\nsigd3h <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n lmexpand = .15,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n UseMethod(\"sigd3h\")\n }\n\n##' @method sigd3h default\n##' @export\nsigd3h.default <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n lmexpand = .15,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n labfam = match.arg(labfam)\n order <- match.arg(order)\n g <- ggpredict(object, terms = term)\n g <- g %>% mutate(obs = 1:nrow(g)) %>%\n rename(\n \"y\" = \"x\",\n \"x\" = \"predicted\",\n \"xlow\" = \"conf.low\",\n \"xup\" = \"conf.high\"\n ) %>%\n select(\"x\", \"y\", \"xlow\", \"xup\", \"obs\")\n if (!is.null(names)) {\n if (length(names) == nrow(g)) {\n g <- mutate(y = names)\n } else{\n stop(\n paste0(\n \"names vector (length \",\n length(names),\n \" has to be the same as the number of parameters (length, \",\n nrow(g),\n \").\\n\"\n )\n )\n }\n }\n fp <- factorplot(object, factor.var = term, pval = 1 - level)\n mat <- matrix(0, nrow = nrow(fp$pval) + 1, ncol = ncol(fp$pval) + 1)\n mat[upper.tri(mat, diag = FALSE)] <-\n fp$pval[upper.tri(fp$pval, diag = TRUE)]\n mat <- t(mat)\n mat[upper.tri(mat, diag = FALSE)] <-\n fp$pval[upper.tri(fp$pval, diag = TRUE)]\n mat <- ifelse(mat < .05, 1, 0)\n diag(mat) <- 0\n\n mat <- as.data.frame(mat)\n names(mat) <- paste0(\"g\", 1:ncol(mat))\n g <- g %>% bind_cols(mat)\n if (order == \"size-ascending\") {\n g <- g %>% arrange(.data$x)\n }\n if (order == \"size-descending\") {\n g <- g %>% arrange(-.data$x)\n }\n\n h <- '\n \n \n \n \n \n \n \n\n \n\n \n\n')\n\n cat(h,\n \"\\n\",\n sep = \"\",\n file = fname,\n append = FALSE)\n\n cat(\n \"var data = \",\n toJSON(g),\n \";\\n\",\n sep = \"\",\n file = fname,\n append = TRUE\n )\n\n cat(b,\n sep = \"\",\n file = fname,\n append = TRUE)\n\n if (return_iFrame) {\n tags$iframe(\n src = fname,\n height = height * 1.05,\n width = width * 1.05,\n style = \"border: none\"\n )\n }\n }\n\n\n\n##' @method sigd3h mcmc.list\n##' @export\nsigd3h.mcmc.list <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n lmexpand = .15,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n object <- do.call(rbind, object)\n\n sigd3h.mcmc(\n object = object,\n term = term,\n fname = fname,\n height = height,\n width = width,\n return_iFrame = return_iFrame,\n level = level,\n ylab = ylab,\n axfont = axfont,\n labfont = labfont,\n lmexpand = lmexpand,\n labfam = labfam,\n colors = colors,\n ptSize = ptSize,\n lineSize = lineSize,\n names = names,\n order = order\n )\n }\n\n\n\n\n##' @method sigd3h mcmc\n##' @export\nsigd3h.mcmc <-\n function(object,\n term,\n fname,\n height = 500,\n width = 625,\n return_iFrame = TRUE,\n level = .95,\n ylab = paste0(\"Effect of \", term),\n axfont = 12,\n labfont = 12,\n lmexpand = .15,\n labfam = c(\"sans-serif\", \"serif\"),\n colors = c(\"#B80000\", \"#000000\", \"#000000\"),\n ptSize = c(7, 5),\n lineSize = c(3, 1.5),\n names = NULL,\n order = c(\"size-descending\", \"size-ascending\", \"natural\")) {\n labfam = match.arg(labfam)\n order <- match.arg(order)\n\n if (!is.null(names)) {\n if (length(names) == ncol(object)) {\n colnames(object) = names\n } else{\n stop(\n paste0(\n \"names vector (length \",\n length(names),\n \" has to be the same as the number of parameters (length, \",\n ncol(object),\n \").\\n\"\n )\n )\n }\n\n }\n g <- data.frame(\n y = colnames(object),\n x = colMeans(object),\n xlow = apply(object, 2, quantile, (1 - level) / 2),\n xup = apply(object, 2, quantile, 1 - (1 - level) / 2),\n obs = 1:ncol(object)\n )\n\n combs <- combn(ncol(object), 2)\n m1 <- object[, combs[1, ]]\n m2 <- object[, combs[2, ]]\n diffs <- m2 - m1\n g0 <- apply(diffs, 2, function(x)\n mean(x > 0))\n\n mat <- matrix(0, nrow = ncol(object), ncol = ncol(object))\n mat[cbind(combs[1, ], combs[2, ])] <- g0\n mat <- t(mat)\n mat[cbind(combs[1, ], combs[2, ])] <- g0\n mat <- ifelse(mat > level | mat < (1 - level), 1, 0)\n diag(mat) <- 0\n mat <- as.data.frame(mat)\n names(mat) <- paste0(\"g\", 1:ncol(mat))\n g <- g %>% bind_cols(mat)\n if (order == \"size-ascending\") {\n g <- g %>% arrange(.data$x)\n }\n if (order == \"size-descending\") {\n g <- g %>% arrange(-.data$x)\n }\n\n h <- '\n \n \n \n \n \n \n \n\n \n\n \n\n')\n\n cat(h,\n \"\\n\",\n sep = \"\",\n file = fname,\n append = FALSE)\n\n cat(\n \"var data = \",\n toJSON(g),\n \";\\n\",\n sep = \"\",\n file = fname,\n append = TRUE\n )\n\n cat(b,\n sep = \"\",\n file = fname,\n append = TRUE)\n\n if (return_iFrame) {\n tags$iframe(\n src = fname,\n height = height * 1.05,\n width = width * 1.05,\n style = \"border: none\"\n )\n }\n }\n\n\n#' Corporatism\n#'\n#' A dataset from Alvarez, Garrett and Lange's 1991 APSR article.\n#'\n#' @format A list with the following variables for 16 countries over 14 years:\n#' \\describe{\n#' \\item{y}{Economic growth}\n#' \\item{country}{Country indicator variable}\n#' \\item{imports}{Imports price movement}\n#' \\item{exports}{Export price movement}\n#' \\item{left}{Leftist government}\n#' \\item{demand}{Demand}\n#' \\item{growth.lag}{Lag of economic growth}\n#' \\item{labor.org}{Labor organizations prevalence, only one observation per country}\n#' }\n#' @source \\url{https://spia.uga.edu/faculty_pages/rbakker/bayes/POLS%20Bayes.htm}\n\"corp\"\n", "meta": {"hexsha": "e1d555ef0b200edc1bb907d23f2806c51b4da4c8", "size": 35813, "ext": "r", "lang": "R", "max_stars_repo_path": "R/daviz_functions.r", "max_stars_repo_name": "davidaarmstrong/daviz", "max_stars_repo_head_hexsha": "919b27d1ab8cc8fc0ef38fd20810dad0767b3059", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/daviz_functions.r", "max_issues_repo_name": "davidaarmstrong/daviz", "max_issues_repo_head_hexsha": "919b27d1ab8cc8fc0ef38fd20810dad0767b3059", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 12, "max_issues_repo_issues_event_min_datetime": "2020-07-30T12:35:23.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-17T20:08:10.000Z", "max_forks_repo_path": "R/daviz_functions.r", "max_forks_repo_name": "davidaarmstrong/daviz", "max_forks_repo_head_hexsha": "919b27d1ab8cc8fc0ef38fd20810dad0767b3059", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4682034976, "max_line_length": 252, "alphanum_fraction": 0.547119761, "num_tokens": 10616, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3224312514868036}} {"text": "library(lattice)\neval.polyglot(path = 'babylonian-demo/fibonacci-complete.js')\n\n# \nplotFunction <- function(func, start, end) {\n x <- seq(from = start, to = end, by = 1)\n # \n y <- lapply(x, (function(n) func(n)))\n svg()\n print(xyplot(y ~ x, type=\"p\", cex=2, pch=16))\n grDevices:::svg.off()\n}\n", "meta": {"hexsha": "7105581dbe06b637336c1d270d096c222e6844ce", "size": 376, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/babylonian-demo/plotFunction-complete.r", "max_stars_repo_name": "hpi-swa/polyglot-live-programming", "max_stars_repo_head_hexsha": "f85082d917fe63f73057a9512a9b5064003de3b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2020-10-30T14:47:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-08T09:46:30.000Z", "max_issues_repo_path": "examples/babylonian-demo/plotFunction-complete.r", "max_issues_repo_name": "hpi-swa/polyglot-live-programming", "max_issues_repo_head_hexsha": "f85082d917fe63f73057a9512a9b5064003de3b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-02T13:51:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-09T12:15:28.000Z", "max_forks_repo_path": "examples/babylonian-demo/plotFunction-complete.r", "max_forks_repo_name": "hpi-swa/polyglot-live-programming", "max_forks_repo_head_hexsha": "f85082d917fe63f73057a9512a9b5064003de3b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-21T09:36:39.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-21T09:36:39.000Z", "avg_line_length": 28.9230769231, "max_line_length": 71, "alphanum_fraction": 0.6170212766, "num_tokens": 126, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369904, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3224312429985551}} {"text": "##%######################################################%##\n# #\n#### 2. Model selection ####\n# #\n##%######################################################%##\n\n# This script carries out the model selection process using the dataset generated\n# in the previous script. Models are run for 2 biodiversity metrics. \n\nrm(list = ls())\n\n\n# load libraries\nlibrary(devtools)\n#install_github(repo = \"timnewbold/StatisticalModels\")\nlibrary(StatisticalModels)\nlibrary(cowplot)\nlibrary(gridGraphics)\n\n\n# directories\ndatadir <- \"1_PREDICTS_PLUS_VARIABLES/\"\noutdir <- \"2_MODEL_SELECTION/\"\nif(!dir.exists(outdir)) dir.create(outdir)\n\n# read in the PREDICTS datasets with landscape variables\n\nload(paste0(datadir, \"/PREDICTS_dataset_TRANS_INSECTS.rdata\")) # final.data.trans\n\n\n#### Run model selection process including landscape variables that are not correlated ####\n\n\n##%######################################################%##\n# #\n#### 1. Species richness ####\n# #\n##%######################################################%##\n\n\n#### 1. Species richness models ####\n\n### A. ALL INSECTS ###\n\n# run the model selection process\n\nsystem.time({sr1 <- GLMERSelect(modelData = final.data.trans, \n responseVar = \"Species_richness\",\n fitFamily = \"poisson\", \n fixedFactors = c(\"Predominant_land_use\", \"Use_intensity\", \"fields\"),\n fixedTerms = list(pest_H_RS = 1, ncrop_RS = 1, percNH_RS = 1),\n randomStruct = \"(1|SS)+(1|SSB)+(1|SSBS)\", \n fixedInteractions = c(\"Predominant_land_use:pest_H_RS\", \n \"Predominant_land_use:ncrop_RS\",\n \"Predominant_land_use:percNH_RS\",\n \"Predominant_land_use:fields\",\n \"Use_intensity:pest_H_RS\", \n \"Use_intensity:ncrop_RS\",\n \"Use_intensity:percNH_RS\",\n \"Use_intensity:fields\",\n \"Predominant_land_use:Use_intensity\"), verbose = F)}) \n# save the output\nsave(sr1, file = paste0(outdir, \"/SPECIESRICHNESS_Model_selection_INSECTS.rdata\"))\n\n# take a look at the model output\nsummary(sr1$model)\n\n# extract the stats produced as part of the model selection process\nsr1stats <- as.data.frame(sr1$stats)\n\n# save these\nwrite.csv(sr1stats, file = paste0(outdir, \"/sr_stats_INSECTS.csv\"), row.names = F)\n\n\n# Non selection version to assess all variables\n\nsr2 <- GLMER(modelData = final.data.trans,responseVar = \"Species_richness\",fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Use_intensity + fields + pest_H_RS + ncrop_RS + percNH_RS + Predominant_land_use:pest_H_RS + Predominant_land_use:ncrop_RS + Predominant_land_use:percNH_RS + Predominant_land_use:fields + Use_intensity:pest_H_RS + Use_intensity:ncrop_RS + Use_intensity:percNH_RS+ Use_intensity:fields + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS)+(1|SSB)+(1|SSBS\")\n\nsummary(sr2$model)\n\n# save the output\nsave(sr2, file = paste0(outdir, \"/RICHNESS_Model_Set_INSECTS.rdata\"))\n\n\n##%######################################################%##\n# #\n#### 2. Abundance ####\n# #\n##%######################################################%##\n\nmodel_data <- final.data.trans[!is.na(final.data.trans$logAbun), ] # 4681 rows\n\nlength(unique(model_data$SS)) # 229\nlength(unique(model_data$SSBS)) # 4681\n\n### A. ALL INSECTS ######SSBS A. ALL INSECTS ###\n\n\nsystem.time({ab1 <- GLMERSelect(modelData = final.data.trans, \n responseVar = \"logAbun\",\n fitFamily = \"gaussian\", \n fixedFactors = c(\"Predominant_land_use\", \"Use_intensity\", \"fields\"),\n fixedTerms = list(pest_H_RS = 1, ncrop_RS = 1, percNH_RS = 1),\n randomStruct = \"(1|SS)+(1|SSB)\", \n fixedInteractions = c(\"Predominant_land_use:pest_H_RS\", \n \"Predominant_land_use:ncrop_RS\",\n \"Predominant_land_use:percNH_RS\",\n \"Predominant_land_use:fields\",\n \"Use_intensity:pest_H_RS\", \n \"Use_intensity:ncrop_RS\",\n \"Use_intensity:percNH_RS\",\n \"Use_intensity:fields\",\n \"Predominant_land_use:Use_intensity\"), verbose = F)}) \n# take a look at the model output\nsummary(ab1$model)\n\n# save the output\nsave(ab1, file = paste0(outdir, \"/ABUNDANCE_Model_Selection_INSECTS.rdata\"))\n\n# extract the stats produced as part of the model selection process\nab1stats <- as.data.frame(ab1$stats)\n\n# save these\nwrite.csv(ab1stats, file = paste0(outdir, \"/ab_stats_INSECTS.csv\"), row.names = F)\n\n\n# Non selection version to assess all variables\n\nab2 <- GLMER(modelData = final.data.trans,responseVar = \"logAbun\",fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Use_intensity + fields + pest_H_RS + ncrop_RS + percNH_RS + Predominant_land_use:pest_H_RS + Predominant_land_use:ncrop_RS + Predominant_land_use:percNH_RS + Predominant_land_use:fields + Use_intensity:pest_H_RS + Use_intensity:ncrop_RS + Use_intensity:percNH_RS+ Use_intensity:fields + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS)+(1|SSB)\")\n\nsummary(ab2$model)\n\n# save the output\nsave(ab2, file = paste0(outdir, \"/ABUNDANCE_Model_Set_INSECTS.rdata\"))\n\n\n\n\n##%######################################################%##\n# #\n#### Run final selected model using REML ####\n# #\n##%######################################################%##\n\n\n\n### selected model for species richness ###\n\n# one model with ncrop, one without #\n\n### A. ALL INSECTS ###\n\n# rerun the selected models, using REML\n\nsrmod_IN <- GLMER(modelData = final.data.trans, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + Tropical + percNH + ncrop + Predominant_land_use:percNH + Predominant_land_use:Use_intensity + Tropical:percNH\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\nsrmod_IN2 <- GLMER(modelData = final.data.trans, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + Tropical + percNH + Predominant_land_use:percNH + Predominant_land_use:Use_intensity + Tropical:percNH\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\n\n\n# Warning messages: none\n\n\n\n# take a look at the model output\nsummary(srmod_IN$model)\nsummary(srmod_IN2$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(srmod_IN$model)\ncoefs2 <- fixef(srmod_IN2$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_INSECTS_ncrops.csv\"), row.names = F)\nwrite.csv(coefs2, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_INSECTS.csv\"), row.names = F)\n\n# save the model output\nsave(srmod_IN, file = paste0(outdir, \"/SRMOD_output_INSECTS_ncrops.rdata\"))\nsave(srmod_IN2, file = paste0(outdir, \"/SRMOD_output_INSECTS.rdata\"))\n\n\n\n\n\n### B. POLLINATORS ###\n\n# selected model for species richness:\n\n\n# rerun the selected models, using REML\n\nsrmod_PO <- GLMER(modelData = final.data.trans.pols, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Use_intensity + percNH + ncrop + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\nsrmod_PO2 <- GLMER(modelData = final.data.trans.pols, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Use_intensity + percNH + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\n\n# Warning messages: none\n\n\n# take a look at the model output\nsummary(srmod_PO$model)\nsummary(srmod_PO2$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(srmod_PO$model)\ncoefs2 <- fixef(srmod_PO2$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_POLLINATORS_ncrop.csv\"), row.names = F)\nwrite.csv(coefs2, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_POLLINATORS.csv\"), row.names = F)\n\n# save the model output\nsave(srmod_PO, file = paste0(outdir, \"/SRMOD_output_POLLINATORS_ncrop.rdata\"))\nsave(srmod_PO2, file = paste0(outdir, \"/SRMOD_output_POLLINATORS.rdata\"))\n\n\n\n\n### C. PEST CONTROLLERS ###\n\n# selected model for species richness:\n\n# rerun the selected models, using REML\n\nsrmod_PC <- GLMER(modelData = final.data.trans.pc, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + ncrop + percNH + landcovers.5k + Predominant_land_use:ncrop + Predominant_land_use:landcovers.5k + Use_intensity:ncrop + Use_intensity:percNH\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\nsrmod_PC2 <- GLMER(modelData = final.data.trans.pc, responseVar = \"Species_richness\", fitFamily = \"poisson\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + pest_H_log + percNH + landcovers.5k + Predominant_land_use:pest_H_log + Use_intensity:percNH\",\n randomStruct = \"(1|SS) + (1|SSB) + (1|SSBS)\", REML = TRUE)\n\n# Warning messages: none\n\n\n\n# take a look at the model output\nsummary(srmod_PC$model)\nsummary(srmod_PC2$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(srmod_PC$model)\ncoefs2 <- fixef(srmod_PC2$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_PESTC_ncrop.csv\"), row.names = F)\nwrite.csv(coefs2, file = paste0(outdir, \"/SPECIESRICHNESS_coefs_PESTC.csv\"), row.names = F)\n\n# save the model output\nsave(srmod_PC, file = paste0(outdir, \"/SRMOD_output_PESTC_ncrop.rdata\"))\nsave(srmod_PC2, file = paste0(outdir, \"/SRMOD_output_PESTC.rdata\"))\n\n\n\n### Selected model for abundance ###\n\n\n### A. ALL INSECTS ###\n\n# selected model:\n\n\n# run selected model with REML\n\nabmod_IN <- GLMER(modelData = final.data.trans, responseVar = \"logAbun\", fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + pest_H_log + Use_intensity:pest_H_log + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB)\", REML = TRUE)\n\nabmod_IN2 <- GLMER(modelData = final.data.trans, responseVar = \"logAbun\", fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + pest_H_log + Use_intensity:pest_H_log + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB)\", REML = TRUE)\n\n# warnings: none\n\n\n# take a look at the model output\nsummary(abmod_IN$model)\nsummary(abmod_IN2$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(abmod_IN$model)\ncoefs2 <- fixef(abmod_IN2$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/ABUNDANCE_coefs_INSECTS_ncrop.csv\"), row.names = F)\nwrite.csv(coefs2, file = paste0(outdir, \"/ABUNDANCE_coefs_INSECTS.csv\"), row.names = F)\n\n# save the model output\nsave(abmod_IN, file = paste0(outdir, \"/ABMOD_output_INSECTS_ncrop.rdata\"))\nsave(abmod_IN, file = paste0(outdir, \"/ABMOD_output_INSECTS.rdata\"))\n\n\n\n\n### B. POLLINATORS ###\n\n# selected model:\n\n\n\n# run selected model with REML\n\nabmod_PO <- GLMER(modelData = final.data.trans.pols, responseVar = \"logAbun\", fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Use_intensity + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB)\", REML = TRUE)\n\n# both models were the same with and without ncrop variable\n\n# warnings: none\n\n\n\n# take a look at the model output\nsummary(abmod_PO$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(abmod_PO$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/ABUNDANCE_coefs_POLLINATORS.csv\"), row.names = F)\n\n# save the model output\nsave(abmod_PO, file = paste0(outdir, \"/ABMOD_output_POLLINATORS.rdata\"))\n\n\n\n### C. PEST CONTROLLERS ###\n\n# selected model:\n\n\n\n\n# run selected model with REML\n\nabmod_PC <- GLMER(modelData = final.data.trans.pc, responseVar = \"logAbun\", fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + percNH + ncrop + landcovers.5k + Predominant_land_use:ncrop + Predominant_land_use:landcovers.5k + Predominant_land_use:percNH + Use_intensity:ncrop + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB)\", REML = TRUE)\n\nabmod_PC2 <- GLMER(modelData = final.data.trans.pc, responseVar = \"logAbun\", fitFamily = \"gaussian\",\n fixedStruct = \"Predominant_land_use + Forest_biome + Use_intensity + percNH + pest_H_log + landcovers.5k + Predominant_land_use:percNH + Predominant_land_use:landcovers.5k + Use_intensity:pest_H_log + Predominant_land_use:Use_intensity\",\n randomStruct = \"(1|SS) + (1|SSB)\", REML = TRUE)\n\n# warnings:\n\n\n\n# take a look at the model output\nsummary(abmod_PC$model)\nsummary(abmod_PC2$model)\n\n# extract the coefficents of the model\ncoefs <- fixef(abmod_PC$model)\ncoefs2 <- fixef(abmod_PC2$model)\n\n# save the coefficients\nwrite.csv(coefs, file = paste0(outdir, \"/ABUNDANCE_coefs_PESTC_ncrop.csv\"), row.names = F)\nwrite.csv(coefs2, file = paste0(outdir, \"/ABUNDANCE_coefs_PESTC.csv\"), row.names = F)\n\n# save the model output\nsave(abmod_PC, file = paste0(outdir, \"/ABMOD_output_PESTC_ncrop.rdata\"))\nsave(abmod_PC2, file = paste0(outdir, \"/ABMOD_output_PESTC.rdata\"))\n\n", "meta": {"hexsha": "f38506f2f573d84a8077b3f7fb96200f93a8ebe5", "size": 14757, "ext": "r", "lang": "R", "max_stars_repo_path": "R/2_Model_Selection.r", "max_stars_repo_name": "CharlieOuthwaite/PREDICTS_InsectsLandscapes", "max_stars_repo_head_hexsha": "f937bdaa82637ffc4eb6bd0f581b29734eac19e9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/2_Model_Selection.r", "max_issues_repo_name": "CharlieOuthwaite/PREDICTS_InsectsLandscapes", "max_issues_repo_head_hexsha": "f937bdaa82637ffc4eb6bd0f581b29734eac19e9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/2_Model_Selection.r", "max_forks_repo_name": "CharlieOuthwaite/PREDICTS_InsectsLandscapes", "max_forks_repo_head_hexsha": "f937bdaa82637ffc4eb6bd0f581b29734eac19e9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2473404255, "max_line_length": 375, "alphanum_fraction": 0.6094057058, "num_tokens": 3623, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.32239181661441113}} {"text": "\n# R CMD BATCH --no-save --no-restore '--args phenotype_file=\"blabla.txt\" output_file=\"blalba.png\" ' analyze_phenotype.r output.txt\nlibrary(plyr)\nlibrary(tidyr)\nlibrary(ggplot2)\n\nargs=(commandArgs(TRUE))\n\nif(length(args)==0){\n print(\"No arguments supplied.\")\n ##supply default values\n phenotype_file = 'phenotypes.txt'\n output_file = paste0(phenotype_file, \".png\", sep=\"\")\n} else {\n for(i in 1:length(args)){\n eval(parse(text=args[[i]]))\n }\n}\n\nwrite(paste(\"phenotype file: \", phenotype_file), stderr())\nwrite(paste(\"output_file: \", output_file), stderr())\n\nerrorfile = paste(phenotype_file, \".err\", sep=\"\");\n\nphenodata = read.csv(phenotype_file,fill=TRUE, sep=\",\", header = TRUE, stringsAsFactors = T, na.strings=\"NA\");\n\nblocks = unique(phenodata$blockNumber)\nprint(paste(\"blocks: \", blocks));\nstudyNames = unique(phenodata$studyName)\naccessions = unique(phenodata$germplasmName)\ndatamatrix <- c()\ndatasetnames <- c()\ntrial_accessions <- c()\nall_accessions = unique(phenodata$germplasmName)\n\ndatamatrix = matrix(nrow = length(all_accessions), ncol=length(studyNames)) # * length(blocks))\nwfAlltrialsdata <- c()\nfor (i in 1:(length(studyNames))) {\n\n trialdata <- phenodata[phenodata[,\"studyName\"]==studyNames[i], ] # & phenodata[,\"blockNumber\"]==n, ]\n \n metadata <- c('studyYear', 'studyDbId', 'studyName', 'studyDesign', 'locationDbId', 'locationName', 'germplasmDbId', 'germplasmSynonyms', 'observationLevel', 'observationUnitDbId', 'observationUnitName', 'plotNumber')\n \n trialdata <- trialdata[, !(names(trialdata) %in% metadata)]\n \n trialdata <- trialdata[, !(names(trialdata) %in% c('replicate', 'blockNumber'))]\n\n trialdata <- ddply(trialdata,\n \"germplasmName\",\n colwise(mean, na.rm=TRUE)\n )\n \n trialdata <- data.frame(trialdata)\n\n trialdata <- trialdata[complete.cases(trialdata), ]\n\n colnames(trialdata)[2]<- make.names(studyNames[i])\n\n if( i == 1) {\n wfAllTrialsData <- trialdata\n } else {\n wfAllTrialsData <- merge(wfAllTrialsData, trialdata, by=\"germplasmName\")\n }\n}\n\n#create a fake 4 trials dataset\n#wfAllTrialsData <- merge(wfAllTrialsData, wfAllTrialsData, by=\"germplasmName\")\n \nnames(wfAllTrialsData) <- make.names(names(wfAllTrialsData))\n\nlfTrialsData <- function (wfTrialsData) {\n lfTrDa <- gather(wfTrialsData, Trials, Trait,\n 2:length(wfTrialsData),\n factor_key=TRUE)\n \n return (lfTrDa)\n}\n\n\ndatamatrix <- data.matrix(wfAllTrialsData)\n\n\nif (nrow(datamatrix)==0) { \n write(\"No data was retrieved from the database for this combination of trials: \", file = errorfile);\n}\nif (ncol(datamatrix) < 2) { \n write(\"No data. Try again\", file = errorfile);\n}\n\n# correlation\n#\npanel.cor <- function(x, y, digits=2, cex.cor)\n{\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(0, 1, 0, 1))\n r <- abs(cor(x, y, use =\"na.or.complete\"))\n txt <- format(c(r, 0.123456789), digits=digits)[1]\n test <- cor.test(x,y,use =\"na.or.complete\")\n Signif <- ifelse(round(test$p.value,3)<0.001,\"p<0.001\",paste(\"p=\",round(test$p.value,3))) \n text(0.5, 0.25, paste(\"r=\",txt))\n text(.5, .75, Signif)\n}\n\n#pairs(data_test,lower.panel=panel.smooth,upper.panel=panel.cor)\n\n#smooth\n\npanel.smooth<-function (x, y, col = \"black\", bg = NA, pch = 18, cex = 0.8, col.smooth = \"red\", span = 2/3, iter = 3, ...) \n{\n points(x, y, pch = pch, col = col, bg = bg, cex = cex)\n ok <- is.finite(x) & is.finite(y)\n if (any(ok)) \n \tlines(stats::lowess(x[ok], y[ok], f = span, iter = iter), \n \tcol = col.smooth, ...)\n }\n\n\n#pairs(data,lower.panel=panel.smooth,upper.panel=panel.smooth)\n\n#histo\npanel.hist <- function(x, ...)\n{\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(usr[1:2], 0, 1.5) )\n h <- hist(x, plot = TRUE)\n breaks <- h$breaks; nB <- length(breaks)\n y <- h$counts; y <- y/max(y)\n rect(breaks[-nB], 0, breaks[-1], y, col=\"red\", ...)\n}\n\nscatterPlot <- function (wfTrialsData) {\n\n lfTrDa <- lfTrialsData(wfTrialsData)\n\n scatter <- ggplot(wfTrialsData, aes_string(x=names(wfTrialsData)[2], y=names(wfTrialsData)[3])) +\n theme(plot.title = element_text(size=18, face=\"bold\", color=\"olivedrab4\", margin = margin(40, 40, 40, 40)),\n plot.margin = unit(c(0.75, 1, 0.75, 1), \"cm\"),\n axis.title.x = element_text(size=14, face=\"bold\", color=\"olivedrab4\"),\n axis.title.y = element_text(size=14, face=\"bold\", color=\"olivedrab4\"),\n axis.text.x = element_text(angle=90, vjust=0.5, size=10, color=\"olivedrab4\"),\n axis.text.y = element_text(size=10, color=\"olivedrab4\")) +\n geom_point(shape=1, color='DodgerBlue') +\n scale_x_continuous(breaks = round(seq(min(lfTrDa$Trait), max(lfTrDa$Trait), by = 2),1)) +\n scale_y_continuous(breaks = round(seq(min(lfTrDa$Trait), max(lfTrDa$Trait), by = 2),1)) +\n geom_smooth(method=lm, se=FALSE) \n\n return(scatter)\n \n}\n\n\nfreqPlot <- function (wfTrialsData) {\n \n lfTrDa <- lfTrialsData(wfTrialsData)\n\n averages <- ddply(lfTrDa, \"Trials\", summarise, traitAverage = mean(Trait))\n \n freq <- ggplot(lfTrDa, aes(x=Trait, fill=Trials)) +\n xlab(\"Trait values\") +\n ylab(\"Frequency\") +\n theme(plot.title = element_text(size=18, face=\"bold\", color=\"olivedrab4\", margin = margin(40, 40, 40, 40)),\n plot.margin = unit(c(0.75, 1, 0.75, 1), \"cm\"),\n axis.title.x = element_text(size=14, face=\"bold\", color=\"olivedrab4\"),\n axis.title.y = element_text(size=14, face=\"bold\", color=\"olivedrab4\"),\n axis.text.x = element_text(angle=90, size=10, color=\"olivedrab4\"),\n axis.text.y = element_text(size=10, color=\"olivedrab4\"),\n legend.title=element_blank(),\n legend.text=element_text(size=12, color=\"olivedrab4\"),\n legend.position=\"bottom\") + \n geom_histogram(binwidth=2, alpha=.5, position=\"identity\") +\n scale_x_continuous(breaks = round(seq(min(lfTrDa$Trait), max(lfTrDa$Trait), by = 2),1)) +\n scale_fill_manual(values=c(\"ForestGreen\", \"DodgerBlue\")) +\n geom_vline(data=averages,\n aes(xintercept=traitAverage, colour=Trials),\n linetype=\"dashed\", size=2)\n\n return(freq)\n}\n\n\n# Multiple plot function: for how to use this function go here:\n# http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/\n\nmultiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n library(grid)\n\n plots <- c(list(...), plotlist)\n\n numPlots = length(plots)\n\n if (is.null(layout)) {\n layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n ncol = cols, nrow = ceiling(numPlots/cols))\n }\n\n if (numPlots==1) {\n print(plots[[1]])\n\n } else {\n grid.newpage()\n pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n\n for (i in 1:numPlots) {\n matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n\n print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n layout.pos.col = matchidx$col))\n }\n }\n}\n\n\ngetTrialsPairs <- function (wfAllTrialsData) {\n combiTrMx <- combn(names(wfAllTrialsData[, 2:length(names(wfAllTrialsData))]), 2)\n nPairs <- dim(combiTrMx)[2]\n\n allPairs <- c()\n for (i in 1:nPairs) {\n pairs <- combiTrMx[, i]\n message(\"pair \" , i, \" \", pairs )\n allPairs[i] <- list(i=pairs)\n }\n\n return (list(\"trialsPairs\"= allPairs, \"pairsCount\"= nPairs))\n}\n\n\nprTr <- getTrialsPairs(wfAllTrialsData)\ntrialsPairs <- prTr[[\"trialsPairs\"]]\npairsCount <- prTr[[\"pairsCount\"]]\n\nmessage(\"pairs count \", pairsCount)\n\ncreateGraphNames <- function (pairsCount) {\n graphNames <- c()\n \n for (i in 1:pairsCount) {\n pf <- paste(\"freq\", i, sep=\"\")\n ps <- paste(\"scatter\", i, sep=\"\")\n \n graphNames[i] <- list(i=c(ps, pf))\n }\n\n return(graphNames)\n}\n\n\npng(output_file, height= pairsCount * 400, width=800)\n\ngraphNames <- createGraphNames(pairsCount)\n\nfor (i in 1:pairsCount) {\n pnames <- graphNames[[i]]\n message(pnames, \" \", pnames[1], \" \", pnames[2])\n \n scatter <- scatterPlot(wfAllTrialsData[, c(\"germplasmName\", trialsPairs[[i]])])\n freq <- freqPlot(wfAllTrialsData[, c(\"germplasmName\", trialsPairs[[i]])])\n\n assign(pnames[1], scatter)\n assign(pnames[2], freq)\n\n}\n\nif (pairsCount == 1) {\n multiplot(scatter1, freq1, cols=2) \n} else if (pairsCount == 3) {\n multiplot(scatter1, freq1, scatter2, freq2, scatter3, freq3, cols=2)\n} else if (pairsCount == 6) { \n multiplot(scatter1, freq1, scatter2, freq2, scatter3, freq3, scatter4, freq4, scatter5, freq5, scatter6, freq6, cols=2) \n}\n\ndev.off()\n\n\n", "meta": {"hexsha": "e47e578241e0e8bedddea4839e88861d500dde5e", "size": 8627, "ext": "r", "lang": "R", "max_stars_repo_path": "R/analyze_phenotype.r", "max_stars_repo_name": "TriticeaeToolbox/sgn", "max_stars_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2015-02-03T15:47:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T13:34:05.000Z", "max_issues_repo_path": "R/analyze_phenotype.r", "max_issues_repo_name": "TriticeaeToolbox/sgn", "max_issues_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2491, "max_issues_repo_issues_event_min_datetime": "2015-01-07T05:49:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:05.000Z", "max_forks_repo_path": "R/analyze_phenotype.r", "max_forks_repo_name": "TriticeaeToolbox/sgn", "max_forks_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-06-30T19:10:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T13:34:09.000Z", "avg_line_length": 31.3709090909, "max_line_length": 219, "alphanum_fraction": 0.6297670105, "num_tokens": 2662, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5273165233795672, "lm_q1q2_score": 0.32239181661441113}} {"text": "## Created on Thursday, December 5, 2013 at 3:24pm EST by WeekendEditor on WeekendEditorMachine.\n## UnCopyright (u) 2013, nobody in particular. All rights reversed. As if you care.\n\n##\n## When is Dodds's Day? When is Weekend Editrix's Day?\n## Who are these \"Dodds\" and \"Weekend Editrix\" people, anyway?\n##\n\n## > cambSunData <- doit(location = \"Cambridge\")\n## > parisSunData <- doit(location = \"Paris\")\n## > athensSunData <- doit(location = \"Athens\")\n## > elkhartSunData <- doit(location = \"Elkhart\")\n##\n## up-arrow is c-x 8 2191\n## degree is C-x 8 b0\n## dash/minus is C-x 8 2212\ndoit <- function(location = \"Cambridge\",\n sunDataFile = sprintf(\"./sun-times-%s-%s.tsv\", format(Sys.time(), \"%Y\"), location),\n plotFile = sub(\"^(.*)\\\\.tsv$\", \"\\\\1.png\", sunDataFile)) {\n ## Dodds himself: http://web.fastermac.net/~dodds/analemma/greeting-message.htm (now defunct)\n ## Data sources: https://www.timeanddate.com/sun/usa/boston\n ## https://www.timeanddate.com/sun/france/paris\n ## https://www.timeanddate.com/sun/greece/athens\n ## General notes: https://analemma.com/\n\n parseDate <- function(d) { as.POSIXct(strptime(d, format = \"%d-%b-%y\")) }\n\n parseTime <- function(d, t) { # Combine date & time into POSIXct\n as.POSIXct(strptime(sprintf(\"%s %s\", as.character(d), as.character(t)),\n format = \"%d-%b-%y %I:%M %p\")) #\n } #\n\n parseAngle <- function(theta) { as.numeric(gsub(\"^([+-]?[0-9\\\\.]+).*$\", \"\\\\1\", theta)) }\n\n significantDays <- function(sunData) { # Dodds, Weekend Editrix, and solstice\n sunDataTimes <- transform(sunData, # Hours since midnight\n SunsetTime = Sunset - Date,\n SunriseTime = Sunrise - Date)\n ## Each one gets several days @ extremum; take the middle day in the range\n dodds <- subset(sunDataTimes, subset = SunsetTime == min(SunsetTime))\n dodds <- dodds[ceiling(nrow(dodds) / 2), \"Date\", drop = TRUE]\n solstice <- subset(sunDataTimes, subset = Day.Sec == min(Day.Sec))\n solstice <- solstice[ceiling(nrow(solstice) / 2), \"Date\", drop = TRUE]\n editrix <- subset(sunDataTimes, subset = SunriseTime == max(SunriseTime))\n editrix <- editrix[ceiling(nrow(editrix) / 2), \"Date\", drop = TRUE]\n c(\"Dodds\" = dodds, \"Solstice\" = solstice, \"Weekend Editrix\" = editrix)\n } #\n\n withPars <- function(bodyFn, ...) { # Call bodyFn with graphics pars set\n oldParList <- NULL # Capture old vals: params changed only\n tryCatch({ oldParList <- par(...); bodyFn() }, finally = par(oldParList))\n } # Guarantee old vals restored @ end\n\n withGraphicsToImageFile <- function(file, width, height, ask, deviceFn, bodyFn) {\n if (is.null(file)) { # If no file, then execute directly\n pixelsPerInch <- par(\"cra\") / par(\"cin\") # px/in horiz & vertical; new device\n dev.off() # on screen wants size in inches!\n dev.new(width = width / pixelsPerInch[[1]], height = height / pixelsPerInch[[2]])\n withPars(bodyFn, \"ask\" = ask) # plotting goes to screen device\n } else # Else there is a file, so execute\n tryCatch({ # w/graphics redirected to a PNG file\n deviceFn(file, width = width, height = height, bg = \"transparent\")\n withPars(bodyFn, \"ask\" = FALSE) # No asking, just go ahead and plot\n }, finally = dev.off()) # Guarantee closure of the file device\n } #\n\n withPNG <- function(file, width, height, ask, bodyFn) {# Capture graphics to .png file\n withGraphicsToImageFile(file, width, height, ask, png, bodyFn)\n } #\n\n drawDateLine <- function(d, lab1, lab2) { # Draw vertical line @ given date,\n parsedD <- as.POSIXct(d) # with labels\n abline(v = parsedD, lty = \"dotted\", col = \"black\") # Black, dotted line\n text(x = parsedD, y = 7.5, srt = 90, adj = c(0, -0.3), labels = lab1)\n if (!is.null(lab2)) # If 2nd label supplied, do that too\n text(x = parsedD, y = 7.5, srt = 90, adj = c(0, +1.2), labels = lab2)\n } #\n\n ## NB: dropped Nov 01-03, which was confusing things with the DST->EST time change.\n sunData <- transform(transform(subset(read.table(sunDataFile, sep = \"\\t\", header = TRUE),\n ## Drop boring columns\n select = -c(Day.Length, Difference, Distance)),\n ## Parse dates & times as POSIX times\n Date = parseDate(Date),\n Sunrise = parseTime(Date, Sunrise),\n Sunset = parseTime(Date, Sunset),\n # Altitude = parseAngle(Altitude),\n Noon = parseTime(Date, Noon)),\n Day.Sec = as.numeric(difftime(Sunset, Sunrise, units = \"secs\")))\n\n sigDays <- significantDays(sunData) # Days of significance:\n doddsDay <- sigDays[[\"Dodds\"]] # - earliest sunset\n solsticeDay <- sigDays[[\"Solstice\"]] # - shortest length\n editrixDay <- sigDays[[\"Weekend Editrix\"]] # - latest sunrise\n cat(\"Days of earliest sunset, shortest length, and latest sunrise:\\n\")\n print(subset(sunData, subset = Date %in% sigDays)) # Show these Very Important Days\n\n withPNG(sprintf(plotFile, location), 700, 450, FALSE, function() {\n withPars(function() { # Save/restore graphics parameters\n pointColors <- c(\"Sunrise\" = \"red\", \"Noon\" = \"green\", \"Sunset\" = \"blue\")\n matplot(x = sunData$\"Date\", # Date of Nov - Jan\n y = subset(transform(sunData, # Time of event (hr since midnight)\n Sunrise = as.numeric(Sunrise - Date),\n Noon = as.numeric(Noon - Date),\n Sunset = as.numeric(Sunset - Date)),\n select = c(Sunrise, Noon, Sunset)),\n type = \"p\", col = \"black\", pch = 21, bg = pointColors, cex = 1.5,\n xaxt = \"n\", xlab = NA, ylab = \"Time of Day (hr since midnight)\",\n main = sprintf(\"%s Sunrise/Noon/Sunset: %s Winter Solstice\",\n location, strftime(sunData[1, \"Date\"], format = \"%Y\")))\n\n ticks <- seq(from = 1, to = nrow(sunData), by = 7)# Weekly ticks on time axis\n axis(1, sunData[ticks, \"Date\"], format(sunData[ticks, \"Date\"], \"%b %d\"))\n\n legend(\"topright\", inset = c(0.02, 0.20), bg = \"antiquewhite\", pch = 21, pt.cex = 2,\n ## Reverse order to keep color labels in same order top-to-bottom as curves\n pt.bg = rev(pointColors), legend = rev(names(pointColors)))\n\n ## Add vertical lines at Very Important Days\n drawDateLine(doddsDay, sprintf(\"Earliest Sunset: %s\", doddsDay), \"Dodds's Day\")\n drawDateLine(solsticeDay, sprintf(\"Shortest Day: %s\", solsticeDay), \"Winter Solstice\")\n drawDateLine(editrixDay, sprintf(\"Latest Sunrise: %s\", editrixDay), \"Weekend Editrix's Day\")\n\n }, \"pty\" = \"m\", # Maximal plotting area\n \"bg\" = \"transparent\", # Transparent background\n \"ps\" = 18, # Larger type size\n \"las\" = 2, # Labels perp to axes\n \"mgp\" = c(2.2, 0.6, 0), # Pull in on tick labels & axis title\n \"mar\" = c(5, 3.5, 2, 0.5)) # Allocate margins\n }) # Done capturing graphics\n\n invisible(sunData) # Return data, invisibly\n} #\n", "meta": {"hexsha": "0085d87eb239342f3e245c2c9140be43b260663f", "size": 8482, "ext": "r", "lang": "R", "max_stars_repo_path": "assets/2021-12-15-analemma-season-sun-times.r", "max_stars_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_stars_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "assets/2021-12-15-analemma-season-sun-times.r", "max_issues_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_issues_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assets/2021-12-15-analemma-season-sun-times.r", "max_forks_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_forks_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 63.2985074627, "max_line_length": 101, "alphanum_fraction": 0.505541146, "num_tokens": 2108, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.32239181661441113}} {"text": "#' Front ends for Xsampler \r\n#'\r\n#' Front ends for Xsampler, for quick implementation of dynamic lattice basis and full Markov basis samplers. XGibbs and XMH are Gibbs and Metropolis-Hastings versions of Xsampler using dynamic lattice bases. XMarkovBasis_Gibbs and XMarkovBasis_MH are versions using a full Markov basis. \r\n#' @param ... As for Xsampler\r\n#' @return A list as for Xsampler\r\n#' @export\r\n#' @examples\r\n#' data(BXno3way)\r\n#' XGibbs(BXno3way$y,BXno3way$A,BXno3way$lambda,combine=T,tune.par=0.5)\r\n\r\nXGibbs <- function (y, A, lambda, Reorder=TRUE, tune.par=0.5, combine=F, x.order=NULL, x.ini=NULL, Model=\"Poisson\", NB.alpha=0, ndraws = 10000, burnin = 2000, verbose = 0, THIN = 1) {\r\n\tXsampler(y=y,A=A,lambda=lambda,U=NULL,Method=\"Gibbs\",combine=combine,Reorder=Reorder,tune.par=tune.par,x.order=x.order,x.ini=x.ini,Model=Model,Proposal=NULL,NB.alpha=NB.alpha,ndraws=ndraws,burnin=burnin,verbose=verbose,THIN=THIN) \r\n}\r\n\r\n\r\n#' Front ends for Xsampler \r\n#'\r\n#' Front ends for Xsampler, for quick implementation of dynamic lattice basis and full Markov basis samplers. XGibbs and XMH are Gibbs and Metropolis-Hastings versions of Xsampler using dynamic lattice bases. XMarkovBasis_Gibbs and XMarkovBasis_MH are versions using a full Markov basis. \r\n#' @param ... As for Xsampler\r\n#' @return A list as for Xsampler\r\n#' @export\r\n#' @examples\r\n#' data(BXno3way)\r\n#' XGibbs(BXno3way$y,BXno3way$A,BXno3way$lambda,combine=T,tune.par=0.5)\r\n\r\nXMH <- function (y, A, lambda, Reorder=TRUE, tune.par=0.5, combine=F, Proposal=\"NonUnif\",x.order=NULL, x.ini=NULL, Model=\"Poisson\", NB.alpha=0, ndraws = 10000, burnin = 2000, verbose = 0, THIN = 1) {\r\n\tXsampler(y=y,A=A,lambda=lambda,U=NULL,Method=\"MH\",combine=combine,Reorder=Reorder,tune.par=tune.par,x.order=x.order,x.ini=x.ini,Model=Model,Proposal=Proposal,NB.alpha=NB.alpha,ndraws=ndraws,burnin=burnin,verbose=verbose,THIN=THIN) \r\n}\r\n\r\n\r\n#' Front ends for Xsampler \r\n#'\r\n#' Front ends for Xsampler, for quick implementation of dynamic lattice basis and full Markov basis samplers. XGibbs and XMH are Gibbs and Metropolis-Hastings versions of Xsampler using dynamic lattice bases. XMarkovBasis_Gibbs and XMarkovBasis_MH are versions using a full Markov basis. \r\n#' @param ... As for Xsampler\r\n#' @return A list as for Xsampler\r\n#' @export\r\n#' @examples\r\n#' data(BXno3way)\r\n#' XGibbs(BXno3way$y,BXno3way$A,BXno3way$lambda,combine=T,tune.par=0.5)\r\n\r\nXMarkovBasis_Gibbs <- function (y, A, lambda, U, x.ini=NULL, Model=\"Poisson\", NB.alpha=0, ndraws = 10000, burnin = 2000, verbose = 0, THIN = 1) {\r\n\tXsampler(y=y,A=A,lambda=lambda,U=U,Method=\"Gibbs\",Reorder=FALSE,x.order=NULL,x.ini=x.ini,Model=Model,Proposal=NULL,NB.alpha=NB.alpha,ndraws=ndraws,burnin=burnin,verbose=verbose,THIN=THIN) \r\n}\r\n\r\n\r\n#' Front ends for Xsampler \r\n#'\r\n#' Front ends for Xsampler, for quick implementation of dynamic lattice basis and full Markov basis samplers. XGibbs and XMH are Gibbs and Metropolis-Hastings versions of Xsampler using dynamic lattice bases. XMarkovBasis_Gibbs and XMarkovBasis_MH are versions using a full Markov basis. \r\n#' @param ... As for Xsampler\r\n#' @return A list as for Xsampler\r\n#' @export\r\n#' @examples\r\n#' data(BXno3way)\r\n#' XGibbs(BXno3way$y,BXno3way$A,BXno3way$lambda,combine=T,tune.par=0.5)\r\n\r\nXMarkovBasis_MH <- function (y, A, lambda, U, x.ini=NULL, Model=\"Poisson\", Proposal=\"NonUnif\", NB.alpha=0, ndraws = 10000, burnin = 2000, verbose = 0, THIN = 1) {\r\n\tXsampler(y=y,A=A,lambda=lambda,U=U,Method=\"MH\",Reorder=FALSE,x.order=NULL,x.ini=x.ini,Model=Model,Proposal=\"Unif\",NB.alpha=NB.alpha,ndraws=ndraws,burnin=burnin,verbose=verbose,THIN=THIN) \r\n}", "meta": {"hexsha": "09a200bc618e4ea52fab9e1d50fc560cdb6a0cd4", "size": 3603, "ext": "r", "lang": "R", "max_stars_repo_path": "R/frontends.r", "max_stars_repo_name": "MartinLHazelton/DynamicLatticeBasis", "max_stars_repo_head_hexsha": "6c9a134acf398c4e80f06f8e837d4298efedca4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/frontends.r", "max_issues_repo_name": "MartinLHazelton/DynamicLatticeBasis", "max_issues_repo_head_hexsha": "6c9a134acf398c4e80f06f8e837d4298efedca4c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/frontends.r", "max_forks_repo_name": "MartinLHazelton/DynamicLatticeBasis", "max_forks_repo_head_hexsha": "6c9a134acf398c4e80f06f8e837d4298efedca4c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 62.1206896552, "max_line_length": 289, "alphanum_fraction": 0.7377185679, "num_tokens": 1216, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.32239181661441113}} {"text": "# Set up\r\nsource('2019-06-19-jsa-type-ch1/init.r')\r\n\r\n# Parameters\r\ntheme = theme_minimal()\r\ndir_plot = \"C:\\\\Users\\\\ejysoh\\\\Dropbox\\\\msc-thesis\\\\research\\\\_figures\\\\_ch2\\\\\"\r\n\r\n\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - Fig. 1 time series of species description\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- Fig. 1 time series of species description\"))\r\n\r\nspecies_per_year <- df[,.(.N), by=.(date.n)][order(date.n)]\r\ntemplate_year <- data.frame(date.n=min(species_per_year$date.n):max(species_per_year$date.n))\r\nspecies_per_year <- merge(template_year, species_per_year, by=\"date.n\", all.x=T, all.y=F)\r\nspecies_per_year[is.na(species_per_year$N),]$N <- 0\r\nspecies_per_year$N_cumsum <- cumsum(species_per_year$N)\r\n\r\nspecies_per_year2 <- melt(species_per_year, \"date.n\", stringsAsFactors=F)\r\nspecies_per_year2$variable <- factor(species_per_year2$variable, c(\"N_cumsum\", \"N\"))\r\npt_sum <- data.table(species_per_year2)[, list(max=max(value)), by=c(\"variable\")]\r\npts <- data.frame(date.n=rep(c(1914, 1919, 1939, 1945), 2), \r\n variable=c(rep(\"N_cumsum\", 4), rep(\"N\", 4)),\r\n value=c(rep(pt_sum[variable==\"N_cumsum\",]$max, 4), \r\n rep(pt_sum[variable==\"N\",]$max, 4)))\r\n\r\nlabs <- c(`N` = \"N species\",\r\n `N_cumsum` = \"Cumulative N species\")\r\np1 <- ggplot(species_per_year2, aes(x=date.n, y=value)) + \r\n facet_wrap(.~variable, nrow=2, scales = \"free_y\", labeller= as_labeller(labs)) +\r\n geom_ribbon(pts[c(1,2,5,6),], mapping=aes(x=date.n, ymin=0, ymax=value), fill=\"red\", alpha=0.2) +\r\n geom_ribbon(pts[c(1,2,5,6),], mapping=aes(x=date.n, ymin=0, ymax=value), fill=\"red\", alpha=0.2) +\r\n geom_ribbon(pts[c(3,4,7,8),], mapping=aes(x=date.n, ymin=0, ymax=value), fill=\"red\", alpha=0.2) +\r\n geom_ribbon(pts[c(3,4,7,8),], mapping=aes(x=date.n, ymin=0, ymax=value), fill=\"red\", alpha=0.2) +\r\n geom_line(size=1) + geom_smooth() +\r\n xlab(\"\") + ylab(\"\") +\r\n theme\r\n\r\nggsave(paste0(dir_plot, 'fig-1.png'), p1, units=\"cm\", width=21, height=10, dpi=300)\r\n\r\n\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - Fig 4. Species richness and area graph\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- Species richness and area graph\"))\r\narea <- read.csv('data/lookup/2019-12-20-richness-area.csv')\r\n\r\np4 = ggplot(area) + \r\n geom_point(aes(x=log(area), y=log(richness))) +\r\n stat_smooth(area, mapping=aes(x=log(area), y=log(richness)), method='lm', formula = y~x) +\r\n xlab(\"log(Area (million sq km))\") + ylab(\"log(N species discovered)\") +\r\n scale_y_continuous(limits=c(2, 10)) + \r\n theme\r\n\r\nggsave(paste0(dir_plot, 'fig-4.png'), p4, units=\"cm\", width=10, height=10, dpi=300)\r\n\r\nsummary(lm(log(richness) ~ log(area), area))\r\n\r\n\r\n\r\n######## EXTRA EDA ########\r\n\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - Time series\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- Time series\"))\r\n\r\n# Individual plots\r\n\r\n# Cumulative bee species across years\r\n## All bees\r\ndf_year <- unique(df_country[,c(\"idx\", \"date.n\")])\r\nsummary_year <- df_year[,.(.N), by=\"date.n\"][order(date.n)]\r\ntemplate_year <- data.frame(date.n=min(summary_year$date.n):max(summary_year$date.n))\r\nsummary_year <- merge(template_year, summary_year, by=\"date.n\", all.x=T, all.y=F)\r\nsummary_year[is.na(summary_year$N),]$N <- 0\r\nsummary_year$N_cumsum <- cumsum(summary_year$N)\r\nplot_year_cumsum <- ggplot(data = summary_year, aes(x=date.n, y=N_cumsum)) + geom_point() + geom_line() + \r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n ggtitle(\"Cumulative number of bee species time series (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\"))\r\nplot_year <- ggplot(data = summary_year, aes(x=date.n, y=N)) + geom_point() + geom_line() + \r\n xlab(\"Year\") + ylab(\"Number of species described in a year\") + theme +\r\n ggtitle(\"Number of species described time series (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\")) + geom_smooth()\r\n\r\n## By family\r\ndf_country2 <- merge(df_country, df[,c(\"idx\", \"family\")], by=\"idx\", all.x=T, all.y=F)\r\ndf_family <- unique(df_country2[,c(\"idx\", \"date.n\", \"family\")])\r\nsummary_year_fam <- df_family[,.(.N), by=c(\"date.n\", \"family\")]\r\ntemplate_year_fam <- expand.grid(date.n=min(summary_year_fam$date.n):max(summary_year_fam$date.n),\r\n family=unique(df_country2$family))\r\nsummary_year_fam <- merge(template_year_fam, summary_year_fam,\r\n by=c(\"date.n\", \"family\"), all.x=T, all.y=F)\r\nsummary_year_fam[is.na(summary_year_fam$N),]$N <- 0\r\nsummary_year_fam <- data.table(summary_year_fam)\r\nsummary_year_fam[, N_cumsum := cumsum(N), by=\"family\"]\r\n\r\nplot_year_fam_cumsum <- ggplot(data = summary_year_fam, aes(x=date.n, y=N_cumsum)) \r\nplot_year_fam_cumsum1 <- plot_year_fam_cumsum + geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n facet_wrap(. ~ family, ncol=3, scales = \"free_y\") +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\"))\r\nplot_year_fam_cumsum2 <- plot_year_fam_cumsum + geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n facet_wrap(. ~ family, ncol=3) +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\"))\r\nplot_year_fam <- ggplot(data = summary_year_fam, aes(x=date.n, y=N)) + \r\n geom_point() + geom_line() + \r\n xlab(\"Year\") + ylab(\"Number of species described in a year\") + theme +\r\n facet_wrap(. ~ family, ncol=3, scales=\"free_y\") +\r\n ggtitle(\"Number of species described time series (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\")) + geom_smooth()\r\n\r\n## By tropics etc\r\nget_first_type <- function(x) strsplit(x, \"/\")[[1]][1]\r\ndf_country2 <- df_country\r\ndf_country2$Latitude_type3 <- sapply(df_country2$Latitude_type, get_first_type)\r\ndf_trop <- unique(df_country2[,c(\"idx\", \"date.n\", \"Latitude_type3\")])\r\nsummary_year_trop <- df_trop[,.(.N), by=c(\"date.n\", \"Latitude_type3\")]\r\ntemplate_year_trop <- expand.grid(\r\n date.n=min(summary_year_fam$date.n):max(summary_year_fam$date.n),\r\n Latitude_type3=unique(df_country2$Latitude_type3))\r\nsummary_year_trop <- merge(template_year_trop, summary_year_trop,\r\n by=c(\"date.n\", \"Latitude_type3\"), all.x=T, all.y=F)\r\nsummary_year_trop[is.na(summary_year_trop$N),]$N <- 0\r\nsummary_year_trop <- data.table(summary_year_trop)\r\nsummary_year_trop[, N_cumsum := cumsum(N), by=\"Latitude_type3\"]\r\n\r\nplot_year_trop_cumsum <- \r\n ggplot(data = summary_year_trop, \r\n aes(x=date.n, y=N_cumsum, col=Latitude_type3)) +\r\n geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\"))\r\nplot_year_trop <- ggplot(data = summary_year_trop, aes(x=date.n, y=N, col=Latitude_type3)) +\r\n # geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\")) + geom_smooth()\r\n\r\n## By family and tropics/ not \r\ndf_country2 <- merge(df_country, df[,c(\"idx\", \"family\")], by=\"idx\", all.x=T, all.y=F)\r\ndf_country2$Latitude_type3 <- sapply(df_country2$Latitude_type, get_first_type)\r\ndf_trop <- unique(df_country2[,c(\"idx\", \"date.n\", \"family\", \"Latitude_type3\")])\r\nsummary_year_fam_trop <- df_trop[, .(.N), by=c(\"date.n\", \"family\", \"Latitude_type3\")]\r\ntemplate_year_fam_trop <- expand.grid(\r\n date.n=min(summary_year_fam$date.n):max(summary_year_fam$date.n),\r\n family=unique(df_country2$family), Latitude_type3=unique(df_country2$Latitude_type3))\r\nsummary_year_fam_trop <- merge(template_year_fam_trop, summary_year_fam_trop,\r\n by=c(\"date.n\", \"family\", \"Latitude_type3\"), all.x=T, all.y=F)\r\nsummary_year_fam_trop[is.na(summary_year_fam_trop$N),]$N <- 0\r\nsummary_year_fam_trop <- data.table(summary_year_fam_trop)\r\nsummary_year_fam_trop[, N_cumsum := cumsum(N), by=c(\"family\", \"Latitude_type3\")]\r\n\r\nplot_year_fam_trop_cumsum <- \r\n ggplot(data = summary_year_fam_trop, \r\n aes(x=date.n, y=N_cumsum, col=Latitude_type3)) + geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n facet_wrap(. ~ family, ncol=3) +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\"))\r\nplot_year_fam_trop <- ggplot(data = summary_year_fam_trop, aes(x=date.n, y=N, \r\n col=Latitude_type3)) +\r\n # geom_point() + geom_line() +\r\n xlab(\"Year\") + ylab(\"Cumulative number of species\") + theme +\r\n facet_wrap(. ~ family, ncol=3, scales = \"free_y\") +\r\n ggtitle(\"Cumulative number of bee species time series for each family (1758-2018)\") + \r\n theme(plot.title = element_text(lineheight=.8, face=\"bold\")) + geom_smooth()\r\n\r\n# Combined plots\r\nplot_year_cumsum\r\nplot_year\r\n\r\nplot_year_fam_cumsum1\r\nplot_year_fam_cumsum2\r\nplot_year_fam\r\n\r\nplot_year_trop_cumsum\r\nplot_year_trop\r\n\r\nplot_year_fam_trop_cumsum\r\nplot_year_fam_trop\r\n\r\n\r\n", "meta": {"hexsha": "6db29840a4bb49678dcdb7c30f85ddd731b1793e", "size": 9650, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch2/plots/plots_main.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch2/plots/plots_main.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch2/plots/plots_main.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.7894736842, "max_line_length": 107, "alphanum_fraction": 0.6448704663, "num_tokens": 2814, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.3223918166144111}} {"text": "#' Read MODFLOW .rch File\n#'\n#' This function reads in a rch file and creates a list\n#' composed of the following vectors:\n#' \\describe{\n#' \\item{NRCHOP}{Atomic Vector of the is the recharge option code. Recharge fluxes are defined in a layer variable, RECH, with one value for\n#' each vertical column. Accordingly, recharge is applied to one cell in each vertical column, and the option code\n#' determines which cell in the column is selected for recharge.\n#' 1\u2014Recharge is only to the top grid layer.\n#' 2\u2014Vertical distribution of recharge is specified in layer variable IRCH.\n#' 3\u2014Recharge is applied to the highest active cell in each vertical column. A constant-head node intercepts\n#' recharge and prevents deeper infiltration.}\n#' \\item{IRCHCB}{Atomic Vector and is flag and a unit number.\n#' If IRCHCB > 0, cell-by-cell flow terms will be written to this unit number when \"SAVE BUDGET\" or a nonzero\n#' value for ICBCFL is specified in Output Control.\n#' If IRCHCB \u2264 0, cell-by-cell flow terms will not be written.}\n#' \\item{RCH}{Data Frame Composed of Layer, Row, Column, Stress Period, and Recharge rate applied to the top of the model domain}\n#' }\n#' @param rootname This is the root name of the rch file\n#' @export\n\nreadrch <- function(rootname = NA){\n if(is.na(rootname)){\n rootname <- MFtools::getroot()\n }\n infl <- paste0(rootname, \".rch\")\n MOD_DIMS <- MFtools::get_dims(rootname)\n NPER <- MOD_DIMS$NPER\n NCOL <- MOD_DIMS$NCOL\n NROW <- MOD_DIMS$NROW\n linin <- readr::read_lines(infl) %>% .[!grepl(\"#\", .)] # READ IN RCH FILE BUT REMOVE COMMENTED LINES\n indx <- 1\n NRCHOP <- linin[indx] %>% MFtools::parse_MF_FW_ELMT(1) %>% as.integer()\n IRCHCB <- linin[indx] %>% MFtools::parse_MF_FW_ELMT(2) %>% as.integer()\n \n HDGLOC <- grep(\"\\\\(\", linin)\n HDG <- linin[HDGLOC]\n UNI <- substr(HDG, start = 1, stop = 10) %>% as.integer()\n MULT <- substr(HDG, start = 11, stop = 20) %>% as.numeric()\n ARR_MULT <- UNI / UNI\n ARR_MULT[is.na(ARR_MULT)] <- 0 \n MULTLOC <- grep(\"^0$\", UNI)\n FRMT <- substr(HDG, start = 21, stop = 30)\n FRMTREP <- regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT)) %>% lapply('[[', 1) %>% unlist() %>% as.integer()\n BLOCK_LENGTH <- (NROW * ceiling(NCOL / FRMTREP))\n BLOCK_START <- HDGLOC + 1\n BLOCK_END <- dplyr::lead(BLOCK_START) - 2\n BLOCK_END <- ifelse(is.na(BLOCK_END), BLOCK_START + BLOCK_LENGTH - 1, BLOCK_END)\n if(length(BLOCK_END) > 1){\n BLOCK_END[length(BLOCK_END)] <- BLOCK_END[length(BLOCK_END) - 1] + BLOCK_LENGTH[length(BLOCK_END)] + 1\n }\n BLOCK_START <- BLOCK_START * ARR_MULT\n BLOCK_END <- BLOCK_END * ARR_MULT\n CELL_INDX <- Map(seq, BLOCK_START, BLOCK_END)\n \n indx <- indx + 1\n BLOCKSIZE <- NROW * NCOL\n BLOCKEND <- ceiling(NPER / 50)\n INRECH_INIRCH_LOC <- HDGLOC - 1\n INRECH <- linin[INRECH_INIRCH_LOC] %>% parse_MF_FW_ELMT(1) %>% as.integer()\n INIRCH <- linin[INRECH_INIRCH_LOC] %>% parse_MF_FW_ELMT(2) %>% as.integer()\n \n indx <- HDGLOC[1]\n VAL <- vector(mode = \"numeric\", length = NPER * BLOCKSIZE)\nif(length(MULTLOC[MULTLOC > 3]) > 0){\n # SPECIFIED CELLS: CELL BOTTOM ELEVATIONS SPECIFED USING MULT\n # SPEC_CELLS ARE THE CELLS THAT ARE DEFINED WHEN UNIT == 0\n SPEC_CELLS <- rep(BLOCKSIZE * (MULTLOC[MULTLOC > 3] - 4), each = BLOCKSIZE) + 1:BLOCKSIZE\n # ARR_CELLS ARE SPEFICIED IN ARRAYS.\n ARR_LOC <- grep(\"^1$\", ARR_MULT)\n ARR_CELL <- rep(BLOCKSIZE * (ARR_LOC[ARR_LOC > 3] - 4), each = BLOCKSIZE) + 1:BLOCKSIZE\n VAL[SPEC_CELLS] <- rep(MULT[MULTLOC[MULTLOC > 3]], each = BLOCKSIZE)\n VAL[ARR_CELL] <- CELL_INDX %>% parse_MF_CELL_INDX(LINE = linin)\n }else{\n VAL <- CELL_INDX %>% MFtools::parse_MF_CELL_INDX(LINE = linin)\n }\n\n RCHdf <- tibble::data_frame(\n LAY = 1 %>% as.integer(),\n ROW = rep(rep(1:NROW, each = NCOL), NPER) %>% as.integer(), \n COL = rep(rep(seq(1, NCOL, 1), NROW), NPER) %>% as.integer(), \n SPER = rep(1:NPER, each = NCOL * NROW) %>% as.integer(),\n RCH = VAL %>% as.numeric())\n rm(VAL)\n RCH <- list(NRCHOP = NRCHOP, \n IRCHCB = IRCHCB, \n RCH = RCHdf)\n return(RCH)\n cat(\"WARNING################################\\n\")\n cat(\"More work is needed on this function if there are more than one stress period\\n\")\n cat(\"Use the readlpf function as a template\\n\")\n cat(\"#######################################\\n\")\n }", "meta": {"hexsha": "a73309c13206cf4164432743351b7c90e0dbcf57", "size": 4718, "ext": "r", "lang": "R", "max_stars_repo_path": "R/readrch.r", "max_stars_repo_name": "dpphat/MFtools", "max_stars_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-12-23T21:35:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-23T14:52:25.000Z", "max_issues_repo_path": "R/readrch.r", "max_issues_repo_name": "dpphat/MFtools", "max_issues_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/readrch.r", "max_forks_repo_name": "dpphat/MFtools", "max_forks_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-10-21T18:07:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-21T18:07:26.000Z", "avg_line_length": 50.7311827957, "max_line_length": 140, "alphanum_fraction": 0.592412039, "num_tokens": 1409, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947155710233, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3223314091503026}} {"text": "#' Deviation Area chart.\n#'\n#' area function will draw area chart for deviation analysis.\n#' @param data input dataframe\n#' @param date date variable\n#' @param value value variable\n#' @param title input data.frame\n#' @param subtitle date variable\n#' @param xtitle value variable\n#' @param ytitle input data.frame\n#' @param caption date variable\n#' @return An object of class \\code{ggplot}\n#' @examples\n#' #prepare data\n#' economics$returns_perc <- c(0, diff(economics$psavert)/economics$psavert[-length(economics$psavert)])\n#' economics<- as.data.frame(economics)\n#'\n#' #area chart\n#' plot<- area(data=economics[1:100,],date = \"date\",value = \"returns_perc\")\n#' plot\n#'\n#' @import ggplot2\n#' @import scales\n#' @import reshape2\n#' @import ggthemes\n#' @import gganimate\n#' @import gapminder\n#' @import ggalt\n#' @import ggExtra\n#' @import ggcorrplot\n#' @import dplyr\n#' @import treemapify\n#' @import ggfortify\n#' @import zoo\n#' @import ggdendro\n#' @export\narea<-function(data,date,value,\n title=NULL,subtitle=NULL,xtitle=NULL,ytitle=NULL,caption=NULL){\n df<- data\n x<- date\n y<- value\n brks <- df[,x][seq(1, length(df[,x]), 12)]\n lbls <- lubridate::year(df[,x][seq(1, length(df[,x]), 12)])\n\n\n p<-ggplot(df, aes_string(x, y)) +\n geom_area() +\n scale_x_date(breaks=brks, labels=lbls) +\n theme_fivethirtyeight() +\n theme(axis.title = element_text(),\n legend.title = element_text(face = 4,size = 10),\n axis.text.x = element_text(angle=90),\n legend.direction = \"horizontal\", legend.box = \"horizontal\")+\n labs(title=title,\n subtitle = subtitle,\n x=xtitle,\n y=ytitle,\n caption=caption)\n\n return(p)\n}\n", "meta": {"hexsha": "e3c96268880baf6c51922a9afd9024c9c8994357", "size": 1686, "ext": "r", "lang": "R", "max_stars_repo_path": "R/area.r", "max_stars_repo_name": "HeeseokMoon/ggedachart", "max_stars_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/area.r", "max_issues_repo_name": "HeeseokMoon/ggedachart", "max_issues_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/area.r", "max_forks_repo_name": "HeeseokMoon/ggedachart", "max_forks_repo_head_hexsha": "1646ac896eca23fd96dd9b2f8b46f4243ee27955", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.1935483871, "max_line_length": 104, "alphanum_fraction": 0.6548042705, "num_tokens": 480, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.32231539495248585}} {"text": "####\n# sets styles for plotting the results\n# of transition matrix analysis\n# key vars are: \"shift\", \"spread\" and \"maintenance\"\n####\n\nlibrary(ggplot2)\n\n# for all line plots \npd <- position_dodge(.5)\nY.lab <- \"Proportion of each transition\"\nshiftcol <- \"#d92120\" # red\nspreadcol <- \"#dea73a\" # gold\nmaintcol <- \"#404096\" # dark blue\nlinecol <- \"gray50\" # mid gray\nerrorcol <- \"gray30\" # dark gray\nzerocol <- \"gray70\" # light gray\n\n# for gen1 subsets\nplot1 <- function(df){\n ggplot(df, aes(\n x=as.character(cohort), \n y=prob, \n color=outcome, \n group=outcome)) +\n geom_errorbar(aes(\n ymin=prob-se, ymax=prob+se),\n width=.5, size=0.8, colour=errorcol, position=pd) +\n geom_line(position=pd, size=2.5) +\n geom_point(position=pd, size=5) +\n xlab(X.lab) + ylab(Y.lab) + labs(color=\"Transition\") +\n ylim(0,1) +\n theme(axis.text=element_text(size=12))\n}\n\n# for gen2 subsets\nplot2 <- function(df){\n ggplot(df, aes(\n x=as.character(cohort), \n y=prob, \n color=outcome, \n group=outcome)) +\n geom_errorbar(aes(\n ymin=prob-se, ymax=prob+se),\n width=.5, size=0.8, colour=errorcol, position=pd) +\n geom_line(position=pd, size=2.5, linetype = \"dashed\") + \n # dashed lines are the only difference between plot2 and plot1\n geom_point(position=pd, size=5) +\n xlab(X.lab) + ylab(Y.lab) + labs(color=\"Transition\") +\n ylim(0,1) +\n theme(axis.text=element_text(size=12))\n}\n", "meta": {"hexsha": "506a04ef2ef1b35f1384aad6bd2c56f18680dc39", "size": 1434, "ext": "r", "lang": "R", "max_stars_repo_path": "tp_plot_styles.r", "max_stars_repo_name": "saralakumari/lmsim", "max_stars_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tp_plot_styles.r", "max_issues_repo_name": "saralakumari/lmsim", "max_issues_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tp_plot_styles.r", "max_forks_repo_name": "saralakumari/lmsim", "max_forks_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0566037736, "max_line_length": 66, "alphanum_fraction": 0.6394700139, "num_tokens": 447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3223153949524858}} {"text": "# Uvoz s spletne strani\n\nlibrary(XML)\n\n# Vrne vektor nizov z odstranjenimi za\u010detnimi in kon\u010dnimi \"prazninami\" (whitespace)\n# iz vozli\u0161\u010d, ki ustrezajo podani poti.\nstripByPath <- function(x, path) {\n unlist(xpathApply(x, path,\n function(y) gsub(\"^\\\\s*(.*?)\\\\s*$\", \"\\\\1\", xmlValue(y))))\n}\n\nuvozi.obsojeni <- function() {\n url.obsojeni <- \"podatki/obsojenir.htm\"\n doc.obsojeni <- htmlTreeParse(url.obsojeni, useInternalNodes=TRUE,\n encoding=\"UTF-8\")\n \n # Poi\u0161\u010demo vse tabele v dokumentu\n tabele <- getNodeSet(doc.obsojeni, \"//table\")\n \n # Iz druge tabele dobimo seznam vrstic () neposredno pod\n # trenutnim vozli\u0161\u010dem\n vrstice <- getNodeSet(tabele[[1]], \".//tr\")\n\n \n # Seznam vrstic pretvorimo v seznam (znakovnih) vektorjev\n # s porezanimi vsebinami celic () neposredno pod trenutnim vozli\u0161\u010dem\n seznam <- lapply(vrstice[5:length(vrstice)-1], stripByPath, \"./td\")\n \n # Iz seznama vrstic naredimo matriko\n matrika <- matrix(unlist(seznam), nrow=length(seznam), byrow=TRUE)\n \n # Imena stolpcev matrike dobimo iz celic () glave (prve vrstice) prve tabele\n colnames(matrika) <- 2006:2013\n \n imena <- unlist(lapply(vrstice[5:length(vrstice)-1], stripByPath, \"./th\"))\n \n # Podatke iz matrike spravimo v razpredelnico\n return(data.frame(apply(matrika, 2, as.numeric), row.names=imena))\n}", "meta": {"hexsha": "94c73db6d0cbcee56dd36b71be4805288ace0d3c", "size": 1363, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/xml.r", "max_stars_repo_name": "CerneK12/APPR-2014-15", "max_stars_repo_head_hexsha": "6855910766d6566c6121a3837def0a6784f32880", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/xml.r", "max_issues_repo_name": "CerneK12/APPR-2014-15", "max_issues_repo_head_hexsha": "6855910766d6566c6121a3837def0a6784f32880", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2015-01-13T01:06:09.000Z", "max_issues_repo_issues_event_max_datetime": "2015-04-06T13:34:44.000Z", "max_forks_repo_path": "lib/xml.r", "max_forks_repo_name": "CerneK12/APPR-2014-15", "max_forks_repo_head_hexsha": "6855910766d6566c6121a3837def0a6784f32880", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.9487179487, "max_line_length": 83, "alphanum_fraction": 0.6742479824, "num_tokens": 483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.554470450236115, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.32231538639319757}} {"text": "source(\"code/libs.r\")\nsource(\"code/helpers.r\")\nsource(\"code/get_data.r\")\n\n#Convert data to matrix and small matrix for testing\nfal_mat = raster_to_matrix(fal_school)\nfal_small = resize_matrix(fal_mat, 0.25)\n\n# Create Basemap based on lat long range of site --------------------------\nlat_range = c(50.158378, 50.162544)\nlong_range = c(-5.102861, -5.090522)\n\n\nutm_bbox = convert_coords(lat = lat_range,\n long = long_range,\n to = crs(fal_school))\n\nextent_zoomed = extent(utm_bbox[1], utm_bbox[2], utm_bbox[3], utm_bbox[4])\nfal_zoom = crop(fal_school, extent_zoomed)\nfal_zoom_mat = raster_to_matrix(fal_zoom)\n\nmaxcolor = \"#e6dbc8\"\nmincolor = \"#b6bba5\"\ncontour_color = \"#7d4911\"\nbasemap = fal_zoom_mat %>%\n height_shade() %>%\n add_overlay(sphere_shade(fal_zoom_mat, texture = \"bw\", colorintensity = 5),\n alphalayer = 0.5) %>%\n add_shadow(lamb_shade(fal_zoom_mat), 0) %>%\n add_shadow(ambient_shade(fal_zoom_mat), 0) %>%\n add_shadow(texture_shade(\n fal_zoom_mat,\n detail = 8 / 10,\n contrast = 9,\n brightness = 11\n ),\n 0.1)\n# Create overlays for DTM data --------------------------------------------\n#Route overlay\nfal_lines = st_transform(p, crs = crs(fal_school))\n\n#OS roads overlay\nosm_bbox = c(long_range[1], lat_range[1], long_range[2], lat_range[2])\nfal_highway = opq(osm_bbox) %>%\n add_osm_feature(\"highway\") %>%\n osmdata_sf()\n\nfal_roads = st_transform(fal_highway$osm_lines, crs = crs(fal_school))\n\nfal_footpaths = subset(fal_roads,\n highway == \"footway\")\n\nfal_cyclepaths = subset(fal_roads,\n highway == \"cycleway\")\n\nfal_roads = subset(\n fal_roads,\n highway = c(\n \"unclassified\",\n \"secondary\",\n \"tertiary\",\n \"residential\",\n \"service\"\n )\n)\n\nfal_footpaths = st_transform(fal_footpaths$geometry, crs = crs(fal_school))\nfal_roads = st_transform(fal_roads$geometry, crs = crs(fal_school))\nfal_cyclepaths = st_transform(fal_cyclepaths$geometry, crs = crs(fal_school))\nresting_area = st_transform(resting_area, crs = crs(fal_school))\n\n\ndd = subset(pp, select = \"geometry\")\nView(dd)\ndd$name = \"Table top\"\ndd = dd[-(16:22),]\ndd = dd[-(1:7),]\ndd = dd[-(8:14),]\ndd = dd[-(8),]\ndd = dd[-(10),]\ndd = dd[-(1:4),]\ndd$name[1] = \"Resting Area\"\ndd$name[2] = \"Drain\"\ndd$name[3] = \"Scrub to clear\"\ndd$name[4] = \"Path to clear\"\ndd$name[5] = \"Resting Area\"\ndd$name[1] = \"Table top\"\n\ndd = st_transform(dd, crs = crs(fal_school))\n# Plot map ----------------------------------------------------------------\nbasemap %>%\n add_overlay(\n generate_line_overlay(\n fal_lines,\n extent = extent_zoomed,\n linewidth = 6,\n color = \"#2d8a91\",\n heightmap = fal_zoom_mat\n )\n ) %>%\n add_overlay(generate_label_overlay(dd, extent = extent_zoomed,\n text_size = 2, point_size = 1, color = \"black\",\n halo_color = \"white\", halo_expand = 10, \n halo_blur = 20, halo_alpha = 0.8,\n seed=1,\n heightmap = fal_zoom_mat, data_label_column = \"name\")) %>% \n add_overlay(\n generate_line_overlay(\n resting_area,\n extent = extent_zoomed,\n linewidth = 6,\n color = \"black\",\n heightmap = fal_zoom_mat\n )\n ) %>% plot_map(title_text = \"Falmouth School Bike Track (Flashman Track and Trails)\", title_offset = c(15,15),\n title_bar_color = \"grey5\", title_color = \"white\", title_bar_alpha = 1)\n\nbasemap %>%\n add_overlay(\n generate_line_overlay(\n fal_lines,\n extent = extent_zoomed,\n linewidth = 6,\n color = \"#2d8a91\",\n heightmap = fal_zoom_mat\n )\n ) %>% add_overlay(\n generate_line_overlay(\n resting_area,\n extent = extent_zoomed,\n linewidth = 6,\n color = \"black\",\n heightmap = fal_zoom_mat\n )\n ) %>%\n add_overlay(generate_label_overlay(dd, extent = extent_zoomed,\n text_size = 2, point_size = 1, \n halo_color = \"white\",halo_expand = 5, \n seed=1,\n heightmap = fal_zoom_mat, data_label_column = \"name\")) %>%\n plot_3d(fal_zoom_mat, windowsize = c(1200, 800))\nrender_camera(\n theta = 240,\n phi = 30,\n zoom = 0.5,\n fov = 60\n)\nrender_snapshot(\n filename = \"Plots/3d-track.png\",\n clear = T\n)\n#Plot only route\nl = points2line_trajectory(pp)\nplot(l)\n", "meta": {"hexsha": "2da4add8ab52b6c95be789e3d283daa576d02f30", "size": 4496, "ext": "r", "lang": "R", "max_stars_repo_path": "code/main.r", "max_stars_repo_name": "natesheehan/flashman-tracks-and-trails", "max_stars_repo_head_hexsha": "2562729e37bec9d6b4d36d378a1b68823971fa47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/main.r", "max_issues_repo_name": "natesheehan/flashman-tracks-and-trails", "max_issues_repo_head_hexsha": "2562729e37bec9d6b4d36d378a1b68823971fa47", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/main.r", "max_forks_repo_name": "natesheehan/flashman-tracks-and-trails", "max_forks_repo_head_hexsha": "2562729e37bec9d6b4d36d378a1b68823971fa47", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0064516129, "max_line_length": 112, "alphanum_fraction": 0.587633452, "num_tokens": 1255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3222661238298851}} {"text": "group_by_commas <- function(n) formatC(n, format='d', big.mark=',')", "meta": {"hexsha": "280c799cf0914e5be3cacd47fe2f93e1a0743018", "size": 67, "ext": "r", "lang": "R", "max_stars_repo_path": "6_kyu/Grouped_by_commas.r", "max_stars_repo_name": "UlrichBerntien/Codewars-Katas", "max_stars_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "6_kyu/Grouped_by_commas.r", "max_issues_repo_name": "UlrichBerntien/Codewars-Katas", "max_issues_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "6_kyu/Grouped_by_commas.r", "max_forks_repo_name": "UlrichBerntien/Codewars-Katas", "max_forks_repo_head_hexsha": "bbd025e67aa352d313564d3862db19fffa39f552", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 67.0, "max_line_length": 67, "alphanum_fraction": 0.6865671642, "num_tokens": 20, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6297745935070806, "lm_q1q2_score": 0.32226611671838923}} {"text": "# wrapper\nwrapper <- function(table, columns, options) {\n\n # initialize output list\n l <- list()\n\n # loop through all columns\n m <- list()\n for (key in names(columns)) {\n # load column data\n column <- as.numeric(columns[key])\n column_data <- suppressWarnings(as.numeric(as.character(table[column][[1]])))\n \n # collect vectors in list\n m <- append(m, list(column_data))\n }\n \n # identify optimal breaks\n hist_data <- hist(unlist(m), plot=FALSE)\n breaks <- hist_data$breaks;\n \n # add as first column\n l <- append(l, list(breaks[2: length(breaks)]))\n \n # loop through all columns\n for (key in seq(m)) {\n # load column data\n column_data <- m[[key]]\n \n # create hist data\n hist_data <- hist(column_data, breaks=breaks, plot=FALSE)\n \n # normalize densities\n count_sum <- sum(hist_data$counts)\n if (count_sum > 0) {\n hist_data$counts = hist_data$counts / count_sum\n }\n \n # collect vectors in list\n l <- append(l, list(hist_data$counts))\n }\n \n # return\n return (l)\n}\n", "meta": {"hexsha": "88dba62d7cdf18553ddd87ca821da9dba2a71b31", "size": 1162, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/charts/histogram.r", "max_stars_repo_name": "ic4f/tools-iuc", "max_stars_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-07-19T05:26:21.000Z", "max_stars_repo_stars_event_max_datetime": "2016-07-19T05:26:21.000Z", "max_issues_repo_path": "tools/charts/histogram.r", "max_issues_repo_name": "ic4f/tools-iuc", "max_issues_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-05-27T20:54:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-04T09:33:30.000Z", "max_forks_repo_path": "tools/charts/histogram.r", "max_forks_repo_name": "willemdek11/tools-iuc", "max_forks_repo_head_hexsha": "dc0a0cf275168c2a88ee3dc47652dd7ca1137871", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-09-12T14:56:37.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-16T00:30:14.000Z", "avg_line_length": 25.2608695652, "max_line_length": 85, "alphanum_fraction": 0.5576592083, "num_tokens": 284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.3222661167183891}} {"text": "# Make a shapefile of our Areas of Interest, for use in the NatCap's InVest software\n# Also make a csv of lat/lon for landing points and grid connection points\n\n\n#######################\n## Load libraries\n#######################\n#require(maptools)\nrequire(RColorBrewer)\n#require(rgdal)\n#require(PBSmapping)\n#require(rgeos) # for gBuffer\nrequire(sf)\n\n##############################################################\n## Read in and process data to create Areas of Interest (AOI)\n##############################################################\nclimgrid <- readRDS('temp/SPsf2.rds') # the projection grid\n\n# label by area of interest regions\nclimgrid$AOI <- NA\nclimgrid$AOI[climgrid$lon < -100 | climgrid$lon > 0] <- 'west'\nclimgrid$AOI[climgrid$lon >= -100 & climgrid$lon < 0] <- 'east'\nsum(is.na(climgrid$AOI)) # 0\n# plot(climgrid['AOI'], lwd=0.001, axes=TRUE) # Aleutians are on the far right\n# plot(climgrid['AOI'], lwd=0.5, xlim=c(-70, -69), ylim=c(41,42), axes=TRUE) # zoom in (Cape Cod)\n\n# project to North American Albers, but in WGS84 rather than NAD83\nclimgridp <- st_transform(climgrid, crs='+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=WGS84 +units=m +no_defs') # North America Albers Equal Area Conic from https://epsg.io/102008, except with WGS84\n# plot(climgridp['AOI'], lwd=0.001, axes=TRUE) # Aleutians are on the far right\n\n# buffer slightly (1000 m) to aid merging the grid cells together\nclimgridbuf <- st_buffer(climgridp, 1000)\n\n# merge all into multipart polygons based on AOI\nclimpoly <- aggregate(climgridbuf[,c('geometry')], by = list(AOI = climgridbuf$AOI), do_union = TRUE, FUN=function(x, ...) return(1))\t\n#\t plot(climpoly['AOI'], lwd=0.01, axes=TRUE)\n# plot(climpoly['AOI'], lwd=1, xlim=c(1.9e6, 2.2e6), ylim=c(4e5,6e5), axes=TRUE) # zoom in (Cape Cod)\n\t\n# buffer whole analysis area\nclimpolyb <- st_buffer(climpoly, dist = 100000) # 100km\n#\tplot(climpolyb, lwd=0.2, axes=TRUE)\n\t\n# write out each region as a separate shapefile\n# will throw an error if file already exists\nfor(i in 1:nrow(climpolyb)){\n\tst_write(climpolyb[i,], paste0('temp/AOI_', climpolyb$AOI[i], '.shp')) # write the .prj file as part of the shapefile. Overwrite\n}\n\n\n\n#######################################\n## Make land and grid connection points\n#######################################\n# set parameters\ncrs <- '+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs' # North America Albers Equal Area Conic from https://epsg.io/102008\ncrslatlong = \"+init=epsg:4326\"\n\n# read in files\ncoastlines <- st_read('data/natcap/NAmainland_lines.shp') # global coast layer from NatCap, trimmed to North America\n#\tplot(coastlines['id'], axes=TRUE) # a bit slow\ntownsUS <- st_read('dataDL/usgs/citiesx020_nt00007/citiesx020.shp') # US National Map cites and towns\n st_crs(townsUS) <- 4269 # set CRS to NAD83\n#\tplot(townsUS['NAME'], pch=16, cex=0.2, axes=TRUE)\n# plot(st_geometry(townsUS), add=TRUE, col='red') # to add to coastlines plot. Not working for some reason.\ntownsCan <- st_read('dataDL/statcan/lpc_000b16a_e/lpc_000b16a_e.shp') # Canadian census population centres\n\n\n\n# project to North American Albers\ncoastlinesp <- st_transform(coastlines, crs = crs)\ntownsUSp <- st_transform(townsUS, crs = crs)\ntownsCanp <- st_transform(townsCan, crs = crs)\n \n# find centroids for Canada\ntownsCanpc <- st_centroid(townsCanp)\n \n plot(st_geometry(coastlinesp), lwd=0.2, axes=TRUE)\n plot(st_geometry(townsUSp), pch=16, cex=0.1, add=TRUE, col='blue') # plots on top of NA well\n plot(st_geometry(townsCanpc), pch=16, cex=0.1, add=TRUE, col='green') # plots on top of NA well\n\n# trim US towns to those >1000 people (Canada is already trimmed)\ntownsUS1000 <- townsUSp[which(townsUSp$POP_2000 >= 1000),]\n\tdim(townsUSp) # 35432\n\tdim(townsUS1000) # 9992\n\n# combine US and Canada\nnames(townsCanpc)[names(townsCanpc)=='PCNAME'] <- 'NAME'\ntowns1000 <- rbind(townsUS1000[,c('NAME', 'geometry')], townsCanpc[,c('NAME', 'geometry')])\n\t\t\n# trim town to those <50km from the NA coast\nnearcoast <- st_is_within_distance(towns1000, coastlinesp, dist = 50*1000, sparse = FALSE) # slow (a few min). units in meters (50km). returns a matrix with columns corresponding to each coastline (includes a few major islands)\nnearcoastany <- rowSums(nearcoast) > 0 # sum across rows: we don't care which coast a town is close to\n\tsum(nearcoast) # 2233\n\tsum(nearcoastany) # 1873. shows that some towns were close to multiple coastlines\n\tplot(st_geometry(towns1000[which(nearcoastany),]), col='red', pch=16, cex=1, add=TRUE) # adds to the plot before\n\ntowns1000nearcoast <- towns1000[which(nearcoastany),]\n\n\n# find nearest point on the coastline to each town. So much faster than the old method of sampling!\n#coastpoint.near <- st_as_sf(rgeos::gNearestPoints(as(towns1000nearcoast,\"Spatial\"), as(coastlinesp,\"Spatial\"))[2,]) # from https://gis.stackexchange.com/questions/288570/find-nearest-point-along-polyline-using-sf-package-in-r. But only returns one point. Would have to implement in a loop\ntowncoastlines <- st_nearest_points(towns1000nearcoast, coastlinesp) # returns LINESTRINGS from first to second geometry\ntowncoastlengths <- st_length(towncoastlines) # length of each line, so that we can find the closest coastline to each town\nnearestptinds <- aggregate(list(ind = towncoastlengths), by = list(town = rep(1:nrow(towns1000nearcoast), each = nrow(coastlinesp))), FUN = function(x) which.min(x)) # find index of shortest line from town to a coast. Works because st_nearest_points returns a vector where y cycles fastest and x cycles slowest\nnearestptinds2 <- nearestptinds$ind + seq(0, length.out = nrow(nearestptinds), by = nrow(coastlinesp)) # convert to an index into towncoastlines\npts <- st_cast(towncoastlines[nearestptinds2], \"POINT\") # gives all start (towns) & end (coastlines) points, alternating\ncoastpts <- pts[seq(2, length(pts), 2)] # just the end points (on coastlines)\n\tlength(coastpts)\n\t\n #plot(st_geometry(towns1000nearcoast[1:10,]), axes = TRUE, col = 'blue') # to plot a few towns\n\tplot(st_geometry(towns1000nearcoast), axes = TRUE, col = 'blue', xlim = c(-2.3e6, -1.8e6), ylim = c(-4e5, 0)) # to plot a few towns\n\t#plot(st_geometry(towns1000nearcoast[1:100,]), axes = TRUE, col = 'blue') # to plot many towns\n\tplot(st_geometry(coastlinesp), add = TRUE)\n plot(st_geometry(coastpts), add = TRUE, col = 'red')\n\n# project back to latlong in order to make a table for NatCap InVEST\ncoastpts.ll <- st_transform(coastpts, crs = crslatlong)\ntowns1000nearcoast.ll <- st_transform(towns1000nearcoast, crs = crslatlong)\n\n# make output table of town and nearest landing point locations\n# format as specified by NatCap InVEST Wave\ntowns.coords <- st_coordinates(towns1000nearcoast.ll)\ncoast.coords <- st_coordinates(coastpts.ll)\n\noutgrid <- data.frame(ID=1:nrow(towns.coords), LAT=towns.coords[,2], LONG=towns.coords[,1], TYPE='GRID', LOCATION=towns1000nearcoast.ll$NAME)\noutland <- data.frame(ID=(1+nrow(towns.coords)):(nrow(towns.coords)+nrow(coast.coords)), LAT=coast.coords[,2], LONG=coast.coords[,1], TYPE='LAND', LOCATION=towns1000nearcoast.ll$NAME)\n\n\t# make sure it looks OK\n\tplot(outland$LONG, outland$LAT, pch=16, cex=0.5)\n\tpoints(outgrid$LONG, outgrid$LAT, col='red', pch=16, cex=0.5) # the towns\n\t\n # combine town and landings points\n out <- rbind(outgrid, outland)\n\n head(out[out$TYPE == 'GRID', ])\n head(out[out$TYPE == 'LAND', ])\n tail(out[out$TYPE == 'GRID', ])\n tail(out[out$TYPE == 'LAND', ])\n\t\n # write out\n write.csv(out, file='output/landgridpts_northamerica.csv', row.names=FALSE)\n\n# make output table of town and nearest landing point locations\n# format as specified by NatCap InVEST Wind\nout2 <- out[, c('ID', 'TYPE', 'LAT', 'LONG')]\nnames(out2)[names(out2)=='LAT'] <- 'LATI'\n head(out2)\n tail(out2)\n \n # write out\n write.csv(out2, file='output/landgridpts_northamerica_wind.csv', row.names=FALSE)\n ", "meta": {"hexsha": "460f7062317f0cecd2803a368b45a9fb399a35c3", "size": 7965, "ext": "r", "lang": "R", "max_stars_repo_path": "code/3.0_make_NatCap_files.r", "max_stars_repo_name": "mpinsky/proj_ranges", "max_stars_repo_head_hexsha": "e2b43fb6b29109d0abc2a14ce79b5619d0e8aa5d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/3.0_make_NatCap_files.r", "max_issues_repo_name": "mpinsky/proj_ranges", "max_issues_repo_head_hexsha": "e2b43fb6b29109d0abc2a14ce79b5619d0e8aa5d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2015-05-15T17:59:03.000Z", "max_issues_repo_issues_event_max_datetime": "2015-09-01T00:06:46.000Z", "max_forks_repo_path": "code/3.0_make_NatCap_files.r", "max_forks_repo_name": "mpinsky/proj_ranges", "max_forks_repo_head_hexsha": "e2b43fb6b29109d0abc2a14ce79b5619d0e8aa5d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.0588235294, "max_line_length": 310, "alphanum_fraction": 0.7000627746, "num_tokens": 2525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727232, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3221610130544784}} {"text": "library(ggplot2)\nlibrary(ggthemes)\n\nsource('colors.r')\n\npdf('../pdfs/valence_poi_line.pdf',width=4.7239,height=3.2927)\n\npoi = 'valence'\n\ndata_by_x = read.csv(file=paste0('../avgs_toppers/avg_',poi,'.csv'), sep=',', header = TRUE)\ndata_by_x$category <- rep('top_x', nrow(data_by_x))\ndata_by_x_left <- subset(data_by_x, year < 1979)\ndata_by_x_right <- subset(data_by_x, year > 1979)\n\ndata_remainder = read.csv(file=paste0('../avgs/avg_',poi,'.csv'), sep=',', header = TRUE)\ndata_remainder$category <- rep('remainder', nrow(data_remainder))\ndata_remainder_left <- subset(data_remainder, year < 1979)\ndata_remainder_right <- subset(data_remainder, year > 1979)\n\ndata <- rbind(data_by_x, data_remainder)\n\noldpar <- par(mar=rep(0,4))\nplot <- ggplot() +\n geom_line(data=data_by_x_left, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_line(data=data_by_x_right, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_line(data=data_remainder_left, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_line(data=data_remainder_right, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_line(data=data_remainder_left, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_line(data=data_remainder_right, size=1, aes_string(x='year',y=paste0('avg_',poi), color='category')) +\n geom_vline(xintercept = 1979, size = 0.5, color=grays[2]) +\n geom_vline(xintercept = 2008, size = 0.5, color=grays[2]) +\n scale_x_continuous(breaks = seq(1968, 2017, by = 10)) +\n # scale_y_continuous(breaks = seq(0, 1, by = 0.1)) +\n theme_wsj(color='white') +\n scale_colour_manual(values = c('#fcde05', grays[3] )) +\n theme(plot.margin=grid::unit(c(0,0,0,0), 'mm'), legend.position='none')\n\npar(reset)\nprint(plot)\npar(reset)\ndev.off()\n", "meta": {"hexsha": "be469fdd6ce6ab9ccf7fccc030a4d2a150fc7c70", "size": 1916, "ext": "r", "lang": "R", "max_stars_repo_path": "completed/poi_line.r", "max_stars_repo_name": "ruddfawcett/spotify-data", "max_stars_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "completed/poi_line.r", "max_issues_repo_name": "ruddfawcett/spotify-data", "max_issues_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "completed/poi_line.r", "max_forks_repo_name": "ruddfawcett/spotify-data", "max_forks_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.619047619, "max_line_length": 115, "alphanum_fraction": 0.6753653445, "num_tokens": 599, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011686727231, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.32216101305447836}} {"text": "# Libraries:\r\n\r\ninstall.packages(c(\"geoscale\", \"strap\"))\r\nlibrary(strap)\r\ninstall.packages(\"devtools\")\r\nlibrary(devtools)\r\ninstall_github(\"graemetlloyd/Claddis\")\r\nlibrary(Claddis)\r\n\r\n# Functions:\r\n\r\nmakedeep <- function(x, y, y_label, quartz=TRUE, main=\"\")\r\n{\r\n y.max=max(sort(y))\r\n y.min=0\r\n young.bin <- -8 # Offset for youngest bin label...allows for age < 0 to center label\r\n plot.min=y.min-.03*(y.max-y.min)\r\n seg.min=y.min-.07*(y.max-y.min)\r\n text.min=y.min-.036*(y.max-y.min)\r\n time.boundaries=c(5.332, 23.03, 33.9, 55.8, 65.5, 99.6, 145.5, 161.2, 175.6, 199.6)\r\n interval.names=c(\"Pl\", \"M\", \"O\", \"E\", \"P\", \"UK\", \"LK\", \"UJ\", \"MJ\", \"LJ\")\r\n interval.midpoint=time.boundaries-diff(c(0, time.boundaries))/2\r\n plot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(plot.min, y.max), type=\"n\", xlab=\"Geologic time (Ma)\", ylab=y_label, main=main)\r\n abline(h=y.min)\r\n segments(c(time.boundaries, 0), y.min, c(time.boundaries, 0), seg.min)\r\n text(interval.midpoint, text.min, labels=interval.names)\r\n lines(x, y)\r\n}\r\n\r\nmakedeeppoly <- function(x, y, y_label, upper95, lower95, quartz=TRUE, main=\"\")\r\n{\r\n y.max=max(c(sort(y), sort(upper95)))\r\n y.min=0\r\n young.bin <- -8 # Offset for youngest bin label...allows for age < 0 to center label\r\n plot.min=y.min-.03*(y.max-y.min)\r\n seg.min=y.min-.07*(y.max-y.min)\r\n text.min=y.min-.036*(y.max-y.min)\r\n time.boundaries=c(5.332, 23.03, 33.9, 55.8, 65.5, 99.6, 145.5, 161.2, 175.6, 199.6)\r\n interval.names=c(\"Pl\", \"M\", \"O\", \"E\", \"P\", \"UK\", \"LK\", \"UJ\", \"MJ\", \"LJ\")\r\n interval.midpoint=time.boundaries-diff(c(0, time.boundaries))/2\r\n plot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(plot.min, y.max), type=\"n\", xlab=\"Geologic time (Ma)\", ylab=y_label, main=main)\r\n abline(h=y.min)\r\n segments(c(time.boundaries, 0), y.min, c(time.boundaries, 0), seg.min)\r\n text(interval.midpoint, text.min, labels=interval.names)\r\n lines(x, y)\r\n polygon(x=c(x, rev(x)), y=c(upper95, rev(lower95)), col=\"grey\", border=NA)\r\n points(x, y, type=\"l\")\r\n}\r\n\r\nmakedeepz <- function(x, y, z, y_label, z_label, quartz=TRUE, main=\"\")\r\n{\r\n\ty.max=max(sort(y))\r\n\ty.min=0\r\n\tz.max=max(sort(z))\r\n\tz.min=0\r\n\tyoung.bin <- -8 # Offset for youngest bin label...allows for age < 0 to center label\r\n\typlot.min=y.min-.03*(y.max-y.min)\r\n\tyseg.min=y.min-.07*(y.max-y.min)\r\n\tytext.min=y.min-.036*(y.max-y.min)\r\n\tzplot.min=z.min-.03*(z.max-z.min)\r\n\ttime.boundaries=c(5.332, 23.03, 33.9, 55.8, 65.5, 99.6, 145.5, 161.2, 175.6, 199.6)\r\n\tinterval.names=c(\"Pl\", \"M\", \"O\", \"E\", \"P\", \"UK\", \"LK\", \"UJ\", \"MJ\", \"LJ\")\r\n\tinterval.midpoint=time.boundaries-diff(c(0, time.boundaries))/2\r\n\tpar(mar=c(5, 4, 4, 4) + 0.1) # Leave space for z axis\r\n\tplot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(yplot.min, y.max), type=\"n\", xlab=\"Geologic time (Ma)\", ylab=y_label, main=main)\r\n\tabline(h=y.min)\r\n\tsegments(c(time.boundaries, 0), y.min, c(time.boundaries, 0), yseg.min)\r\n\ttext(interval.midpoint, ytext.min, labels=interval.names)\r\n\tlines(x, y)\r\n\tpar(new=T)\r\n\tplot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(zplot.min, z.max), axes=F, type=\"n\", xlab=\"\", ylab=\"\")\r\n\tlines(x, z, col=\"dark grey\")\r\n\taxis(4, at=pretty(range(z)))\r\n\tmtext(z_label, 4, 3)\r\n}\r\n\r\nmakephan <- function(x, y, y_label, quartz=TRUE, main=\"\")\r\n{\r\n\ty.max=max(sort(y))\r\n\ty.min=0\r\n\tyoung.bin <- -8 # Offset for youngest bin label...allows for age < 0 to center label\r\n\tplot.min=y.min-.03*(y.max-y.min)\r\n\tseg.min=y.min-.07*(y.max-y.min)\r\n\ttext.min=y.min-.036*(y.max-y.min)\r\n\ttime.boundaries=c(23.03, 65.5, 145.5, 199.6, 251, 299, 359.2, 416, 443.7, 488.3, 542)\r\n\tinterval.names=c(\"Ng\", \"Pg\", \"K\", \"J\", \"Tr\", \"P\", \"C\", \"D\", \"S\", \"O\", \"Cm\")\r\n\tinterval.midpoint=time.boundaries-diff(c(0, time.boundaries))/2\r\n\tplot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(plot.min, y.max), type=\"n\", xlab=\"Geologic time (Ma)\", ylab=y_label, main=main)\r\n\tabline(h=y.min)\r\n\tsegments(c(time.boundaries, 0), y.min, c(time.boundaries, 0), seg.min)\r\n\ttext(interval.midpoint, text.min, labels=interval.names)\r\n\tlines(x, y)\r\n}\r\n\r\nAICc <- function(model, n)\r\n{\r\n\trequire(stats)\r\n\trequire(nlme)\r\n\tp <- length(coef(model))\r\n\tlk <- AIC(model)-(2*p)\r\n\tlk+(2*p*(n/(n-p-1)))\r\n}\r\n\r\nlinear.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodelout1 <- nls(y~a*x, start=list(a=(max(y)/max(x))), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE))\r\n\tmodelout2 <- nls(y~(a*x)+b, start=list(a=(max(y)/max(x)), b=1), algorithm=\"port\", lower=list(a=0, b=0), control=list(warnOnly=TRUE))\r\n\tmodelout3 <- lm(y~x)\r\n\twts <- akaike.wts(c(AICc(modelout1, n=length(x)), AICc(modelout2, n=length(x)), AICc(modelout3, n=length(x))))\r\n\tbest <- grep(TRUE, max(wts) == wts)\r\n\tif(best == 1) model <- modelout1\r\n\tif(best == 2) model <- modelout2\r\n\tif(best == 3) model <- modelout3\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nhyperbolic.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodelout1 <- NA; modelout2 <- NA\r\n\ttry(modelout1 <- nls(y~(a*x)/(b+x), start=list(a=max(y), b=1), algorithm=\"port\", control=list(warnOnly=TRUE)), silent=TRUE)\r\n\ttry(modelout2 <- nls(y~a+((c*x)/(b+x)), start=list(a=1, b=1, c=max(y)), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE)), silent=TRUE)\r\n\tif (is.na(modelout1[1]) == TRUE) modelout1 <- nls(y~(max(y)*x)/(b+x), start=list(b=1), algorithm=\"port\", control=list(warnOnly=TRUE))\r\n\tif (is.na(modelout2[1]) == TRUE) modelout2 <- nls(y~a+((max(y)*x)/(b+x)), start=list(a=1, b=1), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE))\r\n\twts <- akaike.wts(c(AICc(modelout1, n=length(x)), AICc(modelout2, n=length(x))))\r\n\tifelse(wts[1] >= wts[2], model <- modelout1, model <- modelout2)\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nlogarithmic.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodel <- NA\r\n\ttry(model <- nls(y~a+log(b+x), start=list(a=1, b=1), algorithm=\"port\", lower=list(a=0, b=0), control=list(warnOnly=TRUE)), silent=TRUE)\r\n\tif (is.na(model[1]) == TRUE) model <- nls(y~a+log(1+x), start=list(a=1), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE))\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nexponential.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodelout1 <- NA; modelout2 <- NA\r\n\ttry(modelout1 <- nls(y~a*(1-exp(-c*x)), start=list(a=max(y), c=0.1), algorithm=\"port\", lower=list(a=0, c=0), control=list(warnOnly=TRUE)), silent=TRUE)\r\n\ttry(modelout2 <- nls(y~a-(b*exp(-c*x)), start=list(a=max(y), b=max(y), c=0), algorithm=\"port\", control=list(warnOnly=TRUE)), silent=TRUE)\r\n\tif (is.na(modelout1[1]) == TRUE) modelout1 <- nls(y~max(y)*(1-exp(-c*x)), start=list(c=0.000001), algorithm=\"port\", lower=list(c=0), control=list(warnOnly=TRUE))\r\n\tif (is.na(modelout2[1]) == TRUE) modelout2 <- nls(y~max(y)-(max(y)*exp(-c*x)), start=list(c=0.), algorithm=\"port\", control=list(warnOnly=TRUE))\r\n\twts <- akaike.wts(c(AICc(modelout1, n=length(x)), AICc(modelout2, n=length(x))))\r\n\tifelse(wts[1] >= wts[2], model <- modelout1, model <- modelout2)\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nsigmoidal.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodelout1 <- NA; modelout2 <- NA\r\n\ttry(modelout1 <- nls(y~b/(c+exp(-x)), start=list(b=max(y)/100, c=max(y)/10000), algorithm=\"port\", control=list(warnOnly=TRUE)), silent=TRUE)\r\n\ttry(modelout2 <- nls(y~a+(b/(c+exp(-x))), start=list(a=1, b=0.1, c=0.001), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE)), silent=TRUE)\r\n\tif (is.na(modelout1[1]) == TRUE) modelout1 <- nls(y~b/((1/max(y))+exp(-x)), start=list(b=1), algorithm=\"port\", control=list(warnOnly=TRUE))\r\n\tif (is.na(modelout2[1]) == TRUE) modelout2 <- nls(y~a+(max(y)/100)/((max(y)/100)+exp(-x)), start=list(a=1), algorithm=\"port\", lower=list(a=0), control=list(warnOnly=TRUE))\r\n\twts <- akaike.wts(c(AICc(modelout1, n=length(x)), AICc(modelout2, n=length(x))))\r\n\tifelse(wts[1] >= wts[2], model <- modelout1, model <- modelout2)\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\npolynomial.model <- function(x, y)\r\n{\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\trequire(plotrix)\r\n\tmodelout2 <- lm(y~x+I(x^2))\r\n\tmodelout3 <- lm(y~x+I(x^2)+I(x^3))\r\n\tmodelout4 <- lm(y~x+I(x^2)+I(x^3)+I(x^4))\r\n\twts <- akaike.wts(c(AICc(modelout2, n=length(x)), AICc(modelout3, n=length(x)), AICc(modelout4, n=length(x))))\r\n\tbest <- grep(TRUE, max(wts) == wts)\r\n\tif(best == 1) model <- modelout2\r\n\tif(best == 2) model <- modelout3\r\n\tif(best == 3) model <- modelout4\r\n\tsefit.model <- std.error(y-predict(model))*1.96\r\n\tsdfit.model <- sd(y-predict(model))*1.96\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nbest.model <- function(x, y)\r\n{\r\n\tlinmod <- linear.model(x, y)$model # Fit linear model\r\n\thypmod <- hyperbolic.model(x, y)$model # Fit hyperbolic model\r\n\tlogmod <- logarithmic.model(x, y)$model # Fit logarithmic model\r\n\texpmod <- exponential.model(x, y)$model # Fit exponential model\r\n\tsigmod <- sigmoidal.model(x, y)$model # Fit sigmoidal model\r\n\tpolmod <- polynomial.model(x, y)$model # Fit polynomial model\r\n\tbest <- min(c(AICc(linmod, n=length(x)), AICc(hypmod, n=length(x)), AICc(logmod, n=length(x)), AICc(expmod, n=length(x)), AICc(sigmod, n=length(x)), AICc(polmod, n=length(x))))\r\n\tif (AICc(linmod, n=length(x)) == best) model <- linmod; sefit.model <- linear.model(x, y)$sefit.model; sdfit.model <- linear.model(x, y)$sdfit.model\r\n\tif (AICc(hypmod, n=length(x)) == best) model <- hypmod; sefit.model <- hyperbolic.model(x, y)$sefit.model; sdfit.model <- hyperbolic.model(x, y)$sdfit.model\r\n\tif (AICc(logmod, n=length(x)) == best) model <- logmod; sefit.model <- logarithmic.model(x, y)$sefit.model; sdfit.model <- logarithmic.model(x, y)$sdfit.model\r\n\tif (AICc(expmod, n=length(x)) == best) model <- expmod; sefit.model <- exponential.model(x, y)$sefit.model; sdfit.model <- exponential.model(x, y)$sdfit.model\r\n\tif (AICc(sigmod, n=length(x)) == best) model <- sigmod; sefit.model <- sigmoidal.model(x, y)$sefit.model; sdfit.model <- sigmoidal.model(x, y)$sdfit.model\r\n\tif (AICc(polmod, n=length(x)) == best) model <- polmod; sefit.model <- polynomial.model(x, y)$sefit.model; sdfit.model <- polynomial.model(x, y)$sdfit.model\r\n\tresult <- list(model, sefit.model, sdfit.model)\r\n\tnames(result) <- c(\"model\", \"sefit.model\", \"sdfit.model\")\r\n\treturn(result)\r\n}\r\n\r\nrockmodel.predictCI <- function(rockmeasure, diversitymeasure, CI=0.95)\r\n{\r\n\tx <- sort(rockmeasure)\r\n\ty <- sort(diversitymeasure)\r\n\tmodel <- best.model(x, y)$model\r\n\tsefit.model <- best.model(x, y)$sefit.model\r\n\tsdfit.model <- best.model(x, y)$sdfit.model\r\n\tpredicted <- predict(model, list(x=rockmeasure))\r\n\tselowerCI <- predicted-sefit.model\r\n\tseupperCI <- predicted+sefit.model\r\n\tsdlowerCI <- predicted-sdfit.model\r\n\tsdupperCI <- predicted+sdfit.model\r\n\tresult <- list(predicted, selowerCI, seupperCI, sdlowerCI, sdupperCI, model)\r\n\tnames(result) <- c(\"predicted\", \"selowerCI\", \"seupperCI\", \"sdlowerCI\", \"sdupperCI\", \"model\")\r\n\treturn(result)\r\n}\r\n\r\nsphpolyarea <- function(vlat, vlon)\r\n{\r\n\trequire(fossil)\r\n\tsplits <- strsplit(unique(paste(vlon, vlat, sep=\"_\")), \"_\")\r\n\tvlat <- vlon <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(splits)) {\r\n\t\tvlon[i] <- splits[[i]][1]\r\n\t\tvlat[i] <- splits[[i]][2]\r\n\t}\r\n\tvlat <- as.numeric(vlat)\r\n\tvlon <- as.numeric(vlon)\r\n\tpoints <- chull(vlon, vlat)\r\n\tvlon <- vlon[points]\r\n\tvlat <- vlat[points]\r\n\tsum <- 0\r\n\tnv <- length(vlat)\r\n\tR <- 40041.47/(2 * pi)\r\n\twhile (nv >= 3) {\r\n\t\tlat1 <- vlat[1]\r\n\t\tlat2 <- vlat[2]\r\n\t\tlat3 <- vlat[3]\r\n\t\tlong1 <- vlon[1]\r\n\t\tlong2 <- vlon[2]\r\n\t\tlong3 <- vlon[3]\r\n\t\tcx <- deg.dist(lat1, long1, lat2, long2)/R\r\n\t\tbx <- deg.dist(lat1, long1, lat3, long3)/R\r\n\t\tax <- deg.dist(lat2, long2, lat3, long3)/R\r\n\t\tA <- acos((cos(ax) - cos(bx) * cos(cx))/(sin(bx) * sin(cx)))\r\n\t\tB <- acos((cos(bx) - cos(cx) * cos(ax))/(sin(cx) * sin(ax)))\r\n\t\tC <- acos((cos(cx) - cos(ax) * cos(bx))/(sin(ax) * sin(bx)))\r\n\t\tSA <- R^2 * ((A + B + C) - pi)\r\n\t\tsum <- sum+SA\r\n\t\tvlat <- vlat[-2]\r\n\t\tvlon <- vlon[-2]\r\n\t\tnv <- length(vlat)\r\n\t}\r\n\treturn(sum)\r\n}\r\n\r\nsubsample <- function(pool, ntrials, CI = 0.95)\r\n{\r\n spgout <- gout <- sout <- matrix(nrow=ntrials, ncol=length(pool))\r\n stopat <- length(unique(pool)) # No point continuing to pull out species if they have all been sampled\r\n poolholder <- pool\r\n topCI <- ceiling((1-((1-CI)/2))*ntrials)\r\n bottomCI <- max(floor(((1-CI)/2)*ntrials), 1)\r\n for (i in 1:ntrials) {\r\n pool <- poolholder\r\n poolsize <- length(pool)\r\n picked <- vector(mode=\"character\") # Binomial\r\n pickedgen <- vector(mode=\"character\") # Genus only\r\n counter <- 1\r\n while (poolsize > 0 && length(picked) < stopat) {\r\n pickno <- ceiling(runif(1, 0, length(pool)))\r\n picked <- unique(c(picked, pool[pickno]))\r\n pickedgen <- unique(c(pickedgen, strsplit(pool[pickno], \" \")[[1]][1]))\r\n sout[i, counter] <- length(picked)\r\n gout[i, counter] <- length(pickedgen)\r\n spgout[i, counter] <- length(picked)/length(pickedgen)\r\n pool <- pool[-pickno]\r\n poolsize <- length(pool)\r\n counter <- counter+1\r\n }\r\n sout[i, grep(TRUE, is.na(sout[i, ]))] <- length(picked) # Fill in remaining\r\n gout[i, grep(TRUE, is.na(gout[i, ]))] <- length(pickedgen) # Fill in remaining\r\n spgout[i, grep(TRUE, is.na(spgout[i, ]))] <- length(picked)/length(pickedgen) # Fill in remaining\r\n }\r\n smeanCI <- supperCI <- slowerCI <- apply(sout, 2, mean)\r\n gmeanCI <- gupperCI <- glowerCI <- apply(gout, 2, mean)\r\n spgmeanCI <- spgupperCI <- spglowerCI <- apply(spgout, 2, mean)\r\n for (i in 1:length(sout[1, ])) {\r\n supperCI[i] <- sort(sout[, i])[topCI]\r\n slowerCI[i] <- sort(sout[, i])[bottomCI]\r\n gupperCI[i] <- sort(gout[, i])[topCI]\r\n glowerCI[i] <- sort(gout[, i])[bottomCI]\r\n spgupperCI[i] <- sort(spgout[, i])[topCI]\r\n spglowerCI[i] <- sort(spgout[, i])[bottomCI]\r\n }\r\n sout <- rbind(supperCI, smeanCI, slowerCI)\r\n gout <- rbind(gupperCI, gmeanCI, glowerCI)\r\n spgout <- rbind(spgupperCI, spgmeanCI, spglowerCI)\r\n out <- list(sout, gout, spgout)\r\n names(out) <- c(\"sout\", \"gout\", \"spgout\")\r\n return(out)\r\n}\r\n\r\nfive.models <- function(intable, time, log.input=FALSE)\r\n{\r\n tstable <- matrix(ncol=4, nrow=length(time))\r\n if (log.input == FALSE) {\r\n for (i in 1:length(time)) {\r\n tstable[i, 1] <- ifelse(is.nan(mean(sort(intable[, i]))), 0, mean(sort(intable[, i])))\r\n tstable[i, 2] <- ifelse(is.nan(var(sort(intable[, i]))), 0, var(sort(intable[, i])))\r\n tstable[i, 3] <- length(sort(intable[, i]))\r\n tstable[i, 4] <- time[i]\r\n }\r\n }\r\n if (log.input == TRUE) {\r\n for (i in 1:length(time)) {\r\n tstable[i, 1] <- mean(sort(log(intable[, i]))[grep(TRUE, sort(log(intable[, i])) > 0)])\r\n tstable[i, 2] <- var(sort(log(intable[, i]))[grep(TRUE, sort(log(intable[, i])) > 0)])\r\n tstable[i, 3] <- length(sort(log(intable[, i]))[grep(TRUE, sort(log(intable[, i])) > 0)])\r\n tstable[i, 4] <- time[i]\r\n }\r\n }\r\n for (i in length(tstable[, 1]):1) {\r\n kill <- 0; a <- tstable[i, 1]; b <- tstable[i, 2]; c <- tstable[i, 3]\r\n if(is.nan(a) || is.na(a) || a == Inf || a == -Inf || a == 0) kill <- 1\r\n if(is.nan(b) || is.na(b) || b == Inf || b == -Inf || b == 0) kill <- 1\r\n if(is.nan(c) || is.na(c) || c == Inf || c == -Inf || c == 0) kill <- 1\r\n if(kill == 1) tstable <- tstable[-i, ]\r\n }\r\n tstable <- as.paleoTS(tstable[, 1], tstable[, 2], tstable[, 3], tstable[, 4], start.age=max(tstable[, 4]))\r\n threemodels <- fit3models(tstable, silent=T)\r\n twostases <- fitGpunc(tstable, ng=2, oshare=F, silent=T)\r\n threestases <- fitGpunc(tstable, ng=3, oshare=F, silent=T)\r\n results <- cbind(c(threemodels$aic, twostases$AIC, threestases$AIC), c(threemodels$aicc, twostases$AICc, threestases$AICc), round(akaike.wts(c(threemodels$aicc, twostases$AICc, threestases$AICc)), digits=5))\r\n colnames(results) <- c(\"AIC\", \"AICc\", \"Akaike Weights\"); rownames(results) <- c(\"Directional trend\", \"Random walk\", \"Stasis\", \"Punctuation with two stases\", \"Punctuation with three stases\")\r\n result <- list(tstable, threemodels, twostases, threestases, results, log.input)\r\n names(result) <- c(\"tstable\", \"threemodels\", \"twostases\", \"threestases\", \"results\", \"log.input\")\r\n return(result)\r\n}\r\n\r\nplot.stases <- function(fivemodel, rawtable, y_label)\r\n{\r\n makedeep(x=fivemodel$tstable$tt, y=fivemodel$tstable$mm, y_label=y_label)\r\n fivemodel$results\r\n bestmodel <- rownames(fivemodel$results)[grep(TRUE, fivemodel$results[, \"Akaike Weights\"] == max(fivemodel$results[, \"Akaike Weights\"]))]\r\n text(max(fivemodel$tstable$tt), max(fivemodel$tstable$mm), paste(\"Best model: \", bestmodel, sep=\"\"), pos=4)\r\n if (bestmodel == \"Punctuation with three stases\") {\r\n firststasis <- 1:(fivemodel$threestases$shift.start[1]-1)\r\n secondstasis <- fivemodel$threestases$shift.start[1]:(fivemodel$threestases$shift.start[2]-1)\r\n thirdstasis <- fivemodel$threestases$shift.start[2]:length(fivemodel$tstable$mm)\r\n }\r\n if (bestmodel == \"Punctuation with two stases\") {\r\n firststasis <- 1:(fivemodel$twostases$shift.start[1]-1)\r\n secondstasis <- fivemodel$twostases$shift.start[1]:length(fivemodel$tstable$mm)\r\n }\r\n SD <- rawtable\r\n if (fivemodel$log.input == FALSE) {\r\n for (i in 1:length(SD[1, ])) SD[1, i] <- sd(sort(SD[, i]))\r\n }\r\n if (fivemodel$log.input == TRUE) {\r\n for (i in 1:length(SD[1, ])) SD[1, i] <- sd(log(sort(SD[, i])))\r\n }\r\n SD <- 1.96*SD[1, 1:length(fivemodel$tstable$mm)]\r\n if (bestmodel == \"Punctuation with three stases\") {\r\n polygon(c(fivemodel$tstable$tt[firststasis], rev(fivemodel$tstable$tt[firststasis])), c(rep(fivemodel$threestases$par[1]+fivemodel$threestases$par[4], length(firststasis)), rep(fivemodel$threestases$par[1]-fivemodel$threestases$par[4], length(firststasis))), col=\"grey\", border=NA)\r\n polygon(c(fivemodel$tstable$tt[secondstasis], rev(fivemodel$tstable$tt[secondstasis])), c(rep(fivemodel$threestases$par[2]+fivemodel$threestases$par[5], length(secondstasis)), rep(fivemodel$threestases$par[2]-fivemodel$threestases$par[5], length(secondstasis))), col=\"grey\", border=NA)\r\n polygon(c(fivemodel$tstable$tt[thirdstasis], rev(fivemodel$tstable$tt[thirdstasis])), c(rep(fivemodel$threestases$par[3]+fivemodel$threestases$par[6], length(thirdstasis)), rep(fivemodel$threestases$par[3]-fivemodel$threestases$par[6], length(thirdstasis))), col=\"grey\", border=NA)\r\n }\r\n if (bestmodel == \"Punctuation with two stases\") {\r\n polygon(c(fivemodel$tstable$tt[firststasis], rev(fivemodel$tstable$tt[firststasis])), c(rep(fivemodel$twostases$par[1]+fivemodel$twostases$par[4], length(firststasis)), rep(fivemodel$twostases$par[1]-fivemodel$twostases$par[4], length(firststasis))), col=\"grey\", border=NA)\r\n polygon(c(fivemodel$tstable$tt[secondstasis], rev(fivemodel$tstable$tt[secondstasis])), c(rep(fivemodel$twostases$par[2]+fivemodel$twostases$par[5], length(secondstasis)), rep(fivemodel$twostases$par[2]-fivemodel$twostases$par[5], length(secondstasis))), col=\"grey\", border=NA)\r\n }\r\n points(x=fivemodel$tstable$tt, y=fivemodel$tstable$mm, type=\"l\")\r\n points(fivemodel$tstable$tt, fivemodel$tstable$mm, cex=0.5)\r\n for (i in 1:length(fivemodel$tstable$mm)) lines(c(fivemodel$tstable$tt[i], fivemodel$tstable$tt[i]), c(fivemodel$tstable$mm[i]+SD[i], fivemodel$tstable$mm[i]-SD[i]))\r\n if (bestmodel == \"Punctuation with three stases\") {\r\n points(fivemodel$tstable$tt[firststasis], rep(fivemodel$threestases$par[1], length(firststasis)), type=\"l\")\r\n points(fivemodel$tstable$tt[secondstasis], rep(fivemodel$threestases$par[2], length(secondstasis)), type=\"l\")\r\n points(fivemodel$tstable$tt[thirdstasis], rep(fivemodel$threestases$par[3], length(thirdstasis)), type=\"l\")\r\n }\r\n if (bestmodel == \"Punctuation with two stases\") {\r\n points(fivemodel$tstable$tt[firststasis], rep(fivemodel$twostases$par[1], length(firststasis)), type=\"l\")\r\n points(fivemodel$tstable$tt[secondstasis], rep(fivemodel$twostases$par[2], length(secondstasis)), type=\"l\")\r\n }\r\n}\r\n\r\nts.corr <- function(var1, var2)\r\n{\r\n shared.time <- intersect(var1$tstable$tt, var2$tstable$tt)\r\n var1.comp <- var1$tstable$mm[match(shared.time, var1$tstable$tt)]\r\n var2.comp <- var2$tstable$mm[match(shared.time, var2$tstable$tt)]\r\n raw.corr <- cor.test(var1.comp, var2.comp, method=\"spearman\")\r\n firstdiff.corr <- cor.test(diff(var1.comp), diff(var2.comp), method=\"spearman\")\r\n result <- list(raw.corr, firstdiff.corr)\r\n names(result) <- c(\"raw.corr\", \"firstdiff.corr\")\r\n return(result)\r\n}\r\n\r\nmulti.ts.3comp <- function(var1, var2, var3, var2name, var3name, cuts=NA) # First variable is dependent, second and third are explanatory\r\n{\r\n\trequire(paleoTS)\r\n\t# Create comparable vectors (i.e. where all variables are sampled in bin)\r\n\tshared.time <- intersect(intersect(var1$tstable$tt, var2$tstable$tt), var3$tstable$tt)\r\n\tif(!is.na(cuts)[1]) {\r\n\t bottom <- max(cuts)\r\n\t top <- min(cuts)\r\n\t shared.time <- shared.time[intersect(grep(TRUE, shared.time >= top), grep(TRUE, shared.time <= bottom))]\r\n\t}\r\n\tvar1.comp <- var1$tstable$mm[match(shared.time, var1$tstable$tt)]\r\n\tvar2.comp <- var2$tstable$mm[match(shared.time, var2$tstable$tt)]\r\n\tvar3.comp <- var3$tstable$mm[match(shared.time, var3$tstable$tt)]\r\n\tfd.var1.comp <- diff(var1.comp)\r\n\tfd.var2.comp <- diff(var2.comp)\r\n\tfd.var3.comp <- diff(var3.comp)\r\n\tgd.var1.comp <- gen.diff(var1.comp, shared.time)\r\n\tgd.var2.comp <- gen.diff(var2.comp, shared.time)\r\n\tgd.var3.comp <- gen.diff(var3.comp, shared.time)\r\n\t# Fit linear models:\r\n\tlm.var2 <- lm(var1.comp ~ var2.comp)\r\n\tlm.var3 <- lm(var1.comp ~ var3.comp)\r\n\tlm.var2var3 <- lm(var1.comp ~ var2.comp + var3.comp)\r\n\tfd.lm.var2 <- lm(fd.var1.comp ~ fd.var2.comp)\r\n\tfd.lm.var3 <- lm(fd.var1.comp ~ fd.var3.comp)\r\n\tfd.lm.var2var3 <- lm(fd.var1.comp ~ fd.var2.comp + fd.var3.comp)\r\n\tgd.lm.var2 <- lm(gd.var1.comp ~ gd.var2.comp)\r\n\tgd.lm.var3 <- lm(gd.var1.comp ~ gd.var3.comp)\r\n\tgd.lm.var2var3 <- lm(gd.var1.comp ~ gd.var2.comp + gd.var3.comp)\r\n\t# Get proportion explained by each model, plus unexplained:\r\n\tvar2.rsq <- summary(lm.var2var3)$r.squared-summary(lm.var3)$r.squared\r\n\tvar3.rsq <- summary(lm.var2var3)$r.squared-summary(lm.var2)$r.squared\r\n\tvar2var3.rsq <- summary(lm.var2)$r.squared-var2.rsq\r\n\tue.rsq <- 1-(var2.rsq+var3.rsq+var2var3.rsq)\r\n\tfd.var2.rsq <- summary(fd.lm.var2var3)$r.squared-summary(fd.lm.var3)$r.squared\r\n\tfd.var3.rsq <- summary(fd.lm.var2var3)$r.squared-summary(fd.lm.var2)$r.squared\r\n\tfd.var2var3.rsq <- summary(fd.lm.var2)$r.squared-fd.var2.rsq\r\n\tfd.ue.rsq <- 1-(fd.var2.rsq+fd.var3.rsq+fd.var2var3.rsq)\r\n\tgd.var2.rsq <- summary(gd.lm.var2var3)$r.squared-summary(gd.lm.var3)$r.squared\r\n\tgd.var3.rsq <- summary(gd.lm.var2var3)$r.squared-summary(gd.lm.var2)$r.squared\r\n\tgd.var2var3.rsq <- summary(gd.lm.var2)$r.squared-gd.var2.rsq\r\n\tgd.ue.rsq <- 1-(gd.var2.rsq+gd.var3.rsq+gd.var2var3.rsq)\r\n\t# Get AIC and AICc for each model plus Akaike weights:\r\n\tAIC.var2 <- AIC(lm.var2)\r\n\tAIC.var3 <- AIC(lm.var3)\r\n\tAIC.var2var3 <- AIC(lm.var2var3)\r\n\tfd.AIC.var2 <- AIC(fd.lm.var2)\r\n\tfd.AIC.var3 <- AIC(fd.lm.var3)\r\n\tfd.AIC.var2var3 <- AIC(fd.lm.var2var3)\r\n\tgd.AIC.var2 <- AIC(gd.lm.var2)\r\n\tgd.AIC.var3 <- AIC(gd.lm.var3)\r\n\tgd.AIC.var2var3 <- AIC(gd.lm.var2var3)\r\n\tAICc.var2 <- AICc(lm.var2, length(var1.comp))\r\n\tAICc.var3 <- AICc(lm.var3, length(var1.comp))\r\n\tAICc.var2var3 <- AICc(lm.var2var3, length(var1.comp))\r\n\tfd.AICc.var2 <- AICc(fd.lm.var2, length(fd.var1.comp))\r\n\tfd.AICc.var3 <- AICc(fd.lm.var3, length(fd.var1.comp))\r\n\tfd.AICc.var2var3 <- AICc(fd.lm.var2var3, length(fd.var1.comp))\r\n\tgd.AICc.var2 <- AICc(gd.lm.var2, length(gd.var1.comp))\r\n\tgd.AICc.var3 <- AICc(gd.lm.var3, length(gd.var1.comp))\r\n\tgd.AICc.var2var3 <- AICc(gd.lm.var2var3, length(gd.var1.comp))\r\n\twts <- akaike.wts(c(AICc.var2, AICc.var3, AICc.var2var3))\r\n\tfd.wts <- akaike.wts(c(fd.AICc.var2, fd.AICc.var3, fd.AICc.var2var3))\r\n\tgd.wts <- akaike.wts(c(gd.AICc.var2, gd.AICc.var3, gd.AICc.var2var3))\r\n\t# Write formulae for models:\r\n\tfm.var2 <- paste(\"(\", round(coef(lm.var2)[2], 2), \" x \", var2name, \") + \", round(coef(lm.var2)[1], 2), sep=\"\")\r\n\tfm.var3 <- paste(\"(\", round(coef(lm.var3)[2], 2), \" x \", var3name, \") + \", round(coef(lm.var3)[1], 2), sep=\"\")\r\n\tfm.var2var3 <- paste(\"(\", round(coef(lm.var2var3)[2], 2), \" x \", var2name, \") + (\", round(coef(lm.var2var3)[3], 2), \" x \", var3name, \") + \", round(coef(lm.var2var3)[1], 2), sep=\"\")\r\n\tfd.fm.var2 <- paste(\"(\", round(coef(fd.lm.var2)[2], 2), \" x \", var2name, \") + \", round(coef(fd.lm.var2)[1], 2), sep=\"\")\r\n\tfd.fm.var3 <- paste(\"(\", round(coef(fd.lm.var3)[2], 2), \" x \", var3name, \") + \", round(coef(fd.lm.var3)[1], 2), sep=\"\")\r\n\tfd.fm.var2var3 <- paste(\"(\", round(coef(fd.lm.var2var3)[2], 2), \" x \", var2name, \") + (\", round(coef(fd.lm.var2var3)[3], 2), \" x \", var3name, \") + \", round(coef(fd.lm.var2var3)[1], 2), sep=\"\")\r\n\tgd.fm.var2 <- paste(\"(\", round(coef(gd.lm.var2)[2], 2), \" x \", var2name, \") + \", round(coef(gd.lm.var2)[1], 2), sep=\"\")\r\n\tgd.fm.var3 <- paste(\"(\", round(coef(gd.lm.var3)[2], 2), \" x \", var3name, \") + \", round(coef(gd.lm.var3)[1], 2), sep=\"\")\r\n\tgd.fm.var2var3 <- paste(\"(\", round(coef(gd.lm.var2var3)[2], 2), \" x \", var2name, \") + (\", round(coef(gd.lm.var2var3)[3], 2), \" x \", var3name, \") + \", round(coef(gd.lm.var2var3)[1], 2), sep=\"\")\r\n\t# Make table:\r\n\tmodels <- c(var2name, var3name, paste(var2name, \" + \", var3name, sep=\"\"), \"Unexplained\")\r\n\tresults <- cbind(models, c(fm.var2, fm.var3, fm.var2var3, NA), c(fd.fm.var2, fd.fm.var3, fd.fm.var2var3, NA), c(gd.fm.var2, gd.fm.var3, gd.fm.var2var3, NA), c(var2.rsq, var3.rsq, var2var3.rsq, ue.rsq), c(fd.var2.rsq, fd.var3.rsq, fd.var2var3.rsq, fd.ue.rsq), c(gd.var2.rsq, gd.var3.rsq, gd.var2var3.rsq, gd.ue.rsq), c(AIC.var2, AIC.var3, AIC.var2var3, NA), c(fd.AIC.var2, fd.AIC.var3, fd.AIC.var2var3, NA), c(gd.AIC.var2, gd.AIC.var3, gd.AIC.var2var3, NA), c(AICc.var2, AICc.var3, AICc.var2var3, NA), c(fd.AICc.var2, fd.AICc.var3, fd.AICc.var2var3, NA), c(gd.AICc.var2, gd.AICc.var3, gd.AICc.var2var3, NA), c(wts, NA), c(fd.wts, NA), c(gd.wts, NA))\r\n\tcolnames(results) <- c(\"Explanatory variable\", \"Model formula (raw data)\", \"Model formula (first differences)\", \"Model formula (generalised differences)\", \"Proportion variance explained (raw data)\", \"Proportion variance explained (first differences)\", \"Proportion variance explained (generalised differences)\", \"AIC (raw data)\", \"AIC (first differences)\", \"AIC (generalised differences)\", \"AICc (raw data)\", \"AICc (first differences)\", \"AICc (generalised differences)\", \"Akaike weight (raw data)\", \"Akaike weight (first differences)\", \"Akaike weight (generalised differences)\")\r\n\tresult <- list(shared.time, var1.comp, var2.comp, var3.comp, fd.var1.comp, fd.var2.comp, fd.var3.comp, gd.var1.comp, gd.var2.comp, gd.var3.comp, lm.var2, lm.var3, lm.var2var3, fd.lm.var2, fd.lm.var3, fd.lm.var2var3, gd.lm.var2, gd.lm.var3, gd.lm.var2var3, results)\r\n\tnames(result) <- c(\"shared.time\", \"var1.comp\", \"var2.comp\", \"var3.comp\", \"fd.var1.comp\", \"fd.var2.comp\", \"fd.var3.comp\", \"gd.var1.comp\", \"gd.var2.comp\", \"gd.var3.comp\", \"lm.var2\", \"lm.var3\", \"lm.var2var3\", \"fd.lm.var2\", \"fd.lm.var3\", \"fd.lm.var2var3\", \"gd.lm.var2\", \"gd.lm.var3\", \"gd.lm.var2var3\", \"results\")\r\n\treturn(result)\r\n}\r\n\r\nmoving.average <- function(ts.holder, mav)\r\n{\r\n ts <- vector(mode=\"numeric\")\r\n for (i in 1:length(ts.holder[1, ])) ts[i] <- mean(sort(ts.holder[, i]))\r\n trimtb <- floor(mav/2)\r\n mav.vector <- vector(mode=\"numeric\", length=length(ts)-(2*trimtb))\r\n for (i in (trimtb+1):(length(ts)-trimtb)) mav.vector[(i-trimtb)] <- mean(sort(ts[(i-trimtb):(i+trimtb)]))\r\n mav.vector <- as.numeric(gsub(NaN, NA, mav.vector))\r\n mav.vector <- c(rep(NA, trimtb), mav.vector, rep(NA, trimtb))\r\n mav.vector\r\n}\r\n\r\nmoving.averagets <- function(ts, mav)\r\n{\r\n\ttrimtb <- floor(mav/2)\r\n\tmav.vector <- vector(mode=\"numeric\", length=length(ts)-(2*trimtb))\r\n\tfor (i in (trimtb+1):(length(ts)-trimtb)) mav.vector[(i-trimtb)] <- mean(sort(ts[(i-trimtb):(i+trimtb)]))\r\n\tmav.vector <- as.numeric(gsub(NaN, NA, mav.vector))\r\n\tmav.vector <- c(rep(NA, trimtb), mav.vector, rep(NA, trimtb))\r\n\tmav.vector\r\n}\r\n\r\ncorr.plot <- function(x.holder, y.holder, time, log.x=FALSE, log.y=FALSE, fd=FALSE, gd=FALSE, do.mav=FALSE, mav, xlab=\"\", ylab=\"\", main=\"\")\r\n{\r\n\ty <- x <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(x.holder[1, ])) x[i] <- mean(sort(x.holder[, i]))\r\n\tfor (i in 1:length(y.holder[1, ])) y[i] <- mean(sort(y.holder[, i]))\r\n\tif(log.x == FALSE) x1 <- as.numeric(gsub(NaN, NA, x))\r\n\tif(log.x == TRUE) x1 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, x)))))\r\n\tif(log.y == FALSE) y1 <- as.numeric(gsub(NaN, NA, y))\r\n\tif(log.y == TRUE) y1 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, y)))))\r\n\tif(fd == TRUE) {\r\n\t\tx1 <- diff(x1)\r\n\t\ty1 <- diff(y1)\r\n\t}\r\n\tif(gd == TRUE) {\r\n\t\tx1 <- gen.diff(x1, time)\r\n\t\ty1 <- gen.diff(y1, time)\r\n\t}\t\r\n\tif(do.mav == TRUE) {\r\n\t\ttrimtb <- floor(mav/2)\r\n\t\tmav.x1 <- vector(mode=\"numeric\", length=length(x1)-(2*trimtb))\r\n\t\tmav.y1 <- vector(mode=\"numeric\", length=length(y1)-(2*trimtb))\r\n\t\tfor (i in (trimtb+1):(length(x1)-trimtb)) mav.x1[(i-trimtb)] <- mean(sort(x1[(i-trimtb):(i+trimtb)]))\r\n\t\tfor (i in (trimtb+1):(length(y1)-trimtb)) mav.y1[(i-trimtb)] <- mean(sort(y1[(i-trimtb):(i+trimtb)]))\r\n\t\tmav.x1 <- as.numeric(gsub(NaN, NA, mav.x1))\r\n\t\tmav.y1 <- as.numeric(gsub(NaN, NA, mav.y1))\r\n\t\tmav.x1 <- c(rep(NA, trimtb), mav.x1, rep(NA, trimtb))\r\n\t\tmav.y1 <- c(rep(NA, trimtb), mav.y1, rep(NA, trimtb))\r\n\t\tx1 <- x1-mav.x1\r\n\t\ty1 <- y1-mav.y1\r\n\t}\r\n\tplot(x1, y1, xlab=xlab, ylab=ylab, main=main)\r\n\ttext(x=min(sort(x1)), y=max(sort(y1)), labels=paste(\"Spearman rho: \", round(cor.test(x1, y1, method=\"spearman\")$estimate, 2), \" (p = \", format(round(cor.test(x1, y1, method=\"spearman\")$p.value, 4), nsmall = 4), \")\", sep=\"\", collapse=\"\"), adj=c(0, 1))\r\n\tabline(lsfit(x1, y1)$coefficients[1], lsfit(x1, y1)$coefficients[2])\r\n}\r\n\r\nts.corr.plot <- function(var1.holder, var2.holder, var3.holder, log.var1=FALSE, log.var2=FALSE, log.var3=FALSE, var1.name, var2.name, var3.name, time)\r\n{\r\n var1 <- var2 <- var3 <- vector(mode=\"numeric\")\r\n for (i in 1:length(var1.holder[1, ])) var1[i] <- mean(sort(var1.holder[, i]))\r\n for (i in 1:length(var2.holder[1, ])) var2[i] <- mean(sort(var2.holder[, i]))\r\n for (i in 1:length(var3.holder[1, ])) var3[i] <- mean(sort(var3.holder[, i]))\r\n if(log.var1 == FALSE) var1 <- as.numeric(gsub(NaN, NA, var1))\r\n if(log.var1 == TRUE) var1 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var1)))))\r\n if(log.var2 == FALSE) var2 <- as.numeric(gsub(NaN, NA, var2))\r\n if(log.var2 == TRUE) var2 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var2)))))\r\n if(log.var3 == FALSE) var3 <- as.numeric(gsub(NaN, NA, var3))\r\n if(log.var3 == TRUE) var3 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var3)))))\r\n par(mfrow=c(2, 2))\r\n plot(var1, var2, cex=0.5, xlab=var1.name, ylab=var2.name)\r\n abline(a=lsfit(var1, var2)$coefficients[1], b=lsfit(var1, var2)$coefficients[2])\r\n text(x=min(sort(var1)), y=max(sort(var2)), paste(\"rho = \", round(cor.test(var1, var2, method=\"spearman\")$estimate, 2)), adj=c(0, 1))\r\n plot(gen.diff(var1, time), gen.diff(var2, time), cex=0.5, xlab=var1.name, ylab=var2.name)\r\n abline(a=lsfit(gen.diff(var1, time), gen.diff(var2, time))$coefficients[1], b=lsfit(gen.diff(var1, time), gen.diff(var2, time))$coefficients[2])\r\n lines(x=c(0, 0), y=c(-10, 10), col=\"grey\", lty=2)\r\n lines(x=c(-10, 10), y=c(0, 0), col=\"grey\", lty=2)\r\n text(x=min(sort(gen.diff(var1, time))), y=max(sort(gen.diff(var2, time))), paste(\"rho = \", round(cor.test(gen.diff(var1, time), gen.diff(var2, time), method=\"spearman\")$estimate, 2)), adj=c(0, 1))\r\n plot(var1, var3, cex=0.5, xlab=var1.name, ylab=var3.name)\r\n abline(a=lsfit(var1, var3)$coefficients[1], b=lsfit(var1, var3)$coefficients[2])\r\n text(x=min(sort(var1)), y=max(sort(var3)), paste(\"rho = \", round(cor.test(var1, var3, method=\"spearman\")$estimate, 2)), adj=c(0, 1))\r\n plot(gen.diff(var1, time), gen.diff(var3, time), cex=0.5, xlab=var1.name, ylab=var3.name)\r\n abline(a=lsfit(gen.diff(var1, time), gen.diff(var3, time))$coefficients[1], b=lsfit(gen.diff(var1, time), gen.diff(var3, time))$coefficients[2])\r\n lines(x=c(0, 0), y=c(-10, 10), col=\"grey\", lty=2)\r\n lines(x=c(-10, 10), y=c(0, 0), col=\"grey\", lty=2)\r\n text(x=min(sort(gen.diff(var1, time))), y=max(sort(gen.diff(var3, time))), paste(\"rho = \", round(cor.test(gen.diff(var1, time), gen.diff(var3, time), method=\"spearman\")$estimate, 2)), adj=c(0, 1))\r\n}\r\n\r\nts.diffs.plot <- function(var1.holder, var2.holder, var3.holder, log.var1=FALSE, log.var2=FALSE, log.var3=FALSE, diff.type=\"fd\", mav, time)\r\n{\r\n\tvar1 <- var2 <- var3 <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(var1.holder[1, ])) var1[i] <- mean(sort(var1.holder[, i]))\r\n\tfor (i in 1:length(var2.holder[1, ])) var2[i] <- mean(sort(var2.holder[, i]))\r\n\tfor (i in 1:length(var3.holder[1, ])) var3[i] <- mean(sort(var3.holder[, i]))\r\n\tif(log.var1 == FALSE) var1 <- as.numeric(gsub(NaN, NA, var1))\r\n\tif(log.var1 == TRUE) var1 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var1)))))\r\n\tif(log.var2 == FALSE) var2 <- as.numeric(gsub(NaN, NA, var2))\r\n\tif(log.var2 == TRUE) var2 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var2)))))\r\n\tif(log.var3 == FALSE) var3 <- as.numeric(gsub(NaN, NA, var3))\r\n\tif(log.var3 == TRUE) var3 <- as.numeric(gsub(-Inf, NA, log(as.numeric(gsub(NaN, NA, var3)))))\r\n\tif(diff.type == \"fd\") {\r\n\t\tvar1 <- diff(var1)\r\n\t\tvar2 <- diff(var2)\r\n\t\tvar3 <- diff(var3)\r\n\t}\r\n\tif(diff.type == \"gd\") {\r\n\t\tvar1 <- gen.diff(var1, time)\r\n\t\tvar2 <- gen.diff(var2, time)\r\n\t\tvar3 <- gen.diff(var3, time)\r\n\t}\r\n\tif(diff.type == \"mav\") {\r\n\t\ttrimtb <- floor(mav/2)\r\n\t\tmav.var1 <- vector(mode=\"numeric\", length=length(var1)-(2*trimtb))\r\n\t\tmav.var2 <- vector(mode=\"numeric\", length=length(var2)-(2*trimtb))\r\n\t\tmav.var3 <- vector(mode=\"numeric\", length=length(var3)-(2*trimtb))\r\n\t\tfor (i in (trimtb+1):(length(var1)-trimtb)) mav.var1[(i-trimtb)] <- mean(sort(var1[(i-trimtb):(i+trimtb)]))\r\n\t\tfor (i in (trimtb+1):(length(var2)-trimtb)) mav.var2[(i-trimtb)] <- mean(sort(var2[(i-trimtb):(i+trimtb)]))\r\n\t\tfor (i in (trimtb+1):(length(var3)-trimtb)) mav.var3[(i-trimtb)] <- mean(sort(var3[(i-trimtb):(i+trimtb)]))\r\n\t\tmav.var1 <- as.numeric(gsub(NaN, NA, mav.var1))\r\n\t\tmav.var2 <- as.numeric(gsub(NaN, NA, mav.var2))\r\n\t\tmav.var3 <- as.numeric(gsub(NaN, NA, mav.var3))\r\n\t\tmav.var1 <- c(rep(NA, trimtb), mav.var1, rep(NA, trimtb))\r\n\t\tmav.var2 <- c(rep(NA, trimtb), mav.var2, rep(NA, trimtb))\r\n\t\tmav.var3 <- c(rep(NA, trimtb), mav.var3, rep(NA, trimtb))\r\n\t\tvar1 <- var1-mav.var1\r\n\t\tvar2 <- var2-mav.var2\r\n\t\tvar3 <- var3-mav.var3\r\n\t}\r\n\tpar(mfrow=c(2, 1))\r\n\tif(diff.type == \"fd\") {\r\n\t\ttime <- time[1:(length(time)-1)]+diff(time)/2\r\n\t\tplot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var2))^2)), max(sqrt(sort(c(var1, var2))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"First diff.\")\r\n\t\tpoints(time, var2, type=\"l\")\r\n\t}\r\n\tif(diff.type == \"gd\") {\r\n\t\ttime <- time[1:(length(time)-1)]+diff(time)/2\r\n\t\tplot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var2))^2)), max(sqrt(sort(c(var1, var2))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"Gen. diff.\")\r\n\t\tpoints(time, var2, type=\"l\")\r\n\t}\r\n\tif(diff.type == \"mav\") {\r\n\t\tplot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var2))^2)), max(sqrt(sort(c(var1, var2))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"Moving average detrended\")\r\n\t\tpoints(time, var2, type=\"l\")\r\n\t}\r\n\tlines(x=c(max(time)+20, -20), y=c(0, 0), col=\"grey\", lty=2)\r\n\tif(diff.type == \"fd\") plot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var3))^2)), max(sqrt(sort(c(var1, var3))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"First diff.\")\r\n\tif(diff.type == \"gd\") plot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var3))^2)), max(sqrt(sort(c(var1, var3))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"Gen. diff.\")\r\n\tif(diff.type == \"mav\") plot(time, var1, xlim=c(max(time), 0), ylim=c(-max(sqrt(sort(c(var1, var3))^2)), max(sqrt(sort(c(var1, var3))^2))), type=\"l\", col=\"grey\", xlab=\"Time (Ma)\", ylab=\"Moving average detrended\")\r\n\tpoints(time, var3, type=\"l\")\r\n\tlines(x=c(max(time)+20, -20), y=c(0, 0), col=\"grey\", lty=2)\r\n}\r\n\r\nparse.nexustotnt <- function(file)\r\n{\r\n\tX <- scan(file = file, what = \"\", sep = \"\\n\", quiet = TRUE) # Read in NEXUS file\r\n\t# Remove trees:\r\n\tdeleteline <- grep(\"BEGIN TREES\", X)-1\r\n\tX <- X[1:deleteline]\r\n\t# Replace #NEXUS with xread:\r\n\tnexusline <- grep(\"NEXUS\", X)\r\n\tX[nexusline] <- \"xread\"\r\n\t# Replace [!...] with '...':\r\n\ttextline <- grep(\"[\", X, fixed=TRUE)\r\n\tX[textline] <- gsub(\"[!\", \"'\", X[textline], fixed=TRUE)\r\n\tX[textline] <- gsub(\"]\", \"'\", X[textline], fixed=TRUE)\r\n\t# Replace DATA block with char, ntax:\r\n\tntaxline <- grep(\"NTAX\", X)\r\n\tX[ntaxline] <- gsub(\"\\tDIMENSIONS NTAX=\", \"\", X[ntaxline])\r\n\tX[ntaxline] <- gsub(\"NCHAR=\", \"\", X[ntaxline])\r\n\tX[ntaxline] <- gsub(\";\", \"\", X[ntaxline])\r\n\tX[ntaxline] <- paste(strsplit(X[ntaxline], \" \")[[1]][2], strsplit(X[ntaxline], \" \")[[1]][1], sep=\" \")\r\n\t# Delete now redundant starting lines:\r\n\tX <- X[-(3:(ntaxline-1))]\r\n\tformatline <- grep(\"FORMAT\", X)\r\n\tmatrixline <- grep(\"MATRIX\", X)\r\n\tsemicolonline <- grep(\";\", X, fixed=TRUE)[grep(TRUE, grep(\";\", X, fixed=TRUE) > matrixline)][1]\r\n\tif(length(grep(\"(\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE)) > 0) X[(matrixline+1):(semicolonline-1)] <- gsub(\"(\", \"[\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE) # Replace polymorphic ( with [\r\n\tif(length(grep(\")\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE)) > 0) X[(matrixline+1):(semicolonline-1)] <- gsub(\")\", \"]\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE) # Replace polymorphic ) with ]\r\n\tmissingchar <- strsplit(strsplit(X[formatline], \"MISSING=\")[[1]][2], \"\")[[1]][1] # Find character used for missing states\r\n\tgapchar <- strsplit(strsplit(X[formatline], \"GAP=\")[[1]][2], \"\")[[1]][1] # Find character used for gaps\r\n\tif (missingchar != \"?\") X[(matrixline+1):(semicolonline-1)] <- gsub(missingchar, \"?\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE) # Replace with ? if not ?\r\n\tif (gapchar != \"-\") X[(matrixline+1):(semicolonline-1)] <- gsub(gapchar, \"-\", X[(matrixline+1):(semicolonline-1)], fixed=TRUE) # Replace with - if not -\r\n\tX <- X[-matrixline]\r\n\tX <- X[-formatline]\r\n\t# Re-find end of matrix:\r\n\tsemicolonline <- grep(\";\", X, fixed=TRUE)[1]\r\n\t# Search for ordered character list:\r\n\tif (length(grep(\"TYPESET\", X)) > 0) {\r\n\t\torderingline <- grep(\"TYPESET\", X)\r\n\t\tX[orderingline] <- gsub(\"\\tTYPESET * UNTITLED = \", \"\", X[orderingline], fixed=TRUE) # Clean up\r\n\t\tX[orderingline] <- gsub(\";\", \"\", X[orderingline], fixed=TRUE) # Clean up\r\n\t\tunordered <- strsplit(X[orderingline], \", \")[[1]][1] # Get unordered list\r\n\t\tordered <- strsplit(X[orderingline], \", \")[[1]][2] # Get ordered list\r\n\t\tunordered <- gsub(\"unord: \", \"\", unordered, fixed=TRUE) # Clean up\r\n\t\tordered <- gsub(\"ord: \", \"\", ordered, fixed=TRUE) # Clean up\r\n\t\tif(length(grep(\"-\", unordered)) > 0) {\r\n\t\t\twhile(length(grep(\"-\", unordered)) > 0) {\r\n\t\t\t\tunordered <- strsplit(unordered, \" \")[[1]]\r\n\t\t\t\tunordered[grep(\"-\", unordered)[1]] <- paste(c(strsplit(unordered[grep(\"-\", unordered)[1]], \"-\")[[1]][1]:strsplit(unordered[grep(\"-\", unordered)[1]], \"-\")[[1]][2]), collapse=\" \")\r\n\t\t\t\tunordered <- paste(unordered, collapse=\" \")\r\n\t\t\t}\r\n\t\t}\r\n\t\tif(length(grep(\"-\", ordered)) > 0) {\r\n\t\t\twhile(length(grep(\"-\", ordered)) > 0) {\r\n\t\t\t\tordered <- strsplit(ordered, \" \")[[1]]\r\n\t\t\t\tordered[grep(\"-\", ordered)[1]] <- paste(c(strsplit(ordered[grep(\"-\", ordered)[1]], \"-\")[[1]][1]:strsplit(ordered[grep(\"-\", ordered)[1]], \"-\")[[1]][2]), collapse=\" \")\r\n\t\t\t\tordered <- paste(ordered, collapse=\" \")\r\n\t\t\t}\r\n\t\t}\r\n\t\torderline <- paste(\"ccode +\", ordered, \" -\", unordered, \";\", sep=\"\", collapse=\"\")\r\n\t\tendline <- c(orderline, \"proc/;\")\r\n\t} else {\r\n\t\torderline <- paste(\"ccode -\", paste(1:strsplit(X[3], \" \")[[1]][1], collapse=\" \"), \";\", sep=\"\", collapse=\"\")\r\n\t\tendline <- c(orderline, \"proc/;\")\r\n\t}\r\n\tX <- X[-((semicolonline+1):length(X))]\r\n\tX <- c(X, endline)\r\n\treturn(X)\r\n}\r\n\r\npath.lengths <- function(phy)\r\n{\r\n\tntips <- length(phy$tip.label)\r\n\tpathlengths <- vector(mode=\"numeric\")\r\n\tfor (i in 1:ntips) {\r\n\t\ttaxon <- i\r\n\t\tpathedges <- vector(mode=\"numeric\")\r\n\t\tpathedges[1] <- grep(TRUE, phy$edge[, 2] == i)\r\n\t\twhile (phy$edge[pathedges[length(pathedges)], 1] != (ntips+1)) {\r\n\t\t\ti <- grep(TRUE, phy$edge[, 2] == phy$edge[pathedges[length(pathedges)], 1])\r\n\t\t\tpathedges <- c(pathedges, i)\r\n\t\t}\r\n\t\tpathlengths[taxon] <- sum(phy$edge.length[pathedges])\r\n\t}\r\n\tnames(pathlengths) <- phy$tip.label\r\n\treturn(pathlengths)\r\n}\r\n\r\npat.dist.phylo <- function(tree, comp, nchar)\r\n{\r\n\trequire(ape)\r\n\tnt <- Ntip(tree)\r\n\tcchar <- nchar-comp[tree$tip.label, 1]\r\n\tfor (i in 1:length(tree$edge[, 1])) {\r\n\t\tif (tree$edge[i, 2] <= max(nt)) tree$edge.length[i] <- tree$edge.length[i]/cchar[tree$edge[i, 2]]\r\n\t\tif (tree$edge[i, 2] > max(nt)) tree$edge.length[i] <- tree$edge.length[i]/nchar\r\n\t}\r\n\treturn(tree)\r\n}\r\n\r\nSCM <- function(names, ages, tbins)\r\n{\r\n\tout <- matrix(NA, nrow=length(unique(names)), ncol=length(tbins)-1)\r\n\trownames(out) <- sort(unique(names))\r\n\tfor (i in 1:length(out[, 1])) {\r\n\t\tdates <- sort(ages[grep(TRUE, names == rownames(out)[i])])\r\n\t\tfor(j in 1:length(dates)) {\r\n\t\t\tout[i, intersect(grep(TRUE, dates[j] <= tbins[1:(length(tbins)-1)]), grep(TRUE, dates[j] > tbins[2:length(tbins)]))] <- 1\r\n\t\t}\r\n\t\tstart <- min(grep(TRUE, out[i, ] == 1))\r\n\t\tstop <- max(grep(TRUE, out[i, ] == 1))\r\n\t\tout[i, (start+grep(TRUE, is.na(out[i, start:stop]))-1)] <- 0\r\n\t}\r\n\tout.adj <- out\r\n\tfor (i in length(out.adj[, 1]):1) {\r\n\t\tout.adj[i, min(grep(TRUE, out.adj[i, ] == 1))] <- NA\r\n\t\tout.adj[i, max(grep(TRUE, out[i, ] == 1))] <- NA\r\n\t\tif(length(sort(out.adj[i, ])) == 0) out.adj <- out.adj[-i, ]\r\n\t}\r\n\tSCM.taxa.adj <- SCM.tbins.adj <- SCM.taxa <- SCM.tbins <- vector(mode=\"numeric\")\r\n\tfor(i in 1:length(out[1, ])) {\r\n\t\tSCM.tbins[i] <- mean(sort(out[, i]))\r\n\t}\r\n\tfor(i in 1:length(out[, 1])) {\r\n\t\tSCM.taxa[i] <- mean(sort(out[i, ]))\r\n\t}\r\n\tfor(i in 1:length(out.adj[1, ])) {\r\n\t\tSCM.tbins.adj[i] <- mean(sort(out.adj[, i]))\r\n\t}\r\n\tfor(i in 1:length(out.adj[, 1])) {\r\n\t\tSCM.taxa.adj[i] <- mean(sort(out.adj[i, ]))\r\n\t}\r\n\tnames(SCM.taxa) <- rownames(out)\r\n\tnames(SCM.taxa.adj) <- rownames(out.adj)\r\n\ttbin.midpoints <- ((tbins[1:(length(tbins)-1)]-tbins[2:length(tbins)])/2)+tbins[2:length(tbins)]\r\n\tresult <- list(out, SCM.tbins, SCM.taxa, SCM.tbins.adj, SCM.taxa.adj, tbin.midpoints)\r\n\tnames(result) <- c(\"out\", \"SCM.tbins\", \"SCM.taxa\", \"SCM.tbins.adj\", \"SCM.taxa.adj\", \"tbin.midpoints\")\r\n\treturn(result)\r\n}\r\n\r\nmake.phan <- function(x, y, y_label, quartz=TRUE, main=\"\")\r\n{\r\n\ty.max <- max(sort(y))\r\n\ty.min <- 0\r\n\tyoung.bin <- -8 # Offset for youngest bin label...allows for age < 0 to center label\r\n\tplot.min <- y.min-.03*(y.max-y.min)\r\n\tseg.min <- y.min-.07*(y.max-y.min)\r\n\ttext.min <- y.min-.036*(y.max-y.min)\r\n\ttime.boundaries <- c(23.03, 65.5, 145.5, 199.6, 251, 299, 359.2, 416, 443.7)\r\n\tinterval.names <- c(\"Ng\", \"Pg\", \"K\", \"J\", \"Tr\", \"P\", \"C\", \"D\", \"S\")\r\n\tinterval.midpoint <- time.boundaries-diff(c(0, time.boundaries))/2\r\n\tplot(1, 1, xlim=c(max(time.boundaries), 0), ylim=c(plot.min, y.max), type=\"n\", xlab=\"Geologic time (Ma)\", ylab=y_label, main=main)\r\n\tabline(h=y.min)\r\n\tsegments(c(time.boundaries, 0), y.min, c(time.boundaries, 0), seg.min)\r\n\ttext(interval.midpoint, text.min, labels=interval.names)\r\n\tlines(x, y)\r\n}\r\n\r\nrandomisation.phylo <- function(tree, permutations)\r\n{\r\n\tnchang <- sum(tree$edge.length)\r\n\tpermat <- matrix(0, nrow=length(tree$edge.length), ncol=permutations)\r\n\tbrk <- vector(mode=\"numeric\", length=length(tree$edge.length))\r\n\tbrk[1] <- 1/length(tree$edge.length)\r\n\tfor (i in 2:length(brk)) brk[i] <- brk[i-1]+1/length(tree$edge.length)\r\n\tfor (i in 1:permutations) {\r\n\t\trandno <- runif(nchang, min=0, max=1)\r\n\t\trandno <- sort(randno)\r\n\t\tfor (j in 1:length(tree$edge.length)) {\r\n\t\t\tpermat[j, i] <- length(grep(TRUE, randno <= brk[j]))\r\n\t\t\trandno <- randno[-grep(TRUE, randno <= brk[j])]\r\n\t\t}\r\n\t}\r\n\tprobs <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(tree$edge.length)) {\r\n\t\tprobs[i] <- wilcox.test(tree$edge.length[i], permat[i, ], alternative=\"greater\")$p.value\r\n\t}\r\n\tprobs\r\n}\r\n\r\nrandombranch.phylo <- function(tree, ttree, comp, nchar, permutations)\r\n{\r\n\tcharcorr <- c((comp[ttree$tip.label, ]/nchar), rep(1, Nnode(ttree)))\r\n\tttree$edge.length <- charcorr[tree$edge[, 2]]*ttree$edge.length\r\n\tnchang <- sum(tree$edge.length)\r\n\tpermat <- matrix(0, nrow=length(tree$edge.length), ncol=permutations)\r\n\tbrk <- vector(mode=\"numeric\", length=length(ttree$edge.length))\r\n\tbrk[1] <- ttree$edge.length[1]/length(ttree$edge.length)\r\n\tfor (i in 2:length(brk)) brk[i] <- brk[i-1]+ttree$edge.length[i]/length(ttree$edge.length)\r\n\tbrk <- brk/(sum(ttree$edge.length)/length(ttree$edge.length))\r\n\tfor (i in 1:permutations) {\r\n\t\trandno <- runif(nchang, min=0, max=1)\r\n\t\trandno <- sort(randno)\r\n\t\tfor (j in 1:length(tree$edge.length)) {\r\n\t\t\tbreaker <- length(grep(TRUE, randno <= brk[j]))\r\n\t\t\tpermat[j, i] <- breaker\r\n\t\t\tif (breaker > 0) randno <- randno[-(1:breaker)]\r\n\t\t}\r\n\t}\r\n\tsigs <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(tree$edge.length)) {\r\n\t\tifelse(tree$edge.length[i] > sort(permat[i, ])[ceiling(0.95*length(permat[i, ]))], sigs[i] <- 1, sigs[i] <- 0)\r\n\t}\r\n\tsigs\r\n}\r\n\r\nget.branches <- function(node, froms, tos)\r\n{\r\n\tbranches <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(froms)) {\r\n\t\tfor (j in 1:length(node)) {\r\n\t\t\tbranches[length(branches)+1:length(branches)+2] <- grep(node[j], froms)\r\n\t\t\tbranches <- sort(unique(branches))\r\n\t\t}\r\n\t\tnode <- unique(c(node, froms[sort(match(tos[branches], froms))]))\r\n\t}\r\n\tbranches <- vector(mode=\"numeric\")\r\n\tfor (i in 1:length(node)) {\r\n\t\tbranches[length(branches)+1:length(branches)+2] <- grep(node[i], froms)\r\n\t\tbranches <- sort(unique(branches))\r\n\t}\r\n\tbranches\r\n}\r\n\r\nbroken.stick <- function(timeseries, time)\r\n{\r\n\trequire(stats)\r\n\trequire(nlme)\r\n\trequire(paleoTS)\r\n\tif(length(grep(\"1 1\", paste(diff(grep(TRUE, diff(timeseries) == 0)), collapse=\" \"))) > 0) { # Case if 4 values in a row are the same\r\n\t\tprint(\"Some bins deleted due to consecutive equal values (confounding model comparison)\")\r\n\t\tdeletes <- vector(mode=\"numeric\")\r\n\t\tfor(i in 2:length(timeseries)) {\r\n\t\t\tif(timeseries[i-1] == timeseries[i]) deletes[length(deletes)+1] <- i\r\n\t\t}\r\n\t\ttimeseries <- timeseries[-deletes]\r\n\t\ttime <- time[-deletes]\r\n\t}\r\n\ttslength <- length(timeseries)\r\n\tonestick <- lm(timeseries~time) # First fit single stick model\r\n\tonestickAICc <- AICc(onestick, tslength)\r\n\tif(length(timeseries) < 15 && length(timeseries) >= 10) print(\"Time series too short (too few bins) for three stick model\") # Case if broken stick pointless\r\n\tif(length(timeseries) < 10) print(\"Time series too short (too few bins) for two or three stick model\") # Case if broken stick pointless\r\n\tif(length(timeseries) >= 10) { # Case if two stick doable\r\n\t\tsplits <- vector(length=tslength-9, mode=\"numeric\")\r\n\t\tfor (i in 1:(tslength-9)) splits[i] <- (4+i) # Find all splits of at least five bins in length\r\n\t\tAICcsplits <- splits # Set up vector to store AICc of two stick models\r\n\t\tfor(i in 1:length(splits)) { # Fill the above\r\n\t\t\tfirstsplit <- 1:splits[i]\r\n\t\t\tsecondsplit <- (splits[i]+1):tslength\r\n\t\t\tAICcsplits[i] <- AICc(lm(timeseries[firstsplit]~time[firstsplit]), length(firstsplit))+AICc(lm(timeseries[secondsplit]~time[secondsplit]), length(secondsplit))\r\n\t\t}\r\n\t\tAICcsplits <- as.numeric(AICcsplits)\r\n\t\ttwostickAICc <- min(AICcsplits)\r\n\t\tbesttwostick <- splits[grep(TRUE, AICcsplits == min(AICcsplits))] # Establish split with lowest combined AICc\r\n\t\ttwostickone <- lm(timeseries[1:besttwostick]~time[1:besttwostick]) # Fit first stick of two stick model\r\n\t\ttwosticktwo <- lm(timeseries[(besttwostick+1):tslength]~time[(besttwostick+1):tslength]) # Fit second stick of two stick model\r\n\t}\r\n\tif(length(timeseries) >= 15) { # Case if three stick doable\r\n\t\tsplits <- vector(mode=\"character\")\r\n\t\tfor(i in 5:(tslength-10)) {\r\n\t\t\tfor(j in (i+5):(tslength-5)) {\r\n\t\t\t\tsplits[(length(splits)+1)] <- paste(i, \":\", j, sep=\"\", collapse=\"\")\r\n\t\t\t}\r\n\t\t}\r\n\t\tAICcsplits <- splits\r\n\t\tfor(i in 1:length(splits)) {\r\n\t\t\tAICcsplits[i] <- AICc(lm(timeseries[1:strsplit(splits[i], \":\")[[1]][1]]~time[1:strsplit(splits[i], \":\")[[1]][1]]), length(1:strsplit(splits[i], \":\")[[1]][1]))+AICc(lm(timeseries[(as.numeric(strsplit(splits[i], \":\")[[1]][1])+1):strsplit(splits[i], \":\")[[1]][2]]~time[(as.numeric(strsplit(splits[i], \":\")[[1]][1])+1):strsplit(splits[i], \":\")[[1]][2]]), length((as.numeric(strsplit(splits[i], \":\")[[1]][1])+1):strsplit(splits[i], \":\")[[1]][2]))+AICc(lm(timeseries[(as.numeric(strsplit(splits[i], \":\")[[1]][2])+1):tslength]~time[(as.numeric(strsplit(splits[i], \":\")[[1]][2])+1):tslength]), length((as.numeric(strsplit(splits[i], \":\")[[1]][2])+1):tslength))\r\n\t\t}\r\n\t\tAICcsplits <- as.numeric(AICcsplits)\r\n\t\tthreestickAICc <- min(AICcsplits)\r\n\t\tbestthreestick <- splits[grep(TRUE, AICcsplits == min(AICcsplits))] # Establish split with lowest combined AICc\r\n\t\tthreestickone <- lm(timeseries[1:as.numeric(strsplit(bestthreestick, \":\")[[1]][1])]~time[1:as.numeric(strsplit(bestthreestick, \":\")[[1]][1])]) # Fit first stick of three stick model\r\n\t\tthreesticktwo <- lm(timeseries[(as.numeric(strsplit(bestthreestick, \":\")[[1]][1])+1):as.numeric(strsplit(bestthreestick, \":\")[[1]][2])]~time[(as.numeric(strsplit(bestthreestick, \":\")[[1]][1])+1):as.numeric(strsplit(bestthreestick, \":\")[[1]][2])]) # Fit second stick of three stick model\r\n\t\tthreestickthree <- lm(timeseries[(as.numeric(strsplit(bestthreestick, \":\")[[1]][2])+1):tslength]~time[(as.numeric(strsplit(bestthreestick, \":\")[[1]][2])+1):tslength]) # Fit second stick of three stick model\r\n\t}\r\n\tmodelcomp <- cbind(c(onestickAICc, twostickAICc, threestickAICc), round(akaike.wts(c(onestickAICc, twostickAICc, threestickAICc)), 3))\r\n\trownames(modelcomp) <- c(\"One stick\", \"Two stick\", \"Three stick\")\r\n\tcolnames(modelcomp) <- c(\"AICc\", \"Akaike wt\")\r\n\tresult <- list(timeseries, time, onestick, twostickone, twosticktwo, threestickone, threesticktwo, threestickthree, besttwostick, bestthreestick, modelcomp)\r\n\tnames(result) <- c(\"y.values\", \"x.values\", \"onestick.model\", \"twostick.model1\", \"twostick.model2\", \"threestick.model1\", \"threestick.model2\", \"threestick.model3\", \"twostick.split\", \"threestick.split\", \"model.comparison\")\r\n\treturn(result)\r\n}\r\n\r\nmars <- function(x, y)\r\n{\r\n\trequire(earth)\r\n\tAICs <- vector(mode=\"numeric\")\r\n\tfor(i in 3:21) { # Find optimal number of knots using AIC\r\n\t\tmarsout <- earth(x, y, nk=i)\r\n\t\tcuts <- sort(x[match(sort(unique(marsout$cuts)), x)])\r\n\t\trss <- marsout$rss\r\n\t\tAICs[(i-2)] <- (2*(length(cuts)+1))+(length(x)*log(rss))\r\n\t}\r\n\tnk <- max(grep(TRUE, min(AICs) == AICs))+2\r\n\tmarsout <- earth(x, y, nk=nk)\r\n\tprediction <- y-marsout$residuals\r\n\tcuts <- sort(x[match(sort(unique(marsout$cuts)), x)])\r\n\tresiduals <- marsout$residuals\r\n\trss <- marsout$rss\r\n\tslopes <- vector(mode=\"numeric\", length=length(cuts)+1)\r\n\tbreaks <- sort(unique(c(1, match(cuts, x), length(x))))\r\n\tfor(i in 2:length(breaks)) slopes[(i-1)] <- prediction[breaks[i]]-prediction[breaks[(i-1)]]\r\n\tslopes <- round(slopes, 4)\r\n\tAIC <- (2*(length(cuts)+1))+(length(x)*log(rss))\r\n\tlinearRSS <- sum((y-((lsfit(x, y)$coefficients[2]*x)+lsfit(x, y)$coefficients[1]))^2)\r\n\tlinearAIC <- 4+(length(x)*log(linearRSS))\r\n\tifelse(linearAIC <= AIC, best.model <- \"Linear\", best.model <- paste(\"MARS with \", length(cuts), \" hinges\", sep=\"\", collapse=\"\"))\r\n\tresult <- list(prediction, cuts, residuals, rss, slopes, AIC, linearRSS, linearAIC, best.model)\r\n\tnames(result) <- c(\"prediction\", \"cuts\", \"residuals\", \"rss\", \"slopes\", \"AIC\", \"linearRSS\", \"linearAIC\", \"best.model\")\r\n\treturn(result)\r\n}\r\n\r\n\r\n\r\n\r\n# sqs version 2.0 by John Alroy\r\n# performs shareholder quorum subsampling on an array of specimen counts\r\n# can be used to perform classical rarefaction instead of SQS\r\n# written 29 July 2010; version 2.0 completed 14 February 2011\r\n# changes in version 2.0: improved subsampling algorithm; including the dominant \r\n# taxon is now the default; improved reporting of errors and basic statistics\r\n# warning: do not use this program with taxonomic occurrence data drawn from\r\n# multiple published references because it is not designed to count\r\n# single-reference taxa or adjust for long taxonomic lists\r\n# warning: version 1.0 yields estimates that are downwards-biased when q < 0.6\r\n# and abundance distributions are highly uneven\r\n#\r\n# Modified by GTL 22/03/11 to include single publication occurrence correction\r\nsqs <- function(ab, q, trials, method, dominant, p.1)\t{\r\n\t\r\n\tparams <- array(data=NA, dim=0, dimnames=c(\"raw richness\"))\r\n\tif (missing(trials))\t{\r\n\t\ttrials <- 100\r\n\t}\r\n\tif (missing(method))\t{\r\n\t\tmethod <- \"\"\r\n\t} else if (method != \"\" && method != \"rarefaction\" && method != \"CR\")\t{\r\n\t\treturn(print('If the method is rarefaction enter method=\"rarefaction\" or \"CR\"', quote=F))\r\n\t}\r\n\tif ((q <= 0 || q >= 1) && method != \"rarefaction\" && method != \"CR\")\t{\r\n\t\treturn(print(\"If the method is SQS the quota must be greater than zero and less than one\", quote=F))\r\n\t} else if (q < 1 && (method == \"rarefaction\" || method == \"CR\"))\t{\r\n\t\treturn(print(\"If the method is rarefaction the quota must be an integer\", quote=F))\r\n\t}\r\n\tif (missing(dominant))\t{\r\n\t\tdominant <- 0\r\n\t} else if (dominant != \"\" && dominant != \"exclude\" && dominant != \"no\")\t{\r\n\t\treturn(print('To exclude the dominant taxon, enter dominant=\"exclude\" or \"no\"', quote=F))\r\n\t}\r\n\t\r\n\t# compute basic statistics\r\n\tspecimens <- sum(ab)\r\n\tsingletons <- 0\r\n\tdoubletons <- 0\r\n\thighest <- 0\r\n\tfor (i in 1:length(ab))\t{\r\n\t\tif (ab[i] == 1)\t{\r\n\t\t\tsingletons <- singletons + 1\r\n\t\t} else if (ab[i] == 2)\t{\r\n\t\t\tdoubletons <- doubletons + 1\r\n\t\t}\r\n\t\tif (ab[i] > highest)\t{\r\n\t\t\thighest <- ab[i]\r\n\t\t\tmostfrequent <- i\r\n\t\t}\r\n\t}\r\n\t\r\n\tu <- 1 - singletons / specimens\r\n\t\r\n\t# GTL modification starts\r\n\tif (missing(p.1) && dominant == \"exclude\") {\r\n\t\tu <- 1 - singletons/(specimens - highest)\r\n\t}\r\n\tif (missing(p.1) && dominant == \"no\") {\r\n\t\tu <- 1 - singletons/(specimens - highest)\r\n\t}\r\n\tif (!missing(p.1)) {\r\n\t\tu <- (sum(ab) - p.1)/sum(ab)\r\n\t}\r\n\t# GTL modification ends\r\n\r\n\tif (u == 0)\t{\r\n\t\treturn(print(\"Coverage is zero because all taxa are singletons\", quote=F))\r\n\t}\r\n\t\r\n\t# compute raw taxon frequencies (temporarily)\r\n\tfreq <- ab / specimens\r\n\t\r\n\t# standard recursive equation for Fishers alpha\r\n\talpha <- 10\r\n\toldalpha <- 0\r\n\twhile (abs(alpha - oldalpha) > 0.0000001)\t{\r\n\t\toldalpha <- alpha\r\n\t\talpha <- length(ab) / log(1 + specimens/alpha)\r\n\t}\r\n\r\n\tparams[\"raw richness\"] <- length(ab)\r\n\tparams[\"Good's u\"] <- u\r\n\tparams[\"subsampled richness\"] <- NA\r\n\tparams[\"subsampled u\"] <- NA\r\n\tparams[\"Chao 1\"] <- length(ab) + singletons**2/(2* doubletons)\r\n\tparams[\"subsampled Chao 1\"] <- NA\r\n\t# governing parameter of the geometric series distribution\r\n\tparams[\"k\"] <- abs(lm(log(sort(freq)) ~ c(1:length(freq)))$coefficients[2])\r\n\tparams[\"Fisher's alpha\"] <- alpha\r\n\tparams[\"Shannon's H\"] <- -1 * sum(freq * log(freq))\r\n\tparams[\"Hurlbert's PIE\"] <- (1 - sum(freq**2)) * length(ab) / (length(ab) - 1)\r\n\tparams[\"dominance\"] <- highest / specimens\r\n\tparams[\"specimens\"] <- specimens\r\n\tparams[\"singletons\"] <- singletons\r\n\tparams[\"doubletons\"] <- doubletons\r\n\tparams[\"specimens drawn\"] <- 0\r\n\r\n\tif (dominant != \"exclude\" && dominant != \"no\")\t{\r\n\t\thighest <- 0\r\n\t\tmostfrequent <- 0\r\n\t}\r\n\r\n\t# return if the quorum target is higher than overall coverage\r\n\tif ((q > u && method != \"rarefaction\" && method != \"CR\") || (q >= sum(ab)))\t{\r\n\t\treturn(params)\r\n\t}\r\n\t# return if the rarefaction quota is equal to or higher than the\r\n\t# specimen count\r\n\tif (method == \"rarefaction\" && q >= specimens - highest)\t{\r\n\t\treturn(params)\r\n\t}\r\n\r\n\t# compute adjusted taxon frequencies\r\n\tfreq <- ab * u / (specimens - highest)\r\n\r\n\t# create an array in which each cell corresponds to one specimen\r\n\tids <- array()\r\n\tn <- 0\r\n\tfor (i in 1:length(ab))\t{\r\n\t\tfor (j in 1:ab[i])\t{\r\n\t\t\tn <- n + 1\r\n\t\t\tids[n] <- i\r\n\t\t}\r\n\t}\r\n\r\n\t# subsampling trial loop\r\n\t# s will be the subsampled taxon count\r\n\ts <- array(rep(0, trials))\r\n\tsubsingle <- array(rep(0, trials))\r\n\tsubdouble <- array(rep(0, trials))\r\n\tsubchao <- array(rep(0, trials))\r\n\tmostfrequentdrawn <- 0\r\n\tfor (trial in 1:trials)\t{\r\n\t\tpool <- ids\r\n\t\tleft <- length(pool)\r\n\t\tseen <- array(data=rep(0, length(ab)))\r\n\t\tsubfreq <- array(rep(0, length(ab)))\r\n\t\tif (method != \"rarefaction\" && method != \"CR\")\t{\r\n\t\t\tudrawn <- 0\r\n\t\t\twhile (udrawn < q)\t{\r\n\t\t\t\t# draw a specimen\r\n\t\t\t\tx <- floor(runif(1, min=1, max=left+1))\r\n\t\t\t\t# add to frequency and taxon sums if species has\r\n\t\t\t\t# not been drawn previously\r\n\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] + 1\r\n\t\t\t\tif (seen[pool[x]] == 0)\t{\r\n\t\t\t\t\tif (pool[x] != mostfrequent)\t{\r\n\t\t\t\t\t\tudrawn <- udrawn + freq[pool[x]]\r\n\t\t\t\t\t}\r\n\t\t\t\t\tseen[pool[x]] <- 1\r\n\t\t\t\t\t# randomly throw back some draws that put the sum over q\r\n\t\t\t\t\t# (improved algorithm added in version 2.0)\r\n\t\t\t\t\tplus <- 1\r\n\t\t\t\t\tif (udrawn > q)\t{\r\n\t\t\t\t\t\tplus <- 1 - q / udrawn\r\n\t\t\t\t\t}\r\n\t\t\t\t\tif (runif(1) <= plus)\t{\r\n\t\t\t\t\t\ts[trial] <- s[trial] + 1\r\n\t\t\t\t\t} else\t{\r\n\t\t\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] - 1\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\t# decrease pool of specimens not yet drawn\r\n\t\t\t\tpool[x] <- pool[left]\r\n\t\t\t\tleft <- left - 1\r\n\t\t\t}\r\n\t\t} else\t{\r\n\t\t\ti <- 0\r\n\t\t\tdraws <- 0\r\n\t\t\twhile (i < q)\t{\r\n\t\t\t\tdraws <- draws + 1\r\n\t\t\t\tx <- floor(runif(1, min=1, max=length(ids)-draws+2))\r\n\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] + 1\r\n\t\t\t\tif (pool[x] != mostfrequent)\t{\r\n\t\t\t\t\ti <- i + 1\r\n\t\t\t\t}\r\n\t\t\t\tif (seen[pool[x]] == 0)\t{\r\n\t\t\t\t\tseen[pool[x]] <- 1\r\n\t\t\t\t\ts[trial] <- s[trial] + 1\r\n\t\t\t\t}\r\n\t\t\t\tpool[x] <- pool[length(ids)-draws+1]\r\n\t\t\t}\r\n\t\t}\r\n\t\tfor (i in 1:length(ab))\t{\r\n\t\t\tif (subfreq[i] == 1 && i != mostfrequent)\t{\r\n\t\t\t\tsubsingle[trial] <- subsingle[trial] + 1\r\n\t\t\t} else if (subfreq[i] == 2 && i != mostfrequent)\t{\r\n\t\t\t\tsubdouble[trial] <- subdouble[trial] + 1\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (subsingle[trial] > 0 && subdouble[trial] > 0)\t{\r\n\t\t\tsubchao[trial] <- s[trial] + subsingle[trial]**2/(2*subdouble[trial])\r\n\t\t} else\t{\r\n\t\t\tsubchao[trial] <- s[trial]\r\n\t\t}\r\n\t\tparams[\"specimens drawn\"] <- params[\"specimens drawn\"] + sum(subfreq)\r\n\t\tif (mostfrequent != 0)\t{\r\n\t\t\tmostfrequentdrawn <- mostfrequentdrawn + subfreq[mostfrequent]\r\n\t\t}\r\n\t}\r\n\tparams[\"specimens drawn\"] <- params[\"specimens drawn\"] / trials\r\n\t# compute vector of non-zero counts\r\n\toptions(warn=-1)\r\n\ts2 <- sort(sqrt(s-1))^2+1\r\n\toptions(warn=0)\r\n\t# compute geometric mean\r\n\tparams[\"subsampled richness\"] <- exp(mean(log(s2))) * length(s2)/length(s)\r\n\t# use of arithmetic means to compute Goods u is adequate\r\n\tmostfrequentdrawn <- mostfrequentdrawn / trials\r\n\tparams[\"subsampled u\"] <- 1 - mean(subsingle) / (params[\"specimens drawn\"] - mostfrequentdrawn)\r\n\tparams[\"subsampled Chao 1\"] <- exp(mean(log(subchao)))\r\n\treturn(params)\r\n\t\r\n}\r\n\r\n# sqs version 1.0 by John Alroy\r\n# performs shareholder quorum subsampling on an array of specimen counts\r\n# set method=\"rarefaction\" or \"CR\" to perform classical rarefaction instead of SQS\r\n# written 29 July 2010\r\n# Modified by GTL 14/12/10 to include single publication occurrence correction\r\n#sqs <- function(ab, q, trials, method, dominant, p.1)\r\n#{\r\n#\tparams <- array(data=NA, dim=0, dimnames=c(\"raw richness\"))\r\n#\tif (missing(trials)) {\r\n#\t\ttrials <- 100\r\n#\t}\r\n#\tif (missing(method)) {\r\n#\t\tmethod <- \"\"\r\n#\t}\r\n#\tif ((q <= 0 || q >= 1) && method != \"rarefaction\" && method != \"CR\") {\r\n#\t\tprint(\"If the method is SQS the quota must be greater than zero and less than one\")\r\n#\t\treturn(params)\r\n#\t} else if (q < 1 && (method == \"rarefaction\" || method == \"CR\")) {\r\n#\t\tprint(\"If the method is rarefaction the quota must be an integer\")\r\n#\t\treturn(params)\r\n#\t}\r\n#\tif (missing(dominant)) {\r\n#\t\tdominant <- 0\r\n#\t}\r\n#\t\r\n#\t# compute basic statistics\r\n#\tspecimens <- sum(ab)\r\n#\tsingletons <- 0\r\n#\tdoubletons <- 0\r\n#\thighest <- 0\r\n#\tfor (i in 1:length(ab)) {\r\n#\t\tif (ab[i] == 1) {\r\n#\t\t\tsingletons <- singletons + 1\r\n#\t\t} else if (ab[i] == 2) {\r\n#\t\t\tdoubletons <- doubletons + 1\r\n#\t\t}\r\n#\t\tif (ab[i] > highest) {\r\n#\t\t\thighest <- ab[i]\r\n#\t\t\tmostfrequent <- i\r\n#\t\t}\r\n#\t}\r\n#\t# exclude dominant taxon unless told to include it\r\n#\tif (dominant == \"include\") {\r\n#\t\thighest <- 0\r\n#\t}\r\n#\t\r\n#\tif (missing(p.1)) {\r\n#\t\tu <- 1 - singletons/(specimens - highest)\r\n#\t}\r\n#\tif (!missing(p.1)) {\r\n#\t\tu <- (sum(ab) - p.1)/sum(ab)\r\n#\t}\r\n#\tif (u == 0) {\r\n#\t\tprint(\"Coverage is zero because all taxa are singletons\")\r\n#\t\treturn(params)\r\n#\t}\r\n#\t\r\n#\t# compute raw taxon frequencies (temporarily)\r\n#\tfreq <- ab / specimens\r\n#\t\r\n#\t# standard recursive equation for Fishers alpha\r\n#\talpha <- 10\r\n#\toldalpha <- 0\r\n#\twhile (abs(alpha - oldalpha) > 0.0000001) {\r\n#\t\toldalpha <- alpha\r\n#\t\talpha <- length(ab) / log(1 + specimens/alpha)\r\n#\t}\r\n#\t\r\n#\tparams[\"raw richness\"] <- length(ab)\r\n#\tparams[\"Good's u\"] <- u\r\n#\tparams[\"subsampled richness\"] <- NA\r\n#\tparams[\"Chao 1\"] <- length(ab) + singletons**2/(2* doubletons)\r\n#\tparams[\"subsampled Chao 1\"] <- NA\r\n#\t# governing parameter of the geometric series distribution\r\n#\tparams[\"k\"] <- abs(lm(log(sort(freq)) ~ c(1:length(freq)))$coefficients[2])\r\n#\tparams[\"Fisher's alpha\"] <- alpha\r\n#\tparams[\"Shannon's H\"] <- -1 * sum(freq * log(freq))\r\n#\tparams[\"Hurlbert's PIE\"] <- (1 - sum(freq**2)) * length(ab) / (length(ab) - 1)\r\n#\tparams[\"dominance\"] <- highest / specimens\r\n#\tparams[\"specimens\"] <- specimens\r\n#\tparams[\"singletons\"] <- singletons\r\n#\tparams[\"doubletons\"] <- doubletons\r\n#\tparams[\"specimens drawn\"] <- 0\r\n#\t\r\n#\t# return if the quorum target is higher than overall coverage\r\n#\tif ((q > u && method != \"rarefaction\" && method != \"CR\") || (q >= sum(ab))) {\r\n#\t\treturn(params)\r\n#\t}\r\n#\t\r\n#\t# compute adjusted taxon frequencies\r\n#\tfreq <- ab * u / (specimens - highest)\r\n#\t\r\n#\t# create an array in which each cell corresponds to one specimen\r\n#\tids <- array()\r\n#\tn <- 0\r\n#\tfor (i in 1:length(ab)) {\r\n#\t\tfor (j in 1:ab[i]) {\r\n#\t\t\tn <- n + 1\r\n#\t\t\tids[n] <- i\r\n#\t\t}\r\n#\t}\r\n#\t\r\n#\t# subsampling trial loop\r\n#\t# s will be the subsampled taxon count\r\n#\ts <- array(rep(0, trials))\r\n#\tsubchao <- array(rep(0, trials))\r\n#\tfor (trial in 1:trials) {\r\n#\t\tpool <- ids\r\n#\t\tleft <- length(pool)\r\n#\t\tseen <- array(data=rep(0, length(ab)))\r\n#\t\tsubfreq <- array(rep(0, length(ab)))\r\n#\t\t\r\n#\t\tif (method != \"rarefaction\" && method != \"CR\") {\r\n#\t\t\tsumfreq <- 0\r\n#\t\t\tunder <- q\r\n#\t\t\twhile (sumfreq < q) {\r\n#\t\t\t\t# draw a specimen\r\n#\t\t\t\tx <- floor(runif(1, min=1, max=left+1))\r\n#\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] + 1\r\n#\t\t\t\t# add to frequency and taxon sums if species has\r\n#\t\t\t\t# not been drawn previously\r\n#\t\t\t\tif (seen[pool[x]] == 0) {\r\n#\t\t\t\t\tif (pool[x] != mostfrequent || dominant == \"include\") {\r\n#\t\t\t\t\t\tsumfreq <- sumfreq + freq[pool[x]]\r\n#\t\t\t\t\t}\r\n#\t\t\t\t\tseen[pool[x]] <- 1\r\n#\t\t\t\t\t# count the taxon putting the sum over the target\r\n#\t\t\t\t\t# only if the resulting overshoot would be less\r\n#\t\t\t\t\t# than the undershoot created by not counting it\r\n#\t\t\t\t\tif ((sumfreq >= q && sumfreq - q < under) || sumfreq < q) {\r\n#\t\t\t\t\t\ts[trial] <- s[trial] + 1\r\n#\t\t\t\t\t} else {\r\n#\t\t\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] - 1\r\n#\t\t\t\t\t}\r\n#\t\t\t\t\tunder <- q - sumfreq\r\n#\t\t\t\t}\r\n#\t\t\t\t# decrease pool of specimens not yet drawn\r\n#\t\t\t\tpool[x] <- pool[left]\r\n#\t\t\t\tleft <- left - 1\r\n#\t\t\t}\r\n#\t\t} else {\r\n#\t\t\tfor (i in 1:q) {\r\n#\t\t\t\tx <- floor(runif(1, min=1, max=length(ids)-i+2))\r\n#\t\t\t\tsubfreq[pool[x]] <- subfreq[pool[x]] + 1\r\n#\t\t\t\tif (seen[pool[x]] == 0) {\r\n#\t\t\t\t\tseen[pool[x]] <- 1\r\n#\t\t\t\t\ts[trial] <- s[trial] + 1\r\n#\t\t\t\t}\r\n#\t\t\t\tpool[x] <- pool[length(ids)-i+1]\r\n#\t\t\t}\r\n#\t\t}\r\n#\t\tsubsingle <- 0\r\n#\t\tsubdouble <- 0\r\n#\t\tfor (i in 1:length(ab)) {\r\n#\t\t\tif (subfreq[i] == 1) {\r\n#\t\t\t\tsubsingle <- subsingle + 1\r\n#\t\t\t} else if (subfreq[i] == 2) {\r\n#\t\t\t\tsubdouble <- subdouble + 1\r\n#\t\t\t}\r\n#\t\t}\r\n#\t\tif (subsingle > 0 && subdouble > 0) {\r\n#\t\t\tsubchao[trial] <- s[trial] + subsingle**2/(2*subdouble)\r\n#\t\t} else {\r\n#\t\t\tsubchao[trial] <- s[trial]\r\n#\t\t}\r\n#\t\tparams[\"specimens drawn\"] <- params[\"specimens drawn\"] + sum(subfreq)\r\n#\t}\r\n#\t\r\n#\tparams[\"subsampled richness\"] <- exp(mean(log(s)))\r\n#\tparams[\"subsampled Chao 1\"] <- exp(mean(log(subchao)))\r\n#\tparams[\"specimens drawn\"] <- params[\"specimens drawn\"] / trials\r\n#\treturn(params)\r\n#}\r\n\r\ngen.diff <- function(x, time)\r\n{\r\n\t#if(cor.test(time, x)$p.value > 0.05) print(\"Warning: variables not significantly correlated, generalised differencing not recommended\")\r\n\tdt <- x-((lsfit(time, x)$coefficients[2]*time)+lsfit(time, x)$coefficients[1])\r\n\tm <- lsfit(dt[1:(length(dt)-1)], dt[2:length(dt)])$coefficients[2]\r\n\tgendiffs <- dt[1:(length(dt)-1)]-(dt[2:length(dt)]*m)\r\n\tgendiffs\r\n}\r\n\r\ngetthedamndatain <- function(file, sep=\"\\t\", header=F)\r\n{\r\n\tX <- scan(file = file, what = \"\", sep = \"\\n\", quiet = TRUE)\r\n\tdata <- matrix(nrow=length(X), ncol=length(strsplit(X[1], sep)[[1]]))\r\n\tfor(i in 1:length(X)) {\r\n\t\tdata[i, 1:length(strsplit(X[1], sep)[[1]])] <- strsplit(X[i], sep)[[1]]\r\n\t}\r\n\tif(header == TRUE) {\r\n\t\tcolnames(data) <- data[1, ]\r\n\t\tdata <- data[-1, ]\r\n\t}\r\n\treturn(data)\r\n}\r\n\r\nmy.ccf <- function(x, y, xlab, ylab) {\r\n\tcompleteforboth <- intersect(grep(TRUE, !is.na(x)), grep(TRUE, !is.na(y)))\r\n\tdifferences <- diff(completeforboth)\r\n\tif(length(grep(TRUE, differences != 1)) > 0) { # Does gap exist?\r\n\t\tfor(i in 1:length(grep(TRUE, differences != 1))) {\r\n\t\t\tif(i == 1) {\r\n\t\t\t\tsplit <- completeforboth[1:grep(TRUE, differences != 1)[i]]\r\n\t\t\t\tif(length(grep(TRUE, differences != 1)) == 1) {\r\n\t\t\t\t\tsplit.a <- completeforboth[(grep(TRUE, differences != 1)[i]+1):length(completeforboth)]\r\n\t\t\t\t\tif(length(split.a) > length(split)) split <- split.a\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tif(i > 1) {\r\n\t\t\t\tsplit.a <- completeforboth[(grep(TRUE, differences != 1)[(i-1)]+1):grep(TRUE, differences != 1)[i]]\r\n\t\t\t\tif(length(split.a) > length(split)) split <- split.a\r\n\t\t\t}\r\n\t\t\tif(i == length(grep(TRUE, differences != 1)) && i > 1) {\r\n\t\t\t\tsplit.a <- completeforboth[(grep(TRUE, differences != 1)[i]+1):length(completeforboth)]\r\n\t\t\t\tif(length(split.a) > length(split)) split <- split.a\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tif(length(grep(TRUE, differences != 1)) == 0) split <- completeforboth\r\n\tx <- x[split]\r\n\ty <- y[split]\r\n\tplot.title <- paste(xlab, \"vs.\", ylab, \"\\n(Continuous from bin\", split[1], \"to\", split[length(split)], \")\")\r\n\tccf(y, x, main=plot.title)\r\n}\r\n\r\nget.anc.states <- function(nexus.matrix, tree) {\r\n require(ape)\r\n anc.lik.matrix <- matrix(nrow=Nnode(tree), ncol=length(nexus.matrix$matrix[1, ])) # Create matrix to record ancestral state estimates\r\n rownames(anc.lik.matrix) <- c((Ntip(tree)+1):(Ntip(tree)+Nnode(tree))) # Label matrix to record ancestral state estimates\r\n for(i in 1:length(nexus.matrix$matrix[1, ])) { # Cycle through characters:\r\n tipstogo <- rownames(nexus.matrix$matrix)[grep(TRUE, is.na(nexus.matrix$matrix[, i]))] # Find taxa with missing data:\r\n if(length(tipstogo) > 0) chartree <- drop.tip(tree, tipstogo) # Remove tips with missing data\r\n if(length(tipstogo) == 0) chartree <- tree # If no missing data use whole tree \r\n tipvals <- nexus.matrix$matrix[chartree$tip.label, i]\r\n # Create state change matrix (important for ordered/unordered characters:\r\n minval <- as.numeric(nexus.matrix$min.vals[i])\r\n maxval <- as.numeric(nexus.matrix$max.vals[i])\r\n if(maxval-minval == 1) mymodel <- matrix(c(minval, maxval, maxval, minval), 2) # Case for binary character (ordering irrelevant)\r\n if(maxval-minval > 1 && nexus.matrix$ordering[i] == \"unord\") { # Case for unordered multistate character\r\n mymodel <- matrix(1, ncol=(maxval-minval)+1, nrow=(maxval-minval)+1)\r\n for(j in 1:length(mymodel[1, ])) mymodel[j, j] <- 0\r\n }\r\n if(maxval-minval > 1 && nexus.matrix$ordering[i] == \"ord\") { # Case for ordered multistate character\r\n mymodel <- matrix(0, ncol=(maxval-minval)+1, nrow=(maxval-minval)+1)\r\n for(j in 1:length(mymodel[1, ])) {\r\n for(k in 1:length(mymodel[1, ])) {\r\n mymodel[j, k] <- sqrt((diff(c(j, k)))^2)\r\n }\r\n }\r\n }\r\n # Ascertain if polymorphisms are present and if so compute all possible prmutations\r\n if(length(grep(\"&\", tipvals)) > 0) {\r\n permutation.counts <- vector(mode=\"numeric\") # Vector to store permutation numbers for each taxon (i.e. number of possible states)\r\n for(j in 1:length(tipvals)) permutation.counts[j] <- length(strsplit(as.character(tipvals[j]), \"&\")[[1]]) # Get number of permutations for each taxon\r\n permutation.count <- prod(permutation.counts) # Get product of permutation counts (i.e. total number of permutations)\r\n permutations <- matrix(nrow=length(tipvals), ncol=permutation.count) # Make permutations matrix\r\n permutations[grep(TRUE, permutation.counts == 1), 1:permutation.count] <- as.numeric(tipvals[grep(TRUE, permutation.counts == 1)]) # Fill in data for non-polymorphic characters\r\n phase.count <- 1 # Permutation phase\r\n for(j in grep(\"&\", tipvals)) {\r\n perm <- as.numeric(strsplit(as.character(tipvals[j]), \"&\")[[1]])\r\n if(phase.count == 1) permutations[j, ] <- perm\r\n if(phase.count > 1) {\r\n perm <- sort(rep(perm, phase.count))\r\n permutations[j, ] <- perm\r\n }\r\n phase.count <- length(perm)\r\n }\r\n rownames(permutations) <- names(tipvals) # Make sure tip value names are carried over\r\n # Ancestor state estimate for first permutation:\r\n if(length(unique(sort(permutations[, 1]))) > 1) { # If there is more than one state\r\n if(length(mymodel[, 1]) > length(sort(unique(as.numeric(permutations[, 1]))))) { # Case if model has larger spread than actual tipvalues (e.g. spans 0-5 when 2 is not recorded)\r\n anc.lik <- ace(permutations[, 1], chartree, type=\"discrete\", model=mymodel[sort(unique(as.numeric(permutations[, 1])))+1, sort(unique(as.numeric(permutations[, 1])))+1])$lik.anc\r\n }\r\n if(length(mymodel[, 1]) <= length(sort(unique(as.numeric(permutations[, 1]))))) { # Case if model has same spread as tip values (theoretically this should always be true, but in reality not so much)\r\n anc.lik <- ace(permutations[, 1], chartree, type=\"discrete\", model=mymodel[])$lik.anc\r\n }\r\n }\r\n if(length(unique(sort(permutations[, 1]))) == 1) {\r\n anc.lik <- matrix(0, nrow=length(permutations[, 1])-1, ncol=max(c((unique(sort(as.numeric(permutations[, 1])))+1), 2)))\r\n colnames(anc.lik) <- c(0:max(c(unique(sort(as.numeric(permutations[, 1]))), 1)))\r\n anc.lik[, as.character(unique(sort(as.numeric(permutations[, 1]))))] <- rep(1, length(permutations[, 1])-1)\r\n }\r\n for(j in 2:length(permutations[1, ])) { # Cycle through remaining permutations\r\n if(length(unique(sort(permutations[, j]))) > 1) {\r\n if(length(mymodel[, 1]) > length(sort(unique(as.numeric(permutations[, j]))))) { # Case if model has larger spread than actual tipvalues (e.g. spans 0-5 when 2 is not recorded)\r\n anc.lik <- ((anc.lik*(j-1))+ace(permutations[, j], chartree, type=\"discrete\", model=mymodel[sort(unique(as.numeric(permutations[, j])))+1, sort(unique(as.numeric(permutations[, j])))+1])$lik.anc)/j # Get mean ancestral estimations\r\n }\r\n if(length(mymodel[, 1]) <= length(sort(unique(as.numeric(permutations[, j]))))) { # Case if model has same spread as tip values (theoretically this should always be true, but in reality not so much)\r\n anc.lik <- ((anc.lik*(j-1))+ace(permutations[, j], chartree, type=\"discrete\", model=mymodel)$lik.anc)/j # Get mean ancestral estimations\r\n }\r\n }\r\n if(length(unique(sort(permutations[, j]))) == 1) {\r\n anc.lik.perm <- matrix(0, nrow=length(permutations[, j])-1, ncol=max(c((unique(sort(as.numeric(permutations[, j])))+1), 2)))\r\n colnames(anc.lik.perm) <- c(0:max(c(unique(sort(as.numeric(permutations[, j]))), 1)))\r\n anc.lik.perm[, as.character(unique(sort(as.numeric(permutations[, j]))))] <- rep(1, length(permutations[, j])-1)\r\n anc.lik <- ((anc.lik*(j-1))+anc.lik.perm)/j # Get mean ancestral estimations\r\n }\r\n }\r\n }\r\n # If no polymorphisms present just get ancestral likelihoods:\r\n if(length(grep(\"&\", tipvals)) == 0 && length(unique(sort(as.numeric(tipvals)))) > 1) {\r\n if(length(mymodel[, 1]) > length(sort(unique(as.numeric(tipvals))))) { # Case if model has larger spread than actual tipvalues (e.g. spans 0-5 when 2 is not recorded)\r\n anc.lik <- ace(as.numeric(tipvals), chartree, type=\"discrete\", model=mymodel[sort(unique(as.numeric(tipvals)))+1, sort(unique(as.numeric(tipvals)))+1])$lik.anc # Get ancestral state likelihoods for internal nodes\r\n }\r\n if(length(mymodel[, 1]) <= length(sort(unique(as.numeric(tipvals))))) { # Case if model has same spread as tip values (theoretically this should always be true, but in reality not so much)\r\n anc.lik <- ace(as.numeric(tipvals), chartree, type=\"discrete\", model=mymodel)$lik.anc # Get ancestral state likelihoods for internal nodes\r\n }\r\n }\r\n if(length(grep(\"&\", tipvals)) == 0 && length(unique(sort(as.numeric(tipvals)))) == 1) { # Case if all states are the same\r\n anc.lik <- matrix(0, nrow=length(tipvals)-1, ncol=max(c((unique(sort(as.numeric(tipvals)))+1), 2)))\r\n colnames(anc.lik) <- c(0:max(c(unique(sort(as.numeric(tipvals))), 1)))\r\n anc.lik[, as.character(unique(sort(as.numeric(tipvals))))] <- rep(1, length(tipvals)-1)\r\n }\r\n # Convert to just most likely states (i.e. 0 OR 1 for binary):\r\n anc.lik.vector <- vector(mode=\"numeric\")\r\n max.lik <- apply(anc.lik, 1, max)\r\n for(j in 1:length(anc.lik[, 1])) {\r\n anc.lik.vector[j] <- as.numeric(colnames(anc.lik)[match(max.lik[j], anc.lik[j, ])]) # Case if single most likely state\r\n if(length(grep(max.lik[j], anc.lik[j, ])) > 1) anc.lik.vector[j] <- NA # Case if multiple equally most likely states (i.e. replace with NA)\r\n }\r\n chartreenodes <- (Ntip(chartree)+1):(Ntip(chartree)+Nnode(chartree)) # Nodes for character tree\r\n names(anc.lik.vector) <- chartreenodes\r\n # Copy information across to ancestral state matrix for whole tree:\r\n for(j in chartreenodes) {\r\n descs <- sort(chartree$tip.label[FindDescendants(j, chartree)]) # Find character tree descendants\r\n anc.lik.matrix[as.character(FindAncestor(descs, tree)), i] <- anc.lik.vector[as.character(j)]\r\n }\r\n }\r\n return(anc.lik.matrix)\r\n}\r\n\r\nget.bl <- function(state.matrix, tree, weights) {\r\n require(ape)\r\n tree.pd <- tree.nc <- tree.cc <- tree # Trees for patristic distance, number of changes and comparable characters\r\n for(i in 1:length(tree$edge[, 1])) {\r\n node.1 <- tree$edge[i, 1] # Get nodes for branch\r\n node.2 <- tree$edge[i, 2] # Get nodes for branch\r\n compchar <- sort(intersect(grep(TRUE, !is.na(state.matrix[node.1, ])), grep(TRUE, !is.na(state.matrix[node.2, ])))) # Characters that can be compared\r\n if(length(compchar) > 0) {\r\n polychar <- sort(unique(c(grep(\"&\", state.matrix[node.1, compchar]), grep(\"&\", state.matrix[node.2, compchar])))) # Characters with polymorphisms\r\n if(length(polychar) == 0) {\r\n tree.nc$edge.length[i] <- sum(sqrt(((as.numeric(state.matrix[node.1, compchar])-as.numeric(state.matrix[node.2, compchar]))*weights[compchar])^2))\r\n tree.pd$edge.length[i] <- sum(sqrt(((as.numeric(state.matrix[node.1, compchar])-as.numeric(state.matrix[node.2, compchar]))*weights[compchar])^2))/(length(compchar)*weights[compchar])\r\n }\r\n if(length(polychar) > 0) {\r\n diffs <- sqrt(( as.numeric(state.matrix[node.1, compchar])-as.numeric(state.matrix[node.2, compchar])) ^2)\r\n for (j in 1:length(polychar)) {\r\n # Get minimum difference between polymorphic characters:\r\n mindiff <- min(sqrt((as.numeric(strsplit(as.character(state.matrix[node.1, compchar[polychar[j]]]), \"&\")[[1]])-as.numeric(strsplit(as.character(state.matrix[node.2, compchar[polychar[j]]]), \"&\")[[1]]))^2))\r\n diffs[polychar[j]] <- mindiff # Add to differences vector\r\n }\r\n tree.nc$edge.length[i] <- sum(diffs*weights[compchar])\r\n tree.pd$edge.length[i] <- sum(diffs*weights[compchar])/(length(compchar)*weights[compchar])\r\n }\r\n tree.cc$edge.length[i] <- length(compchar)*weights[compchar]\r\n }\r\n if(length(compchar) == 0) {\r\n tree.nc$edge.length[i] <- 0\r\n tree.pd$edge.length[i] <- 0\r\n tree.cc$edge.length[i] <- 0\r\n }\r\n }\r\n result <- list(tree.cc, tree.nc, tree.pd)\r\n\tnames(result) <- c(\"tree.cc\", \"tree.nc\", \"tree.pd\")\r\n\treturn(result)\r\n}\r\n\r\nrbranch.phylo <- function(state.matrix, tree, ttree, cctree, permutations)\r\n{\r\n charcorr <- (length(state.matrix[1, ])-apply(is.na(state.matrix), 1, sum))/length(state.matrix[1, ]) # Get proportion of completeness of nodes/tips\r\n ttree$edge.length <- (cctree$edge.length/length(state.matrix[1, ]))*ttree$edge.length # Normalise time tree branch lengths to reflect comparable characters number\r\n nchang <- sum(tree$edge.length) # Total number of character changes on tree\r\n permat <- matrix(0, nrow=length(tree$edge.length), ncol=permutations) # Matrix to store premutation results\r\n # Apportion branches to uniform distribution (between 0 and 1):\r\n brk <- vector(mode=\"numeric\", length=length(ttree$edge.length))\r\n\tbrk[1] <- ttree$edge.length[1]/length(ttree$edge.length)\r\n for (i in 2:length(brk)) brk[i] <- brk[i-1]+ttree$edge.length[i]/length(ttree$edge.length)\r\n\tbrk <- brk/(sum(ttree$edge.length)/length(ttree$edge.length))\r\n # Assign changes to branches using a uniform distribution:\r\n for (i in 1:permutations) {\r\n\t\trandno <- runif(nchang, min=0, max=1)\r\n\t\trandno <- sort(randno)\r\n\t\tfor (j in 1:length(tree$edge.length)) {\r\n\t\t\tbreaker <- length(grep(TRUE, randno <= brk[j]))\r\n\t\t\tpermat[j, i] <- breaker\r\n\t\t\tif (breaker > 0) randno <- randno[-(1:breaker)]\r\n\t\t}\r\n\t}\r\n sigs <- vector(mode=\"numeric\") # Vector to store significant results\r\n for (i in 1:length(tree$edge.length)) ifelse(tree$edge.length[i] > sort(permat[i, ])[ceiling(0.95*length(permat[i, ]))], sigs[i] <- 1, sigs[i] <- 0) # Significance test for high rates\r\n\treturn(sigs)\r\n}\r\n\r\nread.tnt <- function(file)\r\n{\r\n X <- scan(file = file, what = \"\", sep = \"\\n\", quiet = TRUE) # Read in NEXUS file\r\n if(length(grep(\"mxram\", X)) > 0) { # If there is an nstates line\r\n X <- X[-grep(\"mxram\", X)] # Remove nstates line\r\n }\r\n if(length(grep(\"nstates\", X)) > 0) { # If there is an nstates line\r\n X <- X[-grep(\"nstates\", X)] # Remove nstates line\r\n }\r\n if(length(grep(\"taxname\", X)) > 0) { # If there is a taxname line\r\n X <- X[-grep(\"taxname\", X)] # Remove taxname line\r\n }\r\n X <- X[-grep(TRUE, X == \"xread\")] # Remove xread\r\n if(length(grep(\", \", X)) > 0) {\r\n header <- X[grep(\", \", X)] # Find header text\r\n header <- gsub(\"'\", \"\", header) # Remove quotes\r\n X <- X[-grep(\", \", X)] # Remove header line(s)\r\n } else {\r\n header <- \"\"\r\n }\r\n nchar <- as.numeric(strsplit(X[1], \" \")[[1]][1]) # Get number of characters\r\n ntax <- as.numeric(strsplit(X[1], \" \")[[1]][2]) # Get number of taxa\r\n X <- X[-1] # Delete top line\r\n matrixblock <- X[1:ntax] # Get matrix block\r\n X <- X[-(1:ntax)] # Remove matrix block\r\n MATRIX <- matrix(nrow=ntax, ncol=nchar) # Make matrix\r\n rownames(MATRIX) <- c(1:ntax) # Dummy rownames\r\n for(i in 1:ntax) { # For each taxon\r\n rownames(MATRIX)[i] <- strsplit(matrixblock[i], \" \")[[1]][1] # Extract taxon name\r\n matrixblock[i] <- strsplit(matrixblock[i], \" \")[[1]][length(strsplit(matrixblock[i], \" \")[[1]])] # And character block\r\n chars <- strsplit(matrixblock[i], \"\")[[1]] # Get character vector\r\n j <- 1 # Start value for j\r\n while(j <= length(chars)) { # Go through character vector until all characters have been looked at\r\n if(chars[j] != \"[\") { # If the character is not a polymorphism\r\n MATRIX[i, grep(TRUE, is.na(MATRIX[i, ]))[1]] <- chars[j] # Just store it in the matrix\r\n j <- j+1 # And move to the next one\r\n\t\t\t} else { # If it is a polymorphism\r\n MATRIX[i, grep(TRUE, is.na(MATRIX[i, ]))[1]] <- paste(chars[(j+1):(j+grep(TRUE, chars[j:length(chars)] == \"]\")[1]-2)], collapse=\"&\") # Paste all states into the matrix\r\n j <- j+grep(TRUE, chars[j:length(chars)] == \"]\")[1] # And move to the next character\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tMATRIX <- gsub(\"\\\\?\", NA, MATRIX) # Replace question marks with NAs\r\n\tunq.states <- sort(unique(as.vector(MATRIX))) # Find unique states list\r\n\tif(length(grep(\"&\", unq.states)) > 0) { # If polymorphisms are present\r\n\t\tpolys <- unq.states[grep(\"&\", unq.states)] # Isolate them\r\n\t\tunq.states <- unq.states[-grep(\"&\", unq.states)] # Remove them\r\n\t\tfor(i in 1:length(polys)) { # Go through each polymorphism\r\n\t\t\tunq.states <- c(unq.states, strsplit(polys[i], \"&\")[[1]]) # Split adn add to unqiue states list\r\n\t\t}\r\n\t\tunq.states <- sort(unique(unq.states)) # Get unique states again\r\n\t}\r\n\tif(length(sort(match(LETTERS, unq.states))) > 0) { # If there are letters (greater than ten states)\r\n\t\tlets.found <- unq.states[sort(match(LETTERS, unq.states))] # List leters found\r\n\t\tfor(i in 1:length(lets.found)) { # For each leter found\r\n\t\t\tMATRIX <- gsub(lets.found[i], match(lets.found[i], LETTERS)+10, MATRIX) # Replace with appropriate number\r\n\t\t}\r\n\t}\r\n\tif(length(grep(\"ccode\", X)) > 0) { # If weights and/or orderings are present\r\n\t\tordering <- weights <- vector(mode=\"character\", length=nchar)\r\n\t\twt.ord <- gsub(\";\", \"\", gsub(\"ccode \", \"\", X[grep(\"ccode\", X)])) # Get character weights/orderings\r\n\t\twt.ord <- strsplit(wt.ord, \" \")[[1]]\r\n\t\tchar.nos <- as.numeric(wt.ord[c(1:length(wt.ord))[grep(TRUE, c(1:length(wt.ord)) %% 2 == 0)]])+1 # Get character numbers (even cells)\r\n\t\tchar.vals <- wt.ord[c(1:length(wt.ord))[grep(TRUE, c(1:length(wt.ord)) %% 2 != 0)]] # Get character values (odd cells)\r\n\t\tordering[grep(\"-\", char.vals)] <- \"unord\" # Get unordered characters\r\n\t\tordering[grep(\"\\\\+\", char.vals)] <- \"ord\" # Get ordered characters\r\n\t\tfor(i in 1:nchar) { # For each character\r\n\t\t\tif(length(grep(\"\\\\[\", char.vals[i])) > 0) { # If character is active\r\n weights[i] <- as.numeric(strsplit(char.vals[i], \"/\")[[1]][2]) # Set weight\r\n\t\t\t} else { # If character is inactive\r\n weights[i] <- 0 # Set weight to zero\r\n\t\t\t}\r\n\t\t}\r\n\t} else { # If they are not\r\n\t\tweights <- rep(1, nchar) # Make all characters weighted one\r\n\t\tordering <- rep(\"unord\", nchar) # Make all characters unordered\r\n\t}\r\n\tmax.vals <- min.vals <- vector(mode=\"numeric\", length=nchar) # Min and max value vectors\r\n\tfor(i in 1:nchar) { # For each character\r\n\t\tunq.states <- sort(unique(as.vector(MATRIX[, i])))\r\n\t\tif(length(grep(\"&\", unq.states)) > 0) { # If polymorphisms are present\r\n\t\t\tpolys <- unq.states[grep(\"&\", unq.states)] # Isolate them\r\n\t\t\tunq.states <- unq.states[-grep(\"&\", unq.states)] # Remove them\r\n\t\t\tfor(j in 1:length(polys)) { # Go through each polymorphism\r\n\t\t\t\tunq.states <- c(unq.states, strsplit(polys[j], \"&\")[[1]]) # Split and add to unqiue states list\r\n\t\t\t}\r\n\t\t\tunq.states <- sort(unique(unq.states)) # Get unique states again\r\n\t\t}\r\n\t\tmin.vals[i] <- min(as.numeric(unq.states))\r\n\t\tmax.vals[i] <- max(as.numeric(unq.states))\r\n\t}\r\n\tresult <- list(header, MATRIX, ordering, weights, max.vals, min.vals)\r\n\tnames(result) <- c(\"header\", \"matrix\", \"ordering\", \"weights\", \"max.vals\", \"min.vals\")\r\n\treturn(result)\r\n}\r\n\r\nread.paleodb.occs <- function(file, sep=\", \", header=TRUE)\r\n{\r\n\t# Not sure why I have to do this, but it seems to break otherwise:\r\n\tSys.setlocale('LC_ALL', 'C')\r\n\t\r\n\t# Read in raw data:\r\n\tX <- scan(file=file, what=\"\", sep=\"\\n\", quiet=TRUE)\r\n\t\r\n\t# Whilst there are gaps (empty cells) in the data:\r\n\twhile(length(grep(paste(sep, sep, sep=\"\"), X)) > 0) {\r\n\t\r\n\t\t# Insert NAs into gaps (excluding first and last columns):\r\n\t\tX <- gsub(paste(sep, sep, sep=\"\"), paste(sep, \"NA\", sep, sep=\"\"), X)\r\n\t\r\n\t}\r\n\t\r\n\t# To check ends of lines for gaps go line by line:\r\n\tfor(i in 1:length(X)) {\r\n\t\t\r\n\t\t# If first value is empty replace with NA:\r\n\t\tif(strsplit(X[i], \"\")[[1]][1] == sep) X[i] <- paste(NA, X[i], sep=\"\")\r\n\t\t\r\n\t\t# If last value is empty replace with NA:\r\n\t\tif(strsplit(X[i], \"\")[[1]][length(strsplit(X[i], \"\")[[1]])] == sep) X[i] <- paste(X[i], NA, sep=\"\")\r\n\r\n\t}\r\n\t\r\n\t# Catch separators in text (i.e. when followed by a space):\r\n\tX <- gsub(paste(sep, \" \", sep=\"\"), paste(\"%sEpArAtOr%\", \" \", sep=\"\"), X)\r\n\r\n\t# For each line of the text file:\r\n\tfor(i in 1:length(X)) {\r\n\t\t\r\n\t\t# Break text at quotation marks:\r\n\t\tnew.block <- strsplit(X[i], \"\\\"\")[[1]]\r\n\t\t\r\n\t\t# Find within quotes parts (i.e. even numbers):\r\n\t\tevens <- grep(TRUE, c(1:length(new.block)) %% 2 == 0)\r\n\t\t\r\n\t\t# Replace seperation values present within quotes:\r\n\t\tnew.block[evens] <- gsub(sep, \"%sEpArAtOr%\", new.block[evens])\r\n\t\t\r\n\t\t# Reinsert quotes and store in X:\r\n\t\tX[i] <- paste(new.block, collapse=\"\\\"\")\r\n\t\t\r\n\t}\r\n\t\r\n\t# Create vector to record the number of separator values on each line:\r\n\tseps <- vector(mode=\"numeric\")\r\n\t\r\n\t# For each line:\r\n\tfor(i in 1:length(X)) {\r\n\t\t\r\n\t\t# Count the number of separator values:\r\n\t\tseps[i] <- length(grep(TRUE, strsplit(X[i], \"\")[[1]] == sep))\r\n\t\t\r\n\t}\r\n\t\r\n\t# If there are still separator values inside fields:\r\n\tif(length(unique(seps)) > 0) {\r\n\t\t\r\n\t\t# Identify problem rows:\r\n\t\tproblem.rows <- grep(TRUE, seps > min(seps))\r\n\t\t\r\n\t\t# For each problem row:\r\n\t\tfor(i in problem.rows) {\r\n\t\t\t\r\n\t\t\t# Isolate values by separating them:\r\n\t\t\trow.values <- strsplit(X[i], sep)[[1]]\r\n\t\t\t\r\n\t\t\t# Set vectors for storing values where first or last characters are quotes:\r\n\t\t\tfirst.character.a.quote <- last.character.a.quote <- vector(mode=\"numeric\")\r\n\t\t\t\r\n\t\t\t# For each value:\r\n\t\t\tfor(j in 1:length(row.values)) {\r\n\t\t\t\t\r\n\t\t\t\t# If starts with a quote then store:\r\n\t\t\t\tif(strsplit(row.values[j], \"\")[[1]][1] == \"\\\"\") first.character.a.quote <- c(first.character.a.quote, j)\r\n\t\t\t\t\r\n\t\t\t\t# If ends with a quote then store:\r\n\t\t\t\tif(strsplit(row.values[j], \"\")[[1]][length(strsplit(row.values[j], \"\")[[1]])] == \"\\\"\") last.character.a.quote <- c(last.character.a.quote, j)\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Fields that begin and end with quotes are text and have already been dealt with above:\r\n\t\t\ttext.fields <- intersect(first.character.a.quote, last.character.a.quote)\r\n\t\t\t\r\n\t\t\t# Other quotes are stray and may indicate problematic fields:\r\n\t\t\tstray.quotes <- sort(c(setdiff(first.character.a.quote, last.character.a.quote), setdiff(last.character.a.quote, first.character.a.quote)))\r\n\t\t\t\r\n\t\t\t# Identify fields that contain text (but may not be bookended by quotes):\r\n\t\t\tcontains.text <- grep(\"[A-Z:a-z]\", row.values)\r\n\t\t\t\r\n\t\t\t# Identify initial list of fields that are OK:\r\n\t\t\tOK.fields <- sort(c(text.fields, grep(TRUE, row.values == \"NA\")))\r\n\t\t\t\r\n\t\t\t# Identify initial list of fields that are potentially not OK:\r\n\t\t\tnotOK.fields <- setdiff(c(1:length(row.values)), OK.fields)\r\n\t\t\t\r\n\t\t\t# Update Ok fields to exclude number fields:\r\n\t\t\tOK.fields <- sort(unique(c(OK.fields, setdiff(notOK.fields, contains.text))))\r\n\r\n\t\t\t# Update not OK fields:\r\n\t\t\tnotOK.fields <- setdiff(c(1:length(row.values)), OK.fields)\r\n\r\n\t\t\t# If first value is not OK, but second value is OK:\r\n\t\t\tif(length(grep(TRUE, notOK.fields == 1)) > 0 && length(grep(TRUE, OK.fields == 2))) {\r\n\t\t\t\t\r\n\t\t\t\t# First value is now OK:\r\n\t\t\t\tOK.fields <- c(1, OK.fields) \r\n\t\t\t\t\r\n\t\t\t\t# First value is no longer not OK:\r\n\t\t\t\tnotOK.fields <- notOK.fields[-1]\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# If first value is not OK, but second value is OK:\r\n\t\t\tif(length(grep(TRUE, notOK.fields == length(row.values))) > 0 && length(grep(TRUE, OK.fields == (length(row.values) - 1)))) {\r\n\t\t\t\t\r\n\t\t\t\t# First value is now OK:\r\n\t\t\t\tOK.fields <- c(OK.fields, length(row.values))\r\n\t\t\t\t\r\n\t\t\t\t# First value is no longer not OK:\r\n\t\t\t\tnotOK.fields <- notOK.fields[-grep(TRUE, notOK.fields == length(row.values))]\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# For each remaining not Ok field:\r\n\t\t\tfor(j in notOK.fields) {\r\n\t\t\t\t\r\n\t\t\t\t# Check that it is not an end value:\r\n\t\t\t\tif(j != 1 && j != length(row.values)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# If values either side are OK:\r\n\t\t\t\t\tif(length(grep(TRUE, OK.fields == (j + 1))) && length(grep(TRUE, OK.fields == (j - 1)))) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# First value is now OK:\r\n\t\t\t\t\t\tOK.fields <- sort(c(OK.fields, j))\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# First value is no longer not OK:\r\n\t\t\t\t\t\tnotOK.fields <- notOK.fields[-grep(TRUE, notOK.fields == j)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\twhile(length(notOK.fields) > 1) {\r\n\t\t\t\t\r\n\t\t\t\t\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t\r\n\t\t\r\n\t\t}\r\n\t\t\r\n\t}\r\n\t\r\n\t\r\n\t\r\n\t\r\n\t\r\n\tif(header == TRUE) {\r\n\t\t\r\n\t\tout <- matrix(ncol=min(seps) + 1, nrow=length(X) - 1)\r\n\t\t\r\n\t\tcolnames(out) <- strsplit(X[1], sep)[[1]]\r\n\t\t\r\n\t\tX <- X[-1]\r\n\t\t\r\n\t}\r\n\t\r\n\t\r\n\tif(header == FALSE) out <- matrix(ncol=min(seps) + 1, nrow=length(X))\r\n\t\r\n\t\r\n\t\r\n\tfor(i in 1:length(X)) out[i, ] <- strsplit(X[i], sep)[[1]]\r\n\t\r\n\t\r\n\t\r\n\t\r\n\t# Reinsert non-separating separator characters:\r\n\tout <- gsub(\"%sEpArAtOr%\", sep, out)\r\n\t\r\n\t# Remove now redundant quotes:\r\n\tout <- gsub(\"\\\"\", \"\", out)\r\n\t\r\n\t# Return output as table:\r\n\treturn(out)\r\n\t\r\n}\r\n\r\n# Function to find nodes that can be realistically dated by the Hedman technique:\r\nfind.dateable.nodes <- function(tree, tip.ages) {\r\n\r\n\t# Requires ape library for reading tree structure:\r\n\trequire(ape)\r\n\t\r\n\t# List all internal nodes except root:\r\n\tlist.nodes <- (Ntip(tree) + 2):(Ntip(tree) + Nnode(tree))\r\n\t\r\n\t# Create vector to store nodes that are dateable:\r\n\tdefo.nodes <- vector(mode=\"numeric\")\r\n\t\r\n\t# For each node in the list:\r\n\tfor(i in length(list.nodes):1) {\r\n\t\t\r\n\t\t# Find descendant nodes:\r\n\t\tdescs <- tree$edge[grep(TRUE, tree$edge[, 1] == list.nodes[i]), 2]\r\n\t\t\r\n\t\t# If all immediate descendants are internal nodes than it cannot be dated and is removed:\r\n\t\tif(length(which(descs > Ntip(tree))) == length(descs)) list.nodes <- list.nodes[-i]\r\n\t\t\r\n\t\t# If all immediate descendants are terminal nodes than it can definitely be dated...:\r\n\t\tif(length(which(descs <= Ntip(tree))) == length(descs)) {\r\n\t\t\t\r\n\t\t\t# ...and is retained...:\r\n\t\t\tdefo.nodes <- c(defo.nodes, list.nodes[i])\r\n\t\t\t\r\n\t\t\t# ...but can be removed from list.nodes:\r\n\t\t\tlist.nodes <- list.nodes[-i]\r\n\t\t\r\n\t\t}\r\n\t\r\n\t}\r\n\t\r\n\t# For each internal node with both a terminal and internal node descendant:\r\n\tfor(i in length(list.nodes):1) {\r\n\t\t\r\n\t\t# Find node age:\r\n\t\tnode.age <- max(tip.ages[FindDescendants(list.nodes[i], tree)])\r\n\t\t\r\n\t\t# List its descendants:\r\n\t\tdescs.ages <- descs <- tree$edge[grep(TRUE, tree$edge[, 1] == list.nodes[i]), 2]\r\n\t\t\r\n\t\t# For each descendant:\r\n\t\tfor(j in length(descs.ages):1) {\r\n\t\t\t\r\n\t\t\t# If an internal node date as oldest descendant:\r\n\t\t\tif(descs[j] > Ntip(tree)) descs.ages[j] <- max(tip.ages[FindDescendants(descs[j], tree)])\r\n\t\t\t\r\n\t\t\t# Remove if terminal node:\r\n\t\t\tif(descs[j] <= Ntip(tree)) descs.ages <- descs.ages[-j]\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# If node age is older than any descendant internal nodes:\r\n\t\tif(node.age > max(descs.ages)) {\r\n\t\t\t\r\n\t\t\t# Then can be dated so add to list:\r\n\t\t\tdefo.nodes <- c(defo.nodes, list.nodes[i])\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t}\r\n\t\r\n\t# Output all dateable nodes:\r\n\treturn(defo.nodes)\r\n\r\n}\r\n\r\n# Function that retrieves dates for use as tnodes variable in Hedman function\r\nget.tnodes <- function(node, tree, tip.ages) {\r\n\t\r\n\t# Require ape library:\r\n\trequire(ape)\r\n\t\r\n\t# Number of root node (point at which searching for ancestral nodes stops):\r\n\troot.node <- Ntip(tree) + 1\r\n\t\r\n\t# If the input node is the root node:\r\n\tif(node == root.node) {\r\n\t\t\r\n\t\t# Error and warning:\r\n\t\tstop(\"ERROR: Input node is root node - tnodes has length one!\")\r\n\t\t\r\n\t# If input node is not root node:\r\n\t} else {\r\n\t\t\r\n\t\t# Find first ancestor node:\r\n\t\tinternodes <- tree$edge[match(node, tree$edge[, 2]), 1]\r\n\t\t\r\n\t\t# As long as we have not yet reached the root:\r\n\t\twhile(internodes[length(internodes)] != root.node) {\r\n\t\t\t\r\n\t\t\t# Add next internode to set:\r\n\t\t\tinternodes <- c(internodes, tree$edge[match(internodes[length(internodes)], tree$edge[, 2]), 1])\r\n\t\t}\r\n\t\t\r\n\t\t# Add in original node as that is counted too!:\r\n\t\tinternodes <- c(node, internodes)\r\n\t\t\r\n\t\t# Create tnodes vector for storage:\r\n\t\ttnodes <- vector(mode=\"numeric\")\r\n\t\t\r\n\t\t# Date first (original) node:\r\n\t\ttnodes[1] <- max(tip.ages[FindDescendants(internodes[1], tree)])\r\n\t\t\r\n\t\t# For each internode:\r\n\t\tfor(i in 2:length(internodes)) {\r\n\t\t\t\r\n\t\t\t# Find descendants:\r\n\t\t\tdescs <- tree$edge[grep(TRUE, tree$edge[, 1] == internodes[i]), 2]\r\n\t\t\t\r\n\t\t\t# Remove previously dated node:\r\n\t\t\tdescs <- descs[-match(internodes[(i - 1)], descs)]\r\n\t\t\t\r\n\t\t\t# For each other descendant:\r\n\t\t\tfor(j in 1:length(descs)) {\r\n\t\t\t\t\r\n\t\t\t\t# If descendant node is internal:\r\n\t\t\t\tif(descs[j] > Ntip(tree)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Get maximum possible age:\r\n\t\t\t\t\tdescs[j] <- max(tip.ages[FindDescendants(descs[j], tree)])\r\n\t\t\t\t\t\r\n\t\t\t\t# If descendant node is terminal:\r\n\t\t\t\t} else {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Get maximum possible age:\r\n\t\t\t\t\tdescs[j] <- tip.ages[descs[j]]\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Store in tnodes:\r\n\t\t\ttnodes[i] <- max(descs)\r\n\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Vector for storing node ages to be deleted:\r\n\t\tdeletes <- vector(mode=\"numeric\")\r\n\t\t\r\n\t\t# For each potential tnode:\r\n\t\tfor(i in 2:length(tnodes)) {\r\n\t\t\t\r\n\t\t\t# Find nodes too young for dating and store in deletes:\r\n\t\t\tif(max(tnodes[1:(i-1)]) > tnodes[i]) deletes <- c(deletes, i)\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Remove these nodes (if there are any) from tnodes:\r\n\t\tif(length(deletes) > 0) tnodes <- tnodes[-deletes]\r\n\t\t\r\n\t\t# Give in order from oldest to youngest:\r\n\t\ttnodes <- rev(tnodes)\r\n\t\t\r\n\t\t# Return tnodes:\r\n\t\treturn(tnodes)\r\n\t\t\r\n\t}\r\n\t\r\n}\r\n\r\n# Hedman (2010) method for estimating confidence given ages of ougroups (tnodes is the sequence of ages, from oldest to youngest; t0 is the arbitrary lower stratigraphic bound; resolution is the number of steps to take between the FAD and the lower stratigraphic bound):\r\nHedman.2010 <- function (tnodes, t0, resolution) { # Outputs estimate with two-tailed 95% CIs\r\n \r\n # Check t0 is older than any other node age:\r\n if(!all(t0 > tnodes)) stop(\"t0 must be older than any tnode value.\")\r\n \r\n\t# Store requested resolution:\r\n\trequested.resolution <- resolution\r\n\t\r\n\t# Function returns p.d.f. for node ages\t(t0 is an arbitrary oldest age to consider, tnodes are the ages of oldest known fossil stemming from each node, and tsteps is a vector of arbitrary time steps on which the p.d.f. is calculated):\r\n\tnodeage <- function(tsteps, tnodes, t0) {\r\n\r\n\t\t# Get number of outgroups (tnodes):\r\n\t\tnn <- length(tnodes)\r\n\t\t\r\n\t\t# Get number of time steps (resolution):\r\n\t\tnt <- length(tsteps)\r\n\t\t\r\n\t\t# Initialize array:\r\n\t\tpnodes <- matrix(0, nn, nt)\r\n\t\t\r\n\t\t# First get pdf for node 1, at discrete values:\r\n\t\tii <- which(tsteps > t0 & tsteps < tnodes[1])\r\n\t\t\r\n\t\t# Assume uniform distribution for oldest node:\r\n\t\tpnodes[1, ii] <- 1.0 / length(ii)\r\n\t\t\r\n\t\t# Cycle through remaining nodes:\r\n\t\tfor (i in 2:nn) {\r\n\t\t\t\r\n\t\t\t# Cycle through series of time steps:\r\n\t\t\tfor (j in 1:nt) {\r\n\t\t\t\t\r\n\t\t\t\t# Initialize vector:\r\n\t\t\t\tp21 <- rep(0, nt)\r\n\t\t\t\t\r\n\t\t\t\t# Get p.d.f. for ith node ay discrete values:\r\n\t\t\t\tii <- which(tsteps >= tsteps[j] & tsteps < tnodes[i])\r\n\t\t\t\t\r\n\t\t\t\t\r\n\t\t\t\tp21[ii] <- 1.0 / length(ii) * pnodes[(i - 1), j]\r\n\t\t\t\t\r\n\t\t\t\t# Conditional probability of this age, given previous node age, times probability of previous node age, added to cumulative sum:\r\n\t\t\t\tpnodes[i, ii] <- pnodes[i, ii] + p21[ii]\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Get just the p.d.f. vector for the youngest node:\r\n\t\tout <- pnodes[nn, ]\r\n\t\t\r\n\t\t# Return output:\r\n\t\treturn(out)\r\n\t\t\r\n\t}\r\n\t\r\n\t# Calculate c.d.f. from p.d.f., find median and 95% credibility limits:\r\n\tHedmanAges <- function(tnodes, t0, resolution) {\r\n\t\r\n\t\t# Store input resolution for later reuse:\r\n\t\told.resolution <- resolution\r\n\t\r\n\t\t# Get uniformly spaced p-values (including 95% limits adn median) for calculating CIs:\r\n\t\tCIs <- sort(unique(c(0.025, 0.975, 0.5, c(1:old.resolution) * (1 / old.resolution))))\r\n\t\r\n\t\t# Get enw resolution (may be longer by adding limits and/or median):\r\n\t\tresolution <- length(CIs)\r\n\t\r\n\t\t# Get oldest possible age to consider:\r\n\t\tfirst <- t0\r\n\t\r\n\t\t# Get youngest possible age to consider:\r\n\t\tlast <- min(tnodes)\r\n\t\r\n\t\t# Make tnodes negative for Hedman function:\r\n\t\ttnodes <- -tnodes\r\n\t\r\n\t\t# Get uniformly spaced time steps for Hedman function:\r\n\t\ttsteps <- seq(-first, -last, length=resolution)\r\n\r\n\t\t# Make t0 negative for Hedman function:\r\n\t\tt0 <- -t0\r\n\t\r\n\t\t# Run Hedman function to get p.d.f.:\r\n\t\tvector <- nodeage(tsteps, tnodes, t0)\r\n\t\r\n\t\t# Convert from p-value scale to age scale:\r\n\t\tintegral.vector <- vector * ((abs(first - last)) / resolution)\r\n\t\r\n\t\t# Get sum of probabilities in order to re-scale as p.d.f.:\r\n\t\tprobability.sum <- sum(integral.vector)\r\n\t\r\n\t\t# Get re-scaled p-values for CIs:\r\n\t\tps.CIs <- probability.sum * CIs\r\n\t\r\n\t\t# Cretae empty vector to store dates:\r\n\t\tdate.distribution <- vector(mode=\"numeric\")\r\n\t\r\n\t\t# Set initial value:\r\n\t\tvalue <- 0\r\n\t\r\n\t\t# For each re-scaled p-value:\r\n\t\tfor(i in length(ps.CIs):1) {\r\n\t\t\r\n\t\t\t# Update value:\r\n\t\t\tvalue <- value + integral.vector[i]\r\n\t\t\r\n\t\t\t# If in age window:\r\n\t\t\tif(length(which(value >= ps.CIs)) > 0) {\r\n\r\n\t\t\t\t# Store dates:\r\n\t\t\t\tdate.distribution <- c(date.distribution, rep(tsteps[i], length(which(value >= ps.CIs))))\r\n\t\t\t\r\n\t\t\t\t# Update re-scaled p-values:\r\n\t\t\t\tps.CIs <- ps.CIs[-grep(TRUE, value >= ps.CIs)]\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\r\n\t\t}\r\n\t\r\n\t\t# Add t0 at end if length is short:\r\n\t\twhile(length(date.distribution) < resolution) date.distribution <- c(date.distribution, t0)\r\n\r\n\t\t# Find median of distribution:\r\n\t\tBest.guess <- date.distribution[CIs == 0.5]\r\n\t\t\t\r\n\t\t# Get upper 95% CI:\r\n\t\tBest.guess.lower <- date.distribution[CIs == 0.975]\r\n\t\t\r\n\t\t# Get lower 95% CI:\r\n\t\tBest.guess.upper <- date.distribution[CIs == 0.025]\r\n\t\t\r\n\t\t# Combine results and reverse sign to give palaeo ages:\r\n\t\tresults <- list(-Best.guess, -Best.guess.lower, -Best.guess.upper, -date.distribution[match(c(c(1:old.resolution) * (1 / old.resolution)), CIs)])\r\n\t\t\r\n\t\t# Name subvariables:\r\n\t\tnames(results) <- c(\"Best.guess\", \"Best.guess.lower\", \"Best.guess.upper\", \"Age.distribution\")\r\n\t\t\r\n\t\t# Return result\r\n\t\treturn(results)\r\n\t\t\r\n\t}\r\n\t\r\n\t# Get Hedman ages:\r\n\tout <- HedmanAges(tnodes, t0, resolution)\r\n\t\r\n\t# If Hedman ages represent less than five unique values (leading to downstream problem of flat distributions):\r\n\twhile(length(unique(out$Age.distribution[round(seq(1, length(out$Age.distribution), length.out=requested.resolution))])) < 5) {\r\n\t\t\r\n\t\t# Double the resolution size:\r\n\t\tresolution <- resolution * 2\r\n\t\t\r\n\t\t# Get Hedman ages:\r\n\t\tout <- HedmanAges(tnodes, t0, resolution)\r\n\t\t\r\n\t}\r\n\t\r\n\t# Update output:\r\n\tout$Age.distribution <- out$Age.distribution[round(seq(1, length(out$Age.distribution), length.out=requested.resolution))]\r\n\t\r\n\t# Return output:\r\n\treturn(out)\r\n\t\r\n}\r\n\r\n# Over-arching Hedman tree-dating function:\r\nHedman.tree.dates <- function(tree, tip.ages, outgroup.ages, t0, resolution = 1000, conservative = TRUE) {\r\n\t\r\n# MORE CONDITIONALS NEEDED FOR WHEN RESOLUTION IS LOW\r\n\r\n\t# Load libraries:\r\n\trequire(ape)\r\n\trequire(strap)\r\n \r\n # Check tree is fully bifurcating:\r\n if(!is.binary.tree(tree)) stop(\"Tree must be fully bifurcating.\")\r\n \r\n\t# Ensure tip ages are in tree tip order:\r\n\ttip.ages <- tip.ages[tree$tip.label]\r\n\r\n\t# Find root node:\r\n\troot.node <- Ntip(tree) + 1\r\n\t\r\n\t# Create variables to store age estimates:\r\n\tage.estimates <- matrix(nrow = Nnode(tree), ncol = 3)\r\n\t\r\n\t# Create variables to store age distributions:\r\n\tage.distributions <- matrix(nrow = Nnode(tree), ncol = resolution)\r\n\t\r\n\t# Set column headings:\r\n\tcolnames(age.estimates) <- c(\"Best.guess\", \"Best.guess.lower\", \"Best.guess.upper\")\r\n\r\n\t# Set row names:\r\n\trownames(age.estimates) <- rownames(age.distributions) <- c((Ntip(tree) + 1):(Ntip(tree) + Nnode(tree)))\r\n\t\r\n\t# Vector to store outgroup sequences:\r\n\tog.seq <- vector(mode = \"numeric\")\r\n\r\n\t# Report progress:\r\n\tcat(\"Identifying nodes that are date-able using the Hedman technique_\")\r\n\t\r\n\t# Find dateable conservative nodes:\r\n\tif(conservative) dateable.nodes <- find.dateable.nodes(tree, tip.ages)\r\n\t\r\n\t# Find dateable non-conservative nodes:\r\n\tif(!conservative) dateable.nodes <- c((Ntip(tree) + 1):(Ntip(tree) + Nnode(tree)))\r\n\r\n\t# Report progress:\r\n\tcat(\"Done\\nDating nodes using Hedman technique_\")\r\n\r\n\t# If not using the conservative approach:\r\n\tif(!conservative) {\r\n\r\n\t\t# Create ages matrix:\r\n\t\tages <- cbind(tip.ages, tip.ages)\r\n\r\n\t\t# Add rownames:\r\n\t\trownames(ages) <- names(tip.ages)\r\n\r\n\t\t# Add column names:\r\n\t\tcolnames(ages) <- c(\"FAD\", \"LAD\")\r\n\r\n\t\t# Sort by taxon order in tree:\r\n\t\tages <- ages[tree$tip.label, ]\r\n\r\n\t\t# Date tree using basic algorithm:\r\n\t\tstree <- DatePhylo(tree, ages, 0, \"basic\", FALSE)\r\n\r\n\t\t# Get node ages:\r\n\t\tsages <- GetNodeAges(stree)\r\n\r\n\t\t# Set first outgroup sequence (i.e. root) as this is special case:\r\n\t\tog.seq[1] <- paste(c(outgroup.ages, sages[root.node]), collapse = \"%%\")\r\n\r\n\t\t# For each non-root node:\r\n\t\tfor(i in 2:length(dateable.nodes)) {\r\n\t\t\t\r\n\t\t\t# Identify node:\r\n\t\t\tnode <- dateable.nodes[i]\r\n\t\t\t\r\n\t\t\t# Find preceding node:\r\n\t\t\tinternodes <- tree$edge[match(node, tree$edge[, 2]), 1] \r\n\t\t\t\r\n\t\t\t# Keep going until we reach the root and add next internode to set:\r\n\t\t\twhile(internodes[length(internodes)] != root.node) internodes <- c(internodes, tree$edge[match(internodes[length(internodes)], tree$edge[, 2]), 1])\r\n\t\t\t\r\n\t\t\t# Collate nodes and put in order:\r\n\t\t\tinternodes <- sort(c(node, internodes))\r\n\t\t\t\r\n\t\t\t# Find outgroup age sequence and collaspe to single string and store:\r\n\t\t\tog.seq[i] <- paste(c(outgroup.ages, sages[internodes]), collapse=\"%%\")\r\n\r\n\t\t}\r\n\r\n\t# If using the conservative approach:\r\n\t} else {\r\n\r\n\t\t# Find age estimates for each dateable node:\r\n\t\tfor(i in 1:length(dateable.nodes)) {\r\n\t\t\r\n\t\t\t# Find tnodes for a specific node:\r\n\t\t\ttnodes <- get.tnodes(dateable.nodes[i], tree, tip.ages)\r\n\t\t\r\n\t\t\t# Add additional outgroup tnodes:\r\n\t\t\ttnodes <- c(outgroup.ages, tnodes)\r\n\r\n\t\t\t# Turn outgroup sequence into single string and store:\r\n\t\t\tog.seq[i] <- paste(tnodes, collapse=\"%%\")\r\n\t\t\t\r\n\t\t}\r\n\r\n\t}\r\n\t\r\n\t# Get unique outgroup sequences:\r\n\tunq.og.seq <- unique(og.seq)\r\n\r\n\t# For each unique outgroup sequence:\r\n\tfor(i in 1:length(unq.og.seq)) {\r\n\t\t\r\n\t\t# List nodes with this outgroup sequence:\r\n\t\tnodes <- dateable.nodes[which(og.seq == unq.og.seq[i])]\r\n\t\t\r\n\t\t# Define tnodes:\r\n\t\ttnodes <- as.numeric(strsplit(unq.og.seq[i], \"%%\")[[1]])\r\n\r\n\t\t# Get Hedman dates:\r\n\t\thedman.out <- Hedman.2010(tnodes = tnodes, t0 = t0, resolution = resolution)\r\n\t\t\r\n\t\t# Update age distributions:\r\n for(j in 1:length(nodes)) age.distributions[as.character(nodes[j]), ] <- hedman.out$Age.distribution\r\n\t\t\r\n\t}\r\n\t\r\n\t# Separate check to see if root is dateable (first find root descendants that are tips, if any):\r\n\troot.desc.tips <- sort(tree$tip.label[tree$edge[which(root.node == tree$edge[, 1]), 2]])\r\n\t\r\n\t# Now check that there are descendants that are tips:\r\n\tif(length(root.desc.tips) > 0) {\r\n\t\t\r\n\t\t# Get maximum root descendant age:\r\n\t\troot.tip.age <- max(tip.ages[root.desc.tips])\r\n\r\n\t\t# Now check that descendants are older than any other tips and get Hedman dates for root and update age distributions:\r\n\t\tif(root.tip.age > max(tip.ages[setdiff(tree$tip.label, root.desc.tips)])) age.distributions[as.character(root.node), ] <- Hedman.2010(tnodes = c(outgroup.ages, root.tip.age), t0 = t0, resolution = resolution)$Age.distribution\r\n\t\t\r\n\t}\r\n\r\n\t# If not using the conservative approach:\r\n\tif(conservative == FALSE) {\r\n\t\r\n\t\t# Report progress:\r\n\t\tcat(\"Done\\nIdentifying remaining undated nodes_\")\r\n\t\t\r\n\t\t# Report progress:\r\n\t\tcat(\"Done\\nDating remaining undated nodes using randomisation technique_\")\r\n\r\n\t# If using the conservative approach:\r\n\t} else {\r\n\r\n\t\t# Report progress:\r\n\t\tcat(\"Done\\nIdentifying remaining undated nodes_\")\r\n\t\t\r\n\t\t# Find undated nodes by first establishing actually dated nodes:\r\n\t\tdated.nodes <- sort(dateable.nodes)\r\n\t\t\r\n\t\t# List all internal nodes:\r\n\t\tall.nodes <- (Ntip(tree) + 1):(Ntip(tree) + Nnode(tree))\r\n\t\t\r\n\t\t# Get undated nodes:\r\n\t\tundated.nodes <- setdiff(all.nodes, dated.nodes)\r\n\t\t\r\n\t\t# Find branches that define undated nodes:\r\n\t\tinternal.branches <- tree$edge[which(tree$edge[, 2] > Ntip(tree)), ]\r\n\t\t\r\n\t\t# Is root dated? (default to \"yes\" before actually checking):\r\n\t\troot.dated <- \"Yes\"\r\n\t\t\r\n\t\t# If the root is undated (then it needs to be):\r\n\t\tif(is.na(match(root.node, dated.nodes))) {\r\n\t\t\t\r\n\t\t\t# Update root.dated:\r\n\t\t\troot.dated <- \"no\"\r\n\t\t\t\r\n\t\t\t# Create tnodes from outgroup ages ONLY (this will later be used to constrain the root age through the saem randomisation process as the other undated nodes):\r\n\t\t\ttnodes <- outgroup.ages\r\n\t\t\t\r\n\t\t\t# Date root node:\r\n\t\t\thedman.out <- Hedman.2010(tnodes = tnodes, t0 = t0, resolution = resolution)\r\n\t\t\t\r\n\t\t\t# Add to age estimates:\r\n\t\t\tage.estimates <- rbind(age.estimates, c(hedman.out$Best.guess, hedman.out$Best.guess.lower, hedman.out$Best.guess.upper))\r\n\t\t\t\r\n\t\t\t# Add to age distributions:\r\n\t\t\tage.distributions <- rbind(age.distributions, hedman.out$Age.distribution)\r\n\t\t\t\r\n\t\t\t# Add node name 0:\r\n\t\t\trownames(age.estimates)[length(rownames(age.estimates))] <- rownames(age.distributions)[length(rownames(age.distributions))] <- \"0\"\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Vector for storing strings of sequences of undated node(s) bound by dated nodes:\r\n\t\tanc.strings.trimmed <- anc.strings <- vector(mode = \"character\")\r\n\t\t\r\n\t\t# Work through dated nodes to find sequences of undated nodes bracketed by them:\r\n\t\tfor(i in 1:length(dated.nodes)) {\r\n\t\t\t\r\n\t\t\t# As long as the dated node is not the root (which has no ancestors):\r\n\t\t\tif(dated.nodes[i] != root.node) {\r\n\t\t\t\t\r\n\t\t\t\t# Get ancestors:\r\n\t\t\t\tancestors <- internal.branches[which(internal.branches[, 2] == dated.nodes[i]), 1]\r\n\t\t\t\t\r\n\t\t\t\t# As long as the ancestor is neither the root or a dated node:\r\n\t\t\t\tif(ancestors != root.node && length(sort(match(ancestors, dated.nodes))) == 0) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# And as long as it remains so find next ancestral node:\r\n\t\t\t\t\twhile(ancestors[length(ancestors)] != root.node && length(sort(match(ancestors[length(ancestors)], dated.nodes))) == 0) ancestors <- c(ancestors, internal.branches[match(ancestors[length(ancestors)], internal.branches[, 2]), 1])\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Add initial dated node:\r\n\t\t\t\tancestors <- c(dated.nodes[i], ancestors)\r\n\t\t\t\t\r\n\t\t\t\t# Only need retain those with at least one undated node between dated nodes:\r\n\t\t\t\tif(length(ancestors) > 2) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Add to ancestor strings:\r\n\t\t\t\t\tanc.strings <- c(anc.strings, paste(ancestors, collapse = \" \"))\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Add to trimmed ancestor strings:\r\n\t\t\t\t\tanc.strings.trimmed <- c(anc.strings.trimmed, paste(ancestors[2:(length(ancestors) - 1)], collapse = \" \"))\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# If root is undated then anc.strings with the root present must be modified to include node 0 at the end:\r\n\t\tif(root.dated == \"no\") {\r\n\t\t\t\r\n\t\t\t# Go through each anc.string:\r\n\t\t\tfor(i in 1:length(anc.strings)) {\r\n\t\t\t\t\r\n\t\t\t\t# Get ancestors vector:\r\n\t\t\t\tancestors <- as.numeric(strsplit(anc.strings[i], \" \")[[1]])\r\n\t\t\t\t\r\n\t\t\t\t# If last node is the root:\r\n\t\t\t\tif(ancestors[length(ancestors)] == root.node) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Add 0 node to end:\r\n\t\t\t\t\tancestors <- c(ancestors, 0)\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Re-store in anc.strings:\r\n\t\t\t\t\tanc.strings[i] <- paste(ancestors, collapse = \" \")\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Find oldest node from each ancestral sequence (first set up empty vector):\r\n\t\toldest.nodes <- vector(mode=\"numeric\")\r\n\t\t\r\n\t\t# Go through each anc.string:\r\n\t\tfor(i in 1:length(anc.strings)) {\r\n\t\t\t\r\n\t\t\t# Get ancestors vector:\r\n\t\t\tancestors <- as.numeric(strsplit(anc.strings[i], \" \")[[1]])\r\n\t\t\t\r\n\t\t\t# Store oldest node:\r\n\t\t\toldest.nodes <- c(oldest.nodes, ancestors[length(ancestors)])\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Collapse to unique oldest nodes only:\r\n\t\toldest.nodes <- sort(unique(oldest.nodes))\r\n\t\t\r\n\t\t# Clump anc.strings by shared oldest node (i.e. blocks that will be dated at the same time) - first create empty vector:\r\n\t\tclumped.anc.strings <- vector(mode=\"character\")\r\n\t\t\r\n\t\t# For each oldest node find anc.strings with oldest node and collapse with %%:\r\n\t\tfor(i in 1:length(oldest.nodes)) clumped.anc.strings <- c(clumped.anc.strings, paste(anc.strings[grep(paste(\" \", oldest.nodes[i], sep = \"\"), anc.strings)], collapse = \"%%\"))\r\n\r\n\t\t# Report progress:\r\n\t\tcat(\"Done\\nDating remaining undated nodes using randomisation technique_\")\r\n\t\t\r\n\t\t# Can now date undated nodes using random draws from bounding Hedman dated nodes:\r\n\t\tfor(i in 1:length(clumped.anc.strings)) {\r\n\t\t\t\r\n\t\t\t# First retrieve anc.strings with shared oldest node:\r\n\t\t\tanc.strings <- strsplit(clumped.anc.strings[i], \"%%\")[[1]]\r\n\t\t\t\r\n\t\t\t# Get young dated nodes (for drawing from for upper bounds):\r\n\t\t\tyoung.dated.nodes <- vector(mode=\"numeric\")\r\n\t\t\t\r\n\t\t\t# For each ancestor string find youngest dated node, i.e., lower bounds::\r\n\t\t\tfor(j in 1:length(anc.strings)) young.dated.nodes <- c(young.dated.nodes, as.numeric(strsplit(anc.strings[j], \" \")[[1]][1]))\r\n\t\t\t\r\n\t\t\t# Get age distributions for young dated nodes:\r\n\t\t\tyoung.distributions <- age.distributions[as.character(young.dated.nodes), ]\r\n\t\t\t\r\n\t\t\t# Force into a single row matrix if only a vector:\r\n\t\t\tif(!is.matrix(young.distributions)) young.distributions <- t(as.matrix(young.distributions))\r\n\t\t\t\r\n\t\t\t# Set young distribution half up from young distributions:\r\n\t\t\tyoung.distributions.old.half <- young.distributions\r\n\t\t\t\r\n\t\t\t# Get old half as age distributions for young dated nodes:\r\n\t\t\tfor(j in 1:length(apply(young.distributions, 1, median))) {\r\n\t\t\t\t\r\n\t\t\t\t# As long as the median is not the maximum:\r\n\t\t\t\tif(max(young.distributions[j, ]) > median(young.distributions[j, ])) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Split distribution into older half using median:\r\n\t\t\t\t\ttemp.distribution <- young.distributions[j, which(young.distributions[j, ] > median(young.distributions[j, ]))]\r\n\t\t\t\t\t\r\n\t\t\t\t# If the median is the maximum:\r\n\t\t\t\t} else {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Just use the maximum:\r\n\t\t\t\t\ttemp.distribution <- max(young.distributions[j, ])\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# ???:\r\n\t\t\t\tyoung.distributions.old.half[j, ] <- c(temp.distribution, rep(NA, length(young.distributions[j, ]) - length(temp.distribution)))\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Get old dated node (for drawing from for lower bounds):\r\n\t\t\told.dated.node <- as.numeric(strsplit(anc.strings[1], \" \")[[1]][length(strsplit(anc.strings[1], \" \")[[1]])])\r\n\t\t\t\r\n\t\t\t# Get age distributions for old dated node:\r\n\t\t\told.distribution <- age.distributions[as.character(old.dated.node), ]\r\n\t\t\t\r\n\t\t\t# As long as the median is not the minimum:\r\n\t\t\tif(min(old.distribution) < median(old.distribution)) {\r\n\t\t\t\t\r\n\t\t\t\t# Split distribution using median:\r\n\t\t\t\told.distribution.young.half <- old.distribution[which(old.distribution < median(old.distribution))]\r\n\t\t\t\t\r\n\t\t\t# If the median is the minimum:\r\n\t\t\t} else {\r\n\t\t\t\t\r\n\t\t\t\t# Just use the minimum:\r\n\t\t\t\told.distribution.young.half <- min(old.distribution)\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Get undated nodes (vector for storing output):\r\n\t\t\tundated.nodes <- vector(mode=\"numeric\")\r\n\t\t\t\r\n\t\t\t# For each anc.string:\r\n\t\t\tfor(j in 1:length(anc.strings)) {\r\n\t\t\t\t\r\n\t\t\t\t# Get ancestors:\r\n\t\t\t\tancestors <- strsplit(anc.strings[j], \" \")[[1]]\r\n\t\t\t\t\r\n\t\t\t\t# Store undated nodes:\r\n\t\t\t\tundated.nodes <- c(undated.nodes, as.numeric(ancestors[2:(length(ancestors) - 1)]))\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Collapse to unique nodes only:\r\n\t\t\tundated.nodes <- sort(unique(undated.nodes))\r\n\t\t\t\r\n\t\t\t# Find young constraints for each undated node (vector for storing results):\r\n\t\t\tyoung.constraints <- vector(mode = \"numeric\")\r\n\t\t\t\r\n\t\t\t# For each undated node add young constraints to list:\r\n\t\t\tfor(j in 1:length(undated.nodes)) young.constraints <- c(young.constraints, paste(young.dated.nodes[grep(paste(\" \", undated.nodes[j], sep = \"\"), anc.strings)], collapse = \" \"))\r\n\t\t\t\r\n\t\t\t# Find old constraints for each undated node (i.e. all nodes that are lower in tree as these will potentially be dated first and replace the current oldest age; vector for storing results):\r\n\t\t\told.constraints <- vector(mode=\"numeric\")\r\n\t\t\t\r\n\t\t\t# For each undated node:\r\n\t\t\tfor(j in 1:length(undated.nodes)) {\r\n\t\t\t\t\r\n\t\t\t\t# Get just anc.strings where undated node is present:\r\n\t\t\t\tnode.strings <- anc.strings[grep(paste(\" \", undated.nodes[j], sep=\"\"), anc.strings)]\r\n\t\t\t\t\r\n\t\t\t\t# Vector for storing older nodes:\r\n\t\t\t\tolder.nodes <- vector(mode=\"numeric\")\r\n\t\t\t\t\r\n\t\t\t\t# For each anc.string where the undated node exists ?????:\r\n\t\t\t\tfor(k in 1:length(node.strings)) older.nodes <- c(older.nodes, as.numeric(strsplit(strsplit(node.strings[k], undated.nodes[j])[[1]][2], \" \")[[1]]))\r\n\t\t\t\t\r\n\t\t\t\t# ????:\r\n\t\t\t\told.constraints <- c(old.constraints, paste(unique(sort(older.nodes)), collapse=\" \"))\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Find undated nodes constrained by dated nodes (vector for storage):\r\n\t\t\tyoung.dated.nodes.constrain <- vector(mode = \"character\")\r\n\t\t\t\r\n\t\t\t# For each young dated node constraint find undated nodes which it contrains:\r\n\t\t\tfor(j in 1:length(young.dated.nodes)) young.dated.nodes.constrain[j] <- paste(as.numeric(strsplit(anc.strings[j], \" \")[[1]][2:(length(strsplit(anc.strings[j], \" \")[[1]]) - 1)]), collapse = \" \")\r\n\t\t\t\r\n\t\t\t# Main dating loop (repeats random draw N times, where N is defined by the variable resolution):\r\n\t\t\tfor(j in 1:resolution) {\r\n\t\t\t\t\r\n\t\t\t\t# Modify age distributions used if medians of undated nodes exceed bounds of medians of dated nodes (needs to have been at least two entries already):\r\n\t\t\t\tif(j >= 3 && j >= floor(resolution / 2)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# If there is more than one undated node establish present medians for undated nodes:\r\n\t\t\t\t\tif(length(undated.nodes) > 1) undated.medians <- apply(age.distributions[as.character(undated.nodes), 1:(j - 1)], 1, median)\r\n\t\t\t\t\t\r\n\t\t\t\t\t# If there is only one undated node\r\n\t\t\t\t\tif(length(undated.nodes) == 1) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Establish present median of undated node:\r\n\t\t\t\t\t\tundated.medians <- median(age.distributions[as.character(undated.nodes), 1:(j - 1)])\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Add name for reference later:\r\n\t\t\t\t\t\tnames(undated.medians) <- as.character(undated.nodes)\r\n\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Case if oldest median of the undated nodes exceeds the median of the bounding old dated node:\r\n\t\t\t\t\tif(max(undated.medians) >= median(sort(old.distribution))) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Update active distribution with older half only (to force undated nodes towards median ages consistent with dated nodes):\r\n\t\t\t\t\t\tactive.old.distribution <- old.distribution.young.half\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t# If oldest median ages do not conflict:\r\n\t\t\t\t\t} else {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Draw from full distribution:\r\n\t\t\t\t\t\tactive.old.distribution <- old.distribution\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Set default active young distributions:\r\n\t\t\t\t\tactive.young.distributions <- young.distributions\r\n\t\t\t\t\t\r\n\t\t\t\t\t# For each young (top bounding) dated node:\r\n\t\t\t\t\tfor(k in 1:length(young.dated.nodes)) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Case if youngest median of the undated nodes exceeds the median of the bounding young dated node - update active distribution with younger half only (to force undated nodes towards median ages consistent with dated nodes):\r\n\t\t\t\t\t\tif(min(undated.medians[strsplit(young.dated.nodes.constrain[k], \" \")[[1]]]) <= median(sort(age.distributions[as.character(young.dated.nodes[k]), ]))) active.young.distributions[k, ] <- young.distributions.old.half[k, ]\r\n\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t# Case if still in first half of resolution:\r\n\t\t\t\t} else {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Use uncorrected distribution for upper bound:\r\n\t\t\t\t\tactive.old.distribution <- old.distribution\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Use uncorrected distributions for lower bound:\r\n\t\t\t\t\tactive.young.distributions <- young.distributions\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Special case of last iteration where we want to ensure we draw a date between the constraining medians:\r\n\t\t\t\tif(j == resolution) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Set old distribution to young half only:\r\n\t\t\t\t\tactive.old.distribution <- old.distribution.young.half\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Set default active young distributions:\r\n\t\t\t\t\tactive.young.distributions <- young.distributions\r\n\t\t\t\t\t\r\n\t\t\t\t\t# For each young (top bounding) dated node set all young distribution to old half only:\r\n\t\t\t\t\tfor(k in 1:length(young.dated.nodes)) active.young.distributions[k, ] <- young.distributions.old.half[k, ]\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Modify young and old distributions to remove tails which will always violate node order (young node older than old node and vice versa) to speed up the random draw step below; if part of young distributions are older than the oldest part of the old distribution:\r\n\t\t\t\tif(max(sort(as.vector(active.young.distributions))) >= max(sort(active.old.distribution))) {\r\n\t\t\t\t\r\n\t\t\t\t\t# Find oldest part of old distribution\r\n\t\t\t\t\tupper.limit <- max(sort(active.old.distribution))\r\n\t\t\t\t\t\r\n\t\t\t\t\t# ????:\r\n\t\t\t\t\tactive.young.distributions[which(active.young.distributions >= upper.limit)] <- NA\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Vector to store young distribution minima:\r\n\t\t\t\tactive.young.distribution.mins <- vector(mode=\"numeric\")\r\n\t\t\t\t\r\n\t\t\t\t# For each young node store minima:\r\n\t\t\t\tfor(k in 1:length(active.young.distributions[, 1])) active.young.distribution.mins <- c(active.young.distribution.mins, min(sort(active.young.distributions[k, ])))\r\n\t\t\t\t\r\n\t\t\t\t# If part of old distribution is younger than the youngest part of the youngest young distribution:\r\n\t\t\t\tif(min(sort(active.old.distribution)) <= min(active.young.distribution.mins)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Find lower limit (minimum of minima):\r\n\t\t\t\t\tlower.limit <- min(active.young.distribution.mins)\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Remove values less than or equal to the lower limit from the old distribution:\r\n\t\t\t\t\tactive.old.distribution <- active.old.distribution[grep(FALSE, active.old.distribution <= lower.limit)]\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Draw upper and lower bounds at random from old and young nodes:\r\n\t\t\t\tyoung.random.ages <- vector(mode=\"numeric\")\r\n\t\t\t\t\r\n\t\t\t\t# For each young node draw a random age from its distributon:\r\n\t\t\t\tfor(k in 1:length(young.dated.nodes)) young.random.ages <- c(young.random.ages, sort(active.young.distributions[k, ])[ceiling(runif(1, 0, length(sort(active.young.distributions[k, ]))))])\r\n\t\t\t\t\r\n\t\t\t\t# Repeat for old age:\r\n\t\t\t\told.random.age <- sort(active.old.distribution)[ceiling(runif(1, 0, length(sort(active.old.distribution))))]\r\n\r\n\t\t\t\t# Ensure old node is older than all young nodes; while old node is younger or equal in age to oldest young nodes:\r\n\t\t\t\twhile(old.random.age <= max(young.random.ages)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Re-draw ages as above:\r\n\t\t\t\t\tyoung.random.ages <- vector(mode=\"numeric\")\r\n\t\t\t\t\t\r\n\t\t\t\t\t# For each young node draw a random age from its distributon:\r\n\t\t\t\t\tfor(k in 1:length(young.dated.nodes)) young.random.ages <- c(young.random.ages, sort(active.young.distributions[k, ])[ceiling(runif(1, 0, length(sort(active.young.distributions[k, ]))))])\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Repeat for old age:\r\n\t\t\t\t\told.random.age <- sort(active.old.distribution)[ceiling(runif(1, 0, length(sort(active.old.distribution))))]\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Need to add names so can extract data later:\r\n\t\t\t\tnames(young.random.ages) <- young.dated.nodes\r\n\t\t\t\t\r\n\t\t\t\t# Find oldest (i.e. constraining) young node for each undated node (vector for storing output):\r\n\t\t\t\tyoung.constraints.node <- young.constraints.age <- vector(mode=\"numeric\")\r\n\t\t\t\t\r\n\t\t\t\t# For each undated node:\r\n\t\t\t\tfor(k in 1:length(undated.nodes)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Store young age:\r\n\t\t\t\t\tyoung.constraints.age <- c(young.constraints.age, max(young.random.ages[strsplit(young.constraints[k], \" \")[[1]]]))\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Store young node:\r\n\t\t\t\t\tyoung.constraints.node <- c(young.constraints.node, as.numeric(names(young.random.ages[strsplit(young.constraints[k], \" \")[[1]]])[which(young.random.ages[strsplit(young.constraints[k], \" \")[[1]]] == max(young.random.ages[strsplit(young.constraints[k], \" \")[[1]]]))[1]]))\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Vector for storing node dates:\r\n\t\t\t\tnode.dates <- rep(NA, length(undated.nodes))\r\n\t\t\t\t\r\n\t\t\t\t# Date undated nodes using randomisation process:\r\n\t\t\t\tfor(k in 1:length(unique(young.constraints.node)) ) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Find top.node:\r\n\t\t\t\t\ttop.node <- unique(young.constraints.node)[k]\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Find target (.e. undated) nodes:\r\n\t\t\t\t\ttarget.nodes <- undated.nodes[which(young.constraints.node == top.node)]\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Find bounding dates (potential old constraints):\r\n\t\t\t\t\told.constraining.nodes <- as.numeric(sort(unique(strsplit(paste(old.constraints[match(target.nodes, undated.nodes)], collapse=\" \"), \" \")[[1]])))\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Remove any target nodes present\r\n\t\t\t\t\tif(length(sort(match(target.nodes, old.constraining.nodes))) > 0) old.constraining.nodes <- old.constraining.nodes[-sort(match(target.nodes, old.constraining.nodes))]\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Find youngest old node as actual constraint:\r\n\t\t\t\t\told.constraining.node <- max(old.constraining.nodes)\r\n\t\t\t\t\t\r\n\t\t\t\t\t# If lower bound is the previously dated old node:\r\n\t\t\t\t\tif(old.constraining.node == old.dated.node) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Use that as maximum limit\r\n\t\t\t\t\t\tmax <- old.random.age\r\n\t\t\t\t\t\r\n\t\t\t\t\t# If lower bound is a previously undated node:\r\n\t\t\t\t\t} else {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Use that as maximum limit\r\n\t\t\t\t\t\tmax <- node.dates[match(old.constraining.node, undated.nodes)]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Set minimum age:\r\n\t\t\t\t\tmin <- young.random.ages[as.character(top.node)]\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Get node dates:\r\n\t\t\t\t\tnode.dates[match(target.nodes, undated.nodes)] <- sort(runif(length(target.nodes), min = min, max = max), decreasing = TRUE) # Draw node ages from bounding minima and maxima\r\n\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# Store results:\r\n\t\t\t\tage.distributions[as.character(undated.nodes), j] <- node.dates\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Can now remove node \"0\" (i.e., if root is undated by Hedman method) if present:\r\n\t\tif(root.dated == \"no\") {\r\n\t\t\t\r\n\t\t\t# Remove zero node from distributions:\r\n\t\t\tage.distributions <- age.distributions[-which(rownames(age.distributions) == \"0\"), ]\r\n\t\t\r\n\t\t\t# Remove zero node from estimates:\r\n\t\t\tage.estimates <- age.estimates[-which(rownames(age.estimates) == 0), ]\r\n\t\r\n\t\t}\r\n\r\n\t}\r\n\t\r\n\t# Report progress:\r\n\tcat(\"Done\\nTidying up and returning results_\")\r\n\r\n\t# Create vector of nodes estimated using Hedman method:\r\n\tHedman.estimated <- rep(1, length(rownames(age.estimates)))\r\n\t\r\n\t# Update those not estimated using Hedman method:\r\n\tif(conservative == TRUE) Hedman.estimated[match(setdiff(rownames(age.estimates), dateable.nodes), rownames(age.estimates))] <- 0\r\n\t\r\n\t# Add to age estimates:\r\n\tage.estimates <- cbind(age.estimates, Hedman.estimated)\r\n\t\r\n\t# Sort age distributions in advance of picking confidence intervals:\r\n\tage.distributions <- t(apply(age.distributions, 1, sort))\r\n\t\r\n\t# Add median values to age estimates:\r\n\tage.estimates[, \"Best.guess\"] <- apply(age.distributions, 1, median)\r\n\t\r\n\t# Add upper CI to age estimates:\r\n\tage.estimates[, \"Best.guess.upper\"] <- age.distributions[, ceiling(0.975 * resolution)]\r\n\t\r\n\t# Add lower CI to age estimates:\r\n\tage.estimates[, \"Best.guess.lower\"] <- age.distributions[, max(c(1, floor(0.025 * resolution)))]\r\n\r\n\t# Create vectors to store node ages for each branch:\r\n\tto.ages <- from.ages <- age.estimates[match(tree$edge[, 1], rownames(age.estimates)), \"Best.guess\"]\r\n\t\r\n\t# Get to ages for terminal branches:\r\n\tto.ages[which(tree$edge[, 2] <= Ntip(tree))] <- tip.ages[tree$edge[which(tree$edge[, 2] <= Ntip(tree)), 2]]\r\n\t\r\n\t# Get to ages for internal branches:\r\n\tto.ages[which(tree$edge[, 2] > Ntip(tree))] <- age.estimates[match(tree$edge[which(tree$edge[, 2] > Ntip(tree)), 2], rownames(age.estimates)), \"Best.guess\"]\r\n\t\r\n\t# Update tree with branch lengths scaled to time using median ages:\r\n\ttree$edge.length <- from.ages-to.ages\r\n\t\r\n\t# Update tree to include root time:\r\n\ttree$root.time <- age.estimates[as.character(root.node), \"Best.guess\"]\r\n\t\r\n\t# Collate results:\r\n\tresults <- list(age.estimates, age.distributions, tree)\r\n\t\r\n\t# Add names:\r\n\tnames(results) <- c(\"age.estimates\", \"age.distributions\", \"tree\")\r\n\t\r\n\t# Repprt progress\r\n\tcat(\"Done\")\r\n\t\r\n\t# Return output:\r\n\treturn(results)\r\n\t\r\n}\r\n\r\n# Alroys metric for the \"wobbliness\" of diversity curves:\r\nwobble.index <- function(diversity.vector) {\r\n\t\r\n\t# Calculate wobble index:\r\n\tout <- median(abs(log(diversity.vector[2:(length(diversity.vector) - 1)] ** 2 / (diversity.vector[1:(length(diversity.vector) - 2)] * diversity.vector[3:length(diversity.vector)]))))\r\n\r\n\t# Return result:\r\n\treturn(out)\r\n\r\n}\r\n\r\n# Matts intervalFitting function:\r\nintervalFitting <- function(phy, data, divisions, rootAge, interval.names=NULL, data.names=NULL, bounds=NULL, meserr=NULL) {\r\n \r\n\trequire(mvtnorm)\r\n\t\r\n\t# sort is T because sub-functions assume data are in\r\n\t# this particular order\r\n \r\n\tname.check <- \r\n\tfunction(phy, data, data.names=NULL)\r\n\t{\r\n\t\tif(is.null(data.names)) \r\n\t\t{\r\n\t\t\tif(is.vector(data))\r\n\t\t\tdata.names=names(data)\r\n\t\t\telse\r\n\t\t\tdata.names <- rownames(data)\r\n\t\t}\r\n\t\tt <- phy$tip.label\r\n\t\tr1 <- t[is.na(match(t, data.names))]\r\n\t\tr2 <- data.names[is.na(match(data.names, t))]\r\n\t\t\r\n\t\tr <- list(sort(r1), sort(r2))\r\n\t\t\r\n\t\tnames(r) <- cbind(\"Tree.not.data\", \"Data.not.tree\")\r\n\t\tif(length(r1)==0 && length(r2)==0) return(\"OK\")\r\n\t\telse return(r)\r\n\t}\r\n\t\r\n\ttreedata <- function(phy, data, data.names=NULL, sort=F, warnings=T)\r\n\t{\r\n\t\t\r\n\t\tif(is.vector(data)) data <- as.matrix(data)\r\n\t\tif(is.factor(data)) data <- as.matrix(data)\r\n\t\tif(is.array(data) & length(dim(data))==1) data <- as.matrix(data)\r\n\t\t\r\n\t\tif(is.null(data.names)) {\r\n\t\t\tif(is.null(rownames(data))) {\r\n\t\t\t\tdata.names <- phy$tip.label[1:dim(data)[1]]\r\n\t\t\t\tif(warnings)\r\n\t\t\t\tcat(\"Warning: no tip labels, order assumed to be the same as in the tree\\n\")\r\n\t\t\t} else\r\n\t\t\tdata.names <- rownames(data)\r\n\t\t}\r\n\t\tnc <- name.check(phy, data, data.names)\r\n\t\tif(is.na(nc[[1]][1]) | nc[[1]][1]!=\"OK\") {\r\n\t\t\tif(length(nc[[1]]!=0)) {\r\n\t\t\t\tphy=drop.tip(phy, as.character(nc[[1]]))\r\n\t\t\t\tif(warnings) {\r\n\t\t\t\t\tcat(\"Dropped tips from the tree because there were no matching names in the data:\\n\")\r\n\t\t\t\t\tprint(nc[[1]])\r\n\t\t\t\t\tcat(\"\\n\")\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\tif(length(nc[[2]]!=0)) {\r\n\t\t\t\tm <- match(data.names, nc[[2]])\r\n\t\t\t\tdata=as.matrix(data[is.na(m), ])\r\n\t\t\t\tdata.names <- data.names[is.na(m)]\r\n\t\t\t\tif(warnings) {\r\n\t\t\t\t\tcat(\"Dropped rows from the data because there were no matching tips in the tree:\\n\")\r\n\t\t\t\t\tprint(nc[[2]])\r\n\t\t\t\t\tcat(\"\\n\")\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\torder <- match(data.names, phy$tip.label)\t\r\n\t\t\r\n\t\trownames(data) <- phy$tip.label[order]\r\n\t\t\r\n\t\tif(sort) {\r\n\t\t\t\r\n\t\t\tindex <- match(phy$tip.label, rownames(data))\r\n\t\t\tdata <- as.matrix(data[index, ])\r\n\t\t}\r\n\t\t\r\n\t\tphy$node.label=NULL\r\n\t\t\r\n\t\treturn(list(phy=phy, data=data))\r\n\t}\r\n\t\r\n\ttd <- treedata(phy, data, data.names, sort=T)\r\n \r\n\tntax=length(td$phy$tip.label)\r\n \r\n\tif(is.null(meserr)) {\r\n\t\tme=td$data\r\n\t\tme[]=0\r\n\t\tmeserr=me\r\n\t} else if(length(meserr)==1) {\r\n\t\tme=td$data\r\n\t\tme[]=meserr\r\n\t\tmeserr=me\r\n\t} else if(is.vector(meserr)) {\r\n\t\tif(!is.null(names(meserr))) {\r\n\t\t\to <- match(rownames(td$data), names(meserr))\r\n\t\t\tif(length(o)!=ntax) stop(\"meserr is missing some taxa from the tree\")\r\n\t\t\tmeserr <- as.matrix(meserr[o, ])\r\n\t\t} else {\r\n\t\t\tif(length(meserr)!=ntax) stop(\"No taxon names in meserr, and the number of taxa does not match the tree\")\r\n\t\t\tme <- td$data\r\n\t\t\tme[]=meserr\r\n\t\t\tmeserr=me\r\n\t\t}\r\n\t} else {\r\n\t\tif(!is.null(rownames(meserr))) {\r\n\t\t\to <- match(rownames(td$data), rownames(meserr))\r\n\t\t\tmeserr=meserr[o, ]\r\n\t\t} else {\r\n\t\t\tif(sum(dim(meserr)!=dim(td$data))!=0)\r\n stop(\"No taxon names in meserr, and the number of taxa does not match the tree\")\r\n\t\t\tprint(\"No names in meserr; assuming that taxa are in the same order as tree\")\r\n\t\t}\r\n\t}\r\n\t\r\n\tif (is.null(interval.names)) {\r\n interval.names <- as.character (c(1:(length(divisions)-1)))\r\n\t} else if (is.vector(interval.names)) {\r\n if (length(interval.names) != (length(divisions)-1)) {\r\n stop (\"Number of interval names does not match number of intervals specified by 'divisions'\")\r\n print (\"Number of interval names does not match numbr of intervals specified by 'divisions'\")\r\n } else {\r\n interval.names <- interval.names\r\n }\r\n } else {\r\n stop (\"List of interval names is not a vector\")\r\n print (\"List of interval names is not a vector\")\r\n }\r\n \r\n\t\r\n\t\r\n \r\n #--------------------------------\r\n #--- PREPARE DATA LIST ---\r\n #--------------------------------\r\n ds\t\t\t <- list()\r\n ds$tree \t\t <- td$phy # TIP data\r\n ds$sliceEdge <- timesliceEdge (ds$tree, rootAge, divisions) #time slice tree once!\r\n ds$interval.names <- interval.names\r\n #--------------------------------\r\n #--- IDENTIFY ANCHORING BIN ---\r\n #--------------------------------\r\n\t\r\n\tds$anchorBin <- which.max (colSums (ds$sliceEdge))\r\n \r\n #--------------------------------\r\n #--- SET MODEL SPECIFICATIONS ---\r\n #--------------------------------\r\n cat(\"Fitting interval model\")\r\n #-----------------------------\r\n #--- SET PARAMETER BOUNDS ---\r\n #-----------------------------\r\n #---- DEFAULT BOUNDS\r\n bounds.default\t\t\t <- matrix(c(0.00000001, 20, rep (c(0.000001, 10000), (length(interval.names)-1))), nrow=(length(interval.names)), ncol=2, byrow=TRUE)\r\n rownames(bounds.default) <- c(\"beta\", interval.names[c(2:length(interval.names))]);\r\n colnames(bounds.default) <- c(\"min\", \"max\")\r\n \r\n \t#---- USER DEFINED PARAMETER BOUNDS\r\n \tif (is.null(bounds)) {\r\n \t\tbounds <- bounds.default # USE DEFAULTS\r\n \t}else{\r\n \t\tif (class(bounds)!=\"list\"){\r\n \t\t\tstop(\"Please specify user defined parameter bounds as a list()\")\r\n \t\t}else{\r\n \t\t\tspecified <- !c(is.null(bounds$beta)\r\n )\r\n \t\t\tbounds.user <- matrix(c(bounds$beta), \r\n nrow=sum(specified), ncol=2, byrow=TRUE\r\n )\r\n \t\t\trownames(bounds.user) <- c(\"beta\")[specified]\r\n \t \t\tcolnames(bounds.user) <- c(\"min\", \"max\")\r\n \r\n \t \t\t#---- SET FINAL SEARCH BOUNDS\r\n \t\t\tbounds <- bounds.default\r\n \t\t\tbounds[specified, ] <- bounds.user # Final Bounds\r\n \t\t} # END if list\r\n \t} # END user bound if loop\r\n \t#--------------------------------\r\n #--- APPEND MODEL SETTINGS ---\r\n #--------------------------------\r\n \tds$bounds <- data.frame(t(bounds))\r\n \r\n \t#--------------------------------\r\n #--- FIT MODEL ---\r\n #--------------------------------\r\n result <- list()\r\n for(i in 1:ncol(td$data)) {\r\n \tds$data=td$data[, i]\r\n \tds$meserr=meserr[, i]\r\n \t\tresult[[i]] <- fitContinuousModel(ds, print=print)\r\n \t\tif(!is.null(colnames(td$data))) names(result)[i] <- colnames(td$data)[i] else names(result)[i] <- paste(\"Trait\", i, sep=\"\")\r\n \r\n \t}\r\n \tresult\r\n}\r\n\r\n# Matts insert function:\r\n#----------------------------------\r\n#----- INSERTION FUNCTION -----\r\n#----------------------------------\r\ninsert <- function(v, e, pos){\r\n return(c(v[1:(pos-1)], e, v[(pos):length(v)]))\r\n}\r\n\r\n# Matts fitContinuousModel function:\r\nfitContinuousModel <- function(ds, print=TRUE)\r\n{\r\n\tbounds \t <- ds$bounds\r\n\tn \t\t <- length(ds$data)\r\n \r\n\t#----- MINIMIZE NEGATIVE LOG LIKELIHOOD\r\n \r\n\tbeta.start <- var(ds$data)/max(branching.times(ds$tree))\r\n \r\n \r\n\tout <- NULL\r\n \r\n\ty\t\t\t <- ds$data\t\t\t\t# TIP data\r\n\ttree\t\t <- ds$tree\t\t\t# Tree\r\n\tmeserr\t\t <- ds$meserr\r\n\tn\t\t\t <- length(y)\r\n\tnFactors <- ncol (ds$sliceEdge)-1 #specify the number of factors\r\n\tsliceEdge <- ds$sliceEdge #sliced branches\r\n \tsliceTree <- ds$tree\r\n\tanchorBin <- ds$anchorBin\r\n \r\n \r\n \r\n\t#----------------------------------\r\n\t#----- DEFAULT FIT -----\r\n\t#----------------------------------\r\n \r\n k <- 2+nFactors\r\n \r\n \r\n start=log(c(beta.start, rep (1, nFactors)))\r\n lower=log(bounds[\"min\", ])\r\n upper=log(bounds[\"max\", ])\r\n \r\n foo <- function(x) { #x is a vector of parameters\r\n \r\n \r\n if (anchorBin==1) {\r\n \r\n factors <- exp(c(log(1), x[-1])) #place anchor at start\r\n \r\n }\r\n \r\n else if (anchorBin==length(x)) {\r\n \r\n factors <- exp(c(x[-1], log(1))) #place anchor at end\r\n \r\n }\r\n \r\n else {\r\n \r\n x.x <- x [-1]\r\n \r\n x.x <- insert (x.x, log(1), anchorBin)\r\n \r\n factors <- exp (x.x)\r\n \r\n }\r\n \r\n\t\t\r\n \r\n newBranchLengths <- rowSums (aperm(factors*aperm(sliceEdge)))\r\n sliceTree$edge.length <- newBranchLengths\r\n\t\t\r\n vcv <- vcv.phylo(sliceTree)\r\n vv <- exp(x[1])*vcv\r\n diag(vv) <- diag(vv)+meserr^2\r\n mu <- phylogMean(vv, y)\r\n mu <- rep(mu, n)\r\n dmvnorm(y, mu, vv, log=T)\r\n }\r\n \r\n control <- list()\r\n \r\n control$fnscale <- -1\r\n \r\n o <- optim(foo, p=start, lower=lower, upper=upper, method=\"L\", hessian = T, control = control)\r\n \r\n #----------------------------------\r\n #---Caclulate CIs (approximate)----\r\n #----------------------------------\r\n \r\n fisherInfo <- solve(-o$hessian)\r\n \r\n propSigma <- sqrt(diag(fisherInfo))\r\n \r\n propSigma2 <- propSigma [-1] #removes information for variance parameter\r\n \r\n \r\n \r\n if (anchorBin==1) {\r\n \r\n factors <- c(1, exp(o$par[-1])) #anchor at start\r\n \r\n errors <- c(NA, propSigma2)\r\n \r\n \r\n \r\n }\r\n \r\n else if (anchorBin==(nFactors+1)) {\r\n \r\n factors <- c(exp(o$par[-1]), 1) #anchor at end\r\n \r\n errors <- c(propSigma2, NA)\r\n \r\n \r\n }\r\n \r\n else {\r\n \r\n factors <- exp(o$par[-1])\r\n \r\n factors <- insert (factors, 1, anchorBin)\r\n \r\n errors <- insert (propSigma2, NA, anchorBin)\r\n \r\n }\r\n \r\n \r\n names (factors) <- ds$interval.names\r\n \r\n upperSE <- factors + errors\r\n\t\r\n lowerSE <- factors - errors\r\n \r\n upper95 <- factors + 1.96*errors\r\n\t\r\n lower95 <- factors- 1.96*errors\r\n \r\n \r\n \r\n \r\n results <- list(lnl=o$value, beta= exp(o$par[1]), factors = factors)\r\n \r\n \r\n \r\n \r\n\t#----------------------------------\r\n\t#----- Collect results -----\r\n\t#----------------------------------\r\n \r\n \r\n\tresults$aic <- 2*k-2*results$lnl\r\n\tresults$aicc <- 2*k*(n-1)/(n-k-2)-2*results$lnl\r\n\tresults$k <- k\r\n\tresults$hessian <- o$hessian\r\n\tresults$upperSE <- upperSE\r\n\tresults$lowerSE <- lowerSE\r\n\tresults$upper95 <- upper95\r\n\tresults$lower95 <- lower95\r\n\treturn(results)\r\n \r\n}\r\n\r\n# Matts phylogMean function:\r\nphylogMean <- function(phyvcv, data)\r\n{\r\n\to <- rep(1, length(data))\r\n\tci <- solve(phyvcv)\r\n \r\n\tm1 <- solve(t(o) %*% ci %*% o)\r\n\tm2 <- t(o) %*% ci %*% data\r\n \r\n\treturn(m1 %*% m2)\r\n}\r\n\r\n# Matts timesliceedge function:\r\ntimesliceEdge <- function (tree, rootAge, divisions)\r\n{\r\n require(ape)\r\n require(phytools)\r\n \r\n #Create a new matrix i x j matrix, where i is equal to the number of branches and j is equal to the number of intervals\r\n \r\n sliceEdge <- matrix (nrow = nrow(tree$edge), ncol = (length(divisions)-1))\r\n \r\n nodeAges <- -(nodeHeights (tree) - rootAge)\r\n \r\n #Run some simple checks\r\n \r\n \r\n \r\n for (i in 1:nrow (sliceEdge)) {\r\n \r\n for (j in 1:ncol(sliceEdge)){\r\n \r\n #entirely below interval\r\n if (nodeAges[i, 2] > divisions[j]){\r\n \r\n sliceEdge[i, j] <- 0\r\n \r\n }\r\n \r\n #entirely above interval\r\n else if (nodeAges[i, 1] < divisions[j+1]){\r\n \r\n sliceEdge[i, j] <- 0\r\n \r\n }\r\n \r\n #entirely within interval\r\n else if (nodeAges[i, 1] < divisions[j] && nodeAges[i, 2] > divisions[j+1]){\r\n \r\n sliceEdge[i, j] <- nodeAges[i, 1] - nodeAges[i, 2]\r\n \r\n }\r\n \r\n #extends through entire interval\r\n else if (nodeAges[i, 1] >= divisions[j] && nodeAges[i, 2] <= divisions[j+1]){\r\n sliceEdge[i, j] <- divisions[j]-divisions[j+1]\r\n } else {\r\n #includes beginning of interval, but ends in interval\r\n if (nodeAges[i, 2] > divisions[j+1]){\r\n sliceEdge[i, j] <- divisions[j]-nodeAges[i, 2]\r\n } else { #includes end of interval, but begins in interval\r\n sliceEdge[i, j] <- nodeAges[i, 1]-divisions[j+1]\r\n }\r\n }\r\n }\r\n }\r\n return(sliceEdge)\r\n}\r\n\r\ncollapse.clade <- function(n, tree)\r\n{\r\n # If tree has no branch lengths make all branch lengths equal one:\r\n\tif(is.null(tree$edge.length)) tree$edge.length <- rep(1, length(tree$edge[, 1]))\r\n \r\n # If there are zero length branches make all branches equal to one:\r\n #if(min(tree$edge.length) == 0) tree$edge.length <- rep(1, length(tree$edge[, 1]))\r\n \r\n # Set the collapse cutoff branch length as less than the smallest branch length:\r\n\ttol <- min(tree$edge.length)/2\r\n \r\n # Set all branches ionside clade we want to collapse to zero:\r\n\tfor(i in n) tree$edge.length[GetDescendantEdges(i, tree)] <- 0\r\n \r\n # Collapse those branches:\r\n\ttree <- di2multi(tree, tol)\r\n \r\n # Return the collapsed tree:\r\n\treturn(tree)\r\n}\r\n\r\n# Function to calculate all possible combinations for variables given in var.names:\r\nvar.combns <- function(var.names) {\r\n\trequire(utils)\r\n\tcombos <- vector(mode=\"character\")\r\n\tfor(i in 1:(length(var.names)-1)) {\r\n\t\tmat <- apply(combn(var.names, i), 1, paste)\r\n\t\tif(is.matrix(mat) == FALSE) mat <- as.matrix(mat)\r\n\t\tfor(j in 1:length(mat[, 1])) combos <- c(combos, paste(mat[j, ], collapse=\" + \"))\r\n\t}\r\n\tcombos <- c(combos, paste(var.names, collapse=\" + \"))\r\n\treturn(combos)\r\n}\r\n\r\n# Function to reinsert safely deleted taxa:\r\nstr.reinsert <- function(treefile.in, treefile.out, str.list, multi.placements=\"exclude\")\r\n{\r\n\t\r\n\t# Ensure str list is formatted as characters:\r\n\tstr.list[, \"Junior\"] <- as.character(str.list[, \"Junior\"])\r\n\tstr.list[, \"Senior\"] <- as.character(str.list[, \"Senior\"])\r\n\tstr.list[, \"Rule\"] <- as.character(str.list[, \"Rule\"])\r\n\t\r\n\t# Read in tree file as text:\r\n\ttext <- scan(treefile.in, what=\"\\n\", quiet=TRUE)\r\n\t\r\n\t# Resort str list by Junior taxon:\r\n\tstr.list <- str.list[order(str.list[, \"Junior\"]), ]\r\n\t\r\n\t# Find number of seniors for each junior:\r\n\tnames.and.numbers <- rle(as.character(str.list[, \"Junior\"]))\r\n\t\r\n\t# Make list of taxa that have a single senior:\r\n\tsingle.replacements <- names.and.numbers$values[grep(TRUE, names.and.numbers$lengths == 1)]\r\n\t\r\n\t# Collapse to just those not in a polytomy with other taxa in the str list:\r\n\tsingle.replacements <- single.replacements[is.na(match(single.replacements, str.list[, \"Senior\"]))]\r\n\t\r\n\t# For taxa that have a single replacement:\r\n\tif(length(single.replacements) > 0) {\r\n\t\t\r\n\t\t# For each single replacement taxon:\r\n\t\tfor(i in 1:length(single.replacements)) {\r\n\t\t\t\r\n\t\t\t# Reinsert into tree next to its senior:\r\n\t\t\ttext <- gsub(str.list[match(single.replacements[i], str.list[, \"Junior\"]), \"Senior\"], paste(\"(\", paste(str.list[match(single.replacements[i], str.list[, \"Junior\"]), c(\"Junior\", \"Senior\")], collapse=\", \"), \")\", sep=\"\"), text)\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Remove single replacement taxa from str list:\r\n\t\tstr.list <- str.list[-match(single.replacements, str.list[, \"Junior\"]), ]\r\n\t\t\r\n\t\t# Update names and numbers now taxa have been removed:\r\n\t\tnames.and.numbers <- rle(as.character(sort(str.list[, \"Junior\"])))\r\n\t}\r\n\t\r\n\t# Vector to store taxa that only occur in a single polytomy:\r\n\tpolytomy.taxa <- vector(mode=\"character\")\r\n\t\r\n\t# For each taxon:\r\n\tfor(i in 1:length(names.and.numbers$values)) {\r\n\t\t\r\n\t\t# Get taxon name:\r\n\t\ttaxon.name <- names.and.numbers$values[i]\r\n\t\t\r\n\t\t# Find its seniors:\r\n\t\tseniors <- str.list[grep(TRUE, str.list[, \"Junior\"] == taxon.name), \"Senior\"]\r\n\t\t\r\n\t\t# If its seniors, except for one, are all also juniors then record it:\r\n\t\t# (This finds taxa that only exist in a single polytomy in the original tree)\r\n\t\tif(length(grep(TRUE, is.na(match(seniors, str.list[, \"Junior\"])))) <= 1) polytomy.taxa <- c(polytomy.taxa, taxon.name)\r\n\t}\r\n\t\r\n\t# If there are taxa that only occur in a single polytomy:\r\n\tif(length(polytomy.taxa) > 0) {\r\n\t\t\r\n\t\t# Reorder from most seniors to least:\r\n\t\ttaxa.to.delete <- polytomy.taxa <- polytomy.taxa[order(names.and.numbers$lengths[match(polytomy.taxa, names.and.numbers$values)], decreasing=TRUE)]\r\n\t\t\r\n\t\t# Whilst there are still polytomous taxa in the list:\r\n\t\twhile(length(polytomy.taxa) > 0) {\r\n\t\t\t\r\n\t\t\t# Get taxon name with most juniors:\r\n\t\t\ttaxon.name <- polytomy.taxa[1]\r\n\t\t\t\r\n\t\t\t# Find all of its seniors:\r\n\t\t\tseniors <- str.list[grep(TRUE, str.list[, \"Junior\"] == taxon.name), \"Senior\"]\r\n\t\t\t\r\n\t\t\t# Find senior taxon (already in tree):\r\n\t\t\tsenior.taxon <- seniors[grep(TRUE, is.na(match(seniors, str.list[, \"Junior\"])))]\r\n\t\t\t\r\n\t\t\t# Replace senior with all juniors in polytomy:\r\n\t\t\ttext <- gsub(senior.taxon, paste(\"(\", paste(sort(c(taxon.name, seniors)), collapse=\", \"), \")\", sep=\"\"), text)\r\n\t\t\t\r\n\t\t\t# Remove taxa just dealt with from polytomy.taxa:\r\n\t\t\tpolytomy.taxa <- polytomy.taxa[-sort(match(c(taxon.name, seniors), polytomy.taxa))]\r\n\t\t}\r\n\t\t\r\n\t\t# Trims str list down to remaining taxa:\r\n\t\tfor(i in 1:length(taxa.to.delete)) str.list <- str.list[-grep(TRUE, str.list[, \"Junior\"] == taxa.to.delete[i]), ]\r\n\t}\r\n\t\r\n\t# Only keep going if there are taxa still to reinsert:\r\n\tif(length(str.list[, 1]) > 0) {\r\n\t\t\r\n\t\t# If the user wishes to reinsert remaining taxa at random:\r\n\t\tif(multi.placements == \"random\") {\r\n\t\t\t\r\n\t\t\t# List unique juniors:\r\n\t\t\tunique.juniors <- rle(str.list[, \"Junior\"])$values[order(rle(str.list[, \"Junior\"])$lengths)]\r\n\t\t\t\r\n\t\t\t# For each junior taxon remaining:\r\n\t\t\tfor(i in 1:length(unique.juniors)) {\r\n\t\t\t\t\r\n\t\t\t\t# Isolate junior taxon name:\r\n\t\t\t\tjunior <- unique.juniors[i]\r\n\t\t\t\t\r\n\t\t\t\t# Get a senior for each tree:\r\n\t\t\t\tseniors <- sample(str.list[grep(TRUE, str.list[, \"Junior\"] == junior), \"Senior\"], length(text), replace=TRUE)\r\n\t\t\t\t\r\n\t\t\t\t# Remove junior from str list:\r\n\t\t\t\tstr.list <- str.list[-grep(TRUE, str.list[, \"Junior\"] == junior), ]\r\n\t\t\t\t\r\n\t\t\t\t# Make replacements:\r\n\t\t\t\treplacements <- paste(\"(\", paste(junior, seniors, sep=\", \"), \")\", sep=\"\")\r\n\t\t\t\t\r\n\t\t\t\t# For each tree:\r\n\t\t\t\tfor(j in 1:length(text)) {\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Isolate tree:\r\n\t\t\t\t\ttree.text <- text[j]\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Case if senior ends in a parenthesis:\r\n\t\t\t\t\tif(length(grep(paste(seniors[j], \")\", sep=\"\"), tree.text)) > 0) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Replace senior with combination of junior and senior:\r\n\t\t\t\t\t\ttree.text <- gsub(paste(seniors[j], \")\", sep=\"\"), paste(replacements[j], \")\", sep=\"\"), tree.text)\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Case if senior ends in a comma:\r\n\t\t\t\t\tif(length(grep(paste(seniors[j], \", \", sep=\"\"), tree.text)) > 0) {\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# Replace senior with combination of junior and senior:\r\n\t\t\t\t\t\ttree.text <- gsub(paste(seniors[j], \", \", sep=\"\"), paste(replacements[j], \", \", sep=\"\"), tree.text)\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\t# Update tree text:\r\n\t\t\t\t\ttext[j] <- tree.text\r\n\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\t# Set uninserted list to null (i.e. empty):\r\n\t\t\tuninserted <- NULL\r\n\t\t}\r\n\t\t\r\n\t\t# If the user wishes to exclude remaining taxa:\r\n\t\tif(multi.placements == \"exclude\") {\r\n\t\t\t\r\n\t\t\t# Set uninserted list to remaining juniors:\r\n\t\t\tuninserted <- unique(str.list[, \"Junior\"])\r\n\t\t}\r\n\t\t\r\n\t# If taxa all have a single reinsertion position:\r\n\t} else {\r\n\t\t\r\n\t\t# Set uninserted list to null (i.e. empty):\r\n\t\tuninserted <- NULL\r\n\t}\r\n\t\r\n\t# Write out tree file:\r\n\twrite(text, treefile.out)\r\n\t\r\n\t# Compile output:\r\n\toutput <- list(uninserted)\r\n\t\r\n\t# Name output:\r\n\tnames(output) <- \"unreinserted.taxa\"\r\n\t\r\n\t# Return output:\r\n\treturn(output)\r\n}\r\n\r\nGetNBifurcatingResolutions <- function(tree) {\r\n\t\r\n\t# Require the ape library:\r\n\trequire(ape)\r\n\t\r\n\t# Get a list of the number of descendant edges for each node:\r\n\tNfurcations <- rle(sort(tree$edge[, 1]))$lengths\r\n\t\r\n\t# Reduce this list to just the polytomies (nodes with three or more descendant edges):\r\n\tpolytomies <- Nfurcations[grep(TRUE, Nfurcations > 2)]\r\n\t\r\n\t# If there is at least one polytomy:\r\n\tif(length(polytomies) > 0) {\r\n\t\t\r\n\t\t# Work through each polytomy:\r\n\t\tfor(i in 1:length(polytomies)) {\r\n\t\t\t\r\n\t\t\t# Record the number of descendnat edges for the ith polytomy:\r\n\t\t\tn <- polytomies[i]\r\n\t\t\t\r\n\t\t\t# If tree is rooted:\r\n\t\t\tif(is.rooted(tree)) {\r\n\t\t\t\t\r\n\t\t\t\t# Calculate the number of possible rooted bifurcations for the ith polytomy (equation 1 in Felsenstein 1978):\r\n\t\t\t\tpolytomies[i] <- factorial((2 * n) - 3) / ((2 ^ (n - 2)) * factorial(n - 2))\r\n\t\t\t\t\r\n\t\t\t# If tree is unrooted:\r\n\t\t\t} else {\r\n\t\t\t\t\r\n\t\t\t\t# Calculate the number of possible unrooted bifurcations for the ith polytomy (equation from Casey Dunns slides):\r\n\t\t\t\tpolytomies[i] <- factorial((2 * n) - 5) / ((2 ^ (n - 3)) * factorial(n - 3))\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\r\n\t\t\r\n\t\t# Find product of all polytomies (total number of bifurcating resolutions):\r\n\t\tout <- prod(polytomies)\r\n\t\t\r\n\t# If there are no polytomies:\r\n\t} else {\r\n\t\t\r\n\t\t# Warn the user:\r\n\t\tprint(\"Tree is already fully bifurcating\")\r\n\t\t\r\n\t\t# Record the number of bifurcating resolutions (has to be one):\r\n\t\tout <- 1\r\n\t\t\r\n\t}\r\n\t\r\n\t# Return the number of bifurcating resolutions:\r\n\treturn(out)\r\n\t\r\n}\r\n\r\n# Compile functions; load compiler library:\r\nlibrary(compiler) \r\n\r\n# For each object in memory:\r\nfor(i in 1:length(objects())) {\r\n\t\r\n\t# If the object is a function (i.e. do not try to compile non-function objects):\r\n if(is.function(get(objects()[i]))) {\r\n\t\t\r\n\t\t# Compile function and reassign it to itself:\r\n assign(objects()[i], cmpfun(get(objects()[i]), options=c(suppressAll=TRUE)))\r\n\t\t\r\n }\r\n\t\r\n}\r\n", "meta": {"hexsha": "d40a45277e0bfac57d80faa5970738b37d126cfa", "size": 146539, "ext": "r", "lang": "R", "max_stars_repo_path": "Evolutionary rates analysis (Fig 5d-e and Supplementary Fig 2-4)/functions_7.r", "max_stars_repo_name": "SusanaGutarra/Plesiosaur-hydrodynamics-evolution", 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YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.32216100508746953}} {"text": "\\name{getSignalsFromList}\n\\alias{getSignalsFromList}\n\\title{\nGet Signals from a List\n}\n\\description{\nGet Signals from a List\n}\n\\usage{\ngetSignalsFromList(lt, fun = function(x) mean(x, na.rm = TRUE))\n}\n\\arguments{\n\n \\item{lt}{A list of normalized matrices which are returned by \\code{\\link{normalizeToMatrix}}. Matrices in the list should be generated with same settings (e.g. they should use same target regions, same extension to targets and same number of windows).}\n \\item{fun}{A user-defined function to summarize signals.}\n\n}\n\\details{\nLet's assume you have a list of histone modification signals for different samples and you want\nto visualize the mean pattern across samples. You can first normalize histone mark signals for each sample and then\ncalculate means values across all samples. In following example code, \\code{hm_gr_list} is a list of \\code{GRanges} objects\nwhich contain positions of histone modifications, \\code{tss} is a \\code{GRanges} object containing positions of gene TSS.\n\n \\preformatted{\n mat_list = NULL\n for(i in seq_along(hm_gr_list)) \\{\n mat_list[[i]] = normalizeToMatrix(hm_gr_list[[i]], tss, value_column = \"density\")\n \\} }\n\nIf we compress the list of matrices as a three-dimension array where the first dimension corresponds to genes,\nthe second dimension corresponds to windows and the third dimension corresponds to samples, the mean signal\nacross all sample can be calculated on the third dimension. Here \\code{\\link{getSignalsFromList}} simplifies this job.\n\nApplying \\code{getSignalsFromList()} to \\code{mat_list}, it gives a new normalized matrix which contains mean signals across all samples and can\nbe directly used in \\code{EnrichedHeatmap()}.\n\n \\preformatted{\n mat_mean = getSignalsFromList(mat_list)\n EnrichedHeatmap(mat_mean) }\n\nThe correlation between histone modification and gene expression can\nalso be calculated on the third dimension of the array. In the user-defined function \\code{fun}, \\code{x} is the vector for gene i\nand window j in the array, and \\code{i} is the index of current gene.\n\n \\preformatted{\n mat_corr = getSignalsFromList(mat_list, \n fun = function(x, i) cor(x, expr[i, ], method = \"spearman\")) }\n\nThen \\code{mat_corr} here can be used to visualize how gene expression is correlated to histone modification around TSS.\n\n \\preformatted{\n EnrichedHeatmap(mat_corr) }\n}\n\\value{\nA \\code{\\link{normalizeToMatrix}} object which can be directly used for \\code{\\link{EnrichedHeatmap}}.\n}\n\\author{\nZuguang Gu \n}\n\\examples{\nNULL\n}\n", "meta": {"hexsha": "e2270f5c8e0f8a5241bfaf1503669f528f69bf98", "size": 2553, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/getSignalsFromList.rd", "max_stars_repo_name": "jokergoo/CentralizedHeatmap", "max_stars_repo_head_hexsha": "17e7453693363bdaa2724c0e8c6c4d466f433e3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 156, "max_stars_repo_stars_event_min_datetime": "2015-07-06T04:31:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T09:36:21.000Z", "max_issues_repo_path": "man/getSignalsFromList.rd", "max_issues_repo_name": "jokergoo/CentralizedHeatmap", "max_issues_repo_head_hexsha": "17e7453693363bdaa2724c0e8c6c4d466f433e3f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 65, "max_issues_repo_issues_event_min_datetime": "2015-12-09T21:30:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-23T09:54:00.000Z", "max_forks_repo_path": "man/getSignalsFromList.rd", "max_forks_repo_name": "jokergoo/CentralizedHeatmap", "max_forks_repo_head_hexsha": "17e7453693363bdaa2724c0e8c6c4d466f433e3f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 25, "max_forks_repo_forks_event_min_datetime": "2016-04-20T13:31:24.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-22T03:55:11.000Z", "avg_line_length": 40.5238095238, "max_line_length": 255, "alphanum_fraction": 0.758715237, "num_tokens": 642, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.32216100508746953}} {"text": "# defining an operator\n`%*%` <- function (v, p) {\n r <- 1\n if (p == 1) {\n r <- v\n } else {\n for (i in 1:p) {\n r <- r * v\n }\n }\n r\n}\n\nprint (c(2 %*% 10, 2%*%2, 2%*%3)[1:2])\n\n# j introduced as a global var.\nfor (j in 1:3) {\n print (j)\n}\nprint (j)\n", "meta": {"hexsha": "46a7abcdd9969db7140ebb18e3f78368518ddb0e", "size": 295, "ext": "r", "lang": "R", "max_stars_repo_path": "third_party/universal-ctags/ctags/Units/parser-r.r/r-loop-counters.d/input.r", "max_stars_repo_name": "f110/wing", "max_stars_repo_head_hexsha": "31b259f723b57a6481252a4b8b717fcee6b01ff4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-03-24T16:08:43.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-24T16:08:43.000Z", "max_issues_repo_path": "third_party/universal-ctags/ctags/Units/parser-r.r/r-loop-counters.d/input.r", "max_issues_repo_name": "f110/wing", "max_issues_repo_head_hexsha": "31b259f723b57a6481252a4b8b717fcee6b01ff4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "third_party/universal-ctags/ctags/Units/parser-r.r/r-loop-counters.d/input.r", "max_forks_repo_name": "f110/wing", "max_forks_repo_head_hexsha": "31b259f723b57a6481252a4b8b717fcee6b01ff4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-26T09:00:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-26T09:00:06.000Z", "avg_line_length": 14.0476190476, "max_line_length": 38, "alphanum_fraction": 0.3728813559, "num_tokens": 124, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352403, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3221610050874695}} {"text": "library(ggplot2)\nlibrary(reshape2)\n\n#topic_names <- read.csv(\"../topic_names/final_lda20_fulltext.txt\",header=FALSE,as.is=TRUE)\n#topic_names <- read.csv(\"..//topic_names/abs_lda20_all4.txt\",header=FALSE,as.is=TRUE)\n#topic_names <- read.csv(\"~/work/tmpl/analysis/topic_names/abs_ICFP_lda20_topics.txt\",header=FALSE,as.is=TRUE)\n\n\n#src <- \"2015-01-06_11:06/lda20.csv\"\nsrc <- \"2016-01-27_16:44/lda10.csv\"\n\nsetwd(\"~/tmpl/out/\")\nlda <- data.frame(read.csv(src, header=TRUE,sep=\",\",quote=\"\\\"\"))\n#names(lda)[4:23] <- topic_names[1:20,]\n\n\n\n# for OOP and OOSD\n#s <- data.frame(c(lda[1:3],lda[16],lda[23]))\n#names(s)[4:5] <- c(names(lda)[16],names(lda)[23])\n\n# for languages and control\n#s <- data.frame(c(lda[1:3],lda[10]))\n#names(s)[4] <- names(lda)[10]\n\n# PLDI data history stuff\n#s <- data.frame(c(lda[1:3],lda[4],lda[8]))\n#names(s)[4:5] <- c(names(lda)[4],names(lda)[8])\n\n# POPL topics\n#s <- data.frame(c(lda[1:3],lda[13],lda[22]))\n#names(s)[4:5] <- c(names(lda)[13],names(lda)[22])\n\ns <- data.frame(lda)\n\nd <- melt(s,seq(1,3))\nnames(d)[4:5] <- c(\"Topic\",\"Weight\")\n\np <- ggplot(d, aes(x=Year,y=Weight,colour=Conference))\np + stat_smooth(method='loess') + facet_wrap(~Topic) + theme(strip.text=element_text(size=10)) \n#coord_cartesian(ylim=c(-50,1100))\n#+ coord_cartesian(ylim=c(-5,30)) \n\n# OOPSLA\n# p + stat_smooth(method='loess') + coord_cartesian(ylim=c(-50,1100)) + facet_wrap(~Topic) + theme(strip.text=element_text(size=20)) + coord_cartesian(ylim=c(-5,30)) + geom_vline(xintercept=2006, colour = \"green\", linetype = \"longdash\") + geom_vline(xintercept=2010, colour = \"red\", linetype=\"longdash\")\n\n\n", "meta": {"hexsha": "bdcc032fce48cf5d350c9c9cc744659a6aa2f513", "size": 1599, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/topic_weights/topic_weights.r", "max_stars_repo_name": "mgree/sigplan", "max_stars_repo_head_hexsha": "9f494b5d83ba4652812038b7a5b48fd237beae64", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2015-08-13T19:56:39.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-21T20:30:15.000Z", "max_issues_repo_path": "analysis/topic_weights/topic_weights.r", "max_issues_repo_name": "mgree/sigplan", "max_issues_repo_head_hexsha": "9f494b5d83ba4652812038b7a5b48fd237beae64", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/topic_weights/topic_weights.r", "max_forks_repo_name": "mgree/sigplan", "max_forks_repo_head_hexsha": "9f494b5d83ba4652812038b7a5b48fd237beae64", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2015-04-21T16:01:53.000Z", "max_forks_repo_forks_event_max_datetime": "2015-04-21T16:01:53.000Z", "avg_line_length": 33.3125, "max_line_length": 304, "alphanum_fraction": 0.6685428393, "num_tokens": 573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3221125600738508}} {"text": "#### GO Analysis\nlibrary(goseq)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(ggplot2)\nlibrary(GO.db)\n\ndo_goseq_DE <- function(data, perturbation, control,\n direction = NA, test = \"GO\") {\n foo <- data %>%\n filter(Perturbation == perturbation) %>%\n filter(Control == control)\n if (is.na(direction)) {\n genes <- as.integer(p.adjust(foo$PValue[foo$logFC != 0],\n method = \"BH\"\n ) < .05)\n names(genes) <- foo[foo$logFC != 0, ]$Geneid\n } else if (direction == \"up\") {\n genes <- as.integer(p.adjust(foo$PValue[foo$logFC > 0],\n method = \"BH\"\n ) < .05)\n names(genes) <- foo[foo$logFC > 0, ]$Geneid\n } else if (direction == \"down\") {\n genes <- as.integer(p.adjust(foo$PValue[foo$logFC < 0],\n method = \"BH\"\n ) < .05)\n names(genes) <- foo[foo$logFC < 0, ]$Geneid\n }\n ## Fit the Probability Weighting Function (PWF)\n pwf <- nullp(genes, \"hg19\", \"geneSymbol\")\n print(head(pwf))\n ## Fetch relationship between gene symbols and GO categories\n ## Then use Wallenius approximation\n if (test == \"GO\") {\n go_wall <- goseq(pwf, \"hg19\", \"geneSymbol\")\n return(go_wall)\n } else if (test == \"MF\") {\n go_mf <- goseq(pwf, \"hg19\", \"geneSymbol\", test.cats = c(\"GO:MF\"))\n return(go_mf)\n } else if (test == \"BP\") {\n go_bp <- goseq(pwf, \"hg19\", \"geneSymbol\", test.cats = c(\"GO:BP\"))\n return(go_bp)\n } else if (test == \"hallmarks\") {\n hallmarks <- read.table(\n paste0(\n \"/fh/fast/berger_a/grp/bergerlab_shared/Projects/RNA_eVIP/\",\n \"mSigDB/h.all.v6.2.symbols.gmt\"\n ),\n sep = \"\\t\", fill = TRUE\n )\n names(hallmarks) <- c(\"Category\", \"Gene\", seq(200))\n hallmarks <- data.frame(hallmarks) %>%\n gather(\"foo\", \"Gene\", 3:202) %>%\n dplyr::select(\"Category\", \"Gene\") %>%\n filter(Gene != \"\")\n go_hallmarks <- goseq(pwf, gene2cat = hallmarks)\n go_hallmarks$ontology <- \"mSigH\"\n return(go_hallmarks)\n }\n}", "meta": {"hexsha": "2a3681c5c18938d581f11d3f3e856736e06b7916", "size": 2116, "ext": "r", "lang": "R", "max_stars_repo_path": "R/load_goseq.r", "max_stars_repo_name": "aprilflow/lotk", "max_stars_repo_head_hexsha": "fc165d36ecc04cc566d1bd9da143bd52e5b78429", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/load_goseq.r", "max_issues_repo_name": "aprilflow/lotk", "max_issues_repo_head_hexsha": "fc165d36ecc04cc566d1bd9da143bd52e5b78429", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/load_goseq.r", "max_forks_repo_name": "aprilflow/lotk", "max_forks_repo_head_hexsha": "fc165d36ecc04cc566d1bd9da143bd52e5b78429", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2666666667, "max_line_length": 76, "alphanum_fraction": 0.5278827977, "num_tokens": 616, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3220556577548419}} {"text": "options(stringsAsFactors = F)\nlibrary(reshape2)\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(stringr)\nlibrary(MASS)\nsource(\"../Data/tidy-data/data/xtable.r\")\n\nif (!file.exists(\"../Data/tidy-data/case-study/deaths.rds\")) {\n src <- \"https://github.com/hadley/mexico-mortality/raw/master/deaths/deaths08.csv.bz2\"\n file.download(src, \"../Data/tidy-data/case-study/deaths.csv.bz2\", quiet = TRUE)\n \n deaths <- read.csv(\"../Data/tidy-data/case-study/eaths08.csv.bz2\")\n unlink(\"deaths08.csv.bz2\")\n deaths$hod[deaths$hod == 99] <- NA\n deaths$hod[deaths$hod == 24] <- 0\n deaths$hod[deaths$hod == 0] <- NA\n deaths$hod <- as.integer(deaths$hod) \n deaths <- arrange(deaths, yod, mod, dod, hod, cod)\n deaths <- deaths[c(\"yod\", \"mod\", \"dod\", \"hod\", \"cod\")]\n \n saveRDS(deaths, \"deaths.rds\")\n}\n\ndeaths <- readRDS(\"deaths.rds\")\n\nok <- subset(deaths, yod == 2008 & mod != 0 & dod != 0)\nxtable(ok[c(1, 1:14 * 2000), c(\"yod\", \"mod\", \"dod\", \"hod\", \"cod\")], \n \"raw.tex\")\n\ncodes <- read.csv(\"icd-main.csv\")\ncodes$disease <- sapply(codes$disease, function(x)\n str_c(strwrap(x, width = 30), collapse = \"\\n\"))\nnames(codes)[1] <- \"cod\"\ncodes <- codes[!duplicated(codes$cod), ]\n\n# Display overall hourly deaths\nhod_all <- subset(count(deaths, \"hod\"), !is.na(hod))\nqplot(hod, freq, data = hod_all, geom = \"line\") + \n scale_y_continuous(\"Number of deaths\", labels = function(x) format(x, big.mark = \",\")) + \n xlab(\"Hour of day\")\nggsave(\"overall.pdf\", width = 10, height = 6)\n\n# Count deaths per hour, per disease\nhod2 <- count(deaths, c(\"cod\", \"hod\"))\nhod2 <- subset(hod2, !is.na(hod))\nhod2 <- join(hod2, codes)\nhod2 <- ddply(hod2, \"cod\", transform, prop = freq / sum(freq))\n\n# Compare to overall abundance\noverall <- ddply(hod2, \"hod\", summarise, freq_all = sum(freq))\noverall <- mutate(overall, prop_all = freq_all / sum(freq_all))\n\nhod2 <- join(overall, hod2, by = \"hod\")\n\n# Pick better subset of rows to show\ncods <- join(arrange(count(deaths, \"cod\"), desc(freq)), codes)\nmutate(tail(subset(cods, freq > 100), 30), disease = str_sub(disease, 1, 30))\n\nhod3 <- subset(hod2, cod %in% c(\"I21\", \"N18\", \"E84\", \"B16\") & hod >= 8 & hod <= 12)[1:15, c(\"hod\", \"cod\", \"disease\", \"freq\", \"prop\", \"freq_all\", \"prop_all\")]\n\nxtable(hod3[c(\"hod\", \"cod\", \"freq\")], \"counts.tex\")\nxtable(hod3[c(\"disease\")], \"counts-disease.tex\")\nxtable(hod3[5], \"counts-prop.tex\")\nxtable(hod3[6:7], \"counts-all.tex\")\n\ndevi <- ddply(hod2, \"cod\", summarise, n = sum(freq), \n dist = mean((prop - prop_all)^2))\ndevi <- subset(devi, n > 50)\n\n# Find outliers\nxlog10 <- scale_x_log10(\n breaks = c(100, 1000, 10000), \n labels = c(100, 1000, 10000), \n minor_breaks = log10(outer(1:9, 10^(1:5), \"*\")))\nylog10 <- scale_y_log10(\n breaks = 10 ^ -c(3, 4, 5), \n labels = c(\"0.001\", \"0.0001\", \"0.00001\"),\n minor_breaks = log10(outer(1:9, 10^-(3:6), \"*\")))\n\nqplot(n, dist, data = devi)\nggsave(\"n-dist-raw.pdf\", width = 6, height = 6)\nqplot(n, dist, data = devi) + \n geom_smooth(method = \"rlm\", se = F) + \n xlog10 + \n ylog10\nggsave(\"n-dist-log.pdf\", width = 6, height = 6)\n\ndevi$resid <- resid(rlm(log(dist) ~ log(n), data = devi))\ncoef(rlm(log(dist) ~ log(n), data = devi))\nggplot(devi, aes(n, resid)) + \n geom_hline(yintercept = 1.5, colour = \"grey50\") +\n geom_point() + \n xlog10\nggsave(\"n-dist-resid.pdf\", width = 6, height = 6)\n\nunusual <- subset(devi, resid > 1.5)\nhod_unusual_big <- match_df(hod2, subset(unusual, n > 350))\nhod_unusual_sml <- match_df(hod2, subset(unusual, n <= 350))\n\n# Visualise unusual causes of death\nggplot(hod_unusual_big, aes(hod, prop)) + \n geom_line(aes(y = prop_all), data = overall, colour = \"grey50\") +\n geom_line() + \n facet_wrap(~ disease, ncol = 3)\nggsave(\"unusual-big.pdf\", width = 8, height = 6)\nlast_plot() %+% hod_unusual_sml\nggsave(\"unusual-sml.pdf\", width = 8, height = 4)\n\n", "meta": {"hexsha": "f4e94523d49c1537b99f39a1efa75bbbd35173e1", "size": 3766, "ext": "r", "lang": "R", "max_stars_repo_path": "notes/tidy-data/case-study/case-study.r", "max_stars_repo_name": "namkyodai/2022-UrbanComputation-SUTD", "max_stars_repo_head_hexsha": "fc3921ec4ea8719ebfb04f2646f00c90d886721f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2022-03-16T10:46:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T02:48:39.000Z", "max_issues_repo_path": "notes/tidy-data/case-study/case-study.r", "max_issues_repo_name": "namkyodai/2022-UrbanComputation-SUTD", "max_issues_repo_head_hexsha": "fc3921ec4ea8719ebfb04f2646f00c90d886721f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2022-03-16T06:48:23.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T16:39:15.000Z", "max_forks_repo_path": "notes/tidy-data/case-study/case-study.r", "max_forks_repo_name": "namkyodai/2022-UrbanComputation-SUTD", "max_forks_repo_head_hexsha": "fc3921ec4ea8719ebfb04f2646f00c90d886721f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2022-03-16T10:35:52.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-30T11:12:09.000Z", "avg_line_length": 34.2363636364, "max_line_length": 157, "alphanum_fraction": 0.6386086033, "num_tokens": 1370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555713, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.32196816377828635}} {"text": "# Generates a png image of the median RTD of each pair of ranks\n\nrequire('ggplot2')\nrequire('reshape')\n\nanalyze_all_pairs <- function(raw_data, kernel, kind, outputdir){\n time_cols = c(3:length(raw_data))\n cores_per_host = 8 # Adjust to your needs\n cores_per_socket = 4 # Adjust to your needs\n upper_bound = 600\n top_limit = 12\n title = paste(\"all pairs RTD in microsec (\", kernel , \")\", sep=\"\")\n kernel_fname = gsub(\" \", \"_\", fixed = TRUE, kernel) # replace spaces\n\tplotfile\t\t\t\t = paste(kernel_fname, kind, \"png\", sep=\".\")\n plotfile = paste(outputdir, plotfile, sep=\"/\")\n \n measure_data <- raw_data \n \n # combine all aggregated data\n clear_data <- melt(measure_data) \n # name columns for plotting\n names(clear_data) <- c(\"sender\", \"reciever\", \"data\") \n\n # set outliers to top_limit\n clear_data$data[clear_data$data > top_limit] <- top_limit\n\n # set correct name\n# colnames(clear_data)[3] <- kind\n maj_grid_lines = seq(0, dim(raw_data)[1], cores_per_host)\n min_grid_lines = seq(cores_per_socket, dim(raw_data)[1], cores_per_host)\n pal <- colorRampPalette(c(\"blue\", \"cyan\", \"yellow\", \"red\", \"deeppink\"))\n #plot\n p <- ggplot(clear_data, aes(x=sender-0.5, y=reciever-0.5))\n p <- p + ggtitle(title) + ylab(\"sender rank\") + xlab(\"reciever rank\")\n p <- p + geom_tile(aes(fill=data))\n\tp <- p + scale_fill_gradientn( colours = pal(100), limits = c(0,top_limit), guide = guide_legend(title = kind))\n p <- p + scale_x_continuous(minor_breaks = maj_grid_lines)\n p <- p + scale_y_continuous(minor_breaks = maj_grid_lines)\n\n# mark host and socket boundaries\n# p <- p + geom_vline(xintercept=maj_grid_lines, alpha=1/8, size=.5) # major v lines\n# p <- p + geom_hline(yintercept=maj_grid_lines, alpha=1/8, size=.5) # major h lines\n\n p <- p + theme_bw()\n p <- p + coord_fixed(ratio = 1)\n p <- p + theme(plot.title=element_text(face=\"bold\", size=20))\n ggsave(filename = plotfile, plot=p, units = \"mm\", width = 200, dpi = 400)\n \n return (clear_data)\n}\n", "meta": {"hexsha": "547c0b8041460a5053c9cf468237f3ea05ceb925", "size": 2012, "ext": "r", "lang": "R", "max_stars_repo_path": "rscripts/analyze_all_pairs.r", "max_stars_repo_name": "fmoessbauer/all-pairs", "max_stars_repo_head_hexsha": "512308b0b83083c743b3a3caa0981b788f51c51a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-09-27T16:18:06.000Z", "max_stars_repo_stars_event_max_datetime": "2016-09-27T16:18:06.000Z", "max_issues_repo_path": "rscripts/analyze_all_pairs.r", "max_issues_repo_name": "fmoessbauer/all-pairs", "max_issues_repo_head_hexsha": "512308b0b83083c743b3a3caa0981b788f51c51a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rscripts/analyze_all_pairs.r", "max_forks_repo_name": "fmoessbauer/all-pairs", "max_forks_repo_head_hexsha": "512308b0b83083c743b3a3caa0981b788f51c51a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-08T15:02:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-08T15:02:20.000Z", "avg_line_length": 39.4509803922, "max_line_length": 112, "alphanum_fraction": 0.6645129225, "num_tokens": 607, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3219681550798301}} {"text": "# Shows the different conversations (neighbourhoods) in which a user participated\n# \n# author: Alberto Lumbreras\nlibrary(RSQLite)\n\nplot.user.participations <- function(thread, user, con, database='reddit'){\n \n g <- database.to.graph(thread, con, database)\n gp <- g$gp\n mask <- V(gp)$user==user\n posts <- V(gp)[mask]\n \n for(i in 1:length(posts)){\n eg <- neighborhood.temporal.order(gp, posts[i], 2, 4)\n g <- graph.union(gp,eg)\n \n \n # Color nodes\n root.post <- V(gp)[which(degree(gp, mode='out')==0)]$name\n user.name <- V(gp)[posts[i]]$user\n post.id <- V(gp)[posts[i]]$name\n \n # See if it matches any seen motif\n u <- V(gp)[V(gp)$name==post.id] # identify the ego vertex\n uu <- V(gp)[V(gp)$user==user.name] # posts writen by ego author\n \n # note the <= in case two siblings have the same date\n V(gp)$color[V(gp)$date<=V(gp)[u]$date] <- 1 \n V(gp)$color[V(gp)$date>V(gp)[u]$date] <- 2 \n if(degree(gp, v=u, mode='out')==1){\n p.user.name <- neighbors(gp, u, mode='out')$user\n V(gp)[V(gp)$user==p.user.name]$color <- 3 # posts writen by parent of ego\n }\n V(gp)[uu]$color <- 4\n V(gp)[u]$color <- 5\n V(gp)[V(eg)$name==root.post]$color <- 6\n V(gp)[! V(gp)$name %in% V(eg)$name]$color <- 7\n \n mypalette <- c(\"grey\", \"black\", \"yellow\", \"orange\", \"red\", \"white\", 'green')\n \n # Plot\n gmotif <- as.undirected(gp)\n la = layout_as_tree(gmotif, mode='out', root=which.min(V(gmotif)$date))\n plot(gmotif,\n layout = la,\n vertex.color=mypalette[V(gmotif)$color],\n vertex.label = order(V(gp)$date),\n vertex.label.cex = 1,\n vertex.size= ifelse(V(gp)$color==7, 1, 3),\n edge.arrow.size=0.6)\n title(paste(user))\n }\n}\n\n\ncon <- dbConnect(dbDriver(\"SQLite\"), dbname = paste0(\"./data/\", database, \".db\"))\ndatabase <- 'reddit'\n\nfor (u in 1:length(cluster1)){\n user <- as.character(cluster1[u])\n query <- paste0(\"SELECT DISTINCT(t.threadid) FROM posts p, threads t WHERE t.forum LIKE 'podemos' AND p.thread = t.threadid AND p.user = '\", user, \"'\")\n threads <- dbGetQuery(con, query)$threadid\n \n for(i in 1:length(threads)){\n plot.user.participations(threads[i], user, con)\n \n answer <- readline(\"Continue with user? ([y]/n/exit) \")\n if (answer=='yes' || answer == '')\n {\n next\n }\n else if (answer == 'n'){\n break \n }\n else if (answer == 'exit'){\n stop(\"Bye!\")\n }\n }\n}\n\n\n\n", "meta": {"hexsha": "ef537c92e395c5877aa488b224fb473eb1d37e39", "size": 2446, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_user_participations.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "plot_user_participations.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_user_participations.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4698795181, "max_line_length": 155, "alphanum_fraction": 0.5776778414, "num_tokens": 749, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3219681550798301}} {"text": "# Seasonality analysis\n\nsource('./../../gwloggeR/R/graphics.R')\nsource('./../../gwloggeR/R/fourier.R')\nsys.source('./utils/utils.r', envir = (utils <- new.env()))\n\nlogger.names <- grep('barometer/', gwloggeR.data::enumerate(), value = TRUE)\n\nlogger.names <- setdiff(logger.names, 'barometer/BAOL016X_W1666.csv')\nlogger.names <- setdiff(logger.names, 'barometer/BAOL050X_56819.csv') # high freq manu in range of 80 cmH2O\n\nround_timestamp <- function(ts, scalefactor.sec = 3600*12) {\n as.POSIXct(round(as.numeric(ts)/scalefactor.sec) * scalefactor.sec, origin = '1970-01-01', tz = 'UTC')\n}\n\nread.baro <- function(logger.name) {\n df <- gwloggeR.data::read(logger.name)$df\n if (nrow(df) == 0L) return(df)\n df <- df[!is.na(TIMESTAMP_UTC), ]\n df <- df[!is.na(PRESSURE_VALUE), ]\n df <- df[!duplicated(TIMESTAMP_UTC), ]\n df <- df[, .('PRESSURE_VALUE' = mean(PRESSURE_VALUE)),\n by = .('TIMESTAMP_UTC' = round_timestamp(TIMESTAMP_UTC))]\n\n # Meta data\n df[, 'FILE' := basename(logger.name)]\n df[, 'N' := .N]\n\n data.table::setkey(df, TIMESTAMP_UTC)\n data.table::setattr(df, 'logger.name', logger.name)\n\n df\n}\n\n# from analysis 04\ncompare <- function(df1, df2) {\n if (nrow(df1) == 0L || nrow(df2) == 0L) return(data.table::data.table())\n\n diff.df <- df1[J(df2), .('PRESSURE_DIFF' = x.PRESSURE_VALUE - i.PRESSURE_VALUE, TIMESTAMP_UTC)][!is.na(PRESSURE_DIFF), ]\n\n diff.df\n}\n\n#cl <- parallel::makeCluster(5L)\n#doParallel::registerDoParallel(cl)\n#parallel::stopCluster(cl)\n\ncoefs <- function(fit, lambda) {\n coef <- glmnet::coef.glmnet(fit$glmnet.fit, s = lambda)\n coef[coef[, '1'] != 0,,drop = FALSE]\n}\n\ncoef.periods <- function(coefs) {\n coefs <- coefs[grepl('(sin\\\\(|cos\\\\(.*)', rownames(coefs)), , drop = FALSE]\n periods.sec <- sub(pattern = '(sin|cos)\\\\(([0-9.]+)\\\\)', replacement = '\\\\2', rownames(coefs))\n periods.sec <- as.numeric(periods.sec)\n periods.day <- periods.sec/3600/24\n if (length(periods.day) < 2L) return(round(periods.day))\n idx <- cutree(hclust(dist(periods.day)), h = 10)\n round(aggregate(periods.day, by = list(idx), FUN = mean)$x)\n}\n\n# # DTFT analysis\n#\n# dtft(frequency = 1/(365.25*24*3600), x = df.diff$PRESSURE_DIFF, timestamps = df.diff$TIMESTAMP_UTC)\n# dtft(frequency = 4/diff(as.numeric(range(df.diff$TIMESTAMP_UTC))), x = df.diff$PRESSURE_DIFF, timestamps = df.diff$TIMESTAMP_UTC)\n#\n# period <- seq(from = 12*3600*4, to = diff(as.numeric(range(df.diff$TIMESTAMP_UTC))), length.out = 10000)\n# fd <- sapply(1/period, FUN = dtft,\n# x = df.diff$PRESSURE_DIFF, timestamps = df.diff$TIMESTAMP_UTC)\n# plot(Mod(fd), type = 'l', x = period/3600/24)\n# period[which.max(Mod(fd))]/3600/24\n# table(diff(df.diff$TIMESTAMP_UTC))\n\n# # FFT analysis\n#\n# z <- fft(df.diff$PRESSURE_DIFF)\n# P <- diff(as.numeric(range(df.diff$TIMESTAMP_UTC)))\n# P1 <- P/(length(z) - 1)\n# fft.periods <- 1/((0:(length(z) - 1))*P1/P)\n# plot(Mod(z), type = 'l',\n# x = fft.periods,\n# xlab = 'days')\n# fft.periods[order(Mod(z), decreasing = TRUE)][1:5]\n# spectrum(df.diff$PRESSURE_DIFF)\n# dft(1L, df.diff$PRESSURE_DIFF)\n\nfit.glmnet <- function(y, x, parallel = FALSE) {\n\n # alist stores symbols (i.e. doesn't evaluate the arguments.)\n glmnet.args <- alist(\n y = y, x = x,\n alpha = 1,\n parallel = parallel,\n nlambda = 500L,\n foldid = rep(1:10, each = nrow(x)/10, length.out = nrow(x))\n )\n\n cv.glmnet <- glmnet::cv.glmnet\n\n fit <- utils$cache$as.file(\n fun = 'cv.glmnet', # this way glmnet stores only \"glmnet()\" call in fit\n args = glmnet.args,\n prefix = 'glmnet',\n cache.dir = './drifts/analysis_20'\n )\n\n fit$lambda.2se <- fit$lambda.min + 2*(fit$lambda.1se - fit$lambda.min)\n\n fit\n}\n\nplt.fspec <- function(periods.sec, intensity) {\n force(periods.sec); force(intensity)\n ggplot2::ggplot(mapping = ggplot2::aes(x = periods.sec/3600/24, y = intensity)) +\n ggplot2::geom_line(col = 'black') +\n ggplot2::geom_vline(xintercept = 365.25, col = 'red') +\n #ggplot2::ylab('Intensity') +\n ggplot2::xlab('Period (days)') +\n #ggplot2::ggtitle('Discrete-Time Fourier Transform') +\n ggplot2::theme_light() +\n ggplot2::theme(axis.title.y = ggplot2::element_blank(),\n axis.text.y = ggplot2::element_text(angle = 90, hjust = 0.5))\n}\n\nplt.yearly <- function(TIMESTAMP_UTC, y, ylim = quantile(y, probs = c(0.001, 0.999))) {\n force(TIMESTAMP_UTC); force(y)\n ggplot2::ggplot() +\n ggplot2::geom_point(mapping = ggplot2::aes(x = data.table::yday(TIMESTAMP_UTC), y = y)) +\n ggplot2::xlab('Day') +\n ggplot2::coord_cartesian(ylim = ylim,\n xlim = c(1, 366)) +\n ggplot2::theme_light() +\n ggplot2::theme(axis.title.y = ggplot2::element_blank(),\n axis.text.y = ggplot2::element_text(angle = 90, hjust = 0.5))\n}\n\nplt.hist <- function(x, xlim = quantile(x, probs = c(0.001, 0.999))) {\n force(x)\n binwidth <- 1.5 * IQR(x) / length(x) ^ (1/3)\n ggplot2::ggplot(mapping = ggplot2::aes(x = x)) +\n ggplot2::geom_histogram(binwidth = binwidth, fill = 'black') +\n ggplot2::xlab('PRESSURE_DIFF') +\n ggplot2::coord_cartesian(xlim = xlim) +\n ggplot2::theme_light() +\n ggplot2::theme(#axis.title.x = ggplot2::element_blank(),\n axis.title.y = ggplot2::element_blank(),\n axis.text.y = ggplot2::element_blank())\n}\n\nplt.comp <- function(TIMESTAMP_UTC, Y, Y_FIT_OPT, Y_FIT_1SE, Y_FIT_1SE_LABEL,\n front_layer = NULL, ylim, xlim, Y.col = 'red', Y.lab = 'PRESSURE_DIFF') {\n force(TIMESTAMP_UTC); force(Y)\n force(Y_FIT_OPT); force(Y_FIT_1SE); force(Y_FIT_1SE_LABEL)\n force(ylim); force(xlim)\n ggplot2::ggplot(mapping = ggplot2::aes(x = TIMESTAMP_UTC, y = Y)) +\n front_layer +\n ggplot2::geom_line(col = Y.col, alpha = 0.9) +\n ggplot2::geom_line(mapping = ggplot2::aes(y = Y_FIT_OPT), col = 'blue', size = 1.3) +\n ggplot2::geom_line(mapping = ggplot2::aes(y = Y_FIT_1SE), col = 'green', size = 1.3) +\n ggplot2::coord_cartesian(ylim = ylim,\n xlim = xlim) +\n ggplot2::annotate(\n \"label\", x = as.POSIXct(-Inf, origin = '1970-01-01'), y = Inf,\n size = 4, col = 'green', hjust = 0, vjust = 1, fill = 'grey', label.size = NA,\n label = Y_FIT_1SE_LABEL\n ) +\n ggplot2::ylab(Y.lab) +\n ggplot2::theme_light() +\n ggplot2::theme(axis.text.y = ggplot2::element_text(angle = 90, hjust = 0.5))\n}\n\nperiods.label <- function(coefs) {\n p <- coef.periods(coefs)\n has.trend <- any(grepl(pattern = 'trend', x = rownames(coefs), ignore.case = TRUE))\n str <- if (length(p) == 0L) 'Periods: /' else sprintf('Periods: %s days', paste0(p, collapse = ', '))\n if (has.trend) str <- paste0(str, ' + LinTrend')\n str\n}\n\nreport <- function(logger.name, ref.logger.names, parallel = FALSE) {\n\n print(logger.name)\n\n on.exit(gc(TRUE))\n\n df.logger <- read.baro(logger.name = logger.name)\n data.table::setkey(df.logger, TIMESTAMP_UTC)\n\n if (nrow(df.logger) < 10L) return(invisible(FALSE))\n\n periods <- seq(from = 2*24*3600,\n # take minimum a year for the periods\n to = max(diff(as.numeric(range(df.logger$TIMESTAMP_UTC))), 1.1*365.25*24*3600),\n length.out = 10000)\n\n fbase <- data.matrix(fbasis(df.logger$TIMESTAMP_UTC, trend = TRUE, frequencies = 1/periods))\n\n\n\n # Logger analysis ------------------------------------------------------------\n\n fd.single <- sapply(1/periods, FUN = dtft,\n x = df.logger$PRESSURE_VALUE - median(df.logger$PRESSURE_VALUE), timestamps = df.logger$TIMESTAMP_UTC)\n #plot(Mod(fd.single), type = 'l', x = periods/3600/24, xlab = 'days')\n\n p.single.fspec <- plt.fspec(periods.sec = periods, intensity = Mod(fd.single)/nrow(df.logger))\n\n fit.single <- fit.glmnet(y = df.logger$PRESSURE_VALUE, x = fbase, parallel = parallel)\n #plot(fit.single)\n #coef.periods(coefs(fit.single, lambda = fit.single$lambda.min))\n\n df.logger[, PRESSURE_VALUE_GLMNET_MIN := glmnet::predict.glmnet(fit.single$glmnet.fit, newx = fbase, s = fit.single$lambda.min)]\n df.logger[, PRESSURE_VALUE_GLMNET_1SE := glmnet::predict.glmnet(fit.single$glmnet.fit, newx = fbase, s = fit.single$lambda.1se)]\n\n\n\n # Multi comparison -----------------------------------------------------------\n\n df.diff <- lapply(setdiff(basename(ref.logger.names), basename(logger.name)), function(other.name) {\n df.other <- read.baro(other.name)\n compare(df.logger, df.other)\n })\n df.diff <- data.table::rbindlist(df.diff, use.names = TRUE, fill = TRUE)\n\n df.multi <- df.diff[\n , .(Q.025 = quantile(PRESSURE_DIFF, 0.025),\n Q.5 = quantile(PRESSURE_DIFF, 0.5),\n Q.975 = quantile(PRESSURE_DIFF, 0.975)),\n keyby = TIMESTAMP_UTC]\n\n # fbase is made on logger timestamps. we need to filter that one to get only the multi timestamps\n ridx.multi <- df.logger[, .(TIMESTAMP_UTC, I = 1:.N)][J(df.multi$TIMESTAMP_UTC), I]\n fbase.multi <- fbase[ridx.multi,,drop=FALSE]\n\n fit.multi <- fit.glmnet(y = df.multi$Q.5, x = fbase.multi, parallel = parallel)\n #plot(fit.multi)\n #coefs(fit.multi, lambda = fit.multi$lambda.1se)\n #coef.periods(coefs(fit.multi, lambda = fit.multi$lambda.1se))\n\n df.multi[, Q.5_GLMNET_MIN := glmnet::predict.glmnet(fit.multi$glmnet.fit, newx = fbase.multi, s = fit.multi$lambda.min)]\n df.multi[, Q.5_GLMNET_1SE := glmnet::predict.glmnet(fit.multi$glmnet.fit, newx = fbase.multi, s = fit.multi$lambda.1se)]\n\n fd.multi <- sapply(1/periods, FUN = dtft,\n x = df.multi$Q.5 - median(df.multi$Q.5), timestamps = df.multi$TIMESTAMP_UTC)\n #plot(Mod(fd.multi), type = 'l', x = periods/3600/24, xlab = 'days')\n\n p.multi.fspec <- plt.fspec(periods.sec = periods, intensity = Mod(fd.multi)/nrow(df.multi))\n\n\n\n # Reference comparison -------------------------------------------------------\n\n df.ref <- compare(df.logger, read.baro('KNMI_20200312_hourly'))\n data.table::setkey(df.ref, TIMESTAMP_UTC)\n #plot(df.ref$PRESSURE_DIFF, type = 'l', x = df.ref$TIMESTAMP_UTC)\n\n # fbase is made on logger timestamps. we need to filter that one to get only the ref timestamps\n ridx.ref <- df.logger[, .(TIMESTAMP_UTC, I = 1:.N)][J(df.ref$TIMESTAMP_UTC), I]\n fbase.ref <- fbase[ridx.ref,,drop=FALSE]\n\n fit.ref <- fit.glmnet(y = df.ref$PRESSURE_DIFF, x = fbase.ref, parallel = parallel)\n #plot(fit.ref)\n #coef.periods(coefs(fit.ref, lambda = fit.ref$lambda.1se))\n\n df.ref[, PRESSURE_DIFF_GLMNET_MIN := glmnet::predict.glmnet(fit.ref$glmnet.fit, newx = fbase.ref, s = fit.ref$lambda.min)]\n df.ref[, PRESSURE_DIFF_GLMNET_1SE := glmnet::predict.glmnet(fit.ref$glmnet.fit, newx = fbase.ref, s = fit.ref$lambda.1se)]\n\n fd.ref <- sapply(1/periods, FUN = dtft,\n x = df.ref$PRESSURE_DIFF - median(df.ref$PRESSURE_DIFF), timestamps = df.ref$TIMESTAMP_UTC)\n #plot(Mod(fd.ref), type = 'l', x = periods/3600/24, xlab = 'days')\n\n p.ref.fspec <- plt.fspec(periods.sec = periods, intensity = Mod(fd.ref)/nrow(df.ref))\n\n\n\n # Comparison plots -----------------------------------------------------------\n\n p.single <- plt.comp(\n TIMESTAMP_UTC = df.logger$TIMESTAMP_UTC, Y = df.logger$PRESSURE_VALUE,\n Y_FIT_OPT = df.logger$PRESSURE_VALUE_GLMNET_MIN, Y_FIT_1SE = df.logger$PRESSURE_VALUE_GLMNET_1SE,\n Y_FIT_1SE_LABEL = periods.label(coefs(fit.single, lambda = fit.single$lambda.1se)),\n ylim = quantile(df.logger$PRESSURE_VALUE, probs = c(0.001, 0.999)),\n xlim = range(df.logger$TIMESTAMP_UTC), Y.col = 'black', Y.lab = 'PRESSURE_VALUE'\n )\n\n ylim.multi <- quantile(df.multi$Q.5, probs = c(0.005, 0.995))\n ylim.ref <- quantile(df.ref$PRESSURE_DIFF, probs = c(0.005, 0.995))\n ylim.comp.range <- max(diff(ylim.multi), diff(ylim.ref))\n\n p.multi <- plt.comp(\n TIMESTAMP_UTC = df.multi$TIMESTAMP_UTC, Y = df.multi$Q.5,\n Y_FIT_OPT = df.multi$Q.5_GLMNET_MIN, Y_FIT_1SE = df.multi$Q.5_GLMNET_1SE,\n Y_FIT_1SE_LABEL = periods.label(coefs(fit.multi, lambda = fit.multi$lambda.1se)),\n front_layer = ggplot2::geom_point(data = df.diff, mapping = ggplot2::aes(x = TIMESTAMP_UTC, y = PRESSURE_DIFF), pch = '.'),\n ylim = mean(ylim.multi) + c(-1, +1)*ylim.comp.range/2, xlim = range(df.logger$TIMESTAMP_UTC)\n )\n\n p.ref <- plt.comp(\n TIMESTAMP_UTC = df.ref$TIMESTAMP_UTC, Y = df.ref$PRESSURE_DIFF,\n Y_FIT_OPT = df.ref$PRESSURE_DIFF_GLMNET_MIN, Y_FIT_1SE = df.ref$PRESSURE_DIFF_GLMNET_1SE,\n Y_FIT_1SE_LABEL = periods.label(coefs(fit.ref, lambda = fit.ref$lambda.1se)),\n ylim = mean(ylim.ref) + c(-1, +1)*ylim.comp.range/2, xlim = range(df.logger$TIMESTAMP_UTC))\n\n\n\n # Yearly plots ---------------------------------------------------------------\n\n p.single.yearly <- plt.yearly(TIMESTAMP_UTC = df.logger$TIMESTAMP_UTC, y = df.logger$PRESSURE_VALUE)\n\n p.multi.yearly <- plt.yearly(TIMESTAMP_UTC = df.multi$TIMESTAMP_UTC, y = df.multi$Q.5,\n ylim = mean(ylim.multi) + c(-1, +1)*ylim.comp.range/2)\n\n p.ref.yearly <- plt.yearly(TIMESTAMP_UTC = df.ref$TIMESTAMP_UTC, y = df.ref$PRESSURE_DIFF,\n ylim = mean(ylim.ref) + c(-1, +1)*ylim.comp.range/2)\n\n\n\n # Density plots --------------------------------------------------------------\n\n xlim.hist.multi <- quantile(df.multi$Q.5, probs = c(0.001, 0.999))\n xlim.hist.ref <- quantile(df.ref$PRESSURE_DIFF, probs = c(0.001, 0.999))\n xlim.hist.range <- max(diff(xlim.hist.multi), diff(xlim.hist.ref))\n\n p.multi.hist <- plt.hist(df.multi$Q.5, mean(xlim.hist.multi) + c(-1, +1)*xlim.hist.range/2)\n p.ref.hist <- plt.hist(df.ref$PRESSURE_DIFF, mean(xlim.hist.ref) + c(-1, +1)*xlim.hist.range/2)\n\n p.empty <- ggplot2::ggplot() + ggplot2::theme_void()\n\n\n\n # File export ----------------------------------------------------------------\n\n filename <- sprintf('./drifts/analysis_20/%s.png', tools::file_path_sans_ext(basename(logger.name)))\n dir.create(dirname(filename), showWarnings = FALSE, recursive = TRUE)\n\n layout_matrix <- rbind(c(1, 1, 1, 4, 5, 6),\n c(2, 2, 2, 7, 8, 9),\n c(3, 3, 3, 10, 11, 12))\n\n grob.title <- grid::textGrob(sprintf('%s (#%s observations from %s to %s)',\n basename(logger.name), nrow(df.logger),\n min(df.logger$TIMESTAMP_UTC), max(df.logger$TIMESTAMP_UTC)),\n x = 0.05, hjust = 0)\n\n local({\n png(filename, width = 1280, height = 720)\n on.exit(dev.off())\n print(gridExtra::grid.arrange(p.single, p.multi, p.ref,\n p.single.fspec, p.single.yearly, p.empty,\n p.multi.fspec, p.multi.yearly, p.multi.hist,\n p.ref.fspec, p.ref.yearly, p.ref.hist,\n layout_matrix = layout_matrix,\n top = grob.title))\n })\n\n invisible(TRUE)\n}\n\n# report('BAOL828X_P2_15705.csv', ref.logger.names = logger.names, parallel = TRUE)\n# report('BAOL528X_B_B2152.csv', ref.logger.names = logger.names, parallel = TRUE)\n# invisible(lapply(logger.names, report, ref.logger.names = logger.names))\n\nparallel::setDefaultCluster(parallel::makeCluster(spec = 4L, outfile=''))\nparallel::clusterExport(varlist = ls())\nresults <- parallel::parSapplyLB(X = logger.names, FUN = report, chunk.size = 1,\n ref.logger.names = logger.names)\nparallel::stopCluster(cl = parallel::getDefaultCluster())\n", "meta": {"hexsha": "4f67af899580e574df5648659611e0144e8325e7", "size": 15318, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r/drifts/analysis_20.r", "max_stars_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_stars_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-16T10:47:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-16T10:47:56.000Z", "max_issues_repo_path": "src/r/drifts/analysis_20.r", "max_issues_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_issues_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 61, "max_issues_repo_issues_event_min_datetime": "2019-05-17T21:14:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-26T13:47:40.000Z", "max_forks_repo_path": "src/r/drifts/analysis_20.r", "max_forks_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_forks_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-07-30T10:39:48.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-16T10:48:04.000Z", "avg_line_length": 41.512195122, "max_line_length": 131, "alphanum_fraction": 0.6197284241, "num_tokens": 4705, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.32196815507983007}} {"text": "context(\"annotate\")\n\ntest_that(\"dates in segment annotation work\", {\n dt <- structure(list(month = structure(c(1364774400, 1377993600),\n class = c(\"POSIXct\", \"POSIXt\"), tzone = \"UTC\"), total = c(-10.3,\n 11.7)), .Names = c(\"month\", \"total\"), row.names = c(NA, -2L), class =\n \"data.frame\")\n\n p <- ggplot(dt, aes(month, total)) +\n geom_point() +\n annotate(\"segment\",\n x = as.POSIXct(\"2013-04-01\"),\n xend = as.POSIXct(\"2013-07-01\"),\n y = -10,\n yend = 10\n )\n\n expect_true(all(c(\"xend\", \"yend\") %in% names(layer_data(p, 2))))\n})\n\ntest_that(\"segment annotations transform with scales\", {\n # This should be a visual test, but contriubtion documentation does not\n # explain how to make one\n ggplot(mtcars, aes(wt, mpg)) +\n geom_point() +\n annotate(\"segment\", x = 2, y = 10, xend = 5, yend = 30, colour = \"red\") +\n scale_y_reverse()\n})\n\ntest_that(\"annotation_* has dummy data assigned and don't inherit aes\", {\n custom <- annotation_custom(zeroGrob())\n logtick <- annotation_logticks()\n library(maps)\n usamap <- map_data(\"state\")\n map <- annotation_map(usamap)\n rainbow <- matrix(hcl(seq(0, 360, length.out = 50 * 50), 80, 70), nrow = 50)\n raster <- annotation_raster(rainbow, 15, 20, 3, 4)\n dummy <- dummy_data()\n expect_equal(custom$data, dummy)\n expect_equal(logtick$data, dummy)\n expect_equal(map$data, dummy)\n expect_equal(raster$data, dummy)\n\n expect_false(custom$inherit.aes)\n expect_false(logtick$inherit.aes)\n expect_false(map$inherit.aes)\n expect_false(raster$inherit.aes)\n})\n", "meta": {"hexsha": "d5b8b7be4270fed08cd35c9692696dcca6ceb3be", "size": 1551, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.3/ggplot2/tests/testthat/test-annotate.r", "max_stars_repo_name": "xinbinhuang/bitcoin-analysis", "max_stars_repo_head_hexsha": "9c496fe94100ab5e7293dc5b4328f44c2d1fda76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-06-28T21:04:57.000Z", "max_stars_repo_stars_event_max_datetime": "2017-06-28T21:04:57.000Z", "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/ggplot2/tests/testthat/test-annotate.r", "max_issues_repo_name": "lordbitin/ESWA-2017", "max_issues_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/ggplot2/tests/testthat/test-annotate.r", "max_forks_repo_name": "lordbitin/ESWA-2017", "max_forks_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.6530612245, "max_line_length": 78, "alphanum_fraction": 0.6505480335, "num_tokens": 478, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5888891307678319, "lm_q1q2_score": 0.32196815507983}} {"text": "#\n# Combine the results of the two classification runs with gradient boosted models\n# \n# The population and sampled classifications are in a data frame called \"results\"\n# The tasampled classifications are in a data frame called \"subsets\" since they're subsets of the single large database\n# The combined (non-subsetted) tasampled classification is in a data frame called \"combined_results\"\n#\nlibrary(mmadsenr)\n\n\nresults_files <- c(\n\"classification-combined-tassize-result-gbm-dfonly.RData\",\n\"classification-population-results-gbm-dfOnly.RData\",\n\"classification-ta-sampled-results-gbm-dfonly.RData\",\n\"per-locus-analysis-gbm-dfonly.RData\",\n\"perlocus-tassize-results-gbm-dfonly.RData\"\n)\n\n\n\n\nfor(file in results_files) {\n load(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = file))\n}\n\n\n# Step #1: Add sample size and ta duration columns to the results data frame\nresults$sample_size <- 0\nresults$ta_duration <- 0 # this broadcasts 0 to the entire column\n\n\n# Add an \"experiment group\" to each df before merging, to be used in visually distinguishing the classes\n\nresults$exp_group <- 'Population Census' \nperlocus_results$exp_group <- 'Population Per-Locus Only'\ncombined_tassize_results$exp_group <- 'Time Averaged and Sampled Combined Intervals' \ntassize_perlocus_results$exp_group <- 'Time Averaged and Sampled Per-Locus Only'\ntassize_subsets_results$exp_group <- 'Time Averaged and Sampled'\n\n\n\n\n\n\n\n# now merge the two into a single dataframe\nclassifier_results <- rbind(results, \n perlocus_results, \n combined_tassize_results, \n tassize_perlocus_results, \n tassize_subsets_results)\n\n\n###### Add two useful statistics #######\n\n\nclassifier_results$fdr <- 1.0 - classifier_results$ppv\nclassifier_results$youdensj <- classifier_results$sensitivity + classifier_results$specificity - 1.0\n\n\n\n############## Complete Processing and Save Results ##########3\n\n# save objects from the environment\nimage_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"classification-gbm-merged-dfonly.RData\")\nsave(classifier_results, file=image_file)\n\n\n", "meta": {"hexsha": "f966251676f738f3aaf1c9279198a667a6af3aa1", "size": 2213, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-4/postsimulation/merge-classification-results.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-4/postsimulation/merge-classification-results.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-4/postsimulation/merge-classification-results.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0724637681, "max_line_length": 137, "alphanum_fraction": 0.7406235879, "num_tokens": 519, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3219681471902846}} {"text": "#!/usr/bin/env Rscript\n\nt <- read.table(\"./invest_return.tsv\", sep=\"\\t\", header=FALSE)\n\nx <- as.Date(t$V1, \"%m/%d/%Y\")\ny1 <- as.numeric(gsub(\",\",\"\", t$V2))/1000000\ny2 <- as.numeric(gsub(\",\",\"\", t$V3))/1000000\npng(\"../images/investment_return/investment_return.png\")\ny_range <- range(y1, y2)\n\npar(family = \"HiraKakuProN-W3\")\n# mar \u2013 A numeric vector of length 4, which sets the margin sizes in the following order: bottom, left, top, and right. The default is c(5.1, 4.1, 4.1, 2.1).\n\npar(mar = c(3, 7, 2, 2))\n\n# mgp \u2013 A numeric vector of length 3, which sets the axis label locations relative to the edge of the inner plot window. The first value represents the location the labels (i.e. xlab and ylab in plot), the second the tick-mark labels, and third the tick marks. The default is c(3, 1, 0).\n\npar(mgp = c(3.5, 1, 0))\n\nplot(x, y1, xlab=\"\", ylab=\"\u91d1\u984d[\u767e\u4e07\u5186]\", type=\"o\", \n cex.lab=2, cex.axis=1.5, cex.main=1.5, cex=2, ylim=y_range,\n xaxt=\"n\",\n yaxp = c(10, 50, 4),\n pch=1\n )\nlines(x, y2, xlab=\"\", type=\"o\", pch=16,\n ylab=\"\u91d1\u984d[\u767e\u4e07\u5186]\",\n cex.lab=2, cex.axis=1.5, cex.main=1.5, cex=2, ylim=y_range,\n xaxt=\"n\",\n# 1,000\u4e07\u5186\u304b\u30894,000\u4e07\u5186\u307e\u3067\u30924\u5206\u5272\u3067\n yaxp = c(10, 50, 4)\n )\nx_years = as.Date(c(\"2015/1/1\", \"2016/1/1\", \"2017/1/1\", \"2018/1/1\", \"2019/1/1\", \"2020/1/1\", \"2021/1/1\"), \"%Y/%m/%d\")\naxis(1, x_years, format(x_years, \"%Y/%m\"), cex=2, cex.axis=1.5, cex.main=1.5, cex.lab=2)\n\nlegend(x_years[1], y_range[2], c(\"\u8a55\u4fa1\u984d\", \"\u6295\u8cc7\u984d\"), cex=1.5, pch=c(16, 1))\n", "meta": {"hexsha": "c789c8b684908c692fcb9493bd4c98875f8c4fc3", "size": 1472, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/invest_return_graph.r", "max_stars_repo_name": "programmers-index-investment/programmers-index-investment.github.io", "max_stars_repo_head_hexsha": "85fa0a501ad74236f9a146a303586216d99dad30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-07-12T02:39:45.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-12T02:39:45.000Z", "max_issues_repo_path": "scripts/invest_return_graph.r", "max_issues_repo_name": "programmers-index-investment/programmers-index-investment.github.io", "max_issues_repo_head_hexsha": "85fa0a501ad74236f9a146a303586216d99dad30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/invest_return_graph.r", "max_forks_repo_name": "programmers-index-investment/programmers-index-investment.github.io", "max_forks_repo_head_hexsha": "85fa0a501ad74236f9a146a303586216d99dad30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7837837838, "max_line_length": 287, "alphanum_fraction": 0.6120923913, "num_tokens": 625, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3219681471902846}} {"text": "# Rectangling data\n\n# A. Intro to non-rectangular data\n# 1. Rectangling Star Wars movies\n# Data is taken from Star Wars API and is in JSON format\n\n# Create a movie column from the movie_list\ntibble(movie = movie_list) %>% \n # Unnest the movie column\n unnest_wider(movie)\n\n# Create a tibble with a movie column\ntibble(movie = movie_planets_list) %>% \n # Unnest the movie column\n unnest_wider(movie) %>% \n # Unnest the planets column\n unnest_wider(planets)\n\n# B. From nested values to observations\n# 1. Rectangling Star Wars planets\n# unnest_wider(): common length of lists\n# unnest_longer(): varying length of lists\n\n# Create a tibble from movie_planets_list\ntibble(movie = movie_planets_list) %>% \n # Unnest the movie column in the correct direction\n unnest_wider(movie) %>% \n # Unnest the planets column in the correct direction\n unnest_longer(planets)\n\n# 2. The Solar System's biggest moons\nplanet_df %>% \n # Unnest the moons list column over observations\n unnest_longer(moons) %>% \n # Further unnest the moons column\n unnest_wider(moons) %>% \n # Unnest the moon_data column\n unnest_wider(moon_data) %>% \n # Get the top five largest moons by radius\n slice_max(radius, n = 5)\n\n# C. Selecting nested variables\n# 1. Hoisting Star Wars films\n# hoist(): can be used to select a specify element of the JSON dictionary\ncharacter_df %>% \n # Unnest the metadata column\n unnest_wider(metadata) %>% \n # Unnest the films column\n unnest_longer(films)\n\n# Hoisting to find the same information. Selecting the first film for each film list by row of metadata layer\ncharacter_df %>% \n hoist(metadata, first_film = list(\"films\", 1))\n\n# 2. Hoisting movie ratings\nmovie_df %>% \n # Unnest the movie column\n unnest_wider(movie) %>% \n select(Title, Year, Ratings) %>% \n # Unnest the Ratings column\n unnest_wider(Ratings)\n\n# Hoisting method to extract the Rotten Tomatoes rating for each movie\nmovie_df %>% \n hoist(\n movie,\n title = \"Title\",\n year = \"Year\",\n rating = list(\"Ratings\", \"Rotten Tomatoes\")\n )\n\n# D. Nesting data for modelling\n# 1. Tidy model outputs with broom\n# Linear model\nmodel <- lm(weight_kg ~ waist_circum_m + stature_m, data = ansur_df)\n# Review the model Statistics using broom\nbroom::glance(model)\n# Review the variable fit statistics with broom\nbroom::tidy(model)\n\n# 2. Nesting tibbles\n# Tibble data is added to the DataFrame\nansur_df %>% \n # Group the data by branch, then nest\n group_by(branch) %>% \n nest()\n\nansur_df %>% \n # Group the data by branch and sex, then nest\n group_by(branch, sex) %>% \n nest()\n\n# 3. Modelling on nested dataframes\n# The dplyr, broom, and purrr packages have been pre-loaded for you\n# In the provided code, the purrr package's map() function applies functions on each nested data frame\nansur_df %>%\n # Group the data by sex\n group_by(sex) %>% \n # Nest the data\n nest() %>% \n mutate(\n fit = map(data, function(df) lm(weight_kg ~ waist_circum_m + stature_m, data = df)),\n glanced = map(fit, glance)\n ) %>% \n # Unnest the glanced column\n unnest(glanced)\n", "meta": {"hexsha": "40d4fa5c14b6ad8a3de9256193f1f8c065c20a7e", "size": 3045, "ext": "r", "lang": "R", "max_stars_repo_path": "R/DataAnalyst/Tidyr/rectangling-data.r", "max_stars_repo_name": "James-McNeill/Learning", "max_stars_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/DataAnalyst/Tidyr/rectangling-data.r", "max_issues_repo_name": "James-McNeill/Learning", "max_issues_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/DataAnalyst/Tidyr/rectangling-data.r", "max_forks_repo_name": "James-McNeill/Learning", "max_forks_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4579439252, "max_line_length": 109, "alphanum_fraction": 0.7136288998, "num_tokens": 877, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.615087862571909, "lm_q1q2_score": 0.3219495036538863}} {"text": "#' @title Prepare bootstrapped results for `ggplot` friendly data format.\n#'\n#' @description Helper function to convert the bootstrapped dissimilarities from\n#' matrix format to `ggplot2` friendly data format.\n#'\n#' @param x A matrix of bootstrapped dissimilarities from\n#' \\code{\\link[bootdissim]{boot_dissim}}\n#'\n#' @param probs A numeric vector of two probabilities in the interval `[0, 1]`.\n#' Used for building the lower and upper bounds of the confidence intervals.\n#' Defaults to `c(0.025, 0.975)`, which corresponds to a 95\\\\% confidence\n#' interval.\n#'\n#' @return A list of two data frames to be used with\n#' \\code{\\link[ggplot2]{ggplot}}. First one, `stats_df` is a 4 columns data\n#' frame, containing the sample size/number of sampled interactions\n#' (`spl_size`) at each step and the corresponding mean dissimilarity (`mean`)\n#' together with its bootstrap confidence interval limits (`ci_low`, `ci_up`).\n#' The second one, `lines_df`, contains all the bootstrapped dissimilarities\n#' (`value`) at each bootstrap iteration (`simulation_id`) and its\n#' corresponding sample size/sampled interactions (`spl_size`). Can be used\n#' for enhancing visual effect when plotting the mean bootstrap values.\n#'\n#' @importFrom magrittr %>%\n#' @importFrom data.table melt setDT\n#' @importFrom dplyr mutate\n#' @importFrom tibble rownames_to_column\n#' @importFrom matrixStats rowMeans2 rowQuantiles\n#'\n#' @export\n#'\n#' @md\nget_stats_gg <- function(x, probs = c(0.025, 0.975)){\n if (! is.matrix(x) ) stop(\"Expecting a matrix\")\n\n stats_df <- data.frame(spl_size = as.integer(rownames(x))) %>%\n dplyr::mutate(mean = matrixStats::rowMeans2(x, na.rm = TRUE),\n ci_low = matrixStats::rowQuantiles(x, probs = probs[1], na.rm = TRUE) %>% unname,\n ci_up = matrixStats::rowQuantiles(x, probs = probs[2], na.rm = TRUE) %>% unname)\n\n lines_df <- x %>%\n as.data.frame %>%\n tibble::rownames_to_column(var = \"spl_size\") %>%\n setDT() %>%\n data.table::melt(id.vars = \"spl_size\",\n variable.name = \"simulation_id\") %>%\n dplyr::mutate(spl_size = as.integer(spl_size),\n simulation_id = as.integer(simulation_id))\n\n return(list(stats_df = stats_df,\n lines_df = lines_df))\n}\n\n\n#' @title Plot bootstrapped dissimilarities\n#'\n#' @description Custom plotting function to display the bootstrapped\n#' dissimilarities, the mean line and its confidence intervals.\n#'\n#' @param metrics_stats The output of \\code{\\link[bootdissim]{get_stats_gg}}.\n#'\n#' @param size_boot_lines Size of the line connecting the bootstrapped values.\n#' Default is 0.2. Passed to \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @param alpha_boot_lines Alpha parameter of the line connecting the\n#' bootstrapped values. Default is 0.2. Passed to\n#' \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @param size_ci Size of the line used for the confidence intervals. Default is\n#' 1. Passed to \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @param linetype_ci Type of the line used for the confidence intervals.\n#' Default is 2. Passed to \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @param size_mean Size of the line connecting the bootstrapped mean values of\n#' the metric. Default is 1. Passed to\n#' \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @param linetype_mean Type of the line connecting the bootstrapped mean values\n#' of the metric. Default is 1. Passed to\n#' \\code{\\link[ggplot2:geom_path]{geom_line}}.\n#'\n#' @return A ggplot object.\n#'\n#' @import ggplot2\n#'\n#' @export\n#'\n#' @md\nplot_dissim <- function(metrics_stats,\n size_boot_lines = 0.2,\n alpha_boot_lines = 0.2,\n size_ci = 1,\n linetype_ci = 2,\n size_mean = 1,\n linetype_mean = 1){\n\n stats_df <- metrics_stats[[\"stats_df\"]]\n lines_df <- metrics_stats[[\"lines_df\"]]\n\n plots_gg <- ggplot() +\n # Bootstrap lines\n geom_line(data = lines_df,\n aes(x = spl_size,\n y = value,\n group = simulation_id),\n size = size_boot_lines,\n alpha = alpha_boot_lines) +\n # Lower confidence interval bound\n geom_line(data = stats_df,\n aes(x = spl_size,\n y = ci_low),\n size = size_ci,\n linetype = linetype_ci) +\n # Upper confidence interval bound\n geom_line(data = stats_df,\n aes(x = spl_size,\n y = ci_up),\n size = size_ci,\n linetype = linetype_ci) +\n # Average line\n geom_line(data = stats_df,\n aes(x = spl_size,\n y = mean),\n size = size_mean,\n linetype = linetype_mean)\n\n return(plots_gg)\n}\n", "meta": {"hexsha": "f47cf9ff86342393cf09a42697682d0eea5cdc84", "size": 4852, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ggplot_functions.r", "max_stars_repo_name": "valentinitnelav/bootstrapturnover", "max_stars_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-03-22T19:14:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T17:01:52.000Z", "max_issues_repo_path": "R/ggplot_functions.r", "max_issues_repo_name": "valentinitnelav/bootstrapturnover", "max_issues_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ggplot_functions.r", "max_forks_repo_name": "valentinitnelav/bootstrapturnover", "max_forks_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3230769231, "max_line_length": 99, "alphanum_fraction": 0.6347897774, "num_tokens": 1268, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3219494999607148}} {"text": "2: 1 -> 2\n4: 2 -> 4\n3: 3 -> 4\n", "meta": {"hexsha": "17d35c8ce3332f2c7eb9838b275cf5768eadcc1c", "size": 30, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/03.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/03.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/UndirectedVertexPredecessor/03.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 7.5, "max_line_length": 9, "alphanum_fraction": 0.3, "num_tokens": 24, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3219494999607148}} {"text": "library(gapminder)\r\nlibrary(tidyverse)\r\nlibrary(gganimate)\r\noptions(gganimate.nframes=20)\r\n\r\ngapminder %>%\r\n select(country, pop, year, continent) %>%\r\n group_by(year) %>%\r\n arrange(year, -pop) %>%\r\n mutate(rank=1:n()) %>%\r\n filter(rank <= 10) ->\r\nranked_by_year\r\n\r\nmy_theme <- theme_classic(base_family = \"Times\") +\r\n theme(axis.text.y = element_blank()) +\r\n theme(axis.ticks.y = element_blank()) +\r\n theme(axis.line.y = element_blank()) +\r\n theme(legend.background = element_rect(fill = \"linen\")) +\r\n theme(plot.background = element_rect(fill = \"linen\")) +\r\n theme(panel.background = element_rect(fill = \"linen\"))\r\n\r\nggplot(data = ranked_by_year) +\r\n aes(group = country, fill = continent) +\r\n aes(xmin = 0 ,\r\n xmax = pop / 1000000) +\r\n aes(ymin = rank - .45,\r\n ymax = rank + .45) +\r\n scale_y_reverse() +\r\n scale_x_continuous(\r\n limits = c(-300, 1400),\r\n breaks = c(0, 400, 800, 1200),\r\n labels = c(0, 400, 800, 1200)) +\r\n labs(fill = \"\") +\r\n geom_rect(alpha = .7) +\r\n labs(x = 'Population (millions)') +\r\n aes(label = country, y = rank) +\r\n geom_text(col = \"gray13\",\r\n hjust = \"right\",\r\n x = -50) +\r\n labs(y = \"\") +\r\n scale_fill_viridis_d(option = \"magma\",\r\n direction = -1) +\r\n geom_text(x = 1000 , y = -10,\r\n family = \"Times\",\r\n aes(label = as.character(year)),\r\n size = 30, col = \"grey18\") +\r\n my_theme -> g\r\ng + gganimate::transition_time(year)", "meta": {"hexsha": "d780e24d8e42f11b1f97f03e0f32aabb5e5f67be", "size": 1484, "ext": "r", "lang": "R", "max_stars_repo_path": "src/archive/animated_bar.r", "max_stars_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_stars_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-04-07T21:26:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T22:41:56.000Z", "max_issues_repo_path": "src/archive/animated_bar.r", "max_issues_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_issues_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2019-06-27T19:27:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-20T17:34:13.000Z", "max_forks_repo_path": "src/archive/animated_bar.r", "max_forks_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_forks_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2019-04-07T20:09:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-29T21:46:58.000Z", "avg_line_length": 30.9166666667, "max_line_length": 60, "alphanum_fraction": 0.5687331536, "num_tokens": 431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.321736944089191}} {"text": "##This script was written to clean the data retrieved manually from https://www.dol.gov/whd/state/stateMinWageHis.htm\r\n##On 1/14/2019 and combine it with the January 2019 week three makeover monday data set to use for a visualization\r\n##The goal of the visualization is to contextualize Bureau of Labor Statistics and Department of Labor data to answer:\r\n##Do state minimum wages affect the percentage of workers paid at or below the federal minimum wage?\r\n\r\n##Author: Pauly Russ\r\n##Date: 1/14/2019\r\n\r\n\r\n##load required libraries\r\nlibrary(dplyr)\r\nlibrary(stringr)\r\nlibrary(tidyr)\r\n\r\n##set working directory and load data files\r\nsetwd(\"C:\\\\Users\\\\pruss\\\\Documents\\\\My Tableau Repository\\\\WIP\")\r\nStateMinWages <- read.csv(\"State Minimum Wage.csv\", stringsAsFactors = FALSE)\r\nFedMinWages <- read.csv(\"Minimum Wage.csv\", stringsAsFactors = FALSE)\r\n\r\n##clean up state minimum wage data\r\n\r\n ##first pull the header names and clean those\r\nstateminwagenames <- colnames(StateMinWages)\r\n\r\n ##I saw three patterns: first year colnames started with X, state colname had junk char + .., one state had junk char + .\r\nstateminwagenames <- str_replace_all(stateminwagenames, \"(\u00ef\\\\.\\\\.)|(X)|(\u00c2\\\\.)\", \"\")\r\n\r\n ##second clean data. four strings need to be removed\r\n ##1. [\\c] - some values have a note indicated by an alpha character wrapped in square brackets\r\n ##2. (\\c) - some values have a note indicated by an alpha character wrapped in parenthesis\r\n ##3. \u00c2 - a few instances of this junk character appear in the data set\r\n ##4. Some entries have two values listed separated by a hyphen. I read through the notes and decided to drop the second (higher) value\r\n ## the lower value is indicative of the state's true 'MINIMUM' that can be paid\r\nStateMinWages <- lapply(StateMinWages, str_replace_all, pattern = \"(\\\\[[A-z]\\\\])|(\\\\([A-z]\\\\))|(\u00c2)|( *\\\\- *\\\\d*\\\\.\\\\d*)\", replacement = \"\")\r\n\r\nStateMinWages[2:20] <- lapply(StateMinWages[2:20], str_replace_all, pattern = \"\\\\h\", replacement = \"\")\r\n\r\n ##states with no state minimum were indicated by three dots, replace with 0 so we can use math\r\nStateMinWages <- lapply(StateMinWages, str_replace_all, pattern = \"\\\\.\\\\.\\\\.\", replacement = \"0\")\r\n\r\n ##lapply breaks data frame, put back into 51 row data frame\r\nStateMinWages <- data.frame(matrix(unlist(StateMinWages), nrow=51, byrow=F),stringsAsFactors=FALSE)\r\n\r\n\r\n ##reapply column names\r\ncolnames(StateMinWages) <- stateminwagenames\r\n\r\n## now we prep and combine for final set and calculate some columns\r\n\r\n## first, pivot the state minimum wages into a long format\r\npivotStateMinWages <- reshape(StateMinWages, direction = \"long\", varying = list(colnames(StateMinWages[2:20])))\r\n\r\n## I think the pivot treated years as factors, but they were sequential, so math to fix year values\r\npivotStateMinWages$time = pivotStateMinWages$time + 1999\r\n\r\n## rename some columns\r\ncolnames(pivotStateMinWages)[colnames(pivotStateMinWages)==\"time\"] <- \"year\"\r\ncolnames(pivotStateMinWages)[colnames(pivotStateMinWages)==\"State.or.other\"] <- \"state\"\r\ncolnames(pivotStateMinWages)[colnames(pivotStateMinWages)==\"2000\"] <- \"minwage\"\r\n\r\n## convert the minimum wage to number and make year/state into factors\r\npivotStateMinWages$minwage <- as.numeric(pivotStateMinWages$minwage)\r\npivotStateMinWages$year <- as.factor(as.character(pivotStateMinWages$year))\r\npivotStateMinWages$state <- as.factor(as.character(pivotStateMinWages$state))\r\n\r\n## rename some columns\r\ncolnames(FedMinWages)[1] <- \"year\"\r\ncolnames(FedMinWages)[2] <- \"state\"\r\ncolnames(FedMinWages) <- tolower(colnames(FedMinWages))\r\n\r\n## the federal minimum wage data set had a 'total' set of rows, drop them and convert to factor\r\nFedMinWages <- FedMinWages[-grep('Total', FedMinWages$state),]\r\n\r\n## make state and year into factors\r\nFedMinWages$year <- as.factor(as.character(FedMinWages$year))\r\nFedMinWages$state <- as.factor(as.character(FedMinWages$state))\r\n\r\n\r\n## combine minimum wage table with pivoted state minimums\r\nfinalset <- merge(FedMinWages, pivotStateMinWages)\r\n\r\n##rename some columns\r\ncolnames(finalset)[6] <- \"state.minimum.wage\"\r\ncolnames(finalset)[5] <- \"pcnt.below.fed.min\"\r\ncolnames(finalset)[4] <- \"pcnt.at.fed.min\"\r\n\r\n\r\n## forgot fed minimum wage changed over observed time, below adds that based on the chart at https://www.dol.gov/whd/minwage/chart.htm\r\nfedminovertime <- data.frame(year = unique(finalset$year))\r\nfedminovertime$fed.minimum.wage <- 5.15\r\n\r\nfedminovertime$year <- as.numeric(as.character(fedminovertime$year))\r\nfedminovertime$fed.minimum.wage <- if_else(fedminovertime$year == 2007, fedminovertime$fed.minimum.wage + 0.7, if_else(fedminovertime$year == 2008, fedminovertime$fed.minimum.wage + 1.4, if_else(fedminovertime$year >= 2009, fedminovertime$fed.minimum.wage + 2.1, fedminovertime$fed.minimum.wage)))\r\n\r\n## merge the federal minimum wage over time on to the final set as a column\r\nfinalset <- merge(finalset, fedminovertime)\r\n\r\n\r\n## create a column that quickly indicates whether a state minimum is higher, equal or lower than federal min\r\nfinalset <- finalset%>%\r\n mutate(state.min.vs.fed.min = case_when(\r\n state.minimum.wage < fed.minimum.wage & state.minimum.wage != 0 ~ \"lt\",\r\n state.minimum.wage == fed.minimum.wage | state.minimum.wage == 0 ~ \"eq\",\r\n state.minimum.wage > fed.minimum.wage ~ \"gt\"))\r\n\r\nwrite.csv(finalset, \"enhancedWageData.csv\", sep = \",\", row.names = FALSE, fileEncoding = \"UTF-8\")\r\n", "meta": {"hexsha": "44dd94025aebe61a63eab05406a249a366e8871e", "size": 5402, "ext": "r", "lang": "R", "max_stars_repo_path": "StateMinWageDataCleanse.r", "max_stars_repo_name": "PaulyPedantic/dataCleansingScripts", "max_stars_repo_head_hexsha": "8a86f8f28b45872477e1f11dd16c74a350aedef4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "StateMinWageDataCleanse.r", "max_issues_repo_name": "PaulyPedantic/dataCleansingScripts", "max_issues_repo_head_hexsha": "8a86f8f28b45872477e1f11dd16c74a350aedef4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "StateMinWageDataCleanse.r", "max_forks_repo_name": "PaulyPedantic/dataCleansingScripts", "max_forks_repo_head_hexsha": "8a86f8f28b45872477e1f11dd16c74a350aedef4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.4859813084, "max_line_length": 298, "alphanum_fraction": 0.733061829, "num_tokens": 1453, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3217369362777521}} {"text": "library(tidyverse)\n\n#########\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder = file.path(folder, '..', 'data', 'intermediate', 'model_inputs')\n\nfilename = 'rp_by_distance.csv'\ndata <- read.csv(file.path(folder, filename))\n\ndata$frequency_category = factor(data$frequency_category,\n levels=c(\"6-8 GHz\",\n \"11-15 GHz\",\n \"15-18 GHz\"),\n labels=c(\"6-8\",\n \"11-15\",\n \"15-18\"))\n\ndata$distance_category = factor(data$distance_category,\n levels=c(\"<10 km\",\n \"10-25 km\",\n \"25-40 km\"))\n\nplot1 = \n ggplot(data, aes(received_power_db, frequency_category)) + coord_flip() +\n geom_boxplot() + geom_jitter(width = 0.5, size=.05, alpha=.2) +\n labs(title=\"(A) Simulation Results for the Received Signal based on the FSPL\",\n colour=NULL,\n subtitle = \"Reported for all frequency and distance categories\",\n x = 'Received Signal (dB)', y = \"Frequency (GHz)\") +\n scale_x_continuous(expand = c(0, 0), limits = c(-40,0)) +\n facet_wrap(~distance_category)\n\n############\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nfolder = file.path(folder, '..', 'data', 'intermediate', 'model_inputs')\n\nfilename = 'fresnel_clearances.csv'\ndata <- read.csv(file.path(folder, filename))\n\ndata = data[data$clearance > 1,]\n\ndata$frequency_category = factor(data$frequency_category,\n levels=c(\"6-8 GHz\",\n \"11-15 GHz\",\n \"15-18 GHz\"),\n labels=c(\"6-8\",\n \"11-15\",\n \"15-18\"))\n\nplot2 =\n ggplot(data, aes(clearance, frequency_category)) + coord_flip() +\n geom_boxplot() + geom_jitter(width = 0.5, size=.05, alpha=.2) +\n labs(title=\"(B) Simulation Results for Required Fresnel Clearance Values\",\n colour=NULL,\n subtitle = \"Reported for all frequency and distance categories\",\n x = 'Clearance (M)', y = \"Frequency (GHz)\") +\n scale_x_continuous(expand = c(0, 0), limits = c(0,22.5)) +\n facet_wrap(~distance_category)\n\ncombined <- ggarrange(plot1, plot2, \n ncol = 1, nrow = 2)\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures', 'panel_plot.png')\nggsave(path, units=\"in\", width=7, height=5, dpi=300)\nprint(combined)\ndev.off()\n\n\n\n\n\n\n# folder <- dirname(rstudioapi::getSourceEditorContext()$path)\n# path = file.path(folder, 'figures', 'fresnel_clearances.png')\n# ggsave(path, units=\"in\", width=7, height=4, dpi=300)\n# print(plot)\n# dev.off()\n\n", "meta": {"hexsha": "ba2227f35b5c7ea6dcea4e1b4a56677107c246ad", "size": 2902, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/old/panel.r", "max_stars_repo_name": "edwardoughton/e3nb", "max_stars_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-10T21:45:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T21:45:07.000Z", "max_issues_repo_path": "vis/old/panel.r", "max_issues_repo_name": "edwardoughton/e3nb", "max_issues_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/old/panel.r", "max_forks_repo_name": "edwardoughton/e3nb", "max_forks_repo_head_hexsha": "d03701ba24aad8a723e3e9c138f7f636f7c67573", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-13T16:27:41.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-13T16:27:41.000Z", "avg_line_length": 36.275, "max_line_length": 80, "alphanum_fraction": 0.538938663, "num_tokens": 665, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5926665999540697, "lm_q1q2_score": 0.32173693627775196}} {"text": "library(ggplot2)\nlibrary(ggthemes)\n\npdf('streak_year.pdf',width=12.5417,height=4.9722)\n\ndata = read.csv(file='year_avg_streak.csv', sep=',', header = TRUE)\n\nreset <- par(mar=rep(0,4))\n\nplot <- ggplot(new_data, aes(x = year, y = avg_streak)) +\n geom_line(size = 1, color='#251AED') +\n scale_x_continuous(breaks = seq(1967, 2017, by = 5)) +\n scale_y_continuous(breaks = seq(0, 20, by = 1)) +\n theme_wsj(color='white') +\n guides(alpha = FALSE) +\n theme(plot.margin=grid::unit(c(0,0,0,0), 'mm'))\n\npar(reset)\nprint(plot)\npar(reset)\ndev.off()\n", "meta": {"hexsha": "636a23d36bc60680010cf14b3c4a6ec7705299a4", "size": 579, "ext": "r", "lang": "R", "max_stars_repo_path": "streak_year.r", "max_stars_repo_name": "ruddfawcett/spotify-data", "max_stars_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "streak_year.r", "max_issues_repo_name": "ruddfawcett/spotify-data", "max_issues_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "streak_year.r", "max_forks_repo_name": "ruddfawcett/spotify-data", "max_forks_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.3181818182, "max_line_length": 67, "alphanum_fraction": 0.6200345423, "num_tokens": 191, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32173692846631297}} {"text": "library(caret)\nlibrary(tidyverse)\nlibrary(dplyr)\nlibrary(randomForest)\nlibrary(fpc) # pamk\nlibrary(cluster) # pam\nlibrary(ggplot2,quietly=TRUE)\nsuppressMessages(library(ellipse))\nsuppressMessages(library(RColorBrewer))\nlibrary(reshape2,quietly=TRUE) # Load the reshape2 package (for the melt() function)\n\nsource('robust.clustering.metagenomics.functions.r')\n\n\n#####################################\n### Function process.one.fluxes.file\n#####################################\n# a) To generate subjectID (suffix from file, between 'biomass_' and '.tsv')\n# b) To generate sample ID: subjectID_[0*]seqNum\nprocess.one.fluxes.file <- function(file){\n suffix = gsub('.tsv$','',gsub('^fluxes_','',file))\n table=read.table(file,sep=\"\\t\",header=TRUE)\n table[,grep('time',colnames(table))] <- NULL # Remove repeated time columns (from different strains)\n id.subject=rep(suffix,nrow(table))\n id.num=seq(1,nrow(table),1)\n id.num=str_pad(id.num,5, pad = '0') # Add leading zeros, to easy sort\n id.vector=paste(id.subject, id.num,sep = '_')\n rownames(table)=id.vector\n return(table)\n} # end-function process.one.fluxes.file\n\n#####################################\n### Function rbind.with.rownames\n#####################################\nrbind.with.rownames <- function(datalist) {\n require(plyr)\n temp <- rbind.fill(datalist)\n rownames(temp) <- unlist(lapply(datalist, row.names))\n return(temp)\n} # end-function rbind.with.rownames\n\n#####################################\n### Function cluster names\n#####################################\ncluster_names <- function(df) {\n require(dplyr)\n means <- df %>% \n select(starts_with(\"Biomass\"), cluster) %>%\n group_by(cluster) %>%\n summarize_all(mean)\n signed <- c(as.character(means[which.max(rowSums(means[,2:ncol(means)])),]$cluster),\n as.character(means[which.min(rowSums(means[,2:ncol(means)])),]$cluster))\n df$cluster <- mapvalues(df$cluster, from = signed, to = c(\"growth\", \"stationary\"))\n return(df)\n} # end-function cluster names\n\n#####################################\n### Function rf.10CV\n#####################################\n# Build and evaluate (with 10 Cross-Validation) a model with Random Forest, using caret.\n# Args:\n# df: data frame with all data (including predictors and class variable)\n# formula: object of class formula (i.e. class~ predictors (separated by '+'))\n# classVar: string/number of class variable in 'df'. Default: the last one.\n# ntree: number of trees in the forest\n# Output:\n# finalModel: object with the model in randomForest format, for further evaluations or plots.\nrf.10CV <- function(df,formula,classVar=length(df),ntree=1000){\n require(doMC) # parallel processing; in Windows, maybe doParallel would work\n registerDoMC(cores = 5)\n set.seed(123)\n train_control <- trainControl(method=\"cv\", number=10, savePredictions = TRUE)\n model <- train(form=formula,data=df, trControl=train_control, method=\"rf\", metric='Accuracy',\n ntree=ntree, importance=TRUE, localImp=TRUE, na.action=na.omit,\n allowParallel = T, ncores = 5, nodesize = 42)\n # Define output\n output <- list(\"model\"=model$finalModel, \"pred\"=model$pred)\n # summarize results\n print(model)\n # plot model error\n print(plot(model))\n # Variable Importance\n pdf('varImportance_rf10CV.pdf')\n print(varImpPlot(model$finalModel))\n dev.off()\n print(plot(margin(model$finalModel,df[,classNum])))\n \n return(output)\n}\n\n####################################################\n### plot.rf.var.importance.by.class.andMean.dotplot\n####################################################\n# Plot dotplot with variable importance mean over all classes\n# Args:\n# model: random forest model already build\n# predVar: string of column ID with predictor/variables names values\n# classVar: string of class variable in 'df'\n# title: header of the plot\n# colorVector: vector of colors\n# nBestFeatures: number of top relevant features to show in the plot.\nplot.rf.var.importance.by.class.andMean.dotplot <- function(model,predVar,classVar,title='',colorVector=NULL,nBestFeatures=NULL){\n library(reshape2)\n imp.df <- melt(importance(model)[,1:(length(model$classes)+1)])\n colnames(imp.df)=c(predVar,classVar,'value')\n # a.-Order features\n pred.order=names(sort(importance(model)[,'MeanDecreaseAccuracy'])) # My order according to global MeandDecreaseAccuracy\n imp.df[,predVar] <- factor(imp.df[,predVar], levels = pred.order)\n class.names=levels(imp.df[,classVar])\n levels(imp.df[,classVar]) <- c(class.names[1:(length(class.names)-1)],\"MEAN\")\n imp.df[,classVar] <- factor(imp.df[,classVar])\n # b.- Subset features to show\n if(!is.null(nBestFeatures)){\n imp.df=subset(imp.df,subset=(imp.df[,predVar] %in% tail(pred.order,n=nBestFeatures)))\n }\n p <- ggplot(imp.df, aes_string(x = 'value', y = predVar, group = predVar, colour = classVar)) +\n geom_segment(aes_string(yend=predVar), xend=0, colour=\"grey50\") +\n geom_point( size = 1) +\n theme_bw() +\n facet_grid(reformulate(classVar)) +\n theme(panel.grid.major.y = element_blank()) +\n #theme(text = element_text(size=16)) +\n xlab(paste(predVar,\" importance\",sep='')) +\n theme(axis.text.x = element_text(angle = 270, hjust = 1)) +\n theme(legend.position=\"none\") +\n ggtitle(title)\n if(!is.null(colorVector)){\n p + scale_color_manual(values=colorVector)\n }else{\n p\n }\n return(p)\n}\n\n##########################################################################\n# END FUNCTIONS\n##########################################################################\n\n# 1. READ DATA\n# Read fluxes tables from MMODES\nfile <- list.files(pattern=\"^fluxes\\\\_.*\\\\.tsv$\")\n# Add subject and sample ID per table\ntables.list=lapply(file, process.one.fluxes.file)\n# Concatenate all fluxes data.frames (one per subject time series) in a unique one\ntable.all <- rbind.with.rownames(tables.list)\n# Save\nfluxes_raw <- table.all\nsaveRDS(fluxes, \"fluxes.rds\")\nwrite.table(fluxes,file='allTimeSeries_fluxes.tsv',sep='\\t',quote=FALSE)\nfluxes_raw <- readRDS(\"fluxes.rds\")\n\n# 1.2. FEATURE SELECTION\n# Check columns with NA values\n# fluxes.isna=colnames(fluxes)[colSums(is.na(fluxes)) > 0]\n# NA values are generated if some Perturbations are missing in some simulation.\n# We can afford to remove the rows of these simulations (generally 1 or 0 in the dataset).\nfluxes <- fluxes_raw[complete.cases(fluxes_raw),]\n# Check fluxes with constant values (sd=0)\noutput_sd=apply(fluxes, MARGIN=2, sd)\nfluxes.sd0=colnames(fluxes)[output_sd==0]\n# Remove those fluxes\nif(length(fluxes.sd0)>0){\n fluxes = fluxes[, -which(names(fluxes) %in% fluxes.sd0)]\n}\n# Remove first all of the transporters and exchanges\n# It does not remove knowledge, but interpretations of the inner metabolism is possible\nfluxes <- fluxes %>%\n select(-starts_with(\"EX\"), -starts_with(\"Ex\"), -contains(\"tex_\"), -contains(\"tpp_\"), -contains(\"abcpp\"))\n# Some columns shares identical values across rows. That is because reactions are coupled,\n# belonging to the same pathway\nfluxes <- fluxes[,-which(duplicated(t(fluxes)) == TRUE)]\ncat(\"Predictos variables reduced sized from\", ncol(fluxes_raw), \"to\", ncol(fluxes), \n \"by feature selection.\")\n\n# normalization\nlibrary(scales)\nfluxes.rescale <- apply(fluxes[,1:ncol(fluxes)-1], MARGIN = 2, rescale, to=c(-1,1))\n\n# 2. RANDOM SUFFLE\niniSeed <- 1234\nset.seed(iniSeed)\nselected <- sample(nrow(fluxes.rescale))\nfluxes.rescale.random <- fluxes.rescale[selected,] # for clustering\nfluxes.random <- fluxes[selected,] # for random forest, without normalization\n\n# 3. CLUSTERING\nmaxClus=10\neval.array2d <- array(0,dim=c(maxClus-1,3),dimnames=list(as.character(seq(2,maxClus,1)), list('SI','PS','Jaccard')))\n# Silhouette\nfitPamBest <- pamk(fluxes.rescale.random,krange=2:maxClus)\nsave(fitPamBest,file='fitPamBest.Rdata')\neval.array2d[,'SI']=fitPamBest$crit[2:maxClus]\n# Prediction Strength\nout.pred.str <- prediction.strength(fluxes.rescale.random, Gmin=2, Gmax=maxClus, M=50, clustermethod=claraCBI, classification=\"centroid\")\neval.array2d[,'PS']=out.pred.str$mean.pred[2:maxClus]\n# Jaccard\nfor(k in 2:maxClus){\n cf <- clusterboot(fluxes.rescale.random,B=100,bootmethod=\"boot\",clustermethod=claraCBI,k=k,seed=iniSeed,count=FALSE)\n #print(mean(cf$bootmean))\n eval.array2d[as.character(k),'Jaccard'] <- mean(cf$bootmean)\n}\nsave(eval.array2d,file='eval.arrays.RData')\nplot.robust.clustering.PAM(eval.array2d)\n\n# Select the best k (with the higher SI and (PS or Jac) > their thresholds)\nkBest <- robust.clustering.decision(eval.array2d)\nfit <- pam(fluxes.rescale.random,kBest)\nprint.clustering.results(eval.array2d,fit,kBest)\n\n# Getting a list \nlabels <- as.data.frame(as.factor(fit$cluster))\ncolnames(labels) <- c('cluster')\ndf.fluxes <- as.data.frame(fluxes.random)\ndf <- merge(df.fluxes,labels,by='row.names')\nrow.names(df) <- df$Row.names\ndf$Row.names <- NULL\ndf.out <- subset(df,select=cluster)\nwrite.table(df.out,'sampleId-cluster_pairs_fluxes.txt',quote=FALSE,sep=',',row.names=TRUE)\nrm(df.fluxes,df.out)\n\n# 4. RANDOM FOREST\n# Print medoids with their values in the most relevant features\n# Very important to intepretate the clusters!!\nas_tibble(df[rownames(fit$medoids),])\n\n# Supervised learning after feature selection\n# Random forest with the clusters defined by PAM\nformula <- formula(\"cluster~.\")\nmodel <- rf.10CV(df,formula)\nsaveRDS(model, \"final_RF_bf_model.rsd\")\nsink('model_RF_bf.txt')\ncat('Growth in medoids')\nas_tibble(df[rownames(fit$medoids),]) %>% select(starts_with(\"Biomass\"))\nprint(model)\nsink()\nmod <- model$model\npred <- model$pred\n# fluxes importance independent by predicted class\nplot.rf.var.importance.by.class.andMean.dotplot(mod,'flux','cluster',title='Most relevant fluxes',nBestFeatures=25)\n# see predictions (and in which CV fold it was tested)\n\n", "meta": {"hexsha": "afef8567d8cda4d06beade8adbbd1dcb272a9d86", "size": 9757, "ext": "r", "lang": "R", "max_stars_repo_path": "Src/DockerMDPbiome/extra_clusteringFluxes_BifFae.r", "max_stars_repo_name": "beatrizgj/MDPbiomeGEM", "max_stars_repo_head_hexsha": "0e3622b43ba382e648faf049825a5362a09cf0ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Src/DockerMDPbiome/extra_clusteringFluxes_BifFae.r", "max_issues_repo_name": "beatrizgj/MDPbiomeGEM", "max_issues_repo_head_hexsha": "0e3622b43ba382e648faf049825a5362a09cf0ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Src/DockerMDPbiome/extra_clusteringFluxes_BifFae.r", "max_forks_repo_name": "beatrizgj/MDPbiomeGEM", "max_forks_repo_head_hexsha": "0e3622b43ba382e648faf049825a5362a09cf0ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-05-13T08:52:54.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-13T08:52:54.000Z", "avg_line_length": 40.3181818182, "max_line_length": 137, "alphanum_fraction": 0.6823818797, "num_tokens": 2658, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.32173692846631297}} {"text": "## 2. Reading a CSV Files into a Dataframe ##\n\ndf <- read.csv(\"recent-grads.csv\")\nprint(df)\n\n## 3. Previewing the First Few Rows ##\n\nhead(df)\ntail(df)\n\n## 4. Examining the Internal Structure ##\n\nstr(df)\n\n## 5. Numeric vs. Integer Data Types ##\n\npetro_eng_med_salary <- 110000\nfinance_med_salary <- 47000\n\npet_integer <- is.integer(petro_eng_med_salary)\nfin_integer <- is.integer(finance_med_salary)\n\n## 6. Representing Categorical Values Using Factors ##\n\nmajors <- c('Arts','Biology & Life Science','Business','Computers & Mathematics', 'Engineering',\n 'Health','Humanities & Liberal Arts','Psychology & Social Work','Social Science')\n\nfactor_majors <- factor(majors)\nprint(levels(factor_majors))\nmajor_levels <- levels(factor_majors)\n\n## 7. Selecting Data by Rows ##\n\nrank_1_100 <- df[1:100,]\narchitectural_engineering <- df[19,]\ncomputer_science <- df[21,]\n\n## 8. Selecting Data by Columns ##\n\nselect_df <- df[,c(\"Major\",\"Unemployment_rate\",\"Median\",\"Men\",\"Women\")]\n\n## 9. Selecting Specific Values ##\n\nmech_eng_salary <- df[\"MECHANICAL ENGINEERING\",\"Median\"]\ncomp_sci_salary <- df[\"COMPUTER SCIENCE\",\"Median\"]\nfinance_salary <- df[\"FINANCE\",\"Median\"]\n\n## 10. Using Comparison Operators to Filter Values ##\n\nabove_50 <- df[df$Median > 50000,]\nengineering <- df[df$Major_category == \"Engineering\",]\ngreat_40 <- df[df$ShareWomen > 0.4,]\n\n## 11. Combining Conditions using Logical Operators ##\n\nmajors <- df[df$Median >50000 & df$ShareWomen >0.4, ]\n\n## 12. Sorting A DataFrame ##\n\nmajors <- df[df$Median >50000 & df$ShareWomen >0.4, ]\n\nmajor_choice <- majors[order(majors$Unemployment_rate),]", "meta": {"hexsha": "78589bc11058a28f92afd742aec2eab97ca09e06", "size": 1603, "ext": "r", "lang": "R", "max_stars_repo_path": "R Fundamentals/Introduction to DataFrames-313.r", "max_stars_repo_name": "nairachyut/dataquest-projects", "max_stars_repo_head_hexsha": "0807564bb35f39df21a84c8d97ab8eb3a428fb19", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-23T20:02:07.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-20T13:01:20.000Z", "max_issues_repo_path": "R Fundamentals/Introduction to DataFrames-313.r", "max_issues_repo_name": "nairachyut/dataquest-projects", "max_issues_repo_head_hexsha": "0807564bb35f39df21a84c8d97ab8eb3a428fb19", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R Fundamentals/Introduction to DataFrames-313.r", "max_forks_repo_name": "nairachyut/dataquest-projects", "max_forks_repo_head_hexsha": "0807564bb35f39df21a84c8d97ab8eb3a428fb19", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.8548387097, "max_line_length": 96, "alphanum_fraction": 0.7061759201, "num_tokens": 449, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6334102775181399, "lm_q1q2_score": 0.3216532538800949}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/sinking-marbles-nullutterance-simple/results/\")\nsource(\"rscripts/helpers.r\")\nload(\"data/priors.RData\")\n#r = read.table(\"data/sinking_marbles_nullutterance-simple.tsv\", sep=\"\\t\", header=T)\nr = read.table(\"data/test.csv\", sep=\",\", header=T)\nnrow(r)\nsummary(r)\n#as.data.frame(table(r$assignmentid))[as.data.frame(table(r$assignmentid))$Freq < 120,]\nr$trial = r$slide_number_in_experiment - 2\nr = r[,c(\"assignmentid\",\"workerid\", \"rt\", \"effect\", \"cause\",\"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\",\"quantifier\", \"object_level\", \"response\", \"object\",\"slider_id\",\"num_objects\",\"trial\",\"enjoyment\",\"asses\",\"comments\",\"Answer.time_in_minutes\")]\nrow.names(priors) = paste(priors$effect, priors$object)\nr$Prior = priors[paste(r$effect, r$object),]$response\nr$object_level = factor(r$object_level, levels=c(\"object_high\", \"object_mid\", \"object_low\"))\nr$Proportion = factor(ifelse(r$slider_id == \"all\",\"100\",ifelse(r$slider_id == \"lower_half\",\"1-50\",ifelse(r$slider_id == \"upper_half\",\"51-99\",\"0\"))),levels=c(\"0\",\"1-50\",\"51-99\",\"100\"))\nr$Half = as.factor(ifelse(r$trial < 16, 1, 2))\nr$Quarter = as.factor(ifelse(r$trial < 8, 1, ifelse(r$trial < 16, 2, ifelse(r$trial < 24, 3, 4))))\nsummary(r)\nr$Combination = as.factor(paste(r$cause,r$object,r$effect))\ntable(r$Combination)\n\n# compute normalized probabilities\nnr = ddply(r, .(assignmentid,trial), summarize, normresponse=response/(sum(response)),assignmentid=assignmentid,Proportion=Proportion)\nrow.names(nr) = paste(nr$assignmentid,nr$trial,nr$Proportion)\nr$normresponse = nr[paste(r$assignmentid,r$trial,r$Proportion),]$normresponse\n# test: sums should add to 1\nsums = ddply(r, .(assignmentid,trial), summarize, sum(normresponse))\ncolnames(sums) = c(\"assignmentid\",\"trial\",\"sum\")\nsummary(sums)\nsums[is.na(sums$sum),]\n\nsave(r, file=\"data/r.RData\")\n\n##################\n\nggplot(aes(x=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=rt), data=r) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,50000))\n\nggplot(aes(x=age), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,fill=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=enjoyment), data=r) +\n geom_histogram()\n\nggplot(aes(x=asses), data=r) +\n geom_histogram()\n\nggplot(aes(x=quantifier), data=r) +\n geom_histogram()\n\nggplot(aes(x=Answer.time_in_minutes), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,y=Answer.time_in_minutes,color=gender.1), data=unique(r[,c(\"assignmentid\",\"age\",\"Answer.time_in_minutes\",\"gender.1\")])) +\n geom_point() +\n geom_smooth(method=\"lm\")\n\nunique(r$comments)\n", "meta": {"hexsha": "eea102f0c6f6880422bbdf55cd25ddd0a626ede5", "size": 2632, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/4_sinking-marbles-nullutterance-simple/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/4_sinking-marbles-nullutterance-simple/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/4_sinking-marbles-nullutterance-simple/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2835820896, "max_line_length": 254, "alphanum_fraction": 0.7135258359, "num_tokens": 803, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6334102498375401, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3216532398235579}} {"text": "\r\nbottom.contact.incremental = function( sm, bcp=bcp ) {\r\n ## Algorithm: this is essentially a faster discrete version of the \"smooth method\"\r\n ## .. using an equally spaced x-interval, determine locations where change in slope is maximal\r\n stop( \"Not working well enough for full time use\")\r\n\r\n if(0) {\r\n load(\"~/aegis/data/nets/Scanmar/bottom.contact/results/bc.NED2014102.8.rdata\")\r\n sm =data.frame( Z=bc$Z)\r\n sm$timestamp=bc$timestamp\r\n sm$ts=bc$ts\r\n good = bc$good\r\n sm$Z[ !good] = NA\r\n }\r\n\r\n res = c(NA, NA)\r\n\r\n # use only the subset with data for this step\r\n names( sm) = c(\"Z\", \"timestamp\", \"ts\" ) # Z is used to abstract the variable name such that any variable can be passed\r\n N = nrow(sm)\r\n if ( N < 50 ) return(res) # insufficient data\r\n\r\n # interpolate missing data\r\n sm$Z.cleaned = interpolate.xy.robust( sm[, c(\"ts\", \"Z\")], method=\"sequential.linear\" , probs=bcp$incremental.quants )\r\n\r\n sm$slope = NA\r\n sm$rsq = NA\r\n # n.target = n.target # nrequired to enter into a linear regression model\r\n dta = -bcp$incremental.windowsize:bcp$incremental.windowsize\r\n for ( i in (bcp$incremental.windowsize+1):(N-bcp$incremental.windowsize) ) {\r\n j = i+dta\r\n lmres = lm( Z.cleaned ~ ts, data=sm[j,], na.action=\"na.omit\" )\r\n # sm$rsq[i] = Rsquared(lmres)\r\n sm$slope[i] = coefficients(lmres)[\"ts\"]\r\n }\r\n\r\n sm$slope.cleaned = interpolate.xy.robust( sm[, c(\"ts\", \"slope\")], method=\"sequential.linear\" , probs=bcp$incremental.quants )\r\n\r\n slope.imax = which.max( sm$slope ) # descent has a positive slope start search from maximal slope\r\n slope.imin = which.min( sm$slope ) # ascending limb has a negative slope .. start search from min slope\r\n slope.mid = trunc( (slope.imin + slope.imax) /2 )\r\n slope.modes = modes( sm$slope )\r\n # sm$slope.smoothed = interpolate.xy.robust( sm[, c(\"ts\", \"slope\")], method=\"inla\" )\r\n\r\n if ( !is.finite(slope.modes$sd ) || slope.modes$sd < 1e-3) {\r\n return(res)\r\n }\r\n\r\n i0 = slope.imax\r\n for (i0 in slope.imax:slope.mid) {\r\n if (sm$slope[i0] < slope.modes$ub2 ) break()\r\n }\r\n\r\n i1 = slope.imin\r\n for (i1 in slope.imin:slope.mid) {\r\n if (sm$slope[i1] > slope.modes$lb2 ) break()\r\n }\r\n\r\n res = c( sm$timestamp[i0], sm$timestamp[i1] )\r\n\r\n if (0) {\r\n plot(Z~ts,sm, pch=\".\")\r\n points(Z.cleaned~ts,sm, col=\"red\", pch=20)\r\n abline( v=sm$ts[ c(i0, i1) ], col=\"blue\" )\r\n\r\n plot.new()\r\n plot(slope~ts, sm, pch=20, col=\"green\" )\r\n abline( h=slope.modes, col=\"red\" )\r\n abline( v=sm$ts[ c(i0, i1) ], col=\"blue\" )\r\n }\r\n\r\n sm$slope.smoothed = interpolate.xy.robust( sm[, c(\"ts\", \"slope.cleaned\")], method=\"inla\" , probs=bcp$incremental.quants, target.r2=0.9 )\r\n\r\n return(res)\r\n}\r\n\r\n\r\n", "meta": {"hexsha": "cd08b33a4f02220deda46162606927c7cae4d902", "size": 2705, "ext": "r", "lang": "R", "max_stars_repo_path": "R/bottom.contact.incremental.r", "max_stars_repo_name": "PEDsnowcrab/netmensuration", "max_stars_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/bottom.contact.incremental.r", "max_issues_repo_name": "PEDsnowcrab/netmensuration", "max_issues_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/bottom.contact.incremental.r", "max_forks_repo_name": "PEDsnowcrab/netmensuration", "max_forks_repo_head_hexsha": "7388d7b8458dfe96a059cc11a70e5afb67771370", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-05-04T14:40:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T12:56:48.000Z", "avg_line_length": 34.6794871795, "max_line_length": 139, "alphanum_fraction": 0.6229205176, "num_tokens": 876, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.685949467848392, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.32156668086658613}} {"text": "library(s2dverification)\nlibrary(ncdf4)\nlibrary(multiApply) #nolint\nlibrary(yaml)\nlibrary(abind)\nlibrary(ClimProjDiags) #nolint\nlibrary(RColorBrewer) #nolint\n\nargs <- commandArgs(trailingOnly = TRUE)\nparams <- read_yaml(args[1])\nplot_dir <- params$plot_dir\nrun_dir <- params$run_dir\nwork_dir <- params$work_dir\n\ndir.create(plot_dir, recursive = TRUE)\ndir.create(run_dir, recursive = TRUE)\ndir.create(work_dir, recursive = TRUE)\n\n# setup provenance file and list\nprovenance_file <- paste0(run_dir, \"/\", \"diagnostic_provenance.yml\")\nprovenance <- list()\n\ninput_files_per_var <- yaml::read_yaml(params$input_files)\n\nmodel_names <- lapply(input_files_per_var, function(x) x$model)\nmodel_names <- unname(model_names)\nvar0 <- lapply(input_files_per_var, function(x) x$short_name)\nfullpath_filenames <- names(var0)\nvar0 <- unname(var0)[1]\na <- 1\nb <- params$beta\ng <- 0.1\nnm <- params$number_of_members\nnstartd <- 1\nnleadt <- params$no_of_lead_times\n\n\nvar0 <- unlist(var0)\ndata_nc <- nc_open(fullpath_filenames)\ndata <- ncvar_get(data_nc, var0)\ndata <- InsertDim(InsertDim(data, 1, 1), 1, 1) #nolint\nnames(dim(data)) <- c(\"model\", \"var\", \"lon\", \"lat\", \"time\")\nlat <- ncvar_get(data_nc, \"lat\")\nlon <- ncvar_get(data_nc, \"lon\")\nlon <- unlist(lon)\nlat <- unlist(lat)\nattributes(lon) <- NULL\nattributes(lat) <- NULL\nunits <- ncatt_get(data_nc, var0, \"units\")$value\ncalendar <- ncatt_get(data_nc, \"time\", \"calendar\")$value\nlong_names <- ncatt_get(data_nc, var0, \"long_name\")$value\ntime <- ncvar_get(data_nc, \"time\")\nstart_date <- as.POSIXct(substr(ncatt_get(data_nc, \"time\", \"units\")$value,\n 11, 29))\nnc_close(data_nc)\ntime <- as.Date(time, origin = start_date, calendar = calendar)\n\ndim_names <- names(dim(data))\nlon_dim <- which(names(dim(data)) == \"lon\")\nlat_dim <- which(names(dim(data)) == \"lat\")\ndata <- WeightedMean(data, lat = lat, lon = lon, #nolint\n londim = lon_dim, latdim = lat_dim)\nnames(dim(data)) <- dim_names[-c(lon_dim, lat_dim)]\ntime_dim <- which(names(dim(data)) == \"time\")\n\nToyModel <- function (alpha = 0.1, beta = 0.4, gamma = 1, sig = 1, #nolint\n trend = 0, nstartd = 30, nleadt = 4, nmemb = 10,\n obsini = NULL, fxerr = NULL) {\n if (any(!is.numeric(c(alpha, beta, gamma, sig, trend, nstartd,\n nleadt, nmemb)))) {\n stop(paste(\"Parameters alpha, beta, gamma, sig, trend, nstartd,\",\n \"nleadt and nmemb must be numeric.\"))\n }\n nstartd <- round(nstartd)\n nleadt <- round(nleadt)\n nmemb <- round(nmemb)\n if (!is.null(obsini)) {\n if (!is.numeric(obsini) || !is.array(obsini)) {\n stop(\"Parameter obsini must be a numeric array.\")\n }\n if (length(dim(obsini)) != 4) {\n stop(paste(\"Parameter obsini must be an array with dimensions\",\n \"c(1, 1, nleadt, nstartd).\"))\n }\n if (dim(obsini)[3] != nstartd || dim(obsini)[4] != nleadt) {\n stop(paste0(\"The dimensions of parameter obsini and the parameters \",\n \"nleadt and nstartd must match:\\n dim(obsini) = c(\",\n dim(obsini)[3], \", \", dim(obsini)[4], \")\\n nstartd = \",\n nstartd, \" nleadt = \", nleadt))\n }\n }\n if (!is.null(fxerr)) {\n if (!is.numeric(fxerr)) {\n stop(\"Parameter fxerr must be numeric.\")\n }\n }\n if (nstartd < 0) {\n stop(\"Number of start dates must be positive\")\n }\n if (nleadt < 0) {\n stop(\"Number of lead-times must be positive\")\n }\n if (nmemb < 0) {\n stop(\"Number of members must be positive\")\n }\n\n obs_ano <- obsini\n\n forecast <- array(dim = c(length(gamma), nmemb, nstartd, nleadt))\n for (j in 1 : nstartd) {\n for (f in 1 : nleadt) {\n for (g in 1 : length(gamma)) {\n auto_term <- obs_ano[1, 1, j, f]\n if (is.numeric(fxerr)) {\n conf_term <- fxerr\n }\n else {\n conf_term <- rnorm(nmemb, mean = 0, sd = beta)\n }\n trend_term <- gamma[g] * trend * j\n var_corr <- rnorm(nmemb, mean = 0,\n sd = sqrt(sig - alpha ^ 2 - beta ^ 2))\n forecast[g, , j, f] <- matrix(auto_term, c(nmemb,1)) + #nolint\n matrix(conf_term, c(nmemb, 1)) + matrix(trend_term, c(nmemb, 1))\n }\n }\n }\n list(mod = forecast, obs = obs_ano)\n}\n\nforecast <- ToyModel(alpha = a, beta = b, gamma = g, nmemb = nm, #nolint\n obsini = InsertDim(data, 1, 1), # nolint\n nstartd = 1, nleadt = dim(data)[time_dim])\n\nymin <- min(forecast$mod, na.rm = TRUE)\nymax <- max(forecast$mod, na.rm = TRUE)\n\nfilepng <- paste0(plot_dir, \"/\", \"synthetic_\", gsub(\".nc\", \"\",\n basename(fullpath_filenames)), \".jpg\")\njpeg(filepng, height = 15, width = 20, res = 300, units = \"cm\")\ntitle <- paste(nm, \"synthetic members generated\")\nplot(time, forecast$obs, type = \"l\", ylab = paste(var0, \"(\", units, \")\"),\n main = title, bty = \"n\", ylim = c(ymin, ymax))\nmatlines(time, t(forecast$mod[1, , 1, ]), #nolint\n col = brewer.pal(n = nm, name = \"Blues\"))\nlines(time, forecast$obs, lwd = 2)\ndev.off()\n\n\nobs_data <- forecast$obs\ndata <- forecast$mod[1, , 1, ] #nolint\nnames(dim(data))[c(1, 2)] <- c(\"number\", \"time\")\n\nattributes(time) <- NULL\ndim(time) <- c(time = length(time))\nmetadata <- list(time = list(standard_name = \"time\", long_name = \"time\",\n units = \"days since 1970-01-01 00:00:00\", prec = \"double\",\n dim = list(list(name = \"time\", unlim = FALSE))))\nattr(time, \"variables\") <- metadata\nmetadata <- list(index = list(dim = list(list(name = \"time\", unlim = FALSE,\n prec = \"double\"))))\nnames(metadata)[1] <- var0\nattr(data, \"variables\") <- metadata\nvariable_list <- list(variable = data, time = time)\nnames(variable_list)[1] <- var0\nfilencdf <- paste0(work_dir, \"/\", \"synthetic_\", basename(fullpath_filenames))\nArrayToNetCDF(variable_list, filencdf) #nolint\n\n # Set provenance for output files\n xprov <- list(ancestors = list(fullpath_filenames),\n authors = list(\"bellprat_omar\"),\n projects = list(\"c3s-magic\"),\n caption = title,\n statistics = list(\"other\"),\n realms = list(\"atmos\"),\n themes = list(\"phys\"),\n plot_file = filepng)\n\n provenance[[filencdf]] <- xprov\n\n# Write provenance to file\nwrite_yaml(provenance, provenance_file)\n", "meta": {"hexsha": "d108fe7f56824736c6d478b8be51131558f34c21", "size": 6420, "ext": "r", "lang": "R", "max_stars_repo_path": "esmvaltool/diag_scripts/magic_bsc/toymodel.r", "max_stars_repo_name": "jeromaerts/ESMValTool", "max_stars_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "esmvaltool/diag_scripts/magic_bsc/toymodel.r", "max_issues_repo_name": "jeromaerts/ESMValTool", "max_issues_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "esmvaltool/diag_scripts/magic_bsc/toymodel.r", "max_forks_repo_name": "jeromaerts/ESMValTool", "max_forks_repo_head_hexsha": "09fa55d01e7e571b2fcad5610cad0749fcbe8d73", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0819672131, "max_line_length": 77, "alphanum_fraction": 0.5957943925, "num_tokens": 1914, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494678483918, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.3215666808665861}} {"text": "#!/usr/bin/env Rscript\n#\n# Use generalized linear mixed models to test for differences in allele\n# frequency.\n#\n# Each read is treated as an observation, and pools are treated as random\n# effects. A likelihood ratio test is used to produce a p-value.\n#\n# Usage:\n#\n# snp_by_snp_glmm_test.r \n# \n#\n# The names of the pools in each group should be separated by commas.\n#\n#==============================================================================#\n\n\nlibrary(data.table)\nlibrary(lme4)\n\noptions(warn=1)\n\n#==============================================================================#\n\n## From a sync file row, get read counts.\nget_counts = function(sync_row, pools, pool_names) {\n contig = sync_row[[1]]\n pos = sync_row[[2]]\n ref_allele = toupper(sync_row[[3]])\n counts = structure(vector('list', length=length(pools)),\n names=pools)\n for(pn in pools) {\n x = sync_row[[which(pool_names == pn) + 3]]\n cnts = as.numeric(unlist(strsplit(x, ':')))\n names(cnts) = c('A', 'T', 'C', 'G', 'N', 'D')\n ref_cnt = cnts[ref_allele]\n alt_cnt = (sum(cnts) - cnts['N']) - ref_cnt\n counts[[pn]] = c('ref'=ref_cnt, 'alt'=alt_cnt)\n }\n counts\n}\n\n## Test if this set of counts is variable\nis_variable = function(counts) {\n ref = sum(sapply(counts, function(x) x[1]))\n alt = sum(sapply(counts, function(x) x[2]))\n return((ref != 0) && (alt != 0))\n}\n\nnull_result = list('p'=NaN, 'effect'=NaN, term=NA, converged=NA,\n 'wald_p'=NaN, 'freqs0'=NA, 'freqs1'=NA)\n\n## Run a GLMM with pool as a random effect. Use the default\n## Wald Z-test and likelihood ratio test to get p-values\ntest = function(counts, groups) {\n resp = unlist(sapply(counts, function(x) c(rep(0, x[1]), rep(1, x[2]))))\n n_reads = sapply(counts, sum)\n freqs = structure(round(sapply(counts, function(x) x[1] / sum(x)), 4),\n names=names(counts))\n pools = factor(rep(names(counts), n_reads))\n trt = rep(names(groups),\n sapply(groups, function(x) sum(n_reads[x])))\n freqs0 = paste(freqs[names(freqs) %in% groups[[1]]], collapse=',')\n freqs1 = paste(freqs[names(freqs) %in% groups[[2]]], collapse=',')\n ret = null_result\n if(length(unique(trt)) > 1) {\n ## Random intercept for each pool\n m = glmer(resp ~ trt + (1|pools), family=binomial('logit'))\n mnull = glmer(resp ~ (1|pools), family=binomial('logit'))\n lrt_p = anova(m, mnull)[['Pr(>Chisq)']][2]\n s = summary(m)\n conv = (length(s$optinfo$conv$lme4) == 0) &&\n (length(mnull@optinfo$conv$lme4) == 0)\n p = s$coefficients[2, 4]\n fe = fixef(m)[2]\n ret = list('p'=lrt_p, 'wald_p'=p, 'effect'=fe, 'term'=names(fe),\n 'converged'=as.numeric(conv),\n 'freqs0'=freqs0, 'freqs1'=freqs1)\n }\n ret\n}\n\n#==============================================================================#\n\n## Get arguments to the script\ncargs = commandArgs(trailingOnly=TRUE)\nsync_file_name = cargs[1]\npool_file_name = cargs[2]\ngroup_1 = Filter(function(x) x != '', unlist(strsplit(cargs[3], ',')))\ngroup_2 = Filter(function(x) x != '', unlist(strsplit(cargs[4], ',')))\n\n## Read in data\nread_counts = fread(sync_file_name, sep='\\t', stringsAsFactors=FALSE)\npool_names = scan(pool_file_name, what='character')\n\ntrts = list('group1'=group_1, 'group2'=group_2)\n\n## Test every SNP and print results\ncat('contig\\tpos\\tref\\tp\\twald_p\\teffect\\tterm\\tconverged\\tfreqs0\\tfreqs1\\n', file=stdout())\nfor(i in 1:nrow(read_counts)) {\n row_data = read_counts[i]\n contig = row_data[[1]]\n pos = row_data[[2]]\n ref = row_data[[3]]\n counts = get_counts(row_data, unlist(trts), pool_names)\n if(is_variable(counts)) {\n test_results = tryCatch(test(counts, trts),\n error=function(e) null_result)\n } else {\n test_results = null_result\n }\n\n cat(paste(contig, pos, ref, test_results$p, test_results$wald_p,\n test_results$effect, test_results$term, test_results$converged,\n test_results$freqs0, test_results$freqs1,\n sep='\\t'),\n '\\n', sep='', file=stdout())\n}\n", "meta": {"hexsha": "56b6beebaf1329f21c116863d671c05a95eb57d3", "size": 4270, "ext": "r", "lang": "R", "max_stars_repo_path": "snp_by_snp_glmm_test.r", "max_stars_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_stars_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "snp_by_snp_glmm_test.r", "max_issues_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_issues_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-09-17T11:14:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-17T11:14:13.000Z", "max_forks_repo_path": "snp_by_snp_glmm_test.r", "max_forks_repo_name": "brendane/miscellaneous_bioinfo_scripts", "max_forks_repo_head_hexsha": "91ca3282823495299e4c68aa79bdc1c0225a6d7b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.5833333333, "max_line_length": 92, "alphanum_fraction": 0.5733021077, "num_tokens": 1207, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.32156667484722096}} {"text": "#!/usr/bin/env Rscript\n\nlibrary(SparseM)\nlibrary(peakRAM)\nlibrary(FateID)\nlibrary(RaceID)\n\nprint(packageVersion(\"FateID\"))\nprint(packageVersion(\"RaceID\"))\n\nargs = commandArgs(trailingOnly=TRUE)\nstopifnot(length(args) == 2)\n\nsize <- as.numeric(args[1])\nsplit <- as.numeric(args[2])\nprofiler_root <- \"../../../data/benchmarking/memory_analysis/fateid/\"\nfname = paste(profiler_root, paste(\"fateid\", size, split, sep=\"_\"), \".txt\", sep=\"\")\ndrivers <- paste(\"../../../data/morris_data/\", \"lin_drivers/\", sep=\"\")\n\nselect_driver_genes <- function(valid_genes, ld) {\n selected_genes = {}\n \n ld <- ld[ld[[1]] %in% valid_genes,]\n selected_genes <- list()\n \n for (i in c(2, 3, 4)) {\n c <- colnames(ld)[i]\n genes <- unlist(selected_genes)\n y <- ld[!(ld[[1]] %in% genes), ]\n y <- y[order(y[, c], decreasing=TRUE),]\n selected_genes[[c]] <- as.vector(y$X[seq(1, 3)])\n }\n \n selected_genes\n}\n\nprint(\"Reading data\")\nX <- as(readRDS(\"adata_t.rds\"), \"dgCMatrix\")\nrownames(X) <- readRDS(\"var_names.rds\")\ncolnames(X) <- readRDS(\"obs_names.rds\")\n\nstopifnot(dim(X)[2] == size)\nmintotal = size %/% 1000\n\n\nprint(\"Preprocessing\")\n\nsc <- SCseq(X)\nsc <- filterdata(sc, mintotal=mintotal)\nx <- getfdata(sc)[sc@cluster$features,]\n\nprint(\"Getting marker genes\")\n\nvalid_genes <- rownames(x)\nld <- read.csv(paste(drivers, size, \"_\", split, \".csv\", sep=\"\"))\nmarkers <- select_driver_genes(valid_genes, ld)\n\nminnr = minnrh = size %/% 100\nn = size %/% 500\n\npa <- getPart(x, markers, n=n)\nclustering <- pa$part\nendpoints <- pa$tar\nz <- sc@distances\n\nprint(\"Profiling fateBias\")\nx <- as.matrix(x)\n\nmem <- peakRAM({\n fb <- fateBias(x, clustering,\n endpoints, z=z,\n minnr=minnr, minnrh=minnrh,\n seed=123, use.dist=FALSE)\n})\nsink(fname)\nprint(mem)\nsink()\n", "meta": {"hexsha": "0053daddc9d4d9b18a0cecb8fdc6512e0bce61e1", "size": 1831, "ext": "r", "lang": "R", "max_stars_repo_path": "notebooks/suppl_fig_memory_benchmark/analysis_files/scripts/benchmark_fateid.r", "max_stars_repo_name": "theislab/cellrank_reproducibility", "max_stars_repo_head_hexsha": "cf32dfadb031af01e28abac8c7fd37c2a6073ce8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2020-10-29T12:11:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-19T22:24:19.000Z", "max_issues_repo_path": "notebooks/suppl_fig_memory_benchmark/analysis_files/scripts/benchmark_fateid.r", "max_issues_repo_name": "theislab/cellrank_reproducibility_preprint", "max_issues_repo_head_hexsha": "3dd448483499447b597229142768859f2ff9b5b5", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-10-21T10:41:14.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-12T09:30:21.000Z", "max_forks_repo_path": "notebooks/suppl_fig_memory_benchmark/analysis_files/scripts/benchmark_fateid.r", "max_forks_repo_name": "theislab/cellrank_reproducibility_preprint", "max_forks_repo_head_hexsha": "3dd448483499447b597229142768859f2ff9b5b5", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-01-14T08:07:49.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-17T02:28:59.000Z", "avg_line_length": 23.4743589744, "max_line_length": 83, "alphanum_fraction": 0.6182413981, "num_tokens": 540, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.32149433924204923}} {"text": "#!/usr/bin/env Rscript\n\nrequire(ggplot2)\nrequire(data.table)\nlibrary(reshape2)\nrequire(plyr)\nlibrary(grid)\n# require(Hmisc)\nlibrary(plyr)\n\nsource(\"summarySE.r\")\n\nfile <- \"update_time.log\"\n\nargs <- commandArgs(trailingOnly = TRUE)\nif(length(args) > 0)\n{\n\tfile <- args[1]\n}\n\nalldata <- as.data.frame(read.table(file, header=TRUE, sep=\"\\t\"))\n\nasfactors <- c(\"arg_method\",\"arg_topology\")\nfor (i in asfactors) {\n alldata[[i]] <- as.factor(alldata[[i]])\n}\nalldata$arg_method <- revalue(alldata$arg_method, c(\"cen\"=\"Cent.\", \"ez\"=\"ez-Seg.\"))\nalldata$arg_topology <- revalue(alldata$arg_topology, c(\"b4\"=\"B4\", \"i2\"=\"I2\"))\n\ndata <- alldata\n\n\nXquantified50 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(update_only, probs = 0.50),type=\"Coordination\", perc=50)\n\nXquantified90 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(update_only, probs = 0.90),type=\"Coordination\", perc=90)\n\nXquantified99 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(update_only, probs = 0.99),type=\"Coordination\", perc=99)\n\nCTRquantified50 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(ctr_time, probs = 0.5),type=\"Computation\", perc=50)\n\nCTRquantified90 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(ctr_time, probs = 0.90),type=\"Computation\", perc=90)\n\nCTRquantified99 <- ddply(alldata,c(\"arg_method\",\"arg_topology\"),summarise,time = quantile(ctr_time, probs = 0.99),type=\"Computation\", perc=99)\n\nquantified <- rbind(CTRquantified50, CTRquantified90, CTRquantified99,Xquantified50, Xquantified90, Xquantified99)\n\nquantified <- ddply(quantified,c(\"arg_method\"),transform,group=paste(arg_topology,\" \",perc,\"th-ile\",sep=\"\"))\n\np1 <- ggplot() +\n geom_bar(data=quantified, aes(y = time, x = arg_method, fill = type), stat=\"identity\",\n position='stack',colour=\"black\",\n size=.3) +\n facet_grid( ~ group) +\n labs(y=\"Time [ms]\",fill = \"Update time\") +\n theme_bw() +\n guides(fill = guide_legend(override.aes = list(colour = NULL))) +\n theme(plot.margin=unit(x=c(1,1,1,1),units=\"mm\"),\n axis.title.x=element_blank(),\n axis.text.x = element_text(size=8),\n legend.title = element_blank(), legend.key = element_rect(colour = \"black\"), legend.position=\"top\", legend.margin=unit(-0.225, 'cm')) +\n scale_y_continuous(breaks = c(0,200,400,600,800,1000,1200,1400,1600,1800,2000,2200,2400))\n\nggsave(\"plot-percentile.pdf\", width=6.5, height=2.5)\n", "meta": {"hexsha": "26a6ee0e88bc7f516bee1f73b2d0a9f835c1cced", "size": 2482, "ext": "r", "lang": "R", "max_stars_repo_path": "r-scripts/exp-plotting-percentile.r", "max_stars_repo_name": "faywh/read_ez", "max_stars_repo_head_hexsha": "d26a14aed473291b896664faf284ef3027c8be90", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2017-05-28T21:00:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-05T16:14:07.000Z", "max_issues_repo_path": "r-scripts/exp-plotting-percentile.r", "max_issues_repo_name": "faywh/read_ez", "max_issues_repo_head_hexsha": "d26a14aed473291b896664faf284ef3027c8be90", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r-scripts/exp-plotting-percentile.r", "max_forks_repo_name": "faywh/read_ez", "max_forks_repo_head_hexsha": "d26a14aed473291b896664faf284ef3027c8be90", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-12-27T07:59:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-02T07:47:42.000Z", "avg_line_length": 38.78125, "max_line_length": 144, "alphanum_fraction": 0.7002417405, "num_tokens": 763, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3214943326964557}} {"text": "# TODO: model with subj as third cue.\n\n# source(\"act-s.r\")\n# reset_params()\n \n\n######################################################################\n##\n## Standard reflexive interference model with 4 conditions.\n##\n## Uses csim to compute fan\n## Includes a variation of fan effect size determined by the activation of competing chunk\n## Parameters:\n## abl= antecedent base level activation\n## dbl= distractor base level activation\n## Returns a list of \n## l= latencies by condition\n## m= misretrievals by condition\n## a= activations of antecedent and distractor AT RETRIEVAL for interference conditions\n## b= \"BASE\" activations without fan for interference conditions \n##\n######################################################################\nrefl_model_4cond_orig <- function(iterations=1000, csim=cuesim, ND=1, cweights=c(strWeight(),semWeight()), print=0){\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,8),nrow=4) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n b[1] <- activation(fan=c(1,1), match=c(0,0), bl=blc, times=lp, weights=cweights) # +c +a\n b[2] <- activation(fan=c(NA,1), match=c(-1,0), bl=dbl, times=ldp, weights=cweights) # -c +a\n fs[1] <- fan_strength(b[1],b[2])\n fs[2] <- fan_strength(b[2],b[1])\n a[1,1] <- activation(fan=c(1+(ND*(1+csim)*fs[1]), 1+(ND*fs[1])), match=c(0,0), bl=blc, times=lp, weights=cweights) # +c +a\n a[1,2] <- activation(fan=c(NA, ND+((1)*fs[2])), match=c(-1,0), bl=dbl, times=ldp, weights=cweights) # -c +a\n ##\n ## b) MATCH/NOINT: full match, no fan\n a[2,1] <- activation(fan=c(1,1), match=c(0,0), bl=blc, times=lp, weights=cweights) # +c +a\n a[2,2] <- activation(fan=c(NA,NA), match=c(-1,-1), bl=dbl, times=ldp, weights=cweights) # -c -a\n ##\n ## c) MISMATCH/INT: partial match with fan from partially matching chunk\n b[3] <- activation(fan=c(1,NA), match=c(0,-1), bl=blc, times=lp, weights=cweights) # +c -a\n b[4] <- activation(fan=c(NA,1), match=c(-1,0), bl=dbl, times=ldp, weights=cweights) # -c +a\n fs[3] <- fan_strength(b[3],b[4])\n fs[4] <- fan_strength(b[4],b[3])\n a[3,1] <- activation(fan=c(1+(ND*(1+csim)*fs[3]), NA), match=c(0,-1), bl=blc, times=lp, weights=cweights) # +c -a\n a[3,2] <- activation(fan=c(NA, ND+((1+csim)*fs[4])), match=c(-1,0), bl=dbl, times=ldp, weights=cweights) # -c +a\n ##\n ## d) MISMATCH/NOINT: partial match, no fan\n a[4,1] <- activation(fan=c(1,NA), match=c(0,-1), bl=blc, times=lp, weights=cweights) # +c -a\n a[4,2] <- activation(fan=c(NA,NA), match=c(-1,-1), bl=dbl, times=ldp, weights=cweights) # -c -a\n ##\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,f=f,m=m,baseact=baseact,act=finalact))\n}\n\n\n\nrefl_model_4cond <- function(iterations=1000, csim=cuesim, ND=1, cweights=c(strWeight(),semWeight()), print=0){\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,8),nrow=4) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n res <- final_activation(c(1,1), c(NA,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[1,1] <- res$a1\n a[1,2] <- res$a2\n b[1] <- res$b1\n b[2] <- res$b2\n ##\n ## b) MATCH/NOINT: full match, no fan\n res <- final_activation(c(1,1), c(NA,NA), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[2,1] <- res$a1\n a[2,2] <- res$a2\n # b[1] <- res$b1\n # b[2] <- res$b2\n ##\n ## c) MISMATCH/INT: partial match with fan from partially matching chunk\n res <- final_activation(c(1,NA), c(NA,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[3,1] <- res$a1\n a[3,2] <- res$a2\n b[3] <- res$b1\n b[4] <- res$b2\n ##\n ## d) MISMATCH/NOINT: partial match, no fan\n res <- final_activation(c(1,NA), c(NA,NA), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[4,1] <- res$a1\n a[4,2] <- res$a2\n # b[1] <- res$b1\n # b[2] <- res$b2\n ##\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,f=f,m=m,baseact=baseact,act=finalact))\n}\n\n\n######################################################################\n##\n## Reflexive interference model including +subj as cue\n##\n######################################################################\nrefl_model_4cond_subj_cue <- function(iterations=1000, ND=1, cweights=c(strWeight(),semWeight(), semWeight()), subj=TRUE, print=0){\n SF <- ifelse(subj, 1, NA) ## Distractor subject feature\n SFM <- ifelse(subj, 0, -1) ## Subject feature match\n SFC <- ifelse(subj, 0, 1) ## Cuesim multiplicator\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,8),nrow=4) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n res <- final_activation(c(1,1,1), c(NA,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[1,1] <- res$a1\n a[1,2] <- res$a2\n b[1] <- res$b1\n b[2] <- res$b2\n ##\n ## b) MATCH/NOINT:\n res <- final_activation(c(1,1,1), c(NA,NA,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[2,1] <- res$a1\n a[2,2] <- res$a2\n # b[] <- res$b1\n # b[] <- res$b2\n ##\n ## c) MISMATCH/INT: partial match with fan from partially matching chunk\n res <- final_activation(c(1,NA,1), c(NA,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[3,1] <- res$a1\n a[3,2] <- res$a2\n b[3] <- res$b1\n b[4] <- res$b2\n ##\n ## d) MISMATCH/NOINT: partial match, no fan\n res <- final_activation(c(1,NA,1), c(NA,NA,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[4,1] <- res$a1\n a[4,2] <- res$a2\n # b[] <- res$b1\n # b[] <- res$b2\n ##\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,f=f,m=m,baseact=baseact,act=finalact))\n}\n\n\n\n\n\n\n######################################################################\n##\n## Multiple feature mismatch model (Parker & Phillips, 2014)\n##\n######################################################################\nrefl_model_4cond_mult_feature_mismatch_orig <- function(iterations=1000, csim=cuesim, ND=1, cweights=c(strWeight(),semWeight(),semWeight()), print=0){\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,12),ncol=2) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n b[1] <- activation(fan=c(1,1,1), match=c(0,0,0), bl=blc, times=lp, weights=cweights) # +c +g +n\n b[2] <- activation(fan=c(NA,1,1), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights) # -c +g +n\n fs[1] <- fan_strength(b[1],b[2])\n fs[2] <- fan_strength(b[2],b[1])\n a[1,1] <- activation(fan=c(1+(ND*(1+csim)*fs[1]), 1+(ND*fs[1]), 1+(ND*fs[1])), match=c(0,0,0), bl=blc, times=lp, weights=cweights) \n a[1,2] <- activation(fan=c(NA, ND+(1*fs[2]), ND+(1*fs[2])), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights)\n ##\n ## b) MATCH/NOINT: full match, no fan\n b[3] <- activation(fan=c(1,1,1), match=c(0,0,0), bl=blc, times=lp, weights=cweights) # +c +g +n\n b[4] <- activation(fan=c(NA,NA,1), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -c -g +n\n fs[3] <- fan_strength(b[3],b[4])\n fs[4] <- fan_strength(b[4],b[3])\n a[2,1] <- activation(fan=c(1+(ND*(1+csim)*fs[3]),1+(ND*(1+csim)*fs[3]),1+(ND*fs[3])), match=c(0,0,0), bl=blc, times=lp, weights=cweights) # +c +g +n\n a[2,2] <- activation(fan=c(NA,NA,ND+(1*fs[4])), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -c -g +n\n ##\n ## c) 1MISMATCH/INT: partial match with fan from partially matching chunk\n b[5] <- activation(fan=c(1,NA,1), match=c(0,-1,0), bl=blc, times=lp, weights=cweights) # +c -g +n\n b[6] <- activation(fan=c(NA,1,1), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights) # -c +g +n\n fs[5] <- fan_strength(b[5],b[6])\n fs[6] <- fan_strength(b[6],b[5])\n a[3,1] <- activation(fan=c(1+(ND*(1+csim)*fs[5]), NA, 1+(ND*fs[5])), match=c(0,-1,0), bl=blc, times=lp, weights=cweights)\n a[3,2] <- activation(fan=c(NA, ND+((1+csim)*fs[6]), ND+(1*fs[6])), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights)\n ##\n ## d) 1MISMATCH/NOINT: partial match, no fan\n b[7] <- activation(fan=c(1,NA,1), match=c(0,-1,0), bl=blc, times=lp, weights=cweights) # +c -g +n\n b[8] <- activation(fan=c(NA,NA,1), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -c -g +n\n fs[7] <- fan_strength(b[7],b[8])\n fs[8] <- fan_strength(b[8],b[7])\n a[4,1] <- activation(fan=c(1+(ND*(1+csim)*fs[7]),NA,1+(ND*fs[7])), match=c(0,-1,0), bl=blc, times=lp, weights=cweights) # +c -g +n\n a[4,2] <- activation(fan=c(NA,NA,ND+(1*fs[8])), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -c -g +n\n ##\n ## e) 2MISMATCH/INT: \n b[9] <- activation(fan=c(1,NA,NA), match=c(0,-1,-1), bl=blc, times=lp, weights=cweights) # +c -g -n\n b[10] <- activation(fan=c(NA,1,1), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights) # -c +g +n\n fs[9] <- fan_strength(b[9],b[10])\n fs[10] <- fan_strength(b[10],b[9])\n a[5,1] <- activation(fan=c(1+(ND*(1+csim)*fs[9]), NA, NA), match=c(0,-1,-1), bl=blc, times=lp, weights=cweights)\n a[5,2] <- activation(fan=c(NA, ND+((1+csim)*fs[10]), ND+((1+csim)*fs[10])), match=c(-1,0,0), bl=dbl, times=ldp, weights=cweights)\n ##\n ## f) 2MISMATCH/NOINT: \n b[11] <- activation(fan=c(1,NA,NA), match=c(0,-1,-1), bl=blc, times=lp, weights=cweights) # +c -g -n\n b[12] <- activation(fan=c(NA,NA,1), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -c -g +n\n fs[11] <- fan_strength(b[11],b[12])\n fs[12] <- fan_strength(b[12],b[11])\n a[6,1] <- activation(fan=c(1+(ND*(1+csim)*fs[11]), NA, NA), match=c(0,-1,-1), bl=blc, times=lp, weights=cweights) # +c -g +n\n a[6,2] <- activation(fan=c(NA,NA,ND+((1+csim)*fs[12])), match=c(-1,-1,0), bl=dbl, times=ldp, weights=cweights) # -\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n # finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n # colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n # colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,m=m,f=f,baseact=baseact))\n}\n\n\n\n\n######################################################################\n##\n## Multiple feature mismatch model (Parker & Phillips, 2014)\n##\n######################################################################\nrefl_model_4cond_mult_feature_mismatch <- function(iterations=1000, csim=cuesim, ND=1, cweights=c(strWeight(),semWeight(),semWeight()), print=0){\n SF <- ifelse(subj, 1, NA) ## Distractor subject feature\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,12),ncol=2) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n ## +c +g +n +s\n ## -c +g +n +s\n res <- final_activation(c(1,1,1), c(NA,1,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[1,1] <- res$a1\n a[1,2] <- res$a2\n b[1] <- res$b1\n b[2] <- res$b2\n ##\n ## b) MATCH/NOINT: full match, no fan\n res <- final_activation(c(1,1,1), c(NA,NA,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[2,1] <- res$a1\n a[2,2] <- res$a2\n b[3] <- res$b1\n b[4] <- res$b2\n ##\n ## c) 1MISMATCH/INT: partial match with fan from partially matching chunk\n res <- final_activation(c(1,NA,1), c(NA,1,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[3,1] <- res$a1\n a[3,2] <- res$a2\n b[5] <- res$b1\n b[6] <- res$b2\n ##\n ## d) 1MISMATCH/NOINT: partial match, no fan\n res <- final_activation(c(1,NA,1), c(NA,NA,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[4,1] <- res$a1\n a[4,2] <- res$a2\n b[7] <- res$b1\n b[8] <- res$b2\n ##\n ## e) 2MISMATCH/INT: \n res <- final_activation(c(1,NA,NA), c(NA,1,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[5,1] <- res$a1\n a[5,2] <- res$a2\n b[9] <- res$b1\n b[10] <- res$b2\n ##\n ## f) 2MISMATCH/NOINT: \n res <- final_activation(c(1,NA,NA), c(NA,NA,1), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[6,1] <- res$a1\n a[6,2] <- res$a2\n b[11] <- res$b1\n b[12] <- res$b2\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n # finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n # colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n # colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,m=m,f=f,baseact=baseact))\n}\n\n\n\n\n######################################################################\n##\n## Multiple feature mismatch model (Parker & Phillips, 2014)\n##\n######################################################################\nrefl_model_4cond_mult_feature_mismatch_subj_cue <- function(iterations=1000, csim=cuesim, ND=1, cweights=c(strWeight(),semWeight(),semWeight(),semWeight()), subj=TRUE, print=0){\n SF <- ifelse(subj, 1, NA) ## Distractor subject feature\n ta <- NULL ## collects target activations\n l <- NULL ## collects latencies\n r <- NULL ## collects retrievals\n baseact <- NULL ## collects chunk base activations\n finalact <- NULL ## collects chunk act including fan / interference\n for(i in 1:iterations){\n a <- matrix(rep(NA,12),ncol=2) ## Chunk activations\n b <- NULL\n fs <- NULL\n ##\n ##\n ## a) MATCH/INT: full match with fan from partially matching chunk\n ## +c +g +n +s\n ## -c +g +n +s\n res <- final_activation(c(1,1,1,1), c(NA,1,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[1,1] <- res$a1\n a[1,2] <- res$a2\n b[1] <- res$b1\n b[2] <- res$b2\n ##\n ## b) MATCH/NOINT: full match, no fan\n res <- final_activation(c(1,1,1,1), c(NA,NA,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[2,1] <- res$a1\n a[2,2] <- res$a2\n b[3] <- res$b1\n b[4] <- res$b2\n ##\n ## c) 1MISMATCH/INT: partial match with fan from partially matching chunk\n res <- final_activation(c(1,NA,1,1), c(NA,1,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[3,1] <- res$a1\n a[3,2] <- res$a2\n b[5] <- res$b1\n b[6] <- res$b2\n ##\n ## d) 1MISMATCH/NOINT: partial match, no fan\n res <- final_activation(c(1,NA,1,1), c(NA,NA,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[4,1] <- res$a1\n a[4,2] <- res$a2\n b[7] <- res$b1\n b[8] <- res$b2\n ##\n ## e) 2MISMATCH/INT: \n res <- final_activation(c(1,NA,NA,1), c(NA,1,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[5,1] <- res$a1\n a[5,2] <- res$a2\n b[9] <- res$b1\n b[10] <- res$b2\n ##\n ## f) 2MISMATCH/NOINT: \n res <- final_activation(c(1,NA,NA,1), c(NA,NA,1,SF), N2=ND, csim=cuesim, weights=cweights, bl1=blc, bl2=dbl, times1=lp, times2=ldp)\n a[6,1] <- res$a1\n a[6,2] <- res$a2\n b[11] <- res$b1\n b[12] <- res$b2\n ##\n r <- rbind(r, apply(a,1,retrieve)) ## retrieved chunk index\n maxacts <- apply(a,1,max) ## maximum activation per condition\n l <- rbind(l, latency(maxacts)) ## latencies\n ##\n ta <- rbind(ta, a[,1]) ## target activations\n #\n baseact <- rbind(baseact, b)\n # finalact <- rbind(finalact, c(a[1,],a[3,]))\n }\n ## correct retrievals:\n c <- r \n c[r!=1] <- 0\n ## retrieval failures:\n f <- r \n f[r==0] <- 1; f[r!=0] <- 0\n ## misretrievals:\n m <- 1-c \n m[f==1] <- 0\n ##\n if(print!=0) print(colMeans(l))\n if(print!=0) print(colMeans(m))\n if(print!=0) print(colMeans(f))\n # colnames(baseact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n # colnames(finalact) <- c(\"match.int.ant\",\"match.int.dis\",\"mismatch.int.ant\", \"mismatch.int.dis\")\n return(list(l=l,c=c,m=m,f=f,baseact=baseact))\n}\n\n\n\n\n\n########################################################\n##\n## Test-run models\n##\n########################################################\n# reset_params()\n\n# lf <<- 0.4\n# mp <<- 0.2\n# ans <<- 0.15\n# cuesim <<- -1\n# fsf <<- 0\n# mas <<- 1\n# rth <<- -1.5\n# ldp <<- 1.5\n# cueweighting <<- 1\n# normalizeWeights <<- FALSE\n# model <- refl_model_4cond_mult_feature_mismatch(iterations=3000,print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4],colMeans(l)[5]-colMeans(l)[6]))\n\n\n# ## Original model: inhib/facil\n# gaze\n# rbrt\n# lf <<- .15\n# mp <<- 1.2\n# ans <<- 0.15\n# cuesim <<- 0\n# fsf <<- 0\n# mas <<- 3\n# w <<- 1\n# dbl <<- 0\n# model <- refl_model_4cond(print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4]))\n\n# ## Original model, prominent distr.:\n# dbl <<- 1\n# model <- refl_model_4cond(print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4]))\n\n\n\n# ## Exp1: none/inhib\n# lf <<- .3\n# mp <<- 1\n# ans <<- 0.2\n# cuesim <<- -1\n# fsf <<- 4\n# dbl <<- 0\n# model <- refl_model_4cond(iterations=3000,print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4]))\n\n\n# ## Prominent distr. \n# lf <<- .15\n# mp <<- 1.2\n# ans <<- 0.15\n# cuesim <<- -1\n# fsf <<- 0\n# dbl <<- 1\n# model <- refl_model_4cond(print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4]))\n\n\n# ## Exp2: mult distr\n# lf <<- .15\n# mp <<- 1.2\n# ans <<- 0.15\n# cuesim <<- 0\n# fsf <<- 0\n# mas <<- 3\n# w <<- 1\n# dbl <<- 0\n# model <- refl_model_4cond_mult_distr(print=1)\n# with(model, c(colMeans(l)[1]-colMeans(l)[2],colMeans(l)[3]-colMeans(l)[4]))\n\n\n\n\n\n# ########################################################\n# #########################################################\n\n# ## Exp2: inhib/-\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- -.6\n# fsf <- 4\n# ans <- 0.1\n# model <- similarity_model_3_exp2(1000, csim=cuesim, data=exp2, print=2)\n# fit2(exp2, round(colMeans(model$l)), print=2)\n\n# ## Multiple distr.: inhib/-\n# gaze\n# rbrt\n# lf <- .15\n# mp <- 1.2\n# cuesim <- -.3\n# fsf <- 4\n# ans <- 0.15\n# model <- similarity_model_3_mult_distr(1000, csim=cuesim, data=exp2, print=2)\n# fit(rbrt, round(colMeans(model$l)), print=2)\n\n\n\n# ## none/none\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- -.8\n# fsf <- 4\n# ans <- 0.1\n# similarity_model_3_exp1(1000, csim=cuesim, data=gaze, print=2)\n\n# ## none/facil\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- -.9\n# fsf <- 4\n# ans <- 0.1\n# similarity_model_3_exp1(1000, csim=cuesim, data=gaze, print=2)\n\n# ## inhib/facil\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- -.9\n# fsf <- 1\n# ans <- 0.1\n# similarity_model_3_exp1(1000, csim=cuesim, data=gaze, print=2)\n\n# ## inhib/none\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- -.8\n# fsf <- 1\n# ans <- 0.1\n# similarity_model_3_exp1(1000, csim=cuesim, data=gaze, print=2)\n\n\n# ## inhib/-\n# gaze\n# rbrt\n# lf <- .13\n# mp <- 1.2\n# cuesim <- 0\n# fsf <- 4\n# ans <- 0.1\n# similarity_model_3_exp1(1000, csim=cuesim, data=gaze, print=2)\n\n\n", "meta": {"hexsha": "ec3eecbd85f75510709d153732abc869acecff51", "size": 24260, "ext": "r", "lang": "R", "max_stars_repo_path": "chapters4to6_simulations/chapter_4/model/models-old.r", "max_stars_repo_name": "vasishth/RetrievalModels", "max_stars_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-05T15:49:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-05T15:49:49.000Z", "max_issues_repo_path": "chapters4to6_simulations/chapter_4/model/models-old.r", "max_issues_repo_name": "vasishth/RetrievalModels", "max_issues_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-06-08T10:53:53.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-08T11:14:40.000Z", "max_forks_repo_path": "chapters4to6_simulations/chapter_4/model/models-old.r", "max_forks_repo_name": "vasishth/RetrievalModels", "max_forks_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.8345642541, "max_line_length": 177, "alphanum_fraction": 0.5561005771, "num_tokens": 8904, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3214943326964557}} {"text": "library(data.table)\n\nsource('../../functions.r')\n\nload('../generated/model/mcmc-results.rdata', v=F)\n\nspplist <- fread('../acap-species-list.csv')[base_species == T]\nsetkeyv(spplist, 'code.sra')\n\nload('../generated/pst-samples.rdata', v=F)\nload('../../input-data/grid/grids.rdata')\n\n\n## ** APF\n\nmcsel <- mcmc[variable %in% mc_attributes[role == 'flag_quarter0_spatial0' & vartype == 'apf_t', variable]]\nmcsel <- merge(mcsel, mc_attributes, by = 'variable', all.x=T, all.y=F)\napf_samples <- mcsel[, .(apf = sum(value)), .(species_code, sample)]\n\napf_samples[spplist,\n `:=`(cname = upper1st(i.cname),\n sname = i.Taxa),\n on = c('species_code' = 'code.sra')]\n\nsaveRDS(apf_samples, '../generated/apf-samples.rds')\n\napf_sp_summ <- apf_samples[\n , .(mean = mean(apf),\n median = median(apf),\n sd = sd(apf),\n lcl = quantile(apf, 0.025, names=F),\n ucl = quantile(apf, 0.975, names=F),\n nsamples = .N), .(species_code)]\n\napf_sp_summ[spplist,\n `:=`(common_name = upper1st(i.cname),\n sname = i.Taxa),\n on = c('species_code' = 'code.sra')]\nsetorder(apf_sp_summ, -mean)\n\nfwrite(apf_sp_summ, '../generated/apf-summary.csv')\n\n## * Aggregated spatial APF\n#TBC\nmcsel_space <- mcmc[variable %in% mc_attributes[role == 'quarter0_spatial1' & vartype == 'apf_t', variable]]\nmcsel_space <- merge(mcsel_space, mc_attributes, by = 'variable', all.x=T, all.y=F)\napf_samples_space <- mcsel_space[, .(apf = sum(value)), .(grid_id, sample)]\napf_grid <- merge(apf_samples_space[, .(apf=mean(apf)), grid_id], grid, by='grid_id')\nwrite.csv(apf_grid[, .(longitude=centre.y, latitude=centre.x, captures=apf)], row.names=FALSE, file='../generated/apf-mean-grid.csv')\n\n## ** Risk\n\nrisk_ratio <- merge(pst_samples, apf_samples, by = c('species_code', 'sample'))\nrisk_ratio[, riskratio := apf / pst]\n\n### APF by genus\nmcsel_genus <- mcmc[variable %in% mc_attributes[role == 'quarter0_spatial1' & vartype == 'apf_t', variable]]\nmcsel_genus <- merge(mcsel_genus, mc_attributes, by = 'variable', all.x=T, all.y=F)\napf_samples_genus <- merge(mcsel_genus, spplist[, .(species_code=code.sra, genus=vulgroup)], by='species_code', all.x=T)\napf_genus <- apf_samples_genus[, .(apf=sum(value)), .(sample, genus)][, .(amean=mean(apf), ali=quantile(apf, 0.025), aui=quantile(apf, 0.975)), genus]\nwrite.csv(apf_genus, row.names=FALSE, file='../generated/apf-genus.csv')\n\n\n## ** Summarise\nrisk_summ <- risk_ratio[, .(pst_mean = mean(pst),\n pst_lcl = quantile(pst, 0.025, names = F),\n pst_ucl = quantile(pst, 0.975, names = F),\n apf_mean = mean(apf),\n apf_lcl = quantile(apf, 0.025, names = F),\n apf_ucl = quantile(apf, 0.975, names = F),\n rr_med = median(riskratio),\n rr_lcl = quantile(riskratio, 0.025, names = F),\n rr_ucl = quantile(riskratio, 0.975, names = F),\n rr_p_ov_1 = mean(riskratio > 1)), species_code]\nrisk_summ[, `:=`(pst_ci = sprintf('%s--%s',\n format(round(pst_lcl,1), big.mark=' ', trim=T),\n format(round(pst_ucl,1), big.mark=' ', trim=T)),\n apf_ci = sprintf('%s--%s',\n format(round(apf_lcl), big.mark=' ', trim=T),\n format(round(apf_ucl), big.mark=' ', trim=T)),\n rr_ci = sprintf('%s--%s',\n format(round(rr_lcl,2), big.mark=' ', trim=T),\n format(round(rr_ucl,2), big.mark=' ', trim=T)))]\n\nrisk_summ <- merge(spplist[, .(species_code=code.sra, sname=Taxa, cname)], risk_summ, by = 'species_code')\n\n\nsaveRDS(risk_ratio, '../generated/risk-ratios.rds')\nfwrite(risk_summ, '../generated/risk-ratios-summary.csv')\n \n", "meta": {"hexsha": "00034dee775aac7213920960a4515f684c02f5f4", "size": 3980, "ext": "r", "lang": "R", "max_stars_repo_path": "12-genus-tracking-interaction-fleet/model/risk-calc.r", "max_stars_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_stars_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "12-genus-tracking-interaction-fleet/model/risk-calc.r", "max_issues_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_issues_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "12-genus-tracking-interaction-fleet/model/risk-calc.r", "max_forks_repo_name": "seabird-risk-assessment/abnj-seabird-bycatch-analysis", "max_forks_repo_head_hexsha": "fdb52ca62a9a3a518a60de9c33a7358e5cf43667", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.7362637363, "max_line_length": 150, "alphanum_fraction": 0.5713567839, "num_tokens": 1164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.32149432615086204}} {"text": "#!/usr/bin/env Rscript\n\n#######################################################################\n#######################################################################\n## Created on April. 29, 2021 call edgeR\n## Copyright (c) 2021 Jianhong Ou (jianhong.ou@gmail.com)\n#######################################################################\n#######################################################################\npwd <- getwd()\npwd <- file.path(pwd, \"lib\")\ndir.create(pwd)\n.libPaths(c(pwd, .libPaths()))\n\nlibrary(edgeR)\nwriteLines(as.character(packageVersion(\"edgeR\")), \"edgeR.version.txt\")\n\nbinsize = \"diffhic_bin5000\"\n## load n.cores\nargs <- commandArgs(trailingOnly=TRUE)\nif(length(args)>0){\n binsize <- args[[1]]\n args <- args[-1]\n}\nif(length(args)>0){\n args <- lapply(args, function(.ele) eval(parse(.ele)))\n}else{\n args <- list()\n}\n\n\n## get peaks\npf <- dir(\"peaks\", \"bedpe\", full.names = TRUE)\npeaks <- lapply(pf, read.delim)\n### reduce the peaks\npeaks <- unique(do.call(rbind, peaks)[, c(\"chr1\", \"start1\", \"end1\",\n \"chr2\", \"start2\", \"end2\")])\n\n## get counts\npc <- dir(\"long\", \"bedpe\", full.names = FALSE)\ncnts <- lapply(file.path(\"long\", pc), read.table)\nsamples <- sub(\"(_REP\\\\d+)\\\\.(.*?)\\\\.long.intra.bedpe\", \"\\\\1\", pc)\ncnts <- lapply(split(cnts, samples), do.call, what=rbind)\nsizeFactor <- vapply(cnts, FUN=function(.ele) sum(.ele[, 7], na.rm = TRUE),\n FUN.VALUE = numeric(1))\n\ngetID <- function(mat) gsub(\"\\\\s+\", \"\", apply(mat[, seq.int(6)], 1, paste, collapse=\"_\"))\npeaks_id <- getID(peaks)\ncnts <- do.call(cbind, lapply(cnts, function(.ele){\n .ele[match(peaks_id, getID(.ele)), 7]\n}))\ncnts[is.na(cnts)] <- 0\nnames(peaks_id) <- paste0(\"p\", seq_along(peaks_id))\nrownames(cnts) <- names(peaks_id)\n\npf <- as.character(binsize)\ndir.create(pf)\n\nfname <- function(subf, ext, ...){\n pff <- ifelse(is.na(subf), pf, file.path(pf, subf))\n dir.create(pff, showWarnings = FALSE, recursive = TRUE)\n file.path(pff, paste(..., ext, sep=\".\"))\n}\n\n## write counts\nwrite.csv(cbind(peaks, cnts), fname(NA, \"csv\", \"raw.counts\"), row.names = FALSE)\n## write sizeFactors\nwrite.csv(sizeFactor, fname(NA, \"csv\", \"library.size\"), row.names = TRUE)\n\n## coldata\nsampleNames <- colnames(cnts)\ncondition <- sub(\"_REP.*$\", \"\", sampleNames)\ncoldata <- data.frame(condition=factor(condition),\n row.names = sampleNames)\n## write designtable\nwrite.csv(coldata, fname(NA, \"csv\", \"designTab\"), row.names = TRUE)\n\ncontrasts.lev <- levels(coldata$condition)\n\nif(length(contrasts.lev)>1 || any(table(condition)>1)){\n contrasts <- combn(contrasts.lev, 2, simplify = FALSE)\n ## create DGEList\n group <- coldata$condition\n y <- DGEList(counts = cnts,\n lib.size = sizeFactor,\n group = group)\n\n ## do differential analysis\n names(contrasts) <- vapply(contrasts, \n FUN=paste,\n FUN.VALUE = character(1),\n collapse = \"-\")\n y <- calcNormFactors(y)\n design <- model.matrix(~0+group)\n colnames(design) <- levels(y$samples$group)\n y <- estimateDisp(y,design)\n fit <- glmQLFit(y, design)\n \n ## PCA\n pdf(fname(NA, \"pdf\", \"Multidimensional.scaling.plot-plot\"))\n plotMDS(y)\n dev.off()\n ## plot dispersion\n pdf(fname(NA, \"pdf\", \"DispersionEstimate-plot\"))\n plotBCV(y)\n dev.off()\n ## plot QL dispersions\n pdf(fname(NA, \"pdf\", \"Quasi-Likelihood-DispersionEstimate-plot\"))\n plotQLDisp(fit)\n dev.off()\n \n res <- mapply(contrasts, names(contrasts), FUN = function(cont, name){\n BvsA <- makeContrasts(contrasts = name, levels = design)\n qlf <- glmQLFTest(fit, contrast = BvsA)\n rs <- topTags(qlf, n = nrow(qlf), sort.by = \"none\")\n ## MD-plot\n pdf(fname(name, \"pdf\", \"Mean-Difference-plot\", name))\n plotMD(qlf)\n abline(h=0, col=\"red\", lty=2, lwd=2)\n dev.off()\n ## PValue distribution\n pdf(fname(name, \"pdf\", \"PValue-distribution-plot\", name))\n hist(rs$table$PValue, breaks = 20)\n dev.off()\n ## save res\n res <- as.data.frame(rs)\n res <- cbind(peaks, res[names(peaks_id), ])\n write.csv(res, fname(name, \"csv\", \"edgeR.DEtable\", name), row.names = FALSE)\n ## save metadata\n elementMetadata <- do.call(rbind, lapply(c(\"adjust.method\",\"comparison\",\"test\"), function(.ele) rs[[.ele]]))\n rownames(elementMetadata) <- c(\"adjust.method\",\"comparison\",\"test\")\n colnames(elementMetadata)[1] <- \"value\"\n write.csv(elementMetadata, fname(name, \"csv\", \"edgeR.metadata\", name), row.names = TRUE)\n ## save subset results\n res.s <- res[res$FDR<0.05 & abs(res$logFC)>1, ]\n write.csv(res.s, fname(name, \"csv\", \"edgeR.DEtable\", name, \"padj0.05.lfc1\"), row.names = FALSE)\n ## Volcano plot\n res$qvalue <- -10*log10(res$PValue)\n pdf(fname(name, \"pdf\", \"Volcano-plot\", name))\n plot(x=res$logFC, y=res$qvalue,\n main = paste(\"Volcano plot for\", name),\n xlab = \"log2 Fold Change\", ylab = \"-10*log10(P-value)\",\n type = \"p\", col=NA)\n res.1 <- res[res$padj>=0.05 & abs(res$logFC)<=1, ]\n if(nrow(res.1)>0) points(x=res.1$logFC, y=res.1$qvalue, pch = 20, cex=.5, col=\"gray80\")\n if(nrow(res.s)>0) points(x=res.s$logFC, y=res.s$qvalue, pch = 19, cex=.5, col=ifelse(res.s$logFC>0, \"brown\", \"darkblue\"))\n dev.off()\n res$qvalue <- -10*log10(res$PValue)\n png(fname(name, \"png\", \"Volcano-plot\", name))\n plot(x=res$logFC, y=res$qvalue,\n main = paste(\"Volcano plot for\", name),\n xlab = \"log2 Fold Change\", ylab = \"-10*log10(P-value)\",\n type = \"p\", col=NA)\n res.1 <- res[res$padj>=0.05 & abs(res$logFC)<=1, ]\n if(nrow(res.1)>0) points(x=res.1$logFC, y=res.1$qvalue, pch = 20, cex=.5, col=\"gray80\")\n if(nrow(res.s)>0) points(x=res.s$logFC, y=res.s$qvalue, pch = 19, cex=.5, col=ifelse(res.s$logFC>0, \"brown\", \"darkblue\"))\n dev.off()\n })\n}\n\n\n\n\n", "meta": {"hexsha": "e08dd1ccf0f1a2d49ffd33ffa56a84e5ca49d31f", "size": 5840, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/diffhicar.r", "max_stars_repo_name": "jianhong/hicar_tools", "max_stars_repo_head_hexsha": "2bd20fe7c7defef422a3cda118f9bc13bc48b7f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/diffhicar.r", "max_issues_repo_name": "jianhong/hicar_tools", "max_issues_repo_head_hexsha": "2bd20fe7c7defef422a3cda118f9bc13bc48b7f8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/diffhicar.r", "max_forks_repo_name": "jianhong/hicar_tools", "max_forks_repo_head_hexsha": "2bd20fe7c7defef422a3cda118f9bc13bc48b7f8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-06-07T18:09:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-07T18:09:45.000Z", "avg_line_length": 35.8282208589, "max_line_length": 125, "alphanum_fraction": 0.579109589, "num_tokens": 1746, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040617, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3214696362412742}} {"text": "require(graphics)\nrequire(Matrix)\n\npdf(file=\"plots/Experiment3b.pdf\",\n width=4.5, height=4.0, family=\"serif\", pointsize=14)\n\ndatasets = c(\"Adult\", \"Covtype\", \"USCensus\", \"KDD98\")\nplot_colors <- c(\"orangered\", \"black\", \"orange\", \"cornflowerblue\")\n\ndata = matrix(0, 5, 7); #dataset x params\ndata[1,] = as.matrix(c(0.36,0.68,0.84,0.92,0.96,0.98,0.99))\n\nfor(i in 1:(nrow(data)-1)) {\n for(j in 1:ncol(data)) {\n fname = paste(\"results/Experiment3_\",datasets[i],\"_a\",data[1,j],\".dat\",sep=\"\")\n if( file.exists(fname) ) {\n data[i+1,j] = as.matrix(read.table(fname, sep=\",\"))[1,4]\n }\n else {\n print(paste(fname,\" does not exist\"))\n }\n }\n}\n\ndata[1,] = 1-data[1,]\n\nprint(data)\n\nplot( data[1,], data[5,], \n type=\"o\", \n pch=19, \n cex=1.1,\n col=plot_colors[4], \n ylim = c(1,2e6),\n xlab=\"\", \n ylab=\"\", \n axes=FALSE, \n bg=plot_colors[4],\n log=\"xy\",\n lwd=1.1, \n lty=1\n )\n\n\naxis(2, las=2,at=c(1,10,100,1000,1e4,1e5,1e6), labels=c(\"1\",\"10\",\"100\",\"1000\",\"1e4\",\"1e5\",\"1e6\")) \naxis(1, las=1, at=c(0.01,0.02,0.04,0.08,0.16,0.32,0.64), \n labels=c(\"0.99\",\".98\",\".96\",\".92\",\".84\",\".68\",\".36\")) \nmtext(2, text=\"Top-1 Slice Size\", line=2.7) \nmtext(1, text=expression(paste(\"Weight Parameter \",alpha)), line=2) \n\nlines(data[1,2:7], data[2,2:7], type=\"o\", pch=15, lty=1, lwd=1.1, col=plot_colors[1], bg=plot_colors[1], cex=1.0)\nlines(data[1,2:7], data[3,2:7], type=\"o\", pch=17, lty=1, lwd=1.1, col=plot_colors[2], bg=plot_colors[2], cex=1.0)\nlines(data[1,2:7], data[4,2:7], type=\"o\", pch=25, lty=1, lwd=1.1, col=plot_colors[3], bg=plot_colors[3], cex=1.0)\n\nbox()\t\n\nlegend( \"bottomright\",\n c(\"USCensus\",\"Adult\", \"KDD98\", \"Covtype\"), col=plot_colors[c(3,1,4,2)], \n pch=c(25,15,19,17), lty=c(1), lwd=c(1.1), bty=\"n\", ncol=2, pt.bg=plot_colors[c(3,1,4,2)]);\n\ntext(0.32, 1000, expression(paste(sigma,\" = 0.01n\")))\n\ndev.off() \n", "meta": {"hexsha": "ee39ef974aa4e4604cebeb186c8e7c6e9fe528e9", "size": 1967, "ext": "r", "lang": "R", "max_stars_repo_path": "sigmod2021-sliceline-p218/exp/plotting/Experiment3b.r", "max_stars_repo_name": "damslab/reproducibility", "max_stars_repo_head_hexsha": "f7804b2513859f7e6f14fa7842d81003d0758bf8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-12-10T17:20:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-27T14:38:40.000Z", "max_issues_repo_path": "sigmod2021-sliceline-p218/exp/plotting/Experiment3b.r", "max_issues_repo_name": "damslab/reproducibility", "max_issues_repo_head_hexsha": "f7804b2513859f7e6f14fa7842d81003d0758bf8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sigmod2021-sliceline-p218/exp/plotting/Experiment3b.r", "max_forks_repo_name": "damslab/reproducibility", "max_forks_repo_head_hexsha": "f7804b2513859f7e6f14fa7842d81003d0758bf8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.734375, "max_line_length": 113, "alphanum_fraction": 0.5500762583, "num_tokens": 819, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040617, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.32146963624127417}} {"text": "# This is a test/example rd file\n# Two spheres, pure diffuse surface, one distant light behind eye.\n\n\nDisplay \"Simple Shading\" \"Screen\" \"rgbdouble\"\nFormat 640 480\nCameraFOV 5\nCameraEye 0 0 50\n\nWorldBegin\n\nFarLight 0 0 -1 1.0 1.0 1.0 1.0\n\nKa 0.0\nKd 1.0\n\nTranslate 1.25 0 0\n\nSphere 1.0 -1.0 1.0 360.0\n\nTranslate -2.5 0 0 \n\nSphere 1.0 -1.0 1.0 360.0\n\nWorldEnd\n", "meta": {"hexsha": "3a1fa2486da1da8383fa21f1532210b056711a76", "size": 362, "ext": "rd", "lang": "R", "max_stars_repo_path": "scenes/simple_shading_db.rd", "max_stars_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_stars_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scenes/simple_shading_db.rd", "max_issues_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_issues_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scenes/simple_shading_db.rd", "max_forks_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_forks_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 13.9230769231, "max_line_length": 66, "alphanum_fraction": 0.6906077348, "num_tokens": 162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3214696362412741}} {"text": "library(tidyverse)\nlibrary(reticulate)\n\nnp <- import(\"numpy\")\n\ndata <- np$load(\n \"./outputs/all_scores_mag_models_mnecommonsubjects_interval_shuffle-split.npy\",\n allow_pickle = T)[[1]] %>%\n as.data.frame()\n\ndata[\"mne\"] <- np$load(\n \"./outputs/scores_mag_models_mne_intervals.npy\",\n allow_pickle = T)[[1]][\"mne_shuffle_split\"] %>%\n as.data.frame()\n\ndata$dummy <- NULL\ndata$rand_riemannwass <- NULL\ndata$unsup_riemannwass <- NULL\ndata$sup_riemannwass <- NULL\n\ndata_long <- data %>% gather(key = \"estimator\", value = \"score\")\n# move to long format\ndata_long$estimator <- factor(data_long$estimator)\n\n# set distance types\nest_types <- c(\n # \"dummy\",\n \"log-diag\",\n \"log-diag\",\n \"log-diag\",\n \"log-diag\",\n \"Wasserstein\",\n# \"Wasserstein\",\n# \"Wasserstein\",\n# \"Wasserstein\",\n \"geometric\",\n \"geometric\",\n \"geometric\",\n \"geometric\",\n \"MNE\"\n)\n\n# categorical colors based on: https://jfly.uni-koeln.de/color/\ncolor_cats <- c(\n \"#000000\",\n \"#009D79\",# blueish green\n \"#E36C2F\", #vermillon\n \"#EEA535\", # orange\n \"#0072B2\" #blue\n)\n\n# beef up long data\ndata_long$est_type <- rep(est_types, each = 100) %>%\n factor(., levels = c(\"dummy\", \"log-diag\", \"Wasserstein\", \"geometric\",\n \"MNE\"))\ndata_long$fold <- rep(1:100, times = length(est_types))\n \n# prepare properly sorted x labels\nsort_idx <- apply(data, 2, mean) %>% order()\nlevels_est <- c(\n # \"dummy\",\n \"identity\",\n \"random\",\n \"unsupervised\",\n \"supervised\",\n \"identity\",\n # \"random[r]\",\n # \"unsupervised[r]\",\n# \"SPoC[r]\",\n \"identity\",\n \"random\",\n \"unsupervised\",\n \"supervised\",\n \"biophysics\"\n)[sort_idx]\n\nggplot(data = data_long %>% subset(estimator != \"dummy\"),\n mapping = aes(y = score, x = reorder(estimator, score))) +\n geom_jitter(alpha = 0.5, aes(color = est_type), size = 3.5) +\n geom_boxplot(mapping = aes(fill = est_type), alpha = 0.2,\n outlier.fill = NA, outlier.colour = NA) +\n theme_minimal() + \n labs(y = \"mean absolute error (years)\", x = NULL) +\n theme(text = element_text(family = \"Helvetica\", size = 18),\n legend.position = \"top\", legend.text = element_text( size = 18)) +\n coord_flip() +\n scale_fill_manual(values = color_cats[2:5], name = NULL) +\n scale_color_manual(values = color_cats[2:5], name = NULL) +\n scale_x_discrete(labels = parse(text = levels_est))\n\nggsave(\"./figures/fig1_meg_data_full_intervals.png\", width = 8, height = 6, dpi = 300)\nggsave(\"./figures/fig1_meg_data_full_intervals.pdf\", width = 8, height = 6, dpi = 300)\n", "meta": {"hexsha": "224dd70802bb2e6e67ad599494a3fb788738005e", "size": 2498, "ext": "r", "lang": "R", "max_stars_repo_path": "debug/plot_figure_meg_results_full_intervals.r", "max_stars_repo_name": "DavidSabbagh/meeg_power_regression", "max_stars_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-18T06:10:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-18T06:10:16.000Z", "max_issues_repo_path": "debug/plot_figure_meg_results_full_intervals.r", "max_issues_repo_name": "DavidSabbagh/meeg_power_regression", "max_issues_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "debug/plot_figure_meg_results_full_intervals.r", "max_forks_repo_name": "DavidSabbagh/meeg_power_regression", "max_forks_repo_head_hexsha": "d9cd5e30028ffc24f08a52966c7641f611e92ee6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-01T01:36:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-01T13:44:02.000Z", "avg_line_length": 26.8602150538, "max_line_length": 86, "alphanum_fraction": 0.643714972, "num_tokens": 784, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.32146962893669445}} {"text": "#!/usr/bin/Rscript\n\nstateShipments <- read.csv('../data/StateShipments.csv', skip=2, na.strings=\"\")\ndesel <- c(\"Origin\", \"Total\", \"X\")\ntest <- !(colnames(stateShipments) %in% desel)\nss <- stateShipments[, test]\n\ndesel <- c(\"Total\", \"NA.\")\nrn <- make.names(stateShipments$Origin)\ntest <- !(rn %in% desel)\nss <- ss[test, ]\nrownames(ss) <- rn[test]\nstopifnot(rownames(ss) == colnames(ss))\nss[is.na(ss)] <- 0\n\nkey <- match(colnames(ss), c(make.names(state.name), 'District.of.Columbia',\n 'Mexico', 'Canada', 'Other.states'))\nnms <- c(state.abb, 'DC', 'Mexico', 'Canada', 'otherStates')[key]\n\nrownames(ss) <- colnames(ss) <- nms\nwrite.csv(data.matrix(ss),\n 'shipment-flows-origins-on-rows-dests-on-columns.csv',\n row.names=TRUE)\n", "meta": {"hexsha": "ac4c27115edf439a8061fbd5ec3af786aa81db6c", "size": 770, "ext": "r", "lang": "R", "max_stars_repo_path": "src/clean-shipment-matrix.r", "max_stars_repo_name": "e3bo/2015pedv", "max_stars_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-11T22:33:34.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-11T22:33:34.000Z", "max_issues_repo_path": "src/clean-shipment-matrix.r", "max_issues_repo_name": "e3bo/2015pedv", "max_issues_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2015-10-23T15:48:37.000Z", "max_issues_repo_issues_event_max_datetime": "2015-12-15T18:00:05.000Z", "max_forks_repo_path": "src/clean-shipment-matrix.r", "max_forks_repo_name": "e3bo/2015pedv", "max_forks_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0833333333, "max_line_length": 79, "alphanum_fraction": 0.6168831169, "num_tokens": 227, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.32146962893669445}} {"text": "gdi.data <- read.csv(\"/media/Linux_backup/snpchip/structure/results/wtccc/GDI_DISC_RAND001_100T1D_100NBS_level3_all.csv\");\npdf(\"gdi_test_RAND001_100T1D_100NBS_level3_all.pdf\",paper=\"a4r\",\n width=11,height=8);\nplot(gdi.data$SNPs,gdi.data$GDI, ylab = \"Genome Diagnostic Index\",\n xlab = \"Number of SNPs\");\nplot(gdi.data$SNPs,gdi.data$GDI - c(0,gdi.data$GDI[-(dim(gdi.data)[1])]),\n ylab = \"GDI difference\", xlab = \"Number of SNPs\",\n col = sapply(gdi.data$GDI - c(0,gdi.data$GDI[-(dim(gdi.data)[1])]),\n function(x){\n if(x<0)\n return (\"red\");\n if(x==0)\n return(\"yellow\");\n if(x>0)\n return(\"green\");\n }));\nabline(h=0);\ndummy <- dev.off();\n", "meta": {"hexsha": "91b1535cde711d844e80cd418c637399d01fc50c", "size": 699, "ext": "r", "lang": "R", "max_stars_repo_path": "gdi.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gdi.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gdi.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_forks_repo_licenses": ["ISC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-02T11:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-02T11:22:10.000Z", "avg_line_length": 36.7894736842, "max_line_length": 122, "alphanum_fraction": 0.6194563662, "num_tokens": 241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3214696289366944}} {"text": "#!/usr/bin/env Rscript\n\n# install.packages('devtools')\n# devtools::install_github('wmay/dwnominate')\nlibrary(dwnominate)\nlibrary(pscl)\n\nrcList <- list()\n\nfor (councilPeriod in 22:23) {\n dat <- readLines(paste(\"votes-\", councilPeriod, \".fwf\", sep = \"\"))\n names <- substring(dat, 1, 12)\n dat <- substring(dat, 13)\n rows <- length(dat)\n mat <- matrix(\n NA,\n ncol = nchar(dat[1]),\n nrow = rows\n )\n for(i in 1:rows){\n mat[i,] <- as.numeric(\n unlist(\n strsplit(dat[i], split = character(0))\n )\n )\n }\n rc <- rollcall(\n mat,\n yea = 1,\n nay = 2,\n missing = c(0, 3, 4, 5, 6, 7, 8),\n notInLegis = 9,\n legis.names = names,\n desc = paste(\"DC Council\", councilPeriod)\n )\n summary(rc, verbose = TRUE)\n rcList <- append(rcList, rc)\n}\n\nresults <- dwnominate(rcList)\nplot(results)\n", "meta": {"hexsha": "45218737efe614419f65104a545cb3e5fda84e4e", "size": 920, "ext": "r", "lang": "R", "max_stars_repo_path": "dwnominate/analyze.r", "max_stars_repo_name": "kcivey/dc-council-votes", "max_stars_repo_head_hexsha": "726e6108fd697b5c2be6fc8ab448daa469178e0c", "max_stars_repo_licenses": ["0BSD"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-22T05:53:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-22T05:53:31.000Z", "max_issues_repo_path": "dwnominate/analyze.r", "max_issues_repo_name": "kcivey/dc-council-votes", "max_issues_repo_head_hexsha": "726e6108fd697b5c2be6fc8ab448daa469178e0c", "max_issues_repo_licenses": ["0BSD"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-11-05T20:53:22.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-01T19:35:21.000Z", "max_forks_repo_path": "dwnominate/analyze.r", "max_forks_repo_name": "kcivey/dc-council-votes", "max_forks_repo_head_hexsha": "726e6108fd697b5c2be6fc8ab448daa469178e0c", "max_forks_repo_licenses": ["0BSD"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.9047619048, "max_line_length": 70, "alphanum_fraction": 0.5239130435, "num_tokens": 278, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3214696216321147}} {"text": "#!/usr/bin/env Rscript\nlibrary(scatterD3)\nlibrary(htmlwidgets)\n## https://github.com/juba/scatterD3\n## https://cran.r-project.org/web/packages/scatterD3/scatterD3.pdf\n## https://cran.r-project.org/web/packages/scatterD3/vignettes/introduction.html\n\ncaption = list(title = \"Notes\",\n subtitle = \"\",\n text = \"Builds 50 to 80 ran with excessive coverage reporting set to -Dcoverage=1. The straight line represents the linear regression on throughput.\")\n\ndata <- read.csv(file('stdin'), header=T, as.is=T)\n\n## Derive some data\ndata$labels <- ifelse(data$CLOUD == \"aws-ec2\",\"\", data$CLOUD)\ndata$urls <- sprintf(\"https://tla.msr-inria.inria.fr/build/job/M-HEAD-master-TLC-performances/CLOUD=%s,SPEC=%s/%s/\", data$CLOUD, data$SPEC, data$BUILD_NUMBER)\ndata$tooltips <- sprintf(\"Cloud: %s
Spec: %s
Build number: %s
Commit: %s (%s)\", data$CLOUD, data$SPEC, data$BUILD_NUMBER, data$REVISION, data$BRANCH)\n\n## linear regression of throuphput\nlma <- lm(VALUE ~ BUILD_NUMBER, data=subset(data, MEASUREMENT == \"Throughput\"))\nline <- data.frame(slope = coef(lma)[\"BUILD_NUMBER\"], intercept = coef(lma)[\"(Intercept)\"], stroke = \"red\", stroke_width = 3, stroke_dasharray = \"7,5\")\n\ns <- scatterD3(data = data, x = BUILD_NUMBER, y = VALUE, y_log=T, xlab = \"Build Number\", ylab = \"log(Time&Throughput)\", \n col_var=SPEC, symbol_var = MEASUREMENT,\n url_var = urls, tooltip_text = data$tooltips,\n lab = labels, caption = caption,\n lines = line)\nsaveWidget(s, file=\"index.html\")\n", "meta": {"hexsha": "f9e62aaba28ec425dc006c2549bfb60036bf598b", "size": 1574, "ext": "r", "lang": "R", "max_stars_repo_path": "general/performance/old/performance.r", "max_stars_repo_name": "jonesmartins/tlaplus", "max_stars_repo_head_hexsha": "165deb68c22dfe51afc62571a89b84217fa061a7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-23T23:43:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T23:43:25.000Z", "max_issues_repo_path": "general/performance/old/performance.r", "max_issues_repo_name": "Code-distancing/tlaplus", "max_issues_repo_head_hexsha": "f4d8c3d7455e4315e4a95e070d7ccf84346a45cc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "general/performance/old/performance.r", "max_forks_repo_name": "Code-distancing/tlaplus", "max_forks_repo_head_hexsha": "f4d8c3d7455e4315e4a95e070d7ccf84346a45cc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.275862069, "max_line_length": 186, "alphanum_fraction": 0.6696315121, "num_tokens": 451, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.32146746583132213}} {"text": "library(bayesplot)\nlibrary(ggplot2)\n\n## allow the effect of each NPI to come in gradually\ngradual.npi.effect <- function(stan_data, lambda=0.7) {\n grad.effect <- round(1 - exp(-lambda * 1:20), 2)\n\n ## loop over countries\n for (m in 1:nrow(stan_data$X)) {\n first.day <- apply(stan_data$X[m, , ], 2, function(z) which(z == 1)[1])\n for (p in 1:length(first.day)) {\n if (is.na(first.day[p])) next\n stan_data$X[m, first.day[p] + 1:length(grad.effect) - 1, p] <- grad.effect\n }\n }\n\n return(stan_data)\n}\n\n## save results data\nsave.results <- function(fit, processed_data, modelname) {\n out <- rstan::extract(fit)\n prediction <- out$prediction\n estimated.deaths <- out$E_deaths\n estimated.deaths.cf <- out$E_deaths0\n\n stan_data <- processed_data$stan_data\n dates <- processed_data$dates\n deaths_by_country <- processed_data$deaths_by_country\n reported_cases <- processed_data$reported_cases\n countries <- countries$Regions\n\n save(fit, prediction, dates, reported_cases, deaths_by_country, countries,\n estimated.deaths, estimated.deaths.cf, stan_data, processed_data,\n file=paste0(\"results/\", modelname, \"-stanfit.Rdata\"))\n}\n\n## deaths/counterfactual and MSE\nget.deaths.mse <- function(fit, processed_data, forecast=0) {\n cases.pr <- rstan::extract(fit, \"prediction\")[[1]]\n deaths.pr <- rstan::extract(fit, \"E_deaths\")[[1]]\n deaths.cf <- rstan::extract(fit, \"E_deaths0\")[[1]]\n deaths.ob <- processed_data$deaths_by_country\n dates <- processed_data$dates\n S <- nrow(deaths.pr) # number of samples\n\n ## loop over countries\n cum.deaths.mse <- NULL\n for (m in 1:length(dates)) {\n ## consider only deaths within the modelled period\n N <- length(dates[[m]])\n if (forecast > 0)\n per <- tail(1:N, forecast)\n else\n per <- 1:N\n mse <- apply(deaths.pr[, , m], 1,\n function(z) mean((z[per] - deaths.ob[[m]][per])^2))\n cum.deaths.mse <- rbind(cum.deaths.mse,\n c(sum(colMeans(deaths.pr[, 1:N, m])),\n sum(colMeans(deaths.cf[, 1:N, m])),\n sum(colMeans(cases.pr[, 1:N, m])),\n mean(mse)))\n\n }\n\n cum.deaths.mse <- as.data.frame(cum.deaths.mse)\n rownames(cum.deaths.mse) <- names(dates)\n colnames(cum.deaths.mse) <- c(\"deaths\", \"counterfactual\", \"infections\", \"mse\")\n cum.deaths.mse[, 1:3] <- round(cum.deaths.mse[, 1:3])\n cum.deaths.mse$mse <- round(cum.deaths.mse$mse, 1)\n return(cum.deaths.mse)\n}\n\n## compute ranges of herd immunity threshold from posterior means of parameters\nhit <- function(fit) {\n R0 <- rstan::extract(fit, \"mu\")[[1]]\n alpha <- tryCatch(matrix(rstan::extract(fit, \"a_het\")[[1]],\n nrow(R0), ncol(R0)),\n error=function(e) Inf)\n hit <- 1 - (1 / R0)^(1 - 1 / (1 + alpha))\n list(hit=colMeans(hit), R0=colMeans(R0),\n range.hit=range(colMeans(hit)),\n range.R0=range(colMeans(R0)),\n alpha=mean(alpha))\n}\n\n## create some plots\nplot.mu.rt.rhat <- function(modelname) {\n filename <- paste0(\"results/\", modelname, \"-stanfit.Rdata\")\n print(sprintf(\"loading: %s\", filename))\n load(filename)\n print('Generating mu, rt plots')\n out <- rstan::extract(fit)\n mu <- as.matrix(out$mu)\n colnames(mu) <- countries\n g <- mcmc_intervals(mu, prob=0.9)\n ggsave(sprintf(\"figures/%s-mu.png\", modelname), g, width=4, height=6)\n tmp <- lapply(1:length(countries), function(i) (out$Rt_adj[, stan_data$N[i], i]))\n Rt_adj <- do.call(cbind, tmp)\n colnames(Rt_adj) <- countries\n g <- mcmc_intervals(Rt_adj, prob=0.9)\n ggsave(sprintf(\"figures/%s-final-rt.png\", modelname), g, width=4, height=6)\n\n print(\"Generating rhat plot\")\n g <- mcmc_rhat_hist(rhat(fit))\n ggsave(sprintf(\"figures/%s-rhat.png\", modelname), g, width=4, height=6)\n}\n\n## export Stan's neg_binomial_2_lpmf function as negbim2\ncat(\"Exporting Stan's neg_binomial_2_lpmf\\n\")\nrstan::expose_stan_functions(rstan::stanc(model_code='\nfunctions {\n real negbim2(int[] n, real[] mu, real phi) {\n return neg_binomial_2_lpmf(n | mu, phi);\n }\n}\nmodel{}\n'))\n\ndic <- function(fit, stan_data) {\n\n ## total likelihood\n lik <- rowSums(rstan::extract(fit, \"log_lik\")[[1]])\n\n ## mean deviance\n d1 <- -2 * mean(lik)\n\n ## deviance of the mean\n d2 <- 0\n for (m in 1:stan_data$M) {\n per <- stan_data$EpidemicStart[m]:stan_data$N[m]\n deaths.ob <- stan_data$deaths[per, m]\n deaths.pr <- rstan::extract(fit, \"E_deaths\")[[1]][, per, m]\n phi <- rstan::extract(fit, \"phi\")[[1]]\n d2 <- d2 + negbim2(deaths.ob, colMeans(deaths.pr), mean(phi))\n }\n d2 <- -2 * d2\n\n return(round(c(pD=d1 - d2, pV=mean(var(-2 * lik)) / 2, dic=2 * d1 - d2, deviance=d1), 1))\n}\n\nwaic <- function(fit) {\n loo::waic(rstan::extract(fit, \"log_lik\")[[1]])\n}\n\n## collect summary measures of fit\nfit.summary <- function(fit, processed_data, stan_data, forecast) {\n dic.model <- dic(fit, stan_data)\n hit.model <- hit(fit)\n waic.model <- waic(fit)\n loo.model <- loo(fit)\n mm <- get.deaths.mse(fit, processed_data, forecast)\n mm.sums <- colSums(mm)\n data.frame(\n Deviance=dic.model[\"deviance\"],\n pD=dic.model[\"pD\"],\n DIC=dic.model[\"dic\"],\n R0=round(mean(hit.model$R0), 2),\n alpha=gsub(\"Inf\", \"\\U221E\", round(mean(hit.model$alpha), 2)),\n H=round(mean(hit.model$hit), 2),\n Infections=mm.sums[\"infections\"],\n UK.Infections=mm[\"United_Kingdom\", \"infections\"],\n Deaths=mm.sums[\"deaths\"],\n Counterfactual=mm.sums[\"counterfactual\"],\n row.names=deparse(substitute(fit)), stringsAsFactors=FALSE)\n}\n\n## collect statistics on the sampler behaviour\nsampler.stats <- function(fit) {\n sp <- rstan::get_sampler_params(fit, inc_warmup=FALSE)\n accept.stat <- sapply(sp, function(x) mean(x[, \"accept_stat__\"]))\n stepsize <- sapply(sp, function(x) mean(x[, \"stepsize__\"]))\n divergences <- sapply(sp, function(x) sum(x[, \"divergent__\"]))\n treedepth <- sapply(sp, function(x) max(x[, \"treedepth__\"]))\n gradients <- sapply(sp, function(z) sum(z[, \"n_leapfrog__\"]))\n et <- round(rstan::get_elapsed_time(fit), 2)\n res <- cbind(accept.stat, stepsize, divergences, treedepth, gradients, et)\n avg <- colMeans(res)\n tot <- colSums(res)\n round(rbind(res, all=c(avg[1:2], tot[3], max(res[, 4]), tot[5:7])), 4)\n}\n", "meta": {"hexsha": "d17efb42884ee48d60e04d92c9eb4b108ccebbfe", "size": 6249, "ext": "r", "lang": "R", "max_stars_repo_path": "utils/utils.r", "max_stars_repo_name": "mcol/covid19model-reanalysis", "max_stars_repo_head_hexsha": "a696b0477556ed044a8ec8b01f8399e8f61aa430", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "utils/utils.r", "max_issues_repo_name": "mcol/covid19model-reanalysis", "max_issues_repo_head_hexsha": "a696b0477556ed044a8ec8b01f8399e8f61aa430", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utils/utils.r", "max_forks_repo_name": "mcol/covid19model-reanalysis", "max_forks_repo_head_hexsha": "a696b0477556ed044a8ec8b01f8399e8f61aa430", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-09-28T15:44:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-09-28T15:44:17.000Z", "avg_line_length": 34.3351648352, "max_line_length": 91, "alphanum_fraction": 0.6343414946, "num_tokens": 1979, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331319177488, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.32146745036587066}} {"text": "## https://israeldi.github.io/bookdown/_book/monte-carlo-simulation-of-stock-portfolio-in-r-matlab-and-python.html\n\n##-----------------------------------------------------------------------------\n## setup\nos <- .Platform$OS.type\nif (os == 'windows') {\n ## load generic modules\n source(\"F:\\\\Documents\\\\01_Dave's Stuff\\\\Programs\\\\GitHub_home\\\\R-setup\\\\setup.r\")\n ## identify working folder\n path <- c(\"f:/Documents/01_Dave's Stuff/Programs/GitHub_home/Retirement/\")\n} else {\n ## os == unix\n source('~/GitHub_repos/R-setup/setup.r')\n path <- c('~/GitHub_repos/Retirement/')\n}\n## set working folder\nsetwd(path)\n## load local modules\nr_files <- list.files(paste(path, 'modules/', sep=''), pattern=\"*.[rR]$\", full.names=TRUE)\nfor (f in r_files) {\n ## cat(\"f =\",f,\"\\n\")\n source(f)\n}\n\nis.date <- function(x) inherits(x, 'Date')\n\n##-----------------------------------------------------------------------------\n## SET PARAMETERS FOR MONTE CARLO SIMULATIONS\n## Set number of Monte Carlo Simulations\nmc_rep <- 10\nperiod <- 'years'\n## Set simulation start and approximate end dates\nsim_start <- as.Date(format(Sys.Date(), \"%Y-%m-01\"))\nsim_end <- as.Date('2030-11-30') # must be a day in the period\n\n\n##-----------------------------------------------------------------------------\n## Define starting value and weights for each asset and total value\n## create matrix of values for each account\nvalue0 <- cbind(invest = 100000,\n ira = 200000,\n roth = 300000)\nnacct <- ncol(value0)\ntvalue0 <- sum(value0)\nweights <- c(value0[[1]]/tvalue0,\n value0[[2]]/tvalue0,\n value0[[3]]/tvalue0)\nprint(weights)\n\n## Define asset allocation for each account\nallocation <- '\naccount US_L US_S Inter Fixed Cash\ninvest 50 10 0 30 10\nira 40 30 5 20 5\nroth 50 30 10 10 0\n'\nallocation <- readall(allocation)\nrownames(allocation) <- allocation$account\nallocation$account <- NULL\nallocation <- as.matrix(allocation / 100)\nprint(allocation)\n\n\n##-----------------------------------------------------------------------------\n## OBTAIN BENCHMARK HISTORICAL DATA\n## category ETF description\n## ------------- --- ---------------\n## USL SPY S&P 500\n## USS IWM Russell 2000\n## International EFA MSCI EAFE (TRN)\n## Fixed AGG Bloomberg US Aggregate Bond\n## Cash SHV FTSE 3-month treasury bill\n\n## periods per year\nif (period == 'years') {\n nperiod <- 1\n} else if (period == 'months') {\n nperiod <- 12\n} else if (period == 'weeks') {\n nperiod <- 52\n} else {\n ## days\n ## number of periods will change a bit each year\n ## 365.25 (days on average per year) * 5/7 (proportion work days per week)\n ## - 6 (weekday holidays) - 3*5/7 (fixed date holidays) = 252.75 \u2248 253\n nperiod <- 253\n}\n\nout <- equityget(c('SPY', 'IWM', 'EFA', 'AGG', 'SHV'), from='1995-01-01',\n period=period)\nbenchclose <- out$close\nbenchtwr <- out$twr\ncolnames(benchclose) <- c('US_L', 'US_S', 'Inter', 'Fixed', 'Cash')\ncolnames(benchtwr) <- c('US_L', 'US_S', 'Inter', 'Fixed', 'Cash')\n## plot closing prices and TWR\nplotspace(1,2)\nplotxts(benchclose)\n## plotzoo(benchclose)\nplotxts(benchtwr)\n\n## only keep twr for dates where all are defined\nbenchtwr <- na.omit(benchtwr)\n\n\n##-----------------------------------------------------------------------------\n## DATA FOR EACH ASSET\n\n## Determine mean for each asset and covariance matrix\nreturn <- '\n Date invest ira roth\n1/31/17 0.01 0.02 0.03\n2/28/17 0.02 0.03 0.05\n3/31/17 0.05 0.04 0.30\n4/30/17 0.03 0.05 0.02\n5/31/17 0.05 0.04 0.02\n6/30/17 0.07 0.08 0.01\n7/31/17 0.08 0.07 -0.02\n8/31/17 0.05 0.07 0.02\n9/30/17 0.08 0.10 0.03\n10/31/17 0.09 0.10 0.05\n11/30/17 0.10 0.09 0.02\n12/31/17 0.10 0.09 0.03\n1/31/17 0.06 0.08 0.05\n2/28/17 0.03 0.04 0.06\n3/31/17 0.01 0.05 0.06\n'\n\n## set monthly changes to account\nmonthly_spending <- c( 1000, \n 0, \n 0)\nmonthly_saving <- c( 500, \n 0, \n 0)\nmonthly_change <- monthly_saving - monthly_spending\n\n## calculate changes per period\nperiod_change <- monthly_change * 12 / nperiod\n\n\n## set specific date spending and saving (specify at end of a period)\nsaving <- '\n Date invest ira roth\n2/28/22 0 2000 0 \n3/30/22 0 0 500\n4/30/22 0 1000 1000\n'\nsaving <- readall(saving)\n\nspending <- '\n Date invest ira roth\n1/31/22 1000 0 0\n4/30/22 0 500 0\n'\nspending <- readall(spending)\n\n## convert to XTS\n## note the format used below must match that in the dataframe being read\nsaving <- xts::as.xts(zoo::read.zoo(saving, index.column = 1, format = \"%m/%d/%y\" ))\nspending <- xts::as.xts(zoo::read.zoo(spending, index.column = 1, format = \"%m/%d/%y\" ))\n\n\n\n##-----------------------------------------------------------------------------\n## determine number of timesteps and create date sequence\nif (period == 'years') {\n ntimestep <- as.numeric(format(sim_end, \"%Y\")) - as.numeric(format(sim_start,\"%Y\"))\n ## Create sequence of dates at ends of periods for simulation \n ## (+1 on ntimestep is so start with initial value)\n Date <- seq(as.Date(sim_start), length=ntimestep+1, by=period) - 1\n} else if (period == 'months') {\n ntimestep <- lubridate::interval(sim_start, sim_end) %/% months(1) + 1\n Date <- seq(as.Date(sim_start), length=ntimestep+1, by=period) - 1\n} else if (period == 'weeks') {\n ntimestep <- ceiling( (sim_end - sim_start) / 7 ) # rounds up\n Date <- seq(as.Date(sim_start), length=ntimestep+1, by=period) - 1\n} else {\n ## period = 'days'\n ## install.packages('bizdays')\n holidays <- timeDate::holidayNYSE(year = c(2021:2051))\n bizdays::create.calendar(\"USA\", holiday =holidays, weekdays=c(\"saturday\", \"sunday\"))\n Date <- bizdays::bizseq(sim_start, sim_end, \"USA\")\n}\n## Number of timesteps for the simulation\nntimestep = length(Date) - 1 # minus 1 because Date constains time zero\n\n## split off 1st date since mc will only iterate on future dates\ndate0 <- Date[1]\ndatesim <- Date[2:length(Date)]\n\n\n##-----------------------------------------------------------------------------\n## READ IN DATA FOR EACH ASSET\n\n## ##----------------------\n## ## OLD\n## return_in <- as_tibble(readall(return))\n## \n## ## convert date field to date format\n## return_in$Date <- as.Date(return_in$Date, \"%m/%d/%y\")\n## \n## ## convert return columns as matrix for efficiency later\n## returnm <- as.matrix(return_in[2:ncol(return_in)])\n##\n## use above plus following to get old results\n## benchtwr <- returnm\n## ## OLD\n## ##----------------------\n\n## number of benchmarks used\nnbench <- ncol(benchtwr)\n\n## calculate mean return for each benchmark asset\nmeans <- colMeans(benchtwr)\n\n## Get the Variance Covariance Matrix of benchmark returns\n## pairsdf(as.matrix(benchtwr))\ncovarm <- cov(benchtwr)\nprint(covarm)\n\n## Lower Triangular Matrix from Choleski Factorization, L where L * t(L) = covarm\n## check with as.matrix(L) %*% as.matrix(t(L)) = covarm\n## needed for R_i = mean_i + L_ij * Z_ji\nL = t( chol(covarm) )\nprint(L)\n\n\ndlh need to rethink save_expand and spend_expand\ncurrently, there is no entry for the 0th year,\nbut I say additions and subtractions are at the end of every year\n\n\n\n## create xts object with row for each datesim and column for each account\naccounts <- names(spending)\nzeros <- xts::xts(matrix(0, length(datesim), length(accounts)),\n datesim, dimnames=list(NULL, accounts))\nsave_expand <- zeros\nspend_expand <- zeros\n\n## add user specified entries to saving\nfor (i in 1:nrow(saving)) {\n irow <- birk::which.closest(datesim, zoo::index(saving[i]))\n save_expand[irow,] <- saving[i,]\n}\n\n## add usser speified spending entries\nfor (i in 1:nrow(spending)) {\n irow <- birk::which.closest(datesim, zoo::index(spending[i]))\n spend_expand[irow,] <- spending[i,]\n}\n\n## create single xts object with the total amount added and removed\ninout <- save_expand - spend_expand\n\n## add every period additions and subtractions\nfor (i in 1:nacct) {\n inout[,i] <- inout[,i] + period_change[i]\n}\n\n\n##-----------------------------------------------------------------------------\n## START MONTE CARLO SIMULATION\n\n## initialize variables\n## twr and totalvalue matrices\n## row for each timestep\n## column for each mc sim\ntwr <- matrix(0, ntimestep, mc_rep)\ntotalvalue <- twr\n## same as above for value but with 2nd dimension for each account\nvalue <- array(0, dim=c(ntimestep, nacct, mc_rep))\n\n## Extend means vector to a matrix\n## one row for each benchmark repeated in columns for each timestep\nmeansm <- matrix(rep(means, ntimestep), nrow = nbench)\n\n## set seed if want to repeat exactly\nset.seed(200)\nfor (i in 1:mc_rep) {\n ## do following for each monte carlo simulation\n\n cat('simulation', i, '\\n')\n\n ## start with initial values for each account\n valueold <- value0\n \n ## obtain random z values for each account (rows) for each date increment (columns)\n Z <- matrix( rnorm( nbench * ntimestep ), ncol = ntimestep)\n\n ## simulate returns for each increment forward in time (assumed same as whatever data was)\n sim_benchtwr <- meansm + L %*% Z\n ## to view as a dataframe\n ## dfsim <- as_tibble(as.data.frame(t(sim_benchtwr)))\n\n ## Calculate vector of portfolio returns\n twr_i <- cumprod( weights %*% allocation %*% sim_benchtwr + 1 ) -1 # ntimestep entries\n \n ## Add it to the monte-carlo matrix\n twr[,i] <- twr_i;\n\n ## figure out account values and new weights\n for (j in 1:ntimestep) {\n ## for each time increment\n ## if (j == ntimestep-1) browser()\n cat('simulation', i, '; timestep', j, '\\n')\n \n ## growth due to market\n sb <- sim_benchtwr[,j] # vector of bencmark returns for time j\n growth <- t( allocation %*% sb ) * valueold\n \n ## add or remove funds at end of each period\n in_out <- inout[j,]\n \n ## value for simulation i\n value[j,,i] <- as.numeric(valueold + growth + in_out)\n ## value for each account\n valueold <- value[j,,i]\n ## total value for all accounts combined\n totalvalue[j,i] <- sum(valueold)\n \n ## recalculate weights after adjustments\n weights <- as.numeric(valueold / sum(valueold))\n }\n}\n\n##-----------------------------------------------------------------------------\n## GATHER RESULTS\n\n## put twr results into dataframe\ntwr <- as.data.frame(twr)\n## add row for starting value\nones <- rep(0, ncol(twr))\ntwr <- rbind(ones, twr)\n## ## add date\n## twr <- as_tibble(cbind(Date=Date, twr))\n\n## put total value results into dataframe\ntotalvalue <- as.data.frame(totalvalue)\ntotalvalue <- rbind(sum(value0), totalvalue)\n## totalvalue <- as_tibble(cbind(Date=Date, totalvalue))\n\n## value is size ntimestep x nbench x mc_rep\n## e.g., value[1,,2] returns 1st timestep results for all account values for mc_rep=2\n## value[,,1] returns all timestep results for all account values for mc_rep=1\n## value[,1,] returns all timestep results for 1st account for all mc_reps\n\n\n\n##-----------------------------------------------------------------------------\n## DISPLAY RESULTS\n\n## plot results\nplotspace(2,1)\n\n## PLOT TWR\n## --------\n## first establish plot area\nylim <- range(twr)\nplot(Date, twr$V1, type='n',\n ylab='Simulation Returns',\n ylim=ylim)\nfor (i in 2:ncol(twr)) {\n lines(Date, t(twr[i]), type='l')\n}\n\n## Construct Confidential Intervals for returns\n## first define function\nci <- function(df, conf) {\n ## calculate confidence limit for specified confidence level\n ## note specified conf = 1 - alpha\n ## i.e., if want alpha=0.05 to get 95% conf limit, specify 0.95\n apply(df, 1, function(x) quantile(x, conf))\n}\n## create dataframe of confidence intervals\ndf <- twr\ncis <- as_tibble( data.frame(Date,\n conf_99.9_upper_percent = ci(df, 0.999),\n conf_99.0_upper_percent = ci(df, 0.99),\n conf_95.0_upper_percent = ci(df, 0.95),\n conf_50.0_percent = ci(df, 0.5),\n conf_95.0_lower_percent = ci(df, 0.05),\n conf_99.0_lower_percent = ci(df, 0.01),\n conf_99.9_lower_percent = ci(df, 0.001)) )\n\n## plot confidence intervals on simulation\nlines(cis$Date, cis$conf_99.9_upper_percent, lwd=4, lty=2, col='red')\nlines(cis$Date, cis$conf_50.0_percent , lwd=4, col='red')\nlines(cis$Date, cis$conf_99.9_lower_percent, lwd=4, lty=2, col='red')\nlegend('topleft', \n legend=c('simulation', 'upper 99.99', 'mean', 'lower 99.99'),\n col=c('black', 'red', 'red', 'red'),\n lty=c(1,2,1,2))\n\n\n\n\n\n## PLOT VALUE\n## ----------\n## first establish plot area\nylim <- range(totalvalue)\nplot(Date, totalvalue$V1, type='n',\n ylab='Simulation Value',\n ylim=ylim)\nfor (i in 2:ncol(totalvalue)) {\n lines(Date, t(totalvalue[i]), type='l')\n}\n\n## Construct Confidential Intervals for returns\n## first define function\nci <- function(df, conf) {\n ## calculate confidence limit for specified confidence level\n ## note specified conf = 1 - alpha\n ## i.e., if want alpha=0.05 to get 95% conf limit, specify 0.95\n apply(df, 1, function(x) quantile(x, conf))\n}\n## create dataframe of confidence intervals\ndf <- totalvalue\ncis <- as_tibble( data.frame(Date,\n conf_99.9_upper_percent = ci(df, 0.999),\n conf_99.0_upper_percent = ci(df, 0.99),\n conf_95.0_upper_percent = ci(df, 0.95),\n conf_90.0_upper_percent = ci(df, 0.90),\n conf_50.0_percent = ci(df, 0.50),\n conf_90.0_lower_percent = ci(df, 0.10),\n conf_95.0_lower_percent = ci(df, 0.05),\n conf_99.0_lower_percent = ci(df, 0.01),\n conf_99.9_lower_percent = ci(df, 0.001)) )\n\n## plot confidence intervals on simulation\nlines(cis$Date, cis$conf_99.9_upper_percent, lwd=4, lty=2, col='red')\nlines(cis$Date, cis$conf_50.0_percent , lwd=4, col='red')\nlines(cis$Date, cis$conf_99.9_lower_percent, lwd=4, lty=2, col='red')\nlegend('topleft', \n legend=c('simulation', 'upper 99.99', 'mean', 'lower 99.99'),\n col=c('black', 'red', 'red', 'red'),\n lty=c(1,2,1,2))\n\n\n##-----------------------------------------------------------------------------\n## final results at end of simulation\n## Porfolio Returns statistics at end of simulation\ncum_final <- as.numeric( twr[nrow(twr),] )\ncum_final_stats <- data.frame(mean = mean(cum_final),\n median = median(cum_final),\n sd = sd(cum_final))\nprint(cum_final_stats)\n\nfinal <- cbind(save_expand, spend_expand[2:ncol(spend_expand)],\n ci(twr, 0.001), ci(totalvalue, 0.001))\ncat(' Saving Spending TWR (99.9% LB CI) Value (99.9% LB CI)\\n',\n ' ------------------ ------------------ ------------------ -------------------\\n')\nprint(final)\nci(twr, 0.999)\n\n\n## ## create dataframe of mean and upper/lower ci for each account\n## value <- data.frame(matrix(0, # Create data frame of zeros\n## nrow = length(datesim),\n## ncol = mc_rep))\n## for (i in 1:nbench) {\n## cat('account', i, '\\n')\n## value <- value[,i,]\n## }\n", "meta": {"hexsha": "d324470a6993ab80f8133f5fd6757add4cf154e1", "size": 15841, "ext": "r", "lang": "R", "max_stars_repo_path": "retire.r", "max_stars_repo_name": "dhjelmar/Retirement", "max_stars_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "retire.r", "max_issues_repo_name": "dhjelmar/Retirement", "max_issues_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "retire.r", "max_forks_repo_name": "dhjelmar/Retirement", "max_forks_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9935622318, "max_line_length": 114, "alphanum_fraction": 0.5694716243, "num_tokens": 4462, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.3214674503658705}} {"text": "sd_section(\"Starting Points\",\n \"Introductory material\",\n c(\"ggtern_package\",\n \"ggtern\",\n \"ggplot\"))\n\nsd_section(\"Ternary Coordinates\",\n \"Definitions for the ternary coordinate system.\",\n c(\"coord_tern\")\n)\n\nsd_section(\"Ternary Scales\",\n \"Definitions for the ternary axes.\",\n c(\"scale_X_continuous\",\n 'tern_limits')\n)\n\nsd_section(\"Approved Layers\",\n \"Information on the layers which are available and approve for use.\",\n c(\"approved_layers\")\n)\n\nsd_section(\"Geoms\",\n paste(c(\"Geoms, short for geometric objects, describe the type of plot you will produce.\",\n \"Several of the geoms are accompanied by dedicated stats.\"), collapse=\" \"),\n c(\"geom_crosshair_tern\",\n \"geom_confidence_tern\",\n \"geom_density_tern\",\n \"geom_interpolate_tern\",\n \"geom_Xline\",\n \"geom_Xisoprop\",\n \"geom_errorbarX\",\n \"geom_smooth_tern\",\n \"geom_point_swap\",\n \"geom_mask\",\n \"geom_label_viewport\",\n \"geom_text_viewport\",\n \"geom_mean_ellipse\",\n \"geom_hex_tern\",\n \"geom_tri_tern\",\n \"geom_polygon_closed\"\n )\n)\n\nsd_section(\"Annotation\",\n \"Specialised functions for adding annotations to a plot.\",\n c(\"annotate\",\n \"annotation_raster_tern\")\n)\n\nsd_section(\"Positional Adjustments\",\n \"Position adjustments can be used to fine tune positioning of objects to achieve effects like dodging, jittering and stacking.\",\n c(\"position_nudge_tern\",\n \"position_jitter_tern\")\n)\n\nsd_section(\"Calculations\",\n \"Various calculation routines.\",\n c(\"ternary_transformation\",\n \"mahalanobis_distance\")\n)\n\nsd_section(\"Theme Elements\",\n \"New theme elements, unique to ggtern.\",\n c(\"theme\",\n \"theme_elements\")\n)\n\nsd_section(\"Themes\",\n \"Complete themes available for use.\",\n c(\"theme_complete\",\n \"ggtern_themes\")\n)\n\nsd_section(\"Convenience Functions\",\n \"Functions for the rapid customization of plot appearance.\",\n c(\"theme_convenience_functions\",\n \"theme_arrowlength\",\n \"theme_gridsontop\",\n \"theme_bordersontop\",\n \"theme_clockwise\",\n \"theme_legend_position\",\n \"theme_noarrows\",\n \"theme_nomask\",\n \"theme_novar_tern\",\n \"theme_rotate\",\n \"theme_showgrid\",\n \"theme_showlabels\",\n \"theme_showprimary\",\n \"theme_showtitles\",\n \"theme_ticksoutside\",\n \"theme_ticklength\",\n \"theme_mesh\",\n \"theme_latex\",\n \"theme_zoom_X\"\n )\n)\n\nsd_section(\"Labels\",\n \"Ternary-specific Labels.\",\n c(\"ggtern_labels\",\n \"ggtern_labels_arrow_suffix\",\n \"label_formatter\",\n \"breaks_tern\",\n 'labels_tern')\n)\n\nsd_section(\"Data\",\n \"The following datasets have been included in the present package.\",\n c(\"data_Feldspar\",\n \"data_Fragments\",\n \"data_USDA\",\n \"data_WhiteCells\",\n \"data_SkyeLava\")\n)\n\nsd_section(\"Legend Keys\",\n \"Functions related to the renderin of legend keys.\",\n c('draw_key_tern'))\n\nsd_section(\"Arrangement & Saving\",\n \"The following funcions are useful for saving and printing.\",\n c(\"arrangeGrob\",\n \"ggsave\"))\n\n", "meta": {"hexsha": "7e4b07a012e2568902bdf15dad05eb4292508d04", "size": 3637, "ext": "r", "lang": "R", "max_stars_repo_path": "R-Portable-Mac/library/ggtern/staticdocs/index.r", "max_stars_repo_name": "dpique/aneuvis-desktop", "max_stars_repo_head_hexsha": "fec525abb58cbaeb2dd1a0662160efffe45a8c7d", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R-Portable-Mac/library/ggtern/staticdocs/index.r", "max_issues_repo_name": "dpique/aneuvis-desktop", "max_issues_repo_head_hexsha": "fec525abb58cbaeb2dd1a0662160efffe45a8c7d", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R-Portable-Mac/library/ggtern/staticdocs/index.r", "max_forks_repo_name": "dpique/aneuvis-desktop", "max_forks_repo_head_hexsha": "fec525abb58cbaeb2dd1a0662160efffe45a8c7d", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-04T05:38:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-04T05:38:07.000Z", "avg_line_length": 28.8650793651, "max_line_length": 139, "alphanum_fraction": 0.5570525158, "num_tokens": 743, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3214674492650752}} {"text": "\n\n#set working directory\nsetwd(\"C:\\\\Users\\\\Eva\\\\Google Drive\\\\Admissions\\\\NYU\\\\Admitted\\\\Courses\\\\DSGA 3001_9 Responsible DS\\\\Project\\\\nutritional-label-for-loan-recommendation-system\\\\data\\\\raw\")\n\ndata <- read.csv(\"cs-training.csv\", header=T)\ncolnames (data)\n\n\n##############\n# create validation \n##############\n#\ntrain <- sample(nrow(data), floor(nrow(data) * 0.66)) \ntraining <- data[train, ] \nvalidation <- data[-train, ] \nremove (train) # remove data to free up space \n\n# install.packages(\"DMwR\")\n\n#######################\n# SMOTE\n#######################\n# Literature: http://cran.r-project.org/web/packages/DMwR/DMwR.pdf\n\nrequire(DMwR)\n# must set ydata as factor and has to be placed at the end!!!!!!!!\ndata$ydata <- as.factor(data$ydata)\ndata <- SMOTE(ydata ~ ., data\n ,k = 5\n ,perc.over = 700,perc.under=200)\ntable(data$ydata)\n\n####################################################\n#modelling procedure\n####################################################\n\n################\n#GBM \n################\nrequire(gbm)\n\nmodel <- gbm(\n ydata ~ ., \n distribution = \"adaboost\", \n data = data, \n #var.monotone = NULL,\n n.trees = 5000,\n interaction.depth = 4,\n n.minobsinnode = 30,\n shrinkage = 0.05,\n bag.fraction = 0.2,\n train.fraction = 0.8,\n #cv.folds=5,\n #keep.data = TRUE,\n verbose = TRUE)\n\npredict <- predict.gbm (model, test, n.trees = 5000, type = \"response\")\n\n################\n# Random Forest\n################\nrequire(randomForest)\ndata$ydata <- as.factor(data$ydata)\n\nmodel <- randomForest (ydata ~ ., data, ntree = 250, nodesize = 100)\npredict <- predict (model, test, type = \"prob\")\n\n\n################\n#caret\n################\n#http://cran.r-project.org/web/packages/caret/caret.pdf\nrequire(caret)\nrequire(MASS)\n\ndata$ydata <- as.factor(data$ydata)\nmodel <- train (ydata ~ ., data, method = \"xxx\")\n# replace xxx with a function found in caret\n\npredict <- predict (model, test)\n\n#################\n# write output\n#################\n\nwrite.csv(predict, file = \"predict.csv\", quote = FALSE, row.names = FALSE)\n", "meta": {"hexsha": "4553d115708cc29a2e89a513ecd0f096518378e0", "size": 2055, "ext": "r", "lang": "R", "max_stars_repo_path": "notebooks/ece278/EJ_R Script SMOTE_v1.r", "max_stars_repo_name": "evaezekwem/nutritional-label-for-loan-recommendation-system", "max_stars_repo_head_hexsha": "a07d9514ec5ddc306f913344ceb029285b678764", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "notebooks/ece278/EJ_R Script SMOTE_v1.r", "max_issues_repo_name": "evaezekwem/nutritional-label-for-loan-recommendation-system", "max_issues_repo_head_hexsha": "a07d9514ec5ddc306f913344ceb029285b678764", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "notebooks/ece278/EJ_R Script SMOTE_v1.r", "max_forks_repo_name": "evaezekwem/nutritional-label-for-loan-recommendation-system", "max_forks_repo_head_hexsha": "a07d9514ec5ddc306f913344ceb029285b678764", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-02T06:54:31.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-02T06:54:31.000Z", "avg_line_length": 23.3522727273, "max_line_length": 171, "alphanum_fraction": 0.5625304136, "num_tokens": 561, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3213827032754021}} {"text": "library(\"readxl\")\nlibrary(\"here\")\nlibrary(\"dplyr\")\nlibrary(\"janitor\")\nlibrary(\"tidyr\")\nlibrary(\"ggplot2\")\nlibrary(\"scales\")\nlibrary(\"binom\")\nlibrary(\"lubridate\")\nlibrary(\"rvest\")\nlibrary(\"ggrepel\")\nlibrary(\"viridis\")\nlibrary(\"purrr\")\nlibrary(\"sf\")\n\nurl <- paste0(\"https://www.gov.uk/government/collections/\",\n \"nhs-test-and-trace-statistics-england-weekly-reports\")\nsession <- session(url)\n\nweekly_url <- session %>%\n html_nodes(xpath = \"//div/ul/li/a\") %>%\n html_attr(\"href\") %>%\n grep(\"weekly-statistics\", ., value = TRUE) %>%\n pluck(1)\n\nlatest <- session %>%\n session_jump_to(weekly_url)\n\nurl <- latest %>%\n html_nodes(xpath = \"//div/h3/a\") %>%\n html_attr(\"href\") %>%\n grep(pattern = \"tests.reported\", value = TRUE, ignore.case = TRUE)\n\nfilename <- sub(\"^.*/([^/]+)$\", \"\\\\1\", url)\n\ndir <- tempdir()\ndownload.file(url, file.path(dir, filename))\n\nltlas <- read_excel(file.path(dir, filename), sheet = \"Table_4\")\nheader_row <- which(ltlas[, 1] == \"LTLA\")\n\nltlas <- ltlas %>%\n row_to_names(header_row) %>%\n as_tibble() %>%\n clean_names() %>%\n select(-total) %>%\n mutate(test_result = sub(\"Total number of (positive|negative) LFD tests\",\n \"\\\\1\", test_result)) %>%\n filter(grepl(\"(positive|negative)\", test_result)) %>%\n pivot_longer(names_to = \"date\", starts_with(\"x\")) %>%\n mutate(value = as.integer(value)) %>%\n filter(!is.na(value)) %>%\n mutate(date = as.Date(sub(\"^.+([0-9]{2})_([0-9]{2})_([0-9]{2})$\",\n \"20\\\\3-\\\\2-\\\\1\", date))) %>%\n pivot_wider(names_from = \"test_result\") %>%\n mutate(total = positive + negative) %>%\n filter(!is.na(total))\n\nuncert <- binom.confint(ltlas$positive, ltlas$total, method = \"exact\") %>%\n select(mean, lower, upper)\n\ndf <- ltlas %>%\n bind_cols(uncert) %>%\n filter(date != \"2020-12-24\", date >= \"2021-02-01\")\n\nwr_latest <- df %>%\n mutate(rel_error = (abs(lower - mean) + abs(upper - mean)) / (2 * mean)) %>%\n select(date, ltla, estimate = mean, rel_error)\n\nwr_previous <- wr_latest %>%\n mutate(date = date + 7)\n\nwr <- wr_latest %>%\n inner_join(wr_previous, by = c(\"date\", \"ltla\"),\n suffix = c(\"\", \"_previous\")) %>%\n mutate(ratio = estimate / estimate_previous,\n rel_error = rel_error + rel_error_previous,\n lower_ratio = pmax(0, ratio - rel_error * ratio),\n upper_ratio = ratio + rel_error * ratio) %>%\n select(date, ltla, ends_with(\"ratio\"))\n\ninc_r <- df %>%\n inner_join(wr, by = c(\"ltla\", \"date\")) %>%\n mutate(label = if_else(ltla_name %in% c(\"Bolton\", \"Blackburn with Darwen\",\n \"Liverpool\", \"Tower Hamlets\"),\n ltla_name, NA_character_))\n\nlatest <- inc_r %>%\n filter(date > max(date) - 7) %>%\n mutate(label = if_else(ratio > 2 | mean > 0.01,\n ltla_name, NA_character_))\n\np <- ggplot(latest, aes(y = mean, x = ratio, colour = region_name)) +\n geom_point() +\n geom_errorbarh(alpha = 0.15, aes(xmin = lower_ratio, xmax = upper_ratio)) +\n geom_errorbar(alpha = 0.15, aes(ymin = lower, ymax = upper)) +\n geom_text_repel(aes(label = label), show.legend = FALSE) +\n scale_y_continuous(\"LFD positive prevalence\", labels = scales::percent_format(.5)) +\n xlab(\"Weekly relative growth\") +\n theme_bw() +\n coord_cartesian(xlim = c(0, ceiling(max(latest$ratio)) + 1)) +\n geom_vline(xintercept = 1, linetype = \"dashed\") +\n scale_color_brewer(\"Region\", palette = \"Paired\")\n\nsuppressWarnings(dir.create(here::here(\"figure\")))\nggsave(here::here(\"figure\", \"lfd_prev_growth.svg\"), p, width = 11, height = 7)\n\nlabels <- inc_r %>%\n filter(date == max(date)) %>%\n arrange(desc(mean)) %>%\n head(n = 20) %>%\n mutate(date = date + 2)\n\nlast_10_weeks <- inc_r %>%\n filter(date > max(date) - weeks(10)) %>%\n mutate(label = if_else(ltla %in% labels$ltla & date == max(date),\n ltla_name, NA_character_))\n\np <- ggplot(last_10_weeks, aes(x = date, y = mean,\n colour = region_name,\n group = ltla)) +\n geom_point() +\n geom_line(alpha = 0.2) +\n scale_colour_brewer(\"\", palette = \"Set1\") +\n theme_bw() +\n xlab(\"\") +\n expand_limits(x = max(last_10_weeks$date + 7), y = 0) +\n scale_y_continuous(\"LFD prevalence\", labels = scales::label_percent()) +\n theme(legend.position = \"bottom\") +\n geom_text_repel(aes(label = label), show.legend = FALSE)\n\nggsave(here::here(\"figure\", \"lfd_last_10_weeks.svg\"), p, width = 10, height = 6)\n\np <- ggplot(last_10_weeks, aes(x = date, y = mean,\n colour = region_name,\n group = ltla)) +\n geom_point() +\n geom_line(colour = \"black\", alpha = 0.2) +\n scale_colour_brewer(\"\", palette = \"Set1\") +\n theme_bw() +\n xlab(\"\") +\n expand_limits(x = max(last_10_weeks$date + 7), y = 0) +\n scale_y_continuous(\"LFD prevalence\", labels = scales::label_percent()) +\n theme(legend.position = \"bottom\") +\n geom_text_repel(aes(label = label), show.legend = FALSE) +\n facet_wrap(~ region_name)\n\nggsave(here::here(\"figure\", \"lfd_last_10_weeks_regions.svg\"), p, width = 12, height = 10)\n\nengland_ltla_shape <- st_read(here::here(\"data\", \"Local_Authority_Districts_(December_2021)_UK_BUC\")) %>%\n rename(geo_code = LAD21CD) %>%\n filter(grepl(\"^E\", geo_code))\n\nall_ltlas_dates <-\n expand_grid(ltla = unique(england_ltla_shape$geo_code),\n date = unique(last_10_weeks$date))\n\nlast_10_weeks_all <- last_10_weeks %>%\n right_join(all_ltlas_dates, by = c(\"ltla\", \"date\")) %>%\n replace_na(list(mean = 0))\n\nmap <- england_ltla_shape %>%\n inner_join(last_10_weeks_all %>%\n rename(geo_code = ltla), by = \"geo_code\")\n\np <- ggplot(map, aes(x = LAT, y = LONG, fill = mean)) +\n geom_sf(colour = NA) +\n theme_void() +\n theme(panel.background = element_rect(fill = \"white\", colour = \"white\"),\n\tplot.background = element_rect(fill = \"white\", colour = \"white\")) + \n scale_fill_viridis(\"LFD prevalence\", labels = scales::label_percent()) +\n facet_wrap( ~ date, nrow = 1)\n\nggsave(here::here(\"figure\", \"lfd_last_10_weeks_maps.svg\"), p, width = 12, height = 4)\n\np_testing <- ggplot(df, aes(x = date, y = mean,\n ymin = lower, ymax = upper)) +\n geom_point() +\n geom_line() +\n geom_ribbon(alpha = 0.35) +\n scale_colour_brewer(\"\", palette = \"Dark2\") +\n scale_fill_brewer(\"\", palette = \"Dark2\") +\n theme_bw() +\n expand_limits(y = 0) +\n scale_y_continuous(\"Proportion positive\", labels = scales::percent) +\n facet_wrap(~ltla_name) +\n xlab(\"Final Wednesday of week of data\")\n\nggsave(here::here(\"figure\", \"lfd_ltla.svg\"), p_testing, width = 25, height = 25)\n", "meta": {"hexsha": "6808d272dee817fe5ea61eec75eabfee4372ed8d", "size": 6600, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/lfd_ltla.r", "max_stars_repo_name": "epiforecasts/sars.cov.2.england.prevalence", "max_stars_repo_head_hexsha": "778ae332ca2d8b90d14163a4dc644f56765d310e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-12-16T13:32:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-16T13:43:25.000Z", "max_issues_repo_path": "inst/scripts/lfd_ltla.r", "max_issues_repo_name": "epiforecasts/covid19.lfd.england", "max_issues_repo_head_hexsha": "9da9418a27f222019426516c4fbbb70a0bff80bd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-19T09:05:15.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T14:04:07.000Z", "max_forks_repo_path": "inst/scripts/lfd_ltla.r", "max_forks_repo_name": "epiforecasts/covid19.lfd.england", "max_forks_repo_head_hexsha": "9da9418a27f222019426516c4fbbb70a0bff80bd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7368421053, "max_line_length": 105, "alphanum_fraction": 0.6153030303, "num_tokens": 1958, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710085, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.321382703275402}} {"text": "## Analysis of Full Foodwebs in different nutrient landscapes\nlibrary(plyr)\nlibrary(ggplot2)\nlibrary(igraph)\nlibrary(raster)\nlibrary(matrixStats)\nlibrary(\"viridis\")\n\nrequire(doParallel)\n######\n\n\ncl<-makeCluster(16) ##### WARNING! Number of cores may depend on your machine!\nregisterDoParallel(cl)\n\n\n\ndata.end<-foreach (i=c(1:15,30:44),.combine = \"rbind\") %dopar% {\n \n persistance<-data.frame(Scenario=numeric(0),Nutrient.factor=numeric(0),Nutrient=numeric(0),Web=numeric(0),Landscape=numeric(0),Patch=numeric(0), Species=numeric(0), Persist=numeric(0), Growth1=numeric(0),Growth2=numeric(0), Plant=numeric(0))\n \n local<-read.table(\"../../Simulations/FullWeb/SummaryMass.out\", sep=\",\", header=T)\n\n subset.nutrient<-subset(local,local$nut==i)\nNutrients<- read.table(paste(\"../../Simulations/FullWeb/Nutrients/Nutrient_\", i, \".out\",sep=\"\"),header=F)\n\n for (w in 1)\n {\n subset.web<-subset(subset.nutrient,subset.nutrient$web==w)\n \n for (l in 1:5)\n {\n subset.landscape<-subset(subset.web,subset.web$landscape==l)\n \n for (p in 0:49)\n {\n subset.patch<- subset(subset.landscape, subset.landscape$patch==p)\n \n for (k in 0:39)\n {\n subset.sp<-subset(subset.patch,subset.patch$species==k)\n \n if (subset.sp$biomass.tend.10k==0 && subset.sp$biomass.tend.20k==0 && subset.sp$biomass.tend!=0) \n {\n persist<-NA\n mean.net.growth1<-NA\n mean.net.growth2<-NA\n \n } else {\n persist<-(sum(subset.sp$biomass.tend)>0)*1\n mean.net.growth1<-subset.sp$Mean.net.growth1\n mean.net.growth2<-subset.sp$Mean.net.growth2\n }\n if.basal<-subset.sp$if.basal.spp\n if (i<20)\n {scen<-\"Homogenious\"} else if (i<35 && i>16) {scen<-\"HeterogeneousMeso\"} else if (i<40 && i>34) {scen<-\"HeterogeneousOligo\"} else {scen<-\"HeterogeniousEutro\"}\n \n vec<-data.frame(Scenario=scen,Nutrient.factor=i,Nutrient=Nutrients[p+1,], Web=w,Landscape=l,Patch=p,Species=k,Persist=persist, Growth1=mean.net.growth1, Growth2=mean.net.growth2, Plant=if.basal)\n \n persistance<-rbind(persistance,vec)\n \n \n }\n }\n }\n }\nreturn(persistance) \n}\nstr(data.end)\n\npersistance1<-data.end\nwrite.table(persistance1,\"data.out\")\npersistance1<-read.table(\"data.out\")\npersistence2<-read.table(\"../FullWeb/data.out\")\n\nfor.plot<-ddply(persistance1,.(Scenario,Nutrient.factor,Nutrient,Web,Patch,Landscape, Plant), summarize, Persistance=sum(Persist))\n\n\nplants<-subset(for.plot,for.plot$Plant==1)\nanimals<-subset(for.plot,for.plot$Plant==0)\n\npdf(\"FigPlantAnimal.pdf\",width=12,height=8, onefile = T)\nggplot(plants, aes(x=log10(Nutrient),y=Persistance,color=Scenario)) + geom_point(size=0.2) + geom_smooth(size=0.5) + labs(x=\"Nutrient Supply log10[]\",y=expression(bold(paste(\"Plant \",alpha,\"-Diversity\")))) +\n theme_classic() + theme(axis.text=element_text(size=22),text=element_text(size=22, face=\"bold\"),\n axis.title=element_text(size=22,face=\"bold\")) \nggplot(animals, aes(x=log10(Nutrient),y=Persistance,color=Scenario)) + geom_point(size=0.2) + geom_smooth(size=0.5) + labs(x=\"Nutrient Supply log10[]\",y=expression(bold(paste(\"Animal \",alpha,\"-Diversity\")))) +\n theme_classic() + theme(axis.text=element_text(size=22),text=element_text(size=22, face=\"bold\"),\n axis.title=element_text(size=22,face=\"bold\")) \n\ndev.off()\nfor.plot1<-ddply(persistance1,.(Scenario,Nutrient.factor,Nutrient,Web,Patch,Landscape), summarize, Persistance=sum(Persist))\n\npdf(\"Fig4.pdf\",width=12,height=8)\nggplot(for.plot1, aes(x=log10(Nutrient),y=Persistance,color=Scenario)) + geom_count(position = position_jitter(w = 0.05, h = 0),shape=1, alpha=1, stroke = 0.8) + geom_smooth(size=1) + labs(x=\"Local Nutrient Supply log10[]\",y=expression(bold(paste(alpha,\"-Diversity\")))) +\n theme_classic() + theme(axis.text=element_text(size=22),text=element_text(size=22, face=\"bold\"),\n axis.title=element_text(size=22,face=\"bold\")) \n\ndev.off()\n\nfor.plot1.sub<-subset(for.plot1,for.plot1$Scenario!=\"Homogenious\")\npdf(\"Fig4add.pdf\", width=12, height=4, onefile = T)\nggplot(for.plot1, aes(y=log10(Nutrient),x=Scenario,color=Scenario)) + geom_violin( draw_quantiles = c(0.5), na.rm=T) + stat_summary(fun.y=mean, geom=\"point\", shape=23, size=2) +coord_flip() + theme_classic() + theme(axis.text=element_text(size=22),text=element_text(size=22, face=\"bold\"),\n axis.title=element_text(size=22,face=\"bold\")) \nggplot(for.plot1, aes(y=log10(Nutrient),x=Scenario,color=Scenario)) + geom_violin( draw_quantiles = c(0.5), na.rm=T) +coord_flip() + theme_classic()+ theme(axis.text=element_text(size=22),text=element_text(size=22, face=\"bold\"),\n axis.title=element_text(size=22,face=\"bold\")) \n\ndev.off()\n\nfor.plot2<-ddply(persistance1,.(Scenario,Nutrient.factor,Nutrient,Web,Landscape), summarize, Persistance=mean(Persist))\nggplot(for.plot2, aes(x=as.factor(Nutrient.factor),y=Persistance,color=as.factor(Scenario))) + geom_violin(trim=T, draw_quantiles = c(0.05, 0.5, 0.95), na.rm=T, scale=\"width\")+ ggtitle(\"Gamma Diversity\")\n\n######\ndata.end<-foreach (i=c(21:23),.combine = \"rbind\") %dopar% {\n \n persistance<-data.frame(Scenario=numeric(0),Nutrient.factor=numeric(0),Nutrient=numeric(0),Web=numeric(0),Landscape=numeric(0),Patch=numeric(0), Species=numeric(0), Persist=numeric(0), Growth1=numeric(0),Growth2=numeric(0), Plant=numeric(0))\n \n local<-read.table(\"../Simulations/FullWeb/SummaryMass.out\", sep=\",\", header=T)\n \n subset.nutrient<-subset(local,local$nut==i)\n Nutrients<- read.table(paste(\"../Simulations/FullWeb/Nutrients/Nutrient_\", i, \".out\",sep=\"\"),header=F)\n \n for (w in 1)\n {\n subset.web<-subset(subset.nutrient,subset.nutrient$web==w)\n \n for (l in 1:5)\n {\n subset.landscape<-subset(subset.web,subset.web$landscape==l)\n \n for (p in 0:49)\n {\n subset.patch<- subset(subset.landscape, subset.landscape$patch==p)\n \n for (k in 0:39)\n {\n subset.sp<-subset(subset.patch,subset.patch$species==k)\n \n if (subset.sp$biomass.tend.10k==0 && subset.sp$biomass.tend.20k==0 && subset.sp$biomass.tend!=0) \n {\n persist<-NA\n mean.net.growth1<-NA\n mean.net.growth2<-NA\n \n } else {\n persist<-(sum(subset.sp$biomass.tend)>0)*1\n mean.net.growth1<-subset.sp$Mean.net.growth1\n mean.net.growth2<-subset.sp$Mean.net.growth2\n }\n if.basal<-subset.sp$if.basal.spp\n if (i>16)\n {scen<-\"Heterogenious\"} else {scen<-\"Homogenious\"}\n \n vec<-data.frame(Scenario=scen,Nutrient.factor=i,Nutrient=Nutrients[p+1,], Web=w,Landscape=l,Patch=p,Species=k,Persist=persist, Growth1=mean.net.growth1, Growth2=mean.net.growth2, Plant=if.basal)\n \n persistance<-rbind(persistance,vec)\n \n \n }\n }\n }\n }\n return(persistance) \n}\n\n\npersistance2<-data.end\n\n\n\nfor.plot<-ddply(persistance2,.(Scenario,Nutrient.factor,Nutrient,Web,Patch,Landscape, Plant), summarize, Persistance=mean(Persist))\n\nplants<-subset(for.plot,for.plot$Plant==1)\nanimals<-subset(for.plot,for.plot$Plant==0)\npdf(\"PlotsHeterogenious.pdf\", onefile=T)\nggplot(plants, aes(x=log10(Nutrient),y=Persistance,color=as.factor(Nutrient.factor))) + geom_point(size=0.2) + geom_smooth(size=0.5) + ggtitle(\"Plants\")\nggplot(animals, aes(x=log10(Nutrient),y=Persistance,color=as.factor(Nutrient.factor))) + geom_point(size=0.2) + geom_smooth(size=0.5) + ggtitle(\"Animals\")\n\nfor.plot1<-ddply(persistance2,.(Scenario,Nutrient.factor,Nutrient,Web,Patch,Landscape), summarize, Persistance=mean(Persist))\n\nggplot(for.plot1, aes(x=log10(Nutrient),y=Persistance,color=as.factor(Nutrient.factor))) + geom_point(size=0.2) + geom_smooth(size=0.5) + ggtitle(\"All Species\")\n\n\nfor.plot2<-ddply(persistance2,.(Scenario,Nutrient.factor,Nutrient,Web,Landscape), summarize, Persistance=mean(Persist))\nggplot(for.plot2, aes(x=as.factor(Nutrient.factor),y=Persistance,color=as.factor(Nutrient.factor))) + geom_violin(trim=T, draw_quantiles = c(0.05, 0.5, 0.95), na.rm=T, scale=\"width\") + ggtitle(\"Gamma Diversity\")\ndev.off()\n\n\n", "meta": {"hexsha": "ecc40b8cc89d51a872ab21d3365553899e9544e7", "size": 8415, "ext": "r", "lang": "R", "max_stars_repo_path": "Code-Metafoodweb/R/FullWeb/Fullweb.r", "max_stars_repo_name": "RemoRyser/Metafoodweb", "max_stars_repo_head_hexsha": "524d0a5362d914cafb7cca5efd08ad20f437a57e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-03-25T21:49:06.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T21:49:06.000Z", "max_issues_repo_path": "Code-Metafoodweb/R/FullWeb/Fullweb.r", "max_issues_repo_name": "RemoRyser/Metafoodweb", "max_issues_repo_head_hexsha": "524d0a5362d914cafb7cca5efd08ad20f437a57e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code-Metafoodweb/R/FullWeb/Fullweb.r", "max_forks_repo_name": "RemoRyser/Metafoodweb", "max_forks_repo_head_hexsha": "524d0a5362d914cafb7cca5efd08ad20f437a57e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.7606382979, "max_line_length": 288, "alphanum_fraction": 0.6554961378, "num_tokens": 2520, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3213687054147485}} {"text": "x_files <- list.files(pattern=\"*txt\")\nsample_names <- substr(x_files, 1, nchar(x_files)-10)\nsample_names <- paste(substr(sapply(strsplit(sample_names, \"_\"), \"[[\", 1), 1, 1), \"_\",\n\t\t\t\t\t\tsapply(strsplit(sample_names, \"_\"), \"[[\", 2), \"_\",\n\t\t\t\t\t\tsapply(strsplit(sample_names, \"_\"), \"[[\", 3), \"_\",\n\t\t\t\t\t\tsapply(strsplit(sample_names, \"_\"), \"[[\", 4), sep=\"\")\n\ncoverage_max_to_check <- 60\nplot_max <- coverage_max_to_check + 2\n\noutput1 <- list()\noutput2 <- list()\nmean_coverage <- c()\nfor(a in 1:length(x_files)) {\n a_rep <- scan(x_files[a])\n a_output <- c()\n for(b in 1:plot_max) {\n if(b == plot_max) {\n a_output <- c(a_output, length(a_rep[a_rep >= (b - 1)]))\n } else {\n a_output <- c(a_output, length(a_rep[a_rep == (b - 1)]))\n }\n }\n a_output2 <- a_output / sum(a_output)\n output1[[a]] <- a_output\n output2[[a]] <- a_output2\n mean_coverage <- c(mean_coverage, mean(a_rep))\n}\n\nrm(a_rep)\nsave.image(\"setophaga_coverage.RData\")\n\n\n\n\n\n\n\nload(\"setophaga_coverage.RData\")\n\npar(mfrow=c(7,8))\nfor(a in 1:length(output2)) {\n plot(0:61, output2[[a]], pch=19, cex=0.1, xlab=\"Coverage\", ylab=\"Proportion S.coronata genome\", main=sample_names[a], ylim=c(0,0.3))\n poly.plot <- rbind(cbind(0:61, output2[[a]]), c(61, 0), c(0,0))\n polygon(poly.plot, col=\"gray\")\n abline(v=mean_coverage[a], col=\"red\")\n}\n\nmean_coverage_samples <- cbind(sample_names, mean_coverage)\n\nwrite.table(mean_coverage_samples, \"mean_coverage_samples.txt\", sep = \"\\t\")\n", "meta": {"hexsha": "c109383544b15ad4cd51e8e0786a01b79a088925", "size": 1451, "ext": "r", "lang": "R", "max_stars_repo_path": "prep/plot_coverage.r", "max_stars_repo_name": "jphruska/Seteophaga_gracia_phylogeography", "max_stars_repo_head_hexsha": "09f1685a941363cba790add0e83d5981831bbaf5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "prep/plot_coverage.r", "max_issues_repo_name": "jphruska/Seteophaga_gracia_phylogeography", "max_issues_repo_head_hexsha": "09f1685a941363cba790add0e83d5981831bbaf5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "prep/plot_coverage.r", "max_forks_repo_name": "jphruska/Seteophaga_gracia_phylogeography", "max_forks_repo_head_hexsha": "09f1685a941363cba790add0e83d5981831bbaf5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9038461538, "max_line_length": 134, "alphanum_fraction": 0.6368022054, "num_tokens": 467, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.32115984792011987}} {"text": "#!/usr/bin/env Rscript\noptions(stringAsfactors = FALSE, useFancyQuotes = FALSE)\n\n# Taking the command line arguments\nargs <- commandArgs(trailingOnly = TRUE)\n\nif(length(args)==0)stop(\"No file has been specified!\\n\")\nrequire(xcms)\nrequire(CAMERA)\nrequire(intervals)\n\n\nppmCal<-function(run,ppm)\n{\n return((run*ppm)/1000000)\n}\nmetFragToCamera<-function(metFragSearchResult=NA,cameraObject=NA,ppm=5,MinusTime=5,PlusTime=5,method=\"fast\")\n{\n #metFragSearchResult<-bb\n IDResults<-metFragSearchResult\n #cameraObject<-an\n listofPrecursorsmz<-c()\n listofPrecursorsmz<-IDResults[,\"parentMZ\"]\n listofPrecursorsrt<-IDResults[,\"parentRT\"]\n CamerartLowColumnIndex<-which(colnames(cameraObject@groupInfo)==\"rtmin\")\n CamerartHighColumnIndex<-which(colnames(cameraObject@groupInfo)==\"rtmax\")\n CameramzColumnIndex<-which(colnames(cameraObject@groupInfo)==\"mz\")\n imatch=NA\n if(method==\"regular\")\n {\n MassRun1<-Intervals_full(cbind(listofPrecursorsmz,listofPrecursorsmz))\n\n MassRun2<-Intervals_full(cbind(cameraObject@groupInfo[,CameramzColumnIndex]-\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm),\n cameraObject@groupInfo[,CameramzColumnIndex]+\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm)))\n\n Mass_iii <- interval_overlap(MassRun1,MassRun2)\n\n\n\n TimeRun1<-Intervals_full(cbind(listofPrecursorsrt,listofPrecursorsrt))\n\n TimeRun2<-Intervals_full(cbind(cameraObject@groupInfo[,CamerartLowColumnIndex]-MinusTime,\n cameraObject@groupInfo[,CamerartHighColumnIndex]+PlusTime))\n Time_ii <- interval_overlap(TimeRun1,TimeRun2)\n\n imatch = mapply(intersect,Time_ii,Mass_iii)\n }else if(method==\"fast\")\n {\n featureMzs<-cbind(cameraObject@groupInfo[,CameramzColumnIndex]-\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm),\n cameraObject@groupInfo[,CameramzColumnIndex]+\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm))\n\nfeatureRTs<-cbind(cameraObject@groupInfo[,CamerartLowColumnIndex]-MinusTime,\n cameraObject@groupInfo[,CamerartHighColumnIndex]+PlusTime)\n\nimatch<-list()\nfor(i in 1:length(listofPrecursorsmz))\n{\n mz<-listofPrecursorsmz[i]\n rt<-listofPrecursorsrt[i]\n\n imatch[[i]]<-which(featureMzs[,1]mz & featureRTs[,1]rt)\n\n\n}\n }else if (method==\"par\"){\n featureMzs<-cbind(cameraObject@groupInfo[,CameramzColumnIndex]-\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm),\n cameraObject@groupInfo[,CameramzColumnIndex]+\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm))\n\nfeatureRTs<-cbind(cameraObject@groupInfo[,CamerartLowColumnIndex]-MinusTime,\n cameraObject@groupInfo[,CamerartHighColumnIndex]+PlusTime)\n imatch <-mclapply(c(1:length(listofPrecursorsmz)),FUN =function(x) { which(featureMzs[,1]listofPrecursorsmz[x] & featureRTs[,1]listofPrecursorsrt[x])},mc.cores=ncore)\n\n }else{stop(\"Method should be either fast,par and regular\")}\n\n listOfMS2Mapped<-list()\n for (i in 1:length(imatch)) {\n for(j in imatch[[i]])\n {\n if(is.null(listOfMS2Mapped[[as.character(j)]]))\n {\n listOfMS2Mapped[[as.character(j)]]<-data.frame(IDResults[i,],stringsAsFactors = F)\n }else\n {\n listOfMS2Mapped[[as.character(j)]]<-\n rbind(listOfMS2Mapped[[as.character(j)]],data.frame(IDResults[i,],stringsAsFactors = F))\n }\n\n }\n }\n return(list(mapped=listOfMS2Mapped))\n}\n\n\nppmTol<-5\nrtTol = 10\nhigherTheBetter<-T\nscoreColumn<-\"q.value\"\nimpute<-T\ntypeColumn<-\"type\"\nselectedType<-\"p\"\nrenameCol<-\"rename\"\nrename<-T\nonlyReportWithID<-T\ncombineReplicate<-F\ncombineReplicateColumn<-\"rep\"\niflog<-F\nsampleCoverage<-0\nsampleCoverageMethod<-\"global\"\nncore=1\nIfnormalize<-NA\nscoreInput<-NA\nfor(arg in args)\n{\n argCase<-strsplit(x = arg,split = \"=\")[[1]][1]\n value<-strsplit(x = arg,split = \"=\")[[1]][2]\n\n if(argCase==\"inputcamera\")\n {\n inputCamera=as.character(value)\n }\n if(argCase==\"inputscores\")\n {\n scoreInput=as.character(value)\n }\n if(argCase==\"inputpheno\")\n {\n phenotypeInfoFile=as.character(value)\n }\n if(argCase==\"ppm\")\n {\n ppmTol=as.numeric(value)\n }\n if(argCase==\"rt\")\n {\n rtTol=as.numeric(value)\n }\n if(argCase==\"higherTheBetter\")\n {\n higherTheBetter=as.logical(value)\n }\n if(argCase==\"scoreColumn\")\n {\n scoreColumn=as.character(value)\n }\n if(argCase==\"impute\")\n {\n impute=as.logical(value)\n }\n if(argCase==\"typeColumn\")\n {\n typeColumn=as.character(value)\n }\n if(argCase==\"selectedType\")\n {\n selectedType=as.character(value)\n }\n if(argCase==\"rename\")\n {\n rename=as.logical(value)\n }\n if(argCase==\"renameCol\")\n {\n renameCol=as.character(value)\n }\n if(argCase==\"onlyReportWithID\")\n {\n onlyReportWithID=as.logical(value)\n }\n if(argCase==\"combineReplicate\")\n {\n combineReplicate=as.logical(value)\n }\n if(argCase==\"combineReplicateColumn\")\n {\n combineReplicateColumn=as.character(value)\n }\n if(argCase==\"log\")\n {\n iflog=as.logical(value)\n }\n if(argCase==\"sampleCoverage\")\n {\n sampleCoverage=as.numeric(value)\n }\n if(argCase==\"sampleCoverageMethod\")\n {\n sampleCoverageMethod=as.character(value)\n }\n if(argCase==\"outputPeakTable\")\n {\n outputPeakTable=as.character(value)\n }\n if(argCase==\"outputVariables\")\n {\n outputVariables=as.character(value)\n }\n if(argCase==\"outputMetaData\")\n {\n outputMetaData=as.character(value)\n }\n if(argCase==\"ncore\")\n {\n ncore=as.numeric(value)\n }\n if(argCase==\"normalize\")\n {\n Ifnormalize=as.numeric(value)\n }\n\n}\n\n\n\nload(inputCamera)\ncameraObject<-get(varNameForNextStep)\ncameraPeakList<-getPeaklist(cameraObject)\n\n\nphenotypeInfo<-read.csv(file = phenotypeInfoFile,stringsAsFactors = F)\n#sepScore<-\",\"\n#if(scoreColumn==\"q.value\")\n sepScore=\"\\t\"\n\nif(!is.na(scoreInput))\n{\nmetfragRes<-read.table(file = scoreInput,header = T,sep = sepScore,quote=\"\",stringsAsFactors = F,comment.char = \"\")\n\nmappedToCamera<-metFragToCamera(metFragSearchResult = metfragRes,\n cameraObject = cameraObject,MinusTime = rtTol,PlusTime = rtTol,ppm = ppmTol,method=\"par\")\n\n\n\n VariableData<-data.frame(matrix(\"Unknown\",nrow = nrow(cameraPeakList),\n ncol = (ncol(metfragRes)+1)),stringsAsFactors = F)\n\n\n colnames(VariableData)<-c(\"variableMetadata\",colnames(metfragRes))\n\n VariableData[,\"variableMetadata\"]<-paste(\"variable_\",1:nrow(cameraPeakList),sep=\"\")\n\n for(rnName in rownames(cameraPeakList))\n {\n if(rnName %in% names(mappedToCamera$mapped))\n {\n tmpId<-mappedToCamera$mapped[[rnName]]\n if(higherTheBetter)\n {\n tmpId<-tmpId[which.max(tmpId[,scoreColumn]),]\n }else{\n tmpId<-tmpId[which.min(tmpId[,scoreColumn]),]\n }\n\n VariableData[VariableData[,\"variableMetadata\"]==paste(\"variable_\",rnName,sep=\"\"),\n c(2:ncol(VariableData))]<-tmpId\n }\n }\n\n\n if(impute)\n {\n\n toBeImputed<-rownames(VariableData[VariableData[,2]==\"Unknown\",])\n pcgroups<-cameraPeakList[rownames(cameraPeakList)%in%toBeImputed,\"pcgroup\"]\n\n for(pcgr in pcgroups)\n {\n selectedFeatures<-\n VariableData[,\"variableMetadata\"]%in%paste(\"variable_\",rownames(cameraPeakList[cameraPeakList[,\"pcgroup\"]==pcgr,]),sep=\"\") &\n VariableData[,2]!=\"Unknown\"\n\n if(any(selectedFeatures))\n {\n tmpIDs<-VariableData[selectedFeatures,]\n tmpId<-NA\n if(higherTheBetter)\n {\n tmpId<-tmpIDs[which.max(tmpIDs[,scoreColumn]),]\n }else\n {\n tmpId<-tmpIDs[which.min(tmpIDs[,scoreColumn]),]\n }\n\n\n imputedVariables<- paste(\"variable_\",rownames(cameraPeakList[ cameraPeakList[,\"pcgroup\"]==pcgr,]),sep=\"\")\n\n imputedVariables<- VariableData[,\"variableMetadata\"]%in%imputedVariables & VariableData[,2]==\"Unknown\"\n\n VariableData[imputedVariables,c(2:ncol(VariableData))]<-tmpId[,c(2:ncol(tmpId))]\n }\n }\n\n }\n\n\n\n\n}else{\n\nVariableData<-data.frame(matrix(\"Unknown\",nrow = nrow(cameraPeakList),\n ncol = 2),stringsAsFactors = F)\ncolnames(VariableData)<-c(\"variableMetadata\",\"extraColumn\")\nVariableData[,\"variableMetadata\"]<-paste(\"variable_\",1:nrow(cameraPeakList),sep=\"\")\n}\n\n\n\npeakMatrix<-c()\npeakMatrixNames<-c()\npeakMatrixTMP<-cameraPeakList\ntechnicalReps<-c()\n\nphenotypeInfo<-phenotypeInfo[phenotypeInfo[,typeColumn]==selectedType,]\nfor(i in 1:nrow(cameraObject@xcmsSet@phenoData))\n{\n index<-which(phenotypeInfo==rownames(cameraObject@xcmsSet@phenoData)[i],arr.ind = T)[1]\n if(!is.na(index))\n {\n peakMatrix<-cbind(peakMatrix, peakMatrixTMP[,rownames(cameraObject@xcmsSet@phenoData)[i]])\n if(rename)\n {\n\n peakMatrixNames<-c(peakMatrixNames,phenotypeInfo[index,renameCol])\n }else\n {\n peakMatrixNames<-c(peakMatrixNames,phenotypeInfo[index,1])\n #peakMatrixNames<-c(peakMatrixNames,rownames(cameraObject@xcmsSet@phenoData)[i])\n }\n if(combineReplicate)\n {\n technicalReps<-c(technicalReps,phenotypeInfo[index,combineReplicateColumn])\n }\n\n }\n}\n\n\n\n\npeakMatrix<-data.frame(peakMatrix)\ncolnames(peakMatrix)<-peakMatrixNames\n\n\nsampleMetaData<-c()\nphenotypeInfo<-phenotypeInfo[,!grepl(pattern = \"step_\",x = colnames(phenotypeInfo),fixed=T)]\nif(rename)\n{\n\n sampleMetaData<-phenotypeInfo[,c(renameCol,colnames(phenotypeInfo)[colnames(phenotypeInfo)!=renameCol])]\n}else\n{\n sampleMetaData<-phenotypeInfo\n\n}\ncolnames(sampleMetaData)[1]<-\"sampleMetadata\"\n\ntechnicalReps<-technicalReps[match(sampleMetaData[,1],colnames(peakMatrix))]\npeakMatrix<-peakMatrix[,match(sampleMetaData[,1],colnames(peakMatrix))]\npeakMatrixNames<-colnames(peakMatrix)\nif(combineReplicate)\n{\n newpheno<-c()\n newNames<-c()\n combinedPeakMatrix<-c()\n techs<-unique(technicalReps)\n for(x in techs)\n {\n if(ncol(data.frame(peakMatrix[,technicalReps==x]))>1)\n {\n\n dataTMP<-apply(data.frame(peakMatrix[,technicalReps==x]),MARGIN = 1,FUN = median,na.rm=T)\n newNames<-c(newNames,as.character(unique(peakMatrixNames[technicalReps==x]))[1])\n newpheno<-rbind(newpheno,sampleMetaData[sampleMetaData[,combineReplicateColumn]==x,][1,])\n combinedPeakMatrix<-cbind(combinedPeakMatrix,dataTMP)\n }else\n {\n dataTMP<-peakMatrix[,technicalReps==x]\n newNames<-c(newNames,as.character(unique(peakMatrixNames[technicalReps==x]))[1])\n newpheno<-rbind(newpheno,sampleMetaData[sampleMetaData[,combineReplicateColumn]==x,][1,])\n combinedPeakMatrix<-cbind(combinedPeakMatrix,dataTMP)\n }\n }\n\n peakMatrix<- combinedPeakMatrix\n peakMatrixNames<-newNames\n sampleMetaData<-newpheno[,colnames(newpheno)!=combineReplicateColumn]\n}\n\n\n\npeakMatrix<-data.frame(peakMatrix)\ncolnames(peakMatrix)<-peakMatrixNames\n\nif(iflog)\n{\npeakMatrix<-log2(peakMatrix)\n}\nnormalize.median <- function(x, weights = NULL) {\n\tl <- dim(x)[2]\n\n\tif(is.null(weights)) {\n\t\tfor(j in 1:l)\n\t\t\tx[, j] <- x[, j] - median(x[, j], na.rm = TRUE)\n\t} else {\n\t\tfor(j in 1:l)\n\t\t\tx[, j] <- x[, j] - weighted.median(x[, j], weights[, j],\n\t\t\t\tna.rm = TRUE)\n\t}\n\n\tx\n}\n # normalize.regression\n# based on pek\nnorm.regression <- function(x) {\n\t\ty<-rowMedians(as.matrix(x), na.rm=TRUE)\n\t\tfor(j in 1:ncol(x)){\n\t\tfit<-lm(y~x[,j],na.action=na.exclude)\n\t\tx[,j] <- predict(fit,na.action=na.exclude)\n\t\t}\nreturn(x)\n}\nnormalize.reference <- function(x) {\n{\n\t\tsel <-1\n\t\ty<-1\n\t\tfor(j in 1:ncol(x)){\n\t\tas.matrix(sel)\n\t\tsel[j] <- length(which(!is.na(x[,j])))\n\t\tref<-which.max(sel[])\n\t\t}\n\t\tfor(j in 1:ncol(x))\n\t\t{\n\t\t y[j]<-median(x[, j]-x[,ref], na.rm = TRUE)\n\t\t x[, j] <- (x[, j]-y[j])\n\n}\n\nreturn(x)\n}\n}\n\nif(!is.na(Ifnormalize))\n{\n if(Ifnormalize==1)\npeakMatrix<-limma::normalizeCyclicLoess(peakMatrix)\n if(Ifnormalize==2)\npeakMatrix<-normalize.median(peakMatrix)\n if(Ifnormalize==3)\npeakMatrix<-normalize.reference(peakMatrix)\n if(Ifnormalize==4)\npeakMatrix<-norm.regression(peakMatrix)\n\n}\nif(!onlyReportWithID & !is.na(scoreInput))\n{\n peakMatrix<-peakMatrix[VariableData[,2]!=\"Unknown\",]\n VariableData<-VariableData[VariableData[,2]!=\"Unknown\",]\n}\n\nsep_covWithGroup<-function(X,groups)\n{\n lft_f<-X\n\n\n dt<-list()\n dt_cov<-list()\n for(x in unique(groups))\n {\n assign(x,lft_f[,groups==x])\n assign(paste(x,\"_cov\",sep=\"\"),(apply((!is.na(get(x))),1,function(x1){sum(x1)})/dim(get(x))[2])*100)\n dt[[x]]<-get(x)\n dt_cov[[x]]<-get(paste(x,\"_cov\",sep=\"\"))\n }\n\n\n return(list(dt,dt_cov))\n}\n\nif(sampleCoverage>0)\n{\ncoverageLogical<-rep(T,nrow(peakMatrix))\nif(sampleCoverageMethod==\"global\")\n{\n\ngroups<-rep(\"g\",nrow(sampleMetaData))\ncoverage<-sep_covWithGroup(peakMatrix,groups)\ncoverageLogical<-coverage[[2]]$g>sampleCoverage\n}else{\n\ngroups<-sampleMetaData[,sampleCoverageMethod]\n\ncoverage<-sep_covWithGroup(peakMatrix,groups)\n\nfor(gr in unique(groups))\n{\ncoverageLogical<-coverageLogical & (coverage[[2]][[gr]]>sampleCoverage)\n}\n\n}\npeakMatrix<-peakMatrix[coverageLogical==T,]\nVariableData<-VariableData[coverageLogical==T,]\n}\npeakMatrix<-cbind.data.frame(dataMatrix=VariableData[,\"variableMetadata\"],peakMatrix,stringsAsFactors = F)\nVariableData<-sapply(VariableData, gsub, pattern=\"\\'|#\", replacement=\"\")\nVariableData<-VariableData[apply(is.na(peakMatrix),1,sum)!=(ncol(peakMatrix)-1),]\npeakMatrix<-peakMatrix[apply(is.na(peakMatrix),1,sum)!=(ncol(peakMatrix)-1),]\n#peakMatrix[VariableData[,2]!=\"Unknown\",1]<-VariableData[VariableData[,2]!=\"Unknown\",\"Identifier\"]\n#VariableData[VariableData[,2]!=\"Unknown\",1]<-VariableData[VariableData[,2]!=\"Unknown\",\"Identifier\"]\n\nwrite.table(x = peakMatrix,file = outputPeakTable,\n row.names = F,quote = F,sep = \"\\t\")\nwrite.table(x = VariableData,file = outputVariables,\n row.names = F,quote = F,sep = \"\\t\")\n\nwrite.table(x = sampleMetaData,file = outputMetaData,\n row.names = F,quote = F,sep = \"\\t\")\n", "meta": {"hexsha": "fd47f8ab6d656ec5d8af08042a91d72e74f190d1", "size": 13964, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/prepareOutput.r", "max_stars_repo_name": "MetaboIGNITER/container-camera", "max_stars_repo_head_hexsha": "d364d859df3fb00d921611c6644f99c40779757e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/prepareOutput.r", "max_issues_repo_name": "MetaboIGNITER/container-camera", "max_issues_repo_head_hexsha": "d364d859df3fb00d921611c6644f99c40779757e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/prepareOutput.r", "max_forks_repo_name": "MetaboIGNITER/container-camera", "max_forks_repo_head_hexsha": "d364d859df3fb00d921611c6644f99c40779757e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7509578544, "max_line_length": 251, "alphanum_fraction": 0.6825408192, "num_tokens": 4063, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514778, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32115228960449516}} {"text": "# Calculate warming of the hottest hour\n# Also calculate hottest future hours (add to climatology)\n\n\nrequire(ncdf4)\nrequire(abind)\nrequire(raster)\nrequire(data.table)\n\n###################\n## Read in data\n###################\n\n# read in TXx rcp26\ninfile <- nc_open('data_dl/cmip5/txx_yr_modmean_rcp26_ave.nc') # tasmax for each month\n\t# print(infile)\n\ttasmax26 <- ncvar_get(infile, 'txxETCCDI') # 144 x 72 x 240 lon x lat x years 1861-2100)\n\tdim(tasmax26)\n\tdimnames(tasmax26) <- list(lon=1:144, lat=1:72, time=1:240)\n\tdimnames(tasmax26)[[1]] <- infile$var[['txxETCCDI']]$dim[[1]]$vals # add lon\n\tdimnames(tasmax26)[[2]] <- infile$var[['txxETCCDI']]$dim[[2]]$vals # add lat\n\tdimnames(tasmax26)[[3]] <- 1861:2100 # year of the CMIP5 time dimension\n\tnc_close(infile)\n\n\n# read in tos rcp26\ninfile <- nc_open('data_dl/cmip5/tos_Omon_modmean_rcp26_ave.nc') # tos for each month\n\t# print(infile)\n\ttos26 <- ncvar_get(infile, 'tos') # tos 288 x 144 x 2800 lon x lat x months 1861-2100\n\tdim(tos26)\n\tdimnames(tos26) <- list(lon=1:288, lat=1:144, time=1:2880)\n\tdimnames(tos26)[[1]] <- infile$var[['tos']]$dim[[1]]$vals # add lon\n\tdimnames(tos26)[[2]] <- infile$var[['tos']]$dim[[2]]$vals # add lat\n\tdimnames(tos26)[[3]] <- paste(rep(1861:2100, rep(12, length(1861:2100))), paste('_', 1:12, sep=''), sep='') # year and month of the CMIP5 time dimension\n\tnc_close(infile)\n\n# read in TXx rcp85\ninfile <- nc_open('data_dl/cmip5/txx_yr_modmean_rcp85_ave.nc') # tasmax for each month\n\t# print(infile)\n\ttasmax85 <- ncvar_get(infile, 'txxETCCDI') # 144 x 72 x 240 lon x lat x years 1861-2100)\n\tdim(tasmax85)\n\tdimnames(tasmax85) <- list(lon=1:144, lat=1:72, time=1:240)\n\tdimnames(tasmax85)[[1]] <- infile$var[['txxETCCDI']]$dim[[1]]$vals # add lon\n\tdimnames(tasmax85)[[2]] <- infile$var[['txxETCCDI']]$dim[[2]]$vals # add lat\n\tdimnames(tasmax85)[[3]] <- 1861:2100 # year of the CMIP5 time dimension\n\tnc_close(infile)\n\n\n# read in tos rcp85\ninfile <- nc_open('data_dl/cmip5/tos_Omon_modmean_rcp85_ave.nc') # tos for each month\n\t# print(infile)\n\ttos85 <- ncvar_get(infile, 'tos') # tos 288 x 144 x 2800 lon x lat x months 1861-2100\n\tdim(tos85)\n\tdimnames(tos85) <- list(lon=1:288, lat=1:144, time=1:2880)\n\tdimnames(tos85)[[1]] <- infile$var[['tos']]$dim[[1]]$vals # add lon\n\tdimnames(tos85)[[2]] <- infile$var[['tos']]$dim[[2]]$vals # add lat\n\tdimnames(tos85)[[3]] <- paste(rep(1861:2100, rep(12, length(1861:2100))), paste('_', 1:12, sep=''), sep='') # year and month of the CMIP5 time dimension\n\tnc_close(infile)\n\n# read in DTR (daily temperature range)\nload('temp/dtrclimatology_interp1.25.rdata') # dtr125\n\n\n# read in max hr climatologies\nload('temp/tosmax.rdata') # tosmax 0.25x0.25 from OISST daily + HadDTR\nload('temp/hadex2_txx.rdata') # hadex2txx 2.5x3.75 from HadEX2\n\ttasmax <- hadex2txx\n\n##################################################################################\n# permute CMIP5 dimensions: lat x lon x time\n# lat 90 to -90; lon 0 to 360\n##################################################################################\n# change dimension order\ntasmax26 <- aperm(tasmax26, c(2,1,3))\ntasmax85 <- aperm(tasmax85, c(2,1,3))\ntos26 <- aperm(tos26, c(2,1,3))\ntos85 <- aperm(tos85, c(2,1,3))\n\n# rotate so columns 0 -> 360 lon, rows 90 -> -90 lat\n\t# tasmax26\nlons <- as.numeric(colnames(tasmax26)) # good\n\nlats <- as.numeric(rownames(tasmax26))\nnewlatord <- sort(lats, decreasing=TRUE)\nnewrownms <- as.character(newlatord)\nnewlatord <- as.character(newlatord)\n\ntasmax26 <- tasmax26[newlatord, ,]\n\n\t#image(tasmax26[,,1]) # north pole should be at the left, America at the top\n\n\t# tasmax85\nlons <- as.numeric(colnames(tasmax85)) # good\n\nlats <- as.numeric(rownames(tasmax85))\nnewlatord <- sort(lats, decreasing=TRUE)\nnewrownms <- as.character(newlatord)\nnewlatord <- as.character(newlatord)\n\ntasmax85 <- tasmax85[newlatord, ,]\n\n\t#image(tasmax85[,,1])\n\n\t# tos26\nlons <- as.numeric(colnames(tos26)) # good\n\nlats <- as.numeric(rownames(tos26))\nnewlatord <- sort(lats, decreasing=TRUE)\nnewrownms <- as.character(newlatord)\nnewlatord <- as.character(newlatord)\n\ntos26 <- tos26[newlatord, ,]\n\n\t#image(tos26[,,1])\n\n\t# tos85\nlons <- as.numeric(colnames(tos85)) # good\n\nlats <- as.numeric(rownames(tos85))\nnewlatord <- sort(lats, decreasing=TRUE)\nnewrownms <- as.character(newlatord)\nnewlatord <- as.character(newlatord)\n\ntos85 <- tos85[newlatord, ,]\n\n\t#image(tos85[,,1])\n\n############################\n# Compute tosmax26 and 86 (CMIP5 temperatures)\n# Irrelevant since we compute a delta?\n############################\n# extend dtr125 so that it matches the dimensions of tos85\ndtr125l <- abind(list(dtr125)[rep(1,dim(tos85)[3]/dim(dtr125)[3])], along=3)\n\tdim(dtr125l)\n\tdim(tos26)\n\tdim(tos85)\n\n# add to get daily maximum\ntosmax26 <- tos26 + 0.5*dtr125l\ntosmax85 <- tos85 + 0.5*dtr125l\n\n\n#############################################\n# Calculate CMIP5 anomalies from 1986-2005\n#############################################\nclimtime <- as.character(1986:2005) # year of the climatology period\nfuttime <- as.character(2006:2100) # year the future period\n\n# Reshape CMIP5 to lat x lon x month x year\n\t# not needed for tasmax26 and tasmax85 since already annual TXx\n\t\n\t# tosmax26\nnms <- dimnames(tosmax26)\nmos <- as.numeric(unlist(strsplit(nms[[3]], split='_'))[seq(2,length(nms[[3]])*2,by=2)]) # months\nyrs <- as.numeric(unlist(strsplit(nms[[3]], split='_'))[seq(1,length(nms[[3]])*2,by=2)]) # years\ndm <- dim(tosmax26)\ntosmax26bymo <- array(tosmax26, dim=c(dm[1], dm[2], 12, dm[3]/12), dimnames=list(lat=nms[[1]], lon=nms[[2]], mo=1:12, year=sort(unique(yrs)))) # add a month dimension (3rd dimension)\n\n\t# tosmax85\nnms <- dimnames(tosmax85)\nmos <- as.numeric(unlist(strsplit(nms[[3]], split='_'))[seq(2,length(nms[[3]])*2,by=2)]) # months\nyrs <- as.numeric(unlist(strsplit(nms[[3]], split='_'))[seq(1,length(nms[[3]])*2,by=2)]) # years\ndm <- dim(tosmax85)\ntosmax85bymo <- array(tosmax85, dim=c(dm[1], dm[2], 12, dm[3]/12), dimnames=list(lat=nms[[1]], lon=nms[[2]], mo=1:12, year=sort(unique(yrs)))) # add a month dimension (3rd dimension)\n\n\n# find highest daily max within years (TXx)\ntasmax26yr <- tasmax26 # already annual TXx on land (HadEX2)\ntasmax85yr <- tasmax85\ntosmax26yr <- apply(tosmax26bymo, MARGIN=c(1,2,4), FUN=max)\ntosmax85yr <- apply(tosmax85bymo, MARGIN=c(1,2,4), FUN=max)\n\n# calculate climatology as the highest TXx across 20-year chunk\ntasmax26clim <- apply(tasmax26yr[,,climtime], MARGIN=c(1,2), FUN=max) # climatological average for CMIP5\ntasmax26climlong <- abind(list(tasmax26clim)[rep(1,length(futtime))], along=3) # expand to match future projections\n\tdim(tasmax26climlong) \n\ntasmax85clim <- apply(tasmax85yr[,,climtime], MARGIN=c(1,2), FUN=max) # climatological average for CMIP5\ntasmax85climlong <- abind(list(tasmax85clim)[rep(1,length(futtime))], along=3) # expand to match future projections\n\tdim(tasmax85climlong) \n\ntosmax26clim <- apply(tosmax26yr[,,climtime], MARGIN=c(1,2), FUN=max)\ntosmax26climlong <- abind(list(tosmax26clim)[rep(1,length(futtime))], along=3)\n\tdim(tosmax26climlong)\n\ntosmax85clim <- apply(tosmax85yr[,,climtime], MARGIN=c(1,2), FUN=max)\ntosmax85climlong <- abind(list(tosmax85clim)[rep(1,length(futtime))], along=3)\n\tdim(tosmax85climlong)\n\n\n# calculate deltas by years\ntasmax26dyr <- tasmax26yr[,,futtime] - tasmax26climlong # CMIP5 deltas\ntasmax85dyr <- tasmax85yr[,,futtime] - tasmax85climlong # CMIP5 deltas\ntosmax26dyr <- tosmax26yr[,,futtime] - tosmax26climlong\ntosmax85dyr <- tosmax85yr[,,futtime] - tosmax85climlong\n\n\n# write out for use in future_tsm_byyear.r\nsave(tasmax26dyr, file='temp/tasmax26dyr.rdata')\nsave(tasmax85dyr, file='temp/tasmax85dyr.rdata')\nsave(tosmax26dyr, file='temp/tosmax26dyr.rdata')\nsave(tosmax85dyr, file='temp/tosmax85dyr.rdata')\n\n\n#######################################################\n# average warming by latitude for 2081-2100 and write out\n# for use in figure\n#######################################################\n# Make a \"not ocean\" mask\nsearast <- raster(!is.na(tos26[,,1]), xmn=0, xmx=360, ymn=-90, ymx=90) # 1 for ocean, 0 for land\nlandrast <- raster(tasmax26dyr[,,1], xmn=0, xmx=360, ymn=-90, ymx=90)\nseamask <- round(resample(searast, landrast, method='bilinear')) # includes areas >=50% land as land (floor): 0 is land, 1 is ocean\nseamask <- as.matrix(seamask)\nseamask[seamask==1] <- NA # turn ocean to NA\nseamask <- abind(list(seamask)[rep(1,dim(tasmax26dyr)[3])], along=3) # expand to match future projections\n\n# mask out ocean for terrestrial air temp\ntasmax26dyrmask <- tasmax26dyr + seamask\ntasmax85dyrmask <- tasmax85dyr + seamask\n\n# data.frame to hold results\nlstwarmingbylat <- data.table(lat=as.numeric(dimnames(tasmax26dyr)[[1]]))\nsstwarmingbylat <- data.table(lat=as.numeric(dimnames(tosmax26dyr)[[1]]))\n\n# average by lat\nlstwarmingbylat$tasmax26 <- apply(tasmax26dyrmask[,,as.character(2081:2100)], MARGIN=1, FUN=mean, na.rm=TRUE)\nlstwarmingbylat$tasmax85 <- apply(tasmax85dyrmask[,,as.character(2081:2100)], MARGIN=1, FUN=mean, na.rm=TRUE)\nsstwarmingbylat$tosmax26 <- apply(tosmax26dyr[,,as.character(2081:2100)], MARGIN=1, FUN=mean, na.rm=TRUE)\nsstwarmingbylat$tosmax85 <- apply(tosmax85dyr[,,as.character(2081:2100)], MARGIN=1, FUN=mean, na.rm=TRUE)\n\t\n\t# plot to check\n\tlstwarmingbylat[,plot(lat, tasmax26, type='l', ylim=c(0,6))]\n\tlstwarmingbylat[,lines(lat, tasmax85, lty=2)]\n\tsstwarmingbylat[,lines(lat, tosmax26, col='blue')]\n\tsstwarmingbylat[,lines(lat, tosmax85, col='blue', lty=2)]\n\n# SD by lat\nlstwarmingbylat$tasmax26sd <- apply(tasmax26dyrmask[,,as.character(2081:2100)], MARGIN=1, FUN=sd, na.rm=TRUE)\nlstwarmingbylat$tasmax85sd <- apply(tasmax85dyrmask[,,as.character(2081:2100)], MARGIN=1, FUN=sd, na.rm=TRUE)\nsstwarmingbylat$tosmax26sd <- apply(tosmax26dyr[,,as.character(2081:2100)], MARGIN=1, FUN=sd, na.rm=TRUE)\nsstwarmingbylat$tosmax85sd <- apply(tosmax85dyr[,,as.character(2081:2100)], MARGIN=1, FUN=sd, na.rm=TRUE)\n\n# Sample size by lat\nlstwarmingbylat$tasmax26n <- apply(tasmax26dyrmask[,,as.character(2081)], MARGIN=1, FUN=function(x) sum(!is.na(x)))\nlstwarmingbylat$tasmax85n <- apply(tasmax85dyrmask[,,as.character(2081)], MARGIN=1, FUN=function(x) sum(!is.na(x)))\nsstwarmingbylat$tosmax26n <- apply(tosmax26dyr[,,as.character(2081)], MARGIN=1, FUN=function(x) sum(!is.na(x)))\nsstwarmingbylat$tosmax85n <- apply(tosmax85dyr[,,as.character(2081)], MARGIN=1, FUN=function(x) sum(!is.na(x)))\n\n\n# write out\nwrite.csv(lstwarmingbylat, file='temp/warmingmaxhr_bylat_land.csv')\nwrite.csv(sstwarmingbylat, file='temp/warmingmaxhr_bylat_ocean.csv')\n\n\n\n##################################################################\n# Add deltas to climatology to get future temperatures 2081-2100\n##################################################################\n# average deltas for 2081-2100\ntasmax26d2100 <- apply(tasmax26dyr[,,dimnames(tasmax26dyr)[[3]] %in% 2081:2100], MARGIN=c(1,2), FUN=max)\ntasmax85d2100 <- apply(tasmax85dyr[,,dimnames(tasmax85dyr)[[3]] %in% 2081:2100], MARGIN=c(1,2), FUN=max)\ntosmax26d2100 <- apply(tosmax26dyr[,,dimnames(tosmax26dyr)[[3]] %in% 2081:2100], MARGIN=c(1,2), FUN=max)\ntosmax85d2100 <- apply(tosmax85dyr[,,dimnames(tosmax85dyr)[[3]] %in% 2081:2100], MARGIN=c(1,2), FUN=max)\n\n# create rasters for interpolation\ntasmax26d2100r <- raster(tasmax26d2100, xmn=0, xmx=360, ymn=-90, ymx=90)\ntasmax85d2100r <- raster(tasmax85d2100, xmn=0, xmx=360, ymn=-90, ymx=90)\ntosmax26d2100r <- raster(tosmax26d2100, xmn=0, xmx=360, ymn=-90, ymx=90)\ntosmax85d2100r <- raster(tosmax85d2100, xmn=0, xmx=360, ymn=-90, ymx=90)\n\ntasmaxr <- raster(tasmax, xmn=0, xmx=360, ymn=-90, ymx=90)\ntosmaxr <- raster(tosmax, xmn=0, xmx=360, ymn=-90, ymx=90)\n\n# interpolate to resolution of the climatology (2.5x3.75 on land, 0.25x0.25 in ocean)\ntasmax26d2100_025r <- resample(tasmax26d2100r, tasmaxr, method='bilinear')\ntasmax85d2100_025r <- resample(tasmax85d2100r, tasmaxr, method='bilinear')\ntosmax26d2100_025r <- resample(tosmax26d2100r, tosmaxr, method='bilinear')\ntosmax85d2100_025r <- resample(tosmax85d2100r, tosmaxr, method='bilinear')\n\n# add deltas to climatologies\ntasmax26_2100r <- tasmaxr + tasmax26d2100_025r\ntasmax85_2100r <- tasmaxr + tasmax85d2100_025r\ntosmax26_2100r <- tosmaxr + tosmax26d2100_025r\ntosmax85_2100r <- tosmaxr + tosmax85d2100_025r\n\n# convert to matrices\ntasmax26_2100 <- as.matrix(tasmax26_2100r)\ntasmax85_2100 <- as.matrix(tasmax85_2100r)\ntosmax26_2100 <- as.matrix(tosmax26_2100r)\ntosmax85_2100 <- as.matrix(tosmax85_2100r)\n\n\n\n#############################################\n# Average future temperatures by latitude\n#############################################\n\n# data.frame to hold results\nlstmaxhrlat_2100 <- data.table(lat=yFromRow(tasmax26_2100r, 1:nrow(tasmax26_2100r)))\nsstmaxhrlat_2100 <- data.table(lat=yFromRow(tosmax26_2100r,1:nrow(tosmax26_2100r)))\n\n# average by lat\nlstmaxhrlat_2100$tasmax26 <- apply(tasmax26_2100, MARGIN=1, FUN=mean, na.rm=TRUE)\nlstmaxhrlat_2100$tasmax85 <- apply(tasmax85_2100, MARGIN=1, FUN=mean, na.rm=TRUE)\nsstmaxhrlat_2100$tosmax26 <- apply(tosmax26_2100, MARGIN=1, FUN=mean, na.rm=TRUE)\nsstmaxhrlat_2100$tosmax85 <- apply(tosmax85_2100, MARGIN=1, FUN=mean, na.rm=TRUE)\n\t\n\t# plot to check\n\tlstmaxhrlat_2100[,plot(lat, tasmax26, type='l', ylim=c(0,50))]\n\tlstmaxhrlat_2100[,lines(lat, tasmax85, lty=2)]\n\tsstmaxhrlat_2100[,lines(lat, tosmax26, col='blue')]\n\tsstmaxhrlat_2100[,lines(lat, tosmax85, col='blue', lty=2)]\n\n# SD by lat\nlstmaxhrlat_2100$tasmax26sd <- apply(tasmax26_2100, MARGIN=1, FUN=sd, na.rm=TRUE)\nlstmaxhrlat_2100$tasmax85sd <- apply(tasmax85_2100, MARGIN=1, FUN=sd, na.rm=TRUE)\nsstmaxhrlat_2100$tosmax26sd <- apply(tosmax26_2100, MARGIN=1, FUN=sd, na.rm=TRUE)\nsstmaxhrlat_2100$tosmax85sd <- apply(tosmax85_2100, MARGIN=1, FUN=sd, na.rm=TRUE)\n\n# Sample size by lat\nlstmaxhrlat_2100$tasmax26n <- apply(tasmax26_2100, MARGIN=1, FUN=function(x) sum(!is.na(x)))\nlstmaxhrlat_2100$tasmax85n <- apply(tasmax85_2100, MARGIN=1, FUN=function(x) sum(!is.na(x)))\nsstmaxhrlat_2100$tosmax26n <- apply(tosmax26_2100, MARGIN=1, FUN=function(x) sum(!is.na(x)))\nsstmaxhrlat_2100$tosmax85n <- apply(tosmax85_2100, MARGIN=1, FUN=function(x) sum(!is.na(x)))\n\n# write out\nwrite.csv(lstmaxhrlat_2100, file='temp/lstmaxhrlat_2081-2100.csv')\nwrite.csv(sstmaxhrlat_2100, file='temp/sstmaxhrlat_2081-2100.csv')\n", "meta": {"hexsha": "107238a4967b6faf7e6885ccef06967ac03addac", 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NO", "lm_q1_score": 0.7025300698514777, "lm_q2_score": 0.45713671682749485, "lm_q1q2_score": 0.32115228960449516}} {"text": "(iRecreateData\nRecreateData\np1\n(dp2\nS'min_score'\np3\nI1\nsS'mean_precision'\np4\nF0\nsS'un_mut_num_samples'\np5\nI3\nsS'poss_vals'\np6\n(lp7\nI1\naI2\naI3\naI4\naI5\naI6\naI7\naI8\naI9\naI10\nasS'variance_precision'\np8\nF2.2400000000000007\nsS'un_mut_variance'\np9\nF2.6000000000000001\nsS'sols'\np10\n(dp11\n(F5\nF1\ntp12\n(lp13\n(lp14\nI0\naI0\naI0\naI1\naI1\naI1\naI0\naI0\naI0\naI0\naas(F5\nF4\ntp15\n(lp16\n(lp17\nI0\naI0\naI1\naI0\naI1\naI0\naI1\naI0\naI0\naI0\naas(F5\nF3\ntp18\n(lp19\n(lp20\nI0\naI0\naI1\naI0\naI0\naI2\naI0\naI0\naI0\naI0\naa(lp21\nI0\naI0\naI0\naI2\naI0\naI0\naI1\naI0\naI0\naI0\naassS'simpleData'\np22\n(lp23\n(lp24\nI4\naI5\naI6\naa(lp25\nI3\naI5\naI7\naa(lp26\nI3\naI6\naI6\naa(lp27\nI4\naI4\naI7\naasS'num_samples'\np28\nI3\nsS'debug'\np29\nI01\nsS'variance'\np30\nF2.6000000000000001\nsS'max_score'\np31\nI10\nsS'un_mut_mean'\np32\nI5\nsS'mean'\np33\nI5\nsb.", "meta": {"hexsha": "7011f57a060e8169f81e710db1846740c226cff7", "size": 768, "ext": "rd", "lang": "R", "max_stars_repo_path": "DATA/Demo_model.rd", "max_stars_repo_name": "Gorcenski/corvids", "max_stars_repo_head_hexsha": "a6b6a5559600aeda0bb2fac4bdcbcdb2ec7a90d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-02-01T16:24:16.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-02T19:50:40.000Z", "max_issues_repo_path": "DATA/Demo_model.rd", "max_issues_repo_name": "Gorcenski/corvids", "max_issues_repo_head_hexsha": "a6b6a5559600aeda0bb2fac4bdcbcdb2ec7a90d7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-01-31T02:16:06.000Z", "max_issues_repo_issues_event_max_datetime": "2018-06-15T20:20:44.000Z", "max_forks_repo_path": "DATA/Demo_model.rd", "max_forks_repo_name": "Gorcenski/corvids", "max_forks_repo_head_hexsha": "a6b6a5559600aeda0bb2fac4bdcbcdb2ec7a90d7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-03T21:21:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-03T21:21:13.000Z", "avg_line_length": 5.9534883721, "max_line_length": 23, "alphanum_fraction": 0.7669270833, "num_tokens": 514, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300573952054, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.32115228391027567}} {"text": "for (line in readLines(tail(commandArgs(), n=1))) {\n cat(sapply(strsplit(line, \",\"), function(s) {\n a <- table(s)\n m <- which.max(a)\n if (a[m] > length(s)/2) {\n names(m)\n } else {\n \"None\"\n }\n }), sep=\"\\n\")\n}\n", "meta": {"hexsha": "211bd63c978e799ce2cf432d5cca2d52cbc8932d", "size": 235, "ext": "r", "lang": "R", "max_stars_repo_path": "easy/major_element.r", "max_stars_repo_name": "IlkhamGaysin/ce-challenges", "max_stars_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-06-24T17:09:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-03T11:44:54.000Z", "max_issues_repo_path": "easy/major_element.r", "max_issues_repo_name": "IlkhamGaysin/ce-challenges", "max_issues_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "easy/major_element.r", "max_forks_repo_name": "IlkhamGaysin/ce-challenges", "max_forks_repo_head_hexsha": "ac6b8c1fc4b6c098e380b9d694e93e614f6c969b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.5833333333, "max_line_length": 51, "alphanum_fraction": 0.4765957447, "num_tokens": 77, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290806, "lm_q2_score": 0.6370307944803832, "lm_q1q2_score": 0.3210037481557077}} {"text": "library(devtools)\nlibrary(parallel)\ndevtools::load_all()\nRNGkind(\"L'Ecuyer-CMRG\")\ndata(grid)\n\nseed <- 999983\ns_k <- 1000\ns_n <- 100\ns_m <- 4\n\nsim_type2_trig_1004 <- mclapply(r_grid_trig[-1], r_loop <- function(s_r) {\n type_2(seed, s_k, s_n, s_m, \"trigonometric\", s_r, L = 1000,\n fast.tn = F, semi.iter = F, center.bs = T)\n}, mc.cores = 4)\n\nsave(sim_type2_trig_1004, file = \"sim_type2_trig_1004.RData\")", "meta": {"hexsha": "5c96e3fc197c605319fe2b6e708d4e85080d7839", "size": 415, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation/type2/sim_type2_trig_1004.r", "max_stars_repo_name": "ZhuolinSong/Ftesting", "max_stars_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation/type2/sim_type2_trig_1004.r", "max_issues_repo_name": "ZhuolinSong/Ftesting", "max_issues_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation/type2/sim_type2_trig_1004.r", "max_forks_repo_name": "ZhuolinSong/Ftesting", "max_forks_repo_head_hexsha": "d0ecb36b44d377ab012a6325220f52110aeaf8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.4117647059, "max_line_length": 74, "alphanum_fraction": 0.6722891566, "num_tokens": 160, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.32100374815570765}} {"text": "suppressMessages(library(ggplot2))\nsuppressMessages(library(multcomp))\nsuppressMessages(library(nlme))\nsuppressMessages(library(pastecs))\nsuppressMessages(library(reshape))\nsuppressMessages(library(tidyverse))\nsuppressMessages(library(sjPlot))\nsuppressMessages(library(sjmisc))\nsuppressMessages(library(dplyr))\nsuppressMessages(library(emmeans))\n\noldw <- getOption(\"warn\")\noptions(warn = -1)\n\nprint(\"Plotting main effects in facets...\")\n\nmyData <- read.csv(file=\"output/meltedDataThreeCategories.csv\", header=TRUE, sep=\",\")\n\nmyData<-myData[!(myData$ScoreSystem==\"B\" | myData$ScoreSystem==\"D\"),]\n\nlevels(myData$ScoreSystem)[levels(myData$ScoreSystem) == \"A\"] <- \"Competition\"\nlevels(myData$ScoreSystem)[levels(myData$ScoreSystem) == \"C\"] <- \"Mutual Help\"\n\n#myData$ScoreSystem <- factor(myData$ScoreSystem, levels=rev(levels(myData$ScoreSystem)))\n\nmyData$C <- factor(myData$C , levels=c(\"High\", \"Medium\", \"Low\"))\nmyData$A <- factor(myData$A , levels=c(\"High\", \"Medium\", \"Low\"))\n\nlm0 = lm(takes ~ C * ScoreSystem, data = myData)\nx <- emmip(lm0, C ~ ScoreSystem, engine=\"ggplot\", CIs = TRUE) + labs(x=\"Game Version\", y=\"Average Take Actions\", colour=\"Conscienciousness\") + theme(text = element_text(size=20)) + coord_cartesian(ylim=c(0,4)) + scale_x_discrete(labels=c(\"Competition\", \"Mutual Help\"))\nx + scale_colour_manual(values=c(\"#d7301f\", \"#fc8d59\", \"#fdcc8a\"), labels=c(\"High\", \"Medium\", \"Low\"), guide = guide_legend(reverse = FALSE)) #+ geom_jitter(aes(x = ScoreSystem, y = takes, colour = C), data = myData, pch = 20, width = 0.1, size=2) \n\nsuppressMessages(ggsave(\"plots/interactionEffects/takes/takes.png\", height = 4, width = 10))\n\nlm0 = lm(who ~ A * ScoreSystem, data = myData)\nx <- emmip(lm0, A ~ ScoreSystem, engine=\"ggplot\", CIs = TRUE) + labs(x=\"Game Version\", y=\"Focus\", colour=\"Agreeableness\") + theme(text = element_text(size=20)) + coord_cartesian(ylim=c(1,7)) + scale_x_discrete(labels=c(\"Competition\", \"Mutual Help\"))\nx + scale_colour_manual(values=c(\"#d7301f\", \"#fc8d59\", \"#fdcc8a\"), labels=c(\"High\", \"Medium\", \"Low\"), guide = guide_legend(reverse = FALSE)) + scale_y_reverse(\"Focus\", labels = as.character(c(\"Me 3\",\"2\", \"1\", \"Neutral 0\", \"-1\", \"-2\", \"The Other -3\")), breaks = c(1,2,3,4,5,6,7)) #+ geom_jitter(aes(x = ScoreSystem, y = takes, colour = C), data = myData, pch = 20, width = 0.1, size=2) \n\nsuppressMessages(ggsave(\"plots/interactionEffects/who/who.png\", height = 4, width = 10))\n\nlm0 = lm(what ~ A * ScoreSystem, data = myData)\nx <- emmip(lm0, A ~ ScoreSystem, engine=\"ggplot\", CIs = TRUE) + labs(x=\"Game Version\", y=\"Social Valence\", colour=\"Agreeableness\") + theme(text = element_text(size=20)) + coord_cartesian(ylim=c(1,7)) + scale_x_discrete(labels=c(\"Competition\", \"Mutual Help\"))\nx + scale_colour_manual(values=c(\"#d7301f\", \"#fc8d59\", \"#fdcc8a\"), labels=c(\"High\", \"Medium\", \"Low\"), guide = guide_legend(reverse = FALSE)) + scale_y_reverse(\"Social Valence\", labels = as.character(c(\"Help 3\",\"2\", \"1\", \"Neutral 0\", \"-1\", \"-2\", \"Complicate -3\")), breaks = c(1,2,3,4,5,6,7)) #+ geom_jitter(aes(x = ScoreSystem, y = takes, colour = C), data = myData, pch = 20, width = 0.1, size=2) \n\nsuppressMessages(ggsave(\"plots/interactionEffects/what/what.png\", height = 4, width = 10))\n\noptions(warn = oldw)\n\n", "meta": {"hexsha": "5c43b9c8899829326066c6a2e4744677f447a609", "size": 3238, "ext": "r", "lang": "R", "max_stars_repo_path": "messageAcrossScripts/scripts/plotsGeneratorCHI.r", "max_stars_repo_name": "SamGomes/message-across", "max_stars_repo_head_hexsha": "952f51a45e603802184a53d133e7c4b9dcca8a99", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "messageAcrossScripts/scripts/plotsGeneratorCHI.r", "max_issues_repo_name": "SamGomes/message-across", "max_issues_repo_head_hexsha": "952f51a45e603802184a53d133e7c4b9dcca8a99", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "messageAcrossScripts/scripts/plotsGeneratorCHI.r", "max_forks_repo_name": "SamGomes/message-across", "max_forks_repo_head_hexsha": "952f51a45e603802184a53d133e7c4b9dcca8a99", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 66.0816326531, "max_line_length": 400, "alphanum_fraction": 0.6933292156, "num_tokens": 1022, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.32100374815570765}} {"text": "library(RJSONIO)\nlibrary(factoextra)\n\nroot = \"likelihood_incubation_exp/tuned/\"\nmodel_subdirs = list.dirs(root, recursive=FALSE)\nmodel_subdirs = model_subdirs[!grepl(\"legacy\", model_subdirs)]\n\nmodels = list()\n# unpack each fitted model's parameters\nfor (model_subdir in model_subdirs) {\n model_path = paste(model_subdir, \"model.json\", sep=\"/\")\n model = fromJSON(model_path)\n as_row = c(model$beta, model$tau1, model$tau2, model$N0, model$A0, model$loss)\n models[[model_subdir]] = as_row\n}\nmodels = do.call(\"rbind\", models)\nmodels = data.frame(models)\ncolnames(models) = c(paste0(\"beta\", 1:length(model$beta)), \"tau1\", \"tau2\", \"N0\", \"A0\", \"loss\")\nX = models[colnames(models)[colnames(models) != \"loss\"]]\ncol_sds = apply(X, 2, sd)\ncol_means = apply(X, 2, mean)\nX = sweep(X, 2, col_means, `-`)\nX = sweep(X, 2, col_sds, `/`)\n\n# perform k-means clustering at several numbers of clusters\nwss = c()\nclusterings = list()\nfor (k in 1:20) {\n clusters = kmeans(X, k, nstart=1000)\n wss = c(wss, clusters$tot.withinss)\n # de-normalize clusters\n clusters$centers = sweep(clusters$centers, 2, col_sds, `*`)\n clusters$centers = sweep(clusters$centers, 2, col_means, `+`)\n clusterings[[k]] = clusters\n}\nplot(1:length(wss), wss, \"l\")\n\n# plot per-cluster mean loss\nmean_cluster_losses = aggregate(models$loss, by=list(clusterings[[6]]$cluster), FUN=mean)\nfor (i in 1:5) {\n cluster_losses = models[clusterings[[5]]$cluster == i, \"loss\"]\n plot(1:length(cluster_losses), cluster_losses, main=paste(\"Losses for cluster\", i))\n}", "meta": {"hexsha": "b97482228ba62e5c4da1c2741f46016f268a7915", "size": 1515, "ext": "r", "lang": "R", "max_stars_repo_path": "likelihood_incubation_exp/cluster_fitted.r", "max_stars_repo_name": "secg95/INS_COVID", "max_stars_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "likelihood_incubation_exp/cluster_fitted.r", "max_issues_repo_name": "secg95/INS_COVID", "max_issues_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "likelihood_incubation_exp/cluster_fitted.r", "max_forks_repo_name": "secg95/INS_COVID", "max_forks_repo_head_hexsha": "d3e1e9b2d83de9bbfb246c9be93624ffb4df88a1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2325581395, "max_line_length": 94, "alphanum_fraction": 0.699669967, "num_tokens": 480, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3210037481557076}} {"text": "context(\"itee iterator\")\n\ntest_that(\"itee returns n independent numeric vectors\", {\n # Iterate through each of the iterators without any order in mind\n iter_list <- itee(1:5, n=3)\n expect_equal(nextElem(iter_list[[1]]), 1)\n expect_equal(nextElem(iter_list[[1]]), 2)\n expect_equal(nextElem(iter_list[[1]]), 3)\n\n expect_equal(nextElem(iter_list[[2]]), 1)\n expect_equal(nextElem(iter_list[[2]]), 2)\n \n expect_equal(nextElem(iter_list[[3]]), 1)\n expect_equal(nextElem(iter_list[[3]]), 2)\n\n expect_equal(nextElem(iter_list[[1]]), 4)\n expect_equal(nextElem(iter_list[[1]]), 5)\n expect_error(nextElem(iter_list[[1]]), \"StopIteration\")\n\n expect_equal(nextElem(iter_list[[2]]), 3)\n expect_equal(nextElem(iter_list[[2]]), 4)\n expect_equal(nextElem(iter_list[[2]]), 5)\n expect_error(nextElem(iter_list[[2]]), \"StopIteration\")\n\n expect_equal(nextElem(iter_list[[3]]), 3)\n expect_equal(nextElem(iter_list[[3]]), 4)\n expect_equal(nextElem(iter_list[[3]]), 5)\n expect_error(nextElem(iter_list[[3]]), \"StopIteration\")\n\n # After the iterators are exhausted, ensure that they are truly exhausted\n expect_error(nextElem(iter_list[[1]]), \"StopIteration\")\n expect_error(nextElem(iter_list[[2]]), \"StopIteration\")\n expect_error(nextElem(iter_list[[3]]), \"StopIteration\")\n})\n\n# Based on GitHub Issue #36\ntest_that(\"itee returns n independent numeric vectors based on n iterators\", {\n # Iterate through each of the iterators without any order in mind\n it <- iterators::iter(1:5)\n iter_list <- itee(it, n=3)\n expect_equal(nextElem(iter_list[[1]]), 1)\n expect_equal(nextElem(iter_list[[1]]), 2)\n expect_equal(nextElem(iter_list[[1]]), 3)\n\n expect_equal(nextElem(iter_list[[2]]), 1)\n expect_equal(nextElem(iter_list[[2]]), 2)\n \n expect_equal(nextElem(iter_list[[3]]), 1)\n expect_equal(nextElem(iter_list[[3]]), 2)\n\n expect_equal(nextElem(iter_list[[1]]), 4)\n expect_equal(nextElem(iter_list[[1]]), 5)\n expect_error(nextElem(iter_list[[1]]), \"StopIteration\")\n\n expect_equal(nextElem(iter_list[[2]]), 3)\n expect_equal(nextElem(iter_list[[2]]), 4)\n expect_equal(nextElem(iter_list[[2]]), 5)\n expect_error(nextElem(iter_list[[2]]), \"StopIteration\")\n\n expect_equal(nextElem(iter_list[[3]]), 3)\n expect_equal(nextElem(iter_list[[3]]), 4)\n expect_equal(nextElem(iter_list[[3]]), 5)\n expect_error(nextElem(iter_list[[3]]), \"StopIteration\")\n\n # After the iterators are exhausted, ensure that they are truly exhausted\n expect_error(nextElem(iter_list[[1]]), \"StopIteration\")\n expect_error(nextElem(iter_list[[2]]), \"StopIteration\")\n expect_error(nextElem(iter_list[[3]]), \"StopIteration\")\n})\n", "meta": {"hexsha": "16252fc3862386c81106add33a7db5c86eeb6067", "size": 2606, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-itee.r", "max_stars_repo_name": "ramhiser/itertools2", "max_stars_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2015-02-02T02:54:54.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-20T12:07:34.000Z", "max_issues_repo_path": "tests/testthat/test-itee.r", "max_issues_repo_name": "ramhiser/itertools2", "max_issues_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_issues_repo_licenses": ["Apache-2.0", "MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2015-01-07T15:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2017-02-18T18:01:36.000Z", "max_forks_repo_path": "tests/testthat/test-itee.r", "max_forks_repo_name": "ramhiser/itertools2", "max_forks_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_forks_repo_licenses": ["Apache-2.0", "MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-02-02T05:04:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T02:13:12.000Z", "avg_line_length": 37.2285714286, "max_line_length": 78, "alphanum_fraction": 0.7179585572, "num_tokens": 742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6370307875894138, "lm_q1q2_score": 0.3210037446833056}} {"text": "library(reprtree)\nlibrary(caret)\nlibrary(randomForest)\nsetwd(\"~/Documents/Models/variant-filtering\")\nset.seed(123) # here I fix the seed of the random generator to have the same random numbers if re-run \n\ntrain_table=\"tables/K2H_AllVariants_All_Lib_NoMinAF_noequal_GOF_addVCFfeatures_illuminaBED_WES_samples_annotated_with_coverage_INFO_GENO_status_supp_features.txt\"\ntest_table=\"tables/K2H_AllVariants_All_Lib_NoMinAF_noequal_GOF_addVCFfeatures_illuminaBED_WES_samples_annotated_with_coverage_INFO_GENO_status_supp_features.txt\"\n\n# if we want to test the downsampling\n#train_table=\"downsampling/K2H_AllVariants_All_Lib_downsampling_noequal_GOF_addVCFfeatures_illuminaBED_WES_samples_annotated_with_coverage_INFO_GENO_status_supp_features.txt\"\n#test_table=\"downsampling/K2H_AllVariants_All_Lib_downsampling_noequal_GOF_addVCFfeatures_illuminaBED_WES_samples_annotated_with_coverage_INFO_GENO_status_supp_features.txt\"\n\ntrain_table = read.table(train_table, quote=\"\\\"\", stringsAsFactors=F, sep=\"\\t\", header=T)\ntest_table = read.table(test_table, quote=\"\\\"\", stringsAsFactors=F, sep=\"\\t\", header=T)\n\ntrain_table$IoD = 1 + train_table$SIG * 10^(train_table$ERR)\ntest_table$IoD = 1 + test_table$SIG * 10^(test_table$ERR)\n\ntype=\"snv\"\nif(type==\"snv\"){\n train_table = train_table[which(train_table$TYPE_INFO==\"snv\"),] \n}\nif(type==\"indel\"){\n train_table = train_table[which(train_table$TYPE_INFO==\"ins\" | train_table$TYPE_INFO==\"del\"),] \n}\n\ntrain_table[which(is.infinite(train_table$MIN_DIST)),\"MIN_DIST\"] = 1000000000\ntrain_table[which(is.infinite(train_table$MaxRatioWin)),\"MaxRatioWin\"] = 1000000000\ntest_table[which(is.infinite(test_table$MIN_DIST)),\"MIN_DIST\"] = 1000000000\ntest_table[which(is.infinite(test_table$MaxRatioWin)),\"MaxRatioWin\"] = 1000000000\n\npropTP = as.numeric(table(train_table$status)[\"TP\"] / nrow(train_table))\npropFP = as.numeric(table(train_table$status)[\"FP\"] / nrow(train_table))\n\nmy_features=c(\"status\",\"RVSB\", \"QVAL\",\"AF\",\"ERR_INFO\",\"DP\", \"medianDP_INFO\",\n \"FS\", \"MIN_DIST\", \"AO\", \"QUAL\", \"MaxRatioWin\", \"NbVarWin\", \"IoD\", \"HpLength\", \"N_QVAL_20_50_INFO\")\n\nif(exists(\"dbsnp_status\")){\n if(dbsnp){\n train_table$WES_status = train_table$status\n train_table$status = \"FP\"\n train_table[which(!is.na(train_table$avsnp150)),\"status\"] = \"TP\"\n }\n}\n\nrf = randomForest(as.factor(status) ~ .,\n data = train_table[,my_features],\n importance = TRUE, # to allow us to inspect variable importance\n ntree = 500, sampsize = as.numeric(table(train_table$status)[\"TP\"]) #classwt = c(propTP, propFP), # weighted FP by propTP and TP by propFP\n # ,maxnodes=10, nodesize=20\n ) \n\ntest_table$prediction = predict(rf, test_table)\nsens = sum(test_table$status == \"TP\" & test_table$prediction == \"TP\") / sum(test_table$status == \"TP\")\nfdr = 1 - (sum(test_table$status == \"TP\" & test_table$prediction == \"TP\") / sum(test_table$prediction == \"TP\"))\n\n# look at variable importance\nvarImpPlot(rf)\n\n# contruct the representative tree and plot it\n# definition: Representative trees are, in some sense, trees in the ensemble which are on average the \"closest\" to \n# all the other trees in the ensemble.\ntree = ReprTree(rf, train_table, metric='d2')\nplot(tree)\n\n# plot first tree from the forest : reprtree:::plot.getTree(rf, k = 1)\n\n# use K-fold\nkfold_spec = c()\nkfold_sens = c()\nkfold_TDR = c()\n\nfolds <- createFolds(test_table$status, 10)\n\nfor(i in 1:10){\n print(paste(\"fold: \",i,sep=\"\"))\n test = test_table[folds[[i]],]\n train = test_table[-folds[[i]],]\n \n propTP = as.numeric(table(train$status)[\"TP\"] / nrow(train))\n propFP = as.numeric(table(train$status)[\"FP\"] / nrow(train))\n \n rf_fold = randomForest(as.factor(status) ~ .,\n data = train[,my_features],\n importance = TRUE, # to allow us to inspect variable importance\n ntree = 500, sampsize = as.numeric(table(train$status)[\"TP\"])) #classwt = c(propTP, propFP))\n test$prediction = predict(rf_fold, test)\n kfold_sens = c(kfold_sens, (sum(test$status == \"TP\" & test$prediction == \"TP\") / sum(test$status == \"TP\")) )\n kfold_spec = c(kfold_spec, (sum(test$status == \"FP\" & test$prediction == \"FP\") / sum(test$status == \"FP\")) )\n kfold_TDR = c(kfold_TDR, (sum(test$status == \"TP\" & test$prediction == \"TP\") / sum(test$prediction == \"TP\")) )\n}\n\nif(type==\"snv\") { boxplot(data.frame(\"TDR\"=kfold_TDR, \"sensitivity\"=kfold_sens), col=\"lightgrey\", ylim=c(0.85,1), outpch=20, \n outcex=0.75, axes=F, staplelwd=2, main=type)\naxis(side=3, at=c(1,2), labels=c(\"TDR\",\"sensitivity\"), col = NA, col.ticks = NA)\naxis(side=2, at=c(\"0.85\",\"0.90\",\"0.95\",\"1\")) }\n\nif(type==\"indel\") { boxplot(data.frame(\"TDR\"=kfold_TDR, \"sensitivity\"=kfold_sens), col=\"lightgrey\", ylim=c(0,1), outpch=20, \n outcex=0.75, axes=F, staplelwd=2, main=type)\n axis(side=3, at=c(1,2), labels=c(\"TDR\",\"sensitivity\"), col = NA, col.ticks = NA)\n axis(side=2, at=seq(0,1,by=0.1)) }\n\n# load functions from MLP.r and run plot_ggviolin(kfold_TDR, kfold_sens, type)\n\n# compare with home filters\ntrain_table$prediction_home_filters = \"FP\"\ntrain_table[which(train_table$AF>0.1 & train_table$RVSB<0.95 & train_table$DP>50 & train_table$ERR<(-2)),\"prediction_home_filters\"] = \"TP\"\nsens_home_filter = sum(train_table$status==\"TP\" & train_table$prediction_home_filters==\"TP\") / sum(train_table$status==\"TP\")\nTDR_home_filters = sum(train_table$status == \"TP\" & train_table$prediction_home_filters == \"TP\") / sum(train_table$prediction_home_filters == \"TP\")\n\npoints(1, TDR_home_filters, pch=8, col=\"darkred\", lwd=2)\npoints(2, sens_home_filter, pch=8, col=\"darkred\", lwd=2)\n\nlegend(x=2, y=0.88, c(\"AF>0.1\",\"RVSB<0.95\",\"DP>50\",\"ERR<0.01\"), col=c(\"black\",\"white\",\"white\",\"white\"), pch=4, bty=\"n\", cex=0.75)\n\nsens_rf = sum(train_table$status==\"TP\" & train_table$prediction==\"TP\") / sum(train_table$status==\"TP\")\nTDR_rf = sum(train_table$status == \"TP\" & train_table$prediction == \"TP\") / sum(train_table$prediction == \"TP\")\n\n\n", "meta": {"hexsha": "6290cc7700939fb61b975a034e9126b83a705310", "size": 6045, "ext": "r", "lang": "R", "max_stars_repo_path": "random_forest.r", "max_stars_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_stars_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "random_forest.r", "max_issues_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_issues_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-05-10T07:48:49.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-10T08:17:31.000Z", "max_forks_repo_path": "random_forest.r", "max_forks_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_forks_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.958677686, "max_line_length": 174, "alphanum_fraction": 0.7022332506, "num_tokens": 1811, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6370307806984445, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3210037412109037}} {"text": "##' @title diagnoseBiomass\n##'\n##' @param beem.out output of a beem run\n##' @param true.biomass measured/true biomass (default: the biomass of last iteration)\n##' @param alpha transparency parameter of the biomass lines\n##' @description plot the trace of the fitted error of biomass\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\ndiagnoseBiomass <- function(beem.out, true.biomass=NA, alpha=0.1,...){\n trace.m <- beem.out$trace.m\n nIter <- ncol(trace.m)\n if(any(is.na(true.biomass))){\n true.m <- trace.m[,nIter]\n }else{\n true.m <- true.biomass\n }\n rel.err.m <- (t((trace.m-true.m)/true.m))*100\n col <- rep(rgb(0,0,0,alpha), nrow(trace.m))\n col[beem.out$sample2rm] <- rgb(1,1,0, alpha)\n matplot(rel.err.m, type='l',\n xlab=\"Iterations\",\n ylab=\"Relative difference (%)\",\n main='Biomass trace',\n lty=1, lwd = 3,\n col=col,\n ...\n )\n lines(x=1:ncol(trace.m),y=apply(rel.err.m,1,median), col='red', lwd=5)\n}\n\n##' @title diagnoseFit\n##'\n##' @param beem.out output of a beem run\n##' @param dat input data for the beem run\n##' @param thre threshold of R2 to show the label\n##' @param annotate display the label for the species with R2 > thre\n##' @import ggplot2\n##' @import ggrepel\n##' @description plot the R2 value for each species\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\ndiagnoseFit <- function(beem.out, dat, thre=0.5, annotate=TRUE){\n dat.tss <- tss(dat)\n r_ss <- rowSums(beem.out$err.p^2, na.rm=TRUE)\n if(length(beem.out$sample2rm) > 0 ) {\n dat.tss <- dat.tss[, -beem.out$sample2rm]\n }\n t_ss <- apply(dat.tss, 1, function(x) sum((x[x!=0]-mean(x[x!=0]))^2))\n r2 <- 1-r_ss/t_ss\n if(is.null(rownames(dat.tss))){\n species <- paste0('species', 1:nrow(dat.tss))\n }else{\n species <- rownames(dat)}\n plot.dat <- data.frame(r2=r2, species=species)\n\n p <- ggplot(plot.dat, aes(y=r2, x=species, label=species)) +\n geom_point(size=2) +\n geom_abline(intercept=thre, slope=0, lty=2, size=1.5, col='red') +\n labs(x='Species', y=expression(R^{2})) +\n coord_flip() +\n theme_bw()\n if(annotate){\n p <- p +\n geom_text_repel(data=subset(plot.dat, r2>thre))\n }\n p\n}\n\n##' @title checkPackage\n##'\n##' @param x package to check\n##' @description Check if a package is available\n##' @author Chenhao Li, Niranjan Nagarajan\ncheckPackage <- function(x){\n if (!requireNamespace(x, quietly = TRUE)) {\n stop(paste0(\"Package \\\"\", x, \"\\\" needed for this function to work. Please install it.\"),\n call. = FALSE)\n }\n}\n\n##' @title showInteraction\n##'\n##' @param beem.out output of a beem run\n##' @param dat input data for the beem run\n##' @param layout graph layout (see the documentation for ggraph)\n##' @param node.text.size node text size\n##' @param t.strength the threshold used to limit the number of interactions (strength)\n##' @param t.stab the threshold used to limit the number of interactions (stability)\n##' @description plot the interaction network inferred by beem (using ggraph)\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\nshowInteraction <- function(beem.out, dat, layout='fr', node.text.size=2, t.strength=0.001, t.stab=0.8){\n checkPackage(\"ggraph\")\n checkPackage(\"igraph\")\n suppressMessages(require(igraph))\n suppressMessages(require(ggraph))\n b <- t(beem2param(beem.out)$b.est) ## need transpose\n diag(b) <- 0\n if(!is.null(beem.out$resample)){\n b[t(beem.out$resample$b.stab0, '+', '-')\n E(g)$Strength <- abs(E(g)$I)\n g.simple <- delete.vertices(g, V(g)[degree(g) == 0])\n ggraph(g.simple, layout = layout)+#, circular=TRUE) +\n geom_edge_arc(aes(col=Type, width=Strength),arrow = arrow(length = unit(2, 'mm')),\n curvature = 0.1, alpha=0.8,\n end_cap=circle(1.5, 'mm'), start_cap=circle(1.5, 'mm')) +\n geom_node_point(pch=1, aes(size=RelativeAbundance)) +\n geom_node_text(aes(label = label), size=node.text.size, repel = TRUE) +\n scale_edge_width(range = c(0.5,1.5), guide=FALSE) +\n theme_void()\n}\n\n##' @title showGrowth\n##'\n##' @param beem.out output of a beem run\n##' @param dat input data for the beem run\n##' @description ordered barplot for growth rates\n##' @import ggplot2\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\nshowGrowth <- function(beem.out, dat){\n a <- beem2param(beem.out)$a.est\n ggplot(data.frame(Name=rownames(dat), Growth=a),\n aes(x=reorder(Name, Growth, function(x) x), y=Growth)) +\n geom_bar(stat='identity') +\n labs(x=NULL) + theme_bw() +\n coord_flip()\n}\n\n##' @title pcoa\n##'\n##' @param countData OTU/species abundance table (each row is one species, each column is one site)\n##' @param col a vector of colors for the points\n##' @description perform a PCoA analysis using Bray-Curtis distance\n##' @importFrom vegan vegdist\n##' @import ggplot2\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\npcoa <- function(countData, col='Color'){\n dat.pcoa <- cmdscale(vegan::vegdist(t(countData)), eig=TRUE)\n per.var <- (dat.pcoa$eig/sum(dat.pcoa$eig))[1:2] * 100\n dat <- data.frame(dat.pcoa$points, col=col)\n ggplot(dat, aes(x=X1, y=X2, col=col)) +\n geom_point(size=2) +\n labs(x=paste0('MDS1 (', round(per.var[1], 2),'%)'),\n y=paste0('MDS2 (', round(per.var[2], 2),'%)'))\n}\n\n\n##' @title cluster\n##'\n##' @param countData OTU/species abundance table (each row is one species, each column is one site)\n##' @description perform a hierachical clustering analysis using Bray-Curtis distance\n##' @importFrom vegan vegdist\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @export\ncluster <- function(countData){\n hc <- hclust(vegan::vegdist(t(countData)))\n hc\n}\n\n##' @title auc.b\n##'\n##' @param b.est The estimated interaction network (This can be a correlation matrix and the edges will be ranked based on the absolute value of the correlation coefficents)\n##' @param b.true The true interaction network\n##' @param is.association b.est is an association structure\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @description plot ROC curve with AUC for the interaction graph (interaction signs are ignored)\n##' @export\nauc.b <- function(b.est, b.true, is.association=FALSE, ...){\n checkPackage(\"pROC\")\n diag(b.est) <- NA\n diag(b.true) <- NA\n if(is.association){\n b.true <- interaction2association(b.true)\n est <- b.est[lower.tri(b.est)]\n lab <- b.true[lower.tri(b.true)]\n }else{\n est <- c(b.est)\n lab <- c(b.true)\n }\n est <- abs(est[!is.na(est)])\n lab <- lab[!is.na(lab)]\n lab <- (lab!=0 ) *1\n pROC::plot.roc(lab,est, print.auc = TRUE, ...)\n}\n\n##' @title interaction2association\n##'\n##' @param m an interaction matrix\n##' @author Chenhao Li, Niranjan Nagarajan\n##' @description Convert interaction matrix to association matrix by taking the average of the symmetric entries\ninteraction2association <- function(m){\n m.cp <- m\n tmp <- (m[lower.tri(m)] + t(m)[lower.tri(m)])/2\n m.cp[lower.tri(m.cp)] <- tmp\n m.cp[upper.tri(m.cp)] <- tmp\n m.cp\n}\n\n", "meta": {"hexsha": "2a0731b559617c3af131c9f0c296b7910d20b3ab", "size": 7424, "ext": "r", "lang": "R", "max_stars_repo_path": "R/vis.r", "max_stars_repo_name": "CSB5/BEEM-static", "max_stars_repo_head_hexsha": "83d97e2c1ca8bf9226f291100f75d6298a179688", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-11-22T13:37:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-09T09:45:11.000Z", "max_issues_repo_path": "R/vis.r", "max_issues_repo_name": "CSB5/BEEM-static", "max_issues_repo_head_hexsha": "83d97e2c1ca8bf9226f291100f75d6298a179688", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/vis.r", "max_forks_repo_name": "CSB5/BEEM-static", "max_forks_repo_head_hexsha": "83d97e2c1ca8bf9226f291100f75d6298a179688", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-02-18T06:50:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-18T06:50:07.000Z", "avg_line_length": 35.6923076923, "max_line_length": 173, "alphanum_fraction": 0.627424569, "num_tokens": 2246, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.320952427895305}} {"text": "# input args <- input file, popmap\nargs <- commandArgs(trailingOnly = TRUE)\n\n# file name for parsing chromosome and window bounds\nfilename_simple <- strsplit(args[1], \"windows/\")[[1]][2]\n\n# add functions for calculations\nsource(\"window_stat_calculations.r\")\n\n# define minimum number of sites to keep a fasta file \nmin_sites <- 50000\n\n# no scientific notation\noptions(scipen=999)\n\n# read in input file\ninput_file <- read.table(args[1], sep=\"\\t\", stringsAsFactors=F)\n\n# subset input file \ninput_file_genotypes <- input_file[,4:ncol(input_file)]\n\n# read in populations\npopulations <- read.table(args[2], sep=\"\\t\", stringsAsFactors=F, header=T)\n\n# define output name\noutput_name <- paste(strsplit(args[1], \".simple\")[[1]][1], \"__stats.txt\", sep=\"\")\n\n# write output file\nwrite(c(\"pop1\", \"pop2\", \"stat\", \"chr\", \"start\", \"end\", \"number_sites\", \"number_variable_sites\", \"calculated_stat\"), ncolumns=9, file=output_name, sep=\"\\t\")\n\n# calculate heterozygosity for each individual\nheterozygosity(input_file, populations, output_name, filename_simple)\n\n# calculate differentiation statistics for each pairwise comparison\nall_combinations <- combn(unique(populations$PopName), 2)\n# keep only those where the species is the same in the combination\nall_combinations <- all_combinations[,sapply(strsplit(all_combinations[1,], \"_\"), \"[[\", 1) == sapply(strsplit(all_combinations[2,], \"_\"), \"[[\", 1)]\nfor(a in 1:ncol(all_combinations)) {\n\t# define populations\n\ta_pop1 <- all_combinations[1,a]\n\ta_pop2 <- all_combinations[2,a]\n\t\n\n\t# subset vcf inputs\n\ta_input1 <- input_file_genotypes[,populations$PopName == a_pop1]\n\tif(is.null(dim(a_input1))) { a_input1 <- as.matrix(a_input1) }\n\t# remove sites that are not either invariant or bi-allelic SNPs\n\ta_input1 <- a_input1[nchar(input_file[,2]) == 1 & nchar(input_file[,3]) == 1, ]\n\tif(is.null(dim(a_input1))) { a_input1 <- as.matrix(a_input1) }\n\n\ta_input2 <- input_file_genotypes[,populations$PopName == a_pop2]\n\tif(is.null(dim(a_input2))) { a_input2 <- as.matrix(a_input2) }\n\t# remove sites that are not either invariant or bi-allelic SNPs\n\ta_input2 <- a_input2[nchar(input_file[,2]) == 1 & nchar(input_file[,3]) == 1, ]\n\tif(is.null(dim(a_input2))) { a_input2 <- as.matrix(a_input2) }\n\n\t# determine which sites have missing data and remove them\n\ta_input <- cbind(a_input1, a_input2)\n\tdont_keep <- apply(a_input, 1, any_missing)\n\ta_input1 <- a_input1[dont_keep == FALSE, ]\n\tif(is.null(dim(a_input1))) { a_input1 <- as.matrix(a_input1) }\n\ta_input2 <- a_input2[dont_keep == FALSE, ]\n\tif(is.null(dim(a_input2))) { a_input2 <- as.matrix(a_input2) }\n\t\t\t\n\tdifferentiation(a_input1, a_input2, a_pop1, a_pop2, output_name, filename_simple)\n\t\n}\n", "meta": {"hexsha": "b7f8767e5a3f3f513eaeb28fb4f91ce8460c720b", "size": 2662, "ext": "r", "lang": "R", "max_stars_repo_path": "02_trim_process_genotype/07_calc_windows/calculate_windows.r", "max_stars_repo_name": "jdmanthey/ethiopia_grv_birds", "max_stars_repo_head_hexsha": "aa92cb4cd223c5142466eab64c2ca69da8f032db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "02_trim_process_genotype/07_calc_windows/calculate_windows.r", "max_issues_repo_name": "jdmanthey/ethiopia_grv_birds", "max_issues_repo_head_hexsha": "aa92cb4cd223c5142466eab64c2ca69da8f032db", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "02_trim_process_genotype/07_calc_windows/calculate_windows.r", "max_forks_repo_name": "jdmanthey/ethiopia_grv_birds", "max_forks_repo_head_hexsha": "aa92cb4cd223c5142466eab64c2ca69da8f032db", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.1470588235, "max_line_length": 155, "alphanum_fraction": 0.7235161533, "num_tokens": 754, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3209524278953049}} {"text": "\n\n# Snow crab --- Areal unit modelling of habitat \n\n# -------------------------------------------------\n# Part 1 -- construct basic parameter list defining the main characteristics of the study\n\n\n year.assessment = 2020\n\n p = bio.snowcrab::snowcrab_parameters( \n project_class=\"carstm\", \n yrs=2000:year.assessment, \n areal_units_type=\"tesselation\", \n arstm_model_label = \"nonseparable_space-time_pa_fishable_binomial\",\n selection = list(type = \"presence_absence\")\n )\n\n if (0) {\n\n p$selection$type = \"presence_absence\"\n p$selection$biologicals=list(\n spec_bio=bio.taxonomy::taxonomy.recode( from=\"spec\", to=\"parsimonious\", tolookup=p$groundfish_species_code ),\n sex=0, # male\n mat=1, # do not use maturity status in groundfish data as it is suspect ..\n len= c( 95, 200 )/10, # mm -> cm ; aegis_db in cm\n ranged_data=\"len\"\n )\n p$selection$survey=list(\n data.source = c(\"snowcrab\", \"groundfish\", \"logbook\"),\n yr = p$yrs, # time frame for comparison specified above\n settype = 1, # same as geartype in groundfish_survey_db\n polygon_enforce=TRUE, # make sure mis-classified stations or incorrectly entered positions get filtered out\n strata_toremove = NULL #, # emphasize that all data enters analysis initially ..\n # ranged_data = c(\"dyear\") # not used .. just to show how to use range_data\n )\n p$variabletomodel = \"pa\"\n \n p$carstm_model_label = \"nonseparable_space-time_pa_fishable_binomial\"\n p$carstm_modelengine = \"inla\"\n p$formula = as.formula( paste(\n p$variabletomodel, ' ~ 1 ',\n ' + f( dyri, model=\"ar1\", hyper=H$ar1 ) ',\n ' + f( inla.group( t, method=\"quantile\", n=11 ), model=\"rw2\", scale.model=TRUE, hyper=H$rw2) ',\n ' + f( inla.group( z, method=\"quantile\", n=11 ), model=\"rw2\", scale.model=TRUE, hyper=H$rw2) ',\n ' + f( inla.group( substrate.grainsize, method=\"quantile\", n=11 ), model=\"rw2\", scale.model=TRUE, hyper=H$rw2) ',\n ' + f( inla.group( pca1, method=\"quantile\", n=11 ), model=\"rw2\", scale.model=TRUE, hyper=H$rw2) ',\n ' + f( inla.group( pca2, method=\"quantile\", n=11 ), model=\"rw2\", scale.model=TRUE, hyper=H$rw2) ',\n ' + f( space, model=\"bym2\", graph=slot(sppoly, \"nb\"), scale.model=TRUE, constr=TRUE, hyper=H$bym2 ) '\n ' + f( space_time, model=\"bym2\", graph=slot(sppoly, \"nb\"), group=time_space, scale.model=TRUE, constr=TRUE, hyper=H$bym2, control.group=list(model=\"ar1\", hyper=H$ar1_group)) '\n ) )\n\n p$family = \"binomial\" # \"nbinomial\", \"betabinomial\", \"zeroinflatedbinomial0\" , \"zeroinflatednbinomial0\"\n p$carstm_model_inla_control_familiy = list(control.link=list(model='logit'))\n\n # p$family = \"zeroinflatedbinomial1\", # \"binomial\", # \"nbinomial\", \"betabinomial\", \"zeroinflatedbinomial0\" , \"zeroinflatednbinomial0\"\n # p$carstm_model_inla_control_familiy = NULL\n\n }\n\n\n\n sppoly = areal_units( p=p ) # to reload\n plot( sppoly[, \"au_sa_km2\"] )\n\n\n M = snowcrab.db( p=p, DS=\"carstm_inputs\", redo=TRUE ) # will redo if not found\n\n fit = carstm_model( p=p, data='snowcrab.db( p=p, DS=\"carstm_inputs\" )' ) # 151 configs and long optim .. 19 hrs\n \n\n\n\n # fit = carstm_model( p=p, DS=\"carstm_modelled_fit\")\n\n # extract results\n if (0) {\n # very large files .. slow \n fit = carstm_model( p=p, DS=\"carstm_modelled_fit\" ) # extract currently saved model fit\n plot(fit)\n plot(fit, plot.prior=TRUE, plot.hyperparameters=TRUE, plot.fixed.effects=FALSE )\n }\n\n\n res = carstm_model( p=p, DS=\"carstm_modelled_summary\" ) # to load currently saved results\n res$summary$dic$dic\n res$summary$dic$p.eff\n res$dyear\n\n\n plot_crs = p$aegis_proj4string_planar_km\n coastline=aegis.coastline::coastline_db( DS=\"eastcoast_gadm\", project_to=plot_crs )\n isobaths=aegis.bathymetry::isobath_db( depths=c(50, 100, 200, 400), project_to=plot_crs )\n managementlines = aegis.polygons::area_lines.db( DS=\"cfa.regions\", returntype=\"sf\", project_to=plot_crs )\n\n time_match = list( year=as.character(2020) )\n carstm_map( res=res, \n vn=\"predictions\", \n time_match=time_match , \n coastline=coastline,\n managementlines=managementlines,\n isobaths=isobaths,\n main=paste(\"Habitat probability - mature male \", paste0(time_match, collapse=\"-\") ) \n )\n \n\n # map all :\n vn \"= predictions\"\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"predicted.probability.observation\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n brks = pretty( res[[vn]] )\n\n for (y in res$year ){\n\n time_match = list( year=y )\n fn_root = paste(\"Predicted_abundance\", paste0(time_match, collapse=\"-\"), sep=\"_\")\n fn = file.path( outputdir, paste(fn_root, \"png\", sep=\".\") )\n\n carstm_map( \n res=res, \n vn=vn, \n time_match=time_match, \n breaks =brks,\n# palette=\"-RdYlBu\",\n coastline=coastline,\n isobaths=isobaths,\n managementlines=managementlines,\n main=paste(\"Habitat probability - mature male \", paste0(time_match, collapse=\"-\") ),\n outfilename=fn\n ) \n\n }\n \n\n\n snowcrab.db(p=p, DS=\"carstm_output_compute\" )\n \n RES = snowcrab.db(p=p, DS=\"carstm_output_timeseries\" )\n\n pa = snowcrab.db(p=p, DS=\"carstm_output_spacetime_pa\" )\n\n# plots with 95% PI\n\n outputdir = file.path( p$modeldir, p$carstm_model_label, \"aggregated_biomass_timeseries\" )\n\n if ( !file.exists(outputdir)) dir.create( outputdir, recursive=TRUE, showWarnings=FALSE )\n\n (fn = file.path( outputdir, \"cfa_all.png\"))\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfaall ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Prob of observing snow crab\", xlab=\"\", ylim=c(0,1))\n lines( cfaall_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfaall_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n (fn = file.path( outputdir, \"cfa_south.png\") )\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfasouth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Prob of observing snow crab\", xlab=\"\", ylim=c(0,1))\n lines( cfasouth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfasouth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n (fn = file.path( outputdir, \"cfa_north.png\"))\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfanorth ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Prob of observing snow crab\", xlab=\"\", ylim=c(0,1))\n lines( cfanorth_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfanorth_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n (fn = file.path( outputdir, \"cfa_4x.png\"))\n png( filename=fn, width=3072, height=2304, pointsize=12, res=300 )\n plot( cfa4x ~ yrs, data=RES, lty=\"solid\", lwd=4, pch=20, col=\"slateblue\", type=\"b\", ylab=\"Prob of observing snow crab\", xlab=\"\", ylim=c(0,1))\n lines( cfa4x_lb ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n lines( cfa4x_ub ~ yrs, data=RES, lty=\"dotted\", lwd=2, col=\"slategray\" )\n dev.off()\n\n\n\n\n\n \n# end\n", "meta": {"hexsha": "85b18217b656d60dba7e0d8dd151369db13b96e0", "size": 7362, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/03.habitat_estimation_carstm.r", "max_stars_repo_name": "jae0/bio.snowcrab", "max_stars_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/scripts/03.habitat_estimation_carstm.r", "max_issues_repo_name": "jae0/bio.snowcrab", "max_issues_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/scripts/03.habitat_estimation_carstm.r", "max_forks_repo_name": "jae0/bio.snowcrab", "max_forks_repo_head_hexsha": "07b2daa7ddb0d5281b62b5f3b49b3f6f68230720", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 39.7945945946, "max_line_length": 185, "alphanum_fraction": 0.6407226297, "num_tokens": 2373, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7662936430859597, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.32084478001900096}} {"text": "suppressMessages (library(shiny))\nsuppressMessages (library(ggplot2))\nsuppressMessages (library(ggrepel))\nsuppressMessages (library(scales))\nsuppressMessages (library(DT))\nsuppressMessages (library(tidyr))\nsuppressMessages (library(dplyr))\nsuppressMessages (library(ggkm))\nsuppressMessages (library(Hmisc))\nsuppressMessages (library(quantreg))\n\n\n\nstat_sum_df <- function(fun, geom=\"point\", ...) {\n stat_summary(fun.data=fun, geom=geom, ...)\n}\nstat_sum_single <- function(fun, geom=\"point\", ...) {\n stat_summary(fun.y=fun, geom=geom, ...)\n}\n\nmedian.n <- function(x){\n return(c(y = ifelse(median(x)<0,median(x),median(x)),\n label = round(median(x),2))) \n}\ngive.n <- function(x){\n return(c(y = min(x)*1, label = length(x))) \n}\n\noptions(shiny.maxRequestSize=100*1024^2) \n#options(shiny.reactlog=TRUE) \ntableau10 <- c(\"#1F77B4\",\"#FF7F0E\",\"#2CA02C\",\"#D62728\",\"#9467BD\",\n \"#8C564B\",\"#E377C2\",\"#7F7F7F\",\"#BCBD22\",\"#17BECF\")\n\n\n\nui <- fluidPage(\n titlePanel(\"Hello GHAP HBGDki Member!\"),\n sidebarLayout(\n sidebarPanel(\n tabsetPanel(\n tabPanel(\"Inputs\", \n fileInput(\"datafile\", \"Choose csv file to upload\",\n multiple = FALSE, accept = c(\"csv\")),\n uiOutput(\"ycol\"),uiOutput(\"xcol\"),\n tabsetPanel(id = \"filtercategorize\",\n tabPanel(\"Categorize/Rename\", \n uiOutput(\"catvar\"),\n uiOutput(\"ncuts\"),\n uiOutput(\"catvar2\"),\n uiOutput(\"catvar3\"),\n uiOutput(\"ncuts2\"),\n uiOutput(\"asnumeric\"),\n textOutput(\"bintext\"),\n uiOutput(\"catvar4\"),\n textOutput(\"labeltext\"),\n uiOutput(\"nlabels\")\n ),\n \n tabPanel(\"Combine Variables\", \n uiOutput(\"pastevar\")\n ),\n tabPanel(\"Filters\", \n uiOutput(\"maxlevels\"),\n uiOutput(\"filtervar1\"),\n uiOutput(\"filtervar1values\"),\n uiOutput(\"filtervar2\"),\n uiOutput(\"filtervar2values\"),\n uiOutput(\"filtervar3\"),\n uiOutput(\"filtervar3values\"),\n uiOutput(\"filtervarcont1\"),\n uiOutput(\"fslider1\"),\n uiOutput(\"filtervarcont2\"),\n uiOutput(\"fslider2\"),\n uiOutput(\"filtervarcont3\"),\n uiOutput(\"fslider3\")\n ),\n tabPanel(\"Simple Rounding\",\n uiOutput(\"roundvar\"),\n numericInput(\"rounddigits\",label = \"N Digits\",value = 0,min=0,max=10) \n ),\n tabPanel(\"Reorder Variables\", \n uiOutput(\"reordervar\"),\n conditionalPanel(condition = \"input.reordervarin!='' \" ,\n selectizeInput( \"functionordervariable\", 'The:',\n choices =c(\"Median\",\"Mean\",\"Minimum\",\"Maximum\") ,multiple=FALSE)\n ),\n uiOutput(\"variabletoorderby\"),\n conditionalPanel(condition = \"input.reordervarin!='' \" ,\n checkboxInput('reverseorder', 'Reverse Order ?', value = FALSE) ),\n \n uiOutput(\"reordervar2\"),\n uiOutput(\"reordervar2values\")\n )\n ),\n hr()\n ), # tabsetPanel\n \n \n tabPanel(\"Graph Options\",\n tabsetPanel(id = \"graphicaloptions\",\n tabPanel( \"X/Y Log /Labels\",\n hr(),\n textInput('ylab', 'Y axis label', value = \"\") ,\n textInput('xlab', 'X axis label', value = \"\") ,\n hr(),\n checkboxInput('logy', 'Log Y axis', value = FALSE) ,\n checkboxInput('logx', 'Log X axis', value = FALSE) ,\n conditionalPanel(condition = \"!input.logy\" ,\n checkboxInput('scientificy', 'Comma separated Y axis ticks', value = FALSE)),\n conditionalPanel(condition = \"!input.logx\" ,\n checkboxInput('scientificx', 'Comma separated X axis ticks', value = FALSE)),\n checkboxInput('rotateyticks', 'Rotate/Justify Y axis Ticks ?', value = FALSE),\n checkboxInput('rotatexticks', 'Rotate/Justify X axis Ticks ?', value = FALSE),\n conditionalPanel(condition = \"input.rotateyticks\" , \n sliderInput(\"yticksrotateangle\", \"Y axis ticks angle:\", min=0, max=360, value=c(0),step=10),\n sliderInput(\"ytickshjust\", \"Y axis ticks horizontal justification:\", min=0, max=1, value=c(0.5),step=0.1),\n sliderInput(\"yticksvjust\", \"Y axis ticks vertical justification:\", min=0, max=1, value=c(0.5),step=0.1)\n ),\n conditionalPanel(condition = \"input.rotatexticks\" , \n sliderInput(\"xticksrotateangle\", \"X axis ticks angle:\", min=0, max=360, value=c(20),step=10),\n sliderInput(\"xtickshjust\", \"X axis ticks horizontal justification:\", min=0, max=1, value=c(1),step=0.1),\n sliderInput(\"xticksvjust\", \"X axis ticks vertical justification:\", min=0, max=1, value=c(1),step=0.1)\n )\n \n ),\n tabPanel( \"Graph Size/Zoom\",\n sliderInput(\"height\", \"Plot Height\", min=1080/4, max=1080, value=480, animate = FALSE),\n h6(\"X Axis Zoom only works if facet x scales are not set to be free.\"),\n uiOutput(\"xaxiszoom\")\n \n ),\n \n tabPanel( \"Background Color and Legend Position\",\n selectInput('backgroundcol', label ='Background Color',\n choices=c(\"Gray\" =\"gray97\",\"White\"=\"white\",\"Dark Gray\"=\"grey90\"),\n multiple=FALSE, selectize=TRUE,selected=\"white\"),\n selectInput('legendposition', label ='Legend Position',\n choices=c(\"left\", \"right\", \"bottom\", \"top\",\"none\"),\n multiple=FALSE, selectize=TRUE,selected=\"bottom\"),\n selectInput('legenddirection', label ='Layout of Items in Legends',\n choices=c(\"horizontal\", \"vertical\"),\n multiple=FALSE, selectize=TRUE,selected=\"horizontal\"),\n selectInput('legendbox', label ='Arrangement of Multiple Legends ',\n choices=c(\"horizontal\", \"vertical\"),\n multiple=FALSE, selectize=TRUE,selected=\"vertical\")\n ),\n tabPanel( \"Facets Options\",\n \n uiOutput(\"facetscales\"),\n selectInput('facetspace' ,'Facet Spaces:',c(\"fixed\",\"free_x\",\"free_y\",\"free\")),\n \n selectizeInput( \"facetswitch\", \"Facet Switch to Near Axis:\",\n choices = c(\"x\",\"y\",\"both\"),\n options = list( maxItems = 1 ,\n placeholder = 'Please select an option',\n onInitialize = I('function() { this.setValue(\"\"); }') ) ),\n checkboxInput('facetmargin', 'Show Facet(s) Margin(s) ?'),\n \n selectInput('facetlabeller' ,'Facet Label:',c(\n \"Variable(s) Name(s) and Value(s)\" =\"label_both\",\n \"Value(s)\"=\"label_value\",\n \"Parsed Expression\" =\"label_parsed\"),\n selected=\"label_both\"),\n \n checkboxInput('facetwrap', 'Use facet_wrap?'),\n conditionalPanel(condition = \"input.facetwrap\" ,\n checkboxInput('customncolnrow', 'Control N columns an N rows?')),\n conditionalPanel(condition = \"input.customncolnrow\" ,\n h6(\"An error (nrow*ncol >= n is not TRUE) will show up if the total number of facets/panels is greater than the product of the specified N columns x N rows. Increase the N columns and/or N rows to avoid the error. The default empty values will use ggplot automatic algorithm.\"), \n numericInput(\"wrapncol\",label = \"N columns\",value =NA,min=1,max =10) ,\n numericInput(\"wrapnrow\",label = \"N rows\",value = NA,min=1,max=10) \n )\n \n ) ,\n \n tabPanel( \"Reference Lines\",\n checkboxInput('identityline', 'Identity Line') , \n checkboxInput('horizontalzero', 'Horizontol Zero Line'),\n checkboxInput('customline1', 'Vertical Line'),\n conditionalPanel(condition = \"input.customline1\" , \n numericInput(\"vline\",label = \"\",value = 1) ),\n checkboxInput('customline2', 'Horizontal Line'),\n conditionalPanel(condition = \"input.customline2\" , \n numericInput(\"hline\",label = \"\",value = 1) )\n ),\n tabPanel( \"Additional Themes Options\",\n sliderInput(\"themebasesize\", \"Theme Size (affects all text elements in the plot):\", min=1, max=100, value=c(16),step=1),\n checkboxInput('themetableau', 'Use Tableau Colors and Fills ? (maximum of 10 colours are provided)',value=TRUE),\n conditionalPanel(condition = \"input.themetableau\" ,\n h6(\"If you have more than 10 color groups the plot will not work and you get /Error: Insufficient values in manual scale. ## needed but only 10 provided./ Uncheck Use Tableau Colors and Fills to use default ggplot2 colors.\")),\n checkboxInput('themecolordrop', 'Keep All levels of Colors and Fills ?',value=TRUE) , \n \n checkboxInput('themebw', 'Use Black and White Theme ?',value=TRUE), \n checkboxInput('themeaspect', 'Use custom aspect ratio ?') , \n conditionalPanel(condition = \"input.themeaspect\" , \n numericInput(\"aspectratio\",label = \"Y/X ratio\",\n value = 1,min=0.1,max=10,step=0.01)),\n checkboxInput('sepguides', 'Separate Legend Guides for Median/PI ?',value = TRUE), \n checkboxInput('labelguides', 'Hide the Names of the Guides ?',value = FALSE) \n ) #tabpanel\n )#tabsetpanel\n ), # tabpanel\n #) ,#tabsetPanel(),\n \n tabPanel(\"How To\",\n h5(\"1. Upload your data file in CSV format. R default options for read.csv will apply except for missing values where both (NA) and dot (.) are treated as missing. If your data has columns with other non-numeric missing value codes then they be treated as factors.\"),\n h5(\"2. The UI is dynamic and changes depending on your choices of options, x ,y, filter, group and so on.\"),\n h5(\"3. It is assumed that your data is tidy and ready for plotting (long format).\"),\n h5(\"4. x and y variable(s) input allow numeric and factor variables.\"),\n h5(\"5. You can now select more than one y variable. The data will always be automatically stacked using tidyr::gather and result in yvars and yvalues variables.if you select factor and continuous variables, all selected variables will be transformed to factor. The internal variable yvalues is used for y variable mapping and you can select yvars for facetting. The app automatically select additional row split as yvars and set Facet Scales to free_y. To change Facet Scales and many other options go to Graph Options tab.\"),\n h5(\"6. Inputs, Categorize, Recode into Binned Categories: Include the numeric variable to change to categorical in the list and then choose a number of cuts (default is 3). This helps when you want to group or color by cuts of a continuous variable.\"),\n h5(\"7. Inputs, Categorize, Treat as Categories: This changes a numeric variable to factor without binning. This helps when you want to group or color by numerical variable that has few unique values e.g. 1,2,3.\"),\n h5(\"8. Inputs, Categorize, Custom cuts: You can cut a numeric variable to factor using specified cutoffs, by default the min, median, max are used.\"),\n h5(\"9.Inputs, Categorize, Treat as Numeric: This checkbox recodes categorical/factor variables to numeric values that start with 0. This is useful to recode Yes/No to 1/0 and overlay a logistic smooth. Numeric Codes/values correspondence are shown in text below the checkbox.\"),\n h5(\"10.Inputs, Categorize, Combine Variables: This allows to paste together two variables (e.g. Sex with values: Male and Female and Treatment with values: TRT1, TRT2 and TRT3) to construct a new one called combinedvariable with values: Male TRT1, Male TRT2, Male TRT3, Female TRT1, Female TRT2, Female TRT3. Once specified the combinedvariable becomes available to color, group and any other mapping\"),\n h5(\"11. Inputs, Filters: There is six slots for Filter variables. Filter variables 1, 2,3,4,5 and 6 are applied sequentially. Values shown for filter variable 2 will depend on your selected data exclusions using filter variable 1 and so on. The first three filters accept numeric and non numeric columns while the last three are sliders and only work with numeric variables. For performance improvement the first three filters only show variables with a default maximum number of levels of 500 but you can increase it to your needs.\"),\n \n h5(\"12. New ! You can use additional smoothing functions including linear and logistic fits. Please make sure that data is compatible with the smoothing used i.e. for logistic a 0/1 variable is expected.\"),\n h5(\"13. Additional support to include boxplots. More work/feedback needed. Boxplots grouping might not be what is intented when the x axis variable is continuous, you can change the Group By: variable in the Color/Group/Split/Size/Fill Mappings (?) to better reflect your needs.\"),\n h5(\"14. Initial support to enable Kaplan-Meier Plots.\"),\n h5(\"15. Download the plot using the options on the 'Download' tab. This section is based on code from Mason DeCamillis ggplotLive app.\"),\n h5(\"16. Visualize the data table in the 'Data' tab. You can reorder the columns, filter and much more.\"),\n p(),\n h5(\"Samer Mouksassi 2016\"),\n h5(\"Contact me @ samermouksassi@gmail.com for feedback/Bugs/features requests!\")\n \n )# tabpanel \n )\n ), #sidebarPanel\n mainPanel(\n tabsetPanel(\n tabPanel(\"Plot\" , \n uiOutput('ui_plot'),\n hr(),\n uiOutput(\"clickheader\"),\n tableOutput(\"plot_clickedpoints\"),\n uiOutput(\"brushheader\"),\n tableOutput(\"plot_brushedpoints\"),\n #actionButton(\"plotButton\", \"Update Plot\"),\n uiOutput(\"optionsmenu\") ,\n \n conditionalPanel(\n condition = \"input.showplottypes\" , \n \n fluidRow(\n \n column (12, hr()),\n column (3,\n radioButtons(\"Points\", \"Points/Jitter:\",\n c(\"Points\" = \"Points\",\n \"Jitter\" = \"Jitter\",\n \"None\" = \"None\")),\n conditionalPanel( \" input.Points!= 'None' \",\n sliderInput(\"pointstransparency\", \"Points Transparency:\", min=0, max=1, value=c(0.5),step=0.01),\n checkboxInput('pointignorecol', 'Ignore Mapped Color')\n )),\n column(3,\n conditionalPanel( \" input.Points!= 'None' \",\n sliderInput(\"pointsizes\", \"Points Size:\", min=0, max=4, value=c(1),step=0.1),\n numericInput('pointtypes','Points Type:',16, min = 1, max = 25),\n conditionalPanel( \" input.pointignorecol \",\n selectInput('colpoint', label ='Points Color', choices=colors(),multiple=FALSE, selectize=TRUE, selected=\"black\") \n )\n )\n ), \n column(3,\n radioButtons(\"line\", \"Lines:\",\n c(\"Lines\" = \"Lines\",\n \"None\" = \"None\"),selected=\"None\"),\n conditionalPanel( \" input.line== 'Lines' \",\n sliderInput(\"linestransparency\", \"Lines Transparency:\", min=0, max=1, value=c(0.5),step=0.01),\n checkboxInput('lineignorecol', 'Ignore Mapped Color')\n )\n \n ),\n column(3,\n conditionalPanel( \" input.line== 'Lines' \",\n sliderInput(\"linesize\", \"Lines Size:\", min=0, max=4, value=c(1),step=0.1),\n selectInput('linetypes','Lines Type:',c(\"solid\",\"dotted\")),\n conditionalPanel( \" input.lineignorecol \",\n selectInput('colline', label ='Lines Color', choices=colors(),multiple=FALSE, selectize=TRUE,selected=\"black\") \n )\n ),\n checkboxInput('boxplotaddition', 'Add a Boxplot ? (makes sense if x variable is categorical and\n you Group By a sensible choice. By default the x variable is used for grouping)'),\n checkboxInput('boxplotignoregroup', 'Ignore Mapped Group ? (can me helpful to superpose a loess or median on top of the boxplot)')\n \n ),\n column (12, h6(\"Points and Lines Size will apply only if Size By: in the Color Group Split Size Fill Mappings are set to None\"))\n \n )#fluidrow\n ) ,\n conditionalPanel(\n condition = \"input.showfacets\" , \n fluidRow(\n column (12, hr()),\n column (3, uiOutput(\"colour\"),uiOutput(\"group\")),\n column(3, uiOutput(\"facet_col\"),uiOutput(\"facet_row\")),\n column (3, uiOutput(\"facet_col_extra\"),uiOutput(\"facet_row_extra\")),\n column (3, uiOutput(\"pointsize\"),uiOutput(\"fill\")),\n column (12, h6(\"Make sure not to choose a variable that is in the y variable(s) list otherwise you will get an error Variable not found. These variables are stacked and become yvars and yvalues.\" ))\n \n )\n ),\n \n #rqss quantile regression\n conditionalPanel(\n condition = \"input.showrqss\" , \n \n fluidRow(\n column(12,hr()),\n column(3,\n checkboxInput('Tauvalue', 'Dynamic and Preset Quantiles', value = FALSE),\n h5(\"Preset Quantiles\"),\n checkboxInput('up', '95%'),\n checkboxInput('ninetieth', '90%'),\n checkboxInput('mid', '50%', value = FALSE),\n checkboxInput('tenth', '10%'),\n checkboxInput('low', '5%')\n ),\n column(5,\n sliderInput(\"Tau\", label = \"Dynamic Quantile Value:\",\n min = 0, max = 1, value = 0.5, step = 0.01) ,\n sliderInput(\"Penalty\", label = \"Spline sensitivity adjustment:\",\n min = 0, max = 10, value = 1, step = 0.1) ,\n selectInput(\"Constraints\", label = \"Spline constraints:\",\n choices = c(\"N\",\"I\",\"D\",\"V\",\"C\",\"VI\",\"VD\",\"CI\",\"CD\"), selected = \"N\")\n ),\n column(3,\n checkboxInput('ignorecolqr', 'Ignore Mapped Color'),\n checkboxInput('ignoregroupqr', 'Ignore Mapped Group',value = TRUE),\n checkboxInput('hidedynamic', 'Hide Dynamic Quantile'),\n selectInput('colqr', label ='QR Color', choices=colors(),multiple=FALSE, selectize=TRUE,selected=\"black\")\n )\n \n )#fluidrow\n ),\n \n conditionalPanel(\n condition = \"input.showSmooth\" , \n \n fluidRow(\n column(12,hr()),\n column (3, \n radioButtons(\"Smooth\", \"Smooth:\",\n c(\"Smooth\" = \"Smooth\",\n \"Smooth and SE\" = \"Smooth and SE\",\n \"None\" = \"None\"),selected=\"None\")\n ),\n column (3, \n conditionalPanel( \" input.Smooth!= 'None' \",\n selectInput('smoothmethod', label ='Smoothing Method',\n choices=c(\"Loess\" =\"loess\",\"Linear Fit\"=\"lm\",\"Logistic\"=\"glm\"),\n multiple=FALSE, selectize=TRUE,selected=\"loess\"),\n \n sliderInput(\"loessens\", \"Loess Span:\", min=0, max=1, value=c(0.75),step=0.05)\n ) \n ),\n \n \n \n \n \n column (3, conditionalPanel( \" input.Smooth!= 'None' \",\n checkboxInput('ignorecol', 'Ignore Mapped Color'),\n uiOutput(\"weight\")\n )\n ),\n column (3, conditionalPanel( \" input.Smooth!= 'None' \",\n checkboxInput('ignoregroup', 'Ignore Mapped Group',value = TRUE),\n conditionalPanel( \" input.ignorecol \",\n selectInput('colsmooth', label ='Smooth Color', choices=colors(),multiple=FALSE, selectize=TRUE,selected=\"black\") )\n ) )\n \n )#fluidrow\n )\n ,\n ### Mean CI section\n conditionalPanel(\n condition = \"input.showMean\" , \n \n fluidRow(\n column(12,hr()),\n column (3, \n radioButtons(\"Mean\", \"Mean:\",\n c(\"Mean\" = \"Mean\",\n \"Mean (95% CI)\" = \"Mean (95% CI)\",\n \"None\" = \"None\") ,selected=\"None\") \n ),\n column (3,\n \n conditionalPanel( \" input.Mean== 'Mean (95% CI)' \",\n sliderInput(\"CI\", \"CI %:\", min=0, max=1, value=c(0.95),step=0.01),\n numericInput( inputId = \"errbar\",label = \"CI bar width:\",value = 2,min = 1,max = NA) \n )\n \n )\n ,\n column (3,\n conditionalPanel( \" input.Mean!= 'None' \",\n checkboxInput('meanpoints', 'Show points') ,\n checkboxInput('meanlines', 'Show lines', value=TRUE),\n checkboxInput('meanignorecol', 'Ignore Mapped Color') ,\n conditionalPanel( \" input.meanignorecol \",\n selectInput('colmean', label ='Mean Color', choices=colors(),multiple=FALSE, selectize=TRUE,selected=\"black\") )\n \n ) ),\n \n \n column(3,\n conditionalPanel( \" input.Mean!= 'None' \",\n checkboxInput('meanignoregroup', 'Ignore Mapped Group',value = TRUE),\n sliderInput(\"meanlinesize\", \"Mean(s) Line(s) Size:\", min=0, max=3, value=1,step=0.05)\n ) \n )\n ) #fluidrow\n ), # conditional panel for mean\n \n ### median PI section\n \n \n conditionalPanel(\n condition = \"input.showMedian\" , \n fluidRow(\n column(12,hr()),\n column (3,\n radioButtons(\"Median\", \"Median:\",\n c(\"Median\" = \"Median\",\n \"Median/PI\" = \"Median/PI\",\n \"None\" = \"None\") ,selected=\"None\") ,\n conditionalPanel( \" input.Median!= 'None' \",\n checkboxInput('medianvalues', 'Label Values?') ,\n checkboxInput('medianN', 'Label N?') )\n \n ),\n column (3,\n conditionalPanel( \" input.Median== 'Median' \",\n checkboxInput('medianpoints', 'Show points') ,\n checkboxInput('medianlines', 'Show lines',value=TRUE)),\n conditionalPanel( \" input.Median== 'Median/PI' \",\n sliderInput(\"PI\", \"PI %:\", min=0, max=1, value=c(0.95),step=0.01),\n sliderInput(\"PItransparency\", \"PI Transparency:\", min=0, max=1, value=c(0.2),step=0.01)\n )\n ),\n column (3,\n conditionalPanel( \" input.Median!= 'None' \",\n checkboxInput('medianignorecol', 'Ignore Mapped Color'),\n conditionalPanel( \" input.medianignorecol \",\n selectInput('colmedian', label ='Median Color', choices=colors(),multiple=FALSE, selectize=TRUE,selected=\"black\") )\n \n ) ),\n column (3,\n conditionalPanel( \" input.Median!= 'None' \",\n \n checkboxInput('medianignoregroup', 'Ignore Mapped Group',value = TRUE),\n sliderInput(\"medianlinesize\", \"Median(s) Line(s) Size:\", min=0, max=4, value=c(1),step=0.1)\n \n )\n )\n \n )#fluidrow\n ),\n ### median PI section\n \n ### KM section\n \n \n conditionalPanel(\n condition = \"input.showKM\" , \n fluidRow(\n column(12,hr()),\n column (12, h6(\"KM curves support is currently experimental some features might not work. When a KM curve is added nothing else will be plotted (e.g. points, lines etc.).Color/Fill/Group/Facets are expected to work.\" )),\n column (3,\n radioButtons(\"KM\", \"KM:\",\n c(\"KM\" = \"KM\",\n \"KM/CI\" = \"KM/CI\",\n \"None\" = \"None\") ,selected=\"None\") \n ),\n column (3,\n conditionalPanel( \" input.KM!= 'None' \",\n checkboxInput('censoringticks', 'Show Censoring Ticks?') ,\n conditionalPanel( \" input.KM== 'KM/CI' \",\n sliderInput(\"KMCI\", \"KM CI:\", min=0, max=1, value=c(0.95),step=0.01),\n sliderInput(\"KMCItransparency\", \"KM CI Transparency:\", min=0, max=1, value=c(0.2),step=0.01)\n )\n )),\n \n column (3,\n conditionalPanel( \" input.KM!= 'None' \",\n selectInput('KMtrans', label ='KM Transformation',\n choices=c(\"None\" =\"identity\",\"event\"=\"event\",\"cumhaz\"=\"cumhaz\",\"cloglog\"=\"cloglog\"),\n multiple=FALSE, selectize=TRUE,selected=\"loess\") \n )\n )\n )#fluidrow\n )\n ### KM section\n \n ),#tabPanel1\n tabPanel(\"Download\", \n selectInput(\n inputId = \"downloadPlotType\",\n label = h5(\"Select download file type\"),\n choices = list(\"PDF\" = \"pdf\",\"BMP\" = \"bmp\",\"JPEG\" = \"jpeg\",\"PNG\" = \"png\")),\n \n # Allow the user to set the height and width of the plot download.\n h5(HTML(\"Set download image dimensions
(units are inches for PDF, pixels for all other formats)\")),\n numericInput(\n inputId = \"downloadPlotHeight\",label = \"Height (inches)\",value = 7,min = 1,max = 100),\n numericInput(\n inputId = \"downloadPlotWidth\",label = \"Width (inches)\",value = 7,min = 1,max = 100),\n # Choose download filename.\n textInput(\n inputId = \"downloadPlotFileName\",\n label = h5(\"Enter file name for download\")),\n \n # File downloads when this button is clicked.\n downloadButton(\n outputId = \"downloadPlot\", \n label = \"Download Plot\")\n ),\n \n tabPanel('Data', dataTableOutput(\"mytablex\") \n )#tabPanel2\n )#tabsetPanel\n )#mainPanel\n )#sidebarLayout\n)#fluidPage\nserver <- function(input, output, session) {\n filedata <- reactive({\n infile <- input$datafile\n if (is.null(infile)) {\n # User has not uploaded a file yet\n return(NULL)\n }\n read.csv(infile$datapath,na.strings = c(\"NA\",\".\"))\n \n \n })\n \n \n \n myData <- reactive({\n df=filedata()\n if (is.null(df)) return(NULL)\n })\n \n output$optionsmenu <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n \n fluidRow(\n column (12, h6(\"Select the checkbox(es) for the options to be showed\")),\n hr(),\n column(4,checkboxInput('showplottypes',\n 'Plot types, Points, Lines (?)',\n value = TRUE)),\n column(4,checkboxInput('showfacets',\n 'Color/Group/Split/Size/Fill Mappings (?)',\n value = TRUE) ),\n column(4,checkboxInput('showrqss',\n 'Quantile Regression (?)',\n value = TRUE)),\n column(4,checkboxInput('showSmooth',\n 'Smooth/Linear/Logistic Regressions (?)',\n value = TRUE)),\n column(4,checkboxInput('showMean' , 'Mean CI (?)', value = FALSE)),\n column(4,checkboxInput('showMedian','Median PIs (?)', value = FALSE)),\n column(3,checkboxInput('showKM','Kaplan-Meier (?)', value = FALSE))\n \n )\n })\n \n output$ycol <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n selectInput(\"y\", \"y variable(s):\",choices=items,selected = items[1],multiple=TRUE,selectize=TRUE)\n })\n \n output$xcol <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n selectInput(\"x\", \"x variable:\",items,selected=items[2])\n \n })\n \n outputOptions(output, \"ycol\", suspendWhenHidden=FALSE)\n outputOptions(output, \"xcol\", suspendWhenHidden=FALSE)\n \n output$catvar <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [ MODEDF ]\n selectInput('catvarin',label = 'Recode into Binned Categories:',choices=NAMESTOKEEP2,multiple=TRUE)\n })\n \n \n output$ncuts <- renderUI({\n if (length(input$catvarin ) <1) return(NULL)\n sliderInput('ncutsin',label = 'N of Cut Breaks:', min=2, max=10, value=c(3),step=1)\n })\n \n output$catvar2 <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [ MODEDF ]\n if (length(input$catvarin ) >=1) {\n NAMESTOKEEP2<-NAMESTOKEEP2 [ !is.element(NAMESTOKEEP2,input$catvarin) ]\n }\n \n selectInput('catvar2in',label = 'Treat as Categories:',choices=NAMESTOKEEP2,multiple=TRUE)\n \n })\n \n output$catvar3 <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [ MODEDF ]\n if (length(input$catvarin ) >=1) {\n NAMESTOKEEP2<-NAMESTOKEEP2 [ !is.element(NAMESTOKEEP2,input$catvarin) ]\n }\n if (length(input$catvar2in ) >=1) {\n NAMESTOKEEP2<-NAMESTOKEEP2 [ !is.element(NAMESTOKEEP2,input$catvar2in) ]\n }\n selectizeInput( \"catvar3in\", 'Custom cuts of this variable, defaults to min, median, max before any applied filtering:',\n choices =NAMESTOKEEP2 ,multiple=FALSE,\n options = list( placeholder = 'Please select a variable',\n onInitialize = I('function() { this.setValue(\"\"); }')\n )\n )\n })\n output$ncuts2 <- renderUI({\n df <-filedata()\n if (length(input$catvar3in ) <1) return(NULL)\n if ( input$catvar3in!=\"\"){\n textInput(\"xcutoffs\", label = paste(input$catvar3in,\"Cuts\"),\n value = as.character(paste(\n min(df[,input$catvar3in] ,na.rm=T),\n median(df[,input$catvar3in],na.rm=T),\n max(df[,input$catvar3in],na.rm=T) ,sep=\",\")\n )\n )\n }\n \n })\n \n\n \n output$asnumeric <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n if (length(input$catvar3in ) <1) return(NULL)\n if ( input$catvar3in!=\"\"){\n column(12,\n checkboxInput('asnumericin', 'Treat as Numeric (helpful to overlay a smooth/regression line on top of a boxplot or to convert a variable into 0/1 and overlay a logistic fit', value = FALSE)\n #,checkboxInput('useasxaxislabels', 'Use the Categories Names as x axis label (makes sense only if you really chose it as x axis variable)', value = FALSE), \n #checkboxInput('useasyaxislabels', 'Use the Categories Names as y axis label (makes sense only if you really chose it as y axis variable)', value = FALSE) \n )\n }\n })\n \n \n outputOptions(output, \"catvar\", suspendWhenHidden=FALSE)\n outputOptions(output, \"ncuts\", suspendWhenHidden=FALSE)\n outputOptions(output, \"catvar2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"catvar3\", suspendWhenHidden=FALSE)\n outputOptions(output, \"ncuts2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"asnumeric\", suspendWhenHidden=FALSE)\n\n \n \n \n recodedata1 <- reactive({\n df <- filedata() \n if (is.null(df)) return(NULL)\n if(length(input$catvarin ) >=1) {\n for (i in 1:length(input$catvarin ) ) {\n varname<- input$catvarin[i]\n df[,varname] <- cut(df[,varname],input$ncutsin)\n df[,varname] <- as.factor( df[,varname])\n }\n }\n df\n })\n \n \n recodedata2 <- reactive({\n df <- recodedata1()\n if (is.null(df)) return(NULL)\n if(length(input$catvar2in ) >=1) {\n for (i in 1:length(input$catvar2in ) ) {\n varname<- input$catvar2in[i]\n df[,varname] <- as.factor( df[,varname])\n }\n }\n df\n })\n \n recodedata3 <- reactive({\n df <- recodedata2()\n if (is.null(df)) return(NULL)\n if(input$catvar3in!=\"\") {\n varname<- input$catvar3in\n xlimits <- input$xcutoffs \n nxintervals <- length(as.numeric(unlist (strsplit(xlimits, \",\")) )) -1\n df[,varname] <- cut( as.numeric ( as.character( df[,varname])),\n breaks= as.numeric(unlist (strsplit(xlimits, \",\"))),include.lowest=TRUE)\n df[,\"custombins\"] <- df[,varname] \n \n if(input$asnumericin) {\n df[,varname] <- as.numeric(as.factor(df[,varname]) ) -1 \n }\n }\n \n df\n })\n output$bintext <- renderText({\n df <- recodedata3()\n if (is.null(df)) return(NULL)\n bintextout <- \"\"\n if(input$catvar3in!=\"\") {\n varname<- input$catvar3in\n if(!input$asnumericin){\n bintextout <- levels(df[,\"custombins\"] )\n }\n if(input$asnumericin){\n bintextout <- paste( sort(unique(as.numeric(as.factor(df[,varname]) ) -1)) ,levels(df[,\"custombins\"] ),sep=\"/\") \n }}\n bintextout \n }) \n # xaxislabels <-levels(cut( as.numeric ( as.character( dataedafilter$month_ss)), breaks= as.numeric(unlist (strsplit(ageglimits, \",\") )),include.lowest=TRUE))\n #+ scale_x_continuous(breaks=seq(0,length(xaxislabels)-1),labels=xaxislabels ) useasxaxislabels\n output$catvar4 <- renderUI({\n df <-recodedata3()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [! MODEDF ]\n \n selectizeInput( \"catvar4in\", 'Change labels of this variable:',\n choices =NAMESTOKEEP2 ,multiple=FALSE,\n options = list( placeholder = 'Please select a variable',\n onInitialize = I('function() { this.setValue(\"\"); }')\n )\n )\n })\n \n output$labeltext <- renderText({\n df <- recodedata3()\n if (is.null(df)) return(NULL)\n labeltextout <- \"\"\n if(input$catvar4in!=\"\") {\n varname<- input$catvar4in\n labeltextout <- c(\"Old labels\",levels(df[,varname] ))\n }\n labeltextout \n }) \n \n \n \n \n output$nlabels <- renderUI({\n df <-recodedata3()\n if (length(input$catvar4in ) <1) return(NULL)\n if ( input$catvar4in!=\"\"){\n nlevels <- length( unique( levels(as.factor( df[,input$catvar4in] ))))\n levelsvalues <- levels(as.factor( df[,input$catvar4in] ))\n textInput(\"customvarlabels\", label = paste(input$catvar4in,\"requires\",nlevels,\"new labels,\n type in a comma separated list below\"),\n value =\n # paste(\"\\\"\",as.character(levelsvalues),\"\\\"\",collapse=\", \",sep=\"\")\n #paste(\"'\",as.character(1:nlevels),\"'\",collapse=\", \",sep=\"\")\n paste(as.character(1:nlevels),collapse=\", \",sep=\"\")\n )\n }\n \n })\n \n outputOptions(output, \"catvar4\", suspendWhenHidden=FALSE)\n outputOptions(output, \"nlabels\", suspendWhenHidden=FALSE)\n \n \n recodedata4 <- reactive({\n df <- recodedata3()\n if (is.null(df)) return(NULL)\n if(input$catvar4in!=\"\") {\n varname<- input$catvar4in\n xlabels <- input$customvarlabels \n # xlabels <- c(\"a\",\"b\")\n nxxlabels <- length(as.numeric(unlist (strsplit(xlabels, \",\")) )) -1\n df[,varname] <- as.factor(df[,varname])\n levels(df[,varname]) <- unlist (strsplit(xlabels, \",\") )\n }\n #print(head(df))\n df\n })\n \n \n output$pastevar <- renderUI({\n df <- recodedata4()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [! MODEDF ]\n selectizeInput(\"pastevarin\", \"Combine the categories of these two variables:\", choices = NAMESTOKEEP2,multiple=TRUE,\n options = list(\n maxItems = 2 ,\n placeholder = 'Please select some variables',\n onInitialize = I('function() { this.setValue(\"\"); }'),\n plugins = list('remove_button', 'drag_drop')\n )\n )\n })\n \n \n outputOptions(output, \"pastevar\", suspendWhenHidden=FALSE)\n outputOptions(output, \"bintext\", suspendWhenHidden=FALSE)\n \n \n \n \n output$maxlevels <- renderUI({\n df <-recodedata4()\n if (is.null(df)) return(NULL)\n numericInput( inputId = \"inmaxlevels\",label = \"Max number of unique values for Filter variable (1),(2),(3) (this is to avoid performance issues):\",value = 500,min = 1,max = NA)\n \n })\n outputOptions(output, \"maxlevels\", suspendWhenHidden=FALSE)\n \n \n output$filtervar1 <- renderUI({\n df <-recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df) [ NUNIQUEDF < input$inmaxlevels ]\n selectInput(\"infiltervar1\" , \"Filter variable (1):\",c('None',NAMESTOKEEP ) )\n })\n \n output$filtervar2 <- renderUI({\n df <- recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df) [ NUNIQUEDF < input$inmaxlevels ]\n #NAMESTOKEEP<- NAMESTOKEEP[ NAMESTOKEEP!=input$infiltervar1 ]\n selectInput(\"infiltervar2\" , \"Filter variable (2):\",c('None',NAMESTOKEEP ) )\n })\n \n output$filtervar3 <- renderUI({\n df <- recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df) [ NUNIQUEDF < input$inmaxlevels ]\n #NAMESTOKEEP<- NAMESTOKEEP[ NAMESTOKEEP!=input$infiltervar1 ]# allow nested filters\n #NAMESTOKEEP<- NAMESTOKEEP[ NAMESTOKEEP!=input$infiltervar2 ]\n selectInput(\"infiltervar3\" , \"Filter variable (3):\",c('None',NAMESTOKEEP ) )\n })\n \n \n output$filtervarcont1 <- renderUI({\n df <-recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df)\n NAMESTOKEEP<- NAMESTOKEEP[ is.element ( NAMESTOKEEP,names(df[sapply(df,is.numeric)]))]\n selectInput(\"infiltervarcont1\" , \"Filter continuous (1):\",c('None',NAMESTOKEEP ) )\n })\n output$filtervarcont2 <- renderUI({\n df <-recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df) \n NAMESTOKEEP<- NAMESTOKEEP[ is.element ( NAMESTOKEEP,names(df[sapply(df,is.numeric)]))]\n selectInput(\"infiltervarcont2\" , \"Filter continuous (2):\",c('None',NAMESTOKEEP ) )\n })\n output$filtervarcont3 <- renderUI({\n df <-recodedata4()\n if (is.null(df)) return(NULL)\n NUNIQUEDF <- sapply(df, function(x) length(unique(x)))\n NAMESTOKEEP<- names(df) \n NAMESTOKEEP<- NAMESTOKEEP[ is.element ( NAMESTOKEEP,names(df[sapply(df,is.numeric)]))]\n selectInput(\"infiltervarcont3\" , \"Filter continuous (3):\",c('None',NAMESTOKEEP ) )\n })\n output$filtervar1values <- renderUI({\n df <-recodedata4()\n validate( need(!is.null(df), \"Please select a data set\"))\n \n if (is.null(df)) return(NULL)\n if(input$infiltervar1==\"None\") {return(NULL)}\n if(input$infiltervar1!=\"None\" ) {\n choices <- levels(as.factor(df[,input$infiltervar1]))\n selectInput('infiltervar1valuesnotnull',\n label = paste(\"Select values\", input$infiltervar1),\n choices = c(choices),\n selected = choices,\n multiple=TRUE, selectize=FALSE) \n }\n }) \n \n filterdata <- reactive({\n if (is.null(filedata())) return(NULL)\n df <- recodedata4()\n if (is.null(df)) return(NULL)\n if(is.null(input$infiltervar1)) {\n df <- df \n }\n if(!is.null(input$infiltervar1)&input$infiltervar1!=\"None\") {\n \n df <- df [ is.element(df[,input$infiltervar1],input$infiltervar1valuesnotnull),]\n }\n \n df\n })\n \n output$filtervar2values <- renderUI({\n df <- filterdata()\n if (is.null(df)) return(NULL)\n if(input$infiltervar2==\"None\") {\n selectInput('infiltervar2valuesnull',\n label ='No filter variable 2 specified', \n choices = list(\"\"),multiple=TRUE, selectize=FALSE) \n }\n if(input$infiltervar2!=\"None\"&!is.null(input$infiltervar2) ) {\n choices <- levels(as.factor(as.character(df[,input$infiltervar2])))\n selectInput('infiltervar2valuesnotnull',\n label = paste(\"Select values\", input$infiltervar2),\n choices = c(choices),\n selected = choices,\n multiple=TRUE, selectize=TRUE) \n }\n })\n \n filterdata2 <- reactive({\n df <- filterdata()\n if (is.null(df)) return(NULL)\n if(!is.null(input$infiltervar2)&input$infiltervar2!=\"None\") {\n df <- df [ is.element(df[,input$infiltervar2],input$infiltervar2valuesnotnull),]\n }\n if(input$infiltervar2==\"None\") {\n df \n }\n df\n }) \n output$filtervar3values <- renderUI({\n df <- filterdata2()\n if (is.null(df)) return(NULL)\n if(input$infiltervar3==\"None\") {\n selectInput('infiltervar3valuesnull',\n label ='No filter variable 2 specified', \n choices = list(\"\"),multiple=TRUE, selectize=FALSE) \n }\n if(input$infiltervar3!=\"None\"&!is.null(input$infiltervar3) ) {\n choices <- levels(as.factor(as.character(df[,input$infiltervar3])))\n selectInput('infiltervar3valuesnotnull',\n label = paste(\"Select values\", input$infiltervar3),\n choices = c(choices),\n selected = choices,\n multiple=TRUE, selectize=TRUE) \n }\n })\n \n filterdata3 <- reactive({\n df <- filterdata2()\n if (is.null(df)) return(NULL)\n if(!is.null(input$infiltervar3)&input$infiltervar3!=\"None\") {\n df <- df [ is.element(df[,input$infiltervar3],input$infiltervar3valuesnotnull),]\n }\n if(input$infiltervar3==\"None\") {\n df \n }\n df\n }) \n \n output$fslider1 <- renderUI({ \n df <- filterdata3()\n if (is.null(df)) return(NULL)\n xvariable<- input$infiltervarcont1\n if(input$infiltervarcont1==\"None\" ){\n return(NULL) \n }\n if (!is.numeric(df[,xvariable]) ) return(NULL)\n if(input$infiltervarcont1!=\"None\" ){\n sliderInput(\"infSlider1\", paste(\"Select\",xvariable,\"Range\"),\n min=min(df[,xvariable],na.rm=T),\n max=max(df[,xvariable],na.rm=T),\n value=c(min(df[,xvariable],na.rm=T),max(df[,xvariable],na.rm=T)) \n )\n } \n })\n filterdata4 <- reactive({\n df <- filterdata3()\n if (is.null(df)) return(NULL)\n if(input$infiltervarcont1!=\"None\" ){\n if(is.numeric( input$infSlider1[1]) & is.numeric(df[,input$infiltervarcont1])) {\n df <- df [!is.na(df[,input$infiltervarcont1]),]\n df <- df [df[,input$infiltervarcont1] >= input$infSlider1[1]&df[,input$infiltervarcont1] <= input$infSlider1[2],]\n }\n }\n \n df\n })\n output$fslider2 <- renderUI({ \n df <- filterdata4()\n if (is.null(df)) return(NULL)\n xvariable<- input$infiltervarcont2\n if(input$infiltervarcont2==\"None\" ){\n return(NULL) \n }\n if (!is.numeric(df[,xvariable]) ) return(NULL)\n if(input$infiltervarcont2!=\"None\" ){\n sliderInput(\"infSlider2\", paste(\"Select\",xvariable,\"Range\"),\n min=min(df[,xvariable],na.rm=T),\n max=max(df[,xvariable],na.rm=T),\n value=c(min(df[,xvariable],na.rm=T),max(df[,xvariable],na.rm=T)) \n )\n } \n })\n \n \n filterdata5 <- reactive({\n df <- filterdata4()\n if (is.null(df)) return(NULL)\n if(input$infiltervarcont2!=\"None\" ){\n if(is.numeric( input$infSlider2[1]) & is.numeric(df[,input$infiltervarcont2])) {\n df<- df [!is.na(df[,input$infiltervarcont2]),]\n df<-df [df[,input$infiltervarcont2] >= input$infSlider2[1]&df[,input$infiltervarcont2] <= input$infSlider2[2],]\n }\n }\n \n df\n })\n \n output$fslider3 <- renderUI({ \n df <- filterdata5()\n if (is.null(df)) return(NULL)\n xvariable<- input$infiltervarcont3\n if(input$infiltervarcont3==\"None\" ){\n return(NULL) \n }\n if (!is.numeric(df[,xvariable]) ) return(NULL)\n if(input$infiltervarcont3!=\"None\" ){\n sliderInput(\"infSlider3\", paste(\"Select\",xvariable,\"Range\"),\n min=min(df[,xvariable],na.rm=T),\n max=max(df[,xvariable],na.rm=T),\n value=c(min(df[,xvariable],na.rm=T),max(df[,xvariable],na.rm=T)) \n )\n } \n })\n \n \n filterdata6 <- reactive({\n df <- filterdata5()\n if (is.null(df)) return(NULL)\n if(input$infiltervarcont3!=\"None\" ){\n if(is.numeric( input$infSlider3[1]) & is.numeric(df[,input$infiltervarcont3])) {\n df<- df [!is.na(df[,input$infiltervarcont3]),]\n df<-df [df[,input$infiltervarcont3] >= input$infSlider3[1]&df[,input$infiltervarcont3] <= input$infSlider3[2],]\n }\n }\n \n df\n })\n \n outputOptions(output, \"filtervar1\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervar2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervar3\", suspendWhenHidden=FALSE)\n \n outputOptions(output, \"filtervarcont1\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervarcont2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervarcont3\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervar1values\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervar2values\", suspendWhenHidden=FALSE)\n outputOptions(output, \"filtervar3values\", suspendWhenHidden=FALSE)\n \n outputOptions(output, \"fslider1\", suspendWhenHidden=FALSE)\n outputOptions(output, \"fslider2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"fslider3\", suspendWhenHidden=FALSE)\n \n \n \n output$roundvar <- renderUI({\n df <- filterdata6()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [MODEDF]\n selectizeInput( \"roundvarin\", \"Round the Values to the Specified N Digits:\", choices = NAMESTOKEEP2,multiple=TRUE,\n options = list(\n placeholder = 'Please select some variables',\n onInitialize = I('function() { this.setValue(\"\"); }')\n )\n )\n \n }) \n outputOptions(output, \"roundvar\", suspendWhenHidden=FALSE)\n \n stackdata <- reactive({\n \n df <- filterdata6() \n \n if (is.null(df)) return(NULL)\n if (!is.null(df)){\n validate( need(!is.element(input$x,input$y) , \"Please select a different x variable or remove the x variable from the list of y variable(s)\"))\n # validate(\n # need(!is.null(length(input$y)| length(input$y) <1) , \n # \"Please select a at least one y variable\"))\n \n \n if( all( sapply(df[,as.vector(input$y)], is.numeric)) )\n {\n tidydata <- df %>%\n gather_( \"yvars\", \"yvalues\", gather_cols=as.vector(input$y) ) %>%\n mutate(combinedvariable=\"Choose two variables to combine first\")\n }\n if( any( sapply(df[,as.vector(input$y)], is.factor)) |\n any( sapply(df[,as.vector(input$y)], is.character)))\n {\n tidydata <- df %>%\n gather_( \"yvars\", \"yvalues\", gather_cols=as.vector(input$y) ) %>%\n mutate(yvalues=as.factor(as.factor(as.character(yvalues)) ))%>%\n mutate(combinedvariable=\"Choose two variables to combine first\")\n } \n \n if( all( sapply(df[,as.vector(input$y)], is.factor)) |\n all( sapply(df[,as.vector(input$y)], is.character)))\n {\n tidydata <- df %>%\n gather_( \"yvars\", \"yvalues\", gather_cols=as.vector(input$y) ) %>%\n mutate(yvalues=as.factor(as.character(yvalues) ))%>%\n mutate(combinedvariable=\"Choose two variables to combine first\")\n } \n \n \n }\n \n if( !is.null(input$pastevarin) ) {\n if (length(input$pastevarin) > 1) {\n tidydata <- tidydata %>%\n unite_(\"combinedvariable\" , c(input$pastevarin[1], input$pastevarin[2] ),\n remove=FALSE)\n }\n }\n \n tidydata\n })\n \n rounddata <- reactive({\n if (is.null(df)) return(NULL)\n df <- stackdata()\n if(length(input$roundvarin ) >=1) {\n for (i in 1:length(input$roundvarin ) ) {\n varname<- input$roundvarin[i]\n df[,varname] <- round( df[,varname],input$rounddigits)\n }\n }\n df\n }) \n \n \n output$reordervar <- renderUI({\n df <- rounddata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [ !MODEDF ]\n selectizeInput( \"reordervarin\", 'Reorder This Variable By:',\n choices =NAMESTOKEEP2 ,multiple=FALSE,\n options = list( placeholder = 'Please select a variable',\n onInitialize = I('function() { this.setValue(\"\"); }')\n )\n )\n })\n \n \n \n output$variabletoorderby <- renderUI({\n if (is.null(df)) return(NULL)\n if (length(input$reordervarin ) <1) return(NULL)\n if ( input$reordervarin!=\"\"){\n df <-rounddata()\n yinputs <- input$y\n items=names(df)\n names(items)=items\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP2<- names(df) [ MODEDF ]\n selectInput('varreorderin',label = 'Of this Variable:', choices=NAMESTOKEEP2,multiple=FALSE)\n }\n })\n \n \n\n \n outputOptions(output, \"reordervar\", suspendWhenHidden=FALSE)\n outputOptions(output, \"variabletoorderby\", suspendWhenHidden=FALSE)\n \n \n \n reorderdata <- reactive({\n df <- rounddata()\n if (is.null(df)) return(NULL)\n \n if(length(input$reordervarin ) >=1 &\n length(input$varreorderin ) >=1 & input$reordervarin!=\"\" ) {\n varname<- input$reordervarin[1]\n if(input$functionordervariable==\"Median\" ) {\n df[,varname] <- reorder( df[,varname],df[,input$varreorderin], FUN=function(x) median(x[!is.na(x)]))\n }\n if(input$functionordervariable==\"Mean\" ) {\n df[,varname] <- reorder( df[,varname],df[,input$varreorderin], FUN=function(x) mean(x[!is.na(x)]))\n }\n if(input$functionordervariable==\"Minimum\" ) {\n df[,varname] <- reorder( df[,varname],df[,input$varreorderin], FUN=function(x) min(x[!is.na(x)]))\n }\n if(input$functionordervariable==\"Maximum\" ) {\n df[,varname] <- reorder( df[,varname],df[,input$varreorderin], FUN=function(x) max(x[!is.na(x)]))\n }\n if(input$reverseorder ) {\n df[,varname] <- factor( df[,varname], levels=rev(levels( df[,varname])))\n \n }\n }\n df\n }) \n \n \n output$reordervar2 <- renderUI({\n df <- reorderdata()\n if (is.null(df)) return(NULL)\n MODEDF <- sapply(df, function(x) is.numeric(x))\n NAMESTOKEEP<- names(df) [ !MODEDF ]\n if(length(input$reordervarin ) >=1 ){\n NAMESTOKEEP<- NAMESTOKEEP [ NAMESTOKEEP!=input$reordervarin ]\n \n }\n selectInput(\"reordervar2in\" , \"Custom Reorder this variable:\",c('None',NAMESTOKEEP ) )\n })\n\n output$reordervar2values <- renderUI({\n df <- reorderdata()\n if (is.null(df)) return(NULL)\n if(input$reordervar2in==\"None\") {\n selectInput('reordervar2valuesnull',\n label ='No reorder variable specified', \n choices = list(\"\"),multiple=TRUE, selectize=FALSE) \n }\n if(input$reordervar2in!=\"None\"&!is.null(input$reordervar2in) ) {\n choices <- levels(as.factor(as.character(df[,input$reordervar2in])))\n selectizeInput('reordervar2valuesnotnull',\n label = paste(\"Drag/Drop to reorder\",input$reordervar2in, \"values\"),\n choices = c(choices),\n selected = choices,\n multiple=TRUE, options = list(\n plugins = list('drag_drop')\n )\n ) \n }\n })\n outputOptions(output, \"reordervar2\", suspendWhenHidden=FALSE)\n outputOptions(output, \"reordervar2values\", suspendWhenHidden=FALSE)\n \n reorderdata2 <- reactive({\n df <- reorderdata()\n if (is.null(df)) return(NULL)\n \n if(input$reordervar2in!=\"None\" ) {\ndf [,input$reordervar2in] <- factor(df [,input$reordervar2in],\n levels = input$reordervar2valuesnotnull)\n\n}\n df\n })\n \n output$xaxiszoom <- renderUI({\n df <-reorderdata2()\n if (is.null(df)| !is.numeric(df[,input$x] ) ) return(NULL)\n if (is.numeric(df[,input$x]) &\n input$facetscalesin!=\"free_x\"&\n input$facetscalesin!=\"free\"){\n xvalues <- df[,input$x][!is.na( df[,input$x])]\n xmin <- min(xvalues)\n xmax <- max(xvalues)\n xstep <- (xmax -xmin)/100\n sliderInput('xaxiszoomin',label = 'Zoom to X variable range:', min=xmin, max=xmax, value=c(xmin,xmax),step=xstep)\n \n }\n \n \n })\n outputOptions(output, \"xaxiszoom\", suspendWhenHidden=FALSE)\n \n \n output$colour <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"colorin\", \"Colour By:\",c(\"None\",items,\"yvars\", \"yvalues\",\"combinedvariable\") )\n \n })\n \n \n output$group <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items \n \n if (input$boxplotaddition ){\n items= c(input$x,\"None\",items[items!=input$x], \"yvars\",\"yvalues\",\"combinedvariable\") \n }\n if (!input$boxplotaddition ){\n items= c(\"None\",input$x,items[items!=input$x],\"yvars\", \"yvalues\",\"combinedvariable\") \n }\n selectInput(\"groupin\", \"Group By:\",items)\n })\n \n \n output$facet_col <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"facetcolin\", \"Column Split:\",c(None='.',items,\"yvars\", \"yvalues\",\"combinedvariable\"))\n })\n output$facet_row <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"facetrowin\", \"Row Split:\", c(None=\".\",items,\"yvars\", \"yvalues\",\"combinedvariable\"))\n })\n \n output$facet_col_extra <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"facetcolextrain\", \"Extra Column Split:\",c(None='.',items,\"yvars\", \"yvalues\",\"combinedvariable\"))\n })\n output$facet_row_extra <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n if (length(input$y) > 1 ){\n items= c(\"yvars\",None=\".\",items, \"yvalues\",\"combinedvariable\") \n }\n if (length(input$y) < 2 ){\n items= c(None=\".\",items,\"yvars\", \"yvalues\",\"combinedvariable\") \n }\n selectInput(\"facetrowextrain\", \"Extra Row Split:\",items)\n })\n \n \n output$facetscales <- renderUI({\n if (length(input$y) > 1 ){\n items= c(\"free_y\",\"fixed\",\"free_x\",\"free\") \n }\n if (length(input$y) < 2 ){\n items= c(\"fixed\",\"free_x\",\"free_y\",\"free\") \n }\n selectInput('facetscalesin','Facet Scales:',items)\n })\n outputOptions(output, \"facetscales\", suspendWhenHidden=FALSE)\n \n \n \n output$pointsize <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"pointsizein\", \"Size By:\",c(\"None\",items,\"yvars\", \"yvalues\",\"combinedvariable\") )\n \n })\n \n output$fill <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"fillin\", \"Fill By:\" ,c(\"None\",items,\"yvars\", \"yvalues\",\"combinedvariable\") )\n })\n \n output$weight <- renderUI({\n df <-filedata()\n if (is.null(df)) return(NULL)\n items=names(df)\n names(items)=items\n items= items #[!is.element(items,input$y)]\n selectInput(\"weightin\", \"Weight By:\",c(\"None\",items,\"yvars\", \"yvalues\",\"combinedvariable\") )\n })\n outputOptions(output, \"weight\", suspendWhenHidden=FALSE)\n \n \n output$mytablex = renderDataTable({\n datatable( recodedata4() , # reorderdata2\n extensions = c('ColReorder','Buttons','FixedColumns'),\n options = list(dom = 'Bfrtip',\n searchHighlight = TRUE,\n pageLength=-1 ,\n lengthMenu = list(c(5, 10, 15, -1), c('5','10', '15', 'All')),\n colReorder = list(realtime = TRUE),\n buttons = \n list('colvis', 'pageLength','print','copy', list(\n extend = 'collection',\n buttons = list(\n list(extend='csv' ,filename = 'plotdata'),\n list(extend='excel',filename = 'plotdata'),\n list(extend='pdf' ,filename = 'plotdata')),\n text = 'Download'\n )),\n scrollX = TRUE,scrollY = 400,\n fixedColumns = TRUE\n ), \n filter = 'bottom',\n style = \"bootstrap\")\n })\n \n \n \n plotObject <- reactive({\n validate(\n need(!is.null(reorderdata2()), \"Please select a data set\") \n )\n \n plotdata <- reorderdata2()\n \n \n if(!is.null(plotdata)) {\n \n if (input$themetableau){\n scale_colour_discrete <- function(...) \n scale_colour_manual(..., values = tableau10,drop=!input$themecolordrop)\n scale_fill_discrete <- function(...) \n scale_fill_manual(..., values = tableau10,drop=!input$themecolordrop)\n }\n \n p <- ggplot(plotdata, aes_string(x=input$x, y=\"yvalues\")) \n \n if (input$colorin != 'None')\n p <- p + aes_string(color=input$colorin)\n if (input$fillin != 'None')\n p <- p + aes_string(fill=input$fillin)\n if (input$pointsizein != 'None')\n p <- p + aes_string(size=input$pointsizein)\n \n # if (input$groupin != 'None' & !is.factor(plotdata[,input$x]))\n if (input$groupin != 'None')\n p <- p + aes_string(group=input$groupin)\n if (input$groupin == 'None' & !is.numeric(plotdata[,input$x]) \n & input$colorin == 'None')\n p <- p + aes(group=1)\n \n if (input$Points==\"Points\"&input$pointsizein == 'None'&!input$pointignorecol)\n p <- p + geom_point(,alpha=input$pointstransparency,shape=input$pointtypes,size=input$pointsizes) \n if (input$Points==\"Points\"&input$pointsizein != 'None'&!input$pointignorecol)\n p <- p + geom_point(,alpha=input$pointstransparency,shape=input$pointtypes)\n \n if (input$Points==\"Jitter\"&input$pointsizein == 'None'&!input$pointignorecol)\n p <- p + geom_jitter(,alpha=input$pointstransparency,shape=input$pointtypes,size=input$pointsizes)\n if (input$Points==\"Jitter\"&input$pointsizein != 'None'&!input$pointignorecol)\n p <- p + geom_jitter(,alpha=input$pointstransparency,shape=input$pointtypes)\n \n \n if (input$Points==\"Points\"&input$pointsizein == 'None'&input$pointignorecol)\n p <- p + geom_point(,alpha=input$pointstransparency,shape=input$pointtypes,size=input$pointsizes,colour=input$colpoint) \n if (input$Points==\"Points\"&input$pointsizein != 'None'&input$pointignorecol)\n p <- p + geom_point(,alpha=input$pointstransparency,shape=input$pointtypes,colour=input$colpoint)\n \n if (input$Points==\"Jitter\"&input$pointsizein == 'None'&input$pointignorecol)\n p <- p + geom_jitter(,alpha=input$pointstransparency,shape=input$pointtypes,size=input$pointsizes,colour=input$colpoint)\n if (input$Points==\"Jitter\"&input$pointsizein != 'None'&input$pointignorecol)\n p <- p + geom_jitter(,alpha=input$pointstransparency,shape=input$pointtypes,colour=input$colpoint)\n \n \n \n if (input$line==\"Lines\"&input$pointsizein == 'None'& !input$lineignorecol)\n p <- p + geom_line(,size=input$linesize,alpha=input$linestransparency,linetype=input$linetypes)\n if (input$line==\"Lines\"&input$pointsizein != 'None'& !input$lineignorecol)\n p <- p + geom_line(,alpha=input$linestransparency,linetype=input$linetypes)\n if (input$line==\"Lines\"&input$pointsizein == 'None'&input$lineignorecol)\n p <- p + geom_line(,size=input$linesize,alpha=input$linestransparency,linetype=input$linetypes,colour=input$colline)\n if (input$line==\"Lines\"&input$pointsizein != 'None'& input$lineignorecol)\n p <- p + geom_line(,alpha=input$linestransparency,linetype=input$linetypes,colour=input$colline)\n \n \n if (input$boxplotaddition){\n if (input$groupin != 'None'& !input$boxplotignoregroup ){\n p <- p + aes_string(group=input$groupin)\n p <- p + geom_boxplot()\n }\n if (input$groupin == 'None'){\n p <- p + geom_boxplot(aes(group=NULL))\n } \n if (input$boxplotignoregroup ){\n p <- p + geom_boxplot(aes(group=NULL))\n } \n \n \n }\n \n \n ###### Mean section START \n \n \n if (!input$meanignoregroup) {\n if (!input$meanignorecol) {\n \n if (input$Mean==\"Mean\") {\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\")\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",size=input$meanlinesize)\n \n \n if(input$meanpoints) \n p <- p + \n stat_sum_single(mean, geom = \"point\")\n \n }\n \n if (input$Mean==\"Mean (95% CI)\"){\n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"errorbar\",fun.args=list(conf.int=input$CI),width=input$errbar)\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\")\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\",size=input$meanlinesize)\n if(input$meanpoints) \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"point\")\n \n }\n }\n \n \n if (input$meanignorecol) {\n meancol <- input$colmean\n if (input$Mean==\"Mean\") {\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",col=meancol)\n \n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",col=meancol,size=input$meanlinesize)\n \n if(input$meanpoints) \n p <- p + \n stat_sum_single(mean, geom = \"point\",col=meancol)\n \n }\n \n if (input$Mean==\"Mean (95% CI)\"){\n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"errorbar\",fun.args=list(conf.int=input$CI),width=input$errbar, col=meancol)\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\", col=meancol)\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\", col=meancol,size=input$meanlinesize)\n \n if(input$meanpoints) \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"point\", col=meancol)\n \n }\n }\n }\n \n if (input$meanignoregroup) {\n if (!input$meanignorecol) {\n \n if (input$Mean==\"Mean\") {\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",aes(group=NULL))\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",aes(group=NULL),size=input$meanlinesize)\n \n if(input$meanpoints) \n p <- p + \n stat_sum_single(mean, geom = \"point\",aes(group=NULL))\n \n }\n \n if (input$Mean==\"Mean (95% CI)\"){\n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"errorbar\",fun.args=list(conf.int=input$CI), width=input$errbar,aes(group=NULL))\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\",aes(group=NULL))\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\",aes(group=NULL),size=input$meanlinesize)\n if(input$meanpoints) \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"point\",aes(group=NULL))\n \n }\n }\n \n \n if (input$meanignorecol) {\n meancol <- input$colmean\n if (input$Mean==\"Mean\") {\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",col=meancol,aes(group=NULL))\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(mean, geom = \"line\",col=meancol,aes(group=NULL),size=input$meanlinesize)\n \n \n if(input$meanpoints) \n p <- p + \n stat_sum_single(mean, geom = \"point\",col=meancol,aes(group=NULL))\n \n }\n \n if (input$Mean==\"Mean (95% CI)\"){\n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"errorbar\",fun.args=list(conf.int=input$CI), width=input$errbar, col=meancol, aes(group=NULL))\n if(input$meanlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\",col=meancol,aes(group=NULL))\n if(input$meanlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"line\",col=meancol,aes(group=NULL),size=input$meanlinesize)\n \n if(input$meanpoints) \n p <- p + \n stat_sum_df(\"mean_cl_normal\", geom = \"point\",col=meancol,aes(group=NULL))\n \n }\n }\n }\n ###### Mean section END \n \n ###### Smoothing Section START\n if(!is.null(input$Smooth) ){\n familyargument <- ifelse(input$smoothmethod==\"glm\",\"binomial\",\"gaussian\") \n \n if ( input$ignoregroup) {\n if (!input$ignorecol) {\n spanplot <- input$loessens\n if (input$Smooth==\"Smooth\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,aes(group=NULL))\n \n if (input$Smooth==\"Smooth and SE\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,aes(group=NULL))\n \n if (input$Smooth==\"Smooth\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,aes(group=NULL))+ \n aes_string(weight=input$weightin)\n \n if (input$Smooth==\"Smooth and SE\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,aes(group=NULL))+ \n aes_string(weight=input$weightin)\n }\n if (input$ignorecol) {\n spanplot <- input$loessens\n colsmooth <- input$colsmooth\n if (input$Smooth==\"Smooth\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,col=colsmooth,aes(group=NULL))\n \n if (input$Smooth==\"Smooth and SE\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,col=colsmooth,aes(group=NULL))\n \n if (input$Smooth==\"Smooth\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,col=colsmooth,aes(group=NULL))+ \n aes_string(weight=input$weightin)\n \n if (input$Smooth==\"Smooth and SE\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,col=colsmooth,aes(group=NULL))+ \n aes_string(weight=input$weightin)\n }\n \n }\n \n if ( !input$ignoregroup) {\n if (!input$ignorecol) {\n spanplot <- input$loessens\n if (input$Smooth==\"Smooth\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot)\n \n if (input$Smooth==\"Smooth and SE\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot)\n \n if (input$Smooth==\"Smooth\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot)+ \n aes_string(weight=input$weightin)\n \n if (input$Smooth==\"Smooth and SE\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot)+ \n aes_string(weight=input$weightin)\n }\n if (input$ignorecol) {\n spanplot <- input$loessens\n colsmooth <- input$colsmooth\n if (input$Smooth==\"Smooth\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,col=colsmooth)\n \n if (input$Smooth==\"Smooth and SE\")\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,col=colsmooth)\n \n if (input$Smooth==\"Smooth\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=F,span=spanplot,col=colsmooth)+ \n aes_string(weight=input$weightin)\n \n if (input$Smooth==\"Smooth and SE\"& input$weightin != 'None')\n p <- p + geom_smooth(method=input$smoothmethod,\n method.args = list(family = familyargument),\n size=1.5,se=T,span=spanplot,col=colsmooth)+ \n aes_string(weight=input$weightin)\n }\n \n }\n \n ###### smooth Section END\n }\n \n \n ###### Median PI section START \n if (!input$medianignoregroup) {\n \n if (!input$medianignorecol) {\n \n if (input$Median==\"Median\") {\n if(input$medianlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\")\n \n if(input$medianlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",size=input$medianlinesize)\n \n \n if(input$medianpoints) \n p <- p + \n stat_sum_single(median, geom = \"point\")\n \n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein == 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI) ,size=input$medianlinesize,alpha=input$PItransparency,col=NA)+ \n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI) ,size=input$medianlinesize,alpha=0)\n \n if ( input$sepguides )\n p <- p + \n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n \n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein != 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI), alpha=input$PItransparency,col=NA)+\n stat_sum_df(\"median_hilow\", geom = \"smooth\" ,fun.args=list(conf.int=input$PI),alpha=0)\n \n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n \n }\n \n if (input$Median!=\"None\" & input$medianvalues ) {\n p <- p +\n stat_summary(fun.data = median.n,geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\",\n show.legend=FALSE,size=6)}\n if (input$Median!=\"None\" & input$medianN) {\n p <- p +\n stat_summary(fun.data = give.n, geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\", \n show.legend=FALSE,size=6) \n } \n }\n \n \n \n if (input$medianignorecol) {\n mediancol <- input$colmedian\n if (input$Median==\"Median\") {\n if(input$medianlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",col=mediancol)\n \n if(input$medianlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",col=mediancol,size=input$medianlinesize)\n \n if(input$medianpoints) \n p <- p + \n stat_sum_single(median, geom = \"point\",col=mediancol)\n \n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein == 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\", fun.args=list(conf.int=input$PI), alpha=input$PItransparency,col=NA)+\n stat_sum_df(\"median_hilow\", geom = \"smooth\", fun.args=list(conf.int=input$PI), size=input$medianlinesize,col=mediancol,alpha=0)\n \n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n if (input$Median==\"Median/PI\"&input$pointsizein != 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI),alpha=input$PItransparency,col=NA)+\n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI),col=mediancol,\n alpha=0) \n \n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n if (input$Median!=\"None\" & input$medianvalues ) {\n p <- p +\n stat_summary(fun.data = median.n,geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\",colour=mediancol,\n show.legend=FALSE,size=6)}\n if (input$Median!=\"None\" & input$medianN) {\n p <- p +\n stat_summary(fun.data = give.n, geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\", colour=mediancol,\n show.legend=FALSE,size=6) \n } \n \n }\n }\n \n \n if (input$medianignoregroup) {\n if (!input$medianignorecol) {\n if (input$Median==\"Median\") {\n if(input$medianlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",aes(group=NULL))\n if(input$medianlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",aes(group=NULL),size=input$medianlinesize)\n \n if(input$medianpoints) \n p <- p + \n stat_sum_single(median, geom = \"point\",aes(group=NULL))\n \n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein == 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI),aes(group=NULL),alpha=input$PItransparency,col=NA)+ \n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI),aes(group=NULL),size=input$medianlinesize,alpha=0) \n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein != 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI),aes(group=NULL),alpha=input$PItransparency,col=NA)+ \n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI),aes(group=NULL),alpha=0)\n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n if (input$Median!=\"None\" & input$medianvalues ) {\n p <- p +\n stat_summary(fun.data = median.n, aes(group=NULL),geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\",fill=\"white\",\n show.legend=FALSE,\n size=6)}\n if (input$Median!=\"None\" & input$medianN) {\n p <- p +\n stat_summary(fun.data = give.n, aes(group=NULL), geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\", fill=\"white\",\n show.legend=FALSE,size=6) \n }\n \n \n }\n \n \n if (input$medianignorecol) {\n mediancol <- input$colmedian\n if (input$Median==\"Median\") {\n if(input$medianlines&input$pointsizein != 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",col=mediancol,aes(group=NULL))\n if(input$medianlines&input$pointsizein == 'None') \n p <- p + \n stat_sum_single(median, geom = \"line\",col=mediancol,aes(group=NULL),size=input$medianlinesize)\n \n if(input$medianpoints) \n p <- p + \n stat_sum_single(median, geom = \"point\",col=mediancol,aes(group=NULL))\n \n }\n \n if (input$Median==\"Median/PI\"&input$pointsizein == 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI),aes(group=NULL),alpha=input$PItransparency,col=NA)+ \n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI),col=mediancol,aes(group=NULL),size=input$medianlinesize,alpha=0)\n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n if (input$Median==\"Median/PI\"&input$pointsizein != 'None'){\n p <- p + \n stat_sum_df(\"median_hilow\", geom = \"ribbon\",fun.args=list(conf.int=input$PI),aes(group=NULL),alpha=input$PItransparency,col=NA)+ \n stat_sum_df(\"median_hilow\", geom = \"smooth\",fun.args=list(conf.int=input$PI),col=mediancol,aes(group=NULL),alpha=0)\n \n \n \n if ( input$sepguides )\n p <- p +\n guides(\n color = guide_legend(paste(\"Median\"),\n override.aes = list(shape =NA,fill=NA)),\n fill = guide_legend(paste( 100*input$PI,\"% prediction interval\"),\n override.aes = list(shape =NA ,linetype =0,alpha=0.5 )\n ) )\n }\n \n \n if (input$Median!=\"None\" & input$medianvalues ) {\n p <- p +\n stat_summary(fun.data = median.n, aes(group=NULL),geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\",colour=mediancol,\n show.legend=FALSE,size=6)}\n if (input$Median!=\"None\" & input$medianN) {\n p <- p +\n stat_summary(fun.data = give.n, aes(group=NULL), geom = \"label_repel\",alpha=0.1,\n fun.y = median, fontface = \"bold\", colour=mediancol,\n show.legend=FALSE,size=6) \n }\n \n }\n }\n \n \n \n ###### Median PI section END\n \n \n \n ###### RQSS SECTION START \n if (!input$ignoregroupqr) {\n if (!input$ignorecolqr) {\n if (input$Tauvalue) {\n if(!input$hidedynamic){\n p <- p + stat_quantile(method = \"rqss\",quantiles =input$Tau,size=1.5,\n linetype=\"solid\", \n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty)) \n }\n \n if (input$mid)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.5,size=1.5,\n linetype=\"solid\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$ninetieth)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.90,size=1,\n linetype=\"dashed\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$tenth)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.1,size=1,\n linetype=\"dashed\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$up)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.95,size=1,\n linetype=\"dashed\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$low) \n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.05,size=1,\n linetype=\"dashed\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n \n \n }\n }\n if (input$ignorecolqr) {\n colqr <- input$colqr\n if (input$Tauvalue) {\n if(!input$hidedynamic){\n p <- p + stat_quantile(method = \"rqss\",quantiles =input$Tau,size=1.5,\n linetype=\"solid\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty)) \n }\n \n \n if (input$mid)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.5,size=1.5,\n linetype=\"solid\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$ninetieth)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.90,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$tenth)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.1,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$up)\n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.95,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$low) \n p <- p + stat_quantile(method = \"rqss\",quantiles = 0.05,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n \n \n }\n }\n }\n \n \n if (input$ignoregroupqr) {\n if (!input$ignorecolqr) {\n if (input$Tauvalue) {\n if(!input$hidedynamic){\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles =input$Tau,size=1.5,\n linetype=\"solid\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty)) \n }\n \n if (input$mid)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.5,size=1.5,\n linetype=\"solid\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$ninetieth)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.90,size=1,\n linetype=\"dashed\", \n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$tenth)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.1,size=1,\n linetype=\"dashed\",\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$up)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.95,size=1,\n linetype=\"dashed\", \n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$low) \n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.05,size=1,\n linetype=\"dashed\", \n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n }\n }\n if (input$ignorecolqr) {\n colqr <- input$colqr\n if (input$Tauvalue) {\n if(!input$hidedynamic){\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles =input$Tau,size=1.5,\n linetype=\"solid\",col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty)) \n }\n \n \n if (input$mid)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.5,size=1.5,\n linetype=\"solid\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$ninetieth)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.90,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n if (input$tenth)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.1,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$up)\n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.95,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n if (input$low) \n p <- p + stat_quantile(aes(group=NULL),method = \"rqss\",quantiles = 0.05,size=1,\n linetype=\"dashed\", col=colqr,\n formula=y ~ qss(x, constraint= input$Constraints,\n lambda=input$Penalty))\n \n }\n }\n }\n \n \n ###### RQSS SECTION END\n \n ###### KM SECTION START\n \n if (input$KM!=\"None\") {\n p <- ggplot(plotdata, aes_string(time=input$x, status=\"yvalues\")) \n if (input$colorin != 'None')\n p <- p + aes_string(color=input$colorin)\n if (input$fillin != 'None')\n p <- p + aes_string(fill=input$fillin)\n if (input$groupin != 'None' & !is.factor(plotdata[,input$x]))\n p <- p + aes_string(group=input$groupin)\n }\n \n if (input$KM==\"KM/CI\") {\n p <- p +\n geom_kmband(alpha=input$KMCItransparency,conf.int = input$KMCI,trans=input$KMtrans) }\n \n \n if (input$KM!=\"None\") {\n p <- p +\n geom_smooth(stat=\"km\",trans=input$KMtrans)\n }\n if (input$censoringticks) {\n p <- p +\n geom_kmticks(trans=input$KMtrans)\n }\n \n \n \n \n ###### KM SECTION END\n \n \n facets <- paste(input$facetrowin,'~', input$facetcolin)\n \n if (input$facetrowextrain !=\".\"&input$facetrowin !=\".\"){\n facets <- paste(input$facetrowextrain ,\"+\", input$facetrowin, '~', input$facetcolin)\n } \n if (input$facetrowextrain !=\".\"&input$facetrowin ==\".\"){\n facets <- paste( input$facetrowextrain, '~', input$facetcolin)\n } \n \n if (input$facetcolextrain !=\".\"){\n facets <- paste( facets, \"+\",input$facetcolextrain)\n } \n if (facets != '. ~ .')\n p <- p + facet_grid(facets,scales=input$facetscalesin,space=input$facetspace\n ,labeller=input$facetlabeller,margins=input$facetmargin )\n \n if (facets != '. ~ .' & input$facetswitch!=\"\" )\n \n p <- p + facet_grid(facets,scales=input$facetscalesin,space=input$facetspace,\n switch=input$facetswitch\n , labeller=input$facetlabeller,\n margins=input$facetmargin )\n \n if (facets != '. ~ .'&input$facetwrap) {\n p <- p + facet_wrap( c(input$facetrowextrain ,input$facetrowin,input$facetcolin,input$facetcolextrain ) [\n c(input$facetrowextrain ,input$facetrowin,input$facetcolin,input$facetcolextrain )!=\".\"]\n ,scales=input$facetscalesin)\n \n if (input$facetwrap&input$customncolnrow) {\n p <- p + facet_wrap( c(input$facetrowextrain ,input$facetrowin,input$facetcolin,input$facetcolextrain ) [\n c(input$facetrowextrain ,input$facetrowin,input$facetcolin,input$facetcolextrain )!=\".\"]\n ,scales=input$facetscalesin,ncol=input$wrapncol,nrow=input$wrapnrow)\n }\n }\n \n \n \n \n if (input$logy)\n p <- p + scale_y_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n labels = trans_format(\"log10\", math_format(10^.x))) \n \n if (input$logx)\n p <- p + scale_x_log10(breaks = trans_breaks(\"log10\", function(x) 10^x),\n labels = trans_format(\"log10\", math_format(10^.x))) \n \n \n \n if (input$scientificy )\n p <- p + \n scale_y_continuous(labels=comma )\n \n if (input$scientificx )\n p <- p + \n scale_x_continuous(labels=comma) \n \n \n \n \n if (length(input$y) >= 2 & input$ylab==\"\" ){\n p <- p + ylab(\"Y variable(s)\")\n }\n if (length(input$y) < 2 & input$ylab==\"\" ){\n p <- p + ylab(input$y)\n }\n \n if (input$xlab!=\"\")\n p <- p + xlab(input$xlab)\n if (input$ylab!=\"\")\n p <- p + ylab(input$ylab)\n \n \n if (input$horizontalzero)\n p <- p+\n geom_hline(aes(yintercept=0))\n \n if (input$customline1)\n p <- p+\n geom_vline(xintercept=input$vline)\n \n \n if (input$customline2)\n p <- p+\n geom_hline(yintercept=input$hline)\n \n \n \n if (input$identityline)\n p <- p+ geom_abline(intercept = 0, slope = 1)\n \n if (input$themebw) {\n p <- p+\n theme_bw(base_size=input$themebasesize) \n }\n \n \n if (!input$themebw){\n p <- p +\n theme_gray(base_size=input$themebasesize)+\n theme( \n #axis.title.y = element_text(size = rel(1.5)),\n #axis.title.x = element_text(size = rel(1.5))#,\n #strip.text.x = element_text(size = 16),\n #strip.text.y = element_text(size = 16)\n )\n }\n \n \n p <- p+theme(\n legend.position=input$legendposition,\n legend.box=input$legendbox,\n legend.direction=input$legenddirection,\n panel.background = element_rect(fill=input$backgroundcol))\n \n if (input$labelguides)\n p <- p+\n theme(legend.title=element_blank())\n if (input$themeaspect)\n p <- p+\n theme(aspect.ratio=input$aspectratio)\n if (!input$themetableau){\n p <- p +\n scale_colour_hue(drop=!input$themecolordrop)+\n scale_fill_hue(drop=!input$themecolordrop)\n }\n \n if (grepl(\"^\\\\s+$\", input$ylab) ){\n p <- p + theme(\n axis.title.y=element_blank())\n }\n if (grepl(\"^\\\\s+$\", input$xlab) ){\n p <- p + theme(\n axis.title.x=element_blank())\n }\n \n if (input$rotatexticks ){\n p <- p+\n theme(axis.text.x = element_text(angle = input$xticksrotateangle,\n hjust = input$xtickshjust,\n vjust = input$xticksvjust) )\n \n }\n if (input$rotateyticks ){\n p <- p+\n theme(axis.text.y = element_text(angle = input$yticksrotateangle,\n hjust = input$ytickshjust,\n vjust = input$yticksvjust) ) \n } \n \n if (!is.null(input$xaxiszoomin[1])&\n is.numeric(plotdata[,input$x] )&\n input$facetscalesin!=\"free_x\"&\n input$facetscalesin!=\"free\"\n ){\n p <- p +\n coord_cartesian(xlim= c(input$xaxiszoomin[1],input$xaxiszoomin[2]) )\n }\n \n #p <- ggplotly(p)\n p\n }\n })\n \n output$plot <- renderPlot({\n plotObject()\n })\n \n \n output$ui_plot <- renderUI({ \n plotOutput('plot', width = \"100%\" ,height = input$height,\n click = \"plot_click\",\n hover = hoverOpts(id = \"plot_hover\", delayType = \"throttle\"),\n brush = brushOpts(id = \"plot_brush\"))\n })\n \n output$plotinfo <- renderPrint({\n df<- reorderdata2() \n if (is.null(df)) return(NULL)\n nearPoints( reorderdata2(), input$plot_click, threshold = 5, maxpoints = 5,\n addDist = TRUE) #,xvar=input$x, yvar=input$y\n })\n \n \n output$clickheader <- renderUI({\n df <-reorderdata2()\n if (is.null(df)) return(NULL)\n h4(\"Clicked points\")\n })\n \n output$brushheader <- renderUI({\n df <- reorderdata2()\n if (is.null(df)) return(NULL)\n h4(\"Brushed points\")\n \n })\n \n output$plot_clickedpoints <- renderTable({\n # For base graphics, we need to specify columns, though for ggplot2,\n # it's usually not necessary.\n df<- reorderdata2() \n if (is.null(df)) return(NULL)\n \n res <- nearPoints(reorderdata2(), input$plot_click, input$x, \"yvalues\")\n if (nrow(res) == 0|is.null(res))\n return(NULL)\n res\n })\n output$plot_brushedpoints <- renderTable({\n df<- reorderdata2() \n if (is.null(df)) return(NULL)\n res <- brushedPoints(reorderdata2(), input$plot_brush, input$x,\"yvalues\")\n if (nrow(res) == 0|is.null(res))\n return(NULL)\n res\n })\n \n \n \n \n downloadPlotType <- reactive({\n input$downloadPlotType \n })\n \n observe({\n plotType <- input$downloadPlotType\n plotTypePDF <- plotType == \"pdf\"\n plotUnit <- ifelse(plotTypePDF, \"inches\", \"pixels\")\n plotUnitDef <- ifelse(plotTypePDF, 7, 480)\n \n updateNumericInput(\n session,\n inputId = \"downloadPlotHeight\",\n label = sprintf(\"Height (%s)\", plotUnit),\n value = plotUnitDef)\n \n updateNumericInput(\n session,\n inputId = \"downloadPlotWidth\",\n label = sprintf(\"Width (%s)\", plotUnit),\n value = plotUnitDef)\n \n })\n \n \n # Get the download dimensions.\n downloadPlotHeight <- reactive({\n input$downloadPlotHeight\n })\n \n downloadPlotWidth <- reactive({\n input$downloadPlotWidth\n })\n \n # Get the download file name.\n downloadPlotFileName <- reactive({\n input$downloadPlotFileName\n })\n \n # Include a downloadable file of the plot in the output list.\n output$downloadPlot <- downloadHandler(\n filename = function() {\n paste(downloadPlotFileName(), downloadPlotType(), sep=\".\") \n },\n # The argument content below takes filename as a function\n # and returns what's printed to it.\n content = function(con) {\n # Gets the name of the function to use from the \n # downloadFileType reactive element. Example:\n # returns function pdf() if downloadFileType == \"pdf\".\n plotFunction <- match.fun(downloadPlotType())\n plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())\n print(plotObject())\n dev.off(which=dev.cur())\n }\n )\n \n \n}\n\nshinyApp(ui = ui, server = server, options = list(height = 1000))\n", "meta": {"hexsha": "29980602077ca7d0cdb50976daa8794ccf9cfbf9", "size": 112357, "ext": "r", "lang": "R", "max_stars_repo_path": "app.r", "max_stars_repo_name": "Sage-Bionetworks/ggplotwithyourdata", "max_stars_repo_head_hexsha": "1d2e133861399d0a580089857dc6d2f153340204", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "app.r", "max_issues_repo_name": "Sage-Bionetworks/ggplotwithyourdata", "max_issues_repo_head_hexsha": "1d2e133861399d0a580089857dc6d2f153340204", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-10-05T15:00:54.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-05T15:00:54.000Z", "max_forks_repo_path": "app.r", "max_forks_repo_name": "Sage-Bionetworks/ggplotWithYourData", "max_forks_repo_head_hexsha": "1d2e133861399d0a580089857dc6d2f153340204", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.4274416766, "max_line_length": 550, "alphanum_fraction": 0.4881137802, "num_tokens": 25579, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.32081425095402993}} {"text": "library(tidyverse)\nlibrary(lubridate)\nlibrary(ggplot2)\nlibrary(scales)\n\n# Read in data\nnyt <- data.table::fread(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n# Removes some teritories\nnot_states <- c(\"Northern Mariana Islands\", \"Guam\", \"Virgin Islands\", \"Puerto Rico\")\nnyt <- nyt[which(!(nyt$state %in% not_states)),]\n\n# Read in state code look up table - assuming 50 states + DC\ncodes <- read.csv(\"usa/data/states.csv\", stringsAsFactors = FALSE)\nnames(codes) <- c(\"state\", \"code\")\nnyt <- left_join(nyt, codes, by = \"state\")\n\n# Sort dates\nnyt$date <- ymd(nyt$date)\n\n# Work out cumulative cases by state\ndata <- nyt %>%\n group_by(code, date) %>%\n summarise(\n cumulative_cases = sum(cases),\n cumulative_deaths = sum(deaths)\n )\n\ndata_daily <- NULL\nunique_codes <- unique(data$code)\n\nless10deaths <- c()\nless50deaths <- c()\n# Working out daily deaths and cases\nfor (i in 1:length(unique_codes)){\n subset <- data[which(data$code == unique_codes[i]),]\n # Sort dates into ordere\n subset <- subset[order(subset$date),]\n # Fill in daily deaths\n daily_cases <- vector(length = length(subset$date))\n daily_deaths <- vector(length = length(subset$date))\n for (j in 1:length(subset$date)){\n if (j == 1){\n daily_cases[j] <- subset$cumulative_cases[j]\n daily_deaths[j] <- subset$cumulative_deaths[j]\n } else {\n daily_cases[j] <- subset$cumulative_cases[j] - subset$cumulative_cases[j-1]\n daily_deaths[j] <- subset$cumulative_deaths[j] - subset$cumulative_deaths[j-1]\n \n if (daily_cases[j] < 0){\n daily_cases[j] = 0\n print(sprintf(\"%s has a decrease in cumulative deaths on %s\", unique_codes[i], subset$date[j]))\n }\n if (daily_deaths[j] < 0){\n daily_deaths[j] = 0\n print(sprintf(\"%s has a decrease in cumulative deaths on %s\", unique_codes[i], subset$date[j]))\n }\n }\n }\n subset$daily_cases <- daily_cases\n subset$daily_deaths <- daily_deaths\n data_daily <- rbind(data_daily, subset)\n \n if (max(subset$cumulative_deaths) < 10){\n less10deaths <- c(less10deaths, unique_codes[i])\n }\n if (max(subset$cumulative_deaths) < 30){\n less50deaths <- c(less50deaths, unique_codes[i])\n }\n}\n\nprint(sprintf(\"Last date in data is: %s\", max(data_daily$date)))\nprint(sprintf(\"First date in data is: %s\", min(data_daily$date)))\nprint(\"States with less than 10 cumulative deaths\")\nprint(less10deaths)\nprint(\"States with less than 50 cumulative deaths\")\nprint(less50deaths)\n\n\n#-----------------------------------------------------------------------------\n# Plotting code\np <- ggplot(data_daily) +\n geom_col(aes(x = date, y = cumulative_cases, group = code, fill = code)) +\n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) + \n facet_wrap(~code) +\n theme_bw() + \n ylab(\"Cumulative cases\") +\n xlab(\"\") + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\") \nggsave(\"figures/state_cases.png\", p, height = 10, width = 15)\n\np1 <- ggplot(data_daily) +\n geom_col(aes(x = date, y = daily_cases, group = code, fill = code)) +\n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) + \n facet_wrap(~code) +\n theme_bw() + \n ylab(\"Daily number of cases\") +\n xlab(\"\") + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\") \nggsave(\"usa/figures/state_cases_daily.png\", p1, height = 10, width = 15)\n\np2 <- ggplot(data_daily) +\n geom_col(aes(x = date, y = cumulative_deaths, group = code, fill = code)) +\n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) + \n facet_wrap(~code) +\n ylab(\"Cumulative deaths\") +\n xlab(\"\") + \n theme_bw() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\")\nggsave(\"usa/figures/state_deaths.png\", p2, height = 10, width = 15)\nprint(p2)\n\np3 <- ggplot(data_daily) +\n geom_col(aes(x = date, y = daily_deaths, group = code, fill = code)) +\n scale_x_date(date_breaks = \"2 weeks\", labels = date_format(\"%e %b\")) + \n facet_wrap(~code) +\n ylab(\"Daily number of deaths\") +\n xlab(\"\") + \n theme_bw() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1), \n legend.position = \"None\")\nggsave(\"usa/figures/state_deaths_daily.png\", p3, height = 10, width = 15)\nprint(p3)\n\n\n#-----------------------------------------------------------------------------\n# Padding with zeros\n\ndate_min <- ymd(\"2020-01-01\")\n\nall_data <- NULL\n\nfor (i in 1:length(unique_codes)){\n # Subset per state\n state_subset <- data_daily[which(data_daily$code == unique_codes[i]),]\n # Order by date\n state_subset <- state_subset[order(state_subset$date),]\n # Pad previous dates with zeros\n pad <- state_subset$date[1] - date_min\n pad_dates <- date_min + days(1:pad[[1]]-1)\n padded_data <- data.frame(\"code\" = rep(unique_codes[i], pad),\n \"date\" = pad_dates,\n \"cumulative_cases\" = as.integer(rep(0, pad)),\n \"cumulative_deaths\" = as.integer(rep(0, pad)), \n \"daily_cases\" = as.integer(rep(0, pad)),\n \"daily_deaths\" = as.integer(rep(0, pad)))\n \n state_data <- bind_rows(padded_data, state_subset)\n \n all_data <- bind_rows(all_data, state_data)\n}\n\n# Save RDS\nsaveRDS(all_data, file = \"usa/data/nyt_death_data_padded.rds\")\n\n", "meta": {"hexsha": "3c2b37d3550fadbdc4ecd2f03223fb693cd1a863", "size": 5339, "ext": "r", "lang": "R", "max_stars_repo_path": "usa/code/data-scrape/process-death-data-nyt.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "usa/code/data-scrape/process-death-data-nyt.r", "max_issues_repo_name": "codecheckers/covid19model-report23", "max_issues_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 99, "max_issues_repo_issues_event_min_datetime": "2020-03-30T17:17:04.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-25T13:39:40.000Z", "max_forks_repo_path": "usa/code/data-scrape/process-death-data-nyt.r", "max_forks_repo_name": "codecheckers/covid19model-report23", "max_forks_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 319, "max_forks_repo_forks_event_min_datetime": "2020-03-30T20:38:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-09T16:12:51.000Z", "avg_line_length": 33.7911392405, "max_line_length": 104, "alphanum_fraction": 0.6317662484, "num_tokens": 1517, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3208142509540299}} {"text": "library(tidyverse)\n\na <- rnorm(200)\nb <- rnorm(200)\ndat <- data_frame(a,b) %>%\n mutate(sp = paste0(\"sp\", rep(1:20, each = 10)))\n\nwrite.csv(dat, \"data.csv\", row.names = F)\n", "meta": {"hexsha": "3fce11e38ef48a84178b72bd13b9d02146d34a9f", "size": 172, "ext": "r", "lang": "R", "max_stars_repo_path": "csv_script.r", "max_stars_repo_name": "mattocci27/makeR", "max_stars_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "csv_script.r", "max_issues_repo_name": "mattocci27/makeR", "max_issues_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "csv_script.r", "max_forks_repo_name": "mattocci27/makeR", "max_forks_repo_head_hexsha": "965fef673e5eaa3a06cfed44f93b8c42a2786052", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.1111111111, "max_line_length": 49, "alphanum_fraction": 0.6046511628, "num_tokens": 61, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3208142509540299}} {"text": "# under-sampling and bagging make prediction models\r\n\r\n# setting\r\n```{r}\r\n\r\n########################## Data setting\r\n### Setting\r\nrm(list=ls(all=TRUE))\r\n\r\nlibrary(data.table)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\nlibrary(car)\r\nlibrary(psych)\r\nlibrary(pROC)\r\nlibrary(epitools)\r\nlibrary(tableone)\r\nlibrary(caret)\r\nlibrary(tictoc)\r\nlibrary(Boruta)\r\n\r\n\r\n\r\nlibrary(doParallel)\r\ncl <- makePSOCKcluster(detectCores(all.tests = FALSE, logical = TRUE) - 1)\r\nregisterDoParallel(cl)\r\n\r\n\r\n\r\n\r\n\r\nset.seed(1234)\r\n\r\n\r\n\r\n######################### Input data\r\nData <- fread(\"Data_demo.csv\", encoding = \"UTF-8\" )\r\nData$V1 <- NULL\r\n\r\ncolnames(Data)\r\ndim(Data)\r\n\r\n\r\n\r\nData[, table(TrainTest), ]\r\n\r\nData_train <- Data[TrainTest == \"Train\", , ] #*eligible \r\n\r\n\r\nData_test <- Data[TrainTest == \"Test\", , ] #*eligible \r\n\r\n\r\n\r\nNoise <- function(Var, Data){\r\n\t\tVar + rnorm(mean = 0, sd = sd(Var)/5, n = nrow(Data))\r\n\t}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nRMSE <- function(Obs, Pred){\r\n\tDif <- Pred - Obs\r\n\tRMSE <- round(sqrt(mean(Dif**2)), 2)\r\n\treturn(RMSE)\r\n}\r\n\r\n\r\n\r\nMAE <- function(Obs, Pred){\r\n\tDif <- Pred - Obs\r\n\tMAE <- Dif %>% abs() %>% mean()\t%>% round(., 2)\r\n\treturn(MAE)\r\n}\r\n\r\n\r\n\r\nMAPE <- function(Obs, Pred){\r\n\tDif <- Pred - Obs\r\n\tMAPE <- mean(abs(Dif/Obs)*100) %>% round(., 2)\r\n\treturn(MAPE)\r\n}\r\n\r\n\r\n\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# \r\n```{r}\r\n\r\n# CV methods\r\nCV_method <- \"cv\"\r\nCV_number <- 5\r\nRepeated_number <- 1\r\nTune_number <- 1\r\n\r\n\r\n\r\n# under-sampling parameters\r\nData_train_original <- data.frame(Data_train)\r\n\r\nParameter_grid <- expand.grid(\r\n \tiii_Top_x_percentile = seq(0.90, 0.98, 0.01),\r\n\tiii_N_down = seq(100, 200, 100)\r\n \t) %>% as.data.frame\r\n\r\n\r\n\r\n\r\nPopulation_cut <- Data_train[AMPM == \"PM\" & Population100000 >= 5, ID, ] %>% unique()\r\nPopulation_cut\r\n\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Under sampling setting \r\n```{r}\r\n\r\n\r\n# using variables\r\nVar_use <- c( \r\n\t\"Cluster_pred\", \r\n\t\"ID\",\r\n\t\"Date\",\r\n\r\n\r\n\t\"Population100000\", \r\n\t\"LogPopulation100000\", \t\r\n\t\"Mean_income_city_2015\",\r\n\t\"Green_area_percent\", \r\n\t\"Age_median_city\",\r\n\t\"Population_over_65_100000\",\r\n\t\"SexRatioF\",\r\n\t\r\n\t\r\n\t\"SeverityAll\", \r\n\t\r\n\r\n\t\"RainySeason\", \r\n\t\"DifDateRain\", \r\n\t\"holiday_flag\", \r\n\t\"Hour\", \r\n\t\"Month\", \r\n\t\r\n\t\"precip1Hour\", \r\n\t\"windSpeed_ms\", \r\n\t\"DownwardSolarRadiation_kwm2\", \r\n\t\"relativeHumidity\", \r\n\t\r\n\t\"temperature\", \r\n\t\"temperature_past1d_max_diff\",\r\n\t\"temperature_past1d_mean_diff\", \r\n\t\"temperature_past1d_min_diff\"\r\n)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# formula\r\nForm_temp <- formula( \t\r\n\tSeverityAll ~ \r\n\t\r\n\tPopulation100000 + \r\n\tMean_income_city_2015 + \r\n\tGreen_area_percent + \r\n\tAge_median_city + \r\n\tPopulation_over_65_100000 + \r\n\tSexRatioF + \r\n\t\r\n\t\r\n\t\r\n\t\r\n\tRainySeason + \r\n\tDifDateRain + \r\n\tholiday_flag + \r\n\tHour + \r\n\tMonth + \r\n\t\r\n\tprecip1Hour + \r\n\twindSpeed_ms + \r\n\tDownwardSolarRadiation_kwm2 + \r\n\trelativeHumidity + \r\n\t\r\n\ttemperature + \r\n\ttemperature_past1d_max_diff + \r\n\ttemperature_past1d_mean_diff + \r\n\ttemperature_past1d_min_diff \r\n)\r\n\t\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n# set seed \r\nSize_n <- 36 \r\nSeeds_fix <- vector(mode = \"list\", length = CV_number * Repeated_number + 1)\r\nfor(i in 1:(CV_number * Repeated_number)) Seeds_fix[[i]] <- sample.int(n = 10000, size = Size_n )\r\nSeeds_fix[[(CV_number * Repeated_number + 1)]] <- sample.int(10000, 1)\r\nSeeds_fix\r\n\t\t\t\t\t \r\n\t\t\t\t\t \r\n\r\n\r\n# construct rfeControl object\r\nrfe_control = rfeControl(\r\n\tfunctions = caretFuncs, \r\n\tallowParallel = TRUE,\r\n\tmethod = CV_method,\r\n number = CV_number,\r\n\treturnResamp = \"final\",\r\n\tseeds = Seeds_fix\r\n\t)\r\n\r\n\r\n# construct trainControl object for train method \r\nfit_control = trainControl(\r\n\tallowParallel = TRUE,\r\n\tmethod = CV_method,\r\n\tnumber = CV_number,\r\n\tseeds = Seeds_fix\r\n\t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Under sampling\r\n```{r}\r\n\r\ntic(4)\r\n\r\n# Under sampling\r\nHogeRMSE <- c()\r\nHogeMAPE <- c()\r\n\r\n\r\n\r\nfor(iii in 1:nrow(Parameter_grid)){\r\n\r\n\r\n\r\n# read classifiers\r\nClassifier <- readRDS(\r\n\tpaste(\"Classifier\",\r\n\t\t\"_top_\",\r\n\t\tParameter_grid[iii, \"iii_Top_x_percentile\"],\r\n\t\t\"_percent.rds\", \r\n\t\tsep = \"\"\r\n\t\t)\r\n\t)\r\n\r\n\r\n\r\n# predict classess\r\nData_train_cluster <- Data_train[ID %in% Population_cut, , ]\r\nData_train_cluster$Cluster_pred <- predict(Classifier, Data_train_cluster, pe = \"raw\")\r\n\r\n\r\nData_train_cluster_no <- Data_train[(ID %in% Population_cut) == FALSE, , ]\r\nData_train_cluster_no[, Cluster_pred := \"99\", ]\r\n\r\n\r\nData_train_cluster <- rbind(Data_train_cluster, Data_train_cluster_no)\r\nData_train_cluster <- Data_train_cluster[, Var_use, with = F]\r\n\r\n\r\n\r\n\t\r\n\t\r\n\t\r\n\t\r\n# Under sampling b1-5\r\nTemp_train <- c()\r\nTemp_test <- c()\r\n\r\n\r\nfor(bbb in 1:10){\r\nset.seed(bbb)\r\n\r\nData_train_cluster_auged <- sample_n(\r\n\t\ttbl = Data_train_cluster[Cluster_pred == \"99\", , ], \r\n\t\tsize = Parameter_grid[iii, \"iii_N_down\"],\r\n\t\treplace = F\r\n\t\t)\r\n\t\t\r\n\r\nData_train_cluster_auged_bag <- rbind(Data_train_cluster_auged, Data_train_cluster[Cluster_pred != \"99\", , ]) \r\n\t\r\n\t### Save data\r\n\tfwrite(\r\n\t\tData_train_cluster_auged_bag, \r\n\t\tpaste(\r\n\t\t\t\"Data_train_top_\", \r\n\t\t\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\t\t\"_percent_\", \r\n\t\t\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\t\t\"_under_\",\r\n\t\t\tbbb,\r\n\t\t\t\".csv\", sep = \"\")\r\n\t\t)\t\r\n\r\n\r\n\r\n# xgbTree\r\ntic(6)\r\nxgbTree_temp <- train(\r\n\tform = Form_temp,\r\n\tdata = Data_train_cluster_auged_bag,\r\n method = \"xgbTree\",\r\n\r\n\tobjective=\"reg:squarederror\",\r\n\ttrControl = fit_control,\r\n\tmetric = \"RMSE\", \r\n\tpreProcess = c(\"center\", \"scale\"), \r\n\ttuneLength = 3\r\n\t#tuneGrid = tune_params\r\n\t)\r\nsummary(xgbTree_temp)\r\nxgbTree_temp\r\ntoc(6)\r\n\r\n\r\n\r\n# save models\r\nsaveRDS(xgbTree_temp, \r\n\tpaste(\r\n\t\t\"xgbTree_temp_model_\",\r\n\t\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\t\"_percent_\", \r\n\t\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\t\"_under_\",\r\n\t\tbbb,\r\n\t\t\".rds\", sep = \"\"\r\n\t\t)\r\n\t)\r\n\t\r\n\r\n\t\r\n# Store\r\nTemp_train <- cbind(Temp_train, predict(xgbTree_temp, Data_train, pe = \"raw\"))\r\nTemp_test <- cbind(Temp_test, predict(xgbTree_temp, Data_test, pe = \"raw\"))\r\n\r\n\t\t\r\n}\r\n\r\n\r\n\r\nTemp_train <- data.table(Temp_train)\r\nTemp_train[, Pred := rowMeans(Temp_train), ]\r\nTemp_train[, Pred := ifelse(Pred < 0, 0, Pred), ]\r\n\r\n\r\n\r\n\r\nTemp_test <- data.table(Temp_test)\r\nTemp_test[, Pred := rowMeans(Temp_test), ]\r\nTemp_test[, Pred := ifelse(Pred < 0, 0, Pred), ]\r\n\r\n\r\n\r\n\r\n\r\n\r\n# get performance\r\nData_train[, Pred := Temp_train$Pred, ]\r\nData_test[, Pred := Temp_test$Pred, ]\r\n\r\n\r\n\r\nRMSE_train <- RMSE(Obs = Data_train$SeverityAll, Pred = Data_train$Pred)\r\n\r\nMAE_train <- MAE(Obs = Data_train$SeverityAll, Pred = Data_train$Pred)\r\n\r\nCor_train <- paste(\r\n\tcor.test(Data_train$SeverityAll, Data_train$Pred)$estimate %>% round(., 2),\r\n\t\" (\",\r\n\tcor.test(Data_train$SeverityAll, Data_train$Pred)$conf.int[1] %>% round(., 2),\r\n\t\" to \",\r\n\tcor.test(Data_train$SeverityAll, Data_train$Pred)$conf.int[2] %>% round(., 2),\r\n\t\")\",\r\n\tsep = \"\"\r\n\t)\r\n\r\n\r\n\r\n\r\n# get performance\r\nRMSE_test <- RMSE(Obs = Data_test$SeverityAll, Pred = Data_test$Pred)\r\n\r\nMAE_test <- MAE(Obs = Data_test$SeverityAll, Pred = Data_test$Pred)\r\n\r\nCor_test <- paste(\r\n\tcor.test(Data_test$SeverityAll, Data_test$Pred)$estimate %>% round(., 2),\r\n\t\" (\",\r\n\tcor.test(Data_test$SeverityAll, Data_test$Pred)$conf.int[1] %>% round(., 2),\r\n\t\" to \",\r\n\tcor.test(Data_test$SeverityAll, Data_test$Pred)$conf.int[2] %>% round(., 2),\r\n\t\")\",\r\n\tsep = \"\"\r\n\t)\r\n\t\r\n\r\n\r\n\r\n# Summarize\r\nPerformance <- c(\r\n\tpaste(\r\n\t\"xgbTree_temp_model_\",\r\n\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\"_percent_\", \r\n\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\"_under\", sep = \"\"\r\n\t),\r\n\t\r\n\tRMSE_train \r\n\t, RMSE_test\r\n\t\r\n\t, Cor_train\t\r\n\t, Cor_test\t\r\n\t)\r\n\r\nHogeRMSE <- rbind(HogeRMSE, Performance)\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Organized dataset\r\nData_train_MAPE <- Data_train %>% \r\n\tgroup_by(Date) %>%\r\n\tsummarise(\r\n\t\tObs = sum(SeverityAll),\r\n\t\tPred = sum(Pred)\r\n\t\t) %>% data.table()\r\n\r\nData_train_MAPE[, Year := year(Date), ]\r\n\r\nData_train_MAPE[, Spike :=\r\n\tifelse(Year == 2015 & Obs >= Data_train_MAPE[Year == 2015, quantile(Obs, 0.8), ], 1, \r\n\tifelse(Year == 2016 & Obs >= Data_train_MAPE[Year == 2016, quantile(Obs, 0.8), ], 1,\r\n\tifelse(Year == 2017 & Obs >= Data_train_MAPE[Year == 2017, quantile(Obs, 0.8), ], 1, 0)))\r\n\t, ]\r\nData_train_MAPE[, table(Spike), ]\r\nData_train_MAPE[, table(Spike), ] / nrow(Data_train_MAPE)\r\n\r\n\r\n\r\n# get performance\r\nMAPE_train <- MAPE(\r\n\tObs = Data_train_MAPE[Spike == 1, Obs, ],\r\n\tPred = Data_train_MAPE[Spike == 1, Pred, ]\r\n\t)\r\n\r\nPE_train <- Data_train_MAPE[Spike == 1, round( (abs(sum(Pred) - sum(Obs)) / sum(Obs) ) * 100, 2), ]\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n# Organized dataset\r\nData_test_MAPE <- Data_test %>% \r\n\tgroup_by(Date) %>%\r\n\tsummarise(\r\n\t\tObs = sum(SeverityAll),\r\n\t\tPred = sum(Pred)\r\n\t\t) %>% data.table()\r\n\r\nData_test_MAPE[, Year := year(Date), ]\r\n\r\nData_test_MAPE[, Spike :=\r\n\tifelse(Year == 2018 & Obs >= Data_test_MAPE[Year == 2018, quantile(Obs, 0.8), ], 1, 0)\r\n\t, ]\r\nData_test_MAPE[, table(Spike), ]\r\nData_test_MAPE[, table(Spike), ] / nrow(Data_test_MAPE)\r\n\r\n\r\n\r\n# get performance\r\nMAPE_test <- MAPE(\r\n\tObs = Data_test_MAPE[Spike == 1, Obs, ],\r\n\tPred = Data_test_MAPE[Spike == 1, Pred, ]\r\n\t)\r\n\r\n\r\nPE_est <- Data_test_MAPE[Spike == 1, round( (abs(sum(Pred) - sum(Obs)) / sum(Obs) ) * 100, 2), ]\r\n\t\r\n\r\n\r\n\r\n\r\n# Summarize\r\nPerformance <- c(\r\n\tpaste(\r\n\t\"xgbTree_temp_model_\",\r\n\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\"_percent_\", \r\n\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\"_under\", sep = \"\"\r\n\t),\r\n\t\r\n\tMAPE_train \r\n\t, MAPE_test\r\n\t\r\n\t, PE_train\r\n\t, PE_est\r\n\t)\r\n\r\nHogeMAPE <- rbind(HogeMAPE, Performance)\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n# get predicted values\r\nData_train$Predicted <- Data_train$Pred\r\nData_train$Obserbved <- Data_train$SeverityAll\r\n\r\n\r\n# get predicted values\r\nData_test$Predicted <- Data_test$Pred\r\nData_test$Obserbved <- Data_test$SeverityAll\r\n\r\n\r\n\r\n\r\n## Make figures\r\nggplot(data = Data_train, aes(x = Predicted, y = Obserbved)) +\r\n\tgeom_point() + \r\n\tgeom_smooth(method = lm) + \r\n\tscale_x_continuous(limits = c(0, 100), breaks = seq(0, 100, 10)) + \r\n\tscale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, 10)) + \r\n\txlab(\"\") + \r\n\tylab(\"\") +\r\n\ttheme_classic() \r\n\t\r\nggsave(\r\n\tpaste(\r\n\t\t\"Out_plot_\", \r\n\t\t\"xgbTree_temp_model_\",\r\n\t\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\t\"_percent_\", \r\n\t\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\t\"_under\",\r\n\t\t\"_train.png\", sep = \"\"), \r\n\t\twidth = 4.2, height = 3.2\r\n\t\t) #*action\r\n\r\n\r\n\r\n\r\n\r\n### Make figures taest\r\nggplot(data = Data_test, aes(x = Predicted, y = Obserbved)) +\r\n\tgeom_point() + \r\n\tgeom_smooth(method = lm) + \r\n\tscale_x_continuous(limits = c(0, 100), breaks = seq(0, 100, 10)) + \r\n\tscale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, 10)) + \r\n\txlab(\"\") + \r\n\tylab(\"\") +\r\n\ttheme_classic() \r\n\r\nggsave(\r\n\tpaste(\r\n\t\t\"Out_plot_\", \r\n\t\t\"xgbTree_temp_model_\",\r\n\t\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\t\"_percent_\", \r\n\t\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\t\"_under\",\r\n\t\t\"_test.png\", sep = \"\"), \r\n\t\twidth = 4.2, height = 3.2\r\n\t\t) #*action\r\n\r\n\r\n\r\n\r\n\r\n\r\n### Make Fig train\r\nDataSum_train <- Data_train %>%\r\n\tgroup_by(Date) %>%\r\n\tsummarise(\r\n\t\tObserbved = sum(Obserbved),\r\n\t\tPredicted = sum(Predicted)\r\n\t\t) %>%\r\n\tdata.table()\r\n\r\n\r\n\r\n\r\n\r\nDataSum_test <- Data_test %>%\r\n\tgroup_by(Date) %>%\r\n\tsummarise(\r\n\t\tObserbved = sum(Obserbved),\r\n\t\tPredicted = sum(Predicted)\r\n\t\t) %>%\r\n\tdata.table()\r\n\r\n\r\n\r\n\r\nDataSum <- rbind(DataSum_train, DataSum_test)\r\n\r\n\r\n\r\nDataSum[, Date:=as.Date(Date), ]\r\nDataSum[, YearUse:=year(Date), ]\r\nDataSum <- DataSum[order(Date), , ]\r\nDataSum[, Day:=1:length(Obserbved), by = YearUse]\r\n\r\n\r\n\r\nDataSum[Date == as.Date(\"2015-06-01\"), , ]\r\nDataSum[Date == as.Date(\"2015-07-01\"), , ]\r\nDataSum[Date == as.Date(\"2015-08-01\"), , ]\r\nDataSum[Date == as.Date(\"2015-09-01\"), , ]\r\n\r\n\r\n\r\n\r\n\r\n\r\nggplot(data = DataSum, aes(x = Day), group=factor(YearUse)) +\r\n\tgeom_line(aes(y = Obserbved, x=Day), colour = \"Black\", size = 0.4) + \r\n\tgeom_line(aes(y = Predicted, x=Day), colour = \"Red\", size = 0.4) + \r\n\txlab(\"\") + \r\n\tylab(\"\") +\r\n\r\n\tscale_y_continuous(\r\n\t\tlimits = c(0, 450), \r\n\t\tbreaks = seq(0, 450, 50)\r\n\t\t) + \r\n\r\n\tscale_x_continuous(\r\n\t\tlabel = c(\"Jun.\", \"Jul.\", \"Aug.\", \"Sep.\"), \r\n\t\tbreaks = c(1, 31, 62, 93)\r\n\t\t) + \r\n\ttheme(axis.text.x = element_text(angle = 90, hjust = 1)) + \r\n\tfacet_grid(~YearUse) +\r\n\ttheme_classic()\r\n\r\nggsave(\r\n\tpaste(\r\n\t\t\"Out_time_24_\", \r\n\t\t\"xgbTree_temp_model_\",\r\n\t\tParameter_grid[iii, \"iii_Top_x_percentile\"], \r\n\t\t\"_percent_\", \r\n\t\tParameter_grid[iii, \"iii_N_down\"],\r\n\t\t\"_under\",\r\n\t\t\".png\", sep = \"\"), \r\n\t\twidth = 6.7 * 1.5, height = 3.5 * 0.66\r\n\t\t) #*action\r\n\r\n\r\n\r\n\r\n\r\n}\r\n\r\n\r\nfwrite(HogeRMSE, \"Out_RMSE_under.csv\")\r\nfwrite(HogeMAPE, \"Out_MAPE_under.csv\")\r\n\r\ntoc(4)\r\n\r\n\r\ntoc(1)\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "281beed68b797a686129df8daeebd0694a9d5fc8", "size": 12369, "ext": "r", "lang": "R", "max_stars_repo_path": "all_heatstrokes/R_down_sampling_XGBoost_no_clusters_bagging.r", "max_stars_repo_name": "ssrogata/Prediction-models-for-heatstrokes-in-Japanese-cities", "max_stars_repo_head_hexsha": "8e4b9b93e73dc8582a41c6e7a0392595a0b6330f", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "all_heatstrokes/R_down_sampling_XGBoost_no_clusters_bagging.r", "max_issues_repo_name": "ssrogata/Prediction-models-for-heatstrokes-in-Japanese-cities", "max_issues_repo_head_hexsha": "8e4b9b93e73dc8582a41c6e7a0392595a0b6330f", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "all_heatstrokes/R_down_sampling_XGBoost_no_clusters_bagging.r", "max_forks_repo_name": "ssrogata/Prediction-models-for-heatstrokes-in-Japanese-cities", "max_forks_repo_head_hexsha": "8e4b9b93e73dc8582a41c6e7a0392595a0b6330f", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.0371900826, "max_line_length": 111, "alphanum_fraction": 0.6121756003, "num_tokens": 3896, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.32081424338040665}} {"text": "# comparison with MSVPA\n nox<-2; noy<-3;\nsms<-Read.summary.data()\n\nif (F) {\n file<-file.path(root,'data_Northsea','summary.out')\n\n MSVPA.no<-c('COD','WHG','HAD','POK','HER','SAN','NOP')\n MSVPA.sp<-c('Cod','Whiting','Haddock','Saithe','Herring','Sandeel','Nor. pout')\n MSVPA.prey<-c('Cod','Whiting','Haddock','Herring','Sandeel','Nor. pout')\n}\nif (T) {\n file<-file.path(root,'data_baltic','Compar01.out')\n\n MSVPA.no<-c('COD','HER','SPR')\n MSVPA.sp<-c('Cod','Herring','Sprat')\n MSVPA.prey<-MSVPA.sp\n}\ns<-read.table(file,header=TRUE)\n\nmsvpa<-data.frame(MSVPA.species=s$species, #Species.n=match(s$species,MSVPA.no),\n Species=MSVPA.sp[match(s$species,MSVPA.no)],Age=s$age,Year=s$year,\n MSVPA.M2=s$M2,Quarter=s$quarter,MSVPA.SSB=s$SSB)\nunique(msvpa$Species)\n\nb<-merge(sms,msvpa,by=c('Year','Quarter','Age','Species'))\nunique(b$Species)\n\nM2<-tapply(b$M2,list(b$Year,b$Age,b$Species),sum,na.rm=T)\n\nmsvpa.M2<-tapply(b$MSVPA.M2,list(b$Year,b$Age,b$Species),sum,na.rm=T)\n\n########################## M2 plot (sum af quarterly M (wrong!!) #############\n\n#clear graphical windows\n cleanup()\n\ndev<-\"print\"\ndev<-\"screen\"\nnox<-2; noy<-3;\nnoxy<-nox*noy\npar(ask=TRUE)\n\ncleanup()\n\n#by species\nyear<-as.numeric(dimnames(M2)[[1]])\nfor (sp in MSVPA.prey) {\n\n newplot(dev,nox,noy);\n par(mar=c(3,5,3,2))\n i<-0\n for (age in (fa:5)) {\n a<-age+1\n if (i==noxy) {newplot(dev,nox,noy); i<-0 }\n max.M2<-max(M2[,a,sp],msvpa.M2[,a,sp])\n print(max.M2)\n if (!is.na(max.M2)) if (max.M2>0.001) {\n plot(year,M2[,a,sp],xlab=\" \",ylab=\"M2\",main=paste(sp,' age:',age),type='b', ylim=c(0,max.M2),col=1 )\n lines(year,msvpa.M2[,a,sp],col=4)\n i<-i+1\n }\n a<-age+1\n }\n} \n\n#######################################################\n# M2 plot for report (on file if dev=\"wmf\")\n\n#Reformat output\ndev<-\"print\"\ndev<-\"screen\"\n#dev<-\"wmf\"\n\nnox<-4; noy<-3;\nnoxy<-nox*noy\npar(ask=TRUE)\n\ncleanup()\n#by species\nyear<-as.numeric(dimnames(M2)[[1]])\n i<-0\n pic<-1\n newplot(dev,nox,noy,filename=\"annual_M2_1\")\n par(mar=c(3,5,3,2))\n\n for (sp in (1:nsp)) {\n for (age in (fa:3)) {\n a<-age+1\n if (i==noxy) {pic<-pic+1; newplot(dev,nox,noy,filename=paste(\"annual_M2_\",pic,sep=\"\")); i<-0 }\n max.M2<-max(M2[,a,sp],msvpa.M2[,a,sp])\n if (!is.na(max.M2)) if (max.M2>0.001) {\n plot(year,M2[,a,sp],xlab=\" \",ylab=\"M2\",main=paste(name[sp+1],\" age:\",age),type='b', ylim=c(0,max.M2),col=1 )\n lines(year,msvpa.M2[,a,sp],col=4)\n i<-i+1\n }\n }\n} \n\n\n###################################\n\nnox<-1; noy<-1;\nnoxy<-nox*noy\ni<-0\nnewplot(dev,nox,noy)\n#by species\nyear<-as.numeric(dimnames(M2)[[1]])\nfor (sp in (1:nsp)) {\n if (i==noxy) {newplot(dev,nox,noy); i<-0 }\n max.M2<-max(M2[,,sp],na.rm=T)\n if (!is.na(max.M2)) if (max.M2>0.001) {\n plot(year,M2[,fa+1,sp],xlab=\" \",ylab=\"M2\",main=name[sp+1],type='b', ylim=c(0,max.M2),pch=as.character(0) ) \n i<-i+1\n for (age in ((fa+1):8)) {\n a<-age+1\n max.M2<-max(M2[,a,sp])\n if (!is.na(max.M2)) if (max.M2>0.001) lines(year,M2[,a,sp],type='b',pch=as.character(age),col=a)\n }\n }\n} \n\n############################################################ \n##SSB\n\nc<-subset(b,Quarter==1,drop=T)\n\nSSB<-tapply(c$SSB,list(c$Year,c$Species.n),sum,na.rm=T)/1000\n\nmsvpa.SSB<-tapply(c$MSVPA.SSB,list(c$Year,c$Species.n),sum,na.rm=T)/1000000\n\n\ndev<-\"print\"\ndev<-\"screen\"\n#dev<-\"wmf\"\n\nnox<-2; noy<-2\nnoxy<-nox*noy\npar(ask=TRUE)\n\ncleanup()\n\n\n#by species\n newplot(dev,nox,noy,filename=\"SSB_MSVPA_SMS\");\n i<-0\nyear<-as.numeric(dimnames(SSB)[[1]])\nfor (sp in (1:nsp)) {\n if (i==noxy) {newplot(dev,nox,noy,filename=\"SSB_MSVPA_SMS\"); i<-0 }\n max.SSB<-max(SSB[,sp],msvpa.SSB[,sp])\n plot(year,SSB[,sp],xlab=\" \",ylab=\"SSB (1000t)\",main=name[sp+1],type='b', ylim=c(0,max.SSB),col=1 )\n lines(year,msvpa.SSB[,sp],col=4)\n i<-i+1\n}\nif (dev!=\"screen\") cleanup()\n\n", "meta": {"hexsha": "ed9338fd086654241fa2fb92a5f5b97f5051f8ec", "size": 3833, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/msvpa_compare.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/msvpa_compare.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/msvpa_compare.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.4140127389, "max_line_length": 114, "alphanum_fraction": 0.5669188625, "num_tokens": 1528, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.3206222648499325}} {"text": "\"This script generates the data files required for setting up \nan Instrument (for CDI-IRT administrations) on e-Babylab. \nChange the definitions for 'lang', 'type', and 'resp' as needed.\"\n\n\npacs <- c(\"wordbankr\",\"plyr\",\"dplyr\",\"tidyr\",\"stats4\",\"openxlsx\",\"mirt\",\"mirtCAT\",\n \"sweep\",\"purrr\",\"tidyverse\",\"tictoc\",\"parallel\",\"doParallel\", \"readr\") #\"tictoc\",\"parallel\",\"doParallel\",\ninvisible(lapply(pacs,library,character.only=TRUE))\n\n########## Configure settings here\nlang <- \"German\" # use get_instruments() to see what languages and forms are available on wordbank\ntype <- \"WS\" # \"WS\" or \"WG\"\nresp <- \"production\" # \"comprehension\" or \"production\"\n\nd_poly <- 3\npoly_mode <- \"Flexi\" #\"Fixed\" #\"Fixed\" # or\ncriteria <- \"incl.anyFinite\" # or \"excl.Inf\" \u2014 for evaluation of log = -inf\ngen_list <- c(\"Female\", \"Male\")\n\n########## Functions \n# Get data from word bank\nprep_data <- function(){\n admin_ws <<- get_administration_data(lang, type)\n if(lang == \"English (American)\"){\n admin_ws <<- admin_ws #%>% filter(norming==\"TRUE\")\n }\n data_ws <<- get_instrument_data(lang, type)\n if(lang == \"English (American)\"){\n data_ws <<- data_ws %>% filter(data_id %in% admin_ws$data_id)\n }\n items_ws <<- get_item_data(lang, type)\n \n # reduce datasets\n items_ws <<- subset(items_ws, type==\"word\")\n num_words <<- nrow(items_ws)\n data_ws <<- data_ws %>% filter(num_item_id %in% items_ws$num_item_id)\n if (type == \"WS\" | (type == \"WG\" & resp == \"production\")) {\n data_ws <- data_ws %>% \n mutate(value = ifelse(value %in% c(\"produces\", \"yes\", \"sometimes\", \"often\"),\n 1,\n 0))\n } else if (type == \"WG\" & resp == \"comprehension\") { # WG\n data_ws <- data_ws %>% \n mutate(value = ifelse(value %in% c(\"no\", \"never\", \"not yet\", \"\") | is.na(value),\n 0,\n 1))\n }\n data_ws$value <<- as.numeric(data_ws$value)\n}\n\n# Prepare admin_data_all for simulation (by merging admin_ws and data_ws)\ndata_in <- function(){\n if (type == \"WS\") {\n inner_join(data_ws, select(admin_ws, age, data_id, sex, production), by=\"data_id\")\n } else if (type == \"WG\") {\n inner_join(data_ws, select(admin_ws, age, data_id, sex, all_of(resp)), by=\"data_id\")\n }\n}\n\n# Get percentages for each word in each age\nperc_word <- function(){\n temp <- select(admin_data_all,num_item_id,value,age)\n word_dist <<- temp %>% dplyr::group_by(age,num_item_id) %>% dplyr::summarise(perc = mean(value))\n}\n\n# Order percentage word\nget_order <- function(df){order(df$perc)}\n\n# Model for MLE\nLL <- function(m, sd) {\n R = dnorm(scores, m, sd)\n -sum(log(R))\n}\n\n# Apply mle model\nmle_model <- function(dfdata,...){\n # i <<- i + 1 # troubleshooting\n # print(i) # troubleshooting\n if (type == \"WS\") { # check CDI type\n scores <<- dfdata$production\n scores <<- scores[scores != 0] # remove zeros\n check <<- scores[scores != 0] \n if (length(check) > item_threshold) {\n mle(LL, start = list(m = mean(scores), sd = sd(scores)),\n method = \"L-BFGS-B\")\n } else{\n NaN\n } \n } else if (type == \"WG\") { # check CDI type\n if (resp == \"comprehension\"){\n scores <<- dfdata$comprehension\n scores <<- scores[scores != 0] # remove zeros\n check <<- scores[scores != 0] \n } else if (resp == \"production\"){\n scores <<- dfdata$production\n scores <<- scores[scores != 0] # remove zeros\n check <<- scores[scores != 0]\n }\n if (length(check) > item_threshold) { \n mle(LL, start = list(m = mean(scores), sd = sd(scores)))#, \n # method = \"L-BFGS-B\")\n } else{\n NaN\n }\n }\n}\n\n# Fill in \"missing\" words and Reorder according to word_purr\nfill_in_and_sort <- function(dfage,df){\n # ex <- data.frame(1:num_words)\n sorted <<- word_dist$num_item_id %>% unique() %>% data.frame()\n colnames(sorted) <- \"num_item_id\"\n full_join(sorted, df, by = \"num_item_id\")\n}\n\n# Configuration for polynomial\npoly_cfg <- function(mode = \"Flexi\"){\n \n age <<- admin_data_all$age %>% unique %>% sort\n ageN <- count(admin_data_all,age)\n \n # Measures size\n if (median(as.matrix(ageN[,2])) > 200){\n size <- \"large\"\n } else if (median(as.matrix(ageN[,2])) <= 200 & median(as.matrix(ageN[,2])) > 100){\n size <- \"medium\"\n } else if (median(as.matrix(ageN[,2])) <= 100 & median(as.matrix(ageN[,2])) > 50){\n size <- \"small\"\n } else if (median(as.matrix(ageN[,2])) <= 50) {\n size <- \"tiny\"\n }\n print(size)\n \n if (mode == \"Fixed\"){\n matrix(3, length(age), 1, dimnames = list(c(age), c(\"poly\")))\n \n } else if (mode == \"Flexi\"){\n if(type == \"WS\"){\n # Flexi Poly\n df_mad <- admin_data_all %>% ungroup() %>% select(age,production)\n df_mad <- aggregate(. ~ age, df_mad, function(x) c(median = median(x), mad = mad(x)))\n temp_df <- cbind(df_mad[,1],df_mad$production[,2]) %>%\n data.frame %>%\n mutate(poly = ifelse(X2 < 100, 1, 3))\n # Extract poly num only\n as.matrix(temp_df[,3])\n } else if (type == \"WG\" & resp == \"comprehension\"){\n # Flexi Poly\n df_mad <- admin_data_all %>% select(age,all_of(resp))\n df_mad <- aggregate(. ~ age, df_mad, function(x) c(median = median(x), mad = mad(x)))\n temp_df <- cbind(df_mad[,1],\n (df_mad %>% select(all_of(resp)) %>% data.frame)[,1][,2]) %>% # dynamic columns\n data.frame %>%\n mutate(poly = ifelse(X2 < 100, 1, 3))\n # Extract poly num only\n as.matrix(temp_df[,3])\n \n } else if (type == \"WG\" & resp == \"production\"){\n \n # Flexi Poly Original\n df_mad <- admin_data_all %>% select(age,all_of(resp))\n df_mad <- aggregate(. ~ age, df_mad, function(x) c(median = median(x), mad = mad(x)))\n temp_df <- cbind(df_mad[,1],\n (df_mad %>% select(all_of(resp)) %>% data.frame)[,1][,2]) %>% # dynamic columns\n data.frame %>%\n mutate(poly = ifelse(X2 < 100, 1, 3))\n # Extract poly num only\n as.matrix(temp_df[,3])\n }\n }\n}\n\n# Fit degree of polynomial\npoly_fit_mean <- function(dfage,df,...){\n d_poly <<- idpoly[which(age == dfage)]\n if (any(is.finite(df$mean))) {\n model <<- lm(df$mean~poly(c(1:num_words), d_poly, raw = TRUE))\n pmax(20, predict(model, newdata=data.frame(c(1:num_words))))\n } else {\n NaN\n }\n}\n\npoly_fit_sd <- function(dfage,df,...){\n d_poly <<- idpoly[which(age == dfage)]\n if (any(is.finite(df$sd))) {\n model <<- lm(df$sd~poly(c(1:num_words), d_poly, raw = TRUE))\n pmax(20, predict(model, newdata=data.frame(c(1:num_words))))\n } else {\n NaN\n }\n}\n\n# log PDF of normal distribution\nlog_pdf <- function(df){\n log(dnorm(c(0,1:num_words),df$lm_mean,df$lm_sd), base = exp(1))\n}\n\n# Filter log dnorm pdf\nfilter_log_pdf <- function(df, criteria){\n if(criteria == \"excl.Inf\"){\n \n df$filt <- is.finite(as.numeric(map(df$log,sum)))\n } else if (criteria == \"incl.anyFinite\"){\n \n df$filt <- as.numeric(map(df$log,ls.is.finite))\n } else {\n cat(\"Input error.\")\n }\n}\n\n# Filter log dnorm pdf by Rows\nls.is.finite <- function(df){\n ifelse(any(is.finite(unlist(df)))==TRUE,TRUE,FALSE)}\n\n# sum of log (basis)\nsum_log <- function(df){\n list(reduce(df,`+`) + 1)\n}\n\n# max basis\nmax_basis <- function(df){\n as.numeric(which(unlist(df) == max(unlist(df), na.rm = T)))\n}\n\n# max basis\nmax_basis_test <- function(df, value){\n if (value == 0){\n as.numeric(which(unlist(df) == max(unlist(df), na.rm = T)))\n } else if (value == 1){\n as.numeric(which(unlist(df) == max(unlist(df), na.rm = T))) + 1\n }\n}\n\n# calculate slope\nget_slope <- function(df){\n diff(df)/num_words\n}\n\n# Configuration for CAT simulations\ncat_cfg <- function(mode = \"NoPreCAT\") {\n if (mode == \"NoPreCAT\"){\n CAT_mode <<- list(\"NoPreCAT\", list())\n } else if (mode == \"PreCATFlex\"){\n CAT_mode <<- list(\"PreCATFlex\", list(min_items = 0, max_items = 10,\n criteria = \"MI\", method = \"WLE\",\n response_variance = TRUE))\n } else if (mode == \"PreCAT\") {\n CAT_mode <<- list(\"PreCAT\", list(min_items = 10, max_items = 10,\n criteria = \"MI\", method = \"WLE\"))\n }\n}\n\nremove_dataID <- function(df){df %>% select(-c(data_id))}\n\nfilter_response <- function(df){Filter(var,df)}\n\n# Fit data to IRT\nfit_item_mirt <- function(df){mirt(df, 1, itemtype = \"2PL\", method = \"EM\", SE=TRUE)}\n\n########## Code\n\n# Prepare data\nprep_data() # Extract data from wordbank\nadmin_ws <- admin_ws # %>% filter(!(age < 11 & production > 100)) # extra filter for extreme outliers\n\nadmin_data_all <- data_in() # merging admin_ws and data_ws\n\nword_dist <- perc_word() # percentage for each word in each age\nword_dist$perc <- round(word_dist$perc, digits=2) # rounding\n\ndefault_test <- c(num_words, 400, 200, 100, 50, 25, 10, 5)\ntest <- c(num_words, default_test[default_test < num_words])\n\n# Convert admin_data_all and word_dist for purr and furr\ndata_purr <- admin_data_all %>% group_by(sex, value, age, num_item_id) %>%\n nest() # main set\n\nword_purr <- word_dist %>% select(-c(\"num_item_id\")) %>% group_by(age) %>% nest() %>%\n mutate(data = map(data, get_order))\n\n# Modeling\n#Mod# Maximum likelihood estimation\nitem_threshold <- 5 # min amount of data point needed to fit mle\n\nmle_purr <- data_purr\nmle_purr$model <- pmap(list(data_purr$data), mle_model)\n\nmle_purr$mean <- foreach(i = 1:nrow(mle_purr), .combine = rbind) %do% {\n try(coef(mle_purr$model[[i]])[1], silent=TRUE) %>% as.numeric()\n}\nmle_purr$sd <- foreach(i = 1:nrow(mle_purr), .combine = rbind) %do% {\n try(coef(mle_purr$model[[i]])[2], silent=TRUE) %>% as.numeric()\n}\n\nmle_purr <- mle_purr %>% select(-c(\"model\"))\nmle_purr$mean[which(is.na(mle_purr$mean))] <- NaN\nmle_purr$sd[which(is.na(mle_purr$sd))] <- NaN\n\n#Mod# poly 3 or flexi\nprep_purr <- mle_purr %>% select(-c(\"data\")) %>%\n group_by(sex,value,age) %>% nest()\n\nprep_purr$data2 <- pmap(list(prep_purr$age,prep_purr$data), fill_in_and_sort)\nprep_purr <- prep_purr %>%\n select(-c(\"data\"))\n\nidpoly <- poly_cfg(mode = poly_mode) # polynomial flexible, f(MAD)\n\npoly_purr <- prep_purr\npoly_purr$lm_mean <- pmap(list(prep_purr$age, prep_purr$data2), poly_fit_mean)\npoly_purr$lm_sd <- pmap(list(prep_purr$age, prep_purr$data2), poly_fit_sd)\npoly_purr <- poly_purr %>% unnest %>% nest(-c(sex,value,age,num_item_id))\n\n#Mod# log PDF of normal distribution\nlog_pdf_purr_raw <- poly_purr\nlog_pdf_purr_raw$log <- map(poly_purr$data, log_pdf)\nlog_pdf_purr_raw <- log_pdf_purr_raw %>%\n select(-data)\n\n# Filter log dnorm pdf\nlog_pdf_purr <- log_pdf_purr_raw\nlog_pdf_purr$filt <- filter_log_pdf(log_pdf_purr_raw, criteria) # \"excl.Inf\" or\nlog_pdf_purr <- log_pdf_purr %>% filter(filt == 1)\n\n#Mod# norms: basis for all items\nall_basis <- log_pdf_purr %>% group_by(sex,value,age) %>% summarise(data = sum_log(log))\n\n#Mod# Get Bmin (np_param) & slope for real-data simulation\nparam <- all_basis %>% group_by(sex,value,age) %>% summarise(B = max_basis(data))\nnp_param <- param %>% subset(value == 0)\nslope <- param %>% group_by(sex,age) %>% summarise(slope = get_slope(B))\n\nfor (gen in c(\"Female\", \"Male\")) {\n \n slope2 = slope %>% ungroup() %>% filter(sex == gen) %>% \n select(age, slope) %>% spread(age, slope)\n Bmin = np_param %>% ungroup() %>% filter(sex == gen) %>% \n select(age, B) %>% spread(age, B)\n poly_unnest <- poly_purr %>% unnest()\n \n lm_p_mean = poly_unnest %>% ungroup() %>% filter(sex == gen & value == 1) %>% \n select(age, lm_mean, num_item_id) %>% spread(age, lm_mean) %>% \n mutate(word_id = num_item_id) %>%\n select(-num_item_id) %>% select(word_id, colnames(.[,1:(ncol(.)-1)]))\n lm_p_sd = poly_unnest %>% ungroup() %>% filter(sex == gen & value == 1) %>% \n select(age, lm_sd, num_item_id) %>% spread(age, lm_sd) %>% \n mutate(word_id = num_item_id) %>%\n select(-num_item_id) %>% select(word_id, colnames(.[,1:(ncol(.)-1)]))\n lm_np_mean = poly_unnest %>% ungroup() %>% filter(sex == gen & value == 0) %>% \n select(age, lm_mean, num_item_id) %>% spread(age, lm_mean) %>% \n mutate(word_id = num_item_id) %>%\n select(-num_item_id) %>% select(word_id, colnames(.[,1:(ncol(.)-1)]))\n lm_np_sd = poly_unnest %>% ungroup() %>% filter(sex == gen & value == 0) %>% \n select(age, lm_sd, num_item_id) %>% spread(age, lm_sd) %>% \n mutate(word_id = num_item_id) %>%\n select(-num_item_id) %>% select(word_id, colnames(.[,1:(ncol(.)-1)]))\n \n if (type == \"WS\") {\n fname1 <- paste(\"p_m\", gen, lang, type, sep=\"-\")\n fname1 <- paste(fname1, \"csv\", sep = \".\")\n fname2 <- paste(\"p_sd\", gen, lang, type, sep=\"-\")\n fname2 <- paste(fname2, \"csv\", sep = \".\")\n fname3 <- paste(\"np_m\", gen, lang, type, sep=\"-\")\n fname3 <- paste(fname3, \"csv\", sep = \".\")\n fname4 <- paste(\"np_sd\", gen, lang, type, sep=\"-\")\n fname4 <- paste(fname4, \"csv\", sep = \".\")\n fname5 <- paste(\"BMin\", gen, lang, type, sep=\"-\")\n fname5 <- paste(fname5, \"csv\", sep = \".\")\n fname6 <- paste(\"Slope\", gen, lang, type, sep=\"-\")\n fname6 <- paste(fname6, \"csv\", sep = \".\")\n } else if (type == \"WG\") {\n fname1 <- paste(\"p_m\", gen, lang, type, resp, sep=\"-\")\n fname1 <- paste(fname1, \"csv\", sep = \".\")\n fname2 <- paste(\"p_sd\", gen, lang, type, resp, sep=\"-\")\n fname2 <- paste(fname2, \"csv\", sep = \".\")\n fname3 <- paste(\"np_m\", gen, lang, type, resp, sep=\"-\")\n fname3 <- paste(fname3, \"csv\", sep = \".\")\n fname4 <- paste(\"np_sd\", gen, lang, type, resp, sep=\"-\")\n fname4 <- paste(fname4, \"csv\", sep = \".\")\n fname5 <- paste(\"BMin\", gen, lang, type, resp, sep=\"-\")\n fname5 <- paste(fname5, \"csv\", sep = \".\")\n fname6 <- paste(\"Slope\", gen, lang, type, resp, sep=\"-\")\n fname6 <- paste(fname6, \"csv\", sep = \".\")\n }\n \n readr::write_csv(lm_p_mean, fname1)\n readr::write_csv(lm_p_sd, fname2)\n readr::write_csv(lm_np_mean, fname3)\n readr::write_csv(lm_np_sd, fname4)\n readr::write_csv(Bmin, fname5)\n readr::write_csv(slope2, fname6)\n \n}\n\nfname7 <- paste(\"word_list\", lang, type, resp, sep=\"-\")\nfname7 <- paste(fname7, \"csv\", sep = \".\")\nwordonly <- items_ws %>% mutate(word = definition, word_id = num_item_id) %>%\n select(word_id, word)\nreadr::write_csv(wordonly, fname7)\n\n#### Item Response Theory, IRT\ncat_cfg(mode = \"NoPreCAT\") # NoPreCAT\n\nnum_words <- max(admin_data_all$num_item_id)\ntest <- c(num_words, 400, 200, 100, 50, 25, 10, 5)\n\n# Fit IRT (overall)\ndata_irt <- admin_data_all %>% #filter(between(age, 16, 30)) %>%\n select(value,num_item_id,data_id) %>%\n spread(num_item_id,value) %>% select(-data_id)\ndata_irt <- Filter(var,data_irt)\nitems <- mirt(data_irt, 1, itemtype = \"2PL\", method = \"EM\", SE=TRUE)\n\n# Extract parameters only (a1, d, g, u)\nfor (i in 1:(length(items@ParObjects$pars) - 1)){\n print(i)\n if (i == 1) {\n irtparam <- coef(items)[[i]][1,1:4] \n } else {\n irtparam <- rbind(irtparam, coef(items)[[i]][1,1:4]) \n }\n}\nrownames(irtparam) <- 1:nrow(irtparam)\n\nIRT_Parameters <- irtparam %>% data.frame() %>% \n mutate(word_id = colnames(data_irt) %>% as.numeric()) %>% \n select(word_id, colnames(.[,1:(ncol(.)-1)]))\n\nif (type == \"WG\") {\n fname8 <- paste(\"IRT_Parameters\", lang, type, resp, sep=\"-\")\n fname8 <- paste(fname8, \"csv\", sep = \".\")\n} else if (type == \"WS\") {\n fname8 <- paste(\"IRT_Parameters\", lang, type, sep=\"-\")\n fname8 <- paste(fname8, \"csv\", sep = \".\")\n}\n\nreadr::write_csv(IRT_Parameters, fname8)\n### END of Modeling ###\n\n", "meta": {"hexsha": "1f3ec441ba5a1503aebc8010cddfbf3e5c5a1d38", "size": 15330, "ext": "r", "lang": "R", "max_stars_repo_path": "ipl/media/uploads/instruments/generateInstrumentFiles.r", "max_stars_repo_name": "fkellner/e-Babylab", "max_stars_repo_head_hexsha": "595130ed6a7d2a4ac097836c26a9004ba4aa1d76", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-11-08T13:29:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-02T15:36:49.000Z", "max_issues_repo_path": "ipl/media/uploads/instruments/generateInstrumentFiles.r", "max_issues_repo_name": "fkellner/e-Babylab", "max_issues_repo_head_hexsha": "595130ed6a7d2a4ac097836c26a9004ba4aa1d76", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-05-11T13:21:28.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-15T06:28:12.000Z", "max_forks_repo_path": "ipl/media/uploads/instruments/generateInstrumentFiles.r", "max_forks_repo_name": "fkellner/e-Babylab", "max_forks_repo_head_hexsha": "595130ed6a7d2a4ac097836c26a9004ba4aa1d76", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-03-17T13:10:14.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-12T16:35:00.000Z", "avg_line_length": 34.7619047619, "max_line_length": 115, "alphanum_fraction": 0.6061317678, "num_tokens": 4772, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3206222648499325}} {"text": "REBOL [ Title: \"ebeats\" File: %ebeats.r ]\nbeats: to integer! (now/time - now/zone * 115740 / 100000)\nprint join \"@\" beats / 100 \n\n", "meta": {"hexsha": "f7aaf08a93855fd5b8ac96034739d7ba5a765664", "size": 131, "ext": "r", "lang": "R", "max_stars_repo_path": "rebol/ebeats.r", "max_stars_repo_name": "rubyists/ebeats-implementations", "max_stars_repo_head_hexsha": "72f37f8a851718c8c9a55110c0a1b3ef37023f28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-11-05T20:22:17.000Z", "max_stars_repo_stars_event_max_datetime": "2015-11-05T20:22:17.000Z", "max_issues_repo_path": "rebol/ebeats.r", "max_issues_repo_name": "rubyists/ebeats-implementations", "max_issues_repo_head_hexsha": "72f37f8a851718c8c9a55110c0a1b3ef37023f28", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rebol/ebeats.r", "max_forks_repo_name": "rubyists/ebeats-implementations", "max_forks_repo_head_hexsha": "72f37f8a851718c8c9a55110c0a1b3ef37023f28", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.2, "max_line_length": 58, "alphanum_fraction": 0.641221374, "num_tokens": 50, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.32062225838908404}} {"text": "#!/usr/bin/env Rscript \n#plot tree and shared alleles of dataset only filtered for marker missingness\nargs = commandArgs(trailingOnly=TRUE)\nif (length(args)==0) {\n stop(\"At least one argument must be supplied (input file).n\", call.=FALSE)\n\n\nWD<-getwd()\nsetwd(WD)\noutputfile<-\"Alleles_largedist\"\nlargedist1a<- read.table (\"args[1]\", header = TRUE)\nattach (largedist1a)\nlargedist2a <- as.dist(largedist1a) #this gives a triangular matrix dist 2 is triangular matrix\nlargehca <- hclust(largedist2a, \"average\") #UPGMA\n\npdf (file =paste(outputfile,\"_tree.pdf\", sep=\"\"), width =10, height = 5, pointsize =6)\nplot(largehca, cex=0.8)\ndev.off()\nq()\nN\n", "meta": {"hexsha": "d3c201d9ca440abccb5bb700ba25de582d786db4", "size": 645, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/alleles.r", "max_stars_repo_name": "Hammarn/Popolipo", "max_stars_repo_head_hexsha": "4da2877603f2dcb2ac1fab2175b469b2154f879a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/alleles.r", "max_issues_repo_name": "Hammarn/Popolipo", "max_issues_repo_head_hexsha": "4da2877603f2dcb2ac1fab2175b469b2154f879a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/alleles.r", "max_forks_repo_name": "Hammarn/Popolipo", "max_forks_repo_head_hexsha": "4da2877603f2dcb2ac1fab2175b469b2154f879a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.7142857143, "max_line_length": 96, "alphanum_fraction": 0.7271317829, "num_tokens": 203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.32058913653265636}} {"text": "myfunction <- function() {\n\tx <- rnorm(100)\n\tmean(x)\n}\n\nsecond <- function(x) {\n\tx + rnorm(length(x))\n}", "meta": {"hexsha": "8f0c619672821f1210a2df96caa0b9de5cb73ec9", "size": 103, "ext": "r", "lang": "R", "max_stars_repo_path": "exploratorydataanalysis/mycode.r", "max_stars_repo_name": "mcsosa121/cdsci", "max_stars_repo_head_hexsha": "b8c8eb442eb8b84d2a1752d712317bd1d59e2a0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exploratorydataanalysis/mycode.r", "max_issues_repo_name": "mcsosa121/cdsci", "max_issues_repo_head_hexsha": "b8c8eb442eb8b84d2a1752d712317bd1d59e2a0e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exploratorydataanalysis/mycode.r", "max_forks_repo_name": "mcsosa121/cdsci", "max_forks_repo_head_hexsha": "b8c8eb442eb8b84d2a1752d712317bd1d59e2a0e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.875, "max_line_length": 26, "alphanum_fraction": 0.5825242718, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3205891365326563}} {"text": "rttpyft<-39354\nrttpskj<-92351\nssap10<-94430\nssap16<-82874\n\ndispread<-function(file)\n{\n tailstring<-paste(\"tail -n +3 \",file, sep=\"\")\n print(tailstring)\n options(\"warn\"=-1)\n disp<-read.table(pipe(tailstring),sep=\",\",header=T,check.names=T)\n disp\n}\n\n#udispplot<-function(fit)\n#{\n# file<-paste(fit,\".prn\",sep=\"\")\n# disp<-dispread(file)\n# dispplot(disp,fit,\"Displacement (NMi)\")\n#}\n\nxdispplot<-function(fit)\n{\n file<-paste(fit,\".prn\",sep=\"\")\n disp<-dispread(file)\n dispplot(disp,fit,\"E-W Displacement (NMi)\")\n}\n\nydispplot<-function(fit)\n{\n file<-paste(fit,\".prn\",sep=\"\")\n disp<-dispread(file)\n dispplot(disp,fit,\"N-S Displacement (NMi)\")\n}\n\ndispplot<-function(disp,fit,xtitle)\n{\n require(modreg)\n x<-as.numeric(gsub(\"X\",\"\",gsub(\"X\\\\.\",\"-\",names(disp)[-1])))\n# x<-as.numeric(substring(names(disp)[-1],2))\n# print(x)\n wd<-getwd()\n mtitle<-paste(wd,\"/\",fit,sep=\"\")\n plot(x,disp[1,-1],type='l',xlim<-c(-1000,1000), main= mtitle, \n ylab=\"Number\",xlab=xtitle)\n pts<-3\n fpts<-pts/length(x)\n# print(fpts)\n sdpr<-supsmu(x,as.numeric(as.vector(disp[1,-1])),span=fpts)\n lines(sdpr$x,sdpr$y,col=pts,lwd=2)\n lines(c(0,0),c(0,max(disp[1,-1])))\n lines(x,disp[3,-1])\n lines(x,disp[6,-1])\n lines(x,disp[12,-1])\n\n ps<-paste(fit,\".ps\",sep=\"\")\n dev.copy(device=postscript, file=ps, horizontal=FALSE,\n paper=\"letter\", height=6,width=6,pointsize=12)\n dev.off()\n}\n\nudisplot<-function(fit,releases)\n{\n require(modreg)\n file<-paste(fit,\".prn\",sep=\"\")\n disp<-dispread(file)\n dd<-disp[,-1]\n surv<-apply(dd,1,sum)\n deaths<- -1.0*diff(c(releases,surv))\n tt<-apply(dd,2,function(x)x*deaths/(surv+1e-8))\n x<-as.numeric(substring(names(dd),2))\n dpr<-apply(tt,2,sum)\n wd<-getwd()\n mtitle<-paste(wd,\"/\",fit,sep=\"\")\n plot(x,dpr,type='l',xlim<-c(1,1000),lwd=2, main=mtitle,\n xlab=\"Net Displacement (NMi)\",ylab=\"Number of Fish\")\n points(x,rep(0,length(x)),pch=3)\n\n pts<-2\n fpts<-pts/length(x)\n# print(fpts)\n sdpr<-supsmu(x,dpr,span=fpts)\n lines(sdpr$x,sdpr$y,col=pts,lwd=2)\n\n pts<-6\n fpts<-pts/length(x)\n# print(fpts)\n sdpr<-supsmu(x,dpr,span=fpts)\n lines(sdpr$x,sdpr$y,col=pts)\n\n sdpr<-supsmu(x,dpr)\n lines(sdpr$x,sdpr$y)\n\n cdpr<-cumsum(dpr)/sum(dpr)\n cprmed<-approx(cdpr,x,.5)\n print(paste(\"Median displacement distance = \",cprmed$y,\" NMi\",sep=\"\"))\n lines(x,cdpr*max(dpr))\n# lines(c(cprmed$y,cprmed$y),c(0,0.5*max(dpr)))\n arrows(cprmed$y,0.0,cprmed$y,0.5*max(dpr),code=1,angle=15,length=0.25,col=\"green\")\n\n ps<-paste(fit,\".ps\",sep=\"\")\n dev.copy(device=postscript, file=ps, horizontal=FALSE,\n paper=\"letter\", height=6,width=6,pointsize=12)\n dev.off()\n# wmf<-paste(fit,\".wmf\",sep=\"\")\n# dev.copy(device=win.metafile,filename = wmf, width = 6, height = 6, \n# pointsize=12)\n# dev.off()\n# pngname<-paste(fit,\".png\",sep=\"\")\n# dev.copy(device=png,filename=pngname)\n# dev.off()\n}\n\nudisplot2<-function(fit1,fit2,releases)\n{\n require(modreg)\n file<-paste(fit1,\".prn\",sep=\"\")\n disp<-dispread(file)\n dd<-disp[,-1]\n surv<-apply(dd,1,sum)\n deaths<- -1.0*diff(c(releases,surv))\n tt<-apply(dd,2,function(x)x*deaths/(surv+1e-8))\n x<-as.numeric(substring(names(dd),2))\n dpr<-apply(tt,2,sum)\n sdpr<-supsmu(x,dpr)\n lsty<-\"solid\"\n# plot(sdpr$x,sdpr$y,type='l',xlim<-c(1,1000),lwd=2,lty=lsty,\n# xlab=\"Net Displacement (NMi)\",ylab=\"Number of Fish\")\n cdpr<-cumsum(dpr)/sum(dpr)\n cprmed<-approx(cdpr,x,.5)\n print(paste(\"Median displacement distance = \",cprmed$y,\" NMi\",sep=\"\"))\n# lines(x,cdpr*max(dpr),lty=lsty)\n plot(x,cdpr*max(dpr),type='l',xlim<-c(1,1000),ylim<-c(0,3000),lwd=1,lty=lsty,\n xlab=\"Net Displacement (NMi)\",ylab=\"Number of Fish\")\n arrows(cprmed$y,0.0,cprmed$y,0.5*max(dpr),code=1,angle=15,length=0.25,lwd=1,lty=lsty)\n\n file<-paste(fit2,\".prn\",sep=\"\")\n disp<-dispread(file)\n dd<-disp[,-1]\n surv<-apply(dd,1,sum)\n deaths<- -1.0*diff(c(releases,surv))\n tt<-apply(dd,2,function(x)x*deaths/(surv+1e-8))\n x<-as.numeric(substring(names(dd),2))\n dpr<-apply(tt,2,sum)\n sdpr<-supsmu(x,dpr)\n lsty<-\"dashed\"\n# lines(sdpr$x,sdpr$y,lwd=2,lty=lsty)\n cdpr<-cumsum(dpr)/sum(dpr)\n cprmed<-approx(cdpr,x,.5)\n print(paste(\"Median displacement distance = \",cprmed$y,\" NMi\",sep=\"\"))\n lines(x,cdpr*max(dpr),lty=lsty)\n arrows(cprmed$y,0.0,cprmed$y,0.5*max(dpr),code=1,angle=15,length=0.25,lty=lsty)\n fit<-\"displacements\"\n ps<-paste(fit,\".ps\",sep=\"\")\n dev.copy(device=postscript, file=ps, horizontal=FALSE,\n paper=\"letter\", height=6,width=6,pointsize=12)\n dev.off()\n# wmf<-paste(fit,\".wmf\",sep=\"\")\n# dev.copy(device=win.metafile,filename = wmf, width = 6, height = 6, \n# pointsize=12)\n# dev.off()\n pngname<-paste(fit,\".png\",sep=\"\")\n dev.copy(device=png,filename=pngname)\n dev.off()\n}\n\nempdisplace<-function(file,title)\n{\n recaps<-read.csv(file,header=T)\n print(paste(title, \"; median = \",median(recaps$distnm),sep=\"\"))\n mt<-paste(title, \" (\",median(recaps$distnm),\" NMi)\", sep=\"\")\n fr<-range(0,1000)\n hist(recaps$distnm,breaks=80, xlim=fr, main=mt, xlab=\"Distance Traveled (Nmi)\",\n ylab=\"Number of Tag Returns\")\n}\n\nempdisp3<-function()\n{\n nf<-layout(matrix(c(1,2,3),3,1,byrow=TRUE))\n layout.show(nf)\n empdisplace(\"SSS.csv\", \"SSAP Skipjack\")\n empdisplace(\"RTS.csv\", \"RTTP Skipjack\")\n empdisplace(\"RTY.csv\", \"RTTP Yellowfin\")\n\n dev.copy(device=postscript, file=\"empdisp3.ps\", horizontal=FALSE, \n paper=\"letter\", height=9,width=6,pointsize=12)\n dev.off() \n\n dev.copy(device=png,file=\"empdisp3.png\")\n dev.off() \n\n}\n\ncomboplot<-function()\n{\n releases<-rttpyft\n empfile <-paste(\"RTY.csv\")\n print(empfile)\n title <- paste(\"RTTP Yellowfin\")\n recaps<-read.csv(empfile,header=T)\n fr<-range(0,1000)\n oldpar <- par(no.readonly = TRUE)\n par(mar=c(5, 5, 5, 5) + 0.1)\n hist(recaps$distnm,breaks=80, xlim=fr, main=title, axes=F, xlab=NULL, ylab=NULL)\n axis(1)\n mtext(text = \"Distance Traveled (Nmi)\", side = 1,line=3)\n axis(2)\n mtext(text=\"Recaptured Tags\",side=2,line=3)\n\n require(modreg)\n fit<-paste(\"fit0b_pdispl_q\")\n file<-paste(fit,\".prn\",sep=\"\")\n print(file)\n disp<-dispread(file)\n dd<-disp[,-1]\n surv<-apply(dd,1,sum)\n deaths<- -1.0*diff(c(releases,surv))\n tt<-apply(dd,2,function(x)x*deaths/(surv+1e-8))\n x<-as.numeric(substring(names(dd),2))\n dpr<-apply(tt,2,sum)\n sdpr<-supsmu(x,dpr)\n\n par(new = TRUE)\n plot(sdpr$x,sdpr$y, type = \"n\", xlim=fr,ylim<-c(0,3500), axes = FALSE, xlab=\"\", ylab =\"\")\n lines(sdpr$x,sdpr$y,col=\"blue\",xlab=\"\", ylab =\"\",lwd=2)\n axis(4)\n mtext(text=\"Tags At Liberty\",side=4, col=\"blue\",line=3)\n\n cdpr<-cumsum(dpr)/sum(dpr)\n cprmed<-approx(cdpr,x,.5)\n print(paste(\"Median displacement distance = \",cprmed$y,\" NMi\",sep=\"\"))\n# lines(x,cdpr*max(dpr))\n# lines(c(cprmed$y,cprmed$y),c(0,0.5*max(dpr)))\n# arrows(cprmed$y,0.0,cprmed$y,0.5*max(dpr),code=1,angle=15,length=0.25,col=\"green\")\n box()\n par(oldpar)\n\n dev.copy(device=postscript, file=\"yft_combo.ps\", horizontal=FALSE,\n paper=\"letter\", height=6,width=6,pointsize=12)\n dev.off()\n# wmf<-paste(fit,\".wmf\",sep=\"\")\n# dev.copy(device=win.metafile,filename = wmf, width = 6, height = 6, \n# pointsize=12)\n# dev.off()\n# pngname<-paste(fit,\".png\",sep=\"\")\n# dev.copy(device=png,filename=pngname)\n# dev.off()\n}\n\n", "meta": {"hexsha": "2beb4b98cfe21e7251d7222347c607d70f453f1c", "size": 7143, "ext": "r", "lang": "R", "max_stars_repo_path": "25mt/scripts/old-displace.r", "max_stars_repo_name": "johnoel/tagest", "max_stars_repo_head_hexsha": "be0a6b164683c448c90f0c3952343f5963eece3d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2015-08-26T17:14:02.000Z", "max_stars_repo_stars_event_max_datetime": "2015-11-17T04:18:56.000Z", "max_issues_repo_path": "25mt/scripts/old-displace.r", "max_issues_repo_name": "johnoel/tagest", "max_issues_repo_head_hexsha": "be0a6b164683c448c90f0c3952343f5963eece3d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2015-08-20T00:51:05.000Z", "max_issues_repo_issues_event_max_datetime": "2016-11-16T19:14:48.000Z", "max_forks_repo_path": "25mt/scripts/old-displace.r", "max_forks_repo_name": "johnoel/tagest", "max_forks_repo_head_hexsha": "be0a6b164683c448c90f0c3952343f5963eece3d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2016-11-16T00:52:23.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-10T02:17:40.000Z", "avg_line_length": 28.572, "max_line_length": 91, "alphanum_fraction": 0.6404871903, "num_tokens": 2722, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3204592703345866}} {"text": "library(changepoint)\r\nlibrary(dplyr)\r\n\r\nair_data <- readRDS(\"Air_box.rds\")\r\n\r\nsensor_ID <- unique(air_data$ID)\r\n\r\ndata_f_ID = character(0)\r\ndata_f_CP = numeric(0)\r\ndata_f_lat = numeric(0)\r\ndata_f_lon = numeric(0)\r\nup_down = numeric (0)\r\n\r\nfor(j in 1:length(sensor_ID)){ #length(sensor_ID)\r\n if( j == 127 ) next #error message Data must have at least 4 observations to fit a change point model.\r\n\r\n\tPM25_of_sensor <- subset(air_data, ID == sensor_ID[j]) \r\n\r\n\tdel_index_arr = group_by(PM25_of_sensor, as.Date(f_time, tz = \"Etc/GMT+8\")) %>%\r\n\tsummarise( length(PM25) > 4 ) #change point detection is not available when length is shorter than 4\r\n\r\n\tDay_ID <- unique(as.Date(PM25_of_sensor$f_time, tz = \"Etc/GMT+8\"))\r\n\tdel_index = which(as.data.frame(del_index_arr[,2])[,1] %in% 0)\r\n\r\n\tif(length(del_index) != 0) PM25_of_sensor <- PM25_of_sensor[ -which( Day_ID[del_index] == as.Date(PM25_of_sensor$f_time, tz = \"Etc/GMT+8\") ),]\r\n\t#get Day_ID after deleting the ones with length shorter than 4\r\n\tDay_ID <- unique(as.Date(PM25_of_sensor$f_time, tz = \"Etc/GMT+8\"))\r\n\r\n\r\n\tch_id_group = group_by(PM25_of_sensor, as.Date(f_time, tz = \"Etc/GMT+8\")) %>%\r\n\tsummarise(\r\n\t ifelse(length(cpt.meanvar(PM25,penalty=\"Manual\",pen.value=\"4*log(n)\",method=\"BinSeg\",Q=2,class=FALSE)) == 0, NA, list(cpt.meanvar(PM25,penalty=\"Manual\",pen.value=\"4*log(n)\",method=\"BinSeg\",Q=2,class=FALSE))) \r\n\t )\r\n\r\n\t# numeric_id_in_each_day ch_id_group[[2]][[DAY_i]]\r\n\t# each_day ch_id_group[[1]][[DAY_i]]\r\n\t# sen_of_day <- PM25_of_sensor[which(as.Date(PM25_of_sensor$f_time, tz = \"Etc/GMT+8\") == Day_ID[DAY_i]),]\r\n\t# sen_of_day$f_time[ch_id_group[[2]][[DAY_i]]]\r\n\tif(length(Day_ID)==0) next\r\n\tmy_vec = numeric (0)\r\n\t\r\n\t\r\n\t\r\n\tID_of_full_length = 0\r\n\tfor(Day_i in 1:length(Day_ID)){\r\n\t The_ID = ch_id_group[[2]][[Day_i]]\r\n\t sen_of_day <- PM25_of_sensor[which(as.Date(PM25_of_sensor$f_time, tz = \"Etc/GMT+8\") == Day_ID[Day_i]),]\r\n\t \r\n\t for(k in 1:length(The_ID)){\r\n\t if(is.na( PM25_of_sensor$PM25[ ID_of_full_length + The_ID[k] + 1 ] )){\r\n\t print(ID_of_full_length + The_ID[k] + 1)\r\n\t up_down <- append(up_down, -1) }\r\n\t\t else if( (PM25_of_sensor$PM25[ ID_of_full_length + The_ID[k] ]) > (PM25_of_sensor$PM25[ ID_of_full_length + The_ID[k] + 1 ]) ) {up_down <- append(up_down, 0)}\r\n\t\t else{up_down <- append(up_down, 1)}\r\n\t }\r\n\t my_vec <- append(my_vec, sen_of_day$f_time[The_ID])\r\n\t \r\n\t ID_of_full_length <- ID_of_full_length + length(sen_of_day$PM25)\r\n\t}\r\n\t# make it as dataframe\r\n\tx <- rep(sensor_ID[j], length(my_vec))\r\n\tlat <- rep(PM25_of_sensor$Coor_x[1], length(my_vec))\r\n\tlon <- rep(PM25_of_sensor$Coor_y[1], length(my_vec))\r\n\t\r\n\tdata_f_CP <- append(data_f_CP, my_vec)\r\n\tdata_f_ID <- append(data_f_ID, as.character(x))\r\n\tdata_f_lat <- append(data_f_lat, lat)\r\n\tdata_f_lon <- append(data_f_lon, lon)\r\n}\r\ndf <- data.frame(data_f_ID, data_f_CP, data_f_lat, data_f_lon, up_down)\r\nsaveRDS(df, file = \"C:\\\\Users\\\\anthony\\\\Desktop\\\\Changepoint.rds\")", "meta": {"hexsha": "b7eac3acb80b0f27dfdf4e7228618324ef379d04", "size": 2934, "ext": "r", "lang": "R", "max_stars_repo_path": "lastest_data_out_9_18.r", "max_stars_repo_name": "ninetf135246/R-notes", "max_stars_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lastest_data_out_9_18.r", "max_issues_repo_name": "ninetf135246/R-notes", "max_issues_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lastest_data_out_9_18.r", "max_forks_repo_name": "ninetf135246/R-notes", "max_forks_repo_head_hexsha": "60f797d0157b8beab7d3328f1014480265dba3e1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.323943662, "max_line_length": 212, "alphanum_fraction": 0.6799591002, "num_tokens": 955, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056040203136, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3204592584655265}} {"text": "#' @title is_leap_year\n#' @description unknown\n#' @family abysmally documented\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\nis_leap_year = function (yr) {\n .Deprecated(msg=\"Why not use lubridate::leap_year() instead?\")\n #\\\\test to see if yr is a leap year\n yr%%4 == 0 & (yr%%100 != 0 | yr%%400 == 0 )\n}\n\n", "meta": {"hexsha": "6550911392e6e37d96e843022ae59aa85324ed79", "size": 327, "ext": "r", "lang": "R", "max_stars_repo_path": "R/is_leap_year.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/is_leap_year.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/is_leap_year.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.25, "max_line_length": 64, "alphanum_fraction": 0.6422018349, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.32043083010208784}} {"text": "#install.packages(\"Rcpp\")\r\n#install.packages(\"anytime\")\r\nlibrary(jsonlite)\r\nlibrary(hrbrthemes)\r\nlibrary(tidyverse)\r\nlibrary(dplyr)\r\nlibrary(anytime)\r\n\r\n\r\n# the wallets address accepting ransom payments for petya ransomware from thegaurdian.com\r\n\r\nwallets <- c(\r\n \"1Mz7153HMuxXTuR2R1t78mGSdzaAtNbBWX\"\r\n)\r\n\r\n# easy way to get each wallet info vs bringing in the Rbitcoin package\r\n\r\nsprintf(\"https://blockchain.info/rawaddr/%s\", wallets) %>%\r\n map(jsonlite::fromJSON) -> chains\r\n\r\n# get the current USD conversion (tho the above has this, too)\r\n\r\ncurr_price <- jsonlite::fromJSON(\"https://blockchain.info/ticker\")\r\n\r\n# calculate some basic stats\r\n\r\ntot_bc <- sum(map_dbl(chains, \"total_received\")) / 10e7\r\ntot_usd <- tot_bc * curr_price$USD$last\r\ntot_xts <- sum(map_dbl(chains, \"n_tx\"))\r\n\r\n# This needs to be modified once the counters go above 100 and also needs to\r\n# account for rate limits in the blockchain.info API\r\n\r\npaged <- which(map_dbl(chains, \"n_tx\") > 50)\r\nif (length(paged) > 0) {\r\n sprintf(\"https://blockchain.info/rawaddr/%s?offset=50\", wallets[paged]) %>%\r\n map(jsonlite::fromJSON) -> chains2\r\n}\r\n\r\n# We want hourly data across all transactions\r\n\r\nmap_df(chains, \"txs\") %>%\r\n bind_rows(map_df(chains2, \"txs\")) %>% \r\n mutate(xts = anytime::anytime(time),\r\n xts = as.POSIXct(format(xts, \"%Y-%m-%d %H:00:00\"), origin=\"GMT\")) %>%\r\n count(xts) -> xdf\r\n\r\n# Plot it\r\n\r\nggplot(xdf, aes(xts, y = n)) +\r\n geom_col() +\r\n scale_y_comma(limits = c(0, max(xdf$n))) +\r\n labs(x = \"Day/Time (GMT)\", y = \"# Transactions\",\r\n title = \"Bitcoin Payments-per-hour summary for Petya Ransomware\",\r\n subtitle=sprintf(\"%s transactions to-date; %s total bitcoin; %s USD; Chart generated at: %s EDT\",\r\n scales::comma(tot_xts), tot_bc, scales::dollar(tot_usd), Sys.time())) +\r\n theme_ipsum_rc(grid=\"Y\")", "meta": {"hexsha": "aa987a4069ed26017d6c14ce366ecdfe2cc4e759", "size": 1843, "ext": "r", "lang": "R", "max_stars_repo_path": "bitcoin_ransom_summary_petya.r", "max_stars_repo_name": "mayukhdifferent/Petya_Ransomware_paymenttracking_using_R", "max_stars_repo_head_hexsha": "f0664c264003514ba06fdee08a18dd03b50d9f3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-03-17T16:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-17T16:05:39.000Z", "max_issues_repo_path": "bitcoin_ransom_summary_petya.r", "max_issues_repo_name": "mayukhdifferent/Petya_Ransomware_paymenttracking_using_R", "max_issues_repo_head_hexsha": "f0664c264003514ba06fdee08a18dd03b50d9f3c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bitcoin_ransom_summary_petya.r", "max_forks_repo_name": "mayukhdifferent/Petya_Ransomware_paymenttracking_using_R", "max_forks_repo_head_hexsha": "f0664c264003514ba06fdee08a18dd03b50d9f3c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3333333333, "max_line_length": 105, "alphanum_fraction": 0.6673901248, "num_tokens": 543, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226291, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.32043082260878164}} {"text": "library(Seurat)\n# library(reticulate)\n# use_python(\"/Users/cuizhe/anaconda3/bin/python\",required = TRUE)\nsetwd(\"/Users/cuizhe/project_local/2020-06-21-seurat-rna-seq-10x-5k-v3\")\n# load 10x nextgem pbmc data\ncounts <- Read10X_h5(\"./tmp5k_nextgem3.0.2/5k_pbmc_v3_nextgem_filtered_feature_bc_matrix.h5\")\nrownames(counts) <- make.unique(rownames(counts))\nrna <- CreateSeuratObject(counts = counts, assay = 'RNA', min.cells = 5, min.features = 500, project = '10x_RNA')\nrna <- RenameCells(rna, add.cell.id = 'rna')\nmito.features <- grep(pattern = \"^MT-\", x = rownames(x = rna), value = TRUE)\npercent.mito <- Matrix::colSums(x = GetAssayData(object = rna, slot = 'counts')[mito.features, ]) / Matrix::colSums(x = GetAssayData(object = rna, slot = 'counts'))\nrna$percent.mito <- percent.mito\n\n# QC\nrna <- subset(x = rna, subset = nCount_RNA > 2000 & nCount_RNA < 20000 & percent.mito < 0.2)\n\n# preprocessing\nrna <- NormalizeData(rna)\nrna <- FindVariableFeatures(rna, nfeatures = 3000)\nrna <- ScaleData(rna)\nrna <- RunPCA(rna, npcs = 100)\nrna <- RunTSNE(rna, dims = 1:30)\nrna <- FindNeighbors(rna, dims = 1:30)\nrna <- FindClusters(rna, resolution = 0.4, algorithm = 3)\nrna <- RunUMAP(rna, graph = 'RNA_nn', metric = 'euclidean')\n\nDimPlot(rna, reduction = \"umap\")\nrna.markers <- FindAllMarkers(rna, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)\nView(rna.markers %>% group_by(cluster) %>% top_n(n = 20, wt = avg_logFC))\ntop10 <- rna.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)\nDoHeatmap(rna, features = top10$gene) + NoLegend()\nFeaturePlot(rna, features = c(\"IL7R\",\"CCR7\",\"S100A4\",\"CD14\",\"LYZ\",\"MS4A1\",\"CD8A\",\"FCGR3A\",\"MS4A7\", \"GNLY\",\"NKG7\", \"FCER1A\", \"CST3\",\"PPBP\"))\n\nnew.cluster.ids <- c(\n \"Naive CD4+ T\",\n 'CD14+ Mono',\n 'Memory CD4+',\n 'NK',\n 'NK',\n 'B',\n 'B',\n 'CD8+ T',\n 'FCGR3A+ Mono',\n 'NK',\n 'DC', \n 'Platelet')\n\nnames(x = new.cluster.ids) <- levels(x = rna)\nrna <- RenameIdents(object = rna, new.cluster.ids)\nrna$celltype <- Idents(rna)\nnk.cells <- subset(rna, subset = celltype == 'NK')\ngzmk <- GetAssayData(nk.cells, assay = 'RNA', slot = 'data')['GZMK', ]\nnk.cells$bright <- ifelse(gzmk > 1, 'NK bright', 'NK dim')\nctypes <- as.vector(rna$celltype)\nnames(ctypes) <- names(rna$celltype)\nctypes[Cells(nk.cells)] <- nk.cells$bright\nrna <- AddMetaData(rna, metadata = ctypes, col.name = 'celltype')\nsaveRDS(rna, \"./tmp5k_nextgem3.0.2/pbmc_5k_nextgem.rds\")\n", "meta": {"hexsha": "f7ef0ed9c3db7f9ce4a8c107f538f57f7dc92ffa", "size": 2398, "ext": "r", "lang": "R", "max_stars_repo_path": "10x_assign_label/scRNA-seq-5k-v3/bin/run_seurat_nextgem.r", "max_stars_repo_name": "mrcuizhe/svmATAC", "max_stars_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-09-23T13:14:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T00:35:09.000Z", "max_issues_repo_path": "10x_assign_label/scRNA-seq-5k-v3/bin/run_seurat_nextgem.r", "max_issues_repo_name": "mrcuizhe/svmATAC", "max_issues_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "10x_assign_label/scRNA-seq-5k-v3/bin/run_seurat_nextgem.r", "max_forks_repo_name": "mrcuizhe/svmATAC", "max_forks_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.6440677966, "max_line_length": 164, "alphanum_fraction": 0.6718098415, "num_tokens": 852, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3204196065939125}} {"text": "# Use XGB binary prediction model on raster data\n\nrasterXgb <- function(xgbModel, rData, nLayers) {\n\n for (i in 1:nLayers) {\n rastPred[i,] <- predict(xgbModel, rData[i,], missing=NA)\n print(\"Current rasterXgb loop iteration: \", i)\n }\n}\n", "meta": {"hexsha": "b84d677762679b5fd516a52c877a1938b6a55ecb", "size": 256, "ext": "r", "lang": "R", "max_stars_repo_path": "static/r/rasterXgb.r", "max_stars_repo_name": "Thru-Echoes/PEARL2.0", "max_stars_repo_head_hexsha": "dbc62f8d77c6b846ab8fef3aa3afc03adc8175d4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-09-22T03:40:50.000Z", "max_stars_repo_stars_event_max_datetime": "2017-09-22T03:40:50.000Z", "max_issues_repo_path": "static/r/rasterXgb.r", "max_issues_repo_name": "Thru-Echoes/BirdShader", "max_issues_repo_head_hexsha": "cc7a694956e277a772152a3eec4a29db10ec7e6c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "static/r/rasterXgb.r", "max_forks_repo_name": "Thru-Echoes/BirdShader", "max_forks_repo_head_hexsha": "cc7a694956e277a772152a3eec4a29db10ec7e6c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-09-20T21:04:49.000Z", "max_forks_repo_forks_event_max_datetime": "2017-09-20T21:04:49.000Z", "avg_line_length": 25.6, "max_line_length": 64, "alphanum_fraction": 0.640625, "num_tokens": 77, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316860482763, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.32041960033594047}} {"text": "# Data Visualization with ggplot\n# Reading Datasets with read_csv\n# Video 1.3\n\n# This course assumes that you already have the Tidyverse and ggmap libraries installed\n# on your system. If you do not, uncomment and execute the lines below.\n\n#install.packages(\"tidyverse\")\n#install.packages(\"ggmap\")\n\n# Load the Tidyverse\nlibrary(tidyverse)\n\n# Read the college dataset\ncollege <- read_csv('http://672258.youcanlearnit.net/college.csv')\n\n# Take a look at the data\nsummary(college)\n\n# Convert state, region, highest_degree, control, and gender to factors\ncollege <- college %>%\n mutate(state=as.factor(state), region=as.factor(region),\n highest_degree=as.factor(highest_degree),\n control=as.factor(control), gender=as.factor(gender))\n\n# Take a look at the data\nsummary(college)\n\n# What's going on with loan_default_rate?\nunique(college$loan_default_rate)\n\n# Let's just force that to numeric and the \"NULL\" will convert to N/A\ncollege <- college %>%\n mutate(loan_default_rate=as.numeric(loan_default_rate))\n\n# Take a look at the data\nsummary(college)\n\n", "meta": {"hexsha": "e5b71387cc58006c926a42e51916c80a6c8dc260", "size": 1066, "ext": "r", "lang": "R", "max_stars_repo_path": "ggplot/1_3/read_csv_complete.r", "max_stars_repo_name": "snakexv/R_ggplot2", "max_stars_repo_head_hexsha": "83c70c9ed6dacac2ea34179c11621d46c3fe37d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ggplot/1_3/read_csv_complete.r", "max_issues_repo_name": "snakexv/R_ggplot2", "max_issues_repo_head_hexsha": "83c70c9ed6dacac2ea34179c11621d46c3fe37d6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ggplot/1_3/read_csv_complete.r", "max_forks_repo_name": "snakexv/R_ggplot2", "max_forks_repo_head_hexsha": "83c70c9ed6dacac2ea34179c11621d46c3fe37d6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.3333333333, "max_line_length": 87, "alphanum_fraction": 0.7523452158, "num_tokens": 264, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.6261241842048093, "lm_q1q2_score": 0.32039814166445035}} {"text": "# Information about code:\r\n# This code corresponds to a chapter in my MSc thesis for\r\n# Chapter 3, the section on Gender analysis: data aquisition\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n\r\n# Libraries\r\nlibrary(httr)\r\n\r\n\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\n# Section - get UN data\r\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\r\nprint(paste0(Sys.time(), \" --- get UN data\"))\r\n\r\n# http://ec2-54-174-131-205.compute-1.amazonaws.com/API/Information.php\r\n\r\nindicators <- list(`24106` = \"schl_f\", # GDI: Mean years of schooling (females aged 25 years and above)\r\n `24206` = \"schl_m\", # GDI: Mean years of schooling (males aged 25 years and above)\r\n `120606`= \"lexp_f\", # GDI: Life expectancy at birth, female\r\n `121106`= \"lexp_m\", # GDI: Life expectancy at birth, male\r\n `123306`= \"esch_f\", # GDI: Expected years of schooling, females\r\n `123406`= \"esch_m\", # GDI: Expected years of schooling, males\r\n `123506`= \"gnip_f\", # GDI: Estimated GNI per capita (PPP), female\r\n `123606`= \"gnip_m\", # GDI: Estimated GNI per capita (PPP), male\r\n `137906`= \"gdip_a\" # GDI\r\n)\r\nquery_indicator <- do.call(paste, c(as.list(names(indicators)), sep = \",\"))\r\npath <- paste0(\r\n \"http://ec2-54-174-131-205.compute-1.amazonaws.com/API/HDRO_API.php/indicator_id=\",\r\n query_indicator, \"/year=1990,2017/structure=cyi\")\r\n\r\nrequest <- GET(url = path)\r\nrequest$status_code\r\nresponse <- content(request, as = \"text\", encoding = \"UTF-8\")\r\nparsed_json <- fromJSON(response, flatten = TRUE)\r\n\r\ndf <- parsed_json$indicator_value %>% data.frame() %>% t() %>% data.frame()\r\ndf$col <- rownames(df)\r\ndf <- data.table(df)\r\ndf[, c('country', 'year', 'indicator') := tstrsplit(col, \".\", fixed=TRUE)]\r\nnames(df)[which(names(df)==\".\")] <- \"value\"\r\n\r\ndf_r <- dcast(df[year==2017], country ~ indicator, value.var=\"value\")\r\n\r\nnames(df_r) <- unlist(sapply(names(df_r), function(name) {\r\n # print(name)\r\n if(name %in% names(indicators)) {\r\n indicators[name]\r\n } else { name }\r\n}))\r\n\r\ncols <- c(\"A-3\", \"Country\")\r\nlu <- get_lp_statoid()[, ..cols]\r\ndf_r <- merge(df_r, lu, by.x=\"country\", by.y=\"A-3\", all.x=T, all.y=F); rm(lu)\r\ndf_r <- df_r[!is.na(country)]\r\n\r\nun_path <- \"data/2019-11-11-un-indicators/\"\r\ndir.create(un_path)\r\nfilename_write = paste0(un_path, '2019-11-12-indicators.csv')\r\nwrite.csv(df_r, filename_write, na='', row.names=F, fileEncoding=\"UTF-8\")\r\n", "meta": {"hexsha": "25c57e93e71be84c4be5fd6a2632c6ba425ef557", "size": 2535, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch3-gender/analysis1/data-un.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch3-gender/analysis1/data-un.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch3-gender/analysis1/data-un.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.5573770492, "max_line_length": 104, "alphanum_fraction": 0.5708086785, "num_tokens": 722, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6261241632752915, "lm_q1q2_score": 0.3203981309544686}} {"text": "library(Romp)\n\nn <- 20000\n\ntruth <- 2262\nc <- c_primesbelow(n)\nf77 <- f77_primesbelow(n)\nf90 <- f90_primesbelow(n)\nrcpp <- rcpp_primesbelow(n)\n\nstopifnot(all.equal(truth, c))\nstopifnot(all.equal(truth, f77))\nstopifnot(all.equal(truth, f90))\nstopifnot(all.equal(truth, rcpp))\n\n", "meta": {"hexsha": "018e4c18ba911e5ec55ce28dece62a1c98e5bdae", "size": 276, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/primesbelow.r", "max_stars_repo_name": "wrathematics/Romp", "max_stars_repo_head_hexsha": "1360c966eeb9eb339f1db9580d06ecbaf4a270e6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2015-04-21T12:57:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-14T20:44:28.000Z", "max_issues_repo_path": "tests/primesbelow.r", "max_issues_repo_name": "wrathematics/Romp", "max_issues_repo_head_hexsha": "1360c966eeb9eb339f1db9580d06ecbaf4a270e6", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2017-07-23T15:35:09.000Z", "max_issues_repo_issues_event_max_datetime": "2019-04-21T03:46:02.000Z", "max_forks_repo_path": "tests/primesbelow.r", "max_forks_repo_name": "wrathematics/Romp", "max_forks_repo_head_hexsha": "1360c966eeb9eb339f1db9580d06ecbaf4a270e6", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2016-09-22T14:20:40.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-07T09:15:08.000Z", "avg_line_length": 17.25, "max_line_length": 33, "alphanum_fraction": 0.7210144928, "num_tokens": 97, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32037699047283136}} {"text": "#!/usr/bin/env Rscript\nargs = commandArgs(trailingOnly=TRUE)\n\nfirstFile = args[1]\nnumFiles = as.integer(args[2])\nratioFile = args[3]\n\nnumRatios = ncol(read.table(ratioFile))\nrows = nrow(read.table(firstFile))\ncolm = ncol(read.table(firstFile))\n\nconcatMatrix <- matrix(nrow=numRatios, ncol=colm)\n\nfileName <- substr(firstFile,1, nchar(firstFile)-16)\nnumExec <- substr(firstFile, nchar(firstFile)-13, nchar(firstFile)) \n\nratios = read.table(ratioFile)\n\nfor( h in 1:numRatios){\n currFile = paste( fileName, ratios[,h], numExec, sep=\"\")\n concatMatrix[h,1] = ratios[,h] / 10\n for( i in 2:colm) {\t\n concatMatrix[h,i] = read.table(currFile)[rows,i]\n }\n}\n\noutput <- paste(fileName, numFiles,\"_statRatios.out\",sep=\"\");\n \nwrite.table(format(concatMatrix, digits = 4, scientific = F, trim = T, drop0trailing = T), quote = F, file=output, row.names=F, col.names=F, sep = \"\\t\")\n", "meta": {"hexsha": "cc2010a2883f512ccc9596e3ba92a798ddf93359", "size": 885, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/avrAndstdRat.r", "max_stars_repo_name": "amphybio/odyn-utilities", "max_stars_repo_head_hexsha": "3f598b154b0ae646205e528193e6aa1a5bbf4dbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/avrAndstdRat.r", "max_issues_repo_name": "amphybio/odyn-utilities", "max_issues_repo_head_hexsha": "3f598b154b0ae646205e528193e6aa1a5bbf4dbb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2019-06-04T20:47:45.000Z", "max_issues_repo_issues_event_max_datetime": "2019-06-04T21:15:56.000Z", "max_forks_repo_path": "scripts/avrAndstdRat.r", "max_forks_repo_name": "amphybio/odyn-utilities", "max_forks_repo_head_hexsha": "3f598b154b0ae646205e528193e6aa1a5bbf4dbb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-05-29T17:19:35.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-29T17:19:35.000Z", "avg_line_length": 29.5, "max_line_length": 152, "alphanum_fraction": 0.6847457627, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32037698218554034}} {"text": "# palisades dot lakes at gmail dot com\n# 2018-10-09\n#-----------------------------------------------------------------\nif (file.exists('e:/porta/projects/xfp')) {\n setwd('e:/porta/projects/xfp')\n} else {\n setwd('c:/porta/projects/xfp')\n}\nsource('src/scripts/R/functions.r')\n#-----------------------------------------------------------------\ndata.folder <- file.path('data','interpolate','macro')\nplot.folder <- file.path('plots','interpolate','macro')\ndata.files <- list.files(\n path=data.folder,pattern='*.macro.tsv',full.names=FALSE) \n#-----------------------------------------------------------------\nfor (data.file in data.files) {\n data <- read.tsv(file.path(data.folder,data.file))\n print(nrow(data))\n data$functional <- factor(x=data$functional,levels=functionals)\n knots <- \n data[(data$functional=='argmin') | \n (data$functional=='valueKnot') | \n (data$functional=='slopeKnot') ,]\n curves <- \n data[(data$functional!='argmin') & \n (data$functional!='valueKnot') & \n (data$functional!='slopeKnot') ,]\n dev.on(\n file=file.path(plot.folder,data.file),\n aspect=1016/1856,\n width=1856)\n plot <- ggplot(curves, aes(x=x, y=y, color=functional)) +\n geom_point(data=knots,size=4.0) + \n geom_line(size=1.0) + \n scale_color_manual(values=(functional.colors),drop=FALSE) +\n theme(text=element_text(size=24))#+\n #ggtitle(\"lower is better\")\n print(plot)\n dev.off() }\n#-----------------------------------------------------------------\n", "meta": {"hexsha": "6dac84bb46c34819710df9ce12be2989b4e3e4d4", "size": 1493, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/R/plot.r", "max_stars_repo_name": "palisades-lakes/xfp", "max_stars_repo_head_hexsha": "db12850463cd2033c9814f38e26a820fa8f58bb2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-31T16:45:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-31T16:45:53.000Z", "max_issues_repo_path": "src/scripts/R/plot.r", "max_issues_repo_name": "palisades-lakes/xfp", "max_issues_repo_head_hexsha": "db12850463cd2033c9814f38e26a820fa8f58bb2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/R/plot.r", "max_forks_repo_name": "palisades-lakes/xfp", "max_forks_repo_head_hexsha": "db12850463cd2033c9814f38e26a820fa8f58bb2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4146341463, "max_line_length": 66, "alphanum_fraction": 0.5371734762, "num_tokens": 370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.32037698218554034}} {"text": "library(pheatmap) # heatmap packages\n\n# read the file\nx1= read.delim(\"E:/denboer2009-expr.txt\")\nrownames(x1)= x1[,1]\nx3 = x1[,-1]\nx= as.data.frame(t(x3))\n\n# plot & save heatmap\njpeg(\"Heatmap.jpg\")\npheatmap(x)\ndev.off\n", "meta": {"hexsha": "305b22900c0ae44212ffe8c87d5aad4325245ad2", "size": 217, "ext": "r", "lang": "R", "max_stars_repo_path": "Heatmap/Heatmap.r", "max_stars_repo_name": "ZahraFarajollahi/Gene-expression-data-in-leukemia", "max_stars_repo_head_hexsha": "10fa51d379477353376e83c829cc04346330b1d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-02T12:24:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-02T12:24:01.000Z", "max_issues_repo_path": "Heatmap/Heatmap.r", "max_issues_repo_name": "ZahraFarajollahi/Gene-expression-data-in-leukemia", "max_issues_repo_head_hexsha": "10fa51d379477353376e83c829cc04346330b1d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Heatmap/Heatmap.r", "max_forks_repo_name": "ZahraFarajollahi/Gene-expression-data-in-leukemia", "max_forks_repo_head_hexsha": "10fa51d379477353376e83c829cc04346330b1d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.6923076923, "max_line_length": 41, "alphanum_fraction": 0.6820276498, "num_tokens": 78, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3203577695925053}} {"text": "get.ancestry.structure <- function(node, edge){\n if(!is.vector(node))\n stop(\"'node' must be a vector with length 1 or more\")\n \n if(!is.numeric(node) | !is.numeric(edge))\n stop(\"'node' and 'edge' must be numeric\")\n \n if(!is.matrix(edge))\n stop(\"'edge' must be a matrix\")\n \n if(sum(node %in% edge) == 0)\n stop(\"'node' is not in 'edge'\")\n \n # descendants\n desc <- c()\n \n # node with multiples descendants\n nwmd <- matrix(NA, nrow = length(node), ncol = 2,\n dimnames = list(1:length(node), c('node', 'nwmd')))\n nwmd[ ,1] <- node\n \n for(i in node){\n # descendant node\n dn <- edge[edge[ ,2] %in% i,1]\n # number of descendants\n nod <- edge[edge[ ,1] %in% dn,1]\n while(length(nod)){\n if(length(nod) < 2){\n desc <- c(desc, dn)\n dn <- edge[edge[ ,2] %in% dn,1]\n nod <- edge[edge[ ,1] %in% dn,1]\n } else {\n nwmd[nwmd[ ,1] %in% i,2] <- dn\n break\n }\n }\n }\n\n return(list(u_desc = desc,\n m_desc = nwmd))\n}\n", "meta": {"hexsha": "0e338acdde2cca8fe137bcdededacfc620022b51", "size": 1020, "ext": "r", "lang": "R", "max_stars_repo_path": "drop.clade.label/get.ancestry.structure.r", "max_stars_repo_name": "MarioJose/r-functions", "max_stars_repo_head_hexsha": "a6b2693bd8151769cc799e81bbcc10654a85123e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-06-07T23:19:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-14T11:54:46.000Z", "max_issues_repo_path": "drop.clade.label/get.ancestry.structure.r", "max_issues_repo_name": "MarioJose/r-functions", "max_issues_repo_head_hexsha": "a6b2693bd8151769cc799e81bbcc10654a85123e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "drop.clade.label/get.ancestry.structure.r", "max_forks_repo_name": "MarioJose/r-functions", "max_forks_repo_head_hexsha": "a6b2693bd8151769cc799e81bbcc10654a85123e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.2857142857, "max_line_length": 68, "alphanum_fraction": 0.518627451, "num_tokens": 326, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3203577695925053}} {"text": "#Create some basic summary plots for Wikimedia configuration changes\nlibrary(plyr)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(grid)\nlibrary(scales)\n\nsetwd(dirname(sys.frame(1)$ofile))\ntheme_update(plot.margin = unit(c(0,0,0,0), \"cm\"))\n\ntimes = read.csv(\"times.csv\", header=FALSE)\n\ntimes$date = as.Date(as.POSIXlt(times$V1, origin=\"1970-01-01\"), tz=\"America/New_York\")\nggplot(times, aes(x=date)) + geom_histogram(binwidth=1) + \n scale_x_date(labels=date_format(\"%m/%d/%y\"), breaks=\"2 weeks\") +\n theme(axis.text.x=element_text(angle=45, hjust=1)) +\n scale_y_continuous(\"Number of Configuration Changes\")\nggsave(\"figs/config-changes-bydate.pdf\", width=15, height=6)\n\ntimes$days = weekdays(times$date, abbreviate=TRUE)\ntimes$days = factor(times$days, levels=c(\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"))\nggplot(times, aes(x=days)) + geom_histogram(binwidth=1) +\n scale_y_continuous(\"Number of configuration changes\")\nggsave(\"figs/config-changes-byweekday.pdf\", width=4, height=4)\n\nfiles = read.csv(\"files.csv\", header=FALSE)\nfiles$fno=as.numeric(files$V1)\nfhist=hist(files$fno, breaks=nrow(files), plot=FALSE)\ncounts=data.frame(\n x=seq(1, length(fhist$counts), 1),\n y=sort(fhist$counts, decreasing=TRUE)\n)\nggplot(counts, aes(x, y)) + geom_line() + \n scale_y_log10(\"Number of Changes\") + scale_x_continuous(\"Configuration File Number\")\nggsave(\"figs/config-changes-byfile.pdf\", width=4, height=3)\nggplot(counts, aes(x, y)) + geom_line() + \n scale_y_log10(\"Number of Changes\") + scale_x_log10(\"Configuration File Number\")\nggsave(\"figs/config-changes-byfile-logx.pdf\", width=4, height=3)\n\nftable = sort(table(as.character(files$V1)), decreasing=TRUE)\n", "meta": {"hexsha": "4e47da63c104d636ceea648ec8e60ae3f3e14e8e", "size": 1661, "ext": "r", "lang": "R", "max_stars_repo_path": "plot.r", "max_stars_repo_name": "mrcaps/wikimedia-analysis", "max_stars_repo_head_hexsha": "708d5ce1424f3996007990e7dfbb72c33ee9f294", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-03-05T07:48:09.000Z", "max_stars_repo_stars_event_max_datetime": "2015-03-05T07:48:09.000Z", "max_issues_repo_path": "plot.r", "max_issues_repo_name": "mrcaps/wikimedia-analysis", "max_issues_repo_head_hexsha": "708d5ce1424f3996007990e7dfbb72c33ee9f294", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot.r", "max_forks_repo_name": "mrcaps/wikimedia-analysis", "max_forks_repo_head_hexsha": "708d5ce1424f3996007990e7dfbb72c33ee9f294", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.512195122, "max_line_length": 90, "alphanum_fraction": 0.730885009, "num_tokens": 494, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621764862150634, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3203577612323482}} {"text": "################################################################################\n### R script to compare several conditions with the SARTools and DESeq2 packages\n### Hugo Varet\n### March 20th, 2018\n### designed to be executed with SARTools 1.6.6\n################################################################################\n\n################################################################################\n### parameters: to be modified by the user ###\n################################################################################\nrm(list=ls()) # remove all the objects from the R session\n\nworkDir <- \"~/Git/Project3/SARTools/SARTools.DESeq2.genes.batch.r\" # working directory for the R session\n\nprojectName <- \"SARTools.DESeq2.genes\" # name of the project\nauthor <- \"CheyenneMoore\" # author of the statistical analysis/report\n\ntargetFile <- \"../genes.target.txt\" # path to the design/target file\nrawDir <- \"../\" # path to the directory containing raw counts files\nfeaturesToRemove <- NULL\nvarInt <- \"Treatment\" # factor of interest\ncondRef <- \"Untreated\" # reference biological condition\nbatch <- \"batch\" # blocking factor: NULL (default) or \"batch\" for example\n\nidColumn = 1 # column with feature Ids (usually 1)\ncountColumn = 5 # column with counts (2 for htseq-count, 7 for featurecounts, 5 for RSEM/Salmon, 4 for kallisto)\nrowSkip = 0 # rows to skip (not including header) \n\nfitType <- \"parametric\" # mean-variance relationship: \"parametric\" (default), \"local\" or \"mean\"\ncooksCutoff <- TRUE # TRUE/FALSE to perform the outliers detection (default is TRUE)\nindependentFiltering <- TRUE # TRUE/FALSE to perform independent filtering (default is TRUE)\nalpha <- 0.05 # threshold of statistical significance\npAdjustMethod <- \"BH\" # p-value adjustment method: \"BH\" (default) or \"BY\"\n\ntypeTrans <- \"VST\" # transformation for PCA/clustering: \"VST\" or \"rlog\"\nlocfunc <- \"median\" # \"median\" (default) or \"shorth\" to estimate the size factors\n\ncolors <- c(\"dodgerblue\",\"firebrick1\", # vector of colors of each biological condition on the plots\n \"MediumVioletRed\",\"SpringGreen\")\n\nforceCairoGraph <- FALSE\n\n################################################################################\n### running script ###\n################################################################################\nsetwd(workDir)\n\nif (!require(\"BiocManager\")) install.packages(\"BiocManager\"); library(BiocManager)\nif (!require(\"DESeq2\")) BiocManager::install(\"DESeq2\"); library(DESeq2)\nif (!require(\"edgeR\")) BiocManager::install(\"edgeR\"); library(edgeR)\nif (!require(\"genefilter\")) BiocManager::install(\"genefilter\"); library(genefilter)\n\n# PC Users only, install Rtools https://cran.r-project.org/bin/windows/Rtools/\n\nif (!require(\"devtools\")) install.packages(\"devtools\"); library(devtools)\nif (!require(\"SARTools\")) install_github(\"KField-Bucknell/SARTools\", build_vignettes=TRUE, force=TRUE); library(SARTools)\nif (forceCairoGraph) options(bitmapType=\"cairo\")\n\n# checking parameters\ncheckParameters.DESeq2(projectName=projectName,author=author,targetFile=targetFile,\n rawDir=rawDir,featuresToRemove=featuresToRemove,varInt=varInt,\n condRef=condRef,batch=batch,fitType=fitType,cooksCutoff=cooksCutoff,\n independentFiltering=independentFiltering,alpha=alpha,pAdjustMethod=pAdjustMethod,\n typeTrans=typeTrans,locfunc=locfunc,colors=colors)\n\n# loading target file\ntarget <- loadTargetFile(targetFile=targetFile, varInt=varInt, condRef=condRef, batch=batch)\n\n# loading counts\ncounts <- loadCountData(target=target, rawDir=rawDir, featuresToRemove=featuresToRemove, \n skip=rowSkip, idColumn=idColumn, countColumn=countColumn)\n\n# description plots\nmajSequences <- descriptionPlots(counts=counts, group=target[,varInt], col=colors)\n\n# analysis with DESeq2\nout.DESeq2 <- run.DESeq2(counts=counts, target=target, varInt=varInt, batch=batch,\n locfunc=locfunc, fitType=fitType, pAdjustMethod=pAdjustMethod,\n cooksCutoff=cooksCutoff, independentFiltering=independentFiltering, alpha=alpha)\n\n# PCA + clustering\nexploreCounts(object=out.DESeq2$dds, group=target[,varInt], typeTrans=typeTrans, col=colors)\n\n# summary of the analysis (boxplots, dispersions, diag size factors, export table, nDiffTotal, histograms, MA plot)\nsummaryResults <- summarizeResults.DESeq2(out.DESeq2, group=target[,varInt], col=colors,\n independentFiltering=independentFiltering,\n cooksCutoff=cooksCutoff, alpha=alpha)\n\n# save image of the R session\nsave.image(file=paste0(projectName, \".RData\"))\n\n# generating HTML report\nwriteReport.DESeq2(target=target, counts=counts, out.DESeq2=out.DESeq2, summaryResults=summaryResults,\n majSequences=majSequences, workDir=workDir, projectName=projectName, author=author,\n targetFile=targetFile, rawDir=rawDir, featuresToRemove=featuresToRemove, varInt=varInt,\n condRef=condRef, batch=batch, fitType=fitType, cooksCutoff=cooksCutoff,\n independentFiltering=independentFiltering, alpha=alpha, pAdjustMethod=pAdjustMethod,\n typeTrans=typeTrans, locfunc=locfunc, colors=colors)\n\n", "meta": {"hexsha": "1a00e9ad70a12e1ee8f501bc35ec554c10cb01d5", "size": 6017, "ext": "r", "lang": "R", "max_stars_repo_path": "SARTools/SARTools.DESeq2.genes.batch.r/CopyOfCheyenneMoore_genes_SARTools_DESeq2.r", "max_stars_repo_name": "cheyennelmoore/RNASeqProject", "max_stars_repo_head_hexsha": "86761526603b09592a27a52e433f23d4ff03a381", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SARTools/SARTools.DESeq2.genes.batch.r/CopyOfCheyenneMoore_genes_SARTools_DESeq2.r", "max_issues_repo_name": "cheyennelmoore/RNASeqProject", "max_issues_repo_head_hexsha": "86761526603b09592a27a52e433f23d4ff03a381", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SARTools/SARTools.DESeq2.genes.batch.r/CopyOfCheyenneMoore_genes_SARTools_DESeq2.r", "max_forks_repo_name": "cheyennelmoore/RNASeqProject", "max_forks_repo_head_hexsha": "86761526603b09592a27a52e433f23d4ff03a381", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 60.17, "max_line_length": 150, "alphanum_fraction": 0.5781951138, "num_tokens": 1233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.5, "lm_q1q2_score": 0.320317934281086}} {"text": "user <- Sys.getenv(\"USER\")\nhostname <- readLines(\"/etc/hostname\")\nkernel <- strsplit(readLines(\"/proc/version\"), \" \")[[1]][3]\nterm <- Sys.getenv(\"TERM\")\nshell <- Sys.getenv(\"SHELL\")\ntasks <- length(dir(\"/proc\")[ grepl(\"^[0-9]{1,}$\", dir(\"/proc\")) ])\n\nmem <- readLines(\"/proc/meminfo\")\ntotal <- as.numeric(strsplit(mem[1], \"\\\\D+\")[[1]][-1]) %/% 1000\navail <- as.numeric(strsplit(mem[3], \"\\\\D+\")[[1]][-1]) %/% 1000\n\nuptime <- as.numeric(strsplit(readLines(\"/proc/uptime\"), \" \")[[1]][1])\nd <- trunc(((uptime / 60) / 60) / 24)\nh <- trunc(((uptime / 60) / 60) %% 24)\nm <- trunc((uptime / 60) %% 60)\n\ncat(user, \"@\", hostname,\n \"\\nkernel\\t\", kernel,\n \"\\nterm\\t\", term,\n \"\\nshell\\t\", shell,\n \"\\ntasks\\t\", tasks,\n \"\\nmem\\t\", avail, \"m / \", total, \"m\",\n \"\\nuptime\\t\", d, \"d \", h, \"h \", m, \"m\", \"\\n\",\n sep=\"\")\n", "meta": {"hexsha": "7400fccaf4d8a01abe2d301c0fc3b383ca5f8d68", "size": 820, "ext": "r", "lang": "R", "max_stars_repo_path": "r.r", "max_stars_repo_name": "aosync/fetch", "max_stars_repo_head_hexsha": "9c336c013da9523cdebdfd81bece94e5900fcd01", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-06-12T05:10:29.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-12T14:13:01.000Z", "max_issues_repo_path": "r.r", "max_issues_repo_name": "aosync/fetch", "max_issues_repo_head_hexsha": "9c336c013da9523cdebdfd81bece94e5900fcd01", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-06-24T21:28:51.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-04T10:18:03.000Z", "max_forks_repo_path": "r.r", "max_forks_repo_name": "aosync/fetch", "max_forks_repo_head_hexsha": "9c336c013da9523cdebdfd81bece94e5900fcd01", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-06-15T15:44:19.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-26T07:56:52.000Z", "avg_line_length": 32.8, "max_line_length": 70, "alphanum_fraction": 0.5280487805, "num_tokens": 285, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5, "lm_q1q2_score": 0.320317927419949}} {"text": "#!/usr/bin/env Rscript\n\n#\n# load needed packages (assumption: packages are installed)\n#\nlibrary(dplyr)\n\n#\n# Script Strategy\n#\n# Read and tidy Train UHR source data (test and train sets) -> UHR_Tidy_DF\n# 1) read data in using \"read.table\", since the input file are not completeley \n# clean, i.e. they use both single and double spaces as field delimiters \n# inside each record\n# 2) map activity codes to activity labels\n# 3) change column names, accoding to CodeBook.txt, in order to have\n# descriptive names\n# 4) join data in a single Dataframe\n#\n# Iterations are performed on the two sets of data, using a simple vector to\n# drive them, and consolidating data in target dataframe in order to have\n# code there is more maintanable.\n# Temporary Dataframes are generated, they could be elimited by refactoring the\n# code, in order to reduce memory usage during elaboration, if needed\n\n\n# see https://stackoverflow.com/questions/16979858/reading-text-file-with-multiple-space-as-delimiter-in-r\n\n#\n# define subset of columns, from source, to be selected. Original column name\n# is included, as comment, for reference and review purpose\n#\n Tidy_UHR_Source_Columns <- c(\n 1, # tBodyAcc-mean()-X\n 2, # tBodyAcc-mean()-Y\n 3, # tBodyAcc-mean()-Z\n 4, # tBodyAcc-std()-X\n 5, # tBodyAcc-std()-Y\n 6, # tBodyAcc-std()-Z\n 41, # tGravityAcc-mean()-X\n 42, # tGravityAcc-mean()-Y\n 43, # tGravityAcc-mean()-Z\n 44, # tGravityAcc-std()-X\n 45, # tGravityAcc-std()-Y\n 46, # tGravityAcc-std()-Z\n 81, # tBodyAccJerk-mean()-X\n 82, # tBodyAccJerk-mean()-Y\n 83, # tBodyAccJerk-mean()-Z\n 84, # tBodyAccJerk-std()-X\n 85, # tBodyAccJerk-std()-Y\n 86, # tBodyAccJerk-std()-Z\n 121, # tBodyGyro-mean()-X\n 122, # tBodyGyro-mean()-Y\n 123, # tBodyGyro-mean()-Z\n 124, # tBodyGyro-std()-X\n 125, # tBodyGyro-std()-Y\n 126, # tBodyGyro-std()-Z\n 161, # tBodyGyroJerk-mean()-X\n 162, # tBodyGyroJerk-mean()-Y\n 163, # tBodyGyroJerk-mean()-Z\n 164, # tBodyGyroJerk-std()-X\n 165, # tBodyGyroJerk-std()-Y\n 166, # tBodyGyroJerk-std()-Z\n 201, # tBodyAccMag-mean()\n 202, # tBodyAccMag-std()\n 214, # tGravityAccMag-mean()\n 215, # tGravityAccMag-std()\n 227, # tBodyAccJerkMag-mean()\n 228, # tBodyAccJerkMag-std()\n 240, # tBodyGyroMag-mean()\n 241, # tBodyGyroMag-std()\n 253, # tBodyGyroJerkMag-mean()\n 254, # tBodyGyroJerkMag-std()\n 266, # fBodyAcc-mean()-X\n 267, # fBodyAcc-mean()-Y\n 268, # fBodyAcc-mean()-Z\n 269, # fBodyAcc-std()-X\n 270, # fBodyAcc-std()-Y\n 271, # fBodyAcc-std()-Z\n 345, # fBodyAccJerk-mean()-X\n 346, # fBodyAccJerk-mean()-Y\n 347, # fBodyAccJerk-mean()-Z\n 348, # fBodyAccJerk-std()-X\n 349, # fBodyAccJerk-std()-Y\n 350, # fBodyAccJerk-std()-Z\n 424, # fBodyGyro-mean()-X\n 425, # fBodyGyro-mean()-Y\n 426, # fBodyGyro-mean()-Z\n 427, # fBodyGyro-std()-X\n 428, # fBodyGyro-std()-Y\n 429, # fBodyGyro-std()-Z\n 503, # fBodyAccMag-mean()\n 504, # fBodyAccMag-std()\n 516, # fBodyBodyAccJerkMag-mean()\n 517, # fBodyBodyAccJerkMag-std()\n 529, # fBodyBodyGyroMag-mean()\n 530, # fBodyBodyGyroMag-std()\n 542, # fBodyBodyGyroJerkMag-mean()\n 543 # fBodyBodyGyroJerkMag-std()\n )\n#\n# define Column Names for Tidy Dataset, according to CodeBook\n# \n Tidy_UHR_Column_Names <- c(\n \"Subject\",\n \"ActivityClass\",\n \"Time_BodyAcc_mean_X\",\n \"Time_BodyAcc_mean_Y\",\n \"Time_BodyAcc_mean_Z\",\n \"Time_BodyAcc_std_X\",\n \"Time_BodyAcc_std_Y\",\n \"Time_BodyAcc_std_Z\",\n \"Time_GravityAcc_mean_X\",\n \"Time_GravityAcc_mean_Y\",\n \"Time_GravityAcc_mean_Z\",\n \"Time_GravityAcc_std_X\",\n \"Time_GravityAcc_std_Y\",\n \"Time_GravityAcc_std_Z\",\n \"Time_BodyAccJerk_mean_X\",\n \"Time_BodyAccJerk_mean_Y\",\n \"Time_BodyAccJerk_mean_Z\",\n \"Time_BodyAccJerk_std_X\",\n \"Time_BodyAccJerk_std_Y\",\n \"Time_BodyAccJerk_std_Z\",\n \"Time_BodyGyro_mean_X\",\n \"Time_BodyGyro_mean_Y\",\n \"Time_BodyGyro_mean_Z\",\n \"Time_BodyGyro_std_X\",\n \"Time_BodyGyro_std_Y\",\n \"Time_BodyGyro_std_Z\",\n \"Time_BodyGyroJerk_mean_X\",\n \"Time_BodyGyroJerk_mean_Y\",\n \"Time_BodyGyroJerk_mean_Z\",\n \"Time_BodyGyroJerk_std_X\",\n \"Time_BodyGyroJerk_std_Y\",\n \"Time_BodyGyroJerk_std_Z\",\n \"Time_BodyAccMag_mean\",\n \"Time_BodyAccMag_std\",\n \"Time_GravityAccMag_mean\",\n \"Time_GravityAccMag_std\",\n \"Time_BodyAccJerkMag_mean\",\n \"Time_BodyAccJerkMag_std\",\n \"Time_BodyGyroMag_mean\",\n \"Time_BodyGyroMag_std\",\n \"Time_BodyGyroJerkMag_mean\",\n \"Time_BodyGyroJerkMag_std\",\n \"Freq_BodyAcc_mean_X\",\n \"Freq_BodyAcc_mean_Y\",\n \"Freq_BodyAcc_mean_Z\",\n \"Freq_BodyAcc_std_X\",\n \"Freq_BodyAcc_std_Y\",\n \"Freq_BodyAcc_std_Z\",\n \"Freq_BodyAccJerk_mean_X\",\n \"Freq_BodyAccJerk_mean_Y\",\n \"Freq_BodyAccJerk_mean_Z\",\n \"Freq_BodyAccJerk_std_X\",\n \"Freq_BodyAccJerk_std_Y\",\n \"Freq_odyAccJerk_std_Z\",\n \"Freq_BodyGyro_mean_X\",\n \"Freq_BodyGyro_mean_Y\",\n \"Freq_BodyGyro_mean_Z\",\n \"Freq_BodyGyro_std_X\",\n \"Freq_BodyGyro_std_Y\",\n \"Freq_BodyGyro_std_Z\",\n \"Freq_BodyAccMag_mean\",\n \"Freq_BodyAccMag_std\",\n \"Freq_BodyBodyAccJerkMag_mean\",\n \"Freq_BodyBodyAccJerkMag_std\",\n \"Freq_BodyBodyGyroMag_mean\",\n \"Freq_BodyBodyGyroMag_std\",\n \"Freq_BodyBodyGyroJerkMag_mean\",\n \"Freq_BodyBodyGyroJerkMag_std\"\n )\n \nActivity_Columns <- c(\"ID\") # use descriptive column names\nActivity_Labels_Columns <- c(\"ID\", \"Label\") # use descriptive column names\nSubject_Columns <- c(\"ID\") # use descriptive column names\n\n#\n# create empty dataframe with properly named columns\n#\n\nUHR_Tidy_DF <- read.table(text = \"\", col.names = Tidy_UHR_Column_Names)\n\n#\n# data will be read with path relative to current working directory\n#\n\nUHR_Data_Source_Root <- paste0(getwd(), .Platform$file.sep, \"UCI HAR Dataset\")\n\n#\n# get Activity Labels text\n#\nactivity_labels_filename <- \n paste0(UHR_Data_Source_Root, .Platform$file.sep, \"activity_labels.txt\")\nactivity_label <- \n read.table(\n activity_labels_filename, \n col.names = Activity_Labels_Columns)\n\n# iterate on different data sets and combine them in target Dataframe\n\ndata_sets <- c(\"test\", \"train\" )\n\nfor (src in data_sets) {\n \n print(paste(\"Reading set\", src))\n \n#\n# generate specific source filenames\n#\n subject_filename <- paste0(UHR_Data_Source_Root, .Platform$file.sep, src, .Platform$file.sep, \"subject_\", src, \".txt\")\n activity_filename <- paste0(UHR_Data_Source_Root, .Platform$file.sep, src, .Platform$file.sep, \"y_\", src, \".txt\")\n data_filename <- paste0(UHR_Data_Source_Root, .Platform$file.sep, src, .Platform$file.sep, \"X_\", src, \".txt\")\n\n# \n# get data in separate dataframes \n# \n subject_src <- read.table(subject_filename, col.names = Subject_Columns)\n activity_src <- read.table(activity_filename, col.names = Activity_Columns)\n data_src <- read.table(data_filename)\n\n#\n# decode activities' codes into explict labels\n# \n activity_src$ID <- activity_label$Label[match(activity_src$ID, activity_label$ID)]\n\n#\n# select relevant columns from source data\n#\n data_selection_src <- data_src[, Tidy_UHR_Source_Columns]\n \n#\n# create temporary set Dataframe, with properly named columns\n#\n set_df <- data.frame(subject_src, activity_src, data_selection_src)\n names(set_df) <- names(UHR_Tidy_DF)\n \n print(paste(src, \"data set: \",nrow(set_df)))\n \n#\n# append data to Tidy Dataframe\n# \n UHR_Tidy_DF <- rbind(UHR_Tidy_DF, set_df)\n \n} # scan and build the UHR Tidt\u00ecy Dataframe\n\n#\n# UHR Tidy dataset is ready\n#\nprint(paste(src, \"UHR Tidy data set: \",nrow(UHR_Tidy_DF)))\n\n#\n# Since the dataset is now tidy, we can summarize it usign dplyr pipes!\n# Summarise by average (using mean), against Activty and Subject\n#\n\nUHR_Average_DF <- UHR_Tidy_DF %>% \n group_by(ActivityClass, Subject) %>% \n summarise_all(mean)\n\n#\n# update column names according to CodeBook\n#\n\ncolnames(UHR_Average_DF) <- \n c(colnames(UHR_Average_DF[1:2]), \n paste0(colnames(UHR_Average_DF[3:ncol(UHR_Average_DF)]), \"_avg\"))\n\nprint(paste(src, \"UHR Tidy Averag dataset: \",nrow(UHR_Average_DF)))\n \nprint(\"Done\")\n\n", "meta": {"hexsha": "f56c416e42f1daf02d25672e285af65c9373beea", "size": 8113, "ext": "r", "lang": "R", "max_stars_repo_path": "run_Analysis.r", "max_stars_repo_name": "rbrazioli/UHR_Tidy", "max_stars_repo_head_hexsha": "7a0b37bdb4e887f4822925d7814f5cfd7e5c84e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "run_Analysis.r", "max_issues_repo_name": "rbrazioli/UHR_Tidy", "max_issues_repo_head_hexsha": "7a0b37bdb4e887f4822925d7814f5cfd7e5c84e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "run_Analysis.r", "max_forks_repo_name": "rbrazioli/UHR_Tidy", "max_forks_repo_head_hexsha": "7a0b37bdb4e887f4822925d7814f5cfd7e5c84e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6094890511, "max_line_length": 120, "alphanum_fraction": 0.6906199926, "num_tokens": 2663, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.5, "lm_q1q2_score": 0.320317927419949}} {"text": "library(tidyverse)\nlibrary(RColorBrewer)\nlibrary(rgeos)\nlibrary(maptools)\nlibrary(scales)\n\n#set path\npath_inputs <- \"C:\\\\Users\\\\edwar\\\\Desktop\\\\GitHub\\\\digital_comms\\\\results\\\\digital_transport\"\npath_shapes <- \"C:\\\\Users\\\\edwar\\\\Desktop\\\\GitHub\\\\digital_comms\\\\data\\\\digital_comms\\\\raw\\\\simplified_LADs\"\npath_figures <- \"C:\\\\Users\\\\edwar\\\\Dropbox\\\\DfT ITS Project\\\\figures\"\n\n#set working directory\nsetwd(path_inputs)\n\n# Get the files names\nfiles = list.files(pattern=glob2rx(\"sensitivity_isd_spend_*.csv\"))\n\n# First apply read.csv, then rbind\nall_scenarios = do.call(rbind, lapply(files, function(x) read.csv(x, stringsAsFactors = FALSE)))\n\ncut_down_data <- select(all_scenarios, scenario, strategy, wtp_scenario, isd, \n RAN_units, small_cell_mounting_points, fibre_backhaul_m, length_m, total_tco)\n\ncut_down_data <- cut_down_data %>%\n group_by(scenario, strategy, wtp_scenario, isd) %>%\n summarise(RAN_units = sum(RAN_units),\n small_cell_mounting_points = sum(small_cell_mounting_points),\n fibre_backhaul= sum(fibre_backhaul_m),\n length_m = sum(length_m),\n total_tco = round(sum(total_tco),1))\n\nstrategy_labels <- c(`cellular_V2X_full_greenfield` = \"Greenfield Cellular V2X\", \n `cellular_V2X_NRTS` = \"Cellular V2X with NRTS\", \n `DSRC_full_greenfield` = \"Greenfield DSRC\", \n `DSRC_NRTS` = \"DSRC with NRTS\")\n\nscenario_labels <- c(`high` = \"High (10 Mb/s, \u00a320 ARPU)\",\n `baseline` = \"Baseline (4 Mb/s, \u00a34 ARPU)\",\n `low` = \"Low (1 Mb/s, \u00a32 ARPU)\")\n\naggregate_cost <- cut_down_data %>%\n group_by(scenario, strategy, wtp_scenario, isd) %>%\n mutate(RAN_per_m = round(RAN_units / length_m, 5),\n ISD = round(length_m / RAN_units, 5),\n mounting_points_per_m = round(small_cell_mounting_points / length_m, ),\n fibre_backhaul_m = fibre_backhaul/ length_m,\n length_m = length_m,\n cost_per_m = round(total_tco / length_m, 6),\n total_tco = round(sum(total_tco), 3)) %>%\n select(scenario, strategy, wtp_scenario, isd, RAN_per_m, ISD, mounting_points_per_m, cost_per_m, total_tco)\n\naggregate_cost <- aggregate_cost[(aggregate_cost$ISD < 5000),]\n\naggregate_cost$scenario <- factor(aggregate_cost$scenario,\n levels = c(\"high\",\n \"baseline\",\n \"low\"))\n\nISD <- ggplot(aggregate_cost, aes(x=ISD, y=total_tco/1000000000, group=1)) + geom_line() +\n scale_y_continuous(expand = c(0, 0), limits=c(0,21)) + \n scale_x_continuous(expand = c(0, 0), limits=c(0,4999)) +\n xlab(\"Mean Inter-Site Distance (ISD) (m)\") + \n ylab(\"Investment Cost (Billions GBP)\") + \n labs(title=\"Sensitivity of Total Cost to Mean Inter-Site Distance (ISD)\", subtitle=\"Results reported by scenario and strategy as mean ISD increases\") +\n facet_grid(scenario~strategy, labeller = labeller(strategy = strategy_labels, scenario = scenario_labels))\n\n### EXPORT TO FOLDER\nsetwd(path_figures)\ntiff('ISD.tiff', units=\"in\", width=8, height=9, res=300)\nprint(ISD)\ndev.off()\n\nmean_cost <- aggregate_cost %>%\n group_by(scenario, strategy, wtp_scenario) %>%\n summarise(cost = round(mean(total_tco)/1000000000,2))\n\nrm(aggregate_cost, all_scenarios, cut_down_data, ISD, mean_cost, files)\n\n###############################\n# PENETRATION\n###############################\n\n#set working directory\nsetwd(path_inputs)\n\n# Get the files names\nfiles = list.files(pattern=glob2rx(\"sensitivity_penetration_spend_*.csv\"))\n\n# First apply read.csv, then rbind\nall_scenarios = do.call(rbind, lapply(files, function(x) read.csv(x, stringsAsFactors = FALSE)))\n\ncut_down_data <- select(all_scenarios, scenario, strategy, wtp_scenario, inflection_year, \n annual_CAV_take_up, CAV_revenue)\n\naggregate_cost <- cut_down_data %>%\n group_by(scenario, strategy, wtp_scenario, inflection_year) %>%\n summarise(annual_CAV_take_up = sum(annual_CAV_take_up),\n CAV_revenue = sum(CAV_revenue)/1000000000)\n\nstrategy_labels <- c(`cellular_V2X_full_greenfield` = \"Greenfield Cellular V2X\", \n `cellular_V2X_NRTS` = \"Cellular V2X with NRTS\", \n `DSRC_full_greenfield` = \"Greenfield DSRC\", \n `DSRC_NRTS` = \"DSRC with NRTS\")\n\nscenario_labels <- c(`high` = \"High (10 Mb/s, \u00a320 ARPU)\",\n `baseline` = \"Baseline (4 Mb/s, \u00a34 ARPU)\",\n `low` = \"Low (1 Mb/s, \u00a32 ARPU)\")\n\n# aggregate_cost <- cut_down_data %>%\n# group_by(scenario, strategy, wtp_scenario, inflection_year) %>%\n# mutate(annual_CAV_take_up = sum(annual_CAV_take_up),\n# CAV_revenue = sum(CAV_revenue)) %>%\n# select(scenario, strategy, wtp_scenario, inflection_year, annual_CAV_take_up, CAV_revenue)\n\n#aggregate_cost <- aggregate_cost[(aggregate_cost$ISD < 5000),]\n\naggregate_cost$scenario <- factor(aggregate_cost$scenario,\n levels = c(\"high\",\n \"baseline\",\n \"low\"))\n\naggregate_cost$inflection_year <- round(aggregate_cost$inflection_year + 2019,0) \n\naggregate_cost$inflection_year <- as.factor(aggregate_cost$inflection_year)\n \npenetration_senstivity <- ggplot(aggregate_cost, aes(x=inflection_year, y=CAV_revenue, group=1)) + geom_line() +\n scale_y_continuous(expand = c(0, 0), limits=c(0,0.45)) + \n scale_x_discrete(expand = c(0, 0)) + \n xlab(\"Adoption Rate Inflection year\") + \n ylab(\"Revenue (Billions GBP)\") + \n theme(legend.position = \"bottom\", axis.text.x = element_text(angle = 45, hjust = 1)) + \n labs(title=\"Sensitivity of Revenue to Subscription Adoption Rate\", \n subtitle=\"Results reported by scenario and strategy as adoption rate inflection year varies\",\n xlab=\"Adoption Rate Inflection year\") +\n facet_grid(scenario~strategy, labeller = labeller(strategy = strategy_labels, scenario = scenario_labels))\n\n### EXPORT TO FOLDER\nsetwd(path_figures)\ntiff('penetration_senstivity.tiff', units=\"in\", width=8, height=9, res=300)\nprint(penetration_senstivity)\ndev.off()\n\n", "meta": {"hexsha": "2841fa4a62986185dd730a6c2ee826685ec693a2", "size": 6186, "ext": "r", "lang": "R", "max_stars_repo_path": "data_visualisation/digital_transport/visualise_digital_transport_sensitivity_analysis.r", "max_stars_repo_name": "mchakli/test", "max_stars_repo_head_hexsha": "6f722a0ba8eeaf207984a496ef4d1051d5a13264", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data_visualisation/digital_transport/visualise_digital_transport_sensitivity_analysis.r", "max_issues_repo_name": "mchakli/test", "max_issues_repo_head_hexsha": "6f722a0ba8eeaf207984a496ef4d1051d5a13264", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data_visualisation/digital_transport/visualise_digital_transport_sensitivity_analysis.r", "max_forks_repo_name": "mchakli/test", "max_forks_repo_head_hexsha": "6f722a0ba8eeaf207984a496ef4d1051d5a13264", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.2587412587, "max_line_length": 153, "alphanum_fraction": 0.6587455545, "num_tokens": 1662, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3203179205588119}} {"text": "library(ggplot2)\nlibrary(data.table)\nsource('../utils.r')\nsource('../config.r')\n\ndf <- fread('learning_curves.csv')\ndf[,learning_type := factor(learning_type, levels = c(\"train\", \"validation\"))] \ndf[learning_type==\"train\", learning_type := \"Training\"]\ndf[learning_type==\"validation\", learning_type := \"Testing\"]\ndf <- df[learning_type=='Testing']\n\nfig <- ggplot(\n data = df,\n mapping = aes(x = train_sizes, y = mean, color = learning_type,\n fill = learning_type)) +\n facet_wrap(.~target) +\n geom_line(size = 1.4, show.legend = F) +\n geom_ribbon(mapping = aes(ymin = mean -std, ymax = mean + std),\n alpha = 0.2, size = 0.3, show.legend = F) +\n scale_color_manual(values = with(color_cats, c(`sky blue`))) +\n scale_fill_manual(values = with(color_cats, c(`sky blue`))) +\n my_theme +\n coord_cartesian(xlim = c(0, 3100)) +\n scale_x_continuous(limits = c(0, 3000)) +\n scale_y_continuous(breaks = seq(0, 1, 0.1)) +\n labs(x = \"# training samples\", y = expression(\"out-of-sample\" ~ R^2 ~ \"score\")) +\n theme(legend.title = element_blank(), legend.position=\"bottom\")\n\nmy_ggsave(\"fig_1-supp-1\", fig, width = 11, height = 5)\n", "meta": {"hexsha": "d510a6f2c419d13c4c3932fe4ceb6093aebd8089", "size": 1154, "ext": "r", "lang": "R", "max_stars_repo_path": "figure_1_supp/plot_learning_curves.r", "max_stars_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_stars_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-05-03T13:33:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T11:09:38.000Z", "max_issues_repo_path": "figure_1_supp/plot_learning_curves.r", "max_issues_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_issues_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-09T11:05:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T11:05:01.000Z", "max_forks_repo_path": "figure_1_supp/plot_learning_curves.r", "max_forks_repo_name": "KamalakerDadi/empirical_proxy_measures", "max_forks_repo_head_hexsha": "f501dd3027fa8df29dcac92aec333cc71cbc0acb", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-12-29T20:16:45.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-29T20:16:45.000Z", "avg_line_length": 38.4666666667, "max_line_length": 83, "alphanum_fraction": 0.650779896, "num_tokens": 340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583270090337584, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3203001493802095}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/Dropbox/sinking_marbles/sinking-marbles/models/complex_prior/results/\")\nsource(\"rscripts/helpers.r\")\n\nload(\"data/mp.RData\")\n\nmp = read.table(\"data/parsed_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nnrow(mp)\n\nsummary(mp)\nmp$QUDOpt = as.factor(paste(mp$QUD,mp$SpeakerOptimality))\nsave(mp, file=\"data/mp.RData\")\n\nggplot(mp, aes(x=NumState,y=PosteriorProbability,shape=as.factor(SpeakerOptimality),color=QUD,group=QUDOpt)) +\n geom_point() +\n geom_line() +\n facet_grid(PriorProbability.100~Alternatives)\nggsave(\"graphs/full-dists.pdf\",height=45)\n\nisall = subset(mp, QUD==\"is-all\")\nhowmany = subset(mp, QUD==\"how-many\")\n\nggplot(mp, aes(x=PriorProbability.100,y=PosteriorProbability,shape=as.factor(SpeakerOptimality),color=QUD,group=QUDOpt)) +\n geom_point() +\n geom_line() +\n facet_grid(Alternatives~NumState)\nggsave(\"graphs/byprior.pdf\",width=10,height=10)\n\nall = subset(mp, NumState == 3)\nggplot(all, aes(x=PriorProbability.100,y=PosteriorProbability,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n scale_y_continuous(limits=c(0,1)) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/allprobability-byopt-byqud.pdf\",width=12)\n\nhead(mp)\nnrow(mp)\nhmbasic2 = subset(mp, QUD==\"how-many\" & Alternatives == \"basic\" & SpeakerOptimality == 2 & NumState == 3)\nnrow(hmbasic2)\nsummary(hmbasic2)\nhmbasic2 = droplevels(hmbasic2)\nrow.names(hmbasic2) = paste(hmbasic2$PriorProbability.0,hmbasic2$PriorProbability.1to50,hmbasic2$PriorProbability.51to99,hmbasic2$PriorProbability.100)\nhead(hmbasic2)\nhmbasic2.cprior = hmbasic2\nsave(hmbasic2.cprior, file=\"data/hmbasic2.cprior.RData\")\n", "meta": {"hexsha": "f12ea46008c680e2e928d69df927ce7ddf73b638", "size": 1644, "ext": "r", "lang": "R", "max_stars_repo_path": "models/complex_prior/unsmoothed_binned/results/rscripts/model-predictions.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "models/complex_prior/unsmoothed_binned/results/rscripts/model-predictions.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/complex_prior/unsmoothed_binned/results/rscripts/model-predictions.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.25, "max_line_length": 151, "alphanum_fraction": 0.7597323601, "num_tokens": 523, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.3203001409480639}} {"text": "#' Iterator that replicates elements of an iterable object\n#'\n#' Constructs an iterator that replicates the values of an \\code{object}.\n#'\n#' This function is intended an iterable version of the standard\n#' \\code{\\link[base]{rep}} function. However, as exception, the recycling\n#' behavior of \\code{\\link[base]{rep}} is intentionally not implemented.\n#'\n#' @export\n#' @param object object to return indefinitely.\n#' @param times the number of times to repeat each element in \\code{object}\n#' @param length.out non-negative integer. The desired length of the iterator\n#' @param each non-negative integer. Each element is repeated \\code{each} times\n#' @return iterator that returns \\code{object}\n#' \n#' @examples\n#' it <- irep(1:3, 2)\n#' unlist(as.list(it)) == rep(1:3, 2)\n#' \n#' it2 <- irep(1:3, each=2)\n#' unlist(as.list(it2)) == rep(1:3, each=2)\n#'\n#' it3 <- irep(1:3, each=2, length.out=4)\n#' as.list(it3)\nirep <- function(object, times=1, length.out=NULL, each=NULL) {\n if (!is.null(length.out)) {\n length.out <- as.numeric(length.out)\n if (length(length.out) != 1) {\n stop(\"'length.out' must be a numeric value of length 1\")\n }\n }\n if (!is.null(each)) {\n each <- as.numeric(each)\n if (length(each) != 1) {\n stop(\"'each' must be a numeric value of length 1\")\n }\n }\n\n it <- icycle(object, times=times)\n if (!is.null(each)) {\n it <- irep_each(it, each=each)\n }\n\n islice(it, end=length.out)\n}\n\n#' @export\n#' @rdname irep\nirep_len <- function(object, length.out=NULL) {\n irep(object, times=1, length.out=length.out)\n}\n\nirep_each <- function(object, each=1) {\n each <- as.integer(each)\n iter_obj <- iterators::iter(object)\n \n iter_repeat <- irepeat(iterators::nextElem(iter_obj), times=each)\n\n nextElem <- function() {\n next_elem <- try(iterators::nextElem(iter_repeat), silent=TRUE)\n if (stop_iteration(next_elem)) {\n iter_repeat <<- irepeat(iterators::nextElem(iter_obj), times=each)\n next_elem <- iterators::nextElem(iter_repeat)\n }\n next_elem\n }\n\n it <- list(nextElem=nextElem)\n class(it) <- c(\"abstractiter\", \"iter\")\n it\n}\n", "meta": {"hexsha": "b69123e1c31f80ea9574a0a223441f503caea2f4", "size": 2097, "ext": "r", "lang": "R", "max_stars_repo_path": "R/irep.r", "max_stars_repo_name": "ramhiser/itertools2", "max_stars_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2015-02-02T02:54:54.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-20T12:07:34.000Z", "max_issues_repo_path": "R/irep.r", "max_issues_repo_name": "ramhiser/itertools2", "max_issues_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_issues_repo_licenses": ["Apache-2.0", "MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2015-01-07T15:36:57.000Z", "max_issues_repo_issues_event_max_datetime": "2017-02-18T18:01:36.000Z", "max_forks_repo_path": "R/irep.r", "max_forks_repo_name": "ramhiser/itertools2", "max_forks_repo_head_hexsha": "471515f4e8cf0aa48cc6402741ad3feccca94a9b", "max_forks_repo_licenses": ["Apache-2.0", "MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-02-02T05:04:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T02:13:12.000Z", "avg_line_length": 29.125, "max_line_length": 79, "alphanum_fraction": 0.657606104, "num_tokens": 599, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3203001409480639}} {"text": "\nfishery_model = function( p, DS=\"stan\", plotresults=TRUE, ... ) {\n\n if (0) {\n\n year.assessment=2016\n p = bio.snowcrab::load.environment( year.assessment=year.assessment)\n p$fishery_model = list()\n p$fishery_model$outdir = file.path(project.datadirectory('bio.snowcrab'), \"assessments\", p$year.assessment )\n\n }\n\n\n\n if (DS==\"stan_surplus_production\") {\n return( \"\n data {\n\n int N; // no. years\n int U; // no. regions\n int M; // no. years to project\n int ty;\n real er ;\n real eps ;\n vector[U] Ksd;\n vector[U] rsd;\n vector[U] qsd;\n vector[U] Kmu ;\n vector[U] rmu ;\n vector[U] qmu ;\n matrix[N,U] CAT;\n matrix[N,U] IOA;\n matrix[N,U] missing;\n int missing_n[U];\n int missing_ntot;\n }\n\n transformed data {\n int MN;\n int N1;\n MN = M+N ;\n N1 = N+1;\n }\n\n parameters {\n vector [U] K;\n vector [U] r;\n vector [U] q;\n vector [U] qs;\n vector [U] bosd; // observation error\n vector [U] bpsd; // process error\n vector [U] b0;\n vector [missing_ntot] IOAmissing;\n matrix [M+N,U] bm;\n }\n\n transformed parameters {\n matrix[N,U] Y; // index of abundance\n matrix[N,U] Ymu; // collator used to force positive values for lognormal\n matrix[MN,U] bmmu; // collator used to force positive values for lognormal\n matrix[MN,U] rem; // observed catch\n\n // copy parameters to a new variable (Y) with imputed missing values\n {\n int ii;\n ii = 0;\n for (j in 1:U) {\n for (i in 1:N) {\n Y[i,j] = IOA[i,j];\n if ( missing[i,j] == 1 ) {\n ii = ii+1;\n Y[i,j] = IOAmissing[ii];\n }\n }\n }\n }\n\n // -------------------\n // removals (catch) observation model, standardized to K (assuming no errors in observation of catch!)\n for (j in 1:U) {\n rem[1:N,j] = CAT[1:N,j]/K[j] ;\n rem[(N+1):MN,j] = er*bm[ N:(MN-1),j] ; // forecasts\n }\n\n // -------------------\n // observation model calcs and contraints:\n // Ymu = 'surveyed/observed' residual biomass at time of survey (Bsurveyed)\n // cfanorth(1) and cfasouth(2)\n // This is slightly complicated because a fall / spring survey correction is required:\n // B represents the total fishable biomass available in fishing year y\n // in fall surveys: Btot(t) = Bsurveyed(t) + removals(t)\n // in spring surveys: Btot(t) = Bsurveyed(t) + removals(t-1)\n // spring surveys from 1998 to 2003\n // this is conceptualized in the following time line:\n // '|' == start/end of each new fishing year\n // Sf = Survey in fall\n // Ss = Survey in spring\n // |...(t-2)...|.Ss..(t-1)...|...(t=2004)..Sf.|...(t+1).Sf..|...(t+2)..Sf.|...\n // Cfa 4X -- fall/winter fishery\n // assume similar to a spring fishery but no need for separate q's\n // NOTE: year designation in 4X is for the terminal year: ie. 2001-2002 => 2002\n // Btot(t) = Bsurveyed(t)+ removals(t-1)\n\n for (j in 1:2) {\n Ymu[1,j] = qs[j] * bm[1,j] - rem[1,j] ; // starting year approximation\n Ymu[2:(ty-1),j] = qs[j] * bm[2:(ty-1),j] - rem[1:(ty-2),j] ; //spring surveys\n Ymu[ty,j] = q[j] * bm[ty,j] - (rem[(ty-1),j] + rem[ty,j] )/2.0 ; // transition year .. approximation\n Ymu[(ty+1):N,j] = q[j] * bm[(ty+1):N,j] - rem[(ty+1):N,j] ; // fall surveys\n }\n {\n int k;\n k=3;\n Ymu[1,k] = q[k] * bm[1,k] - rem[1,k] ; // starting year approximation\n Ymu[2:(ty-1),k] = q[k] * bm[2:(ty-1),k] - rem[2:(ty-1),k];\n Ymu[ty:N,k] = q[k] * bm[ty:N,k] - rem[ty:N,k];\n }\n\n for (j in 1:U) {\n for (i in 1:N) {\n Ymu[i,j] = K[j] * fmax( Ymu[i,j], eps); // force positive value\n }\n }\n\n\n // -------------------\n // process model calcs and constraints\n for (j in 1:U) {\n bmmu[1,j] = b0[j] ; // biomass at first year\n for (i in 2:MN) {\n bmmu[i,j] = bm[i-1,j] * ( 1.0 + r[j]*(1-bm[i-1,j]) ) - rem[i-1,j] ;\n }\n }\n for (j in 1:U) {\n for (i in 1:MN) {\n bmmu[i,j] = fmax(bmmu[i,j], eps); // force positive value\n }\n }\n\n\n }\n\n model {\n\n // -------------------\n // priors for parameters\n K ~ normal( Kmu, Ksd ) ;\n r ~ normal( rmu, rsd ) ;\n q ~ normal( qmu, qsd ) ;\n qs ~ normal( qmu, qsd ) ;\n b0 ~ beta( 8, 2 ) ; // starting b prior to first catch event\n bosd ~ cauchy( 0, 0.5 ) ; // slightly informative .. center of mass between (0,1)\n bpsd ~ cauchy( 0, 0.5 ) ;\n\n\n // -------------------\n // biomass observation model\n for (j in 1:U) {\n log(Y[1:N,j]) ~ normal( log(Ymu[1:N,j]), bosd[j] ) ; // stan thinks Y is being transformed due to attempt to impute missing values .. ignore\n }\n\n\n // -------------------\n // biomass process model\n for (j in 1:U) {\n log(bm[1:MN,j]) ~ normal( log(bmmu[1:MN,j]), bpsd[j] ) ;\n }\n\n // could have used lognormal but this parameterization is 10X faster and more stable\n target += - log(fabs(Y)); // required due to log transf above\n target += - log(fabs(bm));\n\n }\n\n generated quantities {\n matrix[MN,U] pd;\n vector[U] MSY;\n vector[U] BMSY;\n vector[U] FMSY;\n matrix[MN,U] B;\n matrix[MN,U] P;\n matrix[MN,U] C;\n\n matrix[MN,U] F;\n matrix[M,U] TAC;\n\n\n // -------------------\n // annual production\n for(j in 1:U) {\n pd[1,j] = bm[2,j]- bm[1,j] + rem[1,j] ; // approximation\n for (i in 2:N ){\n pd[i,j] = (bm[i+1,j]- bm[i-1,j])/2 + rem[i,j] ; // linear interpolation cancels out the bm[i,j] term\n }\n for(i in N1:(MN-1)) {\n pd[i,j] = (bm[i+1,j]- bm[i-1,j])/2 + er * bm[i-1,j] ; // linear interpolation cancels out the bm[i,j] term\n }\n pd[MN,j] = (bm[MN,j]- bm[(MN-1),j]) + er * bm[(MN-1),j] ; // approximation\n }\n\n // -------------------\n // fishing mortality\n // fall fisheries\n\n for (j in 1:3) {\n for (i in 1:N) {\n F[i,j] = 1.0 - rem[i,j] / bm[i,j] ;\n }\n }\n // spring fishery\n// {\n// int j;\n// j=3;\n// F[1,j] = 1.0 - rem[1,j] / bm[1,j] ; // approximation\n// for (i in 2:N) {\n// F[i,j] = 1.0 - rem[i-1,j] / bm[i,j] ;\n // }\n // }\n\n for (j in 1:U) {\n for (i in N1:MN) {\n F[i,j] = 1.0 - er * bm[i-1,j] / bm[i,j] ;\n }\n for (i in 1:MN) {\n F[i,j] = -log( fmax( F[i,j], eps) ) ;\n }\n }\n\n // -------------------\n // parameter estimates for output\n\n for(j in 1:U) {\n MSY[j] = r[j]* exp(K[j]) / 4 ; // maximum height of of the latent productivity (yield)\n BMSY[j] = exp(K[j])/2 ; // biomass at MSY\n FMSY[j] = 2.0 * MSY[j] / exp(K[j]) ; // fishing mortality at MSY\n // BX2MSY[j] = 1.0 - step( bm[N1,j]-0.25 ) ; // test if bm >= 1/2 bmY\n // Bdrop[j] = 1.0 - step( bm[N1,j]-bm[N,j] ) ; // test if bm(t) >= bm(t-1)\n // Fcrash[j] = 4.0 * MSY[j] / exp(K[j]) ; // fishing mortality at which the stock will crash\n }\n\n // recaled estimates\n for(j in 1:U) {\n for(i in 1:MN) {\n B[i,j] = (bm[i,j] - rem[i,j]) * K[j] ;\n P[i,j] = pd[i,j]*K[j] ;\n C[i,j] = rem[i,j]*K[j] ;\n }\n for(i in 1:M) {\n TAC[i,j] = rem[N+i,j]*K[j] ;\n }\n }\n\n }\n \"\n )\n }\n\n if (DS==\"stan_surplus_production_stmv_survey_expanded\") {\n return( \"\n data {\n\n int N; // no. years\n int U; // no. regions\n int M; // no. years to project\n int ty;\n real er ;\n real eps ;\n vector[U] Ksd;\n vector[U] rsd;\n vector[U] qsd;\n vector[U] Kmu ;\n vector[U] rmu ;\n vector[U] qmu ;\n matrix[N,U] CAT;\n matrix[N,U] IOA;\n matrix[N,U] IOAG;\n matrix[N,U] missing;\n int missing_n[U];\n int missing_ntot;\n }\n\n transformed data {\n int MN;\n int N1;\n MN = M+N ;\n N1 = N+1;\n }\n\n parameters {\n vector [U] K;\n vector [U] r;\n vector [U] q;\n vector [U] qs;\n vector [U] q1;\n vector [U] qs1;\n\n vector [U] bosd; // observation error of biomass estimate\n vector [U] bossd; // observation error of catch rate\n vector [U] bpsd; // process error\n vector [U] b0;\n vector [missing_ntot] IOAmissing;\n vector [missing_ntot] IOAGmissing;\n matrix [M+N,U] bm;\n }\n\n transformed parameters {\n matrix[N,U] Y; // index of abundance stmv\n matrix[N,U] Y2; // index of abundance geomean\n matrix[N,U] Ymu; // collator used to force positive values for lognormal\n matrix[MN,U] bmmu; // collator used to force positive values for lognormal\n matrix[MN,U] rem; // observed catch\n\n // copy parameters to a new variable (Y) with imputed missing values\n {\n int ii;\n ii = 0;\n for (j in 1:U) {\n for (i in 1:N) {\n Y[i,j] = IOA[i,j];\n if ( missing[i,j] == 1 ) {\n ii = ii+1;\n Y[i,j] = IOAmissing[ii];\n }\n }\n }\n }\n {\n int ii;\n ii = 0;\n for (j in 1:U) {\n for (i in 1:N) {\n Y2[i,j] = IOAG[i,j];\n if ( missing[i,j] == 1 ) {\n ii = ii+1;\n Y2[i,j] = IOAGmissing[ii];\n }\n }\n }\n }\n\n // -------------------\n // removals (catch) observation model, standardized to K (assuming no errors in observation of catch!)\n for (j in 1:U) {\n rem[1:N,j] = CAT[1:N,j]/K[j] ;\n rem[(N+1):MN,j] = er*bm[ N:(MN-1),j] ; // forecasts\n }\n\n // -------------------\n // observation model calcs and contraints:\n // Ymu = 'surveyed/observed' residual biomass at time of survey (Bsurveyed)\n // cfanorth(1) and cfasouth(2)\n // This is slightly complicated because a fall / spring survey correction is required:\n // B represents the total fishable biomass available in fishing year y\n // in fall surveys: Btot(t) = Bsurveyed(t) + removals(t)\n // in spring surveys: Btot(t) = Bsurveyed(t) + removals(t-1)\n // spring surveys from 1998 to 2003\n // this is conceptualized in the following time line:\n // '|' == start/end of each new fishing year\n // Sf = Survey in fall\n // Ss = Survey in spring\n // |...(t-2)...|.Ss..(t-1)...|...(t=2004)..Sf.|...(t+1).Sf..|...(t+2)..Sf.|...\n // Cfa 4X -- fall/winter fishery\n // assume similar to a spring fishery but no need for separate q's\n // Btot(t) = Bsurveyed(t)+ removals(t-1)\n // NOTE: year designation in 4X is for the terminal year: ie. 2001-2002 => 2002\n\n for (j in 1:2) {\n Ymu[1,j] = qs[j] * bm[1,j] - rem[1,j] ; // starting year approximation\n Ymu[2:(ty-1),j] = qs[j] * bm[2:(ty-1),j] - rem[1:(ty-2),j] ; //spring surveys\n Ymu[ty,j] = q[j] * bm[ty,j] - (rem[(ty-1),j] + rem[ty,j] )/2.0 ; // transition year .. approximation\n Ymu[(ty+1):N,j] = q[j] * bm[(ty+1):N,j] - rem[(ty+1):N,j] ; // fall surveys\n }\n {\n int k;\n k=3;\n Ymu[1,k] = qs[k] * bm[1,k] - rem[1,k] ; // starting year approximation\n Ymu[2:(ty-1),k] = qs[k] * bm[2:(ty-1),k] - rem[1:(ty-2),k];\n Ymu[ty:N,k] = q[k] * bm[ty:N,k] - rem[(ty-1):(N-1),k];\n }\n\n for (j in 1:2) {\n Ymu[1,j] = qs1[j] * bm[1,j] - rem[1,j] ; // starting year approximation\n Ymu[2:(ty-1),j] = qs1[j] * bm[2:(ty-1),j] - rem[1:(ty-2),j] ; //spring surveys\n Ymu[ty,j] = q1[j] * bm[ty,j] - (rem[(ty-1),j] + rem[ty,j] )/2.0 ; // transition year .. approximation\n Ymu[(ty+1):N,j] = q1[j] * bm[(ty+1):N,j] - rem[(ty+1):N,j] ; // fall surveys\n }\n {\n int k;\n k=3;\n Ymu[1,k] = qs1[k] * bm[1,k] - rem[1,k] ; // starting year approximation\n Ymu[2:(ty-1),k] = qs1[k] * bm[2:(ty-1),k] - rem[1:(ty-2),k];\n Ymu[ty:N,k] = q1[k] * bm[ty:N,k] - rem[(ty-1):(N-1),k];\n }\n\n\n for (j in 1:U) {\n for (i in 1:N) {\n Ymu[i,j] = K[j] * fmax( Ymu[i,j], eps); // force positive value\n }\n }\n\n\n // -------------------\n // process model calcs and constraints\n for (j in 1:U) {\n bmmu[1,j] = b0[j] ; // biomass at first year\n for (i in 2:MN) {\n bmmu[i,j] = bm[i-1,j] * ( 1.0 + r[j]*(1-bm[i-1,j]) ) - rem[i-1,j] ;\n }\n }\n for (j in 1:U) {\n for (i in 1:MN) {\n bmmu[i,j] = fmax(bmmu[i,j], eps); // force positive value\n }\n }\n\n\n }\n\n model {\n\n // -------------------\n // priors for parameters\n K ~ normal( Kmu, Ksd ) ;\n r ~ normal( rmu, rsd ) ;\n q ~ normal( qmu, qsd ) ;\n q1 ~ normal( qmu, qsd ) ;\n qs ~ normal( qmu, qsd ) ;\n qs1 ~ normal( qmu, qsd ) ;\n b0 ~ beta( 8, 2 ) ; // starting b prior to first catch event\n bosd ~ cauchy( 0, 0.5 ) ; // slightly informative .. center of mass between (0,1)\n bpsd ~ cauchy( 0, 0.5 ) ;\n bossd ~ cauchy( 0, 0.5 );\n\n // -------------------\n // biomass observation model\n for (j in 1:U) {\n log(Y[1:N,j]) ~ normal( log(Ymu[1:N,j]), bosd[j] ) ; // stan thinks Y is being transformed due to attempt to impute missing values .. ignore\n log(Y2[1:N,j]) ~ normal( log(Ymu[1:N,j]), bossd[j] ) ; // stan thinks Y is being transformed due to attempt to impute missing values .. ignore\n }\n\n\n // -------------------\n // biomass process model\n for (j in 1:U) {\n log(bm[1:MN,j]) ~ normal( log(bmmu[1:MN,j]), bpsd[j] ) ;\n }\n\n // could have used lognormal but this parameterization is 10X faster and more stable\n target += - log(fabs(Y)); // required due to log transf above\n target += - log(fabs(bm));\n target += - log(fabs(Y2)); // required due to log transf above\n }\n\n generated quantities {\n matrix[MN,U] pd;\n vector[U] MSY;\n vector[U] BMSY;\n vector[U] FMSY;\n matrix[MN,U] B;\n matrix[MN,U] P;\n matrix[MN,U] C;\n\n matrix[MN,U] F;\n matrix[M,U] TAC;\n\n\n // -------------------\n // annual production\n for(j in 1:U) {\n pd[1,j] = bm[2,j]- bm[1,j] + rem[1,j] ; // approximation\n for (i in 2:N ){\n pd[i,j] = (bm[i+1,j]- bm[i-1,j])/2 + rem[i,j] ; // linear interpolation cancels out the bm[i,j] term\n }\n for(i in N1:(MN-1)) {\n pd[i,j] = (bm[i+1,j]- bm[i-1,j])/2 + er * bm[i-1,j] ; // linear interpolation cancels out the bm[i,j] term\n }\n pd[MN,j] = (bm[MN,j]- bm[(MN-1),j]) + er * bm[(MN-1),j] ; // approximation\n }\n\n // -------------------\n // fishing mortality\n // force first year estimate assuming catches in year 0 to be similar to year 1\n\n for (j in 1:U) {\n F[1,j] = 1.0 - rem[1,j] / bm[1,j] ;\n for (i in 2:MN) {\n F[i,j] = 1.0 - er * bm[i-1,j] / bm[i,j] ;\n }\n }\n for (j in 1:U) {\n for (i in 1:MN) {\n F[i,j] = -log( fmax( F[i,j], eps) ) ;\n }\n }\n\n // -------------------\n // parameter estimates for output\n\n for(j in 1:U) {\n MSY[j] = r[j]* exp(K[j]) / 4 ; // maximum height of of the latent productivity (yield)\n BMSY[j] = exp(K[j])/2 ; // biomass at MSY\n FMSY[j] = 2.0 * MSY[j] / exp(K[j]) ; // fishing mortality at MSY\n // BX2MSY[j] = 1.0 - step( bm[N1,j]-0.25 ) ; // test if bm >= 1/2 bmY\n // Bdrop[j] = 1.0 - step( bm[N1,j]-bm[N,j] ) ; // test if bm(t) >= bm(t-1)\n // Fcrash[j] = 4.0 * MSY[j] / exp(K[j]) ; // fishing mortality at which the stock will crash\n }\n\n // recaled estimates\n for(j in 1:U) {\n for(i in 1:MN) {\n B[i,j] = (bm[i,j] - rem[i,j]) * K[j] ;\n P[i,j] = pd[i,j]*K[j] ;\n C[i,j] = rem[i,j]*K[j] ;\n }\n for(i in 1:M) {\n TAC[i,j] = rem[N+i,j]*K[j] ;\n }\n }\n\n }\n \"\n )\n}\n\n\n\n if (DS==\"stan_data\" ) {\n sb = snowcrab_tsdata(p=p, assessment_years=2000:p$year.assessment)\n return(sb)\n }\n\n\n\n if (DS==\"stan\" ) {\n\n library(rstan)\n rstan_options(auto_write = TRUE)\n options(mc.cores = parallel::detectCores())\n\n message( \"Output location is: \", p$fishery_model$outdir )\n\n dir.create( p$fishery_model$outdir, recursive=T, showWarnings=F )\n\n f = sampling( p$fishery_model$stancode_compiled, data=p$fishery_model$standata, ... )\n # warmup = 200, # number of warmup iterations per chain\n # control = list(adapt_delta = 0.9),\n # # refresh = 500, # show progress every 'refresh' iterations\n # iter = 1000, # total number of iterations per chain\n # chains = 5, # number of Markov chains\n # cores = 5 # number of cores (using 2 just for the vignette)\n\n res = list( mcmc=extract(f), p=p)\n save(res, file=p$fishery_model$fnres, compress=T)\n return(res)\n\n if (0) {\n\n plot(f)\n print(f)\n traceplot(f)\n\n # extract samples\n e = rstan::extract(f, permuted = TRUE) # return a list of arrays\n m2 = as.array(f)\n\n traceplot(f, pars=c(\"K\"))\n pred=rstan::extract(f)\n\n est=colMeans(pred)\n\n prob=apply(pred,2,function(x) I(length(x[x>0.10])/length(x) > 0.8)*1)\n }\n\n }\n\n\n\n # --------------------------\n\n\n\n if (DS==\"jags\") {\n\n warning( \"Jags method is deprecated. Use stan.\")\n\n require(rjags)\n rjags::load.module(\"dic\")\n rjags::load.module(\"glm\")\n\n if (!exists(\"fishery_model\", p)) p$fishery_model = list()\n if (!exists(\"n.adapt\", p$fishery_model)) p$fishery_model$n.adapt = 5000 # burn-in .. 4000 is enough for the full model\n if (!exists(\"n.iter \", p$fishery_model)) p$fishery_model$n.iter = 10000 # n.iter = 30000,\n if (!exists(\"n.chains\", p$fishery_model)) p$fishery_model$n.chains = 8 # n.chains = 8 ,\n if (!exists(\"n.thin\", p$fishery_model)) p$fishery_model$n.thin = 100 # high autocorrelations\n\n n.iter.total = p$fishery_model$n.iter * p$fishery_model$n.thin\n\n sb = snowcrab_tsdata( p=p, assessment_years=p$assessment_years )\n\n sb$tomonitor = c( \"r\", \"K\", \"q\", \"qs\", \"rmu\", \"rsd\", \"b\",\"bpsd\", \"bosd\",\"b0\", \"b0sd\", \"rem\", \"remsd\", \"remmu\",\"REM\", \"MSY\", \"BMSY\", \"FMSY\", \"Fcrash\", \"Bdrop\", \"BX2MSY\", \"F\", \"TAC\",\n \"C\", \"P\", \"B\" )\n\n sb$jagsmodelname = \"biomassdynamic_nonhyper_2016.bugs\"\n sb$jagsmodel =\n \"\nmodel {\n\n # -------------------\n # define some marginally informative variance priors using CV's (coefficients of variation) as a simple approach\n # uniform distribution seems most stable .. too uninformative?\n # NOTE: eps = a small number non-zero number (essentially equivalent to zero but used to prevent infinity values)\n # uninformative CV's associated with process (bp.) and observation (bo.) errors\n\n # NOTE for lognormals: CV = sqrt(exp( SD ^2) - 1) and CV ~ SD where SD ~ < 0.5\n # of SD = sqrt(log(1+CV^2)) and therefore, in terms of precision:\n # TAU = 1/SD^2 = 1/log(1+CV^2)\n\n\n for (j in 1:U) {\n r[j] ~ dnorm( rmu[j], pow( rsd[j], -2 ) )\n }\n\n\n for (j in 1:U) {\n #qmu[j] ~ dunif( qmin, qmax )\n #qsd[j] ~ dunif( qmu[j] * cvnormalmin, qmu[j] *cvnormalmax ) # catchability coefficient (normal scale)\n q[j] ~ dnorm( qmu[j], pow( qsd[j], -2 ) ) T(qmin, qmax)\n #q[j] ~ dunif( qmin[j] , qmax[j] )\n }\n\n\n for (j in 1:U) {\n K[j] ~ dlnorm( Kmu[j], pow( Ksd[j], -1 )) # T(Kmin[j], Kmax[j] )\n }\n\n\n # -------------------\n # removals (catch) observation model, standardized to K (assuming no errors in observation of catch!)\n for (j in 1:U) {\n for (i in 1:N){\n rem[i,j] <- CAT[i,j]/K[j]\n }\n }\n\n\n # -------------------\n # biomass observation model\n # This is slightly complicated because a fall / spring survey correction is required:\n # B represents the total fishable biomass available in fishing year y\n # in fall surveys: Btot(t) = Bsurveyed(t) + removals(t)\n # in spring surveys: Btot(t) = Bsurveyed(t) + removals(t-1)\n # this is conceptualized in the following time line:\n # '|' == start/end of each new fishing year\n # Sf = Survey in fall\n # Ss = Survey in spring\n # |...(t-2)...|.Ss..(t-1)...|...(t=2004)..Sf.|...(t+1).Sf..|...(t+2)..Sf.|...\n\n for (j in 1:(U)) {\n #botau[j] ~ dunif( pow( log( 1 + pow( cvlognormalmax, 2) ), -1 ), pow( log( 1 + pow( cvlognormalmin, 2) ), -1 ) ) # min/max inverted because it is an inverse scale\n #bosd[j] ~ dunif( bomin[j], bomax[j] )\n bosd[j] ~ dlnorm(bomup[j],pow(bosdp[j],-1))\n botau[j] <- pow( bosd[j], -2 )\n }\n\n for (j in 1:(U-1)) {\n # spring surveys from 1998 to 2003\n IOA[1,j] ~ dlnorm( log( max( q[j] * K[j] * (bm[1,j] - rem[1,j]) , eps)), botau[j] ) # approximation\n for (i in 2:(ty-1)) {\n IOA[i,j] ~ dlnorm( log( max( q[j] * K[j] * (bm[i,j]- rem[(i-1),j]), eps)), botau[j] ) ;\n }\n # transition year\n IOA[ty,j] ~ dlnorm( log( max( q[j] * K[j] * (bm[ty,j] - (rem[(ty-1),j] + rem[ty,j] )/2 ), eps)), botau[j] ) ; # approximation\n # fall surveys\n for (i in (ty+1):N) {\n IOA[i,j] ~ dlnorm( log( max( q[j] * K[j] * (bm[i,j] - rem[i,j]), eps)), botau[j] ) ;\n }\n }\n\n # Cfa 4X -- fall/winter fishery\n # assume similar to a spring fishery but no need for separate q's\n # Btot(t) = Bsurveyed(t)+ removals(t-1)\n # NOTE: year designation in 4X is for the terminal year: ie. 2001-2002 => 2002\n\n IOA[1,cfa4x] ~ dlnorm( log( max( q[cfa4x] * K[cfa4x] * (bm[1,cfa4x] - rem[1,cfa4x]), eps)), botau[cfa4x] ) ; # approximation\n for (i in 2:N) {\n IOA[i,cfa4x] ~ dlnorm( log( max( q[cfa4x] * K[cfa4x] * (bm[i,cfa4x]- rem[(i-1),cfa4x]), eps)), botau[cfa4x] ) ;\n }\n\n\n\n # -------------------\n # biomass process model\n\n for (j in 1:U) {\n #bptau[j] ~ dunif( pow( log( 1 + pow( cvlognormalmax, 2) ), -1 ), pow( log( 1 + pow( cvlognormalmin, 2) ), -1 ) )\n bpsd[j] ~ dunif( bpmin[j], bpmax[j] )\n bptau[j] <- pow( bpsd[j], -2 )\n }\n\n for(j in 1:U) {\n b0[j] ~ dunif( b0min[j], b0max[j] ) # starting b prior to first catch event\n bm[1,j] ~ dlnorm( log( max( b0[j], eps)), bptau[j] ) T(bmin, bmax ) ; # biomass at first year\n for(i in 2:(N+M)) {\n bm[i,j] ~ dlnorm( log( max(bm[i-1,j]*( 1 + r[j]*(1-bm[i-1,j])) - rem[i-1,j] , eps)), bptau[j] ) T(bmin, bmax) ;\n }\n\n # forecasts\n for(i in 1:M) {\n rem[N+i,j] <- er*bm[N+i-1,j]\n }\n }\n\n\n # -------------------\n # monitoring nodes and parameter estimates for output\n for(j in 1:U) {\n Bdrop[j] <- 1 - step( bm[N+1,j]-bm[N,j] ) ; # test if bm(t) >= bm(t-1)\n BX2MSY[j] <- 1 - step( bm[N+1,j]-0.25 ) ; # test if bm >= 1/2 bmY\n MSY[j] <- r[j]* exp(K[j]) / 4 # maximum height of of the latent productivity (yield)\n BMSY[j] <- exp(K[j])/2 # biomass at MSY\n FMSY[j] <- 2 * MSY[j] / exp(K[j]) # fishing mortality at MSY\n Fcrash[j] <- 4 * MSY[j] / exp(K[j]) # fishing mortality at which the stock will crash\n }\n\n\n # -------------------\n # fishing mortality\n # force first year estimate assuming catches in year 0 to be similar to year 1\n for(j in 1:U) {\n for(i in 1:N) {\n F[i,j] <- -log( max(1 - rem[i,j] / bm[i,j], eps))\n }\n for(i in (N+1):(N+M)) {\n F[i,j] <- -log( max(1 - er * bm[i-1,j] / bm[i,j], eps))\n }\n }\n\n\n # -------------------\n # annual production\n for(j in 1:U) {\n pd[1,j] <- bm[2,j]- bm[1,j] + rem[1,j] # approximation\n for (i in 2:(N) ){\n pd[i,j] <- (bm[i+1,j]- bm[i-1,j])/2 + rem[i,j] # linear interpolation cancels out the bm[i,j] term\n }\n for(i in (N+1):(N+M-1)) {\n pd[i,j] <- (bm[i+1,j]- bm[i-1,j])/2 + er * bm[i-1,j] # linear interpolation cancels out the bm[i,j] term\n }\n pd[(N+M),j] <- (bm[(N+M),j]- bm[(N+M-1),j]) + er * bm[(N+M-1),j] # approximation\n }\n\n\n\n # -------------------\n # recaled estimates\n\n for(j in 1:U) {\n for(i in 1:(N+M)) {\n B[i,j] <- (bm[i,j] - rem[i,j]) * K[j]\n P[i,j] <- pd[i,j]*K[j]\n C[i,j] <- rem[i,j]*K[j]\n }\n for(i in 1:M) {\n TAC[i,j] <- rem[N+i,j]*K[j]\n }\n }\n\n}\n\n \"\n\n fn = tempfile()\n cat( sb$jagsmodel, file=fn )\n\n m = jags.model( file=fn, data=sb, n.chains=p$fishery_model$n.chains, n.adapt=p$fishery_model$n.adapt ) # recruitment + spring/summer q's + all observed CVs\n\n tomonitor = intersect( variable.names (m), sb$tomonitor )\n\n y = jags.samples(m, variable.names=tomonitor, n.iter=n.iter.total, thin=p$fishery_model$n.thin) # sample from posterior\n\n jags2stan = function(x){\n # merge chains together\n print(attr(x, \"varname\"))\n xdim = dim(x)\n chain = length(xdim)\n iter = chain-1\n oorder = c(iter, chain, 1:(iter-1))\n o = aperm( x, oorder)\n odim = dim(o)\n dim(o) =c(odim[1]*odim[2], odim[3:length(odim)])\n return( o)\n }\n out = lapply( y, jags2stan )\n\n res = list( mcmc=out, sb=sb, p=p)\n save(res, file=p$fishery_model$fnres, compress=T)\n\n return(res)\n\n\n if (0) {\n\n dic.samples(m, n.iter=p$fishery_model$n.iter ) # pDIC\n\n graphics.off() ; plot.new(); layout( matrix(c(1,2,3), 3, 1 )); par(mar = c(5, 4, 0, 2))\n for( i in 1:3) hist(y$cvr[i,,], \"fd\")\n\n # convergence testing -- by 1000 to 1500 convergence observed by Gelman shrink factor diagnostic\n y = jags.samples(m, variable.names=tomonitor, n.iter=6000, thin=1 )\n\n gelman.plot(y[[\"r\"]])\n gelman.plot(y[[\"K\"]])\n gelman.plot(y[[\"q\"]]) # about 6-8000 runs required to converge\n gelman.plot(y[[\"rsd\"]])\n gelman.plot(y[[\"Ksd\"]])\n gelman.plot(y[[\"bosd\"]])\n gelman.plot(y[[\"bp.p\"]])\n geweke.plot(y[[\"r\"]])\n\n # update if not yet converged\n # update(m, n.iter=p$fishery_model$n.iter ) # above seems enough for convergence but a few more to be sure\n\n # determine autocorrelation thinning\n # y = coda.samples(m, variable.names=c(\"K\", \"r\", \"q\"), n.iter=20000, thin=10) # sample from posterior\n # autocorr.plot(y) # about 10 to 20 required\n # plot(y, ask=T)\n # autocorr(y, lags = c(0, 1, 5, 10, 50), relative=TRUE)\n\n # final sampling from the posteriors\n # y = jags.samples(m, variable.names=tomonitor, n.iter=10000, thin=20) # sample from posterior\n y = jags.samples(m, variable.names=tomonitor, n.iter=n.iter.total, thin=p$fishery_model$n.thin) # sample from posterior\n\n\n\n ##\n b1=apply( y$B[,1,,], 1,quantile , probs=c(0.025,0.5,0.975), na.rm=T )\n f1=apply( y$F[,1,,], 1,quantile , probs=c(0.5), na.rm=T )\n NENS=data.frame(t(b1),F=f1,row.names=1999:2019)\n NENS$U=1-exp(-NENS$F)\n b2=apply( y$B[,2,,], 1,quantile , probs=c(0.025,0.5,0.975), na.rm=T )\n f2=apply( y$F[,2,,], 1,quantile , probs=c(0.5), na.rm=T )\n SENS=data.frame(t(b2),F=f2,row.names=1999:2019)\n SENS$U=1-exp(-SENS$F)\n\n b3=apply( y$B[,3,,], 1,quantile , probs=c(0.025,0.5,0.975), na.rm=T )\n f3=apply( y$F[,3,,], 1,quantile , probs=c(0.5), na.rm=T )\n CFA4X=data.frame(t(b3),F=f3,row.names=1999:2019)\n CFA4X$U=1-exp(-CFA4X$F)\n\n NENS\n SENS\n CFA4X\n\n\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(5, 4, 0, 2))\n\n for( i in 1:3) hist(y$r[i,,], \"fd\")\n for( i in 1:3) hist(y$qsd[i,,], \"fd\")\n for( i in 1:3) hist(y$Ksd[i,,], \"fd\")\n for( i in 1:3) hist(y$b0sd[i,,], \"fd\")\n for( i in 1:3) hist(y$rsd[i,,], \"fd\")\n for( i in 1:3) hist(y$b0[i,,], \"fd\")\n\n }\n\n }\n\n\n # --------------------------\n\n\n if (DS==\"LaplacesDemon\") {\n\n warning( \"LaplacesDemon method is not yet complete\")\n\n require(LaplacesDemonCpp)\n\n\n sb = snowcrab_tsdata( p=p, assessment_years=p$assessment_years )\n\n debug.region=\"cfa4x\"\n debug.region=\"cfasouth\"\n debug.region=\"cfanorth\"\n\n # single region test\n K0est =list()\n K0est[[\"cfanorth\"]] = 5\n K0est[[\"cfasouth\"]] = 65\n K0est[[\"cfa4x\"]] = 5\n\n yrs = as.numeric(rownames( res$B))\n nforecasts = 5\n\n\n Data = list(\n Ndata = length(yrs),\n Nforecasts = nforecasts, # no years for projections\n O = res$B[[debug.region]], # observed index of abundance\n log_O0 = mean( log(res$B[[debug.region]]), na.rm=TRUE ),\n Omax = max( res$B[[debug.region]] *1.5 , na.rm=TRUE),\n removals = res$L[[debug.region]] , # removalsches , assume 20% handling mortality and illegal landings\n log_R0 = log(mean(res$L[[debug.region]], na.rm=TRUE )/K0est[[debug.region]]),\n er = 0.2, # target exploitation rate\n ty = which(yrs==2004) , # index of the transition year (2004) between spring and fall surveys\n log_r0= log(1),\n log_K0= log(K0est[[debug.region]]),\n log_q0= log(mean(res$B[[debug.region]], na.rm=TRUE)/K0est[[debug.region]]),\n S0= 0.6, # normalised\n cv = 0.4,\n smax =1.25,\n eps = 1e-6,\n eps_1 = 1 - 1e-6\n )\n\n\n\n # set up the model\n # set up model for a simple surplus production model\n # to be solved by the Rlibrary: LaplacesDemon (or alternately via penalized Maximum Likelihood)\n\n\n\n # ----------------------------------\n # Identify location and number of missing values -- prediction locations are treated the same way\n\n # compute a few things here that are constant in the model\n Data$N = Data$Ndata + Data$Nforecasts # no years with data + projections\n Data$q0 = exp( Data$log_q0)\n Data$r0 = exp( Data$log_r0)\n Data$K0 = exp( Data$log_K0)\n Data$R0 = exp(Data$log_R0)\n Data$O0 = exp(Data$log_O0)\n Data$O_range = range( Data$O, na.rm=TRUE)\n\n # ----------------------------------\n\n if (is.null( Data$Missing )) {\n mO = which( !is.finite(Data$O))\n mr = which( !is.finite(Data$removals) )\n Data$Missing = list(\n O = mO,\n nO = length(mO) ,\n removals = mr ,\n nremovals = length( mr ),\n n = length( mO) + length(mr)\n )\n }\n\n if (is.null( Data$PGF )) {\n # Parameter Generating Function\n Data$PGF = function(Data) {\n r=rnorm(Data$nregions, Data$r0, sd=Data$cv )\n K=rnorm(1, log(Data$K0), sd=Data$cv ) # log scale\n q=rnorm(1, Data$q0, sd=Data$cv )\n S_sd=runif( 1 )\n O_sd=runif( 1 )\n S=rnorm( Data$N, log(Data$S0), sd=Data$cv ) # log scale\n S0=rnorm(1, log(Data$S0), sd=Data$cv ) # log scale\n out = c(r, K, q, S_sd, O_sd, S, S0)\n if (Data$Missing$nO > 0) {\n Omissing = rnorm( Data$Missing$nO, log(Data$Omissing0), sd=Data$cv )\n out = c( out, Omissing )\n }\n if (Data$Missing$nremovals > 0) {\n removalsmissing = rnorm( Data$Missing$nremovals, log(Data$removalsmissing0), sd=Data$cv )\n out = c( out, removalsmissing )\n }\n return( out )\n }\n Data$PGF = compiler::cmpfun(Data$PGF)\n }\n\n if (is.null( Data$parm.names )) {\n # paramater names and initial values\n pnames = list(\n r = Data$r0,\n K = Data$K0,\n q = Data$q0,\n S_sd = Data$cv,\n O_sd = Data$cv,\n S = rep( Data$S0, Data$N),\n S0 = Data$S0\n )\n if (Data$Missing$nO > 0) pnames$Omissing = rep( Data$Omissing0, Data$Missing$nO )\n if (Data$Missing$nremovals > 0) pnames$removalsmissing = rep( Data$removalsmissing0, Data$Missing$nremovals )\n Data$parm.names = as.parm.names( pnames )\n }\n\n if (is.null( Data$idx )) {\n # index position of parameters\n Data$idx = list(\n r=grep(\"\\\\\", Data$parm.names),\n K=grep(\"\\\\\", Data$parm.names),\n q=grep(\"\\\\\", Data$parm.names),\n S_sd=grep(\"\\\\\", Data$parm.names),\n O_sd=grep(\"\\\\\", Data$parm.names),\n S=grep(\"\\\\\", Data$parm.names),\n S0=grep(\"\\\\\", Data$parm.names)\n )\n if (Data$Missing$nO > 0) Data$idx$Omissing = grep(\"\\\\\", Data$parm.names)\n if (Data$Missing$nremovals > 0) Data$idx$removalsmissing = grep(\"\\\\\", Data$parm.names)\n }\n\n\n if (is.null( Data$mon.names )) {\n Data$mon.names = c(\"LP\", \"r\", \"K\", \"q\" )\n # Data$mon.names = c(\"LP\", \"r\", \"K\", \"q\", paste0(\"S\",1:Data$N), paste0(\"AR\",1:(Data$N-1) ) )\n }\n\n\n if (is.null( Data$Model )) {\n Data$Model = function(parm, Data) {\n\n Spred = Opred = rep(0, Data$N ) # initialize a few storage vectors\n\n # constraints:\n parm[Data$idx$q] = LaplacesDemonCpp::interval( parm[Data$idx$q], Data$eps, 2 );\n parm[Data$idx$r] = LaplacesDemonCpp::interval( parm[Data$idx$r], Data$eps, 2 );\n parm[Data$idx$K] = LaplacesDemonCpp::interval( parm[Data$idx$K], log(Data$eps), log(Data$K0*2) );\n parm[Data$idx$S0] = LaplacesDemonCpp::interval( parm[Data$idx$S0], log(Data$eps), log(Data$smax) );\n parm[Data$idx$S] = LaplacesDemonCpp::interval( parm[Data$idx$S], log(Data$eps), log(Data$smax) );\n\n parm[Data$idx$O_sd] = LaplacesDemonCpp::interval( parm[Data$idx$O_sd], Data$eps, 1)\n parm[Data$idx$S_sd] = LaplacesDemonCpp::interval( parm[Data$idx$S_sd], Data$eps, 1)\n\n # these are SD's on log scale (0,1) is sufficient\n # continue with SD priors as these are now on correct scale\n\n loglik = c()\n loglik[Data$parm.names] = 0\n loglik[Data$idx$q] = LaplacesDemonCpp::dhalfcauchy( parm[Data$idx$q], Data$cv, TRUE ) ;\n loglik[Data$idx$r] = dnorm( parm[Data$idx$r], Data$r0, Data$cv, TRUE ) ;\n loglik[Data$idx$K] = dnorm( parm[Data$idx$K], log(Data$K0), Data$cv, TRUE ) ;\n loglik[Data$idx$S0] = dnorm( parm[Data$idx$S0], log(Data$S0), Data$cv, TRUE ) ;\n loglik[Data$idx$O_sd] = LaplacesDemonCpp::dhalfcauchy( parm[Data$idx$O_sd], Data$cv, TRUE );\n loglik[Data$idx$S_sd] = LaplacesDemonCpp::dhalfcauchy( parm[Data$idx$S_sd], Data$cv, TRUE );\n\n q = parm[Data$idx$q]\n r = parm[Data$idx$r]\n K = exp( parm[Data$idx$K] )\n Spred[1] = exp( parm[Data$idx$S0] );\n S = exp( parm[Data$idx$S] ) ;\n O_sd = parm[Data$idx$O_sd]\n S_sd = parm[Data$idx$S_sd]\n\n R = Data$removals/K ;\n if (exists( \"Missing\", Data ) ) {\n if (Data$Missing$nremovals > 0) {\n R[Data$Missing$removals ] = rlnorm( Data$Missing$nremovals,\n parm[Data$idx$removalsmissing], sd=Data$cv )\n R = .Internal(pmax(na.rm=TRUE, R, Data$eps )) ;\n loglik[Data$idx$removalsmissing] = dnorm( parm[Data$idx$removalsmissing],\n log(R[Data$Missing$removals ]), Data$cv )\n }\n }\n\n AR = 1.0 + r*(1-S[Data$jj]) ; # autocorelation component\n Spred[ Data$ii ] = S[Data$jj] * AR - R[Data$jj] ; # simple logistic\n Spred = .Internal(pmax(na.rm=TRUE, Spred, Data$eps )) ;\n loglik[Data$idx$S] = dnorm( parm[Data$idx$S], log(Spred), S_sd, TRUE );\n\n # Likelihoods for observation model\n i = 1 ; Opred[i] = S[i] - R[i] ;\n i = 2:(Data$ty-1); Opred[i] = S[i] - R[i-1] ;\n i = Data$ty; Opred[i] = S[i] - (R[i-1] + R[i])/2 ;\n i = (Data$ty+1):Data$N ; Opred[i] = S[i] - R[i] ;\n Opred = .Internal(pmax( na.rm=TRUE, K*q*Opred, Data$eps ) ) ;\n if (exists( \"Missing\", Data ) ) {\n if (Data$Missing$nO > 0) {\n Data$O[Data$Missing$O ] = rlnorm( Data$Missing$nO, parm[Data$idx$Omissing], sd=Data$cv )\n Data$O[Data$Missing$O ] = .Internal(pmax(na.rm=TRUE, Data$O[Data$Missing$O ], Data$eps )) ;\n loglik[Data$idx$Omissing] = dnorm( parm[Data$idx$Omissing], log(Data$O[Data$Missing$O ]), Data$cv )\n }\n }\n ll_obs = dnorm( log(Data$O), log(Opred), O_sd, TRUE )\n\n Smon = Rmon = ER = F = B = C = rep(0, Data$MN )\n\n Smon[1:Data$N] = S\n Rmon[1:Data$N] = R\n for( i in (Data$N+1):Data$MN ){\n Smon[i] = Smon[i-1] * (1.0 + r*(1.0-Smon[i-1]) ) - Rmon[i-1]\n Rmon[i] = Smon[i-1] * Data$er\n }\n Smon = .Internal(pmax( na.rm=TRUE, Smon, Data$eps ) )\n Rmon = .Internal(pmax( na.rm=TRUE, Rmon, Data$eps ) )\n\n # monitoring\n ER = Rmon / Smon ;\n B = Smon*K\n C = Rmon*K\n F = -log( 1 - ER) ; # fishing mortality\n\n LL = sum( ll_obs ) # log likelihood (of the data)\n LP = LL + sum( loglik ) # log posterior\n out = list( LP=LP, Dev=-2*LL, Monitor=c(LP, r, K, q), yhat=S*K, parm=parm )\n return( out )\n }\n Data$Model.ML = compiler::cmpfun( function(...) (Data$Model(...)$Dev / 2) ) # i.e. - log likelihood\n Data$Model.PML = compiler::cmpfun( function(...) (- Data$Model(...)$LP) ) #i.e., - log posterior\n Data$Model = compiler::cmpfun(Data$Model) # byte-compiling for more speed .. use RCPP if you want more speed\n }\n\n print (Data$Model( parm=Data$PGF(Data), Data=Data ) ) # test to see if return values are sensible\n\n\n\n # 2. maximum likelihood solution\n f.ml = optim( par=sb$PGF(sb), fn=sb$Model.ML, Data=sb, method=\"BFGS\", control=list(maxit=5000, trace=0), hessian=TRUE )\n names(f.ml$par ) = sb$parm.names\n #print(sqrt( diag( solve(f.ml$hessian) )) ) # assymptotic standard errors\n (f.ml$par)\n\n # 3. penalized maximum likelihood .. better but still a little unstable depending on algorithm\n f.pml = optim( par=sb$PGF(sb), fn=sb$Model.PML, Data=sb, method=\"BFGS\", control=list(maxit=5000, trace=0), hessian=TRUE )\n names(f.pml$par ) = sb$parm.names\n (f.pml$par)\n\n #print(sqrt( diag( solve(f.pml$hessian) )) ) # assymptotic standard errors\n\n\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=1000, Status=100, Thinning=10) #\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=as.initial.values(f), Iterations=1000, Status=100, Thinning=10) #\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=as.initial.values(f), Iterations=5000, Status=10, Thinning=20, Method=\"NUTS\") #\n\n\n Initial.Values <- as.initial.values(f)\n f <- LaplacesDemon(sb$Model, Data=sb, Initial.Values,\n Covar=f$Covar, Iterations=20000, Status=1000, Thinning=1000,\n Algorithm=\"CHARM\", Specs=NULL)\n\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=as.initial.values(f), Iterations=5000, Status=100, Thinning=25, Covar=f$Covar)\n\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=sb$PGF(sb), Method=\"Roptim\", method=\"BFGS\", Stop.Tolerance=1e-9, Iterations = 10000 )\n f\n\n Consort(f)\n plot(f, Data=sb)\n\n PosteriorChecks(f)\n\n\n f0 = LaplacesDemon(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=1000, Status=100, Thinning=1)\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"HAR\", Iterations=1000 )\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"SR1\", Iterations=1000 )\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"TR\", Iterations=10000 )\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"BFGS\", Iterations=1000 )\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"PSO\", Iterations=10000 )\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f0), Method=\"SPG\", Iterations=10000 )\n\n\n\n\n # 4. quick solution: acts as \"burn-in\" .. do a few times in case solution is unstable\n f = LaplaceApproximation(sb$Model, Data=sb, parm=sb$PGF(sb), Iterations=100 )\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Iterations=1000 )\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Method=\"BFGS\", Stop.Tolerance=1e-8, Iterations=1000 )\n\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=5000, Status=10, Thinning=10, Algorithm=\"NUTS\", Covar=f$Covar, Specs=list(A=100, delta=0.6, epsilon=NULL, Lmax=Inf)) # A=burnin, delta=target acceptance rate\n\n f = LaplacesDemon.hpc(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=10, Status=1, Thinning=1, Algorithm=\"NUTS\", Covar=f$Covar, Specs=list(A=100, delta=0.6, epsilon=NULL, Lmax=Inf)) # A=burnin, delta=target acceptance rate\n\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=1000, Status=100, Thinning=1)\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Method=\"Roptim\", Stop.Tolerance=1e-8, Iterations = 5000, method=\"Nelder-Mead\", control=list(maxit=5, reltol=1e-9 ) )\n f\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Method=\"Roptim\", Stop.Tolerance=1e-8, Iterations = 1000, method=\"L-BFGS-B\", control=list(maxit=10, reltol=1e-10 ) )\n\n\n\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f) ) # fast spin up of paramters\n f = LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Method=\"BFGS\", Stop.Tolerance=1e-8 )\n\n\n # NOTES: NM is slow\n # algorithms = c( \"TR\", \"LM\", \"CG\", \"NR\", \"NM\", \"SPG\", \"LBFGS\", \"BFGS\", \"PSO\", \"SR1\", \"HAR\", \"DFP\", \"BHHH\", \"HJ\", \"Rprop\" ) # available\n # algorithms = c( \"CG\", \"NM\", \"SPG\", \"LBFGS\", \"BFGS\", \"PSO\", \"SR1\", \"HAR\", \"DFP\", \"Rprop\" ) # reliably working\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=sb$PGF(sb), Iterations=500, Status=100, Thinning=1)\n\n algorithms = c( \"CG\", \"LBFGS\", \"BFGS\", \"PSO\", \"SR1\", \"HAR\", \"DFP\", \"Rprop\" ) # reliably working\n for (a in algorithms) {\n print(a)\n ft = try( LaplaceApproximation(sb$Model, Data=sb, parm=as.initial.values(f), Method=a, Stop.Tolerance=1e-9, Iterations=5000 ) )\n if (! class(ft) %in% \"try-error\" ) if (ft$Converged) if( ft$LP.Final > f$LP.Final ) {\n f = ft\n f$Method = a\n }\n }\n str(f)\n plot(f, Data=sb)\n\n # MCMC ... look at ACF\n f = LaplacesDemon(sb$Model, Data=sb, Initial.Values=as.initial.values(f), Covar=f$Covar,\n Iterations=1000, Status=100, Thinning=1, Algorithm=\"CHARM\" )\n Consort(f)\n plot(f, Data=sb)\n\n PosteriorChecks(f)\n #caterpillar.plot(f, Parms=\"beta\")\n #plot(f, Data, PDF=FALSE)\n #Pred <- predict(f, Model, Data, CPUs=1)\n\n #summary(Pred, Discrep=\"Chi-Square\")\n #plot(Pred, Style=\"Covariates\", Data=Data)\n #plot(Pred, Style=\"Density\", Rows=1:9)\n #plot(Pred, Style=\"Fitted\")\n #plot(Pred, Style=\"Jarque-Bera\")\n #plot(Pred, Style=\"Predictive Quantiles\")\n #plot(Pred, Style=\"Residual Density\")\n #plot(Pred, Style=\"Residuals\")\n #Levene.Test(Pred)\n #Importance(f, Model, Data, Discrep=\"Chi-Square\")\n\n # MCMC: run with appropriate thinning and other options:\n f = LaplacesDemon(sb$Model, Data=sb, as.initial.values(f),\n Covar=NULL, Iterations=10000, Status=1000, Thinning=1000, Algorithm=\"CHARM\", Specs=NULL)\n\n f = LaplacesDemon(sb$Model, Data=sb, as.initial.values(f),\n Covar=f$Covar , Iterations=50000, Status=10204, Thinning=1000, Algorithm=\"CHARM\", Specs=list(alpha.star=0.44))\n\n\n f = LaplacesDemon(sb$Model, Data=sb,\n Initial.Values=as.initial.values(f), Covar=f$Covar, Iterations=10000, Status=1000, Thinning=100)\n\n f = LaplacesDemon(sb$Model, Data=sb,\n Initial.Values=as.initial.values(f), Covar=f$Covar, Iterations=30000, Status=1000, Thinning=35,\n Algorithm=\"AMWG\", Specs=list(B=NULL, n=1000, Periodicity=35 ) )\n\n f <- LaplacesDemon(sb$Model, Data=sb,\n Initial.Values=as.initial.values(f), Covar=f$Covar, Iterations=10000, Status=1000, Thinning=105,\n Algorithm=\"AFSS\", Specs=list(A=Inf, B=NULL, m=100, n=0, w=1))\n\n # medium speed .. increase Iterations til convergence\n f = VariationalBayes(sb$Model, Data=sb, parm=as.initial.values(f), Iterations=100, Samples=10, CPUs=5 )\n f = VariationalBayes(sb$Model, Data=sb, parm=as.initial.values(f), Iterations=5000, Samples=100, CPUs=5 ,Stop.Tolerance=1e-9)\n\n # slow\n f = IterativeQuadrature(sb$Model, Data=sb, parm=as.initial.values(f), Iterations=10, Algorithm=\"AGH\",\n Specs=list(N=5, Nmax=7, Packages=NULL, Dyn.libs=NULL) )\n\n\n\n }\n\n\n if (DS==\"tmb\") {\n\n warning( \"TMB method is not yet complete\")\n\n require(aegis)\n\n\n sb = snowcrab_tsdata( p=p, assessment_years=p$assessment_years )\n\n\n debug.region=\"cfa4x\"\n debug.region=\"cfanorth\"\n debug.region=\"cfasouth\"\n\n\n # Data\n data=list(N=sb$Ndata, IOA=sb$O, CAT=sb$removals);\n # Parameters: initial values\n parameters=list(log_sigmap=log(0.2),\n log_sigmao=log(0.2),\n log_Q=log(0.1),\n log_r=log(0.8),\n log_K=log(70),\n log_P=rep(log(0.6),sb$Ndata))\n\n #parameters=list(log_tau=rep(log(0.1),sb$U),\n # log_sigma=rep(log(0.1),sb$U),\n # log_Q=rep(log(0.1),sb$U),\n # log_r=rep(log(0.8),sb$U),\n # log_K=log(c(6,70,2)),\n # log_P=matrix(rep(log(0.6),sb$N*sb$U),sb$N,sb$U))\n #\n\n ############################################################################\n # Compile model cpp file in TMB\n ############################################################################\n\n tmb.dir = file.path(project.codedirectory('bio.snowcrab'),\"inst\",\"tmb\")\n setwd(tmb.dir)\n\n library(TMB);\n compile(\"biomassdynamic.cpp\")\n dyn.load(dynlib(\"biomassdynamic\"))\n\n ###########################################################################\n # Estimation\n ###########################################################################\n\n # The objective function\n obj <- MakeADFun(data, parameters, random=\"log_P\", DLL=\"biomassdynamic\")\n\n # Optimize the objective function\n opt <- nlminb(obj$par,obj$fn,obj$gr)\n\n # Report estimates and standard errors\n rep <- sdreport(obj)\n\n MLE <- list(year=1:data$N,B=rep$value[1:data$N], Bsd=rep$sd[1:data$N],predI=rep$value[1:data$N+data$N], predIsd=rep$sd[1:data$N+data$N], K=rep$value[data$N*2+1], Ksd=rep$sd[data$N*2+1], r=rep$value[data$N*2+2],rsd=rep$sd[data$N*2+2],Q=rep$value[data$N*2+3],Qsd=rep$sd[data$N*2+3],sigma=rep$value[data$N*2+4],sigmasd=rep$sd[data$N*2+4],tau=rep$value[data$N*2+5],tausd=rep$sd[data$N*2+5],nll=rep$value[data$N*2+6],nllsd=rep$sd[data$N*2+6])\n\n\n ###########################################################################\n # Results plots\n ###########################################################################\n plot(data$IOA,ylim=c(0,max(MLE$predI+MLE$predIsd)),pch=16,col='red')\n with(MLE,lines(year,predI))\n with(MLE,lines(year,predI+predIsd,lty=2))\n with(MLE,lines(year,predI-predIsd,lty=2))\n\n }\n\n # -----------------------\n\n if (DS==\"delay.difference_jags\") {\n\n warning( \"This is not yet complete .. will eventually convert to Stan for speed and stability ..\")\n\n require(rjags)\n rjags::load.module(\"dic\")\n rjags::load.module(\"glm\")\n\n project.library( \"bio.models\")\n\n ### all data follow this sequence: c(\"cfanorth\", \"cfasouth\", \"cfa4x\")\n\n res = snowcrab_tsdata( p=p, assessment_years=p$assessment_years )\n\n\n sb = list(\n FB0x = c(0.8, 0.6, 0.1), # priors of the starting fishable biomass : N,S,4X\n q0x = c(1, 1, 1), # priors of \"catchability\"\n K0x = c(4, 50, 1 ), # mean carrying capacity estimate\n IOA = as.matrix(res$B), # observed index of fishable biomass\n CAT = as.matrix(res$L) , # observed landings\n REC = as.matrix(res$R) , # observed recruitment\n qREC0 = 1, # q correction for recruitment\n sigma = 0.3, # default upper limit in error (CV) of data\n sigmaB = 0.3, # default upper limit in error (CV) of FB data\n cv = 0.5, # ...\n er = 0.2, # target exploitation rate\n MFB = 0.8, # prior estimate of natural mortality for FB\n MFBsd = 0.8, # prior estimate of SD in natural mortality for FB\n MREC = 0.8, # prior estimate of natural mortality for FB\n MRECsd = 0.8, # prior estimate of SD in natural mortality for FB\n brodycoef = 2, # proportional increase in weight with each age\n brodysd0 = 0.2, # proportional increase in weight with each age\n N = nrow( res$B) , # no years with data\n M = 5, # no years for projections\n R = ncol( res$B), # number of regions\n ty=7, # index of the transition year (2004) between spring and fall surveys\n cfa4x=3, # index of cfa4x\n eps = 1e-4 # small non-zero number\n )\n\n # MCMC/Gibbs parameters\n n.adapt = 5000 # burn-in\n n.chains = 3\n n.thin = 100\n n.iter = 10000\n\n\n if (modelrun == \"simple\" ) {\n ## model 1 --- simple delay difference with observation and process error\n m = jags.model( file=fishery.model.jags ( DS=\"delay.difference\" ), data=sb, n.chains=n.chains, n.adapt=n.adapt )\n coef(m)\n tomonitor = c(\"FB\",\"K\", \"REC\", \"Znat\", \"Ztot\", \"qbiomass\", \"qREC\", \"Catch\", \"Cr\" )\n dic.samples(m, n.iter=n.iter ) # pDIC\n fnres = file.path( project.datadirectory(\"bio.snowcrab\"), \"output\", \"delaydifference.mcmc.simple.rdata\" )\n\n }\n\n\n if (modelrun == \"simple.illegal\" ) {\n ## model 2 --- simple delay difference with observation and process error and illegal landings\n m = jags.model( file=fishery.model.jags ( DS=\"delay.difference.illegal\" ), data=sb, n.chains=n.chains, n.adapt=n.adapt )\n coef(m)\n tomonitor = c(\"FB\",\"K\", \"REC\", \"Znat\", \"Ztot\", \"qbiomass\", \"qREC\", \"Catch\", \"Cr\" )\n dic.samples(m, n.iter=n.iter ) # pDIC\n fnres = file.path( project.datadirectory(\"bio.snowcrab\"), \"output\", \"delaydifference.mcmc.simple.illegal.rdata\" )\n\n }\n\n\n tomonitor = intersect( variable.names (m), tomonitor )\n\n\n # convergence testing -- by 1000 to 1500 convergence observed by Gelman shrink factor diagnostic\n convergence.test = F\n if (convergence.test) {\n y = jags.samples(m, variable.names=tomonitor, n.iter=10000, thin=10)\n gelman.plot(y[[\"Ztot\"]])\n gelman.plot(y[[\"K\"]])\n gelman.plot(y[[\"qbiomass\"]])\n gelman.plot(y[[\"qREC\"]])\n gelman.plot(y[[\"sdK\"]])\n gelman.plot(y[[\"sdo\"]])\n gelman.plot(y[[\"sdp\"]])\n geweke.plot(y[[\"r\"]])\n }\n\n\n # autocorrelation thinning\n y = coda.samples(m, variable.names=c(\"K\", \"r\", \"q\"), n.iter=10000, thin=10) # sample from posterior\n autocorr.plot(y)\n # plot(y, ask=T)\n # autocorr(y, lags = c(0, 1, 5, 10, 50), relative=TRUE)\n\n\n # update if not yet converged\n update(m, n.iter=n.iter ) # above seems enough for convergence but a few more to be sure\n\n\n # final sampling from the posteriors\n n.iter.final = n.iter * n.thin\n # n.iter.final = n.iter\n y = jags.samples(m, variable.names=tomonitor, n.iter=n.iter.final, thin=n.thin) # sample from posterior\n\n\n # save(y, file=fnres, compress=T)\n # load( fnres )\n\n\n # Figures\n dir.output = file.path( dirname(p$ofname), \"figures\", \"bugs\")\n dir.create( dir.output, recursive=T, showWarnings=F )\n\n\n # frequency density of key parameters\n figure.bugs( \"K\", y=y, fn=file.path(dir.output, \"K.density.png\" ) )\n figure.bugs( \"r\", y=y, fn=file.path(dir.output, \"r.density.png\" ) )\n figure.bugs( \"q\", y=y, fn=file.path(dir.output, \"qdensity.png\" ) )\n figure.bugs( \"BMSY\", y=y, fn=file.path(dir.output, \"BMSY.density.png\" ) )\n\n # timeseries\n figure.bugs( type=\"timeseries\", var=\"biomass\", y=y, fn=file.path(dir.output, \"biomass.timeseries.png\" ) )\n figure.bugs( type=\"timeseries\", var=\"fishingmortality\", y=y, fn=file.path(dir.output, \"fishingmortality.timeseries.png\" ) )\n\n # Harvest control rules\n figure.bugs( type=\"hcr\", var=\"default\", y=y, fn=file.path(dir.output, \"hcr.default.png\" ) )\n figure.bugs( type=\"hcr\", var=\"simple\", y=y, fn=file.path(dir.output, \"hcr.simple.png\" ) )\n\n # diagnostics\n figure.bugs( type=\"diagnostics.production\", y=y, fn=file.path(dir.output, \"diagnostic.production.png\" ) )\n figure.bugs( type=\"diagnostics....\", y=y, fn=file.path(dir.output, \"diagnostic. ... .png\" ) )\n\n\n\n\n # densities of biomass estimates for the year.assessment\n for (i in 1:3) plot(density(y$B[ndata,i,,] ), main=\"\")\n qs = apply( y$B[ndata,,,], 1, quantile, probs=c(0.025, 0.5, 0.975) )\n qs\n\n # densities of biomass estimates for the previous year\n for (i in 1:3) plot(density(y$B[ndata-1,i,,] ), main=\"\")\n qs = apply( y$B[ndata-1,,,], 1, quantile, probs=c(0.025, 0.5, 0.975) )\n qs\n\n\n # densities of F in assessment year\n for (i in 1:3) plot(density( y$F[ndata,i,,] ), xlim=c(0.05, 0.5), main=\"\")\n qs = apply( y$F[ndata,,,], 1, quantile, probs=c(0.025, 0.5, 0.975) )\n qs\n\n # densities of F in previous year\n for (i in 1:3) plot(density( y$F[ndata-1,i,,] ), main=\"\")\n qs = apply( y$F[ndata-1,,,], 1, quantile, probs=c(0.025, 0.5, 0.975) )\n qs\n\n # F for table ---\n summary(y$F, median)\n\n\n\n\n # B timeseries with error\n graphics.off()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(5, 4, 0, 2))\n\n vv = \"B\"\n Xm = jags.extract( y, vv, mean )\n X = y[[vv]]\n nR = dim(X)[2]\n for (r in 1:nR) {\n nn = create.histograms.yearly( X[,r,,] )\n nn = nn[ 1:(nrow(nn)-1) ,] # truncate larges and smallest data points as they are not linear increments\n nnr = as.numeric(rownames(nn) )\n if (r==2) nn = nn[ which( nnr < 100 ) ,]\n if (r==3) nn = nn[ which( nnr < 2 ) ,]\n colnames(nn) = yrs\n plot.histograms.temporal( nn, overlaydata=Xm[,r], xlab=\"Year\", ylab=\"Biomass; kt\", barscale=.9, barcol=\"gray\" )\n }\n\n\n\n # F timeseries with error\n graphics.off()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(5, 4, 0, 2))\n\n vv = \"F\"\n Xm = jags.extract( y, vv, mean )\n X = y[[vv]]\n X[ X>1] = 1\n X = X[ 1:length(yrs0), ,, ]\n\n nR = dim(X)[2]\n for (r in 1:nR) {\n nn = create.histograms.yearly( X[,r,,] )\n nn = nn[ 2:(nrow(nn)-1) ,] # truncate larges and smallest data points as they are not linear increments\n uu = scale( nn, center=FALSE, scale=colSums(nn) )\n colnames(nn) = yrs0\n plot.histograms.temporal( nn, overlaydata=Xm[1:length(yrs0),r], xlab=\"Year\", ylab=\"Fishing mortality\", barscale=0.9, barcol=\"gray\" )\n }\n\n\n\n }\n\n\n\n\n\n\n}\n", "meta": {"hexsha": "dccfa22d6afe2e486098689aa45e2b6e95a9a303", "size": 57025, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fishery_model.r", "max_stars_repo_name": "PEDsnowcrab/bio.snowcrab", "max_stars_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/fishery_model.r", "max_issues_repo_name": "PEDsnowcrab/bio.snowcrab", "max_issues_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/fishery_model.r", "max_forks_repo_name": "PEDsnowcrab/bio.snowcrab", "max_forks_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7666021921, "max_line_length": 441, "alphanum_fraction": 0.520420868, "num_tokens": 18590, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7718434978390746, "lm_q2_score": 0.4148988457967688, "lm_q1q2_score": 0.3202369763891728}} {"text": "source(\"./ETL/CountrySources/util.r\")\n\n##########\n# Reads and normalizes data from the Spanish government's official data.\n# Provides diff columns that are grouped by community\n# Input File format:\n# Columns [CCA Codigo ISO, Fecha, Casos, Hospitalizados, UCI, Fallecidos]\n# Final line is text explaining the use of the data\n# Dates are converted from strings to dates\n# Parameters\n# csv_file_name - Full path of the file to read using read.csv\n# Output\n# cv read and normalized to translated names\n# [region_code, date, total_cases, daily_cases, total_hospitalized, daily_hospitalized, total_icu, daily_icu, total_deaths, daily_deaths, total_recovered, daily_recovered]\n##########\nReadSpainCommunityDataFromCSV <- function(csv_file_name) {\n cv <- read.csv(csv_file_name, check.names=FALSE)\n cv <- head(cv, -3) # Remove last row with explanation text\n cv[is.na(cv)] <- 0\n translated_colnames <- c('region_code', 'date', 'total_cases', 'total_hospitalized', 'total_icu', 'total_deaths', 'total_recovered')\n colnames(cv) <- translated_colnames\n cv$date <- as.Date(cv[[\"date\"]], format = \"%d/%m/%Y\")\n #cv$country_code <- 'ES'\n #cv$country_name <- \"Spain\"\n cv$total_active <- (cv$total_cases - cv$total_recovered - cv$total_deaths)\n str(cv$total_active)\n\n return(cv)\n}\n\n\nGenerateDailyDiffs <- function(infection_data, dimension_name) {\n infection_data$daily_cases <- ave(infection_data$total_cases, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n infection_data$daily_hospitalized <- ave(infection_data$total_hospitalized, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n infection_data$daily_icu <- ave(infection_data$total_icu, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n infection_data$daily_deaths <- ave(infection_data$total_deaths, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n infection_data$daily_recovered <- ave(infection_data$total_recovered, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n infection_data$daily_active <- ave(infection_data$total_active, infection_data[[dimension_name]], FUN = function(x) c(NA, diff(x)))\n\n return(infection_data)\n}\n", "meta": {"hexsha": "fb6ba46bc5c1c5ca516283b9273432ad7f5014f9", "size": 2226, "ext": "r", "lang": "R", "max_stars_repo_path": "ETL/CountrySources/spain_gov_etl.r", "max_stars_repo_name": "sbikun/CoronaGraphR", "max_stars_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-16T11:35:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-20T16:37:24.000Z", "max_issues_repo_path": "ETL/CountrySources/spain_gov_etl.r", "max_issues_repo_name": "sbikun/CoronaGraphR", "max_issues_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ETL/CountrySources/spain_gov_etl.r", "max_forks_repo_name": "sbikun/CoronaGraphR", "max_forks_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.0, "max_line_length": 173, "alphanum_fraction": 0.7345013477, "num_tokens": 568, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3202041236470688}} {"text": "### Jinliang Yang\n### Sep 13th, 2015\n\n\nQPCR <- function(doplot=FALSE){\n qpcr <- read.csv(\"data/qPCR_results_validation.csv\")\n qpcr <- subset(qpcr, sample != \"cbf\")\n #qpcr$SampleName <- toupper(qpcr$SampleName)\n \n control <- subset(qpcr, Assay == \"GAPDH\")\n assay <- subset(qpcr, Assay != \"GAPDH\")\n \n ex <- merge(assay, control[, 2:3], by=\"SampleName\")\n ex$exp <- 2^( ex$CqMean.y - ex$CqMean.x)\n \n ex$Assay <- as.character(ex$Assay)\n genes <-unique(ex$Assay)\n \n ####\n ob <- load(\"~/Documents/Github/BDproj/cache/count.RData\")\n \n #countDF <- read.table(\"./results/countDF\")\n targets <- read.csv(\"~/Documents/Github/BDproj/data/target.csv\")\n tem <- as.data.frame(rpkm[genes, ])\n names(tem) <- targets$SampleName\n \n out <- data.frame()\n for(i in 1:10){\n sub1 <- subset(ex, Assay == genes[i])\n sub1$method <- \"qpcr\"\n \n sub2 <- tem[genes[i], ]\n sub2 <- as.data.frame(t(sub2))\n sub2$sampleid <- row.names(sub2)\n sub2$sampleid <- gsub(\"CBF3_sample1\", \"CBF_A\", sub2$sampleid)\n sub2$sampleid <- gsub(\"CBF3_sample2\", \"CBF_B\", sub2$sampleid)\n sub2$sampleid <- gsub(\"CBF3_sample3\", \"CBF_C\", sub2$sampleid)\n sub2$sampleid <- gsub(\"CBF3_4C_sample1\", \"CBF_A_4\", sub2$sampleid)\n sub2$sampleid <- gsub(\"CBF3_4C_sample2\", \"CBF_B_4\", sub2$sampleid)\n sub2$sampleid <- gsub(\"CBF3_4C_sample3\", \"CBF_C_4\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_sample1\", \"BD21_A\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_sample2\", \"BD21_B\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_sample3\", \"BD21_C\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_4C_sample1\", \"BD21_A_4\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_4C_sample2\", \"BD21_B_4\", sub2$sampleid)\n sub2$sampleid <- gsub(\"WT_4C_sample3\", \"BD21_C_4\", sub2$sampleid)\n sub2 <- subset(sub2, sampleid %in% sub1$SampleName)\n \n c1 <- cbind(subset(sub1, temp==4)$exp, subset(sub1, temp==23)$exp)\n \n sub2$temp <- 23\n sub2$temp <- gsub(\".*A_|.*B_|.*C_\", \"\", sub2$sampleid)\n sub2$temp <- gsub(\".*A|.*B|.*C\", \"23\", sub2$temp)\n names(sub2)[1] <- \"exp\"\n sub2$method <- \"rnaseq\"\n \n temp <- merge(sub2, sub1[, c(\"SampleName\", \"Assay\", \"Category\", \"exp\")], by.x=\"sampleid\", by.y=\"SampleName\")\n c2 <- cbind(subset(sub2, temp==4)$exp, subset(sub2, temp==23)$exp)\n \n out <- rbind(out, temp)\n if(doplot){\n par(mfrow=c(2, 5))\n barplot(c1, beside=TRUE, main=genes[i])\n barplot(c2, beside=TRUE, main=genes[i])\n }\n \n }\n return(out)\n \n}\n\n\n######\nres <- QPCR(doplot=FALSE)\nwrite.table(res, \"data/qpcr_rnaseq_cv.csv\", sep=\",\", row.names=FALSE, quote=FALSE)\n\ncor.test(subset(res, temp==4)$exp.x, subset(res, temp==4)$exp.y)\ncor.test(subset(res, temp==23)$exp.x, subset(res, temp==23)$exp.y)\n\ncor.test(res$exp.x, res$exp.y)\n\n", "meta": {"hexsha": "3d6c49b6cab242bb53938007e2b28be3790c0dc7", "size": 2971, "ext": "r", "lang": "R", "max_stars_repo_path": "profiling/2.BD/4.A.1_correlation.r", "max_stars_repo_name": "jyanglab/bd_cold", "max_stars_repo_head_hexsha": "06f32163bcb113ff46cc8aa9a6cb6d562d9008d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "profiling/2.BD/4.A.1_correlation.r", "max_issues_repo_name": "jyanglab/bd_cold", "max_issues_repo_head_hexsha": "06f32163bcb113ff46cc8aa9a6cb6d562d9008d2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "profiling/2.BD/4.A.1_correlation.r", "max_forks_repo_name": "jyanglab/bd_cold", "max_forks_repo_head_hexsha": "06f32163bcb113ff46cc8aa9a6cb6d562d9008d2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.2317073171, "max_line_length": 116, "alphanum_fraction": 0.5789296533, "num_tokens": 1021, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32020412364706874}} {"text": "install.packages(\"VennDiagram\", \nlib = \"./libraries\",\nrepos = 'http://cran.us.r-project.org')\n\n# Load library\nlibrary(VennDiagram, lib.loc = \"./libraries\")\n\n# Generate sets\nsetwd(\"./DEA\")\nsetB_NR <- read.table(file = \"filter_3NR_270_B_vs_NR_10_B.csv\", sep = \",\", header = T)\nsetB0_NR <- read.table(file = \"filter_3NR_270_B0_vs_NR_10_B0.csv\", sep = \",\", header = T)\nsetB_R <- read.table(file = \"filter_3R_270_B_vs_R_10_B.csv\", sep = \",\", header = T)\nsetB0_R <- read.table(file = \"filter_3R_270_B0_vs_R_10_B0.csv\", sep = \",\", header = T)\nsetM_R <- read.table(file = \"filter_3R_270_M_vs_R_10_M.csv\", sep = \",\", header = T)\nsetM_NR <- read.table(file = \"filter_3NR_270_M_vs_NR_10_M.csv\", sep = \",\", header = T)\nsetP_NR <- read.table(file = \"filter_3NR_270_P_vs_NR_10_P.csv\", sep = \",\", header = T)\nsetP_R <- read.table(file = \"filter_3R_270_P_vs_R_10_P.csv\", sep = \",\", header = T)\n# get column of with names of genes deferentially expressed\nset_NR_B <- setB_NR$names\nset_NR_B0 <- setB0_NR$names\nset_R_B <- setB_R$names\nset_R_B0 <- setB0_R$names\nset_R_M <- setM_R$names\nset_NR_M <- setM_NR$names\nset_NR_P <- setP_NR$names\nset_R_P <- setP_R$names\n# make venn diagrams for interesting comparations , max 4 groups\n# venn 1\nvenn.diagram(filename = \"test.venn1\", x = list(set_R_B, set_R_B0, set_R_M, set_R_P),\n category.names = c(\"set_R_P\", \"set_NR_P\", \"set_NR_M\", \"set_R_M\"), height = 6000, width = 6000)\n# venn 2\nvenn.diagram(filename = \"test.venn2\", x = list(set_NR_B, set_NR_B0, set_NR_M, set_R_P),\n category.names = c(\"set_R_P\", \"set_NR_P\", \"set_NR_M\", \"set_R_M\"), height = 6000, width = 6000)\n# venn 3\nvenn.diagram(filename = \"test.venn3\", x = list(set_NR_B, set_R_B0, set_NR_P, set_R_M),\n category.names = c(\"set_R_P\", \"set_NR_P\", \"set_NR_M\", \"set_R_M\"), height = 6000, width = 6000)\n", "meta": {"hexsha": "7410cb009324bb6ca9199638d653482a0635fc09", "size": 1820, "ext": "r", "lang": "R", "max_stars_repo_path": "venn_diagrams.r", "max_stars_repo_name": "labbces/NRGSC", "max_stars_repo_head_hexsha": "2fed53658016fbf421414df1af098cad41499b6c", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-30T15:34:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-30T15:34:58.000Z", "max_issues_repo_path": "venn_diagrams.r", "max_issues_repo_name": "labbces/NRGSC", "max_issues_repo_head_hexsha": "2fed53658016fbf421414df1af098cad41499b6c", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "venn_diagrams.r", "max_forks_repo_name": "labbces/NRGSC", "max_forks_repo_head_hexsha": "2fed53658016fbf421414df1af098cad41499b6c", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-02-05T22:21:37.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-05T22:25:15.000Z", "avg_line_length": 49.1891891892, "max_line_length": 107, "alphanum_fraction": 0.689010989, "num_tokens": 637, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.32020412364706874}} {"text": "#' Construct (but do not fit) an mbm model\n#' \n#' @param x Matrix giving a series of covariates (in columns) for all sites (in rows). \n#' \t\t\tRow names are required. All variables will be included in the model.\n#' @param y Square dissimilarity or distance matrix, can be complete or lower triangular \n#' \t\t\tonly. Row and column names are required and must match the site names in the \n#' \t\t\trows of \\code{x}.\n#' @param y_name A name to give to the y variable\n#' @param link Link function to use\n#' @param likelihood Likelihood function to use\n#' @param lengthscale Either missing (in which case all lengthscales will be optimized) or\n#' \t\t\ta numeric vector of length \\code{ncol(x)+1}. If a vector, the first entry \n#' \t\t\tcorresponds to environmental distance, and entries \\code{i = 1 + (1:n)} \n#' \t\t\tcorrespond to the variable in x[,i]. Values must be \\code{NA} or positive \n#' \t\t\tnumbers; if NA, the corresponding lengthscale will be set via optimization, \n#' \t\t\totherwise it will be fixed to the value given.\n#' @param sparse Should we use the stochastic variational GP.\n#' @param force_increasing Boolean; if true, beta diversity will be constrained to \n#' \t\t\tincrease with environmental distance\n#' @param sparse_inducing Number of inducing inputs to use for the svgp\n#' @param sparse_batch Batchsize to use for the svgp\n#' @param sparse_iter Maximum number of optimizer iterations for svgp\n#' @return An mbm object\n#' @keywords internal\nmake_mbm <- function(y, x, y_name, link, likelihood, lengthscale, sparse, force_increasing, \n\tsparse_inducing = 10, sparse_batch = 10, sparse_iter = 10000) {\n\tif(any(rownames(x) != rownames(y)))\n\t\tstop(\"rownames(x) must equal rownames(y)\")\n\n\tGPy <- reticulate::import(\"GPy\")\n\n\tmodel <- list()\n\tclass(model) <- c('mbm', class(model))\n\tattr(model, 'y_name') <- y_name\n\t\n\t## process covariates\n\tmodel$x <- x\n\tx <- scale(x)\n\tmodel$x_scaling = function(xx) scale(xx, center = attr(x, \"scaled:center\"), \n\t\tscale = attr(x, \"scaled:scale\"))\n\tmodel$x_unscaling = function(xx) xx*attr(x, \"scaled:scale\") + \n \t\tattr(x, \"scaled:center\")\n\txDF <- env_dissim(x)\n\t\n\t## process response transformation & mean function\n\t## if a link function is desired with anything but a vanilla GP, we have to cheat\n\t## by pre-transforming the y-variable\n\tmodel$y <- y\n\tyDF <- reshape2::melt(y,varnames=c('site1', 'site2'), value.name = y_name)\n\tdat <- merge(xDF, yDF, all.x = TRUE, by=c('site1', 'site2'))\n\tif((sparse | force_increasing) & link != 'identity') {\n\t\tmodel <- setup_y_transform(model, link)\n\t\tlink <- \"identity\"\n\t} else {\n\t\tmodel$y_transform <- model$y_rev_transform <- function(y) y\n\t}\n\n\t## add data to obj\n\tx_cols <- which(!colnames(dat) %in% c(y_name))\n\tmodel$response <- model$y_transform(as.matrix(dat[,y_name]))\n\tcovars <- dat[,x_cols]\n\tnames <- grep('site', colnames(covars))\n\tmodel$covariates <- as.matrix(covars[,-names])\n\tmodel$covar_sites <- covars[,names]\n\t\n\t##\n\t## SET UP SVGP\n\t##\n\tif(sparse) {\n\t\tattr(model, \"batchsize\") <- sparse_batch\n\t\tattr(model, \"inducing_inputs\") <- sparse_inducing\n\t\tattr(model, \"svgp_maxiter\") <- sparse_iter\n\t\tlikelihood <- \"gaussian\"\n\t\tmodel$inducing_inputs <- apply(model$covariates, 2, \n\t\t\tfunction(xx) runif(attr(model, \"inducing_inputs\"), min(xx), max(xx)))\n\t}\n\n\t##\n\t## SET UP PYTHON OBJECTS\n\t##\n\tmodel$pyobj <- list()\n\tmodel <- set_mbm_link(model, link)\n\tmodel$likelihood <- likelihood\n\tmodel$lengthscale <- lengthscale\n\n\n\tif(model$likelihood == 'gaussian') {\n\t\tmodel$pyobj$likelihood <- GPy$likelihoods$Gaussian(gp_link = model$pyobj$linkFun)\n\t} else {\n\t\tstop(\"Non-gaussian likelihoods are not supported\")\n\t}\n\n\tif(model$likelihood == 'gaussian' & model$link == 'identity') {\n\t\tmodel$pyobj$inference <- GPy$inference$latent_function_inference$ExactGaussianInference()\n\t\tif(sparse) {\n\t\t\tattr(model, 'inference') <- 'svgp'\n\t\t} else \n\t\t\tattr(model, 'inference') <- 'exact'\n\t} else {\n\t\tmodel$pyobj$inference <- GPy$inference$latent_function_inference$Laplace()\n\t\tattr(model, 'inference') <- 'laplace'\n\t}\n\n\tmodel$pyobj$kernel <- setup_mbm_kernel(dim = ncol(model$covariates), \n\t\tlengthscale = model$lengthscale, sparse = sparse)\n\n \tmodel$pyobj$mf <- set_mean_function(ncol(model$covariates), force_increasing)\n \tattr(model, \"mean_function\") <- if(force_increasing) \"increasing\" else \"0\"\n\n\treturn(model)\t\n}\n\n\n#' Set up link functions for mbm objects\n#' \n#' This is the only supported way for changing link functions in mbm objects; do not try\n#' to do it by hand\n#' @param x mbm model object\n#' @param link Link function to use\n#' @return A copy of the mbm object with the link function set\n#' @keywords internal\nset_mbm_link <- function(x, link) {\n\tGPy <- reticulate::import(\"GPy\")\n\tx$link <- link\n\tif(link == 'identity') {\n\t\tx$pyobj$linkFun <- GPy$likelihoods$link_functions$Identity()\n\t\tx$inv_link <- function(y) y\n\t} else if(link == 'probit') {\n\t\tx$pyobj$linkFun <- GPy$likelihoods$link_functions$Probit()\n\t\tx$inv_link <- function(y) pnorm(y)\n\t} else {\n\t\tstop(link, 'is an unsupported link function')\n\t}\n\treturn(x)\n}\n\n#' Set up MBM kernel\n#' @param dim Kernel dimension (number of variables)\n#' @param lengthscale Lengthscale parameter as from [mbm()]. \n#' \t\tEither NULL (in which case all lengthscales will be optimized) or \n#'\t\ta numeric vector of length \\code{ncol(x)+1}. If a vector, the first entry \n#' \t\tcorresponds to environmental distance, and entries \\code{i = 1 + (1:n)} \n#' \t\tcorrespond to the variable in x[,i]. Values must be \\code{NULL} or positive \n#' \t\tnumbers; if NULL, the corresponding lengthscale will be set via optimization, \n#' \t\totherwise it will be fixed to the value given.\n#' @param prior Prior distribution to use; currently ignored\n#' @param sparse Logical, should a sparse GP be used?\n#' @param which Which parameters to set up, either all, variance params, or lengthscale\n#' @keywords internal\nsetup_mbm_kernel <- function(dim, lengthscale = NULL, prior, sparse,\n\t\twhich = c('all', 'lengthscale', 'variance')) {\n\n\treticulate::source_python(system.file(\"python/kernel.py\", package=\"mbm\"))\n\tk <- make_kernel(dim, sparse)\n\tk <- set_kernel_constraints_py(k, lengthscale, which)\n\treturn(k)\n}\n\n#' Set up MBM mean function\n#' @param dim Kernel dimension (number of variables)\n#' @param useMeanFunction Logical, should the mean function be used?\n#' @keywords internal\nset_mean_function <- function(dim, useMeanFunction) {\n\treticulate::source_python(system.file(\"python/mf.py\", package=\"mbm\"))\n\tmf <- set_mean_function_py(dim, useMeanFunction)\n\treturn(mf)\n}\n\n\n\n#' Set y transformations for an MBM object\n#' @details Sets up y transformations to use instead of a link function for probit links. When\n#' \t\tthe y-data include zeros and ones, an additional step is necessary to squeeze the data\n#' \t\tfrom [0,1], (0,1], or [0,1) to the open interval (0,1). In the [0,1] case, the smithson\n#' \t\tlemon-squeezer is used: y\udbff\udc02' = [y\udbff\udc00(N \u2013 1) + 1/2]/N. Otherwise, eps is first added or\n#' \t\tsubtracted from all values.\n#' @param x an MBM object\n#' @param link character, link function to use\n#' @param eps Constant to add to avoid infinite values for probit\n#' @references Smithson, M. and Verkuilen, J. 2006. A Better Lemon Squeezer? Maximum-Likelihood\n#'\t\tRegression With Beta-Distributed Dependent Variables. Psychological Methods 11(1): 54-71.\n#' @keywords internal\nsetup_y_transform <- function(x, link, eps = 0.001) {\n\tif(link != 'probit')\n\t\tstop(\"Currently only identity or probit links are supported with these options\")\n\n\tif(min(x$y) == 0 & max(x$y) == 1) {\n\t\t# use the smithson transform when we have both 0s and 1s\n\t\tn <- length(model$y)\n\t\tx$y_transform <- function(p) qnorm((p * (n - 1) + 0.5) / n)\n\t\tx$y_rev_transform <- function(q) (pnorm(q) * n - 0.5) / (n-1)\n\t} else {\n\t\teps <- if(min(x$y) == 0) eps else if(max(x$y == 1)) -eps else 0\n\t\tx$y_transform <- function(p) qnorm(p + eps)\n\t\tx$y_rev_transform <- function(q) pnorm(q) - eps\n\t}\n\treturn(x)\n}\n\n", "meta": {"hexsha": "34fbbd139cb56f16f5fdb67dbf0f04f40f570be0", "size": 7841, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make_mbm.r", "max_stars_repo_name": "mtalluto/mbmtools", "max_stars_repo_head_hexsha": "67a01b24ba2bc214081085559500e7b16f8489f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/make_mbm.r", "max_issues_repo_name": "mtalluto/mbmtools", "max_issues_repo_head_hexsha": "67a01b24ba2bc214081085559500e7b16f8489f2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2017-10-19T11:09:18.000Z", "max_issues_repo_issues_event_max_datetime": "2018-06-27T10:08:48.000Z", "max_forks_repo_path": "R/make_mbm.r", "max_forks_repo_name": "mtalluto/mbmtools", "max_forks_repo_head_hexsha": "67a01b24ba2bc214081085559500e7b16f8489f2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-05-25T20:28:51.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-25T20:28:51.000Z", "avg_line_length": 39.205, "max_line_length": 95, "alphanum_fraction": 0.7004208647, "num_tokens": 2233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7185943925708562, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.3201550256041378}} {"text": "subroutine baklo(x,y,w,npetc,wddnfl,spatol,match,\n\t\tetal,s,eta,beta,var,dof,\n\t\tqr,qraux,qpivot,effect,iv,v,iwork,work)\nimplicit double precision(a-h,o-z)\ninteger n,p,q,nit,maxit,qrank\ninteger npetc(7),wddnfl(*),match(*),qpivot(*),iv(*),iwork(*)\n##integer which(q),dwhich(q),degree(q),nef(q),liv(q),lv(q),nvmax(q)\ndouble precision x(*),y(*),w(*),spatol(*),\n\tetal(*),s(*),eta(*),beta(*),var(*),dof(*),\n\tqr(*),qraux(*),v(*),effect(*),work(*)\n#work size: 4*n + sum( nef(k)*(pj+dj+4)+5+3*pj )\t+5*n\n# = 9*n + sum( nef(k)*(pj+dj+4)+5+3*pj )\n\nn=npetc(1)\np=npetc(2)\nq=npetc(3)\nmaxit=npetc(5)\nqrank=npetc(6)\ncall baklo0(x,n,p,y,w,q,wddnfl(1),wddnfl(q+1),wddnfl(2*q+1),\n spatol(1),wddnfl(3*q+1),dof,match,wddnfl(4*q+1),\n etal,s,eta,beta,var,spatol(q+1),\n nit,maxit,qr,qraux,qrank,qpivot,effect,\n work(1),work(n+1),work(2*n+1),work(3*n+1),\n iv,wddnfl(5*q+1),wddnfl(6*q+1),v,wddnfl(7*q+1),\n iwork(1),work(4*n+1))\nnpetc(4)=nit\nnpetc(6)=qrank\nreturn\nend\n\nsubroutine baklo0(x,n,p,y,w,q,which,dwhich,pwhich,span,degree,dof,match,nef,\n\t\t\tetal,s,eta,beta,var,tol,nit,maxit,\n\t\t\tqr,qraux,qrank,qpivot,effect,z,old,sqwt,sqwti,\n\t\t\tiv,liv,lv,v,nvmax,iwork,work)\nimplicit double precision(a-h,o-z)\ninteger n,p,q,which(q),dwhich(q),pwhich(q),degree(q),match(n,q),nef(q),nit,\n\t\tmaxit,qrank,qpivot(p),iv(*),liv(q),lv(q),nvmax(q),iwork(q)\ndouble precision x(n,p),y(n),w(n),span(q),dof(q),\n\t\t\tetal(n),s(n,q),eta(n),beta(p),var(n,q),tol,\n\t\t\tqr(n,p),qraux(p),v(*),effect(n),work(*)\n#work should be sum( nef(k)*(pj+dj+4)+5+3*pj )\t+5*n\ndouble precision z(*),old(*),dwrss,ratio\ndouble precision sqwt(n),sqwti(n)\nlogical anyzwt\ndouble precision deltaf, normf,onedm7\ninteger job,info,slv,sliv,iw,j,dj,pj\nonedm7=1d-7\njob=1101;info=1\nif(q==0)maxit=1 \nratio=1d0\n# fix up sqy's for weighted problems.\nanyzwt=.false.\ndo i=1,n{\n\tif(w(i)>0d0){\n\t\tsqwt(i)=dsqrt(w(i))\n\t\tsqwti(i)=1d0/sqwt(i)\n\t}\n\telse{\n\t\tsqwt(i)=0d0\n\t\tsqwti(i)=0d0\n\t\tanyzwt=.true.\n\t\t}\n\t}\n# if qrank > 0 then qr etc contain the qr decomposition\n# else baklo computes it. \nif(qrank==0){\n\tdo i=1,n{\n\t\tdo j=1,p{\n\t\t\tqr(i,j)=x(i,j)*sqwt(i)\n\t\t\t}\n\t\t}\n\tdo j=1,p{qpivot(j)=j}\n\tcall dqrdca(qr,n,n,p,qraux,qpivot,work,qrank,onedm7)\n\t}\ndo i=1,n{\n\teta(i)=0d0\n\tfor(j=1;j<=q;j=j+1){\n\t\teta(i)=eta(i)+s(i,j)\n\t\t}\n\t}\nnit=0\nwhile ((ratio > tol )&(nit < maxit)){\n\t# first the linear fit\n\tdeltaf=0d0\n\tnit=nit+1\n\tdo i=1,n{\n\t\tz(i)=(y(i)-eta(i))*sqwt(i)\n\t\told(i)=etal(i)\n\t}\n#\tcall dqrsl1(qr,dq,qraux,qrank,sqz,one,work(1),etal,two,three)\n#job=1101 -- computes fits, effects and beta\n\tcall dqrsl(qr,n,n,qrank,qraux,z,work(1),effect(1),beta,\n\t\twork(1),etal,job,info)\n\n# now unsqrt the fits\n#Note: we dont have to fix up the zero weights till the end, since their fits\n#are always immaterial to the computation\n\tdo i=1,n{\n\t\tetal(i)=etal(i)*sqwti(i)\n\t\t}\n\t# now a single non-linear backfitting loop \n\tsliv=1\n\tslv=1\n\tiw=5*n+1\n\tfor(k=1;k<=q;k=k+1){\n\n\t\tj=which(k)\n\t\tdj=dwhich(k)\n\t\tpj=pwhich(k)\n\t\tdo i=1,n{\n\t\t\told(i)=s(i,k)\n\t\t\tz(i)=y(i)-etal(i)-eta(i)+old(i)\n\t\t}\ncall lo1(x(1,j),z,w,n,dj,pj,nvmax(k),span(k),degree(k),match(1,k),\n\t\tnef(k),nit,dof(k),s(1,k),var(1,k),work(iw),\n#\txin,win\n\twork(iw+pj+1),work(iw+nef(k)*dj+pj+1),\n#\tsqwin,sqwini,\n\twork(iw+nef(k)*(dj+1)+pj+2),work(iw + nef(k)*(dj+2)+pj+2),\n#\txqr,qrank,\t\n\twork(iw+nef(k)*(dj+3)+pj+2),work(iw+nef(k)*(pj+dj+4)+pj+2),\n#\tqpivot,qraux,\t\n#\twork(iw+nef(k)*(pj+dj+4)+pj+3),work(iw+nef(k)*(pj+dj+4)+4+2*pj),\n\tiwork(1),work(iw+nef(k)*(pj+dj+4)+4+2*pj),\n\tiv(sliv),liv(k),lv(k),v(slv),\n\twork(1) )\n#work should be sum( nef(k)*(pj+dj+4)+5+3*pj )\t+5*n\n# In the call above I give lo1 pieces of work to use for storing\n# the qr decomposition, and it gets the same undisturbed portion\n# each time. The fact that it is given a double work word for qrank\n# is irrelevant but convenient; it still stores the integer qrank there.\n# I do this because there is a partition like this for each lo() term\n# in the model, and the number of them is variable\n\t\tsliv=sliv+liv(k)\n\t\tslv=slv+lv(k)\n\t\tiw=iw+nef(k)*(pj+dj+4)+5+3*pj\n\t\tdo i=1,n{\n\t\t\teta(i)=eta(i)+s(i,k)-old(i)\n\t\t\t}\n\t\tdeltaf=deltaf+dwrss(n,old,s(1,k),w)\n\t\t}\n\tnormf=0d0\n\tdo i=1,n{\n\t\tnormf=normf+w(i)*eta(i)*eta(i)\n\t\t}\n\tif(normf>0d0){\n\t\tratio=dsqrt(deltaf/normf)\n\t\t}\n\t else {ratio = 0d0}\n\t}\n#now package up the results\ndo j=1,p {work(j)=beta(j)}\ndo j=1,p {beta(qpivot(j))=work(j)}\nif(anyzwt){\n\tdo i=1,n {\n\t\tif(w(i) <= 0d0){\n\t\t\tetal(i)=0d0\n\t\t\tdo j=1,p{\n\t\t\t\tetal(i)=etal(i)+beta(j)*x(i,j)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\t\t\ndo i=1,n\n\teta(i)=eta(i)+etal(i)\n\t\n\nreturn\nend\n", "meta": {"hexsha": "2df87f2250adb48988e3e48f71aef89c020ead12", "size": 4502, "ext": "r", "lang": "R", "max_stars_repo_path": "gam/ratfor/backlo.r", "max_stars_repo_name": "solgenomics/R_libs", "max_stars_repo_head_hexsha": "a936847f6063cdf3d207e8364f05f2e13be2d878", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gam/ratfor/backlo.r", "max_issues_repo_name": "solgenomics/R_libs", "max_issues_repo_head_hexsha": "a936847f6063cdf3d207e8364f05f2e13be2d878", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-08-17T15:14:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-23T21:55:49.000Z", "max_forks_repo_path": "gam/ratfor/backlo.r", "max_forks_repo_name": "solgenomics/R_libs", "max_forks_repo_head_hexsha": "a936847f6063cdf3d207e8364f05f2e13be2d878", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-04T05:34:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-04T05:34:16.000Z", "avg_line_length": 26.3274853801, "max_line_length": 77, "alphanum_fraction": 0.6179475789, "num_tokens": 1971, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7310585786300049, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.3200746249532436}} {"text": "forme_history <- function(data, probs = c(0.025, 0.5, 0.975)) {\n fh <- data |> dplyr::transmute(\n year = year,\n og = og, dettep,\n txppib, tpo,\n tdep=tdeppp*pibpot/qpib,\n spp,\n ci = r_app*dettep,\n ib_po = - tpo + lag(tpo),\n ib_dep = tdeppp - dplyr::lag(tdeppp), \n ib = ib_po + ib_dep,\n qpib, pibpot, tcho, tvnairu, gpib, r_app) |>\n tidyr::pivot_longer(cols = - year, names_to = \"variable\", values_to = \"q0.5\")\n se <- stringr::str_c(\"q\", setdiff(probs, 0.5))\n dplyr::bind_cols(fh, dplyr::bind_cols(purrr::map(se, ~tibble::tibble(!!.x := fh$q0.5)))) |> \n dplyr::arrange(year)\n}\n\ndataandparams <- function(country, start_year, periods = 2050-start_year, draws=100, globals, ameco=NULL) {\n if(is.null(ameco))\n ameco <- globals$ameco\n country_list <- ameco |> dplyr::distinct(country)\n \n countries <- set_names(globals$pays |> dplyr::pull(id))\n countries_ln_fr <- globals$pays |> dplyr::pull(nom_fr, name = id)\n \n data <- ameco |> \n dplyr::filter(country==!!country, unit==0) |>\n dplyr::rename(var = ccode) |> \n dplyr::select(-code, -TITLE, -COUNTRY, -`SUB-CHAPTER`, -UNIT, -csplit2, -csplit3, -unit, -is.prev, -country) |> \n tidyr::drop_na(value) |> \n tidyr::pivot_wider(names_from = var, values_from = value) \n if(!\"tvnairu\" %in% names(data)) \n data <- data |>\n mutate(tvnairu = slider::slide_dbl(tcho, ~mean(.x), .before = 3 ))\n if(!\"pibpot\" %in% names(data)) \n data <- data |>\n mutate(pibpot = slider::slide_dbl(qpib, ~mean(.x), .before = 10),\n og = qpib/pibpot-1)\n data <- data |> \n dplyr::mutate(dplyr::across(.cols=c(irl, irs, tvnairu), .fns = ~ dplyr::if_else(is.na(.x), .x[year==max(year[!is.na(.x)])], .x))) |> \n dplyr::mutate(\n ppib = vpib/qpib,\n gpot = pibpot/dplyr::lag(pibpot) - 1,\n txppib = ppib/dplyr::lag(ppib) - 1, \n gpib = qpib/dplyr::lag(qpib) - 1, \n og = qpib/pibpot-1,\n r_app = ci/dplyr::lag(dette),\n r_inst = irl/100,\n vpibpot = ppib*pibpot,\n tpo = rec_po/vpib,\n tpopp = rec_po/vpibpot,\n tdep = dep_prim/vpib,\n tdeppp = dep_prim/vpibpot,\n cip = ci/vpib,\n soldep=solde/vpib,\n spp=sprim/vpib,\n taut = spp - tpo + tdep,\n tcho = tcho/100,\n tvnairu = tvnairu/100,\n ec = irl/100 - ((ppib/dplyr::lag(ppib,3))^0.33 - 1)- ((pibpot/dplyr::lag(pibpot,3))^0.33 - 1) ) |> \n tibble::as_tibble()\n \n full_historical_data <- data |> \n dplyr::transmute(year,\n dettep = dette/vpib, r_app,\n spp, og, txppib, tpo, tdep, tdeppp, pibpot, qpib, tcho, tvnairu, gpib, ec) |> \n tidyr::drop_na(dettep)\n \n historical_data <- full_historical_data |> \n dplyr::filter(year<=start_year) \n \n years <- c(min(historical_data$year), max(data$year))\n \n init <- data |> \n dplyr::arrange(year) |> \n dplyr::mutate(lagog=dplyr::lag(og),\n lag2og=dplyr::lag(og),\n lagtcho=dplyr::lag(tcho),\n lag2tcho=dplyr::lag(lagtcho),\n lagtvnairu=dplyr::lag(tvnairu),\n lagppib=dplyr::lag(ppib),\n lagpibpot=dplyr::lag(pibpot),\n lag2pibpot=dplyr::lag(lagpibpot),\n lag2ppib=dplyr::lag(lagppib),\n lagqpib = dplyr::lag(qpib),\n lag2qpib = dplyr::lag(lagqpib),\n lagtdeppp = dplyr::lag(tdeppp),\n lag2tdeppp = dplyr::lag(lagtdeppp),\n lagtpo =dplyr::lag(tpo),\n lagci = dplyr::lag(ci),\n lagr_inst = dplyr::lag(r_inst),\n lagdette = dplyr::lag(dette),\n lag2dette = dplyr::lag(lagdette),\n lagdettep = dplyr::lag(dette/vpib),\n lagspp = dplyr::lag(spp),\n lag2spp = dplyr::lag(lagspp),\n lagcip = dplyr::lag(cip),\n lag2cip = dplyr::lag(lagcip)) |> \n dplyr::filter(year==start_year)\n params <- list(\n periods = periods,\n draws = as.numeric(draws), \n infstar = 0.0175,\n nairu = 0.07,\n dstar = init$dette/init$vpib\n )\n \n p_def <- globals$defaults |>\n dplyr::filter(type==\"p\") |>\n dplyr::select(!!country, id) |> \n dplyr::mutate(!!country:=as.numeric(.data[[country]])) |>\n dplyr::pull(!!country, name=id) |> \n as.list()\n \n dstar <- historical_data |>\n dplyr::filter(year==min(start_year, max(year)-3)) |> \n dplyr::pull(dettep)\n \n if(is.null(p_def[[\"dstar\"]]))\n p_def$dstar <- dstar\n \n nairu <- historical_data |>\n dplyr::filter(year==min(start_year, max(year))) |> \n dplyr::pull(tcho)\n \n if(is.null(p_def[[\"nairu\"]]))\n p_def$nairu <- nairu\n \n dci <- globals$defaults |>\n dplyr::filter(type==start_year) |> \n dplyr::select(id, !!country) |> \n dplyr::mutate(!!country:=as.numeric(.data[[country]])) |>\n dplyr::pull(name = id) |> \n as.list()\n \n zn <- function(x) ifelse(is.null(x), 0, x)\n \n p_init <- list(\n i_ogm = init$og,\n i_lagog = init$og,\n i_lag2og = init$lagog,\n i_lagtcho = init$tcho,\n i_lagtvnairu = init$tvnairu,\n i_lagppib = init$ppib,\n i_lag2ppib = init$lagppib,\n i_lag3ppib = init$lag2ppib,\n i_lagqpib = init$qpib,\n i_lag2qpib = init$lagqpib,\n i_lag3qpib = init$lag2qpib,\n i_lagpibpot = init$pibpot,\n i_lag2pibpot = init$lagpibpot,\n i_lag2dette = init$lagdette,\n i_lagtpo = init$tpo + zn(dci$i_tpo),\n i_lagtdeppp = init$tdeppp + zn(dci$i_tdeppp),\n i_lag2tdeppp = init$lagtdeppp,\n i_lagr_app = init$r_app,\n i_lagr_inst = init$r_inst,\n i_lagdette = init$dette,\n i_lagspp = init$spp + zn(dci$i_tpo) - zn(dci$i_tdeppp)/(1+init$og),\n i_lag2spp = init$lagspp,\n i_lagcip = init$cip,\n i_lag2cip = init$lagcip,\n i_lagecart_c = init$ec,\n dette_oneoff = rep(0, periods),\n tx_deriv_dep = rep(0, periods),\n tx_deriv_po = rep(0, periods),\n gpot = rep(init$gpot, periods),\n taut = rep(init$taut, periods)\n )\n \n return(list(countries = countries, \n countries_ln_fr = countries_ln_fr,\n params = params,\n p_init = p_init, \n p_def = p_def,\n historical_data = historical_data, \n full_historical_data = full_historical_data, \n init = init,\n years = years,\n country=country,\n start_year=start_year, \n periods=periods))\n}\n\n# dust_check_params\ndust_fix_params <- function(params, long = c( \"dette_oneoff\", \"phi\", \"gpot\", \"taut\", \"tx_deriv_dep\", \"tx_deriv_po\")) {\n long_exo <- params |> purrr::keep(~length(.x)>1) |> names()\n # pas moyen de r\u00e9cup\u00e9rer cette liste de l'objet dust model\n long_exo <- unique(c(long_exo, long))\n ss <- purrr::map(\n rlang::set_names(long_exo), \n ~{\n if(!is.null(params[[.x]])) \n trimortrick(params[[.x]], params$periods)\n else\n rep(0,params$periods)\n })\n purrr::list_modify(params, !!!ss)\n}\n\n# simulations ---------------\n\ncalc_sim_dust <- function(params, model, probs=c(0.025, 0.5, 0.975), history = NULL, \n long= c(\"dette_oneoff\", \"phi\", \"gpot\", \"taut\", \"tx_deriv_dep\", \"tx_deriv_po\")) {\n p <- params\n p <- dust_fix_params(p, long=long)\n if(is.null(params$seed))\n seed <- NULL\n else\n seed <- as.integer(params$seed)\n m <- model$new(pars=p, step=1, n_particles = p$draws, n_threads = 2L, seed = seed)\n \n state_names <- names(m$info()$index)\n # les variables de sortie sont indicees par _o\n out_i <- which(stringr::str_detect(state_names, \"_o$\")) \n m$set_index(out_i)\n out_names <- state_names[out_i] \n # on simule une annee de plus\n res <- m$simulate(1:(params$periods+1))\n dimnames(res)[[1]] <- out_names\n # on drop le premier step qui ne contient pas d'info\n res <- res[,,-1, drop=FALSE]\n se <- stringr::str_c(\"q\", setdiff(probs, 0.5))\n if(params$draws>1)\n sim <- purrr::map_dfr(out_names, ~{\n m <- matrixStats::colQuantiles(abind::adrop(res[.x,,,drop=FALSE], drop=1), probs=probs, drop=FALSE)\n colnames(m) <- stringr::str_c(\"q\", probs)\n tibble::as_tibble(m) |> \n dplyr::mutate(variable = stringr::str_remove(.x, \"_o$\") , step = 1:nrow(m))\n })\n else \n {\n rr <- tibble::as_tibble(t(abind::adrop(res, drop=2))) |> \n dplyr::mutate(step = dplyr::row_number()) |> \n tidyr::pivot_longer(cols = -step, names_to = \"variable\", values_to = \"q0.5\") |> \n dplyr::mutate(variable = stringr::str_remove(variable,\"_o$\"))\n sim <- dplyr::bind_cols(rr, dplyr::bind_cols(purrr::map(se, ~tibble::tibble(!!.x := rr$q0.5))))\n }\n \n if(!is.null(history))\n {\n start_year <- max(history$year)\n sim <- sim |> \n dplyr::mutate(year = start_year+step) |> \n dplyr::bind_rows(history) |> \n dplyr::arrange(year) |> \n dplyr::select(-step)\n }\n else \n sim <- sim |>\n dplyr::rename(year=step)\n sim\n}\n\nmedianloss1 <- function(params, model, controls, n_threads = 2) {\n p <- params\n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = p$seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names==\"loss_o\")\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$run(params$periods+1)\n res <- matrixStats::rowMedians(res)\n lasso_loss <- sum(purrr::as_vector(params[controls])^2) * params$loss_lasso\n return(log(res+lasso_loss))\n}\n\nmedianloss2 <- function(params, model, controls=c(\"tpo_og\", \"tpo_dstar\", \"tpo_sstar\"), n_threads = 2L) {\n p <- params\n p$seed <- NULL\n periods <- params$periods\n seed <- if(is.null(params$seed)) NULL else as.integer(params$seed) \n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names%in%c(\"loss_nd_o\", \"dettep_o\"))\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$simulate(1:(periods+1))\n res <- res[,,-1, drop=FALSE]\n dimnames(res)[[1]] <- state_names[out_i]\n if(params$draws>1)\n sim <- purrr::map(rlang::set_names(state_names[out_i]), ~{\n matrixStats::colMedians(abind::adrop(res[.x,,,drop=FALSE], drop=1), drop=FALSE)\n })\n else \n sim <-t(abind::adrop(res, drop=2)) |> as_tibble()\n \n loss_part1 <- sim$loss_nd_o[length(sim$loss_nd_o)]\n loss_part2 <- sum((sim$dettep_o[(params$loss_t+1):periods]-params$dstar)^2) * params$loss_d\n lasso_loss <- sum(purrr::as_vector(params[controls])^2) * params$loss_lasso\n return(loss_part1 + loss_part2 + lasso_loss)\n}\n\nconstrainedMedianLoss2 <- function(params, model, controls, ccont, n_threads = 2) {\n if(is.null(ccont))\n return(medianloss2(params, model, controls, n_threads))\n ccc <- purrr::map(\n rlang::set_names(names(ccont)),\n ~min(max(params[[.x]],ccont[[.x]][[1]]), ccont[[.x]][[2]])\n )\n params <- list_modify(params,!!!ccc)\n medianloss2(params, model, names(ccont), n_threads =2 )\n}\n\nmeanloss2 <- function(params, model, controls=c(\"tpo_og\", \"tpo_dstar\", \"tpo_sstar\"), n_threads = 2) {\n p <- params\n periods <- params$periods\n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = p$seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names%in%c(\"loss_nd_o\", \"dettep_o\"))\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$simulate(1:(periods+1))\n res <- res[,,-1, drop=FALSE]\n dimnames(res)[[1]] <- state_names[out_i]\n if(params$draws>1)\n sim <- purrr::map(rlang::set_names(state_names[out_i]), ~{\n matrixStats::colMeans2(abind::adrop(res[.x,,,drop=FALSE], drop=1), drop=FALSE)\n })\n else \n sim <-t(abind::adrop(res, drop=2)) |> as_tibble()\n \n loss_part1 <- sim$loss_nd_o[length(sim$loss_nd_o)]\n loss_part2 <- sum((sim$dettep_o[(params$loss_t+1):periods]-params$dstar)^2) * params$loss_d\n lasso_loss <- sum(purrr::as_vector(params[controls])^2) * params$loss_lasso\n return(log(loss_part1 + loss_part2 + lasso_loss))\n}\n\nmedianloss3 <- function(params, model, controls=c(\"tpo_og\", \"tpo_dstar\", \"tpo_sstar\"), n_threads = 2) {\n p <- params\n p$draws <- NULL\n periods <- params$periods\n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = p$seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names%in%c(\"og_o\", \"dettep_o\"))\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$simulate(1:(periods+1))\n res <- res[,,-1, drop=FALSE]\n dimnames(res)[[1]] <- state_names[out_i]\n if(params$draws>1)\n sim <- purrr::map(rlang::set_names(state_names[out_i]), ~{\n matrixStats::colMedians(abind::adrop(res[.x,,,drop=FALSE], drop=1), drop=FALSE)\n })\n else \n res\n \n loss_part1 <- sum(sim$og_o^2/ (1+params$df)^(0:(periods-1)))\n loss_part2 <- sum((sim$dettep_o[(params$loss_t+1):periods]-params$dstar)^2)\n log(loss_part1 + params$loss_d * loss_part2)\n}\n\nlassomedianloss4 <- function(params, model, controls=c(\"tpo_og\", \"tpo_dstar\", \"tpo_sstar\"), n_threads = 2) {\n p <- params\n p$draws <- NULL\n periods <- params$periods\n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = p$seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names%in%c(\"og_o\", \"dettep_o\"))\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$simulate(1:(periods+1))\n res <- res[,,-1, drop=FALSE]\n dimnames(res)[[1]] <- state_names[out_i]\n if(params$draws>1)\n sim <- purrr::map(rlang::set_names(state_names[out_i]), ~{\n matrixStats::colMedians(abind::adrop(res[.x,,,drop=FALSE], drop=1), drop=FALSE)\n })\n else \n res\n og4l <- sim$og_o^2/ (1+params$loss_df)^(0:(periods-1))\n d4l <- sim$dettep_o[(params$loss_t+1):periods]\n loss_part1 <- sum(og4l[sim$og<0])\n loss_part2 <- sum((d4l[d4l>params$dstar]-params$dstar)^2) * params$loss_d \n lasso_loss <- sum(purrr::as_vector(params[controls])^2)\n log(loss_part1 + loss_part2) + log(1+lasso_loss) * params$loss_lasso\n}\n\nlassomedianloss5 <- function(params, model, controls=c(\"tpo_og\", \"tpo_dstar\", \"tpo_sstar\"), n_threads = 2) {\n p <- params\n p$draws <- NULL\n periods <- params$periods\n m <- model$new(pars=p, step=1, n_particles = params$draws, n_threads = n_threads, seed = p$seed)\n state_names <- names(m$info()$index)\n out_i <- which(state_names%in%c(\"og_o\", \"dettep_o\"))\n m$set_index(out_i)\n # on simule une annee de plus\n res <- m$simulate(1:(periods+1))\n res <- res[,,-1, drop=FALSE]\n dimnames(res)[[1]] <- state_names[out_i]\n if(params$draws>1)\n sim <- purrr::map(rlang::set_names(state_names[out_i]), ~{\n matrixStats::colMedians(abind::adrop(res[.x,,,drop=FALSE], drop=1), drop=FALSE)\n })\n else \n res\n og4l <- sim$og_o^2/ (1+params$loss_df)^(0:(periods-1))\n d4l <- sim$dettep_o[(params$loss_t+1):periods]\n loss_part1 <- sum(og4l)\n loss_part2 <- sum((d4l-params$dstar)^2) * params$loss_d \n lasso_loss <- sum(purrr::as_vector(params[controls])^2)\n log(loss_part1 + loss_part2) + log(1+lasso_loss) * params$loss_lasso\n}\n\nadd_previous <- function(ns, ps) {\n if(!purrr::is_empty(ps))\n {\n dplyr::left_join(ns, ps |> dplyr::select(year, variable, pq0.5 = q0.5), by=c(\"year\", \"variable\"))\n }\n else \n dplyr::mutate(ns, pq0.5 = NA_real_)\n}\n\n# applique le facteur pour l'affichage par exemple des %\n# \u00e0 renseigner dans le fichier sliders.xlsx\ntransform_params <- function(input, globals)\n{\n from_input <- map(set_names(names(globals$sliders)), ~{\n type <- globals$sliders[[.x]][[\"type\"]]\n if(type==\"slider\")\n return(input[[.x]]/globals$sliders[[.x]][[\"facteur\"]])\n if(type==\"pick\")\n return(as.numeric(input[[.x]]))\n if(type==\"pick_str\")\n return(as.character(input[[.x]]))\n if(type==\"date\")\n return(lubridate::year(input[[.x]]))\n if(type==\"check\")\n return(as.logical(input[[.x]]))\n })\n return(from_input)\n}\n\n# inverse la transformation pr\u00e9c\u00e9dente\nundo_transform <- function(p, globals) {\n to_undone <- purrr::map_dbl(\n rlang::set_names(names(globals$sliders)),\n ~globals$sliders[[.x]][[\"facteur\"]])\n to_undone <- to_undone[!is.na(to_undone) & to_undone!=1]\n undone <- map(rlang::set_names(names(to_undone)), ~{\n p[[.x]]*to_undone[[.x]]\n })\n purrr::list_modify(p, !!!undone)\n}\n\n# g\u00e9n\u00e9ralise l'op\u00e9rateur %||%\n`%|||%` <- function (x, y) \n{\n if (purrr::is_empty(x)) \n y\n else x\n}\n\nset_params <- function(inputs, globals, datas)\n{\n s_y <- inputs$start_year\n periods <- inputs$end_year - s_y\n history <- NULL\n # les parametres\n p <- datas$p_def\n p <- purrr::list_modify(p, periods=periods)\n p <- purrr::list_modify(p, !!!datas$p_init)\n p <- purrr::list_modify(p, !!!inputs)\n \n dtpo <- inputs$i_tpo%|||%0\n dtdeppp <- inputs$i_tdeppp%|||%0\n p$i_tpo <- NULL\n p$i_tdeppp <- NULL\n p$i_lagtdeppp <- p$i_lagtdeppp%|||%0 + dtdeppp \n p$i_lagtpo <- p$i_lagtpo%|||%0 + dtpo\n \n # du coup on modifie l'historique\n history <- forme_history(\n datas$historical_data |> \n dplyr::filter(year>=inputs$start_hist))\n full_history <- forme_history(datas$full_historical_data) |>\n select(-q0.025, -q0.975) |>\n rename(full_h = q0.5) \n ses <- names(history |> dplyr::select(dplyr::starts_with(\"q0.\", ignore.case = FALSE)) |> dplyr::select(-q0.5))\n names(ses) <- ses\n \n identities <- purrr::map(ses, ~ function(x) x)\n \n new <- history |> \n dplyr::filter(year==s_y) |>\n dplyr::select(year, q0.5, variable) |> \n tidyr::pivot_wider(names_from = variable, values_from = q0.5) |> \n dplyr::mutate(\n spp = spp + dtpo - dtdeppp/(1+og),\n tpo = tpo + dtpo, \n tdep = tdep + dtdeppp/(1+og),\n ib_dep = ib_dep + dtdeppp,\n ib_po = ib_po + dtpo) |> \n tidyr::pivot_longer(cols=-year, values_to = \"q0.5\", names_to = \"variable\") |> \n dplyr::mutate(dplyr::across(q0.5, .fns = identities, .names=\"{.fn}\"))\n \n history <- history |> \n dplyr::rows_update(new, by=c(\"year\",\"variable\"))\n \n # le potentiel\n p$gpot <- rep(inputs$gpot_sj,periods)\n # les depenses publiques et les po\n p$tx_deriv_dep <- c(rep(inputs$ddep/10, min(10, periods)), rep(0, max(periods-10, 0)))\n p$tx_deriv_po <- c(rep(inputs$dtpo/10, min(10, periods)), rep(0, max(periods-10, 0)))\n \n # checke pas de montecarlo\n if(inputs$go_mc) {\n p$draws <- inputs$draws\n p$ogn <- inputs$og_n\n p$ogn_sigma <- inputs$ogn_sigma\n } else {\n p$draws <- 1\n p$ogn <- 0\n p$ogn_sigma <- 0\n }\n \n # on change les vecteurs \u00e0 la bonne longueur en les coupant ou \u00e9tendant si n\u00e9cessaire\n long_exo <- p |> purrr::keep(~length(.x)>1) |> names()\n ss <- purrr::map(rlang::set_names(long_exo), ~trimortrick(p[[.x]], periods))\n p <- purrr::list_modify(p, !!!ss)\n \n return(list(p=p, h=history, fh=full_history, start_year=s_y, periods=periods, end_year = s_y + periods, start_hist = inputs$start_hist))\n}\n\ncalc_rule_params <- function(globals, params, draws=5000) {\n # on calcule la regle optimale (lineaire) si le switch est frappe\n # pour les lag ajouter \"tpo_1dstar\", \"tpo_1sstar\", \"tpo_1og\"\n # pour les termes quadratiques ajouter \"tpo_dstar2\", \"tpo_sstar2\", \"tpo_og2\"\n # on sort une expression avec la regle budgetaire prete a etre affichee\n controls_c <- list(tpo_sstar = c(0.005 , 0.9), tpo_dstar = c(-0.4, -0.005), tpo_og = c(-1.5, 0))\n controls <- purrr::map_dbl(controls_c, mean)\n par_fr <- best_FR_par(\n params, globals$model,\n controls = controls, \n constraints = controls_c,\n loss = medianloss2,\n draws = draws ,\n dt = 20)\n if(!is.null(par_fr$error))\n return(list(error = TRUE))\n p <- purrr::list_modify(params, !!!par_fr, error=FALSE)\n return(p)\n}\n\ndo_rule_text <- function(tpo_og, tpo_sstar, tpo_dstar) {\n if(is.null(tpo_og)||is.null(tpo_dstar)||is.null(tpo_sstar))\n return(\"\")\n c1 <- signif(tpo_og,2)\n c2 <- signif(tpo_sstar,2)\n c3 <- ifelse(tpo_dstar>0, stringr::str_c(\"+\", signif(tpo_dstar,2)), signif(tpo_dstar,2))\n glue::glue(\n \"ib = {{c1}} \\\\times og + {{c2}} \\\\times (s_p - s^*) \\\\\\\\ {{c3}} \\\\times (d-d^*)\",\n .open=\"{{\", .close=\"}}\")\n}\n\n\ntrimortrick <- function(s,t)\n{\n if(length(s)>=t)\n return(s[1:t])\n return(c(s, rep(dplyr::last(s), t-length(s))))\n}\n\nbest_FR_par <- function(p, model, controls, constraints = NULL, method=\"Nelder-Mead\", loss = medianloss2, draws = 1000, dt = 20) {\n lp <- p\n periods <- lp$loss_t+dt\n if(!is.null(p$seed))\n seed <- p$seed + 1\n else \n seed <- as.integer(floor(runif(1,1,2*10^9)))\n lp <- purrr::list_modify(lp, periods=periods, draws=draws, seed = seed)\n lp <- dust_fix_params(lp)\n ffo <- function(cont) {\n if(is.null(constraints))\n ccc <- cont\n else {\n ccc <- purrr::map(\n rlang::set_names(names(constraints)),\n ~{\n min(max(cont[.x], constraints[[.x]][[1]]), constraints[[.x]][[2]])\n }\n )\n }\n err <- sum((as_vector(ccc)-cont)^2)\n loss(\n params = purrr::list_modify(lp, !!!ccc), \n model = model, \n controls = names(controls),\n n_threads = 2\n ) + err\n }\n \n opt <- try(\n optimx::optimx(\n par=as_vector(controls), \n fn=\\(x) ffo(x),\n method=method,\n control = list(kkt=FALSE, maxit = 2000)\n )\n )\n \n if(\"try-error\"%in%class(opt))\n return(list(error = TRUE))\n \n if(opt$convcode!=0)\n {\n mess <- case_when(\n opt$convcode == 1 ~ \"maxit reached\",\n opt$convcode == 10 ~ \"Nelder-Mead simplex degerenate\")\n message(\"optimisation code {opt$convcode} [{mess}]\" |> glue::glue())\n }\n set_names(c(opt[names(controls)]), names(controls))\n}\n\nbest_FR_par_with_phi <- function(p, model, controls, method=\"Nelder-Mead\", loss=medianloss2, draws = 1000, dt = 40) {\n null_controls <- unique(purrr:::keep(names(p), ~stringr::str_starts(.x, \"tpo\")), names(controls))\n null_controls <- rlang::set_names(rep(0, length(null_controls)), null_controls)\n # on cherche les phi d\u00e9teministes\n lp_det <- purrr::list_modify(p, periods = p$loss_t+dt, ogn_sigma = 0, ogn_ar = 0, loss_lasso = 0, loss_ib = 0, draws = 1, !!!null_controls)\n lp_det <- dust_fix_params(lp_det)\n \n det_with_phi <- function(cont) {\n medianloss1(purrr::list_modify(lp_det, phi = cont), model, \"phi\", 2) \n }\n phic <- lp_det$phi\n det_opt <- optimx::optimx(\n par=phic, \n fn=\\(x) det_with_phi(x),\n method=\"nlm\")\n phi <- unlist(det_opt[1:lp_det$periods])\n names(phi) <- NULL\n lp <- list_modify(p, phi = phi, periods = p$loss_t+dt, draws = draws)\n lp <- dust_fix_params(lp)\n ffo <- function(cont) {\n loss(\n params = purrr::list_modify(lp, !!!cont), \n model = model, \n controls = names(controls),\n n_threads = 4) \n }\n opt <- optimx::optimx(par=controls, fn=\\(x) ffo(x), method=method)\n opt <- set_names(c(opt[names(controls)]), names(controls))\n opt <- list_modify(as.list(null_controls), !!!as.list(opt))\n return(list_modify(list(phi = phi), !!!opt))\n}\n\nbest_FR_par_with_phi_only <- function(p, model, method=\"nlm\", loss=medianloss2, draws = 1000, dt = 40) {\n null_controls <- unique(purrr:::keep(names(p), ~stringr::str_starts(.x, \"tpo\")))\n null_controls <- rlang::set_names(rep(0, length(null_controls)), null_controls)\n lp_sto <- purrr::list_modify(p, periods = p$loss_t+dt, draws = draws, !!!null_controls)\n lp_sto <- dust_fix_params(lp_sto)\n phic <- lp_sto$phi\n with_phi <- function(cont) {\n medianloss1(\n params = purrr::list_modify(lp_sto, phi = cont), \n model = model, \n controls = \"phi\", \n n_threads = 2) \n }\n det_opt <- optimx::optimx(par = phic, \n fn = \\(x) with_phi(x),\n method= method)\n if(det_opt$convcode!=0)\n message(\"Convergence not achieved\")\n phi <- unlist(det_opt[1:lp_sto$periods])\n names(phi) <- NULL\n list_modify(list(phi = phi), !!!null_controls)\n}\n", "meta": {"hexsha": 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YES\n2. NO", "lm_q1_score": 0.7122321720225278, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.3200718921357616}} {"text": "library(ggplot2)\nlibrary(reshape2)\n\nargs <- commandArgs(trailingOnly = TRUE)\n\ninputfile = \"D:/wd/wur/WellG07.cpout\"\ninputfile = args[1]\nplot_file_names = unlist(strsplit(args[2], \",\"))\noutputdir = args[3]\n\nsetwd(outputdir)\n\ninputdata = read.table(inputfile, sep=\"\\t\", header=TRUE, fill=T, comment.char=\"\")\n\ncpout = inputdata[inputdata$ImageNumber == 1,]\n\n# ---------------------- find biggest gap in first image ----------------------\n\ny_freq = data.frame(table(cpout$AreaShape_Center_Y))\nnames(y_freq) = c(\"y\", \"freq\")\ny_freq$y = as.numeric(as.character(y_freq$y))\ny_freq$freq = as.numeric(y_freq$freq)\n\nbiggest_gap_size = 0\nbiggest_gap_start = 0\nbiggest_gap_start_index = 0\nbiggest_gap_end = 0\nbiggest_gap_end_index = 0\n\n#print(y_freq)\n\nfor(i in 1:(nrow(y_freq)-1)){\n gap_size = y_freq[i+1,]$y - y_freq[i,\"y\"]\n if(gap_size > biggest_gap_size){\n biggest_gap_size = gap_size\n biggest_gap_start = y_freq[i,\"y\"]\n biggest_gap_start_index = i\n \n biggest_gap_end_index = i+1\n biggest_gap_end = y_freq[i,\"y\"]\n }\n}\n\n#search up/down through the image for smaller gaps\nwhile((y_freq[biggest_gap_start_index,\"y\"] - y_freq[biggest_gap_start_index-1, \"y\"]) > 1){\n biggest_gap_start_index = biggest_gap_start_index - 1\n}\nbiggest_gap_start = y_freq[biggest_gap_start_index,\"y\"]\n\n\nwhile(y_freq[biggest_gap_end_index + 1,\"y\"] - y_freq[biggest_gap_end_index,\"y\"] > 1){\n biggest_gap_end_index = biggest_gap_end_index + 1\n}\nbiggest_gap_end = y_freq[biggest_gap_end_index,\"y\"]\n\n\n# ---------- for every image, find how many are inside the gap, plot them ----------\n\nimage_numbers = unique(inputdata$ImageNumber)\nnumber_of_images = length(image_numbers)\nresult = data.frame(image=1:number_of_images, inside=1:number_of_images, outside=1:number_of_images, total=1:number_of_images)\nfor(i in image_numbers){\n name = paste(\"Image_\", i, \".cpout\", sep=\"\")\n cpout = inputdata[inputdata$ImageNumber == i,]\n inside_rows = cpout$AreaShape_Center_Y >= biggest_gap_start & cpout$AreaShape_Center_Y <= biggest_gap_end\n inside = sum(inside_rows)\n result[i,\"inside\"] = inside\n total = nrow(cpout)\n result[i,\"total\"] = total\n outside = total - inside\n result[i,\"outside\"] = outside\n \n cpout$col = factor(ifelse(cpout$AreaShape_Center_Y >= biggest_gap_start & cpout$AreaShape_Center_Y <= biggest_gap_end, paste(\"inside -\", inside), paste(\"outside -\", outside)))\n \n p = ggplot(cpout, aes(AreaShape_Center_X, AreaShape_Center_Y))\n p = p + geom_point(aes(colour = col)) #+ scale_colour_manual(values=c(\"red\", \"blue\"))\n p = p + geom_rect(xmin = 0, xmax = Inf, ymin = biggest_gap_start, ymax = biggest_gap_end, fill = \"red\", alpha = 0.0002)\n p = p + ggtitle(paste(\"Nuclei_\", plot_file_names[i], \" - \" , total, sep=\"\"))\n\n \n\n png(paste(\"Nuclei_\", plot_file_names[i], \".png\", sep=\"\"))\n print(p)\n dev.off()\n\tcpout$col = gsub(\" - .*\", \"\", cpout$col)\n\twrite.table(cpout[,c(\"AreaShape_Center_X\", \"AreaShape_Center_Y\", \"col\")], paste(\"Nuclei_\", plot_file_names[i], \".txt\", sep=\"\"), sep=\"\\t\", row.names=F, col.names=T, quote=F)\n}\n\ntest = melt(result, id.vars=c(\"image\"))\npng(\"bar.png\")\nggplot(test, aes(x=image, y = value, fill=variable, colour=variable)) + geom_bar(stat='identity', position='dodge' )\ndev.off()\npng(\"line.png\")\nggplot(test, aes(x=image, y = value, fill=variable, colour=variable)) + geom_line()\ndev.off()\n\nwrite.table(result, \"numbers.txt\", sep=\"\\t\", row.names=F, col.names=T, quote=F)\nwrite.table(result, \"numbers_no_header.txt\", sep=\"\\t\", row.names=F, col.names=F, quote=F)\n\ngrowth = data.frame(image=result$image[2:nrow(result)], inside_growth=diff(result$inside), outside_growth=diff(result$outside), total_growth=diff(result$total))\n\nwrite.table(growth, \"growth.txt\", sep=\"\\t\", row.names=F, col.names=T, quote=F)\n\nsumm <- do.call(data.frame, \n list(mean = apply(growth, 2, mean),\n sd = apply(growth, 2, sd),\n median = apply(growth, 2, median),\n min = apply(growth, 2, min),\n max = apply(growth, 2, max),\n n = apply(growth, 2, length)))\n\nsumm_numeric_colls = sapply(summ, is.numeric)\nsumm[,summ_numeric_colls] = round(summ[,summ_numeric_colls], 2)\nwrite.table(summ, \"summary.txt\", sep=\"\\t\", row.names=T, col.names=NA, quote=F)\n\n# ---------- for follow up analysis ----------\n\nresult=result[,c(\"total\", \"inside\")]\nresult$perc = round((result$inside / result$total) * 100, 2)\n\nwrite.table(result, \"in_out_perc.txt\", sep=\"\\t\", row.names=F, col.names=F)\n\n\n# ---------- csv for d3.js? ----------\n\nwrite.table(inputdata[,c(\"ImageNumber\", \"ObjectNumber\", \"AreaShape_Center_X\", \"AreaShape_Center_Y\")], file=\"objects.csv\", quote=F, sep=\",\", row.names=F, col.names=T)\n\n\n\n\n\n", "meta": {"hexsha": "59a3ab8628c0b61ca9d99ca057e4cc3182409486", "size": 4689, "ext": "r", "lang": "R", "max_stars_repo_path": "after_CP.r", "max_stars_repo_name": "ErasmusMC-Bioinformatics/KREAP", "max_stars_repo_head_hexsha": "c29c895af164359bd67fd5ad8bd4a8c6b3d7a7db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "after_CP.r", "max_issues_repo_name": "ErasmusMC-Bioinformatics/KREAP", "max_issues_repo_head_hexsha": "c29c895af164359bd67fd5ad8bd4a8c6b3d7a7db", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-04-01T10:48:50.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-01T10:48:50.000Z", "max_forks_repo_path": "after_CP.r", "max_forks_repo_name": "ErasmusMC-Bioinformatics/KREAP", "max_forks_repo_head_hexsha": "c29c895af164359bd67fd5ad8bd4a8c6b3d7a7db", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-06-30T14:11:56.000Z", "max_forks_repo_forks_event_max_datetime": "2018-06-30T14:11:56.000Z", "avg_line_length": 35.5227272727, "max_line_length": 177, "alphanum_fraction": 0.6724248241, "num_tokens": 1359, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3200697713049092}} {"text": "library(tidyverse)\nlibrary(data.table)\nlibrary(albersusa)\nlibrary(quantmod)\nlibrary(gganimate)\nlibrary(ggforce)\nlibrary(ggrepel)\nlibrary(extrafont)\nlibrary(uuid)\nlibrary(scales)\n\nloadfonts(device = \"win\")\nfont_family = 'Calibri'\n\n\nus_pop = getSymbols('POP', src = 'FRED', auto.assign = F)\nus_pop_df = \n tibble(\n population = as.numeric(us_pop$POP * 1000),\n date = index(us_pop),\n year = year(date)\n ) %>%\n arrange(date) %>%\n group_by(year) %>%\n summarize(\n year_end_pop = tail(population, 1)\n )\n\nus_sf <- usa_sf(\"laea\")\n\nsetwd(\"~/Public_Policy_Upd/Projects/Voting/data\")\n\nheritage_voterfraud_database = read_csv(\"heritage_voterfraud_database.csv\")\nfraud_stats_by_year = group_by(heritage_voterfraud_database, Year) %>%\n summarize(\n count = n()\n ) \n\n\n##### Get political data #####\n\n\n# MIT data lab \n\nget_margins_by_race = function(grouped_df) {\n\n margins_by_race = grouped_df %>%\n summarize(\n total_votes = totalvotes[1],\n n_candidates = n_distinct(candidate[writein == FALSE]),\n top_vote_count = candidate_votes[1],\n second_vote_count = ifelse(n_candidates > 1, candidate_votes[2], 0)\n ) %>%\n ungroup() %>%\n mutate(\n uncontested = total_votes <= 1 | n_candidates <= 1,\n winning_margin = top_vote_count - second_vote_count,\n winning_margin_pct = winning_margin / total_votes\n )\n \n return(margins_by_race) \n}\n\nhouse_elections = read.csv('1976-2018-house.csv') %>% \n mutate(candidate_votes = as.numeric(candidatevotes %>% str_remove(','))) %>%\n filter(stage == 'gen') %>% \n arrange(state, year, district, -candidate_votes )\n\n\nmargins_by_house_race = get_margins_by_race(house_elections %>% \n group_by(state, year, district)) %>%\n mutate(\n race_id = paste(state, year, district, sep = '_')\n )\n\nhouse_totals_by_state_year = group_by(margins_by_house_race, state, year) %>%\n summarize(\n house_total_votes = sum(total_votes, na.rm = T)\n )\n\ngroup_by(margins_by_house_race, year) %>%\n summarize(\n n_races = n_distinct(race_id)\n )\n\n\n\n# get winning margins for house, senate, and president\n# get total votes for each race as well\n\nsenate_elections = read.csv('1976-2018-senate.csv') %>% \n mutate(candidate_votes = as.numeric(candidatevotes %>% str_remove(','))) %>%\n filter(stage == 'gen') %>% \n arrange(state, year, district, -candidate_votes ) \n\nmargins_by_senate_race = get_margins_by_race(senate_elections %>% group_by(state, year, district))\n\nsenate_totals_by_state_year = group_by(margins_by_senate_race, state, year) %>%\n summarize(\n senate_total_votes = sum(total_votes, na.rm = T)\n )\n\npresidential_elections = read.csv('1976-2016-president.csv') %>% \n mutate(candidate_votes = as.numeric(candidatevotes %>% str_remove(','))) %>%\n arrange(state, year, -candidate_votes ) \n\nmargins_by_presidential_race = get_margins_by_race(presidential_elections %>% group_by(state, year))\npresident_totals_by_state_year = group_by(margins_by_presidential_race, state, year) %>%\n summarize(\n president_total_votes = sum(total_votes, na.rm = T)\n ) \n\ncombined_margin_analysis = bind_rows(\n margins_by_house_race %>% mutate(race = 'House', district = as.character(district)) %>% filter(!uncontested) %>% select(state, year, district, race, winning_margin, winning_margin_pct),\n margins_by_senate_race %>% mutate(race = 'Senate') %>% filter(!uncontested) %>% select(state, year, district, race, winning_margin, winning_margin_pct),\n margins_by_presidential_race %>% mutate(race = 'President', district = 'statewide') %>% select(state, year, district, race, winning_margin, winning_margin_pct)\n) %>%\n mutate(\n race_id = 1:length(year),\n plot_order = 7\n )\nraces_less_1k_margin = filter(combined_margin_analysis, winning_margin <= 1000) %>% nrow()\npercent_races_less_1k_margin = races_less_1k_margin / nrow(combined_margin_analysis)\n\n\n\ntotal_votes_by_year_state = full_join(\n house_totals_by_state_year,\n senate_totals_by_state_year, \n by = c('state', 'year')\n) %>%\n full_join(\n president_totals_by_state_year, by = c('state', 'year')\n ) %>%\n mutate(\n annual_total_votes = pmax(\n house_total_votes, senate_total_votes, president_total_votes, \n na.rm = T\n )\n )\n\ntotal_votes_by_year = group_by(total_votes_by_year_state, year) %>%\n summarize(\n total_votes = sum(annual_total_votes, na.rm = T)\n ) %>%\n bind_rows(\n data.frame(\n year = 2020, \n total_votes = 81283077 + 74222965\n )\n ) %>%\n left_join(us_pop_df)\n\n\n# https://www.weather.gov/safety/lightning-odds\n\nyearly_odds_lightning = 1/1222000\nnot_struck = 1-yearly_odds_lightning\nodds_struck_in_50_years = 1-(not_struck^80)\nodds_struck_in_50_years\n\n\nvoter_fraud_counts_2009_2018 = filter(fraud_stats_by_year, Year >= 2009, Year <= 2018)\ntotal_fraud_cases = sum(voter_fraud_counts_2009_2018$count)\nannual_avg_fraud_cases = total_cases / length(2018:2009)\nlighning_strikes_per_year = 270\nlightning_strikes_over_period = lighning_strikes_per_year * length(2018:2009)\n\ntotal_votes_2009_2018 = filter(total_votes_by_year, between(year, 2009, 2018)) %>% pull(total_votes) %>% sum()\nratio_lightning_fraud = lighning_strikes_per_year / annual_avg_fraud_cases\n\n\nget_circle_radius = function(area) {\n (area / pi)^(1/2)\n}\n\n# title, circle 1, circle 1:2, title, circle 1:3\n# People are more likely to be struck by lightning\\nthan commit voter fraud.\n\ncircle_areas = tibble(\n counts = c(total_fraud_cases, lightning_strikes_over_period, total_votes_2009_2018),\n circle_alphas = c(.9, 0.75, 0.65),\n desc = c('proven cases of voter fraud', \n 'lightning strikes on people', 'votes in federal elections'),\n source = c('Heritage Foundation\\nVoter Fraud Database', 'Weather.gov','MIT Election Lab'),\n circle_r = get_circle_radius(counts),\n circle_desc = sprintf('There were %s %s\\nbetween 2009 and 2018', comma(counts), desc),\n voter_fraud_label = c(NA, 'Voter Fraud', 'Voter fraud is represented\\nby this pixel'),\n voter_fraud_label_y = c(0, min(circle_r) * .25, min(circle_r) * 230),\n circle_label = c('Voter Fraud', 'Lightning Strikes on People', 'Votes in Federal Elections'),\n circle_label_y = c(0, -get_circle_radius(lightning_strikes_over_period) * .85, -get_circle_radius(total_votes_2009_2018) * .85),\n source_label = sprintf('Source: %s', source),\n order = 1:length(counts)\n) %>%\n mutate(\n circle_desc = ifelse(circle_alphas == 0, desc, circle_desc),\n source_label = ifelse(circle_alphas == 0, source, source_label),\n desc_position = ifelse(circle_alphas == 0, 0, circle_r * 1.2),\n desc_size = c(10, 10, 10)\n )\n\nstacked_circle_dat = bind_rows(\n tibble(\n plot_order = 1,\n circle_desc = 'Americans are more likely to be\\nstruck by lightning\\nthan commit voter fraud.',\n desc = circle_desc, \n source_label = 'Chart: Taylor G. White (@t_g_white)',\n desc_position = 0, desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = min(circle_areas$circle_r)\n ),\n filter(circle_areas, order == 1) %>% mutate(plot_order = 2),\n \n filter(circle_areas, order %in% 1:2) %>% \n mutate(plot_order = 3, \n voter_fraud_label = ifelse(order < 2, NA, voter_fraud_label),\n circle_label = ifelse(order < 2, NA, circle_label),\n source_label = ifelse(order < 2, NA, source_label),\n circle_desc = ifelse(order < 2, NA, circle_desc),\n ),\n tibble(\n plot_order = 4,\n circle_desc = 'Voter fraud represents a small proportion of all votes cast.' %>% str_wrap(20),\n desc = \"fraud in federal elections desc\", \n source_label = '',\n desc_position = 0, desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = min(circle_areas$circle_r)\n ),\n filter(circle_areas, order %in% 1:3) %>% \n mutate(plot_order = 5, \n voter_fraud_label = ifelse(order < 3, NA, voter_fraud_label),\n circle_label = ifelse(order < 3, NA, circle_label),\n source_label = ifelse(order < 3, NA, source_label),\n circle_desc = ifelse(order < 3, NA, circle_desc),\n ),\n tibble(\n plot_order = 6,\n circle_desc = \"Voter fraud is rare because it doesn't pay off.\" %>% str_wrap(24),\n desc = \"doesn't pay off\", \n source_label = '',\n desc_position = 0, desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = min(circle_areas$circle_r)\n ),\n tibble(\n plot_order = 7,\n # circle_desc = NA,\n circle_desc = 'Few federal elections are close enough to be impacted by fraud.' %>% str_wrap(32),\n desc = \"close race desc\", \n source_label = '',\n desc_position = 0, \n desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = 0.0001\n ),\n tibble(\n plot_order = 8,\n # circle_desc = NA,\n circle_desc = sprintf('Only %s percent of the %s contested federal elections between 1976-2018 were decided by a thousand votes or less.', percent(percent_races_less_1k_margin, accuracy = 0.01), comma(nrow(combined_margin_analysis))) %>% str_wrap(32),\n desc = \"close race desc\", \n source_label = '',\n desc_position = 0, \n desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = 0.0001\n ),\n tibble(\n plot_order = 9,\n # circle_desc = NA,\n circle_desc = 'Large-scale fraud carries a strong punishment and can be detected easily, which serves as a deterrent.' %>% str_wrap(28),\n desc = \"last panel desc\", \n source_label = '',\n desc_position = 0, \n desc_size = 20, \n voter_fraud_label = NA, \n circle_label = NA, \n circle_label_y = 0,\n voter_fraud_label_y = 0,\n circle_alphas = 0,\n circle_r = 0.0001\n )\n) %>%\n mutate(\n circle_x = 0,\n circle_y = 0\n )\n\n\n\nplot_colors = c('white', '#e41a1c', 'yellow', 'white','#386cb0', 'white', 'white', 'white')\nnames(plot_colors) = c('People are more likely to be\\nstruck by lightning\\nthan commit voter fraud.', \n \"proven cases of voter fraud\", \"lightning strikes on people\", \n \"fraud in federal elections desc\",\n \"votes in federal elections\", \"doesn't pay off\", 'close race desc', \"last panel desc\")\n\n\ncombined_margin_analysis_counts = group_by(combined_margin_analysis, year, winning_margin <= 1000) %>%\n summarize(\n obs = n()\n ) %>%\n ungroup() %>%\n rename(\n close_margin = `winning_margin <= 1000`\n ) %>%\n mutate(\n plot_order = 7\n )\n \n\nseg_dat = data.frame(plot_order = 5, x = 0, y = min(circle_areas$circle_r) * 130)\n\nfraud_anim = ggplot(stacked_circle_dat) +\n geom_circle(aes(x0 = circle_x, y0 = circle_y, r = circle_r, fill = desc, alpha = circle_alphas, group = plot_order), colour = NA, na.rm = T) +\n # geom_bar(data = combined_margin_analysis_counts, aes(year, obs, fill = close_margin, group = plot_order), show.legend = F, stat = 'identity') +\n coord_equal() +\n scale_size(guide = F, range = c(6.5, 9)) +\n scale_alpha(guide = F, range = c(0, .75)) +\n theme_bw() +\n theme_void() +\n scale_fill_manual(guide = F, values = plot_colors) +\n geom_text(aes(x = circle_x, y = desc_position, label = circle_desc, size = desc_size), fontface = 'bold', family = font_family, show.legend = F, na.rm = T) +\n geom_text(aes(x = circle_x, y = -circle_r * 1.2, label = source_label), family = font_family, size = 6, fontface = 'italic') +\n geom_text(aes(x = circle_x, y = voter_fraud_label_y, label = voter_fraud_label), na.rm = T, family = font_family, size = 6) +\n geom_text(aes(x = circle_x, y = circle_label_y, label = circle_label), na.rm = T, family = font_family, size = 6) +\n geom_segment(data = seg_dat, aes(x = 0, xend = 0, y = 0, yend = y, group = plot_order), size = 0.25) +\n transition_states(\n plot_order,\n transition_length = 2,\n state_length = 8\n ) +\n # shadow_mark(exclude_layer = c(2, 3, 4, 5)) +\n view_zoom(pause_length = 40, step_length = 10, nsteps = max(stacked_circle_dat$plot_order), ease = 'sine-in-out')\n\nsetwd('~/Public_Policy_Upd/Projects/Voting/output')\n\nanimate(fraud_anim,\n renderer = gifski_renderer(\"fraud_frequency_comparison_upd.gif\"),\n nframes = 300,\n height = 8, width = 8, units = 'in', type = 'cairo-png', res = 200)\n\n\n\n# consider creating barchart, saving that down separately, and then reading that in as a set of pixels\n# plot the pixels and view_zoom will show it perfectly. \n# http://mfviz.com/r-image-art/\n\n", "meta": {"hexsha": "b23a25a56beb9f0b675890565f301f3aa61b6ca1", "size": 12730, "ext": "r", "lang": "R", "max_stars_repo_path": "Projects/Voting/scripts/analyze_voter_fraud_database.r", "max_stars_repo_name": "vishalbelsare/Public_Policy", "max_stars_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-03-09T01:39:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-08T19:11:44.000Z", "max_issues_repo_path": "Projects/Voting/scripts/analyze_voter_fraud_database.r", "max_issues_repo_name": "vishalbelsare/Public_Policy", "max_issues_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-06-03T20:11:43.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-07T00:03:58.000Z", "max_forks_repo_path": "Projects/Voting/scripts/analyze_voter_fraud_database.r", "max_forks_repo_name": "vishalbelsare/Public_Policy", "max_forks_repo_head_hexsha": "4f57140f85855859ff2e49992f4b7673f1b72857", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-04T22:48:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-09T14:00:22.000Z", "avg_line_length": 34.5923913043, "max_line_length": 255, "alphanum_fraction": 0.6805184603, "num_tokens": 3661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3200697713049092}} {"text": "#' Convert data to target units\n#'\n#' This converts flat data to target units as defined in the dataset. It creates three new columns in your dataset: 1. The converted unit value, 3. the conversion factor used, & 4. a recommended acceptance/rejection based on pairs of input & target units.\n#' All original columns are retained.\n#' @param x Input dataset.\n#' @param input_unit Column name of x with units to convert from (in quotes).\n#' @param target_unit Column name of x with units to convert to (in quotes).\n#' @param value_var Column name of x containing result values (in quotes).\n#' @param conv_val_col Name of new values column for converted values (in quotes).\n\n#' @examples \n#' # Make example data\n#' value=c(abs(rnorm(5,1, 0.1)), rnorm(5, 1000, 100), rnorm(5, 20, 1))\n#' raw_units=c(rep('mg/l',5), rep('ug/L', 5), rep('deg C',5))\n#' preferred_units=c(rep('mg/L', 10), rep('deg C',5))\n#' data=data.frame(value,raw_units,preferred_units)\n#' data\n#' \n#' # Unit conversion\n#' conv_data=convertUnits(data, input_units='raw_units', target_units='preferred_units', value_var='value', conv_val_col='converted_value')\n#' conv_data\n\n#' @export\nconvertUnits=function(x, input_units, target_units='target_unit', value_var, conv_val_col='converted_value'){\n\t\t\n\tif(! input_units %in% names(x)){stop('input_units name not in x')}\n\tif(! target_units %in% names(x)){stop('target_units name not in x')}\n\t\n\tunit_conv_table=read.csv(system.file(\"extdata\", \"unit_conv_table.csv\", package = \"wqTools\"))\n\n\tnames(unit_conv_table)[names(unit_conv_table)=='target_unit'] = paste(target_units)\n\tnames(unit_conv_table)[names(unit_conv_table)=='input_unit'] = paste(input_units)\n\tx=merge(x, unit_conv_table, all.x=T)\n\tx[,input_units]=as.character(x[,input_units])\n\tx[,target_units]=as.character(x[,target_units])\n\t\n\tconv_x=x\n\tconv_x$conversion_flag[is.na(conv_x$conversion_factor) & toupper(conv_x[,input_units])==toupper(conv_x[,target_units])] = \"ACCEPT\"\n\tconv_x$conversion_factor[is.na(conv_x$conversion_factor) & toupper(conv_x[,input_units])==toupper(conv_x[,target_units])] = 1\n\tconv_x$conv_val_col = conv_x[,value_var] * conv_x[,'conversion_factor']\n\tif(any(is.na(conv_x$conv_val_col) & !is.na(conv_x[,value_var]))){\n\t\twarning('Unable to convert one or more unit combinations. These values are expressed as NA.')\n\t}\n\t\n\t\n\tnames(conv_x)[names(conv_x)=='conv_val_col'] = conv_val_col\n\t\n\treturn(conv_x)\n}\n\n", "meta": {"hexsha": "130cd71528db33613bb9b0e6e60ee7fcb8b8ad05", "size": 2390, "ext": "r", "lang": "R", "max_stars_repo_path": "R/convertUnits.r", "max_stars_repo_name": "utah-dwq/udwqTools", "max_stars_repo_head_hexsha": "e5016f35d310fe01d1defb1652bfa103f84d574c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-10-07T16:50:59.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-07T17:44:33.000Z", "max_issues_repo_path": "R/convertUnits.r", "max_issues_repo_name": "utah-dwq/udwqTools", "max_issues_repo_head_hexsha": "e5016f35d310fe01d1defb1652bfa103f84d574c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-01-19T00:39:03.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-23T22:39:49.000Z", "max_forks_repo_path": "R/convertUnits.r", "max_forks_repo_name": "utah-dwq/udwqTools", "max_forks_repo_head_hexsha": "e5016f35d310fe01d1defb1652bfa103f84d574c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-07-26T09:20:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-26T09:20:56.000Z", "avg_line_length": 46.862745098, "max_line_length": 255, "alphanum_fraction": 0.7338912134, "num_tokens": 636, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3200697713049091}} {"text": "#' Greedy ensemble selection for regression model\n#' \n#' \n#' Medley is a package which implements the Caruana et al., 2004 algorithm for\n#' stepwise greedy ensemble selection in R, allowing disparate regression \n#' models to be combined easily.\n#' \n#' @author Martin O'Leary \\email{m.e.w.oleary@@gmail.com}\n#' @name medley\n#' @docType package\nNULL\n", "meta": {"hexsha": "e7ba80b87de195faa643cf8544345dfcded1d559", "size": 348, "ext": "r", "lang": "R", "max_stars_repo_path": "R/medley-package.r", "max_stars_repo_name": "valexandersaulys/medley", "max_stars_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 26, "max_stars_repo_stars_event_min_datetime": "2015-03-18T19:20:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T20:45:40.000Z", "max_issues_repo_path": "R/medley-package.r", "max_issues_repo_name": "valexandersaulys/medley", "max_issues_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/medley-package.r", "max_forks_repo_name": "valexandersaulys/medley", "max_forks_repo_head_hexsha": "e2c07b6780c4a57d8a29142cda6b11c7c1d4f88f", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2015-02-17T03:56:14.000Z", "max_forks_repo_forks_event_max_datetime": "2018-06-14T10:56:34.000Z", "avg_line_length": 29.0, "max_line_length": 78, "alphanum_fraction": 0.7327586207, "num_tokens": 91, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.32006976331715836}} {"text": "setwd('/home/pc-752828/Dev/results-search-master/outputs-normal')\n\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(ggstatsplot)\nlibrary(tcltk)\nlibrary(rstatix)\nlibrary(PMCMRplus)\nlibrary(pgirmess)\n\ndata_norm <- read.csv(file = 'output-n-norm.csv', sep = ',', header = T)\ndata_def <- read.csv(file = 'output-n-price.csv', sep = ',', header = T)\n\nget_df <- function(seed, data) {\n my_ite <- 10\n bo1 <- list()\n bo2 <- list()\n bo3 <- list()\n bo4 <- list()\n rs <- list()\n app <- list()\n seed_l <- list()\n \n for(i in levels(factor(data$name))) {\n r_bo1 <- data[data['name'] == i & data['bo'] == 'bo1' & data['ite'] == my_ite,][seed,]\n r_bo4 <- data[data['name'] == i & data['bo'] == 'bo4' & data['ite'] == my_ite,][seed,]\n r_bo3 <- data[data['name'] == i & data['bo'] == 'bo3' & data['ite'] == my_ite,][seed,]\n r_bo2 <- data[data['name'] == i & data['bo'] == 'bo5' & data['ite'] == my_ite,][seed,]\n r_rs <- data[data['name'] == i & data['bo'] == 'rs' & data['ite'] == my_ite,][seed,]\n bo1 <- append(bo1, levels(factor(r_bo1$best)))\n bo2 <- append(bo2, levels(factor(r_bo2$best)))\n bo3 <- append(bo3, levels(factor(r_bo3$best)))\n bo4 <- append(bo4, levels(factor(r_bo4$best)))\n rs <- append(rs, levels(factor(r_rs$best)))\n app <- append(app, i)\n seed_l <- append(seed_l, seed)\n }\n \n df <- cbind(bo1, bo2, bo3, bo4, rs, app)\n df <- data.frame(df)\n df$bo1 = as.numeric(as.character(df$bo1))\n df$bo2 = as.numeric(as.character(df$bo2))\n df$bo3 = as.numeric(as.character(df$bo3))\n df$bo4 = as.numeric(as.character(df$bo4))\n df$rs = as.numeric(as.character(df$rs))\n \n return(df)\n}\n\ndf_1 <- get_df(1, data_norm)\ndf_2 <- get_df(2, data_norm)\ndf_3 <- get_df(3, data_norm)\ndf_4 <- get_df(4, data_norm)\ndf_5 <- get_df(5, data_norm)\n\ndf_1 <- get_df(1, data_def)\ndf_2 <- get_df(2, data_def)\ndf_3 <- get_df(3, data_def)\ndf_4 <- get_df(4, data_def)\ndf_5 <- get_df(5, data_def)\n\ndf_norm <- rbind(df_1, df_2, df_3, df_4, df_5)\ndf_def <- rbind(df_1, df_2, df_3, df_4, df_5)\n\ndf <- df_norm\n\n\nsummary(df$bo1)\nsummary(df$bo2)\nsummary(df$bo3)\nsummary(df$bo4)\nsummary(df$rs)\n\ndf_1\n\nks.test(df_5$bo1, \"pnorm\")\nks.test(df_5$bo2, \"pnorm\")\nks.test(df_5$bo3, \"pnorm\")\nks.test(df_5$bo4, \"pnorm\")\nks.test(df_5$rs, \"pnorm\")\n\ndf <- df_1\n\ncols <- c(\"BO-6rnd-EIdef\",\"BO-6rnd-EInova\",\"BO-6sel-EIdef\", \"PB3Opt\", \"Ranking Search\", \"ID\")\ncolnames(df) <- cols\ndf$ID = as.character(df$ID)\ndf$ID <- factor(df$ID)\ncols <- c(\"BO-6rnd-EIdef\",\"BO-6rnd-EInova\",\"BO-6sel-EIdef\", \"PB3Opt\", \"Ranking Search\")\nnew_df <- melt(df, id = c(\"ID\"), measured = cols)\nglimpse(new_df)\nfriedman.test(value ~ variable | ID, data = new_df)\n\nnew_df %>% friedman_effsize(value ~ variable | ID)\n\n\n\ncolnames(new_df) <- c(\"ID\", \"Abordagem\", \"Best\")\nfrdAllPairsSiegelTest (new_df$Best, new_df$Abordagem, new_df$ID, p.adjust = \"bonferroni\")\nfrdAllPairsConoverTest(new_df$Best, new_df$Abordagem, new_df$ID, p.adjust = \"bonferroni\")\nfrdAllPairsNemenyiTest(new_df$Best, new_df$Abordagem, new_df$ID, p.adjust = \"bonferroni\")\n\n\nnew_df %>% group_by(Abordagem) %>%\n get_summary_stats(Best, type = \"median_iqr\")\n\n\n\n\nlist_id <- cols\nlist_value <- list(\nlength(which(df$`BO-6rnd-EIdef`==1))/length(df$PB3Opt),\nlength(which(df$`BO-6rnd-EInova`==1))/length(df$PB3Opt),\nlength(which(df$`BO-6sel-EIdef`==1))/length(df$PB3Opt),\nlength(which(df$PB3Opt==1))/length(df$PB3Opt),\nlength(which(df$`Ranking Search`==1))/length(df$PB3Opt)\n)\n\ndf <- cbind(list_id, list_value)\ndf <- data.frame(df)\ndf\n\ndf$list_id = as.character(df$list_id)\ndf$list_value = as.numeric(as.character(df$list_value))\n\nglimpse(df)\n\ntikz('bar1.tex')\n\nggplot(df) +\n geom_bar( aes(x=list_id, y=list_value), stat=\"identity\", alpha=0.7)+\n labs(color = element_blank(), x=\"Abordagem\", y=\"Frequ\u00eancia da Melhor Solu\u00e7\u00e3o ()\")+\n theme_bw()\n\ndev.off()\n\nwrite.csv(x=df_1,file=\"teste-new.csv\", row.names = FALSE)\n\n\n\n\n\n\n\n\np1<-ggplot(new_df, aes(x = value, y= variable, add = \"jitter\"))+\n geom_boxplot(alpha=0.7, colour = \"black\", fill=\"gray\")+\n stat_summary(fun=mean, geom=\"point\", shape=20, size=2, color=\"red\", fill=\"red\") +\n theme(legend.position=\"none\")+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8))+\n ggtitle('a) Boxplot do Melhor Custo Encontrado')+\n labs(color = element_blank(), x=\"Custo Normalizado\", y=\"Frequ\u00eancia\")\n\nylim1<- c(1, 3)\np2<-p1 + coord_cartesian(xlim = ylim1) +\n ggtitle('b) Zoom da Figura a')\n\n\ntikz('boxplot.tex')\ngrid.arrange(p1, p2, nrow = 2, ncol=1, top=\"\", left=\"Abordagem\")\n\ndev.off()\n\n\n\n\n\n\n\n\n\npar(mfrow=c(2, 3))\n\nboxplot(df$bo1,df$bo2, df$bo3, df$bo4, df$rs, xlab = \"Abordagem\", ylab = \"Custo Normalizado\", outline = FALSE)\ngrid(nx=16, ny=16)\nboxplot(df$bo1,df$bo2, df$bo3, df$bo4, df$rs, \n xlab = \"Abordagem\", \n ylab = \"Custo Normalizado\",\n names=c('A1', 'A2', 'A3', 'A4', 'A5'))\nlegend(\"topright\", inset=c(-0.2,0),legend=c(\"A1 - BO-6rnd-EIdef\",\n \"A2 - BO-6rnd-EInova\",\n \"A3 - BO-6sel-EIdef\",\n \"A4 - BO3Opt\",\n \"A5 - Ranking Search\"),\n fill=c(\"white\",\"white\",\"white\",\"white\",\"white\"), bty = \"n\")", "meta": {"hexsha": "078200096062d674baff20099d66a2d93ab15c3e", "size": 5386, "ext": "r", "lang": "R", "max_stars_repo_path": "results-dissertation/r-scripts/results-fred.r", "max_stars_repo_name": "lmcad-unicamp/PB3Opt", "max_stars_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "results-dissertation/r-scripts/results-fred.r", "max_issues_repo_name": "lmcad-unicamp/PB3Opt", "max_issues_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "results-dissertation/r-scripts/results-fred.r", "max_forks_repo_name": "lmcad-unicamp/PB3Opt", "max_forks_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6489361702, "max_line_length": 110, "alphanum_fraction": 0.6195692536, "num_tokens": 1874, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.550607350786733, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.3200697547438055}} {"text": "## library(plyr)\n## library(meffil)\n## library(splitstackshape)\n## library(matrixStats)\n\n#' Calculate CNVs from IDAT files\n#'\n#' Based on the algorithm developed in \\code{R/CopyNumber450k} bioconductor package\n#' \n#' @param samplesheet Output from \\code{meffil.create.samplesheet}\n#' @param cnv.reference Name returned by \\code{\\link{meffil.list.cnv.references}()}.\n#' @param chip Name returned by \\code{\\link{meffil.list.chips()}} (Default: NA).\n#' @param verbose Default = FALSE\n#' @param ... Extra parameters to be passed to \\code{DNAcopy} for segmentation. See details.\n#' \n#' @details\n#' The following default values are being used:\n#' - trim = 0.1\n#' - min.width = 5\n#' - nperm = 10000\n#' - alpha = 0.001\n#' - undo.splits = \"sdundo\"\n#' - undo.SD = 2\n#'\n#' @export\n#' @return Dataframe of segmented results\nmeffil.calculate.cnv <- function(samplesheet, cnv.reference, chip=NA, verbose=FALSE, ...) {\n cnv.reference <- meffil:::get.cnv.reference(cnv.reference)\n l1 <- meffil:::get.index.list(nrow(samplesheet), options(\"mc.cores\")[[1]])\n \n l <- lapply(l1, function(x) {\t\t\n l2 <- mclapply(x, function(i) {\n calculate.cnv(samplesheet$Basename[i],\n samplesheet$Sample_Name[i],\n cnv.reference=cnv.reference,\n chip=chip,\n verbose=verbose, ...)\n })\n names(l2) <- samplesheet$Sample_Name[x]\n return(l2) \n })\n nom <- unlist(sapply(l, names))\n l <- unlist(l, recursive=FALSE)\n names(l) <- nom\n return(l) # list of data frames (output of calculate.cnv), one per sample\n}\n\ncalculate.cnv <- function(bname, samplename=basename(bname), cnv.reference,\n chip=NA, \n trim=0.1, min.width = 5, nperm = 10000, alpha = 0.001,\n undo.splits = \"sdundo\", undo.SD = 2, verbose = TRUE, smoothing=TRUE) {\n msg(\"Reading idat file for\", bname, verbose=verbose)\n rg <- meffil:::read.rg(bname, verbose=verbose)\n \n chip <- meffil:::guess.chip(rg, chip)\n \n featureset <- cnv.reference$featureset\n \n probes <- meffil.probe.info(chip, featureset)\n case <- meffil:::mu.to.cn(meffil:::rg.to.mu(rg, probes))\n \n msg(\"Predicting sex\", verbose=verbose)\n sex <- meffil:::cnv.predict.sex(case, featureset)\n\n if (ncol(cnv.reference$intensity.sex[[sex]]) == 0)\n stop(paste(\"Sample is predicted to be\", \n ifelse(sex == \"M\", \"male\", \"female\"),\n \"but none found in CNV reference.\"))\n \n msg(\"Normalisting against controls\", verbose=verbose)\n int.sex <- case[rownames(cnv.reference$intensity.sex[[sex]])]\n int.sex[order(int.sex)] <- sort(cnv.reference$intensity.sex[[sex]][,1])\n\n int.aut <- case[rownames(cnv.reference$intensity.aut)]\n int.aut[order(int.aut)] <- sort(cnv.reference$intensity.aut[,1])\n \n msg(\"Estimating CNVs\", verbose=verbose)\n int.sex.prop <- log2(int.sex / cnv.reference$control.medians.sex[[sex]])\n int.aut.prop <- log2(int.aut / cnv.reference$control.medians.aut)\n\n features <- meffil:::cnv.features(featureset)\n \n int.sex.prop <- int.sex.prop[names(int.sex.prop) %in% features$name]\n int.aut.prop <- int.aut.prop[names(int.aut.prop) %in% features$name]\n\n p.sex <- features[match(names(int.sex.prop), features$name),c(\"name\",\"chromosome\",\"position\")]\n p.aut <- features[match(names(int.aut.prop), features$name),c(\"name\",\"chromosome\",\"position\")]\n\n cna.sex <- CNA(int.sex.prop, chrom=p.sex$chromosome, maploc=p.sex$position,\n data.type=\"logratio\", sampleid=samplename)\n cna.aut <- CNA(int.aut.prop, chrom=p.aut$chromosome, maploc=p.aut$position,\n data.type=\"logratio\", sampleid=samplename)\n \n if(smoothing) {\n cna.sex <- smooth.CNA(cna.sex, trim=trim)\n cna.aut <- smooth.CNA(cna.aut, trim=trim)\n }\n \n segment.cna.sex <- segment(cna.sex, min.width=min.width, verbose = verbose, nperm = nperm,\n alpha = alpha, undo.splits = undo.splits, undo.SD = undo.SD,\n trim = trim)\n segment.cna.aut <- segment(cna.aut, min.width=min.width, verbose = verbose, nperm = nperm,\n alpha = alpha, undo.splits = undo.splits, undo.SD = undo.SD,\n trim = trim)\n out <- rbind(segment.cna.aut$output, segment.cna.sex$output)\n out$chrom <- ordered(out$chrom, levels = paste(\"chr\", c(1:22, \"X\",\"Y\"), sep=\"\"))\n out$ID <- samplename\n\n msg(\"Calculating p-values\", verbose=verbose)\n out <- meffil:::cnv.pvalue(out, int.aut, int.sex, sex, cnv.reference)\n return(out)\n}\n\nget.index.list <- function(n, mc.cores) {\n mc.cores <- ifelse(is.null(mc.cores) || mc.cores < 1, 1, min(mc.cores, n))\n div <- floor(n / mc.cores)\n rem <- n %% mc.cores\n l1 <- lapply(1:div, function(x) (x-1) * mc.cores + 1:mc.cores)\n if(rem != 0) l1[[div+1]] <- l1[[div]][mc.cores] + 1:rem\n return(l1)\n}\n\nmu.to.cn <- function(mu) {\n mu$M + mu$U\n}\n\ncnv.predict.sex <- function(cn, featureset, sex.cutoff=-2) {\n log.cn <- log2(cn)\n \n sites.x <- meffil.get.x.sites(featureset)\n stopifnot(all(sites.x %in% names(log.cn)))\n x.signal <- median(log.cn[sites.x], na.rm=T)\n \n sites.y <- meffil.get.y.sites(featureset) \n stopifnot(all(sites.y %in% names(log.cn)))\n y.signal <- median(log.cn[sites.y], na.rm=T)\n \n xy.diff <- y.signal-x.signal\n ifelse(xy.diff < sex.cutoff, \"F\", \"M\")\n}\n\ncnv.pvalue <- function(out, int.aut, int.sex, sex, cnv.reference) {\n ## Get the sum of the intensities for the probes in each of the controls\n ## Get the mean sum of intensities\n ## Get the SD of the sum of intensities\n ## Get the sum of intensities of probes in the target\n features <- cnv.features(cnv.reference$featureset)\n for(i in 1:nrow(out)) {\n feature.sub <- subset(features, (chromosome == out$chrom[i]\n & position <= out$loc.end[i]\n & position >= out$loc.start[i]))$name\n if(out$chrom[i] %in% c(\"chrX\", \"chrY\")) {\n control.int.sum <- colSums(cnv.reference$intensity.sex[[sex]][feature.sub, , drop=FALSE])\n sample.sum <- sum(int.sex[feature.sub])\n } else {\n control.int.sum <- colSums(cnv.reference$intensity.aut[feature.sub, , drop=FALSE])\n sample.sum <- sum(int.aut[feature.sub])\n }\n control.mean <- mean(control.int.sum)\n control.sd <- sd(control.int.sum)\n \n z.score <- (sample.sum - control.mean)/control.sd\n pval <- 2 * pnorm(-abs(z.score))\n \n ## Compute p-value (2 sided t-test)\n out$nprobe[i] <- length(feature.sub)\n out$sample.sum[i] <- sample.sum\n out$control.mean[i] <- control.mean\n out$control.sd[i] <- control.sd\n out$z.score[i] <- z.score\n out$pvalue[i] <- pval\n }\n out$adjusted.pvalue <- p.adjust(out$pvalue, method=\"bonferroni\")\n return(out)\n}\n\ncnv.features <- function(featureset) {\n features <- meffil.get.features(featureset)\n sites <- features[which(!is.na(features$chromosome)\n & features$target == \"methylation\"\n & !features$snp.exclude),]\n sites$chromosome <- ordered(sites$chromosome, levels=paste(\"chr\", c(1:22, \"X\", \"Y\"), sep=\"\"))\n sites <- sites[with(sites, order(chromosome, position, decreasing=F)),]\n sites\n}\n", "meta": {"hexsha": "90b07eef2ff38cd45cee7476635903e96555085c", "size": 7513, "ext": "r", "lang": "R", "max_stars_repo_path": "R/cnv.r", "max_stars_repo_name": "RichardJActon/meffil_duplicate", "max_stars_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/cnv.r", "max_issues_repo_name": "RichardJActon/meffil_duplicate", "max_issues_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/cnv.r", "max_forks_repo_name": "RichardJActon/meffil_duplicate", "max_forks_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3924731183, "max_line_length": 101, "alphanum_fraction": 0.590709437, "num_tokens": 2073, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.31998417472789464}} {"text": "library(GenomicRanges)\nlibrary(SnapATAC)\n\nbuenrostro2018_bmat<-readRDS(\"../input/bmat_buenrostro2018.rds\")\nrangeList<-readRDS(\"../input/rangeList_buenrostro2018.rds\")\ncolnames(buenrostro2018_bmat)<-rangeList$name\n\nbuenrostro2018_fullRange<-read.table(\"../input/buenrostro2018_5k_full.txt\")\n\nbuenrostro2018_bmat_full<-matrix(ncol = length(buenrostro2018_fullRange$V1),nrow = nrow(buenrostro2018_bmat))\n\nrownames(buenrostro2018_bmat_full)<-rownames(buenrostro2018_bmat)\ncolnames(buenrostro2018_bmat_full)<-buenrostro2018_fullRange$V1\n\nfor(i in 1:length(buenrostro2018_fullRange$V1)){\n range<-as.character(buenrostro2018_fullRange$V1[i])\n if(range %in% colnames(buenrostro2018_bmat)){\n buenrostro2018_bmat_full[,range]<-buenrostro2018_bmat[,range]\n } else{\n buenrostro2018_bmat_full[,range]<-0\n }\n print(paste0(i,\"/\",length(buenrostro2018_fullRange$V1),\" finished\"))\n}\nbuenrostro2018_bmat_full<-as(buenrostro2018_bmat_full,\"dgCMatrix\")\n\nsaveRDS(buenrostro2018_bmat_full,file=\"../output/buenrostro2018-snap-full.rds\")\n\n\n\n\n\n", "meta": {"hexsha": "624e3456280cf91f742b8f9f51beb6d47938b690", "size": 1048, "ext": "r", "lang": "R", "max_stars_repo_path": "preprocess/Buenrostro2018/scripts/buenrostro2018_bmat_full.r", "max_stars_repo_name": "mrcuizhe/svmATAC", "max_stars_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-09-23T13:14:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T00:35:09.000Z", "max_issues_repo_path": "preprocess/Buenrostro2018/scripts/buenrostro2018_bmat_full.r", "max_issues_repo_name": "mrcuizhe/svmATAC", "max_issues_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "preprocess/Buenrostro2018/scripts/buenrostro2018_bmat_full.r", "max_forks_repo_name": "mrcuizhe/svmATAC", "max_forks_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.75, "max_line_length": 109, "alphanum_fraction": 0.7929389313, "num_tokens": 363, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032313, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3198971237731392}} {"text": "data(\"mtcars\")\nlibrary(dplyr)\narrange(mtcars,mtcars$mpg+mtcars$carb+mtcars$cyl)\n", "meta": {"hexsha": "c3518330faf660f16c6e410635aaad6ae133bb91", "size": 80, "ext": "r", "lang": "R", "max_stars_repo_path": "R Community/R functions Assignments/Assignment6.r", "max_stars_repo_name": "vaibhavkrishna-bhosle/Trendnxt-Projects", "max_stars_repo_head_hexsha": "6c8a31be2f05ec79cfc5086ee09adff161b836ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R Community/R functions Assignments/Assignment6.r", "max_issues_repo_name": "vaibhavkrishna-bhosle/Trendnxt-Projects", "max_issues_repo_head_hexsha": "6c8a31be2f05ec79cfc5086ee09adff161b836ad", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R Community/R functions Assignments/Assignment6.r", "max_forks_repo_name": "vaibhavkrishna-bhosle/Trendnxt-Projects", "max_forks_repo_head_hexsha": "6c8a31be2f05ec79cfc5086ee09adff161b836ad", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.0, "max_line_length": 49, "alphanum_fraction": 0.7875, "num_tokens": 33, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31989712377313917}} {"text": "context(\"Mentor matching\")\n\n# ------------------------------------------------------------------------------\ntest_that(\"Mentor matching\", {\n set.seed(1)\n\n g <- igraph::make_graph(~1--2,1--3,1--4,4--5,6)\n g <- igraph::as_adj(g)\n\n ans <- mentor_matching(g, 2)\n expect_equal(sort(unique(ans$match)), as.character(c(1,4)))\n})\n\n\n# ------------------------------------------------------------------------------\ntest_that(\"Plot mentor\", {\n set.seed(1)\n\n g <- igraph::make_graph(~1--2,1--3,1--4,4--5,6)\n g <- igraph::as_adj(g)\n ans <- mentor_matching(g, 2)\n\n expect_silent(plot(ans))\n})\n", "meta": {"hexsha": "2d7d4c5dead0114128706baf21d83476986ae4ab", "size": 589, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-mentor.r", "max_stars_repo_name": "USCCANA/netdiffuseR", "max_stars_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2015-12-15T02:49:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T02:48:37.000Z", "max_issues_repo_path": "tests/testthat/test-mentor.r", "max_issues_repo_name": "USCCANA/netdiffuseR", "max_issues_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2015-12-17T03:43:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T18:50:22.000Z", "max_forks_repo_path": "tests/testthat/test-mentor.r", "max_forks_repo_name": "USCCANA/netdiffuseR", "max_forks_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-12-28T21:47:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T19:48:08.000Z", "avg_line_length": 23.56, "max_line_length": 80, "alphanum_fraction": 0.4465195246, "num_tokens": 161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.31989712377313917}} {"text": "#' @useDynLib coop R_co_sparse\nco_sparse <- function(n, a, i, j, index, type, use, inverse)\n{\n check.is.flag(inverse)\n \n if (!is.double(a))\n storage.mode(a) <- \"double\"\n if (!is.integer(i))\n storage.mode(i) <- \"integer\"\n if (!is.integer(j))\n storage.mode(j) <- \"integer\"\n \n use <- check_use(use)\n if (use == \"everything\")\n {}\n else if (use == \"all.obs\")\n {\n if (anyNA(a))\n stop(\"missing observations in covar/pcor/cosine\")\n }\n ### TODO\n # else if (use == \"complete.obs\")\n # {\n # if (anyNA(x))\n # {\n # out <- naomit_coo(a, i, j)\n # a <- out[[1]]\n # i <- out[[2]]\n # j <- out[[3]]\n # }\n # }\n else\n stop(\"unsupported 'use' method\")\n \n .Call(R_co_sparse, as.integer(n), a, i, j, as.integer(index), as.integer(type), as.integer(inverse))\n}\n\n\n\n#' @useDynLib coop R_csc_to_coo\ncsc_to_coo <- function(row_ind, col_ptr)\n{\n .Call(R_csc_to_coo, row_ind, col_ptr)\n}\n", "meta": {"hexsha": "28925178f22182a59077902cc92cf8988de91fef", "size": 923, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wrappers_sparse.r", "max_stars_repo_name": "wrathematics/fastco", "max_stars_repo_head_hexsha": "3a0d91311fc172fda52f4f82a10aaf1691a9460e", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 24, "max_stars_repo_stars_event_min_datetime": "2016-03-19T15:43:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T01:37:49.000Z", "max_issues_repo_path": "R/wrappers_sparse.r", "max_issues_repo_name": "wrathematics/fastco", "max_issues_repo_head_hexsha": "3a0d91311fc172fda52f4f82a10aaf1691a9460e", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 15, "max_issues_repo_issues_event_min_datetime": "2016-03-19T16:03:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-24T03:52:44.000Z", "max_forks_repo_path": "R/wrappers_sparse.r", "max_forks_repo_name": "wrathematics/fastco", "max_forks_repo_head_hexsha": "3a0d91311fc172fda52f4f82a10aaf1691a9460e", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2016-07-12T18:15:42.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-20T07:41:53.000Z", "avg_line_length": 20.5111111111, "max_line_length": 102, "alphanum_fraction": 0.5579631636, "num_tokens": 309, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31989711586211583}} {"text": "require('ggplot2')\nrequire('dplyr')\n\ncsv_path <- '/home/sage/play/ores-changes/spring_2017-articles-2017-09-14.csv'\n# csv_path <- '/home/sage/play/ores-changes/visiting_scholars-articles-2017-09-15.csv'\ncampaign_data <- read.csv(csv_path)\ncampaign_data$ores_diff <- with(campaign_data, ores_after - ores_before)\n\n# All articles\nbefore <- select(campaign_data, select=('ores_before'))\nafter <- select(campaign_data, select=('ores_after'))\nnames(before)[1] = 'structural_completeness'\nnames(after)[1] = 'structural_completeness'\nbefore$when <- 'before'\nafter$when <- 'after'\nbefore_after_histogram <- rbind(before, after)\n\npng(filename=\"before-after.png\", width = 600, height = 400)\nggplot(before_after_histogram, aes(structural_completeness, fill=when)) + geom_density(alpha = 0.4, adjust = 1/2)\ndev.off()\n\n# major edits + diff\ndiff <- select(campaign_data, select=('ores_diff'))\n\nmajor_edits <- campaign_data[campaign_data$bytes_added >= 6000, ]\n# major_edits_existing <- major_edits\nmajor_edits_existing <- major_edits[major_edits$ores_before > 0.0, ]\nmajor_diff <- select(major_edits, select=('ores_diff'))\n\nmajor_edits_existing_before <- select(major_edits_existing, select=('ores_before'))\nmajor_edits_existing_after <- select(major_edits_existing, select=('ores_after'))\n\nnames(diff)[1] = 'change_in_structural_completeness'\nnames(major_diff)[1] = 'change_in_structural_completeness'\nnames(major_edits_existing_before)[1] = 'structural_completeness'\nnames(major_edits_existing_after)[1] = 'structural_completeness'\n\nmajor_edits_existing_before$when <- 'before'\nmajor_edits_existing_after$when <- 'after'\n\nmajor_edits_existing_histogram <- rbind(major_edits_existing_before, major_edits_existing_after)\npng(filename=\"before-after-major-edits.png\", width = 600, height = 400)\nggplot(major_edits_existing_histogram, aes(structural_completeness, fill=when)) + geom_density(alpha = 0.4, adjust = 1/2)\ndev.off()\n\n# ggplot(diff, aes(change_in_structural_completeness)) + geom_density(alpha = 0.4, adjust = 1/2)\npng(filename=\"major_articles_improvement.png\", width = 600, height = 400)\nggplot(major_diff, aes(change_in_structural_completeness)) + geom_density(alpha = 0.4, adjust = 1/2)\ndev.off()\n\n# New articles\nnew_articles <- campaign_data[campaign_data$ores_before == 0.0, ]\nnew_articles <- new_articles[new_articles$bytes_added >= 2000, ]\nnew_articles_after <- select(new_articles, select=('ores_after'))\nnames(new_articles_after)[1] = 'structural_completeness'\nnew_articles_after$when <- 'after'\n\npng(filename=\"new-articles.png\", width = 600, height = 400)\nggplot(new_articles_after, aes(structural_completeness, fill=when)) + geom_density(alpha = 0.4, adjust = 1/2)\ndev.off()\n\n# Existing articles\nexisting_articles <- campaign_data[campaign_data$ores_before > 0.0, ]\nexisting_articles_before <- select(existing_articles, select=('ores_before'))\nexisting_articles_after <- select(existing_articles, select=('ores_after'))\n\nnames(existing_articles_before)[1] = 'structural_completeness'\nnames(existing_articles_after)[1] = 'structural_completeness'\nexisting_articles_before$when = 'before'\nexisting_articles_after$when = 'after'\n\nexisting_articles_histogram <- rbind(existing_articles_before, existing_articles_after)\npng(filename=\"existing-before-after.png\", width = 600, height = 400)\nggplot(before_after_histogram, aes(structural_completeness, fill=when)) + geom_density(alpha = 0.4, adjust = 1/2)\ndev.off()\n", "meta": {"hexsha": "544d81e54d476490e08b9d3d3f6522db822a6887", "size": 3413, "ext": "r", "lang": "R", "max_stars_repo_path": "docs/analytics_scripts/ores_changes.r", "max_stars_repo_name": "unnatii/WikiEduDashboard", "max_stars_repo_head_hexsha": "e939521cedf25be3585cdfde8d753f6698da6240", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "docs/analytics_scripts/ores_changes.r", "max_issues_repo_name": "unnatii/WikiEduDashboard", "max_issues_repo_head_hexsha": "e939521cedf25be3585cdfde8d753f6698da6240", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "docs/analytics_scripts/ores_changes.r", "max_forks_repo_name": "unnatii/WikiEduDashboard", "max_forks_repo_head_hexsha": "e939521cedf25be3585cdfde8d753f6698da6240", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9078947368, "max_line_length": 121, "alphanum_fraction": 0.7843539408, "num_tokens": 890, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.585101139733739, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3198971158621158}} {"text": "\nrecode.time.maturity = function( x, time.resolution=\"daily\" ) {\n if (time.resolution == \"daily\" ) {\n zz = 1\n } else if( time.resolution == \"weekly\" ) {\n zz = 52/365\n } else if( time.resolution == \"monthly\" ) {\n zz = 12/365\n }\n x = round( x * zz )\n return(x)\n}\n\n\n\n", "meta": {"hexsha": "80937cb12a5baffaf5dc5acfe0832240e1da27b7", "size": 279, "ext": "r", "lang": "R", "max_stars_repo_path": "R/recode.time.maturity.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/recode.time.maturity.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/recode.time.maturity.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 17.4375, "max_line_length": 63, "alphanum_fraction": 0.5483870968, "num_tokens": 93, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31980702451896953}} {"text": "\n###############\n\nlibrary(data.table)\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(dtplyr)\nlibrary(magrittr)\n\n#############\n\nif(! file.exists(dirname(ofile))) dir.create(dirname(ofile))\n\n\n#################\n# Load data\nload(path_rdata)\n\n########################\n\nto_string = dtpval[, sprintf(\"%s\\np=%.3e\", group, pval.raw_new)]\nnames(to_string) = dtpval[, group]\n\n# Color by p-value\ndt = merge(dt, dtpval[, .(group, isRs)], by = \"group\")\n\n## Reorder by p-value\n# neworder = dtpval %>% arrange(pval.raw_new) %>% select(group) %>% unlist\n# dt[, group := factor(group, levels = neworder)]\n\ntitle = dtpval$id[1]\n\n# labeller = dtpval[, sprintf(\"%s (p=%.3e)\", group, pval.raw)]\n# names(labeller) = dtpval[,group]\n# labeller = as_labeller(labeller)\n\nxmin = dt[, min(bp)]\n\npdfname = ofile\npdf(pdfname, paper=\"a4\", height = 11, width = 8)\n\nymax = dt[, max(depth)]\n\ng = ggplot(dt, aes(x = bp, y = depth))\ng = g + geom_histogram(stat=\"identity\", aes(fill = isRs), width=dt$bp[2]-dt$bp[1])\ng = g + scale_fill_manual(values = c(`TRUE` = \"red\", `FALSE`=\"grey\"))\ng = g + geom_line(aes(x = bp, y = y.rss.pred), size = 0.2)\ng = g + facet_wrap(~group, ncol = 6, strip.position = \"left\", labeller=as_labeller(to_string))\n# g = g + geom_text(data = dtpval, aes(label = sprintf(\"p=%.3e\", pval.raw)), x = xmin, y = ymax, hjust=0, vjust=1)\ng = g + ggtitle(title)\ng = g + scale_y_continuous(position = \"right\")\ng = g + theme_bw()\n# g = g + theme(aspect.ratio = 1/10)\ng = g + theme(legend.position = \"none\")\ng = g + theme(panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_blank(),\n panel.border = element_rect(colour=\"grey\")\n)\ng = g + theme(strip.background = element_blank())\ng = g + theme(strip.text = element_text(size = 2))\ng = g + theme(strip.text.y = element_text(angle = 180))\ng = g + theme(axis.text.x = element_text(size = 2))\ng = g + theme(axis.text.y = element_text(size = 2))\ng = g + theme(panel.spacing.y = unit(0, \"lines\"))\ng = g + theme(axis.ticks = element_line(size = 0.1))\ng = g + theme(axis.ticks.length = unit(0.1, \"lines\"))\ng = g + theme(aspect.ratio = 1/2)\n\nplot(g)\n\n\ng = g + facet_wrap(~group, ncol = 6, strip.position = \"left\", labeller=as_labeller(to_string), scales = \"free_y\")\nplot(g)\n# ymax = dt[, max(depth.sm)]\n\n# g = ggplot(dt, aes(x = bp, y = depth.sm))\n# g = g + geom_histogram(stat=\"identity\", aes(fill = group), width=dt$bp[2]-dt$bp[1])\n# g = g + scale_fill_manual(breaks = groups, values = color_labels)\n# g = g + geom_line(aes(x = bp, y = y.sm.rss.pred))\n# g = g + facet_wrap(~group, ncol = 10, strip.position = \"left\")\n# g = g + geom_text(data = dtpval, aes(label = sprintf(\"p=%.3e\", pval.sm)), x = xmin, y = ymax, hjust=0, vjust=1)\n# g = g + ggtitle(title)\n# g = g + scale_y_continuous(position = \"right\")\n# g = g + theme_bw()\n# # g = g + theme(aspect.ratio = 1/10)\n# g = g + theme(legend.position = \"none\")\n# g = g + theme(panel.grid.major = element_blank(),\n# panel.grid.minor = element_blank(),\n# panel.background = element_blank()\n# )\n\n# print(g)\n\n\ndev.off()\n\n\n", "meta": {"hexsha": "ae438d8e58994d99e4d9339a2bbe9a848fd90362", "size": 3094, "ext": "r", "lang": "R", "max_stars_repo_path": "recursive_splicing/02_replot_fitting/base.r", "max_stars_repo_name": "yuifu/Hayashi2018", "max_stars_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2018-02-14T15:41:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-01T03:05:11.000Z", "max_issues_repo_path": "recursive_splicing/02_replot_fitting/base.r", "max_issues_repo_name": "yuifu/Hayashi2018", "max_issues_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-01-26T05:13:23.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-21T02:39:44.000Z", "max_forks_repo_path": "recursive_splicing/02_replot_fitting/base.r", "max_forks_repo_name": "yuifu/Hayashi2018", "max_forks_repo_head_hexsha": "11456678e6536aac72e35c48564baaa240f344a5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2525252525, "max_line_length": 114, "alphanum_fraction": 0.6085972851, "num_tokens": 974, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31980702451896953}} {"text": "##########################################################################\n# This script plots evolutionary trajectories over time. It will produce #\n# two plots: mean fitness over replicate populations over time, and mean #\n# epistatis over replicate populations over time. #\n# #\n# Author: Dariya K. Sydykova #\n##########################################################################\n\n# load packages necessary to run the following script\nlibrary(tidyverse)\nlibrary(glue)\nlibrary(cowplot)\nlibrary(readr)\nlibrary(here)\n\n# supress scientific notation in R to make plots\noptions(scipen = 999)\n\n# set up input file. Output plot files are generated automatically and are stored in /evolving_q_sim/test_plots\nroot_dir <- here() # get the directory of the root of the project\nbase_name <- \"mu_prob0.01_s0.01\" # set base name to match input and output files\ninfile <- paste(root_dir, \"/evolving_q_sim/processed_results/evolved_q_\", base_name, \".csv\", sep = \"\") # data frame that contains simulation results for the plot\n\n# read in simulation output for evolving q\nt <- read_csv(\n infile,\n col_types = cols(\n time = col_integer(),\n sel_coef = col_double(),\n mu_prob = col_double(),\n Ne = col_integer(),\n L = col_integer(),\n k_start = col_integer(),\n q_start = col_double(),\n q_prob = col_double(),\n q_step = col_double(),\n mean_fitness = col_double(),\n mean_q = col_double(),\n rep = col_character(),\n q_prob_label = col_character()\n )\n)\n\n# extract output for less time points to reduce the data frame\ntimes_wanted <- seq(0, max(t$time), 100000)\nt %>% filter(time %in% times_wanted) -> t_filtered\n\n# # make `rep` (number of replicates) and `q_start` (starting q) into a factor\n# # this allows for grouping on two variables possible later\n# t_final$rep <- factor(t_final$rep)\n# t_final$q_start <- factor(t_final$q_start)\n\n# calculate mean fitness per replicates\nt_filtered %>%\n group_by(q_start, q_prob_label, q_step, time) %>%\n summarise(\n mean_rep_fitness = mean(mean_fitness),\n mean_rep_q = mean(mean_q)\n ) ->\nt_summary\n\n##########################################################################\n# Plotting fitness over time\n##########################################################################\n\n# plot fitness over time for different q mutation rate and delta q\np_fitness1 <-\n t_summary %>%\n filter(q_prob_label == \"0.0001\") %>%\n ggplot(aes(x = time, y = mean_rep_fitness, group = q_start)) +\n geom_line(aes(color = factor(q_start))) +\n scale_y_continuous(\n name = \"mean fitness\",\n limits = c(0, 1.00),\n breaks = seq(0, 1, 0.2)\n ) +\n scale_x_continuous(\n name = \"time (millions)\",\n limits = c(0, 100200000),\n breaks = seq(0, 100200000, 25000000),\n labels = c(\"0\", \"25\", \"50\", \"75\", \"100\")\n ) +\n background_grid(major = \"xy\", minor = \"ny\") +\n draw_text(\n x = 0,\n y = 0,\n hjust = 0,\n vjust = 0,\n text = \"pr(q) = 0.0001, dq = 0.001\",\n size = 12,\n fontface = \"bold\"\n ) +\n scale_color_manual(\n values = c(\n \"#FDE333\",\n \"#C6E149\",\n \"#88D867\",\n \"#38C980\",\n \"#00B691\",\n \"#009F99\",\n \"#008599\",\n \"#00698F\",\n \"#324C7F\",\n \"#432D68\",\n \"#46024E\"\n )\n )\n\np_fitness2 <- t_summary %>%\n filter(q_prob_label == \"0.001\") %>%\n ggplot(aes(x = time, y = mean_rep_fitness, group = q_start)) +\n geom_line(aes(color = factor(q_start))) +\n scale_y_continuous(\n name = \"mean fitness\",\n limits = c(0, 1.00),\n breaks = seq(0, 1, 0.2)\n ) +\n scale_x_continuous(\n name = \"time (millions)\",\n limits = c(0, 100200000),\n breaks = seq(0, 100200000, 25000000),\n labels = c(\"0\", \"25\", \"50\", \"75\", \"100\")\n ) +\n background_grid(major = \"xy\", minor = \"ny\") +\n geom_text(\n x = 0,\n y = 0,\n hjust = 0,\n vjust = 0,\n label = \"mu[q] = 0.001 Delta q = 0.001\",\n size = 12,\n fontface = \"bold\",\n parse = T\n ) +\n scale_color_manual(values = c(\"#FDE333\", \"#C6E149\", \"#88D867\", \"#38C980\", \"#00B691\", \"#009F99\", \"#008599\", \"#00698F\", \"#324C7F\", \"#432D68\", \"#46024E\"))\n\np_fitness3 <-\n t_summary %>%\n filter(q_prob_label == \"0.01\") %>%\n ggplot(aes(x = time, y = mean_rep_fitness, group = q_start)) +\n geom_line(aes(color = factor(q_start))) +\n scale_y_continuous(\n name = \"mean fitness\",\n limits = c(0, 1.00),\n breaks = seq(0, 1, 0.2)\n ) +\n scale_x_continuous(\n name = \"time (millions)\",\n limits = c(0, 100200000),\n breaks = seq(0, 100200000, 25000000),\n labels = c(\"0\", \"25\", \"50\", \"75\", \"100\")\n ) +\n background_grid(major = \"xy\", minor = \"ny\") +\n draw_text(x = 0, y = 0, hjust = 0, vjust = 0, text = \"pr(q) = 0.01, dq = 0.0001\", size = 12, fontface = \"bold\") +\n scale_color_manual(values = c(\"#FDE333\", \"#C6E149\", \"#88D867\", \"#38C980\", \"#00B691\", \"#009F99\", \"#008599\", \"#00698F\", \"#324C7F\", \"#432D68\", \"#46024E\"))\n\n# arrange subfigures\np_fitness <-\n plot_grid(\n p_fitness1 +\n theme(legend.position = \"none\"),\n p_fitness2 +\n theme(axis.title.y = element_blank(), legend.position = \"none\"),\n p_fitness3 +\n theme(axis.title.y = element_blank(), legend.position = \"none\"),\n align = \"vh\",\n hjust = -1,\n ncol = 3,\n nrow = 1\n )\n\n# save the plot\nsave_plot(paste0(root_dir, \"/evolving_q_sim/plots/fitness_v_time_\", base_name, \".png\", sep = \"\"),\n p_fitness,\n base_height = 3,\n base_width = 10\n)\n\n##########################################################################\n# Plotting epistasis over time\n##########################################################################\nt_summary$q_prob_label <- factor(t_summary$q_prob_label,\n labels = c(\n \"list(mu[q]==0.0001, Delta*q==0.001)\",\n \"list(mu[q]==0.001, Delta*q==0.001)\",\n \"list(mu[q]==0.01, Delta*q==0.0001)\"\n )\n)\n\np_epistasis <- t_summary %>%\n ggplot(aes(x = time, y = mean_rep_q, group = q_start)) +\n geom_line(aes(color = factor(q_start))) +\n scale_y_continuous(\n name = \"mean epistasis\",\n limits = c(0, 2.1),\n breaks = seq(0, 2.0, 0.5)\n ) +\n scale_x_continuous(\n name = \"time (millions)\",\n limits = c(0, 100200000),\n breaks = seq(0, 100200000, 25000000),\n labels = c(\"0\", \"25\", \"50\", \"75\", \"100\")\n ) +\n background_grid(major = \"xy\", minor = \"y\") +\n facet_grid(~q_prob_label, labeller = label_parsed) +\n scale_color_manual(values = c(\"#FDE333\", \"#C6E149\", \"#88D867\", \"#38C980\", \"#00B691\", \"#009F99\", \"#008599\", \"#00698F\", \"#324C7F\", \"#432D68\", \"#46024E\")) +\n theme(legend.position = \"none\")\n\n# save the plot\nsave_plot(paste(root_dir, \"/evolving_q_sim/plots/epistasis_v_time_\", base_name, \".png\", sep = \"\"),\n p_epistasis,\n base_height = 4,\n base_width = 12\n)\n\n##########################################################################\n# Plotting epistasis over time (2 trajectories)\n##########################################################################\n\n# create a data frame with labels for the plot\nlabels_df <- data.frame(\n q_start = c(1.4, 1.6, 1.8),\n q_start_label = c(\n \"q[t==0]==1.4\",\n \"q[t==0]==1.6\",\n \"q[t==0]==1.8\"\n )\n)\n\n# filter for all trajectories that start at 1.8, 1.6, or 1.4\nt_filtered %>%\n filter(\n q_prob_label == \"0.001\",\n q_start == 1.8 | q_start == 1.6 | q_start == 1.4\n ) %>% \n left_join(labels_df) -> t_subset1\n\n# plot epistasis over time for different q mutation rate and delta q\nt_summary %>%\n filter(\n q_prob_label == \"list(mu[q]==0.001, Delta*q==0.001)\",\n q_start == 1.8 | q_start == 1.6 | q_start == 1.4\n ) %>%\n left_join(labels_df) -> t_subset2\n\np_epistasis1 <- ggplot() +\n geom_line(data = t_subset1, aes(x = time, y = mean_q, group = rep), color = \"#7d7d7d\", size = 0.3) +\n geom_line(data = t_subset2, aes(x = time, y = mean_rep_q, group = q_start, color = factor(q_start)), size = 1.2) +\n scale_y_continuous(\n name = \"epistasis\",\n limits = c(0, 2.6),\n breaks = seq(0, 2.5, 0.5)\n ) +\n scale_x_continuous(\n name = \"time (millions)\",\n limits = c(0, 100200000),\n breaks = seq(0, 100200000, 25000000),\n labels = c(\"0\", \"25\", \"50\", \"75\", \"100\")\n ) +\n background_grid(major = \"xy\", minor = \"y\") +\n facet_grid(~q_start_label, labeller = label_parsed) +\n scale_color_manual(values = c(\"#00698F\", \"#324C7F\", \"#432D68\")) +\n theme(legend.position = \"none\")\n\n# save the plot\nsave_plot(paste(root_dir, \"/evolving_q_sim/plots/epistasis_v_time_\", base_name, \"_subset.png\", sep = \"\"),\n p_epistasis1,\n base_height = 4,\n base_width = 12\n)", "meta": {"hexsha": "7f5b4f4956f475ae9e80d6408418364cc6f9a321", "size": 8491, "ext": "r", "lang": "R", "max_stars_repo_path": "evolving_q_sim/src/plot_q_trajectories.r", "max_stars_repo_name": "clauswilke/epistasis_evolution", "max_stars_repo_head_hexsha": "25ef74bb772d98ec14a9fcb367be85c56ca308f1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "evolving_q_sim/src/plot_q_trajectories.r", "max_issues_repo_name": "clauswilke/epistasis_evolution", "max_issues_repo_head_hexsha": "25ef74bb772d98ec14a9fcb367be85c56ca308f1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "evolving_q_sim/src/plot_q_trajectories.r", "max_forks_repo_name": "clauswilke/epistasis_evolution", "max_forks_repo_head_hexsha": "25ef74bb772d98ec14a9fcb367be85c56ca308f1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4481481481, "max_line_length": 161, "alphanum_fraction": 0.5656577553, "num_tokens": 2584, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31980701746173923}} {"text": "#' Create a customized summary table\n#'\n#' @param data A data frame\n#' @param y categorical variable name in the data.\n#'\n#' @returnA categorical variable name (quoted or unquoted) in data.\n#' Summary statistics will be calculated separately for each level of the variable\n#' @export\n#'\n#' @examples create_journaltable(data =infert, y = 'case')\ncreate_journaltable <- function(data, y) {\n data %>%\n gtsummary::tbl_summary(\n by = all_of(y),\n type = where(is.character) ~ \"categorical\") %>%\n gtsummary::add_p(test = where(is.numeric) ~ \"t.test\") %>%\n gtsummary::modify_header(update = list(\n label ~ \"**Variable**\"\n ))\n}\n\n\n", "meta": {"hexsha": "22031394f4b0c5b725946087fa00fb6d3c16748e", "size": 652, "ext": "r", "lang": "R", "max_stars_repo_path": "R/create_journaltable_function.r", "max_stars_repo_name": "RotanaRad/rotsurpackage", "max_stars_repo_head_hexsha": "71049c1dea3f003f9c80058819fbced8d8226793", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/create_journaltable_function.r", "max_issues_repo_name": "RotanaRad/rotsurpackage", "max_issues_repo_head_hexsha": "71049c1dea3f003f9c80058819fbced8d8226793", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/create_journaltable_function.r", "max_forks_repo_name": "RotanaRad/rotsurpackage", "max_forks_repo_head_hexsha": "71049c1dea3f003f9c80058819fbced8d8226793", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.347826087, "max_line_length": 83, "alphanum_fraction": 0.6625766871, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6297746074044134, "lm_q1q2_score": 0.31980701746173923}} {"text": "#!/apps/well/R/3.4.3/bin/Rscript\n#$ -cwd\n#$ -q short.qc\n#$ -o ./loglmer/\n#$ -e ./loglmer/\n\nlibrary(MASS)\nlibrary(Matrix)\nlibrary(lme4)\nlibrary(tictoc)\n\n# ---------------------------------------------------------------------------------------\n# IMPORTANT: Input options\n# ---------------------------------------------------------------------------------------\n#\n# The below variables control which simulation is run and how. The variable names match\n# those used in the `LMMPaperSim.py` file and are given as follows:\n#\n# - OutDir: The output directory.\n# - desInd: Integer value between 1 and 3 representing which design to run. The \n# designs are as follows:\n# - Design 1: nlevels=[50], nraneffs=[2]\n# - Design 2: nlevels=[50,10], nraneffs=[3,2]\n# - Design 3: nlevels=[100,50,10], nraneffs=[4,3,2]\n# - nsim: Number of simulations (default=1000)\n# - mode: String indicating whether to run parameter estimation simulations (mode=\n# 'param') or T statistic simulations (mode='Tstat').\n# - reml: Boolean indicating whether to use ML or ReML estimation. \n#\n# ---------------------------------------------------------------------------------------\n# For parameter simulations example use this directory\noutDir <- paste(dirname(rstudioapi::getSourceEditorContext()$path), '..', '..', 'data', 'ParamSimulation', sep=.Platform$file.sep)\n# For T simulations example use this directory\n#outDir <- paste(dirname(rstudioapi::getSourceEditorContext()$path), '..', '..', 'data', 'TstatSimulation', sep=.Platform$file.sep)\ndesInd <- 2\nnsim <- 3\nmode <- 'param'\nreml <- TRUE\n# ---------------------------------------------------------------------------------------\n\n# If we are timing code, don't import lmerTest since it can reduces the performance of lmer\nif (mode=='param'){\n timing <- TRUE\n} else {\n timing <- FALSE \n}\nif (!timing){\n library(lmerTest)\n}\n\n# Loop through the simulations running lmer\nfor (simInd in 1:nsim){\n if (desInd==3){\n \n # Read in the results file\n results <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_results.csv',sep=''))\n \n # Read in the fixed effects design\n X <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_X.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the response vector\n Y <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Y.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the first random factor in the design\n Zfactor0 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zfactor0.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the first random factor in the design\n Zdata0 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zdata0.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the second random factor in the design\n Zfactor1 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zfactor1.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the second random factor in the design\n Zdata1 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zdata1.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the third random factor in the design\n Zfactor2 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zfactor2.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the third random factor in the design\n Zdata2 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design3_Zdata2.csv',sep=''),sep=' ', header=FALSE)\n\n # Reformat Y\n y <- as.matrix(Y[,1])\n \n # Reformat X into columns\n x2 <- as.matrix(X[,2])\n x3 <- as.matrix(X[,3])\n x4 <- as.matrix(X[,4])\n x5 <- as.matrix(X[,5])\n \n # Reformat the raw regressor matrix for the first random factor into columns\n z01 <- as.matrix(Zdata0[,1])\n z02 <- as.matrix(Zdata0[,2])\n z03 <- as.matrix(Zdata0[,3])\n z04 <- as.matrix(Zdata0[,4])\n \n # Reformat the raw regressor matrix for the second random factor into columns\n z11 <- as.matrix(Zdata1[,1])\n z12 <- as.matrix(Zdata1[,2])\n z13 <- as.matrix(Zdata1[,3])\n \n # Reformat the raw regressor matrix for the third random factor into columns\n z21 <- as.matrix(Zdata2[,1])\n z22 <- as.matrix(Zdata2[,2])\n \n # Run the model\n m <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02 + z03 + z04|Zfactor0) + (0 + z11 + z12 + z13|Zfactor1) + (0 + z21 + z22|Zfactor2), REML=reml) #Don't need intercepts in R - automatically assumed\n \n # Get the function which is optimized\n devfun <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02 + z03 + z04|Zfactor0) + (0 + z11 + z12 + z13|Zfactor1) + (0 + z21 + z22|Zfactor2), REML=reml, devFunOnly = TRUE) #Don't need intercepts in R - automatically assumed\n \n # Time lmer from only the devfun point onwards (to ensure fair comparison)\n tic('lmer time')\n opt<-optimizeLmer(devfun)\n t<-toc()\n \n # Calculate time\n lmertime <- t$toc-t$tic\n \n # Record time\n results[1,'lmer']<-lmertime\n\n # Record closest thing we have to number of iterations\n results[2,'lmer'] <- opt$feval\n\n # Record log likelihood\n if (!reml){\n results[3,'lmer']<-logLik(m)[1]\n } else {\n results[3,'lmer']<-logLik(m)[1]\n }\n\n # Record fixed effects estimates\n results[4:8,'lmer'] <- fixef(m)\n\n # Record fixed effects variance estimate\n results[9,'lmer']<-as.data.frame(VarCorr(m))$vcov[20]\n \n # Recover D parameters\n Ds <- as.matrix(Matrix::bdiag(VarCorr(m)))\n \n # Calculate vech(D_0)*sigma2\n vechD0 <- Ds[1:4,1:4][lower.tri(Ds[1:4,1:4],diag = TRUE)]\n\n # Calculate vech(D_1)*sigma2\n vechD1 <- Ds[5:7,5:7][lower.tri(Ds[5:7,5:7],diag = TRUE)]\n\n # Calculate vech(D_2)*sigma2\n vechD2 <- Ds[8:9,8:9][lower.tri(Ds[8:9,8:9],diag = TRUE)]\n \n # Record vech(D_0)\n results[10:19,'lmer']<-vechD0/as.data.frame(VarCorr(m))$vcov[20]\n\n # Record vech(D_1)\n results[20:25,'lmer']<-vechD1/as.data.frame(VarCorr(m))$vcov[20]\n\n # Record vech(D_2)\n results[26:28,'lmer']<-vechD2/as.data.frame(VarCorr(m))$vcov[20]\n\n # Record vech(D_0)*sigma2\n results[29:38,'lmer']<-vechD0\n\n # Record vech(D_1)*sigma2\n results[39:44,'lmer']<-vechD1\n\n # Record vech(D_2)*sigma2\n results[45:47,'lmer']<-vechD2\n \n # If we're not using the simulations to test performance, output everything we can.\n if (!timing){\n\n # Run T statistic inference\n Tresults<-lmerTest::contest1D(m, c(0,0,0,0,1),ddf=c(\"Satterthwaite\"))\n\n # Get the P value\n p<-Tresults$`Pr(>|t|)`\n\n # Get the T statistic\n Tstat<-Tresults$`t value`\n\n # Get the degrees of freedom estimates\n df<-Tresults$df\n \n # Make p-values 1 sided\n if (Tstat>0){\n p <- p/2\n } else {\n p <- 1-p/2\n }\n \n # Record T statsitic\n results[48,'lmer']<-Tstat\n\n # Record P valye\n results[49,'lmer']<-p\n\n # Record degrees of freedom estimate\n results[50,'lmer']<-df\n\n }\n \n # Write results back to csv file\n write.csv(results,paste(outDir,'/Sim',toString(simInd),'_Design3_results.csv',sep=''), row.names = FALSE)\n \n \n } else if (desInd==2){\n \n # Read in the results file\n results <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_results.csv',sep=''))\n \n # Read in the fixed effects design\n X <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_X.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the response vector\n Y <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_Y.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the first random factor in the design\n Zfactor0 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_Zfactor0.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the first random factor in the design\n Zdata0 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_Zdata0.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the second random factor in the design\n Zfactor1 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_Zfactor1.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the second random factor in the design\n Zdata1 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design2_Zdata1.csv',sep=''),sep=' ', header=FALSE)\n \n # Reformat Y\n y <- as.matrix(Y[,1])\n \n # Reformat X into columns\n x2 <- as.matrix(X[,2])\n x3 <- as.matrix(X[,3])\n x4 <- as.matrix(X[,4])\n x5 <- as.matrix(X[,5])\n \n \n # Reformat the raw regressor matrix for the first random factor into columns\n z01 <- as.matrix(Zdata0[,1])\n z02 <- as.matrix(Zdata0[,2])\n z03 <- as.matrix(Zdata0[,3])\n \n # Reformat the raw regressor matrix for the second random factor into columns\n z11 <- as.matrix(Zdata1[,1])\n z12 <- as.matrix(Zdata1[,2])\n \n # Run the model\n m <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02 + z03|Zfactor0) + (0 + z11 + z12|Zfactor1), REML=reml) #Don't need intercepts in R - automatically assumed\n \n # Get the function which is optimized\n devfun <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02 + z03|Zfactor0) + (0 + z11 + z12|Zfactor1), REML=reml, devFunOnly = TRUE) #Don't need intercepts in R - automatically assumed\n \n # Time lmer from only the devfun point onwards (to ensure fair comparison)\n tic('lmer time')\n opt<-optimizeLmer(devfun)\n t<-toc()\n \n # Calculate time\n lmertime <- t$toc-t$tic\n \n # Record time\n results[1,'lmer']<-lmertime\n\n # Record closest thing we have to number of iterations\n results[2,'lmer'] <- opt$feval\n\n # Record log likelihood\n if (!reml){\n results[3,'lmer']<-logLik(m)[1]\n } else {\n results[3,'lmer']<-logLik(m)[1]\n }\n\n # Record fixed effects estimates\n results[4:8,'lmer'] <- fixef(m)\n\n # Record fixed effects variance estimate\n results[9,'lmer']<-as.data.frame(VarCorr(m))$vcov[10]\n \n # Recover D parameters\n Ds <- as.matrix(Matrix::bdiag(VarCorr(m)))\n \n # Calculate vech(D_0)*sigma2\n vechD0 <- Ds[1:3,1:3][lower.tri(Ds[1:3,1:3],diag = TRUE)]\n\n # Calculate vech(D_1)*sigma2\n vechD1 <- Ds[4:5,4:5][lower.tri(Ds[4:5,4:5],diag = TRUE)]\n \n # Record vech(D_0)\n results[10:15,'lmer']<-vechD0/as.data.frame(VarCorr(m))$vcov[10]\n\n # Record vech(D_1)\n results[16:18,'lmer']<-vechD1/as.data.frame(VarCorr(m))$vcov[10]\n \n # Record vech(D_0)*sigma2\n results[19:24,'lmer']<-vechD0\n\n # Record vech(D_1)*sigma2\n results[25:27,'lmer']<-vechD1\n \n # If we're not using the simulations to test performance, output everything we can.\n if (!timing){\n\n # Run T statistic inference\n Tresults<-lmerTest::contest1D(m, c(0,0,0,0,1),ddf=c(\"Satterthwaite\"))\n\n # Get the P value\n p<-Tresults$`Pr(>|t|)`\n\n # Get the T statistic\n Tstat<-Tresults$`t value`\n\n # Get the degrees of freedom estimates\n df<-Tresults$df\n \n # Make p-values 1 sided\n if (Tstat>0){\n p <- p/2\n } else {\n p <- 1-p/2\n }\n \n # Record T statsitic\n results[28,'lmer']<-Tstat\n\n # Record P value\n results[29,'lmer']<-p\n\n # Record degrees of freedom estimate\n results[30,'lmer']<-df\n }\n \n # Write results back to csv file\n write.csv(results,paste(outDir,'/Sim',toString(simInd),'_Design2_results.csv',sep=''), row.names = FALSE)\n \n } else if (desInd==1){\n \n # Read in the results file\n results <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_results.csv',sep=''))\n \n # Read in the fixed effects design\n X <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_X.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the response vector\n Y <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_Y.csv',sep=''),sep=' ', header=FALSE)\n\n # Read in the factor vector for the first random factor in the design\n Zfactor0 <- factor(read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_Zfactor0.csv',sep=''), sep=' ', header=FALSE)[,1])\n\n # Read in the raw regressor for the first random factor in the design\n Zdata0 <- read.csv(file = paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_Zdata0.csv',sep=''),sep=' ', header=FALSE)\n \n # Reformat Y\n y <- as.matrix(Y[,1])\n \n # Reformat X into columns\n x2 <- as.matrix(X[,2])\n x3 <- as.matrix(X[,3])\n x4 <- as.matrix(X[,4])\n x5 <- as.matrix(X[,5])\n \n # Reformat the raw regressor matrix for the first random factor into columns\n z01 <- as.matrix(Zdata0[,1])\n z02 <- as.matrix(Zdata0[,2])\n \n # Run the model\n m <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02|Zfactor0), REML=reml) #Don't need intercepts in R - automatically assumed\n \n # Get the function which is optimized\n devfun <- lmer(y ~ x2 + x3 + x4 + x5 + (0 + z01 + z02|Zfactor0), REML=reml, devFunOnly = TRUE) #Don't need intercepts in R - automatically assumed\n \n # Time lmer from only the devfun point onwards (to ensure fair comparison)\n tic('lmer time')\n opt<-optimizeLmer(devfun)\n t<-toc()\n \n # Calculate time\n lmertime <- t$toc-t$tic\n \n # Record time\n results[1,'lmer']<-lmertime\n\n # Record closest thing we have to number of iterations\n results[2,'lmer'] <- opt$feval\n\n # Record log likelihood\n if (!reml){\n results[3,'lmer']<-logLik(m)[1]\n } else {\n results[3,'lmer']<-logLik(m)[1]\n }\n\n # Record fixed effects estimates\n results[4:8,'lmer'] <- fixef(m)\n\n # Record fixed effects variance estimate\n results[9,'lmer']<-as.data.frame(VarCorr(m))$vcov[4]\n \n # Recover D parameters\n Ds <- as.matrix(Matrix::bdiag(VarCorr(m)))\n \n # Calculate vech(D_0)*sigma2\n vechD0 <- Ds[1:2,1:2][lower.tri(Ds[1:2,1:2],diag = TRUE)]\n \n # Record vech(D_0)\n results[10:12,'lmer']<-vechD0/as.data.frame(VarCorr(m))$vcov[10]\n\n # Record vech(D_0)*sigma2\n results[13:15,'lmer']<-vechD0\n \n # If we're not using the simulations to test performance, output everything we can.\n if (!timing){\n\n # Run T statistic inference\n Tresults<-lmerTest::contest1D(m, c(0,0,0,0,1),ddf=c(\"Satterthwaite\"))\n\n # Get the P value\n p<-Tresults$`Pr(>|t|)`\n\n # Get the T statistic\n Tstat<-Tresults$`t value`\n\n # Get the degrees of freedom estimates\n df<-Tresults$df\n \n # Make p-values 1 sided\n if (Tstat>0){\n p <- p/2\n } else {\n p <- 1-p/2\n }\n \n # Record T statsitic\n results[16,'lmer']<-Tstat\n\n # Record P value\n results[17,'lmer']<-p\n\n # Record degrees of freedom estimate\n results[18,'lmer']<-df\n\n }\n \n # Write results back to csv file\n write.csv(results,paste(outDir,'/Sim',toString(simInd),'_Design',toString(desInd),'_results.csv',sep=''), row.names = FALSE)\n \n }\n}\n\n# Display results\nresults", "meta": {"hexsha": "b6cc0ba0a9113b91a74fbdf94617adf36e88bd10", "size": 15238, "ext": "r", "lang": "R", "max_stars_repo_path": "src/Simulations/LMMPaperSim.r", "max_stars_repo_name": "TomMaullin/LMMPaper", "max_stars_repo_head_hexsha": "0328e75d3bad123632982f3ad86e2209598ab38a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Simulations/LMMPaperSim.r", "max_issues_repo_name": "TomMaullin/LMMPaper", "max_issues_repo_head_hexsha": "0328e75d3bad123632982f3ad86e2209598ab38a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Simulations/LMMPaperSim.r", "max_forks_repo_name": "TomMaullin/LMMPaper", "max_forks_repo_head_hexsha": "0328e75d3bad123632982f3ad86e2209598ab38a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7123893805, "max_line_length": 222, "alphanum_fraction": 0.6038850243, "num_tokens": 4674, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297745935070808, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3198070104045088}} {"text": "#get depth and bulk density weighted NEON micronutrients to 30cm (to match Tedersoo/Bahram priors).\nrm(list=ls())\nlibrary(runjags)\nsource('paths.r')\nsource('NEFI_functions/convert_P.r')\nsource('NEFI_functions/convert_K.r')\n\n#set output path.----\noutput.path <- micronutrient_converted.path\n\n#load raw chemical and physical data.----\nchem <- readRDS(dp1.10008.00_output.path)\nphys <- readRDS(dp1.10047.00_output.path)\n\n#P conversion data sets for downstream.\noxalate_olsen_P.dat <- read.csv(oxalate_olsen_P.conv_data.path)\noxalate_olsen_P.dat <- sapply(oxalate_olsen_P.dat[,-1], as.numeric)\n olsen_AL_P.dat <- read.csv(olsen_AL_P.conv_data.path)\noxalate_olsen_P.dat <- oxalate_olsen_P.dat[,c('Oxalate','Olsen')]\n olsen_AL_P.dat <- olsen_AL_P.dat[,c('Olsen.PICP','P.AL_color')]\n\n\n#for converting K to ammonium lactate\nAA_AL_K.dat <- data.frame(AL <- c(179.57, 202.89, 210.26, 188.07, 197.05, 86.66, 197.98, 195.13, 184.98, 165.99),\n AA <- c(170, 183.09, 197.23, 177.28, 168.88, 83.14, 187.73, 182.01, 190.9, 166.94))\nAA_AL_K.dat <- read.csv('/fs/data3/caverill/NEFI_data/16S/pecan_gen/potassium_AL_AA.csv')\nAA_AL_K.dat <- AA_AL_K.dat[,'K.aa','K.al']\n\n#There are two duplicated \"unique\" horizons in physical table. deal with this.----\n#Generally, one duplicate has some BD data, other duplicate has other BD data.\n#For pair of duplicates, one BD sampling method is \"compliant cavity\" and the other is \"clod\".\n#most horizons sampled using one method or the other, most are clod. These are the only two with both.\n#Lets grab the clod duped observations.\nduped <- phys[duplicated(phys$horizonID),]$horizonID\nphys <- phys[!(phys$horizonID %in% duped & phys$bulkDensSampleType == 'compliant cavity'),]\n\n#merge together chemical and physical dataframes. Only retain horizons that have both physical and chemical data.----\ncol.drop <- colnames(chem)[colnames(chem) %in% colnames(phys)]\ncol.drop <- col.drop[!(col.drop %in% \"horizonID\")]\nchem <- chem[,!(colnames(chem) %in% col.drop)]\nd <- merge(chem, phys, by = 'horizonID')\n\n#They measure bulk density using many methods. Merge these together to a single consensus BD.----\n#first use the clod observations, \"bulkDensOvenDry\nd$bd <- d$bulkDensOvenDry\n#then use compliant cavity data.\nd$bd <- ifelse(is.na(d$bd),d$bulkDensDryWeight/d$bulkDensVolume, d$bd)\n#there are 47 horizons that just don't have data to compute BD. oh well.\n\n#subset to 30cm depth and elements of interest.----\n#We only conside to 5cm depth, because that's what Leho did.\nd <- d[!(d$bulkDensTopDepth >= 5),]\n#6 plots are still duplicated because first horizon doesn't extend to 5cm. This is fine.\n\n#Grab: bulk density, top/bottom depth, horizonID, elements of interest.\nd <- d[,c('horizonID','siteID','plotID','bd','bulkDensTopDepth','bulkDensBottomDepth','mgNh4d','caNh4d','kNh4d','pOxalate','MehlichIIITotP','OlsenPExtractable')]\n\n#Unit conversion.----\n#P is mg/kg (same as Tedersoo/Bahram)\n#others are centimoles / kg. We need to convert to mg/kg to match Tedersoo/Bahram.\n#Multiply centimoles by 100 to get moles. Multiply by MW to get grams. Divide by 1000 to get milligrams.\nmw.Ca <- 40.078\nmw.K <- 39.0983\nmw.Mg <- 24.305\nd$caNh4d <- ((d$caNh4d * 100) * mw.Ca) / 1000\nd$kNh4d <- ((d$kNh4d * 100) * mw.K ) / 1000 \nd$mgNh4d <- ((d$mgNh4d * 100) * mw.Mg) / 1000\n\n\n#Get weighted mean for observations with more than one horizon within 5cm.----\n#this affects 6 profiles.\nd$bulkDensBottomDepth <- ifelse(d$bulkDensBottomDepth > 5, 5, d$bulkDensBottomDepth)\nnutrients <- c('mgNh4d','caNh4d','kNh4d','pOxalate')\nplotID <- as.character(unique(d$plotID))\nsoil.5cm <- list()\nnut.output <- list()\nfor(i in 1:length(nutrients)){\n nut <- list()\n for(k in 1:length(plotID)){\n ag <- d[d$plotID == plotID[k],]\n ag$f.depth <- (ag$bulkDensBottomDepth - ag$bulkDensTopDepth) / 5\n nut[[k]] <- sum(ag[,nutrients[i]]*ag$f.depth, na.rm = T)\n }\n nut <- unlist(nut)\n nut.output[[i]] <- nut\n}\nnut <- do.call(cbind, nut.output)\ncolnames(nut) <- nutrients\n\n#drop new values into data object.\nd <- data.frame(nut,plotID)\nd$siteID <- substr(d$plotID, 1, 4)\n\n#if a micronutrient is zero, set it to the lowest non-zero observation in dataset. otherwise can't log10 transform.\nd$caNh4d <- ifelse(d$caNh4d == 0, min(d$caNh4d[d$caNh4d > 0]), d$caNh4d)\nd$mgNh4d <- ifelse(d$mgNh4d == 0, min(d$mgNh4d[d$mgNh4d > 0]), d$mgNh4d)\n\n#convert Phosphorus to estimate based on different extraction method to match prior.----\nP.flip <- convert_P(d$pOxalate, oxalate_olsen_P.dat, olsen_AL_P.dat, log10_flip = T)\nK.flip <- convert_K(d$kNh4d, AA_AL_K.dat, log10_flip = T)\n\n#log 10 transform ones already measured with the same methods (Ca, Mg).----\nCa <- log10(d$caNh4d)\nMg <- log10(d$mgNh4d)\n\n\n#Save micronutrients and their standard deviations for analysis. Make sure everything on log10 scale to match Tedersoo.----\noutput <- data.frame(d$plotID,d$siteID,P.flip$mean, K.flip$mean, Ca, Mg, P.flip$sd, K.flip$sd)\ncolnames(output) <- c('plotID','siteID','P','K','Ca','Mg','P_sd','K_sd')\nsaveRDS(output,output.path)\n", "meta": {"hexsha": "ea20b178ab8a8018542fee898db8b187e8346ae2", "size": 5032, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/data_construction/NEON_ITS/1._covariate_data_acquisition/soil_micronutrient_workup.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "ITS/data_construction/NEON_ITS/1._covariate_data_acquisition/soil_micronutrient_workup.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ITS/data_construction/NEON_ITS/1._covariate_data_acquisition/soil_micronutrient_workup.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 45.3333333333, "max_line_length": 161, "alphanum_fraction": 0.7104531002, "num_tokens": 1688, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947425132315, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.3197742284357521}} {"text": "row.names(mi) = 1:I\ncolnames(mi) = 1:N\nd_long <- reshape2::melt(t(mi))\n\n### lout: list of output directory\n\nfor (out_dir in lout) {\nload(paste0(out_dir,fname))\ndll = t(mll)\ncolnames(dll) = 1:I\nrow.names(dll) = 1:N\nd_add <- reshape2::melt(dll)\nd_long = plyr::join(d_long, d_add, by = c(\"Var1\",\"Var2\"))\n}\n\nnames(d_long) = c(\"person\",\"item\",\"res\", \"LogLoss_np\", \"LogLoss_no\", \"LogLoss_pn\")\nd_long$LogLoss_diff_np = d_long$LogLoss_no - d_long$LogLoss_np\nd_long$LogLoss_diff_pn = d_long$LogLoss_no - d_long$LogLoss_pn\nd_long$res = factor(d_long$res, labels=c(\"incorrect\",\"correct\"))\n\nll_boxp_np_by <- ggplot(d_long, aes(x=item,y=LogLoss_diff_np,fill=factor(item))) + facet_wrap(~res) +\n geom_boxplot() + theme(legend.position = \"none\") + ggtitle(\"baseline - M1 log loss\")+\n ylab(\"Difference in LogLoss\")\n\nll_boxp_pn_by <- ggplot(d_long, aes(x=item,y=LogLoss_diff_pn,fill=factor(item))) + facet_wrap(~res) +\n geom_boxplot() + theme(legend.position = \"none\") + ggtitle(\"baseline - M2 log loss\") +\n ylab(\"Difference in LogLoss\")\n\nll_boxp_np <- ggplot(d_long, aes(x=item,y=LogLoss_diff_np,fill=factor(item))) +\n geom_boxplot() + theme(legend.position = \"none\") + ggtitle(\"baseline - M1 log loss\")+\n ylab(\"Difference in LogLoss\")\n\nll_boxp_pn <- ggplot(d_long, aes(x=item,y=LogLoss_diff_pn,fill=factor(item))) +\n geom_boxplot() + theme(legend.position = \"none\") + ggtitle(\"baseline - M2 log loss\") +\n ylab(\"Difference in LogLoss\")\n\npdf(pname)\nprint(ll_boxp_np_by)\nprint(ll_boxp_pn_by)\nprint(ll_boxp_np)\nprint(ll_boxp_pn)\ndev.off()\n", "meta": {"hexsha": "d98f66ece4a0d39b68198e08e724a22f4cc5bae4", "size": 1532, "ext": "r", "lang": "R", "max_stars_repo_path": "R/draw_logloss_diff_by.r", "max_stars_repo_name": "Jonghyun-Yun/LSA", "max_stars_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/draw_logloss_diff_by.r", "max_issues_repo_name": "Jonghyun-Yun/LSA", "max_issues_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/draw_logloss_diff_by.r", "max_forks_repo_name": "Jonghyun-Yun/LSA", "max_forks_repo_head_hexsha": "359934190e2f7e3852781f90e15d62575436618b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6279069767, "max_line_length": 101, "alphanum_fraction": 0.7082245431, "num_tokens": 505, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3196862947868583}} {"text": "#plotting variable importance: functional groups.\n#Clear environment, source paths, packages and functions.\nrm(list=ls())\nsource('paths.r')\nlibrary(ggplot2)\n\n#load data.\nd <- readRDS(NEON_dmulti.ddirch_var.importance_fg.path)\nmu <- d$mean\nlo_95 <- d$ci_0.025\nhi_95 <- d$ci_0.975\n\n#drop \"other\" group.----\nmu <- mu[,!(colnames(mu) %in% c('other'))]\nlo_95 <- lo_95[,!(colnames(lo_95) %in% c('other'))] \nhi_95 <- hi_95[,!(colnames(hi_95) %in% c('other'))]\n\n#Global plot settings.----\n#i = 2 #2=Ectomycorrhizal fungi.\npar(mfrow = c(2,2))\nouter.lab.cex <- 1.5\n\n#loop over different fungal groups.----\nfor(i in 2:ncol(d$mean)){\n #plot importance in decreasing order.\n mu <- d$mean[,i][order(d$mean[,i], decreasing = T)]\n hi95 <- d$ci_0.975[,i][order(match(names(d$ci_0.975[,i]), names(mu)))]\n lo95 <- d$ci_0.025[,i][order(match(names(d$ci_0.025[,i]), names(mu)))]\n limy <- c(0,max(mu) * 1.01)\n lab <- names(mu)\n fungi.name <- colnames(d$mean)[i]\n J<-barplot(height = mu,\n beside = true, las = 2,\n ylim = limy,\n cex.names = 0.75, xaxt = \"n\",\n main = paste0(fungi.name,' fungi'),\n border = \"black\", axes = TRUE)\n #add error bars.\n #segments(J, lo95, J, hi95, lwd = 1.5)\n #add variable labels.\n ypos <- -limy[2] / 10\n text(x = J, y = ypos, srt = 45,\n adj = 1, labels = lab, xpd = TRUE)\n}\n#Outer labels.----\nmtext('Variable Importance',side = 2, line = -1.5, cex = outer.lab.cex, outer = T)\n\n\n\n\n", "meta": {"hexsha": "e94fcfaa173606ba6b8a51bc56cc93311b7cd0db", "size": 1466, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/figure_scripts/dmulti-ddirch_forecast_validation_plots/NEON_fg_variable_importance.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ITS/figure_scripts/dmulti-ddirch_forecast_validation_plots/NEON_fg_variable_importance.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "ITS/figure_scripts/dmulti-ddirch_forecast_validation_plots/NEON_fg_variable_importance.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 28.7450980392, "max_line_length": 82, "alphanum_fraction": 0.5968622101, "num_tokens": 526, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3196862947868583}} {"text": " source(\"R/header.r\")\n\n\ntheme_blank <- list(theme(\n\t\t\tpanel.grid.minor = element_blank(),\n panel.grid.major = element_blank(),\n panel.background=element_blank(),\n# panel.background = element_rect(color = \"black\",fill=NA, size = .5, linetype = \"solid\"),\n #panel.border=element_rect(color = \"black\",fill=NA, size = 0.4, linetype = \"solid\"),\n plot.background = element_blank(),\n plot.background=element_blank(),\n \t\t\t\t\t\tpanel.border=element_blank(),\n # panel.border=element_rect(color = \"black\",fill=NA, size = .5, linetype = \"solid\"),\n legend.position=\"right\",\n panel.border = element_blank(),\n axis.line = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks = element_blank(),\n panel.margin = unit(5,\"lines\"),\n panel.margin = unit(0,\"null\"),\n plot.margin = rep(unit(0,\"null\"),4),\n axis.title.x = element_blank(),\n axis.title.y = element_blank(),\n plot.title = element_text()))\n\n\n\n\n country<-new(\"shapefile\",dir=\"data/admin_countries/110m/\",layer=\"ne_110m_admin_0_countries\",\n \t proj_to= to)\ng<-ggplot()\nw_h<-function(lim){\n\treturn(abs((lim[2]-lim[1])/(lim[3]-lim[4])))\n}\na<-readOGR(\"data/shape/110m_cultural/\",\"ne_110m_populated_places\")\na<-spTransform(a,CRS(to))\npp<-as.data.frame(cbind(a@coords,a$POP2010,a$MIN_AREAKM))\nd<-addPoly(country,gg=g,size=0.1,color=\"black\",fill=\"grey\")\nlines_proj<-graticules(20,10,to)\nlines<-lines2dataframe(lines_proj)\n#p<-gratText(lines_proj)\n\nnames(pp)<-c(\"lon\",\"lat\",\"pop\",\"area\")\n\ndd<-d+geom_point(data=pp,aes(lon,lat,size=pop,color=area),alpha=0.6)+\nscale_colour_gradient(low = \"blue\",high=\"red\")+\nscale_size_continuous(range=c(0,10))+theme_opts+coord_equal()+\nggtitle(bquote(\"World Metropolitan Population and Area in 2010\"))+\ngeom_path(data=lines,aes(lon,as.numeric(lat),group=group),color=\"black\",size=0.2)+\n#scale_x_continuous(expand = c(0, 0),breaks=as.vector(p$x[,1]),labels=bquote(.(as.vector(p$x$txt))))+\n#scale_y_continuous(expand = c(0, 0),breaks=as.vector(p$y[,1]), labels=p$y[,2])+\n\nguides(size=guide_legend(\"K Habitants\",title.position=\"top\",direction=\"vertical\",label.position=\"right\"),color=guide_colorbar(title=\"Area KM\u00b2\",direction=\"vertical\"))+\ntheme_blank\n\n\n\nsaveMap(\"point_pj.jpeg\",plot=dd,width=7,height=4,dpi=600)\n\n\n\n\nf<-getShpAttrib(country,slot=c(\"labpt\"),\"pop_est\")\nnames(f)<-c(\"lon\",\"lat\",\"pop\")\n", "meta": {"hexsha": "dfe4b4710ed665fce0370effa025afc8bc4e0c3f", "size": 2739, "ext": "r", "lang": "R", "max_stars_repo_path": "test/test_3.r", "max_stars_repo_name": "sinanshi/visotmed", "max_stars_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T02:17:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T10:32:36.000Z", "max_issues_repo_path": "test/test_3.r", "max_issues_repo_name": "sinanshi/visotmed", "max_issues_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_3.r", "max_forks_repo_name": "sinanshi/visotmed", "max_forks_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.5, "max_line_length": 166, "alphanum_fraction": 0.59437751, "num_tokens": 696, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3196862869532281}} {"text": "# Plot predictions\n# Requires data to be in the same format as spiral\n# - two variables and three classes\n\npng('plot.png')\n\ndata_orig = read.csv('spiral.csv', header=F)\ncolnames(data_orig) <- c('t1','t2','t3','d1','d2')\ndata = read.csv('test_output.csv', header=F)\ncolnames(data) <- c('t1','t2','t3','d1','d2')\n\ndata$result = apply(data[c('t1','t2','t3')],1,which.max)\ncolfn <- function(n){ ifelse(n==1,'red',ifelse(n==2,'green','blue')) }\nplot(data[c('d1','d2')], col=colfn(data['result']))\npoints(data_orig[,c('d1','d2')])\n\ndev.off()\n", "meta": {"hexsha": "bc48aaa8e5088f6c51debb1cc0ae4fd86d63d645", "size": 538, "ext": "r", "lang": "R", "max_stars_repo_path": "plot.r", "max_stars_repo_name": "libroute/spiral_dataset", "max_stars_repo_head_hexsha": "870cc2f5043d61852d4fa4a4cb4ef33c0f809f0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot.r", "max_issues_repo_name": "libroute/spiral_dataset", "max_issues_repo_head_hexsha": "870cc2f5043d61852d4fa4a4cb4ef33c0f809f0e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot.r", "max_forks_repo_name": "libroute/spiral_dataset", "max_forks_repo_head_hexsha": "870cc2f5043d61852d4fa4a4cb4ef33c0f809f0e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8888888889, "max_line_length": 70, "alphanum_fraction": 0.6319702602, "num_tokens": 182, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725053, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3196862869532281}} {"text": "## Turn experiments: indirect vs no reported speech \n\nsource(\"../scripts/collate-word-feats.r\")\n\nload(\"word.pros.dt\")\nload(\"xprospn.dt\")\n\nturn.pros <- get.turn.pros(word.pros)\nrs.pros <- get.rs.pros(word.pros, turn.pros)\nxturn <- get.turn.data(turn.pros, rs.pros, xprospn)\nxturn.ind <- xturn[rstype %in% c(\"I\", \"N\")] \n\nsave(xturn.ind, file=\"xturn.ind\")\nxturn.ind.params.all <- train.test.turn(xturn.ind)\nsave(xturn.ind.params.all, file=\"xturn.ind.params.all\")\n\nturn.ind.feats <- names(xturn.ind.params.all[[\"x0.data\"]])\nprint(turn.ind.feats)\n\nturn.ind.feats <- turn.ind.feats[!(turn.ind.feats %in% c(\"pdur\",\"ndur\",\"pgap\",\"ngap\"))]\nturn.ind.feats <- grep(\"^d[pn]\", turn.ind.feats, invert=T, value=T)\n\nturn.ind.pred.all <- pred.tune(xturn.ind.params.all, feats=turn.ind.feats, outfile=\"pred.turn.ind.all\")\nfs.turn.ind.all <- get.fs.pred(xturn.ind.params.all, feats=turn.ind.feats, outfile=\"fs.turn.ind.all\")\n\n## only F0 and Intensity features\n#pcontext <- turn.ind.feats[grep(\"(F0|I0)\",turn.ind.feats)]\n#turn.ind.pred.pcontext <- pred.tune(xturn.ind.params.all, feats=pcontext, outfile=\"pred.turn.ind.pcontext\")\n#fs.turn.ind.pcontext <- get.fs.pred(xturn.ind.params.all, feats=pcontext, outfile=\"fs.turn.ind.pcontext\")\n\n## F0 and Int, no context\npros <- turn.ind.feats[grep(\"(F0|I0)\",turn.ind.feats)]\nturn.ind.pred.pros <- pred.tune(xturn.ind.params.all, feats=pros, outfile=\"pred.turn.ind.pros\")\nfs.turn.ind.pros <- get.fs.pred(xturn.ind.params.all, feats=pros, outfile=\"fs.turn.ind.pros\")\n\n#lex <- grep(\"^n\\\\.\", turn.ind.feats, value=T)\n#turn.ind.pred.lex <- pred.tune(xturn.ind.params.all, feats=lex, outfile=\"pred.turn.ind.lex\")\n#fs.turn.ind.lex <- get.fs.pred(xturn.ind.params.all, feats=lex, outfile=\"fs.turn.ind.lex\")\n#\n#nonlex <- grep(\"^n\\\\.\", turn.ind.feats, value=T, invert=T)\n#turn.ind.pred.nonlex <- pred.tune(xturn.ind.params.all, feats=nonlex, outfile=\"pred.turn.ind.nonlex\")\n#fs.turn.ind.nonlex <- get.fs.pred(xturn.ind.params.all, feats=nonlex, outfile=\"fs.turn.ind.nonlex\")\n\n\n############################################################################\n### only Intensity features\n#pi0 <- turn.ind.feats[grep(\"I0\",turn.ind.feats)]\n#turn.ind.pred.pi0 <- pred.tune(xturn.ind.params.all, feats=pi0, outfile=\"pred.turn.ind.pi0\")\n#\n### only F0\n#pf0 <- turn.ind.feats[grep(\"F0\",turn.ind.feats)]\n#turn.ind.pred.pf0 <- pred.tune(xturn.ind.params.all, feats=pf0, outfile=\"pred.turn.ind.pf0\")\n#fs.turn.ind.pf0 <- get.fs.pred(xturn.ind.params.all, feats=pf0, outfile=\"fs.turn.ind.pf0\")\n#\n### only Intensity features\n#ti0 <- pros[grep(\"I0\",pros)]\n#turn.ind.pred.ti0 <- pred.tune(xturn.ind.params.all, feats=ti0, outfile=\"pred.turn.ind.ti0\")\n#\n### only F0\n#tf0 <- pros[grep(\"F0\",pros)]\n#turn.ind.pred.tf0 <- pred.tune(xturn.ind.params.all, feats=tf0, outfile=\"pred.turn.ind.tf0\")\n#fs.turn.ind.tf0 <- get.fs.pred(xturn.ind.params.all, feats=tf0, outfile=\"fs.turn.ind.tf0\")\n#\n###########################################################################\n# Models\n###########################################################################\n\nm.turn.ind <- glmer(is.rs ~ (1 | spk)\n\t\t+ to.zscore(mean.normF0) + to.zscore(mean.normI0)\n\t\t+ to.zscore(range.normF0) + to.zscore(range.normI0)\n\t\t+ to.zscore(slope.normF0) + to.zscore(slope.normI0)\n\t\t+ to.zscore(intern.pause)\n\t\t+ to.zscore(abs(dur))\n\t\t, data=xturn.ind, family = binomial(link = \"logit\"))\n\nxtable(get.fixef(m.turn.ind, roundval=2))\n\nsigpros <- c(\"range.normF0\",\"slope.normF0\",\"range.normI0\",\"slope.normI0\",\"intern.pause\")\nturn.ind.pred.sigpros <- pred.tune(xturn.ind.params.all, feats=sigpros, outfile=\"pred.turn.ind.sigpros\")\n", "meta": {"hexsha": "7794bece5f5c02ebe0d235f8587ec7ec661d040e", "size": 3583, 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"max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.7875, "max_line_length": 108, "alphanum_fraction": 0.650851242, "num_tokens": 1066, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3195960933408314}} {"text": "best <- function(state, outcome) {\r\n \r\n ## turn off potential NA warnings\r\n oldw<-getOption(\"warn\")\r\n options(warn=-1)\r\n \r\n \r\n ##Adding simpleCap function for letter capitaliztion\r\n simpleCap <- function(x) {\r\n s <- strsplit(x, \" \")[[1]]\r\n paste(toupper(substring(s, 1,1)), substring(s, 2),\r\n sep=\"\", collapse=\" \")\r\n }\r\n \r\n ## Read outcome data\r\n file<-read.csv(\"outcome-of-care-measures.csv\")\r\n \r\n \r\n ## Check that state and outcome are valid\r\n \r\n ## Checking invalid state\r\n state.list<-file[,\"State\"]\r\n if (!state %in% state.list)\r\n {result<-\"invalid state\"\r\n return (result)}\r\n \r\n ## Checking invalid outcome\r\n outcome.list<-c(\"heart attack\",\"heart failure\",\"pneumonia\")\r\n if (!outcome %in% outcome.list)\r\n {result<- \"invalid outcome\"\r\n return (result)}\r\n \r\n ## Return hospital name in that state with lowest 30-day death\r\n ## rate\r\n hosp.name<-file[,\"Hospital.Name\"] ## set vector for hospitals\r\n outcome.values.init<-file[,paste(\"Hospital.30.Day.Death..Mortality..Rates.from\", sub(\" \",\".\",simpleCap(outcome)),sep=\".\")] ## set vector for outcomeswrite.csv(result.table,\"test.csv\")\r\n outcome.values<- as.numeric(as.character(outcome.values.init))\r\n\r\n ##combine columns together\r\n new.table<-cbind.data.frame(state.list,hosp.name,outcome.values)\r\n \r\n \r\n ##filter on state match\r\n result.table=new.table[state.list==state,]\r\n \r\n \r\n options(warn=oldw)## turn back on warnings\r\n \r\n \r\n ##sort by outcome value and then by hospital name\r\n sort.table = with(result.table,order(outcome.values, hosp.name))\r\n final.table<-result.table[sort.table,]\r\n \r\n ##return top row from table\r\n as.vector(final.table[1,2])\r\n \r\n}\r\n", "meta": {"hexsha": "adc89979c627089bea76b1b04a9b355f0b0f0ecb", "size": 1778, "ext": "r", "lang": "R", "max_stars_repo_path": "best.r", "max_stars_repo_name": "pwachtler/datasciencecoursera", "max_stars_repo_head_hexsha": "67dd2985622890fed82a96c9fe0765cc59be0686", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "best.r", "max_issues_repo_name": "pwachtler/datasciencecoursera", "max_issues_repo_head_hexsha": "67dd2985622890fed82a96c9fe0765cc59be0686", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "best.r", "max_forks_repo_name": "pwachtler/datasciencecoursera", "max_forks_repo_head_hexsha": "67dd2985622890fed82a96c9fe0765cc59be0686", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6551724138, "max_line_length": 187, "alphanum_fraction": 0.6091113611, "num_tokens": 431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3195512558143848}} {"text": "\\name{circos.initialize}\n\\alias{circos.initialize}\n\\title{\nInitialize the circular layout\n}\n\\description{\nInitialize the circular layout\n}\n\\usage{\ncircos.initialize(factors, x = NULL, xlim = NULL, sector.width = NULL)\n}\n\\arguments{\n\n \\item{factors}{A \\code{\\link{factor}} variable or a character vector which represent data categories\r}\n \\item{x}{Data on x-axes, a vector\r}\n \\item{xlim}{Ranges for values on x-axes, see \"details\" section for explanation of the format\r}\n \\item{sector.width}{Width for each sector. The length of the vector should be either 1 which means\r all sectors have same width or as same as the number of sectors. Values for\r the vector are relative, and they will be scaled by dividing their summation. \r By default, it is \\code{NULL} which means the width of sectors correspond to the data\r range in sectors.\r}\n\n}\n\\details{\nThe function allocates the sectors according to the values on x-axis.\r\nThe number of sectors are determined by the \\code{factors} and the order\r\nof sectors are determined by the levels of factors. In this function,\r\nthe start and end position for each sector on the circle (measured by degree)\r\nare calculated according to the values on x-axis or by \\code{xlim}.\n\nIf \\code{x} is set, the length of \\code{x} must be equal to the length of \\code{factors}.\r\nThen the data range for each sector are calculated from \\code{x} by splitting \\code{factors}.\n\nIf \\code{xlim} is set, it should be a vector containing two numbers or a matrix with 2 columns.\r\nIf \\code{xlim} is a 2-element vector, it means all sector share the same \\code{xlim}.\r\nIf \\code{xlim} is a 2-column matrix, the number of rows should be equal to the number of categories\r\nidentified by \\code{factors}, then each row of \\code{xlim} corresponds to the data range for each sector\r\nand the order of rows is corresponding to the order of levels of \\code{factors}. If \\code{xlim} is a matrix\r\nfor which row names cover all sector names, \\code{xlim} is automatically adjusted.\n\nNormally, width of sectors will be calculated internally according to the data range in sectors. But you can\r\nstill set the width manually. However, it is not always a good idea to change the default sector width since\r\nthe width can reflect the range of data in sectors. However, in some cases, it is useful to manually set\r\nthe width such as you want to zoom some part of the sectors.\n\nThe function finally calls \\code{\\link[graphics]{plot}} with enforing aspect ratio to be 1 and be ready for adding graphics.\n}\n\\seealso{\n\\url{http://jokergoo.github.io/circlize_book/book/circular-layout.html}\n}\n\\references{\nGu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics.\n\n}\n\\examples{\ncircos.initialize(factors = sample(letters[1:4], 20, replace = TRUE), xlim = c(0, 1))\ncircos.info()\ncircos.clear()\n\ncircos.initialize(factors = sample(letters[1:4], 20, replace = TRUE), xlim = cbind(1:4, 1:4*2))\ncircos.info()\ncircos.clear()\n\ncircos.initialize(factors = sample(letters[1:4], 20, replace = TRUE), x = rnorm(20))\ncircos.info()\ncircos.clear()\n\n}\n", "meta": {"hexsha": "87a20bbe65595b4f80316091af487a897b7b88b7", "size": 3063, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/circos.initialize.rd", "max_stars_repo_name": "calpan/circlize", "max_stars_repo_head_hexsha": "33f8f23663768367188e50e93d3f9b2b57edd0e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-09-16T12:30:42.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-16T12:30:47.000Z", "max_issues_repo_path": "man/circos.initialize.rd", "max_issues_repo_name": "Nexller/circlize", "max_issues_repo_head_hexsha": "71df6b5316680dcee4d39d3ac7c224fcfc32439b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-08-16T14:55:12.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-16T14:55:12.000Z", "max_forks_repo_path": "man/circos.initialize.rd", "max_forks_repo_name": "Nexller/circlize", "max_forks_repo_head_hexsha": "71df6b5316680dcee4d39d3ac7c224fcfc32439b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.1230769231, "max_line_length": 365, "alphanum_fraction": 0.7538361084, "num_tokens": 786, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.31955125581438476}} {"text": "#!/usr/bin/env Rscript\n\nargs <- commandArgs(trailingOnly = TRUE)\nfilename=as.character(args[1])\nannotationfile=as.character(args[2])\n\ncomputeRegionLengths <- function(data){\n #process\n names(data)=c(\"chr\",\"start\",\"end\",\"label\",\"type\",\"strand\")\n #compute lengths\n singleLengths=apply(data,1,function(x){return(as.numeric(x[3])-as.numeric(x[2])+1)})\n dummyDF=data.frame(label=data$label,lens=singleLengths)\n res=by(dummyDF,dummyDF$label,function(x){sum(x$lens)})\n return(data.frame(region=as.character(names(res)),lens=as.numeric(res)))\n}\n\naddLengthColumnToStats <- function(dfStats,lens){\n Lengths=computeRegionLengths(lens)\n df2=dfStats\n dfStats = merge(df2,Lengths,by=\"region\")\n return(dfStats)\n}\n\n\n#Calculates TPM\ncalcTPM <- function(df, annot) {\n colnames(df) = df[1, ]\n df=df[-c(1),]\n if(class(df)==\"character\"){\n df=t(as.matrix(df)) \n }\n df = addLengthColumnToStats(df, annot)\n df$reads=as.integer(as.character(df$reads))\n df$lens=as.integer(as.character(df$lens))\n df$RPK=(df$reads/df$lens)*1000\n df$TPM=df$RPK/(sum(df$RPK)/1e+06)\n df$RPK=NULL\n df$lens=NULL\n df$reads=as.character(df$reads)\n df$TPM=as.character(df$TPM)\n return(df)\n}\n\nannotations=read.table(annotationfile,header=F,stringsAsFactors=FALSE)\nnames(annotations)=c(\"chr\",\"start\",\"end\",\"label\",\"type\",\"strand\")\noutStats=paste(filename,\"Statistics.dat\",sep=\"\")\ntt=read.table(paste(filename,\"alignedReads.sub.compact\",sep=\"\"),stringsAsFactors=FALSE)\ntt$ID=paste(tt$V1,tt$V2,tt$V3,tt$V4,sep=\"\")\ntt$LM=paste(tt$V2,tt$V3,sep=\"\")\ntt$LS=paste(tt$V2,tt$V4,sep=\"\")\nnomodtt=subset(tt,V5 != \"-\")\nnomodtt$LSEQ=paste(nomodtt$V2,nomodtt$V5,sep=\"\")\ntt$MS=paste(tt$V4,tt$V3,sep=\"\")\nstt=subset(tt,V5 != \"-\")\ntab=table(stt$V5)\nrmin=range(tt$V2)[1]-1\nrmax=range(tt$V2)[2]+1\n\n##create datastructure that counts reads per length per region and strand\nallNames=annotations$label[!duplicated(annotations$label)]\nnames=paste(c((rmin+1):(rmax-1), (rmin+1):(rmax-1)), c(rep(\"+\", rmax-rmin-1), rep(\"-\", rmax-rmin-1)), sep = \"\")\nallCounts=rep(0,length(names)+1)\nfor(name in allNames){\n counts=rep(0,length(names))\n subb=subset(tt,V1 == name)\n idx=which(annotations$label==name)\n strandInfo=annotations$strand[idx][1]\n if(strandInfo==\"-\") {\n subb$LS=sub('\\\\+$','t',subb$LS)\n subb$LS=sub('\\\\-$','+',subb$LS)\n subb$LS=sub('t$','-',subb$LS)\n }\n for (i in 1:length(names)){\n counts[i]=nrow(subset(subb,LS == names[i]))\n }\n allCounts=rbind(allCounts,c(name,counts))\n}\n#remove first dummy row\nallCounts=allCounts[-1,]\n\n#stats function returns line of stats for each matrix of alignments\ncomputeStats <- function(subMatrix,header){\n reads=nrow(subMatrix)\n mod=nrow(subset(subMatrix,V3 == \"Y\"))\n stranded=nrow(subset(subMatrix,V4 == \"-\"))\n Aplus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"A\"))\n Cplus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"C\"))\n Gplus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"G\"))\n Tplus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"T\"))\n Aminus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"A\"))\n Cminus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"C\"))\n Gminus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"G\"))\n Tminus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"T\"))\n if(reads==0){\n ASratio=0\n MODratio=0\n Aratio=0\n Cratio=0\n Gratio=0\n Tratio=0\n }\n else {\n ASratio=round(stranded/reads,2)\n MODratio=round(mod/reads,2)\n Aratio=round(Aminus/(Aplus+Aminus),2)\n Cratio=round(Cminus/(Cplus+Cminus),2)\n Gratio=round(Gminus/(Gplus+Gminus),2)\n Tratio=round(Tminus/(Tplus+Tminus),2)\n }\n return(as.character(c(header,reads,mod,MODratio,stranded,ASratio,Aplus,Cplus,Gplus,Tplus,Aminus,Cminus,Gminus,Tminus,Aratio,Cratio,Gratio,Tratio)))\n}\n\ncomputeStatsNeg <- function(subMatrix,header){\n reads=nrow(subMatrix)\n mod=nrow(subset(subMatrix,V3 == \"Y\"))\n stranded=nrow(subset(subMatrix,V4 == \"+\"))\n Aminus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"A\"))\n Cminus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"C\"))\n Gminus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"G\"))\n Tminus=nrow(subset(subMatrix,V4 == \"+\" & V5 == \"T\"))\n Aplus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"A\"))\n Cplus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"C\"))\n Gplus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"G\"))\n Tplus=nrow(subset(subMatrix,V4 == \"-\" & V5 == \"T\"))\n if(reads==0){\n ASratio=0\n MODratio=0\n Aratio=0\n Cratio=0\n Gratio=0\n Tratio=0\n }\n else {\n ASratio=round(stranded/reads,2)\n MODratio=round(mod/reads,2)\n Aratio=round(Aminus/(Aplus+Aminus),2)\n Cratio=round(Cminus/(Cplus+Cminus),2)\n Gratio=round(Gminus/(Gplus+Gminus),2)\n Tratio=round(Tminus/(Tplus+Tminus),2)\n }\n return(as.character(c(header,reads,mod,MODratio,stranded,ASratio,Aplus,Cplus,Gplus,Tplus,Aminus,Cminus,Gminus,Tminus,Aratio,Cratio,Gratio,Tratio)))\n}\n\n#create table for stats results\nStats=c(\"region\",\"reads\",\"modified\",\"MODratio\",\"antisenseReads\",\"ASratio\",\"A+\",\"C+\",\"G+\",\"T+\",\"A-\",\"C-\",\"G-\",\"T-\",\"Aratio\",\"Cratio\",\"Gratio\",\"Tratio\")\nfor(name in allNames){\nsubtt=subset(tt,V1==name)\nsubtt=subset(subtt, V2 > rmin & V2 < rmax)\n#subtt=subset(subtt, V2 > 16 & V2 <27)\n#add statistics for this subset\nidx=which(annotations$label==name)\nstrandInfo=annotations$strand[idx][1]\nif(strandInfo==\"+\") {\n Stats=rbind(Stats,computeStats(subtt,name))\n} else {\n #For -ve gene direction\n Stats=rbind(Stats,computeStatsNeg(subtt,name))\n}\n}\nStats2=calcTPM(Stats, annotations)\nnames(Stats2)=NULL\nStats2=data.frame(lapply(Stats2, as.character), stringsAsFactors=FALSE)\nthead=c(\"region\",\"reads\",\"modified\",\"MODratio\",\"antisenseReads\",\"ASratio\",\"A+\",\"C+\",\"G+\",\"T+\",\"A-\",\"C-\",\"G-\",\"T-\",\"Aratio\",\"Cratio\",\"Gratio\",\"Tratio\",\"TPM\")\nStats=rbind(thead,as.data.frame(Stats2))\n\n#add the header line of allCounts to the matrix to have the same nrow then Stats\n#allCounts = rbind(names,allCounts)\nallCounts=rbind(c(\"region\",names),allCounts)\n\ndfLenCounts=as.data.frame(allCounts)\ncolnames(dfLenCounts)=allCounts[1,]\ndfLenCounts=dfLenCounts[-1,]\n\ncolnames(Stats)=thead\nStats=Stats[-1,]\n\nmergedStats=merge(Stats,dfLenCounts,by=\"region\")\n\n#Stats=cbind(Stats,allCounts)\nwrite.table(mergedStats,outStats,quote=F,col.names=T,row.names=F,sep=\" \")\n", "meta": {"hexsha": "b1a73800a1920d3ec33110d135509cb3922db8e8", "size": 6115, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/rapidStats.r", "max_stars_repo_name": "skarunan/RAPID", "max_stars_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2018-04-19T09:11:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-17T23:09:40.000Z", "max_issues_repo_path": "bin/rapidStats.r", "max_issues_repo_name": "skarunan/RAPID", "max_issues_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/rapidStats.r", "max_forks_repo_name": "skarunan/RAPID", "max_forks_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2017-10-13T17:23:52.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-08T14:27:43.000Z", "avg_line_length": 33.7845303867, "max_line_length": 156, "alphanum_fraction": 0.6748977923, "num_tokens": 2069, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.31955124848306354}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# set dataset \n\ndataset <- \"Streicher\"\n\n# AI\n#h1 <- \"AIvSA\"\n#h2 <- \"AIvSI\"\n#h3 <- \"AIvAvg\"\n## SA\n#h <- \"SAdGLS\"\n#h1 <- \"SAvSI\"\n#h2 <- \"SAvSI\"\n#h3 <- \"SAvAvg\"\n## SI\n#h <- \"SIdGLS\"\n#h1 <- \"SIvAI\"\n#h2 <- \"SIvSA\"\n#h3 <- \"SIvAvg\"\n\n\n\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\n\n# Read in data\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n\n\nV <- function(df.g,xval.g,yval.g,ylablab.g,ytic.g,hline.g,cc.g,title.g){\n GL_violin <- ggplot(df.g, aes(x=xval.g, y=yval.g, fill=xval.g)) + \n geom_violin(trim=TRUE) + \n geom_point(shape = 18,size=0.5, position = position_jitterdodge(), color='black',alpha=1)+\n scale_color_manual(values=cc.g) + scale_fill_manual(values=cc.g) +\n theme_classic() + \n theme(plot.title = element_text(hjust = 0.5, size=16),\n axis.text = element_text(size=10, color=\"black\"),\n text = element_text(size=14),\n legend.position = \"none\") +\n labs(y=ylablab.g,x=\"\") + \n ggtitle(title.g) +\n coord_cartesian(ylim=ytic.g) +\n scale_y_continuous(breaks = ytic.g) + \n geom_hline(yintercept=hline.g,color=c(\"black\"), linetype=\"dashed\", size=0.5)\n return(GL_violin)\n}\n\n\n\n# Colors \ncolor_S <- \"orange\"\ncolor_TP <- \"springgreen4\"\ncolor_AI <- \"#2BB07FFF\"\ncolor_SA <- \"#38598CFF\"\ncolor_SI <- \"#C2DF23FF\"\n\n# Add column to name type of support \nmLGL <- mutate(mLGL, supportType = case_when(TvS != 'a' ~ \"dGLS\"))\nmLBF <- mutate(mLBF, supportType = case_when(TvS != 'a' ~ \"BF\"))\n\n\ndf.1 <- mLGL\nxval.1 <- mLGL$supportType\nylablab.1 <- \"dGLS value\"\nhline.1 <- c(-0.5,0.5)\n\nsupport <- \"dGLS\"\n\nh1 <- \"AI\"\nyval.1 <- mLGL$AIvSA\nyval.2 <- mLGL$AIvSI\ncc.1 <- c(color_AI)\n\nh2 <- \"SA\"\nyval.3 <- mLGL$AIvSA*(-1) # reversing values \nyval.4 <- mLGL$SAvSI\ncc.2 <- c(color_SA)\n\nh3 <- \"SI\"\nyval.5 <- mLGL$AIvSI*(-1) # reversing values \nyval.6 <- mLGL$SAvSI*(-1) # reversing\ncc.3 <- c(color_SI)\n\n\nmax(c(abs(yval.1),abs(yval.2),abs(yval.3),abs(yval.4),abs(yval.5),abs(yval.6)))\nlimit.1 <- 45\nytic.1 <- c(seq(-limit.1,limit.1,5))\n\n\ntitle.1 <- paste(h1,h2,sep=\"v\") \ntitle.2 <- paste(h1,h3,sep=\"v\") \ntitle.3 <- paste(h2,h1,sep=\"v\") \ntitle.4 <- paste(h2,h3,sep=\"v\") \ntitle.5 <- paste(h3,h1,sep=\"v\") \ntitle.6 <- paste(h3,h2,sep=\"v\") \n\nGL.1 <- V(df.1,xval.1,yval.1,ylablab.1,ytic.1,hline.1,cc.1,title.1)\nGL.2 <- V(df.1,xval.1,yval.2,ylablab.1,ytic.1,hline.1,cc.1,title.2)\nGL.3 <- V(df.1,xval.1,yval.3,ylablab.1,ytic.1,hline.1,cc.2,title.3)\nGL.4 <- V(df.1,xval.1,yval.4,ylablab.1,ytic.1,hline.1,cc.2,title.4)\nGL.5 <- V(df.1,xval.1,yval.5,ylablab.1,ytic.1,hline.1,cc.3,title.5)\nGL.6 <- V(df.1,xval.1,yval.6,ylablab.1,ytic.1,hline.1,cc.3,title.6)\n\nGL <- ggarrange(GL.1, GL.2, GL.3, GL.4, GL.5, GL.6, ncol=6, nrow=1, align=\"h\")\nGL\n\n#ggsave(paste(dataset,\"_violin_scaled_\",h,\".pdf\",sep=\"\"), plot=GL,width = 6, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\nggsave(paste(dataset,\"_violin_wide_\",\"all\",\".pdf\",sep=\"\"), plot=GL,width = 9, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n# Remove variables im gonna duplicate for BF \nrm(h, df.1,xval.1,yval.1,yval.2,yval.3,ylablab.1,ytic.1,hline.1,title.1,title.2,title.3)\n\ndf.1 <- mLBF\nxval.1 <- mLBF$supportType\nylablab.1 <- \"2ln(BF) value\"\nhline.1 <- c(-10,10)\n\nsupport <- \"BF\"\n\nh1 <- \"AI\"\nyval.1 <- mLBF$AIvSA\nyval.2 <- mLBF$AIvSI\ncc.1 <- c(color_AI)\n\nh2 <- \"SA\"\nyval.3 <- mLBF$AIvSA*(-1) # reversing values \nyval.4 <- mLBF$SAvSI\ncc.2 <- c(color_SA)\n\nh3 <- \"SI\"\nyval.5 <- mLBF$AIvSI*(-1) # reversing values \nyval.6 <- mLBF$SAvSI*(-1) # reversing\ncc.3 <- c(color_SI)\n\n\nmax(c(abs(yval.1),abs(yval.2),abs(yval.3),abs(yval.4),abs(yval.5),abs(yval.6)))\nlimit.1 <- 120\nytic.1 <- c(seq(-limit.1,limit.1,10))\n\n\ntitle.1 <- paste(h1,h2,sep=\"v\") \ntitle.2 <- paste(h1,h3,sep=\"v\") \ntitle.3 <- paste(h2,h1,sep=\"v\") \ntitle.4 <- paste(h2,h3,sep=\"v\") \ntitle.5 <- paste(h3,h1,sep=\"v\") \ntitle.6 <- paste(h3,h2,sep=\"v\") \n\nBF.1 <- V(df.1,xval.1,yval.1,ylablab.1,ytic.1,hline.1,cc.1,title.1)\nBF.2 <- V(df.1,xval.1,yval.2,ylablab.1,ytic.1,hline.1,cc.1,title.2)\nBF.3 <- V(df.1,xval.1,yval.3,ylablab.1,ytic.1,hline.1,cc.2,title.3)\nBF.4 <- V(df.1,xval.1,yval.4,ylablab.1,ytic.1,hline.1,cc.2,title.4)\nBF.5 <- V(df.1,xval.1,yval.5,ylablab.1,ytic.1,hline.1,cc.3,title.5)\nBF.6 <- V(df.1,xval.1,yval.6,ylablab.1,ytic.1,hline.1,cc.3,title.6)\n\nBF <- ggarrange(BF.1, BF.2, BF.3, BF.4, BF.5, BF.6, ncol=6, nrow=1, align=\"h\")\nBF\n\nggsave(paste(dataset,\"_violin_wide_allBF\",\".pdf\",sep=\"\"), plot=BF,width = 9, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\nboth <- ggarrange(GL.1,BF.1,GL.2,BF.2, ncol=4,nrow=1,align=\"h\")\nboth\nggsave(paste(dataset,\"_violin_scaled_\",h3,\"_BFdGLS.pdf\",sep=\"\"), plot=BF,width = 6, height = 7, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n", "meta": {"hexsha": "43ad45278b70d62bb2d697c6fe1f240fc3fef830", "size": 4811, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/PrelimScripts/Graphs_Violin_multiTox.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/PrelimScripts/Graphs_Violin_multiTox.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/PrelimScripts/Graphs_Violin_multiTox.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4674556213, "max_line_length": 144, "alphanum_fraction": 0.628351694, "num_tokens": 2069, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.31955124848306354}} {"text": "#install.packages(\"arules\")\nlibrary(arules)\n\nnumcols <- 3;\nclabels <- c(1, 2, 3)\n\nx <- read.csv(\"../data/wine.csv\", header=FALSE)\nfor (i in 1:13) { x[,i] = discretize(x[,i], categories=numcols, labels=clabels) }\nwrite.table(x, file=\"../data/wineDiscrete.csv\", row.names=FALSE, quote=FALSE, na=\"\", col.names=FALSE, sep=\",\")\n\nx <- read.csv(\"../data/iris.csv\", header=FALSE)\nfor (i in 1:4) { x[,i] = discretize(x[,i], categories=numcols, labels=clabels) }\nwrite.table(x, file=\"../data/irisDiscrete.csv\", row.names=FALSE, quote=FALSE, na=\"\", col.names=FALSE, sep=\",\")\n\nx <- read.csv(\"../data/heartDisease.csv\", header=FALSE)\nfor (i in c(1,4,5,8,10)) { x[,i] = discretize(x[,i], categories=numcols, labels=clabels) }\nwrite.table(x, file=\"../data/heartDiseaseDiscrete.csv\", row.names=FALSE, quote=FALSE, na=\"\", col.names=FALSE, sep=\",\")\n", "meta": {"hexsha": "f8bfb7f46f4f0aede355a1f907ec1d0548316dc2", "size": 831, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/discreteize.r", "max_stars_repo_name": "derekrfelson/classifier-demo", "max_stars_repo_head_hexsha": "3469c8d9a15eff143d84b1585bec5d0cc530b2fa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/discreteize.r", "max_issues_repo_name": "derekrfelson/classifier-demo", "max_issues_repo_head_hexsha": "3469c8d9a15eff143d84b1585bec5d0cc530b2fa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/discreteize.r", "max_forks_repo_name": "derekrfelson/classifier-demo", "max_forks_repo_head_hexsha": "3469c8d9a15eff143d84b1585bec5d0cc530b2fa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.1666666667, "max_line_length": 118, "alphanum_fraction": 0.6570397112, "num_tokens": 279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31943735283726377}} {"text": "Display \"Colored Mirrors\" \"Screen\" \"rgbobject\"\n\nFormat 960 480\n\nCameraAt 0 0 10\nCameraEye -30 -40 15\nCameraUp 0 0 1\nCameraFOV 30.0\n\nBackground 0.28 0.56 0.71\n\nWorldBegin\nAmbientLight 1.0 1.0 1.0 0.39\nPointLight 0 0 100 1.0 1.0 1.0 3900\nPointLight -10 10 20 1.0 0.0 0.0 177\nPointLight 10 10 20 0.0 1.0 0.0 177\nPointLight 10 -10 20 0.0 0.0 1.0 177\n\nSurface \"plastic\"\nSpecular 1.0 1.0 1.0 20\n\n#semi-glossy white plastic ground plane\n\nKa 0.7\nKd 0.7\nKs 0.3\n\nPolySet \"P\"\n4 1\n-100.0 -100.0 0.0\n 100.0 -100.0 0.0\n 100.0 100.0 0.0\n-100.0 100.0 0.0\n\n0 1 2 3 -1\n\nXformPush\nTranslate 0 0 5\nSphere 5 -5 5 360\nXformPop\n\n# A red mirror\n\nColor 1.0 0.1 0.1\nSpecular 1.0 0.1 0.1 50\nKa 0.2\nKd 0.2\nKs 0.8\n\nPolySet \"P\"\n4 1\n-10 20 0\n 15 20 0\n 15 20 20\n-10 20 20\n\n0 1 2 3 -1\n\n# A blue mirror\n\nColor 0.1 0.1 1.0\nSpecular 0.1 0.1 1.0 50\nKa 0.2\nKd 0.2\nKs 0.8\n\nPolySet \"P\"\n4 1\n 20 15 0\n 20 -10 0\n 20 -10 20\n 20 15 20 \n\n0 1 2 3 -1\n\n# A green mirror\n\nColor 0.1 1.0 0.1\nSpecular 0.1 1.0 0.1 50\nKa 0.2\nKd 0.2\nKs 0.8\n\nPolySet \"P\"\n4 1\n 14 20 0\n 20 14 0\n 20 14 20\n 14 20 20\n\n0 1 2 3 -1\n\nWorldEnd\n\n", "meta": {"hexsha": "ced4b32f53093d6981e8455a3303d89357b9ad87", "size": 1077, "ext": "rd", "lang": "R", "max_stars_repo_path": "scenes/colored_mirrors_db.rd", "max_stars_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_stars_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scenes/colored_mirrors_db.rd", "max_issues_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_issues_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scenes/colored_mirrors_db.rd", "max_forks_repo_name": "Sergeant-Jaeger/rendering-engine-project", "max_forks_repo_head_hexsha": "2be3d19e422777a27db32f0e908ce5f9848786e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.3368421053, "max_line_length": 46, "alphanum_fraction": 0.6388115135, "num_tokens": 660, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31943735283726377}} {"text": "###############################################################################################################################################\n# fitted method for marssMLE objects; expected value of rhs minus error term\n##############################################################################################################################################\nfitted.marssMLE <- function(object, ...,\n type = c(\"ytt1\", \"ytT\", \"xtT\", \"ytt\", \"xtt1\"),\n interval = c(\"none\", \"confidence\", \"prediction\"),\n level = 0.95,\n output = c(\"data.frame\", \"matrix\"),\n fun.kf = c(\"MARSSkfas\", \"MARSSkfss\")) {\n type <- match.arg.exact(type)\n output <- match.arg(output)\n interval <- match.arg(interval)\n conditioning <- substring(type, 3)\n type <- substr(type, 1, 1)\n # Allow user to force a particular KF function\n if(!missing(fun.kf)) object[[\"fun.kf\"]] <- match.arg(fun.kf)\n\n MLEobj <- object\n if (is.null(MLEobj[[\"par\"]])) {\n stop(\"fitted.marssMLE: The marssMLE object does not have the par element. Most likely the model has not been fit.\", call. = FALSE)\n }\n if (MLEobj[[\"convergence\"]] == 54) {\n stop(\"fitted.marssMLE: MARSSkf (the Kalman filter/smoother) returns an error with the fitted model. Try MARSSinfo('optimerror54') for insight.\", call. = FALSE)\n }\n \n if (interval != \"none\" && (!is.numeric(level) || length(level) != 1 || level > 1 || level < 0)) {\n stop(\"fitted.marssMLE: level must be a single number between 0 and 1.\", call. = FALSE)\n }\n alpha <- 1 - level\n\n # need the model dims in marss form with c in U and d in A\n model.dims <- attr(MLEobj[[\"marss\"]], \"model.dims\")\n TT <- model.dims[[\"x\"]][2]\n n <- model.dims[[\"y\"]][1]\n m <- model.dims[[\"x\"]][1]\n\n if (type == \"y\") {\n if (conditioning == \"T\") hatxt <- MARSSkf(MLEobj)[[\"xtT\"]]\n if (conditioning == \"t1\") hatxt <- MARSSkf(MLEobj)[[\"xtt1\"]]\n if (conditioning == \"t\") hatxt <- MARSSkf(MLEobj)[[\"xtt\"]]\n if (interval != \"none\") {\n if (conditioning == \"T\") hatVt <- MARSSkf(MLEobj)[[\"VtT\"]]\n if (conditioning == \"t1\") hatVt <- MARSSkf(MLEobj)[[\"Vtt1\"]]\n if (conditioning == \"t\") hatVt <- MARSSkf(MLEobj)[[\"Vtt\"]]\n }\n Z.time.varying <- model.dims[[\"Z\"]][3] != 1\n A.time.varying <- model.dims[[\"A\"]][3] != 1\n R.time.varying <- model.dims[[\"R\"]][3] != 1\n H.time.varying <- model.dims[[\"H\"]][3] != 1\n\n val <- matrix(NA, n, TT)\n rownames(val) <- attr(MLEobj$marss, \"Y.names\")\n if (interval != \"none\") se <- val\n\n Zt <- parmat(MLEobj, \"Z\", t = 1)$Z\n At <- parmat(MLEobj, \"A\", t = 1)$A\n Rt <- parmat(MLEobj, \"R\", t = 1)$R\n Ht <- parmat(MLEobj, \"H\", t = 1)$H\n Rt <- Ht %*% tcrossprod(Rt, Ht)\n\n for (t in 1:TT) {\n # parmat returns marss form\n if (Z.time.varying) Zt <- parmat(MLEobj, \"Z\", t = t)$Z\n if (A.time.varying) At <- parmat(MLEobj, \"A\", t = t)$A\n val[, t] <- Zt %*% hatxt[, t, drop = FALSE] + At\n if (interval == \"confidence\") {\n se[, t] <- takediag(Zt %*% tcrossprod(hatVt[, , t], Zt))\n }\n if (interval == \"prediction\") {\n if (R.time.varying) Rt <- parmat(MLEobj, \"R\", t = t)$R\n if (H.time.varying) Ht <- parmat(MLEobj, \"H\", t = t)$H\n if (R.time.varying | H.time.varying) Rt <- Ht %*% tcrossprod(Rt, Ht)\n se[, t] <- takediag(Zt %*% tcrossprod(hatVt[, , t], Zt) + Rt)\n }\n }\n\n # Set up output\n if (output == \"data.frame\") {\n data.names <- attr(MLEobj[[\"model\"]], \"Y.names\")\n data.dims <- attr(MLEobj[[\"model\"]], \"model.dims\")[[\"y\"]]\n model.tsp <- attr(MLEobj[[\"model\"]], \"model.tsp\")\n nn <- data.dims[1]\n TT <- data.dims[2]\n ret <- data.frame(\n .rownames = rep(data.names, each = TT),\n t = rep(seq(model.tsp[1], model.tsp[2], 1 / model.tsp[3]), nn),\n y = vec(t(MLEobj[[\"model\"]]$data)),\n stringsAsFactors = FALSE\n )\n }\n }\n\n if (type == \"x\") {\n if (conditioning == \"T\") hatxt <- MARSSkf(MLEobj)[[\"xtT\"]]\n if (conditioning == \"t1\") hatxt <- MARSSkf(MLEobj)[[\"xtt\"]]\n if (interval != \"none\") {\n if (conditioning == \"T\") hatVt <- MARSSkf(MLEobj)[[\"VtT\"]]\n if (conditioning == \"t1\") hatVt <- MARSSkf(MLEobj)[[\"Vtt\"]]\n }\n\n B.time.varying <- model.dims[[\"B\"]][3] != 1\n U.time.varying <- model.dims[[\"U\"]][3] != 1\n Q.time.varying <- model.dims[[\"Q\"]][3] != 1\n G.time.varying <- model.dims[[\"G\"]][3] != 1\n\n val <- matrix(NA, m, TT)\n rownames(val) <- attr(MLEobj[[\"marss\"]], \"X.names\")\n if (interval != \"none\") se <- val\n\n x0 <- coef(MLEobj, type = \"matrix\")[[\"x0\"]]\n if (interval != \"none\") V0 <- coef(MLEobj, type = \"matrix\")[[\"V0\"]]\n Bt <- parmat(MLEobj, \"B\", t = 1)[[\"B\"]]\n Ut <- parmat(MLEobj, \"U\", t = 1)[[\"U\"]]\n Qt <- parmat(MLEobj, \"Q\", t = 1)[[\"Q\"]]\n Gt <- parmat(MLEobj, \"G\", t = 1)[[\"G\"]]\n Qt <- Gt %*% tcrossprod(Qt, Gt)\n\n if (MLEobj$model$tinitx == 0) {\n val[, 1] <- Bt %*% x0 + Ut\n if (interval == \"confidence\") se[, 1] <- takediag(Bt %*% tcrossprod(V0, Bt))\n if (interval == \"prediction\") se[, 1] <- takediag(Bt %*% tcrossprod(V0, Bt) + Qt)\n }\n if (MLEobj[[\"model\"]][[\"tinitx\"]] == 1) {\n val[, 1] <- x0\n if (interval != \"none\") se[, 1] <- takediag(V0)\n }\n for (t in 2:TT) {\n if (B.time.varying) Bt <- parmat(MLEobj, \"B\", t = t)[[\"B\"]]\n if (U.time.varying) Ut <- parmat(MLEobj, \"U\", t = t)[[\"U\"]]\n val[, t] <- Bt %*% hatxt[, t - 1, drop = FALSE] + Ut\n if (interval == \"confidence\") {\n se[, t] <- takediag(Bt %*% tcrossprod(hatVt[, , t - 1], Bt))\n }\n if (interval == \"prediction\") {\n if (Q.time.varying) Qt <- parmat(MLEobj, \"Q\", t = t)[[\"Q\"]]\n if (G.time.varying) Gt <- parmat(MLEobj, \"G\", t = t)[[\"G\"]]\n if (Q.time.varying | G.time.varying) Qt <- Gt %*% tcrossprod(Qt, Gt)\n se[, t] <- takediag(Bt %*% tcrossprod(hatVt[, , t - 1], Bt) + Qt)\n }\n }\n\n # Set up output\n if (output == \"data.frame\") {\n state.names <- attr(MLEobj[[\"model\"]], \"X.names\")\n state.dims <- attr(MLEobj[[\"model\"]], \"model.dims\")[[\"x\"]]\n model.tsp <- attr(MLEobj[[\"model\"]], \"model.tsp\")\n mm <- state.dims[1]\n TT <- state.dims[2]\n ret <- data.frame(\n .rownames = rep(state.names, each = TT),\n t = rep(seq(model.tsp[1], model.tsp[2], 1 / model.tsp[3]), mm),\n .x = vec(t(hatxt)),\n stringsAsFactors = FALSE\n )\n }\n }\n\n if (interval == \"none\") {\n if (output == \"matrix\") {\n return(val)\n }\n retlist <- list(.fitted = val)\n }\n if (interval == \"confidence\") {\n se[se < 0 & abs(se) < sqrt(.Machine$double.eps)] <- 0\n se <- sqrt(se) # was not sqrt earlier\n retlist <- list(\n .fitted = val,\n .se = se,\n .conf.low = val + qnorm(alpha / 2) * se,\n .conf.up = val + qnorm(1 - alpha / 2) * se\n )\n }\n if (interval == \"prediction\") {\n se[se < 0 & abs(se) < sqrt(.Machine$double.eps)] <- 0\n se <- sqrt(se) # was not sqrt earlier\n retlist <- list(\n .fitted = val,\n .sd = se,\n .lwr = val + qnorm(alpha / 2) * se,\n .upr = val + qnorm(1 - alpha / 2) * se\n )\n }\n if (output == \"matrix\") {\n return(retlist)\n }\n return(cbind(ret, as.data.frame(lapply(retlist, function(x) {\n vec(t(x))\n }))))\n} # end of fitted.marssMLE\n", "meta": {"hexsha": "e8c782c2aca074e2d229d59d372c7ec9c742642e", "size": 7358, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fitted_marssMLE.r", "max_stars_repo_name": "ashaffer/MARSS", "max_stars_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 36, "max_stars_repo_stars_event_min_datetime": "2018-03-07T11:58:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-18T22:19:40.000Z", "max_issues_repo_path": "R/fitted_marssMLE.r", "max_issues_repo_name": "ashaffer/MARSS", "max_issues_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 126, "max_issues_repo_issues_event_min_datetime": "2018-03-15T16:05:51.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-15T02:25:30.000Z", "max_forks_repo_path": "R/fitted_marssMLE.r", "max_forks_repo_name": "ashaffer/MARSS", "max_forks_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2018-04-14T06:01:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T07:48:53.000Z", "avg_line_length": 38.5235602094, "max_line_length": 163, "alphanum_fraction": 0.4944278337, "num_tokens": 2541, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31943735283726377}} {"text": "\ncontext(\"CV scores\")\n\n## linear regression\ntest_that(\"auc_roc returns NA when 1 value\", {\n roc <- auc_roc(factor(1, levels=1:2), 0.5)\n expect_equal( roc, NA_real_ )\n})\n\ntest_that(\"auc_roc returns a number when 2 values\", {\n roc <- auc_roc(factor(c(1,2), levels=1:2), c(0.5, 0.2) )\n expect_equal( roc, 0 )\n})\n\ntest_that(\"auc_pr returns NA when 1 value\", {\n roc <- auc_pr(factor(c(1), levels=1:2), c(0.5) )\n expect_equal( roc, NA_real_ )\n})\n\ntest_that(\"auc_pr returns a number when only one class\", {\n roc <- auc_pr(factor(c(1,1,1), levels=1:2), c(0.5, 0.3, 0.25) )\n expect_equal( roc, NA_real_ )\n})\n\ntest_that(\"auc_pr returns a number when more than 1 value\", {\n roc <- auc_pr(factor(c(1,2), levels=1:2), c(0.5, 0.3) )\n expect_equal( roc, 0.25 )\n})\n", "meta": {"hexsha": "9e81d21033ac909be9f4e3a60edf0783b0ea1775", "size": 759, "ext": "r", "lang": "R", "max_stars_repo_path": "wrappers/R/inst/tests/testthat/test-cv-scores.r", "max_stars_repo_name": "xrounder/optunity", "max_stars_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 401, "max_stars_repo_stars_event_min_datetime": "2015-01-08T00:56:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T09:07:12.000Z", "max_issues_repo_path": "wrappers/R/inst/tests/testthat/test-cv-scores.r", "max_issues_repo_name": "xrounder/optunity", "max_issues_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 67, "max_issues_repo_issues_event_min_datetime": "2015-01-08T09:13:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-05T23:26:36.000Z", "max_forks_repo_path": "wrappers/R/inst/tests/testthat/test-cv-scores.r", "max_forks_repo_name": "xrounder/optunity", "max_forks_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 94, "max_forks_repo_forks_event_min_datetime": "2015-02-04T08:35:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-03T12:40:35.000Z", "avg_line_length": 26.1724137931, "max_line_length": 65, "alphanum_fraction": 0.6442687747, "num_tokens": 279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.31943735283726377}} {"text": "#RUN dipbugs**.r WITH NO SEED UP TO SAMPLING, THEN THIS\r\n\r\n#ch <- 1\r\n\r\n## NEEDED TO RUN IN JAGS INSTEAD OF BUGS (ABSTENTIONS AND ABSENCES AS MISSING)\r\n#rc <- apply(rc, 2, recode, recodes=\"-1=0; 0=NA\") \r\n\r\n## ## SORT BY PARTY 60th\r\n## tmp <- 1:dim(rc)[1]\r\n## tmp <- ifelse ( dipdat$part==\"pan\", tmp,\r\n## ifelse ( dipdat$part==\"pri\", tmp+1000,\r\n## ifelse ( dipdat$part==\"prd\", tmp+2000,\r\n## ifelse ( dipdat$part==\"pt\", tmp+3000,\r\n## ifelse ( dipdat$part==\"pvem\", tmp+4000,\r\n## ifelse ( dipdat$part==\"conve\", tmp+5000,\r\n## ifelse ( dipdat$part==\"panal\", tmp+6000, \r\n## ifelse ( dipdat$part==\"psd\", tmp+7000, tmp+8000 ))))))))\r\n## ## rcold <- rc; dipold <- dipdat\r\n## rc <- rc[,order(tmp)]; dipdat <- dipdat[order(tmp),]\r\n## ## PARTY INDICES (FOR ANCHORS)\r\n## PAN <- length(tmp[tmp<1001]); PRI <- PAN+length(tmp[tmp>1000&tmp<2001]);\r\n## PRD <- PRI+length(tmp[tmp>2000&tmp<3001]); PT <- PRD+length(tmp[tmp>3000&tmp<4001])\r\n## PVEM <- PT+length(tmp[tmp>4000&tmp<5001]); CONVE <- PVEM+length(tmp[tmp>5000&tmp<6001])\r\n## PANAL <- CONVE+length(tmp[tmp>6000&tmp<7001]); PSD <- PANAL+length(tmp[tmp>7000&tmp<8001])\r\n\r\n## SORT BY PARTY 61st\r\ntmp <- 1:nrow(rc)\r\ntmp <- ifelse ( dipdat$part==\"pan\", tmp,\r\n ifelse ( dipdat$part==\"pri\", tmp+1000,\r\n ifelse ( dipdat$part==\"prd\", tmp+2000,\r\n ifelse ( dipdat$part==\"pt\", tmp+3000,\r\n ifelse ( dipdat$part==\"pvem\", tmp+4000,\r\n ifelse ( dipdat$part==\"conve\", tmp+5000,\r\n ifelse ( dipdat$part==\"panal\", tmp+6000, tmp+7000 )))))))\r\nrc <- rc[,order(tmp)]; dipdat <- dipdat[order(tmp),]\r\n## PARTY INDICES (FOR ANCHORS)\r\nPAN <- length(tmp[tmp<1001]); PRI <- PAN+length(tmp[tmp>1000&tmp<2001]);\r\nPRD <- PRI+length(tmp[tmp>2000&tmp<3001]); PT <- PRD+length(tmp[tmp>3000&tmp<4001])\r\nPVEM <- PT+length(tmp[tmp>4000&tmp<5001]); CONVE <- PVEM+length(tmp[tmp>5000&tmp<6001])\r\nPANAL <- CONVE+length(tmp[tmp>6000&tmp<7001])\r\n\r\n## DROPS VOTES NOT IN GACETA\r\n#rcG <- rc[dgaceta==1,]\r\n#votdatG <- votdat[dgaceta==1,]\r\n\r\n## USED FOR SEED IN SEPARATE RUNS\r\n#set.seed(round(ch*runif(1)*1000))\r\n#runif(1) ## RANDOM NUMBER TO CHECK\r\n\r\n##### LOADS SAVED RUNS\r\n###load(\"loops5k.RData\")\r\n\r\n\r\n## BUGS VERSION\r\n#####################################################################################\r\n### Static 2Dimensions, arbitrary three party-anchors, irt paremeterization ###\r\n#####################################################################################\r\n#\r\n## ## FOR 60th\r\n## cat(\"\r\n## model {\r\n## for (j in 1:J){ ## loop over diputados\r\n## for (i in 1:I){ ## loop over items\r\n## #v.hat[j,i] ~ dbern(p[j,i]); ## voting rule\r\n## #p[j,i] <- phi(v.star[j,i]); ## sets 00){tmp0[l] <- 1}\r\n lines(c(l,l), c(tmp2,tmp3), lwd=.25)#, col=dipdat$color[X])\r\n tmp2 <- quantile (tmp-point.x.all[X], 0.25, names=F)\r\n tmp3 <- quantile (tmp-point.x.all[X], 0.75, names=F)\r\n lines(c(l,l), c(tmp2,tmp3), lwd=1)#, col=dipdat$color[X])\r\n }\r\n text(80,4, labels=paste(\"N off limits:\", sum(tmp0)))\r\n }\r\n\r\nj <- 450\r\nmyplot(j)\r\n\r\nsetwd(\"d:/01/Dropbox/MexRollCalls/graphs/thetaVSsampleG\")\r\nfor (j in 1:ncol(rc))\r\n {\r\n pdf( paste(\"dim1-\", j, \".pdf\", sep=\"\"))\r\n myplot(j)\r\n dev.off()\r\n }\r\nsetwd(workdir)\r\n\r\n## 2nd DIMENSION\r\nmyplot <- function(X)\r\n {\r\n tmp0 <- rep(0, 99) ## TO RECEIVE COUNT OF THETAS BEYOND 95% CIs \r\n plot(c(1,99), c(-4,4), type=\"n\", \r\n xlab=\"Sample of Gaceta votes only\",\r\n ylab=\"Deviation from theta\",\r\n main=paste(\"2nd dim.\", dipdat$part[X], \"-\", dipdat$id[X]))\r\n abline(0,0)\r\n for (l in 1:99){\r\n tmp <- get(paste(\"resG\", l, sep=\"\"))\r\n tmp <- tmp[[1]]$sims.list$y[,X] ## COORDENADAS DEL DIP j EN LOOP l\r\n # points(rep(l,100), tmp-point.y.all[X], pch=19, cex=.25, col=dipdat$color[X])\r\n tmp2 <- quantile (tmp-point.y.all[X], 0.025, names=F)\r\n tmp3 <- quantile (tmp-point.y.all[X], 0.975, names=F)\r\n if (tmp3<0 | tmp2>0){tmp0[l] <- 1}\r\n lines(c(l,l), c(tmp2,tmp3), lwd=.25)#, col=dipdat$color[X])\r\n tmp2 <- quantile (tmp-point.y.all[X], 0.25, names=F)\r\n tmp3 <- quantile (tmp-point.y.all[X], 0.75, names=F)\r\n lines(c(l,l), c(tmp2,tmp3), lwd=1)#, col=dipdat$color[X])\r\n }\r\n text(80,4, labels=paste(\"N off limits:\", sum(tmp0)))\r\n }\r\n\r\nj <- 450\r\nmyplot(j)\r\n\r\nsetwd(\"d:/01/Dropbox/MexRollCalls/graphs/thetaVSsampleG\")\r\nfor (j in 1:ncol(rc))\r\n {\r\n pdf( paste(\"dim2-\", j, \".pdf\", sep=\"\"))\r\n myplot(j)\r\n dev.off()\r\n }\r\nsetwd(workdir)\r\n\r\n\r\n\r\n## GRAPH TO REPORT\r\nj <- 100\r\n#setwd(paste(workdir, \"/graphs/\", sep=\"\"))\r\n#pdf(file=\"tmp.pdf\", width=10, height=5)\r\nplot(c(1,ncol(rc)), c(-7,7), type=\"n\", \r\n#plot(c(1,150), c(-4,4), type=\"n\", \r\n# xlab=\"Rank-ordered deputies (by gaceta-only coordinate)\",\r\n# ylab=\"Samples net of gaceta-only coordinate\",\r\n xlab=\"Diputados ordenados por coordenada (s\u00f3lo Gaceta)\",\r\n ylab=\"Cambio de coordenada en la muestra\",\r\n# ylab=\"Deviation from gaceta-only coordinate\",\r\n# main=paste(\"1st dimension\"))\r\n main=paste(\"1a dimensi\u00f3n\"))\r\nabline(0,0)\r\nord <- rank(ips.g.x.61) # point.x.all) ## FOR RANK-ORDERING\r\nfor (j in c(1:length(ord))[order(ord)]){\r\n#for (j in c(1:ncol(rc))[order(ord)]){\r\n#for (j in c(1:150)[order(ord)]){\r\n tmp0 <- res1[[1]]$sims.list$x[,j] ## COORDENADAS DEL DIP j EN LOOP 1\r\n for (l in 2:99){\r\n tmp <- get(paste(\"res\", l, sep=\"\"))\r\n tmp <- tmp[[1]]$sims.list$x[,j] ## COORDENADAS DEL DIP j EN LOOP l\r\n tmp0 <- c(tmp0, tmp) ## COORDENADAS DEL DIP j EN TODOS LOS LOOPS\r\n }\r\n tmp2 <- quantile (tmp0-ips.g.x.61[j], 0.025, names=F)\r\n tmp3 <- quantile (tmp0-ips.g.x.61[j], 0.975, names=F)\r\n lines(c(ord[j],ord[j]), c(tmp2,tmp3), lwd=.25, col=dipdat$color[j])\r\n tmp2 <- quantile (tmp0-ips.g.x.61[j], 0.25, names=F)\r\n tmp3 <- quantile (tmp0-ips.g.x.61[j], 0.75, names=F)\r\n lines(c(ord[j],ord[j]), c(tmp2,tmp3), lwd=1, col=dipdat$color[j])\r\n }\r\n#dev.off()\r\nsetwd(workdir)\r\n\r\nj <- 100\r\nsetwd(paste(workdir, \"/graphs/\", sep=\"\"))\r\npdf(file=\"tmp.pdf\", width=10, height=5)\r\n#plot(c(1,ncol(rc)), c(-6,8), type=\"n\", \r\nplot(c(1,ncol(rc)), c(-7,7), type=\"n\", \r\n#plot(c(1,150), c(-7,7), type=\"n\", \r\n# xlab=\"Rank-ordered deputies (by gaceta-only coordinate)\",\r\n# ylab=\"Samples net of gaceta-only coordinate\",\r\n xlab=\"Diputados ordenados por coordenada (s\u00f3lo Gaceta)\",\r\n ylab=\"Cambio de coordenada en la muestra\",\r\n# ylab=\"Deviation from gaceta-only coordinate\",\r\n# main=paste(\"2nd dimension\"))\r\n main=paste(\"2a dimensi\u00f3n\"))\r\nabline(0,0)\r\nord <- rank(ips.g.y.61) # point.y.all) ## FOR RANK-ORDERING\r\nfor (j in c(1:length(ord))[order(ord)]){\r\n#for (j in c(1:150)[order(ord)]){\r\n tmp0 <- res1[[1]]$sims.list$y[,j] ## COORDENADAS DEL DIP j EN LOOP 1\r\n for (l in 2:99){\r\n tmp <- get(paste(\"res\", l, sep=\"\"))\r\n tmp <- tmp[[1]]$sims.list$y[,j] ## COORDENADAS DEL DIP j EN LOOP l\r\n tmp0 <- c(tmp0, tmp) ## COORDENADAS DEL DIP j EN TODOS LOS LOOPS\r\n }\r\n tmp2 <- quantile (tmp0-ips.g.y.61[j], 0.025, names=F)\r\n tmp3 <- quantile (tmp0-ips.g.y.61[j], 0.975, names=F)\r\n lines(c(ord[j],ord[j]), c(tmp2,tmp3), lwd=.25, col=dipdat$color[j])\r\n tmp2 <- quantile (tmp0-ips.g.y.61[j], 0.25, names=F)\r\n tmp3 <- quantile (tmp0-ips.g.y.61[j], 0.75, names=F)\r\n lines(c(ord[j],ord[j]), c(tmp2,tmp3), lwd=1, col=dipdat$color[j])\r\n }\r\ndev.off()\r\nsetwd(workdir)\r\n\r\n\r\n\r\nmyplot <- function(X)\r\n {\r\n tmp0 <- rep(0, 99) ## TO RECEIVE COUNT OF THETAS BEYOND 95% CIs \r\n plot(c(1,99), c(-4,4), type=\"n\", \r\n xlab=\"Sample of Gaceta votes only\",\r\n ylab=\"Deviation from theta\",\r\n main=paste(\"1st dim.\", dipdat$part[X], \"-\", dipdat$id[X]))\r\n abline(0,0)\r\n for (l in 1:99){\r\n tmp <- get(paste(\"resG\", l, sep=\"\"))\r\n tmp <- tmp[[1]]$sims.list$x[,X] ## COORDENADAS DEL DIP j EN LOOP l\r\n # points(rep(l,100), tmp-point.x.all[X], pch=19, cex=.25, col=dipdat$color[X])\r\n tmp2 <- quantile (tmp-point.x.all[X], 0.025, names=F)\r\n tmp3 <- quantile (tmp-point.x.all[X], 0.975, names=F)\r\n if (tmp3<0 | tmp2>0){tmp0[l] <- 1}\r\n lines(c(l,l), c(tmp2,tmp3), lwd=.25)#, col=dipdat$color[X])\r\n tmp2 <- quantile (tmp-point.x.all[X], 0.25, names=F)\r\n tmp3 <- quantile (tmp-point.x.all[X], 0.75, names=F)\r\n lines(c(l,l), c(tmp2,tmp3), lwd=1)#, col=dipdat$color[X])\r\n }\r\n text(80,4, labels=paste(\"N off limits:\", sum(tmp0)))\r\n }\r\n\r\nj <- 450\r\nmyplot(j)\r\n\r\n", "meta": {"hexsha": "60e9cd5aecd32891e129d55145dc004d78936e09", "size": 22584, "ext": "r", "lang": "R", "max_stars_repo_path": "code/rcAnalysis/loops.r", "max_stars_repo_name": "cblanesg/dipMex", "max_stars_repo_head_hexsha": "91ef8bd76e8c831ffa85ad6f4247d7b2c1b4c9ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-07T05:19:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-07T17:46:55.000Z", "max_issues_repo_path": "code/rcAnalysis/loops.r", "max_issues_repo_name": "cblanesg/dipMex", "max_issues_repo_head_hexsha": "91ef8bd76e8c831ffa85ad6f4247d7b2c1b4c9ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-17T20:19:32.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-17T20:32:18.000Z", "max_forks_repo_path": "code/rcAnalysis/loops.r", "max_forks_repo_name": "cblanesg/dipMex", "max_forks_repo_head_hexsha": "91ef8bd76e8c831ffa85ad6f4247d7b2c1b4c9ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2018-09-11T22:57:32.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-03T15:29:32.000Z", "avg_line_length": 39.0051813472, "max_line_length": 708, "alphanum_fraction": 0.504560751, "num_tokens": 7935, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.31942276098670314}} {"text": "#!/usr/bin/env Rscript\nlibrary(\"rstan\")\nlibrary(\"parallel\")\nlibrary(\"bayesplot\")\nlibrary(\"raster\")\noptions(mc.cores = parallel::detectCores())\n\nload(\"dat/shw_scale.Rdata\")\n\n#### DELETE THIS LINE ONCE EFFORT IS ADDED\nshw_scale$effort <- 1\n\n# set up an environmental data frame for stan\n# start with just cells where we have all data\n# include linear terms plus interactions for region\nenvDatAll <- with(shw_scale, data.frame(intercept=1, bathy=bathy, chla=chla, sst=sst, varsst=varsst,\n dist=dist, east_region = as.integer(region=='E')))\n\nenvDatAll <- within(envDatAll, {\n east_chla <- east_region*chla\n east_sst <- east_region*sst\n east_varsst <- east_region*varsst\n east_dist <- east_region*dist\n})\n\n\n# counts, no gps, and no environmental NAs\n# counts, GPS, and no environmental NAs\n# GPS only, no environmental NAs\ncountRows <- which(!is.na(shw_scale$count) & is.na(shw_scale$ninds) & complete.cases(envDatAll))\ngpsCountRows <- which(!is.na(shw_scale$ninds) & !is.na(shw_scale$count) & complete.cases(envDatAll))\ngpsRows <- which(!is.na(shw_scale$ninds) & is.na(shw_scale$count) & complete.cases(envDatAll))\n\n\n# data for fitting\nenvCount <- envDatAll[countRows,]\nenvCountGPS <- envDatAll[gpsCountRows,]\nenvGPS <- envDatAll[gpsRows,]\n\n# set up remaining stan data\nstanDat <- list(\n nCounts = nrow(envCount),\n nCountsGPS = nrow(envCountGPS),\n nGPS = nrow(envGPS),\n nEnvVars = ncol(envCount),\n envCount = as.matrix(envCount),\n envCountGPS = as.matrix(envCountGPS),\n envGPS = as.matrix(envGPS),\n counts = shw_scale[countRows,'count'],\n countsGPS = shw_scale[gpsCountRows,'count'],\n gpsCounts = shw_scale[gpsCountRows,'ninds'],\n gps = shw_scale[gpsRows,'ninds'],\n surveyEffortCounts = shw_scale[countRows,'effort'],\n surveyEffortCountsGPS = shw_scale[gpsCountRows,'effort'])\n\nenvAbunOnly <- rbind(envCount, envCountGPS)\nstanDatAbun <- list(nCounts = nrow(envAbunOnly),\n nEnvVars = ncol(envAbunOnly),\n envCount = as.matrix(envAbunOnly),\n counts = shw_scale[c(countRows, gpsCountRows),'count'],\n surveyEffortCounts = shw_scale[c(countRows, gpsCountRows),'effort'])\n\nenvGPSOnly <- rbind(envCountGPS, envGPS)\nstanDatGPS <- list(nGPS = nrow(envGPSOnly),\n nEnvVars = ncol(envGPSOnly),\n envGPS = as.matrix(envGPSOnly),\n gps = shw_scale[c(gpsCountRows, gpsRows),'ninds'])\n\n \n# launch stan\nmodFull <- stan('shear.stan', data=stanDat, chains=4, iter=1000, cores=4)\nmodAerial <- stan('shear_abun_only.stan', data=stanDatAbun, chains=4, iter=1000, cores=4)\nmodGPS <- stan('shear_gps_only.stan', data=stanDatGPS, chains=4, iter=1000, cores=4)\n\n# visualize results\nparamsFull <- as.array(modFull)\nbetasFull <- rstan::extract(modFull)$beta\nparamsAerial <- as.array(modAerial)\nbetasAerial <- rstan::extract(modAerial)$beta\nparamsGPS <- as.array(modGPS)\nbetasGPS <- rstan::extract(modGPS)$beta\n\n# look at betas and hyperparams\n# mcmc_dens(paramsFull, pars=c('gpsScale', 'gpsSD'), regex_pars='beta')\n\n# predict abundance for all replicates across entire region\nlogAbunFull <- as.matrix(envDatAll) %*% t(betasFull)\nlogAbunAerial <- as.matrix(envDatAll) %*% t(betasAerial)\nlogAbunGPS <- as.matrix(envDatAll) %*% t(betasGPS)\n\n# make posterior mean and SD rasters\nabunFullMeanRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, exp(rowMeans(logAbunFull))))\nabunFullSDRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, apply(logAbunFull, 1, sd)))\nabunAerialMeanRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, exp(rowMeans(logAbunAerial))))\nabunAerialSDRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, apply(logAbunAerial, 1, sd)))\nabunGPSMeanRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, exp(rowMeans(logAbunGPS))))\nabunGPSSDRas <- rasterFromXYZ(cbind(shw_scale$lon, shw_scale$lat, apply(logAbunGPS, 1, sd)))\n\naCols <- colorRampPalette(rev(c('#032838', '#4E788F', '#80A4B7', '#BAD0D9', '#F1EBDC')), bias=3)(100)\npdf(\"results.pdf\", h=10, w=10)\npar(mfrow=c(3,2), mar=c(0.5,0.5,4,4))\nplot(abunFullMeanRas, col=aCols, xaxt='n', yaxt='n', zlim=c(0, maxValue(abunFullMeanRas)), main=\"Mean Predicted Abundance (Integrated)\")\nplot(abunFullSDRas, col=heat.colors(100), xaxt='n', yaxt='n', zlim=c(0, maxValue(abunFullSDRas)), main=\"SD Predicted Abundance\")\nplot(abunAerialMeanRas, col=aCols, xaxt='n', yaxt='n', zlim=c(0, maxValue(abunAerialMeanRas)), main=\"Mean Predicted Abundance (Aerial Only)\")\nplot(abunAerialSDRas, col=heat.colors(100), xaxt='n', yaxt='n', zlim=c(0, maxValue(abunAerialSDRas)), main=\"SD Predicted Abundance\")\nplot(abunGPSMeanRas, col=aCols, xaxt='n', yaxt='n', zlim=c(0, maxValue(abunGPSMeanRas)), main=\"Mean Predicted Abundance (GPS Only)\")\nplot(abunGPSSDRas, col=heat.colors(100), xaxt='n', yaxt='n', zlim=c(0, maxValue(abunGPSSDRas)), main=\"SD Predicted Abundance\")\ndev.off()", "meta": {"hexsha": "f33193df6d884f3d9c909218e40e249282a4e519", "size": 4751, "ext": "r", "lang": "R", "max_stars_repo_path": "shear.r", "max_stars_repo_name": "mtalluto/gps_integration", "max_stars_repo_head_hexsha": "8a75fae3ce8fa808cdf95e2927e37a9cc5fe1777", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shear.r", "max_issues_repo_name": "mtalluto/gps_integration", "max_issues_repo_head_hexsha": "8a75fae3ce8fa808cdf95e2927e37a9cc5fe1777", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "shear.r", "max_forks_repo_name": "mtalluto/gps_integration", "max_forks_repo_head_hexsha": "8a75fae3ce8fa808cdf95e2927e37a9cc5fe1777", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.9907407407, "max_line_length": 141, "alphanum_fraction": 0.7360555672, "num_tokens": 1560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347362, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.3193195762812715}} {"text": "library(lubridate)\r\noptions(lubridate.week.start=1)\r\n\r\ndays<-500\r\nusers<-100\r\nworkstations<-110\r\nsources<-70\r\n\r\n\r\ntestset<-data.frame()\r\nusers_names<-sample(10001:10000000,size=users)\r\nworkstations_names<-sample(10001:10000000,size=workstations)\r\nsources_names<-sample(10001:10000000,size=sources)\r\n\r\nconnection_type<-function(first_time,users_normal,users_names_normal,testset,mid_day){\r\n type<-c()\r\n if(first_time){\r\n type<-sample(c(2,10),users_normal,replace = T,prob = c(0.95,0.05))\r\n }else{\r\n for(i in 1:users_normal){\r\n types<-testset[users_names_normal[i]==testset[,5] & (testset[,8]==2 | testset[,8]==10),8]\r\n count<-table(types)\r\n prefer<-as.numeric(names(which.max(count)))\r\n if(mid_day){\r\n if(prefer==10){\r\n type[i]<-10\r\n }else{\r\n x<-c(2,7,11)\r\n prob<-c(0.55,0.25,0.2)\r\n type[i]<-sample(x,1,replace = T,prob = prob)\r\n }\r\n }else{\r\n if(prefer==10){\r\n prob<-c(0.05,0.95)\r\n }else{\r\n prob<-c(0.95,0.05)\r\n }\r\n type[i]<-sample(c(2,10),1,replace = T,prob = prob)\r\n } \r\n }\r\n }\r\n return(type)\r\n}\r\n\r\nworkstation_choose<-function(first_time,users_normal,user_names_normal,workstations_names,testset){\r\n workstation<-c()\r\n if(first_time){\r\n workstation<-sample(workstations_names,users_normal)\r\n }else{\r\n for(i in 1:users_normal){\r\n workstations<-testset[users_names_normal[i]==testset[,5] & (testset[,8] %in% c(2,7,10,11)),2]\r\n count<-as.data.frame(table(workstations))\r\n count<-count[order(count$Freq,decreasing = T),]\r\n count[,3]<-match(count[,1],workstations_names)\r\n prob<-c(sample(0.05/length(workstations_names),length(workstations_names),replace = T))\r\n prob_per_occur<-0.95/sum(count$Freq)\r\n for(j in 1:nrow(count)){\r\n prob[count[j,3]]<-prob_per_occur*count[j,2]\r\n }\r\n workstation[i]<-sample(workstations_names,1,prob=prob)\r\n }\r\n }\r\n return(workstation)\r\n}\r\n\r\nsource_allocation<-function(type,users,sources_names,sources){\r\n source<-c()\r\n for(i in 1:users){\r\n if(type[i]==2 || type[i]==7 || type[i]==11){\r\n source[i]<-sources_names[1]\r\n }else{\r\n source[i]<-sample(sources_names[2:sources],1)\r\n }\r\n }\r\n return(source)\r\n}\r\n\r\nsource_port_allocation<-function(source,users,sources_names){\r\n source_port<-c()\r\n for(i in 1:users){\r\n if(source[i]==sources_names[1]){\r\n source_port[i]<-0\r\n }else{\r\n source_port[i]<-sample(c(1024:65534),1)\r\n }\r\n }\r\n return(source_port)\r\n}\r\n\r\nbuild_group<-function(users){\r\n group<-sample(c(1,2,3,4),users,replace=T,prob = c(0.225,0.6,0.15,0.025))\r\n return(group)\r\n}\r\n\r\nbuild_sample<-function(users_normal,users_names_normal,workstations,workstations_names,sources,sources_names,first_time,testset,time,date,mid_day){\r\n type<-connection_type(first_time,users_normal,users_names_normal,testset,mid_day)\r\n workstation<-workstation_choose(first_time,users_normal,user_names_normal,workstations_names,testset)\r\n source<-source_allocation(type,users_normal,sources_names,sources)\r\n source_port<-source_port_allocation(source,users_normal,sources_names)\r\n datetime<-sapply(time,function(x){return(date+seconds(x))})\r\n datetime<-as_datetime(datetime)\r\n new_testset<-data.frame(Event_Typ=4624,Host=workstation,Date=datetime,ID=1,User=users_names_normal,Source=source,Source_Port=source_port,Logon_Type=type)\r\n testset<-rbind(testset,new_testset)\r\n return(testset)\r\n}\r\n\r\nbuild_sample_2<-function(users,users_names,workstations,workstations_names,sources,sources_names,first_time,testset,workstart,workend,lunchstart,lunchend,date,group){\r\n event_anzahl<-c()\r\n time_type<-c()\r\n for(i in 1:length(group)){\r\n if(group[i]==1){\r\n mean<-0\r\n sd<-0\r\n allday<-F\r\n }else if(group[i]==2){\r\n mean<-4\r\n sd<-2\r\n allday<-F\r\n }else if(group[i]==3){\r\n mean<-30\r\n sd<-10\r\n allday<-F\r\n }else{\r\n mean<-100\r\n sd<-20\r\n allday<-T\r\n }\r\n event_anzahl[i]<-round(rnorm(1,mean,sd),digits = 0)\r\n time_type[i]<-allday\r\n }\r\n \r\n for(i in 1:users){\r\n workstation<-c()\r\n source<-c()\r\n source_port<-c()\r\n if(event_anzahl[i]>0){\r\n workstation<-append(workstation,sample(workstations_names,replace=T,event_anzahl[i]))\r\n source<-append(source,sample(sources_names[2:sources],replace=T,event_anzahl[i]))\r\n source_port<-append(source_port,sample(c(1024:65534),replace=T,event_anzahl[i]))\r\n \r\n if(time_type[i]){\r\n time<-sample(c(1:86400),event_anzahl[i])\r\n }else{\r\n time<-sample(c(workstart[i]:lunchstart[i],lunchend[i]:workend[i]),event_anzahl[i])\r\n }\r\n \r\n datetime<-sapply(time,function(x){return(date+seconds(x))})\r\n datetime<-as_datetime(datetime)\r\n new_testset<-data.frame(Event_Typ=4624,Host=workstation,Date=datetime,ID=1,User=users_names[i],Source=source,Source_Port=source_port,Logon_Type=3)\r\n testset<-rbind(testset,new_testset) \r\n }\r\n }\r\n return(testset)\r\n}\r\n\r\nfor (i in 1:days) {\r\n \r\n date<-as_datetime(\"2021-05-31\")+days(i)\r\n \r\n mid_day<-F\r\n \r\n if(i==1){\r\n first_time<-T\r\n group<-build_group(users)\r\n users_not_normal<-which(group %in% 4)\r\n users_normal<-users-length(users_not_normal)\r\n users_names_normal<-users_names[-c(users_not_normal)]\r\n }else{\r\n first_time<-F\r\n }\r\n \r\n if(wday(date)!=6 && wday(date)!=7){\r\n workstart<-rnorm(users,mean=28800,sd=600)\r\n workstart_normal<-workstart[-c(users_not_normal)]\r\n testset<-build_sample(users_normal,users_names_normal,workstations,workstations_names,sources,sources_names,first_time,testset,workstart_normal,date,mid_day)\r\n first_time<-F\r\n mid_day<-T\r\n \r\n lunchstart<-rnorm(users,mean=45000,sd=1800)\r\n lunchstart_normal<-lunchstart[-c(users_not_normal)]\r\n lunchtime<-rnorm(users,mean = 1800,sd=300)\r\n lunchend<-lunchstart+lunchtime\r\n lunchend_normal<-lunchend[-c(users_not_normal)]\r\n testset<-build_sample(users_normal,users_names_normal,workstations,workstations_names,sources,sources_names,first_time,testset,lunchend_normal,date,mid_day)\r\n \r\n worktime<-rnorm(users,mean=28800,sd=300)\r\n workend<-workstart+worktime\r\n workend_normal<-workend[-c(users_not_normal)]\r\n testset<-build_sample_2(users,users_names,workstations,workstations_names,sources,sources_names,first_time,testset,workstart,workend,lunchstart,lunchend,date,group)\r\n \r\n for(j in 1:users_normal){\r\n breaks<-round(rnorm(1,mean=5,sd=2),digits = 0)\r\n if(breaks>0){\r\n break_time<-sample(c(workstart_normal[j]:lunchstart_normal[j],lunchend_normal[j]:workend_normal[j]),breaks)\r\n testset<-build_sample(breaks,rep(users_names_normal[j],breaks),workstations,workstations_names,sources,sources_names,first_time,testset,break_time,date,mid_day)\r\n }\r\n }\r\n }\r\n \r\n}\r\n\r\nwrite.table(testset,\"/home/rmey/Dokumente/my_projekt/Testset/Version_3.csv\",row.names = F,col.names = F,sep=\",\")\r\n\r\n### Adding Testcases\r\nlibrary(lubridate)\r\noptions(lubridate.week.start=1) #Wochentag beginnt Montag\r\nSys.setenv(TZ='UTC')\r\n## Viele Hosts bei einem Nutzer bei dem es untypisch ist, Version 2 nutzen\r\nuser_to_manipulate=1196840\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=\"2021-06-15 08:00:00 UTC\",ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n\r\n#Hosts+1Tag\r\nfor(i in 1:50){\r\n datetime<-as_datetime(as_datetime(\"2021-06-15 00:00:00 UTC\")+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n}\r\n\r\n#Host+ mehrere Tage hintereinander\r\nfor(j in 1:10){\r\n date<-as_datetime(\"2021-06-15 00:00:00 UTC\")+days(j)\r\n if(wday(date)!=6 && wday(date)!=7){\r\n freq<-sample(30:70,1)\r\n for(i in 1:freq){\r\n datetime<-as_datetime(date+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n }\r\n }\r\n}\r\n\r\n#Host+ mehrere Tage, aber nicht hintereinander\r\nfor(j in 1:10){\r\n date<-as_datetime(\"2021-06-15 00:00:00 UTC\")+days(j)\r\n active<-sample(c(1,0),1,prob=c(0.65,0.35))\r\n if(wday(date)!=6 && wday(date)!=7 && active){\r\n freq<-sample(30:70,1)\r\n for(i in 1:freq){\r\n datetime<-as_datetime(date+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n }\r\n }\r\n}\r\n\r\n## Untypische Nutzerzeiten, Version 3\r\n\r\n#AM wocheende aktiv ein Event ein Tag\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=as_datetime(\"2021-06-13 08:00:00 UTC\"),ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n\r\n#An einem Wocheend Tag, aber mehrere Events\r\nfor(i in 1:50){\r\n datetime<-as_datetime(as_datetime(\"2021-06-13 00:00:01 UTC\")+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n}\r\n\r\n#An mehreren Wocheendtagen\r\nfor(j in 1:14){\r\n date<-as_datetime(\"2021-06-15 00:00:00 UTC\")+days(j)\r\n if(wday(date)==6 || wday(date)==7){\r\n freq<-sample(30:70,1)\r\n for(i in 1:freq){\r\n datetime<-as_datetime(date+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n }\r\n }\r\n}\r\n\r\n#An mehreren Wocheendtagen nicht hintereinander\r\nfor(j in 1:14){\r\n date<-as_datetime(\"2021-06-15 00:00:00 UTC\")+days(j)\r\n active<-sample(c(1,0),1,prob=c(0.65,0.35))\r\n if(wday(date)==6 || wday(date)==7){\r\n if(active==1){\r\n freq<-sample(30:70,1)\r\n for(i in 1:freq){\r\n datetime<-as_datetime(date+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,1),Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n } \r\n }\r\n }\r\n}\r\n\r\n# Ein Event au\u00dferhalb der gew\u00f6hnlichen Arbeitszeit\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=\"2021-06-15 23:01:02 UTC\",ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n\r\n#Mehrere Events au\u00dferhalb der gew\u00f6hnlichen Arbeitszeit\r\nfor(i in 1:40){\r\n date<-as_datetime(as_datetime(\"2021-06-15 23:00:00 UTC\")+minutes(i*2))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=date,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n}\r\n\r\n#Mehrere Events, au\u00dferhalb der Arbeitszeit an mehreren Tagen\r\nfor(j in 1:10){\r\n date<-as_datetime(\"2021-06-15 22:00:00 UTC\")+days(j)\r\n if(wday(date)!=6 && wday(date)!=7){\r\n freq<-sample(30:70,1)\r\n for(i in 1:freq){\r\n datetime<-as_datetime(date+minutes(i*15))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n }\r\n }\r\n}\r\n\r\n# Ein Event au\u00dferhalb der Arbeitszeit aller zwei Stunde beginn 16:00\r\nn<-0\r\nfor (i in seq(0,16,2)){\r\n n<-n+1\r\n x<-rbind(testset,data.frame(Event_Typ=4624,Host=3779310,Date=as_datetime(\"2021-06-15 16:00:01 UTC\")+hours(i),ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n write.table(x,paste(\"/home/rmey/Dokumente/my_projekt/Testset/Version_3_Testcase_9_\",n,\".csv\",sep=\"\"),row.names = F,col.names = F,sep=\",\")\r\n}\r\n\r\n\r\n\r\n##Verhaltenswechsel von Interactive zu Remote\r\n\r\n#Ein Remote Event\r\ntestset<-testset[!(testset$User==user_to_manipulate & date(testset$Date)==\"2021-06-15\"),]\r\ndatetime<-as_datetime(\"2021-06-15 08:02:02 UTC\")\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n\r\ndatetime<-as_datetime(\"2021-06-15 11:02:02 UTC\")\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=49992,Logon_Type=10))\r\n\r\n#Mehrere Remotes ein Tag\r\ntestset<-testset[!(testset$User==user_to_manipulate & date(testset$Date)==\"2021-06-15\"),]\r\ndatetime<-as_datetime(\"2021-06-15 08:02:02 UTC\")\r\ntestset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[1],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[1],Source_Port=0,Logon_Type=2))\r\n\r\nfor(i in 1:20){\r\n datetime<-as_datetime(\"2021-06-15 10:02:02 UTC\")+minutes(sample(1:420,1))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=datetime,ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n}\r\n\r\n# Nutzer ist zwei Wochen weg\r\n\r\ntestset<-testset[!(testset$User==user_to_manipulate & testset$Date>=as.Date(\"2021-06-14 00:00:00 UTC\") & testset$Date <=as.Date(\"2021-06-27 23:59:59 UTC\")),]\r\n\r\n# Alle Nutzer fallen weg einer bleibt da an einem Tag\r\n`%notin%` <- Negate(`%in%`)\r\ntestset<-testset[!(testset$User %notin% (user_to_manipulate) & testset$Date>=as.Date(\"2021-06-14 00:00:00 UTC\") & testset$Date=as.Date(\"2021-06-28 00:00:00 UTC\"),]\r\ntestset<-testset[!(testset$User %in% german_location_workers & testset$Date >=as.Date(\"2021-06-28 00:00:00 UTC\")),]\r\n\r\nfor(i in 1:nrow(needs_rework)){\r\n needs_rework[i,3]<-as_datetime(needs_rework[i,3])+hours(1)\r\n}\r\n\r\ntestset<-rbind(testset,needs_rework)\r\n\r\nwrite.table(data.frame(german_location_workers),\"/home/rmey/Dokumente/my_projekt/Testset/Version_3_Testcase_16_german_workers.csv\",row.names = F,col.names = F,sep=\",\")\r\n\r\n# Auswerten dessen\r\n\r\nresult<-read.csv(\"/home/rmey/Dokumente/FindMaliciousEvents_235/Ergebnisse.csv\")\r\nresult_without_manipulation<-read.csv(\"/home/rmey/Dokumente/FindMaliciousEvents_232/Ergebnisse.csv\")\r\nworkers<-read.csv(\"/home/rmey/Dokumente/my_projekt/Testset/Version_3_Testcase_16_german_workers.csv\",header = F)\r\nresult[,1]<-sub(\"^X\",\"\",sub(\"\\\\.[0-9]*$\",\"\",result[,1]))\r\nresult_without_manipulation[,1]<-sub(\"^X\",\"\",sub(\"\\\\.[0-9]*$\",\"\",result_without_manipulation[,1]))\r\n\r\nposition<-data.frame()\r\nfor(i in 1:nrow(workers)){\r\n position[i,1]<-which(result$X==workers[i,1])-which(result_without_manipulation$X==workers[i,1])\r\n}\r\ncolMeans(position)\r\n\r\n# Nutzer wechselt vollst\u00e4ndig ins Home-Office ab dem 05.06.2021\r\ntestset<-testset[!(testset$User==user_to_manipulate & testset$Date>=as.Date(\"2021-06-05 00:00:00 UTC\")),]\r\n\r\nfor (i in 5:days) {\r\n \r\n date<-as_datetime(\"2021-05-31\")+days(i)\r\n \r\n mid_day<-F\r\n \r\n \r\n if(wday(date)!=6 && wday(date)!=7){\r\n workstart<-rnorm(1,mean=28800,sd=600)\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(workstart)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n first_time<-F\r\n mid_day<-T\r\n \r\n lunchstart<-rnorm(1,mean=45000,sd=1800)\r\n lunchtime<-rnorm(1,mean = 1800,sd=300)\r\n lunchend<-lunchstart+lunchtime\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(lunchend)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n \r\n worktime<-rnorm(users,mean=28800,sd=300)\r\n workend<-workstart+worktime\r\n\r\n for(j in 1:1){\r\n breaks<-round(rnorm(1,mean=5,sd=2),digits = 0)\r\n if(breaks>0){\r\n break_time<-sample(c(workstart[j]:lunchstart[j],lunchend[j]:workend[j]),breaks)\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(break_time)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n }\r\n }\r\n }\r\n \r\n}\r\n\r\n\r\n## EIn User wird zweifach verwendet ab dem 15.06.2021, nur zur h\u00e4lfte in der Arbeitszeit, gleich zu den vorher Nutzer aktivit\u00e4ten, nicht am WE\r\nuser_to_manipulate<-4346631\r\nfor (i in 15:days) {\r\n \r\n date<-as_datetime(\"2021-05-31\")+days(i)\r\n \r\n mid_day<-F\r\n \r\n \r\n if(wday(date)!=6 && wday(date)!=7){\r\n workstart<-rnorm(1,mean=43200,sd=600)\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(workstart)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n first_time<-F\r\n mid_day<-T\r\n \r\n lunchstart<-rnorm(1,mean=57600,sd=1800)\r\n lunchtime<-rnorm(1,mean = 1800,sd=300)\r\n lunchend<-lunchstart+lunchtime\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(lunchend)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n \r\n worktime<-rnorm(users,mean=28800,sd=300)\r\n workend<-workstart+worktime\r\n \r\n for(j in 1:1){\r\n breaks<-round(rnorm(1,mean=5,sd=2),digits = 0)\r\n if(breaks>0){\r\n break_time<-sample(c(workstart[j]:lunchstart[j],lunchend[j]:workend[j]),breaks)\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=workstations_names[2],Date=as_datetime(date+seconds(break_time)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=10))\r\n }\r\n }\r\n \r\n network<-round(rnorm(1,mean=5,sd=2),digits = 0)\r\n if(network>0){\r\n break_time<-sample(c(workstart[j]:lunchstart[j],lunchend[j]:workend[j]),network)\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=sample(workstations_names,network),Date=as_datetime(date+seconds(break_time)),ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=3))\r\n }\r\n \r\n }\r\n \r\n}\r\n\r\n\r\n## Alle Hosts haben ab dem 15.06 einen neuen Nutzer mehr mit dem Event Typ 3 mit einem Event typ 3\r\nuser_to_manipulate<-1234567\r\n\r\nfor(i in 15:days){\r\n date<-as_datetime(\"2021-05-31\")+days(i)\r\n for(j in workstations_names){\r\n date<-as_datetime(date+seconds(sample(0:86399,1)))\r\n testset<-rbind(testset,data.frame(Event_Typ=4624,Host=j,Date=date,ID=1,User=user_to_manipulate,Source=sources_names[2],Source_Port=sample(c(1024:65534),1),Logon_Type=3))\r\n }\r\n}\r\n\r\nresult_without<-read.csv(\"/home/rmey/Dokumente/FindMaliciousEvents_351/Ergebnisse.csv\")\r\nresult_with<-read.csv(\"/home/rmey/Dokumente/FindMaliciousEvents_356/Ergebnisse.csv\")\r\n \r\n\r\nresult_without[,1]<-data.frame(Identifier=sub(\"^X\",\"\",sub(\"\\\\.[0-9]*$\",\"\",result_without[,1])))\r\nresult_with[,1]<-data.frame(Identifier=sub(\"^X\",\"\",sub(\"\\\\.[0-9]*$\",\"\",result_with[,1])))\r\n\r\ndiff<-data.frame()\r\nfor(i in 1:nrow(result_with)){\r\n diff[i,1]<-abs(result_with[i,ncol(result_with)]) - abs(result_without[result_without$X==result_with[i,1],ncol(result_without)])\r\n}\r\ncolMeans(diff)\r\n\r\nwrite.table(testset,\"/home/rmey/Dokumente/my_projekt/Testset/Version_3_Testcase_19_v1.csv\",row.names = F,col.names = F,sep=\",\")\r\n", "meta": {"hexsha": "2b0ff9bf01a71d8c4e3b51c81fee737473e84a61", "size": 19395, "ext": "r", "lang": "R", "max_stars_repo_path": "datasets/Simulation_testdata.r", "max_stars_repo_name": "Richl-lab/recognize-unusual-logins", "max_stars_repo_head_hexsha": "d82c685b94acfb9beb4c5e87407373387a27d8a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "datasets/Simulation_testdata.r", "max_issues_repo_name": "Richl-lab/recognize-unusual-logins", "max_issues_repo_head_hexsha": "d82c685b94acfb9beb4c5e87407373387a27d8a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-14T15:13:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-14T15:13:52.000Z", "max_forks_repo_path": "datasets/Simulation_testdata.r", "max_forks_repo_name": "Richl-lab/recognize-unusual-logins", 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YES\n2. YES", "lm_q1_score": 0.63341027751814, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.31917934731792813}} {"text": "physics.convert.to.meters <- function(km) {\n return(km * 1000)\n}", "meta": {"hexsha": "b6c84e0b9523f233c36d417f6affa9e0d3143572", "size": 67, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/conversions.r", "max_stars_repo_name": "AllenHunn/physics", "max_stars_repo_head_hexsha": "db6eb32e4b031b52285a53fbdb0cc4360c8b031a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/conversions.r", "max_issues_repo_name": "AllenHunn/physics", "max_issues_repo_head_hexsha": "db6eb32e4b031b52285a53fbdb0cc4360c8b031a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/conversions.r", "max_forks_repo_name": "AllenHunn/physics", "max_forks_repo_head_hexsha": "db6eb32e4b031b52285a53fbdb0cc4360c8b031a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.3333333333, "max_line_length": 43, "alphanum_fraction": 0.6567164179, "num_tokens": 21, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.63341027751814, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3191793473179281}} {"text": "switchop <- function(s, x, y) {\n if(s < 0) x || y\n else if (s > 0) x && y\n else xor(x, y)\n}\n", "meta": {"hexsha": "bd6be286286bee7a2011575396b763f72458f127", "size": 95, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Short-circuit-evaluation/R/short-circuit-evaluation-3.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Short-circuit-evaluation/R/short-circuit-evaluation-3.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Short-circuit-evaluation/R/short-circuit-evaluation-3.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 15.8333333333, "max_line_length": 31, "alphanum_fraction": 0.4631578947, "num_tokens": 43, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.63341027059799, "lm_q1q2_score": 0.3191793438308218}} {"text": "\n\n\n\n##Fig3D Fig S5\nexo.corfilt2=cor(t(exo.filt2))\nexo.corfilt2.lda=cor(exo.filt2.lda10)\n\n# Make an Igraph object from this matrix:\n\nplot(network)\n\nlibrary(RColorBrewer)\ncoul = brewer.pal(nlevels(as.factor(mtcars$cyl)), \"Set2\")\n# Map the color to cylinders\nmy_color=coul[as.numeric(as.factor(mtcars$cyl))]\n \n# plot\npar(bg=\"grey13\", mar=c(0,0,0,0))\nset.seed(4)\nplot(network,\n vertex.size=12,\n vertex.color=my_color,\n vertex.label.cex=0.7,\n vertex.label.color=\"white\",\n vertex.frame.color=\"transparent\"\n )\n# title a\n\n\n\nrownames(exo.corfilt2)=gsub(\"\\\\d+:\",\"\", rownames(exo.corfilt2))\ncolnames(exo.corfilt2)=gsub(\"\\\\d+:\",\"\", colnames(exo.corfilt2))\n\nrownames(exo.corfilt2.lda)=gsub(\"X\\\\d:\",\"\", rownames(exo.corfilt2.lda))\ncolnames(exo.corfilt2.lda)=gsub(\"X\\\\d+\",\"\", colnames(exo.corfilt2.lda))\n\n\n\n#topn = 10\n\ntoprank.lda = t(apply(exo.filt2.lda10, 1 , function(x,y){x=as.numeric(x); yy=y[order(x,decreasing=T)[1:10]]; yy;}, y=gsub(\"X\\\\d+\", \"\", colnames(exo.filt2.lda10))))\n\ntoprank.cut = t(apply(exo.filt2.lda10, 1 , function(x,y){x=as.numeric(x); xx=x[order(x,decreasing=T)[1:10]]; xx;}, y=gsub(\"X\\\\d+\", \"\", colnames(exo.filt2.lda10))))\n\ntop.dup=as.vector(toprank.lda)[duplicated(as.vector(toprank.lda))]\ntop.uniq=as.vector(toprank.lda)[-which(duplicated(as.vector(toprank.lda)))]\n\n\nmember=rep(\"\",length(unique(as.vector(toprank.lda))))\nnames(member)=unique(as.vector(toprank.lda))\nfor(i in 1:10){\n member[as.character(toprank.lda[i,])] = paste(member[as.character(toprank.lda[i,])], i,sep=\",\")\n\n}\n\nmember=gsub(\"^,\",\"\",member)\n\nfor (x in names(member)){\n asmember[x]\n\n}\n\ntoprank.lda\n##cluster edge betweenness\ncutoff = 0.3\n\nexo.cf2.nw =abs( exo.corfilt2)\nexo.cf2.nw[exo.cf2.nw < cutoff]=0\ncnet=graph_from_adjacency_matrix( exo.cf2.nw, weighted=T, mode=\"undirected\", diag=F)\nV(cnet)$label=V(cnet)$name\n #l =\ncnet=delete.vertices(cnet, degree(cnet)==0)\nV(cnet)[name %in% names(member)]$label= paste(V(cnet)[name %in% names(member)]$label,\"(\", member[V(cnet)[name %in% names(member)]$name],\")\",sep=\"\")\n\n\n#ceb = cluster_edge_betweenness(cnet)\nceb = cluster_label_prop(cnet)\n#ceb=cluster_fast_greedy(cnet)\n\nplot(ceb, cnet, edge.arrow.mode=0, vertex.label=V(cnet)$label, vertex.label.cex=0.5, vertex.size=1) \n\ndev.copy2pdf(file=\"microbe.corRAW.network.c0.2lp.pdf\")\n\n\n\n\ncutoff=0.8\n\n exo.cf2.lda = abs(exo.corfilt2.lda)\nexo.cf2.lda[exo.cf2.lda < cutoff]=0\ncnet2=graph_from_adjacency_matrix( exo.cf2.lda, weighted=T, mode=\"undirected\", diag=F)\n V(cnet2)$label=V(cnet2)$name\n\n#g=cnet #l =\ncnet2=delete.vertices(cnet2, degree(cnet2)==0)\nV(cnet2)[name %in% names(member)]$label= paste(V(cnet2)[name %in% names(member)]$label,\"(\", member[V(cnet2)[name %in% names(member)]$name],\")\",sep=\"\")\n\nceb = cluster_edge_betweenness(cnet2)\n#ceb = cluster_label_prop(cnet2)\n#ceb=cluster_fast_greedy(cnet)\n\nplot(ceb, cnet2, edge.arrow.mode=0, vertex.label=V(cnet2)$label, vertex.label.cex=0.5, vertex.size=1) \ndev.copy2pdf(file=\"microbe.corLDA.network.c0.8.pdf\")\n\n", "meta": {"hexsha": "0b8fec0aadb503774847d7ebe9e5445ed124242e", "size": 3033, "ext": "r", "lang": "R", "max_stars_repo_path": "Figure3/Fig3D.r", "max_stars_repo_name": "dspak/decoasthma", "max_stars_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-04T15:59:58.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-04T15:59:58.000Z", "max_issues_repo_path": "Figure3/Fig3D.r", "max_issues_repo_name": "dspak/decoasthma", "max_issues_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Figure3/Fig3D.r", "max_forks_repo_name": "dspak/decoasthma", "max_forks_repo_head_hexsha": "1b1e67865f45e11a4b87a3bc245d920e38f85473", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T21:05:20.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T21:05:20.000Z", "avg_line_length": 28.6132075472, "max_line_length": 163, "alphanum_fraction": 0.6772172766, "num_tokens": 1031, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.319150439347027}} {"text": "require(raster)\r\nrequire(sp)\r\nrequire(maptools)\r\nrequire(rgdal)\r\n\r\n# -------------------------------------------------------------------------------------------------------\r\n# surface_downwelling_shortwave_flux_in_air\r\n\r\n# alias: surface_downwelling_shortwave_flux\r\n\r\n# The surface called \"surface\" means the lower boundary of the atmosphere. \"shortwave\" means shortwave radiation. \r\n# Downwelling radiation is radiation from above. It does not mean \"net downward\". \r\n# Surface downwelling shortwave is the sum of direct and diffuse solar radiation incident on the surface, and is sometimes called \"global radiation\". \r\n# When thought of as being incident on a surface, a radiative flux is sometimes called \"irradiance\". \r\n# In addition, it is identical with the quantity measured by a cosine-collector light-meter and sometimes called \"vector irradiance\". \r\n# In accordance with common usage in geophysical disciplines, \"flux\" implies per unit area, called \"flux density\" in physics.\r\n# -------------------------------------------------------------------------------------------------------\r\n\r\n# read in the dat file downloaded from the CRU TS20 website\r\nsunp <- read.table(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/10min_sunshine/original_data/TS20/grid_10min_sunp.dat\")\r\n\r\n# change the colnames to something more useable\r\ncolnames(sunp) <- c(\"lat\",\"lon\",\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\")\r\n\r\n# create a shapefile\r\nsunp.pts <- SpatialPoints(coordinates(sunp[,2:1]))\r\n\r\nsunp.spdf <- SpatialPointsDataFrame(sunp.pts, as.data.frame(sunp))\r\n\r\ncolNums <- 3:ncol(sunp)\r\n\r\ntemplate <- raster(\"/Data/Base_Data/Climate/Canada/CRU_Climatology/Temperature/cru_tmean_1/\")\r\nvalues(template) <- NA\r\n\r\n# this loop \r\nfor(i in colNums){\r\n\tprint(i)\r\n\t\r\n\tif(nchar(i) == 1){ d=paste(\"0\",i-2, sep=\"\")\t}else{ d=i-2 }\r\n\t\r\n\trasterize(sunp.spdf, template, field=colnames(sunp)[i] ,filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/10min_sunshine/output_rasters/sunp_cru_10min_\",d,\"_1961_1990.tif\", sep=\"\"), overwrite=T)\r\n\r\n}\r\n\r\n####################################################################################################################\r\n# read in the NetCDF as a stack\r\ncld.ts31 <- stack(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/10min_sunshine/original_data/TS31/cru_ts_3_10.1901.2009.cld.dat.nc\")\r\ncld.ts31.tmp <- brick(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/10min_sunshine/original_data/TS31/cru_ts_3_10.1901.2009.cld.dat.nc\")\r\n\r\n# read in the cru climatology\r\n# this is in SUNP percentage units\r\nsunp.ts20.clim <- stack(list.files(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/10min_sunshine/output_rasters\", pattern=\"tif\", full.names=TRUE))\r\n\r\n# all this does is bring the layernames in as something more descript. The stack layernames are not as good as the brick\r\n# so I give the stack the brick names\r\ncld@layernames <- cld.ts31.tmp@layernames\r\nrm(cld.ts31.tmp) # remove the unneeded brick\r\n\r\n# AKCanada Extent for spatial subsetting\r\nAKCanada.akalb <- raster(\"/workspace/Shared/Michael/ArcGIS_Shared/clipper/alaska_west_canada_clipper_forDownscaling.tif\")\r\n\r\n# project the extent \r\nAKCanada.wgs84 <- projectExtent(AKCanada.akalb, projection(cld.ts31))\r\n\r\n# grab the cell ids at the intersection of the new extent and the original data\r\ncld.ts31.desiredCells <- cellsFromExtent(cld.ts31, extent(AKCanada.wgs84), expand=FALSE)\r\n\r\n# extract those cells and create a new stack \r\ncld.ts31.desiredCells.e <- cld[cld.ts31.desiredCells,drop=F]\r\n\r\n##### TS20 climatology data #########################################################################\r\n# grab the cell ids at the intersection of the new extent and the original data\r\nsunp.ts20.clim.desiredCells <- cellsFromExtent(sunp.ts20.clim, extent(AKCanada.wgs84), expand=FALSE)\r\n# now spatially subset those cells to a new stack\r\nsunp.ts20.clim.desiredCells.e <- sunp.ts20.clim[sunp.ts20.clim.desiredCells,drop=F]\r\n\r\n# would be a good idea to do the sunshine percent to cloud percent conversion\r\n# I think this is working correctly now it is units of percent without the percent sign\r\ncld.ts20.clim.desiredCells.e <- 100 - ts20.clim.desiredCells.e \r\n\r\n# temporally subset the stack object to be 1961-1990 for all 12 months\r\n# these numbers were pre-calculated outside of the code\r\ncld.ts31.subset <- subset(cld.ts31.desiredCells.e, 721:1080)\r\n\r\n# create an empty brick using the extent from the other stack and no layers in it yet.\r\ncld.ts31.clim <- brick(extent(cld.ts31.subset), nrows=nrow(cld.ts31.subset), ncols=ncol(cld.ts31.subset), crs=projection(cld.ts31.subset), nl=1)\r\n\r\n# create climatology\r\nfor(j in 1:12){\r\n\t#monthLayers<-seq(j, nlayers(cld.subset), by= 12)\r\n\tcld.clim.tmp <- mean(subset(cld.ts31.subset, seq(j, nlayers(cld.ts31.subset), by=12)))\r\n\tcld.ts31.clim <- addLayer(cld.ts31.clim, cld.clim.tmp)\r\n}\r\n\r\nyearList <- 1901:2009 \r\n\r\n# create anomalies HISTORICAL\r\n# make an empty brick object\r\ncld.ts31.anom <- brick(extent(cld.ts31.desiredCells.e), nrows=nrow(cld.ts31.desiredCells.e), ncols=ncol(cld.ts31.desiredCells.e), crs=projection(cld.ts31.desiredCells.e), nl=nlayers(cld.ts31.desiredCells.e))\r\n\r\n# this is the centroid values of the cru ts20 That is used as the output points to\r\n# interpolate to from the 0.5 degree to the 10 min spatial resolution of the ts20 climatology \r\nout_xy <- coordinates(cld.ts20.clim.desiredCells.e)\r\n\r\n# calculate and write out the anomalies\r\nfor(i in 1:12){ # go through a list of months\r\n\tprint(paste(\"\tMONTH WORKING = \",i))\r\n\t# use the sequence command to create an index of all of the same month during the entire timeseries\r\n\tmonthList <- seq(i, nlayers(cld.ts31.desiredCells.e), 12)\r\n\t\r\n\t# here we select the month we want from the ts20 climatology\r\n\tcld.ts20.clim.current <- subset(cld.ts20.clim.desiredCells.e,i,drop=T)\r\n\r\n\r\n\t# this line will create a 2 character month for filenaming procedures (if needed)\r\n\tif(nchar(i)<2){ month=paste(\"0\",i,sep=\"\")}else{month=paste(i,sep=\"\")}\r\n\t\r\n\t# here we subset the cld\r\n\tcld.ts31.clim.current <- subset(cld.ts31.clim, i, drop=T)\r\n\t\r\n\tcount=0 # a counter to iterate through the years\r\n\t\r\n\tfor(j in monthList){\r\n\t\tprint(paste(\"anomalies iter \",j))\r\n\t\tcount=count+1\r\n\t\tcld.current <- subset(cld.desiredCells.e, j, drop=T)\r\n\r\n\t\t# this line generates a proportional anomaly\r\n\t\tanom <- cld.current/cld.clim.current\r\n\r\n\t\t# write out the 0.5 resolution anomalies (temporary file)\r\n\t\twriteRaster(anom, filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/anomalies/anomalies_original_resolution/\",\"cld_pct_proportionalAnom_cru_ts31_\", month,\"_\",yearList[count],\".tif\",sep=\"\"), overwrite=T)\r\n\t\t\r\n\r\n\t\t# # # # # # INTERPOLATION SECTION # # # # # # \r\n\t\tin_xy <- coordinates(anom)\r\n\t\tz_in <- getValues(anom)\r\n\r\n\t\txyz <- cbind(in_xy,z_in)\r\n\t\txyz.na <- na.omit(xyz)\r\n\r\n\t\tin_xy <- xyz.na[,1:2]\r\n\t\tz_in <- xyz.na[,3]\r\n\r\n\t\tanom.spline\t<- interp(x=in_xy[,1],y=in_xy[,2],z=z_in,xo=seq(min(out_xy[,1]),max(out_xy[,1]),l=ncol(cld.ts20.clim.desiredCells.e)), yo=seq(min(out_xy[,2]),max(out_xy[,2]),l=nrow(cld.ts20.clim.desiredCells.e),linear=F))\r\n\r\n\t\t# transpose the data here\r\n\t\tnc.interp <- t(anom.spline$z)[,nrow(anom.spline$z):1]\r\n\r\n\t\t# turn that into a new raster object\r\n\t\tnc.interp.r <- raster(nc.interp, xmn=xmin(cld.ts20.clim.desiredCells.e), xmx=xmax(ts20.clim.desiredCells.e), ymn=ymin(ts20.clim.desiredCells.e), ymx=ymax(ts20.clim.desiredCells.e), crs=projection(ts20.clim.desiredCells.e))\r\n\t\t\r\n\t\t# write out the interpolated to 10min resolution anomalies (temporary file)\r\n\t\twriteRaster(anom, filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/anomalies/interpolated_10min/\",\"cld_pct_proportionalAnom_cru_ts31_10min_\", month,\"_\",yearList[count],\".tif\",sep=\"\"), overwrite=T)\r\n\r\n\t\tcld.ts31.downscaled <- cld.ts20.current*anom\r\n\r\n\t\t# write out the downscaled cloud raster\r\n\t\twriteRaster(anom, filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/anomalies/interpolated_10min/\",\"cld_pct_cru_ts31_10min_\", month,\"_\",yearList[count],\".tif\",sep=\"\"), overwrite=T)\r\n\r\n\t}\r\n}\r\n\r\n\r\n\r\n######### RUN ABOVE AS A TEST!!! ################## FIX BELOW!\r\n\r\n\r\n# here we need to create a filelist that follows the chronology of the timeseries\r\nfileList <- character()\r\nfor(i in yearList){\r\n\tprint(i)\r\n\tfor(j in 1:12){\r\n\t\tif(nchar(j)<2){ month=paste(\"0\",j,sep=\"\")}else{month=paste(j,sep=\"\")}\r\n\t\tfileList <- append(fileList, paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/CRU_TS20/anomalies/\",\"cld_anom_cru_ts31_\", month,\"_\",i,\".tif\",sep=\"\"), after=length(fileList))\r\n\t}\r\n}\r\n\r\ncld.anom <- stack(fileList)\r\n\r\n# lets create a new empty brick using the information from the 10min data\r\ncld.anom.interp <- brick(ts20.clim.desiredCells.e, values=F, nl=1)\r\n\r\n# here we need to interpolate the 0.5 resolution anomalies to something that matches the cru ts20 10 min data\r\n# get the centroid xy\r\n# here we create the interpolated anomalies using a spline interpolation down to 10min resolution\r\nfor (f in 1:nlayers(cld.anom)){\r\n\tprint(basename(fileList[f]))\r\n\tinFile <- unlist(strsplit(basename(fileList[f]), \"_\"))\r\n\toutFile <- paste(inFile[1],inFile[2], inFile[3], inFile[4], \"10min_interp\", inFile[5], inFile[6], sep=\"_\")\r\n\t# this is input xy and z data to use in interpolation\r\n\tin_xy <- coordinates(subset(cld.subset.climatology, f, drop=T))\r\n\tz_in <- getValues(subset(cld.subset.climatology, f, drop=T))\r\n\r\n\txyz <- cbind(in_xy,z_in)\r\n\txyz.na <- na.omit(xyz)\r\n\r\n\tin_xy <- xyz.na[,1:2]\r\n\tz_in <- xyz.na[,3]\r\n\r\n\t# this is the centroid values of the cru ts20\r\n\tout_xy <- coordinates(ts20.clim.desiredCells.e)\r\n\r\n\tanom.spline\t<- interp(x=in_xy[,1],y=in_xy[,2],z=z_in,xo=seq(min(out_xy[,1]),max(out_xy[,1]),l=ncol(ts20.clim.desiredCells.e)), yo=seq(min(out_xy[,2]),max(out_xy[,2]),l=nrow(ts20.clim.desiredCells.e),linear=F))\r\n\r\n\t# transpose the data here\r\n\tnc.interp <- t(anom.spline$z)[,nrow(anom.spline$z):1]\r\n\r\n\t# turn that into a new raster object\r\n\tnc.interp.r <- raster(nc.interp, xmn=xmin(ts20.clim.desiredCells.e), xmx=xmax(ts20.clim.desiredCells.e), ymn=ymin(ts20.clim.desiredCells.e), ymx=ymax(ts20.clim.desiredCells.e), crs=projection(ts20.clim.desiredCells.e))\r\n\t# write out the new raster here\r\n\twriteRaster(nc.interp.r, filename=paste(\"/workspace/UA/malindgren/projects/iem/PHASE2_DATA/RSDS_working/September2012_finalRuns/outputs/anomalies/interpolated_10min/\", outFile, sep=\"\"), overwrite=TRUE)\r\n\r\n\t# and add that layer to a new stack as well\r\n\tcld.anom.interp <- addLayer(cld.anom.interp, nc.interp.r)\r\n}\r\n\r\n\r\n# here I am going to downscale the historical data to 10 min (CLOUD COVER)\r\n\r\nfor(m in 1:12){\r\n\tmonthList <- seq(i, nlayers(cld.anom.interp), 12)\r\n\tprint(paste(\"working on: \", f,sep=\"\"))\r\n\r\n\tcld.anom.interp.current <- subset(cld.anom.interp, monthList)\r\n\r\n\tcld.ts20.clim <- subset()\r\n\r\n\r\n\r\n}\r\n\r\n# next loop will involve some calculation between the new cloud cover data and the \r\n\r\n\t# next we need to turn the girr to nirr\r\n\t# nirr = girr * (0.251 + (0.509*(1.0 - clds/100.0)));\r\n\tnirr = cld.subset.v * (0.251 + (0.509*(1.0 - clds/100.0)))\r\n\r\n3. CALCULATE THE HISTORICAL ANOMALIES\r\n4. CALCULATE THE FUTURE ANOMALIES\r\n5. INTERPOLATE THE HISTORICAL / FUTURE ANOMALIES TO MATCH THE TS20\r\n6. MULTIPLY/ADD THE ANOMALIES TO THE CRU TS20 CLIMATOLOGY \r\n\r\n\r\n* WHAT ABOUT THE UNITS?\r\n\r\n\r\n\r\n# get the values of the stack\r\ncld.subset.v <- getValues(cld.subset)\r\n\r\n# apply the function from Dave to create a nirr value using apply()\r\nnirr = cld.subset.v * (0.251 + (0.509*(1.0 - cld.subset.v/100.0)))\r\n\r\n# now we return the modified values to the stack.\r\nvalues(cld.subset2) <- cld.subset.v\r\n\r\n# what we have produced in the above code is the NIRR (essentially the same as RSDS accd to Fengming/Dave)\r\nif (clds > -0.1) {\r\n\tnirr = cld.subset.v * (0.251 + (0.509*(1.0 - clds/100.0))) # basically percent cloudcover\r\n\t}else { \r\n\t\tnirr = -999.9; }\r\n\r\n\r\n", "meta": {"hexsha": "991e7ecb88195982dfba2166e45d4eff16986f32", "size": 12130, "ext": "r", "lang": "R", "max_stars_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/create_sunshine_cru_grids.r", "max_stars_repo_name": "ua-snap/downscale", "max_stars_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-24T21:55:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T16:32:54.000Z", "max_issues_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/create_sunshine_cru_grids.r", "max_issues_repo_name": "ua-snap/downscale", "max_issues_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17, "max_issues_repo_issues_event_min_datetime": "2016-01-04T23:37:47.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-17T20:57:02.000Z", "max_forks_repo_path": "snap_scripts/old_scripts/tem_iem_older_scripts_april2018/tem_inputs_iem/FromSteph_and_Dave_Radiation/StephSept/calcn_startwithlat/create_sunshine_cru_grids.r", "max_forks_repo_name": "ua-snap/downscale", "max_forks_repo_head_hexsha": "3fe8ea1774cf82149d19561ce5f19b25e6cba6fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-09-16T04:48:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-25T03:46:00.000Z", "avg_line_length": 45.7735849057, "max_line_length": 266, "alphanum_fraction": 0.7013190437, "num_tokens": 3553, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.31915043934702697}} {"text": "\nsource(\"helpers.r\")\n\n\n# Parameter setting\nif (length(commandArgs()) > 4) {\n seed_script1 <- as.numeric(commandArgs()[5])\n} else {\n if (!exists(\"seed_script1\")) seed_script1 <- 1\n}\n\nif (length(commandArgs()) > 5) {\n batchj <- as.numeric(commandArgs()[6])\n} else {\n if (!exists(\"batchj\")) batchj <- 4\n}\n\n\n\nrseed = seed_script1\n\n####\n####\n#### Read the Data\n####\nalldata = read.csv(\"alldata.csv\", sep = \",\")\n\nsource(\"helpers.r\")\n\npdfname <- \"UNSC\" # unscaled\ntargetname <- \"BCR5y\"\nvaltyp <- \"LOOCV\" \nerrorfun <- \"aucerror\"\ncormethod <- \"spearman\" \ncl_family <- \"binomial\" \nk <- 50 # number of bootsraps per run and model. k=100 means 100 BSs, so 100 errors, formed to one boxplot with one median\nz <- 1 # number of runs. z=30 means 30 runs, so histograms are averaged over 30 histograms, there are 30 distances of histograms to the average histogram.\ne <- 1000 # end. Look at the e best models at most (default 1000).\nntree <- 200 # number of trees for a random forest\nctype=\"rf\"\n\n\n\npredNames20 = c(\"ATRN\", \"BTD\", \"CADM1\", \"CATD\", \"CERU\", \"CFAH\", \"ECM1\", \"FINC\", \"GOLM1\", \"HYOU1\", \"ICAM1\", \"LG3BP\",\n \"LUM\", \"NCAM1\", \"PIGR\", \"PLXB2\", \"POSTN\", \"TRFE\", \"TSP1\", \"VTNC\", \"ZA2G\")\n\npredNames20 <- sort(predNames20)\n\npredNamesRef_PSA <- c(\"PSA_Dens\")\npredNamesRef_PSABX <- c(predNamesRef_PSA, \"BX_GS\")\n\n\nalldata <- removeNas(alldata, unique(c(predNamesRef_PSABX, predNames20, targetname)))\n\n\n\n\n# Train on one batch ProCOC X\ntrain_data_global <- alldata[which(alldata$reading==1 & alldata$ProCOC==batchj), ]\ntest_data_global <- alldata[which(alldata$reading==1 & alldata$ProCOC!=batchj), ]\npdfname <- paste(c(pdfname, \"_batch\", batchj), collapse = \"\")\npdfname <- paste(c(pdfname, \"_\", errorfun, \"_\", ctype), collapse=\"\")\n\ndat_train <- train_data_global\ndat_test <- test_data_global # for overall validation, will not be used in CV\n\ndat_train <- removeNas(dat_train, unique(c(predNamesRef_PSABX, predNames20, targetname)))\ndat_test <- removeNas(dat_test, unique(c(predNamesRef_PSABX, predNames20, targetname)))\n\nX <- dat_train\n\n\nn <- length(predNames20) # number of predictors from which a combination can be chosen (default 20)\np <- 5 # highest number of predictors in one model (default 5)\n\n# LOAD INDICES\nindexfile <- paste(as.character(n), as.character(p), \".RData\", sep=\"\")\nif (file.exists(indexfile)) {\n load(indexfile)\n} else {\n allInd <- CreateIndices(n, p) \n save(allInd, file=indexfile)\n}\n\n\n\n# BOOTSTRAPS \nindRowNA <- dim(X)[1]+1\nd = dim(X[-indRowNA,])[1]\nallind <- c(1:d)\nbserrorsAllz <- list()\nbsindAll <- allInd\n\ntablesNew <- TRUE\n\n\n\n# calculate reference models PSA\nRF_refs_PSABX <- list()\nGLM_refs_PSABX <- list()\nRF_refs_PSA <- list()\nGLM_refs_PSA <- list()\n\nRF_refs_PSABX_errors <- c()\nGLM_refs_PSABX_errors <- c()\nRF_refs_PSA_errors <- c()\nGLM_refs_PSA_errors <- c()\n\ntrain_inds <- list()\ntest_inds <- list()\nset.seed(rseed)\nfor (i in 1:k) {\n set.seed(i)\n ind <- order(dat_train[,targetname])[sampling::strata(dat_train[order(dat_train[,targetname]),], targetname, round(0.69 * as.numeric(summary(dat_train[, targetname])[2:3])), \"srswor\")$ID_unit]\n ind_oob <- (1:nrow(dat_train))[-ind]\n \n train_inds <- c(train_inds, list(ind))\n test_inds <- c(test_inds, list(ind_oob))\n \n dat_fold_train <- dat_train[ind, ]\n dat_fold_test <- dat_train[ind_oob, ]\n \n \n ##\n ## Random Forest\n ##\n require(randomForest)\n set.seed(rseed)\n rf_fit <- randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(predNamesRef_PSABX, collapse=\"+\")), collapse=\"\")), data.frame(dat_fold_train), importance=TRUE, na.action=na.omit, ntree = ntree, mtry=length(predNamesRef_PSABX))\n #RF_refs_PSABX_errest <- c(RF_refs_PSABX_errest, 1-sum(diag(rf_fit$confusion))/sum(rf_fit$confusion[1:2, 1:2]))\n if (errorfun == \"aucerror\") {\n RF_refs_PSABX_errors <- c(RF_refs_PSABX_errors, errormeasureAUC(dat_fold_test[, targetname], predict(rf_fit, newdata = dat_fold_test, type=\"prob\")[,2]))\n } else if (errorfun == \"surverror\") {\n RF_refs_PSABX_errors <- c(RF_refs_PSABX_errors, errormeasureSURV(predict(rf_fit, newdata = dat_fold_test), dat_fold_test))\n } else {\n RF_refs_PSABX_errors <- c(RF_refs_PSABX_errors, errormeasure(dat_fold_test[, targetname], predict(rf_fit, newdata = dat_fold_test)))\n }\n RF_refs_PSABX <- c(RF_refs_PSABX, list(rf_fit))\n \n set.seed(rseed)\n rf_fit <- randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(predNamesRef_PSA, collapse=\"+\")), collapse=\"\")), data.frame(dat_fold_train), importance=TRUE, na.action=na.omit, ntree = ntree, mtry=length(predNamesRef_PSA))\n if (errorfun == \"aucerror\") {\n RF_refs_PSA_errors <- c(RF_refs_PSA_errors, errormeasureAUC(dat_fold_test[, targetname], predict(rf_fit, newdata = dat_fold_test, type=\"prob\")[,2]))\n } else if (errorfun == \"surverror\") {\n RF_refs_PSA_errors <- c(RF_refs_PSA_errors, errormeasureSURV(predict(rf_fit, newdata = dat_fold_test), dat_fold_test))\n } else {\n RF_refs_PSA_errors <- c(RF_refs_PSA_errors, errormeasure(dat_fold_test[, targetname], predict(rf_fit, newdata = dat_fold_test)))\n }\n RF_refs_PSA <- c(RF_refs_PSA, list(rf_fit))\n \n}\n\n\n\n###\n### Validation Functions\n###\n\ndoClassifierSurvivalFitWithClassifier <- function(data, classifier, tit, classifierPSA=NA, classifierPSABX=NA, legend=TRUE, doPlot=TRUE, plotSurvivalRatherThanROC=TRUE, plotPSA=TRUE, plotPSABX=TRUE, plotNCCN=TRUE, cutoff=0.5, cutoffPSA=0.5, cutoffPSABX=0.5, ctype=\"rf\", mark = 3, lwd=1, ...) {\n require(pROC)\n require(survival)\n \n data. <- data\n \n if (ctype==\"rf\") {\n data.$Score <- predict(classifier, newdata = data., type=\"prob\")[,2]\n } else {\n data.$Score <- predict(classifier, newdata = data., type=\"response\")\n }\n \n rocScore = roc(data.[, targetname], data.$Score, ci=TRUE)\n if (cutoff<0) {\n cutoff = rocScore$thresholds[which.max(rocScore$specificities + rocScore$sensitivities)[1]]\n }\n if (class(data.$Score) == \"numeric\") {\n data.$Score <- factor(data.$Score >= cutoff)\n }\n levels(data.$Score) <- c(\"LOW\", \"HIGH\")\n \n Pred.surv <- survfit(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ Score, data = data.)\n Pred.cox <- coxph(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ Score, data = data.)\n \n if (doPlot) {\n if (plotSurvivalRatherThanROC)\n plot(Pred.surv, col = 1:2, ylab=\"Cumulative Survival\", xlab=\"Months\", main=tit, sub=paste(c(\"n = \", paste(Pred.surv$n, collapse=\" / \"), \", p=\", format(summary(Pred.cox)$logtest[3], digits=3, nsmall=2)), collapse=\"\"), lwd=lwd, mark=mark, ...)\n else {\n plot(rocScore, col = 1, main=tit, sub=paste(c(\"n = \", length(rocScore$cases)+length(rocScore$controls)), collapse=\"\"), mar=c(5.1,4.1,4.2,2.1), ...)\n cis = rocScore$ci[c(1,3)]\n text(x=0.4, y=0.4, labels=paste(c(\"AUC \", format(auc(rocScore), digits=3, nsmall=2), \" [\", format(cis[1], digits=3, nsmall=2), \";\", format(cis[2], digits=3, nsmall=2), \"]\"), collapse=\"\"), pos = 4, col=1)\n }\n \n if (plotPSA && !is.na(classifierPSA)) {\n if (ctype==\"rf\") {\n data.$PSAScore <- predict(classifierPSA, newdata = data., type=\"prob\")[,2]\n } else {\n data.$PSAScore <- predict(classifierPSA, newdata = data., type=\"response\")\n }\n rocPSA = roc(data.[, targetname], data.$PSAScore, ci=TRUE)\n if (cutoffPSA<0) {\n cutoffPSA = rocPSA$thresholds[which.max(rocPSA$specificities + rocPSA$sensitivities)[1]]\n }\n if (class(data.$PSAScore) == \"numeric\") {\n data.$PSAScore <- factor(data.$PSAScore >= cutoffPSA)\n }\n levels(data.$PSAScore) <- c(\"LOW\", \"HIGH\")\n \n PredPSA.surv <- survfit(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ PSAScore, data = data.)\n PredPSA.cox <- coxph(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ PSAScore, data = data.)\n \n if (plotSurvivalRatherThanROC) {\n lines(PredPSA.surv, col = c(\"gray\", \"orange\"), lty=2, mark=mark, lwd=lwd)\n mtext(text=paste(c(\"PSA \", paste(PredPSA.surv$n, collapse=\"/\"), \" (p=\", format(summary(PredPSA.cox)$logtest[3], digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"darkgray\", side = 3, adj = 0, cex=0.7)\n } else {\n plot(rocPSA, col = \"darkgray\", lty=2, add=TRUE, ...)\n mtext(text=paste(c(\"PSA \", paste(c(\"n = \", length(rocPSA$cases)+length(rocPSA$controls)), collapse=\"\"), \" (p=\", format(roc.test(rocScore, rocPSA)$p.value, digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"darkgray\", side = 3, adj = 0, cex=0.7)\n cis = rocPSA$ci[c(1,3)]\n text(x=0.4, y=0.3, labels=paste(c(\"AUC \", format(auc(rocPSA), digits=3, nsmall=2), \" [\", format(cis[1], digits=3, nsmall=2), \";\", format(cis[2], digits=3, nsmall=2), \"]\"), collapse=\"\"), pos = 4, col=\"darkgray\")\n }\n }\n \n if (plotPSABX && !is.na(classifierPSABX)) {\n if (ctype==\"rf\") {\n data.$PSABXScore <- predict(classifierPSABX, newdata = data., type=\"prob\")[,2]\n } else {\n data.$PSABXScore <- predict(classifierPSABX, newdata = data., type=\"response\")\n }\n rocPSABX = roc(data.[, targetname], data.$PSABXScore, ci=TRUE)\n if (cutoffPSABX<0) {\n cutoffPSABX = rocPSABX$thresholds[which.max(rocPSABX$specificities + rocPSABX$sensitivities)[1]]\n }\n if (class(data.$PSABXScore) == \"numeric\") {\n data.$PSABXScore <- factor(data.$PSABXScore >= cutoffPSABX)\n }\n levels(data.$PSABXScore) <- c(\"LOW\", \"HIGH\")\n \n PredPSABX.surv <- survfit(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ PSABXScore, data = data.)\n PredPSABX.cox <- coxph(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ PSABXScore, data = data.)\n \n if (plotSurvivalRatherThanROC) {\n lines(PredPSABX.surv, col = c(\"lightblue3\", \"lightblue4\"), lty=4, mark=mark, lwd=lwd)\n mtext(text=paste(c(\"PSA/BX \", paste(PredPSABX.surv$n, collapse=\"/\"), \" (p=\", format(summary(PredPSABX.cox)$logtest[3], digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"lightblue3\", side = 3, adj = 1, cex=0.7)\n } else {\n plot(rocPSABX, col = \"lightblue3\", lty=4, add=TRUE, ...)\n mtext(text=paste(c(\"PSA/BX \", paste(c(\"n = \", length(rocPSABX$cases)+length(rocPSABX$controls)), collapse=\"\"), \" (p=\", format(roc.test(rocScore, rocPSABX)$p.value, digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"lightblue3\", side = 3, adj = 1, cex=0.7)\n cis = rocPSABX$ci[c(1,3)]\n text(x=0.4, y=0.2, labels=paste(c(\"AUC \", format(auc(rocPSABX), digits=3, nsmall=2), \" [\", format(cis[1], digits=3, nsmall=2), \";\", format(cis[2], digits=3, nsmall=2), \"]\"), collapse=\"\"), pos = 4, col=\"lightblue3\")\n }\n }\n \n if (plotNCCN) {\n # References NCCN\n rocNCCN = roc(data.[, targetname], data.$NCCN_risk_group_Bx, ci=TRUE)\n PredNCCN.surv <- survfit(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ NCCN_risk_group_Bx>2, data = data.)\n PredNCCN.cox <- coxph(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ NCCN_risk_group_Bx>2, data = data.)\n \n if (plotSurvivalRatherThanROC) {\n lines(PredNCCN.surv, col = c(\"palegreen3\", \"palegreen4\"), lty=5, mark=mark, lwd=lwd)\n mtext(text=paste(c(\"NCCN \", paste(PredNCCN.surv$n, collapse=\"/\"), \" (p=\", format(summary(PredNCCN.cox)$logtest[3], digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"palegreen3\", side = 4, adj = 1, cex=0.7)\n } else {\n plot(rocNCCN, col = \"palegreen3\", lty=5, add=TRUE, ...)\n mtext(text=paste(c(\"NCCN \", paste(c(\"n = \", length(rocNCCN$cases)+length(rocNCCN$controls)), collapse=\"\"), \" (p=\", format(roc.test(rocScore, rocNCCN)$p.value, digits=3, nsmall=2), \")\"), collapse=\"\"), col=\"palegreen3\", side = 4, adj = 1, cex=0.7)\n cis = rocNCCN$ci[c(1,3)]\n text(x=0.4, y=0.1, labels=paste(c(\"AUC \", format(auc(rocNCCN), digits=3, nsmall=2), \" [\", format(cis[1], digits=3, nsmall=2), \";\", format(cis[2], digits=3, nsmall=2), \"]\"), collapse=\"\"), pos = 4, col=\"palegreen3\")\n }\n }\n \n if (legend) {\n if (plotSurvivalRatherThanROC && !is.null(names(Pred.surv$strata))) {\n leg = names(Pred.surv$strata)\n leglty = c(1,1)\n legcol = c(\"black\", \"red\")\n \n if (plotPSA && !is.na(classifierPSA)) {\n leg = c(leg, names(PredPSA.surv$strata))\n leglty = c(leglty, c(2,2))\n legcol = c(legcol, c(\"gray\", \"orange\"))\n }\n if (plotPSABX && !is.na(classifierPSABX)) {\n leg = c(leg, names(PredPSABX.surv$strata))\n leglty = c(leglty, c(4,4))\n legcol = c(legcol, c(\"lightblue3\", \"lightblue4\"))\n }\n if (plotNCCN) {\n leg = c(leg, names(PredNCCN.surv$strata))\n leglty = c(leglty, c(5,5))\n legcol = c(legcol, c(\"palegreen3\", \"palegreen4\"))\n }\n legend(\"bottomleft\", leg, col=legcol, lty=leglty, bty=\"n\", cex=0.8)\n }\n }\n }\n \n return (summary(Pred.cox)$logtest[3])\n}\n\ndoClassifierSurvivalFitWithPrednames <- function(datatrain, datatest, predNames, targetname, tit=\"\", ranseed = rseed, ktree = ntree, doPlot=TRUE, cutoff=0.5, cutoffPSA=0.5, cutoffPSABX=0.5, ctype=\"rf\", plotSurvivalRatherThanROC=TRUE, ...) {\n \n set.seed(ranseed)\n if (ctype==\"rf\") {\n require(randomForest)\n classifier = randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(predNames), collapse=\"+\")), collapse=\"\")), data.frame(datatrain), importance=TRUE, na.action=na.omit, ntree = ktree, mtry=length(unique(predNames)))\n } else {\n classifier = glm(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(predNames), collapse=\"+\")), collapse=\"\")), family=binomial, data=data.frame(datatrain))\n }\n \n set.seed(ranseed)\n if (ctype==\"rf\") {\n classifierPSA = randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(\"PSA_Dens\", collapse=\"+\")), collapse=\"\")), data.frame(datatrain), importance=TRUE, na.action=na.omit, ntree = ktree)\n } else {\n classifierPSA = glm(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(\"PSA_Dens\", collapse=\"+\")), collapse=\"\")), family=binomial, data=data.frame(datatrain))\n }\n \n set.seed(ranseed)\n if (ctype==\"rf\") {\n classifierPSABX = randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(c(\"PSA_Dens\", \"BX_GS\")), collapse=\"+\")), collapse=\"\")), data.frame(datatrain), importance=TRUE, na.action=na.omit, ntree = ktree, mtry=2)\n } else {\n classifierPSABX = glm(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(c(\"PSA_Dens\", \"BX_GS\")), collapse=\"+\")), collapse=\"\")), family=binomial, data=data.frame(datatrain))\n }\n \n if (is.na(tit) || tit==\"\") {\n tit = paste(predNames, collapse=\" + \")\n }\n \n return (doClassifierSurvivalFitWithClassifier(datatest, classifier, tit, classifierPSA, classifierPSABX, doPlot=doPlot, cutoff=cutoff, cutoffPSA=cutoffPSA, cutoffPSABX=cutoffPSABX, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...))\n}\n\ndoSurvivalTestWithPrednames <- function(predNames, alldata.=alldata, doPlot=TRUE, ctype=\"rf\", plotSurvivalRatherThanROC=TRUE, ...) {\n par_before <- par(no.readonly = TRUE)\n par(mfrow=c(2,2))\n I <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC!=1), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==1), ], predNames, targetname, tit=\"ProCOC I\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n II <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC!=2), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==2), ], predNames, targetname, tit=\"ProCOC II\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n III <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC!=3), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==3), ], predNames, targetname, tit=\"ProCOC III\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n IV <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC!=4), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==4), ], predNames, targetname, tit=\"ProCOC IV\", legend=TRUE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n if (doPlot) {\n at. = 1.5\n if (plotSurvivalRatherThanROC)\n at. = -10\n mtext(paste(predNames, collapse=\" + \"),line = 24, at = at., col=\"sienna3\")\n mtext(paste(c(\"Batchwise\\nCross-Validated\"), collapse=\"\"),line = 3.5, at = at., col=\"tomato\")\n }\n par(par_before)\n return (c(I=I, II=II, III=III, IV=IV))\n}\n\ndoSurvivalTestWithPrednamesBatchwise <- function(predNames, alldata.=alldata, doPlot=TRUE, ctype=\"rf\", plotSurvivalRatherThanROC=TRUE, ...) {\n par_before <- par(no.readonly = TRUE)\n par(mfrow=c(2,2))\n allbatches <- c()\n for (batch in 1:4) {\n I <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC==batch), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==1), ], predNames, targetname, tit=\"ProCOC I\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n II <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC==batch), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==2), ], predNames, targetname, tit=\"ProCOC II\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n III <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC==batch), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==3), ], predNames, targetname, tit=\"ProCOC III\", legend=FALSE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n IV <- doClassifierSurvivalFitWithPrednames(alldata.[which(alldata.$reading==1 & alldata.$ProCOC==batch), ], alldata.[which(alldata.$reading==1 & alldata.$ProCOC==4), ], predNames, targetname, tit=\"ProCOC IV\", legend=TRUE, doPlot=doPlot, cutoff=-1, cutoffPSA=-1, cutoffPSABX=-1, ctype=ctype, plotSurvivalRatherThanROC=plotSurvivalRatherThanROC, ...)\n if (doPlot) {\n at. = 1.5\n if (plotSurvivalRatherThanROC)\n at. = -10\n mtext(paste(predNames, collapse=\" + \"),line = 24, at = at., col=\"sienna3\")\n mtext(paste(c(\"Trained on Batch \", batch), collapse=\"\"),line = 3.5, at = at., col=\"tomato\")\n }\n allbatches <- c(allbatches, list(c(I=I, II=II, III=III, IV=IV)))\n }\n par(par_before)\n return (allbatches)\n}\n\ndoCV <- function(dat_train, predNames, targetname, rseed = 1, testdata = NA, k_fold = k, ncores = round(0.5*detectCores()), doPlot=FALSE, addToPlot=NA, tit=NA, smoothAUC=FALSE, plotSurvivalRatherThanROC=TRUE, ...) {\n require(doParallel)\n require(pROC)\n require(survival)\n require(randomForestSRC)\n \n registerDoParallel(cores=ncores)\n \n \n CVfolds <- foreach(i=1:k_fold,.combine=\"c\", .packages = c('randomForest', 'boot', 'survival', 'randomForestSRC')) %do% { #inner loop. Change %dopar% to %do% to run sequentially (a normal for loop)\n \n if (is.na(addToPlot)) {\n addToPlot_ = i!=1\n } else {\n addToPlot_ = addToPlot\n }\n if (is.na(tit)) {\n tit_ = paste(predNames, collapse=\" + \")\n } else {\n tit_ = tit\n }\n \n ind <- train_inds[[i]]\n ind_oob <- test_inds[[i]]\n \n dat_fold_train <- dat_train[ind, ]\n dat_fold_test <- dat_train[ind_oob, ]\n \n CVfold <- c()\n \n ##\n ## Random Forest\n ##\n if (ctype==\"rf\") {\n require(randomForest)\n set.seed(rseed)\n CVfold$fit_RF_PSABX <- randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(c(predNamesRef_PSABX, predNames)), collapse=\"+\")), collapse=\"\")), data.frame(dat_fold_train), importance=TRUE, na.action=na.omit, ntree = ntree, mtry=length(unique(c(predNamesRef_PSABX, predNames))))\n if (errorfun == \"aucerror\") {\n CVfold$fit_RF_PSABX_error <- errormeasureAUC(dat_fold_test[, targetname], predict(CVfold$fit_RF_PSABX, newdata = dat_fold_test, type=\"prob\")[,2])\n } \n \n set.seed(rseed)\n CVfold$fit_RF_PSA <- randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(c(predNamesRef_PSA, predNames)), collapse=\"+\")), collapse=\"\")), data.frame(dat_fold_train), importance=TRUE, na.action=na.omit, ntree = ntree, mtry=length(unique(c(predNamesRef_PSA, predNames))))\n if (errorfun == \"aucerror\") {\n CVfold$fit_RF_PSA_error <- errormeasureAUC(dat_fold_test[, targetname], predict(CVfold$fit_RF_PSA, newdata = dat_fold_test, type=\"prob\")[,2], doPlot=(doPlot && !plotSurvivalRatherThanROC), smoothAUC, add=addToPlot_, main=tit_, getROC = FALSE, ...)\n CVfold$fit_RF_PSA_ROC <- errormeasureAUC(dat_fold_test[, targetname], predict(CVfold$fit_RF_PSA, newdata = dat_fold_test, type=\"prob\")[,2], doPlot=FALSE, smoothAUC, add=addToPlot_, main=tit_, getROC = TRUE, ...)\n \n if (!smoothAUC) {\n roc_ = CVfold$fit_RF_PSA_ROC\n cutoff = roc_$thresholds[which.max(roc_$specificities + roc_$sensitivities)[1]]\n dat_fold_test$Score = predict(CVfold$fit_RF_PSA, newdata = dat_fold_test, type=\"prob\")[,2]\n if (class(dat_fold_test$Score) == \"numeric\") {\n dat_fold_test$Score <- factor(dat_fold_test$Score >= cutoff)\n }\n levels(dat_fold_test$Score) <- c(\"LOW\", \"HIGH\")\n \n Pred.surv <- survfit(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ Score, data = dat_fold_test)\n Pred.cox <- coxph(Surv(time=Time_BCR_free_survival, event=Status_BCR, type=\"right\") ~ Score, data = dat_fold_test)\n \n CVfold$fit_RF_PSA_SURV <- Pred.surv\n CVfold$fit_RF_PSA_COX <- Pred.cox\n \n if (doPlot && plotSurvivalRatherThanROC) {\n if (!addToPlot_) {\n plot(Pred.surv, ylab=\"Cumulative Survival\", xlab=\"Months\", main=tit_, ...)\n } else {\n lines(Pred.surv, ...)\n }\n }\n }\n }\n \n set.seed(rseed)\n CVfold$fit_RF <- randomForest(formula(paste(c(\"as.factor(\", targetname, \")~\", paste(unique(c(predNames)), collapse=\"+\")), collapse=\"\")), data.frame(dat_fold_train), importance=TRUE, na.action=na.omit, ntree = ntree, mtry=length(unique(c(predNames))))\n if (errorfun == \"aucerror\") {\n CVfold$fit_RF_error <- errormeasureAUC(dat_fold_test[, targetname], predict(CVfold$fit_RF, newdata = dat_fold_test, type=\"prob\")[,2], getROC=FALSE)\n CVfold$fit_RF_ROC <- errormeasureAUC(dat_fold_test[, targetname], predict(CVfold$fit_RF, newdata = dat_fold_test, type=\"prob\")[,2], getROC=TRUE)\n }\n }\n list(CVfold)\n }\n \n return (CVfolds)\n}\n\n\n\n\n\n", "meta": {"hexsha": "fca5d07ff39a33fe611e92c69fa4587b69dd424a", "size": 23063, "ext": "r", "lang": "R", "max_stars_repo_path": "prostate_Script1.r", "max_stars_repo_name": "jetic83/MSGUIDE", "max_stars_repo_head_hexsha": "a0c9597dfc0c26d1b850108275126d93df6604ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "prostate_Script1.r", "max_issues_repo_name": "jetic83/MSGUIDE", "max_issues_repo_head_hexsha": "a0c9597dfc0c26d1b850108275126d93df6604ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "prostate_Script1.r", "max_forks_repo_name": "jetic83/MSGUIDE", "max_forks_repo_head_hexsha": "a0c9597dfc0c26d1b850108275126d93df6604ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.576754386, "max_line_length": 355, "alphanum_fraction": 0.6540346009, "num_tokens": 7482, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3190555223477802}} {"text": "# GetScalingDocsizeData.r\n\nsource(\"../common/DataUtil.r\")\n\n# G E T D A T A \nrm(results)\nresults <- fndfGetGiantData(\"./\")\n# Get fewer columns to work with, easier to see.\ndat.docsize <- fndfGetDocsizeData(results)\n\n# T A B U L A T E D A T A \n# Tabulate columns by copies and sector lifetime.\ntbl.docsize <- data.frame(with(dat.docsize, \n tapply(mdmlosspct, list(docsize, lifem), FUN=identity)))\n# Re-form the data into a table for printing. \nfoo<-(with(dat.docsize, \n tapply(mdmlosspct, list(docsize, lifem), FUN=identity)))\nfoo2<-cbind(as.numeric(levels(factor(results$docsize))), foo)\ntbl2<-data.frame(foo2)\ncolnames(tbl2)<-c(\"docsize\",as.numeric(colnames(foo2[,2:ncol(foo2)])))\n\n# Pretty-print a table to a file with explanatory headings.\nsOutputFilename <- \"./Data_ScalingDocsize.txt\"\nsTitle <- \"Document losses with single copy and varying document size\"\nsSubtitle <- \"Table of percentage loss of collection tabulated\\n\" %+% \n \"by document size (MB) and sector half-lifetime\" %+% \"\\n\\n\" %+%\n \"Note how document size and sector life scale together\" %+% \"\\n\\n\\n\" %+%\n \" sector half-life (megahours)\"\nsink(sOutputFilename)\ncat(\"MIT Preservation Simulation Project\", \"\\n\\n\")\ncat(sOutputFilename, format(Sys.time(),\"%Y%m%d_%H%M%S%Z\"), \"\\n\\n\")\ncat(sTitle, \"\\n\\n\")\ncat(sSubtitle, \"\\n\\n\")\ncat(\"\\n\")\n# Micah succeeded in bending dcast to his will, thanks; see email.\nlibrary(reshape2)\nbar.small <- dat.docsize[,c(\"lifem\", \"docsize\", \"mdmlosspct\")]\nbar.melted <- melt(bar.small, id=c(\"docsize\", \"lifem\"))\nbar.recast <- dcast(bar.melted, docsize~lifem)\nprint(bar.recast)\nsink()\n\n# Also print the raw table for use in a spreadsheet of comparisons.\nsComparisonDataFilename <- \"Data_Scaling_DocsizeSpreadsheetData.txt\"\nsink(sComparisonDataFilename)\nprint(bar.small, n=nrow(bar.small))\nsink()\n\n# P L O T D A T A \nlibrary(ggplot2)\n\n# Show lines for docsizes 5, 50, 500, 5000 MB; copies=1; over wiiiide range.\nnDocsize <- 5; trows <- dat.docsize[dat.docsize$docsize==nDocsize,]\ngp <- ggplot(data=trows,aes(x=lifem,y=safe(mdmlosspct))) \ngp <- gp + \n scale_x_log10() + scale_y_log10() +\n annotation_logticks()\n\n# docsize 5 MB\ngp <- gp + \n aes(trows, x=(lifem), y=(safe(mdmlosspct))) +\n geom_point(data=trows, \n color=\"red\", size=5, shape=(\"5\")) +\n geom_line(data=trows, \n linetype=\"dashed\", color=\"blue\", size=1)\n\n# docsize 50 MB\nnDocsize <- 50; trows <- dat.docsize[dat.docsize$docsize==nDocsize,]\ngp <- gp + \n aes(trows, x=(lifem), y=(safe(mdmlosspct))) +\n geom_point(data=trows, \n color=\"red\", size=5, shape=(\"B\")) +\n geom_line(data=trows, linetype=\"dashed\", color=\"blue\", size=1) \n\n# docsize 500 MB\nnDocsize <- 500; trows <- dat.docsize[dat.docsize$docsize==nDocsize,]\ngp <- gp + \n aes(trows, x=(lifem), y=(safe(mdmlosspct))) +\n geom_point(data=trows, \n color=\"red\", size=5, shape=(\"C\")) +\n geom_line(data=trows, linetype=\"dashed\", color=\"black\", size=1) \n\n# docsize 5000 MB\nnDocsize <- 5000; trows <- dat.docsize[dat.docsize$docsize==nDocsize,]\ngp <- gp + \n aes(trows, x=(lifem), y=(safe(mdmlosspct))) +\n geom_point(data=trows, \n color=\"red\", size=5, shape=(\"D\")) +\n geom_line(data=trows, linetype=\"dashed\", color=\"black\", size=1) \n\ngp <- gp + ggtitle(\"Larger documents are larger targets to random errors\\n\" %+%\n \"and therefore are lost more frequently.\\n\\n\" %+%\n \"Losses for collection with copies=1\")\ngp <- gp + xlab(\"sector half-life (megahours)\")\ngp <- gp + ylab(\"percent permanent document losses\")\ngp <- gp + theme(\n axis.text=element_text(size=12),\n axis.title=element_text(size=18),\n plot.title=element_text(size=18,face=\"bold\"),\n panel.border = element_rect(color = \"black\", fill=NA, size=1)\n )\n\nplot(gp)\nfnPlotMakeFile(gp, \"baseline-scalingdocsize.png\")\n\n\n\n# Unwind any remaining sink()s to close output files. \nwhile (sink.number() > 0) {sink()}\n", "meta": {"hexsha": "ebff301b9c58dace8cdec26039214508b9d414b7", "size": 4036, "ext": "r", "lang": "R", "max_stars_repo_path": "oldpictures/largerdocs/GetScalingDocsizeData.r", "max_stars_repo_name": "MIT-Informatics/PreservationSimulation", "max_stars_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_stars_repo_licenses": ["X11"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2016-08-24T05:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-12T16:44:48.000Z", "max_issues_repo_path": "oldpictures/largerdocs/GetScalingDocsizeData.r", "max_issues_repo_name": "MIT-Informatics/PreservationSimulation", "max_issues_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_issues_repo_licenses": ["X11"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-20T02:55:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-20T02:55:37.000Z", "max_forks_repo_path": "oldpictures/largerdocs/GetScalingDocsizeData.r", "max_forks_repo_name": "MIT-Informatics/PreservationSimulation", "max_forks_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_forks_repo_licenses": ["X11"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.6909090909, "max_line_length": 79, "alphanum_fraction": 0.646432111, "num_tokens": 1209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3190555150980551}} {"text": "#==============================================================================\n#\tTest for has.offset method in model.interface.\n#==============================================================================\nlibrary(testthat)\nlibrary(model.adapter)\n\n\n#------------------------------------------------------------------------------\n#\tCreate test data.\n#------------------------------------------------------------------------------\ntest.data <- list(\n\t# Test formula with no offset.\n\tcall.no.offset = list(\n\t\tx = substitute(glm(Petal.Length ~ ., data = iris)),\n\t\texpected = character()\n\t),\n\t# Test formula with two offset without and with log.\n\tcall.formula.1 = list(\n\t\tx = substitute(\n\t\t\tglm(\n\t\t\t\tPetal.Length ~ . + offset(iris$Sepal.Width)\n\t\t\t\t+ offset(log(iris$Petal.Width)),\n\t\t\t\tdata = iris\n\t\t\t)\n\t\t),\n\t\texpected = c(\"iris$Sepal.Width\", \"iris$Petal.Width\")\n\t),\n\t# Test formula without '$'.\n\tcall.formula.2 = list(\n\t\tx = substitute(\n\t\t\tglm(Petal.Length ~ . + offset(Sepal.Width), data = iris)\n\t\t),\n\t\texpected = c(\"Sepal.Width\")\n\t),\n\t# Test argument with '$'\n\tcall.argument.1 = list(\n\t\tx = substitute(\n\t\t\tglm(Petal.Length ~ ., offset = iris$Sepal.Width, data = iris)\n\t\t),\n\t\texpected = c(\"iris$Sepal.Width\")\n\t),\n\t# Test argument with log\n\tcall.argument.2 = list(\n\t\tx = substitute(\n\t\t\tglm(Petal.Length ~ ., offset = log(Sepal.Width), data = iris)\n\t\t),\n\t\texpected = c(\"Sepal.Width\")\n\t),\n\t# Test argument with log and '$'\n\tcall.argument.3 = list(\n\t\tx = substitute(\n\t\t\tglm(Petal.Length ~ ., offset = log(iris$Sepal.Width), data = iris)\n\t\t),\n\t\texpected = c(\"iris$Sepal.Width\")\n\t),\n\t# Test argument and formula together.\n\tcall.both = list(\n\t\tx = substitute(\n\t\t\tglm(\n\t\t\t\tPetal.Length ~ . + offset(log(Petal.Width)),\n\t\t\t\toffset = Sepal.Width, data = iris\n\t\t\t)\n\t\t),\n\t\texpected = c(\"Petal.Width\", \"Sepal.Width\")\n\t)\n)\n\nfor (i in names(test.data)) {\n\ttest.data[[gsub(\"^call\", \"object\", i)]] <- test.data[[i]]\n\ttest.data[[i]]$x <- eval(test.data[[i]]$x)\n}\n\n\n#------------------------------------------------------------------------------\n#\tRun tests.\n#------------------------------------------------------------------------------\ntest_that(\n\t\"Testing model.interface.derault$has.offset()\",\n\t{\n\t\tfor (i in names(test.data)) {\n\t\t\texpect_equal(\n\t\t\t\tmodel.adapter:::model.interface.default()$get.offset.names(\n\t\t\t\t\ttest.data[[i]]$x, .GlobalEnv, \"stats\"\n\t\t\t\t),\n\t\t\t\ttest.data[[i]]$expected, info = sprintf(\"While testing %s\", i)\n\t\t\t)\n\t\t}\n\t}\n)\n\n", "meta": {"hexsha": "401d9abceabaa8bcef11315c153dd119c2dacf9d", "size": 2426, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/test__model.interface__get.offset.name.r", "max_stars_repo_name": "Marchen/model.adapter", "max_stars_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/test__model.interface__get.offset.name.r", "max_issues_repo_name": "Marchen/model.adapter", "max_issues_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2018-11-20T10:07:41.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T01:11:12.000Z", "max_forks_repo_path": "tests/test__model.interface__get.offset.name.r", "max_forks_repo_name": "Marchen/model.adapter", "max_forks_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-04T04:46:54.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-04T04:46:54.000Z", "avg_line_length": 26.6593406593, "max_line_length": 79, "alphanum_fraction": 0.4967023908, "num_tokens": 593, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.31905550784832976}} {"text": "## calling library\nsource('https://raw.githubusercontent.com/laurencelin/R-coded-scripts-for-RHESSys-calibration/master/LIB_RHESSys_calibration.R')\n\n\n\n## -------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n\t## generate random parameters for calibraitons and script file for UVA Rivanna (SLURM) job submission\n\n\tRHESSysParamBoundaryDefault # contains all RHESSys calibration parameters\n\t# see https://github.com/RHESSys/RHESSys/wiki for more information\n\n\t\t# add a new parameter / change parameter search boundary\n\t\tRHESSysParamBoundaryDefault$gw1 = c(0.001,0.4) # change boundary values\n\t\tRHESSysParamBoundaryDefault$gw2 = c(0.001,0.4) # change boundary values\n\t\tRHESSysParamBoundaryDefault$rtz = c(0.5,4.0) # e.g., add new parameter and its bounaries \n\t\n\t# use the block below to customize the calibration parameter search\n\titr = 1:1000 # R code; mean from 1 to 1000; you can start from 1001 to 2000 too.\n\tnum=length(itr) \n\tparam = data.frame(itr = itr)\n\tparam$s1 = runif(num, RHESSysParamBoundaryDefault$s1[1],RHESSysParamBoundaryDefault$s1[2])\n\tparam$s2 = runif(num, RHESSysParamBoundaryDefault$s2[1],RHESSysParamBoundaryDefault$s2[2])\n\tparam$sv1 = runif(num, RHESSysParamBoundaryDefault$sv1[1],RHESSysParamBoundaryDefault$sv1[2])\n\tparam$sv2 = runif(num, RHESSysParamBoundaryDefault$sv2[1],RHESSysParamBoundaryDefault$sv2[2])\n\tparam$gw1 = runif(num, RHESSysParamBoundaryDefault$gw1[1],RHESSysParamBoundaryDefault$gw1[2])\n\tparam$gw2 = runif(num, RHESSysParamBoundaryDefault$gw2[1],RHESSysParamBoundaryDefault$gw2[2])\n\t# some additional parameters may be used for calibration\n\tparam$snowEs = runif(num, RHESSysParamBoundaryDefault$snowEs[1],RHESSysParamBoundaryDefault$snowEs[2])\n\tparam$snowTs = runif(num, RHESSysParamBoundaryDefault$snowTs[1],RHESSysParamBoundaryDefault$snowTs[2])\n\t\t\n\t# use the block below to customize the RHESSys model runs\n\tRHESSys_arg = paste(\n\t\t'path_to_rhessys_binary', # (relative) path to RHESSys compiled binary code\n\t\t'-st 2006 1 1 1 -ed 2017 12 1 1', # start time and end time; typical span-up time is 5-year for a 62 ha catchment\n\t\t'-b', # basin scale output\n\t\t'-newcaprise -capr 0.001 -gwtoriparian -capMax 0.01', # some customized flags\n\t\t'-t tecfiles/tec_daily.txt',\n\t\t'-w worldfiles_fc/worldfile', # worldfile\n\t\t'-whdr worldfiles_fc/worldfile.hdr', # worldfile header\n\t\t'-r flows/subTestfc.txt flows/surfTestfc.txt' # flowtable\n\t\t)\n\t\n\t# call a library function to generate a shell/bash file that contains all iterated RHESSys runs for UVA Rivanna\n\tRivannaJobs(\n\t\tRHESSys_arg, # defined above\n\t\t'output', # output folder name in the RHESSys model folder\n\t\tparam, # defined above\n\t\t'../parallelRun18101fc.sh') # the path/name of the shell/bash file (keep it for it would be used later)\t\n\n\n## -------------------------------------------------------------------------------------------------------------------------------------------------------------\n\t## Once all the submitted jobs are done on UVA Rivanna, download all RHESSys outputs and the shell/bash file\n\t\n\t# set paths/names of the project folder and RHESSys model, ...\n\targList$projPath\t\t= \"~/BAISMAN\" # project folder\n\targList$orbFile\t\t\t= \"usgs01583580.csv\" # observed flow time series (certain format is required; see below)\n\targList$startDate\t\t= \"2010-10-1\" # start time for calibration (may not the same as model start time)\n\targList$endDate\t\t\t= \"2017-9-30\" # end time for calibration (may not the same as model end time)\n\targList$RHESSysModel\t\t= \"rhessys_baisman10m\" # RHESSys model folder\n\targList$RHESSysOutput\t\t= \"output_parallelRun18101fc\" # the output folder \n\targList$runScript\t\t= \"parallelRun18101fc.sh\" # the shell/bash file\n\t\n\t# call a library function to caluate a list of fittness and generate a basic plot for each model run.\n\toutputfile = evaluateModel(argList)\n\t\n\t\n\t\n\t\n", "meta": {"hexsha": "a240a467dea005eaed6851f85c13fc375ab41a9f", "size": 3889, "ext": "r", "lang": "R", "max_stars_repo_path": "example.r", "max_stars_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_stars_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-04-25T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-25T22:08:38.000Z", "max_issues_repo_path": "example.r", "max_issues_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_issues_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "example.r", "max_forks_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_forks_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-06-14T13:11:57.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-05T17:07:12.000Z", "avg_line_length": 55.5571428571, "max_line_length": 160, "alphanum_fraction": 0.7019799434, "num_tokens": 1128, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6584175139669995, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.3189243309047778}} {"text": "context(\"Test detection_phenology\")\n\ntest_that(\"Test detection_phenology\", {\n \n sink(file=ifelse(Sys.info()[\"sysname\"] == \"Windows\",\n \"NUL\",\n \"/dev/null\"))\n # Create data\n n <- 15000 #size of dataset\n nyr <- 20 # number of years in data\n nSamples <- 100 # set number of dates\n nSites <- 50 # set number of sites\n set.seed(125)\n \n # Create somes dates\n first <- as.Date(strptime(\"2010/01/01\", \"%Y/%m/%d\")) \n last <- as.Date(strptime(paste(2010+(nyr-1),\"/12/31\", sep=''),\n \"%Y/%m/%d\")) \n dt <- last-first \n rDates <- first + (runif(nSamples)*dt)\n \n # taxa are set as random letters\n taxa <- sample(letters, size = n, TRUE)\n \n # three sites are visited randomly\n site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)\n \n # the date of visit is selected at random from those created earlier\n survey <- sample(rDates, size = n, TRUE)\n \n # format data\n suppressWarnings({visitData <- formatOccData(taxa = taxa,\n site = site,\n survey = survey,\n includeJDay = TRUE)})\n # create some model results\n modelresults <- occDetFunc(taxa_name = 'a',\n n_iterations = 50,\n burnin = 15, \n occDetdata = visitData$occDetdata,\n spp_vis = visitData$spp_vis,\n write_results = FALSE,\n seed = 111,\n modeltype = c('ranwalk','halfcauchy','jul_date'))\n \n # quick check this output is as expected\n expect_equal(head(modelresults$BUGSoutput$sims.list$alpha.p[,1]),\n c(-3.0072164156934, -2.17484348943153, -2.04228261009605, -2.53916362332146, \n -3.18523796966506, -3.02335381838008),\n tolerance = 1e-7)\n \n # create some model results without Julian date\n modelresults2 <- occDetFunc(taxa_name = 'a',\n n_iterations = 50,\n burnin = 15, \n occDetdata = visitData$occDetdata,\n spp_vis = visitData$spp_vis,\n write_results = FALSE,\n seed = 111,\n modeltype = c('ranwalk','halfcauchy'))\n \n # run the function\n results <- detection_phenology(modelresults,\n spname ='a',\n bins = 12,\n density_function = TRUE)\n \n # create expected data\n head_data_bin <- c(1L, 2L, 3L, 4L, 5L, 6L)\n head_data_mean_pDet <- c(0.07952482, 0.07952476, 0.07952399, 0.07951970, 0.07951016, 0.07955624)\n head_data_lower95CI <- c(0.03930188, 0.03930189, 0.03930365, 0.03936658, 0.04011354, 0.04298810)\n head_data_upper95CI <- c(0.1448798, 0.1448798, 0.1448798, 0.1448798, 0.1448798, 0.1448754)\n head_data_JulianDay <- c(1.00000, 34.09091, 67.18182, 100.27273, 133.36364, 166.45455)\n \n \n expect_identical(names(results), c(\"data\", \"layers\", \"scales\", \"mapping\", \"theme\", \"coordinates\", \"facet\", \"plot_env\", \"labels\"))\n expect_identical(names(results$data), c(\"bin\", \"mean_pDet\", \"lower95CI\", \"upper95CI\", \"JulianDay\"))\n expect_equal(head(results$data$bin), head_data_bin, tolerance = 1e-7)\n expect_equal(head(results$data$mean_pDet), head_data_mean_pDet, tolerance = 1e-7)\n expect_equal(head(results$data$lower95CI), head_data_lower95CI, tolerance = 1e-7)\n expect_equal(head(results$data$upper95CI), head_data_upper95CI, tolerance = 1e-7)\n expect_equal(head(results$data$JulianDay), head_data_JulianDay, tolerance = 1e-5)\n \n expect_error(results <- detection_phenology(modelresults2, spname='a', bins=12, density_function = TRUE),\n 'no phenological effect was modelled!')\n \n sink()\n})", "meta": {"hexsha": "c064b1e034265561318de49891ad4e6d81604df2", "size": 3888, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/testdetection_phenology.r", "max_stars_repo_name": "JackHHatfield91/sparta", "max_stars_repo_head_hexsha": "13479a5fdd4263b99efc63b0576ba0933cdd2a22", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/testdetection_phenology.r", "max_issues_repo_name": "JackHHatfield91/sparta", "max_issues_repo_head_hexsha": "13479a5fdd4263b99efc63b0576ba0933cdd2a22", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/testdetection_phenology.r", "max_forks_repo_name": "JackHHatfield91/sparta", "max_forks_repo_head_hexsha": "13479a5fdd4263b99efc63b0576ba0933cdd2a22", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.1818181818, "max_line_length": 131, "alphanum_fraction": 0.5697016461, "num_tokens": 1092, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.31892432441150936}} {"text": "# Experiment 2\n# Shift in fire regime from small-frequent to large-infrequent\n# Shift occurs at different times in the past, and between widely divergent regimes and closer regimes\n\n## Launch model\nlibrary(doParallel)\nlibrary(foreach)\n\nsource(\"cat_face_mortality_pfire_split.r\")\n\n\nregisterDoParallel(cores=16)\n\n######################################################\n######################################################\n\n# Experiment 2: Split regime, mortality on\n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ_1=100 # percentage of years regime 1\nFIRE_PROB_1=5 # Area regime 1\nFIRE_FREQ_2=5 # Percentage of years regime 2\nFIRE_PROB_2=100 # Area regime 2\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=400\n\nSPLIT_YEAR=rep(c(150,200,250,300,350,400,450,500,550,600),each=40) # Sequence of split years\n\noutput_dir=\"e2_range_wide\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ_1=FIRE_FREQ_1\nsim_def_frame$FIRE_FREQ_2=FIRE_FREQ_2\nsim_def_frame$FIRE_PROB_1=FIRE_PROB_1\nsim_def_frame$FIRE_PROB_2=FIRE_PROB_2\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$SPLIT_YEAR=SPLIT_YEAR\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim_split(i)\n\n\n######################################################\n######################################################\n\n# Experiment 2: Split regime, mortality on narror\n############################################\n\nFIELD_SIZE=200 # Number of rows/columns\nSIM_LENGTH=700 # number of years to run\nFIRE_FREQ_1=20 # percentage of years\nFIRE_PROB_1=25\nFIRE_FREQ_2=10\nFIRE_PROB_2=50\nMORTALITY_ON=TRUE # Non-fire Mortality turned on?\nF_MORTALITY_ON=TRUE # Fire-Morality turned on?\n\nMORT_b1 = 20 # First mortality age break\nMORT_b2 = 400 # Second mortality age break\n\nMORT_p1 =.02\nMORT_p2 =.005\nMORT_p3 =.02\n\nMORT_F_b1 = 10 # First fire mortality age break\nMORT_F_b2 = 30 # Second fire mortality age break\nMORT_F_b3 = 400 # Second fire mortality age break\n\nMORT_F_p1 = 0.7\nMORT_F_p2 = 0.3\nMORT_F_p3 = 0.05\nMORT_F_p4 = 0.4\n\nNUMBER_SIMS=400\n\nSPLIT_YEAR=rep(c(150,200,250,300,350,400,450,500,550,600),each=40)\n\noutput_dir=\"e2_range_narrow\"\ndir.create(output_dir)\n#########################################\n\n# Create dataframe to define this sim\n# This can vary between runs\n#\n#########################################\n\n\nsim_def_frame=data.frame(SIM_ID=1:NUMBER_SIMS)\nsim_def_frame$FIELD_SIZE=FIELD_SIZE\nsim_def_frame$SIM_LENGTH=SIM_LENGTH\nsim_def_frame$FIRE_FREQ_1=FIRE_FREQ_1\nsim_def_frame$FIRE_FREQ_2=FIRE_FREQ_2\nsim_def_frame$FIRE_PROB_1=FIRE_PROB_1\nsim_def_frame$FIRE_PROB_2=FIRE_PROB_2\nsim_def_frame$MORTALITY_ON=MORTALITY_ON\nsim_def_frame$F_MORTALITY_ON=F_MORTALITY_ON\nsim_def_frame$MORT_b1 = MORT_b1\nsim_def_frame$MORT_b2 = MORT_b2\nsim_def_frame$MORT_p1 = MORT_p1\nsim_def_frame$MORT_p2 = MORT_p2\nsim_def_frame$MORT_p3 = MORT_p3\nsim_def_frame$MORT_F_b1 = MORT_F_b1\nsim_def_frame$MORT_F_b2 = MORT_F_b2\nsim_def_frame$MORT_F_b3 = MORT_F_b3\nsim_def_frame$MORT_F_p1 = MORT_F_p1\nsim_def_frame$MORT_F_p2 = MORT_F_p2\nsim_def_frame$MORT_F_p3 = MORT_F_p3\nsim_def_frame$MORT_F_p4 = MORT_F_p4\nsim_def_frame$SPLIT_YEAR=SPLIT_YEAR\nsim_def_frame$output_dir = output_dir\n\nwrite.csv(sim_def_frame,paste0(output_dir,\"/sim_list.csv\"))\n\nforeach(i=1:NUMBER_SIMS) %dopar% launch_sim_split(i)\n\n\n", "meta": {"hexsha": "27be05c146badeb378b5155648ed2e628e060be7", "size": 4973, "ext": "r", "lang": "R", "max_stars_repo_path": "experiment_2.r", "max_stars_repo_name": "ozjimbob/FireScar", "max_stars_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-03T05:25:44.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-03T05:47:55.000Z", "max_issues_repo_path": "experiment_2.r", "max_issues_repo_name": "ozjimbob/FireScar", "max_issues_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiment_2.r", "max_forks_repo_name": "ozjimbob/FireScar", "max_forks_repo_head_hexsha": "da4b1a8c5ef13427e01c057e80c7c09cb3d6882f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6975308642, "max_line_length": 102, "alphanum_fraction": 0.6633822642, "num_tokens": 1501, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.31883859721710284}} {"text": ".normalize = import('./normalize')$normalize\n\n#' Quality control function\n#'\n#' @param rawData An `oligo::batch` object\n#' @param NUSE tolerance of meadian NUSE score around 1, set to NA for no QC\n#' @param RLE tolerance of meadian RLE score around 0, set to NA for no QC\n#' @return `rawData` filtered for QC criteria\nqc = function(rawData, NUSE=0.1, RLE=0.1) {\n UseMethod(\"qc\")\n}\n\nqc.list = function(rawData, NUSE=0.1, RLE=0.1) {\n lapply(rawData, .normalize)\n}\n\nqc.FeatureSet = function(rawData, NUSE=0.1, RLE=0.1) {\n plmFit = oligo::fitProbeLevelModel(rawData, target='core')\n\n discard = F\n if (!is.na(NUSE)) {\n med = apply(oligo::NUSE(plmFit, type=\"values\"), 2, function(x) median(x, na.rm=T))\n discard = discard | med > 1+NUSE | med < 1-NUSE\n }\n if (!is.na(RLE)) {\n med = apply(oligo::RLE(plmFit, type=\"values\"), 2, function(x) median(x, na.rm=T))\n discard = discard | med > RLE | med < -RLE\n }\n if (any(discard)) {\n warning(paste(\"Discarding\", sum(discard), \"arrays\"))\n rawData = rawData[,!discard]\n }\n rawData\n}\n\nqc.NChannelSet = function(rawData, NUSE=0.1, RLE=0.1) {\n warning(\"do not know how to QC NChannelSet, skipping\")\n rawData\n}\n", "meta": {"hexsha": "8bc5de0470267bfc906a0b75fb947d498eef541d", "size": 1241, "ext": "r", "lang": "R", "max_stars_repo_path": "process/microarray/qc.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "process/microarray/qc.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "process/microarray/qc.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 31.025, "max_line_length": 90, "alphanum_fraction": 0.6172441579, "num_tokens": 402, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.31883859721710284}} {"text": "#Code for analysing data from Bendix Plugin of VMD\n#List of Files\ninp_list = c(\"S6A\",\"S6B\",\"S6C\",\"S6D\")\ndata=list()\n\n#Reading in the files and storing the data in a list of lists\nfor(i in 1:4)\n{\n\tfile_id = sprintf(\"%s_data\",inp_list[i])\n\tdata[i] = list(read.csv(file_id, sep = '\\t', header = FALSE))\n}\nresidues = readLines('S6_rownames')\n\n#Boxplot of helix curvature of each chain plotted onto same graph\nsetEPS()\npostscript(\"S6_curvature.eps\")\nfor(i in 1:4)\n{\n\tboxplot(data[[i]],col=i+1,ylim=c(0,25),xlab=\"Residue Number\",ylab=\"Curvature\",main=\"Helicity of S6 Helix\",xaxt = 'n',outline=FALSE)\n\tpar(new=TRUE)\n}\ntext(x = seq_along(residues), y = par(\"usr\")[3] - 1, srt = 90, adj = 1, labels = residues, xpd = TRUE)\nlegend(25, 25, c(\"A Chain\",\"B Chain\",\"C Chain\",\"D Chain\"),col=c(2,3,4,5),lty=c(1,1),lwd=c(2.5,2.5))\ndev.off()\n\n#Individual Heatmaps of each chain \nfor(i in 1:4)\n{\n\tplot.new()\n\tframe()\n\tsetEPS()\n\tout_file = sprintf(\"heatmap_%s.eps\",inp_list[i])\n\tpostscript(out_file)\n\tdatamatrix <-as.matrix(data[[i]],headers = FALSE)\n\tcc <- rainbow(ncol(datamatrix), start = 0, end = 0.5)\n\tframes <- rep(\"\", nrow(datamatrix))\n\tframes[seq(1,nrow(datamatrix), 100)] <- seq(1, nrow(datamatrix), 100)\n\theatmap(datamatrix,Rowv = NA, Colv = NA, scale = \"row\",xlab = \"S6 Helix Residue\", ylab = \"Time\",col = cc, ColSideColors = cc,labCol = residues,labRow = frames, cexRow=2,cexCol=1)\n\tdev.off()\n}\n", "meta": {"hexsha": "ec35a7f5a8be891c321e9197f9d149def4c8fbe5", "size": 1389, "ext": "r", "lang": "R", "max_stars_repo_path": "Bendix_boxplot_analysis.r", "max_stars_repo_name": "nairvinayv/Rscripts", "max_stars_repo_head_hexsha": "b740a13149494de9aba95fd8efecda2384d82624", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Bendix_boxplot_analysis.r", "max_issues_repo_name": "nairvinayv/Rscripts", "max_issues_repo_head_hexsha": "b740a13149494de9aba95fd8efecda2384d82624", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Bendix_boxplot_analysis.r", "max_forks_repo_name": "nairvinayv/Rscripts", "max_forks_repo_head_hexsha": "b740a13149494de9aba95fd8efecda2384d82624", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.8780487805, "max_line_length": 180, "alphanum_fraction": 0.6695464363, "num_tokens": 500, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3188385972171028}} {"text": "mut_cats <- unique(agg_5bp_100k$Category2)\ncompare.all <- data.frame()\ncompare.err <- data.frame()\ncompare.aic <- data.frame()\n\nmodsumm<-list()\n\nfor(j in 1:length(mut_cats)) {\n\tcat1 <- mut_cats[j]\n cat(\"Running \", cat1, \"models...\\n\")\n\taggcat <- a3[a3$Category2==cat1,]\n\n\tif(grepl(\"^AT\", cat1)) {\n\t\tbindat <- binsAT\n\t\tmcols <- atcols\n\t} else if(grepl(\"^GC\", cat1)) {\n\t\tbindat <- binsGC\n\t\tmcols <- gccols\n\t} else {\n\t\tbindat <- binscpgGC\n\t\tmcols <- cpggccols\n\t}\n\n aggcatm1 <- merge(aggcat, bindat, by=c(\"CHR\", \"BIN\", \"prop_GC\"), all.x=T)\n\n # Subset 7bp rates for category i and sort\n rcat<-rates5 %>% filter(Category2==cat1) %>% arrange(Sequence)\n\n # Get expected num per window per bin\n z<-as.vector(rcat$rel_prop)*as.matrix(bindat[,6:ncol(bindat)])\n\n # Merge row sums with CHR/BIN\n # CHR BIN EXP\n r6<-cbind(bindat[,c(1,5)],marg=rowSums(z))\n\n bases <- c(\"A\", \"C\", \"G\", \"T\")\n nts <- ifelse(grepl(\"^AT\", cat1), \"A\", \"C\")\n\n b3 <- bases\n if(grepl(\"^cpg\", cat1)){\n b3 <- c(\"G\")\n } else if (grepl(\"^GC\", cat1)){\n b3 <- c(\"A\", \"C\", \"T\")\n }\n\n aggcatm2 <- getSubMotifs(aggcatm1, nts, b3)\n\n # Merge data to include column of marginals\n aggcatm<-merge(aggcatm2, r6, by=c(\"CHR\", \"BIN\"))\n\n # Fix issue where a single bin in Chr5 with 15 AT>GC observations\n # causes glm.nb() to fail to converge\n if(cat1==\"AT_GC\"){\n aggcatm <- aggcatm[aggcatm$obs>15,]\n }\n\n # Get all 5bp motifs to use\n pset <- results %>%\n filter(Category2==cat1, Q!=1)\n\n # Get 5bp motifs to be expanded to constituents\n psetq1 <- results %>%\n filter(Category2==cat1, Q==1)\n\n # Expand significant set to 7\n hierset <- apply(expand.grid(bases, psetq1$Sequence, bases),\n 1, paste, collapse=\"\")\n hiersetrev <- sapply(hierset, revcomp)\n hierset <- paste0(hierset, \"_\", hiersetrev, \"_\")\n\n m5set <- c(as.character(pset$Sequence), as.character(psetq1$Sequence))\n m7set <- c(as.character(pset$Sequence), hierset)\n\n\t# Fit models with all data\n # wm_form <- as.formula(\"obs~exp\")\n # m1_form <- as.formula(\"obs~exp1\")\n gc_form <- as.formula(\"obs~prop_GC\")\n\tfeat_form <- as.formula(paste(\"obs~\",\n\t\tpaste(covnames, collapse=\"+\")))\n\tfull_form <- as.formula(paste(\"obs~\",\n\t\tpaste(m5set, collapse=\"+\"), \"+prop_GC+\",\n\t\tpaste(covnames, collapse=\"+\")))\n\tmotif_form <- as.formula(paste(\"obs~\",\n\t\tpaste(m5set, collapse=\"+\")))\n motif2_form <- as.formula(paste(\"obs~\",\n\t\tpaste(m7set, collapse=\"+\")))\n full_form_int <- as.formula(paste(\"obs~prop_GC*(\",\n\t\tpaste(m5set, collapse=\"+\"), \")+\",\n\t\tpaste(covnames, collapse=\"+\")))\n marg_form <- as.formula(\"obs~marg\")\n logit_form <- as.formula(\"obs~LSUM\")\n\n # Add formulas to list\n forms <- c(gc_form, feat_form,\n full_form, full_form_int,\n motif_form, motif2_form,\n marg_form, logit_form)\n names(forms) <- c(\"gc\", \"features\",\n \"full\", \"full_gc_inter\",\n \"motifs5\", \"motifs5_top7\",\n \"marginal7\", \"logit\")\n\n # Run models for each formula in list\n models <- runMod(forms, aggcatm)\n\n aics <- sapply(models, function(x) AIC(x))\n aicdf <- data.frame(Category2=cat1, model=names(aics), AIC=aics)\n compare.aic <- rbind(compare.aic, aicdf)\n\t# 5-fold cross-validation--may need to update so expected counts are\n\t# re-calculated for each 1/N subset\n\t# gc_cv <- cv.glm(data=aggcatm, glmfit=models$gc, K=5)\n\t# feat_cv <- cv.glm(data=aggcatm, glmfit=models$feat, K=5)\n\t# motif_cv <- cv.glm(data=aggcatm, glmfit=models$motif, K=5)\n\t# full_cv <- cv.glm(data=aggcatm, glmfit=models$full, K=5)\n #\n\t# mspe <- c(gc_cv$delta[2], feat_cv$delta[2], motif_cv$delta[2], full_cv$delta[2])\n\t# rmse <- sqrt(mspe)\n\t# meanct <- mean(aggcat$obs)\n\t# pcterr <- rmse/meanct\n\t# mspe.res <- c(\"GC\", \"features\", \"motifs\", \"motifs+features\")\n\t# mspe.dat <- data.frame(Category2=cat1, res=mspe.res, mspe, rmse, meanct, pcterr)\n\t# compare.err <-rbind(compare.err, mspe.dat)\n\n # Get fitted values from each model and name with CHR/BIN\n fits <- getFits(models, aggcatm)\n\n\tBIN <- as.integer(gsub(\".*\\\\.\", \"\", names(fits$feat)))\n\tCHR <- as.integer(gsub(\"\\\\..*\", \"\", names(fits$feat)))\n\n # Build list of dataframes for each model\n moddat <- buildDF(fits, aggcatm)\n\n # Add column specifying model\n for(i in 1:length(names(moddat))){\n moddat[[i]]$res <- names(moddat)[i]\n }\n\n # Append model predictions to full df\n\tcompare.all <- rbind(compare.all, bind_rows(moddat))\n\tmarg_nm <- aggcatm %>%\n\t\tdplyr::select(CHR, Category2, BIN, exp=marg, obs) %>%\n\t\tmutate(res=\"marg_nm\")\n\tlogit_nm <- aggcatm %>%\n\t\tdplyr::select(CHR, Category2, BIN, exp=LSUM, obs) %>%\n\t\tmutate(res=\"logit_nm\")\n\tcompare.all <- rbind(compare.all, marg_nm, logit_nm)\n\n modsumm[[j]] <- summary(models$full)\n}\n", "meta": {"hexsha": "4f0af59d29afa7268a1127c5fce9e3e2bf663a9a", "size": 4593, "ext": "r", "lang": "R", "max_stars_repo_path": "R/negbin_run.r", "max_stars_repo_name": "theandyb/smaug-genetics", "max_stars_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-09-18T20:54:24.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-16T05:30:06.000Z", "max_issues_repo_path": "R/negbin_run.r", "max_issues_repo_name": "theandyb/smaug-genetics", "max_issues_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-07-24T12:43:50.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-24T12:43:50.000Z", "max_forks_repo_path": "R/negbin_run.r", "max_forks_repo_name": "theandyb/smaug-genetics", "max_forks_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-07-16T20:50:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T10:41:40.000Z", "avg_line_length": 30.62, "max_line_length": 83, "alphanum_fraction": 0.644023514, "num_tokens": 1585, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.31883859052376806}} {"text": "l1 : decjz r0 l2\n inc r-1\n inc r-2\n decjz r-4 l1\nl2 : decjz r-1 l3\nl4 : decjz r-2 l5\n inc r0\n inc r-3\n decjz r-4 l4\nl5 : decjz r-3 l2\n inc r-2\n decjz r-4 l5\nl3 : decjz r-2 end\n decjz r-4 l3\n", "meta": {"hexsha": "7e57a483544bc0e0a527223b02a1bd7b7311b929", "size": 249, "ext": "r", "lang": "R", "max_stars_repo_path": "sample-programs/square.r", "max_stars_repo_name": "dylan-thinnes/register-machine", "max_stars_repo_head_hexsha": "38c0860f5cedd8ba96ecfae2657c2c20b0f4e0e1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sample-programs/square.r", "max_issues_repo_name": "dylan-thinnes/register-machine", "max_issues_repo_head_hexsha": "38c0860f5cedd8ba96ecfae2657c2c20b0f4e0e1", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sample-programs/square.r", "max_forks_repo_name": "dylan-thinnes/register-machine", "max_forks_repo_head_hexsha": "38c0860f5cedd8ba96ecfae2657c2c20b0f4e0e1", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.6, "max_line_length": 20, "alphanum_fraction": 0.4698795181, "num_tokens": 129, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3188256822288364}} {"text": "# Note: when switching between tutorial examples,\n# restart R by clicking Session | Restart R in RStudio\n\nsetwd(\"~/KDD2017R/Code/MRS\")\nsource(\"SetComputeContext.r\")\n\nlibrary(sparklyr)\nlibrary(dplyr)\n\nSys.setenv(SPARK_VERSION=\"2.1.1\")\n\ncc <- rxSparkConnect(interop = \"sparklyr\",\n reset = TRUE,\n consoleOutput = TRUE,\n # numExecutors = 1,\n executorCores = 4,\n driverMem = \"2g\",\n executorMem = \"2g\",\n executorOverheadMem = \"4g\"\n)\n\nsc <- rxGetSparklyrConnection(cc)\n\n\n################################################\n# Specify the data sources\n################################################\n\n\nairlineDF <- sparklyr::spark_read_csv(sc = sc, \n name = \"airline\",\n path = file.path(dataDir, \"AirlineSubsetCsv\"), \n header = TRUE, \n infer_schema = FALSE, # Avoids parsing error\n null_value = \"null\")\n\nweatherDF <- sparklyr::spark_read_csv(sc = sc, \n name = \"weather\",\n path = file.path(dataDir, \"WeatherSubsetCsv\"), \n header = TRUE,\n infer_schema = TRUE,\n null_value = \"null\")\n\n\n################################################\n# Transform the data\n################################################\n\nairlineDF <- airlineDF %>%\n rename(ArrDel15 = ARR_DEL15) %>%\n rename(Year = YEAR) %>%\n rename(Month = MONTH) %>%\n rename(DayOfMonth = DAY_OF_MONTH) %>%\n rename(DayOfWeek = DAY_OF_WEEK) %>%\n rename(Carrier = UNIQUE_CARRIER) %>%\n rename(OriginAirportID = ORIGIN_AIRPORT_ID) %>%\n rename(DestAirportID = DEST_AIRPORT_ID) %>%\n rename(CRSDepTime = CRS_DEP_TIME) %>%\n rename(CRSArrTime = CRS_ARR_TIME)\n\n\n# Keep only the desired columns from the flight data \n\nairlineDF <- airlineDF %>% select(ArrDel15, Year, Month, DayOfMonth, \n DayOfWeek, Carrier, OriginAirportID, \n DestAirportID, CRSDepTime, CRSArrTime)\n\n\n# Round down scheduled departure time to full hour\n\nairlineDF <- airlineDF %>% mutate(CRSDepTime = floor(CRSDepTime / 100))\n\n\nweatherDF <- weatherDF %>%\n rename(OriginAirportID = AirportID) %>%\n rename(Year = AdjustedYear) %>%\n rename(Month = AdjustedMonth) %>%\n rename(DayOfMonth = AdjustedDay) %>%\n rename(CRSDepTime = AdjustedHour)\n\n\n# Average the weather readings by hour\n\nweatherSummary <- weatherDF %>% \n group_by(Year, Month, DayOfMonth, CRSDepTime, OriginAirportID) %>% \n summarise(Visibility = mean(Visibility),\n DryBulbCelsius = mean(DryBulbCelsius),\n DewPointCelsius = mean(DewPointCelsius),\n RelativeHumidity = mean(RelativeHumidity),\n WindSpeed = mean(WindSpeed),\n Altimeter = mean(Altimeter))\n\n\n#######################################################\n# Join airline data with weather at Origin Airport\n#######################################################\n\noriginDF <- left_join(x = airlineDF,\n y = weatherSummary)\n\noriginDF <- originDF %>%\n rename(VisibilityOrigin = Visibility) %>%\n rename(DryBulbCelsiusOrigin = DryBulbCelsius) %>%\n rename(DewPointCelsiusOrigin = DewPointCelsius) %>%\n rename(RelativeHumidityOrigin = RelativeHumidity) %>%\n rename(WindSpeedOrigin = WindSpeed) %>%\n rename(AltimeterOrigin = Altimeter)\n\n\n#######################################################\n# Join airline data with weather at Destination Airport\n#######################################################\n\nweatherSummary <- weatherSummary %>% rename(DestAirportID = OriginAirportID)\n\ndestDF <- left_join(x = originDF,\n y = weatherSummary)\n\nairWeatherDF <- destDF %>%\n rename(VisibilityDest = Visibility) %>%\n rename(DryBulbCelsiusDest = DryBulbCelsius) %>%\n rename(DewPointCelsiusDest = DewPointCelsius) %>%\n rename(RelativeHumidityDest = RelativeHumidity) %>%\n rename(WindSpeedDest = WindSpeed) %>%\n rename(AltimeterDest = Altimeter)\n\n\n#######################################################\n# Register the joined data as a Spark SQL/Hive table\n#######################################################\n\n# NOTE: IGNORE \"Translator is missing window functions\" WARNING\n# https://github.com/rstudio/sparklyr/issues/792\nairWeatherDF <- airWeatherDF %>% sdf_register(\"flightsweather\")\n\ntbl_cache(sc, \"flightsweather\")\n\n\n#######################################################\n# The table of joined data can be queried using SQL\n#######################################################\n\n# Count the number of rows\ntbl(sc, sql(\"SELECT COUNT(*) FROM flightsweather\"))\n\n# Count each distinct value in the ArrDel15 column\ntbl(sc, sql(\"SELECT ArrDel15, COUNT(*) FROM flightsweather GROUP BY ArrDel15\"))\n\n# Count rows by Year and Month\ntbl(sc, sql(\"SELECT Year, Month, COUNT(*) FROM flightsweather GROUP BY Year, Month ORDER BY Year, Month\"))\n\n", "meta": {"hexsha": "ed594f40c251d363adc3b90bcd9ae64501a76c55", "size": 5099, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/MRS/1-Clean-Join.r", "max_stars_repo_name": "Azure/KDD2017R", "max_stars_repo_head_hexsha": "138ce4b6b4f38dae3bc4247e50e220a4d616060b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2017-08-09T23:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-01T14:32:49.000Z", "max_issues_repo_path": "Code/MRS/1-Clean-Join.r", "max_issues_repo_name": "Azure/KDD2017R", "max_issues_repo_head_hexsha": "138ce4b6b4f38dae3bc4247e50e220a4d616060b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2017-08-08T07:22:30.000Z", "max_issues_repo_issues_event_max_datetime": "2017-08-11T22:40:50.000Z", "max_forks_repo_path": "Code/MRS/1-Clean-Join.r", "max_forks_repo_name": "Azure/KDD2017R", "max_forks_repo_head_hexsha": "138ce4b6b4f38dae3bc4247e50e220a4d616060b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2017-08-08T06:50:42.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T11:00:24.000Z", "avg_line_length": 33.5460526316, "max_line_length": 106, "alphanum_fraction": 0.5467738772, "num_tokens": 1119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3188256822288364}} {"text": "2: 1 -> 2\n4: 2 -> 4\n3: 1 -> 3\n", "meta": {"hexsha": "86472ce3b3fddbb0cf8f4d24e82257fb56e4d98a", "size": 30, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/03.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/03.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/03.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 7.5, "max_line_length": 9, "alphanum_fraction": 0.3, "num_tokens": 24, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.31882568222883634}} {"text": "require(ggplot2)\nrequire(xtable)\n\n\n#Arbeitspfad setzen\nsetwd(\"/Volumes/cracker/data/nfs/thomas/experiments/rx-factor-anova-7.6.2011-sender=ma-cca-noiseimmu-weak-cali-channel-driver/data/merged-traces\")\n\na-register <- read.csv(file=\"a-sender=ma-register.csv\", header=TRUE, sep = \" \")\n\n\n#Anova Analyse\n#1.) Normalverteilung pr\u221a\u00bafen\n\n\n#Histogramme erstellen\n\n#ANOVA noise all\nnoise_model_all = aov(-noise ~ as.factor(weak)*as.factor(position), data = noise_all)\ncapture.output(summary(noise_model_all), file=\"anova_all_noise.doc\")\n\nnoise_model_all_rxbusy = aov(rx.busy ~ as.factor(weak)*as.factor(position), data = noise_all)\ncapture.output(summary(noise_model_all_rxbusy), file=\"anova_all_rxbusy.doc\")\n\nnoise_model_all_edbusy = aov(ed.busy ~ as.factor(weak)*as.factor(position), data = noise_all)\ncapture.output(summary(noise_model_all_edbusy), file=\"anova_all_edbusy.doc\")\n\n\n\n\n\n\n\npdf(\"noise_eb.pdf\") \n#par(mfrow = c(1,2))\nhistogram_eb_noise <- qplot(-noise, data=noise_eb, geom=\"histogram\", binwidth=1, facets= rate ~ power)\nhistogram_eb_noise + opts(title='node:eb - noise histograms of all 4 groups of channel and calibration ')\ninteraction.plot(noise_eb$rate,noise_eb$calibration,-noise_eb$noise, legend = TRUE, xlab=\"channel\", ylab=\"noise at EB\", trace.label=\"calibration\", main=\"interaction plot noise at EB\")\ndev.off()\n\npdf(\"noise_vwsf.pdf\") \nhistogram_vws <- qplot(-noise, data=noise_vws, geom=\"histogram\", binwidth=1, facets= rate ~ power)\nhistogram_vws + opts(title='node:vws - noise histograms of all 4 groups of channel and calibration')\ninteraction.plot(noise_vws$channel,noise_vws$calibration,-noise_vws$noise, legend = TRUE, xlab=\"channel\", ylab=\"noise at VWS\", trace.label=\"calibration\", main=\"interaction plot noise at VWS\")\ndev.off()\n\npdf(\"noise_ma.pdf\") \nhistogram_ma <- qplot(-noise, data=noise_ma, geom=\"histogram\", binwidth=1, facets= rate ~ power)\nhistogram_ma + opts(title='node:ma - noise histograms of all 4 groups of channel and calibration')\ninteraction.plot(noise_ma$channel,noise_ma$calibration,-noise_ma$noise, legend = TRUE, xlab=\"channel\", ylab=\"noise at MA\", trace.label=\"calibration\", main=\"interaction plot noise at MA\")\ndev.off()\n\n#ed.busy is negative ---> error !!!!\n#pdf(\"rx-busy_eb.pdf\") \n#histogram_eb_rx <- qplot(rx.busy[rx.busy > 0], data=noise_eb, geom=\"histogram\", binwidth=1, facets= channel ~ calibration)\n#histogram_eb_rx + opts(title='node:eb - rx-busy histograms of all 4 groups of channel and calibration ')\n#interaction.plot(noise_eb$channel,noise_eb$calibration,-noise_eb$rx.busy, legend = TRUE, xlab=\"channel\", ylab=\"rx-busy at EB\", trace.label=\"calibration\", main=\"interaction plot rx-busy at EB\")\n#dev.off()\n\n\n#ANOVA machen EB\nnoise_model_eb = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_eb)\ncapture.output(summary(noise_model_eb), file=\"anova_eb_noise.doc\")\n\nnoise_model_eb_rxbusy = aov(rx.busy ~ as.factor(rate)*as.factor(power), data = noise_eb)\ncapture.output(summary(noise_model_eb_rxbusy), file=\"anova_eb_rxbusy.doc\")\n\nnoise_model_eb_edbusy = aov(ed.busy ~ as.factor(rate)*as.factor(power), data = noise_eb)\ncapture.output(summary(noise_model_eb_edbusy), file=\"anova_eb_edbusy.doc\")\n\n#ANOVA machen VWS\nnoise_model_vws = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_vws)\ncapture.output(summary(noise_model_vws), file=\"anova_vws_noise.doc\")\n\nnoise_model_vws_rxbusy = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_vws)\ncapture.output(summary(noise_model_vws_rxbusy), file=\"anova_vws_rxbusy.doc\")\n\nnoise_model_vws_edbusy = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_vws)\ncapture.output(summary(noise_model_vws_edbusy), file=\"anova_vws_edbusy.doc\")\n\n#ANOVA machen MA\nnoise_model_ma = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_ma)\ncapture.output(summary(noise_model_ma), file=\"anova_ma_noise.doc\")\n\nnoise_model_ma_rxbusy = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_ma)\ncapture.output(summary(noise_model_ma_rxbusy), file=\"anova_ma_rxbusy.doc\")\n\nnoise_model_ma_edbusy = aov(-noise ~ as.factor(rate)*as.factor(power), data = noise_ma)\ncapture.output(summary(noise_model_ma_edbusy), file=\"anova_ma_edbusy.doc\")\n\n#residual histogramme\n\nhist(noise_model_eb$res)\nhist(noise_model_vws$res)\n\n#plot residuals against fitted values to look for bvious trends that are not consistent with the model\ndelivery.res = histogram_eb\nhistogram_eb$M1.Fit = fitted(noise_model_eb)\nhistogram_eb$M1.Resid = resid(noise_model_eb)\n\n#consider the normal probability plot of the model residuals, using the stat_qq() option:\np = qplot(sample = M1.Resid, data= histogram_eb) \np + stat_qq()\n\n\nggplot(delivery.res, aes(M1.Fit, M1.Resid, colour = Service)) + \n geom_point() +\n xlab(\"Fitted Values\") + ylab(\"Residuals\")\n\n\n\n\n\n\n# stuff\n\n#QQ Plot\nqqplot_bib <- qplot(sample = -noise, data=noise_bib, facets = channel ~ calibration)\nqqplot_bib + opts(title='node:bib - QQ-Plot from 64 groups ')\n\nqqplot_eb <- qplot(sample = -noise, data=noise_eb, facets = channel ~ calibration)\nqqplot_eb + opts(title='node:en - QQ-Plot from 64 groups ')\n\n#interactive plot:\ninteraction.plot(noise_eb$rate,noise_eb$power,-noise_eb$noise, legend = TRUE, xlab=\"channel\", ylab=\"noise at EB\", trace.label=\"calibration\", main=\"interaction plot noise at EB\")\ninteraction.plot(noise_vws$rate,noise_vws$power,-noise_vws$noise, legend = TRUE, xlab=\"channel\", ylab=\"noise at VWS\", trace.label=\"calibration\", main=\"interaction plot noise at VWS\")\n\ninteraction.plot(noise_eb$rate,noise_eb$power,noise_eb$rx.busy, legend = TRUE, xlab=\"TX rate MA\", ylab=\"noise at EB\", trace.label=\"TX power MA\", main=\"interaction plot noise at EB\")\ninteraction.plot(noise_vws$rate,noise_vws$power,-noise_vws$rx.busy, legend = TRUE, xlab=\"TX rate MA\", ylab=\"noise at VWS\", trace.label=\"TX power MA\", main=\"interaction plot noise at VWS\")\n\n\ndensity_vws <- qplot(-noise, data=noise_eb, geom=\"density\", binwidth=1, facets= channel ~ calibration)\ndensity_vws + opts(title='node:vws - noise densitys of all 4 groups of channel and calibration')\n\ndensity_eb <- qplot(-noise, data=noise_eb, geom=\"density\", binwidth=1, facets= channel ~ calibration)\ndensity_eb + opts(title='node:eb - noise densitys of all 4 groups of channel and calibration')\n\n\nnoise_model_eb.table = xtable(noise_model_eb)\nprint(noise_model_eb.table, type=\"html\")\n\nnoise_model_eb = aov(-noise ~ rate*power, data = noise_eb)\n\nnoise_model_eb.table = xtable(noise_model_eb)\nprint(noise_model_eb.table, type=\"html\")", "meta": {"hexsha": "dceb3628eef16ff0d00447336d98f314f3520760", "size": 6444, "ext": "r", "lang": "R", "max_stars_repo_path": "patched_RegMon/analysis_scripts/plot_traces/rx-anova-07-06-2011.r", "max_stars_repo_name": "akhila-s-rao/high-fidelity-wireless-measurements", "max_stars_repo_head_hexsha": "64bcaa9e0da5338b9495b63cd7aa67d94eaf3bf5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "patched_RegMon/analysis_scripts/plot_traces/rx-anova-07-06-2011.r", "max_issues_repo_name": "akhila-s-rao/high-fidelity-wireless-measurements", "max_issues_repo_head_hexsha": "64bcaa9e0da5338b9495b63cd7aa67d94eaf3bf5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "patched_RegMon/analysis_scripts/plot_traces/rx-anova-07-06-2011.r", "max_forks_repo_name": "akhila-s-rao/high-fidelity-wireless-measurements", "max_forks_repo_head_hexsha": "64bcaa9e0da5338b9495b63cd7aa67d94eaf3bf5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.75, "max_line_length": 193, "alphanum_fraction": 0.7605524519, "num_tokens": 1872, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7090191337850933, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.31862797641760493}} {"text": "temprad <- function(t, rh, rshort, rdiffuse, sunelev, albedo) {\n .Call('biometeoR_temprad', PACKAGE = 'biometeoR', t, rh, rshort, rdiffuse, sunelev, albedo)\n}\n", "meta": {"hexsha": "07f9bd2a35c0c8e8c1b967ea6da459e5bdaae2c7", "size": 162, "ext": "r", "lang": "R", "max_stars_repo_path": "R/temprad.r", "max_stars_repo_name": "alfcrisci/biometeoR", "max_stars_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:46.000Z", "max_issues_repo_path": "R/temprad.r", "max_issues_repo_name": "alfcrisci/biometeoR", "max_issues_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/temprad.r", "max_forks_repo_name": "alfcrisci/biometeoR", "max_forks_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.5, "max_line_length": 95, "alphanum_fraction": 0.6913580247, "num_tokens": 63, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.3185227755290385}} {"text": "########################################\r\n## cfg\t\t\t\t\t\t\t ##\r\n########################################\r\nsource(\"cfg.r\")\r\ngraphics.off()\r\n\r\nbreaks_default = seq(0.1, 0.8, 0.01)\r\naxis_at = seq(0.1, 0.8, 0.1)\r\n\r\nanomilies = TRUE\r\ntransform = TRUE\r\n########################################\r\n## load and analyes \t\t\t ##\r\n########################################\r\n\r\ndat = dats[[1]][['TreeCover']]\r\nout = makeOrLoadEnsembles()\t\r\nout = selectOutput(out)\r\n\r\naddAnololie <- function(r_exp, r_obs, r_cnt, maxV = 0.8) {\r\n\t\r\n\ttrans <- function(r) { \r\n\t\tr = r / maxV\r\n\t\tr[r > 1] = maxV\r\n\t\t\r\n\t\treturn(r)\r\n\t}\r\n\t\r\n\tr_exp = trans(r_exp)\r\n\tr_obs = trans(r_obs)\r\n\tr_cnt = trans(r_cnt)\r\n\t\r\n\tif (transform) r_obs = logit(r_obs)\r\n\t\r\n\tr_anm = r_obs * r_exp / r_cnt\r\n\t\r\n\tif (transform) r_anm = logistic(r_obs * r_exp / r_cnt, 0, 1)\r\n\treturn(r_anm * maxV)\r\n}\r\n\r\nif (anomilies) aout = lapply(out,addAnololie, dat, out[[1]]) else aout = out\r\n\r\ndout = lapply(out, function(i) i -out[[1]])\r\nplot_hist <- function(r, title, axis = TRUE, yline = NULL, poly = TRUE, maxY = NULL,\r\n\t\t\t\t breaks = breaks_default, hrange = range(breaks), log = '') {\r\n\tprint(maxY)\r\n\tif (log == 'y') yrange = c(0.001, 1.0) else yrange = c(0, 1.2)\r\n\tplot(hrange, yrange, axes = FALSE, type = 'n', xlab = '', ylab = '', log = log)\r\n\tif (axis) axis(1, at = axis_at, labels = axis_at * 100)\r\n\tmtext(title, line = -3)\r\n\tplotPoly <- function(v, alpha = 0.9, poly = TRUE) {\r\n\t\thr = hist(v, breaks = breaks, plot = FALSE)\r\n\t\tx = hr$mids\r\n\t\ty = hr$density\r\n\t\tif (is.null(maxY)) maxY = max(y)\r\n\t\t\r\n\t\ty = y/maxY\r\n\t\tif (log == 'y') y[y < 0.001] = 0.001\r\n\t\tif (poly) {\r\n\t\t\tpolygon(c(x, rev(x)), c(y, rep(0, length(y))),\r\n\t\t\t\t\tborder = NA, col = make.transparent('black', alpha))\r\n\t\t} else {\r\n\t\t\tlines(x, y, col = 'red', lwd = 2.3)\r\n\t\t\tlines(x, y, col = '#BB0000', lwd = 2)\r\n\t\t\tlines(x, y, col = '#FF9999', lwd = 2.1, lty = 2)\r\n\t\t}\r\n\t\treturn(maxY)\r\n\t}\r\n\tvr = layer.apply(r, function(i) i[i > hrange[1] & i < hrange[2]])\r\n\tmaxY_out = lapply(vr, plotPoly, poly = poly)\r\n\tif (!is.null(yline)) plotPoly(yline, poly = FALSE)\r\n\treturn(list(vr, maxY_out))\r\n}\r\n\r\nout = plot_hist(dat, 'obs', poly = FALSE)\r\nvdat = out[[1]][[1]]; maxY = out[[2]][[1]]\r\n\r\ngraphics.off()\r\npng(paste('figs/killer_histergrams', transform, '.png', sep = '-'), height = 9, width = 7, unit = 'in', res = 200)\r\n\r\n\tpar(mfrow = c(4,2), mar = c(0.45, 0.5, 0.1, 0.5), oma = c(2, 0.5, 0.4, 0.5))\r\n\tmapply(plot_hist, aout[5:12], names(dats)[5:12], axis = c(rep(F, 6), T, T), \r\n\t\t MoreArgs = list(vdat, maxY = maxY))\r\n\t \r\ndev.off.gitWatermark()\r\n\r\npng(paste('figs/shift_histergrams', transform, '.png', sep = '-'), height = 9, width = 7, unit = 'in', res = 200)\r\n\r\n\tpar(mfrow = c(4,2), mar = c(0.45, 0.5, 0.1, 0.5), oma = c(2, 0.5, 0.4, 0.5))\r\n\tmapply(plot_hist, dout[5:12], names(dats)[5:12], axis = c(rep(F, 6), T, T), \r\n\t\t MoreArgs = list(maxY = NULL, breaks = seq(0.01, 0.5, 0.01), log = ''))\r\n\t \r\ndev.off.gitWatermark()", "meta": {"hexsha": "15dad65a32f00b9b8513b311c811a7b50a8fe0cf", "size": 2936, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_exp_histergrams.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_exp_histergrams.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_exp_histergrams.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 31.5698924731, "max_line_length": 115, "alphanum_fraction": 0.5252043597, "num_tokens": 1070, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3185227755290385}} {"text": "#Hs liver unique\r\n# Figure Hs_liverunique_TSSgene_numbers\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHsLiver = 1:9200,\r\n\t\tTSSgene = 6943:26051\r\n\t\t),\r\n\tfilename = \"Hs_liverunique_TSSgene_numbers.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"green\"),\r\n\talpha = 0.5,\r\n\tlabel.col = c(\"darkblue\", \"white\", \"darkgreen\"),\r\n\tcex = 2.0,\r\n\tfontfamily = \"arial\",\r\n\tfontface = \"bold\",\r\n\t);\r\n\r\n# Figure Hs_liverunique_TSSgene\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHsLiver = 1:9200,\r\n\t\tTSSgene = 6943:26051\r\n\t\t),\r\n\tfilename = \"Hs_liverunique_TSSgene.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"green\"),\r\n\talpha = 0.5,\r\n\tcex = 0.0,\r\n\tcat.cex = 0.0,\r\n);\r\n\r\n#Hs Testes unique\r\n# Figure Hs_testesunique_TSSgene_numbers\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHstestes = 1:15118,\r\n\t\tTSSgene = 12808:31907\r\n\t\t),\r\n\tfilename = \"Hs_testesunique_TSSgene_numbers.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"red\"),\r\n\talpha = 0.5,\r\n\tlabel.col = c(\"darkblue\", \"white\", \"darkgreen\"),\r\n\tcex = 2.0,\r\n\tfontfamily = \"arial\",\r\n\tfontface = \"bold\",\r\n\t);\r\n\r\n# Figure Hs_testesunique_TSSgene\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHstestes = 1:15118,\r\n\t\tTSSgene = 12808:31907\r\n\t\t),\r\n\tfilename = \"Hs_testesunique_TSSgene.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"red\"),\r\n\talpha = 0.5,\r\n\tcex = 0.0,\r\n\tcat.cex = 0.0,\r\n);\r\n\r\n#Hs Testes and Liver shared\r\n# Figure Hs_testeslivershared_TSSgene_numbers\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHsshared = 1:20847,\r\n\t\tTSSgene = 8031:26277\r\n\t\t),\r\n\tfilename = \"Hs_testeslivershared_TSSgene_numbers.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"orange\"),\r\n\talpha = 0.5,\r\n\tlabel.col = c(\"darkblue\", \"white\", \"darkgreen\"),\r\n\tcex = 2.0,\r\n\tfontfamily = \"arial\",\r\n\tfontface = \"bold\",\r\n\t);\r\n\r\n# Figure Hs_testeslivershared_TSSgene\r\nvenn.diagram(\r\n\tx = list(\r\n\t\tHsshared = 1:20847,\r\n\t\tTSSgene = 8031:26277\r\n\t\t),\r\n\tfilename = \"Hs_testeslivershared_TSSgene.tiff\",\r\ncol = \"transparent\",\r\n\tfill = c(\"blue\", \"orange\"),\r\n\talpha = 0.5,\r\n\tcex = 0.0,\r\n\tcat.cex = 0.0,\r\n);\r\n\r\n###################################\r\n\r\n", "meta": {"hexsha": "b04646f3e08f748ea3ecf80c4d15baa922962e67", "size": 1971, "ext": "r", "lang": "R", "max_stars_repo_path": "obsolete/proj007/Fig3b unique and shared intervals.r", "max_stars_repo_name": "861934367/cgat", "max_stars_repo_head_hexsha": "77fdc2f819320110ed56b5b61968468f73dfc5cb", "max_stars_repo_licenses": ["BSD-2-Clause", "BSD-3-Clause"], "max_stars_count": 87, "max_stars_repo_stars_event_min_datetime": "2015-01-01T03:48:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-23T16:23:24.000Z", "max_issues_repo_path": "obsolete/proj007/Fig3b unique and shared intervals.r", "max_issues_repo_name": "861934367/cgat", "max_issues_repo_head_hexsha": "77fdc2f819320110ed56b5b61968468f73dfc5cb", "max_issues_repo_licenses": ["BSD-2-Clause", "BSD-3-Clause"], "max_issues_count": 189, "max_issues_repo_issues_event_min_datetime": "2015-01-06T15:53:11.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-31T13:19:45.000Z", "max_forks_repo_path": "obsolete/proj007/Fig3b unique and shared intervals.r", "max_forks_repo_name": "CGATOxford/cgat", "max_forks_repo_head_hexsha": "326aad4694bdfae8ddc194171bb5d73911243947", "max_forks_repo_licenses": ["BSD-2-Clause", "BSD-3-Clause"], "max_forks_count": 56, "max_forks_repo_forks_event_min_datetime": "2015-01-13T02:18:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-05T10:00:59.000Z", "avg_line_length": 20.53125, "max_line_length": 57, "alphanum_fraction": 0.6123795028, "num_tokens": 696, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6224593312018545, "lm_q1q2_score": 0.3185227755290385}} {"text": "fitted.ivmodel <- function(object, ...){\n ivmodel <- object\n residual_est = resid(ivmodel)\n stopifnot(\"Number of estimated residuals do not equal number of outcome\"= nrow(residual_est) == nrow(ivmodel$Y))\n \n result = matrix(ivmodel$Y,nrow(residual_est),ncol(residual_est)) - residual_est\n result = result[which(ivmodel$naindex),]\n return(result)\n}\n", "meta": {"hexsha": "8ec1ada44eff0d53be4f5c10630cb2e58f5f8d3f", "size": 356, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fitted.ivmodel.r", "max_stars_repo_name": "hyunseungkang/ivmodel", "max_stars_repo_head_hexsha": "322a6759f381d59a061cd6c39883b2f851c30029", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2018-09-22T13:38:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-23T02:04:23.000Z", "max_issues_repo_path": "R/fitted.ivmodel.r", "max_issues_repo_name": "cran/ivmodel", "max_issues_repo_head_hexsha": "ed4b9ddab888f520dddbb15334044481c0a2c507", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-11-09T19:20:30.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-01T13:44:24.000Z", "max_forks_repo_path": "R/fitted.ivmodel.r", "max_forks_repo_name": "hyunseungkang/ivmodel", "max_forks_repo_head_hexsha": "322a6759f381d59a061cd6c39883b2f851c30029", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-06-01T16:33:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-01T16:33:38.000Z", "avg_line_length": 35.6, "max_line_length": 114, "alphanum_fraction": 0.7275280899, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31851540413116086}} {"text": "\n\n#####################################################################################\n###################### arrange data ###########################################\n#####################################################################################\n\n## import data\nwd = \"D:/Ruo_data/2019_Paper_Publish/Publish\"\nsetwd(wd)\n\nsp_TL.result <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/all_TL_result_16class.csv\",stringsAsFactors = F,sep=\",\",header = T)\nsp_TL.result <-sp_TL.result[,-1]\nsp.mid.L <- as.matrix(read.table(\"./SizeAggregTend_data/compiled/allsp_length_mid_16class.txt\"))\nmature.L <- as.matrix(read.table(\"./SizeAggregTend_data/compiled/mature_L.txt\"))\nsp_name <-c(\"Herring\",\"Cod\",\"Haddock\",\"Whiting\",\"Plaice\",\"Saithe\",\"Mackerel\",\"Sprat\",\"Norwaypout\")\n\n# add information #\n## calculate the standard length \nstd.L <- NULL\nfor(i in 1:9){\n sp.1 <- (sp.mid.L[i,]-mature.L[i,1])/(max(sp.mid.L[i,])-min(sp.mid.L[i,]))\n sp.3 <- (sp.mid.L[i+9,]-mature.L[i,1])/(max(sp.mid.L[i+9,])-min(sp.mid.L[i+9,]))\n std.L <- c(std.L, sp.1, sp.3) \n} \nsp_TL.result$std_L <- std.L\n\n## add species factor\nsp_TL.result$sp_ID <- factor(rep(1:9,each=32))\n\n## add quater factor\nsp_TL.result$quarter <- as.factor(rep(c(rep(\"Q1\",16),rep(\"Q3\",16)),9))\n\n## add spawning information\nsp_TL.result$spwn <- as.factor(rep(c(\"Y\",\"Y\",\"Y\",\"N\",\"Y\",\"N\",\n \"Y\",\"N\",\"Y\",\"N\",\"Y\",\"N\",\n \"N\",\"N\",\"N\",\"Y\",\"Y\",\"N\"),each=16))\n\n## add temperature data\nallsp_T <- read.csv(\"./SizeAggregTend_data/compiled/Temperature_info.csv\")\ncolnames(allsp_T)[2:3] <- c(\"mean_T\",\"cv_T\")\n\nsp_TL.result <- cbind(sp_TL.result,allsp_T[,2:3])\nsp_TL.result$std_T <- unlist(tapply(sp_TL.result$mean_T,\n list(sp_TL.result$sp_ID,sp_TL.result$quarter),\n function(x){(x-median(x))/(max(x)-min(x))}))\nsp_TL.result$std_T_cv <- unlist(tapply(sp_TL.result$cv_T,\n list(sp_TL.result$sp_ID,sp_TL.result$quarter),\n function(x){(x-median(x))/(max(x)-min(x))}))\n## add maturity stage\nsize_at_maturity_stage = read.csv(\"./SizeAggregTend_data/compiled/size_at_maturityStage_byQ.csv\")\nL05 = size_at_maturity_stage$L05\nL95 = size_at_maturity_stage$L95\n\nmat_stage <- NULL\nfor(i in 1:9){\n stage = rep(NA,32)\n stage[which(sp.mid.L[i,]L95[i])] = 3\n stage[which(sp.mid.L[i+9,]>L95[i+9])+16] = 3\n stage[which(is.na(stage))] = 2\n mat_stage = c(mat_stage,stage)\n} \nsp_TL.result$mat_stage <- factor(mat_stage)\n\n## add dummy variable\nsp_TL.result$dum <- 1\n\n## export sp_TL.result\n#write.csv(sp_TL.result,\"./SizeAggregTend_data/output/1_size_TL/all_TL_result_16_compiled.csv\")\n\n\n\n#####################################################################################\n###################### Relationship fitting ###################################\n#####################################################################################\n\nlibrary(\"mgcv\")\nlibrary(\"itsadug\")\nlibrary(\"ggplot2\")\nlibrary(\"dplyr\")\n\n### GLMM ############################################################\n\n## Quarter ##\n# temp_mean #\nfit.QART.temp.lin <- gam(b~std_T*std_L*quarter+s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,method = \"ML\",\n family = Gamma(link = log))\n\n# temp_cv #\nfit.QART.tempCV.lin <- gam(b~std_T_cv*std_L*quarter+s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,method = \"ML\",\n family = Gamma(link = log))\n\n\n## Spawn ##\n# temp_mean #\nfit.SPWN.temp.lin <- gam(b~std_T*std_L*spwn+s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,method = \"ML\",\n family = Gamma(link = log))\n\n# temp_cv #\nfit.SPWN.tempCV.lin <- gam(b~std_T_cv*std_L*spwn+s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,method = \"ML\",\n family = Gamma(link = log))\n\n\n### GAMM ############################################################\n\nfit.QART.temp.smooth.inter <- gam(b~quarter+\n ti(std_T,by=quarter,bs=\"ts\")+\n ti(std_L,by=quarter,bs=\"ts\")+\n ti(std_L,std_T,by=quarter,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,\n method = \"ML\",\n family = Gamma(link = log))\nfit.QART.tempCV.smooth.inter <- gam(b~quarter+\n ti(std_T_cv,by=quarter,bs=\"ts\")+\n ti(std_L,by=quarter,bs=\"ts\")+\n ti(std_L,std_T_cv,by=quarter,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,\n method = \"ML\",\n family = Gamma(link = log))\n\n\nfit.SPWN.temp.smooth.inter <- gam(b~spwn+\n ti(std_T,by=spwn,bs=\"ts\")+\n ti(std_L,by=spwn,bs=\"ts\")+\n ti(std_L,std_T,by=spwn,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,\n method = \"ML\",\n family = Gamma(link = log))\nfit.SPWN.tempCV.smooth.inter <- gam(b~spwn+\n ti(std_T_cv,by=spwn,bs=\"ts\")+\n ti(std_L,by=spwn,bs=\"ts\")+\n ti(std_L,std_T_cv,by=spwn,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,\n method = \"ML\",\n family = Gamma(link = log))\n\n\n# after use shrinkage method, we don't need to do the following part\n# std_L smooth, temp smooth , no smoothing interaction #\n\n#fit.QART.temp.smooth <- gam(b~quarter+\n# s(std_T,by=quarter,bs=\"ts\")+\n# s(std_L,by=quarter,bs=\"ts\")+\n# s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n# data = sp_TL.result,method = \"ML\",#select = F,\n# family = Gamma(link = log))\n#fit.SPWN.temp.smooth <- gam(b~spwn+\n# s(std_T,by=spwn,bs=\"ts\")+\n# s(std_L,by=spwn,bs=\"ts\")+\n# s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n# data = sp_TL.result,method = \"ML\",select = F,\n# family = Gamma(link = log))\n\n\n# no quarter or spawning #\nfit.temp.noQorSPW <- gam(b~s(std_L,bs=\"ts\")+s(std_T,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\")+s(sp_ID,bs=\"re\"),\n data = sp_TL.result,method = \"ML\",select = F,\n family = Gamma(link = log))\nfit.tempCV.noQorSPW <- gam(b~s(std_L,bs=\"ts\")+s(std_T_cv,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\")+s(sp_ID,bs=\"re\"),\n data = sp_TL.result,method = \"ML\",select = F,\n family = Gamma(link = log))\n\n\n# one factor and no random effect #\nfit.size.noRE <- gam(b~s(std_L),\n data = sp_TL.result,method = \"ML\",select = F,\n family = Gamma(link = log))\nfit.temp.noRE <- gam(b~s(std_T),\n data = sp_TL.result,method = \"ML\",select = F,\n family = Gamma(link = log))\nfit.tempCV.noRE <- gam(b~s(std_T_cv),\n data = sp_TL.result,method = \"ML\",select = F,\n family = Gamma(link = log))\n\n\n### Model selection##########################################################\n\nAIC(fit.QART.temp.lin,\n fit.QART.tempCV.lin,\n fit.QART.temp.smooth.inter,\n fit.QART.tempCV.smooth.inter,\n fit.SPWN.temp.lin,\n fit.SPWN.tempCV.lin,\n fit.SPWN.temp.smooth.inter,\n fit.SPWN.tempCV.smooth.inter,\n fit.temp.noQorSPW,\n fit.tempCV.noQorSPW,\n fit.size.noRE,\n fit.temp.noRE,\n fit.tempCV.noRE\n )\n\n### Result using REML #######################################\n\n\nfit.QART.tempCV.smooth.inter_Rslt <- gam(b~quarter+\n ti(std_T_cv,by=quarter,bs=\"ts\")+\n ti(std_L,by=quarter,bs=\"ts\")+\n ti(std_L,std_T_cv,by=quarter,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = sp_TL.result,\n method = \"REML\",\n family = Gamma(link = log)\n #select = T\n)\nsummary(fit.QART.tempCV.smooth.inter_Rslt)\nplot(fit.QART.tempCV.smooth.inter_Rslt)\n\n\n\n##############################################################\n#### propogate error of estimated TL exponents \n#### to relationship analysis\n##############################################################\n\n\n##########\n## NOTE ## To avoid resampling, use the data already save in 1_size_TL\n########## \n\n\n# TL exponents' bootstrap data -1\n#TL_BSdata = apply(sp_TL.result,1,\n# function(x){sample(seq(x[7],x[8],by=0.01),999,replace = T)})\n\n# TL exponents' bootstrap data -2\n#TL_BSdata = NULL\n\n#for(s in 1:9){\n# sp_BS_result_filename =list.files(\"./SizeAggregTend_data/output/1_size_TL/TL999boot\",\n# pattern = sp_name[s],\n# full.names = T)\n# sp_BS_result_filename_Q1 = sp_BS_result_filename[1:16]\n# size_class_Q1 = substr(sp_BS_result_filename_Q1,nchar(sp_BS_result_filename_Q1)-5,nchar(sp_BS_result_filename_Q1)-4)\n# size_class_Q1 = as.numeric(gsub(\"_\", \"\", size_class))\n \n# sp_BS_result_filename_Q3 = sp_BS_result_filename[1:16]\n# size_class_Q3 = substr(sp_BS_result_filename_Q3,nchar(sp_BS_result_filename_Q3)-5,nchar(sp_BS_result_filename_Q3)-4)\n# size_class_Q3 = as.numeric(gsub(\"_\", \"\", size_class))\n \n# for(i in 1:16){\n# size_BS_result_Q1 = read.csv(sp_BS_result_filename[i])\n# BS_resample_data_Q1 = sample(size_BS_result_Q1$V1,999,replace = T)\n# TL_BSdata = rbind(TL_BSdata,c(BS_resample_data_Q1,size_class_Q1[i],1))}\n \n \n# for(i in 1:16){\n# size_BS_result_Q3 = read.csv(sp_BS_result_filename[i+16])\n# BS_resample_data_Q3 = sample(size_BS_result_Q3$V1,999,replace = T)\n# TL_BSdata = rbind(TL_BSdata,c(BS_resample_data_Q3,size_class_Q3[i],3))\n# }\n#}\n\n#TL_BSdata = as.data.frame(TL_BSdata)\n#colnames(TL_BSdata)[1000] = \"size_class\"\n#colnames(TL_BSdata)[1001] = \"quarter\"\n#TL_BSdata$sp_ID = rep(c(1:9),each=32)\n#library(\"dplyr\")\n#TL_BSdata = TL_BSdata %>% arrange(sp_ID,quarter,size_class)\n\n# save bs sample data \n#write.csv(TL_BSdata,\"./SizeAggregTend_data/output/1_size_TL/resample_TL_bs_data.csv\")\n\nTL_BSdata <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/resample_TL_bs_data.csv\")\nTL_BSdata <- TL_BSdata[,-1]\n\n# bootstrap 999 times for model fitting and prediction\nbs_fit_para_summary <- NULL\nbs_fit_smth_summary <- data.frame(temp_cv_Q1=NA,temp_cv_Q3=NA,\n size_Q1=NA,size_Q3=NA,\n inter_Q1=NA,inter_Q3=NA,\n re_slope=NA,re_intercept=NA)\nbs_quarter_diff <- NULL\nbs_pred_result_Q1 <- NULL\nbs_pred_result_Q3 <- NULL\nbs_pred_result_Q1_tempCV <- NULL\nbs_pred_result_Q3_tempCV <- NULL\nbs_pred_result_Q1_inter <- list()\nnewdat <- expand.grid(std_L = seq(-0.69,0.75,length=200),\n std_T_cv = 0,\n quarter = c(\"Q1\",\"Q3\"),\n sp_ID = factor(c(1:9)),\n dum=0)\n\nnewdat_Q1 <- expand.grid(std_L = seq(-0.69,0.75,length=200),\n std_T_cv = 0,\n quarter = c(\"Q1\"),\n sp_ID = factor(c(1:9)),\n dum=0)\nnewdat_Q3 <- expand.grid(std_L = seq(-0.69,0.75,length=200),\n std_T_cv = 0,\n quarter = c(\"Q3\"),\n sp_ID = factor(c(1:9)),\n dum=0)\nnewdat_Q3_tempCV <- expand.grid(std_T_cv = seq(-0.80,0.92,length=200),\n std_L = 0,\n quarter = c(\"Q3\"),\n sp_ID = factor(c(1:9)),\n dum=0)\nnewdat_Q1_inter <- expand.grid(std_L = seq(-0.69,0.75,length=80),\n std_T_cv = seq(-0.80,0.92,length=80),\n quarter = c(\"Q1\"),\n sp_ID = factor(c(1:9)),\n dum=0)\n\nfor (bs in 1:999){\n bs_TL_result <- cbind(TL_BSdata[,bs],sp_TL.result[,9:19])\n colnames(bs_TL_result)[1] <- \"b\"\n bs_fit <- gam(b~quarter+\n ti(std_T_cv,by=quarter,bs=\"ts\")+\n ti(std_L,by=quarter,bs=\"ts\")+\n ti(std_L,std_T_cv,by=quarter,bs=\"ts\")+\n s(std_L,sp_ID,bs=\"re\",by=dum)+s(sp_ID,bs=\"re\",by=dum),\n data = bs_TL_result,\n method = \"REML\",\n family = Gamma(link = log)\n )\n bs_fit_summary <- summary(bs_fit)\n bs_fit_para_summary = rbind(bs_fit_para_summary,bs_fit_summary$p.table[2,])\n bs_fit_smth_summary = rbind(bs_fit_smth_summary,bs_fit_summary$s.table[,4])\n \n # quarter different\n bs_pred_b <- predict(bs_fit, newdata = newdat, type=\"lpmatrix\")\n c1 <- grepl('Q1', colnames(bs_pred_b))\n c3 <- grepl('Q3', colnames(bs_pred_b))\n r1 <- with(newdat, quarter == 'Q1')\n r3 <- with(newdat, quarter == 'Q3')\n X <- bs_pred_b[r1, ] - bs_pred_b[r3, ]\n X[, ! (c1 | c3)] <- 0\n X[, !grepl('^ti\\\\(', colnames(bs_pred_b))] <- 0\n quarter_diff <- X %*% coef(bs_fit)\n quarter_diff <- tapply(quarter_diff,newdat_Q1$std_L,mean)\n bs_quarter_diff <- cbind(bs_quarter_diff,quarter_diff)\n \n \n # Q1 prediction\n bs_pred_b_Q1 <- tapply(predict(bs_fit, newdata = newdat_Q1, type=\"response\"),newdat_Q1$std_L,mean)\n bs_pred_result_Q1 <- cbind(bs_pred_result_Q1,bs_pred_b_Q1)\n \n bs_pred_b_inter <- tapply(predict(bs_fit, newdata = newdat_Q1_inter, type=\"response\"),list(newdat_Q1_inter$std_L,newdat_Q1_inter$std_T_cv),mean)\n bs_pred_result_Q1_inter[[bs]] <- bs_pred_b_inter\n \n # Q3 prediction\n \n bs_pred_b_Q3 <- tapply(predict(bs_fit, newdata = newdat_Q3, type=\"response\"),newdat_Q3$std_L,mean)\n bs_pred_b_Q3_tempCV <- tapply(predict(bs_fit, newdata = newdat_Q3_tempCV, type=\"response\"),newdat_Q3_tempCV$std_T_cv,mean)\n \n bs_pred_result_Q3 <- cbind(bs_pred_result_Q3,bs_pred_b_Q3)\n bs_pred_result_Q3_tempCV <- cbind(bs_pred_result_Q3_tempCV,bs_pred_b_Q3_tempCV)\n \n \n \n if(bs%%50==0){print(bs)}\n}\n\nbs_fit_smth_summary <- bs_fit_smth_summary[-1,]\nwrite.csv(bs_fit_para_summary,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_para_summary.csv\")\nwrite.csv(bs_fit_smth_summary,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_smooth_summary.csv\")\n\nwrite.csv(bs_quarter_diff,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_quarter_difference.csv\")\nwrite.csv(bs_pred_result_Q1,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q1_way1.csv\")\nwrite.csv(bs_pred_result_Q3,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q3_way1.csv\")\nwrite.csv(bs_pred_result_Q1_tempCV,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q1_tempCV_way1.csv\")\nwrite.csv(bs_pred_result_Q3_tempCV,\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q3_tempCV_way1.csv\")\nsave(bs_pred_result_Q1_inter,file=\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q1_inter_way1.RData\")\n\n\n# plot p-value distribution in smooth result\nbs_fit_para_summary <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_para_summary.csv\")\nbs_fit_smth_summary <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_smooth_summary.csv\")\n\nbs_fit_smth_summary <- bs_fit_smth_summary[-1,-1]\nsmth_var = c(\"Temp. CV Q1\",\"Temp. CV Q3\",\"Size Q1\",\"Size Q3\",\"Temp.CV:Size Q1\",\"Temp. CV:Size Q3\",\"Random Slope\",\"Random Intercept\")\nNo_p_sig = apply(bs_fit_smth_summary,2,function(x)length(which(x<=0.05)))\n\njpeg(\"./SizeAggregTend_data/output/fig/Appendix/Fig_bs_relationship_pval.jpeg\", width=6.5, height=3.5, units = \"in\",res=500)\npar(mfrow=c(2,4),mar=c(3,3,3,1),oma=c(2,2,0,0),cex=0.8)\nfor(i in 1:8){\n hist(bs_fit_smth_summary[,i],\n main=\"\",\n xlim=c(0,max(bs_fit_smth_summary[,i])+0.002),xlab = \"p-value\",\n breaks = seq(0,max(bs_fit_smth_summary[,i])+0.001,0.001),\n family=c(\"newrom\"))\n title(paste0(smth_var[i],\"\\n n=\",No_p_sig[i]), adj = 0, line = 1,family=c(\"newrom\"),cex.main=0.9)\n abline(v=0.05,lty=2)\n}\nmtext(\"p-value\",outer = T,side = 1,family=c(\"newrom\"))\nmtext(\"Frequency\",outer = T,side = 2,family=c(\"newrom\"))\ndev.off()\n\n\n######################\n# plot result \n######################\nbs_quarter_diff <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_quarter_difference.csv\")\nbs_pred_result_Q1 <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q1_way1.csv\")\nbs_pred_result_Q3 <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q3_way1.csv\")\nbs_pred_result_Q3_tempCV <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q3_tempCV_way1.csv\")\nload(\"./SizeAggregTend_data/output/1_size_TL/bs_fit_predicted_data_Q1_inter_way1.RData\")\n\n\n# original model prediction\n# quarter different\npred_b <- predict(fit.QART.tempCV.smooth.inter_Rslt, newdata = newdat, type=\"lpmatrix\")\nc1 <- grepl('Q1', colnames(pred_b))\nc3 <- grepl('Q3', colnames(pred_b))\nr1 <- with(newdat, quarter == 'Q1')\nr3 <- with(newdat, quarter == 'Q3')\nX <- pred_b[r1, ] - pred_b[r3, ]\nX[, ! (c1 | c3)] <- 0\nX[, !grepl('^ti\\\\(', colnames(pred_b))] <- 0\nquarter_diff <- X %*% coef(fit.QART.tempCV.smooth.inter_Rslt)\nquarter_diff <- tapply(quarter_diff,newdat_Q1$std_L,mean)\n\npred_b_Q1 <- tapply(predict(fit.QART.tempCV.smooth.inter_Rslt, newdata = newdat_Q1, type=\"response\"),\n newdat_Q1$std_L,mean)\npred_b_Q3 <- tapply(predict(fit.QART.tempCV.smooth.inter_Rslt, newdata = newdat_Q3, type=\"response\"),\n newdat_Q3$std_L,mean)\npred_b_Q3_tempCV <- tapply(predict(fit.QART.tempCV.smooth.inter_Rslt, newdata = newdat_Q3_tempCV, type=\"response\"),\n newdat_Q3$std_L,mean)\n\nbs_quarter_diff <- cbind(bs_quarter_diff[,-1],quarter_diff)\nbs_pred_result_Q1 <- cbind(bs_pred_result_Q1[,-1],pred_b_Q1)\nbs_pred_result_Q3 <- cbind(bs_pred_result_Q3[,-1],pred_b_Q3)\nbs_pred_result_Q3_tempCV <- cbind(bs_pred_result_Q3_tempCV[,-1],pred_b_Q3_tempCV)\n\n\n# bca 95CI\nlibrary(\"coxed\")\nbca_Qdiff_95CI <- apply(bs_quarter_diff,1,bca)\nbca_size_95CI_Q1 <- apply(bs_pred_result_Q1,1,bca)\nbca_size_95CI_Q3 <- apply(bs_pred_result_Q3,1,bca)\nbca_tempCV_95CI_Q3 <- apply(bs_pred_result_Q3_tempCV,1,bca)\n\n\nlibrary(dplyr)\n\n# plot #\njpeg(\"./SizeAggregTend_data/output/fig/Fig_main_effect_with_bs_and_matstage.jpeg\", width=1961, height=800, units = \"px\",res=300)\n\npar(mfrow=c(1,2),oma=c(0,0,1,1),mar=c(4,4,1,1),cex.axis=0.8,cex.lab=0.8)\n\n#Q1\nplot(unique(newdat$std_L),pred_b_Q1,\n type=\"l\",xlim = c(-0.8,0.8),ylim=c(0.5,4),\n xlab=\"Standardized length\",\n ylab=\"Taylor's exponents\",bty=\"l\")\npolygon(c(rev(unique(newdat$std_L)), unique(newdat$std_L)), \n c(rev(bca_size_95CI_Q1[2,]), bca_size_95CI_Q1[1,]), \n col = rgb(50, 50, 50, 40, maxColorValue=255), border = NA)\n\n#for(bs in 1:999){\n# lines(unique(newdat_Q1$std_L),bs_pred_result_Q1[,bs],\n# col=rgb(128, 138, 135, 60, maxColorValue=255),lwd=0.5)}\nlines(unique(newdat$std_L),pred_b_Q1,lwd=2)\n\nsp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==1) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"grey30\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nsp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==2) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"grey80\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nsp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==3) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"black\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nlegend(\"topright\",c(\"immature\",\"maturing\",\"matured\"),pch=1,col=c(\"grey30\",\"grey80\",\"black\"),horiz = F,cex = 0.5)\n\n#points(b~std_L,data=sp_TL.result[which(sp_TL.result$quarter==\"Q1\"),],\n# col=rgb(0, 0, 0, 80, maxColorValue=255),pch=19,cex=0.5)\nmtext(\"(a)\", side = 3, line = 1, outer =F,at =-1,adj=0,cex = 0.9)\n\n\n#Q3\nplot(unique(newdat$std_L),pred_b_Q3,\n type=\"l\",xlim = c(-0.8,0.8),ylim=c(0.5,4),\n xlab=\"Standardized length\",\n ylab=\"Taylor's exponents\",bty=\"l\")\npolygon(c(rev(unique(newdat$std_L)), unique(newdat$std_L)), \n c(rev(bca_size_95CI_Q3[2,]), bca_size_95CI_Q3[1,]), \n col = rgb(50, 50, 50, 40, maxColorValue=255), border = NA)\n\n\n#for(bs in 1:999){\n# lines(unique(newdat_Q3$std_L),bs_pred_result_Q3[,bs],\n# col=rgb(128, 138, 135, 60, maxColorValue=255),lwd=0.5)}\nlines(unique(newdat$std_L),pred_b_Q3,lwd=2)\nsp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==1) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"grey30\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nsp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==2) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"grey80\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nsp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==3) %>% \n with(points(b~std_L,pch=1,cex=0.6,col=\"black\",xlim = c(-0.9,0.9),ylim = c(1,3.5)))\nlegend(\"topright\",c(\"immature\",\"maturing\",\"matured\"),pch=1,col=c(\"grey30\",\"grey80\",\"black\"),horiz = F,cex = 0.5)\n\n#points(b~std_L,\n# data=sp_TL.result[which(sp_TL.result$quarter==\"Q3\"),],\n# col=rgb(0, 0, 0, 80, maxColorValue=255),pch=19,cex=0.5)\n\nmtext(\"(b)\", side = 3, line = 1, outer =F,adj=0,cex = 0.9)\ndev.off()\n\n\n\n# peak location\nMax_b_Q1 <- apply(bs_pred_result_Q1,2,max)\npeak_locat_order_Q1 <- apply(bs_pred_result_Q1,2,function(x)which(x==max(x)))\npeak_locat_Q1 <- seq(-0.69,0.75,length=200)[peak_locat_order_Q1]\nbca(peak_locat_Q1)\npeak_locat_Q1[length(peak_locat_Q1)]\n\nMax_b_Q3 <- apply(bs_pred_result_Q3,2,max)\npeak_locat_order_Q3 <- apply(bs_pred_result_Q3,2,function(x)which(x==max(x)))\npeak_locat_Q3 <- seq(-0.69,0.75,length=200)[peak_locat_order_Q3]\nbca(peak_locat_Q3)\npeak_locat_Q3[length(peak_locat_Q3)]\n\n\n\n#######################################################\n### Plot difference between two quarter \njpeg(\"./SizeAggregTend_data/output/fig/Appendix/Fig_quarter_size_diff.jpeg\", width=6, height=5, units = \"in\",res=600)\n\nplot(unique(newdat$std_L),bs_quarter_diff[,1000],\n type=\"l\",xlim = c(-0.7,0.8),ylim=c(-0.15,0.2),\n xlab=\"Standardized length\",ylab=\"Difference between quarter\",family=c(\"newrom\"),bty=\"l\")\npolygon(c(rev(unique(newdat$std_L)), unique(newdat$std_L)), \n c(rev(bca_Qdiff_95CI[2,]), bca_Qdiff_95CI[1,]), \n col = rgb(50, 50, 50, 40, maxColorValue=255), border = NA)\nabline(h=0)\nabline(v=range(as.numeric(colnames(bca_Qdiff_95CI)[which(bca_Qdiff_95CI[1,]>0)])),lty=2)\nabline(v=range(as.numeric(colnames(bca_Qdiff_95CI)[which(bca_Qdiff_95CI[2,]<0)])),lty=2)\n\n\ndev.off()\n\n\n\n\n\n\n#################################################\n# mature stage effect on the relationship\n#################################################\n\n# dunn-test\nlibrary(\"rstatix\")\ndunn_matstage_Q1 = sp_TL.result%>%\n filter(quarter==\"Q1\") %>%\n dunn_test(b~mat_stage, p.adjust.method = \"BH\")\ndunn_matstage_Q1 <- dunn_matstage_Q1 %>% add_xy_position(x = \"mat_stage\")\n\ndunn_matstage_Q3 = sp_TL.result%>%\n filter(quarter==\"Q3\") %>%\n dunn_test(b~mat_stage, p.adjust.method = \"BH\")\ndunn_matstage_Q3 <- dunn_matstage_Q3 %>% add_xy_position(x = \"mat_stage\")\n\njpeg(\"./SizeAggregTend_data/output/fig/Fig_matstagebyQ_dunntest_Q1.jpeg\", width=980.5, height=700, units = \"px\",res=300)\nlibrary(ggplot2)\nlibrary(\"ggpubr\")\nggboxplot(sp_TL.result[which(sp_TL.result$quarter==\"Q1\"),], \n x = \"mat_stage\", y = \"b\", fill = \"mat_stage\",\n xlab=\"\",ylab=\"Taylor's exponents\") +\n stat_pvalue_manual(dunn_matstage_Q1, hide.ns = FALSE,y.position = c(3.5,3.6,3.7))+\n scale_x_discrete(labels=c(\"immature\",\"maturing\",\"matured\"))+\n theme(legend.position = \"none\",\n axis.text.y = element_text(size=rel(0.9),angle = 90),\n axis.text.x = element_text(size=rel(0.9)),\n axis.title=element_text(size=rel(0.9)))+\n ylim(1.5,3.8)\n\ndev.off()\n\njpeg(\"./SizeAggregTend_data/output/fig/Fig_matstagebyQ_dunntest_Q3.jpeg\", width=980.5, height=700, units = \"px\",res=300)\n\nggboxplot(sp_TL.result[which(sp_TL.result$quarter==\"Q3\"),], \n x = \"mat_stage\", y = \"b\", fill = \"mat_stage\",\n xlab=\"\",ylab=\"Taylor's exponents\") +\n stat_pvalue_manual(dunn_matstage_Q3, hide.ns = FALSE,y.position = c(3.5,3.6,3.7))+\n scale_x_discrete(labels=c(\"immature\",\"maturing\",\"matured\"))+\n theme(legend.position = \"none\",\n axis.text.y = element_text(size=rel(0.9),angle = 90),\n axis.text.x = element_text(size=rel(0.9)),\n axis.title=element_text(size=rel(0.8)))+\n ylim(1.5,3.8)\n\ndev.off()\n\n\n#######\n# examine the relationship in each maturity stage\n#######\n\n# Q1 \n\nlm1.g = sp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==1) %>% \n with(gam(b~std_L+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\nlm2.g = sp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==2) %>% \n with(gam(b~std_L+s(sp_ID,std_L,bs=\"re\")+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\nlm3.g = sp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==3) %>% \n with(gam(b~std_L+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\n\n# Q3\nlm1.3.g = sp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==1) %>% \n with(gam(b~std_L+s(sp_ID,std_L,bs=\"re\")+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\nlm2.3.g = sp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==2) %>% \n with(gam(b~std_L+s(sp_ID,std_L,bs=\"re\")+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\nlm3.3.g = sp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==3) %>% \n with(gam(b~std_L,family = Gamma(link = \"log\")))\n\n\n# bootstrap 999 times for model fitting and prediction\nbs_matstage_lin_result_Q1 <- NULL\nbs_matstage_lin_result_Q3 <- NULL\n\n\nfor (bs in 1:999){\n bs_TL_result <- cbind(TL_BSdata[,bs],sp_TL.result[,9:19])\n colnames(bs_TL_result)[1] <- \"b\"\n \n #Q1\n for(mat in 1:3){\n bs_glm_result <- bs_TL_result %>% filter(quarter==\"Q1\" & mat_stage==mat) %>% \n with(gam(b~std_L+s(sp_ID,std_L,bs=\"re\")+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\n bs_matstage_lin_result_Q1 <- rbind(bs_matstage_lin_result_Q1,c(coef(bs_glm_result)[1:2],mat,1))\n }\n \n #Q3\n for(mat in 1:3){\n bs_glm_result <- bs_TL_result %>% filter(quarter==\"Q3\" & mat_stage==mat) %>% \n with(gam(b~std_L+s(sp_ID,std_L,bs=\"re\")+s(sp_ID,bs=\"re\"),family = Gamma(link = \"log\")))\n bs_matstage_lin_result_Q3 <- rbind(bs_matstage_lin_result_Q3,c(coef(bs_glm_result)[1:2],mat,3))\n }\n if(bs%%50==0){print(bs)}\n}\n\n\nbs_matstage_lin_result_Q1 <- as.data.frame(bs_matstage_lin_result_Q1)\nbs_matstage_lin_result_Q3 <- as.data.frame(bs_matstage_lin_result_Q3)\ncolnames(bs_matstage_lin_result_Q1)[2:4] <- c(\"slope\",\"mat_stage\",\"quarter\")\ncolnames(bs_matstage_lin_result_Q3)[2:4] <- c(\"slope\",\"mat_stage\",\"quarter\")\n\nwrite.csv(bs_matstage_lin_result_Q1,\"./SizeAggregTend_data/output/1_size_TL/bs_lin_matstage_byQ_QART_1.csv\")\nwrite.csv(bs_matstage_lin_result_Q3,\"./SizeAggregTend_data/output/1_size_TL/bs_lin_matstage_byQ_QART_3.csv\")\n\n\n\nbs_matstage_lin_result_Q1 <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_lin_matstage_byQ_QART_1.csv\")\nbs_matstage_lin_result_Q3 <- read.csv(\"./SizeAggregTend_data/output/1_size_TL/bs_lin_matstage_byQ_QART_3.csv\")\n\n# plot\nadd_eq_fuc = function(model){\n cf <- round(coef(model), 2) \n eq <- paste0(\" log(y) = \", cf[1],ifelse(sign(cf[2])==1, \" + \", \" - \"), abs(cf[2]), \"x\")\n return(eq)\n}\n\nlibrary(\"coxed\")\nglm_fit_result <- list(lm1.g,lm2.g,lm3.g,lm1.3.g,lm2.3.g,lm3.3.g)\nmat_stage_name <- c(\"immature\",\"maturing\",\"matured\")\n\n# Q1\njpeg(\"./SizeAggregTend_data/output/fig/Fig_relationship_in_each_matstagebyQ_Q1.jpeg\", width=980.5, height=800, units = \"px\",res=300)\npar(mfrow=c(1,3),mar=c(2,2,2,0),oma=c(3,3,3,1),cex.axis=1.2,cex.lab=0.8) \n\nfor(mat in 1:3){\n sp_TL.result %>% filter(quarter==\"Q1\" & mat_stage==mat) %>% with(plot(b~std_L,ylim=c(1,4),yaxt=\"n\"))\n if(mat==1){axis(side = 2)}\n \n if(summary(glm_fit_result[[mat]])$p.table[2,4]<=0.05){\n lines(seq(-0.8,0.8,0.1),predict(glm_fit_result[[mat]],newdata = data.frame(expand.grid(std_L=seq(-0.8,0.8,0.1),sp_ID=1)),type = \"response\"))\n lm.g.eq <- add_eq_fuc(glm_fit_result[[mat]])\n lm.g.ci <- bs_matstage_lin_result_Q1 %>% filter(mat_stage==mat) %>% with(bca(slope)) %>% round(2)\n mtext(lm.g.eq, 3, line=-1.5,cex=0.35)\n mtext(paste0(\"CI=[\",lm.g.ci[1],\",\",lm.g.ci[2],\"]\"), 3, line=-2.4,cex=0.35)\n }\n mtext(mat_stage_name[mat],3,line=1,cex = 0.8)\n}\nmtext(\"Standardized length\",side=1,line=1,outer = T,cex = 0.8)\nmtext(\"Taylor's exponents\",side=2,line=1,outer = T,cex = 0.8)\nmtext(\"(c)\",side=3,line=1,outer = T,cex = 0.9,adj = 0)\n\ndev.off()\n\n# Q3\njpeg(\"./SizeAggregTend_data/output/fig/Fig_relationship_in_each_matstagebyQ_Q3.jpeg\", width=980.5, height=800, units = \"px\",res=300)\npar(mfrow=c(1,3),mar=c(2,2,2,0),oma=c(3,3,3,1),cex.axis=1.2,cex.lab=0.8) \nfor(mat in 1:3){\n sp_TL.result %>% filter(quarter==\"Q3\" & mat_stage==mat) %>% with(plot(b~std_L,ylim=c(0.5,4),yaxt=\"n\"))\n if(mat==1){axis(side = 2)}\n \n if(summary(glm_fit_result[[mat+3]])$p.table[2,4]<=0.05){\n lines(seq(-0.8,0.8,0.1),predict(glm_fit_result[[mat+3]],newdata = data.frame(expand.grid(std_L=seq(-0.8,0.8,0.1),sp_ID=1)),type = \"response\"))\n lm.g.eq <- add_eq_fuc(glm_fit_result[[mat+3]])\n lm.g.ci <- bs_matstage_lin_result_Q3 %>% filter(mat_stage==mat) %>% with(bca(slope)) %>% round(2)\n mtext(lm.g.eq, 3, line=-1.5,cex=0.35)\n mtext(paste0(\"CI=[\",lm.g.ci[1],\",\",lm.g.ci[2],\"]\"), 3, line=-2.2,cex=0.35)\n }\n mtext(mat_stage_name[mat],3,line=1,cex = 0.8)\n}\n\nmtext(\"Standardized length\",side=1,line=1,outer = T,cex = 0.8)\nmtext(\"Taylor's exponents\",side=2,line=1,outer = T,cex = 0.8)\nmtext(\"(d)\",side=3,line=1,outer = T,cex = 0.9,adj = 0)\n\ndev.off()\n\n", "meta": {"hexsha": "33323598d3fe480f4ef38977e887f33f0be77408", "size": 30268, "ext": "r", "lang": "R", "max_stars_repo_path": "Analysis/Script_2_Relationship_fitting_bVSsize_eachMature.r", "max_stars_repo_name": "ruo-yu-Pan/Hump-shaped-relationship_Aggregation-tendency_vs_bodysize", "max_stars_repo_head_hexsha": 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"max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.3328671329, "max_line_length": 146, "alphanum_fraction": 0.5979252015, "num_tokens": 9645, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3185153972401916}} {"text": "# PROVIDE ANNOTATION FOR GENES\r\nAnnotateGenes <- function(genes){\r\n # Annotate genes.\r\n annotated_data <- cbind(genes, Annotation[rownames(genes),])\r\n return(annotated_data)\r\n}\r\n\r\n# CREATE GENE EXPRESSION FILE (.TXT) FOR GSEA\r\nCreateGeneExpressionFile <- function(df_1, df_2, names){\r\n # Combine expression data of shared genes for two conditions, store in data frame and write to .txt file.\r\n shared_genes <- intersect(row.names(df_1), row.names(df_2))\r\n df_1 <- as.data.frame(df_1[row.names(df_1) %in% shared_genes,])\r\n df_2 <- as.data.frame(df_2[row.names(df_2) %in% shared_genes,])\r\n df <- cbind(shared_genes, NA, df_1, df_2, row.names=shared_genes)\r\n colnames(df) <- c('NAME', 'DESCRIPTION', names)\r\n write.table(df, file='Results/gsea_gene_expression.txt', row.names=F, quote=F, sep='\\t')\r\n}\r\n\r\n# CREATE GENE SETS FILE (.GMX) FOR GSEA\r\nCreateGeneSetsFile <- function(){\r\n max_nrow = 0\r\n # Load unique subclasses from Annotation, filter out duplicate/null values.\r\n subclasses <- as.character(unique(Annotation$subclass))\r\n subclasses <- subclasses[!(duplicated(tolower(subclasses)))]\r\n subclasses <- subclasses[subclasses != '']\r\n # Get corresponding genes per subclass, store in data frame and write to .gmx file.\r\n df <- data.frame(All=row.names(Annotation))\r\n for(index in 1:length(subclasses)){\r\n temp_subset <- subset(Annotation, Annotation$subclass==subclasses[index])['subclass']\r\n genes <- c(NA, row.names(temp_subset))\r\n df$col <- c(genes, rep('', nrow(df)-length(genes)))\r\n names(df)[index+1] = subclasses[index]\r\n if(nrow(temp_subset) > max_nrow){\r\n max_nrow = nrow(temp_subset)\r\n }\r\n }\r\n df <- subset(df, select = -c(All))\r\n df <- df[-c(max_nrow+2:nrow(df)), ]\r\n write.table(df, file='Results/gsea_gene_sets.gmx', row.names=F, quote=F, sep='\\t')\r\n}\r\n\r\n# STORE EXPERIMENTAL CONDITIONS IN FACTOR NAMED GROUP\r\nCreateGroup <- function(conditions){\r\n exp <- rep(conditions, each=2)\r\n group <- factor(exp)\r\n return(group)\r\n}\r\n\r\n# CREATE glmLRT MODEL\r\nCreateModel <- function(strain, data, group){\r\n # Group samples by condition into design matrix.\r\n design <- model.matrix(~0+group, data=data$samples)\r\n colnames(design) <- levels(data$samples$group)\r\n # Create model for top differentially expressed genes.\r\n fit <- glmFit(data, design)\r\n if(strain == 'WCFS1'){\r\n mc <- makeContrasts(exp.r=WCFS1.glc-WCFS1.rib, levels=design)\r\n } else if(strain == 'NC8'){\r\n mc <- makeContrasts(exp.r=NC8.glc-NC8.rib, levels=design)\r\n }\r\n fit <- glmLRT(fit, contrast=mc)\r\n return(fit)\r\n}\r\n\r\n# PERFORM VARIOUS DATA PROCESSING TASKS\r\nDataProcessing <- function(group, start, stop, cpm_filter){\r\n # Create DGEList object for storage of RNA-Seq data.\r\n y <- DGEList(counts=Counts[,start:stop], group=group)\r\n # Filter out genes below cpm_filter.\r\n keep.genes <- rowSums(cpm(y)>cpm_filter) >= 2\r\n y <- y[keep.genes,]\r\n y$samples$lib.size <- colSums(y$counts)\r\n # Determine scale factors using Trimmed Mean of M-values (TMM).\r\n y <- calcNormFactors(y, method='TMM')\r\n # Group samples by condition into design matrix.\r\n design <- model.matrix(~0+group, data=y$samples)\r\n colnames(design) <- levels(y$samples$group)\r\n # Estimate dispersions.\r\n y <- estimateGLMCommonDisp(y,design)\r\n y <- estimateGLMTrendedDisp(y,design, method='power')\r\n y <- estimateGLMTagwiseDisp(y,design)\r\n return(y)\r\n}\r\n\r\n# DETERMINE TOP DIFFERENTIALLY EXPRESSED GENES FOR A GIVEN LOGFC FILTER\r\nDetermineDEGenes <- function(fit, filter_logFC){\r\n df <- topTags(fit, n=nrow(fit))\r\n df <- df$table\r\n df <- subset(df, abs(logFC) >= filter_logFC)\r\n return(df)\r\n}\r\n\r\n# DETERMINE OVERREPRESENTATION OF GENES IN KEGG PATHWAYS\r\nDeterminePathwayOverrep <- function(fit, n_results){\r\n kegg_pathways <- kegga(fit, species.KEGG=KEGG_SPECIES)\r\n top_OR_pathways <- topKEGG(kegg_pathways, number=n_results)\r\n return(top_OR_pathways)\r\n}\r\n\r\n# GET KEGG PATHWAYS FOR GIVEN GENES\r\nGetPathwaysForGenes <- function(genes){\r\n # Set up data frame.\r\n cols <- rownames(genes)\r\n n_pathways <- dbGetQuery(KEGG_dbconn(), 'SELECT COUNT(*) FROM pathway2name')[1,1]\r\n df <- data.frame(matrix(ncol=length(cols), nrow=n_pathways))\r\n colnames(df) <- cols\r\n # Store pathways per gene in data frame.\r\n max_n_pathways = 0\r\n for(index in 1:length(cols)){\r\n gene <- cols[index]\r\n try(query <- keggGet(c(paste('lpl:', gene, sep=''))), silent=T)\r\n if(exists('query')){\r\n pathways <- query[[1]]$PATHWAY\r\n if(!is.null(pathways)){\r\n for(index_2 in 1:length(pathways)){\r\n df[index_2, index] = pathways[index_2]\r\n }\r\n if(length(pathways) > max_n_pathways){\r\n max_n_pathways = length(pathways)\r\n }\r\n }\r\n }\r\n }\r\n df <- df[-c(max_n_pathways+1:nrow(df)), ]\r\n return(df)\r\n}\r\n\r\n# PLOT LOGFC VALUES OF GENES INTO A HEAT MAP\r\nPlotHeatMap <- function(WCFS1_df, NC8_df, n_genes, ...){\r\n # Cuts data frames down to selected number of genes.\r\n WCFS1_df <- WCFS1_df[1:n_genes,]\r\n NC8_df <- NC8_df[1:n_genes,]\r\n shared_genes <- intersect(row.names(WCFS1_df), row.names(NC8_df))\r\n # Reduces data frames to only contain shared genes.\r\n WCFS1_df <- as.data.frame(WCFS1_df[row.names(WCFS1_df) %in% shared_genes,])\r\n NC8_df <- as.data.frame(NC8_df[row.names(NC8_df) %in% shared_genes,])\r\n # Combines data frames into single data frame and provides annotation.\r\n annotation_df <- as.data.frame(Annotation[row.names(Annotation) %in% shared_genes,])\r\n annotation_df <- subset(annotation_df, select=c('name', 'subclass'))\r\n df <- cbind(WCFS1_df['logFC'], NC8_df['logFC'], row.names=shared_genes)\r\n annotation_df <- cbind(df, annotation_df[rownames(df),], row.names=shared_genes)\r\n # Visualizes heat map and sets column and row names.\r\n rownames(df) <- paste(annotation_df[,'name'], annotation_df[,'subclass'], sep=' - ')\r\n colnames(df) <- c('WCFS1', 'NC8')\r\n color_palette <- colorRampPalette(c('green','blue'))(n = 64)\r\n heatmap.2(as.matrix(df),\r\n adjCol=c(NA, 0.5),\r\n cexCol=1.5,\r\n col=color_palette,\r\n Colv=F,\r\n dendrogram='none',\r\n density.info='none',\r\n margins=c(3,22),\r\n notecol='black',\r\n srtCol=0,\r\n trace='none')\r\n}\r\n\r\n# PLOT SAMPLE DISTANCES\r\nPlotSampleDistances <- function(title, data, group, ...){\r\n # Set up colors and symbols for plot.\r\n if(length(levels(group)) == 4){\r\n colors <- rep(c('red', 'red', 'blue', 'blue'), 2)\r\n } else if(length(levels(group)) == 2){\r\n colors <- rep(c('red', 'blue'), 2)\r\n }\r\n par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE)\r\n pch <- rep(c(PCH_1, PCH_2), length(levels(group)))\r\n pch_legend <- rep(c(16, 18), length(levels(group))/2)\r\n # Visualize plot.\r\n plotMDS(data, bg=colors[group], cex=2, col=1, pch=pch[group], xlab='Dimension 1', ylab='Dimension 2')\r\n title(title, line=0.5)\r\n legend('topright', col=colors, inset=c(-0.25,0), legend=levels(group), ncol=1, pch=pch_legend, title='Samples')\r\n}\r\n\r\n# PLOT A GIVEN KEGG PATHWAY WITH LOGFC VALUES OF GENES\r\nPlotKEGGpathway <- function(df, pathway_id, pathway_name, fit, ...){\r\n # Select logFC values from genes in a specific pathway.\r\n matches <- vector('numeric')\r\n for(index in 1:ncol(df)){\r\n temp_vector <- df[,index]\r\n if(pathway_name %in% temp_vector){\r\n matches <- append(matches, colnames(df)[index])\r\n }\r\n }\r\n input_df <- subset(fit$table[row.names(fit$table) %in% matches,], select=logFC)\r\n input <- input_df[,length(input_df)]\r\n names(input) <- row.names(input_df)\r\n # Visualizes logFC values inside KEGG pathway.\r\n pathview(gene.data=input, species=KEGG_SPECIES, pathway=pathway_id, gene.idtype='KEGG')\r\n}\r\n\r\n# WRITE RESULTS TO EXCEL FILE\r\nWriteResults <- function(file_name, annotated_results, sheet_name_1, or_pathways, sheet_name_2, pathways_de_genes, sheet_name_3){\r\n # Write results to sheets in Excel file.\r\n write.xlsx(annotated_results, file=file_name, sheetName=sheet_name_1, col.names=TRUE, row.names=TRUE, append=TRUE, showNA=TRUE)\r\n write.xlsx(or_pathways, file=file_name, sheetName=sheet_name_2, col.names=TRUE, row.names=TRUE, append=TRUE, showNA=TRUE)\r\n write.xlsx(pathways_de_genes, file=file_name, sheetName=sheet_name_3, col.names=TRUE, row.names=FALSE, append=TRUE, showNA=FALSE)\r\n}", "meta": {"hexsha": "281ec5c8ce32c0afa86571251a1039be72dc5e1e", "size": 8234, "ext": "r", "lang": "R", "max_stars_repo_path": "data-analysis-functions.r", "max_stars_repo_name": "kjradem/cou11-data-analysis-in-R", "max_stars_repo_head_hexsha": "3741affb4f40298da834343d1a773533bef23f48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data-analysis-functions.r", "max_issues_repo_name": "kjradem/cou11-data-analysis-in-R", "max_issues_repo_head_hexsha": "3741affb4f40298da834343d1a773533bef23f48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 12, "max_issues_repo_issues_event_min_datetime": "2018-06-01T08:46:51.000Z", "max_issues_repo_issues_event_max_datetime": "2018-06-14T21:54:52.000Z", "max_forks_repo_path": "data-analysis-functions.r", "max_forks_repo_name": "kjradem/cou11-data-analysis-in-R", "max_forks_repo_head_hexsha": "3741affb4f40298da834343d1a773533bef23f48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3768844221, "max_line_length": 132, "alphanum_fraction": 0.6718484333, "num_tokens": 2382, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.31846323491861916}} {"text": "#==============================================================================\n#\tTest for ranger\n#==============================================================================\n\nsource(\"tests.r\")\niris2 <- iris\niris2$Species <- as.character(iris2$Species)\n\ntest.data <- list(\n\tcall = list(\n\t\tsubstitute(\n\t\t\tranger(Sepal.Length ~ ., data = iris, write.forest = TRUE)\n\t\t),\n\t\tsubstitute(ranger(Species ~ ., data = iris, write.forest = TRUE)),\n\t\tsubstitute(\n\t\t\tranger(\n\t\t\t\tSpecies ~ ., data = iris2, write.forest = TRUE,\n\t\t\t\tprobability = TRUE\n\t\t\t)\n\t\t)\n\t),\n\tformula = list(\n\t\tSepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + Species,\n\t\tSpecies ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,\n\t\tSpecies ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width\n\t),\n\tmodel.type = list(\"regression\", \"classification\", \"classification\")\n)\n\ntest.model.adapter(\"ranger\", iris, test.data)\n\nrm(test.data)\n", "meta": {"hexsha": "e53a0b51527b0ef77067ece7ace8ef0ee348b558", "size": 913, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/test__model.adapter__function__ranger.r", "max_stars_repo_name": "Marchen/model.adapter", "max_stars_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/test__model.adapter__function__ranger.r", "max_issues_repo_name": "Marchen/model.adapter", "max_issues_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2018-11-20T10:07:41.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T01:11:12.000Z", "max_forks_repo_path": "tests/test__model.adapter__function__ranger.r", "max_forks_repo_name": "Marchen/model.adapter", "max_forks_repo_head_hexsha": "ace7f78abee9e2ce2b1ee5e09cc8ac59cea66c3e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-04T04:46:54.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-04T04:46:54.000Z", "avg_line_length": 27.6666666667, "max_line_length": 79, "alphanum_fraction": 0.547645126, "num_tokens": 226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.31846323491861916}} {"text": "statesInfo <- read.csv('stateData.csv')\n\nsubset(statesInfo, state.region == 1)\nstateSubset <- statesInfo[statesInfo$illiteracy == 0.5, ]\n\ndim(stateSubset)\nstr(stateSubset)\n\nstateSubset\n", "meta": {"hexsha": "ff8c373111c3ecc5fd9bc824565514da50e51cff", "size": 185, "ext": "r", "lang": "R", "max_stars_repo_path": "src/testfiles/r/input/strings/strings0.r", "max_stars_repo_name": "rahlk/Rosie", "max_stars_repo_head_hexsha": "60dc9d6a5590cdfbafbcbb0a7285db4e496384cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/testfiles/r/input/strings/strings0.r", "max_issues_repo_name": "rahlk/Rosie", "max_issues_repo_head_hexsha": "60dc9d6a5590cdfbafbcbb0a7285db4e496384cc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/testfiles/r/input/strings/strings0.r", "max_forks_repo_name": "rahlk/Rosie", "max_forks_repo_head_hexsha": "60dc9d6a5590cdfbafbcbb0a7285db4e496384cc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5, "max_line_length": 57, "alphanum_fraction": 0.7513513514, "num_tokens": 50, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3184632349186191}} {"text": "library(shiny)\r\nlibrary(ggplot2)\r\nlibrary(TTR)\r\nlibrary(httr)\r\nlibrary(dygraphs)\r\nlibrary(datasets)\r\nlibrary(xts)\r\nlibrary(plyr)\r\n\r\n\r\n# sensor = \"Node_383\"\r\n# channels = \"accelerationx,accelerationy,accelerationz,air:temperature\"\r\n# time_start = \"2014-11-23T07:56:48.006750000+0100\"\r\n# time_end = \"2014-11-25T07:57:00.256750000+0100\"\r\n# \r\n# url <- paste0(\"http://quader.igg.tu-berlin.de/istsos/bridgedemoservice?service=SOS&request=GetObservation&offering=temporary&procedure=\",sensor,\"&observedProperty=\",channels,\"&responseFormat=text/plain&version=1.0.0&\",time_start,\"/\",time_end)\r\n# dataset1 <- read.csv(file=url, header = TRUE)# read csv fil\r\n\r\ndataset1<- read.csv(file=\"Node383.csv\", header = TRUE, stringsAsFactors = F) # read csv file \r\ndataset2 <- read.csv(file=\"Node384.csv\", header = TRUE, skip=16,stringsAsFactors = F ) # read csv file \r\ndataset3 <- read.csv(file=\"Node573.csv\", header = TRUE, skip=16,stringsAsFactors = F ) # read csv file \r\ndataset4 <- read.csv(file=\"Node574.csv\", header = TRUE, skip=15,stringsAsFactors = F) # read csv file\r\n#\r\n#---------------------------------------------------------------------------------------------------------\r\n#---------------------------------------------------------------------------------------------------------\r\ndataset1$Timestamp..GMT. <- sub(\"\\\\.\\\\d*$\",\"\",dataset1$Timestamp..GMT.)\r\ndataset2$Timestamp..GMT. <- sub(\"\\\\.\\\\d*$\",\"\",dataset2$Timestamp..GMT.)\r\ndataset3$Timestamp..GMT. <- sub(\"\\\\.\\\\d*$\",\"\",dataset3$Timestamp..GMT.)\r\ndataset4$Timestamp..GMT. <- sub(\"\\\\.\\\\d*$\",\"\",dataset4$Timestamp..GMT.)\r\n#---------------------------------------------------------------------------------------------\r\n#---------------------------------------------------------------------------------------------\r\ndataset1.summary <- ddply(dataset1, \"Timestamp..GMT.\", summarise,\r\n chan1 = (Channel.1),\r\n chan2 = (Channel.2),\r\n chan3 = (Channel.3),\r\n chan4 = (Channel.4))\r\n\r\ndataset2.summary <- ddply(dataset2, \"Timestamp..GMT.\", summarise,\r\n chan1 = (Channel.1),\r\n chan2 = (Channel.2),\r\n chan3 = (Channel.3),\r\n chan4 = (Channel.4))\r\n\r\ndataset3.summary <- ddply(dataset3, \"Timestamp..GMT.\", summarise,\r\n chan1 = (Channel.1),\r\n chan2 = (Channel.2),\r\n chan3 = (Channel.3),\r\n chan8 = (Channel.8))\r\n\r\ndataset4.summary <- ddply(dataset4, \"Timestamp..GMT.\", summarise,\r\n chan1 = (Channel.1),\r\n chan2 = (Channel.2),\r\n chan8 = (Channel.8))\r\n#--------------------------------------------------------------------------------------------\r\n\r\n#--------------------------------------------------------------------------------------------\r\n# Convert first column to ts:\r\nz <- strptime(dataset1.summary$Timestamp..GMT., \"%d/%m/%Y %H:%M:%OS\")\r\ndataset1.summary$Timestamp..GMT. <- z # replace data frame info\r\n\r\ny <- strptime(dataset2.summary$Timestamp..GMT., \"%d/%m/%Y %H:%M:%OS\")\r\ndataset2.summary$Timestamp..GMT. <- y # replace data frame info\r\n\r\nx <- strptime(dataset3.summary$Timestamp..GMT., \"%d/%m/%Y %H:%M:%OS\")\r\ndataset3.summary$Timestamp..GMT. <- x # replace data frame info\r\n\r\nw <- strptime(dataset4.summary$Timestamp..GMT., \"%d/%m/%Y %H:%M:%OS\")\r\ndataset4.summary$Timestamp..GMT. <- w # replace data frame info\r\n#--------------------------------------------------------------------------------------------\r\n#--------------------------------------------------------------------------------------------\r\n# Make individual time series:\r\ntest1 <- xts(dataset1.summary$chan1, dataset1.summary$Timestamp..GMT.)\r\ntest2 <- xts(dataset1.summary$chan2, dataset1.summary$Timestamp..GMT.)\r\ntest3 <- xts(dataset1.summary$chan3, dataset1.summary$Timestamp..GMT.)\r\ntest4 <- xts(dataset1.summary$chan4, dataset1.summary$Timestamp..GMT.)\r\n\r\ntest21 <- xts(dataset2.summary$chan1, dataset2.summary$Timestamp..GMT.)\r\ntest22 <- xts(dataset2.summary$chan2, dataset2.summary$Timestamp..GMT.)\r\ntest23 <- xts(dataset2.summary$chan3, dataset2.summary$Timestamp..GMT.)\r\ntest24 <- xts(dataset2.summary$chan4, dataset2.summary$Timestamp..GMT.)\r\n\r\ntest31 <- xts(dataset3.summary$chan1, dataset3.summary$Timestamp..GMT.)\r\ntest32 <- xts(dataset3.summary$chan2, dataset3.summary$Timestamp..GMT.)\r\ntest33 <- xts(dataset3.summary$chan3, dataset3.summary$Timestamp..GMT.)\r\ntest34 <- xts(dataset3.summary$chan8, dataset3.summary$Timestamp..GMT.)\r\n\r\ntest41 <- xts(dataset4.summary$chan1, dataset4.summary$Timestamp..GMT.)\r\ntest42 <- xts(dataset4.summary$chan2, dataset4.summary$Timestamp..GMT.)\r\ntest48 <- xts(dataset4.summary$chan8, dataset4.summary$Timestamp..GMT.)\r\n\r\n\r\n#---------------------------------------------------------------------------------------------------------\r\n#---------------------------------------------------------------------------------------------------------\r\n\r\n\r\n##---end of functions-------\r\n## Define server logic required to generate and plot a random distribution\r\nshinyServer(function(input,output) {\r\n\r\n ##---functions----------\r\n linear_regression <- function(x,y){\r\n plot(y ~ x, xlab=\"acceleration.x\",ylab =\"acceleration.y\", main=\"linear regression plotting\")\r\n model = lm(y ~ x)\r\n abline(lm(y ~ x),col=2,lwd=2)\r\n return (model)\r\n }\r\n \r\n\r\n #dygraph(test, main = \"Node383\",) %>% \r\n \r\n#--datset chooseing--\r\n\r\ndataset <- reactive({\r\n if(input$radio == \"Node 383\")\r\n #dataset1 <- cbind(test1,test2,test3,test4)\r\n data <- dataset1\r\n \r\n else if(input$radio == \"Node 384\")\r\n # dataset2<- cbind(test21,test22,test23,test24)\r\n data <- dataset2\r\n \r\n \r\n else if(input$radio == \"Node 573\")\r\n #dataset3<- cbind(test31,test32,test33,test34)\r\n data <- dataset3\r\n \r\n else if(input$radio == \"Node 574\")\r\n # dataset4 <- cbind(test41,test42,test43,test44)\r\n data <- dataset4\r\n \r\n else(\"Error selecting a Node\")\r\n })\r\n#----------------------------------------------------------------------------------------------------------\r\n#----------------------------------------------------------------------------------------------------------\r\nmyData <- function(){\r\n if(input$radio == \"Node 383\"){\r\n test <- cbind(test1,test2,test3,test4)\r\n }\r\n else if(input$radio == \"Node 384\") {\r\n test <- cbind(test21,test22,test23,test24)\r\n }\r\n \r\n else if(input$radio == \"Node 573\")\r\n test <- cbind(test31,test32,test33,test34)\r\n \r\n else if(input$radio == \"Node 574\")\r\n test <- cbind(test41,test42)\r\n #else if\r\n \r\n else(\"Error selecting a Node\")\r\n}\r\n\r\n\r\n#----------------------------------------------------------------------------------------------------------\r\n#----------------------------------------------------------------------------------------------------------\r\ntime<- reactive({\r\n timestamp = dataset()[1:input$samplesize,1]\r\n date = as.character(lapply(strsplit(as.character(timestamp), split=\" \"), \"[\", 1))\r\n hms = as.character(lapply(strsplit(as.character(timestamp), split=\" \"), \"[\", 2))\r\n h = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 1))\r\n m = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 2))\r\n s = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 3))\r\n \r\n time_sec = NULL\r\n for(i in 1:length(timestamp))\r\n {\r\n time_sec[i] = h[i]*3600 + m[i]*60 + s[i]\r\n }\r\n time_sec\r\n# \r\n# date = as.character(lapply(strsplit(as.character(timestamp), split=\"T\"), \"[\", 1))\r\n# hms = as.character(lapply(strsplit(as.character(timestamp), split=\"T\"), \"[\", 2))\r\n# h = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 1))\r\n# m = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 2))\r\n# # s = as.numeric(lapply(strsplit(hms, split=\":\"), \"[\", 3)) # gave NAs introduced by coercion \r\n# \r\n# # Cutting characters manually\r\n# s = as.numeric(substr(hms, 7, 15)) # characters 7 to 15\r\n# \r\n# # Gets UTC component, also cut manually\r\n# utcComp = as.numeric(substr(hms, 17, 18)) \r\n# \r\n# h=h+utcComp # adding UTC to hours value\r\n# \r\n# # Filling time vector\r\n# time_sec = NULL\r\n# for(i in 1:length(timestamp))\r\n# {\r\n# time_sec[i] = h[i]*3600 + m[i]*60 + s[i]\r\n# }\r\n# time_sec\r\n})\r\n\r\nchannel1<- reactive({\r\n dataset()$Channel.1\r\n})\r\n\r\nchannel2<- reactive({\r\n dataset()$Channel.2\r\n})\r\n\r\nchannel3<- reactive({\r\n dataset()$Channel.3\r\n})\r\n\r\nchannel4<- reactive({\r\n dataset()$Channel.4\r\n})\r\n#----datset--------- \r\noutput$plot <- renderPlot(function(){\r\n\r\n p <- ggplot(dataset(),aes_string(x=input$x, y=input$y))+geom_point()\r\n\r\n if(input$color != 'None')\r\n p <- p + aes_string(color=input$color)\r\n\r\n if (input$shape != 'None')\r\n p <- p + aes_string(shape=input$shape)\r\n\r\n facets <- paste(input$facet_row, '~', input$facet_col)\r\n\r\n if (facets != '. ~ .')\r\n p <- p + facet_grid(facets)\r\n\r\n if (input$jitter)\r\n p <- p + geom_jitter()\r\n\r\n if (input$smooth)\r\n p <- p + geom_smooth()\r\n\r\n\r\n print(p)\r\n\r\n})\r\n#--------summary tab-----------------\r\noutput$dataname<- renderText({\r\n paste(\"summary of dataset\", input$radio)\r\n})\r\n\r\noutput$summary <- renderPrint({ \r\n summary(dataset()[,3:6]) \r\n})\r\n\r\n#-------end of summary-------------\r\n#--------------------------------------------------------------------------------------------\r\n\r\n#--------------------------------------------------------------------------------------------\r\n#---------------------Plot Tab--------------------------\r\noutput$plot <- renderDygraph({ \r\n print(summary(myData()))\r\n dygraph(myData()) %>% \r\n dyRoller(rollPeriod = 10)%>% \r\n dyOptions(drawPoints = TRUE, pointSize= 1.3 ,colors = RColorBrewer::brewer.pal(4, \"Set2\"),)%>%\r\n dyLegend(width= 600)%>%\r\n dySeries(\"..1\", axis = \"y\" , label = \"Accelation-x\")%>%\r\n dySeries(\"..2\", axis = \"y\" , label = \"Accelation-y\")%>%\r\n dySeries(\"..3\", axis = \"y\" , label = \"Accelation-z\")%>%\r\n dySeries(\"..4\", axis = \"y2\" , label = \"Temperature\")%>%\r\n dyAxis(\"y\", label = \"Accelation-x y, and z\")%>%\r\n dyAxis(\"y2\", label = \"Temperature\")%>%\r\n dyRangeSelector()\r\n})\r\n\r\n\r\n#output$plot1 <- renderDygraph({\r\n# test2 <- cbind(test21,test22,test23,test24)\r\n# dygraph(test2, main = \"Node384\",)%>%\r\n# dyRangeSelector()\r\n#})\r\n\r\n#output$plot2 <- renderDygraph({\r\n# test3 <- cbind(test31, test32)\r\n# dygraph(test3, main = \"Sensor 3\",) %>% \r\n# dyRangeSelector()\r\n#})\r\n\r\n#output$plot3 <- renderDygraph({\r\n# test4 <- cbind(test41, test42)\r\n# dygraph(test4, main = \"Sensor 4\",) %>% \r\n # dyRangeSelector()\r\n#})\r\n\r\n\r\n#----------------- end of Plot Tab----------------------\r\n#--------------------------------------------------------------------------------------------\r\n\r\n#--------------------------------------------------------------------------------------------\r\n\r\n#--------data tab----------------\r\noutput$datadf <- renderTable({ \r\n dataset()\r\n})\r\noutput$data <- renderTable({ \r\n head(get(dataset()), n=input$samplesize)\r\n})\r\n#-----end of data------------------\r\n\r\n#--------statistic tab----------------\r\noutput$samplesize<- renderText({\r\n paste(input$samplesize, \"observations of dataset\", input$radio)\r\n})\r\n\r\noutput$stats <- renderPrint({ \r\n summary(dataset()[input$samplesize,3:6]) \r\n})\r\n#-----end of statistic------------------\r\n\r\n#----similarity tab--------\r\noutput$plot_acf <- renderPlot({\r\ndata<-channel1()[1:input$samplesize]\r\n ac_res <- acf(data,length(data)/input$acf_lag, type = input$acf_options, na.action = na.pass, col=\"red\") # type should be \"correlation\", \"covariance\", \"partial\"\r\n})\r\n#output$plot_acf <- renderDygraph({\r\n # data<-test1()[1:input$samplesize]\r\n # gygraph(ac_res <- acf(data,length(data)/input$acf_lag, type = input$acf_options, na.action = na.pass, col=\"red\")) # type should be \"correlation\", \"covariance\", \"partial\"\r\n#})\r\n\r\noutput$plot_pacf <- renderPlot({\r\n source('crossCorr_standAlone.r')\r\n})\r\n\r\noutput$plot_ccf <- renderPlot({\r\ndata1= data.frame(time()[1:input$samplesize], channel1()[1:input$samplesize])\r\ndata2= data.frame(time()[1:input$samplesize], channel2()[1:input$samplesize])\r\nsource('crossCorr_function.r')\r\ncrossCorr_function(data1, data2, input$ccf_lag)\r\n})\r\n\r\noutput$datadf <- renderTable({ \r\n data1<- data.frame(time()[1:input$samplesize], dataset()$Channel.1[1:input$samplesize])\r\n data2<- data.frame(time()[1:input$samplesize], dataset()$Channel.2[1:input$samplesize])\r\n data1\r\n})\r\n#-----end of similarity tab----\r\n\r\n##----test tab-------\r\noutput$info<- renderText({\r\n paste(\"Information\",br(),\r\n \"The selected sensor is\",names(dataset()),\"and the selected channel is\",input$radio)\r\n})\r\noutput$str <- renderPrint({\r\n channel1<-dataset()$Channel.1\r\n channel2<-dataset()$Channel.2\r\n a <- linear_regression(channel1,channel2)\r\n summary(a)\r\n})\r\noutput$plot_lm <- renderPlot({\r\n# channel1<-dataset()$Channel.1[1:input$samplesize]\r\n# channel2<-dataset()$Channel.2[1:input$samplesize]\r\n# linear_regression(channel1,channel2) \r\n if(input$reg_options == \"LM\")\r\n source('REGRESSIONS/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR2\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR3\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR4\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR5\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR6\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR7\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR8\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR9\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n else if(input$reg_options == \"PR10\")\r\n source('REGRESSIONs/linearRegression_standalone.r')\r\n}) \r\n\r\nst_test <- eventReactive(input$stat_check, {\r\n source('stationarity_standalone.r')\r\n})\r\n\r\noutput$st_text <- renderText({\r\n st_test()\r\n})\r\noutput$down <- downloadHandler(\r\n filename=function(){\r\n paste(\"untitled\",\".png\",sep=\" \")\r\n }, \r\n content=function(file){\r\n png(file)\r\n plot(dataset()$Channel.1,dataset()$Channel.2)\r\n dev.off()\r\n })\r\n#--------end of test tab\r\n\r\n#------filter tab-----\r\noutput$plot_sma <- renderPlot({\r\n channel1<-dataset()$Channel.1[1:input$samplesize]\r\n channel2<-dataset()$Channel.2[1:input$samplesize]\r\n sma(channel1,10) \r\n})\r\n\r\noutput$text = renderText({\r\n sensor = \"NNode383\"\r\n channels = \"accelerationx,accelerationy,accelerationz,air:temperature\"\r\n time_start = \"2014-05-13T07:56:48.006750000+0100\"\r\n time_end = \"2014-05-13T07:57:00.256750000+0100\"\r\n\r\n url <- paste0(\"http://quader.igg.tu-berlin.de/istsos/test?service=SOS&request=GetObservation&offering=temporary&procedure=\",sensor,\"&observedProperty=\",channels,\"&responseFormat=text/plain&version=1.0.0&\",time_start,\"/\",time_end)\r\n\r\n})\r\n\r\noutput$plot_ma <- renderPlot({\r\n \r\n source('movingAverage_standalone.r')#%>%\r\n})\r\n#---end of filter tab-----\r\n\r\n##-------info tab--------\r\n#output$text = renderText({paste(\"Function Definitions\", \"\\n\", \"You have chosen a range that goes from\", input$range[1], \"to\", input$range[2])})\r\n\r\n#---------end of info tab--------\r\n})\r\n\r\n\r\n\r\n#source('sample.r')\r\n\r\n", "meta": {"hexsha": "0d3d135d661e47994c8193bb31224e6cb7cee537", "size": 15341, "ext": "r", "lang": "R", "max_stars_repo_path": "Apps/R_Shiny_AnaTool/R&Dygraphs/server.r", "max_stars_repo_name": "gisproject2015/Bridge3DModeling", "max_stars_repo_head_hexsha": "d1edde119b33f1cfb1a9440bed78f2936dd21276", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Apps/R_Shiny_AnaTool/R&Dygraphs/server.r", "max_issues_repo_name": "gisproject2015/Bridge3DModeling", "max_issues_repo_head_hexsha": "d1edde119b33f1cfb1a9440bed78f2936dd21276", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Apps/R_Shiny_AnaTool/R&Dygraphs/server.r", "max_forks_repo_name": "gisproject2015/Bridge3DModeling", "max_forks_repo_head_hexsha": "d1edde119b33f1cfb1a9440bed78f2936dd21276", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.1816037736, "max_line_length": 245, "alphanum_fraction": 0.5454012124, "num_tokens": 3906, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3184632349186191}} {"text": "ref <- sapply(\n tools::file_path_sans_ext(grep('20201103', gwloggeR.data::enumerate(partner = 'knmi'), value = TRUE)),\n function(name) {\n df.ref <- gwloggeR.data::read(name)$df\n list('x' = df.ref[!is.na(PRESSURE_VALUE), PRESSURE_VALUE],\n 'timestamps' = df.ref[!is.na(PRESSURE_VALUE), TIMESTAMP_UTC])\n },\n simplify = FALSE, USE.NAMES = TRUE\n)\n\n# INBO Barometers --------------------------------------------------------------\n\nsapply(\n grep('barometer', gwloggeR.data::enumerate(partner = 'inbo'), value = TRUE),\n function(f) {\n ROOT.PATH <- './tests/analytics/detect_drift/inbo/'\n\n df <- gwloggeR.data::read(f)$df\n df <- df[!is.na(TIMESTAMP_UTC),]\n df <- df[!is.na(PRESSURE_VALUE),]\n df <- df[order(TIMESTAMP_UTC),]\n\n if (nrow(df) == 0L) {\n warning(sprintf('Skipping %s: no valid data.', f), call. = FALSE, immediate. = TRUE)\n return(invisible(FALSE))\n }\n\n gwloggeR:::test.detect_function(\n fun = gwloggeR::detect_drift,\n x = df$PRESSURE_VALUE,\n timestamps = df$TIMESTAMP_UTC,\n reference = ref,\n apriori = gwloggeR::Apriori(\"air pressure\", \"cmH2O\"),\n alpha = 1,\n verbose = TRUE,\n plot = TRUE,\n title = toupper(f),\n RESULT.PATH = paste0(ROOT.PATH, f, '.result'),\n ATTRIB.PATH = paste0(ROOT.PATH, f, '.attribs'),\n IMG.PATH = paste0(ROOT.PATH, f, '.png'),\n LOG.PATH = paste0(ROOT.PATH, f, '.log')\n )\n }\n)\n", "meta": {"hexsha": "0cc53a0c78b9151d3b129dd997eb8ed171bcab70", "size": 1424, "ext": "r", "lang": "R", "max_stars_repo_path": "gwloggeR/tests/analytics/detect_drift.r", "max_stars_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_stars_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-16T10:47:56.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-16T10:47:56.000Z", "max_issues_repo_path": "gwloggeR/tests/analytics/detect_drift.r", "max_issues_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_issues_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 61, "max_issues_repo_issues_event_min_datetime": "2019-05-17T21:14:25.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-26T13:47:40.000Z", "max_forks_repo_path": "gwloggeR/tests/analytics/detect_drift.r", "max_forks_repo_name": "DOV-Vlaanderen/groundwater-logger-validation", "max_forks_repo_head_hexsha": "db9bd59c1644bb298206e717a528b4974e3939d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-07-30T10:39:48.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-16T10:48:04.000Z", "avg_line_length": 31.6444444444, "max_line_length": 104, "alphanum_fraction": 0.5786516854, "num_tokens": 435, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6791787121629466, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.31839261406607294}} {"text": "\n\nPortrait<-F # graphical output orientation\n\nfirst.year<- 1963 #first year on plot, negative value means value defined by data\nlast.year<- 2100 #last year on plot\n\n\nHindcastModel<-T; # plot values from the hindcast\nForecastModel<-F; # plot values from the scenarios\n\nredefine.scenario.manually<-F # Define the scenario dir used explecitely in this script or do it externally\n\nop.dir<-data.path\nif (ForecastModel==T & redefine.scenario.manually) {\n scenario<-\"Opti-2_HCR_4_stoch_penBlim_atAgeW__\"; \n output.dir<-data.path \n op.dir<-file.path(data.path,scenario)\n} else if (ForecastModel==T ) {\n output.dir<-scenario.dir \n op.dir<-scenario.dir\n} \n\n\n##########################################################################\n\n\nmy.dev<-'png' # output device: 'screen', 'wmf', 'png', 'pdf'\n#my.dev<-'screen'\n\ncleanup()\nfile.name<-'summary'\n\n\nnox<-3; noy<-1;\nnoxy<-nox*noy\n\nref<-Read.reference.points()\n\nInit.function()\n\n\nMSFD<-read.FLOP.MSFD.control(file=file.path(op.dir,\"op_msfd.dat\"),n.VPA=nsp-first.VPA+1,n.other.pred=first.VPA-1)\n\nif (HindcastModel) {\n dat1<-subset(Read.summary.data(dir=data.path),Year<=last.year &Species.n>=first.VPA)\n if (first.year>0) dat1<-subset(dat1,Year>=first.year )\n \n # mean weight in the sea and in tha catch\n w<-subset(dat1,select=c(Species.n,Year,Quarter,Age,weca,west, N,C.obs ))\n incl<-data.frame(Species.n=first.VPA:nsp,incl=c(MSFD@mean.weight.C.sp))\n w<-subset(merge(incl,w),incl==1) \n w[w$weca<-0,'weca']<-0\n w$N1<-w$N\n w[w$Age==0,'N1']<-0\n w$WC<-w$C.obs*w$weca\n w$WN<-w$N*w$west\n w$WN1<-w$N1*w$west\n \n w<-aggregate(cbind(WC,WN,WN1,N,N1,C.obs)~Species.n+Year,data=w,sum,na.rm=T)\n w$west<-w$WN/w$N\n w$west1<-w$WN1/w$N1\n w$weca<-w$WC/w$C.obs\n w<-subset(w,select=c( Species.n, Year, west,west1,weca))\n\n #Species average M2 and F\n mf<-subset(dat1,select=c(Species.n,Year,Quarter,Age, N.bar,M2,C.hat ))\n mf$deadM2<-mf$N.bar*mf$M2\n inclM<-data.frame(Species.n=first.VPA:nsp,faM=c(MSFD@M2.bar.ages[1,]),laM=c(MSFD@M2.bar.ages[2,]) )\n inclF<-data.frame(Species.n=first.VPA:nsp,faC=c(SMS.control@avg.F.ages[,1]),laC=c(SMS.control@avg.F.ages[,2]) )\n incl<-merge(inclM,inclF)\n mf<-merge(incl,mf)\n mf$inclF<-F\n mf$inclM<-F\n mf[mf$faM>=0 & mf$laM>=0 & mf$Age>=mf$faM & mf$Age<=mf$laM,'inclM']<-T\n mf[mf$faC>=0 & mf$laC>=0 & mf$Age>=mf$faC & mf$Age<=mf$laC,'inclF']<-T\n \n m<-droplevels(subset(mf,inclM==T))\n m$deadM2<-m$N.bar*m$M2\n m<-aggregate(deadM2~Species.n+Year+Age,sum,data=m)\n mn<-subset(dat1,(Age>0 & Quarter==1) | (Age==0 & Quarter==3),select=c(Species.n,Year,Age,N))\n mz<-aggregate(Z~Species.n+Year+Age,sum,data=dat1)\n mm<-merge(mn,mz)\n mm$N.bar<-mm$N*(1-exp(-mm$Z))/mm$Z\n m<-merge(mm,m)\n m$M2<- m$deadM2/m$N.bar\n m<-aggregate(M2~Species.n+Year,mean,data=m)\n \n f<-droplevels(subset(mf,inclF==T))\n f<-aggregate(C.hat~Species.n+Year+Age,sum,data=f)\n fn<-subset(dat1,(Age>0 & Quarter==1) | (Age==0 & Quarter==3),select=c(Species.n,Year,Age,N))\n fz<-aggregate(Z~Species.n+Year+Age,sum,data=dat1)\n ff<-merge(fn,fz)\n ff$N.bar<-ff$N*(1-exp(-ff$Z))/ff$Z\n f<-merge(ff,f)\n f$F<- f$C.hat/f$N.bar\n f<-aggregate(F~Species.n+Year,mean,data=f)\n \n fm<-merge(f,m,all=T)\n \n life.expectancy<-function(fa,la) {\n # life expectancy\n bio<-subset(dat1,select=c(Species.n,Year,Quarter,Age,Z))\n incl<-data.frame(Species.n=first.VPA:nsp,incl=c(MSFD@community.life.expectancy.options['first.age',]))\n incl<-NULL\n for (s in (first.VPA:nsp)) { \n fa.i<-fa[s-first.VPA+1]\n la.i<-la[s]\n if (la.i>=fa.i) incl<-rbind(incl,data.frame(Species.n=s,Age=seq(fa.i,la.i)))\n }\n bio<-merge(bio,incl)\n # a<-data.frame(Species.n=first.VPA:nsp,w=c(MSFD@community.life.expectancy.options['weighting',]))\n # bio<-merge(bio,a)\n \n bio<-bio[order(bio$Species.n,bio$Year,bio$Age,bio$Quarter),]\n min.Year<-min(bio$Year)\n bio$first<- !duplicated(paste(bio$Species.n,bio$Year))\n \n a<-unique(subset(bio,first,select=c(Species.n,Age,Quarter)))\n a$ini.age<-a$Age+(a$Quarter-1)*0.25\n a<-subset(a,select=c(Species.n,ini.age))\n \n bio<-merge(bio,a)\n bio$sumZ<-bio$Z\n bio<-bio[order(bio$Species.n,bio$Year,bio$Age,bio$Quarter),]\n \n \n for (x in (1:dim(bio)[1])) {\n if (!bio[x,'first']) bio[x,'sumZ']<-bio[x,'sumZ']+(bio[x-1,'sumZ'])\n }\n bio$p<-exp(-bio$sumZ)\n \n a<-aggregate(p~Year+Species.n+ini.age,sum,na.rm=T,data=bio)\n a$Expectancy<-a$p/4 + a$ini.age+0.25/2 # to expected life in years (from quarters)\n a$p<-NULL\n a$ini.age<-NULL\n return(a)\n }\n a<-life.expectancy(fa=MSFD@community.life.expectancy.options['first.age',],la=SMS.control@species.info[,'last-age'])\n aa<-MSFD@community.life.expectancy.options['first.age',]\n aa[]<-1\n a1<-life.expectancy(fa=aa,la=SMS.control@species.info[,'last-age'])\n a1$Expectancy1<-a1$Expectancy\n a1$Expectancy<-NULL\n a<-merge(a,a1)\n}\n\n\nadd.set<-strmacro(in1,in2,out,\n expr={\n if (HindcastModel & ForecastModel) out<-c(in1,in2) else if (HindcastModel) out<-c(in1) else if (ForecastModel) out<-c(in2)\n } )\n\n ## HER SKAL HISTORISKE DATA SAMLES MED SCENARIO DATA\n\n\na<-merge(a,w,all = T)\na<-merge(a,fm,all=T)\n# a includes all data\n\na$Species<-sp.names[a$Species.n]\n\nmy.sp<-sort(unique(a$Species.n))\n\ncleanup()\nplotfile<-function(dev='screen',out) {\n if (dev=='screen') X11(width=8, height=12, pointsize=12)\n if (dev=='wmf') win.metafile(filename = paste(out,'.wmf',sep=''), width=8, height=10, pointsize=12)\n if (dev=='png') png(filename =paste(out,'.png',sep=''), width = 1000, height = 1400,units = \"px\", pointsize = 30, bg = \"white\")\n if (dev=='pdf') pdf(file =paste(out,'.pdf',sep=''), width = 8, height = 10,pointsize = 12,onefile=FALSE)\n}\n \n \nfor (sp in my.sp) {\n #sp<-18\n Species<-sp.names[sp] \n b<-droplevels(subset(a,Species.n==sp))\n \n plotfile(dev=my.dev,out=file.path(op.dir,paste('Species_indicator',sp,sep='-')));\n par(mfcol=c(nox,noy))\n par(mar=c(3,4,3,2)) # bottom, left, top, right\n par(mar=c(2,4,3,5)+.1) #bottom, left, top, right\n \n\n # F and M2 \n min.d<-min(min(b$M2,na.rm=T),min(b$F,na.rm=T),na.rm=T)\n max.d<-max(max(b$M2,na.rm=T),max(b$F,na.rm=T),na.rm=T)\n \n plot(b$Year,b$F, ylim=c(min.d,max.d),type='b',xlab='Year',ylab='',lty=1,pch='F',lwd=2,main=paste(Species,' Mean F and M2',sep=':'))\n lines(b$Year,b$M2,lty=2,pch='M',lwd=2,type='b',col=2)\n \n \n # life.expect\n min.d<-min(min(b$Expectancy,na.rm=T),min(b$Expectancy1,na.rm=T),na.rm=T)\n max.d<-max(max(b$Expectancy,na.rm=T),max(b$Expectancy1,na.rm=T),na.rm=T)\n\n plot(b$Year,b$Expectancy,lty=1,pch='0',lwd=2,ylab='year',xlab=' ',type='b',col=1,ylim=c(min.d,max.d),main=\"Life expectancy for age 0 (third quarter) and age 1\") \n lines(b$Year,b$Expectancy1,lty=2,pch='1',lwd=2,type='b',col=2) \n\n \n \n \n min.d<-min(min(b$west,na.rm=T),min(b$weca,na.rm=T),na.rm=T)\n max.d<-max(max(b$west,na.rm=T),max(b$weca,na.rm=T),na.rm=T)\n \n plot(b$Year,b$weca, ylim=c(min.d,max.d),type='b',xlab='Year',col='blue',ylab='kg',lty=1,pch='c',lwd=2,main=paste('Mean weight in the sea (for age>=0 and age>=1) and in the catch (c)',sep=':'))\n lines(b$Year,b$west,lty=2,pch='0',lwd=2,type='b',col=1)\n lines(b$Year,b$west1,lty=2,pch='1',lwd=2,type='b',col=2)\n \n \n \n if (my.dev %in% c('png','wmf','pdf')) dev.off() \n}\n\n", "meta": {"hexsha": "875c649c99469bc7790ad16a2036a03c38cc8ea1", "size": 7289, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/plot_op_species_indicator.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/plot_op_species_indicator.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/plot_op_species_indicator.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9023255814, "max_line_length": 194, "alphanum_fraction": 0.6273837289, "num_tokens": 2753, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.3183926079776295}} {"text": "\ndev<-\"print\"\ndev<-\"screen\"\nnox<-4; noy<-4;\npaper<-FALSE # graphics on paper=file (TRUE) or on screen (FALSE)\nrun.ID<-'B' # file id used for paper output\ncleanup()\n\n\n\nfirst.year.on.plot<-1977\nlast.year.on.plot<-2030\n\nincl.sp<-seq(1,14) # species number to be included\n\n#do.plot.other<-function(sp.plot=c(1),incl.prob=FALSE,incl.closure=FALSE,incl.0.group.M2=FALSE) {\n#########################################\ncleanup()\ni<-0\noth<-Read.other.predator()\noth<-subset(oth,Year>=first.year.on.plot&Year<=last.year.on.plot)\noth<-tapply(oth$Other.bio/1000,list(oth$Species.n,oth$Year,oth$Quarter),sum)\ny<-as.numeric(unlist(dimnames(oth)[2]))\nq<-as.numeric(unlist(dimnames(oth)[3]))\nfor (sp in (incl.sp)) {\n if ((i %% (nox*noy-1))==0) {\n newplot(dev,nox,noy,Portrait=TRUE);\n par(mar=c(3,5,3,2)) \n } \n i<<-i+1\n plot(y,oth[sp,,1],ylab='biomass',main=name[sp+1],ylim=c(0,max(oth[sp,,])),col=1,type='l')\n for (qq in (q)) lines(y,oth[sp,,qq],col=qq)\n} \n \n", "meta": {"hexsha": "535835edc6e4f272705e52f79fa1de6b86f8aa51", "size": 973, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/function/hcr_output_other_predator.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/function/hcr_output_other_predator.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/function/hcr_output_other_predator.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8, "max_line_length": 97, "alphanum_fraction": 0.6145940391, "num_tokens": 342, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.3183926079776295}} {"text": " F01CKJ Example Program Results\n\n Matrix A \n 5.0 8.0 \n 8.0 14.0 \n", "meta": {"hexsha": "3264e435f44af4f99ded3b9dfd0db8ce87385b12", "size": 328, "ext": "r", "lang": "R", "max_stars_repo_path": "simple_examples/baseresults/f01ckje.r", "max_stars_repo_name": "numericalalgorithmsgroup/NAGJavaExamples", "max_stars_repo_head_hexsha": "f625b3f043c5c14a88d7ecbc04374acf75d63c82", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-07-03T22:53:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-04T01:44:03.000Z", "max_issues_repo_path": "simple_examples/baseresults/f01ckje.r", "max_issues_repo_name": "numericalalgorithmsgroup/NAGJavaExamples", "max_issues_repo_head_hexsha": "f625b3f043c5c14a88d7ecbc04374acf75d63c82", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simple_examples/baseresults/f01ckje.r", "max_forks_repo_name": "numericalalgorithmsgroup/NAGJavaExamples", "max_forks_repo_head_hexsha": "f625b3f043c5c14a88d7ecbc04374acf75d63c82", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-07-03T22:55:16.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-02T01:00:53.000Z", "avg_line_length": 54.6666666667, "max_line_length": 132, "alphanum_fraction": 0.131097561, "num_tokens": 37, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3182023277840876}} {"text": "source(\"common.r\")\r\n\r\n# Preprocess data and save interarrival times, processing time distributions, and vector size distributions.\r\n\r\n####################################\r\n## Preprocessing / Sanity Checks. ##\r\n####################################\r\n\r\nclockDuration <- 1 / 3393e6\r\n\r\ndataDir <- \"../data/\"\r\ndataFolders <- dir(dataDir)\r\ndataFolders <- paste(dataDir, \"/\", dataFolders[grepl(\"exp\", dataFolders)], \"/\", sep = \"\")\r\n\r\nallData <- data.table()\r\n\r\nprogbar <- txtProgressBar(style = 3)\r\n\r\nfor (ii in 1:length(dataFolders)) {\r\n \r\n curFileList <- dir(dataFolders[ii])\r\n curFileList <- curFileList[grepl(\"clock.*dat\", curFileList)]\r\n \r\n setTxtProgressBar(progbar, ii / length(dataFolders))\r\n \r\n parts.folder <- \r\n dataFolders[ii] %>%\r\n str_split(\"/\") %>%\r\n unlist() %>%\r\n nth(length(.) - 1) %>% \r\n str_split(\"-|_\") %>% \r\n unlist()\r\n \r\n # => beta\r\n currentMaxFrameSize <- parts.folder[3] %>% as.numeric()\r\n # => pktsize\r\n currentPacketSize <- parts.folder[5] %>% as.numeric()\r\n # => L\r\n currentQueueSize <- parts.folder[7] %>% as.numeric()\r\n \r\n for (jj in 1:length(curFileList)) {\r\n \r\n parts <- str_match(\r\n curFileList[jj],\r\n \"clock-([[:alnum:]]+)-([[:digit:]]+)\\\\.dat\")\r\n \r\n currentRate <- parts[3] %>% as.numeric()\r\n currentExpType <- parts[2]\r\n currentRepetition <- 1\r\n \r\n currentFilePath <- paste(dataFolders[ii], curFileList[jj], sep = \"\")\r\n \r\n currentData <- fread(currentFilePath,\r\n select = c(4, 6, 8, 12),\r\n col.names = c(\"t1\", \"vecsize\", \"t2\", \"t3\")) %>%\r\n mutate(rate = currentRate,\r\n rep = currentRepetition,\r\n exptype = currentExpType,\r\n maxfs = currentMaxFrameSize,\r\n pktsize = currentPacketSize,\r\n qsize = currentQueueSize,\r\n t1 = as.integer64(t1),\r\n t2 = as.integer64(t2),\r\n t3 = as.integer64(t3)) %>%\r\n mutate(proctime = as.double(t3 - t1),\r\n proctime2 = as.double(t3 - t2)) %>%\r\n filter(proctime > 0, vecsize <= currentMaxFrameSize) %>%\r\n # dt1 / dt3 correspond more closely to the paper's / model's definition of B since it's the\r\n # time between consecutive embeddings.\r\n mutate(dt1 = t1 - lag(t1, 1),\r\n dt3 = t3 - lag(t3, 1),\r\n pt1 = lead(t1, 1) - t1) %>%\r\n # FIXME: lead / lag issues with integer64 at margins..\r\n filter(dt1 > 0, dt3 > 0, pt1 < 600000, pt1 > 0)\r\n \r\n allData <- rbind(allData, currentData)\r\n \r\n }\r\n}\r\n\r\nallData <- \r\n allData %>% \r\n mutate(\r\n exptype = factor(exptype),\r\n maxfs = factor(maxfs),\r\n rep = factor(rep),\r\n ratef = factor(rate))\r\n\r\nconfigs <-\r\n allData %>%\r\n group_by(exptype, pktsize, maxfs, qsize) %>%\r\n summarise(n = n()) %>% \r\n mutate(maxfs = as.numeric(as.character(maxfs)))\r\n\r\n############################################################################\r\n## Create linear fits for the batch service times for all configurations. ##\r\n############################################################################\r\n\r\nprogbar <- txtProgressBar(style = 3)\r\n\r\nfor (ii in 1:nrow(configs)) {\r\n \r\n setTxtProgressBar(progbar, ii / nrow(configs))\r\n \r\n ## Get data, filter outliers.\r\n curData <- allData %>%\r\n filter(\r\n exptype == configs[ii, ]$exptype,\r\n pktsize == configs[ii, ]$pktsize,\r\n maxfs == configs[ii, ]$maxfs,\r\n qsize == configs[ii, ]$qsize) %>% \r\n mutate(clocks = as.double(pt1))\r\n \r\n curData.filtered <- data.frame()\r\n \r\n curData$isOutlier <- 0\r\n \r\n # Outlier detection per rate.\r\n for (kk in seq(500, 10e3, 500)) {\r\n \r\n winsize <- 100\r\n \r\n curSlice <- curData %>% \r\n filter(rate == kk)\r\n \r\n medbs <- median(curSlice$vecsize)\r\n \r\n for (jj in 1:floor(nrow(curSlice) / 100)) {\r\n currange <- (((jj-1)*winsize)+1) : (jj * winsize)\r\n curmean <- mean(curSlice$vecsize[currange])\r\n # if (curmean > medbs * 1.1) {\r\n # Second condition to avoid being too strict at low rates.\r\n if (curmean > medbs * 1.1 && abs(curmean - medbs) > 1) {\r\n curSlice$isOutlier[currange] <- 1\r\n }\r\n }\r\n \r\n curData.filtered <- rbind(curData.filtered, curSlice)\r\n \r\n cat(sprintf(\"rate %d, outlier ratio: %f\\n\", kk, nrow(curSlice %>% filter(isOutlier == 1)) / nrow(curSlice)))\r\n \r\n }\r\n \r\n nrow(curData.filtered %>% filter(isOutlier == 1)) / nrow(curData.filtered)\r\n \r\n curData.filtered <- curData.filtered %>% \r\n filter(isOutlier == 0)\r\n \r\n \r\n # Outlier detection done, export stats\r\n \r\n exportBatchSizeDependentMeanProcessingTime(\r\n curData.filtered,\r\n mark = F,\r\n beta = configs[ii, ]$maxfs,\r\n theFun = median,\r\n filename = \r\n sprintf(\"stats/medianE8_filtered_moon_nosleep_all_%s_pktsize_%d_beta_%d_L_%d.csv\",\r\n configs[ii, ]$exptype,\r\n configs[ii, ]$pktsize,\r\n configs[ii, ]$maxfs,\r\n configs[ii, ]$qsize))\r\n \r\n}\r\n", "meta": {"hexsha": "af1d0c137f0f5ef21013a7c617046993f6346531", "size": 5478, "ext": "r", "lang": "R", "max_stars_repo_path": "code/001_preproc_service_time_data.r", "max_stars_repo_name": "lsinfo3/2020-tompecs-vpp-data", "max_stars_repo_head_hexsha": "1f71861af22dc89ad1b61d33d2fd97094a2a752e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/001_preproc_service_time_data.r", "max_issues_repo_name": "lsinfo3/2020-tompecs-vpp-data", "max_issues_repo_head_hexsha": "1f71861af22dc89ad1b61d33d2fd97094a2a752e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/001_preproc_service_time_data.r", "max_forks_repo_name": "lsinfo3/2020-tompecs-vpp-data", "max_forks_repo_head_hexsha": "1f71861af22dc89ad1b61d33d2fd97094a2a752e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2, "max_line_length": 117, "alphanum_fraction": 0.4859437751, "num_tokens": 1349, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3182023195530493}} {"text": "###############################################################################\n# OVERVIEW:\n# Code to combine Medicaid-Medicare & Public housing data to create two tables\n# Public housing includes King County Housing Authority and Seattle Housing Authority data\n# elig_demo: contains time invariant data. Should be 1 row per unique individual.\n# elig_timevar: contains time varying data. Multiple rows per unique individual.\n#\n# STEPS:\n# 01 - Process raw KCHA data and load to SQL database\n# 02 - Process raw SHA data and load to SQL database\n# 03 - Bring in individual PHA datasets and combine into a single file\n# 04 - Deduplicate data and tidy up via matching process\n# 05 - Recode race and other demographics\n# 06 - Clean up addresses\n# 06a - Geocode addresses\n# 07 - Consolidate data rows\n# 08 - Add in final data elements and set up analyses\n# 09 - Join with Medicaid-Medicare eligibility & time varying data ### (THIS CODE) ###\n# 10 - Set up joint housing/Medicaid analyses\n#\n# Alastair Matheson (PHSKC-APDE) | Danny Colombara (PHSKC-APDE)\n# alastair.matheson@kingcounty.gov | dcolombara@kingcounty.gov\n# 2016-08-13, split into separate files 2017-10, rewritten \n# 2019-11-07, rewritten to created normalized mcaid_mcare_pha elig_demo & elig_timevar\n# \n###############################################################################\n\n##### !!!URGENT!!! Make sure the underlying data is up to date! #####\n # Ensure the master cross-walk table linking Medicaid, Medicare, and Public housing IDs is up to date\n # - code 1: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/stage/tables/load_stage.xwalk_apde_mcaid_mcare_pha.r\n # - code 2: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/final/tables/load_final.xwalk_apde_mcaid_mcare_pha.sql\n # - SQL: [PHClaims].[final].[xwalk_apde_mcaid_mcare_pha]\n \n # Ensure the joint Medicaid-Medicare elig_demo table is up to date\n # - code 1: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/stage/tables/load_stage.mcaid_mcare_elig_demo.R\n # - code 2: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/final/tables/load_final.mcaid_mcare_elig_demo.sql\n # - SQL: [PHClaims].[final].[mcaid_mcare_elig_demo]\n \n # Ensure the joing Medicaid-Medicare elig_timevar table is up to date\n # - code 1: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/stage/tables/load_stage.mcaid_mcare_elig_timevar.R\n # - code 2: https://github.com/PHSKC-APDE/claims_data/blob/master/claims_db/phclaims/final/tables/load_final.mcaid_mcare_elig_timevar.sql\n # - SQL: [PHClaims].[final].[mcaid_mcare_elig_timevar]\n\n\n##### Set up global parameter and call in libraries #####\noptions(max.print = 350, tibble.print_max = 30, scipen = 999)\n\nlibrary(odbc) # Used to connect to SQL server\nlibrary(glue) # Used to put together SQL queries\nlibrary(lubridate) # Used to manipulate dates\nlibrary(tidyverse) # Used to manipulate data\nlibrary(data.table) # Used to manipulate data\n\nkc.zips.url <- \"https://raw.githubusercontent.com/PHSKC-APDE/reference-data/master/spatial_data/zip_admin.csv\"\n\nyaml.elig <- \"https://raw.githubusercontent.com/PHSKC-APDE/Housing/master/processing/09_mcaid_mcare_pha_elig_demo.yaml\"\n\nyaml.timevar <- \"https://raw.githubusercontent.com/PHSKC-APDE/Housing/master/processing/09_mcaid_mcare_pha_elig_timevar.yaml\"\n\nsource(\"https://raw.githubusercontent.com/PHSKC-APDE/claims_data/master/claims_db/db_loader/scripts_general/alter_schema.R\")\nsource(\"https://raw.githubusercontent.com/PHSKC-APDE/claims_data/master/claims_db/db_loader/scripts_general/add_index.R\")\n\n\n##### Load data from SQL #####\n### Public Housing ----\n# use stage schema for now but switch to final once QA approach is sorted\ndb_apde51 <- dbConnect(odbc(), \"PH_APDEStore51\")\npha <- setDT(odbc::dbGetQuery(db_apde51, \"SELECT \n pid, start_housing, startdate, enddate, dob_m6, \n gender_new_m6,\n race_new, r_aian_new, r_asian_new, r_black_new, r_nhpi_new, r_white_new, r_hisp_new,\n unit_add_new, unit_apt, unit_apt2, unit_city_new, unit_state_new, unit_zip_new, \n geo_hash_clean, geo_hash_geocode, \n agency_new, operator_type, portfolio_final, subsidy_type, vouch_type_final\n FROM [PH_APDEStore].[stage].[pha] WHERE enddate >= '2012-01-01'\"))\n\n\n ### Medicaid-Medicare-Public Housing ID crosswalk ----\n db_claims51 <- dbConnect(odbc(), \"PHClaims51\")\n xwalk <- setDT(odbc::dbGetQuery(db_claims51, \"SELECT id_apde, pid FROM [PHClaims].[final].[xwalk_apde_mcaid_mcare_pha]\"))\n \n \n ### Joint Medicaid-Medicare elig_demo ----\n elig.mm <- setDT(odbc::dbGetQuery(db_claims51, \"SELECT * FROM [PHClaims].[final].[mcaid_mcare_elig_demo]\"))\n elig.mm[, c(\"last_run\") := NULL]\n \n ### Joint Medicaid-Medicare elig_timevar ----\n timevar.mm <- setDT(odbc::dbGetQuery(db_claims51, \"SELECT * FROM [PHClaims].[final].[mcaid_mcare_elig_timevar]\"))\n timevar.mm[, c(\"contiguous\", \"last_run\", \"cov_time_day\") := NULL]\n \n ### Geo ref table ----\n ref.geo <- setDT(odbc::dbGetQuery(db_claims51, \"\n SELECT geo_add1_clean, geo_city_clean, geo_state_clean, geo_zip_clean, geo_hash_geocode, \n geo_zip_centroid, geo_street_centroid, geo_countyfp10 AS geo_county_code,\n geo_tractce10 AS geo_tract_code, geo_hra_id AS geo_hra_code, \n geo_school_geoid10 AS geo_school_code \n FROM ref.address_geocode\"))\n\n\n##### Clean / prep mm data -----\n # fix dates\n elig.mm[, dob := as.Date(dob)]\n timevar.mm[, `:=` (from_date = as.Date(from_date), to_date = as.Date(to_date))]\n \n \n##### Clean / prep housing data -----\n# merge on id_apde\npha <- merge(pha, xwalk, by = \"pid\", all.x = TRUE, all.y = FALSE)\npha[, pid := NULL]\nrm(xwalk)\n\n# fix dates\ndate.vars <- c(\"start_housing\", \"from_date\", \"to_date\", \"dob\")\nsetnames(pha, c(\"start_housing\", \"startdate\", \"enddate\", \"dob_m6\"), date.vars)\npha[, c(date.vars) := lapply(.SD, as.Date), .SDcols = date.vars]\nrm(date.vars)\n\n# normalize gender\npha[gender_new_m6 == 1, gender_me := \"Female\"] \npha[gender_new_m6 == 2, gender_me := \"Male\"] \npha[, gender_female := 0][gender_me == \"Female\", gender_female := 1]\npha[, gender_male := 0][gender_me == \"Male\", gender_male := 1]\npha[, gender_new_m6 := NULL]\n\n# identify most recent gender\nsetorder(pha, id_apde, -from_date) # sort to identify the most recent time per id\npha[, counter := 1:.N, by = c(\"id_apde\")]\ngender.recent <- pha[counter == 1, .(id_apde, gender_recent = gender_me)] # keep most recent gender\npha <- merge(pha, gender.recent, by = \"id_apde\", all.x = T, all.y = T)\npha[, counter := NULL]\nrm(gender.recent)\n\n# prep race\npha[, race_me := as.character(factor(race_new, \n levels = c(\"AIAN only\", \"Asian only\", \"Black only\", \"Multiple race\", \"NHPI only\", \"White only\", \"\"), \n labels = c(\"AI/AN\", \"Asian\", \"Black\", \"Multiple\", \"NH/PI\", \"White\", \"Unknown\")))]\npha[, race_new := NULL]\n\npha[, race_eth_me := race_me]\npha[r_hisp_new == 1, race_eth_me := \"Latino\"]\n\nsetnames(pha, \n c(\"r_aian_new\", \"r_asian_new\", \"r_black_new\", \"r_nhpi_new\", \"r_white_new\", \"r_hisp_new\"), \n c(\"race_aian\", \"race_asian\", \"race_black\", \"race_nhpi\", \"race_white\", \"race_latino\") )\n\n# identify most recent race\nsetorder(pha, id_apde, -from_date) # sort to identify the most recent time per id\npha[, counter := 1:.N, by = c(\"id_apde\")]\nrace.recent <- pha[counter == 1, .(id_apde, race_recent=race_me, race_eth_recent=race_eth_me)] # keep most recent race\npha <- merge(pha, race.recent, by = \"id_apde\", all.x = T, all.y = T)\npha[, counter := NULL]\nrm(race.recent)\n\n# add in geo_ data\npha <- merge(pha, ref.geo, \n by.x = c(\"unit_add_new\", \"unit_city_new\", \"unit_state_new\", \"unit_zip_new\"),\n by.y = c(\"geo_add1_clean\", \"geo_city_clean\", \"geo_state_clean\", \"geo_zip_clean\"),\n all.x = T, all.y = F)\n\n# ascribe ever KC residence status\npha[, geo_kc_ever := 1] # PHA data is always 1 because everyone lived or lives in either Seattle or King County Pubic Housing\n\n\n##### CREATE MCAID-MCARE-PHA ELIG_DEMO -----\n ### Create PHA elig_demo (most recent row per id) ----\n elig.pha <- copy(pha)\n setorder(elig.pha, id_apde, -from_date) \n elig.pha[, counter := 1:.N, by = c(\"id_apde\")]\n elig.pha <- elig.pha[counter == 1]\n elig.pha[, counter := NULL]\n elig.pha <- elig.pha[, .(id_apde, dob, geo_kc_ever, start_housing, gender_me, gender_recent, gender_female, gender_male, race_me, \n race_eth_me, race_recent, race_eth_recent, race_aian, race_asian, race_black, \n race_nhpi, race_white, race_latino)]\n\n ### Identify IDs in both Mcaid-Mcare & PHA and split from non-linked IDs ----\n linked.id <- intersect(elig.mm$id_apde, elig.pha$id_apde)\n \n elig.pha.solo <- elig.pha[!id_apde %in% linked.id]\n elig.mm.solo <- elig.mm[!id_apde %in% linked.id] \n \n elig.pha.linked <- elig.pha[id_apde %in% linked.id]\n elig.mm.linked <- elig.mm[id_apde %in% linked.id]\n \n ### Combine the data for linked IDs ----\n # some data is assumed to be more reliable in one dataset compared to the other\n linked <- merge(x = elig.mm.linked, y = elig.pha.linked, by = \"id_apde\")\n setnames(linked, names(linked), gsub(\"\\\\.x$\", \".elig.mm\", names(linked))) # clean up suffixes to eliminate confusion\n setnames(linked, names(linked), gsub(\"\\\\.y$\", \".elig.pha\", names(linked))) # clean up suffixes to eliminate confusion\n \n # loop for vars that default to Mcaid-Mcare data\n for(i in c(\"dob\", \"gender_me\", \"gender_female\", \"gender_male\", \"gender_recent\", \"race_eth_recent\", \"race_recent\",\n \"race_me\", \"race_eth_me\", \"race_aian\", \"race_asian\", \"race_black\", \"race_nhpi\", \"race_white\", \"race_latino\")){\n linked[, paste0(i) := get(paste0(i, \".elig.mm\"))] # default is to use Mcaid-Mcare data\n linked[is.na(get(paste0(i))), paste0(i) := get(paste0(i, \".elig.pha\"))] # If NA b/c missing Mcaid-Mcare data, then fill with PHA data\n linked[, paste0(i, \".elig.mm\") := NULL][, paste0(i, \".elig.pha\") := NULL]\n } \n \n # loop for vars that default to PHA data\n for(i in c(\"geo_kc_ever\")){\n linked[, paste0(i) := get(paste0(i, \".elig.pha\"))] # default is to use Mcaid-Mcare data\n linked[is.na(get(paste0(i))), paste0(i) := get(paste0(i, \".elig.mm\"))] # If NA b/c missing Mcaid-Mcare data, then fill with PHA data\n linked[, paste0(i, \".elig.pha\") := NULL][, paste0(i, \".elig.mm\") := NULL]\n } \n \n # add flag for triple linkage (Mcaid-Mcare-PHA)\n linked[, mcaid_mcare_pha := 1]\n \n ### Append the linked to the non-linked ----\n elig <- rbindlist(list(linked, elig.mm.solo, elig.pha.solo), use.names = TRUE, fill = TRUE)\n elig[is.na(mcaid_mcare_pha), mcaid_mcare_pha := 0] # fill in duals flag \n \n ### Prep for pushing to SQL ----\n # recreate race unknown indicator\n elig[, race_unk := 0]\n elig[race_aian==0 & race_asian==0 & race_asian_pi==0 & race_black==0 & race_latino==0 & race_nhpi==0 & race_white==0, race_unk := 1] \n \n # create time stamp\n elig[, last_run := Sys.time()] \n \n # order columns\n setcolorder(elig, c(\"id_apde\", \"mcaid_mcare_pha\", \"apde_dual\"))\n \n # clean objects no longer used\n rm(elig.mm, elig.mm.linked, elig.mm.solo, elig.pha, elig.pha.linked, elig.pha.solo, linked)\n \n \n##### CREATE MCAID-MCARE-PHA ELIG_TIMEVAR -----\n ### Create PHA elig_timevar ----\n timevar.pha <- copy(pha)\n timevar.pha <- timevar.pha[, .(id_apde, from_date, to_date, \n unit_add_new, unit_apt, unit_apt2, unit_city_new, unit_state_new, unit_zip_new, \n geo_zip_centroid, geo_street_centroid, geo_county_code, \n geo_tract_code, geo_hra_code, geo_school_code, \n agency_new, subsidy_type, vouch_type_final, operator_type, portfolio_final)]\n\n ### Identify IDs in both Mcaid-Mcare & PHA and split from non-linked IDs ----\n linked.id <- intersect(timevar.mm$id_apde, timevar.pha$id_apde)\n \n timevar.pha.solo <- timevar.pha[!id_apde %in% linked.id]\n timevar.mm.solo <- timevar.mm[!id_apde %in% linked.id] \n \n timevar.pha.linked <- timevar.pha[id_apde %in% linked.id]\n timevar.mm.linked <- timevar.mm[id_apde %in% linked.id]\n \n ### Linked IDs Part 1: Create master list of time intervals by ID ----\n #-- Create all possible permutations of date interval combinations from mcaid_mcare and pha for each id ----\n linked <- merge(timevar.pha.linked[, .(id_apde, from_date, to_date)], timevar.mm.linked[, .(id_apde, from_date, to_date)], by = \"id_apde\", allow.cartesian = TRUE)\n setnames(linked, names(linked), gsub(\"\\\\.x$\", \".elig.pha\", names(linked))) # clean up suffixes to eliminate confusion\n setnames(linked, names(linked), gsub(\"\\\\.y$\", \".elig.mm\", names(linked))) # clean up suffixes to eliminate confusion\n \n #-- Identify the type of overlaps & number of duplicate rows needed ----\n # As stated in https://github.com/PHSKC-APDE/claims_data/edit/master/claims_db/phclaims/stage/tables/load_stage.mcaid_mcare_elig_timevar.R\n # The code below was validated against a much more time intensive process where a giant table was made for every individual day within the\n # time period being analyzed. This faster / more memory efficient code was found to provide equivalent output.\n temp <- linked %>%\n mutate(overlap_type = case_when(\n # First ID the non-matches\n is.na(from_date.elig.pha) | is.na(from_date.elig.mm) ~ 0,\n # Then figure out which overlapping date comes first\n # Exactly the same dates\n from_date.elig.pha == from_date.elig.mm & to_date.elig.pha == to_date.elig.mm ~ 1,\n # PHA before mcaid_mcare (or exactly the same dates)\n from_date.elig.pha <= from_date.elig.mm & from_date.elig.mm <= to_date.elig.pha & \n to_date.elig.pha <= to_date.elig.mm ~ 2,\n # mcaid_mcare before PHA\n from_date.elig.mm <= from_date.elig.pha & from_date.elig.pha <= to_date.elig.mm & \n to_date.elig.mm <= to_date.elig.pha ~ 3,\n # mcaid_mcare dates competely within PHA dates or vice versa\n from_date.elig.mm >= from_date.elig.pha & to_date.elig.mm <= to_date.elig.pha ~ 4,\n from_date.elig.pha >= from_date.elig.mm & to_date.elig.pha <= to_date.elig.mm ~ 5,\n # PHA coverage only before mcaid_mcare (or mcaid_mcare only after PHA)\n from_date.elig.pha < from_date.elig.mm & to_date.elig.pha < from_date.elig.mm ~ 6,\n # PHA coverage only after mcaid_mcare (or mcaid_mcare only before PHA)\n from_date.elig.pha > to_date.elig.mm & to_date.elig.pha > to_date.elig.mm ~ 7,\n # Anyone rows that are left\n TRUE ~ 8),\n # Calculate overlapping dates\n from_date_o = as.Date(case_when(\n overlap_type %in% c(1, 2, 4) ~ from_date.elig.mm,\n overlap_type %in% c(3, 5) ~ from_date.elig.pha), origin = \"1970-01-01\"),\n to_date_o = as.Date(ifelse(overlap_type %in% c(1:5),\n pmin(to_date.elig.mm, to_date.elig.pha),\n NA), origin = \"1970-01-01\"),\n # Need to duplicate rows to separate out non-overlapping PHA and mcaid_mcare periods\n repnum = case_when(\n overlap_type %in% c(2:5) ~ 3,\n overlap_type %in% c(6:7) ~ 2,\n TRUE ~ 1)\n ) %>%\n select(id_apde, from_date.elig.pha, to_date.elig.pha, from_date.elig.mm, to_date.elig.mm, \n from_date_o, to_date_o, overlap_type, repnum) %>%\n arrange(id_apde, from_date.elig.pha, from_date.elig.mm, from_date_o, \n to_date.elig.pha, to_date.elig.mm, to_date_o)\n \n # Check no unexpected overlap types\n temp %>% group_by(overlap_type) %>% summarise(count = n())\n if (nrow(dplyr::filter(temp, overlap_type == 8)) > 0) {\n warning(\"Unexpected overlap types, check temp data table\")\n }\n \n #-- Expand out rows to separate out overlaps ----\n temp_ext <- setDT(temp[rep(seq(nrow(temp)), temp$repnum), 1:ncol(temp)])\n \n # temp2 <- temp %>% filter(id_apde == 408970) ## DELETE LATER\n # temp_ext <- temp2[rep(seq(nrow(temp2)), temp2$repnum), 1:ncol(temp2)]\n # \n \n #-- Process the expanded data ----\n temp_ext[, rownum_temp := rowid(id_apde, from_date.elig.pha, to_date.elig.pha, from_date.elig.mm, to_date.elig.mm)]\n setorder(temp_ext, id_apde, from_date.elig.pha, to_date.elig.pha, from_date.elig.mm, to_date.elig.mm, from_date_o, \n to_date_o, overlap_type, rownum_temp)\n # Remove non-overlapping dates\n temp_ext[, ':=' (\n from_date.elig.pha = as.Date(ifelse((overlap_type == 6 & rownum_temp == 2) | \n (overlap_type == 7 & rownum_temp == 1), \n NA, from_date.elig.pha), origin = \"1970-01-01\"), \n to_date.elig.pha = as.Date(ifelse((overlap_type == 6 & rownum_temp == 2) | \n (overlap_type == 7 & rownum_temp == 1), \n NA, to_date.elig.pha), origin = \"1970-01-01\"),\n from_date.elig.mm = as.Date(ifelse((overlap_type == 6 & rownum_temp == 1) | \n (overlap_type == 7 & rownum_temp == 2), \n NA, from_date.elig.mm), origin = \"1970-01-01\"), \n to_date.elig.mm = as.Date(ifelse((overlap_type == 6 & rownum_temp == 1) | \n (overlap_type == 7 & rownum_temp == 2), \n NA, to_date.elig.mm), origin = \"1970-01-01\")\n )]\n temp_ext <- unique(temp_ext)\n # Remove first row if start dates are the same or PHA is only one day\n temp_ext <- temp_ext[!(overlap_type %in% c(2:5) & rownum_temp == 1 & \n (from_date.elig.pha == from_date.elig.mm | from_date.elig.pha == to_date.elig.pha))]\n # Remove third row if to_dates are the same\n temp_ext <- temp_ext[!(overlap_type %in% c(2:5) & rownum_temp == 3 & to_date.elig.pha == to_date.elig.mm)]\n \n \n #-- Calculate the finalized date columms----\n # Set up combined dates\n # Start with rows with only PHA or mcaid_mcare, or when both sets of dates are identical\n temp_ext[, ':=' (\n from_date = as.Date(\n case_when(\n (!is.na(from_date.elig.pha) & is.na(from_date.elig.mm)) | overlap_type == 1 ~ from_date.elig.pha,\n !is.na(from_date.elig.mm) & is.na(from_date.elig.pha) ~ from_date.elig.mm), origin = \"1970-01-01\"),\n to_date = as.Date(\n case_when(\n (!is.na(to_date.elig.pha) & is.na(to_date.elig.mm)) | overlap_type == 1 ~ to_date.elig.pha,\n !is.na(to_date.elig.mm) & is.na(to_date.elig.pha) ~ to_date.elig.mm), origin = \"1970-01-01\")\n )]\n # Now look at overlapping rows and rows completely contained within the other data's dates\n temp_ext[, ':=' (\n from_date = as.Date(\n case_when(\n overlap_type %in% c(2, 4) & rownum_temp == 1 ~ from_date.elig.pha,\n overlap_type %in% c(3, 5) & rownum_temp == 1 ~ from_date.elig.mm,\n overlap_type %in% c(2:5) & rownum_temp == 2 ~ from_date_o,\n overlap_type %in% c(2:5) & rownum_temp == 3 ~ to_date_o + 1,\n TRUE ~ from_date), origin = \"1970-01-01\"),\n to_date = as.Date(\n case_when(\n overlap_type %in% c(2:5) & rownum_temp == 1 ~ lead(from_date_o, 1) - 1,\n overlap_type %in% c(2:5) & rownum_temp == 2 ~ to_date_o,\n overlap_type %in% c(2, 5) & rownum_temp == 3 ~ to_date.elig.mm,\n overlap_type %in% c(3, 4) & rownum_temp == 3 ~ to_date.elig.pha,\n TRUE ~ to_date), origin = \"1970-01-01\")\n )]\n # Deal with the last line for each person if it's part of an overlap\n temp_ext[, ':=' (\n from_date = as.Date(ifelse((id_apde != lead(id_apde, 1) | is.na(lead(id_apde, 1))) &\n overlap_type %in% c(2:5) & \n to_date.elig.pha != to_date.elig.mm, \n lag(to_date_o, 1) + 1, \n from_date), origin = \"1970-01-01\"),\n to_date = as.Date(ifelse((id_apde != lead(id_apde, 1) | is.na(lead(id_apde, 1))) &\n overlap_type %in% c(2:5), \n pmax(to_date.elig.pha, to_date.elig.mm, na.rm = TRUE), \n to_date), origin = \"1970-01-01\")\n )]\n # Reorder in preparation for next phase\n setorder(temp_ext, id_apde, from_date, to_date, from_date.elig.pha, from_date.elig.mm, \n to_date.elig.pha, to_date.elig.mm, overlap_type)\n \n \n #-- Label and clean summary interval data ----\n # Identify which type of enrollment this row represents\n temp_ext[, enroll_type := \n case_when(\n (overlap_type == 2 & rownum_temp == 1) | \n (overlap_type == 3 & rownum_temp == 3) |\n (overlap_type == 6 & rownum_temp == 1) | \n (overlap_type == 7 & rownum_temp == 2) |\n (overlap_type == 4 & rownum_temp %in% c(1, 3)) |\n (overlap_type == 0 & is.na(from_date.elig.mm)) ~ \"PHA\",\n (overlap_type == 3 & rownum_temp == 1) | \n (overlap_type == 2 & rownum_temp == 3) |\n (overlap_type == 6 & rownum_temp == 2) | \n (overlap_type == 7 & rownum_temp == 1) | \n (overlap_type == 5 & rownum_temp %in% c(1, 3)) |\n (overlap_type == 0 & is.na(from_date.elig.pha)) ~ \"mcaid_mcare\",\n overlap_type == 1 | (overlap_type %in% c(2:5) & rownum_temp == 2) ~ \"both\",\n TRUE ~ \"x\"\n )]\n # Drop rows from enroll_type == h/m when they are fully covered by an enroll_type == b\n temp_ext[, drop := \n case_when(\n id_apde == lag(id_apde, 1) & !is.na(lag(id_apde, 1)) & \n from_date == lag(from_date, 1) & !is.na(lag(from_date, 1)) &\n to_date >= lag(to_date, 1) & !is.na(lag(to_date, 1)) & \n # Fix up quirk from PHA data where two rows present for the same day\n !(lag(enroll_type, 1) != \"mcaid_mcare\" & lag(to_date.elig.pha, 1) == lag(from_date.elig.pha, 1)) &\n enroll_type != \"both\" ~ 1,\n id_apde == lead(id_apde, 1) & !is.na(lead(id_apde, 1)) & \n from_date == lead(from_date, 1) & !is.na(lead(from_date, 1)) &\n to_date <= lead(to_date, 1) & !is.na(lead(to_date, 1)) & \n # Fix up quirk from PHA data where two rows present for the same day\n !(lead(enroll_type, 1) != \"mcaid_mcare\" & lead(to_date.elig.pha, 1) == lead(from_date.elig.pha, 1)) &\n enroll_type != \"both\" & lead(enroll_type, 1) == \"both\" ~ 1,\n # Fix up other oddities when the date range is only one day\n id_apde == lag(id_apde, 1) & !is.na(lag(id_apde, 1)) & \n from_date == lag(from_date, 1) & !is.na(lag(from_date, 1)) &\n from_date == to_date & !is.na(from_date) & \n ((enroll_type == \"mcaid_mcare\" & lag(enroll_type, 1) %in% c(\"both\", \"PHA\")) |\n (enroll_type == \"PHA\" & lag(enroll_type, 1) %in% c(\"both\", \"mcaid_mcare\"))) ~ 1,\n id_apde == lag(id_apde, 1) & !is.na(lag(id_apde, 1)) & \n from_date == lag(from_date, 1) & !is.na(lag(from_date, 1)) &\n from_date == to_date & !is.na(from_date) &\n from_date.elig.pha == lag(from_date.elig.pha, 1) & to_date.elig.pha == lag(to_date.elig.pha, 1) &\n !is.na(from_date.elig.pha) & !is.na(lag(from_date.elig.pha, 1)) &\n enroll_type != \"both\" ~ 1,\n id_apde == lead(id_apde, 1) & !is.na(lead(id_apde, 1)) & \n from_date == lead(from_date, 1) & !is.na(lead(from_date, 1)) &\n from_date == to_date & !is.na(from_date) &\n ((enroll_type == \"mcaid_mcare\" & lead(enroll_type, 1) %in% c(\"both\", \"PHA\")) |\n (enroll_type == \"PHA\" & lead(enroll_type, 1) %in% c(\"both\", \"mcaid_mcare\"))) ~ 1,\n # Drop rows where the to_date < from_date due to \n # both data sources' dates ending at the same time\n to_date < from_date ~ 1,\n TRUE ~ 0\n )]\n \n temp_ext <- temp_ext[drop == 0 | is.na(drop)]\n \n # Truncate remaining overlapping end dates\n temp_ext[, to_date := as.Date(ifelse(id_apde == lead(id_apde, 1) & !is.na(lead(from_date, 1)) & \n from_date < lead(from_date, 1) & to_date >= lead(to_date, 1),\n lead(from_date, 1) - 1, to_date),\n origin = \"1970-01-01\")]\n \n temp_ext[, ':=' (drop = NULL, repnum = NULL, rownum_temp = NULL)]\n \n # With rows truncated, now additional rows with enroll_type == h/m that \n # are fully covered by an enroll_type == b\n # Also catches single day rows that now have to_date < from_date\n temp_ext[, drop := case_when(id_apde == lag(id_apde, 1) & from_date == lag(from_date, 1) &\n to_date == lag(to_date, 1) & lag(enroll_type, 1) == \"both\" & \n enroll_type != \"both\" ~ 1,\n id_apde == lead(id_apde, 1) & from_date == lead(from_date, 1) &\n to_date <= lead(to_date, 1) & lead(enroll_type, 1) == \"both\" ~ 1,\n id_apde == lag(id_apde, 1) & from_date >= lag(from_date, 1) &\n to_date <= lag(to_date, 1) & enroll_type != \"both\" &\n lag(enroll_type, 1) == \"both\" ~ 1,\n id_apde == lead(id_apde, 1) & from_date >= lead(from_date, 1) &\n to_date <= lead(to_date, 1) & enroll_type != \"both\" &\n lead(enroll_type, 1) == \"both\" ~ 1,\n TRUE ~ 0)]\n temp_ext <- temp_ext[drop == 0 | is.na(drop)]\n linked <- temp_ext[, .(id_apde, from_date, to_date, enroll_type)]\n \n # Catch any duplicates (there are some, have not investigated why yet)\n linked <- unique(linked)\n\n rm(temp, temp_ext)\n \n \n ### Linked IDs Part 2: join mcaid_mcare & PHA data based on ID & overlapping time periods ----\n # foverlaps ... https://github.com/Rdatatable/data.table/blob/master/man/foverlaps.Rd\n #-- structure data for use of foverlaps ----\n linked[, c(\"from_date\", \"to_date\") := lapply(.SD, as.integer), .SDcols = c(\"from_date\", \"to_date\")] \n setkey(linked, id_apde, from_date, to_date) \n \n timevar.pha.linked[, c(\"from_date\", \"to_date\") := lapply(.SD, as.integer), .SDcols = c(\"from_date\", \"to_date\")] \n setkey(timevar.pha.linked, id_apde, from_date, to_date)\n \n timevar.mm.linked[, c(\"from_date\", \"to_date\") := lapply(.SD, as.integer), .SDcols = c(\"from_date\", \"to_date\")] \n setkey(timevar.mm.linked, id_apde, from_date, to_date)\n \n #-- join on the mcaid_mcare linked data ----\n linked <- foverlaps(linked, timevar.mm.linked, type = \"any\", mult = \"all\")\n linked[, from_date := i.from_date] # the complete set of proper from_dates are in i.from_date\n linked[, to_date := i.to_date] # the complete set of proper to_dates are in i.to_date\n linked[, c(\"i.from_date\", \"i.to_date\") := NULL] # no longer needed\n setkey(linked, id_apde, from_date, to_date)\n \n #-- join on the PHA linked data ----\n linked <- foverlaps(linked, timevar.pha.linked, type = \"any\", mult = \"all\")\n linked[, from_date := i.from_date] # the complete set of proper from_dates are in i.from_date\n linked[, to_date := i.to_date] # the complete set of proper to_dates are in i.to_date\n linked[, c(\"i.from_date\", \"i.to_date\") := NULL] # no longer needed \n \n ### Append linked and non-linked data ----\n linked[, c(\"from_date\", \"to_date\") := lapply(.SD, as.Date, origin = \"1970-01-01\"), .SDcols = c(\"from_date\", \"to_date\")]\n timevar <- rbindlist(list(linked, timevar.pha.solo, timevar.mm.solo), use.names = TRUE, fill = TRUE)\n setkey(timevar, id_apde, from_date) # order dual data \n \n ### Collapse data if dates are contiguous and all data is the same ----\n timevar[, gr := cumsum(from_date - shift(to_date, fill=1) != 1), by = c(setdiff(names(timevar), c(\"from_date\", \"to_date\")))] # unique group # (gr) for each set of contiguous dates & constant data \n timevar <- timevar[, .(from_date=min(from_date), to_date=max(to_date)), by = c(setdiff(names(timevar), c(\"from_date\", \"to_date\")))] \n timevar[, gr := NULL]\n setkey(timevar, id_apde, from_date)\n \n ### Prep for pushing to SQL ----\n #-- Create program flags ----\n timevar[, mcare := 0][part_a==1 | part_b == 1 | part_c==1, mcare := 1]\n timevar[, mcaid := 0][!is.na(cov_type), mcaid := 1]\n timevar[, pha := 0][!is.na(agency_new), pha := 1]\n timevar[, apde_dual := 0][mcare == 1 & mcaid == 1, apde_dual := 1]\n timevar[is.na(dual), dual := 0] # is.na(dual)==T when data are only from PHA and/or Mcare\n timevar[apde_dual == 1 , dual := 1] # discussed this change via email with Alastair on 2/21/2020\n timevar[, mcaid_mcare_pha := 0][mcaid == 1 & mcare==1 & pha == 1, mcaid_mcare_pha := 1]\n timevar[, enroll_type := NULL] # kept until now for comparison with the dual flag\n if(nrow(timevar[mcare==0 & mcaid==0 & pha == 0]) > 0) \n stop(\"THERE IS A SERIOUS PROBLEM WITH THE TIMEVAR DATA. Mcaid, Mcare, and PHA should never all == 0\")\n \n #-- Set Mcare/Mcaid related program flag NULLs to zero when person is only in PHA ----\n timevar[mcare == 0 & mcaid == 0 & is.na(part_a), part_a := 0]\n timevar[mcare == 0 & mcaid == 0 & is.na(part_b), part_b := 0]\n timevar[mcare == 0 & mcaid == 0 & is.na(part_c), part_c := 0]\n timevar[mcare == 0 & mcaid == 0 & is.na(partial), partial := 0]\n timevar[mcare == 0 & mcaid == 0 & is.na(buy_in), buy_in := 0]\n timevar[mcare == 0 & mcaid == 0 & is.na(full_benefit), full_benefit := 0] \n timevar[mcare == 0 & mcaid == 0 & is.na(full_criteria), full_criteria := 0] \n \n #-- Create contiguous flag ---- \n # If contiguous with the PREVIOUS row, then it is marked as contiguous. This is the same as mcaid_elig_timevar\n timevar[, prev_to_date := c(NA, to_date[-.N]), by = \"id_apde\"] # MUCH faster than the shift \"lag\" function in data.table\n timevar[, contiguous := 0]\n timevar[from_date - prev_to_date == 1, contiguous := 1]\n timevar[, prev_to_date := NULL] # drop because no longer needed\n \n #-- Create cov_time_date ----\n timevar[, cov_time_day := as.integer(to_date - from_date + 1)]\n \n #-- Select PHA address data over Mcaid-Mcare when available ----\n # street\n timevar[!is.na(unit_add_new), geo_add1 := unit_add_new][, unit_add_new := NULL]\n # apartment\n timevar[!is.na(unit_apt), geo_add2 := unit_apt][, c(\"unit_apt\", \"unit_apt2\") := NULL]\n # city\n timevar[!is.na(unit_city_new ), geo_city := unit_city_new ][, unit_city_new := NULL]\n # state\n timevar[!is.na(unit_state_new ), geo_state := unit_state_new ][, unit_state_new := NULL]\n # zip\n timevar[!is.na(unit_zip_new), geo_zip := unit_zip_new][, unit_zip_new := NULL]\n # other geo_ variables (note that default is already in place, only filling in gaps)\n timevar[is.na(geo_zip_centroid), geo_zip_centroid := i.geo_zip_centroid][, i.geo_zip_centroid := NULL]\n timevar[is.na(geo_street_centroid), geo_street_centroid := i.geo_street_centroid][, i.geo_street_centroid := NULL]\n timevar[is.na(geo_county_code), geo_county_code := i.geo_county_code][, i.geo_county_code := NULL]\n timevar[is.na(geo_tract_code), geo_tract_code := i.geo_tract_code][, i.geo_tract_code := NULL]\n timevar[is.na(geo_hra_code), geo_hra_code := i.geo_hra_code][, i.geo_hra_code := NULL]\n timevar[is.na(geo_school_code), geo_school_code := i.geo_school_code][, i.geo_school_code := NULL]\n \n #-- Add KC flag based on zip code or FIPS code as appropriate---- \n kc.zips <- data.table::fread(\"https://raw.githubusercontent.com/PHSKC-APDE/reference-data/master/spatial_data/zip_admin.csv\")\n timevar[, geo_kc := 0]\n timevar[geo_county_code==\"033\", geo_kc := 1]\n timevar[is.na(geo_county_code) & geo_zip %in% unique(as.character(kc.zips$zip)), geo_kc := 1]\n rm(kc.zips)\n\n #-- create time stamp ----\n timevar[, last_run := Sys.time()] \n \n #-- normalize pha variables ----\n setnames(timevar,\n c(\"agency_new\", \"subsidy_type\", \"vouch_type_final\", \"operator_type\", \"portfolio_final\"),\n c(\"pha_agency\", \"pha_subsidy\", \"pha_voucher\", \"pha_operator\", \"pha_portfolio\"))\n \n timevar[is.na(pha_agency), pha_agency := \"Non-PHA\"]\n timevar[pha_agency == \"Non-PHA\", pha_subsidy := \"Non-PHA\"]\n timevar[pha_agency == \"Non-PHA\", pha_voucher := \"Non-PHA\"]\n timevar[pha_agency == \"Non-PHA\", pha_operator := \"Non-PHA\"]\n timevar[pha_agency == \"Non-PHA\", pha_portfolio := \"Non-PHA\"]\n\n #-- clean up ----\n rm(linked, pha, timevar.mm, timevar.mm.linked, timevar.mm.solo, timevar.pha, timevar.pha.linked, timevar.pha.solo)\n \n \n##### WRITE ELIG_DEMO TO SQL ----\n ### Write to SQL ----\n # Pull YAML from GitHub\n table_config_demo <- yaml::yaml.load(httr::content(httr::GET(yaml.elig)))\n\n # Ensure columns are in same order in R & SQL & that we drop extraneous variables\n keep.elig <- names(table_config_demo$vars)\n elig <- elig[, ..keep.elig]\n \n # Write table to SQL\n # Split into smaller tables to avoid SQL connection issues\n start <- 1L\n max_rows <- 100000L\n cycles <- ceiling(nrow(elig)/max_rows)\n \n lapply(seq(start, cycles), function(i) {\n start_row <- ifelse(i == 1, 1L, max_rows * (i-1) + 1)\n end_row <- min(nrow(elig), max_rows * i)\n \n message(\"Loading cycle \", i, \" of \", cycles)\n if (i == 1) {\n dbWriteTable(db_apde51,\n DBI::Id(schema = table_config_demo$schema, table = table_config_demo$table),\n value = as.data.frame(elig[start_row:end_row]),\n overwrite = T, append = F,\n field.types = unlist(table_config_demo$vars))\n } else {\n dbWriteTable(db_apde51,\n DBI::Id(schema = table_config_demo$schema, table = table_config_demo$table),\n value = as.data.frame(elig[start_row:end_row]),\n overwrite = F, append = T)\n }\n })\n \n \n ### Simple QA ----\n #-- confirm that all rows were loaded to SQL ----\n stage.count <- as.numeric(odbc::dbGetQuery(db_apde51, \"SELECT COUNT (*) FROM stage.mcaid_mcare_pha_elig_demo\"))\n if(stage.count != nrow(elig))\n stop(\"Mismatching row count, error writing data\") \n \n #-- check that rows in stage are not less than the last time that it was created ----\n last_run <- as.POSIXct(odbc::dbGetQuery(db_apde51, \"SELECT MAX (last_run) FROM stage.mcaid_mcare_pha_elig_demo\")[[1]]) # data for the run that was just uploaded\n \n # count number of rows\n previous_rows <- as.numeric(\n odbc::dbGetQuery(db_apde51, \n \"SELECT c.qa_value from\n (SELECT a.* FROM\n (SELECT * FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_demo' AND\n qa_item = 'row_count') a\n INNER JOIN\n (SELECT MAX(qa_date) AS max_date \n FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_demo' AND\n qa_item = 'row_count') b\n ON a.qa_date = b.max_date)c\"))\n \n if(is.na(previous_rows)){previous_rows = 0}\n \n row_diff <- stage.count - previous_rows\n \n if (row_diff < 0) {\n odbc::dbGetQuery(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_demo',\n 'Number new rows compared to most recent run', \n 'FAIL', \n {Sys.time()}, \n 'There were {row_diff} fewer rows in the most recent table \n ({stage.count} vs. {previous_rows})')\",\n .con = db_apde51))\n \n problem.elig.row_diff <- glue::glue(\"Fewer rows than found last time. \n Check metadata.qa_mcaid_mcare_pha for details (last_run = {last_run})\n \\n\")\n } else {\n odbc::dbGetQuery(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_demo',\n 'Number new rows compared to most recent run', \n 'PASS', \n {Sys.time()}, \n 'There were {row_diff} more rows in the most recent table \n ({stage.count} vs. {previous_rows})')\",\n .con = db_apde51))\n \n problem.elig.row_diff <- glue::glue(\" \") # no problem, so empty error message\n \n }\n \n #-- check that the number of distinct IDs not less than the last time that it was created ----\n # get count of unique id \n current.unique.id <- as.numeric(odbc::dbGetQuery(\n db_apde51, \"SELECT COUNT (DISTINCT id_apde) \n FROM stage.mcaid_mcare_pha_elig_demo\"))\n \n previous.unique.id <- as.numeric(\n odbc::dbGetQuery(db_apde51, \n \"SELECT c.qa_value from\n (SELECT a.* FROM\n (SELECT * FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_demo' AND\n qa_item = 'id_count') a\n INNER JOIN\n (SELECT MAX(qa_date) AS max_date \n FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_demo' AND\n qa_item = 'id_count') b\n ON a.qa_date = b.max_date)c\"))\n \n if(is.na(previous.unique.id)){previous.unique.id = 0}\n \n id_diff <- current.unique.id - previous.unique.id\n \n if (id_diff < 0) {\n odbc::dbGetQuery(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_demo',\n 'Number distinct IDs compared to most recent run', \n 'FAIL', \n {Sys.time()}, \n 'There were {id_diff} fewer IDs in the most recent table \n ({current.unique.id} vs. {previous.unique.id})')\",\n .con = db_apde51))\n \n problem.elig.id_diff <- glue::glue(\"Fewer unique IDs than found last time. \n Check metadata.qa_mcaid_mcare_pha for details (last_run = {last_run})\n \\n\")\n } else {\n odbc::dbGetQuery(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_demo',\n 'Number distinct IDs compared to most recent run', \n 'PASS', \n {Sys.time()}, \n 'There were {id_diff} more IDs in the most recent table \n ({current.unique.id} vs. {previous.unique.id})')\",\n .con = db_apde51))\n \n problem.elig.id_diff <- glue::glue(\" \") # no problem, so empty error message\n }\n \n ### Fill qa_mcare_values table ----\n qa.values <- glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha_values\n (table_name, qa_item, qa_value, qa_date, note) \n VALUES ('stage.mcaid_mcare_pha_elig_demo',\n 'row_count', \n {stage.count}, \n {Sys.time()}, \n '')\",\n .con = db_apde51)\n \n DBI::dbExecute(conn = db_apde51, qa.values)\n \n qa.values2 <- glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha_values\n (table_name, qa_item, qa_value, qa_date, note) \n VALUES ('stage.mcaid_mcare_pha_elig_demo',\n 'id_count', \n {current.unique.id}, \n {Sys.time()}, \n '')\",\n .con = db_apde51)\n \n DBI::dbExecute(conn = db_apde51, qa.values2)\n \n \n##### WRITE ELIG_TIMEVAR TO SQL ----\n ### Write to SQL ----\n # Pull YAML from GitHub\n table_config_timevar <- yaml::yaml.load(httr::content(httr::GET(yaml.timevar)))\n\n # Ensure columns are in same order in R & SQL & are limited those specified in the YAML\n keep.timevars <- names(table_config_timevar$vars)\n timevar <- timevar[, ..keep.timevars]\n \n setcolorder(timevar, names(table_config_timevar$vars))\n \n # Write table to SQL\n # Split into smaller tables to avoid SQL connection issues\n start <- 1L\n max_rows <- 100000L\n cycles <- ceiling(nrow(timevar)/max_rows)\n \n lapply(seq(start, cycles), function(i) {\n start_row <- ifelse(i == 1, 1L, max_rows * (i-1) + 1)\n end_row <- min(nrow(timevar), max_rows * i)\n \n message(\"Loading cycle \", i, \" of \", cycles)\n if (i == 1) {\n dbWriteTable(db_apde51,\n DBI::Id(schema = table_config_timevar$schema, table = table_config_timevar$table),\n value = as.data.frame(timevar[start_row:end_row]),\n overwrite = T, append = F,\n field.types = unlist(table_config_timevar$vars))\n } else {\n dbWriteTable(db_apde51,\n DBI::Id(schema = table_config_timevar$schema, table = table_config_timevar$table),\n value = as.data.frame(timevar[start_row:end_row]),\n overwrite = F, append = T)\n }\n })\n\n \n ### Simple QA ----\n #-- confirm that all rows were loaded to SQL ----\n stage.count <- as.numeric(odbc::dbGetQuery(db_apde51, \"SELECT COUNT (*) FROM stage.mcaid_mcare_pha_elig_timevar\"))\n if(stage.count != nrow(timevar))\n stop(\"Mismatching row count, error writing data\") \n \n #-- check that rows in stage are not less than the last time that it was created ----\n last_run <- as.POSIXct(odbc::dbGetQuery(db_apde51, \"SELECT MAX (last_run) FROM stage.mcaid_mcare_pha_elig_timevar\")[[1]]) # data for the run that was just uploaded\n \n # count number of rows\n previous_rows <- as.numeric(\n odbc::dbGetQuery(db_apde51, \n \"SELECT c.qa_value from\n (SELECT a.* FROM\n (SELECT * FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_timevar' AND\n qa_item = 'row_count') a\n INNER JOIN\n (SELECT MAX(qa_date) AS max_date \n FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_timevar' AND\n qa_item = 'row_count') b\n ON a.qa_date = b.max_date)c\"))\n \n if(is.na(previous_rows)){previous_rows = 0}\n \n row_diff <- stage.count - previous_rows\n \n if (row_diff < 0) {\n DBI::dbExecute(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_timevar',\n 'Number new rows compared to most recent run', \n 'FAIL', \n {Sys.time()}, \n 'There were {row_diff} fewer rows in the most recent table \n ({stage.count} vs. {previous_rows})')\",\n .con = db_apde51))\n \n problem.timevar.row_diff <- glue::glue(\"Fewer rows than found last time. \n Check metadata.qa_mcaid_mcare_pha for details (last_run = {last_run})\n \\n\")\n } else {\n DBI::dbExecute(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_timevar',\n 'Number new rows compared to most recent run', \n 'PASS', \n {Sys.time()}, \n 'There were {row_diff} more rows in the most recent table \n ({stage.count} vs. {previous_rows})')\",\n .con = db_apde51))\n \n problem.timevar.row_diff <- glue::glue(\" \") # no problem, so empty error message\n \n }\n \n #-- check that the number of distinct IDs not less than the last time that it was created ----\n # get count of unique id \n current.unique.id <- as.numeric(odbc::dbGetQuery(\n db_apde51, \"SELECT COUNT (DISTINCT id_apde) \n FROM stage.mcaid_mcare_pha_elig_timevar\"))\n \n previous.unique.id <- as.numeric(\n odbc::dbGetQuery(db_apde51, \n \"SELECT c.qa_value from\n (SELECT a.* FROM\n (SELECT * FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_timevar' AND\n qa_item = 'id_count') a\n INNER JOIN\n (SELECT MAX(qa_date) AS max_date \n FROM metadata.qa_mcaid_mcare_pha_values\n WHERE table_name = 'stage.mcaid_mcare_pha_elig_timevar' AND\n qa_item = 'id_count') b\n ON a.qa_date = b.max_date)c\"))\n \n if(is.na(previous.unique.id)){previous.unique.id = 0}\n \n id_diff <- current.unique.id - previous.unique.id\n \n if (id_diff < 0) {\n DBI::dbExecute(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_timevar',\n 'Number distinct IDs compared to most recent run', \n 'FAIL', \n {Sys.time()}, \n 'There were {id_diff} fewer IDs in the most recent table \n ({current.unique.id} vs. {previous.unique.id})')\",\n .con = db_apde51))\n \n problem.timevar.id_diff <- glue::glue(\"Fewer unique IDs than found last time. \n Check metadata.qa_mcaid_mcare_pha for details (last_run = {last_run})\n \\n\")\n } else {\n DBI::dbExecute(\n conn = db_apde51,\n glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha\n (last_run, table_name, qa_item, qa_result, qa_date, note) \n VALUES ({last_run}, \n 'stage.mcaid_mcare_pha_elig_timevar',\n 'Number distinct IDs compared to most recent run', \n 'PASS', \n {Sys.time()}, \n 'There were {id_diff} more IDs in the most recent table \n ({current.unique.id} vs. {previous.unique.id})')\",\n .con = db_apde51))\n \n problem.timevar.id_diff <- glue::glue(\" \") # no problem, so empty error message\n }\n \n ### Fill qa_mcare_values table ----\n qa.values <- glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha_values\n (table_name, qa_item, qa_value, qa_date, note) \n VALUES ('stage.mcaid_mcare_pha_elig_timevar',\n 'row_count', \n {stage.count}, \n {Sys.time()}, \n '')\",\n .con = db_apde51)\n \n DBI::dbExecute(conn = db_apde51, qa.values)\n \n qa.values2 <- glue::glue_sql(\"INSERT INTO metadata.qa_mcaid_mcare_pha_values\n (table_name, qa_item, qa_value, qa_date, note) \n VALUES ('stage.mcaid_mcare_pha_elig_timevar',\n 'id_count', \n {current.unique.id}, \n {Sys.time()}, \n '')\",\n .con = db_apde51)\n \n DBI::dbExecute(conn = db_apde51, qa.values2)\n \n### Print error messages and load to final ----\n #-- create summary of errors\n problems <- glue::glue(\n problem.timevar.row_diff, \"\\n\",\n problem.timevar.id_diff, \"\\n\",\n problem.elig.row_diff, \"\\n\",\n problem.elig.id_diff\n )\n \n if(problems >1){\n message(glue::glue(\"WARNING ... MCAID_MCARE_PHA_ELIG_TIMEVAR OR ELIG_DEMO FAILED AT LEAST ONE QA TEST\", \"\\n\",\n \"Summary of problems in new tables: \", \"\\n\", \n problems))\n } else {\n message(\"Staged MCAID_MCARE_PHA_ELIG_TIMEVAR & ELIG_DEMO passed all QA tests\")\n \n # Load to final schema (permissions should be in place but not working)\n # Use SQL code file for now\n alter_schema_f(conn = db_apde51, \n from_schema = \"stage\", to_schema = \"final\",\n table_name = \"mcaid_mcare_pha_elig_demo\")\n \n alter_schema_f(conn = db_apde51, \n from_schema = \"stage\", to_schema = \"final\",\n table_name = \"mcaid_mcare_pha_elig_timevar\")\n \n \n # Add index\n # (need to update schema name in the config file first)\n table_config_demo$schema <- \"final\"\n table_config_timevar$schema <- \"final\"\n \n add_index_f(db_apde51, table_config = table_config_demo)\n add_index_f(db_apde51, table_config = table_config_timevar)\n }\n\n \n\n \n# the end ---- \n", "meta": {"hexsha": "50572491f7c63218b31f9ff85918e9636d5f28c8", "size": 52670, "ext": "r", "lang": "R", "max_stars_repo_path": "etl/stage/load_stage_mcaid_mcare_pha_elig_demo.r", "max_stars_repo_name": "PHSKC-APDE/Housing", "max_stars_repo_head_hexsha": "0d9fe63faddc90af718afaad4db98d64e2910943", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2016-11-23T18:51:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-03T19:55:28.000Z", "max_issues_repo_path": "etl/stage/load_stage_mcaid_mcare_pha_elig_demo.r", "max_issues_repo_name": "PHSKC-APDE/Housing", "max_issues_repo_head_hexsha": "0d9fe63faddc90af718afaad4db98d64e2910943", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2018-10-02T20:28:23.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-01T20:52:46.000Z", "max_forks_repo_path": "processing/09_mcaid_mcare_pha_join.r", "max_forks_repo_name": "PHSKC-APDE/Housing", "max_forks_repo_head_hexsha": "0d9fe63faddc90af718afaad4db98d64e2910943", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2017-10-03T21:01:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-15T17:41:30.000Z", "avg_line_length": 53.4178498986, "max_line_length": 202, "alphanum_fraction": 0.5659578508, "num_tokens": 14038, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3182023195530493}} {"text": "pdf_file<-\"pdf/dotcharts_overlay.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=7,height=10)\n\nlibrary(Hmisc) # because of dotchart2, only there add=T can be used\npar(omi=c(0.15,0.75,0.95,0.75),mai=c(0.9,1.75,0.25,0),family=\"Lato Light\",las=1) \n\n# Import data and prepare chart\n\nmyData<-read.xls(\"myData/Bechert_Graph.xlsx\",sheet=1, encoding=\"latin1\")\nrow.names(myData)<- myData$Countries\nmyData$Countries<-NULL\n\ntotal<- myData[\"Mean . . . . . . . . . . \",]\nmyData<- myData[rownames(myData)!=\"Mean . . . . . . . . . . \",] \n\nmyDatasort <- myData [order(-myData$Happiness),]\nmyDatasort <-rbind(myDatasort,total)\nattach(myDatasort)\n\nmyC1<-rgb(255,165,0,190,maxColorValue=255)\nmyC2<-rgb(0,0,139,190,maxColorValue=255)\nmyC3<-rgb(100,0,0,190,maxColorValue=255)\n\nmyC1g<-rgb(255,165,0,60,maxColorValue=255)\nmyC2g<-rgb(0,0,139,60,maxColorValue=255)\nmyC3g<-rgb(100,0,0,60,maxColorValue=255)\n\n# Create chart and other elements\n\ndotchart2(Religiosity.,labels=row.names(myDatasort),pch=17,dotsize=4,cex=0.6,cex.labels=0.75,xlab=\"\",col=myC1,xaxis=F,xlim=c(1,100))\ndotchart2(Involvement,labels=row.names(myDatasort),pch=15,dotsize=4,cex=0.6,xlab=\"\",col=myC2,xaxis=F,add=T)\ndotchart2(Happiness,labels=row.names(myDatasort),pch=19,dotsize=4,cex=0.6,xlab=\"\",col=myC3,xaxis=F,add=T)\n\naxis(1)\naxis(3)\t\n\nabline(v=total,col=c(myC1g,myC2g,myC3g),lwd=12)\nlegend(-5,-2.6,c(\"Religious Involvement\"),ncol=1,pch=15,col=myC2,bty=\"n\",cex=1.5,pt.cex=1.5,xpd=T)\nlegend(35,-2.6,c(\"Self-assessed Religiosity\"),ncol=1,pch=17,col=myC1,bty=\"n\",cex=1.5,pt.cex=1.5,xpd=T)\n\nlegend(80,-2.6,c(\"Happiness\"),ncol=1,pch=19,col=myC3,bty=\"n\",cex=1.5,pt.cex=1.5,xpd=T)\n\n\n# Titling\n\nmtext(\"Religious Involvement, Self-assessed Religiosity, and Happiness \",3,line=3.75,adj=0,cex=1.05,family=\"Lato Black\",outer=T)\nmtext(\"(Mean values scaled from 0 to 100, Data from 2008)\",3,line=1.25,adj=0,cex=0.90,font=3,outer=T)\nmtext(\"Source: ISSP Data Report Religious Attitudes and Religious Change\",1,line=-1,adj=1,cex=0.90,font=3,outer=T)\n\ndev.off()\n\n", "meta": {"hexsha": "a24f67faecd42f8002e05bf690a93522215d61c1", "size": 1988, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/dotcharts_overlay.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/dotcharts_overlay.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/dotcharts_overlay.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.2307692308, "max_line_length": 132, "alphanum_fraction": 0.7157947686, "num_tokens": 798, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3181641180781103}} {"text": "library(tidyverse)\nlibrary(lubridate)\nlibrary(data.table)\n\ndf = read.table(\"bank/bank-full.csv\", header = T, sep = \";\")\ndrop = c(\"poutcome\", \"previous\", \"pdays\", \"month\", \"contact\", \"day\")\ndf = df[,!(names(df) %in% drop)]\ndf\nwrite.csv(df, \"bank-full-updated.csv\", row.names = F)\n\ndf = read.table(\"bank/bank-full-updated.csv\", header = T, sep = \",\")\nhead(df)\nunique(df$job , incomparables = FALSE)\nunique(df$marital , incomparables = FALSE)\nunique(df$education , incomparables = FALSE)\nunique(df$default , incomparables = FALSE)\nunique(df$balance , incomparables = FALSE)\nunique(df$housing , incomparables = FALSE)\nunique(df$loan , incomparables = FALSE)\nunique(df$contact , incomparables = FALSE)\nunique(df$day , incomparables = FALSE)\nunique(df$month , incomparables = FALSE)\nunique(df$duration , incomparables = FALSE)\nunique(df$campaign , incomparables = FALSE)\nunique(df$pdays , incomparables = FALSE)\nunique(df$previous , incomparables = FALSE)\nunique(df$poutcome , incomparables = FALSE)\nunique(df$y , incomparables = FALSE)\n\nnames(df)[names(df) == \"y\"] = \"subscribed_term_deposit\"\n\ndf$subscribed_term_deposit[df$subscribed_term_deposit == \"no\"] = 0 \ndf$subscribed_term_deposit[df$subscribed_term_deposit == \"yes\"] = 1\n\ndf$job[df$job == \"unknown\"] = NA \ndf$education[df$education == \"unknown\"] = NA \n\ndf = na.omit(df)\nwrite.csv(df, \"bank-full-updated.csv\", row.names = F)\nhead(df)\ndf = read.table(\"bank-full-updated.csv\", header = T, sep = \",\")\nhead(df)\n", "meta": {"hexsha": "b31f2e5582c6948ef631651243a6ee567903a475", "size": 1460, "ext": "r", "lang": "R", "max_stars_repo_path": "Complete Modules/assignments/done/data mining/cleaning.r", "max_stars_repo_name": "Maks-Drzezdzon/Working-With-Data-L-O", "max_stars_repo_head_hexsha": "86a4b1953d4687cba6cb9b0c2754bc3c801b719b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-01T12:18:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-01T12:18:13.000Z", "max_issues_repo_path": "Complete Modules/assignments/done/data mining/cleaning.r", "max_issues_repo_name": "Maks-Drzezdzon/Working-With-Data-L-O", "max_issues_repo_head_hexsha": "86a4b1953d4687cba6cb9b0c2754bc3c801b719b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 22, "max_issues_repo_issues_event_min_datetime": "2020-10-01T17:52:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-05T22:40:39.000Z", "max_forks_repo_path": "assignments/done/data mining/cleaning.r", "max_forks_repo_name": "Maks-Drzezdzon/Masters-Classes-L-O", "max_forks_repo_head_hexsha": "489f6812d80ca57d86adbaca5d25497939ce33f0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9534883721, "max_line_length": 68, "alphanum_fraction": 0.7095890411, "num_tokens": 416, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3181641180781102}} {"text": "library(dplyr)\n\n###############################################################\n## Global parameters\n###############################################################\nreset_params <- function(){\n lf <<- 0.15 # latency factor\n le <<- 1 # latency exponent\n rth \t <<- -1.5 # retrieval threshold\n bll <<- 0.5 # decay parameter\n ans <<- 0.15 # activation noise\n mas <<- 1 # maximum associative strength \n mp <<- 1.5 # mismatch penalty\n ga <<- 1 # goal source activation\n rand_time <<- 3 # latency variability\n lp <<- 1 # default time since last presentation (sec)\n blc <<- 0 # base-level activation\n ##\n ## Distractor control:\n ldp <<- 1 # last distractor presentation (sec)\n dbl <<- 0 # distractor base-level\n ndistr <<- 1\t\t\t\t# number of distractors\n ##\n ## Cue weighting, cue confusion and activation-sensitivity:\n # cueweights <<- c(1,1) # vector of cue weightings\n cueweighting <<- 1 # Strength of structural cue as ratio str/sem\n normalizeWeights <<- TRUE \n pc <<- 0 # prominence correction factor C\n pco \t <<- 1.3 # prominence correction offset x_0\n cuesim <<- -1 # cue-feature similarity [-1..0]\n cl \t\t\t\t<<- 0 \t\t\t# cue confusion level (0-100)\n}\nreset_params()\n\ncuesim2cl <- function(x=cuesim){\n\tcl <<- (x+1)*100\n\tcl\n}\n\ncl2cuesim <- function(x=cl){\n\tcuesim <<- x/100-1\n\tcuesim\n}\n\nstrWeight <- function(ratio=cueweighting, normalize=normalizeWeights){\n ifelse(normalize, ratio/(ratio+1)*2, ratio)\n}\nsemWeight <- function(ratio=cueweighting, normalize=normalizeWeights){\n ifelse(normalize, 1/(ratio+1)*2, 1)\n}\n\nidnames <- c(\"Set\",\"Iteration\",\"Condition\",\"Target\",\"Distractor\")\nactrnames <- c(\"fan1\",\"fan2\",\"weights\",\"bl1\",\"bl2\",\"times1\",\"times2\",\"noise1\",\"noise2\",\"blact1\",\"blact2\",\"fs1\",\"fs2\",\"act1\",\"act2\",\"activation\",\"latency\",\"retrieved\",\"acc\",\"miss\",\"fail\")\nparamnames <- c(\"lf\",\"le\",\"rth\",\"bll\",\"ans\",\"mas\",\"mp\",\"ga\",\"rand_time\",\"lp\",\"blc\",\"ldp\",\"dbl\",\"ndistr\",\"cueweighting\",\"pc\",\"pco\",\"cuesim\",\"cl\")\n\n\n\n###############################################################\n## RUN MODEL\n###############################################################\n#model <- model_4cond\nrun <- function(model, iterations=1000){\n\t##\n\t## PARAMETER MATRIX\n\t##\n\tprint(\"creating parameter matrix...\")\n\t#\n\tparameters <- list(lf,le,rth,bll,ans,mas,mp,ga,rand_time,lp,blc,ldp,dbl,ndistr,cueweighting,pc,pco,cuesim,cl)\n\t#\n\tn_params <- length(parameters);\n\t#\n\t## The total number of combinations is the product of the number of values\n\t## for each parameter\n\tn_sets <- prod(unlist(lapply(parameters, length)))\n\tn_cond <- length(model$target_fan)\n\ttotal <- iterations*n_sets*n_cond\n\t#\n\tprint(paste(\"Conditions: \",n_cond))\n\tprint(paste(\"Combinations: \",n_sets))\n\tprint(paste(\"Total runs: \",total))\n\t#\n\t## Set up matrix of parameter combinations. Rows are model experiments,\n\t## columns are parameters.\n\tparam_combs <- matrix(nrow=n_sets, ncol=n_params);\n\t#\n\tcumulative_num_combs <- 1;\n\tfor (p in 1:n_params) {\n\t\tparam_combs[,p] <- rep(parameters[p][[1]], each=cumulative_num_combs, length.out=n_sets);\n\t\tcumulative_num_combs <- cumulative_num_combs * length(parameters[p][[1]]);\n\t}\n\t#\n\tparam_matrix <- matrix(data=t(param_combs), nrow=total,ncol=n_params, byrow=TRUE);\n\n\n\n\t##\n\t## DATA TABLE\n\t##\n\tprint(\"creating data table...\")\n\t#\n\tcondnames <- 1:length(model$target_fan)\n\t# c(\"lf\",\"le\",\"bll\",\"ans\",\"mas\",\"mp\",\"rth\",\"rand_time\",\"w\",\"lp\",\"ldp\",\"blc\",\"dbl\",\"pc\",\"pco\",\"cl\",\"cuesim\",\"cueweighting\",\"ndistr\")\n\theader <- c(idnames,paramnames,actrnames)\n#\n\tid_matrix <- matrix(nrow=total, ncol=length(idnames))\n\tactr_matrix <- matrix(nrow=total, ncol=length(actrnames))\n\td <- data.frame(cbind(id_matrix,param_matrix,actr_matrix))\n\tcolnames(d) <- header\n# d$chunk <- rep(chunknames,total/n_chunks)\n\td$Set <- 1:n_sets\n\td$Condition <- rep(condnames, each=n_sets)\n\td$Target <- rep(model$Target, each=n_sets)\n\td$Distractor <- rep(model$Distractor, each=n_sets)\n\td$fan1 <- rep(model$target_fan, each=n_sets)\n\td$fan2 <- rep(model$distractor_fan, each=n_sets)\n\td$Iteration <- rep(1:iterations, each=n_sets*n_cond)\n\td$times1 <- d$lp\n\td$times2 <- d$ldp\n\td$bl1 <- d$blc\n\td$bl2 <- d$dbl\n\td$cl <- (cuesim+1)*100\n\td$weights <- model$weights\n\td$noise1 <- act_r_noise_n(total, d$ans)\n\td$noise2 <- act_r_noise_n(total, d$ans)\n\thead(d)\n\n\n\t##\n\t## ACTIVATION\n\t##\n\tprint(\"computing activations...\")\n\t#\n\tfan1<-matrix(unlist(d$fan1),nrow=total,ncol=length(d$fan1[[1]]),byrow=TRUE)\n\tfan2<-matrix(unlist(d$fan2),nrow=total,ncol=length(d$fan1[[1]]),byrow=TRUE)\n\tweights<-matrix(unlist(d$weights),nrow=total,ncol=2,byrow=TRUE)\n\t#\n\tfan1 <- ifelse(fan1==0,NA,fan1)\n\tfan2 <- ifelse(fan2==0,NA,fan2)\n\tf1 <- ifelse(!is.na(fan1) & fan1>0, 1, 0)\n\tf2 <- ifelse(!is.na(fan2) & fan2>0, 1, 0)\n\tmatch1 <- f1-1\n\tmatch2 <- f2-1\n\td$blact1 <- activation(fan=fan1, match=match1, bl=d$bl1, times=d$times1, weights=weights, noise=d$noise1, W=d$ga, dec=d$bll, S=d$mas, P=d$mp)\n\td$blact2 <- activation(fan=fan2, match=match2, bl=d$bl2, times=d$times2, weights=weights, noise=d$noise2, W=d$ga, dec=d$bll, S=d$mas, P=d$mp)\n\t#\n\td$fs1 <- fan_strength(d$blact1,d$blact2, C=d$pc, x0=d$pco)\n\td$fs2 <- fan_strength(d$blact2,d$blact1, C=d$pc, x0=d$pco)\n #\n print(\"computing cue confusion and fan strength...\")\n #\n\tcueconf1 <- cbind(f2[,-1],f2[,1])*(1+d$cuesim)\n\tffan1 <- fan1*1+(f2+cueconf1)*d$ndistr*d$fs1\n\tcueconf2 <- cbind(f1[,-1],f1[,1])*(1+d$cuesim)\n\tffan2 <- fan2*d$ndistr+(f1+cueconf2)*1*d$fs2\n\t# \n\td$act1 <- activation(fan=ffan1, match=match1, bl=d$bl1, times=d$times1, weights=weights, noise=d$noise1, W=d$ga, dec=d$bll, S=d$mas, P=d$mp)\n\td$act2 <- activation(fan=ffan2, match=match2, bl=d$bl2, times=d$times2, weights=weights, noise=d$noise2, W=d$ga, dec=d$bll, S=d$mas, P=d$mp)\n\t\n\n\t##\n\t## FINAL VALUES\n\t##\n\tprint(\"computing final values...\")\n\td$activation <- ifelse(d$act1>d$act2, d$act1, d$act2)\n\tretrieved <- ifelse(d$act1>d$act2, 1, 2)\n\td$retrieved <- ifelse(d$activation>d$rth, retrieved, 0)\n\td$latency <- latency(d$activation, F=d$lf, f=d$le, tau=d$rth)\n\td$acc <- ifelse(d$retrieved==1, 1, 0)\n\td$miss <- ifelse(d$retrieved==2, 1, 0)\n\td$fail <- ifelse(d$retrieved==0, 1, 0)\n\t#\n\tprint(\"FINISHED\")\n\t#\n\treturn(tbl_df(d))\n}\n\n\n\n\n\n\n\n###############################################################\n## ACT-R\n###############################################################\nactivation <- function(fan=matrix(c(1,1),1,2,T), match=matrix(c(0,0),1,2,T), weights=matrix(c(1,1),1,2,T), times=lp, bl=blc, noise=act_r_noise(ans), W=ga, dec=bll, S=mas, P=mp){\n Wkj <- W*weights/rowSums(weights)\n base_act <- log(times^(-dec)) + bl\n Sji <- S-log(fan)\n Sji <- ifelse(Sji==Inf | is.na(Sji), 0, Sji)\n ifelse(Sji < 0, print(\"!!! WARNING: Sji < 0 !!!\"), T)\n Sji <- ifelse(Sji < 0, 0, Sji)\n Pi <- rowSums(P*match)\n act <- base_act + rowSums(Wkj*Sji) + Pi + noise\n return(act)\n}\n\n\n\nlatency <- function(A, F=lf, f=le, tau=rth){\n t <- ifelse(A>=tau, F*exp(-f*A)*1000, F*exp(-f*tau)*1000)\n round(randomize_time(t))\n}\n\n\nact_r_noise_n <- function(n, s=ans){\n var <- pi^2/3*s^2\n rnorm(n, 0, sqrt(var))\n}\n\nact_r_noise <- function(s=ans){\n var <- pi^2/3*s^2\n rnorm(1, 0, sqrt(var))\n}\n\n\n## Random component ##\nrandomize_time <- function(time, n=rand_time){\n if(n>0) runif(length(time), time*(n-1)/n, time*(n+1)/n) else time\n}\n\n\nfan_strength <- function(a1, a2, C=pc, x0=pco){\n ifelse(C > 0, 1/(1+exp(-C*(x0-(a1-a2)))), 1)\n}\n\n\nnoise_off <- function(){\n ans <<- 0\n rand_time <<- 0\n}\n\nnoise_on <- function(){\n ans <<- 0.15\n rand_time <<- 3\n}\n\n", "meta": {"hexsha": "d762ae8712c9d83ae9e558d2bcb6880e0b4846a1", "size": 7563, "ext": "r", "lang": "R", "max_stars_repo_path": "chapters4to6_simulations/chapter_4/model/act-s.r", "max_stars_repo_name": "vasishth/RetrievalModels", "max_stars_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-05T15:49:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-05T15:49:49.000Z", "max_issues_repo_path": "chapters4to6_simulations/chapter_4/model/act-s.r", "max_issues_repo_name": "vasishth/RetrievalModels", "max_issues_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-06-08T10:53:53.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-08T11:14:40.000Z", "max_forks_repo_path": "chapters4to6_simulations/chapter_4/model/act-s.r", "max_forks_repo_name": "vasishth/RetrievalModels", "max_forks_repo_head_hexsha": "fc2a2843302ae8aef0c7309f1d5dae8a77ebab8d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.7773109244, "max_line_length": 186, "alphanum_fraction": 0.6034642338, "num_tokens": 2569, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3181641180781102}} {"text": "options(scipen = 9999999, digits = 22)\r\nlibrary(dplyr)\r\nlibrary(readxl)\r\nlibrary(readr)\r\nlibrary(bit64)\r\n\r\n\r\n\r\n# SAIL Labs\r\n# ~Mihir\r\n\r\n## Description:\r\n##-------------\r\n#* This file will read all the csv, select the Screenid and keeps the unique userids only.\r\n#* Then you can specify people and number of rows you want to keep in each file. It is meant to\r\n#* help with botometer.\r\n##-------------\r\n\r\n\r\nlibrary(data.table)\r\n\r\n# first read all the files in a folder (Give the path to folder (....), ending /)\r\nsetwd('./htyyt/')\r\n\r\n#filenames\r\nfiles <- list.files( pattern=\"*csv$\")\r\n\r\n#read only columns that we want\r\ntemp <- lapply(files, function(x) data.table::fread(x, select = c(\"Screenid\")))\r\ndata <- data.table::rbindlist(temp, fill = T) #make a df\r\n\r\n\r\n#remove the duplicates userids\r\nn_original <- data[!duplicated(data$Screenid), ]\r\n\r\n#sample 100K with probablity sample without replacements\r\nset.seed(786)\r\nrand_data <- data.frame(Screenid= sample( bit64::as.integer64(n_original$Screenid), nrow(n_original), replace = F), stringsAsFactors = F)\r\n\r\n\r\n\r\n# Here I want 20000 rows per file until last row is reached\r\n# this method forces the execution from innermost, outer () opttional\r\ngroups <- (split(rand_data, (seq(nrow(rand_data))-1) %/% 2537))\r\npeople <- c(\"1_user\", \"2_user\", \"3_user\", \"4_user\", \"5_user\")\r\n\r\n#loop through end of each split, and write file with i\r\nfor (i in seq_along(groups)) {\r\n readr::write_csv(groups[[i]], paste0(\"output_\", people[i], \".csv\")) \r\n}\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "25c2f24a04adc4bd2e4ea8739a39b4ac088ef9f2", "size": 1499, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make_distributable_csv_files.r", "max_stars_repo_name": "opendatasurgeon/covid19-random_scripts", "max_stars_repo_head_hexsha": "0c4696b3544dc0917846a1eb4f60d9e2f722d361", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/make_distributable_csv_files.r", "max_issues_repo_name": "opendatasurgeon/covid19-random_scripts", "max_issues_repo_head_hexsha": "0c4696b3544dc0917846a1eb4f60d9e2f722d361", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/make_distributable_csv_files.r", "max_forks_repo_name": "opendatasurgeon/covid19-random_scripts", "max_forks_repo_head_hexsha": "0c4696b3544dc0917846a1eb4f60d9e2f722d361", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-05-22T14:21:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-01T08:51:13.000Z", "avg_line_length": 27.2545454545, "max_line_length": 138, "alphanum_fraction": 0.6684456304, "num_tokens": 411, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3181641180781101}} {"text": "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n\n# dddddddd \n# d::::::d iiii RRRRRRRRRRRRRRRRR \n# d::::::d i::::i R::::::::::::::::R \n# d::::::d iiii R::::::RRRRRR:::::R \n# d:::::d RR:::::R R:::::R\n# ddddddddd:::::d iiiiiiivvvvvvv vvvvvvvaaaaaaaaaaaaa R::::R R:::::R\n# dd::::::::::::::d i:::::i v:::::v v:::::v a::::::::::::a R::::R R:::::R\n# d::::::::::::::::d i::::i v:::::v v:::::v aaaaaaaaa:::::a R::::RRRRRR:::::R \n# d:::::::ddddd:::::d i::::i v:::::v v:::::v a::::a R:::::::::::::RR \n# d::::::d d:::::d i::::i v:::::v v:::::v aaaaaaa:::::a R::::RRRRRR:::::R \n# d:::::d d:::::d i::::i v:::::v v:::::v aa::::::::::::a R::::R R:::::R\n# d:::::d d:::::d i::::i v:::::v:::::v a::::aaaa::::::a R::::R R:::::R\n# d::::::ddddd::::::ddi::::::i v:::::::v a::::a a:::::a RR:::::R R:::::R\n# d:::::::::::::::::di::::::i v:::::v a:::::aaaa::::::a R::::::R R:::::R\n# d:::::::::ddd::::di::::::i v:::v a::::::::::aa:::aR::::::R R:::::R\n# ddddddddd dddddiiiiiiii vvv aaaaaaaaaa aaaaRRRRRRRR RRRRRRR\n\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n\n# # # load utilities script\n# # # # # # # # # # # # # # # # # # # # # # # # # # #\nsource('utils.r')\n\n# # # Initialize model parameters\n# # # # # # # # # # # # # # # # # # # # # # # # # # #\nmodel <- list(num_blocks = 20,\n\t\t\t num_inits = 5,\n\t\t\t wts_range = 1,\n\t\t\t num_hids = 3,\n\t\t\t learning_rate = 0.15,\n\t\t\t beta_val = 5,\n\t\t\t out_rule = 'sigmoid') # anything else runs linear\n\n# # # The demo below trains the DIVA model on the Shepard, Hovland, \n# # # and Jenkins' elemental types (1961) plus one 4-class problem.\n# # # This demo can be used as a template for your own problem.\n# # # # # # # # # # # # # # # # # # # # # # # # # # #\n\n# # # create training results \ntraining = matrix(rep(0, model$num_blocks * 7), ncol = 7)\n\n# # # initialize model and run it on each SHJ category structure\nfor (category_type in 1:7) { \n\n # # # get shj stimuli\n cases <- demo_cats(category_type)\n model$inputs <- cases$inputs\n model$labels <- cases$labels\n\n # # # train model\n result <- run_diva(model)\n\n# # # add result to training matrix\ntraining[,category_type] <- result$training\n\n}\n\n# # # display results\nprint(training)\ntrain_plot(training)\nsave.image('diva_run.rdata')\n\nwarnings()\n\n\n", "meta": {"hexsha": "81ac62829059805090531e7b2e3b10c6f17da70c", "size": 2928, "ext": "r", "lang": "R", "max_stars_repo_path": "init.r", "max_stars_repo_name": "ghonk/divaR", "max_stars_repo_head_hexsha": "55a0d2f45e71fdb6e2524f128330e40b8dc8f16c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "init.r", "max_issues_repo_name": "ghonk/divaR", "max_issues_repo_head_hexsha": "55a0d2f45e71fdb6e2524f128330e40b8dc8f16c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "init.r", "max_forks_repo_name": "ghonk/divaR", "max_forks_repo_head_hexsha": "55a0d2f45e71fdb6e2524f128330e40b8dc8f16c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.0588235294, "max_line_length": 91, "alphanum_fraction": 0.3490437158, "num_tokens": 1025, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3181641099207704}} {"text": "setwd(\"C:/Users/Bigghost/Documents/GitHub/AutomatedOmicsCompendiumPreparationPipeline/utilities/imputation\")\ndata = read.csv(\"data0.0_50.csv\",header=F)\n\nlibrary(snow)\nlibrary(missForest)\nlibrary(doParallel)\ncl <- makeCluster(8,type=\"SOCK\")\nregisterDoParallel(cl)\nrf_result = missForest(data,verbose = T,parallelize ='variables')\n\n", "meta": {"hexsha": "6dd401c80619b2bb9654fa1a5e23c06f997f7177", "size": 330, "ext": "r", "lang": "R", "max_stars_repo_path": "utilities/imputation/test_rf.r", "max_stars_repo_name": "g-simmons/OCB", "max_stars_repo_head_hexsha": "217d9b8eaaefad97c52741b3eac9c18ae1def51a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "utilities/imputation/test_rf.r", "max_issues_repo_name": "g-simmons/OCB", "max_issues_repo_head_hexsha": "217d9b8eaaefad97c52741b3eac9c18ae1def51a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utilities/imputation/test_rf.r", "max_forks_repo_name": "g-simmons/OCB", "max_forks_repo_head_hexsha": "217d9b8eaaefad97c52741b3eac9c18ae1def51a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.0, "max_line_length": 108, "alphanum_fraction": 0.8, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31808773331795054}} {"text": "library(tidyverse)\nlibrary(stringr)\n\nresults_table <-function(yhat, yhat_rounded, y){\n logical_test <- matrix(0L, dim(y)[1], dim(y)[2])\n logical_test <- ifelse(y == yhat_rounded, \"True\", \"False\")\n \n yhat = as.data.frame(yhat)\n yhat_rounded = as.data.frame(yhat_rounded)\n y = as.data.frame(as.matrix(y)) # y is numeric\n logical_test = as.data.frame(logical_test)\n \n yhat %>%\n cbind(yhat_rounded) %>%\n cbind(y) %>%\n cbind(logical_test) -> result_df\n colnames(result_df) <- c(\"yhat\", \"yhat_rounded\", \"y\", \"logical_test\")\n \n View(result_df)\n\n sum(str_count(result_df$logical_test, \"True\")) -> true_count\n sum(str_count(result_df$logical_test, \"False\")) -> false_count\n \n true_count <- as.double(true_count)\n false_count <-as.double(false_count)\n \n success_perc <- true_count/(true_count + false_count)*100\n \n print(paste0(\"Percentage of rows successfully trained: \", \n as.character(format(round(success_perc, 2), nsmall = 2)), \"%\"))\n \n return(result_df)\n \n}\n", "meta": {"hexsha": "32e02ac43d77ec79cbcff4ef9bba51f7fae2f10c", "size": 1001, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/results_output.r", "max_stars_repo_name": "Fergal-Stapleton/perceptron", "max_stars_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/results_output.r", "max_issues_repo_name": "Fergal-Stapleton/perceptron", "max_issues_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/results_output.r", "max_forks_repo_name": "Fergal-Stapleton/perceptron", "max_forks_repo_head_hexsha": "168bc279e4fe32671cc9fa619e192af8f66bd8fd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6, "max_line_length": 78, "alphanum_fraction": 0.6733266733, "num_tokens": 286, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3180877333179505}} {"text": "# download data 'neuroblastoma' and plot profile.id = 4, chromosome = 4, x=position, y=logratio\nif(!require(neuroblastoma)) {\n install.packages(\"neuroblastoma\")\n}\n\n# import and load neuroblastoma dataset\nlibrary('neuroblastoma')\ndata('neuroblastoma')\n\n# using np as neuro.profiles for profile data\nnp = neuroblastoma$profiles\nxy = np[np$profile.id == 4 & np$chromosome == 2, ]\nx = xy$position\ny = xy$logratio\n\nsvg('Easy/task1a.svg')\nplot(x, y, type='l', main=\"Neuroblastoma Data\", xlab='position in base pair', ylab='normalized logratio of the probe')\ngrid()\nlegend(x=x[5], y=-0.5,legend=\"position vs logpoint data\", col=\"black\", lty=1, cex=0.8, bg='lightblue')\ndev.off()\n", "meta": {"hexsha": "6c21945104cead8f9adebf930115ad2e4d4a6575", "size": 675, "ext": "r", "lang": "R", "max_stars_repo_path": "Easy/task1a.r", "max_stars_repo_name": "avinal/rstats-test", "max_stars_repo_head_hexsha": "83d8b1bdcd1f0f34291ccafec643a0b41ad9a0e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Easy/task1a.r", "max_issues_repo_name": "avinal/rstats-test", "max_issues_repo_head_hexsha": "83d8b1bdcd1f0f34291ccafec643a0b41ad9a0e2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Easy/task1a.r", "max_forks_repo_name": "avinal/rstats-test", "max_forks_repo_head_hexsha": "83d8b1bdcd1f0f34291ccafec643a0b41ad9a0e2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.1428571429, "max_line_length": 118, "alphanum_fraction": 0.7125925926, "num_tokens": 213, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3180877333179505}} {"text": "# (13) #########\n\nprint(\"Not in parallel\")\nnt<-10\nmymat<-matrix(nrow=nt,ncol=nt)\nmymat[1:nt,]<-0\nbsum=0\nbsum<-for(ijk in 1:nt ) { \n\tset.seed(ijk) \n\tmymat[,ijk]<-rnorm(mymat[,ijk])\n}\nprint(bsum)\nprint(mymat)\n#readline(prompt = \"NEXT>\")\n\n", "meta": {"hexsha": "1cc2bfd4f08d24869c9452ce908ad8d0139e0c49", "size": 238, "ext": "r", "lang": "R", "max_stars_repo_path": "r/semantics/13.r", "max_stars_repo_name": "timkphd/examples", "max_stars_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-11-01T00:29:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T19:09:47.000Z", "max_issues_repo_path": "r/semantics/13.r", "max_issues_repo_name": "timkphd/examples", "max_issues_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-09T01:59:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T01:59:47.000Z", "max_forks_repo_path": "r/semantics/13.r", "max_forks_repo_name": "timkphd/examples", "max_forks_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.875, "max_line_length": 32, "alphanum_fraction": 0.6050420168, "num_tokens": 104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3180877333179505}} {"text": "library(tidyverse)\nlibrary(leaflet)\n\nG_PROJ4DEF <- '+proj=lcc +lat_1=49 +lat_2=77 +lat_0=49 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs'\nG_CRS_CODE <- 'EPSG:3978'\n\n\n\n#####################3\n#\n# This is supposed to let me color the points on the leaflet map but it is not working \n#\npal <- leaflet::colorNumeric(viridis_pal(option = \"C\")(10), domain = 1:300)\n\n\n\n#####################################333\n#\n# Aggregate points and resize for the leaflet plot\n#\naggregate_points <- function(data_points, round_digit = 0, size_adjust = 10000){\n data_points %>% \n select(Long, Lat, City) %>% \n mutate_if(is.numeric, round, round_digit) %>% \n group_by(Long, Lat) %>% \n summarise(n = n(), City = paste0(unique(City), collapse = \"; \")) %>% \n #count(Long, Lat, sort = T) %>% \n mutate(Size = sqrt(n)*size_adjust) %>%\n mutate(Popup = paste0(City, \"\\nN=\",n)) \n}\n\n\n\nget_prov_shp <- function(){\n canada_HR_shp <- read_sf(file.path(\"HR000b11a_e_Oct2013_simp.geojson\")) %>% \n st_transform(canada_shp, crs = G_PROJ4DEF)\n}\n\nget_hr_shp <- function(){\n shp <- read_sf(file.path(\"HR000b11a_e_Oct2013_simp.geojson\")) %>% \n st_transform(shp, crs = G_PROJ4DEF)\n}\nget_pr_shp <- function(){\n shp <- read_sf(file.path(\"canada_pt_sim.geojson\")) %>% \n st_transform(shp, crs = G_PROJ4DEF)\n}\n\n\ncanada_HR_shp %>% \n ggplot() +\n geom_sf() +\n theme_map()\n\n\n\ncanada_prov_shp <- read_sf(file.path(\"canada_pt_sim.geojson\")) %>% \n st_transform(canada_shp, crs = G_PROJ4DEF)\n\n\ncanada_prov_shp %>% \n ggplot() +\n geom_sf(alpha = 0, size = 1.25, color = \"black\") +\n theme_map()\n\n\n\n\n\n\n##########################333\n#\n# Get a BASE leaflet map\n#\nget_leaflet_map <- function(){\n\n bnds = c(-7786476.885838887,\n -5153821.09213678,\n 7148753.233541353,\n 7928343.534071138\n )\n \n \n res <- c(\n 38364.660062653464, \n 22489.62831258996, \n 13229.193125052918,\n 7937.5158750317505,\n 4630.2175937685215,\n 2645.8386250105837,\n 1587.5031750063501,\n 926.0435187537042,\n 529.1677250021168,\n 317.50063500127004,\n 185.20870375074085,\n 111.12522225044451,\n 66.1459656252646,\n 38.36466006265346,\n 22.48962831258996,\n 13.229193125052918,\n 7.9375158750317505,\n 4.6302175937685215,\n 2.6458386250105836,\n 1.5875031750063502,\n 0.92604351875370428,\n 0.52916772500211673,\n 0.31750063500127002,\n 0.18520870375074083,\n 0.11112522225044451,\n 0.066145965625264591\n )\n \n orgn <- c(-34655800, 39310000)\n\n \n urlTemplate = \"https://geoappext.nrcan.gc.ca/arcgis/rest/services/BaseMaps/CBMT3978/MapServer/tile/{z}/{y}/{x}?m4h=t\"\n \n tile_attrib <- \"NRCAN\"\n \n epsg3978 <- leafletCRS(\n crsClass = \"L.Proj.CRS\", \n code = G_CRS_CODE,\n proj4def = G_PROJ4DEF,\n origin = orgn,\n bounds = bnds,\n resolutions = res\n )\n \n \n m <- leaflet(options = leafletOptions(worldCopyJump = F, \n crs = epsg3978,\n minZoom = 2, maxZoom = 17)\n ) %>%\n addTiles(urlTemplate = urlTemplate,#x\n attribution = tile_attrib,\n options = tileOptions(continuousWorld = F)\n ) %>% \n # addMarkers(lng = -75.705793, \n # lat = 45.345134,\n # popup = \"My office.\"\n # ) %>%\n setView(lng = -1*(96+(40/60)+(35/3600)), \n lat = 62+(24/60),\n zoom = 3\n ) \n \n \n m\n}\n", "meta": {"hexsha": "21b49ed5b673d146ad57f8f4e376586d4ff7219c", "size": 3414, "ext": "r", "lang": "R", "max_stars_repo_path": "leaftlet_nrc.r", "max_stars_repo_name": "hswerdfe/covid-19-canada-dash", "max_stars_repo_head_hexsha": "e87dce662f919526b73bbd8ce03601c981849d53", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "leaftlet_nrc.r", "max_issues_repo_name": "hswerdfe/covid-19-canada-dash", "max_issues_repo_head_hexsha": "e87dce662f919526b73bbd8ce03601c981849d53", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "leaftlet_nrc.r", "max_forks_repo_name": "hswerdfe/covid-19-canada-dash", "max_forks_repo_head_hexsha": "e87dce662f919526b73bbd8ce03601c981849d53", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9127516779, "max_line_length": 124, "alphanum_fraction": 0.5978324546, "num_tokens": 1178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3180877333179505}} {"text": "suppressMessages(library(fmlr))\n\nif (fml_mpi()){\n m = 2\n n = 3\n g = grid()\n x = mpimat(g, m, n)\n \n source(\"internals/common.r\")\n \n cmp(x$dim(), c(m, n))\n \n \n \n # ------------------------------------------------------------------------------\n # fill\n # ------------------------------------------------------------------------------\n \n x$fill_val(1)\n test = x$to_robj()\n truth = matrix(1, m, n)\n cmp(test, truth)\n \n x$fill_eye()\n test = x$to_robj()\n truth = diag(1, m, n)\n cmp(test, truth)\n \n x$fill_zero()\n test = x$to_robj()\n truth = matrix(0, m, n)\n cmp(test, truth)\n \n x$fill_val(3)\n test = x$to_robj()\n truth = matrix(3, m, n)\n cmp(test, truth)\n \n x$fill_linspace(1, m*n)\n test = x$to_robj()\n truth = matrix(1:(m*n), m, n)\n cmp(test, truth)\n \n \n \n # ------------------------------------------------------------------------------\n # misc\n # ------------------------------------------------------------------------------\n \n # TODO\n # x$fill_linspace(1, m*n)\n # x$rev_rows()\n # test = x$to_robj()\n # truth = matrix(1:(m*n), m, n)[m:1, ]\n # cmp(test, truth)\n # \n # x$rev_cols()\n # test = x$to_robj()\n # truth = matrix(1:(m*n), m, n)[m:1, n:1]\n # cmp(test, truth)\n \n x$fill_linspace(1, 6)\n x$scale(1000)\n test = x$to_robj()\n truth = matrix(1:(m*n) * 1000, m, n)\n cmp(test, truth)\n} # end if (fml_mpi())\n", "meta": {"hexsha": "7d57caf307d3b05bef1f231063e1a0ed78bcc5b3", "size": 1364, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/mpimat.r", "max_stars_repo_name": "fml-fam/fmlr", "max_stars_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-02-06T21:06:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-23T22:34:08.000Z", "max_issues_repo_path": "tests/mpimat.r", "max_issues_repo_name": "wrathematics/fmlr", "max_issues_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-02-19T17:27:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-09T00:30:36.000Z", "max_forks_repo_path": "tests/mpimat.r", "max_forks_repo_name": "wrathematics/fmlr", "max_forks_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.0588235294, "max_line_length": 82, "alphanum_fraction": 0.4017595308, "num_tokens": 452, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704502361149, "lm_q2_score": 0.5736784074525098, "lm_q1q2_score": 0.31808772487093046}} {"text": "#!/usr/bin/env Rscript\n\nargs <- commandArgs(trailingOnly=TRUE)\n\nif(length(args) == 3) {\n\n stopifnot(file.exists(args[2]))\n stopifnot(file.exists(args[3]))\n\n library(DSS)\n require(bsseq)\n\n s1_fn <- args[2]\n s2_fn <- args[3]\n\n dmr_fn <- paste(args[1], \"DMRs.txt\", sep='.')\n dml_fn <- paste(args[1], \"DMLs.txt\", sep='.')\n\n s1 = read.table(s1_fn, header=TRUE)\n s2 = read.table(s2_fn, header=TRUE)\n BSobj = makeBSseqData(list(s1, s2), c('s1', 's2'))\n\n dmlTest = DMLtest(BSobj, group1=c('s1'), group2=c('s2'), smoothing=TRUE)\n dmrs = callDMR(dmlTest, p.threshold=0.05)\n\n write.table(dmrs, file=dmr_fn, quote=F, sep='\\t')\n\n dmls = callDML(dmlTest, p.threshold=1)\n write.table(dmls, file=dml_fn, quote=F, sep='\\t')\n\n}\n\nif(length(args) != 3) {\n cat(\"usage: dss.r \\n\")\n}\n", "meta": {"hexsha": "7dbfeec571feb7116209666a14660709f52a842a", "size": 861, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/dss_2sample.r", "max_stars_repo_name": "adamewing/methylartist", "max_stars_repo_head_hexsha": "6f23f495a47913c061888f30e569f07d12026bc0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28, "max_stars_repo_stars_event_min_datetime": "2021-03-09T19:32:04.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T19:36:42.000Z", "max_issues_repo_path": "scripts/dss_2sample.r", "max_issues_repo_name": "adamewing/tmnt", "max_issues_repo_head_hexsha": "6f23f495a47913c061888f30e569f07d12026bc0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2021-06-21T22:36:10.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-23T14:10:21.000Z", "max_forks_repo_path": "scripts/dss_2sample.r", "max_forks_repo_name": "adamewing/tmnt", "max_forks_repo_head_hexsha": "6f23f495a47913c061888f30e569f07d12026bc0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.9166666667, "max_line_length": 76, "alphanum_fraction": 0.6120789779, "num_tokens": 307, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31806327582762534}} {"text": "\"\"\"\n\n \n \n \n \n \n \n 3\n n0\n \n \n 1\n n1\n \n \n 1\n n2\n \n \n 1\n n3\n \n \n \n \n \n \n \n \n \n\"\"\"", "meta": {"hexsha": "ac4b8789b6225c6129ce124b5a18bf124ab09378", "size": 984, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/SO_isomorphism_bug.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "sandbox/SO_isomorphism_bug.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sandbox/SO_isomorphism_bug.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.1142857143, "max_line_length": 69, "alphanum_fraction": 0.612804878, "num_tokens": 355, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31806326838968485}} {"text": "#Pacote para visualiza\u00e7\u00e3o de dados no RStudio\nlibrary(ggplot2)\n# base de dados - dataset -\nmpg #dados dos carros\n?mpg #info\nView(mpg)\nView(read.csv(\"tabela_fipe.csv\",header = TRUE,encoding = \"UTF-8\"))\n#Gerando grafico\nggplot(mpg, aes(displ,cty))+geom_point()", "meta": {"hexsha": "2c44871d886abbacc4b6d9480aae091586ddfec9", "size": 258, "ext": "r", "lang": "R", "max_stars_repo_path": "analise_carros.r", "max_stars_repo_name": "maledicente/codes-r", "max_stars_repo_head_hexsha": "a3ee9177d3f86e488121e77094202d79482a1b4b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analise_carros.r", "max_issues_repo_name": "maledicente/codes-r", "max_issues_repo_head_hexsha": "a3ee9177d3f86e488121e77094202d79482a1b4b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analise_carros.r", "max_forks_repo_name": "maledicente/codes-r", "max_forks_repo_head_hexsha": "a3ee9177d3f86e488121e77094202d79482a1b4b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6666666667, "max_line_length": 66, "alphanum_fraction": 0.7441860465, "num_tokens": 86, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31806326838968485}} {"text": "library(xlsx)\n\n library(RColorBrewer)\n \n #options(digits=2)\n ##\u8bfb\u53d6t_for\u7684\u5927\u5c0f\nk1<-read.xlsx(\"t_for.xls\", sheetName = \"0.2\", header = TRUE)\nk2<-read.xlsx(\"t_for.xls\", sheetName = \"0.3\", header = TRUE)\nk3<-read.xlsx(\"t_for.xls\", sheetName = \"0.4\", header = TRUE)\nk4<-read.xlsx(\"t_for.xls\", sheetName = \"0.5\", header = TRUE)\n\nv<-4.66e-13 #\u5f2f\u6708\u9762\u4f53\u79ef\u5927\u5c0f\n\nq<-c(2.5e-14, 4.5e-13, 9e-13, 3e-12) #\u6d41\u91cf\u4ece1.5\u5230180\n\nk<-c(0.2,0.3,0.4,0.5) #\u5360\u7a7a\u6bd4\n\npchc<-c(19,22,23,24)\n\nmycolors<-c(\"red\",\"blue\", \"darkgreen\", \"yellow3\")\n\nh<-0.3e-3 #\u5f2f\u6708\u9762\u53d8\u5f62\u5927\u5c0f\n\nduty<-1-k\n\npar(mfrow=c(2,2), mar=c(4,2,2,2), oma=c(2,2,2,2))\n\n\n#######\u5360\u7a7a\u6bd40.2####\nplot(k1[,1], (v+(duty[1]*q[1]))/(k1[,1]*k1[,2]^2), log=\"x\",lwd=2, xlab=expression(log(italic(f[\"v\"])) (Hz)), ylab=expression(italic(F)), main=\"k=0.2\", col=0, pch=pchc[1], ylim=c(0,8e-14))\n\nlines(lowess(k1[,1], (v+(duty[1]*q[1]))/(k1[,1]*k1[,2]^2)), col=mycolors[1], lwd=2.5,lty=2)\n\nfor(i in 1:3){\n\npoints(k1[,1], (v+(duty[1]*q[i+1]))/(k1[,1]*k1[,i+2]^2),pch=pchc[i+1], lwd=2, col=0)\nlines(lowess(k1[,1], (v+(duty[1]*q[i+1]))/(k1[,1]*k1[,i+2]^2)), lwd=2.5, col=mycolors[i+1], lty=2)\n}\n\nlegend(\"topleft\",c(\"1.5nl/min\", \"27nl/min\",\"54nl/min\",\"180nl/min\"), type=\"b\", col=mycolors, pch=pchc,lty=2, lwd=2, bty=\"n\")\n\nabline(v=250, col=\"red\", lwd=2, lty=3)\n#abline(h= (v+(duty[1]*q[2]))/(k1[11,1]*k1[11,3]^2), col=\"blue\", lwd=2, lty=3)\n#abline(h= (v+(duty[1]*q[3]))/(k1[11,1]*k1[11,4]^2), col=\"darkgreen\", lwd=2,lty=3)\n#abline(h= (v+(duty[1]*q[4]))/(k1[11,1]*k1[11,5]^2), col=\"yellow3\", lty=3,lwd=2)\ntext(200, 2e-14,\"250Hz\", col=\"red\", font=2, cex=1)\n#text(20, 1.2e-14,(v+(duty[1]*q[2]))/(k1[11,1]*k1[11,3]^2),font=2, col=\"blue\", cex=1)\n#text(20, 1.4e-14, (v+(duty[1]*q[4]))/(k1[11,1]*k1[11,5]^2), font=2, col=\"darkgreen\", cex=1)\n#text(20, 1.6e-14,(v+(duty[1]*q[4]))/(k1[11,1]*k1[11,5]^2) ,font=2,col=\"yellow3\", cex=1)\n\n#####\u5360\u7a7a\u6bd40.3####\nplot(k2[,1], (v+(duty[2]*q[1]))/(k2[,1]*k2[,2]^2), log=\"x\",lwd=2, xlab=expression(log(italic(f[\"v\"])) (Hz)), ylab=expression(italic(F)), main=\"k=0.3\", col=0, pch=pchc[1], ylim=c(0,3e-14))\n\nlines(lowess(k2[,1], (v+(duty[2]*q[1]))/(k2[,1]*k2[,2]^2)), col=mycolors[1], lwd=2.5,lty=2)\n\nfor(i in 1:3){\n\npoints(k2[,1], (v+(duty[2]*q[i+1]))/(k2[,1]*k2[,i+2]^2),pch=pchc[i+1], lwd=2, col=0)\nlines(lowess(k2[,1], (v+(duty[2]*q[i+1]))/(k2[,1]*k2[,i+2]^2)), lwd=2.5, col=mycolors[i+1], lty=2)\n}\n\nlegend(\"topleft\",c(\"1.5nl/min\", \"27nl/min\",\"54nl/min\",\"180nl/min\"), type=\"b\",col=mycolors, pch=pchc, lwd=2, bty=\"n\")\n\nabline(v=250, col=\"red\", lwd=2,lty=3)\nabline(v=300, col=\"blue\", lwd=2,lty=3)\n#abline(h= (v+(duty[2]*q[2]))/(k2[11,1]*k2[11,3]^2), col=\"blue\", lwd=2, lty=3)\n#abline(h= (v+(duty[2]*q[3]))/(k2[11,1]*k2[11,4]^2), col=\"darkgreen\", lwd=2,lty=3)\n#abline(h= (v+(duty[2]*q[4]))/(k2[11,1]*k2[11,5]^2), col=\"yellow3\", lty=3,lwd=2)\n\ntext(100, 1e-14,\"250Hz\",col=\"red\", font=2, cex=1)\ntext(200, 1.5e-14,\"300Hz\",font=2, col=\"blue\", cex=1)\n#text(20, 1.4e-14,(v+(duty[2]*q[3]))/(k2[11,1]*k2[11,4]^2) ,font=2, col=\"darkgreen\", cex=1)\n#text(20, 1.6e-14,(v+(duty[2]*q[4]))/(k2[11,1]*k2[11,5]^2),font=2,col=\"yellow3\", cex=1)\n\n###\u5360\u7a7a\u6bd40.4###\nplot(k3[,1], (v+(duty[3]*q[1]))/(k3[,1]*k3[,2]^2), log=\"x\", lwd=2, xlab=expression(log(italic(f[\"v\"])) (Hz)), ylab=expression(italic(F)), main=\"k=0.4\", col=0, pch=pchc[1], ylim=c(0,3e-14))\n\nlines(lowess(k3[,1], (v+(duty[3]*q[1]))/(k3[,1]*k3[,2]^2)), col=mycolors[1], lwd=2.5,lty=2)\n\nfor(i in 1:3){\n\npoints(k3[,1], (v+(duty[3]*q[i+1]))/(k3[,1]*k3[,i+2]^2),pch=pchc[i+1], lwd=2, col=0)\nlines(lowess(k3[,1], (v+(duty[3]*q[i+1]))/(k3[,1]*k3[,i+2]^2)), lwd=2.5, col=mycolors[i+1], lty=2)\n}\n\nlegend(\"topleft\",c(\"1.5nl/min\", \"27nl/min\",\"54nl/min\",\"180nl/min\"), type=\"b\",col=mycolors, pch=pchc, lwd=2, bty=\"n\")\n\nabline(v=250, col=\"red\", lwd=2,lty=3)\nabline(v=500, col=\"blue\", lwd=2,lty=3)\n#abline(h= (v+(duty[3]*q[2]))/(k3[11,1]*k3[11,3]^2), col=\"blue\", lwd=2, lty=3)\n#abline(h= (v+(duty[3]*q[3]))/(k3[11,1]*k3[11,4]^2), col=\"darkgreen\", lwd=2,lty=3)\n#abline(h= (v+(duty[3]*q[4]))/(k3[11,1]*k3[11,5]^2), col=\"yellow3\", lty=3,lwd=2)\ntext(100, 1e-14,\"250Hz\", col=\"red\", font=2, cex=1)\ntext(200, 1.5e-14,\"500Hz\",font=2, col=\"blue\", cex=1)\n#text(20, 1.4e-14,(v+(duty[3]*q[3]))/(k3[11,1]*k3[11,4]^2),font=2, col=\"darkgreen\", cex=1)\n#text(20, 1.6e-14,(v+(duty[3]*q[4]))/(k3[11,1]*k3[11,5]^2),font=2,col=\"yellow3\", cex=1)\n\n\n\n####\u5360\u7a7a\u6bd40.5####\nplot(k4[,1], (v+(duty[4]*q[1]))/(k4[,1]*k4[,2]^2),log=\"x\", lwd=2, xlab=expression(log(italic(f[\"v\"])) (Hz)), ylab=expression(italic(F)), main=\"k=0.5\", col=0, pch=pchc[1], ylim=c(0,3e-14))\n\nlines(lowess(k3[,1], (v+(duty[3]*q[1]))/(k3[,1]*k3[,2]^2)), col=mycolors[1], lwd=2.5,lty=2)\n\nfor(i in 1:3){\n\npoints(k3[,1], (v+(duty[3]*q[i+1]))/(k3[,1]*k3[,i+2]^2),pch=pchc[i+1], lwd=2, col=0)\nlines(lowess(k4[,1], (v+(duty[4]*q[i+1]))/(k4[,1]*k4[,i+2]^2)), lwd=2.5, col=mycolors[i+1], lty=2)\n}\nlegend(\"topleft\",c(\"1.5nl/min\", \"27nl/min\",\"54nl/min\",\"180nl/min\"), type=\"b\",col=mycolors, pch=pchc, lwd=2, bty=\"n\")\n\nabline(v=300, col=\"red\", lwd=2,lty=3)\nabline(v=900, col=\"blue\", lwd=2,lty=3)\n#abline(h= (v+(duty[4]*q[2]))/(k4[11,1]*k4[11,3]^2), col=\"blue\", lwd=2, lty=3)\n#abline(h= (v+(duty[4]*q[3]))/(k4[11,1]*k4[11,4]^2), col=\"darkgreen\", lwd=2,lty=3)\n#abline(h= (v+(duty[4]*q[4]))/(k4[11,1]*k4[11,5]^2), col=\"yellow3\", lty=3,lwd=2)\ntext(50, 1e-14,\"300Hz\", col=\"red\", font=2, cex=1)\ntext(200, 1.5e-14,\"900Hz\",font=2, col=\"blue\", cex=1)\n#text(20, 1.4e-14,(v+(duty[4]*q[3]))/(k4[11,1]*k4[11,4]^2),font=2, col=\"darkgreen\", cex=1)\n#text(20, 1.6e-14,(v+(duty[4]*q[4]))/(k4[11,1]*k4[11,5]^2),font=2,col=\"yellow3\", cex=1)\n\n#mtext(\"External forces with Q & K in fv & tfor\"\uff0cside=1, line=0, col=\"red\", cex=1, adj=1,outer=TRUE)", "meta": {"hexsha": "133ee4b313f9ed02527a4c5353139a11058d895d", "size": 5583, "ext": "r", "lang": "R", "max_stars_repo_path": "volume-chap3/force_3.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "volume-chap3/force_3.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "volume-chap3/force_3.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.762295082, "max_line_length": 189, "alphanum_fraction": 0.5640336737, "num_tokens": 2983, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31806326838968485}} {"text": "#!/usr/bin/env Rscript\n\n####################\n#\n# Parameters:\n# 1 - File to scale\n# 2 - Output file to store new dataset\n# 3 - Independent variable\n#\n###################\n\nargs <- commandArgs(trailingOnly = TRUE)\ntraining = read.csv(args[1], check.names=FALSE)\n\n# Saving mu and sigma\nget_scale <- function (x) {\n k = scale(x)\n return(attr(k,\"scaled:scale\"))\n}\nget_center <- function (x) {\n k = scale(x)\n return(attr(k,\"scaled:center\"))\n}\n\nscale_values <- data.frame(apply(training,2, get_scale))\ncenter_values <- data.frame(apply(training,2, get_center))\n\ntraining_scaled=scale(training)\n\n#Shift to the right in order to avoid negative values\ntraining_scaled <- training_scaled + 300\n\n#Replacing original values for some columns\nindependent = args[5]\ntraining_scaled[,independent] <- training[,independent]\n\nif(\"timestamp\" %in% colnames(training_scaled)){\n training_scaled[,\"timestamp\"] <- training$timestamp\n}\nif(\"Core_1CPU\" %in% colnames(training_scaled)){\n training_scaled[,\"Core_1CPU\"] <- training$Core_1CPU\n}\nif(\"Core_2CPU\" %in% colnames(training_scaled)){\n training_scaled[,\"Core_2CPU\"] <- training$Core_2CPU\n}\nif(\"numSockets\" %in% colnames(training_scaled)){\n training_scaled[,\"numSockets\"] <- training$numSockets\n}\n\n# Remove NaN\ntraining_scaled[is.na(training_scaled)] <- 0\n\n#training_scaled <- round(training_scaled,6)\n\nwrite.csv(training_scaled, file = args[2],row.names=FALSE)\nsaveRDS(center_values, file = args[3])\nsaveRDS(scale_values, file = args[4])\n\n", "meta": {"hexsha": "e8e7dbb4dac1d18dc9a5e1bbe83d4f2db0240810", "size": 1472, "ext": "r", "lang": "R", "max_stars_repo_path": "prototype/src/main/resources/scaling_data_training.r", "max_stars_repo_name": "maurocanuto/power-modelling", "max_stars_repo_head_hexsha": "563553a89a9e3ac78f63c303c025ddbfd389cbe1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "prototype/src/main/resources/scaling_data_training.r", "max_issues_repo_name": "maurocanuto/power-modelling", "max_issues_repo_head_hexsha": "563553a89a9e3ac78f63c303c025ddbfd389cbe1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "prototype/src/main/resources/scaling_data_training.r", "max_forks_repo_name": "maurocanuto/power-modelling", "max_forks_repo_head_hexsha": "563553a89a9e3ac78f63c303c025ddbfd389cbe1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.9491525424, "max_line_length": 58, "alphanum_fraction": 0.7126358696, "num_tokens": 390, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3180457648615998}} {"text": "#' @export\r\n Association.plot <- function (data, hydro, strata.group, species = NULL, subset, plot = TRUE, ...) {\r\n attach(data)\r\n if (!missing(subset)) {\r\n data <- data[subset, ]\r\n }\r\n detach(\"data\")\r\n strata.use <- is.element(data$Strata, strata.group$Strata)\r\n nHy = hydro\r\n hydro <- (data[hydro])[strata.use, ]\r\n if (is.null(species)) \r\n species <- rep.int(1, length(hydro))\r\n else species <- (data[species])[strata.use, ]\r\n \r\n Strata <- data$Strata[strata.use]\r\n if (is.null(species)) \r\n species <- rep.int(1, length(hydro))\r\n tempy <- cbind(hydro, species, Strata)\r\n if (any(is.na(hydro))) \r\n tempy <- (na.omit(as.data.frame(tempy)))\r\n hydro <- as.numeric(tempy[, 1])\r\n species <- as.numeric(as.vector(tempy[, 2]))\r\n Strata <- as.numeric(tempy[, 3])\r\n WH <- strata.group$NH\r\n na.strata <- match(strata.group$Strata, unique(Strata))\r\n WH[is.na(na.strata)] <- NA\r\n IWH <- cbind(strata.group$Strata, WH)\r\n IWH <- (na.omit(as.data.frame(IWH)))\r\n WH <- as.numeric(IWH$WH)\r\n IWH <- IWH[[1]]\r\n WH <- (WH/sum(WH))\r\n yhi <- split(species, Strata)\r\n nh <- as.vector(sapply(yhi, length))\r\n yst <- sum(WH * as.vector(sapply(yhi, mean)))\r\n sort.hydro <- unique(sort(hydro))\r\n wh <- WH/(nh * yst)\r\n Whi <- rep(0, length(species))\r\n for (i in seq(along = wh)) Whi[c(Strata == IWH[i])] <- wh[i]\r\n Whi <- Whi * species\r\n gt <- cumsum(sapply(split(Whi, hydro), sum))\r\n res <- list(x = sort.hydro, y = as.vector(gt))\r\n if (plot) {\r\n Association.int.plot(res, xlab=nHy)\r\n invisible(res)\r\n }\r\n else {\r\n class(res) <- \"assocplot\"\r\n res\r\n }\r\n}\r\n", "meta": {"hexsha": "9afb7fc315452f3cd400b8321a7aab32d1b5d86d", "size": 1701, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Association.plot.r", "max_stars_repo_name": "AMCOOK/bio.survey", "max_stars_repo_head_hexsha": "cbca5a4c5c3bd68fd0885c1b84d9fd940ccf9d17", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Association.plot.r", "max_issues_repo_name": "AMCOOK/bio.survey", "max_issues_repo_head_hexsha": "cbca5a4c5c3bd68fd0885c1b84d9fd940ccf9d17", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Association.plot.r", "max_forks_repo_name": "AMCOOK/bio.survey", "max_forks_repo_head_hexsha": "cbca5a4c5c3bd68fd0885c1b84d9fd940ccf9d17", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3529411765, "max_line_length": 102, "alphanum_fraction": 0.5479129924, "num_tokens": 523, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548511303338, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3180457582523323}} {"text": "#' lost_productivity \n#'\n#' Calculate the percentage in worker's lost productivity due to heat conditions.\n#'\n#' @param wbgt numeric Wetbulb globe temperature index in degC\n#' @param tresh numeric treshshold for loss in degC\n#'\n#' @return percentage of productivity lost \n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#'\n#' @keywords Body Surface Area \n#' @export\n#'\n#'\n#'\n#'\n\nlost_productivity=function(wbgt,tresh=33.5) {\n ct$assign(\"wbgt\", as.array(wbgt))\n ct$assign(\"tresh\", as.array(tresh))\n ct$eval(\"var res=[]; for(var i=0, len=wbgt.length; i < len; i++){ res[i]=lost_productivity(wbgt[i],tresh[0])};\")\n res=ct$get(\"res\")\n return(res)\n}\n", "meta": {"hexsha": "44aaa4ec0257cad68c797db0e49031bdbde2f6ed", "size": 719, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lost_productivity.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/lost_productivity.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lost_productivity.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 27.6538461538, "max_line_length": 114, "alphanum_fraction": 0.6884561892, "num_tokens": 226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.661922862511608, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.31803982194926395}} {"text": "## -------------------------- Data preparation --------------------------------\n\n## Source wrapper ---- \n## WARNING: wrapper erase all data in memory, run it with at the beggining;\n## also, it will load several custum functions without which ANYTHING below\n## will work as expected.\n\nsource(\"~/Documentos/2019A/paper_gen_decrement/analysis/r/wrapper.r\")\n\n# Paths for processed data. Check raw2processed_proc.r ----\n\npath_read.general = \"~/Documentos/2019A/paper_gen_decrement/Data/\"\npath_read.BL = path_read.general + \"processed/baseline\"\npath_read.EXP = path_read.general + \"processed/experimental\"\n\n# Paths for results of analysis (data_results) ---- \n\npath_save.general = \"~/Documentos/2019A/paper_gen_decrement/analysis/\"\npath_save.data = path_save.general + \"data_results/\"\n\n# ETC\n## vector of subjects numbers, used in several functions below\nsubjects_vec = 326:333\n## Names of phases\nph_names = c(\"Baseline\", \"Experimental\")\n# Path of BL and EXP to read\npaths = c(path_read.BL, path_read.EXP)\n\n## Extra: individual trial analysis for the FI ----\ndf_bp1_fi <- ind_ana_bp1(\n list_ss = subjects_vec,\n phases_names = ph_names,\n paths = paths,\n sessions = 40,\n max_trials = 2000\n) %>%\n filter(sesion < 39) %>%\n as.data.frame() \n\n\n\ncolores = c('#8c8c8c', \"#8F2727\")\n\nses = unique(df_bp1_fi$sesion)\ncdes = unique(df_bp1_fi$cde)\ntrial = unique(df_bp1_fi$cum_trial)\nphase = unique(df_bp1_fi$phase)\n\ndf1 = df_bp1_fi %>%\n group_by(phase, sesion, cde) %>%\n summarise(bp = mean(bp))\n\npar(mfrow = c(1, 2))\ncdes = c(1, 2)\n\nfor(i in unique(df1$phase)){\n \n \n xmax = max(df1$sesion[df1$phase == i])\n ymax = 45\n ymin = min(df1$bp)\n \n plot(0,0, ylab = '', xlab = '', \n col = 'white', ylim = c(ymin, ymax), xlim = c(0, xmax))\n \n for (k in cdes){\n \n if (k == cdes[1]){\n col = colores[1]\n } else {\n col = colores[2]\n }\n x = df1$sesion[df1$cde == k & df1$phase == i]\n y = df1$bp[df1$cde == k & df1$phase == i]\n points(x,y, pch = 21, bg = col)\n lines(x,y, col = col)\n }\n}\n\n\ndF = data.frame()\n\nfor (i in phase) {\n di = df_bp1_fi[df_bp1_fi$phase == i, ]\n \n for (ii in ses){\n \n dii = di[di$sesion == ii, ]\n \n for (iii in cdes) {\n diii = dii[dii$cde == iii, ]\n sumr = sum(length(diii$r2) > 0 & length(diii$r2 > 0))\n if( sumr > 0) {\n ratio = mean(diii$r2 / diii$r1)\n ratio = log10(ratio)\n dFtmp = data.frame(phase = i, ses = ii, cde = iii, ratio = ratio)\n } else {\n dFtmp = data.frame()\n }\n \n dF = bind_rows(dF, dFtmp)\n }\n }\n}\n\n\npdf('ts_logRatio.pdf',\n height = 4, width = 7)\n\npar(mfrow = c(1, 2))\n\ncdes = c(1, 2)\nfor (pi in phase) {\n \n xmax = max(dF$ses[dF$phase == pi])\n ymax = max(dF$ratio)\n ymin = min(dF$ratio)\n plot(NA,NA, xlim = c(1, xmax), ylim = c(ymin, ymax), axes = F, \n xlab = 'Sessions', ylab = TeX('$\\\\log_{10}$ mean ratio'), \n main = pi, panel.first = grid())\n \n for (cdi in cdes) {\n if (cdi == 1){\n col = colores[1]\n } else {\n col = colores[2]\n }\n x = dF$ses[dF$phase == pi & dF$cde == cdi]\n y = dF$ratio[dF$phase == pi & dF$cde == cdi]\n points(x, y, pch = 21, bg = col)\n lines(x, y, col = col)\n }\n if (pi == phase[1]){\n legend('topleft', legend = c('Delayed','Immediate'),\n col = colores, pch = c(16, 16), bty = 'n')\n }\n axis(1, tck = -0.02, seq(0, xmax, 5))\n axis(2, tck = -0.02)\n}\n\ndev.off()\n\npdf('density_logRatio.pdf',\n height = 4, width = 7)\n\npar(mfrow = c(1, 2),\n mar = c(4,1,3,1))\ncdes = c(1, 2)\n\nfor (pi in phase) {\n \n for (cdi in cdes){\n \n x = dF$ratio[dF$phase == pi & dF$cde == cdi]\n dx1 = density(x)\n \n if(pi == phase[1]){\n xlim = c(1, 1.8)\n ylim = c(0, 12)\n } else {\n xlim = c(1, 1.8)\n ylim = c(0, 12)\n }\n \n if (cdi == 1){\n col = colores[1]\n plot(dx1, type ='l', \n xlim = xlim, ylim = ylim,\n ylab = '', xlab = TeX('$\\\\log_{10}$ mean ratio'),\n main = pi, col = col, \n axes = F, panel.first = grid())\n \n } else {\n col = colores[2]\n lines(dx1, col = col)\n }\n }\n axis(1, tck = -0.02)\n if (pi == phase[1]){\n legend('topleft', legend = c('Delayed','Immediate'),\n col = colores, pch = c(16, 16), bty = 'n')\n }\n \n}\n\ndev.off()\n\n# Quarter life analysis\n\nquants0 <- quants25.75(\n list_ss = subjects_vec,\n phases_names = ph_names,\n paths = paths,\n sessions = 40,\n max_trials = 2000\n) %>%\n filter(sesion < 39) %>%\n as.data.frame() \n\nquants = quants0 %>%\n group_by(cde, phase, sesion) %>%\n summarise(q25 = mean(q25),\n q75 = mean(q75))\n\nses = unique(quants$sesion)\ncdes = unique(quants$cde)\nph = unique(quants$phase)\n\npar(mfrow = c(1, 2),\n \n )\n\nfor (pi in ph){\n if (pi == ph[1]) {\n xlims = c(0, 40)\n } else {\n xlims = c(0, 30)\n }\n ylims = c(30, 80)\n plot(0,0, color = 'white', \n xlab = '', ylab = '',\n xlim = xlims, ylim = ylims)\n \n for (ci in cdes){\n \n if ( ci == cdes[1]) {\n \n col = colores[1]\n \n } else {\n \n col = colores[2]\n }\n \n x = quants$sesion[quants$cde == ci & quants$phase == pi]\n y = quants$q25[quants$cde == ci & quants$phase == pi]\n y1 = quants$q75[quants$cde == ci & quants$phase == pi]\n points(x, y, pch = 21, bg = col)\n points(x, y1, pch = 21, bg = col)\n }\n}\n", "meta": {"hexsha": "1ef3f00e975fa0feaf311ab48afe33cb72fda265", "size": 5332, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/r/results_bp_fi.r", "max_stars_repo_name": "jealcalat/Generalization_decrement_data-analysis", "max_stars_repo_head_hexsha": "5115fcd2749598ee3f8d07886f129738439f809a", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/r/results_bp_fi.r", "max_issues_repo_name": "jealcalat/Generalization_decrement_data-analysis", "max_issues_repo_head_hexsha": "5115fcd2749598ee3f8d07886f129738439f809a", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/r/results_bp_fi.r", "max_forks_repo_name": "jealcalat/Generalization_decrement_data-analysis", "max_forks_repo_head_hexsha": "5115fcd2749598ee3f8d07886f129738439f809a", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.0330578512, "max_line_length": 79, "alphanum_fraction": 0.5431357839, "num_tokens": 1863, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.317973210150966}} {"text": "#Density plot of out of sample R2 values as a function of spatial scale (core-plot-site).\nrm(list=ls())\nsource('paths.r')\nsource('NEFI_functions/zero_truncated_density.r')\n\nlibrary(RCurl)\nscript <- getURL(\"https://raw.githubusercontent.com/colinaverill/NEFI_microbe/master/NEFI_functions/zero_truncated_density.r\", ssl.verifypeer = FALSE)\neval(parse(text = script))\n\n# only include complete cases (no missing data)\ncompl_cases <- F\n\n#set output path.----\nif (compl_cases==T){ output.path <- 'valR2_fg_compl_cases_density_fig.png' \n} else output.path <- 'valR2_fg_density_fig.png' \n#output.path <- NEON_cps_out.of.sample_rsq_density_figure.path\n\n#load in-sample fits.----\npl.cal <- readRDS(prior_16S_all.fg.groups_JAGSfits.path)\n# pl.cal <- readRDS(ted_ITS_prior_phylo.group_JAGSfits)\n# pl.cal$function_group <- fg.cal\n#pl.cal <- readRDS(paste0(scc_gen_16S_dir, \"JAGS_output/prior_phylo_JAGSfit_fewer_taxa.rds\"))\n#phylum.mod <- readRDS(paste0(scc_gen_16S_dir,\"JAGS_output/prior_phylo_JAGSfit_phylum_fewer_taxa_more_burnin.rds\"))\n#pl.cal$phylum <- pl.cal$phylum\n\n\n#get in sample r2 values.----\ncal.rsq <- list()\nfor(i in 1:length(pl.cal)){\n if(names(pl.cal)[i] != 'function_group'){\n y <- pl.cal[[i]]$no.nutr.preds$observed\n x <- pl.cal[[i]]$no.nutr.preds$predicted\n }\n if(names(pl.cal)[i] == 'function_group'){\n y <- pl.cal[[i]]$no.nutr.preds$observed\n x <- pl.cal[[i]]$no.nutr.preds$predicted\n }\n lev.rsq <- list()\n for(k in 1:ncol(y)){\n rsq <- summary(lm(y[,k] ~ x[,k]))$r.squared\n names(rsq) <- colnames(y)[k]\n lev.rsq[[k]] <- rsq\n }\n lev.rsq <- unlist(lev.rsq)\n lev.rsq <- lev.rsq[-grep('other',names(lev.rsq))]\n cal.rsq[[i]] <- lev.rsq\n}\ncal.rsq <- unlist(cal.rsq)\n\n#load forecasts predicted and observed.----\n#functional groups\n# fg.cast <- readRDS(NEON_site_fcast_fg.path)\n# fg.core <- readRDS(NEON_ITS_fastq_taxa_fg.path)\n# #trick out to make work with phylo stuff.\n# fg.core$abundances$geneticSampleID <- NULL\n# fg.plot.truth <- readRDS(NEON_plot.level_fg_obs_fastq.path)\n# fg.site.truth <- readRDS(NEON_site.level_fg_obs_fastq.path)\n# #format fg stuff so we can pop it into the pl list.\n# fg.truth <- list(fg.plot.truth,fg.site.truth)\n# names(fg.truth) <- c('plot.fit','site.fit')\n\n#phylogenetic groups.\n# if (compl_cases==T){ pl.cast <- readRDS(paste0(pecan_gen_16S_dir,\"/NEON_forecast_data/NEON_fcast_comp_cases.rds\"))\n# } else pl.cast <- readRDS(NEON_cps_fcast_all_phylo_16S.path)\npl.truth <- readRDS(NEON_all.fg_plot.site_obs_16S.path)\n\nif (compl_cases== T){\n pl.cast <- readRDS(paste0(pecan_gen_16S_dir,\"NEON_forecast_data/NEON_fcast_fg_comp_cases.rds\"))\n} else pl.cast <- readRDS(NEON_cps_fcast_fg_16S.path)\n#pl.core <- readRDS(NEON_16S_phylo_groups_abundances.path)\n\n# \n# pl.cast <- readRDS(NEON_site_fcast_all_phylo_levels.path)\n# pl.truth <- readRDS(NEON_all.phylo.levels_plot.site_obs_fastq.path)\n# pl.core <- readRDS(NEON_ITS_fastq_all_cosmo_phylo_groups.path)\n#merge in fg stuff\n# pl.core$function_group <- fg.core\n# pl.truth$function_group <- fg.truth\n# pl.cast$function_group <- fg.cast\n\n\n#get core, plot site R2 values out of sample.----\nall.core.rsq <- list()\nall.plot.rsq <- list()\nall.site.rsq <- list()\nfor(i in 1:length(pl.cast)){\n fcast <- pl.cast[[i]]\n core.rsq <- list()\n plot.rsq <- list()\n site.rsq <- list()\n #core.level----\n y <- pl.truth[[i]]$core.fit\n #y <- (pl.core[[i]]$abundances + 1)/pl.core[[i]]$seq_total\n x <- fcast$core.fit$mean\n #make sure row and column orders match.\n rownames(y) <- gsub('-GEN','',rownames(y))\n truth <- y\n map <- readRDS(core_obs_16S.path)\n truth <- as.data.frame(truth)\n truth$deprecatedVialID <- rownames(truth)\n truth1 <- merge(truth, map[,c(\"deprecatedVialID\", \"geneticSampleID\")], by = \"deprecatedVialID\")\n truth1 <- truth1[!duplicated(truth1$geneticSampleID),]\n rownames(truth1) <- gsub('-GEN','',truth1$geneticSampleID)\n truth <- truth1\n y <- truth\n \n y <- y[rownames(y) %in% rownames(x),]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[,colnames(y) %in% colnames(x)]\n x <- x[,colnames(x) %in% colnames(y)]\n x <- x[order(match(rownames(x),rownames(y))),]\n x <- x[,order(match(colnames(x),colnames(y)))]\n #fit model, grab r2.\n for(k in 1:ncol(x)){\n fungi_name <- colnames(x)[k]\n rsq <- summary(lm(y[,k] ~ x[,k]))$r.squared\n names(rsq) <- fungi_name\n core.rsq[[k]] <- rsq\n }\n #plot.level----\n x <- fcast$plot.fit$mean\n y <- pl.truth[[i]]$plot.fit$mean\n #make sure row and column order match.\n rownames(y) <- gsub('\\\\.','_',rownames(y))\n y <- y[rownames(y) %in% rownames(x),]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[,colnames(y) %in% colnames(x)]\n x <- x[,colnames(x) %in% colnames(y)]\n x <- x[order(match(rownames(x),rownames(y))),]\n x <- x[,order(match(colnames(x),colnames(y)))]\n #fit model, grab r2.\n for(k in 1:ncol(y)){\n fungi_name <- colnames(x)[k]\n rsq <- summary(lm(y[,k] ~ x[,k]))$r.squared\n names(rsq) <- fungi_name\n plot.rsq[[k]] <- rsq\n }\n #site.level----\n x <- fcast$site.fit$mean\n y <- pl.truth[[i]]$site.fit$mean\n #make sure row and column order match.\n rownames(y) <- gsub('-GEN','',rownames(y))\n y <- y[rownames(y) %in% rownames(x),]\n x <- x[rownames(x) %in% rownames(y),]\n y <- y[,colnames(y) %in% colnames(x)]\n x <- x[,colnames(x) %in% colnames(y)]\n x <- x[order(match(rownames(x),rownames(y))),]\n x <- x[,order(match(colnames(x),colnames(y)))]\n #fit model, grab r2.\n for(k in 1:ncol(y)){\n fungi_name <- colnames(x)[k]\n rsq <- summary(lm(y[,k] ~ x[,k]))$r.squared\n names(rsq) <- fungi_name\n site.rsq[[k]] <- rsq\n }\n #wrap up for return.----\n all.core.rsq[[i]] <- unlist(core.rsq)\n all.plot.rsq[[i]] <- unlist(plot.rsq)\n all.site.rsq[[i]] <- unlist(site.rsq)\n \n}\ncore.rsq <- unlist(all.core.rsq)\nplot.rsq <- unlist(all.plot.rsq)\nsite.rsq <- unlist(all.site.rsq)\ncore.rsq <- core.rsq[-grep('other',names(core.rsq))]\nplot.rsq <- plot.rsq[-grep('other',names(plot.rsq))]\nsite.rsq <- site.rsq[-grep('other',names(site.rsq))]\n\n#Subset to observations that have a minimum calibration R2 value.----\npass <- cal.rsq[cal.rsq > .1]\ncore.rsq <- core.rsq[names(core.rsq) %in% names(pass)]\nplot.rsq <- plot.rsq[names(plot.rsq) %in% names(pass)]\nsite.rsq <- site.rsq[names(site.rsq) %in% names(pass)]\ncore.d <- zero_truncated_density(core.rsq)\nplot.d <- zero_truncated_density(plot.rsq)\nsite.d <- zero_truncated_density(site.rsq)\n\n#png save line.----\npng(filename=output.path,width=5,height=5,units='in',res=300)\n\n#global plot settings.----\npar(mfrow = c(1,1))\nlimx <- c(0,1)\nlimy <- c(0, 10)\ntrans <- 0.2 #shading transparency.\no.cex <- 1.3 #outer label size.\ncols <- c('purple','cyan','yellow')\npar(mfrow = c(1,1), mar = c(4.5,4,1,1))\n\n#plot.----\nplot(core.d,xlim = c(0, 0.8), ylim = limy, bty = 'n', xlab = NA, ylab = NA, main = NA, yaxs='i', xaxs = 'i', las = 1, lwd = 0)\npolygon(core.d, col = adjustcolor(cols[1],trans))\npolygon(plot.d, col = adjustcolor(cols[2],trans))\npolygon(site.d, col = adjustcolor(cols[3],trans))\nmtext('Density', side = 2, line = 2.2, cex = o.cex)\nmtext(expression(paste(\"Validation R\"^\"2\")), side = 1, line = 2.5, cex = o.cex)\nlegend(x = 0.6, y = 6, legend = c('core','plot','site'), col ='black', pt.bg=adjustcolor(cols,trans), bty = 'n', pch = 22, pt.cex = 1.5)\n\n#end plot.----\ndev.off()\n", "meta": {"hexsha": "fb2fb7e0f7e8ec051ec25dc944c57e6ce45119ae", "size": 7236, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/figure_scripts/NEON_cps_out.of.sample_rsq_density_fg_16S.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "16S/figure_scripts/NEON_cps_out.of.sample_rsq_density_fg_16S.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "16S/figure_scripts/NEON_cps_out.of.sample_rsq_density_fg_16S.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 36.0, "max_line_length": 150, "alphanum_fraction": 0.6597567717, "num_tokens": 2514, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3179732016337714}} {"text": "pieces <- c(\"R\",\"B\",\"N\",\"Q\",\"K\",\"N\",\"B\",\"R\")\n\ngenerateFirstRank <- function() {\n attempt <- paste0(sample(pieces), collapse = \"\")\n while (!check_position(attempt)) {\n attempt <- paste0(sample(pieces), collapse = \"\")\n }\n return(attempt)\n}\n\ncheck_position <- function(position) {\n if (regexpr('.*R.*K.*R.*', position) == -1) return(FALSE)\n if (regexpr('.*B(..|....|......|)B.*', position) == -1) return(FALSE)\n TRUE\n}\n\nconvert_to_unicode <- function(s) {\n s <- sub(\"K\",\"\\u2654\", s)\n s <- sub(\"Q\",\"\\u2655\", s)\n s <- gsub(\"R\",\"\\u2656\", s)\n s <- gsub(\"B\",\"\\u2657\", s)\n s <- gsub(\"N\",\"\\u2658\", s)\n}\n\ncat(convert_to_unicode(generateFirstRank()), \"\\n\")\n", "meta": {"hexsha": "d2558a853f7b830dfd2df7d46c573a62addcf7ab", "size": 660, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Generate-Chess960-starting-position/R/generate-chess960-starting-position.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-05T13:42:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-05T13:42:20.000Z", "max_issues_repo_path": "Task/Generate-Chess960-starting-position/R/generate-chess960-starting-position.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Generate-Chess960-starting-position/R/generate-chess960-starting-position.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.3846153846, "max_line_length": 71, "alphanum_fraction": 0.5545454545, "num_tokens": 218, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3179731936261763}} {"text": "#' Repeats an array along an arbitrary axis\n#'\n#' @param x An array object\n#' @param n Integer, how often to repeat\n#' @param along Along which axis to repeat (default: 1)\n#' @return An array that is repeated `n` times on axis `along`\n#' @export\nrep = function(x, n, along=1) {\n xl = base::rep(list(x), n)\n bind(xl, along=along)\n}\n\n#' @rdname rep\n#' @export\ncrep = function(x, n) rep(x, n, along=2)\n\n#' @rdname rep\n#' @export\nrrep = function(x, n) rep(x, n, along=1)\n", "meta": {"hexsha": "1e128062c49e234678b105fc308f1a71ed113ae3", "size": 490, "ext": "r", "lang": "R", "max_stars_repo_path": "R/rep.r", "max_stars_repo_name": "cran/narray", "max_stars_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17, "max_stars_repo_stars_event_min_datetime": "2016-12-07T16:03:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-20T09:10:42.000Z", "max_issues_repo_path": "R/rep.r", "max_issues_repo_name": "cran/narray", "max_issues_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 28, "max_issues_repo_issues_event_min_datetime": "2016-11-21T09:29:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-11T16:08:02.000Z", "max_forks_repo_path": "R/rep.r", "max_forks_repo_name": "cran/narray", "max_forks_repo_head_hexsha": "916fc5cb4c5d3f110765c262270b2f878c36e432", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-06-21T03:17:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-21T03:17:21.000Z", "avg_line_length": 24.5, "max_line_length": 68, "alphanum_fraction": 0.6163265306, "num_tokens": 158, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6261241842048092, "lm_q1q2_score": 0.31795328925134014}} {"text": "#Extract binary interactions from diet matrix\n bin_inter <- function(x) {\n\n bin.inter <- matrix(nrow=ncol(x)*nrow(x),ncol=3,data=NA)\n colnames(bin.inter) = c(\"Predator\",\"FeedInter\",\"Prey\")\n\n for(i in 1:ncol(x)){\n for(j in 1:nrow(x)){\n bin.inter[((i*nrow(x))-nrow(x))+j,1] <- colnames(x)[i]\n bin.inter[((i*nrow(x))-nrow(x))+j,2] <- as.matrix(x[j,i])\n bin.inter[((i*nrow(x))-nrow(x))+j,3] <- rownames(x)[j]\n }\n }\n\n return(bin.inter)\n }\n", "meta": {"hexsha": "0aa76c4d41d948a2de84d35d7945d4e1df2e4789", "size": 482, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/bin_inter.r", "max_stars_repo_name": "david-beauchesne/Interaction_catalog", "max_stars_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-08-12T11:00:10.000Z", "max_stars_repo_stars_event_max_datetime": "2017-03-09T18:16:12.000Z", "max_issues_repo_path": "Script/bin_inter.r", "max_issues_repo_name": "david-beauchesne/Interaction_catalog", "max_issues_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-08-12T14:42:32.000Z", "max_issues_repo_issues_event_max_datetime": "2016-08-12T15:25:21.000Z", "max_forks_repo_path": "Script/bin_inter.r", "max_forks_repo_name": "david-beauchesne/Interaction_catalog", "max_forks_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2016-08-12T10:46:53.000Z", "max_forks_repo_forks_event_max_datetime": "2016-08-12T10:46:53.000Z", "avg_line_length": 28.3529411765, "max_line_length": 65, "alphanum_fraction": 0.5518672199, "num_tokens": 162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.6261241842048092, "lm_q1q2_score": 0.3179532892513401}} {"text": "# app.r\n# This file contains the server and ui functions of the shiny web app.\n#\n# For running the app form a R terminal use:\n# shiny::runApp(\"D:\\\\Dokumente\\\\GitHub\\\\2D-FEM-Solver\\\\R Shiny Webapp\\\\app.r\")\n\nlibrary(shiny)\nlibrary(shinythemes)\nsource(\"R Shiny Webapp\\\\2D_fem_pre_processor.r\", chdir = TRUE)\n\n# Compile the C++ source and run the solver.\ninit_solver()\n\n# Call C++ functions form the compiled source to calculate all \n# the displacements from the provided input file which contains\n# the mesh data. \ninput_filename <- \"R Shiny Webapp\\\\Mesh_Balken_V2.txt\"\ndisplacements = solve_linear_elastic(input_filename)\nmises_stresses = output_mises_stress()\n\n# Reshape data.\nx <- unname(sapply(displacements, `[[`, 1))\ny <- unname(sapply(displacements, `[[`, 2))\nux <- unname(sapply(displacements, `[[`, 3))\nuy <- unname(sapply(displacements, `[[`, 4))\n\n\n# Define UI for application that draws a histogram\nui <- fluidPage(\n\n # Application title\n titlePanel(\"Mechanical Linear Elastic 2D FEM Solver\"),\n\n # Color theme of the page.\n theme = shinytheme(\"slate\"),\n\n fluidRow(\n # Left sidebar panel for inputs.\n column(2,\n fluidRow(\n wellPanel(\n h4(\"Solver Input Filename\"),\n textOutput(\"text_filename\")\n )\n ),\n\n fluidRow(\n wellPanel(\n # Input: Select a timestep.\n sliderInput(\"bins\",\n \"Time:\",\n min = 0,\n max = 1,\n value = 1),\n \n # Button for updating plots.\n actionButton(\"update\", \"Update Plot\"),\n width = 2\n )\n )\n\n ),\n\n # Main panel.\n column(10,\n wellPanel(\n h4(\"Stress and Displacment Plot\"),\n\n # Plot mesh with stresses and displacements.\n plotOutput(\"distPlot\")\n )\n )\n )\n)\n\n# Define server logic required to draw a histogram\nserver <- function(input, output) {\n\n output$text_filename <- renderText({paste(input_filename)})\n\n output$distPlot <- renderPlot({\n # Generate bins based on input$bins from ui.R\n bins <- input$bins\n\n # Draw plot in app. \n mesh_ggplot(x, y, ux*bins, uy*bins, mises_stresses)\n })\n}\n\n# Run the application \nshinyApp(ui = ui, server = server)\n", "meta": {"hexsha": "b4fec0c8f5115507dc0208f8ee5d79dca2331753", "size": 2452, "ext": "r", "lang": "R", "max_stars_repo_path": "R Shiny Webapp/app.r", "max_stars_repo_name": "JoshuaSimon/2D-FEM-Solver", "max_stars_repo_head_hexsha": "9f3c19b760350338a33445e9817e1c077f4718d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-03-27T12:45:51.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-25T09:41:56.000Z", "max_issues_repo_path": "R Shiny Webapp/app.r", "max_issues_repo_name": "JoshuaSimon/2D-FEM-Solver", "max_issues_repo_head_hexsha": "9f3c19b760350338a33445e9817e1c077f4718d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R Shiny Webapp/app.r", "max_forks_repo_name": "JoshuaSimon/2D-FEM-Solver", "max_forks_repo_head_hexsha": "9f3c19b760350338a33445e9817e1c077f4718d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.652173913, "max_line_length": 78, "alphanum_fraction": 0.5656606852, "num_tokens": 571, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3179532857085877}} {"text": "# Util for cross-validation\nlibrary(caret)\nlibrary(foreach)\nlibrary(doParallel)\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(scales)\nlibrary(pbapply)\nsource(\"utils/accuracy-statistics.r\")\nsource(\"pixel-based/utils/load-sampling-data.r\")\n\n# Pass in the function that takes as input a data.frame and produces a cross-validated data.frame in return\nCrossValidate = function(formula, data, train_function, predict_function, folds=10, fold_column=data[,\"dominant_lc\"],\n covariate_names=names(data), cv_seed=0xfedbeef, oversample=FALSE, packages=NULL, ...)\n{\n set.seed(cv_seed)\n folds = createFolds(fold_column, folds)\n \n #Predictions = NULL\n #for (i in 1:length(folds))\n Predictions = foreach(fold=iter(folds), .combine=rbind, .multicombine=TRUE, .inorder=TRUE, .packages=packages) %dopar%\n {\n #fold = folds[[i]]\n TrainingData = data[-fold,]\n if (oversample)\n TrainingData = Oversample(TrainingData, fold_column=fold_column, seed=cv_seed)\n set.seed(cv_seed)\n Model = train_function(formula=formula, ..., data=TrainingData)\n #print(dim(Model[[1]]))\n #print(dim(Model[[2]]))\n Prediction = predict_function(Model, ..., newdata=data[fold,covariate_names])\n #print(dim(Prediction))\n #Predictions = rbind(Predictions, Prediction)\n }\n Predictions = Predictions[order(unlist(folds)),]\n return(Predictions)\n}\n\n# Train on and predict over ecozone clusters.\nClusterTrain = function(formula, data, train_function=NULL, val_data, predict_function=predict,\n cv_seed=0xfedbeef, include_neighbours=FALSE, cluster_col=\"bc_id\",\n trainpredict_function=NULL, ...)\n{\n if (is.null(train_function) && is.null(trainpredict_function))\n stop(\"Pass either the trainfunction or the trainpredict_function\")\n set.seed(cv_seed)\n \n TrainClusters = if (include_neighbours) ClusterNeighbours() else {\n UniqueECs = unique(data[[cluster_col]])\n UniqueECs = as.character(UniqueECs[!is.na(UniqueECs)])\n ECList = as.list(UniqueECs)\n names(ECList) = UniqueECs\n ECList\n }\n # Remove validation that does not belong to any cluster\n val_data = val_data[!is.na(val_data[[cluster_col]]),]\n \n Prediction = pblapply(TrainClusters, function(x) {\n ClusterRows = val_data[[cluster_col]]==x[[1]]\n if (!is.null(train_function))\n {\n model = train_function(formula=formula, ..., data=data[data[[cluster_col]] %in% x,]) # Train on zone plus (optionally) neighbours\n ResultMat = predict_function(model, ..., newdata=val_data[ClusterRows,]) # Predict on zone only\n } else {\n ResultMat = trainpredict_function(formula=formula, data=data[data[[cluster_col]] %in% x,], newdata=val_data[ClusterRows,], ...)\n }\n if (!is.matrix(ResultMat))\n ResultMat = as.matrix(ResultMat)\n cbind(ResultMat, order=which(ClusterRows))\n })\n ResultOrder = do.call(rbind, Prediction)\n stopifnot(all(!duplicated(ResultOrder[,\"order\"]))) # No point should be in two zones\n ResultInOrder = ResultOrder[order(ResultOrder[,\"order\"]),]\n ResultInOrder = ResultInOrder[,!colnames(ResultInOrder) %in% \"order\"] # Remove order column\n \n return(ResultInOrder)\n}\n\n# Run binary relevance, i.e. one model per class.\n# Formula should be empty on LHS, i.e. paste0(\"~\", paste(Covariates, collapse = \"+\"))\nBinaryRelevance = function(formula, data, train_function, val_data, predict_function=predict,\n seed=0xfedbeef, classes=GetCommonClassNames(), scale=TRUE, filename=NULL, overwrite=FALSE, LeaveZeroes = FALSE, ...)\n{\n if (!is.null(filename) && !overwrite && file.exists(filename))\n {\n Predictions = read.csv(filename)\n if (scale) Predictions = ScalePredictions(Predictions, LeaveZeroes)\n return(Predictions)\n }\n\n Predictions = matrix(ncol=length(classes), nrow=nrow(val_data), dimnames=list(list(), classes))\n \n for (Class in classes)\n {\n print(Class)\n ClassFormula = update.formula(formula, paste0(Class, \" ~ .\"))\n set.seed(seed)\n Model = train_function(ClassFormula, data=data, ...)\n gc(full=TRUE)\n RegPredictions = predict_function(Model, newdata=val_data, ...)\n Predictions[, Class] = RegPredictions\n rm(Model, RegPredictions)\n gc(full=TRUE)\n }\n \n if (!is.null(filename))\n {\n OutDir = dirname(filename)\n if (!dir.exists(OutDir))\n dir.create(OutDir)\n write.csv(Predictions, filename, row.names=FALSE)\n }\n \n if (scale) Predictions = ScalePredictions(Predictions, LeaveZeroes)\n \n return(as.data.frame(Predictions))\n}\n\n# Rescale predictions so that htey add up to 100%\nScalePredictions = function(Predictions, LeaveZeroes = TRUE)\n{\n Predictions = as.matrix(Predictions)\n Predictions = Predictions / rowSums(Predictions) * 100\n # There is a possibility that all classes have been predicted as 0, so we can't normalise.\n # In that case we just keep them as 0%. It won't add up to 100%. Alternatively we can set it to 1/nclass.\n Predictions[is.nan(Predictions)] = if (LeaveZeroes) 0 else 100/ncol(Predictions)\n return(as.data.frame(Predictions))\n}\n\n# Validation metrics and plots\nAccuracyStatisticsPlots = function(predicted, observed, ...)\n{\n # RMSE values and correlation\n AST = AccuracyStatTable(predicted, observed)\n print(AST)\n op = par(mfrow=c(2,2))\n barplot(AST$RMSE, names.arg=rownames(AST), main=\"RMSE\")\n barplot(AST$MAE, names.arg=rownames(AST), main=\"MAE\")\n barplot(AST$ME, names.arg=rownames(AST), main=\"ME\")\n try(corrplot::corrplot(cor(predicted, observed), method=\"ellipse\"))\n par(op)\n}\n\n# Simple oversampling function\nOversample = function(Data, fold_column = Data[[FactorName]], seed=0xfedbeef)\n{\n Factor = fold_column\n MaxSamples = max(table(Factor))\n Result=NULL\n \n set.seed(seed)\n for (ClassName in levels(Factor))\n {\n OneClassOnly = Data[Factor==ClassName,]\n ClassRows = sample(1:nrow(OneClassOnly), MaxSamples, replace=TRUE)\n ClassDF = OneClassOnly[ClassRows,]\n Result = rbind(Result, ClassDF)\n }\n return(Result)\n}\n\n# Plot a 1:1 hexplot, expects a data.frame rather than a matrix, and it should be 0-100 rather than 0-1\nPlotHex = function(predicted, observed, main=\"\")\n{\n hp = ggplot(data.frame(Prediction=unlist(predicted), Truth=unlist(observed)), aes(Truth, Prediction)) +\n geom_hex() +# xlim(0, 100) + ylim(0, 100) +\n scale_fill_gradient2(high=\"red\", mid=muted(\"red\"), low=\"grey90\", midpoint=log(1000), trans=\"log\") +\n #scale_fill_distiller(palette=\"Spectral\", trans=\"log\") + #log scale, 7 was the oranges\n geom_abline(slope=1, intercept=0) + ggtitle(main)\n return(hp)\n}\n\n# Plot 1:1 boxplot\nPlotBox = function(predicted, observed, main=\"\", binpredicted=FALSE, transposeaxes=FALSE,\n varwidth = TRUE, outlier.size = 0.3, outlier.alpha = 0.1, width=0.2, display.n=FALSE)\n{\n #OneToOne = data.frame(Predicted=seq(0, 100, 10), Bins=1:11)\n if (!binpredicted) {\n TruthBins = unlist(observed)\n TruthBins = round(TruthBins, -1)\n ValidationDF = data.frame(Truth=unlist(observed), Bins=as.factor(TruthBins), Predicted=unlist(predicted))\n OneToOne = data.frame(Predicted=as.numeric(levels(ValidationDF$Bins)), Bins=1:length(levels(ValidationDF$Bins)))\n ncount = if (display.n) {\n paste(levels(ValidationDF$Bins),\"\\n(N=\",round(table(ValidationDF$Bins)/1000),\"k)\",sep=\"\")\n } else waiver()\n ggplot(ValidationDF, aes(Bins, Predicted)) +\n stat_boxplot(geom ='errorbar', width=width) +\n geom_boxplot(varwidth = varwidth, outlier.size = outlier.size, outlier.alpha = outlier.alpha) +\n geom_line(data=OneToOne) +\n xlab(\"Reference\") +\n scale_x_discrete(labels=ncount) +\n ggtitle(main)\n } else {\n PredBins = unlist(predicted)\n PredBins = round(PredBins, -1)\n ValidationDF = data.frame(Truth=unlist(observed), Bins=as.factor(PredBins), Predicted=unlist(predicted))\n OneToOne = data.frame(Predicted=as.numeric(levels(ValidationDF$Bins)), Bins=1:length(levels(ValidationDF$Bins)))\n if (!transposeaxes)\n {\n ncount = if (display.n) {\n paste(levels(ValidationDF$Truth),\"\\n(N=\",round(table(ValidationDF$Truth)/1000),\"k)\",sep=\"\")\n } else waiver()\n ggplot(ValidationDF, aes(Truth, Bins)) +\n stat_boxplot(geom ='errorbar', width=width) +\n geom_boxplot(varwidth = varwidth, outlier.size = outlier.size, outlier.alpha = outlier.alpha) +\n geom_line(data=OneToOne, aes(Predicted, Bins)) +\n ylab(\"Predicted\") + xlab(\"Reference\") +\n scale_x_discrete(labels=ncount)+\n ggtitle(main)\n } else {\n ncount = if (display.n) {\n paste(levels(ValidationDF$Bins),\"\\n(N=\",round(table(ValidationDF$Bins)/1000, 1),\"k)\",sep=\"\")\n } else waiver()\n ggplot(ValidationDF, aes(Bins, Truth)) +\n stat_boxplot(geom ='errorbar', width=width) +\n geom_boxplot(varwidth = varwidth, outlier.size = outlier.size, outlier.alpha = outlier.alpha) +\n geom_line(data=OneToOne, aes(Bins, Predicted)) +\n xlab(\"Predicted\") + ylab(\"Reference\") +\n scale_x_discrete(labels=ncount)+\n ggtitle(main)\n }\n }\n}\n\n# Accuracy, Precision, Uncertainty (RMSE, MAE, ME) plot\nAPUPlot = function(predicted, observed)\n{\n GetASTable = function(Class)\n {\n PredClass = if (Class != \"Overall\") predicted[,Class] else unlist(predicted)\n TruthClass = if (Class != \"Overall\") observed[,Class] else unlist(observed)\n PredBins = round(PredClass, -1)\n ValidationDF = data.frame(Truth=TruthClass, Bins=as.factor(PredBins), Predicted=PredClass)\n BinAS = t(sapply(levels(ValidationDF$Bins), function(Bin) {\n ValidationBin = ValidationDF[ValidationDF$Bins == Bin,]\n AS = AccuracyStats(ValidationBin$Predicted, ValidationBin$Truth)\n return(c(unlist(AS), obs=nrow(ValidationBin)/nrow(ValidationDF)*100, obsabs=nrow(ValidationBin), bin=as.numeric(Bin)))\n }))\n BinAS = data.frame(BinAS, class=Class)\n return(BinAS)\n }\n BinAS = lapply(GetCommonClassNames(), GetASTable)\n BinAS = c(list(GetASTable(\"Overall\")), BinAS)\n BinAS = do.call(\"rbind\", BinAS)\n # Exclude too small bins\n BinAS = BinAS[BinAS$obsabs > 10,]\n # Reorder and prettify names\n ClassNames = PrettifyNames(BinAS$class)\n BinAS$class = factor(ClassNames, c(\"Overall\", unique(ClassNames[ClassNames != \"Overall\"])))\n scaleval = 1#1.5 # 300\n ggplot(BinAS, aes(x=bin, y=RMSE)) + geom_line(aes(colour=\"RMSE\")) +\n geom_line(aes(y=MAE, colour=\"MAE\")) + geom_line(aes(y=ME, colour=\"ME\")) +\n geom_line(aes(y=RMSEAdj, colour=\"RMSEAdj\")) +\n geom_col(aes(y=obs/scaleval, fill=\"Density\"), alpha=0, colour=\"black\") +\n scale_y_continuous(sec.axis = sec_axis(~.*scaleval, name = \"Probability density (%)\")) +\n labs(x=\"Predicted fraction (%)\", y=\"Statistic (%)\") +\n scale_colour_discrete(name = 'Statistic', breaks=c(\"RMSE\", \"MAE\", \"ME\", \"RMSEAdj\")) + scale_fill_manual(name = 'Histogram', values=c(\"Density\"=\"white\")) +\n facet_wrap(vars(class), nrow=2)\n}\n\n# Boxplot comparison between different methods\n# predicted_list is a list with the prediction table, with the name being the name of the model\nggplotBox = function(predicted_list, observed, main = \"\", ...)\n{\n ModelData = NULL\n for (i in 1:length(predicted_list))\n {\n predicted = predicted_list[[i]]\n ModelName = names(predicted_list)[i]\n DiffDF = predicted - observed\n DiffMelt = melt(abs(DiffDF), variable.name=\"Class\", value.name=\"AE\", measure.vars=1:length(DiffDF))\n DiffMelt$Model = ModelName\n # Duplicate to add an \"overall\" class\n DiffAll = DiffMelt\n DiffAll$Class = \"Overall\"\n DiffMelt = rbind(DiffAll, DiffMelt)\n ModelData = rbind(ModelData, DiffMelt)\n }\n ModelData$Model = factor(ModelData$Model, levels=names(predicted_list), ordered=TRUE)\n \n ggplot(ModelData, aes(x=Model, y=AE, fill=Class)) + geom_boxplot(...) + ggtitle(main) + \n stat_summary(fun = mean, geom = \"errorbar\", \n aes(ymax = ..y.., ymin = ..y.., group = Class),\n width = 0.75, linetype = \"dotted\", position = position_dodge())\n}\n\nggplotBoxLines = function(predicted_list, observed, main = \"\", ...)\n{\n ModelData = NULL\n for (i in 1:length(predicted_list))\n {\n predicted = predicted_list[[i]]\n ModelName = names(predicted_list)[i]\n DiffDF = predicted - observed\n DiffMelt = melt(abs(DiffDF), variable.name=\"Class\", value.name=\"AE\", measure.vars=1:length(DiffDF))\n DiffMelt$Model = ModelName\n # Duplicate to add an \"overall\" class\n DiffAll = DiffMelt\n DiffAll$Class = \"Overall\"\n DiffMelt = rbind(DiffAll, DiffMelt)\n ModelData = rbind(ModelData, DiffMelt)\n }\n ModelData$Model = factor(ModelData$Model, levels=names(predicted_list), ordered=TRUE)\n \n ggplot(ModelData, aes(x=Model, y=AE, fill=Class)) + ggtitle(main) + \n stat_summary(fun = mean, geom = \"errorbar\", \n aes(ymax = ..y.., ymin = ..y.., group = Class, colour = Class),\n width = 0.75)\n}\n\nggplotBar = function(ModelsToPlot, statistic=\"RMSE\", ylab=NULL, digits=0, textsize=2.5, textvjust=-0.1, ...)\n{\n if (is.null(ylab)) ylab=statistic\n ModelStats=NULL\n for (i in 1:length(ModelsToPlot))\n {\n LMM = AccuracyStatTable(ModelsToPlot[[i]], Truth[,Classes], ...)[statistic]\n LMM[[paste0(statistic,\"R\")]] = round(LMM[[statistic]], digits = digits) # For printing rounded numbers\n LMM$Model=names(ModelsToPlot)[i]\n ClassNames = PrettifyNames(rownames(LMM))\n LMM$Class=factor(ClassNames, c(\"Overall\", ClassNames[ClassNames != \"Overall\"]))\n ModelStats = rbind(ModelStats, LMM)\n }\n ModelStats$Model = factor(ModelStats$Model, levels = names(ModelsToPlot))\n ggplot(ModelStats, aes_string(\"Model\", statistic, fill=\"Class\")) +\n geom_col(position = \"dodge\", colour=\"black\") +\n geom_text(aes_string(label=sprintf(\"%s\", paste0(statistic,\"R\"))), position=position_dodge(width = 0.9), vjust=textvjust, size=textsize) +\n scale_fill_manual(name = \"Class\", values = GetCommonClassColours(TRUE, 0.1)) +\n coord_cartesian(clip = 'off') + ylab(ylab)\n}\n\n# Additional statistics per class: how well we predict 0, 100, 0 0 & observed <= 50\n LRPredictions = predicted[LRidx] > 0 & predicted[LRidx] <= 50\n URidx = observed > 50 & observed < 100\n URPredictions = predicted[URidx] > 0 & predicted[URidx] <= 50\n \n ZeroAccuracy = mean(ZeroPredictions)\n ZeroSD = sd(ZeroPredictions)\n HundredAccuracy = mean(HundredPredictions)\n HundredSD = sd(HundredPredictions)\n LowerRange = mean(LRPredictions)\n LowerSD = sd(LRPredictions)\n UpperRange = mean(URPredictions)\n UpperSD = sd(URPredictions)\n \n Result = data.frame(ZeroAccuracy = ZeroAccuracy, ZeroSD=ZeroSD,\n LowerRange=LowerRange, LowerSD=LowerSD,\n UpperRange=UpperRange, UpperSD=UpperSD,\n HundredAccuracy=HundredAccuracy, HundredSD=HundredSD)\n rownames(Result) = row.name\n return(Result)\n}\n\n# Looped over all classes\nOneToOneStatTable = function(predicted, observed, long=FALSE)\n{\n Result = OneToOneStats(predicted, observed, \"Overall\")\n for (i in 1:ncol(observed))\n {\n Result = rbind(Result, OneToOneStats(predicted[,i], observed[,i], names(observed)[i]))\n }\n \n if (long) return(OneToOneStatTableToLong(Result))\n return(Result)\n}\n\n# Convert stat table to long format\nOneToOneStatTableToLong = function(OTOST)\n{\n LongAcc = OTOST[,seq(1, ncol(OTOST), 2)]\n LongSD = OTOST[,seq(2, ncol(OTOST), 2)]\n LongAcc$class = rownames(LongAcc)\n LongSD$class = rownames(LongSD)\n AccLong = melt(LongAcc, id.vars=\"class\", variable.name=\"statistic\", value.name=\"accuracy\")\n SDLong = melt(LongSD, id.vars=\"class\", variable.name=\"statistic\", value.name=\"sd\")\n Result = cbind(AccLong, sd=SDLong[,3])\n return(Result)\n}\n\nOneToOneStatPlot = function(predicted, observed, main=\"\")\n{\n OOTable = OneToOneStatTable(predicted, observed)\n OOTableLong = OneToOneStatTableToLong(OOTable)\n print(ggplot(OOTableLong, aes(class, accuracy, fill=statistic)) +\n geom_bar(stat=\"identity\", position=\"dodge\") +\n geom_errorbar(aes(ymax=accuracy+sd, ymin=accuracy-sd), position=\"dodge\") +\n ggtitle(main))\n return(OOTable)\n}\n\n# Spatial residual bubbleplot\n# Predicted and observed should be data.frames with 100 as max value\n# none.threshold: What to take as \"no bias\"; ==0 is very rare, and 5% off is fine\nResidualBubblePlot = function(predicted, observed, geometry, none.threshold=5, main=\"\")\n{\n Resids = predicted-observed\n Resids.sf = st_set_geometry(Resids, geometry)\n Resids.long = reshape2::melt(Resids.sf, id.vars=\"geometry\", variable.name=\"class\")\n Resids.long$size = abs(Resids.long$value)\n Resids.long$type = ifelse(Resids.long$value > none.threshold, \"positive\", ifelse(Resids.long$value < -none.threshold, \"negative\", \"none\"))\n ggplot(Resids.long) + geom_sf(aes(colour=type, size=size), alpha=0.5) + \n scale_colour_manual(values=c(positive=\"red\", none=\"green\", negative=\"blue\")) + \n scale_size(range=c(0.1, 1), breaks=c(0, 20, 40, 60, 80)) + \n facet_wrap(\"class\") + ggtitle(main)\n}\n\n# Perform histogram matching for each class\n# extremes is about whether to match extremes; 1 is yes, 0 is not for predicted 0/100, -1 is not for the corresponding quantile\nHistMatchPredictions = function(predicted, training=LoadTrainingAndCovariates(), extremes=1)\n{\n HMPredictions = predicted\n for (Class in names(predicted))\n {\n if (extremes == 1) {\n HMPredictions[,Class] = histmatch(predicted[,Class], training[,Class])\n } else {\n if (extremes == 0) {\n ExtremeRowsP = predicted[,Class] == 0 | predicted[,Class] == 100\n } else if (extremes == -1) {\n PercentileT0 = mean(training[,Class] == 0)\n PercentileT100 = 1-mean(training[,Class] == 100)\n ExtremeRowsP = predicted[,Class] < quantile(predicted[,Class], PercentileT0) |\n predicted[,Class] > quantile(predicted[,Class], PercentileT100)\n }\n ExtremeRowsT = training[,Class] == 0 | training[,Class] == 100\n HMPredictions[!ExtremeRowsP,Class] = histmatch(predicted[!ExtremeRowsP,Class], training[!ExtremeRowsT,Class])\n }\n }\n \n # Sometimes, the histograms match in the way that everything becomes 0, so it's impossible to scale everything.\n # In cases like that, restore original values.\n ZeroRows = apply(HMPredictions, 1, function(x)all(x==0))\n HMPredictions[ZeroRows,] = predicted[ZeroRows,]\n # Scale\n HMPredictions = HMPredictions / rowSums(HMPredictions) * 100\n \n return(HMPredictions)\n}\n\n# Rasterise an SF object\nSfToRaster = function(sfo, xsamplingrate=0.2, ysamplingrate=0.2, layers=GetCommonClassNames(), fun=max, ...)\n{\n xres = (st_bbox(sfo)[\"xmax\"]-st_bbox(sfo)[\"xmin\"])/xsamplingrate\n yres = (st_bbox(sfo)[\"ymax\"]-st_bbox(sfo)[\"ymin\"])/ysamplingrate\n rast = raster()\n sfoextent = extent(sfo)\n sfoextent@xmin = sfoextent@xmin - 0.5*xsamplingrate\n sfoextent@xmax = sfoextent@xmax - 0.5*xsamplingrate\n sfoextent@ymin = sfoextent@ymin - 0.5*ysamplingrate\n sfoextent@ymax = sfoextent@ymax - 0.5*ysamplingrate\n extent(rast) = sfoextent\n ncol(rast) = xres\n nrow(rast) = yres\n PR.ras = rasterize(sfo[layers], rast, fun=fun, ...)\n return(PR.ras)\n}\n", "meta": {"hexsha": "3f445fc5bb2e23670e90886e8beb3f934649c967", "size": 20324, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pixel-based/utils/crossvalidation.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/pixel-based/utils/crossvalidation.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pixel-based/utils/crossvalidation.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 43.7075268817, "max_line_length": 162, "alphanum_fraction": 0.6508561307, "num_tokens": 5536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3179532857085877}} {"text": "#-------------------------------------------------------------------------------\r\n# Licence:\r\n# Copyright (c) 2012-2015 Luzzi Valerio for Gecosistema S.r.l.\r\n#\r\n# The above copyright notice and this permission notice shall be\r\n# included in all copies or substantial portions of the Software.\r\n#\r\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\r\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\r\n# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\r\n# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\r\n# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\r\n# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\r\n# OTHER DEALINGS IN THE SOFTWARE.\r\n#\r\n# Name: time.r\r\n# Purpose: date & time utilities\r\n#\r\n# Author: Luzzi Valerio\r\n#\r\n# Created: 12/10/2015\r\n#-------------------------------------------------------------------------------\r\n\r\n#' isDate\r\n#'\r\n#' This function returns TRUE is text is a Date.\r\n#' @param text - a string\r\n#' @keywords isDate\r\n#' @export\r\n#' @return returns TRUE if \\code{text} is a Date\r\n#' @examples\r\n#' isDate(\"2015-10-12\")\r\nisDate<-function(text){res = tryCatch(length(as.Date(text))>0,error=function(e){return(FALSE)});return(res)}\r\n\r\n#' date2julian\r\n#'\r\n#' This function returns julian number for a date\r\n#' @param date - the orginal date\r\n#' @keywords date2julian,julian\r\n#' @export\r\n#' @return returns the julian number for \\code{date}.\r\n#' @examples\r\n#' date2julian(\"2015-10-12\")\r\ndate2julian<-function(date){return(strftime(date,\"%j\"))}\r\n\r\n#' julian2date\r\n#'\r\n#' This function returns the date for julian number YYYYjjj\r\n#' @param j - the julian number in the form YYYYjjj\r\n#' @keywords julian2date\r\n#' @export\r\n#' @return returns the date number for \\code{j}.\r\n#' @examples\r\n#' julian2date(\"2015221\")\r\njulian2date<-function(j){ j= as.numeric(j);return(strptime(j,\"%Y%j\"))}\r\n\r\n#' dtos\r\n#'\r\n#' This function returns a date string fromatted\r\n#' @param date - the original date\r\n#' @param format - the format.\r\n#' @keywords dtos\r\n#' @export\r\n#' @return returns a date in a string form according to the format.\r\n#' @examples\r\n#' dtos(\"2015-10-12\",\"%d/%m/%Y\")\r\ndtos<-function(date,format=\"%Y-%m-%d\"){\r\n if (is.null(date))\r\n date= Sys.time()\r\n\r\n return(strftime(date,format))\r\n}\r\n", "meta": {"hexsha": "2501e89a316b86d11c7c1365e642d669bd5c5512", "size": 2370, "ext": "r", "lang": "R", "max_stars_repo_path": "R/time.r", "max_stars_repo_name": "valluzzi/gecosistema", "max_stars_repo_head_hexsha": "e5a8f38d08f5a1cfa8294f9706ab34d247b0cd0d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/time.r", "max_issues_repo_name": "valluzzi/gecosistema", "max_issues_repo_head_hexsha": "e5a8f38d08f5a1cfa8294f9706ab34d247b0cd0d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/time.r", "max_forks_repo_name": "valluzzi/gecosistema", "max_forks_repo_head_hexsha": "e5a8f38d08f5a1cfa8294f9706ab34d247b0cd0d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.027027027, "max_line_length": 109, "alphanum_fraction": 0.6345991561, "num_tokens": 610, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.3179532857085877}} {"text": "pdf_file<-\"pdf/hist_eurobarometer_71_1x4.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=12,height=3)\n\npar(omi=c(1,0.5,0.75,0.5),mai=c(0.1,1.25,0.1,0.2),family=\"Lato Light\",las=1) \nsource(\"scripts/inc_datadesign_dbconnect.r\")\n\n# Daten einlesen und Grafik vorbereiten\n\nsql<-\"select * from v_za4972_countries\"\nmyData<-dbGetQuery(con,sql)\nattach(myData)\npar(mfcol=c(1,4))\n\n# Grafik erstellen\n\nfor (i in 2:5) hist(myData[,i],main=names(myData[i]),xlab=\"\")\ndev.off()", "meta": {"hexsha": "dce635144d6d58984496c55f17ff130a6b9b7236", "size": 456, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/hist_eurobarometer_71_1x4.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/hist_eurobarometer_71_1x4.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/hist_eurobarometer_71_1x4.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.8235294118, "max_line_length": 77, "alphanum_fraction": 0.7280701754, "num_tokens": 177, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.546738151984614, "lm_q1q2_score": 0.31782057752181636}} {"text": "context(\"Testing Customer\") \n\n#create a random Customer with user friendly function \n#\t-- showwarnings=TRUE to indicate this. Default is FALSE\n#\t\n# context(\"\\t\\tcreate a random Customer with user friendly function \") \nc1<- new(\"Customer\",showwarnings=FALSE)\n\n\n# context(\"\\t\\tcreate an explizit defined Customer with S4 initializer\") \nc2<- new(\"Customer\",id = \"C02\", label=\"Customer 02\", x = 10, y= 20, demand = 10 )\n\n\n# context(\"\\t\\tcreate a Customer from a data.frame\") \ndf <- data.frame(id = \"C02\", label=\"Customer 02\", x = 14, y= 23, demand = 10 )\nc3 <- new(\"Customer\",df)\n\n\n# context(\"\\t\\tconvert Customer to data.frame\") \ndf2 <- as.data.frame(c3)\n\n# context(\"\\t\\tor convert a data.frame to a Customer\") \nc3a <- as.Customer(df2)\n\nlic3 <- as.list(c3) \nc3b <- as.Customer(lic3) \n#Do some testing\n#\ncontext(\"\\tTest 01: Are Objects created correctly?\") \ntest_that(\"Test for identical objects\", {\n expect_false(identical(c1,c3)) # Customer 1 and 3 should NOT be idenitical\n expect_true(identical(c3,c3a)) # Customer 3 and 3a should be idenitical\n expect_true(identical(c3,c3b)) # Customer 3 and 3b should be idenitical\n \n})\n\ncontext(\"\\tTest 02: Are the Node-Methods for Customer working?\") \ntest_that(\"Node-Methods work for Customer\", {\n \n expect_true(getDistance(c2,c3) == 5) \t\t\t # should be 5\n\t# use a cost-factor: \n expect_true(getDistance(c2,c3, costfactor = 2) == 10) # should be 10 \n})\ncontext(\"\\tTest 03: Are the Customers correctly validated?\") \ntest_that(\"Validation is implemented correctly\", {\n \n \texpect_error(c1$demand <- -12, \"Invalid Object of Type 'Customer': Error with value demand: at least one value is negative. Only positive Values are allowed.\")\n})\n\ncontext(\"done.\") \ncontext(\"--------------------------------------------------\") \n\n", "meta": {"hexsha": "347c9f38189b2cd9fb0b280e36c184d3d2bfb53f", "size": 1781, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/tests/testHNUCustomer.r", "max_stars_repo_name": "felixlindemann/HNUORTools", "max_stars_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/tests/testHNUCustomer.r", "max_issues_repo_name": "felixlindemann/HNUORTools", "max_issues_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/tests/testHNUCustomer.r", "max_forks_repo_name": "felixlindemann/HNUORTools", "max_forks_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.6037735849, "max_line_length": 162, "alphanum_fraction": 0.6743402583, "num_tokens": 495, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31782057752181636}} {"text": "library(plyr)\nlibrary(dplyr)\nlibrary(Nippon)\nlibrary(stringr)\nlibrary(stringi)\nlibrary(jsonlite)\nlibrary(ggplot2)\nlibrary(ggmap)\n\nregister_google(key = \"\")\n\n# \u4e2d\u6587\u5b57\u8f49\u6578\u5b57\nchinese2digits <- function(x){\n vals <- sapply(str_split(x, \"\")[[1]], function(chi_digit){\n mapvalues(chi_digit, c(\"\u96f6\", \"\u4e00\", \"\u4e8c\", \"\u4e09\", \"\u56db\", \"\u4e94\", \"\u516d\", \"\u4e03\", \"\u516b\", \"\u4e5d\",\n \"\u5341\", \"\u767e\", \"\u5343\", \"\u842c\", \"\u5104\"), c(0:10, 10^c(2,3,4,8)), FALSE)\n }) %>% as.integer\n digit_output <- 0\n base_term <- 1\n for (i in rev(seq_along(vals)))\n {\n if (vals[i] >= 10 && i == 1)\n {\n base_term <- ifelse(vals[i] > base_term, vals[i], base_term * vals[i])\n digit_output <- digit_output + vals[i]\n } else if (vals[i] >= 10)\n {\n base_term <- ifelse(vals[i] > base_term, vals[i], base_term * vals[i])\n } else\n {\n digit_output <- digit_output + base_term * vals[i]\n }\n }\n return(digit_output)\n}\n\nshop_data <- read.table(\"~/Desktop/R_Project/shop_data.csv\", header = TRUE, sep = \",\", encoding = \"UTF-8\", stringsAsFactors = FALSE)\nshop_data <- shop_data %>%\n filter(\u5206\u516c\u53f8\u72c0\u614b == \"1\") %>%\n select(\u516c\u53f8\u540d\u7a31, \u5206\u516c\u53f8\u7d71\u4e00\u7de8\u865f, \u5206\u516c\u53f8\u5730\u5740)\n\n# \u8655\u7406\u5730\u5740\n# \u4e0d\u77e5\u9053\u70ba\u4ec0\u9ebc mutate \u5f04\u4e0d\u8d77\u4f86\uff0c\u53ea\u597d\u7528 for\nfor(i in 1:nrow(shop_data)){\n # \u5168\u5f62\u8f49\u534a\u5f62\n shop_data$\u5206\u516c\u53f8\u5730\u5740[i] <- zen2han(as.character(shop_data$\u5206\u516c\u53f8\u5730\u5740[i]))\n\n # \u4e2d\u6587\u5b57\u8f49\u6578\u5b57\n # \u7a0b\u5f0f\u78bc\u4fee\u6539\u81ea\uff1ahttps://www.ptt.cc/bbs/R_Language/M.1466006460.A.4DC.html\n shop_data$\u5206\u516c\u53f8\u5730\u5740[i] <- sapply(shop_data$\u5206\u516c\u53f8\u5730\u5740[i], function(x){\n pattern_starts <- \"[\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d\u5341\u767e\u5343\u842c\u5104]+\u6a13\"\n if (!str_detect(x, pattern_starts))\n return(x)\n stairs <- str_extract(x, pattern_starts)\n x <- str_replace(x, str_c(\"(\\\\d+)(\", pattern_starts, \")\"), \"\\\\1, \\\\2\")\n x <- str_replace(stairs, \"\u6a13\", \"\") %>% chinese2digits %>% str_c(\"\u6a13\") %>%\n {str_replace(x, stairs, .)}\n return(x)\n }) %>% `names<-`(NULL)\n\n shop_data$\u5206\u516c\u53f8\u5730\u5740[i] <- sapply(shop_data$\u5206\u516c\u53f8\u5730\u5740[i], function(x){\n pattern_starts <- \"[\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d\u5341\u767e\u5343\u842c\u5104]+F\"\n if (!str_detect(x, pattern_starts))\n return(x)\n stairs <- str_extract(x, pattern_starts)\n x <- str_replace(x, str_c(\"(\\\\d+)(\", pattern_starts, \")\"), \"\\\\1, \\\\2\")\n x <- str_replace(stairs, \"F\", \"\") %>% chinese2digits %>% str_c(\"F\") %>%\n {str_replace(x, stairs, .)}\n return(x)\n }) %>% `names<-`(NULL)\n\n shop_data$\u5206\u516c\u53f8\u5730\u5740[i] <- sapply(shop_data$\u5206\u516c\u53f8\u5730\u5740[i], function(x){\n pattern_starts <- \"[\u96f6\u4e00\u4e8c\u4e09\u56db\u4e94\u516d\u4e03\u516b\u4e5d\u5341\u767e\u5343\u842c\u5104]+\u865f\"\n if (!str_detect(x, pattern_starts))\n return(x)\n no <- str_extract(x, pattern_starts)\n x <- str_replace(x, str_c(\"(\\\\d+)(\", pattern_starts, \")\"), \"\\\\1, \\\\2\")\n x <- str_replace(no, \"\u865f\", \"\") %>% chinese2digits %>% str_c(\"\u865f\") %>%\n {str_replace(x, no, .)}\n return(x)\n }) %>% `names<-`(NULL)\n}\n\n# \u7570\u9ad4\u5b57\nshop_data <- shop_data %>% mutate(\u5206\u516c\u53f8\u5730\u5740 = {\n \u5206\u516c\u53f8\u5730\u5740 %>%\n str_replace_all(\"\u53f0\", \"\u81fa\") %>%\n str_replace_all(\"\u5dff\", \"\u5e02\")\n})\n\n# \u7e23\u8f44\u5e02\u61c9\u7b97\u5728\u7e23\u5e95\u4e0b\nshop_data <- shop_data %>% mutate(\u5206\u516c\u53f8\u5730\u5740 = {\n \u5206\u516c\u53f8\u5730\u5740 %>%\n str_replace_all(\"^\u7af9\u5317\u5e02\", \"\u65b0\u7af9\u7e23\u7af9\u5317\u5e02\") %>%\n str_replace_all(\"^\u5f70\u5316\u5e02\", \"\u5f70\u5316\u7e23\u5f70\u5316\u5e02\") %>%\n str_replace_all(\"^\u54e1\u6797\u5e02\", \"\u5f70\u5316\u7e23\u54e1\u6797\u5e02\") %>%\n str_replace_all(\"^\u82d7\u6817\u5e02\", \"\u82d7\u6817\u7e23\u82d7\u6817\u5e02\") %>%\n str_replace_all(\"^\u982d\u4efd\u5e02\", \"\u82d7\u6817\u7e23\u982d\u4efd\u5e02\") %>%\n str_replace_all(\"^\u5357\u6295\u5e02\", \"\u5357\u6295\u7e23\u5357\u6295\u5e02\") %>%\n str_replace_all(\"^\u6597\u516d\u5e02\", \"\u96f2\u6797\u7e23\u6597\u516d\u5e02\") %>%\n str_replace_all(\"^\u592a\u4fdd\u5e02\", \"\u5609\u7fa9\u7e23\u592a\u4fdd\u5e02\") %>%\n str_replace_all(\"^\u6734\u5b50\u5e02\", \"\u5609\u7fa9\u7e23\u6734\u5b50\u5e02\") %>%\n str_replace_all(\"^\u5c4f\u6771\u5e02\", \"\u5c4f\u6771\u7e23\u5c4f\u6771\u5e02\") %>%\n str_replace_all(\"^\u5b9c\u862d\u5e02\", \"\u5b9c\u862d\u7e23\u5b9c\u862d\u5e02\") %>%\n str_replace_all(\"^\u82b1\u84ee\u5e02\", \"\u82b1\u84ee\u7e23\u82b1\u84ee\u5e02\") %>%\n str_replace_all(\"^\u81fa\u6771\u5e02\", \"\u81fa\u6771\u7e23\u81fa\u6771\u5e02\") %>%\n str_replace_all(\"^\u99ac\u516c\u5e02\", \"\u6f8e\u6e56\u7e23\u99ac\u516c\u5e02\")\n})\n# \u8655\u7406\u76f4\u8f44\u5e02\u6539\u5236\u5f8c\u7684\u540d\u7a31\nshop_data <- shop_data %>% mutate(\u5206\u516c\u53f8\u5730\u5740 = {\n \u5206\u516c\u53f8\u5730\u5740 %>%\n str_replace_all(\"\u81fa\u5317\u7e23\", \"\u65b0\u5317\u5e02\") %>%\n str_replace_all(\"\u6843\u5712\u7e23\", \"\u6843\u5712\u5e02\") %>%\n str_replace_all(\"\u81fa\u4e2d\u7e23\", \"\u81fa\u4e2d\u5e02\") %>%\n str_replace_all(\"\u81fa\u5357\u7e23\", \"\u81fa\u5357\u5e02\") %>%\n str_replace_all(\"\u9ad8\u96c4\u7e23\", \"\u9ad8\u96c4\u5e02\")\n})\n\nshop_data <- shop_data %>% mutate(city = substring(\u5206\u516c\u53f8\u5730\u5740, 0, 3))\nshop_data <- shop_data %>% mutate_geocode(\u5206\u516c\u53f8\u5730\u5740)\n\n# \u8a08\u7b97\u8d85\u5546\u6578\u91cf\nshop_count <- shop_data %>%\n group_by(\u516c\u53f8\u540d\u7a31) %>%\n dplyr::summarise(n = n_distinct(\u5206\u516c\u53f8\u7d71\u4e00\u7de8\u865f))\nggplot(data=shop_count, mapping=aes(x=\"\u516c\u53f8\u540d\u7a31\", y = n ,fill=\u516c\u53f8\u540d\u7a31)) +\n geom_bar(stat=\"identity\",width = 1, size = 1,position='stack') +\n coord_polar(\"y\", start=0) +\n theme_void() +\n labs(fill='\u8d85\u5546') +\n geom_text(aes( label = scales::percent(n / sum(n))), position = position_stack(vjust = 0.5)) +\n theme(text=element_text(family=\"\u9ed1\u9ad4-\u7e41 \u4e2d\u9ed1\", size=12))\n\ntown_list <- fromJSON(\"~/Desktop/R_Project/town_list.json\")\npopulation <- fromJSON(\"~/Desktop/R_Project/population.json\")\narea <- fromJSON(\"~/Desktop/R_Project/area.json\")\n\n# \u6bcf\u500b\u7e23\u5e02\u7684\u7e3d\u5e97\u6578\nshop_per_city <- shop_data %>% group_by(city, \u516c\u53f8\u540d\u7a31) %>% dplyr::summarise(n = n_distinct(\u5206\u516c\u53f8\u7d71\u4e00\u7de8\u865f))\nggplot(data = shop_per_city, mapping = aes(x = city, y = n, fill = shop_per_city$\u516c\u53f8\u540d\u7a31)) +\n geom_bar(stat = \"identity\") +\n labs(x = '\u7e23\u5e02', y = '\u6578\u91cf', fill='\u8d85\u5546') +\n theme(text=element_text(family=\"\u9ed1\u9ad4-\u7e41 \u4e2d\u9ed1\", size=12), axis.text.x=element_text(angle=45))\n\n# \u5e73\u5747\u6bcf\u5bb6\u5e97\u670d\u52d9\u7684\u4eba\u53e3\u6578\n. <- shop_per_city %>%\n group_by(city) %>%\n summarise(num = sum(n))\npeople_per_shop <- left_join(., population, by = c(\"city\" = \"city\"))\nggplot(data = people_per_shop, mapping = aes(x = city, y = (population / num) / 1e2, fill = people_per_shop$city)) +\n geom_bar(stat = \"identity\") +\n labs(x = '\u7e23\u5e02', y = '\u670d\u52d9\u4eba\u53e3\uff08\u767e\u4eba\uff09', fill='\u7e23\u5e02') +\n geom_text(aes(label = floor((population / num) / 10) / 10), size = 3, position = position_stack(vjust = 0.5)) +\n theme(text=element_text(family=\"\u9ed1\u9ad4-\u7e41 \u4e2d\u9ed1\", size=12), axis.text.x=element_text(angle=45), legend.position=\"none\")\n\n# \u5e73\u5747\u6bcf\u5bb6\u5e97\u670d\u52d9\u7684\u7bc4\u570d\narea_per_shop <- left_join(., area, by = c(\"city\" = \"city\"))\nggplot(data = area_per_shop, mapping = aes(x = city, y = (area / num), fill = area_per_shop$city)) +\n geom_bar(stat = \"identity\") +\n labs(x = '\u7e23\u5e02', y = '\u670d\u52d9\u7bc4\u570d\uff08\u5e73\u65b9\u516c\u91cc\uff09', fill='\u7e23\u5e02') +\n geom_text(aes(label = floor((area / num) * 100) / 100), size = 3, position = position_stack(vjust = 0.5)) +\n theme(text=element_text(family=\"\u9ed1\u9ad4-\u7e41 \u4e2d\u9ed1\", size=12), axis.text.x=element_text(angle=45), legend.position=\"none\")\n\n\n\n# \u7e6a\u88fd\u5730\u5716\nqmplot(lon, lat, data = shop_data, maptype = \"toner-lite\", color = I(\"red\"), zoom = 9)\n\n# \u81fa\u5317: 25.108215, 121.451381 24.989190, 121.570576\ntravel_time_taipei <- c()\nfor(i in 1:200){\n lon <- runif(1, min=121.451381, max=121.570576)\n lat <- runif(1, min=24.989190, max=25.108215)\n dis <- (shop_data$lon - lon) ^ 2 + (shop_data$lat - lat) ^ 2\n . <- mapdist(as.numeric(c(lon, lat)), c(shop_data$\u5206\u516c\u53f8\u5730\u5740[which.min(dis)]), mode=\"walking\")\n if(is.numeric(.$seconds)) travel_time_taipei <- c(travel_time_taipei, .$seconds)\n}\n\n\n# \u96f2\u6797\u5609\u7fa9 23.824379, 120.251754 23.405572, 120.486646\ntravel_time_yunlin_chiayi <- c()\nfor(i in 1:200){\n lon <- runif(1, min=121.451381, max=121.570576)\n lat <- runif(1, min=24.989190, max=25.108215)\n dis <- (shop_data$lon - lon) ^ 2 + (shop_data$lat - lat) ^ 2\n . <- mapdist(as.numeric(c(lon, lat)), c(shop_data$\u5206\u516c\u53f8\u5730\u5740[which.min(dis)]), mode=\"walking\")\n if(is.numeric(.$seconds)) travel_time_yunlin_chiayi <- c(travel_time_yunlin_chiayi, .$seconds)\n}\n\n# \u5b9c\u862d 24.614054, 121.805200 24.786233, 121.654558\ntravel_time_yilan <- c()\nfor(i in 1:200){\n lon <- runif(1, min=121.451381, max=121.570576)\n lat <- runif(1, min=24.989190, max=25.108215)\n dis <- (shop_data$lon - lon) ^ 2 + (shop_data$lat - lat) ^ 2\n . <- mapdist(as.numeric(c(lon, lat)), c(shop_data$\u5206\u516c\u53f8\u5730\u5740[which.min(dis)]), mode=\"walking\")\n if(is.numeric(.$seconds)) travel_time_yilan <- c(travel_time_yilan, .$seconds)\n}\n", "meta": {"hexsha": "30ead3a268ab73eb197a079a410c09250c71c953", "size": 7273, "ext": "r", "lang": "R", "max_stars_repo_path": "code.r", "max_stars_repo_name": "s3131212/Taiwan-Convenience-Store-Distribution", "max_stars_repo_head_hexsha": "0b8de4a05caed6bf9073fb419e7a6b8e3641079c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-02-05T05:15:06.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-05T15:14:39.000Z", "max_issues_repo_path": "code.r", "max_issues_repo_name": "s3131212/Taiwan-Convenience-Store-Distribution", "max_issues_repo_head_hexsha": "0b8de4a05caed6bf9073fb419e7a6b8e3641079c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code.r", "max_forks_repo_name": "s3131212/Taiwan-Convenience-Store-Distribution", "max_forks_repo_head_hexsha": "0b8de4a05caed6bf9073fb419e7a6b8e3641079c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5477386935, "max_line_length": 132, "alphanum_fraction": 0.6214766946, "num_tokens": 2981, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31782057752181636}} {"text": "a <- \\(x) x + 1", "meta": {"hexsha": "18eeeaea386ac1ed64af67cee0b448dd1662ec37", "size": 15, "ext": "r", "lang": "R", "max_stars_repo_path": "testData/parser/r/ShorthandFunction.r", "max_stars_repo_name": "Mervap/Rplugin", "max_stars_repo_head_hexsha": "f6fc7683881127e1b8033786447ebce683659ea3", "max_stars_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_stars_count": 52, "max_stars_repo_stars_event_min_datetime": "2019-10-24T17:16:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-09T22:19:02.000Z", "max_issues_repo_path": "testData/parser/r/ShorthandFunction.r", "max_issues_repo_name": "Mervap/Rplugin", "max_issues_repo_head_hexsha": "f6fc7683881127e1b8033786447ebce683659ea3", "max_issues_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2019-10-24T17:26:36.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-28T12:25:20.000Z", "max_forks_repo_path": "testData/parser/r/ShorthandFunction.r", "max_forks_repo_name": "Mervap/Rplugin", "max_forks_repo_head_hexsha": "f6fc7683881127e1b8033786447ebce683659ea3", "max_forks_repo_licenses": ["MIT", "BSD-2-Clause", "Apache-2.0"], "max_forks_count": 13, "max_forks_repo_forks_event_min_datetime": "2020-02-16T00:07:56.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-21T10:49:52.000Z", "avg_line_length": 15.0, "max_line_length": 15, "alphanum_fraction": 0.2666666667, "num_tokens": 9, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3178205775218163}} {"text": "setwd(\"~/GitHub/bias-study/data\") #machine-dependent... sorry. probably a better way of doing this\nlibrary(ggplot2)\nlibrary(mefa)\nlibrary(lsmeans)\nlibrary(multcomp)\n\nbias_data <- read.csv(\"cleaned_data.csv\")\n#Filter out the people who admitted to cheating\nbias_data <- bias_data[bias_data$id %in% c(\"87912012\", \"30619584\", \"51665387\", \"25917069\", \"50263296\", \"60755967\", \"2893057\", \"28653725\", \"76648162\", \"21045111\", \"72925790\", \"88825154\", \"97626871\", \"29657674\", \"26308857\", \"79273300\", \"77642132\", \"83205605\", \"9782123\", \"28428523\", \"95378753\", \"62759895\", \"45694489\", \"57315921\", \"20505415\", \"73348785\", \"77955785\", \"45840218\", \"79707421\", \"7321301\", \"43548417\", \"89431160\", \"29089701\", \"61154389\", \"71996586\", \"21418137\", \"76395409\", \"79544988\", \"76024785\"),]\nbias_data <- bias_data[bias_data$demographics_cheat == \"no\",]\n#Select the columns for statistics\nid <- rep(as.vector(t(bias_data[\"id\"])), each=2)#airline, states\ncondition <- rep(as.vector(t(bias_data[\"studyCondition\"])), each=2)#airline, states\ncondition <- factor(condition,c(\"onlyNew\", \"difference\", \"both\"))\nvisType <- rep(c(\"airline\", \"states\"), nrow(bias_data))\nfocus <- as.vector(t(bias_data[c(\"focusAirline\", \"focusState\")]))\nsequence <- as.vector(t(bias_data[c(\"seqAirline\", \"seqStates\")]))\ncontrol <- factor(as.factor(ifelse(sequence == 1, 1, 0)), c(\"1\", \"0\"))\norder <- ifelse(rep(as.vector(t(bias_data[\"firstCondition\"])), each=2) == visType, 1, 2)#airline, states\ncleaned_data_frame <- data.frame(id, condition, visType, focus, sequence, order, control)\n\n#-----How many questions when viewing the precise data-----\nhow_many_precise <- cleaned_data_frame\n#Calcualte approximate error\napprox_howMany_answer <- as.vector(t(bias_data[c(\"approx_airline_howMany_answer\", \"approx_states_howMany_answer\")]))\napprox_howMany_approx <- as.vector(t(bias_data[c(\"approx_airline_howMany_approx\", \"approx_states_howMany_approx\")]))\nprecise_howMany_precise <- as.vector(t(bias_data[c(\"precise_airline_howMany_precise\", \"precise_states_howMany_precise\")]))\nprecise_howMany_answer <- as.vector(t(bias_data[c(\"precise_airline_howMany_answer\", \"precise_states_howMany_answer\")]))\n\nhow_many_precise$approximate_error <- (approx_howMany_answer - approx_howMany_approx)/ approx_howMany_approx\nhow_many_precise$expected_bias <- (precise_howMany_precise - approx_howMany_approx)/ precise_howMany_precise\nhow_many_precise$measured_bias <- (precise_howMany_precise - precise_howMany_answer)/ precise_howMany_precise\n\n#One would hope we can remove: visType, focus, sequence, order\n#how_many_precise <- how_many_precise[c(T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, F, T, T, T),] #remove outlier answer\nhow_many_precise_test <- lm(measured_bias ~ expected_bias + approximate_error + visType + condition + focus + sequence + order + (1 | id), data=how_many_precise)\nhow_many_precise_test <- lm(measured_bias ~ control + expected_bias*condition + (1 | id), data=how_many_precise)\n\nhow_many_precise_plot <- ggplot(how_many_precise, aes(expected_bias, measured_bias, color=condition, fill=condition)) + theme(text = element_text(size=16)) + geom_point() + geom_smooth(se=T, method=\"lm\")\nggsave(\"plots/how_many_precise.pdf\", how_many_precise_plot)\n\nhow_many_precise_boxplot <- ggplot(how_many_precise, aes(condition, measured_bias)) + geom_boxplot()\nggsave(\"plots/how_many_precise_box.pdf\", how_many_precise_boxplot)\n\n#-----Comparison questions when viewing the precise data-----\ncompare_precise <- rep(cleaned_data_frame, 3)\n#Calculate approximate error (Only looking at the first approximate answer, since I think this was the intended behavior)\n\napprox_compare_answer <- as.vector(t(bias_data[c(\"approx_airline_howManyCompare_0_answer\", \"approx_states_howManyCompare_0_answer\")]))\napprox_compare_approx <- as.vector(t(bias_data[c(\"approx_airline_howManyCompare_0_approx\", \"approx_states_howManyCompare_0_approx\")]))\nprecise_compare_approx <- as.vector(t(bias_data[c(\"precise_airline_howManyCompare_0_approx\", \"precise_states_howManyCompare_0_approx\", \"precise_airline_howManyCompare_1_approx\", \"precise_states_howManyCompare_1_approx\", \"precise_airline_howManyCompare_2_approx\", \"precise_states_howManyCompare_2_approx\")]))\nprecise_compare_precise <- as.vector(t(bias_data[c(\"precise_airline_howManyCompare_0_precise\", \"precise_states_howManyCompare_0_precise\", \"precise_airline_howManyCompare_1_precise\", \"precise_states_howManyCompare_1_precise\", \"precise_airline_howManyCompare_2_precise\", \"precise_states_howManyCompare_2_precise\")]))\nprecise_compare_answer <- as.vector(t(bias_data[c(\"precise_airline_howManyCompare_0_answer\", \"precise_states_howManyCompare_0_answer\", \"precise_airline_howManyCompare_1_answer\", \"precise_states_howManyCompare_1_answer\", \"precise_airline_howManyCompare_2_answer\", \"precise_states_howManyCompare_2_answer\")]))\nprecise_compare_comparison <- as.vector(t(bias_data[c(\"precise_airline_howManyCompare_0_data\", \"precise_states_howManyCompare_0_data\", \"precise_airline_howManyCompare_1_data\", \"precise_states_howManyCompare_1_data\", \"precise_airline_howManyCompare_2_data\", \"precise_states_howManyCompare_2_data\")]))\napprox_compare_comparison <- as.vector(t(bias_data[c(\"approx_airline_howManyCompare_0_data\", \"approx_states_howManyCompare_0_data\")]))\n\ncompare_precise$approximate_error <- rep((approx_compare_answer - approx_compare_approx)/approx_compare_approx, 3)\ncompare_precise$expected_bias <- (precise_compare_precise - precise_compare_approx)/precise_compare_precise\ncompare_precise$measured_bias <- (precise_compare_precise - precise_compare_answer)/precise_compare_precise\ncompare_precise$approximate_comparison <- rep(approx_compare_comparison, 3)\ncompare_precise$precise_comparison <- rep(precise_compare_comparison)\n\n#One would hope we can remove: visType, focus, sequence, order, approximate_comparison, precise_comparison\ncompare_precise_test <- lm(measured_bias ~ expected_bias + approximate_error + approximate_comparison + precise_comparison + visType + condition + focus + sequence + order + (1 | id), data=compare_precise)\ncompare_precise_test <- lm(measured_bias ~ control + expected_bias*condition + (1 | id), data=compare_precise)\n\ncompare_precise_plot <- ggplot(compare_precise, aes(expected_bias, measured_bias, color=condition, fill=condition)) + theme(text = element_text(size=16)) + geom_point() + geom_smooth(se=T, method=\"lm\")\nggsave(\"plots/compare_precise.pdf\", compare_precise_plot)\n\ncompare_precise_boxplot <- ggplot(compare_precise, aes(condition, measured_bias)) + geom_boxplot()\nggsave(\"plots/compare_precise_box.pdf\", compare_precise_boxplot)\n\n#-----Question of \"how many of X were there?-----\njaccard_precise <- cleaned_data_frame\n\njaccard_precise$expected_bias <- as.vector(t(bias_data[c(\"precise_states_SelectAll_jaccard_answer_precise\", \"precise_airline_SelectAll_jaccard_answer_precise\")]))\njaccard_precise$approximate_error <- as.vector(t(bias_data[c(\"precise_states_SelectAll_jaccard_approx_precise\", \"precise_airline_SelectAll_jaccard_approx_precise\")]))\njaccard_precise$measured_bias <- as.vector(t(bias_data[c(\"precise_states_SelectAll_jaccard_approx_answer\", \"precise_airline_SelectAll_jaccard_approx_answer\")]))\n\njaccard_precise_test <- lm(measured_bias ~ control + expected_bias*condition + (1 | id), data=jaccard_precise)\n\njaccard_precise_plot <- ggplot(jaccard_precise, aes(expected_bias, measured_bias, color=condition, fill=condition)) + theme(text = element_text(size=16)) + geom_jitter() + geom_smooth(se=T, method=\"lm\")\nggsave(\"plots/jaccard_precise_jitter.pdf\", jaccard_precise_plot)\n\njaccard_precise_boxplot <- ggplot(jaccard_precise, aes(condition, measured_bias)) + geom_boxplot()\nggsave(\"plots/jaccard_precise_box.pdf\", jaccard_precise_boxplot)\n", "meta": {"hexsha": "829bf1fd4aa7ed43df18d9cd07faf9adfcac8361", "size": 7689, "ext": "r", "lang": "R", "max_stars_repo_path": "data/analysis.r", "max_stars_repo_name": "domoritz/bias-study", "max_stars_repo_head_hexsha": "05179ab5c8a760444a4f31d29e500a328b057a4b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data/analysis.r", "max_issues_repo_name": "domoritz/bias-study", "max_issues_repo_head_hexsha": "05179ab5c8a760444a4f31d29e500a328b057a4b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2016-11-07T00:13:31.000Z", "max_issues_repo_issues_event_max_datetime": "2016-12-07T22:59:45.000Z", "max_forks_repo_path": "data/analysis.r", "max_forks_repo_name": "domoritz/bias-study", "max_forks_repo_head_hexsha": "05179ab5c8a760444a4f31d29e500a328b057a4b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 88.3793103448, "max_line_length": 509, "alphanum_fraction": 0.8038756665, "num_tokens": 2131, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3178205775218163}} {"text": "#plot tedersoo fits\nrm(list=ls())\nlibrary(data.table)\nsource('NEFI_functions/crib_fun.r')\nd <- readRDS('/fs/data3/caverill/NEFI_microbial/model_fits/ted_frequentist.rds')\n\n\npng(filename='figures/tedersoo_fits.png',width=10,height=10,units='in',res=300)\n\npar(mfrow = c(5,5),\n mai = c(0,0,0,0),\n oma = c(5,5,1,1))\n\nfor(i in 1:length(d)){\n z <- d[[i]]\n plot(z$data[,1] ~ z$data$fitted, ylab = NA,xlab=NA, cex = 0.5, pch = 16)\n abline(lm(z$data[,1] ~ z$data$fitted), lwd =2)\n mtext(paste('r2=',round(z$r.sq,2)), side = 3, line = -5, adj = 0.05, cex = 0.8, col = 'purple')\n mtext(paste(names(d)[i]) , side = 3, line = -3, adj = 0.05, cex = 0.8, col = 'purple')\n}\n\nmtext('observed', side = 2, line = 3, outer = T)\nmtext('fitted' , side = 1, line = 3, outer = T)\n\ndev.off()", "meta": {"hexsha": "f38817127b46bee7b5121a17edba6093c12092bc", "size": 787, "ext": "r", "lang": "R", "max_stars_repo_path": "testing_development/tedersoo_fits/plotting tedersoo fits.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "testing_development/tedersoo_fits/plotting tedersoo fits.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "testing_development/tedersoo_fits/plotting tedersoo fits.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 31.48, "max_line_length": 97, "alphanum_fraction": 0.6010165184, "num_tokens": 326, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3178154945239011}} {"text": "############\n############ Diverse invaders manuscript script,by Hu Jie \n#### Reading data \nngs.r<-read.table(\"otu.raw.txt\", header=T, row.names=1, sep=\"\\t\") ## otu rawdata\nenv<-read.table(\"env.txt\", sep=\"\\t\", header=T, row.names=1) ## soil properties\n\nngs.i<-ngs.r[,1:dim(env)[1]] ## only abundance\nngs.i<-ngs.i[,order(colnames(ngs.i))] ## order the data \ntaxo.i<-ngs.r[,-c(1:dim(env)[1])] ## only taxonomy\n\n#### calculate value for method description \ncolSums(ngs.i)\nmean(colSums(ngs.i))\nmin(colSums(ngs.i))\nmax(colSums(ngs.i))\n\n#### calculate relative abundance of each otu \nngs.relat<-0*ngs.i ## creat a empty matrix\nfor(i in 1:dim(ngs.relat)[2]) ngs.relat[,i]<-ngs.i[,i]/colSums(ngs.i)[i] ## loop to calculate relative abundance\ncolSums(ngs.relat) ## check if calculate correctly\n\n#### Standardization of abundance\nngs.b<-round(ngs.relat*min(colSums(ngs.i)),0) ## recover otu number based relative otu abundance\nngs.rt<-data.frame(ngs.b,taxo.i) ## combine otu and taxonomy\nngs.b.filter<-ngs.rt[,1:dim(env)[1]] ## subset based on dimensionality of env.txt\n\nngs.b$zero.num<-rowSums(ngs.b==0) ## calculate the zero amount of each row\nngs.b$sum<-rowSums(ngs.b) ## calculate sum of each row, it makes some mistakes!!!!\n\ncolb<-data.frame(colSums(ngs.b!=0)) ## OTU richness of each sample\n\nngs.01<-as.data.frame(1*(ngs.b.filter>0)) ## transfer to 0/1 matrix \n\n\n", "meta": {"hexsha": "ced4e5538361190c2c71e0c6b751b8479ffc355d", "size": 1630, "ext": "r", "lang": "R", "max_stars_repo_path": "1-HJngs.r", "max_stars_repo_name": "HuJamie/Downstream-analysis-of-OTU-table", "max_stars_repo_head_hexsha": "f717e1087d613bb40e13367c486df12780a7ac1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-17T13:41:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-17T13:41:14.000Z", "max_issues_repo_path": "1-HJngs.r", "max_issues_repo_name": "HuJamie/Downstream-analysis-of-OTU-table", "max_issues_repo_head_hexsha": "f717e1087d613bb40e13367c486df12780a7ac1b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "1-HJngs.r", "max_forks_repo_name": "HuJamie/Downstream-analysis-of-OTU-table", "max_forks_repo_head_hexsha": "f717e1087d613bb40e13367c486df12780a7ac1b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.5714285714, "max_line_length": 112, "alphanum_fraction": 0.5680981595, "num_tokens": 464, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6406358548398982, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3178154945239011}} {"text": "#####################################################################################\n#\n# Mark Cembrowski, Janelia Research Campus, April 15 2015\n#\n# This script return sgenes that are enriched in one (or more) populations, relative\n# to another set of one or more populations. sampleX will be the enriched population,\n# sampleY will be the depleted population.\n#\n#####################################################################################\n\ngetEnrGenes <- function(sampleX,sampleY,fdr=-1,foldThres=-1,fpkmThres=-1,avgPass=T){\n\t# Populate a matrix with the genes that fit supplied arguments.\n\t# Begin with fold change.\n\tif(foldThres>1){\n\t\tif(avgPass){\n\t\t\tenrXFold <- .apply(fpkmPoolMat[,sampleX],1,min)>\n\t\t\t\t(foldThres* (.apply(fpkmPoolMat[,sampleY],1,max)) )\n\t\t}else{\n\t\t\tminX <- .apply(fpkmRepMat[,grepl(paste(sampleX,collapse='|'),colnames(fpkmRepMat))],1,min)\n\t\t\tmaxY <- .apply(fpkmRepMat[,grepl(paste(sampleY,collapse='|'),colnames(fpkmRepMat))],1,max)\n\t\t\tenrXFold <- minX>(foldThres*maxY)\n\t}\n\t\tenrXFold <- rownames(fpkmPoolMat)[enrXFold]\n\t}else{\n\t\t# Take an effective fold threshold of 1, preventing underexpressed\n\t\t# genes in sampleX from showing up if not screening for fold changes\n\t\t# (e.g., when using FDR to screen). Otherwise, the FDR results\n\t\t# give bidirectional (ie, significantly up AND down regulated) genes\n\t\t# which is undesirable.\n\t\tenrXFold <- .apply(fpkmPoolMat[,sampleX],1,min)>\n\t\t\t\t( .apply(fpkmPoolMat[,sampleY],1,max) )\n\t\tenrXFold <- rownames(fpkmPoolMat)[enrXFold]\n\t}\n\n\t# Next, annotate all genes that cross threshold.\n\tif(fpkmThres>0){\n\t\tif(avgPass){\n\t\t\tenrXFpkm <- .apply(fpkmPoolMat[,sampleX],1,max)>fpkmThres\n\t\t}else{\n\t\t\tenrXFpkm <- fpkmRepMat[,grepl(paste(sampleX,collapse='|'),colnames(fpkmRepMat))]\n\t\t\tenrXFpkm <- .apply(enrXFpkm,1,min)>fpkmThres\n\t\t\t\n\t\t}\n\t\tenrXFpkm <- rownames(fpkmPoolMat)[enrXFpkm]\n\t}else{\n\t\tenrXFpkm <- rownames(fpkmPoolMat)\n\t}\n\t\n\t# Next, annotate all genes that are differentially expressed.\n\tenrXThres <- intersect(enrXFold,enrXFpkm)\n\tfirstTest <- T\n\tif(fdr>0){\n\t\tfor (ii in sampleX){\n\t\t\tfor (jj in sampleY){\n\t\t\t\t# Recover pairwise DE tests. May have a column name of sample1-sample2\n\t\t\t\t# OR sample2-sample1 ; cover both cases.\n\t\t\t\ttheTest <- paste(ii,'.',jj,sep='')\n\t\t\t\ttheCol <- which(colnames(qVal)%in%theTest)\n\t\t\n\t\t\t\tif(length(theCol)<0.1){\n\t\t\t\t\ttheTest <- paste(jj,'.',ii,sep='')\n\t\t\t\t\ttheCol <- which(colnames(qVal)%in%theTest)\n\t\t\t\t}\n\t\t\t\tenrXDeTemp <- qVal$gene_id[qVal[,theCol] 1) tempfsts <- rbind(tempfsts, temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))])\n\t}\n\n\t# save the max windowed fst\n\t# exclude windows with negative midpoints\n\tif(i == 1) maxfst <- tempfsts[POS > 0, max(fst, na.rm = TRUE)]\t\n\tif(i > 1) maxfst <- c(maxfst, tempfsts[POS > 0, max(fst, na.rm = TRUE)])\n}\n\nprint(paste('Max:', max(maxfst, na.rm = TRUE), '; 95th:', quantile(maxfst, prob = 0.95, na.rm = TRUE)))\n\nwrite.csv(maxfst, gzfile(outfilecan), row.names = FALSE)\n\nrm(maxfst)\n\n\n\n# Lof0711\nprint('Starting Lof0711')\nfor(i in 1:nrep){\n\tcat(i); cat(' ')\n\t# create new dataset\n\tinds <- sample(1:nrow(lof0711), nrow(lof0711), replace = FALSE)\n\ttemp <- cbind(lof0711[, ..colnms], lof0711[inds, .(A, B)]) # shuffle FSTs across positions\n\t\n\t# calc fst for each window\n\tfor(j in 1:(winsz/winstp)){\n\t\ttemp2 <- temp[get(paste0('win', j, 'keep')) == 1, ]\n\t\tif(j ==1) tempfsts <- temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))]\n\t\tif(j > 1) tempfsts <- rbind(tempfsts, temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))])\n\t}\n\n\t# save the max windowed fst\n\tif(i == 1) maxfst <- tempfsts[POS > 0, max(fst, na.rm = TRUE)]\t\n\tif(i > 1) maxfst <- c(maxfst, tempfsts[POS > 0, max(fst, na.rm = TRUE)])\n}\n\nprint(paste('Max:', max(maxfst, na.rm = TRUE), '; 95th:', quantile(maxfst, prob = 0.95, na.rm = TRUE)))\n\nwrite.csv(maxfst, gzfile(outfilelof0711), row.names = FALSE)\n\nrm(maxfst)\n\n\n# Lof0714\nprint('Starting Lof0714')\nfor(i in 1:nrep){\n \tcat(i); cat(' ')\n\t# create new dataset\n\tinds <- sample(1:nrow(lof0714), nrow(lof0714), replace = FALSE)\n\ttemp <- cbind(lof0714[, ..colnms], lof0714[inds, .(A, B)]) # shuffle FSTs across positions\n\t\t\n\t# calc fst for each window\n\tfor(j in 1:(winsz/winstp)){\n\t\ttemp2 <- temp[get(paste0('win', j, 'keep')) == 1, ]\n\t\tif(j ==1) tempfsts <- temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))]\n\t\tif(j > 1) tempfsts <- rbind(tempfsts, temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))])\n\t}\n\n\t# save the max windowed fst\n\tif(i == 1) maxfst <- tempfsts[POS > 0, max(fst, na.rm = TRUE)]\t\n\tif(i > 1) maxfst <- c(maxfst, tempfsts[POS > 0, max(fst, na.rm = TRUE)])\n}\n\nprint(paste('Max:', max(maxfst, na.rm = TRUE), '; 95th:', quantile(maxfst, prob = 0.95, na.rm = TRUE)))\n\nwrite.csv(maxfst, gzfile(outfilelof0714), row.names = FALSE)\n\nrm(maxfst)\n\n\n# Lof1114\nprint('Starting Lof1114')\nfor(i in 1:nrep){\n \tcat(i); cat(' ')\n\t# create new dataset\n\tinds <- sample(1:nrow(lof1114), nrow(lof1114), replace = FALSE)\n\ttemp <- cbind(lof1114[, ..colnms], lof1114[inds, .(A, B)]) # shuffle FSTs across positions\n\t\t\n\t# calc fst for each window\n\tfor(j in 1:(winsz/winstp)){\n\t\ttemp2 <- temp[get(paste0('win', j, 'keep')) == 1, ]\n\t\tif(j ==1) tempfsts <- temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))]\n\t\tif(j > 1) tempfsts <- rbind(tempfsts, temp2[, .(fst = sum(A)/sum(B)), by = .(CHROM, POS = get(paste0('win', j)))])\n\t}\n\n\t# save the max windowed fst\n\tif(i == 1) maxfst <- tempfsts[POS > 0, max(fst, na.rm = TRUE)]\t\n\tif(i > 1) maxfst <- c(maxfst, tempfsts[POS > 0, max(fst, na.rm = TRUE)])\n}\n\nprint(paste('Max:', max(maxfst, na.rm = TRUE), '; 95th:', quantile(maxfst, prob = 0.95, na.rm = TRUE)))\n\nwrite.csv(maxfst, gzfile(outfilelof1114), row.names = FALSE)", "meta": {"hexsha": "591bc5929a1b0799ec565ecd4770b0aaed201e91", "size": 13769, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/angsd_fst_siteshuffle_null.r", "max_stars_repo_name": "pinskylab/codEvol", "max_stars_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/angsd_fst_siteshuffle_null.r", "max_issues_repo_name": "pinskylab/codEvol", "max_issues_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-04-11T11:14:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-21T19:57:31.000Z", "max_forks_repo_path": "scripts/angsd_fst_siteshuffle_null.r", "max_forks_repo_name": "pinskylab/codEvol", "max_forks_repo_head_hexsha": "2710c4e4d9223ce02873938fd7aa7fc80f7dd8ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.3602693603, "max_line_length": 214, "alphanum_fraction": 0.6489214903, "num_tokens": 5029, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3176299335587883}} {"text": "# R for Data Science - Chapter 1 \n# Here we will go over the basics of ggplot2 and some of the other exploratory\n# analaysis tools that will be employed throughout the book, Lets begin:\n\nlibrary(tidyverse)\n\n# Imports all of the wonderful tidyverse that the book is based on (Thanks Hadley Wickman)\n\nggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy, color = class))\n\n# We specify that we want to create a baseline graph and layer the geometric points\n# overtop of the specified plane, and with the aes() we specify the aesthetic\n", "meta": {"hexsha": "cb410f2bb7999e81e1e04f3c77634dcfc664dbe0", "size": 544, "ext": "r", "lang": "R", "max_stars_repo_path": "chapter1.r", "max_stars_repo_name": "luke-pritch/R4-DS", "max_stars_repo_head_hexsha": "fc5b5aaeac94811c74bf683f31cf89eb9a304090", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "chapter1.r", "max_issues_repo_name": "luke-pritch/R4-DS", "max_issues_repo_head_hexsha": "fc5b5aaeac94811c74bf683f31cf89eb9a304090", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "chapter1.r", "max_forks_repo_name": "luke-pritch/R4-DS", "max_forks_repo_head_hexsha": "fc5b5aaeac94811c74bf683f31cf89eb9a304090", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.8461538462, "max_line_length": 90, "alphanum_fraction": 0.7555147059, "num_tokens": 132, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3176299335587883}} {"text": "## prog ini 1\n# read data from ASCII file and do some graphical initialisations\n# you have to do this after every new SMS run\n#################################################################\nrm(stom)\nstom<-Read.stomach.data(read.init.function=T)\n\n#stom<-Read.stomach.data()\n\na<-aggregate(list(Pred.avail=stom$Prey.avail.part),\n list(Area=stom$SMS.area,Year=stom$Year,Quarter=stom$Quarter,Predator=stom$Predator,Predator.length.class=stom$Predator.length.class),sum)\nstom<-merge(stom,a)\nstom$stomcon.hat.part<-stom$Prey.avail.part/stom$Pred.avail \nstom$Residual.part<-stom$stom.input-stom$stomcon.hat.part\nstom<-transform(stom,year.range=ifelse(Year<=1981,'1977-81','1982-93'),year=paste(\"Y\",Year,sep=''))\n\nstomAll<-stom # all stomach input irrespectiv of actually use \nstom<-subset(stom,stom.used.all==1) # used stomachs specified for input\n\n\nwrite.csv(stom, file = file.path(data.path,'ASCII_stom_data.csv'),row.names = FALSE)\n\n\n\ntr<-trellis.par.get( \"background\")\n# old value tr$col=\"#909090\"\ntr$col=\"white\"\ntrellis.par.set(\"background\", tr)\n\ndev<-\"print\"\ndev<-\"screen\"\nnox<-4\nnoy<-3\n#################################################################\n\n\nnames(stom)\n \n\n################################################################\n\nstom$ratio<-stom$Predator.size/stom$Prey.size\na<-subset(stom,Prey.no!=0)\na<-a[order(a$ratio),]\nhead(a)\n###\n\na<-subset(stom,Stom.var>0,select=c(Predator,Quarter,Year,Predator.length.mean,Prey,Prey.length.mean,N.samples,stomcon,stan.residual))\na<-a[order(abs(a$stan.residual),decreasing =T),]\nhead(a,20)\n#####\n\nif (F) { # test of output stom proportion=1 (or a serious errror some where)\n a<-aggregate(list(expec=stom$stomcon.hat),list(Year=stom$Year,Quarter=stom$Quarter,Predator.no=stom$Predator.no,Predator=stom$Predator,Predator.size=stom$Predator.length),sum)\n a$expec<-round(a$expec,2)\n a<-a[order(paste(a$Year,a$Quarter,a$Predator.no,a$Predator,a$Predator.size)),]\n a\n}\n###############################################################\n#Number of stomachs;\nns<-unique(subset(stom,select=c(Predator,Predator.no,Predator.length,Predator.length.class,Year,Quarter, N.samples)))\n\n#ns<-droplevels(subset(ns,Predator %in% c(\"W. mackerel\",\"N. mackerel\")))\n\na<-ftable(tapply(ns$N.sample,list(ns$Predator,ns$Year,ns$Quarter,ns$Predator.length),sum,na.rm=T))\ntapply(ns$N.sample,list(ns$Predator,ns$Predator.length),sum,na.rm=T)\ntapply(ns$N.sample,list(ns$Predator),sum,na.rm=T)\n\n##############################################################\n## prog rel 1\n# Observed relativ stomach contents by predator, year and quarter\n unique(stom$Predator)\n \ncleanup()\n\ndev<-\"print\"\n#dev<-\"screen\"\ndev<-\"png\"\nnox<-2\nnoy<-3\n\ni<-0\nb<- tapply(stom$stomcon,list(stom$Prey.no),sum)\nall.prey.col<-sort(as.numeric(dimnames(b)[[1]]),decreasing = TRUE)\nall.names<-rep('aaa',length(all.prey.col))\nfor (s in (1:length(all.prey.col))) all.names[s]<-sp.other.names[all.prey.col[s]+1]\n#stom<-droplevels(subset(stom,Predator=='H. porpoise'))\n#stom<-droplevels(subset(stom,Predator %in% c('G. gurnards','R. radiata','Hake','W.horse mac','N.horse mac')))\n#stom<-droplevels(subset(stom,Predator %in% c('W.horse mac','N.horse mac')))\n\n#stom<-droplevels(subset(stom,Predator %in% c(\"W. mackerel\",\"N. mackerel\")))\n\nstom<-subset(stom,stom.used.like==1) # just used stomachs\n \na<-by(stom,list(stom$Quarter,stom$Year,stom$Predator.no),function(x) {\n if (dim(x)[[1]]>0) {\n b<- tapply(x$stomcon,list(x$Prey.no,x$Predator.length),sum) \n b[is.na(b)]<-0\n prey.names<-as.numeric(dimnames(b)[[1]])\n length.names<-dimnames(b)[[2]]\n #if (x[1,]$Quarter==\"Q1\") {\n if ((i %% (nox*noy-1))==0) {\n newplot(dev,nox,noy,Portrait=F,filename=paste(x[1,]$Year,x[1,]$Quarter,x[1,]$Predator,sep='-'));\n par(mar=c(3,5,3,2)) \n plot.new(); legend(x=0,y=1,all.names,fill=all.prey.col,cex=1.0)\n } \n i<<-i+1\n barplot(b,names=length.names,col=prey.names)\n title(main=paste(x[1,]$Year,x[1,]$Quarter,\" Pred:\",x[1,]$Predator))\n }\n})\nif (dev=='png') cleanup()\n\n\n\n\nby(stom,list(stom$Predator),function(x) {\n ftable(round(tapply(x$stomcon,list(x$Year,x$Quarter,x$Predator.length,x$Prey),sum)*1000,0))\n})\n\n\nby(stom,list(stom$Predator,stom$Quarter),function(x) {\n ftable(round(tapply(x$stomcon,list(x$Year,x$Predator.length,x$Prey),sum)*1000,0))\n})\n\n\n##############################################################\n## prog rel 3\n# Observed length distibution of preys by predator, size, quarter and year\n\ndev<-\"print\"\ndev<-\"screen\"\nnox<-2\nnoy<-3\n\n# select subset\nstom2<-subset(stom,Year==1978 & Quarter==\"Q1\" & Prey.no!=0)\n\ncleanup()\ni<-0\n\nby(stom2,list(stom2$Year,stom2$Quarter,stom2$Predator.no),function(x) {\n\n b<- tapply(x$stomcon,list(x$Predator.length,x$Prey.length.mean,x$Prey),sum)\n\n b[is.na(b)]<-0\n for (l in (1:dim(b)[1])) {\n if ((i %% (nox*noy))==0) {\n newplot(dev,nox,noy,Portrait=TRUE);\n par(mar=c(3,5,3,2))\n }\n i<<-i+1\n bb<-b[l,,]\n print(b)\n barplot(bb,beside = TRUE,col=2)\n title(main=paste(x[1,]$Year,x[1,]$Quarter,x[1,]$Predator,dimnames(b)[[1]][l]))\n }\n})\n\n\n\n\ndev<-\"print\"\ndev<-\"screen\"\nnox<-2\nnoy<-3\n\n\ntapply(stom$Prey.length.mean,list(stom$Predator,stom$Prey),min)\n\n\n# select subset, ONE prey only\nstom2<-subset(stom,Quarter==\"Q3\" & Prey=='Sandeel' & Predator=='Whiting')\nstom2<-subset(stom,Quarter==\"Q3\" & Prey=='Herring')\n\n\n b<- tapply(stom2$Prey.length.mean,list(stom2$Predator,stom2$Prey),sum)\ncleanup()\ni<-0\n\nby(stom2,list(stom2$Year,stom2$Quarter,stom2$Predator.no),function(x) {\n\n # b<- tapply(x$stomcon/x$Prey.weight,list(x$Predator.length,x$Prey.length.mean),sum)\n b<- tapply(x$stomcon,list(x$Predator.length,x$Prey.length.mean),sum)\n\n b[is.na(b)]<-0\n for (l in (1:dim(b)[1])) {\n if ((i %% (nox*noy))==0) {\n newplot(dev,nox,noy,Portrait=TRUE);\n par(mar=c(3,5,3,2))\n }\n i<<-i+1\n bb<-b[l,]\n print(b)\n barplot(bb,beside = TRUE,col=2)\n title(main=paste(x[1,]$Year,x[1,]$Quarter,x[1,]$Predator,dimnames(b)[[1]][l]))\n }\n})\n\n##################################################################################\n## prog rel 4\n# Obsereved and predicted relativ stomach content by predator quarter and year\n\ncleanup()\n\ndev<-\"print\"\ndev<-\"screen\"\n#dev<-\"wmf\"\nnox<-3\nnoy<-3\n\ni<-0\nb<- tapply(stom$stomcon,list(stom$Prey.no),sum)\nall.prey.col<-sort(as.numeric(dimnames(b)[[1]]),decreasing = TRUE)\nall.names<-rep('aaa',length(all.prey.col))\nfor (s in (1:length(all.prey.col))) all.names[s]<-sp.other.names[all.prey.col[s]+1]\n\nby(stom,list(stom$Quarter,stom$Year,stom$Predator.no),function(x) {\n b<- tapply(x$stomcon,list(x$Prey.no,x$Predator.length),sum)\n b[is.na(b)]<-0\n\n c<- tapply(x$stomcon.hat,list(x$Prey.no,x$Predator.length),sum)\n c[is.na(c)]<-0\n\n b<-rbind(b,c)\n prey.no<-as.numeric(dimnames(b)[[1]])\n prey.names<-rep('aaa',length(prey.no))\n for (s in (1:length(prey.names))) prey.names[s]<-sp.other.names[prey.no[s]+1]\n length.names<-dimnames(b)[[2]]\n #if (x[1,]$Quarter==\"Q1\") {\n if ((i %% (nox*noy-1))==0) {\n newplot(dev,nox,noy,Portrait=F,filename=paste('stom',i));\n par(mar=c(3,5,3,2))\n if (dev==\"wmf\" ) par(mar=c(2,4,2,2))\n \n # text(x=0.0,y=0.07,\"lower: observed\",pos=4)\n plot.new();\n title(main=\"upper: expected\\nlower: observed\")\n legend(\"center\",all.names,fill=all.prey.col,cex=1.2)\n\n }\n i<<-i+1\n barplot(b,names=length.names,col=prey.no)\n title(main=paste(x[1,]$Year,x[1,]$Quarter,\" Pred:\",x[1,]$Predator))\n #title(main=paste(x[1,]$Year,x[1,]$Quarter))\n\n abline(h=1,lwd=2)\n})\n\n##############################################################\n\n# Histogram of observations with and without prey size\nstom2<-subset(stom, Prey!='Other' & Prey.length.mean >0 & stomcon>1E-5)\ncleanup()\nnox<-3; noy<-2;\ni<-0\nminStom<-0.2\n\nby(stom2,list(stom2$Predator.no),function(x) {\n xx<-x\n x$stomcon[x$stomcon>minStom]<-minStom\n xx<-with(xx, aggregate(stomcon,list(Predator=Predator,Prey=Prey,Quarter=Quarter,Year=Year,Length=Predator.length),sum))\n xx$x[xx$x>minStom]<-minStom\n print(xx) \n if ((i %% (nox*noy))==0) {\n newplot(dev,nox,noy,Portrait=TRUE);\n par(mar=c(3,5,3,2))\n }\n i<<-i+1\n hist(x$stomcon,freq=F,breaks=50,main=paste(x[1,]$Predator,dim(x)[1],round(sum(x$stomcon)))) \n hist(xx$x,freq=F,breaks=50,main=paste(xx[1,]$Predator,dim(xx)[1],'cond',round(sum(xx$x))))\n})\n\n\n\n##############################################################\n## prog size 1\n#size at length by species\n\ncleanup()\nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$Predator.length.mean,x$Predator.size,xlab='length',ylab='size')\n title(main=paste('pred:',x[1,]$Predator) )\n i<<-i+1\n})\n\nby(stom,list(stom$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n x<-subset(x,Prey.size>0)\n if (dim(x)[1]>0) {\n plot(x$Prey.length.mean,x$Prey.size,xlab='length',ylab='size')\n title(main=paste('prey:',x[1,]$Prey) )\n i<<-i+1\n }\n})\n\n\n\n##############################################################\n#length distribution in the sea\n## prog length 1\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0 \n\na<-subset(stom,Prey.no !=0 & L.N.bar>0,select=c(\"Year\",\"Quarter\",\"Quarter.no\",\"Prey.no\",\"Prey\",\"Prey.length.class\",\"Prey.length.mean\",\"L.N.bar\"))\na<-unique(a)\na<-subset(a,Prey=='Cod')\nby(a,list(a$Quarter,a$Year,a$Prey.no),function(x) {\n if (x[1,]$Quarter==\"Q1\") {\n newplot(dev,nox,noy);\n } \n plot(x$Prey.length.mean,x$L.N.bar,col=x$Quarter.no)\n title(main=paste(x[1,]$Year,x[1,]$Quarter,\" Prey:\",x[1,]$Prey))\n})\n\n\n\n##############################################################\n#length distribution in the sea\n## prog length 2\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\na<-subset(stom,Prey.no !=0,select=c(\"Year\",\"Quarter\",\"Quarter.no\",\"Prey.no\",\"Prey\",\"Prey.length.class\",\"Prey.length.mean\",\"L.N.bar\"))\na<-unique(a)\nby(a,list(a$Year,a$Prey.no),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n xmin<-min(x$Prey.length.mean)\n xmax<-max(x$Prey.length.mean)\n ymin<-min(x$L.N.bar)\n ymax<-max(x$L.N.bar)\n s<-order(x$Quarter.no,x$Prey.length.class)\n x<-x[s,]\n plot(x$Prey.length.mean[x$Quarter.no==1],x$L.N.bar[x$Quarter.no==1],col=x$Quarter.no,type='b',\n xlim=c(xmin,xmax),ylim=c(ymin,ymax),xlab='length',ylab='Nbar')\n title(main=paste(x[1,]$Year,\" Prey:\",x[1,]$Prey))\n for (q in seq(2,4)) {\n lines(x$Prey.length.mean[x$Quarter.no==q],x$L.N.bar[x$Quarter.no==q],col=q)\n }\n i<<-i+1\n})\n\n \n##############################################################\n#length distribution in the sea\n## prog length 3\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\na<-subset(stom,Prey.no !=0 & Prey.length.mean>0, select=c(\"Year\",\"Quarter\",\"Quarter.no\",\"Prey.no\",\"Prey\",\"Prey.length.class\",\"Prey.length.mean\",\"Prey.weight\",\"L.N.bar\"))\na<-unique(a)\nby(a,list(a$Prey.no),function(x) {\n newplot(dev,nox,noy)\n xmin<-min(x$Prey.length.mean)\n xmax<-max(x$Prey.length.mean)\n ymin<-min(x$L.N.bar*x$Prey.weight)\n ymax<-max(x$L.N.bar*x$Prey.weight)\n s<-order(x$Quarter.no,x$Prey.length.class,x$Prey.length.mean)\n x<-x[s,]\n d<-as.data.frame(x)\n print(xyplot(L.N.bar~Prey.length.mean|Prey+as.factor(Year),groups=Quarter,type='b',data=d,ylab='Nbar'),\n layout = c(2, 2))\n})\n \n ##############################################################\n#length distribution in the sea\n## prog length 3\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\na<-subset(stom,Prey.no !=0 & Prey.length.mean>0, select=c(\"Year\",\"Quarter\",\"Quarter.no\",\"Prey.no\",\"Prey\",\"Prey.length.class\",\"Prey.length.mean\",\"Prey.weight\",\"L.N.bar\"))\na<-unique(a)\nby(a,list(a$Prey.no),function(x) {\n newplot(dev,nox,noy)\n xmin<-min(x$Prey.length.mean)\n xmax<-max(x$Prey.length.mean)\n ymin<-min(x$L.N.bar*x$Prey.weight)\n ymax<-max(x$L.N.bar*x$Prey.weight)\n s<-order(x$Quarter.no,x$Prey.length.class,x$Prey.length.mean)\n x<-x[s,]\n d<-as.data.frame(x)\n print(xyplot(L.N.bar*Prey.weight~Prey.length.mean|Prey+as.factor(Year),groups=Quarter,type='b',data=d,ylab='Nbar*mean weight'),\n layout = c(2, 2))\n})\n\n\n##############################################################\n#length distribution in the sea\n## prog length 4\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\na<-subset(stom,Prey.no !=0 & Prey.length.mean>0, select=c(\"Year\",\"Quarter\",\"Quarter.no\",\"Prey.no\",\"Prey\",\"Prey.length.class\",\"Prey.length.mean\",\"Prey.weight\",\"L.N.bar\"))\na<-unique(a)\n\n barchart(stom~as.factor(trunc(PredPrey*2)/2)|Predator, data=a, \n xlab='log(predator weight/prey weight)',ylab='proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.8, lines=2),\n layout = c(1, 3) ,col='grey')\n\n \n\n\n\n ##############################################################\n#Dirichlet \na<-subset(stom,stom.used.like==1,select=c(Predator,\tQuarter,\tYear,\tPredator.length.class,\tPrey,\tQuarter.no,\tSMS.area,\tPredator.no,\tDiri.p,\tDiri.sum.p,\tDiri.like\t,Prey.no,\tPrey.length.class, N.haul))\na<-unique(a)\n\na<-a[order(a$Diri.like),]\nhead(a,20)\ntail(a,20)\n## \n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\na<-subset(stom,Quarter.no>0)\na<-unique(a)\nxyplot(Diri.sum.p~N.samples,group=Predator,\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=a)\n\nxyplot(Diri.like~N.samples,group=Predator,\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=a)\n\n \nxyplot(Diri.like~N.samples|Predator,group=Predator.length.class,\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=a)\n\n\na<-subset(stom,Quarter.no>0 )\n\nxyplot(stomcon.hat*(1-stomcon.hat)/(Diri.sum.p+1)*N.haul ~stomcon.hat,group=Predator, ylab=\"VAR(STOM)\",\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=a)\n\nxyplot(stomcon.hat*(1-stomcon.hat)/(Diri.sum.p+1)*N.haul ~stomcon.hat|Predator, ylab=\"VAR(STOM)\",\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=a)\n\n\n###\nlibrary(MCMCpack)\nrdirichlet<-function(n, alpha) # stolen from library MCMC-pack\n{\n l <- length(alpha)\n x <- matrix(rgamma(l * n, alpha), ncol = l, byrow = TRUE)\n sm <- x %*% rep(1, l)\n return(x/as.vector(sm))\n}\napply(rdirichlet(20, c(1,1,1) ),2,mean)\napply(rdirichlet(20, c(10,10,10) ),2,mean)\napply(rdirichlet(20, c(1,1,1) ),2,sd)\napply(rdirichlet(20, c(10,10,10) ),2,sd)\n\ns<-subset(stom,Quarter.no>0 & Diri.sum.p>0 & Predator=='Cod' & Year==1981)\na<-tapply(s$stomcon.hat,list(s$Year,s$Quarter.no,s$Predator.no,s$Predator.length.class,s$Prey.no,s$Prey.length.class),sum,na.rm=T)\na.in<-tapply(s$stom.input,list(s$Year,s$Quarter.no,s$Predator.no,s$Predator.length.class,s$Prey.no,s$Prey.length.class),sum,na.rm=T)\navail<-tapply(s$stom.used.avail,list(s$Year,s$Quarter.no,s$Predator.no,s$Predator.length.class,s$Prey.no,s$Prey.length.class),sum,na.rm=T)\n\nss<-unique(droplevels(subset(s,select=c(Diri.sum.p,Year,Quarter.no,Predator.no,Predator,Predator.length.class))))\nxyplot(Diri.sum.p~Predator.length.class|Predator, ylab=\"p sum\",\n auto.key = list(space = \"right\", points = T, lines = TRUE),data=ss)\n\n\np_sum<-tapply(ss$Diri.sum.p,list(ss$Year,ss$Quarter.no,ss$Predator.no,ss$Predator.length.class),mean,na.rm=T)\n#ftable(round(p_sum,1))\n\nnrep<-200\ndd<-dimnames(a)\nsumStom<-SMS.dat@sum.stom.like\nmin.stom.cont<-SMS.dat@min.stom.cont \nddd<-NULL\nfor (y in dd[[1]]) {for (q in dd[[2]]) {\n cat('y:',y,' q:',q,'\\n')\n for (pred in dd[[3]]) {\n for (predl in dd[[4]]) {\n obs<-t(a[y,q,pred,predl,,])\n obs<-obs[!is.na(obs)]\n obs<-obs[obs>0]\n if (length(obs)>1) \n bb<-obs*p_sum[y,q,pred,predl]\n dirbb<-rdirichlet(nrep,bb)\n for (i in (1:dim(dirbb)[2])) {\n d<-density(dirbb[,i])\n ddd<-rbind(ddd,data.frame(no=i,year=y,q=q,pred=pred,predl=predl,obs=obs[i],x=d$x,y=d$y))\n }\n }\n}}}\n\n\nddd$labs<-paste(ddd$no,\" mean=\",round(ddd$obs,4),sep='')\nddd$labs2<-paste(ddd$year,ddd$q,ddd$pred,ddd$predl,sep=',')\n\nb<-droplevels(subset(ddd, pred=='17' &predl=='17' & year=='1981' & q=='1'))\n\nxyplot(y~x| labs, ylab=\"density\",data=b,type='l',scales=list(y=list(relation=\"free\")))\n\n\nb<-droplevels(subset(ddd, pred=='17' & year=='1981' & q=='1'))\nxyplot(y~x|labs2, groups=no,ylab=\"density\",data=b,type='l',scales=list(y=list(relation=\"free\")))\n\n\n\n##############################################################\n\n\n# QQplot of Residuals by Predator and prey\n## prog qq 1\n\n\ncleanup() \nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey,stom$Predator),function(x) {\nif (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n qqnorm(x$Residual,main=' ')\n qqline(x$Residual)\n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n})\n\n\n##############################################################\n#SPECIAL CASE COD observed and predicted stomach contents by Predator and prey\n# prog stom 1 cod\n\ncleanup() \nnox<-1; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\nstom2<-subset(stom,Prey=='Cod')\n head(stom2)\nby(stom2,list(stom2$Prey,stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n aa<-log(x$stomcon)\n bb<-log(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='Expected stomach content',ylab='observed',ylim=c(min.val,max.val),xlim=c(min.val,max.val),pch=x$Predator.length.class,\n col=x$Predator.no)\n if (var(bb)>0) abline(lm(aa~bb), lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n})\n\n\n##############################################################\n#observed and predicted stomach contents by Predator and prey\n# prog stom 1\n\ncleanup() \nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey,stom$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n aa<-(x$stomcon)\n bb<-(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='Expected stomach content',ylab='observed',ylim=c(min.val,max.val),xlim=c(min.val,max.val))\n if (var(bb)>0) abline(lm(aa~bb), lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n})\n\n##############################################################\n#log observed and predicted stomach contents by Predator and prey\n## prog stom 2\n\ncleanup() \nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey,stom$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n aa<-log(x$stomcon)\n bb<-log(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='log Expected stomach content',ylab='log observed',ylim=c(min.val,max.val),xlim=c(min.val,max.val))\n if (var(bb)>0) abline(lm(aa~bb), lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n})\n\n##############################################################\n#observed and predicted stomach contents by prey\n## prog stom 3\n\ncleanup() \nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n aa<-(x$stomcon)\n bb<-(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='Expected stomach content',ylab='observed',ylim=c(min.val,max.val),xlim=c(min.val,max.val))\n r2<-''\n if (var(bb)>0) {\n cc<-lm(aa~bb)\n abline(cc, lty=3) \n dd<-summary(cc)\n r2<-paste(\" r^2=\",round(dd[[\"r.squared\"]],2),sep='')\n }\n title(main=paste(\"Prey:\",x[1,]$Prey,r2,sep=\"\"))\n i<<-i+1\n})\n\n \n##############################################################\n#PAPER version. observed and predicted stomach contents by prey\n## prog stom 3\n\ncleanup() \nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n \nby(stom,list(stom$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n # x<-subset(x,stomcon<0.2 &stomcon.hat<0.2)\n x<-subset(x,stomcon>0.0 )\n aa<-(x$stomcon)\n bb<-(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='Expected stomach content',ylab='Observed',ylim=c(min.val,max.val),xlim=c(min.val,max.val),cex=.8)\n r2<-''\n if (var(bb)>0) {\n #cc<-lm(aa~bb)\n cc<-lm(aa~bb,weights=(sqrt(x$N.samples)))\n\n abline(cc, lty=3) \n dd<-summary(cc)\n # r2<-round(dd[[\"r.squared\"]],2)\n r2<-formatC(dd[[\"r.squared\"]],digits = 2, format = \"f\")\n sp<- x[1,]$Prey\n #r2<-paste(\" r^2=\",r2,sep='')\n }\n \n title(main=paste(\" \",sp),adj=0) \n title(bquote(r^2 == .(r2)),adj=1)\n \n #text(x=0,y=max.val*0.9,labels=bquote(r^2 == .(r2)),pos=4,adj=1,cex=1.5)\n i<<-i+1\n})\n\n #theta <- 1.23 ; title(bquote(hat(theta) == .(theta)))\n # substitute(rho == . , list( . = r2))\n\n\n#############################################################\n# log observed and predicted stomach contents by prey\n## prog stom 4\n\ncleanup() \nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n x<-subset(x,stomcon>0.0 )\n\n aa<-log(x$stomcon)\n bb<-log(x$stomcon.hat)\n max.val<-max(aa,bb)\n min.val<-min(aa,bb)\n plot(bb,aa,xlab='log Expected stomach content',ylab='log observed ',\n ylim=c(min.val,max.val),xlim=c(min.val,max.val),col=1)\n r2<-''\n if (var(bb)>0) {\n #cc<-lm(aa~bb,weights=1/x$Stom.var)\n cc<-lm(aa~bb)\n abline(cc, lty=3) \n dd<-summary(cc)\n r2<-paste(\" r^2=\",formatC(dd[[\"r.squared\"]],2,format='f'),sep='')\n }\n title(main=paste(\"Prey:\",x[1,]$Prey,r2,sep=\"\"))\n i<<-i+1\n})\n\n\n\n##############################################################\n# PAPER Residuals by Predator, and expected stom content\n## prog resid 1\n\ncleanup() \nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Predator=='Cod' & Prey==\"Sandeel\")\n#stom2<-subset(stom,Predator=='Cod' )\n\nby(stom2,list(stom2$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$stomcon.hat,x$Residual,xlab='Expected relative stomach content',ylab='Residual')\n abline(h=0, lty=3) \n# title(main=paste(\" Prey:\",x[1,]$Prey,sep=\"\"))\n# title(main=paste(\"Prey:\",x[1,]$Prey))\n\n #abline(lm(x$Residual~x$stomcon.hat),lty=2)\n #if (length(x$Prey)>5 ) lines(smooth.spline(x$stomcon.hat,x$Residual),lty=1)\n i<<-i+1\n})\n\n\n##############################################################\n#Residuals by Predator, and expected stom content\n## prog resid 1\n\ncleanup() \nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nby(stom,list(stom$Prey,stom$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$stomcon.hat,x$Residual,xlab='Expected relative stomach content',ylab='residuals ')\n abline(h=0, lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n# title(main=paste(\"Prey:\",x[1,]$Prey))\n\n #abline(lm(x$Residual~x$stomcon.hat),lty=2)\n #if (length(x$Prey)>5 ) lines(smooth.spline(x$stomcon.hat,x$Residual),lty=1)\n i<<-i+1\n})\n\n\n##############################################################\n#Residuals by Predator and prey, and prey/pred size\n## prog resid 2\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size/x$Prey.size),x$Residual,xlab='log(Pred size/Prey size)',ylab='residuals ')\n abline(h=0, lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n if (x$Prey.no !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Predator.size/x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}}\n)}\n\n##############################################################\n#Residuals and prey, and prey size. Label=quarter\n## prog resid 3\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\n\nby(stom2,list(stom2$Prey.no),function(x) {\nif (x$Prey.no[1] != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals', pch=x$Quarter.no,col=x$Quarter.no)\n abline(h=0, lty=3) \n title(main=paste(\"Prey:\",x[1,]$Prey,sep=\"\"))\n #if (x$Prey.no[1] !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}}\n)}\n\n\n##############################################################\n#Residuals and prey, and prey size. Label=year\n## prog resid 4\n\ncleanup()\nnox<-3; noy<-1;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\n\nby(stom2,list(stom2$Prey.no),function(x) {\nif (x$Prey.no[1] != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals', pch=x$Year-1977,col=x$Year-1977)\n abline(h=0, lty=3) \n title(main=paste(\"Prey:\",x[1,]$Prey,sep=\"\"))\n if (x$Prey.no[1] !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}}\n)}\n\n##############################################################\n#Residuals and prey, and prey size. Label=predator\n## prog resid 5\n\ncleanup()\nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\n\nby(stom2,list(stom2$Prey.no),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals', pch=x$Predator.no,col=x$Predator.no)\n abline(h=0, lty=3) \n title(main=paste(\"Prey:\",x[1,]$Prey,sep=\"\"))\n if (x$Prey.no !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}})}\n\n\n##############################################################\n#Residuals and prey, and prey size and quarter\n## prog resid 6\n\ncleanup()\nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Quarter,stom2$Prey.no),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals ', pch=x$Predator.no,col=x$Predator.no)\n abline(h=0, lty=3) \n title(main=paste(x[1,]$Quarter,\" Prey:\",x[1,]$Prey,sep=\"\"))\n if (x$Prey.no !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}})}\n\n\n\n\n\n##############################################################\n#Residuals and prey, and prey size and quarter\n## prog resid 6b\n\ncleanup()\nnox<-3; noy<-3;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Quarter,stom2$Prey.no),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$Prey.length.class,x$Residual,xlab='prey length class',ylab='residuals ', pch=x$Predator.no,col=x$Predator.no)\n abline(h=0, lty=3)\n title(main=paste(x[1,]$Quarter,\" Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n}})}\n\n##############################################################\n#Residuals by Predator and prey, and prey size\n## prog resid 7\n\nnox<-3; noy<-3;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Predator,stom2$Prey.no),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals ')\n abline(h=0, lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n if (x$Prey.no !=0 & length(x$Prey)>5 ) lines(smooth.spline(log(x$Prey.size),x$Residual),lty=1)\n i<<-i+1\n}})}\n\n##############################################################\n#Residuals by Predator and prey, and prey size and quarter\n## prog resid 8\n\n\nnox<-2; noy<-1;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Quarter,stom2$Prey.no,stom2$Predator),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals ')\n abline(h=0, lty=3) \n title(main=paste(x[1,]$Quarter,\" Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n}})}\n\n\n##############################################################\n#Residuals by Prey and predator, and prey size and quarter\n## prog resid 9\n\n\nnox<-2; noy<-3;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Quarter,stom2$Predator,stom2$Prey.no),function(x) {\nif (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Prey.size),x$Residual,xlab='log(Prey size)',ylab='residuals ')\n abline(h=0, lty=3) \n title(main=paste(x[1,]$Quarter,\", Prey:\",x[1,]$Prey,\" Pred:\",x[1,]$Predator,sep=\"\"))\n i<<-i+1\n}})}\n\n\n\n##############################################################\n# Box-plot of residuals by predator, prey and quarter\n# prog box 1\n\nnox<-2; noy<-2;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\nres.ylim<-c(-0.1,0.1) # Dirichlet\n#res.ylim<-c(-5,5) # log normal\n\nmy.pred<-c(\"Cod\",\"Whiting\",\"Saithe\",\"Haddock\",\"W Mackerel\",\"NS Mackerel\")\nmy.pred<-c(\"Cod\")\n\n#my.pred<-c(\"Cod\")\nstom3<-subset(stom,Predator %in% my.pred,drop=TRUE)\n#if (length(my.pred)==1) a<-glm(Residual~-1+Quarter:Prey,data=stom3,weight=stomcon.hat)\nif (length(my.pred)==1) a<-glm(Residual~-1+Quarter:Prey,data=stom3,weight=N.haul*stomcon.hat)\nif (length(my.pred)>1) a<-glm(Residual~-1+Quarter:Predator:Prey,data=stom3,weight=stomcon.hat)\nsummary(a)\n\nby(stom3,list(stom3$Predator,stom3$Prey),function(x) {\n#if (x$Prey.no != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n boxplot(Residual~Quarter,data=x,ylim=res.ylim)\n\n abline(h=0,col=3)\n title(main=paste(x[1,]$Predator,\"eating\",x[1,]$Prey,sep=\" \"))\n i<<-i+1\n#}\n})\n\n\n##############################################################\n# Box-plot of residuals by predator and quarter\n# prog box 2\n\nnox<-3; noy<-2;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\n# note only other food selected\nstom3<-subset(stom,Prey==\"Other\" & stom$Predator %in% my.pred,drop=TRUE)\n\nby(stom3,list(stom3$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n boxplot(Residual~Quarter,data=x,ylim=c(-1,1))\n abline(h=0,col=3)\n\n title(main=paste(x[1,]$Predator,sep=\"\"))\n i<<-i+1\n})\n\n\n##############################################################\n# Box-plot of residuals by prey and quarter\n# prog box 2\n\nnox<-3; noy<-2;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom3<-subset(stom, stom$Predator %in% my.pred,drop=TRUE)\n\nby(stom3,list(stom3$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n boxplot(Residual~Quarter,data=x,ylim=c(-0.1,0.1))\n abline(h=0,col=3)\n\n title(main=paste(x[1,]$Prey,sep=\"\"))\n i<<-i+1\n})\n\n \n##############################################################\n# Box-plot of residuals by prey and year and quarter\n# prog box 3\n\nnox<-4; noy<-4;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom3<-subset(stom, stom$Predator %in% my.pred,drop=TRUE)\n\nby(stom3,list(stom3$year,stom3$Prey),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n boxplot(Residual~Quarter,data=x,ylim=c(-1,1))\n abline(h=0,col=3)\n\n title(main=paste(x[1,]$year,x[1,]$Prey,sep=\" \"))\n i<<-i+1\n})\n\n##############################################################\n#Frequency of observation by predator-prey size ratio\n## prog size 1\n\ncleanup()\nnox<-4; noy<-4;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model==1)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\nif (x$Prey.no[1] != 0) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 } \n hist(log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(pred-size / prey-size) ')\n abline(h=0, lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,\", Prey:\",x[1,]$Prey,sep=\"\"))\n i<<-i+1\n}}\n)}\n\n \ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & stom$Prey.no != 0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nby(stom2,list(stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 } \n hist(log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(pred-size / prey-size) ')\n abline(h=0, lty=3) \n title(main=paste(\"Pred:\",x[1,]$Predator,sep=\"\"))\n i<<-i+1\n}\n)}\n\n ##################################\n \n \nstom3<-subset(stom,Size.model>0 & Prey.no>0 & N.haul>=10 & Predator !=\"NS Mackerel\" & Predator !=\"W Mackerel\" )\n#stom3<-subset(stom,Size.model>0 & Prey.no>0 & N.haul>10 )\n#stom3<-subset(stom3, !((Predator=='Cod' & Predator.length.mean<400)| (Predator=='Whiting' & Predator.length.mean<200) | (Predator=='Saithe' & Predator.length.mean<400)))\n\n# start with a high fac for calc of mean and var\nfac<-50 \na<-aggregate(list(stom=stom3$stomcon),list(Predator=stom3$Predator,PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*50)/50),sum)\n\nby(a,list(a$Predator),function(x) weighted.mean(x$PredPrey, x$stom))\n\n\n barchart(stom~as.factor(trunc(PredPrey*2)/2)|Predator, data=a, \n xlab='log(predator weight/prey weight)',ylab='proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.8, lines=2),\n layout = c(1, 3) ,col='grey')\n\n \n\n####################################\n# observation by predator-prey size ratio\n## prog size 1b\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\n\n\n\nstom3<-subset(stom, (Size.model==1 | Size.model==11) & Prey.no>0 & !( \n (Predator=='Cod' & Predator.length.mean<400) | \n (Predator=='Whiting' & Predator.length.mean<200) |\n (Predator=='Saithe' & Predator.length.mean<400)))\n\nstom3<-subset(stom, (Size.model==1 | Size.model==11) & Prey.no>0 & Predator %in% c('H. porpoise','Grey seal'))\n \nstom3<-subset(stom, (Size.model==1 | Size.model==11) & Prey.no>0 )\n\n\n# just checking\na<-aggregate(list(stom=stom3$stomcon,stom.input=stom3$stom.input,stom.hat=stom3$stomcon.hat,stom.hat.part=stom3$stomcon.hat.part),\n list(Year=stom3$Year,Quarter=stom3$Quarter,Predator=stom3$Predator,Pred_l=stom3$Predator.length),sum)\n\n# use the individual data points (not the liklihood data) when size mode is 11 \nstom3[stom3$Size.model==11,]$stomcon<- stom3[stom3$Size.model==11,]$stom.input\nstom3[stom3$Size.model==11,]$stomcon.hat<-stom3[stom3$Size.model==11,]$stomcon.hat.part\n\nstom3<-droplevels(stom3)\n\n# start with a high fac for calc of mean and var\nfac<-50 \na<-aggregate(list(stom=stom3$stomcon*5000,stom.hat=stom3$stomcon.hat*5000)\n ,list(Predator=stom3$Predator,PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum)\n\n# by(a,list(a$Predator),function(x) weighted.mean(x$PredPrey, x$stom))\n\nbb<-data.frame(size=rep(a$PredPrey,trunc(a$stom)),Predator=rep(a$Predator,trunc(a$stom)))\nk1<-aggregate(list(mean=bb$size),list(Predator=bb$Predator),mean)\nk2<-aggregate(list(var=bb$size),list(Predator=bb$Predator),var)\nstat<-merge(k1,k2)\n\nbb<-data.frame(size=rep(a$PredPrey,trunc(a$stom.hat)),Predator=rep(a$Predator,trunc(a$stom.hat)))\nk1<-aggregate(list(mean.hat=bb$size),list(Predator=bb$Predator),mean)\nk2<-aggregate(list(var.hat=bb$size),list(Predator=bb$Predator),var)\nstat.hat<-merge(k1,k2)\n\nstat<-merge(stat,stat.hat) \nstat\n\n# decrease fac for a nice plot\nfac<-2\na<-aggregate(list(stom=stom3$stomcon, stomNo=stom3$stomcon/stom3$Prey.weight,stom.hat=stom3$stomcon.hat),list(Predator=stom3$Predator,\n PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum) \nb<-aggregate(list(sumstom=a$stom, sumstomNo=a$stomNo, sumstom.hat=a$stom.hat),list(Predator=a$Predator),sum)\na<-merge(a,b)\na<-merge(a,stat)\na$stom<-a$stom/a$sumstom\na$stomNo<-a$stomNo/a$sumstomNo\na$stom.hat<-a$stom.hat/a$sumstom.hat\n\n## observed\na$headt<-paste(a$Predator,\", mean=\",formatC(a$mean,digits=2,format='f'),\" variance=\",formatC(a$var,digits=2,format='f'),sep='')\n\ncleanup() \ntrellis.device(device = \"windows\", \n color = T, width=9, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\ncleanup()\ntrellis.device(device = \"windows\",\n color = T, width=18, height=18,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|headt, data=a,\n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.5, lines=2),\n scales = list(x = list(rot = 90, cex=0.5), y= list(alternating = 1,cex=0.5)),\n layout = c(3,4) ,col='grey')\n\nif (F) {\n # for the paper\n barchart(stom~as.factor(PredPrey)|headt, data=a, \n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(1,1) ,col='grey')\n }\n\nb1<-a\nb1$type<-paste(a$headt,'\\nObserved',sep='');\n\n## predicted\na$headt<-paste(a$Predator,\" mean=\",formatC(a$mean.hat,digits=2,format='f'),\" var=\",formatC(a$var.hat,digits=2,format='f'),sep='')\n\ntrellis.device(device = \"windows\",\n color = T, width=18, height=22,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n\n barchart(stom.hat~as.factor(PredPrey)|headt, data=a,\n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.5, lines=2),\n scales = list(x = list(rot = 90, cex=0.5), y= list(alternating = 1,cex=0.5)),\n layout = c(3,4) ,col='grey')\n\n\nif (F) {\n\ntrellis.device(device = \"windows\", \n color = T, width=9, height=17,pointsize = 2,\n new = TRUE, retain = FALSE)\n\n # for the paper\n barchart(stom.hat~as.factor(PredPrey)|headt, data=a, \n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=0.8, lines=2),\n scales = list(x = list(rot = 45, cex=0.8), y= list(alternating = 1,cex=0.8)),\n layout = c(1, 3) ,col='grey')\n}\n\nb2<-a\n\nb2$type<-paste(a$headt,'\\nPredicted',sep='');\n\n# both on the same plot\nb2$stom<-b2$stom.hat\naa<-rbind(b1,b2)\n\ntrellis.device(device = \"windows\", \n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|type, data=aa, \n groups = Predator, stack = TRUE, # this line is not needed, but it gives a nicer offset on the Y-axis\n\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 3) ,col='grey')\n\n \n#####################\n# same as above, but with contributions from prey species\n\nfac<-2\na<-aggregate(list(stom=stom3$stomcon, stomNo=stom3$stomcon/stom3$Prey.weight,stom.hat=stom3$stomcon.hat),list(Predator=stom3$Predator,\n Prey=stom3$Prey,\n PredPrey=trunc(log(stom3$Predator.size/stom3$Prey.size)*fac)/fac),sum)\nb<-aggregate(list(sumstom=a$stom, sumstomNo=a$stomNo, sumstom.hat=a$stom.hat),list(Predator=a$Predator),sum)\na<-merge(a,b)\na<-merge(a,stat)\na$stom<-a$stom/a$sumstom\na$stomNo<-a$stomNo/a$sumstomNo\na$stom.hat<-a$stom.hat/a$sumstom.hat\n\n## observed\na$headt<-paste(a$Predator,\", mean=\",formatC(a$mean,digits=2,format='f'),\" variance=\",formatC(a$var,digits=2,format='f'),sep='')\n\n#cleanup()\ntrellis.device(device = \"windows\",\n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE )\n\nb<- tapply(stom3$stomcon,list(stom3$Prey),sum)\ncol<-1:6\nlab<-dimnames(b)[[1]]\n\n#cleanup()\ntrellis.device(device = \"windows\",\n color = T, width=7, height=9,pointsize = 2,\n new = TRUE, retain = FALSE)\n\n\n barchart(stom~as.factor(PredPrey)|headt, data=a,\n groups = Prey, stack = TRUE, col=1:6,\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:5),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(1, 1) )\n\n\nb1<-a\nb1$type<-paste(a$headt,'\\nObserved',sep='');\n\n## predicted\na$headt<-paste(a$Predator,\" mean=\",formatC(a$mean.hat,digits=2,format='f'),\" var=\",formatC(a$var.hat,digits=2,format='f'),sep='')\ntrellis.device(device = \"windows\",\n color = T, width=7, height=9,pointsize = 2,\n new = TRUE, retain = FALSE)\n \nbarchart(stom.hat~as.factor(PredPrey)|headt, data=a,\n groups = Prey, stack = TRUE, col=1:6,\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:6),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(1, 3) )\n\n \nb2<-a\n\nb2$type<-paste(a$headt,'\\nPredicted',sep='');\n\n# both on the same plot\nb2$stom<-b2$stom.hat\naa<-rbind(b1,b2)\naa<-droplevels(aa)\n\ntrellis.device(device = \"windows\", \n color = T, width=17, height=17,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|type, data=aa, \n groups = Prey, stack = TRUE, col=1:6, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:6),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 3) )\n\n \n\n# BALTIC both on the same plot b\nb2$stom<-b2$stom.hat\naa<-rbind(b1,b2)\n\ntrellis.device(device = \"windows\",\n color = T, width=8, height=4,pointsize = 12,\n new = TRUE, retain = FALSE)\n\n barchart(stom~as.factor(PredPrey)|type, data=aa,\n groups = Prey, stack = TRUE, col=1:3, # this line is not needed, but it gives a nicer offset on the Y-axis\n xlab='log(predator weight/prey weight)',ylab='Proportion',\n strip = strip.custom( bg='white'),par.strip.text=list(cex=1, lines=2),\n key = list(text = list(label = lab,col=1), rectangles = TRUE, space = \"right\", col=1:3),\n scales = list(x = list(rot = 45, cex=1), y= list(alternating = 1,cex=1)),\n layout = c(2, 1) )\n\n\n###################\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\n\nby(a,list(a$Predator),function(x) {\n tit<-paste(x[1,]$Predator,' mean=',formatC(x[1,]$mean,digits=2,format='f'), ' var=',formatC(x[1,]$var,digits=2,format='f'),sep='') \n b<- tapply(x$stom,x$PredPrey,sum)\n print(b)\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 } \n barplot(b) \n title(main=tit)\n})\n\n \n ##############################################################\n#observation of prey size against predator size by predator\n## prog size 2aa\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Prey.size))\nmax.ratio<-log(max(stom2$Prey.size))\n\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size),log(x$Prey.size),main=NULL,xlab='log(predator size)',ylab=\"log(prey.size)\",\n ylim=c(min.ratio,max.ratio),col=x$Quarter.no)\n abline(h=0, lty=3)\n aa<-lm(log(Prey.size)~log(Predator.size) ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3)\n title(main=paste(x[1,]$Predator,\" eating \",x[1,]$Prey,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n##############################################################\n#observation of predator-prey size ratio against predator size by predator\n## prog size 2a\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$Predator.size,log(x$Predator.size/x$Prey.size),main=NULL,xlab='predator size',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio),col=x$Quarter.no)\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~Predator.size ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3)\n title(main=paste(x[1,]$Predator,\" eating \",x[1,]$Prey,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n##############################################################\n#observation of predator-prey size ratio against predator size by predator\n## prog size 2b\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0 &stomcon>1E-4)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size),log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(predator size)',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio))\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~log(Predator.size) ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3,lwd=2)\n \n #quantile regression\n for (tau in c(0.025,0.975)) {\n ru<-rq(log(Predator.size/Prey.size)~log(Predator.size) ,data=x, tau=tau)\n abline(ru,col='blue',lty=2)\n }\n # weighted regression\n #aa<-lm(log(Predator.size/Prey.size)~log(Predator.size),weights=stomcon ,data=x)\n #ac<-aa[[\"coefficients\"]]\n #abline(aa, lty=2,col=2)\n\n \n title(main=paste(x[1,]$Predator,\" eating \",x[1,]$Prey,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n##############################################################\n#observation of predator-prey size ratio against predator size by predator\n## prog size 2b paper\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0 &stomcon>1E-4 & Predator=='Cod' & Prey=='Haddock')\n#stom2<-subset(stom,Size.model>0 & Prey.no !=0 &stomcon>1E-4 & Predator=='Cod' & Prey=='Cod')\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Prey.no,stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size),log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(predator weight)',ylab=\"log(predator weight / prey weight)\")\n \n #quantile regression\n for (tau in c(0.025,0.975)) {\n ru<-rq(log(Predator.size/Prey.size)~log(Predator.size) ,data=x, tau=tau)\n abline(ru,col=1,lty=2)\n }\n \n i<<-i+1\n})}\n\n\n##############################################################\n#observation of predator-prey size ratio against predator size by prey\n## prog size 3\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Predator,stom2$Prey.no),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$Predator.size,log(x$Predator.size/x$Prey.size),main=NULL,xlab='predator size',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio),col=x$Quarter.no)\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~Predator.size ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3)\n title(main=paste(x[1,]$Predator,\" eating \",x[1,]$Prey,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n\n##############################################################\n#observation of predator-prey size ratio against predator\n## prog size 4\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(x$Predator.size,log(x$Predator.size/x$Prey.size),main=NULL,xlab='predator size',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio),col=x$Prey.no)\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~Predator.size ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3)\n title(main=paste(x[1,]$Predator,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n##############################################################\n#observation of predator-prey size ratio against predator\n## prog size 5\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size),log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(predator size)',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio))\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~log(Predator.size) ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3)\n #quantile regression\n rl<-rq(log(Predator.size/Prey.size)~log(Predator.size) ,data=x, tau=0.025)\n ru<-rq(log(Predator.size/Prey.size)~log(Predator.size) ,data=x, tau=0.975)\n abline(rl,col='blue',lty=2)\n abline(ru,col='blue',lty=2)\n\n title(main=paste(x[1,]$Predator,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n i<<-i+1\n})}\n\n\n######################################################################\n#observation of predator-prey size ratio against predator\n## prog size 6\n\ncleanup()\nnox<-2; noy<-2;\nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Size.model>0 & Prey.no !=0)\nif (dim(stom2)[1]==0) stop(\"No data, probably because there is no size selection applied in the model\") else {\nmin.ratio<-log(min(stom2$Predator.size/stom2$Prey.size))\nmax.ratio<-log(max(stom2$Predator.size/stom2$Prey.size))\n\nby(stom2,list(stom2$Predator),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n plot(log(x$Predator.size),log(x$Predator.size/x$Prey.size),main=NULL,xlab='log(predator size)',ylab=\"log(pred.size/prey.size)\",\n ylim=c(min.ratio,max.ratio),col=x$Prey.no)\n abline(h=0, lty=3)\n aa<-lm(log(Predator.size/Prey.size)~log(Predator.size) ,data=x)\n ac<-aa[[\"coefficients\"]]\n abline(aa, lty=3,lwd=3)\n title(main=paste(x[1,]$Predator,\n \" a=\",formatC(ac[1],2,format='f'),\" b=\",formatC(ac[2],2,format='f'),sep=\"\"))\n xx<-x\n by(xx,list(xx$Prey.no),function(x) {\n abline(lm(log(Predator.size/Prey.size)~log(Predator.size),data=x))\n })\n i<<-i+1\n})}\n\n\n\n##############################################################\n# Other food observed and estimated by year\n## prog other 1\n\nnox<-4; noy<-4;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Prey.no==0)\n\nby(stom2,list(stom2$Year,stom2$Quarter,stom2$Predator.no),function(x) {\n if (i==noxy) {newplot(dev,nox,noy); i<<-0 }\n max.y<-max(x$stomcon.hat,x$stomcon)\n plot(x$Predator.length.class,x$stomcon,xlab='pred. length class',ylab='', pch=1,col=1,type='b',ylim=c(0,max.y))\n lines(x$Predator.length.class,x$stomcon.hat,pch=2,col=2,type='p')\n title(main=paste(x[1,]$Predator,x[1,]$Year,x[1,]$Quarter))\n i<<-i+1\n})\n\n\n\n\n##############################################################\n# Other food observed and estimated by year\n## prog other 1\n\nnox<-2; noy<-2;\ncleanup() \nnewplot(dev,nox,noy);\ni<-0\n\nstom2<-subset(stom,Prey=='Other')\n\nxyplot(stomcon.hat~Predator.length.class|Quarter*Predator*year, data=stom2,\ngroups=stomcon,\npanel=function(x,y,subscripts,groups) {\n panel.xyplot(x,y,col=1 ,pch=1)\n panel.xyplot(x,groups[subscripts],col=2 ,pch=4)\n})\n\n##############################################################\n# Other food, suitatability\n## prog other 2\n\nxyplot(Suit~Predator.length.class|Quarter*Predator*year, data=stom,subset=(Prey=='Other'),col=2)\n\n\n##############################################################\n#Other food residuals\n## prog other 3\nxyplot(Residual~Predator.length.class|Quarter*Predator, data=stom,subset=(Prey=='Other'),\npanel=function(x,y) {\n panel.xyplot(x,y,col=1 ,pch=1)\n panel.loess(x,y, span=1)\n})\n##############################################################\n\n\n##############################################################\n#Other food residuals\n## prog other 4\nxyplot(Residual~log(Predator.size)|Quarter*Predator, data=stom,subset=(Prey=='Other'),\npanel=function(x,y) {\n panel.xyplot(x,y,col=1 ,pch=1)\n panel.loess(x,y, span=1)\n})\n##############################################################\n\n\n##############################################################\n\n#Total Available food\n## prog avail 2\n#\ns<-tapply(stom$Prey.avail,list(stom$Year,stom$Quarter,stom$Predator,stom$Predator.length.class),sum)\ns1<-arr2df(s)\nnames(s1)<-c('Year','Quarter','Predator','Predator.length','Prey.avail')\ns1<-s1[!is.na(s1$Prey.avail),]\n\nxyplot(Prey.avail~Predator.length|Quarter*Predator,col=2, data=s1)\n\n##############################################################\n\n", "meta": {"hexsha": "ec1ce9a902da7bc40a29097654142c27f9650b87", "size": 55513, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_stomach_data.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/plot_stomach_data.r", 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YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3176299335587883}} {"text": "#' @title Side density distributions\n#'\n#' @description\n#' The [xside] and [yside] variants of \\link[ggplot2]{geom_density} is\n#' [geom_xsidedensity] and [geom_ysidedensity].\n#'\n#' @inheritParams ggplot2::layer\n#' @inheritParams ggplot2::geom_bar\n#' @inheritParams ggplot2::geom_ribbon\n#' @param stat Use to override the default connection between\n#' `geom_density()` and `stat_density()`.\n#' @aliases geom_*sidedensity\n#' @return XLayer or YLayer object to be added to a ggplot object\n#' @examples\n#'\n#' ggplot(mpg, aes(displ, hwy, colour = class)) +\n#' geom_point(size = 2) +\n#' geom_xsidedensity() +\n#' geom_ysidedensity() +\n#' theme(axis.text.x = element_text(angle = 90, vjust = .5))\n#'\n#' ggplot(mpg, aes(displ, hwy, colour = class)) +\n#' geom_point(size = 2) +\n#' geom_xsidedensity(aes(y = after_stat(count)),position = \"stack\") +\n#' geom_ysidedensity(aes(x = after_stat(scaled))) +\n#' theme(axis.text.x = element_text(angle = 90, vjust = .5))\n#'\n#' @export\ngeom_xsidedensity <- function(mapping = NULL, data = NULL,\n stat = \"density\", position = \"identity\",\n ...,\n na.rm = FALSE,\n orientation = \"x\",\n show.legend = NA,\n inherit.aes = TRUE,\n outline.type = \"upper\") {\n mapping <- default_stat_aes(mapping, stat, orientation)\n outline.type <- match.arg(outline.type, c(\"both\", \"upper\", \"lower\", \"full\"))\n l <- layer(\n data = data,\n mapping = mapping,\n stat = stat,\n geom = GeomXsidedensity,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n na.rm = na.rm,\n orientation = orientation,\n outline.type = outline.type,\n ...\n ),\n layer_class = XLayer\n )\n structure(l, class = c(\"ggside_layer\",class(l)))\n}\n\n#' @rdname ggside-ggproto-geoms\n#' @usage NULL\n#' @format NULL\n#' @export\nGeomXsidedensity <- ggplot2::ggproto(\"GeomXsidedensity\",\n ggplot2::GeomDensity,\n default_aes = aes(fill = NA, xfill = NA, weight = 1,\n colour = \"black\", xcolour = NA, alpha = NA,\n size = 0.5, linetype = 1),\n setup_data = function(data, params) {\n data <- parse_side_aes(data, params)\n ggplot2::GeomDensity$setup_data(data, params)\n },\n draw_group = function(data, panel_params, coord, na.rm = FALSE, flipped_aes = FALSE, outline.type = \"both\") {\n data <- use_xside_aes(data)\n ggplot2::GeomDensity$draw_group(data = data, panel_params = panel_params, coord = coord, na.rm = na.rm,\n flipped_aes = flipped_aes, outline.type = outline.type)},\n draw_key = function(data, params, size) {\n data <- use_xside_aes(data)\n ggplot2::GeomDensity$draw_key(data, params, size)\n })\n\n#' @rdname geom_xsidedensity\n#' @export\ngeom_ysidedensity <- function(mapping = NULL, data = NULL,\n stat = \"density\", position = \"identity\",\n ...,\n na.rm = FALSE,\n orientation = \"y\",\n show.legend = NA,\n inherit.aes = TRUE,\n outline.type = \"upper\") {\n mapping <- default_stat_aes(mapping, stat, orientation)\n outline.type <- match.arg(outline.type, c(\"both\", \"upper\", \"lower\", \"full\"))\n l <- layer(\n data = data,\n mapping = mapping,\n stat = stat,\n geom = GeomYsidedensity,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list(\n na.rm = na.rm,\n orientation = orientation,\n outline.type = outline.type,\n ...\n ),\n layer_class = YLayer\n )\n structure(l, class = c(\"ggside_layer\",class(l)))\n}\n\n#' @rdname ggside-ggproto-geoms\n#' @usage NULL\n#' @format NULL\n#' @export\nGeomYsidedensity <- ggplot2::ggproto(\"GeomYsidedensity\",\n ggplot2::GeomDensity,\n default_aes = aes(fill = NA, yfill = NA, weight = 1,\n colour = \"black\", ycolour = NA, alpha = NA,\n size = 0.5, linetype = 1),\n setup_data = function(data, params) {\n data <- parse_side_aes(data, params)\n ggplot2::GeomDensity$setup_data(data, params)\n },\n draw_group = function(data, panel_params, coord, na.rm = FALSE, flipped_aes = FALSE, outline.type = \"both\") {\n data <- use_yside_aes(data)\n ggplot2::GeomDensity$draw_group(data = data, panel_params = panel_params, coord = coord, na.rm = na.rm,\n flipped_aes = flipped_aes, outline.type = outline.type)\n },\n draw_key = function(data, params, size) {\n data <- use_yside_aes(data)\n ggplot2::GeomDensity$draw_key(data, params, size)\n })\n", "meta": {"hexsha": "6ae60507dff7025af3bfd127cb95e0f17f1152c7", "size": 5795, "ext": "r", "lang": "R", "max_stars_repo_path": "R/geom-sidedensity.r", "max_stars_repo_name": "steveped/ggside", "max_stars_repo_head_hexsha": "35d21cd55ddf6f542a8e95fa0d42b105bed0deed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 178, "max_stars_repo_stars_event_min_datetime": "2021-02-19T16:02:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T02:41:58.000Z", "max_issues_repo_path": "R/geom-sidedensity.r", "max_issues_repo_name": "steveped/ggside", "max_issues_repo_head_hexsha": "35d21cd55ddf6f542a8e95fa0d42b105bed0deed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 24, "max_issues_repo_issues_event_min_datetime": "2021-03-03T08:14:21.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T16:08:20.000Z", "max_forks_repo_path": "R/geom-sidedensity.r", "max_forks_repo_name": "steveped/ggside", "max_forks_repo_head_hexsha": "35d21cd55ddf6f542a8e95fa0d42b105bed0deed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2021-03-03T13:24:41.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T16:23:03.000Z", "avg_line_length": 43.5714285714, "max_line_length": 146, "alphanum_fraction": 0.4817946506, "num_tokens": 1237, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.31762992570383236}} {"text": "#' Assign reads to pre-existing bins\n#'\n#' This function exists in AneuFinder, but it is re-bins all reads and ignores\n#' already provided reads. The functionality here is basically the same as:\n#' https://github.com/ataudt/aneufinder/blob/master/R/binReads.R#L269-L303\n#'\n#' @param reads A GRanges object of reads\n#' @param bins A GRanges object of bins\n#' @param min_mapq Min mapping quality (added to object for info)\n#' @param id Sample ID (added to object for info)\n#' @return A GRanges object of the read counts per bin\nbin_reads = function(reads, bins, min_mapq=NULL, id=NULL) {\n grc = function(r) GenomicRanges::countOverlaps(bins, r)\n S4Vectors::mcols(bins) = S4Vectors::DataFrame(\n counts = grc(reads),\n mcounts = grc(reads[BiocGenerics::strand(reads) == '-']),\n pcounts = grc(reads[BiocGenerics::strand(reads) == '+']))\n\n qualityInfo = list(complexity = c(MM=NA),\n coverage = NA,\n spikiness = AneuFinder:::qc.spikiness(bins$counts),\n entropy = AneuFinder:::qc.entropy(bins$counts))\n\n attr(bins, 'qualityInfo') = qualityInfo\n attr(bins, 'min.mapq') = min_mapq\n attr(bins, 'ID') = id\n bins\n}\n", "meta": {"hexsha": "90c111b18196cbdb6bcee1951a8e3f94d7009134", "size": 1235, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/aneufinder/bin_reads.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "tools/aneufinder/bin_reads.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "tools/aneufinder/bin_reads.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 42.5862068966, "max_line_length": 78, "alphanum_fraction": 0.64048583, "num_tokens": 345, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6113819874558604, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.31762597834076645}} {"text": "#' Read MODFLOW .lpf File\n#'\n#' This function reads in a lpf file and creates a list\n#' composed of the following vectors:\n#' \\describe{\n#' \\item{ILPFCB}{is a flag and a unit number. If ILPFCB > 0, cell-by-cell flow terms will be written to this unit\n#' number when \"SAVE BUDGET\" or a non-zero value for ICBCFL is specified in Output Control. The terms that are saved\n#' are storage, constant-head flow, and flow between adjacent cells.\n#' If ILPFCB = 0, cell-by-cell flow terms will not be written.\n#' If ILPFCB < 0, cell-by-cell flow for constant-head cells will be written in the listing file when \"SAVE BUDGET\"\n#' or a non-zero value for ICBCFL is specified in Output Control. Cell-by-cell flow to storage and between\n#' adjacent cells will not be written to any file.}\n#' \\item{HDRY}{is the head that is assigned to cells that are converted to dry during a simulation. Although this value plays\n#' no role in the model calculations, HDRY values are useful as indicators when looking at the resulting heads that are\n#' output from the model. HDRY is thus similar to HNOFLO in the Basic Package, which is the value assigned to cells\n#' that are no-flow cells at the start of a model simulation.}\n#' \\item{NPLPF}{is the number of LPF parameters}\n#' \\item{LAYTYP}{contains a flag for each layer that specifies the layer type.\n#' 0 \u2013 confined\n#' >0 \u2013 convertible\n#' <0 \u2013 convertible unless the THICKSTRT option is in effect. When THICKSTRT is in effect, a negative value of\n#' LAYTYP indicates that the layer is confined, and its saturated thickness will be computed as STRT-BOT.}\n#' \\item{LAYAVG}{contains a flag for each layer that defines the method of calculating interblock transmissivity.\n#' 0\u2014harmonic mean\n#' 1\u2014logarithmic mean\n#' 2\u2014arithmetic mean of saturated thickness and logarithmic-mean hydraulic conductivity.}\n#' \\item{CHANI}{contains a value for each layer that is a flag or the horizontal anisotropy. If CHANI is less than or equal to\n#' 0, then variable HANI defines horizontal anisotropy. If CHANI is greater than 0, then CHANI is the horizontal\n#' anisotropy for the entire layer, and HANI is not read. If any HANI parameters are used, CHANI for all layers must\n#' be less than or equal to 0.}\n#' \\item{LAYVKA}{contains a flag for each layer that indicates whether variable VKA is vertical hydraulic conductivity or\n#' the ratio of horizontal to vertical hydraulic conductivity.\n#' 0\u2014indicates VKA is vertical hydraulic conductivity\n#' not 0\u2014indicates VKA is the ratio of horizontal to vertical hydraulic conductivity, where the horizontal hydraulic\n#' conductivity is specified as HK in item 10.}\n#' \\item{LAYWET}{contains a flag for each layer that indicates whether wetting is active.\n#' 0\u2014indicates wetting is inactive\n#' not 0\u2014indicates wetting is active}\n#' \\item{WETFCT}{is a factor that is included in the calculation of the head that is initially established at a cell when the cell\n#' is converted from dry to wet. (See IHDWET.)\n#' IWETIT\u2014is the iteration interval for attempting to wet cells. Wetting is attempted every IWETIT iteration. If using\n#' the PCG solver (Hill, 1990), this applies to outer iterations, not inner iterations. If IWETIT \u2264 0, the value is changed\n#' to 1.}\n#' \\item{IHDWET}{is a flag that determines which equation is used to define the initial head at cells that become wet:\n#' If IHDWET = 0, equation 5-32A is used: h = BOT + WETFCT (hn - BOT) .\n#' If IHDWET is not 0, equation 5-32B is used: h = BOT + WETFCT(THRESH)}\n#' \\item{PARNAM}{is the name of a parameter to be defined. This name can consist of 1 to 10 characters and is not case\n#' sensitive. That is, any combination of the same characters with different case will be equivalent.}\n#' \\item{PARTYP}{is the type of parameter to be defined. For the LPF Package, the allowed parameter types are:\n#' HK\u2014defines variable HK, horizontal hydraulic conductivity\n#' HANI\u2014defines variable HANI, horizontal anisotropy\n#' VK\u2014defines variable VKA for layers for which VKA represents vertical hydraulic conductivity (LAYVKA=0)\n#' VANI\u2014defines variable VKA for layers for which VKA represents vertical anisotropy (LAYVKA!=0)\n#' SS\u2014defines variable Ss, the specific storage\n#' SY\u2014defines variable Sy, the specific yield\n#' VKCB\u2014defines variable VKCB, the vertical hydraulic conductivity of a Quasi-3D confining layer.}\n#' \\item{Parval}{is the parameter value. This parameter value may be overridden by a value in the Parameter Value File.}\n#' \\item{NCLU}{is the number of clusters required to define the parameter. Each repetition of Item 9 is a cluster (variables\n#' Layer, Mltarr, Zonarr, and IZ). Each layer that is associated with a parameter usually has only one cluster. For\n#' example, parameters which apply to cells in a single layer generally will be defined by just one cluster. However,\n#' having more than one cluster for the same layer is acceptable.}\n#' \\item{Layer}{is the layer number to which a cluster definition applies.}\n#' \\item{Mltarr}{is the name of the multiplier array to be used to define variable values that are associated with a parameter.\n#' The name \u201cNONE\u201d means that there is no multiplier array, and the variable values will be set equal to Parval.}\n#' \\item{Zonarr}{is the name of the zone array to be used to define the cells that are associated with a parameter. The name\n#' \u201cALL\u201d means that there is no zone array, and all cells in the specified layer are part of the parameter.}\n#' \\item{IZ}{is up to 10 zone numbers (separated by spaces) that define the cells that are associated with a parameter. These\n#' values are not used if ZONARR is specified as \u201cALL\u201d. Values can be positive or negative, but 0 is not allowed. The\n#' end of the line, a zero value, or a non-numeric entry terminates the list of values.}\n#' \\item{PROPS}{Data frame of LAY, ROW, COL, HK, HANI, VKA, Ss, Sy, VKCB, WETDRY}\n#' }\n#' @param rootname This is the root name of the lpf file\n#' @export\n#' @examples\n#' readlpf(\"F95\")\n#' $ILPFCB\n#' [1] 50\n#' \n#' $HDRY\n#' [1] -1e+30\n#' \n#' $NPLPF\n#' [1] 0\n#' \n#' $LAYTYP\n#' [1] 1 3 3 3 0 0 0 0\n#' \n#' $LAYAVG\n#' [1] 0 0 0 0 0 0 0 0\n#' \n#' $CHANI\n#' [1] -1 -1 -1 -1 -1 -1 -1 -1\n#' \n#' etc.\n#' \n#' Use this to develop summary statistics of the hydraulic properties\n#' p <- readlpf(\"F95\")\n#' p$PROPS %>% group_by(LAY) %>% summarise(MIN_K = min(HK), MEDIAN_K = median(HK), MAX_K = max(HK))\n#' \n# A tibble: 8 x 4\n# LAY MIN_K MEDIAN_K MAX_K\n# \n# 1 1 0.1000004 3.229593 17.91849\n# 2 2 0.0080000 0.008000 0.00800\n# 3 3 0.0080000 0.008000 0.00800\n# 4 4 0.0080000 0.008000 0.00800\n# 5 5 0.0080000 0.008000 0.00800\n# 6 6 0.0080000 0.008000 0.00800\n# 7 7 0.1000000 6.449597 17.46494\n# 8 8 20.0000000 20.000000 20.00000\n\n\nreadlpf <- function(rootname = NA){\n if(is.na(rootname)){\n rootname <- MFtools::getroot()\n }\n infl <- paste0(rootname, \".lpf\")\n d <- MFtools::readdis(rootname) \n linin <- readr::read_lines(infl)\n indx <- max(grep(\"#\", linin)) + 1\n ILPFCB <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[1]] %>% \n as.integer() \n HDRY <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[2]] %>% \n as.numeric() \n NPLPF <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[3]] %>% \n as.integer() \n indx <- indx + 1\n BLOCKEND <- ceiling(d$NLAY / 50) \n BLOCKEND_NUM <- ceiling(d$NLAY / 20) \n LAYTYP <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.integer() \n indx <- indx + BLOCKEND \n LAYAVG <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.integer() \n indx <- indx + BLOCKEND\n CHANI <- linin[indx + seq(1:BLOCKEND_NUM) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND_NUM\n LAYVKA <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.integer() \n indx <- indx + BLOCKEND\n LAYWET <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.integer()\n indx <- indx + BLOCKEND\n WETFCT <- c(NA) \n IWETIT <- c(NA)\n IHDWET <- c(NA)\n if(sum(LAYWET) > 0){\n WETFCT <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[1]] %>% \n as.numeric() \n IWETIT <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[2]] %>% \n as.integer() \n IHDWET <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[3]] %>% \n as.integer() \n\n indx <- indx + 1 \n }\n PARNAM <- vector(mode = \"character\", length = NPLPF) \n PARTYP <- vector(mode = \"character\", length = NPLPF)\n Parval <- vector(mode = \"numeric\", length = NPLPF) \n NCLU <- vector(mode = \"integer\", length = NPLPF) \n Layer <- c(NULL)\n Mltarr <- c(NULL)\n Zonarr <- c(NULL)\n IZ <- c(NULL)\n if(NPLPF > 0){\n for(Q in 1:NPLPF){\n PARNAM[Q] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[1]] \n PARTYP[Q] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[2]]\n Parval[Q] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[3]] %>%\n as.numeric()\n NCLU[Q] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[4]] %>% \n as.numeric()\n indx <- indx + 1 \n for(ii in 1:NCLU[Q]){\n indx <- indx + 1 \n Layer[ii] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[1]] %>%\n as.integer()\n Mltarr[ii] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[2]] \n Zonarr[ii] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[3]] \n IZ[ii] <- linin[indx] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n .[[4:nchar(linin[indx])]] %>%\n as.integer() \n } \n }\n } \n HKin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY) \n if(min(CHANI) < 0){ \n HANIin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY)\n }else{\n HANIin <- rep(NA, d$NCOL * d$NROW * d$NLAY)\n }\n VKAin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY) \n if(\"TR\" %in% d$SS){\n Ssin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY)\n }else{\n Ssin <- rep(NA, d$NCOL * d$NROW * d$NLAY)\n }\n if((\"TR\" %in% d$SS)&(min(abs(LAYTYP)) == 0)){\n Syin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY)\n }else{\n Syin <- rep(NA, d$NCOL * d$NROW * d$NLAY)\n } \n if(any(d$LAYCBD) != 0){\n VKCBDin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * d$NLAY)\n }else{\n VKCBDin <- rep(NA, d$NCOL * d$NROW * d$NLAY)\n }\n if((sum(LAYWET) == d$NLAY) & (sum(LAYTYP) != 0)){\n WETDRYin <- vector(mode = \"numeric\", length = d$NCOL * d$NROW * sum(LAYWET))\n }else{\n WETDRYin <- rep(NA, d$NCOL * d$NROW * d$NLAY) %>% as.numeric()\n } \n \n for(K in 1:d$NLAY){\n# READ HORIZONTAL HYDRAULIC CONDUCTIVITY\n#########################################################\n # print(paste(\"READING HK: LAYER\", K))\n FROM <- (K - 1) * d$NROW * d$NCOL + 1\n TO <- K * d$NROW * d$NCOL\n \n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n # print(paste(\"FRMTREP[\",K,\"] = \", FRMTREP, sep = \"\"))\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n HKin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n HKin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n# READ HORIZONTAL ANISOTROPY IF CHANI < 0\n########################################################\n # print(paste(\"READING HANI: LAYER\", K))\n if(CHANI[K] <= 0){ \n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- (ceiling(d$NCOL / FRMTREP)) * d$NROW\n indx <- indx + 1 \n if(UNI == 0){\n HANIin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n HANIin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n }\n \n# READ VKA\n########################################################## \n # print(paste(\"READING VKA: LAYER\", K))\n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n VKAin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n VKAin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n \n# READ Ss IF NUMBER OF TRANSIENT > 0\n##########################################################\nif(\"TR\" %in% d$SS){ \n # print(paste(\"READING Ss: LAYER\", K))\n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n Ssin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n Ssin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n }\n \n# READ Sy IF NUMBER OF TRANSIENT > 0 & LAYTYP == UNCONFINED (0)\n##########################################################\nif((\"TR\" %in% d$SS)&(LAYTYP[K] != 0)){ \n # print(paste(\"READING Sy: LAYER\", K))\n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n Syin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n Syin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n }\n\n# READ VKCBD \n##########################################################\nif(d$LAYCBD[K] != 0){ \n # print(paste(\"READING VKBD: LAYER\", K))\n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n rR }else{ \n VKCBDin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n }\n\n# READ WETDRY \n##########################################################\nif((LAYWET[K] != 0) & (LAYTYP[K] != 0)){ \n # print(paste(\"READING WETDRY: LAYER\", K))\n UNI <- substr(linin[indx], start = 1, stop = 10) %>% as.integer()\n MULT <- substr(linin[indx], start = 11, stop = 20) %>% as.numeric()\n FRMT <- substr(linin[indx], start = 21, stop = 30)\n FRMTREP <- as.integer(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[1]]\n FRMTWIDTH <- as.numeric(regmatches(FRMT, gregexpr(\"[[:digit:]]+\", FRMT))[[1]])[[2]]\n BLOCKEND <- ceiling(d$NCOL / FRMTREP) * d$NROW \n \n indx <- indx + 1\n if(UNI == 0){\n WETDRYin[FROM:TO] <- rep(MULT, d$NROW * d$NCOL)\n }else{ \n WETDRYin[FROM:TO] <- linin[indx + seq(1:BLOCKEND) - 1] %>% \n strsplit(\"\\\\s+\") %>% \n unlist() %>% \n subset(. != \"\") %>% \n as.numeric()\n indx <- indx + BLOCKEND\n }\n }\n }\nPROPS <- tibble::data_frame(\n LAY = rep(1:d$NLAY, each = d$NCOL * d$NROW) %>% as.integer(), \n ROW = rep(rep(1:d$NROW, each = d$NCOL), d$NLAY) %>% as.integer(), \n COL = rep(rep(seq(1, d$NCOL, 1), d$NROW), d$NLAY) %>% as.integer(), \n HK = HKin %>% as.numeric(), \n HANI = HANIin %>% as.numeric(), \n VKA = VKAin %>% as.numeric(), \n Ss = Ssin %>% as.numeric(), \n Sy = Syin %>% as.numeric(), \n VKCBD = VKCBDin %>% as.numeric(), \n WETDRY = WETDRYin %>% as.numeric()\n )\nrm(HKin)\nrm(HANIin)\nrm(VKAin)\nrm(Ssin)\nrm(Syin)\nrm(VKCBDin)\nrm(WETDRYin) \nrm(d) \ngc() \n \nPROPLIST <- list(ILPFCB = ILPFCB, \n HDRY = HDRY, \n NPLPF = NPLPF, \n LAYTYP = LAYTYP, \n LAYAVG = LAYAVG, \n CHANI = CHANI, \n LAYVKA = LAYVKA, \n LAYWET = LAYWET, \n WETFCT = WETFCT, \n IWETIT = IWETIT, \n IHDWET = IHDWET, \n PARNAM = PARNAM, \n PARTYP = PARTYP, \n Parval = Parval, \n NCLU = NCLU, \n Layer = Layer, \n Mltarr = Mltarr, \n Zonarr = Zonarr, \n IZ = IZ, \n PROPS = PROPS) \n return(PROPLIST) \n }\n ", "meta": {"hexsha": "ab50caf0644590db2dfc68f3167c31f7f569602a", "size": 21429, "ext": "r", "lang": "R", "max_stars_repo_path": "R/readlpf.r", "max_stars_repo_name": "dpphat/MFtools", "max_stars_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2016-12-23T21:35:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-23T14:52:25.000Z", "max_issues_repo_path": "R/readlpf.r", "max_issues_repo_name": "dpphat/MFtools", "max_issues_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/readlpf.r", "max_forks_repo_name": "dpphat/MFtools", "max_forks_repo_head_hexsha": "fe87cb57f24e3b132a013111d9444e51cd1386aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-10-21T18:07:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-21T18:07:26.000Z", "avg_line_length": 43.2909090909, "max_line_length": 130, "alphanum_fraction": 0.4932568015, "num_tokens": 6433, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3176259709834566}} {"text": "# usage: source(\"benchmark.r\")\n\nlibrary(\"SparkR\")\nlibrary(\"microbenchmark\")\nlibrary(\"magrittr\")\nlibrary(\"ggplot2\")\nlibrary(\"pryr\")\n\ndir_path <- \"/Users/liang/benchmark/results/\"\n\n# For benchmarking spark.lapply, I generate\n\n# lists \n\n# 1. with different types (7):\n# Length = 100,\n# Type = integer, \n# logical, \n# double, \n# character1, \n# character10, \n# character100, \n# character1k. \n\ndata.list.type.double <- runif(100)\ndata.list.type.int <- as.integer(data.list.type.double * 100)\ndata.list.type.logical <- data.list.type.double < 0.5\ndata.list.type.char1 <- sapply(1:100, function(x) { \"a\" })\ndata.list.type.char10 <- sapply(1:100, function(x) { \"0123456789\" })\ndata.list.type.char100 <- sapply(1:100, function(x) { paste(replicate(20, \"hello\"), collapse = \"\") })\ndata.list.type.char1k <- sapply(1:100, function(x) { paste(replicate(200, \"hello\"), collapse = \"\") })\n\n# 2. with different lengths (4):\n# Type = integer,\n# Length = 10,\n# 100,\n# 1k,\n# 10k\n\ndata.list.len.10 <- 1:10\ndata.list.len.100 <- 1:100\ndata.list.len.1k <- 1:1000\ndata.list.len.10k <- 1:10000\n\n# For benchmarking dapply+rnow/dapplyCollect, I generate\n\n# data.frame\n\n# 1. with different types (7):\n# nrow = 100,\n# ncol = 1,\n# Type = integer, \n# logical, \n# double, \n# character1, \n# character10, \n# character100, \n# character1k.\n\ndata.df.type.double <- data.frame(data.list.type.double) %>% createDataFrame\ndata.df.type.int <- data.frame(data.list.type.int) %>% createDataFrame\ndata.df.type.logical <- data.frame(data.list.type.logical) %>% createDataFrame\ndata.df.type.char1 <- data.frame(data.list.type.char1) %>% createDataFrame\ndata.df.type.char10 <- data.frame(data.list.type.char10) %>% createDataFrame\ndata.df.type.char100 <- data.frame(data.list.type.char100) %>% createDataFrame\ndata.df.type.char1k <- data.frame(data.list.type.char1k) %>% createDataFrame\n\n# 2. with different lengths (4):\n# ncol = 1,\n# Type = integer,\n# nrow = 10,\n# 100,\n# 1k,\n# 10k\n\ndata.df.len.10 <- data.frame(data.list.len.10) %>% createDataFrame\ndata.rdf.len.100 <- data.frame(data.list.len.100)\ndata.df.len.100 <- data.rdf.len.100 %>% createDataFrame\ndata.df.len.1k <- data.frame(data.list.len.1k) %>% createDataFrame\ndata.df.len.10k <- data.frame(data.list.len.10k) %>% createDataFrame\n\n# 3. with different ncols (3):\n# nrow = 100,\n# Type = integer,\n# ncol = 1,\n# 10,\n# 100\n\ndata.df.ncol.1 <- data.df.len.100\ndata.df.ncol.10 <- data.frame(rep(data.rdf.len.100, each = 10)) %>% createDataFrame\ndata.df.ncol.100 <- data.frame(rep(data.rdf.len.100, each = 100)) %>% createDataFrame\n\n# For benchmarking gapply+rnow/gapplyCollect, I generate\n\n# data.frame\n\n# 1. with different number of keys (3):\n# ncol = 2,\n# nrow = 1k,\n# Type = ,\n# nkeys = 10,\n# 100,\n# 1000\n\ndata.rand.1k <- runif(1000)\n\ndata.df.nkey.10 <- data.frame(key = rep(1:10, 100), val = data.rand.1k) %>% createDataFrame\ndata.df.nkey.100 <- data.frame(key = rep(1:100, 10), val = data.rand.1k) %>% createDataFrame\ndata.df.nkey.1k <- data.frame(key = 1:1000, val = data.rand.1k) %>% createDataFrame\n\n# data.df.nkey.10 <- repartition(data.df.nkey.10, numPartitions = 10, col = data.df.nkey.10$\"key\")\n# data.df.nkey.100 <- repartition(data.df.nkey.100, numPartitions = 100, col = data.df.nkey.100$\"key\")\n# data.df.nkey.1k <- repartition(data.df.nkey.1k, numPartitions = 100, col = data.df.nkey.1k$\"key\")\n\n\n# 2. with different lengths (4):\n# ncol = 2,\n# Type = ,\n# nkeys = 10,\n# nrow = 10,\n# 100,\n# 1k,\n# 10k\n\ndata.df.nrow.10 <- data.frame(key = 1:10, val = runif(10)) %>% createDataFrame\ndata.df.nrow.100 <- data.frame(key = rep(1:10, 10), val = runif(100)) %>% createDataFrame\ndata.df.nrow.1k <- data.frame(key = rep(1:10, 100), val = data.rand.1k) %>% createDataFrame\ndata.df.nrow.10k <- data.frame(key = rep(1:10, 1000), val = runif(10000)) %>% createDataFrame\n\n# 3. with different key types (3):\n# ncol = 2,\n# nkeys = 10,\n# nrow = 1k,\n# Type = \n# \n# \n\nkey.char10 <- sapply(1:10, function(x) { sprintf(\"%010d\", x) })\nkey.char100 <- sapply(1:10, function(x) { sprintf(\"%0100d\", x) })\n\ndata.df.keytype.int <- data.df.nrow.1k\ndata.df.keytype.char10 <- data.frame(key = rep(key.char10, 100), val = data.rand.1k) %>% createDataFrame\ndata.df.keytype.char100 <- data.frame(key = rep(key.char100, 10), val = data.rand.1k) %>% createDataFrame\n\n# ========== benchmark functions ==============\n\n# plot utils\nplot.box.mbm <- function(mbm_result) {\n\tggplot(mbm_result, aes(x = expr, y = time/1000000)) + \n\tgeom_boxplot(outlier.colour = \"red\", outlier.shape = 1) + labs(x = \"\", y = \"Time (milliseconds)\") +\n\tgeom_jitter(position=position_jitter(0.2), alpha = 0.2) + \n\tcoord_flip()\n}\n\n# dummy function\nfunc.dummy <- function(x) { x }\n\n# group function \ngfunc.mean <- function(key, sdf) {\n\ty <- data.frame(key, mean(sdf$val), stringsAsFactors = F)\n\tnames(y) <- c(\"key\", \"val\")\n\ty\n}\n\n# lapply, type\nrun.mbm.spark.lapply.type <- function() {\n\tmicrobenchmark(\n\t\t\"lapply.int.100\" = { spark.lapply(data.list.type.int, func.dummy) },\n\t\t\"lapply.double.100\" = { spark.lapply(data.list.type.double, func.dummy) },\n\t\t\"lapply.logical.100\" = { spark.lapply(data.list.type.logical, func.dummy) },\n\t\t\"lapply.char1.100\" = { spark.lapply(data.list.type.char1, func.dummy) },\n\t\t\"lapply.char10.100\" = { spark.lapply(data.list.type.char10, func.dummy) },\n\t\t\"lapply.char100.100\" = { spark.lapply(data.list.type.char100, func.dummy) },\n\t\t\"lapply.char1k.100\" = { spark.lapply(data.list.type.char1k, func.dummy) },\n\t\ttimes = 20\n\t)\n}\n\n# lapply, len\nrun.mbm.spark.lapply.len <- function(fast) {\n\tif (fast) {\n\t\tmicrobenchmark(\n\t\t\t\"lapply.len.10\" = { spark.lapply(data.list.len.10, func.dummy) },\n\t\t\t\"lapply.len.100\" = { spark.lapply(data.list.len.100, func.dummy) },\n\t\t\t\"lapply.len.1k\" = { spark.lapply(data.list.len.1k, func.dummy) },\n\t\t\ttimes = 20\n\t\t)\n\t} else {\n\t\tmicrobenchmark(\n\t\t\t\"lapply.len.10\" = { spark.lapply(data.list.len.10, func.dummy) },\n\t\t\t\"lapply.len.100\" = { spark.lapply(data.list.len.100, func.dummy) },\n\t\t\t\"lapply.len.1k\" = { spark.lapply(data.list.len.1k, func.dummy) },\n\t\t\t\"lapply.len.10k\" = { spark.lapply(data.list.len.10k, func.dummy) },\n\t\t\ttimes = 20\n\t\t)\n\t}\n\t\n}\n\n# dapply, type\nrun.mbm.dapply.type <- function() {\n\tmicrobenchmark(\n\t\t\"dapply.int.100\" = { dapply(data.df.type.int, func.dummy, schema(data.df.type.int)) %>% nrow },\n\t\t\"dapply.double.100\" = { dapply(data.df.type.double, func.dummy, schema(data.df.type.double)) %>% nrow },\n\t\t\"dapply.logical.100\" = { dapply(data.df.type.logical, func.dummy, schema(data.df.type.logical)) %>% nrow },\n\t\t\"dapply.char1.100\" = { dapply(data.df.type.char1, func.dummy, schema(data.df.type.char1)) %>% nrow },\n\t\t\"dapply.char10.100\" = { dapply(data.df.type.char10, func.dummy, schema(data.df.type.char10)) %>% nrow },\n\t\t\"dapply.char100.100\" = { dapply(data.df.type.char100, func.dummy, schema(data.df.type.char100)) %>% nrow },\n\t\t\"dapply.char1k.100\" = { dapply(data.df.type.char1k, func.dummy, schema(data.df.type.char1k)) %>% nrow },\n\t\ttimes = 20\n\t)\n}\n\n# dapply, len\nrun.mbm.dapply.len <- function(fast) {\n\tif (fast) {\n\t\tmicrobenchmark(\n\t\t\t\"dapply.len.10\" = { dapply(data.df.len.10, func.dummy, schema(data.df.len.10)) %>% nrow },\n\t\t\t\"dapply.len.100\" = { dapply(data.df.len.100, func.dummy, schema(data.df.len.100)) %>% nrow },\n\t\t\t\"dapply.len.1k\" = { dapply(data.df.len.1k, func.dummy, schema(data.df.len.1k)) %>% nrow },\n\t\t\ttimes = 20\n\t\t)\n\t} else {\n\t\tmicrobenchmark(\n\t\t\t\"dapply.len.10\" = { dapply(data.df.len.10, func.dummy, schema(data.df.len.10)) %>% nrow },\n\t\t\t\"dapply.len.100\" = { dapply(data.df.len.100, func.dummy, schema(data.df.len.100)) %>% nrow },\n\t\t\t\"dapply.len.1k\" = { dapply(data.df.len.1k, func.dummy, schema(data.df.len.1k)) %>% nrow },\n\t\t\t\"dapply.len.10k\" = { dapply(data.df.len.10k, func.dummy, schema(data.df.len.10k)) %>% nrow },\n\t\t\ttimes = 20\n\t\t)\n\t}\n}\n\n# dapply, ncol\nrun.mbm.dapply.ncol <- function() {\n\tmicrobenchmark(\n\t\t\"dapply.ncol.1\" = { dapply(data.df.ncol.1, func.dummy, schema(data.df.ncol.1)) %>% nrow },\n\t\t\"dapply.ncol.10\" = { dapply(data.df.ncol.10, func.dummy, schema(data.df.ncol.10)) %>% nrow },\n\t\t\"dapply.ncol.100\" = { dapply(data.df.ncol.100, func.dummy, schema(data.df.ncol.100)) %>% nrow },\n\t\ttimes = 20\n\t)\n}\n\n# dapplyCollect, type\nrun.mbm.dapplyCollect.type <- function() {\n\tmicrobenchmark(\n\t\t\"dapplyCollect.int.100\" = { dapplyCollect(data.df.type.int, func.dummy) },\n\t\t\"dapplyCollect.double.100\" = { dapplyCollect(data.df.type.double, func.dummy) },\n\t\t\"dapplyCollect.logical.100\" = { dapplyCollect(data.df.type.logical, func.dummy) },\n\t\t\"dapplyCollect.char1.100\" = { dapplyCollect(data.df.type.char1, func.dummy) },\n\t\t\"dapplyCollect.char10.100\" = { dapplyCollect(data.df.type.char10, func.dummy) },\n\t\t\"dapplyCollect.char100.100\" = { dapplyCollect(data.df.type.char100, func.dummy) },\n\t\t\"dapplyCollect.char1k.100\" = { dapplyCollect(data.df.type.char1k, func.dummy) },\n\t\ttimes = 20\n\t)\n}\n\n# dapplyCollect, len\nrun.mbm.dapplyCollect.len <- function(fast) {\n\tif (fast) {\n\t\tmicrobenchmark(\n\t\t\t\"dapplyCollect.len.10\" = { dapplyCollect(data.df.len.10, func.dummy) },\n\t\t\t\"dapplyCollect.len.100\" = { dapplyCollect(data.df.len.100, func.dummy) },\n\t\t\t\"dapplyCollect.len.1k\" = { dapplyCollect(data.df.len.1k, func.dummy) },\n\t\t\ttimes = 20\n\t\t)\n\t} else {\n\t\tmicrobenchmark(\n\t\t\t\"dapplyCollect.len.10\" = { dapplyCollect(data.df.len.10, func.dummy) },\n\t\t\t\"dapplyCollect.len.100\" = { dapplyCollect(data.df.len.100, func.dummy) },\n\t\t\t\"dapplyCollect.len.1k\" = { dapplyCollect(data.df.len.1k, func.dummy) },\n\t\t\t\"dapplyCollect.len.10k\" = { dapplyCollect(data.df.len.10k, func.dummy) },\n\t\t\ttimes = 20\n\t\t)\n\t}\n}\n\n# dapplyCollect, ncol\nrun.mbm.dapplyCollect.ncol <- function() {\n\tmicrobenchmark(\n\t\t\"dapplyCollect.ncol.1\" = { dapplyCollect(data.df.ncol.1, func.dummy) },\n\t\t\"dapplyCollect.ncol.10\" = { dapplyCollect(data.df.ncol.10, func.dummy) },\n\t\t\"dapplyCollect.ncol.100\" = { dapplyCollect(data.df.ncol.100, func.dummy) },\n\t\ttimes = 20\n\t)\n}\n\n\n# gapply, nkey\nrun.mbm.gapply.nkey <- function() {\n\tmicrobenchmark(\n\t\t\"gapply.nkey.10\" = { gapply(data.df.nkey.10, data.df.nkey.10$key, gfunc.mean, schema(data.df.nkey.10)) %>% nrow },\n\t\t\"gapply.nkey.100\" = { gapply(data.df.nkey.100, data.df.nkey.100$key, gfunc.mean, schema(data.df.nkey.100)) %>% nrow },\n\t\t\"gapply.nkey.1k\" = { gapply(data.df.nkey.1k, data.df.nkey.1k$key, gfunc.mean, schema(data.df.nkey.1k)) %>% nrow },\n\t\ttimes = 20\n\t)\n}\n\n\n# gapply, nrow\nrun.mbm.gapply.nrow <- function(fast) {\n\tif (fast) {\n\t\tmicrobenchmark(\n\t\t\t\"gapply.nrow.10\" = { gapply(data.df.nrow.10, data.df.nrow.10$key, gfunc.mean, schema(data.df.nrow.10)) %>% nrow },\n\t\t\t\"gapply.nrow.100\" = { gapply(data.df.nrow.100, data.df.nrow.100$key, gfunc.mean, schema(data.df.nrow.100)) %>% nrow },\n\t\t\t\"gapply.nrow.1k\" = { gapply(data.df.nrow.1k, data.df.nrow.1k$key, gfunc.mean, schema(data.df.nrow.1k)) %>% nrow },\n\t\t\ttimes = 20\n\t\t)\n\t} else {\n\t\tmicrobenchmark(\n\t\t\t\"gapply.nrow.10\" = { gapply(data.df.nrow.10, data.df.nrow.10$key, gfunc.mean, schema(data.df.nrow.10)) %>% nrow },\n\t\t\t\"gapply.nrow.100\" = { gapply(data.df.nrow.100, data.df.nrow.100$key, gfunc.mean, schema(data.df.nrow.100)) %>% nrow },\n\t\t\t\"gapply.nrow.1k\" = { gapply(data.df.nrow.1k, data.df.nrow.1k$key, gfunc.mean, schema(data.df.nrow.1k)) %>% nrow },\n\t\t\t\"gapply.nrow.10k\" = { gapply(data.df.nrow.10k, data.df.nrow.10k$key, gfunc.mean, schema(data.df.nrow.10k)) %>% nrow },\n\t\t\ttimes = 20\n\t\t)\n\t}\n\t\n}\n\n# gapply, keytype\nrun.mbm.gapply.keytype <- function() {\n\tmicrobenchmark(\n\t\t\"gapply.keytype.int\" = { gapply(data.df.keytype.int, data.df.keytype.int$key, gfunc.mean, schema(data.df.keytype.int)) %>% nrow },\n\t\t\"gapply.keytype.char10\" = { gapply(data.df.keytype.char10, data.df.keytype.char10$key, gfunc.mean, schema(data.df.keytype.char10)) %>% nrow },\n\t\t\"gapply.keytype.char100\" = { gapply(data.df.keytype.char100, data.df.keytype.char100$key, gfunc.mean, schema(data.df.keytype.char100)) %>% nrow },\n\t\ttimes = 20\n\t)\n}\n\n\n\n# gapplyCollect, nkey\nrun.mbm.gapplyCollect.nkey <- function() {\n\tmicrobenchmark(\n\t\t\"gapplyCollect.nkey.10\" = { gapplyCollect(data.df.nkey.10, data.df.nkey.10$key, gfunc.mean) },\n\t\t\"gapplyCollect.nkey.100\" = { gapplyCollect(data.df.nkey.100, data.df.nkey.100$key, gfunc.mean) },\n\t\t\"gapplyCollect.nkey.1k\" = { gapplyCollect(data.df.nkey.1k, data.df.nkey.1k$key, gfunc.mean) },\n\t\ttimes = 20\n\t)\n}\n\n\n# gapplyCollect, nrow\nrun.mbm.gapplyCollect.nrow <- function(fast) {\n\tif (fast) {\n\t\tmicrobenchmark(\n\t\t\t\"gapplyCollect.nrow.10\" = { gapplyCollect(data.df.nrow.10, data.df.nrow.10$key, gfunc.mean) },\n\t\t\t\"gapplyCollect.nrow.100\" = { gapplyCollect(data.df.nrow.100, data.df.nrow.100$key, gfunc.mean) },\n\t\t\t\"gapplyCollect.nrow.1k\" = { gapplyCollect(data.df.nrow.1k, data.df.nrow.1k$key, gfunc.mean) },\n\t\t\ttimes = 20\n\t\t)\n\t} else {\n\t\tmicrobenchmark(\n\t\t\t\"gapplyCollect.nrow.10\" = { gapplyCollect(data.df.nrow.10, data.df.nrow.10$key, gfunc.mean) },\n\t\t\t\"gapplyCollect.nrow.100\" = { gapplyCollect(data.df.nrow.100, data.df.nrow.100$key, gfunc.mean) },\n\t\t\t\"gapplyCollect.nrow.1k\" = { gapplyCollect(data.df.nrow.1k, data.df.nrow.1k$key, gfunc.mean) },\n\t\t\t\"gapplyCollect.nrow.10k\" = { gapplyCollect(data.df.nrow.10k, data.df.nrow.10k$key, gfunc.mean) },\n\t\t\ttimes = 20\n\t\t)\n\t}\n\t\n}\n\n# gapplyCollect, keytype\nrun.mbm.gapplyCollect.keytype <- function() {\n\tmicrobenchmark(\n\t\t\"gapplyCollect.keytype.int\" = { gapplyCollect(data.df.keytype.int, data.df.keytype.int$key, gfunc.mean) },\n\t\t\"gapplyCollect.keytype.char10\" = { gapplyCollect(data.df.keytype.char10, data.df.keytype.char10$key, gfunc.mean) },\n\t\t\"gapplyCollect.keytype.char100\" = { gapplyCollect(data.df.keytype.char100, data.df.keytype.char100$key, gfunc.mean) },\n\t\ttimes = 20\n\t)\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "f9b912c939a39b14f616f61e70f6520428eb99ed", "size": 14013, "ext": "r", "lang": "R", "max_stars_repo_path": "sparkr_tests/benchmark.r", "max_stars_repo_name": "morewood/spark-perf", "max_stars_repo_head_hexsha": "ed79fc88ca8b88657709b12bebd818177e1ec537", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sparkr_tests/benchmark.r", "max_issues_repo_name": "morewood/spark-perf", "max_issues_repo_head_hexsha": "ed79fc88ca8b88657709b12bebd818177e1ec537", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sparkr_tests/benchmark.r", "max_forks_repo_name": "morewood/spark-perf", "max_forks_repo_head_hexsha": "ed79fc88ca8b88657709b12bebd818177e1ec537", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6856435644, "max_line_length": 148, "alphanum_fraction": 0.6451866124, "num_tokens": 4714, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31740136032921684}} {"text": "# Run the code below to set up the data\n# Make a scatter plot with x=Male, y=Female\n\nlibrary(tidyverse)\n\n# Load and reshape data\nunisex_data <- read_csv(\"data/unisex.csv\")\n\ncasey <- unisex_data %>% # get just the data for \"Casey\" needed to plot\n filter(name == \"Casey\") %>%\n select(-prop) %>%\n spread(sex, n, fill = 0)\n\n# look at the data\ncasey\n\n# make the plot\n\n", "meta": {"hexsha": "ca07038ef05fe83f38116996ddf94028be77b980", "size": 366, "ext": "r", "lang": "R", "max_stars_repo_path": "exercises/exercise1a.r", "max_stars_repo_name": "nuitrcs/r-ggplot2-april2020", "max_stars_repo_head_hexsha": "c630453775c561b76c9ecf6a7b24f1c9c769f7c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exercises/exercise1a.r", "max_issues_repo_name": "nuitrcs/r-ggplot2-april2020", "max_issues_repo_head_hexsha": "c630453775c561b76c9ecf6a7b24f1c9c769f7c9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exercises/exercise1a.r", "max_forks_repo_name": "nuitrcs/r-ggplot2-april2020", "max_forks_repo_head_hexsha": "c630453775c561b76c9ecf6a7b24f1c9c769f7c9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.2631578947, "max_line_length": 71, "alphanum_fraction": 0.6721311475, "num_tokens": 113, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.31740136032921684}} {"text": "\n#---\n#title: \"pairwise identity from VCF file\"\n#author: \"Tal Dahan-Meir\"\n#date: \"18/08/2021\"\n#---\n\nlibrary(data.table)\n\n\n### load and clean vcf ###\n\nvcf=fread(\"ammiad_845.vcf\")\nvcf_tab_clean = vcf[,10:ncol(vcf)]\nvcf_tab_clean_0_3 = t ( apply (vcf_tab_clean,1,function(x) { as.character(substring(x,1,3)) }) )\nvcf_tab_clean_0_3 = replace(vcf_tab_clean_0_3,vcf_tab_clean_0_3 == \"./.\",\"none\")\ncolnames(vcf_tab_clean_0_3) = colnames(vcf_tab_clean)\n\n\n### get heterozygosity ###\n\nhomoz_het = vcf_tab_clean_0_3\nhomoz_het[,]=\"het\"\nhomoz_het[grepl(\"0/0|1/1|2/2|3/3\",vcf_tab_clean_0_3)] = \"hom\"\nhomoz_het[grepl(\"none\",vcf_tab_clean_0_3)] = \"none\"\nhomoz_frac = apply(homoz_het,2,function(x){100*(sum(x==\"het\")/length(x))})\nnames(homoz_frac) = colnames(homoz_het)\nsort(homoz_frac)\n\n\n### get alternative allele fraction ###\n\nalternative=vcf_tab_clean_0_3\nalternative[,]=\"alt\"\nalternative[grepl(\"0/0\",vcf_tab_clean_0_3)] = \"ref\"\nalt_frac = apply(alternative,2,function(x){100*(sum(x==\"alt\")/length(x))})\nsort(alt_frac)\n\n\n### get covered positions per plant ###\n\ncovered = vcf_tab_clean_0_3\ncovered[,]=\"cov\"\ncovered[grepl(\"none\",vcf_tab_clean_0_3)] = \"not\"\ncovered_frac = apply(covered,2,function(x){100*(sum(x==\"cov\")/length(x))})\ncovered_sum = apply(covered,2,function(x){sum(x==\"cov\")})\nnames(covered_frac) = colnames(covered)\n\n#sort(covered_frac)\n#sort(covered_sum)\n\n\n### calculate pairwise identity ###\n\nvcf_tab_clean_0_3 = replace(vcf_tab_clean_0_3,vcf_tab_clean_0_3 == \"none\",NA)\nsimilarity.matrix<-apply(vcf_tab_clean_0_3,2,function(x)colSums(x==vcf_tab_clean_0_3, na.rm=TRUE))\nNcomparisons_matrix=apply(vcf_tab_clean_0_3,2,function(x)colSums(!is.na(x==vcf_tab_clean_0_3)))\nidentity.matrix=similarity.matrix/Ncomparisons_matrix\n\n \n### plot pairwise identity distribution of Ammiad (removing Zavitan controls) ###\n \nno_z=identity.matrix[!grepl(as.character(\"Zavitan\"),rownames(identity.matrix)),!grepl(\"Zavitan\",colnames(identity.matrix))]\n#hist(no_z[lower.tri(no_z)],breaks=60,col=\"#a1d99b\",border=\"#74C476\")\n \n \n### for every plant, find all plants that has at least X (identity_threshold) identity to it ###\n \nrequire(igraph)\nrequire(data.table)\n \nidentity_threshold=0.981\nmy_list=list()\nfor (i in 1:ncol(identity.matrix))\n{\nrelevant_vector = rownames(identity.matrix)[identity.matrix[,i] >= identity_threshold]\nmy_list[[i]]=relevant_vector\n}\nnames(my_list) = colnames(identity.matrix)\n\n\n\noverlap <- function(u, v) length(intersect(u, v)) / min(length(u), length(v)) > 0.8\nadj <- sapply(my_list, function(u) sapply(my_list, overlap, u)) + 0\ng <- graph_from_adjacency_matrix(adj)\nmemb <- components(g)$membership\ns <- split(my_list, memb)\ngroups <- lapply(s, function(x) unique(unlist(x)))\ncat(paste0(\"getting info from \",length(groups), \" groups\\n\"))\n#length(groups)\n\n \n### assign names to groups ###\n \nnames(groups) <- paste(\"G\",1:length(groups),sep=\"\")\nlengths=as.vector(sapply(groups, length))\n#sort(lengths)\n\n \n### make table with groups and levels ### \n \nm1=max(lengths(groups))\nd1=as.data.frame(do.call(rbind,lapply(groups,'length<-',m1)),stringsAsFactors=FALSE)\nnames(d1)=paste0(\"Level\",seq_along(d1))\ncsv=d1\n group_sizes = apply(csv,2,function(x){sum(!is.na(x))})\n\ngroups_vector = c()\nfor (i in 1:length(group_sizes)){\ngroups_vector = c(groups_vector,rep(i,group_sizes[i]))}\ngroups_list = apply(csv,2,function(x){as.character(x)[!is.na(x)]})\nnames(groups_vector) = unlist(groups_list,use.names=FALSE)\n", "meta": {"hexsha": "86c1786e76537c3f4cc4850a39f6ebdc71758d1c", "size": 3596, "ext": "r", "lang": "R", "max_stars_repo_path": "data_processing/identity/identity.r", "max_stars_repo_name": "ellisztamas/Ammiad-1", "max_stars_repo_head_hexsha": "158d4f1ce8f0b6b4845ac477a377bd9d755cf945", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data_processing/identity/identity.r", "max_issues_repo_name": "ellisztamas/Ammiad-1", "max_issues_repo_head_hexsha": "158d4f1ce8f0b6b4845ac477a377bd9d755cf945", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data_processing/identity/identity.r", "max_forks_repo_name": "ellisztamas/Ammiad-1", "max_forks_repo_head_hexsha": "158d4f1ce8f0b6b4845ac477a377bd9d755cf945", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-17T09:00:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-17T09:00:49.000Z", "avg_line_length": 31.8230088496, "max_line_length": 123, "alphanum_fraction": 0.6896551724, "num_tokens": 1082, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.31734731071361305}} {"text": "#written and kindly provided by Roger Mundry\nglmm.model.stab<-function(model.res, contr=NULL, ind.cases=F, para=F, data=NULL, use=NULL, n.cores=c(\"all-1\", \"all\"), save.path=NULL, load.lib=T, lib.loc=.libPaths()){\n\tprint(\"please carefully evaluate whether the result makes sense, and if not, please contact me\")\n\t#function determining stability of GLMMs (run using lmer or glmer) by excluding levels of random effects, one at a time;\n\t#supports\n\t\t#weights, offset terms and random slopes;\n\t#does not support\n\t\t#correlations between random intercepts and random slopes\n\t\t#any terms calculated in the model formula (e.g., log, function I, etc.); but interactions do work\n\t#latest additions/modifications:\n\t\t#copes also with data sets where a level of a (fixed effects) factor is entirely dropped from the data\n\t\t#new way of catching warnings (are contained in the detailed output table)\n\t\t#includes sample size in the detailed output table\n\t\t#catches errors\n\t\t#use: an argument taking the names of the random effects for which model stability should be evaluated\n\t\t\t#(useful for models with a random effect having a unique case for each level of the data)\n\t#written by Roger Mundry\n\t#modified April 2016 (added dealing with glmer.nb; fixed a bug in the output of the random effects of tthe original model)\n\t#last modified Mar 2017 (added dealing with fixed effects factor levels dropped when dropping levels of random effects)\n\t#last modified June 14 2017 (fixed dealing with random effects comprising correlation parameters)\n\t#last modified July 06 2017 (fixed small bug happening when model revealed an error)\n\tif(load.lib){library(lme4, lib.loc=lib.loc)}\n\tn.cores=n.cores[1]\n model.eq=as.formula(as.character(model.res@call)[2])\n\tif(class(model.res)[1]==\"lmerMod\"){\n REML=model.res@resp$REML==1\n xfam=\"gaussian\"\n }else{\n\t\txfam=model.res@resp$family$family\n }\n if(grepl(x=xfam, pattern=\"Negative Binomial\", fixed=T)){\n\t\txfam=\"neg.bin\"\n\t}\n weights=model.res@resp$weights\n if(length(data)==0){\n\t\tii.data=model.res@frame\n\t\toffs.col=grep(x=names(ii.data), pattern=\"offset(\", fixed=T)\n\t\tif(length(offs.col)>0){\n\t\t\tfor(i in offs.col){\n\t\t\t\t#ii.data[,i]=exp(ii.data[,i])\n\t\t\t\tnames(ii.data)[i]=substr(x=names(ii.data)[i], start=8, stop=nchar(names(ii.data)[i]) -1)\n\t\t\t}\n\t\t}\n\t\twght.col=grep(x=names(ii.data), pattern=\"(weights)\", fixed=T)\n\t\tif(length(wght.col)>0){\n\t\t\tnames(ii.data)[wght.col]=gsub(x=names(ii.data)[wght.col], pattern=\"(\", replacement=\"\", fixed=T)\n\t\t\tnames(ii.data)[wght.col]=gsub(x=names(ii.data)[wght.col], pattern=\")\", replacement=\"\", fixed=T)\n\t\t}else{\n\t\t\tii.data$weights=weights\n\t\t}\n\t}else{\n\t\tii.data=data.frame(data, weights)\n\t}\n\tif(substr(as.character(model.eq)[2], start=1, stop=6)==\"cbind(\"){\n\t\tii.data$weights=1\n\t}\n #if(xfam==\"binomial\"){\n # response=as.character(model.eq)[2]\n # if(sum(names(ii.data)==response)==0)\n #}\n ranefs=names(ranef(model.res))\n\tif(length(use)==0){use=ranefs}\n\tranefs=ranefs[ranefs%in%use]\n xlevels=lapply(ranefs, function(x){return(as.vector(unique(ii.data[ ,x])))})\n ranefs=rep(ranefs, unlist(lapply(xlevels, length)))\n to.do=cbind(ranefs, unlist(xlevels))\n if(ind.cases){\n ii.data=data.frame(ii.data, ic=as.factor(1:nrow(ii.data)))\n to.do=rbind(to.do, cbind(\"ic\", levels(ii.data$ic)))\n }\n\tkeepWarnings <- function(expr) {\n\t\tlocalWarnings <- list()\n\t\tvalue <- withCallingHandlers(expr,\n\t\t\twarning = function(w) {\n\t\t\t\tlocalWarnings[[length(localWarnings)+1]] <<- w\n\t\t\t\tinvokeRestart(\"muffleWarning\")\n\t\t\t}\n\t\t)\n\t\tlist(value=value, warnings=localWarnings)\n\t}\n\tget.ranef<-function(x){#function returning sd associated with random effect in a nicely named vector\n\t\tx=as.data.frame(x)\n\t\ty=lapply(strsplit(as.character(x$grp), split=\"\"), function(y){\n\t\t\tyy=unlist(lapply(strsplit(names(ranef(model.res)), split=\"\"), function(z){\n\t\t\t\tif(length(z)<=length(y)){\n\t\t\t\t\ty=y[1:length(z)]\n\t\t\t\t\treturn(paste(y[y==z], collapse=\"\"))\n\t\t\t\t}else{\n\t\t\t\t\treturn(\"\")\n\t\t\t\t}\n\t\t\t}))\n\t\t\tyy=intersect(yy, names(ranef(model.res)))\n\t\t\tif(length(yy)==0){yy=paste(y, collapse=\"\")}\n\t\t\treturn(yy)\n\t\t})\n\t\tx$grp=y\n\t\tires=x$sdcor\n\t\t#x$var2[is.na(x$var2)]=\"\"\n\t\tnames(ires)=apply(x[, 1:3], 1, paste, collapse=\"@\")\n\t\tnames(ires)[x$grp==\"Residual\"]=\"Residual\"\n\t\treturn(ires)\n\t}\n\tif(length(contr)==0){\n\t\tif(class(model.res)[1]==\"lmerMod\"){\n\t\t\tcontr=lmerControl()\n\t\t}else{\t\n\t\t\tcontr=glmerControl()\n\t\t}\n\t}\n ifun=function(x, model.res, to.do, ii.data, contr, get.ranef){\n #sel.ii.data=subset(ii.data, ii.data[,to.do[x, 1]]!=to.do[x, 2])\n sel.ii.data=ii.data[ii.data[,to.do[x, 1]]!=to.do[x, 2], ]\n\t\tif(class(model.res)[1]==\"lmerMod\"){\n sel.ii.res=try(keepWarnings(lmer(model.eq, data=sel.ii.data, weights=weights, REML=REML, control=contr)), silent=T)\n }else if(xfam==\"binomial\" | xfam==\"poisson\"){\n sel.ii.res=try(keepWarnings(glmer(model.eq, data=sel.ii.data, family=xfam, control=contr)), silent=T)\n }else{\n\t\t\tsel.ii.res=try(keepWarnings(glmer.nb(model.eq, data=sel.ii.data, control=contr)), silent=T)\n\t\t}\n\t\tif(length(save.path)>0){\n\t\t\test.fixed.effects=fixef(sel.ii.res$value)\n\t\t\test.random.effects=as.data.frame(summary(sel.ii.res$value)$varcor)\n\t\t\tmodel.warnings=sel.ii.res$warnings\n\t\t\tn=length(residuals(sel.ii.res$value))\n\t\t\twhat=to.do[x, ]\n\t\t\tsave(file=paste(c(paste(c(paste(c(save.path, \"m\"), collapse=\"/\"), x), collapse=\"_\"), \".RData\"), collapse=\"\"), list=c(\"what\", \"est.fixed.effects\", \"est.random.effects\", \"model.warnings\", \"n\"))\n\t\t}\n if(class(sel.ii.res)!=\"try-error\"){\n\t\t\t#xx=try(list(fere=c(fixef(sel.ii.res$value), get.ranef(summary(sel.ii.res$value)$varcor)), N=length(residuals(sel.ii.res$value)), warnings=paste(unlist(sel.ii.res$warnings)$message, collapse=\"/\")), silent=T)\n\t\t\t#if(class(xx)==\"try-error\"){browser()}\n return(list(fere=c(fixef(sel.ii.res$value), get.ranef(summary(sel.ii.res$value)$varcor)), N=nrow(sel.ii.res$value@frame), warnings=paste(unlist(sel.ii.res$warnings)$message, collapse=\"/\")))\n }else{\n return(list(fere=rep(NA, length(fixef(model.res))+nrow(as.data.frame(summary(model.res)$varcor))), N=NA, warnings=\"error\"))\n }\n }\n if(para){\n\t\t#on.exit(expr = parLapply(cl=cl, X=1:length(cl), fun=function(x){rm(list=ls())}), add = FALSE)\n\t\t#on.exit(expr = stopCluster(cl), add = T)\n require(parallel)\n cl <- makeCluster(getOption(\"cl.cores\", detectCores()))\n\t\tif(n.cores!=\"all\"){\n\t\t\tif(n.cores==\"all-1\"){n.cores=length(cl)-1}\n\t\t\tif(n.cores0 & xx0){\n\t\twarning(paste(c(\"for\", sum(xx>0 & xx\n\n## snpblaster.r -- Calculates location differences to determine which markers can\n## be removed without much loss\n\nusage <- function(){\n cat(\"usage: ./snpblaster.r [-size ]\\n\",\n \"Expects a CSV file with headings: [Marker],Delta,Mutation,Chromosome,Location\\n\",\n sep=\"\");\n}\n\ninfile = FALSE;\n\nwindowSize = 10^6;\nargLoc <- 1;\n\nwhile(!is.na(commandArgs(TRUE)[argLoc])){\n if(file.exists(commandArgs(TRUE)[argLoc])){ # file existence check\n if(infile == FALSE){\n infile <- commandArgs(TRUE)[argLoc];\n } else{\n cat(\"Error: More than one input file specified\\n\");\n usage();\n quit(save = \"no\", status=1);\n }\n } else {\n if(commandArgs(TRUE)[argLoc] == \"-window\"){\n windowSize <- eval(parse(text = commandArgs(TRUE)[argLoc+1]));\n cat(\"Setting window size to \",windowSize,\"\\n\",sep=\"\");\n argLoc <- argLoc + 1;\n }\n }\n argLoc <- argLoc + 1;\n}\n\nif(infile == FALSE){\n cat(\"Error: No input file specified\\n\");\n usage();\n quit(save = \"no\", status=1);\n}\n\nmeanstats.location <- read.csv(infile, row.names = 1);\n\nnum.rowcol <- dim(meanstats.location)[1];\nsnpblast.loc <- matrix(meanstats.location$Location, num.rowcol, num.rowcol);\nsnpblast.cs <- matrix(meanstats.location$Chromosome,num.rowcol, num.rowcol);\nsnpblast.delta <- matrix(meanstats.location$Delta,num.rowcol, num.rowcol);\nsnpblast.names <- matrix(rownames(meanstats.location),num.rowcol,\n num.rowcol);\nsnpblast.mat <- abs(snpblast.loc-t(snpblast.loc));\nsnpblast.mat[snpblast.cs != t(snpblast.cs)] <- NA;\nsnpblast.mat[lower.tri(snpblast.mat, diag = TRUE)] <- NA;\n\nnear.markers <- which(snpblast.mat < windowSize);\nsnpblast.df <- data.frame(Marker1 = snpblast.names[near.markers], Marker2 = t(snpblast.names)[near.markers],\n Chromosome = as.numeric(snpblast.cs[near.markers]),\n Location1 = snpblast.loc[near.markers], Location2 = t(snpblast.loc)[near.markers],\n Delta1 = snpblast.delta[near.markers], Delta2 = t(snpblast.delta)[near.markers],\n Distance = snpblast.mat[near.markers]);\nsnpblast.df <- snpblast.df[order(as.numeric(snpblast.df$Chromosome),snpblast.df$Distance),];\nsnpblast.df$Remove <- as.character(snpblast.df$Marker2);\nsnpblast.df$Remove[snpblast.df$Delta1 < snpblast.df$Delta2] <-\n as.character(snpblast.df$Marker1[snpblast.df$Delta1 < snpblast.df$Delta2]);\n\nwrite.csv(snpblast.df, \"\", row.names = FALSE);\n", "meta": {"hexsha": "9002775ba9662ce7aead5179be708086aab465c2", "size": 2554, "ext": "r", "lang": "R", "max_stars_repo_path": "snpblaster.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "snpblaster.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "snpblaster.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_forks_repo_licenses": ["ISC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-02T11:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-02T11:22:10.000Z", "avg_line_length": 37.5588235294, "max_line_length": 108, "alphanum_fraction": 0.658183242, "num_tokens": 702, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947425132315, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.31721797057111306}} {"text": "# centroid balls\nlibrary(graphics)\nlibrary(plotrix)\n#\n# color transparent darkgreen\ncov.color <- rgb(0,100,0, maxColorValue = 255, alpha = 25)\n#\n# select round in time series\nfor (d in 1:48){\n #d <- 48 # debug\n message(sprintf(\"loop %s of %s\", d, 48))\n #\n #png(filename = paste(\"/home/eric/Downloads/Desktop/data/mex/covid-amlo/graph/nytPosCum\", d, \".png\", sep = \"\"), width = 20, height = 20, units = \"cm\", res = 196)\n pdf(file = paste(\"/home/eric/Downloads/Desktop/data/mex/covid-amlo/graph/nytPosCum\", d, \".pdf\", sep = \"\"), width = 10, height = 7)\n par(mar = c(0,0,0,0))\n plot(shave(nat.map, p = .95), lwd = .5, border = \"gray\")\n title(\"La punta del iceberg\", line = -2)\n title(paste(\"Pruebas positivas, ingreso a cl\u00ednica hasta el\", dates[d]), line = -3, cex.main = .85)\n tmp.ranges <- par(\"usr\") # keep calculated xy ranges to compute ball radius\n radius.base <- (tmp.ranges[2] - tmp.ranges[1]) / 500 # relative radius\n ## # draw municipal borders\n ## for (i in 1:32){\n ## plot(mu.map[[i]], lwd = .25, border = \"lightgray\", add = TRUE)\n ## }\n plot(nat.map, lwd = .5, border = \"darkgray\", add = TRUE)\n plot(nat.map, lwd = .5, lty = 3, border = \"white\", add = TRUE)\n for (i in 1:32){\n #i <- 9 # debug\n #plot(nat.map[nat.map@data$ENT==i,]) # debug\n #tmp.ranges <- par(\"usr\") # keep calculated xy ranges to compute ball radius # debug\n #radius.base <- (tmp.ranges[2] - tmp.ranges[1]) / 500 # relative radius # debug\n sel.col <- which(sub(\"V([0-9]+)\", \"\\\\1\", colnames(mu.map[[i]]@data))==d)\n rad <- radius.base * mu.map[[i]]@data[,sel.col]^.5\n rad[rad==0] <- NA # don't draw zeroes\n #rad <- log(rad, base = 10) # log scale\n for (j in 1:nrow(mu.map[[i]])){\n draw.circle(x = coordinates(mu.map[[i]])[j,1], y = coordinates(mu.map[[i]])[j,2], radius = rad[j],\n border = \"darkgreen\", col = cov.color, lwd = .25)\n }\n }\n # legend\n xl <- -10400000; yl <- 3000000\n incr <- 150000\n draw.circle(x = xl, y = yl+2*incr, radius = radius.base * 1^.5,\n border = \"darkgreen\", col = cov.color, lwd = .5)\n text(xl, yl+2*incr, pos = 4, labels = \"1\", cex = .75)\n draw.circle(x = xl, y = yl+incr, radius = radius.base * 10^.5,\n border = \"darkgreen\", col = cov.color, lwd = .5)\n text(xl, yl+incr, pos = 4, labels = \"10\", cex = .75)\n draw.circle(x = xl, y = yl, radius = radius.base * 100^.5,\n border = \"darkgreen\", col = cov.color, lwd = .5)\n text(xl, yl, pos = NULL, labels = \"100\", cex = .75)\n ## draw.circle(xl,yl-1.5*incr, radius = radius.base * 1000^.5,\n ## border = \"darkgreen\", col = cov.color, lwd = .5)\n ## text(xl, yl-1.5*incr, pos = NULL, labels = \"1000\", cex = .75)\n text(xl, yl+2.9*incr, labels = \"Casos\", font = 2)\n text(-13000000, 1550000, labels = \"Preparado con datos del Sistema Nacional de Vigilancia Epidemiol\u00f3gica por Eric Magar (@emagar)\", col = \"lightgray\", pos = 4, cex = .65)\n dev.off()\n}\n\n\nsetwd(\"/home/eric/Dropbox/data/mex/covid-amlo/graph/tmp\")\nsaveHTML({\n par(mar = c(3, 3, 1, 0.5), mgp = c(2, 0.5, 0), tcl = -0.3, \n cex.axis = 0.8, cex.lab = 0.8, cex.main = 1)\n ani.options(interval = 0.05, nmax = 150)\n brownian.motion(pch = 21, cex = 5, col = \"red\", bg = \"yellow\")\n}, img.name = \"brownian_motion_b\", htmlfile = \"index.html\", navigator = FALSE, \ndescription = c(\"Random walk of 10 points on the 2D plane\", \n \"(without the navigation panel)\"))\n", "meta": {"hexsha": "eaec7e5bab25814f0109292a7bcd14785f99a3f0", "size": 3623, "ext": "r", "lang": "R", "max_stars_repo_path": "code/tmp.r", "max_stars_repo_name": "emagar/covid-mex", "max_stars_repo_head_hexsha": "2a2ac3d19bf3bfe675a1efc12b9f5464b17428b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/tmp.r", "max_issues_repo_name": "emagar/covid-mex", "max_issues_repo_head_hexsha": "2a2ac3d19bf3bfe675a1efc12b9f5464b17428b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/tmp.r", "max_forks_repo_name": "emagar/covid-mex", "max_forks_repo_head_hexsha": "2a2ac3d19bf3bfe675a1efc12b9f5464b17428b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.0281690141, "max_line_length": 174, "alphanum_fraction": 0.5534087773, "num_tokens": 1233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3171515554997103}} {"text": "#!/usr/bin/env Rscript\n\n### Read a CosmoSIS standard output file for a grid sampler, and\n### generate a series of 1- and 2-dimensional likelihood density plots.\n\nsuppressPackageStartupMessages(library(\"optparse\"))\nsuppressPackageStartupMessages(library(\"lattice\"))\nsuppressPackageStartupMessages(library(\"doBy\"))\n\n### Create a dataframe from CosmoSIS grid sampler output. We expect the\n### first line of the output to contain the names of the parameters,\n### separated by spaces, and with section names separated from parameter\n### names by a double-hyphen.\n\nmake.data.frame <- function(fname)\n{\n d <- read.table(fname)\n first <- readLines(fname, n=1)\n first <- sub(\"#\", \"\", first) # Remove comment\n parts <- strsplit(first, \"\\t\")[[1]] # split on tabs\n cols <- sub(\"[a-zA-Z_]+--\", \"\", parts) # remove leading section names\n\n names(d) <- cols\n likes <- exp(d$LIKE)\n norm <- sum(likes)\n d$l <- likes/norm\n return(d)\n}\n\n# Make a 1-d posterior density plot for each variable in the dataframe.\n# We use bw=\"nrd\", which gives a bandwidth calculation according to:\n# Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and\n# Visualization. Wiley.\nmake.1d.density.plots <- function(df, prefix, output, device, verbose)\n{\n cols <- Filter(function(n) { ! n %in% c(\"l\",\"LIKE\") }, names(dframe))\n for(col in cols)\n {\n if (verbose) cat(\"Making 1-d density plot for\", col, \"\\n\")\n form <- as.formula(paste(\"l~\", col, sep=\"\"))\n df.summary <- summaryBy(form, data = df, FUN=sum)\n names(df.summary)[2] <- \"l\" # Replace ugly name generated by summaryBy\n dev.fcn <- get(device)\n filename <- file.path(output, paste(prefix, \"_\", col, \".\", device, sep=\"\"))\n p1 <- xyplot( form\n , df.summary\n , type=\"l\"\n , lwd=2\n , ylab = \"likelihood\"\n , grid = TRUE\n )\n dev.fcn(filename)\n print(p1)\n invisible(dev.off())\n }\n}\n\n# Return the likelihood values corresponding to the boundary of the\n# regions containing the probability contents 'levels'.\nfind.contours <- function(df, levels = c(0.68, 0.95))\n{\n probs.sorted <- sort(df$l, decreasing=TRUE)\n probs.cs <- cumsum(probs.sorted)\n # Get the indices of the first values greater than the given confidence levels.\n indices <- sapply( levels\n , function(x) which(probs.cs>x)[1]\n )\n probs.sorted[indices]\n}\n\n## vmat2df will convert the kind of list returned by kde2d (containing two)\n## vectors and a matrix, named x, y and z) into a dataframe with columns\n## x, y, z.\n#vmat2df <- function(u)\n#{\n# g = expand.grid(u$x, u$y)\n# data.frame( x = g$Var1, y = g$Var2, z = as.numeric(u$z))\n#}\n\n# Make a 2-d density plot of xcol vs. ycol, using data from df.\nmake.2d.density.plot <- function( df, xcol, ycol, prefix, output, device\n , use.color\n )\n{\n form <- as.formula(paste(\"l~\", xcol, \"+\", ycol, sep=\"\"))\n df.summary <- summaryBy(form, data = df, FUN = sum)\n names(df.summary)[3] <- \"l\" # replace the ugly name given by summaryBy\n\n # Find the values of z which correspond to the given confidence levels.\n conf.levels = c(0.68, 0.95)\n zvals = find.contours(df.summary, conf.levels)\n \n dev.fcn <- get(device)\n filename <- file.path( output\n , paste(prefix, \"_\", xcol, \"_\", ycol, \".\", device, sep=\"\"))\n levels <- c(0, zvals, 1)\n labels <- as.character(c(0, conf.levels, 1))\n form <- as.formula(paste(\"l~\", xcol, \"*\", ycol, sep=\"\"))\n p <- contourplot( form, df.summary, at=levels, labels=labels\n , panel=function(...){panel.grid(-1,-1); panel.contourplot(...)}\n , xlab = xcol\n , ylab = ycol\n , region = use.color\n , lwd=2\n , col.regions = function(n,a) rev(terrain.colors(n,a))\n , colorkey = FALSE\n )\n dev.fcn(filename)\n print(p)\n invisible(dev.off())\n}\n\n# For each pair, generate the kde2d result matrix.\n# Determine the values of z at which the 68% and 95% contour lines lie.\n# Transform the matrix to a dataframe.\n# Make the contour plot.\n\nmake.2d.density.plots <- function(df, prefix, output, device, verbose, use.color)\n{\n if (verbose) cat(\"Making 2-d density plots\\n\")\n # Go through all pairs of interesting variables (all but 'LIKE' and 'l',\n # the last two columns).\n n.interesting <- ncol(df)-2\n cols <- names(df)[1:n.interesting]\n pairs <- combn(cols, 2, simplify=FALSE)\n for(pair in pairs)\n {\n xcol <- pair[[1]]\n ycol <- pair[[2]]\n if (verbose) cat(\"Making 2-d density plot of\", xcol, \"vs.\", ycol, \"\\n\")\n make.2d.density.plot(df, xcol, ycol, prefix, output, device, use.color)\n }\n\n}\n\n################################################################################\n###\n### Start of the main program.\n\noption_list <-\n list( make_option( c(\"-v\", \"--verbose\")\n , action=\"store_true\"\n , default = FALSE\n , help = \"Print extra output [%default]\"\n )\n , make_option( c(\"-p\", \"--prefix\")\n , default=\"plot\"\n , type=\"character\"\n , help=\"Prefix for all output files\"\n )\n , make_option( c(\"-o\", \"--output\")\n , default=\".\"\n , type=\"character\"\n , help=\"Directory for all output files\"\n )\n , make_option( c(\"-d\", \"--device\")\n , default=\"png\"\n , type=\"character\"\n , help=\"Graphics device for plots: png or pdf\"\n )\n , make_option( c(\"-f\", \"--fill\")\n , action=\"store_true\"\n , default=FALSE\n , help=\"Color regions in 2-d density plots\"\n )\n , make_option( c(\"-b\", \"--burn\")\n , default = 0\n , type=\"integer\"\n , help=\"Number of burn-in samples to ignore [%default]\"\n )\n )\n\nparser <- OptionParser(option_list=option_list, usage=\"%prog [options] infile\")\nargs <- parse_args(parser, positional_arguments = TRUE)\nopt <- args$options\n\nif (length(args$args) != 1)\n{\n cat(\"Incorrect number of required arguments\\n\\n\")\n print_help(parser)\n stop() \n}\n\ninput.file <- as.character(args$args)\nif (file.exists(input.file) == FALSE )\n{\n cat(\"Unable to open input file\", input.file, \"\\n\")\n stop()\n}\n\nif (opt$output != \".\")\n if (! file.exists(opt$output))\n dir.create(opt$output, recursive=TRUE)\n\nopt$device <- tolower(opt$device)\n\nif (opt$device %in% c(\"png\", \"pdf\") == FALSE)\n{\n cat(\"Device\", opt$device, \"is not supported, using png\\n\")\n opt$device <- \"png\"\n}\n\nif (opt$verbose) \n cat(\"Processing file\", input.file, \"\\n\")\n\ndframe = make.data.frame(input.file)\nmake.1d.density.plots(dframe, opt$prefix, opt$output, opt$device, opt$verbose)\nif (length(dframe) > 3)\n make.2d.density.plots(dframe, opt$prefix, opt$output, opt$device, opt$verbose, opt$fill)\n", "meta": {"hexsha": "e83ba01e8de9a418164b915f53e6b58b9ded326e", "size": 7088, "ext": "r", "lang": "R", "max_stars_repo_path": "cosmosis/plotting/grid_plots.r", "max_stars_repo_name": "ktanidis2/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra", "max_stars_repo_head_hexsha": "07e5d308c6a8641a369a3e0b8d13c4104988cd2b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-06-18T14:11:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T19:19:36.000Z", "max_issues_repo_path": "cosmosis/plotting/grid_plots.r", "max_issues_repo_name": "ktanidis2/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra", "max_issues_repo_head_hexsha": "07e5d308c6a8641a369a3e0b8d13c4104988cd2b", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-02T12:44:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T15:09:48.000Z", "max_forks_repo_path": "cosmosis/plotting/grid_plots.r", "max_forks_repo_name": "ktanidis2/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra", "max_forks_repo_head_hexsha": "07e5d308c6a8641a369a3e0b8d13c4104988cd2b", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2022-03-25T21:26:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T06:37:46.000Z", "avg_line_length": 33.7523809524, "max_line_length": 90, "alphanum_fraction": 0.5718115124, "num_tokens": 1834, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3171515409471788}} {"text": "#########################################################################################\n# #\n# Copyright (c) A_B_Life 2011 #\n# Writer: Chrevan Chen #\n# Program Date: 2011.9.7 #\n# Modifier: Chrevan Chen #\n# Last Modified: 2011.9.16 #\n# This script is used for plotting Pie chart #\n# #\n#########################################################################################\n\nargs <- commandArgs(trailingOnly = TRUE)\t#get the arguments from the command line\noutdir <- args[1]\nfileNumber <- args[2]\n\ndata <- read.table(file = fileNumber,header=F)\ndigit <- data[,2]\t\t# The number of each part,we can read them from a file\nsum <- sum(digit)\t\t\t# The sum number of each part\ndigit <- digit/sum\t\t\t# The percent\nlabel <- data[,1]\t# Label of each part\ncol <- c('#4682B4','#FF8C00','#A0522D','#87CEEB','#6B8E23','#6A5ACD','#778899','#DAA520','#B22222')\npercent <- format(digit*100,digit=2)\t\t\t# record the first three digit of each percent\npercent <- paste(percent,'%',sep='')\t\t\t# Add the symbol \"%\"\npercent <- paste(label,percent,sep=':')\t\t\t# Add the symbol \"%\"\n#pdf('Rplot_Pie_Chart.pdf', width = 9, height = 6)\npng(file=paste(outdir,'/',fileNumber,'_Pie_Chart.png',sep=''),pointsize=20,width=900,height=600)\npar(mar = c(2.1,3.1,3.1,3.1))\n#pie(digit,main=fileNumber,labels=percent,edges=400,radius=0.6,col=rainbow(length(digit)),col.lab='blue',font.lab=3,cex.main=2.5,lwd=3)\npie(digit,main=fileNumber,labels=percent,edges=400,radius=0.8,col=col[1:length(digit)],col.lab='blue',font.lab=3,cex.main=2,lwd=3,cex=1.2)\n#legend(x='bottomleft',legend=label,col=rainbow(length(digit)),bty='n',text.col=rainbow(length(digit)),pch=15,cex=1)\n#dev.off()\n#density=1,angle = 45,cex.lab=3,,cex.axis=2,\n", "meta": {"hexsha": "5829a69a7e640347f5dca383755f5bd466e0b3b4", "size": 2204, "ext": "r", "lang": "R", "max_stars_repo_path": "ABLIRC/bin/public/format/gff/Pie_Chart.r", "max_stars_repo_name": "ablifedev/ABLIRC", "max_stars_repo_head_hexsha": "875278b748a8e22ada2c76c3c76dbf970be4a6a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-02-25T13:08:20.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-25T13:08:20.000Z", "max_issues_repo_path": "ABLIRC/bin/public/format/gff/Pie_Chart.r", "max_issues_repo_name": "ablifedev/ABLIRC", "max_issues_repo_head_hexsha": "875278b748a8e22ada2c76c3c76dbf970be4a6a4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-02-25T13:16:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-25T13:16:03.000Z", "max_forks_repo_path": "ABLIRC/bin/public/format/gff/Pie_Chart.r", "max_forks_repo_name": "ablifedev/ABLIRC", "max_forks_repo_head_hexsha": "875278b748a8e22ada2c76c3c76dbf970be4a6a4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 66.7878787879, "max_line_length": 138, "alphanum_fraction": 0.4823049002, "num_tokens": 565, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.31713495754699644}} {"text": "\n grid.logbook = function(x, res) {\n\n logbook.gridded = grid_lonlat (x, res=res)\n logbook.gridded$gridid = paste(logbook.gridded$lat, logbook.gridded$lon, logbook.gridded$year, sep=\"~\")\n\n v = \"pro_rated_slip_wt_lbs\"\n x = logbook.gridded[is.finite(logbook.gridded[,v]),]\n grid = as.data.frame(xtabs( as.integer(x[,v]) ~ as.factor(x[,\"gridid\"]), exclude=\"\" ))\n names(grid) = c(\"gridid\", \"landings\")\n grid$landings = grid$landings * 0.45359237 # convert to kg\n\n v = \"num_of_traps\"\n x = logbook.gridded[is.finite(logbook.gridded[,v]),]\n effort = as.data.frame(xtabs( as.integer(x[,v]) ~ as.factor(x[,\"gridid\"]), exclude=\"\" ))\n names(effort) = c(\"gridid\", \"notraps\")\n\n grid = merge(grid, effort, by=\"gridid\", all=T, sort=F)\n grid$cpue = grid$landings / grid$notraps\n\n tmp = matrix(unlist(strsplit(as.character(grid$gridid), \"~\")), ncol=3, byrow=T)\n grid$lat = as.numeric(tmp[,1])\n grid$lon = as.numeric(tmp[,2])\n grid$yr = as.numeric(tmp[,3])\n\n# grid = grid[is.finite(grid$yr),]\n\n return(grid)\n }\n\n\n", "meta": {"hexsha": "c00788aa73de3b62abfe0b3ddb9ffb3232b8e52d", "size": 1068, "ext": "r", "lang": "R", "max_stars_repo_path": "R/grid.logbook.r", "max_stars_repo_name": "PEDsnowcrab/bio.snowcrab", "max_stars_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/grid.logbook.r", "max_issues_repo_name": "PEDsnowcrab/bio.snowcrab", "max_issues_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/grid.logbook.r", "max_forks_repo_name": "PEDsnowcrab/bio.snowcrab", "max_forks_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 33.375, "max_line_length": 107, "alphanum_fraction": 0.6179775281, "num_tokens": 358, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6654105720171531, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.31712113822601584}} {"text": "#' @export\n#' @title legend.bubble\n#' @description couldn't accurately describe\n#' @param \\code{x} \n#' @param \\code{y} defaults to NULL\n#' @param \\code{maxradius} defaults to 1\n#' @param \\code{n} defaults to 3\n#' @param \\code{round} defaults to 0\n#' @param \\code{bty} defaults to \"o\"\n#' @param \\code{mab} defaults to 1.2\n#' @param \\code{bg} defaults to NULL\n#' @param \\code{inset} defaults to 0\n#' @param \\code{pch} defaults to 21\n#' @param \\code{pt.bg} defaults to NULL\n#' @param \\code{txt.cex} defaults to 1\n#' @param \\code{txt.col} defaults to NULL\n#' @param \\code{font} defaults to NULL\n#' @family plotting\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\nlegend.bubble <- function (x, y = NULL, z, maxradius = 1, n = 3, round = 0, bty = \"o\", \n mab = 1.2, bg = NULL, inset = 0, pch = 21, pt.bg = NULL, \n txt.cex = 1, txt.col = NULL, font = NULL, ...) \n{\n if (length(z) == 1) \n legend <- round((seq(0, sqrt(z), length.out = n + 1)^2)[-1], \n round)\n else legend <- round(sort(z), round)\n radius <- maxradius * sqrt(legend)/sqrt(max(legend))\n cex <- 2 * radius/par(\"cxy\")[2]/0.375\n box <- legend.box(x, y, maxradius, mab, inset)\n if (bty == \"o\") \n rect(box[1], box[2], box[3], box[4], col = bg)\n x <- (box[1] + box[3])/2\n y <- box[2] - mab * maxradius + maxradius\n for (i in length(radius):1) {\n ri <- radius[i]\n cex <- 2 * ri/par(\"cxy\")[2]/0.375\n points(x, y - ri, cex = cex, pch = pch, bg = pt.bg, ...)\n text(x, y - ri * 2, legend[i], adj = c(0.5, -0.5), cex = txt.cex, \n col = txt.col, font = font)\n }\n}", "meta": {"hexsha": "9f775e7f2f0995e85a46a2d62570437df22e4802", "size": 1635, "ext": "r", "lang": "R", "max_stars_repo_path": "R/legend.bubble.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/legend.bubble.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/legend.bubble.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.023255814, "max_line_length": 88, "alphanum_fraction": 0.5565749235, "num_tokens": 596, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3171211319016423}} {"text": "###VISUALISE MODEL OUTPUTS###\nlibrary(tidyverse)\nlibrary(ggpubr)\nlibrary(kableExtra)\nlibrary(magick)\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\n####################SUPPLY-DEMAND METRICS\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, '..', 'results', 'decile_mno_results_technology_options.csv'))\n\nnames(data)[names(data) == 'GID_0'] <- 'country'\n\n#select desired columns\ndata <- select(data, country, scenario, strategy, confidence, decile, #population, area_km2, \n population_km2, total_estimated_sites, existing_mno_sites,\n total_estimated_sites_km2, existing_mno_sites_km2,\n phone_density_on_network_km2, sp_density_on_network_km2,\n total_mno_revenue, total_mno_cost, cost_per_network_user, \n # cost_per_sp_user\n)\n\ndata <- data[(data$confidence == 50),]\n\ndata$combined <- paste(data$country, data$scenario, sep=\"_\")\n\ndata$country = factor(data$country, levels=c('MWI',\n \"UGA\",\n \"SEN\",\n \"KEN\",\n \"PAK\",\n \"ALB\",\n \"PER\",\n \"MEX\"),\n labels=c(\"Malawi\",\"Uganda\",\n \"Senegal\",\"Kenya\",\n \"Pakistan\",\n \"Albania\",\n \"Peru\",\n \"Mexico\"))\n\ndemand = data[(\n data$scenario == 'S1_25_10_2' &\n data$strategy == '4G_epc_fiber_baseline_baseline_baseline_baseline'\n),]\n\ndemand <- select(demand, country, decile, population_km2, \n phone_density_on_network_km2, \n sp_density_on_network_km2)\n\ndemand <- gather(demand, metric, value, population_km2:sp_density_on_network_km2)\n\ndemand$metric = factor(demand$metric, \n levels=c(\"population_km2\",\n \"phone_density_on_network_km2\",\n \"sp_density_on_network_km2\"),\n labels=c(\"Population Density\",\n \"Modeled Network Phone Density\",\n \"Modeled Network Smartphone Density\"))\n\ndemand_densities <- ggplot(demand, aes(x=decile, y=value, colour=metric, group=metric)) + \n geom_line() +\n scale_fill_brewer(palette=\"Spectral\", name = expression('Cost Type'), direction=1) +\n theme( legend.position = \"bottom\", axis.text.x = element_text(angle = 45, hjust = 1)) + \n labs(colour=NULL,\n title = \"Demand-Side Density Metrics by Population Decile\",\n # subtitle = \"Population and user densities\",\n x = \"Population Decile\", y = \"Density (per km^2)\") + \n scale_x_continuous(expand = c(0, 0.5), breaks = seq(0,100,20)) + \n scale_y_continuous(expand = c(0, 0)) + #, limits = c(0,20)) + \n theme(panel.spacing = unit(0.6, \"lines\")) + expand_limits(y=0) +\n guides(colour=guide_legend(ncol=3)) +\n facet_wrap(~country, scales = \"free\", ncol=4) \n\nsupply = data[(\n data$scenario == 'S1_25_10_2' &\n data$strategy == '4G_epc_fiber_baseline_baseline_baseline_baseline'\n),]\n\nsupply <- select(supply, country, decile, total_estimated_sites_km2, existing_mno_sites_km2)\n\nsupply <- gather(supply, metric, value, total_estimated_sites_km2:existing_mno_sites_km2)\n\nsupply$metric = factor(supply$metric, \n levels=c(\"total_estimated_sites_km2\",\n \"existing_mno_sites_km2\"),\n labels=c(\"Total Site Density\",\n \"Modeled Network Site Density\"))\n\nsupply_densities <- ggplot(supply, aes(x=decile, y=value, colour=metric, group=metric)) + \n geom_line() +\n scale_fill_brewer(palette=\"Spectral\", name = expression('Cost Type'), direction=1) +\n theme( legend.position = \"bottom\", axis.text.x = element_text(angle = 45, hjust = 1)) + \n labs(colour=NULL,\n title = \"Supply-Side Density Metrics by Population Decile\",\n x = \"Population decile\", y = \"Density (per km^2)\") + \n scale_x_continuous(expand = c(0, 0.5), breaks = seq(0,100,20)) + \n scale_y_continuous(expand = c(0, 0)) + #, limits = c(0,20)) + \n theme(panel.spacing = unit(0.6, \"lines\")) + expand_limits(y=0) +\n guides(colour=guide_legend(ncol=3)) +\n facet_wrap(~country, scales = \"free\", ncol=4) \n\ndemand_supply <- ggarrange(demand_densities, supply_densities, \n ncol = 1, nrow = 2, align = c(\"hv\"))\n\n#export to folder\npath = file.path(folder, 'figures_tables', 'a_demand_supply_panel.png')\nggsave(path, units=\"in\", width=8, height=9, dpi=300)\nprint(demand_supply)\ndev.off()\n\n################################\n#technology\ndata_tech <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_technology_options.csv'))\ndata_tech <- select(data_tech, GID_0, scenario, strategy, confidence, societal_cost)\ndata_tech$societal_cost = round(data_tech$societal_cost/1e9,2)\ndata_tech <- data_tech[(data_tech$confidence == 50),]\n\nbaseline <- data_tech %>%\n filter(str_detect(strategy, \"_baseline_baseline_baseline_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nbaseline$strategy_summary = 'baseline'\n\n#business model\ndata_bus_mod <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_business_model_options.csv'))\ndata_bus_mod <- select(data_bus_mod, GID_0, scenario, strategy, confidence, societal_cost)\ndata_bus_mod <- data_bus_mod[(data_bus_mod$confidence == 50),]\n\npassive <- data_bus_mod %>%\n filter(str_detect(strategy, \"_passive_baseline_baseline_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \npassive$strategy_summary = 'passive'\n\nactive <- data_bus_mod %>%\n filter(str_detect(strategy, \"_active_baseline_baseline_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nactive$strategy_summary = 'active'\n\nsrn <- data_bus_mod %>%\n filter(str_detect(strategy, \"_srn_baseline_baseline_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nsrn$strategy_summary = 'srn'\n\n#policy options\ndata_policy <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_policy_options.csv'))\ndata_policy <- select(data_policy, GID_0, scenario, strategy, confidence, societal_cost)\ndata_policy <- data_policy[(data_policy$confidence == 50),]\n\nspectrum_low <- data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_low_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nspectrum_low$strategy_summary = 'spectrum_low'\n\nspectrum_high <- data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_high_baseline\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nspectrum_high$strategy_summary = 'spectrum_high'\n\ntax_low <- data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_baseline_low\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \ntax_low$strategy_summary = 'tax_low'\n\ntax_high <- data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_baseline_high\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \ntax_high$strategy_summary = 'tax_high'\n\n#Mixed options\ndata_mixed <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_mixed_options.csv'))\ndata_mixed <- data_mixed[(data_mixed$confidence == 50),]\ndata_mixed <- select(data_mixed, GID_0, scenario, strategy, confidence, societal_cost)\n\nmixed <- data_mixed %>%\n filter(str_detect(strategy, \"_srn_baseline_low_low\")) %>% \n group_by(GID_0, scenario) %>%\n filter(societal_cost == min(societal_cost)) \nmixed$strategy_summary = 'mixed'\n\n####################\n#Aggregate results\nresults = rbind(baseline, passive, active, srn, \n spectrum_low, spectrum_high,\n tax_low, tax_high, mixed)\n\nresults$tech = sapply(strsplit(results$strategy, \"_\"), \"[\", 1)\nresults$backhaul = sapply(strsplit(results$strategy, \"_\"), \"[\", 3)\n\nrm(data_tech, data_bus_mod, data_policy, baseline, passive, active, srn, \n spectrum_low, spectrum_high, tax_low, tax_high)\n\nresults$GID_0 = factor(results$GID_0,\n levels=c('MWI', 'UGA', 'SEN', 'KEN', 'PAK', 'ALB', 'PER', 'MEX'),\n labels=c('Malawi', 'Uganda', 'Senegal', 'Kenya', 'Pakistan', 'Albania', 'Peru', 'Mexico'))\n\nresults$scenario = factor(results$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\nresults = with(results, results[order(GID_0, scenario, strategy_summary),])\n\nresults$tech_strategy = with(results, paste0(tech, '_', backhaul))\nresults$tech_strategy[results$tech_strategy == '4G_microwave'] <- '4G (W)'\nresults$tech_strategy[results$tech_strategy == '5G_microwave'] <- '5G NSA (W)'\n\nresults = select(results, GID_0, scenario, strategy_summary, tech_strategy)\n\nresults$strategy_summary = as.factor(results$strategy_summary)\nresults$tech_strategy = as.factor(results$tech_strategy)\n\npath = file.path(folder, 'vis_results', 'a_cheapest_strategies.csv')\nwrite.csv(results, path, row.names=FALSE)\n\nnames(results)[names(results)==\"GID_0\"] <- \"Country\"\nnames(results)[names(results)==\"scenario\"] <- \"Scenario\"\n\nresults = results %>%\n pivot_wider(names_from = strategy_summary, values_from = tech_strategy)\n\nresults = results %>%\n mutate(baseline = cell_spec(baseline, \"html\", \n color=ifelse(grepl(\"4G (W)\", baseline, fixed = T), \"blue\", \"black\"))) %>%\n mutate(passive = cell_spec(passive, \"html\", \n color=ifelse(grepl(\"4G (W)\", passive, fixed = T), \"blue\", \"black\"))) %>%\n mutate(active = cell_spec(active, \"html\", \n color=ifelse(grepl(\"4G (W)\", active, fixed = T), \"blue\", \"black\"))) %>%\n mutate(srn = cell_spec(srn, \"html\", \n color=ifelse(grepl(\"4G (W)\", srn, fixed = T), \"blue\", \"black\"))) %>%\n mutate(spectrum_low = cell_spec(spectrum_low, \"html\", \n color=ifelse(grepl(\"4G (W)\", spectrum_low, fixed = T), \"blue\", \"black\"))) %>%\n mutate(spectrum_high = cell_spec(spectrum_high, \"html\", \n color=ifelse(grepl(\"4G (W)\", spectrum_high, fixed = T), \"blue\", \"black\"))) %>%\n mutate(tax_low = cell_spec(tax_low, \"html\", \n color=ifelse(grepl(\"4G (W)\", tax_low, fixed = T), \"blue\", \"black\"))) %>%\n mutate(tax_high = cell_spec(tax_high, \"html\", \n color=ifelse(grepl(\"4G (W)\", tax_high, fixed = T), \"blue\", \"black\"))) %>%\n mutate(mixed = cell_spec(mixed, \"html\", \n color=ifelse(grepl(\"4G (W)\", mixed, fixed = T), \"blue\", \"black\")))\n\nresults = select(results, Country, Scenario, baseline, passive, active, srn, \n spectrum_low, spectrum_high, tax_low, tax_high, mixed)\n\nnames(results)[names(results)==\"baseline\"] <- \"Baseline\"\nnames(results)[names(results)==\"passive\"] <- \"Passive\"\nnames(results)[names(results)==\"active\"] <- \"Active\"\nnames(results)[names(results)==\"srn\"] <- \"SRN\"\nnames(results)[names(results)==\"spectrum_low\"] <- \"Low P.\"\nnames(results)[names(results)==\"spectrum_high\"] <- \"High P.\"\nnames(results)[names(results)==\"tax_low\"] <- \"Low T.\"\nnames(results)[names(results)==\"tax_high\"] <- \"High T.\"\nnames(results)[names(results)==\"mixed\"] <- \"Lowest\"\n\nresults = kable(results, \"html\", escape = F, \n caption = \"Least (Financial) Cost Technology for Universal Coverage\") %>% \n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n add_header_above(\n c(\" \"= 3, \n \"Infrastructure Sharing\" = 3, \n \"Spectrum Pricing\" = 2, \n \"Taxation\" = 2,\n \"Hybrid\" = 1)) \n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(results, file = 'b_best_performing_technology.png', zoom = 1.5)\n\n#################\n#Financial Cost = MNO cost + govt cost\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\n#technology\ndata_tech <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_technology_options.csv'))\ndata_tech <- data_tech[(data_tech$confidence == 50),]\n\ndata_tech$strategy_summary = 'baseline'\ndata_tech$strategy <- as.character(data_tech$strategy)\ndata_tech$tech = sapply(strsplit(data_tech$strategy, \"_\"), \"[\", 1)\ndata_tech$core = sapply(strsplit(data_tech$strategy, \"_\"), \"[\", 2)\ndata_tech$backhaul = sapply(strsplit(data_tech$strategy, \"_\"), \"[\", 3)\n\ndata_tech = data_tech[!(data_tech$backhaul == 'fiber' & data_tech$core == \"nsa\"), ]\ndata_tech = data_tech[!(data_tech$backhaul == 'microwave' & data_tech$core == \"sa\"), ]\n\ndata_tech$tech = NULL\ndata_tech$core = NULL\ndata_tech$backhaul = NULL\n\n#bus_mod\ndata_bus_mod <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_business_model_options.csv'))\ndata_bus_mod <- data_bus_mod[(data_bus_mod$confidence == 50),]\n\ndata_bus_mod$strategy <- as.character(data_bus_mod$strategy)\n\ndata_bus_mod$strategy_summary = sapply(strsplit(data_bus_mod$strategy, \"_\"), \"[\", 4)\n\ndata_bus_mod = data_bus_mod[!(data_bus_mod$strategy_summary == 'baseline'), ]\n\n#policy\ndata_policy <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_policy_options.csv'))\ndata_policy <- data_policy[(data_policy$confidence == 50),]\n\nspectrum_low = data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_low_baseline\"))\nspectrum_low$strategy_summary = 'spectrum_low'\n\nspectrum_high = data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_high_baseline\"))\nspectrum_high$strategy_summary = 'spectrum_high'\n\ntax_low = data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_baseline_low\"))\ntax_low$strategy_summary = 'tax_low'\n\ntax_high = data_policy %>%\n filter(str_detect(strategy, \"baseline_baseline_baseline_high\"))\ntax_high$strategy_summary = 'tax_high'\n\n#mixed\ndata_mixed <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_mixed_options.csv'))\ndata_mixed <- data_mixed[(data_mixed$confidence == 50),]\n\ndata_mixed$strategy <- as.character(data_mixed$strategy)\n\ndata_mixed$strategy_summary = 'mixed'\n\ndata_mixed = data_mixed[!(data_mixed$strategy_summary == 'baseline'), ]\n\n#combine\nresults = rbind(data_tech, data_bus_mod, data_mixed,\n spectrum_low, spectrum_high,\n tax_low, tax_high)\n\nresults$strategy = as.character(results$strategy)\nresults$tech = sapply(strsplit(results$strategy, \"_\"), \"[\", 1)\nresults$core = sapply(strsplit(results$strategy, \"_\"), \"[\", 2)\nresults$backhaul = sapply(strsplit(results$strategy, \"_\"), \"[\", 3)\n\nresults = results[!(results$backhaul == 'fiber' & results$core == \"nsa\"), ]\nresults = results[!(results$backhaul == 'microwave' & results$core == \"sa\"), ]\n\nresults$tech_strategy = with(results, paste0(\n tech, '_', core, '_',backhaul))\n\nrm(data_tech, data_bus_mod, data_policy, data_revenue,\n spectrum_low, spectrum_high, tax_low, tax_high)\n\nresults = select(results, GID_0, scenario, tech_strategy, strategy_summary,\n societal_cost, private_cost, government_cost,\n)\n\nresults$private_cost = signif(results$private_cost/1e9, 2)\nresults$government_cost = signif(results$government_cost/1e9, 2)\nresults$societal_cost = signif(results$societal_cost/1e9, 2)\n\nresults$GID_0 = factor(results$GID_0,\n levels=c('MWI', 'UGA', 'SEN', 'KEN', 'PAK', 'ALB', 'PER', 'MEX'),\n labels=c('Malawi', 'Uganda', 'Senegal', 'Kenya', 'Pakistan', 'Albania', 'Peru', 'Mexico'))\nresults$strategy_summary = factor(results$strategy_summary,\n levels=c('baseline', 'passive', 'active', 'srn',\n 'spectrum_low', 'spectrum_high', 'tax_low', 'tax_high', 'mixed'),\n labels = c('Baseline', 'Passive', 'Active', 'SRN',\n 'Low P.', 'High P.', 'Low T.', 'High T.', 'Mixed'))\nresults$scenario = factor(results$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\nresults$tech_strategy = factor(results$tech_strategy,\n levels=c('4G_epc_microwave', '4G_epc_fiber',\n '5G_nsa_microwave', '5G_sa_fiber'),\n labels=c('4G (W)', '4G (F)', '5G NSA (W)', '5G SA (F)'))\n\nnames(results)[names(results)==\"GID_0\"] <- \"Country\"\nnames(results)[names(results)==\"scenario\"] <- \"Scenario\"\nnames(results)[names(results)==\"tech_strategy\"] <- \"Strategy\"\n\npath = file.path(folder, 'vis_results', 'societal_costs.csv')\nwrite.csv(results, path, row.names=FALSE)\n\n#######################################\nresults_wide <- gather(results, Metric, value, societal_cost:government_cost)\n\nresults_wide = results_wide %>%\n pivot_wider(names_from = strategy_summary, values_from = value)\n\nresults_wide = select(results_wide, Country, Scenario, Strategy, Metric, Baseline)\n\nresults_wide = results_wide %>%\n pivot_wider(names_from = Country, values_from = Baseline)\n\nresults_wide = with(results_wide, results_wide[order(Scenario, Strategy, Metric),])\n\nresults_wide$Metric = factor(results_wide$Metric,\n levels=c('private_cost', 'government_cost', 'societal_cost'),\n labels=c('Private Cost ($Bn)', 'Government Cost ($Bn)','Financial Cost ($Bn)'))\n\nresults_wide = select(results_wide, Scenario, Strategy, Metric,\n Malawi, Uganda, Senegal, Kenya, Pakistan, Albania, Peru, Mexico)\n\ncb <- function(x) {\n range <- max(abs(x))\n width <- round(abs(x / range * 50), 2)\n ifelse(\n x > 0,\n paste0(\n '', x, ''\n ),\n paste0(\n '', x, ''\n )\n )\n}\n\ntable1 = results_wide %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = 'Full Technology Results by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 3, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Infrastructure Sharing Strategy: Baseline.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table1, file='sup_baseline_tech_country_costs.png', zoom = 1.5)\n\n######################################\npath = file.path(folder, 'vis_results', 'sup_baseline_tech_country_costs.csv')\nwrite.csv(results_wide, path, row.names=FALSE)\n\nresults_wide = results_wide[(results_wide$Metric == 'Financial Cost ($Bn)'),]\n\ntable1 = results_wide %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, \n caption = 'Technology Results reported by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 3, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Infrastructure Sharing Strategy: Baseline.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table1, file='c_baseline_tech_country_costs.png', zoom = 1.5)\n\n#######################################\nresults_wide <- gather(results, Metric, value, societal_cost:government_cost)\n\nresults_wide = results_wide[(results_wide$Strategy == '5G NSA (W)'),]\n\nresults_wide = results_wide[\n (results_wide$strategy_summary == \"Baseline\" |\n results_wide$strategy_summary == \"Passive\" |\n results_wide$strategy_summary == \"Active\" |\n results_wide$strategy_summary == \"SRN\" ), ]\n\nresults_wide = results_wide %>%\n pivot_wider(names_from = Country, values_from = value)\n\nresults_wide$Strategy = NULL\n\nnames(results_wide)[names(results_wide)==\"strategy_summary\"] <- \"Strategy\"\n\nresults_wide$Metric = factor(results_wide$Metric,\n levels=c('private_cost', 'government_cost', 'societal_cost'),\n labels=c('Private Cost ($Bn)', 'Government Cost ($Bn)','Financial Cost ($Bn)'))\n\nresults_wide = with(results_wide, results_wide[order(Scenario, Strategy),])\n\nresults_wide = select(results_wide, Scenario, Strategy, Metric,\n Malawi, Uganda, Senegal, Kenya, Pakistan, Albania, Peru, Mexico)\n\ntable2 = results_wide %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = 'Infrastructure Sharing Results by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 3, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table2, file='sup_infra_sharing_country_costs.png', zoom = 1.5)\n\npath = file.path(folder, 'vis_results', 'sup_infra_sharing_country_costs.csv')\nwrite.csv(results_wide, path, row.names=FALSE)\n\n#######################################\nresults_wide = results_wide[(results_wide$Metric == 'Financial Cost ($Bn)'),]\n\ntable2 = results_wide %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = 'Infrastructure Sharing Results by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 3, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table2, file='d_infra_sharing_country_costs.png', zoom = 1.5)\n\n###############################\nresults_wide <- gather(results, Metric, value, societal_cost:government_cost)\n\nresults_wide = results_wide[(results_wide$Strategy == '5G NSA (W)'),]\n\nresults_wide = results_wide[\n (results_wide$strategy_summary == \"Baseline\" |\n results_wide$strategy_summary == \"Low P.\" |\n results_wide$strategy_summary == \"High P.\"), ]\n\nresults_wide = results_wide %>%\n pivot_wider(names_from = Country, values_from = value)\n\nresults_wide$Strategy = NULL\n\nnames(results_wide)[names(results_wide)==\"strategy_summary\"] <- \"Strategy\"\n\nresults_wide$Metric = factor(results_wide$Metric,\n levels=c('private_cost', 'government_cost', 'societal_cost'),\n labels=c('Private Cost ($Bn)', 'Government Cost ($Bn)','Financial Cost ($Bn)'))\n\nresults_wide$Strategy = factor(results_wide$Strategy,\n levels=c('Low P.', 'Baseline', 'High P.'),\n labels=c('Low Prices (-75%)', 'Baseline','High Prices (+100%)'))\n\nresults_wide = with(results_wide, results_wide[order(Scenario, Strategy),])\n\nresults_wide = select(results_wide, Scenario, Strategy, Metric,\n Malawi, Uganda, Senegal, Kenya, Pakistan, Albania, Peru, Mexico)\n\ntable3 = results_wide %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = 'Spectrum Pricing Results by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 3, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Infrastructure Sharing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table3, file='sup_spectrum_pricing_country_costs.png', zoom = 1.5)\n\npath = file.path(folder, 'vis_results', 'sup_spectrum_pricing_country_costs.csv')\nwrite.csv(results_wide, path, row.names=FALSE)\n\n###############################\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\nresults_S2 = read.csv(file.path(folder, 'spectrum_ratio.csv'))\nresults_S2$Scenario = NULL\nspecify_decimal <- function(x, k) trimws(format(round(x, k), nsmall=k))\n\nresults_S2$Pakistan = specify_decimal(results_S2$Pakistan, 1)\nresults_S2$Albania = specify_decimal(results_S2$Albania, 1)\nresults_S2$Peru = specify_decimal(results_S2$Peru, 1)\nresults_S2$Mexico = specify_decimal(results_S2$Mexico, 1)\n\nresults_S2$Pakistan = as.numeric(results_S2$Pakistan)\nresults_S2$Albania = as.numeric(results_S2$Albania)\nresults_S2$Peru = as.numeric(results_S2$Peru)\nresults_S2$Mexico = as.numeric(results_S2$Mexico)\n\ntable4 = results_S2 %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = 'Spectrum Pricing Results by Country', digits=1) %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n row_spec(which(results_S2$Malawi >6), bold = T, color = \"black\", background = \"lightgrey\") %>%\n add_header_above(\n c(\" \"= 2, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Scenario: S2 (<200 Mbps).\",\n \"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Infrastructure Sharing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table4, file='d_S2_spectrum_costs_ratio.png', zoom = 1.5)\n\n\n\n\n\n\n###################NATIONAL COST PROFILE FOR BASELINE\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\ndata <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_technology_options.csv'))\n\ndata <- data[(data$strategy == \"5G_nsa_microwave_baseline_baseline_baseline_baseline\"),]\n# data <- data[(data$scenario == \"S2_200_50_5\"),]\ndata <- data[(data$confidence == 50),]\n\nnames(data)[names(data) == 'GID_0'] <- 'country'\n\ndata <- select(data, scenario, strategy, country, total_ran:total_market_cost)\n\ndata = data %>%\n group_by(scenario, strategy, country) %>%\n mutate(\n perc_ran = total_ran / total_market_cost * 100,\n perc_backhaul = total_backhaul_fronthaul / total_market_cost * 100,\n perc_civils = total_civils / total_market_cost * 100,\n perc_core_network = total_core_network / total_market_cost * 100,\n perc_administration = total_administration / total_market_cost * 100,\n perc_spectrum_cost = total_spectrum_cost / total_market_cost * 100,\n perc_tax = total_tax / total_market_cost * 100,\n perc_profit_margin = total_profit_margin / total_market_cost * 100,\n ) %>%\n select(scenario, strategy, country, perc_ran, perc_backhaul, perc_civils, \n perc_core_network, perc_administration, perc_spectrum_cost,\n perc_tax, perc_profit_margin)\n\ndata <- gather(data, metric, value, perc_ran:perc_profit_margin)#ran:profit_margin)#\n\ndata$metric = factor(data$metric, levels=c(\n 'perc_profit_margin',\n 'perc_tax',\n 'perc_spectrum_cost',\n 'perc_administration',\n 'perc_core_network',\n 'perc_civils',\n 'perc_backhaul',\n 'perc_ran'\n),\nlabels=c(\n \"Profit\",\n \"Tax\",\n \"Spectrum\",\n \"Administration\",\n 'Core',\n \"Civils\",\n \"Backhaul\",\n \"RAN\"\n))\n\ndata$scenario = factor(data$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\ndata$country = factor(data$country, levels=c(\n 'MEX','PER','ALB','PAK','KEN','SEN','UGA','MWI'\n),\nlabels=c(\n 'Mexico','Peru', 'Albania', 'Pakistan', 'Kenya', 'Senegal', 'Uganda','Malawi' \n))\n\ntable5 <- ggplot(data, aes(x=country, y=(value), group=metric, fill=metric)) +\n geom_bar(stat = \"identity\") +\n coord_flip() +\n scale_fill_brewer(palette=\"Spectral\", name = NULL, direction=1) +\n theme(legend.position = \"bottom\", axis.text.x = element_text(angle = 45, hjust = 1)) +\n labs(title = \"Private Cost Composition for 5G NSA (W) by Country\", colour=NULL,\n subtitle = \"Baseline Infrastructure Sharing, Spectrum Pricing and Taxation\",\n x = NULL, y = \"Percentage of Total Private Cost (%)\") +\n scale_y_continuous(expand = c(0, 0)) + \n theme(panel.spacing = unit(0.6, \"lines\")) +\n guides(fill=guide_legend(ncol=8, reverse = TRUE)) +\n facet_wrap(~scenario, scales = \"free\", ncol=3)\n\npath = file.path(folder, 'figures_tables', 'e_percentage_of_total_private_cost.png')\nggsave(path, units=\"in\", width=7, height=4, dpi=300)\nprint(table5)\ndev.off()\n\n###################\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\ndata <- read.csv(file.path(folder, '..', 'results', 'national_market_cost_results_all_options.csv'))\n\ndata <- data[(data$confidence == 50),]\n\nnames(data)[names(data) == 'GID_0'] <- 'country'\n\ndata <- select(data, scenario, strategy, country, societal_cost)\n\nbaseline <- data %>%\n filter(str_detect(strategy, \"5G_nsa_microwave\")) %>% \n group_by(country, scenario) %>% \n filter(str_detect(strategy, \"baseline_baseline_baseline_baseline\")) \nbaseline$type = 'Baseline ($Bn)'\n\ndata <- data %>%\n filter(str_detect(strategy, \"5G_nsa_microwave\")) %>% \n group_by(country, scenario) %>%\n filter(societal_cost == min(societal_cost)) \ndata$type = 'Lowest ($Bn)'\n\nall_data = rbind(data, baseline)\n\nall_data$country = factor(all_data$country,\n levels=c('MWI', 'UGA', 'SEN', 'KEN', 'PAK', 'ALB', 'PER', 'MEX'),\n labels=c('Malawi', 'Uganda', 'Senegal', 'Kenya', \n 'Pakistan', 'Albania', 'Peru', 'Mexico'))\n\nall_data$strategy = NULL\n\nall_data = all_data[!duplicated(all_data[c('scenario', 'country', 'type')]),]\n\nall_data$scenario = factor(all_data$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\nall_data$societal_cost = round(all_data$societal_cost / 1e9, 1)\n\nnames(all_data)[names(all_data) == 'scenario'] <- 'Scenario'\nnames(all_data)[names(all_data) == 'type'] <- 'Strategy'\n\nall_data <- spread(all_data, country, societal_cost)\n\ntable6 = all_data %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, caption = '(A) Financial Cost of Universal Access NPV 2020-2030 by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 2, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Infrastructure Sharing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 1 d.p.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table6, file='f_a_social_cost.png', zoom = 1.5)\n\n\n###################\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\ndata_revenue <- read.csv(file.path(folder, '..', 'results', 'national_mno_results_all_options.csv'))\ndata_revenue <- data_revenue[(data_revenue$confidence == 50),]\ndata_revenue <- select(data_revenue, GID_0, scenario, strategy, total_mno_revenue)\n\ndata <- read.csv(file.path(folder, '..', 'results', 'decile_mno_cost_results_all_options.csv'))\ndata <- data[(data$confidence == 50),]\ndata <- data %>% filter(str_detect(strategy, \"5G_nsa_microwave\")) \n\ndata <- select(data, GID_0, scenario, strategy, decile, total_mno_cost, required_state_subsidy)\n\ndata <- merge(data, data_revenue, by=c('GID_0', 'strategy', 'scenario'))\n\ndata <- data[order(data$GID_0, data$scenario, data$strategy, data$decile),]\n\n#get baseline rows\nbaseline <- data %>% filter(str_detect(strategy, \"baseline_baseline_baseline_baseline\")) \n\nbaseline <- baseline %>%\n group_by(GID_0, scenario) %>%\n mutate(\n total_mno_revenue = total_mno_revenue/1e9,\n cumulative_cost_bn = cumsum(round(total_mno_cost / 1e9, 3)),\n cumulative_subsidy_bn = cumsum(round(required_state_subsidy / 1e9, 2))\n )\nbaseline$type = 'Baseline (%)'\n\nproblems <- baseline %>%\n group_by(GID_0, strategy, scenario) %>%\n filter(total_mno_revenue <= cumulative_cost_bn) %>%\n filter(decile == min(decile)) \n\nbaseline <- baseline %>%\n group_by(GID_0, strategy, scenario) %>%\n filter(total_mno_revenue >= cumulative_cost_bn)\n\n#get baseline rows\nlowest <- filter(data, !strategy %in% \"5G_nsa_microwave_baseline_baseline_baseline_baseline\") \n\nlowest <- lowest[order(lowest$GID_0, lowest$scenario, lowest$strategy, lowest$decile),]\n\nlowest <- lowest %>%\n group_by(GID_0, strategy, scenario) %>%\n mutate(\n total_mno_revenue = total_mno_revenue/1e9,\n cumulative_cost_bn = cumsum(round(total_mno_cost / 1e9, 3)),\n cumulative_subsidy_bn = cumsum(round(required_state_subsidy / 1e9, 2))\n )\nlowest$type = 'Lowest (%)'\n\nlowest <- lowest %>%\n group_by(GID_0, strategy, scenario) %>%\n filter(total_mno_revenue >= cumulative_cost_bn)\n\nall_data = rbind(lowest, baseline)\n\nsubsidy_all = all_data\nsubsidy_all = rbind(subsidy_all, problems)\n\nall_data <- all_data %>%\n group_by(GID_0, scenario, type) %>%\n filter(decile == max(decile)) \n\nall_data = rbind(all_data, problems)\n\nall_data$GID_0 = factor(all_data$GID_0,\n levels=c('MWI', 'UGA', 'SEN', 'KEN', 'PAK', 'ALB', 'PER', 'MEX'),\n labels=c('Malawi', 'Uganda', 'Senegal', 'Kenya', \n 'Pakistan', 'Albania', 'Peru', 'Mexico'))\n\nall_data = select(all_data, scenario, GID_0, decile, type)\n\nall_data = all_data[!duplicated(all_data[c('scenario', 'GID_0', 'type')]),]\n\nall_data$scenario = factor(all_data$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\nnames(all_data)[names(all_data) == 'scenario'] <- 'Scenario'\nnames(all_data)[names(all_data) == 'type'] <- 'Strategy'\n\nall_data <- spread(all_data, GID_0, decile)\n\nall_data[is.na(all_data)] <- 0\n\ncb_inverted <- function(x) {\n range <- max(abs(x))\n width <- round(abs(range / x * 5), 2)\n ifelse(\n x < 100,\n paste0(\n '', x, ''\n ),\n paste0(\n '', x, ''\n )\n\n )\n}\n\ntable7 = all_data %>%\n mutate(\n Malawi = cb_inverted(Malawi),\n Uganda = cb_inverted(Uganda),\n Senegal = cb_inverted(Senegal),\n Kenya = cb_inverted(Kenya),\n Pakistan = cb_inverted(Pakistan),\n Albania = cb_inverted(Albania),\n Peru = cb_inverted(Peru),\n Mexico = cb_inverted(Mexico)\n ) %>%\n kable(escape = F, caption = '(B) Commercially Viable Population Coverage by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 2, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Infrastructure Sharing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 1 d.p.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table7, file='f_b_viable_coverage.png', zoom = 1.5)\n\n#########################################\nsubsidy = ungroup(subsidy_all)\n\nsubsidy <- subsidy %>%\n group_by(GID_0, scenario, type) %>%\n filter(decile == max(decile)) \n\nsubsidy = select(subsidy, scenario, strategy, GID_0, cumulative_subsidy_bn, type)\n\nsubsidy$strategy = NULL\n\nsubsidy = subsidy[!duplicated(subsidy[c('scenario', 'GID_0', 'type')]),]\n\nsubsidy$scenario = factor(subsidy$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\nsubsidy$GID_0 = factor(subsidy$GID_0,\n levels=c('MWI', 'UGA', 'SEN', 'KEN', 'PAK', 'ALB', 'PER', 'MEX'),\n labels=c('Malawi', 'Uganda', 'Senegal', 'Kenya', \n 'Pakistan', 'Albania', 'Peru', 'Mexico'))\n\nnames(subsidy)[names(subsidy) == 'scenario'] <- 'Scenario'\nnames(subsidy)[names(subsidy) == 'type'] <- 'Strategy'\n\nsubsidy$Strategy[subsidy$Strategy == 'Baseline (%)'] <- \"Baseline ($Bn)\"\nsubsidy$Strategy[subsidy$Strategy == 'Lowest (%)'] <- 'Lowest ($Bn)'\n\n# subsidy$required_state_subsidy = round(subsidy$required_state_subsidy / 1e9, 3)\n\nsubsidy <- spread(subsidy, GID_0, cumulative_subsidy_bn)\n\ntable8 = subsidy %>%\n mutate(\n Malawi = cb(Malawi),\n Uganda = cb(Uganda),\n Senegal = cb(Senegal),\n Kenya = cb(Kenya),\n Pakistan = cb(Pakistan),\n Albania = cb(Albania),\n Peru = cb(Peru),\n Mexico = cb(Mexico)\n ) %>%\n kable(escape = F, \n caption = '(C) Government Subsidy to Reach Universal Access NPV 2020-2030 by Country') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 2, \"C1\" = 2, \"C2\" = 2, \"C3\" = 1, \"C4\" = 1, \"C5\" = 1, \"C6\" = 1)) %>%\n footnote(number = c(\"Technology Strategy: 5G NSA with Wireless Backhaul.\",\n \"Infrastructure Sharing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 1 d.p.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table8, file='f_c_subsidy.png', zoom = 1.5)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n##################CLUSTER COSTS\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\nresults <- read.csv(file.path(folder, '..', 'results', 'national_market_results_technology_options.csv'))\nnames(results)[names(results) == 'GID_0'] <- 'iso3'\nresults$metric = 'Baseline'\n\n#mixed\nmixed <- read.csv(file.path(folder, '..', 'results', 'national_market_results_mixed_options.csv'))\nnames(mixed)[names(mixed) == 'GID_0'] <- 'iso3'\nmixed$metric = 'Lowest'\n\nresults = rbind(results, mixed)\n\nclusters <- read.csv(file.path(folder, 'clustering', 'results', 'data_clustering_results.csv'))\nnames(clusters)[names(clusters) == 'ISO_3digit'] <- 'iso3'\nclusters <- select(clusters, iso3, cluster, country)\n\nresults <- merge(results, clusters, x.by='iso3', y.by='iso3', all=FALSE)\n\nresults$cost_per_pop = results$total_market_cost / results$population\n\nmean_pop_cost_by_cluster <- select(results, scenario, strategy, confidence, metric,\n cost_per_pop, cluster)\n\nmean_pop_cost_by_cluster <- mean_pop_cost_by_cluster %>%\n group_by(scenario, strategy, confidence, metric, cluster) %>%\n summarise(mean_cost_per_pop = round(mean(cost_per_pop)))\n\nresults <- merge(clusters, mean_pop_cost_by_cluster, x.by='cluster', y.by='cluster', all=TRUE)\n\ngdp <- read.csv(file.path(folder, 'gdp.csv'))\nnames(gdp)[names(gdp) == 'iso3'] <- 'iso3'\ngdp <- select(gdp, iso3, gdp, income_group)\n\nresults <- merge(results, gdp, by='iso3', all=FALSE)\n\npop <- read.csv(file.path(folder, 'population_2018.csv'))\npop <- select(pop, iso3, population)\npop$iso3 <- as.character(pop$iso3)\n\nresults <- merge(results, pop, by='iso3', all=FALSE)\n\nresults$total_market_cost <- results$mean_cost_per_pop * results$population\n\nresults <- results[(results$confidence == 50),]\n\nresults$scenario = factor(results$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (<25 Mbps)\",\n \"S2 (<200 Mbps)\",\n \"S3 (<400 Mbps)\"))\n\nresults$strategy = factor(results$strategy, levels=c(\n \"4G_epc_microwave_baseline_baseline_baseline_baseline\",\n \"4G_epc_fiber_baseline_baseline_baseline_baseline\",\n \"5G_nsa_microwave_baseline_baseline_baseline_baseline\",\n \"5G_sa_fiber_baseline_baseline_baseline_baseline\",\n '4G_epc_microwave_srn_baseline_low_low',\n '4G_epc_fiber_srn_baseline_low_low',\n '5G_nsa_microwave_srn_baseline_low_low',\n '5G_sa_fiber_srn_baseline_low_low'\n ),\n labels=c(\n \"4G (W)\",\n \"4G (FB)\",\n \"5G NSA (W)\",\n \"5G SA (FB)\",\n \"4G (W)\",\n \"4G (FB)\",\n \"5G NSA (W)\",\n \"5G SA (FB)\"))\n\nresults <- results[complete.cases(results),]\n\n# results <- results[(results$confidence == 'mean'),]\nresults$total_market_cost_dc <- round(results$total_market_cost/1e9, 2)\n\nheadline_costs <- select(results, country, scenario, strategy,\n total_market_cost, gdp, metric)\nheadline_costs <- spread(headline_costs, metric, total_market_cost)\n\nheadline_costs <- headline_costs %>%\n group_by(scenario, strategy) %>%\n summarize(\n `Baseline (US$Tn)` = signif(sum(Baseline)/1e12, 2),\n `Lowest (US$Tn)` = signif(sum(Lowest)/1e12, 2),\n `Baseline (GDP%)` = signif((sum(Baseline)/10) / sum(gdp) * 100, 2),\n `Lowest (GDP%)` = signif((sum(Lowest)/10)/ sum(gdp) * 100, 2)\n )\n\nnames(headline_costs)[names(headline_costs)==\"strategy\"] <- \"Strategy\"\nnames(headline_costs)[names(headline_costs)==\"scenario\"] <- \"Scenario\"\n\ncb <- function(x) {\n range <- max(abs(x))\n width <- round(abs(x / range * 20), 4)\n ifelse(\n x > 0,\n paste0(\n '', x, ''\n ),\n paste0(\n '', x, ''\n )\n )\n}\n\nheadline_costs = ungroup(headline_costs)\n\ntable4 = headline_costs %>%\n mutate(\n `Baseline (US$Tn)` = cb(`Baseline (US$Tn)`),\n `Lowest (US$Tn)` = cb(`Lowest (US$Tn)`),\n `Baseline (GDP%)` = cb(`Baseline (GDP%)`),\n `Lowest (GDP%)` = cb(`Lowest (GDP%)`),\n ) %>%\n kable(escape = F, caption = 'Technology Cost Results for the Developing World') %>%\n kable_classic(\"striped\", full_width = F, html_font = \"Cambria\") %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \" = 2,\n \"Total Cost\" = 2,\n \"10-Year GDP Share\" = 2\n )) %>%\n footnote(number = c(\"Infrastructure Sharing Strategy: Baseline.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table4, file='g_costs_by_income_group.png', zoom = 1.5)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\npath = file.path(folder, 'vis_results', 'headline_costs.csv')\nwrite.csv(headline_costs, path, row.names=FALSE)\n\ninc_group_costs = results[!(results$income_group == 'HIC'),]\n\ninc_group_costs <- select(inc_group_costs, country, scenario, strategy, metric,\n income_group, total_market_cost, gdp, population)\n\ninc_group_costs = inc_group_costs %>%\n group_by(scenario, strategy, metric, income_group) %>%\n summarize(\n population = sum(population),\n total_market_cost = sum(total_market_cost),\n gdp = sum(gdp)\n )\n\ninc_group_costs = inc_group_costs %>% \n gather(econ_metric, value, total_market_cost, gdp)\n\ninc_group_costs$value_dc = round(inc_group_costs$value/1e9, 2)\n\ninc_group_costs$combined <- paste(inc_group_costs$income_group, inc_group_costs$metric, sep=\"_\")\n\ninc_group_costs = ungroup(inc_group_costs)\n\ninc_group_costs <- select(inc_group_costs, scenario, strategy, \n combined, econ_metric, value, population)\n\ninc_group_costs = inc_group_costs %>% spread(econ_metric, value)\n\n# path = file.path(folder, 'vis_results', 'test.csv')\n# write.csv(inc_group_costs, path, row.names=FALSE)\n\ninc_group_costs <- inc_group_costs %>%\n group_by(scenario, strategy, combined) %>%\n summarize(\n `population` = sum(population),\n `total_cost $USDbn` = (sum(total_market_cost)/1e9),\n `(GDP$Bn)` = round(sum(gdp)/1e9),\n `(GDP%)` = signif((sum(total_market_cost)/10) / sum(gdp) * 100, 2),\n )\n\n# path = file.path(folder, 'vis_results', 'test2.csv')\n# write.csv(inc_group_costs, path, row.names=FALSE)\n\ninc_group_costs$combined = factor(inc_group_costs$combined,\n levels=c(\n 'LIC_Baseline',\n 'LIC_Lowest',\n 'LMC_Baseline',\n 'LMC_Lowest',\n 'UMC_Baseline',\n 'UMC_Lowest'\n ),\n labels=c(\n 'Baseline\\nLIC',\n 'Lowest\\nLIC',\n 'Baseline\\nLMIC',\n 'Lowest\\nLMIC',\n 'Baseline\\nUMIC',\n 'Lowest\\nUMIC'\n ))\n\ninc_group_costs <- select(inc_group_costs, scenario, strategy, combined, `(GDP%)`)\n\ninc_group_costs <- spread(inc_group_costs, combined, `(GDP%)`)\n\nnames(inc_group_costs)[names(inc_group_costs)==\"strategy\"] <- \"Strategy\"\nnames(inc_group_costs)[names(inc_group_costs)==\"scenario\"] <- \"Scenario\"\ninc_group_costs = ungroup(inc_group_costs)\n\ncb <- function(x) {\n range <- max(abs(x))\n width <- round(abs(x / range * 20), 4)\n ifelse(\n x > 0,\n paste0(\n '', x, ''\n ),\n paste0(\n '', x, ''\n )\n )\n}\n\ntable5 = inc_group_costs %>%\n mutate(\n `Baseline\\nLIC` = cb(`Baseline\\nLIC`),\n `Lowest\\nLIC` = cb(`Lowest\\nLIC`),\n `Baseline\\nLMIC` = cb(`Baseline\\nLMIC`),\n `Lowest\\nLMIC` = cb(`Lowest\\nLMIC`),\n `Baseline\\nUMIC` = cb(`Baseline\\nUMIC`),\n `Lowest\\nUMIC` = cb(`Lowest\\nUMIC`)\n ) %>%\n kable(escape = F, caption = 'Technology Cost Results for the Developing World') %>%\n kable_classic(\"striped\", html_font = \"Cambria\", full_width = FALSE) %>%\n row_spec(0, align = \"c\") %>%\n add_header_above(\n c(\" \"= 2,\n \"Low\\nIncome\\n(10-Year GDP%)\" = 2,\n \"Lower\\nMiddle Income\\n(10-Year GDP%)\" = 2,\n \"Upper\\nMiddle Income\\n(10-Year GDP%)\" = 2\n )) %>%\n footnote(number = c(\"Infrastructure Sharing Strategy: Baseline.\",\n \"Spectrum Pricing Strategy: Baseline.\",\n \"Taxation Strategy: Baseline.\",\n \"Results rounded to 2 s.f.\"))\n\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\npath = file.path(folder, 'figures_tables')\nsetwd(path)\nkableExtra::save_kable(table5, file='h_costs_by_income_group.png', zoom = 1.5)\n\npath = file.path(folder, 'vis_results', 'costs_by_income_group.csv')\nwrite.csv(inc_group_costs, path, row.names=FALSE)", "meta": {"hexsha": "149b80a3154fb9f353250c8ae24f4578a8533554", "size": 50719, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/best_performing_strategy_final.r", "max_stars_repo_name": "edwardoughton/pytal", "max_stars_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-01-16T12:12:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-04T11:46:00.000Z", "max_issues_repo_path": "vis/best_performing_strategy_final.r", "max_issues_repo_name": "edwardoughton/pytal", "max_issues_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/best_performing_strategy_final.r", "max_forks_repo_name": "edwardoughton/pytal", "max_forks_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-01-15T14:46:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-27T02:42:15.000Z", "avg_line_length": 38.1632806622, "max_line_length": 119, "alphanum_fraction": 0.6309075494, "num_tokens": 13882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251201477015, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.31707996087000273}} {"text": "#' Plot catch curves for catch\r\n#' \r\n#' Creates catch curves to estimate Z from catch data. Useful???\r\n#' @param asap name of the variable that read in the asap.rdat file\r\n#' @param a1 list file produced by grab.aux.files function\r\n#' @param save.plots save individual plots\r\n#' @param od output directory for plots and csv files \r\n#' @param plotf type of plot to save\r\n#' @param first.age youngest age to use in catch curve, -999 finds peak age (defaults to -999)\r\n#' @export\r\n\r\nPlotCatchCurvesForCatch <- function(asap,a1,save.plots,od,plotf,first.age=-999){\r\n # create catch curve plots for catch by fleet\r\n usr <- par(\"usr\"); on.exit(par(usr))\r\n par(oma=c(1,1,1,1),mar=c(4,4,1,0.5))\r\n cohort <- seq(asap$parms$styr-asap$parms$nages-1, asap$parms$endyr+asap$parms$nages-1)\r\n ages <- seq(1,asap$parms$nages)\r\n my.col <- rep(c(\"blue\",\"red\",\"green\",\"orange\",\"gray50\"),50)\r\n for (ifleet in 1:asap$parms$nfleets){\r\n if (asap$parms$nfleets == 1) title1 = \"Catch\"\r\n if (asap$parms$nfleets >= 2) title1 = paste0(\"Catch for Fleet \",ifleet)\r\n \r\n # set up full age range matrices\r\n catch.comp.mat.ob <- matrix(0, nrow=asap$parms$nyears, ncol=asap$parms$nages)\r\n catch.comp.mat.pr <- matrix(0, nrow=asap$parms$nyears, ncol=asap$parms$nages)\r\n \r\n # determine age range for fleet and fill in appropriate ages\r\n age1 <- asap$fleet.sel.start.age[ifleet]\r\n age2 <- asap$fleet.sel.end.age[ifleet]\r\n catch.comp.mat.ob[,age1:age2] <- asap$catch.comp.mats[[(ifleet*4-3)]]\r\n catch.comp.mat.pr[,age1:age2] <- asap$catch.comp.mats[[(ifleet*4-2)]]\r\n \r\n # get catch at age\r\n catchob <- wtprop2caa(asap$catch.obs[ifleet,], asap$WAA.mats[[(ifleet*2-1)]], catch.comp.mat.ob)\r\n catchpr <- wtprop2caa(asap$catch.pred[ifleet,], asap$WAA.mats[[(ifleet*2-1)]], catch.comp.mat.pr)\r\n \r\n # replace zeros with NA and take logs\r\n cob <- rep0log(catchob)\r\n cpr <- rep0log(catchpr)\r\n \r\n # make cohorts\r\n cob.coh <- makecohorts(cob)\r\n cpr.coh <- makecohorts(cpr)\r\n \r\n # drop plus group\r\n cob.coh[,asap$parms$nages] <- NA\r\n cpr.coh[,asap$parms$nages] <- NA\r\n \r\n first.age.label <- 1\r\n if (first.age==1) title1 <- paste0(title1,\" First Age = 1\") \r\n \r\n # determine which ages to use for each cohort (default)\r\n if (first.age == -999){\r\n cob.coh <- find_peak_age(cob.coh)\r\n cpr.coh <- find_peak_age(cpr.coh)\r\n first.age.label <- \"find_peak\"\r\n title1 <- paste0(title1,\" (Peak Age)\") \r\n }\r\n \r\n # or drop youngest ages based on user control\r\n if (first.age > 1) {\r\n cob.coh[,1:(first.age-1)] <- NA\r\n cpr.coh[,1:(first.age-1)] <- NA\r\n title1 <- paste0(title1,\" First Age = \",first.age)\r\n first.age.label <- first.age\r\n }\r\n \r\n # compute Z by cohort\r\n z.ob <- calc_Z_cohort(cob.coh)\r\n z.pr <- calc_Z_cohort(cpr.coh)\r\n \r\n # make the plots\r\n par(mfrow=c(2,1))\r\n plot(cohort,cohort,type='n',ylim=range(c(cob.coh,cpr.coh,0),na.rm=T),xlab=\"\",ylab=\"Log(Catch)\",main=paste0(title1,\" Observed\"))\r\n for (i in 1:length(cob.coh[,1])){\r\n lines(seq(cohort[i],cohort[i]+asap$parms$nages-1),cob.coh[i,],type='p',lty=1,pch=seq(1,asap$parms$nages),col=\"gray50\")\r\n lines(seq(cohort[i],cohort[i]+asap$parms$nages-1),cob.coh[i,],type='l',lty=1,col=my.col[i])\r\n }\r\n \r\n Hmisc::errbar(cohort,z.ob[,1],z.ob[,3],z.ob[,2],xlab=\"Year Class\",ylab=\"Z\",ylim=range(c(z.ob,z.pr,0),na.rm=T))\r\n \r\n if (save.plots) savePlot(paste0(od,\"catch_curve_\",title1,\"_Observed_first_age_\",first.age.label,\".\",plotf), type=plotf)\r\n \r\n plot(cohort,cohort,type='n',ylim=range(c(cob.coh,cpr.coh,0),na.rm=T),xlab=\"\",ylab=\"Log(Catch)\",main=paste0(title1,\" Predicted\"))\r\n for (i in 1:length(cob.coh[,1])){\r\n lines(seq(cohort[i],cohort[i]+asap$parms$nages-1),cpr.coh[i,],type='p',lty=1,pch=seq(1,asap$parms$nages),col=\"gray50\")\r\n lines(seq(cohort[i],cohort[i]+asap$parms$nages-1),cpr.coh[i,],type='l',lty=1,col=my.col[i])\r\n }\r\n \r\n Hmisc::errbar(cohort,z.pr[,1],z.pr[,3],z.pr[,2],xlab=\"Year Class\",ylab=\"Z\",ylim=range(c(z.ob,z.pr,0),na.rm=T))\r\n \r\n if (save.plots) savePlot(paste0(od,\"catch_curve_\",title1,\"_Predicted_first_age_\",first.age.label,\".\",plotf), type=plotf)\r\n \r\n \r\n # write out .csv files for Z, one file for each fleet\r\n asap.name <- a1$asap.name\r\n \r\n colnames(z.ob) <-c(\"Z.obs\",\"low.80%\", \"high.80%\")\r\n write.csv(z.ob, file=paste0(od,\"Z.Ob.Fleet\",ifleet,\"_\",asap.name,\".csv\"), row.names=cohort)\r\n \r\n colnames(z.pr) <-c(\"Z.pred\",\"low.80%\", \"high.80%\")\r\n write.csv(z.pr, file=paste0(od,\"Z.Pr.Fleet.\",ifleet,\"_\",asap.name,\".csv\"), row.names=cohort)\r\n \r\n }\r\n return()\r\n}\r\n", "meta": {"hexsha": "5d30926f77d07fc49046c90fe8754fc21ed4e28a", "size": 4670, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_catch_curves_for_catch.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/plot_catch_curves_for_catch.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/plot_catch_curves_for_catch.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 43.6448598131, "max_line_length": 133, "alphanum_fraction": 0.6164882227, "num_tokens": 1588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3170799541460573}} {"text": "#!/usr/bin/Rscript\nlibrary(psycho) # used for SDT analysis\nlibrary(lme4)\n\n# both blocks of flowers/insects\nflowerFile <- 'data/flower_E5_old_first.csv'\ninsectFile <- 'data/insect_E5_old_first.csv'\n\nargs <- commandArgs(trailingOnly=T)\nif (length(args) < 1) {\n args <- c(flowerFile, insectFile)\n}\n\ncategoryName <- function(isTarget, isFoil) {\n if (isTarget & isFoil) {\n return('both')\n } else if (isTarget) {\n return('learned')\n } else if (isFoil) {\n return('unlearned')\n } else {\n return('neither')\n }\n}\n\nmemoryData <- function(filename) {\n # read the raw data from the csv\n data <- subset(read.csv(filename, header=T),\n\t\t task=='test', select=c(subject, isTarget, isFoil, isOld, wasCorrect))\n\n # count the number of correct/incorrect trials for each condition\n nCorrect <- aggregate(wasCorrect ~ subject + isTarget + isFoil + isOld, data, sum)$wasCorrect\n nIncorrect <- aggregate(wasCorrect ~ subject + isTarget + isFoil + isOld, data,\n function(x) sum(x==0))$wasCorrect\n \n # average performance for each subject for each trial type\n data <- aggregate(wasCorrect ~ subject + isTarget + isFoil + isOld, data, mean)\n data$wasIncorrect <- apply(data, 1, function(x) 1-x['wasCorrect']);\n data$nCorrect <- nCorrect\n data$nIncorrect <- nIncorrect \n \n data$isTarget <- as.numeric(data$isTarget)\n data$isFoil <- as.numeric(data$isFoil)\n data$isOld <- as.numeric(data$isOld)\n \n # rate is for hits (on old trials) and FAs (on new trials)\n data$rate <- apply(data, 1, function(x) ifelse(x['isOld'], x['wasCorrect'], x['wasIncorrect']))\n data$category <- as.factor(apply(data, 1, function(x) categoryName(x['isTarget'], x['isFoil'])))\n data$subject <- as.factor(data$subject)\n data$isTarget <- as.factor(data$isTarget)\n data$isFoil <- as.factor(data$isFoil)\n \n return(data)\n}\n\n# estimates SDT measures of sensitivity & bias for each subject/condition\nrunSDT <- function(data) { \n # group the old/new stimuli by subject and condition\n data <- merge(subset(data, isOld==1),\n subset(data, isOld==0),\n sort=F,\n by=c('subject', 'isTarget', 'isFoil'),\n suffixes=c('.old', '.new'))\n write.csv(data, 'sdt.csv')\n \n # get sensitivty & bias for each subject on each condition\n indices <- psycho::dprime(data$nCorrect.old, data$nIncorrect.new,\n data$nIncorrect.old, data$nCorrect.new)\n sdtData <- cbind(data, indices)\n \n # analyze sensitivity (dprime)\n writeLines('\\n\\nSensitivity (dprime)')\n print(tapply(sdtData$dprime, list(sdtData$isTarget, sdtData$isFoil), mean))\n print(summary(aov(dprime ~ isTarget * isFoil + Error(subject/(isTarget*isFoil)), data=sdtData)))\n\n # analyze bias (c)\n writeLines('\\n\\nBias (C)')\n print(tapply(sdtData$c, list(sdtData$isTarget, sdtData$isFoil), mean))\n print(summary(aov(c ~ isTarget * isFoil + Error(subject/(isTarget*isFoil)), data=sdtData)))\n \n return(sdtData)\n}\n\nanalyze <- function(data) {\n # Run the ANOVA as 2x2 (isLearned x isFoil)\n print(tapply(data$rate, list(data$isTarget, data$isFoil), mean))\n print(summary(aov(rate ~ isTarget * isFoil + Error(subject/(isTarget*isFoil)), data=data)))\n}\n\n\n# analyze data from each file\nfor (i in 1:length(args)) {\n filename <- args[i]\n writeLines(args[i])\n\n memData <- subset(read.csv(filename, header=T), task=='test' & isOld==0,\n select=c('subject', 'isTarget', 'isFoil', 'wasCorrect', 'isOld'))\n memData$subject <- factor(memData$subject)\n memData$isTarget <- factor(memData$isTarget)\n memData$isFoil <- factor(memData$isFoil)\n memData$wasCorrect <- factor(memData$wasCorrect)\n \n print(head(memData))\n model <- glmer(wasCorrect ~ isTarget * isFoil\n + (1 + isTarget|subject) + (1 + isFoil|subject),\n data=memData, family=binomial(link='logit'))\n print(summary(model))\n quit()\n \n #data <- memoryData(filename) \n #hits <- subset(data, isOld==1)\n #FAs <- subset(data, isOld==0) \n #writeLines('\\n\\nHits')\n #analyze(hits)\n #writeLines('\\n\\nFAs')\n #analyze(FAs)\n\n #sdtData <- runSDT(data)\n #writeLines('\\n\\n\\n\\n\\n')\n #write.csv(sdtData, 'sdt.csv')\n}\n", "meta": {"hexsha": "11dd518e1ba13fd86bad29607282d6574fc6f9b2", "size": 4355, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/mturk/hitsFAs.r", "max_stars_repo_name": "IMC-Lab/CategoryLearning", "max_stars_repo_head_hexsha": "84f9f973e267f20f7790dbbd7d3a555e58bb90dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-09-10T22:07:25.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-10T22:07:25.000Z", "max_issues_repo_path": "analysis/mturk/hitsFAs.r", "max_issues_repo_name": "IMC-Lab/CategoryLearning", "max_issues_repo_head_hexsha": "84f9f973e267f20f7790dbbd7d3a555e58bb90dc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/mturk/hitsFAs.r", "max_forks_repo_name": "IMC-Lab/CategoryLearning", "max_forks_repo_head_hexsha": "84f9f973e267f20f7790dbbd7d3a555e58bb90dc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6967213115, "max_line_length": 100, "alphanum_fraction": 0.6291618829, "num_tokens": 1203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3170799541460573}} {"text": "library(shiny)\n\n# Rely on the 'WorldPhones' dataset in the datasets\n# package (which generally comes preloaded).\nlibrary(datasets)\n\n# Define a server for the Shiny app\nshinyServer(function(input, output) {\n \n # Fill in the spot we created for a plot\n output$phonePlot <- renderPlot({\n \n # Render a barplot\n barplot(WorldPhones[,input$region]*1000, \n main=input$region,\n ylab=\"Number of Telephones\",\n xlab=\"Year\")\n })\n})", "meta": {"hexsha": "c5efe4121d4c110207d39d6c398de399b7ab3d83", "size": 561, "ext": "r", "lang": "R", "max_stars_repo_path": "server.r", "max_stars_repo_name": "amedabal/Data-Products", "max_stars_repo_head_hexsha": "d00d615a9ded67b31d2d1f44132624e93f9515ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "server.r", "max_issues_repo_name": "amedabal/Data-Products", "max_issues_repo_head_hexsha": "d00d615a9ded67b31d2d1f44132624e93f9515ed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "server.r", "max_forks_repo_name": "amedabal/Data-Products", "max_forks_repo_head_hexsha": "d00d615a9ded67b31d2d1f44132624e93f9515ed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5263157895, "max_line_length": 57, "alphanum_fraction": 0.5454545455, "num_tokens": 118, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.31683087788656966}} {"text": "library(ggplot2)\nlibrary(reshape2)\nlibrary(cowplot)\n\nfigWidth<-10\nfigHeight<-3\ntextFont<-10\n\n#epoch === repair tool\n#losses === tech(sbfl and profl)\n#Subs == patches (correct, plausible noncorrect)\n#model == result.txt\n\nBasedir=\"./\"\n\n\nSubs<-c(\"correct\",\"incorrectBUTplau\",\"implausible\")\n\n\nlosses<-c(\"SBFL\",\"ProFL\")\n\n\nmodel<-\"result\"\n\n\nepochs<-c(\"kPar\",\"ACS\",\"FixMiner\",\"TBar\",\"Dynamoth\",\"Arja\",\"jMutRepair\",\"Simfix\",\"jKali\",\"AVATAR\")\n\n\nMetrics<-c(\"Top1\")\n\nfor(sub in Subs){\n\tplot_list = list()\n\tcount=1\n\tpdf(paste(c(\"../../Results/FinalResults/RQ3_\",sub,\".pdf\"),collapse=\"\"), width=figWidth, height=figHeight)\n\n\t\tfor(me in Metrics){\n\t\t\t\t\n\t\t\tcombinemet=cbind(epochs)\n\t\t\tfor(loss in losses){\n\t\t\t\tdatafile=paste(Basedir,loss,\"/\",sub,\"/\",model,\".txt\",sep=\"\")\n\t\t\t\tdatamodel<-read.table(datafile,sep = \" \")\n\t\t\t\tcolnames(datamodel) <- c(\"Top1\",\"Top3\",\"Top5\",\"MFR\",\"MAR\")\n\t\t\t\tcombinemet=cbind(combinemet,datamodel[,me])\n\t\t\t}\n\t\t\tcombinemet=data.frame(combinemet)\n\t\t\t#colnames(combinemet) <- c(\"epochs\",\"within-project\",\"cross-project\",\"cross-validation\")\n\t\t\tcolnames(combinemet) <- c(\"Tool\",\"SBFL\",\"ProFL\")\n\t\t\tcombinemet <- melt(combinemet, id.vars='Tool')\n\t\t\tcolnames(combinemet) <- c(\"Tool\",\"model\",\"value\")\n\t\t\tcombinemet$value <- as.numeric(as.character(combinemet$value))\n\t\t\tp<-ggplot(combinemet, aes(x=Tool, y=value, group=model, colour=model,shape=model)) +\n\t\t\t\tgeom_line(aes(linetype=model),size=1.1)+geom_point(size=1.7)+ ylab(me)+theme(text=element_text(size=17))\n\t\t\tplot_list[[count]] = p\n\t\t\tcount=count+1\n\t\t\t\t\n\t\t}\n\t\t\t\n\t\tprow<-plot_grid(plot_list[[1]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[2]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[3]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[4]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#plot_list[[5]]+theme(legend.position=\"none\"),\n\t\t\t\t\t\t#nrow = 1, align = 'h',labels = sub)\n\t\t\t\t\t\tnrow = 1, align = 'h')\n\t\tlegend_b <- get_legend(plot_list[[1]] + theme(legend.position=c(0.13,97),legend.title = element_blank(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tlegend.text = element_text( size = 11)))\n\t\tp <- plot_grid( prow, legend_b, ncol = 1, rel_heights = c(1.1, .01))\n\t\tprint(p)\n\t\tdev.off()\n\t\n}\n", "meta": {"hexsha": "c67ac823675f7ef3eae5229a075e68ec42f5f04d", "size": 2129, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/RQ3/rCode.r", "max_stars_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_stars_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/RQ3/rCode.r", "max_issues_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_issues_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/RQ3/rCode.r", "max_forks_repo_name": "ProdigyXable/UnifiedDebuggingReplicationData", "max_forks_repo_head_hexsha": "838eb19abda1229be844f236114d5c596b9ec14c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5694444444, "max_line_length": 108, "alphanum_fraction": 0.6505401597, "num_tokens": 676, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.31683087788656966}} {"text": "\n# install.packages(\"tseries\")\nlibrary(tseries)\nlibrary(fGarch)\nlibrary(FitARMA)\nlibrary(zoo)\n\nlibrary(stringr)\n\nlibrary(quantmod)\n\nlibrary(tools)\nlibrary(brew)\n\nlibrary(forecast)\n\nlibrary(MSwM)\n\n# This is to concatate a list of list.\n# Total depth should be 2.\npaste0_rec<-function(text_l,sep1){\n res<-\"\"\n for (i in text_l){\n tmp<-paste0(i,collapse=sep1)\n res<-paste0(res,tmp)\n }\n return(res)\n}\n\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\n# Path management for pdf output\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\n# Store in a data.frame\n# path to work.\n# TODO : check if there is like in python a os.path.join\n# Because depending on the OS the \"/\" as a string may not be robust\nconf_add_output_subdir<-function(conf,subdir){\n output_dir<-attr(conf,\"output\")\n basename<-paste0(output_dir,\"/\",subdir)\n attr(conf,subdir)<-basename\n if (! dir.exists(basename)){\n dir.create(basename)\n }\n return(basename)\n}\nconf_make<-function(working_dir=\"./\"){\n output_dir<-paste0(working_dir,\"/output\",collapse=\"\")\n if (! dir.exists(output_dir)){\n dir.create(output_dir)\n }\n conf<-structure(\n data.frame(),\n working_dir=working_dir,\n output=output_dir\n )\n return(conf)\n}\n\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\n# Download quotation\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\n# Need an internet connection\n\n\n# Currently used to retrieve data from the net.\ndownload_quote<-function(name,output_file){\n start=\"1899-12-30\"\n end=\"2017-01-01\"\n quote<-get.hist.quote(instrument = name, start, end,\n quote = c(\"Open\", \"High\", \"Low\", \"Close\"),\n provider = c(\"yahoo\",\"\"), method = NULL,\n origin = \"1899-12-30\", compression = \"d\",\n retclass = c(\"zoo\", \"its\", \"ts\"), quiet = FALSE, drop = FALSE)\n write.zoo(quote,output_file,index.name=\"Date\",sep=\",\")\n}\n\n\nquote_dl<-function(conf,symbol,sourcef){\n # Examples :\n # get_quote(\"DEXUSEU\", \"FRED\")\n basename_dir<-conf_add_output_subdir(conf,symbol)\n #\n csvfile<-paste0(basename_dir,\"/\",symbol,\".csv\")\n #\n res<-getSymbols(symbol,src=sourcef)\n quote<-get(res)\n write.zoo(quote,csvfile,index.name=\"Date\")\n #quote<-read.csv(basename)\n #quote<-read.zoo(basename)\n # retrieve Close, quote$quote.Close\n attr(quote,\"symbol\")<-symbol\n return(quote)\n}\n\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\n# Quotation\n#-----------------------------------------------------------------------------------\n#-----------------------------------------------------------------------------------\nquote_close<-function(quote){\n src<-attr(quote,\"src\")\n v<-quote\n if(src==\"yahoo\"){\n v<-Cl(quote)\n }\n v<-na.omit(v)\n return(v)\n}\nquote_logret<-function(quote){\n src<-attr(quote,\"src\")\n v<-quote\n if(src==\"yahoo\"){\n v<-Cl(quote)\n }\n v<-na.omit(v)\n return(diff(log(v)))\n}\n\nquote_plot_value<-function(conf,seriets){\n serie<-na.omit(seriets)\n #\n pdf(paste0(basename,\".pdf\",collapse=\"\"))\n plot(serie,type=\"l\")\n dev.off()\n pdf(paste0(basename,\"_acf.pdf\",collapse=\"\"))\n acf(serie)\n dev.off()\n pdf(paste0(basename,\"_pacf.pdf\",collapse=\"\"))\n pacf(serie)\n dev.off()\n}\n\nquote_plot<-function(conf,quote){\n symbol<-attr(quote,\"symbol\")\n output_dir<-conf_add_output_subdir(conf,symbol)\n basename<-paste0(output_dir,\"/\",symbol,collapse=\"\")\n #\n quote_plot_value(basename,quote_close(quote))\n quote_plot_value(paste0(basename,\"_logret\"),quote_logret(quote))\n return(basename)\n}\n\n\n# Save various charts (into pdf) on the Time Serie given\n# Original value, ACF and PACF\nretrieve_data_plot<-function(basename,serie){\n pdf(paste0(basename,\".pdf\",collapse=\"\"))\n plot(serie,value,type=\"l\")\n dev.off()\n pdf(paste0(basename,\"_acf.pdf\",collapse=\"\"))\n acf(serie)\n dev.off()\n pdf(paste0(basename,\"_pacf.pdf\",collapse=\"\"))\n pacf(serie)\n dev.off()\n}\n\n# Retrieve time serie from its symbol : \"quote_name\"\nretrieve_data<-function(conf,quote_name,source_name){\n input_dir = attr(conf,\"input_dir\")\n output_dir = attr(conf,\"output_dir\")\n input_csv<-paste0(input_dir,\"/\",quote_name,\".csv\",collapse=\"\",sep=\"\")\n # Download quote if not yet on the disk\n if (! file.exists(input_csv)){\n #download_quote_quantmod(quote_name,source_name) \n download_quote(quote_name,input_dir)\n }\n basename<-paste0(output_dir,\"/\",quote_name,collapse=\"\")\n ###\n data<-read.csv(input_csv)\n #\n date<-as.Date(data$Date)\n quote<-data$Close #data[paste0(quote_name,\".Close\",collapse=\"\")][,]\n retrieve_data_plot(paste0(basename,\"_original\"),date,quote)\n quote_log<-log(quote)\n retrieve_data_plot(paste0(basename,\"_log\"),date,quote_log)\n ret<-c(0,diff(log(quote)))\n retrieve_data_plot(paste0(basename,\"_logret\"),date,ret)\n ret2<-c(0,diff(log(quote)))^2\n retrieve_data_plot(paste0(basename,\"_logret2\"),date,ret2)\n #\n res<- structure(data.frame(date,quote,quote_log,ret,ret2,quote_name),basename=basename)\n return(res)\n}\n\n\n\n\nresolve_garch<-function(basename,quote){\n value<-na.omit(quote_logret(quote))\n\n}\n\nresolve_ar1<-function(val,model){\n r<-arima(val,model)\n # Look if residual are white noises\n plot(qqnorm(r$residuals))\n}\n\n# model should be a vector of size 3 :\n# ARIMA(p,d,q).\n# model <- \n# c(1,0,0) ~ AR(1)\n# c(0,0,1) ~ MA(1)\nplot_arima<-function(value,model,output_dir){\n # \n # ARMA(p,q)\n # y(t) = Beta_1\n # + \\sum_i=1^p \\alpha_i y(t-i) % AR\n # + \\sum_i=1^q \\theta_i e(t-i) % MA\n # + e(t)\n # with e(t) ~ N(0,sigma^2)\n pdf(paste0(output_dir,\"/fit_arima\",model,sep=\"\"))\n fit<-arima(value,order=model)\n fitt<-tsdiag(fit)\n plot(fitt,xlim=c(-1000,1000),ylim=c(-1000,1000))\n dev.off()\n}\n\nresolve_arima_manual<-function(conf,basename,value){\n p<-pacf(value)\n a<-acf(value)\n}\n\n\n\n\nplot_garch<-function(value,model,outputname){\n pdf(paste0(\"./output/\",outputname,sep=\"\"))\n # Note : remove the first element which was 0\n fit<-garch(value,order=model)\n tsdiag(fit)\n dev.off()\n}\n\n#------------------------------------------------------\n# Solver\n#------------------------------------------------------\n\nsolve_ar<-function(basename,value){\n res<-ar(value)\n\n sink(paste0(basename,\"_ar_solving.txt\"))\n print(res)\n sink()\n\n pdf(paste0(basename,\"_ar_solving.pdf\"))\n plot(res$aic,type=\"l\")\n abline(h=0)\n grid()\n dev.off()\n pdf(paste0(basename,\"_ar_param.pdf\"))\n plot(res$ar,type=\"h\")\n abline(h=0)\n grid()\n dev.off()\n\n test_normal_law(paste0(basename,\"_ar_residual\"),na.omit(res$resid))\n #pdf(paste0(basename,\"_ar_residual.pdf\"))\n #plot(res$resid,type=\"h\")\n #grid()\n #dev.off()\n #pdf(paste0(basename,\"_ar_residual_qqnorm.pdf\"))\n #points(qqnorm(res$resid))\n #qqline(res$resid)\n #dev.off()\n}\n\nsolve_ma<-function(basename,value){\n res<-auto.arima(value, max.p=0, stationary=TRUE, seasonal=FALSE)\n sink(paste0(basename,\"_ma_solving.txt\"))\n print(res)\n sink()\n pdf(paste0(basename,\"_ma_param.pdf\"))\n plot(res$ar,type=\"h\")\n abline(h=0)\n grid()\n dev.off()\n test_normal_law(paste0(basename,\"_ma_residual\"),na.omit(res$residuals))\n}\n\n\nsolve_arma<-function(basename,value){\n res<-auto.arima(value, stationary=TRUE, seasonal=FALSE)\n sink(paste0(basename,\"_arma_solving.txt\"))\n print(res)\n sink()\n pdf(paste0(basename,\"_arma_param.pdf\"))\n plot(res$ar,type=\"h\")\n abline(h=0)\n grid()\n dev.off()\n test_normal_law(paste0(basename,\"_arma_residual\"),na.omit(res$residuals))\n}\n\nsolve_msm<-function(basename,value){\n #\n mod<-lm(value~1)\n mod.mswm<-msmFit(mod,k=2,p=1,sw=c(T,T,T), control=list(parallel=F)) \n #\n sink(paste0(basename,\"_msm_solving.txt\"))\n print(mod.mswm)\n sink()\n #\n pdf(paste0(basename,\"_msm_which1.pdf\"))\n plotProb(mod.mswm,which=1)\n dev.off()\n\n pdf(paste0(basename,\"_msm_which2.pdf\"))\n plotProb(mod.mswm,which=2)\n dev.off()\n}\n\n\n\nsolve_all<-function(basename,serie_input){\n pdf(paste0(basename,\".pdf\"))\n plot(serie_input,type=\"l\")\n dev.off()\n serie<-na.omit(serie_input)\n solve_ar(basename,serie)\n solve_ma(basename,serie)\n solve_arma(basename,serie)\n solve_msm(basename,serie)\n}\nsolve_quote<-function(conf,quote){\n #\n symbol<-attr(quote,\"symbol\")\n output_dir<-conf_add_output_subdir(conf,symbol)\n basename<-paste0(output_dir,\"/\",symbol)\n #\n close_level<-quote_close(quote)\n #solve_all(paste0(basename,\"_original\"),close_level)\n # Logret\n logret<-quote_logret(quote)\n #solve_all(paste0(basename,\"_logret\"),logret)\n\n return(basename)\n}\n\n#------------------------------------------------------\n# Examples\n#------------------------------------------------------\n# In order to better understand\n# what looks like AR(1) and MA(1)\n# and its relative ACF and PACF\n# or more generally ARMA(p,q)\n\n#\nar_acf<-function(conf,beta){\n example_dir<-conf_add_output_subdir(conf,\"example_ar\")\n basename<- paste0(example_dir,\"/example_ar1_\",beta,\".pdf\",collapse=\"\",sep=\"\")\n pdf(basename)\n c<-numeric(30)\n gamma<-1\n for (i in index(c)){\n c[i]<-gamma\n gamma<-gamma*beta\n }\n plot(c,type=\"h\")\n abline(h=0)\n grid()\n dev.off()\n return(basename)\n}\n\n# functions to retrieve a path or a string from the model\n# It simplifies the brew template\narima_name<-function(model){\n n=model$n\n ar_l=model$ar\n ma_l=model$ma\n #\n if (is.null(ar_l)){\n name<-paste0_rec(list(\"MA(\",ma_l,\") (n=\",n,\")\"),sep1=\",\")\n } else if (is.null(ma_l)){\n name<-paste0_rec(list(\"AR(\",ar_l,\") (n=\",n,\")\"),sep1=\",\")\n } else {\n name<-paste0_rec(list(\"AR(\",ar_l,\"), MA(\",ma_l,\") (n=\",n,\")\"),sep1=\",\")\n }\n return(name)\n}\narima_file<-function(conf,model){\n example_dir<-conf_add_output_subdir(conf,\"example_arima\")\n index<-arima_name(model)\n #for (c in c(\"\\\\(\",\"\\\\)\",\"=\",\"_ _\",\"__\",\"\\\\.\",\"\\\\,\",\"__\",\"__\")){\n # index<-str_replace_all(index,c[1],\"_\")\n #}\n basename<- paste0(example_dir,\"/example_\",index,collapse=\"\",sep=\"\")\n return(basename)\n}\n\narima_model_name<-function(model){\n ar_l=model$ar\n ma_l=model$ma\n #\n if (is.null(ar_l)){\n name<-paste0(\"MA(\",length(ma_l),\")\",collapse=\"\")\n } else if (is.null(ma_l)){\n name<-paste0(\"AR(\",length(ar_l),\")\",collapse=\"\")\n } else {\n name<-paste0(\"ARMA(\",length(ar_l),\",\",length(ma_l),\")\",collapse=\"\")\n }\n return(name)\n}\n\n# Simulate ARMA model, and save pdf into the example directory\nexample_arima<-function(conf,model){\n ar_l=model$ar\n ma_l=model$ma\n n=model$n\n # Looping for a new id\n basename<-arima_file(conf,model)\n #\n if (length(ar_l) < 1){\n s<-arima.sim(list(ma=ma_l),n)\n } else if (length(ma_l)<1) {\n s<-arima.sim(list(ar=ar_l),n)\n } else {\n s<-arima.sim(list(ar=ar_l,ma=ma_l),n)\n }\n #\n pdf(paste0(basename,\"_sim.pdf\",collapse=\"\"))\n plot(s)\n dev.off()\n pdf(paste0(basename,\"_acf.pdf\",collapse=\"\"))\n acf(s)\n dev.off()\n pdf(paste0(basename,\"_pacf.pdf\",collapse=\"\"))\n pacf(s)\n dev.off()\n \n solve_arma(basename,s)\n return(s)\n}\n\n\nfile_from_normal<-function(conf,n,mean,std){\n example_dir<-conf_add_output_subdir(conf,\"example_normal\")\n basename<-paste0(example_dir,\"/normal_\",n,\"_\",mean,\"_\",std)\n return(basename)\n}\n\ntest_normal_law<-function(basename,res){\n pdf(paste0(basename,\".pdf\",collapse=\"\"))\n plot(res,type=\"l\")\n dev.off()\n #\n pdf(paste0(basename,\"_qqnorm.pdf\",collapse=\"\"))\n points(qqnorm(res))\n qqline(res)\n dev.off()\n #\n pdf(paste0(basename,\"_density.pdf\",collapse=\"\"))\n #xseq<-seq(-std*10+mean,std*10+mean,.01)\n #densities<-dnorm(xseq, mean,std)\n #plot(x=xseq,y=densities,type=\"l\",col=\"green\")\n plot(density(res))\n dev.off()\n # Independance test\n sink(paste0(basename,\"_box_test_ljung.txt\"))\n print(Box.test(res,type='Ljung'))\n sink()\n sink(paste0(basename,\"_shapiro.txt\"))\n print(shapiro.test(res))\n sink()\n}\n\nexample_normal<-function(conf,n,mean,std){\n basename<-file_from_normal(conf,n,mean,std)\n res<-rnorm(n,mean,std)\n test_normal_law(basename,res)\n return(basename)\n}\n\nfile_from_uniform<-function(conf,n,a,b){\n example_dir<-conf_add_output_subdir(conf,\"example_uniform\")\n basename<-paste0(example_dir,\"/uniform_\",n,\"_\",a,\"_\",b)\n return(basename)\n}\nexample_uniform<-function(conf,n,a,b){\n basename<-file_from_uniform(conf,n,a,b)\n res<-runif(n,a,b)\n test_normal_law(basename,res)\n return(basename)\n}\n\n\n#-------------------------------------------------------\n#\n# Main function\n#\n\nmain_report<-function(working_dir=\"./\",doLatex=TRUE)\n{\n conf<-conf_make(working_dir)\n report_dir<-conf_add_output_subdir(conf,\"report\")\n brewfile<-paste0(working_dir,\"/template.brew\",collapse=\"\")\n reportfile<-paste0(report_dir,\"/generated_report.tex\",collapse=\"\")\n brew(brewfile, reportfile)\n #texi2dvi(reportfile,pdf=TRUE)\n if(doLatex){\n texi2pdf(reportfile,clean=TRUE)\n }\n}\n\nif(!interactive()){\n working_dir<-\"./\"\n #\n args = commandArgs(trailingOnly=TRUE)\n if (length(args)>0) {\n # Not yet, because texi2pdf seems to be launch from the PWD (of the terminal)\n #working_dir<-paste0(args[1],\"/\",collapse=\"\")\n }\n \n main_report(working_dir)\n} else {\n working_dir<-\"./\"\n main_report(working_dir,doLatex=FALSE)\n\n}\n\n", "meta": {"hexsha": "a2cf45c6f8db162e0e26d1560d2866dc645818e9", "size": 14083, "ext": "r", "lang": "R", "max_stars_repo_path": "script.r", "max_stars_repo_name": "arabm/multifractal_model", "max_stars_repo_head_hexsha": "4c03c7a3e5120f2e668557796f702dfcdd0132a4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "script.r", "max_issues_repo_name": "arabm/multifractal_model", "max_issues_repo_head_hexsha": "4c03c7a3e5120f2e668557796f702dfcdd0132a4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "script.r", "max_forks_repo_name": "arabm/multifractal_model", "max_forks_repo_head_hexsha": "4c03c7a3e5120f2e668557796f702dfcdd0132a4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.7229601518, "max_line_length": 91, "alphanum_fraction": 0.5770787474, "num_tokens": 3836, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.31683087788656966}} {"text": "library(ggplot2)\nlibrary(ggforce)\nlibrary(Matrix)\nlibrary(methods)\nlibrary(scales) \n#library(igraph)\nlibrary(RColorBrewer)\n#options(stringsAsFactors=FALSE)\n\nargs=commandArgs(T);\nsample=as.character(args[1]);\nworkingDir = \"./\";\nsetwd(workingDir);\n\n\nscore1 <- read.table(paste(\"temp_out/s9_\",sample,\"_score.txt\",sep=\"\"))\ncolor_list<-hue_pal()(2)\n\ndf1<-data.frame(score=score1[,3],coordx=score1[,1],coordy=score1[,2])\ndf1<-df1[with(df1,order(score)),]\n\npl<-ggplot()+\ngeom_point(data=df1,aes(x=coordx,y=coordy,colour=score),size=2,alpha=0.7)+\nannotate(geom = 'text', label = sample, x = -Inf, y = Inf, hjust = -0, vjust = 1.1,size=9)+\n#guides(fill=guide_legend(title=\"abundance\"),title.position = \"center\")+\n#guides(colour=guide_legend(title=\"abundance\"),title.position = \"center\")+\n#guides(colour=FALSE)+\n\nlabs(x=\"Component 1\",y=\"Component 2\")+theme_bw()+\n\n\ntheme(\n\n axis.text.x=element_blank(),\n axis.text.y=element_blank(),\n axis.ticks.x=element_blank(),\n axis.ticks.y=element_blank(),\n axis.title.x=element_blank(),\n axis.title.y=element_blank(),\n\tlegend.text=element_text(size=12), \n\tlegend.title=element_text(size=12), \n\t#legend.position=\"top\",\n # legend.position=\"none\",\n\n # plot.title = element_text(hjust = 0.5),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.background = element_blank())\n#pl<-pl+scale_colour_gradient(name=\"score\",low = \"grey\", high = \"red\")\n#pl<-pl+scale_colour_gradientn(limits = c(0,1),colours=c(\"lightblue\",\"blue\",\"red\"),breaks=c(0.2,0.4,0.6,0.8))\npl<-pl+scale_colour_gradientn(name=\"abundance\",limits = c(0,1),colours=c(\"grey\",\"red\"),breaks=c(0.2,0.4,0.6,0.8))\npl<-pl+scale_fill_discrete(guide=\"none\")\n\n\npdf(paste(\"result/projection_\",sample,\".pdf\",sep=\"\"),width=6,height=5)\npl\ndev.off()\n\n", "meta": {"hexsha": "7190dcc45ad5dd9855da4c71dc131af523e29224", "size": 1816, "ext": "r", "lang": "R", "max_stars_repo_path": "script/s11_plot_on_skeleton.r", "max_stars_repo_name": "pengfeeei/cci", "max_stars_repo_head_hexsha": "9f3415015f0f980af29040244a3c01676350f0a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-02-29T03:42:04.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-18T11:28:37.000Z", "max_issues_repo_path": "script/s11_plot_on_skeleton.r", "max_issues_repo_name": "pengfeeei/cci", "max_issues_repo_head_hexsha": "9f3415015f0f980af29040244a3c01676350f0a2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-09T11:49:14.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-09T11:49:14.000Z", "max_forks_repo_path": "script/s11_plot_on_skeleton.r", "max_forks_repo_name": "pengfeeei/cci", "max_forks_repo_head_hexsha": "9f3415015f0f980af29040244a3c01676350f0a2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-01-20T08:34:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-06T13:16:06.000Z", "avg_line_length": 30.7796610169, "max_line_length": 113, "alphanum_fraction": 0.6855726872, "num_tokens": 565, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3168308778865696}} {"text": "#clearing the workspace\nrm(list=ls())\ngraphics.off()\noptions(show.error.locations = TRUE)\n\n# If we are in a stand alone ubiquity distribution we run \n# from there otherwise we try to load the package\nif(file.exists(file.path('library', 'r_general', 'ubiquity.R'))){\n source(file.path('library', 'r_general', 'ubiquity.R'))\n} else { \n library(ubiquity) }\n\nanalysis_name = 'parent_metabolite';\n# flowctl = 'previous estimate as guess';\n# flowctl = 'plot guess';\n# flowctl = 'plot previous estimate';\n flowctl = 'estimate';\narchive_results = TRUE\n\n# For documentation explaining how to modify the commands below\n# See the \"R Workflow\" section at the link below:\n# http://presentation.ubiquity.grok.tv\n\n# Rebuilding the system (R scripts and compiling C code)\ncfg = build_system(output_directory = file.path(\".\", \"output\"),\n temporary_directory = file.path(\".\", \"transient\"))\n\n# set name | Description\n# -------------------------------------------------------\n# default | Original Estimates\n\n\n# The following will estimate a subset of the parameters:\npnames = c('Vp',\n 'Vt',\n 'Vm',\n 'CLp',\n 'CLm',\n 'Q',\n 'slope_parent',\n 'slope_metabolite');\n\ncfg = system_select_set(cfg, \"default\", pnames)\n\n\n# Specify the output times used for smooth profiles\ncfg=system_set_option(cfg, group = \"simulation\", \n option = \"output_times\", \n seq(0,100,1))\n\n# Loading Datasets\n#\ncfg = system_load_data(cfg, dsname = \"pm_data\", \n data_file = \"pm_data.csv\")\n\n\n# Defining the cohorts\n#\n# Clearing all of the cohorts\ncfg = system_clear_cohorts(cfg);\n \n# One entry for each cohort:\n# For more information type:\n#\n# help system_define_cohort\n#\n# It is necessary to replace the following compontents:\n#\n# CHNAME - cohort name\n# COLNAME - column name in dataset\n# ONAME - output name\n# TIMECOL - column name in dataset with the observation times\n# TS - model timescale corresponding to TIMECOL\n# OBSCOL - column name in dataset with the observation values\n# MODOUTPUT - model output corresponding to OBSCOL\n#\n# Only specify bolus and infusion inputs that are non-zero. Simply ignore\n# those that don't exist for the given cohort. Covariates should be specified\n# to overwrite the default covariate values\n\n#----------------------------------------------------------\n# 10 mpk cohort\ncohort = list(\n name = \"dose_10\",\n cf = list(DOSE = c(10)),\n inputs = NULL,\n outputs = NULL,\n dataset = \"pm_data\")\n\n\n# Bolus inputs for the cohort\ncohort[[\"inputs\"]][[\"bolus\"]] = list()\ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]] = list(TIME=NULL, AMT=NULL)\ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]][[\"TIME\"]] = c( 0) # hours \ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]][[\"AMT\"]] = c(10) # mpk \n\n\n# Defining Parent output\ncohort[[\"outputs\"]][[\"Parent\"]] = list()\n\n# Mapping to data set\ncohort[[\"outputs\"]][[\"Parent\"]][[\"obs\"]] = list(\n time = \"TIME\",\n value = \"PT\",\n missing = -1)\n\n# Mapping to system file\ncohort[[\"outputs\"]][[\"Parent\"]][[\"model\"]] = list(\n time = \"hours\", \n value = \"Cpblood\", \n variance = \"slope_parent*PRED^2\")\n\n# Plot formatting\ncohort[[\"outputs\"]][[\"Parent\"]][[\"options\"]] = list(\n marker_color = \"black\",\n marker_shape = 1,\n marker_line = 1 )\n\n# Defining Metabolite output\ncohort[[\"outputs\"]][[\"Metabolite\"]] = list()\n\n# Mapping to data set\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"obs\"]] = list(\n time = \"TIME\",\n value = \"MT\",\n missing = -1)\n\n# Mapping to system file\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"model\"]] = list(\n time = \"hours\", \n value = \"Cmblood\", \n variance = \"slope_metabolite*PRED^2\")\n\n# Plot formatting\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"options\"]] = list(\n marker_color = \"blue\",\n marker_shape = 1,\n marker_line = 1 )\n\ncfg = system_define_cohort(cfg, cohort)\n#----------------------------------------------------------\n# 30 mpk cohort\ncohort = list(\n name = \"dose_30\",\n cf = list(DOSE = c(30)),\n inputs = NULL,\n outputs = NULL,\n dataset = \"pm_data\")\n\n\n# Bolus inputs for the cohort\ncohort[[\"inputs\"]][[\"bolus\"]] = list()\ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]] = list(TIME=NULL, AMT=NULL)\ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]][[\"TIME\"]] = c( 0) # hours \ncohort[[\"inputs\"]][[\"bolus\"]][[\"Mpb\"]][[\"AMT\"]] = c(30) # mpk \n\n\n# Defining Parent output\ncohort[[\"outputs\"]][[\"Parent\"]] = list()\n\n# Mapping to data set\ncohort[[\"outputs\"]][[\"Parent\"]][[\"obs\"]] = list(\n time = \"TIME\",\n value = \"PT\",\n missing = -1)\n\n# Mapping to system file\ncohort[[\"outputs\"]][[\"Parent\"]][[\"model\"]] = list(\n time = \"hours\", \n value = \"Cpblood\", \n variance = \"slope_parent*PRED^2\")\n\n# Plot formatting\ncohort[[\"outputs\"]][[\"Parent\"]][[\"options\"]] = list(\n marker_color = \"green\",\n marker_shape = 1,\n marker_line = 1 )\n\n# Defining Metabolite output\ncohort[[\"outputs\"]][[\"Metabolite\"]] = list()\n\n# Mapping to data set\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"obs\"]] = list(\n time = \"TIME\",\n value = \"MT\",\n missing = -1)\n\n# Mapping to system file\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"model\"]] = list(\n time = \"hours\", \n value = \"Cmblood\", \n variance = \"slope_metabolite*PRED^2\")\n\n# Plot formatting\ncohort[[\"outputs\"]][[\"Metabolite\"]][[\"options\"]] = list(\n marker_color = \"red\",\n marker_shape = 1,\n marker_line = 1 )\n\ncfg = system_define_cohort(cfg, cohort)\n\n#----------------------------------------------------------\n# performing estimation or loading guess/previous results\npest = system_estimate_parameters(cfg, \n flowctl = flowctl, \n analysis_name = analysis_name, \n archive_results = archive_results)\n\n\n# Simulating the system at the estimates\nerp = system_simulate_estimation_results(pest = pest, cfg = cfg) \n\nplot_opts = c()\n\nplot_opts$outputs$Metabolite$yscale = 'log' \nplot_opts$outputs$Metabolite$ylabel = 'Metabolite'\nplot_opts$outputs$Metabolite$ylim = c(1, 100)\nplot_opts$outputs$Metabolite$xlabel = 'Time (hours)'\n\nplot_opts$outputs$Parent$yscale = 'log'\nplot_opts$outputs$Parent$ylabel = 'Parent'\nplot_opts$outputs$Parent$xlabel = 'Time (hours)'\n\n\n# Plotting the simulated results at the estimates \n# These figures will be placed in output/\nsystem_plot_cohorts(erp, plot_opts, cfg, analysis_name=analysis_name)\n#-------------------------------------------------------\n# Writing the results to a PowerPoint report\n cfg = system_rpt_read_template(cfg, template=\"PowerPoint\")\n cfg = system_rpt_estimation(cfg=cfg, analysis_name=analysis_name)\n system_rpt_save_report(cfg=cfg, output_file=file.path(\"output\",paste(analysis_name, \"-report.pptx\", sep=\"\")))\n#-------------------------------------------------------\n# Writing the results to a Word report\n cfg = system_rpt_read_template(cfg, template=\"Word\")\n cfg = system_rpt_estimation(cfg=cfg, analysis_name=analysis_name)\n system_rpt_save_report(cfg=cfg, output_file=file.path(\"output\",paste(analysis_name, \"-report.docx\", sep=\"\")))\n#-------------------------------------------------------\n\n", "meta": {"hexsha": "1c00e578d68034b5400d9a27036aa464c15e6549", "size": 7726, "ext": "r", "lang": "R", "max_stars_repo_path": "ubiquity_template/examples/R/analysis_parent_metabolite.r", "max_stars_repo_name": "john-harrold/ubiquity-pkpd", "max_stars_repo_head_hexsha": "a320e0261bb3792cafdd9b212e52acdc7911f3ac", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-08-13T00:51:11.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-10T21:15:00.000Z", "max_issues_repo_path": "ubiquity_template/examples/R/analysis_parent_metabolite.r", "max_issues_repo_name": "john-harrold/ubiquity-pkpd", "max_issues_repo_head_hexsha": "a320e0261bb3792cafdd9b212e52acdc7911f3ac", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ubiquity_template/examples/R/analysis_parent_metabolite.r", "max_forks_repo_name": "john-harrold/ubiquity-pkpd", "max_forks_repo_head_hexsha": "a320e0261bb3792cafdd9b212e52acdc7911f3ac", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2015-11-20T18:47:36.000Z", "max_forks_repo_forks_event_max_datetime": "2015-11-20T18:47:36.000Z", "avg_line_length": 32.1916666667, "max_line_length": 111, "alphanum_fraction": 0.5599275175, "num_tokens": 1965, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3168308778865696}} {"text": "###### including libraries in the environment\nlibrary(class)\nlibrary(kknn)\nlibrary(tree)", "meta": {"hexsha": "a754e9c50a9d7588aa428997424787951cd6e61e", "size": 88, "ext": "r", "lang": "R", "max_stars_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/7_Pacakge_Include.r", "max_stars_repo_name": "ParasGarg/Stevens-Computer-Science-Courses-Materials", "max_stars_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 25, "max_stars_repo_stars_event_min_datetime": "2017-03-23T04:51:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-03T21:51:11.000Z", "max_issues_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/7_Pacakge_Include.r", "max_issues_repo_name": "vaishnavimecit/Stevens-Computer-Science-Courses-Materials", "max_issues_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/7_Pacakge_Include.r", "max_forks_repo_name": "vaishnavimecit/Stevens-Computer-Science-Courses-Materials", "max_forks_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2018-05-10T05:17:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-12T05:18:58.000Z", "avg_line_length": 22.0, "max_line_length": 45, "alphanum_fraction": 0.7727272727, "num_tokens": 18, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.31683087788656955}} {"text": "#This is for the user interface of the unnamed NY Philharmonic project\nlibrary(shiny); library(shinythemes); library(visNetwork); library(shinyjs)\nyvec = read.csv(\"philyears.csv\", stringsAsFactors=F, header=F)[,1]\n\n\nshinyUI( fluidPage( \n\theaderPanel(strong(\"New York Philharmonic Coperformance Project\")),\n\tsidebarPanel(h4(strong(\"Data Selection\")),\n\t\tp(\"Coperformance: The ratio of the number programs two composers appear in \n\t\t\ttogether to the number of times they should appear together at random\"\n\t\t),\n\t\thr(),\n\t\tselectizeInput(\"FromSeason\", label=\"From\", choices=yvec, width=\"50%\", multiple=F, selected=\"1842-43\"),\n\t\tselectizeInput(\"ToSeason\", label=\"To\", choices=yvec, width=\"50%\", multiple=F, selected=\"2017-18\"),\n\t\tnumericInput(\"MinPerf\", label=\"Minimum Performances\", min=1,max=1000,step=1,value=400),\n\t\tactionButton(\"makePlot\", \"Plot Network\"),\n\t\thr(),\n\t\th4(strong(\"Download Data\")),\n\t\tdownloadButton(\"djson\", label=\"Download json\"),\n\t\tdownloadButton(\"dcoperf\", label=\"Download matrix\")\n\t),\n\tmainPanel(\n\t\ttextOutput(\"ResMessage\"),\n\t\tverbatimTextOutput(\"ResPrint\"),\n\t\ttextOutput(\"NetMessage\"),\n\t\tvisNetworkOutput(\"network\", height=700)\n\t),\n\ttitle=\"NYPhil_SNA\", theme=shinytheme(\"yeti\"), useShinyjs()\n))\n", "meta": {"hexsha": "192c42893ad22a5794038eb54b0bdca62c83fb01", "size": 1212, "ext": "r", "lang": "R", "max_stars_repo_path": "ui.r", "max_stars_repo_name": "EMurray16/coperformance", "max_stars_repo_head_hexsha": "bdcea2d6957706073e971c92fb229e35caae1051", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-12-04T17:18:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-06T21:02:20.000Z", "max_issues_repo_path": "ui.r", "max_issues_repo_name": "EMurray16/coperformance", "max_issues_repo_head_hexsha": "bdcea2d6957706073e971c92fb229e35caae1051", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-12-20T21:16:37.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-27T13:39:23.000Z", "max_forks_repo_path": "ui.r", "max_forks_repo_name": "EMurray16/coperformance", "max_forks_repo_head_hexsha": "bdcea2d6957706073e971c92fb229e35caae1051", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-12-20T21:09:59.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-20T21:16:59.000Z", "avg_line_length": 40.4, "max_line_length": 104, "alphanum_fraction": 0.7293729373, "num_tokens": 332, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3168308702653742}} {"text": "###############################################################################\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n###############################################################################\n# Evaluting Sample Trading Strategies using Backtesting library in \n# the Systematic Investor Toolbox\n# Copyright (C) 2011 Michael Kapler\n#\n# For more information please visit my blog at www.SystematicInvestor.wordpress.com\n# or drop me a line at TheSystematicInvestor at gmail\n###############################################################################\n\nbt.empty.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\n\tbt.prep(data, align='keep.all', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 0\n\tbuy.hold = bt.run(data, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t\n\tplotbt.custom.report.part1( buy.hold, trade.summary =T)\n\tplotbt.custom.report.part2( buy.hold, trade.summary =T)\n\tplotbt.custom.report.part3( buy.hold, trade.summary =T)\n\n}\n\n###############################################################################\n# How to use execution.price functionality\n###############################################################################\nbt.execution.price.test <- function() \n{\t \n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\n\tbt.prep(data, align='keep.all', dates='1970::')\t\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tnperiods = nrow(prices)\n\t\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Buy & Hold\n\t#****************************************************************** \n\tdata$weight[] = 0\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[] = 1\n\tmodels$buy.hold = bt.run.share(data, clean.signal=T)\n\n\t#*****************************************************************\n\t# MA cross-over strategy\n\t#****************************************************************** \n\tsma.fast = SMA(prices, 50)\n\tsma.slow = SMA(prices, 200)\n\t\tsignal = iif(sma.fast >= sma.slow, 1, -1)\n\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[] = signal\n\tmodels$ma.crossover = bt.run.share(data, clean.signal=T, trade.summary = TRUE)\n\n\t#*****************************************************************\n\t# MA cross-over strategy, add 10c per share commission\n\t#*****************************************************************\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[] = signal\n\tmodels$ma.crossover.com = bt.run.share(data, commission = 0.1, clean.signal=T)\n\t\n\t#*****************************************************************\n\t# MA cross-over strategy:\n\t# Exit trades at the close on the day of the signal\n\t# Enter trades at the open the next day after the signal\t\n\t#****************************************************************** \n\tpopen = bt.apply(data, Op)\t\t\n\tsignal.new = signal\n\t\ttrade.start\t = which(signal != mlag(signal) & signal != 0)\n\t\tsignal.new[trade.start] = 0\n\t\ttrade.start = trade.start + 1\n\t\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$execution.price[trade.start,] = popen[trade.start,]\n\t\tdata$weight[] = signal.new\n\tmodels$ma.crossover.enter.next.open = bt.run.share(data, clean.signal=T, trade.summary = TRUE)\n\t\t\t\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t# put all reports into one pdf file\n\t#pdf(file = 'report.pdf', width=8.5, height=11)\n\t\tmodels = rev(models)\n\t\n\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\t\n\t\t# Plot perfromance\n\t\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\ndev.off()\t\t\t\t\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\t\t\n\t\t# Plot trades\n\t\tplotbt.custom.report.part3(models$ma.crossover, trade.summary = TRUE)\t\t\n\t\t\ndev.off()\t\t\t\t\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\n\t\tplotbt.custom.report.part3(models$ma.crossover.enter.next.open, trade.summary = TRUE)\t\t\n\t\t\ndev.off()\t\t\t\n\t#dev.off()\t\n\n\t\n\t\n\t\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t#*****************************************************************\n\t# Simple example showing the difference in a way commission is integrated into returns\n\t#****************************************************************** \t\n\tcommission = 4\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[201,] = 1\n\t\tdata$weight[316,] = 0\t\t\t\t\n\t\tdata$execution.price[201,] = prices[201,] + commission\n\t\tdata$execution.price[316,] = prices[316,] - commission\t\t\n\tmodels$test.com = bt.run.share(data, clean.signal=T, trade.summary=T)\n\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[201,] = 1\n\t\tdata$weight[316,] = 0\t\t\t\t\t\n\tmodels$test.com.new = bt.run.share(data, commission=commission, trade.summary=T, clean.signal=T)\n\n\tcbind(last(models$test.com$equity), last(models$test.com.new$equity),\n\t\tas.double(prices[316] - commission)/as.double(prices[201] + commission))\n\t\n\tas.double(prices[202]) / as.double(prices[201] + commission)-1\n\tmodels$test.com$equity[202]-1\n\t\n\tas.double(prices[202] - commission) / as.double(prices[201])-1\n\tmodels$test.com.new$equity[202]-1\n\t\n\t\n\t#plotbt.custom.report.part1(models)\n\t\n\t#*****************************************************************\n\t# Example showing the difference in a way commission is integrated into returns\n\t#****************************************************************** \t\t\n\tcommission = 0.1\n\tsma.fast = SMA(prices, 50)\n\tsma.slow = SMA(prices, 200)\n\n\tweight = iif(sma.fast >= sma.slow, 1, -1)\t\n\t\tweight[] = bt.exrem(weight)\n\t\tindex = which(!is.na(weight))\n\t\ttrade.start = index+1\t\t\n\t\ttrade.end = c(index[-1],nperiods)\n\t\ttrade.direction = sign(weight[index])\n\t\t\n\t\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\tdata$weight[] = weight\n\tmodels$test.com.new = bt.run.share(data, commission=commission, trade.summary=T, clean.signal=T)\n\t\n\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\n\t\t\n\t\tindex = which(trade.direction > 0)\n\t\tdata$execution.price[trade.start[index],] = prices[trade.start[index],] + commission\n\t\tdata$execution.price[trade.end[index],] = prices[trade.end[index],] - commission\n\t\t\n\t\tindex = which(trade.direction < 0)\n\t\tdata$execution.price[trade.start[index],] = prices[trade.start[index],] - commission\n\t\tdata$execution.price[trade.end[index],] = prices[trade.end[index],] + commission\n\t\t\n\t\tdata$weight[trade.start,] = trade.direction\n\t\tdata$weight[trade.end,] = 0\t\t\n\t\t\n\tmodels$test.com = bt.run.share(data, clean.signal=T, trade.summary=T)\n\n\t\t\n\t#plotbt.custom.report.part1(models)\n\t\n}\n\n\n###############################################################################\n# How to use commission functionality\n###############################################################################\nbt.commission.test <- function() \n{\t \n\t# cents / share commission\n \t# trade cost = abs(share - mlag(share)) * commission$cps\n\t# fixed commission per trade to more effectively to penalize for turnover\n \t# trade cost = sign(abs(share - mlag(share))) * commission$fixed\n\t# percentage commission\n\t# trade cost = price * abs(share - mlag(share)) * commission$percentage\n\t#\n\t# commission = list(cps = 0.0, fixed = 0.0, percentage = 0/100)\n\t# cps - cents per share i.e. cps = 1.5 is 1.5 cents per share commision\n\t# fixed - fixed cost i.e. fixed = $15 is $15 per trade irrelevant of number of shares\n\t# percentage - percentage cost i.e. percentage = 1/100 is 1% of trade value\t\n\t\n\t\t\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('EEM')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\n\tbt.prep(data, align='keep.all', dates='2013:08::2013:09')\t\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\n\tbuy.date = '2013:08:14'\t\n\tsell.date = '2013:08:15'\n\tday.after.sell.date = '2013:08:16'\t\t\n\t\n\tcapital = 100000\n\tprices = data$prices\n\tshare = as.double(capital / prices[buy.date])\n\t\n\t# helper function to compute trade return\n\tcomp.ret <- function(sell.trade.cost, buy.trade.cost) { round(100 * (as.double(sell.trade.cost) / as.double(buy.trade.cost) - 1), 2) }\n\t\n\t#*****************************************************************\n\t# Zero commission\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[buy.date] = 1\n\t\tdata$weight[sell.date] = 0\n\t\tcommission = 0.0\n\tmodel = bt.run.share(data, commission = commission, capital = capital, silent = T)\n\t\t\n\tcomp.ret( share * prices[sell.date], share * prices[buy.date] )\t\t\n\tcomp.ret( model$equity[day.after.sell.date], model$equity[buy.date] )\t\t\n\t\t\t\n\t#*****************************************************************\n\t# 10c cps commission\n\t# cents / share commission\n \t# trade cost = abs(share - mlag(share)) * commission$cps\t\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[buy.date] = 1\n\t\tdata$weight[sell.date] = 0\n\t\tcommission = 0.1\n\tmodel = bt.run.share(data, commission = commission, capital = capital, silent = T)\n\n\tcomp.ret( share * (prices[sell.date] - commission), share * (prices[buy.date] + commission) )\n\tcomp.ret( model$equity[day.after.sell.date], model$equity[buy.date] )\t\t\n\t\n\t#*****************************************************************\n\t# $5 fixed commission\n\t# fixed commission per trade to more effectively to penalize for turnover\n \t# trade cost = sign(abs(share - mlag(share))) * commission$fixed\t\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[buy.date] = 1\n\t\tdata$weight[sell.date] = 0\n\t\tcommission = list(cps = 0.0, fixed = 5.0, percentage = 0.0)\t\n\tmodel = bt.run.share(data, commission = commission, capital = capital, silent = T)\n\n\tcomp.ret( share * prices[sell.date] - commission$fixed, share * prices[buy.date] + commission$fixed )\n\tcomp.ret( model$equity[day.after.sell.date], model$equity[buy.date] )\t\t\n\t\n\t#*****************************************************************\n\t# % commission\n\t# percentage commission\n\t# trade cost = price * abs(share - mlag(share)) * commission$percentage\t\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[buy.date] = 1\n\t\tdata$weight[sell.date] = 0\n\t\tcommission = list(cps = 0.0, fixed = 0.0, percentage = 1/100)\t\n\tmodel = bt.run.share(data, commission = commission, capital = capital, silent = T)\n\n\tcomp.ret( share * prices[sell.date] * (1 - commission$percentage), share * prices[buy.date] * (1 + commission$percentage) )\n\tcomp.ret( model$equity[day.after.sell.date], model$equity[buy.date] )\t\t\n\n\treturn\n\t\n\t#*****************************************************************\n\t# Not Used\n\t#*****************************************************************\n#\tcomp.ret( as.double(share * prices[sell.date] - commission$fixed)*(share * prices[buy.date] -commission$fixed), share^2 * prices[buy.date]^2 )\t\n#\tas.double(share * prices[sell.date] - commission$fixed) / (share * prices[buy.date]) *\n#\tas.double(share * prices[buy.date] -commission$fixed) / (share * prices[buy.date]) - 1\n#\t\n\t# Say following is time-line 0, A, B, C, 1, 2\n\t# We open share position at 0 and close at 1\n\t# \n\t# Proper Logic\n\t# ret = (share * price1 - commission) / (share * price0 + commission)\t\n\t#\n\t# Current Logic\t\n\t# trade start: cash = price0 * share\n\t# retA = (share * priceA - commission) / (share * price0)\n\t# retB = (share * priceB) / (share * priceA)\n\t# retC = (share * priceC) / (share * priceB)\n\t# ret1 = (share * price1 - commission) / (share * priceC)\n\t# ret2 = (cash - commission) / (cash)\n\t# ret = retA * retB * retC * ret1 * ret2 - 1\n\t\n #*****************************************************************\n # Code Strategies \n\t#******************************************************************\t\t\n obj = portfolio.allocation.helper(data$prices, \n \tperiodicity = 'months', lookback.len = 60, \n \tmin.risk.fns = list(EW=equal.weight.portfolio)\n\t)\n\n commission = list(cps = 0.0, fixed = 0.0, percentage = 0/100) \n models = create.strategies(obj, data, capital = capital, commission = commission )$models\n \n ret = models$EW$ret\n\n commission = list(cps = 0.0, fixed = 0.0, percentage = 4/100) \n models = create.strategies(obj, data, capital = capital, commission = commission )$models\n \n ret = cbind(ret, models$EW$ret)\n \n round(100 * cbind(ret, ret[,1] - ret[,2]),2)\n write.xts(cbind(ret, ret[,1] - ret[,2]), 'diff.csv')\t\n\t\n \n \n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='remove.na', dates='1990::') \n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\t\t\t\t\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = 'months', lookback.len = 60, \n\t\tmin.risk.fns = list(\n\t\t\tEW=equal.weight.portfolio\n\t\t)\n\t)\n\t\n\tcapital = 100000\n\tcommission = list(cps = 0.0, fixed = 0.0, percentage = 0/100)\n\tmodels = create.strategies(obj, data, capital = capital, commission = commission )$models\n\t\t\t\t\n\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n\tstrategy.performance.snapshoot(models, T) \n}\t\n\t\n\t\t\t\n\n\n###############################################################################\n# Cross Pollination from Timely Portfolio\n# http://timelyportfolio.blogspot.ca/2011/08/drawdown-visualization.html\n# http://timelyportfolio.blogspot.ca/2011/08/lm-system-on-nikkei-with-new-chart.html\n###############################################################################\nbt.timelyportfolio.visualization.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='2000::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\tbuy.hold = bt.run(data)\t\n\t\n\t# Strategy\n\tma10 = bt.apply.matrix(prices, EMA, 10)\n\tma50 = bt.apply.matrix(prices, EMA, 50)\n\tma200 = bt.apply.matrix(prices, EMA, 200)\n\tdata$weight[] = NA;\n\t\tdata$weight[] = iif(ma10 > ma50 & ma50 > ma200, 1, \n\t\t\t\t\t\tiif(ma10 < ma50 & ma50 < ma200, -1, 0))\n\tstrategy = bt.run.share(data, clean.signal=F)\n\t\n\t\n\t#*****************************************************************\n\t# Visualization of system Entry and Exit based on\n\t# http://timelyportfolio.blogspot.ca/2011/08/lm-system-on-nikkei-with-new-chart.html\n\t#****************************************************************** \t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tlayout(1)\n\tplota(strategy$eq, type='l', ylim=range(buy.hold$eq,strategy$eq))\n\t\n\t\tcol = iif(strategy$weight > 0, 'green', iif(strategy$weight < 0, 'red', 'gray'))\n\tplota.lines(buy.hold$eq, type='l', col=col)\t\t\n\t\n\t\tplota.legend('strategy,Long,Short,Not Invested','black,green,red,gray')\n\t\t\ndev.off()\t\t\t\n\t#*****************************************************************\t\n\t# Drawdown Visualization \n\t# 10% drawdowns in yellow and 15% drawdowns in orange\n\t# http://timelyportfolio.blogspot.ca/2011/08/drawdown-visualization.html\n\t#*****************************************************************\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tlayout(1:2)\n\tdrawdowns = compute.drawdown(strategy$eq)\n\thighlight = drawdowns < -0.1\n\t\n\tplota.control$col.x.highlight = iif(drawdowns < -0.15, 'orange', iif(drawdowns < -0.1, 'yellow', 0))\t\n\t\n\tplota(strategy$eq, type='l', plotX=F, x.highlight = highlight, ylim=range(buy.hold$eq,strategy$eq))\n\t\tplota.legend('strategy,10% Drawdown,15% Drawdown','black,yellow,orange')\n\t\t\n\tplota(100*drawdowns, type='l', x.highlight = highlight)\n\t\tplota.legend('drawdown', 'black', x='bottomleft')\n\t\ndev.off()\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tplota.control$col.x.highlight = iif(drawdowns < -0.15, 'orange', iif(drawdowns < -0.1, 'yellow', 0))\t\t\t\t\n\thighlight = drawdowns < -0.1\n\n\tplotbt.custom.report.part1(strategy, buy.hold, x.highlight = highlight)\n\t\ndev.off()\t\n\t\t\n\t\n}\n\n\n###############################################################################\n# Improving Trend-Following Strategies With Counter-Trend Entries by david varadi\n# http://cssanalytics.wordpress.com/2011/07/29/improving-trend-following-strategies-with-counter-trend-entries/\n###############################################################################\nbt.improving.trend.following.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\tbuy.hold = bt.run(data)\t\n\n\t# Trend-Following strategy: Long[Close > SMA(10) ]\n\tsma = bt.apply(data, function(x) { SMA(Cl(x), 10) } )\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(prices >= sma, 1, 0)\n\ttrend.following = bt.run(data, trade.summary=T)\t\t\t\n\n\t# Trend-Following With Counter-Trend strategy: Long[Close > SMA(10), DVB(1) CounterTrend ]\n\tdv = bt.apply(data, function(x) { DV(HLC(x), 1, TRUE) } )\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(prices > sma & dv < 0.25, 1, data$weight)\n\t\tdata$weight[] = iif(prices < sma & dv > 0.75, 0, data$weight)\n\ttrend.following.dv1 = bt.run(data, trade.summary=T)\t\t\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(trend.following.dv1, trend.following, buy.hold)\ndev.off()\t\n\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(trend.following.dv1, trend.following, buy.hold)\ndev.off()\t\n\t\n\n\t#*****************************************************************\n\t# Sensitivity Analysis\n\t#****************************************************************** \n\tma.lens = seq(10, 100, by = 10)\n\tdv.lens = seq(1, 5, by = 1)\n\n\t# precompute indicators\n\tmas = matrix(double(), nrow(prices), len(ma.lens))\n\tdvs = matrix(double(), nrow(prices), len(dv.lens))\n\n\tfor(i in 1:len(ma.lens)) {\n\t\tma.len = ma.lens[i]\n\t\tmas[, i] = bt.apply(data, function(x) { SMA(Cl(x), ma.len) } )\n\t}\n\tfor(i in 1:len(dv.lens)) {\n\t\tdv.len = dv.lens[i]\n\t\tdvs[,i] = bt.apply(data, function(x) { DV(HLC(x), dv.len, TRUE) } )\n\t}\n\n\t# allocate matrixes to store backtest results\n\tdummy = matrix(double(), len(ma.lens), 1+len(dv.lens))\n\t\trownames(dummy) = paste('SMA', ma.lens)\n\t\tcolnames(dummy) = c('NO', paste('DV', dv.lens))\n\t\t\n\tout = list()\n\t\tout$Cagr = dummy\n\t\tout$Sharpe = dummy\n\t\tout$DVR = dummy\n\t\tout$MaxDD = dummy\n\t\n\t# evaluate strategies\n\tfor(ima in 1:len(ma.lens)) {\n\t\tsma = mas[, ima]\n\t\tcat('SMA =', ma.lens[ima], '\\n')\n\n\t\tfor(idv in 0:len(dv.lens)) {\t\t\t\n\t\t\tif( idv == 0 ) {\n\t\t\t\tdata$weight[] = NA\n\t\t\t\t\tdata$weight[] = iif(prices > sma, 1, 0)\t\t\t\n\t\t\t} else {\n\t\t\t\tdv = dvs[, idv]\n\t\t\t\t\n\t\t\t\tdata$weight[] = NA\n\t\t\t\t\tdata$weight[] = iif(prices > sma & dv < 0.25, 1, data$weight)\n\t\t\t\t\tdata$weight[] = iif(prices < sma & dv > 0.75, 0, data$weight)\n\t\t\t}\n\t\t\tstrategy = bt.run(data, silent=T)\t\t\t\n\t\t\t\n\t\t\t# add 1 to account for benchmark case, no counter-trend\n\t\t\tidv = idv + 1\n\t\t\tout$Cagr[ima, idv] = compute.cagr(strategy$equity)\n\t\t\tout$Sharpe[ima, idv] = compute.sharpe(strategy$ret)\n\t\t\tout$DVR[ima, idv] = compute.DVR(strategy)\n\t\t\tout$MaxDD[ima, idv] = compute.max.drawdown(strategy$equity)\n\t\t}\n\t}\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\npng(filename = 'plot3.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\n\tlayout(matrix(1:4,nrow=2))\t\n\tfor(i in names(out)) {\n\t\ttemp = out[[i]]\n\t\ttemp[] = plota.format( 100 * temp, 1, '', '' )\n\t\tplot.table(temp, smain = i, highlight = T, colorbar = F)\n\t}\n\t\ndev.off()\t\n\t\n}\t\n\t\n\t\n###############################################################################\n# Simple, Long-Term Indicator Near to Giving Short Signal By Woodshedder \n# http://ibankcoin.com/woodshedderblog/2011/08/28/simple-long-term-indicator-near-to-giving-short-signal/\n###############################################################################\nbt.roc.cross.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\tbuy.hold = bt.run(data)\t\n\n\t\n\t# Strategy: calculate the 5 day rate of change (ROC5) and the 252 day rate of change (ROC252).\n\t# Buy (or cover short) at the close if yesterday the ROC252 crossed above the ROC5 and today the ROC252 is still above the ROC5.\n\t# Sell (or open short) at the close if yesterday the ROC5 crossed above the ROC252 and today the ROC5 is still above the ROC252.\n\troc5 = prices / mlag(prices,5)\n\troc252 = prices / mlag(prices,252)\n\t\n\troc5.1 = mlag(roc5,1)\n\troc5.2 = mlag(roc5,2)\n\troc252.1 = mlag(roc252,1)\n\troc252.2 = mlag(roc252,2)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$SPY[] = iif(roc252.2 < roc5.2 & roc252.1 > roc5.1 & roc252 > roc5, 1, data$weight$SPY)\n\t\tdata$weight$SPY[] = iif(roc252.2 > roc5.2 & roc252.1 < roc5.1 & roc252 < roc5, -1, data$weight$SPY)\n\troc.cross = bt.run(data, trade.summary=T)\t\t\t\t\t\n \n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(roc.cross, buy.hold, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(roc.cross, buy.hold, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part3(roc.cross, buy.hold, trade.summary=T)\ndev.off()\t\n\n\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t\n\t# When shorting always use \ttype = 'share' backtest to get realistic results\n\t# The type = 'weight' backtest assumes that we are constantly adjusting our position\n\t# to keep all cash = shorts\n\tdata$weight[] = NA\n\t\tdata$weight$SPY[] = iif(roc252.2 < roc5.2 & roc252.1 > roc5.1 & roc252 > roc5, 1, data$weight$SPY)\n\t\tdata$weight$SPY[] = iif(roc252.2 > roc5.2 & roc252.1 < roc5.1 & roc252 < roc5, -1, data$weight$SPY)\t\n\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\troc.cross.share = bt.run(data, type='share', trade.summary=T, capital=capital)\t\t\t\t\t\n\t\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(roc.cross.share, roc.cross, buy.hold, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot5.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(roc.cross.share, roc.cross, buy.hold, trade.summary=T)\ndev.off()\t\n\t\n\t\n}\n\n\n\n###############################################################################\n# Rotational Trading Strategies : ETF Sector Strategy\n# http://www.etfscreen.com/sectorstrategy.php\n# http://www.etfscreen.com/intlstrategy.php\n###############################################################################\nbt.rotational.trading.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU,IWB,IWD,IWF,IWM,IWN,IWO,IWP,IWR,IWS,IWV,IWW,IWZ')\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1970::')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = len(tickers) \n\n\t# find month ends\n\tmonth.ends = endpoints(prices, 'months')\n\t\tmonth.ends = month.ends[month.ends > 0]\t\t\n\n\tmodels = list()\n\t\t\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tdates = '2001::'\n\t\t\t\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(prices, n)[month.ends,]\t\n\tmodels$equal.weight = bt.run.share(data, clean.signal=F, dates=dates)\n\t\t\t\n\t\n\t# Rank on 6 month return\n\tposition.score = prices / mlag(prices, 126)\t\n\t\n\t# Select Top 2 funds\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(position.score[month.ends,], 2)\t\n\tmodels$top2 = bt.run.share(data, trade.summary=T, dates=dates)\n\n\t# Seletop Top 2 funds, and Keep then till they are in 1:6 rank\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop.keep(position.score[month.ends,], 2, 6)\t\n\tmodels$top2.keep6 = bt.run.share(data, trade.summary=T, dates=dates)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\t\t\t\n\tstrategy.performance.snapshoot(models, T)\n\t\n\t# Plot Portfolio Turnover for each strategy\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\n\n\t\t\n\t# put all reports into one pdf file\n\tpdf(file = 'report.pdf', width=8.5, height=11)\n\t\tplotbt.custom.report(models, trade.summary=T)\n\tdev.off()\t\n\t\n\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(models)\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part3(models, trade.summary=T)\ndev.off()\t\n\n\t\n\t\t\n}\n\n###############################################################################\n# A Quantitative Approach to Tactical Asset Allocation by M. Faber (2006)\n# http://www.mebanefaber.com/timing-model/\n###############################################################################\nbt.timing.model.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('VTI,VEU,IEF,VNQ,DBC')\t\n\ttickers = spl('VTI,EFA,IEF,ICF,DBC,SHY')\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\t\tfor(i in ls(data)) cat( i, format(index(data[[i]][1,]), '%d%b%y'), '\\n')\n\n\t# extend data for Commodities\n\tCRB = get.CRB()\n\t\tindex = max(which( index(CRB) < index(data$DBC[1,]) ))\n\t\tscale = as.vector(Cl(data$DBC[1,])) / as.vector(Cl(CRB[(index + 1),]))\n\t\ttemp = CRB[1 : (index + 1),] * repmat(scale, index + 1, 6)\t\t\n\tdata$DBC = rbind( temp[1:index,], data$DBC )\n\t\t\n\tbt.prep(data, align='remove.na', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \t\n\tn = len(tickers) \n\t\n\t# ignore cash when selecting funds\n\tposition.score = prices\n\t\tposition.score$SHY = NA\n\t\n\t# find month ends\n\tmonth.ends = date.month.ends(index(prices))\n\t\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(position.score[month.ends,], n)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tequal.weight = bt.run(data, type='share', capital=capital)\n\t\t\n\t# BuyRule, price > 10 month SMA\n\tsma = bt.apply.matrix(prices, SMA, 200)\n\tbuy.rule = prices > sma\n\t\tbuy.rule = ifna(buy.rule, F)\n\t\n\t# Strategy\n\tweight = ntop(position.score[month.ends,], n)\t\n\t\t# keep in cash the rest of the funds\n\t\tweight[!buy.rule[month.ends,]] = 0\n\t\tweight$SHY = 1 - rowSums(weight)\n\n\tdata$weight[] = NA\t\t\n\t\tdata$weight[month.ends,] = weight\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\ttiming = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\t#*****************************************************************\n\t# Code Strategies : Daily\n\t#****************************************************************** \n\tweight = ntop(position.score, n)\t\n\t\t# keep in cash the rest of the funds\n\t\tweight[!buy.rule] = 0\n\t\tweight$SHY = 1 - rowSums(weight)\n\n\tdata$weight[] = NA\n\t\tdata$weight[] = weight\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\ttiming.d = bt.run(data, type='share', trade.summary=T, capital=capital)\t\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\t\t\n\t# put all reports into one pdf file\n\tpdf(file = 'report.pdf', width=8.5, height=11)\n\t\tplotbt.custom.report(timing, timing.d, equal.weight, trade.summary=T)\n\tdev.off()\n\t\n\t#*****************************************************************\n\t# Code Strategies : Daily with Counter-Trend Entries by david varadi\n\t# see bt.improving.trend.following.test\n\t#****************************************************************** \n\tdv = bt.apply(data, function(x) { DV(HLC(x), 1, TRUE) } )\t\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(prices > sma & dv < 0.25, 0.2, data$weight)\n\t\tdata$weight[] = iif(prices < sma & dv > 0.75, 0, data$weight)\n\t\tdata$weight$SHY = 0\n\t\t\n\t\t\n\t\tdata$weight = bt.apply.matrix(data$weight, ifna.prev)\n\t\tdata$weight$SHY = 1 - rowSums(data$weight)\n\t\t\n\t\tdata$weight = bt.exrem(data$weight)\n\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\ttiming.d1 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\n\t# compute turnover\t\n\tmodels = variable.number.arguments(timing.d1, timing.d, timing, equal.weight)\n\t\tsapply(models, compute.turnover, data)\n\t\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\n\tplotbt.custom.report.part1(timing.d1, timing.d, timing, equal.weight)\n\n}\n\n###############################################################################\n# Monthly End-of-the-Month (MEOM) by Quanting Dutchman\n# http://quantingdutchman.wordpress.com/2010/06/30/strategy-2-monthly-end-of-the-month-meom/\n###############################################################################\nbt.meom.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('DIA,EEM,EFA,EWH,EWJ,EWT,EWZ,FXI,GLD,GSG,IEF,ILF,IWM,IYR,QQQ,SPY,VNQ,XLB,XLE,XLF,XLI,XLP,XLU,XLV,XLY,XLK')\t\n\t\n\t# Alternatively use Dow Jones Components\n\t# tickers = dow.jones.components()\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1995-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\n\tbt.prep(data, align='keep.all', dates='1995::')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = ncol(prices)\n\tnperiods = nrow(prices)\n\n\t# Equal Weight\n\tdata$weight[] = ntop(prices, n)\n\tequal.weight = bt.run(data)\t\n\t\n\t\t\n\t# find month ends\n\tmonth.ends = endpoints(prices, 'months')\n\t\tmonth.ends = month.ends[month.ends > 0]\t\t\n\tmonth.ends2 = iif(month.ends + 2 > nperiods, nperiods, month.ends + 2)\n\t\t\t\t\t\t\t\t \n\t# Strategy MEOM - Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(prices, n)[month.ends,]\t\n\t\tdata$weight[month.ends2,] = 0\n\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmeom.equal.weight = bt.run(data, type='share', capital=capital)\n\n\t#*****************************************************************\n\t# Rank1 = MA( C/Ref(C,-2), 5 ) * MA( C/Ref(C,-2), 40 )\n\t#****************************************************************** \n\t\t\t\n\t# BuyRule = C > WMA(C, 89)\n\tbuy.rule = prices > bt.apply.matrix(prices, function(x) { WMA(x, 89) } )\t\t\n\t\tbuy.rule = ifna(buy.rule, F)\n\t\t\n\t# 2-day returns\n\tret2 = ifna(prices / mlag(prices, 2), 0)\n\t\n\t# Rank1 = MA( C/Ref(C,-2), 5 ) * MA( C/Ref(C,-2), 40 )\n\tposition.score = bt.apply.matrix(ret2, SMA, 5) * bt.apply.matrix(ret2, SMA, 40)\n\t\tposition.score[!buy.rule] = NA\n\t\t\t\n\t# Strategy MEOM - top 2 \n\tdata$weight[] = NA;\n\t\tdata$weight[month.ends,] = ntop(position.score[month.ends,], 2)\t\t\n\t\tdata$weight[month.ends2,] = 0\t\t\n\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmeom.top2.rank1 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\t#*****************************************************************\n\t# Rank2 = MA( C/Ref(C,-2), 5 ) * Ref( MA( C/Ref(C,-2), 10 ), -5 )\n\t#****************************************************************** \t\n\t\n\t# Rank2 = MA( C/Ref(C,-2), 5 ) * Ref( MA( C/Ref(C,-2), 10 ), -5 )\n\tposition.score = bt.apply.matrix(ret2, SMA, 5) * mlag( bt.apply.matrix(ret2, SMA, 10), 5)\n\t\tposition.score[!buy.rule] = NA\n\t\n\t# Strategy MEOM - top 2 \n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(position.score[month.ends,], 2)\t\t\n\t\tdata$weight[month.ends2,] = 0\t\t\n\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmeom.top2.rank2 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\t\t\t\n\t# put all reports into one pdf file\n\tpdf(file = 'report.pdf', width=8.5, height=11)\n\t\tplotbt.custom.report(meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\n\tdev.off()\t\n\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part3(meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\ndev.off()\t\n\n\t#*****************************************************************\n\t# Modify MEOM logic - maybe sell in 1 day\n\t#****************************************************************** \n\t\n\tmonth.ends1 = iif(month.ends + 1 > nperiods, nperiods, month.ends + 1)\n\t\t\n\t# Strategy MEOM - top 2, maybe sell in 1 day\n\tdata$weight[] = NA\n\t\tdata$weight[month.ends,] = ntop(position.score[month.ends,], 2)\t\t\n\t\tdata$weight[month.ends2,] = 0\t\t\n\n\t\t# Close next day if Today's Close > Today's Open\n\t\tpopen = bt.apply(data, Op)\n\t\tdata$weight[month.ends1,] = iif((prices > popen)[month.ends1,], 0, NA)\t\t\n\t\t\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmeom.top2.rank2.hold12 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(meom.top2.rank2.hold12, meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\ndev.off()\t\n\npng(filename = 'plot5.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(meom.top2.rank2.hold12, meom.top2.rank2, meom.top2.rank1, meom.equal.weight, equal.weight, trade.summary=T)\ndev.off()\t\n\t\n\t\t\t\n}\n\n\n###############################################################################\n# Intraday Backtest\n# The FX intraday free data was \n# http://www.fxhistoricaldata.com/EURUSD/\n###############################################################################\nbt.intraday.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\n\tEURUSD = getSymbols.fxhistoricaldata('EURUSD', 'hour', auto.assign = F, download=F)\n\tSPY = getSymbols('SPY', src = 'yahoo', from = '1980-01-01', auto.assign = F)\n\t\n\t\n\t#*****************************************************************\n\t# Reference intraday period\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\t\n\tplota(EURUSD['2012:03:06 10::2012:03:06 21'], type='candle', main='EURUSD on 2012:03:06 from 10 to 21')\ndev.off()\t\t\t\n\t\n\t#*****************************************************************\n\t# Plot hourly and daily prices on the same chart\n\t#****************************************************************** \t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\t\n\t# two Y axis plot\t\n\tdates= '2012:01:01::2012:01:11'\n\ty = SPY[dates]\n\tplota(y, type = 'candle', LeftMargin=3)\n\t\t\t\n\ty = EURUSD[dates]\n\tplota2Y(y, ylim = range(OHLC(y), na.rm=T), las=1, col='red', col.axis = 'red')\n\t\tplota.ohlc(y, col=plota.candle.col(y))\n\tplota.legend('SPY(rhs),EURUSD(lhs)', 'black,red', list(SPY[dates],EURUSD[dates]))\n\ndev.off()\t\n\t\n\n\t#*****************************************************************\n\t# Universe: Currency Majors\n\t# http://en.wikipedia.org/wiki/Currency_pair\t\n\t#****************************************************************** \n\ttickers = spl('EURUSD,USDJPY,GBPUSD,AUDUSD,USDCHF,USDCAD')\n\t\n\t#*****************************************************************\n\t# Daily Backtest\n\t#****************************************************************** \n\tdata <- new.env()\n\tgetSymbols.fxhistoricaldata(tickers, 'day', data, download=F)\n\tbt.prep(data, align='remove.na', dates='1990::')\n\t\n\tprices = data$prices \n\tn = len(tickers) \n\tmodels = list()\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, n)\n\tmodels$equal.weight = bt.run.share(data, clean.signal=F)\n\t\n\t# Timing by M. Faber\n\tsma = bt.apply.matrix(prices, SMA, 200)\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, n) * (prices > sma)\n\tmodels$timing = bt.run.share(data, clean.signal=F)\n\t\n\t# Report\n\tmodels = rev(models)\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\npng(filename = 'plot4.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(models)\ndev.off()\t\n\t\n\t#*****************************************************************\n\t# Intraday Backtest\n\t#****************************************************************** \n\tdata <- new.env()\t\n\tgetSymbols.fxhistoricaldata(tickers, 'hour', data, download=F)\t\n\tbt.prep(data, align='remove.na', dates='1990::')\n\t\n\tprices = data$prices \n\tn = len(tickers) \n\tmodels = list()\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, n)\n\tmodels$equal.weight = bt.run.share(data, clean.signal=F)\n\t\n\t# Timing by M. Faber\n\tsma = bt.apply.matrix(prices, SMA, 200)\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, n) * (prices > sma)\n\tmodels$timing = bt.run.share(data, clean.signal=F)\n\t\n\t# Report\n\tmodels = rev(models)\npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\npng(filename = 'plot6.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(models)\ndev.off()\t\n\n}\t\n\t\n\t\n\t\t\t\n\n\n\n###############################################################################\n# Forecast-Free Algorithms: A New Benchmark For Tactical Strategies\n# Rebalancing was done on a weekly basis and quarterly data was used to estimate correlations.\n# http://cssanalytics.wordpress.com/2011/08/09/forecast-free-algorithms-a-new-benchmark-for-tactical-strategies/\n#\n# Minimum Variance Sector Rotation\n# http://quantivity.wordpress.com/2011/04/20/minimum-variance-sector-rotation/\n#\n# The volatility mystery continues\n# http://www.portfolioprobe.com/2011/12/05/the-volatility-mystery-continues/\n###############################################################################\nbt.min.var.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog,lpSolve')\n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tdata.weekly <- new.env()\n\t\tfor(i in tickers) data.weekly[[i]] = to.weekly(data[[i]], indexAt='endof')\n\t\t\t\t\t\n\tbt.prep(data, align='remove.na', dates='1990::')\n\tbt.prep(data.weekly, align='remove.na', dates='1990::')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = ncol(prices)\n\t\n\t# find week ends\n\tweek.ends = endpoints(prices, 'weeks')\n\t\tweek.ends = week.ends[week.ends > 0]\t\t\n\n\t\t\n\t# Equal Weight 1/N Benchmark\n\tdata$weight[] = NA\n\t\tdata$weight[week.ends,] = ntop(prices[week.ends,], n)\t\t\n\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tequal.weight = bt.run(data, type='share', capital=capital)\n\t\t\n\t#*****************************************************************\n\t# Create Constraints\n\t#*****************************************************************\n\tconstraints = new.constraints(n, lb = -Inf, ub = +Inf)\n\t#constraints = new.constraints(n, lb = 0, ub = 1)\n\t\n\t# SUM x.i = 1\n\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints)\t\t\n\n\t\t\n\tret = prices / mlag(prices) - 1\n\tweight = coredata(prices)\n\t\tweight[] = NA\n\t\t\n\tfor( i in week.ends[week.ends >= (63 + 1)] ) {\n\t\t# one quarter = 63 days\n\t\thist = ret[ (i- 63 +1):i, ]\n\t\t\n\t\t# create historical input assumptions\n\t\tia = create.ia(hist)\n\t\t\ts0 = apply(coredata(hist),2,sd)\t\t\n\t\t\tia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))\n\t\t\t\n\t\tweight[i,] = min.risk.portfolio(ia, constraints)\n\t}\n\n\t# Minimum Variance\n\tdata$weight[] = weight\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmin.var.daily = bt.run(data, type='share', capital=capital)\n\n\t#*****************************************************************\n\t# Code Strategies: Weekly\n\t#****************************************************************** \n\t\n\tretw = data.weekly$prices / mlag(data.weekly$prices) - 1\n\tweightw = coredata(prices)\n\t\tweightw[] = NA\n\t\n\tfor( i in week.ends[week.ends >= (63 + 1)] ) {\t\n\t\t# map\n\t\tj = which(index(ret[i,]) == index(retw))\n\t\t\n\t\t# one quarter = 13 weeks\n\t\thist = retw[ (j- 13 +1):j, ]\n\t\t\n\t\t# create historical input assumptions\n\t\tia = create.ia(hist)\n\t\t\ts0 = apply(coredata(hist),2,sd)\t\t\n\t\t\tia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))\n\n\t\tweightw[i,] = min.risk.portfolio(ia, constraints)\n\t}\t\n\t\t\n\tdata$weight[] = weightw\t\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tmin.var.weekly = bt.run(data, type='share', capital=capital, trade.summary = T)\n\t#min.var.weekly$trade.summary$trades\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(min.var.weekly, min.var.daily, equal.weight)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(min.var.weekly, min.var.daily, equal.weight)\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tlayout(1:2)\n\tplotbt.transition.map(min.var.daily$weight)\n\t\tlegend('topright', legend = 'min.var.daily', bty = 'n')\n\tplotbt.transition.map(min.var.weekly$weight)\n\t\tlegend('topright', legend = 'min.var.weekly', bty = 'n')\ndev.off()\t\n\n}\n\n\n###############################################################################\n# Backtest various asset allocation strategies based on the idea\n# Forecast-Free Algorithms: A New Benchmark For Tactical Strategies\n# http://cssanalytics.wordpress.com/2011/08/09/forecast-free-algorithms-a-new-benchmark-for-tactical-strategies/\n#\n# Extension to http://systematicinvestor.wordpress.com/2011/12/13/backtesting-minimum-variance-portfolios/\n###############################################################################\nbt.aa.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog,corpcor,lpSolve')\n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\t#tickers = dow.jones.components()\n\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='remove.na', dates='1990::2011')\n \n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = ncol(prices)\n\t\n\t# find week ends\n\tperiod.ends = endpoints(prices, 'weeks')\n\t\t\tperiod.annual.factor = 52\n\n#\tperiod.ends = endpoints(prices, 'months')\n#\t\t\tperiod.annual.factor = 12\n\n\t\tperiod.ends = period.ends[period.ends > 0]\n\n\t#*****************************************************************\n\t# Create Constraints\n\t#*****************************************************************\n\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\t\n\t# SUM x.i = 1\n\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints)\t\t\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\n\tret = prices / mlag(prices) - 1\n\tstart.i = which(period.ends >= (63 + 1))[1]\n\n\t#min.risk.fns = spl('min.risk.portfolio,min.maxloss.portfolio,min.mad.portfolio,min.cvar.portfolio,min.cdar.portfolio,min.cor.insteadof.cov.portfolio,min.mad.downside.portfolio,min.risk.downside.portfolio,min.avgcor.portfolio,find.erc.portfolio,min.gini.portfolio')\t\n\tmin.risk.fns = spl('min.risk.portfolio,min.maxloss.portfolio')\n\t\n\t# Gini risk measure optimization takes a while, uncomment below to add Gini risk measure\n\t# min.risk.fns = c(min.risk.fns, 'min.gini.portfolio')\n\t\n\tweight = NA * prices[period.ends,]\n\tweights = list()\n\t\t# Equal Weight 1/N Benchmark\n\t\tweights$equal.weight = weight\n\t\t\tweights$equal.weight[] = ntop(prices[period.ends,], n)\t\n\t\t\tweights$equal.weight[1:start.i,] = NA\n\t\t\t\n\t\tfor(f in min.risk.fns) weights[[ gsub('\\\\.portfolio', '', f) ]] = weight\n\t\t\n\trisk.contributions = list()\t\n\t\tfor(f in names(weights)) risk.contributions[[ f ]] = weight\n\t\t\t\n\t# construct portfolios\t\t\t\n\tfor( j in start.i:len(period.ends) ) {\n\t\ti = period.ends[j]\n\t\t\n\t\t# one quarter = 63 days\n\t\thist = ret[ (i- 63 +1):i, ]\n\t\t\n\t\tinclude.index = rep(TRUE, n)\n# new logic, require all assets to have full price history\n#include.index = count(hist)== 63 \n#hist = hist[ , include.index]\n\n\t\t\n\t\t# create historical input assumptions\n\t\tia = create.ia(hist)\n\t\t\ts0 = apply(coredata(hist),2,sd)\t\t\n\t\t\tia$correlation = cor(coredata(hist), use='complete.obs',method='pearson')\n\t\t\tia$cov = ia$correlation * (s0 %*% t(s0))\n\t\t\n\t\t# find optimal portfolios under different risk measures\n\t\tfor(f in min.risk.fns) {\n\t\t\t# set up initial solution\n\t\t\tconstraints$x0 = weights[[ gsub('\\\\.portfolio', '', f) ]][(j-1), include.index]\n\t\t\n\t\t\tweights[[ gsub('\\\\.portfolio', '', f) ]][j, include.index] = match.fun(f)(ia, constraints)\n\t\t}\n\t\t\n\t\t\n\t\t# compute risk contributions implied by portfolio weihgts\n\t\tfor(f in names(weights)) {\n\t\t\trisk.contributions[[ f ]][j, include.index] = portfolio.risk.contribution(weights[[ f ]][j, include.index], ia)\n\t\t}\n\n\t\tif( j %% 10 == 0) cat(j, '\\n')\n\t}\n\t\n\t#*****************************************************************\n\t# Create strategies\n\t#****************************************************************** \t\t\n\tmodels = list()\n\tfor(i in names(weights)) {\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[period.ends,] = weights[[i]]\t\n\t\tmodels[[i]] = bt.run.share(data, clean.signal = F)\n\t}\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\tmodels = rev(models)\n\t\tweights = rev(weights)\n\t\trisk.contributions = rev(risk.contributions)\n\npng(filename = 'plot1.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t# Plot perfromance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Strategy Statistics Side by Side\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\n\n\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Portfolio Turnover for each strategy\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\ndev.off()\t\n\n\t\n\npng(filename = 'plot4.png', width = 600, height = 1600, units = 'px', pointsize = 12, bg = 'white')\t\n\t# Plot transition maps\n\tlayout(1:len(models))\n\tfor(m in names(models)) {\n\t\tplotbt.transition.map(models[[m]]$weight, name=m)\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\ndev.off()\t\n\npng(filename = 'plot5.png', width = 600, height = 1600, units = 'px', pointsize = 12, bg = 'white')\t\n\t# Plot risk contributions\n\tlayout(1:len(risk.contributions))\n\tfor(m in names(risk.contributions)) {\n\t\tplotbt.transition.map(risk.contributions[[m]], name=paste('Risk Contributions',m))\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\ndev.off()\t\n\n\t\npng(filename = 'plot6.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot portfolio concentration stats\n\tlayout(1:2)\t\n\tplota.matplot(lapply(weights, portfolio.concentration.gini.coefficient), main='Gini Coefficient')\n\tplota.matplot(lapply(weights, portfolio.concentration.herfindahl.index), main='Herfindahl Index')\n\t#plota.matplot(lapply(weights, portfolio.turnover), main='Turnover')\ndev.off()\t\n\n\npng(filename = 'plot7.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Compute stats\n\tout = compute.stats(weights,\n\t\tlist(Gini=function(w) mean(portfolio.concentration.gini.coefficient(w), na.rm=T),\n\t\t\tHerfindahl=function(w) mean(portfolio.concentration.herfindahl.index(w), na.rm=T),\n\t\t\tTurnover=function(w) period.annual.factor * mean(portfolio.turnover(w), na.rm=T)\n\t\t\t)\n\t\t)\n\t\n\tout[] = plota.format(100 * out, 1, '', '%')\n\tplot.table(t(out))\ndev.off()\t\t\n\n\npng(filename = 'plot8.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Portfolio Turnover for each strategy\n\tlayout(1)\n\tbarplot.with.labels(sapply(weights, function(w) period.annual.factor * mean(portfolio.turnover(w), na.rm=T)), 'Average Annual Portfolio Turnover')\ndev.off()\t\n\n}\n\nbt.aa.test.new <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog,corpcor,lpSolve')\n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='remove.na', dates='1990::') \n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\t\t\t\n\tcluster.group = cluster.group.kmeans.90\n\t\t\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = 'months', lookback.len = 60, \n\t\tmin.risk.fns = list(\n\t\t\tEW=equal.weight.portfolio,\n\t\t\tRP=risk.parity.portfolio(),\n\t\t\tMD=max.div.portfolio,\t\t\t\t\t\t\n\t\t\t\n\t\t\tMV=min.var.portfolio,\n\t\t\tMVE=min.var.excel.portfolio,\n\t\t\tMV2=min.var2.portfolio,\n\t\t\t\n\t\t\tMC=min.corr.portfolio,\n\t\t\tMCE=min.corr.excel.portfolio,\n\t\t\tMC2=min.corr2.portfolio,\n\t\t\t\n\t\t\tMS=max.sharpe.portfolio(),\n\t\t\tERC = equal.risk.contribution.portfolio,\n\n\t\t\t# target retunr / risk\n\t\t\tTRET.12 = target.return.portfolio(12/100),\t\t\t\t\t\t\t\t\n\t\t\tTRISK.10 = target.risk.portfolio(10/100),\n\t\t\n\t\t\t# cluster\n\t\t\tC.EW = distribute.weights(equal.weight.portfolio, cluster.group),\n\t\t\tC.RP = distribute.weights(risk.parity.portfolio(), cluster.group),\n\t\t\t\n\t\t\t# rso\n\t\t\tRSO.RP.5 = rso.portfolio(risk.parity.portfolio(), 5, 500), \n\t\t\t\n\t\t\t# others\n\t\t\tMMaxLoss = min.maxloss.portfolio,\n\t\t\tMMad = min.mad.portfolio,\n\t\t\tMCVaR = min.cvar.portfolio,\n\t\t\tMCDaR = min.cdar.portfolio,\n\t\t\tMMadDown = min.mad.downside.portfolio,\n\t\t\tMRiskDown = min.risk.downside.portfolio,\n\t\t\tMCorCov = min.cor.insteadof.cov.portfolio\n\t\t)\n\t)\n\t\n\tmodels = create.strategies(obj, data)$models\n\t\t\t\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n # put all reports into one pdf file\n\t#pdf(file = 'filename.pdf', width=8.5, height=11)\n\npng(filename = 'plot1.png', width = 1800, height = 1800, units = 'px', pointsize = 12, bg = 'white')\t\n\t\tstrategy.performance.snapshoot(models, T, 'Backtesting Asset Allocation portfolios')\ndev.off()\n\t\n\t\t\t\n\t\t\n\t# close pdf file\n #dev.off()\t\n \n\t#pdf(file = 'filename.pdf', width=18.5, height=21)\n\t#\tstrategy.performance.snapshoot(models, title = 'Backtesting Asset Allocation portfolios', data = data)\n\t#dev.off()\t\n \n\t# to see last 5 re-balances\n\t# round(100 * last(models$MCDaR$weight[obj$period.ends[-len(obj$period.ends)]+1], 5))\n}\n\n\n\n\n\n###############################################################################\n# Investigate Rebalancing methods:\n# 1. Periodic Rebalancing: rebalance to the target mix every month, quarter, year.\n# 2. Maximum Deviation Rebalancing: rebalance to the target mix when asset weights deviate more than a given percentage from the target mix.\n# 3. Same as 2, but rebalance half-way to target\n###############################################################################\nbt.rebalancing.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\ttickers = spl('SPY,TLT')\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='remove.na', dates='1900::2011')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tnperiods = nrow(prices)\n\ttarget.allocation = matrix(c(0.5, 0.5), nrow=1)\n\t\n\t# Buy & Hold\t\n\tdata$weight[] = NA\t\n\t\tdata$weight[1,] = target.allocation\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tbuy.hold = bt.run(data, type='share', capital=capital)\n\n\t\n\t# Rebalance periodically\n\tmodels = list()\n\tfor(period in spl('months,quarters,years')) {\n\t\tdata$weight[] = NA\t\n\t\t\tdata$weight[1,] = target.allocation\n\t\t\t\n\t\t\tperiod.ends = endpoints(prices, period)\n\t\t\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\t\tdata$weight[period.ends,] = repmat(target.allocation, len(period.ends), 1)\n\t\t\t\t\t\t\n\t\t\tcapital = 100000\n\t\t\tdata$weight[] = (capital / prices) * data$weight\n\t\tmodels[[period]] = bt.run(data, type='share', capital=capital)\t\n\t}\n\tmodels$buy.hold = buy.hold\t\t\t\t\n\t\n\t# Compute Portfolio Turnover \n\tcompute.turnover(models$years, data)\t\t\n\t\n\t# Compute Portfolio Maximum Deviation\n\tcompute.max.deviation(models$years, target.allocation)\t\t\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\n\t# put all reports into one pdf file\n\tpdf(file = 'report.pdf', width=8.5, height=11)\n\t\tplotbt.custom.report(models)\n\tdev.off()\t\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\n\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\t# Plot BuyHold and Monthly Rebalancing Weights\n\tlayout(1:2)\n\tplotbt.transition.map(models$buy.hold$weight, 'buy.hold', spl('red,orange'))\n\t\tabline(h=50)\n\tplotbt.transition.map(models$months$weight, 'months', spl('red,orange'))\n\t\tabline(h=50)\t\t\ndev.off()\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Portfolio Turnover for each Rebalancing method\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\ndev.off()\t\n\n\t#*****************************************************************\n\t# Code Strategies that rebalance based on maximum deviation\n\t#****************************************************************** \n\t\n\t# rebalance to target.allocation when portfolio weights are 5% away from target.allocation\n\tmodels$smart5.all = bt.max.deviation.rebalancing(data, buy.hold, target.allocation, 5/100, 0) \n\t\n\t# rebalance half-way to target.allocation when portfolio weights are 5% away from target.allocation\n\tmodels$smart5.half = bt.max.deviation.rebalancing(data, buy.hold, target.allocation, 5/100, 0.5) \n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\n\t\t\t\npng(filename = 'plot4.png', width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\n\t# Plot BuyHold, Years and Max Deviation Rebalancing Weights\t\n\tlayout(1:4)\n\tplotbt.transition.map(models$buy.hold$weight, 'buy.hold', spl('red,orange'))\n\t\tabline(h=50)\n\tplotbt.transition.map(models$smart5.all$weight, 'Max Deviation 5%, All the way', spl('red,orange'))\n\t\tabline(h=50)\n\tplotbt.transition.map(models$smart5.half$weight, 'Max Deviation 5%, Half the way', spl('red,orange'))\n\t\tabline(h=50)\n\tplotbt.transition.map(models$years$weight, 'years', spl('red,orange'))\n\t\tabline(h=50)\ndev.off()\t\n\npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Portfolio Turnover for each Rebalancing method\n\tlayout(1:2)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', F)\n\tbarplot.with.labels(sapply(models, compute.max.deviation, target.allocation), 'Maximum Deviation from Target Mix')\ndev.off()\n\npng(filename = 'plot6.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Strategy Statistics Side by Side\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\n\t\n\t#*****************************************************************\n\t# Periodic Rebalancing Seasonality\n\t#****************************************************************** \t\t\t\n\t# maQuant annual rebalancing (september/october showed the best results)\t\n\tmonths = spl('Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec')\t\t\n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\tmodels = list()\n\tfor(i in 1:12) {\n\t\tindex = which( date.month(index(prices)[period.ends]) == i )\n\t\tdata$weight[] = NA\t\n\t\t\tdata$weight[1,] = target.allocation\t\t\t\n\t\t\tdata$weight[period.ends[index],] = repmat(target.allocation, len(index), 1)\n\t\t\t\t\t\t\n\t\t\tcapital = 100000\n\t\t\tdata$weight[] = (capital / prices) * data$weight\n\t\tmodels[[ months[i] ]] = bt.run(data, type='share', capital=capital)\t\n\t}\n\npng(filename = 'plot7.png', width = 1200, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\n\t\t\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\n}\n\n\n# Maximum Deviation Rebalancing: rebalance to the target mix when asset weights deviate more than a given percentage from the target mix.\n# Also support rebalancing.ratio, same as above, but rebalance half-way to target\n#' @export \t\nbt.max.deviation.rebalancing <- function\n(\n\tdata,\n\tmodel, \n\ttarget.allocation, \n\tmax.deviation = 3/100, \n\trebalancing.ratio = 0,\t# 0 means rebalance all-way to target.allocation\n\t\t\t\t\t\t\t# 0.5 means rebalance half-way to target.allocation\n\tstart.index = 1,\n\tperiod.ends = 1:nrow(model$weight),\n\tfast = T\t\t\t\t\t\t\t\n) \n{\n\tnperiods = nrow(model$weight)\n\taction.index = rep(F, nperiods)\n\t\n\tstart.index = period.ends[start.index]\n\tstart.index0 = start.index\n\t\n\twhile(T) {\t\n\t\t# find rows that violate max.deviation\n\t\tweight = model$weight\n\t\tindex = apply(abs(weight - rep.row(target.allocation, nperiods)), 1, max) > max.deviation\n\t\tindex = which( index[period.ends] )\n\t\n\t\tif( len(index) > 0 ) {\n\t\t\tindex = period.ends[index]\n\t\t\tindex = index[ index > start.index ]\n\t\t\n\t\t\tif( len(index) > 0 ) {\n\t\t\t\taction.index[index[1]] = T\n\t\t\t\t\n\t\t\t\tdata$weight[] = NA\t\n\t\t\t\t\tdata$weight[start.index0,] = target.allocation\n\t\t\t\t\t\n\t\t\t\t\ttemp = rep.row(target.allocation, sum(action.index))\n\t\t\t\t\tdata$weight[action.index,] = temp + \n\t\t\t\t\t\trebalancing.ratio * (weight[action.index,] - temp)\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t# please note the bt.run.share.ex somehow gives slighly better results\n\t\t\t\tif(fast)\n\t\t\t\t\tmodel = bt.run.share.fast(data)\n\t\t\t\telse\n\t\t\t\t\tmodel = bt.run.share.ex(data, clean.signal=F, silent=T)\n\t\t\t\t\n\t\t\t\tstart.index = index[1]\n\t\t\t} else break\t\t\t\n\t\t} else break\t\t\n\t}\n\t\n\tmodel = bt.run.share.ex(data, clean.signal=F, silent=F)\n\treturn(model)\n}\n\n\n\n\n\n\t\n\n\n\nbt.rebalancing1.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t# SHY - cash\n\ttickers = spl('SPY,TLT,GLD,FXE,USO,SHY')\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='remove.na', dates='1900::2011')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tnperiods = nrow(prices)\n\ttarget.allocation = matrix(rep(1/6,6), nrow=1)\n\t\n\t# Buy & Hold\t\n\tdata$weight[] = NA\t\n\t\tdata$weight[1,] = target.allocation\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tbuy.hold = bt.run(data, type='share', capital=capital)\n\n\t\n\t# Rebalance periodically\n\tmodels = list()\n\tfor(period in spl('months,quarters,years')) {\n\t\tdata$weight[] = NA\t\n\t\t\tdata$weight[1,] = target.allocation\n\t\t\t\n\t\t\tperiod.ends = endpoints(prices, period)\n\t\t\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\t\tdata$weight[period.ends,] = repmat(target.allocation, len(period.ends), 1)\n\t\t\t\t\t\t\n\t\t\tcapital = 100000\n\t\t\tdata$weight[] = (capital / prices) * data$weight\n\t\tmodels[[period]] = bt.run(data, type='share', capital=capital)\t\n\t}\n\tmodels$buy.hold = buy.hold\t\t\t\t\n\t\n\n\t#*****************************************************************\n\t# Code Strategies that rebalance based on maximum deviation\n\t#****************************************************************** \n\t\n\t# rebalance to target.allocation when portfolio weights are 3% away from target.allocation\n\tmodels$smart3.all = bt.max.deviation.rebalancing(data, buy.hold, target.allocation, 3/100, 0) \n\t\n\t# rebalance half-way to target.allocation when portfolio weights are 3% away from target.allocation\n\tmodels$smart3.half = bt.max.deviation.rebalancing(data, buy.hold, target.allocation, 3/100, 0.5) \n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\n\t\t\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\n\t# Plot Portfolio Turnover for each Rebalancing method\n\tlayout(1:2)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', F)\n\tbarplot.with.labels(sapply(models, compute.max.deviation, target.allocation), 'Maximum Deviation from Target Mix')\n\t\n\t\ndev.off()\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Strategy Statistics Side by Side\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\n\t\n}\n\n\n###############################################################################\n# Rotational Trading: how to reduce trades and improve returns by Frank Hassler\n# http://engineering-returns.com/2011/07/06/rotational-trading-how-to-reducing-trades-and-improve-returns/\n###############################################################################\nbt.rotational.trading.trades.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU,IWB,IWD,IWF,IWM,IWN,IWO,IWP,IWR,IWS,IWV,IWW,IWZ')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies : weekly rebalancing\n\t#****************************************************************** \n\tprices = data$prices \n\tn = len(tickers) \n\n\t# find week ends\n\tweek.ends = endpoints(prices, 'weeks')\n\t\tweek.ends = week.ends[week.ends > 0]\t\t\n\n\t\t\n\t# Rank on ROC 200\n\tposition.score = prices / mlag(prices, 200)\t\n\t\tposition.score.ma = position.score\t\t\n\t\tbuy.rule = T\n\n\t# Select Top 2 funds daily\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(position.score, 2)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\t\t\n\ttop2.d = bt.run(data, type='share', trade.summary=T, capital=capital)\n\n\t# Select Top 2 funds weekly\n\tdata$weight[] = NA\n\t\tdata$weight[week.ends,] = ntop(position.score[week.ends,], 2)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.w = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Strategy Metrics Side by Side\n\tplotbt.strategy.sidebyside(top2.d, top2.w, perfromance.fn = 'engineering.returns.kpi')\t\ndev.off()\n\t\n\t#*****************************************************************\n\t# Code Strategies : different entry/exit rank\n\t#****************************************************************** \n\t\n\t# Select Top 2 funds, Keep till they are in 4/6 rank\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop.keep(position.score, 2, 4)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.d.keep4 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop.keep(position.score, 2, 6)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.d.keep6 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\t# Plot Strategy Metrics Side by Side\n\tplotbt.strategy.sidebyside(top2.d, top2.d.keep4, top2.d.keep6, perfromance.fn = 'engineering.returns.kpi')\ndev.off()\n\n\t#*****************************************************************\n\t# Code Strategies : Rank smoothing\n\t#****************************************************************** \n\n\tmodels = list()\n\tmodels$Bench = top2.d\n\tfor( avg in spl('SMA,EMA') ) {\n\t\tfor( i in c(3,5,10,20) ) {\t\t\n\t\t\tposition.score.smooth = bt.apply.matrix(position.score.ma, avg, i)\t\n\t\t\t\tposition.score.smooth[!buy.rule,] = NA\n\t\t\t\n\t\t\tdata$weight[] = NA\n\t\t\t\tdata$weight[] = ntop(position.score.smooth, 2)\t\n\t\t\t\tcapital = 100000\n\t\t\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\t\t\tmodels[[ paste(avg,i) ]] = bt.run(data, type='share', trade.summary=T, capital=capital)\t\t\n\t\t}\n\t}\n\t\t\npng(filename = 'plot3.png', width = 1200, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\t# Plot Strategy Metrics Side by Side\n\tplotbt.strategy.sidebyside(models, perfromance.fn = 'engineering.returns.kpi')\ndev.off()\n\t\n\t#*****************************************************************\n\t# Code Strategies : Combination\n\t#****************************************************************** \n\n\t# Select Top 2 funds daily, Keep till they are 6 rank, Smooth Rank by 10 day EMA\n\tposition.score.smooth = bt.apply.matrix(position.score.ma, 'EMA', 10)\t\n\t\tposition.score.smooth[!buy.rule,] = NA\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop.keep(position.score.smooth, 2, 6)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.d.keep6.EMA10 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\t\n\t# Select Top 2 funds weekly, Keep till they are 6 rank\n\tdata$weight[] = NA\n\t\tdata$weight[week.ends,] = ntop.keep(position.score[week.ends,], 2, 6)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.w.keep6 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\t# Select Top 2 funds weekly, Keep till they are 6 rank, Smooth Rank by 10 week EMA\n\tposition.score.smooth[] = NA\n\t\tposition.score.smooth[week.ends,] = bt.apply.matrix(position.score.ma[week.ends,], 'EMA', 10)\t\n\t\t\tposition.score.smooth[!buy.rule,] = NA\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[week.ends,] = ntop.keep(position.score.smooth[week.ends,], 2, 6)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\t\t\n\ttop2.w.keep6.EMA10 = bt.run(data, type='share', trade.summary=T, capital=capital)\n\t\n\t\t\npng(filename = 'plot4.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\t# Plot Strategy Metrics Side by Side\n\tplotbt.strategy.sidebyside(top2.d, top2.d.keep6, top2.d.keep6.EMA10, top2.w, top2.w.keep6, top2.w.keep6.EMA10, perfromance.fn = 'engineering.returns.kpi')\ndev.off()\n\t\n\n\n\n\n\n\t#*****************************************************************\n\t# Possible Improvements to reduce drawdowns\n\t#****************************************************************** \n\t# Equal Weight\n\tdata$weight[] = ntop(prices, n)\n\tew = bt.run(data)\t\n\n\t# Avoiding severe draw downs\n\t# http://engineering-returns.com/2010/07/26/rotational-trading-system/\n\t# Only trade the system when the index is either above the 200 MA or 30 MA\n\t# Usually these severe draw downs happen bellow the 200MA average and \n\t# the second 30 MA average will help to get in when the recovery happens\t\n\tbuy.rule = (ew$equity > SMA(ew$equity,200)) | (ew$equity > SMA(ew$equity,30))\n\tbuy.rule = (ew$equity > SMA(ew$equity,200))\n\t\tbuy.rule = ifna(buy.rule, F)\n\t\t \t\n\t# Rank using TSI by Frank Hassler, TSI is already smoothed and slow varying, \n\t# so SMA will filter will not very effective\n\t#http://engineering-returns.com/tsi/\n\tposition.score = bt.apply(data, function(x) TSI(HLC(x)) )\t\t\n\t\tposition.score.ma = position.score\n\t\tposition.score[!buy.rule,] = NA\n\t\t\n}\n\n\n\n###############################################################################\n# Charting the Santa Claus Rally\n# http://ibankcoin.com/woodshedderblog/2011/12/15/charting-the-santa-claus-rally/\n#\n# Trading Calendar\n# http://www.cxoadvisory.com/trading-calendar/\n###############################################################################\nbt.december.trading.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na', dates='1970::2011')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = len(tickers) \n\tret = prices / mlag(prices) - 1\n\n\t\n\t# find prices in December\n\tdates = index(prices)\n\tyears = date.year(dates)\t\n\tindex = which(date.month(dates) == 12)\n\t\n\t# rearrange data in trading days\n\ttrading.days = sapply(tapply(ret[index,], years[index], function(x) coredata(x)), function(x) x[1:22])\n\t\t\n\t# average return each trading days, excluding current year\n\tavg.trading.days = apply(trading.days[, -ncol(trading.days)], 1, mean, na.rm=T)\n\tcurrent.year = trading.days[, ncol(trading.days)]\n\t\n\t# cumulative\n\tavg.trading.days = 100 * ( cumprod(1 + avg.trading.days) - 1 )\n\tcurrent.year = 100 * ( cumprod(1 + current.year) - 1 )\n\t\n\t#*****************************************************************\n\t# Create Plot\n\t#****************************************************************** \t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\t# plot\t\n\tpar(mar=c(4,4,1,1))\n\tplot(avg.trading.days, type='b', col=1,\n\t\tylim=range(avg.trading.days,current.year,na.rm=T),\n\t\txlab = 'Number of Trading Days in December',\n\t\tylab = 'Avg % Profit/Loss'\n\t\t)\n\t\tlines(current.year, type='b', col=2)\n\tgrid()\n\tplota.legend('Avg SPY,SPY Dec 2011', 1:2)\ndev.off()\t\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tbuy.hold = bt.run(data, type='share', capital=capital)\n\n\t\n\t# Find Last trading days in November and December\n\tindex = which(date.month(dates) == 11)\n\tlast.day.november = match(tapply(dates[index], years[index], function(x) tail(x,1)), dates)\n\tindex = which(date.month(dates) == 12)\n\tlast.day.december = match(tapply(dates[index], years[index], function(x) tail(x,1)), dates)\n\t\n\t# December\n\tdata$weight[] = NA\t\n\t\tdata$weight[last.day.november,] = 1\n\t\tdata$weight[last.day.december,] = 0\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * data$weight\n\tdecember = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplotbt.custom.report.part1(december, buy.hold, trade.summary=T)\t\ndev.off()\t\n\npng(filename = 'plot3.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(december, buy.hold, trade.summary=T)\t\ndev.off()\t\n\t\n}\n\n\n###############################################################################\n# Seasonality Case Study\n# Historical Seasonality Analysis: What company in DOW is likely to do well in January? \n###############################################################################\nbt.seasonality.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\t\n\ttickers = dow.jones.components()\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\n\tbt.prep(data, align='keep.all', dates='1970::2011')\n\t\t\n\t#*****************************************************************\n\t# Compute monthly returns\n\t#****************************************************************** \n\tprices = data$prices \n\tn = ncol(prices)\t\n\t\n\t# find month ends\n\tmonth.ends = endpoints(prices, 'months')\n\t\n\tprices = prices[month.ends,]\n\tret = prices / mlag(prices) - 1\n\n\t# keep only January\t\n\tret = ret[date.month(index(ret)) == 1, ]\n\t\n\t# keep last 20 years\n\tret = last(ret,20)\n\n\t#*****************************************************************\n\t# Compute stats\n\t#****************************************************************** \n\tstats = matrix(rep(NA,2*n), nc=n)\n\t\tcolnames(stats) = colnames(prices)\n\t\trownames(stats) = spl('N,Positive')\n\t\t\n\tfor(i in 1:n) {\n\t\tstats['N',i] = sum(!is.na(ret[,i]))\n\t\tstats['Positive',i] = sum(ret[,i]>0, na.rm=T)\t\n\t}\n\tsort(stats['Positive',], decreasing =T)\n\t\npng(filename = 'plot1.png', width = 600, height = 200, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplot.table(stats[, order(stats['Positive',], decreasing =T)[1:10]])\ndev.off()\t\n\t\n\t\n\t\n}\n\n\n###############################################################################\n# Volatility Forecasting using Garch(1,1) based\n#\n# Regime Switching System Using Volatility Forecast by Quantum Financier\n# http://quantumfinancier.wordpress.com/2010/08/27/regime-switching-system-using-volatility-forecast/\n###############################################################################\n# Benchmarking Garch algorithms \n# garch from tseries package is faster than garchFit from fGarch package\n###############################################################################\nbt.test.garch.speed <- function() \n{\n\tload.packages('tseries,fGarch,rbenchmark')\t\n\n\ttemp = garchSim(n=252)\n\n\ttest1 <- function() {\n\t\tfit1=garch(temp, order = c(1, 1), control = garch.control(trace = F))\n\t}\n\ttest2 <- function() {\n\t\tfit2=garchFit(~ garch(1,1), data = temp, include.mean=FALSE, trace=F)\n\t}\n\t\t \t\n\tbenchmark(\n\t\ttest1(),\n\t\ttest2(),\n\t\tcolumns=spl('test,replications,elapsed,relative'),\n\t\torder='relative',\n\t\treplications=100\n\t)\n}\n\n###############################################################################\n# One day ahead forecast functions for garch (tseries) and garchFit(fGarch)\n# Sigma[t]^2 = w + a* Sigma[t-1]^2 + b*r[t-1]^2\n# r.last - last return, h.last - last volatility\n###############################################################################\ngarch.predict.one.day <- function(fit, r.last) \n{\n\th.last = tail( fitted(fit)[,1] ,1)\t\t\t\n\tsqrt(sum( coef(fit) * c(1, r.last^2, h.last^2) ))\t\n}\n\n# same as predict( fit, n.ahead=1, doplot=F)[3]\ngarchFit.predict.one.day <- function(fit, r.last) \n{\n\th.last = tail(sqrt(fit@h.t), 1)\n\tsqrt(sum( fit@fit$matcoef[,1] * c(1, r.last^2, h.last^2) ))\n}\n\t\n###############################################################################\n# Forecast Volatility using Garch\n# garch from tseries is fast, but does not consistently converge\n# garchFit from fGarch is slower, but converges consistently\n###############################################################################\nbt.forecast.garch.volatility <- function(ret.log, est.period = 252) \n{\t\t\n\tnperiods = nrow(ret.log)\t\t\n\tgarch.vol = NA * ret.log\n\t\n\tfor( i in (est.period + 1) : nperiods ) {\n\t\ttemp = as.vector(ret.log[ (i - est.period + 1) : i, ])\n\t\tr.last = tail( temp, 1 )\n\t\t\n\t\tfit = tryCatch( garch(temp, order = c(1, 1), control = garch.control(trace = F)),\n\t \t\t\t\terror=function( err ) FALSE, warning=function( warn ) FALSE )\n\t \n\t\tif( !is.logical( fit ) ) {\n\t\t\tif( i == est.period + 1 ) garch.vol[1:est.period] = fitted(fit)[,1]\n\t\t\tgarch.vol[i] = garch.predict.one.day(fit, r.last)\n\t\t} else {\n\t\t\tfit = tryCatch( garchFit(~ garch(1,1), data = temp, include.mean=FALSE, trace=F),\n\t \t\t\t\terror=function( err ) FALSE, warning=function( warn ) FALSE )\n\t \t\t\t\t\n\t\t\tif( !is.logical( fit ) ) {\n\t\t\t\tif( i == est.period + 1 ) garch.vol[1:est.period] = sqrt(fit@h.t)\n\t\t\t\tgarch.vol[i] = garchFit.predict.one.day(fit, r.last)\n\t\t\t} \n\t\t}\t\t\t\n\t\tif( i %% 100 == 0) cat(i, '\\n')\n\t}\n\tgarch.vol[] = ifna.prev(coredata(garch.vol))\n\treturn(garch.vol)\n}\t\n\n###############################################################################\n# Volatility Forecasting using Garch(1,1) based\n###############################################################################\nbt.volatility.garch <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'SPY'\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na', dates='2000::2012')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = len(tickers) \n\tnperiods = nrow(prices)\n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\tbuy.hold = bt.run(data)\t\n\n\t\t\n\t# Mean-Reversion(MR) strategy - RSI2\n\trsi2 = bt.apply.matrix(prices, RSI, 2)\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(rsi2 < 50, 1, -1)\t\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\tmr = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\t\t\t\n\t\t\n\t# Trend Following(TF) strategy - MA 50/200 crossover\n\tsma.short = bt.apply.matrix(prices, SMA, 50)\n\tsma.long = bt.apply.matrix(prices, SMA, 200)\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(sma.short > sma.long, 1, -1)\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\ttf = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tplotbt.custom.report.part1(mr, tf, buy.hold, trade.summary=T)\n\t\ndev.off()\t\t\n\t#*****************************************************************\n\t# Regime Switching Historical\n\t#****************************************************************** \n\t#classify current volatility by percentile using a 252 day lookback period\n\t#The resulting series oscillate between 0 and 1, and is smoothed using a 21 day percentrankSMA (developed by David Varadi) using a 252 day lookback period.\n\t#percentrank(MA(percentrank(Stdev( diff(log(close)) ,21),252),21),250)\n\n\tret.log = bt.apply.matrix(prices, ROC, type='continuous')\n\thist.vol = bt.apply.matrix(ret.log, runSD, n = 21)\n\tvol.rank = percent.rank(SMA(percent.rank(hist.vol, 252), 21), 250)\n\n\t# Regime Switching Historical\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(vol.rank > 0.5, \n\t\t\t\t\t\t\tiif(rsi2 < 50, 1, -1),\n\t\t\t\t\t\t\tiif(sma.short > sma.long, 1, -1)\n\t\t\t\t\t\t)\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\tregime.switching = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\t\n\tplotbt.custom.report.part1(regime.switching, mr, tf, buy.hold, trade.summary=T)\n\t\ndev.off()\t\t\n\n\t#*****************************************************************\n\t# Regime Switching using Garch\n\t#****************************************************************** \t\t\n\tload.packages('tseries,fGarch')\t\n\tgarch.vol = bt.forecast.garch.volatility(ret.log, 252)\t\n\tvol.rank = percent.rank(SMA(percent.rank(garch.vol, 252), 21), 250)\n\n\t# Regime Switching Garch\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(vol.rank > 0.5, \n\t\t\t\t\t\t\tiif(rsi2 < 50, 1, -1),\n\t\t\t\t\t\t\tiif(sma.short > sma.long, 1, -1)\n\t\t\t\t\t\t)\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\tregime.switching.garch = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tplotbt.custom.report.part1(regime.switching.garch, regime.switching, buy.hold, trade.summary=T)\n\t\ndev.off()\t\n}\n\n\n\n\n\n###############################################################################\n# Time Series Matching\n#\n# Based on Jean-Robert Avettand-Fenoel - How to Accelerate Model Deployment using Rook\n# http://www.londonr.org/Sep%2011%20LondonR_AvettandJR.pdf\n###############################################################################\nbt.matching.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'SPY'\n\n\tdata = getSymbols(tickers, src = 'yahoo', from = '1950-01-01', auto.assign = F)\n\n\t#*****************************************************************\n\t# New: logic moved to functions\n\t#****************************************************************** \n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tobj = bt.matching.find(Cl(data), plot=T)\ndev.off()\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tmatches = bt.matching.overlay(obj, plot=T)\ndev.off()\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tbt.matching.overlay.table(obj, matches, plot=T)\ndev.off()\t\n\t\n\n\t#*****************************************************************\n\t# Original logic: Setup search\n\t#****************************************************************** \n\tdata = last(data, 252*10)\n\treference = coredata(Cl(data))\n\t\tn = len(reference)\n\t\tquery = reference[(n-90+1):n]\t\n\t\treference = reference[1:(n-90)]\n\t\t\n\t\tn.query = len(query)\n\t\tn.reference = len(reference)\n\n\t#*****************************************************************\n\t# Compute Distance\n\t#****************************************************************** \t\t\n\tdist = rep(NA, n.reference)\n\tquery.normalized = (query - mean(query)) / sd(query)\n\t\n\tfor( i in n.query : n.reference ) {\n\t\twindow = reference[ (i - n.query + 1) : i]\n\t\twindow.normalized = (window - mean(window)) / sd(window)\n\t\tdist[i] = stats:::dist(rbind(query.normalized, window.normalized))\n\t}\n\n\t#*****************************************************************\n\t# Find Matches\n\t#****************************************************************** \t\t\t\n\tmin.index = c()\n\tn.match = 10\n\t\n\t# only look at the minimums \n\ttemp = dist\n\t\ttemp[ temp > mean(dist, na.rm=T) ] = NA\n\t\t\n\t# remove n.query, points to the left/right of the minimums\n\tfor(i in 1:n.match) {\n\t\tif(any(!is.na(temp))) {\n\t\t\tindex = which.min(temp)\n\t\t\tmin.index[i] = index\n\t\t\ttemp[max(0,index - 2*n.query) : min(n.reference,(index + n.query))] = NA\n\t\t}\n\t}\n\tn.match = len(min.index)\n\t\t\n\t#*****************************************************************\n\t# Plot Matches\n\t#****************************************************************** \t\t\n\tdates = index(data)[1:len(dist)]\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tpar(mar=c(2, 4, 2, 2))\n\tplot(dates, dist, type='l',col='gray', main='Top Matches', ylab='Euclidean Distance', xlab='')\n\t\tabline(h = mean(dist, na.rm=T), col='darkgray', lwd=2)\n\t\tpoints(dates[min.index], dist[min.index], pch=22, col='red', bg='red')\n\t\ttext(dates[min.index], dist[min.index], 1:n.match, adj=c(1,1), col='black',xpd=TRUE)\ndev.off()\t\t\t\n\t\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplota(data, type='l', col='gray', main=tickers)\n\t\tplota.lines(last(data,90), col='blue')\n\t\tfor(i in 1:n.match) {\n\t\tplota.lines(data[(min.index[i]-n.query + 1):min.index[i]], col='red')\n\t\t}\n\t\ttext(index4xts(data)[min.index - n.query/2], reference[min.index - n.query/2], 1:n.match, \n\t\t\tadj=c(1,-1), col='black',xpd=TRUE)\n\t\tplota.legend('Pattern,Match Number','blue,red')\ndev.off()\t\t\t\n\t\n\t#*****************************************************************\n\t# Overlay all Matches\n\t#****************************************************************** \t\t\n\tmatches = matrix(NA, nr=(n.match+1), nc=3*n.query)\n\ttemp = c(rep(NA, n.query), reference, query)\n\tfor(i in 1:n.match) {\n\t\tmatches[i,] = temp[ (min.index[i] - n.query + 1):(min.index[i] + 2*n.query) ]\t\n\t}\n\t#reference[min.index] == matches[,(2*n.query)]\n\t\n\tmatches[(n.match+1),] = temp[ (len(temp) - 2*n.query + 1):(len(temp) + n.query) ]\t\t\n\t#matches[(n.match+1), (n.query+1):(2*n.query)] == query\n\t\n\tfor(i in 1:(n.match+1)) {\n\t\tmatches[i,] = matches[i,] / matches[i,n.query]\n\t}\n\t\t\n\n\t#*****************************************************************\n\t# Plot all Matches\n\t#****************************************************************** \t\t\t\t\n\ttemp = 100 * ( t(matches[,-c(1:n.query)]) - 1)\n\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tpar(mar=c(2, 4, 2, 2))\n\tmatplot(temp, type='l',col='gray',lwd=2, lty='dotted', xlim=c(1,2.5*n.query),\n\t\tmain = paste('Pattern Prediction with', n.match, 'neighbours'),ylab='Normalized', xlab='')\n\t\tlines(temp[,(n.match+1)], col='black',lwd=4)\n\t\n\tpoints(rep(2*n.query,n.match), temp[2*n.query,1:n.match], pch=21, lwd=2, col='gray', bg='gray')\n\t\t\t\t\n\tbt.plot.dot.label <- function(x, data, xfun, col='red') {\n\t\tfor(j in 1:len(xfun)) {\n\t\t\ty = match.fun(xfun[[j]])(data)\n\t\t\tpoints(x, y, pch=21, lwd=4, col=col, bg=col)\n\t\t\ttext(x, y, paste(names(xfun)[j], ':', round(y,1),'%'),\n\t\t\t\tadj=c(-0.1,0), cex = 0.8, col=col,xpd=TRUE)\t\t\t\n\t\t}\n\t}\n\t\n\tbt.plot.dot.label(2*n.query, temp[2*n.query,1:n.match], \n\t\tlist(Min=min,Max=max,Median=median,'Bot 25%'=function(x) quantile(x,0.25),'Top 75%'=function(x) quantile(x,0.75)))\n\tbt.plot.dot.label(n.query, temp[n.query,(n.match+1)], list(Current=min))\ndev.off()\t\n\t\n\t#*****************************************************************\n\t# Table with predictions\n\t#****************************************************************** \t\t\n\ttemp = matrix( double(), nr=(n.match+4), 6)\n\t\trownames(temp) = c(1:n.match, spl('Current,Min,Average,Max'))\n\t\tcolnames(temp) = spl('Start,End,Return,Week,Month,Quarter')\n\t\t\n\t# compute returns\n\ttemp[1:(n.match+1),'Return'] = matches[,2*n.query]/ matches[,n.query]\n\ttemp[1:(n.match+1),'Week'] = matches[,(2*n.query+5)]/ matches[,2*n.query]\n\ttemp[1:(n.match+1),'Month'] = matches[,(2*n.query+20)]/ matches[,2*n.query]\n\ttemp[1:(n.match+1),'Quarter'] = matches[,(2*n.query+60)]/ matches[,2*n.query]\n\t\t\t\n\t# compute average returns\n\tindex = spl('Return,Week,Month,Quarter')\n\ttemp['Min', index] = apply(temp[1:(n.match+1),index],2,min,na.rm=T)\n\ttemp['Average', index] = apply(temp[1:(n.match+1),index],2,mean,na.rm=T)\n\ttemp['Max', index] = apply(temp[1:(n.match+1),index],2,max,na.rm=T)\n\t\n\t# format\n\ttemp[] = plota.format(100*(temp-1),1,'','%')\n\t\t\n\t# enter dates\n\ttemp['Current', 'Start'] = format(index(last(data,90)[1]), '%d %b %Y')\n\ttemp['Current', 'End'] = format(index(last(data,1)[1]), '%d %b %Y')\n\tfor(i in 1:n.match) {\n\t\ttemp[i, 'Start'] = format(index(data[min.index[i] - n.query + 1]), '%d %b %Y')\n\t\ttemp[i, 'End'] = format(index(data[min.index[i]]), '%d %b %Y')\t\n\t}\n\t\t\n\t# plot table\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tplot.table(temp, smain='Match Number')\ndev.off()\t\t\n\t\n}\t\n\n###############################################################################\n# Time Series Matching Backtest\n#\n# New weighting scheme : seight each match by its distance\n###############################################################################\nbt.matching.backtest.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY,^GSPC')\n\n\tdata <- new.env()\n\tquantmod:::getSymbols(tickers, src = 'yahoo', from = '1950-01-01', env = data, auto.assign = T)\n\tbt.prep(data, align='keep.all')\n\n\t# compare common part [ SPY and ^GSPC match only if not adjusted for dividends]\n\t#temp = data$prices['1993:01:29::']\n\t#plot(temp[,1]/as.double(temp[1,1]) - temp[,2]/as.double(temp[1,2]), main='Diff between SPY and ^GSPC')\n\n\t# combine SPY and ^GSPC\n\tscale = as.double( data$prices$SPY['1993:01:29'] / data$prices$GSPC['1993:01:29'] )\n\thist = c(scale * data$prices$GSPC['::1993:01:28'], data$prices$SPY['1993:01:29::'])\n\n\t#*****************************************************************\n\t# Backtest setup:\n\t# Starting January 1994, each month search for the 10 best matches \n\t# similar to the last 90 days in the last 10 years of history data\n\t#\n\t# Invest next month if distance weighted prediction is positive\n\t# otherwise stay in cash\n\t#****************************************************************** \n\t# find month ends\n\tmonth.ends = endpoints(hist, 'months')\n\t\tmonth.ends = month.ends[month.ends > 0]\t\t\n\t\n\tstart.index = which(date.year(index(hist[month.ends])) == 1994)[1]\n\tweight = hist * NA\n\t\n\tfor( i in start.index : len(month.ends) ) {\n\t\t#obj = bt.matching.find(hist[1:month.ends[i],], n.match=10, normalize.fn = normalize.mean, plot=T)\n\t\t#matches = bt.matching.overlay(obj, future=hist[(month.ends[i]+1):(month.ends[i]+22),], plot=T)\n\t\t#bt.matching.overlay.table(obj, matches, weights=NA, plot=T)\n\n\t\tobj = bt.matching.find(hist[1:month.ends[i],], normalize.fn = normalize.first)\n\t\tmatches = bt.matching.overlay(obj)\n\t\t\n\t\t# compute prediction for next month\n\t\tn.match = len(obj$min.index)\n\t\tn.query = len(obj$query)\t\t\t\t\n\t\tmonth.ahead.forecast = matches[,(2*n.query+22)]/ matches[,2*n.query] - 1\n\t\t\n\t\t# Average, mean(month.ahead.forecast[1:n.match]) \n\t\tweights = rep(1/n.match, n.match)\n\t\tavg.direction = weighted.mean(month.ahead.forecast[1:n.match], w=weights)\n\t\t\t\t\n\t\t# Distance weighted average\n\t\ttemp = round(100*(obj$dist / obj$dist[1] - 1))\t\t\n\t\t\tn.weight = max(temp) + 1\n\t\t\tweights = (n.weight - temp) / ( n.weight * (n.weight+1) / 2)\n\t\tweights = weights / sum(weights)\n\t\t\t# barplot(weights)\n\t\tavg.direction = weighted.mean(month.ahead.forecast[1:n.match], w=weights)\n\t\t\n\t\t# Logic\n\t\tweight[month.ends[i]] = 0\n\t\tif( avg.direction > 0 ) weight[month.ends[i]] = 1\n\t\t\n\t\t# print progress\t\t\n\t\tif( i %% 10 == 0) cat(i, '\\n')\n\t}\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\ttickers = 'SPY'\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1950-01-01', env = data, auto.assign = T)\n\tbt.prep(data, align='keep.all')\n\t\n\tprices = data$prices \n\t\n\t# Buy & Hold\t\n\tdata$weight[] = 1\n\tbuy.hold = bt.run(data)\t\n\n\t# Strategy\n\tdata$weight[] = NA\n\t\tdata$weight[] = weight['1993:01:29::']\n\t\tcapital = 100000\n\t\tdata$weight[] = (capital / prices) * bt.exrem(data$weight)\n\ttest = bt.run(data, type='share', capital=capital, trade.summary=T)\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\n\tplotbt.custom.report.part1(test, buy.hold, trade.summary=T)\n\t\ndev.off()\t\t\n\t\n\t\n\t\n}\n\n\n\t\n###############################################################################\n# Time Series Matching helper functions\n###############################################################################\n# functions to normalize data\n###############################################################################\nnormalize.mean <- function(x) { x - mean(x) }\nnormalize.mean.sd <- function(x) { (x - mean(x)) / sd(x) }\nnormalize.first <- function(x) { x/as.double(x[1]) }\n\n###############################################################################\n# functions to compute distance\n###############################################################################\ndist.euclidean <- function(x) { stats:::dist(x) }\n\n###############################################################################\n# Find historical matches similar to the given query(pattern)\n###############################################################################\nbt.matching.find <- function\n(\n\tdata,\t\t\t\t# time series\n\tn.query=90, \t\t# length of pattern i.e. last 90 days\n\tn.reference=252*10, # length of history to look for pattern\n\tn.match=10, \t\t# number of matches to find\n\tnormalize.fn = normalize.mean.sd, \t# function to normalize data\n\tdist.fn = dist.euclidean,\t# function to compute distance\n\tplot=FALSE,\t\t\t# flag to create plot\n\tplot.dist=FALSE,\t# flag to create distance plot\t\n\tlayout = NULL,\t\t# flag to idicate if layout is already set\t\n\tmain = NULL\t\t\t# plot title\n)\n{\n\t#*****************************************************************\n\t# Setup search\n\t#****************************************************************** \n\tdata = last(data, n.reference)\n\treference = coredata(data)\n\t\tn = len(reference)\n\t\tquery = reference[(n - n.query + 1):n]\t\n\t\treference = reference[1:(n - n.query)]\n\t\t\n\t\tmain = paste(main, join(format(range(index(data)[(n - n.query + 1):n]), '%d%b%Y'), ' - '))\n\t\t\t\n\t\tn.query = len(query)\n\t\tn.reference = len(reference)\n\n\t\tdist.fn.name = ''\n\t\tif(is.character(dist.fn)) {\n\t\t\tdist.fn.name = paste('with',dist.fn)\n\t\t\tdist.fn = get(dist.fn)\t\t\t\t\t\n\t\t}\n\t\t\n\t#*****************************************************************\n\t# Compute Distance\n\t#****************************************************************** \t\t\n\tdist = rep(NA, n.reference)\n\tquery.normalized = match.fun(normalize.fn)(query)\t\n\t\n\tfor( i in n.query : n.reference ) {\n\t\twindow = reference[ (i - n.query + 1) : i]\n\t\twindow.normalized = match.fun(normalize.fn)(window)\t\n\t\tdist[i] = match.fun(dist.fn)(rbind(query.normalized, window.normalized))\n\t\t\n\t\t# print progress\t\t\n\t\tif( i %% 100 == 0) cat(i, '\\n')\n\t}\n\n\t#*****************************************************************\n\t# Find Matches\n\t#****************************************************************** \t\t\t\n\tmin.index = c()\n\t\n\t# only look at the minimums \n\ttemp = dist\n\t\ttemp[ temp > mean(dist, na.rm=T) ] = NA\n\t\t\n\t# remove n.query, points to the left/right of the minimums\n\tfor(i in 1:n.match) {\n\t\tif(any(!is.na(temp))) {\n\t\t\tindex = which.min(temp)\n\t\t\tmin.index[i] = index\n\t\t\ttemp[max(0,index - 2*n.query) : min(n.reference,(index + n.query))] = NA\n\t\t}\n\t}\n\tn.match = len(min.index)\n\n\t\n\t#*****************************************************************\n\t# Plot Matches\n\t#****************************************************************** \t\t\n\tif(plot) {\t\n\t\tdates = index(data)[1:len(dist)]\n\t\n\t\tif(is.null(layout)) {\n\t\t\tif(plot.dist) layout(1:2) else layout(1)\t\t\n\t\t}\n\t\tpar(mar=c(2, 4, 2, 2))\n\t\t\n\t\tif(plot.dist) {\n\t\tplot(dates, dist, type='l',col='gray', main=paste('Top Historical Matches for', main, dist.fn.name), ylab='Distance', xlab='')\n\t\t\tabline(h = mean(dist, na.rm=T), col='darkgray', lwd=2)\n\t\t\tpoints(dates[min.index], dist[min.index], pch=22, col='red', bg='red')\n\t\t\ttext(dates[min.index], dist[min.index], 1:n.match, adj=c(1,1), col='black',xpd=TRUE)\n\t\t}\n\t\t\n\t\tplota(data, type='l', col='gray', LeftMargin = 1,\n\t\t\tmain=iif(!plot.dist, paste('Top Historical Matches for', main), NULL)\n\t\t\t)\n\t\t\tplota.lines(last(data,n.query), col='blue')\n\t\t\tfor(i in 1:n.match) {\n\t\t\tplota.lines(data[(min.index[i]-n.query + 1):min.index[i]], col='red')\n\t\t\t}\n\t\t\ttext(index4xts(data)[min.index - n.query/2], reference[min.index - n.query/2], 1:n.match, \n\t\t\t\tadj=c(1,-1), col='black',xpd=TRUE)\n\t\t\tplota.legend(paste('Pattern: ', main, ',Match Number'),'blue,red')\t\n\t}\n\t\n\treturn(list(min.index=min.index, dist=dist[min.index], query=query, reference=reference, dates = index(data), main = main))\n}\n\t\t\n\n###############################################################################\n# Create matrix that overlays all matches one on top of each other\n###############################################################################\n# helper function to plot dots and labels\n###############################################################################\nbt.plot.dot.label <- function(x, data, xfun, col='red') {\n\tfor(j in 1:len(xfun)) {\n\t\ty = match.fun(xfun[[j]])(data)\n\t\tpoints(x, y, pch=21, lwd=4, col=col, bg=col)\n\t\ttext(x, y, paste(names(xfun)[j], ':', round(y,1),'%'),\n\t\t\tadj=c(-0.1,0), cex = 0.8, col=col,xpd=TRUE)\t\t\t\n\t}\n}\n\nbt.matching.overlay <- function\n(\n\tobj, \t\t\t\t# object from bt.matching.find function\n\tfuture=NA,\t\t\t# time series of future, only used for plotting\n \tplot=FALSE,\t\t\t# flag to create plot\n \tplot.index=NA,\t\t# range of data to plot\n\tlayout = NULL\t\t# flag to idicate if layout is already set\t\n)\n{\n\tmin.index = obj$min.index\n\tquery = obj$query\n\treference = obj$reference\n\t\n\tn.match = len(min.index)\n\tn.query = len(query)\n\tn.reference = len(reference)\n\n\t#*****************************************************************\n\t# Overlay all Matches\n\t#****************************************************************** \t\t\n\tmatches = matrix(NA, nr=(n.match+1), nc=3*n.query)\n\ttemp = c(rep(NA, n.query), reference, query, future)\n\tfor(i in 1:n.match) {\n\t\tmatches[i,] = temp[ (min.index[i] - n.query + 1):(min.index[i] + 2*n.query) ]\t\n\t}\n\t#reference[min.index] == matches[,(2*n.query)]\n\t\n\tmatches[(n.match+1),] = temp[ (n.reference + 1):(n.reference + 3*n.query) ]\t\t\n\t#matches[(n.match+1), (n.query+1):(2*n.query)] == query\n\t\n\tfor(i in 1:(n.match+1)) {\n\t\tmatches[i,] = matches[i,] / iif(!is.na(matches[i,n.query]), matches[i,n.query], matches[i,(n.query+1)])\n\t}\n\t\n\t#*****************************************************************\n\t# Plot all Matches\n\t#****************************************************************** \t\t\t\t\n\tif(plot) {\t\t\n\t\ttemp = 100 * ( t(matches[,-c(1:n.query)]) - 1)\n\t\tif(!is.na(plot.index[1])) temp=temp[plot.index,]\n\t\tn = nrow(temp)\n\t\t\n\t\tif(is.null(layout)) layout(1)\n\t\t#par(mar=c(4, 2, 2, 2), ...)\n\t\tpar(mar=c(4, 2, 2, 2))\n\t\t\n\t\tmatplot(temp, type='n',col='gray',lwd=2, lty='dotted', xlim=c(1, n + 0.15*n),\n\t\t\tmain = paste(obj$main,'Historical Pattern Prediction with', n.match, 'neighbours'),ylab='Normalized', xlab = 'Trading Days')\n\t\t\t\n\t\tcol=adjustcolor('yellow', 0.5)\n\t\trect(0, par('usr')[3],n.query, par('usr')[4], col=col, border=col)\n\t\tbox()\n\n\t\t\n\t\tmatlines(temp, col='gray',lwd=2, lty='dotted')\n\t\tlines(temp[,(n.match+1)], col='black',lwd=4)\n\t\t\n\t\t\n\t\t\t\n\t\tpoints(rep(n, n.match), temp[n, 1:n.match], pch=21, lwd=2, col='gray', bg='gray')\n\t\t\t\t\t\t\n\t\tbt.plot.dot.label(n, temp[n, 1:n.match], \n\t\t\tlist(Min=min,Max=max,Median=median,'Bot 25%'=function(x) quantile(x,0.25),'Top 75%'=function(x) quantile(x,0.75)))\n\t\tbt.plot.dot.label(n.query, temp[n.query,(n.match+1)], list(Current=min))\t\n\t}\n\t\n\treturn(matches)\n}\t\n\n\n###############################################################################\n# Create matches summary table\n###############################################################################\nbt.matching.overlay.table <- function\n(\n\tobj, \t\t\t\t# object from bt.matching.find function\n\tmatches, \t\t\t# matches from bt.matching.overlay function\n\tweights=NA, \t\t# weights to compute average\n \tplot=FALSE,\t\t\t# flag to create plot\n\tlayout = NULL\t\t# flag to idicate if layout is already set\t\n)\n{\n\tmin.index = obj$min.index\n\tquery = obj$query\n\treference = obj$reference\n\tdates = obj$dates\n\t\n\tn.match = len(min.index)\n\tn.query = len(query)\n\tn.reference = len(reference)\n\t\n\tif(is.na(weights)) weights = rep(1/n.match, n.match)\n\n\t#*****************************************************************\n\t# Table with predictions\n\t#****************************************************************** \t\t\n\ttemp = matrix( double(), nr=(n.match + 4), 6)\n\t\trownames(temp) = c(1:n.match, spl('Current,Min,Average,Max'))\n\t\tcolnames(temp) = spl('Start,End,Return,Week,Month,Quarter')\n\t\t\n\t# compute returns\n\ttemp[1:(n.match+1),'Return'] = matches[,2*n.query]/ matches[,n.query]\n\ttemp[1:(n.match+1),'Week'] = matches[,(2*n.query+5)]/ matches[,2*n.query]\n\ttemp[1:(n.match+1),'Month'] = matches[,(2*n.query+20)]/ matches[,2*n.query]\n\ttemp[1:(n.match+1),'Quarter'] = matches[,(2*n.query+60)]/ matches[,2*n.query]\n\t\t\t\n\t# compute average returns\n\tindex = spl('Return,Week,Month,Quarter')\n\ttemp['Min', index] = apply(temp[1:(n.match+0),index],2,min,na.rm=T)\n\t#temp['Average', index] = apply(temp[1:(n.match+0),index],2,mean,na.rm=T)\n\ttemp['Average', index] = apply(temp[1:(n.match+0),index],2,weighted.mean,w=weights,na.rm=T)\n\ttemp['Max', index] = apply(temp[1:(n.match+0),index],2,max,na.rm=T)\n\t\n\t# format\n\ttemp[] = plota.format(100*(temp-1),1,'','%')\n\t\t\n\t# enter dates\n\ttemp['Current', 'Start'] = format(dates[(n.reference+1)], '%d %b %Y')\n\ttemp['Current', 'End'] = format(dates[len(dates)], '%d %b %Y')\n\tfor(i in 1:n.match) {\n\t\ttemp[i, 'Start'] = format(dates[min.index[i] - n.query + 1], '%d %b %Y')\n\t\ttemp[i, 'End'] = format(dates[min.index[i]], '%d %b %Y')\t\n\t}\n\t\t\n\t# plot table\n\tif(plot) {\t\t\t\n\t\tif(is.null(layout)) layout(1)\n\t\tplot.table(temp, smain='Match Number')\t\n\t}\n\t\n\treturn(temp)\n}\n\n\n\n###############################################################################\n# Time Series Matching with Dynamic time warping\n#\n# Based on Jean-Robert Avettand-Fenoel - How to Accelerate Model Deployment using Rook\n# http://www.londonr.org/Sep%2011%20LondonR_AvettandJR.pdf\n###############################################################################\n# functions to compute distance\n###############################################################################\n#dist.euclidean <- function(x) { stats:::dist(x) }\ndist.MOdist <- function(x) { MOdist(t(x)) }\ndist.DTW <- function(x) { dtw(x[1,], x[2,])$distance }\n\n\nbt.matching.dtw.test <- function() \n{\n\t#*****************************************************************\n\t# Example of Dynamic time warping from dtw help\n\t#****************************************************************** \n\tload.packages('dtw')\n\t\n\t# A noisy sine wave as query\n\tidx = seq(0,6.28,len=100)\n\tquery = sin(idx)+runif(100)/10\n\t\n\t# A cosine is for reference; sin and cos are offset by 25 samples\n\treference = cos(idx)\n\t\n\t# map one to one, typical distance\n\talignment<-dtw(query, reference, keep=TRUE)\n\talignment$index1 = 1:100\n\talignment$index2 = 1:100\n\npng(filename = 'plot0.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplot(alignment,main='Example of 1 to 1 mapping', type='two',off=3)\ndev.off()\n\n\t# map one to many, dynamic time warping\n\talignment<-dtw(query, reference, keep=TRUE)\n\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplot(alignment,main='Example of 1 to many mapping (DTW)', type='two',off=3)\ndev.off()\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'SPY'\n\n\tdata = getSymbols(tickers, src = 'yahoo', from = '1950-01-01', auto.assign = F)\t\n\n\t#*****************************************************************\n\t# Euclidean distance\t\n\t#****************************************************************** \n\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tobj = bt.matching.find(Cl(data), normalize.fn = normalize.mean, dist.fn = 'dist.euclidean', plot=T)\ndev.off()\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tmatches = bt.matching.overlay(obj, plot.index=1:90, plot=T)\ndev.off()\n\npng(filename = 'plot4.png', width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:2)\n\tmatches = bt.matching.overlay(obj, plot=T, layout=T)\n\tbt.matching.overlay.table(obj, matches, plot=T, layout=T)\ndev.off()\n\n\t#*****************************************************************\n\t# Dynamic time warping distance\t\n\t#****************************************************************** \n\t# http://en.wikipedia.org/wiki/Dynamic_time_warping\n\t# http://dtw.r-forge.r-project.org/\n\t#****************************************************************** \n\npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tobj = bt.matching.find(Cl(data), normalize.fn = normalize.mean, dist.fn = 'dist.DTW', plot=T)\ndev.off()\n\npng(filename = 'plot6.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tmatches = bt.matching.overlay(obj, plot.index=1:90, plot=T)\ndev.off()\n\n\npng(filename = 'plot7.png', width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:2)\n\tmatches = bt.matching.overlay(obj, plot=T, layout=T)\n\tbt.matching.overlay.table(obj, matches, plot=T, layout=T)\ndev.off()\n\n\t#*****************************************************************\n\t# Dynamic time warping distance\t\n\t#****************************************************************** \n\npng(filename = 'plot8.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tobj = bt.matching.find(Cl(data), normalize.fn = normalize.mean, dist.fn = 'dist.DTW1', plot=T)\ndev.off()\n\npng(filename = 'plot9.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tmatches = bt.matching.overlay(obj, plot.index=1:90, plot=T)\ndev.off()\n\n\npng(filename = 'plot10.png', width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:2)\n\tmatches = bt.matching.overlay(obj, plot=T, layout=T)\n\tbt.matching.overlay.table(obj, matches, plot=T, layout=T)\ndev.off()\n\n\n\n\t#*****************************************************************\n\t# Dynamic time warping distance\t\n\t#****************************************************************** \n\npng(filename = 'plot11.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tobj = bt.matching.find(Cl(data), normalize.fn = normalize.mean, dist.fn = 'dist.DDTW', plot=T)\ndev.off()\n\npng(filename = 'plot12.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tmatches = bt.matching.overlay(obj, plot.index=1:90, plot=T)\ndev.off()\n\n\npng(filename = 'plot13.png', width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:2)\n\tmatches = bt.matching.overlay(obj, plot=T, layout=T)\n\tbt.matching.overlay.table(obj, matches, plot=T, layout=T)\ndev.off()\n\n\n\n}\t\t\n\n\n############################################################################### \n# Derivative Dynamic Time Warping by Eamonn J. Keogh and Michael J. Pazzani\n# http://www.cs.rutgers.edu/~mlittman/courses/statai03/DDTW-2001.pdf\n# \n# page 3\n# To align two sequences using DTW we construct an n-by-m matrix where the (ith, jth)\n# element of the matrix contains the distance d(qi,cj) between the two points qi and cj\n# (Typically the Euclidean distance is used, so d(qi,cj) = (qi - cj)2 ).\n# \n# page 6\n# With DDTW the distance measure d(qi,cj) is not Euclidean but rather the square of the \n# difference of the estimated derivatives of qi and cj. \n# This estimate is simply the average of the slope of the line through the point in \n# question and its left neighbor, and the slope of the line through the left neighbor and the\n# right neighbor. Empirically this estimate is more robust to outliers than any estimate\n# considering only two datapoints. Note the estimate is not defined for the first and last\n# elements of the sequence. Instead we use the estimates of the second and next-to-last\n# elements respectively.\n###############################################################################\nderivative.est <- function(x) {\n\tx = as.vector(x)\n\tn = len(x)\n\td = (( x - mlag(x) ) + ( mlag(x,-1)- mlag(x) ) / 2) / 2\n\td[1] = d[2]\n\td[n] = d[(n-1)]\n\td\n}\n\ndist.DDTW <- function(x) { \n\ty = x\n\tx[1,] = derivative.est(x[1,])\n\tx[2,] = derivative.est(x[2,])\n\t\n\talignment = dtw(x[1,], x[2,])\n\tstats:::dist(rbind(y[1,alignment$index1],y[2,alignment$index2]))\n\t#proxy::dist(y[1,alignment$index1],y[2,alignment$index2],method='Euclidean',by_rows=F)\t\n}\t\n\ndist.DTW1 <- function(x) { \n\talignment = dtw(x[1,], x[2,])\n\tstats:::dist(rbind(x[1,alignment$index1],x[2,alignment$index2]))\n}\n\n\nbt.ddtw.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'SPY'\n\n\tdata = getSymbols(tickers, src = 'yahoo', from = '1950-01-01', auto.assign = F)\t\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \n\tload.packages('dtw')\n\t\n\tquery = as.vector(coredata(last(Cl(data['2011::2011']), 60)))\n\treference = as.vector(coredata(last(Cl(data['2010::2010']), 60)))\t\n\t\n\t#*****************************************************************\n\t# Dynamic Time Warping \t\n\t#****************************************************************** \n\talignment = dtw(query, reference, keep=TRUE)\n\npng(filename = 'plot1.ddtw.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplot(alignment,main='DTW Alignment', type='two',off=20)\ndev.off()\n\t\t\n\t\n\t#*****************************************************************\n\t# Derivative Dynamic Time Warping by Eamonn J. Keogh and Michael J. Pazzani\n\t# http://www.cs.rutgers.edu/~mlittman/courses/statai03/DDTW-2001.pdf\n\t#****************************************************************** \n\tderivative.est <- function(x) {\n\t\tx = as.vector(x)\n\t\tn = len(x)\n\t\td = (( x - mlag(x) ) + ( mlag(x,-1)- mlag(x) ) / 2) / 2\n\t\td[1] = d[2]\n\t\td[n] = d[(n-1)]\n\t\td\n\t}\n\t\n\talignment0 = dtw(derivative.est(query), derivative.est(reference), keep=TRUE)\n\talignment$index1 = alignment0$index1\n\talignment$index2 = alignment0$index2\n\t\t\npng(filename = 'plot2.ddtw.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplot(alignment,main='Derivative DTW Alignment', type='two',off=20)\ndev.off()\n\t\n\t\n}\n\t\n\n\n\n\n###############################################################################\n# Position Sizing\n#\n# Money Management Position Sizing\n# http://www.trading-plan.com/money_position_sizing.html\n#\n# Position Sizing is Everything\n# http://www.leighdrogen.com/position-sizing-is-everything/\n###############################################################################\nbt.position.sizing.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\n\tbt.prep(data, align='keep.all', dates='1970::')\t\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tnperiods = nrow(prices)\n\t\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Buy & Hold\n\t#****************************************************************** \n\tdata$weight[] = 0\n\t\tdata$weight[] = 1\n\tmodels$buy.hold = bt.run.share(data, clean.signal=T)\n\n\t#*****************************************************************\n\t# Volatility Position Sizing - ATR\n\t#****************************************************************** \n\tatr = bt.apply(data, function(x) ATR(HLC(x),20)[,'atr'])\n\t\t\n\t# http://www.leighdrogen.com/position-sizing-is-everything/\t\n\t# position size in units = ((porfolio size * % of capital to risk)/(ATR*2)) \n\tdata$weight[] = NA\n\t\tcapital = 100000\n\t\t\n\t\t# risk 2% of capital, assuming 2 atr stop\n\t\tdata$weight[] = (capital * 2/100) / (2 * atr)\n\t\t\n\t\t# make sure you are not commiting more than 100%\n\t\t# http://www.trading-plan.com/money_position_sizing.html\n\t\tmax.allocation = capital / prices\n\t\tdata$weight[] = iif(data$weight > max.allocation, max.allocation,data$weight)\n\t\t\n\tmodels$buy.hold.2atr = bt.run(data, type='share', capital=capital)\t\t\t\t\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\tmodels = rev(models)\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\n\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\tplotbt.custom.report.part2(models)\ndev.off()\t\n\t\t\n\n}\t\n\n\n\n###############################################################################\n# Trading Equity Curve with Volatility Position Sizing\n###############################################################################\nbt.volatility.position.sizing.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'SPY'\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\n\t#*****************************************************************\n\t# Buy and Hold\n\t#****************************************************************** \n\tmodels = list()\n\tprices = data$prices\n\t\n\tdata$weight[] = 1\n\tmodels$buy.hold = bt.run.share(data, clean.signal=T)\n\n\t#*****************************************************************\n\t# Buy and Hold with target 10% Volatility\n\t#****************************************************************** \n\tret.log = bt.apply.matrix(prices, ROC, type='continuous')\n\thist.vol = sqrt(252) * bt.apply.matrix(ret.log, runSD, n = 60)\n\n\tdata$weight[] = 0.1 / hist.vol\n\tmodels$buy.hold.volatility.weighted = bt.run.share(data, clean.signal=T)\n\n\t#*****************************************************************\n\t# Buy and Hold with target 10% Volatility and Max Total leverage 100%\n\t#****************************************************************** \t\t\n\tdata$weight[] = 0.1 / hist.vol\n\t\trs = rowSums(data$weight)\n\t\tdata$weight[] = data$weight / iif(rs > 1, rs, 1) \t\t\t\n\tmodels$buy.hold.volatility.weighted.100 = bt.run.share(data, clean.signal=T)\n\t\t\n\t#*****************************************************************\n\t# Same, rebalanced Monthly\n\t#****************************************************************** \n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\t\n\tdata$weight[] = NA\n\tdata$weight[period.ends,] = 0.1 / hist.vol[period.ends,]\n\t\trs = rowSums(data$weight[period.ends,])\n\t\tdata$weight[period.ends,] = data$weight[period.ends,] / iif(rs > 1, rs, 1) \t\t\t\n\tmodels$buy.hold.volatility.weighted.100.monthly = bt.run.share(data, clean.signal=T)\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t# Plot performance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 1600, height = 1000, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplotbt.custom.report.part2(rev(models))\ndev.off()\t\n\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# Plot Portfolio Turnover for each strategy\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', plotX = F, label='both')\ndev.off()\t\n\t\n\t\n\n\t#*****************************************************************\n\t# Next let's examine other volatility measures\n\t#****************************************************************** \n\tmodels = models[c('buy.hold' ,'buy.hold.volatility.weighted.100.monthly')]\n\n\t\t\n\t# TTR volatility calc types\n\tcalc = c(\"close\", \"garman.klass\", \"parkinson\", \"rogers.satchell\", \"gk.yz\", \"yang.zhang\")\n\t\n\tohlc = OHLC(data$SPY)\n\tfor(icalc in calc) {\n\t\tvol = volatility(ohlc, calc = icalc, n = 60, N = 252)\n\t\t\n\t\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = 0.1 / vol[period.ends,]\n\t\t\trs = rowSums(data$weight[period.ends,])\n\t\t\tdata$weight[period.ends,] = data$weight[period.ends,] / iif(rs > 1, rs, 1) \t\t\t\n\t\tmodels[[icalc]] = bt.run.share(data, clean.signal=T)\n\t}\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot4.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t# Plot performance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\ndev.off()\t\n\t\npng(filename = 'plot5.png', width = 1600, height = 600, units = 'px', pointsize = 12, bg = 'white')\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\n\n\n\t#*****************************************************************\n\t# Volatility Position Sizing applied to MA cross-over strategy's Equity Curve\n\t#****************************************************************** \n\tmodels = list()\t\n\t\n\tsma.fast = SMA(prices, 50)\n\tsma.slow = SMA(prices, 200)\n\tweight = iif(sma.fast >= sma.slow, 1, -1)\n\t\n\tdata$weight[] = weight\n\tmodels$ma.crossover = bt.run.share(data, clean.signal=T)\n\t\t\n\t#*****************************************************************\n\t# Target 10% Volatility\n\t#****************************************************************** \n\tret.log = bt.apply.matrix(models$ma.crossover$equity, ROC, type='continuous')\n\thist.vol = sqrt(252) * bt.apply.matrix(ret.log, runSD, n = 60)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = (0.1 / hist.vol[period.ends,]) * weight[period.ends,]\n\t\t# limit total leverage to 100%\t\t\n\t\trs = rowSums(data$weight[period.ends,])\n\t\tdata$weight[period.ends,] = data$weight[period.ends,] / iif(abs(rs) > 1, abs(rs), 1) \t\t\t\n\tmodels$ma.crossover.volatility.weighted.100.monthly = bt.run.share(data, clean.signal=T)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot6.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t# Plot performance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\ndev.off()\t\n\t\npng(filename = 'plot7.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplotbt.custom.report.part2(rev(models))\ndev.off()\t\n\t\n\t\n\t#*****************************************************************\n\t# Apply Volatility Position Sizing Timing stretegy by M. Faber\n\t#****************************************************************** \n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na', dates='1994::')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\t\tn = ncol(prices)\n\tmodels = list()\n\n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\t\n\t#*****************************************************************\n\t# Equal Weight\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = ntop(prices[period.ends,], n)\n\t\tdata$weight[1:200,] = NA\n\tmodels$equal.weight = bt.run.share(data, clean.signal=F)\n\t\t\t\t\n\t#*****************************************************************\n\t# Timing by M. Faber\n\t#****************************************************************** \n\tsma = bt.apply.matrix(prices, SMA, 200)\n\t\n\tweight = ntop(prices, n) * (prices > sma)\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight[period.ends,]\n\tmodels$timing = bt.run.share(data, clean.signal=F)\n\t\t\n\t#*****************************************************************\n\t# Timing with target 10% Volatility\n\t#****************************************************************** \n\tret.log = bt.apply.matrix(models$timing$equity, ROC, type='continuous')\n\thist.vol = bt.apply.matrix(ret.log, runSD, n = 60)\n\t\thist.vol = sqrt(252) * as.vector(hist.vol)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = (0.1 / hist.vol[period.ends]) * weight[period.ends,]\n\t\trs = rowSums(data$weight)\n\t\tdata$weight[] = data$weight / iif(rs > 1, rs, 1) \t\t\t\t\n\t\tdata$weight[1:200,] = NA\n\tmodels$timing.volatility.weighted.100.monthly = bt.run.share(data, clean.signal=T)\n\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot8.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t# Plot performance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\ndev.off()\t\n\t\npng(filename = 'plot9.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplotbt.custom.report.part2(rev(models))\ndev.off()\t\n\t\t\n\t\n}\t\n\t\n\n\n\n\n###############################################################################\n# Rolling Correlation\n# http://www.activetradermag.com/index.php/c/Trading_Strategies/d/Trading_correlation\n###############################################################################\nbt.rolling.cor.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = sp500.components()$tickers\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1970::')\n\n\tspy = getSymbols('SPY', src = 'yahoo', from = '1970-01-01', auto.assign = F)\n\t\tret.spy = coredata( Cl(spy) / mlag(Cl(spy))-1 )\n\t\n\t#*****************************************************************\n\t# Code Logic\n\t#****************************************************************** \n\tprices = data$prices['1993:01:29::'] \n\t\tnperiods = nrow(prices)\n\t\t\t\n\tret = prices / mlag(prices) - 1\n\t\tret = coredata(ret)\n\t\t\n\t# require at least 100 stocks with prices\n\tindex = which((count(t(prices)) > 100 ))\n\t\tindex = index[-c(1:252)]\n\t\t\n\t# average correlation among S&P 500 components\n\tavg.cor = NA * prices[,1]\n\t\n\t# average correlation between the S&P 500 index (SPX) and its component stocks\n\tavg.cor.spy = NA * prices[,1]\n\t\n\tfor(i in index) {\n\t\thist = ret[ (i- 252 +1):i, ]\n\t\thist = hist[ , count(hist)==252, drop=F]\n\t\t\tnleft = ncol(hist)\n\t\t\n\t\tcorrelation = cor(hist, use='complete.obs',method='pearson')\n\t\tavg.cor[i,] = (sum(correlation) - nleft) / (nleft*(nleft-1))\n\t\t\n\t\tavg.cor.spy[i,] = sum(cor(ret.spy[ (i- 252 +1):i, ], hist, use='complete.obs',method='pearson')) / nleft\n\t\t\n\t\tif( i %% 100 == 0) cat(i, 'out of', nperiods, '\\n')\n\t}\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\npng(filename = 'plot.sp500.cor.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\n\t\n \tsma50 = SMA(Cl(spy), 50)\n \tsma200 = SMA(Cl(spy), 200)\n \t\n \tcols = col.add.alpha(spl('green,red'),50)\n\tplota.control$col.x.highlight = iif(sma50 > sma200, cols[1], cols[2])\n\thighlight = sma50 > sma200 | sma50 < sma200\n\t\t\t\n\tplota(avg.cor, type='l', ylim=range(avg.cor, avg.cor.spy, na.rm=T), x.highlight = highlight,\n\t\t\tmain='Average 252 day Pairwise Correlation for stocks in SP500')\n\t\tplota.lines(avg.cor.spy, type='l', col='blue')\n\t\tplota.legend('Pairwise Correlation,Correlation with SPY,SPY 50-day SMA > 200-day SMA,SPY 50-day SMA < 200-day SMA', \n\t\tc('black,blue',cols))\n\t\t\ndev.off()\t\n\t\t\n}\n\n\n\n###############################################################################\n# Volatility Quantiles\n###############################################################################\nbt.volatility.quantiles.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = sp500.components()$tickers\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\t#save(data, file='data.sp500.components.Rdata') \n\t\t#load(file='data.sp500.components.Rdata') \t\n\t\t\n\t\t# remove companies with less than 5 years of data\n\t\trm.index = which( sapply(ls(data), function(x) nrow(data[[x]])) < 1000 )\t\n\t\trm(list=names(rm.index), envir=data)\n\t\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\n\t\n\t\n\tdata.spy <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data.spy, auto.assign = T)\n\tbt.prep(data.spy, align='keep.all', dates='1994::')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# setdiff(index(data.spy$prices), index(data$prices))\n\t# setdiff(index(data$prices),index(data.spy$prices))\n\tprices = data$prices\n\t\tnperiods = nrow(prices)\n\t\tn = ncol(prices)\n\t\t\t\n\tmodels = list()\n\t\n\t# SPY\n\tdata.spy$weight[] = NA\n\t\tdata.spy$weight[] = 1\n\tmodels$spy = bt.run(data.spy)\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, 500)\n\tmodels$equal.weight = bt.run(data)\n\t\n\t#*****************************************************************\n\t# Create Quantiles based on the historical one year volatility \n\t#****************************************************************** \n\t# setup re-balancing periods\n#\tperiod.ends = 1:nperiods\n\tperiod.ends = endpoints(prices, 'weeks')\n#\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\t# compute historical one year volatility\t\n\tp = bt.apply.matrix(coredata(prices), ifna.prev)\t\n\tret = p / mlag(p) - 1\t\t\n\tsd252 = bt.apply.matrix(ret, runSD, 252)\t\t\n\t\t\n\t# split stocks in the S&P 500 into Quantiles using one year historical Volatility\t\n\tn.quantiles=5\n\tstart.t = which(period.ends >= (252+2))[1]\n\tquantiles = weights = p * NA\t\t\t\n\t\n\tfor( t in start.t:len(period.ends) ) {\n\t\ti = period.ends[t]\n\n\t\tfactor = sd252[i,]\n\t\tranking = ceiling(n.quantiles * rank(factor, na.last = 'keep','first') / count(factor))\n\t\n\t\tquantiles[i,] = ranking\n\t\tweights[i,] = 1/tapply(rep(1,n), ranking, sum)[ranking]\t\t\t\n\t}\n\n\tquantiles = ifna(quantiles,0)\n\t\n\t#*****************************************************************\n\t# Create backtest for each Quintile\n\t#****************************************************************** \n\tfor( i in 1:n.quantiles) {\n\t\ttemp = weights * NA\n\t\ttemp[period.ends,] = 0\n\t\ttemp[quantiles == i] = weights[quantiles == i]\n\t\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[] = temp\n\t\tmodels[[ paste('Q',i,sep='_') ]] = bt.run(data, silent = T)\n\t}\n\trowSums(models$Q_2$weight,na.rm=T)\t\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\t\n\t# put all reports into one pdf file\n\t#pdf(file = 'report.pdf', width=8.5, height=11)\n\t\n\tpng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(models)\t\t\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.strategy.sidebyside(models)\n\tdev.off()\t\t\n\n\t\t\n\t\n\n\t# save summary\n\t#load.packages('abind')\n\t#out = abind(lapply(models, function(m) m$equity))\t\n\t#\tcolnames(out) = names(models)\n\t#write.xts(make.xts(out, index(prices)), 'report.csv')\t\n}\n\n\n\n\n\n###############################################################################\n# Factor Attribution & Value Quantiles\n###############################################################################\nbt.fa.value.quantiles.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = sp500.components()$tickers\n\t#tickers = dow.jones.components()\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\t#save(data, file='data.sp500.components.Rdata') \n\t\t#load(file='data.sp500.components.Rdata') \t\n\t\t\n\t\t# remove companies with less than 5 years of data\n\t\trm.index = which( sapply(ls(data), function(x) nrow(data[[x]])) < 1000 )\t\n\t\trm(list=names(rm.index), envir=data)\n\t\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\ttickers = data$symbolnames\n\t\n\t\n\tdata.spy <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data.spy, auto.assign = T)\n\tbt.prep(data.spy, align='keep.all', dates='1994::')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# setdiff(index(data.spy$prices), index(data$prices))\n\t# setdiff(index(data$prices),index(data.spy$prices))\n\tprices = data$prices\n\t\tnperiods = nrow(prices)\n\t\tn = ncol(prices)\n\t\t\t\n\tmodels = list()\n\t\n\t# SPY\n\tdata.spy$weight[] = NA\n\t\tdata.spy$weight[] = 1\n\tmodels$spy = bt.run(data.spy)\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(prices, n)\n\tmodels$equal.weight = bt.run(data)\n\t\n\t#*****************************************************************\n\t# Compute Factor Attribution for each ticker\n\t#****************************************************************** \n\tperiodicity = 'weeks'\n\t\n\t# load Fama/French factors\n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = F, clean = F)\n\t\n\tperiod.ends = endpoints(data$prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\t# add factors and align\n\tdata.fa <- new.env()\n\t\tfor(i in tickers) data.fa[[i]] = data[[i]][period.ends,]\n\tdata.fa$factors = factors$data / 100\n\tbt.prep(data.fa, align='remove.na')\n\n\t\n\tindex = match( index(data.fa$prices), index(data$prices) )\n\tmeasure = data$prices[ index, ]\n\t\t\n\tfor(i in tickers) {\n\t\tcat(i, '\\n')\n\t\t\n\t\t# Facto Loadings Regression\n\t\tobj = factor.rolling.regression(data.fa, i, 36, silent=T)\n\t\t\n\t\tmeasure[,i] = coredata(obj$fl$estimate$HML)\n\t}\n\t\t\n\t\n\t#*****************************************************************\n\t# Create Value Quantiles\n\t#****************************************************************** \n\tn.quantiles=5\n\tstart.t = 1+36\n\tquantiles = weights = coredata(measure) * NA\t\t\t\n\t\n\tfor( t in start.t:nrow(weights) ) {\n\t\tfactor = as.vector(coredata(measure[t,]))\n\t\tranking = ceiling(n.quantiles * rank(factor, na.last = 'keep','first') / count(factor))\n\t\t#tapply(factor,ranking,sum)\n\t\t\n\t\tquantiles[t,] = ranking\n\t\tweights[t,] = 1/tapply(rep(1,n), ranking, sum)[ranking]\t\t\t\n\t}\n\n\tquantiles = ifna(quantiles,0)\n\t\n\t#*****************************************************************\n\t# Create backtest for each Quintile\n\t#****************************************************************** \n\tfor( i in 1:n.quantiles) {\n\t\ttemp = weights * NA\n\t\t\ttemp[] = 0\n\t\ttemp[quantiles == i] = weights[quantiles == i]\n\t\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[index,] = temp\n\t\tmodels[[ paste('Q',i,sep='_') ]] = bt.run(data, silent = T)\n\t}\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\t\n\t# put all reports into one pdf file\n\t#pdf(file = 'report.pdf', width=8.5, height=11)\n\t\n\tpng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(models)\t\t\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.strategy.sidebyside(models)\n\tdev.off()\t\n\n}\n\t\n\t\n###############################################################################\n# Three Factor Rolling Regression Viewer\n# http://mas.xtreemhost.com/\n############################################################################### \n# New fund regression calculator\n# http://www.bogleheads.org/forum/viewtopic.php?t=11506&highlight=regression\n#\n# Factor loadings?\n# http://www.bogleheads.org/forum/viewtopic.php?t=14629\n#\n# Efficient Frontier: Rolling Your Own: Three-Factor Analysis\n# http://www.efficientfrontier.com/ef/101/roll101.htm\n#\n# Kenneth R French: Data Library\n# http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html\n############################################################################### \n# alpha: how much 'extra' return did the fund have that could not be accounted for by the model. this is almost never large or statistically significant.\n# B(MKT) = market factor: most 100% stock funds have a market factor near 1.0. higher values may indicate leverage. lower values may indicate cash and or bonds.\n# B(SMB) = size factor (small minus big): positive values indicate the fund's average holding is smaller than the market\n# B(HML) = value factor (high minus low): positive values indicate the fund's average holding is more 'value' oriented than the market (based on book to market ratio)\n# R2: measures how well the fund returns match the model (values close to 1.0 indicate a good statistical fit)\n############################################################################### \nfactor.rolling.regression <- function(\n\tdata, \n\tticker = data$symbolnames[-grep('factor', data$symbolnames)], \n\twindow.len = 36,\n\tsilent = F,\n\tcustom.stats.fn = NULL\n) \n{\n\tticker = ticker[1]\n\t\n\t#*****************************************************************\n\t# Facto Loadings Regression over whole period\n\t#****************************************************************** \n\tprices = data$prices\n\t\tnperiods = nrow(prices)\n\t\tdates = index(data$prices)\n\t\n\t# compute simple returns\t\n\thist.returns = ROC(prices[,ticker], type = 'discrete')\t\n\t\thist.returns = hist.returns - data$factors$RF\n\tyout = hist.returns\n\ty = coredata(yout)\n\t\n\txout = data$factors[, -which(names(data$factors) == 'RF')]\n\tx = coredata(xout)\n\t\t\n#\tfit = summary(lm(y~x))\n#\t\test = fit$coefficients[,'Estimate']\n#\t\tstd.err = fit$coefficients[,'Std. Error']\n#\t\tr2 = fit$r.squared\n\n\tok.index = !(is.na(y) | (rowSums(is.na(x)) > 0))\n\tfit = ols(cbind(1,x[ok.index,]),y[ok.index], T)\n\t\test = fit$coefficients\n\t\tstd.err = fit$seb\n\t \tr2 = fit$r.squared\n\n\t \t\t\n\t# Facto Loadings - fl\n\tfl.all = list()\n\t\tfl.all$estimate = c(est, r2)\n\t\tfl.all$std.error = c(std.err, NA)\n\n\t#*****************************************************************\n\t# Facto Loadings Regression over Month window\n\t#****************************************************************** \n colnames = c('alpha', colnames(x), 'R2')\n \n estimate = make.xts(matrix(NA, nr = nperiods, len(colnames)), dates)\n \tcolnames(estimate) = colnames\n fl = list()\n \tfl$estimate = estimate\n \tfl$std.error = estimate\n \t\tif( !is.null(custom.stats.fn) ) {\n \t\t\ttemp = match.fun(custom.stats.fn)(cbind(1,x), y, fit)\n \t\t\tfl$custom = make.xts(matrix(NA, nr = nperiods, len(temp)), dates)\t\n \t\t} \t\n\t\n\t# main loop\n\tfor( i in window.len:nperiods ) {\n\t\twindow.index = (i - window.len + 1) : i\n\t\t\n\t\tif(all(!is.na(y[window.index]))) {\n\t\txtemp = cbind(1,x[window.index,])\n\t\tytemp = y[window.index]\n\t\tfit = ols(xtemp, ytemp, T)\n\t\t\test = fit$coefficients\n\t\t\tstd.err = fit$seb\n\t\t \tr2 = fit$r.squared\t\t\n\t\tfl$estimate[i,] = c(est, r2)\n\t\tfl$std.error[i,] = c(std.err, NA)\n\t\t\n\t\tif( !is.null(custom.stats.fn) )\n\t\t\tfl$custom[i,] = match.fun(custom.stats.fn)(xtemp, ytemp, fit)\n\t\t}\n\t\t\n\t\tif( i %% 10 == 0) if(!silent) cat(i, '\\n')\n\t}\n\n\treturn(list(fl.all = fl.all, fl = fl, window.len=window.len,\n\t\ty=yout, x=xout, RF=data$factors$RF))\n}\n\n\n\n# detail plot for each factor and histogram\nfactor.rolling.regression.detail.plot <- function(obj) {\n\t#setup\n\tn = ncol(obj$fl$estimate)\n\tdates = index(obj$fl$estimate)\n\n\tlayout(matrix(1:(2*n), nc=2, byrow=T))\n\t\n\tfor(i in 1:n) {\t\n\t\t#-------------------------------------------------------------------------\n\t\t# Time plot\n\t\t#-------------------------------------------------------------------------\n\t\test = obj$fl$estimate[,i]\n\t\test.std.error = ifna(obj$fl$std.error[,i], 0)\n\t\t\n\t\tplota(est, \n\t\t\tylim = range( c(\n\t\t\t\trange(est + est.std.error, na.rm=T),\n\t\t\t\trange(est - est.std.error, na.rm=T)\t\t\n\t\t\t\t)))\n\t\n\t\tpolygon(c(dates,rev(dates)), \n\t\t\tc(coredata(est + est.std.error), \n\t\t\trev(coredata(est - est.std.error))), \n\t\t\tborder=NA, col=col.add.alpha('red',50))\n\t\n\t\test = obj$fl.all$estimate[i]\n\t\test.std.error = obj$fl.all$std.error[i]\n\t\n\t\tpolygon(c(range(dates),rev(range(dates))), \n\t\t\tc(rep(est + est.std.error,2),\n\t\t\trep(est - est.std.error,2)),\n\t\t\tborder=NA, col=col.add.alpha('blue',50))\n\t\t\n\t\tabline(h=0, col='blue', lty='dashed')\n\t\t\t\n\t\tabline(h=est, col='blue')\n\t\n\t\tplota.lines(obj$fl$estimate[,i], type='l', col='red')\n\t\t\n\t\t#-------------------------------------------------------------------------\n\t\t# Histogram\n\t\t#-------------------------------------------------------------------------\n\t\tpar(mar = c(4,3,2,1))\n\t\thist(obj$fl$estimate[,i], col='red', border='gray', las=1,\n\t\t\txlab='', ylab='', main=colnames(obj$fl$estimate)[i])\n\t\t\tabline(v=obj$fl.all$estimate[i], col='blue', lwd=2)\n\t}\n}\n\n\n# style plot for 2 given factors\nfactor.rolling.regression.style.plot <- function(obj, \n\txfactor='HML', yfactor='SMB',\n\txlim = c(-1.5, 1.5), ylim = c(-0.5, 1.5)\n) {\n\t# Style chart\t\n\ti = which(colnames(obj$fl$estimate) == xfactor)\n\t\tx = coredata(obj$fl$estimate[,i])\n\t\tx.e = ifna(coredata(obj$fl$std.error[,i]), 0)\n\t\n\t\tx.all = obj$fl.all$estimate[i]\n\t\tx.all.e = obj$fl.all$std.error[i]\n\t\t\n\t\txlab = colnames(obj$fl$estimate)[i]\n\t\t\n\ti = which(colnames(obj$fl$estimate) == yfactor)\n\t\ty = coredata(obj$fl$estimate[,i])\n\t\ty.e = ifna(coredata(obj$fl$std.error[,i]), 0)\n\t\n\t\ty.all = obj$fl.all$estimate[i]\n\t\ty.all.e = obj$fl.all$std.error[i]\n\n\t\tylab = colnames(obj$fl$estimate)[i]\n\t\t\n\t# plot\n\tlayout(1)\n\tplot(x,y, xlab=xlab, ylab = ylab, type='n', las=1,\n\t\txlim = range(c(x + x.e, x - x.e, xlim), na.rm=T),\n\t\tylim = range(c(y + y.e, y - y.e, ylim), na.rm=T),\n\t\tmain = paste('Style, last =', ylab, round(last(y),2), xlab, round(last(x),2))\n\t\t)\t\t\n\tgrid()\n\tabline(h=0)\n\tabline(v=0)\n\t\n\t\t\n\tcol = col.add.alpha('pink',250)\n\trect(x - x.e, y - y.e, x + x.e, y + y.e, col=col, border=NA)\n\t\n\tpoints(x,y, col='red', pch=20)\n\t\tpoints(last(x),last(y), col='black', pch=3)\t\n\tpoints(x.all,y.all, col='blue', pch=15)\n\t\n\tlegend('topleft', spl('Estimates,Last estimate,Overall estimate'),\n\t\tpch = c(20,3,15),\n\t\tcol = spl('red,black,blue'),\n\t\tpt.bg = spl('red,black,blue'),\n\t\tbty='n'\n\t) \n}\n\n\n# re-construct historical perfromance based on factor loadings\n# compare fund perfromance to the\n# - re-constructed portfolio based on the regression over whole period\n# - re-constructed portfolio based on the rolling window regression\nfactor.rolling.regression.bt.plot <- function(obj) {\n\t# setup\n\tticker = colnames(obj$y)\n\t\tn = ncol(obj$fl$estimate)-1\n\t\tnperiods = nrow(obj$fl$estimate)\n\t\n\t# fund, alpha, factors, RF\n\tret = cbind(obj$RF, obj$y, 1, obj$x)\n\t\tcolnames(ret)[1:3] = spl('RF,fund,alpha')\n\tprices = bt.apply.matrix(1+ifna(ret,0),cumprod)\n\t\n\tdata <- new.env()\n\t\tdata$symbolnames = colnames(prices)\t\t\n\t\t\n\tfor(i in colnames(prices)) {\n\t\tdata[[i]] = prices[,i]\n\t\tcolnames(data[[i]]) = 'Close'\n\t}\n\n\tbt.prep(data, align='keep.all')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\t\n\t# create models\n\tmodels = list()\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$fund = 1\n\t\tdata$weight$RF = 1\n\t\tdata$weight[1:obj$window.len,] = NA\n\tmodels[[ticker]] = bt.run.share(data, clean.signal = F)\n\n\tdata$weight[] = NA\n\t\tdata$weight[,3:(n+2)] = t(repmat(obj$fl.all$estimate[1:n], 1, nperiods))\n\t\tdata$weight$RF = 1\n\t\tdata$weight[1:obj$window.len,] = NA\n\tmodels$all.alpha = bt.run.share(data, clean.signal = F)\n\n\tdata$weight[] = NA\n\t\tdata$weight[,3:(n+2)] = t(repmat(obj$fl.all$estimate[1:n], 1, nperiods))\n\t\tdata$weight$RF = 1\n\t\tdata$weight$alpha = NA\n\t\tdata$weight[1:obj$window.len,] = NA\n\tmodels$all = bt.run.share(data, clean.signal = F)\n\n\tdata$weight[] = NA\n\t\tdata$weight[,3:(n+2)] = obj$fl$estimate[,1:n]\n\t\tdata$weight$RF = 1\n\t\tdata$weight[1:obj$window.len,] = NA\n\tmodels$est.alpha = bt.run.share(data, clean.signal = F)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[,3:(n+2)] = obj$fl$estimate[,1:n]\n\t\tdata$weight$RF = 1\n\t\tdata$weight$alpha = NA\t\n\t\tdata$weight[1:obj$window.len,] = NA\n\tmodels$est = bt.run.share(data, clean.signal = F)\n\t\t\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\t# Plot perfromance\n\tlayout(1)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\n\t\n\t# obj$fl.all$estimate[1]*52\n\t# mean(obj$fl$estimate$alpha,na.rm=T)\t\n}\n\n\n# main function to demonstrate factor attribution\nthree.factor.rolling.regression <- function() {\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'VISVX'\n\t#tickers = 'IBM'\n\n\tperiodicity = 'weeks'\n\tperiodicity = 'months'\n\t\t\n\tdata <- new.env()\n\tquantmod::getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\tfor(i in ls(data)) {\n\t\ttemp = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t\t\n\t\tperiod.ends = endpoints(temp, periodicity)\n\t\t\tperiod.ends = period.ends[period.ends > 0]\n\n\t\tif(periodicity == 'months') {\n\t\t\t# reformat date to match Fama French Data\n\t\t\tmonthly.dates = as.Date(paste(format(index(temp)[period.ends], '%Y%m'),'01',sep=''), '%Y%m%d')\n\t\t\tdata[[i]] = make.xts(coredata(temp[period.ends,]), monthly.dates)\n\t\t} else\n\t\t\tdata[[i]] = temp[period.ends,]\n\t}\n\tdata.fund = data[[tickers]]\n\t\n\t#*****************************************************************\n\t# Fama/French factors\n\t#****************************************************************** \n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = T, clean = F)\n\n\t# add factors and align\n\tdata <- new.env()\n\t\tdata[[tickers]] = data.fund\n\tdata$factors = factors$data / 100\n\tbt.prep(data, align='remove.na', dates='1994::')\n\n\t#*****************************************************************\n\t# Facto Loadings Regression\n\t#****************************************************************** \n\tobj = factor.rolling.regression(data, tickers, 36)\n\n\t#*****************************************************************\n\t# Reports\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 1200, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tfactor.rolling.regression.detail.plot(obj)\ndev.off()\n\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfactor.rolling.regression.style.plot(obj)\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\tfactor.rolling.regression.bt.plot(obj)\ndev.off()\t\n\t\n\t#*****************************************************************\n\t# Fama/French factors + Momentum\n\t#****************************************************************** \n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = F, clean = F)\n\n\tfactors.extra = get.fama.french.data('F-F_Momentum_Factor', periodicity = periodicity,download = T, clean = F)\t\n\t\tfactors$data = merge(factors$data, factors.extra$data) \n\t\n\t# add factors and align\n\tdata <- new.env()\n\t\tdata[[tickers]] = data.fund\n\tdata$factors = factors$data / 100\n\tbt.prep(data, align='remove.na', dates='1994::')\n\n\t#*****************************************************************\n\t# Facto Loadings Regression\n\t#****************************************************************** \n\tobj = factor.rolling.regression(data, tickers, 36)\n\n\t#*****************************************************************\n\t# Reports\n\t#****************************************************************** \npng(filename = 'plot4.png', width = 600, height = 1200, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tfactor.rolling.regression.detail.plot(obj)\ndev.off()\n\npng(filename = 'plot5.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfactor.rolling.regression.style.plot(obj)\ndev.off()\t\n\npng(filename = 'plot6.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfactor.rolling.regression.style.plot(obj, xfactor='HML', yfactor='Mom')\ndev.off()\t\n\npng(filename = 'plot7.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\tfactor.rolling.regression.bt.plot(obj)\ndev.off()\t\n\n}\n\n# exmple of using your own factors in the factor attribution\nyour.own.factor.rolling.regression <- function() {\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('EEM,SPY')\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na')\n\t\n\t#*****************************************************************\n\t# Create weekly factor\n\t#****************************************************************** \n\tprices = data$prices\n\t\n\tperiodicity = 'weeks'\n\tperiod.ends = endpoints(prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\thist.returns = ROC(prices[period.ends,], type = 'discrete')\t\n\t\thist.returns = na.omit(hist.returns)\n\t\n\t#Emerging Market over US Market i.e. MSCI EM vs S&P 500 = EEM - SPY\t\n\tEEM_SPY = hist.returns$EEM - hist.returns$SPY\n\t\tcolnames(EEM_SPY) = 'EEM_SPY'\n\t\n\twrite.xts(EEM_SPY, 'EEM_SPY.csv')\n\t\n\t\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = 'VISVX'\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\tfor(i in ls(data)) {\n\t\ttemp = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t\t\n\t\tperiod.ends = endpoints(temp, periodicity)\n\t\t\tperiod.ends = period.ends[period.ends > 0]\n\n\t\tdata[[i]] = temp[period.ends,]\n\t}\n\tdata.fund = data[[tickers]]\n\t\n\t\n\t#*****************************************************************\n\t# Fama/French factors\n\t#****************************************************************** \n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = F, clean = F)\n\n\tfactors.extra = 100 * read.xts('EEM_SPY.csv')\n\t\tfactors$data = merge(factors$data, factors.extra, join='inner')\n\t# add factors and align\n\tdata <- new.env()\n\t\tdata[[tickers]] = data.fund\n\tdata$factors = factors$data / 100\n\tbt.prep(data, align='remove.na')\n\t\n\t#*****************************************************************\n\t# Check Correlations, make sure the new factor is NOT highly correlated \n\t#****************************************************************** \t\n\tload.packages('psych')\npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tpairs.panels(coredata(data$factors))\t\ndev.off()\n\t\n\n\n\n\t\n\t#*****************************************************************\n\t# Facto Loadings Regression\n\t#****************************************************************** \n\tobj = factor.rolling.regression(data, tickers, 36)\n\n\t#*****************************************************************\n\t# Reports\n\t#****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 1200, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tfactor.rolling.regression.detail.plot(obj)\ndev.off()\n\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfactor.rolling.regression.style.plot(obj)\ndev.off()\t\n\npng(filename = 'plot4.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfactor.rolling.regression.style.plot(obj, xfactor='HML', yfactor='EEM_SPY')\ndev.off()\t\n\npng(filename = 'plot5.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\tfactor.rolling.regression.bt.plot(obj)\ndev.off()\t\n\n\n}\n\t\n\n\n\n\n###############################################################################\n# One month reversals\n###############################################################################\nbt.one.month.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = sp500.components()$tickers\n\t#tickers = dow.jones.components()\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\t#save(data, file='data.sp500.components.Rdata') \n\t\t#load(file='data.sp500.components.Rdata') \t\n\t\t\n\t\t# remove companies with less than 5 years of data\n\t\trm.index = which( sapply(ls(data), function(x) nrow(data[[x]])) < 1000 )\t\n\t\trm(list=names(rm.index), envir=data)\n\t\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\ttickers = data$symbolnames\n\t\n\t\n\tdata.spy <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data.spy, auto.assign = T)\n\tbt.prep(data.spy, align='keep.all', dates='1994::')\n\t\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n#save(data, data.spy, tickers, file='data.sp500.components.Rdata') \t\n#load(file='data.sp500.components.Rdata') \t\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\t\t\t\n\t#*****************************************************************\n\t# Setup monthly periods\n\t#****************************************************************** \n\tperiodicity = 'months'\n\t\n\tperiod.ends = endpoints(data$prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\tprices = prices[period.ends, ]\t\t\n\t\t\n\t#*****************************************************************\n\t# Create Benchmarks\n\t#****************************************************************** \t\n\tmodels = list()\n\tn.skip = 36\n\tn.skip = 2\n\t\n\t# SPY\n\tdata.spy$weight[] = NA\n\t\tdata.spy$weight[] = 1\n\t\tdata.spy$weight[1:period.ends[n.skip],] = NA\n\tmodels$spy = bt.run(data.spy)\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = ntop(prices, n)\n\t\tdata$weight[1:period.ends[n.skip],] = NA\t\t\n\tmodels$equal.weight = bt.run(data)\n\t\n\t#*****************************************************************\n\t# Create Reversal Quantiles\n\t#****************************************************************** \n\tone.month = coredata(prices / mlag(prices))\n\n\tmodels = c(models, \n\t\tbt.make.quintiles(one.month, data, period.ends, start.t=1 + n.skip, prefix='M1_'))\n\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\n\tplotbt.custom.report.part1(models[spl('spy,equal.weight,spread')])\ndev.off()\t\t\n\n}\t\n\n\n\n###############################################################################\n# Better one-month reversal\n###############################################################################\n# compute various additional stats\nfactor.rolling.regression.custom.stats <- function(x,y,fit) {\n\tn = len(y)\n\te = y - x %*% fit$coefficients\n\tse = sd(e)\n\treturn(c(e[n], e[n]/se))\n}\n\n# create quintiles\nbt.make.quintiles <- function(\n\tposition.score,\t# position.score is a factor to form Quintiles sampled at the period.ends\n\tdata,\t\t\t# back-test object\n\tperiod.ends,\t\n\tn.quantiles = 5,\n\tstart.t = 2,\t# first index at which to form Quintiles\n\tprefix = ''\n) \n{\n\tn = ncol(position.score)\n\t#*****************************************************************\n\t# Create Quantiles\n\t#****************************************************************** \n\tposition.score = coredata(position.score)\n\tquantiles = weights = position.score * NA\n\t\n\tfor( t in start.t:nrow(weights) ) {\n\t\tfactor = as.vector(position.score[t,])\n\t\tranking = ceiling(n.quantiles * rank(factor, na.last = 'keep','first') / count(factor))\n\t\t\n\t\tquantiles[t,] = ranking\n\t\tweights[t,] = 1/tapply(rep(1,n), ranking, sum)[ranking]\t\t\t\n\t}\n\t\n\tquantiles = ifna(quantiles,0)\n\t\n\t#*****************************************************************\n\t# Create backtest for each Quintile\n\t#****************************************************************** \n\ttemp = weights * NA\n\tmodels = list()\n\tfor( i in 1:n.quantiles) {\n\t\ttemp[] = 0\n\t\ttemp[quantiles == i] = weights[quantiles == i]\n\t\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[period.ends,] = temp\n\t\tmodels[[ paste(prefix,'Q',i,sep='') ]] = bt.run(data, silent = T)\n\t}\n\t\n\t# rowSums(models$M1_Q2$weight,na.rm=T)\t\n\n\t#*****************************************************************\n\t# Create Q1-QN spread\n\t#****************************************************************** \n\ttemp[] = 0\n\ttemp[quantiles == 1] = weights[quantiles == 1]\n\ttemp[quantiles == n.quantiles] = -weights[quantiles == n.quantiles]\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = temp\n\tmodels$spread = bt.run(data, silent = T)\t\n\n\treturn(models)\n}\n\n\n\nbt.fa.one.month.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\tinfo = sp500.components()\n\ttickers = info$tickers\n\t#tickers = dow.jones.components()\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\t#save(data, file='data.sp500.components.Rdata') \n\t\t#load(file='data.sp500.components.Rdata') \t\n\t\t\n\t\t# remove companies with less than 5 years of data\n\t\trm.index = which( sapply(ls(data), function(x) nrow(data[[x]])) < 1000 )\t\n\t\trm(list=names(rm.index), envir=data)\n\t\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\ttickers = data$symbolnames\n\t\tsector = info$sector[match(tickers, info$tickers)]\n\t\n\t\n\tdata.spy <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data.spy, auto.assign = T)\n\tbt.prep(data.spy, align='keep.all', dates='1994::')\n\t\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# setdiff(index(data.spy$prices), index(data$prices))\n\t# setdiff(index(data$prices),index(data.spy$prices))\n#save(data, data.spy, tickers, sector, file='data.sp500.components.Rdata') \t\n#load(file='data.sp500.components.Rdata') \t\n\n\n\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\t\t\t\n\t#*****************************************************************\n\t# Setup monthly periods\n\t#****************************************************************** \n\tperiodicity = 'months'\n\t\n\tperiod.ends = endpoints(data$prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\tprices = prices[period.ends, ]\t\t\n\t\t\n\t#*****************************************************************\n\t# Create Benchmarks\n\t#****************************************************************** \t\n\tmodels = list()\n\tn.skip = 36\n\t\n\t# SPY\n\tdata.spy$weight[] = NA\n\t\tdata.spy$weight[] = 1\n\t\tdata.spy$weight[1:period.ends[n.skip],] = NA\n\tmodels$spy = bt.run(data.spy)\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = ntop(prices, n)\n\t\tdata$weight[1:period.ends[n.skip],] = NA\t\t\n\tmodels$equal.weight = bt.run(data)\n\t\t\t\n\t#*****************************************************************\n\t# Load factors and align them with prices\n\t#****************************************************************** \t\n\t# load Fama/French factors\n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = F, clean = F)\n\t\n\t# align monthly dates\n\tmap = match(format(index(factors$data), '%Y%m'), format(index(prices), '%Y%m'))\n\t\tdates = index(factors$data)\n\t\tdates[!is.na(map)] = index(prices)[na.omit(map)]\n\tindex(factors$data) = as.Date(dates)\n\t\t\n\t\n\t# add factors and align\n\tdata.fa <- new.env()\n\t\tfor(i in tickers) data.fa[[i]] = data[[i]][period.ends, ]\n\t\tdata.fa$factors = factors$data / 100\n\tbt.prep(data.fa, align='remove.na')\n\n\t\n\tindex = match( index(data.fa$prices), index(data$prices) )\n\t\tprices = data$prices[index, ]\n\t\t\n\t#*****************************************************************\n\t# Compute Factor Attribution for each ticker\n\t#****************************************************************** \t\n\n\ttemp = NA * prices\n\tfactors\t= list()\n\t\tfactors$last.e = temp\n\t\tfactors$last.e_s = temp\n\t\n\tfor(i in tickers) {\n\t\tcat(i, '\\n')\n\t\t\n\t\t# Facto Loadings Regression\n\t\tobj = factor.rolling.regression(data.fa, i, 36, silent=T,\n\t\t\tfactor.rolling.regression.custom.stats)\n\n\t\tfor(j in 1:len(factors))\t\t\n\t\t\tfactors[[j]][,i] = obj$fl$custom[,j]\n\t\t\t\n\t}\n\t\n\t# add base strategy\n\tfactors$one.month = coredata(prices / mlag(prices))\n\t\n\t#save(factors, file='data.ff.factors.Rdata') \t\n\tload(file='data.ff.factors.Rdata') \t\n\n\t\n\t#*****************************************************************\n\t# Create Quantiles\n\t#****************************************************************** \n\tquantiles = list()\n\t\n\tfor(name in names(factors)) {\n\t\tcat(name, '\\n')\n\t\tquantiles[[name]] = bt.make.quintiles(factors[[name]], data, index, start.t = 1+36, prefix=paste(name,'_',sep=''))\n\t}\n\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\t\n\t# put all reports into one pdf file\n\t#pdf(file = 'report.pdf', width=8.5, height=11)\n\t\n\tpng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(quantiles$one.month$spread,quantiles$last.e$spread,quantiles$last.e_s$spread)\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.strategy.sidebyside(quantiles$one.month$spread,quantiles$last.e$spread,quantiles$last.e_s$spread)\n\tdev.off()\t\n\n\n\tpng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles$last.e )\n\tdev.off()\t\t\n\t\t\n\tpng(filename = 'plot4.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles$last.e_s )\n\tdev.off()\t\t\n\n\t\n}\n\t\n\n\nbt.fa.sector.one.month.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\tinfo = sp500.components()\n\ttickers = info$tickers\n\t\t#tickers = dow.jones.components()\n\n\t\n\tdata <- new.env()\n\t#getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\tfor(i in tickers) try(getSymbols(i, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T), TRUE)\t\n\t\t#save(data, file='data.sp500.components.Rdata') \n\t\t#load(file='data.sp500.components.Rdata') \t\n\t\t\n\t\t# remove companies with less than 5 years of data\n\t\trm.index = which( sapply(ls(data), function(x) nrow(data[[x]])) < 1000 )\t\n\t\trm(list=names(rm.index), envir=data)\n\t\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='keep.all', dates='1994::')\n\t\ttickers = data$symbolnames\n\t\tsector = info$sector[match(tickers, info$tickers)]\n\t\n\t\n\tdata.spy <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data.spy, auto.assign = T)\n\tbt.prep(data.spy, align='keep.all', dates='1994::')\n\t\n\tsave(data, data.spy, tickers, sector, file='data.sp500.components.Rdata') \t\n\t#load(file='data.sp500.components.Rdata') \t\n\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# setdiff(index(data.spy$prices), index(data$prices))\n\t# setdiff(index(data$prices),index(data.spy$prices))\n\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\t\t\t\n\t#*****************************************************************\n\t# Setup monthly periods\n\t#****************************************************************** \n\tperiodicity = 'months'\n\t#periodicity = 'weeks'\n\t\n\tperiod.ends = endpoints(data$prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\n\tprices = prices[period.ends, ]\t\t\n\t\t\n\t#*****************************************************************\n\t# Create Benchmarks\n\t#****************************************************************** \t\n\tmodels = list()\n\tn.skip = 36\n\t\n\t# SPY\n\tdata.spy$weight[] = NA\n\t\tdata.spy$weight[] = 1\n\t\tdata.spy$weight[1:period.ends[n.skip],] = NA\n\tmodels$spy = bt.run(data.spy)\n\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = ntop(prices, n)\n\t\tdata$weight[1:period.ends[n.skip],] = NA\t\t\n\tmodels$equal.weight = bt.run(data)\n\t\t\t\n\t#*****************************************************************\n\t# Load factors and align them with prices\n\t#****************************************************************** \t\n\t# load Fama/French factors\n\tfactors = get.fama.french.data('F-F_Research_Data_Factors', periodicity = periodicity,download = T, clean = F)\n\t\n\t\n\t# align monthly dates\n\tif(periodicity == 'months') {\n\t\tmap = match(format(index(factors$data), '%Y%m'), format(index(prices), '%Y%m'))\n\t\t\tdates = index(factors$data)\n\t\t\tdates[!is.na(map)] = index(prices)[na.omit(map)]\n\t\tindex(factors$data) = as.Date(dates)\n\t}\t\n\t\n\t# add factors and align\n\tdata.fa <- new.env()\n\t\tfor(i in tickers) data.fa[[i]] = data[[i]][period.ends, ]\n\t\tdata.fa$factors = factors$data / 100\n\tbt.prep(data.fa, align='remove.na')\n\n\t\t\n\tindex = match( index(data.fa$prices), index(data$prices) )\n\t\tprices = data$prices[index, ]\n\t\t\n\t#*****************************************************************\n\t# Compute Factor Attribution for each ticker\n\t#****************************************************************** \t\n\ttemp = NA * prices\n\tfactors\t= list()\n\t\tfactors$last.e = temp\n\t\tfactors$last.e_s = temp\n\t\n\tfor(i in tickers) {\n\t\tcat(i, '\\n')\n\t\t\n\t\t# Facto Loadings Regression\n\t\tobj = factor.rolling.regression(data.fa, i, 36, silent=T,\n\t\t\tfactor.rolling.regression.custom.stats)\n\n\t\tfor(j in 1:len(factors))\t\t\n\t\t\tfactors[[j]][,i] = obj$fl$custom[,j]\n\t\t\t\n\t}\n\n\t# add base strategy\n\tnlag = iif(periodicity == 'months', 1, 4)\n\tfactors$one.month = coredata(prices / mlag(prices, nlag))\t\n\t\t\t\t\t\n\tsave(factors, file='data.ff.factors.Rdata') \t\n\t#load(file='data.ff.factors.Rdata') \t\n\n\t\n\t\n\t#*****************************************************************\n\t# Create Quantiles\n\t#****************************************************************** \n\tquantiles = list()\n\t\n\tfor(name in names(factors)) {\n\t\tcat(name, '\\n')\n\t\tquantiles[[name]] = bt.make.quintiles(factors[[name]], data, index, start.t = 1+36, prefix=paste(name,'_',sep=''))\n\t}\n\n\tquantiles.sn = list()\n\tfor(name in names(factors)) {\n\t\tcat(name, '\\n')\n\t\tquantiles.sn[[name]] = bt.make.quintiles.sector(sector, factors[[name]], data, index, start.t = 1+36, prefix=paste(name,'_',sep=''))\n\t}\n\n\tsave(quantiles, quantiles.sn, file='model.quantiles.Rdata') \t\n\t#load(file='model.quantiles.Rdata') \t \t\n\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\t\n\t# put all reports into one pdf file\n\t#pdf(file = 'report.pdf', width=8.5, height=11)\n\t\n\tpng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(quantiles$one.month$spread,\n\t\t\tquantiles$last.e$spread, quantiles$last.e_s$spread,\n\t\t\tquantiles.sn$one.month$spread.sn,\n\t\t\tquantiles.sn$last.e$spread.sn, quantiles.sn$last.e_s$spread.sn)\t\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2.png', width = 800, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.strategy.sidebyside(quantiles$one.month$spread,\n\t\t\tquantiles$last.e$spread, quantiles$last.e_s$spread,\n\t\t\tquantiles.sn$one.month$spread.sn,\n\t\t\tquantiles.sn$last.e$spread.sn, quantiles.sn$last.e_s$spread.sn)\t\n\tdev.off()\t\n\n\tpng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles.sn$one.month )\n\tdev.off()\t\t\t\t\n\t\n\tpng(filename = 'plot4.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles.sn$last.e_s )\n\tdev.off()\t\t\n\t\n\t\n\t\n\t#*****************************************************************\n\t# Create Report - bt.one.month.test\n\t#****************************************************************** \t\n\tpng(filename = 'plot1a.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\n\t\tplotbt.custom.report.part1(c(models,quantiles$one.month))\n\tdev.off()\t\n\n\tpng(filename = 'plot2a.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\n\t\tplotbt.custom.report.part1(c(models,quantiles$one.month$spread))\n\tdev.off()\t\n\n\t#*****************************************************************\n\t# Create Report - bt.fa.one.month.test\n\t#****************************************************************** \t\t\t\t\t\t\n\tpng(filename = 'plot1b.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(quantiles$one.month$spread,quantiles$last.e$spread,quantiles$last.e_s$spread)\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2b.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.strategy.sidebyside(quantiles$one.month$spread,quantiles$last.e$spread,quantiles$last.e_s$spread)\n\tdev.off()\t\n\n\n\tpng(filename = 'plot3b.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles$last.e )\n\tdev.off()\t\t\n\t\t\n\tpng(filename = 'plot4b.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tplotbt.custom.report.part1(\tquantiles$last.e_s )\n\tdev.off()\t\t\n\t\n}\n\n\n\n\n#position.score = factors[[1]]\n#period.ends\t= index\n#n.quantiles = 5\n#start.t = 1+36\n#prefix = ''\t\n\n# create sector quintiles\t\t\nbt.make.quintiles.sector <- function(\n\tsector,\t\t\t# sector data\n\tposition.score,\t# position.score is a factor to form Quintiles sampled at the period.ends\n\tdata,\t\t\t# back-test object\n\tperiod.ends,\t\n\tn.quantiles = 5,\n\tstart.t = 2,\t# first index at which to form Quintiles\n\tprefix = ''\t\n) \n{\n\t#*****************************************************************\n\t# Re-organize sectors into matrix, assume that sectors are constant in time\n\t#****************************************************************** \n\ttemp = factor(sector)\n\tsector.names = levels(temp)\t\n\t\tn.sectors = len(sector.names)\n\tsectors = matrix(unclass(temp),nr=nrow(position.score),nc=ncol(position.score),byrow=T)\n\t\n\t#*****************************************************************\n\t# Create Quantiles\n\t#****************************************************************** \n\tposition.score = coredata(position.score)\n\tquantiles = weights = position.score * NA\t\t\t\n\t\n\tfor( s in 1:n.sectors) {\n\t\tfor( t in start.t:nrow(weights) ) {\n\t\t\tindex = sectors[t,] == s\n\t\t\tn = sum(index)\n\t\t\t\n\t\t\t# require at least 3 companies in each quantile\n\t\t\tif(n > 3*n.quantiles) {\t\t\t\n\t\t\t\tfactor = as.vector(position.score[t, index])\n\t\t\t\tranking = ceiling(n.quantiles * rank(factor, na.last = 'keep','first') / count(factor))\n\t\t\t\n\t\t\t\tquantiles[t, index] = ranking\n\t\t\t\tweights[t, index] = 1/tapply(rep(1,n), ranking, sum)[ranking]\t\t\t\n\t\t\t}\n\t\t}\n\t}\t\n\t\n\tquantiles = ifna(quantiles,0)\n\t\n\t#*****************************************************************\n\t# Create Q1-QN spread for each Sector\n\t#****************************************************************** \n\tlong = weights * NA\n\tshort = weights * NA\n\tmodels = list()\n\t\n\tfor( s in 1:n.sectors) {\n\t\tlong[] = 0\n\t\tlong[quantiles == 1 & sectors == s] = weights[quantiles == 1 & sectors == s]\n\t\tlong = long / rowSums(long,na.rm=T)\n\t\t\n\t\tshort[] = 0\n\t\tshort[quantiles == n.quantiles & sectors == s] = weights[quantiles == n.quantiles & sectors == s]\n\t\tshort = short / rowSums(short,na.rm=T)\n\t\t\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[period.ends,] = long - short\n\t\tmodels[[ paste(prefix,'spread.',sector.names[s], sep='') ]]\t= bt.run(data, silent = T)\t\n\t}\n\nif(F) {\t\n\t#*****************************************************************\n\t# Create Basic momentum strategy\n\t#****************************************************************** \t\t\t\n\tload.packages('abind')\n\tmodel.prices = abind(lapply(models, function(m) m$equity), along=2)\t\n\t\t#model.prices = make.xts(model.prices, index(data$prices)\n\t\tmodel.prices = model.prices[period.ends,]\n\t\tmodel.returns = model.prices / mlag(model.prices)-1\n\t\tmodel.score = bt.apply.matrix(model.returns, SMA, 6) \n\t\tmodel.vol = bt.apply.matrix(model.returns, runSD, 6) \n\n\t# select top 3 sectors based on the 6 month momentum, risk weighted\t\n\ttop = ntop(model.score, 3)\n\t\ttop = top / model.vol\n\ttop = top / rowSums(top, na.rm=T)\n\ttop = ifna(top,0)\n\t\n\tn = ncol(position.score)\n\tnperiods = nrow(position.score)\n\n\tlong[] = 0\n\tshort[] = 0\n\tfor( s in 1:n.sectors) {\n\t\tscore = matrix(top[,s], nr = nperiods, n)\n\t\tlong[quantiles == 1 & sectors == s] = (weights * score)[quantiles == 1 & sectors == s]\n\t\tshort[quantiles == n.quantiles & sectors == s] = (weights * score)[quantiles == n.quantiles & sectors == s]\t\t\n\t}\n\tlong = long / rowSums(long,na.rm=T)\n\tshort = short / rowSums(short,na.rm=T)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = long - short\n\tmodels$spread.sn.top3 = bt.run(data, silent = T)\t\n\n\t\n#plotbt.custom.report.part1(models)\n#plotbt.strategy.sidebyside(models)\n}\t\n\n\t\n\t#*****************************************************************\n\t# Create Sector - Neutral Q1-QN spread\n\t#****************************************************************** \t\t\n\tlong[] = 0\n\tlong[quantiles == 1] = weights[quantiles == 1]\n\tlong = long / rowSums(long,na.rm=T)\n\t\n\tshort[] = 0\n\tshort[quantiles == n.quantiles] = weights[quantiles == n.quantiles]\n\tshort = short / rowSums(short,na.rm=T)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = long - short\n\tmodels$spread.sn = bt.run(data, silent = T)\t\n\n\treturn(models)\n}\n\n\n\n###############################################################################\n# Yet Another Forecast Dashboard\n###############################################################################\n# extract forecast info\nforecast.helper <- function(fit, h=10, level = c(80,95)) {\n\tout = try( forecast(fit, h=h, level=level), silent=TRUE)\n\tif (class(out)[1] != 'try-error') {\n\t\tout = data.frame(out)\n\t} else {\n\t\ttemp = data.frame(predict(fit, n.ahead=h, doplot=F))\n\t\t\tpred = temp[,1]\n\t\t\tse = temp[,2]\n\t\tqq = qnorm(0.5 * (1 + level/100))\n\t\tout = matrix(NA, nr=h, nc=1+2*len(qq))\n\t\t\tout[,1] = pred\n\t\tfor(i in 1:len(qq))\n\t\t\tout[,(2*i):(2*i+1)] = c(pred - qq[i] * se, pred + qq[i] * se)\n\t\tcolnames(out) = c('Point.Forecast', matrix(c(paste('Lo', level, sep='.'), paste('Hi', level, sep='.')), nr=2, byrow=T))\n\t\tout = data.frame(out)\n\t}\t\n\treturn(out)\n}\n\n# compute future dates for the forecast\nforecast2xts <- function(data, forecast) {\n\t# length of the forecast\n\th = nrow(forecast)\n\tdates = as.Date(index(data))\n \t\t\n\tnew.dates = seq(last(dates)+1, last(dates) + 2*365, by='day')\n\trm.index = date.dayofweek(new.dates) == 6 | date.dayofweek(new.dates) == 0\n \tnew.dates = new.dates[!rm.index]\n \t\t\n \tnew.dates = new.dates[1:h] \t\n \treturn(make.xts(forecast, new.dates))\n}\n\n# create forecast plot\nforecast.plot <- function(data, forecast, ...) {\n\tout = forecast2xts(data, forecast)\n\n \t# create plot\n \tplota(c(data, out[,1]*NA), type='l', \n \t\t\tylim = range(data,out,na.rm=T), ...) \t\t\n \t\n \t# highligh sections\n\tnew.dates = index4xts(out)\n\t\ttemp = coredata(out)\n\n\tn = (ncol(out) %/% 2)\n\tfor(i in n : 1) {\n\t\tpolygon(c(new.dates,rev(new.dates)), \n\t\t\tc(temp[,(2*i)], rev(temp[,(2*i+1)])), \n\t\tborder=NA, col=col.add.alpha(i+2,150))\n\t}\n\t\n\tplota.lines(out[,1], col='red')\n\t\n\tlabels = c('Data,Forecast', paste(gsub('Lo.', '', colnames(out)[2*(1:n)]), '%', sep=''))\n\tplota.legend(labels, fill = c('black,red',col.add.alpha((1:n)+2, 150)))\t\t\t\n}\n\n\nbt.forecast.dashboard <- function() {\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1990-01-01', env = data, auto.assign = T)\n\tbt.prep(data, align='remove.na')\n\t\n\t#*****************************************************************\n\t# Create models\n\t#****************************************************************** \n\tload.packages('forecast,fGarch,fArma')\n\t\n\tsample = last(data$prices$SPY, 200)\t\n\tts.sample = ts(sample, frequency = 12)\n\t\n\t\n\t\n\tmodels = list(\n\t\t# fGarch\t\t\n\t\tgarch = garchFit(~arma(1,15)+garch(1,1), data=sample, trace=F),\n\t\t# fArma\n\t\tarima = armaFit(~ arima(1, 1, 15), data=ts.sample),\t\n\t\t\n\t\t# forecast\n\t\tarma = Arima(ts.sample, c(1,0,1)),\n\t\tarfima = arfima(ts.sample),\n\t\tauto.arima = auto.arima(ts.sample),\n\t\n\t\tbats = bats(ts.sample),\n\t\tHoltWinters = HoltWinters(ts.sample),\n\t\tnaive = Arima(ts.sample, c(0,1,0))\n\t)\n\t\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\t\t\t\n\tpng(filename = 'plot1.png', width = 800, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tlayout(matrix(1:9,nr=3))\n\t\tfor(i in 1:len(models)) {\n\t\t\tout = forecast.helper(models[[i]], 30, level = c(80,95))\t \t\n\t\t\tforecast.plot(sample, out, main = names(models)[i]) \t\n\t\t}\t\n\tdev.off()\t\t\n\t\n\tpng(filename = 'plot2.png', width = 800, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t\tlayout(matrix(1:9,nr=3))\n\t\tfor(i in 1:len(models)) {\n\t\t\tout = forecast.helper(models[[i]], 30, level = c(75,85,95,97,99))\t \t\n\t\t\tforecast.plot(sample, out, main = names(models)[i]) \t\n\t\t}\t\n\tdev.off()\t\t\n\t\n}\n\t\t\n\n\n###############################################################################\n# New 60/40\n# http://gestaltu.blogspot.ca/2012/07/youre-looking-at-wrong-number.html\t\n###############################################################################\nbt.new.60.40.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SHY,IEF,TLT,SPY')\n\n\tdata.all <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1990-01-01', env = data.all, auto.assign = T)\t\n\tfor(i in ls(data.all)) data.all[[i]] = adjustOHLC(data.all[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data.all, align='remove.na')\n\t\n\tprices = data.all$prices\n\t\tn = ncol(prices)\n\t\tnperiods = nrow(prices)\n\tprices = prices/ matrix(first(prices), nr=nperiods, nc=n, byrow=T)\n\t\npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tplota.matplot(prices)\ndev.off()\t\t\n\t\n\t\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \t\t\n\tdata <- new.env()\n\t\tdata$stock = data.all$SPY\n\t\tdata$bond = data.all$TLT\t\n\tbt.prep(data, align='remove.na')\n\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# all bonds began trading at 2002-07-31\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\tnperiods = nrow(prices)\n\t\n\tmodels = list()\n\t\n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\n\n\t\n\t#*****************************************************************\n\t# Traditional, Dollar Weighted 40% Bonds & 60% Stock\n\t#****************************************************************** \t\t\t\n\tweight.dollar = matrix(c(0.4, 0.6), nr=nperiods, nc=n, byrow=T)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.dollar[period.ends,]\n\tmodels$dollar.w.60.40 = bt.run.share(data, clean.signal=F)\n\n\t\n\t#*****************************************************************\n\t# Risk Weighted 40% Bonds & 60% Stock\n\t#****************************************************************** \t\t\t\t\n\tret.log = bt.apply.matrix(prices, ROC, type='continuous')\n\thist.vol = sqrt(252) * bt.apply.matrix(ret.log, runSD, n = 21)\t\n\tweight.risk = weight.dollar / hist.vol\n\t\tweight.risk = weight.risk / rowSums(weight.risk)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.risk[period.ends,]\n\tmodels$risk.w.60.40 = bt.run.share(data, clean.signal=F)\n\t\t\t\t\n\t#*****************************************************************\n\t# Scale Risk Weighted 40% Bonds & 60% Stock strategy to have 6% volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk.w.60.40,\n\t\t\t\t\t\tweight.risk, 6/100, 21, 100/100)[period.ends,]\n\tmodels$risk.w.60.40.target6 = bt.run.share(data, clean.signal=T)\n\t\n\t#*****************************************************************\n\t# Same, plus invest cash into SHY\n\t#****************************************************************** \t\t\t\t\t\n\tweight = target.vol.strategy(models$risk.w.60.40,\n\t\t\t\t\t\tweight.risk, 6/100, 21, 100/100)\n\tdata.all$weight[] = NA\n\t\tdata.all$weight$SPY[period.ends,] = weight$stock[period.ends,]\n\t\tdata.all$weight$TLT[period.ends,] = weight$bond[period.ends,]\n\t\t\n\t\tcash = 1-rowSums(weight)\n\t\tdata.all$weight$SHY[period.ends,] = cash[period.ends]\n\tmodels$risk.w.60.40.target6.cash = bt.run.share(data.all, clean.signal=T)\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\n\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tplotbt.strategy.sidebyside(models)\ndev.off()\t\t\n\t\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tplotbt.custom.report.part1(models)\ndev.off()\t\t\n\t\t\npng(filename = 'plot4.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tplotbt.custom.report.part2(models$risk.w.60.40.target6)\t\t\ndev.off()\t\t\n\t\npng(filename = 'plot5.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tplotbt.custom.report.part2(models$risk.w.60.40.target6.cash)\t\t\ndev.off()\t\t\n\t\n\t\n}\t\t\n\n\n\n\n\n###############################################################################\n# Adaptive Asset Allocation\n# http://www.macquarieprivatewealth.ca/dafiles/Internet/mgl/ca/en/advice/specialist/darwin/documents/darwin-adaptive-asset-allocation.pdf\n# http://cssanalytics.wordpress.com/2012/07/17/adaptive-asset-allocation-combining-momentum-with-minimum-variance/\n###############################################################################\n\n\n#' @export \nbt.aaa.combo <- function\n(\n\tdata,\n\tperiod.ends,\n\tn.top = 5,\t\t# number of momentum positions\n\tn.top.keep = n.top, # only change position if it drops from n.top.keep\n\tn.mom = 6*22,\t# length of momentum look back\n\tn.vol = 1*22 \t# length of volatility look back\n) \n{\n #*****************************************************************\n # Combo: weight positions in the Momentum Portfolio according to Volatliliy\n #*****************************************************************\n prices = coredata(data$prices) \n ret.log = bt.apply.matrix(prices, ROC, type='continuous')\n hist.vol = bt.apply.matrix(ret.log, runSD, n = n.vol) \n \tadj.vol = 1/hist.vol[period.ends,]\n \n momentum = prices / mlag(prices, n.mom)\n \n weight = ntop.keep(momentum[period.ends,], n.top, n.top.keep) * adj.vol\n\t\tn.skip = max(n.mom, n.vol)\n \n data$weight[] = NA\n data$weight[period.ends,] = weight / rowSums(weight, na.rm=T) \n data$weight[1 : n.skip,] = NA \n bt.run.share(data, clean.signal=F, silent=T)\n}\n\n#' @export \nbt.aaa.minrisk <- function\n(\n\tdata,\n\tperiod.ends,\n\tn.top = 5,\t\t# number of momentum positions\n\tn.mom = 6*22,\t# length of momentum look back\n\tn.vol = 1*22 \t# length of volatility look back\n) \n{\n #***************************************************************** \n # Adaptive Asset Allocation (AAA)\n # weight positions in the Momentum Portfolio according to \n # the minimum variance algorithm\n #***************************************************************** \n prices = coredata(data$prices) \n ret.log = bt.apply.matrix(prices, ROC, type='continuous')\n \n momentum = prices / mlag(prices, n.mom)\n \n weight = NA * prices\n weight[period.ends,] = ntop(momentum[period.ends,], n.top)\n\tn.skip = max(n.mom, n.vol)\n \n for( i in period.ends[period.ends >= n.skip] ) {\n \thist = ret.log[ (i - n.vol + 1):i, ]\n \t\n\t\t# require all assets to have full price history\n\t\tinclude.index = count(hist)== n.vol \n\n\t\t# also only consider assets in the Momentum Portfolio\n index = ( weight[i,] > 0 ) & include.index\n n = sum(index)\n \n\t\tif(n > 0) {\t\t\t\t\t\n\t\t\thist = hist[ , index]\n \n\t # create historical input assumptions\n\t ia = create.ia(hist)\n\t s0 = apply(coredata(hist),2,sd) \n\t ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))\n\t \n\t\t\t# create constraints: 0<=x<=1, sum(x) = 1\n\t\t\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\t\t\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints) \n\t\t\t\n\t\t\t# compute minimum variance weights\t\t\t\t \n\t weight[i,] = 0 \n\t weight[i,index] = min.risk.portfolio(ia, constraints)\n }\n }\n\n # Adaptive Asset Allocation (AAA)\n data$weight[] = NA\n data$weight[period.ends,] = weight[period.ends,] \n bt.run.share(data, clean.signal=F, silent=T)\n}\n\n\n# Sensitivity Analysis based on the bt.improving.trend.following.test()\nbt.aaa.sensitivity.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = spl('SPY,EFA,EWJ,EEM,IYR,RWX,IEF,TLT,DBC,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='2004:12::')\n \n \n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n n = ncol(prices)\n \n models = list()\n \n # find period ends\n period.ends = endpoints(prices, 'months')\n period.ends = period.ends[period.ends > 0]\n\n \n\t#*****************************************************************\n\t# Test\n\t#****************************************************************** \n\tmodels = list()\n\t\n\tmodels$combo = bt.aaa.combo(data, period.ends, n.top = 5,\n\t\t\t\t\tn.mom = 180, n.vol = 20)\n\t\t\t\t\t\n\t\t\t\t\t\n\tmodels$aaa = bt.aaa.minrisk(data, period.ends, n.top = 5,\n\t\t\t\t\tn.mom = 180, n.vol = 20)\n\t\t\t\t\t\n\tplotbt.custom.report.part1(models) \n\n\t \n \n\t#*****************************************************************\n\t# Sensitivity Analysis: bt.aaa.combo / bt.aaa.minrisk\n\t#****************************************************************** \n\t# length of momentum look back\n\tmom.lens = ( 1 : 12 ) * 20\n\t# length of volatility look back\n\tvol.lens = ( 1 : 12 ) * 20\n\n\t\n\tmodels = list()\n\t\n\t# evaluate strategies\n\tfor(n.mom in mom.lens) {\n\t\tcat('MOM =', n.mom, '\\n')\n\t\t\n\t\tfor(n.vol in vol.lens) {\n\t\t\tcat('\\tVOL =', n.vol, '\\n')\n\n\t\t\tmodels[[ paste('M', n.mom, 'V', n.vol) ]] = \n\t\t\t\tbt.aaa.combo(data, period.ends, n.top = 5,\n\t\t\t\t\tn.mom = n.mom, n.vol = n.vol)\n\t\t}\n\t}\n\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T, make.plot = F)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t# allocate matrixe to store backtest results\n\tdummy = matrix('', len(vol.lens), len(mom.lens))\n\t\tcolnames(dummy) = paste('M', mom.lens)\n\t\trownames(dummy) = paste('V', vol.lens)\n\t\t\n\tnames = spl('Sharpe,Cagr,DVR,MaxDD')\n\npng(filename = 'plot1.png', width = 1000, height = 1000, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\t\n\tlayout(matrix(1:4,nrow=2))\t\n\tfor(i in names) {\n\t\tdummy[] = ''\n\t\t\n\t\tfor(n.mom in mom.lens)\n\t\t\tfor(n.vol in vol.lens)\n\t\t\t\tdummy[paste('V', n.vol), paste('M', n.mom)] =\n\t\t\t\t\tout[i, paste('M', n.mom, 'V', n.vol) ]\n\t\t\t\t\t\n\t\tplot.table(dummy, smain = i, highlight = T, colorbar = F)\n\n\t}\t\n\t\t\ndev.off()\t\n \n\t#*****************************************************************\n\t# Sensitivity Analysis\n\t#****************************************************************** \t\n\t# evaluate strategies\n\tfor(n.mom in mom.lens) {\n\t\tcat('MOM =', n.mom, '\\n')\n\t\t\n\t\tfor(n.vol in vol.lens) {\n\t\t\tcat('\\tVOL =', n.vol, '\\n')\n\n\t\t\tmodels[[ paste('M', n.mom, 'V', n.vol) ]] = \n\t\t\t\tbt.aaa.minrisk(data, period.ends, n.top = 5,\n\t\t\t\t\tn.mom = n.mom, n.vol = n.vol)\n\t\t}\n\t}\n\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T, make.plot = F)\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot2.png', width = 1000, height = 1000, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\t\t\n\tlayout(matrix(1:4,nrow=2))\t\n\tfor(i in names) {\n\t\tdummy[] = ''\n\t\t\n\t\tfor(n.mom in mom.lens)\n\t\t\tfor(n.vol in vol.lens)\n\t\t\t\tdummy[paste('V', n.vol), paste('M', n.mom)] =\n\t\t\t\t\tout[i, paste('M', n.mom, 'V', n.vol) ]\n\t\t\t\t\t\n\t\tplot.table(dummy, smain = i, highlight = T, colorbar = F)\n\n\t}\t\ndev.off()\t\n \n\n \n}\n\n\nbt.aaa.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = spl('SPY,EFA,EWJ,EEM,IYR,RWX,IEF,TLT,DBC,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\n\t\n\t# contruct another back-test enviroment with split-adjusted prices, do not include dividends\n\t# http://www.fintools.com/wp-content/uploads/2012/02/DividendAdjustedStockPrices.pdf\n\t# http://www.pstat.ucsb.edu/research/papers/momentum.pdf\n\tdata.price <- new.env()\n\t\tfor(i in ls(data)) data.price[[i]] = adjustOHLC(data[[i]], symbol.name=i, adjust='split', use.Adjusted=F)\n\tbt.prep(data.price, align='keep.all', dates='2004:12::')\t\n\t\n\t\n\t# create split and dividend adjusted prices\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='2004:12::')\n \n\t\n\t# flag to indicate whether to use Total(split and dividend adjusted) or Price(split adjusted) prices\n\tuse.total = FALSE\n\t\n\t\n\t#*****************************************************************\n # Sample Plot of Total and Price only time series\n #******************************************************************\t \n if(F) {\n\t\ty = data$prices$TLT\n\t\ty.price = data.price$prices$TLT\n\t\t\ty = y / as.double(y[1])\n\t\t\ty.price = y.price / as.double(y.price[1])\n\t\t\t\n\t\tplota(y, type='l', ylim=range(y, y.price, na.rm=T))\n\t\t\tplota.lines(y.price, col='red')\n\t\tplota.legend('Total,Price', 'black,red')\n\t}\t\t\t\n\t\t\n \n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n n = ncol(prices)\n \n\tprices4mom = iif(use.total, data$prices, data.price$prices)\n\tprices4vol = iif(use.total, data$prices, data.price$prices) \n \n models = list()\n \n # find period ends\n period.ends = endpoints(prices, 'months')\n period.ends = period.ends[period.ends > 0]\n\n\t# Adaptive Asset Allocation parameters\n\tn.top = 5\t\t# number of momentum positions\n\tn.mom = 6*22\t# length of momentum look back\n\tn.vol = 1*22 \t# length of volatility look back\n \n #*****************************************************************\n # Equal Weight\n #******************************************************************\n data$weight[] = NA\n data$weight[period.ends,] = ntop(prices[period.ends,], n) \n models$equal.weight = bt.run.share(data, clean.signal=F)\n\n #*****************************************************************\n # Volatliliy Position Sizing\n #******************************************************************\n ret.log = bt.apply.matrix(prices4vol, ROC, type='continuous')\n hist.vol = bt.apply.matrix(ret.log, runSD, n = n.vol)\n \n adj.vol = 1/hist.vol[period.ends,]\n \n data$weight[] = NA\n data$weight[period.ends,] = adj.vol / rowSums(adj.vol, na.rm=T) \n models$volatility.weighted = bt.run.share(data, clean.signal=F)\n \n #*****************************************************************\n # Momentum Portfolio\n #*****************************************************************\n momentum = prices4mom / mlag(prices4mom, n.mom)\n \n data$weight[] = NA\n data$weight[period.ends,] = ntop(momentum[period.ends,], n.top) \n models$momentum = bt.run.share(data, clean.signal=F)\n \n #*****************************************************************\n # Combo: weight positions in the Momentum Portfolio according to Volatliliy\n #*****************************************************************\n weight = ntop(momentum[period.ends,], n.top) * adj.vol\n \n data$weight[] = NA\n data$weight[period.ends,] = weight / rowSums(weight, na.rm=T) \n models$combo = bt.run.share(data, clean.signal=F,trade.summary = TRUE)\n\n #***************************************************************** \n # Adaptive Asset Allocation (AAA)\n # weight positions in the Momentum Portfolio according to \n # the minimum variance algorithm\n #***************************************************************** \n weight = NA * prices\n weight[period.ends,] = ntop(momentum[period.ends,], n.top)\n \n for( i in period.ends[period.ends >= n.mom] ) {\n \thist = ret.log[ (i - n.vol + 1):i, ]\n \t\n\t\t# require all assets to have full price history\n\t\tinclude.index = count(hist)== n.vol \n\n\t\t# also only consider assets in the Momentum Portfolio\n index = ( weight[i,] > 0 ) & include.index\n n = sum(index)\n \n\t\tif(n > 0) {\t\t\t\t\t\n\t\t\thist = hist[ , index]\n \n\t # create historical input assumptions\n\t ia = create.ia(hist)\n\t s0 = apply(coredata(hist),2,sd) \n\t ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))\n\t \n\t\t\t# create constraints: 0<=x<=1, sum(x) = 1\n\t\t\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\t\t\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints) \n\t\t\t\n\t\t\t# compute minimum variance weights\t\t\t\t \n\t weight[i,] = 0 \n\t weight[i,index] = min.risk.portfolio(ia, constraints)\n }\n }\n\n # Adaptive Asset Allocation (AAA)\n data$weight[] = NA\n data$weight[period.ends,] = weight[period.ends,] \n models$aaa = bt.run.share(data, clean.signal=F,trade.summary = TRUE)\n \n \n #*****************************************************************\n # Create Report\n #****************************************************************** \n #pdf(file = 'report.pdf', width=8.5, height=11)\n \n models = rev(models)\n \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part1(models) \ndev.off()\t\t\n\npng(filename = 'plot2.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part2(models) \ndev.off()\t\t\n\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n\tplotbt.custom.report.part3(models$combo, trade.summary = TRUE) \ndev.off()\t\t\n \npng(filename = 'plot4.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part3(models$aaa, trade.summary = TRUE) \ndev.off() \n\n\n}\n\t\n\nbt.aaa.test.new <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = spl('SPY,EFA,EWJ,EEM,IYR,RWX,IEF,TLT,DBC,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='2004:12::')\n \n \n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n n = ncol(prices)\n \n models = list()\n \n \t#*****************************************************************\n # Code Strategies\n #******************************************************************\n # find period ends\n period.ends = endpoints(prices, 'months')\n #period.ends = endpoints(prices, 'weeks')\n period.ends = period.ends[period.ends > 0]\n\n \nn.mom = 180\nn.vol = 60\nn.top = 4\n \n\tmomentum = prices / mlag(prices, n.mom) \n \n\tmodels$combo = bt.aaa.combo(data, period.ends, n.top = n.top,\n\t\t\t\t\tn.mom = n.mom, n.vol = n.vol)\n\t\t\t\t\t\n\t# bt.aaa.minrisk is equivalent to MV=min.var.portfolio below\n\tmodels$aaa = bt.aaa.minrisk(data, period.ends, n.top = n.top,\n\t\t\t\t\tn.mom = n.mom, n.vol = n.vol)\n\t\t\t\n\t\t\t\t\t\n\tobj = portfolio.allocation.helper(data$prices, period.ends=period.ends,\n\t\tlookback.len = n.vol, universe = ntop(momentum[period.ends,], n.top) > 0,\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio,\n\t\t\t\t\t\tRP=risk.parity.portfolio(),\n\t\t\t\t\t\tMV=min.var.portfolio,\n\t\t\t\t\t\tMD=max.div.portfolio,\n\t\t\t\t\t\tMC=min.corr.portfolio,\n\t\t\t\t\t\tMC2=min.corr2.portfolio,\n\t\t\t\t\t\tMCE=min.corr.excel.portfolio,\n\t\t\t\t\t\tRSO.2 = rso.portfolio(equal.weight.portfolio, 2, 100), \n\t\t\t\t\t\tMS=max.sharpe.portfolio())\n\t) \n\t\n\t#models = c(models, create.strategies(obj, data)$models)\n\tmodels = create.strategies(obj, data)$models\n\t\t\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n # put all reports into one pdf file\n\t#pdf(file = 'filename.pdf', width=8.5, height=11)\n\npng(filename = 'plot2.png', width = 800, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\n\t\tstrategy.performance.snapshoot(models, T)\ndev.off()\n\t\npng(filename = 'plot3.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplotbt.custom.report.part2(models$MS)\ndev.off()\t\n\t\t\t\n\t\t\npng(filename = 'plot4.png', width = 500, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\t\t# Plot Portfolio Turnover for each strategy\n\t\tlayout(1)\n\t\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\ndev.off()\t\n\n\t\t\t\n\t\t\n\t# close pdf file\n #dev.off()\t\n}\n\n\n\n\n#*****************************************************************\n# Random Subspace Optimization(RSO)\n# https://cssanalytics.wordpress.com/2013/10/06/random-subspace-optimization-rso/\n# http://systematicedge.wordpress.com/2013/10/14/random-subspace-optimization-max-sharpe/\n#*****************************************************************\nbt.rso.portfolio.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog,corpcor,lpSolve')\n\ttickers = spl('SPY,EEM,EFA,TLT,IWM,QQQ,GLD')\t\n\ttickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='1998::') \n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\t\t\t\n\t\t\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = 'months', lookback.len = 120, \n\t\tmin.risk.fns = list(\n\t\t\tEW = equal.weight.portfolio,\n\t\t\t# RP = risk.parity.portfolio(),\n\t\t\t\n\t\t\t# MV = min.var.portfolio,\n\t\t\t# RSO.MV = rso.portfolio(min.var.portfolio, 3, 100),\n\t\t\t\n\t\t\tMS = max.sharpe.portfolio(),\t\t\t\n\t\t\tRSO.MS.2 = rso.portfolio(max.sharpe.portfolio(), 2, 100),\n\t\t\tRSO.MS.3 = rso.portfolio(max.sharpe.portfolio(), 3, 100),\n\t\t\tRSO.MS.4 = rso.portfolio(max.sharpe.portfolio(), 4, 100),\n\t\t\tRSO.MS.5 = rso.portfolio(max.sharpe.portfolio(), 5, 100),\n\t\t\tRSO.MS.6 = rso.portfolio(max.sharpe.portfolio(), 6, 100),\n\t\t\tRSO.MS.7 = rso.portfolio(max.sharpe.portfolio(), 7, 100)\n\t\t)\n\t)\n\t\n\tmodels = create.strategies(obj, data)$models\n\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n\tstrategy.performance.snapshoot(models,T)\n\t\t\t\n}\n\n###############################################################################\n# Merging Current Stock Quotes with Historical Prices\n###############################################################################\nbt.current.quote.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = spl('VTI,EFA,SHY')\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data)\n \n \n # look at the data\n\tlast(data$prices, 2)\n\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = spl('VTI,EFA,SHY')\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t\t\n\t\t# current quotes logic\n\t\tquotes = getQuote(tickers)\n\t\tfor(i in ls(data))\n\t\t\tif( last(index(data[[i]])) < as.Date(quotes[i, 'Trade Time']) ) {\n\t\t\t\tdata[[i]] = rbind( data[[i]], make.xts(quotes[i, spl('Open,High,Low,Last,Volume,Last')],\n\t\t\t\t\tas.Date(quotes[i, 'Trade Time'])))\n\t\t\t}\n\n\tbt.prep(data)\n \n\t\n # look at the data\n\tlast(data$prices, 2)\n}\n\n\n###############################################################################\n# Extending Commodity time series\n# with CRB Commodities Index \n# http://www.jefferies.com/cositemgr.pl/html/ProductsServices/SalesTrading/Commodities/ReutersJefferiesCRB/IndexData/index.shtml\n###############################################################################\nbt.extend.DBC.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \t\n\tload.packages('quantmod')\t\t\n\tCRB = get.CRB()\n\t\t\n\ttickers = spl('GSG,DBC')\t\t\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01')\n\t\n\t#*****************************************************************\n\t# Compare different indexes\n\t#****************************************************************** \t\n\tout = na.omit(merge(Ad(CRB), Ad(GSG), Ad(DBC)))\n\t\tcolnames(out) = spl('CRB,GSG,DBC')\n\ttemp = out / t(repmat(as.vector(out[1,]),1,nrow(out)))\n\t\t\npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \t\n\t# Plot side by side\n\tlayout(1:2, heights=c(4,1))\n\tplota(temp, ylim=range(temp))\n\t\tplota.lines(temp[,1],col=1)\n\t\tplota.lines(temp[,2],col=2)\n\t\tplota.lines(temp[,3],col=3)\n\tplota.legend(colnames(temp),1:3)\n\t\t\t\n\t# Plot correlation table\n\ttemp = cor(temp / mlag(temp)- 1, use='complete.obs', method='pearson')\n\t\t\ttemp[] = plota.format(100 * temp, 0, '', '%')\n\tplot.table(temp)\t\ndev.off()\t\t\n\t\n\t\n\t#*****************************************************************\n\t# Create simple equal weight back-test\n\t#****************************************************************** \n\ttickers = spl('GLD,DBC,TLT')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\t\t# extend Gold and Commodity time series\n\t\tdata$GLD = extend.GLD(data$GLD)\t\n\t\tdata$DBC = extend.data(data$DBC, get.CRB(), scale=T)\n\t\t\t\n\tbt.prep(data, align='remove.na')\n \n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n n = ncol(prices)\n \n # find period ends\n period.ends = endpoints(prices, 'months')\n period.ends = period.ends[period.ends > 0]\n \n models = list()\n \n #*****************************************************************\n # Equal Weight\n #******************************************************************\n data$weight[] = NA\n data$weight[period.ends,] = ntop(prices[period.ends,], n) \n models$equal.weight = bt.run.share(data, clean.signal=F)\n\n \n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part1(models) \ndev.off()\t\t\n\npng(filename = 'plot3.png', width = 1200, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part2(models) \ndev.off()\t\n}\n\n\nbt.extend.DBC.update.test <- function() \n{\n #*****************************************************************\n # Load historical data\n #****************************************************************** \n\tload.packages('quantmod')\t\t\n\ttickers = spl('GSG,DBC')\n data = new.env()\n\t getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T) \n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n \n # \"TRJ_CRB\" file was downloaded from the http://www.jefferies.com/Commodities/2cc/389\n # for \"TRJ/CRB Index-Total Return\"\n\ttemp = extract.table.from.webpage( join(readLines(\"TRJ_CRB\")), 'EODValue' )\n\t\ttemp = join( apply(temp, 1, join, ','), '\\n' )\n\tdata$CRB_1 = make.stock.xts( read.xts(temp, format='%m/%d/%y' ) )\n\t\n # \"prfmdata.csv\" file was downloaded from the http://www.crbequityindexes.com/indexdata-form.php\n # for \"TR/J CRB Global Commodity Equity Index\", \"Total Return\", \"All Dates\"\n data$CRB_2 = make.stock.xts( read.xts(\"prfmdata.csv\", format='%m/%d/%Y' ) )\n \t \n\tbt.prep(data, align='remove.na')\n\t \n #*****************************************************************\n # Compare\n #****************************************************************** \npng(filename = 'plot1.png', width = 500, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\t\n\tplota.matplot(scale.one(data$prices))\n\t\ndev.off()\n}\n\n\n\n\n\n###############################################################################\n# Permanent Portfolio\n# http://catallacticanalysis.com/permanent-portfolio/\n# http://systematicinvestor.wordpress.com/2011/12/16/backtesting-rebalancing-methods/\n# http://en.wikipedia.org/wiki/Fail-Safe_Investing#The_Permanent_Portfolio\n###############################################################################\nbt.permanent.portfolio.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY,TLT,GLD,SHY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\t\t# extend GLD with Gold.PM - London Gold afternoon fixing prices\n\t\tdata$GLD = extend.GLD(data$GLD)\n\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\t\n\tprices = data$prices \n\t\tn = ncol(prices)\n\t\tnperiods = nrow(prices)\n\n\t# annual\n\tperiod.ends = endpoints(prices, 'years')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\tperiod.ends.y = c(1, period.ends)\n\n\t# quarterly\n\tperiod.ends = endpoints(prices, 'quarters')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\tperiod.ends.q = c(1, period.ends)\n\t\t\t\t\t\n\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\ttarget.allocation = matrix(rep(1/n,n), nrow=1)\n\t\n\t# Buy & Hold\t\n\tdata$weight[] = NA\t\n\t\tdata$weight[period.ends.y[1],] = target.allocation\n\tmodels$buy.hold = bt.run.share(data, clean.signal=F)\n\n\t\t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends.y,] = ntop(prices[period.ends.y,], n)\n\tmodels$equal.weight.y = bt.run.share(data, clean.signal=F)\n\n\t# Rebalance only when threshold is broken\n\tmodels$threshold.y = bt.max.deviation.rebalancing(data, models$buy.hold, target.allocation, 10/100, 0, period.ends = period.ends.y) \n\n\t#*****************************************************************\n\t# Quarterly\n\t#****************************************************************** \t\n\t# Equal Weight\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends.q,] = ntop(prices[period.ends.q,], n)\n\tmodels$equal.weight.q = bt.run.share(data, clean.signal=F)\n\n\t# Rebalance only when threshold is broken\n\tmodels$threshold.q = bt.max.deviation.rebalancing(data, models$buy.hold, target.allocation, 10/100, 0, period.ends = period.ends.q) \n\t\n\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part1(models) \ndev.off()\t\t\t\t\t\n\t\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.strategy.sidebyside(models)\ndev.off()\t\t\t\n\t\n\t\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n\t# Plot Portfolio Turnover for each Rebalancing method\n\tlayout(1:2)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', F)\n\tbarplot.with.labels(sapply(models, compute.max.deviation, target.allocation), 'Maximum Deviation from Target Mix')\ndev.off()\t\t\t\n\t\n}\n\t\n\n\n\n###############################################################################\n# Additional example for Permanent Portfolio\n# that employs:\n# * risk allocation\n# * volatility targeting\n# * makret filter (10 month SMA)\n# to improve strategy perfromance\n###############################################################################\nbt.permanent.portfolio2.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY,TLT,GLD,SHY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\t\t# extend GLD with Gold.PM - London Gold afternoon fixing prices\n\t\tdata$GLD = extend.GLD(data$GLD)\n\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\t\n\tprices = data$prices \n\t\tn = ncol(prices)\n\n\tperiod.ends = endpoints(prices, 'quarters')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\tperiod.ends = c(1, period.ends)\n\t\t\t\t\t\n\n\tmodels = list()\n\t\n\t\n\t#*****************************************************************\n\t# Dollar Weighted\n\t#****************************************************************** \t\t\t\n\ttarget.allocation = matrix(rep(1/n,n), nrow=1)\n\tweight.dollar = ntop(prices, n)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.dollar[period.ends,]\n\tmodels$dollar = bt.run.share(data, clean.signal=F)\n\t\t\t\t\n\t#*****************************************************************\n\t# Dollar Weighted + 7% target volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$dollar,\n\t\t\t\t\t\tweight.dollar, 7/100, 21, 100/100)[period.ends,]\n\tmodels$dollar.target7 = bt.run.share(data, clean.signal=F)\n\t\n\t#*****************************************************************\n\t# Risk Weighted\n\t#****************************************************************** \t\t\t\t\n\tret.log = bt.apply.matrix(prices, ROC, type='continuous')\n\thist.vol = sqrt(252) * bt.apply.matrix(ret.log, runSD, n = 21)\t\n\tweight.risk = weight.dollar / hist.vol\n\t\tweight.risk = weight.risk / rowSums(weight.risk)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.risk[period.ends,]\n\tmodels$risk = bt.run.share(data, clean.signal=F)\n\n\tif(F) {\n\t\t# risk weighted + 7% target volatility\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk,\n\t\t\t\t\t\t\tweight.risk, 7/100, 21, 100/100)[period.ends,]\n\t\tmodels$risk.target7 = bt.run.share(data, clean.signal=F)\n\t\n\t\t# risk weighted + 5% target volatility\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk,\n\t\t\t\t\t\t\tweight.risk, 5/100, 21, 100/100)[period.ends,]\n\t\tmodels$risk.target5 = bt.run.share(data, clean.signal=F)\n\t}\t\n\t#*****************************************************************\n\t# Market Filter (tactical): 10 month moving average\n\t#****************************************************************** \t\t\t\t\n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\tperiod.ends = c(1, period.ends)\n\n\tsma = bt.apply.matrix(prices, SMA, 200)\n\tweight.dollar.tactical = weight.dollar * (prices > sma)\t\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.dollar.tactical[period.ends,]\n\tmodels$dollar.tactical = bt.run.share(data, clean.signal=F)\n\n\t#*****************************************************************\n\t# Tactical + 7% target volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$dollar.tactical,\n\t\t\t\t\t\tweight.dollar.tactical, 7/100, 21, 100/100)[period.ends,]\n\tmodels$dollar.tactical.target7 = bt.run.share(data, clean.signal=F)\n\t\t\n\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part1(models) \ndev.off()\t\t\t\t\t\n\t\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.strategy.sidebyside(models)\ndev.off()\t \n\t\n}\n\n\n\n\n###############################################################################\n# Additional example for Permanent Portfolio\n# add transaction cost and \n# RR - remove SHY from basket\n###############################################################################\nbt.permanent.portfolio3.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('SPY,TLT,GLD,SHY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\t\t# extend GLD with Gold.PM - London Gold afternoon fixing prices\n\t\tdata$GLD = extend.GLD(data$GLD)\n\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\t\n\tprices = data$prices \n\t\tn = ncol(prices)\n\n\tperiod.ends = endpoints(prices, 'months')\n\t\tperiod.ends = period.ends[period.ends > 0]\t\t\n\t\tperiod.ends = c(1, period.ends)\n\t\t\t\t\t\n\n\tmodels = list()\n\tcommission = 0.1\n\t\n\t#*****************************************************************\n\t# Dollar Weighted\n\t#****************************************************************** \t\t\t\n\ttarget.allocation = matrix(rep(1/n,n), nrow=1)\n\tweight.dollar = ntop(prices, n)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.dollar[period.ends,]\n\tmodels$dollar = bt.run.share(data, commission=commission, clean.signal=F)\n\t\t\t\t\n\t#*****************************************************************\n\t# Dollar Weighted + 7% target volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$dollar,\n\t\t\t\t\t\tweight.dollar, 7/100, 21, 100/100)[period.ends,]\n\tmodels$dollar.target7 = bt.run.share(data, commission=commission, clean.signal=F)\n\t\n\t#*****************************************************************\n\t# Risk Weighted\n\t#****************************************************************** \t\t\t\t\n\tret.log = bt.apply.matrix(prices, ROC, type='continuous')\n\thist.vol = sqrt(252) * bt.apply.matrix(ret.log, runSD, n = 21)\t\n\tweight.risk = weight.dollar / hist.vol\n\t\tweight.risk$SHY = 0 \n\t\tweight.risk = weight.risk / rowSums(weight.risk)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.risk[period.ends,]\n\tmodels$risk = bt.run.share(data, commission=commission, clean.signal=F)\n\n\t#*****************************************************************\n\t# Risk Weighted + 7% target volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk,\n\t\t\t\t\t\tweight.risk, 7/100, 21, 100/100)[period.ends,]\n\tmodels$risk.target7 = bt.run.share(data, commission=commission, clean.signal=F)\n\n\t#*****************************************************************\n\t# Risk Weighted + 7% target volatility + SHY\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk,\n\t\t\t\t\t\tweight.risk, 7/100, 21, 100/100)[period.ends,]\n\t\t\t\t\t\t\n \t\tcash = 1-rowSums(data$weight)\n\t data$weight$SHY[period.ends,] = cash[period.ends]\n\tmodels$risk.target7.shy = bt.run.share(data, commission=commission, clean.signal=F)\n\t\n\t\n\t\n\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.custom.report.part1(models) \ndev.off()\t\t\t\t\t\n\t\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \n plotbt.strategy.sidebyside(models)\ndev.off()\t \n\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white')\t\t \t\n\t# Plot Portfolio Turnover for each strategy\n\tlayout(1)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover')\ndev.off()\t \t\n\t\n\t\n\t\n\t\n\t\n\t\n\t#*****************************************************************\n\t# Market Filter (tactical): 10 month moving average\n\t#****************************************************************** \t\t\t\t\n\tsma = bt.apply.matrix(prices, SMA, 200)\n\tweight.dollar.tactical = weight.dollar * (prices > sma)\t\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.dollar.tactical[period.ends,]\n\tmodels$dollar.tactical = bt.run.share(data, commission=commission, clean.signal=F)\n\n\t#*****************************************************************\n\t# Tactical + 7% target volatility\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$dollar.tactical,\n\t\t\t\t\t\tweight.dollar.tactical, 7/100, 21, 100/100)[period.ends,]\n\tmodels$dollar.tactical.target7 = bt.run.share(data, commission=commission, clean.signal=F)\n\t\t\n\t\t\n\t\n\t\n\t#*****************************************************************\n\t# Risk Weighted + Tactical \n\t#****************************************************************** \t\t\t\t\n\tweight.risk.tactical = weight.risk * (prices > sma)\t\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = weight.risk.tactical[period.ends,]\n\tmodels$risk.tactical = bt.run.share(data, commission=commission, clean.signal=F)\n\t\n\t#*****************************************************************\n\t# Risk Weighted + Tactical + 7% target volatility + SHY\n\t#****************************************************************** \t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = target.vol.strategy(models$risk.tactical,\n\t\t\t\t\t\tweight.risk.tactical, 7/100, 21, 100/100)[period.ends,]\n \t\tcash = 1-rowSums(data$weight)\n\t data$weight$SHY[period.ends,] = cash[period.ends]\t\t\t\t\t\t\n\tmodels$risk.tactical.target7.shy = bt.run.share(data, commission=commission, clean.signal=F)\n\t\n\t\n}\n\n\n\n\n###############################################################################\n# Minimum Correlation Algorithm Example\n###############################################################################\nbt.mca.test <- function() \n{\n\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog')\n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='2002:08::')\n\t\n\t#write.xts(data$prices, 'data.csv')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\t\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'weeks',\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio,\n\t\t\t\t\t\tRP=risk.parity.portfolio(),\n\t\t\t\t\t\tMV=min.var.portfolio,\n\t\t\t\t\t\tMD=max.div.portfolio,\n\t\t\t\t\t\tMC=min.corr.portfolio,\n\t\t\t\t\t\tMC2=min.corr2.portfolio,\n\t\t\t\t\t\tMCE=min.corr.excel.portfolio),\n\t\tcustom.stats.fn = 'portfolio.allocation.custom.stats'\n\t) \n\t\n\t\n\tmodels = create.strategies(obj, data)$models\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \n\tlayout(1:2)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T)\ndev.off()\n\npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \t\n\t# Plot time series of components of Composite Diversification Indicator\n\tcdi = custom.composite.diversification.indicator(obj,plot.table = F)\t\n\t\tout = rbind(colMeans(cdi, na.rm=T), out)\n\t\trownames(out)[1] = 'Composite Diversification Indicator(CDI)'\ndev.off()\t\t\n\t\t\t\t\n\t# Portfolio Turnover for each strategy\n\ty = 100 * sapply(models, compute.turnover, data)\n\t\tout = rbind(y, out)\n\t\trownames(out)[1] = 'Portfolio Turnover'\t\t\n\npng(filename = 'plot3.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \t\t\t\t\n\tperformance.barchart.helper(out, 'Sharpe,Cagr,DVR,MaxDD', c(T,T,T,T))\ndev.off()\t\n\t\npng(filename = 'plot4.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \t\t\n\tperformance.barchart.helper(out, 'Volatility,Portfolio Turnover,Composite Diversification Indicator(CDI)', c(F,F,T))\ndev.off()\t\n\n\t\npng(filename = 'plot5.png', width = 600, height = 1000, units = 'px', pointsize = 12, bg = 'white') \t\t\t\n\t# Plot transition maps\n\tlayout(1:len(models))\n\tfor(m in names(models)) {\n\t\tplotbt.transition.map(models[[m]]$weight, name=m)\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\ndev.off()\t\n\t\npng(filename = 'plot6.png', width = 600, height = 1000, units = 'px', pointsize = 12, bg = 'white') \t\t\t\t\t\n\t# Plot transition maps for Risk Contributions\n\tdates = index(data$prices)[obj$period.ends]\n\tlayout(1:len(models))\n\tfor(m in names(models)) {\n\t\tplotbt.transition.map(make.xts(obj$risk.contributions[[m]], dates), \n\t\tname=paste('Risk Contributions',m))\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\ndev.off()\t\n\t\n\t# plot the most recent weights\n\tplot.table( sapply(models, function(m) round(100*last(m$weight),1)) )\n\t\n}\t\n\n\t\n###############################################################################\n# Minimum Correlation Algorithm Speed test\n###############################################################################\nbt.mca.speed.test <- function() \n{\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tload.packages('quadprog,corpcor')\n\t\n\tn = 100\n\thist = matrix(rnorm(1000*n), nc=n)\n\t\n\t# 0 <= x.i <= 1\n\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\t\tconstraints = add.constraints(diag(n), type='>=', b=0, constraints)\n\t\tconstraints = add.constraints(diag(n), type='<=', b=1, constraints)\n\n\t# SUM x.i = 1\n\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints)\t\t\n\t\t\t\t\t\t\n\t# create historical input assumptions\n\tia = list()\n\t\tia$n = n\n\t\tia$risk = apply(hist, 2, sd)\n\t\tia$correlation = cor(hist, use='complete.obs', method='pearson')\n\t\tia$cov = ia$correlation * (ia$risk %*% t(ia$risk))\n\t\t\t\t\n\t\tia$cov = make.positive.definite(ia$cov, 0.000000001)\n\t\tia$correlation = make.positive.definite(ia$correlation, 0.000000001)\n\t\t\n\t#*****************************************************************\n\t# Time\n\t#*****************************************************************\t\t\t\t\n\tload.packages('rbenchmark')\t\t\t\n\n\tbenchmark(\n\t\tmin.var.portfolio(ia, constraints),\n\t\tmin.corr.portfolio(ia, constraints),\n\t\tmin.corr2.portfolio(ia, constraints),\n\t\t\n\t\t\n\tcolumns=c(\"test\", \"replications\", \"elapsed\", \"relative\"),\n\torder=\"relative\",\n\treplications=100\n\t)\n\t\n\t\n\t#*****************************************************************\n\t# Check the bottle neck\n\t#*****************************************************************\t\t\t\t\n\tRprof()\n\tfor(i in 1:10)\n\t\tmin.corr.portfolio(ia, constraints)\n\tRprof(NULL)\n\tsummaryRprof()\n\n\t\n\t#ia$cov = make.positive.definite.fast(ia$cov)\n\t#ia$correlation = make.positive.definite.fast(ia$correlation)\n\t\n\t#*****************************************************************\n\t# Template for testing speed finding bottle necks\n\t#*****************************************************************\t\t\t\t\t\n\t# time it\n\ttic(12) \t\n\tfor(icount in 1:10) {\n \t\t\n \t\t# inset your code here and adjust number of evalutaions\n \t\t\n\t} \t\t \t\t\n\ttoc(12) \n\t\n\t# determine bottle necks\n\tRprof()\n\tfor(icount in 1:10) {\n \t\t\n \t\t# inset your code here and adjust number of evalutaions\n \t\t\n\t} \t\t\t\t\n\tRprof(NULL)\n\tsummaryRprof() \t\t\n\t\t\n}\t\n\n\n###############################################################################\n# Testing Universal Portfolios - Constant Rebalanced portfolio\n# http://optimallog.blogspot.ca/2012/06/universal-portfolio-part-3.html\n# http://optimallog.blogspot.ca/2012/06/universal-portfolio-part-4.html\n# to call internal function in logopt use logopt:::crp_bh(x) or logopt:::roll.bcrp\n###############################################################################\nbt.crp.test <- function() \n{\n\t#*****************************************************************\n\t# Example from http://optimallog.blogspot.ca/2012/06/universal-portfolio-part-3.html\n\t#****************************************************************** \n\tload.packages('FNN')\n\tload.packages('logopt', 'http://R-Forge.R-project.org')\n\t\n\tload.packages('quantmod')\n\n\tdata(nyse.cover.1962.1984)\n\tx = nyse.cover.1962.1984\n\tx = x[,spl('iroqu,kinar')]\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tdata <- new.env()\n\t\tfor(i in names(x)) {\n\t\t\tdata[[i]] = cumprod(x[,i])\n\t\t\tcolnames(data[[i]]) = 'Close'\n\t\t\t}\n\tbt.prep(data, align='remove.na')\n\t\n\t\n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n\t n = ncol(prices)\n\t\n #*****************************************************************\n # Plot 1\n #******************************************************************\n\tplota(prices$iroqu, col='blue', type='l', ylim=range(prices), main = '\"iroqu\" and \"kinar\"', ylab='')\n\t\tplota.lines(prices$kinar, col='red')\n\t\tgrid()\n\tplota.legend('iroqu,kinar', 'blue,red')\t\n\t\n #*****************************************************************\n # Compute Universal Portfolio\n #******************************************************************\t\n\tuniversal = prices[,1] * 0\t \n\talphas = seq(0,1,by=0.05)\n\tcrps = alphas\n\tfor (i in 1:length(crps)) {\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[] = c(alphas[i], 1-alphas[i])\n\t\tequity = bt.run(data, silent=T)$equity\n\t\t\n\t\tuniversal = universal + equity\n\t\tcrps[i] = last(equity)\n\t}\t \n\tuniversal = universal/length(alphas)\n\t \n #*****************************************************************\n # Plot 2\n #******************************************************************\n\tplot(alphas, crps, col=\"blue\", type=\"l\", ylab=\"\",\n\t\tmain='20 Year Return vs. mix of \"iroqu\" and \"kinar\"',\n\t\txlab='Fraction of \"iroqu\" in Portfolio')\n\tpoints(alphas, crps, pch=19, cex=0.5, col=\"red\")\n\tabline(h=mean(crps), col=\"green\")\n\ttext(0.5,mean(crps)*1.05,labels=\"Return from Universal Portfolio\")\n\t\tgrid()\t\n\n #*****************************************************************\n # Plot 3\n #******************************************************************\n\tplota(prices$iroqu, col='blue', type='l', ylim=range(prices, universal), \n\t\t\tmain = 'Universal Portfolios with \"iroqu\" and \"kinar\"', ylab=\"\")\n\t\tplota.lines(prices$kinar, col='red')\n\t\tplota.lines(universal, col='green')\n\t\tgrid()\n\tplota.legend('iroqu,kinar,universal', 'blue,red,green')\t\n \n\t\n\t\n\t\n\n\n\t# Constant Rebalanced portfolio\n\tcrp.portfolio <- function\n\t(\n\t\tia,\t\t\t\t# input assumptions\n\t\tconstraints\t\t# constraints\n\t)\n\t{\n\t\tbcrp.optim(1 + ia$hist.returns, fast.only = TRUE )\n\t}\n\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog')\n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='keep.all', dates='2002:08::')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'weeks', lookback.len = 460, \n\t\tmin.risk.fns = list(CRP=crp.portfolio)\n\t) \n\t\n\tmodels = create.strategies(obj, data)$models\n\t\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n # quite volatlie for a short lookback.len\n plotbt.custom.report.part2( models$CRP )\n \n \n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\t\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'weeks',\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio,\n\t\t\t\t\t\tRP=risk.parity.portfolio(),\n\t\t\t\t\t\tMC=min.corr.portfolio,\n\t\t\t\t\t\tMC2=min.corr2.portfolio)\n\t) \n\t\n\tmodels = c(models, create.strategies(obj, data)$models)\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n\t# performance is inferior to other algos\n layout(1:2)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T)\n\t\n}\n\n\n\n###############################################################################\n# Interesting that Sep/Nov perfromance changes over different time frames\n# http://www.marketwatch.com/story/an-early-halloween-for-gold-traders-2012-09-26\n# An early Halloween for gold traders\n# Commentary: October is worst month of calendar for gold bullion\n# By Mark Hulbert\n# Watch out, gold traders: Halloween is likely to come early. \n###############################################################################\nbt.october.gold.test <- function() \n{\n #*****************************************************************\n # Load historical data\n #****************************************************************** \n load.packages('quantmod')\n ticker = 'GLD'\n \n data = getSymbols(ticker, src = 'yahoo', from = '1970-01-01', auto.assign = F)\n data = adjustOHLC(data, use.Adjusted=T)\n \n #*****************************************************************\n # Look at the Month of the Year Seasonality\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \n\tmonth.year.seasonality(data, ticker)\ndev.off() \n \n \n \n #*****************************************************************\n # Load long series of gold prices from Bundes Bank\n #****************************************************************** \n data = bundes.bank.data.gold()\n\n #*****************************************************************\n # Look at the Month of the Year Seasonality\n #****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \n\tmonth.year.seasonality(data, 'GOLD', lookback.len = nrow(data))\ndev.off() \n\n\n #*****************************************************************\n # Create file for Seasonality Tool\n #****************************************************************** \n GLD = getSymbols(ticker, src = 'yahoo', from = '1970-01-01', auto.assign = F)\n GLD = adjustOHLC(GLD, use.Adjusted=T)\n \n\n\twrite.xts(extend.data(GLD, data / 10), 'GOLD.csv')\n\n\n\n} \n\n\n\n\n###############################################################################\n# Couch Potato strategy\n# http://www.moneysense.ca/2006/04/05/couch-potato-portfolio-introduction/\n###############################################################################\n\t# helper function to model Couch Potato strategy - a fixed allocation strategy\n\tcouch.potato.strategy <- function\n\t(\n\t\tdata.all,\n\t\ttickers = 'XIC.TO,XSP.TO,XBB.TO',\n\t\tweights = c( 1/3, 1/3, 1/3 ), \t\t\n\t\tperiodicity = 'years',\n\t\tdates = '1900::',\n\t\tcommission = 0.1\n\t) \n\t{ \n\t\t#*****************************************************************\n\t\t# Load historical data \n\t\t#****************************************************************** \n\t\ttickers = spl(tickers)\n\t\tnames(weights) = tickers\n\t\t\n\t\tdata <- new.env()\n\t\tfor(s in tickers) data[[ s ]] = data.all[[ s ]]\n\t\t\n\t\tbt.prep(data, align='remove.na', dates=dates)\n\t\n\t\t#*****************************************************************\n\t\t# Code Strategies\n\t\t#******************************************************************\n\t\tprices = data$prices \n\t\t\tn = ncol(prices)\n\t\t\tnperiods = nrow(prices)\n\t\n\t\t# find period ends\n\t\tperiod.ends = endpoints(data$prices, periodicity)\n\t\t\tperiod.ends = c(1, period.ends[period.ends > 0])\n\t\n\t\t#*****************************************************************\n\t\t# Code Strategies\n\t\t#******************************************************************\n\t\tdata$weight[] = NA\n\t\t\tfor(s in tickers) data$weight[period.ends, s] = weights[s]\n\t\tmodel = bt.run.share(data, clean.signal=F, commission=commission)\n\t\t\n\t\treturn(model)\n\t} \t\n\nbt.couch.potato.test <- function() \n{\n\t#*****************************************************************\n\t# Canadian Version\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\tmap = list()\n\t\tmap$can.eq = 'XIC.TO'\n\t\tmap$can.div = 'XDV.TO'\t\t\n\t\tmap$us.eq = 'XSP.TO'\n\t\tmap$us.div = 'DVY'\t\t\t\n\t\tmap$int.eq = 'XIN.TO'\t\t\n\t\tmap$can.bond = 'XBB.TO'\n\t\tmap$can.real.bond = 'XRB.TO'\n\t\tmap$can.re = 'XRE.TO'\t\t\n\t\tmap$can.it = 'XTR.TO'\n\t\tmap$can.gold = 'XGD.TO'\n\t\t\t\n\tdata <- new.env()\n\tfor(s in names(map)) {\n\t\tdata[[ s ]] = getSymbols(map[[ s ]], src = 'yahoo', from = '1995-01-01', env = data, auto.assign = F)\n\t\tdata[[ s ]] = adjustOHLC(data[[ s ]], use.Adjusted=T)\t\n\t}\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tmodels = list()\n\t\tperiodicity = 'years'\n\t\tdates = '2006::'\n\t\n\tmodels$classic = couch.potato.strategy(data, 'can.eq,us.eq,can.bond', rep(1/3,3), periodicity, dates)\n\tmodels$global = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.bond', c(0.2, 0.2, 0.2, 0.4), periodicity, dates)\n\tmodels$yield = couch.potato.strategy(data, 'can.div,can.it,us.div,can.bond', c(0.25, 0.25, 0.25, 0.25), periodicity, dates)\n\tmodels$growth = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.bond', c(0.25, 0.25, 0.25, 0.25), periodicity, dates)\n\t\n\tmodels$complete = couch.potato.strategy(data, 'can.eq,us.eq,int.eq,can.re,can.real.bond,can.bond', c(0.2, 0.15, 0.15, 0.1, 0.1, 0.3), periodicity, dates)\t\n\t\n\tmodels$permanent = couch.potato.strategy(data, 'can.eq,can.gold,can.bond', c(0.25,0.25,0.5), periodicity, dates)\t\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \n\tlayout(1:2)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T)\ndev.off()\n\t\n\t\n\t\n\t#*****************************************************************\n\t# US Version\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\ttickers = spl('VIPSX,VTSMX,VGTSX,SPY,TLT,GLD,SHY')\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1995-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\n\t\t\n\t\t# extend GLD with Gold.PM - London Gold afternoon fixing prices\n\t\tdata$GLD = extend.GLD(data$GLD)\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tmodels = list()\n\t\tperiodicity = 'years'\n\t\tdates = '2003::'\n\t\n\tmodels$classic = couch.potato.strategy(data, 'VIPSX,VTSMX', rep(1/2,2), periodicity, dates)\n\tmodels$margarita = couch.potato.strategy(data, 'VIPSX,VTSMX,VGTSX', rep(1/3,3), periodicity, dates)\n\tmodels$permanent = couch.potato.strategy(data, 'SPY,TLT,GLD,SHY', rep(1/4,4), periodicity, dates)\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \npng(filename = 'plot2.png', width = 600, height = 600, units = 'px', pointsize = 12, bg = 'white') \n\tlayout(1:2)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\t\n\tout = plotbt.strategy.sidebyside(models, return.table=T)\ndev.off()\n \t\n}\n\n\n\n###############################################################################\n# Regime Detection\n# http://blogs.mathworks.com/pick/2011/02/25/markov-regime-switching-models-in-matlab/\n###############################################################################\nbt.regime.detection.test <- function() \n{\t\n\t#*****************************************************************\n\t# Generate data as in the post\n\t#****************************************************************** \n\tbull1 = rnorm( 100, 0.10, 0.15 )\n\tbear = rnorm( 100, -0.01, 0.20 )\n\tbull2 = rnorm( 100, 0.10, 0.15 )\n\ttrue.states = c(rep(1,100),rep(2,100),rep(1,100))\n\treturns = c( bull1, bear, bull2 )\n\n\n\t# find regimes\n\tload.packages('RHmm')\n\n\ty=returns\n\tResFit = HMMFit(y, nStates=2)\n\tVitPath = viterbi(ResFit, y)\n\t# HMMGraphicDiag(VitPath, ResFit, y)\n\t# HMMPlotSerie(y, VitPath)\n\n\t#Forward-backward procedure, compute probabilities\n\tfb = forwardBackward(ResFit, y)\n\n\t# Plot probabilities and implied states\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:2)\n\tplot(VitPath$states, type='s', main='Implied States', xlab='', ylab='State')\n\t\n\tmatplot(fb$Gamma, type='l', main='Smoothed Probabilities', ylab='Probability')\n\t\tlegend(x='topright', c('State1','State2'), fill=1:2, bty='n')\ndev.off()\t\n\n\t\n\t\t\n\t# http://lipas.uwasa.fi/~bepa/Markov.pdf\n\t# Expected duration of each regime (1/(1-pii)) \n\t#1/(1-diag(ResFit$HMM$transMat))\n\n\n\t#*****************************************************************\n\t# Add some data and see if the model is able to identify the regimes\n\t#****************************************************************** \n\tbear2 = rnorm( 100, -0.01, 0.20 )\n\tbull3 = rnorm( 100, 0.10, 0.10 )\n\tbear3 = rnorm( 100, -0.01, 0.25 )\n\ty = c( bull1, bear, bull2, bear2, bull3, bear3 )\n\tVitPath = viterbi(ResFit, y)$states\n\n\t\n\t# map states: sometimes HMMFit function does not assign states consistently\n\t# let's use following formula to rank states\n\t# i.e. high risk, low returns => state 2 and low risk, high returns => state 1\n\tmap = rank(sqrt(ResFit$HMM$distribution$var) - ResFit$HMM$distribution$mean)\n\tVitPath = map[VitPath]\n\n\t#*****************************************************************\n\t# Plot regimes\n\t#****************************************************************** \n\tload.packages('quantmod')\n\tdata = xts(y, as.Date(1:len(y)))\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tlayout(1:3)\n\t\tplota.control$col.x.highlight = col.add.alpha(true.states+1, 150)\n\tplota(data, type='h', plotX=F, x.highlight=T)\n\t\tplota.legend('Returns + True Regimes')\n\tplota(cumprod(1+data/100), type='l', plotX=F, x.highlight=T)\n\t\tplota.legend('Equity + True Regimes')\n\t\n\t\tplota.control$col.x.highlight = col.add.alpha(VitPath+1, 150)\n\tplota(data, type='h', x.highlight=T)\n\t\tplota.legend('Returns + Detected Regimes')\t\t\t\t\ndev.off()\t\n\n}\n\n\n###############################################################################\n# Regime Detection Pitfalls\n###############################################################################\nbt.regime.detection.pitfalls.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\tdata <- new.env()\n\tgetSymbols('SPY', src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tdata$SPY = adjustOHLC(data$SPY, use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data)\n\t\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\t\n\tnperiods = nrow(data$prices)\n\n\tmodels = list()\n\t\n\trets = ROC(Ad(data$SPY))\n\t\trets[1] = 0\n\t\n\t# use 10 years: 1993:2002 for training\t\n\tin.sample.index = '1993::2002'\n\tout.sample.index = '2003::'\n\t\t\n\tin.sample = rets[in.sample.index]\n\tout.sample = rets[out.sample.index]\n\tout.sample.first.date = nrow(in.sample) + 1\n\n\t#*****************************************************************\n\t# Fit Model\n\t#****************************************************************** \t\t\n\tload.packages('RHmm')\t\n\tfit = HMMFit(in.sample, nStates=2)\n\t\n\t# find states\n\tstates.all = rets * NA\n\tstates.all[] = viterbi(fit, rets)$states\n\t\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(states.all == 1, 0, 1)\n\t\tdata$weight[in.sample.index] = NA\n models$states.all = bt.run.share(data)\n\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t \n\tplotbt.custom.report.part1(models) \ndev.off()\t\n\t\n\t#*****************************************************************\n\t# Find problem - results are too good\n\t#****************************************************************** \t\t\n\t# The viterbi function need to see all data to compute the most likely sequence of states\n\t# or forward/backward probabilities\n\t# http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm\n\t# http://en.wikipedia.org/wiki/Viterbi_algorithm\n\t\n\t# We can use expanding window to determine the states\n\tstates.win1 = states.all * NA\n\tfor(i in out.sample.first.date:nperiods) {\n\t\tstates.win1[i] = last(viterbi(fit, rets[1:i])$states)\n\t\tif( i %% 100 == 0) cat(i, 'out of', nperiods, '\\n')\n\t}\n\t\n\t# Or we can refit model over expanding window as suggested in the\n\t# Regime Shifts: Implications for Dynamic Strategies by M. Kritzman, S. Page, D. Turkington\n\t# Out-of-Sample Analysis, page 8\n\tinitPoint = fit$HMM\n\tstates.win2 = states.all * NA\n\tfor(i in out.sample.first.date:nperiods) {\n\t\tfit2 = HMMFit(rets[2:i], nStates=2, control=list(init='USER', initPoint = initPoint))\n\t\t\tinitPoint = fit2$HMM\n\t\tstates.win2[i] = last(viterbi(fit2, rets[2:i])$states)\n\t\tif( i %% 100 == 0) cat(i, 'out of', nperiods, '\\n')\n\t}\n\n\t#*****************************************************************\n\t# Plot States\n\t#****************************************************************** \t\t\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t \t\n\tlayout(1:3)\n\tcol = col.add.alpha('white',210)\n\tplota(states.all[out.sample.index], type='s', plotX=F)\n\t\tplota.legend('Implied States based on all data', x='center', bty='o', bg=col, box.col=col,border=col,fill=col,cex=2)\n\tplota(states.win1[out.sample.index], type='s')\n\t\tplota.legend('Implied States based on rolling window', x='center', bty='o', bg=col, box.col=col,border=col,fill=col,cex=2)\n\tplota(states.win2[out.sample.index], type='s')\n\t\tplota.legend('Implied States based on rolling window(re-fit)', x='center', bty='o', bg=col, box.col=col,border=col,fill=col,cex=2)\ndev.off()\n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(states.win1 == 1, 0, 1)\n\t\tdata$weight[in.sample.index] = NA\n models$states.win1 = bt.run.share(data)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(states.win2 == 1, 0, 1)\n\t\tdata$weight[in.sample.index] = NA\n models$states.win2 = bt.run.share(data)\n\n\t#*****************************************************************\n\t# Create report\n\t#****************************************************************** \t\t\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t \t\t\n\tplotbt.custom.report.part1(models) \ndev.off()\n\n}\t\n \t \n\n\n\n\n###############################################################################\n# Financial Turbulence Index example based on the\n# Skulls, Financial Turbulence, and Risk Management by M. Kritzman, Y. Li\n# http://www.cfapubs.org/doi/abs/10.2469/faj.v66.n5.3\n#\n# Timely Portfolio series of posts:\n# http://timelyportfolio.blogspot.ca/2011/04/great-faj-article-on-statistical.html\n# http://timelyportfolio.blogspot.ca/2011/04/great-faj-article-on-statistical_26.html\n# http://timelyportfolio.blogspot.ca/2011/04/great-faj-article-on-statistical_6197.html\n###############################################################################\nbt.financial.turbulence.test <- function() \n{\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\n\tfx = get.G10()\n\t\tnperiods = nrow(fx)\n\n\t#*****************************************************************\n\t# Rolling estimate of the Financial Turbulence for G10 Currencies\n\t#****************************************************************** \n\tturbulence = fx[,1] * NA\n\tret = coredata(fx / mlag(fx) - 1)\n\t\n\tlook.back = 252\n\t\n\tfor( i in (look.back+1) : nperiods ) {\n\t\ttemp = ret[(i - look.back + 1):(i-1), ]\n\t\t\t\t\n\t\t# measures turbulence for the current observation\n\t\tturbulence[i] = mahalanobis(ret[i,], colMeans(temp), cov(temp))\n\t\t\n\t\tif( i %% 200 == 0) cat(i, 'out of', nperiods, '\\n')\n\t}\t\n\t\n\t#*****************************************************************\n\t# Plot 30 day average of the Financial Turbulence for G10 Currencies\n\t#****************************************************************** \t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tplota(EMA( turbulence, 30), type='l', \n\t\tmain='30 day average of the Financial Turbulence for G10 Currencies')\ndev.off()\t\n\t\n\t\n}\t\n\t\n\t\n\n###############################################################################\n# Principal component analysis (PCA)\n###############################################################################\t\t\nbt.pca.test <- function()\n{\t\t\t\n\t#*****************************************************************\n\t# Find Sectors for each company in DOW 30\n\t#****************************************************************** \n\ttickers = spl('XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU')\n\ttickers.desc = spl('ConsumerCyclicals,ConsumerStaples,Energy,Financials,HealthCare,Industrials,Materials,Technology,Utilities')\n\t\n\tsector.map = c()\n\tfor(i in 1:len(tickers)) {\n\t\tsector.map = rbind(sector.map, \n\t\t\t\tcbind(sector.spdr.components(tickers[i]), tickers.desc[i])\n\t\t\t)\n\t}\n\tcolnames(sector.map) = spl('ticker,sector')\n\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = dow.jones.components()\n\t\n\tsectors = factor(sector.map[ match(tickers, sector.map[,'ticker']), 'sector'])\n\t\tnames(sectors) = tickers\n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '2000-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\n\t\n\tbt.prep(data, align='keep.all', dates='2012')\n\t\n\t# re-order sectors, because bt.prep can change the order of tickers\n\tsectors = sectors[data$symbolnames]\n\t\n\t# save data for later examples\n\tsave(data, tickers, sectors, file='bt.pca.test.Rdata')\n\t#load(file='bt.pca.test.Rdata')\n\n\t#*****************************************************************\n\t# Principal component analysis (PCA), for interesting discussion\n\t# http://machine-master.blogspot.ca/2012/08/pca-or-polluting-your-clever-analysis.html\n\t#****************************************************************** \n\tprices = data$prices\t\n\tret = prices / mlag(prices) - 1\n\t\n\tp = princomp(na.omit(ret))\n\t\n\tloadings = p$loadings[]\n\tp.variance.explained = p$sdev^2 / sum(p$sdev^2)\n\n\t# plot percentage of variance explained for each principal component\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tbarplot(100*p.variance.explained, las=2, xlab='', ylab='% Variance Explained')\ndev.off()\n\t\n\t#*****************************************************************\n\t# 2-D Plot\n\t#****************************************************************** \t\t\n\tx = loadings[,1]\n\ty = loadings[,2]\n\tz = loadings[,3]\n\tcols = as.double(sectors)\n\t\n\t# plot all companies loadings on the first and second principal components and highlight points according to the sector they belong\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tplot(x, y, type='p', pch=20, col=cols, xlab='Comp.1', ylab='Comp.2')\n\ttext(x, y, data$symbolnames, col=cols, cex=.8, pos=4)\n\t\n\tlegend('topright', cex=.8, legend = levels(sectors), fill = 1:nlevels(sectors), merge = F, bty = 'n') \ndev.off()\n\n\t#*****************************************************************\n\t# 3-D Plot, for good examples of 3D plots\n\t# http://statmethods.wordpress.com/2012/01/30/getting-fancy-with-3-d-scatterplots/\n\t#****************************************************************** \t\t\t\t\n\tload.packages('scatterplot3d') \n\t\n\t# plot all companies loadings on the first, second, and third principal components and highlight points according to the sector they belong\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\ts3d = scatterplot3d(x, y, z, xlab='Comp.1', ylab='Comp.2', zlab='Comp.3', color=cols, pch = 20)\n\t\t\n s3d.coords = s3d$xyz.convert(x, y, z)\n text(s3d.coords$x, s3d.coords$y, labels=data$symbolnames, col=cols, cex=.8, pos=4)\n\t\t\n legend('topleft', cex=.8, legend = levels(sectors), fill = 1:nlevels(sectors), merge = F, bty = 'n') \ndev.off()\n\n\t#*****************************************************************\n\t# Next steps \n\t#*****************************************************************\n # - demonstrate clustering based on the selected Principal components\n # - using PCA for spread trading \n # http://matlab-trading.blogspot.ca/2012/12/using-pca-for-spread-trading.html\n} \n\t\n###############################################################################\n# Link between svd and eigen\n# https://stat.ethz.ch/pipermail/r-help/2001-September/014982.html\n# http://r.789695.n4.nabble.com/eigen-and-svd-td2550210.html\n# X is a matrix of de-mean returns, cov(X) = (t(x) %*% x) / T\n# (svd) X = U D V' ## D are the singular values of X\n# (eigen) X'X = V D^2 V' ## D^2 are the eigenvalues of X'X\n# V is the same in both factorizations. \n###############################################################################\n\n\n###############################################################################\n# The \"Absorption Ratio\" as defined in the \"Principal Components as a Measure of Systemic Risk\" \n# by M. Kritzman,Y. Li, S. Page, R. Rigobon paper\n# http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1633027\n#\n# The \"Absorption Ratio\" is define as the fraction of the total variance explained or absorbed by \n# a finite set of eigenvectors. Let?s, for example, compute the \"Absorption Ratio\" using \n# the first 3 eigenvectors.\n# sum( p$sdev[1:3]^2 ) / sum( sd(na.omit(ret))^2 )\n###############################################################################\n\n\n\n\n###############################################################################\n# Clustering based on the selected Principal components\n###############################################################################\t\t\nbt.clustering.test <- function()\n{\t\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\t\n\t# load data saved in the bt.pca.test() function\n\tload(file='bt.pca.test.Rdata')\n\n\t#*****************************************************************\n\t# Principal component analysis (PCA), for interesting discussion\n\t# http://machine-master.blogspot.ca/2012/08/pca-or-polluting-your-clever-analysis.html\n\t#****************************************************************** \n\tprices = data$prices\t\n\tret = prices / mlag(prices) - 1\n\t\n\tp = princomp(na.omit(ret))\n\t\n\tloadings = p$loadings[]\n\t\n\tx = loadings[,1]\n\ty = loadings[,2]\n\tz = loadings[,3]\t\n\t \n\t#*****************************************************************\n\t# Create clusters\n\t#****************************************************************** \t\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\t# create and plot clusters based on the first and second principal components\n\thc = hclust(dist(cbind(x,y)), method = 'ward')\n\tplot(hc, axes=F,xlab='', ylab='',sub ='', main='Comp 1/2')\n\trect.hclust(hc, k=3, border='red')\ndev.off()\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\t# create and plot clusters based on the first, second, and third principal components\n\thc = hclust(dist(cbind(x,y,z)), method = 'ward')\n\tplot(hc, axes=F,xlab='', ylab='',sub ='', main='Comp 1/2/3')\n\trect.hclust(hc, k=3, border='red')\ndev.off()\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\t# create and plot clusters based on the correlation among companies\n\thc = hclust(as.dist(1-cor(na.omit(ret))), method = 'ward')\n\tplot(hc, axes=F,xlab='', ylab='',sub ='', main='Correlation')\n\trect.hclust(hc, k=3, border='red')\ndev.off()\n\n\t# cor(ret, method=\"pearson\")\n\t# cor(ret, method=\"kendall\")\n\t# cor(ret, method=\"spearman\")\n\t\n\n}\n\t\n\t\n###############################################################################\n# Using Principal component analysis (PCA) for spread trading \n# http://matlab-trading.blogspot.ca/2012/12/using-pca-for-spread-trading.html\n# http://www.r-bloggers.com/cointegration-r-irish-mortgage-debt-and-property-prices/\n###############################################################################\t\t\nbt.pca.trading.test <- function()\n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t# tickers = spl('XLE,USO,XES,XOP')\n\ttickers = dow.jones.components()\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '2009-01-01', env = data, auto.assign = T)\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Principal component analysis (PCA), for interesting discussion\n\t# http://machine-master.blogspot.ca/2012/08/pca-or-polluting-your-clever-analysis.html\n\t#****************************************************************** \n\tprices = last(data$prices, 1000)\n\t\tn = len(tickers) \t\t\n\tret = prices / mlag(prices) - 1\n\t\n\tp = princomp(na.omit(ret[1:250,]))\n\t\n\tloadings = p$loadings[]\n\n\t# look at the first 4 principal components \t\n\tcomponents = loadings[,1:4]\n\t\n\t# normalize all selected components to have total weight = 1\n\tcomponents = components / rep.row(colSums(abs(components)),len(tickers))\n\t\n\t# note that first component is market, and all components are orthogonal i.e. not correlated to market\n\tmarket = ret[1:250,] %*% rep(1/n,n)\n\ttemp = cbind(market, -ret[1:250,] %*% components)\n\t\tcolnames(temp)[1] = 'Market'\t\n\t\t\n\tround(cor(temp, use='complete.obs',method='pearson'),1)\n\n\t# the variance of each component is decreasing\n\tround(100*sd(temp,na.rm=T),1)\n\t\n\t#*****************************************************************\t\n\t# examples of stationarity ( mean-reversion )\n\t# p.value - small => stationary\n\t# p.value - large => not stationary\n\t#*****************************************************************\t\n\tlibrary(tseries)\n\t\n\tlayout(1:2)\n\ttemp = rnorm(100)\n\tplot(temp, type='b', main=adf.test(temp)$p.value)\n\tplot(cumsum(temp), type='b', main=adf.test(cumsum(temp))$p.value)\n\t\t\t\n\t#*****************************************************************\n\t# Find stationary components, Augmented Dickey-Fuller test\n\t# library(fUnitRoots)\n\t# adfTest(as.numeric(equity[,1]), type=\"ct\")@test$p.value\t\n\t#****************************************************************** \t\n\tlibrary(tseries)\n\tequity = bt.apply.matrix(1 + ifna(-ret %*% components,0), cumprod)\n\t\tequity = make.xts(equity, index(prices))\n\t\n\t# test for stationarity ( mean-reversion )\n\tadf.test(as.numeric(equity[,1]))$p.value\n\tadf.test(as.numeric(equity[,2]))$p.value\n\tadf.test(as.numeric(equity[,3]))$p.value\n\tadf.test(as.numeric(equity[,4]))$p.value\n\n\n\t\t\t\n\t#*****************************************************************\n\t# Plot securities and components\n\t#*****************************************************************\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\n\tlayout(1:2)\n\t# add Bollinger Bands\n\ti.comp = 4\t\n\tbbands1 = BBands(rep.col(equity[,i.comp],3), n=200, sd=1)\n\tbbands2 = BBands(rep.col(equity[,i.comp],3), n=200, sd=2)\n\ttemp = cbind(equity[,i.comp], bbands1[,'up'], bbands1[,'dn'], bbands1[,'mavg'],\n\t\t\t\tbbands2[,'up'], bbands2[,'dn'])\n\t\tcolnames(temp) = spl('Comp. 4,1SD Up,1SD Down,200 SMA,2SD Up,2SD Down')\n\t\n\tplota.matplot(temp, main=paste(i.comp, 'Principal component'))\n\t\n\tbarplot.with.labels(sort(components[,i.comp]), 'weights')\ndev.off()\n\t\n\n\t\n # http://www.wekaleamstudios.co.uk/posts/seasonal-trend-decomposition-in-r/\n ts.sample = ts(as.numeric(equity[,i.comp]), frequency = 252)\n fit.stl = stl(ts.sample, s.window=\"periodic\")\n plot(fit.stl) \n \n \n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tlayout(1:2)\n\tplota.matplot(prices, plotX=F)\n\tplota.matplot(equity)\ndev.off()\n\t\t\t\n}\n\n\n\n\t\n###############################################################################\n# Details for the Visual of Current Major Market Clusters post by David Varadi\n# http://cssanalytics.wordpress.com/2013/01/10/a-visual-of-current-major-market-clusters/\n###############################################################################\t\t\nbt.cluster.visual.test <- function()\n{\n #*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\ttickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tbt.prep(data, align='remove.na')\n\n #*****************************************************************\n\t# Create Clusters\n\t#****************************************************************** \n\t# compute returns\n\tret = data$prices / mlag(data$prices) - 1\n\t\tret = na.omit(ret)\t\t\n\n\t# setup period and method to compute correlations\n\tdates = '2012::2012'\n\tmethod = 'pearson'\t# kendall, spearman\n\t\n\tcorrelation = cor(ret[dates], method = method) \n dissimilarity = 1 - (correlation)\n distance = as.dist(dissimilarity)\n \t\n\t# find 4 clusters \n\txy = cmdscale(distance)\n\tfit = kmeans(xy, 4, iter.max=100, nstart=100)\n\t\n\tfit$cluster\n\t\n #*****************************************************************\n\t# Create Plot\n\t#****************************************************************** \t\n\tload.packages('cluster')\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tclusplot(xy, fit$cluster, color=TRUE, shade=TRUE, labels=3, lines=0, plotchar=F, \n\t\tmain = paste('Major Market Clusters over', dates), sub='')\ndev.off()\t\n\n\npng(filename = 'plot2.png', width = 800, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tlayout(matrix(1:8,nc=2))\n\tpar( mar = c(2, 2, 2, 2) )\n\n\tfor(icluster in 2:8)\n\tclusplot(xy, kmeans(xy, icluster, iter.max=100, nstart=100)$cluster, color=TRUE, shade=F, \n\t\tlabels=3, lines=0, plotchar=F, main=icluster, sub='')\ndev.off()\t\n\n}\n\n\n\n###############################################################################\n# Optimal number of clusters\n# http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set\n#\n# R and Data Mining: Examples and Case Studies by Y. Zhao, Chapter 6, Clustering\n# http://cran.r-project.org/doc/contrib/Zhao_R_and_data_mining.pdf\n#\n# http://blog.echen.me/2011/03/19/counting-clusters/\n#\n# Clustergram: visualization and diagnostics for cluster analysis (R code)\n# http://www.r-statistics.com/tag/parallel-coordinates/\n#\n# http://tr8dr.wordpress.com/2009/12/30/equity-clusters/\n# http://www.starklab.org/members/kazmar/2012/01/09/Optimal-number-of-clusters/\n#\n# Morphometrics with R By Julien Claude\n# http://books.google.ca/books?id=hA9ANHMPm14C&pg=PA123&lpg=PA123&dq=optimal+number+of+clusters+elbow+method&source=bl&ots=7P2bnNf5VL&sig=GEgiSL7CfOEU8gsalSsWHbDhGVc&hl=en&sa=X&ei=wmfwUIOtM_Ls2AW2k4FY&ved=0CFQQ6AEwBg#v=onepage&q=optimal%20number%20of%20clusters%20elbow%20method&f=false\n#\n# Choosing the number of clusters\n# http://geomblog.blogspot.ca/2010/03/this-is-part-of-occasional-series-of.html\n# http://geomblog.blogspot.ca/2010/03/choosing-number-of-clusters-ii.html\n###############################################################################\t\t\nbt.cluster.optimal.number.test <- function()\n{\n #*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\ttickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tbt.prep(data, align='remove.na')\n\n #*****************************************************************\n\t# Create Clusters\n\t#****************************************************************** \n\t# compute returns\n\tret = data$prices / mlag(data$prices) - 1\n\t\tret = na.omit(ret)\t\t\n\n\t# setup period and method to compute correlations\n\tdates = '2012::2012'\n\tmethod = 'pearson'\t# kendall, spearman\n\t\n\tcorrelation = cor(ret[dates], method = method) \n dissimilarity = 1 - (correlation)\n distance = as.dist(dissimilarity)\n \t\n\t# get first 2 pricipal componenets\n\txy = cmdscale(distance)\n\t\n #*****************************************************************\n\t# Determine number of clusters\n\t#****************************************************************** \n\tn = ncol(data$prices)\n\t\tn1 = ceiling(n*2/3)\n\n\t# percentage of variance explained by clusters\n\tp.exp = rep(0,n1)\n\t\t\n\t# minimum correlation among all components in each cluster\t\n\tmin.cor = matrix(1,n1,n1) \n\t\n\tfor (i in 2:n1) {\n\t\tfit = kmeans(xy, centers=i, iter.max=100, nstart=100)\n\t\tp.exp[i] = 1- fit$tot.withinss / fit$totss\n\t\t\n\t\tfor (j in 1:i) {\n\t\t\tindex = fit$cluster == j\n\t\t\tmin.cor[i,j] = min(correlation[index,index])\n\t\t}\n\t}\n\t\n\t# minimum number of clusters that explain at least 90% of variance\n\tmin(which(p.exp > 0.9))\n\t\t\t\n\t# minimum number of clusters such that correlation among all components in each cluster is at least 40%\n\t# will not always work\n\tmin(which(apply(min.cor[-1,],1,min,na.rm=T) > 0.4)) + 1\n\n\t# number of clusters based on elbow method\n\tfind.maximum.distance.point(p.exp[-1]) + 1\n\n\t\t\n #*****************************************************************\n\t# Create Plot\n\t#****************************************************************** \t\n\tload.packages('cluster')\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfit = kmeans(xy, 4, iter.max=100, nstart=100)\n\tclusplot(xy, fit$cluster, color=TRUE, shade=TRUE, labels=3, lines=0, plotchar=F, \n\t\tmain = paste('Major Market Clusters over', dates, ', 4 Clusters'), sub='')\ndev.off()\t\n\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\tfit = kmeans(xy, 5, iter.max=100, nstart=100)\n\tclusplot(xy, fit$cluster, color=TRUE, shade=TRUE, labels=3, lines=0, plotchar=F, \n\t\tmain = paste('Major Market Clusters over', dates, ', 5 Clusters'), sub='')\ndev.off()\t\n\n\n# http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Expectation_Maximization_(EM)\n\tload.packages('mclust')\n\tfitBIC = mclustBIC(xy)\n\tplot(fitBIC, legendArgs = list(x = \"topleft\"))\n\t\n\tfit <- summary(fitBIC, data = xy)\n\tmclust2Dplot(data = xy, what = \"density\", identify = TRUE, parameters = fit$parameters, z = fit$z)\t\n\n}\t\t\n\t\n\n###############################################################################\n# Historical Optimal number of clusters\n# based on the bt.cluster.optimal.number.test function\n###############################################################################\nbt.cluster.optimal.number.historical.test <- function()\n{\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\ttickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT')\n\t\tdates='2007:03::'\n\ttickers = dow.jones.components()\n\t\tdates='1970::'\n\t\n\ttickers = sp500.components()$tickers\n\t\tdates='1994::'\t\n\t\n\t\n\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tbt.prep(data, align='keep.all', dates=dates)\n\n\t\n\t#*****************************************************************\n\t# Use following 3 methods to determine number of clusters\n\t# * Minimum number of clusters that explain at least 90% of variance\n\t# cluster.group.kmeans.90\n\t# * Elbow method\n\t# cluster.group.kmeans.elbow\n\t# * Hierarchical clustering tree cut at 1/3 height\n\t# cluster.group.hclust\n\t#****************************************************************** \n\n\t# helper function to compute portfolio allocation additional stats\n\tportfolio.allocation.custom.stats.clusters <- function(x,ia) {\n\t\treturn(list(\n\t\t\tncluster.90 = max(cluster.group.kmeans.90(ia)),\n\t\t\tncluster.elbow = max(cluster.group.kmeans.elbow(ia)),\n\t\t\tncluster.hclust = max(cluster.group.hclust(ia))\n\t\t))\n\t}\n\n\t\n\t#*****************************************************************\n\t# Compute # Clusters\n\t#****************************************************************** \t\t\n\tperiodicity = 'weeks'\n\tlookback.len = 250\n\t\t\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = periodicity, lookback.len = lookback.len,\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio),\n\t\tcustom.stats.fn = portfolio.allocation.custom.stats.clusters\n\t) \t\t\t\n\t\n\t#*****************************************************************\n\t# Create Reports\n\t#****************************************************************** \t\t\n\ttemp = list(ncluster.90 = 'Kmeans 90% variance',\n\t\t ncluster.elbow = 'Kmeans Elbow',\n\t\t ncluster.hclust = 'Hierarchical clustering at 1/3 height')\t\n\t\n\tfor(i in 1:len(temp)) {\n\t\thist.cluster = obj[[ names(temp)[i] ]]\n\t\ttitle = temp[[ i ]]\n\t\npng(filename = paste('plot',i,'.png',sep=''), width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\n\t\tplota(hist.cluster, type='l', col='gray', main=title)\n\t\t\tplota.lines(SMA(hist.cluster,10), type='l', col='red',lwd=5)\n\t\tplota.legend('Number of Clusters,10 period moving average', 'gray,red', x = 'bottomleft')\t\t\t\ndev.off()\t\t\n\t}\n\t\n\n\n}\n\n\n\n\n###############################################################################\n# Seasonality Examples\n# Find January's with return > 4%\n# http://www.avondaleam.com/2013/02/s-annual-performance-after-big-january.html\n###############################################################################\nbt.seasonality.january.test <- function()\n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\tprice = getSymbols('^GSPC', src = 'yahoo', from = '1900-01-01', auto.assign = F)\n\t\tprice = Cl(to.monthly(price, indexAt='endof'))\n\t\t\t\t\n\tret = price / mlag(price) - 1\n\n\t#*****************************************************************\n\t# http://www.avondaleam.com/2013/02/s-annual-performance-after-big-january.html\n\t# Find January's with return > 4%\n\t#****************************************************************** \n\tindex = which( date.month(index(ret)) == 1 & ret > 4/100 )\n\t\n\ttemp = c(coredata(ret),rep(0,12))\n\tout = cbind(ret[index], sapply(index, function(i) prod(1 + temp[i:(i+11)])-1))\n\t\tcolnames(out) = spl('January,Year')\n\n\t#*****************************************************************\n\t# Create Plot\n\t#****************************************************************** \n\npng(filename = 'plot1.png', width = 500, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\n\tcol=col.add.alpha(spl('black,gray'),200)\n\t# https://stat.ethz.ch/pipermail/r-help/2002-October/025879.html\n\tpos = barplot(100*out, border=NA, beside=T, axisnames = F, axes = FALSE,\n\t\tcol=col, main='Annual Return When S&P500 Rises More than 4% in January')\n\t\taxis(1, at = colMeans(pos), labels = date.year(index(out)), las=2)\n\t\taxis(2, las=1)\n\tgrid(NA, NULL)\n\tabline(h= 100*mean(out$Year), col='red', lwd=2)\t\t\n\tplota.legend(spl('January,Annual,Average'), c(col,'red'))\n\n\t\n\t\ndev.off()\t\npng(filename = 'plot2.png', width = 500, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\t\n\t# plot table\n\tplot.table(round(100*as.matrix(out),1))\n\t\ndev.off()\t\n\t\n}\n\n\n###############################################################################\n# Example of the Cluster Portfolio Allocation method\n############################################################################### \nbt.cluster.portfolio.allocation.test <- function()\n{\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\ttickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \n\t# compute returns\n\tret = data$prices / mlag(data$prices) - 1\n\n\t# setup period\n\tdates = '2012::2012'\n\tret = ret[dates]\n\t\n\t#*****************************************************************\n\t# Create Portfolio\n\t#****************************************************************** \t\t\n\tfn.name = 'risk.parity.portfolio'\t\n\tfn.name = 'equal.weight.portfolio'\t\n\t\t\t\n\t\n\tnames = c('risk.parity.portfolio', 'equal.weight.portfolio')\n\t\t\nfor(fn.name in names) {\n\tfn = match.fun(fn.name)\n\n\t# create input assumptions\n\tia = create.ia(ret) \n\t\n\t# compute allocation without cluster, for comparison\n\tweight = fn(ia)\n\t\n\t# create clusters\n\tgroup = cluster.group.kmeans.90(ia)\n\tngroups = max(group)\n\n\tweight0 = rep(NA, ia$n)\n\t\t\t\n\t# store returns for each cluster\n\thist.g = NA * ia$hist.returns[,1:ngroups]\n\t\t\t\n\t# compute weights within each group\t\n\tfor(g in 1:ngroups) {\n\t\tif( sum(group == g) == 1 ) {\n\t\t\tweight0[group == g] = 1\n\t\t\thist.g[,g] = ia$hist.returns[, group == g, drop=F]\n\t\t} else {\n\t\t\t# create input assumptions for the assets in this cluster\n\t\t\tia.temp = create.ia(ia$hist.returns[, group == g, drop=F]) \n\n\t\t\t# compute allocation within cluster\n\t\t\tw0 = fn(ia.temp)\n\t\t\t\n\t\t\t# set appropriate weights\n\t\t\tweight0[group == g] = w0\n\t\t\t\n\t\t\t# compute historical returns for this cluster\n\t\t\thist.g[,g] = ia.temp$hist.returns %*% w0\n\t\t}\n\t}\n\t\t\t\n\t# create GROUP input assumptions\n\tia.g = create.ia(hist.g) \n\t\t\t\n\t# compute allocation across clusters\n\tgroup.weights = fn(ia.g)\n\t\t\t\t\n\t# mutliply out group.weights by within group weights\n\tfor(g in 1:ngroups)\n\t\tweight0[group == g] = weight0[group == g] * group.weights[g]\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t\t\n\tload.packages('RColorBrewer')\n\tcol = colorRampPalette(brewer.pal(9,'Set1'))(ia$n)\n\npng(filename = paste(fn.name,'.plot.png',sep=''), width = 600, height = 800, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\n\tlayout(matrix(1:2,nr=2,nc=1))\n\tpar(mar = c(0,0,2,0))\n\tindex = order(group)\n\tpie(weight[index], labels = paste(colnames(ret), round(100*weight,1),'%')[index], col=col, main=fn.name)\n\tpie(weight0[index], labels = paste(colnames(ret), round(100*weight0,1),'%')[index], col=col, main=paste('Cluster',fn.name))\n\t\ndev.off()\t\n\t\n\t\n}\n\n\n}\t\n\n\n###############################################################################\n# Example of the Cluster Portfolio Allocation method\n############################################################################### \nbt.cluster.portfolio.allocation.test1 <- function()\n{\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod')\n\n\ttickers = spl('GLD,UUP,SPY,QQQ,IWM,EEM,EFA,IYR,USO,TLT')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1900-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\t\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\tperiodicity = 'months'\n\tlookback.len = 250\n\tcluster.group = cluster.group.kmeans.90\n\t\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = periodicity, lookback.len = lookback.len,\n\t\tmin.risk.fns = list(\n\t\t\t\t\t\tEW=equal.weight.portfolio,\n\t\t\t\t\t\tRP=risk.parity.portfolio(),\n\t\t\t\t\t\t\n\t\t\t\t\t\tC.EW = distribute.weights(equal.weight.portfolio, cluster.group),\n\t\t\t\t\t\tC.RP=distribute.weights(risk.parity.portfolio(), cluster.group)\n\t\t\t)\n\t) \t\t\n\t\n\tmodels = create.strategies(obj, data)$models\n\t\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\npng(filename = 'plot1.png', width = 500, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\t\n\tstrategy.performance.snapshoot(models, T)\n\t\ndev.off()\n\t\n\n}\n\t\n\n###############################################################################\n# Examples of 4 ways to load Historical Stock Data\n###############################################################################\nload.hist.stock.data <- function()\n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\n\ttickers = 'MMM, AA, CAT, KO, HPQ'\n\t\ttickers = trim(spl(tickers))\n\t\n\tdata = env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\tbt.prep(data, align='remove.na', fill.gaps = T)\n\n\t#*****************************************************************\n\t# Create test data\n\t#****************************************************************** \n\t\t\n\t# one file per ticker\n\tfor(ticker in tickers)\n\t\twrite.xts(data[[ticker]], paste0(ticker, '.csv'), format='%m/%d/%Y')\n\t\n\t# one file\n\twrite.xts(bt.apply(data, Ad), 'adjusted.csv', format='%m/%d/%Y')\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\tstock.folder = ''\n\t\n\tdata = env()\n\t\t\n\t# load historical data, select data load method\n\tdata.load.method = 'basic'\n\t\n\tif(data.load.method == 'basic') {\t\t\n\t\t# quantmod - getSymbols\n\t\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t}else if(data.load.method == 'basic.local') {\n\t\t# if you saved yahoo historical price files localy\n\t\tgetSymbols.sit(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T, stock.folder = stock.folder)\n\t}else if(data.load.method == 'custom.local') {\n\t\t# custom format historical price files\n\t\tfor(n in tickers) {\n\t\t\tdata[[n]] = read.xts(paste(stock.folder, n, '.csv', sep=''), format='%m/%d/%Y')\n\t\t}\t\n\t}else if(data.load.method == 'custom.one.file') {\n\t\t# read from one csv file, column headers are tickers\n\t\tfilename = 'adjusted.csv'\n\t\tall.data = read.xts(paste(stock.folder, filename, sep=''), format='%m/%d/%Y')\n\t\t\n\t\t# alternatively reading xls/xlsx\n\t\t#load.packages('readxl')\n\t\t#all.data = read.xts(read_excel('adjusted.xls'))\n\t\t\n\t\tfor(n in names(all.data)) {\n\t\t\tdata[[n]] = all.data[,n]\n\t\t\tcolnames(data[[n]]) = 'Close'\n\t\t\tdata[[n]]$Adjusted = data[[n]]$Open = data[[n]]$High = data[[n]]$Low = data[[n]]$Close\n\t\t}\n\t}\t\n\t\n\t\t\t\n\t\t\n\t# prepare data for back test\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\tbt.prep(data, align='remove.na')\n \n \n #*****************************************************************\n # Code Strategies\n #******************************************************************\n prices = data$prices \n n = ncol(prices)\n \n models = list()\n \n # find period ends\n period.ends = endpoints(prices, 'months')\n period.ends = period.ends[period.ends > 0]\n \n\tobj = portfolio.allocation.helper(data$prices, period.ends=period.ends, lookback.len = 250, \n\t\tmin.risk.fns = list(EW=equal.weight.portfolio,\n\t\t\t\t\t\tRP=risk.parity.portfolio(),\n\t\t\t\t\t\tMV=min.var.portfolio,\n\t\t\t\t\t\tMC=min.corr.portfolio)\n\t) \n\t\n\tmodels = create.strategies(obj, data)$models\n\t\t\t\t\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \n \n strategy.performance.snapshoot(models, T)\n\n} \n\n\n###############################################################################\n# Predictive Indicators for Effective Trading Strategies By John Ehlers\n# http://www.stockspotter.com/files/PredictiveIndicators.pdf\n# http://dekalogblog.blogspot.ca/2013/07/my-nn-input-tweak.html\n###############################################################################\njohn.ehlers.custom.strategy.plot <- function(\n\tdata,\n\tmodels,\n\tname,\n\tmain = name,\n\tdates = '::',\n\tlayout = NULL\t\t# flag to idicate if layout is already set\t\n) {\n \t# John Ehlers Stochastic\n stoch = roofing.stochastic.indicator(data$prices)\n \n\t\n # highlight logic based on weight\n weight = models[[name]]$weight[dates]\n \tcol = iif(weight > 0, 'green', iif(weight < 0, 'red', 'white'))\n \tplota.control$col.x.highlight = col.add.alpha(col, 100)\n \thighlight = T\n \n\tif(is.null(layout)) layout(1:2)\n \t \n plota(data$prices[dates], type='l', x.highlight = highlight, plotX = F, main=main)\n \tplota.legend('Long,Short,Not Invested','green,red,white')\n \t\n\tplota(stoch[dates], type='l', x.highlight = highlight, plotX = F, ylim=c(0,1)) \t\n \tcol = col.add.alpha('red', 100)\n abline(h = 0.2, col=col, lwd=3)\n abline(h = 0.8, col=col, lwd=3)\n plota.legend('John Ehlers Stochastic') \n}\n\njohn.ehlers.filter.test <- function() {\n #*****************************************************************\n # Load historical data\n #****************************************************************** \n load.packages('quantmod') \n \n tickers = spl('DG')\n data = new.env()\n getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T) \n for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data)\n\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices \n \n models = list()\n \n # John Ehlers Stochastic\n stoch = roofing.stochastic.indicator(prices)\n \n\t# Day Stochastic\n\tstoch14 = bt.apply(data, function(x) stoch(HLC(x),14)[,'slowD'])\n\t\t\n #*****************************************************************\n # Create plots\n #****************************************************************** \n dates = '2011:10::2012:9'\n \njpeg(filename = 'plot1.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \n layout(1:3)\n \n plota(prices[dates], type='l', plotX=F)\n plota.legend('DG')\n \n plota(stoch[dates], type='l', plotX=F)\n abline(h = 0.2, col='red')\n abline(h = 0.8, col='red')\n plota.legend('John Ehlers Stochastic')\n\n plota(stoch14[dates], type='l')\n abline(h = 0.2, col='red')\n abline(h = 0.8, col='red')\n plota.legend('Stochastic')\ndev.off()\t\n\t \n\t#*****************************************************************\n\t# Code Strategies\n\t#*****************************************************************\n\t# Figure 6: Conventional Wisdom is to Buy When the Indicator Crosses Above 20% and \n\t# To Sell Short when the Indicator Crosses below 80%\n data$weight[] = NA\n data$weight[] = iif(cross.up(stoch, 0.2), 1, iif(cross.dn(stoch, 0.8), -1, NA))\n models$post = bt.run.share(data, clean.signal=T, trade.summary=T)\n\n data$weight[] = NA\n data$weight[] = iif(cross.up(stoch, 0.2), 1, iif(cross.dn(stoch, 0.8), 0, NA))\n models$post.L = bt.run.share(data, clean.signal=T, trade.summary=T)\n \n data$weight[] = NA\n data$weight[] = iif(cross.up(stoch, 0.2), 0, iif(cross.dn(stoch, 0.8), -1, NA))\n models$post.S = bt.run.share(data, clean.signal=T, trade.summary=T)\n \n\t# Figure 8: Predictive Indicators Enable You to Buy When the Indicator Crosses Below 20% and \n\t# To Sell Short when the Indicator Crosses Above 80%\n data$weight[] = NA\n data$weight[] = iif(cross.dn(stoch, 0.2), 1, iif(cross.up(stoch, 0.8), -1, NA))\n models$pre = bt.run.share(data, clean.signal=T, trade.summary=T)\n\n data$weight[] = NA\n data$weight[] = iif(cross.dn(stoch, 0.2), 1, iif(cross.up(stoch, 0.8), 0, NA))\n models$pre.L = bt.run.share(data, clean.signal=T, trade.summary=T)\n\n data$weight[] = NA\n data$weight[] = iif(cross.dn(stoch, 0.2), 0, iif(cross.up(stoch, 0.8), -1, NA))\n models$pre.S = bt.run.share(data, clean.signal=T, trade.summary=T)\n \t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \t\t \njpeg(filename = 'plot2.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\tstrategy.performance.snapshoot(models, T)\ndev.off()\t\t\n\t\t\njpeg(filename = 'plot3.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\tlayout(1:4, heights=c(2,1,2,1))\n\tjohn.ehlers.custom.strategy.plot(data, models, 'post.L', dates = '2013::', layout=T,\n\t\tmain = 'post.L: Buy When the Indicator Crosses Above 20% and Sell when the Indicator Crosses Below 80%')\n\tjohn.ehlers.custom.strategy.plot(data, models, 'pre.L', dates = '2013::', layout=T,\n\t\tmain = 'pre.L: Buy When the Indicator Crosses Below 20% and Sell when the Indicator Crosses Above 80%')\ndev.off()\t\n\t\n\t\n\treturn\n\t\n\t\n\t\n\t\n\t\n\t\n\n \n \n \n \n\n\t# Not used\n\tx = Cl(data$DG)\n dates = '2013'\n\t\n layout(1:2)\n plota(x[dates], type='l')\n plota(my.stochastic.indicator(x)[dates], type='l')\n abline(h = 0.2, col='red')\n abline(h = 0.8, col='red')\n\n\t# Draw arrows corresponding buy/sell signals \n\ttrades = last(models$pre.L$trade.summary$trades,10)\n\tposition = sign(as.double(trades[,'weight']))\n \n\td = index4xts(prices[dates2index(prices, trades[,'entry.date'])])\n\t\tcol = col.add.alpha('green', 50)\n\t\tsegments(d, rep(0.2,len(d)), d, rep(0.25,len(d)), col=col, lwd=5)\n\t\tpoints(d, rep(0.25,len(d)), pch=24, col=col, bg=col, lwd=5)\n\t\n\td = index4xts(prices[dates2index(prices, trades[,'exit.date'])])\n\t\tcol = col.add.alpha('red', 50)\n\t\tsegments(d, rep(0.8,len(d)), d, rep(0.75,len(d)), col=col, lwd=5)\n\t\tpoints(d, rep(0.75,len(d)), pch=25, col=col, bg=col, lwd=5)\n\t\t \n\tlast(models$post$trade.summary$trades,10) \t\n} \n\n\n###############################################################################\n# Calendar-based sector strategy\n#\n# http://www.cxoadvisory.com/2785/calendar-effects/kaeppels-sector-seasonality-strategy/\n# http://www.optionetics.com/marketdata/article.aspx?aid=13623\n# http://www.optionetics.com/marketdata/article.aspx?aid=18343\n#\n# Buy Fidelity Select Technology (FSPTX) at the October close.\n# Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.\n# Switch from FSENX to cash at the May close.\n# Switch from cash to Fidelity Select Gold (FSAGX) at the August close.\n# Switch from FSAGX to cash at the September close.\n# Repeat by switching from cash to FSPTX at the October close.\n#\n# Benchmarks\n# - Vanguard 500 Index Investor (VFINX)\n# - VFINX from the October close through the May close and cash otherwise (VFINX /Cash)\n###############################################################################\nbt.calendar.based.sector.strategy.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('FSPTX,FSENX,FSAGX,VFINX,BIL') \n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\n\t#-------------------------------- \n\t# BIL 30-May-2007 \n\t# load 3-Month Treasury Bill from FRED\n\tTB3M = quantmod::getSymbols('DTB3', src='FRED', auto.assign = FALSE)\t\t\n\tTB3M[] = ifna.prev(TB3M)\t\n\tTB3M = processTBill(TB3M, timetomaturity = 1/4, 261)\t\n\t#-------------------------------- \t\n\t#proxies = list(BIL = data$BIL, TB3M = TB3M)\n\t#proxy.test(proxies)\n\t#proxy.overlay.plot(proxies)\n\t#bt.start.dates(data)\t\t\n\n\t\n\t# extend\t\n\tdata$BIL = extend.data(data$BIL, TB3M, scale=T)\t\n\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\t\t\t\t\n\tbt.prep(data, align='remove.na')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tdates = data$dates\t\n\t \n\tmodels = list()\n\n\t# find period ends\n\tperiod.ends = endpoints(prices, 'months')\n \tperiod.ends = period.ends[period.ends > 0]\t\n\t\n\tmonths = date.month(dates[period.ends])\n\t \t\n\t# control back-test\n\tdates = '::'\n\t# we can use zero lag becuase buy/sell dates are known in advance\n\tdo.lag = 0\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t# Vanguard 500 Index Investor (VFINX)\n\tdata$weight[] = NA\n\t\tdata$weight$VFINX[] = 1\n\tmodels$VFINX = bt.run.share(data, clean.signal=F, dates=dates, do.lag=do.lag) \n\t\n\t\n\t# VFINX from the October[10] close through the May[5] close and cash otherwise (VFINX /Cash)\n\tdata$weight[] = NA\n\t\tdata$weight$VFINX[period.ends] = iif( months >= 10 | months <= 5, 1, 0)\n\t\tdata$weight$BIL[period.ends] = iif( !(months >= 10 | months <= 5), 1, 0)\n\tmodels$VFINX_Cash = bt.run.share(data, clean.signal=F, dates=dates, do.lag=do.lag) \t\n\n\t\n #*****************************************************************\n # Calendar-based sector strategy\n #****************************************************************** \t\n\t# Buy Fidelity Select Technology (FSPTX) at the October close.\n\t# Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.\n\t# Switch from FSENX to cash at the May close.\n\t# Switch from cash to Fidelity Select Gold (FSAGX) at the August close.\n\t# Switch from FSAGX to cash at the September close.\n\t# Repeat by switching from cash to FSPTX at the October close.\n\tdata$weight[] = NA\n\t\t# Buy Fidelity Select Technology (FSPTX) at the October close.\n\t\tdata$weight$FSPTX[period.ends] = iif( months >= 10 | months < 1, 1, 0)\n\t\t\n\t\t# Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.\n\t\tdata$weight$FSENX[period.ends] = iif( months >= 1 & months < 5, 1, 0)\n\t\t\t\t\n\t\t# Switch from cash to Fidelity Select Gold (FSAGX) at the August close.\n\t\tdata$weight$FSAGX[period.ends] = iif( months >= 8 & months < 9, 1, 0)\n\n\t\t# Rest time in Cash\n\t\tdata$weight$BIL[period.ends] = 1 - rowSums(data$weight[period.ends], na.rm = T)\n\tmodels$Sector = bt.run.share(data, clean.signal=F, dates=dates, do.lag=do.lag) \t\n\t\t \n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \njpeg(filename = 'plot1.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\tstrategy.performance.snapshoot(models, T)\ndev.off()\t\t\n\t\n \n\tplotbt.custom.report.part2(models$sector, trade.summary=T)\n\t\t\t\t\n\n\treturn\n\t\n\t\n\t\n\t\n\t\n\t\n\n \n \n \n \n\n\t# Not used\t\n\tdata$weight[] = NA\n\t\t# Buy Fidelity Select Technology (FSPTX) at the October close.\n\t\tdata$weight$FSPTX[period.ends] = iif( months >= 10 | months < 1, 1, 0)\n\t\t\n\t\t# Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.\n\t\tdata$weight$FSENX[period.ends] = iif( months >= 1 & months < 5, 1, 0)\n\t\t\n\t\t# Switch from FSENX to cash at the May close.\n\t\tdata$weight$BIL[period.ends] = iif( months >= 5 & months < 8, 1, data$weight$BIL[period.ends])\t\t\n\t\t\n\t\t# Switch from cash to Fidelity Select Gold (FSAGX) at the August close.\n\t\tdata$weight$FSAGX[period.ends] = iif( months >= 8 & months < 9, 1, 0)\n\t\t\n\t\t# Switch from FSAGX to cash at the September close.\n\t\tdata$weight$BIL[period.ends] = iif( months >= 9 & months < 10, 1, data$weight$BIL[period.ends])\n\t\t\n\t\t# since we have multiple entries to BIL, make sure to close them\n\t\tdata$weight$BIL[period.ends] = ifna(data$weight$BIL[period.ends], 0)\t\t\n\tmodels$sector1 = bt.run.share(data, clean.signal=F) \t\n\t\n}\n\n\n\n\n###############################################################################\n# 7Twelve strategy\n#\n# http://www.7twelveportfolio.com/index.html\n# http://www.mebanefaber.com/2013/08/01/the-712-allocation/\n# http://seekingalpha.com/article/228664-on-israelsens-7twelve-portfolio\n###############################################################################\nbt.7twelve.strategy.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl('VFINX,VIMSX,NAESX,VDMIX,VEIEX,VGSIX,FNARX,QRAAX,VBMFX,VIPSX,OIBAX,BIL') \n\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\n\t#-------------------------------- \n\t# BIL 30-May-2007 \n\t# load 3-Month Treasury Bill from FRED\n\tTB3M = quantmod::getSymbols('DTB3', src='FRED', auto.assign = FALSE)\t\t\n\tTB3M[] = ifna.prev(TB3M)\t\n\tTB3M = processTBill(TB3M, timetomaturity = 1/4, 261)\t\n\t#-------------------------------- \t\n\t# extend\t\n\tdata$BIL = extend.data(data$BIL, TB3M, scale=T)\t\n\t\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\t\t\t\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tmodels = list()\n\t\n\t# Vanguard 500 Index Investor (VFINX)\n\tdata$weight[] = NA\n\t\tdata$weight$VFINX[] = 1\n\tmodels$VFINX = bt.run.share(data, clean.signal=F) \n\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'years',\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio)\n\t) \t\n\tmodels$year = create.strategies(obj, data)$models$EW\n\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'quarters',\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio)\n\t) \t\n\tmodels$quarter = create.strategies(obj, data)$models$EW\n\t\t\n\tobj = portfolio.allocation.helper(data$prices, periodicity = 'months',\n\t\tmin.risk.fns = list(EW=equal.weight.portfolio)\n\t) \t\n\tmodels$month = create.strategies(obj, data)$models$EW\n\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \njpeg(filename = 'plot1.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\tstrategy.performance.snapshoot(models, T)\ndev.off()\t\t\n\t\n \n\treturn\n\t\n\t\n\t\n\t\n\t\n\t\n\n \n \n \n \n\n\t# Not used\t\n\t#http://seekingalpha.com/article/228664-on-israelsens-7twelve-portfolio\n\tmap = list(\n\t us.eq = list(\n\t us.large = list('VFINX', 'VTI'),\n\t us.mid = list('VIMSX', 'VO'),\n\t us.small = list('NAESX', 'VB')\n\t ),\n\t non.us.eq = list(\n\t devel.eq = list('VDMIX', 'EFA'),\t# VGTSX\n\t em.eq = list('VEIEX', 'EEM')\n\t ),\n\t re = list( \n\t \tre = list('VGSIX', 'RWX')\n\t ),\n\t res = list(\n\t \tnat.res = list('FNARX', 'GLD'),\n\t \tcom = list('QRAAX', 'DBC')\t# CRSAX\n\t ),\n\t us.bond = list(\n\t \tus.bond = list('VBMFX', 'AGG'),\n\t \ttips = list('VIPSX', 'TIP')\n\t ),\n\t non.bond = list(\n\t \tint.bond = list('OIBAX', 'BWX')\t# BEGBX\n\t ),\n\t cash = list(\n\t \tcash = list('BIL', 'BIL')\t# VFISX\n\t )\n\t) \n\t\n\tfunds = unlist(lapply(map, function(x) lapply(x, function(y) y[[1]]))) \n\tetfs = unlist(lapply(map, function(x) lapply(x, function(y) y[[2]]))) \n\t\n\tpaste(funds, collapse=',')\n}\t\n\n\n\n###############################################################################\n# One of the biggest challenges for a market neutral strategy is your shorts ripping when a market \n# bottoms and all of the (expensive/low momentum) stocks rip straight up. That is why most factor \n# based long short portfolios rarely survive ? they are long and short the wrong things at market \n# bottoms. \n# \n# Below is french fama momentum data that shows high and low momentum stocks back to the 1920s. \n# Hi mo beats both the market and low mo. One would think a market neutral portfolio would be \n# really low risk, but in reality it has massive drawdowns in the 1920s and 2009. \n# \n# One way to rectify this situation is to simply short less the more the market goes down. \n# Kind of makes sense as you think about it and is probably just prudent risk management. \n# \n# So the modified strategy below starts 100% market neutral, and depending on the drawdown bucket \n# will reduce the shorts all the way to zero once the market has declined by 50% \n# (in 20% steps for every 10% decline in stocks).\n#\n# http://www.mebanefaber.com/2013/10/30/the-problem-with-market-neutral-and-an-answer/\n###############################################################################\nbt.mebanefaber.modified.mn.test <- function() \n{\t\n #*****************************************************************\n # Load historical data\n #****************************************************************** \n\tload.packages('quantmod')\t\t\n\t\n\tdata = new.env()\n\t\t\n\t# load historical market returns\n\ttemp = get.fama.french.data('F-F_Research_Data_Factors', periodicity = '',download = T, clean = T)\n\t\tret = temp[[1]]$Mkt.RF + temp[[1]]$RF\n\t\tprice = bt.apply.matrix(ret / 100, function(x) cumprod(1 + x))\n\tdata$SPY = make.stock.xts( price )\n\t\n\t# load historical momentum returns\n\ttemp = get.fama.french.data('10_Portfolios_Prior_12_2', periodicity = '',download = T, clean = T)\t\t\n\t\tret = temp[[1]]\n\t\tprice = bt.apply.matrix(ret / 100, function(x) cumprod(1 + x))\n\tdata$HI.MO = make.stock.xts( price$High )\n\tdata$LO.MO = make.stock.xts( price$Low )\n\t\n\t# align dates\n\tbt.prep(data, align='remove.na')\n\t\n\t#*****************************************************************\n\t# Create Plots\n\t#*****************************************************************\n\t# plota.matplot(data$prices, log = 'y')\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#*****************************************************************\t\n\tmodels = list()\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$SPY[] = 1\n\tmodels$SPY = bt.run.share(data, clean.signal=T)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$HI.MO[] = 1\n\tmodels$HI.MO = bt.run.share(data, clean.signal=T)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$LO.MO[] = 1\n\tmodels$LO.MO = bt.run.share(data, clean.signal=T)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$HI.MO[] = 1\n\t\tdata$weight$LO.MO[] = -1\n\tmodels$MKT.NEUTRAL = bt.run.share(data, clean.signal=F)\n\n\t#*****************************************************************\n\t# Modified MN\n\t# The modified strategy below starts 100% market neutral, and depending on the drawdown bucket \n\t# will reduce the shorts all the way to zero once the market has declined by 50%\n\t# (in 20% steps for every 10% decline in stocks)\n\t#*****************************************************************\t\n\tmarket.drawdown = -100 * compute.drawdown(data$prices$SPY)\n\t\tmarket.drawdown.10.step = 10 * floor(market.drawdown / 10)\n\t\tshort.allocation = 100 - market.drawdown.10.step * 2\n\t\tshort.allocation[ short.allocation < 0 ] = 0\n\t\t\t\t\n\t# cbind(market.drawdown, market.drawdown.10.step, short.allocation)\n\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight$HI.MO[] = 1\n\t\tdata$weight$LO.MO[] = -1 * short.allocation / 100\n\tmodels$Modified.MN = bt.run.share(data, clean.signal=F)\n\t\n\t#*****************************************************************\n # Create Report\n #*****************************************************************\njpeg(filename = 'plot1.jpg', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \n strategy.performance.snapshoot(models, T)\ndev.off() \n\t\n\t\n}\t\n\n###############################################################################\n# http://www.mebanefaber.com/2013/12/04/square-root-of-f-squared/\n###############################################################################\nbt.mebanefaber.f.squared.test <- function() \n{\t\n #*****************************************************************\n # Load historical data\n #****************************************************************** \n\tload.packages('quantmod')\t\t\n\t\n\tdata = new.env()\n\t\t\n\tdownload = T\n\t\n\t# load historical market returns\n\ttemp = get.fama.french.data('F-F_Research_Data_Factors', periodicity = '',download = download, clean = T)\n\t\tret = cbind(temp[[1]]$Mkt.RF + temp[[1]]$RF, temp[[1]]$RF)\n\t\tprice = bt.apply.matrix(ret / 100, function(x) cumprod(1 + x))\n\tdata$SPY = make.stock.xts( price$Mkt.RF )\n\tdata$SHY = make.stock.xts( price$RF )\n\t\n\t# load historical momentum returns\n\ttemp = get.fama.french.data('10_Industry_Portfolios', periodicity = '',download = download, clean = T)\t\t\n\t\tret = temp[[1]]\n\t\tprice = bt.apply.matrix(ret[,1:9] / 100, function(x) cumprod(1 + x))\n\tfor(n in names(price)) data[[n]] = make.stock.xts( price[,n] )\n\t\n\t# align dates\n\tdata$symbolnames = c(names(price), 'SHY', 'SPY')\n\tbt.prep(data, align='remove.na', dates='2000::')\n\n\tbt.dates = '2001:04::'\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\n\tprices = data$prices \n\tn = ncol(data$prices)\n\t\t\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Benchmark Strategies\n\t#****************************************************************** \t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight$SPY[1] = 1\n\tmodels$SPY = bt.run.share(data, clean.signal=F, dates=bt.dates)\n\t\t\t\n\tweight = prices\n\t\tweight$SPY = NA\n\t\tweight$SHY = NA\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = ntop(weight[], n)\n\tmodels$EW = bt.run.share(data, clean.signal=F, dates=bt.dates)\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t# http://www.mebanefaber.com/2013/12/04/square-root-of-f-squared/\n\t#****************************************************************** \t\t\t\n\tsma = bt.apply.matrix(prices, SMA, 10)\n\t\n\t# create position score\n\tposition.score = sma\n\tposition.score[ prices < sma ] = NA\n\t\tposition.score$SHY = NA\t\n\t\tposition.score$SPY = NA\t\n\t\n\t# equal weight allocation\n\tweight = ntop(position.score[], n)\t\n\t\n\t# number of invested funds\n\tn.selected = rowSums(weight != 0)\n\t\n\t# cash logic\n\tweight$SHY[n.selected == 0,] = 1\n\t\n\tweight[n.selected == 1,] = 0.25 * weight[n.selected == 1,]\n\tweight$SHY[n.selected == 1,] = 0.75\n\t\n\tweight[n.selected == 2,] = 0.5 * weight[n.selected == 2,]\n\tweight$SHY[n.selected == 2,] = 0.5\n\t\n\tweight[n.selected == 3,] = 0.75 * weight[n.selected == 3,]\n\tweight$SHY[n.selected == 3,] = 0.25\n\t\n\t# cbind(round(100*weight,0), n.selected)\t\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = weight\n\tmodels$strategy1 = bt.run.share(data, clean.signal=F, dates=bt.dates)\n\t\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\t\t\n\tstrategy.performance.snapshoot(models, one.page = T)\ndev.off()\n\t\n\t\n}\t\t\n\t\n\n\t\n\n###############################################################################\n# Test for Averaged Input Assumptions and Averaged Momentum created by pierre.c.chretien\n###############################################################################\nbt.averaged.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\t# 10 funds\n\ttickers = spl('Us.Eq = VTI + VTSMX,\n\tEurpoe.Eq = IEV + FIEUX,\n\tJapan.Eq = EWJ + FJPNX,\n\tEmer.Eq = EEM + VEIEX,\n\tRe = RWX + VNQ + VGSIX,\t\t\n\tCom = DBC + QRAAX,\n\tGold = GLD + SCGDX,\n\tLong.Tr = TLT + VUSTX,\n\tMid.Tr = IEF + VFITX,\n\tShort.Tr = SHY + VFISX') \n\t\n\tstart.date = 1998\n\t\n\tdates = paste(start.date,'::',sep='') \n\t\n\tdata <- new.env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T)\n\t\t#bt.start.dates(data)\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='keep.all', dates=paste(start.date-2,':12::',sep=''), fill.gaps = T)\n\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \t\t\n\tprices = data$prices \n\t\tn = ncol(prices)\n\t\tnperiods = nrow(prices)\n\t\t\n\t\t\n\tperiodicity = 'quarters'\n\tperiodicity = 'months'\n\tperiod.ends = endpoints(prices, periodicity)\n\t\tperiod.ends = period.ends[period.ends > 0]\n\t\t\n\tmax.product.exposure = 0.6\t\n\t\n\t#*****************************************************************\n\t# Input Assumptions\n\t#****************************************************************** \t\n\tlookback.len = 40\n\tcreate.ia.fn = create.ia\n\t\n\t# input assumptions are averaged on 20, 40, 60 days using 1 day lag\n\tia.array = c(20,40,60)\n\tavg.create.ia.fn = create.ia.averaged(ia.array, 1)\n\n\t#*****************************************************************\n\t# Momentum\n\t#****************************************************************** \t\n\tuniverse = prices>0\n\t\n\tmom.lookback.len = 120\t\n\tmomentum = prices / mlag(prices, mom.lookback.len) - 1\n\tmom.universe = ifna(momentum > 0, F)\n\t\n\t# momentum is averaged on 20,60,120,250 days using 3 day lag\n\tmom.array = c(20,60,120,250)\t\n\tavg.momentum = momentum.averaged(prices, mom.array, 3)\n\tavgmom.universe = ifna(avg.momentum > 0, F)\n\n\t#*****************************************************************\n\t# Algos\n\t#****************************************************************** \t\n\tmin.risk.fns = list(\n\t\tEW = equal.weight.portfolio,\n\t\tMV = min.var.portfolio,\n\t\tMCE = min.corr.excel.portfolio,\n\t\t\t\t\n\t\tMV.RSO = rso.portfolio(min.var.portfolio, 3, 100, const.ub = max.product.exposure),\n\t\tMCE.RSO = rso.portfolio(min.corr.excel.portfolio, 3, 100, const.ub = max.product.exposure)\n\t)\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\nmake.strategy.custom <- function(name, create.ia.fn, lookback.len, universe, env) {\n\tobj = portfolio.allocation.helper(data$prices, \n\t\tperiodicity = periodicity,\n\t\tuniverse = universe,\n\t\tlookback.len = lookback.len,\n\t\tcreate.ia.fn = create.ia.fn,\n\t\tconst.ub = max.product.exposure,\n\t\tmin.risk.fns = min.risk.fns,\n\t\tadjust2positive.definite = F\n\t)\n\tenv[[name]] = create.strategies(obj, data, prefix=paste(name,'.',sep=''))$models\n}\n\n\n\tmodels <- new.env()\t\n\tmake.strategy.custom('ia.none' , create.ia.fn , lookback.len, universe , models)\n\tmake.strategy.custom('ia.mom' , create.ia.fn , lookback.len, mom.universe , models)\n\tmake.strategy.custom('ia.avg_mom' , create.ia.fn , lookback.len, avgmom.universe, models)\n\tmake.strategy.custom('avg_ia.none' , avg.create.ia.fn, 252 , universe , models)\n\tmake.strategy.custom('avg_ia.mom' , avg.create.ia.fn, 252 , mom.universe , models)\n\tmake.strategy.custom('avg_ia.avg_mom' , avg.create.ia.fn, 252 , avgmom.universe, models)\n\t\n\t#*****************************************************************\n # Create Report\n #*****************************************************************\t\t\nstrategy.snapshot.custom <-\tfunction(models, n = 0, title = NULL) {\n\tif (n > 0)\n\t\tmodels = models[ as.vector(matrix(1:len(models),ncol=n, byrow=T)) ]\t\n\n\tlayout(1:3)\t\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = title)\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\tplotbt.strategy.sidebyside(models)\n\tbarplot.with.labels(sapply(models, compute.turnover, data), 'Average Annual Portfolio Turnover', T)\t\n}\n\n#pdf(file = paste('M.Paramless.Portfolio.Tests.pdf',sep=''), width=8.5, height=11)\n\npng(filename = 'plot1.png', width = 900, height = 900, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# basic vs basic + momentum => momentum filter has better results\n\tmodels.final = c(models$ia.none, models$ia.mom)\n\tstrategy.snapshot.custom(models.final, len(min.risk.fns), 'Momentum Filter')\ndev.off()\n\npng(filename = 'plot2.png', width = 900, height = 900, units = 'px', pointsize = 12, bg = 'white')\t\t\n\t# basic vs basic + avg ia => averaged ia reduce turnover\n\tmodels.final = c(models$ia.none, models$avg_ia.none)\n\tstrategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged Input Assumptions')\ndev.off()\n\npng(filename = 'plot3.png', width = 900, height = 900, units = 'px', pointsize = 12, bg = 'white')\t\t\t\n\t# basic + momentum vs basic + avg.momentum => mixed results for averaged momentum\n\tmodels.final = c(models$ia.mom, models$ia.avg_mom)\n\tstrategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged Momentum')\ndev.off()\n\npng(filename = 'plot4.png', width = 900, height = 900, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\n\t# basic + momentum vs avg ia + avg.momentum\n\tmodels.final = c(models$ia.mom, models$avg_ia.avg_mom)\n\tstrategy.snapshot.custom(models.final, len(min.risk.fns), 'Averaged vs Base')\t\ndev.off()\n\n\n\n}\n\n\n###############################################################################\n# Probabilistic Momentum\n# http://cssanalytics.wordpress.com/2014/01/28/are-simple-momentum-strategies-too-dumb-introducing-probabilistic-momentum/\n# http://cssanalytics.wordpress.com/2014/02/12/probabilistic-momentum-spreadsheet/\n###############################################################################\nbt.probabilistic.momentum.test <- function()\n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = spl('EQ=QQQ,FI=SHY')\n\ttickers = spl('EQ=SPY,FI=TLT')\n\t\t\n\tdata <- new.env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='remove.na', dates='::')\n \n\t\n\t#*****************************************************************\n\t# Setup\n\t#****************************************************************** \n\tlookback.len = 120\n\tlookback.len = 60\n\tconfidence.level = 60/100\n\t\n\t\n\tprices = data$prices\n\t\tret = prices / mlag(prices) - 1 \n\t\t#ret = log(prices / mlag(prices))\n\t\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Simple Momentum\n\t#****************************************************************** \n\tmomentum = prices / mlag(prices, lookback.len)\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = momentum$EQ > momentum$FI\n\t\tdata$weight$FI[] = momentum$EQ <= momentum$FI\n\tmodels$Simple = bt.run.share(data, clean.signal=T) \t\n\n\t#*****************************************************************\n\t# Probabilistic Momentum + Confidence Level\n\t# http://cssanalytics.wordpress.com/2014/01/28/are-simple-momentum-strategies-too-dumb-introducing-probabilistic-momentum/\n\t# http://cssanalytics.wordpress.com/2014/02/12/probabilistic-momentum-spreadsheet/\n\t#****************************************************************** \n\tir = sqrt(lookback.len) * runMean(ret$EQ - ret$FI, lookback.len) / runSD(ret$EQ - ret$FI, lookback.len)\n\tmomentum.p = pt(ir, lookback.len - 1)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(cross.up(momentum.p, confidence.level), 1, iif(cross.dn(momentum.p, (1 - confidence.level)), 0,NA))\n\t\tdata$weight$FI[] = iif(cross.dn(momentum.p, (1 - confidence.level)), 1, iif(cross.up(momentum.p, confidence.level), 0,NA))\n\tmodels$Probabilistic = bt.run.share(data, clean.signal=T) \t\n\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(cross.up(momentum.p, confidence.level), 1, iif(cross.up(momentum.p, (1 - confidence.level)), 0,NA))\n\t\tdata$weight$FI[] = iif(cross.dn(momentum.p, (1 - confidence.level)), 1, iif(cross.up(momentum.p, confidence.level), 0,NA))\n\tmodels$Probabilistic.Leverage = bt.run.share(data, clean.signal=T) \t\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \n strategy.performance.snapshoot(models, T)\ndev.off()\n \n #*****************************************************************\n # Visualize Signal\n #****************************************************************** \n\tcols = spl('steelblue,steelblue1')\n\tprices = scale.one(data$prices)\n \npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white')\n\tlayout(1:3)\n\t\n\tplota(prices$EQ, type='l', ylim=range(prices), plotX=F, col=cols[1], lwd=2)\n\tplota.lines(prices$FI, type='l', plotX=F, col=cols[2], lwd=2)\n\t\tplota.legend('EQ,FI',cols,as.list(prices))\n\n\thighlight = models$Probabilistic$weight$EQ > 0\n\t\tplota.control$col.x.highlight = iif(highlight, cols[1], cols[2])\n\tplota(models$Probabilistic$equity, type='l', plotX=F, x.highlight = highlight | T)\n\t\tplota.legend('Probabilistic,EQ,FI',c('black',cols))\n\t\t\t\t\n\thighlight = models$Simple$weight$EQ > 0\n\t\tplota.control$col.x.highlight = iif(highlight, cols[1], cols[2])\n\tplota(models$Simple$equity, type='l', plotX=T, x.highlight = highlight | T)\n\t\tplota.legend('Simple,EQ,FI',c('black',cols))\t\t\ndev.off()\n\n #*****************************************************************\n # Create PDF Report\n #****************************************************************** \npdf(file = 'Probabilistic.Momentum.Report.pdf', width=8.5, height=11) \n \tstrategy.performance.snapshoot(bt.trim(models), data = data)\ndev.off()\n\n\n\n\n\n\n #*****************************************************************\n # 60 / 40 Idea\n #****************************************************************** \n\n\t#*****************************************************************\n\t# Simple Momentum\n\t#****************************************************************** \n\tmomentum = prices / mlag(prices, lookback.len)\n\t\n\tsignal = momentum$EQ > momentum$FI\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(signal, 60, 40) / 100\n\t\tdata$weight$FI[] = iif(signal, 40, 60) / 100\n\tmodels$Simple = bt.run.share(data, clean.signal=T) \t\n\n\t#*****************************************************************\n\t# Probabilistic Momentum\n\t#****************************************************************** \n\tir = sqrt(lookback.len) * runMean(ret$EQ - ret$FI, lookback.len) / runSD(ret$EQ - ret$FI, lookback.len)\n\tmomentum.p = pt(ir, lookback.len - 1)\n\t\t\n\tsignal = iif(cross.up(momentum.p, confidence.level), 1, iif(cross.dn(momentum.p, (1 - confidence.level)), 0,NA))\n\tsignal = ifna.prev(signal) == 1\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(signal, 60, 40) / 100\n\t\tdata$weight$FI[] = iif(signal, 40, 60) / 100\n\tmodels$Probabilistic = bt.run.share(data, clean.signal=T) \t\n\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \n strategy.performance.snapshoot(models, T)\ndev.off()\t\n\n}\n\n\n###############################################################################\n# Testing Intraday data from http://thebonnotgang.com/tbg/historical-data/\n###############################################################################\n# helper function to load and optionally clean data from thebonnotgang\nbt.load.thebonnotgang.data <- function(Symbols, folder, silent=F, clean=T) \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\t# data from http://thebonnotgang.com/tbg/historical-data/\n\t\n\t# http://stackoverflow.com/questions/14440661/dec-argument-in-data-tablefread\n\t\tSys.localeconv()[\"decimal_point\"]\n\t\tSys.setlocale(\"LC_NUMERIC\", \"French_France.1252\")\n\t\n\tdata <- new.env()\n\tfor(s in spl(Symbols))\n\t\tdata[[s]] = read.xts(paste0(folder,s,'_1m.csv'), \n\t\t\tsep = ';', date.column = 3, format='%Y-%m-%d %H:%M:%S', index.class = c(\"POSIXlt\", \"POSIXt\"))\t\n\t\t\t\t\t\nif(!clean)return(data)\n\t\n\t#*****************************************************************\n\t# Clean data\n\t#****************************************************************** \t\n\tfor(i in ls(data)) {\n\t\t# remove dates with gaps over 4 min\n\t\tdates = index(data[[i]])\n\t\t\tdates.number = as.double(dates)\n\t\tfactor = format(dates, '%Y%m%d')\n\t\tgap = tapply(dates.number, factor, function(x) max(diff(x)))\n\t\tok.index = names(gap[gap <= 4*60])\n\t\tdata[[i]] = data[[i]][ !is.na(match(factor, ok.index)) ]\n\t\nif(!silent)cat(i, 'removing due to gaps:', setdiff(factor,ok.index), '\\n\\n')\n\t\n\t\t\t\t\n\t\t# remove dates with over 7 hours or less than 2 hours of trading\n\t\tdates = index(data[[i]])\n\t\t\tdates.number = as.double(dates)\n\t\tfactor = format(dates, '%Y%m%d')\n\t\tnperiods = len(dates)\n\t\tday.change = which(diff(dates.number) > 5 * 60)\n\t\tday.start = c(1, day.change + 1)\n\t\tday.end = c(day.change, nperiods)\n\t\tok.index = which(dates.number[day.end] - dates.number[day.start] < 7*60*60 &\n\t\t\t\t\t\tdates.number[day.end] - dates.number[day.start] > 2*60*60)\n\t\tok.index = factor[day.start][ok.index]\n\t\tdata[[i]] = data[[i]][ !is.na(match(factor, ok.index)) ]\n\t\t\nif(!silent)cat(i, 'removing due to trading hours:', setdiff(factor,ok.index), '\\n\\n')\t\t\n\t\t\n\t\t# align all trading to start at 9:31\n\t\tdates = index(data[[i]])\n\t\t\tdates.number = as.double(dates)\n\t\tfactor = format(dates, '%Y%m%d')\n\t\tnperiods = len(dates)\n\t\tday.change = which(diff(dates.number) > 5 * 60)\n\t\tday.start = c(1, day.change + 1)\n\t\tday.end = c(day.change, nperiods)\n\n\t\tadd.hours = as.double(format(dates[day.start], '%H')) - 9\t\t\t\t\n\t\tfor(h in which(add.hours != 0))\n\t\t\tdates[day.start[h]:day.end[h]] = dates[day.start[h]:day.end[h]] - add.hours[h]*60*60\n\t\tindex(data[[i]]) = dates\n\t}\t\n\t\n\t\n\tok.index = unique(format(index(data[[ls(data)[1]]]), '%Y%m%d'))\n\tfor(i in ls(data)) {\n\t\tdates = index(data[[i]])\n\t\tfactor = format(dates, '%Y%m%d')\t\n\t\tok.index = intersect(ok.index, unique(factor))\n\t}\n\t\n\t# remove days that are not present in both time series\n\tfor(i in ls(data)) {\n\t\tdates = index(data[[i]])\n\t\tfactor = format(dates, '%Y%m%d')\t\t\n\t\tdata[[i]] = data[[i]][ !is.na(match(factor, ok.index)) ]\n\t\t\nif(!silent)cat(i, 'removing due to not being common:', setdiff(factor,ok.index), '\\n\\n')\t\t\n\t}\n\t\t\n\t#*****************************************************************\n\t# Round to the next minute\n\t#****************************************************************** \n\tfor(i in ls(data))\n\t\tindex(data[[i]]) = as.POSIXct(format(index(data[[i]]) + 60, '%Y-%m-%d %H:%M'), tz = Sys.getenv('TZ'), format = '%Y-%m-%d %H:%M')\n\n\tdata\t\t\n}\n\n# helper function to extract index of day start / end in intraday data\nbt.intraday.day <- function(dates) \n{\n\tdates.number = as.double(dates)\t\t\n\t\n\tnperiods = len(dates)\n\t\n\tday.change = which(diff(dates.number) > 5 * 60)\n\tlist(\n\t\tday.start = c(1, day.change + 1),\n\t\tday.end = c(day.change, nperiods)\n\t)\t\n}\n\n\n\nbt.intraday.thebonnotgang.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\n\t# data from http://thebonnotgang.com/tbg/historical-data/\n\t# please save SPY and GLD 1 min data at the given path\n\tspath = 'c:/Desktop/'\n\t# http://stackoverflow.com/questions/14440661/dec-argument-in-data-tablefread\n\t\tSys.localeconv()[\"decimal_point\"]\n\t\tSys.setlocale(\"LC_NUMERIC\", \"French_France.1252\")\n\t\n\tdata <- new.env()\n\tdata$SPY = read.xts(paste0(spath,'SPY_1m.csv'), \n\t\tsep = ';', date.column = 3, format='%Y-%m-%d %H:%M:%S', index.class = c(\"POSIXlt\", \"POSIXt\"))\n\n\tdata$GLD = read.xts(paste0(spath,'GLD_1m.csv'), \n\t\tsep = ';', date.column = 3, format='%Y-%m-%d %H:%M:%S', index.class = c(\"POSIXlt\", \"POSIXt\"))\n\t\t\t\t\n\t#*****************************************************************\n\t# Create plot for Nov 1, 2012 and 2013\n\t#****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\n\tlayout(c(1,1,2))\t\t\n\tplota(data$SPY['2012:11:01'], type='candle', main='SPY on Nov 1st, 2012', plotX = F)\n\tplota(plota.scale.volume(data$SPY['2012:11:01']), type = 'volume')\t\n\ndev.off()\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\t\n\tlayout(c(1,1,2))\t\t\n\tplota(data$SPY['2013:11:01'], type='candle', main='SPY on Nov 1st, 2013', plotX = F)\n\tplota(plota.scale.volume(data$SPY['2013:11:01']), type = 'volume')\t\n\t\t\ndev.off()\t\t\n\t\n\t#*****************************************************************\n\t# Data check for Gaps in the series Intraday\n\t#****************************************************************** \n\ti = 'GLD'\n\tdates = index(data[[i]])\n\tfactor = format(dates, '%Y%m%d')\n\tgap = tapply(dates, factor, function(x) max(diff(x)))\n\t\n\tgap[names(gap[gap > 4*60])]\n\tdata[[i]]['2013:02:19']\n\n\ti = 'SPY'\n\tdates = index(data[[i]])\n\tfactor = format(dates, '%Y%m%d')\n\tgap = tapply(dates, factor, function(x) max(diff(x)))\n\t\n\tgap[names(gap[gap > 4*60])]\n\tdata[[i]]['2013:02:19']\n\t\n\t#*****************************************************************\n\t# Data check : compare with daily\n\t#****************************************************************** \n\tdata.daily <- new.env()\n\t\tquantmod::getSymbols(spl('SPY,GLD'), src = 'yahoo', from = '1970-01-01', env = data.daily, auto.assign = T) \n \npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\t\t\n\tlayout(1)\t\t\n\tplota(data$GLD, type='l', col='blue', main='GLD')\n\t\tplota.lines(data.daily$GLD, type='l', col='red')\n\tplota.legend('Intraday,Daily', 'blue,red')\t\n\t\ndev.off()\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\n\tplota(data$SPY, type='l', col='blue', main='SPY')\n\t\tplota.lines(data.daily$SPY, type='l', col='red')\n\tplota.legend('Intraday,Daily', 'blue,red')\t\n\ndev.off()\n\n\t#*****************************************************************\n\t# Round to the next minute\n\t#****************************************************************** \n\tGLD.sample = data$GLD['2012:07:10::2012:07:10 09:35']\n\tSPY.sample= data$SPY['2012:07:10::2012:07:10 09:35']\n\t\n\tmerge( Cl(GLD.sample), Cl(SPY.sample) )\n\t\n\t# round to the next minute\n\tindex(GLD.sample) = as.POSIXct(format(index(GLD.sample) + 60, '%Y-%m-%d %H:%M'), format = '%Y-%m-%d %H:%M')\n\tindex(SPY.sample) = as.POSIXct(format(index(SPY.sample) + 60, '%Y-%m-%d %H:%M'), format = '%Y-%m-%d %H:%M')\n\t\n\tmerge( Cl(GLD.sample), Cl(SPY.sample) )\n\t\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tdata = bt.load.thebonnotgang.data('SPY,GLD', spath)\n\t#plota(data$SPY['2013:10:11'], type='candle')\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\tprices = data$prices \n\tdates = data$dates\n\t\tnperiods = nrow(prices)\n\t\n\tmodels = list()\n\n\t#*****************************************************************\n\t# Benchmarks\n\t#****************************************************************** \t\t\t\t\t\t\t\n\tdata$weight[] = NA\n\t\tdata$weight$SPY = 1\n\tmodels$SPY = bt.run.share(data, clean.signal=F)\n\n\tdata$weight[] = NA\n\t\tdata$weight$GLD = 1\n\tmodels$GLD = bt.run.share(data, clean.signal=F)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$SPY = 0.5\n\t\tdata$weight$GLD = 0.5\n\tmodels$EW = bt.run.share(data, clean.signal=F)\n\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T)\t\n \ndev.off()\n\t\n}\t\n\n###############################################################################\n# Strategy Testing Intraday data from http://thebonnotgang.com/tbg/historical-data/\n###############################################################################\nbt.strategy.intraday.thebonnotgang.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\n\t# data from http://thebonnotgang.com/tbg/historical-data/\n\t# please save SPY and GLD 1 min data at the given path\n\tspath = 'c:/Desktop/'\nspath = 'c:/Documents and Settings/mkapler/Desktop/'\nspath = 'c:/Desktop/1car/1shaun/'\n\tdata = bt.load.thebonnotgang.data('SPY,GLD', spath)\n\t\n\tdata1 <- new.env()\t\t\n\t\tdata1$FI = data$GLD\n\t\tdata1$EQ = data$SPY\n\tdata = data1\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\n\tlookback.len = 120\n\tconfidence.level = 60/100\n\t\n\tprices = data$prices\n\t\tret = prices / mlag(prices) - 1 \n\t\t\n\tmodels = list()\n\t\n\t#*****************************************************************\n\t# Simple Momentum\n\t#****************************************************************** \n\tmomentum = prices / mlag(prices, lookback.len)\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = momentum$EQ > momentum$FI\n\t\tdata$weight$FI[] = momentum$EQ <= momentum$FI\n\tmodels$Simple = bt.run.share(data, clean.signal=T) \t\n\n\t#*****************************************************************\n\t# Probabilistic Momentum + Confidence Level\n\t# http://cssanalytics.wordpress.com/2014/01/28/are-simple-momentum-strategies-too-dumb-introducing-probabilistic-momentum/\n\t# http://cssanalytics.wordpress.com/2014/02/12/probabilistic-momentum-spreadsheet/\n\t#****************************************************************** \n\tir = sqrt(lookback.len) * runMean(ret$EQ - ret$FI, lookback.len) / runSD(ret$EQ - ret$FI, lookback.len)\n\tmomentum.p = pt(ir, lookback.len - 1)\n\t\t\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(cross.up(momentum.p, confidence.level), 1, iif(cross.dn(momentum.p, (1 - confidence.level)), 0,NA))\n\t\tdata$weight$FI[] = iif(cross.dn(momentum.p, (1 - confidence.level)), 1, iif(cross.up(momentum.p, confidence.level), 0,NA))\n\tmodels$Probabilistic = bt.run.share(data, clean.signal=T) \t\n\n\tdata$weight[] = NA\n\t\tdata$weight$EQ[] = iif(cross.up(momentum.p, confidence.level), 1, iif(cross.up(momentum.p, (1 - confidence.level)), 0,NA))\n\t\tdata$weight$FI[] = iif(cross.dn(momentum.p, (1 - confidence.level)), 1, iif(cross.up(momentum.p, confidence.level), 0,NA))\n\tmodels$Probabilistic.Leverage = bt.run.share(data, clean.signal=T) \t\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T)\t\n \ndev.off()\n \n #*****************************************************************\n # Hourly Performance\n #****************************************************************** \n strategy.name = 'Probabilistic.Leverage'\n\tret = models[[strategy.name]]$ret\t\n\t\tret.number = 100*as.double(ret)\n\t\t\n\tdates = index(ret)\n factor = format(dates, '%H')\n \npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t \n layout(1:2)\n par(mar=c(4,4,1,1))\n\tboxplot(tapply(ret.number, factor, function(x) x),outline=T, main=paste(strategy.name, 'Distribution of Returns'), las=1)\n\tbarplot(tapply(ret.number, factor, function(x) sum(x)), main=paste(strategy.name, 'P&L by Hour'), las=1)\ndev.off() \t\n\n #*****************************************************************\n # Hourly Performance: Remove first return of the day (i.e. overnight)\n #****************************************************************** \n \tday.stat = bt.intraday.day(dates)\n\tret.number[day.stat$day.start] = 0\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t \t\t\n layout(1:2)\n par(mar=c(4,4,1,1))\n\tboxplot(tapply(ret.number, factor, function(x) x),outline=T, main=paste(strategy.name, 'Distribution of Returns'), las=1)\n\tbarplot(tapply(ret.number, factor, function(x) sum(x)), main=paste(strategy.name, 'P&L by Hour'), las=1)\ndev.off() \t\n\t\n}\n\t\n\t\nbt.pair.strategy.intraday.thebonnotgang.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\n\t# data from http://thebonnotgang.com/tbg/historical-data/\n\t# please save SPY and GLD 1 min data at the given path\n\tspath = 'c:/Desktop/'\nspath = 'c:/Documents and Settings/mkapler/Desktop/'\t\n\tdata = bt.load.thebonnotgang.data('USO,GLD', spath)\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\tprices = data$prices \n\t\tnperiods = nrow(prices)\n\tdates = data$dates\n\t\tday.stat = bt.intraday.day(dates)\n\t\n\tmodels = list()\n \n\t#*****************************************************************\n\t# Construct signal\n\t# http://systematicedge.wordpress.com/2014/02/26/energy-stat-arb/\n\t#****************************************************************** \t\t\t\t\t\t\t\n\tlookback = 120\n\t\n\tstoch = (prices - bt.apply.matrix(prices, runMin, lookback)) / (bt.apply.matrix(prices, runMax, lookback) - bt.apply.matrix(prices, runMin, lookback))\t\n\t\tstoch = bt.apply.matrix(stoch, ifna.prev)\n\t\n\tstat = stoch$USO - stoch$GLD\n\tstat = (stat - runMean(stat,20))/runSD(stat,20)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$USO = iif(stat >= 2, -1, iif(stat <= -2, 1, 0))\n\t\tdata$weight$GLD = iif(stat <= -2, -1, iif(stat >= 2, 1, 0))\n\n\t\tdata$weight[day.stat$day.end,] = 0\n\t\tdata$weight[as.vector(0:(lookback-1) + rep.row(day.stat$day.start,lookback)),] = 0\n\t\t\n\tmodels$P = bt.run.share(data, clean.signal=T, do.lag = 1)\n\n\t#*****************************************************************\n\t# Construct signal\n\t# http://systematicedge.wordpress.com/2014/03/01/energy-stat-arb-part-2/\n\t# lm(y~x+0) <=> ols(x,y)$coefficients\n\t#****************************************************************** \t\t\t\t\t\t\t\n\tbeta = NA * prices[,1]\n\ttemp = coredata(prices[,spl('USO,GLD')])\n\tfor(i in lookback : nperiods) {\n\t\tdummy = temp[(i- lookback +1):i,]\n\t\tbeta[i] = ols(dummy[, 1], dummy[, 2])$coefficients\n\t\tif( i %% 1000 == 0) cat(i, nperiods, round(100*i/nperiods), '\\n')\t\n\t}\n\t\n\tstat = temp[,2] - beta * temp[,1]\n\tstat = -(stat - runMean(stat,20))/runSD(stat,20)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight$USO = iif(stat >= 2, -1, iif(stat <= -2, 1, 0))\n\t\tdata$weight$GLD = iif(stat <= -2, -1, iif(stat >= 2, 1, 0))\n\n\t\tdata$weight[day.stat$day.end,] = 0\n\t\tdata$weight[as.vector(0:(lookback-1) + rep.row(day.stat$day.start,lookback)),] = 0\n\t\t\n\tmodels$P1 = bt.run.share(data, clean.signal=T, do.lag = 1)\n\t\n #*****************************************************************\n # Create Report\n #****************************************************************** \npng(filename = 'plot1a.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T)\t\n \ndev.off()\n\n\n \t\n}\t\n\t\n\t\n\n###############################################################################\n# Calendar Strategy: Month End\n###############################################################################\nbt.calendar.strategy.month.end.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = spl('SPY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T)\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\n\tmodels = list()\n\t\t\n\tuniverse = prices > 0\n\t\n\tkey.date.index = date.month.ends(data$dates, F)\n\tkey.date = NA * prices\n\t\tkey.date[key.date.index,] = T\n\n\t#*****************************************************************\n\t# Strategy\n\t#*****************************************************************\n\tdata$weight[] = NA\n\t\tdata$weight[] = ifna(universe & key.date, F)\n\tmodels$T0 = bt.run.share(data, do.lag = 0, trade.summary=T, clean.signal=T) \n\n\t#*****************************************************************\n\t# Add helper functions\n\t#*****************************************************************\n\tcalendar.strategy <- function(data, signal, universe = data$prices > 0) {\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[] = ifna(universe & signal, F)\n\t\tbt.run.share(data, do.lag = 0, trade.summary=T, clean.signal=T) \t\n\t}\n\t\n\tcalendar.signal <- function(key.date, offsets = 0) {\n\t\tsignal = mlag(key.date, offsets[1])\n\t\tfor(i in offsets) signal = signal | mlag(key.date, i)\n\t\tsignal\n\t}\n\n\t# Trade on key.date\n\tmodels$T0 = calendar.strategy(data, key.date)\n\n\t# Trade next day after key.date\n\tmodels$N1 = calendar.strategy(data, mlag(key.date,1))\n\t# Trade two days next(after) key.date\n\tmodels$N2 = calendar.strategy(data, mlag(key.date,2))\n\n\t# Trade a day prior to key.date\n\tmodels$P1 = calendar.strategy(data, mlag(key.date,-1))\n\t# Trade two days prior to key.date\n\tmodels$P2 = calendar.strategy(data, mlag(key.date,-2))\n\t\n\t# Trade: open 2 days before the key.date and close 2 days after the key.date\t\n\tsignal = key.date | mlag(key.date,-1) | mlag(key.date,-2) | mlag(key.date,1) | mlag(key.date,2)\n\tmodels$P2N2 = calendar.strategy(data, signal)\n\n\t# same, but using helper function above\t\n\tmodels$P2N2 = calendar.strategy(data, calendar.signal(key.date, -2:2))\n\t\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T)\t\n \ndev.off()\n\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\t\n\tstrategy.performance.snapshoot(models, control=list(comparison=T), sort.performance=F)\n\t\ndev.off()\t\n\n\t\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\n\tlast.trades(models$P2)\n\t\ndev.off()\t\n\n\t\n\t#*****************************************************************\n\t# Using new functions\n\t#*****************************************************************\n\tsignals = calendar.signal(key.date, T0=0, N1=1, N2=2, P1=-1, P2=-2, P2N2=-2:2)\n\tmodels = calendar.strategy(data, signals, universe = universe)\n\t\n\tstrategy.performance.snapshoot(models, control=list(main=T))\n\t\n\tstrategy.performance.snapshoot(models, control=list(comparison=T), sort.performance=F)\n\t\t\n\tstrategy.performance.snapshoot(models[\"P2N2\"], control=list(monthly=T))\n\t\n\tstrategy.performance.snapshoot(models, control=list(transition=T))\n\n\tlast.trades(models$P2)\n\n}\n\n\n###############################################################################\n# Calendar Strategy: Option Expiry\n#\n# Op-ex week in December has been the most bullish week of the year for the SPX\n# Buy: December Friday prior to op-ex.\n# Sell X days later: 100K/trade 1984-present\n# http://quantifiableedges.blogspot.com/2011/12/mooost-wonderful-tiiiiiiime-of.html\n# http://quantifiableedges.blogspot.com/2010/12/most-wonderful-tiiiime-of-yearrrrrr.html\n###############################################################################\nbt.calendar.strategy.option.expiry.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = spl('SPY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T)\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\n\tdates = data$dates\t\n\t\n\tmodels = list()\n\t\n\tuniverse = prices > 0\n\t\t\n\t# Find Friday before options expiration week in December\n\tyears = date.year(range(dates))\n\tsecond.friday = third.friday.month(years[1]:years[2], 12) - 7\n\t\tkey.date.index = na.omit(match(second.friday, dates))\n\t\t\t\t\n\tkey.date = NA * prices\n\t\tkey.date[key.date.index,] = T\n\n\t#*****************************************************************\n\t# Strategy\n\t#\n\t# Op-ex week in December has been the most bullish week of the year for the SPX\n\t# Buy: December Friday prior to op-ex.\n\t# Sell X days later: 100K/trade 1984-present\n\t# http://quantifiableedges.blogspot.com/2011/12/mooost-wonderful-tiiiiiiime-of.html\n\t# http://quantifiableedges.blogspot.com/2010/12/most-wonderful-tiiiime-of-yearrrrrr.html\n\t###############################################################################\n\tsignals = list(T0=0)\n\t\tfor(i in 1:15) signals[[paste0('N',i)]] = 0:i\t\n\tsignals = calendar.signal(key.date, signals)\n\tmodels = calendar.strategy(data, signals, universe = universe)\n\t\tnames(models)\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T, sort.performance=F)\n \ndev.off()\n\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\n\t# custom stats\t\n\tout = sapply(models, function(x) list(\n\t\tCAGR = 100*compute.cagr(x$equity),\n\t\tMD = 100*compute.max.drawdown(x$equity),\n\t\tWin = x$trade.summary$stats['win.prob', 'All'],\n\t\tProfit = x$trade.summary$stats['profitfactor', 'All']\n\t\t))\t\n\tperformance.barchart.helper(out, sort.performance = F)\n\t\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\n\tstrategy.performance.snapshoot(models$N15, control=list(main=T))\n\t\ndev.off()\t\t\n\t\n\t#strategy.performance.snapshoot(models['N15'], control=list(transition=T))\n\t\n\t#strategy.performance.snapshoot(models['N15'], control=list(monthly=T))\n\t\t\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\t\n\tlast.trades(models$N15)\n\t\ndev.off()\t\t\n\t\npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\n\ttrades = models$N15$trade.summary$trades\n\t\ttrades = make.xts(parse.number(trades[,'return']), as.Date(trades[,'entry.date']))\n\tlayout(1:2)\n\t\tpar(mar = c(4,3,3,1), cex = 0.8) \n\tbarplot(trades, main='Trades', las=1)\n\tplot(cumprod(1+trades/100), type='b', main='Trades', las=1)\n\t\ndev.off()\t\t\t\n\t\n\t#plotbt.custom.report.part2(models$N15, trade.summary=F)\n\t\n}\t\n\t\n\t\t\n###############################################################################\n# Calendar Strategy: Fed Days\n#\n# http://quantifiableedges.blogspot.ca/search/label/Fed%20Study\n###############################################################################\nbt.calendar.strategy.fed.days.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = spl('SPY')\n\t\t\n\tdata <- new.env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T)\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices\n\t\tn = ncol(prices)\n\t\t\n\tdates = data$dates\t\n\t\n\tmodels = list()\n\t\n\tuniverse = prices > 0\n\t\tuniverse = universe & prices > SMA(prices,100)\n\t\t\n\t# Find Fed Days\n\tinfo = get.FOMC.dates(F)\n\t\tkey.date.index = na.omit(match(info$day, dates))\n\t\n\tkey.date = NA * prices\n\t\tkey.date[key.date.index,] = T\n\t\t\n\t#*****************************************************************\n\t# Strategy\n\t#*****************************************************************\n\tsignals = list(T0=0)\n\t\tfor(i in 1:15) signals[[paste0('N',i)]] = 0:i\t\n\tsignals = calendar.signal(key.date, signals)\n\tmodels = calendar.strategy(data, signals, universe = universe)\n\t\n\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n \n strategy.performance.snapshoot(models, T, sort.performance=F)\n \ndev.off()\n\t\npng(filename = 'plot2.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\n\t# custom stats\t\n\tout = sapply(models, function(x) list(\n\t\tCAGR = 100*compute.cagr(x$equity),\n\t\tMD = 100*compute.max.drawdown(x$equity),\n\t\tWin = x$trade.summary$stats['win.prob', 'All'],\n\t\tProfit = x$trade.summary$stats['profitfactor', 'All']\n\t\t))\t\n\tperformance.barchart.helper(out, sort.performance = F)\n\t\ndev.off()\t\n\npng(filename = 'plot3.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\n\tstrategy.performance.snapshoot(models$N15, control=list(main=T))\n\t\ndev.off()\t\t\n\t\n\t#strategy.performance.snapshoot(models['N15'], control=list(transition=T))\n\t\n\t#strategy.performance.snapshoot(models['N15'], control=list(monthly=T))\n\t\t\n\t\npng(filename = 'plot4.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\t\n\tlast.trades(models$N15)\n\t\ndev.off()\t\t\n\t\npng(filename = 'plot5.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\t\n\t\t\n\ttrades = models$N15$trade.summary$trades\n\t\ttrades = make.xts(parse.number(trades[,'return']), as.Date(trades[,'entry.date']))\n\tlayout(1:2)\n\t\tpar(mar = c(4,3,3,1), cex = 0.8) \n\tbarplot(trades, main='N15 Trades', las=1)\n\tplot(cumprod(1+trades/100), type='b', main='N15 Trades', las=1)\n\t\ndev.off()\t\t\t\n\n}\n\n\n###############################################################################\n# Adjusted Momentum by David Varadi\n#\n# http://cssanalytics.wordpress.com/2014/07/29/vix-adjusted-momentum/\n# http://cssanalytics.wordpress.com/2014/07/30/error-adjusted-momentum/\n# http://www.quintuitive.com/2015/06/21/trading-moving-averages-with-less-whipsaws/\n###############################################################################\nbt.adjusted.momentum.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = spl('SPY,^VIX')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='remove.na', fill.gaps = T)\n\n\tVIX = Cl(data$VIX)\n\n\tbt.prep.remove.symbols(data, 'VIX')\n\t\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices\n\t\t\n\tmodels = list()\n\n\tcommission = list(cps = 0.01, fixed = 10.0, percentage = 0.0)\n\t\n\t\n\t#*****************************************************************\n\t# Buy and Hold\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[] = 1\n\tmodels$buy.hold = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# 200 SMA\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(prices > SMA(prices, 200), 1, 0)\n\tmodels$ma200 = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# 200 ROC\n\t#****************************************************************** \n\troc = prices / mlag(prices) - 1\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(SMA(roc, 200) > 0, 1, 0)\n\tmodels$roc200 = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\t\n\t#*****************************************************************\n\t# 200 VIX MOM\n\t#****************************************************************** \n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(SMA(roc/VIX, 200) > 0, 1, 0)\n\tmodels$vix.mom = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\n\t#*****************************************************************\n\t# 200 ER MOM - the logic is that returns should be weighted more \n\t# when predictability is high, and conversely weighted less when \n\t# predictability is low. In this case, the error-adjusted moving average \n\t# will hopefully be more robust to market noise than a standard moving average.\t\n\t#****************************************************************** \n\tforecast = SMA(roc,10)\n\terror = roc - mlag(forecast)\n\tmae = SMA(abs(error), 10)\n\tsma = SMA(roc/mae, 200)\n\t\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(sma > 0, 1, 0)\n\tmodels$er.mom = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\t\t\n\t# cushioned\n\tstddev = runSD(roc/mae,200)\n\tupper.band = 0\n\tlower.band = -0.05*stddev\n\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(cross.up(sma, upper.band), 1, iif(cross.dn(sma, lower.band), 0, NA))\n\tmodels$er.mom.cushioned = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\n\t\nif(F) {\t\t\n\t#*****************************************************************\n\t# EA, for stdev calculatins assume 0 sample mean\n\t# http://www.quintuitive.com/2015/06/21/trading-moving-averages-with-less-whipsaws/\n\t#****************************************************************** \n\tadj.rets = roc / sqrt(runSum(roc^2,10)/9)\n\t#adj.rets = roc / runSD(roc,10)\n\t\tsma = SMA(adj.rets, 200) \n\t\tstddev = sqrt(runSum(adj.rets^2,200)/199)\n\t\t#stddev = runSD(adj.rets,200)\n \n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(sma > 0, 1, 0)\n\t\t#upper.band = lower.band = 0\n\t\t#data$weight[] = iif(cross.up(sma, upper.band), 1, iif(cross.dn(sma, lower.band), 0, NA))\n\tmodels$ea = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n\n\t#*****************************************************************\n\t# Cushioned EA \n\t#****************************************************************** \n\tupper.band = 0\n\tlower.band = -0.05*stddev\n\n\tdata$weight[] = NA\n\t\tdata$weight[] = iif(cross.up(sma, upper.band), 1, iif(cross.dn(sma, lower.band), 0, NA))\n\tmodels$ea.cushioned = bt.run.share(data, clean.signal=T, commission=commission, trade.summary=T)\n}\t\n\t#*****************************************************************\n\t# Report\n\t#****************************************************************** \n\tlayout(1:2)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\n\tplotbt.strategy.sidebyside(models, perfromance.fn=engineering.returns.kpi)\n\t\n\t\n\t\npng(filename = 'plot1.png', width = 600, height = 500, units = 'px', pointsize = 12, bg = 'white') \t\n\t\n\tstrategy.performance.snapshoot(models, T)\n\t\ndev.off()\n}\t\n\t\n\n \n#*****************************************************************\n# Example showing the signal and execution lags\n#*****************************************************************\nbt.signal.execution.lag.test <- function()\n{\n\tdo.lag = 2\n\tcalc.offset = -1\n\t\n\t# Load test data\n\tdata <- new.env()\n\t\tdate = as.Date('2015-Aug-26','%Y-%b-%d')\n\t\tdates = seq(date-100, date, by=1)\n\t\tdata$TEST = make.stock.xts(make.xts(1:len(dates), dates))\n\tbt.prep(data)\n\t\n\t# Setup\n\tprices = data$prices \n\t\tnperiods = nrow(prices)\n\t\t\t\n\tperiod.ends = endpoints(prices, 'months') + calc.offset\n\t\tperiod.ends = period.ends[(period.ends > 0) & (period.ends <= nperiods)]\n\t\n\t# Code Strategy\n\tdata$weight[] = NA\n\t\tdata$weight[period.ends,] = prices[period.ends,]\n\tmodel = bt.run(data, do.lag = do.lag)\n\t\n\tlast(as.xts(list(WEIGHT = model$weight, SIGNAL = prices)),20)\n}\n\n###############################################################################\n# Execution price: buy low sell high\n###############################################################################\nbt.execution.price.high.low.test <- function\n(\n\tsymbols = 'SPY,XLY,XLP,XLE,XLF,XLV,XLI,XLB,XLK,XLU',\n\tn.top = 4,\n\tmom.lag = 126,\n\tdates = '2001::'\n) \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\t\n\ttickers = spl(symbols)\t\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\n\tbt.prep(data, align='remove.na')\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tprices = data$prices \n\tn = len(tickers) \n\n\t# find month ends\n\tmonth.ends = endpoints(prices, 'months')\n\t\tmonth.ends = month.ends[month.ends > 0]\t\t\n\n\tmodels = list()\n\t\t\t\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\t\t\t\t\n\t# Rank on Momentum lag return\n\tposition.score = prices / mlag(prices, mom.lag)\t\n\t\n\tfrequency = month.ends\n\t# Select Top N funds\n\tweight = ntop(position.score, n.top)\n\t\n\t#*****************************************************************\n\t# Code Strategies, please note that there is only one price per day\n\t# so all transactions happen at selected price\n\t# i.e. below both buys and sells take place at selected price\n\t#****************************************************************** \n\tfor(name in spl('Cl,Op,Hi,Lo')) {\n\t\tfun = match.fun(name)\n\t\n\t\texec.prices = bt.apply(data, fun)\n\t\t\n\t\tdata$weight[] = NA\n\t\t\tdata$execution.price[] = NA\n\t\t \tdata$execution.price[frequency,] = exec.prices[frequency,]\n\t\t \tdata$weight[frequency,] = weight[frequency,]\n\t\tmodels[[name]] = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)\n\t}\t\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tlow.prices = bt.apply(data, Lo)\n\thigh.prices = bt.apply(data, Hi)\n\t\n\t# buy at low price\n\texecution.price = low.prices[frequency,]\n\t\n\t# sell(i.e. weight=0) at high price\n\tindex = (weight[frequency,])==0\n\texecution.price[index] = coredata(high.prices[frequency,])[index]\n\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\t\t\n\t \tdata$execution.price[frequency,] = execution.price\t \t\t \t\n\t \tdata$weight[frequency,] = weight[frequency,]\n\tmodels$Buy.Low.Sell.High = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)\n\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \n\tlow.prices = bt.apply(data, Lo)\n\thigh.prices = bt.apply(data, Hi)\n\t\n\t# buy at high price\n\texecution.price = high.prices[frequency,]\n\t\n\t# sell(i.e. weight=0) at low price\n\tindex = (weight[frequency,])==0\n\texecution.price[index] = coredata(low.prices[frequency,])[index]\n\t\n\tdata$weight[] = NA\n\t\tdata$execution.price[] = NA\t\t\n\t \tdata$execution.price[frequency,] = execution.price\t \t\t \t\n\t \tdata$weight[frequency,] = weight[frequency,]\n\tmodels$Buy.High.Sell.Low = bt.run.share(data, trade.summary=T, dates=dates, silent=T, clean.signal=F)\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t#strategy.performance.snapshoot(models, T)\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\t\t\n\tm = names(models)[1]\n\tplotbt.transition.map(models[[m]]$weight, name=m)\n\t\tlegend('topright', legend = m, bty = 'n')\n\nprint('Strategy Performance:')\t\t\t\nprint(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T))\n\nprint('Monthly Results for', m, ':')\nprint(plotbt.monthly.table(models[[m]]$equity, make.plot = F))\n}\n\n\n\n\n###############################################################################\n# Dual Momentum\n#\n# http://www.scottsinvestments.com/2012/12/21/dual-momentum-investing-with-mutual-funds/\n#\n# http://itawealth.com/2014/11/10/dual-momentum-back-tests-part-1/\n# http://itawealth.com/2014/11/12/dual-momentum-back-tests-part-2-adding-diversification-dual-momentum-strategy/\n# http://itawealth.com/2015/04/20/deciphering-the-dual-momentum-model/\n#\n###############################################################################\nbt.dual.momentum.test <- function() \n{\n\t#*****************************************************************\n\t# Load historical data\n\t#****************************************************************** \n\tload.packages('quantmod')\n\t\t\n\ttickers = '\n\t# Equity Risk = US Equity = VTI; Equity ex US = VEA \n\tEQ.US = VTI\n\tEQ.EX.US = VEA\n\t\n\t# Credit Risk = High Yield Bonds = HYG; Credit Bonds = CIU \n\tHI.YLD = HYG\n\tCREDIT = CIU\n\t\n\t# Real Estate Risk = Equity REITs = VNQ; mortgage REITs = REM \n\tREIT.EQ = VNQ\n\tREIT.MTG = REM\n\t\n\t# Economic Stress = Gold = GLD; Long Term Treasuries =TLT \n\tGOLD = GLD\n\tLT.GOV = TLT\n\t\n\t# Cash: T-Bills (SHY)\n\tCASH = SHY + VFISX\n\t'\n\t\n\t\t\n\tdata = env()\n\tgetSymbols.extra(tickers, src = 'yahoo', from = '1980-01-01', env = data, set.symbolnames = T, auto.assign = T)\n\t\t#print(bt.start.dates(data))\n\t\tfor(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\n\tbt.prep(data, align='keep.all', fill.gaps = T)\n\tdata = bt.prep.trim(data, '2006::')\n\n\t\n\t\n\t# define risk groups, do not define group for CASH\n\trisk.groups = transform(data$prices[1,] * NA, \n\t\tEQ.US=1, EQ.EX.US=1,\n\t\tHI.YLD=2, CREDIT=2,\n\t\tREIT.EQ=3, REIT.MTG=3,\n\t\tGOLD=4, LT.GOV=4\n\t\t)\n\trisk.groups\n\n\t# plot asset history\n\t#plota.matplot(scale.one(data$prices),main='Asset Performance')\n\n\t#*****************************************************************\n\t# Setup\n\t#*****************************************************************\n\tprices = data$prices\n\t\n\tperiod.ends = date.ends(prices, 'month')\n\t\t\n\tmom = prices / mlag(prices, 252)\n\t\t# Absolute momentum logic: do not allocate if momentum is below CASH momentum\n\t\tmom[mom < as.vector(mom$CASH)] = NA\n\t\n\t#*****************************************************************\n\t# Code Strategies\n\t#****************************************************************** \t\t\n\tcustom.weight.portfolio <- function(mom, ntop)\n\t{\n\t\tmom = mom\n\t\tntop = ntop\n\t\tfunction\n\t\t(\n\t\t\tia,\t\t\t\t# input assumptions\n\t\t\tconstraints\t\t# constraints\n\t\t)\n\t\t{\n\t\t\tntop.helper(mom[ia$nperiod, ia$index], ntop)\t\t\t\n\t\t}\t\n\t}\t\n\t\n\t\n\n\t# setup universe\t\n\tuniverse = !is.na(mom)\n\t\tuniverse[,'CASH'] = F\n\t\n\tobj = portfolio.allocation.helper(prices, \n\t\tperiod.ends = period.ends, \n\t\tuniverse = universe,\n\t\tmin.risk.fns = list(\n\t\t\tEW=equal.weight.portfolio,\n\t\t\tDM=distribute.weights(\n\t\t\t\tstatic.weight.portfolio(rep(1,4)), # there are 4 clusters\n\t\t\t\tstatic.group(risk.groups), # predefined groups\n\t\t\t\tcustom.weight.portfolio(mom, 1)\t\t\t\t\n\t\t\t)\n\t\t),\n\t\tadjust2positive.definite = F,\n\t\tsilent=T\n\t) \t\t\n\t\n\t# scale results such that each cluster gets 25% weight\n\tobj$weights$DM = obj$weights$DM * 0.25\n\n\n\t\n\t#*****************************************************************\n\t# Sanity check\n\t#*****************************************************************\t\nif(T) {\n\trisk.groups = as.vector(risk.groups)\n\t\trisk.groups = ifna(risk.groups, 0)\n\t\n\ttest = matrix(0, nr=len(period.ends),nc=ncol(prices))\n\tfor(i in 1:nrow(test)) {\n\t\tfor(g in 1:4) {\n\t\t\tindex = risk.groups == g\n\t\t\ttest[i, index] = ntop.helper(mom[period.ends[i], index], 1) * 0.25 # there are 4 sectors, each one gets 1/4\n\t\t}\n\t}\n\t\n\trange( coredata(obj$weights$DM) - test )\n}\n\t\n\t\n\t#*****************************************************************\n\t# Absolute momentum logic: move reaming allocation to CASH, so that portfolio is fully invested\n\t#*****************************************************************\t\n\tfor(i in names(obj$weights))\n\t\tobj$weights[[i]]$CASH = obj$weights[[i]]$CASH + ( 1 - rowSums(obj$weights[[i]]) )\n\n\t\n\tmodels = create.strategies(obj, data )$models\n \n\n\tstrategy.performance.snapshoot(models, T)\n\n}\n\t\n\t\n\t", "meta": {"hexsha": "3ffee23547e9fe7c50ed319763d728604e321a17", "size": 364665, "ext": "r", "lang": "R", "max_stars_repo_path": "patterns.matching/SIT/bt.test.r", "max_stars_repo_name": "wisonhang/Shiny_report", "max_stars_repo_head_hexsha": "bed828a4c3d88f37ba1cd16f31354541693f64fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "patterns.matching/SIT/bt.test.r", "max_issues_repo_name": "wisonhang/Shiny_report", "max_issues_repo_head_hexsha": "bed828a4c3d88f37ba1cd16f31354541693f64fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "patterns.matching/SIT/bt.test.r", "max_forks_repo_name": "wisonhang/Shiny_report", "max_forks_repo_head_hexsha": "bed828a4c3d88f37ba1cd16f31354541693f64fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.9275862069, "max_line_length": 286, "alphanum_fraction": 0.5107591899, "num_tokens": 95856, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3167051318389201}} {"text": "#Simple manhattan plot of the Ods Ratio (OR) of assoc files\n#Will output a png plot \n#argv[1] Title\n#argv[2] Assoc file location\n\n#'qqman' package needs to be installed\n#(install.packages(\"qqman\")\nlibrary(qqman)\n\n#Able to read given arguments\n#args[1]:Title name for the plot\nargs <- commandArgs(trailing = TRUE)\n\ntitle <- args[1]\nlocation <- args[2]\n\n#Tell R that output should be .png \npng(paste(location, \".OR.png\", sep=\"\"), width = 900 , height = 900, res = 90)\n\n\n#Load in the table to\ntheTable <- read.table(location, header = TRUE) \n#Plot the table into (X=Chromosone possition, Y=log P value)\nmanhattan(theTable, p = \"OR\", logp =FALSE, ylab=\"Ods Ratio\", genomewideline = FALSE, suggestiveline = FALSE, main = title )\n", "meta": {"hexsha": "74d369494577c87d74fa03df3a196f478303aafb", "size": 726, "ext": "r", "lang": "R", "max_stars_repo_path": "rScripts/manORPlot.r", "max_stars_repo_name": "RunarReve/PredictICDwithHPO", "max_stars_repo_head_hexsha": "f9508db4da6f7afda495d8075b0e38ec19ec5d66", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "rScripts/manORPlot.r", "max_issues_repo_name": "RunarReve/PredictICDwithHPO", "max_issues_repo_head_hexsha": "f9508db4da6f7afda495d8075b0e38ec19ec5d66", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "rScripts/manORPlot.r", "max_forks_repo_name": "RunarReve/PredictICDwithHPO", "max_forks_repo_head_hexsha": "f9508db4da6f7afda495d8075b0e38ec19ec5d66", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.04, "max_line_length": 124, "alphanum_fraction": 0.7066115702, "num_tokens": 221, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3167051318389201}} {"text": "tryNA <- function(x){\n x <- try(x)\n if(inherits(x,'try-error')) return(NA)\n x\n}\n\npoLCA <-\n function (formula, data, nclass = 2, maxiter = 1000, graphs = FALSE,\n tol = 0.0000000001, na.rm = TRUE, probs.start = NULL, nrep = 1,\n verbose = TRUE, calc.se = TRUE,impVal=0)\n{\n nanProbs <- integer(0)\n starttime <- Sys.time()\n mframe <- model.frame(formula, data, na.action = NULL)\n mf <- model.response(mframe)\n if (any(mf < 1, na.rm = TRUE) | any(round(mf) != mf, na.rm = TRUE)) {\n cat(\"\\n ALERT: some manifest variables contain values that are not\\n positive integers. For poLCA to run, please recode categorical\\n outcome variables to increment from 1 to the maximum number of\\n outcome categories for each variable. \\n\\n\")\n ret <- NULL\n }\n else {\n data <- data[rowSums(is.na(model.matrix(formula, mframe))) ==\n 0, ]\n if (na.rm) {\n mframe <- model.frame(formula, data)\n y <- model.response(mframe)\n }\n else {\n mframe <- model.frame(formula, data, na.action = NULL)\n y <- model.response(mframe)\n }\n if (any(sapply(lapply(as.data.frame(y), table), length) ==\n 1)) {\n y <- y[, !(sapply(apply(y, 2, table), length) ==\n 1)]\n cat(\"\\n ALERT: at least one manifest variable contained only one\\n outcome category, and has been removed from the analysis. \\n\\n\")\n }\n y[is.na(y)] <- 0\n\n\n x <- model.matrix(formula, mframe)\n N <- nrow(y)\n J <- ncol(y)\n K.j <- t(matrix(apply(y, 2, max)))\n R <- nclass\n S <- ncol(x)\n if (S > 1) {\n calc.se <- TRUE\n }\n eflag <- FALSE\n probs.start.ok <- TRUE\n ret <- list()\n if (R == 1) {\n ret$probs <- list()\n for (j in 1:J) {\n ret$probs[[j]] <- matrix(NA, nrow = 1, ncol = K.j[j])\n for (k in 1:K.j[j]) {\n ret$probs[[j]][k] <- sum(y[, j] == k)/sum(y[,\n j] > 0)\n }\n }\n ret$probs.start <- ret$probs\n ret$P <- 1\n ret$posterior <- ret$predclass <- prior <- matrix(1,\n nrow = N, ncol = 1)\n ret$llik <- sum(log(poLCA:::poLCA.ylik.C(poLCA:::poLCA.vectorize(ret$probs),\n y)))\n if (calc.se) {\n se <- poLCA:::poLCA.se(y, x, ret$probs, prior, ret$posterior)\n ret$probs.se <- se$probs\n ret$P.se <- se$P\n }\n else {\n ret$probs.se <- NA\n ret$P.se <- NA\n }\n ret$numiter <- 1\n ret$probs.start.ok <- TRUE\n ret$coeff <- NA\n ret$coeff.se <- NA\n ret$coeff.V <- NA\n ret$eflag <- FALSE\n if (S > 1) {\n cat(\"\\n ALERT: covariates not allowed when nclass=1; will be ignored. \\n \\n\")\n S <- 1\n }\n }\n else {\n if (!is.null(probs.start)) {\n if ((length(probs.start) != J) | (!is.list(probs.start))) {\n probs.start.ok <- FALSE\n }\n else {\n if (sum(sapply(probs.start, dim)[1, ] == R) !=\n J)\n probs.start.ok <- FALSE\n if (sum(sapply(probs.start, dim)[2, ] == K.j) !=\n J)\n probs.start.ok <- FALSE\n if (sum(round(sapply(probs.start, rowSums),\n 4) == 1) != (R * J))\n probs.start.ok <- FALSE\n }\n }\n ret$llik <- -Inf\n ret$attempts <- NULL\n for (repl in 1:nrep) {\n error <- TRUE\n firstrun <- TRUE\n probs <- probs.init <- probs.start\n while (error) {\n error <- FALSE\n b <- rep(0, S * (R - 1))\n prior <- poLCA:::poLCA.updatePrior(b, x, R)\n if ((!probs.start.ok) | (is.null(probs.start)) |\n (!firstrun) | (repl > 1)) {\n probs <- list()\n for (j in 1:J) {\n probs[[j]] <- matrix(runif(R * K.j[j]),\n nrow = R, ncol = K.j[j])\n probs[[j]] <- probs[[j]]/rowSums(probs[[j]])\n }\n probs.init <- probs\n }\n vp <- poLCA:::poLCA.vectorize(probs)\n iter <- 1\n llik <- matrix(NA, nrow = maxiter, ncol = 1)\n llik[iter] <- -Inf\n dll <- Inf\n while ((iter <= maxiter) & (dll > tol) & (!error)) {\n iter <- iter + 1\n rgivy <- poLCA:::poLCA.postClass.C(prior, vp, y)\n vp$vecprobs <- poLCA:::poLCA.probHat.C(rgivy, y,\n vp)\n if (S > 1) {\n dd <- poLCA:::poLCA.dLL2dBeta.C(rgivy, prior, x)\n b <- b + ginv(-dd$hess) %*% dd$grad\n prior <- poLCA:::poLCA.updatePrior(b, x, R)\n }\n else {\n prior <- matrix(colMeans(rgivy), nrow = N,\n ncol = R, byrow = TRUE)\n }\n nanProbs <- which(is.nan(vp$vecprobs))\n if(length(nanProbs)){\n vp$vecprobs[nanProbs] <- impVal\n if(verbose) message(paste('Replacing NaN probs with',impVal))\n }\n llik[iter] <- tryNA(sum(log(rowSums(prior * poLCA:::poLCA.ylik.C(vp,\n y)))))\n dll <- llik[iter] - llik[iter - 1]\n if (is.na(dll)) {\n error <- TRUE\n }\n else if ((S > 1) & (dll < -0.0000001)) {\n error <- TRUE\n }\n }\n if (!error) {\n if (calc.se) {\n se <- poLCA:::poLCA.se(y, x, poLCA:::poLCA.unvectorize(vp),\n prior, rgivy)\n }\n else {\n se <- list(probs = NA, P = NA, b = NA,\n var.b = NA)\n }\n }\n else {\n eflag <- TRUE\n }\n firstrun <- FALSE\n }\n ret$attempts <- c(ret$attempts, llik[iter])\n #ret$fullllik <- llik\n if (llik[iter] > ret$llik) {\n ret$llik <- llik[iter]\n ret$probs.start <- probs.init\n ret$probs <- poLCA:::poLCA.unvectorize(vp)\n ret$probs.se <- se$probs\n ret$P.se <- se$P\n ret$posterior <- rgivy\n ret$predclass <- apply(ret$posterior, 1, which.max)\n ret$P <- colMeans(ret$posterior)\n ret$numiter <- iter - 1\n ret$probs.start.ok <- probs.start.ok\n if (S > 1) {\n b <- matrix(b, nrow = S)\n rownames(b) <- colnames(x)\n rownames(se$b) <- colnames(x)\n ret$coeff <- b\n ret$coeff.se <- se$b\n ret$coeff.V <- se$var.b\n }\n else {\n ret$coeff <- NA\n ret$coeff.se <- NA\n ret$coeff.V <- NA\n }\n ret$eflag <- eflag\n }\n if (nrep > 1 & verbose) {\n cat(\"Model \", repl, \": llik = \", llik[iter],\n \" ... best llik = \", ret$llik, \"\\n\", sep = \"\")\n flush.console()\n }\n }\n }\n names(ret$probs) <- colnames(y)\n if (calc.se) {\n names(ret$probs.se) <- colnames(y)\n }\n ret$npar <- (R * sum(K.j - 1)) + (R - 1)\n if (S > 1) {\n ret$npar <- ret$npar + (S * (R - 1)) - (R - 1)\n }\n ret$aic <- (-2 * ret$llik) + (2 * ret$npar)\n ret$bic <- (-2 * ret$llik) + (log(N) * ret$npar)\n ret$Nobs <- sum(rowSums(y == 0) == 0)\n if (all(rowSums(y == 0) > 0)) {\n ret$Chisq <- NA\n ret$Gsq <- NA\n ret$predcell <- NA\n }\n else {\n compy <- poLCA:::poLCA.compress(y[(rowSums(y == 0) == 0),\n ])\n datacell <- compy$datamat\n rownames(datacell) <- NULL\n freq <- compy$freq\n if (!na.rm) {\n fit <- matrix(ret$Nobs * (poLCA:::poLCA.ylik.C(poLCA:::poLCA.vectorize(ret$probs),\n datacell) %*% ret$P))\n ret$Chisq <- sum((freq - fit)^2/fit) + (ret$Nobs -\n sum(fit))\n }\n else {\n fit <- matrix(N * (poLCA:::poLCA.ylik.C(poLCA:::poLCA.vectorize(ret$probs),\n datacell) %*% ret$P))\n ret$Chisq <- sum((freq - fit)^2/fit) + (N - sum(fit))\n }\n ret$predcell <- data.frame(datacell, observed = freq,\n expected = fit)\n ret$Gsq <- 2 * sum(freq * log(freq/fit))\n }\n y[y == 0] <- NA\n ret$y <- data.frame(y)\n ret$x <- data.frame(x)\n\n if(length(nanProbs)){\n vp$vecprobs[nanProbs] <- NaN\n ret$probs <- poLCA:::poLCA.unvectorize(vp)\n names(ret$probs) <- colnames(y)\n }\n\n for (j in 1:J) {\n rownames(ret$probs[[j]]) <- paste(\"class \", 1:R,\n \": \", sep = \"\")\n if (is.factor(data[, match(colnames(y), colnames(data))[j]])) {\n lev <- levels(data[, match(colnames(y), colnames(data))[j]])\n colnames(ret$probs[[j]]) <- lev\n ret$y[, j] <- factor(ret$y[, j], labels = lev)\n }\n else {\n colnames(ret$probs[[j]]) <- paste(\"Pr(\", 1:ncol(ret$probs[[j]]),\n \")\", sep = \"\")\n }\n }\n ret$N <- N\n ret$maxiter <- maxiter\n ret$resid.df <- min(ret$N, (prod(K.j) - 1)) - ret$npar\n class(ret) <- \"poLCA\"\n if (graphs)\n plot.poLCA(ret)\n if (verbose)\n poLCA:::print.poLCA(ret)\n ret$time <- Sys.time() - starttime\n\n }\n ret$call <- match.call()\n return(ret)\n}\n\n### automatically orders the classes by prevalence\npoLCAord <-\n function (formula, data, nclass = 2, maxiter = 1000, graphs = FALSE,\n tol = 0.0000000001, na.rm = TRUE, probs.start = NULL, nrep = 1,\n verbose = TRUE, calc.se = TRUE,ordVar=NULL){\n\n mm <- match.call()\n mm <- as.list(mm)\n mm[[1]] <- NULL\n mm$ordVar <- NULL\n mod <- do.call(\"poLCA\",mm)\n\n pp <- if(!is.null(ordVar)) mod$probs[[ordVar]][,1] else mod$P\n if(length(pp)>length(unique(pp))){\n warning(paste0(\"Ties in \", ordVar,\" ordering by prevalence\"))\n pp <- mod$P\n }\n ord <- order(pp,decreasing=TRUE)\n\n probs.start.new <- poLCA::poLCA.reorder(mod$probs.start,ord)\n\n mm$probs.start <- probs.start.new\n mm$nrep <- 1\n\n do.call(\"poLCA\",mm)\n }\n\n", "meta": {"hexsha": "bc518db7b152f10d3e52cb98ec59ff396c470c4e", "size": 11429, "ext": "r", "lang": "R", "max_stars_repo_path": "poLCA.r", "max_stars_repo_name": "adamSales/lcaCode", "max_stars_repo_head_hexsha": "9b618f79ded724e8cc5faa8ab50a400d9e8f8058", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "poLCA.r", "max_issues_repo_name": "adamSales/lcaCode", "max_issues_repo_head_hexsha": "9b618f79ded724e8cc5faa8ab50a400d9e8f8058", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "poLCA.r", "max_forks_repo_name": "adamSales/lcaCode", "max_forks_repo_head_hexsha": "9b618f79ded724e8cc5faa8ab50a400d9e8f8058", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8677419355, "max_line_length": 260, "alphanum_fraction": 0.4004724823, "num_tokens": 3057, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.63341026367784, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31670513183892}} {"text": "# fix problem of raw length changing analysis\n\nbooks <- books %>%\n mutate(bookLength = nchar(text))\n\nwords <- books %>%\n unnest_tokens(word, text) \n\nwords %>%\n filter(word %in% sentiments$word) %>%\n count(title, author, bookLength) %>%\n mutate(EmoRatio = n / bookLength) %>%\n arrange(desc(EmoRatio)) %>%\n View()\n\n# analyze happiest books\n\nhappy_books <- words %>%\n filter(word %in% sentiments$word) %>%\n count(title, author, bookLength) %>%\n mutate(EmoRatio = n / bookLength) %>%\n arrange(EmoRatio) %>%\n slice(1:9)\n\nggplot(books_sentiment %>% filter(title %in% happy_books$title), \n aes(index, sentiment, fill = title)) +\n geom_bar(stat = \"identity\", show.legend = FALSE) +\n facet_wrap(~title, ncol = 3, scales = \"free_x\")", "meta": {"hexsha": "769f47743bfbab4a9897c7998183551b7e2b0fa9", "size": 742, "ext": "r", "lang": "R", "max_stars_repo_path": "labs/lab-solutions.r", "max_stars_repo_name": "rccordell/f19tot", "max_stars_repo_head_hexsha": "7886824b482c43eeb5680bc1fd5c91f13376a8f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "labs/lab-solutions.r", "max_issues_repo_name": "rccordell/f19tot", "max_issues_repo_head_hexsha": "7886824b482c43eeb5680bc1fd5c91f13376a8f8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "labs/lab-solutions.r", "max_forks_repo_name": "rccordell/f19tot", "max_forks_repo_head_hexsha": "7886824b482c43eeb5680bc1fd5c91f13376a8f8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.5, "max_line_length": 65, "alphanum_fraction": 0.6549865229, "num_tokens": 215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.63341024983754, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31670512491877}} {"text": "checkPangram <- function(sentence){\n my.letters <- tolower(unlist(strsplit(sentence, \"\")))\n is.pangram <- all(letters %in% my.letters)\n\n if (is.pangram){\n cat(\"\\\"\", sentence, \"\\\" is a pangram! \\n\", sep=\"\")\n } else {\n cat(\"\\\"\", sentence, \"\\\" is not a pangram! \\n\", sep=\"\")\n }\n}\n", "meta": {"hexsha": "1b4bada6d31674af12a7fcada8810f07bf169644", "size": 288, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Pangram-checker/R/pangram-checker.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Pangram-checker/R/pangram-checker.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Pangram-checker/R/pangram-checker.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 26.1818181818, "max_line_length": 58, "alphanum_fraction": 0.5694444444, "num_tokens": 89, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3166402226127737}} {"text": "########################################################\n##### Author: Diego Valle Jones\n##### Website: www.diegovalle.net\n##### Date Created: Thu Feb 04 13:35:41 2010\n########################################################\n#Shared functions\n\n#Group dates into intervals\ncutDates <- function(df, dates, hack = 0) {\n DateMid <- as.Date(format(df$Date, \"%Y%m15\"),\n \"%Y%m%d\") + hack\n vec <- c(DateMid[1], dates, DateMid[length(DateMid)] + 1000)\n as.numeric(as.factor(cut(DateMid, vec)))\n}\n\n#Get rid of the full name of the states (eg: Veracruz de\n#Ignacio de la Llave changes to Veracruz\ncleanNames <- function(df, varname = \"County\"){\n df[[varname]] <- gsub(\"* de .*\",\"\", df[[varname]])\n df[[varname]]\n}\n\nmonthSeq <- function(st, len){\n #start <- as.Date(st)\n #next.mon <- seq(start, length = len, by='1 month')\n #next.mon - 1\n seq(as.Date(st), length = len, by='1 month')\n}\n\nmonthlyPop <- function() {\n pop <- read.csv(\"conapo-pop-estimates/conapo-states.csv\")\n pop2 <- data.frame(year = rep(1990:2008, each = 12),\n month = rep(1:12))\n pop2$Monthly.Pop[pop2$month == 6] <- unlist(pop[33,2:ncol(pop)])\n pop2$Monthly <- na.spline(pop2$Monthly.Pop, na.rm=FALSE)\n pop2\n}\n\naddHom <- function(df, pop) {\n hom.st <- ddply(df, .(Month.of.Murder, Year.of.Murder),\n function(df) sum(df$Total.Murders))\n hom.st <- hom.st[order(hom.st$Year.of.Murder,\n hom.st$Month.of.Murder),]\n pop$murders <- hom.st$V1\n pop$rate <- (pop$murders / pop$Monthly) * 100000 * 12\n start <- as.Date(\"1990/01/15\")\n next.mon <- seq(start, length = 12*19, by='1 month')\n period <- next.mon - 1\n pop$date <- period\n pop\n}\n\naddTrend <- function(df){\n hom.ts <- ts(df$rate, start=1990, freq = 12)\n hom.stl <- stl(hom.ts, \"per\")\n cbind(df, data.frame(hom.stl$time.series))\n}\n\ncleanHom <- function(df) {\n df <- subset(df, Code == \"#NAME?\" &\n Year.of.Murder != \"Total\" &\n Year.of.Murder != \"No especificado\" &\n Month.of.Murder != \"Total\" &\n Month.of.Murder != \"No especificado\" &\n County != \"Extranjero\"\n )\n df$Year.of.Murder <- as.numeric(gsub('[[:alpha:]]', '',\n df$Year.of.Murder))\n df <- subset(df, Year.of.Murder >= 1990)\n df$County <- iconv(df$County, \"windows-1252\", \"utf-8\")\n col2cvt <- 5:ncol(df)\n df[is.na(df)] <- 0\n df$Total.Murders <- apply(df[ , col2cvt], 1, sum)\n df$Month.of.Murder <- factor(df$Month.of.Murder)\n #The months are in a weird order, so 04=Abril, etc.\n levels(df$Month.of.Murder) <- c(\"04\",\"08\",\"12\",\"01\",\"02\",\"07\",\"06\",\"03\",\"05\",\"11\",\"10\",\"09\")\n df\n}\n\naddMonths <- function(df){\n states <- unique(factor(df$County))\n start <- as.Date(\"1990/1/15\")\n next.mon <- seq(start, length=12*19, by='1 month')\n period <- next.mon\n dates.df <- data.frame(Date = factor(rep(period,\n each = 32)),\n County = states)\n dates <- strptime(as.character(dates.df$Date), \"%Y-%m-%d\")\n dates.df$Month.of.Murder <- dates$mon + 1\n dates.df$Year.of.Murder <- dates$year + 1900\n df$Month.of.Murder <- as.numeric(as.character(df$Month.of.Murder))\n df <- merge(dates.df, df,\n by = c(\"Month.of.Murder\",\n \"Year.of.Murder\", \"County\"),\n all.x = TRUE)\n df[is.na(df)] <- 0\n df\n}\n", "meta": {"hexsha": "b242e106b14db18ca2b6a87a833d0296800b612d", "size": 3423, "ext": "r", "lang": "R", "max_stars_repo_path": "library/utilities.r", "max_stars_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_stars_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2015-05-14T01:06:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-16T12:52:10.000Z", "max_issues_repo_path": "library/utilities.r", "max_issues_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_issues_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "library/utilities.r", "max_forks_repo_name": "diegovalle/Homicide-MX-Drug-War", "max_forks_repo_head_hexsha": "6b1a5257420c4d444324672503c03237c4fca543", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2015-02-05T15:09:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-10T02:19:40.000Z", "avg_line_length": 34.5757575758, "max_line_length": 94, "alphanum_fraction": 0.5404615834, "num_tokens": 1051, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.61878043374385, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.31664022261277364}} {"text": "steadman_outdoor_index <- function(t, rh, wind, rshort, sunelev) {\n .Call('biometeoR_steadman_outdoor_sun', PACKAGE = 'biometeoR', t, rh, wind, rshort, sunelev)\n}\n", "meta": {"hexsha": "e9e86805233ddc98d16f7618161f1e656d564e70", "size": 166, "ext": "r", "lang": "R", "max_stars_repo_path": "R/steadman_outdoor_index.r", "max_stars_repo_name": "alfcrisci/biometeoR", "max_stars_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:46.000Z", "max_issues_repo_path": "R/steadman_outdoor_index.r", "max_issues_repo_name": "alfcrisci/biometeoR", "max_issues_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/steadman_outdoor_index.r", "max_forks_repo_name": "alfcrisci/biometeoR", "max_forks_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.5, "max_line_length": 96, "alphanum_fraction": 0.7228915663, "num_tokens": 58, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.31664021541792986}} {"text": "\n#' Simulate a dyad\n#' @param rates Numeric vector. Vector of rates\n#' @param mimicy Numeric vector. proportion of mimicry (likelihood).\n#' @param last.bite Numeric scalar. Time of last bite.\n#' @export\nsimulate_dyad <- function(\n rates = c(10, 15),\n mimicy = c(.1, .5),\n last.bite = stats::runif(1, 1, 2)\n) {\n\n structure(\n .simulate_dyad(rates, mimicy, last.bite),\n class = \"bite_dyad\"\n )\n}\n\n#' Plotting method\n#' @param x An object of class `bite_dyad`\n#' @param y Ignored.\n#' @param ... Ignored.\n#' @export\nplot.bite_dyad <- function(x, y = NULL, ...) {\n\n # Computing time range\n op <- graphics::par(mar = rep(0, 4))\n on.exit(graphics::par(op))\n ran <- range(x[[1]], x[[2]])\n\n graphics::plot.new()\n graphics::plot.window(xlim = ran, ylim = c(.5, 2.5))\n\n xstart <- diff(ran)*.1+ran[1]\n\n\n\n cols <- c(\"steelblue\", \"tomato\")\n for (i in 1:2) {\n graphics::text(x=xstart, y = i + .1, labels = paste(\"Subject\", i))\n\n for (k in 1:length(x[[i]]))\n graphics::text(\n x = x[[i]][k],\n y = i,\n labels = substitute(t[i]^j, list(i=i, j=k)),\n pos = 1, offset = (k %% 2)*.75 + 1\n )\n\n lines(\n y = rep.int(i, length(x[[i]])), x = x[[i]], col=cols[i],\n type = \"b\",\n lwd=2\n )\n }\n\n\n#\n#\n# text(x=xstart, y = 2.1, labels = \"Subject 2\")\n# lines(y = rep.int(2, length(x[[2]])), x = x[[2]], col=\"tomato\", type = \"b\",\n# lwd=2)\n\n}\n", "meta": {"hexsha": "e736c6100ed25a93fdf9cf90cb396c6e4f0865b0", "size": 1412, "ext": "r", "lang": "R", "max_stars_repo_path": "R/methods.r", "max_stars_repo_name": "gvegayon/biteme", "max_stars_repo_head_hexsha": "e7e2264a69bb3d9173305a0c8ca904be90bdf8ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/methods.r", "max_issues_repo_name": "gvegayon/biteme", "max_issues_repo_head_hexsha": "e7e2264a69bb3d9173305a0c8ca904be90bdf8ba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/methods.r", "max_forks_repo_name": "gvegayon/biteme", "max_forks_repo_head_hexsha": "e7e2264a69bb3d9173305a0c8ca904be90bdf8ba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.7230769231, "max_line_length": 79, "alphanum_fraction": 0.5361189802, "num_tokens": 488, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.31664021541792986}} {"text": "# context(\"Checking data-management methods\")\n\ndata(fakeexperiment)\ndata(faketree)\n\n# As phylo methods -------------------------------------------------------------\n# test_that(\"As phylo conversion and methods\", {\n \n tree <- as.phylo(faketree)\n ans <- new_aphylo(tip.annotation = fakeexperiment[,-1], tree = tree)\n \n expect_silent(print(ans))\n expect_equal(class(as.phylo(ans)), \"phylo\")\n expect_true(is.null(plot(ans)))\n expect_silent(summary(as.phylo(ans)))\n expect_silent(summary(raphylo(10, P=4)))\n# })\n\n# Conversion -------------------------------------------------------------------\n# test_that(\"Can return to the original labeling\", {\n ans0 <- faketree\n phylo <- as.phylo(ans0)\n ans1 <- phylo$edge\n \n \n ans1[] <- with(phylo, c(tip.label, node.label))[ans1[]]\n \n expect_equivalent(ans0, ans1)\n# })\n\n\n", "meta": {"hexsha": "fcdc88251581f43612e8466e5524ae1dbbb50c2b", "size": 825, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/tinytest/test-data-management.r", "max_stars_repo_name": "gvegayon/phylogenetic", "max_stars_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/tinytest/test-data-management.r", "max_issues_repo_name": "gvegayon/phylogenetic", "max_issues_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2017-01-19T19:52:09.000Z", "max_issues_repo_issues_event_max_datetime": "2017-03-29T23:34:21.000Z", "max_forks_repo_path": "inst/tinytest/test-data-management.r", "max_forks_repo_name": "USCbiostats/phylogenetic", "max_forks_repo_head_hexsha": "6cf77f34e7313060327176069e3ad08e6866b827", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.78125, "max_line_length": 80, "alphanum_fraction": 0.5721212121, "num_tokens": 211, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795672, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.316489238977978}} {"text": "##############################################################################\n# Negbin model processing functions\n##############################################################################\n\n#' Run negbin model for each formula specified in formlist\n#' @param formlist List of formula objects\n#' @param data Data on which you wish to run the model(s)\n#' @return List of model objects returned by glm.nb\n#' @importFrom MASS glm.nb\n#' @export\nrunMod <- function(formlist, data){\n\tout <- lapply(formlist, function(x) glm.nb(x, data))\n\tnames(out) <- names(formlist)\n\treturn(out)\n}\n\n#' Build list of fitted values (with CHR/BIN names) for each model\n#' @param modlist list containing model objects\n#' @param data Data to be fitted\n#' @importFrom stats fitted.values\n#' @return list of fitted values\n#' @export\ngetFits <- function(modlist, data){\n\tout <- lapply(modlist,\n\t\tfunction(x){\n\t\t\ty <- fitted.values(x)\n\t\t\tnames(y) <- paste0(data$CHR, \".\", data$BIN)\n\t\t\ty\n\t\t})\n\tnames(out) <- names(modlist)\n\treturn(out)\n}\n\n#' Build list of dataframes for each model\n#' @param fitlist list containing fitted values\n#' @param data Data to be fitted\n#' @return list of fitted values\n#' @export\nbuildDF <- function(fitlist, data){\n\tout <- lapply(fitlist,\n\t\tfunction(x){\n\t\t\tdata.frame(CHR, Category2=cat1, BIN,\n\t\t\t\texp=x,\n\t\t\t\tobs=data$obs,\n\t\t\t\t# res=names(fitlist),\n\t\t\t\tstringsAsFactors=F)\n\t\t}\n\t)\n\tnames(out) <- names(fitlist)\n\treturn(out)\n}\n\n#' Compute standard error for correlations\n#' @param corval correlation\n#' @param ct count?\n#' @return list of fitted values\n#' @export\ncorSE <- function(corval, ct){\n\tsqrt((1-corval^2)/(ct-2))\n}\n", "meta": {"hexsha": "80ef6c827a9cc34242b88d35b5e6b90f30af6728", "size": 1623, "ext": "r", "lang": "R", "max_stars_repo_path": "R/negbin.r", "max_stars_repo_name": "theandyb/smaug", "max_stars_repo_head_hexsha": "842984cdc04de87b1b04c62145a3b9f650ebc300", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/negbin.r", "max_issues_repo_name": "theandyb/smaug", "max_issues_repo_head_hexsha": "842984cdc04de87b1b04c62145a3b9f650ebc300", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/negbin.r", "max_forks_repo_name": "theandyb/smaug", "max_forks_repo_head_hexsha": "842984cdc04de87b1b04c62145a3b9f650ebc300", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.606557377, "max_line_length": 78, "alphanum_fraction": 0.6216882317, "num_tokens": 408, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3164892389779779}} {"text": "library(ggplot2)\n\nload_results <- function(path= \"./synthetic/\"){\n \n if(!dir.exists(\"./synthetic\")){\n stop(\"The results folder was not found! The raw paper results are available at https://www.dropbox.com/sh/xurs5z6z3uzj6vs/AAC8fzABOvkFaWF9sZFuS57Ua?dl=0\")\n }\n \n vf <- list.files(path)\n all<- NULL\n for(i in vf){\n print(i)\n x <- readRDS(paste0(path,i))\n all <- rbind(all, x)\n \n }\n return(all)\n}\n\n#+++++++++++++++++++++++++++++++ Plot the error when MTe vary for a given MTr ++++++++++++++++++++++++++++++++++\n# Used to get the plots of Figure 3\nplot_synthetic <- function(MTr){\n \n all <- readRDS(\"./all_syn_results_icdm21.rds\")\n #all <- load_results(\"./synthetic/\")\n \n vcol <- c(\"dodgerblue2\",\n \"lightsalmon2\",\n \"lightsteelblue\",\n \"plum3\",\n \"pink3\",\n \"olivedrab3\",\n \"navajowhite2\",\n \"lightyellow4\",\n \"indianred\",\n \"green3\",\"#597DBE\", \"#76BF72\", \"#AF85BE\", \"#C76E6E\"\n )\n aux2 <- all[all$Qnt%in%c(\"MS\", \"DyS-TS\", \"PACC\", \"HDy-LP\", \"X\", \"MAX\", \"T50\", \"SORD\", \"ACC\", \"CC\", \"PCC\", \"MS2\", \"SMM\"),]\n aux2 <- aux2[aux2$MFtr==MTr,]\n aux2 <- aggregate(as.numeric(as.vector(aux2$MAE)), by=list(aux2$Qnt, aux2$MFte), FUN=mean)\n \n names(aux2) <- c(\"Quantifier\", \"MFte\", \"MAE\")\n a1 <- aux2[,-2]\n qn <- unique(a1$Quantifier)\n at <- NULL\n for(qi in qn){\n at <- cbind(at, a1[a1$Quantifier==qi,2]) \n }\n at <- apply(at, 2, as.numeric)\n at <- as.matrix(at)\n colnames(at) <- qn\n \n p1 <- ggplot(data = aux2, aes(x=MFte,y=MAE, group= Quantifier, linetype=Quantifier))+\n geom_vline(xintercept = MTr*20, linetype=\"dashed\", \n color = \"black\", size=3.5) +\n geom_line(size=5, aes(color=Quantifier), alpha=0.7)+\n geom_point(size=7, aes(shape=Quantifier, color=Quantifier), stroke=2)+\n scale_shape_manual(values=c(0:14))+\n scale_color_manual(values=vcol)+\n scale_x_discrete(breaks = seq(0.05,0.95, length.out = 10))+\n ylab(\"MAE\") +\n xlab(\"MTe\") +\n guides(col = guide_legend(nrow = 5, byrow = TRUE))+\n theme_bw()+\n theme(\n plot.background = element_blank()\n ,text = element_text(size=50)\n ,legend.position = \"top\"\n ,legend.title = element_blank()\n ,panel.border = element_rect(size = 1.5)\n ,axis.line.x = element_line(size=1)\n ,axis.ticks = element_line(size = 1),\n plot.margin = unit(c(.5,0.1,0.1,0.1), \"cm\")\n )\n #draws x and y axis line\n theme(axis.line = element_line(color = 'black'))\n print(p1)\n return(p1)\n}\n\n\n\nsetwd(dirname(parent.frame(2)$ofile))\n#uncomment the next lines to get the plots presented in Figure 3\n#plot_synthetic(0.05)\n#plot_synthetic(0.25)\n#plot_synthetic(0.5)\n#plot_synthetic(0.75)\n\n\n", "meta": {"hexsha": "a1a696880f1b21c16cfc159d3fc5413e3735b82b", "size": 2726, "ext": "r", "lang": "R", "max_stars_repo_path": "DySyn_synthetic/show_results_synthetic.r", "max_stars_repo_name": "andregustavom/icdm21_paper", "max_stars_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DySyn_synthetic/show_results_synthetic.r", "max_issues_repo_name": "andregustavom/icdm21_paper", "max_issues_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "DySyn_synthetic/show_results_synthetic.r", "max_forks_repo_name": "andregustavom/icdm21_paper", "max_forks_repo_head_hexsha": "ee4f5247ae6574ab69f5a29134846d50d9e305b8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.956043956, "max_line_length": 158, "alphanum_fraction": 0.5865737344, "num_tokens": 908, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3164892389779779}} {"text": "plotdfall <- function(df, xx, size=0.01, type='b', legendloc='topleft') {\n ## plots every parameter in df as a function of x on single plot\n ## df = dataframe\n ## xx = x-axis variable to be specified in quotes\n ## size = datapoint size (0.01 default to not see points but still get line color)\n ## type = 'p' for points\n ## = 'l' for lines <-- creates a single line so only black\n ## = 'b' for both <-- same as above\n \n ## determine location of xx in the dataframe\n xxcol <- which(grepl(xx, names(df))) ## xx column\n xx1 <- df[, xxcol] ## xx values\n\n ## first sort the data by xx in case want lines\n df <- df[order(df[xxcol]),]\n\n ## use melt to create new df with columns: index, series, and value\n ## where every column of old df is now in column value with the\n ## name of the column in series\n df <- reshape2::melt(df, id.vars=xx, variable.name='series')\n\n ## need to plot points and not just line to get color using df$series\n ## do not actually want points, so plotting them small with cex=0.01\n plot(df[1], df$value, col=df$series,\n cex=size, lty=1, type=type,\n xlab=xx, ylab='value')\n grid(col='grey70')\n legend(legendloc,\n legend = unique(df$series),\n col = 1:length(df$series),\n lty = 1)\n}\n\n#plotdfall(mtcars, 'mpg', legendloc='topright', type='b')\n# a <- seq(10,1,-1)\n# b <- seq(21,30)\n# c <- seq(41,50)\n# df <- data.frame(a,b,c)\n# plotdfall(df, 'a')\n\n\n\n#d <- rep('type1', 5)\n#d2 <- rep('type2', 5)\n#d <- c(d, d2)\n#df <- data.frame(a,b,c,d)\n\n#plotspace(1,2)\n#plotdfall(mtcars, 'mpg', legendloc='topright', type='b')\n#df <- select(mtcars, mpg, hp)\n#plotdfall(df, 'mpg', legendloc='topright', type='b')\n\n## out <- equityget(c('SPY', 'IWM', 'EFA', 'AGG', 'SHV'), from='1995-01-01', period='years')\n## twr <- out$twr\n## twrdf <- as.data.frame(twr)\n## twrdf$date <- as.Date( rownames(twrdf) )\n## plotdfall(twrdf, 'date', size=1)\n", "meta": {"hexsha": "773da1ecf147134404e4673b55926b6e59b9868e", "size": 2013, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/plotdfall.r", "max_stars_repo_name": "dhjelmar/Retirement", "max_stars_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/plotdfall.r", "max_issues_repo_name": "dhjelmar/Retirement", "max_issues_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/plotdfall.r", "max_forks_repo_name": "dhjelmar/Retirement", "max_forks_repo_head_hexsha": "2f844025e72d89c241aac5a6bd14780c48bb8dbb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7068965517, "max_line_length": 92, "alphanum_fraction": 0.5846994536, "num_tokens": 641, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3164892389779779}} {"text": "##############################\n# CREATION OF PIE CHARTS FOR INVERSION FREQUENCIES\n##############################\n\n###########################\n#df <- data.frame(status = c(\"N/N\", \"N/I\", \"I/I\"), freq = c(34, 47.9, 17.1))\n#bp <- ggplot(df, aes(x=\"\", y=freq, fill=status)) + geom_bar(width = 1, stat = \"identity\")\n#pie <- bp + coord_polar(\"y\", start=0)\n\n#pie + geom_text(aes(y = freq/3 + c(0, cumsum(freq)[-length(freq)]),label = paste(status,percent(freq/100), sep = \": \")), size=6) + \n# ggtitle(\"inv8_001 - EUR\") + \n# theme_minimal() +\n# theme(legend.position=\"none\", axis.title.x=element_blank(), axis.title.y=element_blank()) +\n# theme(plot.title = element_text(size = 20, face=\"bold\"))\n\n###########################\n\nlibrary(plotrix)\nlibrary(maps)\nlibrary(stringr)\nlibrary(lattice)\n\nsetwd(\"/scratch/itolosana/Rdata/\")\nload(file = \"Allpop_invstatus.Rdata\")\n\n# Select data for an inversion (one plot per inversion)\ninv8 <- invstatus_allpop[,c(1:2, 3)] # need to change this, to better select columns\ninv8$N <- str_count(inv8$inv8_001, \"N\")\ninv8$I <- str_count(inv8$inv8_001, \"I\")\n\n#groups <- unique(inv8$pop)\n############################################ CAMBIAR SUPERPOP DE VIETNAM PQ SON EAS NO SAS\ngroups <- c(\"GBR\", \"FIN\", \"CHS\", \"PUR\", \"CDX\", \"CLM\", \"IBS\", \"PEL\", \"PJL\", \"KHV\", \"ACB\",\n \"GWD\", \"ESN\", \"BEB\", \"MSL\", \"STU\", \"ITU\", \"CEU\", \"YRI\", \"CHB\", \"JPT\", \"LWK\",\n \"ASW\", \"MXL\", \"TSI\", \"GIH\") \n\nlat <- c(55, 60, 33.5, 20, 23, 4, 40, -13, 38, 10, 13, \n 19, 4, 27, 7, 5, 18, 48, 18, 44, 35.6, -4, \n 36, 19.4, 40, 26.5)\nlon <- c(-7, 24.5, 113, -71, 104, -73, -6, -76, 75, 106.8, -57.6, \n -15.3, 10, 90.4, -7, 82, 79, 4.5, 15, 121, 139.7, 39.7, \n -97, -99, 15.5, 69)\n\n\nleglat <- c(60, 60, 28, 29.5, 19, 4, 40, -13, 45, -1, 6, \n 19, -7, 36, -1, -5, 14, 57, 18, 53, 35.6, -15, \n 46, 19.4, 40, 28)\nleglon <- c(-19, 38, 125, -76, 117.5, -87, -19, -88, 65, 112, -46, \n -30, 15, 93, -17, 86, 67, 10, 30, 130, 153, 39.7, \n -103, -114, 29, 57)\n\n\nNfreq <- lapply(groups, function(x){\n Ntot <- sum(inv8[inv8$pop==x,]$N)\n Itot <- sum(inv8[inv8$pop==x,]$I)\n freq <- Ntot/(Ntot+Itot)\n freq\n})\n\nNfreq <- as.numeric(Nfreq)\ndf <- data.frame(groups, lat, lon, leglat, leglon, Nfreq)\n\nlayout(matrix(1:2, nrow=2, ncol=2), widths = c(1,0.5),\n heights = c(3, 1), respect = FALSE)\npar(mar=c(3,4.5,2,4.5))\n\n\nmap(\"world\", col=\"grey90\", border=0, fill=TRUE)\n\nlapply(unique(inv8[inv8$superpop==\"AFR\",]$pop), function(x) {floating.pie(df[groups==x,3],df[groups==x, 2],c((100*(df[groups==x, 6])+0.0001),100-(100*df[groups==x, 6])),r=6,\n col=c(\"goldenrod3\",\"yellow1\")) \n text(df[groups==x,5],df[groups==x,4], labels = x, font = 2, cex=0.7,lwd=1)})\nlapply(unique(inv8[inv8$superpop==\"AMR\",]$pop), function(x) {floating.pie(df[groups==x,3],df[groups==x, 2],c((100*(df[groups==x, 6])+0.0001),100-(100*df[groups==x, 6])),r=6,\n col=c(\"darkred\",\"brown1\"))\n text(df[groups==x,5],df[groups==x,4], labels = x, font = 2, cex=0.7,lwd=1)})\nlapply(unique(inv8[inv8$superpop==\"EAS\",]$pop), function(x) {floating.pie(df[groups==x,3],df[groups==x, 2],c((100*(df[groups==x, 6])+0.0001),100-(100*df[groups==x, 6])),r=6,\n col=c(\"darkgreen\",\"chartreuse2\"))\n text(df[groups==x,5],df[groups==x,4], labels = x, font = 2, cex=0.7,lwd=1)})\nlapply(unique(inv8[inv8$superpop==\"EUR\",]$pop), function(x) {floating.pie(df[groups==x,3],df[groups==x, 2],c((100*(df[groups==x, 6])+0.0001),100-(100*df[groups==x, 6])),r=6,\n col=c(\"dodgerblue4\",\"darkslategray2\"))\n text(df[groups==x,5],df[groups==x,4], labels = x, font = 2, cex=0.7,lwd=1)})\nlapply(unique(inv8[inv8$superpop==\"SAS\",]$pop), function(x) {floating.pie(df[groups==x,3],df[groups==x, 2],c((100*(df[groups==x, 6])+0.0001),100-(100*df[groups==x, 6])),r=6,\n col=c(\"purple4\",\"plum\"))\n text(df[groups==x,5],df[groups==x,4], labels = x, font = 2, cex=0.7,lwd=1)})\n\n\npar(xpd=TRUE)\n\npos <- legend(-46, -80, legend=c(\"Frequency of NI\",\"Frequency of I\"), bty = \"n\", cex = 1.3)\n\npoints(x=rep(pos$text$x, times=2) - c(7,7), \n y=rep(pos$text$y, times=2), \n pch=rep(21, times=2), bg = rep(c(\"goldenrod3\",\"yellow1\"), times=2), col=rep(\"black\", times=2), cex = 1.5)\npoints(x=rep(pos$text$x, times=2) - c(17,17), \n y=rep(pos$text$y, times=2), \n pch=rep(21, times=2), bg=rep(c(\"darkred\",\"brown1\"), times=2), col=rep(\"black\", times=2), cex = 1.5)\npoints(x=rep(pos$text$x, times=2) - c(27,27), \n y=rep(pos$text$y, times=2), \n pch=rep(21, times=2), bg=rep(c(\"darkgreen\",\"chartreuse2\"), times=2), col=rep(\"black\", times=2), cex = 1.5)\npoints(x=rep(pos$text$x, times=2) - c(37,37), \n y=rep(pos$text$y, times=2), \n pch=rep(21, times=2), bg=rep(c(\"dodgerblue4\",\"darkslategray2\"), times=2), col=rep(\"black\", times=2), cex = 1.5)\npoints(x=rep(pos$text$x, times=2) - c(47,47), \n y=rep(pos$text$y, times=2), \n pch=rep(21, times=2), bg=rep(c(\"purple4\",\"plum\"), times=2), col=rep(\"black\", times=2), cex = 1.5)\n\n\ntitle(main=\"Inversion frequencies: inv8_001\", cex.main = 2, font.main= 2)\n\n\n\n\n\ndev.copy(pdf,'inv8_freq.pdf')\ndev.off()\n\nfloating.pie(108.9,19.2,c(Nfreq*100,100-(Nfreq*100)),r=5,col=c(\"blue\",\"lightsteelblue2\"))\ntext(-24,56,expression(bold(\"FIN\")),cex=0.8,lwd=1)\n\n\n\n\n\n\n\n\n\n\n\n\n\npar(xpd=TRUE)\n\ntext(-180,120, \"a)\", cex=2, font=2)\ntext(-180,-120, \"b)\", cex=2, font=2)\n\nlegend(120, 120, c(\"Frequency of NI\", \"Frequency of I\"), col=c(\"blue\",\"lightsteelblue2\"), pch=16)\n\n\n\ndat <- as.data.frame.matrix(tt)\nres$li[\"MKK\", \"Axis1\"] <- 0.45\nres$li[\"LWK\", \"Axis2\"] <- -0.1\nres$li[\"CHD\", \"Axis2\"] <- 0.1\nres$li[\"CHD\", \"Axis1\"] <- 0.125\n\ns.label(res$li, clab = 1.4, lab = rownames(dat), xlim=c(-1,0.5), ylim=c(-0.4, 0.4))\npar(xpd=TRUE)\nres$co[\"X2\",\"Comp1\"] <- 0.18 # asthetical purposes\nres$co[\"X2\",\"Comp2\"] <- 0.05 # asthetical purposes\nfor (i in 1:nrow(res$li))\n{\n text(res$co[i,1], res$co[i,2], ll[i], cex=2, col=\"red\")\n}", "meta": {"hexsha": "f9ea310de455a2d3479d148dce14e9a62261cac2", "size": 6526, "ext": "r", "lang": "R", "max_stars_repo_path": "impute_regions/inversion_frequencies/piecharts_invfreq.r", "max_stars_repo_name": "isglobal-brge/brgeUtils", "max_stars_repo_head_hexsha": "cd93c6afb5fa5f71b2da07b8d5217be2c01eb24b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "impute_regions/inversion_frequencies/piecharts_invfreq.r", "max_issues_repo_name": "isglobal-brge/brgeUtils", "max_issues_repo_head_hexsha": "cd93c6afb5fa5f71b2da07b8d5217be2c01eb24b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "impute_regions/inversion_frequencies/piecharts_invfreq.r", "max_forks_repo_name": "isglobal-brge/brgeUtils", "max_forks_repo_head_hexsha": "cd93c6afb5fa5f71b2da07b8d5217be2c01eb24b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.3766233766, "max_line_length": 173, "alphanum_fraction": 0.5111860251, "num_tokens": 2460, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3164892314358396}} {"text": "#' dewpoint\n#'\n#' Computes the dewpoint temperature in degC following different computational schemes defined by formula of saturation pressure estimation\n#' (\"NOAA\",\"Sonntag\",\"Paroscientific\").\n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Air Relative humidity in percentage.\n#' @param formula character Default is \"NOAA\". \n#' @return dewpoint\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' \n#' @export\n#'\n#'\n#'\n#'\n\ndewpoint=function(t,rh,formula=\"NOAA\") {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n if ( length(formula)==1) {formula=rep(formula,length(t))}\n ct$assign(\"formula\", as.array(formula))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=dewpoint(t[i],rh[i],formula[0])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "1512812ec1afcfb2262e6c1e88d3a159b478145e", "size": 1049, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dewpoint.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/dewpoint.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/dewpoint.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 34.9666666667, "max_line_length": 141, "alphanum_fraction": 0.5710200191, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.31635557027348454}} {"text": "library(Seurat)\nlibrary(SeuratDisk)\nlibrary(dplyr)\nlibrary(cowplot)\nlibrary(argparse)\nlibrary(aricode)\nlibrary(reticulate)\nreticulate::use_python(\"python\")\n\nprint_memory_usage <- function() {\n library(reticulate)\n py_run_string('import psutil; pmem = psutil.Process().memory_info(); print(pmem); rss_mb = pmem.rss/1024')\n return(py$rss_mb)\n}\n\nparser <- ArgumentParser()\nparser$add_argument(\"--h5seurat-path\", type = \"character\", help = \"path to the h5seurat file to be processed\")\nparser$add_argument(\"--resolutions\", type = \"double\", nargs = \"+\", default = c(0.02, 0.03, 0.04, 0.05, 0.08, 0.1, 0.2, 0.25, 0.3, 0.4), help = \"resolution of leiden/louvain clustering\")\nparser$add_argument(\"--subset-genes\", type = \"integer\", default = 3000, help = \"number of features (genes) to select, 0 for don't select\")\nparser$add_argument(\"--n-pcs\", type = \"integer\", default = 30, help = \"number of pcs to use during integration\")\nparser$add_argument(\"--no-eval\", action = \"store_true\", help = \"do not eval\")\nparser$add_argument(\"--reference\", action = \"store_true\", help = \"use the largest batch as reference\")\nparser$add_argument('--ckpt-dir', type = \"character\", help='path to checkpoint directory', default = file.path('..', 'results'))\nparser$add_argument(\"--seed\", type = \"integer\", default = -1, help = \"random seed.\")\n\nargs <- parser$parse_args()\n\nif (args$seed >= 0) {\n set.seed(args$seed)\n}\n\nlibrary(reticulate)\nreticulate::use_python(\"python\")\nmatplotlib <- import(\"matplotlib\")\nmatplotlib$use(\"Agg\")\nsc <- import(\"scanpy\")\nsc$settings$set_figure_params(\n dpi=120,\n dpi_save=250,\n facecolor=\"white\",\n fontsize=10,\n figsize=c(10, 10)\n)\n\ndataset_str <- basename(args$h5seurat_path)\ndataset_str <- substring(dataset_str, 1, nchar(dataset_str) - 9)\ndataset <- LoadH5Seurat(args$h5seurat_path)\n\nckpt_dir <- file.path(args$ckpt_dir, sprintf(\"%s_Seuratv3_%d_seed%d_%s\", dataset_str, args$subset_genes, args$seed, strftime(Sys.time(),\"%m_%d-%H_%M_%S\")))\nif (!dir.exists((ckpt_dir))) {\n dir.create(ckpt_dir)\n}\nscETM <- import(\"scETM\")\nscETM$initialize_logger(ckpt_dir = ckpt_dir)\nanndata <- import(\"anndata\")\n\n\nstart_time <- proc.time()[3]\nstart_mem <- print_memory_usage()\n\ndataset_list <- SplitObject(dataset, split.by = \"batch_indices\")\nbatches <- names(dataset_list)\nprint(batches)\n\nlargest_batch <- NA\nlargest_batch_sample_size <- 0\n\nfor (i in seq_along(batches)) {\n dataset_list[[i]] <- NormalizeData(\n object = dataset_list[[i]],\n verbose = FALSE\n )\n ncells <- dim(dataset_list[[i]])[2]\n if (ncells > largest_batch_sample_size) {\n largest_batch <- i\n largest_batch_sample_size <- ncells\n }\n}\n\nif (args$subset_genes) {\n anchor_features <- args$subset_genes\n} else {\n anchor_features <- rownames(dataset@assays$RNA@data)\n}\n\nif (args$reference){\n anchors <- FindIntegrationAnchors(\n object.list = dataset_list,\n dims = 1:args$n_pcs,\n anchor.features = anchor_features,\n reference = largest_batch\n )\n} else {\n anchors <- FindIntegrationAnchors(\n object.list = dataset_list,\n dims = 1:args$n_pcs,\n anchor.features = anchor_features\n )\n}\n\nintegrated <- IntegrateData(\n anchorset = anchors,\n dims = 1:args$n_pcs\n)\n\nDefaultAssay(object = integrated) <- \"integrated\"\n\nintegrated <- ScaleData(\n object = integrated,\n verbose = FALSE\n)\n\ntime_cost <- proc.time()[3] - start_time\nmem_cost <- print_memory_usage() - start_mem\nwriteLines(sprintf(\"Duration: %.1f s (%.1f min)\", time_cost, time_cost / 60))\n\nintegrated <- RunPCA(integrated, npcs = args$n_pcs)\nfpath <- file.path(ckpt_dir, sprintf(\"%s_Seuratv3_seed%d.h5ad\", dataset_str, args$seed))\nX <- integrated@reductions$pca@cell.embeddings\nprocessed_data <- anndata$AnnData(\n X = X,\n obs = integrated@meta.data\n)\nprocessed_data$write_h5ad(fpath)\n\nif (!args$no_eval) {\n result <- scETM$evaluate(\n processed_data,\n embedding_key = \"X\",\n resolutions = args$resolutions,\n plot_dir = ckpt_dir,\n plot_fname = sprintf(\"%s_Seuratv3_seed%d_eval\", dataset_str, args$seed),\n n_jobs = 1L\n )\n line <- sprintf(\"%s\\tSeuratv3\\t%s\\t%.4f\\t%.4f\\t%.4f\\t%.5f\\t%.5f\\t%.2f\\t%.0f\",\n dataset_str, args$seed,\n result$ari, result$nmi, result$asw, result$ebm, result$k_bet,\n time_cost, mem_cost)\n write(line, file = file.path(args$ckpt_dir, \"table1.tsv\"), append = T)\n}\n", "meta": {"hexsha": "bc44f2fbe9e23a2b363f3ee246be710a3aa121fc", "size": 4411, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/train_Seuratv3.r", "max_stars_repo_name": "hui2000ji/scETM", "max_stars_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 24, "max_stars_repo_stars_event_min_datetime": "2021-07-09T12:59:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T22:31:41.000Z", "max_issues_repo_path": "scripts/train_Seuratv3.r", "max_issues_repo_name": "hui2000ji/scETM", "max_issues_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-09-07T11:14:19.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T01:38:09.000Z", "max_forks_repo_path": "scripts/train_Seuratv3.r", "max_forks_repo_name": "hui2000ji/scETM", "max_forks_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-12-02T23:44:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-11T16:46:45.000Z", "avg_line_length": 31.2836879433, "max_line_length": 185, "alphanum_fraction": 0.6801178871, "num_tokens": 1260, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.31630911332824757}} {"text": "## Some helper functions for evaluating lme models\n\n## Get group level effects in prediction\nget.glevel.effect <- function(u, glevel, gname) {\n#\tu <- ranef(m)\n\n\txf <- 0\n\tif ((glevel %in% rownames(u[[gname]]))) {\n\t\txf <- u[[gname]][glevel,]\n\t} else {\n\t\txf <- 0\n\t#\tprint(\"???\")\n\t}\n\n\treturn(xf)\n}\n\n\n## linear prediction \nget.qs.linear.pred <- function(m, newdata, corpus=F) {\n\n mm <- model.matrix(terms(m),data=newdata) \n u <- mm %*% fixef(m)\n\n\tm.ranef <- ranef(m)\n role.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,role,\"role\")),by=list(pid)]$fx\n group.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,group,\"group\")),by=list(pid)]$fx\n meeting.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,meeting,\"meeting\")),by=list(pid)]$fx\n\tif (corpus==T) {\n \tcorpus.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,corpus,\"corpus\")),by=list(pid)]$fx\n \tpred.fx <- u + role.fx + group.fx + meeting.fx + corpus.fx\n\t} else {\n \tpred.fx <- u + role.fx\n\t}\n return(pred.fx)\n}\n\n\n## Out of date with respect to group level indicators?\nget.eda.linear.pred <- function(m, newdata, corpus=F) {\n\n mm <- model.matrix(terms(m),data=newdata) \n u <- mm %*% fixef(m)\n\n\tm.ranef <- ranef(m)\n annot.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,annot,\"annot\")),by=list(wid,annot)]$fx\n mtype.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,mtype,\"mtype\")),by=list(wid,annot)]$fx\n\tif (corpus==T) {\n \tcorpus.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,corpus,\"corpus\")),by=list(wid,annot)]$fx\n \tpred.fx <- u + annot.fx + mtype.fx + corpus.fx\n\t} else {\n \tpred.fx <- u + annot.fx + mtype.fx \n\t}\n return(pred.fx)\n}\n\n## This needs to be generalized\nget.eda.true.pred <- function(m, newdata, corpus=F, spk=F) {\n\n\tprint(nrow(newdata))\n\t#print(terms(m))\n mm <- model.matrix(terms(m),data=newdata) \n\t#print(nrow(mm))\n u <- mm %*% fixef(m)\n\n\t\n\tm.ranef <- ranef(m)\n\n\tif (spk) {\n\t\tprint(\"HERE spk\")\t\n\t\tmtype.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,mtype,\"mtype\")),by=list(wid.spk,annot)]$fx\n\n \tsum.fx <- u + mtype.fx\n\t\tif (\"annot\" %in% m.ranef) {\n\t\t\tannot.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,annot,\"annot\")),by=list(wid.spk,annot)]$fx\n\t\t\tsum.fx <- sum.fx + annot.fx\n\t\t}\n\t\tif (\"eda.annot\" %in% m.ranef) {\n\t\t\teda.annot.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,eda.annot,\"eda.annot\")),by=list(wid.spk,annot)]$fx\n\t\t\tsum.fx <- sum.fx + eda.annot.fx\n\t\t}\n\t\tif (\"mgroup\" %in% m.ranef) {\n\t\t\tmgroup.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,mgroup,\"mgroup\")),by=list(wid.spk,annot)]$fx\n\t\t\tsum.fx <- sum.fx + mgroup.fx\n\t\t}\n\t\tif (\"corpus\" %in% m.ranef) {\n \t\tcorpus.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,corpus,\"corpus\")),by=list(wid.spk,annot)]$fx\n\t\t\tsum.fx <- sum.fx + corpus.fx\n\n\t\t}\n\t} else {\n\t\tprint(\"HERE\")\t\n\t\tmtype.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,mtype,\"mtype\")),by=list(wid,annot)]$fx\n \tsum.fx <- u + mtype.fx\n\t\tprint(m.ranef)\n\t\tprint(names(m.ranef))\n\t\tif (\"annot\" %in% m.ranef) {\n\t\t\tprint(\"annot\")\n\t\t\tannot.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,annot,\"annot\")),by=list(wid,annot)]$fx\n\t\t\tsum.fx <- sum.fx + annot.fx\n\t\t}\n\t\tif (\"eda.annot\" %in% m.ranef) {\n\t\t\tprint(\"eda.annot\")\n\t\t\teda.annot.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,eda.annot,\"eda.annot\")),by=list(wid,annot)]$fx\n\t\t\tsum.fx <- sum.fx + eda.annot.fx\n\t\t}\n\t\tif (\"mgroup\" %in% m.ranef) {\n\t\t\tprint(\"mgroup\")\n\t\t\tmgroup.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,mgroup,\"mgroup\")),by=list(wid,annot)]$fx\n\t\t\tsum.fx <- sum.fx + mgroup.fx\n\t\t}\n\t\tif (\"corpus\" %in% m.ranef) {\n\t\t\tprint(\"corpus\")\n \t\tcorpus.fx <- newdata[,list(fx=get.glevel.effect(m.ranef,corpus,\"corpus\")),by=list(wid,annot)]$fx\n\t\t\tsum.fx <- sum.fx + corpus.fx\n\n\t\t}\n\t}\n\n \tpred.fx <- invlogit(sum.fx)\n\n return(pred.fx)\n}\n\n\ntrain.logit.model <- function(m, xdata, x.formula) {\n\tglmer(x.formula, data=xdata,family=binomial(link=\"logit\"))\n}\n\ntrain.linear.model <- function(m, xdata, x.formula) {\n\tglmer(x.formula, data=xdata)\n}\n\n## Leave one out cross-validation\nget.loocv.qs <- function(m, xdata, filename=\"curr.loocv.qs\", corpus=F, linear=T) {\n\tprint(filename)\n\tx <- xdata\t\n\tgnames <- unique(xdata$group) \n\txpred <- NULL\n\tmlist <- list()\t\n\n\tfor (gname in gnames) {\n\t\tprint(gname)\n\t\tcurr <- x[group==gname]\n\t\tif (linear) {\n\t\t\tcurr.m <- train.linear.model(m, x[group != gname])\n\t\t\tcurr.pred <- get.qs.linear.pred(curr.m, curr, corpus=corpus)\n\t\t\txpred <- rbind(xpred, data.table(curr[,list(pid, role, meeting, group, A.centre)], \n\t\t\t\t\t\tpred.val=curr.pred[,1])) \n\n\t\t} else {\n\t\t\tprint(\"Oh no, not yet!\")\t\n\t\t\txpred <- curr \n\t\t}\n\t\tmlist[[gname]] <- curr.m\n\t}\t\n\n\tcurrcv <- list(xpred=xpred, mlist=mlist)\n\tif (!is.null(filename)) {\n\t\tprint(filename)\n\t\tsave(currcv, file=filename) \n\t}\t\t\n\treturn(xpred)\t\n}\n\nget.cv.qs <- function(m, xdata, nfolds=5, filename=\"curr.cv.qs\", corpus=F, linear=T) {\n\tprint(filename)\n\tord <- sample.int(nrow(xdata)) \n\tnx <- ceiling(nrow(xdata)/nfolds)\t\n\tfold.starts <- seq(1,nrow(xdata),by=nx)\n\tfold.ends <- c(fold.starts[2:length(fold.starts)]-1, nrow(xdata))\n\tfolds <- data.table(fstart=fold.starts, fend=fold.ends)\n\n\t#x <- xdata\n\tx <- xdata[ord]\t\n\txpred <- NULL\n\tmlist <- list()\t\n\n\tfor (i in c(1:nrow(folds))) {\n\t\tprint(folds[i])\n\t\tcurr <- x[folds$fstart[i]:folds$fend[i]]\n\t\tif (linear) {\n\t\t\tcurr.m <- train.linear.model(m, x[!(folds$fstart[i]:folds$fend[i])])\n\t\t\tcurr.pred <- get.qs.linear.pred(curr.m, curr, corpus=corpus)\n\t\t\txpred <- rbind(xpred, data.table(curr[,list(pid, role, meeting, group, A.centre)], \n\t\t\t\t\t\tpred.val=curr.pred[,1])) \n\n\t\t} else {\n\t\t\tprint(\"Oh no, not yet!\")\t\n\t\t\txpred <- curr \n\t\t}\n\t\tmlist[[folds$fstart[i]]] <- curr.m\n\t}\t\n\n\tcurrcv <- list(xpred=xpred, mlist=mlist, folds=folds, ord=ord)\n\tif (!is.null(filename)) {\n\t\tprint(filename)\n\t\t#save(currcv, file=filename) \n\t}\t\t\n\treturn(xpred)\t\n}\n\nget.cv <- function(m, xdata, x.formula, nfolds=5, filename=NULL, corpus=F, linear=F, spk=F) {\n\tprint(filename)\n\tord <- sample.int(nrow(xdata)) \n\tnx <- ceiling(nrow(xdata)/nfolds)\t\n\tfold.starts <- seq(1,nrow(xdata),by=nx)\n\tfold.ends <- c(fold.starts[2:length(fold.starts)]-1, nrow(xdata))\n\tfolds <- data.table(fstart=fold.starts, fend=fold.ends)\n\n\t#x <- xdata\n\tx <- xdata[ord]\t\n\txpred <- NULL\n\tmlist <- list()\t\n\n\n\tfor (i in c(1:nrow(folds))) {\n\t\tprint(folds[i])\n\t\tcurr <- x[folds$fstart[i]:folds$fend[i]]\n\t\tprint(nrow(curr))\n\t\tif (linear) {\n\t\t\tcurr.m <- train.linear.model(m=m, xdata=x[!(folds$fstart[i]:folds$fend[i])], x.formula=x.formula)\n\t\t\tcurr.pred <- get.eda.linear.pred(curr.m, curr, corpus=corpus)\n\t\t\txpred <- rbind(xpred, data.table(curr[,list(wid,annot,corpus,eda.true,eda.time,link.eda)], \n\t\t\t\t\t\tpred.val=curr.pred[,1])) \n\n\t\t} else {\n\t\t\tcurr.m <- train.logit.model(m=m, xdata=x[!(folds$fstart[i]:folds$fend[i])], x.formula=x.formula)\n\t\t\tprint(\"Here\")\n\t\t\tcurr.pred <- get.eda.true.pred(curr.m, curr, corpus=corpus, spk=spk)\n\t\t\txpred <- rbind(xpred, data.table(curr[,list(wid,annot,corpus,eda.true,eda.time,link.eda)], \n\t\t\t\t\t\tlogit.val=curr.pred[,1])) \n\t\t}\n\t\tmlist[[folds$fstart[i]]] <- curr.m\n\t}\t\n\n\tcurrcv <- list(xpred=xpred, mlist=mlist, folds=folds, ord=ord)\n\tif (!is.null(filename)) {\n\t\tprint(filename)\n\t\t#save(currcv, file=filename) \n\t}\t\t\n\treturn(xpred)\t\n}\n\nget.cv.wrapper <- function(m, N, xdata, x.formula=formula(m), nfolds=10, corpus=F, linear=F, spk=F) {\n\t#get.cv(m, xdata, nfolds, filename=paste(\"/home/clai/kdata/ami/derived/R.outputs/cv.\",N,sep=\"\"), corpus=corpus)\n\tget.cv(m, xdata, x.formula, nfolds, filename=paste(\"cv.\",N,sep=\"\"), corpus=corpus, linear=linear, spk=spk)\n}\n\nget.cv.reps <- function(m, xdata, nfolds=5, nreps=10) {\n\tmclapply(c(1:nreps), get.cv.wrapper, m=m, xdata=xdata, nfolds=nfolds, \n\t \tmc.cores=5)\n\n}\n\nget.f1 <- function(x, dep.var=\"eda.true\") {\n\tif (!is.data.table(x)) {\n\t\tx <- data.table(x)\n\t}\n\tsetnames(x, c(dep.var), c(\"eda.true\"))\n\tif (\"wid\" %in% names(x) & !(\"niteid\" %in% names(x))) {\n\t\tsetnames(x, c(\"wid\"), c(\"niteid\"))\n\t}\t\n\tctab <- x[,length(niteid),by=list(eda.true, logit.val > 0.5)]\n\tbaseline <- x[,length(niteid)/nrow(x),by=list(eda.true)]\n\taccuracy <- ctab[,sum(V1[eda.true == logit.val])/(sum(V1))]\n\tprecision <- ctab[,V1[eda.true & logit.val]/(sum(V1[logit.val]))]\n\tif (length(precision)==0) {precision <- 0}\n\trecall <- ctab[,V1[eda.true & logit.val]/(sum(V1[eda.true]))]\n\tif (length(recall)==0) {recall <- 0}\n\tF1 <- 2* ((precision*recall)/(precision+recall))\n\n\tsetnames(x, c(\"eda.true\"), c(dep.var))\n\n\treturn(list(f1=data.table(baseline, accuracy, precision=precision, recall=recall, F1=F1), ctab=ctab))\n}\n\n\n#---------------------------------------------------------------------\n# Examine model parameters\n\n## Get fixed effects of model m\nget.fixef <- function(m, roundval=3) {\n\tu <- display(m)\n\tv0 <- round(data.table(estimate=u$coef, se=u$se, ymin=u$coef-2*u$se, ymax=u$coef+2*u$se), roundval) \n\tv <- data.table(varx=names(u$coef), v0)\n\treturn(v)\n}\n\nget.ranef.var <- function(m, varname) {\n\n\tu0 <- se.ranef(m)[[varname]]\n\tx0 <- ranef(m)[[varname]]\n\n\tx1 <- data.frame(varx=rownames(x0), x0)\n\tnames(x1)[2] <- varname #\"Intercept\"\n\tx2 <- melt(x1)\n\tnames(x2) <- c(\"varx\",\"varb\", \"estimate\")\n\n\tu1 <- data.frame(varx=rownames(x0), u0)\n\tnames(u1) <- names(x1)\n\tu2 <- melt(u1) \n\tnames(u2) <- c(\"varx\",\"varb\", \"se\")\n\n\tv <- data.frame(x2, u2$se, ymin=x2$estimate-2*u2$se, ymax=x2$estimate+2*u2$se)\n\treturn(v)\n}\n\nplot.fixef.est <- function(v, varname=\"Features\") {\n\tp <- ggplot(v, aes(x=varx, y=estimate, ymin=ymin, ymax=ymax)) + geom_pointrange() + geom_hline(y=0, col=\"grey\")\n\tp <- p + scale_x_discrete(varname) + theme(axis.text.x=element_text(angle=90, hjust=1)) \n\tp\n\n}\nplot.ranef.var <- function(m, varname) {\n\n u0 <- se.ranef(m)[[varname]]\n x0 <- ranef(m)[[varname]]\n\n x1 <- data.frame(varx=rownames(x0), x0)\n names(x1)[2] <- varname #\"Intercept\"\n x2 <- melt(x1)\n names(x2) <- c(\"varx\",\"varb\", \"estimate\")\n\n u1 <- data.frame(varx=rownames(x0), u0)\n names(u1) <- names(x1)\n u2 <- melt(u1)\n names(u2) <- c(\"varx\",\"varb\", \"se\")\n\n v <- data.frame(x2, u2$se, ymin=x2$estimate-2*u2$se, ymax=x2$estimate+2*u2$se)\n\n\n p <- ggplot(v, aes(x=varx, y=estimate, ymin=ymin, ymax=ymax)) + geom_pointrange()\n p <- p + geom_hline(y=0, col=\"grey\")\n p <- p + facet_wrap(~ varb)\n p <- p + scale_x_discrete(varname)\n p\n\n\n}\n\n", "meta": {"hexsha": "afa4a9131a93efccdae1a6e9e45eef62350cf1e6", "size": 10365, "ext": "r", "lang": "R", "max_stars_repo_path": "sarc/scripts/proc-lme.r", "max_stars_repo_name": "laic/rst-prosody", "max_stars_repo_head_hexsha": "72925b0828b7700e366efa667af3e052dff12114", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "sarc/scripts/proc-lme.r", "max_issues_repo_name": "laic/rst-prosody", "max_issues_repo_head_hexsha": "72925b0828b7700e366efa667af3e052dff12114", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sarc/scripts/proc-lme.r", "max_forks_repo_name": "laic/rst-prosody", "max_forks_repo_head_hexsha": "72925b0828b7700e366efa667af3e052dff12114", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3070175439, "max_line_length": 112, "alphanum_fraction": 0.6087795466, "num_tokens": 3579, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3163091133282475}} {"text": "deseq = modules::import_package('DESeq2')\npiano = modules::import_package('piano')\nmodules::import_package('dplyr', attach = TRUE)\n\n#' @export\nprepare_gene_set = function (gene_set)\n piano$loadGSC(gene_set, 'data.frame')\n\n#' @export\ngsa_de = function (data, col_data, contrast, go_genes) {\n stopifnot(inherits(go_genes, 'GSC'))\n data = untidy(select(data, Gene, starts_with('do')))\n col_data = untidy(col_data)\n\n # Annoyingly, we need to recalculate the DE genes here because we previously\n # only stored genes with evidence of DE, not all genes.\n de = deseq_test(data, col_data, contrast) %>%\n deseq$results() %>%\n as.data.frame() %>%\n {.[! is.na(.$padj), ]}\n stats = de[, 'padj', drop = FALSE]\n directions = de[, 'log2FoldChange', drop = FALSE]\n piano$runGSA(stats, directions, gsc = go_genes, verbose = FALSE)\n}\n\n#' @export\nenriched_terms = function (data, direction = c('up', 'down'), alpha = 0.01) {\n direction = switch(direction, up = 'up', 'down' = 'dn',\n stop('Invalid ', sQuote('direction')))\n modules::import_package('lazyeval', attach = TRUE)\n dir_col = as.name(sprintf('p adj (dist.dir.%s)', direction))\n\n piano$GSAsummaryTable(data) %>%\n filter_(interp(~ p < alpha, p = dir_col)) %>%\n select_('Name', padj = dir_col) %>%\n arrange(padj)\n}\n\nuntidy = function (tidy_data, rownames = 1)\n `rownames<-`(as.data.frame(tidy_data[-rownames]), tidy_data[[rownames]])\n\ndeseq_test = function (data, col_data, contrast) {\n cols = rownames(col_data)[col_data[[1]] %in% contrast]\n col_data = col_data[cols, , drop = FALSE]\n # Ensure that the conditions are in the same order as `contrast`; that is,\n # the reference level corresponds to `contrast[1]`.\n # FIXME: DESeq2 bug causes this not to work, need to use relevel instead.\n #col_data[[1]] = factor(col_data[[1]], unique(col_data[[1]]), ordered = TRUE)\n data = data[, cols]\n design = eval(bquote(~ .(as.name(colnames(col_data)[1]))))\n dds = deseq$DESeqDataSetFromMatrix(data, col_data, design)\n GenomicRanges::colData(dds)[[1]] = relevel(GenomicRanges::colData(dds)[[1]],\n contrast[1])\n deseq$DESeq(dds, quiet = TRUE)\n}\n", "meta": {"hexsha": "4da2a5ae2512958c9348151ca79eabcc53aadb47", "size": 2259, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/gsa.r", "max_stars_repo_name": "klmr/codons", "max_stars_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-12-19T00:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2015-12-19T00:54:46.000Z", "max_issues_repo_path": "scripts/gsa.r", "max_issues_repo_name": "klmr/codons", "max_issues_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2016-03-06T14:47:12.000Z", "max_issues_repo_issues_event_max_datetime": "2016-03-06T14:47:12.000Z", "max_forks_repo_path": "scripts/gsa.r", "max_forks_repo_name": "klmr/codons", "max_forks_repo_head_hexsha": "7e5efe08ba91c4891b0820c3e30ebfa5afbf26bc", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3392857143, "max_line_length": 81, "alphanum_fraction": 0.632580788, "num_tokens": 638, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.31630910668801393}} {"text": "#Title: Estimate fitting decay curve\r\n#Auther: Naoto Imamachi\r\n#ver: 1.0.0\r\n#Date: 2015-10-24\r\n\r\n###Estimate_normalization_factor_function###\r\nBridgeRHalfLifeCalcR2Select <- function(InputFile = \"BridgeR_4_Normalized_expression_dataset.txt\",\r\n group, \r\n hour, \r\n InforColumn = 4, \r\n CutoffDataPointNumber = 4,\r\n CutoffDataPoint1 = c(1,2),\r\n CutoffDataPoint2 = c(8,12),\r\n ThresholdHalfLife = c(8,12),\r\n OutputFile = \"BridgeR_5C_HalfLife_calculation_R2_selection.txt\"){\r\n ###Prepare_file_infor###\r\n time_points <- length(hour)\r\n group_number <- length(group)\r\n input_file <- fread(InputFile, header=T)\r\n output_file <- paste(OutputFile,\".log\",sep=\"\")\r\n log_file <- OutputFile\r\n \r\n ###print_header###\r\n cat(\"\",file=output_file)\r\n cat(\"\",file=log_file)\r\n hour_label <- NULL\r\n CutoffDataPoint1 <- sort(CutoffDataPoint1, decreasing = T)\r\n CutoffDataPoint2 <- sort(CutoffDataPoint2, decreasing = F)\r\n CutoffDataPoint1_length <- length(CutoffDataPoint1)\r\n CutoffDataPoint2_length <- length(CutoffDataPoint2)\r\n \r\n for_iteration_number <- CutoffDataPoint1_length + CutoffDataPoint2_length\r\n for(a in 1:group_number){\r\n if(!is.null(hour_label)){\r\n cat(\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", file=log_file, append=T)\r\n }\r\n hour_label <- NULL\r\n for(x in hour){\r\n hour_label <- append(hour_label, paste(\"T\", x, \"_\", a, sep=\"\"))\r\n }\r\n infor_st <- 1 + (a - 1)*(time_points + InforColumn)\r\n infor_ed <- (InforColumn)*a + (a - 1)*time_points\r\n infor <- colnames(input_file)[infor_st:infor_ed]\r\n cat(infor,hour_label, sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n cat(infor,hour_label, sep=\"\\t\", file=log_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=log_file, append=T)\r\n \r\n for_iteration_number2 <- for_iteration_number + 1\r\n for(number in 1:for_iteration_number2){\r\n cat(\"Model\",\"Decay_rate_coef\",\"coef_error\",\"coef_p-value\",\r\n \"R2\",\"Adjusted_R2\",\"Residual_standard_error\",\"half_life\",\r\n sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n }\r\n cat(\"Model\",\"R2\",\"half_life\", sep=\"\\t\", file=output_file, append=T)\r\n cat(\"Model\",\"R2\",\"half_life\", sep=\"\\t\", file=log_file, append=T)\r\n }\r\n cat(\"\\n\", sep=\"\", file=output_file, append=T)\r\n cat(\"\\n\", sep=\"\", file=log_file, append=T)\r\n \r\n ###calc_RNA_half_lives###\r\n ###Function1#################################\r\n half_calc <- function(time_exp_table,label){\r\n data_point <- length(time_exp_table$exp)\r\n if(!is.null(time_exp_table)){\r\n if(data_point >= CutoffDataPointNumber){\r\n if(as.numeric(as.vector(as.matrix(time_exp_table$exp[1]))) > 0){\r\n model <- lm(log(time_exp_table$exp) ~ time_exp_table$hour - 1)\r\n model_summary <- summary(model)\r\n coef <- -model_summary$coefficients[1]\r\n coef_error <- model_summary$coefficients[2]\r\n coef_p <- model_summary$coefficients[4]\r\n r_squared <- model_summary$r.squared\r\n adj_r_squared <- model_summary$adj.r.squared\r\n residual_standard_err <- model_summary$sigma\r\n half_life <- log(2)/coef\r\n if(coef < 0 || half_life >= 24){\r\n half_life <- 24\r\n }\r\n cat(label,coef,coef_error,coef_p,r_squared,adj_r_squared,residual_standard_err,half_life, sep=\"\\t\", file=output_file, append=T)\r\n return(c(half_life,r_squared))\r\n }else{\r\n cat(\"low_expresion\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n return(c(\"NA\",\"NA\"))\r\n }\r\n }else{\r\n cat(\"few_data\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n return(c(\"NA\",\"NA\"))\r\n }\r\n }else{\r\n cat(\"low_expresion\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n return(c(\"NA\",\"NA\"))\r\n }\r\n }\r\n #############################################\r\n \r\n ###Function2#################################\r\n test_R2 <- function(time_point_exp_raw, cutoff_data_point, halflife_Raw, R2_Raw){\r\n test_times <- cutoff_data_point\r\n times_length <- length(test_times)\r\n times_index <- c(times_length)\r\n add_index <- times_length\r\n \r\n R2_list <- c(R2_Raw)\r\n half_list <- c(halflife_Raw)\r\n label_list <- c(\"Raw\")\r\n for(counter in times_length:1){\r\n #excepted_time_points\r\n check_times <- test_times[times_index] #c(24), c(24,12), c(24,12,8)\r\n time_point_exp_del <- NULL\r\n time_point_exp_del_label <- paste(\"Delete_\",paste(check_times,collapse=\"hr_\"),\"hr\",sep=\"\")\r\n label_list <- append(label_list, time_point_exp_del_label) #\r\n time_point_exp_del <- time_point_exp_raw\r\n for(times_list in check_times){\r\n time_point_exp_del <- time_point_exp_del[time_point_exp_del$hour != as.numeric(times_list),]\r\n }\r\n time_point_exp_del <- time_point_exp_del[time_point_exp_del$exp > 0,]\r\n halflife_R2_result <- half_calc(time_point_exp_del, time_point_exp_del_label)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n \r\n R2_list <- append(R2_list, halflife_R2_result[2]) #\r\n half_list <- append(half_list, halflife_R2_result[1]) #\r\n \r\n #Counter\r\n add_index <- add_index - 1\r\n times_index <- append(times_index, add_index)\r\n }\r\n \r\n R2_table <- data.frame(label=label_list, R2=R2_list, half=half_list)\r\n #R2_table <- R2_table[R2_table$R2 != \"NA\",]\r\n #sortlist <- order(R2_table$R2, decreasing = T)\r\n #R2_table <- R2_table[sortlist,]\r\n #result <- as.vector(as.matrix(R2_table[1,]))\r\n #cat(result, sep=\"\\t\", file=output_file, append=T)\r\n return(R2_table)\r\n }\r\n #############################################\r\n \r\n gene_number <- length(input_file[[1]])\r\n for(x in 1:gene_number){\r\n data <- as.vector(as.matrix(input_file[x,]))\r\n for(a in 1:group_number){\r\n if(a != 1){\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=log_file, append=T)\r\n }\r\n infor_st <- 1 + (a - 1)*(time_points + InforColumn)\r\n infor_ed <- (InforColumn)*a + (a - 1)*time_points\r\n exp_st <- infor_ed + 1\r\n exp_ed <- infor_ed + time_points\r\n \r\n gene_infor <- data[infor_st:infor_ed]\r\n cat(gene_infor, sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", file=output_file, append=T)\r\n cat(gene_infor, sep=\"\\t\", file=log_file, append=T)\r\n cat(\"\\t\", file=log_file, append=T)\r\n \r\n exp <- as.numeric(data[exp_st:exp_ed])\r\n cat(exp, sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", file=output_file, append=T)\r\n cat(exp, sep=\"\\t\", file=log_file, append=T)\r\n cat(\"\\t\", file=log_file, append=T)\r\n \r\n ###Raw_data\r\n time_point_exp_raw <- data.frame(hour,exp)\r\n time_point_exp_base <- time_point_exp_raw[time_point_exp_raw$exp > 0, ]\r\n test <- half_calc(time_point_exp_base,\"Exponential_Decay_Model\") #T1/2 - R2\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n \r\n ###Re-calculation of RNA half-life(Default: ThresholdHalfLife - 12hr)\r\n R2_list <- NULL\r\n half_list <- NULL\r\n label_list <- NULL\r\n if(test[1] == \"NA\"){\r\n for(number in 1:for_iteration_number){\r\n cat(\"Notest\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n }\r\n cat(\"Notest\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n cat(\"Notest\",\"NA\",\"NA\", sep=\"\\t\", file=log_file, append=T)\r\n }else if(test[1] < ThresholdHalfLife[1]){ #Default: <12hr => Delete 8,12hr\r\n for(number in 1:CutoffDataPoint1_length){\r\n cat(\"Notest\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n }\r\n R2_table <- test_R2(time_point_exp_raw, CutoffDataPoint2, test[1], test[2]) #test[1], test[2] => T1/2, R2\r\n \r\n }else if(test[1] >= ThresholdHalfLife[1] && test[1] < ThresholdHalfLife[2]){\r\n R2_table1 <- test_R2(time_point_exp_raw, CutoffDataPoint1, test[1], test[2]) #test[1], test[2] => T1/2, R2\r\n R2_table2 <- test_R2(time_point_exp_raw, CutoffDataPoint2, test[1], test[2]) #test[1], test[2] => T1/2, R2\r\n R2_table <- rbind(R2_table1, R2_table2)\r\n \r\n }else if(test[1] >= ThresholdHalfLife[2]){ #Default: >=12hr => Delete 1,2hr\r\n for(number in 1:CutoffDataPoint2_length){\r\n cat(\"Notest\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\",\"NA\", sep=\"\\t\", file=output_file, append=T)\r\n cat(\"\\t\", sep=\"\", file=output_file, append=T)\r\n }\r\n R2_table <- test_R2(time_point_exp_raw, CutoffDataPoint1, test[1], test[2]) #test[1], test[2] => T1/2, R2\r\n \r\n }\r\n ###R2_Selection###\r\n if(test[1] != \"NA\"){\r\n R2_table <- R2_table[R2_table$R2 != \"NA\",]\r\n sortlist <- order(R2_table$R2, decreasing = T)\r\n R2_table <- R2_table[sortlist,]\r\n result <- as.vector(as.matrix(R2_table[1,]))\r\n cat(result, sep=\"\\t\", file=output_file, append=T)\r\n cat(result, sep=\"\\t\", file=log_file, append=T)\r\n }\r\n }\r\n cat(\"\\n\", file=output_file, append=T)\r\n cat(\"\\n\", file=log_file, append=T)\r\n }\r\n}\r\n\r\n###TEST###\r\n#output_file <- \"test.txt\"\r\n#CutoffDataPointNumber <- 4\r\n#x <- c(0,1,2,4,8,12)\r\n#y <- c(1,0.9,0.8,0.5,0.3,0.1)\r\n#table <- data.frame(hour=x,exp=y)\r\n#test_data_point <- c(8,12)\r\n#test_R2(table, test_data_point, 4.5, 0.992)\r\n\r\n# R2_list half_list label_list\r\n#1 0.9919170 4.547501 Delete_12hr\r\n#2 0.9720505 4.378777 Delete_12hr_8hr\r\n\r\n#########################\r\n#test <- c(8,12,24,2,12,21,1)\r\n#test_length <- length(test)\r\n#test_index <- c(test_length)\r\n#add_index <- test_length\r\n\r\n#for(x in test_length:1){\r\n# print(test[test_index])\r\n# add_index <- add_index - 1\r\n# test_index <- append(test_index,add_index)\r\n#}\r\n", "meta": {"hexsha": "0653dc67cef1be92e395f2ee1546961dcc1b4a40", "size": 11331, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Z5C_estimate_fitting_decay_curve_R2_selection.r", "max_stars_repo_name": "ChristophRau/BridgeR", "max_stars_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-10T15:03:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-10T15:03:45.000Z", "max_issues_repo_path": "R/Z5C_estimate_fitting_decay_curve_R2_selection.r", "max_issues_repo_name": "ChristophRau/BridgeR", "max_issues_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Z5C_estimate_fitting_decay_curve_R2_selection.r", "max_forks_repo_name": "ChristophRau/BridgeR", "max_forks_repo_head_hexsha": "d4d68826bc2fc210b409ff3345047def4fd7ede0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-11T14:15:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-11T14:15:15.000Z", "avg_line_length": 46.8223140496, "max_line_length": 148, "alphanum_fraction": 0.5223722531, "num_tokens": 2872, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.3163091066880139}} {"text": "require(graphics)\r\nlibrary(ggplot2)\r\nlibrary(ggthemes)\r\nlibrary(MASS)\r\nlibrary(gridExtra)\r\n\r\nmm1 <- function(...) {\r\n mean_cl_normal(...,mult=1)\r\n}\r\nmm2 <- function(...) {\r\n mean_cl_normal(...,mult=2)\r\n}\r\n\r\n\r\ng1 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease, fill = treatment))+\r\n theme_gray(base_size = 12)+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"errorbar\",width=0.5) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"bar\") + ggFill(color.value = \"Hue\")+ \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_line(color = \"black\"),\r\n panel.grid.major = element_line(color = \"black\"),\r\n panel.border=element_blank(),\r\n axis.title.y=element_blank(),\r\n axis.title.x=element_blank()\r\n )\r\n\r\ng2 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease)) + theme_minimal(base_size = 14)+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"errorbar\",width=0.5) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"bar\") + \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_blank(),\r\n panel.background=element_blank(),\r\n axis.title.y=element_blank(),\r\n axis.title.x=element_blank()\r\n ) \r\n\r\ng3 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease)) + theme_minimal(base_size = 14)+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"errorbar\",width=0.5) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"bar\",width=0.7) + \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_blank(),\r\n panel.grid.major=element_line(size = 0.5, color = \"gray96\"),\r\n panel.background=element_blank(),\r\n axis.title.y=element_blank(),\r\n axis.title.x=element_blank()\r\n ) \r\n\r\ng4 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease)) + theme_minimal(base_size = 14)+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"errorbar\",width=0.5, size = 1) +\r\n stat_summary(fun.data=mean_cl_normal,geom=\"point\",size=3) + \r\n xlab(\"Treatment\") + ylab(\"Effect\") + \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_blank(),\r\n panel.grid.major=element_line(size = 0.5, color = \"gray96\"),\r\n panel.background=element_blank()\r\n ) \r\n\r\n\r\n\r\ng5 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease))+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mm2,geom=\"linerange\", size = 0.7)+ \r\n stat_summary(fun.data=mean_cl_normal,geom=\"point\") + \r\n xlab(\"Treatment\") + ylab(\"Effect\") + \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_blank(),\r\n panel.grid.major=element_line(size = 0.5, color = \"gray96\"),\r\n panel.background=element_blank()\r\n ) \r\n\r\n\r\n\r\ng6 <- ggplot(OrchardSprays,aes(x=treatment,y=decrease))+\r\n scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75,100)) +\r\n stat_summary(fun.data=mm2,geom=\"linerange\", size = 0.7)+ \r\n stat_summary(fun.data=mm1,geom=\"linerange\",size=1.1, color = \"red\", position=position_dodge(width=0))+\r\n stat_summary(fun.data=mean_cl_normal,geom=\"point\") + \r\n xlab(\"Treatment\") + ylab(\"Effect\") + \r\n theme(legend.position=\"none\",\r\n panel.grid.minor=element_blank(),\r\n panel.grid.major=element_line(size = 0.5, color = \"gray96\"),\r\n panel.background=element_blank()\r\n ) \r\n\r\n\r\n\r\ng1 \r\nggsave(file=\"Plot42a.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\ng2 \r\nggsave(file=\"Plot42b.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\ng3 \r\nggsave(file=\"Plot42c.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\ng4 \r\nggsave(file=\"Plot42d.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\ng5 \r\nggsave(file=\"Plot42e.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\ng6 \r\nggsave(file=\"Plot42f.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n", "meta": {"hexsha": "e66c302e4f8057bd48f19aefb5fcaa4255cce9fe", "size": 4022, "ext": "r", "lang": "R", "max_stars_repo_path": "GW_dynamite.r", "max_stars_repo_name": "GraphicsPrinciples/CheatSheet", "max_stars_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 138, "max_stars_repo_stars_event_min_datetime": "2018-08-26T15:02:26.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-20T22:50:33.000Z", "max_issues_repo_path": "GW_dynamite.r", "max_issues_repo_name": "sts-sadr/CheatSheet", "max_issues_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-11-06T12:07:21.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-30T17:48:02.000Z", "max_forks_repo_path": "GW_dynamite.r", "max_forks_repo_name": "sts-sadr/CheatSheet", "max_forks_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 35, "max_forks_repo_forks_event_min_datetime": "2018-11-05T12:46:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-25T12:26:20.000Z", "avg_line_length": 36.8990825688, "max_line_length": 105, "alphanum_fraction": 0.6484336151, "num_tokens": 1237, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5234203489363239, "lm_q2_score": 0.6039318337259584, "lm_q1q2_score": 0.31611021114259513}} {"text": "# SVM\nlibrary(dplyr)\n\n# 3 Models\n\n# read data - remove #\n# data <- read.csv('../input/twitter-sentiment-analysis/Sentiment_Data.csv')\nhead(data)\n\ndata_1 <- data %>% \n select(text, sentiment)\nhead(data_1)\n\nround(prop.table(table(data_1$sentiment)),2)\n\nlibrary(tm)\n# Loading required package: NLP\nlibrary(SnowballC)\ncorpus = VCorpus(VectorSource(data_1$text))\n# a snap shot of the first text stored in the corpus\nas.character(corpus[[1]])\n\ncorpus = tm_map(corpus, content_transformer(tolower))\ncorpus = tm_map(corpus, removeNumbers)\ncorpus = tm_map(corpus, removePunctuation)\ncorpus = tm_map(corpus, removeWords, stopwords(\"english\"))\ncorpus = tm_map(corpus, stemDocument)\ncorpus = tm_map(corpus, stripWhitespace)\nas.character(corpus[[1]])\n\ndtm = DocumentTermMatrix(corpus)\ndtm\ndim(dtm)\n\ndtm = removeSparseTerms(dtm, 0.999)\ndim(dtm)\n\n#Inspecting the the first 10 tweets and the first 15 words in the dataset\ninspect(dtm[0:10, 1:15])\n\nfreq<- sort(colSums(as.matrix(dtm)), decreasing=TRUE)\nfindFreqTerms(dtm, lowfreq=60) #identifying terms that appears more than 60times\n\nlibrary(wordcloud)\npositive <- subset(data_1,sentiment==\"Positive\")\nhead(positive)\nwordcloud(positive$text, max.words = 100, scale = c(3,0.5))\n\nnegative <- subset(data_1,sentiment==\"Negative\")\nhead(negative)\nwordcloud(negative$text, max.words = 100, scale = c(3,0.5))\n\nneutral <- subset(data_1,sentiment==\"Neutral\")\nhead(neutral)\nwordcloud(neutral$text, max.words = 100, scale = c(3,0.5))\n\nlibrary(\"wordcloud\")\n\n# Loading required package: RColorBrewer\nlibrary(RColorBrewer)\nset.seed(1234)\nwordcloud(words = wf$word, freq = wf$freq, min.freq = 1,\n max.words=200, random.order=FALSE, rot.per=0.35, \n colors=brewer.pal(8, \"Dark2\"))\n\nconvert_count <- function(x) {\n y <- ifelse(x > 0, 1,0)\n y <- factor(y, levels=c(0,1), labels=c(\"No\", \"Yes\"))\n y\n}\n\n# Apply the convert_count function to get final training and testing DTMs\ndatasetNB <- apply(dtm, 2, convert_count)\n\ndataset = as.data.frame(as.matrix(datasetNB))\n\ndataset$Class = data_1$sentiment\nstr(dataset$Class)\n\nhead(dataset)\ndim(dataset)\n\n# Splitting data\nset.seed(222)\nsplit = sample(2,nrow(dataset),prob = c(0.75,0.25),replace = TRUE)\ntrain_set = dataset[split == 1,]\ntest_set = dataset[split == 2,] \n\nprop.table(table(train_set$Class))\nprop.table(table(test_set$Class))\n\n# SVM: Support vector machine\nsvm_classifier <- svm(Class~., data=train_set)\nsvm_classifier\n\n# Model evaluation: SVM\nsvm_pred = predict(svm_classifier,test_set)\n\nconfusionMatrix(svm_pred,test_set$Class)", "meta": {"hexsha": "6f13c9fd358fbb389f61847110f0dadaa9a1d18c", "size": 2519, "ext": "r", "lang": "R", "max_stars_repo_path": "svm.r", "max_stars_repo_name": "SiddharthanSingaravel/Identifying-Sentiments-in-HealthTwitter", "max_stars_repo_head_hexsha": "a743fa1c7bf113b0e69a31ebc234a2a3690f6c70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "svm.r", "max_issues_repo_name": "SiddharthanSingaravel/Identifying-Sentiments-in-HealthTwitter", "max_issues_repo_head_hexsha": "a743fa1c7bf113b0e69a31ebc234a2a3690f6c70", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "svm.r", "max_forks_repo_name": "SiddharthanSingaravel/Identifying-Sentiments-in-HealthTwitter", "max_forks_repo_head_hexsha": "a743fa1c7bf113b0e69a31ebc234a2a3690f6c70", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.4444444444, "max_line_length": 80, "alphanum_fraction": 0.7320365224, "num_tokens": 736, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5234203489363239, "lm_q1q2_score": 0.31611020368002246}} {"text": "# Unit tests for the Bioconductor layer\n# \n# Author: Renaud Gaujoux\n# Created: Mar 6, 2013\n###############################################################################\n\n\n# check extension of Bioc access methods\n# => this also serves to check that they are correctly exported\ntest.access <- function(){\n\t\n\tx <- nmfModel(3, 20, 10)\t\n\t.check <- function(fun, val, newval){\n\t\tmsg <- function(...) paste(fun, ': ', ..., sep='')\n\t\tf <- match.fun(fun)\n\t\tcheckIdentical(f(x), val, msg('on fresh object returns NULL'))\n\t\t\n\t\tif( isNumber(newval) ) newval <- paste('aaa', 1:newval)\n\t\tres <- try(eval(parse(text=paste(fun, '(x) <- newval', sep=''))))\n\t\tcheckTrue(!is(res, 'try-error'), msg('setting value works'))\n\t\tcheckIdentical(f(x), newval, msg('new value is correct'))\n\t\tres <- try(eval(parse(text=paste(fun, '(x) <- val', sep=''))))\n\t\tcheckTrue(!is(res, 'try-error'), msg('resetting value works'))\n\t\tcheckIdentical(f(x), val, msg('reset value is correct'))\n\t}\n\t\n\tcheckIdentical(nbasis(x), nmeta(x), 'nmeta is defined')\n\t.check('featureNames', NULL, 20)\n\t.check('sampleNames', NULL, 10)\n\t.check('basisnames', NULL, 3)\n\t.check('metaprofiles', coef(x), rmatrix(coef(x)))\t\n\t.check('metagenes', basis(x), rmatrix(basis(x)))\n\t\n}", "meta": {"hexsha": "c00e7a431197783114fb59cff6d4f438b9ede0f9", "size": 1219, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/NMF/tests/runit.bioc.r", "max_stars_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_stars_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/NMF/tests/runit.bioc.r", "max_issues_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_issues_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-12T14:06:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-10T23:26:27.000Z", "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/NMF/tests/runit.bioc.r", "max_forks_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_forks_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.8529411765, "max_line_length": 79, "alphanum_fraction": 0.6029532404, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31609223341560055}} {"text": "context(\"Network bootstrap\")\n\n# rn(list=ls())\n# library(microbenchmark)\n# library(netdiffuseR)\n#\n# set.seed(1231)\n# g <- rgraph_ba(t=1e2, m=2)\n# index <- sample(1:nnodes(g), nnodes(g), TRUE)\n# E <- g@x\n# g <- g[index,index]\n#\n# microbenchmark(\n# R = netdiffuseR:::bootnet_fillselfR(g, index, E),\n# Cpp = netdiffuseR:::bootnet_fillself(g, index, E), times=1e3,\n# unit= \"relative\"\n# )\n\n# ------------------------------------------------------------------------------\ntest_that(\"Filling zeros\", {\n\n set.seed(123)\n n <- 5\n g <- rgraph_ba(t = n-1, self=FALSE, m = 1)\n\n set.seed(1)\n ans0 <- bootnet(g, function(w, i, ...) length(w@x), R=100,\n resample.args = list(self=FALSE, useR=FALSE))\n\n set.seed(1)\n ans1 <- bootnet(g, function(w, i, ...) length(w@x), R=100,\n resample.args = list(self=FALSE, useR=TRUE))\n\n expect_equal(ans1[-length(ans0)], ans1[-length(ans1)])\n})\n\n# ------------------------------------------------------------------------------\ntest_that(\"Methods\", {\n # Generating the data\n set.seed(1291)\n\n # Static graphs\n graphdg <- rgraph_ba(t=9)\n graphmt <- as.matrix(graphdg)\n\n set.seed(123); ans0 <- resample_graph(graphdg)\n set.seed(123); ans1 <- resample_graph(graphmt)\n\n expect_equal(ans0, ans1)\n\n # Dynamic graphs\n graphls <- lapply(1:3, function(x) rgraph_ba(t=9))\n names(graphls) <- 2001:2003\n toa <- sample(c(2001:2003, NA), 10, TRUE)\n\n graphdn <- as_diffnet(graphls, toa, t0=2001, t1=2003)$graph\n graphar <- lapply(graphls, as.matrix)\n graphar <- array(unlist(graphar), dim=c(10,10,3),\n dimnames = list(1:10, 1:10, 2001:2003))\n\n set.seed(123); ans0 <- resample_graph(graphls)\n set.seed(123); ans1 <- resample_graph(graphdn)\n set.seed(123); ans2 <- resample_graph(graphar)\n\n expect_equivalent(ans0, ans1)\n expect_equivalent(ans0, ans2)\n})\n\n# ------------------------------------------------------------------------------\ntest_that(\"diffnet_bootnet methods\", {\n set.seed(1222)\n x <- rgraph_ba(t=19, m=1)\n ans <- bootnet(x, function(g,...) mean(dgr(g)), R=50)\n\n expect_output(print(ans), \"Network Bootstrap\")\n expect_silent(hist(ans, ask=FALSE))\n expect_s3_class(c(ans, ans), \"diffnet_bootnet\")\n expect_output(print(c(ans, ans)), \": 100\")\n\n})\n# rn(list=ls())\n# library(microbenchmark)\n# library(netdiffuseR)\n#\n# set.seed(1231)\n# g <- rgraph_ba(t=1e2, m=2)\n# index <- sample(1:nnodes(g), nnodes(g), TRUE)\n# E <- g@x\n# g <- g[index,index]\n#\n# microbenchmark(\n# R = netdiffuseR:::bootnet_fillselfR(g, index, E),\n# Cpp = netdiffuseR:::bootnet_fillself(g, index, E), times=1e3,\n# unit= \"relative\"\n# )\n\n# rm(list=ls())\n# library(netdiffuseR)\n# set.seed(123)\n# n <- 5\n# g <- rgraph_ba(t = n-1, self=FALSE, m = 1)\n#\n# set.seed(1); ans0 <- bootnet(g, function(w, ...) ifelse(inherits(w, \"list\"), length(w$graph@x), length(w@x)), R=100)\n# set.seed(1); ans1 <- bootnet(g, function(w, ...) ifelse(inherits(w, \"list\"), length(w$graph@x), length(w@x)), R=100,\n# resample.args = list(self=FALSE, useR=FALSE))\n#\n\n# library(netdiffuseR)\n# n <- 100\n# G <- rgraph_ws(n=n, p = .5, undirected = FALSE, self=FALSE, k=4)\n# G <- list(G,G)\n# Y1 <- sample(c(0,1), n, TRUE)\n# Y2 <- Y1\n# Y2[Y2==0] <- sample(c(0,1), sum(Y2==0), TRUE)\n# X <- runif(n*2)\n# dat <- data.frame(Y=c(Y1, Y2), X, year=c(rep(1,n), rep(2,n)))\n# dat1 <- subset(dat, year==1)\n# dat2 <- subset(dat, year==2)\n#\n# ans1 <- bootnet(G, function(g, idx) {\n# d <- rbind(dat1[idx,], dat2[idx,])\n# suppressMessages(netmatch(d, g, \"year\", \"Y\", \"X\", treat_thr = 3, method=\"cem\")$fATT)\n# }, R=1e3)\n#\n# ans2 <- struct_test(G, function(g, idx) {\n# suppressMessages(netmatch(dat, g, \"year\", \"Y\", \"X\", treat_thr = 3, method=\"cem\")$fATT)\n# }, R=1e3)\n#\n", "meta": {"hexsha": "9f8c74eb1d870819f0c75f13c43d64625b9c957c", "size": 3728, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-bootnet.r", "max_stars_repo_name": "USCCANA/netdiffuseR", "max_stars_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2015-12-15T02:49:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T02:48:37.000Z", "max_issues_repo_path": "tests/testthat/test-bootnet.r", "max_issues_repo_name": "USCCANA/netdiffuseR", "max_issues_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2015-12-17T03:43:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T18:50:22.000Z", "max_forks_repo_path": "tests/testthat/test-bootnet.r", "max_forks_repo_name": "USCCANA/netdiffuseR", "max_forks_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-12-28T21:47:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T19:48:08.000Z", "avg_line_length": 28.8992248062, "max_line_length": 118, "alphanum_fraction": 0.5721566524, "num_tokens": 1260, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6224593312018546, "lm_q1q2_score": 0.31609223341560055}} {"text": "MGRAST_plot_pca <<- function(file_in,\n file_out = \"my_pca\",\n \n num_PCs = 2,\n\n produce_fig = FALSE,\n PC1=\"PC1\",\n PC2=\"PC2\",\n \n image_out = \"my_pca\",\n image_title = image_out,\n figure_width = 950,\n figure_height = 950,\n points_color = \"red\", #c (\"color1\",\"color2\", ... ,\"color_n\") e.g. c(\"red\",\"red\",\"red\")\n figure_res = NA,\n lab_cex= 1,\n axis_cex = 1,\n points_text_cex = .8)\n\n \n{\n\n \n suppressPackageStartupMessages(library(pcaMethods)) # load the neccessary packages (suppress messages)\n \n \n { # sub to import the input_file\n input_object = data.matrix(read.table(file_in, row.names=1, header=TRUE, sep=\"\\t\", comment.char=\"\", quote=\"\"))\n } \n \n number_entries = (dim(input_object)[1]) # get dimensions of input object\n number_samples = (dim(input_object)[2])\n \n my_pcaRes <<- pca(input_object, nPcs = num_PCs) # Run pca and create pcaRes object\n \n # if (produce_fig == TRUE){ # option to produce figure\n # \n # suppressPackageStartupMessages(library(Cairo))\n # \n # CairoPNG(image_out, width = figure_width, height = figure_height, pointsize = 12, res = figure_res , units = \"px\")\n # \n # plot(my_pcaRes@loadings[,PC1],\n # my_pcaRes@loadings[,PC2], \n # cex.axis = axis_cex,\n # cex.lab = lab_cex,\n # main = image_title,\n # type = \"p\",\n # col = points_color,\n # xlab = paste(PC1, \"R^2 =\", round(my_pcaRes@R2[PC1], 4)),\n # ylab = paste(PC2, \"R^2 =\", round(my_pcaRes@R2[PC2], 4))\n # )\n # \n # if (points_color != 0){ \n # points(my_pcaRes@loadings[,PC1], my_pcaRes@loadings[,PC2], col = points_color, pch=19, cex=2 )\n # # color in the points if the points_color option has a value other than NA\n # # pch, integer values that indicate different point types (19 is a filled circle)\n # }\n # \n # text(my_pcaRes@loadings[,PC1], my_pcaRes@loadings[,PC2], labels=rownames(my_pcaRes@loadings), cex = points_text_cex)\n # } \n \n write.table(my_pcaRes@R2, file=file_out, sep=\"\\t\", col.names=FALSE, row.names=TRUE, append = FALSE)\n write.table(loadings(my_pcaRes), file=file_out, sep=\"\\t\", col.names=FALSE, row.names=TRUE, append = TRUE)\n \n}\n\n#write.table(loadings(my_pcaRes), file=gsub(\" \", \"_\", paste(files_out_prefix, \"LOADINGS_matrix.txt\")),\n# sep=\"\\t\", col.names = NA, row.names = TRUE)\n#write.table(scores(my_pcaRes), file=gsub(\" \", \"_\", paste(files_out_prefix, \"SCORES_matrix.txt\")),\n# sep=\"\\t\", col.names = NA, row.names = TRUE)\n\n", "meta": {"hexsha": "8af801c88a7c2ca34ee778e829bac65b2ff19104", "size": 3305, "ext": "r", "lang": "R", "max_stars_repo_path": "src/MGRAST/r/plot_pca.r", "max_stars_repo_name": "wilke/MG-RAST", "max_stars_repo_head_hexsha": "508e4736bafcf2a45d3f67d87dd196890f2da7a0", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 23, "max_stars_repo_stars_event_min_datetime": "2015-01-28T10:36:41.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-20T14:54:52.000Z", "max_issues_repo_path": "src/MGRAST/r/plot_pca.r", "max_issues_repo_name": "wilke/MG-RAST", "max_issues_repo_head_hexsha": "508e4736bafcf2a45d3f67d87dd196890f2da7a0", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2015-03-12T14:51:34.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T17:58:20.000Z", "max_forks_repo_path": "src/MGRAST/r/plot_pca.r", "max_forks_repo_name": "wilke/MG-RAST", "max_forks_repo_head_hexsha": "508e4736bafcf2a45d3f67d87dd196890f2da7a0", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 17, "max_forks_repo_forks_event_min_datetime": "2015-01-14T17:13:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-16T10:18:00.000Z", "avg_line_length": 45.9027777778, "max_line_length": 150, "alphanum_fraction": 0.4732223903, "num_tokens": 797, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3160922334156005}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of uniform prior wonky world model predictions\"\n#' author: \"Judith Degen\"\n#' date: \"January 12, 2014\"\n#' ---\n\nlibrary(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/writing/_2015/_journal_cognition/models/graphs/\")\nsource(\"../rscripts/helpers.r\")\n\n#' get model predictions\nd = read.table(\"../modelresults/parsed_uniform_untruthful_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nd$Type = \"untruthful\"\nuntruthful = d\nd = read.table(\"../modelresults/parsed_uniform_uninformative_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nd$Type = \"uninformative\"\nuninformative = d\n\nd = rbind(untruthful,uninformative)\nd$Type = as.factor(d$Type)\n\ntable(d$Item)\nnrow(d)\nhead(d)\nsummary(d)\n\n# get prior expectations\npriorexpectations = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorexpectations) = paste(priorexpectations$effect,priorexpectations$object)\nd$PriorExpectation = priorexpectations[as.character(d$Item),]$expectation\n\n# get smoothed prior probabilities\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nhead(priorprobs)\nrow.names(priorprobs) = priorprobs$Item\nmpriorprobs = melt(priorprobs, id.vars=c(\"Item\"))\nhead(mpriorprobs)\nrow.names(mpriorprobs) = paste(mpriorprobs$Item,mpriorprobs$variable)\nd$PriorProbability = mpriorprobs[paste(as.character(d$Item),\" X\",d$State,sep=\"\"),]$value\nd$AllPriorProbability = priorprobs[paste(as.character(d$Item)),]$X15\nhead(d)\nd$QUD = NULL\nd$Alternatives = NULL\n\nload(\"../mp-uniform.RData\")\nhead(mp)\nmp$Type = \"cooperative\"\nmp = mp[,names(d)]\nd = rbind(d,mp)\n\nallprobs = droplevels(subset(d, State == 15 & Quantifier == \"some\"))\nggplot(allprobs, aes(x=PriorProbability, y=PosteriorProbability, shape=as.factor(SpeakerOptimality),color=Type)) +\n geom_point() +\n geom_smooth() +\n facet_grid(SpeakerOptimality~WonkyWorldPrior)\nggsave(\"model-predictions-unreliablespeaker.pdf\")\n\nbest = droplevels(subset(allprobs, SpeakerOptimality == 2 & WonkyWorldPrior == .5))\nggplot(best, aes(x=PriorProbability, y=PosteriorProbability,color=Type)) +\n geom_point() +\n geom_smooth() +\n scale_x_continuous(\"Prior probability of all-state\") +\n scale_y_continuous(\"Predicted posterior probability of all-state\")\nggsave(\"model-predictions-unreliablespeaker-spopt2-wwprior.5.pdf\",width=6,height=4)\nggsave(\"../../pics/modelpredictions-unreliablespeaker.pdf\",width=6,height=4)\n", "meta": {"hexsha": "254983f617ccac204d0c486ef6e87c6c8f63d845", "size": 2747, "ext": "r", "lang": "R", "max_stars_repo_path": "writing/_2015/_journal_cognition/models/rscripts/modelpredictions-untruthful.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "writing/_2015/_journal_cognition/models/rscripts/modelpredictions-untruthful.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "writing/_2015/_journal_cognition/models/rscripts/modelpredictions-untruthful.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.0, "max_line_length": 215, "alphanum_fraction": 0.7670185657, "num_tokens": 793, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3160270463246756}} {"text": "\n\neffects.data.export = function( Y ) {\n \n Z = Y$x\n Z$predictedPr = inverse.logit( Y$fit )\n Z$se = Y$se # keep on logit scale as it is meaningless of the Pr. scale.\n Z$upper = inverse.logit( Y$upper )\n Z$lower = inverse.logit( Y$lower )\n\n return (Z)\n\n}\n\n\n", "meta": {"hexsha": "e3e1903b4a636eae5669c7be5845eed12346af57", "size": 261, "ext": "r", "lang": "R", "max_stars_repo_path": "R/effects.data.export.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/effects.data.export.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/effects.data.export.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 16.3125, "max_line_length": 74, "alphanum_fraction": 0.6245210728, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307806984444, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3160270394875408}} {"text": "##\n## Java Information Dynamics Toolkit (JIDT)\n## Copyright (C) 2012, Joseph T. Lizier\n## \n## This program is free software: you can redistribute it and/or modify\n## it under the terms of the GNU General Public License as published by\n## the Free Software Foundation, either version 3 of the License, or\n## (at your option) any later version.\n## \n## This program is distributed in the hope that it will be useful,\n## but WITHOUT ANY WARRANTY; without even the implied warranty of\n## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n## GNU General Public License for more details.\n## \n## You should have received a copy of the GNU General Public License\n## along with this program. If not, see .\n##\n\n# = Example 2 - Transfer entropy on multidimensional binary data =\n\n# Simple transfer entropy (TE) calculation on multidimensional binary data using the discrete TE calculator.\n\n# Load the rJava library and start the JVM\nlibrary(\"rJava\")\n.jinit()\n\n# Change location of jar to match yours:\n# IMPORTANT -- If using the default below, make sure you have set the working directory\n# in R (e.g. with setwd()) to the location of this file (i.e. demos/r) !!\n.jaddClassPath(\"../../infodynamics.jar\")\n\n# Create many columns in a multidimensional array (2 rows by 100 columns),\n# where the next time step (row 2) copies the value of the column on the left\n# from the previous time step (row 1):\ntwoDTimeSeriesRtime1 <- sample(0:1, 100, replace=\"TRUE\")\ntwoDTimeSeriesRtime2 <- c(twoDTimeSeriesRtime1[100], twoDTimeSeriesRtime1[1:99])\ntwoDTimeSeriesR <- rbind(twoDTimeSeriesRtime1, twoDTimeSeriesRtime2)\n\n# Create a TE calculator and run it:\nteCalc<-.jnew(\"infodynamics/measures/discrete/TransferEntropyCalculatorDiscrete\", 2L, 1L)\n.jcall(teCalc,\"V\",\"initialise\") # V for void return value\n# Add observations of transfer across one cell to the right per time step:\ntwoDTimeSeriesJava <- .jarray(twoDTimeSeriesR, \"[I\", dispatch=TRUE)\n.jcall(teCalc,\"V\",\"addObservations\", twoDTimeSeriesJava, 1L)\nresult2D <- .jcall(teCalc,\"D\",\"computeAverageLocalOfObservations\")\ncat(\"The result should be close to 1 bit here, since we are executing copy operations of what is effectively a random bit to each cell here: \", result2D, \"\\n\")\n\n", "meta": {"hexsha": "705a6e2bdd4c4816c2a78f2a3a7672d880c24ead", "size": 2273, "ext": "r", "lang": "R", "max_stars_repo_path": "infodynamics-dist-1.2.1/demos/r/example2TeMultidimBinaryData.r", "max_stars_repo_name": "jmmccracken/ECA", "max_stars_repo_head_hexsha": "1476ef3f86aa7951f9f5fdc3ab27b9170ed77c75", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2016-02-15T19:51:42.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-08T06:09:59.000Z", "max_issues_repo_path": "infodynamics-dist-1.2.1/demos/r/example2TeMultidimBinaryData.r", "max_issues_repo_name": "jmmccracken/ECA", "max_issues_repo_head_hexsha": "1476ef3f86aa7951f9f5fdc3ab27b9170ed77c75", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "infodynamics-dist-1.2.1/demos/r/example2TeMultidimBinaryData.r", "max_forks_repo_name": "jmmccracken/ECA", "max_forks_repo_head_hexsha": "1476ef3f86aa7951f9f5fdc3ab27b9170ed77c75", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-02-21T15:06:12.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-08T06:10:09.000Z", "avg_line_length": 47.3541666667, "max_line_length": 159, "alphanum_fraction": 0.7461504619, "num_tokens": 598, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583270090337583, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3160234358137084}} {"text": "#\r\nlibrary(fields)\r\nlibrary(gplots)\r\nlibrary(lme4)\r\nlibrary(abind)\r\nlibrary(stringr)\r\n\r\nsource(\"~esswein/lib/R/pt.color.r\")\r\nsource(\"~esswein/lib/R/rle_ext.r\")\r\nsource(\"~esswein/lib/R/halt.r\")\r\nsource('~esswein/lib/R/day_of_year.R')\r\nsource('~esswein/lib/R/runinfo.R')\r\nsource('~esswein/lib/R/strssep.R')\r\nsource('~esswein/lib/R/julday.R')\r\nsource('~esswein/lib/R/midpoint.R')\r\nsource('~esswein/lib/R/earthdist.R')\r\nsource('~esswein/lib/R/formula.2.character.R')\r\nsource('~esswein/lib/R/position.labels.R')\r\nadd.alpha <- function (hex.color.list, alpha) sprintf(\"%s%02X\", hex.color.list, floor(alpha * 256))\r\n\r\nif (!exists('do.pdf')) do.pdf = F\r\n\r\nri = runinfo()\r\nhdr = paste('written_by',ri$prog)\r\n\r\nfile.pdf = sub('Regress.rds','PlotRegress.pdf',file.regress)\r\n\r\ntimefrac.color = tim.colors(9)\r\ntimefrac.color = timefrac.color[-6]\r\ntimefrac.color = add.alpha(timefrac.color,.5)\r\nn.timefrac.color = length(timefrac.color)\r\n\r\nif (file.access(file.regress) == 0) {\r\n dat.regress = readRDS(file.regress)\r\n}\r\nif (!exists('dat.regress')) stop('dat.regress not available')\r\nsite.df = dat.regress$site.df\r\nsite.data = dat.regress$site.data\r\ndoylist = unique(sort(site.df$DOY))\r\nmodel.s = as.character(deparse(formula(dat.regress$fit)))\r\nmodel.title = dat.regress$model.title\r\n\r\n# Sites that have some PM2.5 for this region and time period:\r\nsitelist = c()\r\nlonlist = c()\r\nlatlist = c()\r\nfor (i in 1:dim(site.data)[3]) {\r\n if (length(which(!is.na(site.data[,'PM2.5',i]))) > 0) {\r\n sitelist = c(sitelist,dimnames(site.data)[[3]][i])\r\n lonlist = c(lonlist,site.data[1,'lon',i])\r\n latlist = c(latlist,site.data[1,'lat',i])\r\n }\r\n}\r\nlon.ctr = mean(lonlist)\r\nlat.ctr = mean(latlist)\r\ndist.ctr = earthdist(lat.ctr,lon.ctr,latlist,lonlist)\r\nhdist.ctr = earthdist(lat.ctr,lon.ctr,lat.ctr,lonlist)\r\nhdist.ctr[lonlist < lon.ctr] = -hdist.ctr[lonlist < lon.ctr]\r\nvdist.ctr = earthdist(lat.ctr,lon.ctr,latlist,lon.ctr)\r\nvdist.ctr[latlist < lat.ctr] = -vdist.ctr[latlist < lat.ctr]\r\nangle.ctr = atan2(vdist.ctr,hdist.ctr)*180/pi\r\n\r\nsitechroma = 90\r\nsitehue = angle.ctr\r\nsitelum = 90 - 70*(dist.ctr-min(dist.ctr))/(max(dist.ctr)-min(dist.ctr))\r\nsitecolor = hcl(sitehue,sitechroma,sitelum)\r\nptcolor = sitecolor[match(site.df$name,sitelist)]\r\n\r\nif (do.pdf) pdf(file=file.pdf,title=hdr)\r\n\r\n# Coverage plots:\r\nvar = 'PM2.5'\r\n# Same range as for predicted maps:\r\n# There are a few values outside this range.\r\nlim.PM2.5 = c(0,75)\r\nn.time = dim(site.data)[1]\r\nn.site = dim(site.data)[3]\r\n\r\ndaterange.dt = strptime(dat.regress$daterange,format='%Y%m%d',tz='GMT')\r\ndate.tick = pretty(daterange.dt)\r\ndate.tick = gsub('[A-Z]','',date.tick)\r\n\r\nimage.plot(site.data[,var,n.site:1],axes=F,main=str_c(dat.regress$region,' ',var),zlim=lim.PM2.5)\r\n\r\nlim = par('usr')\r\nxtick = midpoint(seq(lim[1],lim[2],len=n.time+1))\r\nytick = midpoint(seq(lim[3],lim[4],len=n.site+1))\r\n####\r\n## Note: for this set of runs only\r\n## TBD: programmaticaly choose dates\r\n####\r\ndoylab = c(324,345,1,25,48)\r\nwdoytick = rle_ext(site.data[,'DOY',1])$start\r\nwdoylab = rep.int(NA,length(doylab))\r\nfor (i in 1:length(doylab)) wdoylab[i] = min(which(site.data[,'DOY',1] == doylab[i]))\r\ndoylab = paste(month.abb[site.data[wdoylab,'month',1]],site.data[wdoylab,'day',1])\r\n\r\nsitelab = dimnames(site.data)[[3]][n.site:1]\r\nsitebase = sub('.*_','',sitelab)\r\nsiterle = rle_ext(sitebase)\r\n# Delete prefix from sites with unduplicated base names;\r\nwsingle = siterle$start[siterle$lengths == 1]\r\nsitelab[wsingle] = sub('^..._','',sitelab[wsingle])\r\nsitelab[grepl('EPA',sitelab)] = paste(sitebase[grepl('EPA',sitelab)],'E')\r\nsitelab[grepl('DAQ',sitelab)] = paste(sitebase[grepl('DAQ',sitelab)],'D')\r\n\r\n# Draw a point off the plot:\r\n# This is necessary to get \"text\" to work.\r\npoints(-10,-10)\r\n\r\ntoffset = -.02\r\nnday = dim(site.data)[1]\r\nxtitle = paste('Day of Year',site.data[1,'day',1],month.name[site.data[1,'month',1]],site.data[1,'year',1],'to',site.data[nday,'day',1],month.name[site.data[nday,'month',1]],site.data[nday,'year',1])\r\ntext(xtick[wdoylab],lim[3]+toffset,lab=doylab,srt=90,xpd=NA,adj=1,cex=1)\r\n# text(mean(xtick),lim[3]+6*toffset,lab=xtitle,xpd=NA)\r\ntext(lim[1]+toffset,ytick,lab=sitelab,xpd=NA,adj=1,cex=.8)\r\naxis(1,at=xtick[wdoytick],label=F,tcl=-.25)\r\n# halt()\r\n\r\nvar = 'Residuals'\r\nresid.by.site = matrix(NA,n.time,n.site)\r\nsites = unique(site.df$name)\r\ndates = unique(sort(site.df$yyyymmddhhmm))\r\ncolnames(resid.by.site) = sites\r\nisite.used = match(site.df$name,sites)\r\nitime.used = match(site.df$yyyymmddhhmm,dates)\r\nresid.by.site[cbind(itime.used,isite.used)] = site.df$residuals\r\nimage.plot(resid.by.site[,n.site:1],axes=F,main=str_c(dat.regress$region,' ',var))\r\n\r\nlim = par('usr')\r\nxtick = midpoint(seq(lim[1],lim[2],len=n.time+1))\r\nytick = midpoint(seq(lim[3],lim[4],len=n.site+1))\r\npoints(-10,-10)\r\ntoffset = -.02\r\ntext(xtick,lim[3]+toffset,lab=site.data[,'DOY',1],srt=90,xpd=NA,adj=1,cex=.6)\r\ntext(lim[1]+toffset,ytick,lab=dimnames(site.data)[[3]][n.site:1],xpd=NA,adj=1,cex=.6)\r\n\r\nplot(lonlist,latlist,col=sitecolor,pch=19,cex=2,xlim=range(pretty(site.df$lon)),ylim=range(pretty(site.df$lat)),main='Site Colors',xlab='',ylab='',bty='n')\r\n position.labels(lonlist,latlist,sitelist,xlab='',ylab='',map=F,cex=2,cex.lab=.8)\r\n\r\n# Scatterplot fitted vs. obs:\r\ntitle = c(dat.regress$region,model.title,str_c('Rval',signif(dat.regress$Rval,3),'RMSerr',signif(dat.regress$RMSerr,3),sep=' '))\r\nplot(fitted(dat.regress$fit),fitted(dat.regress$fit)+residuals(dat.regress$fit),xlim=lim.PM2.5,ylim=lim.PM2.5,xlab='Fitted',ylab='Obs',main=title,col=ptcolor,pch=19)\r\n\r\n# Time series fitted/obs vs day\r\nplot(site.df$jd,fitted(dat.regress$fit)/(fitted(dat.regress$fit)+residuals(dat.regress$fit)),xlab='DOY',ylab='Fitted/Obs',main=title,col=ptcolor,pch=19)\r\n\r\n# Obs vs AOT\r\nif ('AOT' %in% names(dat.regress$model.vars)) {\r\n rval = cor(site.df$AOT,fitted(dat.regress$fit)+residuals(dat.regress$fit),use='complete.obs')\r\n RMSerr = sigma(lm(PM2.5~AOT,data=site.df))\r\n title = c(dat.regress$region,str_c('Rval',signif(rval,3),'RMSerr',signif(RMSerr,3),sep=' '))\r\n plot(site.df$AOT,site.df$PM2.5,xlab='AOT',ylab='Obs',main=title,col=ptcolor,pch=19,ylim=c(0,70))\r\n}\r\n\r\n# Obs vs AOT/CWV\r\nif ('AOT' %in% names(dat.regress$model.vars) & 'CWV' %in% names(dat.regress$model.vars)) {\r\n rval = cor(site.df$AOT/site.df$CWV,fitted(dat.regress$fit)+residuals(dat.regress$fit),use='complete.obs')\r\n RMSerr = sigma(lm(PM2.5~I(AOT/CWV),data=site.df))\r\n title = c(dat.regress$region,str_c('Rval',signif(rval,3),'RMSerr',signif(RMSerr,3),sep=' '))\r\n plot(site.df$AOT/site.df$CWV,fitted(dat.regress$fit)+residuals(dat.regress$fit),xlab='AOT/CWV',ylab='Obs',main=title,col=ptcolor,pch=19)\r\n}\r\n\r\n# Random effects\r\nrandeffects = ranef(dat.regress$fit)\r\nplot(randeffects[[1]][,1],type='h',xlab='DOY',ylab=paste(randeffects$name,'Random Effect'),main=title)\r\npoints(randeffects[[1]][,1],pch=21,bg=\"cadetblue\",cex=0.6)\r\n\r\n### When rest of the script is fixed, move this to end\r\nif (do.pdf) {\r\n dev.off()\r\n cat(file.pdf,'\\n')\r\n}\r\n\r\n\r\n## May want to randomize point plot order, so that points later in time\r\n## do not obscure earlier points so much.\r\n\r\n", "meta": {"hexsha": "3f361a08f3acfdd35d3533ef8fcbe869dffc8e46", "size": 7084, "ext": "r", "lang": "R", "max_stars_repo_path": "Regress.PM2.5.Plot.r", "max_stars_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_stars_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Regress.PM2.5.Plot.r", "max_issues_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_issues_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Regress.PM2.5.Plot.r", "max_forks_repo_name": "RobertBChatfield/AOT_to_PM2.5", "max_forks_repo_head_hexsha": "c6be4af306ce96f4f8330c8f9f911ece210c5f77", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.5754189944, "max_line_length": 200, "alphanum_fraction": 0.6815358554, "num_tokens": 2434, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.566018549837479, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.316023435668827}} {"text": "# Set up the environment\n\n# Makes sure package is not already installed and loaded before it loads it\nprepare.package <- function(package) {\n if(package %in% rownames(installed.packages()) == FALSE) {\n install.packages(package)\n }\n require(package, character.only = TRUE)\n}\n\nsetwd(dirname(parent.frame(2)$ofile))\nprepare.package(\"doBy\")\nprepare.package(\"ggplot2\")\nprepare.package(\"gdata\")\nprepare.package(\"plyr\")\n\n################################################################################\n# First challenge: load in and clean up the data\n################################################################################\n\ncat(c(\"Initializing data...\\n\"), sep=\"\")\n\nif(file.exists(\"./data_set/rollingsales_manhattan.xls\")) {\n mh <- read.xls(\"./data_set/rollingsales_manhattan.xls\",pattern=\"BOROUGH\")\n} else {\n cat(c(\"Can't find data file!\\n\"), sep=\"\")\n}\n\n# Get some information about what we have to work with\nhead(mh)\nsummary(mh)\n\n# Set column names to lower case\nnames(mh) <-tolower(names(mh))\n\n# Creates a numerical version of columns containing commas and/or dollar signs\nmh$sale.price.n <- as.numeric(gsub(\"[^[:digit:]]\", \"\", mh$sale.price))\nmh$gross.sqft <- as.numeric(gsub(\"[^[:digit:]]\", \"\", mh$gross.square.feet))\nmh$land.sqft <- as.numeric(gsub(\"[^[:digit:]]\", \"\", mh$land.square.feet))\n\n# See if we have any invalid values within the newly created columns\ncount(is.na(mh$sale.price.n))\ncount(is.na(mh$gross.sqft))\ncount(is.na(mh$land.sqft))\n\n################################################################################\n# Next, conduct exploratory data analysis in order to find out where there are \n# outlier or missing values, decide how you will treat them, make sure the \n# dates are formatted correctly, make sure values you think are numerical are \n# being treated as such, etc.\n################################################################################\n\n# Making sure dates are formatted correctly\nmh$sale.date <- as.Date(mh$sale.date)\n# Making sure values I think are numerical are being treated as such\nmh$year.built <- as.numeric(as.character(mh$year.built))\n\n# Some exploration to make sure there's nothing weird going on with sale prices\n\n# Total sales on record\nggplot(mh, aes(x=sale.price.n)) + \n geom_histogram(binwidth = diff(range(mh$sale.price.n)))\n# Total sales on record with a price more than 0\nggplot(subset(mh, sale.price.n>0), aes(x=sale.price.n)) + \n geom_histogram(binwidth = diff(range(mh$sale.price.n)))\n# Total sales on record with a price of 0\nggplot(subset(mh, sale.price.n==0), aes(x=gross.sqft)) + \n geom_histogram(binwidth = diff(range(mh$gross.sqft)))\n\n# We shall treat sales with price of 0 as missing values and omit them\n\n# Keep only the actual sales\nmh.sale <- mh[mh$sale.price.n!=0,]\n\n# Let us look for some outliers\nggplot(mh.sale, aes(gross.sqft, sale.price.n)) + geom_point()\nggplot(mh.sale, aes(log(gross.sqft), log(sale.price.n))) + geom_point()\n\n# Seems like we found some let's investigate further and see what we can do\n\n# Factorize building.class.category\nmh.sale$building.class.category <- factor(mh.sale$building.class.category)\n\n# Let us look at building types and see what is most reasonable to work with\nlevels(mh.sale$building.class.category)\n\n# We do want to work with building types that give us gross.sqft and lad.sqft\nsummaryBy(gross.sqft+land.sqft~building.class.category, \n data=mh.sale, FUN=c(min, max))\n\n# It seems most reasonable to work with 1, 2, and 3 family homes\nmh.homes <- mh.sale[which(grepl(\"FAMILY\",mh.sale$building.class.category)),]\n\n# Let us look and see if we still have outliers\nggplot(mh.homes, aes(gross.sqft, sale.price.n)) + geom_point()\nggplot(mh.homes, aes(log(gross.sqft), log(sale.price.n))) + geom_point()\n\n# We seem to have some, let's take a closer look\nsummaryBy(sale.price.n~address, data=subset(mh.homes, sale.price.n<10000), \n FUN=c(length, min, max))\n\n# The summarized values above are definitely outliers and so we shall omit them\nmh.homes$outliers <- (log(mh.homes$sale.price.n) <=5) + 0\nmh.homes <- mh.homes[which(mh.homes$outliers==0),]\n\n# Let us look and see if we still have outliers\nggplot(mh.homes, aes(gross.sqft, sale.price.n)) + geom_point()\nggplot(mh.homes, aes(log(gross.sqft), log(sale.price.n))) + geom_point()\n\n################################################################################\n# Once the data is in good shape, conduct exploratory data analysis to visualize\n# and make compariosons (i) across neighborhoods,\n################################################################################\n\n# Factorize neighborhood\nmh.homes$neighborhood <- factor(mh.homes$neighborhood)\n\n# Let's first see which neighborhoods are included in our data\nlevels(mh.homes$neighborhood)\n\n# Let's explore the data quantitatively first:\n\nmetrics <- function(x) {\n c(length(x), # count\n round(mean(x)), # mean\n round(median(x)), # median\n names(sort(-table(x)))[1], # mode\n round(sd(x)), # standard deviation\n min(x), # minimum\n max(x), # maximum\n max(x)-min(x)) # range\n}\n\n# Sale price across neighborhoods\nsummaryBy(sale.price.n~neighborhood, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Gross square feet across neighborhoods\nsummaryBy(gross.sqft~neighborhood, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Land square feet across neighborhoods\nsummaryBy(land.sqft~neighborhood, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Year built across neighborhoods\nsummaryBy(year.built~neighborhood, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n\n# Let's now explore the data by visualizing it:\n\n# Number of sales of homes across neighborhoods\nggplot(mh.homes, aes(x=neighborhood, fill=neighborhood, ymax=max(..count..))) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/28)) +\n stat_bin(binwidth=1,geom=\"text\",drop=TRUE,aes(label=..count.., vjust=-1))\n\n# Sale price across neighborhoods\n# Histogram\nggplot(mh.homes, aes(x=sale.price.n, fill=neighborhood)) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/60))\n# Box and whisker plot\nggplot(mh.homes, aes(x=neighborhood, y=sale.price.n, fill=neighborhood)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=sale.price.n, color=neighborhood)) + geom_density()\n# Jitter Plot\nggplot(mh.homes, aes(x=neighborhood, y=sale.price.n, color=neighborhood)) + \n geom_jitter()\n# Scatter Plot\nggplot(mh.homes, aes(x=neighborhood, sale.price.n, color=neighborhood)) + \n geom_point()\n# Kernel density estimate\nggplot(mh.homes, aes(x=sale.price.n, fill=neighborhood)) + stat_density()\n\n# Gross square feet across neighborhoods\n# Histogram\nggplot(mh.homes, aes(x=gross.sqft, fill=neighborhood)) + \n geom_histogram(binwidth = diff(range(mh.homes$gross.sqft)/60))\n# Box and whisker plot\nggplot(mh.homes, aes(x=neighborhood, y=gross.sqft, fill=neighborhood)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=gross.sqft, color=neighborhood)) + geom_density()\n# Jitter Plot\nggplot(mh.homes, aes(x=neighborhood, y=gross.sqft, color=neighborhood)) + \n geom_jitter()\n# Scatter Plot\nggplot(mh.homes, aes(x=neighborhood, gross.sqft, color=neighborhood)) + \n geom_point()\n# Kernel density estimate\nggplot(mh.homes, aes(x=gross.sqft, fill=neighborhood)) + stat_density()\n\n# Land square feet across neighborhoods\n# Histogram\nggplot(mh.homes, aes(x=land.sqft, fill=neighborhood)) + \n geom_histogram(binwidth = diff(range(mh.homes$land.sqft)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=neighborhood, y=land.sqft, fill=neighborhood)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=land.sqft, color=neighborhood)) + geom_density()\n# Jitter Plot\nggplot(mh.homes, aes(x=neighborhood, y=land.sqft, color=neighborhood)) + \n geom_jitter()\n# Scatter Plot\nggplot(mh.homes, aes(x=neighborhood, land.sqft, color=neighborhood)) + \n geom_point()\n# Kernel density estimate\nggplot(mh.homes, aes(x=land.sqft, fill=neighborhood)) + stat_density()\n\n# Year built across neighborhoods\n# Histogram\nggplot(mh.homes, aes(x=year.built, fill=neighborhood)) + \n geom_histogram(binwidth = diff(range(mh.homes$year.built)/60))\n# Box and whisker plot\nggplot(mh.homes, aes(x=neighborhood, y=year.built, fill=neighborhood)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=year.built, color=neighborhood)) + geom_density()\n# Jitter Plot\nggplot(mh.homes, aes(x=neighborhood, y=year.built, color=neighborhood)) + \n geom_jitter()\n# Scatter Plot\nggplot(mh.homes, aes(x=neighborhood, year.built, color=neighborhood)) + \n geom_point()\n# Kernel density estimate\nggplot(mh.homes, aes(x=year.built, fill=neighborhood)) + stat_density()\n\n################################################################################\n# and (ii) across time.\n################################################################################\n\n# We only have data for August, 2012 - August 2013\n# Seeing as we don't have full years or comparable months of those years\n# it would be wiser to conduct EDA across months and weekdays only.\n\n# Categorize sale dates by month and weekday\nmh.homes$sale.month <- format(mh.homes$sale.date, \"%B\")\nmh.homes$sale.day <- format(mh.homes$sale.date, \"%A\")\n\n# Factorize sale dates by month and weekday\nmh.homes$sale.month <- \n factor(mh.homes$sale.month, \n levels= c(\"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n \"July\", \"August\", \"September\", \"October\", \"November\",\n \"December\"))\nmh.homes$sale.day <- \n factor(mh.homes$sale.day, \n levels= c(\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"))\n\n# Let's explore the data quantitatively first:\n\n# Sale price across months\nsummaryBy(sale.price.n~sale.month, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Gross square feet across months\nsummaryBy(gross.sqft~sale.month, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Land square feet across months\nsummaryBy(land.sqft~sale.month, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Year built across months\nsummaryBy(year.built~sale.month, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n\n# Sale price across days of the week\nsummaryBy(sale.price.n~sale.day, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Gross square feet across days of the week\nsummaryBy(gross.sqft~sale.day, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Land square feet across days of the week\nsummaryBy(land.sqft~sale.day, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n# Year built across days of the week\nsummaryBy(year.built~sale.day, data=mh.homes, FUN=metrics,\n fun.names=c(\"Count\",\"Mean\",\"Median\",\"Mode\",\"SD\",\"Min\",\"Max\",\"Range\"))\n\n# Let's now explore the data by visualizing it:\n\n# Number of sales of homes\n# By month\nggplot(mh.homes, aes(x=sale.month, fill=sale.month, ymax=max(..count..))) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/12)) +\n stat_bin(binwidth=1,geom=\"text\",drop=TRUE,aes(label=..count.., vjust=-1))\n# By day of the week\nggplot(mh.homes, aes(x=sale.day, fill=sale.day, ymax=max(..count..))) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/7)) +\n stat_bin(binwidth=1,geom=\"text\",drop=TRUE,aes(label=..count.., vjust=-1))\n\n# Sale price across months\n# Histogram\nggplot(mh.homes, aes(x=sale.price.n, fill=sale.month)) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/60))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.month, y=sale.price.n, fill=sale.month)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=sale.price.n, color=sale.month)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.month, sale.price.n, color=sale.month)) + \n geom_point()\n# Sale price across days of the week\n# Histogram\nggplot(mh.homes, aes(x=sale.price.n, fill=sale.day)) + \n geom_histogram(binwidth = diff(range(mh.homes$sale.price.n)/60))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.day, y=sale.price.n, fill=sale.day)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=sale.price.n, color=sale.day)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.day, sale.price.n, color=sale.day)) + geom_point()\n\n# Gross square feet (for sold homes) across months\n# Histogram\nggplot(mh.homes, aes(x=gross.sqft, fill=sale.month)) + \n geom_histogram(binwidth = diff(range(mh.homes$gross.sqft)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.month, y=gross.sqft, fill=sale.month)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=gross.sqft, color=sale.month)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.month, gross.sqft, color=sale.month)) + geom_point()\n# Gross square feet (for sold homes) across days of the week\n# Histogram\nggplot(mh.homes, aes(x=gross.sqft, fill=sale.day)) + \n geom_histogram(binwidth = diff(range(mh.homes$gross.sqft)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.day, y=gross.sqft, fill=sale.day)) + geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=gross.sqft, color=sale.day)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.day, gross.sqft, color=sale.day)) + geom_point()\n\n# Land square feet (for sold homes) across months\n# Histogram\nggplot(mh.homes, aes(x=land.sqft, fill=sale.month)) + \n geom_histogram(binwidth = diff(range(mh.homes$land.sqft)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.month, y=land.sqft, fill=sale.month)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=land.sqft, color=sale.month)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.month, land.sqft, color=sale.month)) + geom_point()\n# Land square feet (for sold homes) across days of the week\n# Histogram\nggplot(mh.homes, aes(x=land.sqft, fill=sale.day)) + \n geom_histogram(binwidth = diff(range(mh.homes$land.sqft)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.day, y=land.sqft, fill=sale.day)) + geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=land.sqft, color=sale.day)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.day, land.sqft, color=sale.day)) + geom_point()\n\n# Year built (for sold homes) across months\n# Histogram\nggplot(mh.homes, aes(x=year.built, fill=sale.month)) + \n geom_histogram(binwidth = diff(range(mh.homes$year.built)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.month, y=year.built, fill=sale.month)) + \n geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=year.built, color=sale.month)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.month, year.built, color=sale.month)) + geom_point()\n# Year built (for sold homes) across days of the week\n# Histogram\nggplot(mh.homes, aes(x=year.built, fill=sale.day)) + \n geom_histogram(binwidth = diff(range(mh.homes$year.built)/30))\n# Box and whisker plot\nggplot(mh.homes, aes(x=sale.day, y=year.built, fill=sale.day)) + geom_boxplot()\n# Density plot\nggplot(mh.homes, aes(x=year.built, color=sale.day)) + geom_density()\n# Scatter Plot\nggplot(mh.homes, aes(x=sale.day, year.built, color=sale.day)) + geom_point()\n\n################################################################################\n# If you have time, start looking for meaningful patters in this dataset.\n################################################################################\n\n# - Most sales happen in Harlem and the Upper East Side.\n# - Housing is most expensive in the Upper East Side.\n# - Housing is least expensive in Harlem.\n# - Houses have the most gross square feet in Gremich and the Upper East Side.\n# - Houses seem about equal in terms of gross square feet elsewhere.\n# - Houses seem about equal in terms of land square feet throughout.\n# - The Upper West side has the newest housing.\n# - Housing was built mostly around the early 1900s.\n# - Harlem has the newest housing.\n# - The amount of sales seem equally distributed around Wednesday.\n# - December has a lot more sales than any other month.\n# - The least amount of sales happen in September and October.\n# - Home sales according to other metrics across time seem equally distributed.\n", "meta": {"hexsha": "b3e926b9ff23967c96a3b94000aa6ea536b7d641", "size": 16744, "ext": "r", "lang": "R", "max_stars_repo_path": "realdirect/realdirect.r", "max_stars_repo_name": "limeonion/Airline-Timeliness", "max_stars_repo_head_hexsha": "c069fb02e8596b1e13526f3733b2b11638cfead8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "realdirect/realdirect.r", "max_issues_repo_name": "limeonion/Airline-Timeliness", "max_issues_repo_head_hexsha": "c069fb02e8596b1e13526f3733b2b11638cfead8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "realdirect/realdirect.r", "max_forks_repo_name": "limeonion/Airline-Timeliness", "max_forks_repo_head_hexsha": "c069fb02e8596b1e13526f3733b2b11638cfead8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.3898734177, "max_line_length": 80, "alphanum_fraction": 0.6703296703, "num_tokens": 4612, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961016, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.31602342749415074}} {"text": "require(Hmisc)\nset.seed(2)\nd <- expand.grid(major=c('Alabama', 'Alaska', 'Arkansas',\n 'Arizona', 'Nevada'),\n minor=c('East', 'West'),\n group=c('Female', 'Male'),\n city=0:2)\nn <- nrow(d)\n# d$x <- (1 : nrow(d)) + runif(n)\nd$num <- round(100*runif(n))\nd$denom <- d$num + round(100*runif(n))\nd$x <- d$num / d$denom\nd\n\nwith(d,\n dotchartpl(x, major, minor, group, city, big=city==0, num=num, denom=denom)\n )\n\n## Same without city, compute Famale - Male differences and conf. intervals\n## Within major groups sort in descending order of differences, show\n## differences with color of Female if positive, Male if negative,\n## add layer with horizontal bar centered at the difference and with\n## width equal to half-width of confidence interval\nd <- subset(d, city==0)\ni <- with(d, order(major, minor, group))\n# xless(d[i, ])\nwith(d,\n dotchartpl(x, major, minor, group, refgroup='Male', num=num, denom=denom,\n xlim=c(0,1))\n )\n\n\n# Original source of aeanonym: HH package\n# aeanonym <- read.table(hh(\"datasets/aedotplot.dat\"), header=TRUE, sep=\",\")\n# Modified to remove denominators from data and to generate raw data\n# (one record per event per subject)\n\nae <-\n structure(list(RAND = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, \n 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, \n 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, \n 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, \n 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c(\"a\", \n \"b\"), class = \"factor\"), PREF = structure(c(12L, 12L, \n 18L, 18L, 26L, 26L, 33L, 33L, 5L, 5L, 27L, 27L, 6L, 6L, 15L, \n 15L, 22L, 22L, 23L, 23L, 31L, 31L, 17L, 17L, 2L, 2L, 3L, 3L, \n 13L, 13L, 25L, 25L, 28L, 28L, 14L, 14L, 4L, 4L, 8L, 8L, 19L, \n 19L, 21L, 21L, 29L, 29L, 10L, 10L, 20L, 20L, 16L, 16L, 32L, 32L, \n 11L, 11L, 1L, 1L, 30L, 30L, 24L, 24L, 9L, 9L, 7L, 7L),\n .Label = tolower(c(\"ABDOMINAL PAIN\", \n \"ANOREXIA\", \"ARTHRALGIA\", \"BACK PAIN\", \"BRONCHITIS\", \"CHEST PAIN\", \n \"CHRONIC OBSTRUCTIVE AIRWAY\", \"COUGHING\", \"DIARRHEA\", \"DIZZINESS\", \n \"DYSPEPSIA\", \"DYSPNEA\", \"FATIGUE\", \"FLATULENCE\", \"GASTROESOPHAGEAL REFLUX\", \n \"HEADACHE\", \"HEMATURIA\", \"HYPERKALEMIA\", \"INFECTION VIRAL\", \"INJURY\", \n \"INSOMNIA\", \"MELENA\", \"MYALGIA\", \"NAUSEA\", \"PAIN\", \"RASH\", \"RESPIRATORY DISORDER\", \n \"RHINITIS\", \"SINUSITIS\", \"UPPER RESP TRACT INFECTION\", \"URINARY TRACT INFECTION\", \n \"VOMITING\", \"WEIGHT DECREASE\")), class = \"factor\"), SAE = c(15L, \n 9L, 4L, 9L, 4L, 9L, 2L, 9L, 8L, 11L, 4L, 11L, 9L, 12L, 5L, 12L, \n 7L, 12L, 6L, 12L, 6L, 12L, 2L, 14L, 2L, 15L, 1L, 15L, 4L, 16L, \n 4L, 17L, 11L, 17L, 6L, 20L, 10L, 23L, 13L, 26L, 12L, 26L, 4L, \n 26L, 13L, 28L, 9L, 29L, 12L, 30L, 14L, 36L, 6L, 37L, 8L, 42L, \n 20L, 61L, 33L, 68L, 10L, 82L, 23L, 90L, 76L, 95L)), .Names = c(\"RAND\", \n \"PREF\", \"SAE\"), class = \"data.frame\", row.names = c(NA, \n -66L))\n\nae$n <- ifelse(ae$RAND == 'a', 212, 188)\nae$p <- ae$SAE / ae$n\n# ae <- subset(ae, p >= 0.05)\nwith(ae, dotchartpl(p, num=SAE, denom=n, minor=PREF, group=RAND, refgroup='a'))\n\n\nn <- 500\nset.seed(1)\nd <- data.frame(\n race = sample(c('Asian', 'Black/AA', 'White'), n, TRUE),\n sex = sample(c('Female', 'Male'), n, TRUE),\n treat = sample(c('A', 'B'), n, TRUE),\n smoking = sample(c('Smoker', 'Non-smoker'), n, TRUE),\n hypertension = sample(c('Hypertensive', 'Non-Hypertensive'), n, TRUE),\n region = sample(c('North America','Europe','South America',\n 'Europe', 'Asia', 'Central America'), n, TRUE))\n\nd <- upData(d, labels=c(race='Race', sex='Sex'))\n\ndm <- addMarginal(d, region)\ns <- summaryP(race + sex + smoking + hypertension ~\n region + treat, data=dm)\n\n## add exclude1=FALSE to include female category\nggplot(s, groups='treat', exclude1=TRUE, abblen=12)\nggplot(s, groups='region')\n\ns$region <- ifelse(s$region == 'All', 'All Regions', as.character(s$region))\n\nwith(s, \n dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,\n big=region == 'All Regions', num=freq, denom=denom)\n)\n\ns2 <- s[- attr(s, 'rows.to.exclude1'), ]\nwith(s2, \n dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,\n big=region == 'All Regions', num=freq, denom=denom)\n)\n\n\n\n\n", "meta": {"hexsha": "605475f0388e5b6ccd55abe841658aca5aae1116", "size": 8122, "ext": "r", "lang": "R", "max_stars_repo_path": "SilveR/R/library/Hmisc/tests/dotchartpl.r", "max_stars_repo_name": "robalexclark/SilveR-Dev", "max_stars_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SilveR/R/library/Hmisc/tests/dotchartpl.r", "max_issues_repo_name": "robalexclark/SilveR-Dev", "max_issues_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SilveR/R/library/Hmisc/tests/dotchartpl.r", "max_forks_repo_name": "robalexclark/SilveR-Dev", "max_forks_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-31T18:42:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-31T18:42:06.000Z", "avg_line_length": 74.5137614679, "max_line_length": 336, "alphanum_fraction": 0.3169170155, "num_tokens": 1939, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3160234274941507}} {"text": "# TECOA166\r\n#Sheetal Singh\r\n\r\n#install.packages('ggplot2')\r\nlibrary(ggplot2)\r\n\r\n# Funcions for perticular action\r\n\r\nscatter_plot_1 <- function(d) {\r\n print(ggplot(data = d) +\r\n geom_point(mapping = aes(x = displ, y = hwy, color = class)))\r\n}\r\n\r\nscatter_plot_2 <- function(d) {\r\n print(ggplot(data = d) +\r\n geom_point(mapping = aes(x = displ, y = hwy)) +\r\n facet_wrap(~ class, nrow = 2))\r\n}\r\n\r\nline_graph <- function(d) {\r\n print(ggplot(data = d) +\r\n geom_line(\r\n mapping = aes(x = displ, y = hwy, color = drv)))\r\n}\r\n\r\ncurve_plot <- function(d) {\r\n print(ggplot(data = d) +\r\n geom_smooth(\r\n mapping = aes(x = displ, y = hwy, color = drv)))\r\n}\r\n\r\nbar_plot <- function(d) {\r\n print(ggplot(d, aes(class, fill = drv)) +\r\n geom_bar(position = \"dodge\"))\r\n}\r\n\r\npie_chart <- function(d) {\r\n print(ggplot(d, aes(x = factor(1), fill = drv)) +\r\n geom_bar(width = 1) +\r\n coord_polar(theta = \"y\"))\r\n}\r\n\r\nbox_plot <- function(d) {\r\n print(ggplot(data = d, mapping = aes(x = class, y = hwy)) +\r\n geom_boxplot(aes(fill=class)))\r\n}\r\n\r\n# Main Program Starts form here\r\ndata<-ggplot2::mpg # Dataset for visualization\r\n#?mpg # to know about mpg dataset\r\nwhile (TRUE)\r\n{\r\n # Display Menu\r\n cat(\"\\n\\nMenu:-\r\n1. Scatter Plot 1\r\n2. Scatter Plot 2\r\n3. Line Graph\r\n4. Curve Plot\r\n5. Bar Plot\r\n6. Pie Chart\r\n7. Box Plot\r\n8. Exit\\n\\n\")\r\n \r\n # Take input form user\r\n ch = as.integer(readline(prompt=\"Enter Your Choice: \")) \r\n \r\n # Perform action specified by user\r\n switch(ch,\r\n scatter_plot_1(data),\r\n scatter_plot_2(data),\r\n line_graph(data),\r\n curve_plot(data),\r\n bar_plot(data),\r\n pie_chart(data),\r\n box_plot(data))\r\n \r\n if(ch==8){break}\r\n}", "meta": {"hexsha": "73c847051d7936826d7f0bf22b65fc490b3a88d3", "size": 1728, "ext": "r", "lang": "R", "max_stars_repo_path": "Data Visualization.r", "max_stars_repo_name": "sangeetasingh17/python", "max_stars_repo_head_hexsha": "02fe83d5188a643a1d95b1a2b5592ae6444e260f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-11T14:42:48.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-11T14:42:48.000Z", "max_issues_repo_path": "Data Visualization.r", "max_issues_repo_name": "sangeetasingh17/python", "max_issues_repo_head_hexsha": "02fe83d5188a643a1d95b1a2b5592ae6444e260f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Data Visualization.r", "max_forks_repo_name": "sangeetasingh17/python", "max_forks_repo_head_hexsha": "02fe83d5188a643a1d95b1a2b5592ae6444e260f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-08-04T20:26:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-18T10:24:43.000Z", "avg_line_length": 22.1538461538, "max_line_length": 66, "alphanum_fraction": 0.5873842593, "num_tokens": 497, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.31602342749415063}} {"text": "# .hlpr_modular_models generic function [helper] ----\r\n.hlpr_modular_models <- function(parameters, ...) UseMethod(\".hlpr_modular_models\", parameters)\r\n\r\n# .hlpr_modular_models param_glm method ----\r\n.hlpr_modular_models.param_glm <- function(parameters, ...) {\r\n dv <- parameters$dv\r\n cv <- parameters$cv\r\n iv <- parameters$iv\r\n mv <- parameters$mv\r\n data <- parameters$data\r\n\r\n if (is.null(cv)) {\r\n cv <- 1\r\n }\r\n \r\n out <- vector(mode = \"list\", length = 3 + length(mv) * 2)\r\n form_cv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\")))\r\n out[[1]] <- glm(formula = form_cv, data = data, family = attr(parameters, \"family\"))\r\n \r\n form_iv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv))\r\n out[[2]] <- update(out[[1]], formula. = form_iv)\r\n \r\n for (i in seq(length(mv))) {\r\n j <- i * 2 + 1\r\n form_mv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", mv[i]))\r\n out[[j]] <- update(out[[1]], formula. = form_mv)\r\n j <- j + 1\r\n form_mv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", mv[i], \"+\", paste0(c(iv, mv[i]), collapse = \":\")))\r\n out[[j]] <- update(out[[1]], formula. = form_mv)\r\n }\r\n \r\n if (parameters$full_model) {\r\n form_full <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", paste0(mv, collapse = \"+\"), \"+\", .hlpr_paste(element1 = iv, element2 = mv, sep = \":\", collapse = \"+\")))\r\n out[[length(out)]] <- update(out[[1]], formula. = form_full)\r\n } else {\r\n out <- out[-length(out)] \r\n }\r\n\r\n return(out)\r\n}\r\n\r\n# .hlpr_modular_models param_glmer method ----\r\n.hlpr_modular_models.param_glmer <- function(parameters, ...) {\r\n dv <- parameters$dv\r\n cv <- parameters$cv\r\n iv <- parameters$iv\r\n mv <- parameters$mv\r\n fe <- paste0(\"(1|\", parameters$fe, \")\")\r\n data <- parameters$data\r\n \r\n if (!is.null(cv)) {\r\n cv <- c(cv, fe)\r\n } else {\r\n cv <- fe\r\n }\r\n \r\n out <- vector(mode = \"list\", length = 3 + length(mv) * 2)\r\n form_cv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\")))\r\n if (attr(parameters, \"family\") == \"gaussian\") {\r\n out[[1]] <- lme4::lmer(formula = form_cv, data = data)\r\n } else {\r\n out[[1]] <- lme4::glmer(formula = form_cv, data = data, fam = attr(parameters, \"family\"))\r\n }\r\n \r\n form_iv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv))\r\n out[[2]] <- update(out[[1]], formula. = form_iv)\r\n \r\n for (i in seq(length(mv))) {\r\n j <- i * 2 + 1\r\n form_mv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", mv[i]))\r\n out[[j]] <- update(out[[1]], formula. = form_mv)\r\n j <- j + 1\r\n form_mv <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", mv[i], \"+\", paste0(c(iv, mv[i]), collapse = \":\")))\r\n out[[j]] <- update(out[[1]], formula. = form_mv)\r\n }\r\n \r\n if (parameters$full_model) {\r\n form_full <- formula(paste0(dv, \"~\", paste0(cv, collapse = \"+\"), \"+\", iv, \"+\", paste0(mv, collapse = \"+\"), \"+\", .hlpr_paste(element1 = iv, element2 = mv, sep = \":\", collapse = \"+\")))\r\n out[[length(out)]] <- update(out[[1]], formula. = form_full)\r\n } else {\r\n out <- out[-length(out)] \r\n }\r\n \r\n return(out)\r\n}\r\n", "meta": {"hexsha": "693449bf6597f0b70c937e937e518f4335ec1b04", "size": 3156, "ext": "r", "lang": "R", "max_stars_repo_path": "R/hlpr_modular_models.r", "max_stars_repo_name": "ha-pu/supportR", "max_stars_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/hlpr_modular_models.r", "max_issues_repo_name": "ha-pu/supportR", "max_issues_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2020-07-10T07:17:20.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-16T09:17:33.000Z", "max_forks_repo_path": "R/hlpr_modular_models.r", "max_forks_repo_name": "ha-pu/supportR", "max_forks_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.6976744186, "max_line_length": 187, "alphanum_fraction": 0.5358048162, "num_tokens": 998, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.31602342749415063}} {"text": "\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(gtable)\nlibrary(grid)\n\n# Given a set of regions plot as lines related to an ID parameter\n# world_data\nPlotDimensionForRegionSet <- function(data, x_col_name, region_ids, region_id_col_name, dimension_name, color_codes) {\n g <- ggplot(data, aes_string(x=x_col_name, y=dimension_name, col=region_id_col_name)) + \n geom_line() +\n scale_color_manual(values = color_codes) +\n geom_smooth()\n return(g)\n}\n\n# Plots lines for all regions in a given data set\nPlotDimensionForRegions <- function(data, x_col_name, region_id_col_name, dimension_name, line_colors) {\n\n}\n\n# Draws 4 graphs to one plot and adds a legend at the bottom\n# spain_communities\nGraphDimensionForCountry <- function(regional_data, country_data, dimension_name, country_name, color_codes) {\n\n total_dimension_name = paste(\"total_\", dimension_name, sep = \"\")\n daily_dimension_name = paste(\"daily_\", dimension_name, sep = \"\")\n\n str(color_codes)\n\n p1 <- ggplot(country_data, aes_string(x=\"date\", y=total_dimension_name)) + geom_line()\n p2 <- ggplot(country_data, aes_string(x=\"date\", y=daily_dimension_name)) + geom_line() + geom_smooth()\n p3 <- ggplot(regional_data, aes_string(x=\"date\", y=total_dimension_name, col=\"region_code\")) + geom_line()\n p4 <- ggplot(regional_data, aes_string(x=\"date\", y=daily_dimension_name, col=\"region_code\")) + geom_line()\n\n if (!is.na(color_codes)) {\n p3 <- p3 + scale_color_manual(values = color_codes)\n p4 <- p4 + scale_color_manual(values = color_codes)\n }\n\n g <- arrangeGrob(\n p1 + theme(legend.position=\"none\"), \n p2 + theme(legend.position=\"none\"),\n p3 + theme(legend.position=\"none\"),\n p4 + theme(legend.position=\"none\"),\n ncol=2,\n nrow=2\n )\n\n legend <- gtable_filter(ggplot_gtable(ggplot_build(p3)), \"guide-box\")\n title <- paste(country_name, dimension_name, sep=\" - \")\n footer <- paste(max(country_data$date))\n final <- grid.arrange(g, legend, ncol = 2, widths=c(1.15, 0.25), bottom = footer, top = textGrob(title, gp=gpar(fontsize=20, font=3)))\n return(final)\n\n}\n\n# Draws a graph for all of a country\nDrawGraphForCountry <- function(country_data, dimension_name, country_name) {\n\n}\n\n# Output all available graphable dimensions as png\n# italy_data, daily_graphs\nOutputAllDimensionsAsPng <- function(regional_data, country_data, country_name, color_codes) {\n empty_columns <- apply(regional_data, 2, function(x) any(is.na(x)))\n\n # Generate output path\n output_path <- paste(\"Output/\", country_name, sep=\"\")\n dir.create(output_path, showWarnings = FALSE)\n\n for(i in GRAPHABLE_COLUMN_ROOT_NAMES) {\n total_name <- paste(\"total_\", i, sep=\"\")\n if(!empty_columns[total_name]) {\n file_name <- paste(output_path, \"/\", i, \".png\", sep=\"\")\n g <- GraphDimensionForCountry(regional_data, country_data, i, country_name, color_codes)\n ggsave(file_name, plot = g, width = 16, height = 10, dpi = 300)\n }\n }\n}\n", "meta": {"hexsha": "cdc1b47bd82264201eae8b6a3b1daecef74ece7e", "size": 3037, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/ggplot/common_graphs.r", "max_stars_repo_name": "sbikun/CoronaGraphR", "max_stars_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-04-16T11:35:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-20T16:37:24.000Z", "max_issues_repo_path": "Graphing/ggplot/common_graphs.r", "max_issues_repo_name": "sbikun/CoronaGraphR", "max_issues_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/ggplot/common_graphs.r", "max_forks_repo_name": "sbikun/CoronaGraphR", "max_forks_repo_head_hexsha": "861172b4574afdeb03c32a446caf8dfb311de4a3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.4938271605, "max_line_length": 138, "alphanum_fraction": 0.6871913072, "num_tokens": 781, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369904, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.31602341917459287}} {"text": "# inc_forecast_interval_05_index.r\nmyLand<-subset(UNPop,UNPop$Country==mySelection[i] & UNPop$Variant==\"Medium variant\")\nmyPrognoses<-subset(UNPop,UNPop$Country == mySelection[i] & Year >= 2010)\nmyPrognosis_L<-subset(myPrognoses,myPrognoses$Variant==\"Low variant\")$Value/1000\nmyPrognosis_M<-subset(myPrognoses,myPrognoses$Variant==\"Medium variant\")$Value/1000\nmyPrognosis_H<-subset(myPrognoses,myPrognoses$Variant==\"High variant\")$Value/1000\nmyYears<-seq(2010,2100,by=5)\nattach(myLand)\nmyBase<-(Value[13]/1000)\n\nplot(axes=F,type=\"n\",xlab=\"\",ylab=\"\",Year,Value/1000,ylim=c(ymin[i],ymax[i]))\npy<-c(0,100,200,300,400,500)\nabline(h=py[2:6], col=\"lightgray\",lty=\"dotted\")\naxis(1,tck=-0.01,col=\"grey\",at=c(1950,2010,2100),cex.axis=1.2) \npy<-c(0,100,200,300,400,500)\nif (mySelection[i]==\"World\")\n{\naxis(2,tck=-0.01,col=\"grey\",at=py,labels=format(py,big.mark=\".\"),cex.axis=1.2) \n}\nxx<-c(myYears,rev(myYears))\nyy<-c(100*myPrognosis_H/basis,rev(100*myPrognosis_L/basis))\n\npolygon(xx,yy,col=rgb(192,192,192,maxColorValue=255),border=F)\n\nlines(Year,100*(Value/1000)/basis,col=\"grey\",lwd=2)\nlines(myYears,100*myPrognosis_H/basis,col=\"black\",lwd=2)\nlines(myYears,100*myPrognosis_L/basis,col=\"orange\",lwd=2)\nlines(myYears,100*myPrognosis_M/basis,col=\"white\",lwd=2)\n\n\n", "meta": {"hexsha": "3f71f14f8a195736e395d143c98e2e57523f3ba5", "size": 1252, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/inc_forecast_interval_05_index.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/inc_forecast_interval_05_index.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/inc_forecast_interval_05_index.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3870967742, "max_line_length": 85, "alphanum_fraction": 0.7420127796, "num_tokens": 482, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31596646458854005}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of uniform prior wonky world model predictions with prior slider DV\"\n#' author: \"Judith Degen\"\n#' date: \"March 12, 2015\"\n#' ---\n\nlibrary(ggplot2)\nlibrary(hydroGOF)\ntheme_set(theme_bw(18))\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/\")\nsource(\"rscripts/helpers.r\")\n\n#' get model predictions\nload(\"data/mp-uniform-priorsliders.RData\")\nload(\"data/wr-uniform-priorsliders.RData\")\n\nd = read.table(\"data/parsed_uniform_priorslider_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nnames(d)\ntable(d$Item)\nnrow(d)\nhead(d)\nsummary(d)\n\nagrmp = aggregate(PosteriorProbability ~ Item + State + SpeakerOptimality + WonkyWorldPrior + Wonky, FUN=\"mean\", data=d)\nnrow(agrmp)\nsummary(agrmp)\nagrmp$CILow = aggregate(PosteriorProbability ~ Item + State + SpeakerOptimality + WonkyWorldPrior + Wonky, FUN=\"ci.low\", data=d)$PosteriorProbability\nagrmp$CIHigh = aggregate(PosteriorProbability ~ Item + State + SpeakerOptimality + WonkyWorldPrior + Wonky, FUN=\"ci.high\", data=d)$PosteriorProbability\n\nmp = ddply(agrmp, .(Item, State, SpeakerOptimality, WonkyWorldPrior), summarise, PosteriorProbability=sum(PosteriorProbability))\nsummary(mp)\nhead(mp)\n#mp[mp$Item == \"ate the seeds birds\" & mp$QUD==\"how-many\" & mp$Alternatives==\"0_basic\" & mp$SpeakerOptimality == 1,]\nwr = ddply(agrmp, .(Item, Wonky, SpeakerOptimality, WonkyWorldPrior), summarise, PosteriorProbability=sum(PosteriorProbability))\nsummary(wr)\nwr[wr$Item == \"ate the seeds birds\" & wr$QUD==\"how-many\" & wr$Alternatives==\"0_basic\" & wr$SpeakerOptimality == 1,]\n\nmp$Item = gsub(\"_\",\" \",mp$Item)\nwr$Item = gsub(\"_\",\" \",wr$Item)\n\n\n# get empirical state posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/16_sinking-marbles-sliders-certain/results/data/r.RData\")\nhead(r)\n# because posteriors come in 4 bins, make Bin variable for model prediction dataset:\nmp$Proportion = as.factor(ifelse(mp$State == 0, \"0\", ifelse(mp$State == 15, \"100\", ifelse(mp$State < 8, \"1-50\", \"51-99\"))))\n\nagr = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=mean)\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Proportion, agr$Quantifier)\nmp$PosteriorProbability_empirical = agr[paste(mp$Item,mp$Proportion,\"some\"),]$normresponse\n\nhead(mp)\n\n# get slider prior probabilities\nload(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/23_sinking-marbles-prior-sliders-exactly/results/data/agr-normresponses.RData\")\n\nsliderprobs = agr\nhead(sliderprobs)\nnrow(sliderprobs)\nrow.names(sliderprobs) = paste(sliderprobs$Item,sliderprobs$slider_id)\nmp$PriorProbability_slider = sliderprobs[paste(as.character(mp$Item),mp$State),]$normresponse\n#mp$AllPriorProbability_slider = priorprobs[paste(as.character(mp$Item)),]$X15\nhead(mp)\nmp[mp$Item == \"sank marbles\" & mp$WonkyWorldPrior == .5 & mp$SpeakerOptimality == 2,]\n\n# get slider expectations\nslexp = ddply(sliderprobs, .(Item), summarise, expectation=sum(slider_id*normresponse))\nsummary(slexp)\nhead(slexp)\nrow.names(slexp) = as.character(slexp$Item)\n\nmp$PriorExpectation_slider = slexp[as.character(mp$Item),]$expectation\nwr$PriorExpectation_slider = slexp[as.character(wr$Item),]$expectation\n\n\nsave(mp, file=\"data/mp-uniform-priorsliders.RData\")\nsave(wr, file=\"data/wr-uniform-priorsliders.RData\")\n\n\n\n\n###### HERE WE START WITH ALL THE INTERESTING PLOTTING\n#plot empirical against predicted distributions for \"some\"\nsome = ddply(mp, .(Item, SpeakerOptimality, PriorExpectation_slider, Proportion, WonkyWorldPrior, PosteriorProbability_empirical), summarise, PosteriorProbability_predicted=sum(PosteriorProbability), PriorProbability_smoothed=sum(PriorProbability_slider))\nnrow(some)\nhead(some)\nmsome = melt(some, measure.vars=c(\"PosteriorProbability_empirical\",\"PosteriorProbability_predicted\",\"PriorProbability_smoothed\"))\nmsome$ptype = as.factor(ifelse(msome$variable == \"PosteriorProbability_empirical\", \"posterior (empirical)\",ifelse(msome$variable == \"PosteriorProbability_predicted\",\"posterior (model)\", \"prior\")))\nhead(msome)\nnrow(msome)\nsummary(msome)\n\ntoplot = droplevels(subset(msome, SpeakerOptimality == 2))#\"0_basic1_lownum2_extra4_twowords5_threewords\"))\nnrow(toplot)\ntoplot$Probability = as.factor(ifelse(toplot$ptype == \"prior\",\"prior\",\"posterior\"))\ntoplot$Prop = factor(toplot$Proportion, levels=c(\"1-50\",\"51-99\",\"100\"))\nggplot(toplot, aes(x=Prop, y=value,color=ptype, group=ptype, size=Probability)) +\n geom_point() +\n geom_line() +\n scale_size_discrete(range=c(1,2)) +\n scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_wrap(WonkyWorldPrior~Item)\nggsave(\"graphs/2_slider_priors/model-empirical-uniform-howmany-2-basic-sliderprior.pdf\",width=35,height=30)\n\n#plot empirical against predicted expectations for \"some\"\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nsummary(r)\nr$Item = as.factor(paste(r$effect, r$object))\nagr = aggregate(ProportionResponse ~ Item + quantifier, data=r, FUN=mean)\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Quantifier)\nmp$PosteriorExpectation_empirical = agr[paste(mp$Item,\"some\"),]$ProportionResponse*15\n\npexpectations = ddply(mp, .(Item,SpeakerOptimality,PriorExpectation_slider, WonkyWorldPrior, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability))\nhead(pexpectations)\nsome=pexpectations\n\n\nlibrary(hydroGOF)\n\ntest = ddply(some, .(SpeakerOptimality, WonkyWorldPrior), summarise, mse=gof(PosteriorExpectation_predicted, PosteriorExpectation_empirical)[\"MSE\",],r=gof(PosteriorExpectation_predicted, PosteriorExpectation_empirical)[\"r\",],R2=gof(PosteriorExpectation_predicted, PosteriorExpectation_empirical)[\"R2\",])\ntest = test[order(test[,c(\"mse\")]),]\nhead(test,10)\ntest = test[order(test[,c(\"r\")],decreasing=T),]\nhead(test,10)\ntest = test[order(test[,c(\"R2\")],decreasing=T),]\nhead(test,10)\nhead(some)\n\n\nggplot(some, aes(x=PosteriorExpectation_predicted, y=PosteriorExpectation_empirical,color=as.factor(SpeakerOptimality), shape=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n# scale_x_continuous(limits=c(0,1)) +\n# scale_y_continuous(limits=c(0,1)) + \n# geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(SpeakerOptimality~WonkyWorldPrior)\nggsave(\"graphs/2_slider_priors/model-empirical-uniform-sliderpior-expectations.pdf\",width=20,height=10)\n\nggplot(some, aes(x=PriorExpectation_slider, y=PosteriorExpectation_predicted,color=as.factor(SpeakerOptimality), shape=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,15)) +\n scale_y_continuous(limits=c(0,15)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(SpeakerOptimality~WonkyWorldPrior)\nggsave(\"graphs/2_slider_priors/model-uniform-priorslider-expectations.pdf\",width=20,height=10)\n\n# plot only empirical\nun = unique(some[,c(\"Item\",\"PriorExpectation_slider\",\"PosteriorExpectation_empirical\")])\nnrow(un)\nggplot(un, aes(x=PriorExpectation_slider,y=PosteriorExpectation_empirical)) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1)\n# scale_x_continuous(limits=c(0,15)) +\n# scale_y_continuous(limits=c(0,15)) \nggsave(\"graphs/2_slider_priors/some_empirical_expectation_priorslider.pdf\",width=7)\n\n#plot empirical against predicted allstate-prbabilities for \"some\"\nallstate = droplevels(subset(mp, State == 15))\n\ntest = ddply(allstate, .(SpeakerOptimality, WonkyWorldPrior), summarise, mse=gof(PosteriorProbability, PosteriorProbability_empirical)[\"MSE\",],r=gof(PosteriorProbability, PosteriorProbability_empirical)[\"r\",],R2=gof(PosteriorProbability, PosteriorProbability_empirical)[\"R2\",])\ntest = test[order(test[,c(\"mse\")]),]\nhead(test,10)\ntest = test[order(test[,c(\"r\")],decreasing=T),]\nhead(test,10)\ntest = test[order(test[,c(\"R2\")],decreasing=T),]\nhead(test,10)\n\nggplot(allstate, aes(x=PosteriorProbability, y=PosteriorProbability_empirical,color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous() +#limits=c(0,1)) +\n scale_y_continuous() +#limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(SpeakerOptimality~WonkyWorldPrior)\nggsave(\"graphs/2_slider_priors/model-empirical-uniform-priorslider-allstateprobs.pdf\",width=20,height=10)\n\n#maybe COGSCI plot basis? plot predicted allstate-prbabilities for \"some\" as a function of prior allstate-probabilities\n\nggplot(allstate, aes(x=PriorProbability_slider, y=PosteriorProbability,color=as.factor(WonkyWorldPrior), shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(SpeakerOptimality~WonkyWorldPrior)\nggsave(\"graphs/2_slider_priors/model-uniform-priorslider-allstateprobs.pdf\",width=20,height=10)\n\nun = unique(allstate[,c(\"PriorProbability_slider\",\"PosteriorProbability_empirical\")])\nnrow(un)\nggplot(un, aes(x=PriorProbability_slider, y=PosteriorProbability_empirical)) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) \nggsave(\"graphs/2_slider_priors/some_empirical_allprobs_priorslider.pdf\",width=7)\n\n# get empirical wonkiness posteriors\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/11_sinking-marbles-normal/results/data/r.RData\")\nhead(r)\nnrow(r)\nr$Item = as.factor(paste(r$effect,r$object))\n\nt = as.data.frame(prop.table(table(r$Item, r$quantifier, r$response), mar=c(1,2)))\nhead(t)\ncolnames(t) = c(\"Item\",\"Quantifier\",\"NormalMarbles\",\"Proportion\")\nt[t$Var1==\"ate the seeds birds\",]\nt$Quantifier = tolower(t$Quantifier)\ntail(t)\nt$Wonky = as.factor(ifelse(t$NormalMarbles == \"yes\",\"false\",\"true\"))\nrow.names(t) = paste(t$Item, t$Quantifier, t$Wonky)\n\nwr$PosteriorProbability_empirical = t[paste(wr$Item, \"some\", wr$Wonky),]$Proportion\nhead(wr)\nwonky = droplevels(subset(wr, Wonky == \"true\"))\n\nhead(wonky)\n#toplot = droplevels(subset(wonky, WonkyWorldPrior == .6 & SpeakerOptimality == 2))\n#head(toplot)\n\nggplot(wonky, aes(x=PriorExpectation_slider, y=PosteriorProbability)) +\n geom_point() +\n geom_smooth() +\n facet_grid(WonkyWorldPrior ~ SpeakerOptimality)\nggsave(file=\"graphs/2_slider_priors/wonkinessplot_priorslider.pdf\")\n\ntest = ddply(wonky, .(SpeakerOptimality, WonkyWorldPrior), summarise, mse=gof(PosteriorProbability, PosteriorProbability_empirical)[\"MSE\",],r=gof(PosteriorProbability, PosteriorProbability_empirical)[\"r\",],R2=gof(PosteriorProbability, PosteriorProbability_empirical)[\"R2\",])\ntest = test[order(test[,c(\"mse\")]),]\nhead(test,10)\ntest = test[order(test[,c(\"r\")],decreasing=T),]\nhead(test,10)\ntest = test[order(test[,c(\"R2\")],decreasing=T),]\nhead(test,10)\n\n", "meta": {"hexsha": "89c6e98b04bee107eb42515dd82f8ab90f3ff815", "size": 11586, "ext": "r", "lang": "R", "max_stars_repo_path": "models/wonky_world/results/rscripts/modelpredictions-uniform-priorsliders.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "models/wonky_world/results/rscripts/modelpredictions-uniform-priorsliders.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/wonky_world/results/rscripts/modelpredictions-uniform-priorsliders.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.344, "max_line_length": 303, "alphanum_fraction": 0.7667011911, "num_tokens": 3401, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31596646458854}} {"text": "# Evaluating the dual taxonomic resolution of observed interactions\ninter_taxo_resolution <- function(tx.list, inter.list, taxo.resolution.accepted){\n # tx.list <- taxon list with taxonomic resolution, taxon should be rownames\n # inter.list <- list of binary interactions with three columns: 1 == predator, 2 == interaction type, 3 == prey\n # taxo.resolution.accepted <- list of taxonomic resolutions accepted for further analyses\n nb.inter <- nrow(inter.list)\n inter.resolution <- character(nb.inter)\n taxo.resol <- unique(tx.list[, 2])\n\n matrix.dims <- dim(inter.list)\n if(is.matrix(inter.list) == FALSE) {inter.list <- as.matrix(inter.list)}\n if(identical(matrix.dims,dim(inter.list)) == FALSE) {stop(\"Verify table structure\")}\n\n # Evaluating taxonomic resolutions of predators and preys forming observed binary interactions\n for(i in 1:nb.inter) {\n pred.tx.resolution <- tx.list[inter.list[i, 1], 2]\n prey.tx.resolution <- tx.list[inter.list[i, 3], 2]\n inter.resolution[i] <- paste(pred.tx.resolution, prey.tx.resolution, sep = \" - \")\n }\n\n inter.list <- cbind(inter.list, inter.resolution)\n\n taxo.combination <- as.vector(outer(taxo.resolution.accepted, taxo.resolution.accepted, paste, sep = \" - \")) # Computes all combinations that could be of interest\n\n # Evaluating which interactions in the list meets our criteria for minimal dual taxonomic resolution\n row.inter.accepted <- numeric()\n for(i in 1:length(taxo.combination)) {\n row.inter.accepted <- c(row.inter.accepted,which(inter.resolution == taxo.combination[i]))\n }\n\n inter.list.tot <- inter.list[row.inter.accepted, ]\n\n return(inter.list.tot)\n}\n", "meta": {"hexsha": "acb11eb630af5914b746c0a40c39ca25457ac98b", "size": 1643, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/inter_taxo_resolution.r", "max_stars_repo_name": "david-beauchesne/Interaction_catalog", "max_stars_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Script/inter_taxo_resolution.r", "max_issues_repo_name": "david-beauchesne/Interaction_catalog", "max_issues_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Script/inter_taxo_resolution.r", "max_forks_repo_name": "david-beauchesne/Interaction_catalog", "max_forks_repo_head_hexsha": "4e6ff0ba5571ae6ed5c5673acfd9e69b3fd53612", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.9428571429, "max_line_length": 165, "alphanum_fraction": 0.7212416312, "num_tokens": 406, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31596646458854}} {"text": "#' @docType package\n#' @name MethylDeconBloodSubtypes\n#' @title Deconvolute array-based methylation data.\n\n#' Fit models.\n#'\n#' @family Model fitting\n#' @param testVector\n#' @param basis\n#' @return A numeric matrix\nfitNonneg.nosum1 <- function (testVector, basis){ \n\tbasisIndex <- c(seq(1,dim(basis)[2]))\n\tbasisSplice <- basis\n\twhile (1) \n\t{\n\t\tafit <- lsfit (basisSplice, testVector,intercept=F)\n\t\tcoef <- afit$coefficients[1:(dim(basisSplice)[2])]\n\t\tcoeff <- c(rep(0,dim(basis)[2]))\n\t\tcoeff[basisIndex] <- coef\n\t\t\n\t\tif (length(which(coeff < 0)) == 0) \n\t\t{\n\t\t\tcoeff2 <- coeff\n\t\t\tif (sum(coeff)>1)\n\t\t\t{\n\t\t\t\tcoeff2 <- coeff/sum(coeff)\n\t\t\t}\n\t\t\treturn(coeff2)\n\t\t} \n\t\t\n\t\telse \n\t\t{\n\t\t\tlow <- which.min(coeff)\n\t\t\tbasisIndex[low] <- 0\n\t\t\tbasisSplice <- basis[,which(basisIndex > 0)]\n\t\t\t\n\t\t\tif ((length(which(coeff > 0)) == 1) && (length(which(coeff != 0)) == 2))\n\t\t\t{\n\t\t\t\tcoeff[which(coeff > 0)] <- 1\n\t\t\t\tcoeff[which(coeff <= 0)] <- 0\n\t\t\t\treturn(coeff)\n\t\t\t\t\n\t\t\t}\n\t\t}\n\t}\n}\n\n## (may want to also hard code in this list using the default options to make code faster)\n#' Get the CpG list for the model for main cell types.\n#'\n#' @family Generate list\n#' @param length.anova An integer numeric\n#' @param length.pairs An integer numeric\n#' @return A character\n#' @export\nget.cpg.list.main <- function(length.anova = 1000, length.pairs = 300){\n\t## get keep list of CpGs for overall ANOVA test for all types\n\tord.anova <- tests.main[,\"anova.p\"]\n\tnames(ord.anova) <- row.names(tests.main)\n\tord.anova <- ord.anova[order(ord.anova)]\n\tnames.ord.anova <- names(ord.anova)\n\t\n\t## get keep list of CpGs for all pairwise comparisons\n\t\n\t## CD4-CD8\n\tord.cd4.cd8 <- tests.main[,\"cd4.cd8.p\"]\n\tnames(ord.cd4.cd8) <- row.names(tests.main)\n\tord.cd4.cd8 <- ord.cd4.cd8[order(ord.cd4.cd8)]\n\tnames.ord.cd4.cd8 <- names(ord.cd4.cd8)\n\t\n\t\n\t## CD4-CD14\n\tord.cd4.cd14 <- tests.main[,\"cd4.cd14.p\"]\n\tnames(ord.cd4.cd14) <- row.names(tests.main)\n\tord.cd4.cd14 <- ord.cd4.cd14[order(ord.cd4.cd14)]\n\tnames.ord.cd4.cd14 <- names(ord.cd4.cd14)\n\t\n\t\n\t## CD4-CD19\n\tord.cd4.cd19 <- tests.main[,\"cd4.cd19.p\"]\n\tnames(ord.cd4.cd19) <- row.names(tests.main)\n\tord.cd4.cd19 <- ord.cd4.cd19[order(ord.cd4.cd19)]\n\tnames.ord.cd4.cd19 <- names(ord.cd4.cd19)\n\t\n\t## CD4-gran\n\tord.cd4.gran <- tests.main[,\"cd4.gran.p\"]\n\tnames(ord.cd4.gran) <- row.names(tests.main)\n\tord.cd4.gran <- ord.cd4.gran[order(ord.cd4.gran)]\n\tnames.ord.cd4.gran <- names(ord.cd4.gran)\n\t\n\t\n\t## CD4-nk\n\tord.cd4.nk <- tests.main[,\"cd4.nk.p\"]\n\tnames(ord.cd4.nk) <- row.names(tests.main)\n\tord.cd4.nk <- ord.cd4.nk[order(ord.cd4.nk)]\n\tnames.ord.cd4.nk <- names(ord.cd4.nk)\n\t\n\t\n\t## CD8-CD14\n\tord.cd8.cd14 <- tests.main[,\"cd8.cd14.p\"]\n\tnames(ord.cd8.cd14) <- row.names(tests.main)\n\tord.cd8.cd14 <- ord.cd8.cd14[order(ord.cd8.cd14)]\n\tnames.ord.cd8.cd14 <- names(ord.cd8.cd14)\n\t\n\t## CD8-CD19\n\tord.cd8.cd19 <- tests.main[,\"cd8.cd19.p\"]\n\tnames(ord.cd8.cd19) <- row.names(tests.main)\n\tord.cd8.cd19 <- ord.cd8.cd19[order(ord.cd8.cd19)]\n\tnames.ord.cd8.cd19 <- names(ord.cd8.cd19)\n\t\n\t## CD8-gran\n\tord.cd8.gran <- tests.main[,\"cd8.gran.p\"]\n\tnames(ord.cd8.gran) <- row.names(tests.main)\n\tord.cd8.gran <- ord.cd8.gran[order(ord.cd8.gran)]\n\tnames.ord.cd8.gran <- names(ord.cd8.gran)\n\t\n\t## CD8-nk\n\tord.cd8.nk <- tests.main[,\"cd8.nk.p\"]\n\tnames(ord.cd8.nk) <- row.names(tests.main)\n\tord.cd8.nk <- ord.cd8.nk[order(ord.cd8.nk)]\n\tnames.ord.cd8.nk <- names(ord.cd8.nk)\n\t\n\n\n\t## CD14-CD19\n\tord.cd14.cd19 <- tests.main[,\"cd14.cd19.p\"]\n\tnames(ord.cd14.cd19) <- row.names(tests.main)\n\tord.cd14.cd19 <- ord.cd14.cd19[order(ord.cd14.cd19)]\n\tnames.ord.cd14.cd19 <- names(ord.cd14.cd19)\n\t\n\t## CD14-gran\n\tord.cd14.gran <- tests.main[,\"cd14.gran.p\"]\n\tnames(ord.cd14.gran) <- row.names(tests.main)\n\tord.cd14.gran <- ord.cd14.gran[order(ord.cd14.gran)]\n\tnames.ord.cd14.gran <- names(ord.cd14.gran)\n\t\n\t## CD14-nk\n\tord.cd14.nk <- tests.main[,\"cd14.nk.p\"]\n\tnames(ord.cd14.nk) <- row.names(tests.main)\n\tord.cd14.nk <- ord.cd14.nk[order(ord.cd14.nk)]\n\tnames.ord.cd14.nk <- names(ord.cd14.nk)\n\t\n\t\n\t\n\t## CD19-gran\n\tord.cd19.gran <- tests.main[,\"cd19.gran.p\"]\n\tnames(ord.cd19.gran) <- row.names(tests.main)\n\tord.cd19.gran <- ord.cd19.gran[order(ord.cd19.gran)]\n\tnames.ord.cd19.gran <- names(ord.cd19.gran)\n\t\n\t## CD19-nk\n\tord.cd19.nk <- tests.main[,\"cd19.nk.p\"]\n\tnames(ord.cd19.nk) <- row.names(tests.main)\n\tord.cd19.nk <- ord.cd19.nk[order(ord.cd19.nk)]\n\tnames.ord.cd19.nk <- names(ord.cd19.nk)\n\t\n\t\n\t## gran-nk\n\tord.gran.nk <- tests.main[,\"gran.nk.p\"]\n\tnames(ord.gran.nk) <- row.names(tests.main)\n\tord.gran.nk <- ord.gran.nk[order(ord.gran.nk)]\n\tnames.ord.gran.nk <- names(ord.gran.nk)\n\t\n\t\n\tinit <- cbind(names.ord.anova,names.ord.cd4.cd8,names.ord.cd4.cd14,names.ord.cd4.cd19,names.ord.cd4.gran,names.ord.cd4.nk,names.ord.cd8.cd14,names.ord.cd8.cd19,names.ord.cd8.gran,names.ord.cd8.nk,names.ord.cd14.cd19,names.ord.cd14.gran,names.ord.cd14.nk,names.ord.cd19.gran,names.ord.cd19.nk,names.ord.gran.nk)\n\n\n\tkeep.anova <- init[1:length.anova,\"names.ord.anova\"]\n\t\n\tlist.rm.cd4.cd8 <- c(init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.cd8 <- unique(list.rm.cd4.cd8)\n\tord.list.rm.cd4.cd8 <- setdiff(init[,\"names.ord.cd4.cd8\"],list.rm.cd4.cd8)\n\t\n\tlist.rm.cd4.cd14 <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.cd14 <- unique(list.rm.cd4.cd14)\n\tord.list.rm.cd4.cd14 <- setdiff(init[,\"names.ord.cd4.cd14\"],list.rm.cd4.cd14)\n\n\n\tlist.rm.cd4.cd19 <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.cd19 <- unique(list.rm.cd4.cd19)\n\tord.list.rm.cd4.cd19 <- setdiff(init[,\"names.ord.cd4.cd19\"],list.rm.cd4.cd19)\n\n\tlist.rm.cd4.gran <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.gran <- unique(list.rm.cd4.gran)\n\tord.list.rm.cd4.gran <- setdiff(init[,\"names.ord.cd4.gran\"],list.rm.cd4.gran)\n\n\tlist.rm.cd4.nk <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.nk <- unique(list.rm.cd4.nk)\n\tord.list.rm.cd4.nk <- setdiff(init[,\"names.ord.cd4.nk\"],list.rm.cd4.nk)\n\n\n\tlist.rm.cd8.cd14 <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd8.cd14 <- unique(list.rm.cd8.cd14)\n\tord.list.rm.cd8.cd14 <- setdiff(init[,\"names.ord.cd8.cd14\"],list.rm.cd8.cd14)\n\t\n\tlist.rm.cd8.cd19 <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd8.cd19 <- unique(list.rm.cd8.cd19)\n\tord.list.rm.cd8.cd19 <- setdiff(init[,\"names.ord.cd8.cd19\"],list.rm.cd8.cd19)\n\t\n\tlist.rm.cd8.gran <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd8.gran <- unique(list.rm.cd8.gran)\n\tord.list.rm.cd8.gran <- setdiff(init[,\"names.ord.cd8.gran\"],list.rm.cd8.gran)\n\t\n\t\n\tlist.rm.cd8.nk <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd8.nk <- unique(list.rm.cd8.nk)\n\tord.list.rm.cd8.nk <- setdiff(init[,\"names.ord.cd8.nk\"],list.rm.cd8.nk)\n\n\tlist.rm.cd14.cd19 <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd14.cd19 <- unique(list.rm.cd14.cd19)\n\tord.list.rm.cd14.cd19 <- setdiff(init[,\"names.ord.cd14.cd19\"],list.rm.cd14.cd19)\n\t\n\t\n\tlist.rm.cd14.gran <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd14.gran <- unique(list.rm.cd14.gran)\n\tord.list.rm.cd14.gran <- setdiff(init[,\"names.ord.cd14.gran\"],list.rm.cd14.gran)\n\t\n\t\n\tlist.rm.cd14.nk <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd14.nk <- unique(list.rm.cd14.nk)\n\tord.list.rm.cd14.nk <- setdiff(init[,\"names.ord.cd14.nk\"],list.rm.cd14.nk)\n\n\n\tlist.rm.cd19.gran <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd19.gran <- unique(list.rm.cd19.gran)\n\tord.list.rm.cd19.gran <- setdiff(init[,\"names.ord.cd19.gran\"],list.rm.cd19.gran)\n\n\n\n\tlist.rm.cd19.nk <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd19.nk <- unique(list.rm.cd19.nk)\n\tord.list.rm.cd19.nk <- setdiff(init[,\"names.ord.cd19.nk\"],list.rm.cd19.nk)\n\n\n\tlist.rm.gran.nk <- c(init[1:length.pairs,\"names.ord.cd4.cd8\"],init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"])\n\tlist.rm.gran.nk <- unique(list.rm.gran.nk)\n\tord.list.rm.gran.nk <- setdiff(init[,\"names.ord.gran.nk\"],list.rm.gran.nk)\n\n\n\tkeep.cd4.cd8 <- ord.list.rm.cd4.cd8[1:length.pairs]\n\tkeep.cd4.cd14 <- ord.list.rm.cd4.cd14[1:length.pairs]\n\tkeep.cd4.cd19 <- ord.list.rm.cd4.cd19[1:length.pairs]\n\tkeep.cd4.gran <- ord.list.rm.cd4.gran[1:length.pairs]\n\tkeep.cd4.nk <- ord.list.rm.cd4.nk[1:length.pairs]\n\t\n\tkeep.cd8.cd14 <- ord.list.rm.cd8.cd14[1:length.pairs]\n\tkeep.cd8.cd19 <- ord.list.rm.cd8.cd19[1:length.pairs]\n\tkeep.cd8.gran <- ord.list.rm.cd8.gran[1:length.pairs]\n\tkeep.cd8.nk <- ord.list.rm.cd8.nk[1:length.pairs]\n\t\n\tkeep.cd14.cd19 <- ord.list.rm.cd14.cd19[1:length.pairs]\n\tkeep.cd14.gran <- ord.list.rm.cd14.gran[1:length.pairs]\n\tkeep.cd14.nk <- ord.list.rm.cd14.nk[1:length.pairs]\n\t\n\tkeep.cd19.gran <- ord.list.rm.cd19.gran[1:length.pairs]\n\tkeep.cd19.nk <- ord.list.rm.cd19.nk[1:length.pairs]\n\t\n\tkeep.gran.nk <- ord.list.rm.gran.nk[1:length.pairs]\t\n\n\t\n\t## merge CpG sets to get final cpg list\n\tcpg.list <- c(keep.anova,keep.cd4.cd8,keep.cd4.cd14,keep.cd4.cd19,keep.cd4.gran,keep.cd4.nk,keep.cd8.cd14,keep.cd8.cd19,keep.cd8.gran,keep.cd8.nk,keep.cd14.cd19,keep.cd14.gran,keep.cd14.nk,keep.cd19.gran,keep.cd19.nk,keep.gran.nk)\n\t\n\tcpg.list <- unique(cpg.list)\n\t\n\treturn(cpg.list)\n}\n\n#' Fit the model for the 6 main cell types\n#'\n#' @family Model fitting\n#' @param data A data frame\n#' @param cpg.list A character\n#' @return A character\n#' @export\nfit.main.nosum1 <- function(data, cpg.list = cpg.list.main.stage1){\n\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.main[match(cpg.list,row.names(med.main)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\n\tcolnames(fit.sub3) <- c(\"CD4\",\"CD8\",\"CD14\",\"CD19\",\"Gran\",\"NK\")\n\n\treturn(fit.sub3)\n}\n\n## Functions to partition the whole blood beta in order to fit the two-stage model (refine CD4/CD8 estimates) \n## \tand also to fit T and B cell subtype models\n#' Partition whole blood beta for two-stage model fit inner\n#'\n#' @family Data partitioning\n#' @param x\n#' @param fit\n#' @param keep.cols\n#' @return A numeric matrix\nget.beta.sub.inner <- function(x, fit, keep.cols){\n\tx <- as.numeric(x)\n\tkeep.len <- length(keep.cols)\n\tsub.cols <- setdiff(seq(from=1,to=dim(fit)[2]),keep.cols)\n\tsub.len <- length(sub.cols)\n\tbeta <- x[(sub.len+1):length(x)]\n\tblood <- x[1:sub.len]\n\t\n\ttemp.sum <- 0\n\tfor (i in (1:sub.len))\n\t{\n\t\ttemp <- fit[,sub.cols[i]]*as.numeric(blood[i])\n\t\ttemp.sum <- temp.sum+temp\n\t}\n\ttemp2 <- beta-temp.sum\n\treturn(temp2)\n}\n\n#' Partition whole blood beta for two-stage model fit\n#' \n#' @family Data partitioning\n#' @param data A data frame\n#' @param blood\n#' @param fit\n#' @param keep.cols\n#' @return A numeric matrix\n#' @export\nget.beta.sub <- function(data, blood = med.main, fit, keep.cols){\n\tper.sum <- apply(fit,1,function(x){sum(x[keep.cols])})\n\t#blood.sub <- blood[,keep.cols]\n\tsub.cols <- setdiff(seq(from=1,to=dim(fit)[2]),keep.cols)\n\tkeep.cpgs <- intersect(row.names(data),row.names(blood))\n\tdata.all <- cbind(blood[keep.cpgs,sub.cols],data[keep.cpgs,])\n\tbeta.sub <- t(apply(data.all,1,get.beta.sub.inner,fit=fit,keep.cols=keep.cols))\n\tbeta.sub2 <- beta.sub/per.sum\n\treturn(beta.sub)\n}\n\n## Stage 2 models to improve CD4/CD8 main cell type estimates -------------\n## Stage 2b (CD4 vs. CD8)\n\n# (should hard code in CpG list also)\n#' Get CpG list for stage 2\n#' \n#' @family Generate list\n#' @param length.pairs An integer numeric\n#' @return A Character\n#' @export\nget.cpg.list.stage2b <- function(length.pairs = 100){\n\t## get keep list of CpGs for all pairwise comparisons\n\t\n\t## CD4-CD8\n\tord.cd4.cd8 <- tests.main[,\"cd4.cd8.p\"]\n\tnames(ord.cd4.cd8) <- row.names(tests.main)\n\tord.cd4.cd8 <- ord.cd4.cd8[order(ord.cd4.cd8)]\n\tnames.ord.cd4.cd8 <- names(ord.cd4.cd8)\n\t\n\t\n\t## CD4-CD14\n\tord.cd4.cd14 <- tests.main[,\"cd4.cd14.p\"]\n\tnames(ord.cd4.cd14) <- row.names(tests.main)\n\tord.cd4.cd14 <- ord.cd4.cd14[order(ord.cd4.cd14)]\n\tnames.ord.cd4.cd14 <- names(ord.cd4.cd14)\n\t\n\t\n\t## CD4-CD19\n\tord.cd4.cd19 <- tests.main[,\"cd4.cd19.p\"]\n\tnames(ord.cd4.cd19) <- row.names(tests.main)\n\tord.cd4.cd19 <- ord.cd4.cd19[order(ord.cd4.cd19)]\n\tnames.ord.cd4.cd19 <- names(ord.cd4.cd19)\n\t\n\t## CD4-gran\n\tord.cd4.gran <- tests.main[,\"cd4.gran.p\"]\n\tnames(ord.cd4.gran) <- row.names(tests.main)\n\tord.cd4.gran <- ord.cd4.gran[order(ord.cd4.gran)]\n\tnames.ord.cd4.gran <- names(ord.cd4.gran)\n\t\n\t\n\t## CD4-nk\n\tord.cd4.nk <- tests.main[,\"cd4.nk.p\"]\n\tnames(ord.cd4.nk) <- row.names(tests.main)\n\tord.cd4.nk <- ord.cd4.nk[order(ord.cd4.nk)]\n\tnames.ord.cd4.nk <- names(ord.cd4.nk)\n\t\n\t\n\t## CD8-CD14\n\tord.cd8.cd14 <- tests.main[,\"cd8.cd14.p\"]\n\tnames(ord.cd8.cd14) <- row.names(tests.main)\n\tord.cd8.cd14 <- ord.cd8.cd14[order(ord.cd8.cd14)]\n\tnames.ord.cd8.cd14 <- names(ord.cd8.cd14)\n\t\n\t## CD8-CD19\n\tord.cd8.cd19 <- tests.main[,\"cd8.cd19.p\"]\n\tnames(ord.cd8.cd19) <- row.names(tests.main)\n\tord.cd8.cd19 <- ord.cd8.cd19[order(ord.cd8.cd19)]\n\tnames.ord.cd8.cd19 <- names(ord.cd8.cd19)\n\t\n\t## CD8-gran\n\tord.cd8.gran <- tests.main[,\"cd8.gran.p\"]\n\tnames(ord.cd8.gran) <- row.names(tests.main)\n\tord.cd8.gran <- ord.cd8.gran[order(ord.cd8.gran)]\n\tnames.ord.cd8.gran <- names(ord.cd8.gran)\n\t\n\t## CD8-nk\n\tord.cd8.nk <- tests.main[,\"cd8.nk.p\"]\n\tnames(ord.cd8.nk) <- row.names(tests.main)\n\tord.cd8.nk <- ord.cd8.nk[order(ord.cd8.nk)]\n\tnames.ord.cd8.nk <- names(ord.cd8.nk)\n\t\n\n\n\t## CD14-CD19\n\tord.cd14.cd19 <- tests.main[,\"cd14.cd19.p\"]\n\tnames(ord.cd14.cd19) <- row.names(tests.main)\n\tord.cd14.cd19 <- ord.cd14.cd19[order(ord.cd14.cd19)]\n\tnames.ord.cd14.cd19 <- names(ord.cd14.cd19)\n\t\n\t## CD14-gran\n\tord.cd14.gran <- tests.main[,\"cd14.gran.p\"]\n\tnames(ord.cd14.gran) <- row.names(tests.main)\n\tord.cd14.gran <- ord.cd14.gran[order(ord.cd14.gran)]\n\tnames.ord.cd14.gran <- names(ord.cd14.gran)\n\t\n\t## CD14-nk\n\tord.cd14.nk <- tests.main[,\"cd14.nk.p\"]\n\tnames(ord.cd14.nk) <- row.names(tests.main)\n\tord.cd14.nk <- ord.cd14.nk[order(ord.cd14.nk)]\n\tnames.ord.cd14.nk <- names(ord.cd14.nk)\n\t\n\t\n\t\n\t## CD19-gran\n\tord.cd19.gran <- tests.main[,\"cd19.gran.p\"]\n\tnames(ord.cd19.gran) <- row.names(tests.main)\n\tord.cd19.gran <- ord.cd19.gran[order(ord.cd19.gran)]\n\tnames.ord.cd19.gran <- names(ord.cd19.gran)\n\t\n\t## CD19-nk\n\tord.cd19.nk <- tests.main[,\"cd19.nk.p\"]\n\tnames(ord.cd19.nk) <- row.names(tests.main)\n\tord.cd19.nk <- ord.cd19.nk[order(ord.cd19.nk)]\n\tnames.ord.cd19.nk <- names(ord.cd19.nk)\n\t\n\t\n\t## gran-nk\n\tord.gran.nk <- tests.main[,\"gran.nk.p\"]\n\tnames(ord.gran.nk) <- row.names(tests.main)\n\tord.gran.nk <- ord.gran.nk[order(ord.gran.nk)]\n\tnames.ord.gran.nk <- names(ord.gran.nk)\n\t\n\t\n\tinit <- cbind(names.ord.cd4.cd8,names.ord.cd4.cd14,names.ord.cd4.cd19,names.ord.cd4.gran,names.ord.cd4.nk,names.ord.cd8.cd14,names.ord.cd8.cd19,names.ord.cd8.gran,names.ord.cd8.nk,names.ord.cd14.cd19,names.ord.cd14.gran,names.ord.cd14.nk,names.ord.cd19.gran,names.ord.cd19.nk,names.ord.gran.nk)\n\n\t\n\tlist.rm.cd4.cd8 <- c(init[1:length.pairs,\"names.ord.cd4.cd14\"],init[1:length.pairs,\"names.ord.cd4.cd19\"],init[1:length.pairs,\"names.ord.cd4.gran\"],init[1:length.pairs,\"names.ord.cd4.nk\"],init[1:length.pairs,\"names.ord.cd8.cd14\"],init[1:length.pairs,\"names.ord.cd8.cd19\"],init[1:length.pairs,\"names.ord.cd8.gran\"],init[1:length.pairs,\"names.ord.cd8.nk\"],init[1:length.pairs,\"names.ord.cd14.cd19\"],init[1:length.pairs,\"names.ord.cd14.gran\"],init[1:length.pairs,\"names.ord.cd14.nk\"],init[1:length.pairs,\"names.ord.cd19.gran\"],init[1:length.pairs,\"names.ord.cd19.nk\"],init[1:length.pairs,\"names.ord.gran.nk\"])\n\tlist.rm.cd4.cd8 <- unique(list.rm.cd4.cd8)\n\tord.list.rm.cd4.cd8 <- setdiff(init[,\"names.ord.cd4.cd8\"],list.rm.cd4.cd8)\t\n\t\n\t\n\t\n\t\n\tkeep.cd4.cd8 <- ord.list.rm.cd4.cd8[1:length.pairs]\n\n\t\n\t## merge CpG sets to get final cpg list\n\tcpg.list <- keep.cd4.cd8\n\t\n\tcpg.list <- unique(cpg.list)\n\t\n\treturn(cpg.list)\n}\n\n#' Fit models for stage 2\n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param fit.main A numeric matrix\n#' @param cpg.list A character\n#' @return A numeric matrix\nfit.stage2b <- function(data, fit.main, cpg.list = cpg.list.2b){\n\n\tt.per <- fit.main[,1]+fit.main[,2]\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\n\tmed.t <- med.main[,c(1,2)]\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.t[match(cpg.list,row.names(med.t)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\tsum.fit <- apply(fit.sub3,1,sum)\n\t\n\tratio <- sum.fit/t.per\n\t\n\tfit.sub3 <- fit.sub3/ratio\t\n\t\n\tfit.new <- cbind(fit.sub3[,1],fit.sub3[,2],fit.main[,3],fit.main[,4],fit.main[,5],fit.main[,6])\n\tcolnames(fit.new) <- c(\"CD4\",\"CD8\",\"CD14\",\"CD19\",\"Gran\",\"NK\")\n\n\treturn(fit.new)\n}\n\n\n### functions for CD4 subtypes -----------------------------------------------\n\n## get CpG list for CD4 subtypes (should hard code in CpG list also)\n#' Get CpG list for CD4 subtypes\n#' @family Generate list\n#' @param length.anova An integer numeric\n#' @param length.pairs An integer numeric\n#' @return A character\nget.cpg.list.cd4.sub <- function(length.anova = 2100, length.pairs = 600){\n\t## get keep list of CpGs for overall ANOVA test for all types\n\tord.anova <- tests.cd4.sub[,\"cd4.anova.p\"]\n\tnames(ord.anova) <- row.names(tests.cd4.sub)\n\tord.anova <- ord.anova[order(ord.anova)]\n\tnames.ord.anova <- names(ord.anova)\n\t\n\t## get keep list of CpGs for all pairwise comparisons\n\t\n\t## Mem-Naive\n\tord.mem.naive <- tests.cd4.sub[,\"cd4.mem.naive.p\"]\n\tnames(ord.mem.naive) <- row.names(tests.cd4.sub)\n\tord.mem.naive <- ord.mem.naive[order(ord.mem.naive)]\n\tnames.ord.mem.naive <- names(ord.mem.naive)\n\t\n\t\n\t## Mem-Reg\n\tord.mem.reg <- tests.cd4.sub[,\"cd4.mem.reg.p\"]\n\tnames(ord.mem.reg) <- row.names(tests.cd4.sub)\n\tord.mem.reg <- ord.mem.reg[order(ord.mem.reg)]\n\tnames.ord.mem.reg <- names(ord.mem.reg)\n\t\n\t\n\t## Naive-Reg\n\tord.naive.reg <- tests.cd4.sub[,\"cd4.naive.reg.p\"]\n\tnames(ord.naive.reg) <- row.names(tests.cd4.sub)\n\tord.naive.reg <- ord.naive.reg[order(ord.naive.reg)]\n\tnames.ord.naive.reg <- names(ord.naive.reg)\n\n\n\tkeep.anova <- names.ord.anova[1:length.anova]\n\t\n\tlist.rm.mem.naive <- c(names.ord.mem.reg[1:length.pairs],names.ord.naive.reg[1:length.pairs])\n\tlist.rm.mem.naive <- unique(list.rm.mem.naive)\n\tord.list.rm.mem.naive <- setdiff(names.ord.mem.naive,list.rm.mem.naive)\n\t\n\tlist.rm.mem.reg <- c(names.ord.mem.naive[1:length.pairs],names.ord.naive.reg[1:length.pairs])\n\tlist.rm.mem.reg <- unique(list.rm.mem.reg)\n\tord.list.rm.mem.reg <- setdiff(names.ord.mem.reg,list.rm.mem.reg)\n\t\n\tlist.rm.naive.reg <- c(names.ord.mem.naive[1:length.pairs],names.ord.mem.reg[1:length.pairs])\n\tlist.rm.naive.reg <- unique(list.rm.naive.reg)\n\tord.list.rm.naive.reg <- setdiff(names.ord.naive.reg,list.rm.naive.reg)\n\t\n\t\n\t\n\tkeep.mem.naive <- ord.list.rm.mem.naive[1:length.pairs]\n\tkeep.mem.reg <- ord.list.rm.mem.reg[1:length.pairs]\n\tkeep.naive.reg <- ord.list.rm.naive.reg[1:length.pairs]\n\t\t\n\t## merge CpG sets to get final cpg list\n\tcpg.list <- c(keep.anova,keep.mem.naive,keep.mem.reg,keep.naive.reg)\n\t\n\tcpg.list <- unique(cpg.list)\n\t\n\treturn(cpg.list)\n}\n\n#(need to partition data into CD4 beta value first)\n#' Fit model for CD4 subtypes on whole blood data\n#' \n#' @family Generate list\n#' @param data A data frame\n#' @param fit.main A numeric matrix\n#' @param cpg.list A character\n#' @return A numeric matrix\n#' @export\nfit.cd4.sub <- function(data, fit.main, cpg.list = cpg.list.cd4) {\n\n\tcd4.per <- fit.main[,1]\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd4.sub[match(cpg.list,row.names(med.cd4.sub)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\tsum.fit <- apply(fit.sub3,1,sum)\n\t\n\tratio <- sum.fit/cd4.per\n\t\n\tfit.sub3 <- fit.sub3/ratio\t\n\tcolnames(fit.sub3) <- c(\"CD4-Naive\",\"CD4-Mem\",\"CD4-Reg\")\n\n\treturn(fit.sub3)\n}\n\n#' Fit model for CD4 subtypes on M450 data from sorted CD4 T cells\n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param cpg.list A character\n#' @return A numeric matrix\nfit.cd4.sorted <- function(data, cpg.list = cpg.list.cd4){\n\t#cd4.per <- fit.main[,1]\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd4.sub[match(cpg.list,row.names(med.cd4.sub)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\t#sum.fit <- apply(fit.sub3,1,sum)\n\t\n\t#ratio <- sum.fit/cd4.per\n\t\n\t#fit.sub3 <- fit.sub3/ratio\t\n\tcolnames(fit.sub3) <- c(\"CD4-Naive\",\"CD4-Mem\",\"CD4-Reg\")\n\n\treturn(fit.sub3)\n}\n\n\n## functions to fit model for CD8 subtypes ------------------------------------\n\n# (should hard code in this CpG list also)\n#' Get CpG list for CD8 subtypes\n#' \n#' @family Generate list\n#' @param length.list An integer numeric\n#' @return A character\n#' @export\nget.cpg.list.cd8.sub <- function(length.list = 300){\n\t## get keep list of CpGs for overall ANOVA test for all types\n\tord.list <- tests.cd8.sub[,\"cd8.mem.naive.p\"]\n\tnames(ord.list) <- row.names(tests.cd8.sub)\n\tord.list <- ord.list[order(ord.list)]\n\tnames.ord.list <- names(ord.list)\n\t\n\t\t## merge CpG sets to get final cpg list\n\tcpg.list <- names.ord.list[1:length.list]\n\t\n\tcpg.list <- unique(cpg.list)\n\t\n\treturn(cpg.list)\n}\n\n# (need to parition data into CD8 beta value first)\n#' Fit model for CD8 subtypes for whole blood data\n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param fit.main A numeric matrix\n#' @param cpg.list A character\n#' @return A numeric matrix\n#' @export\nfit.cd8.sub <- function(data, fit.main, cpg.list = cpg.list.cd8){\n\n\tcd8.per <- fit.main[,2]\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd8.sub[match(cpg.list,row.names(med.cd8.sub)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\tsum.fit <- apply(fit.sub3,1,sum)\n\t\n\tratio <- sum.fit/cd8.per\n\t\n\tfit.sub3 <- fit.sub3/ratio\t\n\tcolnames(fit.sub3) <- c(\"CD8-Naive\",\"CD8-Mem\")\n\n\treturn(fit.sub3)\n}\n\n#' Fit model for CD8 subtypes using M450 data from sorted CD8 T cells\n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param cpg.list A character\n#' @return A numeric matrix\nfit.cd8.sub.sorted <- function(data, cpg.list = cpg.list.cd8){\n\n\t#cd8.per <- fit.main[,2]\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd8.sub[match(cpg.list,row.names(med.cd8.sub)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\t#sum.fit <- apply(fit.sub3,1,sum)\n\t\n\t#ratio <- sum.fit/cd8.per\n\t\n\t#fit.sub3 <- fit.sub3/ratio\t\n\tcolnames(fit.sub3) <- c(\"CD8-Naive\",\"CD8-Mem\")\n\n\treturn(fit.sub3)\n}\n\n## models for CD19 B cell subtypes -----------------------------------------------\n\n#(should hard code in CpG list also)\n#' Function to get CpG list for CD19 B cell subtypes \n#' \n#' @family Model fitting\n#' @param length.pairs An integer numeric\n#' @return A character\n#' @export\nget.cpg.list.cd19.sub <- function(length.pairs = 300){\n\t## get keep list of CpGs for overall ANOVA test for all types\n\tord.anova <- tests.cd19.sub[,\"cd19.naive.mem.p\"]\n\tnames(ord.anova) <- row.names(tests.cd19.sub)\n\tord.anova <- ord.anova[order(ord.anova)]\n\tnames.ord.anova <- names(ord.anova)\n\t\n\t\n\tkeep.anova <- names.ord.anova[1:length.pairs]\n\t\n\tcpg.list <- keep.anova\n\t\n\tcpg.list <- unique(cpg.list)\n\t\n\treturn(cpg.list)\n}\n\n# (need to partition data into B cell beta score first)\n#!! med.cd19.sub not available !!\n#' Fit model for CD19 B cell subtypes for whole blood \n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param fit.main A numeric matrix\n#' @param cpg.list A character\n#' @return A numeric matrix\n#' @export\nfit.cd19.sub <- function(data, fit.main, cpg.list = cpg.list.cd19){\n\n\tcd19.per <- fit.main[,4]\n\t\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd19.sub.2class[match(cpg.list,row.names(med.cd19.sub.2class)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\tsum.fit <- apply(fit.sub3,1,sum)\n\t\n\tratio <- sum.fit/cd19.per\n\t\n\tfit.sub3 <- fit.sub3/ratio\n\t\n\tcolnames(fit.sub3) <- c(\"CD19-Naive\",\"CD19-Mem\")\n\n\treturn(fit.sub3)\n}\n\n\n#' Fit model for CD19 subtypes using M450 data from sorted B cells\n#' \n#' @family Model fitting\n#' @param data A data frame\n#' @param cpg.list A character\n#' @return A numeric matrix\n#' @export\nfit.cd19.sorted <- function(data, cpg.list = cpg.list.cd19){\n\n\tcpg.list <- intersect(cpg.list,row.names(data))\n\t\t\n\tfit.sub3 <- apply(data[match(cpg.list,row.names(data)),],2,fitNonneg.nosum1,med.cd19.sub.2class[match(cpg.list,row.names(med.cd19.sub.2class)),])\n\n\tfit.sub3 <- t(fit.sub3)\n\t\n\tcolnames(fit.sub3) <- c(\"CD19-Naive\",\"CD19-Mem\")\n\n\treturn(fit.sub3)\n}\n\n#' Get stage 1 & 2 estimates and T and B cell subtypes\n#' \n#' @family Subtype estimation\n#' @param data A data frame\n#' @return A numeric matrix\n#' @export \nest.all.wb <- function(data){\n\t## stage 1\n\tfit.stage1 <- fit.main.nosum1(data)\n\t\n\t## stage 2\n\t# t.data <- get.beta.sub(data[cpg.list.2b,],med.main,fit.stage1,c(1,2))\n\t# fit.stage2 <- fit.stage2b(t.data,fit.stage1,cpg.list.2b)\n\t\n\t## cd4 subtypes\n\t# data.cd4.partition <- get.beta.sub(data[cpg.list.cd4,],med.main,fit.stage2,1)\n\t# fit.data.cd4.sub <- fit.cd4.sub(data.cd4.partition,fit.stage2,cpg.list.cd4)\n\n\t# ## cd8 subtypes\n\t# data.cd8.partition <- get.beta.sub(data[cpg.list.cd8,],med.main,fit.stage2,2)\n\t# fit.data.cd8.sub <- fit.cd8.sub(data.cd8.partition,fit.stage2,cpg.list.cd8)\n\n\t# ## cd19 subtypes\n\t# data.cd19.partition <- get.beta.sub(data[cpg.list.cd19,],med.main,fit.stage2,4)\n\t# fit.data.cd19.sub <- fit.cd19.sub(data.cd19.partition,fit.stage2,cpg.list.cd19)\n\n\t# ## merge results together\n\t# res <- cbind(fit.stage2,fit.data.cd4.sub,fit.data.cd8.sub,fit.data.cd19.sub)\n\t\n\t# return(res)\n}\n", "meta": {"hexsha": "f13b75b6510a654c282d5175caf3d9bb92ccdaf9", "size": 33266, "ext": "r", "lang": "R", "max_stars_repo_path": "R/main_functions.r", "max_stars_repo_name": "HudsonAlpha/MethylDeconBloodSubtypes", "max_stars_repo_head_hexsha": "33bd5d95ec6770957106aa9aab421985044b8f47", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-01-03T19:08:01.000Z", "max_stars_repo_stars_event_max_datetime": "2017-01-03T19:08:01.000Z", "max_issues_repo_path": "R/main_functions.r", "max_issues_repo_name": "HudsonAlpha/MethylDeconBloodSubtypes", 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YES\n2. NO", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.44552953503957266, "lm_q1q2_score": 0.31588896500943314}} {"text": "#######DESCRIPTION###############################\n#processes raw stage data from Indian CWC records\n#outputs rdata object and csv\n#################################################\n#load libraries\nlibrary(dplyr)\nlibrary(data.table)\nlibrary(readr)\nlibrary(tidyr)\nlibrary(ncdf4)\nlibrary(tools)\n##user inputs\n#raw file location\ndir_raw = '/Volumes/gonggong/flood forecasting/data/raw/stage/'\n#output location\ndir_dat = '/Volumes/gonggong/flood forecasting/data/'\n#quality control flag - see qc_reference for a description of the flag values and methods\nqc_thresh = 90\n##process raw files\nstage_list = list.files(paste0(dir_raw), pattern = '.nc')\ndate_ref = as.POSIXct('2000-01-01 00:00:00', \"%Y-%m-%d %H:%M:%S\", tz = 'GMT')\ndate_ref = data.table(date = seq(from = as.POSIXct('2000-01-01 00:00:00'), to = as.POSIXct(Sys.Date()), by = 'min'))\ndate_ref$date_ind = 0:(nrow(date_ref) - 1)\nstage_dat = NULL\nfor(i in 1:length(stage_list)){\n\tsta_id_temp = file_path_sans_ext(stage_list[i])\n\tnc_temp = try(nc_open(paste0(dir_raw, stage_list[i])))\n\tif(length(nc_temp) > 1){\n\t\tstage_temp = ncvar_get(nc_temp, 'level')\n\t\tqc_temp = ncvar_get(nc_temp, 'QC')\n\t\tdate_temp = ncvar_get(nc_temp, 'date')\n\n\t\tstage_dat_temp = data.table(sta_id = sta_id_temp, date_ind = date_temp, flag = qc_temp, stage = stage_temp)\n\t\tstage_dat = rbind_list(stage_dat, stage_dat_temp)\n\t}\n}\nstage_dat = stage_dat %>% left_join(date_ref) %>% dplyr::select(-date_ind) %>% dplyr::filter(flag >= qc_thresh)\n#output processed files\nwrite.csv(stage_dat, paste0(dir_dat, 'stage.csv'), row.names = F)\nsaveRDS(stage_dat, paste0(dir_dat, 'stage.csv'))\n", "meta": {"hexsha": "398552f91de0b74f7bf7416c4147f4457dfa89b0", "size": 1600, "ext": "r", "lang": "R", "max_stars_repo_path": "stage_processing.r", "max_stars_repo_name": "dpbroman/floodforecasting", "max_stars_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-22T22:15:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-26T06:52:11.000Z", "max_issues_repo_path": "stage_processing.r", "max_issues_repo_name": "dpbroman/floodforecasting", "max_issues_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stage_processing.r", "max_forks_repo_name": "dpbroman/floodforecasting", "max_forks_repo_head_hexsha": "7bc3ebdadbf1bbcd045da9e5de7dbfe3c70a5333", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.0243902439, "max_line_length": 116, "alphanum_fraction": 0.691875, "num_tokens": 460, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3158715565918786}} {"text": "#!/usr/bin/env Rscript\nlibrary(\"optparse\")\nlibrary(splines)\nlibrary(limma)\nlibrary(edgeR)\nlibrary(xlsx)\n\noption_list = list(\n make_option(c(\"-p\", \"--project-path\"),\n type=\"character\",\n default=NULL,\n help=\"Project path\",\n metavar=\"character\"),\n make_option(c(\"-c\", \"--counts\"),\n type=\"character\",\n default=NULL,\n help=\"Collected counts file ready for R.\",\n metavar=\"character\"),\n make_option(c(\"-s\", \"--strains\"),\n type=\"character\",\n default=NULL,\n help=\"File describing strains and/or conditions.\",\n metavar=\"character\"),\n make_option(c(\"-o\", \"--output\"),\n type=\"character\",\n default=NULL,\n help=\"Output path\",\n metavar=\"character\"),\n make_option(c(\"-f\", \"--filter\"),\n type=\"double\",\n default=0.5,\n help=\"Filter threshold for CPM\",\n metavar=\"double\")\n );\n\nopt <- parse_args(OptionParser(option_list=option_list), convert_hyphens_to_underscores=TRUE);\n# set project path\nsetwd(opt$project_path)\n# create results path\nif (!(dir.exists(opt$output))) {dir.create(opt$output, recursive=TRUE)}\n\n# write edgeR plots on this file\npdf(paste(opt$output, \"edgeR_plots.pdf\", sep=\"/\"))\n\ncounts <- read.delim(opt$counts, header=T, row.names=1, sep=\",\")\ncounts <- counts[ , order(names(counts))]\n\n# Plot mean-difference accross all pairs\n# It's probably not worth doing this \"per replicate\" bases. It might make sense to see it \"per averaged replicates\" bases - which I haven't figured out how.\n\n# box plot for counts\nboxplot(log2(counts+1), las=1)\ntitle('Gene level counts', ylab='log2(count+1)')\n\n# bar plot for total counts\npar(mar=c(5,6,4,2)+0.1,mgp=c(5,1,0)) # to avoid overlaping axis label/title\ntotal_counts <- colSums(counts)\nbarplot(total_counts, las=1)\ntitle('Total mapped read counts', ylab='Read counts')\n\n## Make design matrix\nconditions <- read.csv(opt$strains)\ngroup <- factor(paste(conditions$Strain,conditions$Treatment,sep=\".\"))\ncbind(conditions,group=group)\ndesign <- model.matrix(~0+group)\ncolnames(design) <- levels(group)\n\n## Make new DGEList\ny <- DGEList(counts=counts, group=group)\n\n## This is a manual filter to keep only Features that have a CPM>0.5 in at least two sample. Features with less are\n## considered not expressed and won't be included in comparisons.\n## CPM bound depends very much on the read/genome size. A CPM corresponding to 10 counts should be aimed for. Ideally,\n## this should not be a static value. Since we usually get generous amount of reads, it will be set to 0.5 for now.\n## This may not apply in all conditions, so a switch as an argument might be useful later on.\nkeep <- rowSums(cpm(y)>opt$filter) >= 2\ny <- y[keep, , keep.lib.sizes=FALSE]\n\n## Calculate normalization factor to account for library size\ny <- calcNormFactors(y, method='upperquartile')\n\n# Plot for MDS\nplotMDS(y)\ntitle('Fold-change based multi-dimensional scaling (MDS) plot')\n\n\n## normalize by library size, and estimate dispersion allowing possible trend with average count size\ny <- estimateDisp(y, design, robust=TRUE)\n\n# Plot for MDS\nplotMDS(y, method='BCV')\ntitle('Biological coefficient of variation based multi-dimensional scaling (MDS) plot')\n\n# plot BCV for replicates\nplotBCV(y)\ntitle('Biological coefficient of variation')\n\n## Run an Exact Test for Differences between Two Negative Binomial Groups\n#et <- exactTest(y)\n\nfit <- glmQLFit(y, design, robust=TRUE)\n# generate comparison string based in available groups to make pairwise comparisons for all\nconstr <- paste(combn(levels(group), 2, simplify=TRUE, FUN=function(x) {sprintf(\"%s_vs_%s=%s-%s\", x[1], x[2], x[1], x[2])}), sep=', ')\ncon <- makeContrasts(contrasts=constr, levels=design)\n\n# Create the excel file for results and design\nwb <- createWorkbook()\nsaveWorkbook(wb, paste(opt$output, \"edgeR_pairwise_comparisons.xlsx\", sep='/'))\nwrite.xlsx(con, paste(opt$output, \"edgeR_pairwise_comparisons.xlsx\", sep='/'), sheetName =\"Comparisons\", append=TRUE)\nwrite.xlsx(counts, paste(opt$output, \"edgeR_pairwise_comparisons.xlsx\", sep='/'), sheetName =\"Counts\", append=TRUE)\n\nfor (i in colnames(con)){\n\tlrt = glmLRT(fit, contrast=con[,i])\n\tassign(i, topTags(lrt, n=nrow(y))$table)\n\twrite.xlsx(get(i), paste(opt$output, \"edgeR_pairwise_comparisons.xlsx\", sep='/'), sheetName = i, append = TRUE)\n\tdt <- decideTestsDGE(lrt)\n\tisDE <- as.logical(dt)\n\tDEnames <- rownames(y)[isDE]\n\tplotSmear(lrt, de.tags=DEnames)\n\tabline(h=c(-1,1), col=\"blue\")\n\ttitle(con[1])\n}\ndev.off()\n", "meta": {"hexsha": "5e2db03fa715d135ef9f9ed349220055fd576d16", "size": 5042, "ext": "r", "lang": "R", "max_stars_repo_path": "iLoop_RNAseq_pipeline/scripts/edgeR_script.r", "max_stars_repo_name": "meono/iLoop_RNAseq_pipeline", "max_stars_repo_head_hexsha": "824816f0708ebe8ea704eee9b435fa5157740bdd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "iLoop_RNAseq_pipeline/scripts/edgeR_script.r", "max_issues_repo_name": "meono/iLoop_RNAseq_pipeline", "max_issues_repo_head_hexsha": "824816f0708ebe8ea704eee9b435fa5157740bdd", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "iLoop_RNAseq_pipeline/scripts/edgeR_script.r", "max_forks_repo_name": "meono/iLoop_RNAseq_pipeline", "max_forks_repo_head_hexsha": "824816f0708ebe8ea704eee9b435fa5157740bdd", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.6612903226, "max_line_length": 156, "alphanum_fraction": 0.6233637445, "num_tokens": 1187, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7341195385342972, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.31577957304559046}} {"text": "readcube <- function(fname, drpcat=NULL) {\n require(FITSio)\n ff <- file(fname, \"rb\")\n hd0 <- parseHdr(readFITSheader(ff))\n mangaid <- hd0[grep(\"MANGAID\", hd0)+1]\n plateifu <- hd0[grep(\"PLATEIFU\", hd0)+1]\n ra <- as.numeric(hd0[grep(\"IFURA\", hd0)+1])\n dec <- as.numeric(hd0[grep(\"IFUDEC\", hd0)+1])\n l <- as.numeric(hd0[grep(\"IFUGLON\", hd0)+1])\n b <- as.numeric(hd0[grep(\"IFUGLAT\", hd0)+1]) \n ebv <- as.numeric(hd0[grep(\"EBVGAL\", hd0)+1])\n hd0 <- parseHdr(readFITSheader(ff))\n cra <- as.numeric(hd0[grep(\"CRVAL1\", hd0)+1]) \n cdec <- as.numeric(hd0[grep(\"CRVAL2\", hd0)+1]) \n cpix1 <- as.numeric(hd0[grep(\"CRPIX1\", hd0)+1]) \n cpix2 <- as.numeric(hd0[grep(\"CRPIX2\", hd0)+1]) \n cd1 <- as.numeric(hd0[grep(\"CD1_1\", hd0)+1]) \n cd2 <- as.numeric(hd0[grep(\"CD2_2\", hd0)+1]) \n close(ff)\n \n flux <- readFITS(fname, hdu=1)$imDat\n ivar <- readFITS(fname, hdu=2)$imDat\n mask <- readFITS(fname, hdu=3)$imDat\n lambda <- readFITS(fname, hdu=4)$imDat\n ivar[ivar <= 0] <- NA\n ivar[mask >= 1024] <- NA\n flux[is.na(ivar)] <- NA\n extc <- elaw(lambda, ebv)\n nx <- dim(flux)[1]\n ny <- dim(flux)[2]\n for (ix in 1:nx) {\n for (iy in 1:ny) {\n flux[ix,iy,] <- flux[ix,iy,]*extc\n ivar[ix,iy,] <- ivar[ix,iy,]/extc^2\n }\n }\n snr <- apply(sqrt(pmax(flux,0)^2*ivar), c(1,2), median, na.rm=TRUE)\n xpos <- (1:nx-cpix1)*cd1\n ypos <- (1:ny-cpix2)*cd2\n phi <- function(x,y) atan2(-y, x)\n rho <- function(x,y) sqrt(x^2 + y^2)\n long <- outer(xpos, ypos, phi)\n lat <- atan(180/pi/outer(xpos, ypos, rho))\n dec.f <- 180/pi*asin(sin(lat)*sin(cdec*pi/180) - cos(lat)*cos(long)*cos(cdec*pi/180))\n ra.f <- cra + 180/pi*atan2(cos(lat)*sin(long),\n sin(lat)*cos(cdec*pi/180) + cos(lat)*cos(long)*sin(cdec*pi/180))\n gimg <- readFITS(fname, hdu=8)$imDat*elaw(4640.4,ebv)\n rimg <- readFITS(fname, hdu=9)$imDat*elaw(6122.3,ebv)\n iimg <- readFITS(fname, hdu=10)$imDat*elaw(7439.5,ebv)\n zimg <- readFITS(fname, hdu=11)$imDat*elaw(8897.1,ebv)\n if (!is.null(drpcat)) {\n ind <- which(drpcat$plateifu == plateifu)\n z <- drpcat$nsa_z[ind]\n zdist <- drpcat$nsa_zdist[ind]\n } else {\n z <- zdist <- NA\n }\n list(meta=list(mangaid=mangaid, plateifu=plateifu, ra=ra, dec=dec, l=l, b=b, \n ebv=ebv, z=z, zdist=zdist, cpix=c(cpix1, cpix2)),\n xpos=xpos, ypos=ypos,\n lambda=lambda, ra.f=ra.f, dec.f=dec.f, flux=flux, ivar=ivar, snr=snr,\n gimg=gimg, rimg=rimg, iimg=iimg, zimg=zimg)\n}\n\nreadrss <- function(fname, drpcat=NULL, ndither=3) {\n require(FITSio)\n ff <- file(fname, \"rb\")\n hd0 <- parseHdr(readFITSheader(ff))\n mangaid <- hd0[grep(\"MANGAID\", hd0)+1]\n plateifu <- hd0[grep(\"PLATEIFU\", hd0)+1]\n nexp <- as.numeric(hd0[grep(\"NEXP\", hd0)+1])\n ra <- as.numeric(hd0[grep(\"IFURA\", hd0)+1])\n dec <- as.numeric(hd0[grep(\"IFUDEC\", hd0)+1])\n l <- as.numeric(hd0[grep(\"IFUGLON\", hd0)+1])\n b <- as.numeric(hd0[grep(\"IFUGLAT\", hd0)+1]) \n ebv <- as.numeric(hd0[grep(\"EBVGAL\", hd0)+1])\n close(ff)\n \n flux <- readFITS(fname, hdu=1)$imDat\n ivar <- readFITS(fname, hdu=2)$imDat\n mask <- readFITS(fname, hdu=3)$imDat\n lambda <- as.vector(readFITS(fname, hdu=5)$imDat)\n xpos <- readFITS(fname, hdu=9)$imDat\n ypos <- readFITS(fname, hdu=10)$imDat\n xpos <- apply(xpos, 2, mean)\n ypos <- apply(ypos, 2, mean)\n ivar[ivar <= 0] <- NA\n ivar[mask >= 1024] <- NA\n flux[is.na(ivar)] <- NA\n extc <- elaw(lambda, ebv)\n flux <- flux*extc\n ivar <- ivar/extc^2\n nl <- nrow(flux)\n ns <- nexp/ndither\n npos <- ncol(flux)/ns\n xy <- data.frame(x=xpos, y=ypos)\n txy <- SearchTrees::createTree(xy)\n neighbors <- as.vector(SearchTrees::knnLookup(txy, newdat=xy[1:npos,], k=ns))\n flux <- flux[,neighbors]\n ivar <- ivar[,neighbors]\n snr <- apply(sqrt(pmax(flux,0)^2*ivar), 2, median, na.rm=TRUE)\n flux <- array(t(flux), dim=c(npos, ns, nl))\n ivar <- array(t(ivar), dim=c(npos, ns, nl))\n snr <- matrix(snr, npos, ns)\n xpos <- matrix(xpos[neighbors], npos, ns)\n ypos <- matrix(ypos[neighbors], npos, ns)\n if (!is.null(drpcat)) {\n ind <- which(drpcat$plateifu == plateifu)\n z <- drpcat$nsa_z[ind]\n zdist <- drpcat$nsa_zdist[ind]\n } else {\n z <- zdist <- NA\n }\n list(meta=list(mangaid=mangaid, plateifu=plateifu, nexp=nexp, ra=ra, dec=dec, l=l, b=b, \n ebv=ebv, z=z, zdist=zdist),\n lambda=lambda, flux=flux, ivar=ivar, snr=snr,\n xpos=xpos, ypos=ypos\n )\n}\n\nstackrss <- function(gdat, dz=NULL) {\n meta <- gdat$meta\n ivar <- apply(gdat$ivar, c(1,3), sum, na.rm=TRUE)\n ivar[ivar <= 0] <- NA\n flux <- apply(gdat$flux*gdat$ivar, c(1,3), sum, na.rm=TRUE)/ivar\n snr <- apply(sqrt(pmax(flux,0)^2*ivar), 1, median, na.rm=TRUE)\n nr <- nrow(flux)\n nc <- ncol(flux)\n dim(flux) <- c(nr, 1, nc)\n dim(ivar) <- c(nr, 1, nc)\n snr <- matrix(snr, nr, 1)\n if (!is.null(dz)) {\n dz <- rowMeans(dz, na.rm=TRUE)\n }\n xpos <- rowMeans(gdat$xpos)\n ypos <- rowMeans(gdat$ypos)\n dec.f <- meta$dec + ypos/3600\n ra.f <- meta$ra - xpos/3600/cos(dec.f*pi/180)\n list(meta=meta, lambda=gdat$lambda, flux=flux, ivar=ivar, snr=snr, \n xpos=xpos, ypos=ypos, ra.f=ra.f, dec.f=dec.f, dz=dz)\n}\n\n## Galactic extinction correction from Fitzpatrick (1998): http://arxiv.org/abs/astro-ph/9809387v1\n## This is spline fit portion valid from near-UV to near-IR and R=3.1\n\nelaw <- function(lambda, ebv) {\n il <- c(0,0.377,0.820,1.667,1.828,2.141,2.433,3.704,3.846)\n al <- c(0,0.265,0.829,2.688,3.055,3.806,4.315,6.265,6.591)\n fai <- splinefun(il,al)\n 10^(0.4*fai(10000/lambda)*ebv)\n}\n\n", "meta": {"hexsha": "e0863722300ed9bfe887c20a02fb162d531e0abd", "size": 5793, "ext": "r", "lang": "R", "max_stars_repo_path": "readmanga.r", "max_stars_repo_name": "mlpeck/vrot_stanmodels", "max_stars_repo_head_hexsha": "7c4fbbd24d50aeeb475d7c3b7f323228e5666ad8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "readmanga.r", "max_issues_repo_name": "mlpeck/vrot_stanmodels", "max_issues_repo_head_hexsha": "7c4fbbd24d50aeeb475d7c3b7f323228e5666ad8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "readmanga.r", "max_forks_repo_name": "mlpeck/vrot_stanmodels", "max_forks_repo_head_hexsha": "7c4fbbd24d50aeeb475d7c3b7f323228e5666ad8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.862745098, "max_line_length": 98, "alphanum_fraction": 0.5717244951, "num_tokens": 2238, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.3157512444899741}} {"text": "library(Matrix)\n\nhandler <- function() {\n\treturn(Matrix(1:6, 3, 2)[,2])\n}\n", "meta": {"hexsha": "9243221d09b119dff55afd30a0524b7c2a220d90", "size": 74, "ext": "r", "lang": "R", "max_stars_repo_path": "example/matrix.r", "max_stars_repo_name": "thomaslaber/aws-lambda-r-runtime", "max_stars_repo_head_hexsha": "4a9a473ec4e74da42a4898f824ebfcb0e3c71ff1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "example/matrix.r", "max_issues_repo_name": "thomaslaber/aws-lambda-r-runtime", "max_issues_repo_head_hexsha": "4a9a473ec4e74da42a4898f824ebfcb0e3c71ff1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "example/matrix.r", "max_forks_repo_name": "thomaslaber/aws-lambda-r-runtime", "max_forks_repo_head_hexsha": "4a9a473ec4e74da42a4898f824ebfcb0e3c71ff1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.3333333333, "max_line_length": 30, "alphanum_fraction": 0.6081081081, "num_tokens": 25, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.679178686187839, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.3157512384520399}} {"text": "suppressPackageStartupMessages(library(\"optparse\"))\n\noption_list <- list( \n make_option(c(\"-s\", \"--silent\"), action=\"store_true\", \n help=\"Silent mode to reduce logs\"),\n make_option(c(\"-n\", \"--number\"), type=\"integer\", default=5, \n help=\"Number of number to generate [default %default]\",\n metavar=\"number\"),\n make_option(c(\"-w\", \"--word\"), default=\"word\", \n help = \"Word to use [default \\\"%default\\\"]\")\n )\n\nopt <- parse_args(OptionParser(option_list=option_list))\n\nif ( opt$verbose ) { \n write(\"writing some verbose output to standard error...\\n\", stderr()) \n}\n\nif( opt$generator == \"rnorm\") {\n cat(paste(rnorm(opt$count, mean=opt$mean, sd=opt$sd), collapse=\"\\n\"))\n} else {\n cat(paste(do.call(opt$generator, list(opt$count)), collapse=\"\\n\"))\n}\ncat(\"\\n\")\n", "meta": {"hexsha": "eaac686223c4fa2737690ff7044d8d71979f814d", "size": 802, "ext": "r", "lang": "R", "max_stars_repo_path": "layout/optparse.r", "max_stars_repo_name": "0xdomyz/r_collection", "max_stars_repo_head_hexsha": "29defad610c9b8603fff91370176201f72aaa856", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-25T13:57:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-25T13:57:36.000Z", "max_issues_repo_path": "layout/optparse.r", "max_issues_repo_name": "0xdomyz/r_collection", "max_issues_repo_head_hexsha": "29defad610c9b8603fff91370176201f72aaa856", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "layout/optparse.r", "max_forks_repo_name": "0xdomyz/r_collection", "max_forks_repo_head_hexsha": "29defad610c9b8603fff91370176201f72aaa856", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.08, "max_line_length": 74, "alphanum_fraction": 0.6246882793, "num_tokens": 208, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555714, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.31573875727153}} {"text": "library(shiny)\r\nlibrary(shinyLP)\r\nlibrary(shinyBS)\r\nlibrary(shinyjqui)\r\nlibrary(dplyr)\r\nlibrary(tools)\r\nlibrary(tidyverse)\r\nlibrary(lmtest)\r\nlibrary(lm.beta)\r\nlibrary(ggplot2)\r\nlibrary(RColorBrewer)\t\r\nlibrary(car)\r\nlibrary(data.table)\r\noptions(shiny.reactlog = TRUE)\r\noptions(shiny.usecairo=FALSE)\r\ncolMax <- function(data) sapply(data, max, na.rm = TRUE)\r\nbx.stat <- function(inp){return(boxplot.stats(inp)$stats[c(1,5)])}\r\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\r\nfl_cl <- function(inp){return(c(floor(inp[1]),ceiling(inp[2])))}\r\nsplitVec <- function(vec){nvec <- c()\r\n\ttemp <- c()\r\n\twhile (length(vec) > 2) {\r\n\t\twhile (length(temp) < 3){\r\n\t\t\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t\r\nnormRes <- function(vals){\r\n\tm <- mean(vals)\r\n\tres <- vals-m\r\n\tshapiro.test(res)$p.value > 0.05\r\n\t}\t\r\n\r\ngetColNames <- function(ct){\r\n\tcolLabs <- unlist(lapply(1:ncol(ct), function (i){\r\n\t\t\tpaste(\r\n\t\t\t\tpaste(rownames(ct)[ct[,i]>0], collapse=\", \"),\r\n\t\t\t\t\"vs.\",\r\n\t\t\t\tpaste(rownames(ct)[ct[,i]<0], collapse=\", \")\r\n\t\t\t\t)\r\n\t\t}))\r\n\r\n\treturn(colLabs)\r\n\t}\r\n\t\r\ndoughnut <-\r\nfunction (x0, edges = 200, labels=NULL, outer.radius = 1, \r\n inner.radius=0.75, clockwise = FALSE,\r\n init.angle = ifelse (clockwise, 90, 0), density = NULL, \r\n angle = 45, col = NULL, border = FALSE, lty = NULL, \r\n main = NULL, inside=NULL, ...){\r\n\tif (!is.factor(x0)){stop(\"'x' values must be factors.\")}\r\n\tif (is.null(labels)){labels <- as.character(levels(x0))}\r\n\t\r\n\tnas <- sum(is.na(x0))\r\n\t\r\n\tx <- as.vector(table(x0[!is.na(x0)]))\r\n\t\r\n\tcol = colorRampPalette(brewer.pal(9,\"Spectral\"))(length(labels))\r\n\t\r\n\tx <- c(0, cumsum(x)/sum(x))\r\n\tdx <- diff(x)\r\n\tnx <- length(dx)\r\n\tpar(mar=c(2,2,2,2))\r\n\tplot.new()\r\n\tpin <- par(\"pin\")\r\n\txlim <- ylim <- c(-1, 1)\r\n\tif (pin[1L] > pin[2L])\r\n\t\t{xlim <- (pin[1L]/pin[2L]) * xlim}\r\n\telse {ylim <- (pin[2L]/pin[1L]) * ylim}\r\n\tplot.window(xlim, ylim, \"\", asp = 1)\t\r\n\t\r\n\ttwopi <- if (clockwise)\r\n\t\t-2 * pi\r\n\telse 2 * pi\r\n\tt2xy <- function(t, radius) {\r\n\t\tt2p <- twopi * t + init.angle * pi/180\r\n\t\tlist(x = radius * cos(t2p), \r\n\t\t\t y = radius * sin(t2p))\r\n\t}\r\n\tfor (i in 1L:nx) {\r\n\t\tn <- max(2, floor(edges * dx[i]))\r\n\t\tP <- t2xy(seq.int(x[i], x[i + 1], length.out = n),\r\n\t\t\t\t outer.radius)\r\n\t\tpolygon(c(P$x, 0), c(P$y, 0), density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = col[i], lty = lty[i])\r\n\t\tPout <- t2xy(mean(x[i + 0:1]), outer.radius)\r\n\t\tlab <- as.character(labels[i])\r\n\t\tif (!is.na(lab) && nzchar(lab)) {\r\n\t\t\ttext(1.3 * Pout$x, 1.3 * Pout$y, labels[i], family=\"serif\",\r\n\t\t\t\t xpd = TRUE, adj = ifelse(Pout$x < 0, 1, 0), \r\n\t\t\t\t ...)\r\n\t\t}\r\n\t\t## Add white disc \r\n\t\tPin <- t2xy(seq.int(0, 1, length.out = n*nx),\r\n\t\t\t\t inner.radius)\r\n\t\tpolygon(Pin$x, Pin$y, density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = \"white\", lty = lty[i])\r\n\tif (nas > 0)\r\n\t\ttext(0,0,paste(nas, ifelse(nas>1,\"\\nNAs\", \"\\nNA\")), family=\"serif\", cex=1.25)\r\n\t}\r\n \r\n\ttitle(main = main, ...)\r\n\tinvisible(NULL)\r\n\t}\r\n\r\n\r\neffPlot <-\r\nfunction (eff, type, boundary=2, edges = 100, labels=NULL, outer.radius = 1, \r\n inner.radius=0.75,\r\n init.angle = 0, density = NULL, \r\n angle = 45, col = NULL, border = FALSE, lty = NULL, \r\n main = NULL, inside=NULL, ...){\r\n\tif (!all(is.numeric(c(eff, boundary)))){stop(\"'x' values must be factors.\")}\r\n\tif (is.null(labels)){labels <- c(\"\", \"\")}\r\n\teffb <- eff\r\n\teff <- min(abs(eff), 2)\r\n\r\n\tx <- abs(c(0, (boundary-eff)/boundary, 1))\r\n\trat <- abs(0.5*eff/boundary)\r\n\tefcol <- hsv(max(0.5-rat, 0),1,1)\r\n\tcol <- c(\"grey90\", efcol)\r\n\t\r\n\tdx <- diff(x)\r\n\tnx <- length(dx)\r\n\tpar(mar=c(0,2,1,2))\r\n\tplot.new()\r\n\tpin <- par(\"pin\")\r\n\txlim <- c(-1, 1)\r\n\tylim <- c(0,1)\r\n\tif (pin[1L] > pin[2L])\r\n\t\t{xlim <- (pin[1L]/pin[2L]) * xlim\r\n\t} else {ylim <- (pin[2L]/pin[1L]) * ylim}\r\n\tplot.window(xlim, ylim, \"\", asp = 1)\t\r\n\r\n\tt2xy <- function(t, radius) {\r\n\t\tt2p <- pi * t + init.angle * pi/180\r\n\t\tlist(x = radius * cos(t2p), \r\n\t\t\t y = radius * sin(t2p))\r\n\t}\r\n\tfor (i in 1L:nx) {\r\n\t\tn <- max(50, floor(edges * dx[i]))\r\n\t\tP <- t2xy(seq.int(x[i], x[i + 1], length.out = n),\r\n\t\t\t\t outer.radius)\r\n\t\tpolygon(c(P$x, 0), c(P$y, 0), density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = col[i], lty = lty[i])\r\n\t\tPout <- t2xy(mean(x[i + 0:1]), outer.radius)\r\n\t\tlab <- as.character(labels[i])\r\n\t\tif (!is.na(lab) && nzchar(lab)) {\r\n\t\t\ttext(1.1 * Pout$x, 1.1 * Pout$y, labels[i], family=\"serif\",\r\n\t\t\t\t xpd = TRUE, adj = ifelse(Pout$x < 0, 1, 0))\r\n\t\t}\r\n\t}\r\n\r\n\t\t## Add white disc \r\n\t\tPin <- t2xy(seq.int(0, 1, length.out = n*nx),\r\n\t\t\t\t inner.radius)\r\n\t\tpolygon(Pin$x, Pin$y, density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = \"white\", lty = lty[i])\r\n\t\r\n\ttext(0,0.2,paste(type, \": \", abs(round(effb, 2)), sep=\"\"), family=\"serif\", cex=1.25)\r\n \ttext(-1,-0.2, \"Weak\", family=\"serif\", cex=1.25)\r\n\ttext(1,-0.2,\"Strong\", family=\"serif\", cex=1.25)\r\n\ttitle(main = main, ...)\r\n\tinvisible(NULL)\r\n\t}\r\n\r\ncorPlot <-\r\nfunction (eff, type=\"spearman\", boundary=1, edges = 100, labels=NULL, outer.radius = 1, \r\n inner.radius=0.75,\r\n init.angle = 0, density = NULL, \r\n angle = 45, col = NULL, border = FALSE, lty = NULL, \r\n main = NULL, inside=NULL, ...){\r\n\r\n\tif (type == \"spearman\") {type <- \"Spearman's rho\"} else {type <- \"Pearson's r\"}\r\n\r\n\tif (!all(is.numeric(c(eff, boundary)))){stop(\"'x' values must be factors.\")}\r\n\tif (is.null(labels)){labels <- c(\"\", \"\")}\r\n\teffb <- eff\r\n\t#eff <- min(abs(eff), 2)\r\n\r\n\tx <- c(-1, max(eff-0.02,-1), min(eff+0.02,1), 1)\r\n\tx <- (x/-2)+0.5\r\n\trat <- abs(0.4*eff)\r\n\tefcol <- hsv(max(rat, 0),1,1)\r\n\tcol <- c(\"grey90\", efcol, \"grey90\")\r\n\t\r\n\tdx <- diff(x)\r\n\tprint(dx)\r\n\tnx <- length(dx)\r\n\tpar(mar=c(0,2,1,2))\r\n\tplot.new()\r\n\tpin <- par(\"pin\")\r\n\txlim <- c(-1, 1)\r\n\tylim <- c(0,1)\r\n\tif (pin[1L] > pin[2L])\r\n\t\t{xlim <- (pin[1L]/pin[2L]) * xlim\r\n\t} else {ylim <- (pin[2L]/pin[1L]) * ylim}\r\n\tplot.window(xlim, ylim, \"\", asp = 1)\t\r\n\r\n\tt2xy <- function(t, radius) {\r\n\t\tt2p <- pi * t + init.angle * pi/180\r\n\t\tlist(x = radius * cos(t2p), \r\n\t\t\t y = radius * sin(t2p))\r\n\t}\r\n\tfor (i in 1L:nx) {\r\n\t\tn <- max(50, floor(edges * dx[i]))\r\n\t\tP <- t2xy(seq.int(x[i], x[i + 1], length.out = n),\r\n\t\t\t\t outer.radius)\r\n\t\tpolygon(c(P$x, 0), c(P$y, 0), density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = col[i], lty = lty[i])\r\n\t\tPout <- t2xy(mean(x[i + 0:1]), outer.radius)\r\n\t\tlab <- as.character(labels[i])\r\n\t\tif (!is.na(lab) && nzchar(lab)) {\r\n\t\t\ttext(1.1 * Pout$x, 1.1 * Pout$y, labels[i], family=\"serif\",\r\n\t\t\t\t xpd = TRUE, adj = ifelse(Pout$x < 0, 1, 0))\r\n\t\t}\r\n\t}\r\n\r\n\t\t## Add white disc \r\n\t\tPin <- t2xy(seq.int(0, 1, length.out = n*nx),\r\n\t\t\t\t inner.radius)\r\n\t\tpolygon(Pin$x, Pin$y, density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = \"white\", lty = lty[i])\r\n\t\r\n\ttext(0,0.2,paste(type, \": \", abs(round(effb, 2)), sep=\"\"), family=\"serif\", cex=1.25)\r\n \ttext(-1,-0.2, \"Strong negative\", family=\"serif\", cex=1.25)\r\n\ttext(1,-0.2,\"Strong positive\", family=\"serif\", cex=1.25)\r\n\ttitle(main = main, ...)\r\n\tinvisible(NULL)\r\n\t}\r\n\r\n\t\r\nbend <-\r\nfunction (nas, tot, edges = 100, labels=NULL, outer.radius = 1, \r\n inner.radius=0.75,\r\n init.angle = 0, density = NULL, \r\n angle = 45, col = NULL, border = FALSE, lty = NULL, \r\n main = NULL, inside=NULL, ...){\r\n\tif (!all(is.numeric(c(nas, tot)))){stop(\"'x' values must be factors.\")}\r\n\tif (is.null(labels)){labels <- c(\"\", \"\")}\r\n\t\r\n\tcol <- c(\"grey90\", \"red\")\r\n\tx <- c(0, (tot-nas)/tot, 1)\r\n\tdx <- diff(x)\r\n\tnx <- length(dx)\r\n\tpar(mar=c(0,2,1,2))\r\n\tplot.new()\r\n\tpin <- par(\"pin\")\r\n\txlim <- c(-1, 1)\r\n\tylim <- c(0,1)\r\n\tif (pin[1L] > pin[2L])\r\n\t\t{xlim <- (pin[1L]/pin[2L]) * xlim\r\n\t} else {ylim <- (pin[2L]/pin[1L]) * ylim}\r\n\tplot.window(xlim, ylim, \"\", asp = 1)\t\r\n\r\n\tt2xy <- function(t, radius) {\r\n\t\tt2p <- pi * t + init.angle * pi/180\r\n\t\tlist(x = radius * cos(t2p), \r\n\t\t\t y = radius * sin(t2p))\r\n\t}\r\n\tfor (i in 1L:nx) {\r\n\t\tn <- max(50, floor(edges * dx[i]))\r\n\t\tP <- t2xy(seq.int(x[i], x[i + 1], length.out = n),\r\n\t\t\t\t outer.radius)\r\n\t\tpolygon(c(P$x, 0), c(P$y, 0), density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = col[i], lty = lty[i])\r\n\t\tPout <- t2xy(mean(x[i + 0:1]), outer.radius)\r\n\t\tlab <- as.character(labels[i])\r\n\t\tif (!is.na(lab) && nzchar(lab)) {\r\n\t\t\ttext(1.1 * Pout$x, 1.1 * Pout$y, labels[i], family=\"serif\",\r\n\t\t\t\t xpd = TRUE, adj = ifelse(Pout$x < 0, 1, 0))\r\n\t\t}\r\n\t}\r\n\r\n\t\t## Add white disc \r\n\t\tPin <- t2xy(seq.int(0, 1, length.out = n*nx),\r\n\t\t\t\t inner.radius)\r\n\t\tpolygon(Pin$x, Pin$y, density = density[i], \r\n\t\t\t\tangle = angle[i], border = border[i], \r\n\t\t\t\tcol = \"white\", lty = lty[i])\r\n\t\r\n\ttext(0,0.2,paste(round((1-x[2])*100, 1), \"% missing\", sep=\"\"), family=\"serif\", cex=1.25)\r\n \r\n\ttitle(main = main, ...)\r\n\tinvisible(NULL)\r\n\t}\r\n\t\r\ngetna <- function(cc) {\r\n\tcc[ sample(c(TRUE, NA), prob = c(0.95, 0.05), size = length(cc), replace = TRUE) ]\r\n\t}\r\n\t\r\ngetEffLab <- function(eff) {\r\n\teff <- abs(eff)\r\n\tif (eff <0.8){\r\n\t\tif (eff < 0.2) {return(\"very small\")}\r\n\t\telse if (eff < 0.5) {return(\"small\")}\r\n\t\telse {return(\"medium\")}\r\n\t\t}\r\n\telse {\r\n\t\tif (eff < 1.2) {return(\"large\")}\r\n\t\telse if (eff < 2) {return(\"very large\")}\r\n\t\telse {return(\"huge\")}\t\r\n\t\t}\r\n\t}\r\n\r\ngetCorLab <- function(eff, rel = T) {\r\n\teff <- abs(eff)\r\n\tif (rel == T){if (eff <0.5){\r\n\t\t\tif (eff < 0.1) {return(\"very weak\")}\r\n\t\t\telse if (eff < 0.3) {return(\"weak\")}\r\n\t\t\telse {return(\"medium\")}\r\n\t\t}\r\n\t\telse {\r\n\t\t\treturn(\"strong\")\r\n\t\t\t}\r\n\t\t}\r\n\t\t\r\n\telse {\r\n\t\tif (eff <0.5){\r\n\t\t\tif (eff < 0.1) {return(\"at all\")}\r\n\t\t\telse if (eff < 0.3) {return(\"to a limited degree\")}\r\n\t\t\telse {return(\"reasonably well\")}\r\n\t\t\t}\r\n\t\telse {\r\n\t\t\treturn(\"well\")\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t\r\nranddata <- function(){\r\n\tseed0 <- round(rnorm(100, 100, 25))\r\n\trd <- data.frame(\r\n\t\tgender=c(sample(c(rep(\"female\",10),rep(\"male\",8)), 50, replace=T), \r\n\t\t\tsample(c(rep(\"female\",8),rep(\"male\",10)), 50, replace=T)),\r\n\t\tage=c(round(runif(50,min=10,max=30),0), round(runif(50,min=20,max=45),0)),\r\n\t\teducation=c(sample(c(rep(\"3_university\",7),rep(\"2_high\",5),rep(\"1_elementary\",2),rep(\"4_doctorate\",10)),50,replace=T),\r\n\t\t\tsample(c(rep(\"3_university\",5),rep(\"2_high\",7),rep(\"1_elementary\",10),rep(\"4_doctorate\",2)),50,replace=T)),\r\n\t\tresidence=c(sample(c(rep(\"East\",7),rep(\"Mid\",5),rep(\"West\",1)),50,replace=T),\r\n\t\t\tsample(c(rep(\"East\",2),rep(\"Mid\",7),rep(\"West\",10)),50,replace=T)),\r\n\t\tbirthplace=c(sample(c(rep(\"East\",5),rep(\"Mid\",4),rep(\"West\",1)),50,replace=T),\r\n\t\t\tsample(c(rep(\"East\",3),rep(\"Mid\",8),rep(\"West\",15)),50,replace=T)),\t\r\n\t\trhoticity=c(abs(round(rnorm(50,0.2, 0.1),2)),abs(round(rnorm(50,0.5, 0.1),2))),\r\n\t\tglottalisation=c(sample(c(rep(\"2_preconsonantal\",7), rep(\"3_preconsonantal+initial\",1), rep(\"1_none\",10)),50,replace=T),\r\n\t\t\tsample(c(rep(\"2_preconsonantal\",7), rep(\"3_preconsonantal+initial\",3), rep(\"1_none\",1)),50,replace=T)),\r\n\t\tbefore_class=seed0,\r\n\t\tmid_class=seed0+rnorm(100,10,26),\r\n\t\tafter_class=seed0+rnorm(100,20,25)\r\n\t\t)\r\n\t\t\r\n\t# rd <- as.data.frame(lapply(rd, getna))\r\n\t\r\n\treturn(rd)\r\n\r\n\t}\r\n\r\ngetExp <- function (cdata) {\r\n\texpDat <- data.frame()\r\n\tfor (i in 1:length(rowSums(cdata))){\r\n\t\tfor (j in 1:length(colSums(cdata))) {\r\n\t\t\texpDat[i,j] <- (sum(cdata[i,]) * sum(cdata[,j])) / sum(cdata)\r\n\t\t\texpDat[i,j] <- (sum(cdata[i,]) * sum(cdata[,j])) / sum(cdata)\r\n\t\t\t}\r\n\t\t}\r\n\tcolnames(expDat) <- colnames(cdata)\r\n\treturn(expDat)\r\n\t}\r\n\t\r\nrenderCatPlots <- function(cats, dats, input, output) {\r\n\t\tfor (i in cats) {\r\n\t\t\tlocal({\r\n\t\t\t\tii <- i \r\n\t\t\t\t# need i evaluated here\r\n\t\t\t\toutput[[sprintf('%s_%s', \"plot\", ii)]] <- renderPlot({\r\n\t\t\t\t\tdoughnut(dats[[ii]])\r\n\t\t\t\t})\r\n\t\t\t})\r\n\t\t}\r\n\t}\r\n\r\nrenderNumPlots <- function(nums, dats, input, output) {\r\n\t\tfor (i in nums) {\r\n\t\t\tlocal({\r\n\t\t\t\tii <- i\r\n\t\t\t\tnas <- sum(is.na(dats[[ii]]))\r\n\t\t\t\t\r\n\t\t\t\t# need i evaluated here\r\n\t\t\t\toutput[[sprintf('%s_%s', \"plot\", ii)]] <- renderPlot({\r\n\t\t\t\t\tggplot(dats[!is.na(dats[[ii]]),], aes_string(ii))+ \r\n\t\t\t\t\t\tgeom_histogram(aes(fill=..count..), bins=10) + \r\n\t\t\t\t\t\ttheme_minimal() + xlab(\"\") + ylab(\"\") + \r\n\t\t\t\t\t\ttheme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.position=\"none\") +\r\n\t\t\t\t\t\tscale_y_continuous(labels=c()) + \r\n\t\t\t\t\t\tscale_fill_distiller(palette=\"Oranges\")+\r\n\t\t\t\t\t\tannotate(\"text\", Inf, Inf, label = ifelse(nas>0, paste(nas, ifelse(nas>1,\"NAs\", \"NA\")), \"\"), hjust = 1, vjust = 1, size=6, family=\"serif\")\r\n\t\t\t\t\t\t\r\n\t\t\t\t})\r\n\t\t\t})\r\n\t\t}\r\n\t}\t\r\n\r\ngetSummary <- function(dt.i){\r\n\tsmr <- lapply(levels(dt.i[,2]), function(i){\r\n\t\t m <- mean(dt.i[dt.i[,2]==i,1], na.rm=T)\r\n\t\t s <- sd(dt.i[dt.i[,2]==i,1], na.rm=T)/sqrt(length(dt.i[dt.i[,2]==i,1]))\r\n \t\t return(c(i, m,s))\r\n\t\t})\r\n\tsmr <- do.call(\"rbind\",smr)\t\r\n\tcolnames(smr) <- c(\"l\", \"m\", \"s\")\r\n\tsmr <- as.data.frame(smr)\r\n\tsmr$m <- as.numeric(as.character(smr$m))\r\n\tsmr$s <- as.numeric(as.character(smr$s))\r\n\treturn(smr)\r\n\t}\r\n\t\r\nshinyServer(function(input, output, session) {\r\n\tcookedData <- reactiveValues(cats=NULL, nums=NULL, cooked=NULL)\t\t\t\t\r\n\tplotType <- reactiveValues(current=NULL)\r\n\timportSettings <- reactiveValues(header=TRUE, sep=\"\\t\", quoter='\"', dec=\".\")\t\r\n\ttestSet <- reactiveValues(settings=NULL, vals=NULL, manConts=NULL)\r\n\tsettings <- reactiveValues(na.ignore=\"ignore\", mod=\"real\", cols=\"b&w\", serif=\"serif\")\r\n\tsummaryVals <- reactiveValues(freqs=NULL)\r\n\tanovaGroups <- reactiveValues(level=NULL, finished=FALSE, results=NULL)\r\n\t\r\n\tobserveEvent(input$upFile, {\r\n\t\tsettings$mod <- \"real\"\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(size=\"s\",\r\n\t\t\t\tfileInput(inputId='file1', label='Choose a CSV file',\r\n\t\t\t\t\t\t\t\t\taccept=c('text/csv', \r\n\t\t\t\t\t\t\t\t\t\t\t 'text/comma-separated-values', \r\n\t\t\t\t\t\t\t\t\t\t\t 'text/plain',\r\n\t\t\t\t\t\t\t\t\t\t\t 'text/tab-separated-values',\r\n\t\t\t\t\t\t\t\t\t\t\t 'csv',\r\n\t\t\t\t\t\t\t\t\t\t\t 'tsv')),\r\n\t\t\t\thr(),\r\n\t\t\t\tp(a(\"Download sample data\", href=\"https://onedrive.live.com/download?cid=8BF09AD1C8343122&resid=8BF09AD1C8343122%21258985&authkey=ACWhDNRScGBzKwk\"), style='text-align:center'),\r\n\t\t\t\teasyClose = TRUE\r\n\t\t\t)\r\n\t\t)\r\n\t})\r\n\t\r\n\tobserveEvent(input$saveSettings, {\r\n\t\tsettings$na.ignore <- input$naSetting\r\n\t\tsettings$cols <- input$plotCol\r\n\t\tsettings$serif <- input$serif\r\n\t\t\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$startDemo, {\r\n\t\tsettings$mod <- \"demo\"\r\n\t\tsession$sendCustomMessage('activeNavs', 'Check')\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Check')\r\n\t})\r\n\t\r\n\tobserveEvent(input$file1, {\r\n\t\r\n\t\tif (is.null(rawData())){\r\n\t\t\tshowModal(modalDialog(\r\n\t\t\t\tsize=\"s\",\r\n\t\t\t\ttitle=\"Invalid file\",\r\n\t\t\t\th4(\"This file is not supported\", style=\"color: #ff0000; font-face: bold\"),\r\n\t\t\t\tfileInput(inputId='file1', label='Choose a CSV file',\r\n\t\t\t\t\t\t\t\t\taccept=c('text/csv', \r\n\t\t\t\t\t\t\t\t\t\t\t 'text/comma-separated-values', \r\n\t\t\t\t\t\t\t\t\t\t\t 'text/plain',\r\n\t\t\t\t\t\t\t\t\t\t\t 'text/tab-separated-values',\r\n\t\t\t\t\t\t\t\t\t\t\t 'csv'\r\n\t\t\t\t\t\t\t\t\t\t\t )\r\n\t\t\t\t\t),\r\n\t\t\t\thr(),\r\n\t\t\t\tp(a(\"Download sample data\", href=\"https://onedrive.live.com/download?cid=8BF09AD1C8343122&resid=8BF09AD1C8343122%21258985&authkey=ACWhDNRScGBzKwk\"), style='text-align:center'),\r\n\t\t\t\teasyClose = TRUE,\t\t\r\n\t\t\t\tfade=FALSE))\r\n\t\t\t}\r\n\t\telse {removeModal()}\r\n\t})\t\r\n\t\r\n\trawData <- reactive({\r\n\t\tif (settings$mod == \"demo\"){randdata()}\r\n\t\telse {\r\n\t\t\tinFile <- input$file1\r\n\t\t\t\tif (is.null(inFile))\r\n\t\t\t\t\treturn(NULL)\r\n\t\t\t\tif (file_ext(inFile$name) %in% c(\r\n\t\t\t\t\t'text/csv',\r\n\t\t\t\t\t'text/comma-separated-values',\r\n\t\t\t\t\t'text/tab-separated-values',\r\n\t\t\t\t\t'text/plain',\r\n\t\t\t\t\t'csv',\r\n\t\t\t\t\t'tsv'))\t{read.csv(inFile$datapath, header=importSettings$header, sep=importSettings$sep, quote=importSettings$quoter, dec=importSettings$dec)}\r\n\t\t\t\telse {return(NULL)}}\r\n\t\t})\r\n\t\r\n\tcookData <- function() {\r\n\t\treq(rawData())\r\n\t\tcookedData$cooked <- rawData()\r\n\t\tfor (label in colnames(rawData())) {\r\n\t\t\tif (label %in% cookedData$cats) {\r\n\t\t\t\tif (!is.factor(cookedData$cooked[[label]])){cookedData$cooked[[label]] <- as.factor(cookedData$cooked[[label]])}\r\n\t\t\t\t}\r\n\t\t\tif (label %in% cookedData$nums) {\r\n\t\t\t\tif (is.factor(cookedData$cooked[[label]])){cookedData$cooked[[label]] <- as.numeric(cookedData$cooked[[label]])}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\t\r\n\t\t# colnames(cookedData$cooked) <- gsub(\"_\", \" \", colnames(cookedData$cooked))\r\n\t\t# names(cookedData$cats) <- gsub(\"_\", \" \", names(cookedData$cats))\r\n\t\t# names(cookedData$nums) <- gsub(\"_\", \" \", names(cookedData$nums))\r\n\t\t}\r\n\t\r\n\t# Observer waiting for the upload of a file --> once its done the page Data check is activated and selected\r\n observe({\r\n\t\t\tif (is.null(rawData()) == FALSE) {\r\n\t\t\t\tsession$sendCustomMessage('activeNavs', 'Check')\r\n\t\t\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Check')\r\n\t\t\t}\r\n\t\t})\r\n\t\t\r\n\tobserveEvent(input$bigFriendlyButton, {\r\n\t\tsession$sendCustomMessage('activeNavs', 'Summarize')\r\n\t\tsession$sendCustomMessage('activeNavs', 'Explore')\r\n\t\tsession$sendCustomMessage('activeNavs', 'Analyze')\r\n\t\tcookedData$cats <- if(!is.null(input$cats_order)){input$cats_order} else {TRUE}\r\n\t\tcookedData$nums <- if(!is.null(input$nums_order)){input$nums_order} else {TRUE}\r\n\r\n\t\tcookData()\r\n\t\t\r\n\t\treq(cookedData$cooked)\r\n\t\trenderCatPlots(cookedData$cats, cookedData$cooked, input, output)\r\n\t\trenderNumPlots(cookedData$nums, cookedData$cooked, input, output)\r\n\t\t\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Summarize')\t\r\n\t\t})\r\n\t\t\r\n\tplotinput <- eventReactive(input$doPlot, {\t\r\n\t\tif (length(input$outcome_order)!=1|length(input$pred_order)>2) {\r\n\t\t\tshowModal(\r\n\t\t\t\tmodalDialog(\r\n\t\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\t\"You need one outcome and 0-2 predictors\",\r\n\t\t\t\t\tfooter=NULL,\r\n\t\t\t\t\teasyClose=TRUE\r\n\t\t\t\t\t)\r\n\t\t\t\t)\r\n\t\t\t}\r\n\t\tlist(outcome=input$outcome_order, pred=input$pred_order)\t\t\r\n\t\t})\r\n\t\t\r\n\tobserveEvent(input$bigUnfriendlyButton, {\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Tell us more about your data\",\r\n\t\t\t\ttagList(\r\n\t\t\t\t\t#FOR SOME REASON, THIS DOES NOT WORK WITH T/F AND NEEDS THIS KIND OF WORKAROUND\r\n\t\t\t\t\tselectInput(inputId='header', label='Header',\r\n\t\t\t\t\t\t\t c(\"Header row in the data\"=\"h\",\r\n\t\t\t\t\t\t\t\t \"No header in the data\"=\"nh\"),\r\n\t\t\t\t\t\t\t ifelse(importSettings$header, \"h\", \"nh\")),\r\n\t\t\t\t\tselectInput(inputId='sep', label='Separator',\r\n\t\t\t\t\t\t\t c(Comma=',',\r\n\t\t\t\t\t\t\t\t Semicolon=';',\r\n\t\t\t\t\t\t\t\t Tab='\\t'),\r\n\t\t\t\t\t\t\t importSettings$sep),\r\n\t\t\t\t\tselectInput(inputId='dec', label='Decimal sign',\r\n\t\t\t\t\t\t\t c(\"Point\"='.',\r\n\t\t\t\t\t\t\t\t 'Comma'=','),\r\n\t\t\t\t\t\t\t importSettings$dec),\t\t\t\t\t\t\t \t\t\t\t\t\t\t \r\n\t\t\t\t\tselectInput(inputId='quoter', label='Quote sign',\r\n\t\t\t\t\t\t\t c(None='',\r\n\t\t\t\t\t\t\t\t 'Double Quote'='\"',\r\n\t\t\t\t\t\t\t\t 'Single Quote'=\"'\"),\r\n\t\t\t\t\t\t\t importSettings$quoter)\r\n\t\t\t\t\t\t\t ),\r\n\t\t\t\tfooter=\tactionButton(\"ok\", \"OK\")\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\t\r\n\t\t\r\n\tobserveEvent(input$ok, {\t\t\r\n\t\timportSettings$header <- ifelse(input$header==\"h\", T, F)\r\n\t\timportSettings$quoter <- input$quoter\r\n\t\timportSettings$sep <- input$sep\t\r\n\t\timportSettings$dec <- input$dec\r\n\t\tremoveModal()\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$compSel, {\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\twellPanel(\r\n\t\t\t\t\th4(\"Columns\"),\r\n\t\t\t\t\tp(\"Take several columns from your data and compare them to each other\"),\r\n\t\t\t\t\tactionButton(\"colComp\", \"Proceed\", class=\"btn btn-secondary\")\t\t\t\t\t\t\r\n\t\t\t\t\t),\r\n\t\t\t\twellPanel(\r\n\t\t\t\t\th4(\"Groups\"),\r\n\t\t\t\t\tp(\"Take one column from your data and compare several groups in it\"),\r\n\t\t\t\t\tactionButton(\"grComp\", \"Proceed\", class=\"btn btn-secondary\")\t\t\t\t\t\t\t\r\n\t\t\t\t\t)\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\t\t\r\n\tobserveEvent(input$colComp, {\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Columns ')\r\n\t\ttestSet$settings <- \"colComp\"\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\tanovaGroups$finished <- FALSE\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('sels', 'Columns to compare', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs'), width=\"100%\", item_class=\"success\", placeholder=\"Select two or more columns to compare\"),\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_cols\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\r\n\tobserveEvent(input$colCChange, {\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Columns ')\r\n\t\ttestSet$settings <- \"colComp\"\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\tlabs <- colnames(testSet$vals)\r\n\t\tlabs <- labs[labs %in% c(cookedData$cats, cookedData$nums)]\r\n\t\tcats <- cookedData$cats[!(cookedData$cats %in% labs)]\r\n\t\tnums <- cookedData$nums[!(cookedData$nums %in% labs)]\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('sels', 'Columns to compare', items=labs,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs'), width=\"100%\", item_class=\"success\", placeholder=\"Select two or more columns to compare\"),\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_cols\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\t\t\t\t\r\n\tobserveEvent(input$grComp, {\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('ind', 'Column to evaluate', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'cs'), width=\"100%\", item_class=\"success\", placeholder=\"Select the measured variable\"),\r\n\t\t\t\t\torderInput('pred', 'Column with groups', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'ind'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column defining the groups\"),\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_grs\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\t\t\r\n\t\t\t)\r\n\t\r\n\t\t})\t\r\n\t\r\n\tobserveEvent (input$ok_grs, {\r\n\t\treq(input$ind_order, input$pred_order)\r\n\t\ttestSet$manConts <- NULL\r\n\t\tif (length(input$ind_order) == 1 & length(input$pred_order)==1 & input$pred_order[1] %in% cookedData$cats){\r\n\t\t\r\n\t\t\ttestSet$settings <- \"grComp\"\r\n\t\t\tc1 <- input$ind_order\r\n\t\t\tc2 <- input$pred_order\r\n\t\t\ttestSet$vals <- cookedData$cooked[,c(input$ind_order, input$pred_order)]\r\n\t\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Groups ')\r\n\t\t\tremoveModal()\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {\r\n\t\t\tif (input$pred_order[1] %in% cookedData$nums){\r\n\t\t\t\tshowModal(\r\n\t\t\t\tmodalDialog(\r\n\t\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\t\tp(\"The grouping variable must be a category, not a number\",style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\t\torderInput('ind', 'Column to evaluate', items=input$ind_order,\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the measured variable\"),\r\n\t\t\t\t\t\torderInput('pred', 'Column with groups', items=c(),\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'ind'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column defining the groups\"),\t\t\t\t\t\r\n\t\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfooter=actionButton(\"ok_grs\", \"OK\"), fade = FALSE\r\n\t\t\t\t\t)\t\t\r\n\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\telse{\r\n\t\t\t\tshowModal(\r\n\t\t\t\tmodalDialog(\r\n\t\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\t\tp(\"One measured variable and one grouping variable needed\",style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\t\torderInput('ind', 'Column to evaluate', items=c(),\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the measured variable\"),\r\n\t\t\t\t\t\torderInput('pred', 'Column with groups', items=c(),\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'ind'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column defining the groups\"),\t\t\t\t\t\r\n\t\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfooter=actionButton(\"ok_grs\", \"OK\"), fade = FALSE\r\n\t\t\t\t\t)\t\t\r\n\t\t\t\t)\t\t\t\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t})\r\n\t\r\n\tobserveEvent(input$grCChange, {\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\toutp <- input$ind_order\r\n\t\tpreds <- input$pred_order\r\n\t\tlabs <- unique(c(outp, preds))\r\n\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\t\r\n\t\t\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('ind', 'Column to evaluate', items=input$ind_order,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'cs'), width=\"100%\", item_class=\"success\", placeholder=\"Select the measured variable\"),\r\n\t\t\t\t\torderInput('pred', 'Column with groups', items=input$pred_order,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'ind'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column defining the groups\"),\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'pred', 'ind'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_grs\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\t\t\r\n\t\t\t)\r\n\t\t})\t\r\n\t\t\r\n\tobserveEvent(input$modSet, {\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Model data')\t\t\r\n\t\ttestSet$settings <- \"mod\"\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('outp', 'Column to predict', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column to predict\"),\r\n\t\t\t\t\torderInput('pred', 'Columns with predictors', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'outp'), width=\"100%\", item_class=\"success\", placeholder=\"Select one or more columns as predictors\"),\t\t\t\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_mod\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\t\r\n\r\n\tobserveEvent(input$modChange, {\r\n\t\tupdateNavbarPage(session, 'mainnavbar', selected = 'Model data')\r\n\t\ttestSet$settings <- \"mod\"\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\toutp <- input$outp_order\r\n\t\tpreds <- input$pred_order\r\n\t\tlabs <- unique(c(outp, preds))\r\n\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Select columns\",\r\n\t\t\t\t\torderInput('outp', 'Column to predict', items=outp,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column to predict\"),\r\n\t\t\t\t\torderInput('pred', 'Columns with predictors', items=preds,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'outp'), width=\"100%\", item_class=\"info\", placeholder=\"Select one or more columns as predictors\"),\t\t\t\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_mod\", \"OK\"), fade = FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\r\n\tobserveEvent(input$ok_cols, {\r\n\t\tif (length(input$sels_order)>1 & (all(input$sels_order %in% cookedData$cats)|all(input$sels_order %in% cookedData$nums))) {\r\n\t\t\ttestSet$vals <- cookedData$cooked[,input$sels_order]\r\n\t\t\ttestSet$vals <- testSet$vals[!is.null(testSet$vals)]\r\n\t\t\ttestSet$manConts <- NULL\r\n\t\t\ttestSet$twoCol <- NULL\r\n\t\t\tif (length(input$sels_order) == 2 & all(colnames(testSet$vals) %in% cookedData$nums)) {\r\n\t\t\t\tshowModal(modalDialog(\r\n\t\t\t\t\ttitle=\"Test type\",\r\n\t\t\t\t\th4(\"Decide what to do with these columns\"),\r\n\t\t\t\t\tp(\"Compare distribution - e.g. for counts from a corpus, where each row is a different option\"),\r\n\t\t\t\t\tactionButton(\"compCount\", \"Proceed\"),\r\n\t\t\t\t\tp(\"Compare means - e.g. for measurements, where each row is one participant\"),\r\n\t\t\t\t\tactionButton(\"compMeans\", \"Proceed\"),\r\n\t\t\t\t\tp(\"Compare values - to determine the strength of relationship between two numeric variables\"),\r\n\t\t\t\t\tactionButton(\"correlate\", \"Proceed\"),\r\n\t\t\t\t\tfade=FALSE,\r\n\t\t\t\t\teasyClose=FALSE\r\n\t\t\t\t\t))\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse {removeModal()}\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (length(input$sels_order)<2) {testSet$vals <- NULL\r\n\t\t\tshowModal(modalDialog(\r\n\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\th4(\"Not enough data - select two or more columns\", style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\torderInput('sels', 'Columns to compare', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs'), width=\"100%\", item_class=\"primary\", placeholder=\"Select two or more columns to compare\"),\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_cols\", \"OK\")\t\t\t\r\n\t\t\t\t))\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {testSet$vals <- NULL\r\n\t\t\tshowModal(modalDialog(\r\n\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\th4(\"Can't compare numbers to categories\", style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\torderInput('sels', 'Columns to compare', items=c(),\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs'), width=\"100%\", item_class=\"primary\", placeholder=\"Select two or more columns to compare\"),\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = cookedData$nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cookedData$cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'sels'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\r\n\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_cols\", \"OK\")\t\t\t\r\n\t\t\t\t))\r\n\t\t\t}\r\n\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$compCount, {\r\n\t\ttestSet$twoCol <- \"compCount\"\r\n\t\tshowModal(modalDialog(\r\n\t\t\ttitle=\"Set rownames\",\r\n\t\t\tp(\"Select the column which contains the names of the categories that the counts represent\"),\r\n\t\t\tselectInput(\"namesCol\", label=\"Columns with labels\",\r\n\t\t\t\tchoices=c(cookedData$cats),\r\n\t\t\t\t),\r\n\t\t\tactionButton(\"ok_namesCol\", \"OK\"),\r\n\t\t\tfade=FALSE,\r\n\t\t\teasyClose=FALSE\r\n\t\t\t))\r\n\t\t})\r\n\tobserveEvent(input$ok_namesCol, {\r\n\t\ttestSet$namesCol <- input$namesCol\r\n\t\tremoveModal()\r\n\t\t})\r\n\tobserveEvent(input$compMeans, {\r\n\t\ttestSet$twoCol <- \"compMeans\"\r\n\t\tremoveModal()\r\n\t\t})\r\n\tobserveEvent(input$correlate, {\r\n\t\ttestSet$twoCol <- \"correlate\"\r\n\t\tremoveModal()\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$ok_mod, {\r\n\t\tanovaGroups$finished <- NULL\r\n\t\tif (length(input$outp_order)==1 & length(input$pred_order)>0) {\r\n\t\t\ttestSet$vals <- cookedData$cooked[,c(input$outp_order, input$pred_order)]\r\n\t\t\ttestSet$manConts <- NULL\r\n\t\t\tremoveModal()\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (length(input$outp_order)!=1) {testSet$vals <- NULL\r\n\t\t\tif (length(input$outp_order)!=0 | length(input$outp_order)!=0){\r\n\t\t\t\toutp <- input$outp_order\r\n\t\t\t\tpreds <- input$pred_order\r\n\t\t\t\tlabs <- unique(c(outp, preds))\r\n\t\t\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\t\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\r\n\t\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tif(length(input$outp_order)==0) {outp <- c()} else {outp <- input$outp_order}\r\n\t\t\t\tif(length(input$pred_order)==0) {preds <- c()} else {preds <- input$pred_order}\r\n\t\t\t\tlabs <- unique(c(outp, preds))\r\n\t\t\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\t\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\r\n\t\t\t\t}\r\n\t\t\tshowModal(modalDialog(\r\n\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\th4(\"Select one column to be predicted\", style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\torderInput('outp', 'Column to predict', items=outp,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column to predict\"),\r\n\t\t\t\t\torderInput('pred', 'Columns with predictors', items=preds,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'outp'), width=\"100%\", item_class=\"info\", placeholder=\"Select one or more columns as predictors\"),\t\t\t\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_mod\", \"OK\")\t\t\t\r\n\t\t\t\t))\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {testSet$vals <- NULL\r\n\t\t\tif (length(input$outp_order)!=0 | length(input$outp_order)!=0){\r\n\t\t\t\toutp <- input$outp_order\r\n\t\t\t\tpreds <- input$pred_order\r\n\t\t\t\tlabs <- unique(c(outp, preds))\r\n\t\t\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\t\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\r\n\t\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tif(length(input$outp_order)==0) {outp <- c()} else {outp <- input$outp_order}\r\n\t\t\t\tif(length(input$pred_order)==0) {preds <- c()} else {preds <- input$pred_order}\r\n\t\t\t\tlabs <- unique(c(outp, preds))\r\n\t\t\t\tcats <- cookedData$cats[(!cookedData$cats %in% labs)]\r\n\t\t\t\tnums <- cookedData$nums[(!cookedData$nums %in% labs)]\r\n\t\t\t\t}\r\n\t\t\tshowModal(modalDialog(\r\n\t\t\t\ttitle=\"ERROR\",\r\n\t\t\t\t\th4(\"Select some predictors\", style=\"font-face: bold; color: red\"),\r\n\t\t\t\t\torderInput('outp', 'Column to predict', items=outp,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'pred'), width=\"100%\", item_class=\"success\", placeholder=\"Select the column to predict\"),\r\n\t\t\t\t\torderInput('pred', 'Columns with predictors', items=preds,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'cs', 'outp'), width=\"100%\", item_class=\"info\", placeholder=\"Select one or more columns as predictors\"),\t\t\t\t\t\t\t\t\r\n\t\t\t\t\torderInput('ns', 'Numbers', items = nums,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('cs', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Numeric variables\"),\r\n\t\t\t\t\torderInput('cs', 'Categories', items = cats,\r\n\t\t\t\t\t\t\t\tas_source = FALSE, connect = c('ns', 'outp', 'pred'), width=\"100%\", item_class=\"primary\", placeholder=\"Categoric variables\"),\t\t\t\t\t\t\t\r\n\t\t\t\tfooter=actionButton(\"ok_mod\", \"OK\")\t\t\t\r\n\t\t\t\t))\r\n\t\t\t}\r\n\r\n\t\t})\t\t\r\n\t\t\t\t\r\n\toutput$fulltab <- renderDataTable({\r\n\t\treq(cookedData$cooked, input$selGrs)\r\n\t\tcookedData$cooked\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$handleNAs, {\r\n\t\treq(cookedData$cooked, settings)\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Handle NAs\",\r\n\t\t\t\tp(\"Choose what to do with the missing values. Some tests will not work if they are kept.\"),\r\n\t\t\t\tselectInput(\"naAction\", label=\"\",\r\n\t\t\t\t\tc(\"Ignore missing values\"=\"ignore\",\r\n\t\t\t\t\t\"Keep missing values\"=\"keep\"\r\n\t\t\t\t\t), settings$na.ignore),\r\n\t\t\t\tfooter=actionButton(\"ok_nas\", \"OK\"),\r\n\t\t\t\teasyClose=T\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\r\n\t\t})\r\n\t\t\r\n\tobserveEvent(input$ok_nas, {\r\n\t\tsettings$na.ignore <- input$naAction\r\n\t\tremoveModal()\r\n\t\t\r\n\t\t})\r\n\t\t\r\n\tobserveEvent(input$setContsCol,{\r\n\t\t\r\n\t\treq(testSet$vals)\r\n\t\t\r\n\t\tanovaGroups$finished <- NULL\r\n\t\t\t\t\r\n\t\tif (testSet$settings==\"colComp\") {\r\n\t\t\tdat0 <- testSet$vals\r\n\t\t\tids <- colnames(dat0)\r\n\t\t\tdat <- data.frame(values__=c(), id__=c())\r\n\t\t\tfor (i in ids) {\r\n\t\t\t\tdat <- rbind(dat, data.frame(values__=dat0[, i], id__=rep(i, length(dat0[, i]))))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\tdat$id__ <- factor(dat$id__)\t\t\t\r\n\t\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\t\t\r\n\t\t\r\n\t\t\tgroups <- levels(dat[,2])\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (testSet$settings==\"grComp\"){\r\n\t\t\tdat <- testSet$vals\r\n\t\t\tcolnames(dat) <- c(\"values__\", \"id__\")\t\t\t\r\n\t\t\tdat$id__ <- factor(dat$id__)\r\n\t\t\t\r\n\t\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\t\t\r\n\t\t\t\t\r\n\t\t\tgroups <- levels(dat[,2])\r\n\t\t\t}\r\n\t\t\r\n\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\r\n\t\t\r\n\t\tprint(\"DOING\")\t\t\r\n\t\tanovaGroups$groupMatrix <- data.frame(matrix(ncol=length(groups), nrow=10))\r\n\t\tcolnames(anovaGroups$groupMatrix) <- groups\r\n\t\t\t\t\r\n\t\tanovaGroups$groupMatrix[1,] <- 1\r\n\t\tanovaGroups$level <- 1\t\t\r\n\t\tanovaGroups$groupnos <- c(2,3)\r\n\t\t\t\t\t\t\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Set contrasts\",\r\n\t\t\t\tp(\"You can now divide the labels into groups. To do so, draw the labels into the appropriate bin. Labels left aside will still be used in the overall evaluation.\"),\r\n\t\t\t\tp(style=\"col: 'red'; font-weight: bold\", \"Important: To make sure that the results reported are correct, only draw labels from ONE LINE in each round of contrast set up. Contrasting labels from different rows may lead to incorrect results.\"),\r\n\t\t\t\tfluidRow(\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr1', 'Group 1', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr2\", \"inp1\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\r\n\t\t\t\t\t\t\t)),\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr2', 'Group 2', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr1\", \"inp1\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\r\n\t\t\t\t\t\t\t))\r\n\t\t\t\t\t),\r\n\t\t\t\tfluidRow(\t\t\t\t\t\t\r\n\t\t\t\t\tcolumn(12,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('anovaInp1', 'Labels available', items=groups,\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr1\", \"gr2\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\t\t\t\t\t\r\n\t\t\t\t\t),\r\n\t\t\t\tfooter=tagList(\r\n\t\t\t\t\tactionButton(\"nextConts\", \"Next contrast\"),\r\n\t\t\t\t\tactionButton(\"saveConts\", \"Done\")\r\n\t\t\t\t\t),\r\n\t\t\t\tfade=FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\tobserveEvent(input$setContsGr,{\r\n\t\t\r\n\t\treq(testSet$vals)\r\n\t\t\r\n\t\tanovaGroups$finished <- NULL\r\n\t\t\t\t\r\n\t\tif (testSet$settings==\"colComp\") {\r\n\t\t\tdat0 <- testSet$vals\r\n\t\t\tids <- colnames(dat0)\r\n\t\t\tdat <- data.frame(values__=c(), id__=c())\r\n\t\t\tfor (i in ids) {\r\n\t\t\t\tdat <- rbind(dat, data.frame(values__=dat0[, i], id__=rep(i, length(dat0[, i]))))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\tdat$id__ <- factor(dat$id__)\t\t\t\r\n\t\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\t\t\r\n\t\t\r\n\t\t\tgroups <- levels(dat[,2])\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (testSet$settings==\"grComp\"){\r\n\t\t\tdat <- testSet$vals\r\n\t\t\tcolnames(dat) <- c(\"values__\", \"id__\")\t\t\t\r\n\t\t\tdat$id__ <- factor(dat$id__)\r\n\t\t\t\r\n\t\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\t\t\r\n\t\t\t\t\r\n\t\t\tgroups <- levels(dat[,2])\r\n\t\t\t}\r\n\t\t\r\n\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\r\n\t\t\r\n\t\tprint(\"DOING\")\t\t\r\n\t\tanovaGroups$groupMatrix <- data.frame(matrix(ncol=length(groups), nrow=10))\r\n\t\tcolnames(anovaGroups$groupMatrix) <- groups\r\n\t\t\t\t\r\n\t\tanovaGroups$groupMatrix[1,] <- 1\r\n\t\tanovaGroups$level <- 1\t\t\r\n\t\tanovaGroups$groupnos <- c(2,3)\r\n\t\t\t\t\t\t\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Set contrasts\",\r\n\t\t\t\tp(\"You can now divide the labels into groups. To do so, draw the labels into the appropriate bin. Labels left aside will still be used in the overall evaluation.\"),\r\n\t\t\t\tp(style=\"col: 'red'; font-weight: bold\", \"Important: To make sure that the results reported are correct, only draw labels from ONE LINE in each round of contrast set up. Contrasting labels from different rows may lead to incorrect results.\"),\r\n\t\t\t\tfluidRow(\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr1', 'Group 1', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr2\", \"inp1\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\r\n\t\t\t\t\t\t\t)),\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr2', 'Group 2', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr1\", \"inp1\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\r\n\t\t\t\t\t\t\t))\r\n\t\t\t\t\t),\r\n\t\t\t\tfluidRow(\t\t\t\t\t\t\r\n\t\t\t\t\tcolumn(12,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('anovaInp1', 'Labels available', items=groups,\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = c(\"gr1\", \"gr2\"), width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\t\t\t\t\t\r\n\t\t\t\t\t),\r\n\t\t\t\tfooter=tagList(\r\n\t\t\t\t\tactionButton(\"nextConts\", \"Next contrast\"),\r\n\t\t\t\t\tactionButton(\"saveConts\", \"Done\")\r\n\t\t\t\t\t),\r\n\t\t\t\tfade=FALSE\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\tobserveEvent(input$saveConts, {\r\n\t\tanovaGroups$level <- anovaGroups$level + 1\r\n\t\tif (length(input$gr1_order)>0){anovaGroups$groupMatrix[anovaGroups$level, input$gr1_order] <- anovaGroups$groupnos[1]}\r\n\t\tif (length(input$gr2_order)>0){anovaGroups$groupMatrix[anovaGroups$level, input$gr2_order] <- anovaGroups$groupnos[2]}\r\n\t\t\r\n\t\tif (!is.null(anovaGroups$finished)){anovaGroups$finished <- anovaGroups$finished + 1}\r\n\t\telse {anovaGroups$finished <- 1}\r\n\t\ttestSet$manConts <- TRUE\r\n\t\t\r\n\t\tremoveModal()\r\n\t})\t\r\n\tobserveEvent(input$nextConts, { \r\n\t\tanovaGroups$level <- anovaGroups$level + 1\t\r\n\t\tanovaGroups$groupMatrix[anovaGroups$level, input$gr1_order] <- anovaGroups$groupnos[1]\r\n\t\tanovaGroups$groupMatrix[anovaGroups$level, input$gr2_order] <- anovaGroups$groupnos[2]\r\n\t\tif (testSet$settings==\"colComp\"| (testSet$settings == \"grComp\" & is.numeric(testSet$vals[,1]))){\r\n\t\t\tinps <- c(\"gr1\", \"gr2\", paste(\"anovaInp\", seq(1,max(anovaGroups$groupnos))))\r\n\t\t\tinps <- gsub(\" \", \"\", inps)\r\n\t\t\t\r\n\t\t\tanovaGroups$groupnos <- anovaGroups$groupnos + 2\r\n\t\t\tgroups\t<- colnames(anovaGroups$groupMatrix)\r\n\t\t\t\r\n\t\t\tgroupItems <- list(\"initial\" = c(1,2,3))\r\n\t\t\t\r\n\t\t\tfor (inp in inps[-c(1,2)]) {\r\n\t\t\t\tinpNo <- as.integer(gsub(\"anovaInp\", \"\", inp))\r\n\t\t\t\t# print(inpNo)\r\n\t\t\t\tfil <- colMax(anovaGroups$groupMatrix)==inpNo\r\n\t\t\t\tits <- groups[fil]\r\n\t\t\t\t# print(its)\r\n\t\t\t\tif (length(its) > 0){groupItems[[inp]] <- its}\r\n\t\t\t\t}\t\t\r\n\t\t\t}\t\t\t\t\t\r\n\r\n\t\telse if (testSet$settings == \"grComp\" & is.factor(testSet$vals[,1])){\r\n\t\t\tanovaGroups$groupnos <- anovaGroups$groupnos + 2\r\n\t\t\tgroups\t<- colnames(anovaGroups$groupMatrix)\t\t\r\n\t\t\tinps <- c(\"gr1\", \"gr2\", \"i\")\r\n\t\t\t}\r\n\t\t\r\n\t\t# str(groupItems)\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\ttitle=\"Set contrast\",\r\n\t\t\t\tp(\"You can now divide the labels into groups. To do so, draw the labels into the appropriate bin. Labels left aside will still be used in the overall evaluation.\"),\r\n\t\t\t\tp(style=\"col: 'red'; font-weight: bold\", \"Important: To make sure that the results reported are correct, only draw labels from ONE LINE in each round of contrast set up. Contrasting labels from different rows may lead to incorrect results.\"),\r\n\t\t\t\tfluidRow(\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr1', 'Group 1', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = inps[inps!=\"gr1\"], width=\"100%\", item_class=\"primary\", placeholder=\"\")\r\n\t\t\t\t\t\t\t)),\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput('gr2', 'Group 2', items=c(),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = inps[inps!=\"gr2\"], width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\r\n\t\t\t\t\t\t\t))\r\n\t\t\t\t\t),\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tif (testSet$settings==\"colComp\"| (testSet$settings == \"grComp\" & is.numeric(testSet$vals[,1]))){\r\n\t\t\t\t\t\tlapply(1:length(names(groupItems)[names(groupItems)!=\"initial\"]), function(x) {\r\n\t\t\t\t\t\t\ti <- names(groupItems)[names(groupItems)!=\"initial\"][x]\r\n\t\t\t\t\t\t\t# print(i)\r\n\t\t\t\t\t\t\t# print(groupItems[i])\r\n\t\t\t\t\t\t\t# print(inps[inps!=i])\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\torderInput(i, paste('Comparison ', gsub(\"anovaInp\", \"\", i, fixed=T), sep=\"\", collapse=\"\"), items=groupItems[[i]],\r\n\t\t\t\t\t\t\t\t\tas_source = FALSE, connect = inps[inps!=i], width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t})\t\t\t\t\t\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\telse if (testSet$settings == \"grComp\" & is.factor(testSet$vals[,1])){\r\n\r\n\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\torderInput(\"i\", 'Groups', items=levels(testSet$vals[,2]),\r\n\t\t\t\t\t\t\tas_source = FALSE, connect = inps[inps!=\"i\"], width=\"100%\", item_class=\"primary\", placeholder=\"\")\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t}\t\t\t\t\t\t\r\n\t\t\t\t\t),\r\n\t\t\t\tfooter=tagList(\r\n\t\t\t\t\tactionButton(\"nextConts\", \"Next contrast\"),\r\n\t\t\t\t\tactionButton(\"saveConts\", \"Done\")\r\n\t\t\t\t\t),\r\n\t\t\t\tfade=FALSE\r\n\t\t\t\t)\t\t\t\t\r\n\t\t\t)\r\n\t\r\n\t\t})\r\n\tobserveEvent(input$doATestCol, {\r\n\t\tprint(input$doATestCol)\r\n\t\tprint(is.null(testRes()))})\r\n\tobserveEvent(input$doATestGr, {})\r\n\t\r\n\taovConts <- reactive({\r\n\t\t# reactlog::listDependencies()\t\r\n\t\treq(testSet$vals)\r\n\t\treq(!is.null(input$doATestCol)|!is.null(input$doATestGr))\r\n\t\t\r\n\t\tif (!is.null(input$doATestCol)) {req(input$doATestCol > 0)}\r\n\t\telse {\r\n\t\t\treq(input$doATestGr > 0)\r\n\t\t\t}\r\n\r\n\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\tdat0 <- testSet$vals\r\n\t\t\tids <- colnames(dat0)\r\n\t\t\tdat <- data.frame(values__=c(), id__=c())\r\n\t\t\tfor (i in ids) {\r\n\t\t\t\tdat <- rbind(dat, data.frame(values__=dat0[[i]], id__=rep(i, length(dat0[[i]]))))\r\n\t\t\t\t}\r\n\t\t\tdat$id__ <- factor(dat$id__)\t\t\t\t\t\r\n\t\t\t}\r\n\t\telse if (testSet$settings==\"grComp\"){\r\n\t\t\tdat <- testSet$vals\r\n\t\t\t}\r\n\t\t\r\n\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\t\t\r\n\t\t\t\t\t\t\t\t\r\n\t\t\r\n\t\tif (!is.null(input$setContsGr)){\r\n\t\t\tif (input$setContsGr==0) {manualContrasts <- \"GrFALSE\"}\r\n\t\t\telse {manualContrasts <- \"GrTRUE\"}\r\n\t\t\t}\r\n\t\t\t\r\n\t\tif (!is.null(input$setContsCol)){\r\n\t\t\tif (input$setContsCol==0) {manualContrasts <- \"ColFALSE\"}\r\n\t\t\telse {manualContrasts <- \"ColTRUE\"}\r\n\t\t\t}\r\n\r\n\t\tif (manualContrasts %in% c(\"ColFALSE\",\"GrFALSE\")){\r\n\t\t\tif (testSet$settings==\"colComp\"){\r\n\t\t\t\tprint(\"ContsNO\")\r\n\t\t\t\treturn(contrasts(dat$id__))}\r\n\t\t\telse if (testSet$settings==\"grComp\"){\r\n\t\t\t\tprint(\"ContsNO\")\r\n\t\t\t\treturn(contrasts(dat[,2]))}\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse{\r\n\t\t\treq(!is.null(input$setContsGr)|!is.null(input$setContsCol))\r\n\t\t\t\r\n\t\t\tif (!is.null(input$setContsGr)) {req(input$setContsGr > 0)}\r\n\t\t\telse {\r\n\t\t\t\treq(input$setContsCol > 0)\r\n\t\t\t\t}\t\t\t\r\n\r\n\t\t\treq(anovaGroups$groupMatrix, anovaGroups$finished)\r\n\t\t\t\t\t\t\t\t\t\r\n\t\t\tconts <- anovaGroups$groupMatrix[-1,]\r\n\t\t\tconts <- conts[1:((anovaGroups$groupnos[2]-1)/2),]\r\n\t\t\tcontTable <- as.data.frame(t(conts))\r\n\t\t\tcontTable <- sapply(1:ncol(contTable), function(i){\r\n\t\t\t\tiCol <- contTable[,i]\r\n\t\t\t\tiCol[iCol==0] <- NA\r\n\t\t\t\tifelse(!is.na(iCol),ifelse(iCol%%2==0, -1/sum(iCol%%2==0, na.rm=T), 1/sum(iCol%%2!=0, na.rm=T)),0)\r\n\t\t\t\t})\r\n\t\t\t\t\r\n\t\t\tinps <- c(paste(\"anovaInp\", seq(1,max(anovaGroups$groupnos))))\r\n\t\t\tinps <- gsub(\" \", \"\", inps)\r\n\t\t\t\r\n\t\t\t# anovaGroups$groupnos <- anovaGroups$groupnos + 2\r\n\t\t\tgroups\t<- colnames(anovaGroups$groupMatrix)\r\n\t\t\t\r\n\t\t\tgroupItems <- list(\"initial\" = c(1,2,3))\r\n\t\t\t\r\n\t\t\tfor (inp in inps) {\r\n\t\t\t\tinpNo <- as.integer(gsub(\"anovaInp\", \"\", inp))\r\n\t\t\t\tfil <- colMax(anovaGroups$groupMatrix)==inpNo\r\n\t\t\t\tits <- groups[fil]\r\n\t\t\t\tif (length(its) > 0){groupItems[[inp]] <- its}\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\tgroupItems <- groupItems[!(names(groupItems) %in% c(\"initial\", \"anovaInp1\"))]\r\n\t\t\t\r\n\t\t\trownames(contTable) <- levels(dat[,2])\r\n\t\t\tcolnames(contTable) <- getColNames(contTable)\r\n\t\t\tcontrasts(dat[,2]) <- as.matrix(contTable)\t\t\t\t\r\n\t\t\t# print(contTable)\r\n\t\t\tprint(\"ContsYES\")\r\n\t\t\treturn(contTable)\r\n\t\t\t}\r\n\r\n\t\t})\r\n\r\n\r\n\toutput$outplot <- renderPlot({\r\n\r\n\t\treq(cookedData$cooked, plotinput())\t\t\r\n\t\t\r\n\t\tplotSet <- cookedData$cooked\r\n\t\t\r\n\t\tpars <- plotinput()\t\r\n\t\t\r\n\t\treq(length(pars$outcome)==1)\r\n\t\treq(length(pars$pred)<3)\r\n\t\t\r\n\t\tif (settings$na.ignore==\"ignore\") {plotSet <- plotSet %>% drop_na(c(pars$outcome, pars$pred))}\r\n\t\tcolScale <- \"none\"\r\n\t\t\r\n\t\t# There are no predictors, plot just the outcome\r\n\t\tif (length(pars$pred)<1) {\r\n\t\t\t\r\n\t\t\t# Outcome is a factor\r\n\t\t\tif (pars$outcome %in% cookedData$cats) {\r\n\t\t\t\tplotType$current <- \"bar_dist\"\r\n\t\t\t\tp <- ggplot(plotSet, aes_string(pars$outcome))\r\n\t\t\t\tp <- p + geom_bar()}\r\n\t\t\t\t\r\n\t\t\t# Outcome is numeric\t\r\n\t\t\telse if (pars$outcome %in% cookedData$nums) {\r\n\t\t\t\treq(input$histRange, input$histBins)\r\n\t\t\t\tplotType$current <- \"histogram\"\r\n\t\t\t\tp <- ggplot(plotSet[between(plotSet[[pars$outcome]],input$histRange[1],input$histRange[2]),], aes_string(pars$outcome))\r\n\t\t\t\tp <- p + geom_histogram(bins=input$histBins, fill=\"grey90\")}\r\n\t\t\t}\r\n\t\t\r\n\t\t# There is one predictor\r\n\t\telse if (length(pars$pred)==1){\r\n\t\t\t\r\n\t\t\t# the outcome is numeric\r\n\t\t\tif (pars$outcome %in% cookedData$nums){\r\n\t\t\t\t# The predictor is a factor --> boxplot\r\n\t\t\t\tif (pars$pred %in% cookedData$cats){\r\n\t\t\t\t\treq(input$catVis)\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(pars$pred, pars$outcome))\r\n\t\t\t\t\tif (input$catVis == \"violin\"){p <- p + geom_violin(draw_quantiles=c(0.25,0.5,0.75))}\r\n\t\t\t\t\telse {p <- p + geom_boxplot()}\t\r\n\t\t\t\t\t}\r\n\t\t\t\t# The predictor is numeric --> scatterplot\r\n\t\t\t\telse {\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(pars$pred, pars$outcome))\r\n\t\t\t\t\tp <- p + geom_point()\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t# The outcome is categorical\r\n\t\t\telse if (pars$outcome %in% cookedData$cats){\r\n\t\t\t\r\n\t\t\t\t# The predictor is a factor --> barplot\r\n\t\t\t\tif (pars$pred %in% cookedData$cats){\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(pars$pred, fill=pars$outcome))\r\n\t\t\t\t\tp <- p + geom_bar()\r\n\t\t\t\t\tcolScale <- \"fill_discrete\"\r\n\t\t\t\t\t}\r\n\t\t\t\t# The predictor is numeric --> logistic regression?\r\n\t\t\t\telse {\r\n\t\t\t\t\treq(input$catVis)\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(pars$outcome, pars$pred))\r\n\t\t\t\t\tif (input$catVis == \"violin\"){p <- p + geom_violin(draw_quantiles=c(0.25,0.5,0.75)) + coord_flip()}\r\n\t\t\t\t\telse {p <- p + geom_boxplot() + coord_flip()}\t\t\t\t\t\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\r\n\t\t# There are two predictors\r\n\t\telse if (length(pars$pred)==2){\r\n\t\t\t\r\n\t\t\t# the outcome is numeric\r\n\t\t\tif (pars$outcome %in% cookedData$nums){\r\n\t\t\t\t# The predictor is are factors --> boxplot with subcategories\r\n\t\t\t\tif (all(pars$pred %in% cookedData$cats)){\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$pred[1], y=pars$outcome, fill=pars$pred[2]))\r\n\t\t\t\t\tp <- p + geom_boxplot()\r\n\t\t\t\t\tcolScale <- \"fill_discrete\"\r\n\t\t\t\t\t}\r\n\t\t\t\t# The predictors is numeric --> scatterplot with color gradient\r\n\t\t\t\telse if (all(pars$pred %in% cookedData$nums)){\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$pred[1], y=pars$outcome, color=pars$pred[2]))\r\n\t\t\t\t\tp <- p + geom_point()\r\n\t\t\t\t\tcolScale <- \"fill_gradient\"\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\t# Mixed predictors --> scatterplot with character mapping\r\n\t\t\t\telse {\r\n\t\t\t\t\tfil <- pars$pred %in% cookedData$nums\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$pred[fil], y=pars$outcome))\r\n\t\t\t\t\treq(input$catVis)\r\n\t\t\t\t\tif (input$catVis == \"facet\"){p <- p + geom_point() + facet_wrap(pars$pred[!fil])}\r\n\t\t\t\t\telse {p <- p + geom_point(aes_string(shape=pars$pred[!fil]))+scale_shape_manual(values=c(1,3,5,15,17,0,2,4,6,7,8,9,10,11,12,13,14,16,18,19,20,21,22,23,24,25))}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t# The outcome is categorical\r\n\t\t\telse if (pars$outcome %in% cookedData$cats){\r\n\t\t\t\r\n\t\t\t\t# The predictors are a factor --> faceted barplot\r\n\t\t\t\tif (all(pars$pred %in% cookedData$cats)){\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$pred[1], fill=pars$outcome))\r\n\t\t\t\t\tp <- p + geom_bar() + facet_wrap(pars$pred[2])\r\n\t\t\t\t\tcolScale <- \"fill_discrete\"\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\t# The predictors are all numeric --> logistic regression with color\r\n\t\t\t\telse if (all(pars$pred %in% cookedData$nums)){\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$pred[1], y=pars$outcome, color=pars$pred[2]))\r\n\t\t\t\t\tp <- p + geom_point()\r\n\t\t\t\t\tcolScale <- \"color_gradient\"\r\n\t\t\t\t\t}\t\t\t\t\t\r\n\t\t\t\t# Mixed predictors --> logistic regression with pch\r\n\t\t\t\telse {\r\n\t\t\t\t\treq(input$catVis)\r\n\t\t\t\t\tfil <- pars$pred %in% cookedData$nums\t\t\t\r\n\t\t\t\t\tp <- ggplot(plotSet, aes_string(x=pars$outcome, y=pars$pred[fil]))\r\n\t\t\t\t\t# p <- p + geom_point()\r\n\t\t\t\t\tif (input$catVis == \"violin_col\"){p <- p + geom_violin(aes_string(fill=pars$pred[!fil]), draw_quantiles=c(0.25,0.5,0.75)) + coord_flip()\r\n\t\t\t\t\t\tcolScale <- \"fill_discrete\"}\r\n\t\t\t\t\telse if (input$catVis == \"violin_facet\"){p <- p + geom_violin(draw_quantiles=c(0.25,0.5,0.75)) + coord_flip() + facet_wrap(pars$pred[!fil])}\t\r\n\t\t\t\t\telse if (input$catVis == \"boxplot_col\"){p <- p + geom_boxplot(aes_string(fill=pars$pred[!fil])) + coord_flip()\r\n\t\t\t\t\t\tcolScale <- \"fill_discrete\"}\r\n\t\t\t\t\telse if (input$catVis == \"boxplot_facet\"){p <- p + geom_boxplot() + coord_flip()+facet_wrap(pars$pred[!fil])}\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\t\t\t\r\n\t\t\t}\r\n\t\r\n\t\tp <- p + theme_bw()\r\n\t\tif (settings$cols == \"b&w\"){\r\n\t\t\tif (colScale == \"fill_discrete\"){\r\n\t\t\t\tp <- p + scale_fill_grey(start=0.4, end=0.8)\r\n\t\t\t\t}\r\n\t\t\telse if (colScale == \"fill_gradient\"){\r\n\t\t\t\tp <- p + scale_fill_grey(start=0.4, end=0.8)\r\n\t\t\t\t}\r\n\t\t\telse if (colScale == \"color_gradient\"){\r\n\t\t\t\tp <- p + scale_color_grey(start=0.4, end=0.8)\r\n\t\t\t\t}\t\r\n\t\t\telse if (colScale == \"color_discrete\"){\t\t\r\n\t\t\t\tp <- p + scale_color_grey(start=0.4, end=0.8)\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\telse if (settings$cols == \"colored\"){\r\n\t\t\tgetPalette <- colorRampPalette(brewer.pal(10,\"Spectral\"))\r\n\t\t\tif (colScale == \"fill_discrete\"){\r\n\t\t\t\tp <- p + discrete_scale(\"fill\", \"manual\", getPalette)\r\n\t\t\t\t}\r\n\t\t\telse if (colScale == \"fill_gradient\"){\r\n\t\t\t\tp <- p + scale_fill_gradient(low=\"whitesmoke\", high=\"midnightblue\")\r\n\t\t\t\t}\r\n\t\t\telse if (colScale == \"color_gradient\"){\r\n\t\t\t\tp <- p + scale_color_gradient(low=\"whitesmoke\", high=\"midnightblue\")\r\n\t\t\t\t}\t\r\n\t\t\telse if (colScale == \"color_discrete\"){\t\t\r\n\t\t\t\tp <- p + discrete_scale(\"color\", \"manual\", getPalette )\r\n\t\t\t\t}\t\t\t\t\r\n\t\t\t}\r\n\t\t\r\n\t\tif (settings$serif == \"serif\"){\r\n\t\t\tp <- p + theme(text=element_text(family=\"serif\"))\r\n\t\t\t}\t\t\r\n\t\telse if (settings$serif == \"noserif\") {\r\n\t\t\tp <- p + theme(text=element_text(family=\"sans\"))\t\t\r\n\t\t\t}\r\n\t\treturn(p)\r\n\t})\r\n\t\r\n\toutput$plotChoices <- renderUI({\r\n\t\treq(cookedData$cooked, plotinput())\r\n\t\t\r\n\t\tpars <- plotinput()\t\r\n\t\t\r\n\t\treq(length(pars$outcome)==1)\r\n\t\treq(length(pars$pred)<3)\r\n\t\t\r\n\t\tif (length(pars$pred)<1) {\r\n\t\t\t\r\n\t\t\t# Outcome is a factor\r\n\t\t\tif (pars$outcome %in% cookedData$cats) {}\r\n\t\t\t\t\r\n\t\t\t# Outcome is numeric\t\r\n\t\t\telse if (pars$outcome %in% cookedData$nums) {\r\n\t\t\t\tdataSpan <- range(cookedData$cooked[[pars$outcome]], na.rm=T)\r\n\t\t\t\tdataSpan[1] <- floor(dataSpan[1])\r\n\t\t\t\tdataSpan[2] <- ceiling(dataSpan[2])\r\n\t\t\t\ttagList(column(6,\r\n\t\t\t\t\t\tsliderInput(\"histRange\", \"Range of values in histogram\", min=dataSpan[1], max=dataSpan[2], value=dataSpan)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\tsliderInput(\"histBins\", \"Number of bars displayed in histogram\", min=5, max=25, value=10)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\r\n\t\t# There is one predictor\r\n\t\telse if (length(pars$pred)==1){\r\n\t\t\t\r\n\t\t\t# the outcome is numeric\r\n\t\t\tif (pars$outcome %in% cookedData$nums){\r\n\t\t\t\t# The predictor is a factor --> boxplot\r\n\t\t\t\tif (pars$pred %in% cookedData$cats){\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tradioButtons(\"catVis\", \"Select visualisation style\", choices=c(\"Boxplot\"=\"boxplot\", \"Violin plot\"=\"violin\"), selected=\"violin\")\r\n\t\t\t\t\t\t)\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t# The predictor is numeric --> scatterplot\r\n\t\t\t\telse {}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t# The outcome is categorical\r\n\t\t\telse if (pars$outcome %in% cookedData$cats){\r\n\t\t\t\r\n\t\t\t\t# The predictor is a factor --> barplot\r\n\t\t\t\tif (pars$pred %in% cookedData$cats){}\r\n\t\t\t\t# The predictor is numeric --> logistic regression?\r\n\t\t\t\telse {\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tradioButtons(\"catVis\", \"Select visualisation style\", choices=c(\"Boxplot\"=\"boxplot\", \"Violin plot\"=\"violin\"), selected=\"violin\")\r\n\t\t\t\t\t\t)\t\t\t\t\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\r\n\t\t# There are two predictors\r\n\t\telse if (length(pars$pred)==2){\r\n\t\t\t\r\n\t\t\t# the outcome is numeric\r\n\t\t\tif (pars$outcome %in% cookedData$nums){\r\n\t\t\t\t# The predictors are factors --> boxplot with subcategories\r\n\t\t\t\tif (all(pars$pred %in% cookedData$cats)){}\r\n\t\t\t\t# The predictors are numeric --> scatterplot with color gradient\r\n\t\t\t\telse if (all(pars$pred %in% cookedData$nums)){\t\t\t\t\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\t# Mixed predictors --> scatterplot with character mapping\r\n\t\t\t\telse {\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tradioButtons(\"catVis\", \"Select visualisation style\", choices=c(\"Facet plot\"=\"facet\", \"Character mapping\"=\"charmap\"), selected=\"charmap\")\r\n\t\t\t\t\t\t)\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t# The outcome is categorical\r\n\t\t\telse if (pars$outcome %in% cookedData$cats){\r\n\t\t\t\r\n\t\t\t\t# The predictors are a factor --> faceted barplot\r\n\t\t\t\tif (all(pars$pred %in% cookedData$cats)){}\r\n\t\t\t\t\r\n\t\t\t\t# The predictors are all numeric --> logistic regression with color\r\n\t\t\t\telse if (all(pars$pred %in% cookedData$nums)){}\t\t\t\t\t\r\n\t\t\t\t# Mixed predictors --> logistic regression with pch\r\n\t\t\t\telse {\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tselectInput(\"catVis\", \"Select visualisation style\", choices=c(\"Facetted boxplot\"=\"boxplot_facet\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Facetted violin\"=\"violin_facet\",\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Colored boxplot\"=\"boxplot_col\", \r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Colored violin\"=\"violin_col\"), \r\n\t\t\t\t\t\t\t\tselected=\"violin_col\")\r\n\t\t\t\t\t\t)\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t}\t\t\r\n\t\t})\r\n\t\t\r\n\toutput$settings <- renderUI({\r\n\r\n\t\ttagList(\r\n\t\t\tfluidRow(\r\n\t\t\t\tcolumn(6,\r\n\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\tselectInput(\"naSetting\", label=\"NA action\",\r\n\t\t\t\t\t\t\t\tc(\"Ignore missing values\"=\"ignore\",\r\n\t\t\t\t\t\t\t\t\"Keep missing values\"=\"keep\"),\r\n\t\t\t\t\t\t\t\tsettings$na.ignore\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\thr()\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t),\r\n\t\t\t\tcolumn(6,\r\n\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\th3(\"Plotting options\"),\r\n\t\t\t\t\t\tselectInput(\r\n\t\t\t\t\t\t\t\"plotCol\",\r\n\t\t\t\t\t\t\t\"Colors\",\r\n\t\t\t\t\t\t\tchoices=c(\"Black and white\"=\"b&w\",\r\n\t\t\t\t\t\t\t\t\"Colored\"=\"colored\"),\r\n\t\t\t\t\t\t\tsettings$cols),\r\n\t\t\t\t\t\tselectInput(\r\n\t\t\t\t\t\t\t\"serif\",\r\n\t\t\t\t\t\t\t\"Font\",\r\n\t\t\t\t\t\t\tchoices=c(\"Serif\"=\"serif\",\r\n\t\t\t\t\t\t\t\t\"Sans serif\"=\"noserif\"\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tselected=settings$serif\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\t})\r\n\t\r\n\toutput$naPlot <- renderPlot({\r\n\t\treq(cookedData$cooked)\r\n\t\tnas <- sum(is.na(cookedData$cooked))\r\n\t\t\r\n\t\tif (nas > 0) {bend(nas,nrow(cookedData$cooked)*ncol(cookedData$cooked))}\r\n\t\telse {return(NULL)}\r\n\t\t})\r\n\t\r\n\toutput$naCount <- renderUI({\r\n\t\treq(cookedData$cooked)\r\n\r\n\t\t\tif (sum(is.na(cookedData$cooked))>0){\r\n\t\t\t\ttagList(\r\n\t\t\t\t\th3(\"Overall\", style=\"text-align: center\"),\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\", \r\n\t\t\t\t\t\t\t\tplotOutput(\"naPlot\" ,height=\"200px\", width=\"100%\"),\r\n\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\tactionButton(\"handleNAs\",\"What to do with missing values?\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\telse {invisible()}\r\n\t\t\r\n\t\t})\r\n\t\r\n\toutput$summaryOutput <- renderUI({\r\n\t\treq(cookedData$cooked)\r\n\t\t\r\n\t\twells <- '

Categories

'\r\n\t\t\r\n\t\t# Create rows to display\r\n\t\trows <- list()\r\n\t\tcRow <- c()\r\n\t\tind <- 1\r\n\t\tcRowInd <- 1\r\n\t\trest <- length(cookedData$cats)\r\n\t\tcats <- cookedData$cats[order(seq(-1,-rest))]\r\n\t\t\r\n\t\tfor (label in cats) {\r\n\t\t\twhile(rest >4) {\r\n\t\t\t\twhile (ind <=4){\r\n\t\t\t\t\tcRow <- c(cRow, as.character(cats[rest]))\r\n\t\t\t\t\trest <- rest - 1\r\n\t\t\t\t\tind <- ind + 1\r\n\t\t\t\t\t}\r\n\t\t\t\tind <- 1\r\n\t\t\t\trows[[cRowInd]] <- cRow\r\n\t\t\t\tcRowInd <- cRowInd + 1\r\n\t\t\t\tcRow <- c()\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\tcRow <- as.character(cats[seq(rest,1)])\r\n\t\t\t\trows[[cRowInd]] <- cRow\r\n\t\t\t}\t\t\r\n\t\t\r\n\t\t\r\n\t\tfor (row in rows) {\r\n\t\t\tif (length(row) == 4){\r\n\t\t\t\twells <- paste(wells, '
', sep=\"\")\r\n\t\t\t\tfor (label in row)\t{\r\n\t\t\t\t\tsummarize <- paste(\"

\",label, \"

\",\"
\",\r\n\t\t\t\t\t\tplotOutput(sprintf('%s_%s', \"plot\", label), height=\"200px\", width=\"100%\"),\r\n\t\t\t\t\t\t\"

\", sep=\"\")\r\n\t\t\t\t\t\tfor (level in levels(cookedData$cooked[[label]])) {\r\n\t\t\t\t\t\tsummarize <- paste(summarize,\r\n\t\t\t\t\t\t\tlevel,\r\n\t\t\t\t\t\t\t\"\\t\",\r\n\t\t\t\t\t\t\tsum(cookedData$cooked[[label]]==level, na.rm=T),\r\n\t\t\t\t\t\t\t\"
\",\r\n\t\t\t\t\t\t\tsep=\"\"\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\tsummarize <- paste(summarize, \"

\", sep=\"\")\r\n\t\t\t\t\twells <- paste(wells, '
', summarize, \"
\", sep=\"\")\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\twells <- paste(wells, \"
\", sep=\"\")\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\tif (length(row)!=4){\r\n\t\t\t\twells <- paste(wells, '
',ifelse(length(row)!=3, paste('
\", sep=\"\"), \"\"), sep=\"\")\r\n\t\t\t\tfor (label in row)\t{\r\n\t\t\t\t\tsummarize <- paste(\"

\",label, \"

\",\"
\",\r\n\t\t\t\t\t\tplotOutput(sprintf('%s_%s', \"plot\", label), height=\"200px\", width=\"100%\"),\r\n\t\t\t\t\t\t\"

\", sep=\"\")\r\n\t\t\t\t\t\tfor (level in levels(cookedData$cooked[[label]])) {\r\n\t\t\t\t\t\tsummarize <- paste(summarize,\r\n\t\t\t\t\t\t\tlevel,\r\n\t\t\t\t\t\t\t\"\\t\",\r\n\t\t\t\t\t\t\tsum(cookedData$cooked[[label]]==level, na.rm=T),\r\n\t\t\t\t\t\t\t\"
\",\r\n\t\t\t\t\t\t\tsep=\"\"\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\tsummarize <- paste(summarize, \"

\", sep=\"\")\r\n\t\t\t\t\twells <- paste(wells, '
', summarize, \"
\", sep=\"\")\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\twells <- paste(wells, ifelse(length(row)!=3, paste('
\", sep=\"\"), \"\"),\"
\", sep=\"\")\t\t\t\r\n\r\n\t\t\t\t}\r\n\r\n\t\t\t}\r\n\t\t\t\r\n\t\twells <- paste(wells, '

Numbers

', sep=\"\")\t\r\n\r\n\t\trows <- list()\r\n\t\tcRow <- c()\r\n\t\tind <- 1\r\n\t\tcRowInd <- 1\r\n\t\trest <- length(cookedData$nums)\r\n\t\tnums <- cookedData$nums[order(seq(-1,-rest))]\r\n\t\t\r\n\t\tfor (label in nums) {\r\n\t\t\twhile(rest >4) {\r\n\t\t\t\twhile (ind <=4){\r\n\t\t\t\t\tcRow <- c(cRow, as.character(nums[rest]))\r\n\t\t\t\t\trest <- rest - 1\r\n\t\t\t\t\tind <- ind + 1\r\n\t\t\t\t\t}\r\n\t\t\t\tind <- 1\r\n\t\t\t\trows[[cRowInd]] <- cRow\r\n\t\t\t\tcRowInd <- cRowInd + 1\r\n\t\t\t\tcRow <- c()\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\tcRow <- as.character(nums[seq(rest,1)])\r\n\t\t\t\trows[[cRowInd]] <- cRow\r\n\t\t\t}\t\t\r\n\t\t\r\n\t\tfor (row in rows) {\r\n\t\t\tif (length(row) == 4){\r\n\t\t\t\twells <- paste(wells, '
', sep=\"\")\r\n\t\t\t\tfor (label in row)\t{\r\n\t\t\t\t\tsummarize <- paste(\"

\",label, \"

\",\"

\", sep=\"\")\r\n\t\t\t\t\tmn = round(mean(cookedData$cooked[[label]], na.rm=T),3)\r\n\t\t\t\t\tquants <- round(quantile(cookedData$cooked[[label]], c(0,0.25,0.5,0.75,1), na.rm=T),3)\r\n\t\t\t\t\tnas <- sum(is.na(cookedData$cooked[[label]])|is.nan(cookedData$cooked[[label]]))\r\n\t\t\t\t\tsummarize <- paste(\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\tlabel,\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\t\"
\",\r\n\t\t\t\t\t\tplotOutput(sprintf('%s_%s', \"plot\", label), height=\"200px\", width=\"100%\"),\r\n\t\t\t\t\t\t\"
\",\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\t\t\"Min.:\\t\",\r\n\t\t\t\t\t\t\tquants[1],\r\n\t\t\t\t\t\t\t\"
25%.:\\t\",\r\n\t\t\t\t\t\t\tquants[2],\r\n\t\t\t\t\t\t\t\"
Median.:\\t\",\r\n\t\t\t\t\t\t\tquants[3],\r\n\t\t\t\t\t\t\t\"
Mean.:\\t\",\r\n\t\t\t\t\t\t\tmn,\r\n\t\t\t\t\t\t\t\"
75%.:\\t\",\r\n\t\t\t\t\t\t\tquants[4],\r\n\t\t\t\t\t\t\t\"
Max.:\\t\",\r\n\t\t\t\t\t\t\tquants[5],\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tifelse(nas>1, \r\n\t\t\t\t\t\t\t\tpaste(\"
NAs.:\\t\",nas),\r\n\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\tsep=\"\"\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\tsummarize <- paste(summarize, \"

\", sep=\"\")\r\n\t\t\t\t\t\t\twells <- paste(wells, '
', summarize, \"
\", sep=\"\")\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\twells <- paste(wells, \"
\", sep=\"\")\r\n\t\t\t}\r\n\t\t\t\r\n\t\t\tif (length(row)!=4){\r\n\t\t\t\twells <- paste(wells, '
',ifelse(length(row)!=3, paste('
\", sep=\"\"), \"\"), sep=\"\")\r\n\t\t\t\tfor (label in row)\t{\r\n\t\t\t\t\tsummarize <- paste(\"

\",label, \"

\",\"

\", sep=\"\")\r\n\t\t\t\t\tmn = round(mean(cookedData$cooked[[label]], na.rm=T),3)\r\n\t\t\t\t\tquants <- round(quantile(cookedData$cooked[[label]], c(0,0.25,0.5,0.75,1), na.rm=T),3)\r\n\t\t\t\t\tnas <- sum(is.na(cookedData$cooked[[label]])|is.nan(cookedData$cooked[[label]]))\r\n\t\t\t\t\tsummarize <- paste(\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\tlabel,\t\t\t\t\t\t\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\t\"
\",\r\n\t\t\t\t\t\tplotOutput(sprintf('%s_%s', \"plot\", label), height=\"200px\", width=\"100%\"),\r\n\t\t\t\t\t\t\"
\",\t\t\t\t\t\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\t\t\"Min.:\\t\",\r\n\t\t\t\t\t\t\tquants[1],\r\n\t\t\t\t\t\t\t\"
25%.:\\t\",\r\n\t\t\t\t\t\t\tquants[2],\r\n\t\t\t\t\t\t\t\"
Median.:\\t\",\r\n\t\t\t\t\t\t\tquants[3],\r\n\t\t\t\t\t\t\t\"
Mean.:\\t\",\r\n\t\t\t\t\t\t\tmn,\r\n\t\t\t\t\t\t\t\"
75%.:\\t\",\r\n\t\t\t\t\t\t\tquants[4],\r\n\t\t\t\t\t\t\t\"
Max.:\\t\",\r\n\t\t\t\t\t\t\tquants[5],\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tifelse(nas>1, \r\n\t\t\t\t\t\t\t\tpaste(\"
NAs.:\\t\",nas),\r\n\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\"

\",\r\n\t\t\t\t\t\tsep=\"\"\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\tsummarize <- paste(summarize, \"

\", sep=\"\")\r\n\t\t\t\t\twells <- paste(wells, '
', summarize, \"
\", sep=\"\")\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\twells <- paste(wells, ifelse(length(row)!=3, paste('
\"), \"\"),\"
\", sep=\"\")\t\t\t\r\n\r\n\t\t\t}\t\r\n\t\t\t\r\n\t\t}\t\t\r\n\t\t\t\t\r\n\t\tHTML(wells)\r\n\t\t})\r\n\r\n\toutput$vartype <- renderUI({\r\n\t\treq(rawData())\r\n\t\t\r\n\t\tfil <- sapply(rawData(), class)==\"factor\"\r\n\t\tfil2 <- sapply(rawData(), is.numeric)\r\n\t\ttagList(\r\n\t\t\torderInput('cats', 'Categories', items = colnames(rawData())[fil],\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('nums', 'none'), width=\"100%\", item_class=\"primary\"),\r\n\t\t\torderInput('nums', 'Numbers', items = colnames(rawData())[fil2],\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('cats', 'none'), width=\"100%\", item_class=\"primary\"),\r\n\t\t\torderInput('none', 'Not analysed', items = colnames(rawData())[!(fil|fil2)],\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('cats', 'nums'), width=\"100%\", item_class=\"default\", placeholder=\"e.g. Names\")\t\t\t\t\t\t\r\n\t\t\t)\r\n\t\t})\t\r\n\t\t\r\n\toutput$plotselect <- renderUI({\r\n\t\r\n\t\treq(cookedData$cooked, cookedData$cats, cookedData$nums)\r\n\t\t\r\n\t\ttagList(\r\n\t\t\torderInput('outcome', 'Outcome', items=c(),\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('pred', 'source'), width=\"100%\", item_class=\"primary\", placeholder=\"Choose only the outcome to visualise its distribution\"),\r\n\t\t\torderInput('pred', 'Predictors', items = c(),\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('outcome', 'source'), width=\"100%\", item_class=\"primary\", placeholder=\"Up to two predictors\"),\r\n\t\t\torderInput('source', 'Not displayed', items = c(cookedData$cats, cookedData$nums),\r\n\t\t\t\t\t\tas_source = FALSE, connect = c('outcome', 'pred'), width=\"100%\", item_class=\"primary\")\t\t\t\t\t\t\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t)\r\n\t\t})\t\t\r\n\t\t\r\n\toutput$columnNumber <- renderText({\r\n\t\treq(rawData())\r\n\t\tas.character(ncol(rawData()))\r\n\t\t})\r\n\t\t\r\n\toutput$rowNumber <- renderText({\r\n\t\treq(rawData())\r\n\t\tas.character(nrow(rawData()))\r\n\t\t})\r\n\t\r\n\toutput$testSettingsColComp <- renderUI({\r\n\t\treq(testSettings(), testSet$settings)\r\n\t\tif(testSet$settings==\"colComp\"){\r\n\t\t\ttestSettings()\r\n\t\t\t}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\r\n\toutput$testSettingsGrComp <- renderUI({\r\n\t\treq(testSettings(), testSet$settings)\r\n\t\tif(testSet$settings==\"grComp\"){\r\n\t\t\ttestSettings()\r\n\t\t\t}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\r\n\toutput$testSettingsMod <- renderUI({\r\n\t\treq(testSettings(), testSet$settings)\r\n\t\tif(testSet$settings==\"mod\"){\r\n\t\t\ttestSettings()\r\n\t\t\t}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\t\t\r\n\ttestSettings <- reactive({\r\n\t\treq(testSet$settings, testSet$vals)\r\n\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\tif (ncol(testSet$vals)==2){\r\n\t\t\t\tif (testSet$twoCol == \"compMeans\") {\r\n\t\t\t\t\tif (ncol(testSet$vals)==2 & all(colnames(testSet$vals) %in% cookedData$nums)){\r\n\t\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th3(\"Test setup\"),\r\n\t\t\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\t\t\th4(\"Data\"),\r\n\t\t\t\t\t\t\t\t\t\tcheckboxInput(\"paired\", \"Individual rows form pairs\", FALSE),\r\n\t\t\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\t\t\th4(\"Hypothesis\"),\r\n\t\t\t\t\t\t\t\t\t\tselectInput(\"tails\", paste(\"The mean in\", colnames(testSet$vals)[1], \"is:\"), c(\r\n\t\t\t\t\t\t\t\t\t\t\t\"Lower\"=\"less\",\r\n\t\t\t\t\t\t\t\t\t\t\t\"Different\"=\"two.sided\",\r\n\t\t\t\t\t\t\t\t\t\t\t\"Higher\"=\"greater\"),\r\n\t\t\t\t\t\t\t\t\t\t\tselected=\"two.sided\"\r\n\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\tp(style=\"font-weight:700; font-size: 14px \", paste(\"in comparison to\", colnames(testSet$vals)[2])),\r\n\t\t\t\t\t\t\t\t\t\tactionButton(\"doTestCol\", \"Save\", style=\"background-color: green; color: white\")\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\telse if (testSet$twoCol==\"correlate\"){\r\n\t\t\t\t\t\ttagList(\r\n\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th3(\"Test setup\"),\r\n\t\t\t\t\t\t\t\t\t\thr(),\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\tselectInput(\"method\", paste(\"The relationship between\", colnames(testSet$vals)[1], \"and\", colnames(testSet$vals)[2], \"is:\"), c(\r\n\t\t\t\t\t\t\t\t\t\t\t\"Linear\"=\"pearson\",\r\n\t\t\t\t\t\t\t\t\t\t\t\"Monotonic\"=\"spearman\"),\r\n\t\t\t\t\t\t\t\t\t\t\tselected=\"spearman\"\r\n\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\tp(\"Linear relationships can be visualised as a simple line, connecting the dots on a scatterplot. Monotonic relationships can be represented by a curve or a line.\"),\r\n\t\t\t\t\t\t\t\t\t\tactionButton(\"doCorTest\", \"Proceed\", style=\"background-color: green; color: white\")\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t)\t\t\t\t\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\t\telse {invisible()}\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\telse if (ncol(testSet$vals)>2 & all(colnames(testSet$vals) %in% cookedData$nums)){\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th3(\"Test setup\"),\r\n\t\t\t\t\t\t\t\thr(),\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\th4(\"Hypothesis\"),\r\n\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\tp(\"If your data contains several groups which you planned beforehand, set them up here.\"),\r\n\t\t\t\t\t\t\t\tp(\"Example case: Your data contains non-native (French, German) and native speakers. \r\n\t\t\t\t\t\t\t\t\tYou hypothesized that natives differ from non-natives, and French differ from Germans.\r\n\t\t\t\t\t\t\t\t\"),\r\n\t\t\t\t\t\t\t\tactionButton(\"setContsCol\", \"Planned contrasts\", class=\"btn btn-info\"),\t\r\n\t\t\t\t\t\t\t\tactionButton(\"doATestCol\", \"Proceed\", style=\"background-color: green; color: white\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\telse {invisible()}\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (testSet$settings == \"grComp\"){\r\n\t\t\tif (length(levels(testSet$vals[,2]))==2 & colnames(testSet$vals)[1] %in% cookedData$nums){\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th3(\"Test setup\"),\r\n\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\th4(\"Data\"),\r\n\t\t\t\t\t\t\t\tcheckboxInput(\"paired\", \"Individual rows form pairs\", FALSE),\r\n\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\th4(\"Hypothesis\"),\r\n\t\t\t\t\t\t\t\tselectInput(\"tails\", paste(\"The mean in\", colnames(testSet$vals)[1], \"is:\"), c(\r\n\t\t\t\t\t\t\t\t\t\"Lower\"=\"less\",\r\n\t\t\t\t\t\t\t\t\t\"Different\"=\"two.sided\",\r\n\t\t\t\t\t\t\t\t\t\"Higher\"=\"greater\"),\r\n\t\t\t\t\t\t\t\t\tselected=\"two.sided\"\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tp(style=\"font-weight:700; font-size: 14px \", paste(\"in comparison to\", colnames(testSet$vals)[2])),\r\n\t\t\t\t\t\t\t\tactionButton(\"doTestGr\", \"Save\", style=\"background-color: green; color: white\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (length(levels(testSet$vals[,2]))>2 & colnames(testSet$vals)[1] %in% cookedData$nums){\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th3(\"Test setup\"),\r\n\t\t\t\t\t\t\t\thr(),\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\th4(\"Hypothesis\"),\r\n\t\t\t\t\t\t\t\thr(),\r\n\t\t\t\t\t\t\t\tp(\"If your data contains several groups which you planned beforehand, set them up here.\"),\r\n\t\t\t\t\t\t\t\tp(\"Example case: Your data contains non-native (French, German) and native speakers. \r\n\t\t\t\t\t\t\t\t\tYou hypothesized that natives differ from non-natives, and French differ from Germans.\r\n\t\t\t\t\t\t\t\t\"),\r\n\t\t\t\t\t\t\t\tactionButton(\"setContsGr\", \"Plannned contrasts\", class=\"btn btn-info\"),\t\r\n\t\t\t\t\t\t\t\tactionButton(\"doATestGr\", \"Proceed\", style=\"background-color: green; color: white\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\t\t\t\r\n\t\t\t\t\r\n\t\t\t\r\n\t\t\t}\t\t\t\r\n\r\n\t\telse if (testSet$settings == \"mod\"){\r\n\t\t\tinvisible()\t\t\r\n\t\t\t}\r\n\r\n\t\t\t})\r\n\t\t\t\r\n\tdoChisq <- function(dat, type=\"raw\") {\r\n\t\tif (type==\"raw\"){\r\n\t\t\t# Check that we can do X-squared:\r\n\t\t\t#1. Check that there is an overlap in the levels\r\n\t\t\tc1 <- dat[,1]\r\n\t\t\tc2 <- dat[,2]\r\n\t\t\tgrLabs <- colnames(dat)\t\t\t\r\n\t\t\tif (testSet$settings == \"grComp\"){\r\n\t\t\t\tgrLabs <- levels(c2)\r\n\t\t\t\tc1.x <- c1[c2==levels(c2)[1]]\r\n\t\t\t\tc2.x <- c1[c2==levels(c2)[2]]\r\n\t\t\t\tc1 <- c1.x\r\n\t\t\t\tc2 <- c2.x\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\tif (!any(levels(c1) %in% levels(c2))){\r\n\t\t\t\treturn(list(results=NULL, type=\"applesToOranges\"))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\telse { \r\n\t\t\t\t# Merge the levels in both columns, so that levels not present in col A equal to 0 rather than error, remove NAs\r\n\t\t\t\tlevs <- sort(unique(c(levels(c1), levels(c2))))\r\n\t\t\t\tlevs <- levs[!is.na(levs)]\r\n\t\t\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\t\t\tc1 <- c1[!is.na(c1)]\r\n\t\t\t\t\tc2 <- c2[!is.na(c2)]\r\n\t\t\t\t\t}\r\n\t\t\t\t\t\r\n\t\t\t\tc1 <- factor(c1, levels=levs)\r\n\t\t\t\tc2 <- factor(c2, levels=levs)\t\r\n\t\t\t\t\r\n\t\t\t\tt1 <- as.vector(table(c1))\r\n\t\t\t\tt2 <- as.vector(table(c2))\r\n\t\t\t\t\r\n\t\t\t\tcomp <- t1 < t2\r\n\t\t\t\t\r\n\t\t\t\ttab <- data.frame(t1,t2)\t\t\r\n\t\t\t}\r\n\t\t\r\n\t\t\tforplot <- factor(c(as.character(c1), as.character(c2)))\r\n\t\t\tforplot <- data.frame(id=c(rep(grLabs[1], length(c1)),rep(grLabs[2], length(c2))) ,values=forplot)\t\t\t\r\n\t\t\tp.obj <- ggplot(forplot, aes(id, fill=values)) + geom_bar(position=position_dodge()) + theme_bw()\t\t\r\n\t\t\r\n\t\t}\r\n\r\n\t\telse if (type==\"count\") {\r\n\t\t\treq(testSet$namesCol)\r\n\t\t\tcatnames <- factor(cookedData$cooked[[testSet$namesCol]])\r\n\t\t\t# Check that we can do X-squared:\r\n\t\t\t#1. Check that there is an overlap in the levels\r\n\t\t\tc1 <- dat[,1]\r\n\t\t\tc2 <- dat[,2]\r\n\t\t\tgrLabs <- colnames(dat)\t\t\t\r\n\r\n\t\t\t# Merge the levels in both columns, so that levels not present in col A equal to 0 rather than error, remove NAs\t\t\t\r\n\t\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\t\tfil = !is.na(c1) & !is.na(c2)\r\n\t\t\t\tc1 <- c1[fil]\r\n\t\t\t\tc2 <- c2[fil]\r\n\t\t\t\t}\r\n\t\t\tlevs <- as.character(catnames)\r\n\t\t\t\r\n\t\t\tdat <- data.frame(c1,c2)\t\r\n\r\n\t\t\tforplot <- data.frame(c(c1,c2), c(rep(grLabs[1],length(c1)), rep(grLabs[2],length(c2))), rep(factor(catnames),2))\r\n\t\t\tcolnames(forplot) <- c(\"Frequency\", \"Data\", \"Category\")\r\n\t\t\tp.obj <- ggplot(forplot, aes(Data,y=Frequency, fill=Category))+\r\n\t\t\t\tgeom_col() + \r\n\t\t\t\ttheme_bw()\t\t\r\n\t\t\tcomp <- c1 < c2\t\t\t\r\n\t\t\ttab <- data.frame(c1,c2)\r\n\t\t\r\n\t\t}\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t\r\n\r\n\t\t#There should not be any zeros or expected values below 5 \r\n\t\tcond1 <- sum(tab==0)\r\n\t\tcond2 <- getExp(tab)\r\n\t\t\r\n\t\tif (cond1 == 0 & !any(cond2 < 5)){\r\n\t\t\tt.obj <- suppressWarnings(chisq.test(tab))\r\n\t\t\trownames(t.obj$observed) <- levs\r\n\t\t\tcolnames(t.obj$observed) <- grLabs\r\n\t\t\treturn(list(results=t.obj, type=\"chisq2\", larger=levs[comp], smaller=levs[!comp], plot=p.obj))\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {\t\t\r\n\t\t\tt.obj <- suppressWarnings(fisher.test(tab))\r\n\t\t\tt.obj$observed <- tab\r\n\t\t\trownames(t.obj$observed) <- levs\r\n\t\t\tcolnames(t.obj$observed) <- grLabs\t\r\n\t\t\treturn(list(results=t.obj, type=\"fisher2\", larger=levs[comp], smaller=levs[!comp], plot=p.obj))\r\n\t\t\t}\r\n\t\t\t\r\n\t\t}\t\t\t\r\n\t\r\n\tdoTtest <- function(dat){\r\n\t\t# Check that we can do t-test\r\n\t\tc1 <- dat[,1]\r\n\t\tc2 <- dat[,2]\r\n\t\tgrLabs <- colnames(dat)\r\n\t\t\r\n\t\tif (testSet$settings==\"grComp\"){\r\n\t\t\tgrLabs <- levels(c2)\r\n\t\t\tc1.x <- c1[c2==levels(c2)[1]]\r\n\t\t\tc2.x <- c1[c2==levels(c2)[2]]\r\n\t\t\tc1 <- c1.x\r\n\t\t\tc2 <- c2.x\t\t\t\t\t\t\r\n\t\t\t}\r\n\t\t\r\n\t\tif(testSet$settings==\"colComp\"){req(input$doTestCol > 0)}\r\n\t\t\r\n\t\tif(testSet$settings==\"grComp\"){req(input$doTestGr > 0)}\r\n\t\t\r\n\t\tpars <- isolate(list(tails=input$tails, paired=input$paired))\r\n\t\t\r\n\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\tif (pars$paired) {\r\n\t\t\t\tmask <- !(is.na(c1)|is.na(c2))\r\n\t\t\t\tc1 <- c1[mask]\r\n\t\t\t\tc2 <- c2[mask]\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse {\r\n\t\t\t\tc1 <- c1[!is.na(c1)]\r\n\t\t\t\tc2 <- c2[!is.na(c2)]\r\n\t\t\t\t}\t\t\t\t\r\n\t\t}\r\n\r\n\t\tforplot <- data.frame(\r\n\t\t\tgroup=grLabs,\r\n\t\t\tmeans=c(mean(c1), mean(c2)),\r\n\t\t\tsds=c(sd(c1), sd(c2))\r\n\t\t\t)\r\n\t\t\r\n\t\tlc1 <- length(c1)\r\n\t\tlc2 <- length(c2)\r\n\t\tforplot$se <- forplot$sds/sqrt(c(lc1, lc2))\r\n\r\n\t\tp.obj <- ggplot(forplot, aes(x=group, y=means, fill=group))+\r\n\t\t\tgeom_bar(stat=\"identity\")+\r\n\t\t\tgeom_errorbar(aes(ymin=means-2*se, ymax=means+2*se),\r\n\t\t\t\t\t\t width=.2, # Width of the error bars\r\n\t\t\t\t\t\t position=position_dodge(.9))+\r\n\t\t\ttheme_bw()+\r\n\t\t\tscale_fill_brewer(palette=\"Accent\")\t+\r\n\t\t\tylab(\"mean\")\r\n\t\t\t\r\n\t\tlarger <- forplot[forplot$means==max(forplot$means), \"means\"]\r\n\t\tnames(larger) <- forplot[forplot$means==max(forplot$means), \"group\"]\r\n\t\tsmaller <- forplot[forplot$means==min(forplot$means), \"means\"]\r\n\t\tnames(smaller) <- forplot[forplot$means==min(forplot$means), \"group\"]\t\r\n\t\t\r\n\t\t## TO DO: \r\n\t\t# EFF SIZE: For equal samples, use Cohen's d\r\n\t\tif (lc1==lc2) {\r\n\t\t\teff <- (forplot$means[1] - forplot$means[2])/\r\n\t\t\t\t(sum(forplot$sds*(c(lc1, lc2)-1))/\r\n\t\t\t\t\t(lc1+lc2-2))\r\n\t\t\tnames(eff) <- \"Cohen's d\"\t\t\r\n\t\t\t}\r\n\t\t\t\t\t\t\r\n\t\t# For unequal samples, use Hedge's g\t\r\n\t\telse {eff <- (1-(3/((4*(lc1+lc2))-9)))*((forplot$means[1] - forplot$means[2])/\r\n\t\t\t\t(sum(forplot$sds*(c(lc1, lc2)-1))/\r\n\t\t\t\t\t(lc1+lc2-2)))\r\n\t\t\tnames(eff) <- \"Hedge's d\"\t\t\r\n\t\t\t\t\t}\r\n\t\t\r\n\t\tsumTab <- forplot[, c(\"means\", \"sds\", \"se\")]\r\n\t\tcolnames(sumTab) <- c(\"Mean\", \"SD\", \"SE\")\r\n\t\trownames(sumTab) <- forplot$group\r\n\t\t\r\n\t\tif (pars$paired==TRUE) {\r\n\r\n\t\t\tcond1 <- shapiro.test(c1-c2)$p.value >= 0.05\r\n\t\t\tif (cond1) {\r\n\t\t\t\tt.obj <- suppressWarnings(t.test(c1, c2, paired=T, alternative=pars$tails))\r\n\t\t\t\treturn(list(results=t.obj, type=\"ttest2\", plot=p.obj, larger=larger, smaller=smaller, sumTab=sumTab, eff=eff))\r\n\t\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tt.obj <- suppressWarnings(wilcox.test(c1, c2, paired=T, alternative=pars$tails))\r\n\t\t\t\treturn(list(results=t.obj, type=\"wilcox2\", plot=p.obj, larger=larger, smaller=smaller, sumTab=sumTab, eff=eff))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t}\r\n\r\n\t\telse {\r\n\t\t\tcond1 <- shapiro.test(c1)$p.value >= 0.05\r\n\t\t\tcond2 <- shapiro.test(c2)$p.value >= 0.05\r\n\t\t\tcond3 <- var.test(c1, c2)$p.value >= 0.05\r\n\t\t\t\r\n\t\t\tif (all(cond1, cond2, cond3)) {\r\n\t\t\t\tt.obj <- suppressWarnings(t.test(c1, c2, paired=F, alternative=pars$tails))\r\n\t\t\t\treturn(list(results=t.obj, type=\"ttest2\", plot=p.obj, larger=larger, smaller=smaller, sumTab=sumTab, eff=eff))\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse {\r\n\t\t\t\tt.obj <- suppressWarnings(wilcox.test(c1, c2, paired=F, alternative=pars$tails))\r\n\t\t\t\treturn(list(results=t.obj, type=\"wilcox2\", plot=p.obj, larger=larger, smaller=smaller, sumTab=sumTab, eff=eff))\r\n\t\t\t\t}\t\t\t\t\t\t\r\n\t\t\t}\t\r\n\t\t}\r\n\r\n\tdoAOV <- function(dat0, conts){\r\n\t\t# str(dat0)\r\n\t\t\r\n\t\treq(!is.null(input$doATestCol)|!is.null(input$doATestGr))\r\n\t\tif (!is.null(input$doATestCol)) {\r\n\t\t\ttemp <- input$doATestCol\r\n\t\t\treq(input$doATestCol > 0)}\r\n\t\telse {\r\n\t\t\ttemp <- input$doATestGr\r\n\t\t\treq(input$doATestGr > 0)\r\n\t\t\t}\r\n\t\t\r\n\t\tif (testSet$settings == \"colComp\") {\r\n\t\t\tids <- colnames(dat0)\r\n\t\t\tdat <- data.frame(values__=c(), id__=c())\r\n\t\t\tfor (i in ids) {\r\n\t\t\t\tdat <- rbind(dat, data.frame(values__=dat0[[i]], id__=rep(i, length(dat0[[i]]))))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\tdat$id__ <- factor(dat$id__)\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (testSet$settings == \"grComp\"){\r\n\t\t\tdat <- dat0\r\n\t\t\t}\r\n\t\t\r\n\t\tcolnames(dat) <- c(\"value__\", \"id__\")\r\n\t\tcontrasts(dat$id__) <- conts\r\n\t\t\r\n\t\t\r\n\t\tif (settings$na.ignore==\"ignore\"){dat <- dat[rowSums(is.na(dat))==0,]}\r\n\t\r\n\t\tordering <- as.vector(by(dat[,1], dat[,2], mean))\r\n\t\tnames(ordering) <- levels(dat[,2])\r\n\t\tordering <- sort(ordering, decreasing = TRUE)\r\n\t\t\t\t\r\n\t\tcond1 <- all(as.vector(by(dat[,1], dat[,2], normRes)))\r\n\t\tcond2 <- leveneTest(dat[,1]~dat[,2])$\"Pr(>F)\"[1]>0.05\r\n\t\t\r\n\t\tif (!is.null(testSet$manConts)) {\r\n\t\t\tconts <- colnames(as.data.frame(conts))\r\n\t\t\t}\r\n\t\t\r\n\t\tif (cond1 & cond2){\r\n\t\t\tt.obj <- aov(dat[,1] ~ dat[,2])\r\n\t\t\tsm <- summary.lm(t.obj)\r\n\t\t\t\r\n\t\t\tp.data <- getSummary(dat)\r\n\t\t\tp.obj <- ggplot(p.data , aes(x=l, y=m, fill=l))+\r\n\t\t\t\tgeom_bar(stat=\"identity\")+\r\n\t\t\t\tgeom_errorbar(aes(ymin=m-2*s, ymax=m+2*s),\r\n\t\t\t\t\t\t\t width=.2, # Width of the error bars\r\n\t\t\t\t\t\t\t position=position_dodge(.9))+\r\n\t\t\t\ttheme_bw()+\r\n\t\t\t\tscale_fill_brewer(\"Group\", palette=\"Accent\")\t+\r\n\t\t\t\txlab(\"Group\")+\r\n\t\t\t\tylab(\"Mean\")\r\n\t\t\tprint(\"AOV-DONE\")\r\n\t\t\treturn(list(results=t.obj, sm=sm, type=\"anova\", plot=p.obj, conts=conts, ordering=ordering))\r\n\t\t\t}\r\n\t\t\r\n\t\t#Welch\r\n\t\t# else if (cond1 & !cond2){\r\n\t\t\t# t.obj <- summary.lm(oneway(dat[,2] ~ dat[,1]))\r\n\t\t\t# }\r\n\t\t\r\n\t\t#Kruskal Wallis <-- done as ANOVA on ranks\r\n\t\telse {\r\n\t\t\tdat[,1] <- rank(dat[,1], ties.method=\"average\")\r\n\t\t\tt.obj <- aov(dat[,1] ~ dat[,2])\r\n\t\t\tsm <- summary.lm(t.obj)\r\n\r\n\t\t\tp.data <- getSummary(dat)\r\n\t\t\tp.obj <- ggplot(p.data , aes(x=l, y=m, fill=l))+\r\n\t\t\t\tgeom_bar(stat=\"identity\")+\r\n\t\t\t\tgeom_errorbar(aes(ymin=m-2*s, ymax=m+2*s),\r\n\t\t\t\t\t\t\t width=.2, # Width of the error bars\r\n\t\t\t\t\t\t\t position=position_dodge(.9))+\r\n\t\t\t\ttheme_bw()+\r\n\t\t\t\tscale_fill_brewer(\"Group\", palette=\"Accent\")\t+\r\n\t\t\t\txlab(\"Group\") +\r\n\t\t\t\tylab(\"Mean\")\t\t\t\r\n\t\t\tprint(\"KW-DONE\")\r\n\t\t\treturn(list(results=t.obj, sm=sm, type=\"anovaOnRanks\", plot=p.obj, conts=conts, ordering=ordering))\r\n\t\t\t}\t\t\r\n\t\t}\r\n\t\r\n\tdoBigChisq <- function(dat, conts) {\r\n\t\tprint(\"BIGCHISQ\")\r\n\t\tif (testSet$settings==\"colComp\"){\r\n\t\t\tdat <- as.data.frame(rbindlist(\r\n\t\t\tlapply(\r\n\t\t\t\tcolnames(dat),\r\n\t\t\t\tfunction(i){\r\n\t\t\t\t\tdata.frame(\r\n\t\t\t\t\t\tobservations=dat[[i]],\r\n\t\t\t\t\t\tgroups=rep(i, length(dat[,i]))\r\n\t\t\t\t\t\t\t)}\r\n\t\t\t\t)\r\n\t\t\t))\r\n\t\t\t}\r\n\t\t# else if (testSet$settings==\"grComp\"){\r\n\t\t\t# }\r\n\t\t\r\n\t\t# print(conts)\r\n\t\t\r\n\t\tc1 <- dat[,1]\r\n\t\tc2 <- dat[,2]\r\n\t\t\r\n\t\t\r\n\t\t\r\n\t\tlevs <- sort(levels(c1))\r\n\t\tlevs <- levs[!is.na(levs)]\r\n\t\t\r\n\t\tgrNames <- sort(levels(c2))\r\n\t\tgrNames <- grNames[!is.na(grNames)]\r\n\t\t\r\n\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\tfil <- !(is.na(c1)|is.na(c2))\r\n\t\t\tc1 <- c1[fil]\r\n\t\t\tc2 <- c2[fil]\r\n\t\t\t}\r\n\t\t\t\r\n\t\tdat <- data.frame(values=c1, id=c2)\t\r\n\t\t\r\n\t\ttab <- table(dat)\r\n\t\tp.obj <- ggplot(dat, aes(id, fill=values)) + geom_bar(position=position_dodge()) + theme_bw()\r\n\t\t\r\n\t\t#There should not be any zeros or expected values below 5 \r\n\t\tcond1 <- sum(tab==0)\r\n\t\tcond2 <- getExp(tab)\r\n\r\n\t\tif (!is.null(testSet$manConts)) {\r\n\t\t\tconts <- colnames(as.data.frame(conts))\r\n\t\t\t}\r\n\t\t\r\n\t\tif (cond1 == 0 & !any(cond2 < 5)){\r\n\t\t\tt.obj <- suppressWarnings(chisq.test(tab))\r\n\t\t\trownames(t.obj$observed) <- levs\r\n\t\t\tcolnames(t.obj$observed) <- grNames\r\n\t\t\tprint(\"BIGCHISQ1\")\r\n\t\t\treturn(list(results=t.obj, type=\"chisq\", plot=p.obj, conts=conts))\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {\t\t\r\n\t\t\tt.obj <- suppressWarnings(fisher.test(tab))\r\n\t\t\tt.obj$observed <- tab\r\n\t\t\trownames(t.obj$observed) <- levs\r\n\t\t\tcolnames(t.obj$observed) <- grNames\r\n\t\t\tprint(\"BIGCHISQ2\")\r\n\t\t\treturn(list(results=t.obj, type=\"fisher\", plot=p.obj, conts=conts))\r\n\t\t\t}\t\t\t\t\t\t\r\n\t\t}\r\n\t\r\n\tdoCor <- function(dat){\r\n\t\r\n\t\tc1 <- dat[,1]\r\n\t\tc2 <- dat[,2]\r\n\t\tgrLabs <- colnames(dat)\r\n\t\t\r\n\t\tcond1 <- shapiro.test(c1)$p.value >= 0.05\r\n\t\tcond2 <- shapiro.test(c2)$p.value >= 0.05\t\r\n\t\t\r\n\t\treq(input$doCorTest > 0)\r\n\t\t\r\n\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\tfil <- !(is.na(c1) | is.na(c2))\r\n\t\t\tc1 <- c1[fil]\r\n\t\t\tc2 <- c2[fil]\r\n\t\t\t}\r\n\t\t\r\n\t\tif (cond1 & cond2){\r\n\t\t\tc.obj <- suppressWarnings(cor.test(c1, c2, method=input$method))\r\n\t\t\tmethod <- input$method\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {c.obj <- suppressWarnings(cor.test(c1, c2, method=\"spearman\"))\r\n\t\t\tmethod <- \"spearman\"\r\n\t\t\t}\r\n\t\t\r\n\t\tforplot <- data.frame(x=c1, y=c2)\r\n\t\tp.obj <- ggplot(forplot, aes(x, y))+\r\n\t\t\tgeom_point()+\r\n\t\t\ttheme_bw()+\r\n\t\t\txlab(grLabs[1])+\r\n\t\t\tylab(grLabs[2])\t\t\t\r\n\t\t\r\n\t\treturn(list(results=c.obj, type=method, plot=p.obj))\r\n\t\t}\r\n\t\r\n\ttestRes <- reactive({\r\n\t\treq(testSet$settings, testSet$vals)\r\n\t\t\r\n\t\tif (testSet$settings == \"colComp\") {\r\n\t\t\tif (ncol(testSet$vals)==2){\r\n\t\t\t\tif (all(colnames(testSet$vals) %in% cookedData$cats)) {\r\n\t\t\t\t\t\treturn(doChisq(testSet$vals, type=\"raw\"))\r\n\t\t\t\t\t}\r\n\t\t\t\telse if (all(colnames(testSet$vals) %in% cookedData$nums)) {\r\n\t\t\t\t\r\n\t\t\t\t\tif (testSet$twoCol == \"compMeans\"){\r\n\t\t\t\t\t\treturn(doTtest(testSet$vals))\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\telse if (testSet$twoCol == \"compCount\"){\r\n\t\t\t\t\t\treturn(doChisq(testSet$vals, type=\"count\"))\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\telse if (testSet$twoCol == \"correlate\"){\r\n\t\t\t\t\t\treturn(doCor(testSet$vals))\r\n\t\t\t\t\t\t}\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (ncol(testSet$vals)>2){\r\n\t\t\t\tif (all(colnames(testSet$vals) %in% cookedData$cats)) {\r\n\t\t\t\t\t# ids <- colnames(testSet$vals)\r\n\t\t\t\t\t# dat <- data.frame(id__=c(), values__=c())\r\n\t\t\t\t\t# for (i in ids) {\r\n\t\t\t\t\t\t# dat <- rbind(dat, data.frame(id__=rep(i, length(testSet$vals[, i])), values__=testSet$vals[, i]))\r\n\t\t\t\t\t\t# }\r\n\t\t\t\t\t\r\n\t\t\t\t\t# dat$id__ <- factor(dat$id__)\r\n\t\t\t\t\t# testSet$vals <- dat\t\t\t\t\t\t\t\r\n\t\t\t\t\treturn(doBigChisq(testSet$vals, aovConts()))\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\telse if (all(colnames(testSet$vals) %in% cookedData$nums)) {\r\n\t\t\t\t\treturn(doAOV(testSet$vals, aovConts()))\t\t\t\t\t\r\n\t\t\t\t\t}\t\t\t\t\r\n\t\t\t\t}\t\t\r\n\t\t\t}\r\n\t\t\r\n\t\telse if (testSet$settings == \"grComp\") {\r\n\t\t\tif (length(levels(testSet$vals[,2]))==2){\r\n\t\t\t\tif (all(colnames(testSet$vals) %in% cookedData$cats)) {\r\n\t\t\t\t\t\treturn(doChisq(testSet$vals))\r\n\t\t\t\t\t}\r\n\t\t\t\telse if (colnames(testSet$vals)[1] %in% cookedData$nums) {\r\n\t\t\t\t\t\treturn(doTtest(testSet$vals))\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse {\r\n\t\t\t\tif (all(colnames(testSet$vals) %in% cookedData$cats)) {\r\n\t\t\t\t\treturn(doBigChisq(testSet$vals, aovConts()))\r\n\t\t\t\t\t}\r\n\t\t\t\telse if (colnames(testSet$vals)[1] %in% cookedData$nums){\r\n\t\t\t\t\treturn(doAOV(testSet$vals, aovConts()))\r\n\t\t\t\t\t}\r\n\t\t\t\t}\t\t\t\t\r\n\t\t\t}\t\t\t\r\n\r\n\t\telse if (testSet$settings == \"mod\") {\r\n\r\n\t\t\toutp <- input$outp_order\r\n\t\t\tpreds <- input$pred_order\r\n\t\t\t\r\n\t\t\t\r\n\t\t\tdat <- cookedData$cooked[,c(outp, preds)]\r\n\t\t\tif (settings$na.ignore == \"ignore\"){\r\n\t\t\t\tmask <- rowSums(is.na(dat))==0\r\n\t\t\t\tdat <- dat[mask,]\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t# LINEAR REGRESSION\r\n\t\t\tif (outp %in% cookedData$nums){\r\n\t\t\t\tcord <- dat[colnames(dat)[colnames(dat) %in% cookedData$nums]]\r\n\t\t\t\tcorpreds <- preds[preds %in% cookedData$nums]\r\n\t\t\t\tcoroutp <- outp[outp %in% cookedData$nums]\r\n\r\n\t\t\t\tlin <- cor(cord, method=\"pearson\")[corpreds,coroutp]-cor(cord, method=\"spearman\")[corpreds,coroutp]\r\n\t\t\t\tnames(lin) <- corpreds\r\n\t\t\t\t# print(lin)\r\n\t\t\t\tlin <- names(lin)[lin < -0.1]\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\tmod <- lm(reformulate(termlabels = preds, response = outp), dat)\r\n\t\t\t\tif (length(preds)>1){mColl <- vif(mod)\r\n\t\t\t\t\tif (class(mColl)==\"matrix\"){\t\t\t\t\t\t\r\n\t\t\t\t\t\tmColl <- as.data.frame(mColl)\r\n\t\t\t\t\t\t# print(mColl)\r\n\t\t\t\t\t\tmColl <- mColl[3]**2\r\n\t\t\t\t\t\tmColl <- rownames(mColl)[mColl[,1]>10]\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\telse {\r\n\t\t\t\t\t\tmColl <- names(mColl)[mColl>10]\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\telse {mColl <- NULL}\r\n\t\t\t\t\r\n\t\t\t\tmodplot <- data.frame(rlv=mod$model[[outp]], predicted=mod$fitted)\r\n\t\t\t\tp.obj <- ggplot(modplot, aes(rlv, predicted))+\r\n\t\t\t\t\tgeom_point()+\r\n\t\t\t\t\ttheme_bw()+\r\n\t\t\t\t\t#geom_line(aes(x=rlv, y=rlv))+\r\n\t\t\t\t\txlab(\"Real value in the data\") +\r\n\t\t\t\t\tylab(\"Value predicted by the model\")\r\n\r\n\t\t\t\treturn(list(results=mod, sm=summary(lm.beta(mod)), type=\"linreg\", lin=lin, mColl=mColl, plot=p.obj))\r\n\t\t\t\t# create a summary for factors?\r\n\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t# LOGISTIC REGRESSION/DECISION TREES?\t\r\n\t\t\telse if (outp %in% cookedData$cats) {\r\n\t\t\t\t\r\n\t\t\t\tmod <- glm(reformulate(termlabels = preds, response = outp), dat, family=\"binomial\")\r\n\t\t\t\treturn(list(results=mod, type=\"logreg\"))\r\n\t\t\t\t# create a summary for factors?\r\n\t\t\t\t\r\n\t\t\t\t}\t\r\n\t\t\t\t\r\n\t\t\t}\r\n\r\n\t\t})\r\n\t\t\r\n\t# CREATE THE SUMMARY\r\n\toutput$testOutputColComp <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\treq(testRes())\r\n\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\t\t\t\r\n\t\t\treq(testRes()$type, testSet$vals)\r\n\t\t\ta <- anovaGroups$finished\t\t\r\n\t\t\t\r\n\t\t\tinp <- testRes()\r\n\t\t\tprint(inp)\r\n\t\t\t\r\n\t\t\tif (inp$type %in% c(\"chisq2\", \"fisher2\")) {\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(paste(\"p-value:\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Comparison\"),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonColComp\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotColComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the variables \", paste(sort(c(inp$larger, inp$smaller)), collapse=\", \"), \" was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"chisq2\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p< %g, chi-squared= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that the distribution of these variables \",ifelse(inp$results$p.value<0.05, \"differs\", \"does not substantially differ\"), \" between \",\r\n\t\t\t\t\t\t\t\t\t\t\tn[1], \" and \", n[2], \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"chisq\", \"fisher\")) {\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(paste(\"p-value:\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Comparison\"),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonColComp\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotColComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the variables \", paste(n, collapse=\", \"), \" was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"chisq\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p< %g, chi-squared= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that the distribution of these variables \",ifelse(inp$results$p.value<0.05, \"differs\", \"does not substantially differ\"), \" between \",\r\n\t\t\t\t\t\t\t\t\t\t\tn[1], \" and \", n[2], \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\telse if (inp$type == \"applesToOranges\") {\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"ERROR\"),\r\n\t\t\t\t\t\t\t\tp(\"You are probably comparing columns which contain different data. It is not possible to compare the distribution in the columns as they do not contain the same variables.\"),\r\n\t\t\t\t\t\t\t\tp(\"Change the analysis type, or use the button in the top left corner to select different data.\"),\r\n\t\t\t\t\t\t\t\timg(src=\"aToO.jpg\", width=\"100%\")\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\r\n\t\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"pearson\", \"spearman\")) {\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\tprint(inp)\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\t\tp(sprintf(\"correlation coefficient: %g\", max(round(inp$results$estimate, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\t\tif (inp$results$p.value>0) {p(style=\"font-weight:400\", \"positive\")\r\n\t\t\t\t\t\t\t\t\t\t\t} else {p(style=\"font-weight:400\", \"negative\")}\t,\r\n\t\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: red; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\t\t\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Effect size\"),\r\n\t\t\t\t\t\t\t\t\tplotOutput(\"effectCol\", height=\"200px\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tplotOutput(\"testPlotColComp\")\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The relationship between the variables '\", n[1], \"' and '\", n[2], \"' was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The correlation coefficient between these two variables was \", ifelse(inp$type==\"pearson\", \"r=\", \"rho=\"), round(inp$results$estimate,3),\r\n\t\t\t\t\t\t\t\t\t\t\t\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level of 0.05 \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"pearson\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, t= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, S= %g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3))\r\n\t\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that the correlation coefficient in the population \",ifelse(inp$results$p.value<0.05, \"is\", \"may not be\"), \" different from zero. In other words, it is\",\r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$results$p.value<0.05, \" likely\", \" unlikely\"), \" that knowing the value of '\", n[1], \"' allows to predict '\", n[2], \"' and vice versa. \",\r\n\t\t\t\t\t\t\t\t\t\t\t\"The size of the correlation coefficient suggests a \", ifelse(inp$results$estimate<0, \"negative\", \"positive\"), \" correlation, i.e. as one of the variables increases, the other \",\r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$results$estimate<0, \"decreases.\", \"increases as well.\"), \" Additionally, the relationship is \", getCorLab(inp$results$estimate), \r\n\t\t\t\t\t\t\t\t\t\t\t\"; thus, by knowing the value of one of the variables, it is \", ifelse(abs(inp$results$estimate>0.1),\"\", \"not \"), \"possible to predict the value of the other \", getCorLab(inp$results$estimate, rel=F), \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\r\n\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)}\t\t\t\r\n\t\t\t\r\n\t\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\th4(\"Mean comparison\"),\r\n\t\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonColComp\")\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\t\t\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Effect size\"),\r\n\t\t\t\t\t\t\t\t\tplotOutput(\"effectCol\", height=\"200px\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotColComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the values associated with the groups '\", n[1], \"' and '\", n[2], \"' was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level of 0.05 \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"ttest2\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, t= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, W= %g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3))\r\n\t\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that these groups \",ifelse(inp$results$p.value<0.05, \"do not belong\", \"belong\"), \" to the same population, that means that there is \",\r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$results$p.value<0.05, \"a\", \"no\"), \" reason to distinguish between '\", n[1], \"' and '\", n[2], \"' with respect to their value. \",\r\n\t\t\t\t\t\t\t\t\t\t\t\"The effect size as measured by \", names(inp$eff), \" was \", abs(round(inp$eff,2)), \" corresponding to a \", getEffLab(inp$eff), \" effect; in other words the difference between '\", n[1], \"' and '\", n[2], \"' is\", getEffLab(inp$eff), \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\r\n\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)}\t\t\t\r\n\t\t\t\r\n\t\t\telse if (inp$type == \"anova\"){\r\n\r\n\t\t\t\tif (testSet$settings == \"colComp\"){n <- gsub(\"_\", \" \", colnames(testSet$vals))}\r\n\t\t\t\telse if (testSet$settings == \"grComp\"){n <- levels(testSet$Vals[,2])}\r\n\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\tprint(inp$sm)\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\tp(\"One-way analysis of variance (ANOVA)\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(\"Overall result\"),\r\n\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(pval, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\tif (pval<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Comparison of means\"),\r\n\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonColComp\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\r\n\t\t\t\t##############WIP CONTINUE HERE\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotColComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the scores within the groups \", paste(names(inp$ordering), collapse=\", \"), \" was evaluated with a One-way ANOVA\", \r\n\t\t\t\t\t\t\t\t\t\t\". The fact that the p-value yielded by the test was \", ifelse(pval<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\tsprintf(\"(p<%g, F= %g, df= %g and %g) \", max(round(pval, 4), 0.0001), round(inp$sm$fstatistic[1],3), inp$sm$fstatistic[2], inp$sm$fstatistic[3]),\r\n\t\t\t\t\t\t\t\t\t\t\"suggests that \",ifelse(pval<0.05, \"at least one\", \"none\"), \" of these groups\",ifelse(pval<0.05, \"differs\", \"does not substantially differ\"), \" from the rest.\",\r\n\t\t\t\t\t\t\t\t\t\tifelse(input$setContsCol==0, \"\", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(sum(inp$sm$coefficients[-1,4]<0.05)>0, paste(\" Additionally, the following manually preset contrasts were identified as significant (at p<0.05):\"), \"None of the manually preset contrasts were identified as significant (at p<0.05)\")), sep=\"\"),\r\n\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t), \r\n\t\t\t\t\t\t\t\t{\r\n\t\t\t\t\t\t\t\tif (input$setContsCol>0) {\r\n\t\t\t\t\t\t\t\t\tif (sum(inp$sm$coefficients[-1,4]<0.05)>0){\r\n\t\t\t\t\t\t\t\t\t\to2 <- \"

    \"\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\tfor (i in 2:nrow(inp$sm$coefficients)){\r\n\t\t\t\t\t\t\t\t\t\t\tif (inp$sm$coefficients[i,][4]<0.05){\r\n\t\t\t\t\t\t\t\t\t\t\t\to2 <- paste(o2,\"
  • \", \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tinp$conts[i-1],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"
    • \"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\to2 <- paste(o2, \"

\")\r\n\t\t\t\t\t\t\t\t\t\tHTML(o2)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t}\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\r\n\t\t\telse if (inp$type == \"anovaOnRanks\"){\r\n\t\t\t\tprint(\"AOVonRanks\")\r\n\t\t\t\tif (testSet$settings == \"colComp\"){n <- gsub(\"_\", \" \", colnames(testSet$vals))}\r\n\t\t\t\telse if (testSet$settings == \"grComp\"){n <- levels(testSet$Vals[,2])}\r\n\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\tp(\"Kruskal-Wallis test\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(\"Overall result\"),\r\n\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(pval, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\tif (pval<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Comparison of means\"),\r\n\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonColComp\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\r\n\t\t\t\t##############WIP CONTINUE HERE\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotColComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the scores within the groups \", paste(names(inp$ordering), collapse=\", \"), \" was evaluated with the Kruskal-Wallis test as the data did not allow for a parametric test\", \r\n\t\t\t\t\t\t\t\t\t\t\". The fact that the p-value yielded by the test was \", ifelse(pval<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\tsprintf(\"(p<%g, F= %g, df= %g and %g) \", max(round(pval, 4), 0.0001), round(inp$sm$fstatistic[1],3), inp$sm$fstatistic[2], inp$sm$fstatistic[3]),\r\n\t\t\t\t\t\t\t\t\t\t\"suggests that \",ifelse(pval<0.05, \"at least one\", \"none\"), \" of these groups\",ifelse(pval<0.05, \"differs\", \"does not substantially differ\"), \" from the rest.\",\r\n\t\t\t\t\t\t\t\t\t\tifelse(input$setContsCol==0, \"\", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(sum(inp$sm$coefficients[-1,4]<0.05)>0, paste(\" Additionally, the following manually preset contrasts were identified as significant (at p<0.05):\"), \"None of the manually preset contrasts were identified as significant (at p<0.05)\")), sep=\"\"),\r\n\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t), \r\n\t\t\t\t\t\t\t\t{\r\n\t\t\t\t\t\t\t\tif (input$setContsCol>0) {\r\n\t\t\t\t\t\t\t\t\tif (sum(inp$sm$coefficients[-1,4]<0.05)>0){\r\n\t\t\t\t\t\t\t\t\t\to2 <- \"

    \"\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\tfor (i in 2:nrow(inp$sm$coefficients)){\r\n\t\t\t\t\t\t\t\t\t\t\tif (inp$sm$coefficients[i,][4]<0.05){\r\n\t\t\t\t\t\t\t\t\t\t\t\to2 <- paste(o2,\"
  • \", \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tinp$conts[i-1],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"
    • \"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\to2 <- paste(o2, \"

\")\r\n\t\t\t\t\t\t\t\t\t\tHTML(o2)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t}\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\t\t\t\t\t\t\r\n\t\t\t}\r\n\t\telse {invisible()}\t\t\t\r\n\t\t})\r\n\t\t\r\n\toutput$testOutputGrComp <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\tif (testSet$settings==\"grComp\"){\r\n\t\t\t\t\r\n\t\t\treq(testRes()$type, testSet$vals)\r\n\t\t\t# a <- anovaGroups$finished\t\t\r\n\t\t\t\r\n\t\t\tinp <- testRes()\r\n\t\t\t# print\r\n\t\t\tprint(inp)\r\n\t\t\t\r\n\t\t\tif (inp$type %in% c(\"chisq2\", \"fisher2\")) {\r\n\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(paste(\"p-value:\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Comparison\"),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonGrComp\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotGrComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the variables \", paste(sort(c(inp$larger, inp$smaller)), collapse=\", \"), \" was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"chisq2\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p< %g, chi-squared= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that the distribution of these variables \",ifelse(inp$results$p.value<0.05, \"differs\", \"does not substantially differ\"), \" between \",\r\n\t\t\t\t\t\t\t\t\t\t\tn[1], \" and \", n[2], \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"chisq\", \"fisher\")) {\r\n\t\t\t\t\tn <- levels(testSet$vals[,2])\r\n\t\t\t\t\tprint(\"Drawinng....\")\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(paste(\"p-value:\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Comparison\"),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonGrComp\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotGrComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the variables \", paste(n, collapse=\", \"), \" was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"chisq\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p< %g, chi-squared= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that the distribution of these variables \",ifelse(inp$results$p.value<0.05, \"differs\", \"does not substantially differ\"), \" between \",\r\n\t\t\t\t\t\t\t\t\t\t\tn[1], \" and \", n[2], \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tif (!is.null(testSet$manConts)){\r\n\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\t\th4(\"Manual contrasts\"),\r\n\t\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\t\tp(\"The following manually set contrasts were significant:\",\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\tHTML(paste(\r\n\t\t\t\t\t\t\t\t\t\t\t\"
    \",\r\n\t\t\t\t\t\t\t\t\t\t\tpaste(collapse=\"
  • \")\r\n\t\t\t\t\t\t\t\t\t\t\t,\"
\"\r\n\t\t\t\t\t\t\t\t\t\t\t))\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t}\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t}\t\t\t\r\n\t\t\t\t\t\t\t\t\t\r\n\t\t\telse if (inp$type == \"applesToOranges\") {\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"ERROR\"),\r\n\t\t\t\t\t\t\t\tp(\"You are probably comparing columns which contain different data. It is not possible to compare the distribution in the columns as they do not contain the same variables.\"),\r\n\t\t\t\t\t\t\t\tp(\"Change the analysis type, or use the button in the top left corner to select different data.\"),\r\n\t\t\t\t\t\t\t\timg(src=\"aToO.jpg\", width=\"100%\")\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", levels(testSet$vals[,2]))\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(inp$results$p.value, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\t\tif (inp$results$p.value<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\twellPanel(align=\"center\",\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\th4(\"Mean comparison\"),\r\n\t\t\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonGrComp\")\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\t\t\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Effect size\"),\r\n\t\t\t\t\t\t\t\t\tplotOutput(\"effectGr\", height=\"200px\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotGrComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the values associated with the groups \", n[1], \" and \", n[2], \" was evaluated with \", \r\n\t\t\t\t\t\t\t\t\t\t\tinp$results$method,\". The fact that the p-value yielded by the test was \", ifelse(inp$results$p.value<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\t\" the significance level of 0.05 \", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$type==\"ttest2\", \r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, t= %g, df=%g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3), inp$results$parameter),\r\n\t\t\t\t\t\t\t\t\t\t\t\tsprintf(\"(p= %g, W= %g) \", max(round(inp$results$p.value, 4), 0.0001), round(inp$results$statistic, 3))\r\n\t\t\t\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\t\t\t\t\"suggests that these groups \",ifelse(inp$results$p.value<0.05, \"do not belong\", \"belong\"), \" to the same population, that means that there is \",\r\n\t\t\t\t\t\t\t\t\t\t\tifelse(inp$results$p.value<0.05, \"a\", \"no\"), \" reason to distinguish between \", n[1], \" and \", n[2], \"with respect to their value. \",\r\n\t\t\t\t\t\t\t\t\t\t\t\"The effect size as measured by \", names(inp$eff), \" was \", abs(round(inp$eff,2)), \" corresponding to a \", getEffLab(inp$eff), \" effect; in other words the difference between \", n[1], \" and \", n[2], \"is\", getEffLab(inp$eff), \".\",\r\n\t\t\t\t\t\t\t\t\t\t\tsep=\"\"),\r\n\t\t\t\t\t\t\t\t\t\tfixed=T)\r\n\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t)\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\t\t\t\r\n\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\r\n\t\t\telse if (inp$type == \"anova\"){\r\n\r\n\t\t\t\tn <- levels(testSet$Vals[,2])\r\n\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\tp(\"One-way analysis of variance (ANOVA)\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(\"Overall result\"),\r\n\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(pval, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\tif (pval<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Comparison of means\"),\r\n\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonGrComp\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\r\n\t\t\t\t##############WIP CONTINUE HERE\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotGrComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the scores within the groups \", paste(names(inp$ordering), collapse=\", \"), \" was evaluated with a One-way ANOVA\", \r\n\t\t\t\t\t\t\t\t\t\t\". The fact that the p-value yielded by the test was \", ifelse(pval<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\tsprintf(\"(p<%g, F= %g, df= %g and %g) \", max(round(pval, 4), 0.0001), round(inp$sm$fstatistic[1],3), inp$sm$fstatistic[2], inp$sm$fstatistic[3]),\r\n\t\t\t\t\t\t\t\t\t\t\"suggests that \",ifelse(pval<0.05, \"at least one\", \"none\"), \" of these groups \",ifelse(pval<0.05, \"differs\", \"substantially differs\"), \" from the rest.\",\r\n\t\t\t\t\t\t\t\t\t\tifelse(input$setContsGr==0, \"\", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(sum(inp$sm$coefficients[-1,4]<0.05)>0, paste(\" Additionally, the following manually preset contrasts were identified as significant (at p<0.05):\"), \"None of the manually preset contrasts were identified as significant (at p<0.05)\")), sep=\"\"),\r\n\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t), \r\n\t\t\t\t\t\t\t\t{\r\n\t\t\t\t\t\t\t\tif (input$setContsCol>0) {\r\n\t\t\t\t\t\t\t\t\tif (sum(inp$sm$coefficients[-1,4]<0.05)>0){\r\n\t\t\t\t\t\t\t\t\t\to2 <- \"

    \"\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\tfor (i in 2:nrow(inp$sm$coefficients)){\r\n\t\t\t\t\t\t\t\t\t\t\tif (inp$sm$coefficients[i,][4]<0.05){\r\n\t\t\t\t\t\t\t\t\t\t\t\to2 <- paste(o2,\"
  • \", \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tinp$conts[i-1],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"
    • \"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\to2 <- paste(o2, \"

\")\r\n\t\t\t\t\t\t\t\t\t\tHTML(o2)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t}\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\r\n\t\t\telse if (inp$type == \"anovaOnRanks\"){\r\n\r\n\t\t\t\tn <- levels(testSet$Vals[,2])\r\n\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\ttagList(\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\tp(\"Kruskal-Wallis test\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(\"Overall result\"),\r\n\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(pval, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\tif (pval<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")}\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\th4(\"Comparison of means\"),\r\n\t\t\t\t\t\t\t\tuiOutput(\"freqComparisonGrComp\")\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\r\n\t\t\t\t#############WIP CONTINUE HERE\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\t\tcolumn(3, tableOutput(\"testTable\")),\r\n\t\t\t\t\t\t\t\t\tcolumn(1),\r\n\t\t\t\t\t\t\t\t\tcolumn(8, plotOutput(\"testPlotGrComp\"))\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\r\n\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\tp(gsub(\" .\", \".\",\r\n\t\t\t\t\t\t\t\t\tpaste(\"The distribution of the scores within the groups \", paste(names(inp$ordering), collapse=\", \"), \" was evaluated with the Kruskal-Wallis test as the data did not allow for a parametric test\", \r\n\t\t\t\t\t\t\t\t\t\t\". The fact that the p-value yielded by the test was \", ifelse(pval<0.05, \"below\", \"above\"), \r\n\t\t\t\t\t\t\t\t\t\t\" the significance level \", \r\n\t\t\t\t\t\t\t\t\t\tsprintf(\"(p<%g, F= %g, df= %g and %g) \", max(round(pval, 4), 0.0001), round(inp$sm$fstatistic[1],3), inp$sm$fstatistic[2], inp$sm$fstatistic[3]),\r\n\t\t\t\t\t\t\t\t\t\t\"suggests that \",ifelse(pval<0.05, \"at least one\", \"none\"), \" of these groups\",ifelse(pval<0.05, \"differs\", \"does not substantially differ\"), \" from the rest.\",\r\n\t\t\t\t\t\t\t\t\t\tifelse(input$setContsGr==0, \"\", \r\n\t\t\t\t\t\t\t\t\t\t\tifelse(sum(inp$sm$coefficients[-1,4]<0.05)>0, paste(\" Additionally, the following manually preset contrasts were identified as significant (at p<0.05):\"), \"None of the manually preset contrasts were identified as significant (at p<0.05)\")), sep=\"\"),\r\n\t\t\t\t\t\t\t\t\tfixed=T)\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t), \r\n\t\t\t\t\t\t\t\t{\r\n\t\t\t\t\t\t\t\tif (input$setContsCol>0) {\r\n\t\t\t\t\t\t\t\t\tif (sum(inp$sm$coefficients[-1,4]<0.05)>0){\r\n\t\t\t\t\t\t\t\t\t\to2 <- \"

    \"\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\tfor (i in 2:nrow(inp$sm$coefficients)){\r\n\t\t\t\t\t\t\t\t\t\t\tif (inp$sm$coefficients[i,][4]<0.05){\r\n\t\t\t\t\t\t\t\t\t\t\t\to2 <- paste(o2,\"
  • \", \r\n\t\t\t\t\t\t\t\t\t\t\t\t\tinp$conts[i-1],\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"
    • \"\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t\to2 <- paste(o2, \"

\")\r\n\t\t\t\t\t\t\t\t\t\tHTML(o2)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t}\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\t}\t\t\t\r\n\r\n\t\t\t}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\r\n\toutput$testOutputMod <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\t\r\n\t\tif (testSet$settings == \"mod\"){\r\n\t\t\treq(testRes()$type)\r\n\t\t\t\r\n\t\t\tinp <- testRes()\r\n\t\t\t# print(inp)\r\n\t\t\tif (inp$type == \"linreg\"){\r\n\t\t\t\t\tn <- gsub(\"_\", \" \", colnames(testSet$vals))\r\n\t\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\t\ttagList(\r\n\t\t\t\t\t\tif(length(inp$lin)>0){\r\n\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\t\t\twellPanel(class=\"border-danger\", align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th4(\"Warning: Non-linearity\"),\r\n\t\t\t\t\t\t\t\t\t\tp(paste(\"The relationship between \", colnames(inp$results$model)[1], \" and \", paste(inp$lin, collapse=\", \"), \" is probably not a straight line, but rather a curve. More advanced models would probably be better capable of modelling your data.\", sep=\"\"))\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t},\r\n\t\t\t\t\t\tif (length(inp$mColl)>0){\r\n\t\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\t\tcolumn(3),\r\n\t\t\t\t\t\t\t\tcolumn(6,\r\n\t\t\t\t\t\t\t\t\twellPanel(class=\"border-danger\", align=\"center\",\r\n\t\t\t\t\t\t\t\t\t\th4(\"Warning: Collinearity\"),\r\n\t\t\t\t\t\t\t\t\t\tp(paste(\"The variables \", paste(inp$mColl, collapse=\", \"), \" are strongly related to each other. As a consequence, the evaluation of their significance and their predictions of the value of \", colnames(inp$results$model)[1], \" are unreliable. The significance of the model overall, however, is unaffected.\", sep=\"\"))\r\n\t\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\tcolumn(3)\r\n\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t},\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\t\t\t\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the test\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Test\"),\r\n\t\t\t\t\t\t\t\t\tp(inp$results$method),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(sprintf(\"p-value: %g\", max(round(pval, 4), 0.0001))),\r\n\t\t\t\t\t\t\t\t\tif (pval<0.05) {p(style=\"color: green; font-weight:400\", \"significant\")\r\n\t\t\t\t\t\t\t\t\t\t} else {p(style=\"color: green; font-weight:400\", \"not significant\")},\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tp(sprintf(\"R2: %g\", max(round(inp$sm$r.squared, 4), 0.0001)))\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(4,\r\n\t\t\t\t\t\t\t\t#Summarize the data\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\th4(\"Quick overview\"),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"coefOverview\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tfluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(align=\"center\",\r\n\t\t\t\t\t\t\t\t\tplotOutput(\"testPlotMod\")\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t,fluidRow(\r\n\t\t\t\t\t\t\tcolumn(2),\r\n\t\t\t\t\t\t\tcolumn(8,\r\n\t\t\t\t\t\t\t\twellPanel(\r\n\t\t\t\t\t\t\t\t\th4(\"Summary\"),\r\n\t\t\t\t\t\t\t\t\tactionLink(\"linregSumHelp\", \"How to read this?\"),\r\n\t\t\t\t\t\t\t\t\tbr(),\r\n\t\t\t\t\t\t\t\t\tuiOutput(\"fullSummaryMod\")\r\n\t\t\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t\t\t),\r\n\t\t\t\t\t\t\tcolumn(2)\t\t\t\r\n\t\t\t\t\t\t\t)\r\n\t\t\t\t\t\t)\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\telse {tagList(\r\n\t\t\t\tfluidRow(\r\n\t\t\t\t\tp(\"Not implemented yet\")\r\n\t\t\t\t\t)\r\n\t\t\t\t)}\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse {invisible()}\r\n\t\t})\r\n\t\r\n\tobserveEvent(input$linregSumHelp, {\r\n\t\tshowModal(\r\n\t\t\tmodalDialog(\r\n\t\t\t\tsize=\"l\",\r\n\t\t\t\ttitle=\"Reading the summary\",\r\n\t\t\t\tp(\"This summary first informs you about the model overall. A model that is significant can explain the distribution of your predicted variable better than its mean. The extent to which this model is better is expressed by the R-squared value, which can be between 0-1, the higher the better. In social sciences, R-squared values above 0.5 are usually considered good, as humans are harder to predict than physical processes.\"),\r\n\t\t\t\tp(\"The intercept tells you about the value of the predicted variable a theoretical case would have, if all of its numeric characteristics equaled to zero and categories to default. As this interface takes the alphabetically first category as default, you may need to rename them in order to get the right one recognized as default, e.g. 'college, elementary, high' to '1_elementary, 2_high, 3_college'\"),\r\n\t\t\t\tp(\"If the intercept is significant it means that it is significantly different from the mean, i.e. it makes sense to have it in the model.\"),\r\n\t\t\t\tp(\"The coefficients express the influence each of your predictors has on the outcome. The value in the column 'coefficient' is the number which you should multiply by the value of the predictor (for numbers) or 1 (for categories) and add to the intercept to obtain the prediction for the given case. E.g. if intercept is 0, coefficient of IQ for test scores is 0.15 and for educationNone -1.35, the predicted value for an uneducated participant with IQ 100 would be: 0 (intercept) + 100*0.15 (IQ) + -1.35 (education) --> 13.65\"),\r\n\t\t\t\tp(\"The standardized coefficients translate the values from specific units (IQ points, centimeters) to relative units (standard deviations) which allows the comparison of the individual predictors: the further from 0 the standardized coefficient, the stronger the influence of that predictor on the outcome.\"),\r\n\t\t\t\tp(\"The columns Std. error, and t-value are additional statistics from which the significance of that predictor is calculated and you should report them if you report the p-value.\"),\r\n\t\t\t\tp(\"The significance of individual predictors tells whether they are significantly different from 0. If they were not, it would not be clear whether adjusting for this predictor is better than just taking the mean value.\")\t\t\t\t\r\n\t\t\t\t)\r\n\t\t\r\n\t\t\t)\r\n\t\r\n\t\t})\r\n\t\t\r\n\toutput$freqComparisonColComp <- renderUI({\r\n\t\treq(testSet$settings)\t\t\r\n\t\tif (testSet$settings==\"colComp\"){\t\t\t\r\n\t\t\tfrqComp()}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\t\t\r\n\toutput$freqComparisonGrComp <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\tif (testSet$settings==\"grComp\"){frqComp()}\r\n\t\telse {invisible()}\r\n\t\t})\r\n\t\t\r\n\toutput$coefOverview <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\tif (testSet$settings==\"mod\"){frqComp()}\r\n\t\telse {invisible()}\t\r\n\t\t})\t\r\n\t\t\r\n\toutput$fullSummaryMod <- renderUI({\r\n\t\treq(testSet$settings)\r\n\t\tif (testSet$settings==\"mod\"){\r\n\t\t\treq(testRes(), testSet$vals)\r\n\t\t\tinp <- testRes()\r\n\t\t\tn <- colnames(testSet$vals)\r\n\t\t\tif (inp$type == \"linreg\"){\r\n\t\t\t\tpval <- 1-pf(inp$sm$fstatistic[1], inp$sm$fstatistic[2], inp$sm$fstatistic[3])\r\n\t\t\t\tsmtb <- as.data.frame(inp$sm$coefficients)\r\n\t\t\t\tsmtb$pred <- rownames(smtb)\r\n\t\t\t\tintercept <- smtb[1,]\r\n\t\t\t\tsmtb <- smtb[-1,]\r\n\t\t\t\tsigs <- smtb[smtb[,5]<0.05,]\r\n\t\t\t\tsigs <- sigs[order(abs(sigs[,2]), decreasing=T),]\r\n\t\t\t\tnsigs <- smtb[smtb[,5]>=0.05,]\r\n\t\t\t\tnsigs <- nsigs[order(abs(nsigs[,2]), decreasing=T),]\r\n\t\t\t\tpredcats <- colnames(inp$results$model)[-1]\r\n\t\t\t\tprednums <- predcats[predcats %in% cookedData$nums]\r\n\t\t\t\tpredcats <- predcats[predcats %in% cookedData$cats]\r\n\t\t\t\tdefs <- unlist(lapply(predcats, function(i){levels(testSet$vals[,i])[1]}))\r\n\t\t\t\tdefs <- paste(predcats, defs)\r\n\t\t\t\tdefs <- gsub(\" ^\", \"\", defs)\r\n\t\t\t\tdefs <- gsub(\" \", \" is \", defs)\r\n\t\t\t\tdefs <- paste(defs, collapse=\", \")\r\n\t\t\t\t\r\n\t\t\t\to <- paste(\"

\",paste(toupper(substring(colnames(inp$results$model)[1],1,1)), substring(colnames(inp$results$model)[1],2), collapse=\"\", sep=\"\") , \" was modelled with \", paste(colnames(inp$results$model)[-1], collapse=\", \"), \" as \", ifelse((ncol(inp$results$model)-1)>1, \"predictors.\", \"predictor.\"), \" The model is\", \r\n\t\t\t\t\tifelse(pval<0.05, \"\", \" not\" ), \" significant overall \",sprintf(\"(p<%g, F=%g, df=%g and %g)\", max(round(pval,4),0.0001), max(round(inp$sm$fstatistic[1],4),0.0001), max(round(inp$sm$fstatistic[2],4),0.0001), max(round(inp$sm$fstatistic[3]),4),0.0001),\r\n\t\t\t\t\t\", suggesting that it is\", ifelse(pval<0.05, \"\", \" not\" ), \" a better predictor of \", colnames(inp$results$model)[1], \" than its mean value. The r-squared is \",\r\n\t\t\t\t\tmax(round(inp$sm$r.squared,4),0.0001), \" meaning that the predictors can explain \", 100*max(round(inp$sm$r.squared,4),0.0001),\"% of the variation around the mean.

\",\r\n\t\t\t\t\t\"

The intercept (the value of \", colnames(inp$results$model)[1], \" if\", \r\n\t\t\t\t\tifelse(length(prednums>0), paste(\" \", paste(prednums, collapse=\", \"), ifelse(length(prednums)>1, \" are \", \" is \" ), \"equal to zero\", sep=\"\"), \"\"),\r\n\t\t\t\t\tifelse((length(prednums)>0 & length(predcats)>0), \" and \", \" \"),\r\n\t\t\t\t\tifelse(length(predcats>0), defs, \"\"),\r\n\t\t\t\t\t\") was \", round(intercept[1],4),\"

\", sep=\"\", collapse=\"\")\r\n\t\t\t\tif (length(inp$lin>0)){o <- paste(o, \"

Since some of the predictors are non-linearly related to the outcome variable, the model fit measured by R-squared does not entirely represent the amount of variation around the mean that the they could explain.


\")}\r\n\t\t\t\tif (length(inp$mColl>0)){o <- paste(o, \"

Since the predictors: \", paste(inp$mColl, collapse=\", \"), \"are affected by collinearity, their coefficients and significance values are unreliable.


\")}\r\n\t\t\t\t\r\n\t\t\t\to <- paste(o, \"

Following significant predictors were identified (ordered by relative importance expressed by standardized coefficients)

\")\r\n\t\t\t\to <- paste(o, '')\r\n\t\t\t\to <- paste(o, '')\r\n\t\t\t\tif (nrow(sigs) > 0){\r\n\t\t\t\t\tfor (li in 1:nrow(sigs)){\r\n\t\t\t\t\t\tl <- sigs[li,]\r\n\t\t\t\t\t\to <- paste(o, sprintf('', l[6], l[1], l[2], l[3], l[4], l[5]))}\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\t\to <- paste(o, \"\")\r\n\t\t\t\to <- paste(o, '')\t\t\t\t\r\n\t\t\t\tif (nrow(nsigs) > 0){\r\n\t\t\t\t\tfor (li in 1:nrow(nsigs)){\r\n\t\t\t\t\t\tl <- nsigs[li,]\r\n\t\t\t\t\t\to <- paste(o, sprintf('', l[6], l[1], l[2], l[3], l[4], l[5]))}\t\t\r\n\t\t\t\t\t}\r\n\t\t\t\to <- paste(o, \"
PredictorCoefficientStandardized coefficientStd.errort-valuep-value
%s%g%g%g%g%g

Following non-significant predictors were identified (ordered by relative importance expressed by standardized coefficients)

PredictorCoefficientStandardized coefficientStd.errort-valuep-value
%s%g%g%g%g%g
\")\t\r\n\t\t\t\tHTML(o)\r\n\t\t\t\t}\t\t\r\n\t\t\t}\r\n\t\telse {invisible()}\t\r\n\t\t})\t\r\n\t\t\r\n\tfrqComp <- reactive({\r\n\t\treq(testRes(), testSet$vals)\r\n\t\tinp <- testRes()\r\n\t\tn <- colnames(testSet$vals)\r\n\t\t\r\n\t\t# a <- colnames(testSet$vals)\r\n\t\t\t\t\r\n\t\tif (inp$type %in% c(\"fisher2\", \"chisq2\")) {\t\t\t\r\n\t\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\t\to <- paste(\"

Compared to\", n[1], \"the following changes occur in\", n[2], \"


\")\r\n\t\t\t\to <- paste(o, '
    ')\r\n\r\n\t\t\t\tfor (li in inp$larger){\r\n\t\t\t\t\to <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t\r\n\t\t\t\tfor (li in inp$smaller){\t\r\n\t\t\t\t\to <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\to <- paste(o, \"
\")\r\n\t\t\r\n\t\t\t\tHTML(o)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (testSet$settings == \"grComp\"){\r\n\t\t\t\tn <- levels(testSet$Vals[,2])\r\n\t\t\t\to <- paste(\"

Compared to\", n[1], \"the following changes occur in\", n[2], \"


\")\r\n\t\t\t\to <- paste(o, '
    ')\r\n\r\n\t\t\t\tfor (li in inp$larger){\r\n\t\t\t\t\to <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t\r\n\t\t\t\tfor (li in inp$smaller){\t\r\n\t\t\t\t\to <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\to <- paste(o, \"
\")\r\n\t\t\r\n\t\t\t\tHTML(o)\t\t\t\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\t# WIP\r\n\t\telse if (inp$type %in% c(\"fisher\", \"chisq\")) {\r\n\t\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\t\to <- paste(\"

Compared to\", n[1], \"the following changes occur in\", n[2], \"


\")\r\n\t\t\t\t# o <- paste(o, '
    ')\r\n\r\n\t\t\t\t# for (li in inp$larger){\r\n\t\t\t\t\t# o <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t\r\n\t\t\t\t# for (li in inp$smaller){\t\r\n\t\t\t\t\t# o <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t# o <- paste(o, \"
\")\r\n\t\t\r\n\t\t\t\tHTML(o)\r\n\t\t\t\t}\r\n\t\t\t\t\r\n\t\t\telse if (testSet$settings == \"grComp\"){\r\n\t\t\t\tn <- levels(testSet$Vals[,2])\r\n\t\t\t\to <- paste(\"

Compared to\", n[1], \"the following changes occur in\", n[2], \"


\")\r\n\t\t\t\t# o <- paste(o, '
    ')\r\n\r\n\t\t\t\t# for (li in inp$larger){\r\n\t\t\t\t\t# o <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t\r\n\t\t\t\t# for (li in inp$smaller){\t\r\n\t\t\t\t\t# o <- paste(o, '
  • ', li, \"
    • \")}\r\n\t\t\t\t\t# o <- paste(o, \"
\")\r\n\t\t\r\n\t\t\t\tHTML(o)\t\t\t\r\n\t\t\t}\r\n\t\t}\r\n\t\t\r\n\t\t# else if (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\r\n\t\t\t# o <- '
    '\r\n\r\n\t\t\t# o <- paste(o, '
  • ', sprintf(\"%s: %g\", names(inp$larger), round(inp$larger, 4)), \"
    • \")\t\t\t\r\n\t\t\t# o <- paste(o, '
  • ', sprintf(\"%s: %g\", names(inp$smaller), round(inp$smaller,4)), \"
    • \")\r\n\t\t\t# o <- paste(o, \"
\")\r\n\t\t\t# o <- paste(o, '
')\r\n\r\n\t\t\t# }\r\n\t\t\t\r\n\t\telse if (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\t\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\t\to <- '

'\r\n\r\n\t\t\t\to <- paste(o, '', sprintf(\"%s: %g\", names(inp$larger), round(inp$larger, 4)), \"
\")\t\t\t\r\n\t\t\t\to <- paste(o, '', sprintf(\"%s: %g\", names(inp$smaller), round(inp$smaller,4)), \"
\")\r\n\t\t\t\tHTML(o)\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\telse if (testSet$settings == \"grComp\"){\r\n\t\t\t\to <- '

'\r\n\r\n\t\t\t\to <- paste(o, '', sprintf(\"%s: %g\", names(inp$larger), round(inp$larger, 4)), \"
\")\t\t\t\r\n\t\t\t\to <- paste(o, '', sprintf(\"%s: %g\", names(inp$smaller), round(inp$smaller,4)), \"
\")\r\n\t\t\t\tHTML(o)\t\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (inp$type == \"anova\"){\r\n\t\t\t\to <- '

    '\r\n\t\t\t\tfor (li in 1:length(inp$ordering)) {o <- paste(o, sprintf(\"
  1. %s: %g
    2. \", names(inp$ordering[li]), round(inp$ordering[[li]], 4)), \"
    \")\t}\r\n\t\t\t\to <- paste(o, \"

\")\r\n\r\n\t\t\t\tHTML(o)\t\t\t\t\t\t\t\r\n\t\t\t}\r\n\r\n\t\telse if (inp$type == \"anovaOnRanks\"){\r\n\t\t\t\to <- '

    '\r\n\t\t\t\tfor (li in 1:length(inp$ordering)) {o <- paste(o, sprintf(\"
  1. %s: %g
    2. \", names(inp$ordering[li]), round(inp$ordering[[li]], 4)), \"
    \")\t}\r\n\t\t\t\to <- paste(o, \"

\")\r\n\r\n\t\t\t\tHTML(o)\t\t\t\t\t\t\t\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (inp$type == \"linreg\"){\r\n\t\t\tsmtb <- as.data.frame(inp$sm$coefficients)\r\n\t\t\tsmtb$pred <- rownames(smtb)\r\n\t\t\tintercept <- smtb[1,]\r\n\t\t\tsmtb <- smtb[-1,]\r\n\t\t\tlarger <- smtb[smtb$Estimate>0,]\r\n\t\t\tlarger <- larger[order(abs(larger[,2]), decreasing=T),]\r\n\t\t\tsmaller <- smtb[smtb$Estimate<=0,]\r\n\t\t\tsmaller <- smaller[order(abs(smaller[,2]), decreasing=T),]\r\n\t\t\t# print(larger)\r\n\t\t\t# print(smaller)\r\n\t\t\to <- paste(\"

Following influences have been identified. (Ordered by relative importance, bold predictors are significant)


\")\r\n\t\t\to <- paste(o, '
    ')\r\n\t\t\t\r\n\t\t\tif (nrow(larger) > 0){\r\n\t\t\t\tfor (li in 1:nrow(larger)){\r\n\t\t\t\t\tl <- larger[li,]\r\n\t\t\t\t\to <- paste(o, '
  • ', ifelse(l[5]<0.05, sprintf(\"%s: %g\", l[6], round(l[1], 4)), sprintf(\"%s: %g\", l[6], round(l[1],4))), \"
    • \")}\r\n\t\t\t\t\t\t\t\t\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\tif (nrow(smaller) > 0){\r\n\t\t\t\tfor (li in 1:nrow(smaller)){\r\n\t\t\t\t\tl <- smaller[li,]\r\n\t\t\t\t\to <- paste(o, '
  • ', ifelse(l[5]<0.05, sprintf(\"%s: %g\", l[6], round(l[1], 4)), sprintf(\"%s: %g\", l[6], round(l[1],4))), \"
    • \")}\r\n\t\t\t\t\to <- paste(o, \"
\")\r\n\t\t\t\t}\t\t\r\n\t\t\tHTML(o)\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t}\r\n\t\t})\r\n\t\t\t\t\r\n\toutput$testTable <- renderTable(rownames=T, {\r\n\t\treq(testRes())\r\n\t\tinp <- testRes()\r\n\t\t\r\n\t\tif (inp$type %in% c(\"fisher2\", \"chisq2\")) {\r\n\t\t\tinp$results$observed\r\n\t\t\t}\r\n\t\t\t\r\n\t\telse if (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\t\t\tinp$sumTab\r\n\t\t\t}\r\n\t\t})\r\n\t\t\r\n\toutput$testPlotColComp <- renderPlot({\r\n\t\treq(testRes(), testSet$settings)\r\n\r\n\t\tif (testSet$settings== \"colComp\"){\r\n\t\t\tinp <- testRes()\r\n\t\t\t\r\n\t\t\tif (inp$type %in% c(\"fisher2\", \"chisq2\", \"fisher\", \"chisq\", \"ttest2\", \"wilcox2\", \"anova\", \"anovaOnRanks\", \"spearman\", \"pearson\")) {\r\n\t\t\t\tinp$plot + theme(text=element_text(family=ifelse(settings$serif==\"serif\", \"serif\", \"sans\")))\r\n\t\t\t\t}\t\t\r\n\t\t\t}\r\n\t\telse {invisible()}\t\t\r\n\t\t})\r\n\t\t\t\r\n\toutput$testPlotGrComp <- renderPlot({\r\n\t\treq(testRes(), testSet$settings)\r\n\r\n\t\tif (testSet$settings== \"grComp\"){\r\n\t\t\tinp <- testRes()\r\n\t\t\t\r\n\t\t\tif (inp$type %in% c(\"fisher2\", \"chisq2\", \"fisher\", \"chisq\", \"ttest2\", \"wilcox2\", \"anova\", \"anovaOnRanks\")) {\r\n\t\t\t\tinp$plot + theme(text=element_text(family=ifelse(settings$serif==\"serif\", \"serif\", \"sans\")))\r\n\t\t\t\t}\t\t\r\n\t\t\t\r\n\t\t\t}\r\n\t\telse {invisible()}\t\t\r\n\t\t})\r\n\r\n\toutput$testPlotMod <- renderPlot({\r\n\t\treq(testRes(), testSet$settings)\r\n\t\tif (testSet$settings== \"mod\"){\r\n\t\t\tinp <- testRes()\r\n\t\t\tprint(\"MODELLING\")\r\n\t\t\t# print(inp$type)\r\n\t\t\tif (inp$type %in% c(\"linreg\")) {\r\n\t\t\t\tinp$plot + theme(text=element_text(family=ifelse(settings$serif==\"serif\", \"serif\", \"sans\")))\r\n\t\t\t\t}\r\n\t\t\telse {invisible()}\t\r\n\t\t\t\t\r\n\t\t\t}\r\n\t\telse {invisible()}\t\t\r\n\t\t})\r\n\t\t\t\t\r\n\toutput$effectCol <- renderPlot({\r\n\t\treq(testRes())\r\n\t\tinp <- testRes()\r\n\t\tprint(\"EFFECT\")\r\n\t\t# print(inp$results$estimate)\t\r\n\t\t# print(inp$method)\r\n\t\t\r\n\t\tif (testSet$settings == \"colComp\"){\r\n\t\t\tif (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\t\t\t\teffPlot(inp$eff, names(inp$eff))\r\n\t\t\t\t}\r\n\t\t\t\r\n\t\t\telse if (inp$type %in% c(\"spearman\", \"pearson\")) {\r\n\t\t\t\t# print(inp$results$estimate)\r\n\t\t\t\t# print(inp$method)\r\n\t\t\t\tcorPlot(inp$results$estimate, type=inp$type)\r\n\t\t\t\t}\r\n\t\t\t}\r\n\r\n\t\t})\r\n\t\r\n\toutput$effectGr <- renderPlot({\r\n\t\treq(testRes())\r\n\t\tinp <- testRes()\r\n\t\tprint(\"EFFPLOT\")\r\n\t\t# print(inp)\r\n\t\tif(testSet$settings == \"grComp\"){\r\n\t\t\tif (inp$type %in% c(\"ttest2\", \"wilcox2\")) {\r\n\t\t\t\teffPlot(inp$eff, names(inp$eff))\r\n\t\t\t\t}\r\n\t\t\t}\t\t\t\t\t\r\n\t\t})\t\r\n\t\r\n})\r\n", "meta": {"hexsha": "2fa517a3ceaee8a33d4987c68b629bd214055af5", "size": 135147, "ext": "r", "lang": "R", "max_stars_repo_path": "server.r", "max_stars_repo_name": "zameji/Freddie", "max_stars_repo_head_hexsha": "ace6019c1b91a30f8903f63f33f6553a6cd7d5b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "server.r", "max_issues_repo_name": "zameji/Freddie", "max_issues_repo_head_hexsha": "ace6019c1b91a30f8903f63f33f6553a6cd7d5b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "server.r", "max_forks_repo_name": "zameji/Freddie", "max_forks_repo_head_hexsha": "ace6019c1b91a30f8903f63f33f6553a6cd7d5b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.4850727226, "max_line_length": 534, "alphanum_fraction": 0.5523023079, "num_tokens": 40731, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555714, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.31573875727153}} {"text": "ddplot <- function(drp, well = 1, channel = 1,...){\r\n drp1 <- if(channel == 1){na.omit(drp$Ch1[, well])}else{na.omit(drp$Ch2[, well])}\r\n temp <- sample(c(1:length(drp1)), size = length(drp1), replace = F)\r\n plot(drp1 ~ temp, col = rgb(123, 168, 194, maxColorValue = 255), bty = \"L\", pch = '.' , cex = 5, xlab = \"observation\", ylab = \"FU\", ...)}\r\n\r\ntdplot <- function(drp, well = 1,...){\r\n drp1 <- na.omit(drp$Ch1[, well])\r\n drp2 <- na.omit(drp$Ch2[, well])\r\n plot(drp1 ~ drp2, col = rgb(123, 168, 194, maxColorValue = 255), bty = \"L\", pch = '.' , cex = 5, xlab = \"FU_Ch2\", ylab = \"FU_Ch1\", ...)}\r\n", "meta": {"hexsha": "933692bf130e2b7cb61343d891ea42bf630a21d8", "size": 627, "ext": "r", "lang": "R", "max_stars_repo_path": "MonoColor ddPlot.r", "max_stars_repo_name": "Gromgorgel/ddPCR", "max_stars_repo_head_hexsha": "0d403e7b15f19586f432b4f04cc57121f753d215", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-01-02T11:49:28.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-22T12:25:28.000Z", "max_issues_repo_path": "MonoColor ddPlot.r", "max_issues_repo_name": "Gromgorgel/ddPCR", "max_issues_repo_head_hexsha": "0d403e7b15f19586f432b4f04cc57121f753d215", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-05-16T07:20:02.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-16T07:20:02.000Z", "max_forks_repo_path": "MonoColor ddPlot.r", "max_forks_repo_name": "Gromgorgel/ddPCR", "max_forks_repo_head_hexsha": "0d403e7b15f19586f432b4f04cc57121f753d215", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-05-15T11:28:41.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-19T11:50:12.000Z", "avg_line_length": 62.7, "max_line_length": 144, "alphanum_fraction": 0.5342902711, "num_tokens": 245, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.3157387487413704}} {"text": "# BiocManager::install(c(\"minfi\",\"minfiData\"))\nlibrary(minfi)\n# specify directory\n#minfi_baseDir = paste0(\"/Users/nrigby/Desktop/idats_standard/batch_1052641/\")\nbaseDir = paste0(\"/Users/mmaxmeister/methylprep/docs/example_data/GSE69852/minfi/\")\n# read samplesheet\ntargets = read.metharray.sheet(baseDir)\n# read IDAT's into RGChannelSet\nrgSet <- read.metharray.exp(targets = targets, verbose = TRUE)\n# preprocessRaw\nmSet.raw = preprocessRaw(rgSet)\n# make files\nmeth.raw = getMeth(mSet.raw)\nwrite.csv(file=file.path(baseDir,'minfi_raw_meth.csv'),x=meth.raw,row.names=TRUE)\nunmeth.raw = getUnmeth(mSet.raw)\nwrite.csv(file=file.path(baseDir,'minfi_raw_unmeth.csv'),x=unmeth.raw,row.names=TRUE)\nbetas.raw = getBeta(mSet.raw)\nwrite.csv(file=file.path(baseDir,'minfi_raw_betas.csv'),x=betas.raw,row.names=TRUE)\n# preprocessNoob\nmSet.noob = preprocessNoob(rgSet)\n# make files\nmeth.noob = getMeth(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_meth.csv',x=meth.noob,row.names=TRUE,col.names=TRUE)\nunmeth.noob = getUnmeth(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_unmeth.csv',x=unmeth.noob,row.names=TRUE,col.names=TRUE)\nbetas.noob = getBeta(mSet.noob)\nwrite.csv(file='~/Desktop/idats_standard/minfi_noob_betas.csv',x=betas.noob,row.names=TRUE,col.names=TRUE)\n", "meta": {"hexsha": "5b8f14738b4185bf4621092c512076ef9604a083", "size": 1287, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/processing/test_compare_to_minfi.r", "max_stars_repo_name": "WonyoungCho/methylprep", "max_stars_repo_head_hexsha": "4e34f62be969158453ba9b05b7629433f9bbba8b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2019-08-28T08:27:16.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-11T17:20:01.000Z", "max_issues_repo_path": "tests/processing/test_compare_to_minfi.r", "max_issues_repo_name": "WonyoungCho/methylprep", "max_issues_repo_head_hexsha": "4e34f62be969158453ba9b05b7629433f9bbba8b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2021-04-08T22:02:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-18T17:30:50.000Z", "max_forks_repo_path": "tests/processing/test_compare_to_minfi.r", "max_forks_repo_name": "WonyoungCho/methylprep", "max_forks_repo_head_hexsha": "4e34f62be969158453ba9b05b7629433f9bbba8b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2022-01-26T00:12:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-09T22:43:22.000Z", "avg_line_length": 45.9642857143, "max_line_length": 108, "alphanum_fraction": 0.7816627817, "num_tokens": 386, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31573874874137037}} {"text": "#' Combine datasets from different source files.\n#'\n#' @param data - Normalized, imputed data. Data matrix with observations as rows, features as columns.\n#' @param ref - Reference samples normalized, imputed data.\n#' @return combined.data - Z-transformed data.\n#' @export data.combineData\ndata.combineData = function(ref, data) {\n ref = as.matrix(ref)\n data = as.matrix(data)\n \n unionMets = unique(c(rownames(ref), rownames(data)))\n combined.data = matrix(0, nrow=length(unionMets), ncol=ncol(ref)+ncol(data))\n rownames(combined.data) = unionMets\n colnames(combined.data) = c(colnames(ref), colnames(data))\n for (r in 1:length(unionMets)) {\n if (unionMets[r] %in% rownames(ref)) {\n combined.data[r,colnames(ref)] = as.numeric(ref[unionMets[r], ])\n } else {\n combined.data[r,colnames(ref)] = rep(NA, ncol(ref))\n }\n \n if (unionMets[r] %in% rownames(data)) {\n combined.data[r,colnames(data)] = as.numeric(data[unionMets[r], ])\n } else {\n combined.data[r,colnames(data)] = rep(NA, ncol(data))\n }\n }\n \n return(combined.data)\n}\n", "meta": {"hexsha": "1d639f03c710fdc868891392be08ea13db1472f4", "size": 1075, "ext": "r", "lang": "R", "max_stars_repo_path": "R/data.combineData.r", "max_stars_repo_name": "Xiqi-Li/CTD", "max_stars_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/data.combineData.r", "max_issues_repo_name": "Xiqi-Li/CTD", "max_issues_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/data.combineData.r", "max_forks_repo_name": "Xiqi-Li/CTD", "max_forks_repo_head_hexsha": "3002736b9cc43e5435b8a0bf07535b0146a1e32c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6774193548, "max_line_length": 102, "alphanum_fraction": 0.6641860465, "num_tokens": 304, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.6076631698328916, "lm_q1q2_score": 0.31569397329318305}} {"text": "# Generates VRTs for all the Proba-V imagery to use as a stack.\nlibrary(gdalUtils)\nlibrary(raster)\nlibrary(foreach)\nlibrary(doParallel)\nsource(\"pixel-based/utils/getProbaVInfo.r\")\nsource(\"pixel-based/utils/load-sampling-data.r\")\nsource(\"utils/GetProbaVQCMask.r\")\nsource(\"pixel-based/utils/ProbaVDataDirs.r\")\n\nDataDir = \"/data/MTDA/TIFFDERIVED/PROBAV_L3_S5_TOC_100M\"\nOutputDir = \"../data/pixel-based/raster\"\n\nSamplePoints = LoadGlobalRasterPoints()\n\n# Tiles to process\nTileList = levels(SamplePoints$Tile)#GetTileList(SamplePoints)\nTileList = TileList[!TileList %in% c(\"X00Y08\", \"X00Y09\", \"X00Y11\", \"X01Y05\", \"X02Y07\", \"X02Y08\", \"X02Y09\", \"X03Y08\", \"X03Y09\", \"X04Y08\", \"X06Y05\", \"X08Y07\", \"X10Y10\", \"X10Y13\", \"X11Y13\", \"X13Y01\", \"X14Y03\", \"X14Y09\", \"X14Y12\", \"X15Y13\", \"X23Y07\", \"X23Y08\", \"X23Y09\", \"X23Y12\", \"X24Y12\", \"X25Y12\", \"X29Y07\", \"X31Y06\", \"X32Y06\", \"X33Y06\", \"X34Y08\", \"X35Y07\")] # Remove incomplete tiles\n\n# Generate a list of directories to read from.\n# This list is semi-static: we don't read in more files when they arrive. Else the lengths would differ.\nDataDirsDates = LoadRawDataDirs(RequiredTiles = TileList)\nDataDirs = DataDirsDates$dir\n\nBuildProbaVTileVRT = function(TileFiles, VI, Tile, Band=1, OutputDir=\"../../userdata/master-classification/pixel-based/vrt/raster/\")\n{\n Dates = getProbaVinfo(TileFiles)$date\n if (!dir.exists(OutputDir))\n dir.create(OutputDir)\n VRTFile = file.path(OutputDir, paste0(Tile, \"-\", VI, \"-\", Band, \".vrt\"))\n if (!file.exists(VRTFile))\n {\n gdalbuildvrt(TileFiles, VRTFile, separate=TRUE, b=Band)\n # Workaround for -b not working again\n if (Band != 1)\n system(paste0('sed -i \"s|1|', Band ,'|\" ', VRTFile))\n }\n Result = brick(VRTFile)\n Result = setZ(Result, Dates)\n return(Result)\n}\n\n# Extract time series data from the Proba-V tiles and mask them with the quality control layer.\n# Returns a matrix of extracted, masked values.\n# FilterQC values are based on GetProbaVQCMask(bluegood=TRUE, redgood=TRUE, nirgood=TRUE, swirgood=TRUE, ice=FALSE, cloud=FALSE, shadow=FALSE)\nExtractPixelData = function(ExtractLocations, DataDirs, VI, Tile, Band, QCMatrix, FilterQC=c(240, 248), OutputDir=\"../data/pixel-based\")\n{\n OutputCSVFile = file.path(OutputDir, paste0(paste(Tile, VI, Band, sep=\"-\"), \".csv\"))\n if (file.exists(OutputCSVFile))\n return(read.csv(OutputCSVFile))\n \n TileFiles = list.files(DataDirs, pattern=glob2rx(paste0(\"PROBAV_S5_TOC_\", Tile, \"*\", VI, \".tif\")), full.names=TRUE)\n BandStack = BuildProbaVTileVRT(TileFiles, VI, Tile, Band)\n BandValueMatrix = extract(BandStack, ExtractLocations)\n BandValueMatrix[!QCMatrix %in% FilterQC] = NA\n colnames(BandValueMatrix) = paste0(Band, \"-\", c(getZ(BandStack)))\n rownames(BandValueMatrix) = ExtractLocations$location_id\n if (!dir.exists(OutputDir))\n dir.create(OutputDir)\n write.csv(BandValueMatrix, OutputCSVFile)\n return(BandValueMatrix)\n}\n\nregisterDoParallel(cores=4)\nforeach(Tile=iter(TileList), .inorder=FALSE, .multicombine=TRUE, .verbose=TRUE) %dopar%\n{\n PointsInTile = SamplePoints[SamplePoints$Tile == Tile,]\n TileFiles = list.files(DataDirs, pattern=glob2rx(paste0(\"PROBAV_S5_TOC_\", Tile, \"*\", \"SM\", \".tif\")), full.names=TRUE)\n if (nrow(PointsInTile) < 1) {\n print(paste(\"Skipping tile\", Tile, \"because there are no samples here\"))\n } else if (length(TileFiles) == 0) {\n print(paste(\"Skipping tile\", Tile, \"because there are no files at\", DataDirs))\n } else {\n print(paste(\"Processing tile\", Tile))\n \n # Extract QC values\n QCStack = BuildProbaVTileVRT(TileFiles, \"SM\", Tile)\n print(any(duplicated(getZ(QCStack))))\n print(system.time(\n QCInTile <- extract(QCStack, PointsInTile)\n ))\n \n # Extract NDVI values\n ExtractPixelData(PointsInTile, DataDirs, \"NDVI\", Tile, 1, QCInTile, OutputDir=OutputDir)\n print(\"NDVI extraction complete, moving on to radiometry\")\n \n # Radiometry: do it for 4 bands (Red, NIR, Blue, SWIR)\n for (Band in 1:4)\n ExtractPixelData(PointsInTile, DataDirs, \"RADIOMETRY\", Tile, Band, QCInTile, OutputDir=OutputDir)\n }\n}\n\n# Could merge into one if needed\n#cbind(PointsInTile, NDVIInTile)\n\n", "meta": {"hexsha": "e83f5e3c766638cad0fdcef988aa70d289d6c323", "size": 4336, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pixel-based/optical/old/stack-timeseries.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/pixel-based/optical/old/stack-timeseries.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pixel-based/optical/old/stack-timeseries.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 45.1666666667, "max_line_length": 383, "alphanum_fraction": 0.6861162362, "num_tokens": 1311, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3155681043054963}} {"text": "generateHeatMap <- function(closest_systems_candidates, executionStatus, folderName) {\n\n\n nSystems <- executionStatus$AmountOfRecomendedSystems\n\n if(as.integer(executionStatus$amountOfCandidatesRecomendation) < as.integer(executionStatus$AmountOfRecomendedSystems))\n nSystems <- executionStatus$amountOfCandidatesRecomendation\n\n\n # heatmaps\n closest_systems_candidates$coveragePercent <- NULL\n closest_systems_candidates$coverage <- NULL\n\n closest_systems_candidates[closest_systems_candidates == 2] <- 1\n\n # closest systems\n closest <- closest_systems_candidates[0:nSystems,-1] %>%\n rownames_to_column() %>%\n gather(colname, value, -rowname)\n\n\n closest$rowname <- factor(closest$rowname, levels = unique(closest$rowname), ordered=FALSE)\n\n\n my_plot_2 <- ggplot(closest, aes(x = rowname, y = colname, fill = value)) +\n geom_tile(aes(fill = value), colour = \"red\", size = 3) +\n scale_fill_gradient(low=\"white\", high=\"black\") +\n labs(x = \"Sistemas\", y = \"Caracteristicas\") +\n theme(axis.text.x=element_text(size=60, angle = 90, hjust = 1),\n axis.text.y=element_text(size=60),\n axis.title=element_text(size=80,face=\"bold\"))\n\n ggsave(here(\"R/results\", executionStatus$Tecnique, paste(folderName, \"/heat_map.png\", sep=\"\")),\n my_plot_2, width = 80, height = 40, limitsize = FALSE)\n\n}\n\ngenerateCoverageMap <- function(coveredSystems, executionStatus, folderName, system_candidates) {\n\n nSystems <- executionStatus$AmountOfRecomendedSystems\n\n if(as.integer(executionStatus$amountOfCandidatesRecomendation) < as.integer(executionStatus$AmountOfRecomendedSystems))\n nSystems <- executionStatus$amountOfCandidatesRecomendation\n\n system_candidates <- tibble::rownames_to_column(system_candidates, \"id\")\n coveredSystems$SystemName <- paste(\"(\", system_candidates$id, \") \", coveredSystems$SystemName, sep=\"\")\n\n\n #ploting line graph of covered systems\n ggplot(data=coveredSystems, aes(x=NumberOfSystems, y=FeaturesCovered, label=SystemName))+\n geom_line()+\n geom_point() +\n geom_point(data=subset(coveredSystems, NewFeature > 0), aes(x=NumberOfSystems, y=FeaturesCovered), colour=\"red\", size = 4)+\n scale_y_continuous(breaks=c(0:executionStatus$AmountOfFeatures))+\n scale_x_continuous(breaks=c(0:nSystems))+\n labs(x = \"Quantidade de sistemas\", y = \"Quantidade de caracteristicas atendidas\") +\n geom_text(aes(label=SystemName),hjust=1.2, angle = 90) +\n geom_text(data=subset(coveredSystems, NewFeature > 0),aes(label=SystemName),hjust=1.2, angle = 90, colour = \"red\") +\n geom_segment(aes(x=0,xend=tail(coveredSystems, n=1)$NumberOfSystems, y=executionStatus$AmountOfFeatures, yend=executionStatus$AmountOfFeatures))\n\n\n ggsave(here(\"R/results\", executionStatus$Tecnique, paste(folderName, \"/coverageGraph.png\", sep=\"\")),\n width = 20, height = 20, units = \"cm\")\n}\n", "meta": {"hexsha": "3786e739212135c819e385a49acf1cfa1b92c4b3", "size": 2848, "ext": "r", "lang": "R", "max_stars_repo_path": "R/functions/chart.r", "max_stars_repo_name": "ThiagoInocencio/constituentSystemsDataMining", "max_stars_repo_head_hexsha": "e0508b9cbe29c17032e75f700ed0718123212e51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/functions/chart.r", "max_issues_repo_name": "ThiagoInocencio/constituentSystemsDataMining", "max_issues_repo_head_hexsha": "e0508b9cbe29c17032e75f700ed0718123212e51", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/functions/chart.r", "max_forks_repo_name": "ThiagoInocencio/constituentSystemsDataMining", "max_forks_repo_head_hexsha": "e0508b9cbe29c17032e75f700ed0718123212e51", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.8153846154, "max_line_length": 148, "alphanum_fraction": 0.7401685393, "num_tokens": 772, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3155681043054963}} {"text": "\nlibrary(\"openxlsx\")\nlibrary(\"stringr\")\nlibrary(ComplexHeatmap)\nlibrary(circlize)\nlibrary(colorspace)\nlibrary(GetoptLong)\nlibrary(ggplot2)\nlibrary(gplots)\n# install.packages('OpasnetUtils')\nlibrary(reticulate)\n\nmemory.limit(22000) # be sure you have enough memory to conduct the enrichment analysis\n\n#set the project path\nproject_path = \"./project/\"\n\n# be sure geneframe_id.pkl file is accessible\ngenome_width_dataset_file = paste(project_path,\"data/geneframe_id.pkl\", sep=\"\")\n\nxlgene = py_load_object(genome_width_dataset_file, pickle = \"pickle\")\n\n\n#specify which lncRNA you want to analysis\n# you can specify one of the five lncRNAs: ENSG00000206567.8, ENSG00000187185.4, ENSG00000259641.4, ENSG00000218510.5\uff0c ENSG00000257989.1\nselectedLncRNA = 'ENSG00000206567.8' # by default\n# selectedLncRNA = 'ENSG00000218510.5' \n\nindex = grep(selectedLncRNA, rownames(xlgene)) # the index of specified lncRNA in genome-width dataset\nindex\n\nclinical_entire_cohort = read.xlsx(paste(project_path, \"data/tcga/clinical_entire_cohort.xlsx\",sep=\"\"), sheet = 1, colNames = TRUE)\n# clinical_entire_cohort\n\n#find correlated genes (co-expressed genes), it may takes 10 mins!\nrows = length(rownames(xlgene))\nx = xlgene[index,]\nrealY = t(xlgene[-index,])\n\ncormat = round(cor(t(x),realY, method=\"spearman\"),2) #make correlation matrix\n\n\nlength(colnames(cormat)) #total\nlength(which(cormat >= 0.4)) #number of positively related genes\nlength(which(cormat <= -0.3))#number of negatively related genes\n\nlibrary(scales)\n\n#get positive related genes and negative related genes separately using cutoff 0.4 and -0.4\ndf_positive = realY[,which(cormat >= 0.4)]\ndf_negative = realY[,which(cormat <= -0.3)]\n\n#filter genes by p-values, remove genes which have correlations but not statistic meaning\ncols_positive = c()\nfor ( i in 1: length(colnames(df_positive)))\n {\n if (cor.test(df_positive[,i],t(x)[,1])$p.value < 0.05)\n {\n cols_positive <- c(cols_positive,i)\n } \n}\n \nlength(cols_positive)\n\n# we have a lncRNA that don't have negatively correlated genes, remember to ommit this part when dealing with that lncRNA\ncols_negative = c()\nfor ( i in 1: length(colnames(df_negative)))\n {\n if (cor.test(df_negative[,i],t(x)[,1])$p.value < 0.05)\n {\n cols_negative <- c(cols_negative,i)\n } \n}\n\n\nlength(cols_negative)\n\ndf_sel_positive = df_positive[,cols_positive] #get final selected positively related genes\n\n#get final selected negatively related genes, one of the lncRNAs have no negatively correlated genes,\n#remember ommit this sentence when dealing with it\ndf_sel_negative = df_negative[,cols_negative] \n\n\n#rotate to set cluster (positive 1, negative 2), after that we can combine them\nframe_positive = data.frame(t(df_sel_positive))\nframe_negative = data.frame(t(df_sel_negative))\nframe_positive$cluster <- 1\nframe_negative$cluster <- 2\n\nframe_sel_all = rbind(frame_positive, frame_negative) #combine selected genes, we have cluster to identify them\n\n\n \ndf = frame_sel_all[,1:(length(colnames(frame_sel_all))-1)] #dataframe without cluster column\n\ndf = apply(df, 1, rescale, to=c(-10,10)) #apply scale\n\n\ndf = data.frame(t(df))\ndf$cluster = frame_sel_all$cluster #add cluster back to scaled dataset\n\ndftemp = data.frame(t(df)) #a temp\n\ndftemp$GeneA <- c(t(x)[,1],-100)#add GeneA expression, used for sorting\n\nclinical_entire_cohort$tumor_stage[is.na(clinical_entire_cohort$tumor_stage)] = \"unknown\"\n\nwrite.xlsx(frame_negative[,1:10], paste(project_path,\"results/negative5.xlsx\", sep=\"\"),col.names=TRUE, row.names=TRUE) \n\nempty <- list()\nfor (age in clinical_entire_cohort$age_at_diagnosis)\n{\n if(!is.na(age))\n {\n if(age > 65*365)\n {\n empty <- c(empty, 2)\n } \n else if(age >45*365)\n {\n empty <- c(empty, 1)\n }\n else \n {\n empty <- c(empty, 0)\n }\n }\n else\n {\n empty <- c(empty, 3)\n }\n\n}\n\nlibrary(OpasnetUtils)\n#add patient info\ndftemp$gender = c(sapply(clinical_entire_cohort$gender,switch,\"male\"=1,\"female\"=0, \"unknown\"=2),0)\ndftemp$System = c(clinical_entire_cohort$System,0)\n\ndftemp$age_at_diagnosis = c(empty,0)\n\ndftemp$stage = c(sapply(clinical_entire_cohort$tumor_stage,switch ,\"ivb\"=2,\"i/ii nos\"=1,\"ia\"=1,\"x\"=2,\"iiic\"=2,\"ii\"=1,\"iib\"=1,\"iia\"=1,\"iic\"=1,\"iii\"=2,\"iva\"=2,\"ib\"=1,\"iiib\"=2,\"i\"=1,\"iiia\"=2,\"ivc\"=2,\"iv\"=2,\"0\"=1,\"is\"=1, \"unknown\"=0), 0)\n\n#female cancer heatmap\ndftemp$cancer_type = c(sapply(clinical_entire_cohort$project_id,switch,\"OV\"=0,\"MESO\"=5,\"LAML\"=5,\"LUAD\"=5,\"LGG\"=5,\"UCEC\"=1,\"GBM\"=5,\"CHOL\"=5,\"READ\"=5,\"BLCA\"=5,\"TGCT\"=5,\"PRAD\"=5,\"THYM\"=5,\"UCS\"=5,\"CESC\"=2,\"KIRC\"=5,\"SKCM\"=5,\"DLBC\"=5,\"THCA\"=5,\"PAAD\"=5,\"COAD\"=5,\"HNSC\"=5,\"KIRP\"=5,\"ACC\"=5,\"LUSC\"=5,\"STAD\"=5,\"BRCA\"=3,\"SARC\"=5,\"UVM\"=5,\"KICH\"=5,\"PCPG\"=5,\"ESCA\"=5,\"LIHC\"=5),0)\n\n\nlength(rownames(dftemp))\n\ndftemp = dftemp[dftemp$gender!=2,]\nlength(rownames(dftemp)) # in total of 4231 studies, another one is cluster for enrichment analysis\n\nlength(colnames(dftemp))\n\ndfsort = dftemp[order(dftemp$GeneA),] #sort gene expression, the patients are reordered\nframe = data.frame(t(dfsort)) # rotated for heatmap plotting\npatient_frame = frame[(length(rownames(frame))-5):(length(rownames(frame))),]\n\nclear_frame = frame[1:(length(rownames(frame))-6),]\npt = data.frame(t(patient_frame[,2:length(colnames(frame))]))\npt <- data.frame(sapply(pt, function(x) as.numeric(as.character(x))))\n\npt\n\nlength(rownames(pt))\n\nlength(rownames(pt[pt$age_at_diagnosis==3,]))\n\nclear_frame <- data.frame(sapply(clear_frame, function(x) as.numeric(as.character(x))))\nmedian(pt$GeneA[pt$GeneA > median(pt$GeneA)])\n\nret = paste0(c(\"Gene Group\"), clear_frame$cluster) # two parts of the heatmap\n\nmedian(pt$GeneA)\n\nmin(pt$GeneA)\n\nmax(pt$GeneA)\n\nlength(colnames(clear_frame[,2:length(colnames(clear_frame))]))\n\n#show the heatmapp, the first column is cluster (positive or negative related genes), which is useless when plotting heatmap\n#but, we can use cluster to split the heatmap into two parts, the top part is positively related gene expression, the lower part is negatively related gene expression\n#we set :cluster_rows = FALSE, cluster_columns = FALSE, we do not use any of clustering methods provided by Heatmap function, we just plot the sorted gene expressions\nha1 = HeatmapAnnotation(df = pt,\n col = list(\n gender = c(\"1\"=\"black\",\"0\"=\"pink\"),\n GeneA=colorRamp2(c(min(pt$GeneA),median(pt$GeneA), median(pt$GeneA[pt$GeneA > median(pt$GeneA)]), max(pt$GeneA)), c(\"blue\",\"#ebebeb\", \"#FF5C5C\", \"red\")),\n stage = c(\"1\"=\"#1e90ff\",\"2\"=\"orange\", \"0\"=\"grey\"),\n age_at_diagnosis = c(\"0\" = \"#C0C0C0\", \"1\" = \"#778899\",\"2\"= \"#696969\" , \"3\"=\"#2F4F4F\"), \n cancer_type = c(\"0\"=\"#f24433\",\"1\"=\"#960c14\",\"2\"=\"#d82623\",\"3\"=\"#bc1419\",\"5\"=\"blue\"),\n System = c(\"1\"=\"#1764ab\",\"2\"=\"#bb1419\",\"3\"=\"#157e3a\",\"4\"=\"#404040\",\"5\"=\"#61409b\",\"6\"=\"#f3701b\",\"7\"=\"#4294c3\",\"8\"=\"#99017b\",\"9\"=\"#cccc00\")\n\n ),\n \n annotation_legend_param = list(\n gender = list(title = \"Gender\", at = c(\"1\", \"0\"), labels = c(\"Male\", \"Female\")),\n cancer_type = list(title = \"FRC VS. Others\", at = c(\"0\", \"1\", \"2\", \"3\", \"5\"), labels = c(\"OV\", \"UCEC\", \"CESC\", \"BRCA\", \"Other 29 Cancers\")),\n System = list(title = \"System\", at = c(\"1\", \"2\", \"3\", \"4\", \"5\",\"6\",\"7\",\"8\",\"9\"), labels = c(\"Respiratory system\", \"Reproductive system\", \"Digestive system\", \"Endocrine system\"\n , \"Urinary system\",\"Central nervous system\",\"Immune system\",\"Skin\",\"Others\")),\n stage = list(title = \"Tumor stage\", at = c(\"2\",\"1\", \"0\"), labels = c(\"I&II\",\"III&IV\", \"Not reported\")),\n age_at_diagnosis = list(title = \"Age at diagnosis\", at = c(\"3\",\"2\",\"1\", \"0\"), labels = c(\"Unknown\",\"Age > 65\", \"45 < Age \u226465\", \"Age \u2264 45\")),\n # replace median value with above analysis results\n GeneA = list(title = paste(\"\\n\\n\\n\\n\\n\\nExpression\", sep=\"\"))\n\n )\n )\n\nht = Heatmap(clear_frame[,2:length(colnames(clear_frame))], name = \"Expression\", split = sapply(ret,switch, \"Gene Group2\"=\"C2 - Negative\", \"Gene Group1\"=\"C1 - Positive\"),\n show_row_dend = FALSE,show_column_dend = FALSE, cluster_rows = FALSE,\n cluster_columns = FALSE, show_row_names = FALSE, show_column_names = FALSE,\n show_heatmap_legend = FALSE,\n ,top_annotation=ha1)\n\n\n\np = draw(ht, heatmap_legend_side = \"top\")\ndecorate_annotation(\"GeneA\", {grid.text(\"Expression\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n\ndecorate_annotation(\"gender\", {grid.text(\"Gender\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"System\", {grid.text(\"System\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"stage\", {grid.text(\"Tumor Stage\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\n\ndecorate_annotation(\"cancer_type\", {grid.text(\"FRC VS. Others\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"age_at_diagnosis\", {grid.text(\"Age at diagnosis\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", , gp = gpar(fontsize = 10) )})\n\n\n# save results\ntitle = paste(project_path, \"results/\", selectedLncRNA, \"-Heatmap.tiff\", sep=\"\")\ntiff(title, width=861*3, height=840*3, units=\"px\", res=96*3, compression = \"lzw\")\np = draw(ht, heatmap_legend_side = \"top\")\ndecorate_annotation(\"GeneA\", {grid.text(\"Expression\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\" , gp = gpar(fontsize = 10))})\n\ndecorate_annotation(\"gender\", {grid.text(\"Gender\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"System\", {grid.text(\"System\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"stage\", {grid.text(\"Tumor Stage\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\n\ndecorate_annotation(\"cancer_type\", {grid.text(\"FRC VS. Others\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", gp = gpar(fontsize = 10))})\ndecorate_annotation(\"age_at_diagnosis\", {grid.text(\"Age at diagnosis\", unit(1, \"npc\") + unit(2, \"mm\"), 0.5, default.units = \"npc\", just = \"left\", , gp = gpar(fontsize = 10) )})\n\ndev.off()\n\n", "meta": {"hexsha": "e8053d1e4b5ecf781513123c55301f473dd0f431", "size": 10629, "ext": "r", "lang": "R", "max_stars_repo_path": "GeneEnrichmentAnalysis.r", "max_stars_repo_name": "guoqingbao/PanCancerLncRNA", "max_stars_repo_head_hexsha": "297eca20d1d3e9f97f7df1811f009c8942e1bc0a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-01-25T09:38:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-11T02:12:33.000Z", "max_issues_repo_path": "GeneEnrichmentAnalysis.r", "max_issues_repo_name": "guoqingbao/PanCancerLncRNA", "max_issues_repo_head_hexsha": "297eca20d1d3e9f97f7df1811f009c8942e1bc0a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GeneEnrichmentAnalysis.r", "max_forks_repo_name": "guoqingbao/PanCancerLncRNA", "max_forks_repo_head_hexsha": "297eca20d1d3e9f97f7df1811f009c8942e1bc0a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-14T04:42:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-14T04:42:00.000Z", "avg_line_length": 43.5614754098, "max_line_length": 364, "alphanum_fraction": 0.6574466083, "num_tokens": 3358, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7401743735019594, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.3155523176741963}} {"text": "# For diff-in-diff data, first group by ID, then aggregate by\n# period. Each period has the length of N rows\ndiffInDiffAggregate =\n function(data,\n idCol=\"id\", # Identifier of the group\n xCol=\"x\", # Data to aggregate\n timeCol=\"date\",\n interval=7, # 1 interval=N rows: Each period has N rows\n normalizeByPeriod=NULL, # Set N-th period (1, 2, 3...)\n diffByPeriod=NULL, # Set N-th period (1, 2, 3...)\n FUNC=sum) {\n require(dplyr)\n \n totalAggData <- NULL\n ids <- unique(data[,idCol])\n for (i in 1:length(ids)){\n current <- data[data[, idCol] == ids[i],]\n current <- current[order(current[,timeCol]),]\n aggData <- NULL\n for (j in 1:(nrow(current) / interval)) {\n x <- FUNC(current[seq((j - 1) * interval + 1,\n min(nrow(current), j * interval),\n 1),\n xCol])\n time <- current[(j - 1) * interval + 1, timeCol]\n agg <- data.frame(id=ids[i], time=time, x=x)\n names(agg) <- c(idCol, timeCol, xCol)\n \n if (is.null(aggData)) {\n aggData <- agg\n } else {\n aggData <- rbind(aggData, agg)\n }\n }\n \n if (!is.null(normalizeByPeriod)) {\n base <- aggData[normalizeByPeriod, xCol]\n if (is.null(base) | is.na(base) | base == 0) {\n message(paste(\"Skipped\", aggData[1, idCol], \"because of normalization by zero\"))\n next()\n }\n aggData[, xCol] <- aggData[, xCol] / base\n } else if (!is.null(diffByPeriod)) {\n base <- aggData[diffByPeriod, xCol]\n aggData[, xCol] <- aggData[, xCol] - base\n }\n \n if (is.null(totalAggData)) {\n totalAggData <- aggData\n } else {\n totalAggData <- rbind(totalAggData, aggData)\n }\n }\n return (totalAggData)\n}\n", "meta": {"hexsha": "7b68b0678cac4dc18509d2c93e5f607bf2b3dced", "size": 2206, "ext": "r", "lang": "R", "max_stars_repo_path": "data/aggregate.r", "max_stars_repo_name": "daigotanaka/r-utils", "max_stars_repo_head_hexsha": "787b08973cd4e2d8c6f9de36baa994102cdda65b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-09-27T08:18:45.000Z", "max_stars_repo_stars_event_max_datetime": "2016-09-27T08:18:45.000Z", "max_issues_repo_path": "data/aggregate.r", "max_issues_repo_name": "daigotanaka/r-utils", "max_issues_repo_head_hexsha": "787b08973cd4e2d8c6f9de36baa994102cdda65b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data/aggregate.r", "max_forks_repo_name": "daigotanaka/r-utils", "max_forks_repo_head_hexsha": "787b08973cd4e2d8c6f9de36baa994102cdda65b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.3928571429, "max_line_length": 100, "alphanum_fraction": 0.4415231188, "num_tokens": 530, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.626124191181315, "lm_q1q2_score": 0.3155078434537943}} {"text": "# Class: RCTtoolbox.ttest\n#'\n#' @importFrom dplyr mutate\n#' @importFrom dplyr case_when\n#' @importFrom dplyr case_when\n#' @importFrom glue glue\n#' @importFrom rlang enquos\n#' @importFrom ggplot2 ggplot\n#' @importFrom ggplot2 aes\n#' @importFrom ggplot2 geom_bar\n#' @importFrom ggplot2 geom_errorbar\n#' @importFrom ggplot2 geom_hline\n#' @importFrom ggplot2 geom_text\n#' @importFrom ggplot2 ylim\n#' @importFrom ggplot2 labs\n#' @importFrom ggplot2 coord_flip\n#' @importFrom ggplot2 theme\n#' @importFrom patchwork wrap_plots\n#' @importFrom patchwork plot_layout\n#' @importFrom forcats fct_rev\n#'\nrctplot.RCTtoolbox.ttest <- function(obj,\n zval = 1,\n xlab = \"Experimental Arms\",\n ylab = \"Average (+/- Std.Err.)\",\n title = NULL,\n caption = NULL,\n inplot_lab_format =\n \"{round(mean1, 3)}{pstar}\",\n inplot_lab_size = 5,\n inplot_lab_adjust = 0,\n ylim = NULL,\n flip = FALSE,\n patchwork = TRUE,\n ncol = NULL,\n nrow = NULL,\n family = getOption(\"RCTtoolbox.plot_family\"),\n ...) {\n mean1 <- se1 <- arms <- ymin <- ymax <- label <- NULL\n # plot data\n pdt <- obj$result %>%\n mutate(pstar = case_when(\n pval <= .01 ~ \"***\",\n pval <= .05 ~ \"**\",\n pval <= .1 ~ \"*\",\n TRUE ~ \"\"\n ))\n\n method <- unique(pdt$method)\n if (method == \"Welch t-test\") {\n pdt <- pdt %>%\n mutate(\n ymin = mean1 - se1 * zval,\n ymax = mean1 + se1 * zval\n )\n }\n\n if (!is.null(inplot_lab_format)) {\n pdt <- pdt %>% mutate(label = glue(inplot_lab_format))\n }\n\n # plot arguments\n args <- enquos(\n xlab = xlab,\n ylab = ylab,\n title = title,\n caption = caption,\n inplot_lab_adjust = inplot_lab_adjust,\n ylim = ylim\n )\n\n # plot list\n outcome <- unique(pdt$outcome)\n plist <- lapply(outcome, function(z) {\n input <- subset(pdt, outcome == z)\n args <- lapply(args, eval_tidy, list(outcome = z))\n inplot_lab_pos <- if (method == \"Welch t-test\") {\n max(input$ymax) + inplot_lab_adjust\n } else {\n max(input$mean1) + inplot_lab_adjust\n }\n\n if (flip) {\n p <- ggplot(input, aes(x = fct_rev(arms), y = mean1))\n } else {\n p <- ggplot(input, aes(x = arms, y = mean1))\n }\n\n p <- p +\n geom_bar(stat = \"identity\", fill = \"grey80\", color = \"black\") +\n geom_hline(aes(yintercept = 0))\n\n if (method == \"Welch t-test\") {\n p <- p +\n geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.5)\n }\n\n if (!is.null(input$label)) {\n p <- p +\n geom_text(\n aes(y = inplot_lab_pos, label = label),\n size = inplot_lab_size, family = family\n )\n }\n\n if (!is.null(ylim)) p <- p + ylim(args$ylim)\n\n p <- p +\n labs(\n x = args$xlab,\n y = args$ylab,\n title = args$title,\n caption = args$caption\n )\n\n if (flip) p <- p + coord_flip()\n\n p + simplegg(flip = flip, font_family = family, ...)\n })\n\n # patchwork\n if (patchwork) {\n wrap_plots(plist, ncol = ncol, nrow = nrow) +\n plot_layout(guides = \"collect\") &\n theme(legend.position = \"bottom\")\n } else {\n plist\n }\n}", "meta": {"hexsha": "597fffae504d112af3b0ef5d931f2fd0b9cdb283", "size": 3623, "ext": "r", "lang": "R", "max_stars_repo_path": "R/S3-rctplot.r", "max_stars_repo_name": "KatoPachi/multiarmRCT", "max_stars_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/S3-rctplot.r", "max_issues_repo_name": "KatoPachi/multiarmRCT", "max_issues_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/S3-rctplot.r", "max_forks_repo_name": "KatoPachi/multiarmRCT", "max_forks_repo_head_hexsha": "fe75143c5dc194abac8579aba49814fdb0b3acb6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0852713178, "max_line_length": 82, "alphanum_fraction": 0.4893734474, "num_tokens": 950, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3155078364227857}} {"text": "#!/usr/bin/env Rscript --vanilla\nif (!library(\"getopt\", character.only = TRUE, logical.return = TRUE)) {\n install.packages(\"getopt\", repos = \"http://lib.stat.cmu.edu/R/CRAN\")\n}\nrequire(\"getopt\")\n\n# Setup parameters for the script\nparams = matrix(c(\n 'help', 'h', 0, \"logical\",\n 'input', 'i', 2, \"character\"\n ), ncol=4, byrow=TRUE)\n\n# Parse the parameters\nopt = getopt(params)\n\ndata <- read.csv(file=opt$input,head=TRUE,sep=\",\")\nplot(data$t, data$exponential.mean, \"l\", xlab=\"Time\", ylab=\"Mean\", col=\"tomato\")\nlines(data$expected.exponential.mean, col=\"tomato4\")\nlines(data$uniform.mean, col=\"violetred\")\nlines(data$expected.uniform.mean, col=\"violetred4\")\n", "meta": {"hexsha": "fc9c4b29cd67ae90edb377d329c6d8c8da550930", "size": 665, "ext": "r", "lang": "R", "max_stars_repo_path": "metrics-core/src/test/resources/recency-bias-graph.r", "max_stars_repo_name": "SimpleFinance/metrics", "max_stars_repo_head_hexsha": "2987c00750240fa0739fbe1451228744fdb03286", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2016-10-18T20:44:32.000Z", "max_stars_repo_stars_event_max_datetime": "2017-09-22T13:16:36.000Z", "max_issues_repo_path": "metrics-core/src/test/resources/recency-bias-graph.r", "max_issues_repo_name": "SimpleFinance/metrics", "max_issues_repo_head_hexsha": "2987c00750240fa0739fbe1451228744fdb03286", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2016-10-03T13:57:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-25T22:16:18.000Z", "max_forks_repo_path": "metrics-core/src/test/resources/recency-bias-graph.r", "max_forks_repo_name": "jmhodges/metrics", "max_forks_repo_head_hexsha": "759fd0d27558592e33cd635b2b0b025bcfe589d2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2016-04-20T05:05:07.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-01T21:40:29.000Z", "avg_line_length": 31.6666666667, "max_line_length": 80, "alphanum_fraction": 0.6796992481, "num_tokens": 203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6261241772283034, "lm_q1q2_score": 0.3155078364227857}} {"text": "##############################################################################\n# Interpolate GC effects for Jun\n##############################################################################\nplotdat_gc <- plotdat %>%\n filter(Cov==\"GC\" & Category==\"A>G\") %>%\n mutate(Odds=exp(Est), subtype=paste0(Category, \"_\", Sequence)) %>%\n dplyr::select(Category, Sequence, subtype, Odds) %>%\n filter(Odds<2)\n\nplotdat_exp <- plotdat_gc %>%\n tidyr::expand(subtype, ind=seq(0:10)-1)\n\nplotdat_gc <- merge(plotdat_exp, plotdat_gc, by=\"subtype\") %>%\n mutate(Odds=Odds^ind, pctGC=ind*10, seq3=substr(Sequence, 3, 5))\n\n# Get top 6 positive/negative effects\ntop <- plotdat_gc %>%\n ungroup() %>%\n filter(pctGC==100) %>%\n top_n(6, Odds) %>%\n dplyr::select(subtype)\n\nbottom <- plotdat_gc %>%\n ungroup() %>%\n filter(pctGC==100) %>%\n top_n(-6, Odds) %>%\n dplyr::select(Sequence)\n\ntb <- rbind(top, bottom)\n\nplotdat_gc <- plotdat_gc %>%\n filter(subtype %in% tb$subtype)\n\nggplot(plotdat_gc, aes(x=pctGC, y=log(Odds), colour=subtype, group=subtype))+\n geom_point()+\n geom_line()+\n geom_hline(yintercept=0)+\n scale_x_continuous(expand=c(0,0.4),\n breaks=seq(0, 100, 10),\n labels=seq(0, 100, 10))+\n scale_colour_manual(values=iwhPalette[1:12])+\n ylab(\"log-odds of mutability\")+\n xlab(\"%GC\")+\n theme_bw()+\n theme(legend.position=\"bottom\")\n\nggsave(paste0(parentdir, \"/images/gc_effect.png\"))\n\nsitefile1 <- paste0(parentdir, \"/output/logmod_data/motifs3/CGTAATT.txt\")\nsitefile2 <- paste0(parentdir, \"/output/logmod_data/motifs3/TCGATTG.txt\")\n\nsites1 <- read.table(sitefile1, header=F, stringsAsFactors=F)\nnames(sites1) <- c(\"CHR\", \"POS\", \"Sequence\", mut_cats, \"DP\")\n\nsites1$GC <- gcCol(sites1,\n paste0(parentdir, \"/reference_data/gc10kb.bed\"))\n\nsites2 <- read.table(sitefile2, header=F, stringsAsFactors=F)\nnames(sites2) <- c(\"CHR\", \"POS\", \"Sequence\", mut_cats, \"DP\")\n\nsites2$GC <- gcCol(sites2,\n paste0(parentdir, \"/reference_data/gc10kb.bed\"))\n\nsites1$GP <- \"CGT[A>G]ATT (+)\"\nsites2$GP <- \"TCG[A>G]TTG (-)\"\n\nmround <- function(x,base){\n base*round(x/base)\n}\n\n# sites2 <- sites2 %>%\nrbind(sites1, sites2) %>%\nmutate(GC=mround(GC*100, 0.5)) %>%\ngroup_by(GP, GC, AT_GC) %>%\ntally() %>%\nspread(AT_GC, n) %>%\nsetNames(c(\"GP\", \"GC\", \"nonmut\", \"nERVs\")) %>%\nna.omit() %>%\nmutate(tot=nERVs+nonmut, pctmut=nERVs/tot) %>% #data.frame\nfilter(nERVs>2) %>%\nggplot(aes(x=GC, y=pctmut, colour=GP))+\n geom_point(aes(group=GP, size=nERVs))+\n geom_smooth(method=\"lm\", aes(weight=tot))+\n facet_wrap(~GP)+\n # geom_line()+\n scale_colour_manual(values=iwhPalette[c(6,10)])+\n xlab(\"%GC\")+\n ylab(\"ERV fraction\")+\n theme_bw()\nggsave(paste0(parentdir, \"/images/gc_effect_raw.png\"))\n\n# rbind(sites1, sites2) %>%\n# # mutate(GC=mround(GC*100, 2)) %>%\n# group_by(GP, GC, AT_GC) %>%\n# filter(GC>0.3 & GC<0.6) %>%\n# # tally() %>%\n# # spread(AT_GC, n) %>%\n# # setNames(c(\"GP\", \"GC\", \"nonmut\", \"mut\")) %>%\n# # na.omit() %>%\n# # mutate(tot=mut+nonmut, pctmut=mut/tot) %>% #data.frame\n# # filter(mut>3) %>%\n# ggplot(aes(x=AT_GC, y=GC, fill=GP, group=AT_GC))+\n# # geom_point(alpha=0.3, shape=21)+\n# geom_violin()+\n# facet_wrap(~GP)+\n# # geom_line()+\n# scale_colour_manual(values=iwhPalette[c(6,10)])+\n# xlab(\"%GC\")+\n# ylab(\"mutated\")+\n# theme_bw()\n# ggsave(paste0(parentdir, \"/images/gc_effect_raw2.png\"), width=8, height=6)\n", "meta": {"hexsha": "f273f9680e10fbe12893f65931dd18c909e87d2a", "size": 3311, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/R_deprecated/gc_interp.r", "max_stars_repo_name": "theandyb/smaug-genetics", "max_stars_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-09-18T20:54:24.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-16T05:30:06.000Z", "max_issues_repo_path": "sandbox/R_deprecated/gc_interp.r", "max_issues_repo_name": "theandyb/smaug-genetics", "max_issues_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-07-24T12:43:50.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-24T12:43:50.000Z", "max_forks_repo_path": "sandbox/R_deprecated/gc_interp.r", "max_forks_repo_name": "theandyb/smaug-genetics", "max_forks_repo_head_hexsha": "2e040aafb00bfecb698e83218c87dead07350630", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-07-16T20:50:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T10:41:40.000Z", "avg_line_length": 29.5625, "max_line_length": 78, "alphanum_fraction": 0.6152219873, "num_tokens": 1144, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548782017745, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3155033307976004}} {"text": "#!/usr/bin/env Rscript \n\nload(file = \"/home/stlk/Desktop/DigEc_scripts/df_with_creators.RData\")\n\ndfc$exp = unlist(by(dfc, dfc$creator_id, \n function(x) rank(dfc$state_changed_at, ties.method = \"first\")))\n\nsave(file = \"~/DigEc/df_with_creators.RData\", list = c(\"df_with_creators\"))\n", "meta": {"hexsha": "feab9e292a3c5d3f10edbad5fb08973d654268a5", "size": 300, "ext": "r", "lang": "R", "max_stars_repo_path": "ranks.r", "max_stars_repo_name": "Stalkcomrade/kickstarter_database_topic_modeling", "max_stars_repo_head_hexsha": "0d17357d6ba5e5c06d5f1b7bbb149dbc5fedee5a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ranks.r", "max_issues_repo_name": "Stalkcomrade/kickstarter_database_topic_modeling", "max_issues_repo_head_hexsha": "0d17357d6ba5e5c06d5f1b7bbb149dbc5fedee5a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ranks.r", "max_forks_repo_name": "Stalkcomrade/kickstarter_database_topic_modeling", "max_forks_repo_head_hexsha": "0d17357d6ba5e5c06d5f1b7bbb149dbc5fedee5a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3333333333, "max_line_length": 83, "alphanum_fraction": 0.6833333333, "num_tokens": 87, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.31550332424116706}} {"text": "\n#################################################################################\n## File Name: v3_Analysis0_CodingValidity.R ##\n## Creation Date: 4 Nov 2017 ##\n## Author: Gento Kato ##\n## Project: Foreign Image News Project ##\n## Purpose: Check the Validity of Machine-Coding ##\n#################################################################################\n\n## For Jupyter Notebook (Ignore if Using Other Software) ##\nlibrary(IRdisplay)\n\ndisplay_html(\n'\n
\n \n
'\n)\n\n## Suppress Warning\noptions(warn=-1)\n#options(warn=0) # put it back\n\n\n#################\n## Preparation ##\n#################\n\n## Clear Workspace\nrm(list=ls())\n\n## Library Required Packages\nlibrary(rprojroot); library(xtable);library(repr)\n\n## Set Working Directory (Automatically or Manually) ##\n#setwd(dirname(rstudioapi::getActiveDocumentContext()$path)); setwd(\"../\") #In RStudio\nprojdir <- find_root(has_file(\"README.md\")); projdir; setwd(projdir) #In Atom\n#setwd(\"C:/GoogleDrive/Projects/Agenda-Setting Persuasion Framing/Foreign_Image_News_Project\")\n\nuspred<- read.csv(\"data/uspred_test.csv\")\nchnpred<- read.csv(\"data/chnpred_test.csv\")\nskopred<- read.csv(\"data/skopred_test.csv\")\nnkopred<- read.csv(\"data/nkopred_test.csv\")\n\n\n###########################################################\n## Function to Assess Aggregate Level RF Coding Validity ##\n###########################################################\n\nsimubysampleRF <- function(dtpred,N,seedset=5689){\ndtpred_simu <- data.frame(by10true_neg=rep(NA,N),by10rfp_neg=NA,by10difp_neg=NA,by10rfd_neg=NA,by10difd_neg=NA,\n by50true_neg=NA,by50rfp_neg=NA,by50difp_neg=NA,by50rfd_neg=NA,by50difd_neg=NA,\n by100true_neg=NA,by100rfp_neg=NA,by100difp_neg=NA,by100rfd_neg=NA,by100difd_neg=NA,\n by10true_pos=rep(NA,N),by10rfp_pos=NA,by10difp_pos=NA,by10rfd_pos=NA,by10difd_pos=NA,\n by50true_pos=NA,by50rfp_pos=NA,by50difp_pos=NA,by50rfd_pos=NA,by50difd_pos=NA,\n by100true_pos=NA,by100rfp_pos=NA,by100difp_pos=NA,by100rfd_pos=NA,by100difd_pos=NA)\nfor (i in 1:N){\nset.seed(i+seedset)\nnrs10 <- sample(seq(1,nrow(dtpred),by=1),10)\nnrs50 <- sample(seq(1,nrow(dtpred),by=1),50)\nnrs100 <- sample(seq(1,nrow(dtpred),by=1),100)\ndtpred_simu$by10true_neg[i] <- mean(dtpred[nrs10,]$y_test_neg)\ndtpred_simu$by10rfp_neg[i] <- mean(dtpred[nrs10,]$proby_test_rf_neg)\ndtpred_simu$by10rfd_neg[i] <- mean((dtpred[nrs10,]$proby_test_rf_neg>=0.5)*1)\ndtpred_simu$by50true_neg[i] <- mean(dtpred[nrs50,]$y_test_neg)\ndtpred_simu$by50rfp_neg[i] <- mean(dtpred[nrs50,]$proby_test_rf_neg)\ndtpred_simu$by50rfd_neg[i] <- mean((dtpred[nrs50,]$proby_test_rf_neg>=0.5)*1)\ndtpred_simu$by100true_neg[i] <- mean(dtpred[nrs100,]$y_test_neg)\ndtpred_simu$by100rfp_neg[i] <- mean(dtpred[nrs100,]$proby_test_rf_neg)\ndtpred_simu$by100rfd_neg[i] <- mean((dtpred[nrs100,]$proby_test_rf_neg>=0.5)*1)\ndtpred_simu$by10true_pos[i] <- mean(dtpred[nrs10,]$y_test_pos)\ndtpred_simu$by10rfp_pos[i] <- mean(dtpred[nrs10,]$proby_test_rf_pos)\ndtpred_simu$by10rfd_pos[i] <- mean((dtpred[nrs10,]$proby_test_rf_pos>=0.5)*1)\ndtpred_simu$by50true_pos[i] <- mean(dtpred[nrs50,]$y_test_pos)\ndtpred_simu$by50rfp_pos[i] <- mean(dtpred[nrs50,]$proby_test_rf_pos)\ndtpred_simu$by50rfd_pos[i] <- mean((dtpred[nrs50,]$proby_test_rf_pos>=0.5)*1)\ndtpred_simu$by100true_pos[i] <- mean(dtpred[nrs100,]$y_test_pos)\ndtpred_simu$by100rfp_pos[i] <- mean(dtpred[nrs100,]$proby_test_rf_pos)\ndtpred_simu$by100rfd_pos[i] <- mean((dtpred[nrs100,]$proby_test_rf_pos>=0.5)*1)\n}\n\ndtpred_simu$by10difp_neg <- dtpred_simu$by10rfp_neg - dtpred_simu$by10true_neg \ndtpred_simu$by50difp_neg <- dtpred_simu$by50rfp_neg - dtpred_simu$by50true_neg \ndtpred_simu$by100difp_neg <- dtpred_simu$by100rfp_neg - dtpred_simu$by100true_neg \ndtpred_simu$by10difp_pos <- dtpred_simu$by10rfp_pos - dtpred_simu$by10true_pos \ndtpred_simu$by50difp_pos <- dtpred_simu$by50rfp_pos - dtpred_simu$by50true_pos \ndtpred_simu$by100difp_pos <- dtpred_simu$by100rfp_pos - dtpred_simu$by100true_pos \n\ndtpred_simu$by10difd_neg <- dtpred_simu$by10rfd_neg - dtpred_simu$by10true_neg \ndtpred_simu$by50difd_neg <- dtpred_simu$by50rfd_neg - dtpred_simu$by50true_neg \ndtpred_simu$by100difd_neg <- dtpred_simu$by100rfd_neg - dtpred_simu$by100true_neg \ndtpred_simu$by10difd_pos <- dtpred_simu$by10rfd_pos - dtpred_simu$by10true_pos \ndtpred_simu$by50difd_pos <- dtpred_simu$by50rfd_pos - dtpred_simu$by50true_pos \ndtpred_simu$by100difd_pos <- dtpred_simu$by100rfd_pos - dtpred_simu$by100true_pos \n\nreturn(dtpred_simu)\n}\n\n\n###############################\n## Assess RF Coding Validity ##\n###############################\n\nuspred_simuRF <- simubysampleRF(uspred,1000)\nchnpred_simuRF <- simubysampleRF(chnpred,1000)\nskopred_simuRF <- simubysampleRF(skopred,1000)\nnkopred_simuRF <- simubysampleRF(nkopred,1000)\n\n\ncodesimutab <- data.frame(\n \"Metric\" = NA,\n \"Tone\" = NA,\n \"Country\" = NA,\n \"by10p\" = rep(NA,24), \n \"by10d\" = NA,\n \"by50p\" = NA, \n \"by50d\" = NA,\n \"by100p\" = NA,\n \"by100d\" = NA)\n\ncodesimutab$Metric <- c(\"Correlation\",rep(NA,7),\n \"Av. Distance\",rep(NA,7),\n \"Distance SD\",rep(NA,7))\ncodesimutab$Tone <- c(\"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3),\n \"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3),\n \"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3))\ncodesimutab$Country <- c(rep(c(\"US\",\"China\",\"S.Korea\",\"N.Korea\"),6))\n\n## Correlation (Negative)\ncodesimutab[1,-c(1,2,3)] <- \nc(cor(uspred_simuRF$by10true_neg,uspred_simuRF$by10rfp_neg),\n cor(uspred_simuRF$by10true_neg,uspred_simuRF$by10rfd_neg),\n cor(uspred_simuRF$by50true_neg,uspred_simuRF$by50rfp_neg),\n cor(uspred_simuRF$by50true_neg,uspred_simuRF$by50rfd_neg),\n cor(uspred_simuRF$by100true_neg,uspred_simuRF$by100rfp_neg),\n cor(uspred_simuRF$by100true_neg,uspred_simuRF$by100rfd_neg))\ncodesimutab[2,-c(1,2,3)] <- \n c(cor(chnpred_simuRF$by10true_neg,chnpred_simuRF$by10rfp_neg),\n cor(chnpred_simuRF$by10true_neg,chnpred_simuRF$by10rfd_neg),\n cor(chnpred_simuRF$by50true_neg,chnpred_simuRF$by50rfp_neg),\n cor(chnpred_simuRF$by50true_neg,chnpred_simuRF$by50rfd_neg),\n cor(chnpred_simuRF$by100true_neg,chnpred_simuRF$by100rfp_neg),\n cor(chnpred_simuRF$by100true_neg,chnpred_simuRF$by100rfd_neg))\ncodesimutab[3,-c(1,2,3)] <- \n c(cor(skopred_simuRF$by10true_neg,skopred_simuRF$by10rfp_neg),\n cor(skopred_simuRF$by10true_neg,skopred_simuRF$by10rfd_neg),\n cor(skopred_simuRF$by50true_neg,skopred_simuRF$by50rfp_neg),\n cor(skopred_simuRF$by50true_neg,skopred_simuRF$by50rfd_neg),\n cor(skopred_simuRF$by100true_neg,skopred_simuRF$by100rfp_neg),\n cor(skopred_simuRF$by100true_neg,skopred_simuRF$by100rfd_neg))\ncodesimutab[4,-c(1,2,3)] <- \n c(cor(nkopred_simuRF$by10true_neg,nkopred_simuRF$by10rfp_neg),\n cor(nkopred_simuRF$by10true_neg,nkopred_simuRF$by10rfd_neg),\n cor(nkopred_simuRF$by50true_neg,nkopred_simuRF$by50rfp_neg),\n cor(nkopred_simuRF$by50true_neg,nkopred_simuRF$by50rfd_neg),\n cor(nkopred_simuRF$by100true_neg,nkopred_simuRF$by100rfp_neg),\n cor(nkopred_simuRF$by100true_neg,nkopred_simuRF$by100rfd_neg))\n## Correlation (Positive)\ncodesimutab[5,-c(1,2,3)] <- \n c(cor(uspred_simuRF$by10true_pos,uspred_simuRF$by10rfp_pos),\n cor(uspred_simuRF$by10true_pos,uspred_simuRF$by10rfd_pos),\n cor(uspred_simuRF$by50true_pos,uspred_simuRF$by50rfp_pos),\n cor(uspred_simuRF$by50true_pos,uspred_simuRF$by50rfd_pos),\n cor(uspred_simuRF$by100true_pos,uspred_simuRF$by100rfp_pos),\n cor(uspred_simuRF$by100true_pos,uspred_simuRF$by100rfd_pos))\ncodesimutab[6,-c(1,2,3)] <- \n c(cor(chnpred_simuRF$by10true_pos,chnpred_simuRF$by10rfp_pos),\n cor(chnpred_simuRF$by10true_pos,chnpred_simuRF$by10rfd_pos),\n cor(chnpred_simuRF$by50true_pos,chnpred_simuRF$by50rfp_pos),\n cor(chnpred_simuRF$by50true_pos,chnpred_simuRF$by50rfd_pos),\n cor(chnpred_simuRF$by100true_pos,chnpred_simuRF$by100rfp_pos),\n cor(chnpred_simuRF$by100true_pos,chnpred_simuRF$by100rfd_pos))\ncodesimutab[7,-c(1,2,3)] <- \n c(cor(skopred_simuRF$by10true_pos,skopred_simuRF$by10rfp_pos),\n cor(skopred_simuRF$by10true_pos,skopred_simuRF$by10rfd_pos),\n cor(skopred_simuRF$by50true_pos,skopred_simuRF$by50rfp_pos),\n cor(skopred_simuRF$by50true_pos,skopred_simuRF$by50rfd_pos),\n cor(skopred_simuRF$by100true_pos,skopred_simuRF$by100rfp_pos),\n cor(skopred_simuRF$by100true_pos,skopred_simuRF$by100rfd_pos))\ncodesimutab[8,-c(1,2,3)] <- \n c(cor(nkopred_simuRF$by10true_pos,nkopred_simuRF$by10rfp_pos),\n cor(nkopred_simuRF$by10true_pos,nkopred_simuRF$by10rfd_pos),\n cor(nkopred_simuRF$by50true_pos,nkopred_simuRF$by50rfp_pos),\n cor(nkopred_simuRF$by50true_pos,nkopred_simuRF$by50rfd_pos),\n cor(nkopred_simuRF$by100true_pos,nkopred_simuRF$by100rfp_pos),\n cor(nkopred_simuRF$by100true_pos,nkopred_simuRF$by100rfd_pos))\n## Average Distance (Negative)\ncodesimutab[9,-c(1,2,3)] <-\n c(mean(abs(uspred_simuRF$by10difp_neg)),\n mean(abs(uspred_simuRF$by10difd_neg)),\n mean(abs(uspred_simuRF$by50difp_neg)),\n mean(abs(uspred_simuRF$by50difd_neg)),\n mean(abs(uspred_simuRF$by100difp_neg)),\n mean(abs(uspred_simuRF$by100difd_neg)))\ncodesimutab[10,-c(1,2,3)] <-\n c(mean(abs(chnpred_simuRF$by10difp_neg)),\n mean(abs(chnpred_simuRF$by10difd_neg)),\n mean(abs(chnpred_simuRF$by50difp_neg)),\n mean(abs(chnpred_simuRF$by50difd_neg)),\n mean(abs(chnpred_simuRF$by100difp_neg)),\n mean(abs(chnpred_simuRF$by100difd_neg)))\ncodesimutab[11,-c(1,2,3)] <-\n c(mean(abs(skopred_simuRF$by10difp_neg)),\n mean(abs(skopred_simuRF$by10difd_neg)),\n mean(abs(skopred_simuRF$by50difp_neg)),\n mean(abs(skopred_simuRF$by50difd_neg)),\n mean(abs(skopred_simuRF$by100difp_neg)),\n mean(abs(skopred_simuRF$by100difd_neg)))\ncodesimutab[12,-c(1,2,3)] <-\n c(mean(abs(nkopred_simuRF$by10difp_neg)),\n mean(abs(nkopred_simuRF$by10difd_neg)),\n mean(abs(nkopred_simuRF$by50difp_neg)),\n mean(abs(nkopred_simuRF$by50difd_neg)),\n mean(abs(nkopred_simuRF$by100difp_neg)),\n mean(abs(nkopred_simuRF$by100difd_neg)))\n## Average Distance (Positive)\ncodesimutab[13,-c(1,2,3)] <-\n c(mean(abs(uspred_simuRF$by10difp_pos)),\n mean(abs(uspred_simuRF$by10difd_pos)),\n mean(abs(uspred_simuRF$by50difp_pos)),\n mean(abs(uspred_simuRF$by50difd_pos)),\n mean(abs(uspred_simuRF$by100difp_pos)),\n mean(abs(uspred_simuRF$by100difd_pos)))\ncodesimutab[14,-c(1,2,3)] <-\n c(mean(abs(chnpred_simuRF$by10difp_pos)),\n mean(abs(chnpred_simuRF$by10difd_pos)),\n mean(abs(chnpred_simuRF$by50difp_pos)),\n mean(abs(chnpred_simuRF$by50difd_pos)),\n mean(abs(chnpred_simuRF$by100difp_pos)),\n mean(abs(chnpred_simuRF$by100difd_pos)))\ncodesimutab[15,-c(1,2,3)] <-\n c(mean(abs(skopred_simuRF$by10difp_pos)),\n mean(abs(skopred_simuRF$by10difd_pos)),\n mean(abs(skopred_simuRF$by50difp_pos)),\n mean(abs(skopred_simuRF$by50difd_pos)),\n mean(abs(skopred_simuRF$by100difp_pos)),\n mean(abs(skopred_simuRF$by100difd_pos)))\ncodesimutab[16,-c(1,2,3)] <-\n c(mean(abs(nkopred_simuRF$by10difp_pos)),\n mean(abs(nkopred_simuRF$by10difd_pos)),\n mean(abs(nkopred_simuRF$by50difp_pos)),\n mean(abs(nkopred_simuRF$by50difd_pos)),\n mean(abs(nkopred_simuRF$by100difp_pos)),\n mean(abs(nkopred_simuRF$by100difd_pos)))\n## Distance SD (Negative)\ncodesimutab[17,-c(1,2,3)] <-\n c(sd(abs(uspred_simuRF$by10difp_neg)),\n sd(abs(uspred_simuRF$by10difd_neg)),\n sd(abs(uspred_simuRF$by50difp_neg)),\n sd(abs(uspred_simuRF$by50difd_neg)),\n sd(abs(uspred_simuRF$by100difp_neg)),\n sd(abs(uspred_simuRF$by100difd_neg)))\ncodesimutab[18,-c(1,2,3)] <-\n c(sd(abs(chnpred_simuRF$by10difp_neg)),\n sd(abs(chnpred_simuRF$by10difd_neg)),\n sd(abs(chnpred_simuRF$by50difp_neg)),\n sd(abs(chnpred_simuRF$by50difd_neg)),\n sd(abs(chnpred_simuRF$by100difp_neg)),\n sd(abs(chnpred_simuRF$by100difd_neg)))\ncodesimutab[19,-c(1,2,3)] <-\n c(sd(abs(skopred_simuRF$by10difp_neg)),\n sd(abs(skopred_simuRF$by10difd_neg)),\n sd(abs(skopred_simuRF$by50difp_neg)),\n sd(abs(skopred_simuRF$by50difd_neg)),\n sd(abs(skopred_simuRF$by100difp_neg)),\n sd(abs(skopred_simuRF$by100difd_neg)))\ncodesimutab[20,-c(1,2,3)] <-\n c(sd(abs(nkopred_simuRF$by10difp_neg)),\n sd(abs(nkopred_simuRF$by10difd_neg)),\n sd(abs(nkopred_simuRF$by50difp_neg)),\n sd(abs(nkopred_simuRF$by50difd_neg)),\n sd(abs(nkopred_simuRF$by100difp_neg)),\n sd(abs(nkopred_simuRF$by100difd_neg)))\n## Distance SD (Positive)\ncodesimutab[21,-c(1,2,3)] <-\n c(sd(abs(uspred_simuRF$by10difp_pos)),\n sd(abs(uspred_simuRF$by10difd_pos)),\n sd(abs(uspred_simuRF$by50difp_pos)),\n sd(abs(uspred_simuRF$by50difd_pos)),\n sd(abs(uspred_simuRF$by100difp_pos)),\n sd(abs(uspred_simuRF$by100difd_pos)))\ncodesimutab[22,-c(1,2,3)] <-\n c(sd(abs(chnpred_simuRF$by10difp_pos)),\n sd(abs(chnpred_simuRF$by10difd_pos)),\n sd(abs(chnpred_simuRF$by50difp_pos)),\n sd(abs(chnpred_simuRF$by50difd_pos)),\n sd(abs(chnpred_simuRF$by100difp_pos)),\n sd(abs(chnpred_simuRF$by100difd_pos)))\ncodesimutab[23,-c(1,2,3)] <-\n c(sd(abs(skopred_simuRF$by10difp_pos)),\n sd(abs(skopred_simuRF$by10difd_pos)),\n sd(abs(skopred_simuRF$by50difp_pos)),\n sd(abs(skopred_simuRF$by50difd_pos)),\n sd(abs(skopred_simuRF$by100difp_pos)),\n sd(abs(skopred_simuRF$by100difd_pos)))\ncodesimutab[24,-c(1,2,3)] <-\n c(sd(abs(nkopred_simuRF$by10difp_pos)),\n sd(abs(nkopred_simuRF$by10difd_pos)),\n sd(abs(nkopred_simuRF$by50difp_pos)),\n sd(abs(nkopred_simuRF$by50difd_pos)),\n sd(abs(nkopred_simuRF$by100difp_pos)),\n sd(abs(nkopred_simuRF$by100difd_pos)))\n\ncodesimutab <- as.matrix(cbind(codesimutab[,1:3],round(codesimutab[,-c(1,2,3)],3)))\ncolnames(codesimutab)[-c(1,2,3)] <- c(\"$\\\\overline{p(c|x)}$ by 10\",\"$d(c|x)$ by 10\",\n \"$\\\\overline{p(c|x)}$ by 50\",\"$d(c|x)$ by 50\",\n \"$\\\\overline{p(c|x)}$ by 100\",\"$d(c|x)$ by 100\")\n# display value\nxtable(codesimutab)\ncat('No cases for US-positive have predicted probability larger than 0.5.')\n\n## For the Paper ##\ncolnames(codesimutab)[-c(1,2,3)] <- rep(c(\"$\\\\overline{p(c|x)}$\",\"$d(c|x)$\"),3)\n\naddtorow_cst <- list()\naddtorow_cst$pos <- list(-1,nrow(codesimutab[1:8,]))\naddtorow_cst$command <- \n c(\"\\\\toprule \\n \\\\multicolumn{3}{l}{\\\\it Aggregation Size:} & \\\\multicolumn{2}{c}{By 10} & \\\\multicolumn{2}{c}{By 50} & \\\\multicolumn{2}{c}{By 100} \\\\\\\\ \\n\",\n\"\\\\bottomrule \\n \\\\multicolumn{9}{r}{\\\\scriptsize No cases for US-positive have predicted probability larger than 0.5.}\")\n\n# print(xtable(codesimutab[1:8,]),add.to.row=addtorow_cst,\n# hline.after = c(0),sanitize.colnames.function = identity,\n# floating=FALSE,booktabs=TRUE,include.rownames=F)\n\n##############################################################\n## Function to Assess Aggregate Level Logit Coding Validity ##\n##############################################################\n\nsimubysamplelogit <- function(dtpred,N,seedset=5689){\ndtpred_simu <- data.frame(by10true_neg=rep(NA,N),by10logitp_neg=NA,by10difp_neg=NA,by10logitd_neg=NA,by10difd_neg=NA,\n by50true_neg=NA,by50logitp_neg=NA,by50difp_neg=NA,by50logitd_neg=NA,by50difd_neg=NA,\n by100true_neg=NA,by100logitp_neg=NA,by100difp_neg=NA,by100logitd_neg=NA,by100difd_neg=NA,\n by10true_pos=rep(NA,N),by10logitp_pos=NA,by10difp_pos=NA,by10logitd_pos=NA,by10difd_pos=NA,\n by50true_pos=NA,by50logitp_pos=NA,by50difp_pos=NA,by50logitd_pos=NA,by50difd_pos=NA,\n by100true_pos=NA,by100logitp_pos=NA,by100difp_pos=NA,by100logitd_pos=NA,by100difd_pos=NA)\nfor (i in 1:N){\nset.seed(i+seedset)\nnrs10 <- sample(seq(1,nrow(dtpred),by=1),10)\nnrs50 <- sample(seq(1,nrow(dtpred),by=1),50)\nnrs100 <- sample(seq(1,nrow(dtpred),by=1),100)\ndtpred_simu$by10true_neg[i] <- mean(dtpred[nrs10,]$y_test_neg)\ndtpred_simu$by10logitp_neg[i] <- mean(dtpred[nrs10,]$proby_test_logit_neg)\ndtpred_simu$by10logitd_neg[i] <- mean((dtpred[nrs10,]$proby_test_logit_neg>=0.5)*1)\ndtpred_simu$by50true_neg[i] <- mean(dtpred[nrs50,]$y_test_neg)\ndtpred_simu$by50logitp_neg[i] <- mean(dtpred[nrs50,]$proby_test_logit_neg)\ndtpred_simu$by50logitd_neg[i] <- mean((dtpred[nrs50,]$proby_test_logit_neg>=0.5)*1)\ndtpred_simu$by100true_neg[i] <- mean(dtpred[nrs100,]$y_test_neg)\ndtpred_simu$by100logitp_neg[i] <- mean(dtpred[nrs100,]$proby_test_logit_neg)\ndtpred_simu$by100logitd_neg[i] <- mean((dtpred[nrs100,]$proby_test_logit_neg>=0.5)*1)\ndtpred_simu$by10true_pos[i] <- mean(dtpred[nrs10,]$y_test_pos)\ndtpred_simu$by10logitp_pos[i] <- mean(dtpred[nrs10,]$proby_test_logit_pos)\ndtpred_simu$by10logitd_pos[i] <- mean((dtpred[nrs10,]$proby_test_logit_pos>=0.5)*1)\ndtpred_simu$by50true_pos[i] <- mean(dtpred[nrs50,]$y_test_pos)\ndtpred_simu$by50logitp_pos[i] <- mean(dtpred[nrs50,]$proby_test_logit_pos)\ndtpred_simu$by50logitd_pos[i] <- mean((dtpred[nrs50,]$proby_test_logit_pos>=0.5)*1)\ndtpred_simu$by100true_pos[i] <- mean(dtpred[nrs100,]$y_test_pos)\ndtpred_simu$by100logitp_pos[i] <- mean(dtpred[nrs100,]$proby_test_logit_pos)\ndtpred_simu$by100logitd_pos[i] <- mean((dtpred[nrs100,]$proby_test_logit_pos>=0.5)*1)\n}\n\ndtpred_simu$by10difp_neg <- dtpred_simu$by10logitp_neg - dtpred_simu$by10true_neg \ndtpred_simu$by50difp_neg <- dtpred_simu$by50logitp_neg - dtpred_simu$by50true_neg \ndtpred_simu$by100difp_neg <- dtpred_simu$by100logitp_neg - dtpred_simu$by100true_neg \ndtpred_simu$by10difp_pos <- dtpred_simu$by10logitp_pos - dtpred_simu$by10true_pos \ndtpred_simu$by50difp_pos <- dtpred_simu$by50logitp_pos - dtpred_simu$by50true_pos \ndtpred_simu$by100difp_pos <- dtpred_simu$by100logitp_pos - dtpred_simu$by100true_pos \n\ndtpred_simu$by10difd_neg <- dtpred_simu$by10logitd_neg - dtpred_simu$by10true_neg \ndtpred_simu$by50difd_neg <- dtpred_simu$by50logitd_neg - dtpred_simu$by50true_neg \ndtpred_simu$by100difd_neg <- dtpred_simu$by100logitd_neg - dtpred_simu$by100true_neg \ndtpred_simu$by10difd_pos <- dtpred_simu$by10logitd_pos - dtpred_simu$by10true_pos \ndtpred_simu$by50difd_pos <- dtpred_simu$by50logitd_pos - dtpred_simu$by50true_pos \ndtpred_simu$by100difd_pos <- dtpred_simu$by100logitd_pos - dtpred_simu$by100true_pos \n\nreturn(dtpred_simu)\n}\n\n\n##################################\n## Assess Logit Coding Validity ##\n##################################\n\nuspred_simulogit <- simubysamplelogit(uspred,1000)\nchnpred_simulogit <- simubysamplelogit(chnpred,1000)\nskopred_simulogit <- simubysamplelogit(skopred,1000)\nnkopred_simulogit <- simubysamplelogit(nkopred,1000)\n\ncodesimutabLT <- data.frame(\n \"Metric\" = NA,\n \"Tone\" = NA,\n \"Country\" = NA,\n \"by10p\" = rep(NA,24), \n \"by10d\" = NA,\n \"by50p\" = NA, \n \"by50d\" = NA,\n \"by100p\" = NA,\n \"by100d\" = NA)\n\ncodesimutabLT$Metric <- c(\"Correlation\",rep(NA,7),\n \"Av. Distance\",rep(NA,7),\n \"Distance SD\",rep(NA,7))\ncodesimutabLT$Tone <- c(\"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3),\n \"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3),\n \"Negative\",rep(NA,3),\n \"Positive\",rep(NA,3))\ncodesimutabLT$Country <- c(rep(c(\"US\",\"China\",\"S.Korea\",\"N.Korea\"),6))\n\n## Correlation (Negative)\ncodesimutabLT[1,-c(1,2,3)] <- \nc(cor(uspred_simulogit$by10true_neg,uspred_simulogit$by10logitp_neg),\n cor(uspred_simulogit$by10true_neg,uspred_simulogit$by10logitd_neg),\n cor(uspred_simulogit$by50true_neg,uspred_simulogit$by50logitp_neg),\n cor(uspred_simulogit$by50true_neg,uspred_simulogit$by50logitd_neg),\n cor(uspred_simulogit$by100true_neg,uspred_simulogit$by100logitp_neg),\n cor(uspred_simulogit$by100true_neg,uspred_simulogit$by100logitd_neg))\ncodesimutabLT[2,-c(1,2,3)] <- \n c(cor(chnpred_simulogit$by10true_neg,chnpred_simulogit$by10logitp_neg),\n cor(chnpred_simulogit$by10true_neg,chnpred_simulogit$by10logitd_neg),\n cor(chnpred_simulogit$by50true_neg,chnpred_simulogit$by50logitp_neg),\n cor(chnpred_simulogit$by50true_neg,chnpred_simulogit$by50logitd_neg),\n cor(chnpred_simulogit$by100true_neg,chnpred_simulogit$by100logitp_neg),\n cor(chnpred_simulogit$by100true_neg,chnpred_simulogit$by100logitd_neg))\ncodesimutabLT[3,-c(1,2,3)] <- \n c(cor(skopred_simulogit$by10true_neg,skopred_simulogit$by10logitp_neg),\n cor(skopred_simulogit$by10true_neg,skopred_simulogit$by10logitd_neg),\n cor(skopred_simulogit$by50true_neg,skopred_simulogit$by50logitp_neg),\n cor(skopred_simulogit$by50true_neg,skopred_simulogit$by50logitd_neg),\n cor(skopred_simulogit$by100true_neg,skopred_simulogit$by100logitp_neg),\n cor(skopred_simulogit$by100true_neg,skopred_simulogit$by100logitd_neg))\ncodesimutabLT[4,-c(1,2,3)] <- \n c(cor(nkopred_simulogit$by10true_neg,nkopred_simulogit$by10logitp_neg),\n cor(nkopred_simulogit$by10true_neg,nkopred_simulogit$by10logitd_neg),\n cor(nkopred_simulogit$by50true_neg,nkopred_simulogit$by50logitp_neg),\n cor(nkopred_simulogit$by50true_neg,nkopred_simulogit$by50logitd_neg),\n cor(nkopred_simulogit$by100true_neg,nkopred_simulogit$by100logitp_neg),\n cor(nkopred_simulogit$by100true_neg,nkopred_simulogit$by100logitd_neg))\n## Correlation (Positive)\ncodesimutabLT[5,-c(1,2,3)] <- \n c(cor(uspred_simulogit$by10true_pos,uspred_simulogit$by10logitp_pos),\n cor(uspred_simulogit$by10true_pos,uspred_simulogit$by10logitd_pos),\n cor(uspred_simulogit$by50true_pos,uspred_simulogit$by50logitp_pos),\n cor(uspred_simulogit$by50true_pos,uspred_simulogit$by50logitd_pos),\n cor(uspred_simulogit$by100true_pos,uspred_simulogit$by100logitp_pos),\n cor(uspred_simulogit$by100true_pos,uspred_simulogit$by100logitd_pos))\ncodesimutabLT[6,-c(1,2,3)] <- \n c(cor(chnpred_simulogit$by10true_pos,chnpred_simulogit$by10logitp_pos),\n cor(chnpred_simulogit$by10true_pos,chnpred_simulogit$by10logitd_pos),\n cor(chnpred_simulogit$by50true_pos,chnpred_simulogit$by50logitp_pos),\n cor(chnpred_simulogit$by50true_pos,chnpred_simulogit$by50logitd_pos),\n cor(chnpred_simulogit$by100true_pos,chnpred_simulogit$by100logitp_pos),\n cor(chnpred_simulogit$by100true_pos,chnpred_simulogit$by100logitd_pos))\ncodesimutabLT[7,-c(1,2,3)] <- \n c(cor(skopred_simulogit$by10true_pos,skopred_simulogit$by10logitp_pos),\n cor(skopred_simulogit$by10true_pos,skopred_simulogit$by10logitd_pos),\n cor(skopred_simulogit$by50true_pos,skopred_simulogit$by50logitp_pos),\n cor(skopred_simulogit$by50true_pos,skopred_simulogit$by50logitd_pos),\n cor(skopred_simulogit$by100true_pos,skopred_simulogit$by100logitp_pos),\n cor(skopred_simulogit$by100true_pos,skopred_simulogit$by100logitd_pos))\ncodesimutabLT[8,-c(1,2,3)] <- \n c(cor(nkopred_simulogit$by10true_pos,nkopred_simulogit$by10logitp_pos),\n cor(nkopred_simulogit$by10true_pos,nkopred_simulogit$by10logitd_pos),\n cor(nkopred_simulogit$by50true_pos,nkopred_simulogit$by50logitp_pos),\n cor(nkopred_simulogit$by50true_pos,nkopred_simulogit$by50logitd_pos),\n cor(nkopred_simulogit$by100true_pos,nkopred_simulogit$by100logitp_pos),\n cor(nkopred_simulogit$by100true_pos,nkopred_simulogit$by100logitd_pos))\n## Average Distance (Negative)\ncodesimutabLT[9,-c(1,2,3)] <-\n c(mean(abs(uspred_simulogit$by10difp_neg)),\n mean(abs(uspred_simulogit$by10difd_neg)),\n mean(abs(uspred_simulogit$by50difp_neg)),\n mean(abs(uspred_simulogit$by50difd_neg)),\n mean(abs(uspred_simulogit$by100difp_neg)),\n mean(abs(uspred_simulogit$by100difd_neg)))\ncodesimutabLT[10,-c(1,2,3)] <-\n c(mean(abs(chnpred_simulogit$by10difp_neg)),\n mean(abs(chnpred_simulogit$by10difd_neg)),\n mean(abs(chnpred_simulogit$by50difp_neg)),\n mean(abs(chnpred_simulogit$by50difd_neg)),\n mean(abs(chnpred_simulogit$by100difp_neg)),\n mean(abs(chnpred_simulogit$by100difd_neg)))\ncodesimutabLT[11,-c(1,2,3)] <-\n c(mean(abs(skopred_simulogit$by10difp_neg)),\n mean(abs(skopred_simulogit$by10difd_neg)),\n mean(abs(skopred_simulogit$by50difp_neg)),\n mean(abs(skopred_simulogit$by50difd_neg)),\n mean(abs(skopred_simulogit$by100difp_neg)),\n mean(abs(skopred_simulogit$by100difd_neg)))\ncodesimutabLT[12,-c(1,2,3)] <-\n c(mean(abs(nkopred_simulogit$by10difp_neg)),\n mean(abs(nkopred_simulogit$by10difd_neg)),\n mean(abs(nkopred_simulogit$by50difp_neg)),\n mean(abs(nkopred_simulogit$by50difd_neg)),\n mean(abs(nkopred_simulogit$by100difp_neg)),\n mean(abs(nkopred_simulogit$by100difd_neg)))\n## Average Distance (Positive)\ncodesimutabLT[13,-c(1,2,3)] <-\n c(mean(abs(uspred_simulogit$by10difp_pos)),\n mean(abs(uspred_simulogit$by10difd_pos)),\n mean(abs(uspred_simulogit$by50difp_pos)),\n mean(abs(uspred_simulogit$by50difd_pos)),\n mean(abs(uspred_simulogit$by100difp_pos)),\n mean(abs(uspred_simulogit$by100difd_pos)))\ncodesimutabLT[14,-c(1,2,3)] <-\n c(mean(abs(chnpred_simulogit$by10difp_pos)),\n mean(abs(chnpred_simulogit$by10difd_pos)),\n mean(abs(chnpred_simulogit$by50difp_pos)),\n mean(abs(chnpred_simulogit$by50difd_pos)),\n mean(abs(chnpred_simulogit$by100difp_pos)),\n mean(abs(chnpred_simulogit$by100difd_pos)))\ncodesimutabLT[15,-c(1,2,3)] <-\n c(mean(abs(skopred_simulogit$by10difp_pos)),\n mean(abs(skopred_simulogit$by10difd_pos)),\n mean(abs(skopred_simulogit$by50difp_pos)),\n mean(abs(skopred_simulogit$by50difd_pos)),\n mean(abs(skopred_simulogit$by100difp_pos)),\n mean(abs(skopred_simulogit$by100difd_pos)))\ncodesimutabLT[16,-c(1,2,3)] <-\n c(mean(abs(nkopred_simulogit$by10difp_pos)),\n mean(abs(nkopred_simulogit$by10difd_pos)),\n mean(abs(nkopred_simulogit$by50difp_pos)),\n mean(abs(nkopred_simulogit$by50difd_pos)),\n mean(abs(nkopred_simulogit$by100difp_pos)),\n mean(abs(nkopred_simulogit$by100difd_pos)))\n## Distance SD (Negative)\ncodesimutabLT[17,-c(1,2,3)] <-\n c(sd(abs(uspred_simulogit$by10difp_neg)),\n sd(abs(uspred_simulogit$by10difd_neg)),\n sd(abs(uspred_simulogit$by50difp_neg)),\n sd(abs(uspred_simulogit$by50difd_neg)),\n sd(abs(uspred_simulogit$by100difp_neg)),\n sd(abs(uspred_simulogit$by100difd_neg)))\ncodesimutabLT[18,-c(1,2,3)] <-\n c(sd(abs(chnpred_simulogit$by10difp_neg)),\n sd(abs(chnpred_simulogit$by10difd_neg)),\n sd(abs(chnpred_simulogit$by50difp_neg)),\n sd(abs(chnpred_simulogit$by50difd_neg)),\n sd(abs(chnpred_simulogit$by100difp_neg)),\n sd(abs(chnpred_simulogit$by100difd_neg)))\ncodesimutabLT[19,-c(1,2,3)] <-\n c(sd(abs(skopred_simulogit$by10difp_neg)),\n sd(abs(skopred_simulogit$by10difd_neg)),\n sd(abs(skopred_simulogit$by50difp_neg)),\n sd(abs(skopred_simulogit$by50difd_neg)),\n sd(abs(skopred_simulogit$by100difp_neg)),\n sd(abs(skopred_simulogit$by100difd_neg)))\ncodesimutabLT[20,-c(1,2,3)] <-\n c(sd(abs(nkopred_simulogit$by10difp_neg)),\n sd(abs(nkopred_simulogit$by10difd_neg)),\n sd(abs(nkopred_simulogit$by50difp_neg)),\n sd(abs(nkopred_simulogit$by50difd_neg)),\n sd(abs(nkopred_simulogit$by100difp_neg)),\n sd(abs(nkopred_simulogit$by100difd_neg)))\n## Distance SD (Positive)\ncodesimutabLT[21,-c(1,2,3)] <-\n c(sd(abs(uspred_simulogit$by10difp_pos)),\n sd(abs(uspred_simulogit$by10difd_pos)),\n sd(abs(uspred_simulogit$by50difp_pos)),\n sd(abs(uspred_simulogit$by50difd_pos)),\n sd(abs(uspred_simulogit$by100difp_pos)),\n sd(abs(uspred_simulogit$by100difd_pos)))\ncodesimutabLT[22,-c(1,2,3)] <-\n c(sd(abs(chnpred_simulogit$by10difp_pos)),\n sd(abs(chnpred_simulogit$by10difd_pos)),\n sd(abs(chnpred_simulogit$by50difp_pos)),\n sd(abs(chnpred_simulogit$by50difd_pos)),\n sd(abs(chnpred_simulogit$by100difp_pos)),\n sd(abs(chnpred_simulogit$by100difd_pos)))\ncodesimutabLT[23,-c(1,2,3)] <-\n c(sd(abs(skopred_simulogit$by10difp_pos)),\n sd(abs(skopred_simulogit$by10difd_pos)),\n sd(abs(skopred_simulogit$by50difp_pos)),\n sd(abs(skopred_simulogit$by50difd_pos)),\n sd(abs(skopred_simulogit$by100difp_pos)),\n sd(abs(skopred_simulogit$by100difd_pos)))\ncodesimutabLT[24,-c(1,2,3)] <-\n c(sd(abs(nkopred_simulogit$by10difp_pos)),\n sd(abs(nkopred_simulogit$by10difd_pos)),\n sd(abs(nkopred_simulogit$by50difp_pos)),\n sd(abs(nkopred_simulogit$by50difd_pos)),\n sd(abs(nkopred_simulogit$by100difp_pos)),\n sd(abs(nkopred_simulogit$by100difd_pos)))\n\ncodesimutabLT <- as.matrix(cbind(codesimutabLT[,1:3],round(codesimutabLT[,-c(1,2,3)],3)))\ncolnames(codesimutabLT)[-c(1,2,3)] <- c(\"$\\\\overline{p(c|x)}$ by 10\",\"$d(c|x)$ by 10\",\n \"$\\\\overline{p(c|x)}$ by 50\",\"$d(c|x)$ by 50\",\n \"$\\\\overline{p(c|x)}$ by 100\",\"$d(c|x)$ by 100\")\n# display value\nxtable(codesimutabLT)\ncat('No cases for US-positive have predicted probability larger than 0.5.')\n\n## For the Paper\ncolnames(codesimutabLT)[-c(1,2,3)] <- rep(c(\"$\\\\overline{p(c|x)}$\",\"$d(c|x)$\"),3)\n\naddtorow_cst <- list()\naddtorow_cst$pos <- list(-1,nrow(codesimutabLT[1:8,]))\naddtorow_cst$command <- \n c(\"\\\\toprule \\n \\\\multicolumn{3}{l}{\\\\it Aggregation Size:} & \\\\multicolumn{2}{c}{By 10} & \\\\multicolumn{2}{c}{By 50} & \\\\multicolumn{2}{c}{By 100} \\\\\\\\ \\n\",\n\"\\\\bottomrule \\n \\\\multicolumn{9}{r}{\\\\scriptsize No cases for US-positive have predicted probability larger than 0.5.}\")\n\n# print(xtable(codesimutabLT[1:8,]),add.to.row=addtorow_cst,\n# hline.after = c(0),sanitize.colnames.function = identity,\n# floating=FALSE,booktabs=TRUE,include.rownames=F)\n\n\n####################\n## Save the Table ##\n####################\n\nsave(codesimutab,addtorow_cst,file=\"./outputs/codesimutab.RData\")\nsave(codesimutabLT,addtorow_cst,file=\"./outputs/codesimutabLT.RData\")\n\n", "meta": {"hexsha": "0b4b5b62002bf8f099b0c9d521c80d1b0ce3d33d", "size": 29971, "ext": "r", "lang": "R", "max_stars_repo_path": "codes/v3_Analysis0_CodingValidity.r", "max_stars_repo_name": "gentok/Foreign_Image_News_Project", "max_stars_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "codes/v3_Analysis0_CodingValidity.r", "max_issues_repo_name": "gentok/Foreign_Image_News_Project", "max_issues_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "codes/v3_Analysis0_CodingValidity.r", "max_forks_repo_name": "gentok/Foreign_Image_News_Project", "max_forks_repo_head_hexsha": "625acb25d4e9ae57a104c24b293bb16b17f1a479", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.724522293, "max_line_length": 160, "alphanum_fraction": 0.7252343932, "num_tokens": 11316, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.31550332424116706}} {"text": "library(ggplot2)\nlibrary(reshape2)\nlibrary(scales)\n#library(devtools)\n\n#devtools::install_github('cttobin/ggthemr')\n\nhistory.data <- read.csv(file = \"history.csv\", sep = \";\")\nhistory.data <- head(history.data, 30)\n\ndata <- melt(history.data, id=\"Iteration\")\np <- ggplot(data=data, aes(x=Iteration, y=value, colour=variable)) \np <- p + labs(colour = \"Users\")\np <- p + geom_line() \np <- p + geom_point()\np <- p + scale_x_continuous(breaks = pretty_breaks())\np <- p + labs(title=\"PageRank growth over time\", x = \"Iteration\", y = \"PageRank\")\np <- p + theme_light()\n\np\n", "meta": {"hexsha": "193bfcdd1fbe1f10d69a07f5007db002f608e681", "size": 564, "ext": "r", "lang": "R", "max_stars_repo_path": "PageRank-History/History.r", "max_stars_repo_name": "Vreyesm/Twitter-Pagerank", "max_stars_repo_head_hexsha": "a0dd444769b3257f596ee010a93521de2a2dec17", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "PageRank-History/History.r", "max_issues_repo_name": "Vreyesm/Twitter-Pagerank", "max_issues_repo_head_hexsha": "a0dd444769b3257f596ee010a93521de2a2dec17", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "PageRank-History/History.r", "max_forks_repo_name": "Vreyesm/Twitter-Pagerank", "max_forks_repo_head_hexsha": "a0dd444769b3257f596ee010a93521de2a2dec17", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.8571428571, "max_line_length": 81, "alphanum_fraction": 0.6719858156, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5389832354982645, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3153597131862265}} {"text": "suppressPackageStartupMessages(library(dplyr))\nsuppressPackageStartupMessages(library(ggplot2))\nsuppressPackageStartupMessages(library(svglite))\nsuppressPackageStartupMessages(library(RColorBrewer))\nsuppressPackageStartupMessages(library(cowplot))\n\nfig_extensions <- c('.png', '.pdf', '.svg')\n\n# Define the dataset to compile results for\nsvcca_file <- file.path(\"results\", \"svcca_within_mean_correlation_weights.tsv.gz\")\nsvcca_df <- readr::read_tsv(svcca_file,\n col_types = readr::cols(\n .default = readr::col_character(),\n svcca_mean_similarity = readr::col_double()))\nprint(dim(svcca_df))\nhead(svcca_df, 2)\n\ntable(svcca_df$shuffled)\n\n # Make sure factors are in order\nsvcca_df$z_dim <- factor(\n svcca_df$z_dim,\n levels = sort(as.numeric(paste(unique(svcca_df$z_dim))))\n)\n\nalgorithms <- c(\"pca\", \"ica\", \"nmf\", \"dae\", \"vae\")\n\nsvcca_df$algorithm_1 <- factor(\n svcca_df$algorithm_1,\n levels = algorithms\n)\n\nsvcca_df$algorithm_2 <- factor(\n svcca_df$algorithm_2,\n levels = algorithms\n)\n\nrecoded <- list(\"pca\" = \"PCA\",\n \"ica\" = \"ICA\",\n \"nmf\" = \"NMF\",\n \"dae\" = \"DAE\",\n \"vae\" = \"VAE\")\n\nsvcca_df$algorithm_1 <- svcca_df$algorithm_1 %>%\n dplyr::recode(!!!recoded)\n\nsvcca_df$algorithm_2 <- svcca_df$algorithm_2 %>%\n dplyr::recode(!!!recoded)\n\n# Switch the order of the algorithms in the shuffled dataset to\n# enable plotting in the lower triangle on the same figure\nshuffled_df <- svcca_df %>% dplyr::filter(shuffled == 'shuffled')\nsignal_df <- svcca_df %>% dplyr::filter(shuffled != 'shuffled')\n\nalg1_shuff <- shuffled_df$algorithm_1\nalg2_shuff <- shuffled_df$algorithm_2\n\nshuffled_df$algorithm_1 <- alg2_shuff\nshuffled_df$algorithm_2 <- alg1_shuff\n\nsvcca_switched_df <- dplyr::bind_rows(signal_df, shuffled_df)\n\nbox_theme <- theme(panel.grid.minor = element_line(size = 0.1),\n panel.grid.major = element_line(size = 0.3),\n strip.text = element_text(size = 7),\n strip.background = element_rect(colour = \"black\",\n fill = \"#fdfff4\"),\n axis.text.x = element_text(angle = 90,\n size = 3.2),\n axis.text.y = element_text(size = 6),\n axis.title = element_text(size = 7),\n plot.title = element_text(hjust = 0.5,\n size = 9))\n\n# Visualize across algorithm similarity\noptions(repr.plot.width = 15, repr.plot.height = 9)\n\n\nacross_algorithm_plots = list()\nfor(dataset in c(\"TARGET\", \"TCGA\", \"GTEX\")) {\n\n svcca_subset_df <- svcca_switched_df %>%\n dplyr::filter(dataset == !!dataset)\n\n out_figure <- file.path(\"figures\", paste0(\"stability_within_z_\", dataset))\n plot_title <- paste0(\"SVCCA Mean Correlations\\n\", dataset)\n\n g <- ggplot(svcca_subset_df,\n aes(x = z_dim,\n y = svcca_mean_similarity,\n linetype = shuffled)) +\n geom_boxplot(outlier.size = 0.04,\n lwd = 0.2,\n fill = 'grey75') +\n facet_grid(algorithm_1 ~ algorithm_2) +\n xlab(\"k Dimension\") +\n ylab(\"SVCCA Mean Similarity\") +\n scale_linetype_manual(name = \"Signal:\",\n values = c(\"dotted\", \"solid\"),\n labels = c(\"signal\" = \"Real\",\n \"shuffled\" = \"Permuted\")) +\n ylim(c(0, 1)) +\n ggtitle(plot_title) +\n theme_bw() +\n box_theme +\n guides(linetype = guide_legend(\"none\"))\n\n across_algorithm_plots[[dataset]] <- g\n\n for(extension in fig_extensions) {\n gg_file <- paste0(out_figure, extension)\n ggsave(plot = g,\n filename = gg_file,\n height = 120,\n width = 170,\n units = \"mm\",\n dpi = 500)\n }\n\n print(g)\n\n}\n\nsvcca_subset_df <- svcca_df %>%\n dplyr::group_by(algorithm_1, algorithm_2, dataset, z_dim, shuffled) %>%\n dplyr::summarize_at('svcca_mean_similarity', mean)\n\nsignal_data = svcca_subset_df %>%\n dplyr::filter(shuffled == 'signal')\nshuffled_data = svcca_subset_df %>%\n dplyr::filter(shuffled == 'shuffled')\n\nfull_svcca_data = signal_data %>%\n dplyr::full_join(shuffled_data,\n by = c('algorithm_1', 'algorithm_2', 'dataset', 'z_dim'),\n suffix = c('_signal', '_shuffled'))\n\nfull_svcca_data <- full_svcca_data %>%\n dplyr::mutate(\n svcca_diff = svcca_mean_similarity_signal - svcca_mean_similarity_shuffled\n )\n\ng <- ggplot(full_svcca_data,\n aes(x = z_dim,\n y = svcca_diff,\n color = dataset)) +\n geom_point(alpha = 0.6,\n size = 0.25) +\n facet_grid(algorithm_1 ~ algorithm_2) +\n xlab(\"k Dimension\") +\n ylab(\"SVCCA Mean Similarity\") +\n scale_color_manual(name = \"Dataset\",\n values = c(\"#66c2a5\",\n \"#fc8d62\",\n \"#8da0cb\"),\n labels = c(\"GTEX\" = \"GTEX\",\n \"TCGA\" = \"TCGA\",\n \"TARGET\" = \"TARGET\")) +\n ylim(c(0, 1)) +\n ggtitle(paste0('SVCCA Mean Similarity\\nSignal Difference')) +\n theme_bw() +\n box_theme +\n theme(legend.title = element_text(size = 8),\n legend.text = element_text(size = 7)) +\n guides(color = guide_legend(override.aes = list(\"size\" = 1,\n \"alpha\" = 1)))\n\n\nacross_algorithm_plots[['signal_difference']] <- g\n\nout_figure = file.path(\"figures\", \"within_z_signal_difference\")\n\nfor(extension in fig_extensions) {\n gg_file <- paste0(out_figure, extension)\n ggsave(plot = g,\n filename = gg_file,\n height = 120,\n width = 170,\n units = \"mm\",\n dpi = 500)\n}\n\nprint(g)\n\nmyPalette <- colorRampPalette(rev(brewer.pal(9, \"YlOrRd\")))\n\nheat_theme <- theme(panel.grid.minor = element_line(size = 0.1),\n panel.grid.major = element_line(size = 0.3),\n axis.text.x = element_text(angle = 90,\n size = 3.5),\n axis.text.y = element_text(size = 3.5),\n plot.title = element_text(hjust = 0.5,\n size = 9),\n legend.title = element_text(size = 8),\n legend.text = element_text(size = 7),\n legend.key.size = unit(1, \"lines\"),\n strip.text = element_text(size = 7),\n strip.background = element_rect(colour = \"black\",\n fill = \"#fdfff4\"))\n\nacross_dimension_plots = list()\nfor (dataset in c(\"TARGET\", \"TCGA\", \"GTEX\")) {\n\n # Setup filename\n data_file <- paste0(\"svcca_across_z_\", dataset, \"_mean_correlation.tsv.gz\")\n data_file <- file.path('results', data_file)\n \n # Load file\n svcca_df <- readr::read_tsv(data_file,\n col_types = readr::cols(\n .default = readr::col_character(),\n svcca_mean_similarity = readr::col_double(),\n z_dim_a = readr::col_integer(),\n z_dim_b = readr::col_integer()))\n\n # Setup title and output file name\n plot_title <- paste0(\"SVCCA Across Z\\nMean Correlations - \", dataset)\n out_figure <- paste0(\"stability_across_z_\", dataset)\n out_figure <- file.path(\"figures\", out_figure)\n\n # Make sure factors are in order\n svcca_df$z_dim_a <-\n factor(svcca_df$z_dim_a,\n levels =\n sort(as.numeric(paste(unique(svcca_df$z_dim_a))))\n )\n\n svcca_df$z_dim_b <-\n factor(svcca_df$z_dim_b,\n levels =\n sort(as.numeric(paste(unique(svcca_df$z_dim_b))))\n )\n\n svcca_df$algorithm <- factor(svcca_df$algorithm, levels = algorithms)\n svcca_df$algorithm <- svcca_df$algorithm %>% dplyr::recode(!!!recoded)\n \n svcca_df$svcca_mean_similarity <- as.numeric(paste(svcca_df$svcca_mean_similarity))\n \n # Aggregate over each seed\n svcca_df <- svcca_df %>%\n dplyr::group_by(dataset, algorithm, z_dim_a, z_dim_b) %>%\n dplyr::summarize(svcca_mean = mean(svcca_mean_similarity))\n\n # Plot and save results\n g <- ggplot(svcca_df, aes(z_dim_a, z_dim_b)) +\n geom_tile(aes(fill = svcca_mean), colour = \"white\") +\n scale_fill_gradientn(name = \"SVCCA Mean Similarity\",\n colours = myPalette(100),\n values = scales::rescale(c(1, 0.99, 0.9)),\n limits = c(0, 1)) +\n facet_wrap(~ algorithm, scales = \"free\") +\n ggtitle(plot_title) +\n theme_bw(base_size = 9) +\n heat_theme +\n xlab(\"k Dimension\") +\n ylab(\"k Dimension\") +\n scale_x_discrete(expand = c(0, 0)) +\n scale_y_discrete(expand = c(0, 0))\n\n across_dimension_plots[[dataset]] <- g\n\n for(extension in fig_extensions) {\n gg_file <- paste0(out_figure, extension)\n ggsave(plot = g,\n filename = gg_file,\n height = 100,\n width = 170,\n units = \"mm\",\n dpi = 500)\n }\n\n print(g)\n}\n\nxmin <- 0.81\nxmax <- 0.91\nymin <- 0.1\nymax <- 0.2\n\n# TCGA is the Main Figure\nacross_z_legend <- cowplot::get_legend(across_dimension_plots[['TCGA']])\n\nplot_a <- across_algorithm_plots[[\"TCGA\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Algorithm (TCGA)\"))\n\nplot_b <- across_dimension_plots[[\"TCGA\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Dimension (TCGA)\"))\n\nmain_plot <- (\n cowplot::plot_grid(\n plot_a,\n plot_b,\n labels = c(\"a\", \"b\"),\n ncol = 1,\n nrow = 2\n )\n)\n\nmain_plot <- main_plot + annotation_custom(grob = across_z_legend,\n xmin = xmin,\n xmax = xmax,\n ymin = ymin,\n ymax = ymax)\n\nmain_plot\n\nfor(extension in fig_extensions) {\n fig_file <- paste0(\"stability_summary_TCGA\", extension)\n fig_file <- file.path(\"figures\", fig_file)\n cowplot::save_plot(filename = fig_file,\n plot = main_plot,\n base_height = 200,\n base_width = 170,\n units = \"mm\",\n dpi = 500)\n}\n\ngtex_across_gg <- across_algorithm_plots[[\"GTEX\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Algorithm (GTEX)\"))\n\ntarget_across_gg <- across_algorithm_plots[[\"TARGET\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Algorithm (TARGET)\"))\n\nmain_plot <- (\n cowplot::plot_grid(\n gtex_across_gg,\n target_across_gg,\n labels = c(\"a\", \"b\"),\n ncol = 1,\n nrow = 2\n )\n)\n\nmain_plot\n\nfor(extension in fig_extensions) {\n fig_file <- paste0(\"supplementary_stability_across_algorithm_GTEX_TARGET\",\n extension)\n fig_file <- file.path(\"figures\", fig_file)\n cowplot::save_plot(filename = fig_file,\n plot = main_plot,\n base_height = 200,\n base_width = 170,\n units = \"mm\",\n dpi = 500)\n}\n\ngtex_z_gg <- across_dimension_plots[[\"GTEX\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Dimension (GTEX)\"))\n\ntarget_z_gg <- across_dimension_plots[[\"TARGET\"]] +\n theme(legend.position = 'none') +\n ggtitle(paste0(\"SVCCA Across Dimension (TARGET)\"))\n\nmain_plot <- (\n cowplot::plot_grid(\n gtex_z_gg,\n target_z_gg,\n labels = c(\"a\", \"b\"),\n ncol = 1,\n nrow = 2\n )\n)\n\n\nmain_plot <- main_plot + annotation_custom(grob = across_z_legend,\n xmin = xmin,\n xmax = xmax,\n ymin = ymin,\n ymax = ymax)\n\nmain_plot\n\nfor(extension in fig_extensions) {\n fig_file <- paste0(\"supplementary_stability_across_dimension_GTEX_TARGET\",\n extension)\n fig_file <- file.path(\"figures\", fig_file)\n cowplot::save_plot(filename = fig_file,\n plot = main_plot,\n base_height = 200,\n base_width = 170,\n units = \"mm\",\n dpi = 500)\n}\n", "meta": {"hexsha": "cdea0e4752f40ef7b6891e1c46153d42706c931e", "size": 13172, "ext": "r", "lang": "R", "max_stars_repo_path": "5.analyze-stability/scripts/nbconverted/3.stability-visualize.r", "max_stars_repo_name": "hiraksarkar/BioBombe", "max_stars_repo_head_hexsha": "d65716a3a00db1dc255bf70c31f7b0caa2f1bf65", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-12-04T21:35:59.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-04T21:35:59.000Z", "max_issues_repo_path": "5.analyze-stability/scripts/nbconverted/3.stability-visualize.r", "max_issues_repo_name": "hiraksarkar/BioBombe", "max_issues_repo_head_hexsha": "d65716a3a00db1dc255bf70c31f7b0caa2f1bf65", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 33, "max_issues_repo_issues_event_min_datetime": "2018-03-26T19:33:58.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-07T14:49:36.000Z", "max_forks_repo_path": "5.analyze-stability/scripts/nbconverted/3.stability-visualize.r", "max_forks_repo_name": "hiraksarkar/BioBombe", "max_forks_repo_head_hexsha": "d65716a3a00db1dc255bf70c31f7b0caa2f1bf65", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-03-22T18:21:41.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-23T10:35:45.000Z", "avg_line_length": 33.7743589744, "max_line_length": 87, "alphanum_fraction": 0.5261919223, "num_tokens": 3099, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31535970452053885}} {"text": "\ncode 'cf_reprice_gap'\nname '\u91cd\u5b9a\u4ef7\u7f3a\u53e3'\ndimensions {\n\taccbook fetchParents:true\n\trepriceGap null\n}\ndataRequest {\n\ttable 'repriceGap',{\n\t\tallDimensions (['organ','accbook','currency','repriceGap'])\n\t}\n\tfields 'sum(endingBalance) endingBalance'\n\tgroupFields 'accbook,repriceGap'\n\tcontextParams (['date','organ','currency','staticModel'])\n}\nheads {\n\taccbook {\n\t\tparams {\n\t\t\treprice true\n\t\t}\n\t}\n}\ndataRequest StaticModelRgaplimitDataRequest\n\nderivedHeads{\n accbook ([\n new Head(name:'\u91cd\u5b9a\u4ef7\u7f3a\u53e3',aggregate:true,\n formula:{\n def grids=thisColGrids({parent==null})\n def a=grids.find{it.rowHeadGrid.model?.alType=='A'}\n def l=grids.find{it.rowHeadGrid.model?.alType=='L'}\n if(a && l){\n a-l\n }\n },\n evaluatedCallback:{\n //\u4e0b\u9650\u989d\n def llimit=v0([riquidGap:params['riquidGap'],bal:'lvalue',dataRequest:'rgaplimit'])\n //\u4e0a\u9650\u989d\n def ulimit=v0([riquidGap:params['riquidGap'],bal:'hvalue',dataRequest:'rgaplimit'])\n if((llimit!=null && valueulimit)){\n if(properties==null){properties=[:]}\n properties['cssClass']='rgap_out_of_limit'\n }\n }\n ),\n new Head(name:'\u7f3a\u53e3\u7d2f\u8ba1',aggregate:true,\n formula:{\n (col==0)? higher:(left+higher)\n },\n evaluatedCallback:{\n //\u4e0b\u9650\u989d\n def llimit=v0([riquidGap:params['riquidGap'],bal:'accumLvalue',dataRequest:'rgaplimit'])\n //\u4e0a\u9650\u989d\n def ulimit=v0([riquidGap:params['riquidGap'],bal:'accumHvalue',dataRequest:'rgaplimit'])\n if((llimit!=null && valueulimit)){\n if(properties==null){properties=[:]}\n properties['cssClass']='rgap_out_of_limit'\n }\n }\n )\n ])\n}\n\ndataGrids {\n apply selectRows({children!=null}),{\n\t\tformula {sumChildren()}\n\t}\n}\n", "meta": {"hexsha": "133396a36571a46e61b3bd826060946b3e1faa27", "size": 2153, "ext": "rd", "lang": "R", "max_stars_repo_path": "demo-untidy/cf_reprice_gap.rd", "max_stars_repo_name": "wushexu/jyreport", "max_stars_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-04-13T01:51:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-20T03:19:05.000Z", "max_issues_repo_path": "demo-untidy/cf_reprice_gap.rd", "max_issues_repo_name": "wushexu/jyreport", "max_issues_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo-untidy/cf_reprice_gap.rd", "max_forks_repo_name": "wushexu/jyreport", "max_forks_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-27T04:06:48.000Z", "max_forks_repo_forks_event_max_datetime": "2016-08-05T03:04:09.000Z", "avg_line_length": 30.7571428571, "max_line_length": 108, "alphanum_fraction": 0.5183464933, "num_tokens": 530, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31535970452053885}} {"text": "#LOAD PACKAGES####\nlibrary(Hmisc)\nlibrary(excel.link)\nlibrary(tidyverse)\nlibrary(glue)\nlibrary(uuid)\nlibrary(ggplot2)\n\n#define folder to be read from\ndatafolder = './result'\nfilename = '2021-03-13_linked_data.csv'\n\n#read linked datasets and remove missing rows by first dataset MRN column\ndf = read.csv(paste0(datafolder, '/', filename))\n\nnew_df = df %>% drop_na(MRN__1)\n\nfor (i in c('MRN__1', 'ZIP__1')){\n cond = new_df[i] != ''\n new_df = new_df[cond,]\n}\n\n#census tract conversion (input)\n#in order to convert physical address into census tract number on census website,\n#dataframe is clean to meet the format required by the API\n#https://geocoding.geo.census.gov/geocoder/geographies/addressbatch?form\n#index | address | city | state | zip code\ncol = c('ADDR__1', 'CITY__1', 'ZIP__1') #note that state information is missing\nCT_df = new_df[,col] %>% add_column(STATE__1 = '', .after='CITY__1')\nwrite.table(CT_df, paste0(datafolder, '/', 'census_input.csv'), sep=',', col.names=FALSE)\n\n#clean census tract import\n#reading converted csv file\nfname = paste0(datafolder, '/', 'GeocodeResults.csv')\nct_import = read.csv(fname, header=FALSE)# %>% order('V1')\nct_sorted = ct_import[order(ct_import$V1),]\n\n#get tie (there are several matching states [match, tie, no_match])\n#while enough address information is provided for entries marked as tie\n#the conversion has not happened, thus the dataframe with only the\n#tied results are submitted again for re-evaluation (work in progress)\ntie_idx = ct_sorted[ct_sorted$V3 == 'Tie',]$V1\nct_tie = CT_df[match(tie_idx, rownames(CT_df)),]\nwrite.table(ct_tie, paste0(datafolder, '/', 'census_tiebreaker.csv'), sep=',', col.names=FALSE)\n\n#select match (V11: census tract number, V12: census block number)\n#removes unsuccessful match by V12 column\nct_select = ct_sorted[ct_sorted$V12 != '',] %>% drop_na()\ndf_ct_select = new_df[match(ct_select$V1, rownames(new_df)),]\n\n#evaluate positivity rate by date\ncols = c('UUID__1','LAB_DATE__1', 'LAB_RESULT__1','AGE__1')\ndf_result = df_ct_select[,cols]\ndf_result$census = ct_select$V11\n\nresult_table = df_result %>% group_by(LAB_DATE__1) %>% \n summarise(age = mean(AGE__1), \n pos_rate = sum(LAB_RESULT__1) / length(LAB_RESULT__1))\n\nresult_table = result_table[order(as.Date(result_table$LAB_DATE__1, format='%m/%d/%Y')),]\nresult_table$LAB_DATE__1 = factor(result_table$LAB_DATE__1, levels=result_table$LAB_DATE__1)\n\n#generate figure for positivity rate over time\nggplot(data=result_table, aes(x=LAB_DATE__1, y=pos_rate, group=1)) + \n geom_line() + geom_point() + labs(y='Positivity Rate (positive cases over total number)',title='Positivity Rate over time') +\n theme(axis.text.x = element_text(angle = 45,size=8, hjust = 1), \n axis.title.x = element_blank(), axis.title.y=element_text(size=10),\n plot.title = element_text(hjust=0.5))\n\nplotfolder = './figs/'\nggsave(paste0(plotfolder, 'pos_time.png'), device=png(), width=10, height=4)\n\n\n#evaluate positivity rate by census tract number\nresult_table = df_result %>% group_by(census) %>% \n summarise(age = mean(AGE__1), \n pos_rate = sum(LAB_RESULT__1) / length(LAB_RESULT__1))\n\nresult_table = result_table[order(as.character(result_table$census)),]\n\n#generate figure for positivity rate by census number\nggplot(data=result_table, aes(x=census, y=pos_rate, group=1)) +\n geom_point() + labs(x='Census Block Number', y='Positivity Rate (positive cases over total number)', title='Positivity Rate by Census Block Number') +\n theme(axis.text.x = element_blank(),\n axis.title.x = element_text(size=9), axis.title.y=element_text(size=9),\n plot.title = element_text(hjust=0.5))\n\nplotfolder = './figs/'\nggsave(paste0(plotfolder, 'pos_census.png'), device=png(), width=10, height=4)\n", "meta": {"hexsha": "7904b7beb021f6e561f1dc4479393a1198cfb507", "size": 3805, "ext": "r", "lang": "R", "max_stars_repo_path": "fastLink_positivity_rate.r", "max_stars_repo_name": "jshinm/probabilistic-linkage", "max_stars_repo_head_hexsha": "a8ecdbfeeac6387adb364ae23e5df36d2128d2f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fastLink_positivity_rate.r", "max_issues_repo_name": "jshinm/probabilistic-linkage", "max_issues_repo_head_hexsha": "a8ecdbfeeac6387adb364ae23e5df36d2128d2f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fastLink_positivity_rate.r", "max_forks_repo_name": "jshinm/probabilistic-linkage", "max_forks_repo_head_hexsha": "a8ecdbfeeac6387adb364ae23e5df36d2128d2f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.2777777778, "max_line_length": 152, "alphanum_fraction": 0.7195795007, "num_tokens": 1046, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31535970452053885}} {"text": "context(\"Facetting\")\n\ndf <- data.frame(x = 1:3, y = 3:1, z = letters[1:3])\n\ntest_that(\"facets split up the data\", {\n l1 <- ggplot(df, aes(x, y)) + geom_point() + facet_wrap(~ z)\n d1 <- pdata(l1)[[1]]\n\n expect_that(d1$PANEL, equals(factor(1:3)))\n\n l2 <- ggplot(df, aes(x, y)) + geom_point() + facet_grid(. ~ z)\n l3 <- ggplot(df, aes(x, y)) + geom_point() + facet_grid(z ~ .)\n\n d2 <- pdata(l2)[[1]]\n d3 <- pdata(l3)[[1]]\n\n expect_that(d2, equals(d3))\n expect_that(sort(names(d2)), equals(sort(c(\"x\", \"y\", \"group\", \"PANEL\"))))\n expect_that(d2$PANEL, equals(factor(1:3)))\n})\n\n\ntest_that(\"facets with free scales scale independently\", {\n l1 <- ggplot(df, aes(x, y)) + geom_point() +\n facet_wrap(~ z, scales = \"free\")\n d1 <- cdata(l1)[[1]]\n expect_that(length(unique(d1$x)), equals(1))\n expect_that(length(unique(d1$y)), equals(1))\n\n l2 <- ggplot(df, aes(x, y)) + geom_point() +\n facet_grid(. ~ z, scales = \"free\")\n d2 <- cdata(l2)[[1]]\n expect_that(length(unique(d2$x)), equals(1))\n expect_that(length(unique(d2$y)), equals(3))\n\n l3 <- ggplot(df, aes(x, y)) + geom_point() +\n facet_grid(z ~ ., scales = \"free\")\n d3 <- cdata(l3)[[1]]\n expect_that(length(unique(d3$x)), equals(3))\n expect_that(length(unique(d3$y)), equals(1))\n})\n\n\ntest_that(\"shrink parameter affects scaling\", {\n l1 <- ggplot(df, aes(1, y)) + geom_point()\n r1 <- pranges(l1)\n\n expect_that(r1$x[[1]], equals(c(1, 1)))\n expect_that(r1$y[[1]], equals(c(1, 3)))\n\n l2 <- ggplot(df, aes(1, y)) + stat_summary(fun.y = \"mean\")\n r2 <- pranges(l2)\n expect_that(r2$y[[1]], equals(c(2, 2)))\n\n l3 <- ggplot(df, aes(1, y)) + stat_summary(fun.y = \"mean\") +\n facet_null(shrink = FALSE)\n r3 <- pranges(l3)\n expect_that(r3$y[[1]], equals(c(1, 3)))\n})\n", "meta": {"hexsha": "0054d01b0c0ac321b98b8da3a0f03dac974ca810", "size": 1738, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-facet-.r", "max_stars_repo_name": "lehoangha/GSOE9712_S115_RA", "max_stars_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-facet-.r", "max_issues_repo_name": "lehoangha/GSOE9712_S115_RA", "max_issues_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-facet-.r", "max_forks_repo_name": "lehoangha/GSOE9712_S115_RA", "max_forks_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.9666666667, "max_line_length": 75, "alphanum_fraction": 0.5960874568, "num_tokens": 614, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.31535970452053885}} {"text": "library(ggplot2)\nlibrary(data.table)\nlibrary(ggsci)\nlibrary(ftplottools)\nlibrary(here)\n\npillar2 = fread(here(\"data\", \"pillar2.csv\"))\ncog = fread(here(\"data\", \"cog.csv\"))\nons = fread(here(\"data\", \"ons.csv\"))\nsamples = fread(here(\"data\", \"samples.csv\"))\nct = fread(here(\"data\", \"ct_data.csv\"))\n\n## Figure 1A\ng = ggplot(pillar2[date >= as.Date(\"2021-01-31\") & Region_name == \"London\"])\ng = g + geom_line(data=ons[area == \"LONDON\"], aes(x=date,y=pos_me * population,colour=\"S+ Infections (ONS)\"))\ng = g + geom_ribbon(data=ons[area == \"LONDON\"], aes(x=date,y=pos_me*population, ymin = pos_li*population, ymax = pos_ui*population), alpha = 0.1,fill=\"blue\")\ng = g + geom_line(aes(y=spos,x=date,colour=\"S+ Cases (Pillar 2)\"))\ng = g + geom_line(data=cog[Region_name == \"London\"],aes(y=pos,x=date,colour=\"Non-B.1.1.7 Sequences (COG-UK)\"))\ng = g + geom_ribbon(data=samples[date >= as.Date(\"2021-01-31\") & Region_name == \"London\"],aes(x=date,y=spos, ymin = spos_li, ymax = spos_ui), alpha = 0.1,fill=\"red\") \ng = g + scale_y_log10(\"Number of people\", breaks=c(10,20,50,100,200,500,1000,2000,5000,10000)) \ng = g + xlab(\"\")\ng = g + scale_x_date(date_labels = \"%-d %b\", date_breaks = \"2 week\") \ng = g + scale_color_lancet(limits=c(\"S+ Infections (ONS)\",\"S+ Cases (Pillar 2)\",\"Non-B.1.1.7 Sequences (COG-UK)\"))\ng = g + scale_fill_lancet() + ft_theme()\ng = g + theme(legend.title = element_blank()) \ng\n\n## Figure 1B\ng = ggplot(pillar2[date >= as.Date(\"2021-01-31\")])\ng = g + geom_line(data=ons[date >= as.Date(\"2021-01-31\")], aes(x=date,y=pos_me * population,colour=\"S+ Infections (ONS)\"))\ng = g + geom_ribbon(data=ons[date >= as.Date(\"2021-01-31\")], aes(x=date,y=pos_me*population, ymin = pos_li*population, \n ymax = pos_ui*population), alpha = 0.1,fill=\"blue\") \ng = g + geom_line(aes(y=spos,x=date,colour=\"S+ Cases (Pillar 2)\"))\ng = g + geom_line(data=cog,aes(y=pos,x=date,colour=\"Non-B.1.1.7 Sequences (COG-UK)\"))\ng = g + geom_ribbon(data=samples[date >= as.Date(\"2021-01-31\")],aes(x=date,y=spos, ymin = spos_li, ymax = spos_ui), alpha = 0.1,\n fill=\"red\") \ng = g + scale_y_log10(\"Number of people\") #, breaks=c(10,20,50,100,200,500,1000,2000,5000,10000)) \ng = g + xlab(\"\")\ng = g + scale_color_lancet(limits=c(\"S+ Infections (ONS)\",\"S+ Cases (Pillar 2)\",\"Non-B.1.1.7 Sequences (COG-UK)\")) + scale_fill_lancet() + ft_theme()\ng = g + theme(legend.title = element_blank()) \ng = g + facet_wrap(~ Region_name) #, scales=\"free\")\ng = g + scale_x_date(date_labels = \"%b\", date_breaks = \"1 month\") \ng\n\n## Appendix Figure A\ng = ggplot(pillar2[date >= as.Date(\"2021-01-31\") & Region_name == \"London\"])\ng = g + geom_line(aes(y=frac,x=date,colour=\"Pillar 2\"))\ng = g + geom_ribbon(data=samples[date >= as.Date(\"2021-01-31\") & Region_name == \"London\"],aes(x=date,y=frac, ymin = \n lower_ci, ymax = upper_ci), alpha = 0.1,fill=\"red\") \ng = g + scale_y_continuous(\"Fraction S+\\n\",labels = scales::label_percent(accuracy = 1L),expand=c(0,0)) + expand_limits(y=0)\ng = g + xlab(\"\")\ng = g + geom_line(data=ons[area == \"LONDON\"],aes(x=date,y=pos_me / (pos_me + neg_me),colour=\"ONS\"))\ng = g + geom_line(data=cog[Region_name == \"London\"],aes(x=date,y=frac,colour=\"COG-UK\"))\ng = g + geom_ribbon(data=cog[Region_name == \"London\"],aes(x=date,y=frac, ymin = lower_ci, ymax = upper_ci), alpha = 0.25,fill=\"green\") \ng = g + scale_color_lancet(limits=c(\"ONS\",\"Pillar 2\",\"COG-UK\"))\ng = g + scale_fill_lancet() + ft_theme()\ng = g + theme(legend.title = element_blank()) \ng = g + scale_x_date(date_labels = \"%-d %b\", date_breaks = \"2 week\")\ng = g + geom_ribbon(data=ons[area == \"LONDON\"],aes(x=date,y=pos_me / (pos_me + neg_me), ymin = fraction.lo, ymax = fraction.hi), alpha = 0.1,fill=\"blue\")\ng\n\n## Appendix Figure B\ng = ggplot(pillar2[date >= as.Date(\"2021-01-31\")])\ng = g + geom_line(aes(y=frac,x=date,colour=\"Pillar 2\"))\ng = g + geom_ribbon(data=samples[date >= as.Date(\"2021-01-31\") ],aes(x=date,y=frac, ymin = lower_ci, ymax = upper_ci), alpha = 0.2,fill=\"red\") \ng = g + scale_y_continuous(\"Fraction S+\\n\",labels = scales::label_percent(accuracy = 1L),expand=c(0,0)) + expand_limits(y=0)\ng = g + xlab(\"\")\ng = g + geom_line(data=ons[date >= as.Date(\"2021-01-31\")],aes(x=date,y=pos_me / (pos_me + neg_me),colour=\"ONS\"))\ng = g + geom_line(data=cog,aes(x=date,y=frac,colour=\"COG-UK\"))\ng = g + geom_ribbon(data=cog,aes(x=date,y=frac, ymin = lower_ci, ymax = upper_ci), alpha = 0.2,fill=\"green\") \ng = g + scale_color_lancet(limits=c(\"ONS\",\"Pillar 2\",\"COG-UK\"))\ng = g +scale_fill_lancet() + ft_theme()\ng = g + theme(legend.title = element_blank()) \ng = g + geom_ribbon(data=ons,aes(x=date,y=pos_me / (pos_me + neg_me), ymin = fraction.lo, ymax = fraction.hi), alpha = 0.1,fill=\"blue\")\ng = g + scale_x_date(date_labels = \"%b\", date_breaks = \"1 month\") \ng = g + facet_wrap(~ Region_name) #, scales=\"free\")\ng\n\n## Figure 2 and Appendix Figure 3\nct <- ct[,gene := factor(gene, levels = c(\"ORF1ab (Ct<=30)\",\"N (Ct<=30)\",\"MS2 control\" ))]\nfor(i in unique(ct$phec_name)){\n p <- ggplot(ct[ct$phec_name==i,],aes(x=Date,y=ct_mean,colour=sneg_g)) +\n # labs(colour=\"SGTF\") +\n geom_ribbon(aes(ymin=ct_mean-1.96*se,ymax=ct_mean+1.96*se, linetype=NA,fill=sneg_g),alpha=0.3,show.legend = FALSE) +\n geom_line(aes(y=ct_mean),size=1) +\n facet_wrap(~gene) +\n theme_classic() +\n scale_x_date(date_labels = \"%d %b\", date_breaks = \"3 week\") +\n ylab(\"Mean Ct value\") +\n theme(panel.grid.major = element_line(colour=\"lightgrey\",linetype=\"dashed\", size = 0.1),\n strip.background = element_blank(),strip.text.x = element_text(size = 12)) +\n scale_fill_lancet() + \n scale_color_lancet() +\n ft_theme() +\n theme(axis.text.x = element_text( angle = 45,hjust = 1)) +\n theme(legend.title=element_blank()) +\n ggtitle(i) \n # if (i==\"London\"){\n # p <- p + scale_x_date(date_labels = \"%b %d\", date_breaks = \"3 week\")\n # }\n ggsave(paste0(here(),\"/figures/\",i,\"_ct.pdf\"),p, width=8, height = 5)\n}\n", "meta": {"hexsha": "e22521463efea661478508963579b2710268fd35", "size": 6089, "ext": "r", "lang": "R", "max_stars_repo_path": "plots.r", "max_stars_repo_name": "ImperialCollegeLondon/SARS_CoV_2-_varinats_uk", "max_stars_repo_head_hexsha": "ecb0b51591b3ca45fc54c4434048d6afe331f3e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-05-04T10:34:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-21T05:20:36.000Z", "max_issues_repo_path": "plots.r", "max_issues_repo_name": "ImperialCollegeLondon/SARS_CoV_2_variants_uk", "max_issues_repo_head_hexsha": "ecb0b51591b3ca45fc54c4434048d6afe331f3e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plots.r", "max_forks_repo_name": "ImperialCollegeLondon/SARS_CoV_2_variants_uk", "max_forks_repo_head_hexsha": "ecb0b51591b3ca45fc54c4434048d6afe331f3e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 59.1165048544, "max_line_length": 166, "alphanum_fraction": 0.6260469699, "num_tokens": 2068, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.585101139733739, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31535969672172537}} {"text": "context(\"%>%: multi-argument functions on right-hand side\")\n\ntest_that(\"placement of lhs is correct in different situations\", {\n \n # When not to be placed in first position and in the presence of\n # non-placeholder dots, e.g. in formulas.\n case0a <- \n lm(Sepal.Length ~ ., data = iris) %>% coef\n \n case1a <- \n iris %>% lm(Sepal.Length ~ ., .) %>% coef\n \n case2a <-\n iris %>% lm(Sepal.Length ~ ., data = .) %>% coef\n \n expect_that(case1a, is_equivalent_to(case0a))\n expect_that(case2a, is_equivalent_to(case0a))\n \n # In first position and used in arguments\n case0b <-\n transform(iris, Species = substring(Species, 1, 1))\n \n case1b <-\n iris %>% transform(Species = Species %>% substr(1, 1))\n \n case2b <-\n iris %>% transform(., Species = Species %>% substr(., 1, 1))\n \n expect_that(case1b, is_equivalent_to(case0b))\n expect_that(case2b, is_equivalent_to(case0b))\n \n # LHS function values\n case0c <-\n aggregate(. ~ Species, iris, function(x) mean(x >= 5))\n \n case1c <-\n (function(x) mean(x >= 5)) %>% \n aggregate(. ~ Species, iris, .)\n \n expect_that(case1c, is_equivalent_to(case0c))\n \n # several placeholder dots\n expect_true(iris %>% identical(., .))\n \n \n # \"indirect\" function expressions \n expect_that(1:100 %>% iris[., ], is_identical_to(iris[1:100, ]))\n \n})\n", "meta": {"hexsha": "abb1c17838d3d198a8f1110a195fdf205bd2eca8", "size": 1325, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-multiple-arguments.r", "max_stars_repo_name": "zeehio/magrittr", "max_stars_repo_head_hexsha": "024064675ca88123c35ab7916a2df4ff285fa213", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 559, "max_stars_repo_stars_event_min_datetime": "2016-10-24T03:31:52.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T18:02:41.000Z", "max_issues_repo_path": "tests/testthat/test-multiple-arguments.r", "max_issues_repo_name": "zeehio/magrittr", "max_issues_repo_head_hexsha": "024064675ca88123c35ab7916a2df4ff285fa213", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 136, "max_issues_repo_issues_event_min_datetime": "2016-10-25T10:23:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-14T08:00:43.000Z", "max_forks_repo_path": "tests/testthat/test-multiple-arguments.r", "max_forks_repo_name": "zeehio/magrittr", "max_forks_repo_head_hexsha": "024064675ca88123c35ab7916a2df4ff285fa213", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 132, "max_forks_repo_forks_event_min_datetime": "2016-10-25T10:15:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-16T15:32:56.000Z", "avg_line_length": 26.5, "max_line_length": 66, "alphanum_fraction": 0.6294339623, "num_tokens": 411, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.6113819732941511, "lm_q1q2_score": 0.3152407215445012}} {"text": "# Copyright 2020 The MuLT Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\nlibrary(ggplot2)\n\ndf <- read.csv('../output/fill/accuracy_fish_aucs.csv', sep=',')\ndf$category <- c(rep('Copy Number Deletions', 3), c('Copy Number Gains'), rep('Hyperdiploid Copy Number Gains', 9), rep('Translocations', 8), c('Flag'))\n\nfish.levels <- c(\"Copy Number Deletions\", \"Copy Number Gains\", \"Hyperdiploid Copy Number Gains\", \"Translocations\", \"Flag\")\ndf$category <- factor(df$category, levels = fish.levels)\n#df$category <- fish.levels\ndf <- df[order(df$category),]\n# df$category <- factor(df$category, levels = fish.levels)\n\ndf$var <- factor(df$var, levels = df$var[rev(order(df$category))])\n\ngraph <- ggplot(df, \n aes(x = var, \n y = auc, \n colour = category,\n fill = category)) + \n geom_bar(stat = \"identity\", alpha = .6, size = 0.3) +\n ylim(0,1) + \n scale_fill_hue(name = \"FISH Legend\", \n labels = fish.levels) +\n scale_colour_hue(guide = \"none\") +\n scale_y_continuous(labels = scales::percent) +\n xlab(NULL) + ylab(\"AUC\") +\n theme(legend.background = element_rect(colour = \"black\", size = .2),\n panel.grid.major.y = element_line(size = .15),\n text = element_text(size = 7),\n legend.key.size = unit(8, \"pt\"),\n legend.position = 'right',\n axis.text.x = element_text(size = 6, angle = 45),\n axis.title.x = element_text(vjust = 1)) + \n coord_flip()\n", "meta": {"hexsha": "1a06c10439530f37c6e5178fc71b961e94dbd0d1", "size": 2118, "ext": "r", "lang": "R", "max_stars_repo_path": "r/plot.auc.gen.fish.r", "max_stars_repo_name": "urielcaire/mult", "max_stars_repo_head_hexsha": "45c0cba69153442be2cee6309d46d55086445e5c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2020-10-13T01:27:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-22T13:42:26.000Z", "max_issues_repo_path": "r/plot.auc.gen.fish.r", "max_issues_repo_name": "urielcaire/mult", "max_issues_repo_head_hexsha": "45c0cba69153442be2cee6309d46d55086445e5c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r/plot.auc.gen.fish.r", "max_forks_repo_name": "urielcaire/mult", "max_forks_repo_head_hexsha": "45c0cba69153442be2cee6309d46d55086445e5c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-10-12T13:40:41.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T05:28:59.000Z", "avg_line_length": 43.2244897959, "max_line_length": 152, "alphanum_fraction": 0.6142587347, "num_tokens": 530, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.31524072154450117}} {"text": "library(testthat) \n\nsource(\"../src/data_structures.r\")\n\ntest_that(\"TreatmentDictionary\",{\n expect_error(TreatmentDictionary(TRUE,2),\n \"Elements 1 of class(elements_true) == class(elements_false) are not true\",fixed=TRUE)\n expect_error(TreatmentDictionary(rep(0.5,times=99),rep(1,times=100)),\n \"length(elements_true) not equal to length(elements_false)\",fixed=TRUE)\n treated = TreatmentDictionary(rep(0.5,times=100),rep(1,times=100))\n \n})\n\ntest_that(\"NumericTreatmentDictionary\",{\n expect_error(NumericTreatmentDictionary(TRUE,2),\n \"Elements 1 of class(elements_true) == class(elements_false) are not true\",fixed=TRUE)\n expect_error(NumericTreatmentDictionary(3,\"string\"),\n \"Elements 1 of class(elements_true) == class(elements_false) are not true\",fixed=TRUE)\n expect_error(NumericTreatmentDictionary(rep(TRUE,times=100),rep(1,times=100)),\n \"Elements 1 of class(elements_true) == class(elements_false) are not true\",fixed=TRUE)\n expect_error(NumericTreatmentDictionary(rep(0.5,times=100),rep(FALSE,times=100)),\n \"Elements 1 of class(elements_true) == class(elements_false) are not true\",fixed=TRUE)\n expect_error(NumericTreatmentDictionary(rep(0.5,times=99),rep(1,times=100)),\n \"length(elements_true) not equal to length(elements_false)\",fixed=TRUE)\n expect_error(NumericTreatmentDictionary(\"string\",\"string\"),\n \"elements_true is not a numeric or integer vector\")\n\n treated = NumericTreatmentDictionary(rep(0.5,times=100),rep(1,times=100))\n \n})\n\ntest_that(\"init_observed\",{\n W=rep(c(TRUE,FALSE),times=50)\n treated = NumericTreatmentDictionary(rep(0.5,times=100),rep(1,times=100))\n expect_error(init_observed(4,W),\"is(object = treated, class2 = \\\"numeric_treatment_dictionary\\\") is not TRUE\",fixed = TRUE)\n expect_error(init_observed(treated,\"value\"),\"is.logical(W) is not TRUE\",fixed = TRUE)\n expect_error(init_observed(treated,c(TRUE,FALSE)),\"length(W) not equal to length(treated$`TRUE`)\",fixed = TRUE)\n \n observed=init_observed(treated,W)\n \n expect_equal(observed$'TRUE'[W], (rep(0.5,times=50)))\n expect_equal(observed$'TRUE'[!W], (rep(1,times=50)))\n expect_equal(observed$'FALSE'[W], (rep(1,times=50)))\n expect_equal(observed$'FALSE'[!W], (rep(0.5,times=50)))\n\n})\n\ntest_that(\"getN_treated\",{\n expect_error(getN_treated(\"rr\"),\"W is not a numeric or integer vector\")\n\n W=c(rep(c(1,0),times=20),1,1)\n N_treated = getN_treated(W)\n \n expect_equal(N_treated$`TRUE`,(22))\n expect_equal(N_treated$`FALSE`,(20))\n \n})\n\ntest_that(\"Counterfactuals\",{\n treated <-NumericTreatmentDictionary(c(1,1,1),c(2,2,2))\n W <- c(TRUE,TRUE,FALSE)\n counterfactuals <- Counterfactuals(treated,W)\n expect_equal(get_index(counterfactuals,3)$treated$`TRUE`,1)\n expect_equal(get_index(counterfactuals,3)$treated$`FALSE`,2)\n expect_equal(get_index(counterfactuals,3)$observed$`TRUE`,2)\n expect_equal(get_index(counterfactuals,3)$observed$`FALSE`,1)\n \n expect_equal(get_elements_by_treatment(counterfactuals,W,TRUE,TRUE),c(1,1))\n expect_equal(get_elements_by_treatment(counterfactuals,W,FALSE,FALSE),1)\n expect_equal(get_elements_by_treatment(counterfactuals,W,FALSE,TRUE),2)\n expect_equal(get_elements_by_treatment(counterfactuals,W,TRUE,FALSE),c(2,2))\n\n treated <-NumericTreatmentDictionary(c(1,2,3),c(4,5,6))\n W <- c(TRUE,FALSE,FALSE)\n counterfactuals <- Counterfactuals(treated,W)\n \n expect_equal(get_index(counterfactuals,c(2,1,1))$treated$`TRUE`,c(2,1,1))\n expect_equal(get_index(counterfactuals,c(2,1,1))$treated$`FALSE`,c(5,4,4))\n})\n\ntest_that(\"get_elements_by_treatment.vector\",{\n\n expect_equal(get_elements_by_treatment(1,TRUE,TRUE),1)\n expect_equal(get_elements_by_treatment(1,TRUE,FALSE),numeric(0))\n \n Y<-c(1,2,3,4,5)\n W<-c(TRUE,FALSE,TRUE,TRUE,TRUE)\n exp<-c(1,3,4,5)\n expect_equal(get_elements_by_treatment(Y,W,TRUE),exp)\n expect_equal(get_elements_by_treatment(Y,W,FALSE),2)\n})\n\n\ntest_that(\"get_elements_by_treatment.matrix\",{\n m<-rbind(c(1,2),c(3,4),c(5,6))\n W <- c(TRUE,FALSE,TRUE)\n exp <-rbind(c(1,2),c(5,6))\n expect_equal(get_elements_by_treatment(m,W,TRUE),exp)\n expect_equal(get_elements_by_treatment(m,W,FALSE),c(3,4))\n})\n\ntest_that(\"add_counterfactuals\",{\n treated <-NumericTreatmentDictionary(c(1,1,1),c(2,2,2))\n W <- c(TRUE,FALSE,TRUE)\n c1 <- Counterfactuals(treated,W)\n c2 <- Counterfactuals(treated,W)\n \n sum<-add_counterfactuals(c1,c2)\n expect_equal(sum$treated$`TRUE`,c(2,2,2))\n expect_equal(sum$treated$`FALSE`,c(4,4,4))\n \n treated <-NumericTreatmentDictionary(c(1,1,1)*2,c(2,2,2)*2)\n c3<-Counterfactuals(treated,W)\n \n expect_equal(sum$treated$`TRUE`,c3$treated$`TRUE`)\n expect_equal(sum$treated$`FALSE`,c3$treated$`FALSE`)\n expect_equal(sum$observed$`TRUE`,c3$observed$`TRUE`)\n expect_equal(sum$observed$`FALSE`,c3$observed$`FALSE`)\n})\n\n\n\n", "meta": {"hexsha": "6634ae37802f02844ec9f5eee2b8c296eb470313", "size": 4830, "ext": "r", "lang": "R", "max_stars_repo_path": "src/R/test/test_data_structures.r", "max_stars_repo_name": "naskoD/bachelorThesis", "max_stars_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-04T09:53:36.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-04T09:53:36.000Z", "max_issues_repo_path": "src/R/test/test_data_structures.r", "max_issues_repo_name": "naskoD/bachelorThesis", "max_issues_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/R/test/test_data_structures.r", "max_forks_repo_name": "naskoD/bachelorThesis", "max_forks_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2682926829, "max_line_length": 125, "alphanum_fraction": 0.7246376812, "num_tokens": 1483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.611381973294151, "lm_q1q2_score": 0.31524072154450117}} {"text": "\r\nlibrary( \"rmacrolite\" ) \r\n\r\n# # Setup MACRO directory (if needed)\r\n# rmacroliteSetModelVar( \"C:/swash/macro\" )\r\n\r\n\r\n\r\n# Path to an example par-file\r\npar_file_path <- system.file( \"par-files\", \r\n \"chat_winCer_GW-X_900gHa_d182.par\", \r\n package = \"rmacrolite\" ) \r\n\r\n# Import the example par-file\r\npar_file <- rmacroliteImportParFile( \r\n file = par_file_path ) \r\n\r\n\r\n\r\n# Fetch the current parametrization\r\nrmacroliteDiffCoef( x = par_file ) \r\n # [1] 5.2e-10\r\n\r\n# Modify the parameter\r\npar_file2 <- par_file\r\nrmacroliteDiffCoef( x = par_file2 ) <- 5.00E-10\r\n\r\n# Check the result\r\ndc <- rmacroliteDiffCoef( x = par_file2 )\r\ndc \r\n # [1] 5e-10\r\n\r\n# Internal control\r\nif(dc != 5.00E-10 ){ \r\n stop( \"Test of rmacroliteDiffCoef() failed\" ) } \r\n\r\n# Clean-up\r\nrm( par_file_path, par_file, par_file2, dc )\r\n\r\n", "meta": {"hexsha": "b75dd6ac795583918398d16d426ddcab35a0aaf4", "size": 830, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/examples/rmacroliteDiffCoef-example.r", "max_stars_repo_name": "julienmoeys/rmacrolite", "max_stars_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/examples/rmacroliteDiffCoef-example.r", "max_issues_repo_name": "julienmoeys/rmacrolite", "max_issues_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/examples/rmacroliteDiffCoef-example.r", "max_forks_repo_name": "julienmoeys/rmacrolite", "max_forks_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.75, "max_line_length": 53, "alphanum_fraction": 0.6385542169, "num_tokens": 283, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3152407215445011}} {"text": "#' Proportion size in MSDem\n#'\n#' This function allows you to calculate population size by sex or by regions using the MSDem output\n#' @param res1 data\n#' @param agel lower limit - default is 0\n#' @param ageu upper limit - default it 'max'\n#' @param period specific periods - default is NULL\n#' @param sex specific sex - default is NULL\n#' @param bysex group by sex - default is FALSE\n#' @param reg specific region(s)\n#' @param byreg group by regions - default is FALSE\n#' @param resi specific residence(s)\n#' @param byresi group by residences - default is FALSE\n#' @return R list with five data.table 1) var_def 2) state_space 3) mig_dom 4) mig_int 5) results\n#' @keywords read\n#' @export\n#' @examples\n\npropkc <- function(resX=res,\n period = NULL,\n agel=0,ageu=\"max\",byage=FALSE,\n sex=NULL, bysex=FALSE,\n reg=NULL, byreg=FALSE,\n resi=NULL,byresi=FALSE) {\n #Try this for with residence\n #need to try all possible combinations\n\n # #need res data\n # if(length(grep(\"res\",ls()))==0) print(\"Run fread_all() first\")\n\n var_def <- resX$var_def\n names(var_def)[1] <-c(\"varname\")\n # head(var_def)\n # unique(var_def$varname)\n ages = var_def[varname==\"age\"][,values:=as.numeric(values)]\n if(ageu == \"max\") ageu = max(ages$values)\n\n periods = var_def[varname==\"period\"][,values:=as.numeric(values)]$values\n periods = c(periods,max(periods)+unique(diff(periods)))\n if(!is.null(period)) periods = period\n\n sexes = var_def[varname==\"sex\"]$values\n if(!is.null(sex)) sexes = sex\n\n regions = var_def[varname==\"region\"]$values\n if(!is.null(reg)) regions = reg\n\n group.var = c(\"age\",\"period\")\n if(bysex) group.var = c(group.var,\"sex\")\n if(byreg) group.var = c(group.var,\"region\")\n\n group.var.minus.age = setdiff(group.var,\"age\")\n resX <- resX$results\n\n if(any(grep(\"resi\",unique(var_def$varname)))) {\n residences = var_def[varname==\"resi\"]$values\n if(!is.null(resi)) residences = resi\n if(byres) key.cols = c(group.var,\"residence\")\n res.pop = resX[period%in%periods&age%in%seq(agel,ageu)&sex%in%sexes®%in%regions&resi%in%residences\n ,.(pop=sum(pop)),by=group.var]\n } else {\n res.prop <- resX[period%in%periods & sex%in%sexes & region%in%regions,.(pop=sum(pop)),by=group.var][,.(age=age,prop=prop.table(pop)),by=group.var.minus.age]\n if(!byage) res.prop <- res.prop[age%in%seq(agel,ageu),.(age=paste(agel,ageu+4,sep=\"to\"),prop=sum(prop)),group.var.minus.age]\n }\n return(res.prop)\n }\n", "meta": {"hexsha": "21077f3f47fa0bcf81671abda897904f9eb6e755", "size": 2840, "ext": "r", "lang": "R", "max_stars_repo_path": "R/propkc.r", "max_stars_repo_name": "kcsamir/mdpop", "max_stars_repo_head_hexsha": "1d1330b0fbbae8ea1eb893b3f6a3c5d91f48b73f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-09T11:50:05.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-09T11:50:05.000Z", "max_issues_repo_path": "R/propkc.r", "max_issues_repo_name": "kcsamir/mdpop", "max_issues_repo_head_hexsha": "1d1330b0fbbae8ea1eb893b3f6a3c5d91f48b73f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-08-20T10:32:42.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-20T10:53:29.000Z", "max_forks_repo_path": "R/propkc.r", "max_forks_repo_name": "kcsamir/mdpop", "max_forks_repo_head_hexsha": "1d1330b0fbbae8ea1eb893b3f6a3c5d91f48b73f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.3880597015, "max_line_length": 170, "alphanum_fraction": 0.5781690141, "num_tokens": 758, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.31524071424244177}} {"text": "library(ggplot2)\nlibrary(dplyr)\n\ndata = read.csv('./ld14-intercom-log.csv')\n#data$Timestamp = floor(data$Timestamp / (24 * 60 * 60 * 1000)) * 24 * 60 * 60 * 1000\n#data = aggregate(. ~ Timestamp + Group, data = data, max)\ndates = unique(data$Timestamp)\ngroups = unique(data$Group)\ncombinations = expand.grid(Timestamp = dates, Group = groups)\n\ndata = data %>%\n\tfull_join(combinations, by = c(\"Timestamp\" = \"Timestamp\", \"Group\" = \"Group\")) %>%\n\tmutate(Count = ifelse(is.na(Count), 0, Count)) %>%\n\tarrange(Timestamp, Group) %>%\n\t#mutate(Group = factor(Group, levels = c(\"Sole Founders (1st time)\", \"In a Team (1st time)\", \"Sole Founders (2nd time)\", \"In a Team (2nd time)\", \"Sole Founders (3rd time)\", \"In a Team (3rd time)\", \"Sole Founders (4th time)\", \"In a Team (4th time)\", \"Sole Founders (5th time)\", \"In a Team (5th time)\")))\n\tmutate(Group = factor(Group, levels = c(\"Sole Founders\", \"In a Team\")))\n\nggplot(data, aes(x = as.POSIXct(Timestamp/1000, origin=\"1970-01-01\"), y = Count, fill = Group)) +\n\tgeom_area() +\n\tgeom_vline(aes(xintercept = as.POSIXct(1586131200, origin=\"1970-01-01\")), color=\"purple\") +\n\tgeom_text(aes(x=as.POSIXct(1586131200, origin=\"1970-01-01\") + 60000, label=\"Start of Form\", y=60), color=\"purple\", angle=90, size=4) +\n\tgeom_vline(aes(xintercept = as.POSIXct(1584144000, origin=\"1970-01-01\")), color=\"purple\") +\n\tgeom_text(aes(x=as.POSIXct(1584144000, origin=\"1970-01-01\") + 60000, label=\"KOWE\", y=60), color=\"purple\", angle=90, size=4) +\n\ttheme_bw(base_size=14) +\n\tscale_fill_brewer(palette = 'Paired') +\n\txlab(\"Time\") +\n\tylab(\"Count\")\n\nggsave('./plot.png', width = 10, height = 5)\n", "meta": {"hexsha": "abb064285c67f3ae2c099cac1ced7ccd6962736b", "size": 1614, "ext": "r", "lang": "R", "max_stars_repo_path": "gen-plot.r", "max_stars_repo_name": "yousefamar/ef-scripts", "max_stars_repo_head_hexsha": "932b3bc88a3a5724b891fc7b961034da5e07952d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gen-plot.r", "max_issues_repo_name": "yousefamar/ef-scripts", "max_issues_repo_head_hexsha": "932b3bc88a3a5724b891fc7b961034da5e07952d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gen-plot.r", "max_forks_repo_name": "yousefamar/ef-scripts", "max_forks_repo_head_hexsha": "932b3bc88a3a5724b891fc7b961034da5e07952d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.8, "max_line_length": 303, "alphanum_fraction": 0.6635687732, "num_tokens": 564, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.672331699179286, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.3151827985589447}} {"text": "################################################\n## 2-2_Compute-metrics_scrublet.r\n## - Compute doublet scores per sample\n## - Save an txt file with doublet scores per sample [doublet-scores-sampleName]\n################################################\n\nsuppressWarnings(library(Seurat))\nsuppressWarnings(library(scDblFinder))\n\nmessage(\"reloading old matrices...\")\nscdata_list <- readRDS(\"/output/pre-doublet-scdata_list.rds\")\n\nmessage(\"Loading configuration...\")\nconfig <- RJSONIO::fromJSON(\"/input/meta.json\")\n\n# Check which samples have been selected. Otherwiser we are going to use all of them. \nif (length(config$samples)>0){\n samples <- config$samples\n}else{\n samples <- names(scdata_list)\n}\n\nscdata_list <- scdata_list[samples]\n\n# compute_doublet_scores function\n#' @description Save the result of doublets scores per sample. \n#' @param scdata Raw sparse matrix with the counts for one sample.\n#' @param sample_name Name of the sample that we are preparing.\n#'\n#' @return \ncompute_doublet_scores <- function(scdata, sample_name) {\n \n message(\"Sample --> \", sample_name, \"...\")\n\n edpath <- paste0(\"/output/pre-emptydrops-\", sample_name, \".rds\")\n if (file.exists(edpath)) {\n edout <- readRDS(edpath)\n keep <- which(edout$FDR <= 0.001)\n scdata <- scdata[, keep]\n }\n\n scdata_DS <- scDblFinder(scdata, dbr = NULL, trajectoryMode = FALSE)\n df_doublet_scores <- data.frame(Barcodes=rownames(scdata_DS@colData), doublet_scores=scdata_DS@colData$scDblFinder.score,\n doublet_class = scdata_DS@colData$scDblFinder.class)\n\n write.table(df_doublet_scores, file = paste(\"/output/doublet-scores-\", sample_name, \".csv\", sep = \"\"), row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \"\\t\")\n}\n\nmessage(\"calculating probability of barcodes being doublets...\")\nfor (sample_name in names(scdata_list)) {\n compute_doublet_scores(scdata_list[[sample_name]], sample_name)\n}\n\nmessage(\"Step 2-2 completed.\")\n", "meta": {"hexsha": "e6664a03a0d33cf1107ba4d67863705a9de8f066", "size": 1950, "ext": "r", "lang": "R", "max_stars_repo_path": "src/2-2_Compute-metrics_doublets.r", "max_stars_repo_name": "biomage-ltd/data-ingest", "max_stars_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-23T17:41:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-10T20:50:49.000Z", "max_issues_repo_path": "src/2-2_Compute-metrics_doublets.r", "max_issues_repo_name": "biomage-ltd/data-ingest", "max_issues_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2021-01-07T11:34:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-22T15:46:46.000Z", "max_forks_repo_path": "src/2-2_Compute-metrics_doublets.r", "max_forks_repo_name": "biomage-ltd/data-ingest", "max_forks_repo_head_hexsha": "cbac0d5aae262afa6afdd2ee74b8ef7c58e745f6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-10T23:17:30.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-10T23:17:30.000Z", "avg_line_length": 35.4545454545, "max_line_length": 171, "alphanum_fraction": 0.6820512821, "num_tokens": 483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316860482762, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.31518279240325037}} {"text": "from <- dplyr::dense_rank(sample.int(1E5, replace = TRUE))\nto <- dplyr::dense_rank(sample.int(1E5, replace = TRUE))\n\n# regular R:\n# 10,000 length vector ~ 0.5 sec\n# 30,000 length vector ~ 4 sec\n# 100,000 length vector ~ 75 secs\nprofvis::profvis(r <- group_edges(from, to))\n\n# Rcpp:\n# 10,000 length vector ~ 0.02 sec\n# 30,000 length vector ~ 0.25 sec\n# 100,000 length vector ~ 2 sec\n# 300,000 length vector ~ 15 sec\nprofvis::profvis(r <- group_edges_rcpp(from - 1L, to - 1L))\n\nsort(table(group_edges(from, to)), decreasing = TRUE)\n\nstart_profiler(\"src/profile.out\")\nr <- group_edges_rcpp(from - 1L, to - 1L)\nstop_profiler()\n\n# google-pprof --text src/lshr.so src/profile.out\n", "meta": {"hexsha": "0b9c062e7ab1b84dd5caec2b73b36789533e416a", "size": 674, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/examples/speed-test.r", "max_stars_repo_name": "zamorarr/lshr", "max_stars_repo_head_hexsha": "e09d31671d2668d2664e74b4d39e654d9b10b66f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/examples/speed-test.r", "max_issues_repo_name": "zamorarr/lshr", "max_issues_repo_head_hexsha": "e09d31671d2668d2664e74b4d39e654d9b10b66f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/examples/speed-test.r", "max_forks_repo_name": "zamorarr/lshr", "max_forks_repo_head_hexsha": "e09d31671d2668d2664e74b4d39e654d9b10b66f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0833333333, "max_line_length": 59, "alphanum_fraction": 0.6958456973, "num_tokens": 230, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.31511365724104956}} {"text": "REBOL [\n Title: \"Generates Red/System maths tests\"\n\tAuthor: \"Peter W A Wood\"\n\tFile: \t %make-maths-auto-test.r\n\tVersion: 0.1.3\n\tTabs:\t 4\n\tRights: \"Copyright (C) 2011-2015 Peter W A Wood. All rights reserved.\"\n\tLicense: \"BSD-3 - https://github.com/red/red/blob/origin/BSD-3-License.txt\"\n]\n\nmake-test: func [\n test-string [string!]\n /setup\n test-setup [string!]\n /local\n rule\n char\n mold-char\n new-test-string\n cs\n][\n cs: complement charset [#\"#\"]\n mold-char: func [\n char [char!]\n ][\n join {#\"^^(} [copy/part tail to-hex to integer! to char! char -2 {)\"}]\n ]\n rule: [\n (new-test-string: copy \"\")\n any [\n copy char [{#\"} thru {\"}] (append new-test-string mold-char load char) \n |\n copy string [some cs] (append new-test-string string)\n |\n skip\n ]\n ]\n test-number: test-number + 1\n append tests join { --test-- \"maths-auto-} [test-number {\"^(0A)}]\n if setup [append tests test-setup]\n append tests \" --assert \"\n \n ;; convert all char! literals to hex format\n parse/all test-string rule\n \n ;; check if the first value is a byte!\n either byte-first? [\n append tests reform [expected \"= (as integer! (\" new-test-string \"))^(0A)\"]\n ][\n append tests reform [expected \"= (\" new-test-string \")^(0A)\"]\n ]\n \n]\n\npreprocess: func [\n s [string!]\n /local\n calc\n rules\n sb\n ns\n nothing-changed\n division-sign\n][\n\n calc: func [\n a [char!]\n op [word!]\n b [char!]\n /local \n res\n ][\n ;; Ensure all typecasts are to integer! not char!\n a: to integer! a \n \n switch op [\n + [res: a + b]\n - [res: a - b]\n * [res: a * b]\n div-sign [res: to integer! (a / b)]\n ]\n \n ;; adjust the return value to emulate 8-bit arithmetic with overflow\n either res >= 0 [\n res: res // 256\n ][\n until [\n res: res + 256\n res > 0\n ]\n ]\n to char! res\n ]\n \n rules: [\n any [\n [set a char! set op word! set b char! (\n acc: calc a op b\n replace ns join \"(\" [mold a \" \" op \" \" mold b \")\"] mold acc\n replace ns join \"\" [mold a \" \" op \" \" mold b ] mold acc\n nothing-changed: false\n )] |\n [set a integer! 'div-sign set b integer! (\n replace ns join \"\" [mold a \" \" op \" \" mold b ]\n join \"(to integer! divide \"[mold a \" \" mold b \")\"]\n )] |\n [set p paren! (parse to block! p rules)] |\n skip\n ]\n end\n ]\n\n ns: copy s\n replace/all ns \" / \" \" div-sign \" \n until [\n nothing-changed: true\n sb: to block! load ns\n parse sb rules\n nothing-changed\n ]\n \n ns\n]\n\n;; initialisations \ntests: copy \"\" ;; string to hold generated tests\ntest-number: 0 ;; number of the generated test\nmake-dir %auto-tests/\nfile-out: %auto-tests/maths-auto-test.reds\n\n\n;; tests & data - test formulae, test data, test formulae, test data, etc.\ntests-and-data: [\n [\n \"(v * v) * v\"\n \"(v - v) - v\"\n \"(v * v) - v\"\n \"(v - v) * v\"\n \"v * v * v\"\n \"v - v - v\"\n \"v - v * v\"\n \"(v / v) + v\"\n ] \n [\n [1 1 1]\n [2 2 2]\n [256 256 256]\n [257 257 257]\n [255 256 257]\n [-256 256 256]\n [257 -257 257]\n [255 256 -257]\n [-256 -256 -256]\n [-257 -257 -257]\n [-255 -256 -257]\n [#\"^(02)\" #\"^(02)\" #\"^(02)\"]\n [#\"^(07)\" #\"^(08)\" #\"^(03)\"]\n [1 #\"^(0A)\" 100]\n [2 #\"^(10)\" 256]\n [#\"^(FD)\" #\"^(FE)\" #\"^(FF)\"]\n [#\"^(AA)\" 34 #\"^(99)\"]\n ]\n [\n \"(v * v) * (v * v)\"\n \"(v - v) - (v - v)\"\n \"(v * v) - (v - v)\"\n \"(v - v) * (v - v)\"\n \"(v - v) - (v * v)\"\n \"(v * v) * (v - v)\"\n \"(v - v) * (v * v)\"\n \"(v * v) - (v * v)\"\n \"v + v + v + v\"\n \"v / v * v / v\"\n ]\n [\n [1 1 1 1]\n [2 2 2 2]\n [256 256 256 256]\n [257 257 257 257]\n [#\"^(FF)\" 256 257 258]\n [#\"^(FC)\" #\"^(FD)\" #\"^(FE)\" #\"^(FF)\"]\n ]\n [\n \"((v * v) * (v * v)) * ((v * v) * (v * v))\"\n \"((v - v) * (v * v)) * ((v * v) * (v * v))\"\n \"((v * v) - (v * v)) * ((v * v) * (v * v))\"\n \"((v * v) * (v - v)) * ((v * v) * (v * v))\"\n \"((v * v) * (v * v)) - ((v * v) * (v * v))\"\n \"((v * v) * (v * v)) * ((v - v) * (v * v))\"\n \"((v * v) * (v * v)) * ((v * v) - (v * v))\"\n \"((v * v) * (v * v)) * ((v * v) * (v - v))\"\n \"((v - v) * (v * v)) * ((v * v) * (v * v))\"\n \"((v - v) * (v * v)) * ((v * v) * (v * v))\"\n \"((v - v) - (v * v)) * ((v * v) * (v * v))\"\n \"((v - v) * (v - v)) * ((v * v) * (v * v))\"\n \"((v - v) * (v * v)) - ((v * v) * (v * v))\"\n \"((v - v) * (v * v)) * ((v - v) * (v * v))\"\n \"((v - v) * (v * v)) * ((v * v) - (v * v))\"\n \"((v - v) * (v * v)) * ((v * v) * (v - v))\"\n \"((v * v) - (v * v)) * ((v * v) * (v * v))\"\n \"((v - v) - (v * v)) * ((v * v) * (v * v))\"\n \"((v * v) - (v * v)) * ((v * v) * (v * v))\"\n \"((v * v) - (v - v)) * ((v * v) * (v * v))\"\n \"((v * v) - (v * v)) - ((v * v) * (v * v))\"\n \"((v * v) - (v * v)) * ((v - v) * (v * v))\"\n \"((v * v) - (v * v)) * ((v * v) - (v * v))\"\n \"((v * v) - (v * v)) * ((v * v) * (v - v))\"\n \"((v * v) * (v - v)) - ((v * v) * (v * v))\"\n \"((v - v) * (v - v)) * ((v - v) * (v * v))\"\n \"((v * v) * (v - v)) * ((v * v) - (v * v))\"\n \"((v * v) * (v - v)) * ((v * v) * (v - v))\"\n \"((v * v) * (v - v)) - ((v * v) * (v * v))\"\n \"((v * v) * (v - v)) * ((v - v) * (v * v))\"\n \"((v * v) * (v - v)) * ((v * v) - (v * v))\"\n \"((v * v) * (v - v)) * ((v * v) * (v - v))\"\n \"((v - v) - (v - v)) * ((v * v) * (v * v))\"\n \"((v - v) - (v * v)) - ((v * v) * (v * v))\"\n \"((v - v) - (v * v)) * ((v - v) * (v * v))\"\n \"((v - v) - (v - v)) * ((v * v) - (v * v))\"\n \"((v - v) * (v - v)) - ((v * v) * (v - v))\"\n \"((v - v) * (v * v)) - ((v - v) * (v * v))\"\n \"((v - v) * (v * v)) - ((v * v) - (v * v))\"\n \"((v - v) * (v * v)) - ((v * v) * (v - v))\"\n \"((v - v) * (v * v)) * ((v - v) - (v * v))\"\n \"((v - v) * (v * v)) * ((v - v) * (v - v))\"\n \"((v - v) * (v * v)) * ((v * v) - (v - v))\"\n \"((v * v) - (v - v)) - ((v * v) * (v * v))\"\n \"((v * v) - (v - v)) * ((v - v) * (v * v))\"\n \"((v * v) - (v - v)) * ((v * v) - (v * v))\"\n \"((v * v) - (v - v)) * ((v * v) * (v - v))\"\n \"((v * v) * (v - v)) - ((v - v) * (v * v))\"\n \"((v * v) * (v - v)) - ((v * v) - (v * v))\"\n \"((v * v) * (v - v)) - ((v * v) * (v - v))\"\n \"((v * v) * (v - v)) * ((v - v) - (v * v))\"\n \"((v * v) * (v - v)) * ((v - v) * (v - v))\"\n \"((v * v) * (v * v)) - ((v - v) - (v * v))\"\n \"((v * v) * (v * v)) - ((v - v) * (v - v))\"\n \"((v * v) * (v * v)) - ((v * v) - (v - v))\"\n \"((v * v) * (v * v)) * ((v - v) - (v - v))\"\n \"((v - v) * (v - v)) - ((v - v) * (v * v))\"\n \"((v - v) * (v - v)) - ((v * v) - (v * v))\"\n \"((v - v) * (v - v)) - ((v * v) * (v - v))\"\n \"((v - v) - (v - v)) - ((v * v) * (v * v))\"\n \"((v - v) - (v - v)) * ((v - v) * (v * v))\"\n \"((v - v) - (v - v)) * ((v * v) - (v * v))\"\n \"((v - v) - (v - v)) * ((v * v) * (v - v))\"\n \"((v - v) - (v * v)) * ((v - v) - (v * v))\"\n \"((v - v) - (v * v)) * ((v - v) * (v - v))\"\n \"((v * v) - (v - v)) - ((v - v) * (v * v))\"\n \"((v * v) * (v - v)) - ((v * v) - (v - v))\"\n \"((v * v) * (v - v)) - ((v - v) - (v - v))\"\n \"((v * v) * (v * v)) - ((v - v) - (v - v))\"\n \"((v - v) * (v - v)) - ((v - v) * (v - v))\"\n \"((v - v) - (v - v)) - ((v - v) - (v - v))\"\n ]\n [\n [1 1 1 1 1 1 1 1]\n [256 256 256 256 256 256 256 256]\n [257 257 257 257 257 257 257 257]\n [-256 -256 -256 -256 -256 -256 -256 -256]\n [-257 -257 -257 -257 -257 -257 -257 -257]\n [#\"^(01)\" #\"^(02)\" #\"^(03)\" #\"^(01)\" #\"^(02)\" #\"^(03)\" #\"^(01)\" #\"^(02)\"]\n [1 2 #\"^(03)\" 4 5 6 7 8]\n [#\"^(F8)\" #\"^(F9)\" #\"^(FA)\" #\"^(FB)\" #\"^(FC)\" #\"^(FD)\" #\"^(FE)\" #\"^(FF)\"]\n ]\n]\n\n;;;;;;;;;;;;;;;; start of template ;;;;;;;;;;;;;;;;;;;;;;;;;;\ntemplate: {\nRed/System [\n Title: \"Red/System auto-generated maths tests\"\n Author: \"Peter W A Wood\"\n File: %maths-auto-test.reds\n License: \"BSD-3 - https://github.com/dockimbel/Red/blob/origin/BSD-3-License.txt\"\n]\n\ncomment {\n This file is generated by make-maths-auto-test.r\n Do not edit this file directly.\n}\n;;;;;;;;;;;;;;;; end of template ;;;;;;;;;;;;;;;;;;;;;;;;;;;;\n\n;make-length:$LENGTH$\n\n#include %../../../../../quick-test/quick-test.reds\n\ns: declare struct! [\n a [integer!]\n b [integer!]\n c [integer!]\n d [integer!]\n e [integer!]\n f [integer!]\n g [integer!]\n h [integer!]\n]\n\nident: func [i [integer!] return: [integer!]][i]\n\n~~~start-file~~~ \"Auto-generated tests for maths\"\n\n===start-group=== \"Auto-generated tests for maths\"\n\n}\n;;;;;;;;;;;;;;;; end of template;;;;;;;;;;;;;;;;;;;;;;;;;;\n\n;; start of executable code\nheader: copy template\nreplace header \"$LENGTH$\" length? read %make-maths-auto-test.r \n\nwrite/binary file-out header\n\ntests: copy \"\"\n\nforeach [formulae data] tests-and-data [\n foreach test-formula formulae [\n foreach test-data data [\n test-string: copy test-formula\n foreach test-value test-data [\n replace test-string \"v\" mold test-value\n ]\n \n byte-first?: either char! = type? first test-data [true] [false]\n \n rebol-test-string: preprocess test-string\n \n ;; parse the expression and perform the calculation as Red/System would\n ;; only write a test if REBOL produces a valid result \n if attempt [expected: do load rebol-test-string][\n \n expected: to integer! expected\n \n ;; test with literal values\n make-test test-string\n \n ;; if the data contains byte! values don't create the other tests\n if not find test-string \"#\" [\n \n ;; test using integer variables\n test-setup: copy \"\"\n test-string: copy test-formula\n variable-names: copy [\"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\"]\n foreach test-value test-data [\n append test-setup join \" \" [\n first variable-names \": \" mold test-value \"^(0A)\"\n ]\n replace test-string \"v\" first variable-names\n variable-names: next variable-names\n ]\n make-test/setup test-string test-setup\n \n ;; test using integer/path \n test-setup: copy \"\"\n test-string: copy test-formula\n variable-names: copy [\"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\"]\n foreach test-value test-data [\n append test-setup join \" s/\" [\n first variable-names \": \" mold test-value \"^(0A)\"\n ]\n replace test-string \"v\" join \"s/\" [first variable-names]\n variable-names: next variable-names\n ]\n make-test/setup test-string test-setup\n \n ;; test using function call\n test-string: copy test-formula\n foreach test-value test-data [\n replace test-string \"v\" join \"(ident \" [mold test-value \")\"]\n ]\n make-test test-string\n ]\n ]\n ]\n ]\n recycle\n] \nwrite/append/binary file-out tests\ntests: copy \"\"\n\n;; write file epilog\nappend tests \"^(0A)===end-group===^(0A)^(0A)\"\nappend tests {~~~end-file~~~^(0A)^(0A)}\n\nwrite/append/binary file-out tests\n \nprint [\"Number of assertions generated\" test-number]\n\n\n\n\n\n\n", "meta": {"hexsha": "e63559dcbd64a8318f202624dfa44c2f71d41026", "size": 11135, "ext": "r", "lang": "R", "max_stars_repo_path": "system/tests/source/units/make-maths-auto-test.r", "max_stars_repo_name": "0xflotus/red", "max_stars_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_stars_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_stars_count": 5234, "max_stars_repo_stars_event_min_datetime": "2015-01-01T12:59:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:28:22.000Z", "max_issues_repo_path": "system/tests/source/units/make-maths-auto-test.r", "max_issues_repo_name": "0xflotus/red", "max_issues_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_issues_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_issues_count": 3406, "max_issues_repo_issues_event_min_datetime": "2015-01-02T08:53:02.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T17:47:35.000Z", "max_forks_repo_path": "system/tests/source/units/make-maths-auto-test.r", "max_forks_repo_name": "0xflotus/red", "max_forks_repo_head_hexsha": "d329c17bfe905cdc1917969e9ac649f586542626", "max_forks_repo_licenses": ["BSL-1.0", "BSD-3-Clause"], "max_forks_count": 509, "max_forks_repo_forks_event_min_datetime": "2015-01-27T21:26:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T10:10:32.000Z", "avg_line_length": 28.3333333333, "max_line_length": 83, "alphanum_fraction": 0.4033228559, "num_tokens": 4110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3151136562307398}} {"text": "#' emis_atm\n#'\n#' Calculate air emissivity.\n#'\n#' @param tk numeric Air temperature in degK.\n#' @param rh numeric Relative humidity in percentage.\n#' @return air emissivity \n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords emis_atm \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nemis_atm=function(tk,rh) {\n ct$assign(\"t\", as.array(tk))\n ct$assign(\"rh\", as.array(rh/100))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=emis_atm(ta[i],rh[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n\n", "meta": {"hexsha": "6cd57aa71928e612aec893f74ba1143ed928c033", "size": 710, "ext": "r", "lang": "R", "max_stars_repo_path": "R/emis_atm.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/emis_atm.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/emis_atm.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 24.4827586207, "max_line_length": 120, "alphanum_fraction": 0.5338028169, "num_tokens": 205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3151136485091581}} {"text": "#' Create frescalo weights file\n#' \n#' Create the weights file required to run frescalo, as outlined in (Hill,\n#' 2011). For more information on frescalo see \\code{\\link{frescalo}}. This function\n#' takes a table of geographical distances between sites and a table of numeric data\n#' from which to calculate similarity (for example, landcover or abiotic data)\n#'\n#' @param distances a dataframe giving the distance between sites in a long format with three\n#' columns. The first column give the ID of the first site, the second column gives\n#' the ID of the second site and the third column gives the distance between them.\n#' The table should include reciprocal data i.e. both rows 'A, B, 10' and 'B, A, 10'\n#' should exist. \n#' @param attributes a dataframe of numeric attributes of each site. The first column must contain\n#' the site IDs and all other columns are used to calculate the similarity between\n#' sites using dist() and method 'euclidean'.\n#' @param dist_sub the number of neighbours to include after ranking by distance. In Hill\n#' (2011), this is set to 200 and is the default here\n#' @param sim_sub the number of neighbours to include after ranking by similarity. This is\n#' the final number of sites that will be included in a neighbourhood. In Hill (2011),\n#' this is set to 100 and is the default here.\n#' @param normalise Logical. If \\code{TRUE} each attribute is divided by its maximum value to \n#' produce values between 0 and 1. Default is \\code{FALSE}\n#' @param verbose Logical, should progress be printed to console. Defaults to TRUE\n#' @return A dataframe is returned in a format that can be used directly in frescalo() or \n#' sparta(). The dataframe has three columns giving the target cell, the neighbourhood\n#' cell, and the weight.\n#' @keywords trends, frescalo, weights\n#' @references Hill, Mark. Local frequency as a key to interpreting species occurrence data when\n#' recording effort is not known. 2011. \\emph{Methods in Ecology and Evolution}, 3 (1), 195-205.\n#' @import sp\n#' @importFrom reshape2 melt\n#' @export\n#' @examples\n#' \\dontrun{\n#'\n# # I'm going to create some made up data\n#' mySites <- paste('Site_', 1:100, sep = '')\n#'\n#' # Build a table of distances\n#' myDistances <- merge(mySites, mySites) \n#' \n#' # add random distances\n#' myDistances$dist <- runif(n = nrow(myDistances), min = 10, max = 10000) \n#'\n#' # to be realistic the distance from a site to itself should be 0\n#' myDistances$dist[myDistances$x == myDistances$y] <- 0\n#'\n#' # Build a table of attributes\n#' myHabitatData <- data.frame(site = mySites,\n#' grassland = runif(length(mySites), 0, 1),\n#' woodland = runif(length(mySites), 0, 1),\n#' heathland = runif(length(mySites), 0, 1),\n#' urban = runif(length(mySites), 0, 1),\n#' freshwater = runif(length(mySites), 0, 1))\n#'\n#' # This pretend data is supposed to be proportional cover so lets \n#' # make sure each row sums to 1\n#' multiples <- apply(myHabitatData[,2:6], 1, sum)\n# \n#' for(i in 1:length(mySites)){\n#' myHabitatData[i,2:6] <- myHabitatData[i,2:6]/multiples[i]\n#' }\n#'\n#' # Create the weights file\n#' weights <- createWeights(distances = myDistances,\n#' attributes = myHabitatData,\n#' dist_sub = 20,\n#' sim_sub = 10)\n#' }\n \ncreateWeights<-function(distances,\n attributes,\n dist_sub=200,\n sim_sub=100,\n normalise=FALSE,\n verbose=TRUE){\n \n # Error checks\n errorChecks(dist = distances, sim = attributes, dist_sub = dist_sub, sim_sub = sim_sub)\n\n # Ensure site names are characters not factors\n distances[,1] <- as.character(distances[,1])\n distances[,2] <- as.character(distances[,2])\n attributes[,1] <- as.character(attributes[,1])\n \n # rename the columns\n colnames(distances) <- c('site1', 'site2' ,'distance')\n \n # Check that sites are in both drop those that are not - give a warning\n unique_dist_sites <- unique(c(distances[,1], distances[,2]))\n unique_sim_sites <- unique(attributes[,1])\n distmiss <- unique_dist_sites[!unique_dist_sites %in% unique_sim_sites]\n simmiss <- unique_sim_sites[!unique_sim_sites %in% unique_dist_sites]\n missing <- unique(c(distmiss,simmiss))\n \n if(length(missing) > 0){\n warning(paste(\"The following sites were in only one of 'attributes' and 'distances' and so have been excluded from the weights file:\",toString(missing)))\n distances <- distances[!distances[,1] %in% missing,]\n distances <- distances[!distances[,2] %in% missing,]\n attributes <- attributes[!attributes[,1] %in% missing,]\n }\n \n #normalise if required\n if(normalise){\n for(i in 2:length(colnames(attributes))){\n mx <- max(attributes[,i])\n attributes[,i] <- attributes[,i] / mx\n }\n }\n \n #convert attribute table into a long distance table\n if(verbose) cat('Creating similarity distance table...')\n row.names(attributes) <- attributes[,1]\n sim_distance <- dist(attributes[,2:length(names(attributes))], diag = TRUE, upper = TRUE) \n sim_distance <- melt(as.matrix(sim_distance))\n sim_distance$value <- (sim_distance$value/max(sim_distance$value))\n sim_distance[,1] <- as.character(sim_distance[,1])\n sim_distance[,2] <- as.character(sim_distance[,2])\n colnames(sim_distance) <- c('site1', 'site2', 'similarity')\n if(verbose) cat('Complete\\n')\n \n if(verbose) cat('Creating weights file...\\n0%\\n')\n \n total <- length(unique(distances[,1]))\n pb <- txtProgressBar(min = 0, max = total, style = 3)\n \n # Taking each cell in turn calculate the weights\n weights_list <- lapply(unique(distances[,1]), function(i){\n \n # select for target cell\n sim_foc <- sim_distance[sim_distance$site1 == i, ]\n dist_foc <- distances[distances$site1 == i, ]\n \n # For this focal cell rank all others by distance\n dist_foc$rankdist <- rank(dist_foc$distance, ties.method = \"first\")\n \n # Take the top 'dist_sub' closest (dist_sub defaults to 200)\n dist_foc <- dist_foc[dist_foc$rankdist <= dist_sub, ]\n \n # Of these take the 'sim_sub' top by similarity distance (sim_sub defaults to 100)\n ranks <- merge(x = dist_foc, y = sim_foc, by = c('site1', 'site2'), all.x = TRUE, all.y = FALSE)\n ranks$rankflor <- rank(ranks$similarity, ties.method = \"first\")\n ranks <- ranks[ranks$rankflor <= sim_sub, ]\n \n # Calculate similarity by distance and flora\n ranks$distsim <- (1 - (((ranks$rankdist - 1)^2) / (dist_sub)^2))^4\n ranks$florsim <- (1 - (((ranks$rankflor - 1)^2) / (sim_sub)^2))^4\n \n # Calculate weights\n ranks$weight <- ranks$distsim*ranks$florsim\n \n if(verbose) setTxtProgressBar(pb, grep(paste0('^', i, '$'), unique(distances[,1])))\n \n # Merge back with all data\n return(data.frame(target = ranks$site1,\n neighbour = ranks$site2,\n weight = round(ranks$weight, 4)))\n \n })\n\n weights_master <- do.call(rbind, weights_list)\n close(pb)\n \n if(verbose) cat('Complete\\n')\n \n return(weights_master) \n \n}", "meta": {"hexsha": "245ae2242eb987a486bd8da35342de95f0418b36", "size": 7271, "ext": "r", "lang": "R", "max_stars_repo_path": "R/createWeights.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "R/createWeights.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "R/createWeights.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 43.2797619048, "max_line_length": 157, "alphanum_fraction": 0.6473662495, "num_tokens": 1884, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.315113648509158}} {"text": "# CHAPTER 7\n# Backtesting with Quantstrat\n\nlibrary(quantmod)\n\n#################\n# Initial setup #\n#################\n\n# Suppresses warnings\noptions(\"getSymbols.warning4.0\" = FALSE)\n\n# Do some house cleaning\nrm(list = ls(.blotter), envir = .blotter)\n\n# Set the currency and the timezone\ncurrency('USD')\nSys.setenv(TZ = \"UTC\")\n\n# Define symbols of interest\nsymbols <- c(\"XLB\", #SPDR Materials sector\n \"XLE\", #SPDR Energy sector\n \"XLF\", #SPDR Financial sector\n \"XLP\", #SPDR Consumer staples sector\n \"XLI\", #SPDR Industrial sector\n \"XLU\", #SPDR Utilities sector\n \"XLV\", #SPDR Healthcare sector\n \"XLK\", #SPDR Tech sector\n \"XLY\", #SPDR Consumer discretionary sector\n \"RWR\", #SPDR Dow Jones REIT ETF\n \"EWJ\", #iShares Japan\n \"EWG\", #iShares Germany\n \"EWU\", #iShares UK\n \"EWC\", #iShares Canada\n \"EWY\", #iShares South Korea\n \"EWA\", #iShares Australia\n \"EWH\", #iShares Hong Kong\n \"EWS\", #iShares Singapore\n \"IYZ\", #iShares U.S. Telecom\n \"EZU\", #iShares MSCI EMU ETF\n \"IYR\", #iShares U.S. Real Estate\n \"EWT\", #iShares Taiwan\n \"EWZ\", #iShares Brazil\n \"EFA\", #iShares EAFE\n \"IGE\", #iShares North American Natural Resources\n \"EPP\", #iShares Pacific Ex Japan\n \"LQD\", #iShares Investment Grade Corporate Bonds\n \"SHY\", #iShares 1-3 year TBonds\n \"IEF\", #iShares 3-7 year TBonds\n \"TLT\" #iShares 20+ year Bonds\n)\n\n# SPDR ETFs first, iShares ETFs afterwards\nif(!\"XLB\" %in% ls()) {\n # If data is not present, get it from yahoo\n suppressMessages(getSymbols(symbols, from = from,\n to = to, src = \"yahoo\", adjust = TRUE))\n}\n\n# Define the instrument type\nstock(symbols, currency = \"USD\", multiplier = 1)\n\n###############################################\n# The first strategy: A simple trend follower #\n###############################################\n\"lagATR\" <- function(HLC, n = 14, maType, lag = 1, ...) {\n ATR <- ATR(HLC, n = n, maType = maType, ...)\n ATR <- lag(ATR, lag)\n out <- ATR$atr\n colnames(out) <- \"atr\"\n return(out)\n}\n\n\"osDollarATR\" <- function(orderside, tradeSize, pctATR,\n maxPctATR = pctATR, data, timestamp,\n symbol, prefer = \"Open\", portfolio, integerQty = TRUE,\n atrMod = \"\", rebal = FALSE, ...) {\n if(tradeSize > 0 & orderside == \"short\"){\n tradeSize <- tradeSize * -1\n }\n \n pos <- getPosQty(portfolio, symbol, timestamp)\n atrString <- paste0(\"atr\", atrMod)\n atrCol <- grep(atrString, colnames(mktdata))\n \n if(length(atrCol) == 0) {\n stop(paste(\"Term\", atrString,\n \"not found in mktdata column names.\"))\n }\n \n atrTimeStamp <- mktdata[timestamp, atrCol]\n if(is.na(atrTimeStamp) | atrTimeStamp == 0) {\n stop(paste(\"ATR corresponding to\", atrString,\n \"is invalid at this point in time. Add a logical\n operator to account for this.\"))\n }\n \n dollarATR <- pos * atrTimeStamp\n \n desiredDollarATR <- pctATR * tradeSize\n remainingRiskCapacity <- tradeSize *\n maxPctATR - dollarATR\n \n if(orderside == \"long\"){\n qty <- min(tradeSize * pctATR / atrTimeStamp,\n remainingRiskCapacity / atrTimeStamp)\n } else {\n qty <- max(tradeSize * pctATR / atrTimeStamp,\n remainingRiskCapacity / atrTimeStamp)\n }\n \n if(integerQty) {\n qty <- trunc(qty)\n }\n if(!rebal) {\n if(orderside == \"long\" & qty < 0) {\n qty <- 0\n }\n if(orderside == \"short\" & qty > 0) {\n qty <- 0 }\n }\n if(rebal) {\n if(pos == 0) {\n qty <- 0\n } \n }\n return(qty)\n}\n\nrequire(quantstrat)\nrequire(PerformanceAnalytics)\n\ninitDate = \"1990-01-01\"\nfrom = \"2003-01-01\"\nto = \"2013-12-31\"\noptions(width = 70)\n\n# To rerun the strategy, rerun everything below this line\n# demoData.R contains all of the data-related boilerplate.\nsource(file.path(here::here(), \"Quantitative Trading\", \"common.r\"))\n\n# Trade sizing and initial equity settings\ntradeSize <- 10000\ninitEq <- tradeSize * length(symbols)\n\nstrategy.st <- \"Clenow_Simple\"\nportfolio.st <- \"Clenow_Simple\"\naccount.st <- \"Clenow_Simple\"\nrm.strat(portfolio.st)\nrm.strat(strategy.st)\n\ninitPortf(portfolio.st, symbols = symbols,\n initDate = initDate, currency = 'USD')\n\ninitAcct(account.st, portfolios = portfolio.st,\n initDate = initDate, currency = 'USD', initEq = initEq)\n\ninitOrders(portfolio.st, initDate = initDate)\n\nstrategy(strategy.st, store=TRUE)\n\n\n##################################\n# Backtesting the first strategy #\n##################################\nnLag = 252\npctATR = 0.02\nperiod = 10\n\nnamedLag <- function(x, k = 1, na.pad = TRUE, ...) {\n out <- lag(x, k = k, na.pad = na.pad, ...)\n out[is.na(out)] <- x[is.na(out)]\n colnames(out) <- \"namedLag\"\n return(out)\n}\n\nadd.indicator(strategy.st, name = \"namedLag\",\n arguments = list(x = quote(Cl(mktdata)), k = nLag),\n label = \"ind\")\n\nadd.indicator(strategy.st, name = \"lagATR\",\n arguments = list(HLC = quote(HLC(mktdata)), n = period),\n label = \"atrX\")\n\ntest <- applyIndicators(strategy.st, mktdata = OHLC(XLB))\nhead(round(test, 2), 253)\n\n# Signals\nadd.signal(strategy.st, name = \"sigCrossover\",\n arguments = list(columns = c(\"Close\", \"namedLag.ind\"),\n relationship = \"gt\"),\n label = \"coverOrBuy\")\n\nadd.signal(strategy.st, name = \"sigCrossover\",\n arguments = list(columns = c(\"Close\", \"namedLag.ind\"),\n relationship = \"lt\"),\n label = \"sellOrShort\")\n\n# Long rules\nadd.rule(strategy.st, name = \"ruleSignal\",\n arguments = list(sigcol = \"coverOrBuy\",\n sigval = TRUE, ordertype = \"market\",\n orderside = \"long\", replace = FALSE,\n prefer = \"Open\", osFUN = osDollarATR,\n tradeSize = tradeSize, pctATR = pctATR,\n atrMod = \"X\"), type = \"enter\", path.dep = TRUE)\n\nadd.rule(strategy.st, name = \"ruleSignal\",\n arguments = list(sigcol = \"sellOrShort\",\n sigval = TRUE, orderqty = \"all\",\n ordertype = \"market\", orderside = \"long\",\n replace = FALSE, prefer = \"Open\"),\n type = \"exit\", path.dep = TRUE)\n\n# Short rules\nadd.rule(strategy.st, name = \"ruleSignal\",\n arguments = list(sigcol = \"sellOrShort\",\n sigval = TRUE, ordertype = \"market\",\n orderside = \"short\", replace = FALSE,\n prefer = \"Open\", osFUN = osDollarATR,\n tradeSize = -tradeSize, pctATR = pctATR,\n atrMod = \"X\"), type = \"enter\", path.dep = TRUE)\n\nadd.rule(strategy.st, name = \"ruleSignal\",\n arguments = list(sigcol = \"coverOrBuy\",\n sigval = TRUE, orderqty = \"all\",\n ordertype = \"market\", orderside = \"short\",\n replace = FALSE, prefer = \"Open\"),\n type = \"exit\", path.dep = TRUE)\n\n# Get begin time\nt1 <- Sys.time()\nout <- applyStrategy(strategy = strategy.st,\n portfolios = portfolio.st)\n\n# Record end time\nt2 <- Sys.time()\nprint(t2 - t1)\n\napplyStrategy(out)\n", "meta": {"hexsha": "ef2e57f04e417a6eae80e5b8b91435d77fc38235", "size": 7472, "ext": "r", "lang": "R", "max_stars_repo_path": "Quantitative Trading/trend_follow.r", "max_stars_repo_name": "bmoretz/Quantitative-Investments", "max_stars_repo_head_hexsha": "25d9a7199f212787dd9ae05f7af9e7407591c5bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-03-26T05:47:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-18T21:50:54.000Z", "max_issues_repo_path": "Quantitative Trading/trend_follow.r", "max_issues_repo_name": "bmoretz/Quantitative-Investments", "max_issues_repo_head_hexsha": "25d9a7199f212787dd9ae05f7af9e7407591c5bc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Quantitative Trading/trend_follow.r", "max_forks_repo_name": "bmoretz/Quantitative-Investments", "max_forks_repo_head_hexsha": "25d9a7199f212787dd9ae05f7af9e7407591c5bc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-11-03T09:23:54.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-18T21:50:55.000Z", "avg_line_length": 31.0041493776, "max_line_length": 80, "alphanum_fraction": 0.5457708779, "num_tokens": 1977, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678319, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.31511364850915796}} {"text": "library(devtools)\nlibrary(parallel)\ndevtools::load_all()\nRNGkind(\"L'Ecuyer-CMRG\")\ndata(grid)\n\nseed <- 999983\ns_k <- 1000\ns_n <- 100\ns_m <- 4\n\nnew_plain_trig_1004 <- mclapply(new_r_grid_trig, r_loop <- function(s_r) {\n stephanie_type2(seed, s_k, s_n, s_m, \"trigonometric\", s_r, L = 1000, err = 0.25, i_face = F, approx = T, truncate.tn = 1)\n}, mc.cores = 2)\n\nsave(new_plain_trig_1004, file = \"new_plain_trig_1004.RData\")", "meta": {"hexsha": "ffacff0f0b6867794e67dcbe1d4e6077888ad204", "size": 422, "ext": "r", "lang": "R", "max_stars_repo_path": "simulation/plain/plain_type2_025/new/an_plain_trig_1004.r", "max_stars_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_stars_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "simulation/plain/plain_type2_025/new/an_plain_trig_1004.r", "max_issues_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_issues_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "simulation/plain/plain_type2_025/new/an_plain_trig_1004.r", "max_forks_repo_name": "ZhuolinSong/Goodness-of-fit-test-for-sparse-functional-data", "max_forks_repo_head_hexsha": "5f5c51e91b5b369edef7b14f4a181f4d91542754", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.375, "max_line_length": 125, "alphanum_fraction": 0.7037914692, "num_tokens": 164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31488731065087294}} {"text": "#' @inheritParams layer\n#' @inheritParams geom_point\n#' @inheritParams stat_density\n#' @param scale if \"area\" (default), all violins have the same area (before trimming\n#' the tails). If \"count\", areas are scaled proportionally to the number of\n#' observations. If \"width\", all violins have the same maximum width.\n#' @section Computed variables:\n#' \\describe{\n#' \\item{density}{density estimate}\n#' \\item{scaled}{density estimate, scaled to maximum of 1}\n#' \\item{count}{density * number of points - probably useless for violin plots}\n#' \\item{violinwidth}{density scaled for the violin plot, according to area, counts\n#' or to a constant maximum width}\n#' \\item{n}{number of points}\n#' \\item{width}{width of violin bounding box}\n#' }\n#' @seealso [geom_violin()] for examples, and [stat_density()]\n#' for examples with data along the x axis.\n#' @export\n#' @rdname geom_violin\nstat_ydensity <- function(mapping = NULL, data = NULL,\n geom = \"violin\", position = \"dodge\",\n ...,\n bw = \"nrd0\",\n adjust = 1,\n kernel = \"gaussian\",\n trim = TRUE,\n scale = \"area\",\n na.rm = FALSE,\n orientation = NA,\n show.legend = NA,\n inherit.aes = TRUE) {\n scale <- arg_match0(scale, c(\"area\", \"count\", \"width\"))\n\n layer(\n data = data,\n mapping = mapping,\n stat = StatYdensity,\n geom = geom,\n position = position,\n show.legend = show.legend,\n inherit.aes = inherit.aes,\n params = list2(\n bw = bw,\n adjust = adjust,\n kernel = kernel,\n trim = trim,\n scale = scale,\n na.rm = na.rm,\n ...\n )\n )\n}\n\n\n#' @rdname ggplot2-ggproto\n#' @format NULL\n#' @usage NULL\n#' @export\nStatYdensity <- ggproto(\"StatYdensity\", Stat,\n required_aes = c(\"x\", \"y\"),\n non_missing_aes = \"weight\",\n\n setup_params = function(data, params) {\n params$flipped_aes <- has_flipped_aes(data, params, main_is_orthogonal = TRUE, group_has_equal = TRUE)\n\n params\n },\n\n extra_params = c(\"na.rm\", \"orientation\"),\n\n compute_group = function(self, data, scales, width = NULL, bw = \"nrd0\", adjust = 1,\n kernel = \"gaussian\", trim = TRUE, na.rm = FALSE, flipped_aes = FALSE) {\n if (nrow(data) < 2) {\n cli::cli_warn(\"Groups with fewer than two data points have been dropped.\")\n return(new_data_frame())\n }\n range <- range(data$y, na.rm = TRUE)\n modifier <- if (trim) 0 else 3\n bw <- calc_bw(data$y, bw)\n dens <- compute_density(data$y, data$w, from = range[1] - modifier*bw, to = range[2] + modifier*bw,\n bw = bw, adjust = adjust, kernel = kernel)\n\n dens$y <- dens$x\n dens$x <- mean(range(data$x))\n\n # Compute width if x has multiple values\n if (length(unique(data$x)) > 1) {\n width <- diff(range(data$x)) * 0.9\n }\n dens$width <- width\n\n dens\n },\n\n compute_panel = function(self, data, scales, width = NULL, bw = \"nrd0\", adjust = 1,\n kernel = \"gaussian\", trim = TRUE, na.rm = FALSE,\n scale = \"area\", flipped_aes = FALSE) {\n data <- flip_data(data, flipped_aes)\n data <- ggproto_parent(Stat, self)$compute_panel(\n data, scales, width = width, bw = bw, adjust = adjust, kernel = kernel,\n trim = trim, na.rm = na.rm\n )\n\n # choose how violins are scaled relative to each other\n data$violinwidth <- switch(scale,\n # area : keep the original densities but scale them to a max width of 1\n # for plotting purposes only\n area = data$density / max(data$density),\n # count: use the original densities scaled to a maximum of 1 (as above)\n # and then scale them according to the number of observations\n count = data$density / max(data$density) * data$n / max(data$n),\n # width: constant width (density scaled to a maximum of 1)\n width = data$scaled\n )\n data$flipped_aes <- flipped_aes\n flip_data(data, flipped_aes)\n }\n\n)\n\ncalc_bw <- function(x, bw) {\n if (is.character(bw)) {\n if (length(x) < 2) {\n cli::cli_abort(\"{.arg x} must contain at least 2 elements to select a bandwidth automatically\")\n }\n\n bw <- switch(\n to_lower_ascii(bw),\n nrd0 = stats::bw.nrd0(x),\n nrd = stats::bw.nrd(x),\n ucv = stats::bw.ucv(x),\n bcv = stats::bw.bcv(x),\n sj = ,\n `sj-ste` = stats::bw.SJ(x, method = \"ste\"),\n `sj-dpi` = stats::bw.SJ(x, method = \"dpi\"),\n cli::cli_abort(\"{.var {bw}} is not a valid bandwidth rule\")\n )\n }\n bw\n}\n", "meta": {"hexsha": "d74b4f738bee7c67e3e91a79b6f2d98e70489b09", "size": 4703, "ext": "r", "lang": "R", "max_stars_repo_path": "R/stat-ydensity.r", "max_stars_repo_name": "sthagen/tidyverse-ggplot2", "max_stars_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/stat-ydensity.r", "max_issues_repo_name": "sthagen/tidyverse-ggplot2", "max_issues_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/stat-ydensity.r", "max_forks_repo_name": "sthagen/tidyverse-ggplot2", "max_forks_repo_head_hexsha": "31dce56e38c091725e16a577867ffd547352de85", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1197183099, "max_line_length": 106, "alphanum_fraction": 0.5734637465, "num_tokens": 1232, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31488731065087294}} {"text": "#install.packages(\"ggplot2\")\n#install.packages(\"RColorBrewer\")\n#install.packages(\"zoo\")\n\nlibrary(\"RColorBrewer\")\nrequire(\"ggplot2\")\nlibrary(zoo)\n\npar(mfrow=c(1,1))\n\nsetwd(\"C:/Code/climData/glaser2010\")\n\n\np0 <- read.csv(\"https://raw.githubusercontent.com/climdata/glaser2019/master/csv/p_1500_2xxx.csv\", sep=\",\", na = \"NA\")\nspifull <- read.csv(\"https://raw.githubusercontent.com/climdata/dwdSPI/master/csv/spi_de.csv\", sep=\",\", na = \"NA\")\n\np1 <- data.frame()\nmonthNames = c(\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\")\nfor(i in 1:length(monthNames)) {\n pnew <- data.frame(year = p0$year, month = i)\n pnew$prec0 <- p0[,monthNames[i]]\n p1 <- rbind(p1, pnew)\n}\n\np1$time = p1$year + (p1$month+0.5)/12.0\n\nspinew <- subset(spifull, spifull$time>max(p1$time))\nspinew <- spinew[, c(\"year\",\"month\",\"time\",\"spi1\")]\nnames(spinew)[names(spinew) == 'spi1'] <- 'prec0'\nspinew <- spinew[order(spinew$time),]\n\n### DOES NOT WORK !!!\nfor(i in length(spinew$prec0)) {\n if(spinew$prec0[i] > 3.0) {\n spinew$prec0[i] = 3.0\n }\n if(spinew$prec0[i] < -3.0) {\n spinew$prec0[i] = -3.0\n } \n}\n\np1 <- rbind(p1, spinew)\np1 <- p1[order(p1$time),]\n\n\nmp <- ggplot(p1, aes(year, month))\nmp + geom_raster(aes(fill=prec0))+\n theme_classic(base_size=80) +\n #theme_classic() +\n labs(x=\"Year\", y=\"Month\", title=\"\", subtitle=\"\") +\n scale_y_continuous(breaks=c(1,6,12))+\n scale_x_continuous(limits=c(1500,2020)) + \n scale_fill_gradient2(low=\"#AA6010\", mid=\"#FCF0C2\", high=\"#23AB30\",\n limits=c(-3,3)) +\n theme( legend.key.width = unit(2,\"cm\")) +\n guides(fill=guide_legend(title=\"PI\", reverse = TRUE)) ", "meta": {"hexsha": "ec12144d6980893d14f0e5139a5ad6ee97c842d0", "size": 1631, "ext": "r", "lang": "R", "max_stars_repo_path": "source/pi_1500_2018.r", "max_stars_repo_name": "climdata/playground", "max_stars_repo_head_hexsha": "0fa75f2ffaa1f5e8473860c1f552b4f83e02df40", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "source/pi_1500_2018.r", "max_issues_repo_name": "climdata/playground", "max_issues_repo_head_hexsha": "0fa75f2ffaa1f5e8473860c1f552b4f83e02df40", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/pi_1500_2018.r", "max_forks_repo_name": "climdata/playground", "max_forks_repo_head_hexsha": "0fa75f2ffaa1f5e8473860c1f552b4f83e02df40", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.125, "max_line_length": 118, "alphanum_fraction": 0.6327406499, "num_tokens": 583, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3148873037022067}} {"text": "library(istack)\nlibrary(ggplot2)\n# library(devtools)\n# setwd('/Users/gaot/Desktop/istack')\n\n# cancer types and treatments\nset.seed(2018-12-09)\ncancers = simulate_cancers(15)\n\np = istack(cancers, \n var = 'Disease', \n group = 'Treatment', \n icon = \"https://upload.wikimedia.org/wikipedia/commons/d/d8/Person_icon_BLACK-01.svg\",\n size = 0.025,\n asp = 3)\n\np + theme(panel.grid.major.x = element_line(colour = \"grey\", linetype = 'dashed')) +\n ggtitle('Cancer Treatments')\n\n# mtcars\np = istack(mtcars, \n var = 'carb', \n group = 'gear', \n icon = \"https://upload.wikimedia.org/wikipedia/commons/7/7e/Car_icon_transparent.png\",\n size = 0.12,\n asp = 1)\n\np + theme(panel.grid.major.y = element_line(colour = \"grey\", linetype = 'dashed')) + \n coord_flip() + ggtitle('mtcars')\n", "meta": {"hexsha": "311d31a29a71d3a7ea254771d3f4dbcef0a9d1a9", "size": 866, "ext": "r", "lang": "R", "max_stars_repo_path": "test.r", "max_stars_repo_name": "teng-gao/istack", "max_stars_repo_head_hexsha": "713ab603179c662420ca1a8144e5f257d98e8837", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-02-13T00:15:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-15T08:18:56.000Z", "max_issues_repo_path": "test.r", "max_issues_repo_name": "teng-gao/istack", "max_issues_repo_head_hexsha": "713ab603179c662420ca1a8144e5f257d98e8837", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-08-17T13:19:27.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-17T13:19:27.000Z", "max_forks_repo_path": "test.r", "max_forks_repo_name": "teng-gao/istack", "max_forks_repo_head_hexsha": "713ab603179c662420ca1a8144e5f257d98e8837", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8666666667, "max_line_length": 97, "alphanum_fraction": 0.6073903002, "num_tokens": 254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.31483006197816854}} {"text": "# Set up\nsource('2019-06-19-jsa-type-ch1/init.r')\n\n# Libraries\nlibrary(zoo)\nlibrary(ggrepel)\n\nlibrary(forecast)\nlibrary(tseries)\n\nlibrary(Kendall)\n\nlibrary(segmented)\n\n# Parameters\ntheme <- theme_minimal(base_size=7)\nyear_end <- 2018\ndir_plot <- \"C:\\\\Users\\\\ejysoh\\\\Dropbox\\\\msc-thesis\\\\research\\\\_figures\\\\_ch1\\\\\"\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - Fig. 1\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/fig-1.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - Fig. 2\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/fig-2.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - Fig. 3\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/fig-3.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - Fig. 4 Prop species describing <=N species\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/fig-4.r\")\n\n\n#########################################################################################\n# Supporting Information\n#########################################################################################\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2A Table 1 - Paragraph on taxonomic effort\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-A.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2B Fig - Histogram of active years\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-B-fig-1.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2C Fig 1\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-C-fig-1.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2D Table 1\n# Describers profile - one large monograph towards end of life, or many small?\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-D-table-1.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2E Fig 2,3\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-E-fig-2,3.r\")\n\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n# Section - SI 2F Tables - Ancillary info on describers\n# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\nsource(\"2019-06-19-jsa-type-ch1/plots_main/si-F-tables.r\")\n\n", "meta": {"hexsha": "6d09ed8abfae5406dc009bf1f3836bca69e8e3fb", "size": 2564, "ext": "r", "lang": "R", "max_stars_repo_path": "2019-06-19-jsa-type-ch1/plots_main.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2019-06-19-jsa-type-ch1/plots_main.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2019-06-19-jsa-type-ch1/plots_main.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7368421053, "max_line_length": 89, "alphanum_fraction": 0.3763650546, "num_tokens": 784, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.31483006197816854}} {"text": "library(\"dplyr\")\n\n\nfind_start_positions <- function(pop_df, start_positions = 0.5) {\n\t# set small time interval:\n\tall_gens_list <- unique(pop_df$Generation)\n\tdelta <- abs(min(1E-2 * min(diff(all_gens_list)), 1E-4 * (max(all_gens_list) - min(all_gens_list))))\n\tstart_positions <- max(start_positions, delta)\n\tstart_positions <- min(start_positions, 1 - delta)\n\tstart_positions <- max(start_positions, 0.5) # Manual change to account for very large datasets (LTEE)\n\treturn(start_positions)\n}\n\nargs = commandArgs(trailingOnly=TRUE)\n#filename <- \"/home/cld100/Documents/github/muller_diagrams/5G/tables/5_genotypes.timeseries.ggmuller.populations.tsv\"\nfilename <- args[1]\npop_df <- read.csv(filename, sep = \"\\t\", header = TRUE)\nresult <- find_start_positions(pop_df)\n\nwrite(result, args[2])", "meta": {"hexsha": "26486740ff40f410155c0284396d473f67c7fa29", "size": 786, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/data/scripts_ggmuller/rscript.adjustpopulations.addstartpoints.findstartpositions.r", "max_stars_repo_name": "andreashirley/Lolipop", "max_stars_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-04-18T15:43:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-19T18:43:23.000Z", "max_issues_repo_path": "tests/data/scripts_ggmuller/rscript.adjustpopulations.addstartpoints.findstartpositions.r", "max_issues_repo_name": "andreashirley/Lolipop", "max_issues_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-05-04T16:09:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-13T03:52:56.000Z", "max_forks_repo_path": "tests/data/scripts_ggmuller/rscript.adjustpopulations.addstartpoints.findstartpositions.r", "max_forks_repo_name": "cdeitrick/muller_diagrams", "max_forks_repo_head_hexsha": "5b87b00a2c7ccbeeb3876bddb32e54aedf6bdf6d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-03-23T17:12:56.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-24T22:22:12.000Z", "avg_line_length": 39.3, "max_line_length": 118, "alphanum_fraction": 0.7519083969, "num_tokens": 210, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3148300531811561}} {"text": "#Joyce with R : nuage de mots\n\n# Etape 1 : Ouvrir R\n\n# Etape 2 : Charger les librairies necessaires\n\nlibrary(tm)\nlibrary(SnowballC)\nlibrary(wordcloud)\n\n# Etape 3 : Charger les donnees, situees sur PATH\n\nsetwd('PATH') \ntexts <-Corpus(DirSource(\"PATH\"), readerControl = list(language=\"lat\")) \n\n# Verifier que les donnes ont bel et bien ete chargees. \n\nsummary(texts)\n\n# Etape 4 : Nettoyer les donnees.\n\ntexts <- tm_map(texts, removeNumbers)\ntexts <- tm_map(texts, removePunctuation)\ntexts <- tm_map(texts , stripWhitespace)\ntexts <- tm_map(texts, content_transformer(tolower))\ntexts <- tm_map(texts, removeWords, stopwords(\"english\"))\ntexts <- tm_map(texts, stemDocument, language = \"english\")\n\n# Etape 5 : Cr\u00e9er une matrice.\n\nTDM <- TermDocumentMatrix(texts)\n\n# Etape 6 : Enlever les termes n'\u00e9tant pas utiliser suffisament de fois.\n\nTDM.common = removeSparseTerms(TDM, 0.1)\n\n# Etape 8 : preparer pour le decompte des mots\n\nTDM.dense <- as.matrix(TDM.common)\n\n# Etape 9 : creer le nuage de mots\n\npalette <- brewer.pal(9,\"BuGn\")[-(1:4)]\nwordcloud(rownames(TDM.dense), rowSums(TDM.dense), min.freq = 1, color = palette)\n\n", "meta": {"hexsha": "0f6519198e2b5914348ce61f8bd240d0108f059e", "size": 1119, "ext": "r", "lang": "R", "max_stars_repo_path": "RwithJoyce.r", "max_stars_repo_name": "AliceBourbaki/JoycewithR", "max_stars_repo_head_hexsha": "57f96897cce74d29cc8918946a6aa9c9fbdebdbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "RwithJoyce.r", "max_issues_repo_name": "AliceBourbaki/JoycewithR", "max_issues_repo_head_hexsha": "57f96897cce74d29cc8918946a6aa9c9fbdebdbe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RwithJoyce.r", "max_forks_repo_name": "AliceBourbaki/JoycewithR", "max_forks_repo_head_hexsha": "57f96897cce74d29cc8918946a6aa9c9fbdebdbe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.3260869565, "max_line_length": 81, "alphanum_fraction": 0.7247542449, "num_tokens": 343, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3148300531811561}} {"text": "# calculate_compensation_paper.r\n#\n# Copyright (c) 2020 VIB (Belgium) & Babraham Institute (United Kingdom)\n#\n# Software written by Carlos P. Roca, as research funded by the European Union.\n#\n# This software may be modified and distributed under the terms of the MIT\n# license. See the LICENSE file for details.\n\n\n# Runs a calculation of compensation with autospill, creating all figures and\n# tables used in autospill paper.\n#\n# Requires being called as a batch script with the following two arguments:\n# control.dir directory with the set of single-color controls\n# control.def.file csv file defining the names and channels of the\n# single-color controls\n\n\nlibrary( autospill )\n\n\n# get directory and csv file with definition of control dataset\n\nargs <- commandArgs( TRUE )\n\nif ( length( args ) != 2 ) {\n cat( \"ERROR: no arguments with directory and csv file with definition of control dataset\",\n file = stderr() )\n stop()\n}\n\ncontrol.dir <- args[[ 1 ]]\ncontrol.def.file <- args[[ 2 ]]\n\n\n# set parameters\n\nasp <- get.autospill.param( \"paper\" )\n\n\n# read flow controls\n\nflow.control <- read.flow.control( control.dir, control.def.file, asp )\n\n\n# gate events before calculating spillover\n\nflow.gate <- gate.flow.data( flow.control, asp )\n\n\n# get initial spillover matrices from untransformed and transformed data\n\nmarker.spillover.unco.untr <- get.marker.spillover( TRUE, flow.gate,\n flow.control, asp )\nmarker.spillover.unco.tran <- get.marker.spillover( FALSE, flow.gate,\n flow.control, asp )\n\n\n# get spillover and compensation matrices with positive and negative\n# populations\n\nspillover.error.posnegpop <- process.posnegpop( marker.spillover.unco.untr,\n flow.gate, flow.control, asp )\n\n\n# refine spillover matrix iteratively\n\nrefine.spillover.result <- refine.spillover( marker.spillover.unco.untr,\n marker.spillover.unco.tran, flow.gate, flow.control, asp )\n\n\n# plot results together for slope error and skewness\n\nplot_result.together( flow.control, asp )\n\n\n# replot convergence adding spillover error from the calculation with positive\n# and negative populations\n\nplot_convergence( refine.spillover.result$convergence,\n spillover.error.posnegpop$error$slop, asp )\n\n\n# output session info\n\nsessionInfo()\n\n", "meta": {"hexsha": "2a1b66e581e7fbd0f42eb32c83baa7c41dee72f2", "size": 2257, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/batch/calculate_compensation_paper.r", "max_stars_repo_name": "DillonHammill/autospill", "max_stars_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-07T21:48:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-12T03:00:59.000Z", "max_issues_repo_path": "inst/batch/calculate_compensation_paper.r", "max_issues_repo_name": "DillonHammill/autospill", "max_issues_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-09-10T08:08:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-29T23:41:00.000Z", "max_forks_repo_path": "inst/batch/calculate_compensation_paper.r", "max_forks_repo_name": "DillonHammill/autospill", "max_forks_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-09-05T14:15:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-12T14:36:42.000Z", "avg_line_length": 25.3595505618, "max_line_length": 94, "alphanum_fraction": 0.7403633141, "num_tokens": 542, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3148300531811561}} {"text": "rm(list = ls())\nwdir <- c(\"~/Dropbox/data/elecs/MXelsCalendGovt/elecReturns\")\nsetwd(wdir)\n\ndf <- read.csv(\"dfSeccion2012.csv\", header=TRUE)\ndf$casillasNoInstaladas <- NULL; df$casillasNoEntregadas <- NULL; df$ord <- NULL\ndf$listanomcasilla <- NULL # OJO: averiguar diferencia con pr$lisnom\ndf <- df[order(df$edon,df$seccion),]\ndf$efec <- df$pan+df$pri+df$prd+df$pvem+df$pt+df$mc+df$panal+df$pripvem+df$prdptmc+df$prdpt+df$prdmc+df$ptmc\n\npr <- read.csv(\"prSeccion2012.csv\", header=TRUE)\npr$ord <- NULL\npr$listanomcasilla <- NULL # OJO: averiguar diferencia con pr$lisnom\npr <- pr[order(pr$edon,pr$seccion),]\npr$efec <- pr$pan+pr$pri+pr$prd+pr$pvem+pr$pt+pr$mc+pr$panal+pr$pripvem+pr$prdptmc+pr$prdpt+pr$prdmc+pr$ptmc\n\n# VERIFY SECCIONES IN BOTH DATASETS MATCH (SUM MUST BE ZERO)\ntmp <- data.frame(df=df$edon*10000+df$seccion, pr=pr$edon*10000+pr$seccion); tmp$dif <- tmp$df-tmp$pr\nsum(tmp$dif)\nrm(tmp)\n\nel <- data.frame(edon=df$edon, disn=df$disn, munn=df$munn, seccion=df$seccion, urbrur=df$urbrur, lisnom=df$lisnom, df.pan=df$pan, df.pri=df$pri, df.prd=df$prd, df.pt=df$pt, df.pvem=df$pvem, df.mc=df$mc, df.panal=df$panal, df.pripvem=df$pripvem, df.prdptmc=df$prdptmc, df.prdpt=df$prdpt, df.prdmc=df$prdmc, df.ptmc=df$ptmc, df.efec=df$efec, df.nul=df$nul)\nrm(df)\nel$pr.pan <- pr$pan; el$pr.pri <- pr$pri; el$pr.prd <- pr$prd; el$pr.pt <- pr$pt; el$pr.pvem <- pr$pvem; el$pr.mc <- pr$mc; el$pr.panal <- pr$panal; el$pr.pripvem <- pr$pripvem; el$pr.prdptmc <- pr$prdptmc; el$pr.prdpt <- pr$prdpt; el$pr.prdmc <- pr$prdmc; el$pr.ptmc <- pr$ptmc; el$pr.efec <- pr$efec; el$pr.nul <- pr$nul\nrm(pr)\nhead(el)\n\n#AQUI NECESITO INFO SOCIODEMOGRAFICA DE LAS SECCIONES!!!!\n#AQUI NECESITO MATRIZ DE VECINDAD DE LAS SECCIONES!!!!\n\n# COALICIONES COMO UdeA\nel$df.coalpri <- el$df.pri+el$df.pvem+el$df.pripvem\nel$df.coalprd <- el$df.prd+el$df.pt+el$df.mc+el$df.prdptmc+el$df.prdpt+el$df.prdmc+el$df.ptmc\nel$pr.coalpri <- el$pr.pri+el$pr.pvem+el$pr.pripvem\nel$pr.coalprd <- el$pr.prd+el$pr.pt+el$pr.mc+el$pr.prdptmc+el$pr.prdpt+el$pr.prdmc+el$pr.ptmc\n##\n## DF\nxx <- data.frame(pan=el$df.pan, pri=el$df.coalpri, prd=el$df.coalprd, panal=el$df.panal)\nxxx <- apply(xx, 1, max)\nwin.df.coal <- rep(NA, nrow(xx))\nwin.df.coal[xxx==xx[,1]] <- \"pan\"; win.df.coal[xxx==xx[,2]] <- \"pri\"; win.df.coal[xxx==xx[,3]] <- \"prd\"; win.df.coal[xxx==xx[,4]] <- \"panal\" \n##\n## MATRIX OF SECTION PARTY RANKS\nf = function(x) { 5-rank(x, na.last = FALSE, ties.method = \"random\") }\nrank.df <- as.data.frame(t(apply(xx, 1, f)))\nrank.pr <- as.data.frame(t(apply(xx, 1, f)))\n## VOTES FOR 2nd, 3rd PARTIES IN SECTION\nxxx <- cbind(rank.df, xx)\nf = function(x) { as.numeric(x[c(rep(FALSE,4),x[1:4]==2)]) } ## x is a vote vector, returns second largest element\nv2nd.df.coal <- apply(xxx, 1, f)\nf = function(x) { as.numeric(x[c(rep(FALSE,4),x[1:4]==3)]) } ## x is a vote vector, returns third largest element\nv3rd.df.coal <- apply(xxx, 1, f)\nrm(xx,xxx)\n## Secci\u00f3n SF Ratios\nsf.df.coal <- v3rd.df.coal/v2nd.df.coal\n##\n## PR\nxx <- data.frame(pan=el$pr.pan, pri=el$pr.coalpri, prd=el$pr.coalprd, panal=el$pr.panal)\nxxx <- apply(xx, 1, max)\nwin.pr.coal <- rep(NA, nrow(xx))\nwin.pr.coal[xxx==xx[,1]] <- \"pan\"; win.pr.coal[xxx==xx[,2]] <- \"pri\"; win.pr.coal[xxx==xx[,3]] <- \"prd\"; win.pr.coal[xxx==xx[,4]] <- \"panal\" \n##\n## MATRIX OF SECTION PARTY RANKS\nf = function(x) { 5-rank(x, na.last = FALSE, ties.method = \"random\") }\nrank.pr <- as.data.frame(t(apply(xx, 1, f)))\nrank.pr <- as.data.frame(t(apply(xx, 1, f)))\n## VOTES FOR 2nd, 3rd PARTIES IN SECTION\nxxx <- cbind(rank.pr, xx)\nf = function(x) { as.numeric(x[c(rep(FALSE,4),x[1:4]==2)]) } ## x is a vote vector, returns second largest element\nv2nd.pr.coal <- apply(xxx, 1, f)\nf = function(x) { as.numeric(x[c(rep(FALSE,4),x[1:4]==3)]) } ## x is a vote vector, returns third largest element\nv3rd.pr.coal <- apply(xxx, 1, f)\nrm(xx,xxx)\n## Secci\u00f3n SF Ratios\nsf.pr.coal <- v3rd.pr.coal/v2nd.pr.coal\n##\n## PLOT SF ratios by seccion\nhist(sf.df.coal)\nhist(sf.pr.coal)\n\n#PLOT MARGINS\nmg <- data.frame(df.pripan=el$df.coalpri-el$df.pan, df.priprd=el$df.coalpri-el$df.coalprd, df.prdpan=el$df.coalprd-el$df.pan, pr.pripan=el$pr.coalpri-el$pr.pan, pr.priprd=el$pr.coalpri-el$pr.coalprd, pr.prdpan=el$pr.coalprd-el$pr.pan)\n#PRI-PAN\nuno.1 <- round(sum(mg$df.pripan>=0 & mg$pr.pripan>=0 & mg$df.pripan=0 & mg$pr.pripan>=0 & mg$df.pripan>=mg$pr.pripan)/nrow(mg),digits=2)\ndos <- round(sum(mg$df.pripan>=0 & mg$pr.pripan<0)/nrow(mg),digits=2)\ntres.1 <- round(sum(mg$df.pripan<0 & mg$pr.pripan<0 & mg$df.pripan>=mg$pr.pripan)/nrow(mg),digits=2)\ntres.2 <- round(sum(mg$df.pripan<0 & mg$pr.pripan<0 & mg$df.pripan=0)/nrow(mg),digits=2)\n#plot(c(min(mg$df.pripan), max(mg$df.pripan)), c(min(mg$pr.pripan), max(mg$pr.pripan)), type=\"n\")\nplot(c(-2000,2000), c(-2000,2000), type=\"n\", xlab = \"PRI-PAN diputados margin\", ylab = \"Pe\u00f1a-V\u00e1zquez Mota margin\")\npoints(mg$df.pripan, mg$pr.pripan, pch=\".\", cex=1)\nabline(a=0,b=1,col=\"red\")\nabline(h=0, col=\"red\"); abline(v=0, col=\"red\");\n#abline(lm(formula= mg$pr.pripan ~ mg$df.pripan))\ntext(500,1500,uno.1)\ntext(1500,500,uno.2)\ntext(1000,-1000,dos)\ntext(-500,-1500,tres.1)\ntext(-1500,-500,tres.2)\ntext(-1000,1000,cuatro)\n##\nres.pripan <- mg$pr.pripan-mg$df.pripan\nres.pripan.plus <- res.pripan[res.pripan>0]\nres.pripan.minus <- res.pripan[res.pripan<=0]\nlength(res.pripan); length(res.pripan.plus); length(res.pripan.minus)\nmean(res.pripan.plus)\nmean(res.pripan.minus)\nmean(res.pripan)\n##\n## FOR USE IN TEXT: Distinguishing positive residuals from the 45-degree line (points above the line) from negative residuals is illustrative. The mean positive residual is 42.5---implying that in secciones where Pe\u00f1a overperformed his party's deputy candidate, his margin over the PAN was 42.5 votes larger than the PRI--PAN deputy margin. At $-44-7$, the mean negative residual was larger, but a much larger number of secciones above (about 38,500) than below (about 28,000) gave Pe\u00f1a a mean margin 6 votes larger in each secci\u00f3n than his fellow deputies. \n\nplot(mg$df.pripan[res.pripan>0],res.pripan.plus)\nplot(mg$df.pripan[res.pripan<=0],res.pripan.minus)\npoints(mg$df.pripan, mg$pr.pripan, pch=\".\", cex=1)\n\n\n\n#PRI-PRD\nuno.1 <- round(sum(mg$df.priprd>=0 & mg$pr.priprd>=0 & mg$df.priprd=0 & mg$pr.priprd>=0 & mg$df.priprd>=mg$pr.priprd)/nrow(mg),digits=2)\ndos <- round(sum(mg$df.priprd>=0 & mg$pr.priprd<0)/nrow(mg),digits=2)\ntres.1 <- round(sum(mg$df.priprd<0 & mg$pr.priprd<0 & mg$df.priprd>=mg$pr.priprd)/nrow(mg),digits=2)\ntres.2 <- round(sum(mg$df.priprd<0 & mg$pr.priprd<0 & mg$df.priprd=0)/nrow(mg),digits=2)\n#plot(c(min(mg$df.priprd), max(mg$df.priprd)), c(min(mg$pr.priprd), max(mg$pr.priprd)), type=\"n\")\nplot(c(-2000,2000), c(-2000,2000), type=\"n\", xlab = \"PRI-PRD diputados margin\", ylab = \"Pe\u00f1a-AMLO margin\")\npoints(mg$df.priprd, mg$pr.priprd, pch=\".\", cex=1)\nabline(a=0,b=1,col=\"red\")\nabline(h=0, col=\"red\"); abline(v=0, col=\"red\");\n#abline(lm(formula= mg$pr.priprd ~ mg$df.priprd))\ntext(500,1500,uno.1)\ntext(1500,500,uno.2)\ntext(1000,-1000,dos)\ntext(-500,-1500,tres.1)\ntext(-1500,-500,tres.2)\ntext(-1000,1000,cuatro)\n\n#PRD-PAN\nuno.1 <- round(sum(mg$df.prdpan>=0 & mg$pr.prdpan>=0 & mg$df.prdpan=0 & mg$pr.prdpan>=0 & mg$df.prdpan>=mg$pr.prdpan)/nrow(mg),digits=2)\ndos <- round(sum(mg$df.prdpan>=0 & mg$pr.prdpan<0)/nrow(mg),digits=2)\ntres.1 <- round(sum(mg$df.prdpan<0 & mg$pr.prdpan<0 & mg$df.prdpan>=mg$pr.prdpan)/nrow(mg),digits=2)\ntres.2 <- round(sum(mg$df.prdpan<0 & mg$pr.prdpan<0 & mg$df.prdpan=0)/nrow(mg),digits=2)\n#plot(c(min(mg$df.prdpan), max(mg$df.prdpan)), c(min(mg$pr.prdpan), max(mg$pr.prdpan)), type=\"n\")\nplot(c(-2000,2000), c(-2000,2000), type=\"n\", xlab = \"PRD-PAN diputados margin\", ylab = \"AMLO-V\u00e1zquez Mota margin\")\npoints(mg$df.prdpan, mg$pr.prdpan, pch=\".\", cex=1)\nabline(a=0,b=1,col=\"red\")\nabline(h=0, col=\"red\"); abline(v=0, col=\"red\");\n#abline(lm(formula= mg$pr.prdpan ~ mg$df.prdpan))\ntext(500,1500,uno.1)\ntext(1500,500,uno.2)\ntext(1000,-1000,dos)\ntext(-500,-1500,tres.1)\ntext(-1500,-500,tres.2)\ntext(-1000,1000,cuatro)\n\n## Seccion shares\n#el$df.pan.sh <- el$df.pan / el$df.efec\n#el$df.coalpri.sh <- el$df.coalpri / el$df.efec\n#el$df.coalprd.sh <- el$df.coalprd / el$df.efec\n#el$pr.pan.sh <- el$pr.pan / el$pr.efec\n#el$pr.coalpri.sh <- el$pr.coalpri / el$pr.efec\n#el$pr.coalprd.sh <- el$pr.coalprd / el$pr.efec\n## TRIPLOT HERE\nlibrary(vcd)\nxx <- el$df.pan+el$df.coalpri+el$df.coalprd\nxxx <- data.frame(PRD=el$df.coalprd/xx, PAN=el$df.pan/xx, PRI=el$df.coalpri/xx)\ncolor <- rep(\"black\", length(win.df.coal));\nfor (i in 1:length(win.df.coal)){\n color[i] <- ifelse(win.df.coal[i]==\"pan\", \"blue\",\n ifelse(win.df.coal[i]==\"pri\", \"red\",\n ifelse(win.df.coal[i]==\"prd\", \"gold\", \"black\")))\n }\ncolor <- color[is.na(xxx[,1])==FALSE]; xxx <- xxx[is.na(xxx[,1])==FALSE,]\ngdir <- c(\"~/Dropbox/data/elecs/MXelsCalendGovt/elRef/graphs\")\nsetwd(gdir)\npdf(file=\"ternary.df.pdf\",width=7, height=7)\nternaryplot(xxx, scale = 1, dimnames_position = \"corner\", labels = \"outside\", pch=\".\", main=\"Deputies\", col=color)\ndev.off()\nsetwd(wdir)\n\nxx <- el$pr.pan+el$pr.coalpri+el$pr.coalprd\nxxx <- data.frame(AMLO=el$pr.coalprd/xx, JVM=el$pr.pan/xx, Pe\u00f1a=el$pr.coalpri/xx)\ncolor <- rep(\"black\", length(win.pr.coal));\nfor (i in 1:length(win.pr.coal)){\n color[i] <- ifelse(win.pr.coal[i]==\"pan\", \"blue\",\n ifelse(win.pr.coal[i]==\"pri\", \"red\",\n ifelse(win.pr.coal[i]==\"prd\", \"gold\", \"black\")))\n }\ncolor <- color[is.na(xxx[,1])==FALSE]; xxx <- xxx[is.na(xxx[,1])==FALSE,]\nlibrary(vcd)\ngdir <- c(\"~/Dropbox/data/elecs/MXelsCalendGovt/elRef/graphs\")\nsetwd(gdir)\n#pdf(file=\"ternary.pr.pdf\",width=7, height=7)\nternaryplot(xxx, scale = 1, dimnames_position = \"corner\", labels = \"outside\", pch=\".\", main=\"President\", col=color)\n#dev.off()\nsetwd(wdir)\n\n##OJO: ESTOS SON DATOS DEL CONTEO 2005. EN /mapas/secciones/edo/ HAY DBF CON CENSO 2010\nlibrary(foreign)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ags/01_ags_lengind.dbf\")\nlengind <- data.frame(edon=tmp$ENTIDAD, seccion=tmp$SECCION, habli=tmp$HABLEIN1)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bc/02_bc_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bcs/03_bcs_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cam/04_camp_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/coa/05_coah_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/col/06_col_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cps/07_chiap_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cua/08_chih_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/df/09_df_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/dgo/10_dgo_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gua/11_gto_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gue/12_gro_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/hgo/13_hgo_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/jal/14_jal_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mex/15_mex_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mic/16_mich_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mor/17_mor_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nay/18_nay_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nl/19_nl_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/oax/20_oax_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/pue/21_pue_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/que/22_qro_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/qui/23_qroo_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/san/24_slp_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/sin/25_sin_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/son/26_son_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tab/27_tab_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tam/28_tamp_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tla/29_tlax_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ver/30_ver_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/yuc/31_yuc_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/zac/32_zac_lengind.dbf\")\nold <- nrow(lengind); new <- nrow(tmp);\nlengind[(old+1):(old+new),1] <- tmp$ENTIDAD; lengind[(old+1):(old+new),2] <- tmp$SECCION; lengind[(old+1):(old+new),3] <- tmp$HABLEIN1; \nrm(tmp,old,new)\n\nlibrary(foreign)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ags/01_ags_viv.dbf\")\nviv <- data.frame(edon=tmp$ENTIDAD, disn=tmp$DISTRITO, munn=tmp$MUNICIPIO, seccion=tmp$SECCION, vivTot=tmp$VIV_TOT, tierra=tmp$MAT_PISO1,\n tv=tmp$DIS_TELE1, compu=tmp$DIS_COMP1, sinluz=tmp$DIS_ELEC2, aguaRio=tmp$DIS_AGUA7, sinWc=tmp$DIS_SANI2+tmp$CON_AGUA5,\n avgRoomOccup=tmp$PTOPERVIV/tmp$PCUARDOM)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bc/02_bc_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bcs/03_bcs_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cam/04_camp_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/coa/05_coah_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/col/06_col_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cps/07_chiap_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cua/08_chih_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAP_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/df/09_df_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAP_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/dgo/10_dgo_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gua/11_gto_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gue/12_gro_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/hgo/13_hgo_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/jal/14_jal_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mex/15_mex_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAP_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mic/16_mich_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mor/17_mor_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nay/18_nay_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nl/19_nl_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/oax/20_oax_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/pue/21_pue_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAP_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/que/22_qro_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/qui/23_qroo_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/san/_4_slp_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/sin/25_sin_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/son/26_son_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tab/27_tab_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tam/28_tamp_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tla/_9_tlax_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ver/30_ver_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/yuc/31_yuc_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/zac/32_zac_viv.dbf\")\nold <- nrow(viv); new <- nrow(tmp);\nviv[(old+1):(old+new),1] <- tmp$ENTIDAD; viv[(old+1):(old+new),2] <- tmp$DISTRITO; viv[(old+1):(old+new),3] <- tmp$MUNICIPIO; viv[(old+1):(old+new),4] <- tmp$SECCION; viv[(old+1):(old+new),5] <- tmp$VIV_TOT; viv[(old+1):(old+new),6] <- tmp$MAT_PISO1; viv[(old+1):(old+new),7] <- tmp$DIS_TELE1; viv[(old+1):(old+new),8] <- tmp$DIS_COMP1; viv[(old+1):(old+new),9] <- tmp$DIS_ELEC2; viv[(old+1):(old+new),10] <- tmp$DIS_AGUA7; viv[(old+1):(old+new),11] <- tmp$DIS_SANI2+tmp$CON_AGUA5; viv[(old+1):(old+new),12] <- tmp$PTOPERVIV/tmp$PCUARDOM;\nrm(tmp,old,new)\n\nlibrary(foreign)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ags/01_ags_pob.dbf\")\npob <- data.frame(edon=tmp$ENTIDAD, seccion=tmp$SECCION, pobTot=tmp$POB_TOT, pob15plus=tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03, shWomen=tmp$SEXO2/tmp$POB_TOT, young15.29=tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06, old65plus=tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21, imss=tmp$IMSS1, issste=tmp$ISSSTE1, segPop=tmp$SEGU_POP1, analfab=tmp$ALFABET2, rural=tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06, sinPrim=tmp$NIV_ESCO00+tmp$NIV_ESCO01)\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bc/02_bc_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/bcs/03_bcs_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cam/04_camp_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/coa/05_coah_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/col/06_col_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cps/07_chiap_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/cua/08_chih_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/df/09_df_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/dgo/10_dgo_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gua/11_gto_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/gue/12_gro_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/hgo/13_hgo_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/jal/14_jal_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mex/_5_mex_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mic/16_mich_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/mor/17_mor_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nay/18_nay_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/nl/19_nl_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/oax/20_oax_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/pue/21_pue_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/que/22_qro_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/qui/23_qroo_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/san/24_slp_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/sin/_5_sin_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/son/26_son_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tab/27_tab_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tam/28_tamp_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/tla/29_tlax_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/ver/30_ver_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/yuc/31_yuc_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\ntmp <- read.dbf(\"~/Dropbox/data/elecs/MXelsCalendGovt/censos/secciones/zac/_2_zac_pob.dbf\")\nold <- nrow(pob); new <- nrow(tmp);\npob[(old+1):(old+new),1] <- tmp$ENTIDAD; pob[(old+1):(old+new),2] <- tmp$SECCION; pob[(old+1):(old+new),3] <- tmp$POB_TOT; pob[(old+1):(old+new),4] <- tmp$POB_TOT-tmp$EDQUI01-tmp$EDQUI02-tmp$EDQUI03; pob[(old+1):(old+new),5] <- tmp$SEXO2/tmp$POB_TOT; pob[(old+1):(old+new),6] <- tmp$EDQUI04+tmp$EDQUI05+tmp$EDQUI06; pob[(old+1):(old+new),7] <- tmp$EDQUI14+tmp$EDQUI15+tmp$EDQUI16+tmp$EDQUI17+tmp$EDQUI18+tmp$EDQUI19+tmp$EDQUI20+tmp$EDQUI21; pob[(old+1):(old+new),8] <- tmp$IMSS1; pob[(old+1):(old+new),9] <- tmp$ISSSTE1; pob[(old+1):(old+new),10] <- tmp$SEGU_POP1; pob[(old+1):(old+new),11] <- tmp$ALFABET_2; pob[(old+1):(old+new),12] <- tmp$TAM_LOC01+tmp$TAM_LOC02+tmp$TAM_LOC03+tmp$TAM_LOC04+tmp$TAM_LOC05+tmp$TAM_LOC06; pob[(old+1):(old+new),13] <- tmp$NIV_ESCO00+tmp$NIV_ESCO01;\nrm(tmp,old,new)\n", "meta": {"hexsha": "28dda13021db18c521a936ace91c4318b0ecf953", "size": 69047, "ext": "r", "lang": "R", "max_stars_repo_path": "code/seccElec.r", "max_stars_repo_name": "RicardoTM96/elecRetrns", "max_stars_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-02-20T01:40:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-24T18:53:36.000Z", "max_issues_repo_path": "code/seccElec.r", "max_issues_repo_name": "RicardoTM96/elecRetrns", "max_issues_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-10-27T04:24:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T22:21:37.000Z", "max_forks_repo_path": "code/seccElec.r", "max_forks_repo_name": "RicardoTM96/elecRetrns", "max_forks_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2018-04-04T21:36:47.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-19T02:29:37.000Z", "avg_line_length": 138.9275653924, "max_line_length": 782, "alphanum_fraction": 0.6637507785, "num_tokens": 30559, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.31483005318115603}} {"text": "#importing the dataset\n\nsetwd(\"git_repos/Advance-Sentiment-Analysis/scripts/classifiers/kmeans/\")\n\ndataset <- read.csv(\"sentiment.csv\")\n\n#selecting the columns of concern from the dataset in another variable\n\n# %>% is called the pipe symbol\n\nlibrary(magrittr) # need to run every time you start R and want to use %>%\nlibrary(dplyr) # alternative, this also loads %>%\n\npreprocessed_data <- dataset %>%\n select(AS = airline_sentiment, ASC = airline_sentiment_confidence, NRC = negativereason_confidence)\n\n#substituting null values with the mean values of the column\n\nMeanNRC<-mean(preprocessed_data$NRC, na.rm=TRUE)\npreprocessed_data$NRC[which(is.na(preprocessed_data$NRC))] <- MeanNRC\n\n#creating a CSV file to store the columns of concern\n\nwrite.csv(preprocessed_data,\"preprocessed_dataset.csv\",row.names = F)\n\n#importing the refined dataset\n\nKMeans<-read.csv(\"preprocessed_dataset.csv\")\nKMeans_2<-KMeans[-1]\nKMeans_3<-as.data.frame(scale(KMeans_2))\nKMeans_3\n\n#finding the mean and standard deviation\n\nsapply(KMeans_2,mean)\nsapply(KMeans_2,sd)\nsapply(KMeans_3,mean)\nsapply(KMeans_3,sd)\n\n#applying KMeans to the datasets\n\n# NbClust package provides 30 indices for determining the number of clusters and proposes to user \n# the best clustering scheme from the different results obtained by varying all combinations of number of \n# clusters, distance measures, and clustering methods.\n\nlibrary(NbClust)\n\nwssplot <- function(data, nc=15, seed=1234){\n wss <- (nrow(data)-1)*sum(apply(data,2,var))\n for (i in 2:nc){\n set.seed(seed)\n wss[i] <- sum(kmeans(data, centers=i)$withinss)}\n plot(1:nc, wss, type=\"b\", xlab=\"Number of Clusters\",\n ylab=\"Within groups sum of squares\")}\n\n\n# nc <- NbClust(KMeans_3, min.nc=2, max.nc=15, method=\"kmeans\")\n# print(table(nc$Best.n[1,]))\n\n#plotting the graph with the clusters\n\nwssplot(KMeans_3,nc=30,seed=1234)\n\n#visualising the results\n\n# nc=6 as using the elbow method the appropriate number of clusters should be 6\n\nbase_kmeans<-kmeans(KMeans_3,6)\nbase_kmeans\nbase_kmeans$centers\nbase_kmeans$size", "meta": {"hexsha": "62f0d1ae2979fd9927291d9d047eceb95b908337", "size": 2045, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/classifiers/kmeans/KMeans.r", "max_stars_repo_name": "amanparmar17/Advance-Sentiment-Analysis", "max_stars_repo_head_hexsha": "7ce463644c1fd5b76e00299840ded49502702291", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/classifiers/kmeans/KMeans.r", "max_issues_repo_name": "amanparmar17/Advance-Sentiment-Analysis", "max_issues_repo_head_hexsha": "7ce463644c1fd5b76e00299840ded49502702291", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/classifiers/kmeans/KMeans.r", "max_forks_repo_name": "amanparmar17/Advance-Sentiment-Analysis", "max_forks_repo_head_hexsha": "7ce463644c1fd5b76e00299840ded49502702291", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8028169014, "max_line_length": 106, "alphanum_fraction": 0.7574572127, "num_tokens": 578, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3147506762406312}} {"text": "#MeanDecreaseGini\nlibrary(ggplot2)\ndata <- read.csv(\"MeanDecreaseGini.csv\", header = TRUE)\nggplot(data, aes(reorder(row.names(data), data$Value), data$Value))+ geom_bar(stat = \"identity\", fill = \"steelblue\")+coord_flip()\n\n#MeanDecreaseAccuracy\nlibrary(ggplot2)\ndata <- read.csv(\"MeanDecreaseAccuracy.csv\", header = TRUE)\nggplot(data, aes(reorder(row.names(data), data$Value), data$Value))+ geom_bar(stat = \"identity\", fill = \"steelblue\")+coord_flip()\n", "meta": {"hexsha": "0f1502f48556711133994d882d3eb057290672b3", "size": 451, "ext": "r", "lang": "R", "max_stars_repo_path": "Variable importance ranking/varImp.r", "max_stars_repo_name": "yuelinnnnnnn/urban_landmarks_rf", "max_stars_repo_head_hexsha": "d5ad792317463490b453374b2b65baedcdf65486", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-11T09:24:19.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-11T09:24:19.000Z", "max_issues_repo_path": "Variable importance ranking/varImp.r", "max_issues_repo_name": "yuelinnnnnnn/urban_landmarks_rf", "max_issues_repo_head_hexsha": "d5ad792317463490b453374b2b65baedcdf65486", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Variable importance ranking/varImp.r", "max_forks_repo_name": "yuelinnnnnnn/urban_landmarks_rf", "max_forks_repo_head_hexsha": "d5ad792317463490b453374b2b65baedcdf65486", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.1, "max_line_length": 129, "alphanum_fraction": 0.7405764967, "num_tokens": 126, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.31475067624063113}} {"text": "# -*- coding: utf-8 -*-\n\n#' Created on Fri Apr 13 15:38:28 2018\n#' R version 3.4.3 (2017-11-30)\n#' \n#' @group Group 2, DM2 2018 Semester 2\n#' @author: Martins T.\n#' @author: Mendes R.\n#' @author: Santos R.\n#'\n\n# Libs --------------------------------------------------------------------\noptions(warn=-1)\nsource(\"src/packages.r\")\ninclude_packs(c(\"dygraphs\",\"d3heatmap\",\"rockchalk\",\"forcats\",\"rJava\",\n \"xlsxjars\",\"xlsx\",\"tidyverse\",\"stringi\",\"stringr\",\"ggcorrplot\",\n \"sm\",\"lubridate\",\"magrittr\",\"ggplot2\",\"openxlsx\",\"RColorBrewer\",\n \"psych\",\"treemap\",\"data.table\",\"pROC\",\"class\",'gmodels','klaR',\n \"C50\",\"caret\",'gmodels',\"DMwR\",\"recipes\",\"epiR\",\"pubh\",\"leaps\",\n \"MASS\",\"gbm\",\"autoimage\",\"randomForest\",\"settings\",\"factoextra\",\n \"dummies\",\"RRF\",\"h2o\",\"tidyquant\",\"unbalanced\"))\n\n\n# Load data ---------------------------------------------------------------\n#Load normalized data xlsx\nsource(\"src/wrangling.r\")\nnormalizedDataset <- xlsx::read.xlsx('datasets/normalizedDataset.xlsx',1, header= TRUE)\n\n#backup set\ntemp<-normalizedDataset\n\n\n\n# Logistic CV 13 Predictors ----------------------------------------------\n\n\n###' Debug\nnormalizedDataset <- temp\n\ninclude <- c(\"DistanceHomeOffice\",\"isAfterHours\",\n \"RoleSatisfaction\",\"isSingle\",\n \"isMarried\",\"Age\",\n \"FacilitiesSatisfaction\",\"avgSatisfaction\",\n \"TenureWorking\",\"NumCompaniesWorked\",\n \"LastPromotion\",\"MonthlyIncome\",\"isDepartIT\",\n \"isChurn\")\n\n\nnormalizedDataset <- normalizedDataset[ , (names(normalizedDataset) %in% include)]\n\n\n\nh2o.init()\n\n# Data Partition \nset.seed(375); trainingRowIndex <- sample(1:nrow(normalizedDataset),\n 0.69*nrow(normalizedDataset))\n#1000 records\ntrainData <- normalizedDataset[trainingRowIndex, ]\n#remaining 450 records\ntestData <- normalizedDataset[-trainingRowIndex, ]\n\n\n# \ntrainData$isChurn <- ifelse(trainData$isChurn==1,\"Yes\",\"No\")\ntestData$isChurn <- ifelse(testData$isChurn==1,\"Yes\",\"No\")\n\n\n# \n# #Save test_labels\ntestData$isChurn <- as.factor(testData$isChurn)\ntrainData$isChurn<- as.factor(trainData$isChurn)\n\n\n\n\n\n# #Save test_labels\ntest_labels <- as.factor(testData$isChurn)\n\n\n#drop target from testData, not mandatory \ndrops <- c(\"isChurn\")\ntestData <- testData[ , !(names(testData) %in% drops)]\n\n\n\n\ny <- \"isChurn\"\nx <- setdiff(names(trainData), y)\n\ntrain_h2o <- as.h2o(trainData)\ntest_h2o <- as.h2o(testData)\n\n\n\ndl <- h2o.deeplearning(x = x, \n y = y,\n training_frame= train_h2o,\n nfolds = 10, \n stopping_rounds = 10,\n epochs=400,\n overwrite_with_best_model = T,\n activation = \"Tanh\",\n input_dropout_ratio = .1,\n hidden = c(10,10),\n distribution = \"AUTO\",\n stopping_metric = \"logloss\")\n\n\ndl\n\n\npredictions<- as.data.frame(predict(dl,test_h2o))\n\ntestData$pred<-as.factor(predictions$predict)\ntestData$isChurn<-test_labels\ncm <- caret::confusionMatrix(testData$pred, testData$isChurn, positive = 'Yes',\n dnn=c(\"Pred\",\"Actual\"))\n\ndraw_confusion_matrix_boot(cm)\n\n\nperf_h2o <- h2o.performance(dl ,test_h2o ) \n\n# Plot ROC Curve\nleft_join(h2o.tpr(perf_h2o), h2o.fpr(perf_h2o)) %>%\n mutate(random_guess = fpr) %>%\n select(-threshold) %>%\n ggplot(aes(x = fpr)) +\n geom_area(aes(y = tpr, fill = \"AUC\"), alpha = 0.5) +\n geom_point(aes(y = tpr, color = \"TPR\"), alpha = 0.25) +\n geom_line(aes(y = random_guess, color = \"Random Guess\"), size = 1, linetype = 2) +\n theme_tq() +\n scale_color_manual(\n name = \"Key\", \n values = c(\"TPR\" = palette_dark()[[1]],\n \"Random Guess\" = palette_dark()[[2]])\n ) +\n scale_fill_manual(name = \"Fill\", values = c(\"AUC\" = palette_dark()[[5]])) +\n labs(title = \"ROC Curve\", \n subtitle = \"Model is performing much better than random guessing\") +\n annotate(\"text\", x = 0.25, y = 0.65, label = \"Better than guessing\") +\n annotate(\"text\", x = 0.75, y = 0.25, label = \"Worse than guessing\")\n\n", "meta": {"hexsha": "968141e654c383fc2f08ec6ffe7fd4ffd5fb89bf", "size": 4198, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/h2o.r", "max_stars_repo_name": "tmartins1996/r-binary-classification", "max_stars_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/h2o.r", "max_issues_repo_name": "tmartins1996/r-binary-classification", "max_issues_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/h2o.r", "max_forks_repo_name": "tmartins1996/r-binary-classification", "max_forks_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7534246575, "max_line_length": 87, "alphanum_fraction": 0.5778942354, "num_tokens": 1125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160666, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.31475066901944776}} {"text": "library(data.table)\nlibrary(sf)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\nlibrary(rgdal)\n\ndir.create('figs', showWarnings=F)\n\nsource('../parameters.r')\n\neff.obs <- fread('../input-data/fishing-effort-observed.csv')\neff.tot <- fread('../input-data/fishing-effort-total.csv')\neff.obs[, quarter := ((month-1) %/% 3)+1]\neff.tot[, quarter := ((month-1) %/% 3)+1]\n\neff.obs <- eff.obs[, .(effort = sum(effort)), .(fishery_group, quarter, grid_id)]\neff.tot <- eff.tot[year %in% years_prediction, .(effort = sum(effort) / length(years_prediction)),\n .(fishery_group, quarter, grid_id)]\n\nsetnames(eff.obs, 'grid_id', 'GRID_ID')\nsetnames(eff.tot, 'grid_id', 'GRID_ID')\n\neff.obs[, effort := effort / 1000]\neff.tot[, effort := effort / 1000]\n\ngrid <- read_sf('../input-data/southern-hemisphere-5-degree.shp')\nworld <- read_sf('../input-data/world.shp')\n\nw <- as(world, 'Spatial')\nwf <- fortify(w)\ng <- as(grid, 'Spatial')\ngf <- fortify(g)\n\n\n## * Gridded distributions\n\ngridfg <- as(grid, 'Spatial')\ngridfg <- spChFIDs(gridfg, as.character(gridfg$GRID_ID))\ngf <- fortify(gridfg, id='GRID_ID')\n\nworldfg <- as(world, 'Spatial')\nwf <- fortify(worldfg)\n\nmake_map <- function(griddens) {\n gridfg[['density']] <- NA_real_\n gridfg[['density']] <- griddens[\n match(gridfg$GRID_ID, GRID_ID), effort]\n gridfg[is.na(gridfg$density), 'density'] <- 0\n gf$density <- gridfg$density[match(gf$id, as.character(gridfg$GRID_ID))]\n brks <- pretty(gridfg$density)\n\n g <- ggplot() +\n geom_polygon(data = gf, aes(x = long, y = lat, fill = density, group=group),\n colour = '#CCCCCC', size = 0.1, na.rm=T) +\n geom_polygon(data = wf, aes(x = long, y = lat, group=group),\n fill = '#AAAAAA', colour = NA, size = 0.1, na.rm=T) +\n scale_fill_gradientn(name = expression(paste(\"Density\\n(birds / km\"^2, \")\")),\n colours = brewer.pal(9, 'BuPu'), na.value=NA, limits = c(0, max(brks)),\n trans = 'sqrt', breaks = brks) +\n theme_void() +\n theme(legend.position=c(0.92, 0.85),\n legend.margin = margin(5, 2, 2, 2, unit='mm'),\n legend.background=element_rect(fill = '#00000011'),\n legend.title.align = 0,\n legend.text.align = 0,\n legend.key.width = unit(0.5, 'cm')) +\n coord_map(\"ortho\", orientation = c(-90, 178, 0))\n return(g)\n}\n\nfgs <- c(unique(eff.obs$fishery_group), 'ALL')\nfg=fgs[2]\nefftype='obs'\nfor (efftype in c('tot', 'obs')) {\n eff <- get(sprintf('eff.%s', efftype))\n for (fg in fgs) {\n cat(fg, '\\n')\n quart=2\n for (quart in 1:4) {\n cat('\\tquarter', quart, '\\n')\n if (fg != 'ALL') {\n griddens <- eff[fishery_group == fg & quarter == quart]\n } else griddens <- eff[quarter == quart, .(fishery_group = fg, effort = sum(effort)), .(GRID_ID)]\n g <- make_map(griddens)\n ggsave(sprintf('figs/gridded-distribution_%s-effort_%s_quarter%i.png', efftype, fg, quart), width = 7, height = 7)\n }\n }\n}\n", "meta": {"hexsha": "eec79605af0e648fd63e9da570daf23af84892e7", "size": 3191, "ext": "r", "lang": "R", "max_stars_repo_path": "maps/effort-maps.r", "max_stars_repo_name": "dragonfly-science/seabird-risk-assessment", "max_stars_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-02-22T20:16:08.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-22T20:16:08.000Z", "max_issues_repo_path": "maps/effort-maps.r", "max_issues_repo_name": "seabird-risk-assessment/seabird-risk-assessment", "max_issues_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-10-09T08:18:07.000Z", "max_issues_repo_issues_event_max_datetime": "2017-10-29T22:13:43.000Z", "max_forks_repo_path": "maps/effort-maps.r", "max_forks_repo_name": "dragonfly-science/seabird-risk-assessment", "max_forks_repo_head_hexsha": "97d1bd13d3b3eee88ab9b9428dfc6eafc1acd305", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-04-18T22:56:04.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-16T13:57:07.000Z", "avg_line_length": 35.8539325843, "max_line_length": 126, "alphanum_fraction": 0.5603259166, "num_tokens": 921, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3146629536707037}} {"text": "##-- Acc_sampled should be set a priori to this document, since the code may be run multiple times, we do not set it back to nothing here\n## testvalidation is set in the master_shiny file (n>15 per group ?)\nout_sel_ori = out_sel\n\nif (testvalidation){\n ##inputs are trainset, testset, bestk\n trainsetp = trainset\n for (i in 1:Npermutation) {\n #permute trainset:\n \n #taking all permutation is impossible when we get more than 5 animals per group, so we sample one possibility\n trainsetp$groupingvar = sample (trainset$groupingvar)\n #create svm model: we tune only in one kernel:\n obj <-\n tune.svm(\n groupingvar ~ .,\n data = trainsetp,\n gamma = 4 ^ (-5:5),\n cost = 4 ^ (-5:5),\n tune.control(sampling = \"cross\"),\n kernel = bestk[[1]]\n )\n \n svm.model <-\n svm(\n groupingvar ~ .,\n data = trainsetp,\n cost = obj$best.parameters$cost,\n gamma = obj$best.parameters$gamma,\n kernel = bestk[[1]]\n )\n \n svm.pred <- predict(svm.model, testset %>% select(-groupingvar))\n SVMprediction_res = table(pred = svm.pred, true = testset$groupingvar)\n #SVMprediction = as.data.frame(SVMprediction_res)\n \n #Accuracy of grouping and plot\n temp = classAgreement (SVMprediction_res)\n Acc_sampled = c(Acc_sampled, temp$kappa)\n }\n}\n\nif (!testvalidation){\n #out_sel_ori is taken from the svm with real groups\n for (i in 1:Npermutation) {\n out_sel =out_sel_ori\n out_sel$groupingvar = sample (out_sel_ori$groupingvar)\n #-- we perform the same analysis, but grouping was disturbed:\n source (\"Rcode/2_out_svm.r\")\n ##-- we save the result in the Acc_sampled file\n Acc_sampled = c(Acc_sampled, temp$kappa)\n write_lines(Acc_sampled, path = paste0(Outputs,\"/Bseq\",Name_project,groupingby,\"_permutation.csv\"))\n }\n} ", "meta": {"hexsha": "4f8b8a9e87e485c920204ebc4418482685089cd2", "size": 1849, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/Rcode/multidimensional_analysis_perm_svm.r", "max_stars_repo_name": "jcolomb/HCS_analysis", "max_stars_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-09-27T08:57:12.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-22T08:44:06.000Z", "max_issues_repo_path": "analysis/Rcode/multidimensional_analysis_perm_svm.r", "max_issues_repo_name": "jcolomb/HCS_analysis", "max_issues_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 36, "max_issues_repo_issues_event_min_datetime": "2017-09-27T10:42:45.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-01T08:47:42.000Z", "max_forks_repo_path": "analysis/Rcode/multidimensional_analysis_perm_svm.r", "max_forks_repo_name": "jcolomb/HCS_analysis", "max_forks_repo_head_hexsha": "4bad7b048eae47ce19a8095862dee8908061379a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-12-10T12:45:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-19T14:28:05.000Z", "avg_line_length": 34.2407407407, "max_line_length": 137, "alphanum_fraction": 0.6560302866, "num_tokens": 517, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7122321964553657, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.3145737349395595}} {"text": "library(Seurat)\nsource(\"R/functions/gg_color_hue.r\")\n\ndn.fig = \"figures/male_vs_female\"\ndir.create(dn.fig, showWarnings = F, recursive = T)\n\nfn = \"data/H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE_labels.rds\"\nh1 = readRDS(fn)\n\ndf.subj = h1@meta.data %>% dplyr::mutate(response = str_remove(adjmfc.time, \"d0 \")) %>% \n dplyr::select(subject=sampleid, response) %>% \n distinct() %>% \n dplyr::arrange(subject)\n\nfn.info = \"../generated_data/CHI/CHI_sample_info_2_CD38hi.txt\"\ninfo = fread(fn.info) %>% dplyr::mutate(subject = as.character(subject)) %>% \n dplyr::filter(time==0)\n\nfn.tgsig = \"results/sig_scores/scores_TGSig_0.txt\"\ndf.tgsig = fread(fn.tgsig) %>% \n dplyr::rename(TGSig = `pseudo-bulk`) %>% \n dplyr::mutate(subject = as.character(subject))\n\nfn.sle = \"results/sig_scores/scores_SLE.sig_0.txt\"\ndf.sle = fread(fn.sle) %>% \n dplyr::rename(SLE.sig = `pseudo-bulk`) %>% \n dplyr::mutate(subject = as.character(subject))\n\ndf = df.subj %>% \n left_join(info, by=\"subject\") %>% \n left_join(df.tgsig, by=\"subject\") %>% \n left_join(df.sle, by=\"subject\") %>% \n dplyr::mutate(gender = factor(gender)) %>% \n dplyr::mutate(Response = factor(Response, levels=c(\"low\",\"high\"))) %>% \n gather(\"Sig\",\"Score\", c(TGSig, SLE.sig)) %>% \n dplyr::mutate(Sig = fct_inorder(Sig))\n\n\ndf.w = df %>% \n group_by(Sig) %>% \n do(broom::tidy(wilcox.test(Score ~ gender, data=., exact=F, \n paired=F, alternative = \"two\"))) %>% \n ungroup() %>% \n dplyr::mutate(label = glue::glue(\"p = {format(p.value, digits=2)}\"))\n\nclr = gg_color_hue(2)\n\nggplot(df, aes(gender, Score)) +\n geom_boxplot(aes(fill=gender), alpha=1, outlier.colour = NA) +\n geom_dotplot(binaxis = \"y\", stackdir = \"center\", aes(fill=Response)) +\n # geom_jitter(aes(col=Response), width = 0.2, height = 0, size=2) +\n facet_wrap(~Sig, nrow=1) +\n scale_fill_manual(values=c(clr[1], \"black\",\"white\", clr[2])) +\n xlab(\"Gender\") +\n geom_text(data=df.w, aes(label=label), x=1.5, y=Inf, hjust=0.5, vjust = 1.1, size=3, inherit.aes = F) +\n theme_bw() + theme(legend.position=\"none\") +\n theme(panel.border = element_blank(), strip.background = element_blank(), \n strip.text.x = element_text(size=12),\n panel.spacing = unit(0,\"mm\"), panel.grid.major.x = element_blank(),\n axis.ticks = element_blank())\nggsave(file.path(dn.fig, \"TGSig_SLE.sig_vs_sex.png\"), w=4, h=4)\nggsave(file.path(dn.fig, \"TGSig_SLE.sig_vs_sex.pdf\"), w=4, h=4, useDingbats=F)\n", "meta": {"hexsha": "dca97f42c880376c3b8157f6ad953ed0eda7cfa0", "size": 2445, "ext": "r", "lang": "R", "max_stars_repo_path": "citeseq/R/TGSig_SLE.sig_vs_sex.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "citeseq/R/TGSig_SLE.sig_vs_sex.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "citeseq/R/TGSig_SLE.sig_vs_sex.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 38.8095238095, "max_line_length": 105, "alphanum_fraction": 0.6523517382, "num_tokens": 830, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6654105454764747, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.31452857001571866}} {"text": "###Image segmentation part####\n###Using package EBImage : install via Bioconductor###\n#install.packages(\"BiocManager\")\n#BiocManager::install(version = \"3.10\")\n#BiocManager::install(\"EBImage\")\n###Things to load###\nlibrary(Momocs)\nlibrary(EBImage)\nsource(\"/home/samuel/Documents/Rfunctions1.txt\") #Text file containing code of function from Claude (2008). Morphometrics with R.\n##########################################################################################\n\n##########################\n### Floodfill approach ###\n##########################\n\n###Works with background of heterogeneous colors###\n\nsetwd(\"Bureau/collab_Jules_papillons/test/gray\") #Working directory contains grayscale images only.\n\n#List of files\nfiles <- list.files()\n\n#Create lists to fill with output from loop\nGray_images <- vector(\"list\", length=length(files))\nBW_images <- vector(\"list\", length=length(files))\nBW_final <- vector(\"list\", length=length(files))\nshapes.1000 <- vector(\"list\", length=length(files))\n\nstart <- Sys.time() #Starting time of image treatment\n\nfor (i in 1:length(files)) { #Start of loop image treatment\n\n message(i, \" \", files[i]) #To check advancement of loop\n\n x2 <- readImage(files[i]) #Input grayscale image, with any type of background\n\n#Floodfill background, starting from top-left-most pixel (assuming it is part of background). Some aliasing will happen for clear backgrounds\n e <- floodFill(x2, pt=c(1,1), col=\"black\", tol=0.1) #Default tolerance = 0 not good for heterogeneous background.\n\n#Convert grayscale with black background to B/W image\n f <- bwlabel(e)\n\n#Produce vector with all 'objects' in image, with corresponding number of px.\n tf <- table(f)[-1] #First object is background, remove from vector\n\n#Index of all objects to remove, i.e. all, except background and object with max number of px (hopefully the shape of interest).\n obj_rm <- c(1:max(f))[-which.max(tf)] \n\n#Remove parasite objects\n frm <- rmObjects(f, obj_rm)\n\n Gray_images[[i]] <- e \n BW_images[[i]] <- f\n BW_final[[i]] <- frm #Keeping these lists in memory is a bad idea... ~1GB for 5 pictures...\n\n} #End of image treatment loop.\n\nend <- Sys.time() #Ending time of image treatment\nruntime <- end-start #Total time\n\n##########################################\n### Finding points of contour of shape ###\n##########################################\n\n###With Momocs###\nfor (i in 1:length(BW_final)) {\n#Negative (for Momocs, object is black on white background)\n nh <- max(BW_final[[i]])-BW_final[[i]]\n\n#Extract matrix from 'Image' object to switch from EBImage to Momocs\n img <- t(imageData(nh))\n\n#Find first black pixel in matrix = start point within shape\n x <- which(img==0, arr.ind=T)[1,]\n\n#Extract contour, using Momocs function 'import_Conte'\n contour1<-import_Conte(img=img, x=x)\n\n#Subsample points\n shapes.1000[[i]] <- coo_sample(contour1, 200)\n}\n\n\n##########################\n### Synthetic function ###\n##########################\n\n# x : vector with file names, eg list.files()[1:10]\n# NLM : Number of landmarks to represent the contour\n# tol : tolerance level for floodfilling step\n\nauto.LM.cont <- function(x=list.files(), NLM=200, tol=0.1) {\n shapes <- vector(\"list\", length=length(x))\n start <- Sys.time() #Starting timer\n for (i in 1:length(x)) { #Start of loop image treatment\n message(i, \" \", x[i]) #To check advancement of loop\n x2 <- readImage(x[i]) #Input grayscale image, with any type of background\n#Floodfill background, starting from top-left-most pixel.\n e <- floodFill(x2, pt=c(1,1), col=\"black\", tol=tol)\n#Convert grayscale with black background to B/W image\n f <- bwlabel(e)\n#Produce vector with all 'objects' in image, with corresponding number of px.\n tf <- table(f)[-1] #First object is background, remove from vector\n#Index of all objects to remove, i.e. all, except background and object with max number of px.\n obj_rm <- c(1:max(f))[-which.max(tf)] \n#Remove parasite objects\n frm <- rmObjects(f, obj_rm)\n#Negative (for Momocs, object is black on white background)\n nh <- max(frm)-frm\n#Extract matrix from 'Image' object to switch from EBImage to Momocs\n img <- t(imageData(nh))\n#Find first black pixel in matrix = start point within shape\n first.px <- which(img==0, arr.ind=T)[1,]\n#Extract contour, using Momocs function 'import_Conte'\n contour <- import_Conte(img=img, x=first.px)\n#Subsample points\n shapes[[i]] <- coo_sample(contour, NLM)\n } #End of 'for' loop\n\n end <- Sys.time() #Ending timer\n runtime <- end-start\n list(runtime=runtime, shapes=shapes)\n\n} #End of function\n\n", "meta": {"hexsha": "5a9a9363a410f97fb585351e900c78bbee489295", "size": 4594, "ext": "r", "lang": "R", "max_stars_repo_path": "Streamlined_papillons.r", "max_stars_repo_name": "sginot/Image-Treatment-Data-Extraction", "max_stars_repo_head_hexsha": "f8dc597014b03f53fd2d698d42e0f80209d874a1", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Streamlined_papillons.r", "max_issues_repo_name": "sginot/Image-Treatment-Data-Extraction", "max_issues_repo_head_hexsha": "f8dc597014b03f53fd2d698d42e0f80209d874a1", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Streamlined_papillons.r", "max_forks_repo_name": "sginot/Image-Treatment-Data-Extraction", "max_forks_repo_head_hexsha": "f8dc597014b03f53fd2d698d42e0f80209d874a1", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4603174603, "max_line_length": 141, "alphanum_fraction": 0.6569438398, "num_tokens": 1147, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3145090531165991}} {"text": "# Script to create VPC\n# ShinyMixR will add a vector with the selected models, e.g.:\n# models <- c(\"run1\",\"run2\")\nlibrary(ggplot2)\nlapply(models,function(x){\n res <- readRDS(paste0(\"./shinyMixR/\",x,\".res.rds\"))\n dir.create(paste0(\"./analysis/\",x),showWarnings=FALSE)\n R3port::html_plot(nlmixr::vpc(res,nsim=500,show=list(obs_dv=TRUE)),out=paste0(\"./analysis/\",x,\"/vpc.plot.html\"),show=FALSE,title=\"VPC\")\n})\n", "meta": {"hexsha": "8ebf19cbe987d8babb60e493f47643ee099a7fba", "size": 411, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/Other/vpc.plot.r", "max_stars_repo_name": "RichardHooijmaijers/shinyMixR", "max_stars_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-02-21T12:58:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-19T14:22:46.000Z", "max_issues_repo_path": "inst/Other/vpc.plot.r", "max_issues_repo_name": "RichardHooijmaijers/shinyMixR", "max_issues_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2018-05-29T13:01:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-17T14:25:31.000Z", "max_forks_repo_path": "inst/Other/vpc.plot.r", "max_forks_repo_name": "RichardHooijmaijers/shinyMixR", "max_forks_repo_head_hexsha": "0803ab6bdb25b4d03fe0550d6d0cdda0022d7c23", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-06-30T08:07:46.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-17T20:59:33.000Z", "avg_line_length": 41.1, "max_line_length": 137, "alphanum_fraction": 0.6909975669, "num_tokens": 140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102636778403, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.31423092333412467}} {"text": "#!/usr/bin/env Rscript\n\n############################################################################################################\n# Convert ASTER L1T HDF-EOS VNIR/TIR datasets from Radiance \n# and exports GeoTIFF files.\n# Author: Alfonso Crisci \n# Contact: a.crisci@ibimet.cnr.it\n# Organization: Istituto di Biometeorologia\n# Date last modified: 24-11-2017\n\n# DESCRIPTION:\n# This script is used for batch processing of ASTER Images\n# The script uses an ASTER L1T HDF-EOS file (.hdf) as the input.\n# It based on the work of Cole Krehbiel\n# https://git.earthdata.nasa.gov/projects/LPDUR/repos/aster-l1t\n\n# USAGE: Rscript aster_layer_calc.r path/filename name_city city type\n\n\n##############################################################################################################\n\nin_dir <- args[1]\nname <- args[2]\ncity <- args[3]\ntype <- args[4]\n\nemis=0.95\n\nmessage(paste(in_dir,name,city,type))\n\nif (length(args) !=4) {\n stop(\"At least four argument must be supplied (working dir, input file, name of output directory )\\n\", call.=FALSE)\n} \n\n\n\ncalc_lst=function(rbright,emis=0.92,c2micro=14388,Lwave=10.16,kelvin=F) {\n temp=rbright / ( 1 + ( Lwave * (rbright / c2micro)) * log(emis))\n if ( kelvin==F) { temp=temp-273.15}\n return(temp)\n} \n\n\ncalc_tbright=function(r,band=13) {\n k=band-9\n ucc <- c(0.006822, 0.00678, 0.00659, 0.005693, 0.005225)\n k1 <- c(3024, 2460, 1909, 890, 646.4)\n k2 <- c(1733, 166, 1581, 1357, 1273)\n tir_rad <- ((r - 1) * ucc[k])\n sat_brit <- k2[k]/log((k1[k]/tir_rad) + 1)\n return(sat_brit)\n }\n\nretrieve_aster_hdf=function(file,user='',password=\"\") \n {download.file(url=paste0(\"http://e4ftl01.cr.usgs.gov/ASTT/AST_L1T.003/\",\n substr(file,16,19),\".\",\n substr(file,12,13),\".\",\n substr(file,14,15),\"/\",file,'.hdf'),\n destfile=paste0(file,'.hdf'),\n method=\"wget\",\n extra=paste0(\"--http-user=\",user,\" --http-password=\",password))\n }\n\naster_date_hdf=function(file) {aster_date=as.Date(paste(substr(file,16,19),substr(file,12,13),substr(file,14,15),sep=\"-\"))\n return(aster_date)\n }\n\n#############################################################################################################\n# Convert ASTER L1T HDF-EOS VNIR/TIR datasets from Radiance \n# and exports as GeoTIFF files.\n#-------------------------------------------------------------------------------\n# Author: Cole Krehbiel\n# Contact: LPDAAC@usgs.gov \n# Organization: Land Processes Distributed Active Archive Center\n# Date last modified: 03-06-2017\n#-------------------------------------------------------------------------------\n# DESCRIPTION:\n# This script demonstrates how to convert ASTER L1T data from Digital Number(DN)\n# to radiance (w/m2/sr/\ufffdm), and from radiance into Top of Atmosphere (TOA) \n# reflectance. \n\n# The script uses an ASTER L1T HDF-EOS file (.hdf) as the input and outputs \n# georeferenced tagged image file format (GeoTIFF) files for each of the VNIR \n# and SWIR science datasets contained in the original ASTER L1T file.\n##############################################################################################################\n\nrequire(rgdal)\nrequire(raster)\nrequire(gdalUtils)\nrequire(tiff)\nrequire(rgdal)\n\n\nargs = commandArgs(trailingOnly=TRUE)\n\n\n\n#############################################################################################################\n# Set up calculations\n# 1. DN to Radiance (Abrams, 1999)\n# Radiance = (DN-1)* Unit Conversion Coefficient\n\n# 2. Radiance to TOA Reflectance\n# Reflectance_TOA = (pi*Lrad*d2)/(esuni*COS(z))\n\n# Define the following:\n# Unit Conversion Coefficient = ucc\n# pi = pi\n# Radiance,Lrad = rad\n# esuni = esun \n# z = solare\n\n# Order for ucc (Abrams, 1999) is: Band 1 high, normal, low; Band 2 h, n, l; \n# b3 h, n, l (3N & 3B the same) \n# Construct a dataframe for the UCC values:\n\nbands <- c('1', '2', '3N', '3B', '4', '5', '6', '7', '8', '9')\ngain_names <- c('Band', 'High Gain', 'Normal', 'Low Gain 1', 'Low Gain 2')\nucc_vals <- matrix( c(0.676, 1.688, 2.25, 0, 0.708, 1.415, 1.89, 0, 0.423, \n 0.862, 1.15, 0, 0.423, 0.862, 1.15, 0, 0.1087, 0.2174, \n 0.2900, 0.2900, 0.0348, 0.0696, 0.0925, 0.4090, 0.0313,\n 0.0625, 0.0830, 0.3900, 0.0299, 0.0597, 0.0795, 0.3320,\n 0.0209,0.0417, 0.0556, 0.2450, 0.0159, 0.0318, 0.0424, \n 0.2650), nrow = 10, ncol = 4, byrow = TRUE)\nucc <- data.frame( bands, ucc_vals )\n\nnames(ucc) <- gain_names\n\n# Remove unneccessary variables\nrm(bands,gain_names,ucc_vals)\n\n# Thome et al (B) is used, which uses spectral irradiance values from MODTRAN\n# Ordered b1, b2, b3N, b4, b5...b9\n\nirradiance <- c(1848,1549,1114,225.4,86.63,81.85,74.85,66.49,59.85)\n\n#############################################################################################################\n# Next, define functions for calculations\n# Write a function to convert degrees to radians\n\ncalc_radians <- function(x) {(x * pi) / (180)}\n \n# Write a function to calculate the Radiance from DN values\ncalc_radiance <- function(x){(x - 1) * ucc1}\n\n# Write a function to calculate the TOA Reflectance from Radiance\n\ncalc_reflectance <- function(x){\n (pi * x * (earth_sun_dist^2)) / (irradiance1 * sin(pi * sza / 180))\n }\n\n\n\n\n\n\n\nold_dir=getwd()\n\n#############################################################################################################\n\nsetwd(in_dir)\n\n# Maintains the original filename\nfile_name <- paste0(name,\".hdf\")\n \n# if (!file.exists(file_list)) {retrieve_aster_hdf(file_list)}}\n\n# Create and set output directory\n\n\nout_dir <- paste(in_dir,'/',city,\"_\",as.character(aster_date_hdf(file_name)),'/', sep='')\n\nsuppressWarnings(dir.create(out_dir))\n\n###################################################################\n# grab DOY from the filename and convert to day of year\n\n month <- substr(file_name, 12, 13)\n day <- substr(file_name, 14, 15)\n year <- substr(file_name, 16, 19)\n date <- paste(year, month, day, sep = '-') \n doy <- as.numeric(strftime(date, format = '%j'))\n \n# Remove unneccessary variables\n\n rm(month, day, year, date)\n \n # need SZA--calculate by grabbing solar elevation info \n\n md <- gdalinfo(file_name)\n sza <- md[grep('SOLARDIRECTION=', md)]\n clip3 <- regexpr(', ', sza) \n sza <- as.numeric((substr(sza, (clip3 + 2), 10000)))\n \n # Need the gain designation for each band\n gain_01 <- gsub(' ', '', strsplit(md[grep('GAIN.*=01', md)], ',')[[1]][[2]])\n gain_02 <- gsub(' ', '', strsplit(md[grep('GAIN.*=02', md)], ',')[[1]][[2]])\n gain_04 <- gsub(' ', '', strsplit(md[grep('GAIN.*=04', md)], ',')[[1]][[2]])\n gain_05 <- gsub(' ', '', strsplit(md[grep('GAIN.*=05', md)], ',')[[1]][[2]])\n gain_06 <- gsub(' ', '', strsplit(md[grep('GAIN.*=06', md)], ',')[[1]][[2]])\n gain_07 <- gsub(' ', '', strsplit(md[grep('GAIN.*=07', md)], ',')[[1]][[2]])\n gain_08 <- gsub(' ', '', strsplit(md[grep('GAIN.*=08', md)], ',')[[1]][[2]])\n gain_09 <- gsub(' ', '', strsplit(md[grep('GAIN.*=09', md)], ',')[[1]][[2]])\n gain_03b <- gsub(' ', '', strsplit(md[grep('GAIN.*=3B', md)], ',')[[1]][[2]])\n gain_03n <- gsub(' ', '', strsplit(md[grep('GAIN.*=3N', md)], ',')[[1]][[2]])\n \n # Calculate Earth Sun Distance (Achard and D'Souza 1994; Eva and Lambin, 1998)\n earth_sun_dist <- (1 - 0.01672 * cos(calc_radians(0.9856 * (doy - 4))))\n #-----------------------------------------------------------------------------\n # Define CRS\n # Define Upper left and lower right--need for x, y min/max\n # For offset (pixel size / 2), needs to be defined for ASTER pixel \n # resolutions (15, 30)\n \n # Grab LR and UL values\n lr <- substr(md[grep('LOWERRIGHTM', md)], 15, 50)\n ul <- substr(md[grep('UPPERLEFTM', md)], 14, 50)\n clip4 <- regexpr(', ' , ul) \n clip5 <- regexpr(', ', lr) \n \n # Define LR and UL x and y values for 15m VNIR Data\n ul_y <- as.numeric((substr(ul, 1, (clip4 - 1)))) + 7.5\n ul_x <- as.numeric((substr(ul, (clip4 + 2), 10000))) - 7.5\n lr_y <- as.numeric((substr(lr, 1, (clip5 - 1)))) - 7.5\n lr_x <- as.numeric((substr(lr, (clip5 + 2) , 10000))) + 7.5\n \n # Define LR and UL x and y values for 30m SWIR Data\n ul_y_30m <- as.numeric((substr(ul, 1, (clip4 - 1)))) + 15\n ul_x_30m <- as.numeric((substr(ul, (clip4 + 2), 10000))) - 15\n lr_y_30m <- as.numeric((substr(lr, 1, (clip5 - 1)))) - 15\n lr_x_30m <- as.numeric((substr(lr, (clip5 + 2) , 10000))) + 15\n \n # Define LR and UL x and y values for 90m TIR Data\n ul_y_90m <- as.numeric((substr(ul, 1, (clip4 - 1)))) + 45\n ul_x_90m <- as.numeric((substr(ul, (clip4 + 2), 10000))) - 45\n lr_y_90m <- as.numeric((substr(lr, 1, (clip5 - 1)))) - 45\n lr_x_90m <- as.numeric((substr(lr, (clip5 + 2) , 10000))) + 45\n\n # Define UTM zone\n utm_row <- grep('UTMZONECODE', md)\n utm_zone <- substr(md[utm_row[1]], 1, 50)\n clip6 <- regexpr('=', utm_zone) \n utm_zone <- substr(utm_zone, clip6 + 1, 50)\n \n # Configure extent properties (15m VNIR)\n y_min <- min(ul_y, lr_y)\n y_max <- max(ul_y, lr_y)\n x_max <- max(ul_x, lr_x)\n x_min <- min(ul_x, lr_x)\n \n # Configure extent properties (30m SWIR)\n\n y_min_30m <- min(ul_y_30m, lr_y_30m)\n y_max_30m <- max(ul_y_30m, lr_y_30m)\n x_max_30m <- max(ul_x_30m, lr_x_30m)\n x_min_30m <- min(ul_x_30m, lr_x_30m)\n\n # Configure extent properties (90m TIR)\n\n y_min_90m <- min(ul_y_90m, lr_y_90m); \n y_max_90m <- max(ul_y_90m, lr_y_90m)\n x_max_90m <- max(ul_x_90m, lr_x_90m); \n x_min_90m <- min(ul_x_90m, lr_x_90m)\n \n \n raster_dims_15m <- extent(x_min, x_max, y_min, y_max)\n raster_dims_30m <- extent(x_min_30m, x_max_30m, y_min_30m, y_max_30m)\n raster_dims_90m <- extent(x_min_90m, x_max_90m, y_min_90m, y_max_90m)\n \n # Compile Cordinate Reference System string to attach projection information\n crs_string <- paste('+proj=utm +zone=', utm_zone, ' +datum=WGS84 +units=m \n +no_defs +ellps=WGS84 +towgs84=0,0,0', sep = '')\n \n # Remove unneccessary variables\n rm(clip4, clip5, clip6, lr, lr_x, lr_y, md, ul, ul_x, ul_y, utm_zone,\n x_min, x_max, y_min, y_max, utm_row)\n \n # Get a list of sds names\n sds <- get_subdatasets(file_name)\n \n # Limit loop to SDS that contain VNIR/SWIR data (9 max)\n \n match_vnir <- grep('VNIR_Swath', sds)\n match_tir <- grep('TIR_Swath', sds) \n \n###################################################################################################################\u00e0\n\n if (length(match_vnir) > 1){\n for (k in min(match_vnir):max(match_vnir)) {\n \n # Isolate the name of the first sds\n sub_dataset <- sds[k]\n \n # Get the name of the specific SDS\n clip2 <- max(unlist((gregexpr(':', sub_dataset)))) \n \n # Generate output name for tif\n new_file_name <- strsplit(file_name, '.hdf')\n tif_name <- paste(out_dir, new_file_name, '_', substr(sub_dataset, \n (clip2 + 1), 10000),'.tif', sep='')\n sd_name <- paste(new_file_name, substr(sub_dataset, (clip2 + 1), 10000), \n sep = '_')\n sub_name <- paste(new_file_name, 'ImageData', sep = '_')\n ast_band_name <- gsub(sub_name, '', sd_name)\n \n # Extract specified SDS and export as Geotiff\n \n gdal_translate(file_name, tif_name, sd_index=k)\n \n aster_file <- raster(tif_name, crs = crs_string)\n \n if (ast_band_name == '1'){\n # Need to know which gain value you use\n if (gain_01 == 'HGH'){\n ucc1 <- ucc[1, 2] \n }else if(gain_01 == 'NOR'){\n ucc1 <- ucc[1, 3] \n }else{\n ucc1 <- ucc[1, 4] \n }\n irradiance1 <- irradiance[1]\n \n # Define Extent\n extent(aster_file) <- raster_dims_15m\n \n }else if (ast_band_name == '2'){\n # Need to know which gain value you use\n if (gain_02 == 'HGH'){\n ucc1 <- ucc[2, 2] \n }else if(gain_02 == 'NOR'){\n ucc1 <- ucc[2, 3] \n }else{\n ucc1 <- ucc[2, 4] \n }\n irradiance1 <- irradiance[2]\n \n # Define Extent\n extent(aster_file) <- raster_dims_15m\n \n } else if (ast_band_name == '3N'){\n # Need to know which gain value you use\n if (gain_03n == 'HGH'){\n ucc1 <- ucc[3, 2] \n } else if(gain_03n == 'NOR'){\n ucc1 <- ucc[3, 3] \n } else{\n ucc1 <- ucc[3, 4] \n }\n irradiance1 <- irradiance[3]\n \n # Define Extent\n extent(aster_file) <- raster_dims_15m\n\n }\n\n\n # Set up output file names\n ref_out_name <- gsub(paste(ast_band_name, '.tif', sep = ''), \n paste(ast_band_name, '_reflectance.tif', sep = ''), \n tif_name)\n rad_out_name <- gsub(paste(ast_band_name, '.tif', sep = ''), \n paste(ast_band_name, '_radiance.tif', sep = ''),\n tif_name)\n # Convert DN to large raster layer\n aster_file <- calc(aster_file, fun =function(x){x})\n \n # Export the DN raster layer file (Geotiff format) to the output directory\n writeRaster(aster_file, filename = tif_name, options = 'INTERLEAVE=BAND',\n NAflag = 0, format = 'GTiff', datatype = 'INT1U', \n overwrite = TRUE)\n \n # Convert from DN to Radiance\n rad <- calc(aster_file, calc_radiance)\n rad[rad == calc_radiance(0)] <- 0\n rm(aster_file)\n # export the raster layer file (Geotiff format) to the output directory\n writeRaster(rad, filename = rad_out_name, options = 'INTERLEAVE=BAND', \n NAflag = 0, format = 'GTiff', datatype = 'FLT8S', \n overwrite = TRUE)\n \n # Convert from Radiance to TOA Reflectance\n ref <- calc(rad, calc_reflectance)\n rm(rad)\n # export the raster layer file (Geotiff format) to the output directory\n \n writeRaster(ref, filename = ref_out_name, options = 'INTERLEAVE=BAND', \n NAflag = 0, format = 'GTiff', datatype = 'FLT8S', \n overwrite = TRUE)\n \n # Remove unneccessary variables\n \n rm(ucc1, irradiance1, ref_out_name, rad_out_name, ref, sub_dataset,sd_name, sub_name, tif_name, new_file_name)\n \n }\n }\n \n###################################################################################################################\u00e0\n \n if (length(match_tir) > 0) {\n \n for (k in min(match_tir):max(match_tir)){\n \n # Isolate the name of the first sds\n sub_dataset<- sds[k]\n \n # Get the name of the specific SDS\n clip2 <- max(unlist((gregexpr(':', sub_dataset)))) \n \n # Generate output name for tif\n new_file_name <- strsplit(file_name, '.hdf')\n tif_name <- paste(out_dir, new_file_name, '_', substr(sub_dataset, \n (clip2 + 1), 10000),'.tif', sep='')\n sd_name <- paste(new_file_name, substr(sub_dataset, (clip2 + 1), 10000),\n sep = '_')\n sub_name <- paste(new_file_name, 'ImageData', sep = '_')\n ast_band_name <- gsub(sub_name, '', sd_name)\n \n # Extract specified SDS and export as Geotiff\n \n gdal_translate(file_name, tif_name, sd_index=k, output_Raster = FALSE)\n \n # Open geotiff and add projection (CRS)\n \n aster_file <- suppressWarnings(raster(readGDAL(tif_name,as.is=T)))\n proj4string(aster_file)=crs_string\n extent(aster_file) <- raster_dims_90m\n\n # Convert to large raster layer\n aster_file <- calc(aster_file, fun =function(x){x} )\n \n # Export the raster layer file (Geotiff format) to the output directory\n writeRaster(aster_file, filename = tif_name, options = 'INTERLEAVE=BAND',\n format = 'GTiff', datatype = 'INT2U', overwrite = TRUE, \n NAflag = 0)\n # Remove unneccessary variables\n \n \n rm(aster_file, sub_dataset, sd_name, sub_name, tif_name, new_file_name)\n }\n } \n \n###################################################################################################################\u00e0\n# Remove unneccessary variables\n \n \n rm( earth_sun_dist, doy,gain_01,gain_02, gain_03b, \n gain_03n, gain_04, gain_05, gain_06, gain_07, gain_08, gain_09, sza,\n ast_band_name, clip2, crs_string, file_name,sds, lr_x_30m, lr_x_90m, \n lr_y_30m, lr_y_90m, raster_dims_15m, \n raster_dims_30m, raster_dims_90m, ul_x_30m, ul_x_90m, ul_y_30m, ul_y_90m,\n x_max_30m, x_max_90m, x_min_30m, x_min_90m, y_max_30m, y_max_90m,y_min_30m,\n y_min_90m)\n\n###################################################################################################################\u00e0\nres=list()\n\nif (length(match_vnir) > 0 ) {\n blue_radiance=raster(paste0(out_dir,name,\"_\",\"ImageData1_radiance.tif\"))\n blue_reflectance=raster(paste0(out_dir,name,\"_\",\"ImageData1_reflectance.tif\"))\n green_radiance=raster(paste0(out_dir,name,\"_\",\"ImageData2_radiance.tif\"))\n green_reflectance=raster(paste0(out_dir,name,\"_\",\"ImageData2_reflectance.tif\"))\n red_radiance=raster(paste0(out_dir,name,\"_\",\"ImageData3N_radiance.tif\"))\n red_reflectance=raster(paste0(out_dir,name,\"_\",\"ImageData3N_reflectance.tif\"))\n ASTERVNIR=stack(blue_radiance,blue_reflectance, green_radiance,green_reflectance,red_radiance,red_reflectance)\n # writeRaster(ASTERVNIR, filename=paste0(out_dir,name,\"_stackVNIR.tif\"), overwrite=TRUE)\n}\n\nif (length(match_tir) > 0 ) {\n \n res=list()\n \n res$b13_tbright=calc_tbright(suppressWarnings(raster(readGDAL(paste0(out_dir,name,\"_\",\"ImageData13.tif\"),as.is=T))),band=13)\n res$b14_tbright=calc_tbright(suppressWarnings(raster(readGDAL(paste0(out_dir,name,\"_\",\"ImageData14.tif\"),as.is=T))),band=14)\n \n res$b13_LST=calc_lst(res$b13_tbright,emis=emis,Lwave=10.6)\n res$b14_LST=calc_lst(res$b14_tbright,emis=emis,Lwave=11.3)\n \n writeRaster(res$b13_LST, filename=paste0(out_dir,name,\"_\",type,\"_b13_LST.tif\"), overwrite=TRUE)\n writeRaster(res$b14_LST, filename=paste0(out_dir,name,\"_\",type,\"_b14_LST.tif\"), overwrite=TRUE)\n writeRaster(res$b13_tbright, filename=paste0(out_dir,name,\"_\",type,\"_b13_TB.tif\"), overwrite=TRUE)\n writeRaster(res$b14_tbright, filename=paste0(out_dir,name,\"_\",type,\"_b14_TB.tif\"), overwrite=TRUE)\n}\n\n\nsetwd(out_dir)\nolds=list.files(pattern=name, full.names = F)\nnews=gsub(name,paste0(city,\"_\",as.character(aster_date_hdf(name)),\"_\",type),olds)\nnews=gsub(paste0(\"_\",type,\"_\",type),news)\nfile.rename(olds,news)\nsaveRDS(res,file = paste0(city,\"_\",as.character(aster_date_hdf(file_name)),\"_\",type,'.rds'))\n\nsetwd(old_dir)\n\n\n###################################################################################################################\u00e0\n# References\n# ABRAMS, M., HOOK, S., and RAMACHANDRAN, B., 1999, Aster user handbook,\n# Version 2, NASA/Jet Propulsion Laboratory, Pasadena, CA, at \n# https://asterweb.jpl.nasa.gov/content/03_data/04_Documents/\n# aster_user_guide_v2.pdf\n\n# ARCHARD, F., AND D'SOUZA, G., 1994, Collection and pre-processing of \n# NOAA-AVHRR 1km resolution data for tropical forest resource assessment. \n# Report EUR 16055, European Commission, Luxembourg, at \n# http://bookshop.europa.eu/en/collection-and-pre-processing-of-noaa-avhrr-1-km\n# -resolution-data-for-tropical-forest-resource-assessment-pbCLNA16055/\n\n# EVA, H., AND LAMBIN, E.F., 1998, Burnt area mapping in Central Africa using \n# ATSR data, International Journal of Remote Sensing, v. 19, no. 18, 3473-3497, \n# at http://dx.doi.org/10.1080/014311698213768\n\n# Thome, K.J., Biggar, S.F., and Slater, P.N., 2001, Effects of assumed solar\n# spectral irradiance on intercomparisons of earth-observing sensors. In \n# International Symposium on Remote Sensing, International Society for Optics\n# and Photonics, pp. 260-269, at http://dx.doi.org/10.1117/12.450668.\n###################################################################################################################\u00e0\n", "meta": {"hexsha": "7f135a034bde881a66becb24aed06d85a539359c", "size": 20298, "ext": "r", "lang": "R", "max_stars_repo_path": "code/aster_layer_calc.r", "max_stars_repo_name": "meteosalute/Parma_urban_imperviouness", "max_stars_repo_head_hexsha": "317f0dc34316fe4f52d7da58d7f2672ef6fc9ced", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-10-29T01:33:41.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-29T01:33:41.000Z", "max_issues_repo_path": "aster_layer_calc.r", "max_issues_repo_name": "alfcrisci/ASTER_data_retrieve", "max_issues_repo_head_hexsha": "c497b6b02e7774d6c3ad453317133665f2ca8e64", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "aster_layer_calc.r", "max_forks_repo_name": "alfcrisci/ASTER_data_retrieve", "max_forks_repo_head_hexsha": "c497b6b02e7774d6c3ad453317133665f2ca8e64", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.1853281853, "max_line_length": 135, "alphanum_fraction": 0.5603507735, "num_tokens": 6063, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102498375401, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.31423091646803714}} {"text": "#Area of interest \nwdpaid <- '-8.02_51.76_-2.63_55.79'\n#wdpaid <- 'minlon_minlat_maxlon_maxlat'\nwdpaidsplit <- unlist(strsplit(wdpaid, \"[_]\"))\nxmin <- as.numeric(wdpaidsplit[1])\nymin <- as.numeric(wdpaidsplit[2])\nxmax <- as.numeric(wdpaidsplit[3])\nymax <- as.numeric(wdpaidsplit[4])\n\n#Libraries\n\nlibrary(icesDatras)\nlibrary(rworldmap)\nlibrary(rworldxtra)\n\nHH <- data.frame()\nfor (i in getSurveyList()){\n tmp <- getDATRAS(record = \"HH\", i, years=1998:2018,1:4)\n HH <- rbind(HH,tmp)\n}\n\nHH <- dplyr::mutate(HH, Long=((ShootLong+HaulLong)/2), Lat=((ShootLat+HaulLat)/2))\n\ncoast <- rworldmap::getMap(resolution = \"high\")\ncoast <- raster::crop(coast, raster::extent(xmin-0.1,xmax+0.1,ymin-0.1,ymax+0.1))\n\n#Subset hauls in our area\nHH.in <- HH[HH$Longxmin & HH$Latymin,]\ncoast2<-st_as_sf(coast)\nsp<-st_as_sf(HH.in,coords=c(\"Long\",\"Lat\"),crs=crs(coast2))\n\n#Points index to keep \nSeaPt<-!apply(st_intersects(sp,coast2,sparse=F),1,any)\nHH.in <- HH.in[SeaPt,]\n\n#ggplot(HH.in)+geom_point(aes(x=Long,y=Lat,col=Survey))\n\n#Which campaign do we keep\ncampaign <- names(table(HH.in$Survey)[which(table(HH.in$Survey)==max(table(HH.in$Survey)))])\nquarter <- as.integer(names(table(HH.in$Quarter[HH.in$Survey==campaign])[which(table(HH.in$Quarter[HH.in$Survey==campaign])==max(table(HH.in$Quarter[HH.in$Survey==campaign])))]))\n\n#Load campaign for last 10 years\nHH <- getDATRAS(record = \"HH\", campaign, years=2008:2018,quarter)\nHL <- getDATRAS(record = \"HL\", campaign, years=2008:2018,quarter)\n", "meta": {"hexsha": "daa93e19b03ed9cbdfddbf09c7be2d7a351fac5c", "size": 1505, "ext": "r", "lang": "R", "max_stars_repo_path": "OH_2020/Script/Irish_sea/04_getdata_ices.r", "max_stars_repo_name": "ldbk/SeineMSP", "max_stars_repo_head_hexsha": "1e85bd87d9f8aea5937b2b8d5773e137df9417b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-04-06T13:25:34.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-06T13:25:34.000Z", "max_issues_repo_path": "OH_2020/Script/Irish_sea/04_getdata_ices.r", "max_issues_repo_name": "ldbk/SeineMSP", "max_issues_repo_head_hexsha": "1e85bd87d9f8aea5937b2b8d5773e137df9417b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "OH_2020/Script/Irish_sea/04_getdata_ices.r", "max_forks_repo_name": "ldbk/SeineMSP", "max_forks_repo_head_hexsha": "1e85bd87d9f8aea5937b2b8d5773e137df9417b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-07-08T08:56:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-08T08:56:00.000Z", "avg_line_length": 33.4444444444, "max_line_length": 178, "alphanum_fraction": 0.703654485, "num_tokens": 534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102498375401, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.31423091646803714}} {"text": " #'---\n#'title: \"4.1 Opportunistic Learning Model\"\n#'author: \"Denis A. Engemann\"\n#'date: \"9/17/2019\"\n#'output:\n#' html_document:\n#' code_folding:\n#' hide\n#' md_document:\n#' variant:\n#' markdown_github\n#'---\n\n#+ config\nlibrary(ggplot2)\nlibrary(ggbeeswarm)\nlibrary(ggrepel)\n\n# seed!\nset.seed(42)\n\n# imports `color_cats` and `get_label_from_marker`\nsource('./utils.r')\nsource('./config.r')\n\nPREDICTIONS <- './data/age_stacked_predictions_megglobal.csv'\nPREDICTIONS2 <- './data/age_stacked_predictions__na_coded.csv'\nPREDICTIONS3 <- './data/age_stacked_predictions_meglocal.csv'\n\nIMPORTANCES <- \"./outputs/age_stacked_importance_8.csv\"\ndata_pred_wide <- read.csv(PREDICTIONS)\n\n#' Another important topic concersn opportunistic learning from the data\n#' that is available despite missing data. The naive approach would be\n#' to throw away any data point (subject) for which not all modalities\n#' (MRI, fMRI, MEG) are present. Instead one can feature-code missingness and\n#' let the random forest learn from missingness, if possible. Ensuing important\n#' questions are:\n#'\n#' 1. Does an opportunistically trained model perform as well as a\n#' conservatively one on the subsets of complete data?\n#'\n#' 2. Does an opportunistically trained model perform as well on the subsets\n#' of partially complete data as a conservative model, trained only on that\n#' data.\n#'\n#' The first one can be easily answered in the current stacking framework.\n#' One simply has to remove the nans from the prediction and compare the\n#' conservative model with the nan-coded model.\n#'\n#' The second question would require computing the local models that take all\n#' data available for a given modality, retain the indices, and evaluate the\n#' nan-coded model on the same indices. For fairness, one variant would require\n#' to do unimodal stacking here.\n#'\n#' Let us investigate the first idea. If that one does not work we are in\n#' trouble anyways.\n#' Our first task is gonna be to make sure we understand in the NA-coded\n#' dataset, which predictions were based on missing values.\n#' It is important to understand that the computation was setup such that\n#' in any case, the linear predictions contain nans and the union of all\n#' subjects that had at least one modality or variable was retained.\n#' What *is* different between the two datasets is the stacking columns,\n#' which either contain nans or which do not contain nans. Let's make sure\n#' all makes sense:\n\n#+ load_nan_analysis\n\nstacked_keys <- c(\n \"MEG_handcrafted\",\n \"MEG_powers\",\n \"MEG_powers_cross_powers\",\n \"MEG_powers_cross_powers_handrafted\",\n \"MEG_cat_powers_cross_powers_correlation\",\n \"MEG_cat_powers_cross_powers_correlation_handcrafted\",\n \"MEG_cross_powers_correlation\",\n \"MEG_powers_cross_powers_correlation\",\n \"MEG_all\",\n \"ALL\",\n \"ALL_no_fMRI\",\n \"MRI\",\n \"ALL_MRI\",\n \"fMRI\"\n)\n\nstacked_selection <- c(\n \"ALL\",\n \"ALL_MRI\",\n \"ALL_no_fMRI\",\n \"MRI\",\n \"fMRI\",\n \"MEG_all\"\n)\n\ndata_pred_stacked_sel <- preprocess_prediction_data(\n df_wide = data_pred_wide, stack_sel = stacked_selection, drop_na = T)\n\ndata_pred_stacked_sel_nona <- preprocess_prediction_data(\n data_pred_wide, stacked_selection, drop_na = F)\n\ndata_pred_wide_na <- read.csv(PREDICTIONS2)\ndata_pred_stacked_sel_na <- preprocess_prediction_data(\n data_pred_wide_na, stacked_selection, drop_na = F)\n\n#' let's now test that the numbers make sense.\n\n#+ test_nan_index\nn_total <- 674 # number of total camcan subjects \nn_global <- 536 # number of common subjects across modalities\nn_missing_global <- n_total - n_global\n\nn_repeats <- length(unique(data_pred_wide$repeat_idx))\n# drop first idnex column temporarily\nstopifnot(n_total - sum(is.na(rowSums(data_pred_wide[,-1]))) / n_repeats == n_global)\nstopifnot(nrow(data_pred_stacked_sel_na) == nrow(data_pred_stacked_sel_nona))\n\n# The na-coded case has predictions for all subjects\nstack_cols <- grepl('stack', names(data_pred_wide))\nstopifnot(\n sum(is.na(rowSums(subset(data_pred_wide_na,\n repeat_idx == 0)[,stack_cols]))) == 0)\nstopifnot(\n sum(is.na(rowSums(subset(\n data_pred_wide, repeat_idx == 0)[, stack_cols]))) == n_missing_global)\n\n#' Now we need make sure that we really only have data for subjects with\n#' at least one or two linear inputs.\n\n#+ test_nan_index2\nlin_col_idx <- xor(\n names(data_pred_wide_na) %in% c('age', 'repeat_idx', 'fold_idx', 'repeat.'),\n !stack_cols)\n\nrow_nans <- rowSums(is.na(subset(data_pred_wide_na)[, lin_col_idx]))\nstopifnot(max(row_nans) < sum(lin_col_idx))\nggplot(data = data.frame(nans = row_nans[1:n_total]), mapping = aes(x = nans)) +\n geom_histogram() +\n scale_x_sqrt() +\n scale_y_sqrt(breaks = c(c(1, 10), seq(0, 100, 20), seq(100, 500, 50))) +\n labs(x = 'row-wise # NA', y = 'count [subjects]')\nggsave('./figures/elements_fig2_diagnostics_na_dist_rowise.png', dpi = 300)\n\n#' Ok, we're all set. Interestingly there is a larger subgroup of subejcts\n#' With more than 50 missing inputs. _We need to be careful here not to distort\n#' the evaluation of performance with these subjects_.\n#' Based on what we just saw, we can proceed as follows: 1) we get the global\n#' missingness index from the stacked variables of the global dataset 2) we get\n#' the modality-wise missingness index from the linear variables of any dataset.\n#' With these, we can then subset the na-coded data and compare performance.\n\n#+ na_codes_q1\ngood_mask_global <- !is.na(data_pred_stacked_sel_nona$pred)\n\ndata_opp_learn <- data_pred_stacked_sel_nona\ndata_opp_learn$MAE_na <- data_pred_stacked_sel_na$MAE\ndata_opp_learn$pred_na <- data_pred_stacked_sel_na$pred\ndata_opp_learn <-data_opp_learn[good_mask_global,]\n\n# let's print a nice table. Which, with Knitr, becomse a kable ...\n\ncolors_opp_learn <- setNames(\n with(color_cats, c(black, orange, `blueish green`, blue, violet, vermillon)),\n c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG'))\n\ndata_opp_learn$family <- factor(\n data_opp_learn$family,\n levels = c('Multimodal', 'MRI & fMRI', 'MRI & MEG', 'MRI', 'fMRI', 'MEG'),\n labels = c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG'))\n\nopp_learn_1_agg <- aggregate(\n cbind(MAE, MAE_na) ~ family, data = data_opp_learn, FUN = mean)\nknitr::kable(opp_learn_1_agg)\n\nfig2f <- ggplot(data = data_opp_learn,\n mapping = aes(x = pred, y = pred_na, color = family)) +\n geom_point(alpha = 0.3, show.legend = F) +\n facet_wrap(~family) +\n # reuse definitions from above.\n scale_color_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn) +\n scale_fill_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn) +\n scale_x_continuous(breaks = seq(20, 80, 10),\n labels = seq(20, 80, 10)) +\n xlab(\"Age prediction (completed-modality cases)\") + \n ylab(\"Age prediction (at least one modality)\")\nprint(fig2f)\n\nfname <- \"./figures/elements_fig2f_supplement_na_coding\"\nggsave(paste0(fname, \".pdf\"), plot = fig2f,\n width = save_width, height = save_height)\nggsave(paste0(fname, \".png\"), plot = fig2f,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \".png\"), dpi = 200)\n\n#' This looks like there is no evidence that the second opportunistic model\n#' performed worse on the common subjects. Let us now approach the second\n#' question: do modality-wise predictions suffer? For this we must consider\n#' the outputs computed with then `local` option.\n#' There won't be much to check this time, except dimensions.\n#' The logic here boils down to, pushing over from each sub-model the missing\n#' value indices to the nan-coded model.\n\n#+ na_codes_q2\ndata_pred_wide_local <- read.csv(PREDICTIONS3)\ndata_pred_stacked_sel_local <- preprocess_prediction_data(\n data_pred_wide_local, stacked_selection, drop_na = F)\n\nstopifnot(dim(data_pred_stacked_sel_local) == dim(data_pred_stacked_sel_na))\nna_mask <- is.na(data_pred_stacked_sel_local$pred)\n\ndata_opp_learn2 <- data_pred_stacked_sel_na\nnames(data_opp_learn2)[names(data_opp_learn2) == \"MAE\"] <- 'MAE_na'\ndata_opp_learn2$pred[na_mask] <- NA\ndata_opp_learn2$MAE_local <- data_pred_stacked_sel_local$MAE\ndata_opp_learn2$pred_local <- data_pred_stacked_sel_local$pred\n\n\ndata_opp_learn2$family <- factor(\n data_opp_learn2$family,\n levels = c('Multimodal', 'MRI & fMRI', 'MRI & MEG', 'MRI', 'fMRI', 'MEG'),\n labels = c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG'))\n\n# let's print a nice table. Which, with Knitr, becomse a kable ...\nopp_learn_2_agg <- aggregate(\n cbind(MAE_na, MAE_local) ~ family, data = data_opp_learn2, FUN = mean)\nknitr::kable(opp_learn_2_agg)\n\nfig2g <- ggplot(data = data_opp_learn2,\n mapping = aes(x = pred, y = pred_local, color = family)) +\n geom_point(alpha = 0.3, show.legend = F) +\n facet_wrap(~family) +\n # reuse definitions from above.\n scale_color_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn) +\n scale_fill_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn) +\n scale_x_continuous(breaks = seq(20, 80, 10),\n labels = seq(20, 80, 10)) +\n xlab(\"Age prediction (locallly-completed cases)\") +\n ylab(\"Age prediction (at least one modality)\")\nprint(fig2g)\n\nfname <- \"./figures/elements_fig2g_supplement_na_coding.\"\nggsave(paste0(fname, \"pdf\"), plot = fig2g,\n width = save_width, height = save_height)\nggsave(paste0(fname, \"png\"), plot = fig2g,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\ndata_opp_learn3 <- data_pred_stacked_sel_na\ndata_opp_learn3$MAE_full <- with(data_pred_stacked_sel_na,\n rep(MAE[marker == \"ALL\"], times = length(unique(marker))))\ndata_opp_learn3$pred_full <- with(data_pred_stacked_sel_na,\n rep(MAE[marker == \"ALL\"], times = length(unique(marker))))\n\ndata_opp_learn3$MAE_full[na_mask] <- NA\ndata_opp_learn3$pred_full[na_mask] <- NA\ndata_opp_learn3$MAE_local <- data_pred_stacked_sel_local$MAE\ndata_opp_learn3$pred_local <- data_pred_stacked_sel_local$pred\n\ndata_opp_learn3$family <- factor(\n data_opp_learn3$family,\n levels = c('Multimodal', 'MRI & fMRI', 'MRI & MEG', 'MRI', 'fMRI', 'MEG'),\n labels = c(\"MRI, fMRI, MEG\", 'MRI, fMRI', 'MRI, MEG', 'MRI', 'fMRI', 'MEG'))\n\nopp_learn_3_agg <- aggregate(\n cbind(MAE_full, MAE_local) ~ family, data = data_opp_learn3,\n FUN = mean)\n\nknitr::kable(opp_learn_3_agg)\n\nopp_learn_agg <- data.frame(\n family = factor(c(as.character(opp_learn_1_agg$family),\n as.character(opp_learn_2_agg$family),\n as.character(opp_learn_3_agg$family))),\n MAE_na = c(opp_learn_1_agg$MAE_na, opp_learn_2_agg$MAE_na,\n opp_learn_3_agg$MAE_full),\n MAE = c(opp_learn_1_agg$MAE, opp_learn_2_agg$MAE_local,\n opp_learn_3_agg$MAE_local),\n comparison = factor(rep(c(\"common\",\n \"common extra\",\n \"full vs reduced\"),\n each = 6))\n)\n\nfig4a <- ggplot(data = opp_learn_agg,\n mapping = aes(x = MAE, y = MAE_na)) +\n geom_point(\n size = 5,\n alpha = 0.8,\n fill = \"white\",\n stroke = 1,\n mapping = aes(shape = comparison, color = family)) +\n geom_line(size=0.5, mapping = aes(linetype = comparison)) +\n ylab(expression(MAE[opportunistic])) +\n xlab(expression(MAE[available])) +\n coord_fixed(ylim = c(4.5, 7), xlim = c(4.5, 7)) +\n scale_linetype_manual(\n breaks = c(\"common\", \"common extra\", \"full vs reduced\"),\n labels = c(\"common\", \"common extra\", \"full vs reduced\"),\n values = c(\"solid\", \"dotted\", \"dashed\"),\n name = \"comparison\") +\n scale_shape_manual(\n breaks = c(\"common\", \"common extra\", \"full vs reduced\"),\n labels = c(\"common\", \"common extra\", \"full vs reduced\"),\n values = c(21, 22, 23),\n name = \"comparison\") +\n scale_color_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn,\n name = \"stacking model\") +\n scale_fill_manual(breaks = names(colors_opp_learn),\n labels = names(colors_opp_learn),\n values = colors_opp_learn,\n name = \"stacking model\")\n\nprint(fig4a)\n\nfname <- \"./figures/elements_fig4a.\"\nggsave(paste0(fname, \"pdf\"), plot = fig4a,\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"), plot = fig4a,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n\n#' There seems to be a third case to look for.\n#' Let us look at how the performance of the full model, trained\n#' opportunistically, compares with sub-models on their corresponding subsets\n#' of cases. This means we need to repeat the predictions of our full-model\n#' for each submodel, and then carry over the submodels nans.\n#' Remember that we have consumed the folds, there are 10 predictions one for \n#' each repetition.\n\n# aggregate by subject, but modify function to return nans.\n# Nay, we just use te na.action argument.\ndata_pred_wide_na_summ <- aggregate(\n . ~ X, data = data_pred_wide_na, FUN = mean, na.action = NULL)\n#' na_coded3\n\n#' now we have a dataset in which we can split by nan-group\n#' time to move to tidy format.\n\ndata_pred_wide_na_summ$nan_group <- as.factor(sapply(\n seq(nrow(data_pred_wide_na_summ)),\n function(ii,\n these_names = names(data_pred_wide_na_summ),\n na_data = is.na(data_pred_wide_na_summ)) {\n sum(which(na_data[ii,]))\n }))\n\ndata_pred_wide_na_summ$nan_group_cnt <- sapply(\n data_pred_wide_na_summ$nan_group,\n function(nan_group, data = data_pred_wide_na_summ) {\n nrow(data[data$nan_group == nan_group,])\n }\n)\n\ndata_pred_wide_na_summ$nan_type <- as.factor(sapply(\n seq(nrow(data_pred_wide_na_summ)),\n function(ii,\n these_names = names(data_pred_wide_na_summ),\n na_data = is.na(data_pred_wide_na_summ)) {\n no_stack <- !grepl(\"stacked\", these_names)\n out_names <- these_names[!na_data[ii, no_stack]]\n out <- c()\n if (\"Cortical.Thickness\" %in% out_names |\n \"Cortical.Surface.Area\" %in% out_names |\n \"Subcortical.Volumes\" %in% out_names) {\n out <- c(out, 'aMRI')\n }\n if (\"Connectivity.Matrix..MODL.256.tan\" %in% out_names){\n out <- c(out, 'fMRI')\n }\n if (any(grepl(\"mne\", out_names))) {\n out <- c(out, expression(MEG[src]))\n }\n if (\"MEG.1.f.gamma\" %in% out_names |\n \"MEG.alpha_peak\" %in% out_names |\n \"MEG.1.f.low\" %in% out_names |\n \"MEG.aud\" %in% out_names |\n \"MEG.vis\" %in% out_names |\n \"MEG.audvis\" %in% out_names) {\n out <- c(out, expression(MEG[sens]))\n }\n return(paste(out, collapse = ','))\n}))\n\ndata_pred_wide_na_summ_long <- preprocess_prediction_data(\n data_pred_wide_na_summ, stacked_keys)\n\n\nnan_types <- as.character(unique(data_pred_wide_na_summ_long$nan_type))\nlegend_nan_type <- setNames(\n c(\n expression(MEG[sens]),\n expression(MEG),\n expression(MRI~fMRI),\n expression(MRI~fMRI~MEG[sens]),\n expression(MRI~fMRI~MEG),\n expression(fMRI~MEG[sens])\n ),\n c(\n \"MEG[sens]\",\n \"MEG[src],MEG[sens]\",\n \"aMRI,fMRI\",\n \"aMRI,fMRI,MEG[sens]\",\n \"aMRI,fMRI,MEG[src],MEG[sens]\",\n \"fMRI,MEG[sens]\"\n)\n)\n\nmae_by_nan_type <- aggregate(\n MAE ~ nan_type,\n data = subset(data_pred_wide_na_summ_long,\n marker == \"ALL\"),\n FUN = mean)\n\nlegend_nan_type <- legend_nan_type[order(mae_by_nan_type$MAE)]\n\ngroup_counts <- aggregate(\n nan_group_cnt ~ nan_group_cnt : nan_group,\n data = subset(data_pred_wide_na_summ_long,\n marker == \"ALL\"),\n FUN = function(x) {x[[1]]})$nan_group_cnt\n\ncolor_values <- setNames(viridisLite::viridis(6),\n names(legend_nan_type))\n\nfig4b <- ggplot(data = subset(data_pred_wide_na_summ_long,\n marker == \"ALL\"),\n mapping = aes(\n y = MAE,\n color = nan_type,\n fill = nan_type,\n x = reorder(nan_type, MAE, mean))) +\n geom_beeswarm(cex = 0.4, size = 0.5, alpha = 0.5) +\n stat_summary(geom = \"boxplot\", fun.data = my_quantiles,\n alpha = 0.5, size = 0.7, width = 0.8) +\n stat_summary(geom = \"errorbar\", fun.data = my_quantiles,\n alpha = 0.5, size = 0.5, width = 0.5) +\n stat_summary(geom = 'text',\n mapping = aes(label = sprintf(\"%1.1f\", ..y..)),\n fun.y = mean, size = 2.3, show.legend = FALSE,\n position = position_nudge(x = -0.5)) +\n coord_flip() +\n guides(\n color = F,\n fill = F,\n shape = F) +\n scale_fill_manual(\n breaks = names(color_values),\n values = color_values) +\n scale_color_manual(\n breaks = names(legend_nan_type),\n values = color_values) +\n xlab(\"Available inputs\") +\n scale_x_discrete(labels = legend_nan_type) +\n theme(\n legend.position = 'right',\n legend.justification = 'left',\n legend.text.align = 0)\nprint(fig4b)\n\nfname <- \"./figures/elements_fig4b.\"\nggsave(paste0(fname, \"pdf\"), plot = fig4b,\n width = save_width, height = save_height, useDingbats = F)\nembedFonts(file = paste0(fname, \"pdf\"), outfile = paste0(fname, \"pdf\"))\nggsave(paste0(fname, \"png\"), plot = fig4b,\n width = save_width, height = save_height,\n dpi = 300)\nknitr::include_graphics(paste0(fname, \"png\"), dpi = 200)\n\n#+ session_info\nprint(sessionInfo())", "meta": {"hexsha": "f553ffc028220ecfe99b6a0ac6733ff3cde2fd53", "size": 17890, "ext": "r", "lang": "R", "max_stars_repo_path": "figure_opp_learn.r", "max_stars_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_stars_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-11-11T21:26:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-18T17:18:45.000Z", "max_issues_repo_path": "figure_opp_learn.r", "max_issues_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_issues_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-03-14T07:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-14T07:56:17.000Z", "max_forks_repo_path": "figure_opp_learn.r", "max_forks_repo_name": "dengemann/engemann-2020-multimodal-brain-age", "max_forks_repo_head_hexsha": "ceffb1e01658e31d19dfc4dc0be7aff1d6d21af5", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-06-10T08:34:04.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T01:37:08.000Z", "avg_line_length": 36.9628099174, "max_line_length": 85, "alphanum_fraction": 0.676020123, "num_tokens": 5025, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6187804337438502, "lm_q1q2_score": 0.3142240456389593}} {"text": "## plot S-map coefficients\nplot_mode = \"box\" # 'series' or 'box'\n\nsetwd(paste0(wd, 'script'))\nsource(\"utils/plot.r\")\n\nlapply(EDM_lib_var, function(item, colors=cl, shapes=sh, mode=plot_mode){\n if (is.na(item$Smap1)[1]){\n return(NULL)\n }\n \n species = item$species\n smap_model = grep(\"Smap\", names(item))\n num_smap_model = length(smap_model)\n for (i in 1:num_smap_model){\n save_path = paste0(wd, smap_path, mode, \"_\", species, i, \".eps\")\n \n setEPS()\n postscript(save_path)\n showtext_begin() ## call this function after opening a device\n smapplot = plotSmapCoeff(smap_result_list=item[[smap_model[i]]],\n species=species,\n colors=colors,\n shapes=shapes,\n mode=mode)\n print(smapplot)\n dev.off()\n \n #file_name_eps = paste0(save_path, \".eps\")\n #ggsave(filename=file_name_eps, plot=smapplot, width=9, height=6, units=\"in\")\n file_name_png = paste0(save_path, \".png\")\n ggsave(filename=file_name_png, plot=smapplot, width=9, height=6, units=\"in\")\n }\n})\n\nif (plot_mode == \"series\"){\n smap_timeseries_legend(lib_var=library_var, colors=cl, shapes=sh)\n} else if (plot_mode == \"box\"){\n smap_boxplot_legend(lib_var=library_var, colors=cl)\n}\n\nfile_name_eps = paste0(wd, smap_path, plot_mode, \"_legend\", \".eps\")\nggsave(filename = file_name_eps)\n", "meta": {"hexsha": "48f3947678d57fe11857e10c51cec84c3b749cbc", "size": 1484, "ext": "r", "lang": "R", "max_stars_repo_path": "script/plot.r", "max_stars_repo_name": "snakepowerpoint/SpatialVariability", "max_stars_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "script/plot.r", "max_issues_repo_name": "snakepowerpoint/SpatialVariability", "max_issues_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "script/plot.r", "max_forks_repo_name": "snakepowerpoint/SpatialVariability", "max_forks_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7272727273, "max_line_length": 85, "alphanum_fraction": 0.5950134771, "num_tokens": 397, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6187804337438501, "lm_q1q2_score": 0.3142240456389592}} {"text": "# Run init.r before other scripts\nrm(list=ls())\n # for use in R console.\n # set own relevant directory if working in R console, otherwise ignore if in terminal\nsetwd(\"/Users/davidbeauchesne/Dropbox/PhD/PhD_obj2/Structure_Comm_EGSL/EGSL_species_distribution/\")\n# -----------------------------------------------------------------------------\n# PROJECT:\n# Evaluating the structure of the communities of the estuary\n# and gulf of St.Lawrence\n# -----------------------------------------------------------------------------\n\n# -----------------------------------------------------------------------------\n# DETAILS:\n# The goal of this script is to run JSDMs from the HMSC package on data from\n# the annual northern Gulf DFO trawl survey\n# -----------------------------------------------------------------------------\nlibrary(sp)\nlibrary(rgdal)\nlibrary(rgeos)\nlibrary(reshape2)\n# source('../../../PhD_RawData/Script/Function/coordTOsppt.r')\nsource('../../MEGA/MEGAsync/PhDData/Unclassified/Scripts/script/Function/coordTOsppt.r')\n\n\n# Importing data required data from RawData\n northPluri <- readRDS('./RData/northPluriCor.rds')\n\n# Extracting environmental data for northPluri stations\n # At some point I will need to decide whether the extractions will be at the beginning, the end, the middle, or all along the trawl path\n # For now, I will take the middle of the path\n # For the code for the line, beginning or end, see script to format rawdata in './PhD/PhD_RawData/'\n matCoordMid <- northPluri[,c('LoDeTow','LoFiTow','LaDeTow','LaFiTow','ID')] # middle trawl session\n proj <- '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'\n spptmid <- coordTOsppt(matCoord = matCoordMid, data = northPluri, proj = proj, type = \"2\")\n\n# Import environmental data\n # epipelagic <- readOGR(dsn = \"../../../PhD_RawData/data/Epipelagic_habitats_DFO\", layer = \"St_Lawrence_coastal_and_epipelagic_habitats\")\n # benthic <- readOGR(dsn = \"../../../PhD_RawData/data/Benthic_habitats_DFO\", layer = \"Quadrillage_10X10km_Marin\")\n epipelagic <- readOGR(dsn = \"../../MEGA/MEGAsync/PhDData/Unclassified/Epipelagic_habitats_DFO\", layer = \"St_Lawrence_coastal_and_epipelagic_habitats\")\n benthic <- readOGR(dsn = \"../../MEGA/MEGAsync/PhDData/Unclassified/Benthic_habitats_DFO\", layer = \"Quadrillage_10X10km_Marin\")\n\n# Transform point projection to environmental data projection\n spptmid <- spTransform(spptmid, CRSobj = CRS(proj4string(epipelagic)))\n\n# Intersect points with environmental data\n epiStations <- over(spptmid, epipelagic)\n bentStations <- over(spptmid, benthic)\n\n# Bind environmental covariables with northPluri data\n epiVar <- c('SSTWK30','SSAL_MEAN','FWRINFSR','STEMMEAN','BTEMMEAN','SANDBEACH','MUDFLAT','MARSH','SEDIMENT_C','HAB_C_E', 'TURBIDMEAN', 'SSAL_MIN', 'SSAL_MAX', 'BSAL_MEAN', 'BSAL_MIN', 'BSAL_MAX', 'STEMMIN', 'STEMMAX', 'BTEMMIN', 'BTEMMAX')\n bentVar <- c('Bathy_Mean','Geomorph_1','O2_Sat_Mea','SalMoyMoy','TempMoyMoy','SS_Code','SS_Desc_An','Megahabita','MHVar_3x3','SalMinMoy', 'SalMaxMoy', 'TempMinMoy', 'TempMaxMoy')\n Prof <- rowMeans(northPluri[, c('Prof_1','Prof_2')])\n northPluri <- cbind(northPluri, Prof, epiStations[, epiVar], bentStations[, bentVar])\n\n# Check for correlation between environmental covariables\n northPluri_wide <- dcast(northPluri,\n formula = No_Rel + No_Stn + DatDeTow + DatFiTow + HreDeb +\n HreFin + LaDeTow + LoDeTow + LaFiTow + LoFiTow +\n Prof_1 + Prof_2 + Prof + SSTWK30 + SSAL_MEAN +\n FWRINFSR + STEMMEAN + BTEMMEAN + SANDBEACH + MUDFLAT +\n MARSH + SEDIMENT_C + HAB_C_E + Bathy_Mean +\n Geomorph_1 + O2_Sat_Mea + SalMoyMoy + TempMoyMoy +\n SS_Code + Megahabita + MHVar_3x3 + SalMinMoy + SalMaxMoy +\n TempMinMoy + TempMaxMoy + TURBIDMEAN + SSAL_MIN + SSAL_MAX +\n BSAL_MEAN + BSAL_MIN + BSAL_MAX + STEMMIN + STEMMAX + BTEMMIN +\n BTEMMAX ~ EspGen,\n value.var = 'EspGen',\n fun.aggregate = length)\n\n corVar <- c('SSTWK30','SSAL_MEAN','FWRINFSR','STEMMEAN','BTEMMEAN','SANDBEACH','MUDFLAT','MARSH','Bathy_Mean','O2_Sat_Mea','SalMoyMoy','TempMoyMoy','Prof','SalMinMoy', 'SalMaxMoy', 'TempMinMoy', 'TempMaxMoy','TURBIDMEAN', 'SSAL_MIN', 'SSAL_MAX', 'BSAL_MEAN', 'BSAL_MIN', 'BSAL_MAX', 'STEMMIN', 'STEMMAX', 'BTEMMIN', 'BTEMMAX')\n\n round(cor(northPluri_wide[, corVar], use = 'na.or.complete'), 2)\n\n# After correlation Check\n # Bind environmental covariables with northPluri data\n northPluri <- readRDS('./RData/northPluriCor.rds')\n epiVar <- c('SSAL_MEAN','STEMMEAN','BTEMMEAN','HAB_C_E','STEMMIN','BTEMMIN')\n bentVar <- c('Bathy_Mean','O2_Sat_Mea','SalMoyMoy','TempMoyMoy','Megahabita','MHVar_3x3')\n Prof <- rowMeans(northPluri[, c('Prof_1','Prof_2')])\n northPluri <- cbind(northPluri, Prof, epiStations[, epiVar], bentStations[, bentVar])\n\n# Save analysis dataset (overwrite pre-existing data, no need to )\n saveRDS(northPluri, file = './RData/northPluriCor.rds')\n\n# Visual of variables to expert plus 'Bathy_Mean'\n rbPal <- colorRampPalette(c('#bfceda','#0b416c'))\n fig <- \"../../../Wiki/docs/img/\"\n\n # Benthic habitats\n jpeg(paste(fig,'benthic.jpeg',sep=''), width = 8, height = 5.33, res = 100, units = 'in')\n par(mfrow = c(2,3), mar = c(0,0,0,0),pin = c(4,2),pty = \"m\",xaxs = \"i\",xaxt = 'n',xpd = FALSE,yaxs = \"i\",yaxt = 'n')\n for(i in 1:length(bentVar)) {\n data <- benthic@data[, bentVar[i]]\n if(class(data) == 'factor') {\n nCol <- length(unique(data))\n cols <- rbPal(nCol)[data]\n } else {\n cols <- rbPal(50)[as.numeric(cut(data, breaks = 50))]\n }\n plot(benthic, col = cols, border = cols)\n text(x = 350000, y = 925000, labels = paste(bentVar[i]))\n }\n dev.off()\n\n # Epipelagic habitats\n jpeg(paste(fig,'epipelagic.jpeg',sep=''), width = 8, height = 5.33, res = 100, units = 'in')\n par(mfrow = c(2,3), mar = c(0,0,0,0),pin = c(4,2),pty = \"m\",xaxs = \"i\",xaxt = 'n',xpd = FALSE,yaxs = \"i\",yaxt = 'n')\n for(i in 1:length(epiVar)) {\n data <- epipelagic@data[, epiVar[i]]\n if(class(data) == 'factor') {\n nCol <- length(unique(data))\n cols <- rbPal(nCol)[data]\n } else {\n cols <- rbPal(50)[as.numeric(cut(data, breaks = 50))]\n }\n plot(epipelagic, col = cols, border = cols)\n text(x = 350000, y = 925000, labels = paste(epiVar[i]))\n }\n dev.off()\n\n# Intersect study grid with environmental data\n egsl_grid <- readOGR(dsn = \"../../../PhD_obj0/Study_Area/RData/\", layer = \"egsl_grid\")\n\n benthic@data <- benthic@data[, bentVar]\n epipelagic@data <- epipelagic@data[, epiVar]\n\n egsl_gridBent <- aggregate(x = benthic, by = egsl_grid, FUN = mean, areaWeighted = T)\n egsl_gridEpi <- aggregate(x = epipelagic, by = egsl_grid, FUN = mean, areaWeighted = T)\n\n# Get cell centroid\n egslCentroid <- gCentroid(egsl_grid, byid = T)\n proj <- '+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs'\n egslCentroid <- spTransform(egslCentroid, CRSobj = CRS(proj))\n\n# Save data only, to be used to produce predictions of taxa distribution in the St. Lawrence\n egsl_grid@data <- cbind(egsl_grid@data, egslCentroid@coords, egsl_gridBent@data, egsl_gridEpi@data)\n saveRDS(egsl_grid@data, file = './RData/egsl_grid.rds')\n\n# # Visualise grid data\n# data <- egsl_grid@data[, 'O2_Sat_Mea']\n# cols <- rbPal(50)[as.numeric(cut(data, breaks = 50))]\n# plot(egsl_grid, col = cols, border = cols)\n", "meta": {"hexsha": "ab807d45a7847403b2179b281520f7d404008ba5", "size": 7856, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/2_northPluri_envCov.r", "max_stars_repo_name": "david-beauchesne/EGSL_species_distribution", "max_stars_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-10T12:54:25.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-10T12:54:25.000Z", "max_issues_repo_path": "Script/2_northPluri_envCov.r", "max_issues_repo_name": "david-beauchesne/EGSL_species_distribution", "max_issues_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Script/2_northPluri_envCov.r", "max_forks_repo_name": "david-beauchesne/EGSL_species_distribution", "max_forks_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.9370629371, "max_line_length": 330, "alphanum_fraction": 0.6098523422, "num_tokens": 2419, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31422403849901687}} {"text": "library(fgsea)\n\n# load data and model genes using Soma_all.genes_enet_run.r\n\n# load eNetXploer results for all somamers\ndn = \"results/Soma_all.genes\"\nfn = \"enet_data.Rdata\"\nload(file.path(dn, fn), verbose = T)\n\nsoma.ranked = result$feature_coef_wmean[,\"a0\"] %>% abs()\n\n# load genes from previous model\nmod.list = readRDS(\"data_generated/model_genes.rds\")\n\n\nfres = fgsea(mod.list, soma.ranked, nperm=500)\n\nplotEnrichment(mod.list$`70 genes`, soma.ranked) + \n labs(title=paste0(\"70 proteins, p = \", format(fres[1, \"pval\"], digits=3)))\ndev.copy(png, \"figures/Soma_70genes_enrichment_plot.png\", w=600, h=300)\ndev.off()\n\nplotEnrichment(mod.list$`8 genes`, soma.ranked) + \n labs(title=paste0(\"8 proteins, p = \", format(fres[2, \"pval\"], digits=3)))\ndev.copy(png, \"figures/Soma_8genes_enrichment_plot.png\", w=600, h=300)\ndev.off()\n", "meta": {"hexsha": "f12f6efd74efb25bc2b27009c03bcef640bc974e", "size": 825, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Soma_all.genes_enet_enrichment.r", "max_stars_repo_name": "kotliary/pregnancy", "max_stars_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-03T12:52:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-03T12:52:51.000Z", "max_issues_repo_path": "R/Soma_all.genes_enet_enrichment.r", "max_issues_repo_name": "kotliary/pregnancy", "max_issues_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Soma_all.genes_enet_enrichment.r", "max_forks_repo_name": "kotliary/pregnancy", "max_forks_repo_head_hexsha": "e2c1c212b67e8ed2452487efab1f77c29b1b9fb5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.5555555556, "max_line_length": 76, "alphanum_fraction": 0.7175757576, "num_tokens": 269, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.31416402489226614}} {"text": "if(!require(stringi)) install.packages(\"stringi\")\nif(!require(curl)) install.packages(\"curl\")\nif(!require(devtools)) install.packages(\"devtools\")\ndevtools::install_github(\"petermeissner/wikipediatrend\")\ndevtools::install_github(\"twitter/AnomalyDetection\")\nif(!require(Rcpp) install.packages(\"Rcpp\")\nif(!require(ggplot2)) install.packages(\"ggplot2\")\n \nlibrary(wikipediatrend) ## Library containing API wikipedia access \nlibrary(AnomalyDetection)\nlibrary(ggplot2)\n\n## Download wiki webpage \"fifa\" \nnpt = wp_trend(\"Nguy\u1ec5n Ph\u00fa Tr\u1ecdng\", from=\"2013-01-01\", lang = \"en\")\nntd = wp_trend(\"Nguy\u1ec5n T\u1ea5n D\u0169ng\", from=\"2013-01-01\", lang = \"en\")\nnsh = wp_trend(\"Nguy\u1ec5n Sinh H\u00f9ng\", from=\"2013-01-01\", lang = \"en\")\ntts = wp_trend(\"Tr\u01b0\u01a1ng T\u1ea5n Sang\", from=\"2013-01-01\", lang = \"en\")\n\nnpt$title=\"Nguyen Phu Trong\"\nntd$title=\"Nguyen Tan Dung\"\nnsh$title=\"Nguyen Sinh Hung\"\ntts$title=\"Truong Tan Sang\"\ndat=do.call(\"rbind\",list(npt,ntd,nsh,tts))\n\n## Plotting data\nggplot(dat, aes(x=date, y=count, color=title)) + geom_line()\n", "meta": {"hexsha": "6680acd78361595d787e7f5e77c2f3a0c1fb3118", "size": 1003, "ext": "r", "lang": "R", "max_stars_repo_path": "wikitrend.r", "max_stars_repo_name": "hoangvietanh/wikitrend", "max_stars_repo_head_hexsha": "c09997e0427dba0215c4f001e1e0b2db0c164e28", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "wikitrend.r", "max_issues_repo_name": "hoangvietanh/wikitrend", "max_issues_repo_head_hexsha": "c09997e0427dba0215c4f001e1e0b2db0c164e28", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "wikitrend.r", "max_forks_repo_name": "hoangvietanh/wikitrend", "max_forks_repo_head_hexsha": "c09997e0427dba0215c4f001e1e0b2db0c164e28", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.1481481481, "max_line_length": 69, "alphanum_fraction": 0.7278165503, "num_tokens": 340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630722, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31415080791790806}} {"text": "# 3. faza: Vizualizacija podatkov\r\n\r\n# UVOD: UVRSTITVE LIVERPOOLA V PREMIER LIGI, OBDOBJE JURGENA KLOPPA\r\n\r\nsezone_Klopp <- zgodovina_uvrstitev %>% \r\n filter(Sezona %in% c(\"2016-17\",\"2017-18\",\"2018-19\",\"2019-20\",\"2020-21\"))\r\n\r\nzgodovina_uvrstitev_graf <- ggplot(data = zgodovina_uvrstitev, aes(x=Sezona, y=Uvrstitev, group=1)) +\r\n geom_point() + geom_line() + scale_y_reverse(breaks = 1*1:8) +\r\n theme(axis.text.x = element_text(angle = 90)) + geom_point(data=sezone_Klopp, aes(x=Sezona,y=Uvrstitev),color='red') +\r\n geom_line(data=sezone_Klopp, aes(x=Sezona,y=Uvrstitev),color='red') + ggtitle(\"Ligaske uvrstitve Liverpoola v Premier ligi (polne sezone Jurgena Kloppa rdece)\")\r\nprint(zgodovina_uvrstitev_graf)\r\nuvrstitve_Klopp <- sezone_Klopp$Uvrstitev\r\n\r\n# ANALIZA USPESNOSTI V LIGI GLEDE NA DODATNE TEKME\r\n\r\nvse_tekme <- c(last(rezultati2021$`Odigrane tekme`),last(rezultati1920$`Odigrane tekme`),last(rezultati1819$`Odigrane tekme`),\r\n last(rezultati1718$`Odigrane tekme`),last(rezultati1617$`Odigrane tekme`))\r\nligaske_tekme <- c(38,38,38,38,38) #v ligi je 20 mostev, torej vsako sezono po 38 tekem\r\nst_dodatnih_tekem <- vse_tekme - ligaske_tekme #neligaske tekme - to bo x os\r\ntocke <- c(rezultati2021$Zmage[1]*3 + rezultati2021$Remiji[1],rezultati1920$Zmage[1]*3 + rezultati1920$Remiji[1],\r\n rezultati1819$Zmage[1]*3 + rezultati1819$Remiji[1],rezultati1718$Zmage[1]*3 + rezultati1718$Remiji[1],\r\n rezultati1617$Zmage[1]*3 + rezultati1617$Remiji[1]) #to bo y os\r\n\r\nplot(st_dodatnih_tekem,tocke, xlim = c(8,22), xlab = \"Neligaske tekme\", ylab = \"tocke v ligi\", main = \"Uspesnost v ligi glede na dodatne tekme\") +\r\npoints(st_dodatnih_tekem, tocke, col=\"red\", pch = 16) +\r\ngrid()\r\n\r\nfor (i in 1:length(ligaske_tekme))\r\n {\r\n text(st_dodatnih_tekem[i]+0.5,tocke[i]+0.5, uvrstitve_Klopp[i])\r\n text(st_dodatnih_tekem[i]+0.70,tocke[i]-0.15, \".,\")\r\n text(st_dodatnih_tekem[i]+1.65,tocke[i]+0.5, sezone_Klopp$Sezona[i])\r\n}\r\n\r\n\r\n# OBREMENJOST POZICIJ\r\nvratarji <- nastopi1920 %>% \r\n filter(nastopi1920$`Igralno mesto` == \"GK\")\r\n\r\nbranilci <- nastopi1920 %>% \r\n filter(nastopi1920$`Igralno mesto` == \"DF\")\r\n\r\nvezisti <- nastopi1920 %>% \r\n filter(nastopi1920$`Igralno mesto` == \"MF\")\r\n\r\nnapadalci <- nastopi1920 %>% \r\n filter(nastopi1920$`Igralno mesto` == \"FW\")\r\n\r\nprikaz_obremenjenosti <- ggplot(data=nastopi1920, aes(x=Igralec, y=nastopi1920$\"Skupaj vse tekme\")) +\r\n theme(axis.text.x = element_text(angle = 90)) +\r\n geom_point(data=vratarji, aes(Igralec, vratarji$`Skupaj vse tekme`), color=\"blue\") +\r\n geom_point(data=branilci, aes(Igralec, branilci$`Skupaj vse tekme`), color=\"red\") +\r\n geom_point(data=vezisti, aes(Igralec, vezisti$`Skupaj vse tekme`), color=\"black\") +\r\n geom_point(data=napadalci, aes(Igralec, napadalci$`Skupaj vse tekme`), color=\"green\")\r\nprint(prikaz_obremenjenosti)\r\n\r\n#izboljsava: tiste z manj kot 5 zacetimi tekmami vrzemo ven\r\nnastopi1920mod <- nastopi1920 %>% filter(nastopi1920$`Skupaj prva postava` >= 5) #mod - modifikacija\r\nnastopi1920mod <- nastopi1920mod[order(nastopi1920mod$`Igralno mesto`), ]\r\nvratarjimod <- nastopi1920mod %>% filter(nastopi1920mod$`Igralno mesto` == \"GK\")\r\nbranilcimod <- nastopi1920mod %>% filter(nastopi1920mod$`Igralno mesto` == \"DF\")\r\nvezistimod <- nastopi1920mod %>% filter(nastopi1920mod$`Igralno mesto` == \"MF\")\r\nnapadalcimod <- nastopi1920mod %>% filter(nastopi1920mod$`Igralno mesto` == \"FW\")\r\n\r\nprikaz_obremenjenosti_mod <- ggplot(data=nastopi1920mod, aes(x=Igralec, y=nastopi1920mod$`Skupaj vse tekme`)) +\r\n theme(axis.text.x = element_text(angle = 90)) +\r\n scale_x_discrete(limits=nastopi1920mod$Igralec) +\r\n geom_point(data=vratarjimod, aes(Igralec, vratarjimod$`Skupaj vse tekme`), color=\"blue\") +\r\n geom_point(data=branilcimod, aes(Igralec, branilcimod$`Skupaj vse tekme`), color=\"red\") +\r\n geom_point(data=vezistimod, aes(Igralec, vezistimod$`Skupaj vse tekme`), color=\"black\") +\r\n geom_point(data=napadalcimod, aes(Igralec, napadalcimod$`Skupaj vse tekme`), color=\"green\") +\r\n geom_point(data=vratarjimod, aes(Igralec, vratarjimod$`Skupaj prva postava`), color=\"blue\", pch=2) +\r\n geom_point(data=branilcimod, aes(Igralec, branilcimod$`Skupaj prva postava`), color=\"red\", pch=2) +\r\n geom_point(data=vezistimod, aes(Igralec, vezistimod$`Skupaj prva postava`), color=\"black\", pch=2) +\r\n geom_point(data=napadalcimod, aes(Igralec, napadalcimod$`Skupaj prva postava`), color=\"green\", pch=2) + \r\n ggtitle(\"Pregled nastopov v sezoni 2019-20\") + xlab(\"Igralci\") + ylab(\"Stevilo odigranih tekem\")\r\nprint(prikaz_obremenjenosti_mod)\r\n\r\n## ZEMLJEVID ZASTOPANOSTI DRZAV V SAMPIONSKI SEZONI 2019/20\r\n\r\nigralci1920$Igralec[6] <- \"Virgil van Dijk\"\r\nigralci1920$Igralec[17] <- \"Georginio Wijnaldum\"\r\nigralci1920$Igralec[18] <- \"James Milner\"\r\nigralci1920$Igralec[20] <- \"Jordan Henderson\"\r\nigralci1920$Narodnost[4] <- \"Ireland\"\r\n\r\nuk <- function(niz){\r\n if (niz == \"Scotland\" || niz == \"Wales\" || niz == \"England\" || niz == \"Northern Ireland\"){\r\n niz = \"United Kingdom\"\r\n }\r\n return(niz)\r\n}\r\nigralci2021$Narodnost <- unlist(lapply(igralci2021$Narodnost,uk)) #Narodnost zelimo kot namesto\r\nigralci1920$Narodnost <- unlist(lapply(igralci1920$Narodnost,uk)) # oblike \r\nigralci1819$Narodnost <- unlist(lapply(igralci1819$Narodnost,uk))\r\nigralci1718$Narodnost <- unlist(lapply(igralci1718$Narodnost,uk))\r\nigralci1617$Narodnost <- unlist(lapply(igralci1617$Narodnost,uk))\r\n\r\ndata(\"World\")\r\n#by mora biti list, zato dodamo list(stolpec)\r\ntmp1 <- aggregate(x=igralci1920[, colnames(igralci1920) != \"Narodnost\"], by=list(igralci1920$Narodnost), FUN=paste)\r\ntmp1 <- tmp1 %>% select('Group.1', 'Igralec')\r\ntmp1 <- tmp1 %>%\r\n mutate('Igralec' = gsub(\"[a-z]\\\\(\", \"\", Igralec)) %>%\r\n mutate('Igralec' = gsub(\"\\\\)\", \"\", Igralec)) %>%\r\n mutate('Igralec' = gsub(\"\\\"\",\"\", Igralec))\r\ntmp2 <- merge(y = tmp1,x = World, by.x= 'sovereignt', by.y = 'Group.1')\r\n\r\ntmap_mode('view')\r\nzemljevid_1920 <- function(){\r\n World <- tm_shape(tmp2) + tm_fill('sovereignt', popup.vars=\"Igralec\", legend.show=FALSE)\r\n #v tm_shape vzamemo tmp2, da obarva le izbrane drzave\r\n return(World)\r\n}\r\nprint(zemljevid_1920())\r\n\r\n# KARTONI - PRIMERJAVA LIVERPOOL IN LIGA PO SEZONAH\r\nkartoni_sezona <- c(sezone_Klopp$Sezona)\r\nrumeni_liverpool <- c(last(kartoni1617$Rumeni),last(kartoni1718$Rumeni),last(kartoni1819$Rumeni),\r\n last(kartoni1920$Rumeni),last(kartoni2021$Rumeni))\r\nrdeci_liverpool <- c(last(kartoni1617$Rdeci),last(kartoni1718$Rdeci),last(kartoni1819$Rdeci),\r\n last(kartoni1920$Rdeci),last(kartoni2021$Rdeci))\r\nrumeni_liga <- c(sum(liga_kartoni1617$yellow_cards)/20,sum(liga_kartoni1718$yellow_cards)/20,\r\n sum(liga_kartoni1819$yellow_cards)/20,sum(liga_kartoni1920$yellow_cards)/20,\r\n sum(liga_kartoni2021$yellow_cards)/20)\r\nrdeci_liga <- c(sum(liga_kartoni1617$red_cards)/20,sum(liga_kartoni1718$red_cards)/20,\r\n sum(liga_kartoni1819$red_cards)/20,sum(liga_kartoni1920$red_cards)/20,\r\n sum(liga_kartoni2021$red_cards)/20)\r\nrazpredelnica_kartoni <- data.frame(kartoni_sezona,rumeni_liverpool, rdeci_liverpool,rumeni_liga,rdeci_liga)\r\nkartoni_graf <- ggplot(data = razpredelnica_kartoni, aes(x=kartoni_sezona, y=rumeni_liverpool,group=1)) +\r\n geom_point(color=\"gold1\") + geom_line(color=\"gold1\") + geom_point(aes(x=kartoni_sezona, y=rdeci_liverpool),color=\"red\") + \r\n geom_line(aes(x=kartoni_sezona, y=rdeci_liverpool),color=\"red\") + \r\n geom_point(aes(x=kartoni_sezona, y=rumeni_liga),color=\"goldenrod4\") +\r\n geom_line(aes(x=kartoni_sezona, y=rumeni_liga),color=\"goldenrod4\") + \r\n geom_point(aes(x=kartoni_sezona, y=rdeci_liga),color=\"darkorchid4\") + \r\n geom_line(aes(x=kartoni_sezona, y=rdeci_liga),color=\"darkorchid4\") + \r\n ggtitle(\"Pregled kartonov po sezonah\") + labs(x = \"Sezona\", y = \"Stevilo kartonov\")\r\nprint(kartoni_graf)", "meta": {"hexsha": "acaa9daa9903275211fb5dcbb15621a515768a8f", "size": 7861, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "petertiselj/APPR-2019-20", "max_stars_repo_head_hexsha": "6cbb72c29e063cc891b471d52b16e9bb7c5e2a0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "petertiselj/APPR-2019-20", "max_issues_repo_head_hexsha": "6cbb72c29e063cc891b471d52b16e9bb7c5e2a0b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-08-31T19:53:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-03T18:02:47.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "petertiselj/APPR-2019-20", "max_forks_repo_head_hexsha": "6cbb72c29e063cc891b471d52b16e9bb7c5e2a0b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 56.5539568345, "max_line_length": 163, "alphanum_fraction": 0.7135224526, "num_tokens": 3103, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3141508004314963}} {"text": "\\name{cumuEff}\n\\alias{cumuEff}\n\\title{Calculate Cumulative or sub-gr Treatment Effects}\n\\description{Calculate Cumulative or sub-gr Treatment Effects}\n\\usage{cumuEff(x, cumu = TRUE, id = NULL, period = NULL)} \n\\arguments{\n \\item{x}{a \\code{\\link{gsynth}} object.}\n \\item{cumu}{a logical flag indicating whether to calculate cumulative effects or not.}\n \\item{id}{a string vector speicfying a sub-group of treated units that treatment \n effects are to be averaged on. }\n \\item{period}{a two-element numeric vector specifying the range of term during which treatment effects are to be accumulated. If left blank, atts at all post-treatment \n periods will be calculated.}\n}\n\\value{\n \\item{catt}{esimated (cumulative) atts.}\n \\item{est.catt}{uncertainty estimates for \\code{catt}.}\n}\n\n\\author{\n Yiqing Xu , Stanford University\n \n Licheng Liu , M.I.T.\n}\n\\references{ \n Jushan Bai. 2009. \"Panel Data Models with Interactive Fixed\n Effects.\" Econometrica 77:1229--1279.\n\n Yiqing Xu. 2017. \"Generalized Synthetic Control Method: Causal Inference\n with Interactive Fixed Effects Models.\" Political Analysis, Vol. 25, \n Iss. 1, January 2017, pp. 57-76. \n}\n\\seealso{\n \\code{\\link{gsynth}}\n}\n\n\n", "meta": {"hexsha": "1ba0a9bd9043100fb8e8a9fd014b578a77fb2b24", "size": 1239, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/cumuEff.rd", "max_stars_repo_name": "huigangchen/gsynth", "max_stars_repo_head_hexsha": "16262069e19f2bbacc26748f00925c4a2d695556", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "man/cumuEff.rd", "max_issues_repo_name": "huigangchen/gsynth", "max_issues_repo_head_hexsha": "16262069e19f2bbacc26748f00925c4a2d695556", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/cumuEff.rd", "max_forks_repo_name": "huigangchen/gsynth", "max_forks_repo_head_hexsha": "16262069e19f2bbacc26748f00925c4a2d695556", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4864864865, "max_line_length": 170, "alphanum_fraction": 0.7344632768, "num_tokens": 364, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3141507929450845}} {"text": "p <- ggplot(data = ledger_data, aes(x = date, y = amount, colour = account)) + geom_line()\n\nggsave(file = \"line-amount-vs-date-by-account.svg\", plot = p7, width = 10, height = 4)\n", "meta": {"hexsha": "f33763f70b0b8fc155b5fce3e48e2c0b5117a499", "size": 179, "ext": "r", "lang": "R", "max_stars_repo_path": "Support/lib/r/line-amount-vs-date-by-account.r", "max_stars_repo_name": "lifepillar/Ledger.tmbundle", "max_stars_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2015-11-05T08:56:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-07T00:19:12.000Z", "max_issues_repo_path": "Support/lib/r/line-amount-vs-date-by-account.r", "max_issues_repo_name": "lifepillar/Ledger.tmbundle", "max_issues_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-10-02T05:58:02.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-02T07:14:12.000Z", "max_forks_repo_path": "Support/lib/r/line-amount-vs-date-by-account.r", "max_forks_repo_name": "lifepillar/Ledger.tmbundle", "max_forks_repo_head_hexsha": "33a99502db980c538b21e2012ea2efcad3288003", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2016-09-08T18:30:38.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T06:21:52.000Z", "avg_line_length": 44.75, "max_line_length": 90, "alphanum_fraction": 0.6536312849, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3138783242166127}} {"text": "\nsource('./_function_task_expand_name.r')\n\nexpand.name = function (df) {\n names = data.frame(name=unique(df$name))\n \n df.expand.name = names %>%\n rowwise() %>%\n mutate(\n model=revalue(extract.by.split(name, 1), model.full.to.short, warn_missing=FALSE),\n operation=revalue(extract.by.split(name, 2), operation.full.to.short, warn_missing=FALSE), # op\n \n regualizer.scaling = regualizer.get.type(extract.by.split(name, 3), 1), # rs\n \n regualizer.scaling.start=regualizer.scaling.get(extract.by.split(name, 4), 1),\n regualizer.scaling.end=regualizer.scaling.get(extract.by.split(name, 4), 2),\n \n regualizer=regualizer.get.part(extract.by.split(name, 5), 1),\n regualizer.z=regualizer.get.part(extract.by.split(name, 5), 2),\n regualizer.oob=regualizer.get.part(extract.by.split(name, 5), 3),\n regualizer.l2=regualizer.get.part(extract.by.split(name, 5), 4),\n \n interpolation.range=range.full.to.short(extract.by.split(name, 6)),\n extrapolation.range=range.full.to.short(extract.by.split(name, 7)),\n\n input.size=dataset.get.part(extract.by.split(name, 8), 1, 4),\n seq.length=dataset.get.part(extract.by.split(name, 8), 2, NA),\n subset.ratio=dataset.get.part(extract.by.split(name, 8), 3, NA),\n overlap.ratio=dataset.get.part(extract.by.split(name, 8), 4, NA),\n\n hidden.size=as.integer(substring(extract.by.split(name, 9, 'h2'), 2)),\n learning.rate=as.numeric(substring(extract.by.split(name, 11, 'lr-0.001'), 4)),\n batch.size=as.integer(substring(extract.by.split(name, 12), 2)),\n seed=as.integer(substring(extract.by.split(name, 13), 2)),\n )\n \n df.expand.name$name = as.factor(df.expand.name$name)\n df.expand.name$operation = factor(df.expand.name$operation, c('$\\\\times$', '$\\\\mathbin{/}$', '$+$', '$-$', '$\\\\sqrt{z}$', '$z^2$'))\n df.expand.name$model = as.factor(df.expand.name$model)\n df.expand.name$interpolation.range = as.factor(df.expand.name$interpolation.range)\n df.expand.name$extrapolation.range = as.factor(df.expand.name$extrapolation.range)\n \n #return(df.expand.name)\n return(merge(df, df.expand.name))\n}\n", "meta": {"hexsha": "c66049c1273f8fc6d2508743dd6d9958b17da723", "size": 2150, "ext": "r", "lang": "R", "max_stars_repo_path": "export/_function_recurrent_expand_name.r", "max_stars_repo_name": "hoedt/stable-nalu", "max_stars_repo_head_hexsha": "64b3d240db8bff4da857d955f213ef3c7e38e035", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "export/_function_recurrent_expand_name.r", "max_issues_repo_name": "hoedt/stable-nalu", "max_issues_repo_head_hexsha": "64b3d240db8bff4da857d955f213ef3c7e38e035", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "export/_function_recurrent_expand_name.r", "max_forks_repo_name": "hoedt/stable-nalu", "max_forks_repo_head_hexsha": "64b3d240db8bff4da857d955f213ef3c7e38e035", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.7391304348, "max_line_length": 133, "alphanum_fraction": 0.6697674419, "num_tokens": 617, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3138783160255456}} {"text": "\n cleanup()\n \n a<-Read.summary.data(read.init.function=F)\n \n #b<-filter(a,((Age ==0 & Quarter %in% c(3,4)) |(Age ==1 & Quarter %in% c(1,2,3,4))) & Species=='Herring')\n \n b<-a \n by(b,list(b$Species),function(x) {\n X11(w=9,h=12)\n print(ggplot(x,aes(Year,west)) +\n theme_bw() +\n geom_point() +\n #geom_smooth(method = \"loess\") +\n #geom_smooth(method = \"lm\") +\n #facet_wrap(~ paste0(Species,' Age:',Age,' Q:',Quarter), scale=\"free_y\") +\n facet_wrap(~ paste0(Species,' Age:',Age,' Q:',Quarter)) +\n labs(x=\"Year\", y=\"Weight\",title=\"\"))\n })\n\n ", "meta": {"hexsha": "d562d1de9a2f621df4b305ddb8290f4681572a53", "size": 654, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/plot_mean_weights.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/plot_mean_weights.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/plot_mean_weights.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1428571429, "max_line_length": 109, "alphanum_fraction": 0.4862385321, "num_tokens": 196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5583269796369905, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.3138783077624596}} {"text": "\n# Map 1-based optional input ports to variables\ndataset1 <- maml.mapInputPort(1) # class: data.frame\n\n# Connect the zip port to the xgboost_plus_trained_model.zip file\ninstall.packages(\"src/magrittr_1.5.zip\",lib=\"src/\",repos=NULL)\ninstall.packages(\"src/xgboost_0.4-3.zip\",lib=\"src/\",repos=NULL)\ninstall.packages(\"src/jsonlite_0.9.19.zip\",lib=\"src/\",repos=NULL)\nlibrary(xgboost,lib.loc=\"src/\")\nlibrary(Matrix)\nlibrary(jsonlite)\nlibrary(plyr)\nlibrary('geosphere')\n\nmodel <- xgb.load(\"src/xgboost_small.model\")\n\nhandToVector <- function(hand){\n cardarray <- rep(0,52)\n cardID <- 2:14\n names(cardID) <- c(\"2\", \"3\", \"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"T\",\"J\",\"Q\",\"K\",\"A\")\n suiteID <- 1:4\n names(suiteID) <- c(\"s\",\"d\",\"c\",\"h\")\n for (i in 1:length(hand)){\n card <- hand[i]\n cardarray[ cardID[[substr(card,0,1)]]-1 + suiteID[[substr(card,nchar(card),nchar(card))]]*13-13] <- 1 \n }\n return(cardarray)\n}\n\nhandValue <- function(hand){ \n predicted <- predict(model, t(as.matrix(handToVector(hand))))\n probs<- t(matrix(predicted, nrow=10, ncol=length(predicted)/10))\n #print(probs)\n predicted_label <- max.col(t(predicted)) - 1\n #print(probs)\n return(predicted_label)\n}\n\n#Pull the cardDF from the input data string\ncardDF<-fromJSON(gsub(\"([\\\\])\",\"\", dataset1$cardData))\n#Add in the CurHandID, CardScore, and Distance variables\ncardDF['CurHandID'] <- cardDF['HandID']\ncardDF['Distance'] <- 0.0\n\n#Score the hands as they are currently configured.\ncardDF<-ddply(cardDF,c(\"HandID\"),\n transform,\n CardScore = handValue(FaceValue)/length(FaceValue))\n\n#Build the handDF to hold the hand information during computation \nhandDF <- unique(cardDF[c('HandID','HandLocName','HandLocSub','HandLocLat','HandLocLon')])\ndrops <- c('HandLocName','HandLocSub','HandLocLat','HandLocLon')\ncardDF<-cardDF[ , !(names(cardDF) %in% drops)]\n\n\n\n#####################################################################################\n#\n#Build the card swap Monte Carlo algorithm\n#Algorithm from https://github.com/toddwschneider/shiny-salesman/blob/master/helpers.R\n#\n#####################################################################################\n\n\n#Temperature anneal based on an s-curve\ncurrent_temperature <- function(iter, s_curve_amplitude, s_curve_center, s_curve_width) {\n s_curve_amplitude * s_curve(iter, s_curve_center, s_curve_width)\n}\n#Calculate s-curve\ns_curve <- function(x, center, width) {\n 1 / (1 + exp((x - center) / width))\n}\n\n#Function to carry out a card swap and check that it is legal (no duplicated cards in a hand)\ncardSwap <- function(candidate_config, hand_info){\n legalswap <- FALSE\n firstcard <- NULL\n secondcard <- NULL\n \n while (!legalswap){\n swaphands <- sample(unique(candidate_config['CurHandID'])[,],2)\n swapcards <- c(candidate_config[candidate_config['CurHandID']==swaphands[[1]],][sample(nrow(candidate_config[candidate_config['CurHandID']==swaphands[[1]],]), 1),'CardID'],\n candidate_config[candidate_config['CurHandID']==swaphands[[2]],][sample(nrow(candidate_config[candidate_config['CurHandID']==swaphands[[2]],]), 1),'CardID'])\n\n #run the swap using a temp card\n firstcard <- candidate_config[candidate_config$CardID == swapcards[[1]],]\n secondcard <- candidate_config[candidate_config$CardID == swapcards[[2]],]\n candidateDF <- candidate_config\n candidateDF[candidateDF$CardID == swapcards[[1]],'CurHandID'] <- secondcard$CurHandID\n candidateDF[candidateDF$CardID == swapcards[[2]],'CurHandID'] <- firstcard$CurHandID\n #Check to see if it is a legal swap\n legalswap <- !any(duplicated(candidateDF[candidateDF$CurHandID == firstcard$CurHandID,'FaceValue'])) ||\n !any(duplicated(candidateDF[candidateDF$CurHandID == secondcard$CurHandID,'FaceValue']))\n\n }\n \n #Score the new configurations\n hand1<-candidateDF[candidateDF$CurHandID==swaphands[[1]],'FaceValue']\n candidateDF[candidateDF$CurHandID==swaphands[[1]],'CardScore'] <- handValue(hand1)/length(hand1)\n hand2<-candidateDF[candidateDF$CurHandID==swaphands[[2]],'FaceValue']\n candidateDF[candidateDF$CurHandID==swaphands[[2]],'CardScore'] <- handValue(hand2)/length(hand2)\n\n #Move the cards to their new locations and update the displacement vector\n tempcard <- firstcard\n firstcard['CurHandID'] <- secondcard['CurHandID']\n secondcard['CurHandID'] <- tempcard['CurHandID']\n\n p1<-hand_info[hand_info$HandID == firstcard$CurHandID ,c('HandLocLon','HandLocLat')]\n p2<-hand_info[hand_info$HandID == firstcard$HandID ,c('HandLocLon','HandLocLat')]\n candidateDF[candidateDF$CardID==firstcard$CardID,'Distance'] <- distGeo(p1,p2)/1000 #Returns distance in meters, we want kilometers\n\n p1<-hand_info[hand_info$HandID == secondcard$CurHandID ,c('HandLocLon','HandLocLat')]\n p2<-hand_info[hand_info$HandID == secondcard$HandID ,c('HandLocLon','HandLocLat')]\n candidateDF[candidateDF$CardID==secondcard$CardID,'Distance'] <- distGeo(p1,p2)/1000 #Returns distance in meters, we want kilometers\n\n return(list(candidate_config=candidateDF,hands=swaphands,cards=swapcards)) \n}\n\n#Split the anneal into pieces that are handled one at a time\nrun_intermediate_anneal <- function(hand_config, \n hand_score, \n best_config, \n best_score,\n starting_iteration, \n number_of_iterations, \n s_curve_amplitude, \n s_curve_center, \n s_curve_width,\n hand_info) {\n \n score_history <- rep(0,number_of_iterations)\n\n for(i in 1:number_of_iterations) {\n \n iter <- starting_iteration + i \n temp <- current_temperature(iter, s_curve_amplitude, s_curve_center, s_curve_width)\n \n #try a swap: if it is a good swap, we'll keep it later\n swap <- cardSwap(hand_config,hand_info)\n candidate_config <- swap$candidate_config\n \n #score the overall configuration based on this update\n candidate_score <- sum(candidate_config$CardScore)\n \n #the change in score is now dependent on the distance traveled - more distance means less benefit for trading\n \n subtractors <- candidate_config[candidate_config$CardID == swap$cards[[1]],'CardOccLevel'] + \n candidate_config[candidate_config$CardID == swap$cards[[1]],'CardTransMult'] * \n candidate_config[candidate_config$CardID == swap$cards[[1]],'Distance'] +\n candidate_config[candidate_config$CardID == swap$cards[[2]],'CardOccLevel'] + \n candidate_config[candidate_config$CardID == swap$cards[[2]],'CardTransMult'] * \n candidate_config[candidate_config$CardID == swap$cards[[2]],'Distance'] \n \n #The final score is the candidate score minus the subtractors\n delta <- candidate_score - hand_score - subtractors\n\n if (temp > 0 ) {\n #This tends to 1 \n ratio <- exp( delta / temp)\n } else {\n #At zero temp, we only keep good flips\n ratio <- as.numeric(delta > 0)\n }\n \n #Get a random number: if it is less than our ratio, keep the flip\n if (runif(1) < ratio) {\n hand_config <- candidate_config\n hand_score <- candidate_score\n\n if (hand_score > best_score) {\n best_config <- hand_config\n best_score <- hand_score\n \n }\n }\n score_history[i] <- hand_score\n }\n\n return(list(hand_config=hand_config, hand_score=hand_score, best_config=best_config, best_score=best_score, score_history=score_history)) \n}\n\n\n#Determine the number of temperature steps to take based on the number of hands\nprint_iterations <- length(unique(cardDF$HandID))\nsteps_per_iteration <- 300 #This seems to work well\nt_max <- 10\nnumber_of_iterations <- print_iterations * steps_per_iteration\nstarting_iteration <- 1\nt_center <-number_of_iterations/4\nt_width <-number_of_iterations/16\n\n#Set the current configuration as our input cardDF\nhand_config<-cardDF\noriginal_score <- sum(cardDF$CardScore)\nhand_score<-sum(cardDF$CardScore)\nbest_config<-hand_config\nbest_score<-hand_score\nanneal_results <- NULL\nscore_history <- rep(0,print_iterations*print_iterations)\n\nfor (i in 1:print_iterations){\n start.time <- Sys.time()\n\n iter <- steps_per_iteration*(i-1)\n anneal_results<-run_intermediate_anneal(hand_config, hand_score, best_config, best_score,\n iter, \n steps_per_iteration, \n t_max, \n t_center, \n t_width,\n handDF)\n hand_config <- anneal_results$hand_config\n hand_score <- anneal_results$hand_score\n best_config <- anneal_results$best_config\n best_score <- anneal_results$best_score\n score_history[(iter+1):(iter+steps_per_iteration)]<-anneal_results$score_history\n end.time <- Sys.time()\n time.taken <- end.time - start.time\n paste(\"Round\",i,\"of\",print_iterations,\"with score\",best_score,\"in time:\",format(time.taken,digits=2)) \n}\nplot(score_history)\n\npaste('New Score:',anneal_results$best_score)\nbest_config <-anneal_results$best_config\npaste('Number of cards trading:', nrow(best_config[best_config$HandID !=best_config$CurHandID ,]),'of',nrow(best_config))\n\nupdatedCardDF <- anneal_results$best_config\n\n# Get only the cards where they have moved\nmovedDF <- updatedCardDF[updatedCardDF$HandID != updatedCardDF$CurHandID,]\n\n# Add in the rest of the hand information we will need to move the card\ngetLocName <- function(x){\n handDF[handDF$HandID == x['CurHandID'], 'HandLocName']\n}\ngetLocSub <- function(x){\n handDF[handDF$HandID == x['CurHandID'], 'HandLocSub']\n}\ngetLocLat <- function(x){\n handDF[handDF$HandID == x['CurHandID'], 'HandLocLat']\n}\ngetLocLon <- function(x){\n handDF[handDF$HandID == x['CurHandID'], 'HandLocLon']\n}\n\nmovedDF['MoveHandLocName'] <- apply(movedDF, 1, function(x) getLocName(x))\nmovedDF['MoveHandLocSub'] <- apply(movedDF, 1, function(x) getLocSub(x))\nmovedDF['MoveHandLocLat'] <- apply(movedDF, 1, function(x) getLocLat(x))\nmovedDF['MoveHandLocLon'] <- apply(movedDF, 1, function(x) getLocLon(x))\n\nnames(movedDF)[names(movedDF) == 'CurHandID'] <- 'MoveHandID'\noutputDF <- dataset1\n\noutputDF['BatchFinishedTime'] <- strftime(as.POSIXlt(Sys.time(), \"UTC\", \"%Y-%m-%dT%H:%M:%S\") , \"%Y-%m-%dT%H:%M:%S%z\")\noutputDF['OriginalScore'] <- original_score\noutputDF['FinalScore'] <- anneal_results$best_score\noutputDF['cardData']<-paste(toJSON(movedDF))\n\n# Select data.frame to be sent to the output Dataset port\nmaml.mapOutputPort(\"outputDF\");", "meta": {"hexsha": "f363c68b8553ee2678a1ca945b5eccf809f3dcdc", "size": 11081, "ext": "r", "lang": "R", "max_stars_repo_path": "src/AzureML_HandOptimizer.r", "max_stars_repo_name": "madsenmj/ml-iot-poker-network", "max_stars_repo_head_hexsha": "42a74dd6a34f2ec5b57885150ba1d4ce7c3a81ed", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-03-01T19:58:18.000Z", "max_stars_repo_stars_event_max_datetime": "2018-03-01T19:58:18.000Z", "max_issues_repo_path": "src/AzureML_HandOptimizer.r", "max_issues_repo_name": "madsenmj/ml-iot-poker-network", "max_issues_repo_head_hexsha": "42a74dd6a34f2ec5b57885150ba1d4ce7c3a81ed", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/AzureML_HandOptimizer.r", "max_forks_repo_name": "madsenmj/ml-iot-poker-network", "max_forks_repo_head_hexsha": "42a74dd6a34f2ec5b57885150ba1d4ce7c3a81ed", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.6192307692, "max_line_length": 180, "alphanum_fraction": 0.6456998466, "num_tokens": 2790, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7634837743174788, "lm_q2_score": 0.41111086923216794, "lm_q1q2_score": 0.31387647810431507}} {"text": "# Usage:\n# $ Rscript histogram.r file.dat\n\nargs <- commandArgs( trailingOnly=TRUE )\nifileName <- args[1]\nrm(args)\n\nX11(width=8.0, height=3.5)\n\nmy.csv.data <<-\nread.table(\n\tfile=ifileName,\n\theader=FALSE,\n\tsep='\\n',\n\tquote='\\\"',\n\tdec='.',\n\tfill=FALSE,\n\tcomment.char=\"#\",\n\tna.strings=\"NA\",\n\tnrows=-1,\n\tskip=0,\n\tcheck.names=TRUE,\n\tstrip.white=FALSE,\n\tblank.lines.skip=TRUE\n)\n\nhist(\n\tmy.csv.data[[\"V1\"]],\n\tbreaks=40,\n\tfreq=FALSE,\n\tlabels=FALSE,\n\tlty=\"solid\",\n\tdensity=-1,\n\tborder=\"black\",\n\tcol=\"red\",\n\txlab=\"X\",\n\tylab=\"Frequency\",\n\tmain=\"\",\n\taxes=FALSE\n)\n\naxis(2)\naxis(1, at=seq(-10,10,by=1), tick=TRUE)\n#xaxp=c(2, 9, 7)\n#axis(2, xaxp=c(2, 9, 7))\n\nbox()\n\nSys.sleep(100)\n", "meta": {"hexsha": "9979720149ea6141e30c9c70d1a3e7c7728e5e55", "size": 665, "ext": "r", "lang": "R", "max_stars_repo_path": "utils/histogram.r", "max_stars_repo_name": "nfaguirrec/scift", "max_stars_repo_head_hexsha": "2a38018bd495ad42e57a674f525041c4a854dccd", "max_stars_repo_licenses": ["BSD-3-Clause-Open-MPI", "BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-28T02:04:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-28T02:04:45.000Z", "max_issues_repo_path": "utils/histogram.r", "max_issues_repo_name": "bbw7561135/scift", "max_issues_repo_head_hexsha": "0e81e7d29889970d35cc563cc45e9a87fd1cf06d", "max_issues_repo_licenses": ["BSD-3-Clause-Open-MPI", "BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-10-14T21:21:21.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-15T04:37:58.000Z", "max_forks_repo_path": "utils/histogram.r", "max_forks_repo_name": "bbw7561135/scift", "max_forks_repo_head_hexsha": "0e81e7d29889970d35cc563cc45e9a87fd1cf06d", "max_forks_repo_licenses": ["BSD-3-Clause-Open-MPI", "BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-30T04:43:43.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-30T04:43:43.000Z", "avg_line_length": 13.3, "max_line_length": 40, "alphanum_fraction": 0.6210526316, "num_tokens": 247, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704796847396, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.31384056872063604}} {"text": "###### Data Sorting (STEP 2nd)\n#setwd(\"F:/Stevens/CS 513 - Knowledge Discovery & Data Mining/Project/Flight Delay Prediction/\")\n\n### cleaning environment\nrm(list = ls())\n\n######\n###### 2013, 2014 and 2015\n######\n\n### read whole database of month june and year 2013, 2014, & 2015\ndatabase_13_14_15 <- read.csv(\"Databases/Database_NY_June_2013_2014_2015.csv\");\nView(database_13_14_15);\nattach(database_13_14_15);\n\n### fetched important fields of whole database of month june and year 2013, 2014, & 2015\ndataset_original_13_14_15 <- data.frame(DAY_OF_MONTH, DAY_OF_WEEK, CARRIER, \n ORIGIN, DEST, ARR_DELAY, ARR_DEL15,\n CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY,\n DIV_AIRPORT_LANDINGS)\nView(dataset_original_13_14_15)\nwrite.csv(dataset_original_13_14_15, file = \"Dataframes/Dataset_Original_NY_June_2013_2014_2015.csv\", row.names = FALSE); # saving dataset_original dataframe\ndetach(database_13_14_15); # detaching database dataframe\nattach(dataset_original_13_14_15); # attaching dataset_original dataframe\n\n\n### factorizing dataset (assigning every character value as numeric value)\n# numbering CARRIER using level factor\nCARRIER <- as.factor(CARRIER)\nlevels(CARRIER) <- 1:length(levels(CARRIER))\n# numbering ORIGIN using level factor\nORIGIN <- as.factor(ORIGIN)\nlevels(ORIGIN) <- 1:length(levels(ORIGIN))\n# numbering DEST using level factor\nDEST <- as.factor(DEST)\nlevels(DEST) <- 1:length(levels(DEST))\n\n# refreshing work dataframe\ndataset_factorized_13_14_15 <- data.frame(DAY_OF_MONTH, DAY_OF_WEEK, CARRIER, \n ORIGIN, DEST, ARR_DELAY, ARR_DEL15,\n CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY,\n DIV_AIRPORT_LANDINGS);\nView(dataset_factorized_13_14_15);\nwrite.csv(dataset_factorized_13_14_15, file = \"Dataframes/Dataset_Factorized_NY_June_2013_2014_2015.csv\", row.names = FALSE); \n# saving dataset_original dataframe\ndetach(dataset_original_13_14_15); # detaching database dataframe\n\n### removing unnecessary variables\nrm(database_13_14_15);\nrm(dataset_original_13_14_15);\nrm(dataset_factorized_13_14_15);\nrm(CARRIER);\nrm(DEST);\nrm(ORIGIN);\n\n\n######\n###### 2016\n######\n\n### read whole database of month june and year 2016\ndatabase_16 <- read.csv(\"Databases/Database_NY_June_2016.csv\");\nView(database_16);\nattach(database_16);\n\n### fetched important fields of whole database of month june and year 2016\ndataset_original_16 <- data.frame(DAY_OF_MONTH, DAY_OF_WEEK, CARRIER, \n ORIGIN, DEST, ARR_DELAY, ARR_DEL15,\n CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY,\n DIV_AIRPORT_LANDINGS)\nView(dataset_original_16)\nwrite.csv(dataset_original_16, file = \"Dataframes/Dataset_Original_NY_June_2016.csv\", row.names = FALSE); # saving dataset_original dataframe\ndetach(database_16); # detaching database dataframe\nattach(dataset_original_16); # attaching dataset_original dataframe\n\n\n### factorizing work dataset\n# numbering CARRIER using level factor\nCARRIER <- as.factor(CARRIER)\nlevels(CARRIER) <- 1:length(levels(CARRIER))\n# numbering ORIGIN using level factor\nORIGIN <- as.factor(ORIGIN)\nlevels(ORIGIN) <- 1:length(levels(ORIGIN))\n# numbering DEST using level factor\nDEST <- as.factor(DEST)\nlevels(DEST) <- 1:length(levels(DEST))\n\n# refreshing work dataframe\ndataset_factorized_16 <- data.frame(DAY_OF_MONTH, DAY_OF_WEEK, CARRIER, \n ORIGIN, DEST, ARR_DELAY, ARR_DEL15,\n CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY,\n DIV_AIRPORT_LANDINGS);\nView(dataset_factorized_16);\nwrite.csv(dataset_factorized_16, file = \"Dataframes/Dataset_Factorized_NY_June_2016.csv\", row.names = FALSE); \n# saving dataset_original dataframe\ndetach(dataset_original_16); # detaching database dataframe\n\n### removing unnecessary variables\nrm(database_16);\nrm(dataset_original_16);\nrm(dataset_factorized_16);\nrm(CARRIER);\nrm(DEST);\nrm(ORIGIN)\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "7f2e3b750beb0d6d4849b738fbc7daee05abdfd4", "size": 4515, "ext": "r", "lang": "R", "max_stars_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/2_Data_Sorting.r", "max_stars_repo_name": "ParasGarg/Stevens-Computer-Science-Courses-Materials", "max_stars_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 25, "max_stars_repo_stars_event_min_datetime": "2017-03-23T04:51:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-03T21:51:11.000Z", "max_issues_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/2_Data_Sorting.r", "max_issues_repo_name": "vaishnavimecit/Stevens-Computer-Science-Courses-Materials", "max_issues_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CS 513 - Knowledge Discovery & Data Mining/Project/R/2_Data_Sorting.r", "max_forks_repo_name": "vaishnavimecit/Stevens-Computer-Science-Courses-Materials", "max_forks_repo_head_hexsha": "13015e6e83471d89ae29474857fe83a81994420f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2018-05-10T05:17:05.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-12T05:18:58.000Z", "avg_line_length": 38.5897435897, "max_line_length": 157, "alphanum_fraction": 0.6733111849, "num_tokens": 1070, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5544704649604274, "lm_q1q2_score": 0.3138405603864024}} {"text": "\n# -------------------------------------\n# fecundity estimated from predicted mean fecundity (based upon allometric relationship)\n# and kriged estimated numerical abundance of females ... does not directly account for allometry\n\n fecundity.indirect = function( p, outdir, all.areas=T ) {\n stop(\"Broken: Fix me\")\n\n x = snowcrab.db(DS=\"det.initial\")\n y = snowcrab.db(DS=\"set.biologicals\")[, c(\"trip\", \"set\", \"yr\")]\n x = merge(x, y, by= c(\"trip\",\"set\"), all.x=T, all.y=F, sort=F)\n\n primiparous = filter.class( x, \"primiparous\")\n multiparous = filter.class( x, \"multiparous\")\n\n # estimates of mean fecundity of females in a given year\n fem = data.frame( years= sort(unique( as.numeric(as.character(x$yr)) ) ))\n fem$primi.mean = tapply( x$fecundity[primiparous], INDEX=list( yr=x$yr[primiparous] ), FUN=mean, na.rm=T, simplify=T )\n fem$primi.sd = tapply( x$fecundity[primiparous], INDEX=list( yr=x$yr[primiparous] ), FUN=sd, na.rm=T, simplify=T )\n fem$primi.n = tapply( x$fecundity[primiparous], INDEX=list( yr=x$yr[primiparous] ), FUN=function(d) {length( which(!is.na(d))) }, simplify=T )\n fem$multi.mean = tapply( x$fecundity[multiparous], INDEX=list( yr=x$yr[multiparous] ), FUN=mean, na.rm=T, simplify=T )\n fem$multi.sd = tapply( x$fecundity[multiparous], INDEX=list( yr=x$yr[multiparous] ), FUN=sd, na.rm=T, simplify=T )\n fem$multi.n = tapply( x$fecundity[multiparous], INDEX=list( yr=x$yr[multiparous] ), FUN=function(d) {length( which(!is.na(d))) }, simplify=T )\n\n\n p$vars.to.model = c( \"totno.female.primiparous\", \"totno.female.multiparous\" )\n p$yrs = 1998:p$year.assessment\n \n K = interpolation.db( DS=\"interpolation.simulation\", p=p )\n\n# K = K[ -which( K$yr <= 1998 ), ]\n\n K$vars = as.character(K$vars)\n # K = K[ which(K$vars %in% vars ) ,]\n\n# total abundance estimates\n\n a.sum = function(x, y) tapply( x, y, sum, na.rm=T )\n a.sum( K$total, list(K$yr, K$vars) )\n\n offsets = c(NA,NA)\n\n fem = merge( fem, K[ which( K$vars==\"totno.female.primiparous\") ,], by.x=\"years\", by.y=\"yr\", sort=T, all.x=T, all.y=F )\n fem$v = NULL\n fem = merge( fem, K[ which( K$vars==\"totno.female.multiparous\") ,], by.x=\"years\", by.y=\"yr\", sort=T, all.x=T, all.y=F,\n suffixes=c(\".primi\", \".multi\") )\n fem$v = NULL\n\n # fem[ which(is.na(fem)) ] = 0\n\n fem$total.egg.primi = fem$primi.mean * fem$total.primi\n fem$total.egg.multi = fem$multi.mean * fem$total.multi\n fem$total.egg.all = fem$total.egg.primi + fem$total.egg.mult\n\n # propagate the errors from above operations\n fem$total.egg.primi.sd = fem$total.egg.primi * sqrt( (fem$primi.sd / fem$primi.mean)^2 + ( (0.2*fem$total.primi) /fem$total.primi)^2 )\n fem$total.egg.multi.sd = fem$total.egg.multi * sqrt( (fem$multi.sd / fem$multi.mean)^2 + ( (0.2*fem$total.multi)/fem$total.multi)^2 )\n fem$total.egg.all.sd = sqrt(fem$total.egg.primi.sd ^2 + fem$total.egg.multi.sd^2)\n\n yval = log10(fem$total.egg.all)\n yval[ !is.finite(yval) ] = NA\n fem$upper = log10( fem$total.egg.all + 2 * fem$total.egg.all.sd )\n fem$lower = log10( fem$total.egg.all - 2 * fem$total.egg.all.sd )\n\n datarange = range( c(yval, fem$upper, fem$lower), na.rm=T)\n yrs = range( fem$years, na.rm=T )\n yrs = c(-0.5, 0.5) + yrs\n\n fn = file.path( outdir, \"fecundity_indirect\" )\n\n # Cairo( file=fn, type=\"pdf\", bg=\"white\", units=\"in\", width=6, height=8 )\n\n # plot( fem$years, yval, type=\"b\", xlab=\"Year\", ylab=\"Potential total egg production (log10)\", xlim=yrs, ylim=datarange )\n # arrows( x0=fem$years, y0=fem$upper, x1=fem$years, y1=fem$lower, angle=90, code=3, length=0.05 )\n\n # dev.off()\n # cmd( \"convert -trim -quality 9 -geometry 200% -frame 2% -mattecolor white -antialias \", paste(fn, \"pdf\", sep=\".\"), paste(fn, \"png\", sep=\".\") )\n\n if (all.areas) {\n areas = c(\"cfa4x\", \"cfasouth\", \"cfanorth\" )\n regions = c(\"4X\", \"S-ENS\", \"N-ENS\")\n } else {\n areas = c(\"cfasouth\", \"cfanorth\" )\n regions = c(\"S-ENS\", \"N-ENS\")\n }\n\n n.regions = length(regions)\n n.areas = length(areas)\n\n\n td = fem[ which( as.numeric(as.character(years(fem$datestamp.multi))) == p$year.assessment),]\n\n td$yr = td$years\n td$total = log10(td$total.egg.all)\n td$ubound = log10(td$total.egg.all + 2* td$total.egg.all.sd)\n td$lbound = log10(td$total.egg.all - 2* td$total.egg.all.sd)\n td$region = td$region.primi\n\n xlim = range( td$yr, na.rm=T )\n xlim[1] = xlim[1]-0.5\n xlim[2] = xlim[2]+0.5\n\n yy = \"ln( Total potential fecundity ) \"\n convert = 10^6 # convert from\n eps = 10^-4\n\n\n varstocheck = c(\"total.egg.all\", \"ubound\", \"lbound\")\n for (vs in varstocheck) {\n td[,vs] = td[,vs] / convert\n kk = which(td[,vs] <= eps)\n if (length(kk)>0) td[kk,vs] = 0\n }\n\n td = td[ which( is.finite(td$total)) ,]\n td = td[order(td$yr),]\n td$region = factor(td$region, levels=areas, labels=regions)\n\n fn = file.path( outdir, \"fecuncity_indirect\" )\n Cairo( file=fn, type=\"pdf\", bg=\"white\", units=\"in\", width=6, height=8 )\n\n setup.lattice.options()\n pl = xyplot( total~yr|region, data=td, lbound=td$lbound, ubound=td$ubound,\n layout=c(1,3), xlim=xlim, scales = list(y = \"free\"),\n main=\"Potential Egg Production\", xlab=\"Year\",\n ylab=expression(paste(\"Potential Egg Production ( x\", 10^6, \")\")),\n panel = function(x, y, subscripts, lbound, ubound, ...) {\n # larrows(x, lbound[subscripts], x, ubound[subscripts], angle = 90, code = 3, length=0.02, lwd=3)\n panel.abline(h=mean(y, na.rm=T), col=\"gray40\", lwd=1.5,...)\n panel.xyplot(x, y, type=\"b\", pch=19, lwd=1.5, lty=\"11\", col=\"black\", ...)\n# panel.loess(x,y, span=0.15, lwd=2.5, col=\"darkblue\", ... )\n }\n )\n print(pl)\n dev.off()\n\n cmd( \"convert -trim -quality 9 -geometry 200% -frame 2% -mattecolor white -antialias \", paste(fn, \"pdf\", sep=\".\"), paste(fn, \"png\", sep=\".\") )\n\n table.view( fem)\n\n\n\n\n\n\n return(fem)\n\n }\n\n\n", "meta": {"hexsha": "e70bc16a8d594bd585f6f99e5dbaa1c3b2f945f3", "size": 5978, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fecundity.indirect.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/fecundity.indirect.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/fecundity.indirect.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 39.5894039735, "max_line_length": 151, "alphanum_fraction": 0.6030444965, "num_tokens": 2076, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3137883890606057}} {"text": "install.packages(\"viridis\")\n\nlibrary(viridis)\n\nsource(\"../utility_scripts/data_prep.r\")\nsource(\"../utility_scripts/annotation_defaults.r\")\n\nget_breed_percentage_at_level <- function(levels_filter) {\n breed_percentages <- scent_data %>%\n filter(level %in% levels_filter) %>%\n select(breed, team) %>%\n distinct(team, .keep_all=TRUE) %>%\n mutate(total_dogs = n()) %>%\n group_by(breed) %>%\n summarize(number_of_dogs_this_breed = n(), total_dogs = total_dogs) %>%\n ungroup() %>%\n distinct(breed, .keep_all=TRUE) %>%\n mutate(percentage_this_breed = 100 * (number_of_dogs_this_breed / total_dogs)) %>%\n arrange(desc(percentage_this_breed))\n}\n\nsummit_data <- get_breed_percentage_at_level(\"Summit\") %>%\n select(breed, number_of_dogs_this_breed, percentage_this_breed)\nsummit_breeds <- summit_data$breed\n\nnw1l1_data <- get_breed_percentage_at_level(c(\"NW1\", \"L1\")) %>%\n filter(breed %in% summit_breeds) %>%\n select(breed, number_of_dogs_this_breed, percentage_this_breed) %>%\n filter(number_of_dogs_this_breed > 100)\n\ndata <- nw1l1_data %>%\n rename(number_of_dogs_this_breed_nw1l1 = number_of_dogs_this_breed,\n percentage_this_breed_nw1l1 = percentage_this_breed) %>%\n left_join(\n summit_data %>%\n rename(number_of_dogs_this_breed_summit = number_of_dogs_this_breed,\n percentage_this_breed_summit = percentage_this_breed, ),\n by=\"breed\") %>%\n mutate(improved_percentage =\n 100 * (percentage_this_breed_summit - percentage_this_breed_nw1l1)/percentage_this_breed_nw1l1) %>%\n filter(improved_percentage > 0) %>%\n mutate(percentage_achievers = 100 * number_of_dogs_this_breed_summit / number_of_dogs_this_breed_nw1l1) %>%\n arrange(desc(percentage_achievers)) %>%\n select(breed, number_of_dogs_this_breed_nw1l1, number_of_dogs_this_breed_summit, percentage_achievers)\nwrite_csv(breed_percentage_data, \"test.csv\")\n\nplot <- ggplot(data=data) +\n geom_col(mapping = aes(\n x = percentage_achievers,\n y = fct_reorder(breed, percentage_achievers, min),\n fill = fct_reorder(breed, percentage_achievers, min))) +\n scale_fill_viridis(option=\"turbo\", name=\"Breed\", discrete=TRUE) +\n coord_cartesian(xlim=c(0,6)) +\n scale_x_continuous(breaks = c(0, 1, 2, 3, 4, 5, 6), labels = c(\"\", \"1%\", \"2%\", \"3%\", \"4%\", \"5%\", \"6%\")) +\n annotate(geom=\"curve\", x=4.1, y=12, xend=4.8, yend =11.7, curvature =-0.6, arrow=arrow(angle=10), size = 0.75,\n color = \"orange\") +\n annotate(geom=\"curve\", x=3.25, y=11, xend=4.1, yend =10, curvature = 0.1, arrow=arrow(angle=10), size = 0.75,\n color = \"orange\") +\n annotate(geom=\"text\", x = 4.2, y = 10.4,\n label = paste(\"4% of all English Springer Spaniels\\nthat entered L1/NW1 trials\\nmade it to Summit level\\n\\nAs\",\n \"compared to 3.2% of all\\nLabrador Retrievers that entered\\nL1/NW1 trials\"),\n hjust = \"left\", family = \"mono\", size = 4) +\n annotate(geom=\"text\", x = 3, y = 4,\n label = paste(\"The breeds shown are those that:\\n(1) had 100+ dogs at the L1/NW1 level and\\n(2) had 1+ dog(s)\",\n \"at the Summit level and\\n(3) rose above other breeds, i.e.\\n showed a net increase in\",\n \"proportion of dogs\\n from that breed from L1/NW1 to Summit\"),\n hjust = \"left\", family = \"mono\", size = 4) +\n theme_minimal() +\n theme(\n text = element_text(\"mono\"),\n legend.position = 'none',\n legend.key.height = unit(1.2, \"cm\"),\n axis.title = element_blank(),\n axis.text = element_text(size = 9)\n )\n\nannotated_plot <- getAnnotatedPlot(\n plot,\n title=\"Breed-wise Success\",\n subtitle=\"Percentage of dogs from a given breed that made it to Summit level\")\nannotated_plot\n\nggsave(\"breedwise_success.png\", plot=annotated_plot, path=\".\", width=4194, height=2621, units=\"px\", bg=\"white\")\n\n", "meta": {"hexsha": "4373e87ad9a11f5bc947b365624a14bd0c24ec86", "size": 3824, "ext": "r", "lang": "R", "max_stars_repo_path": "breeds/breedwise_success.r", "max_stars_repo_name": "saylibenadikar/snoot-scoop", "max_stars_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "breeds/breedwise_success.r", "max_issues_repo_name": "saylibenadikar/snoot-scoop", "max_issues_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "breeds/breedwise_success.r", "max_forks_repo_name": "saylibenadikar/snoot-scoop", "max_forks_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9882352941, "max_line_length": 122, "alphanum_fraction": 0.6780857741, "num_tokens": 1156, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3137883826719046}} {"text": "#Making a spatial forecast based on the prior data to NEON sites at core, plot and site levels.\n#downstream this will log transform map, which prevents this from generalizing beyond the dirichlet example.\n#This script depends on the following packages: DirichletReg.\n#clear environment, source paths, packages and functions.\nrm(list=ls())\nsource('paths.r')\nsource('NEFI_functions/tic_toc.r')\nsource('NEFI_functions/precision_matrix_match.r')\nsource('NEFI_functions/ddirch_forecast.r')\n\n#set output path.----\noutput.path <- NEON_site_fcast_all_groups_1k_rare.path\n\n#load model results.----\n#mod 1 is data from maps.\n#mod 2 is site-specific data, no maps.\n#mod 3 is all covariates.\nall.mod <- readRDS(ted_ITS_prior_all.groups_JAGSfits.path)\n#mod <- readRDS(ted_ITS.prior_20gen_JAGSfit)\n#mod <- mod[[3]] #just the all predictor case.\n\n#get core-level covariate means and sd.----\ndat <- readRDS(hierarch_filled.path)\ncore_mu <- dat$core.core.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#merge together.\nplot_mu$siteID <- NULL\ncore.preds <- merge(core_mu , plot_mu)\ncore.preds <- merge(core.preds, site_mu)\ncore.preds$relEM <- NULL\nnames(core.preds)[names(core.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get core-level SD.\ncore_sd <- dat$core.core.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nplot_sd$siteID <- NULL\ncore.sd <- merge(core_sd , plot_sd)\ncore.sd <- merge(core.sd, site_sd)\ncore.sd$relEM <- NULL\nnames(core.sd)[names(core.sd)==\"b.relEM\"] <- \"relEM\"\n\n#get plot-level covariate means and sd.----\ncore_mu <- dat$core.plot.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#merge together, .\nplot_mu$siteID <- NULL\nplot.preds <- merge(core_mu,plot_mu)\nplot.preds <- merge(plot.preds,site_mu)\nplot.preds$relEM <- NULL\nnames(plot.preds)[names(plot.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get plot-level SD.\ncore_sd <- dat$core.plot.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together\nplot_sd$siteID <- NULL\nplot.sd <- merge(core_sd,plot_sd)\nplot.sd <- merge(plot.sd,site_sd)\nplot.sd$relEM <- NULL\nnames(plot.sd)[names(plot.sd)=='b.relEM'] <- \"relEM\"\n\n#get site-level covariate means and sd.----\ncore_mu <- dat$core.site.mu\nplot_mu <- dat$plot.site.mu\nsite_mu <- dat$site.site.mu\n#merge together\nsite.preds <- merge(core_mu, plot_mu)\nsite.preds <- merge(site.preds,site_mu)\nnames(site.preds)[names(site.preds)=='b.relEM'] <- \"relEM\"\n\n#get site-level SD.\ncore_sd <- dat$core.site.sd\nplot_sd <- dat$plot.site.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nsite.sd <- merge(core_sd,plot_sd)\nsite.sd <- merge(site.sd,site_sd)\nnames(site.sd)[names(site.sd)=='b.relEM'] <- \"relEM\"\n\n#Get forecasts from ddirch_forecast.----\nphylo.output <- list()\n\ncat('Making forecasts...\\n')\ntic()\nfor(i in 1:length(all.mod)){\n mod <- all.mod[[i]]\n core.fit <- ddirch_forecast(mod=mod, cov_mu=core.preds, cov_sd=core.sd, names=core.preds$sampleID, n.samp = 1000, map.transform = T, relEM.transform = T)\n plot.fit <- ddirch_forecast(mod=mod, cov_mu=plot.preds, cov_sd=plot.sd, names=plot.preds$plotID , n.samp = 1000, map.transform = T, relEM.transform = T)\n site.fit <- ddirch_forecast(mod=mod, cov_mu=site.preds, cov_sd=site.sd, names=site.preds$siteID , n.samp = 1000, map.transform = T, relEM.transform = T)\n \n #store output as a list and save.----\n output <- list(core.fit,plot.fit,site.fit,core.preds,plot.preds,site.preds,core.sd,plot.sd,site.sd)\n names(output) <- c('core.fit','plot.fit','site.fit',\n 'core.preds','plot.preds','site.preds',\n 'core.sd','plot.sd','site.sd')\n phylo.output[[i]] <- output\n cat(paste0(i,' of ',length(all.mod),' forecasts complete. '))\n toc()\n}\ncat('All forecasts complete.')\ntoc()\n\n#Save output.----\nnames(phylo.output) <- names(all.mod)\nsaveRDS(phylo.output, output.path)\n", "meta": {"hexsha": "f73e7ee91ae0dd1f0141364b048cf622db268a7e", "size": 3806, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/02._global_validation_fcast_ITS.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "data_analysis/ITS/02._global_validation_fcast_ITS.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data_analysis/ITS/02._global_validation_fcast_ITS.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 34.2882882883, "max_line_length": 155, "alphanum_fraction": 0.7062532843, "num_tokens": 1131, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3137883826719046}} {"text": "# Load packages\nlibrary(rliger)\nlibrary(dplyr)\nlibrary(Seurat)\nlibrary(SeuratDisk)\nlibrary(SeuratWrappers)\nlibrary(aricode)\nlibrary(argparse)\nlibrary(reticulate)\nreticulate::use_python(\"python\")\n\nprint_memory_usage <- function() {\n library(reticulate)\n py_run_string('import psutil; pmem = psutil.Process().memory_info(); print(pmem); rss_mb = pmem.rss/1024')\n return(py$rss_mb)\n}\n\nparser <- ArgumentParser()\nparser$add_argument(\"--h5seurat-path\", type = \"character\", help = \"path to the h5seurat file to be processed\")\nparser$add_argument(\"--resolutions\", type = \"double\", nargs = \"+\", default = c(0.002, 0.004, 0.006, 0.01, 0.015, 0.02, 0.025, 0.03), help = \"resolution of leiden/louvain clustering\")\nparser$add_argument(\"--subset-genes\", type = \"integer\", default = 3000, help = \"number of features (genes) to select, 0 for don't select\")\nparser$add_argument(\"--no-eval\", action = \"store_true\", help = \"do not eval\")\nparser$add_argument(\"--seurat\", action = \"store_true\", help = \"use seurat for preprocessing\")\nparser$add_argument(\"--ckpt-dir\", type = \"character\", help=\"path to checkpoint directory\", default = file.path(\"..\", \"results\"))\nparser$add_argument(\"--seed\", type = \"integer\", default = -1, help = \"random seed.\")\nargs <- parser$parse_args()\n\nif (args$seed >= 0) {\n set.seed(args$seed)\n}\n\nlibrary(reticulate)\nreticulate::use_python(\"python\")\nmatplotlib <- import(\"matplotlib\")\nmatplotlib$use(\"Agg\")\nsc <- import(\"scanpy\")\nsc$settings$set_figure_params(\n dpi=120,\n dpi_save=250,\n facecolor=\"white\",\n fontsize=10,\n figsize=c(10, 10)\n)\n\n# Load dataset\ndataset_str <- basename(args$h5seurat_path)\ndataset_str <- substring(dataset_str, 1, nchar(dataset_str) - 9)\nmodel_name <- if (args$seurat) \"LigerSeurat\" else \"Liger\"\nseurat_obj <- LoadH5Seurat(args$h5seurat_path)\nmetadata <- seurat_obj@meta.data\nbatches <- names(table(metadata$batch_indices))\nprint(batches)\ngenes_use <- row.names(seurat_obj@assays$RNA@data)[rowSums(seurat_obj@assays$RNA@data) > 0]\n\nckpt_dir <- file.path(args$ckpt_dir, sprintf(\"%s_%s%d_seed%d_%s\", dataset_str, model_name, args$subset_genes, args$seed, strftime(Sys.time(),\"%m_%d-%H_%M_%S\")))\nif (!dir.exists((ckpt_dir))) {\n dir.create(ckpt_dir)\n}\nscETM <- import(\"scETM\")\nscETM$initialize_logger(ckpt_dir = ckpt_dir)\nanndata <- import(\"anndata\")\n\nfpath <- file.path(ckpt_dir, sprintf(\"%s_%s_seed%d.h5ad\", dataset_str, model_name, args$seed))\n\n# Run algo, print result and save images\nstart_time <- proc.time()[3]\nstart_mem <- print_memory_usage()\n\nif (args$seurat) {\n dataset <- NormalizeData(seurat_obj)\n if (args$subset_genes)\n dataset <- FindVariableFeatures(dataset, nfeatures = args$subset_genes)\n dataset <- ScaleData(dataset, split.by = \"batch_indices\", do.center = FALSE)\n dataset <- RunOptimizeALS(dataset,\n k = 20,\n lambda = 5,\n split.by = \"batch_indices\",\n rand.seed = if (args$seed >= 0) args$seed else 1\n )\n dataset <- RunQuantileNorm(dataset, knn_k = 20, split.by = \"batch_indices\")\n} else {\n dataset_list <- list()\n for (i in seq_along(batches)) {\n matrix_data <- seurat_obj@assays$RNA@data[genes_use, metadata$batch_indices == batches[[i]]]\n dataset_list[[i]] <- matrix_data\n }\n names(dataset_list) <- batches\n dataset <- createLiger(dataset_list, remove.missing = F)\n dataset <- normalize(dataset)\n if (args$subset_genes)\n dataset <- selectGenes(dataset, num.genes = args$subset_genes)\n dataset <- scaleNotCenter(dataset)\n dataset <- optimizeALS(dataset, k = 20, lambda = 5,\n rand.seed = if (args$seed >= 0) args$seed else 1)\n dataset <- quantile_norm(dataset, knn_k = 20)\n}\n\ntime_cost <- proc.time()[3] - start_time\nmem_cost <- print_memory_usage() - start_mem\nwriteLines(sprintf(\"Duration: %.1f s (%.1f min)\", time_cost, time_cost / 60))\n\nif (args$seurat) {\n X <- dataset@reductions$iNMF@cell.embeddings\n obs <- metadata\n processed_data <- anndata$AnnData(\n X = X,\n obs = obs\n )\n} else {\n X <- dataset@H.norm\n obs <- metadata\n uns <- list(V = dataset@V, W = dataset@W)\n processed_data <- anndata$AnnData(\n X = X,\n obs = obs,\n uns = uns\n )\n}\nprocessed_data$write_h5ad(fpath)\n\nif (!args$no_eval) {\n scETM <- import(\"scETM\")\n result <- scETM$evaluate(\n processed_data,\n embedding_key = \"X\",\n resolutions = args$resolutions,\n plot_dir = ckpt_dir,\n plot_fname = sprintf(\"%s_%s_seed%d_eval\", dataset_str, model_name, args$seed),\n n_jobs = 1L\n )\n line <- sprintf(\"%s\\t%s\\t%s\\t%.4f\\t%.4f\\t%.4f\\t%.5f\\t%.5f\\t%.2f\\t%.0f\",\n dataset_str, model_name, args$seed,\n result$ari, result$nmi, result$asw, result$ebm, result$k_bet,\n time_cost, mem_cost)\n write(line, file = file.path(args$ckpt_dir, \"table1.tsv\"), append = T)\n}\n", "meta": {"hexsha": "6796d0d4bb28aabf3bf7bf34bb63796b3beebee8", "size": 4843, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/train_Liger.r", "max_stars_repo_name": "hui2000ji/scETM", "max_stars_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 24, "max_stars_repo_stars_event_min_datetime": "2021-07-09T12:59:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-04T22:31:41.000Z", "max_issues_repo_path": "scripts/train_Liger.r", "max_issues_repo_name": "hui2000ji/scETM", "max_issues_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-09-07T11:14:19.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T01:38:09.000Z", "max_forks_repo_path": "scripts/train_Liger.r", "max_forks_repo_name": "hui2000ji/scETM", "max_forks_repo_head_hexsha": "0a34c345d70b262ebc38e033bae683fa4929ed3e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-12-02T23:44:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-11T16:46:45.000Z", "avg_line_length": 35.3503649635, "max_line_length": 182, "alphanum_fraction": 0.6706586826, "num_tokens": 1405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.31378838267190456}} {"text": "options(warn=-1)\r\noptions(scipen=999)\r\n\r\nsuppressPackageStartupMessages(library(VGAM))\r\nsuppressPackageStartupMessages(library(data.table))\r\n#suppressPackageStartupMessages(library(ggplot2))\r\nsuppressPackageStartupMessages(library(argparse))\r\nsuppressPackageStartupMessages(library(bbmle))\r\nsuppressPackageStartupMessages(library(survcomp)) # Bioconductor \r\n\r\n# Variant calling functions\r\n# FUNCTION\r\ndist_SNP2 <- function(CHROM,POS,CALLS){\r\n distances <- abs(CALLS$POS - POS)\r\n distances <- distances[distances > 0]\r\n CLOSEST_SNP <- min(distances, na.rm = T)\r\n return(CLOSEST_SNP)\r\n}\r\n\r\ndist_SNP <- function(CALLS, SOMATIC_LIKE){\r\n FINAL <- ''\r\n NAMES <- names(table(CALLS$CHROM))\r\n for (chrom in NAMES){\r\n CALLS2 <- CALLS[CALLS$CHROM == chrom,]\r\n SOMATIC_LIKE2 <- SOMATIC_LIKE[SOMATIC_LIKE$CHROM == chrom,]\r\n \r\n CALLS2$DIST <- apply(CALLS2, 1, function(x) dist_SNP2(x[\"CHROM\"], as.numeric(x[\"POS\"]), SOMATIC_LIKE2))\r\n FINAL <- rbind(FINAL, CALLS2) \r\n }\r\n \r\n FINAL <- FINAL[!FINAL$CHROM == '',]\r\n \r\n return(FINAL)\r\n}\r\n\r\nestBetaParams <- function(mu, var) {\r\n alpha <- ((1 - mu) / var - 1 / mu) * mu ^ 2\r\n beta <- alpha * (1 / mu - 1)\r\n return(params = list(alpha = alpha, beta = beta))\r\n}\r\n\r\nMODEL_BETABIN_DP_HQ <- function(DATA){\r\n result <- tryCatch({mle2(ALT_COUNT~dbetabinom.ab(size=DP_HQ,shape1,shape2),\r\n data=DATA,\r\n method=\"Nelder-Mead\",\r\n skip.hessian=TRUE,\r\n start=list(shape1=1,shape2=round(mean(DATA$DP_HQ))),\r\n control=list(maxit=1000))}, \r\n error = function(e) {(estBetaParams(mean(DATA$ALT_COUNT/DATA$DP_HQ, na.rm = T),var(DATA$ALT_COUNT/DATA$DP_HQ, na.rm = T)))})\r\n PARAM1 <- ifelse(is.null(coef(result)[[1]]),result[[1]], coef(result)[[1]])\r\n PARAM2 <- ifelse(is.null(coef(result)[[2]]),result[[2]], coef(result)[[2]])\r\n \r\n return (c(PARAM1, PARAM2)) \r\n} \r\n\r\nP_VAL <- function(SNP, a, b){\r\n p_val <- pzoibetabinom.ab(SNP$ALT_COUNT-1, SNP$DP_HQ, a, b, lower.tail=F)\r\n p_val[p_val < 0] <- 0\r\n return(p_val)\r\n}\r\n\r\nparser <- ArgumentParser()\r\n\r\n# setting parameters\r\nparser$add_argument(\"-t\", \"--tumor_file\", type=\"character\", help=\"Tumor - Read count input file\", metavar=\"file\", nargs=1, required=TRUE)\r\nparser$add_argument(\"-n\", \"--normal_file\", type=\"character\", help=\"Normal - Read count input file\", nargs=1, default = NULL)\r\nparser$add_argument(\"-tr\", \"--train_file\", type=\"character\", help=\"File to trian error rate distribution [optional]\", nargs=1, default = NULL)\r\nparser$add_argument(\"-o\", \"--out_file\", type=\"character\", help=\"output_file\", metavar=\"file\", nargs=1, required=TRUE)\r\nparser$add_argument(\"-b\", \"--bed\", type=\"character\", help=\"Bed file with positions to ignore for modelling\", nargs=1, default = NULL)\r\n\r\n# reading parameters\r\nargs <- parser$parse_args()\r\n\r\nTD <- args$tumor_file\r\n\r\nND <- args$normal_file\r\n\r\nTR <- args$train_file\r\n \r\nbed <- args$bed \r\n\r\nOUTF <- args$out_file\r\n\r\nprint (paste(\"Normal file:\", ND, sep=\" \"))\r\nprint (paste(\"Tumor file:\", TD, sep=\" \"))\r\nprint (paste(\"Out file:\", OUTF))\r\n\r\n\r\n# Training set\r\nif (is.null(TR)){\r\n CONTROL <- as.data.frame(fread(TD))\r\n CONTROL$AB <- CONTROL$ALT_COUNT/CONTROL$DP_HQ\r\n CONTROL <- CONTROL[CONTROL$AB < 0.1,] # Focusing only to non-germline sites\r\n \r\n if (nrow(CONTROL) > 500000) {\r\n CONTROL <- CONTROL[sample(nrow(CONTROL), 500000),]\r\n }\r\n\r\n if (!is.null(bed)){\r\n \r\n BED <- as.data.frame(fread(bed,header = F))\r\n colnames(BED)[1] <- 'CHROM'\r\n colnames(BED)[2] <- 'POS'\r\n \r\n CONTROL <- CONTROL[CONTROL$AB < 0.1 & CONTROL$DP_HQ > 10 & !paste(CONTROL$CHROM,CONTROL$POS,sep = ',') %in% paste(BED$CHROM,BED$POS,sep = ','),]\r\n \r\n } else {\r\n CONTROL <- CONTROL[CONTROL$AB < 0.1 & CONTROL$DP_HQ > 10 & CONTROL$ALT_COUNT <= quantile(CONTROL$ALT_COUNT,0.99),]\r\n }\r\n} else {\r\n CONTROL <- as.data.frame(fread(TR))\r\n CONTROL$AB <- CONTROL$ALT_COUNT/CONTROL$DP_HQ\r\n CONTROL <- CONTROL[CONTROL$AB < 0.1,]\r\n}\r\n\r\n#CONTROL <- CALLS[CALLS$AB < 0.1 & CALLS$DP_HQ > 0 & CALLS$AB > 0,] \r\n#CONTROL <- CONTROL[CONTROL$AB < 0.1 & CONTROL$DP_HQ > 10 & CONTROL$AB > 0,] \r\n#CONTROL <- CONTROL[CONTROL$AB < 0.1 & CONTROL$DP_HQ > 10,] \r\n\r\n## NUCLEOTIDE CHANGES\r\nTG <- c(\"T>G\",\"A>C\")\r\nTA <- c(\"T>A\",\"A>T\")\r\nTC <- c(\"T>C\",\"A>G\")\r\nGT <- c(\"G>T\",\"C>A\")\r\nGA <- c(\"G>A\",\"C>T\")\r\nGC <- c(\"G>C\",\"C>G\")\r\n\r\n\r\n###### SPLIT THEM IN BASE QUALITY RANGES\r\n# Groups of qualities\r\n\r\n# # ONLY INCLUDING VARIANTS \r\n# CONTROL_TG <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TG & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n# CONTROL_TA <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TA & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n# CONTROL_TC <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TC & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n# CONTROL_GT <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GT & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n# CONTROL_GA <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GA & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n# CONTROL_GC <- CONTROL[paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GC & CONTROL$VARIANT_TYPE == \"SNP\",]\r\n\r\n# # ONLY INCLUDING VARIANTS BUT INDELS SEPARATED\r\nCONTROL_TG <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TG & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('T','A') & CONTROL$ALT_COUNT == 0),]\r\n\r\nCONTROL_TA <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TA & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('T','A') & CONTROL$ALT_COUNT == 0),]\r\n\r\nCONTROL_TC <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% TC & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('T','A') & CONTROL$ALT_COUNT == 0),]\r\n\r\nCONTROL_GT <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GT & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('G','C') & CONTROL$ALT_COUNT == 0),]\r\n\r\nCONTROL_GA <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GA & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('G','C') & CONTROL$ALT_COUNT == 0),]\r\n\r\nCONTROL_GC <- CONTROL[(paste(CONTROL$REF,CONTROL$ALT,sep='>') %in% GC & CONTROL$VARIANT_TYPE == \"SNP\")\r\n | (CONTROL$REF %in% c('G','C') & CONTROL$ALT_COUNT == 0),]\r\n\r\n## Estimating parameters for each indels\r\nCONTROL_Indel_A <- CONTROL[(CONTROL$REF %in% c('T','A'))\r\n | (CONTROL$REF %in% c('T','A') & nchar(CONTROL$ALT) > 1)\r\n | (CONTROL$ALT %in% c('T','A') & nchar(CONTROL$REF) > 1),]\r\n\r\nCONTROL_Indel_G <- CONTROL[(CONTROL$REF %in% c('G','C'))\r\n | (CONTROL$REF %in% c('G','C') & nchar(CONTROL$ALT) > 1)\r\n | (CONTROL$ALT %in% c('G','C') & nchar(CONTROL$REF) > 1),]\r\n\r\n\r\n\r\n## Estimating parameters for each nucleotide change when we only have 1 duplicate per barcode group\r\nFIT_DP1.TG <- MODEL_BETABIN_DP_HQ(CONTROL_TG)\r\n\r\nFIT_DP1.TA <- MODEL_BETABIN_DP_HQ(CONTROL_TA)\r\n\r\nFIT_DP1.TC <- MODEL_BETABIN_DP_HQ(CONTROL_TC)\r\n\r\nFIT_DP1.GT <- MODEL_BETABIN_DP_HQ(CONTROL_GT)\r\n\r\nFIT_DP1.GA <- MODEL_BETABIN_DP_HQ(CONTROL_GA)\r\n\r\nFIT_DP1.GC <- MODEL_BETABIN_DP_HQ(CONTROL_GC)\r\n\r\nFIT_DP1.Indel_A <- MODEL_BETABIN_DP_HQ(CONTROL_Indel_A)\r\n\r\nFIT_DP1.Indel_G <- MODEL_BETABIN_DP_HQ(CONTROL_Indel_G)\r\n\r\n\r\n# Getting parameters\r\na_DP1.GA <- (FIT_DP1.GA)[[1]]\r\nb_DP1.GA <- (FIT_DP1.GA)[[2]]\r\n\r\na_DP1.GC <- (FIT_DP1.GC)[[1]]\r\nb_DP1.GC <- (FIT_DP1.GC)[[2]]\r\n\r\na_DP1.GT <- (FIT_DP1.GT)[[1]]\r\nb_DP1.GT <- (FIT_DP1.GT)[[2]]\r\n\r\na_DP1.TA <- (FIT_DP1.TA)[[1]]\r\nb_DP1.TA <- (FIT_DP1.TA)[[2]]\r\n\r\na_DP1.TC <- (FIT_DP1.TC)[[1]]\r\nb_DP1.TC <- (FIT_DP1.TC)[[2]]\r\n\r\na_DP1.TG <- (FIT_DP1.TG)[[1]]\r\nb_DP1.TG <- (FIT_DP1.TG)[[2]]\r\n\r\na_DP1.Indel_A <- (FIT_DP1.Indel_A)[[1]]\r\nb_DP1.Indel_A <- (FIT_DP1.Indel_A)[[2]]\r\n\r\na_DP1.Indel_G <- (FIT_DP1.Indel_G)[[1]]\r\nb_DP1.Indel_G <- (FIT_DP1.Indel_G)[[2]]\r\n\r\nrm(CONTROL)\r\nrm(CONTROL_GA)\r\nrm(CONTROL_GC)\r\nrm(CONTROL_GT)\r\nrm(CONTROL_TA)\r\nrm(CONTROL_TC)\r\nrm(CONTROL_TG)\r\nrm(CONTROL_Indel_A)\r\nrm(CONTROL_Indel_G)\r\n\r\n############\r\n#### SNP Calling\r\n############\r\nCALLS <- as.data.frame(fread(TD))\r\nCALLS$AB <- CALLS$ALT_COUNT/CALLS$DP_HQ\r\n\r\n# Groups of nt changes\r\nCALLS_TG <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% TG,]\r\nCALLS_TA <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% TA,]\r\nCALLS_TC <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% TC,]\r\nCALLS_GT <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% GT,]\r\nCALLS_GA <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% GA,]\r\nCALLS_GC <- CALLS[paste(CALLS$REF,CALLS$ALT,sep='>') %in% GC,]\r\n\r\n#### FOR GA group\r\nCALLS_GA$P_VAL <- P_VAL(CALLS_GA, a_DP1.GA, b_DP1.GA)\r\nCALLS_GA$P_VAL_adj <- p.adjust(CALLS_GA$P_VAL, method = \"bonferroni\")\r\n\r\n#### FOR GC group\r\nCALLS_GC$P_VAL <- P_VAL(CALLS_GC, a_DP1.GC, b_DP1.GC)\r\nCALLS_GC$P_VAL_adj <- p.adjust(CALLS_GC$P_VAL, method = \"bonferroni\")\r\n\r\n#### FOR GT group\r\nCALLS_GT$P_VAL <- P_VAL(CALLS_GT, a_DP1.GT, b_DP1.GT)\r\nCALLS_GT$P_VAL_adj <- p.adjust(CALLS_GT$P_VAL, method = \"bonferroni\")\r\n\r\n#### FOR TA group\r\nCALLS_TA$P_VAL <- P_VAL(CALLS_TA, a_DP1.TA, b_DP1.TA)\r\nCALLS_TA$P_VAL_adj <- p.adjust(CALLS_TA$P_VAL, method = \"bonferroni\")\r\n\r\n#### FOR TC group\r\nCALLS_TC$P_VAL <- P_VAL(CALLS_TC, a_DP1.TC, b_DP1.TC)\r\nCALLS_TC$P_VAL_adj <- p.adjust(CALLS_TC$P_VAL, method = \"bonferroni\")\r\n\r\n#### FOR TG group\r\nCALLS_TG$P_VAL <- P_VAL(CALLS_TG, a_DP1.TG, b_DP1.TG)\r\nCALLS_TG$P_VAL_adj <- p.adjust(CALLS_TG$P_VAL, method = \"bonferroni\")\r\n\r\n# Merge SNP calls\r\nSNP <- rbind(CALLS_GA, CALLS_GC, CALLS_GT, CALLS_TA, CALLS_TC, CALLS_TG)\r\n\r\n\r\n############\r\n#### Indel Calling\r\n############\r\n\r\nIndel_A <- CALLS[(CALLS$REF %in% c('T','A') & nchar(CALLS$ALT) > 1 & CALLS$ALT_COUNT > 0) |\r\n (CALLS$ALT %in% c('T','A') & nchar(CALLS$REF) > 1 & CALLS$ALT_COUNT > 0),]\r\n\r\nIndel_G <- CALLS[(CALLS$REF %in% c('G','C') & nchar(CALLS$ALT) > 1 & CALLS$ALT_COUNT > 0) |\r\n (CALLS$ALT %in% c('G','C') & nchar(CALLS$REF) > 1 & CALLS$ALT_COUNT > 0),]\r\n\r\n# Indel_A\r\nif ( nrow(Indel_A) > 0 ){\r\n Indel_A$P_VAL <- P_VAL(Indel_A, a_DP1.Indel_A, b_DP1.Indel_A)\r\n Indel_A$P_VAL_adj <- p.adjust(Indel_A$P_VAL, method = \"bonferroni\")\r\n}\r\n\r\n# Indel_G\r\nif ( nrow(Indel_G) > 0 ){\r\n Indel_G$P_VAL <- P_VAL(Indel_G, a_DP1.Indel_G, b_DP1.Indel_G)\r\n Indel_G$P_VAL_adj <- p.adjust(Indel_G$P_VAL, method = \"bonferroni\")\r\n}\r\n\r\n############\r\n#### Merge indels and SNPs\r\n############\r\n\r\n#### TO BE DONE\r\nVARIANTS <- rbind(SNP,Indel_A,Indel_G)\r\n\r\n## Fisher strand filter\r\nVARIANTS$FISHER <- apply(VARIANTS,1,function(x) fisher.test(matrix(c(as.numeric(x[\"Ref_fwd\"]), as.numeric(x[\"Alt_fwd\"]), as.numeric(x[\"Ref_rev\"]), as.numeric(x[\"Alt_rev\"])), nrow = 2))[[1]])\r\nVARIANTS$FISHER <- p.adjust(VARIANTS$FISHER, method = \"bonferroni\")\r\n\r\n\r\n## DP1-DP2 strand filter\r\n#VARIANTS$FISHER_RATIO <- apply(VARIANTS,1,function(x) fisher.test(matrix(c(as.numeric(x[\"DP_DP1\"]), as.numeric(x[\"DP_DP2\"]), as.numeric(x[\"ALT_COUNT_DP1\"]), as.numeric(x[\"ALT_COUNT_DP2\"])), nrow = 2))[[1]])\r\n#VARIANTS$P_RATIO <- pbinom(VARIANTS$ALT_COUNT_DP2,VARIANTS$ALT_COUNT,prob = MU, lower.tail = T)\r\n\r\n# Distance to somatic like mutations (those ones that are outside of the Error Distribution)\r\nSOMATIC_LIKE <- VARIANTS[VARIANTS$P_VAL_adj < 0.1,]\r\n\r\nVARIANTS <- dist_SNP(VARIANTS,SOMATIC_LIKE)\r\n\r\nCONTROL <- NULL\r\n\r\n\r\n############\r\n#### Merging results with normal tissue sites\r\n############\r\n\r\n# Loading normal file \r\n\r\nif (!is.null(ND)){\r\n ND <- args$normal_file\r\n \r\n ND <- as.data.frame(fread(ND))\r\n \r\n ND$AB <- ND$ALT_COUNT/ND$DP_HQ\r\n\r\n # Getting fastly alpha and beta for beta-binamial distribution from normal tissue\r\n FIT_DP1.ND <- MODEL_BETABIN_DP_HQ(ND[ND$AB < 0.1,])\r\n\r\n a_DP1.ND <- FIT_DP1.ND[[1]]\r\n b_DP1.ND <- FIT_DP1.ND[[2]]\r\n\r\n #### FOR TC group\r\n ND$P_VAL <- P_VAL(ND, a_DP1.ND, b_DP1.ND)\r\n \r\n ND_NO_VARIANTS <- ND[ND$ALT_COUNT == 0,]\r\n ND_VARIANTS <- ND[ND$ALT_COUNT > 0,]\r\n \r\n ND_NO_VARIANTS$nP_VAL_adj <- 1\r\n ND_VARIANTS$nP_VAL_adj <- p.adjust(ND_VARIANTS$P_VAL, method = \"bonferroni\")\r\n \r\n ND <- rbind(ND_NO_VARIANTS,ND_VARIANTS)\r\n \r\n # Keep only columns that we are interested\r\n nd <- ND[,c(\"CHROM\",\"POS\", \"DP_HQ\",\"ALT_COUNT\", \"nP_VAL_adj\")]\r\n colnames(nd) <- c(\"CHROM\",\"POS\",\"DP_HQ_N\",\"ALT_COUNT_N\",\"nP_VAL_adj\")\r\n \r\n nd$AB_N <- nd$ALT_COUNT_N / nd$DP_HQ_N\r\n \r\n # Merge with posible somatic variants (VARIANTS)\r\n nd_temp <- nd[nd$POS %in% VARIANTS$POS,]\r\n \r\n FINAL <- merge(VARIANTS,nd_temp, by=c(\"CHROM\",\"POS\"),all.x = T)\r\n} else {\r\n FINAL <- VARIANTS\r\n}\r\n\r\n############\r\n#### Printing results\r\n############\r\n\r\nwrite.table(x=FINAL, file=OUTF, sep='\\t', quote=F, row.names=F,col.names=T)\r\n", "meta": {"hexsha": "ba55c8f393b7a729c5f7dc3c2117e74cf8526121", "size": 12683, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/Step1/tsv_betabinomial.r", "max_stars_repo_name": "Francesc-Muyas/RnaMosaicMutationFinder", "max_stars_repo_head_hexsha": "c8d56603dcc8f5fe70c80b77300ca15fb97acf44", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-01-15T03:37:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-15T03:37:30.000Z", "max_issues_repo_path": "scripts/Step1/tsv_betabinomial.r", "max_issues_repo_name": "yandgong307/RnaMosaicMutationFinder", "max_issues_repo_head_hexsha": "c8d56603dcc8f5fe70c80b77300ca15fb97acf44", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/Step1/tsv_betabinomial.r", "max_forks_repo_name": "yandgong307/RnaMosaicMutationFinder", "max_forks_repo_head_hexsha": "c8d56603dcc8f5fe70c80b77300ca15fb97acf44", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-09-29T13:20:17.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-13T08:41:12.000Z", "avg_line_length": 35.2305555556, "max_line_length": 208, "alphanum_fraction": 0.6201214224, "num_tokens": 4322, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3137805619932754}} {"text": "##plots=group\n##Layer=vector\n##Data=Field Layer\n##Data_Name=string data\n##showplots\n\nlibrary(ggplot2)\n\nDF <- ggplot(NULL, aes(x= Layer[[Data]])) +\n geom_histogram(fill= \"red\", \n colour= \"black\") +\n labs(x = Data_Name)\n\nplot(DF)\n", "meta": {"hexsha": "b49885bf632ae9b70f5793adb7bd7d517c6a07c8", "size": 258, "ext": "rsx", "lang": "R", "max_stars_repo_path": "Rscripts/Histogram.rsx", "max_stars_repo_name": "klauswiese/QGIS-R", "max_stars_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/Histogram.rsx", "max_issues_repo_name": "klauswiese/QGIS-R", "max_issues_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/Histogram.rsx", "max_forks_repo_name": "klauswiese/QGIS-R", "max_forks_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 17.2, "max_line_length": 43, "alphanum_fraction": 0.5968992248, "num_tokens": 75, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982043529715, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3137805488189715}} {"text": "processing_Read_pipeup_at_5p_end_of_identified_small_RNA_loci<- function(fprefix='input',wdir=\".\") {\r\n\r\nstart_time <- proc.time()\r\ncat(\"Read pileup at 5' end of segmented loci (M3.09) start\", date(), \"\\n\") \r\n \r\n# Plots for Module 3 Analysis and visualization of SPAR output \r\n# Session: Segmentation characteristics \r\n\r\n# Module_3_Figure_9(Figure 3.09) \t\r\n# Description: Read pipeup at the 5p end point of segmented loci across all sncRNA classes\r\n# input: input_annot.with_conservation.xls, input.unannot.final.with_conservation.xls\r\n# output: Read_pipeup_at_5p_end_of_identified_small_RNA_loci.png / Read_pipeup_at_5p_end_of_identified_small_RNA_loci.pdf\r\n\r\n# parameters for the plot \r\ndatafile1=paste(wdir, \"/\", fprefix, \"_annot.with_conservation.xls\", sep=\"\")\r\ndatafile2=paste(wdir, \"/\", fprefix, \"_unannot.with_conservation.xls\", sep=\"\")\r\nBASENAME=\"Read_pipeup_at_5p_end_of_identified_small_RNA_loci\"\r\nPLOTTITLE=\"Normalized entropy at 5p end of loci\"\r\nXTITLE=\"sncRNA_classes\"\r\nYTITLE=\"Normalized_peak_entropy_5p_read_end\"\r\n\r\n# libraries needed \r\n#suppressPackageStartupMessages(library(reshape2))\r\n#suppressPackageStartupMessages(library(ggplot2))\r\n#suppressPackageStartupMessages(library(plyr))\r\nlibrary(reshape2)\r\nlibrary(ggplot2)\r\nlibrary(plyr)\r\n#args<-commandArgs(TRUE)\r\n#datafile1=args[1] # input file 1\r\n#datafile2=args[2] # input file 2\r\n#wdir=args[3] # output / working directory\r\n#if (length(args)<1) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<2) { stop(\"ERROR: No input! USAGE: script inputfile \")}\r\n#if (length(args)<3) { wdir=\".\" } \r\n#use current dir if no \r\n#working dir has been specified\r\n\r\n# output image file\r\npngfile= paste(wdir, \"/../figures/\", paste(BASENAME,\".png\",sep=\"\"), sep=\"\")\r\n#pdffile= paste(wdir, \"/\", paste(BASENAME,\".pdf\",sep=\"\"), sep=\"\")\r\nprint(pngfile)\r\nflush.console()\r\n\r\n# Read in data\r\nD = read.table(datafile1,sep='\\t',header=T,comment.ch=\"\")\r\nE = read.table(datafile2,sep='\\t',header=T,comment.ch=\"\")\r\nE$annotRNAclass = \"unannot\"\r\n#DE = rbind(D[,c(1:20,25,29:31)],E) \r\nDE = rbind(D,E)\r\n\r\nDE$Peak_length = DE$peakChrEnd-DE$peakChrStart\r\nDX = DE[DE$Peak_length<=44,]\r\nDXE = DE[,c(\"annotRNAclass\",\"peakProportionOfReadsAtMostCommon5pPosition\")]\r\nDXE_melt = melt(DXE,id=1) \r\ncolnames(DXE_melt)= c(XTITLE,\"Category\",YTITLE)\r\n\r\ndata_summary <- function(x) {\r\n m <- mean(x)\r\n ymin <- m-sd(x)\r\n ymax <- m+sd(x)\r\n return(c(y=m,ymin=ymin,ymax=ymax))\r\n}\r\n\r\n\r\n# make plot \r\npng(pngfile,width = 7, height = 7, units = 'in', res = 300, type=\"cairo\")\r\noptions(scipen=10000)\r\nprint(ggplot(DXE_melt, aes(x=DXE_melt[,XTITLE], y=DXE_melt[,YTITLE])) + \r\n\t\tstat_summary(fun.data=data_summary) + theme_classic()+ \r\n\t\t# stat_summary(fun.data=\"mean_sdl\", geom=\"crossbar\", width=0.5)+\r\n\t\t# geom_boxplot(width=.1)\r\n\t\tgeom_boxplot(outlier.shape = NA,position=position_dodge(0.75))+\r\n\t\tggtitle(PLOTTITLE) + xlab(XTITLE)+ylab(YTITLE)+\r\n\t\tylim(0,1)+\r\n\t\ttheme(text = element_text(size=20),axis.text.x = element_text(angle=90, vjust=0.5, hjust=1)))\r\n#ylim1=boxplot.stats(DXE_melt[,YTITLE])$stats[c(1, 5)]\r\n\r\n#print(p0+coord_cartesian(ylim = ylim1*1.05))\r\ndev.off()\r\n\r\ncat(\"Total time for read pileup at 5' end of loci (M3.09) analysis:\", (proc.time() - start_time)[['elapsed']], \"seconds (\", date(), \")\\n\")\r\n\r\n}\r\n\r\n# processing_Read_pipeup_at_5p_end_of_identified_small_RNA_loci(fprefix=fprefix,wdir=wdir)\r\n", "meta": {"hexsha": "d36f378caca9b78d467634ff9533caad51f967d3", "size": 3386, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/R/module3/M3.09_processing_Read_pipeup_at_5p_end_of_identified_small_RNA_loci.r", "max_stars_repo_name": "ConYel/spar_pipeline", "max_stars_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-12-03T10:07:54.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-03T10:07:54.000Z", "max_issues_repo_path": "scripts/R/module3/M3.09_processing_Read_pipeup_at_5p_end_of_identified_small_RNA_loci.r", "max_issues_repo_name": "ConYel/spar_pipeline", "max_issues_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-12-09T03:48:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-08T13:35:31.000Z", "max_forks_repo_path": "scripts/R/module3/M3.09_processing_Read_pipeup_at_5p_end_of_identified_small_RNA_loci.r", "max_forks_repo_name": "ConYel/spar_pipeline", "max_forks_repo_head_hexsha": "26685700f498b256c795a33c4923b65f70d76bcf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9195402299, "max_line_length": 139, "alphanum_fraction": 0.7114589486, "num_tokens": 1058, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3137650671384208}} {"text": "## 2. Programming in R ##\n\n125-3\n\n## 3. Literal values: Logicals ##\n\nTRUE\nFALSE\nT\nF\n\n## 4. Literal values: Numerics ##\n\nTRUE\nFALSE\n0.012\n\n## 5. Literal values: Integers ##\n\nFALSE\n125\n\n## 6. Literal values: Character ##\n\n\"Disagree\"\n\"Disagree\"\n\"Disagree\"\n\"Agree\"\n\n## 7. Arithmetic operators: simple expression ##\n\n24.90 - 21.15\n3L * 24.90\nFALSE\n\n## 8. R syntax ##\n\nFALSE\nTRUE\nFALSE", "meta": {"hexsha": "15617a3377417a13e77bf90e5330c1fb1d74ff4c", "size": 379, "ext": "r", "lang": "R", "max_stars_repo_path": "Data Analyst in R/Step 1 - Introduction to R/1. Introduction to Data Analysis in R/1. Introduction to Programming in R.r", "max_stars_repo_name": "MyArist/Dataquest", "max_stars_repo_head_hexsha": "d0ee0a2a5e9d1f69f09bf0f6c32f382b6fa46b18", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2020-07-27T12:04:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-01T04:39:33.000Z", "max_issues_repo_path": "Data Analyst in R/Step 1 - Introduction to R/1. Introduction to Data Analysis in R/1. Introduction to Programming in R.r", "max_issues_repo_name": "myarist/Dataquest", "max_issues_repo_head_hexsha": "d0ee0a2a5e9d1f69f09bf0f6c32f382b6fa46b18", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Data Analyst in R/Step 1 - Introduction to R/1. Introduction to Data Analysis in R/1. Introduction to Programming in R.r", "max_forks_repo_name": "myarist/Dataquest", "max_forks_repo_head_hexsha": "d0ee0a2a5e9d1f69f09bf0f6c32f382b6fa46b18", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2021-03-30T06:45:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-17T03:55:02.000Z", "avg_line_length": 9.475, "max_line_length": 48, "alphanum_fraction": 0.6490765172, "num_tokens": 135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3137650671384207}} {"text": "#' simulateGP: Functions for Simulating Genotype-Phenotype Relationships\n#'\n#' This package is a collection of utilities relating to simulating genotype phenotype maps\n#' The simulations are largely used for Mendelian randomisation, and link up with the TwoSampleMR R package\n#'\n#' **Full documentation available here:** [https://explodecomputer.github.io/simulateGP](https://explodecomputer.github.io/simulateGP)\n#'\n#' @name simulateGP-package\n#' @aliases simulateGP simulategp\n#' @docType package\nNULL\n", "meta": {"hexsha": "1f597cdd98d4049007a6bb02e1afd1bdb5d98b40", "size": 504, "ext": "r", "lang": "R", "max_stars_repo_path": "R/simulateGP-package.r", "max_stars_repo_name": "explodecomputer/simulateGP", "max_stars_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-05T16:58:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-05T16:58:46.000Z", "max_issues_repo_path": "R/simulateGP-package.r", "max_issues_repo_name": "aeaswar81/simulateGP", "max_issues_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-20T16:14:15.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-20T16:14:15.000Z", "max_forks_repo_path": "R/simulateGP-package.r", "max_forks_repo_name": "aeaswar81/simulateGP", "max_forks_repo_head_hexsha": "9dd10532644f9ddb1ce5067d253f473fe4aaeb92", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-03-10T19:27:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-05T16:59:02.000Z", "avg_line_length": 42.0, "max_line_length": 134, "alphanum_fraction": 0.7896825397, "num_tokens": 115, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3137650671384207}} {"text": "# server.R\nload('model.rda')\n\nshinyServer(function(input, output) {\n output$roc_plot <- \n renderPlot({roc_plot})\n \n \n output$predicted_diabetes <-\n renderText({\n features <- data.frame(\n pregnant = input$pregnant,\n glucose = input$glucose,\n pressure = input$pressure,\n triceps = input$triceps,\n insulin = input$insulin,\n mass = input$mass,\n pedigree = input$pedigree,\n age = input$age\n )\n print(features)\n paste('Risk of diabetes:',\n predict(model, features, type = \"prob\")[[2]])\n })\n \n output$explainer <- \n renderPlot({\n features <- data.frame(\n pregnant = input$pregnant,\n glucose = input$glucose,\n pressure = input$pressure,\n triceps = input$triceps,\n insulin = input$insulin,\n mass = input$mass,\n pedigree = input$pedigree,\n age = input$age\n )\n \n explainer <- lime(test.data, model, n_bins = 5)\n \n explanation <- lime::explain(x = features,\n explainer = explainer,\n n_permutations = 5000,\n dist_fun = 'gower',\n kernel_width = .75,\n n_features = 8,\n feature_select = 'highest_weights',\n labels = 'pos')\n # explanation[, 2:9]\n \n plot_features(explanation, ncol = 1)\n \n \n })\n \n \n})\n", "meta": {"hexsha": "1ae56da301b5e57a75a2a2042e98d408b0926804", "size": 1545, "ext": "r", "lang": "R", "max_stars_repo_path": "server.r", "max_stars_repo_name": "johnaclouse/shiny-random-forest", "max_stars_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-02-27T09:53:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-07T23:50:53.000Z", "max_issues_repo_path": "server.r", "max_issues_repo_name": "johnaclouse/shiny-random-forest", "max_issues_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "server.r", "max_forks_repo_name": "johnaclouse/shiny-random-forest", "max_forks_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-08-16T16:20:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-27T09:53:00.000Z", "avg_line_length": 26.6379310345, "max_line_length": 70, "alphanum_fraction": 0.4796116505, "num_tokens": 336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.31375546462626147}} {"text": "library(raster)\r\nlibrary(gitBasedProjects)\r\nlibrary(rasterPlot)\r\nlibrary(rasterExtras)\r\nsource(\"libs/return_multiple_from_functions.r\")\r\n\r\nsetupProjectStructure()\r\ndata_dir = 'data/driving_Data/'\n\r\nfnames_xy = c(\"MAP (mm/yr)\" = \"MAP_MSWEP.nc\",\n\t \"Tree cover (%)\" = \"TreeCover.nc\")\n\t\t\t \nxyscale = c(1, 100) \n\nfnames_z = c(\"None\" = \"TreeCover.nc\",\n \"Temperature (~DEG~C)\" = \"MAT.nc\",\n \"Annual burnt area (%)\" = \"BurntArea.nc\",\n \"Rainfall distribution\" = \"MADD_MSWEP.nc\",\n \"Max. Temperature (~DEG~C)\" = \"MTWM.nc\",\n\t \"Population density (/k~m2~)\" = \"PopDen.nc\",\n\t \"Urban area (%)\" = \"urban.nc\",\n\t \"Cropland cover (%)\" = \"crop.nc\",\n\t \"Pasture cover (%)\" = \"pas.nc\")\n\nzscale = c(1, 1, 1200, 1, 1, 1, 100, 100, 100)\n\ntas_colour = c('#5e4fa2', '#66c2a5', '#96A558', '#Ae904b', '#f46d43', '#9e0142')\t \ncols = list(c(\"black\"),\n tas_colour,\n c(\"black\", \"#999900\", \"#FF0000\"),\n c('#00000b', '#05A0FF'),\n tas_colour,\n\t\t c(\"black\", \"blue\"),\n\t\t c(\"black\", \"purple\"),\n\t\t c(\"black\", \"green\"),\n\t\t c(\"black\", \"brown\"))\n\nlimits = list(None = NaN,\n tas = c(22, 24, 26, 28),\n fire = c(0.1, 1, 2, 5, 10, 20, 50),\n drought = seq(0.2, 0.8, 0.2),\n tasmax = c(26, 28, 30, 32),\n\t popdens = c(1, 10, 100, 1000),\n\t urban = c(0.01, 0.1, 1, 10),\n\t crop = c(2, 5, 10, 20, 40),\n\t pas = c(2, 5, 10, 20, 40))\n\t\t\t \nextent = c(-180, 180, -30, 30)\n\nopenFiles <- function(fnames, scale) {\n\tfnames = paste(data_dir, fnames, sep = '/')\n\tdat = lapply(fnames, function(i) raster(i))\n\t#dat = lapply(dat, mean)\n\t\n\tdat = lapply(dat, raster::crop, extent(extent))\n\tdat = mapply('*', dat, scale)\n\treturn(dat)\t\n}\n\nc(x, y) := openFiles(fnames_xy, xyscale)\ny[is.na(x)] = NaN\nc(xlab, ylab) := names(fnames_xy)\n\nzs = openFiles(fnames_z, zscale)\nznames = names(fnames_z)\n\nplot_3way <- function(z, name, limits, cols, xaxis, yaxis) {\n mask = !is.na(x + y + z)\n x = x[mask]\n y = y[mask]\n z = z[mask]\n\n plot(x, y, xlab = '', ylab = '', xlim = c(0, 4000), axes = FALSE, type = 'n')\n\t\n if (name != \"None\") { \n\tz0 = z\n\tz = cut_results(z, limits)\n \tcols = colsFull =\n\t make_col_vector(cols, ncols = length(limits) + 1, whiteAt0 = FALSE)\n\t \n\tcols = make.transparent(cols, 0.99)\n\tcols = cols[z]\n for (cex in c(1, 0.8, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.03, 0.02, 0.01))\n points(x, y, col = cols, cex = cex, pch = 20)\n } else {\n mask = x > 0 & y > 0\n x = x[mask]\n y = y[mask]\n cols = blues9[unlist(mapply(rep, 1:9, 9 + (1:9)^3))]\n cols = densCols(x,y, colramp = colorRampPalette(cols))\n points(y~x, col = cols, pch = 20)\n }\n \n if (xaxis) axis(1)\n if (yaxis) axis(2)\n if (name != \"None\") {\n mtext.units(name)\n\t\n\tto = rep('-', length(limits) - 1)\n limits = c('<', limits, '>')\n\tto = c(' ', to, ' ')\n legend = paste(head(limits, -1), to, limits[-1], sep = '')\n\tlegend('topleft', legend, pch = 18, col = colsFull, cex = 0.8)\n }\n}\n\n\npng('figs/MAP_vs_Tree_vs_vars.png', width = 7, height = 7, units = 'in', res = 300)\r\n lmat = rbind(c(3:5, 0), c(6:8, 0), c(9:11, 1), c(0, 0, 2, 0))\n\n layout(lmat, widths = c(1, 1, 1, 0.5), heights = c(1, 1, 1, 0.5))\n\tpar(mar = c(0, 1, 3, 0), oma = c(3.5, 3.5, 0, 0))\n\n\tyhist = hist(y[y > 0], 100, plot = FALSE)\n\tbarplot(yhist$density, horiz = TRUE, axes = FALSE)\n\n\txhist = hist(x[x > 0 & x < 4000], 100, plot = FALSE)\n\tbarplot(xhist$density, axes = FALSE, ylim = rev(range(xhist$density)))\n par(mar = c(0, 0, 2, 0))\n\tmapply(plot_3way, zs, znames, limits, cols, c(F, F, F, F, F, F, T,T, T), \n c(T, F, F, T, F, F, T, F, F))\n\n\tmtext.units(xlab, side = 1, line = -4, outer = TRUE, adj = 1.5/3.5)\n\tmtext.units(ylab, outer = TRUE, side = 2, line = 2.3, adj = 1.5/3.5)\n\ndev.off()#.gitWatermark()\n\n", "meta": {"hexsha": "a10746c306122831dfc581c6875ced508b874423", "size": 4068, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_MA_vs_fire.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_MA_vs_fire.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_MA_vs_fire.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 31.78125, "max_line_length": 96, "alphanum_fraction": 0.5027040315, "num_tokens": 1538, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318194686359, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3137554572192785}} {"text": "# James Rekow\r\n\r\ntreatmentDFListArrayCreator = function(M = 80, N = 20, iStrength = 1, univ = 1, sigmaMax = 0.1,\r\n thresholdMult = 10 ^ (-1), maxSteps = 10 ^ 4, tStep = 10 ^ (-2),\r\n intTime = 1, interSmplMult = 0.01, lambda = 10 ^ (-1),\r\n synthM = floor(M / 4), numSynthChrt = 10, numSample = 100,\r\n maxIters = 100){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS:\r\n \r\n source(\"conGraphCreator.r\")\r\n source(\"DOCNSDFListCreator.r\")\r\n \r\n lambdaVec = c(0.05, 0.1, 0.15)\r\n graphTypeVec = c(\"smallWorld\", \"scaleFree\", \"gnp\", \"gnm\")\r\n meanDegreeVec = c(2, 4, 6)\r\n \r\n x1 = 1:length(lambdaVec)\r\n x2 = 1:length(graphTypeVec)\r\n x3 = 1:length(meanDegreeVec)\r\n \r\n g = function(x, y) sapply(1:length(x), function(n) list(append(x[[n]], y[[n]])))\r\n \r\n coordArray = outer(x1, outer(x2, x3, g), g)\r\n \r\n produceTreatmentDF = function(coords){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS:\r\n \r\n coords = unlist(coords)\r\n lambda = lambdaVec[coords[1]]\r\n graphType = graphTypeVec[coords[2]]\r\n meanDegree = meanDegreeVec[coords[3]]\r\n \r\n conGraph = conGraphCreator(graphType = graphType, meanDegree = meanDegree, v = M)\r\n \r\n \r\n \r\n } # end produceTreatmentDF function\r\n \r\n return(treatmentDFListArray)\r\n \r\n} # end treatmentDFListArrayCreator function\r\n", "meta": {"hexsha": "15f3dbc0bfcae9afaaad12c16e0a47870291e85c", "size": 1421, "ext": "r", "lang": "R", "max_stars_repo_path": "treatmentDFArrayCreator.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "treatmentDFArrayCreator.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "treatmentDFArrayCreator.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6041666667, "max_line_length": 104, "alphanum_fraction": 0.5341308937, "num_tokens": 426, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593452091673, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3136611049543905}} {"text": "# Copyright Syncfusion Inc. 2001 - 2016. All rights reserved.\n# Use of this code is subject to the terms of our license.\n# A copy of the current license can be obtained at any time by e-mailing\n# licensing@syncfusion.com. Any infringement will be prosecuted under\n# applicable laws. \n\n\n# If you are not familiar with R you can obtain a quick introduction by downloading\n# R Succinctly for free from Syncfusion - http://www.syncfusion.com/resources/techportal/ebooks/rsuccinctly\n# R Succinctly is also included with this installation and is available here\n# Installed Drive :\\Program Files (x86)\\Syncfusion\\Essential Studio\\XX.X.X.XX\\Infrastructure\\EBooks\\R_Succintly.pdf OF R Succinctly\n# Uncomment below lines to install necessary packages if not installed already\n#install.packages(\"gbm\")\n#install.packages(\"devtools\")\n#install.packages(\"rJava\")\n#install.packages(\"githubinstall\")\n#install.packages(\"KMsurv\")\n#install.packages(\"survival\")\n#Use 'r2pmml' package version v0.3.0 or below since, the function 'coxph' has been deprecated in later versions. \n#library(githubinstall)\n#gh_install_packages(\"jpmml/r2pmml\", ref=\"0.3.0\")\n\n# Load below packages\nlibrary(devtools)\nlibrary(gbm)\nlibrary(rJava)\nlibrary(r2pmml)\nlibrary(KMsurv) #This package is specifically loaded for bfeed data shipped within it. \nlibrary(survival)\n\n# Here we directly load the bfeed dataset installed with the \"KMsurv\" package.\ndata(bfeed)\n\n# rename column names for bfeed dataset from KMsurv package \nbfeedOriginal <- setNames(bfeed, c(\"Duration\", \"Bfeed_Indicator\", \"Race\", \"Is_Poor\", \"Smoker\", \"Alcoholic\", \"Age\", \"Year\", \"Education_Level\", \"Prenatal_Care\"))\n\n# Code below demonstrates loading the same dataset from a CSV file shipped with our installer.\n# Please check installed samples (Data) location to set actual working directory \n# Uncomment below lines and comment out the code to read data from CSV file.\n# setwd(\"C:/actual_data_location\")\n# bfeed= read.csv(\"bfeed.csv\")\n\n# Divide dataset for training and test\ntrainData=bfeedOriginal[1:741,]\ntestData=bfeedOriginal[742:927,]\n\n# Applying Genenalized Boosting Model - coxph distribution to predict Survival\nbfeed_GBM = gbm(Surv(Duration,Bfeed_Indicator)~Race+Is_Poor+Smoker+Alcoholic+Age+Year+Education_Level+Prenatal_Care, \n data=trainData, distribution=\"coxph\",cv.folds=5,verbose=FALSE)\n\n# Display the predicted results and create cross table to check on accuracy\n# Predict \"Survival\" column probability for test data set\nbfeedTestProbabilities = predict.gbm(bfeed_GBM, newdata=testData, n.trees=100,type=\"response\")\n# Display predicted probabilities\nbfeedTestProbabilities\n\n# PMML generated will be of version v4.2 since, older version(v0.3.0) 'r2pmml' has been loaded. \nr2pmml(bfeed_GBM ,\"Bfeed.pmml\")\n\n# The code below is used for evaluation purpose. \n# The model is applied for original bfeed data set and predicted results are saved in \"ROutput.csv\"\n# \"ROutput.csv\" file used for comparing the R results with PMML Evaluation engine results\n\n# Applying GBM model to entire dataset and save the results in a CSV file\nbfeedEntirePrediction = predict.gbm(bfeed_GBM, type = \"response\",bfeedOriginal)\n\n# Save predicted value in a data frame\nresult = data.frame(bfeedEntirePrediction)\nnames(result) = c(\"Predicted_Survival\")\n\n# Write the results in a CSV file\nwrite.csv(result, \"ROutput.csv\" , quote=F)\n\n\n", "meta": {"hexsha": "6065852aa8e2b58e44ee0cbf14a735d2036b40ca", "size": 3345, "ext": "r", "lang": "R", "max_stars_repo_path": "common/Analytics/Gradient Boosting Model/BfeedCoxph/Model/Bfeed.r", "max_stars_repo_name": "aTiKhan/winforms-demos", "max_stars_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 103, "max_stars_repo_stars_event_min_datetime": "2018-11-08T07:10:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T06:20:59.000Z", "max_issues_repo_path": "common/Analytics/Gradient Boosting Model/BfeedCoxph/Model/Bfeed.r", "max_issues_repo_name": "aTiKhan/winforms-demos", "max_issues_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2018-11-12T20:02:11.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-15T06:45:35.000Z", "max_forks_repo_path": "common/Analytics/Gradient Boosting Model/BfeedCoxph/Model/Bfeed.r", "max_forks_repo_name": "aTiKhan/winforms-demos", "max_forks_repo_head_hexsha": "ba27b6748fa0723dcd42906ee58cb0e944291e51", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 95, "max_forks_repo_forks_event_min_datetime": "2018-10-23T08:37:10.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T07:56:59.000Z", "avg_line_length": 44.6, "max_line_length": 159, "alphanum_fraction": 0.7811659193, "num_tokens": 871, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.31366109789601915}} {"text": "#!/usr/bin/Rscript\n\nlibrary(tidyverse)\nlibrary(Rfast)\n#library(vegan) #needs to installed globally\n## file loading\n\nmgnify_associations <- read_delim(\"/data/databases/scripts/gathering_data/mgnify/mgnify_markergene_associations.tsv\", delim = \"\\t\", col_names = F) %>% select(-10)\ncolnames(mgnify_associations) <- c(\"type_1\",\"term_1\",\"type_2\",\"term_2\",\"source\",\"evidence\",\"MI\",\"status\",\"url\")\n\nmgnify_sample_entity_sources <- read_delim(\"/data/databases/scripts/gathering_data/mgnify/sample_entity_sources.tsv\", delim = \"\\t\", col_names = F)\ncolnames(mgnify_sample_entity_sources) <- c(\"sample_id\",\"type\",\"term_id\")\n\nmgnify_taxon_sample <- read_delim(\"/data/databases/scripts/gathering_data/mgnify/taxon_sample_abundance.tsv\", delim = \"\\t\", col_names = F)\ncolnames(mgnify_taxon_sample) <- c(\"ncbi_id\",\"sample_id\")\n\n\n## load taxonomy\n\nncbi_tax_rank <- read_delim(\"ncbi_tax_rank.tsv\", delim = \"\\t\", col_names = F)\ncolnames(ncbi_tax_rank) <- c(\"ncbi_id\",\"rank\")\n\nncbi_species_kingdom <- read_delim(\"categories.tsv\",delim = \"\\t\", col_names = F)\ncolnames(ncbi_species_kingdom) <- c(\"kingdom\",\"species_level\",\"ncbi_id\")\n\nmgnify_taxon_sample <- mgnify_taxon_sample %>% left_join(ncbi_tax_rank, by=c(\"ncbi_id\"=\"ncbi_id\"))\n\n## Mgnify terms and the samples they are tagged summary \n\nmgnify_sample_entity_sources_samples <- mgnify_sample_entity_sources %>% group_by(type,sample_id) %>% summarise(total_terms=n()) # the tagger can assign multiple terms of a specific type (e.g multiple ENVO terms) \n\nmgnify_sample_entity_sources_terms <- mgnify_sample_entity_sources %>% group_by(type,term_id) %>% summarise(total_samples=n())\nmgnify_sample_entity_sources_summary <- mgnify_sample_entity_sources %>% group_by(type,term_id) %>% summarise(total_samples=n()) %>% group_by(type,total_samples) %>% summarize(total_terms=n())\n\nmgnify_sample_entity_sources_summary_plot <- ggplot()+\n geom_point(data = mgnify_sample_entity_sources_summary, aes(x=total_samples,y=total_terms, colour=factor(type)))+\n ylab(\"Number of terms that have the same background size\")+\n xlab(\"Number of Samples (background of terms)\")+\n labs(colour=\"Types\")+\n ggtitle(\"Mgnify terms and the samples they are tagged distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_sample_entity_sources_summary_plot.png\",plot = mgnify_sample_entity_sources_summary_plot,device = \"png\")\n\n\nmgnify_sample_entity_sources_summary_log_plot <- ggplot()+\n geom_point(data = mgnify_sample_entity_sources_summary, aes(x=total_terms,y=log2(total_samples), colour=factor(type)))+\n xlab(\"Number of terms\")+\n ylab(\"log2 of Number of Samples\")+\n labs(colour=\"Types\")+\n ggtitle(\"Mgnify terms and the samples they are tagged distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_sample_entity_sources_summary_log_plot.png\",plot = mgnify_sample_entity_sources_summary_log_plot,device = \"png\")\n\n\n## Mgnify taxon NCBI ids and the samples they are associated with\n\n\nmgnify_taxon_sample_summary_ncbi_per_sample <- mgnify_taxon_sample %>% group_by(ncbi_id) %>% summarise(total_samples=n()) %>% group_by(total_samples) %>% summarise(total_ncbi_ids=n())\n\nmgnify_taxon_sample_summary_ncbi_per_sample_plot <- ggplot()+\n geom_point(data = mgnify_taxon_sample_summary_ncbi_per_sample, aes(x=total_samples,y=total_ncbi_ids))+\n xlab(\"NCBI ids background size (no. of samples)\")+\n ylab(\"Number of NCBI ids\")+\n ggtitle(\"Mgnify NCBI ids and the samples they are tagged distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_taxon_sample_summary_ncbi_per_sample_plot.png\",plot = mgnify_taxon_sample_summary_ncbi_per_sample_plot,device = \"png\")\n\n\n### NCBI ids per sample id distribution\n\nmgnify_taxon_sample_summary_sample_per_ncbi <- mgnify_taxon_sample %>% group_by(sample_id) %>% summarise(total_ncbi_ids=n()) %>% group_by(total_ncbi_ids) %>% summarise(total_samples=n()) %>% mutate(cumsum_total_samples=cumsum(total_samples))\n\n## test of normality of the log normal transformation\n\nshapiro.test(log(mgnify_taxon_sample_summary_sample_per_ncbi$total_ncbi_ids))\n\n\nmgnify_taxon_sample_summary_sample_per_ncbi_plot <- ggplot()+\n geom_point(data = mgnify_taxon_sample_summary_sample_per_ncbi, aes(x=total_ncbi_ids,y=total_samples))+\n xlab(\"Size of Samples (no. NCBI ids)\")+\n ylab(\"Number of Samples\")+\n ggtitle(\"Mgnify NCBI ids and the samples they contain distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_taxon_sample_summary_sample_per_ncbi_plot.png\",plot = mgnify_taxon_sample_summary_sample_per_ncbi_plot,device = \"png\")\n\n### cumulative distribution of the sample size in terms of NCBI ids\n\nmgnify_taxon_sample_summary_sample_per_ncbi_cumulative_plot <- ggplot()+\n geom_point(data = mgnify_taxon_sample_summary_sample_per_ncbi, aes(x=total_ncbi_ids,y=cumsum_total_samples))+\n xlab(\"Size of Samples (no. NCBI ids)\")+\n ylab(\"Cumulative Number of Samples\")+\n ggtitle(\"Mgnify NCBI ids and the samples contain cumulative distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_taxon_sample_summary_sample_per_ncbi_cumulative_plot.png\",plot = mgnify_taxon_sample_summary_sample_per_ncbi_cumulative_plot,device = \"png\")\n\nsummary(mgnify_taxon_sample_summary_sample_per_ncbi) \n\nmgnify_taxon_sample_summary_sample_per_ncbi_log_plot <- ggplot()+\n geom_point(data = mgnify_taxon_sample_summary_sample_per_ncbi, aes(x=log(total_ncbi_ids),y=log(total_samples)))+\n xlab(\"ln - Size of Samples (no. NCBI ids)\")+\n ylab(\"ln - Number of Samples\")+\n ggtitle(\"Mgnify NCBI ids and the samples they are tagged distribution\")+\n theme_bw()\n\nggsave(filename = \"plots/mgnify_taxon_sample_summary_sample_per_ncbi_log_plot.png\",plot = mgnify_taxon_sample_summary_sample_per_ncbi_log_plot,device = \"png\")\n\n#### test per ncbi taxonomic rank \n\nmgnify_taxon_sample %>% distinct(ncbi_id,rank) %>% group_by(rank) %>% summarize(total_ids=n())\n\nmgnify_taxon_sample_summary_sample_per_ncbi_per_rank <- mgnify_taxon_sample %>% group_by(sample_id,rank) %>% summarise(total_ncbi_ids=n()) %>% group_by(total_ncbi_ids,rank) %>% summarise(total_samples=n())\n\nmgnify_taxon_sample_summary_sample_per_ncbi_per_rank_plot <- ggplot()+\n geom_point(data = mgnify_taxon_sample_summary_sample_per_ncbi_per_rank, aes(x=total_ncbi_ids,y=total_samples))+\n xlab(\"Number of NCBI ids\")+\n ylab(\"Number of Samples\")+\n ggtitle(\"Mgnify NCBI ids and the samples they are tagged distribution\")+\n theme_bw()+\n facet_grid(rows=vars(rank), scales = \"free\")\n\nggsave(filename = \"plots/mgnify_taxon_sample_summary_sample_per_ncbi_per_rank_plot.png\",plot = mgnify_taxon_sample_summary_sample_per_ncbi_per_rank_plot,device = \"png\")\n\n\n## Mgnify and all associations including their score. The original output file\n\n### Score of Lars \n#summary(mgnify_associations$score)\n\n#mgnify_associations_score_distribution <- mgnify_associations %>% group_by(score,type_2) %>% summarise(number_associations=n())\n#\n#mgnify_associations_score_distribution_plot <- ggplot()+\n# geom_point(data = mgnify_associations_score_distribution, aes(x=score, y=number_associations, colour=factor(type_2)))\n#\n#ggsave(filename = \"plots/mgnify_associations_score_distribution_plot.png\",plot = mgnify_associations_score_distribution_plot,device = \"png\")\n#\n#mgnify_associations_score_distribution_log_plot <- ggplot()+\n# geom_point(data = mgnify_associations_score_distribution, aes(x=score, y=log2(number_associations), colour=factor(type_2)))\n#\n#ggsave(filename = \"plots/mgnify_associations_score_distribution_log_plot.png\",plot = mgnify_associations_score_distribution_log_plot,device = \"png\")\n\n### Evidence\n\n### count term_1 samples for background_term_1\n\nmgnify_taxon_sample_background_term_1 <- mgnify_taxon_sample %>% group_by(ncbi_id) %>% summarise(background_term_1=n())\n\nmgnify_associations_evidence <- mgnify_associations %>% extract(evidence, c(\"samples\", \"background_term_2\"), \"([[:digit:]]+) of ([[:digit:]]+)*.\") %>% left_join(mgnify_taxon_sample_background_term_1, by=c(\"term_1\"=\"ncbi_id\")) %>% left_join(ncbi_tax_rank, by=c(\"term_1\"=\"ncbi_id\")) %>% left_join(ncbi_species_kingdom, by=c(\"term_1\"=\"ncbi_id\"))\n\nmgnify_associations_evidence$samples <- as.numeric(mgnify_associations_evidence$samples)\nmgnify_associations_evidence$background_term_2 <- as.numeric(mgnify_associations_evidence$background_term_2)\n\n#### Associations and their taxonomic rank \ntable(mgnify_associations_evidence$rank)\n\n### backgrounds are not correlated\ncor(mgnify_associations_evidence$background_term_1,mgnify_associations_evidence$background_term_2)\n### score summary\n\n\n\n\n\n\n\nmgnify_associations_evidence_summary <- mgnify_associations_evidence %>% group_by(type_2,background_term_2) %>% summarise(count_associations=n()) #%>% group_by(type_2,count_samples) %>% summarise(count_background=n())\n\n### find the pearson correlation of the number of associations with the size of the background of the NCBI ids. The correlation is executed for each association type seperately.\n\nmgnify_associations_evidence_summary_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence_summary, aes(x=background_term_2, y=count_associations, colour=factor(type_2)))\n\nggsave(filename = \"plots/mgnify_associations_evidence_summary_plot_term_2.png\",plot = mgnify_associations_evidence_summary_plot,device = \"png\")\n\n\nmgnify_associations_evidence_summary_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence_summary, aes(x=background_term_2, y=count_associations, colour=factor(type_2)))+\n scale_x_continuous(limits=c(0,1000),breaks=seq(0,5000,100))\nggsave(filename = \"plots/mgnify_associations_evidence_summary_plot_term_2_x_limits.png\",plot = mgnify_associations_evidence_summary_plot,device = \"png\")\n\nmgnify_associations_evidence_summary_term_1 <- mgnify_associations_evidence %>% group_by(type_2,background_term_1) %>% summarise(count_associations=n()) #%>% group_by(type_2,count_samples) %>% summarise(count_background=n())\n\nmgnify_associations_evidence_summary_plot_term_1 <- ggplot()+\n geom_point(data = mgnify_associations_evidence_summary_term_1, aes(x=background_term_1, y=count_associations, colour=factor(type_2)))\n\nggsave(filename = \"plots/mgnify_associations_evidence_summary_plot_term_1.png\",plot = mgnify_associations_evidence_summary_plot_term_1,device = \"png\")\n\n\nmgnify_associations_evidence_summary_term_1 %>% group_by(type_2) %>% group_modify(~ tibble(cor(.x$background_term_1,.x$count_associations)))\n### background terms scatter plot\n\nmgnify_associations_evidence_backgrounds_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence,aes(x=background_term_1,y = background_term_2,colour=factor(type_2)))\nggsave(filename = \"plots/mgnify_associations_evidence_backgrounds_plot.png\",plot = mgnify_associations_evidence_backgrounds_plot,device = \"png\")\n\n### Jaccard index as association Score\n\nmgnify_associations_evidence <- mgnify_associations_evidence %>% mutate(jaccard=samples/(background_term_2+background_term_1-samples))\n\nmgnify_associations_evidence_jaccard_summary <- mgnify_associations_evidence %>% group_by(jaccard,type_2) %>% summarise(count_associations=n())\n\nmgnify_associations_evidence_jaccard_summary_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence_jaccard_summary, aes(x=jaccard, y=count_associations, colour=factor(type_2)))+\n theme_bw()\nggsave(filename = \"plots/mgnify_associations_evidence_jaccard_summary_plot.png\",plot = mgnify_associations_evidence_jaccard_summary_plot,device = \"png\")\n\n### Mutual Information\n\ntotal_count_metadata_entries <- mgnify_sample_entity_sources %>% group_by(type,sample_id) %>% summarise(total_terms=n()) %>% group_by(type) %>% summarise(total_samples=n())\n\ntotal_count_samples_with_ncbi_ids <- length(unique(mgnify_taxon_sample$sample_id))\n\nmgnify_associations_evidence_MI_summary <- mgnify_associations_evidence %>% group_by(MI,type_2) %>% summarise(count_associations=n())\n\nmgnify_associations_evidence_MI_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence_MI_summary, aes(x=MI,y=count_associations, colour=factor(type_2)))+\n theme_bw()\nggsave(filename = \"plots/mgnify_associations_evidence_MI_plot.png\",plot = mgnify_associations_evidence_MI_plot,device = \"png\")\n\n## check which samples don't have NCBI ID\nsamples_without_ncbi_ids <- mgnify_sample_entity_sources %>% distinct(sample_id) %>% filter(!(sample_id %in% unique(mgnify_taxon_sample$sample_id)))\n\nsamples_without_metadata <- mgnify_taxon_sample %>% distinct(sample_id) %>% filter(!(sample_id %in% unique(mgnify_sample_entity_sources$sample_id)))\ncolnames(mgnify_sample_entity_sources) <- c(\"sample_id\",\"type\",\"term_id\")\n\ntotal_samples <- length(unique(c(unique(mgnify_sample_entity_sources$sample_id),unique(mgnify_taxon_sample$sample_id))))\n\n### Empirical Mutual Information calculation\n\nmgnify_associations_evidence <- mgnify_associations_evidence %>% mutate(joint=samples/total_samples) %>% mutate(random=(background_term_1/total_samples)*(background_term_2/total_samples)) %>% mutate(mutual_info=joint*log(joint/random)) %>% mutate(PMI=log(joint/random)) %>% mutate(norm_MI=(MI/max(MI))*100)\n\nmgnify_associations_evidence_norm_MI_plot <- mgnify_associations_evidence %>% group_by(norm_MI, type_2) %>% summarise(count_associations=n()) %>% ggplot() +\n geom_point(aes(x=norm_MI, y=count_associations, colour=factor(type_2)))+\n theme_bw()\nggsave(filename = \"plots/mgnify_associations_evidence_norm_MI_summary_plot.png\",plot = mgnify_associations_evidence_norm_MI_plot,device = \"png\")\n\nmgnify_associations_evidence_mutual_info_summary <- mgnify_associations_evidence %>% group_by(mutual_info,type_2) %>% summarise(count_associations=n())\n\nmgnify_associations_evidence_mutual_mutual_info_summary_plot <- ggplot()+\n geom_point(data = mgnify_associations_evidence_mutual_info_summary, aes(x=mutual_info, y=count_associations, colour=factor(type_2)))+\n theme_bw()\nggsave(filename = \"plots/mgnify_associations_evidence_mutual_mutual_info_summary_plot.png\",plot = mgnify_associations_evidence_mutual_mutual_info_summary_plot,device = \"png\")\n\n", "meta": {"hexsha": "df7afb5bde5a09c35b471b696c938f67d0031b99", "size": 13892, "ext": "r", "lang": "R", "max_stars_repo_path": "descriptives_mgnify.r", "max_stars_repo_name": "lab42open-team/prego_statistics", "max_stars_repo_head_hexsha": "b461f0fa61e7e4251b295e954e5c61f588c01860", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "descriptives_mgnify.r", "max_issues_repo_name": "lab42open-team/prego_statistics", "max_issues_repo_head_hexsha": "b461f0fa61e7e4251b295e954e5c61f588c01860", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "descriptives_mgnify.r", "max_forks_repo_name": "lab42open-team/prego_statistics", "max_forks_repo_head_hexsha": "b461f0fa61e7e4251b295e954e5c61f588c01860", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 56.0161290323, "max_line_length": 342, "alphanum_fraction": 0.804995681, "num_tokens": 3690, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.31366109789601915}} {"text": "library(assertthat)\n\nsource(\"data_structures.r\")\nsource(\"utilities.r\")\n\nsample_synthetic_data<-function(data,F,residuals,equal_share_tr_assignment_resampling){\n \n assert_that(is(data,\"data\"))\n assert_that(is.list(F)&&is(F[[1]],\"counterfactuals\"))\n assert_that(is.vector(residuals)&&is.numeric(residuals))\n assert_that(is.logical(equal_share_tr_assignment_resampling))\n \n #sampling from the residuals\n sampled_residuals<-sample(residuals,data$N,replace = TRUE)\n sampled_residuals\n \n if(equal_share_tr_assignment_resampling){\n draw_sample<-sample_equal_share_indices(data,data$N,TRUE)\n }\n else{\n draw_sample<-sample(1:data$N,data$N,replace = TRUE)\n }\n \n data_sample<-get_index(data,draw_sample)\n \n s_X<-data_sample$X\n s_W<-data_sample$W*1\n \n #applying the (X,Y) sample to the model and adding residuals\n s_Y<-get_index(F[[length(F)]],draw_sample)$observed$`TRUE`+sampled_residuals\n s_Y\n \n #combing the data\n Data(s_X,s_W,s_Y)\n}\n\nsample_synthetic_data_sets<-function(data,F,residuals,equal_share_tr_assignment_resampling,\n N_samples){\n assert_that(is(data,\"data\"))\n assert_that(is.list(F)&&is(F[[1]],\"counterfactuals\"))\n assert_that(is.vector(residuals)&&is.numeric(residuals))\n assert_that(is.logical(equal_share_tr_assignment_resampling))\n assert_integer(N_samples)\n \n synth_data_sets<-vector(mode = \"list\",length = N_samples)\n \n for(i in 1:N_samples){\n synth_data_sets[[i]]<-sample_synthetic_data(data,F,residuals,equal_share_tr_assignment_resampling)\n }\n \n return (synth_data_sets)\n}\n\nsample_equal_share_indices<-function(data,N,replace){\n assert_that(is.data.frame(data)||is(data,\"data\"))\n assert_integer(N)\n \n #equal number of observations in each group\n N_treated<-ceiling(N/2)\n N_control<-floor(N/2)\n \n W_int<-data$W*1\n #finding treated and control indices\n ind_treated<-which(W_int==1)\n ind_control<-which(W_int==0)\n \n #sampling from this indices\n if(length(ind_treated)!=1){\n #sample samples from 1:x, if one element\n ind_treated<-sample(ind_treated,N_treated,replace = replace)\n ind_control<-sample(ind_control,N_control,replace = replace) \n }\n \n #getting them together and permutating them \n indices<-sample(c(ind_treated,ind_control),N,replace = FALSE)\n \n return (indices)\n}\n\nsample_equal_share_data<-function(data,N){\n \n assert_that(is.data.frame(data))\n assert_integer(N)\n \n indices<-sample_equal_share_indices(data,N,FALSE)\n return (data[indices,])\n}\n\nsample_equal_share_generated_assignments<-function(N){\n assert_integer(N)\n \n N_treated<-ceiling(N/2)\n N_control<-floor(N/2)\n \n W<-sample(c(rep(1,times=N_treated),rep(0,times=N_control)),N,replace = FALSE)\n return (W)\n}", "meta": {"hexsha": "447db23447285d9c9675ff4aae85a20e0826bead", "size": 2708, "ext": "r", "lang": "R", "max_stars_repo_path": "src/R/src/sampling.r", "max_stars_repo_name": "naskoD/bachelorThesis", "max_stars_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-04T09:53:36.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-04T09:53:36.000Z", "max_issues_repo_path": "src/R/src/sampling.r", "max_issues_repo_name": "naskoD/bachelorThesis", "max_issues_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/R/src/sampling.r", "max_forks_repo_name": "naskoD/bachelorThesis", "max_forks_repo_head_hexsha": "028ffe0990df9fc72f43024eae67d968dbfb7ae6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9175257732, "max_line_length": 102, "alphanum_fraction": 0.7341211226, "num_tokens": 732, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6224593312018545, "lm_q1q2_score": 0.3136610978960191}} {"text": "#' Convert Data\n#'\n#' Convert (by numericising, centring or standardising) the variables of a \n#' data.frame or elements of a list into a list of integer or numeric values \n#' ready for analysis by \n#' JAGS, OpenBUGS or WinBUGS.\n#' \n#' Numericising, centring and standardising represent increasing conversion of a\n#' variable. A variable is first converted into a numeric value (numericising).\n#' Next the mean is subtracted from all the values (centring). Finally all the \n#' values are divided by the standard deviation (standardising).\n#' \n#' Numericising has no effect on numeric and integer values. Factors are \n#' numericized\n#' by converting to an integer.\n#' Date and POSIXt variables are numericised by converting to integer values\n#' and subtracting the integer value of 1999-12-31 or 1999-12-31 23:59:59 UTC, \n#' respectively.\n#' \n#' Centring has no affect on factors while integer, Date and POSIXt values are subtracted \n#' from the rounded mean. \n#' Similarly, standardising has no affect on factors while centred integer, \n#' Date and POSIXt values are divided by their respective standard deviation.\n#'\n#' @param data the data frame or list of data on which the conversion is based\n#' @param numericise a logical scalar or a character vector of the variables to \n#' numericise\n#' @param centre a logical scalar or a character vector of the variables \n#' to centre\n#' @param standardise a logical scalar or a character vector of the variables\n#' to standardise\n#' @param dat the data frame or data list to convert. If dat is NULL the dataset \n#' passed as the data argument is converted\n#' @return The converted data frame\n#' @examples\n#' data <- data.frame(numeric = 1:10 + 0.1, integer = 1:10, \n#' factor = factor(1:10), date = as.Date(\"2000-01-01\") + 1:10,\n#' posixt = ISOdate(2000,1,1) + 1:10)\n#' convert_data(data)\n#' convert_data(data, centre = TRUE)\n#' convert_data(data, standardise = TRUE)\n#' convert_data(data, numericise = FALSE, standardise = \"date\")\n#' convert_data(data, numericise = FALSE, centre = \"date\", standardise = \"date\")\n#' @export\nconvert_data <- function (data, numericise = TRUE, centre = FALSE, \n standardise = FALSE, dat = NULL) {\n \n assert_that(is_convertible_data_frame(data) || is_convertible_data_list(data))\n assert_that(is.null(dat) || is_convertible_data_frame(dat) ||\n is_convertible_data_list(dat))\n assert_that(is.null(dat) || (is_convertible_data_frame(data) && \n is_convertible_data_frame(dat)) ||\n (is_convertible_data_list(data) && is_convertible_data_list(dat)))\n \n assert_that(is.flag(numericise) || is.character(numericise) || is.null(numericise))\n assert_that(is.flag(centre) || is.character(centre) || is.null(centre))\n assert_that(is.flag(standardise) || is.character(standardise) || is.null(standardise))\n\n \n if (is.null(dat))\n dat <- data\n \n names_data <- names(data)\n names_dat <- names(dat)\n \n if (is.logical(numericise)) {\n if (numericise) {\n numericise <- names_data\n } else\n numericise <- NULL\n }\n if (is.logical(centre)) {\n if (centre) {\n centre <- names_data\n } else\n centre <- NULL\n }\n if (is.logical(standardise)) {\n if (standardise) {\n standardise <- names_data\n } else\n standardise <- NULL\n } \n \n numericise <- numericise[!numericise %in% c(centre, standardise)]\n centre <- centre[!centre %in% standardise]\n \n all <- c(numericise, centre, standardise)\n \n x <- all[!all %in% names_data]\n if(length(x))\n message(paste(c(\"the following variables are not in data:\", x),collapse = \" \"))\n \n x <- names_dat[!names_dat %in% names_data]\n if (length(x))\n message(paste(c(\"the following variables are in dat but not data: \", x), collapse = \" \"))\n \n con_terms <- list()\n \n for(name in names_data) {\n if (name %in% names_dat) {\n\n variable <- variable(data[[name]]) \n \n dat[[name]] <- convert_variable(\n variable, \n dat[[name]], \n numericise = name %in% numericise,\n centre = name %in% centre,\n standardise = name %in% standardise\n )\n }\n }\n return (dat)\n}\n", "meta": {"hexsha": "adc379dbaa02f0fd70ec5b0a9e342a3e53995425", "size": 4166, "ext": "r", "lang": "R", "max_stars_repo_path": "R/convert-data.r", "max_stars_repo_name": "poissonconsulting/jaggernaut", "max_stars_repo_head_hexsha": "59b3e66db1e615efc514dafdd55e50f5d6e17270", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-01-05T17:02:50.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-09T13:04:47.000Z", "max_issues_repo_path": "R/convert-data.r", "max_issues_repo_name": "poissonconsulting/jaggernaut", "max_issues_repo_head_hexsha": "59b3e66db1e615efc514dafdd55e50f5d6e17270", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 37, "max_issues_repo_issues_event_min_datetime": "2015-02-02T19:31:36.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-03T16:13:16.000Z", "max_forks_repo_path": "R/convert-data.r", "max_forks_repo_name": "poissonconsulting/jaggernaut", "max_forks_repo_head_hexsha": "59b3e66db1e615efc514dafdd55e50f5d6e17270", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.9137931034, "max_line_length": 93, "alphanum_fraction": 0.671387422, "num_tokens": 1066, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31365188029418983}} {"text": "\n# Configuration\ntrainSetRatio <- 1\nmodelNameStr <- \"shakeDetectionClassifier\"\nclassLabel <- \"label\"\n\n# Load required libraries\nlibrary(caret)\n#library(rattle)\n\nprint(\"Done\")\n\n# Reading in the sensor and label dataset\nprint(\"Reading input datasets ...\")\n\na.sensor <- read.csv(\"data/a.sensor.csv\", colClasses=\"numeric\")\nm.sensor <- read.csv(\"data/m.sensor.csv\", colClasses=\"numeric\")\np.sensor <- read.csv(\"data/p.sensor.csv\", colClasses=\"numeric\")\n\na.lbl <- read.csv(\"data/a.lbl.csv\", colClasses=\"numeric\")\nm.lbl <- read.csv(\"data/m.lbl.csv\", colClasses=\"numeric\")\np.lbl <- read.csv(\"data/p.lbl.csv\", colClasses=\"numeric\")\n\nprint(\"Summary of sensor data:\")\nsummary(a.sensor)\nsummary(m.sensor)\nsummary(p.sensor)\n\nprint(\"Summary of label data:\")\nsummary(a.lbl)\nsummary(m.lbl)\nsummary(p.lbl)\n\nprint(\"Done.\")\n\n## Transform label dataset into \",\" format\ntransformLabelData <- function(lbl){\n lbl.modified <- cbind(lbl[lbl[, 2] == 0, 1], lbl[lbl[, 2] == 1, 1])\n return(lbl.modified)\n}\n\n## Transforming the label dataset into 'start_time,end_time' format\nprint(\"Transforming label dataset into 'start_time,end_time' format ...\")\na.lbl.modified <- transformLabelData(a.lbl)\nm.lbl.modified <- transformLabelData(m.lbl) \np.lbl.modified <- transformLabelData(p.lbl)\n\nprint(\"Summary of label data after transformation:\")\nsummary(a.lbl.modified)\n\nprint(\"Done\")\n\n## Create a labelled training set from sensor and label datasets\ncreateTrainingDataset <- function(sensor, lbl.modified){\n label <- sapply(sensor[, 1], \n function(ts)\n any(apply(lbl.modified, 1, \n function(shake_ts, test_ts)\n test_ts >= shake_ts[1] && \n test_ts <= shake_ts[2], test_ts=ts)))\n label[label == TRUE] <- \"SHAKE\"\n label[label == FALSE] <- \"NO_SHAKE\"\n sensor.labelled <- cbind(sensor[, -1], label)\n return(sensor.labelled)\n}\n \n# Create a labelled training datasets\nprint(\"Creating labelled training dataset ...\")\na.sensor.labelled <- createTrainingDataset(a.sensor, a.lbl.modified)\nm.sensor.labelled <- createTrainingDataset(m.sensor, m.lbl.modified)\np.sensor.labelled <- createTrainingDataset(p.sensor, p.lbl.modified)\n\n# Combining the labelled training datasets\nsensor.labelled <- rbind(a.sensor.labelled, m.sensor.labelled, p.sensor.labelled)\n\nprint(\"Summary of labelled training dataset:\")\nsummary(sensor.labelled)\n\n# Split data into training and test set\ntrainSet = NULL\ntestSet = NULL\n\nprint(\"Spliting data into train set and test set ...\")\nif(trainSetRatio == 1){\n trainSet <- sensor.labelled\n testSet <- sensor.labelled\n\n} else if(trainSetRatio < 1){\n inTrain <- createDataPartition(sensor.labelled[, classLabel], p = trainSetRatio, list=FALSE)\n\n trainSet <- sensor.labelled[inTrain,]\n testSet <- sensor.labelled[-inTrain,]\n}\n \nprint(\"Done\")\n\n# Create classLabel ~ predictor1 + predictor2 .. string\npredictors <- colnames(sensor.labelled)[-ncol(sensor.labelled)]\n\n# Print predictors\nprint(\"Predictors:\")\nprint(predictors)\n\n# Uncomment relevant parts of below code for up/ down sampling training data\n\n# print(paste(\"Before up/ down sampling: nrow(trainSet):\", \n# nrow(trainSet), \"nrow(testSet):\", nrow(testSet)))\n\n# trainSet <- upSample(trainSet[, predictors], \n# as.factor(trainSet[, classLabel]), \n# list=FALSE, yname=classLabel)\n# trainSet <- downSample(trainSet[, predictors], \n# as.factor(trainSet[, classLabel]), \n# list=FALSE, yname=classLabel)\n\n# print(paste (\"After up/ down sampling: nrow(trainSet):\", nrow(trainSet), \"nrow(testSet):\", nrow(testSet)))\n\n# Training the classifier\nprint(\"Training the classifier ...\")\n\n# Custom summary function for trainControl\ntrainControlSumFuncCustom <- function(data, lev=NULL, model=NULL){\n if(!all(levels(data[, \"pred\"]) == levels(data[, \"obs\"]))){\n print(\"ERROR: Levels of observed and predicted data do not match\")\n q()\n }\n \n precision <- posPredValue(data[, \"pred\"], data[, \"obs\"], \n positive=\"SHAKE\")\n \n recall <- sensitivity(data[, \"pred\"], data[, \"obs\"], \n positive=\"SHAKE\")\n \n f1score <- 2 * ((precision * recall) / (precision + recall))\n\n out <- c(precision, recall, f1score)\n names(out) <- c(\"Precision\", \"Recall\", \"F1-Score\")\n\n return(out)\n}\n\nfitControl <- trainControl(method=\"repeatedcv\", \n number=10, repeats=10, \n classProbs=TRUE, \n summaryFunction=trainControlSumFuncCustom, \n selectionFunction=\"best\")\n\nrpart.grid <- expand.grid(cp=c(0.002, 0.0025, 0.003, 0.0035, 0.004, 0.0045, 0.005, 0.0055, 0.006, 0.0065))\n\nrpartFit <- train(trainSet[, predictors], \n as.factor(as.character(trainSet[, classLabel])), \n method=\"rpart\", \n trControl=fitControl, \n metric=\"F1-Score\", \n tuneGrid = rpart.grid)\n\n# Print the classifier details\nprint(rpartFit)\nprint(\"Done\")\n\n# Predict classes without probabilities on test data\npredClasses <- predict(rpartFit, newdata=testSet[, predictors])\n\n# Print prediction details on test data\n# print(\"Predictions:\")\n# print(predClasses)\n\n# Predict classes with probabilities on test data\n# predClassesProbs <- predict(rpartFit, newdata=testSet[, predictors], type=\"prob\")\n\n# Print prediction details on test data with probabilities\n# print(\"Class Probabilities:\")\n# predClassesProbs\n\n# Print confusion matrix\nprint(\"Confusion matrix\")\nconfusionMatrix(data=predClasses, positive=\"SHAKE\", as.factor(testSet[, classLabel]))\n\n# Plot classification tree\n# fancyRpartPlot(rpartFit$finalModel)\n\nprint(\"Done.\")\n", "meta": {"hexsha": "911276e5e3e2d49f78de787016df2c57606c9dbb", "size": 5922, "ext": "r", "lang": "R", "max_stars_repo_path": "shake-detection-classifier/shake_detection.r", "max_stars_repo_name": "cijogeorge/workspace-machine-learning", "max_stars_repo_head_hexsha": "a8d16d4b1fa6a9549eff28307e3e2f73bc661038", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shake-detection-classifier/shake_detection.r", "max_issues_repo_name": "cijogeorge/workspace-machine-learning", "max_issues_repo_head_hexsha": "a8d16d4b1fa6a9549eff28307e3e2f73bc661038", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "shake-detection-classifier/shake_detection.r", "max_forks_repo_name": "cijogeorge/workspace-machine-learning", "max_forks_repo_head_hexsha": "a8d16d4b1fa6a9549eff28307e3e2f73bc661038", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3606557377, "max_line_length": 108, "alphanum_fraction": 0.6450523472, "num_tokens": 1390, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.31365187232406144}} {"text": "prepare_paths <- function() {\n keys <- c('hyperparameters', \n 'input', \n 'data',\n 'model')\n\n values <- c('input/config/hyperparameters.json', \n 'input/config/inputdataconfig.json', \n 'input/data/',\n 'model/')\n \n paths <- as.list(values)\n names(paths) <- keys\n \n return(paths);\n}\n\nPATHS <- prepare_paths()\n\nget_path <- function(key) {\n output <- paste('/opt/ml/', PATHS[[key]], sep=\"\")\n \n return(output);\n}\n\n\n#* @get /ping\nfunction(res) {\n res$body <- \"OK\"\n\n return(res)\n}\n\n\nload_model <- function() {\n model <- NULL\n \n filename <- paste0(get_path('model'), 'model')\n print(filename)\n \n model <- readRDS(filename)\n \n return(model)\n}\n\n\n#* @post /invocations\nfunction(req, res) {\n print(req$postBody)\n model <- load_model()\n \n payload_value <- as.double(req$postBody)\n X_test <- data.frame(payload_value)\n colnames(X_test) <- \"X\"\n \n print(summary(model))\n y_test <- predict(model, X_test)\n output <- y_test[[1]]\n print(output)\n \n res$body <- toString(output)\n return(res)\n}\n", "meta": {"hexsha": "c49cd73cb7e884c157f34772e36ccf7773c75a3d", "size": 1109, "ext": "r", "lang": "R", "max_stars_repo_path": "Chapter02/ml-r/api.r", "max_stars_repo_name": "MARKOM/sagemaker-cookbook", "max_stars_repo_head_hexsha": "e75fda9493421a013eba5d547d2838dedf46c176", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19, "max_stars_repo_stars_event_min_datetime": "2021-09-14T05:23:52.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T18:20:58.000Z", "max_issues_repo_path": "Chapter02/ml-r/api.r", "max_issues_repo_name": "MARKOM/sagemaker-cookbook", "max_issues_repo_head_hexsha": "e75fda9493421a013eba5d547d2838dedf46c176", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter02/ml-r/api.r", "max_forks_repo_name": "MARKOM/sagemaker-cookbook", "max_forks_repo_head_hexsha": "e75fda9493421a013eba5d547d2838dedf46c176", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 15, "max_forks_repo_forks_event_min_datetime": "2021-10-07T03:41:44.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-06T00:17:25.000Z", "avg_line_length": 17.328125, "max_line_length": 53, "alphanum_fraction": 0.561767358, "num_tokens": 277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.31353899913203714}} {"text": "library(ggplot2)\nlibrary(reshape2)\n\nalnplot <- function(d, file, ylab) {\n d$id <- rownames(d)\n d$id <- factor(c(\"activated-1\", \"nonactivated-1\", \"activated-2\", \"nonactivated-2\"), levels=c(\"nonactivated-1\",\"activated-1\",\"nonactivated-2\",\"activated-2\"))\n m <- melt(d)\n pdf(file)\n print(ggplot(m, aes(x=id, y=value, fill=variable)) + xlab('Sample') + ylab(ylab) + guides(fill=guide_legend(title=\"Mapping class\")) + geom_bar(stat = \"identity\", position=\"fill\"))\n dev.off()\n}\n\nd <- read.csv('data_aln.csv')\nrownames(d) <- factor(c(\"activated-1\", \"nonactivated-1\", \"activated-2\", \"nonactivated-2\"), levels=c(\"nonactivated-1\",\"activated-1\",\"nonactivated-2\",\"activated-2\"))\nalnplot(as.data.frame(data.matrix(d)[,1] * data.matrix(d)[,2:7]), 'data_aln.cnt.pdf', 'Read ratio')\n", "meta": {"hexsha": "dec94ec9f252ea18d71dace84c87497f4735a5cd", "size": 772, "ext": "r", "lang": "R", "max_stars_repo_path": "seq/rna/star.r", "max_stars_repo_name": "chr1swallace/cd4-pchic", "max_stars_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "seq/rna/star.r", "max_issues_repo_name": "chr1swallace/cd4-pchic", "max_issues_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "seq/rna/star.r", "max_forks_repo_name": "chr1swallace/cd4-pchic", "max_forks_repo_head_hexsha": "ccdb757c5c3760eb914b2cb3f0e9f06ef9e24af7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.25, "max_line_length": 181, "alphanum_fraction": 0.670984456, "num_tokens": 245, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.31350102996167467}} {"text": "\r\n\r\n\r\n\r\nhplot1 = histogram(~ act | id ,data=act_table,breaks=bins, type=\"percent\",col=0)\r\nhplot1 = update(hplot1,main=\"Activity Histogram (Individuals)\", ylab=\"frequency\", xlab=\"activity time [s]\",layout=c(3,3))\r\nprint(hplot1)\r\n", "meta": {"hexsha": "ced2f5dd70c88c87e7087f58599dd730258766d6", "size": 228, "ext": "r", "lang": "R", "max_stars_repo_path": "CeTrAn/scripts/unused/Activity_individual.r", "max_stars_repo_name": "brembslab/CeTrAn", "max_stars_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2015-02-26T12:51:15.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-21T08:36:32.000Z", "max_issues_repo_path": "CeTrAn/scripts/unused/Activity_individual.r", "max_issues_repo_name": "brembslab/CeTrAn", "max_issues_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2015-01-09T13:08:07.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-18T13:31:53.000Z", "max_forks_repo_path": "CeTrAn/scripts/unused/Activity_individual.r", "max_forks_repo_name": "brembslab/CeTrAn", "max_forks_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-01-09T13:33:40.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-21T12:55:17.000Z", "avg_line_length": 28.5, "max_line_length": 122, "alphanum_fraction": 0.6929824561, "num_tokens": 71, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6076631556226291, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3133232250901217}} {"text": "library(ggplot2)\nload(url(\"http://www.clutchmemes.com/cs125/avgGOP.RData\"))\nload(url(\"http://www.clutchmemes.com/cs125/avgDEM.RData\"))\nload(url(\"http://www.clutchmemes.com/cs125/pres_trendDF.melt.RData\"))\nhead(goppolls)\n\n\n\ngopgtrends <- pres_trendDF.melt[pres_trendDF.melt$candidate=='Trump' | pres_trendDF.melt$candidate=='Carson' | pres_trendDF.melt$candidate=='Rand',]\ngoppolls <- averagedGOPPoll.melt[averagedGOPPoll.melt$candidate=='Trump' | averagedGOPPoll.melt$candidate=='Carson' | averagedGOPPoll.melt$candidate=='Rand.Paul',]\ngopbubbleDF <- data.frame(Date=gopgtrends$date, Candidate=gopgtrends$candidate, Searched=gopgtrends$searched, Percentage=goppolls$percentage)\n#adding a polling percentage column\n\ngopbubbleDF$Percentage[1] <- 0\n#Fixing the Percentage data so that there are no more NA's\nfor(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){\n if(i > 1){\n if(is.nan(gopbubbleDF$Percentage[i])){\n if(!is.nan(gopbubbleDF$Percentage[i-1])){\n z <- gopbubbleDF$Percentage[i-1]\n }else{\n for(n in gopbubbleDF$Percentage[i:length(gopbubbleDF$Percentage)]){\n if(!is.na(n)) {\n z <- n\n break\n }\n }\n }\n gopbubbleDF$Percentage[i] <- z\n }\n }\n}\n\nchangeInPercentage <- c(0)\nfor(i in seq_along(gopbubbleDF$Percentage[1:length(gopbubbleDF$Percentage)])){\n if(i > 1){\n z <- gopbubbleDF$Percentage[i] - gopbubbleDF$Percentage[i-1]\n changeInPercentage <- append(changeInPercentage, z)}\n}\n\ngopbubbleDF <- cbind(gopbubbleDF, Change.In.Polling.Percentage=changeInPercentage)\n\nsave(gopbubbleDF, file=\"gopbubbledf.RData\")", "meta": {"hexsha": "0b0932c261050cbb0b36a8b08fed202bd72951c0", "size": 1750, "ext": "r", "lang": "R", "max_stars_repo_path": "makeGOPBubble.r", "max_stars_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_stars_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "makeGOPBubble.r", "max_issues_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_issues_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "makeGOPBubble.r", "max_forks_repo_name": "gilgameshskytrooper/PresidentialPlot", "max_forks_repo_head_hexsha": "47545411e7db4f7fe1b1d7dea0c5dee965702b10", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.6976744186, "max_line_length": 163, "alphanum_fraction": 0.664, "num_tokens": 490, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.31331261199117677}} {"text": "b: a -> b (5)\nc: b -> c (6)\ne: c -> e (3)\nd: a -> d (1)\n", "meta": {"hexsha": "0fbf4dc65c00b2257f40250d5e8ea8d566696a39", "size": 56, "ext": "r", "lang": "R", "max_stars_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/02.r", "max_stars_repo_name": "TXCodeDancer/OpenSource", "max_stars_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/02.r", "max_issues_repo_name": "TXCodeDancer/OpenSource", "max_issues_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Demos/QuikGraph/Tests/Cases/Observers/VertexPredecessor/02.r", "max_forks_repo_name": "TXCodeDancer/OpenSource", "max_forks_repo_head_hexsha": "18c6442be2a8b6459a46c5021dd9b68698811811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.2, "max_line_length": 13, "alphanum_fraction": 0.2857142857, "num_tokens": 32, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.31331261199117677}} {"text": "require(DAAG, quietly=T)\nrequire(expm, quietly=T)\nrequire(reader, quietly=T)\nrequire(scales, quietly=T)\n\n####################MCMC UTILITIES############################\n\n#reads a popsize file\nread.popsize = function(popsizefile) {\n\tparams = read.table(popsizefile)\n\tpopSizeFun = function(t) {\n\t\tprev_ind = sapply(t,function(s) { min(which(params[,3]<=s)) } )\n\t\tprev_time = params[prev_ind,3]\n\t\tprev_time[prev_time==-Inf] = 0\n\t\tprev_size = params[prev_ind,1]\n\t\trate = params[prev_ind,2]\n\t\tpopSizes = prev_size*exp(rate*(t-prev_time))\n\t\treturn(popSizes)\n\t}\n\treturn(popSizeFun)\n}\n\n#make a command string for the MCMC\nmake_command_string = function(sam_times,sam_sizes,sam_counts,outFile,dt=.001,n=500000,F=20,s=100,f=1000,e=ceiling(10000*runif(1)),popsize_file = \"~/Desktop/Selection_Recombination/test.pop\",age=TRUE) {\n\toptions(scipen=999)\n\tcommand_string = paste(\"-X\",paste(sam_counts,collapse=\",\"),\"-N\",paste(sam_sizes,collapse=\",\"),\"-T\",paste(sam_times,collapse=\",\"),\"-n\",n,\"-d\",dt,\"-F\",F,\"-f\",f,\"-s\",s,\"-P\",popsize_file,\"-e\", e, \"-a\", \"-o\",outFile,sep=\" \")\n\toptions(scipen=0)\n\treturn(command_string)\n}\n\n#reads sampled paths from MCMC\n#outname should be the PREFIX, it automatically reads both times and trajectories\nread.path = function(outname, lines, skip) {\n\ttraj = n.readLines(paste0(outname, '.traj.gz'), n=lines, skip=skip, header=F)\n\ttraj = lapply(traj, function(x) {temp = as.numeric(unlist(strsplit(x,split=\" \"))); temp[2:length(temp)]})\n\ttime = n.readLines(paste0(outname, '.time.gz'), n=lines, skip=skip, header=F)\n\ttime = lapply(time, function(x) {temp = as.numeric(unlist(strsplit(x,split=\" \"))); temp[2:length(temp)]})\n\treturn(list(traj=traj,time=time))\n}\n\n#plots posterior distribution of paths from MCMC\nplot.posterior.paths = function(paths,sam_freqs,sam_times,sam_size,units,ylim=c(0,1),truePath=c(),trueTime=c(),dt=.001,plot.ages=T, burnin = 0, xlim=NULL) {\n\n\t#find the oldest age\n\toldest = min(sapply(paths$time,min))\n\t#find the time of the most recent sample\n\tpresent = max(paths$time[[1]])\n\t#make the vector of times to sample\n\tpost_times = seq(oldest,present,dt)\n\t#make the matrix to hold the observations from each path\n\tpost_paths = matrix(nrow=length(paths$traj),ncol=length(post_times))\n\t#loop over every sample path, make it as long as necessary and then run a spline function\n\tages = c()\n\tfor (i in 1:length(paths$traj)) {\n\t\tages = c(ages,min(paths$time[[i]]))\n\t\tfake_time = seq(oldest-.5,min(paths$time[[i]])-.001,length=1000)\n\t\tfake_traj = rep(0,1000)\n\t\tcur_traj = c(fake_traj,paths$traj[[i]])\n\t\tcur_time = c(fake_time,paths$time[[i]])\n\t\t#cur_spline = splinefun(cur_time,cur_traj)\n\t\tcur_spline = approxfun(cur_time,cur_traj)\n\t\tpost_paths[i,] = cur_spline(post_times)\n\t}\n\tpath_quantiles = t(apply(post_paths,2,quantile,probs=c(.05,.25,.5,.75,.95)))\n\tpar(mar = c(5, 4, 4, 4) + 0.3)\n\tfive_percent_age = quantile(ages,.05)\n\tsd_age = sd(ages)\n\tfirst_time_ind = min(which(post_times>five_percent_age-sd_age))\n\tx.step <- 5000/units\n\tif (is.null(xlim)) {\n\t\tmatplot(as.numeric(post_times[first_time_ind:length(post_times)]),path_quantiles[first_time_ind:length(post_times),],type=\"l\",lty=1,col=c(3,2,1,2,3),xlab=\"kyr BP\",ylab=\"\",ylim=ylim, axes=F)\n\t x.start <- round(post_times[first_time_ind]/x.step) * x.step\n\t} else {\n\t\tmatplot(as.numeric(post_times[first_time_ind:length(post_times)]),path_quantiles[first_time_ind:length(post_times),],type=\"l\",lty=1,col=c(3,2,1,2,3),xlab=\"kyr BP\",ylab=\"\",ylim=ylim,xlim=xlim, axes=F)\n\t x.start <- floor(min(xlim)/x.step) * x.step\n\t}\n\n\t# add Y-axis\n\taxis(4)\n\tmtext(\"Allele frequency\",side=4,line=3)\n\n\t# add x-axis\n\tx.ticks <- seq(x.start, present, by=x.step)\n\taxis(side=1, at=x.ticks, labels=paste(x.ticks*-units/1000))\n\n\t# add the sample points, proportional to the log of their size\n\tpoints(sam_times,sam_freqs,pch=19,col=alpha(\"black\", 0.6), cex=sam_size)\n\n\tif (length(truePath)>0 && length(trueTime) > 0) {\n\t\tlines(trueTime,truePath,lty=2)\n\t}\n\tif (plot.ages) {\n\t\tfirst_nonzero_ind = min(which(sam_freqs>0))\n\t\tfirst_nonzero_time = sam_times[first_nonzero_ind]\n\t\tprint(c(first_nonzero_ind,first_nonzero_time))\n\t\tfirst_nonzero_post = min(which(post_times>=first_nonzero_time))\n\t\tage_dens = density(ages,to=first_nonzero_time)\n\t\tage_dens_spline = splinefun(age_dens$x,age_dens$y)\n\t\tpar(new=T)\n\t\tprint(\"Trying to plot density of times\")\n\t\tprint(c(first_time_ind,first_nonzero_post))\n\t\tif (is.null(xlim)) {\n\t\t\tplot(post_times[first_time_ind:length(post_times)],c(age_dens_spline(post_times[first_time_ind:first_nonzero_post]),rep(0,length(post_times)-first_nonzero_post)),type=\"l\",col=\"blue\",axes=FALSE,bty=\"n\",xlab=\"\",ylab=\"\")\n\t\t} else {\n\t\t\tplot(post_times[first_time_ind:length(post_times)],c(age_dens_spline(post_times[first_time_ind:first_nonzero_post]),rep(0,length(post_times)-first_nonzero_post)),type=\"l\",col=\"blue\",axes=FALSE,bty=\"n\",xlab=\"\",ylab=\"\",xlim=xlim)\n\t\t}\n\t\taxis(side=2,at=pretty(range(c(age_dens_spline(post_times[1:first_nonzero_post]),rep(0,length(post_times)-first_nonzero_post)))))\n\t\tmtext(\"Density\",side=2,line=3)\n\t}\n\tinvisible(list(quantiles=path_quantiles,sam_freqs=sam_freqs,sam_times=sam_times,post_times = post_times))\n}\n\n##############SIMULATIONS##########################\n\n#Simulate a diploid Wright-Fisher population using an Euler scheme\n#popSize is a popsize file read using read.popsize\nsim_wf_diploid_popsize = function(a,t_1,t_2,popSize = function(t){1}, alpha2 = 0,h=.5,t_len=1000) {\n\t\twf = vector()\n\twf[1] = a\n\tt = seq(t_1,t_2,length=t_len)\n\tfor (i in 2:length(t)) {\n\t\twf[i] = wf[i-1]+alpha2*wf[i-1]*(1-wf[i-1])*(wf[i-1]+h*(1-2*wf[i-1]))*(t[i]-t[i-1])+sqrt(wf[i-1]*(1-wf[i-1])/popSize(t[i-1]))*sqrt(t[i]-t[i-1])*rnorm(1,0,1)\n\t\twf[i] = min(wf[i],1)\n\t\twf[i] = max(wf[i],0)\n\t\tif (wf[i] == 0) {\n\t\t\twf = c(wf,rep(0,t_len-i))\n\t\t\tbreak\n\t\t} else if (wf[i] == 1) {\n\t\t\twf = c(wf,rep(1,t_len-i))\n\t\t\tbreak\n\t\t}\n\t}\n\treturn(rbind(t,wf))\n}\n\n#generates sample data from a single path\nsample_data_from_path = function(path,sample_times,sample_sizes) {\n\t#nb: path[1,] is the time, path[2,] is the trajectory\n\tfirst_in_t = max(which(sample_times < path[1,1]))+1\n\tsample_inds = sapply(sample_times[first_in_t:length(sample_times)], function(s){max(which(path[1,]<=s))})\n\tsample_counts = c(rep(0,first_in_t-1), rbinom(length(sample_inds),prob=path[2,sample_inds],size=sample_sizes))\n\tfreq = c(rep(0,first_in_t-1), path[2,sample_inds])\n\treturn(list(times=sample_times,counts=sample_counts,sizes=sample_sizes,freq=freq))\n}\n\n#completely dumb rejection sampler for the age of the allele\n#requires ages to come from a finite span of time between ancient and recent\nrejection_sample_age = function(n,popSize,ancient,recent=0,M=1) {\n\tcur_sam = 0\n\tdat = numeric(n)\n\twhile(cur_sam < n) {\n\t\ttest = runif(1,ancient,recent)\n\t\tu = runif(1)\n\t\tif (u 0) {\n\t\t\t\tif (t_1[i]0) > 0) {\n\t\t\t\t\tseg=TRUE\n\t\t\t\t} else if (!one_nonzero) {\n\t\t\t\t\tseg=TRUE\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tpaths[[i]] = test\n\t\tsample_counts[i,] = cur_counts\n\t}\n\tfreq = t(t(sample_counts)/sample_sizes)\n\treturn(list(counts=sample_counts,sizes=sample_sizes,times=sample_times,paths=paths,freq=freq))\n}\n\n\n#Generate a command string from a list of sims\n#... are arguments to be passed to make_command_string\nmake_command_string_from_sims = function(sim_data, outPrefix, ...) {\n\tcmd_string = c()\n\tif (is.matrix(sim_data$counts)) {\n\t\tfor (i in 1:nrow(sim_data$counts)) {\n\t\t\tcmd_string = c(cmd_string, make_command_string(sim_data$times,sim_data$sizes,sim_data$counts[i,],paste(outPrefix,i,sep=\"_\"),...))\n\t\t}\n\t} else {\n\t\tcmd_string = c(cmd_string, make_command_string(sim_data$times,sim_data$sizes,sim_data$counts,outPrefix,...))\n\t}\n\treturn(cmd_string)\n}\n\n#bin the data between a and b into num_bin bins\n#last_alone = TRUE means the last one is its own bin\nbin_data = function(sim_data, a=-.1, b=0, num_bin = 4, bins = NULL, last_alone = TRUE, remove_empty = TRUE) {\n\tif (!is.vector(bins)) {\n\t\tbins = seq(a,b,len=num_bin+1)\n\t} else {\n\t\tnum_bin = length(bins)-1\n\t}\n\twhich.bin = .bincode(sim_data$times,bins)\n\tnew_times = (bins[1:(length(bins)-1)]+bins[2:length(bins)])/2\n\tnum_sim = nrow(sim_data$counts)\n\tnew_counts = matrix(0,nrow=nrow(sim_data$counts),ncol=length(new_times))\n\tnew_sizes = rep(0,length(new_times))\n\tnew_freqs = new_counts\n\tfor (i in 1:length(new_times)) {\n\t\tcur_times = (which.bin==i)\n\t\tnew_counts[,i] = rowSums(matrix(sim_data$counts[,cur_times],nrow=num_sim))\n\t\tnew_sizes[i] = sum(sim_data$sizes[cur_times])\n\t}\n\tif (last_alone) {\n\t\tlast = length(sim_data$times)\n\t\tnew_times = c(new_times,sim_data$times[last])\n\t\tnew_sizes = c(new_sizes,sim_data$sizes[last])\n\t\tnew_counts = cbind(new_counts,sim_data$counts[,last])\n\t\tnew_sizes[which.bin[last]] = new_sizes[which.bin[last]] - sim_data$sizes[last]\n\t\tnew_counts[,which.bin[last]] = new_counts[,which.bin[last]] - sim_data$counts[,last]\n\t}\n\tnew_freqs = t(t(new_counts)/new_sizes)\n\tnew_data = sim_data\n\tnew_data$counts = new_counts\n\tnew_data$sizes = new_sizes\n\tnew_data$freq = new_freqs\n\tnew_data$times = new_times\n\tnew_data$lower = bins[1:num_bin]\n\tnew_data$upper = bins[2:(num_bin+1)]\n\tif (last_alone) {\n\t\tnew_data$lower = c(new_data$lower,sim_data$times[last])\n\t\tnew_data$upper = c(new_data$upper,sim_data$times[last])\n\t}\n\treturn(new_data)\n}\n\nmake_input_matrix_from_sims = function(sim_data,lower=sim_data$times,upper=sim_data$times) {\n\tinFiles = list()\n\tempty_bins = sim_data$sizes==0\n\tnum_bins = length(sim_data$sizes[!empty_bins])\n\tfor (i in 1:nrow(sim_data$counts)) {\n\t\tcurInput = matrix(nrow=num_bins,ncol=4)\n\t\tcurInput[,1] = sim_data$counts[i,!empty_bins]\n\t\tcurInput[,2] = sim_data$sizes[!empty_bins]\n\t\tcurInput[,3] = lower[!empty_bins]\n\t\tcurInput[,4] = upper[!empty_bins]\n\t\tinFiles[[i]] = curInput\n\t}\n\treturn(inFiles)\n}\n\n################HMM LIKELIHOOD#####################\n\n#computes the likelihood\nwf_iterate_likelihood_diploid = function(sample_count,sample_size,sample_time,alpha,h,age,N=100) {\n\t#convert times into generations\n\tsample_time_gen = floor(2*N*sample_time)\n\t#convert age to generations\n\tage_gen = floor(2*N*age)\n\t#convert alpha to s\n\ts = alpha/(2*N)\n\t#make the wf matrix for the parameters\n\twf_matrix = construct_wf_matrix_diploid(s,h,N)\n\n\n\t#figure out which samples are older than the allele age\n\tfirst_sample = min(which(sample_time_gen>age_gen))\n\tif (first_sample != 1 && sample_count[first_sample-1]>0) {\n\t\tprint(\"ERROR: sample with more than 0 copies of the derived allele before age\")\n\t\treturn(0)\n\t}\n\n\t#precompute all necessary transition matrices\n\tuniqueBetween = unique(c(diff(sample_time_gen),sample_time_gen[first_sample]-age_gen))\n\twfTrans = lapply(uniqueBetween,function(s){wf_matrix%^%s})\n\n\t#now it's just a hidden markov model\n\tfreqs = 0:(2*N)/(2*N)\n\tinitial_states = c(0,1,rep(0,2*N-1))\n\ttime_between = sample_time_gen[first_sample]-age_gen\n\ttransMat = wfTrans[[which(uniqueBetween==time_between)]]\n\tlike = initial_states%*%t(transMat)*dbinom(sample_count[first_sample],sample_size[first_sample],freqs)\n\tif (first_sample != length(sample_size)) {\n\t\tfor (i in (first_sample+1):length(sample_size)) {\n\t\t\ttime_between = sample_time_gen[i]-sample_time_gen[i-1]\n\t\t\ttransMat = wfTrans[[which(uniqueBetween==time_between)]]\n\t\t\tlike = like%*%t(transMat)*dbinom(sample_count[i],sample_size[i],freqs)\n\t\t}\n\t}\n\tif (any(is.na(like))) {\n\t\tlike = 1e-300\n\t}\n\treturn(sum(like))\n}\n\n#Builds the transition matrix\nconstruct_wf_matrix_diploid = function(s,h,N) {\n\tstarts = seq(0,2*N,1)\n\tends = seq(0,2*N,1)\n\twf_matrix = sapply(starts,function(i){dbinom(ends,2*N,eta_diploid(i,s,h,N))})\n\treturn(wf_matrix)\n}\n\neta_diploid = function(i,s,h,N) {\n\t((1+s)*i^2+(1+s*h)*i*(2*N-i))/((1+s)*i^2+2*(1+s*h)*i*(2*N-i)+(2*N-i)^2)\n}\n\n#############TRANSFORMATIONS####################\n\n#Fisher's angular transformation\nfisher_wf = function(path) {\n\tacos(1-2*path)\n}\n\n#inverts Fisher's angular transformation\ninv_wf = function(path) {\n\t(1-cos(path))/2\n}\n\n#############DIFFUSION FUNCTIONS##################\n\n#infinitesimal mean of the transformed Wright-Fisher diffusion\nmu_wf_diploid = function(x,gam,h) {\n\t1/4*(gam*sin(x)*(1+(2*h-1)*cos(x))-2/tan(x))\n}\n\n#potential function of transformed Wright-Fisher diffusion\nH_wf_diploid = function(x,gam,h) {\n\t-1/8*(gam*cos(x)*(2+(2*h-1)*cos(x))+4*log(sin(x)))\n}\n\n#derivative of infinitesimal mean of transformed Wright-Fisher diffusion\ndmudx_wf_diploid = function(x,gam,h) {\n\t1/4*(gam*cos(x)+(2*h-1)*gam*cos(2*x)+2*1/sin(x)^2)\n}\n\n#infinitesimal mean of the transformed Wright-Fisher diffusion\n#relative to Bes(0)\nmu_wf_diploid_bes0 = function(x,gam,h) {\n\tres = 1/4*(gam*sin(x)*(1+(2*h-1)*cos(x))-2/tan(x))- -1/2*1/x\n\tres[x==0] = 0\n\treturn(res)\n}\n\n#squared infinitesimal mean of transformed Wright-Fisher diffusion\n#relative to Bes(0)\nmu_squared_wf_diploid_bes0 = function(x,gam,h) {\n\tres = (1/4*(gam*sin(x)*(1+(2*h-1)*cos(x))-2/tan(x)))^2- (-1/2*1/x)^2\n\tres[x==0] = -1/6*(1+3*gam*h)\n\treturn(res)\n}\n\n#potential function of transformed Wright-Fisher diffusion\n#relative to Bes(0)\nH_wf_diploid_bes0 = function(x,gam,h) {\n\tres = -1/8*(gam*cos(x)*(2+(2*h-1)*cos(x))+4*log(sin(x))) - -1/2*log(x)\n\tres[x==0] = -1/8*gam*(1+2*h)\n\treturn(res)\n}\n\n#derivative of infinitesimal mean of transformed Wright-Fisher diffusion\n#relative to Bes(0)\ndmudx_wf_diploid_bes0 = function(x,gam,h) {\n\tres = 1/4*(gam*cos(x)+(2*h-1)*gam*cos(2*x)+2*1/sin(x)^2)-1/(2*x*x)\n\tres[x==0] = 1/6*(1+3*gam*h)\n\treturn(res)\n}\n\n##############GIRSANOV FUNCTIONS##################\n\n#Likelihood of transformed Wright-Fisher path with two different selection coefficients\ngirsanov_wfwf = function(path,t_vec, alpha1, alpha2, h1, h2) {\n\tm1 = H_wf_diploid(path[length(path)],alpha1,h1) -\n\t\tH_wf_diploid(path[1],alpha1,h1) -\n\t\t1/2*riemann_integral(dmudx_wf_diploid(path,alpha1,h1),t_vec) -\n\t\t1/2*riemann_integral(mu_wf_diploid(path,alpha1,h1)^2,t_vec)\n\tm2 = H_wf_diploid(path[length(path)],alpha2,h2) -\n\t\tH_wf_diploid(path[1],alpha2,h2) -\n\t\t1/2*riemann_integral(dmudx_wf_diploid(path,alpha2,h2),t_vec) -\n\t\t1/2*riemann_integral(mu_wf_diploid(path,alpha2,h2)^2,t_vec)\n\treturn(m1-m2)\n}\n\n#Likelihood of Wright-Fisher path relative to Bes(0)\ngirsanov_wfbes0_limit = function(path,t_vec,alpha,h) {\n\tm1 = H_wf_diploid_bes0(path[length(path)],alpha,h) -\n\t\tH_wf_diploid_bes0(path[1],alpha,h) -\n\t\t1/2*riemann_integral(dmudx_wf_diploid_bes0(path,alpha,h),t_vec) -\n\t\t1/2*riemann_integral(mu_squared_wf_diploid_bes0(path,alpha,h),t_vec)\n\treturn(m1)\n}\n", "meta": {"hexsha": "71cb7fa708c1a98896bb977601a8a7b9a6b7d7b3", "size": 15602, "ext": "r", "lang": "R", "max_stars_repo_path": "rscript/mcmc_path_utilities.r", "max_stars_repo_name": "ekirving/alleletraj", "max_stars_repo_head_hexsha": "0b5e68608a9f8a218de6b7c36b108a41819c8f84", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": 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YES\n2. YES", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3133126041720593}} {"text": "#-------PAMGuide.R\n\n# Computes calibrated or relative acoustic metrics from WAV audio files.\n \n# This code accompanies the manuscript: \n \n# Merchant et al. (2015). Measuring Acoustic Habitats. Methods \n# in Ecology and Evolution\n \n# and follows the equations presented in Appendix S1. It is not necessarily\n# optimised for efficiency or concision.\n\n###############################################################################\n######\tSee Appendix S1 of the above manuscript for detailed instructions #####\n###############################################################################\n \n# Copyright (c) 2014 The Authors.\n \n# Author: Nathan D. Merchant. Last modified 22 Sep 2014\n\n# PREREQUISITES: PAMGuide.R uses package \"tuneR\", which can be installed\n# using the R Package Installer:\n\n## Install tuneR if not installed---------------------------------------------\n\nif (!is.element('tuneR', installed.packages()[,1])){\t\t\t#if tuneR is not installed\nr <- getOption(\"repos\")\t\t\t\t\t\t\t\t#assign R mirror for download\nr[\"CRAN\"] <- \"http://cran.us.r-project.org\"\noptions(repos = r)\nrm(r)\ninstall.packages('tuneR', dep = TRUE)\t\t\t#install tuneR\nrequire('tuneR', character.only = TRUE)}\n\nlibrary(tuneR)\t\t\t\t\t\t\t\t\t#load tuneR package\n\n\n## Begin PAMGuide--------------------------------------------------------------\n\nPAMGuide <- function(fullfile='',...,atype='PSD',plottype='Both',envi='Air',calib=0,ctype = 'TS',Si=-159,Mh=-36,G=0,vADC=1.414,r=50,N=Fs,winname='Hann',lcut=Fs/N,hcut=Fs/2,timestring=\"\",outdir=dirname(fullfile),outwrite=0,disppar=1,welch=\"\",chunksize=\"\",linlog = \"Log\", isvector=0, y=\"\", Fs=\"\", Nbit=24 ){\n\n#graphics.off()\t\t\t\t\t\t\t\t\t#close plot windows\naid <- 0\t\t\t\t\t\t\t\t\t\t#reset metadata code\nif (calib == 0) {aid <- aid + 20\t\t\t\t#add calibration element to metadata code\n\t} else {aid <- aid + 10}\nif (timestring != \"\"){aid <- aid + 1000} else {aid <- aid + 2000}\t#add time stamp element to metadata code\n\n\n## Select input file and get file info-----------------------------------------\n\n#fullfile <- file.choose()\t\t\t\nifile <- basename(fullfile)\t\t\t\t\t\t#file name\nif (isvector == 0){\n\tfIN <- readWave(fullfile,header = TRUE)\t\t\t#read file header\t\n\tFs <- fIN[[1]]\t\t\t\t\t\t\t\t\t#sampling frequency\n\tNbit <- fIN[[3]]\t\t\t\t\t\t\t\t#bit depth\n\txl <- fIN[[4]]\t\t\t\t\t\t\t\t\t#length of file in samples\t\t\t\n\txlglo <- xl\t\t\t\t\t\t\t\t\t\t#back-up file length\n}\nelse {\n\t#Nbit <- 10\n\txbit <- y\n\txl <- length(xbit) \n\t#Fs <- 48000\n\txlglo <- xl\n\tcat('File length:',xl,'samples =',xl/Fs,'s\\n')\n}\n\n\n## Read time stamp data if provided-----------------------------------------\n\nif (timestring != \"\") {tstamp <- as.POSIXct(strptime(ifile,timestring) ,origin=\"1970-01-01\")\n\tif (disppar == 1){cat('Time stamp start time: ',format(tstamp),'\\n')}\n\t} \nif (timestring == \"\"){tstamp <- \"\"}\t\t#compute time stamp in R POSIXct format\n\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n## Display user-defined settings------------------------------------------\n\nif (disppar == 1){\n\tcat('File name:',ifile,'\\n')\n\tcat('File length:',xl,'samples =',xl/Fs,'s\\n')\n\tcat('Analysis type:',atype,'\\n')\n\tcat('Plot type:',plottype,'\\n')\n\tif (calib == 1){\n\t\tif (ctype == 'EE'){\n\t\t\tcat('End-to-end system sensitivity =',sprintf('%.01f',Si),'dB\\n')\n\t\t\tif (envi == 'Wat') {cat('In-air measurement\\n')}\n\t\t\tif (envi == 'Wat') {cat('Underwater measurement\\n')}}\n\t\tif (ctype == 'RC'){\n\t\t\tcat('System sensitivity of recorder (excluding transducer) =',sprintf('%.01f',Si),'dB\\n')}\n\t\tif (ctype == 'TS' || ctype == 'RC'){\n\t\tif (envi == 'Air') {cat('In-air measurement\\n')\n\t\t\tcat('Microphone sensitivity:',Mh,'dB re 1 V/Pa\\n')\n\t\t\tMh <- Mh - 120}\t\t#convert to dB re 1 V/uPa\n\t\tif (envi == 'Wat') {cat('Underwater measurement\\n')\n\t\t\tcat('Hydrophone sensitivity:',Mh,'dB re 1 V/uPa\\n')}}\n\t\tif (ctype == 'TS'){\t\n\t\tcat('Preamplifier gain:',G,'dB\\n')\n\t\tcat('ADC peak voltage:',vADC,'V\\n')}\n\t} else {cat('Uncalibrated analysis. Output in relative units.\\n')\n\t\t\t}\n\tcat('Time segment length:',N,'samples =',N/Fs,'s\\n')\n\tcat('Window function:',winname,'\\n')\n\tcat('Window overlap:',r,'%\\n')\n}\nr<-r/100\n\n## Read input file------------------------------------------------------------------\n\nif (chunksize == \"\"){nchunks = 1\t\t\t\t#if loading whole file, nchunks = 1\n\t} else if (chunksize != \"\") {\t\t\t\t#if loading file in stages\n\t\tnchunks <- ceiling(xl/(Fs*as.numeric(chunksize)))\t#number of chunks of length chunksize in file\n\t}\n\t\nfor (q in 1:nchunks){\t\t\t\t\t\t\t#loop through file chunks\n\tif (nchunks == 1){\t\t\t\t\t\t\t#if loading whole file at once\nt1=proc.time()\t\t\t\t\t\t\t\t\t#start timer\ncat('Loading input file... ')\nif (isvector == 0){\n\t# Read the wave file:\n\txbit <- readWave(fullfile)\n\txbit <- xbit@left/(2^(Nbit-1))\t\t\t\t\t#convert to full scale (+/- 1) via bit depth\n\t} else {\n\t\t# Take the user-specified input vector.\n\n\txbit <- xbit/(2^(Nbit-1))\t\t\t\t\t#convert to full scale (+/- 1) via bit depth\n}\t\t\t\t\t\t#read file\ncat('done in',(proc.time()-t1)[3],'s.\\n')\n\n\n} else if (nchunks > 1) {\t\t\t\t\t\t#if loading file in stages\n\tif (q == nchunks){\t\t\t\t\t\t\t#load qth chunk\n\t\txbit <- readWave(fullfile,from=((q-1)*as.numeric(chunksize)*Fs+1),to=xlglo,units=\"samples\")\n\t\txbit <- xbit@left/(2^(Nbit-1))\t\t\t#convert to full scale (+/- 1) via bit depth\n\t\txl <- length(xbit)\t\t\t\t\t\t#final chunk length\n\t} else {\n\t\txbit <- readWave(fullfile,from=((q-1)*as.numeric(chunksize)*Fs+1),to=(q*as.numeric(chunksize)*Fs),units=\"samples\")\n\t\txbit <- xbit@left/(2^(Nbit-1))\t\t\t#convert to full scale (+/- 1) via bit depth\n\t\txl <- length(xbit)\t\t\t\t\t\t#chunk length\n\t}\n}\n\nif (envi == 'Air'){pref<-20; aid <- aid+100}\t#set reference pressure depending for in-air or underwater\nif (envi == 'Wat'){pref<-1; aid <- aid+200}\n\n\t\n## Compute system sensitivity if provided-----------------------------------------------\n\nif (calib == 1){\n\tif (ctype == 'EE') {\t\t#'EE' = end-to-end calibration\n\t\tS <- Si}\n\tif (ctype == 'RC') {\t\t#'RC' = recorder calibration with separate transducer sensitivity defined by Mh\n\t\tS <- Si + Mh}\n\tif (ctype == 'TS') {\t\t#'TS' = manufacturer's specifications\n\t\tMh\n\t\tG\n\t\t20*log10((1/vADC))\n\t\tS <- Mh + G + 20*log10(1/vADC);\t\t#EQUATION 4\n\t} \n\n\tif (disppar == 1){cat('System sensitivity correction factor, S = ',sprintf('%.01f',S),' dB\\n')}\n} else {S <- 0}\t\t\t\t#if not calibrated (calib == 0), S is zero\n\n## Compute waveform if selected----------------------------------------------------\n\nif (atype == 'Waveform') {\n\tif (calib == 1){\na <- xbit/(10^(S/20)) \t\t\t\t#EQUATION 21\t\n} else {a <- xbit/(max(xbit))}\nt <- seq(1/Fs,length(a)/Fs,1/Fs)\t#time vector\ntana = proc.time()\n}\n\n## Compute DFT-based metrics if selected----------------------------------------\n\nif (atype != 'Waveform') {\n\tif (nchunks == 1){\n\tif (atype == 'PSD'){cat('Computing PSD...')}\n\tif (atype == 'PowerSpec'){cat('Computing power spectrum...')}\n\tif (atype == 'TOL'){cat('Computing TOLs by DFT method...')}\n\tif (atype == 'Broadband'){cat('Computing broadband level...')}\n\ttana = proc.time()}\n\n# Divide signal into data segments (corresponds to EQUATION 5)\nN = round(N)\t\t\t\t\t\t#ensure N is an integer\nnsam = ceiling((xl)-r*N)/((1-r)*N)\t#number of segments of length N with overlap r\nxgrid <- matrix(nrow = N,ncol = nsam)\t#initialise grid of segmented data for analysis\nfor (i in 1:nsam) {\t\t\t\t\t#make grid of segmented data for analysis\n\tloind <- (i-1)*(1-r)*N+1\n\thiind <- (i-1)*(1-r)*N+N\n\txgrid[,i] = xbit[loind:hiind]\n}\n\nM <- length(xgrid[1,])\n\n# Apply window function (corresponds to EQUATION 6)\nif (winname == 'Rectangular') {\t\t\t#rectangular (Dirichlet) window\n\tw <- matrix(1,1,N)\n\talpha <- 1 }\t\t\t\t\t#scaling factor\nif (winname == 'Hann') {\t\t\t#Hann window\n\tw <- (0.5 - 0.5*cos(2*pi*(1:N)/N))\n\talpha <- 0.5 }\t\t\t\t\t#scaling factor\nif (winname == 'Hamming') {\t\t\t#Hamming window\n\tw <- (0.54 - 0.46*cos(2*pi*(1:N)/N))\n\talpha <- 0.54 }\t\t\t\t\t#scaling factor\nif (winname == 'Blackman') {\t\t#Blackman window\n\tw <- (0.42 - 0.5*cos(2*pi*(1:N)/N) + 0.08*cos(4*pi*(1:N)/N))\n\talpha <- 0.42 }\t\t\t\t\t#scaling factor\n\nxgrid <- xgrid*w/alpha \t\t\t\t#apply window function\n\n\n#Compute DFT (corresponds to EQUATION 7)\nX <- abs(mvfft(xgrid))\n\n#Compute power spectrum (EQUATION 8)\nP <- (X/N)^2\n\n#Compute single-side power spectrum (EQUATION 9)\nPss <- 2*P[0:round(N/2)+1,]\n\n#Compute frequencies of DFT bins\nf <- floor(Fs/2)*seq(1/(N/2),1,len=N/2)\nflow <- which(f >= lcut)[1]\t\t\t#find index of lcut frequency\nfhigh <- max(which(f <= hcut))\t\t#find index of hcut frequency\nf <- f[flow:fhigh]\t\t\t\t\t#limit frequencies to user-defined range\nnf <- length(f)\t\t\t\t\t\t#number of frequency bins\n\n\n#Compute PSD in dB if selected\nif (atype == 'PSD') {\nB <- (1/N)*(sum((w/alpha)^2))\t\t#noise power bandwidth (EQUATION 12)\ndelf <- Fs/N;\t\t\t\t\t\t#frequency bin width\na <- 10*log10((1/(delf*B))*Pss[flow:fhigh,]/(pref^2))-S\n}\t\t\t\t\t\t\t\t\t#PSD (EQUATION 11)\n\n#Compute power spectrum in dB if selected\nif (atype == 'PowerSpec') {\t\t\t\n\ta <- 10*log10(Pss[flow:fhigh,]/(pref^2))-S\n}\t\t\t\t\t\t\t\t\t#EQUATION 10\n\n#Compute broadband level if selected\nif (atype == 'Broadband') {\n\ta <- 10*log10(colSums(Pss[flow:fhigh,])/(pref^2))-S\n}\t\t\t\t\t\t\t\t\t#EQUATION 17\n\n#Compute 1/3-octave band levels if selected\nif (atype == 'TOL') {\n\tif (lcut <25){\t\t\t\t\t#limit TOL analysis to > 25 Hz\n\tlcut <- 25}\n\t\n\t#Generate 1/3-octave freqeuncies\n\tlobandf <- floor(log10(lcut))\t#lowest power of 10 for TOL computation\n\thibandf <- ceiling(log10(hcut))\t#highest power of 10 for TOL computation\n\tnband <- 10*(hibandf-lobandf)+1\t#number of 1/3-octave bands\n\tfc <- matrix(0,nband)\t\t\t#initialise 1/3-octave frequency vector\n\tfc[1] <- 10^lobandf;\n\t\n\t#Calculate centre frequencies (corresponds to EQUATION 13)\n\tfor (i in 2:nband) {\n\t\tfc[i] <- fc[i-1]*10^0.1}\t\n\tfc <- fc[which(fc >= lcut)[1]:max(which(fc <= hcut))]\n\t\n\tnfc <- length(fc)\t\t\t\t#number of 1/3-octave bands\n\t\n\t#Calculate boundary frequencies of each band (EQUATIONS 14-15)\n\tfb <- fc*10^-0.05\t\t\t\t#lower bounds of 1/3-octave bands\n\tfb[nfc+1] <- fc[nfc]*10^0.05\t#upper bound of highest band\n\tif (max(fb) > hcut) {\t\t\t#if upper bound exceeds highest\n\t\tnfc <- nfc-1\t\t\t\t# frequency in DFT, remove\n\t\tfc <- fc[1:nfc]}\n\t\n\t#Calculate TOLs (corresponds to EQUATION 16)\n\tP13 <- matrix(nrow = M,ncol = nfc)\n\tfor (i in 1:nfc) {\n\t\tfli <- which(f >= fb[i])[1]\n\t\tfui <- max(which(f < fb[i+1]))\n\t\tfor (k in 1:M) {\n\t\t\tfcl <- sum(Pss[fli:fui,k])\n\t\t\tP13[k,i] <- fcl\n\t\t}\n\t}\n\ta <- t(10*log10(P13/(pref^2)))-S\n}\n\n# Compute time vector\ntint <- (1-r)*N/Fs\nttot <- M*tint-tint\nt <- seq(0,ttot,tint)\n}\n\nif (nchunks>1){\t\t\t\t\t\t\t\t#if loading in stages, concatenate analyses in each loop iteration\n\tif (q == 1){\n\t\tnewa <- a\n\t\tnewt <- t\n\t\tcat('Chunk size:',chunksize,'s\\nAnalysing in',nchunks,'chunks. Analysing chunk 1')\n\t} else if (q > 1){\n\t\tdima <- dim(a)\n\t\tnewa <- cbind(newa,a)\n\t\tif (timestring != \"\"){\n\t\t\tnewt <- c(newt,t)\n\t\t} else {\n\t\t\tnewt <- c(newt,t+(q-1)*as.numeric(chunksize))\n\t\t} \n\t\tcat(' ',q)\n\t}\n} else {cat('done in',(proc.time()-tana)[3],'s.\\n')}\n}\n\nif (nchunks>1){a <- newa\t\t\t\t\t#reassign output array\n\tt <- newt\n\tcat('\\n')}\n\n# If not calibrated, scale relative dB to zero\nif (calib == 0 & atype != 'Waveform') {a <- a-max(a)}\n\nif (tstamp != \"\"){t <- t+tstamp\n\ttdiff <- max(t)-min(t)\t\t\t\t\t#define time format for x-axis of time plot\n\tif (tdiff < 10){\n\t\ttform <- \"%H:%M:%S:%OS3\"}\n\telse if (tdiff > 10 & tdiff < 86400){ \n\t\ttform <- \"%H:%M:%S\"}\n\telse if (tdiff > 86400 & tdiff < 86400*7){ \n\t\ttform <- \"%H:%M \\n %d %b\"}\n\telse if (tdiff > 86400*7){tform <- \"%d %b %y\"}\n}\n\n## Construct output array------------------------------------------------\n\nif (atype == 'PSD' | atype == 'PowerSpec') {\t \t\t\t\t\n\tA <- cbind(t,t(a))\n\tA <- rbind(c(0,f),A)\n\tif (atype == 'PSD'){aid <- aid + 1}\n\tif (atype == 'PowerSpec'){aid <- aid + 2}\n\tA[1,1] <- aid\n}\n\nif (atype == 'TOL') {\n\tA <- cbind(t,t(a))\n\tA <- rbind(c(0,fc),A)\n\taid <- aid + 3\n\tA[1,1] <- aid\n\tf <- fc\n\t}\n\nif (atype == 'Broadband') {\n\tA <- t(rbind(t,a))\n\taid <- aid + 4\n\tA[1,1] <- aid\n}\n\nif (atype == 'Waveform') {\n\tA <- t(rbind(t,a))\t\t#define output array\n\tA <- rbind(c(0,0),A)\t#add zero top row for metadata\n\taid <- aid + 5\t\t\t#add index to metadata for Waveform\n\tA[1,1] <- aid\t\t\t#encode output array with metadata\n}\n\n## Reduce time resolution if selected------------------------------------------\n\ndimA <- dim(A)\t\t\t\t#dimensions of output array\n\nif (welch != \"\" && atype != \"Waveform\"){\n\tlout <- ceiling(dimA[1]/welch)+1\t#length of new, Welch-averaged, output array\n\tAWelch <- matrix(, nrow = lout, ncol = dimA[2])\t#initialise Welch array\n\tAWelch[1,] <- A[1,]\t\t\t\t\t#assign frequency vector\n\ttint <- A[3,1] - A[2,1]\t\t\t\t#time interval between segments\n\tcat('Welch factor =',welch,'x\\nNew time resolution =',welch,'(Welch factor) x',N/Fs,'s (time segment length) x',r*100,'% (overlap) =',welch*tint,'s\\n')\n\tif (lout == 2){\t\t\t\t\t\t#if Welch factor too large for number of time data points, abort averaging\n\t\tstop(paste('Welch factor is greater than half the number of samples. Set welch <=',dimA[1]/2,', or reduce N.',sep=\"\"),call.=FALSE)\n\t}\telse {\n\t\tfor (i in 2:lout) {\t\t\t\t\t\t#loop through Welch segments for averaging\n\t\t\tstt <- A[2,1] + (i-2)*tint*welch\t#start time\n\t\t\tett <- stt + welch*tint\t\t\t\t#end time\n\t\t\tstiv <- which(A[2:dimA[1]]>=stt)\n\t\t\tsti <- min(stiv)+1\t\t\t\t\t#start index\n\t\t\tetiv <- which(A[2:dimA[1]]= self$begin_year && dat[row_idx,\"year\"] <= self$end_year) {\n dat[row_idx, self$name] <- self$amount\n } else {\n dat[row_idx, self$name] <- 0\n }\n \n # accumulate total spending\n dat[row_idx, \"total_spending\"] <- dat[row_idx, \"total_spending\"] + dat[row_idx, self$name]\n \n return(dat[row_idx,])\n }\n))\n\n\nSpendingSWR <- R6Class(\"SpendingSWR\", list(\n name = NULL,\n initial_rate = NULL,\n initialize = function(name, initial_rate) {\n stopifnot(is.character(name), length(name) == 1)\n stopifnot(is.numeric(initial_rate), length(initial_rate) == 1)\n self$initial_rate <- initial_rate\n self$name <- paste(\"spending\", name, sep=\"_\")\n },\n augment = function(data) {\n data[,self$name] = NA_real_\n return(data)\n },\n update = function(dat, row_idx) {\n if(row_idx == 2) {\n # The first year of retirement, compute initial amount based on a SWR\n dat[row_idx, self$name] <- dat[1, \"total_savings\"] * self$initial_rate\n } else {\n # All subsequent years, just adjust for inflation\n inflation <- dat[row_idx-1, \"inflation\"]\n dat[row_idx, self$name] <- dat[row_idx-1, self$name] * (1+inflation)\n }\n # accumulate total spending\n dat[row_idx, \"total_spending\"] <- dat[row_idx, \"total_spending\"] + dat[row_idx, self$name]\n\n return(dat[row_idx,])\n }\n))\n\n\nSpendingInflationAdjusted <- R6Class(\"SpendingInflationAdjusted\", list(\n name = NULL,\n initial_amt = NULL, #This is in 2020 dollars\n begin_year = NULL,\n end_year = NULL,\n initialize = function(name, initial_amt, begin_year, end_year) {\n stopifnot(is.character(name), length(name) == 1)\n stopifnot(is.numeric(initial_amt), length(initial_amt) == 1)\n stopifnot(is.numeric(begin_year), length(begin_year) == 1)\n stopifnot(is.numeric(end_year), length(end_year) == 1)\n self$name <- paste(\"spending\", name, sep=\"_\")\n self$initial_amt <- initial_amt\n self$begin_year <- begin_year\n self$end_year <- end_year\n },\n augment = function(data) {\n data[,self$name] = NA_real_\n return(data)\n },\n update = function(dat, row_idx) {\n if(dat[row_idx,\"year\"] >= self$begin_year && dat[row_idx,\"year\"] <= self$end_year) {\n inflation_series <- dat[2:row_idx, \"inflation\"] \n dat[row_idx, self$name] <- self$initial_amt * prod(1+inflation_series)\n } else {\n dat[row_idx, self$name] <- 0\n }\n # accumulate total spending\n dat[row_idx, \"total_spending\"] <- dat[row_idx, \"total_spending\"] + dat[row_idx, self$name]\n \n return(dat[row_idx,])\n }\n))\n\n\nAssetAllocationConstant <- R6Class(\"AssetAllocationConstant\", list(\n equities = NULL,\n fixed_income = NULL,\n initialize = function(equities = 0.8, fixed_income = 0.2) {\n stopifnot(is.numeric(equities), length(equities) == 1)\n stopifnot(is.numeric(fixed_income), length(fixed_income) == 1)\n stopifnot(equities+fixed_income == 1)\n self$equities <- equities\n self$fixed_income <- fixed_income\n },\n augment = function(dat) {\n return(dat)\n },\n update = function(dat, row_idx) {\n row <- dat[row_idx,]\n prev_row <- dat[row_idx-1,]\n \n equities_allocation <- self$equities\n bonds_allocation <- self$fixed_income\n \n # TODO: there's probably something smart to do about putting bonds in the \n # taxable account to reduce taxes?? This assumes that each account has \n # the target asset allocation.\n \n # This code is commong to all asset allocation schemes and should be factored out?\n row$taxable_account_value <- row$taxable_account_value + \n row$taxable_account_value * equities_allocation * row$sp500_growth +\n row$taxable_account_value * equities_allocation * row$dividend_yield +\n row$taxable_account_value * bonds_allocation * row$ten_yr_treasury_yield \n \n row$tax_deferred_account_value <- row$tax_deferred_account_value + \n row$tax_deferred_account_value * equities_allocation * row$sp500_growth +\n row$tax_deferred_account_value * equities_allocation * row$dividend_yield + \n row$tax_deferred_account_value * bonds_allocation * row$ten_yr_treasury_yield\n \n row$total_savings <- row$taxable_account_value + row$tax_deferred_account_value\n \n dat[row_idx,] <- row\n return(dat[row_idx,])\n }\n))\n\n\nIncomeConstant <- R6Class(\"IncomeConstant\", list(\n name = NULL,\n amount = NULL,\n \n initialize = function(name, amount) {\n stopifnot(is.character(name), length(name) == 1)\n stopifnot(is.numeric(amount), length(amount) == 1)\n self$amount <- amount\n self$name <- paste(\"income\", name, sep=\"_\")\n },\n \n augment = function(data) {\n data[, self$name] = NA_real_\n return(data)\n },\n \n update = function(dat, row_idx) {\n dat[row_idx, self$name] <- self$amount\n # accumulate total income\n dat[row_idx, \"total_income\"] <- dat[row_idx, \"total_income\"] + dat[row_idx, self$name]\n \n return(dat[row_idx,])\n }\n))\n \nIncomeInflationAdjusted <- R6Class(\"IncomeInflationAdjusted\", list(\n name = NULL,\n initial_amt = NULL, #This is in retirement_year dollars\n begin_year = NULL,\n end_year = NULL,\n initialize = function(name, initial_amt, begin_year, end_year) {\n stopifnot(is.character(name), length(name) == 1)\n stopifnot(is.numeric(initial_amt), length(initial_amt) == 1)\n stopifnot(is.numeric(begin_year), length(begin_year) == 1)\n stopifnot(is.numeric(end_year), length(end_year) == 1)\n self$name <- paste(\"income\", name, sep=\"_\")\n self$initial_amt <- initial_amt\n self$begin_year <- begin_year\n self$end_year <- end_year\n },\n augment = function(data) {\n data[,self$name] = NA_real_\n return(data)\n },\n update = function(dat, row_idx) {\n if(dat[row_idx,\"year\"] >= self$begin_year && dat[row_idx,\"year\"] <= self$end_year) {\n inflation_series <- dat[2:row_idx, \"inflation\"] \n dat[row_idx, self$name] <- self$initial_amt * prod(1+inflation_series)\n } else {\n dat[row_idx, self$name] <- 0\n }\n # accumulate total income\n dat[row_idx, \"total_income\"] <- dat[row_idx, \"total_income\"] + dat[row_idx, self$name]\n \n return(dat[row_idx,])\n }\n))\n\n\nWithdrawTaxableFirst <- R6Class(\"WithdrawTaxableFirst\", list(\n augment = function(data) {\n return(data)\n },\n \n update = function(dat, row_idx) {\n stopifnot(row_idx >= 2)\n\n row <- dat[row_idx,]\n prev_row <- dat[row_idx-1,]\n \n amount <- row$total_spending\n\n if (prev_row$taxable_account_value > amount) {\n taxable_withdrawal <- amount\n tax_deferred_withdrawal <- 0\n } else {\n taxable_withdrawal <- prev_row$taxable_account_value\n tax_deferred_withdrawal <- amount - taxable_withdrawal\n } \n \n row$taxable_account_value <- prev_row$taxable_account_value - taxable_withdrawal\n # TODO: Take taxes into account. Also have to deal with required minimum distributions.\n row$tax_deferred_account_value <- prev_row$tax_deferred_account_value - tax_deferred_withdrawal\n \n dat[row_idx,] <- row\n return(dat[row_idx,]) \n }\n))\n\n\nPersonalInfo <- R6Class(\"PersonalInfo\", lock_objects = FALSE, list(\n retirement_start_year = NULL,\n age_at_retirement = NULL,\n spouse_age_at_retirement = NULL,\n taxable_acct_value_at_retirement = NULL,\n tax_deferred_acct_value_at_retirement = NULL,\n \n initialize = function(retirement_start_year,\n age_at_retirement,\n spouse_age_at_retirement,\n taxable_acct_value_at_retirement,\n tax_deferred_acct_value_at_retirement) {\n \n stopifnot(is.numeric(retirement_start_year), length(retirement_start_year) == 1)\n stopifnot(is.numeric(age_at_retirement), length(age_at_retirement) == 1)\n stopifnot(is.numeric(spouse_age_at_retirement), length(spouse_age_at_retirement) == 1)\n stopifnot(is.numeric(taxable_acct_value_at_retirement), length(taxable_acct_value_at_retirement) == 1)\n stopifnot(is.numeric(tax_deferred_acct_value_at_retirement), length(tax_deferred_acct_value_at_retirement) == 1)\n \n self$retirement_start_year <- retirement_start_year\n self$age_at_retirement <- age_at_retirement\n self$spouse_age_at_retirement <- spouse_age_at_retirement\n self$taxable_acct_value_at_retirement <- taxable_acct_value_at_retirement\n self$tax_deferred_acct_value_at_retirement <- tax_deferred_acct_value_at_retirement\n \n # TODO: set from actuarial tables\n self$age_at_death <- 95\n self$spouse_age_at_death <- 95\n self$years_in_retirement <- max(self$age_at_death-self$age_at_retirement,\n self$spouse_age_at_death-self$spouse_age_at_retirement)\n \n },\n augment = function(data) {\n data %>% mutate(\n taxable_account_value = c(self$taxable_acct_value_at_retirement, \n rep(NA, self$years_in_retirement-1)),\n tax_deferred_account_value = c(self$tax_deferred_acct_value_at_retirement, \n rep(NA, self$years_in_retirement-1)),\n total_savings = taxable_account_value + tax_deferred_account_value)\n return(data)\n }\n))\n", "meta": {"hexsha": "a0d9d295c2725b55b83013b8b793f9edf78056a9", "size": 9820, "ext": "r", "lang": "R", "max_stars_repo_path": "configuration.r", "max_stars_repo_name": "jrauser/finsim", "max_stars_repo_head_hexsha": "c9e4ce857028aa390b230ba49197897252ff09a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "configuration.r", "max_issues_repo_name": "jrauser/finsim", "max_issues_repo_head_hexsha": "c9e4ce857028aa390b230ba49197897252ff09a0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "configuration.r", "max_forks_repo_name": "jrauser/finsim", "max_forks_repo_head_hexsha": "c9e4ce857028aa390b230ba49197897252ff09a0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-08-06T00:54:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-06T00:54:40.000Z", "avg_line_length": 35.0714285714, "max_line_length": 116, "alphanum_fraction": 0.6804480652, "num_tokens": 2635, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.31282343380725797}} {"text": "Square <- function(x) {\r\n return(x^2)\r\n}\r\n\r\ncat(\"R program running with fortran\")\r\nprint(Square(8))\r\n\r\nwhile (TRUE) {\r\n Sys.sleep(3)\r\n}\r\n", "meta": {"hexsha": "5f3b91d286fd68f1957a9cfc1db287fd76859d3f", "size": 145, "ext": "r", "lang": "R", "max_stars_repo_path": "fixtures/simple_fortran_required/simple.r", "max_stars_repo_name": "Rushikesh-m/r-buildpack", "max_stars_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2018-07-05T20:52:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-17T22:28:19.000Z", "max_issues_repo_path": "fixtures/simple_fortran_required/simple.r", "max_issues_repo_name": "Rushikesh-m/r-buildpack", "max_issues_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 107, "max_issues_repo_issues_event_min_datetime": "2018-02-06T18:21:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-11T00:44:13.000Z", "max_forks_repo_path": "fixtures/simple_fortran_required/simple.r", "max_forks_repo_name": "Rushikesh-m/r-buildpack", "max_forks_repo_head_hexsha": "66c59e283e5bb9978fb708ecb08fd8353d28dee1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 23, "max_forks_repo_forks_event_min_datetime": "2018-03-27T05:17:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-07T21:57:00.000Z", "avg_line_length": 13.1818181818, "max_line_length": 38, "alphanum_fraction": 0.5655172414, "num_tokens": 40, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. Yes\n2. Yes", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3128234261417961}} {"text": "library(Matrix)\nbuenrostro2018Data<-readRDS(\"../input/buenrostro2018-snap-full.rds\")\nlabels<-read.table(\"../input/metadata_buenrostro2018_sorted.tsv\",header = TRUE,sep = \"\\t\",check.names = FALSE)\n\nenhanced_buenrostro2018Data<-buenrostro2018Data\ncutOff<-0.1\nfor(label in levels(labels$label)){\n barcode<-labels[labels$label == label,]$barcode\n cell_data<-buenrostro2018Data[rownames(buenrostro2018Data) %in% barcode,]\n nonZeroColumnList_cell<-diff(cell_data@p)/nrow(cell_data)\n candidateRange<-which(nonZeroColumnList_cell>=cutOff)\n j<-1\n for(i in candidateRange){\n enhanced_buenrostro2018Data[rownames(enhanced_buenrostro2018Data) %in% barcode,i] = 1\n print(paste0(label,\":\",j,\"/\",length(candidateRange),\" finished\"))\n j<-j+1\n }\n}\nsaveRDS(enhanced_buenrostro2018Data,file = paste0('../output/buenrostro2018-snap-full_enh',cutOff,'.rds'))\n\n\n", "meta": {"hexsha": "a853910197e5c4679c531ca362335601a7472328", "size": 882, "ext": "r", "lang": "R", "max_stars_repo_path": "intra-dataset/Buenrostro2018/bin/2_rangeEnhance.r", "max_stars_repo_name": "mrcuizhe/svmATAC", "max_stars_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-09-23T13:14:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T00:35:09.000Z", "max_issues_repo_path": "intra-dataset/Buenrostro2018/bin/2_rangeEnhance.r", "max_issues_repo_name": "mrcuizhe/svmATAC", "max_issues_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intra-dataset/Buenrostro2018/bin/2_rangeEnhance.r", "max_forks_repo_name": "mrcuizhe/svmATAC", "max_forks_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.0909090909, "max_line_length": 110, "alphanum_fraction": 0.7392290249, "num_tokens": 277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6406358548398982, "lm_q2_score": 0.4882833952958346, "lm_q1q2_score": 0.3128118503494749}} {"text": "###plot pysim5g lookup tables\n# install.packages(\"tidyverse\")\nlibrary(tidyverse)\nlibrary(plyr)\nlibrary(ggpubr)\n#####################\n\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\n#get path to full tables via the data folder\nfull_tables <- file.path(folder, '..', 'data', 'raw', 'pysim5g', 'full_tables')\n\n#get a list of all files in the folder ending in .csv\nmyfiles = list.files(path=full_tables, pattern=\"*.csv\", full.names=TRUE)\n\n#import data for all files in file list\ndata = ldply(myfiles, read_csv)\n\n# lut = file.path(folder, '..', 'data', 'raw', 'pysim5g', 'capacity_lut_by_frequency.csv')\n# data = read.csv(lut)\n\ndata = data[data$transmittion_type == '1x1' |\n data$transmittion_type == '2x2'\n ,]\n\ndata = data[data$frequency_GHz == 2.1 |\n data$frequency_GHz == 0.8 |\n data$frequency_GHz == 0.7 |\n data$frequency_GHz == 1.8\n,]\n\n# data <- data[!(data$generation == \"5G\"),]\n\n# #drop results over 5km distance\n# data = data[data$inter_site_distance_m <= 10000,]\n#drop results over 5km distance\ndata = data[data$r_distance <= 7500,]\n\n#turn env into factor and relabel\ndata$environment = factor(data$environment, levels=c(\"urban\",\n \"suburban\",\n \"rural\"),\n labels=c(\"Urban\",\n \"Suburban\",\n \"Rural\"))\n\ndata$combined <- paste(data$generation, data$frequency_GHz, sep=\"_\")\n\ndata$combined = factor(data$combined,\n levels=c(\"3G_1.8\",\n \"3G_2.1\",\n \"4G_0.7\",\n \"4G_0.8\",\n \"4G_1.8\",\n \"4G_2.1\"),\n labels=c(\"1.8 (3G)\",\n \"2.1 (3G)\",\n \"0.7 (4G)\",\n \"0.8 (4G)\",\n \"1.8 (4G)\",\n \"2.1 (4G)\"))\nunique(data$combined)\n#subset the data for plotting\ndata = select(data, inter_site_distance_m, r_distance, environment,\n combined, spectral_efficiency_bps_hz, capacity_mbps)\n\nse = ggplot(data, aes(x=r_distance/1000, y=spectral_efficiency_bps_hz,\n colour=factor(combined))) +\n # geom_point(size=0.1) +\n geom_smooth(size=0.5) +\n scale_x_continuous(expand = c(0, 0), limits=c(0,7.5)) +\n scale_y_continuous(expand = c(0, 0), limits=c(0,10.5)) +\n theme(legend.position=\"bottom\") + guides(colour=guide_legend(ncol=7)) +\n labs(title = 'Mean Spectral Efficiency by Frequency and Technology',\n x = 'Cell Radius (km)', y='Spectral Efficiency (Bps/Hz)',\n colour='Frequency (GHz)') +\n facet_wrap(~environment)\n\npath = file.path(folder, 'figures', 'se_panel.png')\nggsave(path, units=\"in\", width=7, height=3.5)\nprint(se)\ndev.off()\n", "meta": {"hexsha": "dafcd6161c76e840d151d4094f0f55f0164b594b", "size": 3029, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/pysim5g_capacity_vs_distance.r", "max_stars_repo_name": "edwardoughton/ictp4d", "max_stars_repo_head_hexsha": "0e36b3c4515e57cc9210bd22f2ab761f2aa750d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-02-07T19:36:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-20T16:46:02.000Z", "max_issues_repo_path": "vis/pysim5g_capacity_vs_distance.r", "max_issues_repo_name": "edwardoughton/ictp4d", "max_issues_repo_head_hexsha": "0e36b3c4515e57cc9210bd22f2ab761f2aa750d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/pysim5g_capacity_vs_distance.r", "max_forks_repo_name": "edwardoughton/ictp4d", "max_forks_repo_head_hexsha": "0e36b3c4515e57cc9210bd22f2ab761f2aa750d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0595238095, "max_line_length": 90, "alphanum_fraction": 0.5278969957, "num_tokens": 765, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.31281184364911624}} {"text": "CrossValidationSplit<-function(input.file,output.file,response.col=\"ResponseBinary\",n.folds=10,stratify=FALSE){\n\n#Description:\n#this code takes as input an mds file with the first line being the predictor or\n#response name, the second line an indicator of predictors to include and the\n#third line being paths where tif files can be found. An output file and a\n#response column must also be specified. Given a number of folds, a new\n#column is created indicating which fold each observation will be assigned to\n#if a Split Column is also found (test/train split) then only the train portion\n#will be assigned a fold. Optional stratification by response is also available\n#Background points are ignored\n#by this module (they are read in, written out, but not assigned to cv folds.\n#Output is written to a csv that can be used by the\n#SAHM R modules.\n\n#Written by Marian Talbert 9/29/2011\n\n if(n.folds<=1 | n.folds%%1!=0) stop(\"n.folds must be an integer greater than 1\")\n browser()\n\n #Read input data and remove any columns to be excluded\n dat.in<-read.csv(input.file,header=FALSE,as.is=TRUE)\n dat<-as.data.frame(dat.in[4:dim(dat.in)[1],])\n names(dat)<-dat.in[1,]\n\n response<-dat[,match(tolower(response.col),tolower(names(dat)))]\n\n if(sum(as.numeric(response)==0)==0 && !is.null(stratify)) stop(\"The ratio of presence to absence cannot be set with only presence data\")\n\n #Ignoring background data that might be present in the mds\n\n bg.dat<-dat[response==-9999,]\n\n if(dim(bg.dat)[1]!=0){\n dat<-dat[-c(which(response==-9999,arr.ind=TRUE)),]\n dat.in<-dat.in[-c(which(response==-9999,arr.ind=TRUE)+3),]\n response<-response[-c(which(response==-9999,arr.ind=TRUE))]\n bg.dat$TrainSplit=\"\"\n }\n\n #this splits the training set\n split.mask<-dat[,match(tolower(\"split\"),tolower(names(dat)))]==\"train\"\n index<-seq(1:nrow(dat))[split.mask]\n if(stratify==TRUE){\n dat[,ncol(dat)+1]<-NA\n for(i in 1:names(table(response))){\n index.i<-index[response[split.mask]==names(table(response))[i]]\n index.i<-index.i[order(runif(length(index.i)))]\n dat[index.i,ncol(dat)]<-c(rep(seq(1:n.folds),each=floor(length(index.i)/n.folds)),sample(seq(1:n.folds),size=length(index.i)%%n.folds,replace=FALSE))\n }\n } else{\n index<-index[order(runif(length(index)))]\n dat[index,ncol(dat)+1]<-c(rep(seq(1:n.folds),each=floor(length(index)/n.folds)),sample(seq(1:n.folds),size=length(index)%%n.folds,replace=FALSE))\n }\n\n #inserting data must be done in 3 steps because dat.in isn't a proper dataframe in that\n #not all elements in a column are of the same type\n dat.in<-dat.in[c(1:3,rownames(dat)),] #removing rows that weren't selected for the test train split\n dat.in[4:(dim(dat.in)[1]),(dim(dat.in)[2]+1)]<-dat$TrainSplit\n dat.in[c(1,3),(dim(dat.in)[2])]<-c(\"Split\",\"\")\n dat.in[2,(dim(dat.in)[2])]<-1\n\n if(dim(bg.dat)[1]!=0) {\n names(bg.dat)<-names(dat.in)\n dat.in<-rbind(dat.in,bg.dat)}\n\n #write output files for R modules\n write.table(dat.in,file=output.file,row.names=FALSE,col.names=FALSE,sep=\",\",quote=FALSE)\n\n\n }\n\n\n #Reading in command line arguments\n Args <- commandArgs(T)\n print(Args)\n #assign default values\n\n responseCol <- \"responseBinary\"\n trainProp=.7\n RatioPresAbs=NULL\n #replace the defaults with passed values\n for (arg in Args) {\n \targSplit <- strsplit(arg, \"=\")\n \targSplit[[1]][1]\n \targSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"p\") trainProp <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"m\") RatioPresAbs <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"o\") output.file <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"i\") infil <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"rc\") responseCol <- argSplit[[1]][2]\n }\n\n RatioResAbs<-as.numeric(RatioPresAbs)\n trainProp<-as.numeric(trainProp)\n\n\t#Run the Test training split with these parameters\n\tCrossValidationSplit(input.file=infil,output.file=output.file,response.col=responseCol,\n trainProp=trainProp,RatioPresAbs=RatioPresAbs)\n", "meta": {"hexsha": "a87e42593e7ee4cec5488723adf03e45c8cc6de7", "size": 4351, "ext": "r", "lang": "R", "max_stars_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/CrossValidationSplit.r", "max_stars_repo_name": "celiafish/VisTrails", "max_stars_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 83, "max_stars_repo_stars_event_min_datetime": "2015-01-05T14:50:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-17T19:45:26.000Z", "max_issues_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/CrossValidationSplit.r", "max_issues_repo_name": "celiafish/VisTrails", "max_issues_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 254, "max_issues_repo_issues_event_min_datetime": "2015-01-02T20:39:19.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T17:16:44.000Z", "max_forks_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/CrossValidationSplit.r", "max_forks_repo_name": "celiafish/VisTrails", "max_forks_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 40, "max_forks_repo_forks_event_min_datetime": "2015-04-17T16:46:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-28T22:43:24.000Z", "avg_line_length": 43.51, "max_line_length": 167, "alphanum_fraction": 0.6265226385, "num_tokens": 1142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723317123102956, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.31256806953721744}} {"text": "#' @title solver\n#' @description unknown\n#' @family abysmally documented\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\nsolver = function(pars,fn,hess=TRUE,...)\n{\n fit = optim(pars,fn,hessian=hess,...)\n if(hess){\n fit$VarCov = solve(fit$hessian) #Variance-Covariance\n fit$SDs = sqrt(diag(fit$V)) #Standard deviations\n fit$Correlations = fit$V/(fit$S %o% fit$S) #Parameter correlation\n } \n return(fit)\n}\n", "meta": {"hexsha": "c1f090d78fbd3fe71dd1c04b9c400ecd78f5d172", "size": 474, "ext": "r", "lang": "R", "max_stars_repo_path": "R/solver.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/solver.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/solver.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.625, "max_line_length": 70, "alphanum_fraction": 0.6097046414, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6723316991792861, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3125680634325904}} {"text": "merge.dictionaries <- function(dictionary.base, dictionary){\n # Returns:\n # - A new dictionary\n # - A mapping vector to map elements from the second dictionary to the base dictionary\n\n dictionary.global <- dictionary.base\n last.pos <- length(dictionary.global)\n mapping <- vector()\n for(i in 1:length(dictionary)){\n dupl <- FALSE\n \n # search a similar motif in the dictionary\n for(j in 1:length(dictionary.base)){\n if(vcount(dictionary[[i]]) != vcount(dictionary.base[[j]])){\n next\n }\n if(is_isomorphic_to(dictionary[[i]], dictionary.base[[j]], method='vf2')){\n # if already in the base dict, save a pointer to it\n dupl <- TRUE\n mapping[i] <- j\n cat(\"\\n\", i, \" -> \", j)\n break\n }\n }\n \n # if not found among motifs in the dictionary, give it a new entry\n if(!dupl){\n new.pos <- last.pos + 1\n dictionary.global[[new.pos]] <- dictionary[[i]] # copy motif graph\n mapping[i] <- new.pos\n cat(\"\\nnew \", i, \" -> \", new.pos)\n last.pos <- last.pos + 1\n }\n }\n \n return(list(dict = dictionary.global, \n mapping = mapping))\n}", "meta": {"hexsha": "fad9046aa64b5a8b2f79c126528a6eb2ac7cbfe8", "size": 1151, "ext": "r", "lang": "R", "max_stars_repo_path": "R/merge_indices.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "R/merge_indices.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/merge_indices.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2894736842, "max_line_length": 89, "alphanum_fraction": 0.583840139, "num_tokens": 288, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3125228910109688}} {"text": "# plot DFSC comparison graphs, R = 24, N = 24\nsurgeD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/surge24.csv\", header = TRUE)\nnomD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/nom24.csv\", header = TRUE)\ntotalD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/total24.csv\", header = TRUE)\n\noutString = \"./Desktop/Git/daniel_Diesel/output/24Plot.png\"\npng(file = outString, width= 8, height = 12.5, units = 'in',res = 300);\npar(mfrow=c(3,1));\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,surgeD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Surge Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,surgeD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,surgeD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,surgeD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,surgeD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,nomD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Nominal Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,nomD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,nomD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,nomD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,nomD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,totalD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Total Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,totalD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,totalD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,totalD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,totalD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\ndev.off()\n\n# plot DFSC comparison graphs, R = 12, N = 12\nsurgeD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/surge12.csv\", header = TRUE)\nnomD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/nom12.csv\", header = TRUE)\ntotalD <- read.csv(\"./Desktop/Git/daniel_Diesel/output/total12.csv\", header = TRUE)\n\noutString = \"./Desktop/Git/daniel_Diesel/output/12Plot.png\"\npng(file = outString, width= 8, height = 12.5, units = 'in',res = 300);\npar(mfrow=c(3,1));\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,surgeD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Surge Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,surgeD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,surgeD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,surgeD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,surgeD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,nomD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Nominal Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,nomD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,nomD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,nomD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,nomD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\npar(mar = c(5,5,2.5,2.5));\nplot(0:384,totalD$TotalD[1:385], type = \"l\", xlab = \"Datetime\", ylab = \"Demand\", main = \"Total Demand\",\n col = \"#000000\", lwd = 2, cex.main = 1.5, cex.lab = 1.5, axes = F);\naxis(cex.axis = 1.5,side=1,at=c(0,24,48,72,96,120,144,168,192,216,240,264,288,312,336,360,384),\n labels=c(\"09-06\",\"09-07\",\"09-08\",\"09-09\",\"09-10\",\"09-11\",\"09-12\",\"09-13\",\"09-14\",\"09-15\",\"09-16\",\"09-17\",\"09-18\",\"09-19\",\"09-20\",\"09-21\",\"09-22\"));\naxis(cex.axis = 1.5,side=2);\nlines(0:384,totalD$GEFS[1:385], col = \"#377EB8\", lwd = 2);\nlines(0:384,totalD$GAVG[1:385], col = \"#E41A1C\", lwd = 2);\nlines(0:384,totalD$NDFD[1:385], col = \"#4DAF4A\", lwd = 2);\nlines(0:384,totalD$REAL[1:385], col = \"#984EA3\", lwd = 2);\nlegend(\"topleft\",c(\"Total\",\"Shortage:GEFS\",\"Shortage:GAVG\",\"Shortage:NDFD\",\"Shortage:PI\"), , col = c(\"#000000\",\"#377EB8\",\"#E41A1C\",\"#4DAF4A\",\"#984EA3\"),pch = 20,cex = 1.5);\n\ndev.off()", "meta": {"hexsha": "7017987dbf67410fd1e898a713c36e2a1e7a3252", "size": 6247, "ext": "r", "lang": "R", "max_stars_repo_path": "IO/plotDFSC.r", "max_stars_repo_name": "haoxiangyang89/DFSC_FL", "max_stars_repo_head_hexsha": "6bc78a077ce8061a1173ca6e42219fd69e9e373a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "IO/plotDFSC.r", "max_issues_repo_name": "haoxiangyang89/DFSC_FL", "max_issues_repo_head_hexsha": "6bc78a077ce8061a1173ca6e42219fd69e9e373a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "IO/plotDFSC.r", "max_forks_repo_name": "haoxiangyang89/DFSC_FL", "max_forks_repo_head_hexsha": "6bc78a077ce8061a1173ca6e42219fd69e9e373a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 68.6483516484, "max_line_length": 172, "alphanum_fraction": 0.6050904434, "num_tokens": 2822, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3125228910109688}} {"text": "#NEON core-scale cross-validation.\n#There was a problem in MAP values in prior that resulted in no convergence and wack values. Need to try again. All paths should work!\n#Fit MULTINOMIAL dirlichet models to all groups of fungi from 50% of NEON core-scale observations.\n#Not going to apply hierarchy, because it would not be a fair comparison to the Tedersoo model.\n#Missing data are allowed.\n#clear environment\nrm(list = ls())\nlibrary(runjags)\nlibrary(data.table)\nlibrary(doParallel)\nsource('paths.r')\nsource('NEFI_functions/ddirch_site.level_JAGS.r')\nsource('NEFI_functions/crib_fun.r')\nsource('NEFI_functions/tic_toc.r')\n\n#detect and register cores.----\nn.cores <- detectCores()\nregisterDoParallel(cores=n.cores)\n\n#set output path.----\n output.path <- core.CV_NEON_dmulti.ddirch_all.path\ncalval_data.path <- core.CV_NEON_cal.val_data.path\n\n#set cal-val split.----\ncal.val_split <- 0.7 #70% calibration, 30% validation.\n\n#load NEON core-scale data.----\n#NOTE: MAP MEANS AND SDS MUST BE DIVIDED BY 1000.\n#WE SHOULD REALLY MOVE THIS TO DATA PRE-PROCESSING.\ndat <- readRDS(hierarch_filled.path)\n#y <- readRDS(tedersoo_ITS_common_phylo_groups_list_1k.rare.path)\n#pl.truth <- readRDS(NEON_all.phylo.levels_plot.site_obs_fastq_1k_rare.path) #this has the plot and site values for NEON.\ny <- readRDS(NEON_ITS_fastq_all_cosmo_phylo_groups_1k_rare.path)\n\n\n#get core-level covariate means and sd.----\ncore_mu <- dat$core.core.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n\n#merge together.\nplot_mu$siteID <- NULL\ncore.preds <- merge(core_mu , plot_mu)\ncore.preds <- merge(core.preds, site_mu)\ncore.preds$relEM <- NULL\nnames(core.preds)[names(core.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get core-level SD.\ncore_sd <- dat$core.core.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nplot_sd$siteID <- NULL\ncore.sd <- merge(core_sd , plot_sd)\ncore.sd <- merge(core.sd, site_sd)\ncore.sd$relEM <- NULL\nnames(core.sd)[names(core.sd)==\"b.relEM\"] <- \"relEM\"\n#IMPORTANT: Reduce magnitude of MAP!\n#log transform map means and standard deviations, magnitudes in 100s-1000s break JAGS code.\ncore.preds$map <- core.preds$map / 1000\ncore.sd $map <- core.sd $map / 1000\n\n#Get relEM back to 0-100.\ncore.preds$relEM <- boot::inv.logit(core.preds$relEM)\n\n#Split into calibration / validation data sets.----\nset.seed(420)\nID <- rownames(y$phylum$abundances)\ncal.ID <- sample(ID, round(length(ID) * cal.val_split))\nval.ID <- ID[!(ID %in% cal.ID)]\n\n#loop through y values. Wasn't an easy way to loop through levels of list.\ny.cal <- list()\ny.val <- list()\nfor(i in 1:length(y)){\n lev <- y[[i]]\n lev.cal <- list()\n lev.val <- list()\n lev.cal$abundances <- lev$abundances[rownames(lev$abundances) %in% cal.ID,]\n lev.val$abundances <- lev$abundances[rownames(lev$abundances) %in% val.ID,]\n lev.cal$rel.abundances <- lev$rel.abundances[rownames(lev$rel.abundances) %in% cal.ID,]\n lev.val$rel.abundances <- lev$rel.abundances[rownames(lev$rel.abundances) %in% val.ID,]\n lev.cal$seq_total <- lev$seq_total[names(lev$seq_total) %in% cal.ID]\n lev.val$seq_total <- lev$seq_total[names(lev$seq_total) %in% val.ID]\n lev.cal$group_frequencies <- lev$group_frequencies\n lev.val$group_frequencies <- lev$group_frequencies\n #return to larger list.\n y.cal[[i]] <- lev.cal\n y.val[[i]] <- lev.val\n}\nnames(y.cal) <- names(y)\nnames(y.val) <- names(y)\n\n#Drop groups that just aren't observed frequently enough (>30% of samples) to fit a decent model (mostly zeros).\nfreq.filter <- list()\nfor(i in 1:length(y.cal)){\n check <- y.cal[[i]]$rel.abundances\n abundance.check <- list()\n for(j in 1:ncol(check)){\n z <- check[,j]\n abundance.check[[j]] <- sum(z > 0.01)/ length(z)\n }\n abundance.check <- unlist(abundance.check)\n names(abundance.check) <- colnames(check)\n abundance.check <- abundance.check[abundance.check >= 0.3]\n freq.filter[[i]] <- abundance.check\n}\nnames(freq.filter) <- names(y.cal)\n\n#update your lists.\nfor(i in 1:length(y.cal)){\n z.cal <- y.cal[[i]]$rel.abundances\n z.val <- y.val[[i]]$rel.abundances\n z.cal <- z.cal[,colnames(z.cal) %in% names(freq.filter[[i]])]\n z.val <- z.val[,colnames(z.val) %in% names(freq.filter[[i]])]\n z.cal[,1] <- 1 - rowSums(z.cal[,2:ncol(z.cal)])\n z.val[,1] <- 1 - rowSums(z.val[,2:ncol(z.val)])\n if(mean(rowSums(z.cal)) != 1){cat('Warning: rowSums of calibration data do not sum to 1.')}\n if(mean(rowSums(z.val)) != 1){cat('Warning: rowSums of calibration data do not sum to 1.')}\n y.cal[[i]]$rel.abundances <- z.cal\n y.val[[i]]$rel.abundances <- z.val\n}\n\n#split x means and sd's.\nx_mu.cal <- core.preds[core.preds$sampleID %in% gsub('-GEN','',cal.ID),]\nx_mu.val <- core.preds[core.preds$sampleID %in% gsub('-GEN','',val.ID),]\nx_sd.cal <- core.sd [core.sd $sampleID %in% gsub('-GEN','',cal.ID),]\nx_sd.val <- core.sd [core.sd $sampleID %in% gsub('-GEN','',val.ID),]\n\n#match the order.\nx_mu.cal <- x_mu.cal[order(match(x_mu.cal$sampleID, gsub('-GEN','',rownames(y.cal$phylum$abundances)))),]\nx_sd.cal <- x_sd.cal[order(match(x_sd.cal$sampleID, gsub('-GEN','',rownames(y.cal$phylum$abundances)))),]\nx_mu.val <- x_mu.val[order(match(x_mu.val$sampleID, gsub('-GEN','',rownames(y.val$phylum$abundances)))),]\nx_sd.val <- x_sd.val[order(match(x_sd.val$sampleID, gsub('-GEN','',rownames(y.val$phylum$abundances)))),]\n\n\n#subset to predictors of interest, drop in intercept.----\nrownames(x_mu.cal) <- rownames(y.cal$phylum$abundances)\nintercept <- rep(1, nrow(x_mu.cal))\nx_mu.cal <- cbind(intercept, x_mu.cal)\nx_mu.cal <- x_mu.cal[,c('intercept','pH','pC','cn','relEM','map','mat','NPP','forest','conifer')]\n\n\n#save calibration/valiation data sets.----\ndat.cal <- list(y.cal, x_mu.cal, x_sd.cal)\ndat.val <- list(y.val, x_mu.val, x_sd.val)\nnames(dat.cal) <- c('y.cal','x_mu.cal','x_sd.cal')\nnames(dat.val) <- c('y.val','x_mu.val','x_sd.val')\ndat.out <- list(dat.cal, dat.val)\nnames(dat.out) <- c('cal','val')\nsaveRDS(dat.out, core.CV_NEON_cal.val_data.path)\n\n#fit model using function in parallel loop.-----\n#for running production fit on remote.\ncat('Begin model fitting loop...\\n')\ntic()\noutput.list<-\n foreach(i = 1:length(y)) %dopar% {\n y.group <- y.cal[[i]]$abundances\n y.group <- y.group + 1\n y.group <- y.group/rowSums(y.group)\n fit <- site.level_dirlichet_jags(y=y.group,x_mu=x_mu.cal, #x_sd=x_sd.cal,\n adapt = 200, burnin = 3000, sample = 3000, \n #adapt = 200, burnin = 200, sample = 200, #testing\n parallel = T, parallel_method = 'parallel') #setting parallel rather than rjparallel. \n return(fit) #allows nested loop to work.\n }\ncat('Model fitting loop complete! ')\ntoc()\n\n\n#name the items in the list\nnames(output.list) <- names(y.cal)\n\n#save output.----\ncat('Saving fit...\\n')\nsaveRDS(output.list, output.path)\ncat('Script complete. \\n')\n", "meta": {"hexsha": "b98660a1a7145bcb57cb127324b14254efcabd13", "size": 6925, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/2._NEON_all.groups_ddirch_core_CV_fit.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/2._NEON_all.groups_ddirch_core_CV_fit.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/2._NEON_all.groups_ddirch_core_CV_fit.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 38.687150838, "max_line_length": 134, "alphanum_fraction": 0.6720577617, "num_tokens": 2115, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.3124359434975043}} {"text": "## Written by Eilis\n## Summarize CNV calls\n## identify any overlapping with known SCZ loci or pathogenic CNVs\n\nfindOverlapsMinProp<-function(query, subject, pThres){\n\t# function to find overlap between two Granges based on minimum proportion\n\thits <- findOverlaps(query, subject)\n\toverlaps <- pintersect(query[queryHits(hits)], subject[subjectHits(hits)])\n\tpercentOverlap <- width(overlaps) / width(subject[subjectHits(hits)])\n\thits <- hits[percentOverlap > pThres]\n\thitsOut <- query[queryHits(hits)]\n\tmcols(hitsOut)$Locus<-subject$Locus[subjectHits(hits)]\n\tmcols(hitsOut)$hg38<-subject$hg38[subjectHits(hits)]\n\treturn(hitsOut)\n}\n\n\nlibrary(GenomicRanges)\nargs<-commandArgs(trailingOnly = TRUE)\nfileName<-args[1]\nsuperPop<-args[2]\nfolder<-dirname(fileName)\n\nsetwd(\"/gpfs/mrc0/projects/Research_Project-MRC190311/SNPdata/CNV/\")\ndat<-read.table(\"PennCNVOutput/SCZ_GCModel_MergedFiltered_AnnoGencodev29.rawcnv\", stringsAsFactors=FALSE)\n\npheno<-read.table(\"../MRC2_UpdatePheno.txt\")\n\ndat$V2<-as.numeric(gsub(\"numsnp=\", \"\",dat$V2))\ndat$V3<-as.numeric(gsub(\",\", \"\", gsub(\"length=\", \"\",dat$V3)))\n\ntable(dat$V4)\n\n## summarise CNV calls\n\npar(mfrow = c(3,2))\nhist(dat$V2, xlab = \"number of markers\", breaks = 35, main = \"All\")\nhist(dat$V3/1000, xlab = \"length (kb)\", breaks = 35, main = \"All\")\nindex.del<-which(dat$V4 == \"state2,cn=1\")\nhist(dat$V2[index.del], xlab = \"number of markers\", breaks = 35, main = \"Deletions\")\nhist(dat$V3[index.del]/1000, xlab = \"length (kb)\", breaks = 35, main = \"Deletions\")\nindex.dup<-which(dat$V4 == \"state5,cn=3\")\nhist(dat$V2[index.dup], xlab = \"number of markers\", breaks = 35, main = \"Duplications\")\nhist(dat$V3[index.dup]/1000, xlab = \"length (kb)\", breaks = 35, main = \"Duplications\")\n\n\n## summarise CNVs by person\npar(mfrow = c(3,2))\ntotByPerson<-aggregate(dat$V3/1000, by = list(dat$V5), sum)\nmuByPerson<-aggregate(dat$V3/1000, by = list(dat$V5), mean)\nhist(totByPerson[,2], xlab = \"Combined length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"All\")\nhist(muByPerson[,2], xlab = \"Mean length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"All\")\ntotByPerson<-aggregate(dat$V3[index.del]/1000, by = list(dat$V5[index.del]), sum)\nmuByPerson<-aggregate(dat$V3[index.del]/1000, by = list(dat$V5[index.del]), mean)\nhist(totByPerson[,2], xlab = \"Combined length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"Deletions\")\nhist(muByPerson[,2], xlab = \"Mean length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"Deletions\")\ntotByPerson<-aggregate(dat$V3[index.dup]/1000, by = list(dat$V5[index.dup]), sum)\nmuByPerson<-aggregate(dat$V3[index.dup]/1000, by = list(dat$V5[index.dup]), mean)\nhist(totByPerson[,2], xlab = \"Combined length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"Duplications\")\nhist(muByPerson[,2], xlab = \"Mean length of CNVs\", ylab = \"Number of samples\", breaks = 35, main = \"Duplications\")\n\n\n## convert to GRanges; do sep for deletions and duplications\nindex<-which(dat$V4 == \"state2,cn=1\")\nallCNVs.del<-GRanges(dat$V1[index])\nmcols(allCNVs.del)$SampleID <- dat$V5[index]\nmcols(allCNVs.del)$Type <- dat$V4[index]\nindex<-which(dat$V4 == \"state5,cn=3\")\nallCNVs.dup<-GRanges(dat$V1[index])\nmcols(allCNVs.dup)$SampleID <- dat$V5[index]\nmcols(allCNVs.dup)$Type <- dat$V4[index]\n\n## do any overlap known scz cnv loci ## list taken from Rees et al. Br J Psychiatry (merge tables 1 & 2)\nsczLoci<-read.csv(\"../../References/CNV/SCZ_CNVloci.csv\", skip = 1, stringsAsFactors = FALSE)\n## filter to those significant in MetaAnalysis\nsczLoci<-sczLoci[which(sczLoci$significantMeta == \"*\"),]\nsczLoci.hg38.del<-GRanges(sczLoci$hg38[grep(\"del\", sczLoci$Locus)])\nmcols(sczLoci.hg38.del)$Locus<-sczLoci$Locus[grep(\"del\", sczLoci$Locus)]\nmcols(sczLoci.hg38.del)$hg38<-sczLoci$hg38[grep(\"del\", sczLoci$Locus)]\nsczLoci.hg38.dup<-GRanges(sczLoci$hg38[grep(\"dup\", sczLoci$Locus)])\nmcols(sczLoci.hg38.dup)$Locus<-sczLoci$Locus[grep(\"dup\", sczLoci$Locus)]\nmcols(sczLoci.hg38.dup)$hg38<-sczLoci$hg38[grep(\"dup\", sczLoci$Locus)]\n\npThres<-0.9 ## set as minimum overlap required\noverlapDel<-findOverlapsMinProp(allCNVs.del, sczLoci.hg38.del, pThres)\noverlapDup<-findOverlapsMinProp(allCNVs.dup, sczLoci.hg38.dup, pThres)\noutput<-rbind(data.frame(overlapDel), data.frame(overlapDup))\nwrite.csv(output, \"CNVsoverlappingKnownSCZRiskLoci.csv\")\n\n## do any overlap known ID cnv loci ## list taken from Rees et al. JAMA Psychiatry 2016 (eTable 2)\nidLoci<-read.csv(\"../../References/CNV/IDCNVLoci.csv\", stringsAsFactors = FALSE)\nidLoci<-idLoci[which(idLoci$hg38 != \"\"),] ## 1 region I couldn't lift over\nidLoci.hg38.del<-GRanges(idLoci$hg38[grep(\"del\", idLoci$Syndrome)])\nmcols(idLoci.hg38.del)$Locus<-idLoci$Syndrome[grep(\"del\", idLoci$Syndrome)]\nmcols(idLoci.hg38.del)$hg38<-idLoci$hg38[grep(\"del\", idLoci$Syndrome)]\nidLoci.hg38.dup<-GRanges(idLoci$hg38[grep(\"dup\", idLoci$Syndrome)])\nmcols(idLoci.hg38.dup)$Locus<-idLoci$Syndrome[grep(\"dup\", idLoci$Syndrome)]\nmcols(idLoci.hg38.dup)$hg38<-idLoci$hg38[grep(\"dup\", idLoci$Syndrome)]\n\npThres<-0.9 ## set as minimum overlap required\noverlapDel<-findOverlapsMinProp(allCNVs.del, idLoci.hg38.del, pThres)\noverlapDup<-findOverlapsMinProp(allCNVs.dup, idLoci.hg38.dup, pThres)\noutput<-rbind(data.frame(overlapDel), data.frame(overlapDup))\nwrite.csv(output, \"CNVsoverlappingIDRiskLoci.csv\")\n\n## do any overlap known pathogenic cnv loci ## list taken from Kendall et al 2017 Biol Psychiatry\npathLoci<-read.table(\"../../References/CNV/PathogenicCNVLoci.txt\", stringsAsFactors = FALSE, sep = \"\\t\", header = TRUE)\npathLoci<-pathLoci[which(pathLoci$hg38 != \"\"),] ## 1 region I couldn't lift over\npathLoci.hg38.del<-GRanges(pathLoci$hg38[grep(\"del\", pathLoci$CNV.locus)])\nmcols(pathLoci.hg38.del)$Locus<-pathLoci$CNV.locus[grep(\"del\", pathLoci$CNV.locus)]\nmcols(pathLoci.hg38.del)$hg38<-pathLoci$hg38[grep(\"del\", pathLoci$CNV.locus)]\npathLoci.hg38.dup<-GRanges(pathLoci$hg38[grep(\"dup\", pathLoci$CNV.locus)])\nmcols(pathLoci.hg38.dup)$Locus<-pathLoci$CNV.locus[grep(\"dup\", pathLoci$CNV.locus)]\nmcols(pathLoci.hg38.dup)$hg38<-pathLoci$hg38[grep(\"dup\", pathLoci$CNV.locus)]\n\npThres<-0.9 ## set as minimum overlap required\noverlapDel<-findOverlapsMinProp(allCNVs.del, pathLoci.hg38.del, pThres)\noverlapDup<-findOverlapsMinProp(allCNVs.dup, pathLoci.hg38.dup, pThres)\noutput<-rbind(data.frame(overlapDel), data.frame(overlapDup))\nwrite.csv(output, \"CNVsoverlappingPathogenicCNV.csv\")\n\n\n\n", "meta": {"hexsha": "bfc46f676c600c8077b2c05ade30fd316293b21e", "size": 6382, "ext": "r", "lang": "R", "max_stars_repo_path": "SNPdata/summarizeCNVCalls.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "SNPdata/summarizeCNVCalls.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "SNPdata/summarizeCNVCalls.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.4677419355, "max_line_length": 119, "alphanum_fraction": 0.7328423692, "num_tokens": 2205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056040203135, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.31243593192560926}} {"text": "#######################################################################################################\n# MARSShatyt function\n# Expectations involving hatyt\n#######################################################################################################\nMARSShatyt <- function(MLEobj, only.kem = TRUE) {\n MODELobj <- MLEobj[[\"marss\"]]\n if (!is.null(MLEobj[[\"kf\"]])) {\n kfList <- MLEobj$kf\n } else {\n kfList <- MARSSkf(MLEobj)\n }\n model.dims <- attr(MODELobj, \"model.dims\")\n n <- model.dims$data[1]\n TT <- model.dims$data[2]\n m <- model.dims$x[1]\n\n # create the YM matrix\n YM <- matrix(as.numeric(!is.na(MODELobj[[\"data\"]])), n, TT)\n # Make sure the missing vals in y are zeroed out if there are any\n y <- MODELobj$data\n y[YM == 0] <- 0\n\n # set-up matrices for hatxt for 1:TT and 1:TT-1\n IIz <- list()\n IIz$V0 <- makediag(as.numeric(takediag(parmat(MLEobj, \"V0\", t = 1)$V0) == 0), m)\n # bad notation; should be hatxtT\n hatxt <- kfList$xtT # 1:TT\n E.x0 <- (diag(1, m) - IIz$V0) %*% kfList$x0T + IIz$V0 %*% parmat(MLEobj, \"x0\", t = 1)$x0\n hatxtp <- cbind(kfList$xtT[, 2:TT, drop = FALSE], NA)\n hatVt <- kfList$VtT\n hatVtpt <- array(NA, dim = dim(kfList$Vtt1T))\n hatVtpt[, , 1:(TT - 1)] <- kfList$Vtt1T[, , 2:TT, drop = FALSE]\n if (!only.kem) {\n hatxtt1 <- kfList$xtt1\n hatxtt <- kfList$xtt\n hatxt1 <- cbind(E.x0, kfList$xtT[, 1:(TT - 1), drop = FALSE])\n hatVtt1 <- kfList$Vtt1\n hatVtt <- kfList$Vtt\n hatVtt1T <- kfList$Vtt1T\n }\n\n msg <- NULL\n\n # Construct needed identity matrices\n I.n <- diag(1, n)\n\n # Note diff in param names from S&S;B=Phi, Z=A, A not in S&S\n time.varying <- c()\n pari <- list()\n for (elem in c(\"R\", \"Z\", \"A\")) { # only params needed for this function\n if (model.dims[[elem]][3] == 1) { # not time-varying\n pari[[elem]] <- parmat(MLEobj, elem, t = 1)[[elem]] # by default parmat uses marss form\n if (elem == \"R\") {\n if (length(pari$R) == 1) diag.R <- unname(pari$R) else diag.R <- takediag(unname(pari$R))\n # isDiagonal is rather expensive; this test is faster\n is.R.diagonal <- all(pari$R[!diag(nrow(pari$R))] == 0) # = isDiagonal(pari$R)\n }\n } else {\n time.varying <- c(time.varying, elem)\n } # which elements are time varying\n } # end for loop over elem\n\n\n # initialize - these are for the forward, Kalman, filter\n # for notation purposes, 't' represents current point in time, 'TT' represents the length of the series\n # notation is horrible and leaves off the time conditioning.\n # In most, not all cases, it is 1:TT. If the last time is the conditioning, notation should be\n # hatyt = hatytT, hatyxt = hatytxtT, hatyxtt1T=hatytxt1T, hatyxttp=hatytxtpT, hatyxtt=hatytxtt\n # hatOt = hatOtT, hatOtt1=is ok,\n hatyt <- matrix(0, n, TT)\n hatOt <- array(0, dim = c(n, n, TT))\n hatyxt <- hatyxttp <- array(0, dim = c(n, m, TT))\n\n if (!only.kem) {\n # hatvarEytT = variance of the expected value of ytT\n hatytt1 <- hatytt <- matrix(0, n, TT)\n hatOtt1 <- hatOtt <- array(0, dim = c(n, n, TT))\n hatyxtt1T <- hatyxtt1 <- hatyxtt <- array(0, dim = c(n, m, TT))\n hatvarEytT <- hatvarytT <- hatvarEytt1 <- hatvarytt1 <- array(0, dim = c(n, n, TT))\n }\n\n for (t in 1:TT) {\n for (elem in time.varying) {\n pari[[elem]] <- parmat(MLEobj, elem, t = t)[[elem]]\n if (elem == \"R\") {\n if (length(pari$R) == 1) diag.R <- unname(pari$R) else diag.R <- takediag(unname(pari$R))\n # isDiagonal is rather expensive; this test is faster\n is.R.diagonal <- all(pari$R[!diag(nrow(pari$R))] == 0)\n # is.R.diagonal = isDiagonal(pari$R)\n }\n }\n if (!only.kem) {\n # For conditioning on data up to t-1, the data at time t do not factor in so Delta.r and I.2 not needed\n hatytt1[, t] <- pari$Z %*% hatxtt1[, t, drop = FALSE] + pari$A\n t.Z <- matrix(pari$Z, m, n, byrow = TRUE)\n hatvarEytt1[, , t] <- pari$Z %*% hatVtt1[, , t] %*% t.Z\n hatvarytt1[, , t] <- pari$R + hatvarEytt1[, , t]\n hatOtt1[, , t] <- hatvarytt1[, , t] + tcrossprod(hatytt1[, t, drop = FALSE])\n hatyxtt1[, , t] <- tcrossprod(hatytt1[, t, drop = FALSE], hatxtt1[, t, drop = FALSE]) + pari$Z %*% hatVtt1[, , t]\n }\n\n if (all(YM[, t] == 1)) { # none missing\n hatyt[, t] <- y[, t, drop = FALSE]\n hatOt[, , t] <- tcrossprod(hatyt[, t, drop = FALSE])\n hatyxt[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxt[, t, drop = FALSE])\n hatyxttp[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxtp[, t, drop = FALSE])\n if (!only.kem) {\n hatytt[, t] <- hatyt[, t]\n hatOtt[, , t] <- hatOt[, , t]\n hatyxtt1T[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxt1[, t, drop = FALSE])\n hatyxtt[, , t] <- tcrossprod(hatytt[, t, drop = FALSE], hatxtt[, t, drop = FALSE])\n # don't need to update hatvarEytT[, , t] nor hatvarytT[, , t] since it will be 0\n }\n } else {\n I.2 <- I.r <- I.n\n I.2[YM[, t] == 1, ] <- 0 # 1 if YM=0 and 0 if YM=1\n I.r[YM[, t] == 0 | diag.R == 0, ] <- 0 # if Y missing or R = 0, then 0\n Delta.r <- I.n\n if (is.R.diagonal) Delta.r <- I.n - I.r\n if (!is.R.diagonal && any(YM[, t] == 1 & diag.R != 0)) {\n mho.r <- I.r[YM[, t] == 1 & diag.R != 0, , drop = FALSE]\n t.mho.r <- I.r[, YM[, t] == 1 & diag.R != 0, drop = FALSE]\n Rinv <- try(chol(mho.r %*% pari$R %*% t.mho.r))\n # Catch errors before entering chol2inv\n if (inherits(Rinv, \"try-error\")) {\n return(list(ok = FALSE, errors = \"Stopped in MARSShatyt: chol(R) error.\\n\"))\n }\n Rinv <- chol2inv(Rinv)\n Delta.r <- I.n - pari$R %*% t.mho.r %*% Rinv %*% mho.r\n }\n hatyt[, t] <- y[, t, drop = FALSE] - Delta.r %*% (y[, t, drop = FALSE] - pari$Z %*% hatxt[, t, drop = FALSE] - pari$A)\n t.DZ <- matrix(Delta.r %*% pari$Z, m, n, byrow = TRUE)\n hatOt[, , t] <- I.2 %*% (Delta.r %*% pari$R + Delta.r %*% pari$Z %*% hatVt[, , t] %*% t.DZ) %*% I.2 + tcrossprod(hatyt[, t, drop = FALSE])\n hatyxt[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxt[, t, drop = FALSE]) + Delta.r %*% pari$Z %*% hatVt[, , t]\n hatyxttp[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxtp[, t, drop = FALSE]) + Delta.r %*% tcrossprod(pari$Z, hatVtpt[, , t])\n\n if (!only.kem) {\n hatytt[, t] <- y[, t, drop = FALSE] - Delta.r %*% (y[, t, drop = FALSE] - pari$Z %*% hatxtt[, t, drop = FALSE] - pari$A)\n hatOtt[, , t] <- I.2 %*% (Delta.r %*% pari$R + Delta.r %*% pari$Z %*% hatVtt[, , t] %*% t.DZ) %*% I.2 + tcrossprod(hatytt[, t, drop = FALSE])\n hatyxtt1T[, , t] <- tcrossprod(hatyt[, t, drop = FALSE], hatxt1[, t, drop = FALSE]) + Delta.r %*% pari$Z %*% hatVtt1T[, , t]\n hatyxtt[, , t] <- tcrossprod(hatytt[, t, drop = FALSE], hatxtt[, t, drop = FALSE]) + Delta.r %*% pari$Z %*% hatVtt[, , t]\n hatvarEytT[, , t] <- I.2 %*% (Delta.r %*% pari$Z %*% hatVt[, , t] %*% t.DZ) %*% I.2\n hatvarytT[, , t] <- I.2 %*% (Delta.r %*% pari$R + Delta.r %*% pari$Z %*% hatVt[, , t] %*% t.DZ) %*% I.2\n }\n }\n } # for loop over time\n if (only.kem) {\n rtn.list <- list(ytT = hatyt, OtT = hatOt, yxtT = hatyxt, yxttpT = hatyxttp)\n } else {\n rtn.list <- list(\n ytT = hatyt, OtT = hatOt, var.ytT = hatvarytT, var.EytT = hatvarEytT, \n yxtT = hatyxt, yxtt1T = hatyxtt1T, yxttpT = hatyxttp,\n ytt1 = hatytt1, Ott1 = hatOtt1, var.ytt1 = hatvarytt1, var.Eytt1 = hatvarEytt1, \n yxtt1 = hatyxtt1,\n ytt = hatytt, Ott = hatOtt, yxtt = hatyxtt\n )\n }\n return(c(rtn.list, list(ok = TRUE, errors = msg)))\n}\n", "meta": {"hexsha": "809e074ca9c91e166087db27ab1413733e9a300c", "size": 7566, "ext": "r", "lang": "R", "max_stars_repo_path": "R/MARSShatyt.r", "max_stars_repo_name": "ashaffer/MARSS", "max_stars_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 36, "max_stars_repo_stars_event_min_datetime": "2018-03-07T11:58:49.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-18T22:19:40.000Z", "max_issues_repo_path": "R/MARSShatyt.r", "max_issues_repo_name": "ashaffer/MARSS", "max_issues_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 126, "max_issues_repo_issues_event_min_datetime": "2018-03-15T16:05:51.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-15T02:25:30.000Z", "max_forks_repo_path": "R/MARSShatyt.r", "max_forks_repo_name": "ashaffer/MARSS", "max_forks_repo_head_hexsha": "62c874483d58a4ffeb354888b606e4d3cf355838", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2018-04-14T06:01:35.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T07:48:53.000Z", "avg_line_length": 47.2875, "max_line_length": 149, "alphanum_fraction": 0.5333068993, "num_tokens": 2904, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056040203135, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.31243593192560926}} {"text": "# ALL SHAPES\n# - Adzes, axes and wedzes, basic analysis of whole dataset\n\n# LIBRARIES ====================================================================\nlibrary(Momocs)\nlibrary(dplyr)\nlibrary(readr)\nlibrary(gridExtra)\nlibrary(grid)\n\n# INPUT ========================================================================\ninNPA <- list.files(\"Morpho/Adze/ProfileAll/\", full.names = TRUE)\ninNSA <- list.files(\"Morpho/Adze/SideView/\", full.names = TRUE)\ninNTA <- list.files(\"Morpho/Adze/TopView/\", full.names = TRUE)\n\ninNPW <- list.files(\"Morpho/Wed/Profile/\", full.names = TRUE)\ninNSW <- list.files(\"Morpho/Wed/Side/\", full.names = TRUE)\ninNTW <- list.files(\"Morpho/Wed/Top/\", full.names = TRUE)\n\ninNPx <- list.files(\"Morpho/Axe/ForProfile/\", full.names = TRUE)\ninNSx <- list.files(\"Morpho/Axe/ForSideView/\", full.names = TRUE)\ninNTx <- list.files(\"Morpho/Axe/ForTopView/\", full.names = TRUE)\n\ninNProf <- c(inNPA, inNPx, inNPW)\ninNSide <- c(inNSA, inNSx, inNSW)\ninNTop <- c(inNTA, inNTx, inNTW)\n\ninProf <- import_jpg(jpg.path = inNProf, auto.notcentered = TRUE, \n threshold = 0.5, verbose = TRUE)\ninSide <- import_jpg(jpg.path = inNSide, auto.notcentered = TRUE, \n threshold = 0.5, verbose = TRUE)\ninTop <- import_jpg(jpg.path = inNTop, auto.notcentered = TRUE, \n threshold = 0.5, verbose = TRUE)\n\n# Clean mem\nrm(list = \"inNProf\", \"inNSide\", \"inNTop\")\nrm(list = \"inNPA\", \"inNSA\", \"inNTA\")\nrm(list = \"inNPW\", \"inNSW\", \"inNTW\")\nrm(list = \"inNPx\", \"inNSx\", \"inNTx\")\n\n# Fac slot data\nprime <- read_csv(\"../Db.PRIME.csv\")\nsecon <- select(prime, -Lokalita, -Note, -Ident)\n\nsecon$Lok <- as.factor(secon$Lok)\nsecon$Orig <- as.factor(secon$Orig)\nsecon$Cult <- as.factor(secon$Cult)\nsecon$Dat <- as.factor(secon$Dat)\nsecon$RM <- as.factor(secon$RM)\nsecon$Morph <- as.factor(secon$Morph)\nsecon$OpSeq <- as.factor(secon$OpSeq)\nsecon$SalVar <- as.factor(secon$SalVar)\nsecon$SalProfVys <- as.factor(secon$SalProfVys)\nsecon$SalProfTvar <- as.factor(secon$SalProfTvar)\nsecon$SalTylBok <- as.factor(secon$SalTylBok)\nsecon$StUpev <- as.factor(secon$StUpev)\nsecon$StPrac <- as.factor(secon$StPrac)\nsecon$Pouzitelnost <- as.factor(secon$Pouzitelnost)\nsecon$Half <- as.factor(secon$Half)\n\nfacProf <- filter(secon, Prof == TRUE) %>% select(-(Top:Prof))\nfacSide <- filter(secon, Side == TRUE) %>% select(-(Top:Prof))\nfacTop <- filter(secon, Top == TRUE) %>% select(-(Top:Prof))\n\nfacProf <- arrange(facProf, Morph, ID)\nfacSide <- arrange(facSide, Morph, ID)\nfacTop <- arrange(facTop, Morph, ID)\n\n# MORPHOMETRICS ================================================================\n# Outlines ---------------------------------------------------------------------\noutProf <- Out(inProf, fac = facProf)\noutSide <- Out(inSide, fac = facSide)\noutTop <- Out(inTop, fac = facTop)\n\npanel(outProf, fac = \"Orig\", names = TRUE)\npanel(outSide, fac = \"Orig\", names = TRUE)\npanel(outTop, fac = \"Orig\", names = TRUE)\n\n# Clean mem.\nrm(list = \"prime\", \"secon\")\nrm(list = \"facProf\", \"facSide\", \"facTop\")\nrm(list = \"inProf\", \"inSide\", \"inTop\")\n\n# Save =========================================================================\nsave(outProf, file = \"./Archive/AllShapes/Dt.outProf\")\nsave(outSide, file = \"./Archive/AllShapes/Dt.outSide\")\nsave(outTop, file = \"./Archive/AllShapes/Dt.outTop\")\n\nload(file = \"./Archive/AllShapes/Dt.outProf\")\nload(file = \"./Archive/AllShapes/Dt.outSide\")\nload(file = \"./Archive/AllShapes/Dt.outTop\")\n\n# Manipulate -------------------------------------------------------------------\nmanProf <- outProf %>% \n coo_slidedirection(\"N\") %>% \n coo_center() %>% \n coo_smooth(50) %>% \n coo_sample(200)\n\nmanSide <- outSide %>% \n coo_alignxax() %>%\n coo_slidedirection(\"W\") %>% \n coo_center() %>% \n coo_smooth(200) %>% \n coo_sample(400)\n\nmanTop <- outTop %>% \n coo_alignxax() %>%\n coo_slidedirection(\"W\") %>% \n coo_center() %>% \n coo_smooth(200) %>% \n coo_sample(400)\n\n# Elliptical Fourier Transform -------------------------------------------------\n# - normalized side and top view - \"better\" results\nefProf <- efourier(manProf, nb.h = 11, norm = FALSE, smooth.it = 0, \n start = TRUE, verbose = TRUE)\nefSide <- efourier(manSide, nb.h = 14, norm = TRUE, smooth.it = 0, \n start = TRUE, verbose = TRUE)\nefTop <- efourier(manTop, nb.h = 11, norm = TRUE, smooth.it = 0, \n start = TRUE, verbose = TRUE)\n\n# ANALYSIS =====================================================================\n# PCA --------------------------------------------------------------------------\npcProf <- PCA(efProf)\npcSide <- PCA(efSide)\npcTop <- PCA(efTop)\n\n# Scree plots\nscree(pcProf, 1:5)\nscree_min(pcProf, 0.98)\nsp <- scree_plot(pcProf, 1:4)\npdf(file = \"./AllShapes/pcContribProf.pdf\", height = 5)\nPCcontrib(pcProf, nax = 1:3, sd.r = c(-1, -0.5, 0, 0.5, 1))\ndev.off()\n\nscree(pcSide, 1:5)\nscree_min(pcSide, 0.98)\nss <- scree_plot(pcSide, 1:4)\npdf(file = \"./AllShapes/pcContribSide.pdf\", height = 2.5, width = 10)\nPCcontrib(pcSide, sd.r = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5))\ndev.off()\n\nscree(pcTop, 1:5)\nscree_min(pcTop, 0.98)\nst <- scree_plot(pcTop, 1:4)\npdf(file = \"./AllShapes/pcContribTop.pdf\", height = 2.5, width = 10)\nPCcontrib(pcTop, nax = 1:3, sd.r = c(-1, -0.5, 0, 0.5, 1))\ndev.off()\n\ntp <- textGrob(\"(a) Prof (profil)\")\nts <- textGrob(\"(b) Side (bokorys)\")\ntt <- textGrob(\"(c) Top (n\u00e1rys)\")\n\nlay <- rbind(c(1,3,5),\n c(1,3,5),\n c(1,3,5),\n c(1,3,5),\n c(1,3,5),\n c(2,4,6))\n\npdf(file = \"./AllShapes/screePlots.pdf\", width = 10, height = 4)\ngrid.arrange(sp, tp, ss, ts, st, tt, layout_matrix = lay)\ndev.off()\n\nrm(list = \"sp\", \"ss\", \"st\", \"tp\", \"ts\", \"tt\", \"lay\")\n\n# Plot PCA ---------------------------------------------------------------------\nplot(pcProf, fac = \"Morph\")\n\n# KMEANS -----------------------------------------------------------------------\n# Count number of clusters K and perform kmeans clustering \n# - elbow method (wss - within-clusters sum of squares)\n# - stolen and modified from rbloggers (DataScience+, by Sunny Anand)\nk.max <- 8\n\n# Prof\nwssProf <- sapply(1:k.max, \n function(k){kmeans(pcProf$x, k, nstart=50,\n iter.max = 15)$tot.withinss})\n\nplot(1:k.max, wssProf,\n type=\"b\", pch = 19, frame = FALSE, \n xlab=\"Number of clusters K (Prof)\",\n ylab=\"Total within-clusters sum of squares\")\n\n# Side\nwssSide <- sapply(1:k.max, \n function(k){kmeans(pcSide$x, k, nstart=50,\n iter.max = 15)$tot.withinss})\n\nplot(1:k.max, wssSide,\n type=\"b\", pch = 19, frame = FALSE, \n xlab=\"Number of clusters K (Side)\",\n ylab=\"Total within-clusters sum of squares\")\n\n# Top\nwssTop <- sapply(1:k.max, \n function(k){kmeans(pcTop$x, k, nstart=50,\n iter.max = 15)$tot.withinss})\n\nplot(1:k.max, wssTop,\n type=\"b\", pch = 19, frame = FALSE, \n xlab=\"Number of clusters K (Top)\",\n ylab=\"Total within-clusters sum of squares\")\n\n# K means clustering\nKMEANS(pcProf, 4, nax = 1:2)\nKMEANS(pcSide, 2)\nKMEANS(pcTop, 3)\n\n# Hierarchical clustering ------------------------------------------------------\nCLUST(pcProf, fac = \"SalProfTvar\", type = \"phylogram\",\n hclust_method = \"ward.D2\", tip_labels = \"SalProfTvar\")\nCLUST(pcSide, fac = \"SalTylBok\", type = \"phylogram\",\n hclust_method = \"ward.D2\", tip_labels = \"SalTylBok\")\nCLUST(pcTop, fac = \"RM\", type = \"phylogram\",\n hclust_method = \"ward.D2\", tip_labels = \"RM\")\n\n# # Linear dicriminant analysis ------------------------------------------------\n# plot(LDA(pcProf, fac = \"Morph\"))\n# plot(LDA(pcSide, fac = \"Morph\"))\n# plot(LDA(pcTop, fac = \"Cult\"))\n\n# END ==========================================================================\ngraphics.off()\nrm(list = ls())\ngc()\n.rs.restartR()\nq(\"no\")\n", "meta": {"hexsha": "012ad0309e03e9c4477b2e83402b91942e95a8ec", "size": 7828, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/AllShapes.r", "max_stars_repo_name": "PetPaj/DiplomaThesis", "max_stars_repo_head_hexsha": "5d1bc646223dca743d090610f656e15d82a701d3", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-08T10:02:36.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-08T10:02:36.000Z", "max_issues_repo_path": "Scripts/AllShapes.r", "max_issues_repo_name": "petrpajdla/DiplomaThesis", "max_issues_repo_head_hexsha": "5d1bc646223dca743d090610f656e15d82a701d3", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/AllShapes.r", "max_forks_repo_name": "petrpajdla/DiplomaThesis", "max_forks_repo_head_hexsha": "5d1bc646223dca743d090610f656e15d82a701d3", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.8874458874, "max_line_length": 80, "alphanum_fraction": 0.5533980583, "num_tokens": 2407, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277513, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3123489177711926}} {"text": "library(jsonlite)\n\nmain <- function() {\n\n#### Fetch command line arguments\nmyArgs <- commandArgs(trailingOnly = TRUE)\n\n#### myArgs is a character vector of all arguments\n###print(myArgs)\n###print(class(myArgs))\ndata<-fromJSON(myArgs[2])\n##print(\"JSON \")\n##print(data[2])\n##print(class(data[1]))\ndd<-rnorm(10)\n#print(dd)\nx2_imp <-lapply(dd, function(x){x*x})\n#print(x2_imp)\nresult<-lapply( data, function(x) {x**3})\n\nprint(list(result))\nresult<-toJSON(result)\nprint(result[1])\nprint(class(result))\nreturn(result)\n# args <- commandArgs(trailingOnly = TRUE)\n\n# for (filename in args) {\n# dat <- read.csv(file = filename, header = FALSE)\n# mean_per_patient <- apply(dat, 1, mean)\n# cat(mean_per_patient, sep = \"\\n\")\n# }\n}\n\nmain()", "meta": {"hexsha": "1b7e168c0b2c505a859380b6f437e8136ba51173", "size": 737, "ext": "r", "lang": "R", "max_stars_repo_path": "src/solver_models/r/model1.r", "max_stars_repo_name": "gduin/apiMathModel", "max_stars_repo_head_hexsha": "35dcbd2ce9c5b2c36ca0cf1d5c4045891a413231", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/solver_models/r/model1.r", "max_issues_repo_name": "gduin/apiMathModel", "max_issues_repo_head_hexsha": "35dcbd2ce9c5b2c36ca0cf1d5c4045891a413231", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/solver_models/r/model1.r", "max_forks_repo_name": "gduin/apiMathModel", "max_forks_repo_head_hexsha": "35dcbd2ce9c5b2c36ca0cf1d5c4045891a413231", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.0571428571, "max_line_length": 53, "alphanum_fraction": 0.6675712347, "num_tokens": 208, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31234891777119256}} {"text": "#!/usr/bin/env Rscript\n\n# Read the inputs\n# 1. bed depth file\n# 2. reference lengths per line tsv\n# 3. strata key\n# 4. output file (.png or .pdf)\nargs=commandArgs(trailingOnly=TRUE)\nif(length(args)<4) {\n stop(\"Must supply input depth, reference lengths, strata key, and output files\\n\",call.=FALSE)\n}\n\n# decide output type\nfilex = substr(args[4],nchar(args[4])-2,nchar(args[4]))\nif(filex==\"pdf\") {\n pdf(args[4],bg=\"#FFFFFF\")\n} else if (filex==\"png\") {\n png(args[4],bg=\"#FFFFFF\")\n} else {\n stop(\"Unsupported type for output file.\\n\",call.=FALSE)\n}\n\n\n\nmytrans<-function(pos,clen,minlen,maxlen) {\n # find range it should cover\n tot = max((9/10)*(clen/maxlen)+(1/10),(1/10))\n return((pos/clen)*tot)\n}\n\nd<-read.csv(args[2],sep=\"\\t\",header=FALSE)\n#ordered_names = as.vector(d[order(names(chrlens)),1])\n#ordered_lens = as.vector(d[order(names(chrlens)),2])\nordered_names = as.vector(d[order(d[,1]),1])\nordered_lens = as.vector(d[order(d[,1]),2])\nminlen = min(ordered_lens)\nmaxlen = max(ordered_lens)\nchr = cbind.data.frame(ordered_names,ordered_lens)\nlongest = max(chr[,2])\n\ndstrata<-read.csv(args[3],header=TRUE,sep=\"\\t\")\n\nlayout(rbind(c(1,2)),heights=c(1),widths=c(4,1))\npar(mar=c(5,1,1,1))\nplot(1,type=\"n\",xlim=c(1,length(ordered_names)),ylim=c(0,1),bty=\"n\",xaxt=\"n\",yaxt=\"n\",xlab=\"\",ylab=\"\")\npar(las=2)\nmtext(ordered_names,side=1,at=1:length(ordered_names))\npar(las=1)\nfor (i in 1:length(ordered_names)) {\n clen = ordered_lens[i]\n rect(i-0.4,0,i+0.4,mytrans(chr[i,2],clen,minlen,maxlen))\n}\ne<-read.csv(gzfile(args[1]),sep=\"\\t\",header=FALSE)\nf<-e[order(e[,4]),]\ncolfunc<-colorRampPalette(c(\"#00000088\",\"#0000FF88\",\"#FF000088\"))\ncolarray<-colfunc(length(dstrata[,1]))\ncind = i\nfor(i in 1:length(f[,1])) {\n cind = match(f[i,1],ordered_names)\n clen = ordered_lens[cind]\n #print(f[i,4])\n fcol = colarray[f[i,4]]\n #print(fcol)\n rect(cind-0.4,mytrans(f[i,2],clen,minlen,maxlen),cind+0.4,mytrans(f[i,3],clen,minlen,maxlen),col=fcol,border=fcol,lwd=0.01)\n}\npar(mar=c(10,0,10,5))\nplot(1,type=\"n\",xlim=c(0,2),ylim=c(0,1),xaxt=\"n\",yaxt=\"n\",xlab=\"\",ylab=\"\",bty=\"n\")\nmindepth = min(dstrata[1,])\nmaxdepth = max(dstrata[1,])\nz = 0\nstep = 1/length(f[,4])\nlegend(-1,1,legend=dstrata[,1],fill=rev(colarray),xpd=TRUE,bty='n')\ndev.off()\n", "meta": {"hexsha": "1a604b21d0fa5cbcafafc683c28b3a6ecf38a549", "size": 2245, "ext": "r", "lang": "R", "max_stars_repo_path": "alignqc/plot_depthmap.r", "max_stars_repo_name": "jason-weirather/AlignQC", "max_stars_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 38, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T02:45:24.000Z", "max_issues_repo_path": "alignqc/plot_depthmap.r", "max_issues_repo_name": "jason-weirather/AlignQC", "max_issues_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2016-10-19T02:10:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-24T17:34:50.000Z", "max_forks_repo_path": "alignqc/plot_depthmap.r", "max_forks_repo_name": "jason-weirather/AlignQC", "max_forks_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2016-11-01T11:51:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-30T01:08:55.000Z", "avg_line_length": 30.3378378378, "max_line_length": 125, "alphanum_fraction": 0.6659242762, "num_tokens": 836, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878696277512, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31234891777119256}} {"text": "# TODO: Allow empty range field, Allow plate ranges\n#\nwellrange <- function(range,first=\"row\"){\n\t#Check format\n\tif (!grepl(\"[A-Za-z]+\\\\d+-[A-Za-z]+\\\\d+\",range)) {\n\t\twarning(\"Range must be of format A2-C4\\n\")\n\t}\n\t#Split out the start and end of the range\n\trange <- unlist(strsplit(range,\"-\"))\n\tif (length(range) > 2) { warning(\"More than one dash in range given\") }\n\trange_start = range[1]\n\trange_end = range[2]\n\trange_start_row = toupper(sub(\"\\\\d+\",\"\",range_start))\n\trange_start_col = as.integer(sub(\"[A-Za-z]+\",\"\",range_start))\n\trange_end_row = toupper(sub(\"\\\\d+\",\"\",range_end))\n\trange_end_col = as.integer(sub(\"[A-Za-z]+\",\"\",range_end))\n\t# Append A for 1536 well plates\n\tletters = c(LETTERS,paste(\"A\",LETTERS,sep=\"\"))\n\trow_range = letters[which(letters==range_start_row):which(letters==range_end_row)]\n\tcol_range = seq(from=range_start_col, to=range_end_col)\n\n\tif (first==\"row\") {\n\t\tfirst = row_range\n\t\tsecond = col_range\n\t\tlabel1=\"Row\"\n\t\tlabel2=\"Column\"\n\t} else {\n\t\tfirst = col_range\n\t\tsecond = row_range\n\t\tlabel2=\"Row\"\n\t\tlabel1=\"Column\"\n\t}\n\toutput = data.frame(matrix(nrow=1,ncol=2))\n\tfor (i in first) {\n\t\tfor (j in second) {\n\t\t\toutput <- rbind(output,c(i,j))\n\t\t}\n\t}\n\tnames(output) <- c(label1,label2)\n\t#First row has NA values\n\toutput <- output[-1,]\n\t#Sort by first then second (i.e. row then column default)\n\t#nchar needed to handle AA, AB... rows\n\toutput[order(nchar(output[,1]),output[,1],as.integer(output[,2])),]\n}\n\ncondition_wells <- function(wells,cname,value) {\n\tdf <- wellrange(as.character(wells))\n\tdf[,as.character(cname)]=value\n\tdf\n\n}\n\nadd_zero_to_single <- function(x) {\n\tif (as.integer(x) < 10) {\n\t\tpaste(\"0\",as.character(x),sep=\"\")\n\t} else {\n\t\tx\n\t}\n}\n\nannotate_plate <- function(df,type=96,plate=1,area=2,condition=3,value=4) {\n# For stuff replicated across plates, can use this with by functions\n# DF provided needs Plate, Area, Condition, Value columns with indexes as in default args\n\n\t# set up the plate types\n\tnplates = length(unique(df[,plate]))\n\tif (type == 96) {\n\t\tfullplate = \"A1-H12\"\n\t} else {\n\t\tif (type == 384) {\n\t\t\tfullplate <- \"A1-P24\"\n\t\t} else {\n\t\t\tif (type == 1536) {\n\t\t\t\tfullplate <- \"A1-AF48\"\n\t\t\t}\n\t\t}\n\t}\n\twells = wellrange(fullplate)\n\t# Apply the annotation\n\t# If Area is empty, apply to whole plate\n\tdf[df[,area] == \"\",area] <- fullplate\n\n\t# If Plates field is empty, apply to all plates\n\tplate_empty <- df[is.na(df[,plate]),]\n\tif (nrow(plate_empty) != 0) {\n\t# Remove old empty plate rows\n\t\tdf <- df[!is.na(df[,plate]),]\n\n\t# Makes a list of duplicate data frames, one for each plate\n\t\tnew_rows <- lapply(unique(df[,plate]), function(x,pe) { temp = pe; temp$Plate = x; temp },pe=plate_empty)\n\n\t# Add the new ones\n\t\tnew_rows$new = df\n\t\tdf <- do.call(rbind,new_rows)\n\n\t}\n\tplate_ranges <- df[grep(\"-\",df[,plate]),]\n\t# TODO: get range, make duplicate DF rows for each plate in range (as above)\n\n\t# Remove any lines where the condition or value is NA\n\n\t# Set up the plates data frame\n\tlst = list()\n\tfor (i in unique(df[,plate])) {\n\t\tlst[[i]] = wells\n\t\tlst[[i]][,\"Plate\"] <- i\n\t}\n\tplates <- do.call(rbind, lst)\n\n\t#anno is a list of data frames\n\t#Now merge all of these with the plates data frame\n\tannolist = list()\n\tfor (i in unique(df[,condition])) {\n\t\tanno <- df[df[,condition] == i,]\n\t\t# Expand well ranges\n\t\tanno <- apply(anno,1,function(x) {dft <- condition_wells(x[area],x[condition],x[value]); dft$Plate = x[plate]; dft})\n\t#\tanno$plates <- plates\n\t#\tannolist[[i]] <- Reduce(function(x,y) merge(x,y,by=c(\"Plate\",\"Row\",\"Column\"),all=T), anno)\n\t\ttempplates <- plates\n\t\tfor (j in anno) {\n\t\t\ttempplates <- merge(tempplates,j,all=T)\n\t\t}\n\t\t# Generates NAs for some reason, remove\n\t\ttempplates <- tempplates[!is.na(tempplates[,4]),]\n\t\tannolist[[i]] <- tempplates\n\t}\n\tred <- Reduce(function(x,y) merge(x,y,all=T),annolist)\n\tred <- red[!apply(red,1,function(x) any(is.na(x))),]\n\t# Add leading zeros and make a Well column by combining row + zerocolumn\n\tred$Well <- paste(red$Row, sapply(red$Column,add_zero_to_single),sep=\"\")\n\tred[order(nchar(red$Row),red$Row,as.integer(red$Column)),]\n}\n", "meta": {"hexsha": "83fe87cf77feda09623a317d95ae8e2c78cebeb0", "size": 4018, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/bio-plates/plate_annotate.r", "max_stars_repo_name": "stveep/bioruby-plates", "max_stars_repo_head_hexsha": "478e482552ad2dfd67aa64b9f6032f84031d771a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/bio-plates/plate_annotate.r", "max_issues_repo_name": "stveep/bioruby-plates", "max_issues_repo_head_hexsha": "478e482552ad2dfd67aa64b9f6032f84031d771a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/bio-plates/plate_annotate.r", "max_forks_repo_name": "stveep/bioruby-plates", "max_forks_repo_head_hexsha": "478e482552ad2dfd67aa64b9f6032f84031d771a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.9850746269, "max_line_length": 118, "alphanum_fraction": 0.6580388253, "num_tokens": 1253, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.31234891060511166}} {"text": "\nlibrary(\"RCurl\")\nlibrary(\"rjson\")\nsource('C:\\\\Program Files\\\\R\\\\R-3.3.1\\\\library\\\\Functions.R');\n\t\t\t\ngetPIPointWebId <- function(piDataArchive, piPointName) {\t\n path = paste(c(\"\\\\\\\\\", piDataArchive, \"\\\\\" , piPointName))\n\tcompl_url= paste(c(\"/points?path=\", path),collapse=\"\");\n\tx = getHttpRequest(compl_url)\n\tx$WebId\n}\n\n\ngetInterpolatedValues <- function(startTime, endTime, interval, webId1, webId2, webId3) {\n\twebIdsString= paste(c(webId1, webId2, webId3),collapse=\"&webId=\");\n\tprint(webIdsString)\n\tcompl_url= paste(c(\"/streamsets/interpolated?starttime=\",startTime,\"&endtime=\",endTime,\"&interval=\",interval,\"&webId=\", webIdsString),collapse=\"\");\n\tcompl_url\n\tx = getHttpRequest(compl_url)\n}\n\ngetHttpRequest <- function(compl_url) {\n\tbase_url = 'https://marc-web-sql.marc.net/piwebapi'\t\n\turl = paste(c(base_url, compl_url),collapse=\"\");\n\tw=getURL(url,httpheader = c(Authorization = \"Basic bsdgyYy5hZG06a2s=\"), ssl.verifypeer = FALSE);\n\tx=fromJSON(w);\n}\n\ntag1name = 'FAC.OAK.Weather-Inside_Humidity-Val.PV';\ntag2name = 'FAC.OAK.Weather-Inside_Temperature-Val.PV';\ntag3name = 'FAC.OAK.Weather-Outside_Temperature-Val.PV';\nwebId1 = getPIPointWebId('marc-pi2016',tag1name); \nwebId2 = getPIPointWebId('marc-pi2016',tag2name); \nwebId3 = getPIPointWebId('marc-pi2016',tag3name); \nvalues = getInterpolatedValues('1-Oct-2012','1-Nov-2012','1h', webId1, webId2, webId3);\n\ni=1;\nv1 = 1: length(values$Items[[1]]$Items)\nv2 = 1: length(values$Items[[2]]$Items)\nv3 = 1: length(values$Items[[3]]$Items)\n\nfor (value in values$Items[[1]]$Items){\n v1[i] = value$Value\n i = i + 1 \n}\n\ni=1;\nfor (value in values$Items[[2]]$Items){\n v2[i] = value$Value\n i = i + 1 \n}\n\ni=1;\nfor (value in values$Items[[3]]$Items){\n v3[i] = value$Value\n i = i + 1 \n}\n\n\nimpact3<-data.frame(v1,v2,v3);\ntn3<-c(tag1name,tag2name,tag3name);\nPI_Multi_Correlation3(impact3,tn3);\n\n", "meta": {"hexsha": "59e1751c535ab3d7b6012c18b5026a4585755a4f", "size": 1843, "ext": "r", "lang": "R", "max_stars_repo_path": "Chapter10 - Retrieving PI Data into R/rCurl/piwebapi.r", "max_stars_repo_name": "AnnuRawat/Integrate-R-to-PI", "max_stars_repo_head_hexsha": "0167a91482f73a54fa2e2129c7e8696baa19b2a8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Chapter10 - Retrieving PI Data into R/rCurl/piwebapi.r", "max_issues_repo_name": "AnnuRawat/Integrate-R-to-PI", "max_issues_repo_head_hexsha": "0167a91482f73a54fa2e2129c7e8696baa19b2a8", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter10 - Retrieving PI Data into R/rCurl/piwebapi.r", "max_forks_repo_name": "AnnuRawat/Integrate-R-to-PI", "max_forks_repo_head_hexsha": "0167a91482f73a54fa2e2129c7e8696baa19b2a8", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.796875, "max_line_length": 148, "alphanum_fraction": 0.6961475855, "num_tokens": 652, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.31234891060511166}} {"text": "\\name{merge_dendrogram}\n\\alias{merge_dendrogram}\n\\title{\nMerge Dendrograms\n}\n\\description{\nMerge Dendrograms\n}\n\\usage{\nmerge_dendrogram(x, y, only_parent = FALSE, ...)\n}\n\\arguments{\n\n \\item{x}{The parent dendrogram.}\n \\item{y}{The children dendrograms. They are connected to the leaves of the parent dendrogram. So the length of \\code{y} should be as same as the number of leaves of the parent dendrogram.}\n \\item{only_parent}{Whether only returns the parent dendrogram where the height and node positions have been adjusted by children dendrograms.}\n \\item{...}{Other arguments.}\n\n}\n\\details{\nDo not retrieve the order of the merged dendrogram. It is not reliable.\n}\n\\examples{\nm1 = matrix(rnorm(100), nr = 10)\nm2 = matrix(rnorm(80), nr = 8)\nm3 = matrix(rnorm(50), nr = 5)\ndend1 = as.dendrogram(hclust(dist(m1)))\ndend2 = as.dendrogram(hclust(dist(m2)))\ndend3 = as.dendrogram(hclust(dist(m3)))\ndend_p = as.dendrogram(hclust(dist(rbind(colMeans(m1), colMeans(m2), colMeans(m3)))))\ndend_m = merge_dendrogram(dend_p, list(dend1, dend2, dend3))\ngrid.dendrogram(dend_m, test = TRUE)\n\ndend_m = merge_dendrogram(dend_p, list(dend1, dend2, dend3), only_parent = TRUE)\ngrid.dendrogram(dend_m, test = TRUE)\n\nrequire(dendextend)\ndend1 = color_branches(dend1, k = 1, col = \"red\")\ndend2 = color_branches(dend2, k = 1, col = \"blue\")\ndend3 = color_branches(dend3, k = 1, col = \"green\")\ndend_p = color_branches(dend_p, k = 1, col = \"orange\")\ndend_m = merge_dendrogram(dend_p, list(dend1, dend2, dend3))\ngrid.dendrogram(dend_m, test = TRUE)\n}\n", "meta": {"hexsha": "403e6666341271556848334e800453ab878f3b16", "size": 1530, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/merge_dendrogram.rd", "max_stars_repo_name": "zhongmicai/complexHeatmap", "max_stars_repo_head_hexsha": "02ad1d0a5097d21f748c4bab5f97d1505cdd8642", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-07-30T13:07:46.000Z", "max_stars_repo_stars_event_max_datetime": "2020-07-30T13:07:46.000Z", "max_issues_repo_path": "man/merge_dendrogram.rd", "max_issues_repo_name": "songyang1992/ComplexHeatmap", "max_issues_repo_head_hexsha": "38cd0ae5391aedd5c1e4733e61de51490019a8bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/merge_dendrogram.rd", "max_forks_repo_name": "songyang1992/ComplexHeatmap", "max_forks_repo_head_hexsha": "38cd0ae5391aedd5c1e4733e61de51490019a8bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0, "max_line_length": 190, "alphanum_fraction": 0.7294117647, "num_tokens": 497, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.3123489106051116}} {"text": " \n#==========\n#convert sp lines to dataframe\n#==========\n lines2dataframe<-function(lines){\n\t a<-data.frame()\n\t k<-1\n\t for(i in 1:length(lines)){\n\t\t for(j in 1:length(lines@lines[[i]]@Lines)){\n\t\t\t l<-data.frame(lines@lines[[i]]@Lines[[j]]@coords,\n\t\t\t\t\t\trep(k,length(lines@lines[[i]]@Lines[[j]]@coords[,1])),\n\t\t\t\t\t\trep(lines@lines[[i]]@ID,length(lines@lines[[i]]@Lines[[j]]@coords[,1])))\n\t\t\t \n\t\t\t a<-rbind(a,l)\t\t\t \n\t\t\t k<-k+1\n\t\t}\n\t}\n\t\t\t\t\t\t\n\tnames(a)<-c(\"lon\",\"lat\",\"group\",\"id\")\n\treturn(a)\n}", "meta": {"hexsha": "840121ea6311e6fd3376a3cccc7062ce85b0b89d", "size": 500, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lines2dataframe.r", "max_stars_repo_name": "sinanshi/visotmed", "max_stars_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T02:17:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T10:32:36.000Z", "max_issues_repo_path": "R/lines2dataframe.r", "max_issues_repo_name": "sinanshi/visotmed", "max_issues_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lines2dataframe.r", "max_forks_repo_name": "sinanshi/visotmed", "max_forks_repo_head_hexsha": "cdea8bf0428145d310e77963d237343c554204d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.8095238095, "max_line_length": 78, "alphanum_fraction": 0.526, "num_tokens": 166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118493816806, "lm_q2_score": 0.6150878625719088, "lm_q1q2_score": 0.312348905024866}} {"text": "install.packages(\"WDI\")\ninstall.packages(\"wbstats\")\nlibrary(WDI)\nlibrary(wbstats)\n\n\ndf = data.frame(\n Year=c(2013, 2013, 2013), \n Country=c(\"Arab World\",\"Caribbean States\", \"Central Europe\"),\n LifeExpectancy=c(71, 72, 76))\n\ndf\nhead(df)\ndf[2, \"Country\"]\nsummary(df)\n\ndf$Year <- as.factor(df$Year)\n\n\ndf <- data.frame(\n \n# data.table(\n wb(indicator = c(\"SP.POP.TOTL\",\n \"SP.DYN.LE00.IN\",\n \"SP.DYN.TFRT.IN\"), mrv = 60)\n) \n\ndf$date <- as.factor(df$date)\n\n\nsummary(df)\n\n# Installation of Rserve\ninstall.packages(\"Rserve\")\nlibrary(Rserve)\nRserve()\n\n# Iris\nhead(iris)\nIrisBySpecies <- split(iris,iris$Species)\n\n\noutput <- list()\n\nfor(n in names(IrisBySpecies)){\n \n ListData <- IrisBySpecies[[n]]\n \n output[[n]] <- data.frame(species=n,\n \n MinPetalLength=min(ListData$Petal.Length),\n \n MaxPetalLength=max(ListData$Petal.Length),\n \n MeanPetalLength=mean(ListData$Petal.Length),\n \n NumberofSamples=nrow(ListData))\n \n output.df <- do.call(rbind,output)\n \n}\n\nprint(output.df)\n\n", "meta": {"hexsha": "f200fc0212918f0e7843333f526650d69011972a", "size": 1228, "ext": "r", "lang": "R", "max_stars_repo_path": "Chapter02/Packt Chapter 2.r", "max_stars_repo_name": "M155K4R4/TabR", "max_stars_repo_head_hexsha": "907a4d10589f29ab6a3774ef4c3fdeca98becfbc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2018-01-10T08:22:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-21T23:21:45.000Z", "max_issues_repo_path": "Chapter02/Packt Chapter 2.r", "max_issues_repo_name": "M155K4R4/TabR", "max_issues_repo_head_hexsha": "907a4d10589f29ab6a3774ef4c3fdeca98becfbc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Chapter02/Packt Chapter 2.r", "max_forks_repo_name": "M155K4R4/TabR", "max_forks_repo_head_hexsha": "907a4d10589f29ab6a3774ef4c3fdeca98becfbc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 29, "max_forks_repo_forks_event_min_datetime": "2017-08-24T16:20:22.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-26T10:03:58.000Z", "avg_line_length": 18.8923076923, "max_line_length": 72, "alphanum_fraction": 0.5390879479, "num_tokens": 309, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3121852455350198}} {"text": "#' Flexible RD Plot\n#' @export\n#' \nrdplot <- function(object, ...) {\n UseMethod(\"rdplot\")\n}\n\n#'\n#' @param object object\n#' @param usemod numeric vector. which models do you want to plot?\n#' @param treat_label string vector of treatment and control label\n#' @param ate_label_size In-plot text size\n#' @param ate_label_digits decimal places of in-plot ATE result.\n#' @param ate_label_format string of label format of in-plot ATE result.\n#' The format produces labels according to a unique grammar.\n#' If you want to embed a numerical value related to the estimation result,\n#' you need to enclose it in {}.\n#' \\itemize{\n#' \\item `{estimate}`: embed coefficient value (Local ATE)\n#' \\item `{std.error}`: embed standard error of coefficient\n#' \\item `{statistic}`: embed z-score of coefficient\n#' \\item `{p.value}`: embed p-value of coefficient\n#' \\item `{star}`: show *** if p-value <= 0.01;\n#' ** if p-value <= 0.05; * if p-value <= 0.1\n#' }\n#' @param outcome_label Outcome label in plot title\n#' @param ylim numeric vector of limits of y-axis\n#' @param vjust numeric. Adjust in-plot text vertically\n#' @param hjust numeric. Adjust in-plot text horizontally\n#' @param xlab label of x-axis\n#' @param ylab label of y-axis\n#' @param patchwork logical.\n#' add a list of plots into one composition by `patchwork::wrap_plots`\n#' @param ncol the dimensions of the grid to create.\n#' argument of `patchwork::wrap_plots`\n#' @param nrow the dimensions of the grid to create.\n#' argument of `patchwork::wrap_plots`\n#' @param \\dots arguments of [simplegg()]\n#'\n#' @importFrom stats aggregate\n#' @importFrom stats predict\n#' @importFrom patchwork wrap_plots\n#' @importFrom patchwork plot_layout\n#' @importFrom ggplot2 theme\n#' @importFrom rlang quo\n#' @importFrom rlang enquos\n#' @importFrom rlang quo_is_missing\n#' @importFrom rlang eval_tidy\n#' @export\n#' @examples\n#' running <- sample(1:100, size = 1000, replace = TRUE)\n#' cov1 <- rnorm(1000, sd = 2); cov2 <- rnorm(1000, mean = -1)\n#' y0 <- running + cov1 + cov2 + rnorm(1000, sd = 10)\n#' y1 <- 2 + 1.5 * running + cov1 + cov2 + rnorm(1000, sd = 10)\n#' y <- ifelse(running <= 50, y1, y0)\n#' bin <- ifelse(y > mean(y), 1, 0)\n#' w <- sample(c(1, 0.5), size = 1000, replace = TRUE)\n#' raw <- data.frame(y, bin, running, cov1, cov2, w)\n#'\n#' set_optDiscRD(\n#' y + bin ~ running,\n#' discRD.cutoff = 50,\n#' discRD.assign = \"smaller\"\n#' )\n#'\n#' est <- global_lm(data = raw, weights = w)\n#'\n#' rdplot(\n#' est,\n#' usemod = c(1, 2),\n#' treat_label = c(\"Treated\", \"Control\"),\n#' outcome_label = switch(x,\n#' \"1\" = \"Simulated outcome\",\n#' \"2\" = \"Simulated outcome > 0\"\n#' ),\n#' ylab = \"Weighted Average\",\n#' ate_label_size = 4\n#' )\n#'\n#' @name rdplot\n#'\nrdplot.list_global_lm <- function(object,\n usemod,\n treat_label = c(\"treated\", \"control\"),\n ate_label_size = 5,\n ate_label_digits = 3,\n ate_label_format,\n outcome_label,\n ylim,\n vjust = 0,\n hjust = 0,\n xlab = \"Running variable\",\n ylab = \"Average\",\n patchwork = TRUE,\n ncol = NULL,\n nrow = NULL,\n ...) {\n # observed data aggregated by mass points\n x <- i <- NULL\n vars <- c(\"outcome\", \"weights\", \"d\")\n aggregate_quo <- rlang::quo({\n data <- recover_data(object, i)\n data$outcome <- data$outcome * data$weights\n agdt <- aggregate(data[, vars], by = list(x = data$x), mean)\n agdt$outcome <- agdt$outcome / agdt$weights\n agdt$x <- agdt$x + object$result[[i]]$RD.info$cutoff\n agdt$d <- factor(agdt$d, levels = c(1, 0), labels = treat_label)\n agdt\n })\n\n # prediction data\n prediction_quo <- rlang::quo({\n data <- recover_data(object, i)\n newdt <- data[, !(names(data) %in% vars)]\n newdt <- newdt[!duplicated(newdt), ]\n predict(object$result[[i]], newdata = newdt)\n })\n\n # subset condition for prediction data\n condmake <- rlang::quo({\n switch(object$result[[i]]$RD.info$assign,\n \"greater\" = rlang::quo(x >= 0),\n \"smaller\" = rlang::quo(x <= 0)\n )\n })\n\n # In-plot label about result of local ATE\n if (missing(ate_label_format)) {\n label <- \"Local ATE: {estimate}{star}({std.error})\"\n } else {\n label <- ate_label_format\n }\n\n label_quo <- rlang::quo({\n res <- object$result[[i]]\n label_maker(res, label, ate_label_digits)\n })\n\n # Other arguments for plot manipulation\n pargs <- rlang::enquos(\n ate_label_size = ate_label_size,\n outcome_label = outcome_label,\n ylim = ylim,\n vjust = vjust,\n hjust = hjust,\n xlab = xlab,\n ylab = ylab\n )\n\n pargs <- Filter(Negate(rlang::quo_is_missing), pargs)\n\n # Arguments for template\n tmparg <- list(...)\n\n # draw plots\n if (missing(usemod)) usemod <- seq_len(length(object$result))\n\n plist <- lapply(usemod, function(m) {\n # eval arguments\n args <- list()\n args$aggregate <- rlang::eval_tidy(aggregate_quo, list(i = m))\n pred <- rlang::eval_tidy(prediction_quo, list(i = m))\n cond <- rlang::eval_tidy(condmake, list(i = m))\n bool <- rlang::eval_tidy(cond, pred)\n args$predict1 <- pred[bool, ]\n args$predict0 <- pred[!bool, ]\n args$ate_label <- rlang::eval_tidy(label_quo, list(i = m))\n args$cutoff <- object$result[[m]]$RD.info$cutoff\n\n # eval pargs\n eval_pargs <- lapply(pargs, rlang::eval_tidy, list(x = as.character(m)))\n args <- append(args, eval_pargs)\n\n # draw plot\n do.call(\"rdplot_internal_cutoff\", append(args, tmparg))\n })\n\n if (patchwork) {\n patchwork::wrap_plots(plist, ncol = ncol, nrow = nrow) +\n patchwork::plot_layout(guides = \"collect\") &\n ggplot2::theme(legend.position = \"bottom\")\n } else {\n plist\n }\n}\n\n#'\n#' @name rdplot\n#' @param force logical. Whether to ignore error about estimation\n#'\n#' @importFrom stats aggregate\n#' @importFrom patchwork wrap_plots\n#' @importFrom patchwork plot_layout\n#' @importFrom ggplot2 theme\n#' @importFrom rlang quo\n#' @importFrom rlang enquos\n#' @importFrom rlang quo_is_missing\n#' @importFrom rlang eval_tidy\n#' @export\n#' @examples\n#' nonpara <- local_lm(\n#' submod = 1,\n#' data = raw,\n#' kernel = \"uniform\",\n#' bw = 5,\n#' order = 1:3\n#' )\n#'\n#' rdplot(\n#' nonpara,\n#' usemod = 2,\n#' outcome_label = switch(x,\n#' \"1\" = \"Order = 1\",\n#' \"2\" = \"Order = 2\",\n#' \"3\" = \"Order = 3\"\n#' )\n#' )\n#'\nrdplot.list_local_lm <- function(object,\n usemod,\n treat_label = c(\"treated\", \"control\"),\n ate_label_size = 5,\n ate_label_digits = 3,\n ate_label_format,\n outcome_label,\n ylim,\n vjust = 0,\n hjust = 0,\n xlab = \"Running variable\",\n ylab = \"Average\",\n patchwork = TRUE,\n ncol = NULL,\n nrow = NULL,\n force = TRUE,\n ...) {\n # observed data aggregated by mass points\n i <- NULL\n vars <- c(\"outcome\", \"sweight\", \"d\")\n aggregate_quo <- rlang::quo({\n data <- recover_data(object, i)\n data$outcome <- data$outcome * data$sweight\n agdt <- aggregate(data[, vars], by = list(x = data$x), mean)\n agdt$outcome <- agdt$outcome / agdt$sweight\n agdt$x <- agdt$x + object$result[[i]]$RD.info$cutoff\n agdt$d <- factor(agdt$d, levels = c(1, 0), labels = treat_label)\n agdt\n })\n\n # prediction data\n predict1_quo <- rlang::quo({\n fit <- fit_local_lm(\n object$result[[i]]$treat, extend = 0, force = force\n )\n colnames(fit)[colnames(fit) == \"yhat\"] <- \"yhat1\"\n fit\n })\n\n predict0_quo <- rlang::quo({\n fit <- fit_local_lm(\n object$result[[i]]$control, extend = 0, force = force\n )\n colnames(fit)[colnames(fit) == \"yhat\"] <- \"yhat0\"\n fit\n })\n\n # In-plot label about result of local ATE\n if (missing(ate_label_format)) {\n label <- \"Local ATE: {estimate}{star}({std.error})\"\n } else {\n label <- ate_label_format\n }\n\n label_quo <- rlang::quo({\n res <- object$result[[i]]\n label_maker(res, label, ate_label_digits)\n })\n\n # Other arguments for plot manipulation\n pargs <- rlang::enquos(\n ate_label_size = ate_label_size,\n outcome_label = outcome_label,\n ylim = ylim,\n vjust = vjust,\n hjust = hjust,\n xlab = xlab,\n ylab = ylab\n )\n\n pargs <- Filter(Negate(rlang::quo_is_missing), pargs)\n\n # Arguments for template\n tmparg <- list(...)\n\n # draw plots\n if (missing(usemod)) usemod <- seq_len(length(object$result))\n\n plist <- lapply(usemod, function(m) {\n # eval arguments\n args <- list()\n args$aggregate <- rlang::eval_tidy(aggregate_quo, list(i = m))\n args$predict1 <- rlang::eval_tidy(predict1_quo, list(i = m))\n args$predict0 <- rlang::eval_tidy(predict0_quo, list(i = m))\n args$ate_label <- rlang::eval_tidy(label_quo, list(i = m))\n args$cutoff <- object$result[[m]]$RD.info$cutoff\n\n # eval pargs\n eval_pargs <- lapply(pargs, rlang::eval_tidy, list(x = as.character(m)))\n args <- append(args, eval_pargs)\n\n # draw plot\n do.call(\"rdplot_internal_cutoff\", append(args, tmparg))\n })\n\n if (patchwork) {\n patchwork::wrap_plots(plist, ncol = ncol, nrow = nrow) +\n patchwork::plot_layout(guides = \"collect\") &\n ggplot2::theme(legend.position = \"bottom\")\n } else {\n plist\n }\n}\n", "meta": {"hexsha": "0927a601ca041905eb48dbed3734267e772ef538", "size": 9940, "ext": "r", "lang": "R", "max_stars_repo_path": "R/discRD-rdplot.r", "max_stars_repo_name": "KatoPachi/discreteRD", "max_stars_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/discRD-rdplot.r", "max_issues_repo_name": "KatoPachi/discreteRD", "max_issues_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/discRD-rdplot.r", "max_forks_repo_name": "KatoPachi/discreteRD", "max_forks_repo_head_hexsha": "f041c8a3ae3cea42f2db3286f95d2fc9a0893f4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8695652174, "max_line_length": 77, "alphanum_fraction": 0.5673038229, "num_tokens": 2765, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31218523802557263}} {"text": "# Legibility and Clarity\r\n\r\nrm(list=ls())\r\nlibrary(ggplot2)\r\nlibrary(dplyr)\r\nlibrary(reshape2)\r\nlibrary(gridExtra)\r\nlibrary(survival)\r\nlibrary(ggfortify)\r\n\r\n\r\n\r\n#######################################################\r\n## Novartis look and feel functions\r\n#######################################################\r\nNovartis.Color.Palette <- structure(\r\n c( \"#0460A9\", \"#CDDFEE\", \"#9BBFDD\", \"#68A0CB\", \"#03487F\", \"#023054\",\r\n \"#E74A21\", \"#FADBD3\", \"#F5B7A6\", \"#F1927A\", \"#AD3819\", \"#742510\",\r\n \"#EC9A1E\", \"#FBEBD2\", \"#F7D7A5\", \"#F4C278\", \"#B17416\", \"#764D0F\",\r\n \"#8D1F1B\", \"#E8D2D1\", \"#D1A5A4\", \"#BB7976\", \"#6A1714\", \"#46100E\",\r\n \"#7F7F7F\", \"#E5E5E5\", \"#CCCCCC\", \"#B2B2B2\", \"#5F5F5F\", \"#404040\",\r\n \"#CCCCCC\", \"#F5F5F5\", \"#EBEBEB\", \"#E0E0E0\", \"#999999\", \"#666666\",\r\n \"#404040\", \"#D9D9D9\", \"#B3B3B3\", \"#8C8C8C\", \"#303030\", \"#202020\"\r\n ),.Dim = c(6L, 7L),\r\n .Dimnames = list(c(\"Hue\", \"Tint3\", \"Tint2\", \"Tint1\", \"Shade1\", \"Shade2\"),\r\n c(\"Novartis Blue\", \"Sienna\", \"Apricot\", \"Carmine\", \r\n \"Gray\", \"Light Gray\", \"Dark Gray\")))\r\n\r\nggColor <- function(n.colors = 50, color.values = c(\"Hue\", \"Tint1\", \"Shade1\"), \r\n color.hues = c(1:4)){\r\n color.vec <- as.vector(t(Novartis.Color.Palette[color.values,color.hues]))\r\n \r\n rep.colors <- 1\r\n if(n.colors > length(color.vec)){\r\n rep.colors <- ceiling(n.colors/length(color.vec))\r\n }\r\n \r\n ggcolor <- scale_color_manual(values = rep(color.vec,rep.colors))\r\n return(ggcolor)\r\n}\r\n\r\nggFill <- function(n.colors = 42, color.values = c(\"Hue\", \"Tint1\", \"Shade1\"), \r\n color.hues = c(1:4)){\r\n color.vec <- as.vector(t(Novartis.Color.Palette[color.values,color.hues]))\r\n \r\n rep.colors <- 1\r\n if(n.colors > length(color.vec)){\r\n rep.colors <- ceiling(n.colors/length(color.vec))\r\n }\r\n \r\n ggcolor <- scale_fill_manual(values = rep(color.vec,rep.colors))\r\n return(ggcolor)\r\n}\r\n\r\n\r\n## Set theme\r\ntheme_set(theme_minimal(base_size=18))\r\nth <- theme(panel.grid.minor=element_blank(),\r\n panel.grid.major=element_blank(),\r\n axis.title.y=element_blank(),\r\n axis.text.y=element_blank(),\r\n axis.title.x=element_blank(),\r\n axis.text.x=element_blank(),\r\n strip.background = element_rect(fill = \"lightgrey\", colour = \"grey50\"),\r\n panel.background = element_rect(fill = \"white\", colour = \"grey50\")\r\n)\r\n\r\n\r\n\r\n###############################################################################\r\n## Annotated dose response curve \r\n## Label axes with clear measurement units and \r\n## provide annotations that support the message.\r\n###############################################################################\r\n\r\n## make data\r\nset.seed(12345666)\r\nDose = seq(0,10,0.1)\r\nDAY = floor(Dose/24)\r\nK1 = 0.2\r\nK2 = 0.8\r\nResponse = 100*(Dose/1)/(Dose/1 + 1) \r\nmy.data <- data.frame(Dose = Dose, Response = Response,\r\n ymin = Response - 0.1*Response - 5, \r\n ymax = Response + 0.1*Response + 5,\r\n obs = Response + \r\n 5*rnorm(length(Response)) + \r\n 0.1*rnorm(length(Response))*Response,\r\n DAY = DAY)\r\n\r\n## plot\r\na1 <- ggplot(data = my.data, aes(x = Dose, y = Response)) + \r\n geom_point(data = my.data[Dose%in%c(0.1,1,2.5,5,10),], \r\n aes(x=Dose, y=obs),size = 4) + \r\n geom_errorbar(data = my.data[Dose%in%c(0.1,1,2.5,5,10),], \r\n aes(x=Dose, ymin=obs-5-0.1*obs, ymax = obs+5+0.1*obs),size = 1) + \r\n xlab(\"Dose\") + \r\n ylab(\"Response\") + \r\n coord_cartesian(ylim = c(-10,120)) + \r\n geom_line(size = 1) +\r\n geom_ribbon(aes(ymin = ymin, ymax=ymax), fill = rgb(0.5,0.5,0.5), alpha = 0.5) +\r\n geom_point(data = my.data[Dose%in%c(0.1,1,2.5,5,10),], \r\n aes(x=Dose, y=obs),size = 4) + \r\n geom_errorbar(data = my.data[Dose%in%c(0.1,1,2.5,5,10),], \r\n aes(x=Dose, ymin=obs-5-0.1*obs, ymax = obs+5+0.1*obs),size = 1) +\r\n theme_bw(base_size = 16) + \r\n theme(panel.grid.minor=element_blank(),\r\n panel.grid.major=element_blank(),\r\n legend.position=\"none\",\r\n axis.text.x=element_text(size = 12)\r\n ) \r\n \r\nggsave(a1, file=\"GWLeg_a1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n## Add informative annotations\r\na2 <- a1 + geom_hline(yintercept = 80, color = \"red\", linetype = \"dashed\", size = 1, alpha = 0.4)+\r\n geom_ribbon(data = data.frame(x=c(0,10), y=c(80,80), ymin = c(70,70), ymax = c(90,90)),\r\n aes(x = x, y=y, ymin = ymin, ymax = ymax), fill = \"red\", alpha = 0.2)+\r\n scale_x_continuous(breaks = c(0.1,1,2.5,5,10), labels = c(0.1,1,2.5,5,10)) +\r\n geom_line(data = data.frame(x = c(2.5, 8), y = c(-7,-7)), aes(x=x, y=y), color = \"red\") +\r\n geom_point(data = data.frame(x = 4, y=-7), aes(x=x, y=y), color = \"red\", size = 2) +\r\n annotate(\"text\", label = \"Target dose\", x = 8, y = 0, color = \"red\") +\r\n annotate(\"text\", label = \"Active control\", x = 8, y = 60, color = \"red\") +\r\n xlab(\"Dose (mg)\") + ylab(\"Response\") +\r\n theme(panel.grid.major=element_line(color = \"lightgrey\", size = 0.4)) \r\n \r\na2\r\nggsave(a2, file=\"GWLeg_a2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n\r\n###############################################################################\r\n## Simple survival plot\r\n## Use font size to create a visual hierarchy\r\n###############################################################################\r\n\r\n## data\r\ndf <- lung\r\ndf$sex <- plyr::mapvalues(df$sex, c(1,2),c(\"Male\",\"Female\"))\r\ndf$sex <- factor(df$sex, levels = c(\"Male\",\"Female\"))\r\nfit <- survfit(Surv(time, status) ~ sex, data = df)\r\n\r\nb1 <-autoplot(fit, conf.int = FALSE, censor = FALSE, surv.size = 2) + \r\n theme_minimal() + \r\n ggColor() + \r\n labs(title=\"Survival Analysis\") + \r\n xlab(\"Time (years)\") + \r\n ylab(\"Survival Rate (%)\")+\r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n panel.grid.minor.y=element_blank(),\r\n panel.grid.major.y=element_blank(),\r\n panel.background=element_blank(),\r\n axis.text.x=element_text(size=10),\r\n axis.text.y=element_text(size=12),\r\n legend.title = element_blank(),\r\n legend.position=c(0.75,0.75),\r\n legend.text = element_text(size = 12),\r\n title=element_text(size=8),\r\n axis.title.x = element_text(size = 16, face = \"bold\"),\r\n axis.title.y = element_text(size = 12)\r\n ) \r\nggsave(b1, file=\"GWLeg_b1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\nb2 <-autoplot(fit, conf.int = FALSE, censor = FALSE, surv.size = 2) + \r\n theme_minimal() + ggColor() + \r\n labs(title=\"Survival Analysis\") + \r\n xlab(\"Time (years)\") + \r\n ylab(\"Survival Rate (%)\")+\r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n #axis.text.y=element_blank(),\r\n panel.grid.minor.y=element_blank(),\r\n panel.grid.major.y=element_blank(),\r\n panel.background=element_blank(),\r\n axis.text.x=element_text(size=10),\r\n axis.text.y=element_text(size=12),\r\n legend.title = element_blank(),\r\n legend.position=c(0.75,0.75),\r\n legend.text = element_text(size = 12),\r\n title=element_text(face=\"bold\", size=14)\r\n ) \r\n\r\nggsave(b2, file=\"GWLeg_b2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n####################################################################################\r\n## Simple bar chart\r\n## Don't use too small or too condensed text, Dont capitalize everything\r\n####################################################################################\r\n\r\n# create toy data\r\nDF <- data.frame(\r\n lab = c(\"C4\", \"C3\", \"C2\", \"C1\"),\r\n value = c(0.05,0.15,0.3,0.5),\r\n Category = c(4,3,2,1)\r\n)\r\n\r\nc1 <- ggplot(DF, aes(reorder(lab,value), value)) +\r\n geom_bar(width = 0.85, stat = \"identity\", color = \"white\") +\r\n geom_text(aes(x=lab, y=value, label=value),nudge_y=0.1, size=6.5) +\r\n scale_y_continuous(limits = c(0, 1)) +\r\n labs(title=\"Subgroup incidence rate\") +\r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n axis.title=element_blank(),\r\n panel.grid.minor.y=element_blank(),\r\n panel.grid.major.y=element_blank(),\r\n axis.text.x=element_text(size=18),\r\n axis.text.y=element_text(size=10),\r\n legend.position=\"none\",\r\n title=element_text(face=\"bold\", size=8)\r\n ) + \r\n coord_flip()\r\n\r\nggsave(c1, file=\"GWLeg_c1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n\r\nc2 <- ggplot(DF, aes(reorder(lab,value), value)) +\r\n geom_bar(width = 0.65, stat = \"identity\",color = \"white\") +\r\n geom_text(aes(x=lab, y=value, label=value),nudge_y=0.15, size=4) +\r\n scale_y_continuous(limits = c(0, 1)) +\r\n labs(title=\"Subgroup \\nincidence rate\") +\r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n axis.title=element_blank(),\r\n panel.grid.minor.y=element_blank(),\r\n panel.grid.major.y=element_blank(),\r\n panel.background=element_blank(),\r\n axis.text.x=element_text(size=10),\r\n axis.text.y=element_text(size=12),\r\n legend.position=\"none\",\r\n title=element_text(face=\"bold\", size=14)\r\n ) + \r\n coord_flip()\r\n\r\nggsave(c2, file=\"GWLeg_c2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n############################################################################\r\n## Simple forest plot\r\n## Use sans serif fonts\r\n############################################################################\r\n\r\n# Make data\r\ndf <- data.frame(Dose=c(100, 200, 400, 800))\r\ndf$Response <- df$Dose/(df$Dose + 200)\r\ndf$ymin <- df$Response*exp(0.2)\r\ndf$ymax <- df$Response*exp(-0.2)\r\n\r\n# plot\r\nd1 <- ggplot(data = df) + \r\n geom_point(aes(x = Dose, y = Response), size = 4) + \r\n geom_errorbar(aes(x= Dose, ymin = ymin, ymax = ymax)) + \r\n scale_y_continuous(limits = c(0, 1)) +\r\n ggtitle(\"Dose Reponse\") + \r\n theme_minimal(base_size = 18) + \r\n theme(title=element_text(face=\"bold\",size=18, family=\"serif\"),\r\n plot.title = element_text(hjust = 0.5),\r\n axis.title = element_text( family=\"serif\"), \r\n axis.text = element_text(family = \"serif\", size = 12)\r\n ) \r\n\r\nggsave(d1, file=\"GWLeg_d1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\nd2 <- ggplot(data = df) + \r\n geom_point(aes(x = Dose, y = Response), size = 4) + \r\n geom_errorbar(aes(x= Dose, ymin = ymin, ymax = ymax)) + \r\n scale_y_continuous(limits = c(0, 1)) +\r\n ggtitle(\"Dose Reponse\") + \r\n theme_minimal(base_size = 18) + \r\n theme(title=element_text(size=18),\r\n axis.text = element_text(size = 12),\r\n plot.title = element_text(hjust = 0.5)\r\n ) \r\n\r\nggsave(d2, file=\"GWLeg_d2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n######################################################################################\r\n## Simple bar chart\r\n## Display text with enough contrast to be visible. \r\n## Favor dark text on light backgrounds over light on dark whenever possible. \r\n######################################################################################\r\n\r\n# create toy data\r\nDF <- data.frame(lab = c(\"Category 4\", \"Category 3\", \"Category 2\", \"Category 1\"),\r\n value = c(0.05,0.15,0.3,0.5),\r\n Category = c(4,3,2,1)\r\n )\r\n\r\ne1 <- ggplot(DF[DF$lab!=\"Category 4\",], aes(reorder(Category,lab), value)) +\r\n geom_bar(width = 0.65, stat = \"identity\",color = \"white\") +\r\n geom_text(aes(x=Category, y=value, label=value*100,fontface=\"bold\"),\r\n nudge_y=-0.05, size=7, color = \"white\") +\r\n scale_y_continuous(limits = c(0, 1)) +\r\n geom_hline(yintercept = 0, colour = \"wheat4\", linetype=1, size=0.8)+ \r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n axis.text.y=element_blank(),\r\n axis.text.x = element_blank(),\r\n axis.title=element_blank(),\r\n panel.grid.minor.x=element_blank(),\r\n panel.grid.major.x=element_blank(),\r\n panel.background=element_blank(),\r\n legend.position=\"none\",\r\n title=element_text(face=\"bold\", size=14)\r\n ) \r\n\r\n\r\nggsave(e1, file=\"GWLeg_e1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n\r\ne2 <- ggplot(DF[DF$lab!=\"Category 4\",], aes(reorder(Category,lab), value)) +\r\n geom_bar(width = 0.65, stat = \"identity\",color = \"white\") +\r\n geom_text(aes(x=Category, y=value, label=value*100),\r\n nudge_y=0.05, size=7, color = \"black\") +\r\n scale_y_continuous(limits = c(0, 1)) +\r\n geom_hline(yintercept = 0, colour = \"wheat4\", linetype=1, size=0.8) + \r\n theme(plot.title = element_text(hjust = 0.5),\r\n axis.ticks = element_blank(),\r\n axis.line = element_blank(),\r\n axis.text.y=element_blank(),\r\n axis.text.x = element_blank(),\r\n axis.title=element_blank(),\r\n panel.grid.minor.x=element_blank(),\r\n panel.grid.major.x=element_blank(),\r\n panel.background=element_blank(),\r\n legend.position=\"none\",\r\n title=element_text(face=\"bold\", size=14)\r\n ) \r\n\r\n\r\nggsave(e2, file=\"GWLeg_e2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n##########################################################################################\r\n## Simple scatter plot\r\n## Bold and italics should only be used for layering. \r\n## Emphasizing everything means nothing gets emphasized.\r\n##########################################################################################\r\n\r\n## make data\r\nset.seed(12345666)\r\nmy_data <- data.frame(Bodyweight = 90 + 20*c(-runif(50), runif(50)))\r\nmy_data$Exposure <- ((my_data$Bodyweight/90)^-0.75)*exp(0.1*rnorm(length(my_data$Bodyweight)))\r\nmy_data$BODYWEIGHT = my_data$Bodyweight\r\nmy_data$EXPOSURE <- my_data$Exposure\r\n\r\n\r\nf1 <- ggplot(my_data, aes(x = BODYWEIGHT, y = EXPOSURE)) + \r\n geom_smooth(method = \"lm\")+\r\n geom_point() + \r\n theme_minimal(base_size = 18 ) + \r\n ggtitle(\"EXPOSURE \\nVS BODYWEIGHT\") +\r\n theme(panel.grid.minor=element_blank(),\r\n legend.position=\"none\",\r\n axis.ticks = element_blank(),\r\n axis.text.y=element_blank(),\r\n axis.text.x=element_blank(),\r\n axis.title = element_text(face = \"bold.italic\",size = 18),\r\n plot.title = element_text(hjust = 0.5, face = \"bold.italic\",size = 18)\r\n )\r\n\r\nggsave(f1, file=\"GWLeg_f1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n\r\nf2 <- ggplot(my_data, aes(x = Bodyweight, y = Exposure)) + \r\n geom_smooth(method = \"lm\")+\r\n geom_point() + \r\n theme_minimal(base_size = 16 ) + \r\n ggtitle(\"Exposure \\nvs. Bodyweight\")+\r\n theme(panel.grid.minor=element_blank(),\r\n legend.position=\"none\",\r\n axis.ticks = element_blank(),\r\n axis.text.y=element_blank(),\r\n axis.text.x=element_blank(),\r\n plot.title = element_text(hjust = 0.5)\r\n ) \r\n\r\nggsave(f2, file=\"GWLeg_f2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\n\r\n######################################################################################\r\n## Simple bar chart\r\n## Try not to set types at an angle to avoid clashing as this decreases legibility. \r\n## First think of alternative solutions such as transposing the graph, abbreviations, \r\n## or reducing the number of labels to what is essential.\r\n########################################################################################\r\n\r\n# make data\r\ndf <- data.frame(trt=c(\"Treatment 1\", \"Treatment 2\", \"Treatment 3\"),\r\ncause=c(4,6,10),\r\nhighlight = c(2,1,2))\r\n\r\ng1 <- ggplot(df, aes(x=trt, y=cause)) +\r\n geom_bar(width=0.5, stat = \"identity\") + \r\n theme(axis.title=element_blank()) +\r\n scale_y_continuous(breaks=c(0, 5, 10))\r\n\r\nggsave(g1, file=\"GWLeg_g1.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\ng2 <- ggplot(df, aes(x=trt, y=cause)) +\r\n geom_bar(width=0.5, stat = \"identity\") + \r\n theme(axis.title=element_blank()) +\r\n scale_y_continuous(breaks=c(0, 5, 10)) + \r\n theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),\r\n axis.text.y=element_text(size=12))\r\n\r\nggsave(g2, file=\"GWLeg_g2.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n\r\ng3 <- ggplot(df, aes(x=trt, y=cause)) +\r\n geom_bar(width=0.5, stat = \"identity\") + \r\n theme(axis.title=element_blank(),\r\n axis.text.x=element_text(size=12),\r\n axis.text.y=element_text(size=12)) +\r\n scale_y_continuous(breaks=c(0, 5, 10)) + \r\n coord_flip() \r\n\r\nggsave(g3, file=\"GWLeg_g3.png\", width = 80, height = 80, units = \"mm\", dpi = 300)\r\n\r\n", "meta": {"hexsha": "aa443d7754c6794e373126e482381ff141623edc", "size": 16722, "ext": "r", "lang": "R", "max_stars_repo_path": "GW_legibility.r", "max_stars_repo_name": "GraphicsPrinciples/CheatSheet", "max_stars_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 138, "max_stars_repo_stars_event_min_datetime": "2018-08-26T15:02:26.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-20T22:50:33.000Z", "max_issues_repo_path": "GW_legibility.r", "max_issues_repo_name": "sts-sadr/CheatSheet", "max_issues_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-11-06T12:07:21.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-30T17:48:02.000Z", "max_forks_repo_path": "GW_legibility.r", "max_forks_repo_name": "sts-sadr/CheatSheet", "max_forks_repo_head_hexsha": "7e23d05c6624cecac37a31606c0290a29ad18ca1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 35, "max_forks_repo_forks_event_min_datetime": "2018-11-05T12:46:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-25T12:26:20.000Z", "avg_line_length": 38.4413793103, "max_line_length": 99, "alphanum_fraction": 0.5444922856, "num_tokens": 4859, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5234203340678568, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.3121852366669731}} {"text": "calcTargetClusterQuals <- function( seuratObj, targetCellType = \"M\"){\n\n# this snippet calculates the cluster quality of the target cluster, see Supplementary Methods\n# the clusters are assigned cell by cell by isTargetCell.r\n# written by Vsevolod J. Makeev 2017 - 2021\n\nsource(\"R/isTargetCell.r\")\n\ncellClusters \t<- sapply( levels( seuratObj@ident), function(x) WhichCells( seuratObj, ident = x)) \nclusterQuals\t<- sapply( cellClusters, function(x) { nCells <- length(which( sapply( x, isTargetCell, seuratObj = seuratObj, targetCellType = targetCellType))); if(nCells ==0) 0 else ( 1 + nCells)/sqrt(1+ length(x))})\n\nreturn( clusterQuals)\n}\n", "meta": {"hexsha": "d197699c906501ed3a193a649724df24d910090c", "size": 639, "ext": "r", "lang": "R", "max_stars_repo_path": "R/calcTargetClusterQuals.r", "max_stars_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_stars_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/calcTargetClusterQuals.r", "max_issues_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_issues_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/calcTargetClusterQuals.r", "max_forks_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_forks_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.6428571429, "max_line_length": 216, "alphanum_fraction": 0.744913928, "num_tokens": 185, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3121095014503377}} {"text": "#' @title Rank abundance plot\n#'\n#' @description Generates a rank abundance curve (rank abundance vs cumulative read abundance), optionally with standard deviation from mean intervals.\n#'\n#' @param data (\\emph{required}) Data list as loaded with \\code{\\link{amp_load}}.\n#' @param group_by (\\emph{required}) Group the samples by a variable in the metadata.\n#' @param showSD Show standard deviation from mean or not. (\\emph{default:} \\code{TRUE})\n#' @param log10_x Log10-transform the x-axis or not. Often most variation is observed among the most abundant OTU's, log10-transforming the x-axis will highlight this better. (\\emph{default:} \\code{TRUE})\n#'\n#' @return A ggplot2 object.\n#' @import ggplot2\n#'\n#' @export\n#'\n#' @details Currently only OTU level is supported.\n#'\n#' @seealso\n#' \\code{\\link{amp_load}}\n#'\n#' @examples\n#' # Load example data\n#' data(\"AalborgWWTPs\")\n#'\n#' # Rank abundance plot\n#' amp_rankabundance(AalborgWWTPs, group_by = \"Plant\", showSD = TRUE, log10_x = TRUE)\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\n#' @author Mads Albertsen \\email{MadsAlbertsen85@@gmail.com}\namp_rankabundance <- function(data,\n group_by,\n showSD = TRUE,\n log10_x = TRUE) {\n ### Data must be in ampvis2 format\n #is_ampvis2(data)\n \n # melt\n d <- amp_export_long(\n normaliseTo100(data),\n metadata_vars = group_by,\n tax_levels = \"OTU\"\n )\n \n # group up and summarise\n d[, groupSum := sum(count), by = c(\"OTU\", group_by)]\n setorderv(d, c(group_by, \"groupSum\"), order = -1)\n d[, prop := groupSum / sum(groupSum) * 100, by = group_by]\n d[, cumProp := cumsum(prop), by = group_by]\n d[, rank := as.integer(!duplicated(groupSum)), by = c(\"OTU\", group_by)]\n d[, rank := cumsum(rank), by = group_by]\n d[, sd := sd(count, na.rm = TRUE), by = c(\"OTU\", group_by)]\n d <- d[, .SD[.N], by = c(\"OTU\", group_by)]\n \n # generate plot\n plot <- ggplot(\n d,\n aes_string(\n x = \"rank\",\n y = \"cumProp\",\n group = group_by,\n color = group_by\n )\n ) +\n geom_line(size = 1) +\n ylim(0, 100) +\n xlab(\"Rank abundance\") +\n ylab(\"Cumulative read abundance (%)\") +\n theme_classic() +\n {\n if (isTRUE(showSD)) {\n geom_ribbon(\n aes_string(\n ymin = \"cumProp - sd\",\n ymax = \"cumProp + sd\",\n fill = group_by\n ),\n alpha = 0.2,\n size = 0\n )\n }\n } +\n {\n if (isTRUE(log10_x)) {\n scale_x_log10()\n }\n }\n return(plot)\n}", "meta": {"hexsha": "2e5e9db25cda82fe4321825261092286e7e6644f", "size": 2564, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/amp_rankabundance.r", "max_stars_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_stars_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/amp_rankabundance.r", "max_issues_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_issues_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/amp_rankabundance.r", "max_forks_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_forks_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8139534884, "max_line_length": 204, "alphanum_fraction": 0.5897035881, "num_tokens": 735, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3121095014503377}} {"text": "args<-commandArgs(TRUE)\ndataset <- read.csv(args[1])\nfilename = gsub(\".csv\", \"\", args[1])\nfileNameEt <- paste(filename, \"_AVG.csv\", sep=\"\")\navgLat <- rowMeans(cbind(dataset$LAT0,dataset$LAT1 ,dataset$LAT2 ,dataset$LAT3 ,dataset$LAT4,dataset$LAT5,dataset$LAT6,dataset$LAT7,dataset$LAT8,dataset$LAT9))\navgA <- rowMeans(cbind(dataset$MEMA0,dataset$MEMA1 ,dataset$MEMA2 ,dataset$MEMA3 ,dataset$MEMA4,dataset$MEMA5,dataset$MEMA6,dataset$MEMA7,dataset$MEMA8,dataset$MEMA9))\navgB <- rowMeans(cbind(dataset$MEMB0,dataset$MEMB1 ,dataset$MEMB2 ,dataset$MEMB3 ,dataset$MEMB4,dataset$MEMB5,dataset$MEMB6,dataset$MEMB7,dataset$MEMB8,dataset$MEMB9))\nwrite.table(cbind(dataset, avgLat, avgB, avgA), append=TRUE, sep=\";\", dec=\",\", row.names = FALSE, file = fileNameEt )", "meta": {"hexsha": "cf7eb906f1fc21f2e65ac45ebffe4e08748795ee", "size": 754, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/report/avgReportAppender.r", "max_stars_repo_name": "streamreasoning/HeavenTeststand", "max_stars_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/report/avgReportAppender.r", "max_issues_repo_name": "streamreasoning/HeavenTeststand", "max_issues_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/report/avgReportAppender.r", "max_forks_repo_name": "streamreasoning/HeavenTeststand", "max_forks_repo_head_hexsha": "0400f790e9d2eee0bb3b46db19d714011a2c26c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 94.25, "max_line_length": 167, "alphanum_fraction": 0.7599469496, "num_tokens": 266, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3120498179904941}} {"text": "library(tidyverse)\nlibrary(mosaic)\nlibrary(cli)\n\n# pipe fn to import surveys and do light tidying\nimport_survey <- . %>%\n read_csv(na = c(\"\", \"NA\", \"N/A\", \"N / A\"),\n col_types = cols(\n \"OptIn Date\" = col_date(format = \"%d/%m/%Y\"),\n HoursDay = col_integer(),\n Rooms = col_integer(),\n Windows = col_integer(),\n \"People in House\" = col_number(),\n Temperature = col_number(),\n \"Temperature (C)\" = col_number(),\n \"Relative Humidity\" = col_number(),\n \"Heat Index\" = col_number(),\n \"Heat Index (C)\" = col_number(),\n .default = col_factor(NULL, include_na = FALSE))) %>%\n clean_names() %>%\n slice(-n()) %>%\n select(-starts_with(\"x1\")) %>%\n # normalise col names for weather in some surveys\n rename_with(~ str_replace(.x, \"_c$\", \"\"), .cols = ends_with(\"_c\"))\n\n# formatting fns for readable labels\n\nlabel_risk <- function(x) {\n pct_risk <- scales::percent(abs(x - 1))\n moreless <- ifelse(x > 1, \"more\", \"less\")\n paste(pct_risk, moreless, \"likely\")\n}\n\nlabel_range <- function(x) {\n paste(\n scales::percent(attr(x, \"lower.RR\")), \"to\",\n scales::percent(attr(x, \"upper.RR\")))\n}\n\nrr_statsig <- function(x) {\n !between(1, attr(x, \"lower.RR\"), attr(x, \"upper.RR\"))\n}\n\nrisk_subtitle <- function(risk, disadvantage, advantage, bad_outcome,\n statsig = FALSE, rr = FALSE) {\n\n statsig_emoji <- if_else(rr_statsig(risk), '\u2611\ufe0f', '\u274c')\n rr_text <- glue(\n \" (RR: {label_risk(attr(risk, 'lower.RR'))}\", \" to\",\n \" {label_risk(attr(risk, 'upper.RR'))})\")\n\n glue(\n \"{if_else(statsig, statsig_emoji, '')}\",\n \"Surveyed respondents in Indonesia, Pakistan, India and Cameroon
\",\n \"{disadvantage} were **{label_risk(attr(risk, 'RR'))}** to {bad_outcome}
\",\n \"than people {advantage}.\",\n \"{if_else(rr, rr_text, '')}\"\n # \" (RR: {label_risk(attr(risk, 'lower.RR'))} to {label_risk(attr(risk, 'upper.RR'))})\"\n )\n}\n\nrate_diff_plot <- function(df, predictor, outcome, bad_outcome, good_outcome,\n disadvantage, advantage, phrase_disadvantage, phrase_advantage,\n phrase_bad_outcome) {\n\n # build the contingency table:\n # \"people w/ X disadvantage were Y% more/less likely to experience Z bad\n # outcome\"\n contingency_table <- data.frame(\n bad_outcome = c(\n df %>% filter({{ bad_outcome }}, {{ advantage }}) %>% pull(n),\n df %>% filter({{ bad_outcome }}, {{ disadvantage }}) %>% pull(n)),\n good_outcome = c(\n df %>% filter({{ good_outcome }}, {{ advantage }}) %>% pull(n),\n df %>% filter({{ good_outcome }}, {{ disadvantage }}) %>% pull(n)))\n rownames(contingency_table) <- c(\"advantage\", \"disadvantage\")\n print(contingency_table)\n\n # calculate the relative risk\n risk <- orrr(contingency_table)\n if (between(1, attr(risk, \"lower.RR\"), attr(risk, \"upper.RR\"))) {\n cli_bullets(c(\n \"x\" = \"This risk difference is not statistically significant at 95% CI\",\n \"i\" = paste(\"RR:\", attr(risk, \"lower.RR\"), \"to\", attr(risk, \"upper.RR\"))\n ))\n }\n\n # ceate the plot\n risk_plot <- ggplot(filter(df, {{ bad_outcome }})) +\n aes(y = predictor_prop, x = {{ predictor }}, fill = {{ predictor }}) +\n geom_col() +\n geom_richtext(\n family = \"Body 360info\",\n aes(label = glue(\n \"**{scales::percent(predictor_prop)} of respondents**
\",\n \"({n} of {predictor_n} surveyed)\")),\n colour = \"white\", fill = NA, label.colour = NA,\n hjust = \"inward\", nudge_y = -0.01, size = 5.5) +\n scale_y_continuous(labels = scales::label_percent(accuracy = 1)) +\n scale_fill_manual(\n values = c(pal_360[[\"blue\"]], pal_360[[\"darkblue\"]])) +\n coord_flip() +\n labs(subtitle = risk_subtitle(risk, phrase_disadvantage, phrase_advantage,\n phrase_bad_outcome))\n\n return(risk_plot)\n}\n", "meta": {"hexsha": "f644a8d1a3a3d4701318a1c73cef43484dd15baa", "size": 3753, "ext": "r", "lang": "R", "max_stars_repo_path": "util.r", "max_stars_repo_name": "360-info/report-hotter-and-hotter", "max_stars_repo_head_hexsha": "b8fab4a16ba929f80f4b69a0e7b0633eed214158", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "util.r", "max_issues_repo_name": "360-info/report-hotter-and-hotter", "max_issues_repo_head_hexsha": "b8fab4a16ba929f80f4b69a0e7b0633eed214158", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "util.r", "max_forks_repo_name": "360-info/report-hotter-and-hotter", "max_forks_repo_head_hexsha": "b8fab4a16ba929f80f4b69a0e7b0633eed214158", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.75, "max_line_length": 91, "alphanum_fraction": 0.6083133493, "num_tokens": 1091, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442250928250376, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3120498047559398}} {"text": "### Tool version\n#- R v3.3.0: Used to analyze sequencing library results\n#- DSS v2.14.0 :Used to call differentially methylated regions\n#- DESeq2 v1.12.4: Used to call differentially expressed genes\n#- DiffBind v 2.2.12: Used to call differentially accessible regions/peaks\n\n####QC for individual WGBS Replicates####\nsetwd() # use the directory where outputs from convert_DSS.py are saved as working directory\n# Setting up plotting basics\nsource(\"PlotSetUp.r\")\n## Load methylation profile \ns15_R1 <- read.table(\"15somite_NCC_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns15_R2 <- read.table(\"15somite_NCC_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns24_R1 <- read.table(\"24hpf_NCC_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns24_R2 <- read.table(\"24hpf_NCC_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nMel_R1 <- read.table(\"Mel_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nMel_R2 <- read.table(\"Mel_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nIri_R1 <- read.table(\"Iri_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nIri_R2 <- read.table(\"Iri_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\n## Calculate methylation level at each CpG site, and round the resulting ratio to 2 digits to the right of decimal.\ns15_R1$M <- round(s15_R1$X/s15_R1$N, digits = 2)\ns15_R2$M <- round(s15_R2$X/s15_R2$N, digits = 2)\ns24_R1$M <- round(s24_R1$X/s24_R1$N, digits = 2)\ns24_R2$M <- round(s24_R2$X/s24_R2$N, digits = 2)\nMel_R1$M <- round(Mel_R1$X/Mel_R1$N, digits = 2)\nMel_R2$M <- round(Mel_R2$X/Mel_R2$N, digits = 2)\nIri_R1$M <- round(Iri_R1$X/Iri_R1$N, digits = 2)\nIri_R2$M <- round(Iri_R2$X/Iri_R2$N, digits = 2)\n\n# Averge methylation\nfilter = 5 # Using 5 as cutoff. Only include CpGs with a coverage no less than 5.\n## Calculate average methylation for each dataset\nme <- data.frame(c(\"15somite Rep1\",\"15somite Rep2\",\"24hpf Rep1\",\"24hpf Rep2\",\"Mel Rep1\",\"Mel Rep2\",\"Iri Rep1\",\"Iri Rep2\"),c(mean(s15_R1[s15_R1$N >= filter,]$M),mean(s15_R2[s15_R2$N >= filter,]$M),mean(s24_R1[s24_R1$N >= filter,]$M),mean(s24_R2[s24_R2$N >= filter,]$M),mean(Mel_R1[Mel_R1$N >= filter,]$M),mean(Mel_R2[Mel_R2$N >= filter,]$M),mean(Iri_R1[Iri_R1$N >= filter,]$M),mean(Iri_R2[Iri_R2$N >= filter,]$M)))\ncolnames(me) <- c(\"Type\", \"Methylation\")\nme$Type <- factor(me$Type, levels = c(\"15somite Rep1\",\"15somite Rep2\",\"24hpf Rep1\",\"24hpf Rep2\",\"Mel Rep1\",\"Mel Rep2\",\"Iri Rep1\",\"Iri Rep2\"))\np <- ggplot(data=me, aes(x=Type, y=Methylation, fill=Type)) +geom_bar(stat=\"identity\",colour = \"black\", position=position_dodge())+ggtitle(\"Average Methylation across samples (coverage filter >=5)\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Library\", y = \"Average Methylation\")+scale_y_continuous(lim = c(0,1))+scale_fill_manual(values = mypalette)+scale_color_manual(values=mypalette)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+geom_text(data=me,aes(x=Type,y=Methylation+0.03,label=paste(round(Methylation,3)*100,\"%\",sep =\"\")),size=8)+ theme(legend.position=\"none\")\np\n\n# Number of CpG covered \n## Calculate number of CpGs covered in each sample with coverage cutoff at 0,5,10\nco <- data.frame(c(rep(\"Mel-R1\", 3),rep(\"Mel-R2\", 3),rep(\"Iri-R1\", 3),rep(\"Iri-R2\", 3),rep(\"24hpf-R1\",3),rep(\"24hpf-R2\", 3),rep(\"s15-R1\", 3),rep(\"s15-R2\", 3)),c(nrow(Mel_R1[Mel_R1$N>0,]), nrow(Mel_R1[Mel_R1$N>=5,]), nrow(Mel_R1[Mel_R1$N>=10,]),nrow(Mel_R2[Mel_R2$N>0,]), nrow(Mel_R2[Mel_R2$N>=5,]), nrow(Mel_R2[Mel_R2$N>=10,]),nrow(Iri_R1[Iri_R1$N>0,]), nrow(Iri_R1[Iri_R1$N>=5,]), nrow(Iri_R1[Iri_R1$N>=10,]),nrow(Iri_R2[Iri_R2$N>0,]), nrow(Iri_R2[Iri_R2$N>=5,]), nrow(Iri_R2[Iri_R2$N>=10,]),nrow(s24_R1[s24_R1$N>0,]), nrow(s24_R1[s24_R1$N>=5,]), nrow(s24_R1[s24_R1$N>=10,]),nrow(s24_R2[s24_R2$N>0,]), nrow(s24_R2[s24_R2$N>=5,]), nrow(s24_R2[s24_R2$N>=10,]),nrow(s15_R1[s15_R1$N>0,]), nrow(s15_R1[s15_R1$N>=5,]), nrow(s15_R1[s15_R1$N>=10,]),nrow(s15_R2[s15_R2$N>0,]), nrow(s15_R2[s15_R2$N>=5,]), nrow(s15_R2[s15_R2$N>=10,])),c(rep(c(\">0\",\">=5\",\">=10\"),8)))\ncolnames(co) <- c(\"Type\", \"count\",\"Filter\")\nco$Type <- factor(co$Type, levels = c(\"s15-R1\",\"s15-R2\",\"24hpf-R1\",\"24hpf-R2\",\"Mel-R1\",\"Mel-R2\",\"Iri-R1\",\"Iri-R2\"))\nco$Filter <- factor(co$Filter, levels = c(\">0\",\">=5\",\">=10\"))\np <- ggplot(data=co, aes(x=Type, y=count, fill=Filter)) +geom_bar(stat=\"identity\",colour = \"black\", position=position_dodge())+ggtitle(paste(\"CpG coverage cutoff\",sep =\"\"))+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Library\", y = \"CpG count\")+scale_fill_manual(values = mypalette2)+scale_color_manual(values=mypalette2)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))\np\n\n# Methylation distribution\n## Only include CpGs with a coverage no less than 5.\nfilter = 5\nmeth <- data.frame(c(rep(\"Mel-R1\", nrow(Mel_R1[Mel_R1$N >= filter,])),rep(\"Mel-R2\", nrow(Mel_R2[Mel_R2$N >= filter,])),rep(\"Iri-R1\", nrow(Iri_R1[Iri_R1$N >= filter,])),rep(\"Iri-R2\", nrow(Iri_R2[Iri_R2$N >= filter,])),rep(\"24hpf-R1\", nrow(s24_R1[s24_R1$N >= filter,])),rep(\"24hpf-R2\", nrow(s24_R2[s24_R2$N >= filter,])),rep(\"s15-R1\", nrow(s15_R1[s15_R1$N >= filter,])),rep(\"s15-R2\", nrow(s15_R2[s15_R2$N >= filter,]))), c(Mel_R1[Mel_R1$N >= filter,]$M,Mel_R2[Mel_R2$N >= filter,]$M,Iri_R1[Iri_R1$N >= filter,]$M,Iri_R2[Iri_R2$N >= filter,]$M,s24_R1[s24_R1$N >= filter,]$M,s24_R2[s24_R2$N >= filter,]$M,s15_R1[s15_R1$N >= filter,]$M,s15_R2[s15_R2$N >= filter,]$M))\ncolnames(meth) <- c(\"Type\", \"Meth\")\na <-ggplot(meth, aes(Meth, fill = Type, colour = Type)) + geom_density(alpha = 0.05, adjust = 3)+ggtitle(paste(\"Pigment CpG methylation distribution (coverage filter >=\",filter,\")\",sep =\"\"))+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Coverage\", y = \"Density\")+scale_fill_manual(values = mypalette)+scale_color_manual(values=mypalette)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))\na\n\n# Principal component analysis \n## Only include CpGs with a coverage no less than 5.\nfilter = 5\nm<-Reduce(function(x, y) merge(x, y, by=c(\"chr\",\"pos\")), list(Mel_R1[Mel_R1$N >= filter,c(1,2,5)],Mel_R2[Mel_R2$N >= filter,c(1,2,5)],Iri_R1[Iri_R1$N >= filter,c(1,2,5)],Iri_R2[Iri_R2$N >= filter,c(1,2,5)],s24_R1[s24_R1$N >= filter,c(1,2,5)],s24_R2[s24_R2$N >= filter,c(1,2,5)],s15_R1[s15_R1$N >= filter,c(1,2,5)],s15_R2[s15_R2$N >= filter,c(1,2,5)]))\nnames(m) <- c(\"chr\",\"start\",\"Mel-R1\",\"Mel-R2\",\"Iri-R1\",\"Iri-R2\",\"24hpf-R1\",\"24hpf-R2\",\"s15-R1\",\"s15-R2\")\nm.pca <-prcomp(m[,3:10])\nsummary(m.pca)\n\n#Importance of components:\n# PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8\n#Standard deviation 0.7544 0.16948 0.1023 0.07458 0.07282 0.06331 0.06319 0.05850\n#Proportion of Variance 0.9025 0.04555 0.0166 0.00882 0.00841 0.00636 0.00633 0.00543\n#Cumulative Proportion 0.9025 0.94805 0.9647 0.97347 0.98188 0.98824 0.99457 1.00000\n\n# Plot samples on PC axes \nloadings <- data.frame(m.pca$rotation, .names = row.names(m.pca$rotation))\na<-ggplot(loadings, aes(x = PC1, y = PC2))+geom_text(data=loadings, \n mapping=aes(x = PC1, y = PC2, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC1 (Variance = 90.3%)\", y = \"PC2 (Variance = 4.6%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC1 vs PC2\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette)+scale_color_manual(values=mypalette)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nb<-ggplot(loadings, aes(x = PC1, y = PC3))+geom_text(data=loadings, \n mapping=aes(x = PC1, y = PC3, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC1 (Variance = 90.3%)\", y = \"PC3 (Variance = 1.7%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC1 vs PC3\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette)+scale_color_manual(values=mypalette)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nc<-ggplot(loadings, aes(x = PC2, y = PC3))+geom_text(data=loadings, \n mapping=aes(x = PC2, y = PC3, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC2 (Variance = 4.6%)\", y = \"PC3 (Variance = 1.7%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC2 vs PC3\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette)+scale_color_manual(values=mypalette)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nmultiplot(a,b,c, cols = 3)\n\n# Plot pair-wise correlation between samples\ngsc_cor <- cor(m[,3:10])\ncorrplot(gsc_cor, order=\"hclust\", addrect=4, method =\"number\")\ncorrplot.mixed(gsc_cor, order=\"hclust\", addrect=4)\ncorrplot(gsc_cor, order=\"hclust\", addrect=4)\n\n\n#### QC for replicate-combined WGBS ####\n# Combine replicates\n## Combine 15somite NCC Rep1 with Rep2\ns15pos_combined <- merge(s15_R1,s15_R2,by=c(\"chr\",\"pos\"), all = TRUE)\ns15pos_combined[is.na(s15pos_combined)]<-0\ns15pos_combined$N <- s15pos_combined$N.x+s15pos_combined$N.y\ns15pos_combined$X <- s15pos_combined$X.x+s15pos_combined$X.y\ns15pos_combined <-s15pos_combined[,c(1,2,9,10)]\nwrite.table(s15pos_combined, \"15somite_NCC_Combined_DSS.txt\",col.names = T,row.names = F, sep = \"\\t\", quote = F)\n\n## Combine 24hpf NCC Rep1 with Rep2\ns24pos_combined <- merge(s24_R1,s24_R2,by=c(\"chr\",\"pos\"), all = TRUE)\ns24pos_combined[is.na(s24pos_combined)]<-0\ns24pos_combined$N <- s24pos_combined$N.x+s24pos_combined$N.y\ns24pos_combined$X <- s24pos_combined$X.x+s24pos_combined$X.y\ns24pos_combined <-s24pos_combined[,c(1,2,9,10)]\nwrite.table(s24pos_combined, \"24hpf_NCC_Combined_DSS.txt\",col.names = T,row.names = F, sep = \"\\t\", quote = F)\n\n## Combine Mel Rep1 with Rep2\nMel_combined <- merge(Mel_R1,Mel_R2,by=c(\"chr\",\"pos\"), all = TRUE)\nMel_combined[is.na(Mel_combined)]<-0\nMel_combined$N <- Mel_combined$N.x+Mel_combined$N.y\nMel_combined$X <- Mel_combined$X.x+Mel_combined$X.y\nMel_combined <-Mel_combined[,c(1,2,9,10)]\nwrite.table(Mel_combined, \"Mel_Combined_DSS.txt\",col.names = T,row.names = F, sep = \"\\t\", quote = F)\n\n## Combine Iri Rep1 with Rep2\nIri_combined <- merge(Iri_R1,Iri_R2,by=c(\"chr\",\"pos\"), all = TRUE)\nIri_combined[is.na(Iri_combined)]<-0\nIri_combined$N <- Iri_combined$N.x+Iri_combined$N.y\nIri_combined$X <- Iri_combined$X.x+Iri_combined$X.y\nIri_combined <-Iri_combined[,c(1,2,9,10)]\nwrite.table(Iri_combined, \"Iri_Combined_DSS.txt\",col.names = T,row.names = F, sep = \"\\t\", quote = F)\n\n# Calculate methylation level and round that to two digits to the right of decimal\ns15pos_combined$M <- round(s15pos_combined$X/s15pos_combined$N, digits = 2)\ns24pos_combined$M <- round(s24pos_combined$X/s24pos_combined$N, digits = 2)\nMel_combined$M <- round(Mel_combined$X/Mel_combined$N, digits = 2)\nIri_combined$M <- round(Iri_combined$X/Iri_combined$N, digits = 2)\n\n# Average methylation\n## Only include CpGs with a coverage no less than 5.\nfilter = 5 \nme <- data.frame(c(\"15somite\",\"24hpf\",\"Mel\",\"Iri\"),c(mean(s15pos_combined[s15pos_combined$N >= filter,]$M),mean(s24pos_combined[s24pos_combined$N >= filter,]$M),mean(Mel_combined[Mel_combined$N >= filter,]$M),mean(Iri_combined[Iri_combined$N >= filter,]$M)))\ncolnames(me) <- c(\"Type\", \"Methylation\")\nme$Type <- factor(me$Type, levels = c(\"15somite\",\"24hpf\",\"Mel\",\"Iri\"))\n# Plot average methylation\np1 <- ggplot(data=me, aes(x=Type, y=Methylation, fill=Type)) +geom_bar(stat=\"identity\",colour = \"black\", position=position_dodge())+ggtitle(\"Average Methylation across samples (coverage filter >=5)\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Library\", y = \"Average Methylation\")+scale_y_continuous(lim = c(0,1))+scale_fill_manual(values = mypalette[c(1,3,5,7)])+scale_color_manual(values=mypalette[c(1,3,5,7)])+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+geom_text(data=me,aes(x=Type,y=Methylation+0.03,label=paste(round(Methylation,3)*100,\"%\",sep =\"\")),size=8)+ theme(legend.position=\"none\")\np1\n\n# Number of CpG covered \n## Coverage cutoff at 0, 5, 10.\nco <- data.frame(c(rep(\"Mel\", 3),rep(\"Iri\", 3),rep(\"24hpf\", 3),rep(\"s15\", 3)),c(nrow(Mel_combined[Mel_combined$N>0,]), nrow(Mel_combined[Mel_combined$N>=5,]), nrow(Mel_combined[Mel_combined$N>=10,]),nrow(Iri_combined[Iri_combined$N>0,]), nrow(Iri_combined[Iri_combined$N>=5,]), nrow(Iri_combined[Iri_combined$N>=10,]),nrow(s24pos_combined[s24pos_combined$N>0,]), nrow(s24pos_combined[s24pos_combined$N>=5,]), nrow(s24pos_combined[s24pos_combined$N>=10,]),nrow(s15pos_combined[s15pos_combined$N>0,]), nrow(s15pos_combined[s15pos_combined$N>=5,]), nrow(s15pos_combined[s15pos_combined$N>=10,])),c(rep(c(\">0\",\">=5\",\">=10\"),4)))\ncolnames(co) <- c(\"Type\", \"count\",\"Filter\")\nco$Type <- factor(co$Type, levels = c(\"s15\",\"24hpf\",\"Mel\",\"Iri\"))\nco$Filter <- factor(co$Filter, levels = c(\">0\",\">=5\",\">=10\"))\n# Plot CpG coverage cutoff\np2 <- ggplot(data=co, aes(x=Type, y=count, fill=Filter)) +geom_bar(stat=\"identity\",colour = \"black\", position=position_dodge())+ggtitle(paste(\"CpG coverage cutoff\",sep =\"\"))+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Library\", y = \"CpG count\")+scale_fill_manual(values = mypalette2)+scale_color_manual(values=mypalette2)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))\np2\n\n# Methylation distribution\n## Only include CpGs with a coverage no less than 5.\nfilter = 5\nmeth <- data.frame(c(rep(\"Mel\", nrow(Mel_combined[Mel_combined$N >= filter,])),rep(\"Iri\", nrow(Iri_combined[Iri_combined$N >= filter,])),rep(\"24hpf\", nrow(s24pos_combined[s24pos_combined$N >= filter,])),rep(\"s15\", nrow(s15pos_combined[s15pos_combined$N >= filter,]))), c(Mel_combined[Mel_combined$N >= filter,]$M,Iri_combined[Iri_combined$N >= filter,]$M,s24pos_combined[s24pos_combined$N >= filter,]$M,s15pos_combined[s15pos_combined$N >= filter,]$M))\ncolnames(meth) <- c(\"Type\", \"Meth\")\nmeth$Type <- factor(meth$Type, levels = c(\"s15\",\"24hpf\",\"Mel\",\"Iri\"))\n# Plot CpG methylation distribution\np3 <-ggplot(meth, aes(Meth, fill = Type, colour = Type)) + geom_density(alpha = 0.1, adjust = 4)+ggtitle(paste(\"Pigment CpG methylation distribution (coverage filter >=\",filter,\")\",sep =\"\"))+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n labs(x = \"Coverage\", y = \"Density\")+scale_fill_manual(values = mypalette[c(1,3,5,7)])+scale_color_manual(values=mypalette[c(1,3,5,7)])+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))\np3\n\n# PCA\n## Only include CpGs with a coverage no less than 5.\nfilter = 5\nm<-Reduce(function(x, y) merge(x, y, by=c(\"chr\",\"pos\")), list(Mel_combined[Mel_combined$N >= filter,c(1,2,5)],Iri_combined[Iri_combined$N >= filter,c(1,2,5)],s24pos_combined[s24pos_combined$N >= filter,c(1,2,5)],s15pos_combined[s15pos_combined$N >= filter,c(1,2,5)]))\nnames(m) <- c(\"chr\",\"start\",\"Mel\",\"Iri\",\"24hpf\",\"s15\")\nm.pca <-prcomp(m[,3:6])\nsummary(m.pca)\n\n#Importance of components:\n# PC1 PC2 PC3 PC4\n#Standard deviation 0.5325 0.12170 0.07764 0.06329\n#Proportion of Variance 0.9194 0.04802 0.01954 0.01299\n#Cumulative Proportion 0.9194 0.96747 0.98701 1.00000\n\n# PCA plot\nloadings <- data.frame(m.pca$rotation, .names = row.names(m.pca$rotation))\na<-ggplot(loadings, aes(x = PC1, y = PC2))+geom_text(data=loadings, \n mapping=aes(x = PC1, y = PC2, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC1 (Variance = 91.9%)\", y = \"PC2 (Variance = 4.8%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC1 vs PC2\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette[c(1,3,5,7)])+scale_color_manual(values=mypalette[c(1,3,5,7)])+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nb<-ggplot(loadings, aes(x = PC1, y = PC3))+geom_text(data=loadings, \n mapping=aes(x = PC1, y = PC3, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC1 (Variance = 91.9%)\", y = \"PC3 (Variance = 2%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC1 vs PC3\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette[c(1,3,5,7)])+scale_color_manual(values=mypalette[c(1,3,5,7)])+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nc<-ggplot(loadings, aes(x = PC2, y = PC3))+geom_text(data=loadings, \n mapping=aes(x = PC2, y = PC3, label = .names, colour = .names), fontface = \"bold\", size = 8) +\n labs(x = \"PC2 (Variance = 4.8%)\", y = \"PC3 (Variance = 2%)\")+ggtitle(\"CpG Methylation PCA plot of replicates (Cov >=5): PC2 vs PC3\")+theme(plot.title = element_text(hjust = 0.5, face = \"bold\",size = 16), axis.title=element_text(size=12,face = \"bold\"),axis.text.x = element_text(face = \"bold\",size = 12),axis.text.y = element_text(face = \"bold\",size = 12))+\n scale_fill_manual(values = mypalette[c(1,3,5,7)])+scale_color_manual(values=mypalette[c(1,3,5,7)])+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.position=\"none\")\nmultiplot(a,b,c, cols = 3)\n# Sample correlation plot\ngsc_cor <- cor(m[,3:6])\ncorrplot(gsc_cor, order=\"hclust\", addrect=4, method =\"number\")\ncorrplot.mixed(gsc_cor, order=\"hclust\", addrect=4)\ncorrplot(gsc_cor, order=\"hclust\", addrect=4)\n\n\n#### Differential DNA methylation analysis with DSS####\n# Load package for differential analysis\nlibrary(DSS)\n# Create an object of BSseq class, \nBSobj <-makeBSseqData(list(s15pos_combined,s24pos_combined,Mel_combined,Iri_combined), c(\"s15\",\"24hpf\",\"Mel\",\"Iri\"))\n\n# Conduct differential methylation loci (DML) tests between each two samples\ndml15v24 <- DMLtest(BSobj, group1 = c(\"s15\"), group2 = c(\"24hpf\"), smoothing = TRUE)\ndml24vMel <- DMLtest(BSobj, group1 = c(\"24hpf\"), group2 = c(\"Mel\"), smoothing = TRUE)\ndml24vIri <- DMLtest(BSobj, group1 = c(\"24hpf\"), group2 = c(\"Iri\"), smoothing = TRUE)\ndmlMelvIri <- DMLtest(BSobj, group1 = c(\"Mel\"), group2 = c(\"Iri\"), smoothing = TRUE)\ndml15vMel <- DMLtest(BSobj, group1 = c(\"s15\"), group2 = c(\"Mel\"), smoothing = TRUE)\ndml15vIri <- DMLtest(BSobj, group1 = c(\"s15\"), group2 = c(\"Iri\"), smoothing = TRUE)\n\n# get DMRs using delta 0.3 and p value 0.01\nDMR15v24_d30_p0.01<- callDMR(dml15v24, delta = 0.30, p.threshold= 0.01)\nDMR24vMel_d30_p0.01<- callDMR(dml24vMel, delta = 0.30, p.threshold= 0.01)\nDMR24vIri_d30_p0.01<- callDMR(dml24vIri, delta = 0.30, p.threshold= 0.01)\nDMRMelvIri_d30_p0.01<- callDMR(dmlMelvIri, delta = 0.30, p.threshold= 0.01)\nDMR15vMel_d30_p0.01<- callDMR(dml15vMel, delta = 0.30, p.threshold= 0.01)\nDMR15vIri_d30_p0.01<- callDMR(dml15vIri, delta = 0.30, p.threshold= 0.01)\n\n# save called DMRs to files\nwrite.table(DMR15v24_d30_p0.01, \"DMR15v24_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\nwrite.table(DMR24vMel_d30_p0.01, \"DMR24vMel_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\nwrite.table(DMR24vIri_d30_p0.01, \"DMR24vIri_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\nwrite.table(DMRMelvIri_d30_p0.01, \"DMRMelvIri_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\nwrite.table(DMR15vMel_d30_p0.01, \"DMR15vMel_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\nwrite.table(DMR15vIri_d30_p0.01, \"DMR15vIri_d30_p0.01_wSMOOTHING.txt\", row.names = F, col.names = F, sep = \"\\t\",quote =F)\n\n \n \n## Generate WGBS signal bigwig files for Fig.3d and SupFig.7a ##\n# Import \"DSS.txt\" WGBS profile files, which are generated from \"WGBS/WGBS_01_preprocessing.sh\"\n## Notice! Please read the original output files but not use the variable above, otherwize the column information will be different\ns15_R1 <- read.table(\"15somite_NCC_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns15_R2 <- read.table(\"15somite_NCC_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns24_R1 <- read.table(\"24hpf_NCC_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\ns24_R2 <- read.table(\"24hpf_NCC_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nMel_R1 <- read.table(\"Mel_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nMel_R2 <- read.table(\"Mel_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nIri_R1 <- read.table(\"Iri_Rep1_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\nIri_R2 <- read.table(\"Iri_Rep2_DSS.txt\",sep = \"\\t\", header = T, stringsAsFactors = F)\n\n# Merge WGBS data from replicated experiment and create an object of BSseq class.\nBSobj <-makeBSseqData(list(s15_R1[,c(1:4)],s15_R2[,c(1:4)],s24_R1[,c(1:4)],s24_R2[,c(1:4)],Mel_R1[,c(1:4)],Mel_R2[,c(1:4)],Iri_R1[,c(1:4)],Iri_R2[,c(1:4)]), c(\"s15_R1\",\"s15_R2\",\"24hpf_R1\",\"24hpf_R2\",\"Mel_R1\",\"Mel_R2\",\"Iri_R1\",\"Iri_R2\"))\n\ndml15v24 <- DMLtest(BSobj, group1 = c(\"s15_R1\",\"s15_R2\"), group2 = c(\"24hpf_R1\",\"24hpf_R2\"), smoothing = TRUE)\ndml24vMel <- DMLtest(BSobj, group1 = c(\"24hpf_R1\",\"24hpf_R2\"), group2 = c(\"Mel_R1\",\"Mel_R2\"), smoothing = TRUE)\ndml24vIri <- DMLtest(BSobj, group1 = c(\"24hpf_R1\",\"24hpf_R2\"), group2 = c(\"Iri_R1\",\"Iri_R2\"), smoothing = TRUE)\ndmlMelvIri <- DMLtest(BSobj, group1 = c(\"Mel_R1\",\"Mel_R2\"), group2 = c(\"Iri_R1\",\"Iri_R2\"), smoothing = TRUE)\n\ndml15 <- dml15v24[,c(1,2,3)] #smoothed values\ndml24 <- dml24vMel[,c(1,2,3)]\ndmlMel <- dml24vMel[,c(1,2,4)]\ndmlIri <- dml24vIri[,c(1,2,4)]\n\n# Generate smoothed CpG methylation bed file\ndml15$chr <- as.character(dml15$chr)\ndml15$end <- as.integer(dml15$pos+1)\nwrite.table(dml15[,c(1,2,4,3)],\"SmoothedCpG_Methylation_s15.bed\", sep = \"\\t\", col.names = F, row.names =F, quote =F)\n\ndml24$chr <- as.character(dml24$chr)\ndml24$end <- as.integer(dml24$pos+1)\nwrite.table(dml24[,c(1,2,4,3)],\"SmoothedCpG_Methylation_s24.bed\", sep = \"\\t\", col.names = F, row.names =F, quote =F)\n\ndmlMel$chr <- as.character(dmlMel$chr)\ndmlMel$end <- as.integer(dmlMel$pos+1)\nwrite.table(dmlMel[,c(1,2,4,3)],\"SmoothedCpG_Methylation_Mel.bed\", sep = \"\\t\", col.names = F, row.names =F, quote =F)\n\ndmlIri$chr <- as.character(dmlIri$chr)\ndmlIri$end <- as.integer(dmlIri$pos+1)\nwrite.table(dmlIri[,c(1,2,4,3)],\"SmoothedCpG_Methylation_Iri.bed\", sep = \"\\t\", col.names = F, row.names =F, quote =F)\n\n# Convert BedGraph to BigWig using UCSC tool in bash code\nsystem(\"bedGraphToBigWig SmoothedCpG_Methylation_s15.bed danRer10.chrom.sizes SmoothedCpG_Methylation_s15.bw\")\nsystem(\"bedGraphToBigWig SmoothedCpG_Methylation_s24.bed danRer10.chrom.sizes SmoothedCpG_Methylation_s24.bw\")\nsystem(\"bedGraphToBigWig SmoothedCpG_Methylation_Mel.bed danRer10.chrom.sizes SmoothedCpG_Methylation_Mel.bw\")\nsystem(\"bedGraphToBigWig SmoothedCpG_Methylation_Iri.bed danRer10.chrom.sizes SmoothedCpG_Methylation_Iri.bw\")\n\n", "meta": {"hexsha": "85ce5894006425c1368783057404500711b67439", "size": 25706, "ext": "r", "lang": "R", "max_stars_repo_path": "WGBS/WGBS_02_DML-DMR.r", "max_stars_repo_name": "joshhjang/zebrafish_pigment_cell_dev", "max_stars_repo_head_hexsha": "f4c0e3f81619778cf4e13955b23209c108ba9b94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "WGBS/WGBS_02_DML-DMR.r", "max_issues_repo_name": "joshhjang/zebrafish_pigment_cell_dev", "max_issues_repo_head_hexsha": "f4c0e3f81619778cf4e13955b23209c108ba9b94", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "WGBS/WGBS_02_DML-DMR.r", "max_forks_repo_name": "joshhjang/zebrafish_pigment_cell_dev", "max_forks_repo_head_hexsha": "f4c0e3f81619778cf4e13955b23209c108ba9b94", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-03T17:45:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-09T00:35:31.000Z", "avg_line_length": 90.1964912281, "max_line_length": 857, "alphanum_fraction": 0.6948961332, "num_tokens": 9208, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737473266735, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.31202952184555555}} {"text": "potFit <- function(data, day = NULL, month = NULL, year = NULL, initialYear = NULL, aggregation = \"annual\", nYears, nReplicates = 1, numPerYear = switch(aggregation, annual = 365, c(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)), threshold = NULL, nCovariates = 0, covariatesByYear = NULL, propMissing = 0, thresholdByYear = NULL, dataScaling = 1, locationModel = NULL, scaleModel = NULL, shapeModel = NULL, returnParams = FALSE, rvInterval = 20, newData = NULL, rvDifference = NULL, multiDayEventHandling = NULL, upper.tail = TRUE, optimMethod = \"Nelder-Mead\"){\n\n # data, day, month, and year are 1-d arrays giving the observed values and corresponding day index and month and year for the exceedances; note that the day index is NOT the day of the year but a continuous index with arbitrary starting value for the first day of the first year. 'day' is required when 'multiDayEventHandling' is not NULL; 'month' is required for seasonal or monthly analyses. 'year' is required when using covariates as 'covariatesByYear' are matched to 'data' based on 'year' and 'initialYear'. With replicated data for which multiDayEventHandling is not NULL, these should be ordered in blocks such that all the exceedances for the first replicate are first, then for the second replicate, etc. That will prevent events from different replicates as being treated as the same event in all but unusual circumstances.\n # initialYear indicates the first year of the dataset; in some cases this may not be the minimum value in 'year', as no exceedances may have occurred in the first year; required when covariates are used\n # aggregation should be one of \"annual\", \"seasonal\", or \"monthly\", indicating the stratification. If monthly or seasonal, separate results will be reported for each stratum (i.e., each month or season)\n # nYears is the number of years (blocks) in the full dataset (the dataset before the exceedances are extracted)\n # nReplicates is the number of replicate data sets; primarily for use with model output where you can run the model multiple times to get independent replicates\n # numPerYear is the number of days in each year. For annual analyses this should be about 365 (i.e., the average of the number of days in the years, including leap years). For seasonal and monthly analyses, this should be a 1-d array with 12 values, one for each month; the default values assume leap years are one-quarter of the years in the full dataset, thus 28.25 days per February. Strictly speaking this should not vary by year as the interpretation of return values is affected, though the effect of leap years should be minimal. \n # threshold is a scalar for annual analyses, a 1-d array of 4 values for seasonal (winter, spring, summer, fall thresholds) or a 1-d array of 12 values for monthly analyses (Jan thru Dec thresholds). However, if the thresholds vary by year, threshold should be NULL and thresholdByYear should be specified\n # nCovariates indicates the number of covariates provided through 'covariatesByYear' (note that any subset of the covariates that are provided may be used in the location, scale, and shape modeling, as specified in locationModel, scaleModel, shapeModel)\n # covariatesByYear must be provided if 'nCovariates' is non-zero and must specify the covariate values by year (year x covariate x (optionally) stratum), with the year index varying fastest and stratum index varying slowest\n # thresholdByYear is an (optional) 1-d array of threshold values by year (year x (optionally) stratum), with the year index varying fastest. If the threshold changes over time, this must be included, in which case threshold should be NULL\n # propMissing is either 0 or a 1-d array indicating the proportion of missing values (year x month (for monthly/seasonal analyses)), with the year index varying fastest. This should include values for all years between the first and last years - years that are entirely missing should have a value of 1\n # dataScaling is a positive-valued scalar used to scale the data values for more robust optimization performance. When multiplied by the values, it should produce values with magnitude around 1\n # locationModel, scaleModel, and shapeModel are vectors indicating the indices of the covariates to be used in the location, scale and shape parameterization. The values are used to select columns from the 'covariatesByYear' array after they are transformed to a multidimensional array with the second dimension indexing the covariates\n # returnParams is a boolean indicating whether to return the fitted parameter values and their standard errors; WARNINGS: (1) Parameter values for models with covariates must be interpreted based on transforming each covariate by subtracting the mean of the yearly values (from 'covariatesByYear') and dividing by the difference of the max and min of the yearly values. This scales the covariates for better numerical performance in the optimization. (2) parameter values for models with covariates for the scale parameter must interpreted based on the log transformation of the scale parameter\n # rvInterval: the timespan for which return values should be calculated. For example a rvInterval of 20 years corresponds to the value of an event that occurs with probability 1/20 in any year and therefore occurs on average every 20 years\n # newData should be a 1-d array of the same form as 'covariatesByYear', providing covariate values (observation x covariate) for which return values are desired. Values will be calculated for each stratum.\n # rvDifference should be a 1-d array of covariate values for two sets of covariates for which the difference in return values is desired (set x covariate), with the set index varying fastest; i.e. provide the first covariate for each set, then the second covariate for each set, etc. The difference is computed as the return value for the second set minus the return value for the first set. Values will be calculated for each stratum.\n # multiDayEventHandling should be NULL, \"noruns\", or a number. If 'noruns' is specified, only the maximum (or minimum if upper.tail = FALSE) value within a set of exceedances occuring on consecutive days is included. If a number, this should indicate the block size within which to allow only the largest (or smallest if upper.tail = FALSE) value\n # upper.tail indicates whether one is working with exceedances over a high threshold (TRUE) or exceedances under a low threshold (FALSE); in the latter case, the function works with the negative of the values and the threshold, changing the sign of the resulting location parameters\n # optimMethod is passed to the R's optimization routine optim() and should specify one of options for the 'method' argument of optim(). It is advisable to try multiple methods: Nelder-Mead and BFGS are commonly used methods; Nelder-Mead uses only function evaluations, while BFGS is a quasi-Newton method that also uses derivative information.\n\n # may want to have thresholdByMonth for cases where threshold varies only by stratum and not by observation \n \n if(!upper.tail){ # modeling exceedances below a threshold is equivalent to modeling the negative of exceedances above the negative of the threshold. Location parameter values will be the negative of those on the original scale, but are corrected before returning parameter values to the user.\n data = -data\n if(!is.null(threshold))\n threshold = -threshold\n if(!is.null(thresholdByYear))\n thresholdByYear = -thresholdByYear\n }\n \n n <- length(data) \n if(!is.null(day) && length(day) != n)\n stop(\"'day' must be of same length as 'data', with one value per observation\")\n if(!is.null(month) && length(month) != n)\n stop(\"'month' must be of same length as 'data', with one value per observation\")\n if(!is.null(year) && length(year) != n)\n stop(\"'year' must be of same length as 'data', with one value per observation\")\n if(!is.null(threshold))\n if((aggregation == \"annual\" && length(threshold) != 1) ||\n (aggregation == \"seasonal\" && length(threshold) != 4) ||\n (aggregation == \"monthly\" && length(threshold) != 12))\n stop(\"'threshold' must contain one value for annual, four values for seasonal, or 12 values for montly analysis\")\n\n \n if(!is.null(multiDayEventHandling) && is.null(day))\n stop(\"'day' is required when multi-day events are excluded\")\n if(aggregation != \"annual\" && is.null(month))\n stop(\"'month' is required when monthly or seasonal analyses are requested\")\n\n # determine strata-specific information\n nStrata = 1\n if(aggregation == \"monthly\"){\n nStrata = 12\n if(length(propMissing) > 1 && !(length(propMissing) %in% (nYears*c(1,12))))\n stop(\"'propMissing' should be a scalar, a vector of one value per year, or a vector with values for each month and year\")\n propMissing = array(propMissing, c(nYears, 12))\n if(length(numPerYear) != 12)\n stop(\"'numPerYear' should have one value for each month\")\n }\n if(aggregation == \"seasonal\"){\n nStrata = 4\n if(length(propMissing) > 1 && !(length(propMissing) %in% (nYears*c(1,12))))\n stop(\"'propMissing' should be a scalar, a vector of one value per year, or a vector with values for each month and year\")\n propMissing = array(propMissing, c(nYears, 12))\n if(length(numPerYear) != 12)\n stop(\"'numPerYear' should have one value for each month\")\n }\n if(aggregation == \"annual\"){\n propMissing = array(propMissing, c(nYears, 1))\n if(length(numPerYear) != 1)\n stop(\"'numPerYear' should be a single value for annual analyses\")\n }\n \n data <- data * dataScaling\n if(!is.null(threshold))\n threshold = threshold * dataScaling\n if(!is.null(thresholdByYear))\n thresholdByYear = thresholdByYear * dataScaling\n\n # check dimensionality of input arrays\n if(!is.null(covariatesByYear) && !(length(covariatesByYear) %in% (nYears*nCovariates*c(1,nStrata))))\n stop(\"'covariatesByYear must have covariate values for each covariate for each year (and optionally stratum)\")\n if(!is.null(thresholdByYear) && !(length(thresholdByYear) %in% (nYears*c(1,nStrata))))\n stop(\"'thresholdByYear must have values for each year (and optionally stratum)\")\n if(!is.null(newData) && length(newData) %% nCovariates != 0)\n stop(\"length of newData should be a multiple of 'nCovariates'\")\n if(!is.null(rvDifference) && length(rvDifference) != 2*nCovariates)\n stop(\"length of rvDifference is not equal to two times the number of covariates\")\n\n # manipulate input arrays to have appropriate number of dimensions and get covariate vals for obs\n if(!is.null(covariatesByYear)) {\n covariatesByYear <- array(covariatesByYear, c(nYears, nCovariates, nStrata))\n if(aggregation == \"annual\")\n covariates <- matrix(covariatesByYear[(year - initialYear + 1), , 1], ncol = nCovariates)\n if(aggregation == \"seasonal\"){ # move December to next year\n monthToSeason <- c(1,1,2,2,2,3,3,3,4,4,4,1)\n lastDec = year == (initialYear + nYears - 1) & month == 12\n data = data[!lastDec]\n n <- length(data)\n month = month[!lastDec]\n year = year[!lastDec]\n day = day[!lastDec]\n year[month == 12] = year[month == 12] + 1 \n covariates <- matrix(covariatesByYear[cbind(\n rep((year - initialYear + 1), nCovariates),\n rep(1:nCovariates, each = n),\n rep(monthToSeason[month], nCovariates))],\n ncol = nCovariates)\n }\n if(aggregation == \"monthly\")\n covariates <- matrix(covariatesByYear[cbind(\n rep((year - initialYear + 1), nCovariates),\n rep(1:nCovariates, each = n),\n rep(month, nCovariates))],\n ncol = nCovariates)\n } else{\n covariates <- NULL\n }\n if(!is.null(thresholdByYear))\n thresholdByYear <- array(thresholdByYear, c(nYears, nStrata))\n if(!is.null(newData)){\n m = length(newData)/nCovariates\n newData <- array(newData, c(m, nCovariates))\n }\n if(!is.null(rvDifference))\n rvDifference <- array(rvDifference, c(2, nCovariates))\n\n if(nCovariates && is.null(covariatesByYear))\n stop(\"'covariatesByYear' is required for nonstationary modeling\")\n\n if(is.null(rvInterval) && (!is.null(newData) || !is.null(rvDifference)))\n stop(\"'rvInterval' must be specified\")\n \n # seasonalize\n if(aggregation == 'seasonal' && !is.null(covariatesByYear)){\n seasons <- c('DJF', 'MAM', 'JJA', 'SON')\n dec = 12\n months = list(DJF = c(1,2,12), MAM = 3:5, JJA = 6:8, SON = 9:11)\n # put propMissing for first year based on missing Dec and shift other years\n propMissing[2:nYears, dec] = propMissing[1:(nYears-1), dec]\n propMissing[1, dec] = 1\n season = rep(1, n)\n for(seas in 2:4)\n season[month %in% months[[seas]]] = seas\n \n tmp = array(0, c(nYears, 4))\n for(seas in 1:4){\n wgts = numPerYear\n wgts[-months[[seas]]] = 0\n tmp[ , seas] = (propMissing%*%wgts) / sum(wgts)\n }\n propMissing = tmp\n numPerYear = c(sum(numPerYear[months[[1]]]), sum(numPerYear[months[[2]]]), sum(numPerYear[months[[3]]]), sum(numPerYear[months[[4]]]))\n }\n\n \n # remove multi-day events\n if(is.character(multiDayEventHandling) && multiDayEventHandling == \"noruns\")\n data = removeRuns(data, day)\n if(is.numeric(multiDayEventHandling))\n data = withinBlockScreen(data, day, blockLen = multiDayEventHandling)\n\n if(!is.null(month))\n month = month[!is.na(data)]\n if(!is.null(year))\n year = year[!is.na(data)]\n \n if(aggregation == \"seasonal\")\n season = season[!is.na(data)]\n\n if(nCovariates)\n covariates <- covariates[!is.na(data), , drop = FALSE]\n data = data[!is.na(data)]\n \n nParam <- 3 + length(locationModel) + length(scaleModel) + length(shapeModel)\n\n if(!is.null(locationModel) && !validateIndices(locationModel, nCovariates))\n stop(\"'locationModel' values do not provide legitimate indices of covariates\")\n \n if(!is.null(scaleModel) && !validateIndices(scaleModel, nCovariates))\n stop(\"'scaleModel' values do not provide legitimate indices of covariates\")\n \n if(!is.null(shapeModel) && !validateIndices(shapeModel, nCovariates))\n stop(\"'shapeModel' values do not provide legitimate indices of covariates\")\n\n if(nCovariates){\n for(p in 1:nCovariates){ # shift and scale to (-.5, .5) for better numeric properties in estimation\n if(!is.null(newData))\n newData[ , p] <- normalize(newData[, p], mean(covariatesByYear[ , p, ]), min(covariatesByYear[ , p, ]), max(covariatesByYear[ , p, ]))\n if(!is.null(rvDifference))\n rvDifference[ , p] <- normalize(rvDifference[, p], mean(covariatesByYear[ , p, ]), min(covariatesByYear[ , p, ]), max(covariatesByYear[ , p, ])) \n covariates[ , p] <- normalize(covariates[ , p], mean(covariatesByYear[ , p, ]), min(covariatesByYear[ , p, ]), max(covariatesByYear[ , p, ]))\n covariatesByYear[ , p, ] <- normalize(covariatesByYear[ , p, ])\n }\n }\n # do I need to save the original covariates or at least mean and divisor of normalization?\n\n mulink <- siglink <- shlink <- identity\n link = \"c(identity, identity, identity)\"\n if(!is.null(scaleModel)){\n siglink <- exp\n link = \"c(identity, exp, identity)\"\n }\n \n NAlist <- list(mle = rep(NA, nParam), se = rep(NA, nParam), cov = matrix(NA, nParam, nParam)) # rep(NA, nParam)\n\n pot.fit.wrap = function(xdat, threshold, npy, ydat, ydatByYear, propMissing, nYears, thresholdByYear){\n fit = try(pp.fit2(xdat, threshold = threshold, npy = npy, ydat = ydat, mul = locationModel, sigl = scaleModel, shl = shapeModel, mulink = mulink, siglink = siglink, shlink = shlink, show = FALSE, exceedancesOnly = TRUE, nBlocks = nYears, propMissingByBlock = propMissing, ydatByBlock = ydatByYear, thresholdByBlock = thresholdByYear))\n if(!is(fit, 'try-error') && !fit$flag && !fit$conv){\n return(fit[c(\"mle\", \"se\", \"cov\")])\n } else{\n return(NAlist)\n }\n }\n\n mle <- se <- array(NA, c(nParam, nStrata))\n covmat <- array(NA, c(nParam, nParam, nStrata))\n\n for(j in 1:nStrata){\n tmpdata = data\n tmpcovariates = covariates\n tmpcovariatesByYear = covariatesByYear\n if(nCovariates)\n tmpcovariatesByYear = matrix(covariatesByYear[ , , j], nc = nCovariates) \n if(aggregation == \"monthly\"){\n tmpdata = data[month == j]\n if(nCovariates){\n tmpcovariates = covariates[month == j, , drop = FALSE]\n } \n } \n if(aggregation == \"seasonal\"){\n tmpdata = data[season == j]\n if(nCovariates){\n tmpcovariates = covariates[season == j, , drop = FALSE]\n } \n }\n if(nCovariates && nReplicates > 1)\n tmpcovariatesByYear = matrix(rep(c(t(tmpcovariatesByYear)), nReplicates), ncol = nCovariates, byrow = TRUE)\n\n output = pot.fit.wrap(tmpdata, threshold[j], numPerYear[j], tmpcovariates, tmpcovariatesByYear, rep(propMissing[ , j], nReplicates), nYears*nReplicates, rep(thresholdByYear[ , j], nReplicates)) \n mle[ , j] <- output$mle\n if(!upper.tail) # location parameters for lower tail are the negative of those computed based on negative of data values\n mle[1:(length(locationModel)+1), ] <- -mle[1:(length(locationModel)+1), ]\n se[ , j] <- output$se\n covmat[ , , j] <- output$cov\n }\n \n results <- list()\n numLocScaleParams = 2 + length(locationModel) + length(scaleModel)\n # rescale parameters so on scale of original data\n mle[1:numLocScaleParams, ] <- mle[1:numLocScaleParams, ] / dataScaling\n se[1:numLocScaleParams, ] <- se[1:numLocScaleParams, ] / dataScaling\n\n if(returnParams){\n if(aggregation == 'seasonal')\n attributes(mle)$dimnames[[2]] <- attributes(se)$dimnames[[2]] <- seasons\n results$mle <- mle[ , , drop = TRUE] \n results$se.mle <- se[ , , drop = TRUE]\n }\n\n # get return values for newData observations\n # perhaps make this more efficient with an apply, but it needs to pass in both mle and cov\n if(!is.null(newData)){\n rv <- array(0, c(m, nStrata, 2))\n for(i in 1:m)\n for(j in 1:nStrata){\n fit = list(mle = mle[ , j], cov = covmat[ , , j], model = list(locationModel, scaleModel, shapeModel), link = link)\n class(fit) = \"pp.fit\"\n rv[i, j, ] <- returnValue(fit, rvInterval, newData[i, ]) \n }\n if(aggregation == \"seasonal\")\n attributes(rv)$dimnames[[2]] <- seasons\n results$returnValue <- rv[ , , 1] \n results$se.returnValue <- rv[ , , 2] \n }\n\n # get stationary return value\n if(is.null(newData) && !is.null(rvInterval) && is.null(covariates)){\n rv <- array(0, c(nStrata, 2))\n for(j in 1:nStrata){\n fit = list(mle = mle[ , j], cov = covmat[ , , j], model = list(locationModel, scaleModel, shapeModel), link = link)\n class(fit) = \"pp.fit\"\n rv[j, ] <- returnValue(fit, rvInterval, rvCovariates = NULL) \n }\n if(aggregation == \"seasonal\")\n attributes(rv)$dimnames[[1]] <- seasons\n results$returnValue <- rv[ , 1]\n results$se.returnValue <- rv[ , 2]\n }\n\n # get return value difference\n if(!is.null(rvDifference)){\n rvDiff <- array(0, c(nStrata, 2))\n for(j in 1:nStrata){\n fit = list(mle = mle[ , j], cov = covmat[ , , j], model = list(locationModel, scaleModel, shapeModel), link = link)\n class(fit) = \"pp.fit\"\n rvDiff[j, ] <- returnValueDiff(fit, rvInterval, rvDifference) \n }\n if(aggregation == \"seasonal\")\n attributes(rvDiff)$dimnames[[1]] <- seasons\n results$returnValueDiff <- rvDiff[ , 1] \n results$se.returnValueDiff <- rvDiff[ , 2]\n }\n \n return(results)\n}\n\n", "meta": {"hexsha": "6db86f7e3d89f4d7870d5705c9b50536491041d0", "size": 19624, "ext": "r", "lang": "R", "max_stars_repo_path": "operators/PeaksOverThreshold/r_src/potVisit.r", "max_stars_repo_name": "ahota/visit_ospray", "max_stars_repo_head_hexsha": "d80b2e18ff5654d04bfb56ae4d6f42e45f87c9b9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "operators/PeaksOverThreshold/r_src/potVisit.r", "max_issues_repo_name": "ahota/visit_ospray", "max_issues_repo_head_hexsha": "d80b2e18ff5654d04bfb56ae4d6f42e45f87c9b9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "operators/PeaksOverThreshold/r_src/potVisit.r", "max_forks_repo_name": "ahota/visit_ospray", "max_forks_repo_head_hexsha": "d80b2e18ff5654d04bfb56ae4d6f42e45f87c9b9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 63.0996784566, "max_line_length": 836, "alphanum_fraction": 0.6918059519, "num_tokens": 5341, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737214979745, "lm_q2_score": 0.45713671682749485, "lm_q1q2_score": 0.31202951003830887}} {"text": "\r\ncat('\\n---Polygenic Burden (PB) - Large Effect Variant (LEV) SCAN (PB-LEV-SCAN)---\\n')\r\ncat('---version 1.0---\\n')\r\n\r\nlibrary('ggplot2')\r\nlibrary(\"optparse\")\r\n\r\noptions(scipen = 10) \r\n\r\noption_list = list(\r\n \r\n #path\r\n make_option(\"--plink_path\", action=\"store\", default=\"plink\", type='character',\r\n help=\"Path to plink1.9 [%default]\"),\r\n make_option(\"--plink2_path\", action=\"store\", default=\"plink2\", type='character',\r\n help=\"Path to plink2 [%default]\"),\r\n make_option(\"--maf_distribution_path\", action=\"store\", default=NA, type='character',\r\n help=\"Path to the maf distribution file (estimated from empirical data) [required]\"),\r\n make_option(\"--ldsc_path\", action=\"store\", default=NA, type='character',\r\n help=\"Path to the ldsc file (estimated from empirical data) [required]\"),\r\n make_option(\"--tmp_folder\", action=\"store\", default=NA, type='character',\r\n help=\"tmp folder for intermediate files [required]. Please vary the tmp folder names (e.g., tmp_1; tmp_2; tmp_3; etc.) when you have multiple jobs to run in parallel. Otherwise it will overwrite each other\"),\r\n make_option(\"--out_folder\", action=\"store\", default=NA, type='character',\r\n help=\"path for output folder [required]\"),\r\n make_option(\"--out_prefix\", action=\"store\", default=NA, type='character',\r\n help=\"prefix for output [required]\"),\r\n \r\n #model\r\n make_option(\"--genetic_architecture\", action=\"store\", default='polygenic', type='character',\r\n help=\"polygenic=polygenic architecture \\nNegativeSelection=with negative selection \\nLDAK=LD-adjusted kinship\"),\r\n make_option(\"--disease_model\", action=\"store\", default='LTM', type='character',\r\n help=\"LTM=liability threshold model\\nlogit=logit model\"),\r\n \r\n #parameter\r\n make_option(\"--n_simu\", action=\"store\", default=500, type='integer',\r\n help=\"simulation times\"),\r\n make_option(\"--sample_size\", action=\"store\", default=10000, type='integer',\r\n help=\"number of sample size\"),\r\n make_option(\"--h2_PB\", action=\"store\", default=0.3, type='double',\r\n help=\"heritability due to common variant based polygenicity\"),\r\n make_option(\"--h2_LEV\", action=\"store\", default=NA, type='double',\r\n help=\"heritability due to large effect variant(s). Use h2_LEV or OR_LEV. If both h2_LEV and OR_LEV are provided, OR_LEV will be ignored\"),\r\n make_option(\"--OR_LEV\", action=\"store\", default=NA, type='double',\r\n help=\"OR of large effect variant. Use h2_LEV or OR_LEV. If both h2_LEV and OR_LEV are provided, OR_LEV will be ignored\"),\r\n make_option(\"--freq_LEV\", action=\"store\", default=0.01, type='double',\r\n help=\"allele frequency of large effect variant(s)\"),\r\n make_option(\"--prevalence\", action=\"store\", default=0.01, type='double',\r\n help=\"disease prevalence in population\"),\r\n make_option(\"--pi0\", action=\"store\", default=0, type='double',\r\n help=\"proportion of non-causal common, small effect variants\"),\r\n make_option(\"--seed\", action=\"store\", default=1, type='integer',\r\n help=\"seed for sampling\"),\r\n \r\n #other options\r\n make_option(\"--generate_a_figure\", action=\"store_true\", default=TRUE,\r\n help=\"Generate a figure comparsion the number of LEV-carriers among patients with different polygenic burdens [default: %default]\"),\r\n make_option(\"--clean_tmp\", action=\"store_true\", default=TRUE,\r\n help=\"Delete the tmp folder and all temporary files [default: %default]\")\r\n \r\n)\r\n\r\nopt = parse_args(OptionParser(option_list=option_list))\r\n\r\n#input\r\nplink_path = opt$plink_path\r\nplink2_path = opt$plink2_path\r\nmaf_distribution_path = opt$maf_distribution_path\r\nldsc_path = opt$ldsc_path\r\ntmp_folder = opt$tmp_folder\r\nout_folder = opt$out_folder\r\nout_prefix = opt$out_prefix\r\n\r\ngenetic_architecture = opt$genetic_architecture\r\ndisease_model = opt$disease_model\r\n\r\nN_simulation = opt$n_simu\r\nN_samples = opt$sample_size\r\nh2_PB = opt$h2_PB\r\nfreq_LEV = opt$freq_LEV\r\nk = opt$prevalence\r\nseed = opt$seed\r\npi0 = opt$pi0\r\ngenerate_a_figure = opt$generate_a_figure\r\nclean_tmp = opt$clean_tmp\r\n\r\nif(is.na(opt$h2_LEV) & is.na(opt$OR_LEV)){\r\n h2_LEV = 0.01\r\n beta_LEV = sqrt(h2_LEV/2/freq_LEV/(1-freq_LEV))\r\n}else if(!is.na(opt$h2_LEV) & is.na(opt$OR_LEV)){\r\n h2_LEV = opt$h2_LEV\r\n beta_LEV = sqrt(h2_LEV/2/freq_LEV/(1-freq_LEV))\r\n}else if(is.na(opt$h2_LEV) & !is.na(opt$OR_LEV)){\r\n #r2=var(ln(OR)*G)/(var(ln(OR)*G)+3.29) ref Hong S. Lee, Gen Epi\r\n h2_LEV = (log(opt$OR_LEV,2.718)^2 * 2 * freq_LEV * (1-freq_LEV)) / (log(opt$OR_LEV,2.718)^2 * 2 * freq_LEV * (1-freq_LEV) + 3.29)\r\n beta_LEV = sqrt(h2_LEV/2/freq_LEV/(1-freq_LEV))\r\n}else if(!is.na(opt$h2_LEV) & !is.na(opt$OR_LEV)){\r\n h2_LEV = opt$h2_LEV\r\n beta_LEV = sqrt(h2_LEV/2/freq_LEV/(1-freq_LEV))\r\n warning('Please only provide h2_LEV or OR_LEV, or OR_LEV will be ignored')\r\n}\r\n\r\n\r\n#load empirical ldsc\r\ncat('INFO loading ldsc from empirical dataset\\n')\r\nldsc_raw<-read.table(ldsc_path,header = T,stringsAsFactors = F)\r\n\r\n#make tmp dir\r\nif(!dir.exists(tmp_folder)){dir.create(tmp_folder)}\r\n#make output dir\r\nif(!dir.exists(out_folder)){dir.create(out_folder)}\r\n\r\n#-----------------------------------------------\r\n\r\n#df for simulation results\r\nsimu<-data.frame(n_simu=seq(1,N_simulation),wilcox_p=NA,p1=NA,p2=NA,p3=NA,p4=NA,p5=NA,p6=NA,p7=NA,p8=NA,p9=NA,p10=NA)\r\n\r\ncat('INFO start simulation\\n')\r\n\r\nfor (i in 1:N_simulation){\r\n #if(i %in% c(1,seq(1,1000)*10+1)){\r\n cat(paste0('INFO ',i,' / ',N_simulation,' \\n'))\r\n #}\r\n \r\n ###------simulate genotype for common causal variants------\r\n \r\n #use plink to simulate genotype data. Note: N_sample/2 doesn't means half cases and half control. See plink website for details.\r\n cmd=paste0('plink --simulate ',maf_distribution_path,' --simulate-ncases ',N_samples/2,' --simulate-ncontrols ',N_samples/2,' --seed ',seed,' --silent --make-bed --out ',tmp_folder,'/tmp_geno; plink --bfile ',tmp_folder,'/tmp_geno --silent --freq --out ',tmp_folder,'/tmp_freq')\r\n system(cmd,wait = T,ignore.stdout=T,ignore.stderr=T)\r\n \r\n #10k samples ~30 secs\r\n #100k samples ~3 mins\r\n \r\n #df of effect size for small effect common variants\r\n df<-read.table(paste0(tmp_folder,'/tmp_freq.frq'),stringsAsFactors = F,header = T)\r\n N_causal_SNPs=nrow(df)\r\n \r\n #ldsc, matching the correlation between ldsc and MAF from empirical data\r\n df$id=seq(1,nrow(df))\r\n df<-df[order(df$MAF),]\r\n ldsc<-ldsc_raw[sample(nrow(ldsc_raw),nrow(df),replace = T),]\r\n ldsc<-ldsc[order(ldsc$MAF),]\r\n df$ldsc=ldsc$ldscore\r\n \r\n # a Infinitesimal architecture\r\n # beta_poly~(0,h2_PB/N_caucal_SNPs)\r\n if(genetic_architecture=='polygenic'){\r\n set.seed(i)\r\n df$effect=(1-pi0)*rnorm(N_causal_SNPs,0,(h2_PB/N_causal_SNPs)^0.5)\r\n \r\n # b Negative selection\r\n # beta_poly~N(O,k_constant([f(1-f)]^(1+alpha)))\r\n }else if(genetic_architecture=='NegativeSelection'){\r\n set.seed(i)\r\n df$effect=df[,'effect']<-sapply(df$MAF,function(f) rnorm(1,mean=0,sd=((f*(1-f))^0.63)^0.5))\r\n #find k constant\r\n k_constant=(h2_PB/N_causal_SNPs/var(df[,'effect']))\r\n #multiply by k constant\r\n df[,'effect']<-df[,'effect']*k_constant^0.5*(1-pi0)\r\n \r\n # c LD-adjusted kinship\r\n # beta_poly~N(O,k_constant([f(1-f)]^(1+alpha)*(1/(1+ldsc))))\r\n }else if(genetic_architecture=='LDAK'){\r\n set.seed(i)\r\n df$effect=df[,'effect']<-mapply(function(f,ld) rnorm(1,mean=0,sd=(((f*(1-f))^0.75)*1/(1+ld))^0.5), df$MAF, df$ldsc)\r\n #find k constant\r\n k_constant=(h2_PB/N_causal_SNPs/var(df[,'effect']))\r\n #multiply by k constant\r\n df[,'effect']<-df[,'effect']*k_constant^0.5*(1-pi0)\r\n }\r\n \r\n \r\n #write weight file\r\n write.table(df[,c('SNP','A1','effect')],paste0(tmp_folder,'/tmp.weight'),row.names = F,col.names = F,sep='\\t',quote = F)\r\n \r\n #use plink to calculate the PB (polygenic burden, i.e.,PRS of common variants)\r\n cmd=paste0('plink2 --bfile ',tmp_folder,'/tmp_geno --score ',tmp_folder,'/tmp.weight variance-standardize --out ',tmp_folder,'/tmp.score')\r\n system(cmd,wait = T,ignore.stdout=T,ignore.stderr=T)\r\n \r\n #load PB results (A)\r\n PB=read.table(paste0(tmp_folder,'/tmp.score.sscore'),stringsAsFactors = F)\r\n PB$IID=PB$V1\r\n PB$A=PB$V6*2*N_causal_SNPs\r\n PB<-PB[,c('IID','A')]\r\n \r\n \r\n ###------simulate genotype for the large effect variants (LEV)------\r\n set.seed(seed+10)\r\n R_raw_genotype = rbinom(n = N_samples, size = 2, prob = freq_LEV)\r\n #genetic risk due to LEV (R)\r\n R<- R_raw_genotype * beta_LEV\r\n \r\n #merge A and R\r\n LEV<-data.frame(IID=PB$IID,R=R,R_raw_genotype=R_raw_genotype)\r\n combined<-merge(PB,LEV,by='IID')\r\n \r\n #x=A+R total genetic risk\r\n combined$x=combined$A+combined$R\r\n \r\n #h2_e error/environment\r\n h2_e=max(1-h2_PB-var(combined$R),0)\r\n \r\n # #\r\n # print(h2_PB)\r\n # print(h2_e)\r\n # print(sum(combined$x))\r\n # print(nrow(combined))\r\n \r\n #disease liability (L) L=A+R+e\r\n set.seed(i+100)\r\n combined$L=combined$x+rnorm(nrow(combined),0,h2_e^0.5)\r\n \r\n #threshold for liability-threshold model (LTM)\r\n t=quantile(combined$L,1-k)[[1]]\r\n \r\n #disease probability\r\n combined$prob=pnorm(-(t-combined$x)/h2_e^0.5)\r\n \r\n #generate binary trait\r\n if(disease_model=='LTM'){\r\n combined$y_binary<-ifelse(combined$L>t,1,0)\r\n }else if (disease_model=='logit'){\r\n set.seed(i*10)\r\n combined$y_binary<-sapply(combined$prob,function(x) rbinom(1,1,x))\r\n }else{\r\n stop('unknown disease model, please provide \"LTM\" or \"logit\"')\r\n }\r\n \r\n #LEV carrier\r\n combined$carrier<-ifelse(combined$R_raw_genotype!=0,1,0)\r\n \r\n #check the 2 by 2 table\r\n table_check<-table(combined$y_binary,combined$carrier)\r\n \r\n #simulated cases (patients)\r\n cases<-combined[combined$y_binary==1,]\r\n \r\n #test PB-LEV in cases only\r\n if(length(which(combined$y_binary==1 & combined$carrier==1))>0 & length(which(combined$y_binary==1 & combined$carrier==0))>0){\r\n #test PB-LEV correlation, one side\r\n ans_inverse<-wilcox.test(cases[cases$carrier==1,'A'],cases[cases$carrier==0,'A'],alternative = c(\"less\"))\r\n simu[i,'wilcox_p']<-ans_inverse$p.value\r\n }else{\r\n #not enough samples in the 2 by 2 table\r\n simu[i,'wilcox_p']<-1\r\n }\r\n \r\n #group cases in to 10 equally sized bins based on their PB risk\r\n cases$group <- as.numeric(cut(cases$A, 10))\r\n \r\n #pool cases from each simulation\r\n if(i==1){\r\n cases_pool=cases\r\n }else{\r\n cases_pool=rbind(cases_pool,cases)\r\n }\r\n \r\n #number of LEV-carriers for each bin\r\n tmp_c<-data.frame(Group.1=seq(1,10))\r\n tmp_c<-merge(tmp_c,as.data.frame(aggregate(cases$carrier,list(cases$group),sum)),all=T)\r\n #number of samples in each bin\r\n tmp_n<-as.data.frame(table(cases$group))\r\n #merge\r\n tmp_c<-merge(tmp_c,tmp_n,by=1,all=T)\r\n tmp_c[is.na(tmp_c)]<-0\r\n colnames(tmp_c)<-c('group','x','n') #'group'; # of carriers; # of cases\r\n #number of LEV-carriers per 1000 cases\r\n simu[i,3:12]<-round(tmp_c$x/tmp_c$n*1000,2)\r\n \r\n}\r\n\r\ncat(paste0('INFO simulation finished\\n'))\r\n\r\ncat(paste0('INFO saving results\\n'))\r\n\r\n#save cases results\r\nwrite.table(cases_pool,paste0(out_folder,'/',out_prefix,'_cases.txt'),quote = F,sep='\\t',row.names = F)\r\n\r\n#save simu results\r\nwrite.table(simu,paste0(out_folder,'/',out_prefix,'_simu.txt'),quote = F,sep='\\t',row.names = F)\r\n\r\n#result df\r\noutput<-data.frame(genetic_architecture=genetic_architecture,\r\n disease_model=disease_model,\r\n h2_PB=h2_PB,\r\n OR_LEV=2.718^beta_LEV,\r\n freq_LEV=freq_LEV,\r\n prevalence=k,\r\n pi0=pi0,\r\n N_samples=N_samples,\r\n N_simulation=N_simulation,\r\n seed=seed,\r\n utility=length(which(simu[,'wilcox_p']<0.05))/nrow(simu)\r\n)\r\n\r\n#mean and sd for the number of LEV-carriers per 1000 cases\r\nfor(j in 1:10){\r\n output[1,paste0('mean_p',j)]<-mean(simu[,paste0('p',j)],na.rm = T)\r\n output[1,paste0('sd_p',j)]<-sd(simu[,paste0('p',j)],na.rm = T)\r\n}\r\n\r\n#save result\r\nwrite.table(output,paste0(out_folder,'/',out_prefix,'.txt'),quote = F,sep='\\t',row.names = F)\r\n\r\n\r\n\r\n#---figure generator (LEV_carriers_per_1000_cases_comparison)---\r\n\r\n\r\nif(generate_a_figure){\r\n \r\n cat(paste0('INFO figure generating\\n'))\r\n \r\n #get the x-coordinates for each cSEV-PB bins\r\n p_tmp<-ggplot(data=cases_pool,aes(x=A))+ #use the A from last simulation\r\n geom_histogram(bins = 10)\r\n \r\n #df for # carriers in each bin\r\n carrier_info<-data.frame(pos=ggplot_build(p_tmp)$data[[1]]$x,\r\n mean=as.numeric(output[1,paste0('mean_p',seq(1,10))]),\r\n sd=as.numeric(output[1,paste0('sd_p',seq(1,10))]))\r\n carrier_info$lower=carrier_info$mean-carrier_info$sd\r\n carrier_info$upper=carrier_info$mean+carrier_info$sd\r\n carrier_info$lower=ifelse(carrier_info$lower<0,0,carrier_info$lower)\r\n carrier_info$upper=ifelse(carrier_info$upper>1000,1000,carrier_info$upper)\r\n \r\n #find ylim (the highest density)\r\n y_density_max<-max(ggplot_build(p_tmp)$data[[1]]$density)*1.2\r\n \r\n #coeff for adjusting dual y-axis\r\n coeff=max(carrier_info$upper,na.rm = T)/y_density_max\r\n \r\n #plot\r\n p<-ggplot(data=carrier_info)+\r\n \r\n #cSEV-PB distribution\r\n geom_histogram(data=cases_pool,aes(x=A,y=..density..), colour=\"black\", fill=\"white\",bins=10)+\r\n geom_density(data=cases_pool,aes(x=A),alpha=.2, fill=\"#FF6666\",bw=0.2)+\r\n \r\n #mean of 'number of LEV-carriers per 1000 cases' \r\n geom_point(aes(x=carrier_info$pos,y=carrier_info$mean/coeff),color='dodgerblue3')+\r\n \r\n #error bar for 'number of LEV-carriers per 1000 cases' (+- 1sd)\r\n geom_pointrange(aes(x=carrier_info$pos,y=carrier_info$mean/coeff,ymin=carrier_info$lower/coeff, ymax=carrier_info$upper/coeff),color='dodgerblue3')+\r\n \r\n scale_y_continuous(\r\n # Features of the first axis\r\n name = \"density\",\r\n #limits for density\r\n limits = c(0,y_density_max),\r\n # Add a second axis and specify its features\r\n sec.axis = sec_axis(~.*coeff, name=\"number of LEV-carriers per 1000 cases\")\r\n )+\r\n \r\n xlab('polygenic burden (PB)')+\r\n \r\n theme(legend.title = element_blank(),\r\n panel.grid =element_blank(),\r\n panel.background = element_blank(),\r\n panel.border = element_blank(),\r\n axis.line = element_line(colour = \"black\"),\r\n legend.position = 'none',\r\n axis.title.y.right = element_text(color = 'dodgerblue3'),\r\n axis.text.y.right = element_text(color = 'dodgerblue3'))\r\n \r\n #figure\r\n pdf(paste0(out_folder,'/',out_prefix,'_LEV_carriers_per_1000_cases_comparison_figure.pdf'),height = 5,width = 5)\r\n suppressWarnings(print(p))\r\n dev.off()\r\n \r\n}\r\n\r\nif(clean_tmp){\r\n cat(paste0('INFO cleaning up tmp folder\\n'))\r\n cmd=paste0('rm -r ',tmp_folder)\r\n system(cmd,wait = F)\r\n}\r\n\r\ncat(paste0('INFO finished.\\n'))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "3f5f03505d692bb6446bfae026ab34b204d03601", "size": 14895, "ext": "r", "lang": "R", "max_stars_repo_path": "src/PB-LEV-SCAN_1.0.r", "max_stars_repo_name": "gamazonlab/Polygenic_Background_Rare_Variant_Axis", "max_stars_repo_head_hexsha": "f86f3e385ae458e738ce9dcd0c0dbaa6e376fac4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2021-08-05T14:31:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T01:58:29.000Z", "max_issues_repo_path": "src/PB-LEV-SCAN_1.0.r", "max_issues_repo_name": "gamazonlab/Polygenic_Background_Rare_Variant_Axis", "max_issues_repo_head_hexsha": "f86f3e385ae458e738ce9dcd0c0dbaa6e376fac4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/PB-LEV-SCAN_1.0.r", "max_forks_repo_name": "gamazonlab/Polygenic_Background_Rare_Variant_Axis", "max_forks_repo_head_hexsha": "f86f3e385ae458e738ce9dcd0c0dbaa6e376fac4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-08-10T16:27:27.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-10T16:27:27.000Z", "avg_line_length": 37.9974489796, "max_line_length": 281, "alphanum_fraction": 0.6504196039, "num_tokens": 4486, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6654105587468141, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.31193823279863797}} {"text": "petsc_matprinter_fmt <- function(fmt=\"default\")\n{\n fmt_choices <- c(\"default\", \"matlab\", \"dense\", \"impl\", \"info\", \"info_detail\", \"common\", \"index\", \"symmodu\", \"vtk\", \"native\", \"basic\", \"lg\", \"contour\")\n fmt <- match.arg(tolower(fmt), fmt_choices)\n \n fmt_int <- .Call(sbase_petsc_printer_lookup_code, fmt)\n .Call(sbase_petsc_matprinter_fmt, as.integer(fmt_int))\n \n invisible()\n}\n\n\n\npetsc_matprinter <- function(dim, ldim, data, row_ptr, col_ind, fmt=\"default\")\n{\n petsc_matprinter_fmt(fmt=fmt)\n \n .Call(sbase_petsc_matprinter, dim, ldim, data, row_ptr, col_ind)\n \n invisible()\n}\n\n\n", "meta": {"hexsha": "dad53437428adeab1e91065da06927184d9bca24", "size": 592, "ext": "r", "lang": "R", "max_stars_repo_path": "R/petsc_printing.r", "max_stars_repo_name": "wrathematics/pbdSBASE", "max_stars_repo_head_hexsha": "24d5b2fba223cd96b46bb07c3d5ca6373fbb877a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/petsc_printing.r", "max_issues_repo_name": "wrathematics/pbdSBASE", "max_issues_repo_head_hexsha": "24d5b2fba223cd96b46bb07c3d5ca6373fbb877a", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/petsc_printing.r", "max_forks_repo_name": "wrathematics/pbdSBASE", "max_forks_repo_head_hexsha": "24d5b2fba223cd96b46bb07c3d5ca6373fbb877a", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.6666666667, "max_line_length": 152, "alphanum_fraction": 0.6841216216, "num_tokens": 180, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3118106498231194}} {"text": "#mtcars is a cars dataset\n# Print the first observations of mtcars\nhead(mtcars)\n\n# Print the last observations of mtcars\ntail(mtcars)\n\n# Print the dimensions of mtcars\ndim(mtcars)\n\n# Investigate the structure of the mtcars data set\nstr(mtcars)", "meta": {"hexsha": "f7b03b367ec35d0ee7403de4c1105f498dfdf25a", "size": 243, "ext": "r", "lang": "R", "max_stars_repo_path": "R/6.1/dataframe.r", "max_stars_repo_name": "applecool/DataScience", "max_stars_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/6.1/dataframe.r", "max_issues_repo_name": "applecool/DataScience", "max_issues_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/6.1/dataframe.r", "max_forks_repo_name": "applecool/DataScience", "max_forks_repo_head_hexsha": "2d166cc18ced32d9bf01620d83555d70c688a627", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.25, "max_line_length": 50, "alphanum_fraction": 0.7818930041, "num_tokens": 70, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3118106498231194}} {"text": "# whitinga-selection: whitinga-selection\n# Authors: Amy Marshall, Jason Gush\n# whitinga-selection.r 2021-03-08 09:01:01Z\n\nwhitinga_selection <- function(dataframe, ma = 5, pa = 3, ff = 15, fe = 30){\n\t# draw M\u0101ori fellows, i.e. Ethnicity is flagged as M\u0101ori or M\u0101ori/Pacific Nations\n\tmaori_fellows <- dataframe %>% filter(str_detect(Ethnicity, \"Ma\")) %>% slice_sample(n = ma) %>% mutate(ballot = \"M\\u101ori\")\n\t\n\t# exclude already drawn fellows from the pool\n\tpool <- dataframe %>% anti_join(maori_fellows, by = c(\"Gender\", \"Ethnicity\", \"id\"))\n\n\t# draw Pacific Nations fellows, i.e. Ethnicity is flagged as Pacific Nations or M\u0101ori/Pacific Nations\n\tpacific_fellows <- pool %>% filter(str_detect(Ethnicity, \"Pa\")) %>% slice_sample(n = pa) %>% mutate(ballot = \"Pacific\")\n\n\tfellows <- bind_rows(maori_fellows, pacific_fellows)\n\tpool <- pool %>% anti_join(fellows, by = c(\"Gender\", \"Ethnicity\", \"id\"))\n\n\t# determine how many more female fellows are needed\n\tcount_female = fellows %>% filter(Gender == \"F\") %>% nrow()\n\tfemale_fellows_to_select = ff - count_female\t\n\n\t# randomly sort remaining applicant pool\n\tshuffled_rows <- sample(nrow(pool))\n\tpool_sorted <- pool[shuffled_rows,]\n\t\n\t# select top sorted female and gender diverse applicants until the minimum target\n\t# of female fellows has been drawn\n\tpriv_pool <- pool_sorted %>% filter(Gender == \"F\" | Gender == \"GD\") \n\thead_counter = female_fellows_to_select\n\tfemales_drawn = 0\n\t\n\twhile (females_drawn < female_fellows_to_select && nrow(fellows) + head_counter < fe) {\n\t\tnew_fellows <- priv_pool %>% slice_head(n = head_counter) %>% mutate(ballot = \"Pool\")\n\t\tfemales_drawn <- new_fellows %>% filter(Gender == \"F\") %>% nrow()\n\t\t# if the slice is too small, i.e., as one or more gender diverse fellows were drawn\n\t\t# in slice, increase the slice size by one and redraw\n\t\thead_counter = head_counter + 1\n\t}\n\t\n\tfellows <- bind_rows(fellows, new_fellows)\n\t\n\tpool_sorted <- pool_sorted %>% anti_join(new_fellows, by = c(\"Gender\", \"Ethnicity\", \"id\"))\n\n\t# draw from the top of the sort (now largely/exclusively male or non-reponse) all remaining fellows\n\tfellows_still_to_draw = fe - nrow(fellows)\n\tnew_new_fellows <- pool_sorted %>% slice_head(n = fellows_still_to_draw) %>% mutate(ballot = \"Pool\")\n\t\n\tfellows <- bind_rows(fellows, new_new_fellows)\n\t\n\treturn(fellows)\n}", "meta": {"hexsha": "884fb305c3ab6031f79e7d27b172027230bf935b", "size": 2311, "ext": "r", "lang": "R", "max_stars_repo_path": "whitinga-selection.r", "max_stars_repo_name": "Royal-Society-of-New-Zealand/whitinga-selection", "max_stars_repo_head_hexsha": "71459996e5ca7ae776cd54ba662c27048ceffb32", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-04-12T00:08:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-03T04:14:34.000Z", "max_issues_repo_path": "whitinga-selection.r", "max_issues_repo_name": "Royal-Society-of-New-Zealand/whitinga-selection", "max_issues_repo_head_hexsha": "71459996e5ca7ae776cd54ba662c27048ceffb32", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "whitinga-selection.r", "max_forks_repo_name": "Royal-Society-of-New-Zealand/whitinga-selection", "max_forks_repo_head_hexsha": "71459996e5ca7ae776cd54ba662c27048ceffb32", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.3137254902, "max_line_length": 125, "alphanum_fraction": 0.7152747728, "num_tokens": 684, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.31181064239247486}} {"text": "\nSys.setlocale(\"LC_ALL\", \"C\")\n\nrequire(ggplot2)\nlibrary(scales) \n\narea <- function(){\n\n set.seed(3)\n\n df = data.frame(\n VisitWeek = rep(as.Date(seq(Sys.time(),\n length.out = 7, by = \"1 day\")), 4),\n ThingAge = rep(1:4, each = 7),\n MyMetric = c(sample(80,7)/10 ,sample(8,7)/10 ,sample(8,7)/10, sample(8,7)/10) )\n\n print(df)\n\n plt <- ggplot(df, aes(x = VisitWeek, y = MyMetric)) +\n geom_area(aes(fill = factor(ThingAge)), alpha=0.7, position = \"fill\") +\n #scale_x_continuous(breaks=df$VisitWeek, labels= df$VisitWeek) +\n xlab(\"\u65e5\u671f\") +\n scale_x_date(breaks = unique(df$VisitWeek)) +\n \n ylab(\"\u65f6\u957f\u767e\u5206\u6bd4(%)\") +\n scale_y_continuous(labels = percent) + \n \n ggtitle(\"\u91cd\u70b9\u8bd5\u9a8c\u8bbe\u5907\u4e00\u5468\u72b6\u6001\u6c47\u603b\") +\n theme(plot.title = element_text(lineheight=3, face=\"bold\", color=\"black\", size=12) )+\n \n scale_fill_manual(values=c(\"#2ECC40\", \"#FFDC00\", \"#FF4136\", \"#85144B\"), \n labels=c(\"\u8bd5\u9a8c\u4e2d\",\"\u95f2\u7f6e\",\"\u6545\u969c\",\"\u7ef4\u62a4\"),\n guide = guide_legend(title = \"\u56fe\u4f8b\")) +\n theme(axis.text.x = element_text(angle=15, hjust=1, vjust=1, size=7),\n axis.title=element_text(size=10,face=\"bold\") )\n\n ggsave(\"output/status_sum_area1.png\", width=6, height=3.5)\n\n}\n\narea()", "meta": {"hexsha": "a3e5e0853ed52260bca12887f89eeb6a6fa069cc", "size": 1383, "ext": "r", "lang": "R", "max_stars_repo_path": "R/status_area.r", "max_stars_repo_name": "mabotech/mabo.task", "max_stars_repo_head_hexsha": "96752a5ae94349a46e3b6f9369cc0933d5e37be0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/status_area.r", "max_issues_repo_name": "mabotech/mabo.task", "max_issues_repo_head_hexsha": "96752a5ae94349a46e3b6f9369cc0933d5e37be0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/status_area.r", "max_forks_repo_name": "mabotech/mabo.task", "max_forks_repo_head_hexsha": "96752a5ae94349a46e3b6f9369cc0933d5e37be0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7317073171, "max_line_length": 97, "alphanum_fraction": 0.5162689805, "num_tokens": 410, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.31180728585121426}} {"text": "#! /usr/bin/Rscript\nargs <- commandArgs(trailingOnly = TRUE)\n\nheadersTXT = c(\n\"Miranda_score\",\n\"miR_ID\",\n\"mRNA_ID\",\n\"Start_position\",\n\"End_position\",\n\"Seed_match_6mer2\",\n\"miR_match_P01\",\n\"Seed_match_7mer2\",\n\"Seed_match_7mer1\",\n\"Seed_MFE\",\n\"X3p_MFE\",\n\"Target_UC_comp\",\n\"miR_match_P09\",\n\"miR_match_P02\",\n\"Seed_GU\",\n\"miR_match_P07\",\n\"miR_match_P19\",\n\"miR_match_P15\"\n)\n\nread.table(args[1],sep=\"\\t\", header=TRUE) -> features\nfeatures = subset(features, select = headersTXT)\n\n#library(seqinr)\n#read.fasta(file=args[2], as.string=TRUE) -> fa\n#fa=fa[which(!duplicated(names(fa)))]\n#utr_length = t(sapply(names(fa), function(i) {\n#\tc(i, nchar(fa[[i]][1]))\n#}))\n\n#remove records with UTRs not in fasta file\n#features = subset(features, mRNA_ID %in% names(fa))\n\nattach(features)\nfeatures_numeric = subset(features, select = -c(miR_ID,mRNA_ID))\naggregate(features_numeric, by=list(miR_ID,mRNA_ID), sum) -> features_sum\naggregate(1:dim(features_numeric)[1], by=list(miR_ID,mRNA_ID), length) -> number_sites\ncolnames(number_sites)[3] <- \"number_sites\"\naggregate(features_numeric, by=list(miR_ID,mRNA_ID), mean) -> features_mean\naggregate(features_numeric, by=list(miR_ID,mRNA_ID), max) -> features_max\naggregate(features_numeric, by=list(miR_ID,mRNA_ID), min) -> features_min\ndetach(features)\n\nfeatures_sum2 = features_sum\nfeatures_mean2 = features_mean\nfeatures_max2 = features_max\nfeatures_min2 = features_min\n\ncolnames(features_sum2) <- paste0(colnames(features_sum2), \".sum\")\ncolnames(features_mean2) <- paste0(colnames(features_mean2), \".mean\")\ncolnames(features_max2) <- paste0(colnames(features_max2), \".max\")\ncolnames(features_min2) <- paste0(colnames(features_min2), \".min\")\noutput = merge(number_sites, features_sum2, by=c(1,2))\noutput = merge(output, features_mean2, by=c(1,2))\noutput = merge(output, features_max2, by=c(1,2))\noutput = merge(output, features_min2, by=c(1,2))\n\nselected_features = \nc(\"Group.1\",\n\"Group.2\",\n\"Miranda_score.max\",\n\"Seed_match_6mer2.mean\",\n\"miR_match_P01.min\",\n\"Seed_match_7mer2.max\",\n\"Seed_match_7mer1.mean\",\n\"Seed_MFE.min\",\n\"X3p_MFE.mean\",\n\"Target_UC_comp.mean\",\n\"miR_match_P09.mean\",\n\"miR_match_P02.min\",\n\"Seed_GU.mean\",\n\"miR_match_P07.mean\",\n\"Start_position.min\",\n\"miR_match_P19.min\",\n\"miR_match_P15.min\"\n)\n\noutput2 <- output[,selected_features]\nwrite.table(output2, file=paste0(args[2], \".csv\"), sep=\",\", row.names=FALSE)\n", "meta": {"hexsha": "528da104a21a42bd224de5690288d08ced957875", "size": 2352, "ext": "r", "lang": "R", "max_stars_repo_path": "Core/calc_utr_features_selected.r", "max_stars_repo_name": "lanagarmire/MirMark", "max_stars_repo_head_hexsha": "19339ee7dbff9bdfd627b642f3df81358cf8c5f6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2015-03-29T08:44:03.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-29T14:21:05.000Z", "max_issues_repo_path": "Core/calc_utr_features_selected.r", "max_issues_repo_name": "lanagarmire/MirMark", "max_issues_repo_head_hexsha": "19339ee7dbff9bdfd627b642f3df81358cf8c5f6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-01-05T07:26:05.000Z", "max_issues_repo_issues_event_max_datetime": "2017-01-05T07:26:05.000Z", "max_forks_repo_path": "Core/calc_utr_features_selected.r", "max_forks_repo_name": "lanagarmire/MirMark", "max_forks_repo_head_hexsha": "19339ee7dbff9bdfd627b642f3df81358cf8c5f6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0, "max_line_length": 86, "alphanum_fraction": 0.7461734694, "num_tokens": 713, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438502, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.31180727876618697}} {"text": "#' Rand seq\n#'\n#' Rand seq.\n#'\n#' @param BSgenome BSgenome object.\n#' @param nsamp Numeric number of samples.\n#' @param flank Numeric length of flank on each side of region.\n#' @inheritParams ezlimma::roast_contrasts\n\nsample_bsgenome_regions <- function(BSgenome, nsamp, flank, seed = 123){\n bed.samp <- vector(\"list\", nsamp)\n set.seed(seed)\n for(samp.ind in seq_along(bed.samp)){\n prob <- BSgenome@seqinfo@seqlengths / sum(as.numeric(BSgenome@seqinfo@seqlengths))\n idx <- sample(length(BSgenome@seqinfo), 1, prob = prob)\n chr <- BSgenome@seqinfo@seqnames[idx]\n seqlength <- BSgenome@seqinfo@seqlengths[idx]\n start <- sample(seqlength - 2*flank, 1)\n end <- start + 2*flank\n name <- paste0(\"random_seq_\", samp.ind)\n bed.samp[[samp.ind]] <- data.frame(chr=chr, start = start, end = end, name = name)\n }\n bed.samp <- Reduce(rbind, bed.samp)\n rownames(bed.samp) <- bed.samp$name\n gr.samp <- methods::as(bed.samp, \"GRanges\")\n\n # test\n seqs.samp <- BSgenome::getSeq(BSgenome, gr.samp)\n stopifnot(names(seqs.samp) == names(gr.samp))\n\n return(seqs.samp)\n}\n", "meta": {"hexsha": "cea9db5d89d7e5c896b25ad527b049849981f2d0", "size": 1083, "ext": "r", "lang": "R", "max_stars_repo_path": "R/sample_bsgenome_regions.r", "max_stars_repo_name": "sneha-matrix/jdcbioinfo", "max_stars_repo_head_hexsha": "c3e9e4e1c1345504c2e91069a335b6d2634b19a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-11-12T01:39:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-12T02:29:40.000Z", "max_issues_repo_path": "R/sample_bsgenome_regions.r", "max_issues_repo_name": "sneha-matrix/jdcbioinfo", "max_issues_repo_head_hexsha": "c3e9e4e1c1345504c2e91069a335b6d2634b19a5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/sample_bsgenome_regions.r", "max_forks_repo_name": "sneha-matrix/jdcbioinfo", "max_forks_repo_head_hexsha": "c3e9e4e1c1345504c2e91069a335b6d2634b19a5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-03-17T15:15:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-17T15:15:13.000Z", "avg_line_length": 32.8181818182, "max_line_length": 86, "alphanum_fraction": 0.676823638, "num_tokens": 350, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3118072787661869}} {"text": "library(dplyr)\nlibrary(tidyr)\nlibrary(splitstackshape)\nlibrary(lubridate)\n\n### dane wej\u015bciowe (???):\n### wyszukiwanie plik\u00f3w:\npath <- \".\"\nfile <- \"s_1.csv\"\nsep <- \",\"\nheader <- TRUE\n### filtrowanie danych z pliku:\ncol_1 <- \"sequence\"\ncol_2 <- \"protein.id\"\ncol_3 <- \"percolator.q.value\"\nq_value <- 0.01\n### separowanie danych w kolumnie:\ncol <- col_2\nsep_2 <- \",\"\n\n###==(spos\u00f3b F.1)=== funckja wybieraj\u0105ca plik/pliki i przypisuj\u0105ca dane \nload_file <- function(path, file, sep, header){\n lst_file <- list.files(path, pattern = file)\n lst_table <- lapply(lst_file, read.table, header = header, sep = sep, stringsAsFactors = FALSE)\n do.call(rbind, lst_table)\n}\n\n# ##==(spos\u00f3b F.2)=== funckja pozwalaj\u0105ca u\u017cytkownikowi wybiera\u0107 jeden plik z okna i przypisuj\u0105ca dane\n# load_file_2 <- function(sep, header){\n# file_ <- file.choose()\n# table_all <- read.table(file_, header = header, sep = sep)\n# }\n\ntable_all <- load_file(path, file, sep, header)\n\n### funckja filtruje po col_3 dla warto\u015bci mniejszej od q_value i wybierania kolumny col_1 i col_2\nfilter_table <- function(table_all, col_1, col_2, col_3, q_value){\n table_filtr <- filter(table_all, table_all[[col_3]] < q_value)\n table_filtr <- select(table_filtr, col_1, col_2)\n}\n\ntable_filtr <- filter_table(table_all, col_1, col_2, col_3, q_value)\n\n### funckja rozseparowuje dane w kolumnie\nseparate_table <- function(table, col, sep){\n table_separate <- cSplit(table, col, sep)\n}\n\ntable_separate <- separate_table(table_filtr, col, sep_2)\n\n### funckja \u0142\u0105cz\u0105ca powsta\u0142e nowe kolumny w jedn\u0105 tabelk\u0119 o dw\u00f3ch kolumnach\n### oraz odrzuca wiersze z warto\u015bci\u0105 \nconnect_table <- function(table_separate){\n table_protein <- table_separate[,1:2]\n for (i in seq(from = 3, to = dim(table_separate)[2])){\n table_protein <- rbind(table_protein,\n filter(select(table_separate, 1, i),\n table_separate[[colnames(table_separate)[i]]] != \"\"),\n use.names = FALSE)\n }\n table_protein\n}\n\ntable_protein <- connect_table(table_separate)\n\n### usuwanie powt\u00f3rzonych wierszy\nunique_table <- function(table_protein){\n table_protein <- unique(table_protein)\n}\n\ntable_protein <- unique_table(table_protein)\n\n### zmiana nazw kolumn\nchange_name_cols <- function(table, name_cols){\n colnames(table) <- name_cols\n table\n}\n\ntable_protein <- change_name_cols(table_protein, c(\"peptides\", \"proteins\"))\n\n### tabela peptyd-bia\u0142ko posortowana po danej nazwie kolumny\nsort_table <- function(table_protein, name_col){\n table_sort <- arrange(table_protein, table_protein[[name_col]])\n}\n\ntable_peptide_protein <- sort_table(table_protein, \"proteins\")\n\n#========================(start: spos\u00f3b T.1)================================================================\n### tworzenie data frame ilo\u015bci wyst\u0119powania danego peptydu w danym bia\u0142ku\n### zamiana klasy kolumn z factor na wektor\ntable_0_1 <- function(table_protein){\n table_protein[[colnames(table_protein)[1]]] <- as.vector(table_protein[[colnames(table_protein)[1]]])\n table_protein[[colnames(table_protein)[2]]] <- as.vector(table_protein[[colnames(table_protein)[2]]])\n table_protein_0_1 <- as.data.frame.matrix(table(table_protein))\n}\n\ntable_protein_0_1 <- table_0_1(table_protein)\n\n# ###======(spos\u00f3b T.1.1 z macierz\u0105 0 i 1):\n# ### zamiana data frame na macierz\n# matrix_0_1 <- function(table_protein_0_1){\n# matrix_protein_0_1 <- as.matrix(table_protein_0_1)\n# }\n# \n# matrix_protein_0_1 <- matrix_0_1(table_protein_0_1)\n# \n# ### funckja obliczaj\u0105ca iloczyn macierzy\n# matrix_diag <- function(matrix_protein_0_1){\n# t(matrix_protein_0_1) %*% matrix_protein_0_1\n# }\n# \n# matrix_qty_peptide <- matrix_diag(matrix_protein_0_1)\n# \n# ### zamiana macierzy peptyd\u00f3w na tabel\u0119 ilo\u015bci peptyd\u00f3w\n# qty_table <- function(matrix_qty){\n# table_qty <- data.frame(rownames(matrix_qty), diag(matrix_qty))\n# rownames(table_qty) <- NULL\n# table_qty\n# }\n# \n# table_qty_peptide <- qty_table(matrix_qty_peptide)\n# \n# ### zmiana nazw kolumn\n# table_qty_peptide <- change_name_cols(table_qty_peptide, c(\"proteins\", \"qty_peptide\"))\n# \n# ### iloczyn macierzy dla ilo\u015bci bia\u0142ek\n# matrix_qty_protein <- matrix_diag(t(matrix_protein_0_1))\n# \n# ### tabela ilo\u015bci bia\u0142ek\n# table_qty_protein <- qty_table(matrix_qty_protein)\n# \n# ### zmiana nazw kolumn\n# table_qty_protein <- change_name_cols(table_qty_protein, c(\"peptides\", \"qty_protein\"))\n\n###========(spos\u00f3b T.1.2 bez macierzy 0 i 1):\n### funckja tworz\u0105ca tabel\u0119 ilo\u015bci peptyd\u00f3w dla danego bia\u0142ka\nqty_peptide <- function(table_protein_0_1){\n table_qty_peptide <- data.frame(colnames(table_protein_0_1), as.vector(apply(table_protein_0_1, 2, sum)))\n}\n\ntable_qty_peptide <- qty_peptide(table_protein_0_1)\n\n### zmiana nazw kolum\ntable_qty_peptide <- change_name_cols(table_qty_peptide, c(\"proteins\", \"qty_peptides\"))\n\n### funckja tworz\u0105ca tabel\u0119 ilo\u015bci bia\u0142ek dla danego peptydu\nqty_protein <- function(table_protein_0_1){\n table_qty_peptide <- data.frame(rownames(table_protein_0_1), as.vector(apply(table_protein_0_1, 1, sum)))\n}\n\ntable_qty_protein <- qty_protein(table_protein_0_1)\n\n### zmiana nazw kolumn\ntable_qty_protein <- change_name_cols(table_qty_protein, c(\"peptides\", \"qty_proteins\"))\n\n### podsumowanie (tabele wynikowe):\nhead(table_qty_peptide)\nhead(table_qty_protein)\nhead(table_peptide_protein)\n\n### funckja znajdowania wsp\u00f3lnych peptyd\u00f3w / filtrowanie po bia\u0142kach ???\n\n ### niebardzo rozumiemy jak to ma dzia\u0142a\u0107\n\n# ###=========================(koniec: spos\u00f3b T.1)=======================================================\n# \n# ###=========================(start: spos\u00f3b T.2)========================================================\n# ### sztuczne stworzenie kolumny zawierania peptyd\u00f3w w bia\u0142kach\n# include_col <- function(table_protein){\n# table_protein <- mutate(table_protein, include = rep(1, dim(table_protein)[1]))\n# }\n# \n# table_protein_include <- include_col(table_protein)\n# \n# ### tworzenie data.frame zawierania, gdzie nazwami kolumn s\u0105 nazwy bia\u0142ek,\n# ### posortowane\n# data_0_1 <-function(table_protein_include){\n# data_protein_0_1 <- as.data.frame(spread(table_protein_include, colnames(table_protein)[2], include, fill = 0))\n# }\n# \n# data_protein_0_1 <- data_0_1(table_protein_include)\n# \n# ### usuwamy pierwsz\u0105 kolumn\u0119 oraz usuwamy nazwy kolumn,\n# ### aby otrzyma\u0107 macierz 0 i 1 jako liczby\n# matrix_0_1 <- function(data_protein_0_1){\n# matrix_protein_0_1 <- data_protein_0_1[,2:dim(data_protein_0_1)[2]]\n# colnames(matrix_protein_0_1) <- NULL\n# matrix_protein_0_1 <- as.matrix(matrix_protein_0_1)\n# }\n# \n# matrix_protein_0_1 <- matrix_0_1(data_protein_0_1)\n# \n# \n# ### funcja iloczyn macierzy i transpozycji macierzy\n# matrix_diag <- function(matrix_protein_0_1){\n# t(matrix_protein_0_1) %*% matrix_protein_0_1\n# }\n# \n# matrix_qty_peptide_2 <- matrix_diag(matrix_protein_0_1)\n# \n# ### zamiana macierzy na tabel\u0119 ilo\u015bci peptydow/ilo\u015bci bia\u0142ek\n# table_qty <- function(matrix, table_peptide_protein, name_col){\n# table_peptide_protein <- sort_table(table_peptide_protein, name_col)\n# col_sort <- table_peptide_protein[[name_col]]\n# table_qty <- data.frame(unique(col_sort), diag(matrix))\n# }\n# \n# table_qty_peptide_2 <- table_qty(matrix_qty_peptide_2, table_peptide_protein, \"proteins\")\n# \n# ### zmiana nazwy kolumn\n# table_qty_peptide_2 <- change_name_cols(table_qty_peptide_2, c(\"proteins\", \"qty_peptides\"))\n# \n# ### macierz ilo\u015bci bia\u0142ek, korzystamy z funckji matrix_diag:\n# matrix_qty_protein_2 <- matrix_diag(t(matrix_protein_0_1))\n# \n# ### zamiana macierzy ilo\u015bci bia\u0142ek na tabel\u0119 ilo\u015bci bia\u0142ek\n# table_qty_protein_2 <- table_qty(matrix_qty_protein_2, table_peptide_protein, \"peptides\")\n# \n# ### zmiana nazwy kolumn\n# table_qty_protein_2 <- change_name_cols(table_qty_protein_2, c(\"peptides\", \"qty_proteins\"))\n# \n# ### podsumowanie T.2 (tabele wynikowe):\n# head(table_peptide_protein)\n# head(table_qty_peptide_2)\n# head(table_qty_protein_2)\n# \n# ###============(koniec: spos\u00f3b T.2)=================================================================================\n\n\n", "meta": {"hexsha": "314e768c5b4c61b24da09bd1fe3647691bca6244", "size": 8025, "ext": "r", "lang": "R", "max_stars_repo_path": "KrzyszkiewiczFoks/proteins.r", "max_stars_repo_name": "4netaf/StudentsProject2019", "max_stars_repo_head_hexsha": "57a844a342266898725c101dda2a22c1480245ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-11-19T22:59:48.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-19T22:59:48.000Z", "max_issues_repo_path": "KrzyszkiewiczFoks/proteins.r", "max_issues_repo_name": "4netaf/StudentsProject2019", "max_issues_repo_head_hexsha": "57a844a342266898725c101dda2a22c1480245ba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "KrzyszkiewiczFoks/proteins.r", "max_forks_repo_name": "4netaf/StudentsProject2019", "max_forks_repo_head_hexsha": "57a844a342266898725c101dda2a22c1480245ba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8913043478, "max_line_length": 118, "alphanum_fraction": 0.702305296, "num_tokens": 2446, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583270090337583, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.31172904081007774}} {"text": "# Using Tidyverse to explore data\n\n# A. Import\n# 1. Read csv file\n# Load readr\nlibrary(readr)\n\n# Create bakeoff but skip first row\nbakeoff <- read_csv(\"bakeoff.csv\", skip = 1)\n\n# Print bakeoff\nbakeoff\n\n# 2. Assign missing values\n# Load dplyr\nlibrary(dplyr)\n\n# Filter rows where showstopper is UNKNOWN \nbakeoff %>% \n filter(showstopper == \"UNKNOWN\")\n\n# Edit to add list of missing values\nbakeoff <- read_csv(\"bakeoff.csv\", skip = 1,\n na = c(\"\", \"NA\", \"UNKNOWN\"))\n\n# Filter rows where showstopper is NA \nbakeoff %>%\n filter(is.na(showstopper))\n\n# B. Know your data\n# 1. Arrange and glimpse. \nbakeoff %>% \n arrange(us_airdate) %>% \n glimpse()\n\n# 2. Summarize the data\n# Load skimr\nlibrary(skimr)\n\n# Edit to filter, group by, and skim\nbakeoff %>% \n filter(!is.na(us_season)) %>% \n group_by(us_season) %>% \n skim()\n\n# C. Count with your data\n# 1. Distinct and count\n# View distinct results\nbakeoff %>%\n distinct(result)\n\n# Count rows for each result\nbakeoff %>% \n count(result)\n\n# Count whether or not star baker\nbakeoff %>% \n count(result == \"SB\")\n\n# 2. Count episodes\n# Count the number of rows by series and episode\nbakeoff %>%\n count(series, episode)\n\n# Add second count by series\nbakeoff %>% \n count(series, episode) %>%\n count(series)\n\n# 3. Count bakers\n# Count the number of rows by series and baker\nbakers_by_series <- bakeoff %>% \n count(series, baker)\n \n# Print to view\nbakers_by_series\n \n# Count again by series\nbakers_by_series %>% \n count(series)\n \n# Count again by baker\nbakers_by_series %>%\n count(baker, sort = TRUE)\n\n# 4. Plot counts\nggplot(bakeoff, aes(episode)) + \n geom_bar() + \n facet_wrap(~series)\n", "meta": {"hexsha": "fa505f45b59e73cedb08a15370821b1a0b8deff1", "size": 1672, "ext": "r", "lang": "R", "max_stars_repo_path": "R/DataAnalyst/Tidyverse/CleanTidy/explore.r", "max_stars_repo_name": "James-McNeill/Learning", "max_stars_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/DataAnalyst/Tidyverse/CleanTidy/explore.r", "max_issues_repo_name": "James-McNeill/Learning", "max_issues_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/DataAnalyst/Tidyverse/CleanTidy/explore.r", "max_forks_repo_name": "James-McNeill/Learning", "max_forks_repo_head_hexsha": "3c4fe1a64240cdf5614db66082bd68a2f16d2afb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5777777778, "max_line_length": 48, "alphanum_fraction": 0.6674641148, "num_tokens": 506, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.3117290326035732}} {"text": "\n# install packages from CRAN\np_needed <- c(\"readr\", # imports spreadsheet data\n \"haven\", # imports SPSS, Stata and SAS files\n \"labelled\", # helpers to work with variable labels\n \"dplyr\", # provides neat functions for data frame manipulation,\n \"tidyr\", # suite for tidying data\n \"ggplot2\", # advanced graphics suite\n \"gridExtra\", # more functionality for ggplot2 plot\n \"stargazer\", # nice formatting of regression table output\n \"babynames\", # US baby names provided by the SSA\n \"nycflights13\", # dataset on all 336776 flights departing from NYC in 2013\n \"wooldridge\", # datasets used in Wooldridge\n \"car\", # functions from \"Compaion to Applied Regression\"\n \"lmtest\", # functions for additional regression diagnostics (including RESET test),\n \"broom\", # tidying model output\n \"interplot\", # plot effects of interaction terms\n \"margins\", # calculate and visualize marginal effects\n \"interflex\", # interaction diagnostics and flexible estimation\n \"xts\", # working with time-series data\n \"tseries\", # functions for time series analysis\n \"lubridate\", # working with dates and times\n \"plm\", # panel data econometrics\n \"pcse\", # panel-corrected standard errors \u00e0 la Beck and Katz (1995)\n \"readxl\", # import Excel data\n \"ggthemes\",\n \"Zelig\",\n \"ISLR\",\n \"janitor\"\n)\npackages <- rownames(installed.packages())\np_to_install <- p_needed[!(p_needed %in% packages)]\nif (length(p_to_install) > 0) {\n install.packages(p_to_install)\n}\nlapply(p_needed, require, character.only = TRUE)\n", "meta": {"hexsha": "287d05e2fb03d1bb8e9060a5f4a1390676eefd88", "size": 1806, "ext": "r", "lang": "R", "max_stars_repo_path": "code/packages.r", "max_stars_repo_name": "simonmunzert/stats-II-hertie-2017", "max_stars_repo_head_hexsha": "f577db8b0b5f2599e7896907233ef6871d793253", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/packages.r", "max_issues_repo_name": "simonmunzert/stats-II-hertie-2017", "max_issues_repo_head_hexsha": "f577db8b0b5f2599e7896907233ef6871d793253", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/packages.r", "max_forks_repo_name": "simonmunzert/stats-II-hertie-2017", "max_forks_repo_head_hexsha": "f577db8b0b5f2599e7896907233ef6871d793253", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2017-09-18T08:04:57.000Z", "max_forks_repo_forks_event_max_datetime": "2018-03-22T08:28:12.000Z", "avg_line_length": 48.8108108108, "max_line_length": 97, "alphanum_fraction": 0.6018826135, "num_tokens": 405, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31171026583597455}} {"text": "#' @useDynLib rotp R_totp\ntotp_wrapper = function(key, interval=30L, digits=6L)\n{\n .Call(R_totp, key, interval, digits)\n}\n\n\n\n#' totp\n#' \n#' Implementation of the Time-based One-Time Password algorithm.\n#' \n#' @param key\n#' The secret key.\n#' @param interval\n#' The interval of time in seconds.\n#' @param digits\n#' The number of digits of the return.\n#' \n#' @return\n#' An integer with \\code{digits} digits.\n#' \n#' @examples\n#' rotp::totp(\"asdf\")\n#' \n#' @references \\url{https://en.wikipedia.org/wiki/Time-based_One-time_Password_algorithm}\n#' @export\ntotp = function(key, interval=30, digits=6)\n{\n check.is.string(key)\n check.is.posint(interval)\n check.is.posint(digits)\n \n if (ndigits(digits) > 10)\n stop(\"argument 'digits' too large\")\n \n interval = as.integer(interval)\n digits = as.integer(digits)\n \n totp_wrapper(key, interval, digits)\n}\n", "meta": {"hexsha": "3cb99172ff834a0d175f5a300847d9784cf398c2", "size": 855, "ext": "r", "lang": "R", "max_stars_repo_path": "R/totp.r", "max_stars_repo_name": "wrathematics/rotp", "max_stars_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_stars_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-06-08T09:54:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-26T13:04:01.000Z", "max_issues_repo_path": "R/totp.r", "max_issues_repo_name": "wrathematics/rotp", "max_issues_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_issues_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/totp.r", "max_forks_repo_name": "wrathematics/rotp", "max_forks_repo_head_hexsha": "df3da2da5160e81e996c128e6ff842c3c1550d63", "max_forks_repo_licenses": ["BSD-2-Clause", "OpenSSL", "Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.3571428571, "max_line_length": 89, "alphanum_fraction": 0.6795321637, "num_tokens": 241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31171026583597455}} {"text": "# Install Packages\ninstall.packages('wbstats')\ninstall.packages('gganimate')\ninstall.packages('viridis')\ninstall.packages('tidyverse')\ndevtools::install_github(\"tidyverse/tidyr\")\ninstall.packages('gifski')\ninstall.packages('png')\n\n# libraries needed\n\nlibrary(tidyverse)\nlibrary(ggplot2)\nlibrary(viridis)\nlibrary(gganimate)\nlibrary(wbstats)\nlibrary(tidyr)\n\n\n# Sample World Bank Data\ndata = wbstats::wb(indicator = c(\"SP.DYN.LE00.IN\", \"NY.GDP.PCAP.CD\", \"SP.POP.TOTL\"), \n country = \"countries_only\", startdate = 1960, enddate = 2019)\n\n# pull the country data down from the World Bank - three indicators\nanim = wbstats::wb(indicator = c(\"SP.DYN.LE00.IN\", \"NY.GDP.PCAP.CD\", \"SP.POP.TOTL\"), \n country = \"countries_only\", startdate = 1960, enddate = 2019) %>% \n # pull down mapping of countries to regions and join\n dplyr::left_join(wbstats::wbcountries() %>% \n dplyr::select(iso3c, region)) %>% \n # spread the three indicators\n tidyr::pivot_wider(id_cols = c(\"date\", \"country\", \"region\"), names_from = indicator, values_from = value) %>% \n # plot the data\n ggplot2::ggplot(aes(x = log(`GDP per capita (current US$)`), y = `Life expectancy at birth, total (years)`,\n size = `Population, total`)) +\n ggplot2::geom_point(alpha = 0.5, aes(color = region)) +\n ggplot2::scale_size(range = c(.1, 16), guide = FALSE) +\n ggplot2::scale_x_continuous(limits = c(2.5, 12.5)) +\n ggplot2::scale_y_continuous(limits = c(30, 90)) +\n viridis::scale_color_viridis(discrete = TRUE, name = \"Region\", option = \"viridis\") +\n ggplot2::labs(x = \"Log GDP per capita\",\n y = \"Life expectancy at birth\") +\n ggplot2::theme_classic() +\n ggplot2::geom_text(aes(x = 30, y = 60, label = date), size = 14, color = 'black', family = 'Oswald') +\n # animate it over years\n gganimate::transition_states(date, transition_length = 1, state_length = 1) +\n gganimate::ease_aes('cubic-in-out')\n\ngganimate::anim_save(animation=anim, 'xp-rosling-2019-v2.gif')\n", "meta": {"hexsha": "1b6e736b1d6ab63b6addbffca70ce049a9de3f9d", "size": 2027, "ext": "r", "lang": "R", "max_stars_repo_path": "gganimatev1.r", "max_stars_repo_name": "xmpuspus/HansRosling-animated-chart", "max_stars_repo_head_hexsha": "5dc02fa1d6ad328bdb8be3ce6294544436b5c344", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gganimatev1.r", "max_issues_repo_name": "xmpuspus/HansRosling-animated-chart", "max_issues_repo_head_hexsha": "5dc02fa1d6ad328bdb8be3ce6294544436b5c344", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gganimatev1.r", "max_forks_repo_name": "xmpuspus/HansRosling-animated-chart", "max_forks_repo_head_hexsha": "5dc02fa1d6ad328bdb8be3ce6294544436b5c344", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3673469388, "max_line_length": 112, "alphanum_fraction": 0.6640355205, "num_tokens": 615, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31171026583597455}} {"text": "# This function deconvolves the azimuth as stored in the \r\n\r\nget_angle_IN <- function(bearings, degrees, dists) {\r\n # This function is used in 'step.one.clean.bind_v1.1.R', it converts the\r\n # text azimuth strings to numeric, 360 degree values.\r\n # This is the vector that will store the values.\r\n angl <- degrees\r\n \r\n # This is a special case, generally where the tree is plot center.\r\n angl[degrees == '0' & dists =='0'] <- 0\r\n \r\n # This is a short function that takes cares of NAs in boolean functions, it's\r\n # just a simple wrapper for the boolean function that sets the NA values in\r\n # the vector to FALSE.\r\n fx.na <- function(x) { x[ is.na( x ) ] <- FALSE; x }\r\n \r\n # Given the text azimuths in the dataset, return the quadrant values.\r\n # This gives a boolean index of the quadrant\r\n \r\n north <- fx.na(bearings == 'NNA'| bearings == 'NAN' | bearings =='N'|bearings =='N99999'|bearings =='N88888')\r\n east <- fx.na(bearings == 'NAE' | bearings ==\"ENA\" | bearings =='E'|bearings =='99999E'|bearings =='88888E')\r\n south <- fx.na(bearings == 'SNA' | bearings ==\"NAS\"| bearings == 'S'|bearings =='S99999'|bearings =='S88888')\r\n west <- fx.na(bearings == 'NAW' | bearings ==\"WNA\" | bearings == 'W'|bearings =='99999W'|bearings =='88888W')\r\n #north <- fx.na( regexpr('N', bearings) > 0 )\r\n #east <- fx.na( regexpr('E', bearings) > 0 | bearings == 'EAST')\r\n #south <- fx.na( regexpr('S', bearings) > 0 | bearings == 'SOUTH')\r\n #west <- fx.na( regexpr('W', bearings) > 0 | bearings == 'WEST')\r\n \r\n ne <- fx.na( (north & east) | bearings == 'NE')\r\n se <- fx.na( (south & east) | bearings == 'SE')\r\n sw <- fx.na( (south & west) | bearings == 'SW')\r\n nw <- fx.na( (north & west) | bearings == 'NW') \r\n \r\n # The cell is in a quadrant, regardless of which.\r\n quad <- ne | se | sw | nw\r\n \r\n # Special case of the trees with a unidirectional direction.\r\n uni <- (!quad) & (north | south | east | west) \r\n \r\n angl[ uni & north ] <- 0\r\n angl[ uni & south ] <- 180\r\n angl[ uni & east ] <- 90 \r\n angl[ uni & west ] <- 270\r\n \r\n # The problem is that some notes have either N04E, N 4E or N4E, or NE!\r\n #strlen <- nchar(bearings)\r\n #strlen[is.na(bearings)] <- NA\r\n \r\n #######not sure if I need this section\r\n \r\n #angl[quad & strlen == 2] <- 45\r\n #angl[quad & strlen == 3] <- as.numeric(substr(azimuth[ quad & strlen == 3 ], 2, 2))\r\n #angl[quad & strlen == 4 & !substr(azimuth, 2, 3) == ' '] <- \r\n # as.numeric(substr(azimuth[quad & strlen == 4 & !substr(azimuth, 2, 3) == ' '], 2, 3))\r\n \r\n # Special case of double spaces in the azimuth.\r\n #angl[quad & strlen == 4 & substr(azimuth, 2, 3) == ' '] <- 45\r\n \r\n ##########\r\n \r\n # Another set of special cases:\r\n angl[ fx.na(bearings == 'NORT') ] <- 0\r\n angl[ fx.na(bearings == 'EAST') ] <- 90\r\n angl[ fx.na(bearings == 'WEST') ] <- 270\r\n angl[ fx.na(bearings == 'SOUT') ] <- 180\r\n \r\n angl[ ne ] <- angl[ ne ]\r\n angl[ se ] <- 180 - angl[ se ]\r\n angl[ sw ] <- 180 + angl[ sw ]\r\n angl[ nw ] <- 360 - angl [ nw ]\r\n \r\n return(angl)\r\n \r\n}", "meta": {"hexsha": "eaf0e8114f3293da232da09ac705bfb41b9a0970", "size": 3056, "ext": "r", "lang": "R", "max_stars_repo_path": "RCode/get_angle_IN.r", "max_stars_repo_name": "jsta/YellowRiver_Indiana", "max_stars_repo_head_hexsha": "40ec6cf459c925f31643aae64197215a602d13d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-24T15:58:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-24T15:58:13.000Z", "max_issues_repo_path": "RCode/get_angle_IN.r", "max_issues_repo_name": "PalEON-Project/YellowRiver_Indiana", "max_issues_repo_head_hexsha": "40ec6cf459c925f31643aae64197215a602d13d4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-02-24T16:21:29.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-24T16:21:29.000Z", "max_forks_repo_path": "RCode/get_angle_IN.r", "max_forks_repo_name": "jsta/YellowRiver_Indiana", "max_forks_repo_head_hexsha": "40ec6cf459c925f31643aae64197215a602d13d4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-02-24T16:02:58.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-24T16:02:58.000Z", "avg_line_length": 41.2972972973, "max_line_length": 112, "alphanum_fraction": 0.5723167539, "num_tokens": 1029, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.31171026583597455}} {"text": "## Unit conversion\n\nconvert <- function(data, unitCol, values){\n \n## convert units to character vector from factor\ndata[,unitCol] <- as.character(data[,unitCol])\n \ndata <- data[!is.na(data[,values]),] ## omit missing values\n \n## Switch text of mg/m2 to mg/m3 \ndata[data[,unitCol]==\"mg/m2\",unitCol] <- \"mg/m3\"\n\n## Switch text ppm to mg/L because equivalent\ndata[data[,unitCol]==\"ppm\",unitCol] <- \"mg/L\"\n\n## Switch text mg/m3 to mg/L\ndata[data[,unitCol]==\"mg/m3\",unitCol] <- \"ug/L\"\n\n## Convert ug/L to mg/L\ndata[data[,unitCol]==\"ug/L\",values] <- data[data[,unitCol]==\"ug/L\",values]/1000\ndata[data[,unitCol]==\"ug/L\",unitCol] <- \"mg/L\" ## switch text\n\n## Round ug/L values to the 10,000th because accuracy below a microgram unlikely\ndata[,values] <- round(data[,values],5)\n\n\n## Omit other units\ndata <- data[data[,unitCol] == \"mg/L\",]\n\n\ndata <- data[,c(\"uniqueID\",unitCol,values)]\n\nreturn(data)\n}\n", "meta": {"hexsha": "c4378ca697f4b4e2189c25d5ed90987f132a1740", "size": 898, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/unitConvert.r", "max_stars_repo_name": "afilazzola/ChlorophyllDataPaper", "max_stars_repo_head_hexsha": "539e0dff9da34ba7b8267147648926f220b202dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/unitConvert.r", "max_issues_repo_name": "afilazzola/ChlorophyllDataPaper", "max_issues_repo_head_hexsha": "539e0dff9da34ba7b8267147648926f220b202dc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/unitConvert.r", "max_forks_repo_name": "afilazzola/ChlorophyllDataPaper", "max_forks_repo_head_hexsha": "539e0dff9da34ba7b8267147648926f220b202dc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6571428571, "max_line_length": 80, "alphanum_fraction": 0.6581291759, "num_tokens": 269, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3117102658359745}} {"text": "args <- commandArgs(trailingOnly = TRUE)\nxyz <- read.csv(file=args[1])\npdf(args[2])\nscatterplot3d::scatterplot3d(xyz, color=\"blue\", pch=19, xlab=\"Statistical Distance\", ylab=\"Sample Size\", zlab=\"Match Percentage\", type=\"h\")\n", "meta": {"hexsha": "72c842ababbb457425966f173c8c3ad7a717f249", "size": 224, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/3d-plotter.r", "max_stars_repo_name": "chris-wood/ccn-eavesdropper-simulator", "max_stars_repo_head_hexsha": "e291a1b06ab7f35c40e99f3b42cc7398908b2acb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/3d-plotter.r", "max_issues_repo_name": "chris-wood/ccn-eavesdropper-simulator", "max_issues_repo_head_hexsha": "e291a1b06ab7f35c40e99f3b42cc7398908b2acb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/3d-plotter.r", "max_forks_repo_name": "chris-wood/ccn-eavesdropper-simulator", "max_forks_repo_head_hexsha": "e291a1b06ab7f35c40e99f3b42cc7398908b2acb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-10T19:09:21.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-10T19:09:21.000Z", "avg_line_length": 44.8, "max_line_length": 139, "alphanum_fraction": 0.7232142857, "num_tokens": 68, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3117102658359745}} {"text": "test_that(\"define_level_slope_variances\",{\n result <- define_level_slope_variances(\"variable_of_interest_name\")\n expect_equal(result, 'variable_of_interest_name_level ~~ variable_of_interest_name_level')\n})\n\n\ntest_that(\"define_level_slope_variances - with include_slopes true\",{\n result <- define_level_slope_variances(\"pop\", TRUE)\n pasteResult <- paste(\n 'pop_level ~~ pop_level',\n 'pop_level ~~ pop_slope',\n 'pop_slope ~~ pop_slope',\n sep = \"\\n\"\n )\n expect_equal(result, pasteResult)\n})\n\n\ntest_that(\"define_level_slope_variances - with include_slopes true and constrain_group_variances_to_be_equal is TRUE\",{\n result <- define_level_slope_variances(\"pop\", TRUE, TRUE)\n pasteResult <- paste(\n 'pop_level ~~ c(LV,LV)*pop_level',\n 'pop_level ~~ c(LSV,LSV)*pop_slope',\n 'pop_slope ~~ c(SV,SV)*pop_slope',\n sep = \"\\n\"\n )\n expect_equal(result, pasteResult)\n})\n", "meta": {"hexsha": "ddccf2f9077a1740f3b2533890e7670464e561ee", "size": 890, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-variances.r", "max_stars_repo_name": "epf02013/r2sem", "max_stars_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test-variances.r", "max_issues_repo_name": "epf02013/r2sem", "max_issues_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testthat/test-variances.r", "max_forks_repo_name": "epf02013/r2sem", "max_forks_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6896551724, "max_line_length": 119, "alphanum_fraction": 0.7292134831, "num_tokens": 228, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3117102658359745}} {"text": "# Stephen Turner\n# http://StephenTurner.us/\n# http://GettingGeneticsDone.blogspot.com/\n\n# Daniel Capurso\n# UCSF\n# http://www.linkedin.com/in/dcapurso\n\n# Last major update: June 10, 2013\n# R code for making manhattan plots and QQ plots from plink output files. \n\n### This is for testing purposes. ######################################\n# \tset.seed(42)\n# \tnchr=22\n# \tnsnps=1000\n# \td = data.frame(\n# \t\tSNP=sapply(1:(nchr*nsnps), function(x) paste(\"rs\",x,sep='')),\n# \t\tCHR=rep(1:nchr,each=nsnps), \n# \t\tBP=rep(1:nsnps,nchr), \n# \t\tP=runif(nchr*nsnps)\n# \t)\n# \t### d[d$SNP=='rs20762',]$P = 1e-29\n# \ttop_snps = c('rs13895','rs20762')\n# \tsurrounding_snps = list(\tas.character(d$SNP[13795:13995]),\n# \t\t\t\t\t\t\tas.character(d$SNP[20662:20862]))\n# \t\n# \tpvector = d$P\n# \tnames(pvector) = d$SNP\n# \t\n# \t#CALL:\n# \t#manhattan(d,annotate=top_snps,highlight=surrounding_snps)\n# \t#qq(pvector,annotate=top_snps,highlight=top_snps)\n#########################################################################\n\n# manhattan plot using base graphics\nmanhattan <- function(dataframe, limitchromosomes=NULL,pt.col=c('gray10','gray50'),pt.bg=c('gray10','gray50'),\n\tpt.cex=0.45,pch=21,cex.axis=0.95,gridlines=F,gridlines.col='gray83',gridlines.lty=1,gridlines.lwd=1,ymax=8, ymax.soft=T, annotate=NULL,annotate.cex=0.7,annotate.font=3,\n\tsuggestiveline=-log10(1e-5), suggestiveline.col='blue', suggestiveline.lwd=1.5, suggestiveline.lty=1, \n\tgenomewideline=-log10(5e-8), genomewideline.col='red', genomewideline.lwd=1.5, genomewideline.lty=1, \n\thighlight=NULL,highlight.col=c('green3','magenta'),highlight.bg=c('green3','magenta'), ...) {\n\t#============================================================================================\n\t######## Check data and arguments\n d = na.omit(dataframe) # omit NAs\n \n if (!(\"CHR\" %in% names(d) & \"BP\" %in% names(d) & \"P\" %in% names(d))) stop(\"Make sure your data frame contains columns CHR, BP, and P\")\n if (TRUE %in% is.na(suppressWarnings(as.numeric(d$CHR)))) warning('non-numeric, non-NA entries in CHR column of dataframe. attempting to remove..')\n if (TRUE %in% is.na(suppressWarnings(as.numeric(d$BP)))) warning('non-numeric, non-NA entries in BP column of dataframe. attempting to remove..')\n\tif (TRUE %in% is.na(suppressWarnings(as.numeric(d$P)))) warning('non-numeric, non-NA entries in P column of dataframe. attempting to remove..')\n \n\td = d[!is.na(suppressWarnings(as.numeric(d$CHR))),] # remove rows with non-numeric, non-NA entries\n d = d[!is.na(suppressWarnings(as.numeric(d$BP))),]\n d = d[!is.na(suppressWarnings(as.numeric(d$P))),]\n \n\t\n\tif (!is.null(annotate)){\n\t\tif ('SNP' %in% names(d)){\n\t\t\tif (FALSE %in% (annotate %in% d$SNP)) stop (\"D'oh! Annotate vector must be a subset of the SNP column.\")\n\t\t} else {\n\t\t\tstop(\"D'oh! Dataframe must have a column $SNP with rs_ids to use annotate feature.\")\n\t\t}\n\t}\n\tif (!is.numeric(annotate.cex) | annotate.cex<0) annotate.cex=0.7\n\tif (!is.numeric(annotate.font)) annotate.font=3\n\t\n\tif (is.character(gridlines.col[1]) & !(gridlines.col[1] %in% colors())) gridlines.col = 'gray83'\n\tif (!is.numeric(pt.cex) | pt.cex<0) pt.cex=0.45\n\tif (is.character(pt.col) & (FALSE %in% (pt.col %in% colors()))) pt.col = c('gray10','gray50')\n\tif (is.character(pt.bg) & (FALSE %in% (pt.bg %in% colors()))) pt.bg = F\n\tif (is.character(highlight.col) & (FALSE %in% (highlight.col %in% colors()))) highlight.col = c('green3','magenta')\n\tif (is.character(highlight.bg) & (FALSE %in% (highlight.bg %in% colors()))) highlight.bg = F\n\tif (is.character(suggestiveline.col[1]) & !(suggestiveline.col[1] %in% colors())) suggestiveline.col = 'blue'\n\tif (is.character(genomewideline.col[1]) & !(genomewideline.col[1] %in% colors())) genomewideline.col = 'red'\n\t\t\n if(!is.null(limitchromosomes)){\n \tif (TRUE %in% is.na(suppressWarnings(as.numeric(limitchromosomes)))){\n \t\tstop('limitchromosomes argument is not numeric') \n \t} else { \n \t\td = d[d$CHR %in% as.numeric(limitchromosomes), ]\n \t}\n }\n \n\n ######################\n \n # Set positions, ticks, and labels for plotting\n d=subset(d[order(d$CHR, d$BP), ], (P>0 & P<=1)) # sort, and keep only 00 & d<1] # only Ps between 0 and 1\n \n \n\tif (!is.null(highlight) | !is.null(annotate)){\n\t\tif (is.null(names(d))) stop(\"P-value vector must have names to use highlight or annotate features.\")\n\t\td = d[!is.na(names(d))]\n\t\tif (!is.null(highlight) & FALSE %in% (highlight %in% names(d))) stop (\"D'oh! Highlight vector must be a subset of names(pvector).\")\n\t\tif (!is.null(annotate) & FALSE %in% (annotate %in% names(d))) stop (\"D'oh! Annotate vector must be a subset of names(pvector).\")\n\t}\n\t\n\td = d[order(d,decreasing=F)] # sort\n\to = -log10(d)\n \n e = -log10( ppoints(length(d) ))\n if (!is.null(highlight) | !is.null(annotate)) names(e) = names(o) = names(d)\n\t\n\tif (!is.numeric(ymax) | ymax6.0] <- 6.0\n\t#ymax=6.0+0.2\n\t#xmax=6.0+0.2\n\tplot(0,xlab=expression(Expected~~-log[10](italic(p))),ylab=expression(Observed~~-log[10](italic(p))),\n\t\t\tcol=F,las=1,xaxt='n',xlim=c(xmin,xmax),ylim=c(ymin,ymax),bty='n',xaxs='i',yaxs='i',cex.axis=cex.axis)\n # added DE\n\taxis(side=1,labels=seq(0,6,1),at=seq(0,6,1),cex.axis=cex.axis,lwd=0,lwd.ticks=1)\n\t#axis(side=1,labels=seq(0,max(e),1),at=seq(0,max(e),1),cex.axis=cex.axis,lwd=0,lwd.ticks=1)\n\t\n\t# Grid lines\n\tif (isTRUE(gridlines)){\n\t\tyvals = par('yaxp')\n\t\tyticks = seq(yvals[1],yvals[2],yvals[2]/yvals[3])\n\t\tabline(v=seq(0,max(e),1),col=gridlines.col[1],lwd=gridlines.lwd,lty=gridlines.lty)\n\t\tabline(h=yticks,col=gridlines.col[1],lwd=gridlines.lwd,lty=gridlines.lty)\n\t}\n\t\n\t #Confidence intervals\n\t find_conf_intervals = function(row){\n\t \ti = row[1]\n\t \tlen = row[2]\n\t \tif (i < 10000 | i %% 100 == 0){\n\t \t\treturn(c(-log10(qbeta(0.95,i,len-i+1)), -log10(qbeta(0.05,i,len-i+1))))\n\t \t} else { # Speed up\n\t \t\treturn(c(NA,NA))\n\t \t}\n\t }\n\n\t # Find approximate confidence intervals\n\tif (isTRUE(confidence)){\n\t\t#print('Plotting confidence intervals.')\n\t\tci = apply(cbind( 1:length(e), rep(length(e),length(e))), MARGIN=1, FUN=find_conf_intervals)\n\t \tbks = append(seq(10000,length(e),100),length(e)+1)\n\t\t# added DE\n\t \t#bks = append(seq(1000,length(e),10),length(e)+1)\n\t\tfor (i in 1:(length(bks)-1)){\n\t \t\tci[1, bks[i]:(bks[i+1]-1)] = ci[1, bks[i]]\n\t \t\tci[2, bks[i]:(bks[i+1]-1)] = ci[2, bks[i]]\n\t\t}\n\t\tcolnames(ci) = names(e)\n\t\t# Extrapolate to make plotting prettier (doesn't affect intepretation at data points)\n\t\tslopes = c((ci[1,1] - ci[1,2]) / (e[1] - e[2]), (ci[2,1] - ci[2,2]) / (e[1] - e[2]))\n\t\textrap_x = append(e[1]+xspace,e) #extrapolate slightly for plotting purposes only\n\t\textrap_y = cbind( c(ci[1,1] + slopes[1]*xspace, ci[2,1] + slopes[2]*xspace), ci)\n\t\t\n\t\t# added DE\n\t\tpolygon(c(extrap_x, rev(extrap_x)), c(extrap_y[1,], rev(extrap_y[2,])),col = confidence.col[1], border = \"red\")\t\n\t\t#polygon(c(extrap_x, rev(extrap_x)), c(extrap_y[1,], rev(extrap_y[2,])),col = confidence.col[1], border = confidence.col[1])\t\n\t}\n\t\n\t# Points (with optional highlighting)\n\t#print('Plotting data points.')\n\tfills = rep(pt.bg,length(o))\n\tborders = rep(pt.col,length(o))\n\tnames(fills) = names(borders) = names(o)\n\tif (!is.null(highlight)){\t\n\t\tborders[highlight] = rep(NA,length(highlight))\n\t\tfills[highlight] = rep(NA,length(highlight))\n\t}\n\tpoints(e,o,pch=pch,cex=pt.cex,col=borders,bg=fills)\n\t# added DE\n\ttitle(main=title)\n\ttext(2,1,paste(\"lambda=\", round(lambda, 3), \"; lambda_1000=\", round(lambda_1000, 3), \"\\nN(pvals)=\", length(pvector), sep=\"\"), cex=1.5, pos=4)\n\t\n\tif (!is.null(highlight)){\n\t\tpoints(e[highlight],o[highlight],pch=pch,cex=pt.cex,col=highlight.col,bg=highlight.bg)\n\t}\n\t\n\t#Abline\n # added DE\n\t#segments(0,0,extrap_x,extrap_y,col=abline.col,lwd=abline.lwd,lty=abline.lty)\n\tabline(0,1,col=abline.col,lwd=abline.lwd,lty=abline.lty)\n\t\n\t# Annotate SNPs\n\tif (!is.null(annotate)){\n\t\tx = e[annotate] # x will definitely be the same\n\t\ty = -0.1 + apply(rbind(o[annotate],ci[1,annotate]),2,min)\n\t\ttext(x,y,labels=annotate,srt=90,cex=annotate.cex,adj=c(1,0.48),font=annotate.font)\t\t\n\t}\n\t# Box\n\tbox()\n}\n\n\n\n\n\n\n\n\n# Old ggplot2 code --------------------------------------------------------\n#\n## manhattan plot using ggplot2\n# gg.manhattan = function(dataframe, title=NULL, max.y=\"max\", suggestiveline=0, genomewideline=-log10(5e-8), size.x.labels=9, size.y.labels=10, annotate=F, SNPlist=NULL) {\n# library(ggplot2)\n# if (annotate & is.null(SNPlist)) stop(\"You requested annotation but provided no SNPlist!\")\n# \td=dataframe\n# \t#limit to only chrs 1-23?\n# \td=d[d$CHR %in% 1:23, ]\n# \tif (\"CHR\" %in% names(d) & \"BP\" %in% names(d) & \"P\" %in% names(d) ) {\n# \t\td=na.omit(d)\n# \t\td=d[d$P>0 & d$P<=1, ]\n# \t\td$logp = -log10(d$P)\n# \t\td$pos=NA\n# \t\tticks=NULL\n# \t\tlastbase=0\n# \t\t#new 2010-05-10\n# \t\tnumchroms=length(unique(d$CHR))\n# \t\tif (numchroms==1) {\n# \t\t\td$pos=d$BP\n# \t\t} else {\n# \t\t\n# \t\t\tfor (i in unique(d$CHR)) {\n# \t\t\t\tif (i==1) {\n# \t\t\t\t\td[d$CHR==i, ]$pos=d[d$CHR==i, ]$BP\n# \t\t\t\t}\telse {\n# \t\t\t\t\tlastbase=lastbase+tail(subset(d,CHR==i-1)$BP, 1)\n# \t\t\t\t\td[d$CHR==i, ]$pos=d[d$CHR==i, ]$BP+lastbase\n# \t\t\t\t}\n# \t\t\t\tticks=c(ticks, d[d$CHR==i, ]$pos[floor(length(d[d$CHR==i, ]$pos)/2)+1])\n# \t\t\t}\n# \t\t\tticklim=c(min(d$pos),max(d$pos))\n# \n# \t\t}\n# \t\tmycols=rep(c(\"gray10\",\"gray60\"),max(d$CHR))\n# \t\tif (max.y==\"max\") maxy=ceiling(max(d$logp)) else maxy=max.y\n# \t\tif (maxy<8) maxy=8\n# \t\tif (annotate) d.annotate=d[as.numeric(substr(d$SNP,3,100)) %in% SNPlist, ]\n# \t\tif (numchroms==1) {\n# \t\t\tplot=qplot(pos,logp,data=d,ylab=expression(-log[10](italic(p))), xlab=paste(\"Chromosome\",unique(d$CHR),\"position\"))\n# \t\t}\telse {\n# \t\t\tplot=qplot(pos,logp,data=d, ylab=expression(-log[10](italic(p))) , colour=factor(CHR))\n# \t\t\tplot=plot+scale_x_continuous(name=\"Chromosome\", breaks=ticks, labels=(unique(d$CHR)))\n# \t\t\tplot=plot+scale_y_continuous(limits=c(0,maxy), breaks=1:maxy, labels=1:maxy)\n# \t\t\tplot=plot+scale_colour_manual(value=mycols)\n# \t\t}\n# \t\tif (annotate) \tplot=plot + geom_point(data=d.annotate, colour=I(\"green3\")) \n# \t\tplot=plot + opts(legend.position = \"none\") \n# \t\tplot=plot + opts(title=title)\n# \t\tplot=plot+opts(\n# \t\t\tpanel.background=theme_blank(), \n# \t\t\tpanel.grid.minor=theme_blank(),\n# \t\t\taxis.text.x=theme_text(size=size.x.labels, colour=\"grey50\"), \n# \t\t\taxis.text.y=theme_text(size=size.y.labels, colour=\"grey50\"), \n# \t\t\taxis.ticks=theme_segment(colour=NA)\n# \t\t)\n# \t\tif (suggestiveline) plot=plot+geom_hline(yintercept=suggestiveline,colour=\"blue\", alpha=I(1/3))\n# \t\tif (genomewideline) plot=plot+geom_hline(yintercept=genomewideline,colour=\"red\")\n# \t\tplot\n# \t}\telse {\n# \t\tstop(\"Make sure your data frame contains columns CHR, BP, and P\")\n# \t}\n# }\n# \n# ## QQ plot using ggplot2\n# gg.qq = function(pvector, title=NULL, spartan=F) {\n# \tlibrary(ggplot2)\n# \to = -log10(sort(pvector,decreasing=F))\n# \t#e = -log10( 1:length(o)/length(o) )\n# \te = -log10( ppoints(length(pvector) ))\n# \tplot=qplot(e,o, xlim=c(0,max(e)), ylim=c(0,max(o))) + stat_abline(intercept=0,slope=1, col=\"red\")\n# \tplot=plot+opts(title=title)\n# \tplot=plot+scale_x_continuous(name=expression(Expected~~-log[10](italic(p))))\n# \tplot=plot+scale_y_continuous(name=expression(Observed~~-log[10](italic(p))))\n# \tif (spartan) plot=plot+opts(panel.background=theme_rect(col=\"grey50\"), panel.grid.minor=theme_blank())\n# \tplot\n# }\n# \n# ## Make a qq and manhattan plot\n# gg.qqman = function(data=\"plinkresults\") {\n# \tmyqqplot = ggqq(data$P)\n# \tmymanplot = ggmanhattan(data)\n# \tggsave(file=\"qqplot.png\",myqqplot,w=5,h=5,dpi=100)\n# \tggsave(file=\"manhattan.png\",mymanplot,width=12,height=9,dpi=100)\n# }\n# \n# ## make qq and manhattan plots for a list of files\n# gg.qqmanall= function(command=\"ls *assoc\") {\n# \tfilelist=system(command,intern=T)\n# \tdatalist=NULL\n# \tfor (i in filelist) {datalist[[i]]=read.table(i,T)}\n# \thighestneglogp=ceiling(max(sapply(datalist, function(df) max(na.omit(-log10(df$P))))))\n# \tprint(paste(\"Highest -log10(P) = \",highestneglogp),quote=F)\n# \tstart=Sys.time()\n# \tfor (i in names(datalist)) {\n# \t\tmyqqplot=ggqq(datalist[[i]]$P, title=i)\n# \t\tggsave(file=paste(\"qqplot-\", i, \".png\", sep=\"\"),myqqplot, width=5, height=5,dpi=100)\n# \t\tmymanplot=ggmanhattan(datalist[[i]], title=i, max.y=highestneglogp)\n# \t\tggsave(file=paste(\"manhattan-\", i, \".png\", sep=\"\"),mymanplot,width=12,height=9,dpi=100)\n# \t}\n# \tend=Sys.time()\n# \tprint(elapsed<-end-start)\n# }\n", "meta": {"hexsha": "b3d8cf47e3671008caf45b10a661222e8cdf436e", "size": 19957, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/qqman_dev2.r", "max_stars_repo_name": "jkaessens/gwas-assoc", "max_stars_repo_head_hexsha": "1053c94222701f108362e33c99155cfc148f4ca2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/qqman_dev2.r", "max_issues_repo_name": "jkaessens/gwas-assoc", 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YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3117102658359745}} {"text": "# 2. faza obdelave podatkov: Uvoz\n\n# Uvoz podatkov\n\ndec <- grp <- c()\ndec[6:9] <- grp[10:19] <- \".\"\ndec[10:19] <- grp[6:9] <- \",\"\n\nnat <- lapply(6:18, function(leto)\n sprintf(\"podatki/N_%02d.csv\", leto) %>%\n read_delim(delim=\";\", na=c(\"\", \"#\", \"*\", \"**\", \"***\"), trim_ws=TRUE,\n locale=locale(encoding=\"UTF-8\", decimal_mark=dec[leto],\n grouping_mark=grp[leto]),\n col_names=FALSE, skip=1) %>% # imena stolpcev se razlikujejo, zato bomo podali svoja\n transmute(leto=2000+leto, occ_code=factor(X1),\n occ_title=str_to_sentence(X2) %>% str_replace(\"[*]\", \"\"),\n emp=X4, h_mean=X6, a_mean=X7, h_median=X11, a_median=X16)) %>%\n bind_rows(read_csv2(\"podatki/N_19.csv\", na=c(\"\", \"#\", \"*\", \"**\", \"***\")) %>%\n transmute(leto=2019, occ_code, occ_title, emp=tot_emp,\n h_mean, a_mean, h_median, a_median)) %>%\n unique() # nekatere vrstice se ponovijo, tako da se teh znebimo\n\nkode<- nat %>% select(occ_code, occ_title) %>%\n group_by(occ_code) %>% slice_head(n=1) \n\nt_e <- nat %>% select(leto,occ_code,emp)\n\nnat.ha <- nat %>% select(occ_code, leto, h_mean, a_mean, h_median, a_median) %>%\n pivot_longer(c(-occ_code, -leto), names_to=\"podatek_sredina\", values_to=\"vrednost\") %>%\n separate(podatek_sredina, c(\"podatek\", \"sredina\"), sep=\"_\") %>%\n pivot_wider(names_from=podatek, values_from=vrednost) \n\nh_mean_c <- nat.ha %>% \n mutate(state=\"United States\") %>% \n .[c(1,2,6,3,4,5)]\n\nst <- lapply(6:18, function(leto)\n sprintf(\"podatki/S_%02d.csv\", leto) %>%\n read_delim(delim=\";\", na=c(\"\", \"#\", \"*\", \"**\", \"***\"),\n locale=locale(encoding=\"UTF-8\", decimal_mark=dec[leto],\n grouping_mark=grp[leto])) %>%\n transmute(leto=2000+leto, state=STATE, occ_code=OCC_CODE, emp=TOT_EMP,\n h_mean=H_MEAN, a_mean=A_MEAN, h_median=H_MEDIAN, a_median=A_MEDIAN)) %>%\n bind_rows(read_csv2(\"podatki/S_19.csv\", na=c(\"\", \"#\", \"*\", \"**\", \"***\")) %>%\n transmute(leto=2019, state=area_title, occ_code, emp=tot_emp,\n h_mean, a_mean, h_median, a_median)) %>% unique()\n\n\nt_e_s <- st %>% select(leto, state, occ_code, emp)\n\nst.ha <- st %>% select(occ_code, leto, state, h_mean, a_mean, h_median, a_median) %>%\n pivot_longer(c(-occ_code, -leto, -state), names_to=\"podatek_sredina\", values_to=\"vrednost\") %>%\n separate(podatek_sredina, c(\"podatek\", \"sredina\"), sep=\"_\") %>%\n pivot_wider(names_from=podatek, values_from=vrednost)\n\n# Tabela iz wikipedije: \n\nlink <- \"https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP_per_capita\"\nstran <- html_session(link) %>% read_html()\ntabela <- stran %>% html_nodes(xpath=\"//table[@class='wikitable sortable']\") %>%\n .[[1]] %>% html_table(dec=\",\") \ntabela[[1]] <- parse_number(tabela[[1]], na=\"\u2014\") \ntabela2 <- tabela %>% drop_na(Rank)\ntabela[[1]] <- NULL\ntabela <- tabela %>% pivot_longer(2:9, names_to = \"leto\") %>% rename(GDP=value)\ntabela[[2]] <- parse_number(tabela[[2]])\nGDP_by_state <- tabela\n\n\n# SLAB\u0160I UVOZ \n#uvozi <- function(ime_datoteke){\n# ime <- paste0(\"podatki/\", ime_datoteke, \".csv\")\n# tabela <- read.csv2(ime, fileEncoding = \"UTF-8\" , na=c(\"#\",\"*\",\"**\",\"\",\"***\"),) %>%\n# select(1:2, 4,6,7,11,16) \n# return(tabela)\n#}\n# nat6 <- uvozi(\"N_06\")\n# \n# nat7 <- uvozi(\"N_07\")\n# \n# nat8 <- uvozi(\"N_08\")\n# \n# nat9 <- uvozi(\"N_09\")\n# \n# nat10 <- uvozi(\"N_10\")\n# \n# nat11 <- uvozi(\"N_11\")\n# \n# nat12 <- uvozi(\"N_12\")\n# \n# nat13 <- uvozi(\"N_13\")\n# \n# nat14 <- uvozi(\"N_14\")\n# \n# nat15 <- uvozi(\"N_15\")\n# \n# nat16 <- uvozi(\"N_16\")\n# \n# nat17 <- uvozi(\"N_17\")\n# \n# nat18 <- uvozi(\"N_18\")\n\n\n\n# total employment\n# \n# employment1 <- function(tabela,leto){\n# total_employment <- tabela %>% \n# select(occ_title,tot_emp) %>% \n# rename(emp=tot_emp) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(emp) %>%\n# .[c(1,3,2)] \n# return(total_employment)\n# }\n# \n# employment2 <- function(tabela,leto){\n# total_employment <- tabela %>% \n# select(OCC_TITLE,TOT_EMP) %>% \n# rename(occ_title=OCC_TITLE, emp=TOT_EMP) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(emp) %>%\n# .[c(1,3,2)] \n# return(total_employment)\n# }\n# \n# total_employment1 <- employment1(nat6,2006)\n# \n# total_employment11 <- employment1(nat7,2007)\n# \n# total_employment2 <- employment1(nat8,2008)\n# \n# total_employment22 <- employment1(nat9,2009)\n# \n# total_employment3 <- employment2(nat10,2010)\n# \n# total_employment33 <- employment2(nat11,2011)\n# \n# total_employment4 <- employment2(nat12,2012)\n# \n# total_employment44 <- employment2(nat13,2013)\n# \n# total_employment5 <- employment2(nat14,2014)\n# \n# total_employment55 <- employment2(nat15,2015)\n# \n# total_employment6 <- employment2(nat16,2016)\n# \n# total_employment66 <- employment2(nat17,2017)\n# \n# total_employment7 <- employment2(nat18,2018)\n# \n# \n# t_e <- rbind(total_employment1, total_employment2, total_employment3, total_employment4, \n# total_employment5, total_employment6, total_employment7, total_employment11,\n# total_employment22, total_employment33, total_employment44, total_employment55,\n# total_employment66) %>%\n# mutate(emp2=parse_number(emp, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(emp2) %>% \n# select(1,2,4) %>%\n# rename(emp=emp2)\n\n# # H_MEAN\n# \n# hmean1 <- function(tabela,leto){\n# h_mean <- tabela %>% \n# select(occ_title,h_mean) %>% \n# rename(HM=h_mean) %>% \n# mutate(leto=c(leto)) %>% \n# .[c(1,3,2)] %>%\n# drop_na(HM)\n# h_mean$HM <- as.numeric(as.character(h_mean$HM))\n# return(h_mean)\n# }\n# \n# hmean2 <- function(tabela,leto){\n# h_mean <- tabela %>% \n# select(OCC_TITLE,H_MEAN) %>% \n# rename(occ_title=OCC_TITLE, HM=H_MEAN) %>% \n# mutate(leto=c(leto)) %>% \n# .[c(1,3,2)] %>%\n# drop_na(HM)\n# return(h_mean)\n# }\n# \n# \n# h_mean1 <- hmean1(nat6, 2006)\n# \n# h_mean11 <- hmean1(nat7, 2007)\n# \n# h_mean2 <- hmean1(nat8, 2008)\n# \n# h_mean22 <- hmean1(nat9, 2009)\n# \n# h_mean3 <- hmean2(nat10,2010)\n# \n# h_mean33 <- hmean2(nat11,2011)\n# \n# h_mean4 <- hmean2(nat12,2012)\n# \n# h_mean44 <- hmean2(nat13,2013)\n# \n# h_mean5 <- hmean2(nat14,2014)\n# \n# h_mean55 <- hmean2(nat15,2015)\n# \n# h_mean6 <- hmean2(nat16,2016)\n# \n# h_mean66 <- hmean2(nat17,2017)\n# \n# h_mean7 <- hmean2(nat18,2018)\n# \n# \n# h_mean <- rbind(h_mean1,h_mean2,h_mean3,h_mean4,h_mean6,h_mean7,\n# h_mean11, h_mean22, h_mean33, h_mean44, h_mean55, h_mean66) %>%\n# arrange(HM) \n# \n# h_mean_c <- h_mean %>%\n# mutate(STATE=\"United States\") %>%\n# rename(OCC_TITLE=occ_title) %>%\n# .[c(4,1,2,3)]\n \n# # A_MEAN\n# \n# amean1 <- function(tabela,leto){\n# a_mean <- tabela %>% \n# select(occ_title,a_mean) %>% \n# rename(AM=a_mean) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(AM) %>%\n# .[c(1,3,2)] \n# return(a_mean)\n# }\n# \n# amean2 <- function(tabela,leto){\n# a_mean <- tabela %>% \n# select(OCC_TITLE,A_MEAN) %>% \n# rename(occ_title=OCC_TITLE, AM=A_MEAN) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(AM) %>%\n# .[c(1,3,2)] \n# return(a_mean)\n# }\n# \n# a_mean1 <- amean1(nat6, 2006)\n# \n# a_mean2 <- amean1(nat8, 2008)\n# \n# a_mean3 <- amean2(nat10,2010)\n# \n# a_mean4 <- amean2(nat12,2012)\n# \n# a_mean5 <- amean2(nat14,2014)\n# \n# a_mean6 <- amean2(nat16,2016)\n# \n# a_mean7 <- amean2(nat18,2018)\n# \n# a_mean <- rbind(a_mean1,a_mean2,a_mean3,a_mean4,a_mean5,a_mean6,a_mean7) %>%\n# mutate(AM2=parse_number(AM, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(AM2) %>% \n# select(1,2,4) %>%\n# rename(AM=AM2)\n# \n# H_MEDIAN\n\n\n# hmedian1 <- function(tabela,leto){\n# h_med <- tabela %>% \n# select(occ_title,h_median) %>% \n# rename(HME=h_median) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(HME) %>%\n# .[c(1,3,2)] \n# return(h_med)\n# }\n# \n# hmedian2 <- function(tabela,leto){\n# h_med <- tabela %>% \n# select(OCC_TITLE,H_MEDIAN) %>% \n# rename(occ_title=OCC_TITLE, HME=H_MEDIAN) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(HME) %>%\n# .[c(1,3,2)] \n# h_med$HME <- as.numeric(as.character(h_med$HME))\n# return(h_med)\n# }\n# \n# \n# \n# h_median1 <- hmedian1(nat6,2006)\n# \n# h_median2 <- hmedian1(nat8,2008)\n# \n# h_median3 <- hmedian2(nat10,2010)\n# \n# h_median4 <- hmedian2(nat12,2012)\n# \n# h_median5 <- hmedian2(nat14,2014)\n# \n# h_median6 <- hmedian2(nat16,2016)\n# \n# h_median7 <- hmedian2(nat18,2018)\n# \n# h_med <- h_med <- rbind(h_median1, h_median2, h_median3, h_median4, h_median5, h_median6, h_median7) %>%\n# arrange(HME) \n# \n# # A_MEDIAN\n# \n# \n# amedian1 <- function(tabela,leto){\n# a_med <- tabela %>% \n# select(occ_title,a_median) %>% \n# rename(AME=a_median) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(AME) %>%\n# .[c(1,3,2)] \n# return(a_med)\n# }\n# \n# amedian2 <- function(tabela,leto){\n# a_med <- tabela %>% \n# select(OCC_TITLE,A_MEDIAN) %>% \n# rename(occ_title=OCC_TITLE, AME=A_MEDIAN) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(AME) %>%\n# .[c(1,3,2)] \n# return(a_med)\n# }\n# \n# a_median1 <- amedian1(nat6,2006)\n# \n# a_median2 <- amedian1(nat8,2008)\n# \n# a_median3 <- amedian2(nat10,2010)\n# \n# a_median4 <- amedian2(nat12,2012)\n# \n# a_median5 <- amedian2(nat14,2014)\n# \n# a_median6 <- amedian2(nat16,2016)\n# \n# a_median7 <- amedian2(nat18,2018)\n# \n# a_med <- rbind(a_median1, a_median2, a_median3, a_median4, a_median5, a_median6, a_median7) %>%\n# mutate(AME2=parse_number(AME, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(AME2) %>% \n# select(1,2,4) %>%\n# rename(AME=AME2)\n# # STATE DATA \n# \n# uvozi_s1 <- function(ime_datoteke){\n# ime <- paste0(\"podatki/\", ime_datoteke, \".csv\")\n# tabela <- read.csv2(ime, fileEncoding = \"UTF-8\" , na=c(\"#\",\"*\",\"**\",\"\",\"***\"),) %>%\n# select(3,5,7,9,10,14,19) \n# return(tabela)\n# }\n# \n# uvozi_s2 <- function(ime_datoteke){\n# ime <- paste0(\"podatki/\", ime_datoteke, \".csv\")\n# tabela <- read.csv2(ime, fileEncoding = \"UTF-8\" , na=c(\"#\",\"*\",\"**\",\"\",\"***\"),) %>%\n# select(3,5,7,11,12,16,21) \n# return(tabela)\n# }\n# \n# st6 <- uvozi_s1(\"S_06\")\n# \n# st8 <- uvozi_s1(\"S_08\")\n# \n# st10 <- uvozi_s2(\"S_10\")\n# \n# st12 <- uvozi_s2(\"S_12\")\n# \n# st14 <- uvozi_s2(\"S_14\")\n# \n# st16 <- uvozi_s2(\"S_16\")\n# \n# st18 <- uvozi_s2(\"S_18\")\n# \n# # total employment\n# \n# \n# employment3 <- function(tabela,leto){\n# total_employment_state <- tabela %>% \n# select(STATE,OCC_TITLE,TOT_EMP) %>% \n# rename(emp=TOT_EMP) %>% \n# mutate(leto=c(leto)) %>% \n# .[c(1,3,2,4)] %>%\n# drop_na(emp) \n# return(total_employment_state)\n# }\n# \n# total_employment_state_1 <- employment3(st6,2006)\n# \n# total_employment_state_2 <- employment3(st8,2008)\n# \n# total_employment_state_3 <- employment3(st10,2010)\n# \n# total_employment_state_4 <- employment3(st12,2012)\n# \n# total_employment_state_5 <- employment3(st14,2014)\n# \n# total_employment_state_6 <- employment3(st16,2016)\n# \n# total_employment_state_7 <- employment3(st18,2018)\n# \n# t_e_s <- rbind(total_employment_state_1,total_employment_state_2,\n# total_employment_state_3, total_employment_state_4,total_employment_state_5,\n# total_employment_state_6,total_employment_state_7) %>%\n# mutate(emp2=parse_number(emp, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(emp2) %>% \n# select(1,3,4,5) %>%\n# rename(emp=emp2) \n\n# h. mean by state\n\n# hmean3 <- function(tabela,leto){\n# hmean_state <- tabela %>% \n# select(STATE,OCC_TITLE,H_MEAN) %>% \n# rename(HM=H_MEAN) %>% \n# mutate(leto=c(leto)) %>% \n# .[c(1,2,4,3)] %>%\n# drop_na(HM)\n# hmean_state$HM <- as.numeric(as.character(hmean_state$HM))\n# return(hmean_state)\n# }\n# \n# h_mean_state_1 <- hmean3(st6, 2006)\n# \n# h_mean_state_2 <- hmean3(st8, 2008)\n# \n# h_mean_state_3 <- hmean3(st10,2010)\n# \n# h_mean_state_4 <- hmean3(st12,2012)\n# \n# h_mean_state_5 <- hmean3(st14,2014)\n# \n# h_mean_state_6 <- hmean3(st16,2016)\n# \n# h_mean_state_7 <- hmean3(st18,2018)\n# \n# h_mean_s <- rbind(h_mean_state_1, h_mean_state_2,h_mean_state_3,h_mean_state_4,\n# h_mean_state_5,h_mean_state_6,h_mean_state_7) %>% arrange(HM) \n# \n# \n# # a. mean. by state\n# \n# amean3 <- function(tabela,leto){\n# amean_state <- tabela %>% \n# select(STATE,OCC_TITLE,A_MEAN) %>% \n# rename(AM=A_MEAN) %>% \n# mutate(leto=c(leto)) %>% \n# .[c(1,2,4,3)] %>%\n# drop_na(AM)\n# return(amean_state)\n# }\n# \n# a_mean_state_1 <- amean3(st6, 2006)\n# \n# a_mean_state_2 <- amean3(st8, 2008)\n# \n# a_mean_state_3 <- amean3(st10,2010)\n# \n# a_mean_state_4 <- amean3(st12,2012)\n# \n# a_mean_state_5 <- amean3(st14,2014)\n# \n# a_mean_state_6 <- amean3(st16,2016)\n# \n# a_mean_state_7 <- amean3(st18,2018)\n# \n# a_mea_s <- rbind(a_mean_state_1, a_mean_state_2, a_mean_state_3, a_mean_state_4, \n# a_mean_state_5, a_mean_state_6, a_mean_state_7) %>%\n# mutate(AM2=parse_number(AM, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(AM2) %>% \n# select(1,2,3,5) %>%\n# rename(AM=AM2)\n# \n# # H_MEDIAN\n# \n# hmedian3 <- function(tabela,leto){\n# h_median_state <- tabela %>% \n# select(STATE,OCC_TITLE,H_MEDIAN) %>% \n# rename(HME=H_MEDIAN) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(HME) %>%\n# .[c(1,2,4,3)] %>%\n# drop_na(HME)\n# h_median_state$HME <- as.numeric(as.character(h_median_state$HME))\n# return(h_median_state)\n# }\n# \n# \n# h_median_state_1 <- hmedian3(st6,2006)\n# \n# h_median_state_2 <- hmedian3(st8,2008)\n# \n# h_median_state_3 <- hmedian3(st10,2010)\n# \n# h_median_state_4 <- hmedian3(st12,2012)\n# \n# h_median_state_5 <- hmedian3(st14,2014)\n# \n# h_median_state_6 <- hmedian3(st16,2016)\n# \n# h_median_state_7 <- hmedian3(st18,2018)\n# \n# \n# ###### TU JE NEKAJ NAROBE PRI SUMMERISE JE TREBA \u0160E POPRAVITI\n# \n# h_med_s <- rbind(h_median_state_1, h_median_state_2, h_median_state_3, \n# h_median_state_4, h_median_state_5, h_median_state_6, h_median_state_7) %>%\n# arrange(HME)\n# \n# # A_MEDIAN\n# \n# amedian3 <- function(tabela,leto){\n# a_median_state <- tabela %>% \n# select(STATE,OCC_TITLE,A_MEDIAN) %>% \n# rename(AME=A_MEDIAN) %>% \n# mutate(leto=c(leto)) %>% \n# drop_na(AME) %>%\n# .[c(1,2,4,3)] \n# return(a_median_state)\n# }\n# \n# a_median_state_1 <- amedian3(st6,2006)\n# \n# a_median_state_2 <- amedian3(st8,2008)\n# \n# a_median_state_3 <- amedian3(st10,2010)\n# \n# a_median_state_4 <- amedian3(st12,2012)\n# \n# a_median_state_5 <- amedian3(st14,2014)\n# \n# a_median_state_6 <- amedian3(st16,2016)\n# \n# a_median_state_7 <- amedian3(st18,2018)\n# \n# a_med_s <- rbind(a_median_state_1, a_median_state_2, a_median_state_3, \n# a_median_state_4, a_median_state_5, a_median_state_6, a_median_state_7) %>%\n# mutate(AME2=parse_number(AME, locale=locale(decimal_mark = \",\",grouping_mark = \".\") )) %>% \n# arrange(AME2) %>% \n# select(1,2,3,5) %>%\n# rename(AME=AME2)\n# \n# \n# # Tabela iz wikipedije: https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP_per_capita\n# \n# link <- \"https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP_per_capita\"\n# stran <- html_session(link) %>% read_html()\n# tabela <- stran %>% html_nodes(xpath=\"//table[@class='wikitable sortable']\") %>%\n# .[[1]] %>% html_table(dec=\",\") \n# tabela[[1]] <- parse_number(tabela[[1]], na=\"\u2014\") \n# tabela2 <- tabela %>% drop_na(Rank)\n# tabela[[1]] <- NULL\n# tabela <- tabela %>% pivot_longer(2:9, names_to = \"leto\") %>% rename(GDP=value)\n# tabela[[2]] <- parse_number(tabela[[2]])\n# GDP_by_state <- tabela\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "70cf57c4f0c632befd7afefbe740da040bff8768", "size": 17527, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "MatejRojec/APPR-2020-21", "max_stars_repo_head_hexsha": 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"num_tokens": 5962, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3117102658359745}} {"text": "# ==============================================================================\n# FUNCTIONS\n# ==============================================================================\n## Function to produce histrogram in a pairs plot\npanel.hist <- function(x, ...) {\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(usr[1:2], 0, 1.5) )\n h <- hist(x, plot = FALSE)\n breaks <- h$breaks\n nB <- length(breaks)\n y <- h$counts\n y <- y/max(y)\n rect(breaks[-nB], 0, breaks[-1], y, col='blue', cex.main = 0.8,...)\n}\n\n## Function to produce kernel density estimate in a pairs plot\npanel.density <- function(x, ...) {\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(usr[1:2], 0, 1.5) )\n den <- density(x, na.rm = T)\n rug(x, ticksize = 0.06, lwd = 1, col = 'red')\n y <- den$y; y <- y/max(y)\n x <- den$x\n lines(x, y)\n}\n\n## Function to produce correlation coefficients in a pairs plot\npanel.cor <- function(x, y, digits=2, prefix=\"\", cex.cor, ...) {\n usr <- par(\"usr\"); on.exit(par(usr))\n par(usr = c(0, 1, 0, 1))\n r1 <- abs(cor(x, y, use ='pairwise.complete.obs', method = 'pearson'))\n r2 <- abs(cor(x, y, use ='pairwise.complete.obs', method = 'kendall'))\n txt1 <- format(c(r1, 0.123456789), digits=digits)[1]\n txt2 <- format(c(r2, 0.123456789), digits=digits)[1]\n txt1 <- paste('Pearson = ', txt1, sep='')\n txt2 <- paste('Kendall = ',txt2, sep='')\n txt <- paste(prefix, txt1, txt2, sep=\" \")\n if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)\n #text(0.5, 0.5, txt, cex = 1)\n text(0.5, 0.6, txt1)\n text(0.5, 0.4, txt2)\n}\n\n## Function to calculate the deviance (2*likelihood ratio) of two mixed models\npboot<-function(m0,m1){\n s <- simulate(m0)\n L0<-logLik(refit(m0,s))\n L1<-logLik(refit(m1,s))\n 2*(L1-L0)\n}\n\nimputeLRT <- function(h0, h1, imputed.data.object) {\n if (!(length(fixef(h0)) < length(fixef(h1))))\n stop(\"The first argument needs the smaller model.\")\n imputed.data.list <- imputed.data.object$imputations\n m <- length(imputed.data.list)\n h0.models <- lapply(imputed.data.list,\n function (dataset) {\n update(h0, data = dataset)\n })\n h0.dev.funs <- lapply(imputed.data.list,\n function (dataset) {\n update(h0, data = dataset,\n devFunOnly = TRUE)\n })\n Q.bar.0 <-\n colMeans(do.call(rbind,\n lapply(h0.models,\n function (fit) {\n c(getME(fit, \"theta\"), fixef(fit))\n })))\n h1.models <- lapply(imputed.data.list,\n function (dataset) {\n update(h1, data = dataset)\n })\n h1.dev.funs <- lapply(imputed.data.list,\n function (dataset) {\n update(h1, data = dataset,\n devFunOnly = TRUE)\n })\n Q.bar.1 <-\n colMeans(do.call(rbind,\n lapply(h1.models,\n function (fit) {\n c(getME(fit, \"theta\"), fixef(fit))\n })))\n d.prime.m.bar <- mean(unlist(lapply(1:m,\n function(i) {\n anova(h0.models[[i]],\n h1.models[[i]])$Chisq[2]\n })))\n d.L.bar <- mean(unlist(lapply(1:m,\n function(i) {\n h0.dev.funs[[i]](Q.bar.0) -\n h1.dev.funs[[i]](Q.bar.1)\n })))\n p0 <- length(Q.bar.0)\n p1 <- length(Q.bar.1)\n k <- p1 - p0\n rL <- (m + 1) / ((m - 1) * k) * (d.prime.m.bar - d.L.bar) # 3.8\n D.L <- d.L.bar / ((1 + rL) * k) # 3.7\n v <- k * (m - 1)\n w <- ifelse(v > 4, # 2.7\n 4 + (v - 4) * (1 + (1 - v/2) / rL)^2,\n v / 2 * (1 + 1/k) * (1 + 1/rL)^2)\n Pval <- 1 - pf(D.L, k, w)\n # browser()\n return(list(\n D.L = D.L,\n Pval = Pval,\n rL = rL,\n d.L.bar = d.L.bar,\n d.prime.m.bar = d.prime.m.bar,\n# Q.bar.0 = Q.bar.0,\n# Q.bar.1 = Q.bar.1,\n k = k,\n w = w,\n p0 = p0,\n p1 = p1,\n m = m))\n}\n\nimputeLRT.c <- cmpfun(imputeLRT)\n", "meta": {"hexsha": "c6e521c0f9f6631115d2b9cfaedabb7ca03ad5fb", "size": 4636, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/functions.r", "max_stars_repo_name": "SteveLane/blistering-barnacles", "max_stars_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/functions.r", "max_issues_repo_name": "SteveLane/blistering-barnacles", "max_issues_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2017-09-12T05:04:30.000Z", "max_issues_repo_issues_event_max_datetime": "2017-11-08T22:37:45.000Z", "max_forks_repo_path": "scripts/functions.r", "max_forks_repo_name": "SteveLane/blistering-barnacles", "max_forks_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-08-28T04:09:34.000Z", "max_forks_repo_forks_event_max_datetime": "2017-08-28T04:09:34.000Z", "avg_line_length": 37.3870967742, "max_line_length": 80, "alphanum_fraction": 0.4053062985, "num_tokens": 1296, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073802837478, "lm_q2_score": 0.5660185351961016, "lm_q1q2_score": 0.31165398285636975}} {"text": "GetRaws <- function(df.list, var.codes) {\n \n # This function takes a list of hilda dataframes (1 for each wave) and hilda\n # variable codes, and returns a tibble in long format of all records, with the \n # hilda code, xwaveid, waveid, and variable value. If complete == TRUE then\n # only records with valid responses in all variables are returned\n \n require(dplyr)\n \n for (df in df.list) { # for every wave\n waveid <- substr(colnames(df)[2],1,1) # get the waveid (a, b, c, ..p)\n wave.codes <- paste0(waveid, var.codes) # update the variable codes\n wave.codes <- c('xwaveid', wave.codes) # add xwaveid to track individuals\n \n col.index <- which(colnames(df) %in% wave.codes) # find the column number\n df.vars <- df[, col.index] # get the columns\n \n df.vars %<>% \n # select(wave.codes) %>% # this errors if no columns found\n gather(code, val, -xwaveid) %>% \n mutate(wave = waveid) #-> df.vars\n \n if (exists('df.long')) {\n df.long <- suppressWarnings(bind_rows(df.long, df.vars))\n } else {\n df.long <- df.vars\n }\n \n }\n # remove waveid from codes\n df.long %<>%filter(!is.na(code)) %>%mutate_at('code', funs(substring(code, 2, nchar(code)))) #-> df.long\n \n # # Recode wave as year\n # df.long$wave <- apply(df.long, 1, function(x) (which(letters == x['wave']) + 2000))\n #\n \n return(df.long)\n \n }\n", "meta": {"hexsha": "78442bddc33ed7af08f76e7504c57956cda0c1ba", "size": 1373, "ext": "r", "lang": "R", "max_stars_repo_path": "src/data_management/GetRaws.r", "max_stars_repo_name": "Abraham-newbie/Life-Events", "max_stars_repo_head_hexsha": "80a6ef45833edb0e5b99530bcbdeac58a8698d39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/data_management/GetRaws.r", "max_issues_repo_name": "Abraham-newbie/Life-Events", "max_issues_repo_head_hexsha": "80a6ef45833edb0e5b99530bcbdeac58a8698d39", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data_management/GetRaws.r", "max_forks_repo_name": "Abraham-newbie/Life-Events", "max_forks_repo_head_hexsha": "80a6ef45833edb0e5b99530bcbdeac58a8698d39", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.325, "max_line_length": 106, "alphanum_fraction": 0.6212672979, "num_tokens": 407, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.31165398285636964}} {"text": " \nlibrary(HiCdatR)\n\ngene_frags_raw <- read.table(\"gene_by_fragment.tsv\")\nres <- aggregate(V8 ~ V4, FUN=unique, data=gene_frags_raw, simplify=FALSE)\ngene_bins <- res$V8\nnames(gene_bins) <- res$V4\n\n#load our contact matrix and normalize results\nf.source.organism.specific.code(\"../FL1.R\")\nM <- f.load.one.sample(\".\", \"mapped_contacts_10k_r.txt\", 1e4, repetitions=50)\n\n#generate teh gene-by-gene matrix from the contact matrix\n#ther _must_ by a faster way to do this...\nngene <- length(gene_bins)\nG <- matrix(0, nrow=ngene, ncol=ngene)\n\nfor(i in 1:ngene){\n for(j in i:ngene){\n v <- mean(M[ gene_bins[[i]], gene_bins[[j]] ])\n G[i,j] <- v\n G[j,i] <- v\n }\n if( i %% 500 == 0){\n # this takes a while, make sure it is still running...\n message(i)\n }\n}\n\n#util functions to work on intervals \ngenomic_interval <- function(chrom, start, end){\n structure(list(chrom, as.integer(start),as.integer(end)), .Names=c(\"chrom\", \"start\", \"end\"))\n}\n\nbed_to_interval <- function(row){\n genomic_interval(row[[\"chrom\"]], row[[\"start\"]], row[[\"end\"]])\n}\n\ngdist <- function(a,b){\n if(is.null(a) | is.null(b)){\n return(Inf)\n }\n if(a$chrom != b$chrom){\n return(Inf)\n }\n gap <- if (a$end > b$end) a$start - b$end else b$start - a$end\n if(gap <= 0){\n #overlap\n return(0)\n }\n gap\n}\n\nfrags_dist <- function(fragA, fragB){\n min(sapply(fragA, function(x) sapply(fragB, function(y) gdist(x,y))))\n}\n\n\ngenes_full <- read_bed(\"../../annotation/M3_full.bed\")\ngene_intervals <- apply( genes_full, 1, bed_to_interval)\ngene_intervals_ordered <- gene_intervals[rownames(G)]\nngene <- length(gene_intervals_ordered)\n# 1-D genome distance\nD <- matrix(0, nrow=ngene, ngene)\nfor(i in 1:ngene){\n for(j in 1:ngene){\n D[i,j] <- gdist( gene_intervals_ordered[[i]], gene_intervals_ordered[[j]] )\n }\n if(i %% 100 == 0){\n message(i)\n }\n}\n\n\ncuts <- c(0,1, 5000, 20000, 100000, 1e6, 10e6, Inf)\n\nresample_contacts <- function(nsamp, G, D){\n x <- sample(nrow(G), nsamp)\n tapply(G[x,x], cut(D[x,x], breaks=cuts, include.lowest=TRUE), mean, na.rm=TRUE)\n}\n\nplot_v_null <- function(gene_set, G, D,nrep, return_df=TRUE){\n idx <- rownames(G) %in% gene_set\n n <- sum(idx)\n sim <- replicate(nrep, resample_contacts(n, G, D))\n null <- t(apply(sim, 1, quantile, c(0.025, 0.5, 0.975), na.rm=TRUE))\n bin <- cut(D[idx,idx], breaks=cuts, include.lowest=TRUE)\n obs <- tapply(G[idx,idx], bin, mean, na.rm=TRUE)\n df0 <- data.frame(null, obs, as.numeric(table(bin)))\n names(df0) <- c(\"lower\", \"median\", \"upper\", \"obs\", \"nobs\")\n df0$bin <- 1:(length(cuts)-1)\n print(ggplot(df0, aes(bin, obs, ymax=upper, ymin=lower)) + geom_ribbon(fill=\"grey80\", alpha=0.6) + geom_line() + scale_y_log10())\n if(return_df){\n return(df0)\n }\n}\n \n\n\n\n\n}\n```\n", "meta": {"hexsha": "dfc51c42cf71b0531011dfbc17661348a1d82c11", "size": 2813, "ext": "r", "lang": "R", "max_stars_repo_path": "HiC/gene_gene_contacts.r", "max_stars_repo_name": "dwinter/fl1_genome_scripts", "max_stars_repo_head_hexsha": "14c4dd661fefc6c29e4f9e1a87ec143bbbb5a803", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "HiC/gene_gene_contacts.r", "max_issues_repo_name": "dwinter/fl1_genome_scripts", "max_issues_repo_head_hexsha": "14c4dd661fefc6c29e4f9e1a87ec143bbbb5a803", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "HiC/gene_gene_contacts.r", "max_forks_repo_name": "dwinter/fl1_genome_scripts", "max_forks_repo_head_hexsha": "14c4dd661fefc6c29e4f9e1a87ec143bbbb5a803", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-08-13T22:32:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-13T22:32:40.000Z", "avg_line_length": 27.0480769231, "max_line_length": 133, "alphanum_fraction": 0.6210451475, "num_tokens": 912, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3116539745084412}} {"text": "\nsum_longitudinal <- function(dat_in, src, tp_vec, var) {\n # populate var list\n if (src != \"dyad\") {\n label_list <- list(\n continuous ~ \"COST Score\",\n binary ~ \"COST Binary\",\n categorical ~ \"COST Categorical\"\n )\n } else {\n label_list <- list(\n continuous ~ \"Difference in COST\",\n binary ~ \"Difference in Financial Toxicity\",\n categorical ~ \"Difference in Experience of Financial Toxicity\"\n )\n }\n\n # select var_label from var_list\n if (var == \"continuous\") {\n label_var <- label_list[[1]]\n } else if (var == \"binary\") {\n label_var <- label_list[[2]]\n } else if (var == \"categorical\") {\n label_var <- label_list[[3]]\n } else {\n stop(\"Var not in Var List for Function\")\n }\n\n # Build summary table\n dat <- dat_in %>%\n filter(src == {{ src }}) %>%\n filter(redcap_event_name %in% tp_vec) %>%\n tbl_summary(\n by = redcap_event_name,\n include = var,\n label = label_var,\n type = list(where(is.logical) ~ \"categorical\")\n )\n\n # label p function -> doesn't work right\n if (var == \"continuous\") {\n dat <- dat %>%\n add_p(test = continuous ~ \"paired.t.test\", group = partid)\n } else if (var == \"binary\") {\n dat <- dat %>%\n add_p(test = binary ~ \"mcnemar.test\", group = partid)\n } else {\n dat <- dat %>%\n add_p(test = categorical ~ \"mcnemar.test\", group = partid)\n }\n\n return(dat)\n}\n", "meta": {"hexsha": "0fc9cfa67931bfa3af4a88d4b3d94be5f94b19d3", "size": 1568, "ext": "r", "lang": "R", "max_stars_repo_path": "Functions/longitudinal.r", "max_stars_repo_name": "RiversPharmD/AZFinTox", "max_stars_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Functions/longitudinal.r", "max_issues_repo_name": "RiversPharmD/AZFinTox", "max_issues_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Functions/longitudinal.r", "max_forks_repo_name": "RiversPharmD/AZFinTox", "max_forks_repo_head_hexsha": "609f74faffc7eb3eebd83d33b5d79ce239863821", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.037037037, "max_line_length": 74, "alphanum_fraction": 0.5159438776, "num_tokens": 386, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3116539745084412}} {"text": "# ***********************************************************\r\n# xmeans.r\r\n# ***********************************************************\r\nxmeans <- setRefClass (\r\n\tClass = \"xmeans\",\r\n\r\n\tcontains = c (\"s_kmeans\", \"xmeans_sub\"),\r\n\r\n\tfields = c (\"num_split_cluster\" = \"integer\",\r\n\t\t\t\t\"stack\" = \"list\",\r\n\t\t\t\t\"ignore.covar\" = \"logical\",\r\n\t\t\t\t\"ISHIOKA\" = \"logical\",\r\n\t\t\t\t\"Cluster\" = \"integer\"),\r\n\r\n\tmethods = list (\r\n\t\tinitialize = function (ignore.covar_ = TRUE,\r\n\t\t\t\t\t\t\t ISHIOKA_ = TRUE)\r\n\t\t{\r\n\t\t\taddPackages ()\r\n\t\t\tinitFields (num_split_cluster = 0L,\r\n\t\t\t\t\t\tstack = list (),\r\n\t\t\t\t\t\tignore.covar = ignore.covar_,\r\n\t\t\t\t\t\tISHIOKA = ISHIOKA_)\r\n\t\t},\r\n\t\tishioka_xmeans = function (DATA_)\r\n\t\t{\r\n\t\t\t.self$splitData (.self$cleanData (DATA_))\r\n\t\t\tif (ISHIOKA)\r\n\t\t\t\t.self$mergeSplitedData ()\r\n\t\t\t.self$assignCluster ()\r\n\t\t},\r\n\t\tsplitData = function (data)\r\n\t\t{\r\n\t\t\t# simple x-means\r\n\t\t\tsplit_data <- split2Cluster (data, ignore.covar)\r\n\t\t\tif (split_data$doSPLIT) {\r\n\t\t\t\tsplitData (split_data$d1)\r\n\t\t\t\tsplitData (split_data$d2)\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tnum_split_cluster <<- num_split_cluster + 1L\r\n\t\t\t\tstack[[num_split_cluster]] <<- data\r\n\t\t\t}\r\n\t\t},\r\n\t\tmergeSplitedData = function () {\r\n\t\t\tind_order <- .self$getDataSize_order (.self$getDataSize_eachCluster ())\r\n\t\t\tnum_cluster <- length (ind_order)\r\n\t\t\tisMerge_eachCluster <- numeric (num_cluster)\r\n\t\t\tfor (i in seq_len (num_cluster - 1)) {\r\n\t\t\t\ti_ <- ind_order[sprintf (\"%d\", i)]\r\n\t\t\t\tif (isMerge_eachCluster[i_] == 1)\r\n\t\t\t\t\tnext\r\n\t\t\t\tfor (j in (i+1):num_cluster) {\r\n\t\t\t\t\tj_ <- ind_order[sprintf (\"%d\", j)]\r\n\t\t\t \t\tif (isMerge_eachCluster[j_] == 0) {\r\n\t\t\t\t\t\tmerge_data <- merge2Cluster (stack[[i_]], stack[[j_]])\r\n\t\t\t\t\t\tif (merge_data$doMERGE) {\r\n\t\t\t\t\t\t\tstack[[i_]] <<- merge_data$D\r\n\t\t\t\t\t\t\tisMerge_eachCluster[j_] <- 1\r\n\t\t\t\t\t\t\tbreak\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tstack <<- stack [isMerge_eachCluster == 0]\r\n\t\t\tnum_split_cluster <<- length (stack)\r\n\t\t},\r\n\t\tcleanData = function (data_)\r\n\t\t{\r\n\t\t\tout <- na.omit (data_ [, sapply (data_, is.numeric)])\r\n\t\t\trownames (out) <- 1:nrow (out)\r\n\t\t\treturn (out)\r\n\t\t},\r\n\t\tgetDataSize_eachCluster = function () {\r\n\t\t\tout <- sapply (stack, nrow)\r\n\t\t\treturn (out)\r\n\t\t},\r\n\t\tgetDataSize_order = function (DataSize_eachCluster)\r\n\t\t{\r\n\t\t\tout <- seq_len (length (DataSize_eachCluster))\r\n\t\t\tout <- setNames (out, order (DataSize_eachCluster))\r\n\t\t\treturn (out)\r\n\t\t},\r\n\t\tassignCluster = function ()\r\n\t\t{\r\n\t\t\tout <- numeric (sum (.self$getDataSize_eachCluster ()))\r\n\t\t\tind <- lapply (stack, function (d) as.integer (rownames(d)))\r\n\t\t\tfor (i in seq_along (ind))\r\n\t\t\t\tout[ind[[i]]] <- i\r\n\t\t\tCluster <<- as.integer (out)\r\n\t\t}\r\n\t)\r\n)\r\n\r\n", "meta": {"hexsha": "b92b6942ab4fa096418110b742f954bf95f7cd45", "size": 2719, "ext": "r", "lang": "R", "max_stars_repo_path": "xmeans.r", "max_stars_repo_name": "nonsabotage/xmeans-r", "max_stars_repo_head_hexsha": "9df05c1ecaf51017b60a1ae65f0d9e37adce8bfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "xmeans.r", "max_issues_repo_name": "nonsabotage/xmeans-r", "max_issues_repo_head_hexsha": "9df05c1ecaf51017b60a1ae65f0d9e37adce8bfc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "xmeans.r", "max_forks_repo_name": "nonsabotage/xmeans-r", "max_forks_repo_head_hexsha": "9df05c1ecaf51017b60a1ae65f0d9e37adce8bfc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6210526316, "max_line_length": 85, "alphanum_fraction": 0.533652078, "num_tokens": 793, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3116539745084412}} {"text": "# Uvoz s spletne strani\n\nlibrary(XML)\n\n# Vrne vektor nizov z odstranjenimi za\u010detnimi in kon\u010dnimi \"prazninami\" (whitespace)\n# iz vozli\u0161\u010d, ki ustrezajo podani poti.\nstripByPath <- function(x, path) {\n unlist(xpathApply(x, path,\n function(y) gsub(\"[^[:alnum:] .,;'`\u2018\u2019!+-/&()\\n]\", \"\",\n gsub(\"\\\\[[^]]*?\\\\]\", \"\",\n gsub(\"^\\\\s*(.*?)\\\\s*$\", \"\\\\1\", xmlValue(y))))))\n}\n\n\n uvozi.bike <- function(){\n url.bike <- \"http://en.wikipedia.org/wiki/List_of_bicycle_sharing_systems\"\n doc.bike <- htmlTreeParse(url.bike, useInternalNodes=TRUE)\n\n\n# Pobri\u0161emo nevidno vsebino\nfor (t in getNodeSet(doc.bike, \"//span[@style='display:none']|//span[@class='sortkey']\")) {\n xmlValue(t) <- \"\" \n} \n\n# Poi\u0161\u010demo vse tabele v dokumentu\ntabele <- getNodeSet(doc.bike, \"//table\") \n\n# Iz druge tabele dobimo seznam vrstic () neposredno pod\n# trenutnim vozli\u0161\u010dem\nvrstice <- getNodeSet(tabele[[1]], \"./tr\") \n\n# Seznam vrstic pretvorimo v seznam (znakovnih) vektorjev\n# s porezanimi vsebinami celic () neposredno pod trenutnim vozli\u0161\u010dem\nseznam <- lapply(vrstice[2:length(vrstice)], stripByPath, \"./td\") \nseznam <- lapply(seznam, function(x) x[-11])\n\n# Iz seznama vrstic naredimo matriko\nmatrika <- matrix(unlist(seznam), nrow=length(seznam), byrow=TRUE) \n\n# Imena stolpcev matrike dobimo iz celic glave () prve vrstice\ncolnames(matrika) <- gsub(\"\\n\", \" \", stripByPath(vrstice[[1]], \".//th\")[-11]) \n\n# Podatke iz matrike spravimo v razpredelnico\nreturn(data.frame(matrika[,1:6],\n Year.inaugurated = as.numeric(gsub(\".*?([0-9]{4}).*\", \"\\\\1\",\n matrika[,7])),\n apply(gsub(\".*?([0-9]*).*\", \"\\\\1\",\n gsub(\",\", \"\", matrika[,8:9])), 2, as.numeric)))\n}", "meta": {"hexsha": "2738b5c5100f19b4c3827ce0da3979aa0cb0587f", "size": 1835, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/xml.r", "max_stars_repo_name": "damjanm/APPR-2014-15", "max_stars_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/xml.r", "max_issues_repo_name": "damjanm/APPR-2014-15", "max_issues_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-01-09T19:00:02.000Z", "max_issues_repo_issues_event_max_datetime": "2015-03-01T17:23:23.000Z", "max_forks_repo_path": "lib/xml.r", "max_forks_repo_name": "damjanm/APPR-2014-15", "max_forks_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-29T19:30:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-29T19:30:52.000Z", "avg_line_length": 37.4489795918, "max_line_length": 91, "alphanum_fraction": 0.578746594, "num_tokens": 600, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3116539745084412}} {"text": "\n#example of 'one liners' where its safer to break the explicit syntax rules in the name of style\n#turn a DNA sequence into a lower case vector\ns2v = function(dna_string) tolower(strsplit(dna_string, \"\")[[1]]) \n#turn a vector of DNA sequence into a string\nv2s = function(dna_vec) paste(dna_vec, collapse = \"\")\n\n#get a random DNA nucleotide\n#the argument with a default lets this be\n#used in solving two problems!\nrandom_bp = function(exclude_base = NULL){\n bps = c('a', 't', 'g', 'c')\n if(!is.null(exclude_base)){\n bps = bps[bps != exclude_base]\n }\n sample(bps, 1)\n}\n\n\n# a simplified version of a function to introduce errors into DNA sequences\n#by using functions, I've been able to use the random_bp to accomplish to related\n#but different tasks with minimal effort\nerror_introduce = function(dna_string, global_mutation_rate = 0.01, global_indel_rate = 0.01){\n\n\torg_vec = s2v(dna_string)\n\tnew_seq = c() \n\n\tfor(i in 1:length(org_vec)){\n\t\tb = org_vec[[i]]\n\t\tprob = runif(1)\n\t if(prob < global_mutation_rate){ \n \t\t\t#point mutation \n \t\t\tnew_b = random_bp(exclude_base=b) \n \t\t\tnew_seq = c(new_seq,new_b) \n\t } else if ((global_mutation_rate < prob) && (prob<(global_indel_rate+global_mutation_rate))){ \n #indel \n in_prob = runif(1) \n if(in_prob<0.5){ \n #insertion \n #add the base \n new_seq = c(new_seq, b) \n #insert a base after \n new_seq = c(new_seq, random_bp()) \n }else{ \n #deletion \n #don't add anything so base is skipped\n #this 'else' statement could be omitted for brevity\n \tnext\n } \n\t }else{ \n \t\t new_seq = c(new_seq, b) \n\t\t} \n\t} \n\n\toutput = v2s(new_seq)\n\treturn(output)\n}\n\n\n\n#reusing the 'building blocks' I've made to easily do something else\nrandom_add = function(seq, side = 3 , max = 100){\n #side says where the addition is made\n #1 = front\n #2 = back\n #3 = both\n front_seq = c()\n back_seq = c()\n if(side == 1 || side == 3){\n for(i in 1:sample.int(max, 1)){\n front_seq = c(front_seq, random_bp())\n }\n }\n if(side == 2 || side == 3){\n for(i in 1:sample.int(max, 1)){\n back_seq = c(back_seq, random_bp())\n }\n }\n return(v2s(c(front_seq, seq, back_seq)))\n}\n\n\n\ndna_string = \"ctctacttgatttttggtgcatgagcaggaatagttggaatagctttaagtttactaattcgcgctgaactaggtcaacccggatctcttttaggggatgatcagatttataatgtgatcgtaaccgcccatgcctttgtaataatcttttttatggttatacctgtaataattggtggctttggcaattgacttgttcctttaataattggtgcaccagatatagcattccctcgaataaataatataagtttctggcttcttcctccttcgttcttacttctcctggcctccgcaggagtagaagctggagcaggaaccggatgaactgtatatcctcctttagcaggtaatttagcacatgctggcccctctgttgatttagccatcttttcccttcatttggccggtatctcatcaattttagcctctattaattttattacaactattattaatataaaacccccaactatttctcaatatcaaacaccattatttgtttgatctattcttatcaccactgttcttctactccttgctctccctgttcttgcagccggaattacaatattattaacagaccgcaacctcaacactacattctttgaccccgcagggggaggggacccaattctctatcaacactta\"\nerror_introduce(dna_string)\n\nerror_introduce(\"ctctacttgatttttggtgcatgagcaggaatagttggaatagctttaagt\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#you can put those nice little pipes you make into a function to turn\n#them into simple and reusable one liners!\nlibrary(tidyverse)\n#this is matt's code from last week that uses mtcars to make a new col\nmutate_df = function(df){\n df %>%\n mutate(NEW_COLUMN = mpg + cyl) %>%\n rename(new_column_new_you = NEW_COLUMN) %>%\n # rename can also be used to change the names of fixed positions\n arrange(hp) %>%\n dplyr::select(-new_column_new_you)\n \n return(df)\n}\n\n#all the detail is abstracted away to just this!\nnew_df = mutate_df(mtcars)\n\n\n\n", "meta": {"hexsha": "058fd8bbf4c4cf7ed586a71d93446267cd5f3b49", "size": 3636, "ext": "r", "lang": "R", "max_stars_repo_path": "UGRU-practical_examples.r", "max_stars_repo_name": "CNuge/RUsersGroup", "max_stars_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-26T16:52:42.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-19T21:32:38.000Z", "max_issues_repo_path": "UGRU-practical_examples.r", "max_issues_repo_name": "CNuge/RUsersGroup", "max_issues_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "UGRU-practical_examples.r", "max_forks_repo_name": "CNuge/RUsersGroup", "max_forks_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-09-21T13:02:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-07T16:43:22.000Z", "avg_line_length": 26.347826087, "max_line_length": 666, "alphanum_fraction": 0.7004950495, "num_tokens": 1164, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185205547239, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.31165396644679083}} {"text": "getwd()\nworkingDir <- \"/home/luke/Documents/E_FEM_clean/E_FEM\"\nsetwd(workingDir)\n\nrequire(haven)\nrequire(dplyr)\n\nbaseline <- read_dta('output/ELSA_Baseline/ELSA_Baseline_summary.dta')\n\ncohort <- read_dta('output/ELSA_cohort/ELSA_cohort_summary.dta')\n\n\n\n###\ncommit3 <- read_dta('output/COMMIT_cSmoken3/COMMIT_cSmoken3_summary.dta')\n\ncommit30 <- read_dta('output/COMMIT_cSmoken30/COMMIT_cSmoken30_summary.dta')\n\n\n## Visualise the 3% intervention\nplot(cohort$year, cohort$n_smoken_all, type='l', col='red')\nlines(commit3$year, commit3$n_smoken_all, col='green')\n\nplot(commit3$year, commit3$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(commit3$year, commit3$n_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$n_lunge_all, col='red')\n\nplot(commit3$year, commit3$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(commit3$year, commit3$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(commit3$year, commit3$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\n\n## Visualise the 30% intervention\nplot(cohort$year, cohort$n_smoken_all, type='l', col='red')\nlines(commit30$year, commit30$n_smoken_all, col='green')\n\nplot(commit30$year, commit30$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(commit30$year, commit30$n_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$n_lunge_all, col='red')\n\nplot(commit30$year, commit30$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(commit30$year, commit30$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\n\n\n\n## Now work with the PSmoke_stopMult intervention\nsmoke_stop <- read_dta('output/Smoke_Stop_Cohort150/Smoke_Stop_Cohort150_summary.dta')\n\nplot(smoke_stop$year, smoke_stop$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$n_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\nplot(smoke_stop$year, smoke_stop$n_smoke_stop_all, type='l', col='green')\nlines(cohort$year, cohort$n_smoke_stop_all, col='red')\n\n\n###########\nsmoke_stop_pop <- read_dta('output/Smoke_Stop_Pop/Smoke_Stop_Pop_summary.dta')\n\nplot(smoke_stop_pop$year, smoke_stop_pop$p_smoken_all, type='l', col='green')\nlines(baseline$year, baseline$p_smoken_all, col='red')\n\nplot(smoke_stop_pop$year, smoke_stop_pop$n_smoken_all, type='l', col='green')\nlines(baseline$year, baseline$n_smoken_all, col='red')\n\nplot(smoke_stop_pop$year, smoke_stop_pop$p_lunge_all, type='l', col='green')\nlines(baseline$year, baseline$p_lunge_all, col='red')\n\nplot(smoke_stop_pop$year, smoke_stop_pop$p_cancre_all, type='l', col='green')\nlines(baseline$year, baseline$p_cancre_all, col='red')\n\nplot(smoke_stop_pop$year, smoke_stop_pop$p_diabe_all, type='l', col='green')\nlines(baseline$year, baseline$p_diabe_all, col='red')\n\n\n\n###########\nSmokeStopInt <- read_dta('output/SmokeStopIntervention/SmokeStopIntervention_summary.dta')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$n_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(SmokeStopInt$year, SmokeStopInt$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\ntmp <- SmokeStopInt$m_endpop_all / SmokeStopInt$m_endpop_all[1]\ntmp2 <- cohort$m_endpop_all / cohort$m_endpop_all[1]\n\nplot(SmokeStopInt$year, tmp, type='l', col='green')\nlines(cohort$year, tmp2, col='red')\n\n\n#################################################\nsmoke_stop_init <- read_dta('output/Smoke_Stop_Cohort_Init/Smoke_Stop_Cohort_Init_summary.dta')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$p_smoken_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$n_smoken_all, type='l', col='green')\nlines(cohort$year, cohort$n_smoken_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_lunge_all, type='l', col='green')\nlines(cohort$year, cohort$p_lunge_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_cancre_all, type='l', col='green')\nlines(cohort$year, cohort$p_cancre_all, col='red')\n\nplot(smoke_stop_init$year, smoke_stop_init$p_diabe_all, type='l', col='green')\nlines(cohort$year, cohort$p_diabe_all, col='red')\n\n\n\n", "meta": {"hexsha": "325f6d29ac76fea4e34eaa766cebe1a75d64b055", "size": 5136, "ext": "r", "lang": "R", "max_stars_repo_path": "FEM_R/chron_disease_vis.r", "max_stars_repo_name": "ld-archer/E_FEM", "max_stars_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-11-22T10:59:33.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-10T10:32:02.000Z", "max_issues_repo_path": "FEM_R/chron_disease_vis.r", "max_issues_repo_name": "ld-archer/E_FEM", "max_issues_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 39, "max_issues_repo_issues_event_min_datetime": "2019-11-22T10:39:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T15:32:18.000Z", "max_forks_repo_path": "FEM_R/chron_disease_vis.r", "max_forks_repo_name": "ld-archer/E_FEM", "max_forks_repo_head_hexsha": "7db846a17f3c57e98b619d7a9c5860d3a71ccc1c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.1780821918, "max_line_length": 95, "alphanum_fraction": 0.7548676012, "num_tokens": 1582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555714, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3115601939549779}} {"text": "# -*- coding: utf-8 -*-\n\n#' Created on Fri Apr 13 15:38:28 2018\n#' R version 3.4.3 (2017-11-30)\n#' \n#' @group Group 2, DM2 2018 Semester 2\n#' @author: Martins T.\n#' @author: Mendes R.\n#' @author: Santos R.\n#'\n\n# Libs --------------------------------------------------------------------\noptions(warn=-1)\nsource(\"src/packages.r\")\ninclude_packs(c(\"dygraphs\",\"d3heatmap\",\"rockchalk\",\"forcats\",\"rJava\",\n \"xlsxjars\",\"xlsx\",\"tidyverse\",\"stringi\",\"stringr\",\"ggcorrplot\",\n \"sm\",\"lubridate\",\"magrittr\",\"ggplot2\",\"openxlsx\",\"RColorBrewer\",\n \"psych\",\"treemap\",\"data.table\",\"pROC\",\"class\",'gmodels','klaR',\n \"C50\",\"caret\",'gmodels',\"DMwR\",\"recipes\",\"epiR\",\"pubh\",\"leaps\",\n \"MASS\",\"gbm\",\"autoimage\",\"randomForest\",\"settings\",\"factoextra\"\n ,\"dummies\",\"ROCR\",\"neuralnet\",\"sparseLDA\",\"adabag\",\"RWeka\",\n \"Metrics\",\"lattice\"))\n\n\n\n# Performance on ROC Comparison --------------------------------------------------------------\n\n#Linear Models Comparison\nfiles <- paste(\"models/\",list.files(path=\"models/\", pattern =\"glm|lda\"),sep=\"\")\nfor (i in 1:length(files)){load(files[i])};rm(i)\n#Log Models Comparison -------------------------------------------------------\n\n\nLresults <- resamples(list(\"Log 13\"=glm13, \n \"Log 10\"=glm10, \n \"Log 7\"=glm7))\n # , \n # \"Lda 13\"=lda13, \n # \"Lda 10\"=lda10, \n # \"Lda 7\"=lda7))\n\n# Boxplot \npng(filename=\"presentations/LOGmodels.png\",width=1000, height=750)\nbwplot(Lresults, scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"LOG Models Comparison\")\ndev.off()\n\nbwplot(Lresults , scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)),\n main=\"Log Models Comparison\")\n\n#Lda Models Comparison -------------------------------------------------------\n\n\nLresults <- resamples(list(\n \"Lda 13\"=lda13,\n \"Lda 10\"=lda10,\n \"Lda 7\"=lda7))\n\n# Boxplot \npng(filename=\"presentations/LDAmodels.png\",width=1000, height=750)\nbwplot(Lresults, scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"LDA Models Comparison\")\ndev.off()\n\nbwplot(Lresults , scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)),\n main=\"LDA Models Comparison\")\n\n\n\n#Non-Linear Models Comparison\nfiles <- paste(\"models/\",list.files(path=\"models/\", pattern =\"svm|nn\"),sep=\"\")\nfor (i in 1:length(files)){load(files[i])};rm(i)\n#SVM Models Comparison -------------------------------------------------------\n\n\nNLresults <- resamples(list(\"Svm 13\"=svm13, \n \"Svm 10\"=svm10, \n \"Svm 7\"=svm7))\n # , \n # \"NN 13\"=nn13, \n # \"NN 10\"=nn10, \n # \"NN 7\"=nn7))\n# Boxplot \npng(filename=\"presentations/SVMmodels.png\",width=1000, height=750)\nbwplot(NLresults, main=\"SVM Models Comparison\",\n scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)))\ndev.off()\n\nbwplot(NLresults, main=\"SVM Models Comparison\",\n scales=list(cex=1.5,x=\"free\",tck=c(0,0), y=list(cex=3)))\n\n#NN Models Comparison -------------------------------------------------------\n\n\nNLresults <- resamples(list(\n \"NN 13\"=nn13, \n \"NN 10\"=nn10, \n \"NN 7\"=nn7))\n# Boxplot \npng(filename=\"presentations/NNmodels.png\",width=1000, height=750)\nbwplot(NLresults, main=\"NN Models Comparison\",\n scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)))\ndev.off()\n\nbwplot(NLresults, main=\"NN Models Comparison\",\n scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)))\n\n\n\n#Ensemble Models Comparison \nfiles <- paste(\"models/\",list.files(path=\"models/\", pattern =\"cart|rf\"),sep=\"\")\nfor (i in 1:length(files)){load(files[i])};rm(i)\n#CART Models Comparison -------------------------------------------------------\n\n\nEresults <- resamples(list(\"CART 13\"=cart13, \n \"CART 10\"=cart10, \n \"CART 7\"=cart7))\n # , \n # \"RF 13\"=rf13, \n # \"RF 10\"=rf10, \n # \"RF 7\"=rf7))\n# Boxplot \npng(filename=\"presentations/CARTModels.png\",width=1000, height=750)\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"CART Models Comparison\")\ndev.off()\n\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"CART Models Comparison\")\n\n#RF Models Comparison -------------------------------------------------------\n\nEresults <- resamples(list( \n \"RF 13\"=rf13, \n \"RF 10\"=rf10, \n \"RF 7\"=rf7))\n# Boxplot \npng(filename=\"presentations/RFModels.png\",width=1000, height=750)\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"RF Models Comparison\")\ndev.off()\n\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"RF Models Comparison\")\n\n\n\n\n#Final Models Comparison -------------------------------------------------------\n\nEresults <- resamples(list( \n \"SVM 13\"=svm13, \n \"NN 10\"=nn10, \n \"NN 7\"=nn7))\n# Boxplot \npng(filename=\"presentations/FinalModels.png\",width=1000, height=750)\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), cex.axis = 3,main=\"Final Models Comparison\")\ndev.off()\n\nbwplot(Eresults, scales=list(cex=1,x=\"free\",tck=c(0,0), y=list(cex=3)), main=\"Final Models Comparison\")\n", "meta": {"hexsha": "f03666d29d53e0e659673fed10499363ec81ba69", "size": 5628, "ext": "r", "lang": "R", "max_stars_repo_path": "src/evaluation.r", "max_stars_repo_name": "tmartins1996/r-binary-classification", "max_stars_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/evaluation.r", "max_issues_repo_name": "tmartins1996/r-binary-classification", "max_issues_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/evaluation.r", "max_forks_repo_name": "tmartins1996/r-binary-classification", "max_forks_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0769230769, "max_line_length": 116, "alphanum_fraction": 0.5058635394, "num_tokens": 1581, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3115601855377086}} {"text": "dir_script <- '2020-08-31-jsa-type-v2/'\r\nsource(paste0(dir_script, \"subset.r\"))\r\n\r\ndir_figs <- \"C:/Users/ejysoh/Dropbox/msc-thesis/research/thesis-amended-v1/figs-tables/\"\r\n\r\n\r\ndf_describers <- get_des()\r\ndf_describers <- df_describers[spp_N_1st_auth_s >=1]\r\n\r\ndim(df_describers)\r\nnames(df_describers)\r\n\r\ndf_describers[, c(\"ns_spp_N\", \"spp_N\", \"syn_spp_N\")]\r\n\r\np1 <- ggplot(df_describers, aes(spp_N)) + \r\n geom_histogram(binwidth=1) +\r\n scale_x_continuous(breaks=seq(0, 100, 10), limits=c(0, 100)) +\r\n theme_minimal() +\r\n xlab(\"\\nNumber of species described (valid species and synonyms)\") +\r\n ylab(\"Number of PTEs\")\r\n\r\ndim(df_describers[spp_N<=10])[1]/dim(df_describers)[1]\r\n\r\nggsave(paste0(dir_figs, 'fig.ch1-hist.png'), p1, units=\"cm\", width=21, height=8, dpi=300)\r\n\r\nage <- df_describers[!is.na(dob.describer), c(\"dob.describer\", \"min\")]\r\nage$age_at_first_pub <- as.integer(age$min) - as.integer(age$dob.describer)\r\np2 <- ggplot(age, aes(age_at_first_pub)) + \r\n geom_histogram(binwidth=1) +\r\n # scale_x_continuous(minbreaks=seq(0, 100, 10), limits=c(0, 100)) +\r\n theme_minimal() +\r\n xlab(\"\\nAge of first species description\") + # includes valid species and synonyms\r\n ylab(\"Number of PTEs\")\r\n\r\nmedian(age$age_at_first_pub)\r\nlength(age$age_at_first_pub)\r\n\r\nggsave(paste0(dir_figs, 'fig.ch1-hist-age.png'), p2, units=\"cm\", width=10, height=8, dpi=300)\r\n\r\nage_at_death <- df_describers[!(is.na(dob.describer) |is.na(dod.describer)), c(\"dob.describer\", \"dod.describer\")]\r\nage_at_death$age <- \r\n as.integer(age_at_death$dod.describer) - as.integer(age_at_death$dob.describer)\r\n\r\n\r\nduration <- df_describers[, c(\"min\", \"max\")]\r\nduration$duration_career <- \r\n as.integer(duration$max) - as.integer(duration$min)\r\n\r\nsummary(duration$duration_career)\r\n\r\np3 <- ggplot(duration, aes(duration_career)) + \r\n geom_histogram(binwidth=1) +\r\n # scale_x_continuous(minbreaks=seq(0, 100, 10), limits=c(0, 100)) +\r\n theme_minimal() +\r\n xlab(\"\\nDuration between first and last species description (years)\") + # includes valid species and synonyms\r\n ylab(\"Number of PTEs\")\r\n\r\nggsave(paste0(dir_figs, 'fig.ch1-hist-career.png'), p3, units=\"cm\", width=21, height=8, dpi=300)\r\n\r\n\r\nduration <- df_describers[!(is.na(dob.describer) |is.na(dod.describer)), c(\"min\", \"max\")]\r\nduration$duration_career <- \r\n as.integer(duration$max) - as.integer(duration$min)\r\n\r\nsummary(duration$duration_career)\r\n\r\np4 <- ggplot(duration, aes(duration_career)) + \r\n geom_histogram(binwidth=1) +\r\n # scale_x_continuous(minbreaks=seq(0, 100, 10), limits=c(0, 100)) +\r\n theme_minimal() +\r\n xlab(\"\\nDuration between first and last species description (years)\") + # includes valid species and synonyms\r\n ylab(\"Number of PTEs\")\r\n\r\nggsave(paste0(dir_figs, 'fig.ch1-hist-career-w-dod.png'), p4, units=\"cm\", width=21, height=8, dpi=300)\r\n", "meta": {"hexsha": "ff688ef3fd76c5239cf50e152809b7836525d869", "size": 2855, "ext": "r", "lang": "R", "max_stars_repo_path": "20211023-thesis-amendments/ch1.r", "max_stars_repo_name": "eunices/masters-thesis-code", "max_stars_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "20211023-thesis-amendments/ch1.r", "max_issues_repo_name": "eunices/masters-thesis-code", "max_issues_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "20211023-thesis-amendments/ch1.r", "max_forks_repo_name": "eunices/masters-thesis-code", "max_forks_repo_head_hexsha": "6eb3a5543880644fbd24305b0670bf7c1b350a3d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.0666666667, "max_line_length": 114, "alphanum_fraction": 0.688966725, "num_tokens": 905, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632979641571, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3114289522404949}} {"text": "rm(list = ls())\n\nlibrary(shiny)\nlibrary(htmlwidgets)\nlibrary(C3)\nlibrary(FrissIntroJSBasic)\nlibrary(plyr)\n\nFrissHeader <- list(\n tags$a(href = \"http://friss.eu/en\",\n tags$img(src=\"friss.svg\", id = \"FrissLogo\")\n ),\n singleton(includeCSS(\"www/friss.css\")),\n singleton(includeScript(\"www/style.js\")),\n singleton(includeCSS(\"www/app.css\"))\n)\n\n# Load help contents\nhelpData <- read.csv(\"help.csv\")\n\n###\n### get some fake data: random walk with drift\n###\n\n# see http://stackoverflow.com/questions/24272862/generate-random-walk-with-drift-and-or-trend-in-r\n\ngetSeries <- function( n = 100, drift = 0.1, walk = 4, scale = 100){\n y <- scale * cumsum(rnorm(n= n, mean = drift, sd=sqrt(walk)))\n return(y + abs(min(y)))\n}\n\n# make sure we get the same data each time\nset.seed(100)\n\n# helpers to center text\nh3c <- function(text){\n h3(text, style = \"text-align:center\")\n}\n\nh4c <- function(text){\n h4(text, style = \"text-align:center\")\n}\n\n\n", "meta": {"hexsha": "898fdfb734ae9c07fba24d3ff25d9f04a2302508", "size": 942, "ext": "r", "lang": "R", "max_stars_repo_path": "tutorials/materials4/C3_demo_IntroJS/global.r", "max_stars_repo_name": "xlhaw/shinyJsTutorials", "max_stars_repo_head_hexsha": "d66c5569a626f90e9d0398b38a41d0c565995378", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 186, "max_stars_repo_stars_event_min_datetime": "2016-02-01T00:44:26.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T09:25:10.000Z", "max_issues_repo_path": "tutorials/materials4/C3_demo_IntroJS/global.r", "max_issues_repo_name": "amrrs/shinyJsTutorials", "max_issues_repo_head_hexsha": "d66c5569a626f90e9d0398b38a41d0c565995378", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 16, "max_issues_repo_issues_event_min_datetime": "2016-05-08T10:26:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-05T08:03:19.000Z", "max_forks_repo_path": "tutorials/materials4/C3_demo_IntroJS/global.r", "max_forks_repo_name": "amrrs/shinyJsTutorials", "max_forks_repo_head_hexsha": "d66c5569a626f90e9d0398b38a41d0c565995378", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 154, "max_forks_repo_forks_event_min_datetime": "2016-05-04T13:59:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-23T11:14:34.000Z", "avg_line_length": 20.9333333333, "max_line_length": 99, "alphanum_fraction": 0.6645435244, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3113992812082249}} {"text": "library(testit)\n\n# Data\ndata <- kornbrot_table1\ndata_long <- reshape(\n data = kornbrot_table1,\n direction = \"long\",\n varying = c(\"placebo\", \"drug\"),\n v.names = c(\"time\"),\n idvar = \"subject\",\n times = c(\"placebo\", \"drug\"),\n timevar = \"treatment\",\n new.row.names = seq_len(prod(length(c(\"placebo\", \"drug\")), nrow(kornbrot_table1)))\n)\ndata_long$subject <- factor(data_long$subject)\ndata_long$treatment <- factor(data_long$treatment, levels = c(\"placebo\", \"drug\"))\n\n#-------------------------------------------------------------------------------\n# Argument checks return errors\n#-------------------------------------------------------------------------------\nassert(\n \"rdt() errors if 'data' is not data.frame.\",\n has_error(rdt(data = 1, formula = placebo ~ drug))\n)\nassert(\n \"rdt() errors if 'formula' is not a formula.\",\n has_error(rdt(data = data, formula = data))\n)\nassert(\n \"rdt() errors if 'zero.method' is incorrect.\",\n has_error(rdt(data = data, formula = placebo ~ drug, zero.method = \"wilcoxon\"))\n)\nassert(\n \"rdt() errors if 'distribution' is incorrect.\",\n has_error(rdt(data = data, formula = placebo ~ drug, distribution = \"exactt\"))\n)\nassert(\n \"rdt() errors if 'alternative' is incorrect.\",\n has_error(rdt(data = data, formula = placebo ~ drug, alternative = \"lesser\"))\n)\nassert(\n \"rdt() errors if variable names not found in 'data'.\",\n has_error(rdt(data = data, formula = placebo ~ drug2))\n)\nassert(\n \"rdt() errors if y~x formula is misformed.\",\n has_error(rdt(data = data, formula = placebo ~ drug + subject))\n)\nassert(\n \"rdt() errors if y~x|block formula has extra variable.\", {\n\n tmp <- data_long\n tmp$xtra <- seq_len(nrow(data_long))\n\n has_error(rdt(data = tmp, formula = time ~ treatment + xtra | subject))\n})\nassert(\n \"rdt() errors if y~x|block formula uses wrong block.\", {\n\n tmp <- data_long\n tmp$xtra <- seq_len(nrow(data_long))\n\n has_error(rdt(data = tmp, formula = time ~ treatment | xtra))\n})\nassert(\n \"rdt() errors if y~x|block formula has more than 2 blocking factors.\", {\n\n tmp <- data_long\n tmp$treatment <- factor(c(\"a\", rep(\"b\", 12), rep(\"c\", 13)))\n\n has_error(rdt(data = tmp, formula = time ~ treatment | treatment))\n})\n\n#-------------------------------------------------------------------------------\n# p-value matches Kornbrot 1990\n#-------------------------------------------------------------------------------\nassert(\n \"rdt() returns expected p-value for wide format data.\", {\n\n res <- rdt(\n data = data,\n formula = placebo ~ drug,\n alternative = \"greater\",\n distribution = \"asymptotic\"\n )\n isTRUE(all.equal(res$p.value, 0.1238529, tolerance = 1e-6))\n})\n\nassert(\n \"rdt() returns expected p-value for transformed wide format data.\", {\n\n res <- rdt(\n data = data,\n formula = I(60/placebo) ~ I(60/drug),\n alternative = \"less\",\n distribution = \"asymptotic\"\n )\n isTRUE(all.equal(res$p.value, 0.1238529, tolerance = 1e-6))\n})\n\nassert(\n \"rdt() returns expected p-value for long format data.\", {\n\n res <- rdt(\n data = data_long,\n formula = time ~ treatment | subject,\n alternative = \"greater\",\n distribution = \"asymptotic\"\n )\n isTRUE(all.equal(res$p.value, 0.1238529, tolerance = 1e-6))\n})\n\nassert(\n \"rdt() returns expected p-value for transformed long format data.\", {\n\n res <- rdt(\n data = data_long,\n formula = I(60/time) ~ treatment | subject,\n alternative = \"less\",\n distribution = \"asymptotic\"\n )\n isTRUE(all.equal(res$p.value, 0.1238529, tolerance = 1e-6))\n})\n\n#-------------------------------------------------------------------------------\n# Blocking handles random rows\n#-------------------------------------------------------------------------------\nassert(\n \"rdt() blocking correctly handles random rows.\", {\n\n data_long2 <- data_long[sample(seq_len(nrow(data_long)), nrow(data_long)), ]\n\n res1 <- rdt(\n data = data_long,\n formula = time ~ treatment | subject\n )\n res2 <- rdt(\n data = data_long2,\n formula = time ~ treatment | subject\n )\n\n isTRUE(all.equal(res1$p.value, res2$p.value))\n})\n\n#-------------------------------------------------------------------------------\n# Return summaries are correct\n# 1. Formula\n# - y ~ x\n# - y ~ x | block\n# 2. Alternative\n# - two.sided\n# - greater\n# - less\n# 3. Distribution\n# - exact\n# - asymptotic\n# - approximate\n# 4. zero.method\n# - Wilcoxon\n# - Pratt\n#-------------------------------------------------------------------------------\nassert(\n \"rdt() returns correct formula.\", {\n\n # Formula\n res <- rdt(\n data = data,\n formula = placebo ~ drug\n )\n res2 <- rdt(\n data = data_long,\n formula = time ~ treatment | subject\n )\n\n res$formula == \"placebo ~ drug\" &\n res2$formula == \"time ~ treatment | subject\"\n})\n\nassert(\n \"rdt() returns correct alternative.\", {\n\n # Formula\n res <- rdt(\n data = data,\n formula = placebo ~ drug,\n alternative = \"two.sided\"\n )\n res2 <- rdt(\n data = data,\n formula = placebo ~ drug,\n alternative = \"greater\"\n )\n res3 <- rdt(\n data = data,\n formula = placebo ~ drug,\n alternative = \"less\"\n )\n\n res$alternative == \"True location shift of ranks (placebo - drug) is not equal to 0\" &\n res2$alternative == \"True location shift of ranks (placebo - drug) is greater than 0\" &\n res3$alternative == \"True location shift of ranks (placebo - drug) is less than 0\"\n})\n\nassert(\n \"rdt() returns correct distribution.\", {\n\n # Formula\n res <- rdt(\n data = data,\n formula = placebo ~ drug,\n distribution = \"exact\"\n )\n res2 <- rdt(\n data = data,\n formula = placebo ~ drug,\n distribution = \"asymptotic\"\n )\n res3 <- rdt(\n data = data,\n formula = placebo ~ drug,\n distribution = \"approximate\"\n )\n\n res$method == \"Kornbrot's Rank Difference Test using the Exact Wilcoxon-Pratt Signed-Rank Test\" &\n res2$method == \"Kornbrot's Rank Difference Test using the Asymptotic Wilcoxon-Pratt Signed-Rank Test\" &\n res3$method == \"Kornbrot's Rank Difference Test using the Approximate Wilcoxon-Pratt Signed-Rank Test\"\n})\n\nassert(\n \"rdt() returns correct zero-difference method.\", {\n\n # Formula\n res <- rdt(\n data = data,\n formula = placebo ~ drug,\n zero.method = \"Wilcoxon\"\n )\n res2 <- rdt(\n data = data,\n formula = placebo ~ drug,\n zero.method = \"Pratt\"\n )\n\n res$method == \"Kornbrot's Rank Difference Test using the Asymptotic Wilcoxon Signed-Rank Test\" &\n res2$method == \"Kornbrot's Rank Difference Test using the Asymptotic Wilcoxon-Pratt Signed-Rank Test\"\n})\n\n", "meta": {"hexsha": "e07402e50ae69362b766d756ec0d3168a3c0f736", "size": 6526, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testit/test-rdt.r", "max_stars_repo_name": "cran/rankdifferencetest", "max_stars_repo_head_hexsha": "f83ee1e13233b0fcd9f72939ceec0b65f95862b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testit/test-rdt.r", "max_issues_repo_name": "cran/rankdifferencetest", "max_issues_repo_head_hexsha": "f83ee1e13233b0fcd9f72939ceec0b65f95862b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/testit/test-rdt.r", "max_forks_repo_name": "cran/rankdifferencetest", "max_forks_repo_head_hexsha": "f83ee1e13233b0fcd9f72939ceec0b65f95862b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.314516129, "max_line_length": 107, "alphanum_fraction": 0.5752375115, "num_tokens": 1731, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.31139928120822485}} {"text": "##CTE534_Gr\u00e1ficos=group\n##Imagen=raster\n##Banda=number 1\n##showplots\nlibrary(ggplot2)\n\nqplot(c(Imagen[[Banda]][]), geom=\"histogram\") + \n xlab(names(Imagen[[Banda]])) + \n ylab(\"Frecuencia\")\n", "meta": {"hexsha": "4d998e49812b80096517c558813dd51ff950bad5", "size": 207, "ext": "rsx", "lang": "R", "max_stars_repo_path": "Rscripts/Histogramas.rsx", "max_stars_repo_name": "klauswiese/QGIS-R", "max_stars_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/Histogramas.rsx", "max_issues_repo_name": "klauswiese/QGIS-R", "max_issues_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/Histogramas.rsx", "max_forks_repo_name": "klauswiese/QGIS-R", "max_forks_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.7, "max_line_length": 48, "alphanum_fraction": 0.6328502415, "num_tokens": 72, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6926419958239132, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.31126821124423737}} {"text": "## 1. Occurrence points ####\n\n## Get occurrence points, set up modelling area\n\nsetwd(\"\")\n\n# sirgas for BR\nsirgas <- \"+proj=poly +lat_0=0 +lon_0=-54 +x_0=5000000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs\"\nwgs <- \"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0\" # as in arcgis\n\nlibrary(sf)\noptions(sf_max.plot=1)\n\n## import points\npcl.all <- read.csv(\"occ/Paraclaravis.csv\", stringsAsFactors = F)\nstr(pcl.all)\n\npcl <- subset(pcl.all, !is.na(Lat) | !is.na(Lon))\nstr(pcl)\nsummary(pcl)\n\nplot(pcl$Lon, pcl$Lat, asp = 1)\n\n# convert to sf\npcl.sf <- st_as_sf(pcl, coords = c(\"Lon\", \"Lat\"), crs = wgs)\n\n# write to shapefile\n# st_write(pcl.sf, \"gis/wgs/pcl.shp\")\n\ntable(cut(pcl$Year, 8))\nbarplot(table(cut(pcl$Year, 8)))\n\ngetData(\"ISO3\")\n\ncty <- c(\"PRY\", \"BRA\", \"ARG\")\ncty1List <- list()\ncty0List <- list()\n\nfor(i in seq_along(cty)){\n \n cty1.sp <- raster::getData(\"GADM\", country = cty[i], path = \"gis/wgs\", level = 1)\n cty0.sp <- raster::getData(\"GADM\", country = cty[i], path = \"gis/wgs\", level = 0)\n \n cty1 <- st_as_sf(cty1.sp)\n cty0 <- st_as_sf(cty0.sp)\n \n cty1 <- st_transform(cty1, wgs)\n cty0 <- st_transform(cty0, wgs)\n \n cty1List[[i]] <- cty1\n cty0List[[i]] <- cty0\n \n rm(cty1.sp, cty0.sp, cty1, cty0)\n \n}\n\ncty1 <- do.call(rbind, cty1List)\ncty0 <- do.call(rbind, cty0List)\n\ncty1.sir <- st_transform(cty1, sirgas)\ncty0.sir <- st_transform(cty0, sirgas)\n\nrm(cty1List, cty0List, i, cty1, cty0)\n\ncty0\n\nplot(st_geometry(cty0), col = NA)\nplot(st_geometry(pcl.sf), col = \"darkred\", pch= 20, add = T)\n\nplot(st_geometry(pcl.sf), col = \"darkred\", pch= 20, add = F)\nplot(st_geometry(cty0), col = NA, add = T)\n\n\n## Make sure all points are within ecoregion, country, etc\n## convert all to sirgas (check UTM cells if applicable)\n\npcl.sir <- st_transform(pcl.sf, sirgas)\n\n# Make model area\n# import BL shapefile\n## Downloaded from IUCN - requires permission from Birdlife\nbl <- st_read(\"gis/wgs/claravis_geof_BL.shp\")\nbl.sirgas <- st_transform(bl, sirgas)\n# plot(st_geometry(bl), add = T)\n\n# buffer around unioned points\npcl.buff <- st_buffer(st_convex_hull(st_union(pcl.sir)), dist = 50000)\n\nplot(st_geometry(pcl.sir), col = \"darkred\", pch= 20, add = F)\nplot(st_geometry(cty0.sir), col = NA, add = T)\nplot(st_geometry(pcl.buff), col = NA, add = T)\n\nplot(st_geometry(bl.sirgas), border = \"darkgreen\", add = T)\n\n## add to BL extnte (union BL first)\naoi <- st_buffer(st_convex_hull(st_union(st_union(bl.sirgas), pcl.buff)), 5000)\n\n## get extents form pcl.buff for aoi\naoi\nplot(aoi, add = T, border = \"blue\")\n\nst_bbox((aoi))\n\naoi.ext <- adjExt(st_bbox(aoi), d = 1000, outF = \"Extent\")\n\nr <- raster(aoi.ext, crs = sirgas, res = 1000)\nr\n\nr[] <- 1\n\nmsk.aoi <- raster::mask(r, st_as_sf(aoi))\nplot(msk.aoi)\n\nsave(pcl.sir, msk.aoi, cty1.sir, cty0.sir, bl.sirgas, file = \"gis/occ_aoi.rdata\")\n\n## EOO in different time bands\nload(\"gis/occ_aoi.rdata\")\n\nch_all <- st_convex_hull(st_union(pcl.sir))\n\nst_area(ch_all) / 1000000\n# 834,642.8 # km2\n\nch_1990 <- st_convex_hull(st_union(subset(pcl.sir, Year >= 1990)))\nst_area(ch_1990) /1000000\n# 277,650 km2\nsum(pcl.sir$Year >= 1990) # 14\n\nch_2005 <- st_convex_hull(st_union(subset(pcl.sir, Year >= 2005)))\nst_area(ch_2005) / 1000000\n# 95,045.97 km\nsum(pcl.sir$Year >= 2005) # 6\n\nst_write(ch_all, \"gis/s_sir/ch_all.shp\")\nst_write(ch_2005, \"gis/s_sir/ch_2005.shp\", delete_layer = T)\nst_write(ch_1990, \"gis/s_sir/ch_1990.shp\", delete_layer = T)\n\n\n## Absence data... \n## from ebird_zeros_v3_2020_pcl.r\nload(\"occ/ebird/ebird_absences.rdata\") # ebd.zf, ebird.data, ebird.a\nrm(ebd.zf)\n# ebird.a is sf object of just PWGD absences\n\n## cut to study areas\nmsk.aoi\nplot(msk.aoi)\naoi.Ind <- raster::extract(msk.aoi, ebird.a)\ntable(aoi.Ind, useNA = \"always\")\nhead(aoi.Ind==1)\n\n# filter just in study area\nebird.aoi <- ebird.a[!is.na(aoi.Ind),]\n\nplot(ebird.aoi, add = T, pch = \".\")\n\nsave(ebird.aoi, file = \"ebirdAbsences_in_aoi.rdata\")\n\nst_write(ebird.aoi, \"gis/s_sir/absences_aoi.shp\", delete_layer = TRUE)\n\nbarplot(table(ebird.aoi$year), las = 2)\n\ntable(cut(ebird.aoi$year, breaks = c(0, 1990, 2000, 2010, 2020)))\n", "meta": {"hexsha": "611fff23431255d7b3ac060297b95ca8727bc83d", "size": 4076, "ext": "r", "lang": "R", "max_stars_repo_path": "code/s1_occurrence_pts_v1.r", "max_stars_repo_name": "Cdevenish/Paraclaravis_project", "max_stars_repo_head_hexsha": "c9c869de39090864775f62feef801957f2ca6249", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/s1_occurrence_pts_v1.r", "max_issues_repo_name": "Cdevenish/Paraclaravis_project", "max_issues_repo_head_hexsha": "c9c869de39090864775f62feef801957f2ca6249", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/s1_occurrence_pts_v1.r", "max_forks_repo_name": "Cdevenish/Paraclaravis_project", "max_forks_repo_head_hexsha": "c9c869de39090864775f62feef801957f2ca6249", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.8536585366, "max_line_length": 123, "alphanum_fraction": 0.6763984298, "num_tokens": 1572, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.3112603118600591}} {"text": "# install.packages(\"devtools\", dependencies = TRUE, INSTALL_opts = '--no-lock')\n# install.packages(\"rjson\")\n# library(devtools)\n\n# if(\"devtools\" %in% rownames(installed.packages()) == FALSE) {\n# install.packages(\"devtools\", dependencies = TRUE, INSTALL_opts = '--no-lock')\n# }\n# library(\"devtools\")\n#if(\"SPEI\" %in% rownames(installed.packages()) == FALSE) {\n# install.packages('SPEI', dependencies = TRUE, INSTALL_opts = '--no-lock')\n#}\nlibrary(\"SPEI\")\n#if(\"rjson\" %in% rownames(installed.packages()) == FALSE) {\n# install.packages(\"rjson\", dependencies = TRUE, INSTALL_opts = '--no-lock')\n#}\nlibrary(\"rjson\")\n\nargs <- commandArgs(trailingOnly = TRUE)\nname <- args[1]\nlat <- as.numeric(args[2])\n\nrcp <- c(\"rcp26\", \"rcp45\", \"rcp85\")\nyears <- c(2050, 2100)\nspeiData <- data.frame(matrix(ncol = 7, nrow = 60))\ncolnames(speiData) <- c(\"date\", paste0(\"rcp26\", \"_\", years), paste0(\"rcp45\", \"_\", years), paste0(\"rcp85\", \"_\", years))\nspeiData[\"date\"] <- format(seq(as.Date(\"2046-01-01\"), as.Date(\"2050-12-01\"), \"month\"), \"%Y-%m\")\n\n# Calculate potential evapotranspiration using penman for a combined time frame \n# between 2046 - 2050 and 2096 - 2100\nfor(r in rcp) {\n for(year in years) {\n data <- fromJSON(file = file.path(\"./portfolio/climate_risk_dash/data/temp\", paste0(name, \"_\", r, \"_\", year, \".json\")))\n pen <- penman(Tmin = data$tasmin, Tmax = data$tasmax, U2 = data$sfcWind, lat = lat, Rs = data$rsds, RH = data$hurs, P = data$ps)\n p <- data$pr - pen\n speiData[paste0(r, \"_\", year)] <- spei(p, 12)$fitted\n print(year)\n }\n}\nwrite.csv(speiData, file = file.path(\"./portfolio/climate_risk_dash/report\", paste0(name, \"_speiData.csv\")))\n", "meta": {"hexsha": "770804166f5f14be41bc3e51bb089a95f584e7d7", "size": 1683, "ext": "r", "lang": "R", "max_stars_repo_path": "portfolio/climate_risk_dash/spei.r", "max_stars_repo_name": "ericjwei/portfolio", "max_stars_repo_head_hexsha": "7aa8560d322f28333f68a1b32be32609974f3aa9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "portfolio/climate_risk_dash/spei.r", "max_issues_repo_name": "ericjwei/portfolio", "max_issues_repo_head_hexsha": "7aa8560d322f28333f68a1b32be32609974f3aa9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "portfolio/climate_risk_dash/spei.r", "max_forks_repo_name": "ericjwei/portfolio", "max_forks_repo_head_hexsha": "7aa8560d322f28333f68a1b32be32609974f3aa9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.075, "max_line_length": 136, "alphanum_fraction": 0.6405228758, "num_tokens": 531, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.31126030402151633}} {"text": "source('https://raw.githubusercontent.com/laurencelin/Date_analysis/master/LIB_dailytimeseries3.R')\nsource('https://raw.githubusercontent.com/laurencelin/Date_analysis/master/LIB_misc.r')\nsource('https://raw.githubusercontent.com/laurencelin/R-coded-scripts-for-RHESSys-calibration/master/LIB_RHESSys_modelBehavior7.R')\nsource('https://raw.githubusercontent.com/laurencelin/R-coded-scripts-for-RHESSys-calibration/master/LIB_RHESSys_modelFittness7.R')\nsource('https://raw.githubusercontent.com/laurencelin/R-coded-scripts-for-RHESSys-calibration/master/LIB_RHESSys_modelPlot_7.r')\nlibrary(MASS)\n\n\n\n## -------------------------------------------------------------------------------------------------------------------------------------------------------------\narg=commandArgs(T)\n\n\n\n## -------------------------------------------------------------------------------------------------------------------------------------------------------------\n\nstartingDate=as.Date(\"1990-10-1\") #, \nendingDate=as.Date(\"2011-9-30\") #, \nperiod=seq.Date(from=startingDate, to=endingDate ,by=\"day\") \n\ncalobs_ = read.csv(paste('path',sep=\"\"),stringsAsFactors=F);\ncalobsNonZero = sapply(calobs_[,'mmd'],can.be.numeric) & !is.na(calobs_[,'mmd']) & calobs_[,'mmd']> 0 & sapply(calobs_[,'mmd'],can.be.numeric); \ncalobs = calobs_[calobsNonZero,]\ncalobs.date0 = as.Date(paste(calobs$day, calobs$month, calobs$year,sep=\"-\"),format=\"%d-%m-%Y\")\n\n\nrhessys_SingleFile = read.table(\"path\",header=T,skip=0,sep=' ')\n\n\t\n\trhessys_SingleFile.date=as.Date(paste(rhessys_SingleFile$day, rhessys_SingleFile$month, rhessys_SingleFile$year,sep=\"-\"),format=\"%d-%m-%Y\")\n\tplotTime = intersectDate(list(rhessys_SingleFile.date, calobs.date0, period)) ## \"2010-10-01\" \"2017-09-30\"\n\trhessys.dtsm = match(plotTime, rhessys_SingleFile.date)\n\tcalobs.dtsm = match(plotTime, calobs.date0)\n\tDTStable = dailyTimeSeries(plotTime)\n\tmatchYears = range(DTStable$wy)\n\t\n\tw = modelFittness( as.numeric(calobs[calobs.dtsm,'mmd']), rhessys_SingleFile[rhessys.dtsm,], DTStable);\n\tround(w$FittnessList,2)\n\n\t##---------------------------------------- Diagnosis\n\tdev.new(width=14, height=8)\n\tlayout(matrix(1:4,nrow=4)); \n\tpar(mar=c(0,3,1,3))\n\tplot(plotTime, rhessys_SingleFile[rhessys.dtsm,'precip'], col='lightblue', lwd=2,type='l', ylab='',xaxt='n',bty='n', ylim=rev(range(rhessys_SingleFile[rhessys.dtsm,'precip'])))\n\tabline(h=0)\n\tpar(new = TRUE)\n\tplot(plotTime, rhessys_SingleFile[rhessys.dtsm,'tmin'], col=t_col('lightblue1'), bty='l',lty=2, type='l', ylim=c(-20,40), yaxt='n', bty='n',xaxt='n')\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'tmax'], col= t_col('lightpink'), lty=2)\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'tavg'], col= t_col('gray'))\n\tabline(h=0, lty=2, col='gray')\n\taxis(4)\n\tpar(mar=c(0,3,0,3))\n\tupper = max(rhessys_SingleFile[rhessys.dtsm,'streamflow'],calobs[calobs.dtsm,'mmd'])\n\tlower = min(rhessys_SingleFile[rhessys.dtsm,'streamflow'],calobs[calobs.dtsm,'mmd'])\n\tplot(plotTime, rhessys_SingleFile[rhessys.dtsm,'streamflow'], col='blue', lwd=2,type='l', ylab='', ylim=c(lower ,upper),bty='n',xaxt='n')\n\tlines(plotTime, calobs[calobs.dtsm,'mmd'], col='red')\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'pet'], col='gray')\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'evap']+rhessys_SingleFile[rhessys.dtsm,'trans'], col='green')\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'X.sat_area'], lty=2,col=gray(0.8))\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'snowpack'],col='lightblue')\n\tpar(mar=c(0,3,0,3))\n\tplot(plotTime, rhessys_SingleFile[rhessys.dtsm,'sat_def_z'],type='l', ylab='',xaxt='n',bty='n', ylim=rev(range(rhessys_SingleFile[rhessys.dtsm,'sat_def_z'])))\n\tpar(mar=c(3,3,0,3))\n\tupper = max(rhessys_SingleFile[rhessys.dtsm,'streamflow'],calobs[calobs.dtsm,'mmd'])\n\tlower = min(rhessys_SingleFile[rhessys.dtsm,'streamflow'],calobs[calobs.dtsm,'mmd'])\n\tplot(plotTime, calobs[calobs.dtsm,'mmd'], col='red', type='l',log='y',bty='l', ylim=c(lower ,upper))\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'streamflow'], col='blue' )\n\tlines(plotTime, rhessys_SingleFile[rhessys.dtsm,'baseflow'], col='darkblue',lty=2 )\n\t##---------------------------------------- \n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n", "meta": {"hexsha": "8c13b3bcc0e3ffed102096863e41c8ff3b31a4bc", "size": 4169, "ext": "r", "lang": "R", "max_stars_repo_path": "template_RHESSys_fittness_quick.r", "max_stars_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_stars_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-04-25T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-25T22:08:38.000Z", "max_issues_repo_path": "template_RHESSys_fittness_quick.r", "max_issues_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_issues_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "template_RHESSys_fittness_quick.r", "max_forks_repo_name": "laurencelin/R-coded-scripts-for-RHESSys-calibration", "max_forks_repo_head_hexsha": "add43866727b54e502aa18b226624dac5dd6ea3f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-06-14T13:11:57.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-05T17:07:12.000Z", "avg_line_length": 53.4487179487, "max_line_length": 177, "alphanum_fraction": 0.663228592, "num_tokens": 1393, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3112296656009273}} {"text": "##CTE534_Composite=group\n##Imagen=raster\n##Azul=number 1\n##Verde=number 2\n##Rojo=number 3\n##Stretch=selection linear;histograma\n##showplots\nRGB <- raster::stack(Imagen[[Rojo]], Imagen[[Verde]], Imagen[[Azul]])\nif (Stretch == 0) {\nraster::plotRGB(RGB, axes =TRUE, stretch = \"lin\", main = \"Composici\u00f3n en Color Verdadero\")\nbox()\ngrid(lwd=1, col=\"black\")\n} else if (Stretch == 1) {\nraster::plotRGB(RGB, axes =TRUE, stretch = \"hist\", main = \"Composici\u00f3n en Color Verdadero\")\nbox()\ngrid(lwd=1, col=\"black\")\n}\n", "meta": {"hexsha": "79baa6fc329255d5cbf1d16894ecd548b9049125", "size": 504, "ext": "rsx", "lang": "R", "max_stars_repo_path": "Rscripts/ColorVerdadero.rsx", "max_stars_repo_name": "klauswiese/QGIS-R", "max_stars_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/ColorVerdadero.rsx", "max_issues_repo_name": "klauswiese/QGIS-R", "max_issues_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/ColorVerdadero.rsx", "max_forks_repo_name": "klauswiese/QGIS-R", "max_forks_repo_head_hexsha": "d4da3aa782427c082c82299b7374dfc79843f019", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0, "max_line_length": 91, "alphanum_fraction": 0.6865079365, "num_tokens": 174, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31122966560092724}} {"text": "# plotting half-deep intervals and depth along an assembly\n\nread_scaffold_lengths <- function(lengthsFilename,scaffoldsOfInterest=NULL)\n\t#\n\t# Read a file containing scaffold names and lengths. Result is either\n\t# (a) reduced to scaffolds of interest, in order or (b) sorted by\n\t# decreasing length. A column is added, giving offsets for which the\n\t# scaffolds can be arranged along a number line.\n\t#\n\t# typical input:\n\t#\tscaffold_100_arrow_ctg1 29829\n\t#\tscaffold_101_arrow_ctg1 29808\n\t#\tscaffold_102_arrow_ctg1 28782\n\t#\tscaffold_103_arrow_ctg1 27584\n\t#\tscaffold_104_arrow_ctg1 27050\n\t# ...\n\t#\n\t# returns, e.g.\n\t#\t name length offset\n\t#\t super_scaffold_1 215872496 0\n\t#\t super_scaffold_2 165051286 215872496\n\t#\tscaffold_2_arrow_ctg1 125510139 380923782\n\t#\t super_scaffold_Z 84526827 506433921\n\t#\tscaffold_5_arrow_ctg1 82438829 590960748\n\t# ...\n\t#\n\t{\n\tscaffolds = read.table(lengthsFilename,header=F,colClasses=c(\"character\",\"integer\"))\n\tcolnames(scaffolds) <- c(\"name\",\"length\")\n\n\tif (!is.null(scaffoldsOfInterest))\n\t\t{\n\t\t# pick ordered subset\n\t\tscaffolds = scaffolds[scaffolds$name %in% scaffoldsOfInterest,]\n\t\tscaffoldsToNumber = 1:length(scaffoldsOfInterest)\n\t\tnames(scaffoldsToNumber) = scaffoldsOfInterest\n\t\tscaffolds = scaffolds[order(scaffoldsToNumber[scaffolds$name]),]\n\t\t}\n\telse\n\t\t{\n\t\t# sort by decreasing length\n\t\tscaffolds = scaffolds[order(-scaffolds$length),]\n\t\t}\n\n\tscaffolds[,\"offset\"] = rev(sum(as.numeric(scaffolds$length))-cumsum(rev(as.numeric(scaffolds$length))))\n\n\tscaffolds;\n\t}\n\n\nlinearized_scaffolds <- function(scaffolds)\n\t#\n\t# Create scaffold-to-number-line mapping; input is of the form returned by\n\t# read_scaffold_lengths().\n\t#\n\t{\n\tscaffoldToOffset <- scaffolds$offset\n\tnames(scaffoldToOffset) <- scaffolds$name\n\n\tscaffoldToOffset\n\t}\n\n\nread_depth <- function(depthFilename,scaffoldToOffset=NULL)\n\t#\n\t# Read a file containing coverage depth. Typically the depth has been\n\t# averaged over non-overlapping windows, with each window represented by\n\t# a single location. Genomic locations are origin-1, and intervals are\n\t# closed.\n\t#\n\t# A scaffold-to-offset can be provided, as would be produced by\n\t# linearized_scaffolds(). Two columns are added, converting each interval\n\t# to positions along a number line.\n\t#\n\t# typical input:\n\t#\tscaffold_100_arrow_ctg1 1 1 16.727\n\t#\tscaffold_100_arrow_ctg1 1001 1001 19.365\n\t#\tscaffold_100_arrow_ctg1 2001 2001 20.701\n\t#\tscaffold_100_arrow_ctg1 3001 3001 22.764\n\t#\tscaffold_100_arrow_ctg1 4001 4001 20.971\n\t# ...\n\t#\n\t# returns, e.g.\n\t#\t scaffold start end depth s e\n\t#\tscaffold_100_arrow_ctg1 1 1 16.727 1177990031 1177990031\n\t#\tscaffold_100_arrow_ctg1 1001 1001 19.365 1177991031 1177991031\n\t#\tscaffold_100_arrow_ctg1 2001 2001 20.701 1177992031 1177992031\n\t#\tscaffold_100_arrow_ctg1 3001 3001 22.764 1177993031 1177993031\n\t#\tscaffold_100_arrow_ctg1 4001 4001 20.971 1177994031 1177994031\n\t# ...\n\t#\n\t{\n\tdepth = read.table(depthFilename,header=F,colClasses=c(\"character\",\"integer\",\"integer\",\"numeric\"))\n\tcolnames(depth) <- c(\"scaffold\",\"start\",\"end\",\"depth\")\n\n\tif (!is.null(scaffoldToOffset))\n\t\t{\n\t\tdepth = depth[depth$scaffold %in% names(scaffoldToOffset),]\n\t\tdepth[,\"s\"] = scaffoldToOffset[depth$scaffold] + depth$start\n\t\tdepth[,\"e\"] = scaffoldToOffset[depth$scaffold] + depth$end\n\t\t}\n\n\tdepth\n\t}\n\n\nread_halfdeep <- function(halfDeepFilename,scaffoldToOffset=NULL)\n\t#\n\t# Read a file containing a list of genomic intervals. The intervals are\n\t# origin-1 and closed.\n\t#\n\t# A scaffold-to-offset can be provided, as would be produced by\n\t# linearized_scaffolds(). Two columns are added, converting each interval\n\t# to positions along a number line.\n\t#\n\t# typical input:\n\t#\tscaffold_100_arrow_ctg1 9001 10000\n\t#\tscaffold_101_arrow_ctg1 12001 14000\n\t#\tscaffold_103_arrow_ctg1 2001 14000\n\t#\tscaffold_106_arrow_ctg1 1 3000\n\t#\tscaffold_106_arrow_ctg1 19001 20000\n\t# ...\n\t#\n\t# returns, e.g.\n\t#\t scaffold start end s e\n\t#\tscaffold_100_arrow_ctg1 9001 10000 1177999031 1178000030\n\t#\tscaffold_101_arrow_ctg1 12001 14000 1178031860 1178033859\n\t#\tscaffold_103_arrow_ctg1 2001 14000 1178110244 1178122243\n\t#\tscaffold_106_arrow_ctg1 1 3000 1178189381 1178192380\n\t#\tscaffold_106_arrow_ctg1 19001 20000 1178208381 1178209380\n\t# ...\n\t#\n\t{\n\thalfDeep = read.table(halfDeepFilename,header=F,colClasses=c(\"character\",\"integer\",\"integer\"))\n\tcolnames(halfDeep) <- c(\"scaffold\",\"start\",\"end\")\n\tif (!is.null(scaffoldToOffset))\n\t\t{\n\t\thalfDeep = halfDeep[halfDeep$scaffold %in% names(scaffoldToOffset),]\n\t\thalfDeep[,\"s\"] = scaffoldToOffset[halfDeep$scaffold] + halfDeep$start\n\t\thalfDeep[,\"e\"] = scaffoldToOffset[halfDeep$scaffold] + halfDeep$end\n\t\t}\n\t\n\thalfDeep\n\t}\n\n\nread_percentiles <- function(percentilesFilename)\n\t#\n\t# Extract percentile values from a shell script.\n\t#\n\t# typical input:\n\t#\texport percentile40=52.698\n\t#\texport percentile50=54.818\n\t#\texport percentile60=56.938\n\t#\texport halfPercentile40=26.349\n\t#\texport halfPercentile50=27.409\n\t#\texport halfPercentile60=28.469\n\t#\n\t# returns, e.g.\n\t#\t percentile40 percentile50 percentile60\n\t#\t 52.698 54.818 56.938\n\t#\t halfPercentile40 halfPercentile50 halfPercentile60 \n\t#\t 26.349 27.409 28.469 \n\t#\n\t{\n\tpercentilesCommand = paste(\"cat\",percentilesFilename,'| sed \"s/^export //\" | tr \"=\" \" \"')\n\tpercentiles = read.table(pipe(percentilesCommand),header=F,colClasses=c(\"character\",\"numeric\"))\n\tcolnames(percentiles) <- c(\"name\",\"value\")\n\tpercentileToValue <- percentiles$value\n\tnames(percentileToValue) <- percentiles$name\n\n\tpercentileToValue\n\t}\n\n\nhalfdeep_plot <- function(scaffolds,depth,halfDeep,percentileToValue,\n assemblyName=\"\",\n scaffoldsToPlot=NULL,\n plotFilename=NULL,\n tickSpacing=10000000,\n width=17,height=7,pointsize=18,\n yLabelSpace=7,\n maxDepth=NA,\n scaffoldInterval=NULL)\n\t#\n\t# Plot half-deep intervals and depth along an assembly\n\t#\n\t# scaffolds: As returned by read_scaffold_lengths().\n\t# depth: As returned by read_depth().\n\t# halfDeep: As returned by read_halfdeep(). If this is NULL,\n\t# no half-deep information is displayed.\n\t# percentileToValue: As returned by read_percentiles()\n\t# assemblyName: Name of the assembly. This contributes to the\n\t# plain title, and can contribute to the plot\n\t# file name.\n\t# Example: \"bAlcTor1.pri.cur.20190613\"\n\t# scaffoldsToPlot=NULL: A vector of scaffolds to restrict the plot to.\n\t# By default, everything in scaffolds[] is\n\t# plotted.\n\t# plotFilename=NULL: File to plot the data to. If this is NULL, the\n\t# data is plotted on the screen.\n\t# tickSpacing: Spacing of evenly-spaced ticks along the\n\t# horizontal axis. If this is zero, these ticks\n\t# are inhibited. The default is 10Mbp.\n\t# width,height,pointsize: These are passed through to whatever function\n\t# creates the plot window.\n\t# yLabelSpace: Space below the plot. This can be increased to\n\t# accommodate longer scaffold names.\n\t# maxDepth: Depth greater than this is not shown in the plot;\n\t# i.e. the vertical axis stops at this value. By\n\t# default this is 1.5*median depth.\n\t# scaffoldInterval subinterval to restrict the plot to. Typically\n\t# this would only be used when only one scaffold\n\t# is to be plotted. This is a (start,end) pair,\n\t# origin-zero, half-open.\n\t#\n\t{\n\t# if we have a scaffold subset, reduce our copy of the data to that subset\n\n\tif (!is.null(scaffoldInterval))\n\t\tstop(\"scaffoldInterval is not implemented yet\")\n\n\tshowHalfDeep = !is.null(halfDeep)\n\n\tif (!is.null(scaffoldsToPlot))\n\t\t{\n\t\t# validate the names in the subset\n\n\t\tbadNames = scaffoldsToPlot[!(scaffoldsToPlot %in% scaffolds$name)]\n\t\tif (length(badNames) > 0)\n\t\t\tstop(paste(\"bad scaffold name(s):\",paste(scaffoldsToPlot,collapse=\", \")))\n\n\t\t# pick ordered subset\n\t\tscaffolds = scaffolds[scaffolds$name %in% scaffoldsToPlot,]\n\t\tscaffoldsToNumber = 1:length(scaffoldsToPlot)\n\t\tnames(scaffoldsToNumber) = scaffoldsToPlot\n\t\tscaffolds = scaffolds[order(scaffoldsToNumber[scaffolds$name]),]\n\t\tscaffolds[,\"offset\"] = rev(sum(as.numeric(scaffolds$length))-cumsum(rev(as.numeric(scaffolds$length))))\n\t\tscaffoldToOffset = linearized_scaffolds(scaffolds)\n\n\t\t# reduce to ordered subset\n\t\tdepth = depth[depth$scaffold %in% names(scaffoldToOffset),]\n\t\tdepth[,\"s\"] = scaffoldToOffset[depth$scaffold] + depth$start\n\t\tdepth[,\"e\"] = scaffoldToOffset[depth$scaffold] + depth$end\n\n\t\tif (showHalfDeep)\n\t\t\t{\n\t\t\thalfDeep = halfDeep[halfDeep$scaffold %in% names(scaffoldToOffset),]\n\t\t\thalfDeep[,\"s\"] = scaffoldToOffset[halfDeep$scaffold] + halfDeep$start\n\t\t\thalfDeep[,\"e\"] = scaffoldToOffset[halfDeep$scaffold] + halfDeep$end\n\t\t\t}\n\t\t}\n\n\t# fetch percentile values (used only for drawing and labeling)\n\n\tdepth50 = percentileToValue[\"percentile50\"]\n\thalfDepthLo = percentileToValue[\"halfPercentile40\"]\n\thalfDepthHi = percentileToValue[\"halfPercentile60\"]\n\tdepthClip = ifelse(is.na(maxDepth),1.5*depth50,maxDepth)\n\tdepth50Str = sprintf(\"%.1f\",depth50)\n\thalfDepthLoStr = sprintf(\"%.1f\",halfDepthLo)\n\thalfDepthHiStr = sprintf(\"%.1f\",halfDepthHi)\n\tdepthClipStr = sprintf(\"%.1f\",depthClip)\n\n\t# (housekeeping)\n\n\tscaffoldTicks = 1 + c(scaffolds$offset,sum(as.numeric(scaffolds$length)))\n\tscaffoldCenters = (as.numeric(scaffoldTicks[1:nrow(scaffolds)])+as.numeric(scaffoldTicks[2:(nrow(scaffolds)+1)])) / 2\n\tif (showHalfDeep)\n\t\thalfDeepCenters = (as.numeric(halfDeep$s)+as.numeric(halfDeep$e))/2\n\n\txlim = c(1,max(scaffoldTicks))\n\tylim = c(0,depthClip)\n\n\tdepthColor = if (showHalfDeep) rgb(.6,.6,.6) else \"black\"\n\thalfDepthColor = \"black\"\n\thalfDeepMarkerColor = \"red\"\n\tdepthLimitsColor = \"blue\"\n\n\tguns = col2rgb(halfDeepMarkerColor) / 255\n\thalfDeepOverlayColor = rgb(guns[1],guns[2],guns[3],alpha=.3)\n\n\t# open plot window or file\n\n\tturnDeviceOff = F\n\tif (is.null(plotFilename))\n\t\t{\n\t\tquartz(width=width,height=height)\n\t\t}\n\telse\n\t\t{\n\t\tprint(paste(\"drawing to\",plotFilename))\n\t\tpdf(file=plotFilename,width=width,height=height,pointsize=pointsize)\n\t\tturnDeviceOff = T\n\t\t}\n\n\t# create empty plot\n\n\tif (showHalfDeep)\n\t\t{\n\t\ttitle = paste(\"half-deep intervals (red overlay) in \",assemblyName,\n\t\t \"\\nmedian=\",depth50Str,\n\t\t \" 40%ile/2=\",halfDepthLoStr,\n\t\t \" 60%ile/2=\",halfDepthHiStr,\n\t\t sep=\"\")\n\t\tylab = paste(\"aligned read depth in 1Kbp windows (gray, clipped at \",depthClipStr,\")\",sep=\"\")\n\t\t}\n\telse\n\t\t{\n\t\ttitle = paste(\"coverage depth in \",assemblyName,\"\\nmedian=\",depth50Str,sep=\"\")\n\t\tylab = paste(\"aligned read depth in 1Kbp windows (clipped at \",depthClipStr,\")\",sep=\"\")\n\t\t}\n\n\tpar(mar=c(yLabelSpace,4,2.5,0.2)+0.1) # BLTR\n\toptions(scipen=10)\n\tplot(NA,xlim=xlim,ylim=ylim,main=title,xaxt=\"n\",xlab=\"\",ylab=ylab)\n\n\t# add horizontal axis\n\n\tif ((showHalfDeep) && (nrow(halfDeep) > 0))\n\t\t{\n\t\taxis(1,at=halfDeepCenters,labels=F,line=-0.5,col=halfDeepMarkerColor) # interval markers\n\t\taxis(1,at=c(-0.2*max(scaffoldTicks),1.2*max(scaffoldTicks)),labels=F,line=-0.5,col=\"white\") # erase unwanted horizonal 'axis'\n\t\t}\n\n\tif ((tickSpacing > 0) & (xlim[2]>=tickSpacing)) # equal-spaced ticks\n\t\taxis(1,at=seq(tickSpacing,xlim[2],by=tickSpacing),labels=F,col=\"gray\")\n\taxis(1,at=scaffoldTicks,labels=F,tck=-0.04) # scaffold ticks\n\taxis(1,at=scaffoldCenters,tick=F,labels=scaffolds$name, # scaffold labels\n\t\t las=2,cex.axis=0.7)\n\n\t# draw depth as gray, and depth in half-deep intervals as black\n\n\tpoints(depth$s,depth$depth,col=depthColor,pch=19,cex=0.3)\n\n\tif (showHalfDeep)\n\t\t{\n\t\thalfsies = (depth$depth>=halfDepthLo) & (depth$depth<=halfDepthHi)\n\t\tpoints(depth$s[halfsies],depth$depth[halfsies],col=halfDepthColor,pch=19,cex=0.1)\n\t\t}\n\n\t# draw half-deep intervals as red overlay rectangles; note that R often\n\t# does a poor job at filling narrow rectangles, so we also draw each\n\t# rectangle as a centered line; and, because the depth plot will overshoot\n\t# the upper limit, we multiply that limit by 1.2 here\n\n\tif ((showHalfDeep) && (nrow(halfDeep) > 0))\n\t\t{\n\t\trect(halfDeep$s,ylim[1],halfDeep$e,ylim[2]*1.2,border=NA,col=halfDeepOverlayColor) # LBRT\n\n\t\thalfDeepCentersX = matrix(nrow=3,ncol=length(halfDeepCenters))\n\t\thalfDeepCentersX[1,] = halfDeepCenters\n\t\thalfDeepCentersX[2,] = halfDeepCenters\n\t\thalfDeepCentersX[3,] = NA\n\t\tdim(halfDeepCentersX) = NULL\n\t\thalfDeepCentersY = matrix(nrow=3,ncol=length(halfDeepCenters))\n\t\thalfDeepCentersY[1,] = ylim[1]\n\t\thalfDeepCentersY[2,] = ylim[2]*1.2\n\t\thalfDeepCentersY[3,] = NA\n\t\tdim(halfDeepCentersY) = NULL\n\t\tlines(halfDeepCentersX,halfDeepCentersY,col=halfDeepOverlayColor)\n\t\t}\n\n\t# add horizontal lines to show median and half-deep limits\n\n\tlines(xlim,c(depth50,depth50),col=depthLimitsColor,lwd=2,lty=2)\n\tif (showHalfDeep)\n\t\t{\n\t\tlines(xlim,c(halfDepthHi,halfDepthHi),col=depthLimitsColor,lwd=2,lty=2)\n\t\tlines(xlim,c(halfDepthLo,halfDepthLo),col=depthLimitsColor,lwd=2,lty=2)\n\t\t}\n\n\ttext(0,depth50,\"median \",adj=1,cex=0.7,col=depthLimitsColor)\n\tif (showHalfDeep)\n\t\t{\n\t\ttext(0,halfDepthLo,\"half-40th \",adj=1,cex=0.7,col=depthLimitsColor)\n\t\ttext(0,halfDepthHi,\"half-60th \",adj=1,cex=0.7,col=depthLimitsColor)\n\t\t}\n\n\t# close the plot\n\n\tif (turnDeviceOff) dev.off()\n\t}\n\n\nhalfdeep_read_and_plot <- function(lengthsFilename,depthFilename,halfDeepFilename,\n percentilesFilename,\n plotFilenameTemplate=NULL,\n tickSpacing=10000000,\n width=17,height=7,pointsize=18,\n yLabelSpace=7)\n\t#\n\t# Plot half-deep intervals and depth, for several assemblies\n\t#\n\t{\n\tscaffolds = read_scaffold_lengths(lengthsFilename)\n\tscaffoldToOffset = linearized_scaffolds(scaffolds)\n\tdepth = read_depth(depthFilename,scaffoldToOffset)\n\thalfDeep = read_halfdeep(halfDeepFilename,scaffoldToOffset)\n\tpercentileToValue = read_percentiles(percentilesFilename)\n\t\n\thalfdeep_plot(scaffolds,depth,halfDeep,percentileToValue,assembly,\n\t plotFilenameTemplate=plotFilenameTemplate,\n\t tickSpacing=tickSpacing,\n width=width,height=height,pointsize=pointsize,\n yLabelSpace=yLabelSpace)\n\t}\n\n\nread_control_freec <- function(controlFreecFilename,scaffoldToOffset=NULL)\n\t#\n\t# Read a file containing the copy number ouput from ControlFREEC.\n\t#\n\t# A scaffold-to-offset can be provided, as would be produced by\n\t# linearized_scaffolds(). One columns are added, converting each window's\n\t# on-scaffold position to a position along a number line.\n\t#\n\t# typical input:\n\t#\tChromosome Start Ratio MedianRatio CopyNumber\n\t#\tSUPER_2 1 42.2814 22.2608 45\n\t#\tSUPER_2 1001 31.0994 22.2608 45\n\t#\tSUPER_2 2001 30.5009 22.2608 45\n\t#\tSUPER_2 3001 26.9936 22.2608 45\n\t#\tSUPER_2 4001 33.8109 22.2608 45\n\t#\tSUPER_2 5001 27.2106 22.2608 45\n\t# ...\n\t#\n\t# returns, e.g.\n\t#\tscaffold start Ratio MedianRatio CopyNumber s\n\t#\tSUPER_2 1 42.2814 22.2608 45 200529156\n\t#\tSUPER_2 1001 31.0994 22.2608 45 200530156\n\t#\tSUPER_2 2001 30.5009 22.2608 45 200531156\n\t#\tSUPER_2 3001 26.9936 22.2608 45 200532156\n\t#\tSUPER_2 4001 33.8109 22.2608 45 200533156\n\t#\tSUPER_2 5001 27.2106 22.2608 45 200534156\n\t# ...\n\t#\n\t{\n\tcontrolFreec = read.table(controlFreecFilename,header=T,colClasses=c(\"character\",\"numeric\",\"numeric\",\"numeric\",\"numeric\"))\n\tcolnames(controlFreec) <- c(\"scaffold\",\"start\",\"Ratio\",\"MedianRatio\",\"CopyNumber\")\n\tif (!is.null(scaffoldToOffset))\n\t\t{\n\t\tcontrolFreec = controlFreec[controlFreec$scaffold %in% names(scaffoldToOffset),]\n\t\tcontrolFreec[,\"s\"] = scaffoldToOffset[controlFreec$scaffold] + controlFreec$start\n\t\t}\n\n\tcontrolFreec\n\t}\n\n\ncontrol_freec_plot <- function(scaffolds,depth,controlFreec,percentileToValue,\n assemblyName=\"\",\n scaffoldsToPlot=NULL,\n plotFilename=NULL,\n tickSpacing=10000000,\n tickLabels=F,\n width=17,height=7,pointsize=18,\n yLabelSpace=7,\n maxDepth=NA,\n scaffoldInterval=NULL)\n\t#\n\t# Plot half-deep intervals and depth along an assembly\n\t#\n\t# scaffolds: As returned by read_scaffold_lengths().\n\t# depth: As returned by read_depth().\n\t# controlFreec: As returned by read_control_freec(). If this is\n\t# NULL, no controlFreec copy number information is\n\t# displayed.\n\t# percentileToValue: As returned by read_percentiles()\n\t# assemblyName: Name of the assembly. This contributes to the\n\t# plain title, and can contribute to the plot\n\t# file name.\n\t# Example: \"bAlcTor1.pri.cur.20190613\"\n\t# scaffoldsToPlot=NULL: A vector of scaffolds to restrict the plot to.\n\t# By default, everything in scaffolds[] is\n\t# plotted.\n\t# plotFilename=NULL: File to plot the data to. If this is NULL, the\n\t# data is plotted on the screen.\n\t# tickSpacing: Spacing of evenly-spaced ticks along the\n\t# horizontal axis. If this is zero, these ticks\n\t# are inhibited. The default is 10Mbp.\n\t# tickLabels: If true, add numeric labels to the horizontal\n\t# axis.\n\t# width,height,pointsize: These are passed through to whatever function\n\t# creates the plot window.\n\t# yLabelSpace: Space below the plot. This can be increased to\n\t# accommodate longer scaffold names.\n\t# maxDepth: Depth greater than this is not shown in the plot;\n\t# i.e. the vertical axis stops at this value. By\n\t# default this is 1.5*median depth.\n\t# scaffoldInterval subinterval to restrict the plot to. Typically\n\t# this would only be used when only one scaffold\n\t# is to be plotted. This is a (start,end) pair,\n\t# origin-zero, half-open.\n\t#\n\t{\n\t# if we have a scaffold subset, reduce our copy of the data to that subset\n\n\tif (!is.null(scaffoldInterval))\n\t\t{\n\t\tif (is.null(scaffoldsToPlot))\n\t\t\t{\n\t\t\tif (length(scaffoldsToPlot) > 1)\n\t\t\t\tstop(\"scaffoldInterval cannot be used with more than one scaffold\")\n\t\t\t}\n\t\telse\n\t\t\t{\n\t\t\tif (length(unique(scaffolds$name)) > 1)\n\t\t\t\tstop(\"scaffoldInterval cannot be used with more than one scaffold\")\n\t\t\t}\n\t\t}\n\n\tshowControlFreec = !is.null(controlFreec)\n\n\tif (is.null(scaffoldsToPlot))\n\t\t{\n\t\tscaffoldLen = sum(scaffolds$length)\n\t\t}\n\telse\n\t\t{\n\t\t# validate the names in the subset\n\n\t\tbadNames = scaffoldsToPlot[!(scaffoldsToPlot %in% scaffolds$name)]\n\t\tif (length(badNames) > 0)\n\t\t\tstop(paste(\"bad scaffold name(s):\",paste(scaffoldsToPlot,collapse=\", \")))\n\n\t\t# pick ordered subset\n\t\tscaffolds = scaffolds[scaffolds$name %in% scaffoldsToPlot,]\n\t\tscaffoldsToNumber = 1:length(scaffoldsToPlot)\n\t\tnames(scaffoldsToNumber) = scaffoldsToPlot\n\t\tscaffolds = scaffolds[order(scaffoldsToNumber[scaffolds$name]),]\n\t\tscaffolds[,\"offset\"] = rev(sum(as.numeric(scaffolds$length))-cumsum(rev(as.numeric(scaffolds$length))))\n\t\tscaffoldToOffset = linearized_scaffolds(scaffolds)\n\n\t\t# reduce to ordered subset\n\t\tdepth = depth[depth$scaffold %in% names(scaffoldToOffset),]\n\t\tdepth[,\"s\"] = scaffoldToOffset[depth$scaffold] + depth$start\n\t\tdepth[,\"e\"] = scaffoldToOffset[depth$scaffold] + depth$end\n\n\t\tif (showControlFreec)\n\t\t\t{\n\t\t\tcontrolFreec = controlFreec[controlFreec$scaffold %in% names(scaffoldToOffset),]\n\t\t\tcontrolFreec[,\"s\"] = scaffoldToOffset[controlFreec$scaffold] + controlFreec$start\n\t\t\t}\n\n\t\tscaffoldLen = sum(scaffolds$length[scaffolds$name==scaffoldsToPlot])\n\t\t}\n\n\t# fetch percentile values (used only for drawing and labeling)\n\n\tdepth50 = percentileToValue[\"percentile50\"]\n\thalfDepthLo = percentileToValue[\"halfPercentile40\"]\n\thalfDepthHi = percentileToValue[\"halfPercentile60\"]\n\tdepthClip = ifelse(is.na(maxDepth),1.5*depth50,maxDepth)\n\tdepth50Str = sprintf(\"%.1f\",depth50)\n\thalfDepthLoStr = sprintf(\"%.1f\",halfDepthLo)\n\thalfDepthHiStr = sprintf(\"%.1f\",halfDepthHi)\n\tdepthClipStr = sprintf(\"%.1f\",depthClip)\n\n\t# (housekeeping)\n\n\tscaffoldTicks = 1 + c(scaffolds$offset,sum(as.numeric(scaffolds$length)))\n\tscaffoldCenters = (as.numeric(scaffoldTicks[1:nrow(scaffolds)])+as.numeric(scaffoldTicks[2:(nrow(scaffolds)+1)])) / 2\n\n\tCNSpacing = depthClip / 32\n\n\tif (!is.null(scaffoldInterval))\n\t\t{\n\t\txlim = scaffoldInterval\n\t\tdepth = depth[(depth$s>=xlim[1])&(depth$s<=xlim[2]),]\n\t\tcontrolFreec = controlFreec[(controlFreec$s>=xlim[1])&(controlFreec$s<=xlim[2]),]\n\t\tscaffoldCenters = (scaffoldInterval[1]+scaffoldInterval[2])/2\n\t\t}\n\telse\n\t\t{\n\t\txlim = c(1,max(scaffoldTicks))\n\t\t}\n\n\tylim = if (showControlFreec) c(-5*CNSpacing,depthClip) else c(0,depthClip)\n\tylimLong = ylim\n\tylimLong[1] = ylimLong[1] - CNSpacing\n\n\tdepthColor = rgb(.6,.6,.6)\n\thalfDepthColor = \"black\"\n\tdepthLimitsColor = \"blue\"\n\tclippedColor = \"red\"\n\n\t# clipping\n\n\tdepth$clipped = ifelse(depth$depth<=depthClip,depth$depth,depthClip)\n\tdepth$color = ifelse(depth$depth<=depthClip,depthColor,clippedColor)\n\n\tcontrolFreec$CNclipped = ifelse(controlFreec$CopyNumber<=2,controlFreec$CopyNumber,3)\n\n\t# open plot window or file\n\n\tturnDeviceOff = F\n\tif (is.null(plotFilename))\n\t\t{\n\t\tquartz(width=width,height=height,pointsize=pointsize)\n\t\t}\n\telse\n\t\t{\n\t\tprint(paste(\"drawing to\",plotFilename))\n\t\tpdf(file=plotFilename,width=width,height=height,pointsize=pointsize)\n\t\tturnDeviceOff = T\n\t\t}\n\n\t# create empty plot\n\n\tif (assemblyName == \"\")\n\t\ttitle = paste(\"Copy Number\\n(per ControlFREEC)\",sep=\"\")\n\telse\n\t\ttitle = paste(\"Copy Number on \",assemblyName,\"\\n(per ControlFREEC)\",sep=\"\")\n\tylab = \"depth (black/gray) and CN (blue/red)\"\n\n\tpar(mar=c(yLabelSpace,4,2.5,0.2)+0.1) # BLTR\n\toptions(scipen=10)\n\tplot(NA,xlim=xlim,ylim=ylim,main=title,xaxt=\"n\",xlab=\"\",ylab=ylab)\n\n\t# add horizontal axis\n\n\tif ((tickSpacing > 0) & (xlim[2]>=tickSpacing)) # equal-spaced ticks\n\t\t{\n\t\tleftTick = xlim[1] + (tickSpacing-1) - ((xlim[1] + (tickSpacing-1)) %% tickSpacing)\n\t\tticks = seq(leftTick,xlim[2],by=tickSpacing)\n\t\taxis(1,at=ticks,labels=F,col=\"gray\")\n\t\tif (tickLabels)\n\t\t\t{\n\t\t\tlabeledTicks = ticks[abs(scaffoldCenters-ticks)>=tickSpacing/4]\n\t\t\taxis(1,at=labeledTicks,tick=F, # tick labels\n\t\t\t labels=prettyNum(labeledTicks,big.mark=\",\",scientific=FALSE),\n\t\t\t\t las=2,pos=ylim[1]-0.5,cex.axis=0.5)\n\t\t\t}\n\t\t}\n\taxis(1,at=scaffoldTicks,labels=F,tck=-0.08) # scaffold ticks\n\taxis(1,at=scaffoldCenters,tick=F,labels=scaffolds$name, # scaffold labels\n\t\t las=2,cex.axis=0.7)\n\n\t# draw depth as gray, and depth in half-deep intervals as black\n\n\tpoints(depth$s,depth$clipped,col=depth$color,pch=19,cex=0.3)\n\n\thalfsies = (depth$depth>=halfDepthLo) & (depth$depth<=halfDepthHi)\n\tpoints(depth$s[halfsies],depth$depth[halfsies],col=halfDepthColor,pch=19,cex=0.1)\n\n\tfor (ix in 1:length(scaffoldTicks))\n\t\tlines(c(scaffoldTicks[ix],scaffoldTicks[ix]),ylimLong,col=\"black\",lwd=1,lty=2)\n\n\t# add horizontal lines to show median and half-deep limits\n\n\tlines(xlim,c(depth50,depth50),col=depthLimitsColor,lwd=2,lty=2)\n\tlines(xlim,c(halfDepthHi,halfDepthHi),col=depthLimitsColor,lwd=2,lty=2)\n\tlines(xlim,c(halfDepthLo,halfDepthLo),col=depthLimitsColor,lwd=2,lty=2)\n\n\ttext(xlim[1],depth50, \"median \", adj=1,cex=0.5,col=depthLimitsColor)\n\ttext(xlim[1],halfDepthLo,\"half-40th \",adj=1,cex=0.5,col=depthLimitsColor)\n\ttext(xlim[1],halfDepthHi,\"half-60th \",adj=1,cex=0.5,col=depthLimitsColor)\n\n\t# add controlFreec copy number information\n\t# from bottom up, rows are copy number = 0, 1, 2, >2\n\n\tif (showControlFreec)\n\t\t{\n\t\tfor (cn in -2:-5)\n\t\t\t{\n\t\t\tlines(xlim,c(cn*CNSpacing,cn*CNSpacing),col=\"red\",lty=1)\n\t\t\ttext(xlim[1]-0.005*(xlim[2]-xlim[1]),cn*CNSpacing,cex=0.4,adj=1,\n\t\t\t ifelse(cn==-2,\"CN>2\",paste(\"CN=\",cn+5,sep=\"\")))\n\t\t\t}\n\t\tpoints(controlFreec$s,(controlFreec$CNclipped-5)*CNSpacing,pch=16,cex=0.5,\n\t\t\t col=ifelse(controlFreec$CNclipped==1,\"blue\",\"red\"))\n\t\t}\n\n\t# close the plot\n\n\tif (turnDeviceOff) dev.off()\n\t}\n\n", "meta": {"hexsha": "567b84bb06d3e0875b5d02d6c9badeb661bd79ba", "size": 24990, "ext": "r", "lang": "R", "max_stars_repo_path": "halfdeep.r", "max_stars_repo_name": "makovalab-psu/HalfDeep", "max_stars_repo_head_hexsha": "dce570b05e8d8c108853a912e33eb7456acaccbe", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "halfdeep.r", "max_issues_repo_name": "makovalab-psu/HalfDeep", "max_issues_repo_head_hexsha": "dce570b05e8d8c108853a912e33eb7456acaccbe", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "halfdeep.r", "max_forks_repo_name": "makovalab-psu/HalfDeep", "max_forks_repo_head_hexsha": "dce570b05e8d8c108853a912e33eb7456acaccbe", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-14T12:24:46.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-14T12:24:46.000Z", "avg_line_length": 36.75, "max_line_length": 128, "alphanum_fraction": 0.6670268107, "num_tokens": 7585, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.5, "lm_q1q2_score": 0.31122966560092724}} {"text": "Pts2TreeShp <- function(datafile,shape_dir,shape_name,class_col=8,x_col=1,\n y_col=2,z_col=3){\n#\n# A function that takes a pointcloud dataset in csv format and returns\n# a ShapeFile of the convex hulls of each tree labelled by a given \n# number in class_col. Each shape also has associated x,y,z data of\n# the top point of each tree in the dbf file\n#\n# Data is then saved out to the ShapeFile layer as set by the arguments\n# shape_dir and shape_name\n#\n# Arguments\n#\n# datafile: .csv file of points\n# shape_dir: name of directory to be created to store ShapeFile data\n# shape_name: name of ShapeFile layer to be created\n# class_col: column number containing numerical label for tree \n# membership of each point (use 0 if not a member of tree)\n# x_col: column number containing x co-cordinates of each point\n# y_col: column number containing y co-cordinates of each point\n# z_col: column number containing z co-cordinates of each point \n\n# Some input checking\nif(!is.character(shape_dir)){\n stop('Argument shape_dir must be a string')\n}\nif(!is.character(shape_name)){\n stop('Argument shape_name must be a string')\n} \n\n# get dependencies\nif(!require(sp)){\n install.packages('sp')\n}\nlibrary(sp)\nif(!require(rgdal)){\n install.packages('rgdal')\n}\nlibrary(rgdal)\n\n# read data\ndata <- read.csv(datafile,header=F)\n\n# remove unclassified points\nzero_idx <- which(data[,class_col]==0)\nif(length(zero_idx!=0)){\n data <- data[-zero_idx,] \n}\n\n# If no points classified then end here\nif(nrow(data)==0){\n return(NA)\n}\n\n# Get tree indices\ntr_ids <- sort(unique(data[,class_col]))\n\n# Set up objects to collect data\nall_poly <- list()\nx <- numeric(length(tr_ids))\ny <- numeric(length(tr_ids))\nz <- numeric(length(tr_ids))\n\n# get each tree\nfor(i in 1:length(tr_ids)){\n \n #get tree id\n j <- tr_ids[i]\n \n # Get convex hull indices\n ths_tr <- data[data[,class_col]==j,]\n ths_hl <- chull(ths_tr[,c(x_col,y_col)])\n ths_hl <- c(ths_hl,ths_hl[1])\n \n # convert to spatial polygon, as an island not hole\n ths_poly <- sp::Polygon(cbind(ths_tr[ths_hl,1],ths_tr[ths_hl,2]),hole=F) \n ths_polys <- sp::Polygons(list(ths_poly),as.character(i))\n \n # Add to our list\n all_poly[[length(all_poly)+1]] <- ths_polys\n \n # Get location of tree top\n ths_mxid <- which.max(ths_tr[,z_col])\n x[i] <- max(ths_tr[ths_mxid,x_col])\n y[i] <- max(ths_tr[ths_mxid,y_col])\n z[i] <- max(ths_tr[ths_mxid,z_col])\n \n # clean up\n rm(ths_tr,ths_hl,ths_poly,ths_polys,ths_mxid)\n}\n\n# Compiles all polygons and data in a spdf\nall_polys <- sp::SpatialPolygons(all_poly,1:length(tr_ids))\nfinal_polys <- sp::SpatialPolygonsDataFrame(all_polys,\n data=data.frame(x=x, y=y,z=z,\n row.names=row.names(all_polys)))\n\n# Save out the result\nrgdal::writeOGR(obj=final_polys,dsn=shape_dir,layer=shape_name,driver=\"ESRI Shapefile\")\n}", "meta": {"hexsha": "1797aca6cd951191a48ba69b0a1a1bbfd2640c58", "size": 3215, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/R/Pts2TreeShp.r", "max_stars_repo_name": "jonvw28/MCGC", "max_stars_repo_head_hexsha": "e7e09d2c8ecf2f42e5d72db83c1c771f4ebebc52", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2020-04-30T22:21:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-31T02:22:24.000Z", "max_issues_repo_path": "scripts/R/Pts2TreeShp.r", "max_issues_repo_name": "jonvw28/MCGC", "max_issues_repo_head_hexsha": "e7e09d2c8ecf2f42e5d72db83c1c771f4ebebc52", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/R/Pts2TreeShp.r", "max_forks_repo_name": "jonvw28/MCGC", "max_forks_repo_head_hexsha": "e7e09d2c8ecf2f42e5d72db83c1c771f4ebebc52", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2019-11-08T21:58:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-28T14:15:15.000Z", "avg_line_length": 32.15, "max_line_length": 92, "alphanum_fraction": 0.6227060653, "num_tokens": 835, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.31122307323323206}} {"text": "args<-commandArgs(trailingOnly=TRUE)\nif(length(args)<2){\n\tprint(\"Usage: Rscript --vanilla script-branch-clade-analysis.r treefile_to_analyse clade_out all_trees_results\")\n\tquit()\n}\nprint(\"STARTING analysis with R script\")\n\nlibrary(ape)\n\n#-------------------------------------------------\ngetSubtreeInfo=function(t,cl,out){\n\n\t#print(\"entered getSubtreeInfo\")\n\t# Check if the clade is monophyletic\n\tcheck_cl=is.monophyletic(t,cl)\n # Get a subtree structure\n subt=keep.tip(t,cl)\n\n if(check_cl){\n write.tree(file=out,subt)\n subtree=readLines(out)\n }else{\n subtree=\"NonMon\"\n }\n\n\t# Num of edges < 10^(-5) in subclade\n\tcheck_e_len=length(which(subt$edge.length[]<0.00001))\n\n\t# Check if H, N, D are < 10^(-5)\n\tx=t$edge.length[which.edge(t,\"HUMAN\")]\n\ty=t$edge.length[which.edge(t,\"NEANDERTHAL\")]\n\tz=t$edge.length[which.edge(t,\"DENISOVAN\")]\n\t\n\tcheck_H_len=ifelse(x<0.00001, TRUE, FALSE)\n\tcheck_N_len=ifelse(y<0.00001, TRUE, FALSE)\n\tcheck_D_len=ifelse(z<0.00001, TRUE, FALSE)\n\n\tcheck_external=c(check_H_len,check_N_len,check_D_len)\n\n\t# Create a list with all info\n\tclade_info=list(\"mon\" = check_cl, \"subtree\" = subtree, \"edges\" = check_e_len, \"external\" = check_external)\n\treturn(clade_info)\n}\n#-------------------------------------------------\n#print(\"READING tree\")\nt=read.tree(args[1])\nt_out=args[2]\nfile_out=args[3]\n\n\n# Clades of interest\nclade=list(\n\t\"c1\" = c(\"HUMAN\",\"NEANDERTHAL\",\"DENISOVAN\"),\n\t\"c2\" = c(\"HUMAN\",\"NEANDERTHAL\"),\n\t\"c3\" = c(\"NEANDERTHAL\",\"DENISOVAN\"),\n\t\"c4\" = c(\"HUMAN\",\"DENISOVAN\"),\n\t\"c5\" = c(\"GORGO\",\"PANTR\",\"HUMAN\",\"NEANDERTHAL\",\"DENISOVAN\")\n)\n\n#print(\"Checking monophyly of the 5 clades\")\ncheck_clade=c(\n\tis.monophyletic(t,clade$c1),\n\tis.monophyletic(t,clade$c2),\n\tis.monophyletic(t,clade$c3),\n\tis.monophyletic(t,clade$c4),\n\tis.monophyletic(t,clade$c5)\n)\n\n\nsubt_out_1=paste(t_out,\"subtr_1\",sep=\".\")\nsubt_out_2=paste(t_out,\"subtr_2\",sep=\".\")\n\n#print(\"Getting subtree info\")\nsubtree_info_1=getSubtreeInfo(t,clade$c1,subt_out_1)\nsubtree_info_2=getSubtreeInfo(t,clade$c5,subt_out_2)\n\nedges_other=subtree_info_1$edges-length(which(subtree_info_1$external[]==TRUE))\ncheck_e_len=length(which(t$edge.length[]<0.00001))\ntree_newick=readLines(args[1])\n\n#print(\"outputing results\")\n\nsink(file=file_out,append=TRUE)\ncat(paste(\n\targs[1],\n\t\"|\",\n\tpaste(\"cladeHND_\",check_clade[1],sep=\"\"),\n\tifelse(check_clade[2],\"HN\",FALSE),\n\tifelse(check_clade[3],\"ND\",FALSE),\n\tifelse(check_clade[4],\"HD\",FALSE),\n\tsubtree_info_1$subtree,\n\tpaste(\"edgesHND_\",subtree_info_1$edges,sep=\"\"),\n\tpaste(\"H+\",subtree_info_1$external[1],sep=\"\"),\n\tpaste(\"N+\",subtree_info_1$external[2],sep=\"\"),\n\tpaste(\"D+\",subtree_info_1$external[3],sep=\"\"),\n\tpaste(\"other_\",edges_other,sep=\"\"),\n\t\"|=====|\",\n\tpaste(\"cladeHNDPPG_\",check_clade[5],sep=\"\"),\n\tsubtree_info_2$subtree,\n\tpaste(\"edgesHNDPPG_\",subtree_info_2$edges,sep=\"\"),\n\ttree_newick,\n\tpaste(\"edgesTREE_\",check_e_len,sep=\"\"),\n\t'\\n',\nsep=\" \"))\n\n\nsink()\n", "meta": {"hexsha": "2fe1a18e716a4040b96b619501bbbd88caace9e3", "size": 2950, "ext": "r", "lang": "R", "max_stars_repo_path": "3.tree_inference/step_2-check_if_one_of_9/script-branch-clade-analysis.r", "max_stars_repo_name": "OlgaChern/scripts_evo_gen_brain_evo", "max_stars_repo_head_hexsha": "60ece9c3303f60e0ac856efd800e6798a46aefdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "3.tree_inference/step_2-check_if_one_of_9/script-branch-clade-analysis.r", "max_issues_repo_name": "OlgaChern/scripts_evo_gen_brain_evo", "max_issues_repo_head_hexsha": "60ece9c3303f60e0ac856efd800e6798a46aefdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3.tree_inference/step_2-check_if_one_of_9/script-branch-clade-analysis.r", "max_forks_repo_name": "OlgaChern/scripts_evo_gen_brain_evo", "max_forks_repo_head_hexsha": "60ece9c3303f60e0ac856efd800e6798a46aefdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.3148148148, "max_line_length": 113, "alphanum_fraction": 0.6691525424, "num_tokens": 962, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.31122307323323206}} {"text": "#' Check date format\n#'\n#' Checks to see if format is YYYY-MM-DD. Also performs a few other date checks.\n#'\n#' @param date character\n#' @keywords WRTDS flow\n#' @return condition logical TRUE or FALSE if checks passed or failed \n#' @export\n#' @examples\n#' date <- '1985-01-01'\n#' dateFormatCheck(date)\n#' dateWrong <- '1999/1/7'\n#' dateFormatCheck(dateWrong)\ndateFormatCheck <- function(date){ # checks for the format YYYY-MM-DD\n parts <- strsplit(date,\"-\",fixed=TRUE)\n condition <- FALSE\n if (length(parts[[1]])>1) {\n if (nchar(parts[[1]][1]) == 4 && nchar(parts[[1]][2]) == 2 && nchar(parts[[1]][3]) == 2){\n testYear <- as.numeric(parts[[1]][1])\n testMonth <- as.numeric(parts[[1]][2])\n testDay <- as.numeric(parts[[1]][3])\n if (!is.na(testYear) && !is.na(testMonth) && !is.na(testDay)){\n if (testMonth <= 12 && testDay <= 31){\n condition <- TRUE\n } \n } \n }\n }\n return(condition)\n}\n", "meta": {"hexsha": "e2311735b6440d6bc547039e7152b844466e1197", "size": 955, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dateFormatCheck.r", "max_stars_repo_name": "ldecicco-USGS/EGRET", "max_stars_repo_head_hexsha": "88a270ef012f1bdc0c94b89c8e07884073cd2796", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 73, "max_stars_repo_stars_event_min_datetime": "2015-07-25T18:55:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T18:29:43.000Z", "max_issues_repo_path": "R/dateFormatCheck.r", "max_issues_repo_name": "ldecicco-USGS/EGRET", "max_issues_repo_head_hexsha": "88a270ef012f1bdc0c94b89c8e07884073cd2796", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 166, "max_issues_repo_issues_event_min_datetime": "2015-01-30T21:45:52.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-17T14:53:32.000Z", "max_forks_repo_path": "R/dateFormatCheck.r", "max_forks_repo_name": "ldecicco-USGS/EGRET", "max_forks_repo_head_hexsha": "88a270ef012f1bdc0c94b89c8e07884073cd2796", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 43, "max_forks_repo_forks_event_min_datetime": "2015-01-29T17:46:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T18:42:55.000Z", "avg_line_length": 30.8064516129, "max_line_length": 93, "alphanum_fraction": 0.5842931937, "num_tokens": 281, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3110543703595648}} {"text": "# --------------------------------------------------- #\n# Author: Marius D. Pascariu\n# License: MIT\n# Last update: Thu Nov 07 11:33:27 2019\n# --------------------------------------------------- #\n\n#' @details \n#' To learn more about the package, start with the vignettes:\n#' \\code{browseVignettes(package = \"ungroup\")}\n#' \\insertNoCite{*}{ungroup}\n#' @references \\insertAllCited{}\n#' @importFrom Rcpp sourceCpp\n#' @importFrom stats optimise qnorm quantile fitted aggregate nlminb AIC BIC\n#' @importFrom utils tail\n#' @importFrom MortalitySmooth MortSmooth_bbase\n#' @importFrom graphics axis barplot legend lines plot abline par\n#' @importFrom rgl axes3d box3d open3d surface3d title3d\n#' @importFrom pbapply startpb setpb closepb\n#' @import Rdpack\n#' @name ungroup\n#' @useDynLib ungroup\n#' @aliases NULL\n#' @docType package\n\"_PACKAGE\"\n", "meta": {"hexsha": "3115af1f28cc5aaf1e36b1bf0f9d361626599c8a", "size": 835, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ungroup-package.r", "max_stars_repo_name": "timriffe/ungroup", "max_stars_repo_head_hexsha": "57e8cb50737a6abd8cdf1f709eeaf752747d423f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/ungroup-package.r", "max_issues_repo_name": "timriffe/ungroup", "max_issues_repo_head_hexsha": "57e8cb50737a6abd8cdf1f709eeaf752747d423f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ungroup-package.r", "max_forks_repo_name": "timriffe/ungroup", "max_forks_repo_head_hexsha": "57e8cb50737a6abd8cdf1f709eeaf752747d423f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4, "max_line_length": 76, "alphanum_fraction": 0.6491017964, "num_tokens": 228, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.31105437035956474}} {"text": "# R Code\n# Ploting the results\n# L. Garcia, A. Lorena, M. de Solto 2017\n# Ploting the complexity measures\n\ndefault <- function() {\n\n par = element_text(size=14, colour=\"black\")\n\n plot = theme_bw() + \n theme(legend.position=\"top\", \n text=par, axis.text=par, strip.text=par, legend.text=par\n )\n\n return(plot)\n}\n\ndistribution <- function(data) {\n\n data = apply(data[CLASSIFIERS], 1, which.max)\n data = melt(table(CLASSIFIERS[data]))\n\n pdf(\"wins1.pdf\", width=4, height=4)\n plot = ggplot(data, aes(x=Var1, y=value)) + \n geom_bar(colour=\"black\", fill=\"white\", stat=\"identity\", \n position=\"dodge\", width=0.6) + \n geom_hline(yintercept=0) + \n default() + ylab(\"Number of wins\") + \n xlab(\"\") + guides(fill=FALSE)\n print(plot)\n dev.off()\n}\n\nhistogram <- function(data) {\n\n table = data[CLASSIFIERS]\n table = melt(table)\n\n pdf(\"wins2.pdf\", width=4, height=4)\n plot = ggplot(table, aes(x=variable, y=value)) + \n geom_boxplot() + \n default() + ylab(\"Accuracy\") + \n xlab(\"\")\n print(plot)\n dev.off()\n}\n\nboxplot <- function(data) {\n\n data = data.frame(do.call(\"rbind\", data))\n data$Classifier = rep(CLASSIFIERS, each=141)\n\n table = melt(data)\n table$group = c(rep(\"a\", 1692), rep(\"b\", 1128))\n\n pdf(\"boxplot.pdf\", width=8, height=9)\n plot = ggplot(table, aes(x=variable, y=value, fill=group)) + \n geom_boxplot() + facet_wrap( ~ Classifier, ncol=2) + \n default() +scale_fill_manual(values=c(\"white\", \"grey\")) + \n xlab(\"\") + ylab(\"MSE\") + guides(fill=FALSE) + \n ylim(0, 0.1)\n print(plot)\n dev.off()\n}\n\nperformance <- function(data, aux) {\n\n foo = lapply(1:141, function(i) {\n tmp = do.call(\"rbind\", lapply(aux, \"[\", i,))\n goo = apply(tmp, 2, which.max)\n unlist(data[i,CLASSIFIERS[goo]])\n })\n \n foo = do.call(\"rbind\", foo)\n foo = apply(foo, 2, sum)\n foo = rbind(foo[1:3] - foo[4], foo[1:3] - foo[5])\n colnames(foo) = REGRESSORS[1:3]\n rownames(foo) = c(\"Random\", \"Default\")\n foo = foo*100\n\n table = melt(foo)\n\n pdf(\"performance.pdf\", width=6, height=4)\n plot = ggplot(table, aes(x=Var2, y=value)) + \n geom_bar(colour=\"black\", fill=\"white\", stat=\"identity\", \n position=\"dodge\", width=0.6) + facet_wrap( ~ Var1, ncol=4) + \n geom_hline(yintercept=0) + default() + \n ylab(\"Percentage increase of accuracy\") + xlab(\"\")\n print(plot)\n dev.off()\n\n}\n\nfeatures <- function(data) {\n\n aux = do.call(\"cbind\",\n lapply(CLASSIFIERS, function(c) {\n randomForest(formulae(c), data)$importance\n })\n )\n aux = rev(sort(rowMeans(aux)))[1:5]\n table = melt(aux)\n table$Var1 = factor(rownames(table), levels=rownames(table))\n\n pdf(\"features.pdf\", width=5, height=5)\n plot = ggplot(table, aes(x=Var1, y=value)) + \n geom_bar(colour=\"black\", fill=\"white\", stat=\"identity\", \n position=\"dodge\", width=0.6) + default() + \n theme(axis.text.x=element_text(size=14, angle=90, hjust=1, vjust=0.3)) + \n geom_hline(yintercept=0) + \n ylab(\"Measure Importance\") + xlab(\"\")\n print(plot)\n dev.off()\n}\n", "meta": {"hexsha": "8f7f7ebedac962fb04e15d25a333088424b7fbe7", "size": 3047, "ext": "r", "lang": "R", "max_stars_repo_path": "source/meta/exp/plot/plot.r", "max_stars_repo_name": "lpfgarcia/complex", "max_stars_repo_head_hexsha": "6a14c33ea6c5de40aba6d61a56ff24dea633726c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-03-31T15:04:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-08T15:31:09.000Z", "max_issues_repo_path": "source/meta/exp/plot/plot.r", "max_issues_repo_name": "lpfgarcia/complex", "max_issues_repo_head_hexsha": "6a14c33ea6c5de40aba6d61a56ff24dea633726c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/meta/exp/plot/plot.r", "max_forks_repo_name": "lpfgarcia/complex", "max_forks_repo_head_hexsha": "6a14c33ea6c5de40aba6d61a56ff24dea633726c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.4956521739, "max_line_length": 79, "alphanum_fraction": 0.602559895, "num_tokens": 975, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.31105437035956474}} {"text": "# This function gets certain downstream/upstream genes in given kegg pathways\n\n## === input ===\n### file: pathway file name;\n### id: list of kegg pathway ids (in the form of hsaXXXXXX)\n### gene = NA: default as return all the genes\n### direction: 1 for downstream; -1 for upstream\n### step = 20: default as all the upstream/downstream genes; if specified, iterate given steps\n\n## == output ===\n### genelist: certain downstream/upstream genes in given kegg pathways\n\nGetGeneList <- function(file, id, gene = NA, direction = 1, steps = 20) {\n edges.file = read.delim(file, header = T, sep = \",\")\n filter = rep(F, dim(edges.file)[1])\n for (i in 1:length(id)) {\n filter = filter | (as.character(edges.file$Source) == id[i])\n }\n filter = filter & !(as.character(edges.file$Type) == \"downstream\") # here we don't need \"pathway nodes\"\n filter = filter & !(as.character(edges.file$Subtype1) == \"binding/association\") # here we don't consider binding as downstream\n trimmed.file = edges.file[filter,]\n \n if (is.na(gene)) {\n result = unique(c(as.character(trimmed.file$node1), as.character(trimmed.file$node2)))\n } else {\n # generate matrix representing linkages\n node1.list = as.character(trimmed.file$node1)\n node2.list = as.character(trimmed.file$node2)\n all_nodes = unique(c(node1.list, node2.list))\n linkage.matrix = matrix(0, nrow = length(all_nodes), ncol = length(all_nodes), dimnames = list(all_nodes, all_nodes))\n for (j in 1:dim(trimmed.file)[1]) {\n linkage.matrix[node1.list[j], node2.list[j]] = 1\n }\n # get required genes\n if (direction == -1) {\n linkage.matrix = t(linkage.matrix)\n }\n result = c()\n current_number = 0\n for (cnt in 1:steps) {\n if (length(gene) > 1) {\n gene = colnames(linkage.matrix)[colSums(linkage.matrix[gene,]) > 0]\n } else {\n gene = colnames(linkage.matrix)[linkage.matrix[gene,] == 1]\n }\n result = c(result, gene)\n result = unique(result)\n tmp_cnt = length(result)\n if(current_number == tmp_cnt) {\n break\n } else {\n current_number = tmp_cnt\n }\n }\n }\n \n result\n}\n\n\n", "meta": {"hexsha": "e67829eeb0f41b4928ba189bedc1cfcf2a33cd21", "size": 2135, "ext": "r", "lang": "R", "max_stars_repo_path": "ProteinSpace/Networks/SignalPathway/src/GetGeneList.r", "max_stars_repo_name": "ShaoGroup/PICheM", "max_stars_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-03-17T02:51:38.000Z", "max_stars_repo_stars_event_max_datetime": "2016-03-17T02:51:38.000Z", "max_issues_repo_path": "ProteinSpace/Networks/SignalPathway/src/GetGeneList.r", "max_issues_repo_name": "ShaoGroup/PICheM", "max_issues_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ProteinSpace/Networks/SignalPathway/src/GetGeneList.r", "max_forks_repo_name": "ShaoGroup/PICheM", "max_forks_repo_head_hexsha": "de077cfdcf07aac0c394b0a00f418a2084707c9a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0, "max_line_length": 128, "alphanum_fraction": 0.6388758782, "num_tokens": 587, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.31105156596637634}} {"text": "\n\nrequire(rgeos)\nrequire(overpass)\nrequire(rgdal)\nrequire(raster)\nlibrary(tidyverse)\n\nbasin = c('Indus')\n\nsetwd('P:/is-wel/indus/message_indus')\n\n# Grab the basin boundaries\nbasin.spdf = readOGR( paste( getwd(), 'input', sep = '/' ), 'Indus_bcu', verbose = FALSE )\npids_original <- as.character(basin.spdf@data$PID )\nbasin.spdf = spTransform( basin.spdf, CRS(\"+proj=longlat\") ) # temp[which(as.character(temp$BASIN) == basin | as.character(temp$PID) == '2256'),] for Karachi basin\nbasin.sp = gUnaryUnion( basin.spdf )\nbasin.sp = SpatialPolygons(list(Polygons(Filter(function(f){f@ringDir==1},basin.sp@polygons[[1]]@Polygons),ID=1)))\nbuff.sp = gBuffer( basin.sp, width=0.1 ) \nproj4string(basin.sp) = proj4string(basin.spdf)\nproj4string(buff.sp) = proj4string(basin.spdf)\n\n# include the riparian countries outside the basin, to have external routes\nextra_basin.spdf = readOGR( paste( getwd(), 'input', sep = '/' ), 'indus_extra_basin_rip_countries', verbose = FALSE )\nextra_basin.spdf <- extra_basin.spdf[,(3)]\nnames(extra_basin.spdf) = \"PID\"\nproj4string(extra_basin.spdf) = proj4string(basin.spdf)\nextra_basin.spdf = spTransform( extra_basin.spdf, CRS(\"+proj=longlat\") )\n\n# subbasin polygons\nbasin_pid.spdf <- basin.spdf[,(1)]\nmrg.spdf <- rbind(basin_pid.spdf,extra_basin.spdf)\nsubbasins.sp = gUnaryUnion(mrg.spdf, id = mrg.spdf@data$PID)\nsubbasins.sp = SpatialPolygons(lapply(1:length(subbasins.sp),function(x){Polygons(Filter(function(f){f@ringDir==1},subbasins.sp@polygons[[x]]@Polygons),ID=x)}))\nproj4string( subbasins.sp ) = proj4string( basin.spdf )\n\n# get data from osm if not already available locally\nif( !file.exists( paste( 'input/basin_transmission', paste(basin, 'osm_power_lines.shp', sep='_' ), sep = '/' ) ) )\n\t{\n\t\n\t# Grab electricity transmission data corresponding to extent from basin polygon\n\tltypes = c('line','cable','minor_line')\n\tpower_lines = lapply( ltypes, function(ltype){\n\t\text = paste( '(',paste(bbox(basin.spdf)[2,1] , bbox(basin.spdf)[1,1], bbox(basin.spdf)[2,2], bbox(basin.spdf)[1,2],sep=','),')',sep='')\n\t\tpth = paste( '[out:xml][timeout:100];(node[\"power\"=\"',ltype,'\"]',ext,';way[\"power\"=\"',ltype,'\"]',ext,';relation[\"power\"=\"',ltype,'\"]',ext,';);out body;>;out skel qt;', sep = '' ) \n\t\tfrb = overpass_query(pth)\n\t\tif( !is.null(frb) )\n\t\t\t{\n\t\t\tproj4string(frb) = proj4string(basin.spdf)\n\t\t\tfrb = frb[ which( unlist( row.names(frb) ) %in% unlist( sapply( unlist( row.names( gIntersection( frb, buff.sp , byid=TRUE ) ) ), function(xxx){ unlist( strsplit( xxx, '[ ]') )[1] } ) ) ), 'voltage' ]\n\t\t\t}\n\t\treturn(frb)\t\n\t\t} )\n\tnames(power_lines) = ltypes\n\tpower_lines[ sapply( power_lines, is.null ) ] = NULL\n\tpower_lines = do.call( rbind, power_lines )\n\n\t# Output to shapefile\n\twriteOGR( power_lines, 'input/basin_transmission', paste(basin,'osm_power_lines',sep='_'), driver=\"ESRI Shapefile\", overwrite_layer=TRUE )\n\t}\n\n\n# Electricity transmission - stylized St Claire Curve for converting kV to MW\nx1 = c(69,138,230,345,500,765)\ny1 = 0.8 * c(12,50,140,375,900,2200)\nmod1 = nls(y1 ~ a1 + a2*x1 + a3*x1^2, start = list(a1 = 0, a2 = 0, a3 = 1)) # model for converting kV to MW\n\n# Clean up transmission data using an minimum of 66kV and then fit to MW using model\n## HOW HAS THE OSM_POWER_LINES.SHP BEEN CREATED? should have the script, for transferrability\nbasin_pline.sldf = readOGR( 'input/basin_transmission', paste(basin,'osm_power_lines',sep='_') )\t\nbasin_pline.sldf = basin_pline.sldf[ -1 * which( is.na(basin_pline.sldf$voltage) ), ]\nbasin_pline.sldf$voltage = as.numeric( as.character( basin_pline.sldf$voltage ) ) / 1000\nbasin_pline.sldf = basin_pline.sldf[ which( basin_pline.sldf@data$voltage > 60 ), ] \nbasin_pline.sldf$MW = unlist( lapply( 1:length(basin_pline.sldf), function(zz){ \n\treturn( round( \tcoef(mod1)[1] + \n\t\t\t\t\tcoef(mod1)[2] * basin_pline.sldf@data$voltage[zz] + \n\t\t\t\t\tcoef(mod1)[3] * basin_pline.sldf@data$voltage[zz]^2 ) ) \n\t} ) )\n\n# Get intersections between spatial lines and basin polygons - t\t\nsubbasins.sp = spTransform( subbasins.sp, crs(basin_pline.sldf) )\ninters = lapply( 1:length(basin_pline.sldf), function(x){ which( gIntersects(basin_pline.sldf[x,], subbasins.sp,byid=TRUE)) } ) \n\n# Initialize matrix tracking connections and installed capacity\nelectricity_transmission_MW = matrix(data = 0, ncol = length(subbasins.sp), nrow = length(subbasins.sp))\nln = rep(NA,length(inters))\nfor(x in 1:length(inters))\n\t{\t\n\tres = inters[[x]]\n\tln[x] = length(res)\n\tif( length(res) > 1 )\n\t\t{\n\t\tif( length(res) == 2 )\n\t\t\t{\n\t\t\telectricity_transmission_MW[ res[1], res[2] ] = electricity_transmission_MW[ res[1], res[2] ] + basin_pline.sldf$MW[x] \n\t\t\telectricity_transmission_MW[ res[2], res[1] ] = electricity_transmission_MW[ res[2], res[1] ] + basin_pline.sldf$MW[x] \n\t\t\t}else\n\t\t\t{\n\t\t\t# Manual check\n\t\t\t# cols = c('red','green','blue','orange')\n\t\t\t# windows()\n\t\t\t# plot(subbasins.sp[c(res)], main = paste('x','=',x,basin_pline.sldf$voltage[x], 'kV',sep=' '), border = 'white',col = cols)\n\t\t\t# plot(basin_pline.sldf,col='gray',add=TRUE)\n\t\t\t# plot(basin_pline.sldf[x,],add=TRUE, col = 'black', lwd = 4)\n\t\t\t# legend('topleft',legend = unlist(lapply(1:length(res),function(ww){paste(res[ww], as.character(basin.spdf@data$BCU[c(res[ww])]), sep = ' - ')})), fill = cols )\n\t\t\ttemp = combn(length(res),2)\n\t\t\tfor(kkk in 1:ncol(temp))\n\t\t\t\t{\n\t\t\t\tline_features = disaggregate(crop( as(basin_pline.sldf[x,],'SpatialLines'), gUnaryUnion( subbasins.sp[ c( res[ temp[1,kkk] ],res[ temp[2,kkk] ] ) ] ) ),byid=TRUE)\n\t\t\t\tfor(lll in 1:length(line_features))\n\t\t\t\t\t{\n\t\t\t\t\tif( ( length( crop( line_features[lll], subbasins.sp[ c( res[ temp[1,kkk] ]) ] ) ) > 0 ) & ( length( crop( line_features[lll], subbasins.sp[ c( res[ temp[2,kkk] ]) ] ) ) > 0 ) )\n\t\t\t\t\t\t{\n\t\t\t\t\t\tprint(temp[,kkk])\n\t\t\t\t\t\telectricity_transmission_MW[ res[ temp[1,kkk] ], res[ temp[2,kkk] ] ] = electricity_transmission_MW[ res[ temp[1,kkk] ], res[ temp[2,kkk] ] ] + basin_pline.sldf@data$MW[x] \n\t\t\t\t\t\telectricity_transmission_MW[ res[ temp[2,kkk] ], res[ temp[1,kkk] ] ] = electricity_transmission_MW[ res[ temp[2,kkk] ], res[ temp[1,kkk] ] ] + basin_pline.sldf@data$MW[x] \n\t\t\t\t\t\t}\n\t\t\t\t\t}\t\n\t\t\t\t}\n\t\t\t}\n\t\t}\n}\n\n# update PID naming \nelectricity_transmission_MW = data.frame( electricity_transmission_MW )\nnames_et <- c( as.character( basin.spdf$PID), as.character( extra_basin.spdf$PID)) \n# names(electricity_transmission_MW) = names_et\n# row.names(electricity_transmission_MW) = names_et\n\n# instead\npid_mrg<- data.frame( round(coordinates(mrg.spdf),2) ) %>% \n mutate(coords = paste(X1,X2,sep = '.')) %>% \n mutate(pid_4subb = names_et) %>% \n select(coords,pid_4subb)\n\npid_correspondence<- data.frame( round(coordinates(subbasins.sp),2) ) %>% \n mutate(coords = paste(X1,X2,sep = '.')) %>% \n left_join(pid_mrg) %>% \n select(-X1,-X2)\n\nnames(electricity_transmission_MW) = pid_correspondence$pid_4subb\nrow.names(electricity_transmission_MW) = pid_correspondence$pid_4subb\n\n# Make transmission network\nstarts = NULL\nends = NULL\nmw = NULL\ntrns.df2 = NULL\nfor(ii in 1:ncol(electricity_transmission_MW))\n\t{\n\tfor(jj in ii:ncol(electricity_transmission_MW))\n\t\t{\n\t\tif(electricity_transmission_MW[ii,jj]>0)\n\t\t\t{\n\t\t\tstarts = c(starts,ii)\n\t\t\tends = c(ends,jj)\n\t\t\tmw = c(mw,electricity_transmission_MW[ii,jj])\n\t\t\ttrns.df2 = rbind(trns.df2,data.frame(value = electricity_transmission_MW[ii,jj], tec = paste0(names(electricity_transmission_MW)[ii],'|',row.names(electricity_transmission_MW)[jj]),starts = names(electricity_transmission_MW)[ii], ends = row.names(electricity_transmission_MW)[jj],stringsAsFactors = FALSE ) )\n\t\t\t}\n\t\t}\n\t}\t\n\t\n# Get adjacent and downstream bcus\ngt = gTouches( basin.spdf, byid=TRUE )\nadjacent.list = lapply( 1:length(basin.spdf), function( iii ){ return( basin.spdf@data$PID[ which( gt[,iii] ) ] ) } )\ndownstream.list = lapply( 1:length(basin.spdf), function( iii ){ if( !is.na( basin.spdf@data$DOWN[iii] ) ){ return( basin.spdf@data$PID[ which( as.character( basin.spdf@data$ID ) == as.character( basin.spdf@data$DOWN[iii] ) ) ] ) }else{ return( 'SINK' ) } } )\nnames(adjacent.list) = basin.spdf@data$PID\nnames(downstream.list) = basin.spdf@data$PID\nflow_routes = sapply( 1:length( downstream.list ), function( iii ){ paste( names( downstream.list )[[ iii ]], downstream.list[[ iii ]], sep = '|' ) } ) \nadj1 = unlist( sapply( 1:length( adjacent.list ), function( iii ){ paste( names( adjacent.list )[[ iii ]], adjacent.list[[ iii ]], sep = '|' ) } ) )\nadj2 = unlist( sapply( 1:length( adjacent.list ), function( iii ){ paste( adjacent.list[[ iii ]], names( adjacent.list )[[ iii ]], sep = '|' ) } ) )\ninds = NULL\nfor(iii in 1:length(adj2)){ jjj = which( adj1 == adj2[iii] ) ; if( jjj > iii ){ inds = c(inds,jjj) } }\nadj3 = unlist( sapply( 1:length( adjacent.list ), function( iii ){ paste( names( adjacent.list )[[ iii ]],'|',adjacent.list[[ iii ]], sep = '' ) } ) )\nadjacent_routes = adj3[ -1 * inds ]\nexport_routes = c( \t\"PAK_2|PAK\",\n \"PAK_4|PAK\",\n \"PAK_5|PAK\",\n \"PAK_6|PAK\",\n \"PAK_10|PAK\",\n \"PAK_12|PAK\",\n \"PAK_13|CHN\",\n \"CHN_2|CHN\",\n \"CHN_1|CHN\",\n \"CHN_3|CHN\",\n \"AFG_2|AFG\",\n \"AFG_1|AFG\",\n \"PAK_7|AFG\",\n \"PAK_8|AFG\",\n \"PAK_10|IND\",\n \"IND_4|IND\")\n\nlibrary(tmap)\ntm_shape(basin.spdf)+\n tm_borders(col = NA, lwd = 1.5, lty = \"solid\", alpha = NA)+\n tm_polygons(col = 'REGION', lty = \"solid\", alpha = 0.2)+\n tm_text(\"PID\", size=\"AREA\", root=5)+\n tm_shape(extra_basin.spdf)+\n tm_borders(col = NA, lwd = 2.5, lty = \"solid\", alpha = NA)+\n tm_text(\"PID\", color = 'red')\n\nall_routes <- c(adjacent_routes,export_routes)\ntrns_out.df <- data.frame(tec = all_routes, value = 0,stringsAsFactors = F) %>% \n filter(!tec %in% trns.df2$tec) %>% \n bind_rows(trns.df2 %>% select(tec,value) ) %>% \n mutate(node = gsub('\\\\|.*','',tec)) %>% \n mutate(year_all = 2015) %>% \n mutate(units='MW') %>%\n select(node,tec,year_all,value,units)\n\nwrite.csv(trns_out.df,file.path(getwd(),'input/basin_transmission/existing_routes.csv'),row.names = F)\n\ntrueCentroids = gCentroid(basin.spdf,byid=TRUE)\n#proj4string(trueCentroids) = \"+proj=longlat +ellps=WGS84 +units=m\"\ntrueCentroids <- spTransform(trueCentroids,CRS(\"+proj=lcc +lat_1=26 +lat_0=26 +lon_0=74 +k_0=0.99878641 +x_0=2743196.4 +y_0=914398.8 +a=6377301.243 +b=6356100.230165384 +towgs84=283,682,231,0,0,0,0 +units=m +no_defs\" ))\ndistp = gDistance(trueCentroids,byid = T)\ndist = as.data.frame(distp*gt)\n\nnames(dist) = as.character(pid_correspondence$pid_4subb)[1:24]\nrow.names(dist) = as.character(pid_correspondence$pid_4subb)[1:24]\n\nstarts = NULL\nends = NULL\ndf = NULL\nfor(ii in 1:ncol(dist))\n{\n for(jj in ii:ncol(dist))\n {\n if(dist[ii,jj]>0)\n {\n starts = c(starts,ii)\n ends = c(ends,jj)\n df = rbind(df,data.frame(dist = dist[ii,jj], route = paste0(names(dist)[ii],'|',row.names(dist)[jj]), country = gsub('_.*','',names(dist)[ii])) )\n }\n }\n}\n\n#need to add external routes, but can set an average number\nlibrary(data.table)\ndist.df <- df %>% \n\tmutate(dist = dist/1000) %>% \n\tmutate(mean = mean(dist)) %>% \n\tgroup_by(country) %>% \n\tmutate(cnt_mean = mean(dist)) %>% \n\tungroup()\n\ndist.out <- dist.df %>% \n\tselect(dist,route) %>% \n\trename(tec = route)\n\nwrite.csv(dist.out,'P:/is-wel/indus/message_indus/input/PID_distances_km.csv',row.names = F)\n", "meta": {"hexsha": "5d178f2f584c11b1affb8800c043f75b2f93f738", "size": 11585, "ext": "r", "lang": "R", "max_stars_repo_path": "MESSAGEix/input_data_scripts/transmission.r", "max_stars_repo_name": "amirsarikhani/NEST", "max_stars_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-07-15T19:28:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-24T04:45:43.000Z", "max_issues_repo_path": "MESSAGEix/input_data_scripts/transmission.r", "max_issues_repo_name": "amirsarikhani/NEST", "max_issues_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-19T15:49:34.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-19T15:49:34.000Z", "max_forks_repo_path": "MESSAGEix/input_data_scripts/transmission.r", "max_forks_repo_name": "amirsarikhani/NEST", "max_forks_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-05-20T08:50:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T03:35:08.000Z", "avg_line_length": 45.9722222222, "max_line_length": 311, "alphanum_fraction": 0.6476478205, "num_tokens": 3868, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370308082623217, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.31105156596637634}} {"text": "# This example shows you how to use the R6 class ParameterFile. That being said, it is quite simple to just copy-paste the parameter you want from the logfile type 2 rather than using my ParameterFile interface if you prefer.\n# Note that the graph will likely not be good looking. You will need to play around with the ggnet function to get exactly the graph you want. The purpose of this script is just to show you a small example of what you could do to graph parameters that are being printed on the logfile type 2\n\n\n###########################\n##### Instal packages #####\n###########################\n\nneeded_packages <- c(\"network\", \"ggplot2\", \"GGally\")\nneed_to_install_packages = setdiff(needed_packages, rownames(installed.packages()))\nif (length(need_to_install_packages) > 0) {\n\tcat(paste(\"I will try to install the following package(s):\", paste(need_to_install_packages, collapse=\", \")))\n install.packages(need_to_install_packages)\n}\nfor (needed_package in needed_packages)\n{\n\trequire(needed_package, character.only=TRUE)\n}\n\n\n###################################\n##### Load log file of type 2 #####\n###################################\n\nparamFile = ParameterFile$new(\"/Users/remi/Documents/Biologie/programming/PopGenSimulator/SimBit/log.log\")\n\n##########################\n##### Get parameters #####\n##########################\n\ngenerations = paramFile$readParameter(\"__GenerationChange\") # Contains a vector of the generations at which there were temporal changes (with the @G marker)\ndispMatrices = paramFile$readDispersalMatrixOverTime() # Contains a list of dispersal matrix. Each element of the list is for a different @G marker\npatchCapacities = paramFile$readParameter(\"__patchCapacity\") # Contains a list of vector of patch capacity. Each element of the list is for a different @G marker\n\n######################################################################################\n##### Loop over the generations at which there were changes for those parameters #####\n######################################################################################\n\npdf(\"DemographyGraphs.pdf\")\nfor (generation_index in 1:length(generations))\n{\n\tdispMatrix = dispMatrices[[generation_index]]\n\tpatchCapacity = patchCapacities[[generation_index]]\n\tgeneration = generations[generation_index]\n\n\t#################\n\t##### Graph #####\n\t#################\n\n\tnet = network(dispMatrix, directed = TRUE)\n\t#net %v% \"patchID\" = paste(\"patch \", 1:nrow(dispMatrix))\n\tprint(ggnet(net, node.alpha=0.5, label = 1:nrow(dispMatrix), weight = patchCapacity, arrow.size = 8, arrow.gap = 0) + ggtitle(paste(\"From generation\", generation)))\n}\ndev.off()\n\n\n", "meta": {"hexsha": "e81867b805335ec3e3032ff0e4eed99c9a69d130", "size": 2641, "ext": "r", "lang": "R", "max_stars_repo_path": "GraphParameters/example.r", "max_stars_repo_name": "RemiMattheyDoret/SimBit", "max_stars_repo_head_hexsha": "ed0e64c0abb97c6c889bc0adeec1277cbc6cbe43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2017-06-06T23:02:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T20:13:05.000Z", "max_issues_repo_path": "GraphParameters/example.r", "max_issues_repo_name": "RemiMattheyDoret/SimBit", "max_issues_repo_head_hexsha": "ed0e64c0abb97c6c889bc0adeec1277cbc6cbe43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-06-06T23:08:05.000Z", "max_issues_repo_issues_event_max_datetime": "2017-06-07T09:28:08.000Z", "max_forks_repo_path": "GraphParameters/example.r", "max_forks_repo_name": "RemiMattheyDoret/SimBit", "max_forks_repo_head_hexsha": "ed0e64c0abb97c6c889bc0adeec1277cbc6cbe43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.3333333333, "max_line_length": 291, "alphanum_fraction": 0.642180992, "num_tokens": 560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370308082623216, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.31105156596637623}} {"text": "#' The full human linear model underlying PROGENy\n#'\n#' HGNC gene symbols in rows, pathways in columns. Pathway activity inference\n#' works by matrix multiplication of gene expression with the model.\n#'\n#' @format The full human model contains 22479 genes, associated pathways,\n#' weight and the p-value. \n#' \\describe{\n#' \\item{gene}{gene names in HGNC symbols}\n#' \\item{pathway}{names of PROGENy pathways}\n#' \\item{weight}{z-scores for a given gene}\n#' \\item{p.value}{significance of gene in pathway}\n#' }\n#' @keywords datasets\n#' @name model_human_full\n#' @examples get(\"model_human_full\", envir = .GlobalEnv)\n#' @source \\url{https://www.nature.com/articles/s41467-017-02391-6}\nNULL\n\n#' The full mouse linear model underlying PROGENy\n#'\n#' MGI gene symbols in rows, pathways in columns. Pathway activity inference\n#' works by matrix multiplication of gene expression with the model.\n#'\n#' @format The full mouse model contains 17426 genes, associated pathways,\n#' weight and the p-value. \n#' \\describe{\n#' \\item{gene}{gene names in HGNC symbols}\n#' \\item{pathway}{names of PROGENy pathways}\n#' \\item{weight}{z-scores for a given gene}\n#' \\item{p.value}{significance of gene in a pathway}\n#' }\n#' @keywords datasets\n#' @name model_mouse_full\n#' @examples get(\"model_mouse_full\", envir = .GlobalEnv)\n#' @source \\url{https://www.ncbi.nlm.nih.gov/pubmed/31525460}\nNULL\n", "meta": {"hexsha": "2ab7d40e526c3dc887f1f255841719bd3f1293ec", "size": 1416, "ext": "r", "lang": "R", "max_stars_repo_path": "R/data.r", "max_stars_repo_name": "jan-glx/progeny", "max_stars_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/data.r", "max_issues_repo_name": "jan-glx/progeny", "max_issues_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/data.r", "max_forks_repo_name": "jan-glx/progeny", "max_forks_repo_head_hexsha": "6c53e093a9d8dca112892aa9b340740096accd91", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.2631578947, "max_line_length": 77, "alphanum_fraction": 0.7055084746, "num_tokens": 371, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.31105155923688455}} {"text": "## functions for estimate cell counts edited to take matrices rather than mset\n## relaxed p value threshold to select probes to 1e-4\n\nlibrary(genefilter)\nlibrary(quadprog)\n\nvalidationCellType <- function(Y, pheno, modelFix, modelBatch=NULL,\n L.forFstat = NULL, verbose = FALSE){\n N <- dim(pheno)[1]\n pheno$y <- rep(0, N)\n xTest <- model.matrix(modelFix, pheno)\n sizeModel <- dim(xTest)[2]\n M <- dim(Y)[1]\n \n if(is.null(L.forFstat)) {\n L.forFstat <- diag(sizeModel)[-1,] # All non-intercept coefficients\n colnames(L.forFstat) <- colnames(xTest) \n rownames(L.forFstat) <- colnames(xTest)[-1] \n }\n\n ## Initialize various containers\n sigmaResid <- sigmaIcept <- nObserved <- nClusters <- Fstat <- rep(NA, M)\n coefEsts <- matrix(NA, M, sizeModel)\n coefVcovs <- list()\n\n if(verbose)\n cat(\"[validationCellType] \")\n for(j in 1:M) { # For each CpG\n ## Remove missing methylation values\n ii <- !is.na(Y[j,])\n nObserved[j] <- sum(ii)\n pheno$y <- Y[j,]\n \n if(j%%round(M/10)==0 && verbose)\n cat(\".\") # Report progress\n \n try({ # Try to fit a mixed model to adjust for plate\n if(!is.null(modelBatch)) {\n fit <- try(lme(modelFix, random=modelBatch, data=pheno[ii,]))\n OLS <- inherits(fit,\"try-error\") # If LME can't be fit, just use OLS\n } else\n OLS <- TRUE\n\n if(OLS) {\n fit <- lm(modelFix, data=pheno[ii,])\n fitCoef <- fit$coef\n sigmaResid[j] <- summary(fit)$sigma\n sigmaIcept[j] <- 0\n nClusters[j] <- 0\n } else { \n fitCoef <- fit$coef$fixed\n sigmaResid[j] <- fit$sigma\n sigmaIcept[j] <- sqrt(getVarCov(fit)[1])\n nClusters[j] <- length(fit$coef$random[[1]])\n }\n coefEsts[j,] <- fitCoef\n coefVcovs[[j]] <- vcov(fit)\n \n useCoef <- L.forFstat %*% fitCoef\n useV <- L.forFstat %*% coefVcovs[[j]] %*% t(L.forFstat)\n Fstat[j] <- (t(useCoef) %*% solve(useV, useCoef))/sizeModel\n })\n }\n if(verbose)\n cat(\" done\\n\")\n ## Name the rows so that they can be easily matched to the target data set\n rownames(coefEsts) <- rownames(Y)\n colnames(coefEsts) <- names(fitCoef)\n degFree <- nObserved - nClusters - sizeModel + 1\n\n ## Get P values corresponding to F statistics\n Pval <- 1-pf(Fstat, sizeModel, degFree)\n \n out <- list(coefEsts=coefEsts, coefVcovs=coefVcovs, modelFix=modelFix, modelBatch=modelBatch,\n sigmaIcept=sigmaIcept, sigmaResid=sigmaResid, L.forFstat=L.forFstat, Pval=Pval,\n orderFstat=order(-Fstat), Fstat=Fstat, nClusters=nClusters, nObserved=nObserved,\n degFree=degFree)\n \n out\n}\n\n\npickCompProbes <- function(rawbetas, cellInd, cellTypes = NULL, numProbes = 50, probeSelect = probeSelect) {\n\t## p is matrix of beta values\n\t## cellInd is vector denoting cell type \n splitit <- function(x) {\n split(seq(along=x), x)\n }\n \n # p <- getBeta(mSet)\n # pd <- as.data.frame(colData(mSet))\n if(!is.null(cellTypes)) {\n if(!all(cellTypes %in% as.character(cellInd)))\n stop(\"elements of argument 'cellTypes' is not part of 'cellInd'\")\n keep <- which(as.character(cellInd) %in% cellTypes)\n rawbetas <- rawbetas[,keep]\n\t\tcellInd<-cellInd[keep]\n }\n ## make cell type a factor \n cellInd <- factor(cellInd)\n ffComp <- rowFtests(rawbetas, cellInd)\n prof <- sapply(splitit(cellInd), function(i) rowMeans(rawbetas[,i]))\n r <- matrixStats::rowRanges(rawbetas)\n compTable <- cbind(ffComp, prof, r, abs(r[,1] - r[,2]))\n names(compTable)[1] <- \"Fstat\"\n names(compTable)[c(-2,-1,0) + ncol(compTable)] <- c(\"low\", \"high\", \"range\") \n tIndexes <- splitit(cellInd)\n tstatList <- lapply(tIndexes, function(i) {\n x <- rep(0,ncol(rawbetas))\n x[i] <- 1\n return(rowttests(rawbetas, factor(x)))\n })\n \n if (probeSelect == \"any\"){\n probeList <- lapply(tstatList, function(x) {\n y <- x[x[,\"p.value\"] < 1e-4,]\n yAny <- y[order(abs(y[,\"dm\"]), decreasing=TRUE),] \n c(rownames(yAny)[1:(numProbes*2)])\n })\n } else {\n probeList <- lapply(tstatList, function(x) {\n y <- x[x[,\"p.value\"] < 1e-4,]\n yUp <- y[order(y[,\"dm\"], decreasing=TRUE),]\n yDown <- y[order(y[,\"dm\"], decreasing=FALSE),]\n c(rownames(yUp)[1:numProbes], rownames(yDown)[1:numProbes])\n })\n }\n \n trainingProbes <- unique(unlist(probeList))\n rawbetas <- rawbetas[trainingProbes,]\n \n pMeans <- colMeans(rawbetas)\n names(pMeans) <- cellInd\n \n form <- as.formula(sprintf(\"y ~ %s - 1\", paste(levels(cellInd), collapse=\"+\")))\n phenoDF <- as.data.frame(model.matrix(~cellInd-1))\n colnames(phenoDF) <- sub(\"cellInd\", \"\", colnames(phenoDF))\n if(ncol(phenoDF) == 2) { # two group solution\n X <- as.matrix(phenoDF)\n coefEsts <- t(solve(t(X) %*% X) %*% t(X) %*% t(rawbetas))\n } else { # > 2 group solution\n tmp <- validationCellType(Y = rawbetas, pheno = phenoDF, modelFix = form)\n coefEsts <- tmp$coefEsts\n }\n \n out <- list(coefEsts = coefEsts, compTable = compTable,\n sampleMeans = pMeans)\n return(out)\n}\n\nprojectCellType <- function(Y, coefCellType, contrastCellType=NULL, nonnegative=TRUE, lessThanOne=FALSE){ \n if(is.null(contrastCellType))\n Xmat <- coefCellType\n else\n Xmat <- tcrossprod(coefCellType, contrastCellType) \n \n nCol <- dim(Xmat)[2]\n if(nCol == 2) {\n Dmat <- crossprod(Xmat)\n mixCoef <- t(apply(Y, 2, function(x) { solve(Dmat, crossprod(Xmat, x)) }))\n colnames(mixCoef) <- colnames(Xmat)\n return(mixCoef)\n } else {\n nSubj <- dim(Y)[2]\n \n mixCoef <- matrix(0, nSubj, nCol)\n rownames(mixCoef) <- colnames(Y)\n colnames(mixCoef) <- colnames(Xmat)\n \n if(nonnegative){\n if(lessThanOne) {\n Amat <- cbind(rep(-1, nCol), diag(nCol))\n b0vec <- c(-1, rep(0, nCol))\n } else {\n Amat <- diag(nCol)\n b0vec <- rep(0, nCol)\n }\n for(i in 1:nSubj) {\n obs <- which(!is.na(Y[,i])) \n Dmat <- crossprod(Xmat[obs,])\n mixCoef[i,] <- solve.QP(Dmat, crossprod(Xmat[obs,], Y[obs,i]), Amat, b0vec)$sol\n }\n } else {\n for(i in 1:nSubj) {\n obs <- which(!is.na(Y[,i])) \n Dmat <- crossprod(Xmat[obs,])\n mixCoef[i,] <- solve(Dmat, t(Xmat[obs,]) %*% Y[obs,i])\n }\n }\n return(mixCoef)\n }\n}\n\n", "meta": {"hexsha": "267ce8cb379ac48e91f8b2142e82541621f0e29a", "size": 6910, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/FunctionsForBrainCellProportionsPrediction.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DNAm/FunctionsForBrainCellProportionsPrediction.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "DNAm/FunctionsForBrainCellProportionsPrediction.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.618556701, "max_line_length": 108, "alphanum_fraction": 0.5397973951, "num_tokens": 2039, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.3109634841071231}} {"text": "# Creating the functions\nshowdensity2 <- function(pop, sample=999){\n if(pop@type==\"PA\"){\n pop_meth <- lapply(1:4, function(x) poppr:::.sampling(pop, ifelse(x !=4, sample, sample*2), type=pop@type, quiet=F, method=x))\n }\n else{\n pop_meth <- lapply(1:4, function(x) poppr:::.sampling(seploc(pop), ifelse(x !=4, sample, sample*2), type=pop@type, quiet=F, method=x))\n }\n df <- NULL\n methlev <- c(\"Original (multilocus)\", \"Permutataion\", \"Parametric Bootstrap\", \"Non-Parametric Bootstrap\")\n invisible(lapply(1:4, function(x) df <<- rbind(df, data.frame(list(Value=unlist(pop_meth[[x]]), Index=rep(c(\"Ia\",\"rbarD\"), each=ifelse(x!=4,sample,sample*2)), Method=rep(methlev[x], sample*ifelse(x!=4,2,4)))))))\n method_plot <- ggplot(data=df, aes(Value, color=Method)) + \n geom_density(aes(fill=Method), alpha=0.1) + \n facet_wrap(. ~ Index, scales=\"free\") + theme_classic()\n print(method_plot)\n return(list(DataFrame=df, Plot=method_plot))\n}\n\nshowdensity <- function(pop, sample=999){\n Indexfac <- factor(1:2, levels=1:2, labels=c(\"I[A]\",\"bar(r)[D]\"))\n if(pop@type==\"PA\"){\n pop_meth <- lapply(1:4, function(x) poppr:::.sampling(pop, sample, type=pop@type, quiet=F, method=x))\n }\n else{\n pop_meth <- lapply(1:4, function(x) poppr:::.sampling(seploc(pop), sample, type=pop@type, quiet=F, method=x))\n }\n df <- NULL\n methlev <- c(\"Original (multilocus)\", \"Permutataion\", \"Parametric Bootstrap\", \"Non-Parametric Bootstrap\")\n invisible(lapply(1:4, function(x) df <<- rbind(df, data.frame(list(Value=unlist(pop_meth[[x]]), Index=rep(Indexfac, each=sample), Method=rep(methlev[x], sample*2))))))\n method_plot <- ggplot(data=df, aes(Value, color=Method)) + \n geom_density(aes(fill=Method), alpha=0.1) + \n geom_rug() +\n facet_wrap(~ Index, nrow=1, scales=\"free\") + theme_classic()\n print(method_plot)\n return(list(DataFrame=df, Plot=method_plot))\n}\n\n\n\nshowplots <- function(x, name){\n cat(\"|\", name,\"\\n\")\n themed <- theme(axis.text.x=element_text(size = 10, angle=45, hjust=1, vjust = 1), legend.position = \"bottom\")\n thickens <- x + labs(title=paste(name, \"over 1000 data sets\")) + xlab(\"Sex Rate\") + ylab(name) + themed\n# if(name %in% IndexNames[c(2,10)]){\n# \tthickens <- thickens + geom_hline(aes(yintercept = 0.05, color = \"red\")) + annotate(\"text\", x = 0, y = 0.08, label = \"p = 0.05\", color = \"red\") + coord_trans(y = \"log2\")\n# }\n ggsave(filename=paste(name,\"pdf\",sep=\".\"), plot=thickens, width = 16, height = 8, units=\"in\", dpi=300)\n return\n}\npvalprop.rd <- function(df, alpha=0.05){\n sam <- levels(df$Samp.Size)\n sex <- levels(df$Sex.Rate)\n #cat(sam, \"\\n\")\n #cat(sex, \"\\n\")\n derp <- sapply(sex, function(x, alpha){\n #cat(\"x:\",x,\"\\n\")\n temp <- df[df$Sex.Rate == x, ]\n head(temp)\n vapply(sam, function(y, alpha){\n #cat(\"y:\",y,\"\\n\")\n #res <- sum(temp[temp$Samp.Size == y, ]$p.Ia <= 0.05, na.rm=TRUE)\n res <- sum(temp[temp$Samp.Size == y, ]$p.rD <= alpha, na.rm=TRUE)\n return(res)\n }, 1, alpha)\n }, alpha)\n rownames(derp) <- sam\n return(derp)\n}\npvalprop.Ia <- function(df, alpha=0.05){\n sam <- levels(df$Samp.Size)\n sex <- levels(df$Sex.Rate)\n #cat(sam, \"\\n\")\n #cat(sex, \"\\n\")\n derp <- sapply(sex, function(x, alpha){\n #cat(\"x:\",x,\"\\n\")\n temp <- df[df$Sex.Rate == x, ]\n head(temp)\n vapply(sam, function(y, alpha){\n #cat(\"y:\",y,\"\\n\")\n res <- sum(temp[temp$Samp.Size == y, ]$p.Ia <= alpha, na.rm=TRUE)\n #res[2] <- sum(temp[temp$Samp.Size == y, ]$p.rD <= 0.05, na.rm=TRUE)\n return(res)\n }, 1, alpha)\n }, alpha)\n rownames(derp) <- sam\n return(derp)\n}\n\nsplitbymethod <- function(meth, index, alpha=0.05){\n mlist <- NULL\n mlist$ml <- meth[meth$Method == levels(meth$Method)[1], ]\n mlist$perm <- meth[meth$Method == levels(meth$Method)[2], ]\n mlist$pb <- meth[meth$Method == levels(meth$Method)[3], ]\n mlist$npb <- meth[meth$Method == levels(meth$Method)[4], ]\n if(index==\"Ia\"){\n lapply(mlist, pvalprop.Ia, alpha)\n }\n else{\n lapply(mlist, pvalprop.rd, alpha) \n }\n}\n\n# Starting the script\n\nlibrary(poppr)\nlibrary(ggplot2)\nx <- getfile(mult=TRUE, pattern=\"^final.+?csv$\")\nsetwd(x$path)\nan <- lapply(x$files, read.table, header=TRUE)\n\n#==============================================================================#\n# Edit this for the number of methods you use.\n#\nmethan <- lapply(1:4, function(y) cbind(an[[y]], list(Method=rep(y, 40000))))\nmeth <- rbind(methan[[1]], methan[[2]], methan[[3]], methan[[4]])\n#\n# Got it?\n#==============================================================================#\n\n\n\n\nmeth$Sex.Rate <- round(meth$Sex.Rate, 5)\nmeth$Sex.Rate <- factor(meth$Sex.Rate)\nmeth$Method <- factor(meth$Method)\nmeth$Samp.Size <- factor(paste(\"n =\",meth$Samp.Size))\nmeth$Samp.Size <- factor(meth$Samp.Size, levels(meth$Samp.Size)[c(1,3:4,2)])\n\n\n\n\n#==============================================================================#\n# Hey, this is where you should make sure that you have the correct methods.\n#\nmethlev <- c(\"Original (multilocus)\", \"Permutataion\", \"Parametric Bootstrap\", \"Non-Parametric Bootstrap\")\nsamlev <- levels(meth$Samp.Size)\nsexlev <- levels(meth$Sex.Rate)\n#\n#\n#==============================================================================#\n\nlevels(meth$Method) <- methlev\nIndexNames <- c(\"I[A]\", \"I[A] p-value\", \"Resampled I[A] min\", \"Resampled Ia max\", \"Resampled Ia median\", \"Resampled Ia mean\", \"Resampled Ia variance\", \n \"Resampled Ia standard deviation\", \"rbarD\", \"rbarD p-value\", \"Resampled rbarD min\", \"Resampled rbarD max\", \"Resampled rbarD median\", \"Resampled rbarD mean\", \n \"Resampled rbarD variance\", \"Resampled rbarD standard deviation\")\nnames(IndexNames) <- names(meth)[10:25]\nPlots <- NULL\ncat(\"\\n\\nCreating Plots\\n\\n\")\n\nthemes <- theme_bw() + \n theme(legend.position = \"bottom\", \n axis.text.x = element_text(angle = 45, hjust=1, vjust=1, family = \"Helvetica\"), \n axis.text.y = element_text(family = \"Helvetica\"),\n axis.title = element_text(size = rel(2)),\n panel.grid.major.x = element_blank(), \n strip.background = element_rect(color = \"black\", fill = \"white\"), \n strip.text = element_text(face = \"bold\", family = \"Helvetica\"), \n plot.title = element_text(face = \"bold\", size = rel(2), vjust = 1, \n family = \"Helvetica\"))\n\ntheme2 <- theme_bw() + \n theme(legend.position = c(1, 1), \n legend.justification = c(1, 1), \n legend.title = element_text(size = rel(1), family = \"Helvetica\"),\n axis.text.x = element_text(angle = 45, hjust=1, vjust=1, family = \"Helvetica\"), \n axis.text.y = element_text(family = \"Helvetica\"), \n axis.title.y = element_text(angle = 0, face = \"bold\", size = rel(2)), \n axis.title.x = element_text(angle = 0, face = \"bold\", size = rel(2)),\n panel.grid.major.x = element_blank(), \n plot.title = element_text(face = \"bold\", size = rel(2), vjust = 1, \n family = \"Helvetica\")\n )\n\ntheme3 <- theme_bw() + \n theme(#legend.position = c(0, 1), \n #legend.justification = c(0, 1), \n legend.position = \"top\",\n legend.title = element_text(size = rel(1), family = \"Helvetica\"),\n axis.text.x = element_text(angle = 45, hjust=1, vjust=1, family = \"Helvetica\"), \n axis.text.y = element_text(family = \"Helvetica\"), \n axis.title.y = element_text(angle = 90, face = \"bold\", size = rel(2)), \n axis.title.x = element_text(angle = 0, face = \"bold\", size = rel(2)),\n panel.grid.major.x = element_blank(), \n plot.title = element_text(face = \"bold\", size = rel(2), vjust = 1, \n family = \"Helvetica\")\n )\n\nmeth2 <- meth[-which(is.na(meth$rbarD)), ]\n\nIagrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"Ia\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = expression(I[A])) + xlab(\"Sex Rate\") + theme2 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(I[A], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nrbarDgrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"rbarD\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + labs(y = expression(bar(r)[d])) + xlab(\"Sex Rate\") + theme2 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(bar(r)[d], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nIapvalgrey <- ggplot(meth2[meth2$Method == levels(meth2$Method)[2], ], aes_string(x = \"Sex.Rate\", y = \"p.Ia\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1) + labs(y = expression(paste(I[A], \" p-value (log scale)\"))) + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) + coord_trans(y = \"log2\") + geom_hline(aes(yintercept = 0.05), linetype = 2) + annotate(\"text\", x = 0, y = 0.04, label = \"p = 0.05\")\n\nrbarDpvalgrey <- ggplot(meth2[meth2$Method == levels(meth2$Method)[2], ], aes_string(x = \"Sex.Rate\", y = \"p.rD\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1) + labs(y = expression(paste(bar(r)[d], \" p-value (log scale)\"))) + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) + coord_trans(y = \"log2\") + geom_hline(aes(yintercept = 0.05), linetype = 2) + annotate(\"text\", x = 0, y = 0.04, label = \"p = 0.05\")\n\nE5grey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"E.5\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = expression(E[5])) + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(E[5], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nHexpgrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"Hexp\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = \"Expected Heterozygosity\") + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(E[5], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nHgrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"H\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = \"Shannon-Wiener Index\") + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(E[5], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nGgrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"G\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = \"Stoddart and Taylor's Index\") + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(E[5], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\nMgrey <- ggplot(meth2, aes_string(x = \"Sex.Rate\", y = \"MLG\", fill = \"Samp.Size\")) + geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) + theme_bw() + labs(y = \"Multilocus genotypes\") + xlab(\"Sex Rate\") + theme3 + labs(fill = \"Sample Size\") + scale_fill_grey(start = 0.4, end = 1) #+ labs(title = expression(paste(E[5], \" over 1000 data sets\")), size = rel(2), face = \"bold\") \n\n\nquantsrd <- lapply(levels(meth2$Sex.Rate), function(x) lapply(levels(meth2$Samp.Size), function(y){\n p <- shapiro.test(meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y & meth2$Method == methlev[1], ]$rbarD)$p.value\n derp <- qqnorm(meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y & meth2$Method == methlev[1], ]$rbarD, main = paste(\"rbarD, p-value:\",p,\"\\nSample Size:\",y,\"Sex Rate:\",x))\n qqline(derp$y)\n}))\n\n\nkruskalrd <- sapply(levels(meth2$Sex.Rate), function(x){\n kruskal.test(meth2[meth2$Sex.Rate == x & \n meth2$Method == methlev[1], ]$p.rD, \n meth2[meth2$Sex.Rate == x & \n meth2$Method == methlev[1], ]$Samp.Size)$p.value\n})\n\nkruskalia <- sapply(levels(meth2$Sex.Rate), function(x){\n kruskal.test(meth2[meth2$Sex.Rate == x & \n meth2$Method == methlev[1], ]$p.Ia, \n meth2[meth2$Sex.Rate == x & \n meth2$Method == methlev[1], ]$Samp.Size)$p.value\n})\nkruskal <- data.frame(list(Ia = kruskalia,rbarD = kruskalrd))\n\n\nlapply(names(meth)[10:25], function(derp){\n cat(\"|\", derp,\"\\n\")\n Plots[[derp]] <<- ggplot(meth, aes_string(x = \"Sex.Rate\", y = derp, fill = \"Method\")) + \n #geom_violin(trim=FALSE) + \n geom_boxplot(outlier.shape = 20, outlier.size = 1, notch = TRUE) +\n facet_wrap(~ Samp.Size, nrow = 2) + \n \tthemes + \n \tylab(IndexNames[derp]) +\n \txlab(\"Sex Rate\")\n return(0)\n })\nnames(Plots) <- IndexNames\n\n\ncat(\"\\n\\nPrinting Plots (May take a little while)\\n\\n\")\ninvisible(lapply(IndexNames, function(x) showplots(Plots[[x]], x)))\n\n\ncat(\"\\n\\nCreating the Probability of Rejection plots...rbarD\\n\\n\")\nrd.tot <- splitbymethod(meth, \"rbarD\", alpha=0.05)\nrd.comb <- NULL\nIa.comb <- NULL\nlapply(rd.tot, function(x) rd.comb <<- rbind(rd.comb, x))\nrd.comb <- as.vector(rd.comb/1000)\n\nrd.df <- data.frame(list(Percent_Reject = rd.comb, \n Samp.Size = rep(levels(meth$Samp.Size), 10*length(methlev)), \n Sex.Rate = rep(levels(meth$Sex.Rate), each=4*length(methlev)), \n Method = rep(rep(levels(meth$Method), each=4), 10))\n )\nrd.df$Sex.Rate <- factor(rd.df$Sex.Rate, levels(rd.df$Sex.Rate)[c(1,9:10,2:8)])\nrd.df$Samp.Size <- factor(rd.df$Samp.Size, levels(rd.df$Samp.Size)[c(1,3:4,2)])\nrd.df$Method <- factor(rd.df$Method, methlev)\n\ncat(\"\\n\\nCreating the Probability of Rejection plots...Ia\\n\\n\")\n\nIa.tot <- splitbymethod(meth, \"Ia\", alpha=0.05)\nlapply(Ia.tot, function(x) Ia.comb <<- rbind(Ia.comb, x))\nIa.comb <- as.vector(Ia.comb/1000)\nIa.df <- data.frame(list(Percent_Reject = Ia.comb, \n Samp.Size = rep(levels(meth$Samp.Size), 10*length(methlev)), \n Sex.Rate = rep(levels(meth$Sex.Rate), each=4*length(methlev)), \n Method = rep(rep(levels(meth$Method), each=4), 10))\n )\nIa.df$Sex.Rate <- factor(Ia.df$Sex.Rate, levels(Ia.df$Sex.Rate)[c(1,9:10,2:8)])\nIa.df$Samp.Size <- factor(Ia.df$Samp.Size, levels(Ia.df$Samp.Size)[c(1,3:4,2)])\nIa.df$Method <- factor(Ia.df$Method, methlev)\n\npng(\"Probability_of_Rejection%03d.png\", width=1024, height=1225)\nrd.plot <- ggplot(rd.df, aes(x = Sex.Rate, y = Percent_Reject, color=Method))\nrd.plot <- rd.plot + geom_point() + geom_hline(aes(yintercept=0.050), color=\"red\") + \n geom_hline(aes(yintercept=0.950), color=\"blue\") + facet_grid(Samp.Size ~ .) +\n labs(title=expression(paste(\"Probability of Rejecting the Null Hypothesis with \", bar(r)[D]))) + \n xlab(\"Sex Rate\") + ylab(\"Probability of Null Hypothesis Rejection\") + \n annotate(\"text\", x = 1, y = 0.10, label=\"5% rejection\", color=\"red\") +\n annotate(\"text\", x = 10, y = 0.90, label=\"95% rejection\", color=\"blue\")\nprint(rd.plot + geom_line(aes(group = Method)) + theme_bw())\n\nIa.plot <- ggplot(Ia.df, aes(x = Sex.Rate, y = Percent_Reject, color=Method))\nIa.plot <- Ia.plot + geom_point() + geom_hline(aes(yintercept=0.050), color=\"red\") + \n geom_hline(aes(yintercept=0.950), color=\"blue\") + facet_grid(Samp.Size ~ .) +\n labs(title=expression(paste(\"Probability of Rejecting the Null Hypothesis with \", I[A]))) + \n xlab(\"Sex Rate\") + ylab(\"Probability of Null Hypothesis Rejection\") + \n\tannotate(\"text\", x = 1, y = 0.10, label=\"5% rejection\", color=\"red\") +\n annotate(\"text\", x = 10, y = 0.90, label=\"95% rejection\", color=\"blue\")\nprint(Ia.plot + geom_line(aes(group = Method)) + theme_bw()) \n\n\nrd.plot.method <- ggplot(rd.df, aes(x = Sex.Rate, y = Percent_Reject, color=Samp.Size))\nrd.plot.method <- rd.plot.method + geom_point() + geom_hline(aes(yintercept=0.050), color=\"red\") + \n geom_hline(aes(yintercept=0.950), color=\"blue\") + facet_grid(Method ~ .) +\n labs(title=expression(paste(\"Probability of Rejecting the Null Hypothesis with \", bar(r)[D]))) + \n xlab(\"Sex Rate\") + ylab(\"Probability of Null Hypothesis Rejection\") + \n annotate(\"text\", x = 1, y = 0.10, label=\"5% rejection\", color=\"red\") +\n annotate(\"text\", x = 10, y = 0.90, label=\"95% rejection\", color=\"blue\")\nprint(rd.plot.method + geom_line(aes(group = Samp.Size)) + theme_bw())\n\nIa.plot.method <- ggplot(Ia.df, aes(x = Sex.Rate, y = Percent_Reject, color=Samp.Size))\nIa.plot.method <- Ia.plot.method + geom_point() + geom_hline(aes(yintercept=0.050), color=\"red\") + \n geom_hline(aes(yintercept=0.950), color=\"blue\") + facet_grid(Method ~ .) +\n labs(title=expression(paste(\"Probability of Rejecting the Null Hypothesis with \", I[A]))) + \n xlab(\"Sex Rate\") + ylab(\"Probability of Null Hypothesis Rejection\") + \n annotate(\"text\", x = 1, y = 0.10, label=\"5% rejection\", color=\"red\") +\n annotate(\"text\", x = 10, y = 0.90, label=\"95% rejection\", color=\"blue\")\nprint(Ia.plot.method + geom_line(aes(group = Samp.Size)) + theme_bw())\ndev.off()\n\n#==============================================================================#\n#=====================================ROC PLOTS================================#\n#alphacut <- c(seq(0,0.2,0.001), seq(0.3,1,0.1))\n# alphacut <- seq(0,1,0.001)\n# nalpha <- length(alphacut)\n# \n# \n# cat(\"Constructing Ia ROC data. This will take a while....\\n\")\n# print(system.time(Ia.tot2 <- lapply(alphacut, function(x) splitbymethod(meth, \"Ia\", alpha=x))))\n# cat(\"Constructing rbarD ROC data. Again, this will take a while...\\n\")\n# print(system.time(rd.tot2 <- lapply(alphacut, function(x) splitbymethod(meth, \"rbarD\", alpha=x))))\n# Ia.comb2 <- NULL\n# rd.comb2 <- NULL\n# cat(\"Combining data sets...\\n\")\n# print(system.time(lapply(Ia.tot2, function(y) lapply(y, function(x) Ia.comb2 <<- rbind(Ia.comb2, x)))))\n# print(system.time(lapply(rd.tot2, function(y) lapply(y, function(x) rd.comb2 <<- rbind(rd.comb2, x)))))\n# \n# \n# \n# Ia.comb2 <- as.vector(Ia.comb2/1000)\n# Ia.df2 <- data.frame(list(Percent_Reject = Ia.comb2, \n# Samp.Size = rep(rep(levels(meth$Samp.Size), 10*length(methlev)), nalpha), \n# Sex.Rate = rep(rep(levels(meth$Sex.Rate), each=4*length(methlev)), each=nalpha), \n# Method = rep(rep(rep(levels(meth$Method), each=4), 10), nalpha))\n# )\n# Ia.df2$Sex.Rate <- factor(Ia.df2$Sex.Rate, levels(Ia.df2$Sex.Rate)[c(1,9:10,2:8)])\n# Ia.df2$Samp.Size <- factor(Ia.df2$Samp.Size, levels(Ia.df2$Samp.Size)[c(1,3:4,2)])\n# Ia.df2$Method <- factor(Ia.df2$Method, methlev)\n# Ia.df2$alpha <- rep(rep(alphacut, each = 16), 10)\n# Ia.df2n <- Ia.df2[Ia.df2$Sex.Rate == 1, ]\n# Ia.df2 <- Ia.df2[Ia.df2$Sex.Rate != 1, ]\n# #nullidx <- unlist(lapply(unique(Ia.df2n$alpha), function(x) rep(which(Ia.df2n$alpha == x), 9)))\n# #Ia.df2$Null_Percent <- Ia.df2n$Percent_Reject[nullidx]\n# Ia.df2$Null_Percent <- rep(Ia.df2n$Percent_Reject, 9)\n# write.csv(Ia.df2, file=\"Ia_ROC.csv\", row.names=FALSE)\n# \n# rd.comb2 <- as.vector(rd.comb2/1000)\n# rd.df2 <- data.frame(list(Percent_Reject = rd.comb2, \n# Samp.Size = rep(rep(levels(meth$Samp.Size), 10*length(methlev)), nalpha), \n# Sex.Rate = rep(rep(levels(meth$Sex.Rate), each=4*length(methlev)), each=nalpha), \n# Method = rep(rep(rep(levels(meth$Method), each=4), 10), nalpha))\n# )\n# rd.df2$Sex.Rate <- factor(rd.df2$Sex.Rate, levels(rd.df2$Sex.Rate)[c(1,9:10,2:8)])\n# rd.df2$Samp.Size <- factor(rd.df2$Samp.Size, levels(rd.df2$Samp.Size)[c(1,3:4,2)])\n# rd.df2$Method <- factor(rd.df2$Method, methlev)\n# rd.df2$alpha <- rep(rep(alphacut, each = 16), 10)\n# rd.df2n <- rd.df2[rd.df2$Sex.Rate == 1, ]\n# rd.df2 <- rd.df2[rd.df2$Sex.Rate != 1, ]\n# #nullidx <- unlist(lapply(unique(rd.df2n$alpha), function(x) rep(which(rd.df2n$alpha == x), 9)))\n# #rd.df2$Null_Percent <- rd.df2n$Percent_Reject[nullidx]\n# rd.df2$Null_Percent <- rep(rd.df2n$Percent_Reject, 9)\n# write.csv(rd.df2, file=\"rd_ROC.csv\", row.names=FALSE)\n\n\n#==============================================================================#\n# Reading in and printing ROC curves\n#==============================================================================#\n\nIa.df2 <- read.csv(\"Ia_ROC.csv\", header=TRUE)\nIa.df2$Sex.Rate <- factor(Ia.df2$Sex.Rate, levels(meth$Sex.Rate))\nIa.df2$Samp.Size <- factor(Ia.df2$Samp.Size, levels(meth$Samp.Size))\nIa.df2$Method <- factor(Ia.df2$Method, methlev)\n\nlibrary(pracma)\nsamlev <- levels(Ia.df2$Samp.Size)\n\nIa.plot <- ggplot(Ia.df2, aes(x = Null_Percent, y = Percent_Reject, xlim=1))\nIa.plot <- Ia.plot + \n # geom_point(aes(color=Method, alpha=alpha)) + \n geom_line(aes(color=Method, linetype=Samp.Size)) + \n facet_wrap(~ Sex.Rate, nrow=3) +\n # facet_grid(Sex.Rate ~ Samp.Size) +\n labs(title=expression(paste(\"ROC analysis of \", I[A]))) + \n xlab(\"False Positive Fraction\") + ylab(\"True Positive Fraction\")\nprint(Ia.plot + theme_classic())\nggsave(\"ROCIa.pn\", width=12, height=6, units=\"in\")\nrd.df2 <- read.csv(\"rd_ROC.csv\", header=TRUE)\nrd.df2$Sex.Rate <- factor(rd.df2$Sex.Rate, levels(meth$Sex.Rate))\nrd.df2$Samp.Size <- factor(rd.df2$Samp.Size, levels(meth$Samp.Size))\nrd.df2$Method <- factor(rd.df2$Method, methlev)\n\nrd.plot <- ggplot(rd.df2, aes(x = Null_Percent, y = Percent_Reject, xlim=1))\nrd.plot <- rd.plot + \n# geom_point(aes(color=Method, alpha=alpha)) + \n geom_line(aes(color=Method, linetype=Samp.Size)) + \n facet_wrap(~ Sex.Rate, nrow=3) +\n# facet_grid(Sex.Rate ~ Samp.Size) +\n labs(title=expression(paste(\"ROC analysis of \", bar(r)[D]))) + \n xlab(\"False Positive Fraction\") + ylab(\"True Positive Fraction\")\nprint(rd.plot + theme_classic())\nggsave(\"ROCrd.png\", width=12, height=6, units=\"in\")\n\n#==============================================================================#\n# Constructing data for ANOVA of the area under the ROC curve\n#==============================================================================#\n\nAUC.rd <- lapply(levels(rd.df2$Sex.Rate), function(w) sapply(methlev, function(x) sapply(samlev, function(y) trapz(rd.df2[rd.df2$Samp.Size == y & rd.df2$Method == x & rd.df2$Sex.Rate == w, ]$Null_Percent, rd.df2[rd.df2$Samp.Size == y & rd.df2$Method == x & rd.df2$Sex.Rate == w, ]$Percent_Reject))))\n\nnames(AUC.rd) <- levels(rd.df2$Sex.Rate)\nAUC.rd.df <- data.frame(list(AUC = unlist(AUC.rd), Method = rep(rep(methlev, each=4), 10), Sex.Rate=rep(names(AUC.rd), each=16), Samp.Size = rep(rep(samlev, 4), 10)))\n\nAUC.Ia <- lapply(levels(Ia.df2$Sex.Rate), function(w) sapply(methlev, function(x) sapply(samlev, function(y) trapz(Ia.df2[Ia.df2$Samp.Size == y & Ia.df2$Method == x & Ia.df2$Sex.Rate == w, ]$Null_Percent, Ia.df2[Ia.df2$Samp.Size == y & Ia.df2$Method == x & Ia.df2$Sex.Rate == w, ]$Percent_Reject))))\n\nnames(AUC.Ia) <- levels(Ia.df2$Sex.Rate)\nAUC.Ia.df <- data.frame(list(AUC = unlist(AUC.Ia), Method = rep(rep(methlev, each=4), 10), Sex.Rate=rep(names(AUC.Ia), each=16), Samp.Size = rep(rep(samlev, 4), 10)))\n\nAUC.rd.df <- read.table(\"rd_AUROCC.csv\", sep = \",\", head = TRUE)\nAUC.Ia.df <- read.table(\"Ia_AUROCC.csv\", sep = \",\", head = TRUE)\n\nIa.TP <- aov(Percent_Reject ~ Sex.Rate + Samp.Size * Method, data=Ia.df2)\nIa.FP <- aov(Null_Percent ~ Sex.Rate + Samp.Size*Method, data=Ia.df2)\nIa.ROC.aov <- aov(AUC ~ Sex.Rate + Samp.Size + Method, data=AUC.Ia.df)\nrd.TP <- aov(Percent_Reject ~ Sex.Rate + Samp.Size + Method, data=rd.df2)\nrd.FP <- aov(Null_Percent ~ Sex.Rate + Samp.Size + Method, data=rd.df2)\nrd.ROC.aov <- aov(AUC ~ Sex.Rate + Samp.Size + Method, data=AUC.rd.df)\n\n\n#==============================================================================#\n# Comparing the different methods using the original data set with the kruskal\n# wallace test.\n#\n\nkruskalia.samp <- sapply(levels(meth2$Sex.Rate), function(x) sapply(samlev, function(y){\n kruskal.test(meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y, ]$p.Ia, \n meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y, ]$Method)$p.value\n}))\n\nkruskalrd.samp <- sapply(levels(meth2$Sex.Rate), function(x) sapply(samlev, function(y){\n kruskal.test(meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y, ]$p.rD, \n meth2[meth2$Sex.Rate == x & meth2$Samp.Size == y, ]$Method)$p.value\n}))\n\nlibrary(lawstat)\nlevene.boot <- c(\n `AUC for Ia` = levene.test(AUC.Ia.df$AUC, AUC.Ia.df$Method)$p.value,\n `AUC for rbarD` = levene.test(AUC.rd.df$AUC, AUC.rd.df$Method)$p.value,\n `False Positive rbarD` = levene.test(rd.df2$Null_Percent, rd.df2$Method)$p.value,\n `True Positive rbarD` = levene.test(rd.df2$Percent_Reject, rd.df2$Method)$p.value,\n `False Positive Ia` = levene.test(Ia.df2$Null_Percent, Ia.df2$Method)$p.value,\n `True Positive Ia` = levene.test(Ia.df2$Percent_Reject, Ia.df2$Method)$p.value,\n `p-value Ia` = levene.test(meth2$p.Ia, meth2$Method)$p.value,\n `p-value rbarD` = levene.test(meth2$p.rD, meth2$Method)$p.value\n)\n\nkruskal.boot <- c(\n `AUC for Ia` = kruskal.test(AUC.Ia.df$AUC, AUC.Ia.df$Method)$p.value,\n `AUC for rbarD` = kruskal.test(AUC.rd.df$AUC, AUC.rd.df$Method)$p.value,\n `False Positive rbarD` = kruskal.test(rd.df2$Null_Percent, rd.df2$Method)$p.value,\n `True Positive rbarD` = kruskal.test(rd.df2$Percent_Reject, rd.df2$Method)$p.value,\n `False Positive Ia` = kruskal.test(Ia.df2$Null_Percent, Ia.df2$Method)$p.value,\n `True Positive Ia` = kruskal.test(Ia.df2$Percent_Reject, Ia.df2$Method)$p.value,\n `p-value Ia` = kruskal.test(meth2$p.Ia, meth2$Method)$p.value,\n `p-value rbarD` = kruskal.test(meth2$p.rD, meth2$Method)$p.value\n)\n\n\nlevene.no.boot <- c(\n `AUC for Ia` = levene.test(AUC.Ia.df[AUC.Ia.df$Method != methlev[4], ]$AUC, AUC.Ia.df[AUC.Ia.df$Method != methlev[4], ]$Method)$p.value,\n `AUC for rbarD` = levene.test(AUC.rd.df[AUC.rd.df$Method != methlev[4], ]$AUC, AUC.rd.df[AUC.rd.df$Method != methlev[4], ]$Method)$p.value,\n `False Positive rbarD` = levene.test(rd.df2[rd.df2$Method != methlev[4], ]$Null_Percent, rd.df2[rd.df2$Method != methlev[4], ]$Method)$p.value,\n `True Positive rbarD` = levene.test(rd.df2[rd.df2$Method != methlev[4], ]$Percent_Reject, rd.df2[rd.df2$Method != methlev[4], ]$Method)$p.value,\n `False Positive Ia` = levene.test(Ia.df2[Ia.df2$Method != methlev[4], ]$Null_Percent, Ia.df2[Ia.df2$Method != methlev[4], ]$Method)$p.value,\n `True Positive Ia` = levene.test(Ia.df2[Ia.df2$Method != methlev[4], ]$Percent_Reject, Ia.df2[Ia.df2$Method != methlev[4], ]$Method)$p.value,\n `p-value Ia` = levene.test(meth2[meth2$Method != methlev[4], ]$p.Ia, meth2[meth2$Method != methlev[4], ]$Method)$p.value,\n `p-value rbarD` = levene.test(meth2[meth2$Method != methlev[4], ]$p.rD, meth2[meth2$Method != methlev[4], ]$Method)$p.value\n)\n\nkruskal.no.boot <- c(\n`AUC for Ia` = kruskal.test(AUC.Ia.df[AUC.Ia.df$Method != methlev[4], ]$AUC, AUC.Ia.df[AUC.Ia.df$Method != methlev[4], ]$Method)$p.value,\n`AUC for rbarD` = kruskal.test(AUC.rd.df[AUC.rd.df$Method != methlev[4], ]$AUC, AUC.rd.df[AUC.rd.df$Method != methlev[4], ]$Method)$p.value,\n`False Positive rbarD` = kruskal.test(rd.df2[rd.df2$Method != methlev[4], ]$Null_Percent, rd.df2[rd.df2$Method != methlev[4], ]$Method)$p.value,\n`True Positive rbarD` = kruskal.test(rd.df2[rd.df2$Method != methlev[4], ]$Percent_Reject, rd.df2[rd.df2$Method != methlev[4], ]$Method)$p.value,\n`False Positive Ia` = kruskal.test(Ia.df2[Ia.df2$Method != methlev[4], ]$Null_Percent, Ia.df2[Ia.df2$Method != methlev[4], ]$Method)$p.value,\n`True Positive Ia` = kruskal.test(Ia.df2[Ia.df2$Method != methlev[4], ]$Percent_Reject, Ia.df2[Ia.df2$Method != methlev[4], ]$Method)$p.value,\n`p-value Ia` = kruskal.test(meth2[meth2$Method != methlev[4], ]$p.Ia, meth2[meth2$Method != methlev[4], ]$Method)$p.value,\n`p-value rbarD` = kruskal.test(meth2[meth2$Method != methlev[4], ]$p.rD, meth2[meth2$Method != methlev[4], ]$Method)$p.value\n)\n\nlevene.df <- data.frame(list(`With Non-Parametric Bootstrap` = levene.boot, `Without Non-Parametric Bootstrap` = levene.no.boot))\n\nkruskal.df <- data.frame(list(`With Non-Parametric Bootstrap` = kruskal.boot, `Without Non-Parametric Bootstrap` = kruskal.no.boot))\n\n\n\n\n\n\nsummary(Ia.TP)\nsummary(Ia.FP)\nsummary(Ia.ROC.aov)\nsummary(rd.TP)\nsummary(rd.FP)\nsummary(rd.ROC.aov)\n#==============================================================================#\n\n#==============================================================================#\n\n\n\n\n\n#==============================================================================#\n#==============================================================================#\n#==============================================================================#\n\n\n\n# Creating plots looking at the differences between max values of distribution\n# and observed values.\n\nrddiff <- ggplot(meth, aes(x = (rbarD - Rd.max), y = rbarD, color = Sex.Rate))\n\nIadiff <- ggplot(meth, aes(x = (Ia - Ia.max), y = Ia, color = Sex.Rate))\n\ncat(\"Printing Observed vs. Distance from maximum value...\\n\")\n\npng(\"Distances%d.png\", width=1024, height=1225)\nprint(rddiff + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum\") + ylab(expression(bar(r)[D])) + xlab(expression(bar(r)[D] - paste(scriptstyle(max), bar(r)[D]))) + theme_classic())\n\nprint(rddiff + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum\") + ylab(expression(bar(r)[D])) + xlab(expression(bar(r)[D] - paste(scriptstyle(max), ,bar(r)[D]))) + theme_bw())\n\n\nprint(Iadiff + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum\") + ylab(expression(I[A])) + xlab(expression(I[A] - paste(scriptstyle(max), I[A]))) + theme_bw())\n\nprint(Iadiff + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum\") + ylab(expression(I[A])) + xlab(expression(I[A] - paste(scriptstyle(max), I[A]))) + theme_bw())\ndev.off()\n\n# Standard Deviations from the mean\nrddiff_sd <- ggplot(meth, aes(x = (rbarD / Rd.sd), y = (rbarD), color=Sex.Rate))\nIadiff_sd <- ggplot(meth, aes(x = (Ia / Ia.sd), y = (Ia), color=Sex.Rate))\n\n\ncat(\"Printing plots of Observed vs. SD ratio...\\n\")\n\npng(\"SDplots%d.png\", width=1024, height=1225)\nprint(\nrddiff_sd + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(bar(r)[D])) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw()\n)\n\nprint(\nrddiff_sd + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(bar(r)[D])) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw()\n)\n\nprint(\nIadiff_sd + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(I[A])) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw()\n)\n\nprint(\nIadiff_sd + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(I[A])) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw()\n)\ndev.off()\n\n\n# Standard Deviations from the SD\nrddiff_mn <- ggplot(meth, aes(x = (rbarD / Rd.sd), y = (Rd.sd), color=Sex.Rate))\nIadiff_mn <- ggplot(meth, aes(x = (Ia / Ia.sd), y = (Ia.sd), color=Sex.Rate))\n\n\ncat(\"Printing SD vs SD Ratio...\\n\")\n\npng(\"SDplots_2_%d.png\", width=1024, height=1225)\n\nprint(rddiff_mn + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed (units = standard deviations) \\n vs. \\n Standard Deviation\") + ylab(expression(SD(bar(r)[D]))) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw())\n\nprint(rddiff_mn + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed (units = standard deviations) \\n vs. \\n Standard Deviation\") + ylab(expression(SD(bar(r)[D]))) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw())\n\nprint(Iadiff_mn + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed (units = standard deviations) \\n vs. \\n Standard Deviation\") + ylab(expression(SD(I[A]))) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw())\n\nprint(Iadiff_mn + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed (units = standard deviations) \\n vs. \\n Standard Deviation\") + ylab(expression(SD(I[A]))) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw())\ndev.off()\n\n\n# Analyzing at alpha == 0.05\n\nmeth95 <- meth[which(meth$p.rD == 0.05 | meth$p.Ia == 0.05), ]\n\nrddiff95 <- ggplot(meth95, aes(x = (rbarD - Rd.max), y = rbarD, color = Sex.Rate))\nIadiff95 <- ggplot(meth95, aes(x = (Ia - Ia.max), y = Ia, color = Sex.Rate))\nrddiff95_sd <- ggplot(meth95, aes(x = (rbarD / Rd.sd), y = (Rd.sd), color=Sex.Rate))\nIadiff95_sd <- ggplot(meth95, aes(x = (Ia / Ia.sd), y = (Ia.sd), color=Sex.Rate))\n\n\nggsave(rddiff95 + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum \\n at p = 0.05\") + ylab(expression(bar(r)[D])) + xlab(expression(bar(r)[D] - paste(scriptstyle(max), ,bar(r)[D]))) + theme_bw(), file=\"rddiff95.png\", width=8, height=9.57)\nggsave(\nIadiff95 + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Distance of observed value from theoretical distribution maximum \\n at p = 0.05\") + ylab(expression(I[A])) + xlab(expression(I[A] - paste(scriptstyle(max), I[A]))) + theme_bw(), file=\"Iadiff95.png\", width=8, height=9.57)\n\nprint(\nrddiff95_sd + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(bar(r)[D])) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw()\n)\nprint(\nrddiff95_sd + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(bar(r)[D])) + xlab(expression(frac(bar(r)[D],SD(bar(r)[D])))) + theme_bw()\n)\nprint(\nIadiff95_sd + geom_point() + facet_grid(Method ~ Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(I[A])) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw()\n)\nprint(\nIadiff95_sd + geom_point() + facet_grid(Sex.Rate ~ Method + Samp.Size) + labs(title=\"Observed/SD(Observed) \n vs. \n Observed\") + ylab(expression(I[A])) + xlab(expression(frac(I[A],SD(I[A])))) + theme_bw()\n)\n\n", "meta": {"hexsha": "d32b0e63f158ace548d99d8f69fdc30b983fd652", "size": 34356, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/old/plotting.r", "max_stars_repo_name": "zkamvar/clonal-inference-simulations", "max_stars_repo_head_hexsha": "dfab4a5b61f65f777b3aa09f6188557dc331a2af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/old/plotting.r", "max_issues_repo_name": "zkamvar/clonal-inference-simulations", "max_issues_repo_head_hexsha": "dfab4a5b61f65f777b3aa09f6188557dc331a2af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/old/plotting.r", "max_forks_repo_name": "zkamvar/clonal-inference-simulations", "max_forks_repo_head_hexsha": "dfab4a5b61f65f777b3aa09f6188557dc331a2af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.1892744479, "max_line_length": 481, "alphanum_fraction": 0.6204156479, "num_tokens": 11278, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3109513413422851}} {"text": "#' Network data formats\n#'\n#' List of accepted graph formats\n#'\n#' @name netdiffuseR-graphs\n#' @details The \\pkg{netdiffuseR} package can handle different types of graph\n#' objects. Two general classes are defined across the package's functions:\n#' static graphs, and dynamic graphs.\n#' \\itemize{\n#' \\item{In the case of \\strong{static graphs}, these are represented as adjacency\n#' matrices of size \\eqn{n\\times n}{n * n} and can be either \\code{\\link{matrix}}\n#' (dense matrices) or \\code{\\link[Matrix:dgCMatrix-class]{dgCMatrix}}\n#' (sparse matrix from the \\pkg{\\link[Matrix:Matrix]{Matrix}} package). While\n#' most of the package functions are defined for both classes, the default output\n#' graph is sparse, i.e. \\code{dgCMatrix}.}\n#' \\item{With respect to \\strong{dynamic graphs}, these are represented by either\n#' a \\code{\\link{diffnet}} object, an \\code{\\link{array}} of size\n#' \\eqn{n\\times n \\times T}{n * n * T}, or a list of size \\eqn{T}\n#' with sparse matrices (class \\code{dgCMatrix}) of size \\eqn{n\\times n}{n * n}.\n#' Just like the static graph case, while most of the functions accept both\n#' graph types, the default output is \\code{dgCMatrix}.}\n#' }\n#' @section diffnet objects:\n#' In the case of \\code{diffnet}-class objects, the following arguments can be omitted\n#' when calling fuictions suitable for graph objects:\n#' \\itemize{\n#' \\item{\\code{toa}: Time of Adoption vector}\n#' \\item{\\code{adopt}: Adoption Matrix}\n#' \\item{\\code{cumadopt}: Cumulative Adoption Matrix}\n#' \\item{\\code{undirected}: Whether the graph is directed or not}\n#' }\n#'\n#' @section Objects' names:\n#' When possible, \\pkg{netdiffuseR} will try to reuse graphs dimensional names,\n#' this is, \\code{\\link{rownames}}, \\code{\\link{colnames}}, \\code{\\link{dimnames}}\n#' and \\code{\\link{names}} (in the case of dynamic graphs as lists). Otherwise,\n#' when no names are provided, these will be created from scratch.\n#' @include imports.r\n#' @author George G. Vega Yon\n#' @family graph formats\nNULL\n\n\nas_generic_graph <- function(graph) UseMethod(\"as_generic_graph\")\n\n# Method for igraph objects\nas_generic_graph.igraph <- function(graph) {\n\n # If multiple then warn\n if (igraph::any_multiple(graph))\n warning(\"The -igraph- object has multiple edges. Only one of each will be retrieved.\")\n if (\"weight\" %in% igraph::graph_attr_names(graph)) {\n adjmat <- igraph::as_adj(graph, attr=\"weight\")\n } else {\n adjmat <- igraph::as_adj(graph)\n }\n\n # Converting to dgCMatrix\n env <- environment()\n ans <- new_generic_graph()\n suppressWarnings(add_to_generic_graph(\"ans\", \"graph\", list(`1`=adjmat), env))\n meta <- c(classify_graph(adjmat), list(\n self = any(igraph::is.loop(graph)),\n undirected = FALSE, # For now we will assume it is undirected\n multiple = FALSE, # And !multiple\n class = \"igraph\"\n ))\n add_to_generic_graph(\"ans\", \"meta\", meta, env)\n\n return(ans)\n}\n\nnew_generic_graph <- function() {\n list(graph=NULL, meta=NULL)\n}\n\n# This function adds an element checking that the slot exits\nadd_to_generic_graph <- function(gg,nam,val,env=environment()) {\n obj <- get(gg, envir = env)\n if (!(nam %in% names(obj))) stop(nam,\" unknown slot.\")\n obj[[nam]] <- val\n assign(gg,obj,envir = env)\n invisible(NULL)\n}\n\n# Method for network objects\nas_generic_graph.network <- function(graph) {\n # If multiple then warn\n if (network::is.multiplex(graph))\n warning(\"The -network- object has multiple edges. These will be added up.\")\n\n # Converting to an adjacency matrix (dgCMatrix)\n adjmat <- edgelist_to_adjmat(\n network::as.edgelist(graph),\n undirected = !network::is.directed(graph),\n multiple = network::is.multiplex(graph),\n self = network::has.loops(graph)\n )\n\n ord <- network::network.vertex.names(graph)\n ord <- match(ord, rownames(adjmat))\n adjmat <- adjmat[ord,ord]\n\n env <- environment()\n ans <- new_generic_graph()\n suppressWarnings(add_to_generic_graph(\"ans\", \"graph\", list(`1`=adjmat), env))\n\n meta <- c(classify_graph(adjmat), list(\n self = network::has.loops(graph),\n undirected = !network::is.directed(graph),\n multiple = network::is.multiplex(graph),\n class = \"network\"\n ))\n\n add_to_generic_graph(\"ans\", \"meta\", meta, env)\n\n return(ans)\n}\n\nstopifnot_graph <- function(x)\n stop(\"No method for graph of class -\",class(x),\"- for \", deparse(sys.call()) #match.call()\n ,\". Please refer to the manual 'netdiffuseR-graphs'.\")\n\n#' Analyze an R object to identify the class of graph (if any)\n#' @template graph_template\n#' @details This function analyzes an R object and tries to classify it among the\n#' accepted classes in \\pkg{netdiffuseR}. If the object fails to fall in one of\n#' the types of graphs the function returns with an error indicating what (and\n#' when possible, where) the problem lies.\n#'\n#' The function was designed to be used with \\code{\\link{as_diffnet}}.\n#' @seealso \\code{\\link{as_diffnet}}, \\code{\\link{netdiffuseR-graphs}}\n#' @return Whe the object fits any of the accepted graph formats, a list of attributes including\n#' \\item{type}{Character scalar. Whether is a static or a dynamic graph}\n#' \\item{class}{Character scalar. The class of the original object}\n#' \\item{ids}{Character vector. Labels of the vertices}\n#' \\item{pers}{Integer vector. Labels of the time periods}\n#' \\item{nper}{Integer scalar. Number of time periods}\n#' \\item{n}{Integer scalar. Number of vertices in the graph}\n#' Otherwise returns with error.\n#' @author George G. Vega Yon\n#' @export\nclassify_graph <- function(graph) {\n\n # Diffnet object\n if (inherits(graph, \"diffnet\")) {\n return(classify_graph(graph$graph))\n } else if (inherits(graph, \"matrix\") || inherits(graph, \"dgCMatrix\")) { # Static graphs\n # Step 0: Should have length\n d <- dim(graph)\n if (!d[1])\n stop(\"Nothing to do. Empty matrix.\")\n\n # Step 1: Should be square\n if (d[1] != d[2])\n stop(\"-graph- must be a square matrix\\n\\tdim(graph) = c(\",\n paste0(d, collapse=\",\"),\").\")\n\n # Step 3: Should be numeric\n m <- mode(graph)\n if (!inherits(graph, \"dgCMatrix\") && !(m %in% c(\"numeric\", \"integer\")))\n stop(\"-graph- should be either numeric or integer.\\n\\tmode(graph) = \\\"\",\n m, \"\\\".\")\n\n # Step 4: Dimension names\n ids <- rownames(graph)\n if (!length(ids)) ids <- 1:d[1]\n\n return(invisible(list(\n type=\"static\",\n class=\"matrix\",\n ids=ids,\n pers=1,\n nper=1,\n n=d[1]\n )))\n }\n # Dynamic graphs (list) ------------------------------------------------------\n else if (inherits(graph, \"list\")) {\n # Step 0: Should have length!\n t <- length(graph)\n if (t < 2)\n stop(\"-graph- must be at least of length 2.\")\n\n # Step 1: All should be of class -dgCMatrix-\n c <- sapply(graph, inherits, \"dgCMatrix\")\n if (!all(c))\n stop(\"The following elements are not of class -dgCMatrix-:\\n\\t\",\n paste0(which(!c), collapse=\", \"),\".\")\n\n # Step 2.1: All must be square matrices\n d <- lapply(graph, dim)\n s <- sapply(d, function(x) x[1] == x[2])\n\n # Step 2.2: It must have some people!\n if (!d[[1]][1])\n stop(\"Nothing to do. Empty graph.\")\n\n if (!all(s))\n stop(\"The following adjmat are not square:\\n\\t\",\n paste0(which(!s), collapse=\", \"),\".\")\n\n # Step 3: All must have the same dimension\n e <- unlist(d, TRUE) == d[[1]][1]\n if (!all(e))\n stop(\"The dimensions of all slices must be equal. \",\n \"The following elements don't coincide with the first slice:\\n\\t\",\n paste0(which(!e), collapse=\", \"),\".\")\n\n # Step 4.1: Individual's ids\n ids <- rownames(graph[[1]])\n if (!length(ids)) ids <- 1:d[[1]][1]\n\n # Step 4.2 Time ids\n suppressWarnings(pers <- as.integer(names(graph)))\n if (!length(pers)) pers <- 1:t\n else {\n # Step 4.2.1: Must be coercible into integer\n if (any(is.na(pers))) stop(\"names(graph) should be either numeric or integer.\")\n\n # Step 4.2.1: Must keep uniqueness\n if (length(unique(pers)) != t) stop(\"When coercing names(graph) into integer,\",\n \"some slices acquired the same name.\")\n }\n\n return(invisible(list(\n type=\"dynamic\",\n class=\"list\",\n ids=as.character(ids),\n pers=pers,\n nper=t,\n n=d[[1]][1])\n ))\n\n }\n # Dynamic graphs (array) -----------------------------------------------------\n else if (inherits(graph, \"array\")) {\n # Step 0: it should have length!\n d <- dim(graph)\n if (d[3] < 2)\n stop(\"-graph- must be at least of length 2.\")\n\n # Step 1: there must be some people\n if (!d[1])\n stop(\"Nothing to do. Empty matrix.\")\n\n # Step 2: It must be square\n if (d[1] != d[2])\n stop(\"Each adjmat in -graph- must be a square matrix\\n\\tdim(graph) = c(\",\n paste0(d, collapse=\",\"),\").\")\n\n # Step 3: Should be numeric\n m <- mode(graph)\n if (!(m %in% c(\"numeric\", \"integer\")))\n stop(\"-graph- should be either numeric or integer.\\n\\tmode(graph) = \\\"\",\n m, \"\\\".\")\n\n # Step 4: Dimension names\n ids <- rownames(graph)\n if (!length(ids)) ids <- 1:d[1]\n\n pers <- as.numeric(dimnames(graph)[[3]])\n if (!length(pers)) pers <- 1:d[3]\n else {\n # Step 4.2.1: Must be coercible into integer\n suppressWarnings(alters <- as.integer(floor(pers)))\n if (any(is.na(alters))) stop(\"names(graph) should be either numeric or integer.\")\n\n # Step 4.2.1: Must keep uniqueness\n if (length(unique(alters)) != length(pers))\n stop(\"When coercing names(graph) into integer,\",\n \"some slices acquired the same name.\")\n pers <- alters\n }\n\n return(invisible(list(\n type=\"dynamic\",\n class=\"array\",\n ids=ids,\n pers=pers,\n nper=d[3],\n n=d[1])\n ))\n }\n\n # Other case (ERROR) ---------------------------------------------------------\n stop(\"Not an object allowed in netdiffuseR. It must be either:\\n\\t\",\n \"matrix, dgCMatrix, list or array.\\n\", \"Please refer to ?\\\"netdiffuseR-graphs\\\" \")\n}\n\n# Auxiliar function to check if there's any attribute of undirectedness\ncheckingUndirected <- function(graph, warn=TRUE, default=getOption(\"diffnet.undirected\")) {\n\n # Ifendifying the class of graph\n if (inherits(graph, \"diffnet\")) undirected <- graph$meta$undirected\n else undirected <- attr(graph, \"undirected\")\n\n if (warn)\n if (length(undirected) && undirected != FALSE)\n warning(\"The entered -graph- will now be directed.\")\n\n if (!length(undirected)) undirected <- default\n\n invisible(undirected)\n\n}\n", "meta": {"hexsha": "f8af83c72e3a516267cc2e7d05983be797a17529", "size": 10579, "ext": "r", "lang": "R", "max_stars_repo_path": "R/graph_data.r", "max_stars_repo_name": "USCCANA/netdiffuseR", "max_stars_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2015-12-15T02:49:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T02:48:37.000Z", "max_issues_repo_path": "R/graph_data.r", "max_issues_repo_name": "USCCANA/netdiffuseR", "max_issues_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2015-12-17T03:43:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T18:50:22.000Z", "max_forks_repo_path": "R/graph_data.r", "max_forks_repo_name": "USCCANA/netdiffuseR", "max_forks_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-12-28T21:47:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T19:48:08.000Z", "avg_line_length": 34.4592833876, "max_line_length": 96, "alphanum_fraction": 0.6271859344, "num_tokens": 2887, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.31095132679903065}} {"text": "# Download and preprocess GLSDEM\n\n# URLs: ftp://ftp.glcf.umd.edu/glcf/GLSDEM/Degree_tiles/n060/GLSDEM_n060e025/GLSDEM_n060e025.tif.gz\nlibrary(R.utils)\nlibrary(landsat)\nsource(\"utils/raster-utils.r\")\nsource(\"utils/dem-statistics.r\")\n\nGLSDir = \"../../userdata/dem/glsdem\"\nTilesN = 55:61\nTilesE = 28:29\n\ndems = list()\nfor(n in TilesN)\n{\n for(e in TilesE)\n {\n filename = paste0(\"GLSDEM_n0\", n, \"e0\", e, \".tif\")\n url = paste0(\"ftp://ftp.glcf.umd.edu/glcf/GLSDEM/Degree_tiles/n0\", n,\n \"/GLSDEM_n0\", n, \"e0\", e, \"/\", filename, \".gz\")\n outfile = paste0(GLSDir, \"/\", filename)\n outfilegz = paste0(outfile, \".gz\")\n if(!file.exists(outfilegz) && !file.exists(outfile))\n download.file(url, outfilegz, \"wget\")\n if(file.exists(outfilegz) && file.size(outfilegz) > 0)\n gunzip(paste0(outfile, \".gz\"))\n if(file.exists(outfile) && file.size(outfile) > 0)\n dems = list(dems, raster(outfile))\n }\n}\ndems = unlist(dems)\n\nGLSMosaic = mosaic(dems, fun=mean, filename=paste0(GLSDir, \"/mosaic.grd\"), overwrite=TRUE, tolerance=0.5)\n# USGS doesn't provide rasters for pure sea since it's 0 by definition, so fill it if needed\n#DEMMosaic[is.na(DEMMosaic)] = 0\n#GLSMosaic = reclassify(GLSMosaic, cbind(NA, 0))\n#writeRaster(GLSMosaic, filename=paste0(GLSDir,\"/mosaic.tif\"), overwrite=TRUE)\nPVExample = raster(\"/data/MTDA/TIFFDERIVED/PROBAV_L3_S5_TOC_100M/20160711/PROBAV_S5_TOC_20160711_100M_V001/PROBAV_S5_TOC_X20Y01_20160711_100M_V001_NDVI.tif\")\nCalculateDEMStatistics(GLSMosaic, GLSDir, PVExample)\n\n# R3241-R5414\n# L2333-K4421-K5442\n# K-R 2-5\n# Filelist obtained via bash script:\n# rsync -arPv rsync://tiedostot.kartat.kapsi.fi/mml/korkeusmalli/hila_10m/etrs-tm35fin-n2000 | grep \\.tif | awk '{print $5}' | grep -P \"[K-R]\" > filelist.txt\n\n", "meta": {"hexsha": "fcfb11441d4a5f4dd5636d3802976e76b6ca4047", "size": 1813, "ext": "r", "lang": "R", "max_stars_repo_path": "src/raster-based/elevation/get-dem.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/raster-based/elevation/get-dem.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/raster-based/elevation/get-dem.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 38.5744680851, "max_line_length": 157, "alphanum_fraction": 0.6828461114, "num_tokens": 616, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494421679929, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.31091471180218766}} {"text": "#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n# DATA WRANGLING ----\n#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n# 1. Merge data from different rounds \n#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n# This function merges data from different rounds and merge them into\n# a single data.frame for a given year.\n\nmerge_cartola_data <- function(year){\n year <- as.character(year)\n require(dplyr)\n setwd(paste0(\"data/\", year))\n files <- list.files(pattern = \"rodada\")\n \n list_of_data_frames <- lapply(files, function(x){\n read.csv(x, header = TRUE, stringsAsFactors = FALSE)\n })\n \n df <- do.call(bind_rows, list_of_data_frames)\n return(df)\n}\n\n# Let's open 2018's data\ncartola <- merge_cartola_data(2018)\nsetwd(\"../../\")\n\n#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n# 2. Wrangle scouts from Cartola API, aggregated format ----\n#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n## Carregar pacotes\nlibrary(dplyr)\nlibrary(tidyr)\n\n# Sort data by id and round \ncartola <- \n cartola %>%\n arrange(atletas.atleta_id, - atletas.rodada_id)\n\ndf <- cartola\n\n# Create a data.frame to manipulate scouts\ndf <- df[, c(6,16:31)]\n\n# Convert NA values into zeroes\ndf[,2:17] <- sapply(df[,2:17], function(x) ifelse(is.na(x), 0, x))\n\ndf <- df %>%\n group_by(atletas.atleta_id) %>%\n mutate_all(funs(abs(diff(c(.,0)))))\n\ndf$atletas.rodada_id <- cartola$atletas.rodada_id\n\n# Remove aggregated scouts statistics\ncartola <- cartola[, -c(16:33)]\n\n# Join with proper scouts\ndf <- left_join(df, cartola, by = c(\"atletas.atleta_id\", \"atletas.rodada_id\"))\n\n# Arrange variable columns\ndf <- df[, c(1, 18, 20:31, 2:17)]\n\ncartola <- df\nrm(df)\n", "meta": {"hexsha": "a9d3138b8e9d64e0f6fec0be861b45878587d019", "size": 1742, "ext": "r", "lang": "R", "max_stars_repo_path": "assignments/course_2/assignment_4/caRtola-master/src/R/data_wrangling.r", "max_stars_repo_name": "jessequinn/coursera_applied_data_science_with_python_specialization", "max_stars_repo_head_hexsha": "9fec44dc79cf9f524b14b967266d15b6a3bf8495", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2022-01-05T04:42:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-01T02:21:18.000Z", "max_issues_repo_path": "assignments/course_2/assignment_4/caRtola-master/src/R/data_wrangling.r", "max_issues_repo_name": "jessequinn/coursera_applied_data_science_with_python_specialization", "max_issues_repo_head_hexsha": "9fec44dc79cf9f524b14b967266d15b6a3bf8495", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assignments/course_2/assignment_4/caRtola-master/src/R/data_wrangling.r", "max_forks_repo_name": "jessequinn/coursera_applied_data_science_with_python_specialization", "max_forks_repo_head_hexsha": "9fec44dc79cf9f524b14b967266d15b6a3bf8495", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-03-08T07:50:01.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-08T07:50:01.000Z", "avg_line_length": 25.6176470588, "max_line_length": 78, "alphanum_fraction": 0.5442020666, "num_tokens": 479, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.31081121748456}} {"text": "##################\r\n## cfg\t\t\t##\r\n##################\r\nsource(\"cfg.r\")\r\ngraphics.off()\r\n\r\nlimits_TC = c(1, 10, 20, 30, 40, 50, 60, 70)\r\ncols_TC = c(\"white\", \"#AAAA00\", \"#003300\")\r\n\r\ndlimits_TC = c(-40, -20, -10, -5, -2, -1, 1, 2, 5, 10, 20, 40)\r\ndcols_TC = c(\"#1A001A\", \"#330033\", \"#662366\", \"#AA78AA\", \"#eed9ee\", \"#ffffe5\", \"#d9f0a3\", \"#78c679\", \"#238443\", \"#004529\", \"#002211\")\r\n\r\nlimits_BA = c(0.1, 1, 2, 3, 5, 10, 20, 30, 50)\r\ncols_BA = c('#ffffcc','#fed976','#fd8d3c','#e31a1c','#800026')\r\n\r\ndlimits_BA = c(-10, -5, -2, -1, -0.1, -0.01, 0.01, 0.1, 2, 5, 10)\r\ndcols_BA = rev(c('#a50026','#d73027','#f46d43','#fdae61','#fee090','#ffffbf','#e0f3f8','#abd9e9','#74add1','#4575b4','#313695'))\r\n\r\n\r\nitems = c(2:3, 5:6)\r\nyrs = c(2002, 2008, 2014)\r\n\r\nJULES_dir = \"../jules_outputs/\"\r\n\r\nJULES_control = c(\"u-bk543\", \"u-bi607_CropFrag\")\r\nJULES_experiments = list(mortExps = c(\"u-bk757\", \"u-bk809\", \"u-bk716\",\r\n \"u-bk807\", \"u-bk811\", \"u-bk812\", \"u-bk812\"),\r\n INFERNOExps = c(\"u-bi607_CropFrag\", \"u-bi607_FUEL_RH\",\r\n \"u-bi607_FUEL\", \"u-bi607_RH\"))\r\n\r\nExperiment_names = list(c(\"A MAP\", \"B Burnt area\", \"C Rainfall distribution\",\r\n \"D Temperature stress\", \"G Cropland\", \"H Pasture\",\r\n \"I All land use\"),\r\n c(\"Cropland Fragmentation\", \"Fuel and RH\", \"Fuel\", \"RH\"))\r\n\r\n\r\nlmat = list(rbind(c(0,1), c(2,2), 3:4, 5:6, 0, 7:8,c(9, 0), 10), NULL)\r\nheights = list(c(1, 0.33, 1, 1, 1, 1, 1, 0.33), NULL)\r\ngrab_cache = TRUE\r\n\r\n######################\r\n## open\t ##\r\n######################\t\r\n\r\nplotExperimentSet <- function(name, Exp_names, lmat = NULL, heights = 1, normalise = FALSE, ...) { \r\n c(Jules_TC_control, Jules_TC_exp, Jules_BA_control, Jules_BA_exp, Jules_dout, Jules_PFTs) :=\r\n openJulesExperimentSet(name, yrs = yrs, grab_cache = grab_cache,...)\r\n if (normalise) {\r\n mortExpD = layer.apply(Jules_TC_exp, function(i) (i - Jules_TC_control)/ max(addLayer(i, Jules_TC_control)))\r\n } else {\r\n mortExpD = (Jules_TC_exp - Jules_TC_control)\r\n }\r\n \r\n mortExpD = lapply(layer.apply(mortExpD, function(i) c(i)), function(i) i[[1]])\r\n\r\n fname = paste0('figs/JULES_mort_maps', '-', name)\r\n \r\n if (length(mortExpD) == 1){ nrow = 1; ncol = 1\r\n } else { ncol = 2; nrow = ceiling(length(mortExpD)/2)}\r\n \r\n if (is.null(lmat)) {\r\n lmat = matrix(c(1:length(mortExpD), 0)[1:(ncol * nrow)], ncol = ncol, nrow =nrow)\r\n lmat = rbind(c(1, 0), c(2, 0), lmat + 2, max(lmat) + 3)\r\n heights = c(1, 0.5, rep(1, nrow), 0.5)\r\n }\r\n height = (4.75/6.5) * 5 * ((nrow(lmat)-3.5) + 1.6)/4.6\r\n \r\n plotMe <- function(fnamei, control, experiment, limits, cols, dlimits, dcols) {\r\n fname = paste0(fname, '-', fnamei, '.png')\r\n png(fname, height = height, width = 4.75 * ncol/2, units = 'in', res = 300)\r\n layout(lmat, heights = heights)\r\n par(mar = c(0, 0, 0, 0), oma = c(0, 1, 1.5, 1))\r\n\r\n plotStandardMap(control, limits = limits/100, cols = cols, \r\n 'Control', mtext_line = -.8)\r\n par(mar = c(0.3, 0, 0,0))\r\n add_raster_legend2(cols = cols, limits = limits,transpose = FALSE, srt = 0,\r\n plot_loc = c(0.15, 0.85, 0.7, 0.9), add = FALSE, \r\n labelss = c(0, limits, 100), units = '%')\r\n par(mar = rep(0, 4))\r\n mapply(plotStandardMap, experiment, Exp_names, \r\n MoreArgs = list(limits = dlimits/100, cols = dcols, mtext_line = -0.8))\r\n par(mar = c(0.3, 0, 0,0))\r\n add_raster_legend2(cols = dcols, limits = dlimits, extend_min = TRUE, extend_max = TRUE,\r\n transpose = FALSE, srt = 0, units= '%',\r\n plot_loc = c(0.02, 0.98, 0.7, 0.9), add = FALSE)\r\n dev.off()\r\n\r\n }\r\n \r\n plotMe('TC', Jules_TC_control, mortExpD, limits = limits_TC, cols = cols_TC, dlimits = dlimits_TC, dcols = dcols_TC)\r\n Jules_BA_exp[[2]][] = 0.0\r\n mortExpD = layers2list(Jules_BA_exp - Jules_BA_control)\r\n plotMe('BA', Jules_BA_control, mortExpD,\r\n limits = limits_BA, cols = cols_BA, dlimits = dlimits_BA, dcols = dcols_BA)\r\n browser()\r\n}\r\n\r\nmapply(plotExperimentSet, paste0(names(JULES_experiments), '-normalise'),\r\n Experiment_names, JULES_control, JULES_experiments, lmat = lmat, heights = heights, \r\n MoreArgs = list(normalise = TRUE))\r\n\r\nmapply(plotExperimentSet, names(JULES_experiments), Experiment_names, JULES_control, JULES_experiments, lmat = lmat, heights = heights, \r\n MoreArgs = list(normalise = FALSE))\r\n", "meta": {"hexsha": "0a82690e481564122449cb3bdb8161139019a866", "size": 4793, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_jules_diff_maps.r", "max_stars_repo_name": "douglask3/savanna_fire_feedback_test", "max_stars_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_jules_diff_maps.r", "max_issues_repo_name": "douglask3/savanna_fire_feedback_test", "max_issues_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_jules_diff_maps.r", "max_forks_repo_name": "douglask3/savanna_fire_feedback_test", "max_forks_repo_head_hexsha": "3c8cedcafd6b841efea6e8813ca674039336f19e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-13T12:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-13T12:28:00.000Z", "avg_line_length": 45.6476190476, "max_line_length": 137, "alphanum_fraction": 0.5284790319, "num_tokens": 1667, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.31081121748456}} {"text": "context(\"dalda\")\n\ntest_that(\"dalda: misspecified arguments\", {\n\tdata(iris)\n\t# wrong variable names\n\texpect_error(dalda(formula = Species ~ V1, data = iris, wf = \"gaussian\", bw = 10))\n\t# wrong class\n\texpect_error(dalda(formula = iris, data = iris, wf = \"gaussian\", bw = 10))\n\t#expect_error(dalda(iris, data = iris, wf = \"gaussian\", bw = 10))\n\t# target variable also in x\n\texpect_error(dalda(grouping = iris$Species, x = iris, wf = \"gaussian\", bw = 10)) \t\t\t\t\t## system singular\n\texpect_warning(dalda(Species ~ Species + Petal.Width, data = iris, wf = \"gaussian\", bw = 10)) ## warning, Species on RHS removed\n\t# missing x\n\texpect_error(dalda(grouping = iris$Species, wf = \"gaussian\", bw = 10))\n\t## itr\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, itr = -5), throws_error(\"'itr' must be >= 1\"))\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, itr = 0), throws_error(\"'itr' must be >= 1\"))\n\t## wrong method argument\n\t# missing quotes\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, method = ML))\n\t# method as vector\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, method = c(\"ML\",\"unbiased\")))\n\t# expect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, method = c(\"ML\",\"unbiased\")), throws_error(\"'arg' must be of length 1\"))\n})\n\n\ntest_that(\"dalda throws a warning if grouping variable is numeric\", {\n\tdata(iris)\n\t# formula, data\n\texpect_that(dalda(formula = Sepal.Length ~ ., data = iris, wf = \"gaussian\", bw = 10), gives_warning(\"'grouping' was coerced to a factor\"))\n\texpect_error(dalda(formula = Petal.Width ~ ., data = iris, wf = \"gaussian\", bw = 10))\t## system singular\n\t# grouping, x\n\texpect_that(dalda(grouping = iris[,1], x = iris[,-1], wf = \"gaussian\", bw = 10), gives_warning(\"'grouping' was coerced to a factor\"))\n\texpect_error(dalda(grouping = iris[,4], x = iris[,-1], wf = \"gaussian\", bw = 10)) \t## system singular\n})\n\n\ntest_that(\"dalda works if only one predictor variable is given\", {\n\tdata(iris)\n\tfit <- dalda(Species ~ Petal.Width, data = iris, wf = \"gaussian\", bw = 5)\n\texpect_equal(ncol(fit$means), 1)\t\n\texpect_equal(dim(fit$cov), rep(1, 2))\t\n})\n\n\ntest_that(\"dalda: training data from only one class\", {\n\tdata(iris)\n\texpect_that(dalda(Species ~ ., data = iris, bw = 2, subset = 1:50), throws_error(\"training data from only one group given\"))\n\texpect_that(dalda(grouping = iris$Species, x = iris[,-5], bw = 2, subset = 1:50), throws_error(\"training data from only one group given\"))\n})\n\n\ntest_that(\"dalda: one training observation\", {\n\tdata(iris)\n\t# one training observation\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 1), throws_error(\"training data from only one group given\"))\t\n\t# one training observation in one predictor variable\n\texpect_that(dalda(Species ~ Petal.Width, data = iris, wf = \"gaussian\", bw = 1, subset = 1), throws_error(\"training data from only one group given\"))\n})\n\n\ntest_that(\"dalda: initial weighting works correctly\", {\n\tdata(iris)\n\t## check if weighted solution with initial weights = 1 equals unweighted solution\n\tfit1 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2)\n\tfit2 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, weights = rep(1,150))\n\texpect_equal(fit1[-9],fit2[-9])\n\t## returned weights\t\n\ta <- rep(1,150)\n\tnames(a) <- 1:150\n\texpect_equal(fit1$weights[[1]], a)\n\texpect_equal(fit1$weights, fit2$weights)\n\t## weights and subsetting\n\t# formula, data\n\texpect_that(fit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\ta <- rep(1,50)\n\tnames(a) <- 11:60\n\texpect_equal(fit$weights[[1]], a)\n\t# formula, data, weights\n\ta <- rep(1:3,50)[11:60]\n\ta <- a/sum(a) * length(a)\n\tnames(a) <- 11:60\n\texpect_that(fit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, weights = rep(1:3, 50), subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit$weights[[1]], a)\n\t# x, grouping\n\ta <- rep(1,50)\n\tnames(a) <- 11:60\n\texpect_that(fit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit$weights[[1]], a)\t\n\t# x, grouping, weights\n\ta <- rep(1:3,50)[11:60]\n\ta <- a/sum(a) * length(a)\n\tnames(a) <- 11:60\n\texpect_that(fit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2, weights = rep(1:3, 50), subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit$weights[[1]], a)\n\t## wrong specification of weights argument\n\t# weights in a matrix\n\tweight <- matrix(seq(1:150), nrow = 50)\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, weights = weight))\n\t# weights < 0\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, weights = rep(-5, 150)))\n\t# weights true/false\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, weights = TRUE))\n})\n\n\ntest_that(\"dalda breaks out of for-loop if only one class is left\", {\n\texpect_that(fit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, k = 50), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_equal(fit$itr, 3)\n\texpect_equal(length(fit$weights), 4)\n\tset.seed(123)\n\texpect_that(fit <- dalda(Species ~ ., data = iris, wf = \"biweight\", bw = 0.9, subset = 51:150), gives_warning(\"training data from only one group, breaking out of iterative procedure\"))\n\texpect_equal(fit$itr, 2)\n\texpect_equal(length(fit$weights), 3)\n})\n#sapply(fit$weights, function(x) return(list(sum(x[1:50]), sum(x[51:100]), sum(x[101:150]))))\n\ntest_that(\"dalda: subsetting works\", {\n\tdata(iris)\n\t# formula, data\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = 1:80), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris[1:80,], wf = \"gaussian\", bw = 2), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9],fit2[-9])\n\ta <- rep(1,80)\n\tnames(a) <- 1:80\n\texpect_equal(fit1$weights[[1]], a)\n\t# formula, data, weights\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, weights = rep(1:3, each = 50), wf = \"gaussian\", bw = 2, subset = 1:80), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris[1:80,], weights = rep(1:3, each = 50)[1:80], wf = \"gaussian\", bw = 2), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9],fit2[-9])\n\ta <- rep(80, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\tb <- rep(1:3, each = 50)[1:80]\n\tb <- b/sum(b) * length(b)\n\tnames(b) <- 1:80\n\texpect_equal(fit1$weights[[1]], b)\n\t# x, grouping\n\texpect_that(fit1 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 2, subset = 1:80), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = iris$Species[1:80], x = iris[1:80,-5], wf = \"gaussian\", bw = 2), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9],fit2[-9])\n\ta <- rep(1,80)\n\tnames(a) <- 1:80\n\texpect_equal(fit1$weights[[1]], a)\n\t# x, grouping, weights\n\texpect_that(fit1 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 2, weights = rep(1:3, each = 50), subset = 1:80), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = iris$Species[1:80], x = iris[1:80,-5], wf = \"gaussian\", bw = 2, weights = rep(1:3, each = 50)[1:80]), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9],fit2[-9])\n\ta <- rep(80, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\tb <- rep(1:3, each = 50)[1:80]\n\tb <- b/sum(b) * length(b)\n\tnames(b) <- 1:80\n\texpect_equal(fit1$weights[[1]], b)\n\t# wrong specification of subset argument\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = iris[1:10,]))\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = FALSE))\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 0))\n\texpect_error(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = -10:50))\n})\n\n\ntest_that(\"dalda: NA handling works correctly\", {\n\t### NA in x\n\tdata(iris)\n\tirisna <- iris\n\tirisna[1:10, c(1,3)] <- NA\n\t## formula, data\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## formula, data, weights\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\n\t## x, grouping\n\t# na.fail\n\texpect_that(dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping, weights\n\t# na.fail\n\texpect_that(dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t\n\t### NA in grouping\n\tirisna <- iris\n\tirisna$Species[1:10] <- NA\n\t## formula, data\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## formula, data, weights\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = irisna, wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping\n\t# na.fail\n\texpect_that(dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping, weights\n\t# na.fail\n\texpect_that(dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = irisna$Species, x = irisna[,-5], wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\n\t### NA in weights\n\tweights <- rep(1:3,50)\n\tweights[1:10] <- NA\n\t## formula, data, weights\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 6:60, weights = weights, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 6:60, weights = weights, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 11:60, weights = weights), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping, weights\n\t# na.fail\n\texpect_that(dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = weights, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = 6:60, weights = weights, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = 11:60, weights = weights), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\n\t### NA in subset\n\tsubset <- 6:60\n\tsubset[1:5] <- NA\n\t## formula, data\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = subset, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = subset, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## formula, data, weights\n\t# na.fail\n\texpect_that(dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = subset, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = subset, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-c(9, 18)], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping\n\t# na.fail\n\texpect_that(dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = subset, na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = subset, na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = 11:60), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n\t## x, grouping, weights\n\t# na.fail\n\texpect_that(dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = subset, weights = rep(1:3, 50), na.action = na.fail), throws_error(\"missing values in object\"))\n\t# check if na.omit works correctly\n\texpect_that(fit1 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = subset, weights = rep(1:3, 50), na.action = na.omit), gives_warning(\"group virginica is empty\"))\n\texpect_that(fit2 <- dalda(grouping = iris$Species, x = iris[,-5], wf = \"gaussian\", bw = 10, subset = 11:60, weights = rep(1:3, 50)), gives_warning(\"group virginica is empty\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\ta <- rep(50, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, length), a)\n})\n\n\ntest_that(\"dalda: try all weight functions\", {\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 0.5) \n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(0.5)) \n\tfit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 0.5) \n\tfit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = gaussian(0.5)) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\t\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 0.5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5, k = 30)), gives_warning(\"for at least one class all weights are zero\")) \n\texpect_that(fit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 0.5, k = 30), gives_warning(\"for at least one class all weights are zero\")) \n\texpect_that(fit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = gaussian(0.5, 30)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(30, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\t\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"epanechnikov\", bw = 5)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5))\n\tfit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"epanechnikov\", bw = 5)\n\tfit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = epanechnikov(5)) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(150, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"rectangular\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = rectangular(5, 30)), gives_warning(\"for at least one class all weights are zero\")) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(30, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"triangular\", bw = 5)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = triangular(5))\n\tfit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"triangular\", bw = 5)\n\tfit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = triangular(5)) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(150, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"biweight\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = biweight(5, k = 30)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"biweight\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = biweight(5, 30)), gives_warning(\"for at least one class all weights are zero\")) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(30, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"optcosine\", bw = 5)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = optcosine(5))\n\tfit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"optcosine\", bw = 5)\n\tfit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = optcosine(5)) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(150, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"cosine\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = cosine(5, k = 30)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit3 <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"cosine\", bw = 5, k = 30), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit4 <- dalda(x = iris[,-5], grouping = iris$Species, wf = cosine(5, 30)), gives_warning(\"for at least one class all weights are zero\")) \n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit3[-9], fit4[-9])\n\texpect_equal(fit2[c(1:8,10:15)], fit4[c(1:8,10:15)])\n\ta <- rep(30, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n})\n\n\ntest_that(\"dalda: local solution with rectangular window function and large bw and global solution coincide\", {\n\tfit1 <- wlda(formula = Species ~ ., data = iris)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = rectangular(20))\n\texpect_equal(fit1[-c(7,9)], fit2[-c(7,9:15)])\n\texpect_equal(fit1$weights, fit2$weights[[1]])\n})\n\n\ntest_that(\"dalda: arguments related to weighting misspecified\", {\n\t# bw, k not required\r\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5), gives_warning(c(\"argument 'k' is ignored\", \"argument 'bw' is ignored\")))\r\n\tfit2 <- dalda(Species ~ ., data = iris, wf = gaussian(0.5))\r\n\texpect_equal(fit1[-9], fit2[-9])\r\n\r\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5), gives_warning(\"argument 'bw' is ignored\"))\t\n\tfit2 <- dalda(Species ~ ., data = iris, wf = gaussian(0.5))\r\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$k, NULL)\n\texpect_equal(fit1$nn.only, NULL)\t\r\n\texpect_equal(fit1$bw, 0.5)\t\n\texpect_equal(fit1$adaptive, FALSE)\t\n\r\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30), gives_warning(c(\"argument 'k' is ignored\", \"argument 'bw' is ignored\")))\r\n\texpect_that(fit2 <- dalda(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30), gives_warning(\"argument 'k' is ignored\"))\r\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$k, NULL)\n\texpect_equal(fit1$nn.only, NULL)\t\n\texpect_equal(fit1$bw, NULL)\t\n\texpect_equal(fit1$adaptive, NULL)\t\n\r\n\texpect_that(fit1 <- dalda(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5), gives_warning(\"argument 'bw' is ignored\"))\r\n\tfit2 <- dalda(Species ~ ., data = iris, wf = function(x) exp(-x))\r\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$k, NULL)\n\texpect_equal(fit1$nn.only, NULL)\t\n\texpect_equal(fit1$bw, NULL)\t\n\texpect_equal(fit1$adaptive, NULL)\t\n\r\n\t# missing quotes\r\n\texpect_error(dalda(formula = Species ~ ., data = iris, wf = gaussian)) ## error because length(weights) and nrow(x) are different\r\n\r\n\t# bw, k missing\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = gaussian()), throws_error(\"either 'bw' or 'k' have to be specified\"))\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = gaussian(), k = 10), throws_error(\"either 'bw' or 'k' have to be specified\"))\r\n\texpect_that(dalda(Species ~ ., data = iris), throws_error(\"either 'bw' or 'k' have to be specified\"))\n\t\n\t# bw < 0\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = -5), throws_error(\"'bw' must be positive\"))\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"cosine\", k = 10, bw = -50), throws_error(\"'bw' must be positive\"))\r\n\t\n\t# bw vector\r\n\texpect_that(dalda(formula = Species ~., data = iris, wf = \"gaussian\", bw = rep(1, nrow(iris))), gives_warning(\"only first element of 'bw' used\"))\r\n\t\r\n\t# k < 0\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", k =-7, bw = 50), throws_error(\"'k' must be positive\"))\r\n\n\t# k too small\r\n\t#expect_error(dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", k = 5, bw = 0.005))\r\n\n\t# k too large\r\n\texpect_that(dalda(formula = Species ~ ., data = iris, k = 250, wf = \"gaussian\", bw = 50), throws_error(\"'k' is larger than 'n'\"))\r\n\n\t# k vector\r\n\texpect_that(dalda(formula = Species ~., data = iris, wf = \"gaussian\", k = rep(50, nrow(iris))), gives_warning(\"only first element of 'k' used\"))\r\n})\n\n\ntest_that(\"dalda: weighting schemes work\", {\n\t## wf with finite support\n\t# fixed bw\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", bw = 5)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, 5)\n\texpect_equal(fit1$k, NULL)\n\texpect_equal(fit1$nn.only, NULL)\n\texpect_true(!fit1$adaptive)\n\n\t# adaptive bw, only knn \n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", k = 50), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = rectangular(k = 50)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, NULL)\n\texpect_equal(fit1$k, 50)\n\texpect_equal(fit1$bw, NULL)\n\texpect_true(fit1$nn.only)\n\texpect_true(fit1$adaptive)\n\ta <- rep(50, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\t# fixed bw, only knn\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", bw = 5, k = 50)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, 5)\n\texpect_equal(fit1$k, 50)\n\texpect_true(fit1$nn.only)\n\texpect_true(!fit1$adaptive)\n\ta <- rep(50, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\t\n\t# nn.only not needed\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", bw = 5, nn.only = TRUE), gives_warning(\"argument 'nn.only' is ignored\"))\n\n\t# nn.only has to be TRUE if bw and k are both given\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"rectangular\", bw = 5, k = 50, nn.only = FALSE), throws_error(\"if 'bw' and 'k' are given argument 'nn.only' must be TRUE\"))\n\t\n\t## wf with infinite support\n\t# fixed bw\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 0.5)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, 0.5)\n\texpect_equal(fit1$k, NULL)\n\texpect_equal(fit1$nn.only, NULL)\n\texpect_true(!fit1$adaptive)\n\ta <- rep(150, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, function(x) sum(x > 0)), a)\n\n\t# adaptive bw, only knn\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", k = 50), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(k = 50)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, NULL)\n\texpect_equal(fit1$k, 50)\n\texpect_equal(fit1$nn.only, TRUE)\n\texpect_true(fit1$adaptive)\n\ta <- rep(50, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\n\t# adaptive bw, all obs\n\tfit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", k = 50, nn.only = FALSE)\n\tfit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, NULL)\n\texpect_equal(fit1$k, 50)\n\texpect_equal(fit1$nn.only, FALSE)\n\texpect_true(fit1$adaptive)\n\ta <- rep(150, 4)\n\tnames(a) <- 0:3\n\texpect_equal(sapply(fit1$weights, function(x) sum(x > 0)), a)\n\n\t# fixed bw, only knn\n\texpect_that(fit1 <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 1, k = 50), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_that(fit2 <- dalda(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50)), gives_warning(\"for at least one class all weights are zero\"))\n\texpect_equal(fit1[-9], fit2[-9])\n\texpect_equal(fit1$bw, 1)\n\texpect_equal(fit1$k, 50)\n\texpect_equal(fit1$nn.only, TRUE)\n\texpect_true(!fit1$adaptive)\n\ta <- rep(50, 3)\n\tnames(a) <- 1:3\n\texpect_equal(sapply(fit1$weights[2:4], function(x) sum(x > 0)), a)\n\t\n\t# nn.only has to be TRUE if bw and k are both given\n\texpect_that(dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 1, k = 50, nn.only = FALSE), throws_error(\"if 'bw' and 'k' are given argument 'nn.only' must be TRUE\"))\n})\t\n\r\n\n#=================================================================================================================\ncontext(\"predict.dalda\")\n\ntest_that(\"predict.dalda works correctly with formula and data.frame interface and with missing newdata\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\t## formula, data\n\tfit <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n \tpred <- predict(fit)\n \texpect_equal(rownames(pred$posterior), rownames(iris)[ran]) \t\n \texpect_equal(names(pred$class), rownames(iris)[ran]) \t\n\t## formula, data, newdata\n\tfit <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = ran) \n \tpred <- predict(fit, newdata = iris[-ran,])\n \texpect_equal(rownames(pred$posterior), rownames(iris)[-ran]) \t\n \texpect_equal(names(pred$class), rownames(iris)[-ran]) \t\n\t## grouping, x\n\tfit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2, subset = ran) \n \tpred <- predict(fit)\n \texpect_equal(rownames(pred$posterior), rownames(iris)[ran])\n \texpect_equal(names(pred$class), rownames(iris)[ran])\n\t## grouping, x, newdata\n\tfit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2, subset = ran) \n \tpred <- predict(fit, newdata = iris[-ran,-5])\n \texpect_equal(rownames(pred$posterior), rownames(iris)[-ran])\n \texpect_equal(names(pred$class), rownames(iris)[-ran])\n})\n\n\ntest_that(\"predict.dalda: retrieving training data works\", {\n\tdata(iris)\n\t## no subset\n\t# formula, data\n\tfit <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 2)\n \tpred1 <- predict(fit)\n \tpred2 <- predict(fit, newdata = iris)\n \texpect_equal(pred1, pred2)\n\t# y, x\n\tfit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2) \n \tpred1 <- predict(fit)\n \tpred2 <- predict(fit, newdata = iris[,-5])\n \texpect_equal(pred1, pred2)\n\t## subset\n\tran <- sample(1:150,100)\n\t# formula, data\n\tfit <- dalda(formula = Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n \tpred1 <- predict(fit)\n \tpred2 <- predict(fit, newdata = iris[ran,])\n \texpect_equal(pred1, pred2)\n\t# y, x\n\tfit <- dalda(x = iris[,-5], grouping = iris$Species, wf = \"gaussian\", bw = 2, subset = ran) \n \tpred1 <- predict(fit)\n \tpred2 <- predict(fit, newdata = iris[ran,-5])\n \texpect_equal(pred1, pred2)\n})\n\n\ntest_that(\"predict.dalda works with missing classes in the training data\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\texpect_that(fit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 10, subset = 1:100), gives_warning(\"group virginica is empty\"))\n\texpect_equal(length(fit$prior), 2)\n\ta <- rep(50, 2)\n\tnames(a) <- names(fit$counts)\n\texpect_equal(fit$counts, a)\n\texpect_equal(fit$N, 100)\n\texpect_equal(nrow(fit$means), 2)\n\tpred <- predict(fit, newdata = iris[-ran,])\n\texpect_equal(nlevels(pred$class), 3)\n\texpect_equal(ncol(pred$posterior), 2)\n\t# a <- rep(0,50)\n\t# names(a) <- rownames(pred$posterior)\n\t# expect_equal(pred$posterior[,3], a)\n})\n\n\ntest_that(\"predict.dalda works with one single predictor variable\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\tfit <- dalda(Species ~ Petal.Width, data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n\texpect_equal(ncol(fit$means), 1)\n\texpect_equal(dim(fit$cov), rep(1, 2))\n\tpred <- predict(fit, newdata = iris[-ran,])\n})\n\n\ntest_that(\"predict.dalda works with one single test observation\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\tfit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n \tpred <- predict(fit, newdata = iris[5,])\n\texpect_equal(length(pred$class), 1)\n\texpect_equal(dim(pred$posterior), c(1, 3))\n\ta <- factor(\"setosa\", levels = c(\"setosa\", \"versicolor\", \"virginica\"))\n\tnames(a) = \"5\"\n\texpect_equal(pred$class, a)\n\tpred <- predict(fit, newdata = iris[58,])\n\texpect_equal(length(pred$class), 1)\n\texpect_equal(dim(pred$posterior), c(1, 3))\n\ta <- factor(\"versicolor\", levels = c(\"setosa\", \"versicolor\", \"virginica\"))\n\tnames(a) = \"58\"\n\texpect_equal(pred$class, a)\n})\t\n\n\ntest_that(\"predict.dalda works with one single predictor variable and one single test observation\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\tfit <- dalda(Species ~ Petal.Width, data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n\texpect_equal(ncol(fit$means), 1)\n\texpect_equal(dim(fit$cov), rep(1, 2))\n\tpred <- predict(fit, newdata = iris[5,])\n\texpect_equal(length(pred$class), 1)\n\texpect_equal(dim(pred$posterior), c(1, 3))\n})\n\n \ntest_that(\"predict.dalda: NA handling in newdata works\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\tirisna <- iris\n\tirisna[1:17,c(1,3)] <- NA\n\tfit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 50, subset = ran)\n\texpect_warning(pred <- predict(fit, newdata = irisna))\n\texpect_equal(all(is.na(pred$class[1:17])), TRUE)\n\texpect_equal(all(is.na(pred$posterior[1:17,])), TRUE)\t\n})\n\n\ntest_that(\"predict.dalda: misspecified arguments\", {\n\tdata(iris)\n\tran <- sample(1:150,100)\n\tfit <- dalda(Species ~ ., data = iris, wf = \"gaussian\", bw = 2, subset = ran)\n # errors in newdata\n expect_error(predict(fit, newdata = TRUE))\n expect_error(predict(fit, newdata = -50:50))\n # errors in prior\n expect_error(predict(fit, prior = rep(2,length(levels(iris$Species))), newdata = iris[-ran,]))\n expect_error(predict(fit, prior = TRUE, newdata = iris[-ran,]))\n expect_error(predict(fit, prior = 0.6, newdata = iris[-ran,]))\n})\n\n#=================================================================================================================\n# mod <- dalda(Species ~ Sepal.Length + Sepal.Width, data = iris, wf = \"gaussian\", bw = 0.5)\n# x1 <- seq(4,8,0.05)\n# x2 <- seq(2,5,0.05)\n# plot(iris[,1], iris[,2], col = iris$Species, cex = mod$weights[[1]])\n# plot(iris[,1], iris[,2], col = iris$Species, cex = mod$weights[[2]])\n# plot(iris[,1], iris[,2], col = iris$Species, cex = mod$weights[[3]])\n# plot(iris[,1], iris[,2], col = iris$Species, cex = mod$weights[[4]])\n# legend(\"bottomright\", legend = levels(iris$Species), col = as.numeric(unique(iris$Species)), lty = 1)\n\n# iris.grid <- expand.grid(Sepal.Length = x1, Sepal.Width = x2)\n# pred <- predict(mod, newdata = iris.grid)\n# prob.grid <- pred$posterior\n# contour(x1, x2, matrix(prob.grid[,1], length(x1)), add = TRUE, label = colnames(prob.grid)[1])\n# contour(x1, x2, matrix(prob.grid[,2], length(x1)), add = TRUE, label = colnames(prob.grid)[2])\n# contour(x1, x2, matrix(prob.grid[,3], length(x1)), add = TRUE, label = colnames(prob.grid)[3])\n", "meta": {"hexsha": 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"9b7444bc0556e3aafae6661b534727cd8c8818df", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.4141689373, "max_line_length": 200, "alphanum_fraction": 0.6514123695, "num_tokens": 12534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093585306514, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3108112087998422}} {"text": "# user options\nnox<-2; noy<-3;\npaper<-T # if paper==T output on file, else screen\ncleanup()\n\nSMS.option<-\" \"\n\nfirst.year.in.mean<-2020 # years where output is independent of initial conditions and used as \"equilibrium\"\nlast.year.in.mean<-2029\n\ndo.simulation<-T # do the simulations and create data files for plots\n\nread.condense<-T\ndo.plot.condense<-T\n\nread.detailed<-T\ncutof.year.detailed<-2008\ndo.plot.detailed<-T \n\ninclude.probability<-T\n\n\n######### end user options #########\n\n\n\n#do.HCR.batch<-function() {\n\n#Read refence points from file reference_points.in\nref<-Read.reference.points()\nblim<-ref[,\"Blim\"]\nbpa<-ref[,\"Bpa\"]\nT1<-blim\nT2<-bpa\n\n# files for output\nssb.out<-'HCR_SSB.dat'\nyield.out<-'HCR_yield.dat'\nF.out<-'HCR_F.dat'\nprob.out<-'HCR_prob.dat'\n\nssb.out.all<-'mcout_SSB.out.all'\nyield.out.all<-'mcout_yield.out.all'\nF.out.all<-'mcout_mean_F.out.all'\nprob.out.all<-'HCR_prob.dat.all'\n\nif (do.simulation) {\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat.gem')\n\n sp.name<-control@species.names\n \n percentiles<-c(0.025,0.05,0.10,0.25,0.50,0.75,0.95,0.975)\n\n # headers in output files\n heading<-\"targetF Freduction TACconstraint Trigger2\"\n cat(paste(heading,\"Species.n \"),file=ssb.out)\n cat(paste(\"SSB\",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=ssb.out,append=T); cat('\\n',file=ssb.out,append=T) \n cat(paste(heading,\"Species.n \"),file=yield.out)\n cat(paste(\"y\",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=yield.out,append=T); cat('\\n',file=yield.out,append=T) \n cat(paste(heading,\"Species.n \"),file=F.out)\n cat(paste(\"F\",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=F.out,append=T); cat('\\n',file=F.out,append=T) \n cat(paste(heading,\"Species.n \",\" p.T1 p.T2 \\n\"),file=prob.out)\n\n\n iter<-0\n \n HCR@no.MCMC.iterations<-1 #no of iterations\n HCR@last.prediction.year<-2030 \n \n targetFs<-seq(0.18,0.35,0.02)\n Freductions<-c(20,30, 40, 50)\n TAC2<- c(800, 750, 700,600)*1000\n \n #Freductions<-50\n #TACconstraints<-seq(15,45,10)\n TACconstraints<-0\n Trigger2s<-seq(2500000,3000000,250000)\n \n i<-0\n for (targetF in (targetFs)) for (Freduction in (Freductions)) for (TACconstraint in (TACconstraints)) for (Trigger2 in (Trigger2s)) i<-1+i\n print(paste(\"You have asked for\",i,\"runs. Do you want to continue? (Y/N):\")) \n a<-readLines(n=1)\n if(a=='N') stop(\"Script stopped'\")\n print('OK')\n tot.rep<-i\n \n for (targetF in (targetFs)) for (Freduction in (Freductions)) for (TACconstraint in (TACconstraints)) for (Trigger2 in (Trigger2s)){\n iter<-iter+1\n print(paste(\"targetF:\",targetF,\" Freduction:\",Freduction,\" TACconstraint:\",TACconstraint,\" Trigger2;\",Trigger2))\n print(paste(\"run no.:\",iter, \"out of a total of\",tot.rep,\"runs\")) \n \n HCR@constant.F[1]<-targetF\n \n HCR@intermidiate.TAC['second',1]<-TAC2[grep(Freduction,Freductions) ]\n \n HCR@real.time[\"dist\",1]<-Freduction\n \n if (TACconstraint>0 ) {\n HCR@TAC.constraints[\"min\",1]<-1-TACconstraint/100 \n HCR@TAC.constraints[\"max\",1]<-1+TACconstraint/100 \n } else {\n HCR@TAC.constraints[\"min\",1]<-0\n HCR@TAC.constraints[\"max\",1]<-0 \n }\n \n HCR@Trigger[\"T2\",1]<-Trigger2 \n HCR@Trigger.a.b[\"FT12b\",1]<-(targetF-HCR@Trigger.a.b[\"FT1b\",1])/ (HCR@Trigger[\"T2\",1]-HCR@Trigger[\"T1\",1])\n HCR@Trigger.a.b[\"FT2a\",1]<-targetF\n HCR@Trigger.a.b[\"FT2b\",1]<-0.0\n \n write.FLSMS.predict.control(HCR,file='HCR_options.dat')\n\n #run SMS\n shell(paste( file.path(root,\"program\",\"sms.exe\"),\"-mceval\",SMS.option,sep=\" \"), invisible = TRUE)\n\n condense_file<-function(filename){\n file<-file.path(data.path,filename)\n a<-read.table(file,header=TRUE)\n a<-subset(a,Year>cutof.year.detailed)\n a<-data.frame(a,targetF=targetF, Freduction=Freduction, TACconstraint=TACconstraint, Trigger2=Trigger2)\n \n file<-paste(file,\".all\",sep='')\n if (iter==1) write.table(a, file =file, row.names = F,col.names = T,quote = F) else write.table(a, file =file, row.names =F,col.names =F, quote =F,append=T)\n }\n \n condense_file(\"mcout_SSB.out\")\n condense_file(\"mcout_recruit.out\")\n condense_file(\"mcout_mean_F.out\")\n condense_file(\"mcout_yield.out\")\n\n a<-Read.MCMC.SSB.rec.data()\n a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)\n\n b<-tapply(a$SSB,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(targetF,Freduction, TACconstraint, Trigger2 ,i,' '),file=ssb.out,append=TRUE)\n cat(b[[i]],file=ssb.out,append=TRUE)\n cat('\\n',file=ssb.out,append=TRUE)\n }\n \n dummy<-by(a,list(a$Species.n),function(x) {\n q<-tapply(x$SSB,list(x$Year,x$Repetion,x$Iteration),sum)\n sp<-x[1,\"Species.n\"]\n q[q>T2[sp]]<-0\n q[q>0]<-1\n p.T2<-sum(q)/(dim(q)[1]*dim(q)[2]*dim(q)[3])\n\n q<-tapply(x$SSB,list(x$Year,x$Repetion,x$Iteration),sum) \n q[q>T1[sp]]<-0\n q[q>0]<-1\n p.T1<-sum(q)/(dim(q)[1]*dim(q)[2]*dim(q)[3])\n cat(paste(targetF,Freduction, TACconstraint, Trigger2,sp,p.T1,p.T2,'\\n'),file=prob.out,append=TRUE)\n })\n\n a<-Read.MCMC.F.yield.data(dir=data.path)\n a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)\n b<-tapply(a$Yield,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(targetF,Freduction, TACconstraint, Trigger2,i,' '),file=yield.out,append=TRUE)\n cat(b[[i]],file=yield.out,append=TRUE)\n cat('\\n',file=yield.out,append=TRUE)\n }\n\n b<-tapply(a$mean.F,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(targetF,Freduction, TACconstraint, Trigger2,i,' '),file=F.out,append=TRUE)\n cat(b[[i]],file=F.out,append=TRUE)\n cat('\\n',file=F.out,append=TRUE)\n } \n }\n} # end do.simulations\n\n\n\nif (read.condense) {\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')\n\n sp.name<-control@species.names\n\n ssb<-read.table(ssb.out,header=TRUE)\n yield<-read.table(yield.out,header=TRUE)\n proba<-read.table(prob.out,header=TRUE)\n fi<-read.table(F.out,header=TRUE)\n \n a<-merge(ssb,yield)\n a<-merge(a,proba)\n a<-merge(a,fi)\n condensed<-data.frame(a,targetF.fac=as.factor(a$targetF), Freduction.fac=as.factor(a$Freduction),\n TACconstraint.fac=as.factor(a$TACconstraint), Trigger2.fac=as.factor(a$Trigger2/1000))\n }\n \n \nif (read.detailed) {\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')\n\n sp.name<-control@species.names\n Years<- c(2009,2010,2011,2012,2013,2014,2015, 2018, 2020, 2027,2029)\n ssb<-read.table(ssb.out.all,header=TRUE)\n ssb<-subset(ssb,Year %in% Years)\n \n cat(\"Year targetF Freduction TACconstraint Trigger2 p.T2 p.T1 Species.n\\n\",file=prob.out.all)\n dummy<-by(ssb,list(ssb$Species.n),function(x) {\n q<-tapply(x$SSB,list(x$Repetion,x$Iteration,x$Year,x$targetF,x$Freduction,x$TACconstraint,x$Trigger2),sum)\n sp<-x[1,\"Species.n\"]\n q[q>T2[sp]]<-0\n q[q>0]<-1\n p.T2<-apply(q,c(3,4,5,6,7),sum)/ (dim(q)[1]*dim(q)[2])\n \n q<-tapply(x$SSB,list(x$Repetion,x$Iteration,x$Year,x$targetF,x$Freduction,x$TACconstraint,x$Trigger2),sum)\n q[q>T1[sp]]<-0\n q[q>0]<-1\n p.T1<-apply(q,c(3,4,5,6,7),sum)/ (dim(q)[1]*dim(q)[2])\n\n a<-arr2dfny(p.T2,name=\"p.T2\")\n b<-arr2dfny(p.T1,name=\"p.T1\")\n a<-data.frame(merge(a,b),Species.n=sp)\n names(a)[1:5]<-list(\"Year\", \"targetF\",\"Freduction\", \"TACconstraint\", \"Trigger2\")\n \n write.table(a,row.names=F,col.names=F,quote=F,file=prob.out.all,append=T)\n })\n \n #proba<-read.table(prob.out.all,header=TRUE)\n \n yield<-read.table(yield.out.all,header=TRUE)\n yield<-subset(yield,Year %in% Years)\n \n fi<-read.table(F.out.all,header=TRUE)\n fi<-subset(fi,Year %in% Years)\n \n a<-merge(ssb,yield)\n a<-merge(a,fi)\n detailed<-data.frame(a,Year.fac=as.factor(a$Year),targetF.fac=as.factor(a$targetF), Freduction.fac=as.factor(a$Freduction),TACconstraint.fac=as.factor(a$TACconstraint), Trigger2.fac=as.factor(a$Trigger2/1000))\n print(summary(detailed))\n }\n \n\n #### plot function\n my.dev<-function(filen) { \n \t win.metafile(filename = filen, width=14,height=14,restoreConsole = TRUE, pointsize=12)\n }\n\nif (do.plot.detailed) {\n\n PlotYears<- c(2009,2010,2011,2012, 2020, 2027)\n #PlotYears<- c(2010,2012,2015, 2020, 2025,2029)\n ### Yield\n res<-subset(detailed,(Year %in% PlotYears))\n\n a<-subset(res,Year>2008 ,select=c(yield,targetF,Freduction,Year,Trigger2,TACconstraint))\n # tapply(a$yield,list(a$targetF,a$Freduction,a$Year,a$Trigger2,a$TACconstraint),mean) \n a<-aggregate(a,list(a$targetF,a$Freduction,a$Year,a$Trigger2,a$TACconstraint),mean)\n max(a$yield)/1000\n \n resTAC<-data.frame(a,Year.fac=as.factor(a$Year),targetF.fac=as.factor(a$targetF), \n Freduction.fac=as.factor(a$Freduction),TACconstraint.fac=as.factor(a$TACconstraint), Trigger2.fac=as.factor(a$Trigger2/1000))\n\nmy.fontsize<-22\nmy.dev(\"TAC_SMS2.wmf\") \n \n print(contourplot(yield/1000 ~ targetF * Freduction | Year.fac*Trigger2.fac, data = resTAC, \n cuts = 10, region = TRUE,\n at=seq(0,800,100),\n # at=seq(200,500,50),\n \n xlab = \"F target\", par.settings=list(fontsize=list(text=my.fontsize)),\n ylab = \"% F reduction\", col.regions=rev(heat.colors(1000)), pretty=T,\n main = paste(my.stock.dir,\": TAC\")) \n ) \n if (paper) cleanup()\n \n ### SSB\n a<-subset(res,Year>2008 ,select=c(SSB,targetF,Freduction,Year,Trigger2,TACconstraint))\n #tapply(a$SSB,list(a$targetF,a$Freduction,a$Year,a$Trigger2,a$TACconstraint),mean)\n min(a$SSB)/1000 \n max(a$SSB)/1000 \n a<-aggregate(a,list(a$targetF,a$Freduction,a$Year,a$Trigger2,a$TACconstraint),mean)\n max(a$SSB)/1000 \n resTAC<-data.frame(a,Year.fac=as.factor(a$Year),targetF.fac=as.factor(a$targetF), \n Freduction.fac=as.factor(a$Freduction),TACconstraint.fac=as.factor(a$TACconstraint), Trigger2.fac=as.factor(a$Trigger2/1000))\n my.dev(\"SSB_SMS.wmf\") \n print(contourplot(SSB/1000 ~ targetF * Freduction | Year.fac*Trigger2.fac, data = resTAC, \n cuts = 10, region = TRUE,at=seq(1400,2800,100),\n xlab = \"F target\", par.settings=list(fontsize=list(text=my.fontsize)),\n ylab = \"% F reduction\", col.regions=rev(heat.colors(1000)), pretty=T,\n main = paste(my.stock.dir,\": SSB\")) \n ) \n \n if (paper) cleanup()\n} \n\nif (do.plot.condense) {\n\n a<-read.table(prob.out.all,header=TRUE)\n a<-subset(a,(Year %in% PlotYears))\n a<-data.frame(a,Year.fac=as.factor(a$Year),targetF.fac=as.factor(a$targetF), \n Freduction.fac=as.factor(a$Freduction),TACconstraint.fac=as.factor(a$TACconstraint), Trigger2.fac=as.factor(a$Trigger2/1000))\n #print(summary(a))\n #a<-subset(a,TACconstraint==45) \n \n \nmy.dev(\"Plim_SMS.wmf\") \n \n # tapply(a$p.T1,list(a$targetF,a$Freduction,a$Year,a$Trigger2,a$TACconstraint),max)\nmax(a$p.T1)\n\n print(contourplot(p.T1 ~ targetF * Freduction | Year.fac*Trigger2.fac, data = a,\n region = TRUE,at=seq(0,0.6,0.05),\n xlab = \"F target\", par.settings=list(fontsize=list(text=my.fontsize)),\n ylab = \"% F reduction\", col.regions=rev(heat.colors(1000)), pretty=T,\n main = paste(my.stock.dir,\"Prob(SSB<=Blim)\"))\n ) \n \n if (paper) cleanup()\n \n # user options\n nox<-1; noy<-1;\n if (paper) dev<-\"wmf\" else dev<-\"screen\"\n newplot(dev,nox,noy,dir=data.path,filename=paste(\"Batch2_\",sep=''),Portrait=T);\n xlab.title<-'Cod F'\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')\n\n sp.name<-control@species.names\n \n ssb<-read.table(ssb.out,header=TRUE)\n yield<-read.table(yield.out,header=TRUE)\n prob<-read.table(prob.out,header=TRUE)\n fi<-read.table(F.out,header=TRUE)\n\n a<-merge(ssb,yield)\n a<-merge(a,prob)\n a<-merge(a,fi)\n \n} #end do.plot.condense\n\n#}\n#do.HCR.batch()\n\n\n", "meta": {"hexsha": "d7eca094a0bc2f778e497ec6a8f5e9c46448a6bc", "size": 12392, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_bw_contour.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_bw_contour.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_bw_contour.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.3048433048, "max_line_length": 211, "alphanum_fraction": 0.6445287282, "num_tokens": 4072, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3106163408055177}} {"text": "library(phyloseq)\nlibrary(microbiome)\n#library(microbiomeutilities)\nlibrary(DirichletMultinomial)\nlibrary(reshape2)\nlibrary(magrittr)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(pheatmap)\nlibrary(ggpubr)\nlibrary(ggsci)\n# getwd()\n# count_tab<-read.table(\"Combined_BALF_GO_Terms_counts.txt\", header = T, sep = \"\\t\", row.names = 1)\n# tax_tab<-read.table(\"Combined_BALF_GO_Terms_tax.txt\", header = T, sep = \"\\t\", row.names = 1)\n# sam_tab<-read.table(\"Combined_BALF_GO_Terms_metadata.txt\", header = T, sep = \"\\t\", row.names = 1)\n# count<-otu_table(count_tab,taxa_are_rows = T)\n# tax<-tax_table(as.matrix(tax_tab),errorIfNULL = T)\n# sam<-sample_data(sam_tab)\n#pseq<-bio_bac_physeq\npseq <- phyloseq(bio_bac_counts_phy, bio_bac_tax_phy, sample_data(bio_bac_sam))\n\ntest<-as.data.frame(tax_table(pseq))\nhead(test$name, n=1000L)\npseq\npseq<-subset_samples(pseq, sample_type!=\"neg_control\")\npseq<-subset_samples(pseq, sample_type!=\"Unknown\")\npseq\n\nsummarize_phyloseq(pseq)\nmeta(pseq)$case\n##########################################\nmypal2<- pal_startrek(\"uniform\", alpha = 1)(7)\nmypal <- pal_aaas(\"default\", alpha = 1)(7)\nmypal2\nmypal3<-rbind(mypal, mypal2)\nmypal3\n\nmypal3<-c(\"#3B4992FF\", \"#BB0021FF\", \"#5F559BFF\", \"#CC0C00FF\", \"#EE0000FF\",\"#008B45FF\", \"#631879FF\", \"#008280FF\", \"#5C88DAFF\", \"#84BD00FF\", \"#FFCD00FF\", \"#7C878EFF\", \"#00B5E2FF\", \"#00AF66FF\")\np <- plot_frequencies(sample_data(pseq), \"publication\",\"sample_type\")+scale_fill_manual(values = mypal3)\n\nq <- plot_frequencies(sample_data(pseq), \"publication\",\"case\")+scale_fill_manual(values =c( \"#008B45FF\",\"#3B4992FF\" ,\"#EE0000FF\"))#green-blue-red \n\n#########################################\n# Pre-processing\n#########################################\n#maybe I want to subset for the depth....not sure yet\n#pseq_prune<-subset_taxa(physeq = pseq_prune,depth >10)\npseq_prune <- prune_taxa(taxa_sums(pseq) > 1, pseq)\npseq_prune <- prune_samples(sample_sums(pseq_prune) > 1, pseq_prune)\npseq_prune\n\n#rownames clean still\npseq_prune<-tax_glom(physeq = pseq_prune, taxrank = \"name\")\npseq_prune\n####################Do NOT EXECUTE THIS CODE#################################################\n#write.table(x = rownames(data.frame(otu_table(pseq_prune))), file = \"test.tsv\",sep = \"\\t\")\n#this was causing major issues with the rownames\ntaxa_names(pseq_prune)<-get_taxa_unique(pseq_prune,taxonomic.rank = \"name\" )\n############################################################################################\n\n########################################\n#DMM modeling\n#########################################\ndat <- abundances(pseq_prune)\ncount <- as.matrix(t(dat))\ndim(dat)\ndim(count)\ndmn\nfit<-mclapply(1:7,FUN = dmn, count = count, verbose=TRUE)\nfit\n#Check the model fit with different number of mixture componenets using standard information criteria\nlplc <- sapply(fit, laplace) # AIC / BIC / Laplace\naic <- sapply(fit, AIC) # AIC / BIC / Laplace\nbic <- sapply(fit, BIC) # AIC / BIC / Laplace\nplot(lplc, type=\"b\", xlab=\"Number of Dirichlet Components\", ylab=\"Model Fit\")\nplot(aic)\nplot(bic)\n#lines(aic, type=\"b\", lty = 2)\n#lines(bic, type=\"b\", lty = 3)\nbest <- fit[[which.min(lplc)]]\nbest\nbest <-fit[[4]]\nheatmapdmn(count, fit[[1]], best,ntaxa = 30,\n transform = sqrt, lblwidth = 0.1 * nrow(count))\nmixturewt(best)\nass <- apply(mixture(best), 1, which.max)\nass\nwrite.table(ass,\"3_greoup_GO_term_sample_DMM_groups.tsv\",sep=\"\")\nwrite.table(fitted(best),\"3_group_GO_term_DMM2_contributions.tsv\", sep=\"\\t\")\n\nphyseq<-pseq_prune\nmeta<-data.frame(sample_data(physeq))\nmeta$dmm<-ass\nfor (k in seq(ncol(fitted(best))))\n{\n d <- melt(fitted(best))\n colnames(d) <- c(\"GO\", \"cluster\", \"value\")\n d <- subset(d, cluster == k) %>%\n # Arrange GOs by assignment strength\n arrange(value) %>%\n mutate(GO = factor(GO, levels = unique(GO))) %>%\n # Only show the most important drivers\n filter(abs(value) > quantile(abs(value), 0.995))\n\n p <- ggplot(d, aes(x = GO, y = value)) +\n geom_bar(stat = \"identity\") +\n coord_flip() +\n labs(title = paste(\"Top drivers in : GO Terms cluster type \", k, sep = \"\"))\n #paste(p,k, sep = \"\")<-p\n #print(k)\n print(p)\n #print(paste(p,k, sep=\"\"))\n}\n\ndim(d)\npseq_prune\nsample_data(pseq_prune)$dmn<-ass\nlibrary(phyloseq)\nlibrary(speedyseq)\ntmp<-psmelt(pseq_prune)\ntmp<-as_tibble(tmp)\n\ntmp\nd2<-tmp %>%\n select(case,name,Abundance, dmn)%>%\n group_by(name,case, dmn) %>%\n summarise(avg = mean(Abundance),std = sd(Abundance)) %>%\n group_by(name, case,dmn, std)%>%\n arrange(desc(std))\nd2\n#d3<-tidyr::spread(d2,dmn, avg)\nd3<-tidyr::spread(d2,case,avg)\nd3\nd3[4:6]\nlibrary(mosaic)\n#d3$tot<-sd(d3[3:5])\n#d3$tot<-rowSums(d3[3:5], na.rm = T)\nd3<-d3%>%arrange(desc(std))\nrank<-as.character(d3$name)\n\nd4<-d3[1:500,]\n\nd4<-d4%>%\n gather(data = d4,avg,4:6)\n\ncolnames(d4)<-c(\"name\",\"dmn\",\"std\", \"group\",\"avg\")\n\n#d4<-d3%>%group_by(name,group)\nd4<-d4%>%filter(!is.na(avg))\n\nd4\nggballoonplot(data = d4, y =\"name\",x = \"group\", size = \"avg\", facet.by = \"dmn\",fill = \"avg\")+scale_fill_viridis_c()\n\nlibrary(mosaic)\n\nmeta(pseq_prune)\nhead(tmp$OTU)\nhead(tmp$Abundance)\nhead(tmp$dmn)\n\n\n\n\n\nmy_tbl<-tally(~case+ dmn+publication,data =meta(pseq_prune),format = 'count')\nmy_tbl\na<-as_tibble(my_tbl)\ngetwd()\n\nggballoonplot(data = a, y =\"case\",x = \"dmn\", facet.by = \"publication\",size = \"n\", fill = \"n\")+\n scale_fill_viridis_c(option = \"C\")+\n ggsave(filename = \"pub_vs_disease_vs_dmm_dotplot.png\",\n device = \"png\",\n #width = \"8\",\n #height = \"6\",\n #units = \"in\",\n dpi = 600,\n path = \"D:/github/microbial/Rdata/GO_term_analysis/Figures/\")\nmy_tbl<-tally(publication ~ dmn,data =meta(pseq_prune),format = 'count')\nmy_tbl\na<-as_tibble(my_tbl)\nggballoonplot(data = a, y =\"publication\",x = \"dmn\", size = \"n\", fill = \"n\")+scale_fill_viridis_b(option = \"B\")\n\n###########################################\n###Dont forget to save you shit HERE#######\n###########################################\nsave.image(file = \"go_terms_dmm.rdata\")\n\n\n\ncount<-abundances(pseq_prune)\n#count\n#rowMeans(count,na.rm = T)\n\nselect <- order(rowMeans(count),\n decreasing=TRUE)[1:20]\nselect\n#sample_data(pseq_prune)<-sam\ndim(sam)\ndim(sample_data(pseq_prune))\ndf<-as.data.frame(sam)\ndf<-as_tibble(df)\ndf<-df%>%select(dmn)#,dmn,body_site)\ndf <-sample_data(pseq_prune)$dmn\n\ndf<-as.character(df)\ndf<-as.data.frame(df)\ndf$Dataset<-sam$publication\n\ndf$sample_type<-sample_data(pseq_prune)$sample_type\nselect2<-(count)[select,]\ncolnames(select2) <- colnames(otu_table(pseq_prune))\ncolnames(select2)\nrow.names(df)\nrow.names(df) <- colnames(select2)\nass\nmypal <- pal_aaas(\"default\", alpha = 1)(10)\nmypal\nlibrary(\"scales\")\nunique(df)\nshow_col(mypal)\ndf\nmosaicplot(df)\n# Specify colors\nann_colors = list(\n df=c(\"1\"=\"#3B4992FF\",\"2\"=\"#EE0000FF\",\"3\"=\"#008B45FF\",\"4\"=\"#631879FF\"),\n Dataset=c(\"Chen\"=\"#3B4992FF\",\"Wu\"=\"#EE0000FF\",\"Zhou\"=\"#631879FF\", \n \"Xiong\"=\"#008280FF\", \"Shen\"=\"#631879FF\",\"Michalovich\"=\"#BB0021FF\",\n \"Huang\"=\"#000000\",\"Ren\"=\"#111111\"),\n sample_type =c(\"COVID_19\"=\"#3B4992FF\",\n \"Community_acquired_pneumonia\"=\"#EE0000FF\", \n \"Healthy\"=\"#008B45FF\", \n \"neg_control\"=\"#000000\",\n \"Smoker\"=\"#631879FF\",\n \"Asthma\"= \"#008280FF\", \n \"Asthma_Smoker\"=\"#BB0021FF\",\n \"Asthma_Ex_smoker\"=\"#5F559BFF\",\n \"Obese\"=\"#A20056FF\", \n \"Obese_Asthma\"=\"#808180FF\",\n \"Obese_Smoker\"=\"#1B1919FF\",\n \"Obese_Asthma_Smoker\"=\"#1111111\",\n \"Unknown\"=\"#222222\"))\nann_colors\ndf_row<-as.data.frame(fitted(best))\ncolnames(df_row)L-c()\nlibrary(RColorBrewer)\nlibrary(viridis)\nxx <- pheatmap(mat = select2,\n color = viridis(256),\n annotation_col=df, \n annotation_colors = ann_colors,\n clustering_distance_rows = \"euclidean\",\n clustering_distance_cols = \"euclidean\",\n annotation_row = df_row)\nxx\npheatmap(mat, \n color = colorRampPalette(rev(brewer.pal(n = 7, name =\"RdYlBu\")))(100), \n kmeans_k = NA, \n breaks = NA, \n border_color = \"grey60\",\n cellwidth = NA, \n cellheight = NA, \n scale = \"none\", \n cluster_rows = TRUE,\n cluster_cols = TRUE, \n clustering_distance_rows = \"euclidean\",\n clustering_distance_cols = \"euclidean\", \n clustering_method = \"complete\",\n clustering_callback = identity2, \n cutree_rows = NA, \n cutree_cols = NA,\n treeheight_row = ifelse((class(cluster_rows) == \"hclust\") || cluster_rows,\n 50, 0), treeheight_col = ifelse((class(cluster_cols) == \"hclust\") ||\n cluster_cols, 50, 0), \n legend = TRUE,\n legend_breaks = NA,\n legend_labels = NA,\n annotation_row = NA, \n annotation_col = NA,\n annotation = NA, \n annotation_colors = NA, \n annotation_legend = TRUE,\n annotation_names_row = TRUE, \n annotation_names_col = TRUE,\n drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA,\n fontsize = 10, fontsize_row = fontsize, fontsize_col = fontsize,\n angle_col = c(\"270\", \"0\", \"45\", \"90\", \"315\"), display_numbers = F,\n number_format = \"%.2f\", number_color = \"grey30\", fontsize_number = 0.8\n * fontsize, gaps_row = NULL, gaps_col = NULL, labels_row = NULL,\n labels_col = NULL, filename = NA, width = NA, height = NA,\n silent = FALSE, na_col = \"#DDDDDD\", ...)", "meta": {"hexsha": "ff6a4058fe00a5485a450977aad3ef6383364b6e", "size": 9634, "ext": "r", "lang": "R", "max_stars_repo_path": "archive/Rdata/GO_term_analysis/go_term.r", "max_stars_repo_name": "COV-IRT/-microbial", "max_stars_repo_head_hexsha": 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"num_tokens": 2923, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3106163408055177}} {"text": "## FILE: tasampled-three-models-refactored.r\n## EXPERIMENT: equifinality-5\n## PROJECT: experiment-ctmixtures\n## AUTHOR: Mark E. Madsen\n## DATE: 5.20.15\n\n# Purpose: To take simulated output from the CTMixtures simulation, which is time averaged and sampled,\n# and perform gradient boosted classification to measure our ability to distinguish between neutral,\n# conformist, and anticonformist cultural transmission given the availability of 26 predictor variables.\n# The models are fit using the Caret package to find the optimal model tuning (via 5 repeated of 10-fold CV),\n# and the final model assessed using a 20% test hold-out sample. Test sample results are given by a\n# confusion matrix and ROC analysis, which are saved into a set of results objects and saved as binary\n# images to the filesystem for later use in paper preparation.\n# mmadsenr brings in GBM and other crucial analytic libraries\n\n\n# In this analysis, we allow the model to use the sample size and assemblage duration as predictors\n# See combined-tasampled-duration-hidden.r for the same analysis, but withdrawing the ability of the model\n# to use duration (thus combining data from all durations). \n\n\n\nlibrary(mmadsenr)\nlibrary(caret)\nlibrary(doMC)\nlibrary(futile.logger)\nlibrary(dplyr)\n# not sure if the following is necessary anymore\nlibrary(ggthemes)\n\n\n# Overall timing\nptm <- proc.time()\n\n\n\n################# Training and Tuning Parameters #############\n## Caret training and tuning parameter grid\ngbm_grid <- expand.grid(interaction.depth = (1:6)*2,\n n.trees = (2:10)*50,\n shrinkage = 0.05,\n n.minobsinnode = 10)\ntraining_control <- trainControl(method=\"repeatedcv\",\n number=10, repeats=5)\n# make this repeatable - comment this out or change it to get a fresh analysis result\nseed_value <- 58132133\nset.seed(seed_value)\n# Set up sampling of train and test data sets\ntraining_set_fraction <- 0.8\ntest_set_fraction <- 1.0 - training_set_fraction\n\n\n##################### Helper Functions ####################\n\n# Subset of the larger data set (usually the training set) with a given sample size and time\n# averaging duration\nget_tassize_subset_ssize_tadur <- function(df, ssize, tadur) {\n df_tassize_subset <- dplyr::filter(df, sample_size == ssize, ta_duration == tadur)\n df_tassize_subset\n}\n\n\nget_experiment_names <- function(comparison_prefix, tassize_subsets) {\n n <- nrow(tassize_subsets)\n experiment_names <- character(n)\n # Add experiment names to the tassize_subsets since I didn't do this in the original analysis\n for( i in 1:n) {\n experiment_names[i] <- paste0(comparison_prefix, \": Sample Size: \", tassize_subsets[i, \"sample_size\"],\n \" Duration: \", tassize_subsets[i, \"ta_duration\"])\n }\n experiment_names\n}\n\n\ndo_model_fit_and_test <- function(comparison,exp_name, ssize, ta_dur, target_label, subset_df, gbm_grid, training_control, exclude_columns) {\n results <- NULL\n model <- train_gbm_classifier(subset_df, training_set_fraction, target_label, gbm_grid, training_control, exclude_columns, verbose=FALSE)\n # use the test data split by the train_randomforest function and calculate tuned model predictions\n # and then get the confusion matrix and fitting metrics\n test_data <- model$test_data\n predictions <- predict(model$tunedmodel, newdata=test_data)\n cm <- confusionMatrix(predictions, test_data[[target_label]])\n stats <- get_parsed_binary_confusion_matrix_stats(cm)\n roc <- calculate_roc_binary_classifier(model$tunedmodel, model$test_data, target_label, exp_name)\n stats$auc <- unlist(roc$auc@y.values)\n stats$sample_size <- ssize\n stats$ta_duration <- ta_dur\n stats$elapsed <- model$elapsed\n stats$experiments <- exp_name\n stats$exp_group <- comparison\n # add the CM, ROC, and other information to the results list\n results$sample_size <- ssize\n results$ta_duration <- ta_dur\n results$cm <- cm\n results$roc <- roc\n results$model <- model\n results$stats <- stats\n results$elapsed <- model$elapsed\n results\n}\n\n\n####################### Main Program #######################\n#log_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\", filename = \"tasampled-classification.log\")\nlog_file <- \"/mnt/experiment-ctmixtures/equifinality-5/tasampled-classification.log\"\nflog.appender(appender.file(log_file), name='cl')\nflog.info(\"================ TA and Sampled Classification Analysis =================\", name='cl')\nflog.info(\"RNG seed to replicate this analysis: %s\", seed_value, name='cl')\n\n# set up parallel processing - use all the cores (unless it's a dev laptop under OS X) - from mmadsenr\nnum_cores <- get_parallel_cores_given_os(dev=TRUE)\nflog.info(\"Number of cores used in analysis: %s\", num_cores, name='cl')\nregisterDoMC(cores = num_cores)\n\n# load the input data\nclargs <- commandArgs(trailingOnly = TRUE)\nif(length(clargs) == 0) {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\", filename = \"equifinality-5-tasampled-data.rda\")\n} else {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\", filename = \"equifinality-5-tasampled-data.rda\", args = clargs)\n}\n\npop_data_file <- \"/mnt/experiment-ctmixtures/equifinality-5/equifinality-5-tasampled-data.rda\"\nload(pop_data_file)\nflog.info(\"Loaded data file %s with %.0f rows\",pop_data_file, nrow(eq5_ta_sampled_df),name='cl')\n\n\n## To Test, uncomment this\n# test_tasampled_indices <- createDataPartition(eq5_ta_sampled_df$model_class_label, p = 0.005, list=FALSE)\n# eq5_ta_sampled_df <- eq5_ta_sampled_df[test_tasampled_indices,]\n# flog.info(\"Test downsample to %.0f rows\", nrow(eq5_ta_sampled_df), name='cl')\n\n\n# Prepare data subsets for the three comparisons to be performed for each\neq5_neutral_conformist_df <- subset(eq5_ta_sampled_df, eq5_ta_sampled_df$model_class_label == \"neutral\" | eq5_ta_sampled_df$model_class_label == \"conformist\")\neq5_neutral_conformist_df$two_class_label <- factor(eq5_neutral_conformist_df$model_class_label, levels=unique(eq5_neutral_conformist_df$model_class_label))\neq5_neutral_anticonformist_df <- subset(eq5_ta_sampled_df, eq5_ta_sampled_df$model_class_label == \"neutral\" | eq5_ta_sampled_df$model_class_label == \"anticonformist\")\neq5_neutral_anticonformist_df$two_class_label <- factor(eq5_neutral_anticonformist_df$model_class_label, levels=unique(eq5_neutral_anticonformist_df$model_class_label))\neq5_ta_sampled_df$two_class_label <- factor(ifelse(eq5_ta_sampled_df$model_class_label == 'neutral', 'neutral', 'biased'))\n\n# get grid of the sample size and TA duration combinations, to tassize_subset the data set\nsample_sizes <- unique(eq5_ta_sampled_df$sample_size)\nta_durations <- unique(eq5_ta_sampled_df$ta_dur)\ntassize_subsets <- expand.grid(sample_size = sample_sizes, ta_duration = ta_durations)\nflog.info(\"tassize_subsets: number of subset combinations to analyze per experimental comparison: %.0f\", nrow(tassize_subsets),name='cl')\n\n\n################# neutral vs. conformism ##############\nflog.info(\"Starting neutral vs. conformist subsets\",name='cl')\ntassize_neutral_conformist_results <- data.frame()\ntassize_neutral_conformist_roc <- NULL\ntassize_neutral_conformist_roc_ssize_20 <- NULL\ntassize_neutral_conformist_roc_ssize_10 <- NULL\ntassize_neutral_conformist_model <- NULL\ntassize_neutral_conformist_cm <- NULL\n\nexperiment_names <- get_experiment_names(\"Neutral vs Conformist\", tassize_subsets)\ncomparison <- \"Neutral versus Conformist\"\n\nfor( i in 1:nrow(tassize_subsets)) {\n exclude_columns <- c(\"simulation_run_id\", \"innovation_rate\", \"model_class_label\", \"sample_size\", \"ta_duration\")\n target_label <- \"two_class_label\"\n exp_name <- experiment_names[i]\n ssize <- tassize_subsets[i, \"sample_size\"]\n ta_dur <- tassize_subsets[i, \"ta_duration\"]\n subset_df <- get_tassize_subset_ssize_tadur(eq5_neutral_conformist_df,\n ssize,\n ta_dur)\n result <- do_model_fit_and_test(comparison,experiment_names[i], ssize, ta_dur, target_label, subset_df, gbm_grid, training_control, exclude_columns)\n tassize_neutral_conformist_cm[[exp_name]] <- result$cm\n tassize_neutral_conformist_model[[exp_name]] <- result$model\n tassize_neutral_conformist_roc[[exp_name]] <- result$roc\n tassize_neutral_conformist_results <- rbind(tassize_neutral_conformist_results, result$stats)\n if(ssize == 20) {\n tassize_neutral_conformist_roc_ssize_20[[exp_name]] <- result$roc\n }\n if(ssize == 10) {\n tassize_neutral_conformist_roc_ssize_10[[exp_name]] <- result$roc\n }\n logrow <- sprintf(\"row %.0f: sample size: %2.0f ta duration: %3.0f numrows: %.0f elapsed: %.4f\", i, ssize, ta_dur, nrow(subset_df), result$elapsed)\n flog.info(\"%s\", logrow, name='cl')\n}\n\n\n################# neutral vs. anticonformism ##############\nflog.info(\"Starting neutral vs. anticonformist subsets\",name='cl')\n\ntassize_neutral_anticonformist_results <- data.frame()\ntassize_neutral_anticonformist_roc <- NULL\ntassize_neutral_anticonformist_roc_ssize_20 <- NULL\ntassize_neutral_anticonformist_roc_ssize_10 <- NULL\ntassize_neutral_anticonformist_model <- NULL\ntassize_neutral_anticonformist_cm <- NULL\n\nexperiment_names <- get_experiment_names(\"Neutral vs Anticonformist\", tassize_subsets)\ncomparison <- \"Neutral versus Anticonformist\"\n\nfor( i in 1:nrow(tassize_subsets)) {\n exclude_columns <- c(\"simulation_run_id\", \"innovation_rate\", \"model_class_label\", \"sample_size\", \"ta_duration\")\n target_label <- \"two_class_label\"\n exp_name <- experiment_names[i]\n ssize <- tassize_subsets[i, \"sample_size\"]\n ta_dur <- tassize_subsets[i, \"ta_duration\"]\n subset_df <- get_tassize_subset_ssize_tadur(eq5_neutral_anticonformist_df,\n ssize,\n ta_dur)\n result <- do_model_fit_and_test(comparison,experiment_names[i], ssize, ta_dur, target_label, subset_df, gbm_grid, training_control, exclude_columns)\n tassize_neutral_anticonformist_cm[[exp_name]] <- result$cm\n tassize_neutral_anticonformist_model[[exp_name]] <- result$model\n tassize_neutral_anticonformist_roc[[exp_name]] <- result$roc\n tassize_neutral_anticonformist_results <- rbind(tassize_neutral_anticonformist_results, result$stats)\n if(ssize == 20) {\n tassize_neutral_anticonformist_roc_ssize_20[[exp_name]] <- result$roc\n }\n if(ssize == 10) {\n tassize_neutral_anticonformist_roc_ssize_10[[exp_name]] <- result$roc\n }\n logrow <- sprintf(\"row %.0f: sample size: %2.0f ta duration: %3.0f numrows: %.0f elapsed: %.4f\", i, ssize, ta_dur, nrow(subset_df), result$elapsed)\n flog.info(\"%s\", logrow, name='cl')\n}\n\n\n##################### neutral vs. both conformism and anticonformism ##############\nflog.info(\"Starting neutral vs. biased subsets\",name='cl')\n\ntassize_neutral_biased_results <- data.frame()\ntassize_neutral_biased_roc <- NULL\ntassize_neutral_biased_roc_ssize_20 <- NULL\ntassize_neutral_biased_roc_ssize_10 <- NULL\ntassize_neutral_biased_model <- NULL\ntassize_neutral_biased_cm <- NULL\n\nexperiment_names <- get_experiment_names(\"Neutral vs Biased\", tassize_subsets)\ncomparison <- \"Neutral versus Combined Biases\"\n\nfor( i in 1:nrow(tassize_subsets)) {\n exclude_columns <- c(\"simulation_run_id\", \"innovation_rate\", \"model_class_label\", \"sample_size\", \"ta_duration\")\n target_label <- \"two_class_label\"\n exp_name <- experiment_names[i]\n ssize <- tassize_subsets[i, \"sample_size\"]\n ta_dur <- tassize_subsets[i, \"ta_duration\"]\n subset_df <- get_tassize_subset_ssize_tadur(eq5_ta_sampled_df,\n ssize,\n ta_dur)\n result <- do_model_fit_and_test(comparison,experiment_names[i], ssize, ta_dur, target_label, subset_df, gbm_grid, training_control, exclude_columns)\n tassize_neutral_biased_cm[[exp_name]] <- result$cm\n tassize_neutral_biased_model[[exp_name]] <- result$model\n tassize_neutral_biased_roc[[exp_name]] <- result$roc\n tassize_neutral_biased_results <- rbind(tassize_neutral_biased_results, result$stats)\n if(ssize == 20) {\n tassize_neutral_biased_roc_ssize_20[[exp_name]] <- result$roc\n }\n if(ssize == 10) {\n tassize_neutral_biased_roc_ssize_10[[exp_name]] <- result$roc\n }\n logrow <- sprintf(\"row %.0f: sample size: %2.0f ta duration: %3.0f numrows: %.0f elapsed: %.4f\", i, ssize, ta_dur, nrow(subset_df), result$elapsed)\n flog.info(\"%s\", logrow, name='cl')\n}\n\n\n################## Save all results #####################\nif(length(clargs) == 0) {\n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\",\n filename = \"classification-ta-sampled-results-gbm.RData\")\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\",\n filename = \"classification-ta-sampled-results-gbm-dfonly.RData\")\n} else {\n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\",\n filename = \"classification-ta-sampled-results-gbm.RData\", args = clargs)\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-5\",\n filename = \"classification-ta-sampled-results-gbm-dfonly.RData\", args = clargs)\n}\nflog.info(\"Saving results of analysis to R environment snapshot: %s\", image_file, name='cl')\nsave(tassize_neutral_conformist_cm,\n tassize_neutral_conformist_results,\n tassize_neutral_conformist_model,\n tassize_neutral_conformist_roc,\n tassize_neutral_conformist_roc_ssize_10,\n tassize_neutral_conformist_roc_ssize_20, \n tassize_neutral_anticonformist_results,\n tassize_neutral_anticonformist_model,\n tassize_neutral_anticonformist_roc,\n tassize_neutral_anticonformist_roc_ssize_20,\n tassize_neutral_anticonformist_roc_ssize_10,\n tassize_neutral_anticonformist_cm,\n tassize_neutral_biased_results,\n tassize_neutral_biased_model,\n tassize_neutral_biased_roc,\n tassize_neutral_biased_roc_ssize_20,\n tassize_neutral_biased_roc_ssize_10,\n tassize_neutral_biased_cm,\n file=image_file)\nflog.info(\"Saving just data frame of results of analysis to R environment snapshot: %s\", image_file_results, name='cl')\nsave(tassize_neutral_conformist_results,tassize_neutral_anticonformist_results,tassize_neutral_biased_results, file=image_file_results)\n# End\nflog.info(\"Analysis complete\", name='cl')\ntotal_time <- proc.time() - ptm\nflog.info(\"Elapsed time in analysis: %.3f\", total_time[3],name='cl')\n\n\n", "meta": {"hexsha": "fb635c05be494337a95c054fea6a42cc52846066", "size": 14524, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-5/modelfitting/tasampled-three-models-refactored.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-5/modelfitting/tasampled-three-models-refactored.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-5/modelfitting/tasampled-three-models-refactored.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.2524916944, "max_line_length": 168, "alphanum_fraction": 0.7378821261, "num_tokens": 3887, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.3106163338835146}} {"text": "#------------------------------------------------------------------------------\n#\tDefine constants.\n#------------------------------------------------------------------------------\n\n# Name of parameters used when calling optimal.cutpoints.\nOPTIMAL_CUTPOINT_PARAMETERS = c(\"methods\", \"op.prev\", \"control\")\n\n# Name conversion table between the names of metrics in this package\n# and names used in the result of optimal.cutpoints.\nNAME_CONVERSION_TABLE <- c(\n\tcutoff = \"threshold\", Se = \"sensitivity\", Sp = \"specificity\",\n\tPPV = \"ppv\", NPV = \"npv\", DLR.Positive = \"dlr.positive\",\n\tDLR.Negative = \"dlr.negative\", FP = \"fp\", FN = \"fn\"\n)\n\n\n#------------------------------------------------------------------------------\n#\tA reference class calculating metrics for classification models.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator <- R6::R6Class(\n\t\"classification.metrics.calculator\",\n\tprivate = list(\n\t\tcutpoint.options = NULL,\n\t\tpositive.class = NULL\n\t)\n)\n\n\n#------------------------------------------------------------------------------\n#\tInitialize an object from cv.models object.\n#\n#\tArgs:\n#\t\tobject:\n#\t\t\ta cv.models object. Only fields in the fields of this class\n#\t\t\tare used.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"public\", \"initialize\",\n\tfunction(object) {\n\t\t# Check error.\n\t\tif (is.null(object$cutpoint.options$methods)) {\n\t\t\tstop(\"'methods' should be specified in 'cutpoint.options'.\")\n\t\t}\n\t\tif (length(object$cutpoint.options$methods) == 0) {\n\t\t\tmsg = paste(\n\t\t\t\t\"'methods' of 'cutpoint.options' should have at least\",\n\t\t\t\t\"one element.\"\n\t\t\t)\n\t\t\tstop(msg)\n\t\t}\n\t\t# Copy field from the object.\n\t\tprivate$positive.class <- determine.positive.class(object)\n\t\tprivate$cutpoint.options <- object$cutpoint.options\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tGet probability from the result of cross validation.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"get.probability\",\n\tfunction(fit) {\n\t\treturn(fit$prediction[, private$positive.class])\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tGet binary response from the result of cross validation.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"get.binary.response\",\n\tfunction(fit) {\n\t\treturn(as.numeric(fit$response == private$positive.class))\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tPrepare arguments for optimal.cutpoints.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"prepare.optimal.cutpoints.args\",\n\tfunction(fit) {\n\t\t# Prepare options for optimal.cutpoints().\n\t\targs <- private$cutpoint.options[OPTIMAL_CUTPOINT_PARAMETERS]\n\t\targs$X <- prediction ~ response\n\t\targs$data <- data.frame(\n\t\t\tresponse = private$get.binary.response(fit),\n\t\t\tprediction = private$get.probability(fit)\n\t\t)\n\t\targs$ci.fit <- FALSE\n\t\t# Because we can specify only negative class in optimal.cutpoints(),\n\t\t# using the following option to specify positive class.\n\t\targs$tag.healthy <- 0\n\t\treturn(args)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tRun optimal.cutpoints.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"run.optimal.cutpoints\",\n\tfunction(fit) {\n\t\targs <- private$prepare.optimal.cutpoints.args(fit)\n\t\tresult <- do.call(OptimalCutpoints::optimal.cutpoints, args)\n\t\treturn(result)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tParse result of optimal.cutpoints.\n#\n#\tArgs:\n#\t\tobject:\n#\t\t\tan object of optimal.cutpoints.\n#\t\tmethod:\n#\t\t\ta method for which result of optimal.cutpoints is parsed.\n#\n#\tReturns:\n#\t\ta matrix of 1 row with each column represents each metric.\n#\t\t\tmatrix(threshold, sensitivity, ...)\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"parse.optimal.cutpoints.result\",\n\tfunction(object, method) {\n\t\t# Extract threshold and metrics from the result of optimal.cutpoints.\n\t\t# Because field name \"Global\" is only used for the first method,\n\t\t# access element of list by index (1) instead of name (\"Global\").\n\t\tcutoff <- object[[method]][[1]]$optimal.cutoff\n\t\tif (any(sapply(cutoff, length) > 2)) {\n\t\t\t# Not sure there is a possibility to be here.\n\t\t\tmsg <- paste0(\n\t\t\t\t\"Single optimal threshold was not determined \",\n\t\t\t\t\"by specified settings.\\n\",\n\t\t\t\t\"The first threshold was used for further calculation.\\n\",\n\t\t\t\t\"If this is a problem, change the 'cutpoint.options' option\\n\",\n\t\t\t\t\"of the cv.models to find single optimal threshold.\\n\",\n\t\t\t\t\"For the further information, please consult the manual of \",\n\t\t\t\t\"optimal.cutpoitns.\"\n\t\t\t)\n\t\t\twarning(msg)\n\t\t\tcutoff <- lapply(cutoff, \"[\", 1)\n\t\t}\n\t\t# Convert the result to a matrix.\n\t\tresult <- do.call(cbind, lapply(cutoff, c))\n\t\tcolnames(result) <- NAME_CONVERSION_TABLE[colnames(result)]\n\t\t# Join AUC to the result.\n\t\tauc <- object[[method]][[1]]$measures.acc$AUC[\"AUC\"]\n\t\tresult <- cbind(result, auc = auc)\n\t\treturn(result)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate number of true positive (TP).\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.tp\",\n\tfunction(fit, metrics) {\n\t\tn.positive <- sum(private$get.binary.response(fit))\n\t\ttp <- n.positive - metrics[, \"fn\"]\n\t\treturn(tp)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate number of true negative (TN).\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.tn\",\n\tfunction(fit, metrics) {\n\t\tbin <- private$get.binary.response(fit)\n\t\tn.negative <- length(bin) - sum(bin)\n\t\ttn <- n.negative - metrics[, \"fp\"]\n\t\treturn(tn)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate accuracy.\n#\n#\tArgs:\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.accuracy\",\n\tfunction(metrics) {\n\t\ttp <- metrics[, \"tp\"]\n\t\ttn <- metrics[, \"tn\"]\n\t\tfp <- metrics[, \"fp\"]\n\t\tfn <- metrics[, \"fn\"]\n\t\taccuracy <- (tp + tn) / (tp + tn + fp + fn)\n\t\treturn(accuracy)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate informedness.\n#\n#\tArgs:\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.informedness\",\n\tfunction(metrics) {\n\t\treturn(metrics[, \"sensitivity\"] + metrics[, \"specificity\"] - 1)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate markedness.\n#\n#\tArgs:\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.markedness\",\n\tfunction(metrics) {\n\t\treturn(metrics[, \"ppv\"] + metrics[, \"npv\"] - 1)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate Matthew's correlation coefficient (MCC).\n#\n#\tArgs:\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.mcc\",\n\tfunction(metrics) {\n\t\ttp <- metrics[, \"tp\"]\n\t\ttn <- metrics[, \"tn\"]\n\t\tfp <- metrics[, \"fp\"]\n\t\tfn <- metrics[, \"fn\"]\n\t\tdenominator <- sqrt((tp + fn) * (tp + fp) * (tn + fp) * (tn + fn))\n\t\tmcc <- (tp * tn - fp * fn) / denominator\n\t\treturn(mcc)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate log-likelihood.\n#\n#\tOriginal program was obtained from Lawson et al. 2014.\n#\tPrevalence, thresholds and the performance of presence-absence models.\n#\tMethods in Ecology and Evolution 5:54-64.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.loglik\",\n\tfunction(fit) {\n\t\tp <- private$get.probability(fit)\n\t\ty <- private$get.binary.response(fit)\n\t\tloglik <- sum(log(p * y + (1 - p) * (1 - y)))\n\t\treturn(loglik)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate Likelihood based R squared.\n#\n#\tOriginal program was obtained from Lawson et al. 2014.\n#\tPrevalence, thresholds and the performance of presence-absence models.\n#\tMethods in Ecology and Evolution 5:54-64.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\" and \"prediction\" fields.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.r.squared.loglik\",\n\tfunction(fit) {\n\t\tp <- private$get.probability(fit)\n\t\ty <- private$get.binary.response(fit)\n\t\tloglik.p <- sum(log(p * y + (1 - p) * (1 - y)))\n\t\tloglik.n <- sum(log(mean(p) * y + (1 - mean(p)) * (1 - y)))\n\t\trsq <- (loglik.p - loglik.n) / (1 - loglik.n)\n\t\treturn(rsq)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate Cohen's Kappa statistic.\n#\n#\thttps://en.wikipedia.org/wiki/Cohen%27s_kappa\n#\n#\t\t\t\t\t\tresponse\n#\t\t\t\t\t\tTRUE\tFALSE\n#\tpredicted\tTRUE\ttp: a\tfp: b\n#\t\t\t\tFALSE\tfn: c\tfn: d\n#\n#\tArgs:\n#\t\tmetrics:\n#\t\t\tresult of 'parse.optimal.cutpoints.result' method.\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calc.kappa\",\n\tfunction(metrics) {\n\t\ttp <- metrics[, \"tp\"]\n\t\ttn <- metrics[, \"tn\"]\n\t\tfp <- metrics[, \"fp\"]\n\t\tfn <- metrics[, \"fn\"]\n\t\tn <- tp + tn + fp + fn\n\t\tpo = (tp + tn) / n\n\t\tpe.true = (tp + fp) / n * (tp + fn) / n\n\t\tpe.false = (fn + tn) / n * (fp + tn) / n\n\t\tpe = pe.true + pe.false\n\t\tkappa = (po - pe) / (1 - pe)\n\t\treturn(kappa)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate all metrics for single method of optimal.cutpoints.\n#\n#\tArgs:\n#\t\tmethod:\n#\t\t\ta method by which result is calculated.\n#\t\tobject:\n#\t\t\tcv.models object.\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\", \"prediction\" and \"index\" fields.\n#\n#\tReturns:\n#\t\ta matrix of 1 row with each column represents each metric.\n#\t\t\tmatrix(threshold, sensitivity, ...)\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"private\", \"calculate.metrics.for.single.method\",\n\tfunction(method, object, fit) {\n\t\t# Calculate metrics using optimal.cutpoints.\n\t\tmetrics <- private$parse.optimal.cutpoints.result(object, method)\n\t\t# Calculate TP and TN first.\n\t\tmetrics <- cbind(\n\t\t\tmetrics,\n\t\t\ttp = private$calc.tp(fit, metrics),\n\t\t\ttn = private$calc.tn(fit, metrics)\n\t\t)\n\t\t# Calculate other metrics including that depend on TP and TN.\n\t\tmetrics <- cbind(\n\t\t\tmetrics,\n\t\t\taccuracy = private$calc.accuracy(metrics),\n\t\t\tinformedness = private$calc.informedness(metrics),\n\t\t\tmarkedness = private$calc.markedness(metrics),\n\t\t\tmcc = private$calc.mcc(metrics),\n\t\t\tloglik = private$calc.loglik(fit),\n\t\t\trsq.loglik = private$calc.r.squared.loglik(fit),\n\t\t\tkappa = private$calc.kappa(metrics)\n\t\t)\n\t\trownames(metrics) <- NULL\n\t\treturn(metrics)\n\t}\n)\n\n\n#------------------------------------------------------------------------------\n#\tCalculate all metrics.\n#\n#\tArgs:\n#\t\tfit:\n#\t\t\ta list having result of one fold of cross validation\n#\t\t\twith \"response\", \"prediction\" and \"index\" fields.\n#\n#\tReturns:\n#\t\tlist(\n#\t\t\tmethod1 = matrix(threshold, sensitivity, ...),\n#\t\t\tmethod2 = matrix(threshold, sensitivity, ...),\n#\t\t\t...\n#\t\t)\n#------------------------------------------------------------------------------\nclassification.metrics.calculator$set(\n\t\"public\", \"calculate.metrics\",\n\tfunction(fit) {\n\t\toc.result <- private$run.optimal.cutpoints(fit)\n\t\tmethods <- private$cutpoint.options$methods\n\t\tmetrics <- lapply(\n\t\t\tmethods, private$calculate.metrics.for.single.method,\n\t\t\toc.result, fit\n\t\t)\n\t\tnames(metrics) <- private$cutpoint.options$methods\n\t\treturn(metrics)\n\t}\n)\n", "meta": {"hexsha": "746725cbb4df99eb553a7f8d649ff1fb4508ecf7", "size": 13656, "ext": "r", "lang": "R", "max_stars_repo_path": "R/classification.metrics.r", "max_stars_repo_name": "Marchen/cv.models", "max_stars_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-01T15:45:35.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-01T15:45:35.000Z", "max_issues_repo_path": "R/classification.metrics.r", "max_issues_repo_name": "Marchen/cv.models", "max_issues_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-03-11T04:21:27.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-10T12:18:14.000Z", "max_forks_repo_path": "R/classification.metrics.r", "max_forks_repo_name": "Marchen/cv.models", "max_forks_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-16T03:30:36.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-16T03:30:36.000Z", "avg_line_length": 30.7567567568, "max_line_length": 79, "alphanum_fraction": 0.5288517868, "num_tokens": 3066, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.31053096909250844}} {"text": "# /* !/usr/bin/Rscript */\n\n#' # Titanic Data Analysis\n#' This file is the main entry point into the analysis, which is broken\n#' down by concern to aid maintenance and extensibility.\n#'\n#' - *clean.r* -- Data pre-processing\n#' - *explore.r* -- Data inspection\n#' - *create.r* -- Feature engineering\n#' - *model.r* -- Model fitting\n#' - *validate.r* -- Model validation\n#'\n#' ## Setup\n#' Prep the R environment:\n#' - Suppress package messages and warnings when generating Knitr docs\n#' - Teardown previous user vars in workspace\n#' - Set working directory to script directory\n#' - Reset plot setup\n#' - Set global seed value\n#' - load dependencies\n# /*\nwriteLines('Beginning Titanic Data Analysis')\nwriteLines('-------------')\nwriteLines('\\nStart: Reset workspace, load dependencies & source data')\n# */\n\n#+ suppress-warnings, include = FALSE\nif (isTRUE(getOption(\"knitr.in.progress\"))) {\n knitr::opts_chunk$set(message = FALSE, warning = FALSE)\n} else {\n setwd(dirname(sys.frame(1)$ofile))\n}\n\nrm(list=ls())\npar(mfrow=c(1,1))\nSEED <- 83;\n\nlibrary(Amelia) # for missmap()\nlibrary(tree) # for exploring\nlibrary(caret) # for modelling\nlibrary(caretEnsemble) # for modelling\nlibrary(discern) # for analysis - install_github('andybeeching/discern')\n\n#' ### Obtain data\n#' Kaggle doesn't permit direct downloads, so assume manually placed in\n#' the \"data\" directory. Additionally, save the data as a fresh\n#' workspace.\n# TRAIN_DATA_URL <- \"https://www.kaggle.com/c/titanic-gettingStarted/download/train.csv\"\n# TEST_DATA_URL <- \"https://www.kaggle.com/c/titanic-gettingStarted/download/test.csv\"\n# download.file(TRAIN_DATA_URL, destfile=\"../data/train.csv\", method=\"curl\")\n# download.file(TEST_DATA_URL, destfile=\"../data/test.csv\", method=\"curl\")\nts <- date()\nraw <- read.csv('../data/train.csv')\ntest <- read.csv('../data/test.csv')\n\n# /*\n# reference modules to prepare data for modelling\n# - NOTE: model.r and analyse.r generally used independently due to\n# runtime cost of training models and running validation tests.\n# */\nif (isTRUE(getOption(\"knitr.in.progress\"))) {\n knitr::spin_child('clean.r')\n knitr::spin_child('explore.r')\n knitr::spin_child('create.r')\n knitr::spin_child('model.r')\n knitr::spin_child('validate.r')\n} else {\n source('clean.r')\n source('explore.r')\n source('create.r')\n source('model.r')\n source('validate.r')\n}\n\n#' ### Prediction\n#' Predict the response variable *Survived* for the original test\n#' dataset. Through submission the best result was in fact random\n#' forest, so this is the model referenced in the script.\nSurvived <- predict(rfFit, newdata = test.munged)\n\n#' Re-level vector to binary response, associate with passenger ID,\n#' and write CSV file.\nSurvived <- round(Survived)\nlevels(Survived) <- c(0, 1)\noutput <- as.data.frame(Survived)\noutput$PassengerId <- test$PassengerId\nwrite.csv(output[,c(\"PassengerId\", \"Survived\")],\n file=\"../Titanic_predictions.csv\", row.names=FALSE, quote=FALSE)\n\n", "meta": {"hexsha": "b352be7051bf1cc363e4bc489048cc67b88c3776", "size": 2996, "ext": "r", "lang": "R", "max_stars_repo_path": "src/main.r", "max_stars_repo_name": "andybeeching/kaggle-titanic", "max_stars_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-07-13T14:20:50.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-13T14:20:50.000Z", "max_issues_repo_path": "src/main.r", "max_issues_repo_name": "andybeeching/kaggle-titanic", "max_issues_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/main.r", "max_forks_repo_name": "andybeeching/kaggle-titanic", "max_forks_repo_head_hexsha": "df1000173aa9e7e5846611fd0be1223e709fbe06", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2888888889, "max_line_length": 88, "alphanum_fraction": 0.6949265688, "num_tokens": 768, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678319, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3105309690925084}} {"text": "#' @title make.xyz\n#' @description couldn't accurately describe\n#' @param \\code{x} \n#' @param \\code{y} \n#' @param \\code{z} \n#' @param \\code{group} \n#' @param \\code{FUN} \n#' @family data structures\n#' @keywords manip\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\nmake.xyz <- function (x, y, z, group, FUN = sum, ...) \n{\n Z <- tapply(z, list(paste(x, y, sep = \", \"), group), FUN, \n ...)\n Z <- ifelse(is.na(Z), 0, Z)\n XY <- rownames(Z)\n tempfun <- function(XY, i) {\n as.numeric(unlist(lapply(strsplit(XY, \", \"), function(x) x[i])))\n }\n X <- tempfun(XY, 1)\n Y <- tempfun(XY, 2)\n return(list(x = X, y = Y, z = Z))\n}\n\n", "meta": {"hexsha": "8ec8fe13e3ed6ae3286b3f4814771e94d2fef69c", "size": 670, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make.xyz.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/make.xyz.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/make.xyz.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.7692307692, "max_line_length": 72, "alphanum_fraction": 0.5373134328, "num_tokens": 234, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5888891307678319, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3105309690925084}} {"text": "# In RStudio, Ctrl+A then Run\n\nlibrary(ggplot2)\nlibrary(dplyr)\n\nsetwd(\"~/GitHub/nzpolls/graphing\") # replace with own working directory\n\n# Read data\ncsvData <- read.csv(\"local-data.csv\")\npollingData <- arrange(csvData, desc(as.Date(endDate, '%Y-%m-%d')))\n\n# Plot data\nspansize <- 0.65 # higher = smoother\nggplot(pollingData, aes(x = as.Date(endDate, '%Y-%m-%d'))) +\n theme_bw() +\n\n # Labour\n geom_point(aes(y = LAB, colour = \"LAB\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = LAB, colour = \"LAB\"), span = spansize, se = FALSE) +\n # National\n geom_point(aes(y = NAT, colour = \"NAT\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = NAT, colour = \"NAT\"), span = spansize, se = FALSE) +\n # Green\n geom_point(aes(y = GRN, colour = \"GRN\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = GRN, colour = \"GRN\"), span = spansize, se = FALSE) +\n # ACT\n geom_point(aes(y = ACT, colour = \"ACT\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = ACT, colour = \"ACT\"), span = spansize, se = FALSE) +\n # NZ First\n geom_point(aes(y = NZF, colour = \"NZF\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = NZF, colour = \"NZF\"), span = spansize, se = FALSE) +\n # Maori\n geom_point(aes(y = MRI, colour = \"MRI\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = MRI, colour = \"MRI\"), span = spansize, se = FALSE) +\n # TOP\n geom_point(aes(y = TOP, colour = \"TOP\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = TOP, colour = \"TOP\"), span = spansize, se = FALSE) +\n # New Conservative\n geom_point(aes(y = NCP, colour = \"NCP\"), size = 2, alpha = 0.3) +\n geom_smooth(aes(y = NCP, colour = \"NCP\"), span = spansize, se = FALSE) +\n\n # Y-axis\n scale_y_continuous(limits = c(0, 55), breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60), minor_breaks = waiver(), expand = c(0, 0)) +\n # X-axis\n scale_x_date(date_breaks = \"4 months\", date_labels = \"%b '%y\", minor_breaks = \"1 month\") +\n # Axis styling\n theme(\n plot.background = element_rect(fill = \"white\", color = NA),\n axis.text.x = element_text(angle = 0, vjust = 0.5, size = 10),\n axis.text.y = element_text(size = 12),\n axis.title.y = element_text(size = 12)\n ) +\n # Axis labels\n labs(y = \"Party vote (%)\", x = NULL) +\n # Colors and key\n scale_color_manual(\n name = \"\",\n # Legend\n labels = c(\n \"LAB\" = \"Labour\",\n \"NAT\" = \"National\",\n \"ACT\",\n \"GRN\" = \"Greens\",\n \"NZF\" = \"NZ First\",\n \"MRI\" = \"Maori\",\n \"TOP\" = \"Opportunities\",\n \"NCP\" = \"New Conservative\"\n ),\n # Color mapping\n values = c(\n \"LAB\" = \"#D82A20\",\n \"NAT\" = \"#004278\",\n \"ACT\" = \"#FDE401\",\n \"GRN\" = \"#098137\",\n \"NZF\" = \"#000000\",\n \"MRI\" = \"#B2001A\",\n \"TOP\" = \"#32DAC3\",\n \"NCP\" = \"#00AEEF\"\n )\n ) +\n theme(\n legend.position = \"bottom\",\n legend.text = element_text(size = 12)\n )\n\n# Save as 800x500 SVG\n", "meta": {"hexsha": "0567bb8e958324cd20a0f4843ed19a2ce5740645", "size": 2838, "ext": "r", "lang": "R", "max_stars_repo_path": "graphing/graphing.r", "max_stars_repo_name": "Nixinova/nzpolls", "max_stars_repo_head_hexsha": "104f3b585de26314ab4f5efd0868a219decdbb0f", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "graphing/graphing.r", "max_issues_repo_name": "Nixinova/nzpolls", "max_issues_repo_head_hexsha": "104f3b585de26314ab4f5efd0868a219decdbb0f", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graphing/graphing.r", "max_forks_repo_name": "Nixinova/nzpolls", "max_forks_repo_head_hexsha": "104f3b585de26314ab4f5efd0868a219decdbb0f", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6206896552, "max_line_length": 146, "alphanum_fraction": 0.5687103594, "num_tokens": 1009, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3104670268366856}} {"text": "clomin <- function(t, rh, wind, trad) {\n .Call('biometeoR_clomin', PACKAGE = 'biometeoR', t, rh, wind, trad)\n}\n\n", "meta": {"hexsha": "c8cf4985b9ff912bfad3708f4e98098fbc7ce467", "size": 115, "ext": "r", "lang": "R", "max_stars_repo_path": "R/clomin.r", "max_stars_repo_name": "alfcrisci/biometeoR", "max_stars_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:46.000Z", "max_issues_repo_path": "R/clomin.r", "max_issues_repo_name": "alfcrisci/biometeoR", "max_issues_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/clomin.r", "max_forks_repo_name": "alfcrisci/biometeoR", "max_forks_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.0, "max_line_length": 71, "alphanum_fraction": 0.6260869565, "num_tokens": 41, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3104670196452017}} {"text": "## Main fitting function.\n## - Arguments:\n## - capthist: Capture history matrix.\n## - ids: A vector of IDs, where each element indicates which individual is associated with each capture history.\n## - traps: Matrix of detector locations.\n## - mask: Mask object with points for numerical integration over activity centre locations. Must include an attribute called \"area\", providing the area covered by a single point.\n## - detfn: The detection function to use; either \"hn\" (halfnormal) or \"hhn\" (hazard halfnormal). If signal strengths are provided, then this argument is ignored and a signal strength detection function is used.\n## - start: A named vector of start values for numerical maximisation. See the model parameters below for names.\n## - toa: Time of arrival matrix in the same structure as the capthist object (optional).\n## - ss: Signal strength matrix in the same structure as the capthist object (optional).\n## - speed_sound: The speed of sound in metres per second.\n## - ss_cutoff: A signal strength cutoff. See Stevenson et al (2015).\n##\n## For multisession models, capthist, ids, traps, mask, toa, and ss can be lists, where each component of the list corresponds to a single session.\n## \n## Model parameters:\n##\n## Mandatory:\n## - D: Animals per hectare.\n## - lambda: Expected number of calls per individual.\n##\n## When using the halfnormal detection function:\n## - g0: Detection function intercept.\n## - sigma: Detection function spatial scale.\n##\n## When using the hazard halfnormal detection function:\n## - lambda0: Detection function intercept on the hazard scale.\n## - sigma: Detection function spatial scale.\n##\n## When time-of-arrival data are provided:\n## - sigma_toa: Measurement error of times-of-arrival.\n##\n## When signal strength data are provided:\n## - b0_ss: Source signal strength.\n## - b1_ss: Signal strength loss per metre.\n## - sigma_ss: Measurement error of signal strengths.\n\ncuerate.scr.fit <- function(capthist, ids, traps, mask, detfn = NULL, start, toa = NULL, ss = NULL , speed_sound = 330, ss_cutoff = 0, trace = FALSE){\n ## Indicator for whether or not signal strengths are used.\n use_ss <- !is.null(ss)\n ## Indicator for whether or not times of arrival are used.\n use_toa <- !is.null(toa)\n if (is.list(capthist)){\n multi.sess <- TRUE\n } else {\n multi.sess <- FALSE\n capthist <- list(capthist)\n ids <- list(ids)\n traps <- list(traps)\n mask <- list(mask)\n if (use_toa){\n toa <- list(toa)\n }\n if (use_ss){\n ss <- list(ss)\n }\n }\n n.sessions <- length(capthist)\n aMask <- maskDists <- toa_ssq <- vector(\"list\", n.sessions)\n if (!use_toa){\n toa <- vector(\"list\", n.sessions)\n }\n if (!use_ss){\n ss <- vector(\"list\", n.sessions)\n }\n for (i in 1:n.sessions){\n ## ids must be 1:N animals.\n ids[[i]] = as.numeric(factor(ids[[i]]))\n ## Area of a single mask point.\n aMask[[i]] <- attr(mask[[i]], \"area\")\n \n ## Calculating distances between mask points and detectors.\n maskDists[[i]] <- eucdist(mask[[i]], traps[[i]])\n if (use_toa){\n ## Creating TOA sum of squares matrix.\n toa_ssq[[i]] <- make_toa_ssq(toa[[i]], eucdist(traps[[i]], mask[[i]]), speed_sound)\n } else {\n ## Dummy objects if not used.\n toa[[i]] <- toa_ssq[[i]] <- matrix(0, nrow = 1, ncol = 1)\n }\n if (!use_ss){\n ss[[i]] <- matrix(0, nrow = 1, ncol = 1)\n }\n }\n ## Indicator for detection function.\n if (is.null(detfn) & !use_ss){\n stop(\"A detection function must be selected.\")\n }\n if (use_ss){\n if (!is.null(detfn)){\n warning(\"The choice of detection function is being ignored because signal strengths have been provided.\")\n }\n detfn <- \"ss\"\n hn <- FALSE\n } else if (detfn == \"hn\"){\n hn <- TRUE\n } else if (detfn == \"hhn\"){\n hn <- FALSE\n } else {\n stop(\"The argument detfn must either be 'hn' or 'hhn'.\")\n }\n ## Converting parameters to link scale.\n start.link <- numeric(6)\n names(start.link) <- c(\"D\", \"df1\", \"df2\", \"lambda\", \"sigma_toa\", \"sigma_ss\")\n start.link[\"D\"] <- log(start[\"D\"])\n start.link[\"lambda\"] <- log(start[\"lambda\"])\n if (use_ss){\n names(start.link)[c(2, 3)] <- c(\"b0_ss\", \"b1_ss\")\n start.link[\"b0_ss\"] <- log(start[\"b0_ss\"])\n start.link[\"b1_ss\"] <- log(start[\"b1_ss\"])\n start.link[\"sigma_ss\"] <- log(start[\"sigma_ss\"])\n } else if (hn){\n names(start.link)[c(2, 3)] <- c(\"g0\", \"sigma\")\n start.link[\"g0\"] <- qlogis(start[\"g0\"])\n start.link[\"sigma\"] <- log(start[\"sigma\"])\n } else {\n names(start.link)[c(2, 3)] <- c(\"lambda0\", \"sigma\")\n start.link[\"lambda0\"] <- log(start[\"lambda0\"])\n start.link[\"sigma\"] <- log(start[\"sigma\"])\n }\n if (use_toa){\n start.link[\"sigma_toa\"] <- log(start[\"sigma_toa\"])\n }\n start <- c(start[1:4], start[\"sigma_toa\"][use_toa], start[\"sigma_ss\"][use_ss])\n start.link <- c(start.link[1:4], start.link[\"sigma_toa\"][use_toa], start.link[\"sigma_ss\"][use_ss])\n n.pars <- length(start)\n par.names <- names(start)\n ## Fitting model.\n fit <- nlminb(start.link, scr.nll.cuerate.multi,\n caps = capthist,\n aMask = aMask,\n maskDists = maskDists,\n ID = ids,\n toa = toa,\n toa_ssq = toa_ssq,\n use_toa = use_toa,\n ss = ss,\n use_ss = use_ss,\n ss_cutoff = ss_cutoff,\n hn = hn,\n trace = trace,\n par_names = par.names)\n ## Approximating Hessian.\n hess <- optimHess(fit$par, scr.nll.cuerate.multi,\n caps = capthist,\n aMask = aMask,\n maskDists = maskDists,\n ID = ids,\n toa = toa,\n toa_ssq = toa_ssq,\n use_toa = use_toa,\n ss = ss,\n ss_cutoff = ss_cutoff,\n use_ss = use_ss,\n hn = hn,\n trace = trace,\n par_names = par.names)\n \n ## Calculating confidence intervals\n ## - Using the (sqrt of) diagonals of (-ve) Hessian obtained from optimHess\n ## - i.e. Information matrix\n ## - Wald CIs calculated by sapply() loop\n ## - Loops through each of fitted parameters and calculates lower/upper bounds\n ## Note: fitted pars must be on LINK scale\n ## : if matrix is singular, none of the SEs or CIs are calculated (inherits/try statement)\n fittedPars = fit$par\n names(fittedPars) <- par.names\n if(inherits(try(solve(hess), silent = TRUE), \"try-error\")) {\n ## Hessian is singular\n warning(\"Warning: singular hessian\")\n ## SE and Wald CIs not calculated\n se = NA\n waldCI = matrix(NA, nrow = length(fittedPars), ncol = 2)\n ## But columns still need to be returned (if/when simulations are run)\n cnames = c(\"Estimate\", \"SE\", \"Lower\", \"Upper\")\n } else {\n ## Calculating basic Wald CIs\n se = sqrt(diag(solve(hess)))\n waldCI.link = t(sapply(1:length(fittedPars),\n function(i) fittedPars[i] + (c(-1, 1) * (qnorm(0.975) * se[i]))))\n G.mult <- numeric(n.pars)\n waldCI <- 0*waldCI.link\n ## Back-transforming the confidence limits.\n for (i in par.names){\n if (i %in% c(\"D\", \"b0_ss\", \"b1_ss\", \"sigma_ss\", \"lambda0\", \"sigma\", \"lambda\", \"sigma_toa\")){\n waldCI[par.names == i, ] <- exp(waldCI.link[par.names == i, ])\n G.mult[par.names == i] <- exp(fittedPars[i])\n fittedPars[i] <- exp(fittedPars[i])\n }\n if (i == \"g0\"){\n waldCI[par.names == i, ] <- plogis(waldCI.link[par.names == i, ])\n G.mult[par.names == i] <- dlogis(fittedPars[i])\n fittedPars[i] <- plogis(fittedPars[i])\n }\n }\n ## Using the delta method to get the standard errors\n ## - G = jacobian matrix of partial derivatives of back-transformed\n ## - i.e. log(D) -> exp(D) -- deriv. --> exp(D)\n ## - Note: 1st deriv of plogis (CDF) = dlogis (PDF)\n G = diag(length(fittedPars)) * G.mult\n se = sqrt(diag(G %*% solve(hess) %*% t(G)))\n }\n results = cbind(fittedPars, se, waldCI)\n dimnames(results) = list(par.names, c(\"Estimate\", \"SE\", \"Lower\", \"Upper\"))\n ## Returning a list with everything.\n list(results = results, capthist = capthist, \n mask = mask, aMask = aMask, maskDists = maskDists, \n speed_sound = speed_sound, ids = ids,\n traps = traps, detfn = detfn, toa = toa, toa_ssq = toa_ssq, hess = hess)\n \n}\n\nscr.nll.cuerate.multi <- function(pars, caps, aMask, maskDists, ID, toa, toa_ssq,\n use_toa, ss, use_ss, ss_cutoff, hn, trace, par_names){\n n.sessions <- length(caps)\n sess.nll <- numeric(n.sessions)\n for (i in 1:n.sessions){\n sess.nll[i] <- scr_nll_cuerate(pars, caps[[i]], aMask[[i]], maskDists[[i]],\n ID[[i]], toa[[i]], toa_ssq[[i]], use_toa, ss[[i]],\n use_ss, ss_cutoff, hn, trace, par_names)\n }\n sum(sess.nll)\n}\n", "meta": {"hexsha": "73b2fcaaa018de4a4b1de53be66901cd7ad92f13", "size": 9638, "ext": "r", "lang": "R", "max_stars_repo_path": "fit-cuerate-scr.r", "max_stars_repo_name": "b-steve/scr-cuerate", "max_stars_repo_head_hexsha": "f775ab3fd2e6d41b1c6ab32e0d13257464432441", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fit-cuerate-scr.r", "max_issues_repo_name": "b-steve/scr-cuerate", "max_issues_repo_head_hexsha": "f775ab3fd2e6d41b1c6ab32e0d13257464432441", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fit-cuerate-scr.r", "max_forks_repo_name": "b-steve/scr-cuerate", "max_forks_repo_head_hexsha": "f775ab3fd2e6d41b1c6ab32e0d13257464432441", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.0267857143, "max_line_length": 220, "alphanum_fraction": 0.5505291554, "num_tokens": 2563, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7217431943271999, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.3104559213997218}} {"text": "# Run init.r before other scripts\nrm(list=ls())\n # for use in R console.\n # set own relevant directory if working in R console, otherwise ignore if in terminal\nsetwd(\"/Users/davidbeauchesne/Dropbox/PhD/PhD_obj2/Structure_Comm_EGSL/EGSL_species_distribution\")\n# -----------------------------------------------------------------------------\n# PROJECT:\n# Evaluating the structure of the communities of the estuary\n# and gulf of St.Lawrence\n# -----------------------------------------------------------------------------\n\n# Studying the HMSC package, contact is G. Blanchet for inquiries\n\n# Dependencies\n library(Rcpp)\n library(RcppArmadillo)\n library(coda)\n\n# Installing the package\n # library(devtools)\n # install_github(\"guiblanchet/HMSC\")\n library(HMSC)\n\n# Other libraries\n # install.packages('beanplot', dependencies = T)\n # install.packages('corrplot', dependencies = T)\n # install.packages('circlize', dependencies = T)\n library(beanplot)\n library(corrplot)\n library(circlize)\n\n# First part of the workflow:\n # 1. Setting up an HMSC object by de\fning the model structure (e.g. whether traits or random effects are to be included) and organizing the data (typically imported from \fles with standard R commands).\n # 2. De\fning the priors required for Bayesian inference.\n # 3. Initiating the model parameters\n # 4. Running the actual estimation scheme. This involves setting the Markov chain Monte Carlo (MCMC) sampler (e.g. by de\fning the number of iterations and how they will be thinned). As running the estimation scheme may take some time, we recommend the user to save the HMSC object to a \fle outside of R (e.g. called \\model.RData\"). This object includes the model structure, the data, and the full posterior distribution.\n\n# Second part of the workflow:\n # No set order, but the user might typically wish to do the following:\n # - Assess the convergence of the MCMC chains,\n # - Produce posterior summaries (e.g. posterior means and quantiles) such as tables or plots,\n # - Measure the explanatory power of the model,\n # - Perform a variance partitioning among the \ffixed and random eff\u000bects,\n # - Make predictions.\n\n# Structure of the HMSC model\n # For ecologically meaningful analyses, the minimal set of data needed are either of the following:\n # 1. The occurrence matrix for one species as well as the environmental covariate matrix, in which case the HMSC model corresponds to a traditional single-species model.\n # 2. The occurrence matrix for several species, in which case the HMSC model corresponds to a model-based ordination.\n\n# Formatting data\n # species: numeric values\n # environmental covariates: numeric values\n # random effects: factor if single random effect, data frame if multiple random effects\n # traits: numeric values\n # phylogeny: square symmetric correlation or covariance matrix\n # autocorrelated random eff\u000bect: either a data frame with the \ffirst column a factor while the other columns are spatial or temporal coordinates or a list of data frame following the structure presented previously if there are multiple autocorrelated random eff\u000bects.\n\n # Typically highly recommended to scale (center and divide by the standard deviation) the environmental covariates and the traits so that their mean is zero and their variance one.\n # this removes the potential e\u000bects units can have on the parameter estimation\n # the default priors are compatible with the scaled covariates\n\n# Example 1\n # 1. Reading the data\n # Community matrix\n spComm <- read.csv(\"./documentation/HMSC-data/simulated/Y.csv\")\n # Environmental covariates\n env <- read.csv(\"./documentation/HMSC-data/simulated/X.csv\")\n # Random effects\n sitePlot <- read.csv(\"./documentation/HMSC-data/simulated/Pi.csv\")\n\n # 2. Creating HMSC data\n # Convert all columns of Pi to a factor\n for(i in 1:ncol(sitePlot)) {\n sitePlot[,i] <- as.factor(sitePlot[,i])\n }\n\n simulEx1 <- as.HMSCdata(Y = spComm, X = env, Random = sitePlot, interceptX = FALSE, scaleX = FALSE)\n\n # Alternatively, load the data directly. Available for this example, but I will have to do it myself for my own data\n data(\"simulEx1\")\n\n # 3. Defining prior distribution\n # We are using uninformative priors for this part. However, the user can modify this if desired.\n simulEx1prior <- as.HMSCprior(simulEx1)\n\n # 4. Setting initial model parameters\n simulEx1param <- as.HMSCparam(simulEx1, simulEx1prior)\n\n # 5. Setting the values of the true parameters\"\n # This is only possible with simulated data for which we know the actual parameter values. Otherwise, it is impossible.\n data(\"simulParamEx1\")\n\n # 6. Performing the MCMC sampling\n model <- hmsc(simulEx1,\n param = simulEx1param,\n priors = simulEx1prior,\n family = \"probit\",\n niter = 10000,\n nburn = 1000,\n thin = 10)\n\n # Simpler version when uninformative priors are sufficient and a priori parameters do not need to be set\n model <- hmsc(simulEx1,\n family = \"probit\",\n niter = 10000,\n nburn = 1000,\n thin = 10)\n\n # 7. Producing MCMC trace and density plots\n # Mixing objects\n mixingParamX <- as.mcmc(model, parameters = \"paramX\")\n mixingMeansParamX <- as.mcmc(model, parameters = \"meansParamX\")\n mixingMeansVarX <- as.mcmc(model, parameters = \"varX\")\n mixingParamLatent <- as.mcmc(model, parameters = \"paramLatent\")\n\n plot(mixingMeansParamX, col = \"blue\")\n\n # 8. Producing posterior summaries\n # Violin plot\n par(mar=c(6,4,1,1))\n mixingParamXDF <- as.data.frame(mixingParamX)\n beanplot(mixingParamXDF, las = 2)\n points(1:30, as.vector(simulParamEx1$param$paramX), pch=19, col=\"blue\", cex=2)\n\n # Box plot\n par(mar=c(6,4,1,1))\n boxplot(mixingParamXDF, las = 2)\n points(1:30, as.vector(simulParamEx1$param$paramX), pch=19, col=\"blue\", cex=2)\n\n # Average\n average <- apply(model$results$estimation$paramX, 1:2, mean)\n # 95% confidence intervals\n CI.025 <- apply(model$results$estimation$paramX, 1:2, quantile, probs = 0.025)\n CI.975 <- apply(model$results$estimation$paramX, 1:2, quantile, probs = 0.975)\n\n # Summary table\n paramXCITable <- cbind(unlist(as.data.frame(average)),\n unlist(as.data.frame(CI.025)),\n unlist(as.data.frame(CI.975)))\n colnames(paramXCITable) <- c(\"average\", \"lowerCI\", \"upperCI\")\n rownames(paramXCITable) <- paste(rep(colnames(average), each = nrow(average)), \"_\", rep(rownames(average), ncol(average)), sep=\"\")\n\n # Print summary table\n paramXCITable\n\n # Credible intervals\n par(mar=c(7,4,1,1))\n plot(0, 0, xlim = c(1, nrow(paramXCITable)), ylim = range(paramXCITable), type = \"n\", xlab = \"\", ylab = \"\", main=\"paramX\", xaxt=\"n\")\n axis(1,1:30,rownames(paramXCITable),las=2)\n abline(h = 0,col = \"grey\")\n arrows(x0 = 1:nrow(paramXCITable), x1 = 1:nrow(paramXCITable), y0 = paramXCITable[, 2], y1 = paramXCITable[, 3], code = 3, angle = 90, length = 0.05)\n points(1:nrow(paramXCITable), paramXCITable[,1], pch = 15, cex = 2)\n points(1:nrow(paramXCITable), as.vector(simulParamEx1$param$paramX),col = \"blue\", pch = 19, cex = 2)\n\n # 9. Variance partitioning\n variationPart <- variPart(model, c(rep(\"climate\", 2), \"habitat\"))\n\n Colour <- c(\"orange\", \"blue\", \"darkgreen\", \"purple\")\n barplot(t(variationPart), col=Colour, las=1)\n legend(\"bottomleft\",\n legend = c(paste(\"Fixed climate (mean = \", round(mean(variationPart[, 1]), 4)*100, \"%)\", sep=\"\"),\n paste(\"Fixed habitat (mean = \", round(mean(variationPart[, 2]), 4)*100, \"%)\", sep=\"\"),\n paste(\"Random site (mean = \", round(mean(variationPart[, 3]), 4)*100, \"%)\", sep=\"\"),\n paste(\"Random plot (mean = \", round(mean(variationPart[, 4]), 4)*100, \"%)\", sep=\"\")),\n fill = Colour,\n bg=\"white\")\n\n # 10. Association networks\n # Extract all estimated associatin matrix\n assoMat <- corRandomEff(model)\n # Average\n siteMean <- apply(assoMat[, , , 1], 1:2, mean)\n plotMean <- apply(assoMat[, , , 2], 1:2, mean)\n\n #=======================\n ### Associations to draw\n #=======================\n #--------------------\n ### Site level effect\n #--------------------\n # Build matrix of colours for chordDiagram\n siteDrawCol <- matrix(NA, nrow = nrow(siteMean), ncol = ncol(siteMean))\n siteDrawCol[which(siteMean > 0.4, arr.ind=TRUE)]<-\"red\"\n siteDrawCol[which(siteMean < -0.4, arr.ind=TRUE)]<-\"blue\"\n # Build matrix of \"significance\" for corrplot\n siteDraw <- siteDrawCol\n siteDraw[which(!is.na(siteDraw), arr.ind = TRUE)] <- 0\n siteDraw[which(is.na(siteDraw), arr.ind = TRUE)] <- 1\n siteDraw <- matrix(as.numeric(siteDraw), nrow = nrow(siteMean), ncol = ncol(siteMean))\n #--------------------\n ### Plot level effect\n #--------------------\n # Build matrix of colours for chordDiagram\n plotDrawCol <- matrix(NA, nrow = nrow(plotMean), ncol = ncol(plotMean))\n plotDrawCol[which(plotMean > 0.4, arr.ind=TRUE)]<-\"red\"\n plotDrawCol[which(plotMean < -0.4, arr.ind=TRUE)]<-\"blue\"\n # Build matrix of \"significance\" for corrplot\n plotDraw <- plotDrawCol\n plotDraw[which(!is.na(plotDraw), arr.ind = TRUE)] <- 0\n plotDraw[which(is.na(plotDraw), arr.ind = TRUE)] <- 1\n plotDraw <- matrix(as.numeric(plotDraw), nrow = nrow(plotMean), ncol = ncol(plotMean))\n\n # plotDraw plots\n par(mfrow=c(1,2))\n # Matrix plot\n Colour <- colorRampPalette(c(\"blue\", \"white\", \"red\"))(200)\n corrplot::corrplot(siteMean, method = \"color\", col = Colour, type = \"lower\", diag = FALSE, p.mat = siteDraw, tl.srt = 45)\n # Chord diagram\n circlize::chordDiagram(siteMean, symmetric = TRUE, annotationTrack = c(\"name\", \"grid\"), grid.col = \"grey\", col = siteDrawCol)\n\n # siteDraw plots\n par(mfrow=c(1,2))\n # Matrix plot\n Colour <- colorRampPalette(c(\"blue\", \"white\", \"red\"))(200)\n corrplot::corrplot(plotMean, method = \"color\", col = Colour, type = \"lower\", diag = FALSE, p.mat = plotDraw, tl.srt = 45)\n # Chord diagram\n circlize::chordDiagram(plotMean, symmetric = TRUE, annotationTrack = c(\"name\", \"grid\"), grid.col = \"grey\", col = plotDrawCol)\n\n # 11. Computing the explanatory power of the model\n # Prevalence\n prevSp <- colSums(simulEx1$Y)\n # Coefficient of multiple determination\n R2 <- Rsquared(model, averageSp = FALSE)\n R2comm <- Rsquared(model, averageSp = TRUE)\n # Draw figure\n par(mar=c(5,6,0.5,0.5))\n plot(prevSp, R2, xlab = \"Prevalence\", ylab = expression(R^2), pch=19, las=1,cex.lab = 2)\n abline(h = R2comm, col = \"blue\", lwd = 2)\n\n # Extract all MCMC of paramX\n model$results$estimation$paramX\n\n ### Full joint probability distribution\n fullPost <- jposterior(model)\n\n # 12. Generating predictions for training data\n # predictions that are not conditional on the occurrences of other species\n predTrain <- predict(model)\n\n #13. Generating predictions for new data\n # New environmental covariates\n newPred <- 100\n nEnv <- ncol(simulEx1$X)\n Xnew <- matrix(nrow = newPred, ncol = nEnv)\n colnames(Xnew) <- colnames(simulEx1$X)\n Xnew[, 1] <- 1\n Xnew[, 2] <- mean(simulEx1$X[, 2])\n Xnew[, 3] <- seq(min(simulEx1$X[, 3]), max(simulEx1$X[, 3]), length = newPred)\n\n # New site- and plot-level random effect\n RandomSel <- sample(200, 100)\n RandomNew <- simulEx1$Random[RandomSel, ]\n for(i in 1:ncol(RandomNew)) {\n RandomNew[, i] <- as.factor(as.character(RandomNew[, i]))\n }\n colnames(RandomNew) <- colnames(simulEx1$Random)\n\n # Organize the data into an HMSCdata object\n dataVal <- as.HMSCdata(X = Xnew, Random = RandomNew, scaleX = FALSE, interceptX = FALSE)\n\n # Generate predictions\n predVal <- predict(model, newdata = dataVal)\n\n # Plot predictions\n plot(0, 0, type=\"n\", xlim = range(dataVal$X[, 3]), ylim = c(0, 1),\n xlab = \"Environmental covariate 3\",\n ylab = \"Probability of occurrence\")\n Colours <- rainbow(ncol(predVal))\n for(i in 1:ncol(predVal)) {\n lines(dataVal$X[, 3], predVal[, i], col = Colours[i], lwd=3)\n }\n", "meta": {"hexsha": "cb536710e5166b8ae01936d328762834dbda1c27", "size": 13404, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/HMSC.r", "max_stars_repo_name": "david-beauchesne/EGSL_species_distribution", "max_stars_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-10T12:54:25.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-10T12:54:25.000Z", "max_issues_repo_path": "Script/HMSC.r", "max_issues_repo_name": "david-beauchesne/EGSL_species_distribution", "max_issues_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Script/HMSC.r", "max_forks_repo_name": "david-beauchesne/EGSL_species_distribution", "max_forks_repo_head_hexsha": "490ff78c43e8597786c9ab55f9db1b8ddb458acd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.0989010989, "max_line_length": 424, "alphanum_fraction": 0.5957923008, "num_tokens": 3463, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.310422474121176}} {"text": "#Assign spatial products to Delgado data.\n#Climate from worldclim2. N-dep from NADP.\n#These are based on functions written by Colin. \nrm(list = ls())\nlibrary(data.table)\nsource('paths.r')\nsource('NEFI_functions/extract_ndep.r')\nsource('NEFI_functions/worldclim2_grab.r')\nsource('NEFI_functions/arid_extract.r')\nsource('NEFI_functions/extract_pH.r')\nsource('NEFI_functions/extract_npp.r')\nsource('NEFI_functions/extract_ndep_global.r')\nsource('NEFI_functions/extract_EM.r')\nsource('NEFI_functions/extract_soil_moist.r')\n\n# set output path\noutput.path <- \"/projectnb/talbot-lab-data/NEFI_data/16S/scc_gen/prior_abundance_mapping/Delgado/delgado_metadata_spatial.rds\"\n\nd <- read.csv(\"/projectnb/talbot-lab-data/NEFI_data/16S/scc_gen/prior_abundance_mapping/Delgado/delgado_metadata.csv\")\n\n# subset to northern temperate lats\nd <- d[d$Latitude < 66.5 & d$Latitude > 23.5,]\n# change column names\ncolnames(d) <- tolower(colnames(d))\nsetnames(d, c(\"soil_c\", \"soil_c_n_ratio\", \"npp2003_2015\",\"ph\"), c(\"pC\", \"cn\", \"NPP\",\"pH\"))\n\n# create sample ID\nd$sampleID <- paste0(\"site\", d$id_environmental)\nrownames(d) <- d$sampleID\n\n# extract % basal area of ecto trees\nrelEM <- extract_EM(d$latitude, d$longitude)\n\n#extract worldclim.\nclim <- worldclim2_grab(d$latitude,d$longitude, worldclim2_folder = \"/projectnb/talbot-lab-data/spatial_raster_data/WorldClim2/\")\nclim$aridity <- arid_extract(d$latitude,d$longitude, folder = \"/projectnb/talbot-lab-data/spatial_raster_data/Global_Aridity/aridity/\")\n\nmoisture <- extract_soil_moist(d$latitude, d$longitude, path = '/projectnb/talbot-lab-data/NEFI_data/covariate_data/soil_moisture_raster/soilmoisture/w001001.adf')\n#extract N deposition. Half of these are NA because our Ndep product only covers the United States.\n#ignore the warnings.\nndep <- extract_ndep(d$longitude,d$latitude, folder = \"/projectnb/talbot-lab-data/spatial_raster_data/CASTNET_Ndep/\")\n\n#extract pH\nph_estim <- extract_pH(d$latitude,d$longitude, folder = \"/projectnb/talbot-lab-data/spatial_raster_data/SoilGrids_uncertainty/\")\n\n# \"recent\" because the delgado values are averaged over 15 years\nNPP_recent <- extract_npp(d$latitude, d$longitude, path=\"/projectnb/talbot-lab-data/NEFI_data/covariate_data/NPP/w001001.adf\")\n\n# from Ackerman et al.: https://conservancy.umn.edu/handle/11299/197613\nndep.glob <- extract.ndep.global(d$latitude, d$longitude)\n\n#put together all spatial products\nspatial <- cbind(relEM, ndep, ph_estim,clim, NPP_recent, ndep.glob, moisture)\n\n#remove any columns from previous spatial extractions, update with new extraction.\nd <- as.data.frame(d)\nd <- cbind(d,spatial)\n\n#save\nsaveRDS(d,output.path)\n", "meta": {"hexsha": "dda44be71b10d88a2e62c65cd97adaada2c891a5", "size": 2630, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/data_construction/delgado/3._add_spatial_products.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "16S/data_construction/delgado/3._add_spatial_products.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "16S/data_construction/delgado/3._add_spatial_products.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 43.1147540984, "max_line_length": 163, "alphanum_fraction": 0.7779467681, "num_tokens": 788, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6406358548398979, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3103112493582077}} {"text": "# Project: Surveyer\n# Description: Land and Engineering Surveying utilities\n# Authors: Milutin Pejovic, Petar Bursac, Milan Kilibarda, Branislav Bajat, Aleksandar Sekulic\n# Date:\n\n# Functions:\n\nread_surveynet <- function(file, dest_crs = NA, axes = c(\"Easting\", \"Northing\")){\n # TODO: Ako se definise neki model tezina za koji ne postoje podaci stavi warning. Npr. za model \"sd_dh\" mora da postoji i sd.apriori koji se pretvara u sd0.\n\n # TODO: Set warning if there are different or not used points in two elements of survey.net list.\n # TODO: Check if any point has no sufficient measurements to be adjusted.\n # setting columns type\n points_col_type <- c(\"numeric\", \"text\", \"numeric\", \"numeric\", \"numeric\", \"logical\", \"logical\", \"logical\")\n obs_col_type <- c(\"text\", \"text\", rep(\"numeric\", 19))\n # reading data\n points <- readxl::read_xlsx(path = file, sheet = \"Points\", col_types = points_col_type) %>% mutate_at(., .vars = c(\"Name\"), as.character)\n observations <- readxl::read_xlsx(path = file, sheet = \"Observations\", col_types = obs_col_type) %>% mutate_at(., .vars = c(\"from\", \"to\"), as.character)\n\n if(sum(rowSums(is.na(points[, c(\"x\", \"y\")])) != 0) != 0){\n warning(\"Network has no spatial coordinates\")}else{\n # Creating sf class from observations\n observations$x_from <- points$x[match(observations$from, points$Name)]\n observations$y_from <- points$y[match(observations$from, points$Name)]\n observations$x_to <- points$x[match(observations$to, points$Name)]\n observations$y_to <- points$y[match(observations$to, points$Name)]\n\n points <- points %>% as.data.frame() %>% sf::st_as_sf(coords = c(\"x\",\"y\"), remove = FALSE)\n if(which(axes == \"Easting\") == 2){points <- points %>% dplyr::rename(x = y, y = x)}\n\n observations <- observations %>% dplyr::mutate(id = seq.int(nrow(.))) %>% split(., f = as.factor(.$id)) %>%\n lapply(., function(row) {lmat <- matrix(unlist(row[c(\"x_from\", \"y_from\", \"x_to\", \"y_to\")]), ncol = 2, byrow = TRUE)\n st_linestring(lmat)}) %>%\n sf::st_sfc() %>%\n sf::st_sf('ID' = seq.int(nrow(observations)), observations, 'geometry' = .)\n }\n\n\n\n #if(sum(rowSums(is.na(observations[, c(\"HzD\", \"HzM\", \"HzS\")])) != 0) != 0){stop(\"There is uncomplete observations\")}\n\n\n observations <- observations %>% dplyr::mutate(Hz = HzD + HzM/60 + HzS/3600,\n Vz = VzD + VzM/60 + VzS/3600,\n tdh = SD*cos(Vz*pi/180),\n distance = (!is.na(HD) | !is.na(SD)) & !is.na(sd_dist),\n direction = !is.na(Hz) & !is.na(sd_Hz),\n diff_level = (!is.na(dh) | !is.na(tdh)))\n\n # In network design, observation is included if measurement standard is provided\n if(observations %>% purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>% dplyr::select(HzD, HzM, HzS) %>% is.na() %>% all()){\n observations$direction[!is.na(observations$sd_Hz)] <- TRUE\n }\n if(observations %>% purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>% dplyr::select(HD, SD) %>% is.na() %>% all()){\n observations$distance[!is.na(observations$sd_dist)] <- TRUE\n }\n if(observations %>% purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>% dplyr::select(dh) %>% is.na() %>% all()){\n observations$diff_level[(!is.na(observations$dh) | !is.na(observations$sd_dh) | !is.na(observations$d_dh) | !is.na(observations$n_dh))] <- TRUE\n }\n\n # Eliminacija merenih duzina i visinsih razlika izmedju fiksnih tacaka duzina izmedju\n # checking for fixed points\n # TODO what in case of no-fixed points\n fixed_points <- points %>% dplyr::filter(FIX_2D | FIX_1D) %>% .$Name\n\n if(length(fixed_points) > 1){\n observations[observations$from %in% fixed_points & observations$to %in% fixed_points, \"distance\"] <- FALSE\n observations[observations$from %in% fixed_points & observations$to %in% fixed_points, \"diff_level\"] <- FALSE\n }\n\n # Setting coordinate system\n if(!is.na(dest_crs)){\n observations %<>% sf::st_set_crs(dest_crs)\n points %<>% sf::st_set_crs(dest_crs)\n }\n\n\n # Creating list\n survey_net <- list(points, observations)\n names(survey_net) <- c(\"points\", \"observations\")\n return(survey_net)\n}\n\n\ndec2dms <- function(ang){\n deg <- floor(ang); minut <- floor((ang-deg)*60); sec <- ((ang-deg)*60-minut)*60\n return(paste(deg, minut, round(sec, 0), sep = \" \"))\n}\n\n\ndist <- function(pt1_coords, pt2_coords){\n dEasting <- as.numeric(pt2_coords[1] - pt1_coords[1])\n dNorthing <- as.numeric(pt2_coords[2] - pt1_coords[2])\n distance <- sqrt(dEasting^2 + dNorthing^2)\n return(distance)\n}\n\n\n\nni <- function(pt1_coords, pt2_coords, type = list(\"dec\", \"dms\", \"rad\")){\n ## check if the type exists:\n if(length(type) > 1){ type <- type[[1]]}\n if(!any(type %in% list(\"dms\", \"dec\", \"rad\"))){stop(paste(type, \"method not available.\"))}\n\n ## body\n dEasting <- as.numeric(pt2_coords[1] - pt1_coords[1])\n dNorthing <- as.numeric(pt2_coords[2] - pt1_coords[2])\n atg <- ifelse(dNorthing < 0, atan(dEasting/dNorthing)*180/pi + 180, atan(dEasting/dNorthing)*180/pi)\n ang <- ifelse(atg < 0, atg + 360, atg)\n\n deg <- floor(ang); minut <- floor((ang-deg)*60); sec <- ((ang-deg)*60-minut)*60\n\n if(type == \"dms\"){\n ang <- c(deg, minut, sec)\n names(ang) <- c(\"deg\",\"min\",\"sec\")\n }\n if(type == \"rad\"){\n ang <- ang*pi/180\n }\n return(ang)\n}\n\n### coeficients for distances #####################################################\ncoef_d <- function (pt1, pt2, pts, units) {\n units.table <- c(\"mm\" = 1000, \"cm\" = 100, \"m\" = 1)\n pt1 <- as.numeric(pt1)\n pt2 <- as.numeric(pt2)\n coords <- pts\n vec_d <- c(rep(0, dim(pts)[1]*2))#c(rep(0, length(coords)))\n\n y_coords <- coords[, 2]\n x_coords <- coords[, 1]\n y1 <- pt1[2]\n x1 <- pt1[1]\n y2 <- pt2[2]\n x2 <- pt2[1]\n dy1 <- (y_coords-y1)\n dx1 <- (x_coords-x1)\n dy2 <- (y_coords-y2)\n dx2 <- (x_coords-x2)\n i <- which(dy1 == dx1 & dy1 == 0 & dx1 == 0)\n j <- which(dy2 == dx2 & dy2 == 0 & dx2 == 0)\n\n dy <- (y2-y1)*units.table[units]\n dx <- (x2-x1)*units.table[units]\n d <- sqrt(dy^2+dx^2)\n\n A <- (-dy/d)\n B <- (-dx/d)\n A1 <- -A\n B1 <- -B\n\n vec_d[2*i-1] <- B\n vec_d[2*j-1] <- B1\n vec_d[2*i] <- A\n vec_d[2*j] <- A1\n return(vec_d)\n}\n\n### coeficients for directions (pravac) #####################################################\n\ncoef_p <- function (pt1, pt2, pts, units) {\n units.table <- c(\"mm\" = 1000, \"cm\" = 100, \"m\" = 1)\n pt1 <- as.numeric(pt1)\n pt2 <- as.numeric(pt2)\n coords <- pts\n vec_p <- c(rep(0, dim(pts)[1]*2))#c(rep(0, length(coords)))\n ro <- 180/pi*3600\n\n y_coords <- coords[, 2]\n x_coords <- coords[, 1]\n\n y1 <- pt1[2]\n x1 <- pt1[1]\n y2 <- pt2[2]\n x2 <- pt2[1]\n\n dy1 <- (y_coords-y1)\n dx1 <- (x_coords-x1)\n dy2 <- (y_coords-y2)\n dx2 <- (x_coords-x2)\n\n i <- which(dy1 == dx1 & dy1 == 0 & dx1 == 0)\n j <- which(dy2 == dx2 & dy2 == 0 & dx2 == 0)\n\n dy <- (y2-y1)*units.table[units]\n dx <- (x2-x1)*units.table[units]\n d <- sqrt(dy^2 + dx^2)\n\n A <- (ro*dx/d^2)\n B <- (-ro*dy/d^2)\n A1 <- -(ro*dx/d^2)\n B1 <- -(-ro*dy/d^2)\n\n vec_p[2*i-1] <- B\n vec_p[2*j-1] <- B1\n vec_p[2*i] <- A\n vec_p[2*j] <- A1\n return(vec_p)\n}\n\n# net.points <- survey.net[[1]]\nfix_params2D <- function(net.points){\n rep(as.logical(c(apply(cbind(net.points$FIX_2D), 1, as.numeric))), each = 2)\n}\n\n# survey.net <- brana\nAmat <- function(survey.net, units){\n\n if(any(survey.net[[2]]$direction)){\n A_dir <- survey.net[[2]] %>% dplyr::filter(direction) %>% st_coordinates() %>% as.data.frame() %>% mutate_at(vars(L1), as.factor) %>%\n split(., .$L1) %>%\n lapply(., function(x) coef_p(pt1 = x[1, 1:2], pt2 = x[2, 1:2], pts = st_coordinates(survey.net[[1]][, 1:2]), units = units)) %>%\n do.call(rbind, .)\n }else{\n A_dir <- NULL\n }\n\n if(any(survey.net[[2]]$distance)){\n A_dist <- survey.net[[2]] %>% dplyr::filter(distance) %>% st_coordinates() %>% as.data.frame() %>% mutate_at(vars(L1), as.factor) %>%\n split(., .$L1) %>%\n lapply(., function(x) coef_d(pt1 = x[1, 1:2], pt2 = x[2, 1:2], pts = st_coordinates(survey.net[[1]][, 1:2]), units = units)) %>%\n do.call(rbind, .)\n }else{\n A_dist <- NULL\n }\n\n station.names <- survey.net[[2]] %>% dplyr::filter(direction) %>% dplyr::select(from) %>% st_drop_geometry() %>% unique() %>% unlist(use.names = FALSE)\n\n if(!all(is.na(survey.net[[2]]$sd_Hz))){\n Z_mat <- survey.net[[2]] %>% dplyr::filter(direction) %>%\n tidyr::spread(key = from, value = direction, fill = FALSE) %>%\n dplyr::select(as.character(station.names)) %>%\n st_drop_geometry() %>%\n as.matrix()*1\n }else{\n Z_mat <- NULL\n }\n\n fix <- fix_params2D(net.points = survey.net[[1]])\n if(!is.null(A_dir) & !is.null(A_dist)){\n rest_mat <- matrix(0, nrow = dim(A_dist)[1], ncol = dim(Z_mat)[2])\n }else{\n rest_mat <- NULL\n }\n\n A <- cbind(rbind(A_dir, A_dist)[, !fix], rbind(Z_mat, rest_mat))\n\n # Removing zero rows (measured distances between the fixed points)\n # A <- A[apply(A !=0, 1, any), , drop=FALSE]\n\n sufix <- c(\"dx\", \"dy\")\n colnames(A) <- c(paste(rep(survey.net[[1]]$Name, each = 2), rep(sufix, length(survey.net[[1]]$Name)), sep = \"_\")[!fix], paste(colnames(Z_mat), \"z\", sep = \"_\"))\n return(A)\n}\n\n# Weights matrix\n# Wmat je ista, samo je promenjen naziv standarda. Stavljeni su \"sd_Hz\" i \"sd_dist\".\nWmat <- function(survey.net, sd.apriori = 1, res.units = \"mm\"){\n res.unit.lookup <- c(\"mm\" = 1, \"cm\" = 10, \"m\" = 1000)\n #TODO: Omoguciti zadavanje i drugih kovariacionih formi izmedju merenja.\n obs.data <- rbind(survey.net[[2]] %>% st_drop_geometry() %>%\n dplyr::filter(direction) %>%\n dplyr::select(from, to, standard = sd_Hz) %>%\n dplyr::mutate(type = \"direction\"),\n survey.net[[2]] %>% st_drop_geometry() %>%\n dplyr::filter(distance) %>%\n dplyr::select(from, to, standard = sd_dist) %>%\n dplyr::mutate(type = \"distance\", standard = standard/res.unit.lookup[res.units])\n )\n return(diag(sd.apriori^2/obs.data$standard^2))\n}\n\n# Funkcija koja izdvaja elemente Qx matrice u listu za elipsu svake tacke\nQxy <- function(Qx, fix){\n k = 0\n Qxxx <- as.list(rep(NA, length(fix)))\n for(i in 1:length(Qxxx)){\n k = 2*fix[i] + k\n if (fix[i]){\n Qxxx[[i]] <- cbind(c(Qx[k-1, k-1], Qx[k-1, k]), c(Qx[k, k-1], Qx[k, k]))\n } else {\n Qxxx[[i]] <- diag(rep(fix[i]*1, 2))\n }\n }\n return(Qxxx)\n}\n\n# # Funkcija koja izdvaja elemente Qx matrice u listu za elipsu svake tacke\n# Qxy <- function(Qx, n, fixd = fix){\n# k = 0\n# fixd <- cbind(fixd, fixd[,1] + fixd[, 2])\n# Qxxx <- as.list(rep(NA, dim(fixd)[1]))\n# for(i in 1:length(Qxxx)){\n# k = fixd[i, 1] + fixd[i, 2] + k\n# if(fixd[i, 3] == 1){\n# Qxxx[[i]] <- diag(fixd[i, c(1, 2)])*Qx[k, k]\n# }\n# else if (fixd[i, 3] == 2){\n# Qxxx[[i]] <- cbind(c(Qx[k-1, k-1], Qx[k-1, k]), c(Qx[k, k-1], Qx[k, k]))\n# } else {\n# Qxxx[[i]] <- diag(fixd[i, c(1, 2)])\n# }\n# }\n# return(Qxxx)\n# }\n# #\n\nerror.ellipse <- function(Qxy, prob = NA, sd.apriori = 1, teta.unit = list(\"deg\", \"rad\")) {\n Qee <- Qxy[1, 1]\n Qnn <- Qxy[2, 2]\n Qen <- Qxy[1, 2]\n if(any(c(Qee, Qnn) == 0)){\n A <- 0\n B <- 0\n teta <- 0\n ellipse <- c(A, B, teta)\n }else{\n k <- sqrt((Qnn - Qee)^2 + 4*Qen^2)\n lambda1 <- 0.5*(Qee + Qnn + k)\n lambda2 <- 0.5*(Qee + Qnn - k)\n if(is.na(prob)){\n A <- sd.apriori*sqrt(lambda1)\n B <- sd.apriori*sqrt(lambda2)\n }else{\n A <- sd.apriori*sqrt(lambda1*qchisq(prob, df = 2))\n B <- sd.apriori*sqrt(lambda2*qchisq(prob, df = 2))\n }\n teta <- ifelse((Qnn - Qee) == 0, 0, 0.5*atan(2*Qen/(Qnn - Qee)))\n teta <- ifelse(teta >= 0, teta, teta + 2*pi)\n teta <- ifelse(teta >= pi, teta - pi, teta)\n if(teta.unit[[1]] == \"deg\"){\n ellipse <- c(A, B, teta*180/pi)\n }else{\n ellipse <- c(A, B, teta)\n }\n }\n return(ellipse)\n}\n#\n\nrot = function(a) matrix(c(cos(a), sin(a), -sin(a), cos(a)), 2, 2)\n\nsf.ellipse <- function(ellipse.param, scale = 10){\n ellipse <- nngeo::st_ellipse(ellipse.param, ey = ellipse.param$A*scale, ex = ellipse.param$B*scale)\n geom.ellipse = st_geometry(ellipse)\n ellipse.cntrd = st_centroid(geom.ellipse)\n ellipse.rot <- (geom.ellipse - ellipse.cntrd) * rot(ellipse.param$teta*pi/180) + ellipse.cntrd\n ellipse.sf <- st_sf(Name = ellipse.param$Name, A = ellipse.param$A, B = ellipse.param$B, teta = ellipse.param$teta, Geometry = ellipse.rot)\n return(ellipse.sf)\n}\n\nsigma.xy <- function(Qxy.mat, sd.apriori){\n sigma <- diag(diag(as.numeric(sd.apriori), 2, 2)%*%diag(sqrt(diag(Qxy.mat)), 2, 2))\n}\n\n# st.survey.net <- makis.snet[[2]] %>% dplyr::filter(from == \"OM20\")\n# st.survey.net <- brana.snet[[2]] %>% dplyr::filter(from == \"T1\")\n# st.survey.net <- avala[[2]] %>% dplyr::filter(from == \"S2\")\n# st.survey.net <- A.survey.net[[2]] %>% dplyr::filter(from == \"C\")\n# st.survey.net <- Gorica.survey.net[[2]] %>% dplyr::filter(from == \"1\")\n# st.survey.net <- ab[[2]] %>% dplyr::filter(from == \"P2\")\n# st.survey.net <- mreza_sim[[2]] %>% dplyr::filter(from == \"M10\")\n# st.survey.net <- zadatak1.snet[[2]] %>% dplyr::filter(from == \"T2\")\n# st.survey.net <- cut45[[2]] %>% dplyr::filter(from == \"C53\")\n\nfdir_st <- function(st.survey.net, units.dir = \"sec\"){\n units.table <- c(\"sec\" = 3600, \"min\" = 60, \"deg\" = 1)\n st.survey.net <- st.survey.net %>% split(., f = as.factor(.$to)) %>%\n lapply(., function(x) {x$ni = ni(pt1_coords = as.numeric(x[, c(\"x_from\", \"y_from\")]), pt2_coords = as.numeric(x[, c(\"x_to\", \"y_to\")]), type = \"dec\"); return(x)}) %>%\n do.call(rbind,.) %>%\n dplyr::mutate(z = Hz-ni) %>%\n dplyr::arrange(ID)\n #st.survey.net$z <- ifelse(st.survey.net$z < 0 & st.survey.net$z < -0.01, st.survey.net$z + 360, st.survey.net$z)\n st.survey.net$z <- ifelse(st.survey.net$z < 0, st.survey.net$z + 360, st.survey.net$z)\n z0_mean <- mean(st.survey.net$z)\n f <- st.survey.net$ni + z0_mean - st.survey.net$Hz\n f <- ifelse(f < -1, f + 360 , f)\n f <- ifelse(f > 359, f - 360, f)\n f <- f*units.table[units.dir]\n return(f)\n}\n# ab <- survey.survey.sim\n# survey.net = ab\n\n\n# fdir_st <- function(st.survey.net, units.dir = \"sec\"){\n# units.table <- c(\"sec\" = 3600, \"min\" = 60, \"deg\" = 1)\n# st.survey.net <- st.survey.net %>% split(., f = as.factor(.$to)) %>%\n# lapply(., function(x) {x$ni = ni(pt1_coords = as.numeric(x[, c(\"x_from\", \"y_from\")]), pt2_coords = as.numeric(x[, c(\"x_to\", \"y_to\")]), type = \"dec\"); return(x)}) %>%\n# do.call(rbind,.) %>%\n# dplyr::mutate(z = Hz-ni) %>%\n# dplyr::arrange(ID)\n# st.survey.net$z <- ifelse(st.survey.net$z < 1, st.survey.net$z + 360, st.survey.net$z)\n# z0_mean <- mean(st.survey.net$z)\n# st.survey.net$Hz0 <- z0_mean + st.survey.net$ni\n# st.survey.net$Hz0 <- ifelse(st.survey.net$Hz0 > 359, st.survey.net$Hz0 - 360, st.survey.net$Hz0)\n# st.survey.net$Hz <- ifelse(st.survey.net$Hz < 1, st.survey.net$Hz + 360, st.survey.net$Hz)\n# st.survey.net$Hz <- ifelse(st.survey.net$Hz >= 360, st.survey.net$Hz - 360, st.survey.net$Hz)\n# st.survey.net$f <- (st.survey.net$Hz0 - st.survey.net$Hz)*units.table[units.dir]\n#\n# f <- (st.survey.net$Hz0 - st.survey.net$Hz)*units.table[units.dir]\n# return(f)\n# }\n\n\n# survey.net[[2]] %>% dplyr::filter(from == \"M2\") %>% fdir_st() %>% round(.,2)\n\n# fmat(survey.net = survey.net) %>% round(., 2)\n\nfmat <- function(survey.net, units.dir = \"sec\", units.dist = \"mm\"){\n f_dir <- survey.net[[2]] %>% dplyr::filter(direction == TRUE) %>% st_drop_geometry() %>% split(.,factor(.$from, levels = unique(.$from))) %>%\n lapply(., function(x) fdir_st(x, units.dir = units.dir)) %>%\n do.call(\"c\",.) %>% as.numeric() %>% as.vector()\n dist.units.table <- c(\"mm\" = 1000, \"cm\" = 100, \"m\" = 1)\n survey.net[[2]] <- survey.net[[2]] %>% dplyr::filter(distance) %>% st_drop_geometry() %>%\n dplyr::mutate(dist0 = sqrt((x_from-x_to)^2+(y_from-y_to)^2))\n f_dist <- (survey.net[[2]]$dist0 - survey.net[[2]]$HD)*dist.units.table[units.dist]\n return(c(f_dir, f_dist))\n}\n\nmodel_adequacy_test <- function(sd.apriori, sd.estimated, df, prob){\n if(sd.estimated > sd.apriori){\n F.estimated <- sd.estimated^2/sd.apriori^2\n F.quantile <- qf(p = prob, df1 = df, df2 = 10^1000)\n }else{\n F.estimated <- sd.apriori^2/sd.estimated^2\n F.quantile <- qf(p = prob, df1 = 10^1000, df2 = df)\n }\n if(F.estimated < F.quantile){\n note <- \"Model is correct\"; print(note)\n }else{\n note <- \"Model is not correct. Please check Baarda test statistics for individual observations. Suggestion: Remove one observation with the highest statistics\"; print(note)\n }\n return(list(F.estimated < F.quantile, \"F_test\" = F.estimated, \"Crital value F-test\" = F.quantile, note))\n}\n\nmodel_adequacy_test.shiny <- function(sd.apriori, sd.estimated, df, prob){\n if(sd.estimated > sd.apriori){\n F.estimated <- sd.estimated^2/sd.apriori^2\n F.quantile <- qf(p = prob, df1 = df, df2 = 10^1000)\n }else{\n F.estimated <- sd.apriori^2/sd.estimated^2\n F.quantile <- qf(p = prob, df1 = 10^1000, df2 = df)\n }\n\n mlist <- list(F.estimated = F.estimated, F.quantile = F.quantile,\n model = if(F.estimated < F.quantile){\n paste(\"sd.estimated =\", round(sd.estimated, 2), \"/ sd.apriori =\", round(sd.apriori, 2), \"/ Model is correct\", sep = \" \")} else{\n paste(\"sd.estimated =\", round(sd.estimated, 2), \"/ sd.apriori =\", round(sd.apriori, 2), \"/ Model is not correct\", sep = \" \")\n }\n )\n return(mlist)\n}\n\n\nAmat1D <- function(survey.net){\n used_points <- unique(c(survey.net$observations$from, survey.net$observations$to))\n point_names <- unique(survey.net$points$Name)\n point_names <- point_names[point_names %in% used_points]\n if(!all(used_points %in% point_names)){stop(\"Some points are missed\")}\n\n Amat <- data.frame(matrix(0, ncol = length(point_names), nrow = dim(survey.net$observations)[1]))\n names(Amat) <- point_names\n for(i in 1:dim(Amat)[1]){\n Amat[i, survey.net$observations$from[i]] <- -1\n Amat[i, survey.net$observations$to[i]] <- 1\n }\n Amat <- Amat[, !survey.net$points$FIX_1D] #%>% select(-fixed_points)\n Amat <- as.matrix(Amat)\n return(Amat)\n}\n\nWmat1D <- function(survey.net, wdh_model = list(\"sd_dh\", \"d_dh\", \"n_dh\", \"E\"), sd0 = 1, d0 = NA, n0 = 1, res.units = \"mm\"){\n res.unit.lookup <- c(\"mm\" = 1, \"cm\" = 10, \"m\" = 1000)\n \"%!in%\" <- Negate(\"%in%\")\n if(wdh_model %!in% c(\"sd_dh\", \"d_dh\", \"n_dh\", \"E\")){stop(\"Model of weigths is not properly specified, see help\")}\n wdh_model <- wdh_model[[1]]\n if(is(survey.net[[2]], \"sf\")){survey.net[[2]] <- survey.net[[2]] %>% st_drop_geometry()}\n\n survey.net[[2]] %<>%\n dplyr::mutate(weigth = case_when(\n wdh_model == \"sd_dh\" ~ (sd0/res.unit.lookup[res.units])^2/sd_dh^2,\n wdh_model == \"d_dh\" ~ 1/d_dh,\n wdh_model == \"n_dh\" ~ n0/n_dh,\n wdh_model == \"E\" ~ 1\n )\n )\n return(diag(survey.net[[2]]$weigth))\n}\n\nfmat1D <- function(survey.net, units = units){\n unit.lookup <- c(\"mm\" = 1000, \"cm\" = 100, \"m\" = 1)\n survey.net[[2]]$h_from <- survey.net[[1]]$h[match(survey.net[[2]]$from, survey.net[[1]]$Name)]\n survey.net[[2]]$h_to <- survey.net[[1]]$h[match(survey.net[[2]]$to, survey.net[[1]]$Name)]\n f <- ((survey.net[[2]]$h_to-survey.net[[2]]$h_from)-survey.net[[2]]$dh)*unit.lookup[units]\n return(f)\n}\n\n\n\n# adjust = TRUE; survey.net = prva; dim_type = \"1D\"; sd.apriori = 0.7; wdh_model = \"n_dh\"; n0 = 1; maxiter = 1; prob = 0.95; coord_tolerance = 1e-3; result.units = \"mm\"; ellipse.scale = 1; output = \"spatial\"; teta.unit = \"dec\"; units.dir = \"sec\"; units.dist = \"mm\"; use.sd.estimated = TRUE; all = TRUE\nadjust.snet <- function(adjust = TRUE, survey.net, dim_type = list(\"1D\", \"2D\"), sd.apriori = 1, wdh_model = list(\"n_dh\", \"sd_dh\", \"d_dh\", \"E\"), n0 = 1, maxiter = 50, prob = 0.95, output = list(\"spatial\", \"report\"), coord_tolerance = 1e-3, result.units = list(\"mm\", \"cm\", \"m\"), ellipse.scale = 1, teta.unit = list(\"deg\", \"rad\"), units.dir = \"sec\", use.sd.estimated = TRUE, all = TRUE){\n\n dim_type <- dim_type[[1]]\n output <- output[[1]]\n \"%!in%\" <- Negate(\"%in%\")\n if(!adjust){use.sd.estimated <- FALSE}\n units <- result.units[[1]]\n res.unit.lookup <- c(\"mm\" = 1000, \"cm\" = 100, \"m\" = 1)\n disp.unit.lookup <- c(\"mm\" = 2, \"cm\" = 3, \"m\" = 4)\n dir.unit.lookup <- c(\"sec\" = 3600, \"min\" = 60, \"deg\" = 1)\n if(is.na(sf::st_crs(survey.net$points) == TRUE)) {\n net.crs <- 3857\n }else(\n net.crs <- st_crs(survey.net$points)\n )\n \n # TODO: This has to be solved within the read.surveynet function\n used.points <- survey.net[[1]]$Name[survey.net[[1]]$Name %in% unique(c(survey.net[[2]]$from, survey.net[[2]]$to))]\n if(!!any(used.points %!in% survey.net[[1]]$Name)) stop(paste(\"There is no coordinates for point\", used.points[which(used.points %!in% survey.net[[1]]$Name)]), sep = \" \")\n survey.net[[1]] <- survey.net[[1]][which(survey.net[[1]]$Name %in% used.points), ]\n\n # Model\n if(dim_type == \"2D\"){\n observations <- tidyr::pivot_longer(survey.net[[2]] %>% purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>% dplyr::select(from, to, direction, distance), cols = c(direction, distance), names_to = \"type\", values_to = \"used\") %>%\n dplyr::mutate(across(.cols = \"type\", .fns = ~factor(., levels = c(\"direction\", \"distance\")))) %>%\n dplyr::arrange(type) %>%\n dplyr::filter(used == TRUE) %>%\n dplyr::mutate(from_to = stringr::str_c(.$from, .$to, sep = \"_\"))\n\n # tidyr::gather zamenjeno sa tidyr::pivot_longer\n #tidyr::gather(survey.net[[2]] %>% purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>% dplyr::select(from, to, direction, distance), key = type, value = used, -c(from, to)) %>% dplyr::filter(used == TRUE) %>% dplyr::mutate(from_to = str_c(.$from, .$to, sep = \"_\"))\n\n fix.mat <- !rep(survey.net[[1]]$FIX_2D, each = 2)\n stations <- observations %>% dplyr::filter(type == \"direction\") %>% .$from %>% unique()\n if(length(fix.mat) != sum(fix.mat)){\n df <- (dim(observations)[1] - (sum(fix.mat) + length(stations))) #abs(diff(dim(A.mat)))\n }else{\n df <- (dim(observations)[1] - (sum(fix.mat) + length(stations))) + 3\n }\n if(adjust){\n max.coord.corr <- 1\n iter <- 0\n coords.iter_0 <- as.vector(t(cbind(survey.net[[1]]$x, survey.net[[1]]$y)))[fix.mat]\n while (max.coord.corr > coord_tolerance && iter < maxiter) {\n iter <- iter + 1\n coords.iter.inc <- as.vector(t(cbind(survey.net[[1]]$x, survey.net[[1]]$y)))[fix.mat]\n A.mat <- Amat(survey.net, units = units)\n W.mat <- Wmat(survey.net, sd.apriori = sd.apriori, res.units = units)\n rownames(A.mat) <- observations$from_to\n colnames(W.mat) <- observations$from_to\n rownames(W.mat) <- observations$from_to\n # MNK solution\n N.mat <- crossprod(A.mat, W.mat) %*% A.mat\n Qx.mat <- tryCatch(\n {\n x = Qx.mat = solve(N.mat)\n },\n error = function(e) {\n x = Qx.mat = MASS::ginv(N.mat)\n })\n colnames(Qx.mat) <- colnames(N.mat)\n rownames(Qx.mat) <- rownames(N.mat)\n f.mat <- fmat(survey.net = survey.net, units.dir = units.dir, units.dist = units)\n n.mat <- crossprod(A.mat, W.mat) %*% f.mat\n x.mat <- -Qx.mat %*% n.mat\n v.mat <- A.mat%*%x.mat + f.mat\n Ql.mat <- A.mat %*% tcrossprod(Qx.mat, A.mat)\n Qv.mat <- solve(W.mat) - Ql.mat\n r <- Qv.mat%*%W.mat\n coords.est <- coords.iter.inc + x.mat[1:sum(fix.mat)]/res.unit.lookup[units]\n survey.net[[1]][!survey.net[[1]]$FIX_2D, c(\"x\", \"y\")] <- matrix(coords.est, ncol = 2, byrow = TRUE)\n survey.net[[1]] <- survey.net[[1]] %>% st_drop_geometry() %>% sf::st_as_sf(coords = c(\"x\",\"y\"), remove = FALSE)\n survey.net[[2]]$x_from <- survey.net[[1]]$x[match(survey.net[[2]]$from, survey.net[[1]]$Name)]\n survey.net[[2]]$y_from <- survey.net[[1]]$y[match(survey.net[[2]]$from, survey.net[[1]]$Name)]\n survey.net[[2]]$x_to <- survey.net[[1]]$x[match(survey.net[[2]]$to, survey.net[[1]]$Name)]\n survey.net[[2]]$y_to <- survey.net[[1]]$y[match(survey.net[[2]]$to, survey.net[[1]]$Name)]\n survey.net[[2]] <- survey.net[[2]] %>% dplyr::mutate(id = seq.int(nrow(.))) %>% split(., f = as.factor(.$id)) %>%\n lapply(., function(row) {lmat <- matrix(unlist(row[c(\"x_from\", \"y_from\", \"x_to\", \"y_to\")]), ncol = 2, byrow = TRUE)\n st_linestring(lmat)}) %>%\n sf::st_sfc() %>%\n sf::st_sf(survey.net[[2]], 'geometry' = .)\n max.coord.corr <- max(coords.est-coords.iter.inc)\n }\n x.mat <- (coords.est-coords.iter_0)*res.unit.lookup[units] #x.mat[1:sum(fix.mat)]\n sd.estimated <- sqrt((crossprod(v.mat, W.mat) %*% v.mat)/(df))\n model_adequacy <- model_adequacy_test(sd.apriori, sd.estimated, df, prob = prob)\n\n observations <- survey.net[[2]] %>%\n sf::st_drop_geometry() %>%\n dplyr::select(ID:dh, \"Hz\", \"Vz\", \"tdh\") %>%\n purrr::discard(~all(is.na(.x))) %>%\n dplyr::right_join(., observations[, c(\"from\", \"to\", \"type\")], by = c(\"from\", \"to\")) %>%\n dplyr::arrange(type) %>%\n dplyr::mutate(Observations = if_else(type == \"distance\", paste(HD), paste(HzD, HzM, HzS, sep = \" \"))) %>%\n dplyr::mutate(res = v.mat, f = f.mat, Kl = c(sd.apriori^2)*diag(Ql.mat), Kv = c(sd.apriori^2)*diag(Qv.mat), rii = diag(r) ) %>%\n dplyr::mutate(Adj_meas = if_else(type == \"direction\", Hz + res/dir.unit.lookup[units.dir], HD + res/res.unit.lookup[units])) %>%\n dplyr::mutate(Adj_meas = if_else(type == \"direction\" & Adj_meas < 0, Adj_meas + 360, Adj_meas + 0),\n Baarda.test = as.numeric(abs(v.mat)/c(sd.apriori)*(sqrt(diag(Qv.mat))))) %>%\n dplyr::mutate(across(.cols = c(\"res\", \"f\", \"Kl\", \"Kv\", \"rii\", \"Baarda.test\"), ~round(.x, disp.unit.lookup[units]))) %>%\n dplyr::mutate(Adj.observations = if_else(type == \"distance\", paste(HD), dec2dms(Adj_meas)))\n\n if(model_adequacy[[1]] & use.sd.estimated){sigma_apriori <- sd.apriori; sd.apriori <- sd.estimated}\n\n # Results\n coords.inc <- data.frame(parameter = colnames(A.mat)[1:sum(fix.mat)], coords.inc = as.numeric(x.mat))\n coords.inc <- coords.inc %>%\n tidyr::separate(.,col = parameter, into = c(\"Name\", \"inc.name\"), sep = \"_d\") %>%\n tidyr::pivot_wider(., names_from = c(inc.name), values_from = c(coords.inc)) %>%\n dplyr::mutate_all(., ~replace(., is.na(.), 0)) %>%\n dplyr::rename(dx = x, dy = y)\n\n\n # TODO: Ovo treba razdvojiti u slucaju da je report ili sp!!!\n point.adj.results <- dplyr::left_join(survey.net[[1]], coords.inc, by = \"Name\") %>%\n dplyr::mutate(across(.cols = c(\"dx\", \"dy\"), ~replace(., is.na(.), 0))) %>%\n sf::st_drop_geometry() %>%\n dplyr::mutate(x0 = x - dx/res.unit.lookup[units], y0 = y - dy/res.unit.lookup[units]) %>%\n dplyr::mutate(across(.cols = c(\"dx\", \"dy\"), ~round(.x, disp.unit.lookup[units]))) %>%\n sf::st_as_sf(coords = c(\"x\",\"y\"), remove = FALSE) %>%\n dplyr::select(id, Name, x0, y0, dx, dy, x, y, h, FIX_2D, Point_object, geometry)\n # TODO: Gubi se projekcija!!!!\n # TODO: Srediti oko velikog i malog X i Y.\n\n }else{\n A.mat <- Amat(survey.net, units = units)\n W.mat <- Wmat(survey.net, sd.apriori = sd.apriori)\n rownames(A.mat) <- observations$from_to\n colnames(W.mat) <- observations$from_to\n rownames(W.mat) <- observations$from_to\n # MNK solution\n N.mat <- crossprod(A.mat, W.mat) %*% A.mat\n Qx.mat <- tryCatch(\n {\n x = Qx.mat = solve(N.mat)\n },\n error = function(e) {\n x = Qx.mat = MASS::ginv(N.mat)\n })\n colnames(Qx.mat) <- colnames(N.mat)\n rownames(Qx.mat) <- rownames(N.mat)\n Ql.mat <- A.mat %*% tcrossprod(Qx.mat, A.mat)\n Qv.mat <- solve(W.mat) - Ql.mat\n r <- Qv.mat%*%W.mat\n\n observations <- observations %>% dplyr::mutate(Kl = c(sd.apriori^2)*diag(Ql.mat), Kv = c(sd.apriori^2)*diag(Qv.mat), rii = diag(r)) %>%\n dplyr::mutate(across(where(is.numeric), ~round(.x, disp.unit.lookup[units])))\n }\n # Computing error ellipses\n Qxy.list <- Qxy(Qx.mat, fix = !survey.net[[1]]$FIX_2D)\n ellipses <- lapply(Qxy.list, function(x) error.ellipse(x, prob = prob, sd.apriori = sd.apriori, teta.unit = teta.unit[[1]]))\n ellipses <- do.call(rbind, ellipses) %>%\n as.data.frame() %>%\n dplyr::select(A = V1, B = V2, teta = V3) %>%\n mutate(Name = used.points) %>%\n dplyr::mutate(across(where(is.numeric), ~round(.x, disp.unit.lookup[units])))\n # Computing parameters sigmas\n sigmas <- lapply(Qxy.list, function(x) sigma.xy(x, sd.apriori = sd.apriori)) %>%\n do.call(rbind,.) %>%\n as.data.frame() %>%\n dplyr::select(sx = V1, sy = V2) %>% #TODO: proveriti da li ovde treba voditi racuna o redosledu sx i sy.\n dplyr::mutate(sp = sqrt(sx^2 + sy^2), Name = used.points) %>%\n dplyr::mutate(across(where(is.numeric), ~round(.x, disp.unit.lookup[units])))\n\n if(adjust){\n if(output == \"spatial\"){\n points <- merge(point.adj.results, ellipses, by = \"Name\") %>% merge(., sigmas) %>% dplyr::arrange(id)\n points %<>% sf::st_set_crs(., net.crs)#st_crs(survey.net[[2]]))\n }else{\n points <- merge(sf::st_drop_geometry(point.adj.results), ellipses, by = \"Name\") %>% merge(., sigmas) %>% dplyr::arrange(id)\n }\n }else{\n if(output == \"spatial\"){\n points <- merge(survey.net[[1]], ellipses, by = \"Name\") %>% merge(., sigmas) %>% dplyr::arrange(id)\n points %<>% sf::st_set_crs(., net.crs)#st_crs(survey.net[[2]]))\n }else{\n points <- merge(sf::st_drop_geometry(survey.net[[1]]), ellipses, by = \"Name\") %>% merge(., sigmas) %>% dplyr::arrange(id)\n }\n }\n\n if(output == \"spatial\"){\n # Preparing ellipses as separate sf outcome\n # TODO Proveriti da li elipse uzimaju definitivne koordinate ili priblizne!\n ellipse.net <- do.call(rbind, lapply(split(points, factor(survey.net[[1]]$Name, levels = points$Name)), function(x) sf.ellipse(x, scale = ellipse.scale)))\n ellipse.net <- merge(ellipse.net, sigmas)\n ellipse.net %<>% sf::st_set_crs(., net.crs)#st_crs(survey.net[[2]]))\n\n points <- list(net.points = points, ellipse.net = ellipse.net)\n }\n\n\n# ====================================== 1D ====================================\n }else{\n wdh = wdh_model[[1]]\n observations <- survey.net[[2]] %>%\n purrr::when(is(., \"sf\") ~ st_drop_geometry(.), ~.) %>%\n dplyr::filter(diff_level) %>%\n dplyr::select(from, to, dh, all_of(wdh)) %>%\n dplyr::mutate(from_to = stringr::str_c(.$from, .$to, sep = \"_\"))\n\n\n fix.mat <- !(survey.net[[1]]$FIX_1D)\n\n A.mat <- Amat1D(survey.net)\n W.mat <- Wmat1D(survey.net = survey.net, wdh_model = wdh, n0 = 1, res.units = units)\n rownames(A.mat) <- observations$from_to\n colnames(W.mat) <- observations$from_to\n rownames(W.mat) <- observations$from_to\n if(length(fix.mat) != sum(fix.mat)){\n df <- (dim(observations)[1] - sum(fix.mat)) #abs(diff(dim(A.mat)))\n }else{\n df <- (dim(observations)[1] - sum(fix.mat)) + 1\n }\n # MNK solution\n N.mat <- crossprod(A.mat, W.mat) %*% A.mat\n Qx.mat <- tryCatch(\n {\n x = Qx.mat = solve(N.mat)\n },\n error = function(e) {\n x = Qx.mat = MASS::ginv(N.mat)\n })\n colnames(Qx.mat) <- colnames(N.mat)\n rownames(Qx.mat) <- rownames(N.mat)\n Ql.mat <- A.mat %*% tcrossprod(Qx.mat, A.mat)\n Qv.mat <- solve(W.mat) - Ql.mat\n r <- Qv.mat%*%W.mat\n if(adjust){\n max.coord.corr <- 1\n iter <- 0\n coords.iter_0 <- as.vector(survey.net[[1]]$h)[fix.mat]\n while (max.coord.corr > coord_tolerance && iter < maxiter) {\n iter <- iter + 1\n coords.iter.inc <- survey.net[[1]]$h[fix.mat]\n f.mat <- fmat1D(survey.net = survey.net, units = units)\n n.mat <- crossprod(A.mat, W.mat) %*% f.mat\n x.mat <- -Qx.mat %*% n.mat\n v.mat <- A.mat%*%x.mat + f.mat\n survey.net[[1]]$h[fix.mat] <- survey.net[[1]]$h[fix.mat] + x.mat/res.unit.lookup[units]\n max.coord.corr <- max(abs(survey.net[[1]]$h[fix.mat]-coords.iter.inc))\n }\n x.mat <- x.mat[1:sum(fix.mat)] #\n h.inc <- (survey.net[[1]]$h[fix.mat]-coords.iter_0)*res.unit.lookup[units]\n sd.estimated <- sqrt((crossprod(v.mat, W.mat) %*% v.mat)/(df))\n sd.apriori <- sd.apriori/(1000/res.unit.lookup[units])\n model_adequacy <- model_adequacy_test(sd.apriori, sd.estimated, df, prob = prob)\n if(use.sd.estimated){sigma_apriori <- sd.apriori; sd.apriori <- sd.estimated}\n }\n # Results\n if(adjust){\n h.inc <- data.frame(Name = as.character(colnames(A.mat)), dh = as.numeric(h.inc), sd_h = c(sd.apriori)*sqrt(diag(Qx.mat)), stringsAsFactors = FALSE)\n points <- dplyr::left_join(survey.net[[1]], h.inc, by = \"Name\") %>%\n dplyr::mutate(across(.cols = c(\"dh\", \"sd_h\"), ~replace(., is.na(.), 0))) %>%\n dplyr::mutate(h0 = h - dh/res.unit.lookup[units]) %>%\n dplyr::mutate(across(.cols = c(\"dh\"), ~round(.x, disp.unit.lookup[units]))) %>%\n dplyr::select(id, Name, x, y, h0, dh, h, sd_h, FIX_1D, Point_object)\n\n\n observations <- observations %>%\n dplyr::mutate(residuals = as.numeric(v.mat),\n adj.dh = dh + residuals/res.unit.lookup[units],\n f = f.mat, Kl = c(sd.apriori^2)*diag(Ql.mat),\n Kv = c(sd.apriori^2)*diag(Qv.mat), rii = diag(r),\n Baarda.test = as.numeric(abs(v.mat)/c(sd.apriori)*(sqrt(diag(Qv.mat))))) %>%\n dplyr::mutate(across(.cols = c(\"residuals\", \"f\", \"Kl\", \"Kv\", \"rii\", \"Baarda.test\"), ~round(.x, disp.unit.lookup[units])))\n\n\n }else{\n h.inc <- data.frame(Name = as.character(colnames(A.mat)), sd_h = c(sd.apriori)*sqrt(diag(Qx.mat)), stringsAsFactors = FALSE)\n points <- dplyr::left_join(survey.net[[1]], h.inc, by = \"Name\") %>%\n dplyr::mutate(across(.cols = c(\"sd_h\"), ~replace(., is.na(.), 0))) %>%\n dplyr::mutate(across(.cols = c(\"sd_h\"), ~round(.x, disp.unit.lookup[units]))) %>%\n dplyr::select(id, Name, x, y, h, sd_h, FIX_1D, Point_object)\n\n observations <- observations %>%\n dplyr::mutate(Kl = c(sd.apriori^2)*diag(Ql.mat),\n Kv = c(sd.apriori^2)*diag(Qv.mat),\n rii = diag(r)) %>%\n dplyr::mutate(across(where(is.numeric), ~round(.x, disp.unit.lookup[units])))\n }\n }\n if(adjust){\n matrices = list(A = A.mat, W = W.mat, Qx = Qx.mat, Ql = Ql.mat, Qv = Qv.mat, f = f.mat)\n }else{\n matrices = list(A = A.mat, W = W.mat, Qx = Qx.mat, Ql = Ql.mat, Qv = Qv.mat)\n }\n\n if(output == \"spatial\"){\n if(sum(rowSums(is.na(survey.net[[1]][, c(\"x\", \"y\")])) != 0) == 0){\n observations <- observations %>%\n dplyr::mutate(x_from = survey.net[[1]]$x[match(observations$from, survey.net[[1]]$Name)],\n y_from = survey.net[[1]]$y[match(observations$from, survey.net[[1]]$Name)],\n x_to = survey.net[[1]]$x[match(observations$to, survey.net[[1]]$Name)],\n y_to = survey.net[[1]]$y[match(observations$to, survey.net[[1]]$Name)])\n\n observations <- observations %>%\n dplyr::mutate(id = seq.int(nrow(.))) %>%\n split(., f = as.factor(.$id)) %>%\n lapply(., function(row) {lmat <- matrix(unlist(row[c(\"x_from\", \"y_from\", \"x_to\", \"y_to\")]), ncol = 2, byrow = TRUE)\n st_linestring(lmat)}) %>%\n sf::st_sfc() %>%\n sf::st_sf('ID' = seq.int(nrow(observations)), observations, 'geometry' = .) %>%\n dplyr::select(-c(x_from, y_from, x_to, y_to))\n observations %<>% sf::st_set_crs(.,net.crs)#st_crs(survey.net[[2]]))\n\n }\n }\n\n if(dim_type == \"2D\"){\n if(adjust){\n Adjustment_summary = list(Type = if(sum(!fix.mat) == 0){\"inner constrained\"}else{\"constrained\"},\n Dimensions = dim_type,\n \"Fixed points\" = if(sum(survey.net[[1]]$FIX_2D) != 0){survey.net[[1]]$Name[survey.net[[1]]$FIX_2D]}else{\"None\"},\n \"Number of stations\" = length(stations),\n \"Number of Directions\" = sum(observations$type == \"direction\"),\n \"Number of Distances\" = sum(observations$type == \"distance\"),\n \"Unknown coordinates\" = sum(fix.mat),\n \"Unknown orientations\" = observations %>% dplyr::filter(type == \"direction\") %>% .$from %>% unique(.) %>% length(),\n \"Degrees of freedom\" = df,\n \"Number of iterations\" = iter,\n \"Max.coordinate correction in last iteration:\" = max.coord.corr,\n \"sigma apriori\" = if(model_adequacy[[1]] & use.sd.estimated){sigma_apriori}else{sd.apriori},\n \"sigma aposteriori\" = sd.estimated,\n \"Testing Probability\" = prob,\n \"F-test\" = model_adequacy[[2]],\n \"Crital value F-test\" = model_adequacy[[3]],\n \"Test decision\" = model_adequacy[[4]])\n results <- list(Summary = Adjustment_summary, Points = points, Observations = observations %>% dplyr::select(ID, from, to, type, Observations, residuals = res, Adj.observations, Kl, Kv, rii, Baarda.test), Matrices = matrices)\n\n }else{\n Adjustment_summary = list(Type = if(sum(!fix.mat) == 0){\"inner constrained\"}else{\"constrained\"},\n Dimensions = dim_type,\n \"Fixed points\" = if(sum(survey.net[[1]]$FIX_2D) != 0){survey.net[[1]]$Name[survey.net[[1]]$FIX_2D]}else{\"None\"},\n \"Number of stations\" = length(stations),\n Directions = sum(observations$type == \"direction\"),\n Distances = sum(observations$type == \"distance\"),\n \"Unknown coordinates\" = sum(fix.mat),\n \"Unknown orientations\" = observations %>% dplyr::filter(type == \"direction\") %>% .$from %>% unique(.) %>% length(),\n \"Degrees of freedom\" = df,\n \"sigma apriori\" = sd.apriori)\n results <- list(Summary = Adjustment_summary, Points = points, Observations = observations, Matrices = matrices)\n\n }\n }else{\n if(adjust){\n Adjustment_summary = list(Type = if(sum(!fix.mat) == 0){\"inner constrained\"}else{\"constrained\"},\n Dimensions = dim_type,\n \"Fixed points\" = if(sum(survey.net[[1]]$FIX_1D) != 0){survey.net[[1]]$Name[survey.net[[1]]$FIX_1D]}else{\"None\"},\n \"Weightening model\" = wdh_model[[1]],\n \"Number of measured height differences\" = dim(observations)[1],\n \"Unknown heights\" = sum(fix.mat),\n \"Degrees of freedom\" = df,\n \"Number of iterations\" = iter,\n \"Max.coordinate correction in last iteration:\" = max.coord.corr,\n \"sigma apriori\" = if(model_adequacy[[1]] & use.sd.estimated){sigma_apriori}else{sd.apriori},\n \"sigma aposteriori\" = sd.estimated,\n \"Testing Probability\" = prob,\n \"F-test\" = model_adequacy[[2]],\n \"Crital value F-test\" = model_adequacy[[3]],\n \"Test decision\" = model_adequacy[[4]])\n results <- list(Summary = Adjustment_summary, Points = points, Observations = observations, Matrices = matrices)\n\n }else{\n Adjustment_summary = list(Type = if(sum(!fix.mat) == 0){\"inner constrained\"}else{\"constrained\"},\n Dimensions = dim_type,\n \"Fixed points\" = if(sum(survey.net[[1]]$FIX_1D) != 0){survey.net[[1]]$Name[survey.net[[1]]$FIX_1D]}else{\"None\"},\n \"Weightening model\" = wdh_model,\n \"Number of measured height differences\" = dim(observations)[1],\n \"Unknown heights\" = sum(fix.mat),\n \"Degrees of freedom\" = df)\n #\"Number of iterations\" = iter,\n #\"Max.height correction in last iteration:\" = max.coord.corr,\n #\"sigma apriori\" = sigma_apriori,\n #\"sigma aposteriori\" = sd.estimated,\n #\"Testing Probability\" = prob,\n #\"F-test\" = model_adequacy[[2]],\n #\"Crital value F-test\" = model_adequacy[[3]],\n #\"Test decision\" = model_adequacy[[4]])\n results <- list(Summary = Adjustment_summary, Points = points, Observations = observations, Matrices = matrices)\n\n }\n }\n\n if(!all){\n results <- results[-4]\n }else{\n results <- results\n }\n\n return(results)\n}\n\n\n\n##################\n# plot_surveynet\n##################\n\n# Function for data geovisualisation trough package ggplot2 and mapview\n# Parameters:\n# 1. snet - object from function read_surveynet (can be NULL)\n# 2. snet.adj - object from function adjust.snet (can be NULL)\n# 3. webmap - plot 2d net using mapview package\n# 4. net.1D - 2d net indicator\n# 5. net.2D - 1d net indicator\n# 6. ellipse.scale - criteria argument\n# 7. result.units - criteria argument\n# 8. sp_bound - criteria argument\n# 9. rii_bound - criteria argument\n\n# snet = dns.snet\n# snet = brana.snet\n# net.2D = TRUE\n# snet.adj = brana.snet.adj\n\nplot_surveynet <- function(snet = NULL, snet.adj = NULL, webmap = FALSE, net.1D = FALSE, net.2D = FALSE, ellipse.scale = 10, result.units = \"mm\", sp_bound = 2, rii_bound = 0.3, epsg = 3857){\n\n if(!is.null(snet)){\n\n points <- snet$points\n observations <- snet$observations\n\n if(net.2D == TRUE) {\n points %<>% dplyr::mutate(Point_type = dplyr::case_when(Point_object == FALSE ~ \"Geodetic network\",\n Point_object == TRUE ~ \"Points at object\"))\n observations %<>% dplyr::mutate(Observation_type = dplyr::case_when(distance == TRUE & direction == FALSE ~ \"Distance\",\n distance == FALSE & direction == TRUE ~ \"Direction\",\n distance == TRUE & direction == TRUE ~ \"Both\",\n distance == FALSE & direction == FALSE ~ \"None\"))\n\n if(webmap == TRUE){\n\n if(is.na(sf::st_crs(points)) == TRUE) {\n points %<>% sf::st_set_crs(., epsg)\n }\n\n if(is.na(sf::st_crs(observations)) == TRUE) {\n observations %<>% sf::st_set_crs(., epsg)\n }\n\n points <- st_transform(points, epsg)\n observations <- st_transform(observations, epsg)\n\n webmap.net <- mapview(points, zcol = \"Point_type\", col.regions = c(\"red\",\"grey\")) + mapview(observations, zcol = \"Observation_type\")\n return(webmap.net)\n\n } else {\n plot.net <- ggplot() +\n geom_sf(data=observations, aes(color = Observation_type),size=0.5,stroke=0.5)+\n geom_sf(data=points, aes(fill = Point_type), shape = 24, size=2, stroke=0.5) +\n geom_sf_text(data=points, aes(label=Name,hjust = 1.5, vjust =1.5))+\n xlab(\"\\nEasting [m or \u00b0]\") +\n ylab(\"Northing [m or \u00b0]\\n\") +\n ggtitle(\"GEODETIC 2D NETWORK\")+\n labs(subtitle = \"Points and Observational plan\")+\n guides(col = guide_legend())+\n theme_bw()+\n theme(legend.position = 'bottom')\n\n return(plot.net)\n }\n }\n\n if(net.1D == TRUE){\n observations %<>% dplyr::mutate(from_to = paste(from, to, sep = \"-\"))\n observations$id <- row_number(observations$from_to)\n if(!is.null(observations$dh)){\n p.plot <- ggplotly(ggplot()+\n geom_point(data = points,\n aes(x = id,\n y = h,\n colour = h))+\n scale_colour_gradient(low=\"blue\",\n high=\"red\")+\n geom_ribbon(data = points,\n aes(x = id,\n ymin = mean(h),\n ymax = h),\n fill=\"blue\",\n alpha=.2)+\n geom_text(data = points,\n aes(x = id,\n y = h,\n label=Name),\n nudge_x = 0,\n nudge_y = 0.55)+\n scale_y_continuous(limits = c(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0)),\n breaks = seq(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0),\n by = 1))+\n xlab(\"ID\") +\n ylab(\"h [m]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"h [m]\")+\n theme_bw(),#+\n #ylim(min(points$h)-sd(points$h),\n # max(points$h)+sd(points$h)),\n showlegend = T\n )\n\n o.plot <- ggplotly(\n ggplot()+\n geom_point(data = observations,\n aes(x = id,\n y = dh,\n colour = dh))+\n scale_colour_gradient(low=\"orange\",\n high=\"red\")+\n geom_area(data = observations,\n aes(x = id,\n y = dh),fill=\"blue\", alpha=.2)+\n geom_text(data = observations,\n aes(x = id,\n y = dh,\n label = from_to),\n nudge_x = 0,\n nudge_y = 0.03)+\n scale_y_continuous(limits = c(round(min(observations$dh)-sd(observations$dh), 0), round( max(observations$dh)+sd(observations$dh),0)),\n breaks = seq(round(min(observations$dh)-sd(observations$dh), 0), round( max(observations$dh)+sd(observations$dh),0),\n by = 0.5))+\n xlab(\"ID\") +\n ylab(\"dh [m]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"dh [m]\")+\n theme_bw(),#+\n #ylim(min(observations$dh)-sd(observations$dh),\n # max(observations$dh)+sd(observations$dh)),\n showlegend = T\n )\n\n plot.1d.net <- plotly::subplot(style(p.plot, showlegend = FALSE),\n style(o.plot, showlegend = TRUE),\n nrows = 2,\n shareX = FALSE,\n shareY = FALSE,\n titleX = TRUE,\n titleY = TRUE)\n return(plot.1d.net)\n\n }else{\n p.plot <- ggplotly(ggplot()+\n geom_point(data = points,\n aes(x = id,\n y = h,\n colour = h))+\n scale_colour_gradient(low=\"blue\",\n high=\"red\")+\n geom_ribbon(data = points,\n aes(x = id,\n ymin = mean(h),\n ymax = h),\n fill=\"blue\",\n alpha=.2)+\n geom_text(data = points,\n aes(x = id,\n y = h,\n label=Name),\n nudge_x = 0,\n nudge_y = 0.55)+\n scale_y_continuous(limits = c(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0)),\n breaks = seq(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0),\n by = 1))+\n xlab(\"ID\") +\n ylab(\"h [m]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"h [m]\")+\n theme_bw(),#+\n #ylim(min(points$h)-sd(points$h),\n #max(points$h)+sd(points$h)),\n showlegend = T\n )\n\n\n return(p.plot)\n\n }\n }\n }\n\n if(!is.null(snet.adj)){\n if(net.2D == TRUE) {\n points <- snet.adj$Points$net.points\n observations <- snet.adj$Observations\n ellipses <- snet.adj$Points$ellipse.net\n if(webmap == TRUE){\n\n if(is.na(sf::st_crs(points)) == TRUE) {\n points %<>% sf::st_set_crs(., epsg)\n }\n\n if(is.na(sf::st_crs(observations)) == TRUE) {\n observations %<>% sf::st_set_crs(., epsg)\n }\n\n if(is.na(sf::st_crs(ellipses)) == TRUE) {\n ellipses %<>% sf::st_set_crs(., epsg)\n }\n\n points %<>% sf::st_transform(., epsg)\n observations %<>% sf::st_transform(., epsg)\n ellipses %<>% sf::st_transform(., epsg)\n\n points %<>% dplyr::mutate(Point_type = dplyr::case_when(Point_object == FALSE ~ \"Geodetic network\",\n Point_object == TRUE ~ \"Points at object\"))\n\n ellipses %<>% dplyr::mutate(fill = dplyr::case_when(\n sp < sp_bound ~ paste(\"<\",sp_bound),\n sp > sp_bound ~ paste(\">\",sp_bound),\n sp == sp_bound ~ paste(\"=\",sp_bound)))\n\n observations %<>% dplyr::mutate(fill = dplyr::case_when(\n rii < rii_bound ~ paste(\"<\",rii_bound),\n rii > rii_bound ~ paste(\">\",rii_bound),\n rii == rii_bound ~ paste(\"=\",rii_bound)\n ))\n\n pink2 = colorRampPalette(c('deeppink', 'orange'))\n\n observation.dir <- observations %>%\n dplyr::filter(type == \"direction\")\n observation.dis <- observations %>%\n dplyr::filter(type == \"distance\")\n\n if(length(observation.dis$ID) == 0){\n webmap.net.adj <- mapview(points, zcol = \"Point_type\", col.regions = c(\"red\",\"grey\"), layer.name = \"Points_type\") +\n mapview(ellipses, zcol = \"fill\", col.regions = c(\"yellow\", \"red\"), layer.name = paste(\"StDev Position [\",result.units,\"]\", sep = \"\"))+ #+\n #mapview(observations, zcol = \"fill\", color = c(\"red\", \"orange\"), layer.name = \"Reliability measure rii [/]\")+\n mapview(observation.dir, zcol = \"fill\", color = pink2, layer.name = \"Reliability measure rii [/] - direction\") # , at = seq(0,1,rii_bound)\n }else if(length(observation.dir$ID) == 0){\n webmap.net.adj <- mapview(points, zcol = \"Point_type\", col.regions = c(\"red\",\"grey\"), layer.name = \"Points_type\") +\n mapview(ellipses, zcol = \"fill\", col.regions = c(\"yellow\", \"red\"), layer.name = paste(\"StDev Position [\",result.units,\"]\", sep = \"\"))+ #+\n #mapview(observations, zcol = \"fill\", color = c(\"red\", \"orange\"), layer.name = \"Reliability measure rii [/]\")+\n mapview(observation.dis, zcol = \"fill\", color = pink2, layer.name = \"Reliability measure rii [/] - distance\") # , at = seq(0,1,rii_bound)\n }else{\n webmap.net.adj <- mapview(points, zcol = \"Point_type\", col.regions = c(\"red\",\"grey\"), layer.name = \"Points_type\") +\n mapview(ellipses, zcol = \"fill\", col.regions = c(\"yellow\", \"red\"), layer.name = paste(\"StDev Position [\",result.units,\"]\", sep = \"\"))+ #+\n #mapview(observations, zcol = \"fill\", color = c(\"red\", \"orange\"), layer.name = \"Reliability measure rii [/]\")+\n mapview(observation.dir, zcol = \"fill\", color = pink2, layer.name = \"Reliability measure rii [/] - direction\")+ # , at = seq(0,1,rii_bound)\n mapview(observation.dis, zcol = \"fill\", color = pink2, layer.name = \"Reliability measure rii [/] - distance\") # , at = seq(0,1,rii_bound)\n }\n\n\n # webmap.net.adj <- mapview(points, zcol = \"Point_type\", col.regions = c(\"red\",\"grey\"), layer.name = \"Points_type\") +\n # mapview(ellipses, zcol = \"fill\", col.regions = c(\"yellow\", \"red\"), layer.name = paste(\"StDev Position [\",result.units,\"]\", sep = \"\"))+ #+\n # #mapview(observations, zcol = \"fill\", color = c(\"red\", \"orange\"), layer.name = \"Reliability measure rii [/]\")+\n # mapview(observation.dir, zcol = \"fill\", color = pink2, at = seq(0,1,rii_bound), layer.name = \"Reliability measure rii [/] - direction\")+\n # mapview(observation.dis, zcol = \"fill\", color = pink2, at = seq(0,1,rii_bound), layer.name = \"Reliability measure rii [/] - distance\")\n\n return(webmap.net.adj)\n\n } else {\n ellipses %<>% dplyr::rename(`StDev Position` = sp)\n adj.net_plot <- ggplot() +\n geom_sf(data = observations)+\n geom_sf(data=ellipses, aes(fill = `StDev Position`))+\n geom_sf_text(data=ellipses, aes(label=Name,hjust = 1.5, vjust = 1.5))+\n xlab(\"\\nEasting [m or \u00b0]\") +\n ylab(\"Northing [m or \u00b0]\\n\") +\n ggtitle(\"GEODETIC 2D NETWORK\")+\n labs(subtitle = \"Adjusted observational plan - net quality\",\n caption = paste(\"Ellipse scale = \", ellipse.scale))+\n guides(col = guide_legend())+\n theme_bw()+\n theme(legend.position = 'bottom')\n\n return(adj.net_plot)\n }\n }\n\n if(net.1D == TRUE){\n points <- snet.adj$Points\n observations <- snet.adj$Observations\n observations$id <- row_number(observations$from_to)\n if(!is.null(points$h)){\n # ADJUST PLOT\n p.plot <- plotly::ggplotly(ggplot()+\n geom_crossbar(data = points,\n aes(x = id,\n y = h,\n ymin = h-sd(h)/2,\n ymax = h+sd(h)/2,\n fill = sd(h)), fatten = 0)+\n scale_fill_gradient(low=\"green\",\n high=\"red\")+\n geom_point(data = points,\n aes(x = id,\n y = h,\n colour = h))+\n scale_colour_gradient(low=\"blue\",\n high=\"red\",\n guide=FALSE)+\n geom_ribbon(data = points,\n aes(x = id,\n ymin = mean(h),\n ymax = h),fill=\"blue\", alpha=.2)+\n geom_text(data=points, aes(x = id, y = h, label=Name), nudge_x = 0, nudge_y = 0.55)+\n scale_y_continuous(limits = c(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0)),\n breaks = seq(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0),\n by = 1))+\n xlab(\"ID\") +\n ylab(\"h [m]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"h [m]\", fill = \"sd_h [m]\")+\n theme_bw()+\n theme(axis.title.x = element_blank()),#+\n #ylim(min(points$h)-sd(points$h),\n # max(points$h)+sd(points$h)),\n showlegend = TRUE\n )\n o.plot <- ggplotly(\n ggplot()+\n geom_point(data = observations,\n aes(x = id,\n y = dh,\n colour = dh))+\n geom_area(data = observations,\n aes(x = id,\n y = dh),fill=\"blue\", alpha=.2)+\n scale_colour_gradient(low=\"orange\",\n high=\"red\", guide = FALSE)+\n geom_text(data=observations, aes(x = id, y = dh, label=from_to), nudge_x = 0, nudge_y = 0.03)+\n scale_y_continuous(limits = c(round(min(observations$dh)-sd(observations$dh), 0), round( max(observations$dh)+sd(observations$dh),0)),\n breaks = seq(round(min(observations$dh)-sd(observations$dh), 0), round( max(observations$dh)+sd(observations$dh),0),\n by = 0.05))+\n xlab(\"ID\") +\n ylab(\"Residuals [mm]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"Residuals [mm]\")+\n theme_bw(),#+\n #ylim(min(observations$f)-sd(observations$f),\n # max(observations$f)+sd(observations$f)),\n showlegend = TRUE\n )\n plot.1d.net <- plotly::subplot(style(p.plot, showlegend = FALSE),\n style(o.plot, showlegend = TRUE),\n nrows = 2,\n shareX = FALSE,\n shareY = FALSE,\n titleX = TRUE,\n titleY = TRUE)\n return(plot.1d.net)\n\n\n\n\n }else{\n\n # DESIGN PLOT\n plot.1d.net <- ggplotly(ggplot()+\n geom_crossbar(data = points,\n aes(x = id,\n y = h,\n ymin = h-sd(h)/2,\n ymax = h+sd(h)/2,\n fill = sd(h)), fatten = 0)+\n scale_fill_gradient(low=\"green\",\n high=\"red\")+\n geom_point(data = points,\n aes(x = id,\n y = h,\n colour = h))+\n scale_colour_gradient(low=\"blue\",\n high=\"red\")+\n geom_ribbon(data = points,\n aes(x = id,\n ymin = mean(h),\n ymax = h), fill = \"blue\", alpha=.2)+\n geom_text(data=points, aes(x = id, y = h, label=Name), nudge_x = 0, nudge_y = 0.55)+\n scale_y_continuous(limits = c(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0)),\n breaks = seq(round(min(points$h)-sd(points$h),0), round(max(points$h)+sd(points$h),0),\n by = 1))+\n xlab(\"ID\") +\n ylab(\"h [m]\") +\n ggtitle(\"GEODETIC 1D NETWORK\")+\n labs(colour = \"h [m]\", fill = \"sd_h [m]\")+\n theme_bw(),#+\n #ylim(min(points$h)-sd(points$h),\n # max(points$h)+sd(points$h)),\n showlegend = TRUE\n )\n\n return(plot.1d.net)\n\n }\n\n }\n\n\n\n\n }\n\n\n}\n", "meta": {"hexsha": "119f5aea376153f3661994add412a8c7969d6a7f", "size": 60229, "ext": "r", "lang": "R", "max_stars_repo_path": "R/deprecated/functions.r", "max_stars_repo_name": "pejovic/Surveyor", "max_stars_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-14T22:40:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-14T22:40:36.000Z", 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YES\n2. NO", "lm_q1_score": 0.6406358411176238, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3103112427114114}} {"text": "##############################################################\r\n# Description: Functions that do calculations for VIVED.\r\n# Usage: source(\"Vived.calc.fxns.r\")\r\n# Author: Haley Hunter-Zinck\r\n# Date: October 6, 2016\r\n# Dependencies:\r\n##############################################################\r\n\r\n# determine if a parameter combo is illegal\r\nillegal=function(start, end, metric, agg, disp)\r\n{\r\n\tmsg=\"\"\r\n\tADJUST_RANGE=\"\\nPlease adjust date range.\"\r\n\tSELECT_DIFFERENT=\"\\nPlease select another metric or aggregation view.\"\r\n\t\r\n\t# check parameters\r\n\tif(seconds(start,end)<0)\r\n\t{\r\n\t\t# bad date range\r\n\t\tmsg=paste(\"End date is before start date.\",ADJUST_RANGE,\"\\n\",sep=\"\")\r\n\t} else if((metric==BOARD_RO || metric==BOARD_RA || metric==BOARD_AO) && disp==DISCHARGE)\r\n\t{\r\n\t\t# can't calculate boarding time of discharged patients\r\n\t\tmsg=paste(\"Cannot calculate boarding time for discharged patients.\",SELECT_DIFFERENT,\"\\n\",sep=\"\")\r\n\t} \t\r\n\treturn(msg)\r\n}\r\n\r\n# update an list object to include only the indeces requested\r\nupdateObj=function(obj, indeces)\r\n{\r\n\tupt=list()\r\n\tfor(i in 1:length(obj))\r\n\t{\r\n\t\tupt[[i]]=obj[[i]][indeces]\r\n\t}\r\n\tnames(upt)=names(obj)\r\n\treturn(upt)\r\n}\r\n\r\n# calculate occupancy for the given object\r\ncalcOcc=function(vStart,vEnd, iStart, iEnd)\r\n{\r\n\t# setup\r\n\tMAX=1e3\r\n\tnmin=round(minutes(iStart,iEnd))\r\n\tbreaks=unique(c(seq(from=1,to=nmin,by=MAX),nmin+1))\r\n\tsums=list()\r\n\r\n\t# get visit intervals, expressing start and end of interval as minutes since filter start time\r\n\tvisitIntervals=round((cbind(as.double(vStart),as.double(vEnd))-as.double(iStart))/60)\r\n\r\n\t# calculate occ for each large time period\t\r\n\tsubIntervals=c()\r\n\tfor(i in 2:length(breaks))\r\n\t{\r\n\t\t# look at any visit intervals that overlap break interval\r\n\t\tindeces=which(!(visitIntervals[,2]=breaks[i]))\r\n\t\tif(length(indeces)>0)\r\n\t\t{\r\n\t\t\t# determine occupancy in the break interval\r\n\t\t\tmatOcc=apply(matrix(visitIntervals[indeces,],nrow=length(indeces)), 1, occInInterval, interval=c(breaks[i-1],breaks[i]))\r\n\t\t\tsubIntervals[[i-1]]=rowSums(matOcc)\r\n\t\t} else\r\n\t\t{\r\n\t\t\tsubIntervals[[i-1]]=rep(0,breaks[i]-breaks[i-1])\r\n\t\t}\r\n\t}\r\n\r\n\t# collapse all into a single vector\r\n\treturn(unlist(subIntervals))\r\n}\r\n\r\n# calculate occupancy for a given interval of minutes\r\n# NOTE: both visit and interval have two elements\r\n# representing the start end end in minutes from\r\n# a standard reference point\r\noccInInterval=function(visit, interval)\r\n{\r\n\t# convert timestamps to hours since start\r\n\tvStart=visit[1]\r\n\tvEnd=visit[2]\r\n\tiStart=interval[1]\r\n\tiEnd=interval[2]\r\n\r\n\t# truncate visit if necessary\r\n\tif(vStart < iStart)\r\n\t{\r\n\t\tvStart=iStart\r\n\t}\r\n\tif(vEnd>=iEnd)\r\n\t{\r\n\t\tvEnd=iEnd\r\n\t}\r\n\t\r\n\tif(DEBUG)\r\n\t{\r\n\t\tif(vStart-iStart<0)\r\n\t\t{\r\n\t\t\tcat(\"vStart-iStart\\n\")\r\n\t\t\tprint(visit)\r\n\t\t}\r\n\t\t\r\n\t\tif(vEnd-vStart<0)\r\n\t\t{\r\n\t\t\tcat(\"vEnd-vStart\\n\")\r\n\t\t\tprint(visit)\r\n\t\t}\r\n\t\tif(iEnd-vEnd<0)\r\n\t\t{\r\n\t\t\tcat(\"iEnd-vEnd\\n\")\r\n\t\t\tprint(visit)\r\n\t\t}\r\n\t}\r\n\t\r\n\t# update intervals\r\n\tvisitOcc=c(rep(0,vStart-iStart),rep(1,vEnd-vStart),rep(0,iEnd-vEnd))\r\n\treturn(visitOcc)\r\n}\r\n\r\n# calculate the number of visit for each day in the requested time period\r\ncalcNumVisit=function(obj, start, nday, dow, sHourNum, eHourNum, sMinNum, eMinNum, holidays, agg)\r\n{\r\n\t# get visits that start within time range\r\n\tindeces=which(is.element(format(obj$ti,\"%H:%M\"),getMinutes(sHourNum, eHourNum, sMinNum, eMinNum)))\r\n\r\n\t# format all days\r\n\tif(agg==HOUR)\r\n\t{\r\n\t\t# get day by hour counts of visits\r\n\t\tnvisit=table(format(obj$ti[indeces],\"%Y-%m-%d\"),as.double(format(obj$ti[indeces],\"%H\")))\r\n\t\t\r\n\t\t# add missing hours\r\n\t\tmissingHours=setdiff(getHours(sHourNum, eHourNum), as.double(colnames(nvisit)))\r\n\t\tif(length(missingHours>0))\r\n\t\t{\r\n\t\t\tcolLabels=c(colnames(nvisit),missingHours)\r\n\t\t\tnvisit=cbind(nvisit,matrix(0,nrow=nrow(nvisit),ncol=length(missingHours)))\r\n\t\t\tcolnames(nvisit)=colLabels\r\n\t\t\tnvisit=nvisit[,order(as.double(colnames(nvisit)))]\r\n\t\t}\r\n\t} else\r\n\t{\r\n\t\tnvisit=table(format(obj$ti[indeces],\"%Y-%m-%d\"))\r\n\t}\r\n\tdays=format(start+24*3600*c(0:(nday-1)),\"%Y-%m-%d\")\r\n\t\r\n\t# determine missing days\r\n\tmissing=setdiff(days,rownames(nvisit))\r\n\t\r\n\t# only keep missing days if they are within user's dow selection\r\n\tif(length(dow)0)\r\n\t{\r\n\t\tiHoliday=which(is.element(missing, holidays))\r\n\t\tif(length(iHoliday)>0)\r\n\t\t{\r\n\t\t\tmissing=missing[-iHoliday]\r\n\t\t}\r\n\t}\r\n\t\r\n\t# add missing days\r\n\tif(length(missing)>0)\r\n\t{\r\n\t\tif(agg==HOUR)\r\n\t\t{\r\n\t\t\ttoAdd=matrix(0,nrow=length(missing), ncol=ncol(nvisit))\r\n\t\t\trownames(toAdd)=missing\r\n\t\t\tnvisit=rbind(nvisit,toAdd)\r\n\t\t\tnvisit[order(rownames(nvisit)),]\r\n\t\t} else\r\n\t\t{\r\n\t\t\ttoAdd=rep(0,length(missing))\r\n\t\t\tnames(toAdd)=missing\r\n\t\t\tnvisit=append(nvisit,toAdd)\r\n\t\t\tnvisit=nvisit[order(names(nvisit))]\r\n\t\t}\r\n\t}\r\n\t\r\n\t# return number of visits per day\r\n\treturn(nvisit)\r\n}\r\n\r\n# calculate the amount of time in intervals between pairs of timestamps\r\ncalcTimeIntervals=function(t1, t2, maxTime)\r\n{\r\n\t# calculate time interval in minutes\r\n\tvisitMetric=(as.double(t2)-as.double(t1))/60\r\n\t\r\n\t# set unrealistic time intervals to missing\r\n\tif(length(which(visitMetric>maxTime))>0)\r\n\t{\r\n\t\tvisitMetric[which(visitMetric>maxTime)]=NA\r\n\t}\r\n\t\r\n\t# label by initial timestamp and return\r\n\tnames(visitMetric)=t1\r\n\treturn(visitMetric)\r\n}\r\n\r\n# calculate the requested occupancy subsetted by intervals\r\ncalcOccSubset=function(obj, metric, start, end)\r\n{\r\n\t# initialize\r\n\tocc=list()\r\n\r\n\tif(metric==OCC_SPLIT)\r\n\t{\t\t\r\n\t\t# get required metrics for status\r\n\t\ttBoard=(as.double(obj$to)-as.double(obj$td))\r\n\t\tiDis=which(is.na(obj$td))\r\n\t\tiAdmFast=which(!is.na(obj$td) & tBoard<=BOARD_THRESH)\r\n\t\tiAdmSlow=which(!is.na(obj$td) & tBoard>BOARD_THRESH)\r\n\t\t\r\n\t\t# occupancy per minute for each visit interval type\r\n\t\tocc$io=calcOcc(obj$ti[iDis], obj$to[iDis], start, end)\r\n\t\tocc$id=calcOcc(obj$ti[c(iAdmFast,iAdmSlow)], obj$td[c(iAdmFast,iAdmSlow)], start, end)\r\n\t\tocc$do=calcOcc(obj$td[c(iAdmFast,iAdmSlow)], c(obj$to[iAdmFast],(obj$td[iAdmSlow]+BOARD_THRESH)), start, end)\r\n\t\tocc$bo=calcOcc(obj$td[iAdmSlow]+BOARD_THRESH+1, obj$to[iAdmSlow], start, end)\r\n\t} else if(metric==OCC_ACUITY)\r\n\t{\r\n\t\t# occupancy per minute for each visit by acuity level\r\n\t\tocc=list()\r\n\t\tuValue=arrangeUnknown(sort(unique(obj$acuity)),uValue=UNKNOWN_STRING,uLast=FALSE)\r\n\t\tfor(i in 1:length(uValue))\r\n\t\t{\r\n\t\t\tindeces=which(obj$acuity==uValue[i])\r\n\t\t\tocc[[i]]=calcOcc(obj$ti[indeces], obj$to[indeces], start, end)\r\n\t\t}\r\n\t\tnames(occ)=uValue\r\n\t} else if(metric==OCC_DISP)\r\n\t{\r\n\t\t# get occupancy by disposition\r\n\t\tuDisp=names(sort(table(obj$disp),decreasing=FALSE,na.last=FALSE))\r\n\t\tuDisp=arrangeUnknown(uDisp,uValue=UNKNOWN_STRING,uLast=FALSE)\r\n\t\tfor(disp in uDisp)\r\n\t\t{\r\n\t\t\tindeces=which(obj$disp==disp)\r\n\t\t\tocc[[disp]]=calcOcc(obj$ti[indeces], obj$to[indeces], start, end)\r\n\t\t}\r\n\t} else if(metric==OCC_AGE)\r\n\t{ \r\n\t\t# get occupancy by age group\r\n\t\tuAge=as.character(sort(factor(unique(obj$age),levels=as.character(MAP_AGE)),decreasing=FALSE))\r\n\t\tuAge=arrangeUnknown(uAge,uValue=UNKNOWN_STRING,uLast=FALSE)\r\n\t\tfor(age in uAge)\r\n\t\t{\r\n\t\t\tindeces=which(obj$age==age)\r\n\t\t\tif(length(indeces)>0)\r\n\t\t\t{\r\n\t\t\t\tocc[[as.character(age)]]=calcOcc(obj$ti[indeces], obj$to[indeces], start, end)\r\n\t\t\t}\r\n\t\t}\r\n\t} else if(metric==OCC_PROG)\r\n\t{\r\n\t\tocc$it=calcOcc(obj$ti, obj$tt, start, end)\r\n\t\tocc$ts=calcOcc(obj$tt, obj$ts, start, end)\r\n\t\tocc$sd=calcOcc(obj$ts, obj$tdisp, start, end)\r\n\t\tocc$dop=calcOcc(obj$tdisp, obj$to, start, end)\r\n\t}\r\n\r\n\treturn(occ)\r\n}\t\r\n\r\n\r\n# calculate the number of hours between two timestamps\r\nweeks=function(t1,t2)\r\n{\r\n\treturn(days(t1,t2)/7)\r\n}\r\n\r\n# for each timestamp in a vector, get the date\r\n# that corresponds to the start that timestamps\r\n# week where the day of the week that is the start\r\n# of the week is determined by the user\r\nweekLabel=function(vec, dowStart=\"Mon\")\r\n{\r\n\t# get dow numbers\r\n\tdowStartNum=which(LABEL_DOW==dowStart)-1\r\n\tdowVecNum=as.double(format(vec,\"%w\"))\r\n\t\r\n\t# get start of the week date for each\r\n\tdowDiffNum=dowVecNum-dowStartNum\r\n\tdowDiffNum[which(dowDiffNum<0)]=7+dowDiffNum[which(dowDiffNum<0)]\r\n\tvecWeek=format(vec-dowDiffNum*24*3600,\"%Y-%m-%d\")\r\n\treturn(vecWeek)\r\n}\r\n\r\n# calculate the number of hours between two timestamps\r\ndays=function(t1,t2)\r\n{\r\n\treturn(hours(t1,t2)/24)\r\n}\r\n\r\n# calculate the number of hours between two timestamps\r\nhours=function(t1,t2)\r\n{\r\n\treturn(minutes(t1,t2)/60)\r\n}\r\n\r\n# calculate the number of hours between two timestamps\r\nminutes=function(t1,t2)\r\n{\r\n\treturn(seconds(t1,t2)/60)\r\n}\r\n\r\n# calculate seconds between two timestamps\r\nseconds=function(t1,t2)\r\n{\r\n\treturn(as.double(t2)-as.double(t1))\r\n}\r\n\r\n# map labels to a another label set\r\nmapLabel=function(vec, from, to)\r\n{\r\n\tmap=setNames(to,from)\r\n\treturn(map[vec])\r\n}\r\n\r\n# return the number of non-NA elements in a vector\r\nnumNotNa=function(vec)\r\n{\r\n\treturn(length(which(!is.na(vec))))\r\n}\r\n\r\ngetNumBed=function(locName)\r\n{\r\n\t# just return NA if locName unknown\r\n\tif(is.na(locName))\r\n\t{\r\n\t\treturn(NA)\r\n\t}\r\n\r\n\t# get number of beds\r\n\teHeader=scan(FILE_ED_FACTS, what=\"character\", nlines=1, sep=\",\", quiet=QUIET)\r\n\temat=matrix(scan(FILE_ED_FACTS, what=\"character\", skip=1, sep=\",\", quiet=QUIET), ncol=length(eHeader), byrow=TRUE)\r\n\tcIndex=which(eHeader==COL_NBED)\r\n\trIndex=which(emat[,which(eHeader==COL_LOC)]==locName)\r\n\tif(length(rIndex)==1)\r\n\t{\r\n\t\tif(emat[rIndex,cIndex]==\"\")\r\n\t\t{\r\n\t\t\tif(DEBUG)\r\n\t\t\t{\r\n\t\t\t\tcat(\"Number of beds for location \", locName, \" is blank in file \", FILE_ED_FACTS, \"\\n\",sep=\"\")\r\n\t\t\t}\r\n\t\t\tnbed=NA\r\n\t\t} else\r\n\t\t{\r\n\t\t\tif(DEBUG)\r\n\t\t\t{\r\n\t\t\t\tcat(\"Number of beds for location \", locName, \" is \",emat[rIndex,cIndex],\" in file \", FILE_ED_FACTS, \"\\n\",sep=\"\")\r\n\t\t\t}\r\n\t\t\tnbed=as.double(emat[rIndex,cIndex])\r\n\t\t}\r\n\t} else\r\n\t{\r\n\t\tif(DEBUG)\r\n\t\t{\r\n\t\t\tcat(length(rIndex), \" row(s) matching location \",locName,\" in file \", FILE_ED_FACTS, \"\\n\", sep=\"\")\r\n\t\t}\r\n\t\tnbed=NA\r\n\t}\r\n\r\n\treturn(nbed)\r\n}\r\n\r\n# get list of valid hours in the hour time range\r\ngetHours=function(sHourNum, eHourNum)\r\n{\r\n\t# initialize\r\n\thours=c()\r\n\t\r\n\t# get valid hours in time range\r\n\tif(sHourNum0)\r\n\t\t{\r\n\t\t\tmat[,which(TABLE_COL_NAMES==MED_NON_ZERO)]=round(median(vec[which(vec!=0)],na.rm=TRUE),digits=NDIGITS)\r\n\t\t\tmat[,which(TABLE_COL_NAMES==MEAN_NON_ZERO)]=round(mean(vec[which(vec!=0)],na.rm=TRUE),digits=NDIGITS)\r\n\t\t} else\r\n\t\t{\r\n\t\t\tmat[,which(TABLE_COL_NAMES==MED_NON_ZERO)]=NA\r\n\t\t\tmat[,which(TABLE_COL_NAMES==MEAN_NON_ZERO)]=NA\r\n\t\t}\r\n\t} else\r\n\t{\r\n\t\t# get ordering of unique categories\r\n\t\tif(is.factor(cats))\r\n\t\t{\r\n\t\t\tuCats=intersect(as.character(levels(cats)),unique(as.character(cats)))\r\n\t\t\tcats=as.character(cats)\r\n\t\t} else\r\n\t\t{\r\n\t\t\tuCats=sort(unique(as.character(cats)))\r\n\t\t}\r\n\t\t\t\t\t\r\n\t\t# initialize to calculate statistics over categories\r\n\t\tmat=matrix(NA,ncol=length(TABLE_COL_NAMES), nrow=length(uCats), dimnames=list(rownames=uCats,colnames=myColNames))\r\n\t\tmat=matrix(NA,ncol=length(TABLE_COL_NAMES), nrow=length(uCats), dimnames=list(rownames=uCats,colnames=myColNames))\r\n\t\t\r\n\t\t# calculate count\r\n\t\tamat=aggregate(vec~cats,FUN=numNotNa)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==COUNT)]=amat[aIndeces[which(!is.na(aIndeces))],2]\r\n\t\t\r\n\t\t# calculate median over categories\r\n\t\tamat=aggregate(vec~cats,FUN=median, na.rm=TRUE)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MED)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\r\n\t\t# calculate mean over categories\r\n\t\tamat=aggregate(vec~cats,FUN=mean, na.rm=TRUE)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MEAN)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\r\n\t\t# calculate max over categories\r\n\t\tamat=aggregate(vec~cats,FUN=max, na.rm=TRUE)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MAX)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\r\n\t\t# calculate min over categories\r\n\t\tamat=aggregate(vec~cats,FUN=min, na.rm=TRUE)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MIN)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\r\n\t\t# calculate variance over categories\r\n\t\tamat=aggregate(vec~cats,FUN=var, na.rm=TRUE)\r\n\t\taIndeces=match(uCats,amat[,1])\r\n\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==VAR)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\t\t\r\n\t\tif(length(nonZeroIndeces)>0)\r\n\t\t{\r\n\t\t\t# calculate median over non-zero elements\r\n\t\t\tamat=aggregate(vec[nonZeroIndeces]~cats[nonZeroIndeces],FUN=median, na.rm=TRUE)\r\n\t\t\taIndeces=match(uCats,amat[,1])\r\n\t\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MED_NON_ZERO)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t\t\r\n\t\t\t# calculate mean over non-zero elements\r\n\t\t\tamat=aggregate(vec[nonZeroIndeces]~cats[nonZeroIndeces],FUN=mean, na.rm=TRUE)\r\n\t\t\taIndeces=match(uCats,amat[,1])\r\n\t\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MEAN_NON_ZERO)]=round(amat[aIndeces[which(!is.na(aIndeces))],2],digits=NDIGITS)\r\n\t\t} else\r\n\t\t{\r\n\t\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MED_NON_ZERO)]=NA\r\n\t\t\tmat[which(!is.na(aIndeces)),which(TABLE_COL_NAMES==MEAN_NON_ZERO)]=NA\r\n\t\t}\r\n\t}\r\n\t\r\n\treturn(mat)\r\n}\r\n\r\n# get the user-friendly minute of the day label (hh:mm)\r\ngetMinLabel=function(vec)\r\n{\r\n\thour=sprintf(\"%02d\",floor((vec-1)/60))\r\n\tminute=sprintf(\"%02d\",(vec-1)%%60)\r\n\tformatted=apply(cbind(hour,minute),1,paste,collapse=\":\")\r\n\treturn(formatted)\r\n}\r\n\r\n# descretize age by decade\r\ndiscretizeAge=function(vec)\r\n{\r\n\t# get decades for ages\r\n\tdecades=floor(as.double(vec)/10)*10\r\n\tdescritizedAge=as.character(decades)\r\n\t\r\n\t# set boundary cases\r\n\tdescritizedAge[which(decades<0 | is.na(decades))]=UNKNOWN_STRING\r\n\t#descritizedAge[which(decades<20 & decades>=0)]=\"<20\"\r\n\tdescritizedAge[which(decades>=90)]=\"90+\"\r\n\t\r\n\t# set non-boundary cases\r\n\t#indeces=which(decades<90 & decades!=UNKNOWN_STRING)\r\n\t#descritizedAge[indeces]=paste(decades[indeces],\"s\",sep=\"\")\r\n\t\r\n\treturn(descritizedAge)\r\n}\r\n\r\n# extract location name from location label\r\ngetLocName=function(loc)\r\n{\r\n\treturn(strsplit(loc,INSTITUTION_DELIM)[[1]][3])\r\n}\r\n\r\n# return indeces of visits that represent patient returns in less than nhour\r\nreturnedIndex=function(obj,nhour,returnInitial,rAdmit)\r\n{\r\n\t# initialize\r\n\ttIndeces=c()\r\n\r\n\t# identify repeat patients\r\n\tids=apply(cbind(obj$station,obj$psid),1,paste,collapse=\"-\")\r\n\ttab=table(ids)\r\n\treturns=names(tab)[which(tab>1)]\r\n\t\r\n\t# for returns, get those returning in less than nhour\r\n\tif(length(returns)>0)\r\n\t{\r\n\t\tfor(i in 1:length(returns))\r\n\t\t{\t\r\n\t\t\t# get intervals\r\n\t\t\trIndeces=which(ids==returns[i])\r\n\t\t\tintervals=as.double(obj$ti[rIndeces][2:length(rIndeces)])-as.double(obj$to[rIndeces][1:(length(rIndeces)-1)])\r\n\t\t\t\r\n\t\t\t# append visits that are quick returns\r\n\t\t\tif(rAdmit)\r\n\t\t\t{\r\n\t\t\t\t# require that return visit be an admit\r\n\t\t\t\tiIndeces=which(intervalsRETURN_HOUR_MIN*3600 & is.element(obj$disp[rIndeces[2:length(rIndeces)]],DISP_ADMIT))\r\n\t\t\t} else\r\n\t\t\t{\r\n\t\t\t\t# no requirements other than interval of time\r\n\t\t\t\tiIndeces=which(intervalsRETURN_HOUR_MIN*3600)\r\n\t\t\t}\r\n\t\t\tif(length(iIndeces)>0)\r\n\t\t\t{\r\n\t\t\t\tif(returnInitial)\r\n\t\t\t\t{\r\n\t\t\t\t\t# return index of initial visit in quick return\r\n\t\t\t\t\ttIndeces=append(tIndeces,rIndeces[iIndeces])\r\n\t\t\t\t} else\r\n\t\t\t\t{\r\n\t\t\t\t\t# return index of return visit in quick return\r\n\t\t\t\t\ttIndeces=append(tIndeces,rIndeces[iIndeces+1])\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\t\r\n\treturn(tIndeces)\r\n}\r\n\r\n# return the fraction of the vector's elements that are NA\r\nfracNA=function(vec)\r\n{\r\n\tif(length(vec)==0)\r\n\t{\r\n\t\treturn(NA)\r\n\t}\r\n\treturn(length(which(is.na(vec)))/length(vec))\r\n}\r\n\r\n# remove rows and columns that are all NA\r\ncleanTable=function(myTable, agg)\r\n{\r\n\t# initialize\r\n\tcleaned=myTable\r\n\t\r\n\t# get rows that are all NA\r\n\tif(is.element(agg,MD))\r\n\t{\r\n\t\tif(ncol(myTable)>2)\r\n\t\t{\r\n\t\t\tmat=myTable[,2:ncol(myTable)]\r\n\t\t} else\r\n\t\t{\r\n\t\t\tmat=matrix(myTable[,2:ncol(myTable)],ncol=1,byrow=FALSE)\r\n\t\t}\r\n\t\t\r\n\t} else\r\n\t{\r\n\t\tif(ncol(myTable)>1)\r\n\t\t{\r\n\t\t\tmat=myTable\r\n\t\t} else\r\n\t\t{\r\n\t\t\tmat=matrix(myTable[,2:ncol(myTable)],ncol=1,byrow=FALSE)\r\n\t\t}\r\n\t}\r\n\trNas=apply(mat,1,fracNA)\r\n\trIndeces=which(rNas==1)\r\n\t\r\n\t# remove empty rows\r\n\tif(length(rIndeces)>0)\r\n\t{\r\n\t\tcleaned=myTable[-rIndeces,]\r\n\t}\r\n\r\n\treturn(cleaned)\r\n}\r\n\r\n# get all federal holidays within a given time span\r\n# start and end should be POSIXct timestamps#\r\ngetFedHolidays=function(start,end)\r\n{\r\n\t# initialize\r\n\tholidays=list()\r\n\tstart=as.POSIXct(start,tz=TZ)\r\n\tend=as.POSIXct(end,tz=TZ)\r\n\r\n\t# new year's day (date)\r\n\tholidays=as.character(getDateHoliday(month=1, day=1, start, end))\r\n\t\r\n\t# mlk (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=1, week=3, dow=1, start, end))\r\n\t\r\n\t# washington's bday (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=2, week=3, dow=1, start, end))\r\n\t\r\n\t# memorial day (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=5, week=0, dow=1, start, end))\r\n\t\r\n\t# independence day (date)\r\n\tholidays=append(holidays, getDateHoliday(month=7, day=4, start, end))\r\n\t\r\n\t# labor day (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=9, week=1, dow=1, start, end))\r\n\t\r\n\t# columbus day (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=10, week=2, dow=1, start, end))\r\n\t\r\n\t# veterans day (date)\r\n\tholidays=append(holidays, getDateHoliday(month=11, day=11, start, end))\r\n\t\r\n\t# thanksgiving (pattern)\r\n\tholidays=append(holidays, getPatternHoliday(month=11, week=4, dow=4, start, end))\r\n\t\r\n\t# christmas (date)\r\n\tholidays=append(holidays, getDateHoliday(month=12, day=25, start, end))\r\n\r\n\treturn(holidays)\r\n}\r\n\r\n# get holidays for a holiday that always occurs on a particular date\r\ngetDateHoliday=function(month, day, start, end)\r\n{\r\n\t# initialize\r\n\tholidays=list()\r\n\r\n\t# get year of start and end\r\n\tsYear=as.double(format(start,\"%Y\"))\r\n\teYear=as.double(format(end,\"%Y\"))\r\n\t\r\n\t# add all holidays within years\r\n\tholidays=as.POSIXct(paste(sYear:eYear,\"-\",sprintf(\"%02d\",month),\"-\",sprintf(\"%02d\",day),sep=\"\"), tz=TZ)\r\n\tif(length(holidays)==0)\r\n\t{\r\n\t\treturn(c())\r\n\t}\r\n\t\r\n\t# check for boundary years\r\n\tif(start>holidays[[1]])\r\n\t{\r\n\t\tif(length(holidays)==1)\r\n\t\t{\r\n\t\t\treturn(c())\r\n\t\t}\r\n\t\tholidays=holidays[2:length(holidays)]\r\n\t}\r\n\tif(end0)\r\n\t{\r\n\t\tfor(i in 1:length(satIndeces))\r\n\t\t{\r\n\t\t\tholidays[[satIndeces[i]]]=holidays[[satIndeces[i]]]-24*3600\r\n\t\t}\r\n\t}\r\n\tif(length(sunIndeces)>0)\r\n\t{\r\n\t\tfor(i in 1:length(sunIndeces))\r\n\t\t{\r\n\t\t\tholidays[[sunIndeces[i]]]=holidays[[sunIndeces[i]]]+24*3600\r\n\t\t}\r\n\t}\r\n\t\t\r\n\treturn(as.character(holidays))\r\n}\r\n\r\n# get holidays for a holiday that occurs on the nth -day of every month\r\n# month, week, dow should all be numeric\r\n# dow=0 is Sunday, dow=6 is Saturday\r\n# for last week of the month, use week=0\r\ngetPatternHoliday=function(month, week, dow, start, end)\r\n{\r\n\t# initialize\r\n\tholidays=list()\r\n\r\n\t# get year of start and end\r\n\tsYear=as.double(format(start,\"%Y\"))\r\n\teYear=as.double(format(end,\"%Y\"))\r\n\t\r\n\t# for each year\r\n\tfor(y in sYear:eYear)\r\n\t{\r\n\t\t# calculate week dow of month\r\n\t\tmDates=as.POSIXct(paste(y,\"-\",sprintf(\"%02d\",month),\"-\",c(1:numDayInMonth(y,month)),sep=\"\"), tz=TZ)\r\n\t\tmDows=as.double(format(mDates,\"%w\"))\r\n\t\tindeces=which(mDows==dow)\r\n\t\t\r\n\t\t# get actual holiday\r\n\t\tif(week==0)\r\n\t\t{\r\n\t\t\t# last -day of the month\r\n\t\t\tholidays[[y-sYear+1]]=mDates[indeces[length(indeces)]]\r\n\t\t} else\r\n\t\t{\r\n\t\t\t# other -day of the month\r\n\t\t\tholidays[[y-sYear+1]]=mDates[indeces[week]]\r\n\t\t}\r\n\t}\r\n\t\t\r\n\t# check for boundary years\r\n\tif(start>holidays[[1]])\r\n\t{\r\n\t\tif(length(holidays)==1)\r\n\t\t{\r\n\t\t\treturn(c())\r\n\t\t}\r\n\t\tholidays=holidays[2:length(holidays)]\t\r\n\t}\r\n\tif(end 0){\n visFile=paste0(prefix, \".sig_genename.pdf\")\n #if(!file.exists(visFile)){\n siggenes<-read.table(sigGenenameFile, sep=\"\\t\", stringsAsFactors = F)\n colnames(siggenes)<-\"gene\"\n \n sigoutFile<-paste0(edgeRfolder, \"/\", edgeRres[prefix, \"sigFile\"])\n sigout<-read.csv(sigoutFile, header=T, stringsAsFactors = F, row.names=1)\n \n designFile<-paste0(edgeRfolder, \"/\", edgeRres[prefix, \"designFile\"])\n design<-read.csv(designFile, stringsAsFactors = F, header=T)\n \n all_cells<-design$Cell\n \n cell_obj<-subset(obj, cells=design$Cell)\n cell_obj$Group=design$Group\n cell_obj$DisplayGroup=design$DisplayGroup\n \n designUniq<-unique(design[,c(\"Group\", \"DisplayGroup\")])\n rownames(designUniq)<-designUniq$Group\n \n controlGroup<-designUniq[\"control\",\"DisplayGroup\"]\n sampleGroup<-designUniq[\"sample\",\"DisplayGroup\"]\n \n coords<-data.frame(cell_obj@reductions$umap@cell.embeddings)\n xlim<-c(min(coords$UMAP_1-0.1), max(coords$UMAP_1+0.1))\n ylim<-c(min(coords$UMAP_2-0.1), max(coords$UMAP_2+0.1))\n \n visFile=paste0(prefix, \".sig_genename.pdf\")\n pdf(file=visFile, onefile = T, width=10, height=10)\n siggene<-siggenes$gene[1]\n for (siggene in siggenes$gene){\n logFC<-sigout[siggene, \"logFC\"]\n FDR<-sigout[siggene,\"FDR\"]\n \n geneexp=FetchData(cell_obj,vars=c(siggene))\n colnames(geneexp)<-\"Gene\"\n colorRange<-c(min(geneexp), max(geneexp))\n fix.sc <- scale_color_gradientn(colors=c(\"lightgrey\", \"blue\"), limits = colorRange)\n \n geneexp$Group<-cell_obj$DisplayGroup\n geneexp$Sample<-cell_obj$orig.ident\n \n title<-paste0(siggene, ' : logFC = ', round(logFC, 2), \", FDR = \", formatC(FDR, format = \"e\", digits = 2))\n \n if(bBetweenCluster){\n p0<-ggplot(geneexp, aes(x=Group, y=Gene, col=Group)) + geom_violin() + geom_jitter(width = 0.2) + facet_grid(~Sample) + theme_bw() + NoLegend() + xlab(\"\") + ylab(\"Gene Expression\") + theme(strip.background=element_blank())\n \n p1<-DimPlot(cell_obj, reduction = \"umap\", label=T, group.by=\"DisplayGroup\") + NoLegend() + ggtitle(\"Cluster\") + theme(plot.title = element_text(hjust=0.5)) + xlim(xlim) + ylim(ylim)\n \n p2<-FeaturePlot(object = cell_obj, features=as.character(siggene), order=T)\n }else{\n p0<-ggplot(geneexp, aes(x=\"1\", y=Gene, color=Group)) + geom_violin() + geom_jitter(width = 0.2) + facet_grid(~Sample) + theme_bw() + xlab(\"\") + ylab(\"Gene Expression\") + theme(strip.background=element_blank(), axis.text.x = element_blank())\n \n subcells<-colnames(cell_obj)[cell_obj$DisplayGroup == controlGroup]\n subobj<-subset(cell_obj, cells=subcells)\n p2<-FeaturePlot(object = subobj, features=siggene, order=T) + ggtitle(paste0(\"Control: \", controlGroup))\n p2<-suppressMessages(expr = p2 + xlim(xlim) + ylim(ylim) + fix.sc)\n \n subcells<-colnames(cell_obj)[cell_obj$DisplayGroup == sampleGroup]\n subobj<-subset(cell_obj, cells=subcells)\n p1<-FeaturePlot(object = subobj, features=siggene, order=T) + ggtitle(paste0(\"Sample: \", sampleGroup))\n p1<-suppressMessages(expr = p1 + xlim(xlim) + ylim(ylim) + fix.sc)\n }\n p<-ggarrange(p0, # First row with scatter plot\n ggarrange(p1, p2, ncol = 2, labels = c(\"B\", \"C\")), # Second row with box and dot plots\n nrow = 2, \n labels = \"A\" # Labels of the scatter plot\n ) \n g<-ggpubr::annotate_figure(\n p = p,\n top = ggpubr::text_grob(label = title, face = 'bold', size=20)\n )\n print(g)\n #break\n }\n dev.off()\n #}\n }\n curDF<-data.frame(\"prefix\"=prefix, \"sigGeneVisFile\"=visFile, \"sigGene\"=sigGene, \"totalGene\"=totalGene, \"cluster\"=cellType, \"comparison\"=comparison)\n if(is.null(result)){\n result<-curDF\n }else{\n result<-rbind(result, curDF)\n }\n}\n\nwrite.csv(result, file=paste0(outFile, \".vis.files.csv\"), quote=F)\n\nresult$sigRate<-result$sigGene * 100.0 / result$totalGene\n\nif(!bBetweenCluster){\n allcoords<-data.frame(obj@reductions$umap@cell.embeddings)\n allcoords$Cluster=obj[[cluster_name]]\n \n for (comp in unique(result$comparison)) {\n compRes = result[result$comparison == comp,]\n rownames(compRes)=compRes$cluster\n \n obj$sigRate=compRes[unlist(obj[[cluster_name]]), \"sigRate\"]\n \n pdf(paste0(outFile, \".\", comp, \".sigGenePerc.pdf\"), width=14, height=7)\n p1<-DimPlot(obj, group.by = cluster_name, label=T) + NoLegend() + ggtitle(\"Cluster\") + theme(plot.title = element_text(hjust=0.5))\n p2<-FeaturePlot(obj, feature=\"sigRate\", cols=c(\"lightgrey\", \"red\")) + ggtitle(\"Percentage of DE genes in each cluster\") + theme(plot.title = element_text(hjust=0.5))\n g<-ggarrange(p1, p2, ncol = 2, labels = c(\"A\", \"B\"))\n print(g)\n dev.off()\n }\n}\n\n", "meta": {"hexsha": "9412aa8a9e4e20a7331a9372351ffc9881b08f3a", "size": 6106, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/scRNA/edgeRvis.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/scRNA/edgeRvis.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/scRNA/edgeRvis.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 42.4027777778, "max_line_length": 250, "alphanum_fraction": 0.6351130036, "num_tokens": 1773, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.3103032397479641}} {"text": "library(bsseq)\nlibrary(pheatmap)\nlibrary(RColorBrewer)\n\n\n## load CpH data\nload('/dcl01/lieber/ajaffe/lab/brain-epigenomics/bsseq/bsobj_by_chr/allChrs_postNatal_cleaned_nonCG_noHomogenate_highCov.Rdata')\nBSobj_ch = BSobj\nmeth_ch =getMeth(BSobj_ch, type = 'raw')\nmethMap_ch = granges(BSobj_ch)\n\n## load dmrs\nload(\"/dcl01/lieber/ajaffe/lab/brain-epigenomics/bumphunting/bumps_bsseqSmooth_Neuron_interaction_250_perm.Rdata\")\nsigInt = bumps$table[bumps$table$fwer < 0.05,]\nload(\"/dcl01/lieber/ajaffe/lab/brain-epigenomics/bumphunting/bumps_bsseqSmooth_Neuron_age_250_perm.Rdata\")\nsigAge = bumps$table[bumps$table$fwer < 0.05,]\nload(\"/dcl01/lieber/ajaffe/lab/brain-epigenomics/bumphunting/bumps_bsseqSmooth_Neuron_cell_250_perm.Rdata\")\nsigCT = bumps$table[bumps$table$fwer < 0.05,]\nload(\"/dcl01/lieber/ajaffe/lab/brain-epigenomics/bumphunting/rda/limma_Neuron_CpGs_minCov_3_ageInfo_dmrs.Rdata\")\ndmrs = split(dmrs, dmrs$k6cluster_label)\n\n## subset to the DMRs\noo_ch = lapply(c(list(CellType = makeGRangesFromDataFrame(sigCT), Age = makeGRangesFromDataFrame(sigAge), Interaction = makeGRangesFromDataFrame(sigInt)), as.list(dmrs)), function(x) findOverlaps(x, methMap_ch))\n\n# first cluster all\nmeth_DMR_ch = lapply(oo_ch, function(x) meth_ch[queryHits(x),])\ndd_ch = lapply(meth_DMR_ch, function(x) dist(t(x)))\ndd_ch_mat = lapply(dd_ch, as.matrix)\nfor (i in 1:length(dd_ch_mat)) { colnames(dd_ch_mat[[i]]) = rownames(dd_ch_mat[[i]]) = paste(pd$Cell.Type, pd$Age.Bin, pd$Working.Num, sep = \":\") }\n\n\n# mean meth in each DMR\nmeanMeth_ch = lapply(oo_ch, function(oo) do.call(\"rbind\", lapply(split(subjectHits(oo), queryHits(oo)), function(ii) colMeans(t(t(meth_ch[ii,]))))))\ndd_ch_mean = lapply(meanMeth_ch, function(x) dist(t(x)))\ndd_ch_mean_mat = lapply(dd_ch_mean, as.matrix)\nfor (i in 1:length(dd_ch_mean_mat)) { colnames(dd_ch_mean_mat[[i]]) = rownames(dd_ch_mean_mat[[i]]) = paste(pd$Cell.Type, pd$Age.Bin, pd$Working.Num, sep = \":\") }\n\n\npdf(\"/dcl01/lieber/ajaffe/lab/brain-epigenomics/non-CpG/figures/nonCG_heatmap_within_DMRs.pdf\")\ncolors <- colorRampPalette(rev(brewer.pal(9, \"Blues\")) )(255)\nfor (i in 1:length(dd_ch_mat)) {\n print(pheatmap(dd_ch_mat[[i]], clustering_distance_rows = dd_ch[[i]], clustering_distance_cols = dd_ch[[i]],\n col = colors, main = paste0(\"all CpH - Euclidean Distance - \", c(\"Cell Type\",\"Age\",\"Interaction\",\"Group 1 Interaction\",\"Group 2 Interaction\", \"Group 3 Interaction\", \"Group 4 Interaction\",\n \"Group 5 Interaction\", \"Group 6 Interaction\")[i], \" DMRs\")))\n print(pheatmap(dd_ch_mean_mat[[i]], clustering_distance_rows = dd_ch_mean[[i]], clustering_distance_cols = dd_ch_mean[[i]],\n col = colors, main = paste0(\"mean mCpH - Euclidean Distance - \", c(\"Cell Type\", \"Age\", \"Interaction\", \"Group 1 Interaction\", \"Group 2 Interaction\", \"Group 3 Interaction\", \"Group 4 Interaction\",\n \"Group 5 Interaction\", \"Group 6 Interaction\")[i], \" DMRs\")))\n}\ndev.off()\n\n", "meta": {"hexsha": "3550087ed713df2d1d1c3db23a925f6f87aed75e", "size": 3083, "ext": "r", "lang": "R", "max_stars_repo_path": "non-CpG/code/check_CpH_in_all_DMRs.r", "max_stars_repo_name": "LieberInstitute/brain-epigenomics", "max_stars_repo_head_hexsha": "fac4a3232436fd22dad0dcd8372c3fd6c39ebe0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-20T00:45:47.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-26T15:50:01.000Z", "max_issues_repo_path": "non-CpG/code/check_CpH_in_all_DMRs.r", "max_issues_repo_name": "LieberInstitute/brain-epigenomics", "max_issues_repo_head_hexsha": "fac4a3232436fd22dad0dcd8372c3fd6c39ebe0c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-05-08T15:43:12.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-17T23:54:55.000Z", "max_forks_repo_path": "non-CpG/code/check_CpH_in_all_DMRs.r", "max_forks_repo_name": "LieberInstitute/brain-epigenomics", "max_forks_repo_head_hexsha": "fac4a3232436fd22dad0dcd8372c3fd6c39ebe0c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-11-09T04:37:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-08T03:49:45.000Z", "avg_line_length": 60.4509803922, "max_line_length": 211, "alphanum_fraction": 0.701589361, "num_tokens": 918, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6619228625116081, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.3103032334950538}} {"text": "library(rstudioapi)\nlibrary(tidyverse)\nlibrary(patchwork)\nlibrary(dplyr)\nlibrary(drjacoby)\n\nsetwd(dirname(getActiveDocumentContext()$path)) \n\nsource(\"R/create_profile.R\")\n\ndata <- \"Imperial\"\n#data <- \"PHE\"\n\n##################################\n##### LOAD THE PARAMETERS AND DATA\n##################################\n\nif(data==\"Imperial\") {\n load(\"../Model Fits/imp_v2_20211219_AZPD2=FALSE_SB=TRUE_NewDecay=TRUE_mcmc_chain.Rdata\") \n all_data <- read.csv(\"../Model Fits/Imperial_VE.csv\")\n}\n\nif(data==\"PHE\") {\n load(\"../Model Fits/phe_20211219_AZPD2=FALSE_SB=TRUE_NewDecay=TRUE_mcmc_chain.Rdata\") \n all_data <- read.csv(\"../Model Fits/PHE_Data_Long_Omicron_add_2.csv\")\n}\n\nchain <- sample_chains(mcmc, 10000)\n\nposterior_median <- chain %>%\n summarise( \n across(where(is.numeric), median)\n )\n\nname <- c(\"AZ\",\"PF\")\n \nlog10_d2_AZ <- log10(32/59)- posterior_median$fold_red_AZ \nlog10_d2_PF <- log10(223/94) - posterior_median$fold_red_PF\n\nd1_AZ <- 10^( log10_d2_AZ + posterior_median$d1_AZ)\nd1_PF <- 10^( log10_d2_PF + posterior_median$d1_PF)\nfold_red_AZ <- 10^( posterior_median$fold_red_AZ) \nfold_red_PF <- 10^( posterior_median$fold_red_PF)\n\nd3_PF <- 10^(log10_d2_PF + posterior_median$bst_PF)\n\nab_50_PF <- 10^( log10_d2_PF + posterior_median$ni50) \nab_50_severe_PF <- 10^( log10_d2_PF + posterior_median$ns50)\nab_50_death_PF <- 10^( log10_d2_PF + posterior_median$nd50)\n\nab_50_AZ <- 10^( log10_d2_PF + posterior_median$ni50 ) \nab_50_severe_AZ <- 10^( log10_d2_PF + posterior_median$ns50 ) \nab_50_death_AZ <- 10^( log10_d2_PF + posterior_median$nd50 ) \n\nab_50 <-c(ab_50_AZ, ab_50_PF)\nab_50_severe <-c(ab_50_severe_AZ, ab_50_severe_PF)\nab_50_death <-c(ab_50_death_AZ, ab_50_death_PF)\n\nk <- posterior_median$k\nhl_s <- posterior_median$hl_s\nhl_l <- posterior_median$hl_l\nperiod_s <- posterior_median$period_s\n#t_period_l <- posterior_median$period_l\n\n# fixed parameter\nstd10 <- 0.44 # Pooled standard deviation of antibody level on log10 scale\n\nom_red <- 10^(posterior_median$om_red)\nvfr <- c(1,om_red)\n\nmu_ab_d1 <- c(d1_AZ,d1_PF)\nmu_ab_d2 <- c(32/59, 223/94)/c(fold_red_AZ,fold_red_PF)\nmu_ab_d3 <- c(d3_PF,d3_PF) \ndose_3_fold_increase <- mu_ab_d3/mu_ab_d2\n\n# transforms\ndr_s <- -log(2)/hl_s # Corresponding decay rate in days for half life above\ndr_l <- -log(2)/hl_l\n\n# Timing of doses\nmax_t <- 365*2 # number of days to model\nt <- 0:max_t\nt_d2 <- 84 # timing of second dose relative to first\nt_d3 <- 180 # timing of third dose relative to second dose\n\nparam_list <- data.frame(name,mu_ab_d1,mu_ab_d2, mu_ab_d3, t_d2, t_d3, dr_s, dr_l,period_s,ab_50,ab_50_severe) \n \n## manipulate data for plotting\n## remove data points not used in fitting\n\nall_data <- all_data %>%\n mutate(t_orig=floor((t_max+t_min)/2)) %>%\n filter(t_orig>=14) %>%\n mutate(t_fit= case_when( dose==1 ~ t_orig-21,\n dose>1 ~ t_orig-14)) %>%\n filter((dose==1 & t_fit%\n mutate(t= case_when( dose==1 ~ t_fit,\n dose==2 ~ t_d2+t_fit,\n dose==3 ~ t_d2+t_d3+t_fit )) %>%\n mutate(type=case_when( endpoint==1 ~ \"Efficacy\",\n endpoint==2 ~ \"Efficacy_Severe\",\n endpoint==3 ~ \"Death\")) %>%\n filter(endpoint==1 | endpoint == 2) \n\n# initialise other parameters\n\nnt <- NULL\nr1_summary <- NULL\nsummary_stats <- NULL\nplots <- NULL\nplotlist <- list()\n\n##########################\n###### OUTPUT OPTIONS\n##########################\n\noutput_plots <- 1 # boolean for generating plots\nnum_ind <- 100 # number of individuals to simulate - keep to 100 for plots, 1000 or more for summary statistics\n\n#####################################################################################\n### loop through the vaccines to calculate the profiles of NAT and efficacy over time\n#####################################################################################\n\nm <- 2 ### set to 1 for delta, 2 for omicron\n\n\nfor (j in 1:2){\n\n r2 <- NULL\n r2_summary <- NULL\n sub2 <- NULL\n \n mu_ab_d1 <- param_list$mu_ab_d1[j]/vfr[m]\n mu_ab_d2 <- param_list$mu_ab_d2[j]/vfr[m]\n mu_ab_d3 <- param_list$mu_ab_d3[j]/vfr[m]\n dr_s <- param_list$dr_s[j]\n dr_l <- param_list$dr_l[j]\n period_s <- param_list$period_s[j]\n t_d2 <- param_list$t_d2[j]\n t_d3 <- param_list$t_d3[j]\n t_d4 <- param_list$t_d4[j]\n ab_50 <- param_list$ab_50[j]\n ab_50_severe <- param_list$ab_50_severe[j]\n\n ### generate array of simulations and process into plots\n r1 <- NULL\n for (i in 1:num_ind){\n out <- draw(mu_ab_d1 = mu_ab_d1, mu_ab_d2 = mu_ab_d2, mu_ab_d3 = mu_ab_d3, std10 = std10, ab_50 = ab_50, ab_50_severe = ab_50_severe, \n dr_s = dr_s, dr_l = dr_l, period_s = period_s, t = t, t_d2 = t_d2, t_d3, k = k)\n sub <- data.frame(t = t, run = rep(i, length(t)), Titre = out$titre, Efficacy = out$VE, Efficacy_Severe = out$VE_severe)\n r1 <- rbind(r1, sub)\n }\n out0 <- draw(mu_ab_d1 = mu_ab_d1, mu_ab_d2 = mu_ab_d2, mu_ab_d3 = mu_ab_d3, std10 = 0, ab_50 = ab_50, ab_50_severe = ab_50_severe, \n dr_s = dr_s, dr_l = dr_l, period_s = period_s, t = t, t_d2 = t_d2, t_d3, k = k)\n sub0<- data.frame(t = t, run = 0, variant=m, Titre = out0$titre, Efficacy = out0$VE, Efficacy_Severe = out0$VE_severe)\n\n ## cross-check code\n # look <- sub0 %>%\n # filter(t==t_d2+t_d3)\n \n sub0 <- sub0 %>%\n mutate(Efficacy = Efficacy * 100,\n Efficacy_Severe = Efficacy_Severe * 100) %>%\n pivot_longer(cols = c(\"Titre\", \"Efficacy\", \"Efficacy_Severe\"), names_to = \"type\") %>%\n mutate(type = factor(type, levels = c(\"Titre\", \"Efficacy\", \"Efficacy_Severe\"))) \n\n look <- sub0 %>% \n filter(t==20 )\n \n r1 <- r1 %>%\n mutate(variant=m) %>%\n mutate(Efficacy = Efficacy * 100,\n Efficacy_Severe = Efficacy_Severe * 100) %>%\n pivot_longer(cols = c(\"Titre\", \"Efficacy\", \"Efficacy_Severe\"), names_to = \"type\") %>%\n mutate(type = factor(type, levels = c(\"Titre\", \"Efficacy\", \"Efficacy_Severe\")))\n \n r1_summary <- r1 %>%\n group_by(type, t) %>%\n summarise(median = median(value),\n upper = quantile(value, 0.975),\n lower = quantile(value, 0.025),\n ) %>%\n mutate(name=name[j]) %>%\n mutate(vaccine = name[j]) %>%\n mutate(variant=m)\n \n sub2 <- rbind(sub2, sub0)\n r2 <- rbind(r2,r1)\n r2_summary <- rbind(r2_summary, r1_summary)\n \n if(j==1){\n uk_data <- all_data %>%\n filter(vaccine==\"AZ\") \n }\n if(j==2) {\n uk_data <- all_data %>%\n filter(vaccine==\"PF\" | vaccine==\"Pfizer\")\n }\n if(j==3) {\n uk_data <- all_data %>%\n filter(vaccine==\"MD\")\n }\n \n r2_summary <- r2_summary %>%\n left_join(uk_data,by=c(\"t\",\"type\",\"variant\"))\n \n r2_summary <- r2_summary %>%\n left_join(sub2,by=c(\"t\",\"type\",\"variant\")) %>%\n # dont allow data CIs to be <0\n mutate(L95 = if_else(L95 < 0, 0, L95))\n\n summary_stats <-rbind(summary_stats,r2_summary)\n \n ###################################\n ##### generate plots\n ###################################\n \n if(output_plots == 1){\n \n g1 <- ggplot(data = filter(r2, type == \"Titre\", variant==m)) +\n geom_line(aes(x = t, y = value, group = run), col = \"grey\") +\n geom_line(data = filter(r2_summary, type == \"Titre\", variant==m), aes(x = t, y = value), size = 1,col = \"black\") + \n geom_ribbon(data = filter(r2_summary, type == \"Titre\", variant==m), aes(x = t, ymin = lower, ymax = upper), alpha = 0.2, fill = \"darkgreen\") +\n labs(x = \"time (days)\", y = \"NAT\") +\n scale_x_continuous(breaks = c(0, 365, 365*2, 365*3, 365*4)) +\n scale_y_log10(limits = c(1e-3,1e2)) +\n theme_bw() +\n theme(strip.background = element_rect(fill = NA, color = \"white\"),\n panel.border = element_blank(),\n axis.line = element_line(),\n axis.text.x=element_text(angle=60, hjust = 1))\n \n g2 <- ggplot(data = filter(r2, type == \"Efficacy\", variant==m), na.rm=FALSE) +\n geom_line(aes(x = t, y = value, group = run), col = \"grey\") +\n geom_line(data = filter(r2_summary, type == \"Efficacy\", variant==m), aes(x = t, y = value), size = 1,col = \"black\") + \n geom_ribbon(data = filter(r2_summary, type == \"Efficacy\", variant==m), aes(x = t, ymin = lower, ymax = upper), alpha = 0.2, fill = \"darkblue\") +\n geom_point(data = filter(r2_summary, type == \"Efficacy\", variant==m),aes(x = t, y=VE), color = \"blue\", size = 1.5) +\n geom_errorbar(data = filter(r2_summary, type == \"Efficacy\"),aes(x = t, ymin=L95, ymax=U95), color = \"blue\", width=15) +\n lims(y = c(0, 100)) +\n labs(x = \"time (days)\", y = \"vaccine efficacy mild (%)\") +\n scale_x_continuous(breaks = c(0, 365, 365*2, 365*3, 365*4)) +\n theme_bw() +\n theme(strip.background = element_rect(fill = NA, color = \"white\"),\n panel.border = element_blank(),\n axis.line = element_line(),\n axis.text.x=element_text(angle=60, hjust = 1))\n \n g3 <- ggplot(data = filter(r2, type == \"Efficacy_Severe\", variant==m)) +\n geom_line(aes(x = t, y = value, group = run), col = \"grey\") +\n geom_line(data = filter(r2_summary, type == \"Efficacy_Severe\", variant==m), aes(x = t, y = median), size = 1) +\n geom_ribbon(data = filter(r2_summary, type == \"Efficacy_Severe\", variant==m), aes(x = t, ymin = lower, ymax = upper), alpha = 0.4, fill = \"darkgrey\") +\n geom_point(data = filter(r2_summary, type == \"Efficacy_Severe\", variant==m),aes(x = t, y = VE), color = \"black\", size = 1.5) +\n geom_errorbar(data = filter(r2_summary, type == \"Efficacy_Severe\"),aes(x = t, ymin=L95, ymax=U95), color = \"black\", width=15) +\n lims(y = c(0, 100)) +\n labs(x = \"time (days)\", y = \"vaccine efficacy severe (%)\") +\n scale_x_continuous(breaks = c(0, 365, 365*2, 365*3, 365*4)) +\n theme_bw() +\n theme(strip.background = element_rect(fill = NA, color = \"white\"),\n panel.border = element_blank(),\n axis.line = element_line(),\n axis.text.x=element_text(angle=60, hjust = 1))\n \n plots[[j*3-2]] <- g1\n plots[[j*3-1]] <- g2\n plots[[j*3]] <- g3\n \n }\n}\n\n## plots for the 3 vaccines, natural infection, and natural infection followed by Pfizer ##\n\nplot_AZ_profiles_delta <- (plots[[1]] | plots[[2]] | plots[[3]]) \nplot_PF_profiles_delta <- (plots[[4]] | plots[[5]] | plots[[6]]) \n\nplot_AZ_profiles_omicron <- (plots[[1]] | plots[[2]] | plots[[3]]) \nplot_PF_profiles_omicron <- (plots[[4]] | plots[[5]] | plots[[6]]) \n\n\ntext = paste(\"AZ primary PF boost: Delta\")\ncap1<- ggplot() + annotate(\"text\", x = 0, y = 0, size=4, label = text) + theme_void()+ plot_layout(tag_level = 'new')\n\ntext = paste(\"AZ primary PF boost: Omicron\")\ncap2<- ggplot() + annotate(\"text\", x = 0, y = 0, size=4, label = text) + theme_void()+ plot_layout(tag_level = 'new')\n\ntext = paste(\"PF primary PF boost: Delta\")\ncap3<- ggplot() + annotate(\"text\", x = 0, y = 0, size=4, label = text) + theme_void()+ plot_layout(tag_level = 'new')\n\ntext = paste(\"PF primary PF boost: Omicron\")\ncap4<- ggplot() + annotate(\"text\", x = 0, y = 0, size=4, label = text) + theme_void()+ plot_layout(tag_level = 'new')\n\ncombined <- cap1 / plot_AZ_profiles_delta / cap2 / plot_AZ_profiles_omicron /\n cap3 / plot_PF_profiles_delta / cap4 / plot_PF_profiles_omicron +\n plot_annotation(tag_levels = \"A\") + plot_layout(heights = c(2,5,2,5,2,5,2,5))\ncombined\n\nif(data==\"Imperial\") {\n ggsave(\"../Figures/Figure 1 Imperial.png\", combined, height = 13, width = 10)\n}\n\nif(data==\"PHE\") {\n ggsave(\"../Figures/Figure 1 PHE.png\", combined, height = 13, width = 10)\n}\n\n", "meta": {"hexsha": "62e85fe4f6d5c25595446f76845ac6321e3ce470", "size": 12114, "ext": "r", "lang": "R", "max_stars_repo_path": "0_antibody_model_fitting/Plotting and Table Code/Figure 1.r", "max_stars_repo_name": "mrc-ide/global_covid_vaccine_booster_paper", "max_stars_repo_head_hexsha": "fac5ddabb688cca4efa635b3c1be98262e29fcab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "0_antibody_model_fitting/Plotting and Table Code/Figure 1.r", "max_issues_repo_name": "mrc-ide/global_covid_vaccine_booster_paper", "max_issues_repo_head_hexsha": "fac5ddabb688cca4efa635b3c1be98262e29fcab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "0_antibody_model_fitting/Plotting and Table Code/Figure 1.r", "max_forks_repo_name": "mrc-ide/global_covid_vaccine_booster_paper", "max_forks_repo_head_hexsha": "fac5ddabb688cca4efa635b3c1be98262e29fcab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8486842105, "max_line_length": 157, "alphanum_fraction": 0.5798249959, "num_tokens": 3733, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300573952054, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.3102885620194823}} {"text": "#\n# This is a Shiny web application. You can run the application by clicking\n# the 'Run App' button above.\n#\n# Find out more about building applications with Shiny here:\n#\n# http://shiny.rstudio.com/\n#\n\nlibrary(shiny)\nlibrary(ggplot2)\n\n# Define UI for application that draws a histogram\nui <- fluidPage(\n\n # Application title\n titlePanel(\"Regrex1 Scatter Data\"),\n\n # Sidebar with a slider input for number of bins \n sidebarLayout(\n sidebarPanel(\n fileInput(\"file1\", \"Choose CSV File\",\n multiple = FALSE,\n accept = c(\"text/csv\",\n \"text/comma-separated-values,text/plain\",\n \".csv\")),\n \n actionButton(\"go\", \"Linear Regression\"),\n \n tags$hr(),\n \n checkboxInput(\"header\", \"Header\", TRUE),\n radioButtons(\"sep\", \"Separator\",\n choices = c(Comma = \",\",\n Semicolon = \";\",\n Tab = \"\\t\"),\n selected = \",\"),\n \n radioButtons(\"quote\", \"Quote\",\n choices = c(None = \"\",\n \"Double Quote\" = '\"',\n \"Single Quote\" = \"'\"),\n selected = '\"'),\n \n tags$hr(),\n \n radioButtons(\"disp\", \"Display\",\n choices = c(Head = \"head\",\n All = \"all\"),\n selected = \"head\")\n \n ),\n\n # Show a plot of the generated distribution\n mainPanel(\n plotOutput(\"distPlot\"),\n \n h3(\"r-squared\"),\n \n textOutput(\"rsquared\"),\n \n h3(\"slope\"),\n \n textOutput(\"slope\"),\n \n h3(\"y-intercept\"),\n \n textOutput(\"intercept\"),\n \n plotOutput(\"distPlot_lm\"),\n \n tableOutput(\"contents\")\n )\n )\n)\n\n# Define server logic required to draw a histogram\nserver <- function(input, output) {\n\n dataInput <- reactive({\n req(input$file1)\n \n df <- read.csv(input$file1$datapath,\n header = input$header,\n sep = input$sep,\n quote = input$quote)\n return(df)\n })\n \n output$contents <- renderTable({\n \n # input$file1 will be NULL initially. After the user selects\n # and uploads a file, head of that data file by default,\n # or all rows if selected, will be shown.\n \n if(input$disp == \"head\") {\n return(head(dataInput()))\n }\n else {\n return(dataInput())\n }\n \n })\n# Make plot reactive to go button\n \n newlinmod <- eventReactive(input$go, {\n lm(dataInput()$y~dataInput()$x)\n })\n \n output$distPlot <- renderPlot({\n plot(dataInput()$x,dataInput()$y)\n })\n \n \n output$rsquared <- renderText({\n print(round(summary(newlinmod())$r.squared, 2))\n })\n \n output$slope <- renderText({\n coefs <- coef(newlinmod())\n print(round(coefs[2],2))\n })\n \n output$intercept <- renderText({\n coefs <- coef(newlinmod())\n print(round(coefs[1], 2))\n })\n \n output$distPlot_lm <- renderPlot({\n plot(dataInput()$x,dataInput()$y)\n rmse <- round(sqrt(mean(resid(newlinmod())^2)), 2)\n coefs <- coef(newlinmod())\n b0 <- round(coefs[1], 2)\n b1 <- round(coefs[2],2)\n r2 <- round(summary(newlinmod())$r.squared, 2)\n eqn <- bquote(italic(y) == .(b0) + .(b1)*italic(x) * \",\" ~~ \n r^2 == .(r2) * \",\" ~~ RMSE == .(rmse))\n abline(newlinmod())\n text(1, 14, eqn, pos = 4)\n })\n \n }\n \n\n\n# Run the application \nshinyApp(ui = ui, server = server)\n", "meta": {"hexsha": "854fd7a8e41f6b45e6bb4939abdab0ca09be9b23", "size": 4032, "ext": "r", "lang": "R", "max_stars_repo_path": "shiny_scatter/Test.r", "max_stars_repo_name": "bdeng360/Shiny_App", "max_stars_repo_head_hexsha": "76a5375820f4a620cccf3a4e7048d9f1ce1b10e9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "shiny_scatter/Test.r", "max_issues_repo_name": "bdeng360/Shiny_App", "max_issues_repo_head_hexsha": "76a5375820f4a620cccf3a4e7048d9f1ce1b10e9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "shiny_scatter/Test.r", "max_forks_repo_name": "bdeng360/Shiny_App", "max_forks_repo_head_hexsha": "76a5375820f4a620cccf3a4e7048d9f1ce1b10e9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.2432432432, "max_line_length": 74, "alphanum_fraction": 0.4459325397, "num_tokens": 880, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.31021375855086397}} {"text": "[comment This code is written in REBOL 3.]\n[comment This code is written in REBOL 3.]\nengScript_add: func [engScript_a, engScript_b] [\n return (engScript_a + engScript_b)\n]", "meta": {"hexsha": "c131db7f9de4d2af7ed87b3b79e470da32a26c5a", "size": 175, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/EngScript_examples/REBOL/basic_functions.r", "max_stars_repo_name": "jarble/Polyglot-code-generator", "max_stars_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2015-11-30T06:04:27.000Z", "max_stars_repo_stars_event_max_datetime": "2016-06-08T23:45:26.000Z", "max_issues_repo_path": "examples/EngScript_examples/REBOL/basic_functions.r", "max_issues_repo_name": "jarble/Polyglot-code-generator", "max_issues_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/EngScript_examples/REBOL/basic_functions.r", "max_forks_repo_name": "jarble/Polyglot-code-generator", "max_forks_repo_head_hexsha": "bd46b9d2325b72428d915c5907c7439c7fa8a9ee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0, "max_line_length": 48, "alphanum_fraction": 0.7485714286, "num_tokens": 55, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3102137585508639}} {"text": "#library(BGVAR)\nlibrary(reshape2)\nlibrary(panelvar)\nlibrary(dtw)\nlibrary(ggplot2)\nlibrary(stringr)\n\n#library(pacman)\n#p_load(tidyverse,panelvar)\n\n#setwd('Projects/covid-analysis/')\n#setwd('C:/Users/nasko/dev/nars/covid-analysis/bin/rscripts')\ndf <- read.csv('../../data/tidy/cases_mobility_activity.csv')\ndf <- df[, c(2,4,8:186)] #36:95\n\n# Infer missing Apple data\ndf[df$transportation_type=='driving','X5.11.2020'] = df[df$transportation_type=='driving','X5.10.2020']\ndf[df$transportation_type=='driving','X5.12.2020'] = df[df$transportation_type=='driving','X5.10.2020']\ndf[df$transportation_type=='walking','X5.11.2020'] = df[df$transportation_type=='walking','X5.10.2020']\ndf[df$transportation_type=='walking','X5.12.2020'] = df[df$transportation_type=='walking','X5.10.2020']\ndf[df$transportation_type=='transit','X5.11.2020'] = df[df$transportation_type=='transit','X5.10.2020']\ndf[df$transportation_type=='transit','X5.12.2020'] = df[df$transportation_type=='transit','X5.10.2020']\n\n\n# Convert dataframe\nmelted.df <- melt(df, id.vars = c('region', 'transportation_type'))\n# m.data <- dcast(melted.df, region + variable~transportation_type, value.var=\"value\")\n# colnames(m.data) = c('Country', 'Date', 'cov', 'car', 'groc', 'parks', 'home', 'reta', 'tran', 'tstop', 'walk', 'work' )\n\n# Convert countries to factors\n# m.data$Country <- as.factor(m.data$Country)\n\n\nmelted.df <- na.omit(melted.df)\n\n# Rename transportation type\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='grocery_and_pharmacy_percent_change_from_baseline'] <- 'groc'\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='transit_stations_percent_change_from_baseline'] <- 'tran'\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='residential_percent_change_from_baseline'] <- 'home'\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='retail_and_recreation_percent_change_from_baseline'] <- 'reta'\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='workplaces_percent_change_from_baseline'] <- 'work'\nmelted.df[c(\"transportation_type\")][melted.df[c(\"transportation_type\")]=='parks_percent_change_from_baseline'] <- 'parks'\n\n# Drop Apple variables\nmelted.df<-melted.df[!(melted.df$transportation_type==\"driving\" | melted.df$transportation_type==\"walking\" | melted.df$transportation_type==\"transit\"),]\n\n# Convert numbers to numeric\n# for (i in seq(3, length(c('Country', 'Date', 'cov', 'car', 'groc', 'parks', 'home', 'reta', 'tran', 'tstop', 'walk', 'work' ) ) )) {\n# m.data[,i] = as.numeric(m.data[,i], na.pass=TRUE)\n# }\n\n# Correct Google (add 100 to baseline)\n# m.data[,c( 'groc', 'parks', 'home', 'reta', 'tstop', 'work' )] = m.data[,c( 'groc', 'parks', 'home', 'reta', 'tstop', 'work' )] + 100\n\n# Remove Apple data\n# endovars <- c('car', 'tran', 'walk', 'groc', 'parks', 'home', 'reta', 'tstop', 'work' )\n# m.data <- subset(m.data, select = c('Country', 'Date', endovars) )\n# m.data <- na.omit(m.data)\n\n# Plot Google data\ncolors = c( # '#ffff99', ##d8ac93', # '#ffff99', #or yellowversions \n '#66c2a5',\n '#fc8d62',\n '#8da0cb',\n '#e78ac3',\n '#a6d854',\n '#ffd92f',\n '#e5c494')\n\n# Change date format to POSIX\n# m.data$Date <- as.POSIXct(str_remove(m.data1$Date, \"X\"),format=\"%m.%d.%Y\")\n\ngoogle.names <- c(\"Work\", \"Transit Stops\", \"Groceries\", \"Retail\", \"Home\", \"Parks\")\nlims <- as.POSIXct(strptime(c(\"2020-02-15\", \"2020-07-15\"), format = \"%Y-%m-%d\"))\n\n# Create facet grid\nmelted.df$variable <- as.POSIXct(str_remove(melted.df$variable, \"X\"),format=\"%m.%d.%Y\")\nggplot(data = melted.df, aes(x = variable, y = value, color = region, group = region)) +\n # geom_point(size=1, alpha=.4) + \n theme(legend.position=\"none\") +\n scale_x_datetime(limits = lims) +\n geom_smooth(method = \"gam\", size=.5, alpha=.25)+\n facet_grid(rows = vars(transportation_type), labeller = labeller(.cols = google.names))#, labeller = labeller(.cols = google.names))\n #facet_wrap( vars(transportation_type), ncol = 1 )\n\nggsave(\"../../results/facet-grid-test3.png\", width = 14, height = 4, units=\"in\", dpi=\"retina\" )\n \n# ggplot(data = m.data1, aes(x = Date, y = value)) + geom_line() + scale_x_datetime(limits = lims)\n", "meta": {"hexsha": "f18d38176fdf8894b4eddddf1f0a4869a6c7c41e", "size": 4195, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/rscripts/facet-plots.r", "max_stars_repo_name": "naskoap/covid-analysis", "max_stars_repo_head_hexsha": "26a418ed3d46a6014a3f59e2415cc62ea85d7f3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/rscripts/facet-plots.r", "max_issues_repo_name": "naskoap/covid-analysis", "max_issues_repo_head_hexsha": "26a418ed3d46a6014a3f59e2415cc62ea85d7f3c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-06-25T14:55:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-25T14:55:46.000Z", "max_forks_repo_path": "bin/rscripts/facet-plots.r", "max_forks_repo_name": "naskoap/covid-analysis", "max_forks_repo_head_hexsha": "26a418ed3d46a6014a3f59e2415cc62ea85d7f3c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.1348314607, "max_line_length": 152, "alphanum_fraction": 0.6851013111, "num_tokens": 1359, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5926665999540697, "lm_q1q2_score": 0.3102137585508639}} {"text": "\nlibrary(\"igraph\");\nedges = read.table('iteracion5/noticias_word-topic-counts.6.5.txt.edgeslist', sep=',', header=F);\nnet = graph.data.frame(edges, directed=FALSE, vertices=NULL);\nE(net)$weight = edges$V3\nwrite.graph(net, 'noticias_composition.6.5.txt.gml', \"gml\");\n", "meta": {"hexsha": "fc639bc37563b7a50a8112c12c015aff2eb90c70", "size": 268, "ext": "r", "lang": "R", "max_stars_repo_path": "treemap/doingGML.r", "max_stars_repo_name": "j3nnn1/topic_model", "max_stars_repo_head_hexsha": "d7121cf50150455349e437c7c7d16e120beba249", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "treemap/doingGML.r", "max_issues_repo_name": "j3nnn1/topic_model", "max_issues_repo_head_hexsha": "d7121cf50150455349e437c7c7d16e120beba249", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "treemap/doingGML.r", "max_forks_repo_name": "j3nnn1/topic_model", "max_forks_repo_head_hexsha": "d7121cf50150455349e437c7c7d16e120beba249", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.2857142857, "max_line_length": 99, "alphanum_fraction": 0.7201492537, "num_tokens": 79, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.629774621301746, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3099675967827764}} {"text": "#' Munch coordinates data\n#'\n#' This function \"munches\" lines, dividing each line into many small pieces\n#' so they can be transformed independently. Used inside geom functions.\n#'\n#' @param coord Coordinate system definition.\n#' @param data Data set to transform - should have variables `x` and\n#' `y` are chopped up into small pieces (as defined by `group`).\n#' All other variables are duplicated as needed.\n#' @param range Panel range specification.\n#' @param segment_length Target segment length\n#' @keywords internal\n#' @export\ncoord_munch <- function(coord, data, range, segment_length = 0.01) {\n if (coord$is_linear()) return(coord$transform(data, range))\n\n # range has theta and r values; get corresponding x and y values\n ranges <- coord$backtransform_range(range)\n\n # Convert any infinite locations into max/min\n # Only need to work with x and y because for munching, those are the\n # only position aesthetics that are transformed\n data$x[data$x == -Inf] <- ranges$x[1]\n data$x[data$x == Inf] <- ranges$x[2]\n data$y[data$y == -Inf] <- ranges$y[1]\n data$y[data$y == Inf] <- ranges$y[2]\n\n # Calculate distances using coord distance metric\n dist <- coord$distance(data$x, data$y, range)\n dist[data$group[-1] != data$group[-nrow(data)]] <- NA\n if (!is.null(data$subgroup)) {\n dist[data$subgroup[-1] != data$subgroup[-nrow(data)]] <- NA\n }\n\n # Munch and then transform result\n munched <- munch_data(data, dist, segment_length)\n coord$transform(munched, range)\n}\n\n# For munching, only grobs are lines and polygons: everything else is\n# transformed into those special cases by the geom.\n#\n# @param dist distance, scaled from 0 to 1 (maximum distance on plot)\n# @keyword internal\nmunch_data <- function(data, dist = NULL, segment_length = 0.01) {\n n <- nrow(data)\n\n if (is.null(dist)) {\n data <- add_group(data)\n dist <- dist_euclidean(data$x, data$y)\n }\n\n # How many endpoints for each old segment, not counting the last one\n extra <- pmax(floor(dist / segment_length), 1)\n extra[is.na(extra)] <- 1\n # Generate extra pieces for x and y values\n # The final point must be manually inserted at the end\n x <- c(unlist(mapply(interp, data$x[-n], data$x[-1], extra, SIMPLIFY = FALSE)), data$x[n])\n y <- c(unlist(mapply(interp, data$y[-n], data$y[-1], extra, SIMPLIFY = FALSE)), data$y[n])\n\n # Replicate other aesthetics: defined by start point but also\n # must include final point\n id <- c(rep(seq_len(nrow(data) - 1), extra), nrow(data))\n aes_df <- data[id, setdiff(names(data), c(\"x\", \"y\")), drop = FALSE]\n\n new_data_frame(c(list(x = x, y = y), unclass(aes_df)))\n}\n\n# Interpolate.\n# Interpolate n-1 evenly spaced steps (n points) from start to\n# (end - (end - start) / n). end is never included in sequence.\ninterp <- function(start, end, n) {\n if (n == 1) return(start)\n start + seq(0, 1, length.out = n + 1)[-(n + 1)] * (end - start)\n}\n\n# Euclidean distance between points.\n# NA indicates a break / terminal points\ndist_euclidean <- function(x, y) {\n n <- length(x)\n\n sqrt((x[-n] - x[-1]) ^ 2 + (y[-n] - y[-1]) ^ 2)\n}\n\n# Compute central angle between two points.\n# Multiple by radius of sphere to get great circle distance\n# @arguments longitude\n# @arguments latitude\ndist_central_angle <- function(lon, lat) {\n # Convert to radians\n lat <- lat * pi / 180\n lon <- lon * pi / 180\n\n hav <- function(x) sin(x / 2) ^ 2\n ahav <- function(x) 2 * asin(x)\n\n n <- length(lat)\n ahav(sqrt(hav(diff(lat)) + cos(lat[-n]) * cos(lat[-1]) * hav(diff(lon))))\n}\n\n\n# Polar dist.\n# Polar distance between points. This does not give the straight-line\n# distance between points in polar space. Instead, it gives the distance\n# along lines that _were_ straight in cartesian space, but have been\n# warped into polar space. These lines are all spiral arcs, circular\n# arcs, or segments of rays.\ndist_polar <- function(r, theta) {\n\n # Pretending that theta is x and r is y, find the slope and intercepts\n # for each line segment.\n # This is just like finding the x-intercept of a line in cartesian coordinates.\n lf <- find_line_formula(theta, r)\n\n # Rename x and y columns to r and t, since we're working in polar\n # Note that 'slope' actually means the spiral slope, 'a' in the spiral\n # formula r = a * theta\n lf <- rename(lf, c(x1 = \"t1\", x2 = \"t2\", y1 = \"r1\", y2 = \"r2\",\n yintercept = \"r_int\", xintercept = \"t_int\"))\n\n # Re-normalize the theta values so that intercept for each is 0\n # This is necessary for calculating spiral arc length.\n # If the formula is r=a*theta, there's a big difference between\n # calculating the arc length from theta = 0 to pi/2, vs.\n # theta = 2*pi to pi/2\n lf$tn1 <- lf$t1 - lf$t_int\n lf$tn2 <- lf$t2 - lf$t_int\n\n # Add empty distance column\n lf$dist <- NA_real_\n\n # There are three types of lines, which we handle in turn:\n # - Spiral arcs (r and theta change)\n # - Circular arcs (r is constant)\n # - Rays (theta is constant)\n\n # Get spiral arc length for segments that have non-zero, non-infinite slope\n # (spiral_arc_length only works for actual spirals, not circle arcs or rays)\n # Use the _normalized_ theta values for arc length calculation\n # Also make sure to ignore NA's because they cause problems when used on left\n # side assignment.\n idx <- !is.na(lf$slope) & lf$slope != 0 & !is.infinite(lf$slope)\n idx[is.na(idx)] <- FALSE\n lf$dist[idx] <-\n spiral_arc_length(lf$slope[idx], lf$tn1[idx], lf$tn2[idx])\n\n # Get circular arc length for segments that have zero slope (r1 == r2)\n idx <- !is.na(lf$slope) & lf$slope == 0\n lf$dist[idx] <- lf$r1[idx] * (lf$t2[idx] - lf$t1[idx])\n\n # Get radial length for segments that have infinite slope (t1 == t2)\n idx <- !is.na(lf$slope) & is.infinite(lf$slope)\n lf$dist[idx] <- lf$r1[idx] - lf$r2[idx]\n\n # Find the maximum possible length, a spiral line from\n # (r=0, theta=0) to (r=1, theta=2*pi)\n max_dist <- spiral_arc_length(1 / (2 * pi), 0, 2 * pi)\n\n # Final distance values, normalized\n abs(lf$dist / max_dist)\n}\n\n# Given n points, find the slope, xintercept, and yintercept of\n# the lines connecting them.\n#\n# This returns a data frame with length(x)-1 rows\n#\n# @param x A vector of x values\n# @param y A vector of y values\n# @examples\n# find_line_formula(c(4, 7), c(1, 5))\n# find_line_formula(c(4, 7, 9), c(1, 5, 3))\nfind_line_formula <- function(x, y) {\n slope <- diff(y) / diff(x)\n yintercept <- y[-1] - (slope * x[-1])\n xintercept <- x[-1] - (y[-1] / slope)\n new_data_frame(list(x1 = x[-length(x)], y1 = y[-length(y)],\n x2 = x[-1], y2 = y[-1],\n slope = slope, yintercept = yintercept, xintercept = xintercept))\n}\n\n# Spiral arc length\n#\n# Each segment consists of a spiral line of slope 'a' between angles\n# 'theta1' and 'theta2'. Because each segment has its own _normalized_\n# slope, the ending theta2 value may not be the same as the starting\n# theta1 value of the next point.\n#\n# @param a A vector of spiral \"slopes\". Each spiral is defined as r = a * theta.\n# @param theta1 A vector of starting theta values.\n# @param theta2 A vector of ending theta values.\n# @examples\n# spiral_arc_length(a = c(0.2, 0.5), c(0.5 * pi, pi), c(pi, 1.25 * pi))\nspiral_arc_length <- function(a, theta1, theta2) {\n # Archimedes' spiral arc length formula from\n # http://mathworld.wolfram.com/ArchimedesSpiral.html\n 0.5 * a * (\n (theta1 * sqrt(1 + theta1 * theta1) + asinh(theta1)) -\n (theta2 * sqrt(1 + theta2 * theta2) + asinh(theta2)))\n}\n", "meta": {"hexsha": "2feb4e4b18e7012fca5bb987689f8751182ebadc", "size": 7414, "ext": "r", "lang": "R", "max_stars_repo_path": "R/coord-munch.r", "max_stars_repo_name": "netique/ggplot2", "max_stars_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3746, "max_stars_repo_stars_event_min_datetime": "2016-10-31T17:39:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T02:50:11.000Z", "max_issues_repo_path": "R/coord-munch.r", "max_issues_repo_name": "netique/ggplot2", "max_issues_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3277, "max_issues_repo_issues_event_min_datetime": "2016-11-01T19:23:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:44:20.000Z", "max_forks_repo_path": "R/coord-munch.r", "max_forks_repo_name": "netique/ggplot2", "max_forks_repo_head_hexsha": "7cf02ae2d6d851f7f685dc9a83d98e595f145c95", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1693, "max_forks_repo_forks_event_min_datetime": "2016-11-02T07:26:55.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T01:51:19.000Z", "avg_line_length": 36.8855721393, "max_line_length": 92, "alphanum_fraction": 0.6722417049, "num_tokens": 2233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.629774621301746, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3099675967827764}} {"text": "#' Read German Credit dataset\n#'\n#' Reads German Credit dataset from UCI repository.\n#'\n\niv.readgd<- function() {\n\ngerman_data <- read.table(file=\"http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data\",\n sep=\" \", header=FALSE, stringsAsFactors=TRUE)\nnames(german_data) <- c('ca_status','duration','credit_history','purpose','credit_amount','savings',\n 'present_employment_since','installment_rate_income','status_sex','other_debtors',\n 'present_residence_since','property','age','other_installment','housing','existing_credits',\n 'job','liable_maintenance_people','telephone','foreign_worker','gb')\n\n\n# Status of existing checking account\ngerman_data$ca_status <- factor(german_data$ca_status, levels=c(\"A11\",\"A12\",\"A13\",\"A14\"), \n labels = c(\"(;0DM)\",\"<0DM;200DM)\",\"<200DM;)\",\"No Acc.\"))\n\n# Credit history\ngerman_data$credit_history <- factor(german_data$credit_history, levels=c(\"A30\",\"A31\",\"A32\",\"A33\",\"A34\"),\n labels = c(\n \"no credits\", #\"no credits taken/all credits paid back duly\",\n \"paid off\", #\"all credits at this bank paid back duly\",\n \"all paid\", #\"existing credits paid back duly till now\",\n \"delay\", #\"delay in paying off in the past\",\n \"critical\" #\"critical account/other credits existing (not at this bank)\"))\n ))\n# Purpose\ngerman_data$purpose <- factor(german_data$purpose, levels=c(\"A40\",\"A41\",\"A42\",\"A43\",\"A44\",\"A45\",\"A46\",\n # \"A47\",\n \"A48\",\"A49\",\"A410\"),\n labels=c(\n \"car (new)\",\n \"car (used)\",\n \"furniture/equipment\",\n \"radio/television\",\n \"domestic appliances\",\n \"repairs\",\n \"education\",\n # \"vacation\",\n \"retraining\",\n \"business\",\n \"others\" \n ))\n\n# Savings account/bonds\ngerman_data$savings <- factor(german_data$savings, levels=c(\"A61\",\"A62\",\"A63\",\"A64\",\"A65\"),\n labels=c(\n \"(;100DM)\",\n \"<100;500)\",\n \"<500;1000)\",\n \"<1000;)\",\n \"unknown / no savings account\"\n ))\n\n# Present employment since\ngerman_data$present_employment_since <- factor(german_data$present_employment_since, levels=c(\"A71\",\"A72\",\"A73\",\"A74\",\"A75\"),\n labels=c(\n \"unemployed\",\n \"(;1)\",\n \"<1;4)\",\n \"<4;7)\",\n \"<7;)\"\n ))\n# Personal status and sex\ngerman_data$status_sex <- factor(german_data$status_sex, levels=c(\"A91\",\"A92\",\"A93\",\"A94\",\"A95\"),\n labels=c(\n \"male:div./sep.\", #\"male:divorced/separated\",\n \"female:div./sep./marr.\",#\"female:divorced/separated/married\",\n \"male:single\",\n \"male:marr/wid.\", # male:married/widowed\n \"female:single\"\n ))\n\n# Other debtors / guarantors\ngerman_data$other_debtors <- factor(german_data$other_debtors, levels=c(\"A101\",\"A102\",\"A103\"),\n labels=c(\n \"none\",\n \"co-applicant\",\n \"guarantor\" \n ))\n# Property\ngerman_data$property <- factor(german_data$property, levels=c(\"A121\",\"A122\",\"A123\",\"A124\"),\n labels=c(\n \"real estate\", \n \"svngs. agrrement\", # if not A121 : building society savings agreement/ life insurance \n \"car or other\", # if not A121/A122 : car or other, not in attribute 6 \n \"unknown/no\")) # unknown / no property \n# Other installment plans\ngerman_data$other_installment <- factor(german_data$other_installment, levels=c(\"A141\",\"A142\",\"A143\"),\n labels=c(\n \"bank\",\n \"stores\",\n \"none\"))\n\n# Housing\ngerman_data$housing <- factor(german_data$housing, levels=c(\"A151\",\"A152\",\"A153\"),\n labels=c(\n \"rent\",\n \"own\",\n \"for free\"))\n\n# Job\ngerman_data$job <- factor(german_data$job, levels = c(\"A171\",\"A172\",\"A173\",\"A174\"),\n labels=c(\n \"unemp./unsk. nonr.\", # unemployed/ unskilled - non-resident\n \"unsk. res.\", # unskilled - resident\n \"skilled/off.\", #\"skilled employee / official\n \"mng/self emp, hig qual.\" # management/ self-employed/ highly qualified employee/ officer\n ))\n\n# Telephone\ngerman_data$telephone <- factor(german_data$telephone, levels=c(\"A191\",\"A192\"),\n labels=c(\n \"none\",\n \"yes\"))\n# Foreign worker\ngerman_data$foreign_worker <- factor(german_data$foreign_worker, levels=c(\"A201\",\"A202\"),\n labels=c(\n \"yes\",\n \"no\"))\n\n# g/b\ngerman_data$gb <- factor(german_data$gb, levels=c(2,1), labels=c(\"bad\",\"good\"))\n\ngerman_data\n\n}", "meta": {"hexsha": "6f0717ec40a23307771e0c35583f3027fa2de183", "size": 6652, "ext": "r", "lang": "R", "max_stars_repo_path": "Classification & Clustering/others/read_german_data.r", "max_stars_repo_name": "SpekBin/DataScienceR", "max_stars_repo_head_hexsha": "3ae1881663d3e1d429fa3617d51506301b94466c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1932, "max_stars_repo_stars_event_min_datetime": "2015-12-15T13:43:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T15:22:25.000Z", "max_issues_repo_path": "Classification & Clustering/others/read_german_data.r", "max_issues_repo_name": "SpekBin/DataScienceR", "max_issues_repo_head_hexsha": "3ae1881663d3e1d429fa3617d51506301b94466c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-05-05T12:54:12.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-05T12:54:12.000Z", "max_forks_repo_path": "Classification & Clustering/others/read_german_data.r", "max_forks_repo_name": "SpekBin/DataScienceR", "max_forks_repo_head_hexsha": "3ae1881663d3e1d429fa3617d51506301b94466c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 915, "max_forks_repo_forks_event_min_datetime": "2015-12-19T05:20:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T08:43:11.000Z", "avg_line_length": 51.5658914729, "max_line_length": 125, "alphanum_fraction": 0.4027360192, "num_tokens": 1223, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6297745935070808, "lm_q2_score": 0.4921881357207955, "lm_q1q2_score": 0.3099675831025719}} {"text": "# (1) #########\n\nprint(\"Do a very simple loop, not in parallel\")\n\nnt<-10\nasum=0\nfor(ijk in 1:nt ){ \n\tasum<-asum+ijk\n}\nprint(asum)\n#readline(prompt = \"NEXT>\")\n\n", "meta": {"hexsha": "cab8cb0335f47806519354deea331932f19ea595", "size": 159, "ext": "r", "lang": "R", "max_stars_repo_path": "r/semantics/01.r", "max_stars_repo_name": "timkphd/examples", "max_stars_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-11-01T00:29:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T19:09:47.000Z", "max_issues_repo_path": "r/semantics/01.r", "max_issues_repo_name": "timkphd/examples", "max_issues_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-09T01:59:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T01:59:47.000Z", "max_forks_repo_path": "r/semantics/01.r", "max_forks_repo_name": "timkphd/examples", "max_forks_repo_head_hexsha": "04c162ec890a1c9ba83498b275fbdc81a4704062", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.2307692308, "max_line_length": 47, "alphanum_fraction": 0.5911949686, "num_tokens": 59, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.30994656581204544}} {"text": "#' rpartitions\n#'\n#' @name rpartitions\n#' @docType package\nNULL\n", "meta": {"hexsha": "97a72a40bc889edb1efc61ab2876e928f6519d8b", "size": 64, "ext": "r", "lang": "R", "max_stars_repo_path": "rpartitions/R/rpartitions-package.r", "max_stars_repo_name": "klocey/partitions", "max_stars_repo_head_hexsha": "0ce57b75007f9608f55b0a835410f0d0c1de5246", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2015-12-27T07:06:23.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-09T19:22:59.000Z", "max_issues_repo_path": "rpartitions/R/rpartitions-package.r", "max_issues_repo_name": "klocey/partitions", "max_issues_repo_head_hexsha": "0ce57b75007f9608f55b0a835410f0d0c1de5246", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2016-04-28T05:36:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-31T22:07:15.000Z", "max_forks_repo_path": "rpartitions/R/rpartitions-package.r", "max_forks_repo_name": "klocey/partitions", "max_forks_repo_head_hexsha": "0ce57b75007f9608f55b0a835410f0d0c1de5246", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 10.6666666667, "max_line_length": 20, "alphanum_fraction": 0.6875, "num_tokens": 22, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.6150878555160665, "lm_q1q2_score": 0.30994656581204544}} {"text": "# get_transformation.r\n#\n# Copyright (c) 2020 VIB (Belgium) & Babraham Institute (United Kingdom)\n#\n# Software written by Carlos P. Roca, as research funded by the European Union.\n#\n# This software may be modified and distributed under the terms of the MIT\n# license. See the LICENSE file for details.\n\n\n# Returns two lists with transformation parameters per marker, for direct and\n# inverse tranformation.\n\nget.transformation <- function( flow.control, asp )\n{\n if ( ! is.null( asp$transformation.parameter.file.name ) &&\n file.exists( asp$transformation.parameter.file.name ) )\n {\n transformation.param <- read.csv(\n asp$transformation.parameter.file.name, stringsAsFactors = FALSE )\n\n check.critical(\n sort( transformation.param$dye ) ==\n sort( flow.control$marker.original ),\n \"wrong dye name in tranformation parameters\"\n )\n\n transf <- lapply( flow.control$marker.original, function( mo ) {\n mo.idx <- which( transformation.param$dye == mo )\n flowjo_biexp(\n channelRange = transformation.param$length[ mo.idx ],\n maxValue = transformation.param$max.range[ mo.idx ],\n pos = transformation.param$pos[ mo.idx ],\n neg = transformation.param$neg[ mo.idx ],\n widthBasis = transformation.param$width[ mo.idx ],\n inverse = FALSE\n ) } )\n\n transf.inv <- lapply( flow.control$marker.original, function( mo ) {\n mo.idx <- which( transformation.param$dye == mo )\n flowjo_biexp(\n channelRange = transformation.param$length[ mo.idx ],\n maxValue = transformation.param$max.range[ mo.idx ],\n pos = transformation.param$pos[ mo.idx ],\n neg = transformation.param$neg[ mo.idx ],\n widthBasis = transformation.param$width[ mo.idx ],\n inverse = TRUE\n ) } )\n }\n else\n {\n transf <- lapply( flow.control$marker.original, function( mo )\n flowjo_biexp(\n channelRange = asp$default.transformation.param$length,\n maxValue = asp$default.transformation.param$max.range,\n pos = asp$default.transformation.param$pos,\n neg = asp$default.transformation.param$neg,\n widthBasis = asp$default.transformation.param$width,\n inverse = FALSE\n ) )\n\n transf.inv <- lapply( flow.control$marker.original, function( mo )\n flowjo_biexp(\n channelRange = asp$default.transformation.param$length,\n maxValue = asp$default.transformation.param$max.range,\n pos = asp$default.transformation.param$pos,\n neg = asp$default.transformation.param$neg,\n widthBasis = asp$default.transformation.param$width,\n inverse = TRUE\n ) )\n }\n\n names( transf ) <- flow.control$marker.original\n names( transf.inv ) <- flow.control$marker.original\n\n list( transf, transf.inv )\n}\n\n", "meta": {"hexsha": "3335e8a3e00172d4d26b5b5e1d056a2994e870aa", "size": 3099, "ext": "r", "lang": "R", "max_stars_repo_path": "R/get_transformation.r", "max_stars_repo_name": "DillonHammill/autospill", "max_stars_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-07T21:48:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-12T03:00:59.000Z", "max_issues_repo_path": "R/get_transformation.r", "max_issues_repo_name": "DillonHammill/autospill", "max_issues_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2020-09-10T08:08:01.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-29T23:41:00.000Z", "max_forks_repo_path": "R/get_transformation.r", "max_forks_repo_name": "DillonHammill/autospill", "max_forks_repo_head_hexsha": "2cd601ea9481688497f0800e7c873bbf71ba5f1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2020-09-05T14:15:12.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-12T14:36:42.000Z", "avg_line_length": 39.2278481013, "max_line_length": 79, "alphanum_fraction": 0.5976121329, "num_tokens": 644, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3099465587010803}} {"text": "#https://www.r-bloggers.com/creating-guis-in-r-with-gwidgets/\n#https://www.rdocumentation.org/packages/gWidgets/versions/0.0-54\n\nlibrary(gWidgets)\nlibrary(gWidgetstcltk)\n\nOnWindows = TRUE\n\nif(OnWindows){\n setwd(\"//20X/LOL/R\")\n dir = \"//20X/LoL/R\"\n lib_dir <- \"//20X/LoL/Windows/library\" #On Windows\n}else{\n dir = \"/home/tom/LoL/R\"\n lib_dir <- \"/usr/lib/R/library\" #On Linux \n}\n\nStats <- c()\n\nData <- read.csv(\"info_gui.csv\", header = TRUE, row.names=1, stringsAsFactors=FALSE)\nData_Saves <- read.csv(\"ItemSav.csv\", header=FALSE, stringsAsFactors=FALSE)\n\nDamage_Reduction <- function(Resistance, Penetration_Percent, Penetration_Flat){\n \n Resistance <- Resistance-(Resistance*Penetration_Percent)\n Resistance <- Resistance-Penetration_Flat\n if(Resistance > 0){\n Damage_Reduction = 100/(100+Resistance)\n } else{\n Damage_Reduction = 2-(100/(100-(Resistance)))\n }\n #cat(sprintf(\"PP: %s\\tPF: %s\\tDR: %s\\t\\n\\n\", Penetration_Percent, Penetration_Flat, Damage_Reduction))\n return(Damage_Reduction)\n}\nLethality <- function(Level, Lethality){\n return(Lethality * (0.6+0.4*(Level/18)))\n}\n\n\nDamages <- function(Build, Enemy_Info){\n Q = svalue(Q_Slider)\n W = svalue(W_Slider)\n E = svalue(E_Slider)\n R = svalue(R_Slider)\n Level = svalue(gp2_Level_Slider)\n \n R_Sc\t \t= (0.1+(0.1*Level))\n Y_Sc \t\t= (0.1+(0.1*Level))\n B_Sc \t\t= (0.17+(0.17*Level))\n P_Sc \t\t= (0.43+(0.43*Level))\n \n R_Let = 1.6\n P_Let = 3.2\n \n R_ASc = (0.13+(0.13*Level))\n Y_ASc = 0.06+(0.06*Level)\n B_ASc = 0.04+(0.04*Level)\n P_ASc = 0.25+(0.25*Level)\n \n R_AD = 0.95\n Y_AD = 0.43\n B_AD = 0.28\n P_AD = 2.25\n \n R_AP\t\t= 0.59\n Y_AP \t\t= 0.59\n B_AP\t\t= 1.19\n P_AP\t\t= 4.95\n \n R_APFP \t\t= 0.87\n B_APFP \t\t= 0.63\n P_APFP \t\t= 2.01\n \n B_CDR_Sc = (0.09+(0.09*Level))/100\n P_CDR_Sc = (0.28+(0.28*Level))/100\n Stack = 1\n \n Stats \t<- c()\n for(i in (1:length(Build))){\n for(col in (1:ncol(Data[Build[i],]))){\n Data[Build[i], col] <- eval(parse(text=Data[Build[i], col]))\n }\n Stats \t<- c(Stats, as.numeric(as.vector(Data[Build[i],])))\n }\n \n Stats <- array(Stats, dim = c(21, length(Stats)/21, 1))\n Magic_Damage_Reduction_Percent <- c(Stats[2,,])\n \n Attack_Damage_Reduction_Percent <- c(Stats[6,,])\n Stats <- apply(Stats, c(1), sum)\n Stats <- array(Stats, dim = c(21, 1, 1))\n \n Damage_Modifiers \t<- c((1-0.03), (1-.02), (1-0.02))\n Total_Damage\t\t<- 0\n \n if(\"Karthus\" %in% Build){\n Magic_Damage_Reduction_Percent <- c(Magic_Damage_Reduction_Percent, c(1-0.15))\n }\n \n Magic_Damage_Reduction_Percent <- Magic_Damage_Reduction_Percent[Magic_Damage_Reduction_Percent != 0]\n Magic_Damage_Reduction_Percent <- 1-(prod(Magic_Damage_Reduction_Percent))\n Stats[2] <- Magic_Damage_Reduction_Percent\n \n \n Attack_Damage_Reduction_Percent <- Attack_Damage_Reduction_Percent[Attack_Damage_Reduction_Percent != 0]\n Attack_Damage_Reduction_Percent <- 1-(prod(Attack_Damage_Reduction_Percent))\n Stats[6] <- Attack_Damage_Reduction_Percent\n Stats[3] <- ceiling(Stats[3])\n Magic_Damage_Reduction\t= Damage_Reduction(Enemy_Info[3], Stats[2], Stats[3])\n Attack_Damage_Reduction\t= Damage_Reduction(Enemy_Info[2], Stats[6], Lethality(Enemy_Info[1], Stats[7]))\n \n if(Stats[16] > 0.40){\n Stats[16] <- 0.4\n }\n if(\"Seraphs_Embrace\" %in% Build){\n Stats[1] \t\t<- Stats[1] + (Stats[12]*0.03)\n }\n if(\"Rabadons_Deathcap\" %in% Build){\n #cat(sprintf(\"Rabadons Deathcap found in build\\nAP: %s\\tIncreasedAP: %s\\n\", Stats[1], Stats[1]+Stats[1]*0.35))\n Stats[1] \t\t<- Stats[1] + (Stats[1]*0.35)\n }\n\n Stats[10] \t<- Stats[10]+((Stats[10]*Stats[11])/100)\n Stats[20]\t<- Stats[20]+((Stats[20]*Stats[19])/100)\n \n \n hasAbyssal <- FALSE\n if(\"Abyssal_Mask\" %in% Build){\n hasAbyssal <- TRUE\n }\n Damage_Modifier \t<- 1-(prod(Damage_Modifiers))\n \n CSB <- 2\n \n DM <- Damage_Modifier\n MDR <- Magic_Damage_Reduction\n ADR <- Attack_Damage_Reduction\n \n CDR <- function(CD){\n return(CD - (CD*Stats[16]))\n }\n DamageBP <- function(D, isMagic){\n B <- DM\n if(isMagic){\n P <- MDR\n }else{\n P <- ADR\n }\n if(hasAbyssal & isMagic){\n D <- D+D*0.10\n }\n return((D+D*B)*P)\n }\n if(\"Infinity_Edge\" %in% Build){\n CSB <- CSB + 0.5\n }\n if(\"Liandrys_Torment\" %in% Build){\n Liandrys_Torment_Damage\t<- DamageBP(Enemy_Info[4]*0.04, TRUE)\n Total_Damage \t\t<- Total_Damage + Liandrys_Torment_Damage\n }\n if(\"Lich_Bane\" %in% Build){\n print(\"Found Lich Bane\")\n cat(sprintf(\"Lich Bane\\n\\tDamage: %s\\tDamageBP: %s\", \n Stats[1]*0.5+Stats[5]*0.75, \n DamageBP(Stats[1]*0.5+Stats[5]*0.75, TRUE)))\n \n Total_Damage <- Total_Damage + DamageBP(Stats[1]*0.5+Stats[5]*0.75, TRUE)\n }\n if(\"Ludens_Echo\" %in% Build){\n Ludens_Echo_Damage\t<- DamageBP(100+Stats[1]*0.10, TRUE)\n Total_Damage \t<- Total_Damage + Ludens_Echo_Damage\n }\n if(TRUE %in% grepl(\"Ferocity\", Build)){\n Keystone = \"Deathfire Grasp\"\n Deathfire_Grasp\t<- DamageBP(4+Stats[1]*0.125 + Stats[5]*0.225, TRUE)\n Total_Damage \t<- Total_Damage + Deathfire_Grasp\n }\n if(\"Cunning\" %in% Build){\n Keystone = \"Thunderlord's Decree\"\n Thunderlords_Decree\t<- DamageBP((10*Level)+Stats[5]*0.30 + Stats[1]*0.10, TRUE)\n Total_Damage \t<- Total_Damage + Thunderlords_Decree\n }\n Stats[1] <- ceiling(Stats[1])\n RndUp <- function(){\n A1 <<- ceiling(A1)\n A2 <<- ceiling(A2)\n A3 <<- ceiling(A3)\n A4 <<- ceiling(A4)\n #cat(sprintf(\"A1: %s\\tA2: %s\\tA3: %s\\tA4: %s\\n\\n\", A1, A2, A3, A4))\n }\n RndDown <- function(){\n A1 <<- floor(A1)\n A2 <<- floor(A2)\n A3 <<- floor(A3)\n A4 <<- floor(A4)\n \n }\n if(\"Annie\" %in% Build){\n A1_CDR <- CDR(4)\n A2_CDR <- CDR(8)\n A3_CDR <- CDR(10)\n A4_CDR <- CDR(120-(20*R))\n \n A1 <- 80+(35*Q) + Stats[1]*0.80\n A2 <- 75+(45*W) + Stats[1]*0.85\n A3 <- 20+(10*E) + Stats[1]*0.10\n A4 <- 150+(125*R) + Stats[1]*0.65\n #cat(sprintf(\"A1: %s\\tA2: %s\\tA3: %s\\tA4: %s\\n\\n\", A1, A2, A3, A4))\n A1 <- DamageBP(A1, TRUE)\n A2 <- DamageBP(A2, TRUE)\n A3 <- DamageBP(A3, TRUE)\n A4 <- DamageBP(A4, TRUE)\n }\n if(\"Jhin\" %in% Build){\n Bonus_AD <- 0.02\n Bonus_AD <- Bonus_AD + (Stats[9] %/% 0.1 * 0.04)\n Bonus_AD <- Bonus_AD + (Stats[11] %/% 0.1 * 0.025)\n for(x in 1:Level){\n if(x < 11){\n Bonus_AD <- Bonus_AD + 0.01\n }else{\n Bonus_AD <- Bonus_AD + 0.04\n }\n }\n \n Stats[5] <- Stats[5] + (Stats[5] * Bonus_AD)\n A1_CDR <- CDR(7-(0.5*Q))\n A2_CDR <- CDR(14)\n A3_CDR <- CDR(28)\n A4_CDR <- CDR(120-(15*R))\n \n A1 <- 45+(25*Q) + Stats[1]*0.60 + (Stats[5]*(0.40+0.05*Q))\n A2 <- 50+(35*W) + Stats[5]*0.50\n A3 <- 20+(60*E) + Stats[1]*1.0 + Stats[5]*1.2\n A4 <- 50+(65*R) + Stats[5]*0.2\n \n A1 <- DamageBP(A1, FALSE)\n A2 <- DamageBP(A2, FALSE)\n A3 <- DamageBP(A3, TRUE)\n A4 <- DamageBP(A4, FALSE)\n }\n if(\"Xerath\" %in% Build){\n A1_CDR <- CDR(9-(1*Q))\n A2_CDR <- CDR(14-(1*W))\n A3_CDR <- CDR(13-(0.5*E))\n A4_CDR <- CDR(130-(15*R))\n\n A1 <- 80+(40*Q) + (Stats[1]*0.75)\n A2 <- 90+(45*W) + (Stats[1]*0.90)\n A3 <- 80+(30*E) + (Stats[1]*0.45)\n A4 <- 200+(40*R)+ (Stats[1]*0.43)\n \n A1 <- DamageBP(A1, TRUE)\n A2 <- DamageBP(A2, TRUE)\n A3 <- DamageBP(A3, TRUE)\n A4 <- DamageBP(A4, TRUE)\n \n }\n if(\"Karthus\" %in% Build){\n A1_CDR\t\t<- CDR(1)\n A2_CDR\t\t<- CDR(18)\n A3_CDR\t\t<- 1\n A4_CDR\t\t<- CDR(200-(20*R))\n \n A1\t\t\t<- (40+(20*Q)+Stats[1]*0.30)*2\n A2\t\t\t<- 0\n A3\t\t\t<- 30+(20*E)+Stats[1]*0.20\n A4\t\t\t<- 250+(150*R)+Stats[1]*0.60\n \n A1 <- DamageBP(A1, TRUE)\n A2 <- DamageBP(A2, TRUE)\n A3 <- DamageBP(A3, TRUE)\n A4 <- DamageBP(A4, TRUE)\n }\n if(Q == -1){\n A1 = 0\n }\n if(W == -1){\n A2 = 0\n }\n if(E == -1){\n A3 = 0\n }\n if(R == -1){\n A4 = 0\n }\n \n Total_Combo\t\t<- Total_Damage + (A1 + A2 + A3 + A4)\n \n A1_DPS\t\t<- A1/A1_CDR\n A2_DPS\t\t<- A2/A2_CDR\n A3_DPS\t\t<- A3/A3_CDR\n A4_DPS\t\t<- A4/A4_CDR\n \n AS <- Stats[10]+(Stats[10]*Stats[11])\n \n if(\"Jhin\" %in% Build){\n CSB <- CSB - 0.25\n AS <- Stats[10]\n }\n RndDown()\n \n AA <- DamageBP(Stats[5]*CSB, FALSE)\n abilities \t\t<- c(\n AA, \n AS, \n A1, \n A1_DPS, \n A2, \n A2_DPS, \n A3, \n A3_DPS, \n A4, \n A4_DPS, \n Stats[1], \n Stats[5], \n Total_Combo)\n damages \t\t<- array(c(abilities), dim = c(13, 1, 1))\n return(damages)\n}\n\nEnemy_Info = c(17,100,100,1500)\nChampion = \"Xerath\"\n\nBuild1\t\t<- as.character(as.vector(Data_Saves[1,]))\nBuild2\t\t<- as.character(as.vector(Data_Saves[2,]))\nBuild3\t\t<- as.character(as.vector(Data_Saves[3,]))\nBuild4\t\t<- as.character(as.vector(Data_Saves[4,]))\n\nwin <- gwindow(\"Table\")\n\ngp_Build <- ggroup(container=win)\ngp1_Items <- ggroup(container=win)\ngp2_Stats <- ggroup(container=win)\ngp4_ItemSets <- ggroup(horizontal = TRUE, container=win)\n\ngp_Runes <- gedit(\n text = \"Runes\", \n container = gp_Build,\n handle = function(h, ...)\n {\n svalue(gp_Runes) <- UpdateItem(svalue(gp_Runes), svalue(ItemSetSelector), 8) \n }\n)\n\ngp_Masteries <- gedit(\n text = \"Masteries\", \n container = gp_Build,\n handle = function(h, ...)\n {\n svalue(gp_Masteries) <- UpdateItem(svalue(gp_Masteries), svalue(ItemSetSelector), 9) \n }\n)\n\nUpdateItem <- function(ItemInput, ItemSet, ItemNumber){\n Default <- eval(parse(text = sprintf(\"Build%s[%s]\", ItemSet, ItemNumber)))\n if(TRUE %in% is.na(Data[ItemInput,])){\n return(Default)\n }else{\n CE1 <- sprintf('Build%s[%s] <<- \"%s\"', ItemSet, ItemNumber, ItemInput)\n CE2 <- sprintf('svalue(gp4_ItemSet%s) <- Build%s', ItemSet, ItemSet)\n \n eval(parse(text = CE1))\n eval(parse(text = CE2))\n \n UpdateItemSav <- array(c(Build1, Build2, Build3, Build4), dim = c(4,9,1))\n write(UpdateItemSav, sprintf(\"%s/ItemSav.csv\", dir), ncol = 9, sep=\",\")\n UpdateTable()\n return(ItemInput)\n }\n}\n\ngp1_i1 <- gedit(\n text = \"Item1\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i1) <- UpdateItem(svalue(gp1_i1), svalue(ItemSetSelector), 1)\n }\n)\ngp1_i2 <- gedit(\n text = \"Item2\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i2) <- UpdateItem(svalue(gp1_i2), svalue(ItemSetSelector), 2) \n }\n)\ngp1_i3 <- gedit(\n text = \"Item3\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i3) <- UpdateItem(svalue(gp1_i3), svalue(ItemSetSelector), 3) \n }\n)\ngp1_i4 <- gedit(\n text = \"Item4\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i4) <- UpdateItem(svalue(gp1_i4), svalue(ItemSetSelector), 4) \n }\n)\ngp1_i5 <- gedit(\n text = \"Item5\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i5) <- UpdateItem(svalue(gp1_i5), svalue(ItemSetSelector), 5) \n }\n)\ngp1_i6 <- gedit(\n text = \"Item6\", \n container = gp1_Items,\n handle = function(h, ...)\n {\n svalue(gp1_i6) <- UpdateItem(svalue(gp1_i6), svalue(ItemSetSelector), 6) \n }\n)\n\n\n\ngp2_Champion <- gedit(\n text = \"Champion\", \n container = gp_Build,\n handle = function(h, ...)\n {\n svalue(gp2_Champion) <- UpdateItem(svalue(gp2_Champion), svalue(ItemSetSelector), 7) \n }\n)\n\ngp2_Level <- glabel(\"Level\", container = gp2_Stats)\ngp2_Level_Slider <- gslider(\n from = 0, \n to = 17, \n by = 1, \n value = 17, \n container=gp2_Stats,\n handler = function(h, ...)\n {\n #UpdateTable()\n svalue(gp2_Level) <- as.character(svalue(gp2_Level_Slider))\n }\n)\n\nQ_Label <- glabel(\"Q\", container = gp2_Stats)\nQ_Slider <- gslider(\n from = -1, \n to = 4, \n by = 1, \n value = 4, \n container=gp2_Stats,\n handler = function(h, ...)\n {\n #UpdateTable()\n svalue(Q_Label) <- as.character(svalue(Q_Slider))\n }\n)\n\nW_Label <- glabel(\"W\", container = gp2_Stats)\nW_Slider <- gslider(\n from = -1, \n to = 4, \n by = 1, \n value = 4, \n container=gp2_Stats,\n handler = function(h, ...)\n {\n #UpdateTable()\n svalue(W_Label) <- as.character(svalue(W_Slider))\n }\n)\nE_Label <- glabel(\"E\", container = gp2_Stats)\nE_Slider <- gslider(\n from = -1, \n to = 4, \n by = 1, \n value = 4, \n container=gp2_Stats,\n handler = function(h, ...)\n {\n #UpdateTable()\n svalue(E_Label) <- as.character(svalue(E_Slider))\n }\n)\nR_Label <- glabel(\"R\", container = gp2_Stats)\nR_Slider <- gslider(\n from = -1, \n to = 2, \n by = 1, \n value = 2, \n container=gp2_Stats,\n handler = function(h, ...)\n {\n #UpdateTable()\n svalue(R_Label) <- as.character(svalue(R_Slider))\n }\n)\nUpdateItemSlots <- function(){\n \n}\nUpdateStats <- function(){\n a1 <- Damages(Build1, Enemy_Info)\n a2 <- Damages(Build2, Enemy_Info)\n a3 <- Damages(Build3, Enemy_Info)\n a4 <- Damages(Build4, Enemy_Info)\n \n \n #cat(sprintf(\"a1: %s\\na2: %s\\na3: %s\\na4: %s\\n\\n\\n\", a1, a2, a3, a4))\n row.names \t\t<- c(\n \"Auto Attack\", \n \" -DPS- \", \n \"Q\", \n \" -DPS- \", \n \"W\", \n \" -DPS- \", \n \"E\", \n \" -DPS- \", \n \"R\", \n \" -DPS- \", \n \"AP\", \n \"AD\", \n \"Total Damage\")\n row.height\t\t<- length(row.names)\n \n Comparisons <- c(row.names,a1,a2,a3,a4)\n col.width <- length(Comparisons)/row.height\n column.names \t\t<- c(as.character(1:col.width))\n matrix.names \t\t<- c(\"Main\")\n Comparisons <- array(Comparisons, dim = c(row.height, col.width, 1), dimnames = list(row.names, column.names, matrix.names))\n \n return(Comparisons)\n}\nUpdateTable <- function(){\n gp3_gtable[] <- UpdateStats()\n}\n\ngp3_gtable <- gtable(UpdateStats(), container=win)\n\nbtnRefresh <- gbutton(\n \"Refresh\", \n container=gp_Build,\n handler = function(h, ...)\n {\n gp3_gtable[] <- UpdateStats()\n }\n)\n\nItemSetSelector <- gcombobox(\n c(1,2,3,4),\n selected = 1,\n container = gp_Build,\n handler = function(h, ...)\n {\n \n }\n)\n\ngp4_ItemSet1 <- glabel(text = Build1, container=gp4_ItemSets)\ngp4_ItemSet2 <- glabel(text = Build2, container=gp4_ItemSets)\ngp4_ItemSet3 <- glabel(text = Build3, container=gp4_ItemSets)\ngp4_ItemSet4 <- glabel(text = Build4, container=gp4_ItemSets)\n", "meta": {"hexsha": "b260092597ac85a1e6d1ec3a8ef0d9dc908d4048", "size": 13772, "ext": 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YES\n2. NO", "lm_q1_score": 0.7401743620390163, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.3099087177700532}} {"text": "# Unit test for getRNG\n# \n# Author: Renaud Gaujoux\n###############################################################################\n\nlibrary(stringr)\n\ntest.getRNG <- function(){\n\n\tRNGkind('default', 'default')\n\ton.exit( RNGrecovery() )\n\t\n\tchecker <- function(x, y, ..., msg=NULL, drawRNG=TRUE){\n\t\t\n\t\tif( drawRNG ) runif(10)\n\t\tfn <- getRNG\n\t\toldRNG <- RNGseed()\n\t\tif( !missing(x) ){\n\t\t\td <- fn(x, ...)\n\t\t\tcl <- str_c(class(x), '(', length(x), ')')\n\t\t}else{\n\t\t\td <- fn()\n\t\t\tcl <- 'MISSING'\n\t\t}\n\t\tnewRNG <- RNGseed()\n\t\t.msg <- function(x) paste(cl, ':', x, '[', msg, ']')\n\t\tcheckIdentical(oldRNG, newRNG, .msg(\"does not change RNG\"))\n\t\tcheckIdentical(d, y, .msg(\"result is correct\") )\n\t}\n\t\n\tset.seed(123456)\n\tseed123456 <- .Random.seed\n\tchecker(, seed123456, msg=\"No arguments: returns .Random.seed\", drawRNG=FALSE)\n\tchecker(123456, seed123456, msg=\"Single numeric argument: returns .Random.seed as it would be after setting the seed\")\n\tchecker(123456, 123456, num.ok=TRUE, msg=\"Single numeric argument + num.ok: returns argument unchanged\")\n\tchecker(.Random.seed, .Random.seed, msg=\"Integer seed argument: returns its argument unchanged\")\n\tchecker(as.numeric(.Random.seed), .Random.seed, msg=\"Numeric seed argument: returns its argument as an integer vector\")\n\tchecker(2:3, 2:3, msg=\"Integer INVALID seed vector argument: returns its argument unchanged\")\n\tchecker(c(2,3), c(2L,3L), msg=\"Numeric INVALID seed vector argument: returns its argument as an integer vector\")\n\tchecker(1L, 1L, msg=\"Single integer = Encoded RNG kind: returns it unchanged\")\n\tchecker(1000L, 1000L, msg=\"Invalid single integer = Encoded RNG kind: returns it unchanged\")\n\t\n}\n\ntest.setRNG <- function(){\n\t\n\tRNGkind('default', 'default')\n\ton.exit( RNGrecovery() )\n\t\n\tchecker <- function(x, y, tset, drawRNG=TRUE){\n\t\n\t\ton.exit( RNGrecovery() )\n\t\t\n\t\tif( drawRNG ) runif(10)\n\t\toldRNG <- RNGseed()\n\t\td <- force(x)\n\t\tnewRNG <- RNGseed()\n\t\t\n\t\tmsg <- function(x, ...) paste(tset, ':', ...)\n\t\tcheckTrue(!identical(oldRNG, newRNG), msg(\"changes RNG\"))\n\t\tcheckIdentical(getRNG(), y, msg(\"RNG is correctly set\") )\n\t\tcheckIdentical(d, oldRNG, msg(\"returns old RNG\") )\n\t}\n\t\n\tset.seed(123456)\n\trefseed <- .Random.seed\n\tchecker(setRNG(123456), refseed, \"Single numeric: sets current RNG with seed\")\n\t\n\t# setting kind with a character string\n\tset.seed(123)\n\tRNGkind('Mar')\n\trefseed <- .Random.seed\n\tRNGrecovery()\n\tset.seed(123)\n\tchecker(setRNG('Mar'), refseed, \"Single character: change RNG kind\", drawRNG=FALSE)\n\t\n\t# setting kind with a character string\n\tset.seed(123)\n\tRNGkind('Mar', 'Ahrens')\n\trefseed <- .Random.seed\n\tRNGrecovery()\n\tset.seed(123)\n\tchecker(setRNG('Mar', 'Ahrens'), refseed, \"Two character strings: change RNG kind and normal kind\", drawRNG=FALSE)\n\tRNGrecovery()\n\tset.seed(123)\n\tchecker(setRNG(c('Mar', 'Ahrens')), refseed, \"2-long character vector: change RNG kind and normal kind\", drawRNG=FALSE)\n\t\n\t# setting kind\n\tset.seed(123456, kind='Mar')\n\trefseed <- .Random.seed\n\tchecker(setRNG(123456, kind='Mar'), refseed, \"Single numeric + kind: change RNG kind + set seed\")\n\t\n\t# setting Nkind\n\tset.seed(123456, normal.kind='Ahrens')\n\trefseed <- .Random.seed\n\tchecker(setRNG(123456, normal.kind='Ahrens'), refseed\n\t\t\t\t, \"Single numeric + normal.kind: change RNG normal kind + set seed\")\n\t\n\t# setting kind and Nkind\n\tset.seed(123456, kind='Mar', normal.kind='Ahrens')\n\trefseed <- .Random.seed\n\tchecker(setRNG(123456, kind='Mar', normal.kind='Ahrens'), refseed\n\t\t\t, \"Single numeric + kind + normal.kind: change RNG all kinds + set seed\")\n\t\n\t# with seed length > 1\n\trefseed <- as.integer(c(201, 0, 0))\n\tchecker(setRNG(refseed), refseed, \"numeric vector: directly set seed\")\n\t\n\trefseed <- .Random.seed\n\tcheckException( setRNG(2:3), \"numeric vector: throws an error if invalid value for .Random.seed\")\n\tcheckIdentical( .Random.seed, refseed, \".Random.seed is not changed in case of an error in setRNG\")\n \n oldRNG <- getRNG()\n checkException(setRNG(1234L), \"Error with invalid integer seed\")\n checkIdentical(oldRNG, getRNG(), \"RNG still valid after error\")\n checkException(setRNG(123L), \"Error with invalid RNG kind\")\n checkIdentical(oldRNG, getRNG(), \"RNG still valid after error\")\n\n # changes in R >= 3.0.2: invalid seeds only throw warning\n if( testRversion('> 3.0.1') ){\n oldRNG <- getRNG()\n checkWarning(setRNG(1234L, check = FALSE), \"\\\\.Random\\\\.seed.* is not .* valid\"\n , \"Invalid integer kind: Warning only if check = FALSE\")\n checkIdentical(1234L, getRNG(), \"RNG has new invalid integer value\")\n setRNG(oldRNG)\n checkWarning(setRNG(123L, check = FALSE), \"\\\\.Random\\\\.seed.* is not .* valid\"\n , \"Invalid kind: Warning only if check = FALSE\")\n checkIdentical(123L, getRNG(), \"RNG has new invalid RNG kind\")\n \n }\n\t\n}\n\n", "meta": {"hexsha": "fddb18e99274bb003eba2bc676d9dfd5565638b8", "size": 4846, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNG.r", "max_stars_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_stars_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNG.r", "max_issues_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_issues_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-12T14:06:52.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-10T23:26:27.000Z", "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.5/rngtools/tests/runit.RNG.r", "max_forks_repo_name": "Chicago-R-User-Group/2017-n3-Meetup-RStudio", "max_forks_repo_head_hexsha": "71a3204412c7573af2d233208147780d313430af", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.1641791045, "max_line_length": 120, "alphanum_fraction": 0.6537350392, "num_tokens": 1401, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.30957582814909296}} {"text": "#' @title Precipitable Water Model Data Analysis Module\n#' @author Spencer Riley and Vicki Kelsey\n#' @docs https://git.io/fjVHo\n#' @help To get a list of arguments run [Rscript model.r --help]\n\n## Necessary Libraries for the script to run, for installation run install.sh\nlibrary(argparse)\nlibrary(crayon)\nlibrary(RColorBrewer)\nlibrary(plotrix)\nsuppressPackageStartupMessages(library(pacviz))\nsuppressMessages(library(Hmisc))\nlibrary(yaml)\noptions(warn=-1)\n\n## Custom Colors for cmd line features\nred \t\t<- make_style(\"red1\")\norange \t\t<- make_style(\"orange\")\nyellow \t\t<- make_style(\"gold2\")\ngreen \t\t<- make_style(\"lawngreen\")\ncloudblue \t<- make_style(\"lightskyblue\")\n\n## Used for argument parsing run Rscript model.r --help\nparser <- ArgumentParser(formatter_class='argparse.RawTextHelpFormatter')\nparser$add_argument('--dir', help=\"Directory path to data folder\", default=\"../data/\")\nparser$add_argument(\"--set\", type=\"character\", default=FALSE,\n\thelp=\"Select plot sets: \\\\n\\ [t]ime series\\\\n\\ [a]nalytics\\\\n\\ [c]harts\\\\n\\ [i]ndividual sensors\")\nparser$add_argument(\"--poster\", action=\"store_true\", default=FALSE,\n\thelp=\"Produces poster plots\")\nparser$add_argument(\"--dev\", action=\"store_true\", default=FALSE,\n\thelp=\"Development plots\")\nparser$add_argument(\"-d\", \"--data\", action=\"store_true\", default=FALSE,\n\thelp=\"Produces two columned datset including mean temp and PW\")\nparser$add_argument(\"-o\", \"--overcast\", action=\"store_true\", default=FALSE,\n\thelp=\"Shows plots for days with overcast condition. \\\\n\\ (Used with --set [t/a/i] and --data)\")\nparser$add_argument(\"-1st\", \"--first_time\", action=\"store_true\", default=FALSE,\n\thelp=\"Notes for first time users\")\nparser$add_argument(\"-i\", \"--instrument\", action=\"store_true\", default=FALSE,\n\thelp=\"Prints out sensor data stored in instruments.txt\")\nparser$add_argument(\"-ml\", action=\"store_true\",\n\thelp=\"Outs a datafile to use with the machine learning algorithm\")\nparser$add_argument(\"--pacman\", action=\"store_true\",\n\thelp=\"Produces Pacman plots.\")\nargs <- parser$parse_args()\n\n## Command Prompt \"Start of Program\" and 1st time user stuff\nif(args$first_time){\n\tcat(bold(cloudblue(\" \\t\\t**** Welcome First Time Users ****\\t\\t\\t\\n\")))\n\tcat(bold(cloudblue(paste(rep(\"\\t _ _\\t\\t\\t\", 2, collapse=\"\")))))\n\tcat(\"\\n\")\n\tcat(bold(cloudblue(paste(rep(\"\\t ( ` )_\\t\\t\", 2, collapse=\"\")))))\n\tcat(\"\\n\")\n\tcat(bold(cloudblue(paste(rep(\"\\t ( ) `)\\t\\t\",2, collapse=\"\")))))\n\tcat(\"\\n\")\n\tcat(bold(cloudblue(paste(rep(\"\\t(_ (_ . _) _)\\t\", 2, collapse=\"\")))))\n\tcat(\"\\n\")\n\tcat(bold(cloudblue(\"Some Notes:\\n\")))\n\tcat((green(\"\\t- Arguments: Rscript model.r -h or Rscript model.r --help.\\n\")))\n\tcat((yellow(\"\\t- Issues/Bugs?: https://git.io/fjKRx.\\n\")))\n\tquit()\n\t}else{\n\t\tcat(bold(cloudblue(paste(replicate(65, \"-\"), collapse=\"\"), \"\\n\")))\n\t\tcat(bold(cloudblue(\"\\t\\t Precipitable-water Model Analysis Tool \\t\\t\\t\\n\")))\n\t\tcat(bold(cloudblue(paste(replicate(65, \"-\"), collapse=\"\"), \"\\n\")))\n\t\tcat(bold(green(\"First time users are recommended to run the program with the -1st argument\\n\")))\n\t\tcat(bold(green(\"Ex: Rscript model.r -1st\\n\")))\n\t\tcat(bold(cyan(\"\\t\\t>>>>>>>>> Program Start <<<<<<<<\\n\\n\")))\n}\n\n#' @title save\n#' @description A general function that will save plots\n#' @param func the plotting function that will be saved\n#' @param name the name of the file with the plots\n#' @return A pdf of the plots\n#' @export\nsave <- function(func, name){\n\tpdf(name);func;invisible(graphics.off())\n}\n## Imports data from master_data.csv\nfname <- read.table(paste(args$dir,\"master_data.csv\", sep=\"\"), sep=\",\", header=TRUE, strip.white=TRUE)\n## Imports sensor information from instruments.txt\nconfig\t\t<- yaml.load_file(paste(args$dir,\"_pmat.yml\", sep=\"\"))\n\n# Processing functions\nsource(\"./pmat_processing.r\")\n# Pushes returned values to the variable overcast\novercast <- overcast.filter(col_con, col_date, col_com, pw_name, snsr_name)\n# Analysis functions\nsource(\"./pmat_analysis.r\")\nclear_sky.results <- clear_sky.analysis(overcast)\novercast.results <- overcast.analysis(overcast)\nif(args$overcast){\n\texp_reg <- exp.regression(as.numeric(unlist(overcast.results$snsr_sky_calc)), overcast.results$avg)\n}else{\n\texp_reg <- exp.regression(as.numeric(unlist(clear_sky.results$snsr_sky_calc)), clear_sky.results$avg)\n}\n# Plotting functions\nsource(\"./pmat_plots.r\")\n\nif(args$instrument){\n\tprint(sensor)\n}\nif(args$data){\n\tif(args$ml){\n\t\tml_pw <- ml_pw_avg <- ml_temp <- ml_temp_avg <- ml_rh <- list()\n\t\t## Average PW\n\t\tfor(a in 1:length(col_pw)){\n\t\t\tml_pw[[ paste(\"ml_pw\", a, sep=\"\") ]] <- as.numeric(unlist(fname[col_pw[a]]))\n\t\t}\n\t\tfor(a in ml_pw){\n\t\t\tfor(b in 1:(length(unlist(ml_pw))/length(ml_pw))){\n\t\t\t\tml_pw_avg[[ paste(\"ml_pw_avg\", b, sep=\"\") ]] <- append(x=ml_pw_avg[[ paste(\"ml_pw_avg\", b, sep=\"\") ]], value=na.omit(c(a[b])))\n\t\t\t}\n\t\t}\n\t\tfor(a in 1:(length(unlist(ml_pw))/length(ml_pw))){\n\t\t\tml_pw_avg[[ paste(\"ml_pw_avg\", a, sep=\"\") ]] <- mean(ml_pw_avg[[ paste(\"ml_pw_avg\", a, sep=\"\") ]])\n\t\t}\n\t\t# Average Temperature\n\t\tfor(a in 1:length(col_sky)){\n\t\t\tml_temp[[ paste(\"ml_temp\", a, sep=\"\") ]] <- as.numeric(unlist(fname[col_sky[a]]))\n\t\t\tml_temp[[ paste(\"ml_temp\", a, sep=\"\") ]] <- replace(ml_temp[[ paste(\"ml_temp\", a, sep=\"\") ]], ml_temp[[ paste(\"ml_temp\", a, sep=\"\") ]] == \"-Inf\", NaN)\n\t\t}\n\n\t\tfor(a in ml_temp){\n\t\t\tfor(b in 1:(length(unlist(ml_temp))/length(ml_temp))){\n\t\t\t\tml_temp_avg[[ paste(\"ml_temp_avg\", b, sep=\"\") ]] <- append(x=ml_temp_avg[[ paste(\"ml_temp_avg\", b, sep=\"\") ]], value=na.omit(c(a[b])))\n\t\t\t}\n\t\t}\n\t\tfor(a in 1:(length(unlist(ml_temp))/length(ml_temp))){\n\t\t\tml_temp_avg[[ paste(\"ml_temp_avg\", a, sep=\"\") ]] <- mean(ml_temp_avg[[ paste(\"ml_temp_avg\", a, sep=\"\") ]])\n\t\t}\n\t\t## Relative Humidity\n\t\tfor(a in 1:length(col_rh)){\n\t\t\tml_rh[[ paste(\"ml_rh\", a, sep=\"\") ]] <- as.numeric(unlist(fname[col_rh[a]]))\n\t\t}\n\t# Pulls the data\n\t\tavg_temp\t<- as.numeric(unlist(ml_temp_avg))\n\t\tavg_pw \t\t<- as.numeric(unlist(ml_pw_avg))\n\t\tavg_rh \t\t<- as.numeric(unlist(ml_rh))\n\t\tdate \t\t<- as.Date(fname[ ,col_date], \"%m/%d/%Y\")\n\t\tcond \t\t<- fname[,col_con]\n\t# Pulls the data\n\t\tnorm \t\t<- na.omit(data.frame(list(x=date, y1=avg_temp, y2=avg_pw, y3=avg_rh, c=cond)))\n\t\tdata \t\t<- data.frame(list(date=c(norm$x),avg_temp=c(norm$y1), avg_pw=c(norm$y2), avg_rh=c(norm$y3), cond=c(norm$c)))\n\t\tcolnames(data) <- c(\"date\", \"avg_temp\", \"avg_pw\", \"avg_rh\", \"condition\")\n\t# Writes the data to a csv\n\t\twrite.csv(data, file=sprintf(\"../data/ml_data.csv\"), row.names=FALSE)\n\t\tcat(green(sprintf(\"Data sent to data/ml_data.csv\\n\")))\n\t}else{\n\t\tif (args$overcast){\n\t# Pulls the data\n\t\t\tavg_temp\t<- as.numeric(unlist(snsr_sky_calco))\n\t\t\tavg_pw \t\t<- avgo\n\t# Pulls the data\n\t\t\tnorm \t\t<- data.frame(list(x=over_date, y1=avg_temp, y2=avg_pw))\n\t# Removes the NaN data\n\t\t\tnorm \t\t<- norm[-c(which(avg_pw %in% NaN)), ]\n\t\t\tnorm \t\t<- norm[-c(which(avg_temp %in% NaN)), ]\n\t# Adds data to a data frame with column names\n\t\t\tdata \t\t<- data.frame(list(date=c(norm$x), avg_temp=c(norm$y1), avg_pw=c(norm$y2)))\n\t\t\tcolnames(data) <- c(\"date\", \"avg_temp\", \"avg_pw\")\n\t# Writes the data to a csv\n\t\t\twrite.csv(data, file=sprintf(\"../data/data_overcast.csv\"), row.names=FALSE)\n\t\t\tcat(green(sprintf(\"Data sent to data/data_overcast.csv\\n\")))\n\t\t}else{\n\t# Pulls the data\n\t\t\tavg_temp\t<- as.numeric(unlist(snsr_sky_calc))\n\t\t\tavg_pw \t\t<- avg\n\t# Pulls the data\n\t\t\tnorm \t\t<- data.frame(list(x=clear_date, y1=avg_temp, y2=avg_pw))\n\t# Removes the NaN data\n\t\t\tnorm \t\t<- norm[-c(which(avg_pw %in% NaN)), ]\n\t\t\tnorm \t\t<- norm[-c(which(avg_temp %in% NaN)), ]\n\n\t\t\tdata \t\t<- data.frame(list(date=c(norm$x),avg_temp=c(norm$y1), avg_pw=c(norm$y2)))\n\t\t\tcolnames(data) <- c(\"date\", \"avg_temp\", \"avg_pw\")\n\t# Writes the data to a csv\n\t\t\twrite.csv(data, file=sprintf(\"../data/data_clearsky.csv\"), row.names=FALSE)\n\t\t\tcat(green(sprintf(\"Data sent to data/data_clearsky.csv\\n\")))\n\n\t\t}\n\t}\n}\nif(args$set == \"i\"){\n\tif (args$overcast){\n\t# Overcast Condition\n\t\tcat(magenta(\"Condition:\"), \"Overcast\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/sensor_overcast.pdf\") # File name of saved pdf\n\t}else{\n\t# Clear Sky condition\n\t\tcat(magenta(\"Condition:\"), \"Clear Sky\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/sensor.pdf\") # File name of saved pdf\n\n\t}\n\t# Plots available with this option\n\tfor(i in 1:length(unique(snsr_tag))){\n\t\tcat(green(sprintf(\"[%s]\", i)), sprintf(\"Sky-Ground Time Series: %s\\n\", gsub(\"_\", \" \",unique(snsr_tag)[i])))\n\t}\n\t# Saves plots\n\tsave(c(instr(overcast=args$overcast)), sname_pub)\n\n\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", sname_pub)))\n}else if(args$set == \"t\"){\n\tif (args$overcast){\n\t# Overcast Condition\n\t\tcat(magenta(\"Condition:\"), \"Overcast\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/time_series_overcast.pdf\") # File name of saved pdf\n date <- overcast.results$date\n\t}else{\n\t# Clear Sky condition\n\t\tcat(magenta(\"Condition:\"), \"Clear Sky\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/time_series.pdf\") # File name of saved pdf\n\t\tdate <- clear_sky.results$date\n\t}\n\t# Plots available with this option\n\tcat(green(\"[1]\"), \"Sky Temperature Time Series\\n\")\n\tcat(green(\"[2]\"), \"Ground Temperature Time Series\\n\")\n\tcat(green(\"[3]\"), \"Change in Temperature between Sky and Ground Time Series\\n\")\n\tcat(green(\"[4]\"), \"Precipitable Water Time Series\\n\")\n\tcat(green(\"[5]\"), \"Sky Temperature - Precipitable Water Time Series\\n\")\n\tcat(green(\"[6]\"), \"Temporal Mean Precipitable Water Time Series\\n\")\n\tcat(green(\"[7]\"), \"Locational Mean Precipitable Water Time Series\\n\")\n\tcat(green(\"[8]\"), \"Mean Precipitable Water Time Series\\n\")\n\tcat(green(\"[9]\"), \"Precipitable Water - RH Time Series\\n\")\n\tcat(green(\"[10]\"), \"Sky Temperature - RH Time Series\\n\")\n\t# Saves plots\n\tfor (i in list(sname_pub)){\n\t\tsave(c(time_series.plots(date, args$overcast)), i)\n\t\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", i)))\n\t}\n}else if(args$set == \"a\"){\n\tif(args$overcast){\n\t# Overcast condition\n\t\tcat(magenta(\"Condition:\"), \"Overcast\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/analytics_overcast.pdf\") # File name of saved pdf\n\t}else{\n\t# Clear Sky condition\n\t\tcat(magenta(\"Condition:\"), \"Clear Sky\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/analytics.pdf\") # File name of saved pdf\n\n\t}\n\t# Plots available with this option\n\tcat(green(\"[1]\"), \"Correlation between PW and Temperature\\n\")\n\tcat(green(\"[2]\"), \"Correlation between Locational Mean PW and Temperature\\n\")\n\tcat(green(\"[3]\"), \"Correlation between Temporal Mean PW and Temperature\\n\")\n\tcat(green(\"[4]\"), \"Total Mean PW and Temperature\\n\")\n\tcat(green(\"[5]\"), \"Residual of the Mean PW and Temperature Model\\n\")\n\t# Saves plots\n\tfor (i in list(sname_pub)){\n\t\tsave(c(analytical.plots(args$overcast, exp_reg)), i)\n\t\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", i)))\n\t}\n\tout <- as.yaml(list(seed=c(exp_reg$seed), \n\t\t\t\t\t\tdata=list(clear=list(count=c(length(clear_sky.results$date))),\n\t\t\t\t\t\t\t\t overcast=list(count=c(length(overcast.results$date)))\n\t\t\t\t\t\t\t\t ),\n\t\t\t\t\t\tanalysis=list(coeff=list(A=c(round(exp(coef(exp_reg$model)[1]), 4)),\n\t\t\t\t\t\t\t\t\t\t\t\t B=c(round(coef(exp_reg$model)[2],4))), \n\t\t\t\t\t\t\t\t\t rsme=c(round(exp_reg$rsme, 4)), \n\t\t\t\t\t\t\t\t\t rstd=c(round(exp_reg$S, 4)),\n\t\t\t\t\t\t\t\t\t accu=c(round(exp_reg$acc * 100, 2))\n\t\t\t\t\t\t\t\t\t )))\n\tcat(out)\n\twrite_yaml(out, paste(args$dir,\"_output.yml\", sep=\"\"))\n}else if(args$set == \"c\"){\n\t# Plots available with this option\n\tfor (i in 1:length(snsr_name)){\n\t\tcat(green(sprintf(\"[%s]\", i)), sprintf(\"Overcast Condition Percentage: %s\\n\", gsub(\"_\", \" \",snsr_name[i])))\n\t}\n\t# Saves plots\n\tsname_pub \t<- sprintf(\"../figs/results/charts.pdf\")\n\n\tsave(c(charts()), sname_pub)\n\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", sname_pub)))\n}\nif(args$pacman){\n\tif (args$overcast){\n\t# Overcast Condition\n\t\tcat(magenta(\"Condition:\"), \"Overcast\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/pacman_overcast.pdf\") # File name of saved pdf\n\t}else{\n\t# Clear Sky condition\n\t\tcat(magenta(\"Condition:\"), \"Clear Sky\\n\")\n\t\tsname_pub <- sprintf(\"../figs/results/pacman.pdf\")\n\n\t}\n\tcat(green(\"[1]\"), \"Total Mean PW and Temperature\\n\")\n\tcat(green(\"[2]\"), \"Pac-Man Residual Plot\\n\")\n\tsave(c(pac.plots(args$overcast)), sname_pub)\n\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", sname_pub)))\n}\nif(args$poster){\n\t# Plots available with this option\n\tcat(green(\"[1]\"), \"Sky-Ground-Delta Temperature Time Series\\n\")\n\tcat(green(\"[2]\"), \"Analytical Plots\\n\")\n\tcat(green(\"[3]\"), \"Condiiton Distrbuion by Sensor\\n\")\n\t# Saves plots\n\tsname_pub \t<- sprintf(\"../figs/results/poster.pdf\")\n\tsave(c(poster.plots()), sname_pub)\n\n\tcat(green(sprintf(\"Plot set downloaded to %s\\n\", sname_pub)))\n}\nif(args$dev){\n\tcat(\"No Plots in this set\\n\")\n}\n## Ends the script\nif(file.exists(\"Rplots.pdf\")){file.remove(\"Rplots.pdf\")}\n# End of program\ncat(bold(cyan(\"\\n\\t\\t>>>>>>> Program Complete <<<<<<<\\n\"))); quit()", "meta": {"hexsha": "a47be925b737d56f83ecae157604e86baab3a0f5", "size": 12620, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pmat_run.r", "max_stars_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_stars_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-04-07T21:26:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T22:41:56.000Z", "max_issues_repo_path": "src/pmat_run.r", "max_issues_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_issues_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2019-06-27T19:27:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-20T17:34:13.000Z", "max_forks_repo_path": "src/pmat_run.r", "max_forks_repo_name": "physicsgoddess1972/Precipitable-Water-Model", "max_forks_repo_head_hexsha": "5280067fac90d1ed6dfe2a2ad589f444d81f95b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2019-04-07T20:09:46.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-29T21:46:58.000Z", "avg_line_length": 40.8414239482, "max_line_length": 153, "alphanum_fraction": 0.6671156894, "num_tokens": 3756, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704502361149, "lm_q2_score": 0.5583269943353745, "lm_q1q2_score": 0.30957581992811184}} {"text": "#' wetbulb\n#'\n#' This function calculate the natural wetbulb temperature.\n#'\n#' @param t numeric Air temperature in degC.\n#' @param rh numeric Relative humidity in percentage.\n#' @param wind numeric Mean Wind speed in meter per second.\n#' @param pair numeric Air pressure in millibar or hPa. Default 1010 hPa.\n#' @param solar numeric Global solar radiation in Watt on mq.\n#' @param zenith numeric Zenith angle in decimal degrees.\n#' @param alb_sfc numeric Mean albedo of surroundings. Default is 0.4.\n#' @param fdir numeric Ratio of directed solar respect to the diffuse.Default is 0.8.\n#' @param maxair numeric Upper bound of search range referred to air temperature in degC. Default is 10.\n#' @param minair numeric Lower bound of search range referred to air temperature in degC. Default is 10.\n#' @param prec numeric Precision of outcomes.Default is 0.01. \n#' @return natural wetbulb in degC\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords wetbulb \n#' @export\n#'\n\nwetbulb=function(t,rh,wind,solar=0,zenith=0,pair,alb_sfc=0.4,fdir=0.8,maxair=10,minair=2,prec=0.01) {\n if (is.null(press)) {press=1010}\n if (length(solar) != length(t)) {solar=rep(solar,length(t));\n zenith=rep(zenith,length(t));\n }\n irad=1\n ct$assign(\"t\", as.array(t))\n ct$assign(\"rh\", as.array(rh))\n ct$assign(\"wind\", as.array(wind))\n ct$assign(\"pair\", as.array(pair))\n ct$assign(\"alb_sfc\", as.array(alb_sfc))\n ct$assign(\"fdir\", as.array(fdir))\n ct$assign(\"irad\", as.array(irad))\n ct$assign(\"maxair\", as.array(maxair))\n ct$assign(\"minair\", as.array(minair))\n ct$assign(\"prec\", as.array(prec))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++) { res[i]=natural_wetbulb(t[i],rh[i],wind[i],solar[i],zenith[i],pair[0],alb_sfc[0],fdir[0],irad[0],maxair[0],minair[0],prec[0])};\")\n res=ct$get(\"res\")\n return(ifelse(!is.numeric(res),NA,res))\n}\n\n", "meta": {"hexsha": "1848bf0d2021a3548e50904d8ea99e5736251ae8", "size": 2434, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wetbulb.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/wetbulb.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/wetbulb.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 54.0888888889, "max_line_length": 217, "alphanum_fraction": 0.5521774856, "num_tokens": 626, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3095579173440292}} {"text": "\n\n no.grid.logbook = function(x) {\n\n x$landings = x$pro_rated_slip_wt_lbs * 0.45359237 # convert to kg\n x$effort = x$num_of_traps\n x$cpue = x$landings / x$effort\n x$yr = x$year\n\n return(x)\n }\n\n\n", "meta": {"hexsha": "52622b75a3224a2fecab1767d57bbe05b6cef8e9", "size": 212, "ext": "r", "lang": "R", "max_stars_repo_path": "R/no.grid.logbook.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/no.grid.logbook.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/no.grid.logbook.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 15.1428571429, "max_line_length": 70, "alphanum_fraction": 0.608490566, "num_tokens": 84, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.30955791097646085}} {"text": "library(irr)\n\n\nxassetCountrySector <- read.table( \"Raters_CSV.tsv\", sep=\"\\t\", header=TRUE)\ndfa <- xassetCountrySector[,c(1,2)]\n\nprint(kappa2(dfa)$value)", "meta": {"hexsha": "6875129aff27721705003c517be1853108ef0d7f", "size": 152, "ext": "r", "lang": "R", "max_stars_repo_path": "Kappa.r", "max_stars_repo_name": "johnnymoretti/IAA_ClauseBased_Evaluator", "max_stars_repo_head_hexsha": "4a2abcb43ec5dc1f301fec8cc98aeeab49f3b74c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Kappa.r", "max_issues_repo_name": "johnnymoretti/IAA_ClauseBased_Evaluator", "max_issues_repo_head_hexsha": "4a2abcb43ec5dc1f301fec8cc98aeeab49f3b74c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Kappa.r", "max_forks_repo_name": "johnnymoretti/IAA_ClauseBased_Evaluator", "max_forks_repo_head_hexsha": "4a2abcb43ec5dc1f301fec8cc98aeeab49f3b74c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.7142857143, "max_line_length": 75, "alphanum_fraction": 0.7171052632, "num_tokens": 48, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947155710233, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.30955791097646085}} {"text": "# Extract list of triads where a user participates.\n#\n# author: Alberto Lumbreras\n#\n#https://www.reddit.com/r/europe/CasualConversation\n#https://www.reddit.com/r/europe/\n#https://www.reddit.com/r/datascience\n#https://www.reddit.com/r/science/\n#https://www.reddit.com/r/france\n#https://www.reddit.com/r/catalunya\n#https://www.reddit.com/r/es\nlibrary(parallel)\nlibrary(doParallel)\nlibrary(foreach)\nlibrary(ggplot2)\nlibrary(ggbiplot)\nlibrary(gplots)\n\nlibrary(dplyr)\nlibrary(reshape2)\nlibrary(igraph)\nlibrary(ggbiplot)\n#library(RSQLite)\n#library(GGally)\n\nsource('R/load_participations.r')\nsource('R/count_motifs.r')\nsource('R/normalize_counts.r')\nsource('R/clustering.r')\nsource('R/plotting.r')\n\n##########################################################\n# Load Data\n##########################################################\nload('./R_objects/dfposts_podemos.Rda')\ndf.posts$date <- as.numeric(df.posts$date)\ndf.posts <- data.frame(df.posts) %>% arrange(date)\ndf.posts <- df.posts[1:75000,] # Paper\n#df.posts <- df.posts[1:5000,] # Debug\n\ndf.threads <- plyr::count(df.posts, \"thread\")\ndf.users <- plyr::count(df.posts, 'user') \nnames(df.threads)[2] <- \"length\"\nnames(df.users)[2] <- \"posts\"\n\n# Print dates range\nstart.date <- as.POSIXct(min(as.numeric(df.posts$date)), origin = \"1970-01-01\") \nend.date <- as.POSIXct(max(as.numeric(df.posts$date)), origin = \"1970-01-01\")\nprint(paste(\"Start date:\", start.date))\nprint(paste(\"End date:\", end.date))\n\ncat(\"Nunmber of posts:\", nrow(df.posts))\ncat('Number of threads: ', nrow(df.threads))\ncat('Number of users: ', nrow(df.users))\ncat('Number of active users', nrow(filter(df.users, posts>MIN_POSTS)))\n\n#########################################################\n# Compute neighborhood around every post\n#########################################################\n# Only long threads\n#df.threads <- filter(df.threads, length>10)\n\nchunks <- split(df.threads$thread, ceiling(seq_along(df.threads$thread)/1000))\nlength(chunks)\n\nif(FALSE){\n \n chunks <- \"t3_2bmb4v\"\n \n # Profiling\n library(profvis)\n prof <- profvis({\n res.seq <- count_motifs_by_post(as.vector(unlist(chunks))[1], \n database='reddit',\n neighbourhood='struct')\n })\n \n # sequential\n res.seq <- count_motifs_by_post(as.vector(unlist(chunks)), \n database='reddit',\n neighbourhood='time')\n}\n\n# parallel\nncores <- detectCores() - 2\ncl<-makeCluster(ncores, outfile=\"\", port=11439)\nregisterDoParallel(cl)\npck <- c('RSQLite', 'data.table', 'changepoint', 'digest')\nres.parallel <- foreach(i=1:length(chunks), .packages = pck)%dopar%{\n source('R/extract_from_db.r')\n count_motifs_by_post(chunks[[i]], \n database='reddit',\n neighbourhood='time', chunk.id=i)\n}\nstopCluster(cl)\nres <- merge.motif.counts(res.parallel)\n\nsave(res, file='./R_objects/res_time_75000_podemos.Rda') \n# save(res, file='./R_objects/res_order_75000_podemos.Rda') \n#save(res, file='./R_objects/res_struct_75000_podemos.Rda') \n# save(res, file='./R_objects/res_time_75000_gameofthrones.Rda') \n# save(res, file='./R_objects/res_order_75000_gameofthrones.Rda') \n#save(res, file='./R_objects/res_struct_75000_gameofthrones.Rda') \n# save(res, file='./R_objects/res_time_75000_4chan.Rda')\n# save(res, file='./R_objects/res_order_75000_4chan.Rda')\n#save(res, file='./R_objects/res_struct_75000_4chan.Rda')\n#save(res, file='./R_objects/res_struct_10000_4chan.Rda')\n#save(res, file='./R_objects/res_struct_5000_podemos.Rda')\n\n\n#load(\"res_time_75000.Rda\")\n\n#save(res, file='./R_objects/res_order_2_4_75000_4chan.Rda') \n\n#save(res,file=\"res_time_75000_4chan.Rda\")\n#load(\"res_time_75000.Rda\")\n\n#save(res,file=\"res_order_2_4_75000_gameofthrones.Rda\")\n#load(\"res_order_2_4_75000_gameofthrones.Rda\")\n\n#load('res_2_4_order_75000.Rda') \n\n# Plot found motifs and their frequency\n#plot.motif.counts(res)\n\n#dev.copy(png, 'neighbourhoods_time.png')\n#dev.off()\n", "meta": {"hexsha": "69ffe9b93224888906c657697e158a3e0792ea9d", "size": 4122, "ext": "r", "lang": "R", "max_stars_repo_path": "pipeline_extract_neighbourhoods.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "pipeline_extract_neighbourhoods.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pipeline_extract_neighbourhoods.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.203125, "max_line_length": 172, "alphanum_fraction": 0.6331877729, "num_tokens": 1102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.30953852211854904}} {"text": "# vim: shiftwidth=2 tabstop=2\n##------------------------------------------------------------------------------\n#' binR\n#' \n#' Bin a dataset using a formula and a breaks function\n#' \n#' Bin or dkiscretize a dataset using a formula.\n#' \n#' @author Vilhelm von Ehrenheim\n#' @import rpart\n#' @export\n##------------------------------------------------------------------------------\nbinR_rpart <- function(fx, data, cumulative=F) {\n mframe <- model.frame(fx, data)[ ,-1,drop=F] # drop dependent variable\n vars <- names(mframe)\n response <- all.vars(fx[[2]])\n\n if (length(response) != 1) stop(\"Incorrently defined response variable\")\n\n breaks = list()\n\n dat <- foreach(d=vars, .combine=data.frame) %dopar% {\n breaks[[d]] <- rpart_breaks(as.formula(paste(response, '~', d)), data)\n\n lb <- ifelse(all(mframe[ ,d] >= 0), 0, -Inf)\n ub <- ifelse(all(mframe[ ,d] <= 0), 0, Inf)\n breaks[[d]] <- sort(c(lb, breaks[[d]], ub))\n names(breaks[[d]]) <- NULL\n }\n\n out <- list(breaks=breaks, vars=vars)\n class(out) <- \"binR\"\n out\n}\n", "meta": {"hexsha": "f7079103e14e1f98cfe187230c88ccc21dec035a", "size": 1036, "ext": "r", "lang": "R", "max_stars_repo_path": "R/binR_rpart.r", "max_stars_repo_name": "while/binR", "max_stars_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/binR_rpart.r", "max_issues_repo_name": "while/binR", "max_issues_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/binR_rpart.r", "max_forks_repo_name": "while/binR", "max_forks_repo_head_hexsha": "3afa5530385ee971cce8ab7bcb804f2dc2533ddf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.6, "max_line_length": 80, "alphanum_fraction": 0.5193050193, "num_tokens": 296, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.309538522118549}} {"text": "\nx_files <- list.files(pattern=\"*txt\")\nsample_names <- substr(x_files, 3, nchar(x_files)-10)\n\ncoverage_max_to_check <- 40\nplot_max <- coverage_max_to_check + 2\n\noutput1 <- list()\noutput2 <- list()\nmean_coverage <- c()\nmedian_coverage <- c()\nfor(a in 1:length(x_files)) {\n\ta_rep <- scan(x_files[a])\n\ta_output <- c()\n\tfor(b in 1:plot_max) {\n\t\tif(b == plot_max) {\n\t\t\ta_output <- c(a_output, length(a_rep[a_rep >= (b - 1)]))\n\t\t} else {\n\t\t\ta_output <- c(a_output, length(a_rep[a_rep == (b - 1)]))\n\t\t}\n\t}\n\ta_output2 <- a_output / sum(a_output)\n\toutput1[[a]] <- a_output\n\toutput2[[a]] <- a_output2\n\tmean_coverage <- c(mean_coverage, mean(a_rep))\n median_coverage <- c(median_coverage, median(a_rep))\n\n}\n\nrm(a_rep)\nsave.image(\"alb_coverage.RData\")\n\n\n\n\n\n\n\nload(\"alb_coverage.RData\")\n\npar(mfrow=c(4,5))\nfor(a in 1:length(output2)) {\n\tplot(0:41, output2[[a]], pch=19, cex=0.1, xlab=\"Coverage\", ylab=\"Proportion F. albicollis genome\", main=sample_names[a], ylim=c(0,0.14))\n\tpoly.plot <- rbind(cbind(0:41, output2[[a]]), c(41, 0), c(0,0))\n\tpolygon(poly.plot, col=\"gray\")\n\tabline(v=mean_coverage[a], col=\"red\")\n}\n", "meta": {"hexsha": "a885cafb8ffecc0f1a188a7b6a201204110c0456", "size": 1100, "ext": "r", "lang": "R", "max_stars_repo_path": "04b_plot_coverage.r", "max_stars_repo_name": "jdmanthey/Alb_Rift_genomes1", "max_stars_repo_head_hexsha": "3e39a134c9e2b8ace78cb980bd779b5b3c74e2dd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "04b_plot_coverage.r", "max_issues_repo_name": "jdmanthey/Alb_Rift_genomes1", "max_issues_repo_head_hexsha": "3e39a134c9e2b8ace78cb980bd779b5b3c74e2dd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "04b_plot_coverage.r", "max_forks_repo_name": "jdmanthey/Alb_Rift_genomes1", "max_forks_repo_head_hexsha": "3e39a134c9e2b8ace78cb980bd779b5b3c74e2dd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9166666667, "max_line_length": 137, "alphanum_fraction": 0.6545454545, "num_tokens": 370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.30953642752984306}} {"text": "#! /usr/bin/Rscript --vanilla\n\n# install.packages(\"multcomp\")\n# Download tar from http://cran.r-project.org/web/packages/nparcomp/nparcomp.pdf\n# sudo R CMD INSTALL nparcomp_2.0.tar.gz\n\nlibrary(\"rjson\")\nlibrary(\"nparcomp\")\n\nargs = commandArgs(TRUE)\n\nbugginess <- fromJSON(args[2])\nquantile = fromJSON(args[3])\n\ndf <- data.frame(bugginess, quantile)\n\nres <- mctp( df$bugginess~df$quantile, data=df, type=\"Tukey\",asy.method=\"fisher\")\n\nprint(res$Analysis)\n\n\n\n\n", "meta": {"hexsha": "735382062b78e2fd358fcbca6af67f86e1934118", "size": 457, "ext": "r", "lang": "R", "max_stars_repo_path": "Risk/R/multcomp.r", "max_stars_repo_name": "phoxicle/bugger", "max_stars_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-01-26T13:23:09.000Z", "max_stars_repo_stars_event_max_datetime": "2016-01-26T13:23:09.000Z", "max_issues_repo_path": "Risk/R/multcomp.r", "max_issues_repo_name": "phoxicle/bugger", "max_issues_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Risk/R/multcomp.r", "max_forks_repo_name": "phoxicle/bugger", "max_forks_repo_head_hexsha": "70d33c752e00189c3e3f3258a259de14a81853ab", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.0416666667, "max_line_length": 81, "alphanum_fraction": 0.7199124726, "num_tokens": 136, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3095364209658642}} {"text": "## Visualization script for climate scenarios\nlibrary(tidyverse)\nlibrary(ggpubr)\n# install.packages(\"viridis\")\nlibrary(viridis)\n\n\n# ####################ECONOMIC\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, '..','results', 'sites_affected_by_failures_GHA.csv'))\n\ndata = data[data$climatescenario == 'rcp4p5' | data$climatescenario == \"rcp8p5\",]\n\ndata = select(data, floodtype, subsidence, year, returnperiod, climatescenario, technology, cost)\n\ndata = data %>% \n group_by(floodtype, subsidence, year, returnperiod, climatescenario, technology) %>% \n summarise(cost = sum(cost))\n\nwtsub = data[data$subsidence == \"wtsub\",] #remove inuncoast\nwtsub$subsidence = NULL\nmodel_mean = data[data$subsidence != \"wtsub\",] \n\nmodel_mean = model_mean %>% \n group_by(floodtype, climatescenario, year, returnperiod, technology) %>% \n summarise(cost = mean(cost)) \n\ndata = rbind(wtsub, model_mean) #historical\n\ndata$interaction = paste(data$year, data$climatescenario)\n\ndata$floodtype = factor(data$floodtype,\n levels=c(\"inuncoast\",\"inunriver\"),\n labels=c(\"Coastal Flooding\", \"Riverine Flooding\")\n)\n\ndata$interaction = factor(data$interaction,\n levels=c(\"2030 rcp4p5\",\n \"2050 rcp4p5\",\n \"2080 rcp4p5\",\n \"2030 rcp8p5\",\n \"2050 rcp8p5\",\n \"2080 rcp8p5\"),\n labels=c(\"2030 RCP4.5\",\n \"2030 RCP8.5\",\n \"2050 RCP4.5\",\n \"2050 RCP8.5\",\n \"2080 RCP4.5\",\n \"2080 RCP8.5\")\n)\n\ndata$returnperiod = factor(data$returnperiod,\n levels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\"),\n labels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\")\n)\n\ndata$technology = factor(data$technology,\n levels=c(\"GSM\",\"UMTS\",\n \"LTE\",\"NR\"),\n labels=c(\"2G GSM\",\"3G UMTS\",\n \"4G LTE\",\"5G NR\")\n)\n\ndata$cost = (data$cost/1e6)\n\ntotals <- data %>%\n group_by(floodtype, interaction, returnperiod) %>%\n summarize(value = round(sum(cost), 1))\n\nggplot(data, aes(x=interaction, y=cost)) +\n geom_bar(stat = \"identity\", aes(fill=technology)) + \n scale_color_manual(values=c(\"#0072B2\", \"#D55E00\")) +\n geom_text(aes(x=interaction, y=value, label=value), #, color=\"#ffffff\"\n size = 2, data = totals, vjust=-.5, #hjust=1 ,\n position=position_stack(), \n show.legend = FALSE\n ) +\n theme( legend.position = \"bottom\",\n axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8),\n # text = element_text(size=8),\n ) +\n labs(colour=NULL,\n title = \"Estimated Economic Damage to Cellular Voice/Data Infrastructure\",\n subtitle = \"Reported by Hazard, Forecast Year, Climate Scenario, Return Period and Technology\", \n x = NULL, y = \"Economic Damage ($US Millions)\", fill=NULL) +\n theme(panel.spacing = unit(0.6, \"lines\")) + expand_limits(y=0) +\n guides(linetype=guide_legend(ncol=2, title='Scenario'),\n shape=guide_legend(ncol=2, title='Scenario'),\n color=guide_legend(ncol=2, title='Scenario')) +\n scale_fill_viridis_d() +\n # scale_x_discrete(expand = c(0, 0.15)) +\n scale_y_continuous(expand = c(0, 0), limits=c(0,38)) +\n facet_wrap(floodtype~returnperiod, scales = \"free_y\", ncol=4)\n\npath = file.path(folder, 'figures', 'GHA', 'GHA_overall_economic_cost_by_event.png')\nggsave(path, units=\"in\", width=8, height=5.5, dpi=300)\n\n\n# ####################SUPPLY-DEMAND METRICS\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, '..', 'results', 'coverage_final_GHA.csv'))\n\ndata$interaction = paste(data$year, data$climatescenario)\n\ndata$floodtype = factor(data$floodtype,\n levels=c(\"inuncoast\",\"inunriver\"),\n labels=c(\"Coastal Flooding\", \"Riverine Flooding\")\n)\n\ndata$interaction = factor(data$interaction,\n levels=c(\"2030 rcp4p5\",\n \"2050 rcp4p5\",\n \"2080 rcp4p5\",\n \"2030 rcp8p5\",\n \"2050 rcp8p5\",\n \"2080 rcp8p5\"),\n labels=c(\"2030 RCP4.5\",\n \"2030 RCP8.5\",\n \"2050 RCP4.5\",\n \"2050 RCP8.5\",\n \"2080 RCP4.5\",\n \"2080 RCP8.5\")\n)\n\ndata$returnperiod = factor(data$returnperiod,\n levels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\"),\n labels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\")\n)\n\ndata$technology = factor(data$technology,\n levels=c(\"GSM\",\"UMTS\",\n \"LTE\",\"NR\"),\n labels=c(\"2G GSM\",\"3G UMTS\",\n \"4G LTE\",\"5G NR\")\n)\n\ndata$covered_difference = data$covered_difference / 1e6\n\ndata = data[data$subsidence == \"model_mean\" | data$subsidence == \"wtsub\",]\n\ntotals <- data %>%\n group_by(floodtype, interaction, returnperiod) %>%\n summarize(value = round(sum(covered_difference), 2))\n\nggplot(data, aes(x=interaction, y=covered_difference)) +\n geom_bar(stat = \"identity\", aes(fill=technology)) + \n scale_color_manual(values=c(\"#0072B2\", \"#D55E00\")) +\n geom_text(aes(x=interaction, y=value, label=value), #, color=\"#ffffff\"\n size = 2, data = totals, vjust=-.5, #hjust=1 ,\n position=position_stack(), \n show.legend = FALSE\n ) +\n theme(legend.position = \"bottom\",\n axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8)) +\n labs(colour=NULL,\n title = \"Mean Modeled Change in Population Coverage Against Historic Baseline\",\n subtitle = \"Reported by Hazard, Forecast Year, Climate Scenario, Return Period and Technology\", \n x = NULL, y = \"Change in Population Coverage against the\\nHistorical Baseline (Millions)\", fill=NULL) +\n theme(panel.spacing = unit(0.6, \"lines\")) + expand_limits(y=0) +\n guides(linetype=guide_legend(ncol=2, title='Scenario'),\n shape=guide_legend(ncol=2, title='Scenario'),\n color=guide_legend(ncol=2, title='Scenario')) +\n scale_fill_viridis_d() +\n # scale_x_discrete(expand = c(0, 0.15)) +\n scale_y_continuous(expand = c(0.1, 0.1)) + #, limits=c(0,2)\n facet_wrap(floodtype~returnperiod, scales = \"free_y\", ncol=4)\n\npath = file.path(folder, 'figures', 'GHA', 'GHA_coverage_difference.png')\nggsave(path, units=\"in\", width=8, height=5.5, dpi=300)\n\n########################################################################\n\n\n# ####################SUPPLY-DEMAND METRICS\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder, '..', 'results', 'econ_final_GHA.csv'))\n\ndata$interaction = paste(data$year, data$climatescenario)\n\ndata$floodtype = factor(data$floodtype,\n levels=c(\"inuncoast\",\"inunriver\"),\n labels=c(\"Coastal Flooding\", \"Riverine Flooding\")\n)\n\ndata$interaction = factor(data$interaction,\n levels=c(\"2030 rcp4p5\",\n \"2050 rcp4p5\",\n \"2080 rcp4p5\",\n \"2030 rcp8p5\",\n \"2050 rcp8p5\",\n \"2080 rcp8p5\"),\n labels=c(\"2030 RCP4.5\",\n \"2030 RCP8.5\",\n \"2050 RCP4.5\",\n \"2050 RCP8.5\",\n \"2080 RCP4.5\",\n \"2080 RCP8.5\")\n)\n\ndata$returnperiod = factor(data$returnperiod,\n levels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\"),\n labels=c(\"100-year\",\"250-year\",\n \"500-year\",\"1000-year\")\n)\n\ndata$technology = factor(data$technology,\n levels=c(\"GSM\",\"UMTS\",\n \"LTE\",\"NR\"),\n labels=c(\"2G GSM\",\"3G UMTS\",\n \"4G LTE\",\"5G NR\")\n)\n\ndata$cost_difference = data$cost_difference / 1e6\n\ndata = data[data$subsidence == \"model_mean\" | data$subsidence == \"wtsub\",]\n\ntotals <- data %>%\n group_by(floodtype, interaction, returnperiod) %>%\n summarize(value = round(sum(cost_difference), 1))\n\nggplot(data, aes(x=interaction, y=cost_difference)) +\n geom_bar(stat = \"identity\", aes(fill=technology)) + \n scale_color_manual(values=c(\"#0072B2\", \"#D55E00\")) +\n geom_text(aes(x=interaction, y=value, label=value), #, color=\"#ffffff\"\n size = 2, data = totals, vjust=-.5, #hjust=1 ,\n position=position_stack(), \n show.legend = FALSE\n ) +\n theme( legend.position = \"bottom\",\n axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size=8),\n # text = element_text(size=8),\n ) +\n labs(colour=NULL,\n title = \"Mean Modeled Change in Estimated Economic Damage Against Historic Baseline\",\n subtitle = \"Reported by Hazard, Forecast Year, Climate Scenario, Return Period and Technology\", \n x = NULL, y = \"Change in Flooding Damage Costs against the\\nHistorical Baseline (Millions)\", fill=NULL) +\n theme(panel.spacing = unit(0.6, \"lines\")) + expand_limits(y=0) +\n guides(linetype=guide_legend(ncol=2, title='Scenario'),\n shape=guide_legend(ncol=2, title='Scenario'),\n color=guide_legend(ncol=2, title='Scenario')) +\n scale_fill_viridis_d() +\n scale_y_continuous(expand = c(0.1, 0.1)) + #, limits=c(0,2)\n facet_wrap(floodtype~returnperiod, scales = \"free_y\", ncol=4)\n\npath = file.path(folder, 'figures', 'GHA', 'GHA_econ_cost_against_baseline.png')\nggsave(path, units=\"in\", width=8, height=5.5, dpi=300)\n\n# ####################COVERAGE BY DECILES\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder,'..','data','processed','GHA', 'coverage_by_decile.csv'))\n\n# data$population[data$technology == \"NR\" | data$decile == 10 | data$covered == 0] <- 0\n\ndata$covered = factor(data$covered,\n levels=c(1,0),\n labels=c(\"Covered\",\"Uncovered\")\n)\n\ndata$technology = factor(data$technology,\n levels=c('GSM', 'UMTS', 'LTE', 'NR'),\n labels=c(\"2G GSM\",\"3G UMTS\", \"4G LTE\", \"5G NR\")\n)\n\ndata$decile = factor(data$decile,\n levels=c(10,20,30,40,50,60,70,80,90,100),\n labels=c('0 -\\n10%','10 -\\n20%','20 -\\n30%','30 -\\n40%',\n '40 -\\n50%','50 -\\n60%',\n '60 -\\n70%','70 -\\n80%','80 -\\n90%','90 -\\n100%'))\n\ndata$population = data$population / 1e6\n\ntotals <- data %>%\n group_by(decile, covered, technology) %>%\n summarize(value2 = round(population, 1))\n\n# totals$value2[totals$technology == \"5G NR\" | totals$covered == \"uncovered\" | totals$decile == \"0-10\"] <- 0\n\nplot1 = ggplot(data, aes(x=factor(decile), y=population, fill=covered)) +\n geom_bar(stat=\"identity\", position=position_dodge()) +\n geom_text(aes(ymax=0, x=decile, y=value2 + 1, label=value2), #, color=\"#ffffff\"\n size = 2.25, data = totals, vjust=1, #hjust=1 ,\n position=position_dodge(width = 1), show.legend = FALSE\n ) +\n theme(legend.position = \"bottom\") +\n labs(colour=NULL,\n title = \"(A) Population Coverage by Population Density Decile and Technology\",\n subtitle = \"Coverage is defined as a Signal-to-Interference-plus-Noise-Ratio (SINR) >0 dB\",\n x = 'Population Density Decile\\n(0-10% contains the most populated 10% of 1 km^2 tiles)',\n y = \"Population Coverage (Millions)\",\n fill=NULL, color=NULL) +\n theme(panel.spacing = unit(0.6, \"lines\"),\n axis.text.x = element_text(angle = 15, hjust=1)) +\n expand_limits(y=0) +\n guides() +\n scale_y_continuous(expand = c(0, 0), limits = c(0, 17.5)) +\n scale_fill_viridis_d(direction=1) +\n facet_wrap(~technology)\n\n# path = file.path(folder, 'figures', 'GHA', 'GHA_coverage_by_deciles.png')\n# ggsave(path, units=\"in\", width=8, height=6, dpi=300)\n\n# ####################COVERAGE BY DECILES\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\ndata <- read.csv(file.path(folder,'..','data','processed','GHA', 'coverage_by_decile.csv'))\n\n# data$population[data$technology == \"NR\" | data$decile == 10 | data$covered == 0] <- 0\n\ndata$covered = factor(data$covered,\n levels=c(0, 1),\n labels=c(\"Uncovered\", \"Covered\")\n)\n\ndata$technology = factor(data$technology,\n levels=c('GSM', 'UMTS', 'LTE', 'NR'),\n labels=c(\"2G GSM\",\"3G UMTS\", \"4G LTE\", \"5G NR\")\n)\n\ndata$decile = factor(data$decile,\n levels=c(10,20,30,40,50,60,70,80,90,100),\n labels=c('0 -\\n10%','10 -\\n20%','20 -\\n30%','30 -\\n40%',\n '40 -\\n50%','50 -\\n60%',\n '60 -\\n70%','70 -\\n80%','80 -\\n90%','90 -\\n100%'))\n\ndata = data %>%\n group_by(technology,decile) %>%\n mutate(countT= sum(population)) %>%\n group_by(covered) %>%\n mutate(perc=round(100*population/countT))\n\nplot2 = ggplot(data, aes(x=factor(decile), y=perc, fill=covered)) +\n geom_bar(stat=\"identity\") +\n geom_text(aes(x=decile, y=perc, label = paste0(perc,\"%\")),#, color=\"#ffffff\"\n size = 2.25, data = data, vjust=-.75, hjust=.5, \n show.legend = FALSE, position = position_stack(),\n ) +\n theme(legend.position = \"bottom\") +\n labs(colour=NULL,\n title = \"(B) Percentage Population Coverage by Population Density Decile and Technology\",\n subtitle = \"Coverage is defined as a Signal-to-Interference-plus-Noise-Ratio (SINR) >0 dB\",\n x = 'Population Density Decile\\n(0-10% contains the most populated 10% of 1 km^2 tiles)',\n y = \"Population Coverage (%)\",\n fill=NULL) +\n theme(panel.spacing = unit(0.6, \"lines\"),\n axis.text.x = element_text(angle = 15, hjust=1)) +\n expand_limits(y=0) +\n guides(linetype=guide_legend(ncol=2, title='Scenario'),\n shape=guide_legend(ncol=2, title='Scenario'),\n color=guide_legend(ncol=2, title='Scenario')) +\n scale_y_continuous(expand = c(0, 0), limits = c(0, 100)) +\n scale_fill_viridis_d(direction=-1) +\n facet_wrap(~technology)\n\n############\nggarrange(\n plot1,\n plot2,\n # labels = c(\"A\", \"B\"),\n common.legend = TRUE,\n legend = 'bottom',\n ncol = 1, nrow = 2)\n\npath = file.path(folder, 'figures', 'GHA', 'GHA_panel_plot.png')\nggsave(path, units=\"in\", width=8, height=10, dpi=300)\n\n\n\n\n\n\n\n", "meta": {"hexsha": "5d6c8be8f579a9ff2a4d572d4d8fe061565d6403", "size": 15321, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/vis.r", "max_stars_repo_name": "edwardoughton/open-rigbi", "max_stars_repo_head_hexsha": "c861bbc469c9dbab7a2214a0cedcca68d96ef1c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vis/vis.r", "max_issues_repo_name": "edwardoughton/open-rigbi", "max_issues_repo_head_hexsha": "c861bbc469c9dbab7a2214a0cedcca68d96ef1c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/vis.r", "max_forks_repo_name": "edwardoughton/open-rigbi", "max_forks_repo_head_hexsha": "c861bbc469c9dbab7a2214a0cedcca68d96ef1c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2125984252, "max_line_length": 112, "alphanum_fraction": 0.5476796554, "num_tokens": 4058, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3095364209658642}} {"text": "getLineageCoords <- function(seuratObj, slingObj, dimRed){\n\ncellClust\t<- sapply(rownames(slingObj@clusterLabels), function(x) colnames(slingObj@clusterLabels)[which(as.logical(slingObj@clusterLabels[x, ]))])\n\t\t#vector containng cluster ids named with cognate cells\n\nlineageNames\t<- names(slingObj@lineages)\nlineageCoords\t<- sapply( lineageNames, function(x){\n \t\t\tsapply( slingObj@lineages[[x]], \n\t\t\t\tfunction(y) apply( seuratObj@dr[[ dimRed ]]@cell.embeddings[ names(cellClust)[cellClust == y], ], 2, mean)) })\nreturn(lineageCoords)\n}\n", "meta": {"hexsha": "2ec75309335564ea8293a149c3571d21e696e948", "size": 536, "ext": "r", "lang": "R", "max_stars_repo_path": "R/getLineageCoords.r", "max_stars_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_stars_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/getLineageCoords.r", "max_issues_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_issues_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/getLineageCoords.r", "max_forks_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_forks_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6666666667, "max_line_length": 148, "alphanum_fraction": 0.7462686567, "num_tokens": 158, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3094690712196819}} {"text": "#EXORDIUM\nlibrary(tm)\nlibrary(topicmodels)\n\nsetwd(\"whatever\") #Adjust this as necessary, obviously.\n\nSys.setlocale(locale=\"C\")\n\n#INPUT\ngoodpath <- \"whatever\"\ndocs <- Corpus(DirSource(goodpath), readerControl = list(reader=readPDF))\n\n# alternate method\n# filenames <- list.files(getwd(),pattern=\"*.pdf\")\n# files <- readPDF(control = list(text = \"-layout\"))(elem = list(uri = filename),\n# language = \"en\",\n# id = \"id1\")\n\n#TEXT CLEANING\ndocs <- tm_map(docs, content_transformer(tolower), lazy=TRUE)\ndocs <- tm_map(docs, removePunctuation, lazy=TRUE)\ndocs <- tm_map(docs, removeNumbers, lazy=TRUE)\ndocs <- tm_map(docs, removeWords, stopwords(\"english\"), lazy=TRUE)\ndocs <- tm_map(docs, stripWhitespace, lazy=TRUE)\n#docs <- tm_map(docs, stemDocument, lazy=TRUE)\n\n#DOCUMENT TERM MATRIX FORMATION\ndtm <- DocumentTermMatrix(docs)\n#rownames(dtm) <- filenames\nfreq <- colSums(as.matrix(dtm))\nlength(freq)\nord <- order(freq, decreasing = TRUE)\nfreq[ord]\n\n#GIBBS PARAMETERS\nburnin <- 4000\niter <- 300\nthin <- 500\nseed <- list(2003, 5, 63, 100001, 765)\nnstart <- 5\nbest <- TRUE\n\n#NO. OF TOPICS\nk <- 5\n\n#CLEARING OUT DOCUMENTS WITHOUT TERMS FROM MATRIX\nrowTotals <- apply(dtm , 1, sum)\ndtm <- dtm[rowTotals > 0, ]\n\n#LDA PROPER\nldaOutput <- LDA(dtm, k, method=\"Gibbs\", control=list(nstart = nstart, seed = seed, best = best, burnin = burnin, iter = iter, thin = thin))\nldaOutput.terms <- as.matrix(terms(ldaOutput, 6)) #2nd argument here denotes the number of terms to display per topic.\nwrite.csv(ldaOutput.terms, file = paste(\"LDAGibbs\", k, \"TopicsTerms.csv\"))\ntopicProbabilities <- as.data.frame(ldaOutput@gamma)\nwrite.csv(topicProbabilities, file = paste(\"LDAGibbs\", k, \"TopicProbabilities.csv\"))\n", "meta": {"hexsha": "20ea537e58a48cd76417fce74f90ab5666d4b261", "size": 1782, "ext": "r", "lang": "R", "max_stars_repo_path": "LDA on PDF files.r", "max_stars_repo_name": "mstrickland256/Basics", "max_stars_repo_head_hexsha": "7025e8bc03c40d65e71bcab783d2162be828ace7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "LDA on PDF files.r", "max_issues_repo_name": "mstrickland256/Basics", "max_issues_repo_head_hexsha": "7025e8bc03c40d65e71bcab783d2162be828ace7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LDA on PDF files.r", "max_forks_repo_name": "mstrickland256/Basics", "max_forks_repo_head_hexsha": "7025e8bc03c40d65e71bcab783d2162be828ace7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.8214285714, "max_line_length": 140, "alphanum_fraction": 0.677328844, "num_tokens": 500, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.30946907121968187}} {"text": "setwd(\"C:/Users/Matthew/Dropbox/PhD/7_Side_Projects/16_raycast/Paper/Drafts/Figures/plots/\")\n\n# Import libraries and functions\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(readr)\nsource('code/defaults.R')\n\n# Import data\ndataset_pt_series <- read_delim(\"demo/data/point-series.csv\", \";\", escape_double = FALSE, trim_ws = TRUE)\n# Reshape data\ndataset_pt_series <- melt(dataset_pt_series, id.vars = c('Minimum Neighbours'))\n\npt_series <- ggplot(dataset_pt_series, aes(y = value, x = `Minimum Neighbours`)) +\n geom_point(aes(shape = variable, color = variable), size = 2) +\n scale_shape_manual(values = c(1, 8, 18, 16)) +\n scale_color_manual(values = c('royalblue3', 'black', 'orangered1', 'orange1')) +\n scale_x_continuous(name='Minimum number of neighbors', breaks = seq(0,7,1)) + \n scale_y_continuous(name = 'Detection rate', labels = scales::percent, breaks = seq(0,1,0.1), limits = (c(0,1))) +\n theme_pub_light()\npt_series\n\n# Save plot\nggsave(filename = \"point-series.png\", path = \"demo/plots\", plot = pt_series, \n width = 85, height = 65, units = 'mm', dpi = 600)\n\n", "meta": {"hexsha": "4f4944477ea2f171b261f033bd930122efd12f9e", "size": 1077, "ext": "r", "lang": "R", "max_stars_repo_path": "demo/create_plots.r", "max_stars_repo_name": "mmmatthew/ggplot-templates", "max_stars_repo_head_hexsha": "1ba81c571d8c27d88e14a331b833db4320a704ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "demo/create_plots.r", "max_issues_repo_name": "mmmatthew/ggplot-templates", "max_issues_repo_head_hexsha": "1ba81c571d8c27d88e14a331b833db4320a704ca", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/create_plots.r", "max_forks_repo_name": "mmmatthew/ggplot-templates", "max_forks_repo_head_hexsha": "1ba81c571d8c27d88e14a331b833db4320a704ca", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8888888889, "max_line_length": 115, "alphanum_fraction": 0.7084493965, "num_tokens": 324, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3094690638448387}} {"text": "library(memuse, quietly=TRUE)\nlibrary(okcpuid, quietly=TRUE)\nlibrary(compiler, quietly=TRUE)\n\n\nlinpack_benchmark()\n\n", "meta": {"hexsha": "30bc61dd4a306518002b4566d5fe0986bcf2e46b", "size": 116, "ext": "r", "lang": "R", "max_stars_repo_path": "demo/linpack.r", "max_stars_repo_name": "wrathematics/okcpuid", "max_stars_repo_head_hexsha": "2b5eef2a08a2be58db941c84b0771abefddff5ac", "max_stars_repo_licenses": ["Intel", "BSD-2-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2016-07-21T14:30:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-06T14:07:34.000Z", "max_issues_repo_path": "demo/linpack.r", "max_issues_repo_name": "wrathematics/okcpuid", "max_issues_repo_head_hexsha": "2b5eef2a08a2be58db941c84b0771abefddff5ac", "max_issues_repo_licenses": ["Intel", "BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo/linpack.r", "max_forks_repo_name": "wrathematics/okcpuid", "max_forks_repo_head_hexsha": "2b5eef2a08a2be58db941c84b0771abefddff5ac", "max_forks_repo_licenses": ["Intel", "BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.5, "max_line_length": 31, "alphanum_fraction": 0.7931034483, "num_tokens": 30, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.30946906384483863}} {"text": "\\docType{data}\n\\name{savunma}\n\\alias{savunma}\n\\title{Savunma istatistikleri tablosu}\n\\format{140.921 sat\u0131r ve 18 s\u00fctundan olu\u015fan bir data.frame\n\\describe{\n\\item{oyuncu}{\u00d6zg\u00fcn oyuncu kodu}\n\\item{y\u0131l}{Y\u0131l}\n\\item{tak\u0131m_s\u0131ras\u0131}{Oyuncunun bir sezon i\u00e7inde tak\u0131mlar aras\u0131 yapt\u0131\u011f\u0131 ge\u00e7i\u015f say\u0131s\u0131}\n\\item{tak\u0131m}{Tak\u0131m kodu (fakt\u00f6r)}\n\\item{lig}{Lig kodu (fakt\u00f6r) (AA, AL, FL, NL, PL, UA)}\n\\item{mevki}{Savunma oyuncusunun mevkisi}\n\\item{oyun}{Oyun say\u0131s\u0131}\n\\item{ba\u015flama}{Oyuna ba\u015flama say\u0131s\u0131}\n\\item{oyun_s\u00fcresi}{Sahada kald\u0131\u011f\u0131 s\u00fcre}\n\\item{putout}{Oyundan \u00e7\u0131karma}\n\\item{asist}{Asist say\u0131s\u0131}\n\\item{hata}{Oyuncu taraf\u0131ndan yap\u0131lan kural hatas\u0131 say\u0131s\u0131}\n\\item{double_play}{Savunma oyuncular\u0131n\u0131n h\u00fccum oyuncular\u0131n\u0131 art arda d\u0131\u015far\u0131da b\u0131rakmas\u0131}\n\\item{ka\u00e7an_top}{Tutucunun ba\u015far\u0131s\u0131z oldu\u011fu, tutamad\u0131\u011f\u0131 top say\u0131s\u0131}\n\\item{karavana}{At\u0131c\u0131n\u0131n topu do\u011fru b\u00f6lgeye f\u0131rlatamad\u0131\u011f\u0131 at\u0131\u015f say\u0131s\u0131}\n\\item{kale_\u00e7alma}{Tutucular\u0131n rakipten kale \u00e7alma say\u0131s\u0131}\n\\item{yakalanma}{Kale \u00e7alerken yakalanma say\u0131s\u0131}\n\\item{b\u00f6lge_notu}{Savunmac\u0131n\u0131n b\u00f6lgelere g\u00f6re ka\u00e7 ko\u015fuyu engelledi\u011fini g\u00f6steren genel bir ba\u015far\u0131 \u00f6l\u00e7\u00fcs\u00fc}\n}}\n\\usage{savunma}\n\\description{Savunma istatistikleri}\n\\seealso{\\code{\\link[Lahman]{Fielding}}}\n\\keyword{datasets}\n", "meta": {"hexsha": "e254c5c639b3548ddf311ed319206923c72a71d7", "size": 1194, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/savunma.rd", "max_stars_repo_name": "botan/veriler", "max_stars_repo_head_hexsha": "772733a88024f4a3306a3a0bf9ab1182ce7f446f", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-30T13:15:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-30T13:15:26.000Z", "max_issues_repo_path": "man/savunma.rd", "max_issues_repo_name": "botan/veriler", "max_issues_repo_head_hexsha": "772733a88024f4a3306a3a0bf9ab1182ce7f446f", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/savunma.rd", "max_forks_repo_name": "botan/veriler", "max_forks_repo_head_hexsha": "772733a88024f4a3306a3a0bf9ab1182ce7f446f", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8, "max_line_length": 104, "alphanum_fraction": 0.7906197655, "num_tokens": 494, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555713, "lm_q2_score": 0.5660185351961016, "lm_q1q2_score": 0.3094639362827904}} {"text": "library(shiny)\n\n# Define UI for random distribution application \nshinyUI(pageWithSidebar(\n \n headerPanel(\"Stock Explorer\"),\n \n sidebarPanel(\n \n helpText(\"Select a stock to examine. \n Information will be collected from yahoo finance.\"),\n \n textInput(\"symb\", \"Symbol\", \"GOOG\"),\n \n dateRangeInput(\"dates\", \n \"Compare to historic returns from\",\n start = \"2013-01-01\", end = \"2013-09-05\"),\n \n actionButton(\"get\", \"Get Stock\"),\n \n br(),\n br(),\n \n uiOutput(\"newBox\")\n \n ),\n \n # Show a tabset that includes a plot, summary, and table view\n # of the generated distribution\n mainPanel(\n tabsetPanel(\n tabPanel(\"Charts\", plotOutput(\"chart\")), \n tabPanel(\"Model\", div(h3(textOutput(\"ks\"))), \n div(h3(textOutput(\"ksp\"))), \n plotOutput(\"hist\")), \n tabPanel(\"VaR\", h3(textOutput(\"text3\"))),\n id = \"tab\"\n )\n )\n))", "meta": {"hexsha": "c857d1d7341fecaa25424f21352595883f3e2aec", "size": 943, "ext": "r", "lang": "R", "max_stars_repo_path": "Resources/StockReturns/ui.r", "max_stars_repo_name": "JeyDi/RShinyEfficientFrontier", "max_stars_repo_head_hexsha": "adc940d6ab2cb34877a03c0defa00b22c92dd4c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Resources/StockReturns/ui.r", "max_issues_repo_name": "JeyDi/RShinyEfficientFrontier", "max_issues_repo_head_hexsha": "adc940d6ab2cb34877a03c0defa00b22c92dd4c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Resources/StockReturns/ui.r", "max_forks_repo_name": "JeyDi/RShinyEfficientFrontier", "max_forks_repo_head_hexsha": "adc940d6ab2cb34877a03c0defa00b22c92dd4c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-04T14:01:48.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-04T14:01:48.000Z", "avg_line_length": 23.575, "max_line_length": 63, "alphanum_fraction": 0.5652173913, "num_tokens": 243, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381667555713, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.30946393628279034}} {"text": " #cut window from final display data frame\ncutWindow<-function(data,proj,window=NULL){\n\tif(is.null(window)){\n\t\treturn(data)\n\t}\n\telse{\n\t\tcorners<-data.frame(lon=c(window[1],window[2]),lat=c(window[3],window[4]))\n\t\tif(proj!=\"NA\"){\n\t\t\tcorners<-SpatialPoints(corners,CRS(proj_latlon))\n\t\t\tcorners<-as.data.frame(spTransform(corners,CRS(proj)))\n\t\t}\n\t\tdata<-data[data$lon>corners$lon[1]&data$loncorners$lat[1]&data$lat%\n filter(starost == \"Starostne skupine - SKUPAJ\",\n status == status.skupaj,\n spol != \"Spol - SKUPAJ\"),\n aes(x = leto, y = stopnja, fill = spol))+\n geom_col(position = \"dodge\") +\n facet_grid(. ~ oznake[element], space = \"free\")+\n ggtitle(naslov_graf2)\n\n#3.Graf: samoocene splosnega zadovoljstva \u017eivljenja\nnaslov_graf3 <- \"Samoocena splo\u0161nega zadovoljstva \u017eivljenja\"\nEncoding(naslov_graf3)<-\"UTF-8\"\ngraf3 <- ggplot(data = tabela_zadovoljstvo, aes(x=leto, y = odstotek, colour = ocena ))+ \n geom_line()+\n xlab(\"Leto\") + ylab(\"Odstotek\") +\n ggtitle(naslov_graf3)\n\n\n#4.Graf : Breme stanovanjskih stro\u0161kov\nnaslov_graf4 <- \"Breme stanovanjskih stro\u0161kov\"\nEncoding(naslov_graf4)<- \"UTF-8\"\ngraf4 <- ggplot(data = breme_stanovanjskih_stroskov,\n aes(x = leto, y = odstotek, color = velikost.bremena)) +\n geom_line() + facet_grid(. ~ gospodinjstvo) + xlab(\"Leto\") + ylab(\"Odstotek\") +\n ggtitle(naslov_graf4) + guides(color = guide_legend(\"Velikost bremena\")) +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5))\n\n#4.2 Graf: dele\u017e prebivalcev katerim stanovanjski stro\u0161ki predstavljajo preveliko breme:\nnaslov_graf42 <- \"Dele\u017e prebivalcev katerim stanovanjski stro\u0161ki predstavljajo preveliko breme\"\nEncoding(naslov_graf42)<- \"UTF-8\"\ngraf42 <- ggplot(data= delez, aes(x =leto, y = stopnja))+\n geom_line()+\n facet_grid(. ~ drzava)+\n ggtitle(naslov_graf42)\n\n\ngraf123 <- ggplot(data = delez %>% filter(drzava == \"Slovenia\"),\n aes(x = leto, y = stopnja))+\n geom_point() \n#5.Graf: Stopnje prenaseljenosti v eu \n\ngraf5 <- ggplot(data= prenaseljenost, aes(x = timegeo, y = stopnja))+\n geom_col() + facet_grid(. ~ leto)\n\n\n# Uvozimo zemljevid.\nsvet <- uvozi.zemljevid(\"http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_0_countries.zip\",\n \"ne_50m_admin_0_countries\", encoding = \"UTF-8\") %>%\n pretvori.zemljevid() %>% filter(CONTINENT %in% c(\"Europe\"))\n\n#NASLOVA\nnaslov <- \"Stopnja prenaseljenosti za mo\u0161ke v Evropi\"\nEncoding(naslov) <- \"UTF-8\"\nnaslov2 <- \"Stopnja prenaseljenosti za \u017eenske v Evropi\"\nEncoding(naslov2)<- \"UTF-8\"\n#ZEMLJEVID: Stopnja prenaseljenosti za moske\nzemljevid_moski <-ggplot() + geom_polygon(data = prenaseljenost%>% filter(spol == \"moski\", leto == \"2016\") %>% \n mutate(SOVEREIGNT = parse_factor(timegeo,levels(svet$SOVEREIGNT)))%>%\n right_join(svet, by = c(\"timegeo\" = \"NAME_LONG\")),\n aes(x= long, y = lat,\n group = group,\n fill = stopnja)) +\n coord_cartesian(xlim = c(-22, 40), ylim = c(30, 70)) +\n ggtitle(naslov)\n# ZEMLJEVID: Stopnja prenaseljenosti za \u017eenske\n\nzemljevid_zenske <-ggplot() + geom_polygon(data = prenaseljenost%>% filter(spol == \"zenske\", leto == \"2016\") %>% \n mutate(SOVEREIGNT = parse_factor(timegeo,levels(svet$SOVEREIGNT)))%>%\n right_join(svet, by = c(\"timegeo\" = \"NAME_LONG\")),\n aes(x= long, y = lat,\n group = group,\n fill = stopnja)) +\n coord_cartesian(xlim = c(-22, 40), ylim = c(30, 70)) +\n ggtitle(naslov2)\n\n\n\n\n\n", "meta": {"hexsha": "4e01992b6ac16fdfb7a205de4ab5d12a5a961d40", "size": 4570, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "ajdastare/APPR-2017-18", "max_stars_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "ajdastare/APPR-2017-18", "max_issues_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-12-14T16:18:12.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-14T09:19:16.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "ajdastare/APPR-2017-18", "max_forks_repo_head_hexsha": "08c4d959271a9d95379b0853b7b1bd7c26272226", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.1132075472, "max_line_length": 140, "alphanum_fraction": 0.6172866521, "num_tokens": 1514, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3094639279221546}} {"text": "library(TMB)\nlibrary(mvtnorm)\nlibrary(matrixcalc)\nlibrary(corpcor)\n\nload(\"pitTotal_Snake_condensed.Rdata\") #Load the pit tag data.\n\n#Subset the data based on the input variables\npitTotal$year <- \n as.numeric(as.character(pitTotal$year))\npitTotal <- pitTotal[pitTotal$year<=lastYr,]\n\nif(!is.null(myTrans)){\n pitTotal <-\n pitTotal[pitTotal$trans_dam==myTrans,]\n}\nif(!is.null(rear)){\n pitTotal <-\n pitTotal[pitTotal$rear_type==rear,]\n}\nk_name <- paste(pitTotal$trans_dam,pitTotal$rear_type)\npitTotal$k <- as.numeric(as.factor(k_name))-1\npitTotal$yr <- pitTotal$year - eStartYr\n\n#Load the data, subset it and transform is based on the input in the wrapper_modelRuns.r\nload(\"envData.rData\")\ntmpenv <- envdata[envdata$year>=eStartYr & envdata$year<=eLastYr,]\nif(eLastYr>max(envdata$year)){\n projEnv <- as.data.frame(matrix(NA,eLastYr-max(envdata$year),ncol(tmpenv)))\n projEnv[,1] <- (max(envdata$year)+1):eLastYr\n names(projEnv) <- names(tmpenv)\n tmpenv <- rbind(tmpenv,projEnv)\n}\nnames(tmpenv)[1] <- \"Year\"\n\n# Jinky step to get the most recent coastal summer data\nload(paste0(\"envData_7302018.Rdata\"))\ntmpenv$ersstWAcoast.sum[tmpenv$year%in%envdata$Year] <- envdata$ersstWAcoast.sum[tmpenv$year%in%envdata$Year]\n\n#Get the scaling data\nenv_mu <- apply(tmpenv,2,function(x){return(mean(na.omit(x)))})\nenv_sc <- apply(tmpenv,2,function(x){return(sd(na.omit(x)))})\n# env_sc <- rep(1,ncol(tmpenv))\nenv_mu_2000_2015 <- apply(tmpenv[tmpenv$Year>=2000 & tmpenv$Year<=2015,],2,function(x){mean(na.omit(x))})\nenv_sc_2000_2015 <- apply(tmpenv[tmpenv$Year>=2000 & tmpenv$Year<=2015,],2,function(x){sd(na.omit(x))})\nenv_mu_1980_2015 <- apply(tmpenv[tmpenv$Year>=1980 & tmpenv$Year<=2015,],2,function(x){mean(na.omit(x))})\nenv_sc_1980_2015 <- apply(tmpenv[tmpenv$Year>=1980 & tmpenv$Year<=2015,],2,function(x){sd(na.omit(x))})\nStationary_baseline_difference <- env_mu_1980_2015[myVars] - env_mu_2000_2015[myVars]\n\n#Z-score variables and years\ntmpenv[,names(tmpenv)!=\"Year\"] <- t((t(tmpenv[,names(tmpenv)!=\"Year\"]) - env_mu[names(tmpenv)!=\"Year\"])/env_sc[names(tmpenv)!=\"Year\"])\n\n#Index years starting with zero\ntmpenv$Year <- tmpenv$Year - eStartYr\n\n#Get rid of NAs\nsubData <- tmpenv[,myVars]\nsubData[is.na(subData)] <- -10000\n\n\n#Create increment limit\nnte = nrow(subData)\nnvar = ncol(subData)\n\nk_name <- k_name[pitTotal$julian>=minJ & pitTotal$julian<=maxJ]\npitTotal <- pitTotal[pitTotal$julian>=minJ & pitTotal$julian<=maxJ,]\n\n#marine variables for pit analysis using only 2000-2015 mean and standard deviation\nxm <- t((t(pitTotal[,marVars])-env_mu_2000_2015[marVars])/\n env_sc_2000_2015[marVars])\n\nload(\"gridData.rData\")\n#Timing across days and years\nretroTiming <- apply(gridData[\"inriver\",,,,],c(2,1),sum)\nretroTiming <- t(retroTiming)/colSums(retroTiming)\nretroTiming <- t(retroTiming[gridYears%in%eStartYr:(startYr-1),1:365%in%minJ:maxJ])\n\n#This is the calibration timing for Lisa's calibration from ~1998:2015\ncalibration_timing <- NA\ncalibration_years <- NA\nif(calibration_flag==1){\n calibration_timing <- read.table(calibration_file, header=TRUE)\n if(myTrans==\"In-river\"){\n calibration_timing <- calibration_timing[calibration_timing$MigrationType==\"inriver\",]\n }\n if(myTrans==\"Transport\"){\n calibration_timing <- calibration_timing[calibration_timing$MigrationType==\"transportation\",]\n }\n calibration_years <- calibration_timing$Year[calibration_timing$Year<=lastYr]-eStartYr\n \n calibration_timing <- calibration_timing[calibration_timing$Year<=lastYr,minJ:maxJ+3]\n calibration_timing <- calibration_timing/rowSums(calibration_timing)\n}\n\n#This is for projecting into the future.\nobsTiming <- matrix(0,length(minJ:maxJ),length(startYr:lastYr))\nfor(j in minJ:maxJ){\n for(yy in startYr:lastYr){\n obsTiming[j-minJ+1,yy-startYr+1] <- sum(pitTotal$Total[pitTotal$year==yy & pitTotal$julian==j])\n }\n}\nobsTiming <- t(t(obsTiming)/colSums(obsTiming))\ntiming <- cbind(retroTiming,obsTiming)\nprojTiming <- matrix(rowSums(timing)/sum(timing),length(minJ:maxJ),length((lastYr+1):eLastYr))\ntiming <- cbind(timing,projTiming)\n\n#You need and offest for years if you are doing the projection\nif(retro)\n yShift <- 0\nif(!retro)\n yShift <- startYr-eStartYr\n\n#Data list\ndata <- list( yShift = yShift\n ,yr = pitTotal$yr\n ,j = pitTotal$julian-minJ\n ,s_n = pitTotal$Total\n ,s_k = pitTotal$resMatrix[,1] #pitTotal.txt\n ,k = pitTotal$k\n ,nvar = nvar\n ,env = as.matrix(subData)\n ,timing = timing \n ,xm = as.matrix(xm)\n ,marVars = tmpMarVars-1\n ,sd=0.0001\n ,re_j = re_j\n ,re_t = re_t\n ,re_jt = re_jt\n ,cov_pars = cov_pars\n ,retro = retro\n ,env_mu = as.vector(env_mu[myVars])\n ,env_sc = as.vector(env_sc[myVars])\n ,env_mu_2000_2015 = as.vector(env_mu_2000_2015[myVars])\n ,env_sc_2000_2015 = as.vector(env_sc_2000_2015[myVars])\n ,calibration_flag = calibration_flag\n ,calibration_timing = as.matrix(calibration_timing)\n ,calibration_years = calibration_years\n ,Stationary_baseline_difference=Stationary_baseline_difference\n)\n\n# env_mu <- apply(tmpenv,2,function(x){return(mean(na.omit(x)))})\n# env_sc <- apply(tmpenv,2,function(x){return(sd(na.omit(x)))})\n# # env_sc <- rep(1,ncol(tmpenv))\n# env_mu_2000_2015 <- apply(tmpenv[tmpenv$Year>=2000 & tmpenv$Year<=2015,],2,function(x){mean(na.omit(x))})\n# env_sc_2000_2015 <- apply(tmpenv[tmpenv$Year>=2000 & tmpenv$Year<=2015,],2,function(x){sd(na.omit(x))})\n\nnk_dim <- max(data$k)+1\n#Parameter list\nparameters <- list(mu_s = rep(0,nk_dim)\n ,frho_j = rep(0,nk_dim)\n ,frho_t = rep(0,nk_dim)\n ,frho1_jt = rep(0,nk_dim)\n ,frho2_jt = rep(0,nk_dim)\n ,fpsi_j = rep(0,nk_dim)\n ,fpsi_t = rep(0,nk_dim)\n ,fpsi_jt = rep(0,nk_dim)\n ,eps_j = matrix(0,length(minJ:maxJ),nk_dim)\n ,eps_t = matrix(0,length(eStartYr:eLastYr)-yShift,nk_dim)\n ,eps_jt = array(0,c(nk_dim,length(minJ:maxJ),length(eStartYr:eLastYr)-yShift))\n ,beta_mar = matrix(0,length(tmpMarVars),nk_dim)\n ,frho_x = rep(0)\n ,frho_Rx = rep(0,nvar*(nvar-1)/2)\n ,fpsi_x = rep(0,nvar)\n ,eps_x = matrix(0,nvar,length(eStartYr:eLastYr))\n)\n", "meta": {"hexsha": "ab88119d8b7af12c17320c972bc662a8be7b641b", "size": 6485, "ext": "r", "lang": "R", "max_stars_repo_path": "SAR model/create_DataAndPars.r", "max_stars_repo_name": "lisa439/LCM", "max_stars_repo_head_hexsha": "4d6ab2fcba92ad1bf46cc899ba88478af918d4c5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-18T20:19:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-18T20:19:45.000Z", "max_issues_repo_path": "SAR model/create_DataAndPars.r", "max_issues_repo_name": "lisa439/LCM", "max_issues_repo_head_hexsha": "4d6ab2fcba92ad1bf46cc899ba88478af918d4c5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SAR model/create_DataAndPars.r", "max_forks_repo_name": "lisa439/LCM", "max_forks_repo_head_hexsha": "4d6ab2fcba92ad1bf46cc899ba88478af918d4c5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.0662650602, "max_line_length": 134, "alphanum_fraction": 0.6687740941, "num_tokens": 2035, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30939021687192503}} {"text": "require(shiny)\nrequire(shinyWidgets)\nrequire(tidyverse)\nrequire(sf)\nrequire(data.table)\nrequire(ggplot2)\nrequire(viridis)\n\n# Read and wrangle the case data \nx <- fread(\"ltla_2021-05-17_Cases.csv\")\nx[, Lacode := areaCode]\nx[, `% Change In Number Of New Cases` := newCasesBySpecimenDateChangePercentage]\n\n# Read and prep the shape data\nBackground <- st_read(\n \"LocalAuthorities-lowertier.gpkg\",\n layer=\"7 Background\")\n\n# Prep the label alignment\nGroup_labels <- st_read(\n \"LocalAuthorities-lowertier.gpkg\",\n layer=\"1 Group labels\") %>% \n mutate(just=if_else(LabelPosit==\"Left\", 0, 1))\n\n# merge on the case data to the shapes\nltladata <- st_read(\n \"LocalAuthorities-lowertier.gpkg\",\n layer=\"4 LTLA-2019\") %>% \n left_join(x, by=\"Lacode\")\n\nsetDT(ltladata)\n\n\n# Define UI for app that draws a histogram ----\nui <- fluidPage(\n shinyWidgets::setBackgroundColor(\"#19323C\"),\n shinyWidgets::chooseSliderSkin(\"Modern\", color = \"#19323C\"),\n tags$head(tags$style(\n HTML('\n #sidebar {\n background-color: white;\n }\n\n body, label, input, button, select { \n font-family: \"Arial\";\n }')\n )),\n # App title ----\n\n # Sidebar layout with input and output definitions ----\n sidebarLayout(\n \n # Sidebar panel for inputs ----\n sidebarPanel(width = 2, id = 'sidebar',\n \n # Input: Slider for the number of bins ----\n sliderInput(inputId = \"date\",\n label = \"Date:\",\n min = as.Date(\"2020-02-01\",\"%Y-%m-%d\"),\n max = as.Date(Sys.Date(),\"%Y-%m-%d\"),\n value = as.Date(\"2020-12-01\"),\n timeFormat=\"%Y-%m-%d\")\n \n ),\n \n # Main panel for displaying outputs ----\n mainPanel(\n \n # Output: Histogram ----\n plotOutput(outputId = \"distPlot\")\n \n )\n )\n)\n\n\n# Define server logic required to draw a histogram ----\nserver <- function(input, output) {\n \n \n\n \n # Histogram of the Old Faithful Geyser Data ----\n # with requested number ofs bins\n # This expression that generates a histogram is wrapped in a call\n # to renderPlot to indicate that:\n #\n # 1. It is \"reactive\" and therefore should be automatically\n # re-executed when inputs (input$bins) change\n # 2. Its output type is a plot\n output$distPlot <- renderPlot({\n \n\n ggplot()+\n geom_sf(\n data = Background,\n aes(geometry = geom),\n fill = NA, size = .5, color = 'grey40')+\n geom_sf(\n data=ltladata[date == input$date], # subset the data table\n aes(geometry=geom, fill=`% Change In Number Of New Cases`),\n colour=\"Black\",\n size=.35)+\n geom_sf_text(\n data=Group_labels,\n aes(geometry=geom, label= Group.labe, hjust=just),\n size=rel(2.4),\n colour=\"white\",\n family = 'DIN Next LT Pro Light') + \n guides(fill = guide_colourbar(\n ticks = FALSE,\n barwidth = 15,\n barheight = 0.5,\n title.position = 'top')) + \n theme_void() +\n labs(title = 'Percentage Change In Daily Covid-19 Infections\\nBy Lower Tier Local Authority',\n caption = \"Visualisation by Joe O'Reilly (josephedwardoreilly.github.com)\") + \n theme(\n plot.background = element_rect(fill = '#19323C',color = NA),\n panel.background = element_rect(fill = '#19323C', color = NA),\n legend.justification = 'top',\n plot.margin = margin(10, 20, 10, 20),\n legend.position = 'bottom',\n legend.title = element_text(\n size = 8, hjust = 0.5,\n family = 'DIN Next LT Pro Bold',\n color = 'white'),\n legend.text = element_text(\n size = 7,\n family = 'DIN Next LT Pro Light',\n color = 'white'),\n plot.title = element_text(\n family = 'DIN Next LT Pro Bold',\n color = 'white',\n size = 11),\n plot.title.position = \"plot\",\n plot.caption = element_text(\n family = 'DIN Next LT Pro Light',\n color = 'white',\n size = 7,\n hjust = 1,\n margin = margin(10, 0, 0, 0)),\n plot.caption.position = 'plot') + \n scale_fill_viridis(\n option = \"turbo\", \n breaks = c(-100, 0, 100),\n labels = c('Less than -100 %', '0 %', 'Greater than 100 %'),\n limits = c(-100, 100), oob = scales::squish) \n \n }, width = 500, height = 717.5175)\n \n}\n\nshinyApp(ui, server)\n\n", "meta": {"hexsha": "1b374a2350ddfab6c061dca9e91c05654b8a6fc0", "size": 4409, "ext": "r", "lang": "R", "max_stars_repo_path": "Covid_Map_Shiny/app.r", "max_stars_repo_name": "josephedwardoreilly/DataViz", "max_stars_repo_head_hexsha": "f04091150cfa0d50c39d88903b51f0abff3d5a39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Covid_Map_Shiny/app.r", "max_issues_repo_name": "josephedwardoreilly/DataViz", "max_issues_repo_head_hexsha": "f04091150cfa0d50c39d88903b51f0abff3d5a39", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Covid_Map_Shiny/app.r", "max_forks_repo_name": "josephedwardoreilly/DataViz", "max_forks_repo_head_hexsha": "f04091150cfa0d50c39d88903b51f0abff3d5a39", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.2628205128, "max_line_length": 99, "alphanum_fraction": 0.5745066909, "num_tokens": 1169, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30939021687192503}} {"text": "#!/usr/bin/env Rscript\n\n# Copyright (C) 2017 by\n# Thomas E. Gorochowski , Voigt Lab, MIT\n# All rights reserved\n# Released under MIT license (see LICENSE.txt)\n\n# Arguments:\n# 1. count_matrix_filename\n# 2. group1, e.g., 1,2,5,6\n# 3. group2, e.g., 3,4,7,8\n# 4. library_size_matrix\n# 5. output_file_prefix\nargs <- commandArgs(TRUE)\narg_count_matrix <- args[1]\narg_group1 <- as.integer(unlist(strsplit(args[2], \",\")))\narg_group2 <- as.integer(unlist(strsplit(args[3], \",\")))\narg_library_size_matrix <- args[4]\narg_output_file_prefix <- args[5]\n\n# We use edgeR for DE analysis\nlibrary(edgeR)\n\n# Load the data file\ncount_matrix <- read.table(arg_count_matrix, header=T, row.names=1, com='')\n\n# Extract those columns we are interested in\ncol_ordering <- c(arg_group1, arg_group2)\ncount_matrix <- count_matrix[,col_ordering]\nconditions <- factor(c(rep(\"group_1\", length(arg_group1)), rep(\"group_2\", length(arg_group2))))\n\n# Load the actual mapped reads\nlibrary_size_matrix <- read.table(arg_library_size_matrix, header=T, row.names=1, com='')\n\n# Get the library sizes (total counts in annotated genes)\nlib_sizes <- as.vector(library_size_matrix$total_mapped_reads)[col_ordering]\n\n# Calculate normalisation factors using TMM\nexpr <- DGEList(counts=count_matrix, group=conditions, lib.size=lib_sizes)\nexpr <- calcNormFactors(expr)\nexpr <- estimateCommonDisp(expr)\nexpr <- estimateTagwiseDisp(expr)\nde_data <- exactTest(expr)\nde_results <- topTags(de_data, n=length(count_matrix[,1]))\n\n# write the output to a text file\nwrite.table(as.matrix(de_results$table), col.names=NA, quote=FALSE, file=paste0(arg_output_file_prefix, \".de.analysis.txt\"), sep=\"\\t\")\n", "meta": {"hexsha": "cc97a1d72fd459e79f086f35432d2f23a75de336", "size": 1662, "ext": "r", "lang": "R", "max_stars_repo_path": "genetic-analyzer/bin/de_analysis.r", "max_stars_repo_name": "VoigtLab/MIT-BroadFoundry", "max_stars_repo_head_hexsha": "92e969c66353d2983aefdaddeff97b8fb2a7fd30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2018-05-11T08:31:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-18T13:04:00.000Z", "max_issues_repo_path": "genetic-analyzer/bin/de_analysis.r", "max_issues_repo_name": "VoigtLab/MIT-BroadFoundry", "max_issues_repo_head_hexsha": "92e969c66353d2983aefdaddeff97b8fb2a7fd30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "genetic-analyzer/bin/de_analysis.r", "max_forks_repo_name": "VoigtLab/MIT-BroadFoundry", "max_forks_repo_head_hexsha": "92e969c66353d2983aefdaddeff97b8fb2a7fd30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-01-27T20:22:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-01T08:50:16.000Z", "avg_line_length": 34.625, "max_line_length": 134, "alphanum_fraction": 0.7484957882, "num_tokens": 454, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.61878043374385, "lm_q2_score": 0.5, "lm_q1q2_score": 0.309390216871925}} {"text": "# This is code to run analyses for the protocol for ParkProReakt;\r\n# https://innovationsfonds.g-ba.de/projekte/neue-versorgungsformen/parkproreakt-proaktive-statt-reaktive-symptomerkennung-bei-parkinson-patientinnen-und-patienten.432\r\n# Code developed by David Pedrosa\r\n\r\n# Version 1.0 # 2021-12-27\r\n\r\n## First specify the packages of interest\r\npackages = c(\"readxl\", \"tableone\", \"dplyr\", \"tidyverse\", \"rstatix\", \"Hmisc\") #\r\n\r\n## Now load or install&load all\r\npackage.check <- lapply(\r\n packages,\r\n FUN = function(x) {\r\n if (!require(x, character.only = TRUE)) {\r\n install.packages(x, dependencies = TRUE)\r\n library(x, character.only = TRUE)\r\n }\r\n }\r\n)\r\n\r\n## In case of multiple people working on one project, this helps to create an automatic script\r\nusername = Sys.info()[\"login\"]\r\n\r\nif (username == \"dpedr\") {\r\n\twdir = \"D:/innofonds_protocol\"\r\n} else if (username == \"david\") {\r\n\twdir = \"/media/storage/innofonds_protocol/\"\r\n}\r\nsetwd(wdir)\r\ndf \t\t\t\t <- read_excel(file.path(wdir, \"ambulanz.G20.2019to2021.xlsx\")) # read data frame from xlsx-file\r\n\r\ndf$gender \t\t\t<- as.factor(df$gender)\r\nlevels(df$gender) \t<- c(\"male\", \"female\")\r\n\r\ndf$icd \t\t\t\t\t<- as.factor(df$icd) # factors according to: https://gesund.bund.de/en/icd-code-search/g20-9\r\ndf$icd_cat \t\t<- as.factor(df$icd) # factors according to: https://gesund.bund.de/en/icd-code-search/g20-9\r\nlevels(df$icd_cat) \t\t<- c(\t\"Primary Parkinson disease with no or mild impairment - with no fluctuations\", \r\n\t\t\t\t\t\t\t\"Primary Parkinson disease with no or mild impairment - with fluctuations\", \r\n\t\t\t\t\t\t\t\"Primary Parkinson disease with moderate to severe impairment - with no fluctuation \", \r\n\t\t\t\t\t\t\t\"Primary Parkinson disease with moderate to severe impairment - with fluctuations\",\r\n\t\t\t\t\t\t\t\"Primary Parkinson disease with severest impairment - with no fluctuation \",\r\n\t\t\t\t\t\t\t\"Primary Parkinson disease with severest impairment - with fluctuations\",\r\n\t\t\t\t\t\t\t\"Primary Parkinson disease, unspecified - with no fluctuation\",\r\n\t\t\t\t\t\t\t\"Primary Parkinson disease, unspecified - with fluctuations\")\r\n\r\n\r\n# ==================================================================================================\r\n## Create TableOne for specific values\r\nVars \t\t\t<- c(\"icd\", \"age\", \"gender\", \"h&y\", \"bdi\", \"moca\", \"pdq8\")\r\nnonnormalVars \t<- c(\"h&y\") # avoids returning mean in favor of median \r\nfactVars \t\t<- c(\"gender\", \"icd\", \"h&y\") # Here only values with categorial (ordinal distribution should be added) \r\ntableOne \t\t<- CreateTableOne(vars=Vars, factorVars=factVars, data=df) \r\nprint(tableOne, nonnormal=c(\"h&y\"))\r\n\r\n# \r\ndf_corr <- df %>%\r\n select(c(\"h&y\", \"age\", \"bdi\", \"moca\", \"pdq8\"))\r\nrcorr(as.matrix(sapply(df_corr, as.numeric)))\r\n\r\n", "meta": {"hexsha": "c769dc38b599e0a2906c08e8520d8ae46a15ab90", "size": 2694, "ext": "r", "lang": "R", "max_stars_repo_path": "demographics_patientsPHU.r", "max_stars_repo_name": "dpedrosac/ParkProReakt-Randomization", "max_stars_repo_head_hexsha": "1faa84375d59b2ac12b23a71e97aea5ea7b00ae0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-21T09:53:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-21T09:53:56.000Z", "max_issues_repo_path": "demographics_patientsPHU.r", "max_issues_repo_name": "dpedrosac/ParkProReakt-Randomization", "max_issues_repo_head_hexsha": "1faa84375d59b2ac12b23a71e97aea5ea7b00ae0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demographics_patientsPHU.r", "max_forks_repo_name": "dpedrosac/ParkProReakt-Randomization", "max_forks_repo_head_hexsha": "1faa84375d59b2ac12b23a71e97aea5ea7b00ae0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-02-21T09:54:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-21T09:54:03.000Z", "avg_line_length": 44.9, "max_line_length": 167, "alphanum_fraction": 0.6581291759, "num_tokens": 726, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30939020984181914}} {"text": "library(dplyr)\nlibrary(purrr)\n\nspecies <- read.csv(\"data/MasterSpeciesList_clean.csv\", header=TRUE, stringsAsFactors = FALSE) %>%\n filter(!is.na(species))\n\nbycity <- species %>%\n split(.$City) %>%\n map(select, species)\n\n#all cities\n\ninallcities <- Reduce(intersect, bycity) #species that occur in all cities - the most common city community\n#\n# > inallcities\n# species\n# 1 Acer negundo\n# 2 Achillea millefolium\n# 3 Ambrosia artemisiifolia\n# 4 Arctium minus\n# 5 Calystegia sepium\n# 6 Campanula rapunculoides\n# 7 Capsella bursa-pastoris\n# 8 Cirsium arvense\n# 9 Convolvulus arvensis\n# 10 Cornus canadensis\n# 11 Cornus sericea\n# 12 Corylus cornuta\n# 13 Eleocharis palustris\n# 14 Equisetum arvense\n# 15 Erigeron canadensis\n# 16 Euphorbia cyparissias\n# 17 Euthamia graminifolia\n# 18 Glechoma hederacea\n# 19 Helianthus annuus\n# 20 Hesperis matronalis\n# 21 Hordeum jubatum\n# 22 Humulus lupulus\n# 23 Leucanthemum vulgare\n# 24 Linaria vulgaris\n# 25 Linnaea borealis\n# 26 Lotus corniculatus\n# 27 Lythrum salicaria\n# 28 Maianthemum stellatum\n# 29 Matricaria discoidea\n# 30 Medicago lupulina\n# 31 Medicago sativa\n# 32 Melilotus albus\n# 33 Melilotus officinalis\n# 34 Panicum capillare\n# 35 Phalaris arundinacea\n# 36 Plantago major\n# 37 Polygonum aviculare\n# 38 Populus tremuloides\n# 39 Portulaca oleracea\n# 40 Prunus serotina\n# 41 Rorippa palustris\n# 42 Rudbeckia hirta\n# 43 Sambucus racemosa\n# 44 Senecio vulgaris\n# 45 Sinapis arvensis\n# 46 Sisymbrium altissimum\n# 47 Solidago canadensis\n# 48 Sonchus arvensis\n# 49 Symphoricarpos albus\n# 50 Tanacetum vulgare\n# 51 Taraxacum officinale\n# 52 Trifolium hybridum\n# 53 Trifolium repens\n# 54 Viburnum opulus\n# 55 Vicia cracca\n\n# what % of the total species in a city is the most common species list\npercent_common <- species %>%\n select(-quadrat) %>%\n distinct() %>%\n group_by(City) %>%\n summarize(percent_common = (nrow(inallcities)/n())*100) %>%\n arrange(desc(percent_common))\n\nvulgar <- ggplot(percent_common, aes(x=City, y=percent_common, fill=City)) +\n geom_col() +\n labs(x=\"\", y=\"Vulgarity index\") +\n ylim(c(0,100)) +\n theme_classic(base_size=18) +\n theme(legend.position = \"none\")\n\nggsave(\"figures/city_vulgarity.png\", vulgar, width = 16, height=9, units=\"in\")\n\n# Percent of a city's species that are in the set of species that occur in all cities\n# City percent_common\n# \n# 1 Winnipeg 16.4\n# 2 Edmonton 9.15\n# 3 Halifax 5.11\n# 4 Toronto 4.31\n# 5 Montreal 3.56\n# 6 Vancouver 2.10\n\n\n# all species that occur in more than one quadrat\nmorethan1 <- species %>%\n group_by(species) %>%\n summarize(count=n()) %>% # how many quadrats does a species occur in\n filter(count > 1)\n\n", "meta": {"hexsha": "6b74f14b53f9fea350290ce9b5fccd4aff84d913", "size": 3058, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/speciescombinations.r", "max_stars_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_stars_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-07T10:18:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-11T14:44:19.000Z", "max_issues_repo_path": "scripts/speciescombinations.r", "max_issues_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_issues_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2020-05-11T14:56:22.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-05T12:15:24.000Z", "max_forks_repo_path": "scripts/speciescombinations.r", "max_forks_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_forks_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-08T14:15:36.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-08T14:15:36.000Z", "avg_line_length": 28.5794392523, "max_line_length": 107, "alphanum_fraction": 0.635382603, "num_tokens": 1012, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30939020984181914}} {"text": "# plot_scatter.r\n#\n# Copyright (c) 2020 VIB (Belgium) & Babraham Institute (United Kingdom)\n#\n# Software written by Carlos P. Roca, as research funded by the European Union.\n#\n# This software may be modified and distributed under the terms of the MIT\n# license. See the LICENSE file for details.\n\n\n# Makes scatter plots of compensated and uncompensated data, with regression\n# and reference lines, and a label showing slope error.\n\nplot.scatter <- function(\n expr.data.unco.x, expr.data.unco.y, expr.data.comp.x, expr.data.comp.y,\n spillover.unco.inte, spillover.unco.coef,\n spillover.comp.inte, spillover.comp.coef, spillover.comp.slop,\n marker.limit.range, marker.proper.limit.range, samp, marker, marker.proper,\n scale.untransformed, figure.file.label, flow.gate, flow.control, asp\n)\n{\n expr.data.ggp <- data.frame(\n x = c( expr.data.unco.x, expr.data.comp.x ),\n y = c( expr.data.unco.y, expr.data.comp.y ),\n z = factor( rep( c( \"unco\", \"comp\" ),\n each = length( expr.data.unco.x ) ),\n levels = c( \"unco\", \"comp\" ) ),\n w = rep( 1 : length( expr.data.unco.x ) %in%\n flow.gate[[ samp ]], 2 )\n )\n\n marker.slope.unco <- 1 / spillover.unco.coef\n marker.intercept.unco <- - spillover.unco.inte / spillover.unco.coef\n marker.x.intercept.unco <- spillover.unco.inte\n\n marker.slope.comp <- 1 / spillover.comp.coef\n marker.intercept.comp <- - spillover.comp.inte / spillover.comp.coef\n marker.x.intercept.comp <- spillover.comp.inte\n\n x.transform <- flow.control$transform[[\n flow.control$marker.original[ match( marker, flow.control$marker ) ] ]]\n y.transform <- flow.control$transform[[\n flow.control$marker.original[ match( marker.proper,\n flow.control$marker ) ] ]]\n\n x.transform.inv <- flow.control$transform.inv[[\n flow.control$marker.original[ match( marker, flow.control$marker ) ] ]]\n y.transform.inv <- flow.control$transform.inv[[\n flow.control$marker.original[ match( marker.proper,\n flow.control$marker ) ] ]]\n\n marker.limit.adaptor <- diag( 2 ) +\n matrix( c( 0.01 * c( 1, -1 ), 0.05 * c( -1, 1 ) ),\n nrow = 2, byrow = TRUE )\n marker.limit.plot <- as.vector( marker.limit.adaptor %*%\n marker.limit.range )\n\n marker.proper.limit.adaptor <- diag( 2 ) +\n matrix( c( 0.01 * c( 1, -1 ), 0.05 * c( -1, 1 ) ),\n nrow = 2, byrow = TRUE )\n marker.proper.limit.plot <- as.vector(\n marker.proper.limit.adaptor %*%\n marker.proper.limit.range )\n\n if ( ! scale.untransformed )\n {\n marker.limit.plot <- x.transform.inv( marker.limit.plot )\n marker.proper.limit.plot <- y.transform.inv(\n marker.proper.limit.plot )\n }\n\n marker.limit.factor <- log(\n max( abs( marker.proper.limit.plot ) ) /\n max( abs( marker.limit.plot ) ) )\n\n if ( marker.limit.factor > 1 )\n marker.limit.plot <- as.vector(\n matrix( c(\n ( marker.limit.factor + 1 ) / 2,\n ( 1 - marker.limit.factor ) / 2,\n ( 1 - marker.limit.factor ) / 2,\n ( marker.limit.factor + 1 ) / 2 ),\n nrow = 2, byrow = TRUE ) %*%\n marker.limit.plot )\n\n if ( ! scale.untransformed )\n {\n marker.limit.plot <- x.transform( marker.limit.plot )\n marker.proper.limit.plot <- y.transform(\n marker.proper.limit.plot )\n }\n\n for ( scale.same in c( TRUE, FALSE ) )\n if ( scale.same || asp$scatter.plot.scale.other )\n {\n if ( xor( scale.same, ! scale.untransformed ) )\n {\n if ( scale.untransformed ) {\n gg.scale.x <- scale_x_continuous(\n limits = marker.limit.plot )\n gg.scale.y <- scale_y_continuous(\n limits = marker.proper.limit.plot )\n }\n else {\n gg.scale.x <- scale_x_continuous(\n limits = x.transform.inv( marker.limit.plot ) )\n gg.scale.y <- scale_y_continuous(\n limits = y.transform.inv( marker.proper.limit.plot ) )\n }\n }\n else\n {\n if ( scale.untransformed ) {\n marker.limit.plot.untr <- marker.limit.plot\n marker.limit.plot.tran <- x.transform(\n marker.limit.plot )\n }\n else {\n marker.limit.plot.untr <- x.transform.inv(\n marker.limit.plot )\n marker.limit.plot.tran <- marker.limit.plot\n }\n\n marker.breaks.exp <- min( floor( log10( max( abs(\n marker.limit.plot.untr ) ) /\n asp$scatter.scale.breaks.coef ) ), 3 )\n marker.breaks.untr <-\n get.scale.breaks.untransformed(\n marker.limit.plot.untr,\n marker.breaks.exp )\n marker.breaks.tran <- x.transform(\n marker.breaks.untr )\n\n if ( scale.untransformed ) {\n marker.proper.limit.plot.untr <-\n marker.proper.limit.plot\n marker.proper.limit.plot.tran <- y.transform(\n marker.proper.limit.plot )\n }\n else {\n marker.proper.limit.plot.untr <-\n y.transform.inv( marker.proper.limit.plot )\n marker.proper.limit.plot.tran <-\n marker.proper.limit.plot\n }\n\n marker.proper.breaks.exp <- min( floor( log10( max(\n abs( marker.proper.limit.plot.untr ) ) /\n asp$scatter.scale.breaks.coef ) ), 3 )\n marker.proper.breaks.untr <-\n get.scale.breaks.untransformed(\n marker.proper.limit.plot.untr,\n marker.proper.breaks.exp )\n marker.proper.breaks.tran <- y.transform(\n marker.proper.breaks.untr )\n\n gg.scale.x <- scale_x_continuous(\n breaks = marker.breaks.tran,\n labels = marker.breaks.untr,\n limits = marker.limit.plot.tran )\n\n gg.scale.y <- scale_y_continuous(\n breaks = marker.proper.breaks.tran,\n labels = marker.proper.breaks.untr,\n limits = marker.proper.limit.plot.tran )\n }\n\n if ( marker == marker.proper )\n {\n if ( asp$scatter.ref.line.unco )\n gg.ref.line.unco <- geom_abline( slope = 1,\n intercept = 0,\n color = asp$scatter.ref.line.color,\n linetype = \"dashed\",\n size = asp$scatter.ref.line.size.factor *\n asp$figure.scatter.line.size )\n else\n gg.ref.line.unco <- geom_blank()\n\n gg.ref.line.comp <- geom_abline( slope = 1,\n intercept = 0,\n color = asp$scatter.ref.line.color,\n linetype = \"dashed\",\n size = asp$scatter.ref.line.size.factor *\n asp$figure.scatter.line.size )\n }\n else\n {\n if ( asp$scatter.ref.line.unco )\n {\n if ( scale.same )\n marker.x.intercept.unco.plot <- marker.x.intercept.unco\n else\n marker.x.intercept.unco.plot <- ifelse(\n scale.untransformed,\n x.transform( marker.x.intercept.unco ),\n x.transform.inv( marker.x.intercept.unco )\n )\n\n gg.ref.line.unco <- geom_vline(\n xintercept = marker.x.intercept.unco.plot,\n color = asp$scatter.ref.line.color,\n linetype = \"dashed\",\n size = asp$scatter.ref.line.size.factor *\n asp$figure.scatter.line.size )\n }\n else\n gg.ref.line.unco <- geom_blank()\n\n if ( scale.same )\n marker.x.intercept.comp.plot <- marker.x.intercept.comp\n else\n marker.x.intercept.comp.plot <- ifelse(\n scale.untransformed,\n x.transform( marker.x.intercept.comp ),\n x.transform.inv( marker.x.intercept.comp )\n )\n\n gg.ref.line.comp <- geom_vline(\n xintercept = marker.x.intercept.comp.plot,\n color = asp$scatter.ref.line.color,\n linetype = \"dashed\",\n size = asp$scatter.ref.line.size.factor *\n asp$figure.scatter.line.size )\n }\n\n if ( scale.same )\n {\n if ( is.infinite( marker.slope.unco ) )\n gg.slope.line.unco <- geom_vline(\n xintercept = marker.x.intercept.unco,\n color = asp$scatter.expr.color.unco,\n size = asp$figure.scatter.line.size\n )\n else\n gg.slope.line.unco <- geom_abline(\n slope = marker.slope.unco,\n intercept = marker.intercept.unco,\n color = asp$scatter.expr.color.unco,\n size = asp$figure.scatter.line.size\n )\n\n if ( is.infinite( marker.slope.comp ) )\n gg.slope.line.comp <- geom_vline(\n xintercept = marker.x.intercept.comp,\n color = asp$scatter.expr.color.comp,\n size = asp$figure.scatter.line.size\n )\n else\n gg.slope.line.comp <- geom_abline(\n slope = marker.slope.comp,\n intercept = marker.intercept.comp,\n color = asp$scatter.expr.color.comp,\n size = asp$figure.scatter.line.size )\n }\n else\n {\n gg.slope.line.unco <- geom_blank()\n gg.slope.line.comp <- geom_blank()\n }\n\n gg.error.label <- annotate(\n \"label\",\n label = sprintf( \"%.4g\", spillover.comp.slop ),\n size = asp$figure.scatter.error.label.size,\n x = sum( c( 1 - asp$figure.scatter.error.label.pos.x,\n asp$figure.scatter.error.label.pos.x ) *\n gg.scale.x$limits ),\n y = sum( c( 1 - asp$figure.scatter.error.label.pos.y,\n asp$figure.scatter.error.label.pos.y ) *\n gg.scale.y$limits ),\n label.size = NA,\n alpha = 0.8\n )\n\n gg.label <- labs(\n x = flow.control$scatter.and.marker.label[ marker ],\n y = flow.control$scatter.and.marker.label[ marker.proper ]\n )\n\n gg.scale.color <- scale_color_manual(\n values = c( asp$scatter.expr.color.unco,\n asp$scatter.expr.color.comp ),\n guide = \"none\"\n )\n\n data.ggp <- expr.data.ggp\n\n if ( ! scale.same )\n {\n if ( scale.untransformed )\n {\n data.ggp$x <- x.transform( data.ggp$x )\n data.ggp$y <- y.transform( data.ggp$y )\n }\n else\n {\n data.ggp$x <- x.transform.inv( data.ggp$x )\n data.ggp$y <- y.transform.inv( data.ggp$y )\n }\n }\n\n the.plot <- ggplot( data.ggp, aes( .data$x, .data$y,\n color = .data$z ) ) +\n geom_point(\n size = 0.9 * asp$figure.scatter.point.size,\n stroke = 0.1 * asp$figure.scatter.point.size,\n alpha = ifelse( data.ggp$w,\n asp$figure.scatter.alpha.gate.in,\n asp$figure.scatter.alpha.gate.out ) ) +\n gg.slope.line.unco +\n gg.ref.line.unco +\n gg.slope.line.comp +\n gg.ref.line.comp +\n gg.error.label +\n gg.scale.x +\n gg.scale.y +\n gg.label +\n gg.scale.color +\n theme_bw() +\n theme( plot.margin = margin( asp$figure.margin,\n asp$figure.margin, asp$figure.margin,\n asp$figure.margin ),\n axis.ticks = element_line(\n size = asp$figure.panel.line.size ),\n axis.text = element_text(\n size = asp$figure.scatter.axis.text.size ),\n axis.title = element_text(\n size = asp$figure.scatter.axis.title.size ),\n panel.border = element_rect(\n size = asp$figure.panel.line.size ),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank() )\n\n figure.file.name <- sprintf(\n \"%s_%s_%s.png\", marker, figure.file.label,\n ifelse( xor( scale.same, ! scale.untransformed ),\n \"linear\", \"bi-exp\" )\n )\n\n ggsave(\n file.path( flow.control$figure.scatter.dir[ samp ],\n figure.file.name ),\n plot = the.plot, width = asp$figure.width,\n height = asp$figure.height\n )\n\n if ( asp$make.thumbnail )\n {\n if ( scale.same )\n {\n if ( marker == marker.proper )\n {\n if ( asp$scatter.ref.line.unco )\n gg.ref.line.unco$aes_params$size <-\n asp$scatter.ref.line.size.factor *\n asp$thumbnail.scatter.line.size\n\n gg.ref.line.comp$aes_params$size <-\n asp$scatter.ref.line.size.factor *\n asp$thumbnail.scatter.line.size\n }\n else\n {\n if ( asp$scatter.ref.line.unco )\n gg.ref.line.unco$aes_params$size <-\n asp$scatter.ref.line.size.factor *\n asp$thumbnail.scatter.line.size\n\n gg.ref.line.comp$aes_params$size <-\n asp$scatter.ref.line.size.factor *\n asp$thumbnail.scatter.line.size\n }\n }\n\n if ( scale.same )\n {\n if ( is.infinite( marker.slope.unco ) )\n gg.slope.line.unco$aes_params$size <-\n asp$thumbnail.scatter.line.size\n else\n gg.slope.line.unco$aes_params$size <-\n asp$thumbnail.scatter.line.size\n\n if ( is.infinite( marker.slope.comp ) )\n gg.slope.line.comp$aes_params$size <-\n asp$thumbnail.scatter.line.size\n else\n gg.slope.line.comp$aes_params$size <-\n asp$thumbnail.scatter.line.size\n }\n\n gg.error.label$aes_params$size <-\n asp$thumbnail.scatter.error.label.size\n gg.error.label$data$x <- sum(\n c( 1 - asp$thumbnail.scatter.error.label.pos.x,\n asp$thumbnail.scatter.error.label.pos.x ) *\n gg.scale.x$limits\n )\n gg.error.label$data$y <- sum(\n c( 1 - asp$thumbnail.scatter.error.label.pos.y,\n asp$thumbnail.scatter.error.label.pos.y ) *\n gg.scale.y$limits\n )\n\n the.plot <- ggplot( data.ggp, aes( .data$x, .data$y,\n color = .data$z ) ) +\n geom_point(\n size = 0.9 * asp$thumbnail.scatter.point.size,\n stroke = 0.1 * asp$thumbnail.scatter.point.size,\n alpha = ifelse( data.ggp$w,\n asp$thumbnail.scatter.alpha.gate.in,\n asp$thumbnail.scatter.alpha.gate.out ) ) +\n gg.slope.line.unco +\n gg.ref.line.unco +\n gg.slope.line.comp +\n gg.ref.line.comp +\n gg.error.label +\n gg.scale.x +\n gg.scale.y +\n gg.label +\n gg.scale.color +\n theme_bw() +\n theme( plot.margin = margin( asp$thumbnail.margin,\n asp$thumbnail.margin, asp$thumbnail.margin,\n asp$thumbnail.margin ),\n axis.ticks = element_line(\n size = asp$thumbnail.panel.line.size ),\n axis.text = element_text(\n size = asp$thumbnail.scatter.axis.text.size ),\n axis.title = element_text(\n size = asp$thumbnail.scatter.axis.title.size ),\n panel.border = element_rect(\n size = asp$thumbnail.panel.line.size ),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank() )\n\n figure.file.name <- sprintf(\n \"%s_%s_%s_thumbnail.png\", marker, figure.file.label,\n ifelse( xor( scale.same, ! scale.untransformed ),\n \"linear\", \"bi-exp\" )\n )\n\n ggsave(\n file.path( flow.control$figure.scatter.dir[ samp ],\n figure.file.name ),\n plot = the.plot, width = asp$thumbnail.width,\n height = asp$thumbnail.height\n )\n } # make.thumbnail\n } # scale.same TRUE or FALSE\n}\n\n", "meta": {"hexsha": "edaff324c6c5b98594f87a229dc4dffc047e648c", "size": 18900, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_scatter.r", "max_stars_repo_name": "hally166/autospill", "max_stars_repo_head_hexsha": "8e1f6f74fbafec5b91ed278260fbdb6678d482a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2020-08-07T21:48:31.000Z", 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YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.30938463226649343}} {"text": "#'### Functions to plot p-values for a set of pairwise differences\n#+\n\"plotPvalues.data.frame\" <- function(object, p = \"p\", x, y, \n gridspacing = 0, show.sig = FALSE, alpha = 0.10,\n sig.size = 3, sig.colour = \"black\", \n sig.face = \"plain\", sig.family = \"\", \n triangles = \"both\", \n title = NULL, axis.labels = NULL, axis.text.size = 12, \n colours = RColorBrewer::brewer.pal(3, \"Set2\"), \n ggplotFuncs = NULL, printPlot = TRUE, ...)\n #Plots a data.frame of p-values that has columns x, y, p\n{ \n if (!all(c(p,x,y) %in% names(object)))\n stop(\"One or more of the columns p, x and y are not in object\")\n options <- c(\"both\", \"upper\", \"lower\")\n tri.opt <- options[check.arg.values(triangles, options)]\n if (!(alpha %in% c(0.05, 0.10)))\n stop(\"alpha must be either 0.05 or 0.10\")\n plt <- NULL\n \n if (all(is.na(object[[p]])))\n warning(\"All p-values are NA for this plot\")\n else\n {\n object <- na.omit(object)\n object[x] <- factor(object[[x]])\n object[y] <- factor(object[[y]])\n labs <- sort(levels(object[[x]]))\n # if (any(labs != sort(levels(object[[y]]))))\n # stop(\"The row and column labels of differences are not the same\")\n plt <- ggplot(object, aes_string(x = x, y = y, fill=p)) +\n geom_tile() +\n scale_fill_gradientn(colours=colours, \n values = c(0, 0.001, 0.01, 0.05, 0.10, 1), \n limits=c(0,1)) +\n labs(x=axis.labels, y=axis.labels, title=title) + \n theme_bw() +\n theme(axis.text.x=element_text(angle=90, hjust=1, vjust=0.5, size=axis.text.size),\n axis.text.y=element_text(size=axis.text.size),\n plot.title=element_text(face=\"bold\"),\n panel.grid = element_blank(),\n legend.position = \"right\", \n legend.key.height=unit(2,\"lines\"), legend.key.width=unit(1.5,\"lines\"), \n aspect.ratio = 1) +\n guides(fill=guide_colourbar(title = \"p\", nbin=50))\n if (show.sig)\n { \n object$sig <- ifelse(object[p] > alpha, \"\",\n# ifelse(object[p] > 0.05, \".\",\n ifelse(object[p] > 0.01, \"*\",\n ifelse(object[p] > 0.001, \"**\",\n \"***\")))#)\n if (alpha == 0.1)\n if (any(object[p] <= alpha & object[p] > 0.05))\n object$sig[(object[p] <= alpha & object[p] > 0.05)] <- \".\"\n plt <- plt + geom_text(data=object, aes_string(label=\"sig\"), \n size=sig.size, colour = sig.colour, \n fontface = sig.face, family = sig.family)\n }\n \n if (gridspacing[1] > 0)\n {\n if (length(gridspacing) > 1)\n {\n grids <- cumsum(gridspacing)+0.5\n } else\n {\n nlabs <- length(labs)\n grids <- seq(gridspacing + 0.5, nlabs, gridspacing)\n }\n if (tri.opt == \"lower\")\n plt <- plt + geom_hline(yintercept = grids) + geom_vline(xintercept = grids - 1)\n else\n {\n if (tri.opt == \"upper\")\n plt <- plt + geom_hline(yintercept = grids - 1) + geom_vline(xintercept = grids)\n else\n plt <- plt + geom_hline(yintercept = grids) + geom_vline(xintercept = grids)\n }\n }\n \n if (!is.null(plt) && !is.null(ggplotFuncs))\n for (f in ggplotFuncs)\n plt <- plt + f\n \n if (!is.null(plt) && printPlot)\n print(plt)\n }\n invisible(plt)\n}\n\n\"plotPvalues.alldiffs\" <- function(object, sections = NULL, \n gridspacing = 0, factors.per.grid = 0, \n show.sig = FALSE, alpha = 0.10, \n sig.size = 3, sig.colour = \"black\", \n sig.face = \"plain\", sig.family = \"\",\n triangles = \"both\", \n title = NULL, axis.labels = TRUE, axis.text.size = 12, \n sep=\",\", colours = RColorBrewer::brewer.pal(3, \"Set2\"), \n ggplotFuncs = NULL, printPlot = TRUE, \n sortFactor = NULL, sortParallelToCombo = NULL, \n sortNestingFactor = NULL, sortOrder = NULL, decreasing = FALSE, \n ...)\n #Plots a matrix of p-values or, when predictions are for combinations of two \n #factors, produces a plot for each levels combination of nominated section factors \n #object is an all.diffs object with p.differences to be plotted\n #show.sig is a logical indicating whether to put stars onto plot\n #title is a character string giving the plot main title\n{ \n #Check that a valid object of class alldiffs\n validalldifs <- validAlldiffs(object) \n if (is.character(validalldifs))\n stop(validalldifs)\n object <- renameDiffsAttr(object)\n \n if (is.null(object$p.differences))\n stop(\"The p.differences component of object cannot be NULL\")\n if (all(is.na(object$p.differences)))\n stop(\"All p.differences are NA\")\n options <- c(\"both\", \"upper\", \"lower\")\n tri.opt <- options[check.arg.values(triangles, options)]\n \n \n #Sort alldiffs components, if required\n if (!is.null(sortFactor))\n object <- sort(object, decreasing = decreasing, sortFactor = sortFactor, \n sortParallelToCombo = sortParallelToCombo, sortNestingFactor = sortNestingFactor, \n sortOrder = sortOrder)\n \n classify <- attr(object, which = \"classify\")\n #Get differences and convert to a data.frame\n if (tri.opt == \"upper\") \n object$p.differences[lower.tri(object$p.differences)] <- NA\n else \n {\n if (tri.opt == \"lower\")\n object$p.differences[upper.tri(object$p.differences)] <- NA\n }\n p <- object$p.differences\n rownames(p) <- colnames(p) <- NULL #needed because reshape::melt throws a warning re type.convert\n p <- within(reshape::melt(p), \n { \n X1 <- factor(X1, labels=dimnames(object$p.differences)[[1]])\n X2 <- factor(X2, labels=levels(X1))\n })\n names(p)[match(\"value\", names(p))] <- \"p\"\n\n #prepare for plotting\n n <- nrow(p)\n facs <- fac.getinTerm(classify)\n if (any(is.na(match(facs, names(object$predictions)))))\n stop(\"Some factors in the classify for object are not in the predictions\")\n else\n facs[match(facs, names(object$predictions))] <- facs\n nfacs <- length(facs)\n #Function to calculate gridspacing\n autogridspace <- function(object, plotfacs, factors.per.grid = 0)\n {\n gridspace = 0\n if (length(plotfacs) > 1) #only compute gridspace for more than one factor per section\n {\n gridfacs <- plotfacs[order(1:(length(plotfacs)-factors.per.grid), decreasing = TRUE)]\n gridspace <- as.vector(table(object[gridfacs]))\n gridspace <- gridspace[-length(gridspace)]\n }\n return(gridspace)\n }\n \n #Do plots\n if (is.null(sections))\n { \n pairname <- NULL\n if (axis.labels)\n {\n pairname <- fac.getinTerm(classify)\n pairname <- paste(pairname, collapse = \", \")\n\n }\n #Do single plot\n if (factors.per.grid > 0)\n gridspacing <- autogridspace(object = object$predictions, plotfacs = facs, \n factors.per.grid = factors.per.grid)\n if (is.null(title))\n plotPvalues.data.frame(object = p, x = \"X1\", y = \"X2\", \n gridspacing = gridspacing, show.sig = show.sig, alpha = alpha, \n sig.size = sig.size, sig.colour = sig.colour, \n sig.face = sig.face, sig.family = sig.family, \n triangles = triangles, \n axis.labels = pairname, colours = colours, \n printPlot = printPlot, \n axis.text.size = axis.text.size, ggplotFuncs = ggplotFuncs)\n else\n plotPvalues.data.frame(object = p, x = \"X1\", y = \"X2\", \n gridspacing = gridspacing, show.sig = show.sig, alpha = alpha, \n sig.size = sig.size, sig.colour = sig.colour, \n sig.face = sig.face, sig.family = sig.family, \n triangles = triangles, \n title = title, axis.labels = pairname, \n colours = colours, printPlot = printPlot, \n axis.text.size = axis.text.size, ggplotFuncs = ggplotFuncs)\n } else #have sections\n { \n #Prepare for sectioning\n sec.pos <- match(sections, facs)\n pairdiffs <- facs[-match(sections, facs)]\n pd.pos <- match(pairdiffs, facs)\n facOrg <- function(p.fac, p, object, facs, sections, pairdiffs, sep)\n {\n facs.levs <- strsplit(as.character(p[[p.fac]]), split=sep, fixed=TRUE)\n facs.levs <- as.data.frame(do.call(rbind, facs.levs), stringsAsFactors = FALSE)\n names(facs.levs) <- facs\n facs.levs <- as.data.frame(lapply(facs, \n function(fac, facs.levs, predictions)\n {\n levs <- levels(factor(predictions[[fac]]))\n new.fac <- factor(facs.levs[[fac]], levels = levs)\n return(new.fac)\n }, \n facs.levs = facs.levs, \n predictions = object$predictions\n ), stringsAsFactors = FALSE)\n names(facs.levs) <- facs\n p$sections <- fac.combine(facs.levs[sections], combine.levels = TRUE)\n p[p.fac] <- fac.combine(facs.levs[pairdiffs], combine.levels = TRUE)\n return(p)\n }\n \n p <- facOrg(\"X1\", p, object, facs, sections = sections, pairdiffs = pairdiffs, sep = sep)\n names(p)[match(c(\"sections\"), names(p))] <- \"sections1\"\n p <- facOrg(\"X2\", p, object, facs, sections = sections, pairdiffs = pairdiffs, sep = sep)\n names(p)[match(c(\"sections\"), names(p))] <- \"sections2\"\n\n sect.lev <- levels(p$sections1)\n if (!all(sect.lev == levels(p$sections2)))\n stop(\"Sectioning levels in rows and columns of p.differences do not match\")\n \n #Do plot for each section\n sec.name <- pairname <- NULL\n if (axis.labels)\n {\n secname <- paste(sections, collapse = ', ')\n pairname <- paste(pairdiffs, collapse = ', ')\n }\n if (factors.per.grid > 0)\n object$predictions$sections <- fac.combine(object$predictions[sections], \n combine.levels = TRUE)\n for (j in sect.lev)\n { \n psect <- p[p$sections1==j & p$sections2==j, ]\n if (factors.per.grid > 0)\n {\n objsect <- object$predictions[object$predictions$sections == j,]\n gridspacing <- autogridspace(object = objsect, plotfacs = pairdiffs, \n factors.per.grid = factors.per.grid)\n }\n if (is.null(title))\n plotPvalues.data.frame(object = psect, x = \"X1\", y = \"X2\", \n gridspacing = gridspacing, show.sig = show.sig, alpha = alpha, \n sig.size = sig.size, sig.colour = sig.colour, \n sig.face = sig.face, sig.family = sig.family, \n triangles = triangles, \n title = paste(\"Plot of p-values for \",\n secname,\" = \",j, sep = \"\"),\n axis.labels = pairname, colours = colours, \n printPlot = printPlot, \n axis.text.size = axis.text.size, ggplotFuncs = ggplotFuncs)\n else\n plotPvalues.data.frame(object = psect, x = \"X1\", y = \"X2\", \n gridspacing = gridspacing, show.sig = show.sig, alpha = alpha, \n sig.size = sig.size, sig.colour = sig.colour, \n sig.face = sig.face, sig.family = sig.family, \n triangles = triangles, \n title = paste(title,\" - \", secname,\" = \", j, sep=\"\"),\n axis.labels = pairname, colours = colours, \n printPlot = printPlot, \n axis.text.size = axis.text.size, ggplotFuncs = ggplotFuncs)\n \n } \n }\n invisible(p)\n}\n", "meta": {"hexsha": "827c40f89722088f28ab4b22fcbfe3d6da37aa0f", "size": 12695, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plotPvalues.r", "max_stars_repo_name": "briencj/asremlPlus", "max_stars_repo_head_hexsha": "108fb4d50e644fa4c46c8265ac9c1583ce10c2d5", "max_stars_repo_licenses": ["MIT", "BSD-3-Clause"], "max_stars_count": 12, "max_stars_repo_stars_event_min_datetime": "2018-05-14T19:51:27.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T07:18:38.000Z", "max_issues_repo_path": "R/plotPvalues.r", "max_issues_repo_name": "briencj/asremlPlus", "max_issues_repo_head_hexsha": "108fb4d50e644fa4c46c8265ac9c1583ce10c2d5", "max_issues_repo_licenses": ["MIT", "BSD-3-Clause"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-02-04T02:01:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-09T10:17:23.000Z", "max_forks_repo_path": "R/plotPvalues.r", "max_forks_repo_name": "briencj/asremlPlus", "max_forks_repo_head_hexsha": "108fb4d50e644fa4c46c8265ac9c1583ce10c2d5", "max_forks_repo_licenses": ["MIT", "BSD-3-Clause"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2018-04-23T08:49:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-12T11:31:27.000Z", "avg_line_length": 45.0177304965, "max_line_length": 99, "alphanum_fraction": 0.5120913746, "num_tokens": 3105, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5698526514141572, "lm_q1q2_score": 0.3093520812712465}} {"text": "library(ggplot2)\r\nlibrary(ggvis)\r\nlibrary(plotly)\r\n# 3. faza: Vizualizacija podatkov\r\n\r\n# Uvozimo zemljevid.\r\n#zemljevid <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/OB.zip\", \"OB\",\r\n# pot.zemljevida=\"OB\", encoding=\"Windows-1250\")\r\n#levels(zemljevid$OB_UIME) <- levels(zemljevid$OB_UIME) %>%\r\n# { gsub(\"Slovenskih\", \"Slov.\", .) } %>% { gsub(\"-\", \" - \", .) }\r\n#zemljevid$OB_UIME <- factor(zemljevid$OB_UIME, levels=levels(obcine$obcina))\r\n#zemljevid <- fortify(zemljevid)\r\n\r\n# Izra\u0102\u201e\u0139\u00a4unamo povpre\u0102\u201e\u0139\u00a4no velikost dru\u00c4\u0105\u00c4\u013eine\r\n#povprecja <- druzine %>% group_by(obcina) %>%\r\n# summarise(povprecje=sum(velikost.druzine * stevilo.druzin) / sum(stevilo.druzin))\r\n\r\n######\r\nggplot(t1) + aes(x=igralec, y=cleanSheet) + geom_bar(stat=\"identity\")\r\ng1 <- ggplot(t1) + aes(igralec=igralec, ekipa=ekipa, x=appearances, y=cleanSheet, color=drzava) + geom_point() + ggtitle(\"Tekme brez prejetega zadetka\") + xlab(\"\u0160tevilo nastopov\") + ylab(\"Tekme brez prejetega zadetka\")+labs(colour= \"dr\u017eava\")\r\ng11 <- ggplotly(g1)\r\nggplot(t2) + aes(x=penaltyFaced, y=penaltySave, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Posredovanje pri enajstmetrovkah\") + xlab(\"\u0139\u00a0tevilo enajstmetrovk\") + ylab(\"\u0139\u00a0tevilo obranjenih enajstmetrovk\") # + geom_line(data=t2, aes(x=penaltyFaced, y=penaltySave), color=\"green\")\r\nggplot(t2) + aes(x=savedShotsFromInsideTheBox, y=goalsConcededInsideTheBox, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Posredovanje pri strelih znotraj kazenskega prostora\") + xlab(\"Obranjeni streli znotraj kazenskega prostora\") + ylab(\"Prejeti zadetki znotraj kazenskega prostora\")\r\nggplot(t2) + aes(x=savedShotsFromOutsideTheBox, y=goalsConcededOutsideTheBox, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Posredovanje pri strelih izven kazenskega prostora\") + xlab(\"Obranjeni streli izven kazenskega prostora\") + ylab(\"Prejeti zadetki izven kazenskega prostora\")\r\nggplot(t3) + aes(x=runsOut, y=successfulRunsOut, color=drzava) + geom_point() + geom_text(aes(label=ifelse(successfulRunsOut>18,as.character(igralec),'')),hjust=0, vjust=0, size=3) + ggtitle(\"Iztekanja\") + xlab(\"\u0139\u00a0tevilo iztekov\") + ylab(\"\u0139\u00a0tevilo uspe\u0139\u02c7nih iztekov\")\r\nggplot(t3) + aes(x=highClaims, y=crossesNotClaimed, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Posredovanje pri predlo\u0139\u013ekih\") + xlab(\"\u0139\u00a0tevilo ujetih predlo\u0139\u013ekov\") + ylab(\"\u0139\u00a0tevilo napak pri predlo\u0139\u013ekih\")\r\nggplot(t4) + aes(x=ekipa, y=totalPasses, size=accuratePassesPercentage, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Podaje\") + xlab(\"Ekipa\") + ylab(\"\u0139\u00a0tevilo podaj\")\r\nggplot(t4) + aes(x=accurateLongBallsPercentage, y=accurateLongBalls, size=accurateLongBallsPercentage, color=drzava) + geom_point() + geom_text(aes(label=igralec),hjust=0, vjust=0, size=3) + ggtitle(\"Dolge \u0139\u02ddoge\") + xlab(\"Odstotek natan\u00c4\u0164nosti pri dolgih \u0139\u013eogah\") + ylab(\"\u0139\u00a0tevilo dolgih \u0139\u013eog\")\r\n\r\nt5 <- t1 %>% group_by(drzava) %>% summarise(cleanSheet_drzava=sum(cleanSheet))\r\nggplot(t5) + aes(x=drzava, y=cleanSheet_drzava, fill=drzava) + geom_bar(stat=\"identity\") + guides(fill=FALSE) + ggtitle(\"Tekme brez prejetega zadetka, glede na dr\u0139\u013eave\") + xlab(\"\") + ylab(\"Tekme brez prejetega zadetka\")\r\n\r\nt6 <- t2 %>% group_by(drzava) %>% summarise(penaltySave_drzava=sum(penaltySave))\r\nggplot(t6) + aes(x=drzava, y=penaltySave_drzava, fill=drzava) + geom_bar(stat=\"identity\") + guides(fill=FALSE) + ggtitle(\"Obranjena enajstmetrovke, glede na dr\u0139\u013eave\") + xlab(\"\") + ylab(\"\u0139\u00a0tevilo obranjenih enajstmetrovk\")\r\n\r\nt6.2 <- t2 %>% group_by(drzava) %>% summarise(penaltyFaced_drzava=sum(penaltyFaced))\r\npenaltyFaced_drzava <- t6.2[[2]]\r\nt6.3 <- cbind(t6, penaltyFaced_drzava)\r\nt6.3[,2] <- ifelse(t6.3$drzava == 'Germany', t6.3[,2] * (380/340), t6.3[,2])\r\nt6.3[,3] <- ifelse(t6.3$drzava == 'Germany', t6.3[,3] * (380/340), t6.3[,3])\r\nggplot(t6.3) + aes(x=penaltyFaced_drzava, y=penaltySave_drzava) + geom_point() + geom_text(aes(label=drzava),hjust=0, vjust=0) + ggtitle(\"Enajstmetrovke, glede na dr\u0139\u013eave\") #+ geom_smooth(method = \"lm\")\r\n\r\n\r\nggplot(t6.3, aes(x=drzava)) + \r\n geom_col(aes(y=penaltyFaced_drzava, fill=\"Neobranjene enajstmetrovke\")) + \r\n geom_col(aes(y=penaltySave_drzava, fill=\"Obranjene enajstmetrovke\")) + \r\n xlab(\"\") + ylab(\"Vse enajstmetrovke v ligi\") + ggtitle(\"Enajstmetrovke glede na dr\u0139\u013eave\")\r\n\r\n\r\nt7 <- t2 %>% group_by(drzava) %>% summarise(goalsConcededOutsideTheBox_drzava=sum(goalsConcededOutsideTheBox))\r\nggplot(t7) + aes(x=drzava, y=goalsConcededOutsideTheBox_drzava, fill=drzava) + geom_bar(stat=\"identity\") + guides(fill=FALSE)\r\n\r\nt8 <- t2 %>% group_by(drzava) %>% summarise(goalsConcededInsideTheBox_drzava=sum(goalsConcededInsideTheBox))\r\nggplot(t8) + aes(x=drzava, y=goalsConcededInsideTheBox_drzava, fill=drzava) + geom_bar(stat=\"identity\") + guides(fill=FALSE)\r\n\r\ngoalsConcededInsideTheBox_drzava<- t8[[2]]\r\nt9 <- cbind(t7, goalsConcededInsideTheBox_drzava)\r\n\r\nt9[,2] <- ifelse(t9$drzava == 'Germany', t9[,2] * (380/340), t9[,2])\r\nt9[,3] <- ifelse(t9$drzava == 'Germany', t9[,3] * (380/340), t9[,3])\r\n\r\nggplot(t9, aes(x=drzava)) + \r\n geom_col(aes(y=goalsConcededOutsideTheBox_drzava+goalsConcededInsideTheBox_drzava, fill=\"Zadetki izven 16m\")) + \r\n geom_col(aes(y=goalsConcededInsideTheBox_drzava, fill=\"Zadetki znotraj 16m\")) + \r\n xlab(\"\") + ylab(\"\u0139\u00a0tevilo vseh zadetkov v ligi\") + ggtitle(\"Prejeti zadetki glede na dr\u0139\u013eave\")\r\n\r\n\r\n\r\n# #tortni diagram za ita, za cleansheete \r\n# bp<- ggplot(podatki_v_ap_cs_ita, aes(x=\"\", y=cleanSheet, fill=igralec))+\r\n# geom_bar(width = 1, stat = \"identity\")\r\n# bp\r\n# pie <- bp + coord_polar(\"y\", start=0)\r\n# pie\r\n#za vse\r\nbp<- ggplot(t6, aes(x=\"\", y=penaltySave_drzava, fill=drzava))+\r\n geom_bar(width = 1, stat = \"identity\")\r\nbp\r\npie <- bp + coord_polar(\"y\", start=0)\r\npie\r\n\r\n\r\n# Uvozimo zemljevid.\r\nzemljevid <- uvozi.zemljevid(\"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/110m_cultural.zip\",\r\n \"ne_110m_admin_0_countries\", encoding=\"UTF-8\")\r\n\r\n# a <- data.frame(zemljevid) %>% filter(CONTINENT==\"Europe\")\r\n# tm_shape(merge(a,\r\n# t2 %>% group_by(drzava) %>% summarise(penaltySave=sum(penaltySave)),\r\n# by.x=\"SOVEREIGNT\", by.y=\"drzava\")) +\r\n# tm_polygons(\"penaltySave\") + ggtitle(\"Obranjena enajstmetrovke, glede na dr\u0139\u013eave\")\r\n# \r\n# \r\n# tm_shape(merge(zemljevid,\r\n# t1 %>% group_by(drzava) %>% summarise(cleanSheet=sum(cleanSheet)),\r\n# by.x=\"SOVEREIGNT\", by.y=\"drzava\")) +\r\n# tm_polygons(\"cleanSheet\") + ggtitle(\"Tekme brez prejetega zadetka, glede na dr\u0139\u013eave\")\r\n\r\nzemljevid1 <- zemljevid[zemljevid$CONTINENT == \"Europe\",]\r\ntm_shape(merge(zemljevid1,\r\n t1 %>% group_by(drzava) %>% summarise(cleanSheet=sum(cleanSheet)),\r\n by.x=\"SOVEREIGNT\", by.y=\"drzava\"), xlim=c(-15, 35), ylim=c(32, 72)) +\r\n tm_polygons(\"cleanSheet\") + ggtitle(\"Tekme brez prejetega zadetka, glede na dr\u0139\u013eave\")\r\n\r\n\r\ntm_shape(merge(zemljevid1,\r\n t2 %>% group_by(drzava) %>% summarise(penaltySave=sum(penaltySave)),\r\n by.x=\"SOVEREIGNT\", by.y=\"drzava\"), xlim=c(-15, 35), ylim=c(32, 72)) +\r\n tm_polygons(\"penaltySave\") + ggtitle(\"Obranjene enajstmetrovke, glede na dr\u0139\u013eave\")\r\n\r\n\r\nggplotly(ggplot(t1) + aes(igralec=igralec, ekipa= ekipa, x=appearances, y=cleanSheet, color=drzava) + geom_point() + ggtitle(\"Tekme brez prejetega zadetka\") + xlab(\"\u0139\u00a0tevilo nastopov\") + ylab(\"Tekme brez prejetega zadetka\") + geom_smooth(aes(group=drzava), method = \"lm\", se=FALSE) +labs(colour= \"dr\u017eava\"))\r\n\r\n# gg <- ggplotly(p)\r\n# gg\r\n\r\n\r\n\r\n", "meta": {"hexsha": "125a9ae815260247f4c578c705337bbe7bafe946", "size": 7741, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija_moja.r", "max_stars_repo_name": "mperbil/APPR-2019-20", "max_stars_repo_head_hexsha": "f0b3ef47e9fa1c4011e5dca8003b793b391b0792", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija_moja.r", 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YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30935208127124647}} {"text": "context(\"Creating aesthetic mappings\")\n\ntest_that(\"function aes\", {\n expect_equal(aes(x = mpg, y = wt),\n structure(list(x = bquote(mpg), y = bquote(wt)), class = \"uneval\"))\n\n expect_equal(aes(x = mpg ^ 2, y = wt / cyl),\n structure(list(x = bquote(mpg ^ 2), y = bquote(wt / cyl)), class = \"uneval\"))\n\n})\n\ntest_that(\"function aes_string\", {\n expect_equal(aes_string(x = \"mpg\", y = \"wt\"),\n structure(list(x = bquote(mpg), y = bquote(wt)), class = \"uneval\"))\n\n expect_equal(aes_string(x = \"mpg ^ 2\", y = \"wt / cyl\"),\n structure(list(x = bquote(mpg ^ 2), y = bquote(wt / cyl)), class = \"uneval\"))\n})\n\ntest_that(\"function aes_all\", {\n expect_equal(aes_all(names(mtcars)),\n structure(\n list(\n mpg = bquote(mpg),\n cyl = bquote(cyl),\n disp = bquote(disp),\n hp = bquote(hp),\n drat = bquote(drat),\n wt = bquote(wt),\n qsec = bquote(qsec),\n vs = bquote(vs),\n am = bquote(am),\n gear = bquote(gear),\n carb = bquote(carb)),\n class = \"uneval\"))\n\n expect_equal(aes_all(c(\"x\", \"y\", \"col\", \"pch\")),\n structure(list(x = bquote(x), y = bquote(y), colour = bquote(col), shape = bquote(pch)), class = \"uneval\"))\n})\n\ntest_that(\"function aes_auto\", {\n df <- data.frame(x = 1, y = 1, colour = 1, label = 1, pch = 1)\n expect_equal(aes_auto(df),\n structure(list(colour = bquote(colour), label = bquote(label), shape = bquote(pch), x = bquote(x), y = bquote(y)), class = \"uneval\"))\n\n expect_equal(aes_auto(names(df)),\n structure(list(colour = bquote(colour), label = bquote(label), shape = bquote(pch), x = bquote(x), y = bquote(y)), class = \"uneval\"))\n\n df <- data.frame(xp = 1:3, y = 1:3, colour = 1:3, txt = letters[1:3], foo = 1:3)\n expect_equal(aes_auto(df, x = xp, label = txt),\n structure(list(colour = bquote(colour), y = bquote(y), x = bquote(xp), label = bquote(txt)), class = \"uneval\"))\n expect_equal(aes_auto(names(df), x = xp, label = txt),\n structure(list(colour = bquote(colour), y = bquote(y), x = bquote(xp), label = bquote(txt)), class = \"uneval\"))\n expect_equal(aes_auto(x = xp, label = txt, data = df),\n structure(list(colour = bquote(colour), y = bquote(y), x = bquote(xp), label = bquote(txt)), class = \"uneval\"))\n\n df <- data.frame(foo = 1:3)\n expect_equal(aes_auto(df, x = xp, y = yp),\n structure(list(x = bquote(xp), y = bquote(yp)), class = \"uneval\"))\n expect_equal(aes_auto(df), structure(setNames(list(), character(0)), class = \"uneval\"))\n})\n\n", "meta": {"hexsha": "07449aa964e3f4d2d8428a3f5e054e16dd510e8e", "size": 2755, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-aes.r", "max_stars_repo_name": "lehoangha/GSOE9712_S115_RA", "max_stars_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-aes.r", "max_issues_repo_name": "lehoangha/GSOE9712_S115_RA", "max_issues_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.2.1/ggplot2/tests/test-aes.r", "max_forks_repo_name": "lehoangha/GSOE9712_S115_RA", "max_forks_repo_head_hexsha": "f797a32c9bd1a9c906177ab4749cf8196f88e044", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.7301587302, "max_line_length": 148, "alphanum_fraction": 0.5372050817, "num_tokens": 785, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.30935208127124647}} {"text": "# Convert the output GeoTIFF to KMZ\n\nlibrary(raster)\n\nInputFile = \"../data/pixel-based/predictions/randomforest-median-threestep-walltowall.tif\"\nOutputFile = \"../output/global-lc-fraction-map.kmz\"\n\nTIFFFile = brick(InputFile)\nTIFFFile = TIFFFile[[-1]]\nKML(TIFFFile, OutputFile, maxpixels = ncell(TIFFFile), blur=1, col=gray.colors(101, 0, 1), zlim=c(0, 100), overwrite=TRUE)\n", "meta": {"hexsha": "0f7f01398f1bd5961d8ac60c5ba16808026a73e6", "size": 375, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pixel-based/post-classification/tiff-to-kmz.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/pixel-based/post-classification/tiff-to-kmz.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pixel-based/post-classification/tiff-to-kmz.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 34.0909090909, "max_line_length": 122, "alphanum_fraction": 0.7466666667, "num_tokens": 121, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3093520733398365}} {"text": "REBOL []\n\nname: 'Crypt\nloadable: no ;tls depends on this, so it has to be builtin\nsource: %crypt/mod-crypt.c\nincludes: reduce [\n ;\n ; Added so `#include \"bigint/bigint.h` can be found by %rsa.h\n ; and `#include \"rsa/rsa.h\" can be found by %dh.c\n ;\n src-dir/extensions/crypt\n %prep/extensions/crypt ;for %tmp-extensions-crypt-init.inc\n]\ndepends: [\n [\n %crypt/aes/aes.c\n\n ; May 2018 update to MSVC 2017 added warnings about Spectre\n ; mitigation. The JPG code contains a lot of code that would\n ; trigger slowdown. It is not a priority to rewrite, given\n ; that some other vetted 3rd party JPG code should be used.\n ;\n ;-- https://stackoverflow.com/q/50399940\n ]\n\n [\n %crypt/bigint/bigint.c\n\n ; See above remarks on Spectre. This may be a priority to\n ; address, if bigint is used in INTEGER!.\n ;\n ;-- https://stackoverflow.com/q/50399940\n ]\n\n %crypt/dh/dh.c\n %crypt/rc4/rc4.c\n %crypt/rsa/rsa.c\n %crypt/sha256/sha256.c\n]\n", "meta": {"hexsha": "68c1da1182b704d42c189b8a5306770afc678050", "size": 1076, "ext": "r", "lang": "R", "max_stars_repo_path": "src/extensions/crypt/make-spec.r", "max_stars_repo_name": "iArnold/ren-c", "max_stars_repo_head_hexsha": "35418a15faf842499bcf51d0e066035b31a1bc53", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/extensions/crypt/make-spec.r", "max_issues_repo_name": "iArnold/ren-c", "max_issues_repo_head_hexsha": "35418a15faf842499bcf51d0e066035b31a1bc53", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/extensions/crypt/make-spec.r", "max_forks_repo_name": "iArnold/ren-c", "max_forks_repo_head_hexsha": "35418a15faf842499bcf51d0e066035b31a1bc53", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.9, "max_line_length": 69, "alphanum_fraction": 0.6152416357, "num_tokens": 320, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410572017153, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3093504205392397}} {"text": "library(ggplot2)\n\ntabledir <- './tables'\nfigdir <- './figures'\n\n# Note: These plots are generated from 5kb bins files, and intersections of\n# bigWig files with bed files provided as supplementary data: IAPEz_consensus,\n# ChromHMM17, RepMasker_lt200bp_all and TSS_hi.\n\n# These tables can be downloaded from the extra data folder.\n\n# Figure 6F - 5kb bins Scatterplot H3.3 wildtype vs Smarcad1KD vs H3K9me3,\n# IAP-overlapping bins highlighted\n\nbins.df <- read.csv(paste(tabledir,\n 'fig06_f_H33WT_vs_Smarcad1KD_vs_H3K9me3_5kb_bins.csv',\n sep='/'))\n\niap.bins.df <- read.csv(paste(tabledir,\n 'fig06_f_H33WT_vs_Smarcad1KD_vs_H3K9me3_5kb_bins_at_IAP.csv',\n sep='/'))\n\n# Drop zeros in the input\nbins.df <- bins.df[bins.df$Navarro2020_WT_Input > 0 & bins.df$Navarro2020_Smarcad1KD_Input > 0, ]\n\nbins.df$H33wt_norm <- bins.df$Navarro2020_WT_H33 / bins.df$Navarro2020_WT_Input\nbins.df$H33smarcad1KD_norm <- bins.df$Navarro2020_Smarcad1KD_H33 / bins.df$Navarro2020_Smarcad1KD_Input\n\n# Drop zeros in smarcad1KD norm, if any\nbins.df <- bins.df[bins.df$H33smarcad1KD_norm > 0, ]\n\nbins.df$H33fc_norm <- log2(bins.df$H33wt_norm / bins.df$H33smarcad1KD_norm)\n\niap.bins.df$H33wt_norm <- iap.bins.df$Navarro2020_WT_H33 / iap.bins.df$Navarro2020_WT_Input\niap.bins.df$H33smarcad1KD_norm <- iap.bins.df$Navarro2020_Smarcad1KD_H33 / iap.bins.df$Navarro2020_Smarcad1KD_Input\niap.bins.df$H33fc_norm <- log2(iap.bins.df$H33wt_norm / iap.bins.df$H33smarcad1KD_norm)\n\nggplot(bins.df, aes(x=H33fc_norm, y=Shi2019_H3K9me3)) + \n geom_bin2d(binwidth=c(0.02,0.02)) +\n geom_point(data=iap.bins.df, aes(x=H33fc_norm, y=Shi2019_H3K9me3), color='#32a472', size=0.8, alpha=0.6) +\n scale_fill_gradient(low='#dddddd', high='#333333') + #, high='#bb3215') +\n theme_classic() +\n xlim(-3, 3) +\n ylim(0, 30) +\n xlab(\"log2(H33 WT/ Smarcad1KD)\") +\n ylab(\"Shi2019 H3K9me3 RPGC\") +\n geom_vline(xintercept=0, linetype='dashed') +\n ggtitle(\"Smarcad1 vs H33 WT / Smarcad1KD (5kb bins)\")\n\nggsave(paste(figdir, 'fig06_f_H33wt_vs_Smarcad1KD_vs_H3K9me3_scatterplot.png', sep='/'))\n\n\n\n", "meta": {"hexsha": "43585e93f2dccdb456fce2dfaef76db95b9b6bb0", "size": 2145, "ext": "r", "lang": "R", "max_stars_repo_path": "src/fig_6f_h33wt_vs_smarcad1kd_vs_h3k9me3_scatterplot.r", "max_stars_repo_name": "elsasserlab/publicchip", "max_stars_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-28T15:13:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-28T15:13:33.000Z", "max_issues_repo_path": "src/fig_6f_h33wt_vs_smarcad1kd_vs_h3k9me3_scatterplot.r", "max_issues_repo_name": "elsasserlab/publicchip", "max_issues_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/fig_6f_h33wt_vs_smarcad1kd_vs_h3k9me3_scatterplot.r", "max_forks_repo_name": "elsasserlab/publicchip", "max_forks_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7222222222, "max_line_length": 115, "alphanum_fraction": 0.717016317, "num_tokens": 781, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6654105454764747, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.30935040820043663}} {"text": "# 2. faza: Uvoz podatkov\n\nsource(\"lib/libraries.r\")\n\nsl <- locale(\"sl\", decimal_mark=\",\", grouping_mark=\".\")\n\n#1. Tabela najemni\u0161kih poslov:\n\n\n\nfor (i in 2013:2020){\n\n najem.delistavb = read_csv2(sprintf(\"podatki/ETN_SLO_NAJ_%s_20211127/ETN_SLO_NAJ_%s_delistavb_20211127.csv\",i, i),\n col_types = cols(\n .default = col_guess(),\n \"Uporabna povr\u0161ina dela stavbe\" = col_double()\n ))\n \n najem.posli = read_csv2(sprintf(\"podatki/ETN_SLO_NAJ_%s_20211127/ETN_SLO_NAJ_%s_posli_20211127.csv\", i, i))\n \n zdruzeno = left_join(najem.posli, najem.delistavb, by=\"ID Posla\")\n \n \n \n zdruzeno = zdruzeno %>% relocate(\n \"id.posla\" = \"ID Posla\",\n \"obcina\" = colnames(zdruzeno)[22],\n \"mesecna.najemnina\" = \"Pogodbena najemnina\",\n \"povrsina\" = \"Povr\u0161ina oddanih prostorov\",\n \"uporabna.povrsina\" = \"Uporabna povr\u0161ina dela stavbe\",\n \"tip.stavbe\" = \"Vrsta oddanih prostorov\",\n \"leto.izgradnje\" = \"Leto izgradnje stavbe\",\n )\n \n #odstranimo veckratne vnose in odstranimo nezeljene podatke:\n prestej.vnose = function(v){\n stetje = c()\n vektor = v\n for (el in vektor){\n stetje = c(stetje, length(which(vektor == el)))\n }\n return(stetje)\n }\n \n zdruzeno = zdruzeno %>% filter(prestej.vnose(id.posla) == 1) %>% \n select(id.posla, obcina, mesecna.najemnina, povrsina, uporabna.povrsina, tip.stavbe, leto.izgradnje) %>%\n drop_na()\n \n #dodamo letnico:\n \n zdruzeno = zdruzeno %>% mutate(leto.posla = i)\n \n if (i == 2013){\n tabela.najemnin = zdruzeno\n } else{\n tabela.najemnin = rbind(tabela.najemnin, zdruzeno)\n }\n \n}\n\n\ntabela.najemnin = tabela.najemnin %>% mutate(obcina = tolower(obcina)) %>% mutate(obcina = str_to_sentence(obcina)) %>%\n select(-id.posla)\n\netn.sifrant.najemnine = tibble(\n \"tip.stavbe\" = c(1:16),\n \"etn.tip.prostora\" = c(\n \"Stanovanjska hisa\", \n \"Stanovanje\", \n \"Parkirni prostor\",\n \"Garaza\",\n \"Pisarniski prostori\",\n \"Prostori za poslovanje s strankami\",\n \"Prostori za zdravstveno dejavnost\",\n \"Trgovski ali storitveni lokal\",\n \"Gostinski lokal\",\n \"Prostori za sport, kulturo ali izobrazevanje\",\n \"Industrijski prostori\",\n \"Turisticni nastanitveni objekt\",\n \"Kmetijski objekt\",\n \"Tehnicni ali pomozni prostori\",\n \"Drugo\",\n \"Stanovanjska soba ali sobe\")\n )\n\ntabela.najemnin = tabela.najemnin %>% left_join(etn.sifrant.najemnine, by=\"tip.stavbe\") %>% select(\n obcina, mesecna.najemnina, povrsina, uporabna.povrsina, tip.prostora = etn.tip.prostora, leto.izgradnje, leto.posla\n) \n\ntabela.najemnin = tabela.najemnin %>% filter(tip.prostora != \"Stanovanjska soba ali sobe\") %>% mutate(uporabna.povrsina = as.numeric(uporabna.povrsina)) %>% \n mutate(tip.prostora = as.factor(tip.prostora))\n\n#odstranjevanje nesmiselnih vnosov:\n\ntabela.najemnin = tabela.najemnin %>% filter(povrsina >= (uporabna.povrsina) & uporabna.povrsina >= 0.7 * povrsina) %>% \n filter(povrsina != 0) %>% \n mutate(najemnina.na.kvadratni.meter = mesecna.najemnina / povrsina) %>% \n filter(najemnina.na.kvadratni.meter < 100 & najemnina.na.kvadratni.meter > 0.5) %>% select(-najemnina.na.kvadratni.meter)\n\n#print(tabela.najemnin)\n \n#2. Tabela kupoprodajnih poslov:\n\nfor (i in 2013:2020){\n \n nakup.delistavb = read_csv2(sprintf(\"podatki/ETN_SLO_KUP_%s_20211127/ETN_SLO_KUP_%s_delistavb_20211127.csv\",i, i))\n \n nakup.posli = read_csv2(sprintf(\"podatki/ETN_SLO_KUP_%s_20211127/ETN_SLO_KUP_%s_posli_20211127.csv\", i, i))\n \n zdruzeno = left_join(nakup.posli, nakup.delistavb, by=\"ID Posla\")\n \n zdruzeno = zdruzeno %>% relocate(\n \"id.posla\" = \"ID Posla\",\n \"obcina\" = colnames(zdruzeno)[18],\n \"prodajna.cena\" = colnames(zdruzeno)[5],\n \"povrsina\" = colnames(zdruzeno)[34],\n \"uporabna.povrsina\" = colnames(zdruzeno)[47],\n \"tip.stavbe\" = \"Vrsta dela stavbe\",\n \"leto.izgradnje\" = \"Leto izgradnje dela stavbe\",\n )\n \n #odstranimo veckratne vnose in odstranimo nezeljene podatke:\n prestej.vnose = function(v){\n stetje = c()\n vektor = v\n for (el in vektor){\n stetje = c(stetje, length(which(vektor == el)))\n }\n return(stetje)\n }\n \n zdruzeno = zdruzeno %>% filter(prestej.vnose(id.posla) == 1) %>% \n select(id.posla, obcina, prodajna.cena, povrsina, uporabna.povrsina, tip.stavbe, leto.izgradnje) %>%\n drop_na()\n \n #dodamo letnico:\n \n zdruzeno = zdruzeno %>% mutate(leto.posla = i)\n \n if (i == 2013){\n tabela.nakupov = zdruzeno\n } else{\n tabela.nakupov = rbind(tabela.nakupov, zdruzeno)\n }\n \n}\n\n\n\netn.sifrant.nakupi = tibble(\n \"tip.stavbe\" = c(1:15),\n \"etn.tip.prostora\" = c(\n \"Stanovanjska hisa\", \n \"Stanovanje\", \n \"Parkirni prostor\",\n \"Garaza\",\n \"Pisarniski prostori\",\n \"Prostori za poslovanje s strankami\",\n \"Prostori za zdravstveno dejavnost\",\n \"Trgovski ali storitveni lokal\",\n \"Gostinski lokal\",\n \"Prostori za sport, kulturo ali izobrazevanje\",\n \"Industrijski prostori\",\n \"Turisticni nastanitveni objekt\",\n \"Kmetijski objekt\",\n \"Tehnicni ali pomozni prostori\",\n \"Drugo\")\n)\n\ntabela.nakupov = tabela.nakupov %>% mutate(obcina = tolower(obcina)) %>%mutate(obcina = str_to_sentence(obcina)) %>% \n select(-id.posla)\n\ntabela.nakupov = tabela.nakupov %>% left_join(etn.sifrant.nakupi, by=\"tip.stavbe\") %>%\n select(obcina, prodajna.cena, povrsina, uporabna.povrsina, tip.prostora = etn.tip.prostora, leto.izgradnje, leto.posla)\n\ntabela.nakupov = tabela.nakupov %>% mutate(tip.prostora = as.factor(tip.prostora))\n\n#odstranjevanje nesmiselnih vnosov:\ntabela.nakupov = tabela.nakupov %>% filter(povrsina >= (uporabna.povrsina) & uporabna.povrsina >= 0.7 * povrsina) %>% \n filter(prodajna.cena >= 10) %>% filter(prodajna.cena / povrsina <= 10000)\n\n\n\n\n\n\n\n\n\n\n\n#3 Tabela ob\u010din:\n\nobcine = read_csv(\n \"podatki/obcine.csv\", \n skip = 2,\n locale = locale(encoding = \"Windows-1250\"),\n col_types = cols(\n .default = col_guess(),\n \"2008\" = col_double(),\n \"2009\" = col_double(),\n \"2010\" = col_double(),\n \"2011\" = col_double(),\n \"2012\" = col_double(),\n \"2013\" = col_double(),\n \"2014\" = col_double(),\n \"2021\" = col_double()\n )\n )\n\n\nobcine = pivot_longer(obcine,\n cols = (colnames(obcine)[3:16]),\n names_to = \"leto\",\n values_to = \"vrednost\"\n )\n\n\n\n\nobcine_meritve1 = obcine %>% filter(MERITVE == \"Povr\u0161ina (km2) - 1. januar\") %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], povrsina = vrednost)\nobcine_meritve2 = obcine %>% filter(MERITVE == \"\u0160tevilo prebivalcev - 1. januar\") %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], stevilo.prebivalcev = vrednost)\nobcine_meritve3 = obcine %>% filter(MERITVE == \"Skupni prirast\") %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], skupni.prirast = vrednost)\nobcine_meritve4 = obcine %>% filter(MERITVE == \"Indeks staranja - 1. januar\") %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], indeks.staranja = vrednost)\nobcine_meritve5 = obcine %>% filter(MERITVE == obcine$MERITVE[14264]) %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], stevilo.studentov = vrednost)\nobcine_meritve6 = obcine %>% filter(MERITVE == \"Stopnja delovne aktivnosti (%)\") %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], stopnja.delovne.aktivnosti = vrednost)\nobcine_meritve7 = obcine %>% filter(MERITVE == obcine$MERITVE[20873]) %>% mutate(povrsina = vrednost) %>% select(leto, obcina = colnames(obcine)[2], bruto.mesecna.placa = vrednost)\n\n\nzdruzi = obcine_meritve1\nzdruzi = left_join(zdruzi, obcine_meritve2, by = c(\"leto\", \"obcina\"))\nzdruzi = left_join(zdruzi, obcine_meritve3, by = c(\"leto\", \"obcina\"))\nzdruzi = left_join(zdruzi, obcine_meritve4, by = c(\"leto\", \"obcina\"))\nzdruzi = left_join(zdruzi, obcine_meritve5, by = c(\"leto\", \"obcina\"))\nzdruzi = left_join(zdruzi, obcine_meritve6, by = c(\"leto\", \"obcina\"))\ntabela.obcin = left_join(zdruzi, obcine_meritve7, by = c(\"leto\", \"obcina\"))\n\n\ntabela.obcin = tabela.obcin %>% filter(leto != 2021) %>% filter(leto >= 2013) %>%\n mutate(obcina = str_to_sentence(obcina)) %>% filter(obcina != \"Slovenija\")\n\n\ntabela.obcin = tabela.obcin %>% mutate(obcina = str_replace(obcina, \"([:alpha:]*)/[:alpha:]*\", \"\\\\1\")) %>%\n mutate(obcina = str_replace(obcina, \"([:alpha:]*)\\\\s-\\\\s([:alpha:]*)\", \"\\\\1-\\\\2\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "e903ef390deafa773e37ced13a3ca9fd6e89de1b", "size": 8497, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "LeonBahovec/APPR-2021-22", "max_stars_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "uvoz/uvoz.r", "max_issues_repo_name": "LeonBahovec/APPR-2021-22", "max_issues_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-02-03T10:36:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-05T07:49:07.000Z", "max_forks_repo_path": "uvoz/uvoz.r", "max_forks_repo_name": "LeonBahovec/APPR-2021-22", "max_forks_repo_head_hexsha": "6927c854ea3a492d3b40c13fac5917b4fa320470", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4703703704, "max_line_length": 198, "alphanum_fraction": 0.6531717077, "num_tokens": 3272, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.3092837141638457}} {"text": "#rm(list=ls(all=TRUE))\n#data_fn=\"tmp/\"\n#folder_fn=\"results-benchmark-binary\"\n#results_fn=paste(data_fn,folder_fn,\"/raw\",sep=\"\")\n#output_fn=paste(data_fn,folder_fn,\"/results-bars.pdf\",sep=\"\")\n#outtxt_fn=paste(data_fn,folder_fn,\"/results-text.txt\",sep=\"\")\n\nconfigs.threads=1\n#configs.loop=10\n#configs.recursion=c(10)\n#configs.labels=c(\"No Probe\",\"Inactive Probe\",\"Collecting Data\",\"Writing Data\")\nconfigs.count=length(configs.labels)\n#results.count=2000000\n#results.skip=1000000\n\n#bars.minval=500\n#bars.maxval=600\n\n## \"[ recursion , config , loop ]\"\nresultsBIG <- array(dim=c(length(configs.recursion),configs.count,configs.threads*configs.loop*(results.count-results.skip)),dimnames=list(configs.recursion,configs.labels,c(1:(configs.threads*configs.loop*(results.count-results.skip)))))\nfor (cr in configs.recursion) {\n for (cc in (1:configs.count)) {\n for (cl in (1:configs.loop)) {\n results_fn_temp=paste(results_fn, \"-\", cl, \"-\", cr, \"-\", cc, \".csv\", sep=\"\")\n for (ct in (1:configs.threads)) {\n results=read.csv2(results_fn_temp,nrows=(results.count-results.skip),skip=(ct-1)*results.count+results.skip,quote=\"\",colClasses=c(\"NULL\",\"numeric\"),comment.char=\"\",col.names=c(\"thread_id\",\"duration_nsec\"),header=FALSE)\n resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(((cl-1)*configs.threads*(results.count-results.skip)+1):(cl*configs.threads*(results.count-results.skip)))] <- results[[\"duration_nsec\"]]/(1000)\n }\n rm(results,results_fn_temp)\n }\n }\n}\n\npdf(output_fn, width=8, height=5, paper=\"special\")\nplot.new()\nplot.window(xlim=c(min(configs.recursion)-0.5,max(configs.recursion)+0.5),ylim=c(bars.minval,bars.maxval))\naxis(1,at=configs.recursion)\naxis(2)\ntitle(xlab=\"Recursion Depth (Number of Executions)\",ylab=expression(paste(\"Execution Time (\",mu,\"s)\")))\nfor (cr in configs.recursion) {\n printvalues = matrix(nrow=6,ncol=configs.count,dimnames=list(c(\"mean\",\"ci95%\",\"md25%\",\"md50%\",\"md75%\",\"through\"),c(1:configs.count)))\n for (cc in (1:configs.count)) {\n printvalues[\"mean\",cc]=mean(resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(1:(results.count-results.skip))])\n printvalues[\"ci95%\",cc]=qnorm(0.975)*sd(resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(1:(results.count-results.skip))])/sqrt(length(resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(1:(results.count-results.skip))]))\n printvalues[c(\"md25%\",\"md50%\",\"md75%\"),cc]=quantile(resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(1:(results.count-results.skip))],probs=c(0.25,0.5,0.75))\n printvalues[\"through\",cc]=((results.count-results.skip) * 1000*1000) / sum(resultsBIG[(1:length(configs.recursion))[configs.recursion==cr],cc,c(1:(results.count-results.skip))])\n }\n #meanvalues\n for (cc in (configs.count:2)) {\n rect(cr-0.3,printvalues[\"mean\",cc-1],cr+0.5,printvalues[\"mean\",cc])\n }\n rect(cr-0.3,0,cr+0.5,printvalues[\"mean\",1])\n for (cc in (1:configs.count)) {\n lines(c(cr+0.41,cr+0.49),c(printvalues[\"mean\",cc]+printvalues[\"ci95%\",cc],printvalues[\"mean\",cc]+printvalues[\"ci95%\",cc]),col=\"red\")\n lines(c(cr+0.45,cr+0.45),c(printvalues[\"mean\",cc]-printvalues[\"ci95%\",cc],printvalues[\"mean\",cc]+printvalues[\"ci95%\",cc]),col=\"red\")\n lines(c(cr+0.41,cr+0.49),c(printvalues[\"mean\",cc]-printvalues[\"ci95%\",cc],printvalues[\"mean\",cc]-printvalues[\"ci95%\",cc]),col=\"red\")\n }\n #median\n for (cc in (configs.count:2)) {\n rect(cr-0.4,printvalues[\"md50%\",cc-1],cr+0.4,printvalues[\"md50%\",cc],col=\"white\",border=\"black\")\n rect(cr-0.4,printvalues[\"md50%\",cc-1],cr+0.4,printvalues[\"md50%\",cc],angle=45,density=cc*10)\n }\n rect(cr-0.4,0,cr+0.4,printvalues[\"md50%\",1],col=\"white\",border=\"black\")\n rect(cr-0.4,0,cr+0.4,printvalues[\"md50%\",1],angle=45,density=10)\n for (cc in (1:configs.count)) {\n lines(c(cr-0.39,cr-0.31),c(printvalues[\"md75%\",cc],printvalues[\"md75%\",cc]),col=\"red\")\n lines(c(cr-0.35,cr-0.35),c(printvalues[\"md25%\",cc],printvalues[\"md75%\",cc]),col=\"red\")\n lines(c(cr-0.39,cr-0.31),c(printvalues[\"md25%\",cc],printvalues[\"md25%\",cc]),col=\"red\")\n }\n for (cc in (2:configs.count)) {\n labeltext=formatC(printvalues[\"md50%\",cc]-printvalues[\"md50%\",cc-1],format=\"f\",digits=1)\n rect(cr-(strwidth(labeltext)*0.5),printvalues[\"md50%\",cc]-strheight(labeltext),cr+(strwidth(labeltext)*0.5),printvalues[\"md50%\",cc],col=\"white\",border=\"black\")\n text(cr,printvalues[\"md50%\",cc],labels=labeltext,cex=0.75,col=\"black\",pos=1,offset=0.1)\n }\n resultstext=formatC(printvalues,format=\"f\",digits=4,width=8)\n print(resultstext)\n write(paste(\"Recursion Depth: \", cr),file=outtxt_fn,append=TRUE)\n write.table(resultstext,file=outtxt_fn,append=TRUE,quote=FALSE,sep=\"\\t\",col.names=FALSE)\n}\ninvisible(dev.off())\n", "meta": {"hexsha": "eda349a94f8df9ac5e8b0a6d03dc94dd0f7104d0", "size": 4766, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/2014-icpe/r/bar.r", "max_stars_repo_name": "DaGeRe/moobench-fork", "max_stars_repo_head_hexsha": "6aec6a8591190af1826c9d5e0ba9b2cc425722c2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/2014-icpe/r/bar.r", "max_issues_repo_name": "DaGeRe/moobench-fork", "max_issues_repo_head_hexsha": "6aec6a8591190af1826c9d5e0ba9b2cc425722c2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/2014-icpe/r/bar.r", "max_forks_repo_name": "DaGeRe/moobench-fork", "max_forks_repo_head_hexsha": "6aec6a8591190af1826c9d5e0ba9b2cc425722c2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 58.8395061728, "max_line_length": 262, "alphanum_fraction": 0.6915652539, "num_tokens": 1539, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.30928371416384565}} {"text": "####################################################################################################\n# \n# Mark Cembrowski, Janelia Research Campus, April 15 2015\n#\n# This script plots a scatterplot, coming the gene expression across two different populations.\n# Additional options allow labeling of specific genes.\n#\n# INPUT:\n# sampleX: character. String corresponding to the sample_name, plotted on x axis.\n# sampleY: character. String corresponding to the sample_name, plotted on y axis.\n#\n# OPTIONAL INPUT: \n# gsnList=vector(): character vector. A list of specific genes to highlight.\n# fdr=-1: numeric. FDR value to use if using DE as a means of labeling data points.\n# foldThres=-1: numeric. Minimum fold change between populations in order to label points.\n# fpkmThres=-1: numeric. Minimum FPKM value for a gene to be considered enriched.\n# avgPass=T: logical. If T, allows genes to be enriched whenever the average value between the\n#\ttwo samples exceeds provided threshold. If F, requires enrichment to be present across\n#\tall replicates, instead of the average, a stricter comparison.\n# maxX,maxY,minX,minY=-1: numerics. Override axis limits on x and y axes.\n# label=T: logical. Switch to control whether highlighted genes are labeled.\n#\n# OUTPUT:\n# A scatterplot is rendered and the corresponding matrix is returned.\n#\n#####################################################################################################\n\nfpkmScatter <- function(sampleX,sampleY,gsnList=vector(),fdr=-1,foldThres=-1,fpkmThres=-1,avgPass=T,\n\t\t\t\tmaxX=-1,maxY=-1,minX=-1,minY=-1,label=T){\n\n\t# Set parameters corresponding to the type of analysis.\n\tapriori <- F\n\texplore <- F\n\tif(length(gsnList)>0.1){ apriori <- T }\n\tif(fdr>0||foldThres>0){ explore <- T }\n\tif(apriori&&explore){\n\t\t# Check to make sure that the user also has not supplied a list of genes to plot.\n\t\tif(length(gsnList)>0.1){\n\t\t\tstop('Cannot simultaneously do exploratory and a priori labeling')\n\t\t}\n\t}\n\n\t# Produce an empty output, if not labeling any genes.\n\tif( (!apriori) && (!explore) ){\n\t\tenrX <- c()\n\t\tenrY <- c()\n\t}\n\n\t# Obtain enrichment for provided genes, if doing a priori analysis.\n\tif(apriori){\n\t\texpX <- fpkmPoolMat[unlist(symToId(gsnList)),sampleX]\n\t\texpY <- fpkmPoolMat[unlist(symToId(gsnList)),sampleY]\n\t\tpriorRat <- expX/expY\n\t\tenrX <- unlist(symToId(gsnList[priorRat>1]))\n\t\tenrY <- unlist(symToId(gsnList[priorRat<1]))\n\t}\n\t\n\t# Recover list of genes to plot, if doing an exploratory analysis.\n\tif(explore){\n\t\tenrX <- getEnrGenes(sampleX,sampleY,fdr=fdr,foldThres=foldThres,fpkmThres=fpkmThres,\n\t\t\tavgPass=avgPass)\n\t\tenrY <- getEnrGenes(sampleY,sampleX,fdr=fdr,foldThres=foldThres,fpkmThres=fpkmThres,\n\t\t\tavgPass=avgPass)\n\t}\n\t\n\t# Generate an informative table for output.\n\tenrichedRegion <- c(rep(sampleX,length(enrX)),rep(sampleY,length(enrY)))\n\n\tgeneId <- c(enrX,enrY)\n\tgeneShortName <- idToSym(geneId)\n\tfoldDiff <- fpkmPoolMat[geneId,sampleX]/fpkmPoolMat[geneId,sampleY]\n\tfoldDiff <- pmax(foldDiff,1/foldDiff) # Take convention that is >1.\n\tsampleXFpkm <- fpkmPoolMat[geneId,sampleX]\n\tsampleYFpkm <- fpkmPoolMat[geneId,sampleY]\n\n\tenrOut <- as.data.frame(cbind(enrichedRegion,geneId,geneShortName,foldDiff,\n\t\tsampleXFpkm,sampleYFpkm))\n\tenrOut$foldDiff <- as.numeric(as.character(enrOut$foldDiff))\n\tenrOut <- enrOut[with(enrOut,order(enrichedRegion,-foldDiff)),]\t\n\tcolnames(enrOut)[5] <- paste(sampleX,'Fpkm',sep='')\n\tcolnames(enrOut)[6] <- paste(sampleY,'Fpkm',sep='')\n\n\tif(apriori){\n\t\ttoHighlight <- unlist(symToId(gsnList))\n\t}else{\n\t\tif(explore){\n\t\t\ttoHighlight <- enrOut$geneId\n\t\t}else{\n\t\t\ttoHighlight <- vector()\n\t\t}\n\t}\n\thighlight <- rownames(fpkmPoolMat)%in%toHighlight\n\n\t# Prep matrix for plotting.\n\tdf <- fpkmPoolMat[,c(sampleX,sampleY)]\n\tdf <- cbind(df,highlight)\t\n\n\n\t# Do additive smoothing for logarithmic plot.\n\tdf[,sampleX] <- df[,sampleX] + 1\n\tdf[,sampleY] <- df[,sampleY] + 1\n\n\t# Sort so that highlighted genes show up on top layers of plot.\n\tdf <- df[order(df$highlight),]\n\n\t# Attach gene short names to data frame.\n\tdf[,'gene_short_name'] <- idToSym(rownames(df))\n\t\n\t# Adjust column names to properly interact with aes_string.\n\tcolnames(df)[c(1,2)] <- c('fpkmX','fpkmY')\n\n\t# Determine correlation coefficient.\n\txCorr <- round(cor(x=df[,'fpkmX'],y=df[,'fpkmY']),digits=2)\n\n\t# Plot. NOTE: need to use aes_string, as we're plotting inside a function!\n\tscatPlot <- ggplot(df,aes_string(x='fpkmX',y='fpkmY'))\n\tscatPlot <- scatPlot + scale_colour_manual(values=c(\"black\",\"red\"))\n\tscatPlot <- scatPlot + geom_abline(slope=1,linetype='dashed',weight=0.1)\n\n\t# Plot most points as transparent, highlighted genes as nontransparent\n\tscatPlot <- scatPlot + geom_point(data=subset(df,highlight==0),aes(colour=factor(highlight)),size=1,alpha=0.1)\n\tscatPlot <- scatPlot + geom_point(data=subset(df,highlight==1),aes(colour=factor(highlight)),size=1)\n\t\n\n\ttheMax <- max(df[,c('fpkmX','fpkmY')])\n\ttheMin <- 0.9\n\ttheMaxX <- theMax\n\ttheMaxY <- theMax\n\ttheMinX <- theMin\n\ttheMinY <- theMin\n\tif (maxX>0){theMaxX <- maxX}\n\tif (maxY>0){theMaxY <- maxY}\n\tif (minX>0){if (minX0){if (minY% filter(tabela1_obrnjena == \"15 do 19 let\"), aes(x = LETO, y = POVPRECNO_TRAJANJE_ENE_ODSOTNOSTI, color=\"15 do 19 let\")) + \n geom_line(data = tabela1_obrnjena %>% filter(tabela1_obrnjena == \"25 do 29 let\"), aes(x = LETO, y = POVPRECNO_TRAJANJE_ENE_ODSOTNOSTI, color = \"25 do 29 let\")) +\n geom_line(data = tabela1_obrnjena %>% filter(tabela1_obrnjena == \"50 do 54 let\"), aes(x = LETO, y = POVPRECNO_TRAJANJE_ENE_ODSOTNOSTI, color=\"50 do 54 let\")) + \n geom_line(data = tabela1_obrnjena %>% filter(tabela1_obrnjena == \"60 do 64 let\"), aes(x = LETO, y = POVPRECNO_TRAJANJE_ENE_ODSOTNOSTI, color = \"60 do 64 let\")) +\n ggtitle(\"Povpre\u010dno trajanje ene odsotnosti glede na starost\") +\n xlab(\"Leto\") + ylab(\"Povpre\u010dno trajanje ene odsotnosti\") + scale_x_continuous(breaks = c(2005:2020)) +\n scale_color_manual(name = \"Legenda\", values = c(\"15 do 19 let\" = \"red\",\"25 do 29 let\" = \"green\", \"50 do 54 let\" = \"blue\", \"60 do 64 let\"=\"black\")) +\n theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1))\n\n\n#----------------------------------------------------------------------------------------\n \n#Tabela 2: Kazalniki po razlogih bolni\u0161kega stale\u017ea \n\n\n# Tortni diagram glede na razloge\n\ntortni.diagram <- ggplot(tabela2_obrnjena2020) + \n aes(x=\"\", y=PRIMERI, fill= RAZLOGI) + \n geom_col(width=1) + \n coord_polar(theta=\"y\") + \n xlab(\"\") + ylab(\"\") +\n scale_fill_manual(values=c(\"darkgreen\", terrain.colors(10)), name = \"Razlogi po \u0161tevilu primerov (deljeno s 1000)\", \n guide = guide_legend(reverse = TRUE)) +\n labs(title = \"Razlogi bolni\u0161ke odsotnosti v letu 2020\")\n \n\n\n#primerjava razloga - bolezni skozi leta\n\ng3 <- ggplot(tabela2_obrnjena_bolezen)+\n aes(x = LETO, y = PRIMERI) + \n geom_col(position = 'dodge', fill = \"slategray3\") + \n labs(x = \"Leto\", y = \"\u0160tevilo primerov bolezni (deljeno s 1000)\", \n title = \"\u0160tevilo primerov bolezni po letih\") \n\n#----------------------------------------------------------------------------------------\n\n#Tabela 3: Kazalniki bolni\u0161kega stale\u017ea po statisti\u010dnih regijah\n\n\n# Zemljevid\n\nzemljevid <- uvozi.zemljevid(\"https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_SVN_shp.zip\", \"gadm36_SVN_1\",\n encoding=\"UTF-8\")\nzemljevid1 <- tm_shape(merge(zemljevid, regije , by.x=\"NAME_1\", by.y=\"REGIJA\" )) + \n tm_polygons(\"STEVILO_PRIMEROV_NA_100_ZAPOSLENIH\",title=\"\u0160tevilo primerov\",palette=\"PuRd\") + \n tm_style(\"white\") +\n tm_layout(main.title=\"\u0160tevilo primerov na 100 zaposlenih v letu 2020\") + \n tm_text(text='NAME_1', size=0.6)\n\n#----------------------------------------------------------------------------------------\n#Tabela 4: Izgubljeni koledarski dnevi po gospodarskih dejavnosti\n\ng4 <- ggplot(tabela4_obrnjena2020,\n aes(x=GOSPODARSKA_DEJAVNOST, y=ODSTOTEK_BS)) +\n geom_col(position = 'dodge', fill=\"violetred3\") + \n coord_flip() + \n labs(x = \"Gospodarska dejavnost\", y = \"Odstotek BS\", \n title = \"Bolni\u0161ki stale\u017e v gospodarskih dejavnostih\") +\n theme(plot.caption = element_text(hjust = 0, face= \"italic\"), \n plot.title.position = \"plot\")\n\n", "meta": {"hexsha": "a8e9443c9c3b7fb93edc6375280a7eac74d5b475", "size": 3262, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/moja_vizualizacija.r", "max_stars_repo_name": "KlaraDoler/APPR-2020-21", "max_stars_repo_head_hexsha": "216397db05176550a5e6e9010963c124754e6d4b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/moja_vizualizacija.r", "max_issues_repo_name": "KlaraDoler/APPR-2020-21", "max_issues_repo_head_hexsha": "216397db05176550a5e6e9010963c124754e6d4b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-08-30T09:17:59.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-02T11:03:21.000Z", "max_forks_repo_path": "vizualizacija/moja_vizualizacija.r", "max_forks_repo_name": "KlaraDoler/APPR-2020-21", "max_forks_repo_head_hexsha": "216397db05176550a5e6e9010963c124754e6d4b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.9436619718, "max_line_length": 163, "alphanum_fraction": 0.6290619252, "num_tokens": 1144, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.30920304354481526}} {"text": "# This function is pretty broken because it doesn't consider the difference in scale between axes.\n# It also probably doesn't need to always (ever) shorten both ends at least for margin labels...\nshorten <- function (x0, y0, x1, y1, rad) {\n line.lengths <- sqrt((x1-x0)^2+(y1-y0)^2)\n pct.short <- rad/line.lengths\n \n new.pt <- list()\n new.pt$x1 <- x0+(x1-x0)*pct.short\n new.pt$x0 <- x1+(x0-x1)*pct.short\n new.pt$y1 <- y0+(y1-y0)*pct.short\n new.pt$y0 <- y1+(y0-y1)*pct.short\n \n return (new.pt)\n}\n\n#' Label points from the margin\n#' \n#' Labels are sorted according to the axis along which they are labeled.\n#' This could be made a lot better since this often ends up in crossed lines..\n#' \n#' @examples\n#' y <- rnorm(100)\n#' x <- runif(100)\n#' plot(x, y, pch=20, bty='n')\n#' label.pts <- tail(order(y), 10)\n#' marginlabels(x[label.pts], y[label.pts], margin=3, lty=3, rad=0.05)\n#' @export\nmarginlabels <- function(x, y = NULL, labels=seq_along(x), margin=4,\n col='black', lty=1, lwd=1, pch=1, pch.cex=1, las=2,\n rad=0.15, ...) {\n \n len <- length(labels)\n \n if ( missing(y) || is.null(y) ) {\n y <- seq_along(labels)\n }\n \n if ( length(x) != len ) x <- rep(x, len)\n if ( length(y) != len ) y <- rep(y, len)\n \n if ( margin == 1 || margin == 3 ) {\n new.order <- order(x)\n x <- x[new.order]\n y <- y[new.order]\n labels <- labels[new.order]\n if ( !missing(col) && length(col) == len ) col <- col[new.order]\n label.x <- seq(par('usr')[1], par('usr')[2], length.out=len+2)[-c(1, len+2)]\n label.y <- par('usr')[if ( margin == 2) 3 else 4]\n tick.pos <- label.x\n } else {\n new.order <- order(y)\n x <- x[new.order]\n y <- y[new.order]\n labels <- labels[new.order]\n if ( !missing(col) && length(col) == len ) col <- col[new.order]\n label.y <- seq(par('usr')[3], par('usr')[4], length.out=len+2)[-c(1, len+2)]\n label.x <- par('usr')[if ( margin == 1) 1 else 2]\n tick.pos <- label.y\n }\n \n points(x, y, pch=pch, col=col, cex=pch.cex)\n \n connect.lines <- shorten(x, y, label.x, label.y, pch.cex*rad)\n connect.lines$lty <- lty\n connect.lines$lwd <- lwd\n connect.lines$x0 <- label.x\n connect.lines$y0 <- label.y\n \n do.call(segments, connect.lines)\n \n axis(margin, at=tick.pos, labels, las=las, lwd=0, lwd.tick=lwd, lty=lty, line=0)\n}", "meta": {"hexsha": "8ce00c36bdb2bb7a06bc74ee147059fb0b84446f", "size": 2344, "ext": "r", "lang": "R", "max_stars_repo_path": "R/margin_labels.r", "max_stars_repo_name": "sushilashenoy/zoom.plot", "max_stars_repo_head_hexsha": "036aa60980fdf7d86b5168f08e63aa13ca1f9e4b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2015-03-30T22:17:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-14T22:13:21.000Z", "max_issues_repo_path": "R/margin_labels.r", "max_issues_repo_name": "sushilashenoy/zoom.plot", "max_issues_repo_head_hexsha": "036aa60980fdf7d86b5168f08e63aa13ca1f9e4b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2015-03-30T22:17:37.000Z", "max_issues_repo_issues_event_max_datetime": "2016-09-23T16:18:28.000Z", "max_forks_repo_path": "R/margin_labels.r", "max_forks_repo_name": "sushilashenoy/zoom.plot", "max_forks_repo_head_hexsha": "036aa60980fdf7d86b5168f08e63aa13ca1f9e4b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-20T19:14:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-20T19:14:21.000Z", "avg_line_length": 32.5555555556, "max_line_length": 98, "alphanum_fraction": 0.5780716724, "num_tokens": 791, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3092030356877352}} {"text": "\ncode 'cf_reprice_gap_freq_cross'\nname '\u91cd\u5b9a\u4ef7\u7f3a\u53e3\u3001\u5468\u671f\u4ea4\u53c9'\ndimensions {\n\taccbook {\n\t\tfetchParents true\n\t\tsubDimension 'repriceFreq'\n\t}\n\trepriceGap null\n}\ndataRequest {\n\ttable 'repriceGap'\n\tcontextParams (['date','organ','currency','staticModel'])\n}\nheads {\n\taccbook {\n\t\tparams {\n\t\t\treprice true\n\t\t}\n\t}\n}\ndataGrids {\n\tapply selectRows({children!=null}),{\n\t\tformula {sumChildren()}\n\t}\n}\n", "meta": {"hexsha": "fe7d4237f5cd6a290151e752371cbc562fdb2743", "size": 378, "ext": "rd", "lang": "R", "max_stars_repo_path": "demo-untidy/cf_reprice_gap_freq_cross.rd", "max_stars_repo_name": "wushexu/jyreport", "max_stars_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-04-13T01:51:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-20T03:19:05.000Z", "max_issues_repo_path": "demo-untidy/cf_reprice_gap_freq_cross.rd", "max_issues_repo_name": "wushexu/jyreport", "max_issues_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo-untidy/cf_reprice_gap_freq_cross.rd", "max_forks_repo_name": "wushexu/jyreport", "max_forks_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-27T04:06:48.000Z", "max_forks_repo_forks_event_max_datetime": "2016-08-05T03:04:09.000Z", "avg_line_length": 14.0, "max_line_length": 58, "alphanum_fraction": 0.6931216931, "num_tokens": 119, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.538983220687684, "lm_q1q2_score": 0.30920303568773516}} {"text": "###\n# calculates the age distribution of \n# all speakers in the sample, both Subjects and Elders\n# and plots as a histogram of decades of birth,\n# with color coding of Subject vs. Elder\n###\n\nrm(list=ls())\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(ggplot2)\nsource('inread.r')\n\n# read in data\neverybody <- inread(\"data/mbc_everybody.csv\")\n# prepare variables\neverybody$role <- factor(everybody$role, \n\tordered = TRUE, levels = c(\"gen1\", \"gen2\"))\neverybody$Birthdecade=as.factor(floor((everybody$Birthyear)/10)*10)\n\n# make long table for plot\ntallies <- as.data.frame(\n\ttally(na.omit(everybody) %>% \n\tgroup_by(Birthdecade, role)))\n# save out underlying data\nwrite.csv(tallies, \n\tfile = \"analysis/age_dist.csv\", \n\tfileEncoding = \"UTF-8\", row.names = FALSE)\n\n# elements for plot\ngen1col <- \"#dea73a\" # elders - gold\ngen2col <- \"#404096\" # interviewees - blue\ngen1lab <- \"Interviewee's caregiver\"\ngen2lab <- \"Direct interviewee\"\ncolors <- c(gen1col, gen2col) \nn2 <- length(everybody$PersonID[everybody$role %in% \"gen2\"])\nn1 <- length(everybody$PersonID[everybody$role == \"gen1\"])\nnn <- length(everybody$PersonID)\nnphrase <- paste(\"N =\", nn, \n\t\"(\", n2, \"interviewees and\", \n\tn1, \"elders/caregivers)\", sep=\" \")\ncaption <- \"Birthyear Distribution in Sample\"\n\n# plot\naplot <- ggplot(data = tallies, \n aes(x = Birthdecade, y = n, fill = role)) + \n geom_col() +\n scale_fill_manual(values = colors, name = \"Role\",\n labels = c(gen1lab, gen2lab), \n guide = guide_legend()) +\n labs(title=paste(caption, nphrase, sep=\"\\n\"), \n x = \"Decade of Birth\", y = \"Number of People\") +\n theme(axis.text=element_text(size=12), \n axis.title=element_text(size=12)) \n\n# save plot\nggsave(plot=aplot, file=\"figures/age_dist.pdf\", width=8, height=5, units=\"in\")\nggsave(plot=aplot, file=\"figures/age_dist.jpg\", width=8, height=5, units=\"in\")\n", "meta": {"hexsha": "a2b41bec7ec3ef4c9a0253cfc51790418976cede", "size": 1849, "ext": "r", "lang": "R", "max_stars_repo_path": "age_dist.r", "max_stars_repo_name": "saralakumari/lmsim", "max_stars_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "age_dist.r", "max_issues_repo_name": "saralakumari/lmsim", "max_issues_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "age_dist.r", "max_forks_repo_name": "saralakumari/lmsim", "max_forks_repo_head_hexsha": "802f013f72fc023728b1b8dad36f0dbd34b1cb9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3389830508, "max_line_length": 78, "alphanum_fraction": 0.6798269335, "num_tokens": 563, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.30920303568773516}} {"text": "load(\"Figure3.RData\")\n\npdf(\"Figure3.pdf\", width=6, height=6)\n plot(x=as.numeric(xi), y=as.numeric(yi), main=ti, type=\"n\", xlim=c(-1,1), ylim=c(7,13),\n xlab = \"Intensity Contrast\", ylab=\"Intensity Average\")\n text(x=as.numeric(xi), y=as.numeric(yi), as.character(vi), cex=0.4, col=pal[as.character(vi)]) \n text(x=contj, y=avgj, texti, cex=0.8)\n coveredj = texti == \"13\"\n points(x=contj[coveredj], y=avgj[coveredj], pch=19, col=\"red\", cex=1.6)\ndev.off()", "meta": {"hexsha": "94e043c7d1adb6986f1714054a74fa06fe8a23f3", "size": 460, "ext": "r", "lang": "R", "max_stars_repo_path": "code/Figure3.r", "max_stars_repo_name": "simecek/High-Resolution-Mapping-of-Reference-Populations", "max_stars_repo_head_hexsha": "9084043fc91c6cdb68c34190d10730b7a1c1426b", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/Figure3.r", "max_issues_repo_name": "simecek/High-Resolution-Mapping-of-Reference-Populations", "max_issues_repo_head_hexsha": "9084043fc91c6cdb68c34190d10730b7a1c1426b", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/Figure3.r", "max_forks_repo_name": "simecek/High-Resolution-Mapping-of-Reference-Populations", "max_forks_repo_head_hexsha": "9084043fc91c6cdb68c34190d10730b7a1c1426b", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.0, "max_line_length": 98, "alphanum_fraction": 0.6456521739, "num_tokens": 182, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.30920303568773516}} {"text": "#' @title hist_bivariate\n#' @description unknown\n#' @family abysmally documented\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\nhist_bivariate = function(x,y, method=\"fd\", xrange=NULL, yrange=NULL) {\n \n xhist <- hist(x, plot=F, method )\n yhist <- hist(y, plot=F, method )\n\n layout(mat=matrix(c(2,0,1,3),2,2,byrow=TRUE), widths=c(3,1), heights=c(1,3), respect=TRUE)\n\n if (is.null(xrange)) xrange <- range(x, na.rm=T)\n if (is.null(yrange)) yrange <- range(y, na.rm=T)\n\n plot(x, y, xlim=xrange, ylim=yrange, xlab=\"\", ylab=\"\", axes=F)\n axis(1)\n axis(2)\n\n par(mar=c(0,3.5,3.5,1))\n barplot(xhist$counts, axes=F, space=0)\n\n par(mar=c(5,0,5,1))\n barplot(yhist$counts, axes=F, space=0, horiz=TRUE)\n return(NULL)\n}\n\n\n", "meta": {"hexsha": "6bf34b046c3d741480ced2bbc7abd359fe4c3aa3", "size": 739, "ext": "r", "lang": "R", "max_stars_repo_path": "R/hist_bivariate.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/hist_bivariate.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/hist_bivariate.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.4827586207, "max_line_length": 92, "alphanum_fraction": 0.6359945873, "num_tokens": 273, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3092030356877351}} {"text": "# Seurat v2\nfor(j in 0:8){\nn=table(pbmc_c@ident)\nident=j\nn[names(n) %in% c(0:ident)]=0\nn=n[-1]\no=n[n>0]\nident2=names(o[1])\nbim <- FindMarkers(pbmc_c, ident.1 = ident, ident.2 =ident2, only.pos = F, test.use = \"bimod\")\ndeg=bim[bim$p_val_adj < 0.01,]\ndeg1=deg[abs(deg$avg_logFC) >= 1,]\ndegs=dim(deg)\ndegs1=dim(deg1)\n\nfor(i in c(names(o[-1]))){\nbim <- FindMarkers(pbmc_c, ident.1 = ident, ident.2 = i, only.pos = F, test.use = \"bimod\")\n deg=bim[bim$p_val_adj < 0.01,]\ndeg1=deg[abs(deg$avg_logFC) >= 1,]\ndegs=cbind(degs,dim(deg))\ndegs1=cbind(degs1,dim(deg1))\n}\nprint(degs1)\n}\n\n#Seurat v3\ncompareClusters<-function(obj){\n len<-length(levels(Idents(obj)))\n for(j in 0:len){\n n=table(Idents(obj))\n ident=j\n n[names(n) %in% c(0:ident)]=0\n n=n[-1]\n o=n[n>0]\n ident2=names(o[1])\n bim <- FindMarkers(obj, ident.1 = ident, ident.2 =ident2, only.pos = F, test.use = \"bimod\")\n deg=bim[bim$p_val_adj < 0.01,]\n deg1=deg[abs(deg$avg_logFC) >= 1,]\n degs=dim(deg)\n degs1=dim(deg1)\n\n for(i in c(names(o[-1]))){\n bim <- FindMarkers(obj, ident.1 = ident, ident.2 = i, only.pos = F, test.use = \"bimod\")\n deg=bim[bim$p_val_adj < 0.01,]\n deg1=deg[abs(deg$avg_logFC) >= 1,]\n degs=cbind(degs,dim(deg))\n degs1=cbind(degs1,dim(deg1))\n }\n print(degs1)\n }\n}\n", "meta": {"hexsha": "1f4ba230b6352ccf6c03516046dd8246faed698b", "size": 1302, "ext": "r", "lang": "R", "max_stars_repo_path": "functions/compareClusters.r", "max_stars_repo_name": "TongZhou2017/scTools", "max_stars_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "functions/compareClusters.r", "max_issues_repo_name": "TongZhou2017/scTools", "max_issues_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "functions/compareClusters.r", "max_forks_repo_name": "TongZhou2017/scTools", "max_forks_repo_head_hexsha": "0a478d9108ad349827e2276a93b786009efc0aba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5294117647, "max_line_length": 96, "alphanum_fraction": 0.6052227343, "num_tokens": 532, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3092030271912249}} {"text": "#################################################################################################################################\n# Aportion pop to catchments using huff probs #\n#################################################################################################################################\n\nlibrary(data.table)\n\npop_dz15 <- fread(\"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_dz/dz_pop_15.csv\")\nsetnames(pop_dz15, \"dz11\", \"lsoa11\")\npop_lsoa15 <- fread(\"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_lsoa/pop_lsoa_15.csv\")\npop_msoa15 <- fread(\"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_msoa/msoa_pop_15.csv\")\n\nlsoa_probs <- fread(\"~/Dropbox/liverpool/retail_typology/data/lsoa_probs.csv\")\nmsoa_probs <- fread(\"~/Dropbox/liverpool/retail_typology/data/msoa_probs.csv\")\n\npop_lsoa15_probs <- pop_lsoa15[lsoa_probs, on = \"lsoa11\", nomatch = 0]\npop_lsoa15_probs[, weighted_pop := total_pop * mean_huff_prob]\n\npop_msoa15_probs <- pop_msoa15[msoa_probs, on = \"msoa11\", nomatch = 0]\npop_msoa15_probs[, weighted_pop := total_pop * mean_huff_prob]\n\npop_dz15_probs <- pop_dz15[lsoa_probs, on = \"lsoa11\", nomatch = 0]\npop_dz15_probs[, weighted_pop := total_pop * mean_huff_prob]\n\nfwrite(pop_dz15_probs, \"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_dz15_probs.csv\")\nfwrite(pop_lsoa15_probs, \"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_lsoa15_probs.csv\")\nfwrite(pop_msoa15_probs, \"~/Dropbox/liverpool/retail_typology/data/pop_est/pop_msoa15_probs.csv\")\n", "meta": {"hexsha": "93cea6086b3c917d8989d7ad5ce2683658c30fd3", "size": 1583, "ext": "r", "lang": "R", "max_stars_repo_path": "create_variables/population.r", "max_stars_repo_name": "mpavlis/retail_typology", "max_stars_repo_head_hexsha": "6f311f8a345e2b81b3df84090376a66e30173892", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "create_variables/population.r", "max_issues_repo_name": "mpavlis/retail_typology", "max_issues_repo_head_hexsha": "6f311f8a345e2b81b3df84090376a66e30173892", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "create_variables/population.r", "max_forks_repo_name": "mpavlis/retail_typology", "max_forks_repo_head_hexsha": "6f311f8a345e2b81b3df84090376a66e30173892", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 58.6296296296, "max_line_length": 129, "alphanum_fraction": 0.6216045483, "num_tokens": 417, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832058771035, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.30920302719122483}} {"text": "REBOL [\n Title: \"Get-img-alpha-bounds\"\n Date: 30-Nov-2010/10:51:04+1:00\n Author: \"Oldes\"\n\tpurpose: {finds the smallest area which contains alpha information (not transparent)}\n]\nget-img-alpha-bounds: func[\n\t{Returns the smallest area which contains alpha information (not transparent)\n\t in format [ofsx ofsy width height]}\n\timg [image!] \"Image to examine\"\n\t/local h w h- w- c r p vertical-b horizontal-b\n][\n\th: img/size/y\n\tw: img/size/x\n\tw-: w - 1\n\th-: h - 1\n\thorizontal-b: copy []\n\tc: 0\n\tparse/all/case img/alpha [\n\t\tw [\n\t\t\tp: [\n\t\t\t\th- [#{FF} w- skip] #{FF} (c: c + 1) [end | (p: next p) :p]\n\t\t\t\t|\n\t\t\t\t(\n\t\t\t\t\teither c > 0 [\n\t\t\t\t\t\tappend horizontal-b c\n\t\t\t\t\t\tc: 0\n\t\t\t\t\t][\tif empty? horizontal-b [insert horizontal-b 0] ]\n\t\t\t\t\tp: next p\n\t\t\t\t) :p\n\t\t\t]\n\t\t]\n\t\t(\n\t\t\tif c > 0 [append horizontal-b c]\n\t\t\tif 2 > length? horizontal-b [append horizontal-b 0]\n\t\t) to end\n\t]\n\tvertical-b: copy []\n\tr: 0\n\tparse/all/case img/alpha [\n\t\th [\n\t\t\t[\n\t\t\t\tw #{FF} (r: r + 1)\n\t\t\t\t|\n\t\t\t\tw skip (\n\t\t\t\t\teither r > 0 [\n\t\t\t\t\t\tappend vertical-b r\n\t\t\t\t\t\tr: 0\n\t\t\t\t\t][\tif empty? vertical-b [insert vertical-b 0] ]\n\t\t\t\t)\n\t\t\t]\n\t\t]\n\t\t(\n\t\t\tif r > 0 [append vertical-b r]\n\t\t\tif 2 > length? vertical-b [append vertical-b 0]\n\t\t)\n\t]\n\t;probe vertical-b\n\t;probe horizontal-b\n\treduce [\n\t\tfirst horizontal-b\n\t\tfirst vertical-b\n\t\timg/size/x - (first horizontal-b) - (last horizontal-b)\n\t\timg/size/y - (first vertical-b) - (last vertical-b)\n\t]\n]", "meta": {"hexsha": "dcf9aea2b0034460f2865e14a24ae26c4a891cd1", "size": 1410, "ext": "r", "lang": "R", "max_stars_repo_path": "projects/get-img-alpha-bounds/latest/get-img-alpha-bounds.r", "max_stars_repo_name": "Oldes/rs", "max_stars_repo_head_hexsha": "d96d7ba96e9fd2a6ac998ed3be98212feb21df6e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2015-02-02T10:08:01.000Z", "max_stars_repo_stars_event_max_datetime": "2018-03-30T13:43:53.000Z", "max_issues_repo_path": "projects/get-img-alpha-bounds/latest/get-img-alpha-bounds.r", "max_issues_repo_name": "Oldes/rs", "max_issues_repo_head_hexsha": "d96d7ba96e9fd2a6ac998ed3be98212feb21df6e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "projects/get-img-alpha-bounds/latest/get-img-alpha-bounds.r", "max_forks_repo_name": "Oldes/rs", "max_forks_repo_head_hexsha": "d96d7ba96e9fd2a6ac998ed3be98212feb21df6e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.3636363636, "max_line_length": 86, "alphanum_fraction": 0.580141844, "num_tokens": 498, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3090419474270079}} {"text": "#===============================================================================\n#\tcv.best.models.r\n#\n#\tAuther: Michio Oguro\n#\n#\tDescription:\n#\t\tExtract model(s) with best predictive ability from cv.models object.\n#===============================================================================\n\n\n#-------------------------------------------------------------------------------\n#'\t(Internal) Find the Index of Best Metrics\n#'\n#'\tThis function find the index of metrics indicating best predictive ability.\n#'\n#'\t@param metrics\n#'\t\ta matrics of model performance metrics.\n#-------------------------------------------------------------------------------\nfind.best.metrics.index <- function(metrics) {\n\tminimize <- c(\"mse\", \"rmse\", \"fn\", \"fp\")\n\tfor (i in colnames(metrics)) {\n\t\tif (i %in% minimize) {\n\t\t\tmetrics[[i]] <- -metrics[[i]]\n\t\t}\n\t}\n\treturn(which.max.multi(metrics))\n}\n\n\n#-------------------------------------------------------------------------------\n#'\tExtract a Model from cv.models Object\n#'\n#'\tExtract a model from \\code{\\link{cv.models}} object having multiple models\n#'\tcreated by hyper-parameter selection.\n#'\n#'\t@param object a \\code{cv.models} object.\n#'\t@param index model index.\n#'\t@param criteria reserved, but currently not used.\n#'\t@export\n#-------------------------------------------------------------------------------\nextract.result <- function(object, index, criteria = NULL) {\n\t# Fix random number before using random process.\n\tset.seed.if.possible(object)\n\tbest <- object\n\tbest$grid <- NULL\n\tbest$grid.predict <- NULL\n\tbest$metrics <- best$cv.results[[index]]$metrics\n\tbest$criteria <- criteria\n\tbest$call <- best$cv.results[[index]]$call\n\tbest$fits <- best$cv.results[[index]]$fits\n\tbest$cv.group <- best$cv.results[[index]]$cv.group\n\tbest$model <- eval(best$call, envir = best$envir)\n\tbest$cv.results <- NULL\n\tclass(best) <- \"cv.result\"\n\treturn(best)\n}\n\n\n#-------------------------------------------------------------------------------\n#'\tFind best performing model(s)\n#'\n#'\tFind model(s) with best performance.\n#'\tThe result can have multiple models if there are ties.\n#'\n#'\t@param object\n#'\ta \\code{cv.models} object.\n#'\n#'\t@param criteria\n#'\tnames of performance measures by which best model(s) are determined.\n#'\n#'\t@export\n#-------------------------------------------------------------------------------\nfind.best.models <- function(object, criteria) {\n\tif (missing(criteria)) {\n\t\tcriteria <- ifelse(\n\t\t\tobject$adapter$model.type == \"regression\", \"q.squared\", \"mcc\"\n\t\t)\n\t}\n\tif (is.null(object$grid) & is.null(object$grid.predict)) {\n\t\tbest.index <- 1\n\t} else {\n\t\tmetrics <- extract.metrics(object)[criteria]\n\t\tbest.index <- find.best.metrics.index(metrics)\n\t}\n\tresult <- lapply(\n\t\tbest.index, extract.result, object = object, criteria = criteria\n\t)\n\tclass(result) <- \"cv.best.models\"\n\treturn(result)\n}\n", "meta": {"hexsha": "3cd9f6787539a82bd81003e47cf7b0a202a8e65c", "size": 2834, "ext": "r", "lang": "R", "max_stars_repo_path": "R/cv.best.models.r", "max_stars_repo_name": "Marchen/cv.models", "max_stars_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-01T15:45:35.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-01T15:45:35.000Z", "max_issues_repo_path": "R/cv.best.models.r", "max_issues_repo_name": "Marchen/cv.models", "max_issues_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-03-11T04:21:27.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-10T12:18:14.000Z", "max_forks_repo_path": "R/cv.best.models.r", "max_forks_repo_name": "Marchen/cv.models", "max_forks_repo_head_hexsha": "70af64f72933a4172d229413ff43034a53c93163", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-11-16T03:30:36.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-16T03:30:36.000Z", "avg_line_length": 31.1428571429, "max_line_length": 80, "alphanum_fraction": 0.5296400847, "num_tokens": 599, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3090419474270079}} {"text": "args = commandArgs()\nrange=strsplit(basename(args[3]), \".\", fixed=TRUE)[[1]][1]\noutpdf = paste(\"./plots/extendtimes\", range, \".pdf\", sep=\"\") \n\ne <- read.table(args[3])\npdf(outpdf, width=8, height=6)\nplot(density(e[,2] + e[,3] + e[,4]), ylim=c(0,0.8), xlim=c(0,10), frame=FALSE,\naxes=FALSE, main=\"Circuit extension time\", xlab=\"Time [s]\")\naxis(1, at=0:10)\naxis(2)\nlines(density(e[,2]), col=\"red\")\nlines(density(e[,3]), col=\"darkgreen\")\nlines(density(e[,4]), col=\"blue\")\ntext(x=1.1, y=0.7, labels=\"1st hop\", col=\"red\")\ntext(x=2.65, y=0.55, labels=\"2nd hop\", col=\"darkgreen\")\ntext(x=3.68, y=0.27, labels=\"3rd hop\", col=\"blue\")\ntext(x=5.9, y=0.13, labels=\"All hops\")\nabline(v=1:10, lty=3)\ndev.off()\n\n", "meta": {"hexsha": "4f289149bdde0b63e13236407ec82dae46783cf6", "size": 696, "ext": "r", "lang": "R", "max_stars_repo_path": "CircuitAnalysis/BuildTimes/extend_plot.r", "max_stars_repo_name": "isislovecruft/torflow", "max_stars_repo_head_hexsha": "666689ad18d358d764a35d041a7b16adb8d3287c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CircuitAnalysis/BuildTimes/extend_plot.r", "max_issues_repo_name": "isislovecruft/torflow", "max_issues_repo_head_hexsha": "666689ad18d358d764a35d041a7b16adb8d3287c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-12-18T15:58:40.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-26T16:52:51.000Z", "max_forks_repo_path": "CircuitAnalysis/BuildTimes/extend_plot.r", "max_forks_repo_name": "isislovecruft/torflow", "max_forks_repo_head_hexsha": "666689ad18d358d764a35d041a7b16adb8d3287c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1428571429, "max_line_length": 78, "alphanum_fraction": 0.6221264368, "num_tokens": 270, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.30904194742700786}} {"text": "library(ggplot2)\nlibrary(reshape2)\n\nargs <- commandArgs(TRUE)\n\nfile <- args[1]\nout.plot <- args[2]\ncol.rank <- as.numeric(args[3])\nsig.value <- as.numeric(args[4])\n\nlimit <- 5\n# top.pct <- 5\n\ndata <- read.delim(file,header=T)\n\nif(limit > max(data[,col.rank])){\n limit <- max(data[,col.rank])\n}\n#sig.value <- as.numeric(quantile(data[,col.rank],prob=1-top.pct/100))\n#limit <- max(data[,col.rank])\n\ndata$sig <- \"NA\"\ndata[data[,col.rank] >= sig.value,]$sig <- \"top\"\ndata$sig <- as.factor(data$sig)\ndata$mask <- data[,col.rank]\ndata[data[,col.rank] >= sig.value,]$mask <- sig.value\ndata[data[,col.rank] < sig.value,col.rank] <- NA\n\ndata$chr <- as.factor(gsub(\"Chr\",\"\",data[,1]))\nchrs <- as.character(sort(as.numeric(levels(data$chr))))\ndata$chr <- factor(data$chr, levels=chrs)\n\n# \u8bbe\u7f6e\u67d3\u8272\u4f53\u957f\u5ea6\u3001\u6570\u91cf\u3001\u4e2d\u4f4d\u65ad\u70b9\uff08breakpoint\uff09\u4f5c\u4e3ax\u8f74\u7684tick\nchrcount <- length(chrs)\nbreakpoints <- levels(data$chr)\n\n# \u8bbe\u7f6e\u65b0\u5750\u6807\uff08\u7d2f\u52a0\u5750\u6807\uff09\ndata$POS <- data[,2]\ngenome.len <- 0\n\n# \u5faa\u73af\u8ba1\u7b97\uff0c\u83b7\u53d6\u6bcf\u4e00\u6761\u67d3\u8272\u4f53\u7684\u957f\u5ea6\u3001\u4e2d\u95f4\u70b9\uff0c\u540c\u65f6\u8ba1\u7b97\u7d2f\u52a0\u5750\u6807\nfor(i in 1:chrcount){\n chrname <- chrs[i]\n chrlen <- max(data[data$chr==chrname,2])\n breakpoints[i] <- chrlen/2 + genome.len\n data[data$chr==chrname,]$POS <- data[data$chr==chrname,2] + genome.len\n genome.len <- genome.len + chrlen\n}\n\n# \u8bbe\u7f6e\u67d3\u8272\u4f53\u989c\u8272\uff0c\u9700\u8981\u4e24\u5957\u989c\u8272\uff0c\u5206\u522b\u662f\u67d3\u8272\u4f53\u533a\u5206\u8272\u4ee5\u53ca\u9ad8\u4eae\u8272\ncolorchr <- rep(c(\"#228B22\",\"grey\"),round(chrcount/2)+1)[1:chrcount]\ncolorhl <- c(\"grey\",\"red\")\ncolorset <- c(colorchr,colorhl)\ncolorset.rank <- colorset\n\ncolor.ranks <- match(sort(c(chrs,\"top\",\"NA\")),c(chrs,\"NA\",\"top\"))\n\nfor(i in 1:length(color.ranks)){\n rank <- color.ranks[i]\n colorset.rank[i] <- colorset[rank]\n}\n\npdf(out.plot,width=8,height=1.5)\n\nggplot(data) + \n geom_bar(aes(x=POS, y=data[,col.rank], color=sig),stat=\"identity\", position=\"identity\",size=0.5) + \n geom_bar(aes(x=POS, y=mask,color=data$chr),stat=\"identity\",size=0.5) +\n scale_color_manual(values = colorset.rank) +\n coord_cartesian(ylim=c(1,limit)) + \n xlab(\"CHROMOSOME\") +\n ylab(\"value\") +\n theme_bw() +\n theme(axis.line = element_line(colour = \"black\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.border = element_blank(),\n panel.background = element_blank(),\n legend.position = \"none\") +\n geom_hline(yintercept=sig.value, linetype=\"dashed\", color = \"red\") +\n# \u8bbe\u7f6e\u9608\u503c\u7ebf\n scale_x_continuous(breaks=as.numeric(breakpoints),labels=chrs) \n# \u5173\u952e\u8bed\u53e5\uff0c\u8bbe\u7f6e\u65ad\u70b9\n\ndev.off()\n", "meta": {"hexsha": "f88400ca90083f8b75e9b5da6e7b0991ddb74509", "size": 2343, "ext": "r", "lang": "R", "max_stars_repo_path": "ggBarManhattan.PiRatio.r", "max_stars_repo_name": "zhuochenbioinfo/ggomics", "max_stars_repo_head_hexsha": "f67a20187e6f07a55bd0c414a0f25c4fb77283b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-28T01:11:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-28T01:11:53.000Z", "max_issues_repo_path": "ggBarManhattan.PiRatio.r", "max_issues_repo_name": "zhuochenbioinfo/ggomics", "max_issues_repo_head_hexsha": "f67a20187e6f07a55bd0c414a0f25c4fb77283b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ggBarManhattan.PiRatio.r", "max_forks_repo_name": "zhuochenbioinfo/ggomics", "max_forks_repo_head_hexsha": "f67a20187e6f07a55bd0c414a0f25c4fb77283b3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5647058824, "max_line_length": 101, "alphanum_fraction": 0.6623986342, "num_tokens": 811, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3090419401312994}} {"text": "outpath <- commandArgs(TRUE) ## task0/reports/logreg.r_holdout_confusion.txt\n\ntask_name = gsub(\"(task.*)/reports/(.*\\\\.r)_holdout_confusion.txt\",\"\\\\1\", outpath)\nmod_name = gsub(\"(task.*)/reports/(.*\\\\.r)_holdout_confusion.txt\",\"\\\\2\", outpath)\nmod_path = paste0(task_name, \"/models/\", mod_name, \"data\")\n\nload(mod_path) # gets pre-trained `mod`` object\n\ndata_path = paste0(task_name, \"/data/processed/\", \"/test.rdata\")\nload(data_path) \n\nmod_funcs = paste0(task_name, \"/src/models/\", mod_name)\nsource(mod_funcs)\n\nscored = predict_model(mod, X, Y, type=\"class\")\nconfusion <- table(Y, scored)\n\ndir.create(dirname(outpath), showWarnings = FALSE)\nwrite.csv(confusion, file = outpath)\n\nsource(\"common/src/eval/eval_db/dbapi.r\")\n\nm = data.frame(confusion)\nm$row_id = 1:nrow(m)\nm$field_name = paste0(m$Y, \"_\", m$scored)\nm$value = m$Freq\nm = m[,-c(1:3)]\n\nresult_name = \"holdout_confusion\"\nlog_model_result(task_name, mod_name, result_name, m)\ndbDisconnect(conn)", "meta": {"hexsha": "89a2b362c90bd5f2dba9c502a85def8221fd022a", "size": 951, "ext": "r", "lang": "R", "max_stars_repo_path": "common/src/eval/eval_model.r", "max_stars_repo_name": "dmarx/make_for_datascience", "max_stars_repo_head_hexsha": "fc9987de96bab25c67f6e68f2af63a72bd5dc587", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-09-20T17:52:11.000Z", "max_stars_repo_stars_event_max_datetime": "2019-09-30T17:40:22.000Z", "max_issues_repo_path": "common/src/eval/eval_model.r", "max_issues_repo_name": "dmarx/make_for_datascience", "max_issues_repo_head_hexsha": "fc9987de96bab25c67f6e68f2af63a72bd5dc587", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 29, "max_issues_repo_issues_event_min_datetime": "2017-06-15T16:58:27.000Z", "max_issues_repo_issues_event_max_datetime": "2017-10-22T06:48:16.000Z", "max_forks_repo_path": "common/src/eval/eval_model.r", "max_forks_repo_name": "dmarx/make_for_datascience", "max_forks_repo_head_hexsha": "fc9987de96bab25c67f6e68f2af63a72bd5dc587", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6774193548, "max_line_length": 82, "alphanum_fraction": 0.7129337539, "num_tokens": 284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30901686245007903}} {"text": "source(\"_common.r\")\n\nx_ob = readLines(\"../runs/summit/dgemm/openblas.txt\")\ndf_ob = process(x_ob, gpu=FALSE)\n\nx_essl = readLines(\"../runs/summit/dgemm/essl.txt\")\ndf_essl = process(x_essl, gpu=FALSE)\n\ndf = rbind(\n cbind(df_ob, Backend=\"OpenBLAS\"),\n cbind(df_essl, Backend=\"ESSL\")\n)\n\n\nplotter(df, color=nthreads, linetype=Backend) + \n labs(color=\"Number of Threads\", linetype=\"Backend\") +\n ggtitle(\"Square Matrix Product\", subtitle=\"OpenBLAS vs ESSL from R\")\n\nggsave(last_plot(), file=\"openblas_vs_essl.pdf\")\n", "meta": {"hexsha": "4ec574b8a396dba83cd4196bcff1e6d0bdcfb7fb", "size": 510, "ext": "r", "lang": "R", "max_stars_repo_path": "plot/essl.r", "max_stars_repo_name": "wrathematics/matprodbench", "max_stars_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-02-04T16:08:53.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-04T16:08:53.000Z", "max_issues_repo_path": "plot/essl.r", "max_issues_repo_name": "wrathematics/matprodbench", "max_issues_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot/essl.r", "max_forks_repo_name": "wrathematics/matprodbench", "max_forks_repo_head_hexsha": "2023dedb20b41025ca73b0832a846973cd1b52e4", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5, "max_line_length": 70, "alphanum_fraction": 0.7137254902, "num_tokens": 163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30901686245007903}} {"text": "### replace $ and ,. Turn () into negatives\ncleanDollars <- function(x){\n\tx <- gsub(\"\\\\(\", \"-\", x)\n \tx <- as.numeric(gsub(\"\\\\$|,|\\\\)\", \"\", x))\n \tx\n}\n\n\n### compute totals as of a certain date. \nfecAsOf <- function(fec, daysOut, dropQ1 = F){\n\n\tknown <- fec[fec$daysOut > daysOut, ]\n\n\t### don't have Q1 data yet.\n\tif(dropQ1) print(\"hi buddy. just fyi, i'm not using any Q1 fec data, because i think you don't have it yet in 2014\")\n\tif(dropQ1) known <- subset(known, daysOut > 203)\n\n#TODO will generate unfair comparisons when one candidate files earlier than the other. be careful around filing deadlines\n\ttotals <- aggregate(list(money = known$money), list(fecid = known$cand_id, year = known$cycle), sum, na.rm = T)\n\ttotals\n}\n\n\n\ncleanUpResults <- function(results){\n\n\t### clean up some things\n\tresults$candidate <- str_trim(results$candidate)\n\tresults$votesmartid[results$votesmartid==\"#N/A\"] <- NA\n\tresults$fecid[results$fecid==\"#N/A\"] <- NA\n\n\t### add ranks to results\n\tresults <- results[order(results$office),]\n\tresults$rank <- unlist(by(-1*results$vote, results$office, rank, ties.method = \"average\", na.last = \"keep\"))\n\n\t### simplify party codes\n\tresults$party2 <- results$party\n\tdemids <- c(\"D\", \"DEM\", \"Democrat \", \"D/WF/IDP Combined Parties\", \"Democratic-Farmer-Labor\", \"DFL\", \"D*\", \"DNL\", \"Democratic-Nonpartisan League\")\n\trepids <- c(\"R\", \"REP\", \"R/CRV Combined Parties\", \"Republican \", \"Independent-Republican\")\n\tresults$party2[results$party2 %in% demids] <- \"Democrat\"\n\tresults$party2[results$party2 %in% repids] <- \"Republican\"\n\tresults$party2[!results$party2%in%c(\"Democrat\", \"Republican\")] <- \"other\"\n\n\t### add rank by party\n\tresults <- results[order(results$office, results$party2),]\n\tresults$partyrank <- unlist(by(-1*results$vote, paste0(results$office, results$party2), rank, ties.method = \"random\", na.last = \"keep\"))\n\tresults$party2 <- paste0(results$party2, results$partyrank)\n\n\t### add a total vote\n\tresults <- merge(results, aggregate(list(tvote = results$vote), list(office = results$office), sum, na.rm = T), by = \"office\", all = T)\n\tresults$pctvote <- 100*results$vote/results$tvote\n\tresults <- results[order(results$vote, decreasing = T), ]\n\n}\n\n\n\n## latest cook rating\ncookAsOf <- function(data, daysOut){\n\tknown <- data[data$days_til_election > daysOut,]\n\tnames(known)[1] <- \"office\"\n\tknown <- known[order(known$days_til_election),]\n\tknown <- known[!duplicated(known$office), ]\n\tknown <- known[,c(\"office\", \"cook_rating\")]\n\tknown\n}\n\n\n\nthingAsOf <- function(data, daysOut, thing, collapseBy){\n\tknown <- data[data$daysOut >= daysOut,]\n\tknown <- plyr:::arrange(known, as.numeric(daysOut))\n\tweightingSchemes(known, thing, collapseBy)\n}\n\n\n\ncleanUpPresApproval <- function(data, var1 = \"A0\", var2 = \"B0\", signed = T) {\n\n\tdata$enddate <- as.Date(data$enddate, format = \"%Y-%m-%d\")\n\n\tif(is.null(data$knownDate)) data$knownDate <- NA\n\n\tdata$knownDate <- as.Date(data$knownDate, format = \"%Y-%m-%d\")\n\tif(all(is.na(data$knownDate))) data$knownDate <- data$enddate\n\n\tdata <- subset(data, !is.na(enddate))\n\tdata$cycle <- as.numeric(format(data$enddate, \"%Y\"))\n\tdata$cycle <- data$cycle + data$cycle%%2 ## broken for days past election day\n\n\t## two versions of approval ratings\n\tdata$net <- data[,var1] - data[,var2]\n\tdata$pctapp <- 100*data[,var1]/(data[,var1] + data[,var2])\n\n\t## sign the approval ratings\n\tif(signed){\n\t\treps <- which(data$presparty==\"R\")\n\t\tdata$net[reps] <- -1*data$net[reps]\n\t\tdata$pctapp[reps] <- 100 - data$pctapp[reps]\n\t}\n\n\tdata$daysOut <- electionDay(data$cycle) - data$knownDate\n\tbad <- data$daysOut < 0\n\tdata$cycle[bad] <- data$cycle[bad] + 2\n\tdata$daysOut <- electionDay(data$cycle) - data$knownDate\n\tdata\n}\n\n\n\ncleanUpPartyFav <- function(data){\n\t## weirdness so we can use same function as other ones\n\tdata$enddate <- as.character(format(as.Date(data$EndDate, format = \"%m/%d/%Y\"), format = \"%Y-%m-%d\"))\n\tdata$A0 <- data$Favorable.dem - data$Unfavorable.dem\n\tdata$B0 <- data$Favorable.rep - data$Unfavorable.rep\n\tcleanUpPresApproval(data, signed = F)\n}\n\n\n\ncleanUpPVI <- function(pvi){\n\n\tpvi2 <- pvi[ ,grep(\"pvi|postal\", names(pvi))]\n\tpvi2 <- reshape(pvi2, direction = \"long\", varying = list(1:16))\n\tpvi2$year <- 1952 + 4*(16 - pvi2$time)\n\tnames(pvi2)[3] <- \"pvi\"\n\tpvi2 <- pvi2[,c(\"postal\", \"year\", \"pvi\")]\n\n\tpvi3 <- pvi2\n\tpvi3$year <- pvi3$year + 2\n\n\tx <- rbind(pvi2, pvi3)\n\trownames(x) <- NULL\n\tna.exclude(x)\n}\n\n\n\nrealraces <- function(reduced, results){\n\tthese <- c(\"vote.Democrat1\", \"vote.Republican1\")\n\treduced[,these][is.na(reduced[,these])] <- 0\n\treduced$demPctMajor <- 100*reduced$vote.Democrat1/(reduced$vote.Democrat1 + reduced$vote.Republican1)\n\treduced$demMarginAll <- 100*(reduced$vote.Democrat1 - reduced$vote.Republican1)/reduced$tvote\n\n\treduced$realrace <- as.numeric(reduced$demPctMajor > 10 & reduced$demPctMajor < 90)\n\treduced$mostlyARepAndDem <- 1 \n\treduced$mostlyARepAndDem [reduced$office %in% subset(results, rank < 3 & party2 == \"other1\")$office ] <- 0\n\n\t## is it really a standard 2-party deal?\n\treduced$sigThirdParty <- 0\n\treduced$sigThirdParty [reduced$office %in% subset(results, party2 == \"other1\" & pctvote > 20)$office ] <- 1\n\treduced$sigThirdParty10 <- 0\n\treduced$sigThirdParty10[reduced$office %in% subset(results, party2 == \"other1\" & pctvote > 10)$office ] <- 1\n\tproblematicRecodes <- subset(results, xx%in%c(\"2012MEKing\", \"2010FLCrist\", \"2010AKMurkowski\", \"2006CTLieberman\"))$office\n\treduced$sigThirdParty10[reduced$office%in%problematicRecodes] <- 1\n\n\treduced$fakeParty <- 0\n\treduced$fakeParty[reduced$office%in%problematicRecodes] <- 1\n\n\t#reduced$mostlyARepAndDem[ (reduced$vote.Democrat1 + reduced$vote.Republican1) / reduced$tvote < .80 ] <- 0\n\treduced\n}\n\n\n\nweightingSchemes <- function(known, var, id, lastX = 4){\n\tknown$thing <- known[,var]\n\n\tzz <- ddply(known, id, function(X) data.frame(\n\t\t\tx1 = mean(X$thing, na.rm = T), ### mean of everything this election cycle\n\t\t\tx2 = weighted.mean(X$thing, .9^(as.numeric(X$daysOut)/30), na.rm = T), ## time weighting. crazy slow decay.\n\t\t\tx2b = weighted.mean(X$thing, .5^(as.numeric(X$daysOut)/14), na.rm = T), ## half-life of two weeks.\n\t\t\tx2c = weighted.mean(X$thing, .5^(as.numeric(X$daysOut)/30), na.rm = T),\n\t\t\tx3 = X$thing[!is.na(X$thing)][1], ## 1 most recent\n\t\t\tx4 = mean(X$thing[order(X$daysOut) < lastX], na.rm = T) ## 3 most recent\n\t))\n\tzz\n}\n\n\n\naddLags <- function(oldreults, reduced){\n\toldresults <- oldresults[,c(\"year\", \"class\", \"postal\", \"offyear\", \"office\", \"demPctMajor\")]\n\n\tfor(i in names(reduced)){\n\t\tif(!i%in%names(oldresults)){\n\t\t\toldresults[,i] <- NA\n\t\t}\n\t}\n\n\toldresults <- oldresults[,names(reduced)]\n\toldresults <- subset(oldresults, !is.na(postal))\n\treduced <- rbind(reduced, oldresults)\n\n\t#### make lags.\n\treduced$lastResultsThisSeat <- NA\n\treduced$lastResultsThisOffOn <- NA\n\treduced$lastResultsThisState <- NA\n\treduced <- reduced[order(reduced$year, decreasing = T),]\n\n\t### terrible; to improve, handle multi races per state.\n\t### may want to only do competitive raes? \n\tfor(i in 1:nrow(reduced)){\n\t\treduced$lastResultsThisSeat[i] <- subset(reduced, postal == reduced$postal[i] & class == reduced$class[i] & year < reduced$year[i] & demPctMajor!=0)$demPctMajor[1]\n\t\treduced$lastResultsThisOffOn[i] <- subset(reduced, postal == reduced$postal[i] & offyear == reduced$offyear[i] & year < reduced$year[i] & demPctMajor!=0)$demPctMajor[1]\n\t\treduced$lastResultsThisState[i] <- subset(reduced, postal == reduced$postal[i] & year < reduced$year[i] & demPctMajor!=0)$demPctMajor[1]\n\t}\n\n\treduced <- subset(reduced, year > 1990)\n\treduced\n}\n\n\n\nprepareTheData <- function(results, reduced, presApproval, genBallot, partyFav, pvi, fec, incApproval, lastsen, cookRatings, oldresults){\n\n\tresults <- cleanUpResults(results)\n\n\t##############\n\t### fec clean up\n\t##############\n\n\t## moved to fec-updates.r\n\n\t##############\n\t### bio collapse. \n\t##############\n\n\ttemp <- names(results)\n\tbio <- results[ ,grep(\"city.council\", temp):grep(\"us.senate\", temp)]\n\tbio <- data.frame(nothing = 1, bio)\n\tresults$experience <- apply(bio, 1, function(x) max(which(x == 1))) - 1\n\n\t##############\n\t### 2014, all possible combos\n\t##############\n\n\tfinishedRaces <- subset(results, year < 2014)\n\tfinishedRaces$matchup <- 1\n\n\tresults2014 <- subset(results, year == 2014)\n\tthese <- unique(results2014$office)\n\n\t## order by decreasing money, so lower matchup numbers come first\n\t## temp <- aggregate(list(money = fec$money), list(fecid = fec$cand_id), sum)\n\t## results2014 <- results2014[order(temp$money[match(results2014$fecid, temp$fecid)], decreasing = T),]\n\n\tcombos2014 <- NULL\n\tfor(i in these){\n\n\t\ttemp <- subset(results2014, office == i)\n\t\tdems <- temp[grep(\"Democrat\", temp$party2),]$slug\n\t\treps <- temp[grep(\"Republican\", temp$party2),]$slug\n\n\t\tfor(j in 1:length(dems)){\n\t\t\tfor(k in 1:length(reps)){\n\n\t\t\t\tthisdem <- subset(temp, slug == dems[j])\t\n\t\t\t\tif(nrow(thisdem)>0)\n\t\t\t\t\tthisdem$party2 <- \"Democrat1\"\n\n\t\t\t\tthisrep <- subset(temp, slug == reps[k])\n\t\t\t\tif(nrow(thisrep)>0)\n\t\t\t\t\tthisrep$party2 <- \"Republican1\"\n\n\t\t\t\tnewmatchup <- rbind(thisdem, thisrep)\n\t\t\t\tnewmatchup$matchup <- paste(j, k, sep = \"-\")\n\t\t\t\tcombos2014 <- rbind(combos2014, newmatchup)\n\t\t\t}\n\t\t}\n\t}\n\n\tcombos2014 <- combos2014[-c(grep(\"0-|-0\", combos2014$matchup)),]\n\tresults <- rbind(finishedRaces, combos2014)\n\n\t##############\n\t### drop to a single row per race, keeping data for only the top Democrat and the top Republican\n\t##############\n\treduced <- subset(results, party2 %in% c(\"Democrat1\", \"Republican1\"))\n\treduced <- reshape(reduced, direction = \"wide\", idvar = c(\"office\", \"year\", \"state\", \"postal\", \"class\", \"tvote\", \"matchup\"), timevar = \"party2\", drop = c(\"partyrank\", \"party\"))\n\treduced$serious.Republican1[is.na(reduced$candidate.Republican1) & reduced$year == 2014] <- 1\n\treduced$serious.Democrat1[is.na(reduced$candidate.Democrat1) & reduced$year == 2014] <- 1\n\n\t### some dummys (whether our race is real and offyear), and lagged results by seat\n\treduced <- realraces(reduced, results)\n\treduced$lastparty <- lastsen$lastparty[match(reduced$office, lastsen$nextoffice)]\n\treduced$offyear <- as.numeric(reduced$year%%4 != 0)\n\treduced <- addLags(oldresults, reduced)\n\n\treduced$win <- \"\"\n\treduced$win[reduced$win.Democrat1 == 1] <- \"D\"\n\treduced$win[reduced$win.Republican1 == 1] <- \"R\"\n\treduced$win[reduced$win.other1 == 1] <- \"O\"\n\n\t##############\n\t### identify states where the senator (sitting or newly elected) usually (since 1992) doesn't match the presidential vote.\n\t##############\n\ttemp <- cbind(postal = pvi$postal, pvi[,grep(\"diff\", names(pvi))] )\n\ttemp2 <- reshape(temp, idvar = \"postal\", direction = \"long\", varying = list(2:ncol(temp)))\t\n\ttemp2$year <- as.numeric(gsub(\"diff\", \"\", names(temp)[-c(1)][temp2$time]))\n\tnames(temp2)[3] <- \"diff\"\n\ttemp2$presparty <- ifelse(temp2$diff > 0, \"D\", \"R\")\n\ttempsen <- lastsen[,c(\"Party\", \"term\", \"state\")]\n\tnames(tempsen) <- c(\"party\", \"year\", \"postal\")\n\ttemp3 <- merge(tempsen, temp2, by = c(\"year\", \"postal\"))\n\txx <- strsplit(as.character(temp3$party), split = \";\")\n\ttemp3$party <- sapply(xx, function(x) paste(unique(x), collapse = \";\"))\n\ttemp3 <- subset(temp3, temp3$year > 1992)\n\tdiffsen <- aggregate(list(diffsen = temp3$party!=temp3$presparty), list(postal = temp3$postal), mean)\n\tdiffsen <- diffsen[order(diffsen$diffsen),]\n\treduced$presSenMostlyOpposite <- 0\n\treduced$presSenMostlyOpposite [reduced$postal %in% diffsen$postal[diffsen$diffsen > .5]] <- 1\n\n\t##############\n\t### median senate vote\n\t##############\n\tmedMargin2 <- with(reduced, aggregate(list(x = reduced$demMarginAll), list(postal = reduced$postal), median, na.rm = T))\n\treduced$medMargin2 <- medMargin2$x[match(reduced$postal, medMargin2$postal)]\n\n\t##############\n\t### does sitting president party matter (esp in offyear?) - prob not enough data to actaully investigate this\n\t##############\n \toldpres <- data.frame(year = seq(1990, 2014, by = 2), sittingPres = \"D\", stringsAsFactors = F)\n \toldpres$sittingPres[oldpres$year %in% c(1990, 1992, 2002, 2004, 2006, 2008)] <- \"R\"\n \treduced <- merge(reduced, oldpres, by = \"year\")\t\n\n\t##############\n\t### compute margins, name dates consistently, etc. for generic poll data\n\t##############\n\tpresApproval <- cleanUpPresApproval(presApproval)\n\tgenBallot <- cleanUpPresApproval(genBallot, var1 = \"Democrats\", var2 = \"Republicans\", signed = F)\n\tpartyFav <- cleanUpPartyFav(partyFav)\n\tpvi <- cleanUpPVI(pvi)\n\n\t##############\n\t### add some dates to things\n\t##############\n\tincApproval$DATEOUT <- as.Date(incApproval$knownDate, format = \"%Y-%m-%d\") ### switched from incApproval$DATEOUT to incApproval$knownDate\n\tincApproval$daysOut <- electionDay(as.numeric(sapply(strsplit(incApproval$relevant.office, split = \"-\"), function(x) x[5]))) - incApproval$DATEOUT\n\n\tsave(results, reduced, presApproval, genBallot, partyFav, pvi, fec, incApproval, lastsen, cookRatings, oldresults, file = \"fundydata.rdata\")\n\n}\n\n\n\nprepData <- function(fec2, cookRatings2, incApproval2, presApproval2, genBallot2, partyFav2, reduced){\n\t\n\t##############\n\t### put it all together\n\t##############\n\n\tmakeid <- function(data, var, xx = \"year\") paste(data[,xx], data[,var])\n\tgetMoney <- function(xx) fec2$money[match(makeid(reduced, xx), makeid(fec2, \"fecid\"))];\n\n\treduced$money.Democrat1 <- getMoney(\"fecid.Democrat1\")\n\treduced$money.Republican1 <- getMoney(\"fecid.Republican1\")\n\treduced$cook <- cookRatings2$cook_rating[ match(reduced$office, cookRatings2$office) ]\n\treduced$incApproval <- incApproval2$x2[ match(reduced$office, incApproval2$relevant.office) ]\n\treduced$presApproval <- presApproval2$x2b[ match(reduced$year, presApproval2$cycle) ]\n\treduced$genBallot <- genBallot2$x2c[ match(reduced$year, genBallot2$cycle) ]\n\treduced$partyFav <- partyFav2$x2b[ match(reduced$year, partyFav2$cycle) ]\n\treduced$pvi <- pvi$pvi [ match(makeid(reduced, \"postal\"), makeid(pvi, \"postal\")) ]\n\n\treduced$incumbent <- \"open\"\n\treduced$incumbent[ which(reduced$incumbent.Democrat1 == 1) ] <- \"D\"\n\treduced$incumbent[ which(reduced$incumbent.Democrat1 == \"a\") ] <- \"Da\"\n\treduced$incumbent[ which(reduced$incumbent.Republican1 == 1) ] <- \"R\"\n\treduced$incumbent[ which(reduced$incumbent.Republican1 == \"a\") ] <- \"Ra\"\n\treduced$open <- reduced$incumbent == \"open\"\n\treduced$open2 <- !reduced$incumbent %in% c(\"D\", \"R\")\n\n\t### calculate senate percents on a rolling basis\n\tlastsen2 <- NULL\n\tfor(year in seq(1992, 2014, by = 2)){\n\t\ttemp <- subset(lastsen, term < year & term >= year - 40)\n\t\tdemsplits = ddply(temp, \"state\", function(X) {\n\t\t\tgg <- strsplit(as.character(X$Party), split = \";\")\n\t\t\tl <- sapply(gg, length)\n\t\t\tweight <- 1/rep(l, times = l)\n\t\t\tparty = unlist(gg)\n\t\t\tweighted.mean(party !=\"R\" , weight)} )\n\t\tdemsplits$year <- year\n\t\tlastsen2 <- rbind(lastsen2, demsplits)\n\t}\n\tnames(lastsen2)[2] <- \"pctDemLast20\"\n\treduced$pctDemLast20 <- lastsen2$pctDemLast20[ match( paste(reduced$year, reduced$postal), paste(lastsen2$year, lastsen2$state) )]\n\n\t## bad filling in missing ratings\n\treduced$temp <- ifelse(reduced$incumbent %in% c(\"D\", \"Da\"), reduced$demPctMajor, 100 - reduced$demPctMajor)\n\tmissingratings <- which(is.na(reduced$incApproval) & reduced$incumbent != \"open\")\n\ttemp <- reduced[missingratings,]\n\treduced$incApproval[missingratings] <- predict( lm(incApproval ~ pvi + temp, data = subset(reduced, realrace == 1)), newdata = temp )\n\treduced$incApproval[is.na(reduced$incApproval)] <- 0\n\n\treduced$cook <- reduced$cook - 4\n\treduced$cook[is.na(reduced$cook)] <- 0\n\treduced$cook <- as.factor(reduced$cook)\n\treduced$cook <- relevel(reduced$cook, ref = \"0\")\n\n\tfixme <- c(\"money.Democrat1\", \"money.Republican1\", \"experience.Democrat1\", \"experience.Republican1\")\n\treduced[,fixme][is.na(reduced[,fixme])] <- 0\n\n\treduced$demMoney <- 100 * with(reduced, money.Democrat1 / (money.Democrat1 + money.Republican1) )\n\treduced$demMoney[is.na(reduced$demMoney)] <- 50 ## no one has reported money.\n\treduced$demMoney <- reduced$demMoney - 50\n\n\treduced$netExp <- reduced$experience.Democrat1 - reduced$experience.Republican1\n\treduced$netExp2 <- floor(reduced$experience.Democrat1/3) - floor(reduced$experience.Republican1/3)\n\n\treduced$incApproval2 <- reduced$incApproval\n\treps <- reduced$incumbent%in%c(\"R\", \"Ra\")\n\treduced$incApproval2[reps] <- -1 * reduced$incApproval2[reps]\n\n\treduced$incumbentSimple <- substr(reduced$incumbent, 1, 1)\n\treduced$incumbentSimple <- relevel(as.factor(reduced$incumbentSimple), ref = 3)\n\n\treduced$notARace <- with(reduced, as.character(cook)%in%c(\"-3\", \"3\") | is.na(candidate.Democrat1) | is.na(candidate.Republican1))\n\n\treduced$norep <- is.na(reduced$candidate.Republican1)\n\treduced$nodem <- is.na(reduced$candidate.Democrat1)\n\treduced\n}\n", "meta": {"hexsha": "c9c9a22e96892b2c0dc012dce55b82ac7eb7ec53", "size": 16498, "ext": "r", "lang": "R", "max_stars_repo_path": "fundamentals/FunctionsFundamentals.r", "max_stars_repo_name": "yhat/leo-senate-model", "max_stars_repo_head_hexsha": "04b2c1dfd54d74a636e0592ed88f874d581ebd78", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fundamentals/FunctionsFundamentals.r", "max_issues_repo_name": "yhat/leo-senate-model", "max_issues_repo_head_hexsha": "04b2c1dfd54d74a636e0592ed88f874d581ebd78", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fundamentals/FunctionsFundamentals.r", "max_forks_repo_name": "yhat/leo-senate-model", "max_forks_repo_head_hexsha": "04b2c1dfd54d74a636e0592ed88f874d581ebd78", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-05T21:48:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-05T21:48:13.000Z", "avg_line_length": 38.9103773585, "max_line_length": 177, "alphanum_fraction": 0.6791732331, "num_tokens": 5272, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.30901686245007903}} {"text": "## RNA-seq analysis with DESeq2\n## modified from example from Stephen Turner, @genetics_blog\n\nlibrary(DESeq2)\nlibrary(RColorBrewer)\nlibrary(gplots)\n\n\n## args[1] == full path to Charts directory for this analysis\n## args[2] == full path to featureCounts csv for this analysis\n## args[3] == comma separated string of conditions to compare and rep counts:\n## \"c1,c2,c1reps,c2reps\"\n## args[4] == script directory\n##\nargs = commandArgs(trailingOnly=TRUE)\nsetwd(args[1])\nsource(paste(paste(args[4],\"/\",sep=\"\"),\"rld_pca.r\",sep=\"\"))\nsource(paste(paste(args[4],\"/\",sep=\"\"),\"maplot.r\",sep=\"\"))\nsource(paste(paste(args[4],\"/\",sep=\"\"),\"volcanoplot.r\",sep=\"\"))\n\n\n## preprocess phase --------------------------------------------------\n\n## Read featureCounts matrix\ncountdata <- read.table(args[2], header=TRUE, row.names=1)\n\n## Remove first five columns (chr, start, end, strand, length)\ncountdata <- countdata[ ,6:ncol(countdata)]\n\n## Remove .bam or .sam from filenames\ncolnames(countdata) <- gsub(\"\\\\.[sb]am$\", \"\", colnames(countdata))\n\n## Convert to matrix\ncountdata <- as.matrix(countdata)\nhead(countdata)\n\n## Assign condition\nc1c2reps <- strsplit(args[3], \",\", fixed = TRUE)[[1]]\nc1 <- c1c2reps[[1]]\nc2 <- c1c2reps[[2]]\nc1reps <- c1c2reps[[3]]\nc2reps <- c1c2reps[[4]]\nprefix <- paste(c1,c2,sep=\"-\")\n\n##(condition <- factor(c(rep(c1, reps), rep(c2, reps))))\n##(condition <- factor(c(rep(\"Condition1\",2),rep(\"Condition2\",4))))\n(condition <- factor(c(rep(c2, c2reps), rep(c1, c1reps))))\n\n\n## Analysis phase --------------------------------------------------\n\n\n## Create a coldata frame and instantiate the DESeqDataSet. See\n## ?DESeqDataSetFromMatrix\n##\n(coldata <- data.frame(row.names=colnames(countdata), condition))\ndds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition)\ndds\n\n## Run the DESeq pipeline\ndds <- DESeq(dds)\n\n\n# Plot dispersions\npng(paste(prefix,\"qc-dispersions.png\",sep=\"-\"), 1000, 1000, pointsize=20)\nplotDispEsts(dds, main=\"Dispersion plot\")\ndev.off()\n\n## Regularized log transformation for clustering/heatmaps, etc\nrld <- rlogTransformation(dds)\nhead(assay(rld))\npng(paste(prefix,\"histogram-assay.png\",sep=\"-\"), 1000, 1000, pointsize=20)\nhist(assay(rld))\ndev.off()\n\n\n## Colors for plots below via RColorBrewer\n(mycols <- brewer.pal(8, \"Dark2\")[1:length(unique(condition))])\n\n## Create heatmap of sample distance\nsampleDists <- as.matrix(dist(t(assay(rld))))\npng(paste(prefix,\"qc-heatmap-samples.png\",sep=\"-\"), w=1000,h=1000, pointsize=20)\nheatmap.2(as.matrix(sampleDists), key=F, trace=\"none\",\n col=colorpanel(100, \"black\", \"white\"),\n ColSideColors=mycols[condition], RowSideColors=mycols[condition],\n margin=c(10, 10), main=\"Sample Distance Matrix\")\ndev.off()\n\n\n## Do some PCA\n## Could do with built-in DESeq2 function:\n## DESeq2::plotPCA(rld, intgroup=\"condition\")\npng(paste(prefix,\"qc-pca.png\",sep=\"-\"), 1000, 1000, pointsize=20)\nrld_pca(rld, colors=mycols, intgroup=\"condition\", xlim=c(-75, 35))\ndev.off()\n\n\n## Get differential expression results\nres <- results(dds)\ntable(res$padj<0.05)\n## Order by adjusted p-value\nres <- res[order(res$padj), ]\n## Merge with normalized count data\nresdata <- merge(as.data.frame(res), as.data.frame(counts(dds, normalized=TRUE)), by=\"row.names\", sort=FALSE)\nnames(resdata)[1] <- \"Gene\"\nhead(resdata)\n## Write results\nwrite.csv(resdata, file=paste(prefix,\"DGE-results.csv\",sep=\"-\"))\n\n\n## MA plot\n## Could do with built-in DESeq2 function:\n## DESeq2::plotMA(dds, ylim=c(-1,1), cex=1)\npng(paste(prefix,\"DGE-maplot.png\",sep=\"-\"), 1500, 1000, pointsize=20)\nmaplot(resdata, main=\"MA Plot\")\ndev.off()\n\n\n## Volcano plot...\npng(paste(prefix,\"DGE-volcanoplot.png\",sep=\"-\"), 1200, 1000, pointsize=20)\nvolcanoplot(resdata, lfcthresh=1, sigthresh=0.05, textcx=.8, xlim=c(-2.3, 2))\ndev.off()\n", "meta": {"hexsha": "e1a4c0ccebac97c284f29c9b4a50751b111aa709", "size": 3795, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/deseq2-rnaseq.r", "max_stars_repo_name": "jsa-aerial/aerobio", "max_stars_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-01-23T16:08:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-17T19:40:12.000Z", "max_issues_repo_path": "Scripts/deseq2-rnaseq.r", "max_issues_repo_name": "jsa-aerial/aerobio", "max_issues_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 13, "max_issues_repo_issues_event_min_datetime": "2017-06-08T19:17:52.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-04T21:26:30.000Z", "max_forks_repo_path": "Scripts/deseq2-rnaseq.r", "max_forks_repo_name": "jsa-aerial/aerobio", "max_forks_repo_head_hexsha": "9d845355874c304b5e739c81ca3a7b7cd78dbbd9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-03T02:18:02.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-03T02:18:02.000Z", "avg_line_length": 30.8536585366, "max_line_length": 109, "alphanum_fraction": 0.6740447958, "num_tokens": 1143, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.309016862450079}} {"text": "library(raster)\n\nfiles = list.files('.')\nfiles = files[grep('.tif$', files)]\nprint(files)\n\nfor (f in files){\n rast = raster(f)\n new = rast[68:nrow(rast), 68:ncol(rast), drop=F]\n new_fname = gsub('.tif', '_90x90.tif', f)\n print(paste('Now writing:', new_fname))\n writeRaster(new, new_fname, 'GTiff')\n}\n", "meta": {"hexsha": "cc59947bf52354bd5405f411fa90a1398b32bdf9", "size": 316, "ext": "r", "lang": "R", "max_stars_repo_path": "reduce_size_yos_data/cut_down_rast.r", "max_stars_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_stars_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "reduce_size_yos_data/cut_down_rast.r", "max_issues_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_issues_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "reduce_size_yos_data/cut_down_rast.r", "max_forks_repo_name": "drewhart/geonomics_methods_paper_ancillary_code", "max_forks_repo_head_hexsha": "cd403b8200ca8f55d5f41f97cdacf162359ea066", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.5714285714, "max_line_length": 52, "alphanum_fraction": 0.6202531646, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953506426082, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30901685466803364}} {"text": "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Phylogenetic analyses of fraction feeding and lambda\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nlibrary(ape) # Phylogenetics\nlibrary(MCMCglmm)\nlibrary(phylobase)\nlibrary(plyr)\nlibrary(curl)\nlibrary(phylosignal) # for nicer plots (http://www.francoiskeck.fr/phylosignal/demo_plots.html)\n# Fix \"Peer certificate cannot be authenticated with given CA certificates\" error\n# library(Rphylopars) # for inference with missing data and within-species variation\nlibrary(httr)\nlibrary(stringr) # word()\nlibrary(quantreg)\nlibrary(sfsmisc)\nset_config(config(ssl_verifypeer = 0L))\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nrm(list=ls()) # clears workspace\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nsource('R/FracFeed-Functions.r') # Convenience functions\n#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nload('Output/GAM/GAM_fit_noTaxInf.Rdata') # load 'fit' of non-taxonomicmodel\n length(fitted(fit))\n \nload('Data/FracFeed_DataPhylo.Rdata') # load full post-ToL Frac Feeding dataset\n nrow(fdat)\n length(unique(fdat$Consumer.identity))\n nrow(fdat[-fit$na.action,]) # removing species for which GAM couldn't be fit due to lack of information on all variables\n\nload('Data/FracFeed_DynamicsDataPhylo.Rdata') # load full post-ToL Dynamics dataset\n nrow(ddat)\n length(unique(ddat$Consumer.identity))\n \nload('Output/Phylo/Taxa_comb_matched.Rdata') # load taxonomic identities\n nrow(taxa)\n \n###############################\n# Set up full tree construction\n###############################\ntree<-tol_induced_subtree(taxa$ott_id,label_format = 'name')\nsave(tree,file='Output/Phylo/Tree_comb.Rdata')\n\nlength(tree$tip.label)\nlength(unique(c(ddat$Consumer.identity,fdat$Consumer.identity)))\n\n#######################################################################\n# Merge Fraction feeding dataset w/ non-taxonomic model-fit estimates\n#######################################################################\nfdat$fit <- fdat$resid <- rep(NA,nrow(fdat))\nfdat$fit[-fit$na.action] <- fitted(fit)\nfdat$resid[-fit$na.action] <- residuals(fit)\n\n#######################################################\n# Calculate data averages by consumer identity\n# (phylobase 'trees' can only have one datum per taxon)\n#######################################################\nsfdat<-ddply(fdat,.(Consumer.identity),summarise,fF.m=mean(fF),fF.sd=sd(fF),fit.m=mean(fit),fit.sd=sd(fit),resid.m=mean(resid), resid.sd=sd(resid),n=length(fF))\nsfdat$fF.sd[is.na(sfdat$fF.sd)]<-0\nsfdat$fit.sd[is.na(sfdat$fit.sd)]<-0\nsfdat$resid.sd[is.na(sfdat$resid.sd)]<-0\n\n# Residuals are very heavy-tailed, so created log10-transformed residuals\nsfdat$resid.m.log10<-sfdat$resid.m\n neg.res<-which(sfdat$resid.m.log10<0 & !is.na(sfdat$resid.m.log10))\n pos.res<-which(sfdat$resid.m.log10>0 & !is.na(sfdat$resid.m.log10))\n sfdat$resid.m.log10[neg.res]<- -log10(abs(sfdat$resid.m.log10[neg.res]))\n sfdat$resid.m.log10[pos.res]<- log10(sfdat$resid.m.log10[pos.res])\nnrow(sfdat)\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# Average dynamics information\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nsddat <- ddply(ddat, .(Consumer.identity), summarise, lambda.m=mean(lambda),lambda.sd=sd(lambda), sigma.m=mean(sigma),sigma.sd=sd(sigma), epsilon.m=mean(epsilon),epsilon.sd=sd(epsilon), lambda.sigma2.m=mean(lambda.sigma2), lambda.sigma2.sd=sd(lambda.sigma2))\n# Using 2*lambda / sigma^2 (Butler & King, 2004)\nsddat$lambda.sd[is.na(sddat$lambda.sd)]<-0\nsddat$sigma.sd[is.na(sddat$sigma.sd)]<-0\nsddat$epsilon.sd[is.na(sddat$epsilon.sd)]<-0\nsddat$lambda.sigma2.sd[is.na(sddat$lambda.sigma2.sd)]<-0\nnrow(sddat)\n\n# scdat<-ddply(cdat,.(Consumer.identity),summarise,OUSS_FAP=mean(OUSS_FAP),OUSS_PM=mean(OUSS_PM),OUSS_lambda=mean(OUSS_lambda),OUSS_lambda.sd=sd(OUSS_lambda),FracCycle= sum(OUSS_Cycles)/length(OUSS_Cycles)) # This was for use on results provided by Louca & Doebeli (which requires loading 'cdat' in 'FracFeed_CycleDataPhylo.Rdata'\n# nrow(scdat)\n\n#~~~~~~~~~~~~~~~~\n# Merge summaries\n#~~~~~~~~~~~~~~~~\nsdat<-merge(sfdat,sddat,all=TRUE)\nnrow(sdat)==length(tree$tip.label)\n\nrownames(sdat)<-sdat$Consumer.identity\nsdat<-sdat[,-1]\n\n#####################\n# Match data to tree\n#####################\n# Check to ensure all taxa are present in both tree and data\na<-sort(tree$tip.label)\nb<-sort(rownames(sdat))\na[a%!in%b]\nb[b%!in%a]\n\n# Set branch lengths using Grafen's (1989) method (much-improved fan visualization)\nbtree<-tree<-compute.brlen(tree, method='Grafen', power=1)\n\ntree<-as(tree,'phylo4')\n# nodeLabels(tree)\n\ntree_data<-phylo4d(tree,tip.data=sdat,rownamesAsLabels=TRUE, match.data=TRUE,missing.data=NULL,extra.data=NULL)\ntree_data<-reorder(tree_data,order='preorder')\nsave(tree_data,file='Output/Phylo/Tree_w.CombData.Rdata')\n\n# Data-specific trees\nftaxa<-rownames(sdat)[!is.na(sdat$fF.m)]\nftree_data<-subset(tree_data,tips.include=ftaxa)\nsave(ftree_data,file='Output/Phylo/Tree_w.FracFData.Rdata')\n\nctaxa<-rownames(sdat)[!is.na(sdat$lambda.m)]\nctree_data<-subset(tree_data,tips.include=ctaxa)\nsave(ctree_data,file='Output/Phylo/Tree_w.CycleData.Rdata')\n\nfctaxa<-rownames(sdat)[which(!is.na(sdat$lambda.m)&!is.na(sdat$fF.m))]\nfctree_data<-subset(tree_data,tips.include=fctaxa)\nsave(fctree_data,file='Output/Phylo/Tree_w.CombTaxaData.Rdata')\n\n##################\n# Plot phylogenies\n##################\ncolpal <- colorRampPalette(c(\"blue\", \"yellow\", \"red\"))\n# ~~~~~~~~~~~~\n# Without data -- all species in combined datasets\n# ~~~~~~~~~~~~\npdf('Output/Phylo/Phylogeny.pdf',width=20,height=300)\n plot(tree, show.node.label=TRUE,show.tip.label=TRUE)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With data - only those with Fraction Feeding data\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\ntree_dat<-ftree_data\nftraits<-c('fF.m','fit.m','resid.m.log10')\nxlims<-matrix(c(0,1,0,1,range(sdat$resid.m.log10,na.rm=TRUE)),nrow=2)\nxlims[1,3]<-floor(xlims[1,3]); xlims[2,3]<-ceiling(xlims[2,3])\n\n\n# pdf('Output/Phylo/Phylogeny_fF_dot.pdf',width=20,height=120)\n# multiplot.phylo4d(tree_dat,trait=ftraits,data.xlim=xlims,plot.type='dotplot',tree.ratio=0.4,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=F,grid.col='grey90',grid.lty=1,show.box=TRUE,dot.cex=1)\n# dev.off()\n\npdf('Output/Phylo/Phylogeny_fF_col.pdf',width=20,height=120)\n multiplot.phylo4d(tree_dat,trait=ftraits,data.xlim=xlims,plot.type='gridplot',tree.ratio=0.8,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=F,grid.col='grey90',grid.lty=1,show.box=FALSE,cell.col=colpal(100))\ndev.off()\n\npdf('Output/Phylo/Phylogeny_fF_col_fan.pdf',width=25,height=20)\n multiplot.phylo4d(tree_dat,trait=ftraits,tree.type='fan',plot.type='gridplot',data.xlim=xlims,tree.ratio=0.6,show.trait=FALSE,tip.cex=0.2,tree.ladderize=TRUE,cell.col=colpal(100),tree.open.angle = 180,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=FALSE)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With data - only those with Cycling data\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\ntree_dat<-ctree_data\nctraits<-c('fF.m','lambda.m','sigma.m','lambda.sigma2.m')\nxlims<-matrix(c(0,1,0,max(sddat$lambda.m,na.rm=TRUE),0,max(sddat$sigma.m,na.rm=TRUE)),nrow=2)\nxlims[2,2]<-ceiling(xlims[2,2]);xlims[2,3]<-ceiling(xlims[2,3])\n\npdf('Output/Phylo/Phylogeny_Cycle_dot.pdf',width=20,height=120)\nmultiplot.phylo4d(tree_dat,trait=ctraits,data.xlim=xlims,plot.type='dotplot',tree.ratio=0.4,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=F,grid.col='grey90',grid.lty=1,show.box=TRUE,dot.cex=1)\ndev.off()\n\npdf('Output/Phylo/Phylogeny_Cycle_col.pdf',width=20,height=120)\nmultiplot.phylo4d(tree_dat,trait=ctraits,data.xlim=xlims,plot.type='gridplot',tree.ratio=0.8,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=F,grid.col='grey90',grid.lty=1,show.box=FALSE,cell.col=colpal(100))\ndev.off()\n\npdf('Output/Phylo/Phylogeny_Cycle_col_fan.pdf',width=25,height=20)\nmultiplot.phylo4d(tree_dat,trait=ctraits,tree.type='fan',plot.type='gridplot',data.xlim=xlims,tree.ratio=0.6,show.trait=FALSE,tip.cex=0.2,tree.ladderize=TRUE,cell.col=colpal(100),tree.open.angle = 180,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=FALSE)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With data - both data sets - all taxa\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\ntree_dat<-tree_data\ntraits<-c('fF.m','lambda.sigma2.m')\nxlims<-matrix(c(0,1,0,max(sddat$lambda.m,na.rm=TRUE)),nrow=2)\nxlims[2,2]<-ceiling(xlims[2,2])\n\npdf('Output/Phylo/Phylogeny_Comb_col_fan.pdf',width=25,height=20)\nmultiplot.phylo4d(tree_dat,trait=traits,tree.type='fan',plot.type='gridplot',data.xlim=xlims,tree.ratio=0.6,show.trait=FALSE,tip.cex=0.2,tree.ladderize=TRUE,cell.col=colpal(100),tree.open.angle = 180,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=FALSE)\ndev.off()\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# With data - both data sets - taxa in both data sets\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\ntree_dat<-fctree_data\ntraits<-c('fF.m','lambda.sigma2.m')\nxlims<-matrix(c(0,1,0,max(sddat$lambda.m,na.rm=TRUE)),nrow=2) # Should adjust maximum to subset of shared species\nxlims[2,2]<-ceiling(xlims[2,2])\n\npdf('Output/Phylo/Phylogeny_CombTaxa_col_fan.pdf',width=25,height=20)\nmultiplot.phylo4d(tree_dat,trait=traits,tree.type='fan',plot.type='gridplot',data.xlim=xlims,tree.ratio=0.6,show.trait=FALSE,tip.cex=1,tree.ladderize=TRUE,cell.col=colpal(100),tree.open.angle = 180,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=FALSE)\ndev.off()\n\n################################\n# Produce plots for major clades\n################################\nFocal.Nodes<-c('Teleostei','Cnidaria','Amphibia','Tetrapoda','Amniota','Arthropoda','Carnivora','Eucarida','Gnathostomata')\n\ntraits<-c('fF.m','lambda.sigma2.m')\nxlims<-matrix(c(0,1,0,1),nrow=2)\n\nfor(s in 1:length(Focal.Nodes)){ \n Select.Node<-Focal.Nodes[s]\n temp.tree_dat<-subset(tree_data,node.subtree=Select.Node)\n \n pdf(paste0('Output/Phylo/Phylogeny_Comb_col_fan_',Select.Node,'.pdf'),width=25,height=20)\n multiplot.phylo4d(temp.tree_dat,trait=traits,tree.type='fan',plot.type='gridplot',data.xlim=xlims,tree.ratio=0.6,show.trait=FALSE,tip.cex=1,tree.ladderize=TRUE,cell.col=colpal(100),tree.open.angle = 180,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=FALSE)\n \n tryCatch({ # Wrap in this to avoid warning error stopping loop\n \n multiplot.phylo4d(temp.tree_dat,trait=traits,data.xlim=xlims,plot.type='gridplot',tree.ratio=0.8,trait.bg.col=NA,grid.vertical=FALSE,grid.horizontal=F,grid.col='grey90',grid.lty=1,show.box=FALSE,cell.col=colpal(100))\n \n }, error=function(e){cat(\"ERROR :\",conditionMessage(e), \"\\n\")})\n \n dev.off()\n print(s)\n}\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n##########################################\n# Quantify strength of phylogenetic signal\n##########################################\n\n# feed.cg<-phyloCorrelogram(ftree_data,trait='Feeding')\n# save(feed.cg,file='Output/Phylo/phyloCorr_feedcg.Rdata')\n# plot(feed.cg)\n# \n# resil.cg<-phyloCorrelogram(ctree_dat,trait='OUSS_lambda')\n# save(resil.cg,file='Output/Phylo/phyloCorr_feedcg.Rdata')\n# plot(resil.cg)\n# \n\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \n# library(phylolm)\n# \n# set.seed(123456)\n# tre = rcoal(1000)\n# taxa = sort(tre$tip.label)\n# b0=0; b1=1;\n# x <- rTrait(n=1, phy=tre,model=\"BM\",parameters=list(ancestral.state=0,sigma2=10))\n# y <- b0 + b1*x + rTrait(n=1,phy=tre,model=\"lambda\",parameters=list(ancestral.state=0,sigma2=1,lambda=0.5))\n# \n# \n# x[1:5]<-NA\n# y[20:25]<-NA\n# \n# library(Rphylopars)\n# \n# dat = data.frame(species = taxa,trait=y[taxa],pred=x[taxa])\n# fit = phylopars.lm(trait~pred,trait_data=dat,tree=tre,model='lambda')\n# summary(fit)\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~", "meta": {"hexsha": "d508ff0c35b442dde389a77f754ccb51abdd3c0b", "size": 12075, "ext": "r", "lang": "R", "max_stars_repo_path": "dev/R/FracFeed-Analyses-Phylo.r", "max_stars_repo_name": "marknovak/FracFeed", "max_stars_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dev/R/FracFeed-Analyses-Phylo.r", "max_issues_repo_name": "marknovak/FracFeed", "max_issues_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dev/R/FracFeed-Analyses-Phylo.r", "max_forks_repo_name": "marknovak/FracFeed", "max_forks_repo_head_hexsha": "68a919d79cb38d49dbf34d7d4cff8108401d7a43", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.3947368421, "max_line_length": 330, "alphanum_fraction": 0.6318012422, "num_tokens": 3664, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953506426082, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30901685466803364}} {"text": "# source(\"~/Dropbox/research/climate-trace-shipping/lib/load.r\")\n# map <- load.ship.metadata()\n\nres <- matrix(0,nr=8, nc=4)\ncolnames(res) <- c('Speed','Speedservice','Speedmax','Sum')\nmap$Speed[is.na(map$Speed)] <- 0\nmap$Speedservice[is.na(map$Speedservice)] <- 0\nmap$Speedmax[is.na(map$Speedmax)] <- 0\nres[,1:3] <- c(1,1,1,1,0,0,0,0,1,1,0,0,1,1,0,0,1,0,1,0,1,0,1,0)\n\nres[1,4] <- sum(map$Speed > 0 & map$Speedservice > 0 & map$Speedmax > 0)\nres[2,4] <- sum(map$Speed > 0 & map$Speedservice > 0 & map$Speedmax == 0)\nres[3,4] <- sum(map$Speed > 0 & map$Speedservice == 0 & map$Speedmax > 0)\nres[4,4] <- sum(map$Speed > 0 & map$Speedservice == 0 & map$Speedmax == 0)\nres[5,4] <- sum(map$Speed == 0 & map$Speedservice > 0 & map$Speedmax > 0)\nres[6,4] <- sum(map$Speed == 0 & map$Speedservice > 0 & map$Speedmax == 0)\nres[7,4] <- sum(map$Speed == 0 & map$Speedservice == 0 & map$Speedmax > 0)\nres[8,4] <- sum(map$Speed == 0 & map$Speedservice == 0 & map$Speedmax == 0)\n\n# results: Speed always present when others present; conclusion: use Speed\n# Speed Speedservice Speedmax Sum\n# [1,] 1 1 1 26558\n# [2,] 1 1 0 57603\n# [3,] 1 0 1 6081\n# [4,] 1 0 0 0\n# [5,] 0 1 1 0\n# [6,] 0 1 0 0\n# [7,] 0 0 1 0\n# [8,] 0 0 0 37890", "meta": {"hexsha": "534120b9f43425d5b26e4edb269f6435d8ba5206", "size": 1408, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/dev/explore_speed_features.r", "max_stars_repo_name": "knights-lab/climate-trace-shipping", "max_stars_repo_head_hexsha": "2ffa4e0cbea2dd8351de12850b9b3508981d3aeb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/dev/explore_speed_features.r", "max_issues_repo_name": "knights-lab/climate-trace-shipping", "max_issues_repo_head_hexsha": "2ffa4e0cbea2dd8351de12850b9b3508981d3aeb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/dev/explore_speed_features.r", "max_forks_repo_name": "knights-lab/climate-trace-shipping", "max_forks_repo_head_hexsha": "2ffa4e0cbea2dd8351de12850b9b3508981d3aeb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.5517241379, "max_line_length": 75, "alphanum_fraction": 0.5255681818, "num_tokens": 578, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7799928900257127, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.3089303536592743}} {"text": "library(ggplot2)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(rstan)\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(gdata)\nlibrary(EnvStats)\nlibrary(matrixStats)\nlibrary(scales)\nlibrary(gridExtra)\nlibrary(ggpubr)\nlibrary(bayesplot)\nlibrary(cowplot)\n\nsource(\"utils/geom-stepribbon.r\")\n#---------------------------------------------------------------------------\n\n \nload(paste0('results/',\"base\",'-',Sys.Date(),'.Rdata'))\n \ndata_interventions <- read.csv(\"interventions.csv\", \n stringsAsFactors = FALSE)\ncovariates <- data_interventions[1, c(1,2,3,4,5,6, 7, 8)]\n \nN <- length(dates[[1]])\ncountry <- \"MEX\"\n \npredicted_cases <- colQuantiles(prediction[,1:N,1], probs=.5)\npredicted_cases_li <- colQuantiles(prediction[,1:N,1], probs=.025)\npredicted_cases_ui <- colQuantiles(prediction[,1:N,1], probs=.975)\npredicted_cases_li2 <- colQuantiles(prediction[,1:N,1], probs=.25)\npredicted_cases_ui2 <- colQuantiles(prediction[,1:N,1], probs=.75)\n \n \nestimated_deaths <- colQuantiles(estimated.deaths[,1:N,1], probs=.5)\nestimated_deaths_li <- colQuantiles(estimated.deaths[,1:N,1], probs=.025)\nestimated_deaths_ui <- colQuantiles(estimated.deaths[,1:N,1], probs=.975)\nestimated_deaths_li2 <- colQuantiles(estimated.deaths[,1:N,1], probs=.25)\nestimated_deaths_ui2 <- colQuantiles(estimated.deaths[,1:N,1], probs=.75)\n \nrt <- colQuantiles(out$Rt[,1:N,1],probs=.5)\nrt_li <- colQuantiles(out$Rt[,1:N,1],probs=.025)\nrt_ui <- colQuantiles(out$Rt[,1:N,1],probs=.975)\nrt_li2 <- colQuantiles(out$Rt[,1:N,1],probs=.25)\nrt_ui2 <- colQuantiles(out$Rt[,1:N,1],probs=.75)\n \n\ncovariates_country <- covariates[which(covariates$\u00ef..Country == country), 2:8] \n \ncovariates_country$lockdown = NULL\ncovariates_country$sport = NULL \ncovariates_country$travel_restrictions = NULL \ncovariates_country_long <- gather(covariates_country[], key = \"key\", \n value = \"value\")\ncovariates_country_long$x <- rep(NULL, length(covariates_country_long$key))\nun_dates <- unique(covariates_country_long$value)\n \n for (k in 1:length(un_dates)){\n idxs <- which(covariates_country_long$value == un_dates[k])\n max_val <- round(max(rt_ui)) + 0.3\n for (j in idxs){\n covariates_country_long$x[j] <- max_val\n max_val <- max_val - 0.3\n }\n }\n \n \n covariates_country_long$value <- mdy(covariates_country_long$value) \n covariates_country_long$country <- rep(country, \n length(covariates_country_long$value))\n \n data_country <- data.frame(\"time\" = as_date(as.character(dates[[1]])),\n \"country\" = rep(country, length(dates[[1]])),\n \"reported_cases\" = reported_cases[[1]], \n \"reported_cases_c\" = cumsum(reported_cases[[1]]), \n \"predicted_cases_c\" = cumsum(predicted_cases),\n \"predicted_min_c\" = cumsum(predicted_cases_li),\n \"predicted_max_c\" = cumsum(predicted_cases_ui),\n \"predicted_min_c2\" = cumsum(predicted_cases_li2),\n \"predicted_max_c2\" = cumsum(predicted_cases_ui2),\n \"predicted_cases\" = predicted_cases,\n \"predicted_min\" = predicted_cases_li,\n \"predicted_max\" = predicted_cases_ui,\n \"predicted_min2\" = predicted_cases_li2,\n \"predicted_max2\" = predicted_cases_ui2,\n \"deaths\" = deaths_by_country[[1]],\n \"deaths_c\" = cumsum(deaths_by_country[[1]]),\n \"estimated_deaths_c\" = cumsum(estimated_deaths),\n \"death_min_c\" = cumsum(estimated_deaths_li),\n \"death_max_c\"= cumsum(estimated_deaths_ui),\n \"death_min_c2\" = cumsum(estimated_deaths_li2),\n \"death_max_c2\"= cumsum(estimated_deaths_ui2),\n \"estimated_deaths\" = estimated_deaths,\n \"death_min\" = estimated_deaths_li,\n \"death_max\"= estimated_deaths_ui,\n \"death_min2\" = estimated_deaths_li2,\n \"death_max2\"= estimated_deaths_ui2,\n \"rt\" = rt,\n \"rt_min\" = rt_li,\n \"rt_max\" = rt_ui,\n \"rt_min2\" = rt_li2,\n \"rt_max2\" = rt_ui2)\n\n\n data_cases_95 <- data.frame(data_country$time, data_country$predicted_min_c, \n data_country$predicted_max_c)\n names(data_cases_95) <- c(\"time\", \"cases_min\", \"cases_max\")\n data_cases_95$key <- rep(\"nintyfive\", length(data_cases_95$time))\n data_cases_50 <- data.frame(data_country$time, data_country$predicted_min_c2, \n data_country$predicted_max_c2)\n names(data_cases_50) <- c(\"time\", \"cases_min\", \"cases_max\")\n data_cases_50$key <- rep(\"fifty\", length(data_cases_50$time))\n data_cases <- rbind(data_cases_95, data_cases_50)\n levels(data_cases$key) <- c(\"ninetyfive\", \"fifty\")\n \n p1 <- ggplot(data_country) +\n geom_bar(data = data_country, aes(x = time, y = reported_cases_c), \n fill = \"coral4\", stat='identity', alpha=0.5) + \n geom_ribbon(data = data_cases_95, \n aes(x = time, ymin = cases_min, ymax = cases_max, fill = key)) +\n geom_line(aes(x=time,y=predicted_cases_c,color=\"Predicci\u00f3n\"))+\n xlab(\"\") +\n ylab(\"N\u00famero acumulado de casos confirmados\") +\n scale_y_continuous(labels = scales::comma_format(accuracy = 1),n.breaks = 10)+\n scale_x_date(date_breaks = \"3 days\", labels = date_format(\"%e %b\")) + \n scale_fill_manual(name = \"\", labels = c(\"95% CrI\"),\n values = c(alpha(\"deepskyblue4\", 0.35))) + \n scale_color_manual(name = \"\",\n values =\"black\") +\n theme_pubr() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1)) + \n guides(fill=guide_legend(ncol=1))\n \n data_deaths_95 <- data.frame(data_country$time, data_country$death_min_c, \n data_country$death_max_c)\n names(data_deaths_95) <- c(\"time\", \"death_min\", \"death_max\")\n data_deaths_95$key <- rep(\"nintyfive\", length(data_deaths_95$time))\n data_deaths_50 <- data.frame(data_country$time, data_country$death_min_c2, \n data_country$death_max_c2)\n names(data_deaths_50) <- c(\"time\", \"death_min\", \"death_max\")\n data_deaths_50$key <- rep(\"fifty\", length(data_deaths_50$time))\n data_deaths <- rbind(data_deaths_95, data_deaths_50)\n levels(data_deaths$key) <- c(\"ninetyfive\", \"fifty\")\n \n \n p2 <- ggplot(data_country, aes(x = time)) +\n geom_bar(data = data_country, aes(y = deaths_c, fill = \"reported\"),\n fill = \"coral4\", stat='identity', alpha=0.5) +\n geom_line(aes(y=estimated_deaths_c,color=\"Predicci\u00f3n\"))+\n geom_ribbon(\n data = data_deaths_95,\n aes(ymin = death_min, ymax = death_max, fill = key)) +\n scale_x_date(date_breaks = \"3 days\", labels = date_format(\"%e %b\")) +\n scale_fill_manual(name = \"\", labels = c(\"95% CrI\"),\n values = c(alpha(\"deepskyblue4\", 0.35))) + \n scale_color_manual(name = \"\",\n values =\"black\")+\n scale_y_continuous(labels = scales::comma_format(accuracy = 1),\n n.breaks = 10)+\n xlab(\"\")+\n theme_pubr() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1)) + \n guides(fill=guide_legend(ncol=1))+\n ylab(\"N\u00famero acumulado de muertes\")\n \n \n plot_labels <- c(\"Cancelaci\u00f3n de eventos\",\n \"Cierre de escuelas\",\n \"Aislamiento despu\u00e9s de presentar s\u00edntomas\",\n \"Distanciamiento Social\")\n \n # Plotting interventions\n data_rt_95 <- data.frame(data_country$time, \n data_country$rt_min, data_country$rt_max)\n names(data_rt_95) <- c(\"time\", \"rt_min\", \"rt_max\")\n data_rt_95$key <- rep(\"nintyfive\", length(data_rt_95$time))\n data_rt_50 <- data.frame(data_country$time, data_country$rt_min2, \n data_country$rt_max2)\n names(data_rt_50) <- c(\"time\", \"rt_min\", \"rt_max\")\n data_rt_50$key <- rep(\"fifty\", length(data_rt_50$time))\n data_rt <- rbind(data_rt_95, data_rt_50)\n levels(data_rt$key) <- c(\"ninetyfive\", \"fifth\")\n \n p3 <- ggplot(data_country) +\n geom_stepribbon(data = data_rt_95, aes(x = time, ymin = rt_min, ymax = rt_max, \n group = key,\n fill = key)) +\n geom_hline(yintercept = 1, color = 'black', size = 0.1) + \n geom_step(aes(x=time,y=rt,linetype=\"Predicci\u00f3n\"))+\n scale_linetype_manual(name = \"\",\n values =\"solid\")+\n geom_segment(data = covariates_country_long,\n aes(x = value, y = 0, xend = value, yend = max(x)), \n linetype = \"dashed\", colour = \"black\", alpha = 0.75) +\n geom_point(data = covariates_country_long, aes(x = value, \n y = x, \n group = key, \n shape = key, \n col = key), size = 2) +\n xlab(\"\") +\n scale_y_continuous(n.breaks = 10)+\n ylab(\"N\u00famero efectivo de reproducci\u00f3n\") +\n scale_fill_manual(name = \"\", labels = c(\"95% CrI\"),\n values = c(alpha(\"deepskyblue4\", 0.55))) + \n scale_shape_manual(name = \"Intervenciones\", labels = plot_labels,\n values = c(21, 22, 23, 24, 25, 12)) + \n scale_colour_discrete(name = \"Intervenciones\", labels = plot_labels) + \n scale_x_date(date_breaks = \"3 days\", labels = date_format(\"%e %b\"), \n limits = c(data_country$time[1], \n data_country$time[length(data_country$time)])) + \n theme_pubr() + \n theme(axis.text.x = element_text(angle = 45, hjust = 1)) +\n theme(legend.position=\"right\")\n \n p <- plot_grid(p1, p2, p3, ncol = 3, rel_widths = c(1, 1, 2))\n", "meta": {"hexsha": "83a929d88ac2c157933e91e5e54bef6830ee6e87", "size": 10329, "ext": "r", "lang": "R", "max_stars_repo_path": "blog/mex/jp_def/plot-3-panel.r", "max_stars_repo_name": "jadm333/covid19_blog", "max_stars_repo_head_hexsha": "a100027308bd833eb30b1676ae0de2cc7ac370c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "blog/mex/jp_def/plot-3-panel.r", "max_issues_repo_name": "jadm333/covid19_blog", "max_issues_repo_head_hexsha": "a100027308bd833eb30b1676ae0de2cc7ac370c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "blog/mex/jp_def/plot-3-panel.r", "max_forks_repo_name": "jadm333/covid19_blog", "max_forks_repo_head_hexsha": "a100027308bd833eb30b1676ae0de2cc7ac370c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.599078341, "max_line_length": 83, "alphanum_fraction": 0.5702391325, "num_tokens": 2647, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646140788307, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.30891681702819573}} {"text": "#Evolutionary models of continuous trait evolution in R\n\n## Read data & tree\nlibrary(phytools)\nlibrary(geiger)\n\ntree<-read.tree(\"TutorialData/anole.gp.tre\",tree.names=T)\ngroup<-read.csv('TutorialData/anole.gp.csv', row.names=1, header=TRUE,colClasses=c('factor'))\ngp<-as.factor(t(group)); names(gp)<-row.names(group)\nsvl<-read.csv('TutorialData/anole.svl2.csv', row.names=1, header=TRUE)\nsvl<-as.matrix(treedata(phy = tree,data = svl, warnings=FALSE)$data)[,1] #match data to tree\n\n##BM plot data\ntree.col<-contMap(tree,svl,plot=FALSE) #runs Anc. St. Est. on branches of tree\nplot(tree.col,legend=0.7*max(nodeHeights(tree)),\n fsize=c(0.7,0.9))\n\n#PLOT data\ntree.col<-contMap(tree,svl,plot=FALSE) #runs Anc. St. Est. on branches of tree\nplot(tree.col,type=\"fan\",legend=0.7*max(nodeHeights(tree)),\n fsize=c(0.7,0.9))\ncols<-setNames(palette()[1:length(unique(gp))],sort(unique(gp)))\ntiplabels(pie=model.matrix(~gp-1),piecol=cols,cex=0.3)\nadd.simmap.legend(colors=cols,prompt=FALSE,x=0.8*par()$usr[1],\n y=-max(nodeHeights(tree)-.6),fsize=0.8)\n\n\n## 1: Some evolutionary models in GEIGER\n #NOTE: may need to adjust the bounds of the search: see help file\nfit.BM1<-fitContinuous(tree, svl, model=\"BM\") #Brownian motion model\nfit.BMtrend<-fitContinuous(tree, svl, model=\"trend\") #Brownian motion with a trend\nfit.EB<-fitContinuous(tree, svl, model=\"EB\") #Early-burst model\nfit.lambda<-fitContinuous(tree, svl, \n bounds = list(lambda = c(min = exp(-5), max = 2)), model=\"lambda\") #Lambda model\noptions(warn=-1)\nfit.K<-fitContinuous(tree, svl, model=\"kappa\") #Early-burst model\noptions(warn=-1)\nfit.OU1<-fitContinuous(tree, svl,model=\"OU\") #OU1 model\n\n#Examine AIC\nc(fit.BM1$opt$aic,fit.BMtrend$opt$aic,fit.EB$opt$aic,fit.lambda$opt$aic,fit.OU1$opt$aic)\n #NOTE: none of these generate dAIC > 4. So go with simplest model (BM1)\n\n# Compare LRT formally (NOT needed in this case, but done so anyway)\nLRT<-(2*(fit.BM1$opt$lnL-fit.OU1$opt$lnL))\nprob<-pchisq(LRT, 1, lower.tail=FALSE)\nLRT\nprob\n\n## 2: More complex models: Multiple Ornstein-Uhlenbeck Peaks\nlibrary(OUwie)\ndata<-data.frame(Genus_species=names(svl),Reg=gp,X=svl) #input data.frame for OUwie\n\nfitBM1<-OUwie(tree,data,model=\"BM1\",simmap.tree=TRUE)\nfitOU1<-OUwie(tree,data,model=\"OU1\",simmap.tree=TRUE) \n tree.simmap<-make.simmap(tree,gp) # perform & plot stochastic maps (we would normally do this x100)\nfitOUM<-OUwie(tree.simmap,data,model=\"OUM\",simmap.tree=TRUE)\n\nfitBM1 \nfitOU1\nfitOUM #OUM is strongly preferred (examine AIC)\n\n#How it SHOULD be run\n#trees.simmap<-make.simmap(tree = tree,x = gp,nsim = 100) # 100 simmaps\n#fitOUM.100<-lapply(1:100, function(j) OUwie(trees.simmap[[j]],data,model=\"OUM\",simmap.tree=TRUE))\n#OUM.AIC<-unlist(lapply(1:100, function(j) fitOUM.100[[j]]$AIC))\n#hist(OUM.AIC)\n#abline(v=fitBM1$AIC, lwd=2) #add value for BM1\n\n## 3: More complex models: Multiple Evolutionary Rates\n\n# 3A: BM1 vs BMM: Comparing evolutionary rates\ntree.simmap<-make.simmap(tree,gp) # perform & plot stochastic maps (we would normally do this x100)\nBMM.res<-brownie.lite(tree = tree.simmap,x = svl,test=\"simulation\")\nBMM.res\n\n# 3B: Identifying rate shifts on phylogeny\n #Bayesian MCMC: single rate shift (Revell et al. 2012: Evol.)\nBM.MCMC<-evol.rate.mcmc(tree=tree,x=svl, quiet=TRUE)\npost.splits<-minSplit(tree,BM.MCMC$mcmc) #summarize\nMCMC.post<-posterior.evolrate(tree=tree,mcmc=BM.MCMC$mcmc,tips = BM.MCMC$tips,ave.shift = post.splits)\n #Plot rescaled to rates\nave.rates(tree,post.splits,extract.clade(tree, node=post.splits$node)$tip.label,\n colMeans(MCMC.post)[\"sig1\"], colMeans(MCMC.post)[\"sig2\"],post.splits)\n\n #Reversible-jump MCMC: multiple rate shifts (Eastman et al. 2011: Evol.)\nBM.RJMC<-rjmcmc.bm(phy = tree,dat = svl)\n#Run again for plotting\nr <- paste(sample(letters,9,replace=TRUE),collapse=\"\")\nrjmcmc.bm(phy=tree, dat=svl, prop.width=1.5, ngen=20000, samp=500, filebase=r, simple.start=TRUE, type=\"rbm\")\noutdir <- paste(\"relaxedBM\", r, sep=\".\")\nps <- load.rjmcmc(outdir)\ndev.new()\nplot(x=ps, par=\"shifts\", burnin=0.25, legend=TRUE, show.tip=FALSE, edge.width=2)\n\n\n\n################### FOR LECTURE:\n#1: what does lambda do?\nTreeLambda0 <- rescale(tree, model = \"lambda\", 0)\nTreeLambda5 <- rescale(tree, model = \"lambda\", 0.5)\n\npar(mfcol = c(1, 3))\nplot(tree, show.tip.label = FALSE)\nplot(TreeLambda5,show.tip.label = FALSE)\nplot(TreeLambda0,show.tip.label = FALSE)\npar(mfcol=c(1,1))\n\n \n", "meta": {"hexsha": "5dfad6b765511f336187b951d5db8bd531a03518", "size": 4435, "ext": "r", "lang": "R", "max_stars_repo_path": "practicals/TutorialData/FitEvolModels.r", "max_stars_repo_name": "EEOB-Macroevolution/EEOB-565X-Spring2018", "max_stars_repo_head_hexsha": "da192d6744dfaaa68a7f02d6a00ed2642192d43a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "practicals/TutorialData/FitEvolModels.r", "max_issues_repo_name": "EEOB-Macroevolution/EEOB-565X-Spring2018", "max_issues_repo_head_hexsha": "da192d6744dfaaa68a7f02d6a00ed2642192d43a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "practicals/TutorialData/FitEvolModels.r", "max_forks_repo_name": "EEOB-Macroevolution/EEOB-565X-Spring2018", "max_forks_repo_head_hexsha": "da192d6744dfaaa68a7f02d6a00ed2642192d43a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3181818182, "max_line_length": 109, "alphanum_fraction": 0.7098083427, "num_tokens": 1514, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3087982402547768}} {"text": "# 3. faza: Vizualizacija podatkov\n\n# ZEMLJEVID SVETA S PODATKI O BREZPOSELNOSTI\n\nbrezposelnost_svet <- function(){\n svet <- uvozi.svet()\n brezposelnost <- uvozi.zaposlenost()\n podatki <- merge(y = brezposelnost,x = svet, by.x='name', by.y = 'name')\n podatki %>% select(name, 'Brezposelnost')\n svet2 <- tm_shape(podatki) + tm_polygons('Brezposelnost')\n tmap_mode('view')\n return(svet2)\n}\nbrezposelnost_svet <- brezposelnost_svet()\n\n\n# HISTOGRAM ZADOVOLJSTVA PREBIVALCEV ZA LETO 2013 IN 2018\n\nzadovoljstvo_2013 <- function(){\n ocene <- uvozi.rating()\n stare_ocene <- ocene %>% filter(leto == 2013) %>% select(Drzava, Ocena)\n stare_ocene[,-1] <-round(stare_ocene[,-1]*2)/2\n \n histogram <- ggplot(stare_ocene, aes(x=Ocena)) +\n geom_histogram(binwidth=0.5, fill=\"#c0392b\", alpha=0.75) +\n fte_theme() +\n labs(title=\"Ocena zadovoljstva prebivalcev evropskih dr\u017eav z \u017eivljenjem leta 2013\",\n x=\"Ocena zadovoljstva\", y=\"\u0160tevilo dr\u017eav\") +\n scale_x_continuous(breaks = seq(0,10, by=0.5), limits = c(4.5,9)) +\n scale_y_continuous(breaks = seq(0,26, by=2), limits = c(0,12)) + \n geom_hline(yintercept=0, size=0.4, color=\"black\") + \n geom_vline(xintercept=mean(stare_ocene$Ocena))\n return(histogram)\n}\nzadovoljstvo_2013 <- zadovoljstvo_2013()\n\n\nzadovoljstvo_2018 <- function(){\n ocene <- uvozi.rating()\n nove_ocene <- ocene %>% filter(leto == 2018) %>% select(Drzava, Ocena)\n nove_ocene[,-1] <-round(nove_ocene[,-1]*2)/2\n \n histogram <- ggplot(nove_ocene, aes(x=Ocena)) +\n geom_histogram(binwidth=0.5, fill=\"#c0392b\", alpha=0.75) +\n fte_theme() +\n labs(title=\"Ocena zadovoljstva prebivalcev evropskih dr\u017eav z \u017eivljenjem leta 2018\",\n x=\"Ocena zadovoljstva\", y=\"\u0160tevilo dr\u017eav\") +\n scale_x_continuous(breaks = seq(0,10, by=0.5), limits = c(4.5,9)) +\n scale_y_continuous(breaks = seq(0,26, by=2), limits = c(0,12)) + \n geom_hline(yintercept=0, size=0.4, color=\"black\") +\n geom_vline(xintercept=mean(nove_ocene$Ocena))\n return(histogram)\n}\nzadovoljstvo_2018 <- zadovoljstvo_2018()\n\n\n# OSNOVNI GRAF, KI PRIKAZUJE GIBANJE POVPRECNEGA BDP V EVROPI\n\ngibanje_BDP <- function(){\n BDP <- uvozi.BDP()\n bdp_po_letih <- aggregate(BDP$'BDP per capita', by=list(leto=BDP$leto), FUN=mean)\n bdp_po_letih$x <- as.numeric(bdp_po_letih$x)\n g <- ggplot(bdp_po_letih, aes(x=leto, y=x, group = 1)) +\n geom_point(color=\"#c0392b\") +\n geom_path(color = \"black\") +\n ylim(20000, 30000)+\n fte_theme() +\n labs(x=\"Leto\", y=\"Realni BDP v \u20ac\", title=\"Realni povprecni BDP v \u20ac na prebivalca v Evropi\")\n return(g)\n}\ngibanje_BDP <- gibanje_BDP()\n\n# ZEMLJEVID EVROPE GLEDE NA BDP\n\nzemljevid_evrope_BDP <- function(){\n evropa <- uvozi.svet() %>% filter (continent == 'Europe')\n BDP <- uvozi.BDP()\n BDP <- BDP %>% filter (leto == 2018) %>% select('Drzava', 'BDP per capita')\n podatki <- merge(y = BDP,x = evropa, by.x='name', by.y = 'Drzava')\n evropa <- tm_shape(podatki) + tm_polygons('BDP per capita')\n tmap_mode('view')\n return(evropa)\n}\nzemljevid_evrope_BDP <- zemljevid_evrope_BDP()\n", "meta": {"hexsha": "4431900ee81be44139feecb47b1276db7dfafe3e", "size": 3163, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "BulaRebula/APPR-2019-20", "max_stars_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-03-01T11:20:57.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-01T11:20:57.000Z", "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "BulaRebula/APPR-2019-20", "max_issues_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2019-12-15T15:58:06.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-23T17:59:16.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "BulaRebula/APPR-2019-20", "max_forks_repo_head_hexsha": "5977dfa4e86173ca0650906065ff2e9bc2a09563", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.2117647059, "max_line_length": 103, "alphanum_fraction": 0.6474865634, "num_tokens": 1203, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.3087936591497362}} {"text": "# Base plot of raster, but with cut limits\n# 'Plot raster with limits\n\n#' Plot raster with limits\n#'\n#' Base plot of raster, with inlusion of lower and higher plot limits\n#' \n#' @param x raster\n#' @param lcut Minimum value to plot, all below will be similarly coloured. Set to \\code{NA} to disable.\n#' @param hcut Maximum value to plot, all above will be similarly coloured. Set to \\code{NA} to disable.\n#' @param setNA Value in raster to set as \\code{NA} in output. Set to \\code{NA} to disable.\n#' @param useimage Use \\code{image.plot} to make the plot rather than \\code{plot.raster} (logical).\n#' \n#' @return no function return\n#' @export\nplotRast = function(x, lcut=NA, hcut=NA, setNA=NA, useimage=F, col=pkrf::ramp('parula',256), ...) {\n \n require(raster)\n \n # perfom cutting\n if ((!is.na(lcut)) & (!is.na(lcut))){\n x[xhcut] <- hcut\n zlimits <- c(lcut,hcut)\n \n }else if (!is.na(lcut)){\n x[xhcut] <- hcut\n zlimits <- c(raster::cellStats(x,min), hcut)\n \n }else{\n zlimits <- raster::cellStats(x, range)\n }\n \n # set NA\n if (!is.na(setNA)){\n x[x==setNA] <- NA\n }\n \n # plot the image\n if (useimage){\n raster::image(x, zlim=zlimits, col=col, ...)\n }else{\n raster::plot(x, zlim=zlimits, col=col, ...)\n }\n \n}\n\n\n\n#' @rdname plotRast\n#' @export\npkRastplot <- plotRast\n", "meta": {"hexsha": "80d0fd14480219512e63fce3f752403162d88f42", "size": 1440, "ext": "r", "lang": "R", "max_stars_repo_path": "R/pkRastplot.r", "max_stars_repo_name": "kraaijenbrink/pkrf", "max_stars_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/pkRastplot.r", "max_issues_repo_name": "kraaijenbrink/pkrf", "max_issues_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/pkRastplot.r", "max_forks_repo_name": "kraaijenbrink/pkrf", "max_forks_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-03T16:13:24.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-03T16:13:24.000Z", "avg_line_length": 25.2631578947, "max_line_length": 104, "alphanum_fraction": 0.6194444444, "num_tokens": 474, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3087936505213935}} {"text": "\n figure.timeseries.snowcrab.habitat.temperatures = function(p) {\n \n\n td = bio.snowcrab::interpolation.db( p=p, DS=\"habitat.temperatures\" )\n \n td$t = td$temperature\n td$t.sd = td$temperature.sd\n\n areas = c(\"cfa4x\", \"cfasouth\", \"cfanorth\" )\n regions = c(\"4X\", \"S-ENS\", \"N-ENS\")\n td$region = factor(td$region, levels=areas, labels=regions)\n td$ubound = td$t + td$t.sd \n td$lbound = td$t - td$t.sd \n td = td[is.finite(td$t) ,]\n td = td[order(td$region,td$yr),]\n xlim=range(td$yr); xlim[1]=xlim[1]-0.5; xlim[2]=xlim[2]+0.5\n ylim= range( c(td$t, td$ubound, td$lbound) ); ylim[1]=ylim[1]-0.5; ylim[2]=ylim[2]+0.5\n \n fn = file.path( p$annual.results, \"timeseries\", \"interpolated\", \"mean.bottom.temp.snowcrab.habitat\" )\n \n dir.create( dirname(fn), recursive=T, showWarnings=F )\n \n png( filename=paste(fn, \"png\", sep=\".\"), width=3072, height=2304, pointsize=40, res=300 )\n\n setup.lattice.options()\n pl = xyplot( t~yr|region, data=td, ub=td$ubound, lb=td$lbound,\n layout=c(1,3), xlim=xlim, ylim=ylim, cex=3, # scales = list(y = \"free\"),\n main=\"Temperature in potential habitats\", xlab=\"Year\", ylab=\"Celsius\",\n panel = function(x, y, subscripts, ub, lb, ...) {\n panel.abline(h=mean(y, na.rm=T), col=\"gray40\", lwd=2, ...)\n larrows(x, lb[subscripts],\n x, ub[subscripts],\n angle = 90, code = 3, length=0.005, lwd=1, col=\"darkgray\")\n panel.xyplot(x, y, type=\"b\", lwd=4, pch=\".\", col=\"black\", ...)\n# panel.loess(x,y, span=0.15, lwd=2)\n }\n )\n print( pl )\n dev.off()\n \n means = tapply(td$t, td$region, mean, na.rm=T)\n print(\"mean temperatures:\")\n print(means)\n print(\"SD:\")\n print( tapply(td$t, td$region, sd, na.rm=T) ) \n print( \"latest year:\" )\n print( td$t[ td$yr==p$year.assessment ])\n #table.view( td )\n return(fn) \n }\n\n\n", "meta": {"hexsha": "22e9f53a23320ad4a29b8937c81bacc0e5ff48c5", "size": 1937, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure.timeseries.snowcrab.habitat.temperatures.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/figure.timeseries.snowcrab.habitat.temperatures.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure.timeseries.snowcrab.habitat.temperatures.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.8703703704, "max_line_length": 106, "alphanum_fraction": 0.5601445534, "num_tokens": 654, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3087936505213935}} {"text": "#' frostime\n#'\n#' Given a temperature t (Celsius) and wind ( m/sec) frost time following Wind Chill Frostbite Chart.\n#'\n#' @param t numeric Air temperature in Celsius degrees.\n#' @param wind numeric the wind speed in meter per seconds [m/s].\n#' @return time of frosting\n#'\n#'\n#' @author Istituto per la Bioeconomia CNR Firenze Italy Alfonso Crisci \\email{alfonso.crisci@@ibe.cnr.it}\n#' @keywords frostime \n#' @references ISO 9920:2007 Ergonomics of the thermal environment -- Estimation of thermal insulation and water vapour resistance of a clothing ensemble.\n#' @export\n#'\n#'\n#'\n#'\n\nfrostime=function(t,wind) {\n ct$assign(\"t\", as.array(t))\n ct$assign(\"wind\", as.array(wind))\n ct$eval(\"var res=[]; for(var i=0, len=t.length; i < len; i++){ res[i]=frostime(t[i],wind[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n\n\n", "meta": {"hexsha": "3125bc5d0f112f19eb7ce067016a773ecefa99c4", "size": 958, "ext": "r", "lang": "R", "max_stars_repo_path": "R/frostime.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/frostime.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/frostime.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 33.0344827586, "max_line_length": 154, "alphanum_fraction": 0.6022964509, "num_tokens": 251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3087274486712879}} {"text": "library(tidyverse)\n\nfocus_subject <- \"computing\" # use a vector for multiple patterns\nscience_subjects <- c(\"bio\", \"chem\", \"physics\")\nstem_subjects <- c(\"math\", \"statistic\", \"engineering\", \"comput\", \"technical\", \"bio\", \"chem\", \"physics\", \"physiology\")\n\ndefault_highlights <- c(\"focus\" = \"maroon\", \"other\" = \"black\")\ndefault_highlights\n\ngender_options <- c(\"male-\", \"female-\", \"NotKnown-\", \"NA-\", \"NotApplicable-\")\ngender_options_formatted <- c(\"male\", \"female\", \"NotKnown\", \"NA\", \"NotApplicable\")\n\n# definitions moved into common_interaction\n#gender_colour_scheme <- c(\"female\" = \"purple\", \"male\" = \"darkgreen\")\n#gender_shape_icons <- c(\"female\" = -0x2640L, \"male\" = -0x2642L) # \\u2640 and \\u2642 )\n# see also https://www.telegraph.co.uk/women/business/women-mean-business-interactive\n\nredundant_column_flags <- c(\"-Passes\", \"-percentage*\", \"-COMP\", \"-PassesUngradedCourses\")\n\npath_to_file_store <- \"sta_it_402/data\"\n\n# todo - complete and include levels - \nsqa_qualification_list <- read_csv(\"sta_it_402/data/demographic_data/sqa_qualifications.csv\", trim_ws = T) %>%\n suppressMessages()\nsqa_qualification_list\n\n\n# default file for normalising pupil counts across years\nschool_rolls_from_1966 <- read_csv(\"base/index_factors_census_pupils_from_1966.csv\", trim_ws = T) %>%\n suppressMessages()\nbase_year <- 1986\n\n\n", "meta": {"hexsha": "7c88ad85109e342e4a0618b8bc71f79214a92198", "size": 1367, "ext": "r", "lang": "R", "max_stars_repo_path": "code/r/base/it-402-dc-common_vars.r", "max_stars_repo_name": "aba-sah/sta-it402-dresscode", "max_stars_repo_head_hexsha": "e64f413b4126a40e08489f7048dd634a59e449f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/r/base/it-402-dc-common_vars.r", "max_issues_repo_name": "aba-sah/sta-it402-dresscode", "max_issues_repo_head_hexsha": "e64f413b4126a40e08489f7048dd634a59e449f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-02-28T00:03:20.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-18T10:26:55.000Z", "max_forks_repo_path": "code/r/base/it-402-dc-common_vars.r", "max_forks_repo_name": "aba-sah/sta-it402-dresscode", "max_forks_repo_head_hexsha": "e64f413b4126a40e08489f7048dd634a59e449f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-01-01T17:00:32.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-01T17:00:32.000Z", "avg_line_length": 40.2058823529, "max_line_length": 117, "alphanum_fraction": 0.7000731529, "num_tokens": 366, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982043529716, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3087274421902151}} {"text": "###\n### figure 4: pie chart\n###\n\nrm(list = ls()) # This clears everything from memory.\n\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(cowplot)\n\nsetwd(\"~/Dropbox/BCI_Turnover\")\nload(\"BCI_turnover20150611.RData\")\nsource(\"~/Dropbox/MS/TurnoverBCI/TurnoverBCImain/source.R\")\n# prepare data set\nab.data <- as.data.frame(sapply(D20m,function(x)apply(x,2,sum)))\nab.data$sp <- rownames(ab.data)\ntrait.temp <- data.frame(sp=rownames(trait),\n moist=trait$Moist,\n slope=trait$sp.slope.mean,\n slope.sd = trait$sp.slope.sd,\n convex= -trait$sp.convex.mean, # convavity\n convex.sd=trait$sp.convex.sd,\n WSG=trait$WSG,\n slope10=trait$slope_size_10,\n slope20=trait$slope_size_20,\n slope30=trait$slope_size_30,\n slope40=trait$slope_size_40,\n slope50=trait$slope_size_50,\n slope60=trait$slope_size_60,\n slope70=trait$slope_size_70,\n slope80=trait$slope_size_80,\n slope90=trait$slope_size_90,\n slope100=trait$slope_size_100,\n convex10=trait$convex_size_10,\n convex20=trait$convex_size_20,\n convex30=trait$convex_size_30,\n convex40=trait$convex_size_40,\n convex50=trait$convex_size_50,\n convex60=trait$convex_size_60,\n convex70=trait$convex_size_70,\n convex80=trait$convex_size_80,\n convex90=trait$convex_size_90,\n convex100=trait$convex_size_100)\n\nab.t.data <- merge(ab.data,trait.temp,by=\"sp\")\nrownames(ab.t.data) <- ab.t.data$sp\nab.t.data2 <- na.omit(ab.t.data)\n\n\n#this may be useful way to detect species.\n#using the product of delta abundance and deviaiton from mean (or median) trait for each species\nWSGab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n delta_ab2 = ab.t.data$census_2010 - ab.t.data$census_1982,\n delta_ab3 = ab.t.data$census_2010/ab.t.data$census_1982,\n WSG = ab.t.data$WSG,\n WSG_delta =ab.t.data$WSG - mean(ab.t.data$WSG,na.rm=T))\nWSGab$index <- as.numeric(scale(WSGab$delta_ab)) * as.numeric(scale(WSGab$WSG_delta))\n\nWSGab <- WSGab[order(WSGab$index),]\n\n\n\n# moistab\nmoistab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n moist = ab.t.data2$moist,\n moist_delta =ab.t.data2$moist - mean(ab.t.data2$moist))\nmoistab$index <- as.numeric(scale(moistab$delta_ab)) * as.numeric(scale(moistab$moist_delta))\n\n# moistab$index2 <- moistab$delta_ab * moistab$moist_delta\n\nmoistab <- moistab[order(moistab$index),]\n\n# convex100ab\nconvexab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n convex = ab.t.data2$convex,\n convex_delta =ab.t.data2$convex - mean(ab.t.data2$convex))\nconvexab$index <- as.numeric(scale(convexab$delta_ab)) * as.numeric(scale(convexab$convex_delta))\n\n# convexab$index2 <- convexab$delta_ab * convexab$_delta\n\nconvexab <- convexab[order(convexab$index),]\n\n# slope100ab\nslopeab <- data.frame(sp = ab.t.data2$sp,\n delta_ab = ab.t.data2$census_2010/sum(ab.t.data2$census_2010) - ab.t.data2$census_1982/sum(ab.t.data2$census_1982),\n slope = ab.t.data2$slope,\n slope_delta =ab.t.data2$slope - mean(ab.t.data2$slope))\nslopeab$index <- as.numeric(scale(slopeab$delta_ab)) * as.numeric(scale(slopeab$slope_delta))\n\nslopeab <- slopeab[order(slopeab$index),]\n\n\n\n#using the product of delta abundance and deviaiton from mean (or median) trait for each species\nWSGab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n WSG = ab.t.data$WSG,\n WSG_delta =ab.t.data$WSG - mean(WSG100[[1]])) %>%\n mutate(WSG_delta2 = WSG - mean(WSG, na.rm = T))\n\nWSGab$index <- WSGab$delta_ab * WSGab$WSG_delta*100\n\nWSGab <- WSGab[order(WSGab$index),]\n\n# moistab\nmoistab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n moist = ab.t.data$moist,\n moist_delta = ab.t.data$moist - mean(Moist100[[1]]))\nmoistab$index <- moistab$delta_ab * moistab$moist_delta*100\n\nmoistab <- moistab[order(moistab$index),]\n\n# convex100ab\nconvexab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n convex = ab.t.data$convex,\n convex_delta =ab.t.data$convex + mean(convex100[[1]])) # concave\nconvexab$index <- convexab$delta_ab * convexab$convex_delta*100\n\nconvexab <- convexab[order(convexab$index),]\n# convexab <- convexab[order(convexab$convex),]\n\n# slopeab\nslopeab <- data.frame(sp = ab.t.data$sp,\n delta_ab = ab.t.data$census_2010/sum(ab.t.data$census_2010) - ab.t.data$census_1982/sum(ab.t.data$census_1982),\n slope = ab.t.data$slope,\n slope_delta =ab.t.data$slope - mean(slope100[[1]]))\nslopeab$index <- slopeab$delta_ab * slopeab$slope_delta*100\n\nslopeab <- slopeab[order(slopeab$index),]\n\n# sp list for appendix ====================================================\n\ntaxa <- read.csv(\"~/Dropbox/MS/TurnoverBCI/nomenclature_R_20120305_Rready-2.csv\")\n\nsp_list <- WSGab %>%\n mutate(Wood_density = round(index, 4)) %>%\n mutate(Abundance_change = round(delta_ab, 4)) %>%\n select(sp, Abundance_change, Wood_density, -index) %>%\n full_join(., moistab, by = \"sp\") %>%\n mutate(Moisture = round(index, 4)) %>%\n select(-index) %>%\n full_join(., convexab, by = \"sp\") %>%\n mutate(Concavity = round(index, 4)) %>%\n select(-index) %>%\n full_join(., slopeab, by = \"sp\") %>%\n mutate(Slope = round(index, 4), sp6 = sp) %>%\n left_join(., taxa, by = \"sp6\") %>%\n select(sp, family, genus, species,\n Abundance_change, Wood_density, Moisture, Concavity, Slope) %>%\n arrange(sp) %>%\n mutate(species = paste(genus, species)) %>%\n select(-genus)\n\nwrite.csv(sp_list, \"/Users/mattocci/Dropbox/MS/TurnoverBCI/sp_list.csv\")\n\n\n\n# ========================================\nWSGab <- WSGab %>%\n mutate(WSG_index = index)\n\nmoistab <- moistab %>%\n mutate(moist_index = index)\n\nconvexab <- convexab %>%\n mutate(convex_index = index)\n\nslopeab <- slopeab %>%\n mutate(slope_index = index)\n\nfig_dat <- full_join(WSGab, moistab, by = \"sp\") %>%\n full_join(., convexab, by = \"sp\") %>%\n full_join(., slopeab, by = \"sp\") %>%\n tidyr::gather(., \"trait\", \"val\", c(WSG_index, slope_index, moist_index, convex_index)) %>%\n dplyr::select(., c(sp, trait, val)) %>%\n na.omit %>%\n mutate(sig = ifelse(val < 0, \"Negative\", \"Positive\")) %>%\n mutate(val2 = abs(val)) %>%\n mutate(trait_sig = paste(trait, sig, sep = \"_\"))\n\n fig_dat %>% group_by(trait) %>%\n summarise(mean = sum(val2, na.rm = T))\n\ntemp0 <- tapply(fig_dat$val2, fig_dat$trait, mean, na.rm = T) %>% rep(each = 2)\n\ntemp <- fig_dat %>% group_by(trait, sig) %>%\n summarise(mean = mean(val2, na.rm = T)) %>%\n mutate(trait_sig = paste(trait, sig, sep = \"_\")) %>%\n as_data_frame %>%\n dplyr::select(., c(mean, trait_sig)) %>%\n mutate(mean2 = mean / temp0)\n\n# temp[3,1] <- 100\n\n# high slope\n# PIPECA\n\n# large change\n# PSYCLI\n\n# high abund\n# HYBAPR\n# FARAOC\n\nsp_vec <- c(\"PIPECO\", \"POULAR\", \"PIPECA\", \"PSYCLI\", \"HYBAPR\", \"FARAOC\")\n\n# moist\n# TET2PA\n\n#convex\n# SWARS1\n# ALSEBL\n\n# slope\n# HYBAPR\nlibrary(grDevices)\n\nn <- 2\nhues <- seq(15, 375, length=n+1)\ncols_hex <- sort(hcl(h=hues, l=65, c=100)[1:n])\n\nsp_vec2 <- c(\"PIPECO\", \"POULAR\", \"TET2PA\", \"SWARS1\", \"ALSEBL\", \"HYBAPR\")\n\nsp_vec2 <- c(\"FARAOC\", \"HYBAPR\", \"PIPECO\", \"POULAR\")\n\n#sp\nsp1 <- sp_list %>% arrange(desc(Wood_density)) %>% head(6) %>% .$sp %>% as.character\nsp2 <- sp_list %>% arrange(desc(Moisture)) %>% head(1) %>% .$sp %>% as.character\nsp3 <- sp_list %>% arrange(desc(Concavity)) %>% head(6) %>% .$sp %>% as.character\nsp4 <- sp_list %>% arrange((Slope)) %>% head(3) %>% .$sp %>% as.character\n\nsp_vec3 <- c(sp1,sp2,sp3,sp4) %>% unique\n\nfig_dat2 <- full_join(fig_dat, temp, by = \"trait_sig\") %>%\n arrange(val2) %>%\n mutate(trait = factor(trait, levels = c(\"WSG_index\", \"moist_index\", \"convex_index\", \"slope_index\"))) %>%\n mutate(trait2 = factor(trait, labels = c(\"Wood density\", \"Moisture\", \"Concavity\", \"Slope\"))) %>%\n mutate(sig = factor(sig, levels = c(\"Positive\", \"Negative\"))) %>%\n mutate(col = ifelse(sp == \"POULAR\", \"POULAR\",\n ifelse(sp == \"PIPECO\", \"PIPECO\", \"Other species\"))) %>%\n mutate(col = factor(col, levels = c(\"PIPECO\", \"POULAR\", \"Other species\"))) %>%\n mutate(sp2 = ifelse(sp %in% sp_vec, as.character(sp), \"Other species\")) %>%\n mutate(sp3 = ifelse(sp %in% sp_vec2, as.character(sp), \"Other species\")) %>%\n mutate(sp2 = factor(sp2, levels = c(\"FARAOC\", \"HYBAPR\", \"PIPECA\", \"PIPECO\", \"POULAR\", \"PSYCLI\", \"Other species\"))) %>%\n mutate(sp3 = factor(sp3, levels = c(\"FARAOC\", \"HYBAPR\", \"PIPECO\", \"POULAR\", \"Other species\"))) %>%\n mutate(sp4 = ifelse(sp %in% sp_vec3, as.character(sp), \"Other species\")) %>%\n mutate(sp4 = factor(sp4, levels = c(sort(sp_vec3), \"Other species\")))\n\n\n\nlab_dat <- data_frame(lab = paste(\"(\", letters[1:12], \")\", sep = \"\"),\n y = 20,\n x = rep(c(0.21, -1.9, -0.29, 0.5), 3),\n size = rep(c(\"50ha\", \"1ha\", \"0.04ha\"), each = 4),\n trait = rep(c(\"WSG\", \"moist\", \"convex\", \"slope\"), 3),\n sp2 = \"Other species\") %>%\n mutate(size = factor(size, levels = c(\"50ha\", \"1ha\", \"0.04ha\"))) %>%\n mutate(trait = factor(trait, levels = c(\"WSG\", \"moist\", \"convex\", \"slope\"))) %>%\n mutate(trait2 = factor(trait, labels = c(\"Wood~density ~(g~cm^{-3})\", \"Moisture\", \"Concavity~(m)\", \"Slope~(degrees)\")))\n\n\n\n# highliting 4 species\npostscript(\"/Users/mattocci/Dropbox/MS/TurnoverBCI/TurnoverBCI_MS/fig/pie_4_species.eps\", width = 6, height = 3.6)\n\nn <- 4\nhues <- seq(15, 375, length=n+1)\ncols_hex <- sort(hcl(h=hues, l=65, c=100)[1:n])\n\n\nlab_dat <- fig_dat2 %>% count(trait2, sig) %>% as.data.frame %>%\n mutate(x = 1.4, y = 0.8) %>%\n mutate(n2 = paste(\"n =\", n, sep = \" \"))\n\n\n ggplot(fig_dat2) +\n geom_bar(aes(y = val2, x = mean2/2,\n fill = as.factor(sp3), width = mean2), position = \"fill\", stat=\"identity\", color = \"white\", size = 0.02) +\n geom_text(data = lab_dat, aes(label = n2, x = 1.25, y = 1),\n size = 4, vjust = 0) +\n facet_grid(sig ~ trait2) +\n coord_polar(theta=\"y\") +\n scale_fill_manual(values = c(cols_hex, \"gray\"),\n guide = guide_legend(title.position = \"top\",\n title = \"Species\",)) +\n theme_bw() +\n theme(axis.text.x = element_blank(),\n axis.text.y = element_blank(),\n axis.ticks = element_blank()) +\n xlab(\"\") + ylab(\"\") +\n theme(legend.position = \"bottom\",\n legend.margin = unit(-0.2, \"cm\"),\n panel.grid = element_blank())\n\ndev.off()\n\n\n", "meta": {"hexsha": "821c014305d6e8f1ea45253509ed840c47483de0", "size": 11121, "ext": "r", "lang": "R", "max_stars_repo_path": "FigCode/fig_pie.r", "max_stars_repo_name": "mattocci27/TurnoverBCImain", "max_stars_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FigCode/fig_pie.r", "max_issues_repo_name": "mattocci27/TurnoverBCImain", "max_issues_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FigCode/fig_pie.r", "max_forks_repo_name": "mattocci27/TurnoverBCImain", "max_forks_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.702970297, "max_line_length": 135, "alphanum_fraction": 0.6211671612, "num_tokens": 3634, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982043529716, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.3087274421902151}} {"text": "source(\"FunctionsForBrainCellProportionsPrediction.r\")\n\nlibrary(bigmelon)\nlibrary(pheatmap)\nlibrary(glmnet)\n\nsetwd(dataDir)\n\ngfile<-openfn.gds(gdsFile, readonly = FALSE)\n\n## filter samples\nQCSum<-read.csv(paste0(qcOutFolder,\"PassQCStatusAllSamples.csv\"), stringsAsFactors = FALSE, row.names = 1)\n\npassQC<-rownames(QCSum)[which(QCSum$predLabelledCellType == \"TRUE\")]\nQCmetrics<-read.gdsn(index.gdsn(gfile, \"QCdata\"))\nQCmetrics<-QCmetrics[match(passQC, QCmetrics$Basename),]\n\nrawbetas<-betas(gfile)[,]\nrawbetas<-rawbetas[,match(passQC, colnames(rawbetas))]\n\ncellTypes<-unique(QCmetrics$Cell.type)\ncellTypes<-cellTypes[!is.na(cellTypes)]\n## sort so colours lines up correctly\ncellTypes<-sort(cellTypes)\ncol_pal<-c(\"darkgreen\", \"darkblue\", \"darkmagenta\", \"deeppink\", \"darkgray\") ## assumes celltypes are order alphabetically\n\n#############\n### First ref panel all Exeter FANS fractions including double negative\n#############\n\nload(\"RefDataForCellCompEstimation.rdata\")\ncounts.all <- projectCellType(rawbetas[rownames(braincelldata), ], braincelldata)\n\n## plot results\npdf(paste0(qcOutFolder, \"PredictedCellComposition.pdf\"),width = 12)\npar(mfrow = c(2,3))\nfor(each in cellTypes){\n\tbarplot(t(counts.all[which(QCmetrics$Cell.type == each),])*100, col = col_pal, main = each, ylab = \"% estimated\", names.arg = rep(\"\", sum(QCmetrics$Cell.type == each)))\n}\nplot(0,1, type = \"n\", xlab = \"\", ylab = \"\", axes = FALSE)\nlegend(\"center\", colnames(counts.all), col = col_pal[1:ncol(counts.all)], pch = 15)\n\npar(mfrow = c(2,2))\nfor(i in 1:ncol(counts.all)){\n\tboxplot(counts.all[,i]*100 ~ QCmetrics$Cell.type, col = col_pal, ylab = paste(\"%\", colnames(counts.all)[i], \"estimated\"), xlab = \"Cell type\")\n}\ndev.off()\n\n#aggregate(counts , by = list(QCmetrics$Cell.type), mean)\n\n\n#################\n### Second ref panel all Exeter FANS fractions excluding double negative\n#################\n\nload(\"RefDataForCellCompEstimationNoDoubleNeg.rdata\")\ncounts.nodneg <- projectCellType(rawbetas[rownames(braincelldata), ], braincelldata)\n\n## plot results\npdf(paste0(qcOutFolder, \"PredictedCellCompositionNoDoubleNeg.pdf\"),width = 12)\npar(mfrow = c(2,3))\nfor(each in cellTypes){\n\tbarplot(t(counts.nodneg[which(QCmetrics$Cell.type == each),])*100, col = col_pal[-1], main = each, ylab = \"% estimated\", names.arg = rep(\"\", sum(QCmetrics$Cell.type == each)))\n}\nplot(0,1, type = \"n\", xlab = \"\", ylab = \"\", axes = FALSE)\nlegend(\"center\", colnames(counts.nodneg), col = col_pal[c(1:ncol(counts.nodneg))+1], pch = 15)\n\npar(mfrow = c(2,2))\nfor(i in 1:ncol(counts.nodneg)){\n\tboxplot(counts.nodneg[,i]*100 ~ QCmetrics$Cell.type, col = col_pal, ylab = paste(\"%\", colnames(counts.nodneg)[i], \"estimated\"), xlab = \"Cell type\")\n}\n\ndev.off()\n\n## compare output with and without double negative\npdf(paste0(qcOutFolder, \"ComparePredictedCellCompositionWithoutDoubleNeg.pdf\"),width = 12, height = 4)\npar(mfrow = c(1,3))\nfor(each in c(\"IRF8\",\"NeuN\",\"Sox10\")){\n\tplot(counts.all[,each], counts.nodneg[,each], pch = 16,main = each, xlab = \"With DNeg\", ylab = \"Without DNeg\", col = col_pal[as.factor(QCmetrics$Cell.type)])\n\tabline(a = 0, b = 1)\n}\ndev.off()\n\n##############\n### Third EpiGABA FANS samples\n##############\n\nload(\"RefDataEpiGABAForCellCompEstimation.rdata\")\ncounts.epigaba <- projectCellType(rawbetas[intersect(rownames(braincelldata), rownames(rawbetas)), ], braincelldata[intersect(rownames(braincelldata), rownames(rawbetas)),])\n\n## plot results\npdf(paste0(qcOutFolder, \"PredictedCellCompositionEpiGabaRef.pdf\"),width = 12)\npar(mfrow = c(2,3))\nfor(each in cellTypes){\n\tbarplot(t(counts.epigaba[which(QCmetrics$Cell.type == each),])*100, col = c(\"red\", \"blue\", \"green\"), main = each, ylab = \"% estimated\", names.arg = rep(\"\", sum(QCmetrics$Cell.type == each)))\n}\nplot(0,1, type = \"n\", xlab = \"\", ylab = \"\", axes = FALSE)\nlegend(\"center\", colnames(counts.epigaba), col = c(\"red\", \"blue\", \"green\"), pch = 15)\n\npar(mfrow = c(2,2))\nfor(i in 1:ncol(counts.epigaba)){\n\tboxplot(counts.epigaba[,i]*100 ~ QCmetrics$Cell.type, col = c(\"red\", \"blue\", \"green\"), ylab = paste(\"%\", colnames(counts.epigaba)[i], \"estimated\"), xlab = \"Cell type\")\n}\n\ndev.off()\n\npdf(paste0(qcOutFolder, \"CompareCellCompositionEpiGabaBDRRef.pdf\"),width = 10, height = 13)\npar(mfrow = c(ncol(counts.all), ncol(counts.epigaba)))\nfor(i in 1:ncol(counts.all)){\n\tfor(j in 1:ncol(counts.epigaba)){\n\t\tplot(counts.all[,i], counts.epigaba[,j], pch = 16, xlab = colnames(counts.all)[i], ylab = colnames(counts.epigaba)[j], col = col_pal[as.factor(QCmetrics$Cell.type)])\n\t\tabline(a = 0, b = 1)\n\t}\n}\ndev.off()\n\n##############\n### Fourth ref panel Merge EpiGABA and Exeter FANS samples\n##############\n\nload(\"RefDataForCellCompEstimationAll.rdata\")\ncounts.merge <- projectCellType(rawbetas[intersect(rownames(braincelldata), rownames(rawbetas)), ], braincelldata[intersect(rownames(braincelldata), rownames(rawbetas)),])\n\n## plot results\npdf(paste0(qcOutFolder, \"PredictedCellCompositionMergedRef.pdf\"),width = 12)\npar(mfrow = c(2,3))\nfor(each in cellTypes){\n\tbarplot(t(counts.merge[which(QCmetrics$Cell.type == each),])*100, col = c(\"red\", \"blue\", \"green\", col_pal[2:4]), main = each, ylab = \"% estimated\", names.arg = rep(\"\", sum(QCmetrics$Cell.type == each)))\n}\nplot(0,1, type = \"n\", xlab = \"\", ylab = \"\", axes = FALSE)\nlegend(\"center\", colnames(counts.merge), col = c(\"red\", \"blue\", \"green\", col_pal[2:4]), pch = 15)\n\npar(mfrow = c(2,3))\nfor(i in 1:ncol(counts.merge)){\n\tboxplot(counts.merge[,i]*100 ~ QCmetrics$Cell.type, col = c(\"red\", \"blue\", \"green\", col_pal[2:4]), ylab = paste(\"%\", colnames(counts.merge)[i], \"estimated\"), xlab = \"Cell type\")\n}\n\ndev.off()\n\n##############\n### Fifth ref c(\"GABAneurons\",\"GLIA\",\"GLUneurons\",\"Sox10\",\"IRF8\")\n### Fourth ref panel Merge EpiGABA and Exeter FANS samples\n##############\n\nload(\"RefDataForCellCompEstimationAllNoNeuN.rdata\")\ncounts.merge4 <- projectCellType(rawbetas[intersect(rownames(braincelldata), rownames(rawbetas)), ], braincelldata[intersect(rownames(braincelldata), rownames(rawbetas)),])\n\n## plot results\npdf(paste0(qcOutFolder, \"PredictedCellCompositionMergedRefNoNeuN.pdf\"),width = 12)\npar(mfrow = c(2,3))\nfor(each in cellTypes){\n\tbarplot(t(counts.merge4[which(QCmetrics$Cell.type == each),])*100, col = c(\"red\", \"blue\", \"green\", col_pal[c(2,4)]), main = each, ylab = \"% estimated\", names.arg = rep(\"\", sum(QCmetrics$Cell.type == each)))\n}\nplot(0,1, type = \"n\", xlab = \"\", ylab = \"\", axes = FALSE)\nlegend(\"center\", colnames(counts.merge4), col = c(\"red\", \"blue\", \"green\", col_pal[c(2,4)]), pch = 15)\n\npar(mfrow = c(2,3))\nfor(i in 1:ncol(counts.merge4)){\n\tboxplot(counts.merge4[,i]*100 ~ QCmetrics$Cell.type, col = c(\"red\", \"blue\", \"green\", col_pal[c(2,4)]), ylab = paste(\"%\", colnames(counts.merge4)[i], \"estimated\"), xlab = \"Cell type\")\n}\n\ndev.off()\n\n## compare with and without NeuN+ve\npdf(paste0(qcOutFolder, \"ComparePredictedCellCompositionWithoutNeuN.pdf\"),width = 12, height = 8)\npar(mfrow = c(2,3))\nfor(each in c(\"GABAneurons\",\"GLIA\",\"GLUneurons\",\"Sox10\",\"IRF8\")){\n\tplot(counts.merge[,each], counts.merge4[,each], pch = 16,main = each, xlab = \"With NeuN\", ylab = \"Without NeuN\", col = col_pal[as.factor(QCmetrics$Cell.type)])\n\tabline(a = 0, b = 1)\n}\ndev.off()", "meta": {"hexsha": "39558d1cc794e05d925c76b8157b2520256ffe6c", "size": 7138, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/calcBrainCellProportions.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", 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"num_tokens": 2301, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3085785743427913}} {"text": "#options(error = recover, warn = 2)\n#options(error = function() {traceback(2, max.lines=100); if(!interactive()) quit(save=\"no\", status=1, runLast=T)})\n#sink(stdout(), type=\"message\")\n#https://stackoverflow.com/questions/7485514/can-you-make-r-print-more-detailed-error-messages\n#http://adv-r.had.co.nz/Exceptions-Debugging.html\n\n##################################################\n## Project: Comparison of BCI Papers\n## Script purpose: Compare Frustration/Fun/Motivation/Control level between BCI papers\n## Date: 01/07/2019\n## Author: Bastian Ilso\n##################################################\nlibrary(gsheet)\nlibrary(dplyr)\nlibrary(ggplot2)\n\n# data import and prep ---------\nurl <- 'docs.google.com/spreadsheets/d/1EU_GgYr4gQ42Eks8QA5KPyyKwED1eQQRSUTx_PspfEM#gid=1516753331'\npapers.data <- gsheet2tbl(url)\n\n#import kiwi data \ndata<-read.csv(\"kiwiQuantReport.csv\",header=TRUE,sep = \",\")\ndata[is.na(data$shamChange),]$shamChange<-0\n#<-data[!data$PID==2,]\ndata$shamRateCont<-as.numeric(data$shamRate)\ndata<-data[order(data$PID, data$shamRate),]\ndata$shamRate = factor(data$shamRate, levels=c(\"0\", \"15\", \"30\"))\ndata$FrustNormalized = (data$FrustEpisode-1)/6\ndata$controlNormalized = (data$controlEpisode-1)/6\ndfDataForPercFrustPlot<-papers.data[which(papers.data$Paper=='Laar-Hamster' | papers.data$Paper=='MED8-Kiwi'),]\ndata$Paper<-\"MED8-KiwiRaw\"\n#<- papers.data[papers.data$Paper=='Laar-Hamster' | papers.data$Paper=='MED8-Kiwi',]\n\n##Plot Level of Control vs Frustration Level ---------\n##Plot Level of Control vs Frustration Level\npapers.data %>%\n filter(!is.na(Frustration)) %>%\n group_by(Paper) %>%\n ggplot(aes(x=Control, y=Frustration, color=Paper)) +\n scale_colour_manual(values=c(\"#D55E00\", \"#009E73\", \"#0072B2\", \"#ad4141\", \"#ad4141\", \"#ad4141\")) +\n scale_x_continuous(labels = scales::percent, limit=c(0,1)) +\n #xlim(0,1) +\n ylim(0,1) +\n geom_line(size=1.25) +\n geom_point(size=3.5) +\n theme_bw() +\n theme(legend.position = \"none\") +\n theme(panel.grid.major = element_line(size = 0.45, colour = \"#d8d8d8\"), panel.grid.minor = element_blank()) + \n theme(text = element_text(size = 12))\n\n## SMALL VERSION ---------\n##Plot Level of Control vs Frustration Level\npapers.data %>%\n filter(!is.na(Frustration)) %>%\n filter(Paper != \"MED8-Kiwi\") %>%\n filter(Paper != \"McCrea-Hockey\") %>%\n group_by(Paper) %>%\n ggplot(aes(x=Control, y=Frustration, color=Paper)) +\n scale_colour_manual(values=c(\"#D55E00\", \"#009E73\", \"#0072B2\", \"#ad4141\", \"#ad4141\", \"#ad4141\")) +\n scale_x_continuous(labels = scales::percent, limit=c(0,1)) +\n #xlim(0,100) +\n ylim(0,1) +\n geom_line(size=1.25) +\n geom_point(size=1.75) +\n theme_bw() +\n theme(panel.grid.major = element_line(size = 0.45, colour = \"#d8d8d8\"), panel.grid.minor = element_blank()) + \n theme(legend.position = \"none\") +\n theme(text = element_text(size = 12))\n\n##Plot Level of Control vs Perceived Control --------\npapers.data %>%\n filter(!is.na(`Perceived Control`)) %>%\n group_by(Paper) %>%\n ggplot(aes(x=Control, y=`Perceived Control`, color=Paper)) +\n scale_colour_manual(values=c(\"#000000\", \"#da995fff\", \"#f6b3b3\", \"#e79557\", \"#ad4141\", \"#da5f5fff\", \"#009E73\", \"#0072B2\")) + \n scale_x_continuous(labels = scales::percent, limit=c(0,1)) +\n #xlim(0,1) + \n ylim(0,1) +\n #geom_hline(yintercept=1) +\n geom_line(size=1.25) +\n geom_point(size=3.5) +\n theme_bw() +\n theme(legend.position = \"none\") +\n theme(panel.grid.major = element_line(size = 0.45, colour = \"#d8d8d8\"), panel.grid.minor = element_blank()) + \n theme(text = element_text(size = 12))\n\n## SMALL VERSION --------\n##Plot Level of Control vs Perceived Control\npapers.data %>%\n filter(!is.na(`Perceived Control`)) %>%\n filter(Paper != \"MED8-Kiwi\") %>%\n filter(Paper != \"MED8-Kiwi-Interp\") %>%\n filter(Paper != \"McCrea-Hockey\") %>%\n filter(Paper != \"Greville-Causal1\") %>%\n filter(Paper != \"Greville-Causal2\") %>%\n filter(Paper != \"Greville-Causal3\") %>%\n group_by(Paper) %>%\n ggplot(aes(x=Control, y=`Perceived Control`, color=Paper)) +\n scale_colour_manual(values=c(\"#000000\", \"#009c73\", \"#ad4141\", \"#ad4141\", \"#009c73\", \"#009c73\",\"#009c73\", \"#009c73\")) + \n scale_x_continuous(labels = scales::percent, limit=c(0,1)) +\n #xlim(0,1) + \n ylim(0,1) +\n geom_line(size=1.25) +\n geom_point(size=1.75) +\n theme_bw() +\n theme(panel.grid.major = element_line(size = 0.45, colour = \"#d8d8d8\"), panel.grid.minor = element_blank()) + \n theme(legend.title = element_blank()) +\n theme(legend.position = \"none\") +\n theme(text = element_text(size = 12))\n\n\n##Plot Perceived Control vs Frustration --------\npapers.data %>%\n filter(!is.na(`Perceived Control`)) %>%\n group_by(Paper) %>%\n ggplot(aes(x=`Perceived Control`, y=Frustration, color=Paper)) +\n scale_colour_manual(values=c(\"#000000\", \"#ad4141\", \"#f6b3b3\", \"#e79557\", \"#009E73\", \"#ad4141\", \"#009E73\", \"#0072B2\")) + \n xlim(0,1) +\n ylim(0,1) +\n geom_line(size=1.25) +\n geom_point(size=3.5) +\n theme_bw() +\n theme(legend.position = \"none\") +\n theme(panel.grid.major = element_line(size = 0.45, colour = \"#d8d8d8\"), panel.grid.minor = element_blank()) + \n theme(text = element_text(size = 12))\n\n##Plot Perceived Control vs Frustration plus raw data --------\n ggplot()+\n xlim(0,1) + ylim(0,1) +\ngeom_point(data=dfDataForPercFrustPlot, mapping=aes(x=`Perceived Control`,y=Frustration, color=as.factor(Paper), group=as.factor(Paper)), size=3.5) +\n geom_line(size=1.25) +\n geom_point(data = data,mapping=aes(x=controlNormalized,y=FrustNormalized,color=as.factor(shamRate), group=as.factor(Paper)),position = \"jitter\",width = 0.1, height = 0.1)+\n geom_smooth(data=data,mapping=aes(x=controlNormalized,y=FrustNormalized,color=shamRate,group=shamRate),method = lm, se = FALSE)+\n theme_bw() +\n theme(text = element_text(size = 12)) \n\n##Plot Perceived Control vs Frustration ony raw data med8 Kiwi --------\nggplot()+\n xlim(0,1) + ylim(0,1) +\n # geom_point(data=dfDataForPercFrustPlot, mapping=aes(x=`Perceived Control`,y=Frustration, color=as.factor(Paper), group=as.factor(Paper)), size=3.5) +\n geom_line(size=1.25) +\n geom_point(data = data,mapping=aes(x=controlNormalized,y=FrustNormalized,size=as.numeric(as.character(shamRate)),color=as.numeric(as.character(shamRate))),position = \"jitter\",width = 0.05, height = 0.05)+\n geom_smooth(data=data,mapping=aes(x=controlNormalized,y=FrustNormalized,group=shamRate,color=as.numeric(as.character(shamRate))),method = lm, se = FALSE)+\n theme_bw() +\n theme(text = element_text(size = 12)) \n \n#aes(linetype=sex, color=sex)\n#geom_smooth(geom_smooth(method = \"lm\", se = FALSE, linetype = \"dashed\")\n\n##Plot Level of Control vs Fun/Motivation --------\npapers.data %>% mutate(Motivation.Fun = coalesce(Fun, Motivation)) %>%\n filter(!is.na(Motivation.Fun)) %>%\n group_by(Paper) %>%\n ggplot(aes(x=Control, y=Motivation.Fun, color=Paper)) +\n scale_colour_manual(values=c(\"#D55E00\", \"#009E73\", \"#CC79A7\", \"#0072B2\")) + \n xlim(0,1) +\n ylim(0,1) +\n geom_point() +\n geom_line() \n\n\n## correlation/regression analysis of previous work\ncor(papers.data[papers.data$Paper==\"Laar-Hamster\",]$`Perceived Control`,papers.data[papers.data$Paper==\"Laar-Hamster\",]$`Frustration`)\ncor(papers.data[papers.data$Paper==\"MED8-Kiwi\",]$`Perceived Control`,papers.data[papers.data$Paper==\"MED8-Kiwi\",]$`Frustration`)\n\nresLaar<- cor.test(papers.data[papers.data$Paper==\"Laar-Hamster\",]$`Perceived Control`, papers.data[papers.data$Paper==\"Laar-Hamster\",]$`Frustration`, \n method = \"pearson\")\nresLaar\nresKiwi <- cor.test(papers.data[papers.data$Paper==\"MED8-Kiwi\",]$`Perceived Control`, papers.data[papers.data$Paper==\"MED8-Kiwi\",]$`Frustration`, \n method = \"pearson\")\nsummary(lm(`Frustration`~`Perceived Control`, data=papers.data[papers.data$Paper==\"Laar-Hamster\",]))\n", "meta": {"hexsha": "5fd49f09e4cc6bf8851d6a808931eac4f1576ffb", "size": 7755, "ext": "r", "lang": "R", "max_stars_repo_path": "experiment-data/papers-data.r", "max_stars_repo_name": "med-material/bci-kiwi-runner", "max_stars_repo_head_hexsha": "2e5a86c202aa725822790e5bf2f8b23837ec36c9", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiment-data/papers-data.r", "max_issues_repo_name": "med-material/bci-kiwi-runner", "max_issues_repo_head_hexsha": "2e5a86c202aa725822790e5bf2f8b23837ec36c9", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2019-09-09T08:13:21.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-03T11:16:01.000Z", "max_forks_repo_path": "experiment-data/papers-data.r", "max_forks_repo_name": "med-material/bci-kiwi-runner", "max_forks_repo_head_hexsha": "2e5a86c202aa725822790e5bf2f8b23837ec36c9", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2019-10-14T08:45:20.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-11T15:29:18.000Z", "avg_line_length": 45.0872093023, "max_line_length": 206, "alphanum_fraction": 0.6715667311, "num_tokens": 2494, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3085785671266515}} {"text": "library(Seurat)\n\nfn = \"data/H1_day0_scranNorm_adtbatchNorm_dist_clustered_TSNE_labels.rds\"\nh1 = readRDS(fn)\n\nsig.list = readRDS(\"sig/sig.list.RDS\")\n\ndf.subj = h1@meta.data %>% dplyr::mutate(response = str_remove(adjmfc.time, \"d0 \")) %>% \n dplyr::select(subject=sampleid, response) %>% \n dplyr::mutate(subject = as.numeric(subject)) %>% \n distinct() %>% \n dplyr::arrange(subject)\n\nsigs = c(rep(\"CD40.act\",3), rep(\"SLE.sig\",4), rep(\"IFN26\",2), \"TGSig\", \"LI.M165\")\ncl.name = c(\"C3.1.0\", \"C1\", \"C6\", \"C1\", \"C6\", \"C7\", \"C2\", \"C9\", \"C2.1.0\", \"C9\", \"C9\")\ncl.level = str_count(cl.name, \"\\\\.\")+1\nsig.label = glue::glue(\"{sigs} ({cl.name})\")\nsig.label = sig.label %>% str_replace(\"SLE.sig\", \"SLE-Sig\") %>% \n str_replace(\"CD40.act\", \"CD40act\") %>% str_replace(\"IFN26\", \"IFN-I-DCact\")\n\ndn = \"results/sig_scores\"\n\ndf.scores = df.subj\nfor(s in seq_along(sig.label)) {\n cat(sig.label[s],\"\\n\")\n fn = glue::glue(\"{dn}/scores_{sigs[s]}_{cl.level[s]}.txt\")\n df = fread(fn) %>% dplyr::select(\"subject\",cl.name[s])\n df.scores = left_join(df.scores, df, by=\"subject\")\n}\nnames(df.scores)[-(1:2)] = sig.label\n\n\n# add CD38++ frequencies in flow\nfn.flow = file.path(\"../generated_data/CHI/CHI_sample_info_2_CD38hi.txt\")\ndf.flow = fread(fn.flow) %>%\n dplyr::filter(time==0) %>%\n dplyr::select(subject, `CD38hi (flow)` = CD38hi)\n\n# add microarray scores\nfn.ma = file.path(\"../generated_data/CHI/CHI_cd38_ge_sig_score_day0.txt\")\ndf.ma = fread(fn.ma) %>% \n dplyr::select(subject, `TGSig (microarray)` = score)\n\nfn.ma2 = file.path(\"results/sig_scores/scores_SLE.sig_MA.txt\")\ndf.ma2 = fread(fn.ma2) %>% \n dplyr::select(subject, `SLE-Sig (microarray)` = SLE.sig)\n\ndf.scores = df.scores %>% \n left_join(df.flow, by=\"subject\") %>% \n left_join(df.ma, by=\"subject\") %>% \n left_join(df.ma2, by=\"subject\")\n \nfwrite(df.scores, \"results/Score_table.txt\", sep=\"\\t\")\n\n", "meta": {"hexsha": "4f44135d0815e589cd61c8213a697ac44f2ce5dc", "size": 1842, "ext": "r", "lang": "R", "max_stars_repo_path": "citeseq/R/scores_for_correlation.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "citeseq/R/scores_for_correlation.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "citeseq/R/scores_for_correlation.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 33.4909090909, "max_line_length": 88, "alphanum_fraction": 0.6454940282, "num_tokens": 667, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.30857855991051164}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/sinking-marbles-production/results/\")\nsource(\"rscripts/helpers.r\")\nload(\"data/priors.RData\")\nr = read.table(\"data/sinking_marbles_nullutterance-production.txt\", sep=\"\\t\", header=T)\nnrow(r)\nnames(r)\nr$trial = r$slide_number_in_experiment - 2\nr = r[,c(\"assignmentid\",\"workerid\", \"rt\", \"effect\", \"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\", \"object_level\", \"response\", \"object\",\"slider_id\",\"num_objects\",\"trial\",\"enjoyment\",\"asses\",\"comments\",\"Answer.time_in_minutes\",\"num_objects_affected\")]\nrow.names(priors) = paste(priors$effect, priors$object)\nr$Prior = priors[paste(r$effect, r$object),]$response\nr$object_level = factor(r$object_level, levels=c(\"object_high\", \"object_mid\", \"object_low\"))\nr$Quantifier = r$slider_id\nr$Half = as.factor(ifelse(r$trial < 16, 1, 2))\nr$Quarter = as.factor(ifelse(r$trial < 8, 1, ifelse(r$trial < 16, 2, ifelse(r$trial < 24, 3, 4))))\nsummary(r)\nr$Combination = as.factor(paste(r$cause,r$object,r$effect))\ntable(r$Combination)\n\n# compute normalized probabilities\nnr = ddply(r, .(assignmentid,trial), summarize, normresponse=response/(sum(response)),assignmentid=assignmentid,Quantifier=Quantifier)\nrow.names(nr) = paste(nr$assignmentid,nr$trial,nr$Quantifier)\nr$normresponse = nr[paste(r$assignmentid,r$trial,r$Quantifier),]$normresponse\n# test: sums should add to 1\nsums = ddply(r, .(assignmentid,trial), summarize, sum(normresponse))\ncolnames(sums) = c(\"assignmentid\",\"trial\",\"sum\")\nsummary(sums)\nsums[is.na(sums$sum),]\n\nr$Proportion = r$num_objects_affected/r$num_objects\nr$ProportionBin = 3\nr[r$Proportion < 1,]$ProportionBin = cut(r[r$Proportion < 1,]$Proportion,breaks=quantile(r[r$Proportion < 1,]$Proportion, probs=seq(0,1,.5)))\n\nsave(r, file=\"data/r.RData\")\n\n##################\n\nggplot(aes(x=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=rt), data=r) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,50000))\n\nggplot(aes(x=age), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,fill=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=enjoyment), data=r) +\n geom_histogram()\n\nggplot(aes(x=asses), data=r) +\n geom_histogram()\n\nggplot(aes(x=Answer.time_in_minutes), data=r) +\n geom_histogram()\n\nggplot(aes(x=Proportion), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,y=Answer.time_in_minutes,color=gender.1), data=unique(r[,c(\"assignmentid\",\"age\",\"Answer.time_in_minutes\",\"gender.1\")])) +\n geom_point() +\n geom_smooth()\n\nunique(r$comments)\n\ntable(r$num_objects_affected,r$num_objects)\n\n########################################\n# compute speaker probabilities for model\n########################################\n# first exclude all the trials on which there were no affected objects\natleastone = subset(r, Proportion > 0)\natleastone = droplevels(atleastone)\nnrow(atleastone)\nsummary(atleastone)\n\n# simplest model:\nr$State = as.factor(ifelse(r$Proportion == 1, \"all\", \"not-all\"))\ntable(r$State)\n\nprobs = aggregate(normresponse ~ Quantifier + State, FUN=mean, data=r)\nprobs # probs for model\npriors = unique(r$Prior)\nsort(unique(round(priors/100,2))) # priors to run model with\nlength(sort(unique(round(priors/100,2))) ) # 52 unique priors\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "15fc5dea2363d10b63465b6ee6f8be15173c201c", "size": 3238, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/6_sinking-marbles-production/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/6_sinking-marbles-production/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/6_sinking-marbles-production/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.7070707071, "max_line_length": 256, "alphanum_fraction": 0.709697344, "num_tokens": 951, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3085650299174423}} {"text": "library(raster)\nlibrary(WatershedTools)\nlibrary(sp)\nlibrary(data.table)\nlibrary(RSQLite)\n\nmetabDB <- dbConnect(RSQLite::SQLite(), \"../metabolismDB/metabolism.sqlite\")\ndbExecute(metabDB, \"PRAGMA foreign_keys = ON\")\n\nmetadat <- fread(\"catchment_list.csv\")[catchment == \"vjosa\"]\nshareDir <- file.path(metadat$dir, metadat$catchment, metadat$version)\n\ndrain <- raster(file.path(shareDir, \"drainage.tif\"))\naccum <- raster(file.path(shareDir, \"accumulation.tif\"))\nelev <- raster(file.path(shareDir, \"filled_dem.tif\"))\nvjosaChannel <- raster(file.path(shareDir, \"vjosa_stream.tif\"))\ncatchmentAreas <- raster(file.path(shareDir, \"catchment_area.tif\"))\n\n\n# get discharge\nqry <- \"SELECT sites.siteName, (expeditions.watershed || expeditions.season || expeditions.year) \n\tAS expedition, date, hydromorphology.value AS discharge, x, y, epsg FROM hydromorphology \n\tLEFT JOIN sites ON siteID = sites.id \n\tLEFT JOIN variableLookUp ON variableCode = variableLookUp.id \n\tLEFT JOIN expeditions on expeditionID = expeditions.id \n\tWHERE variableLookUp.variable LIKE 'Q' AND expedition LIKE 'Vjosa%'\"\nqSites <- data.table(dbGetQuery(metabDB, qry))\ncoordinates(qSites) <- c('x', 'y')\nproj4string(qSites) <- CRS(paste0(\"+init=epsg:\", qSites$epsg[1]))\nqSites$catchmentArea <- raster::extract(catchmentAreas, qSites)\nqSites <- as.data.table(qSites)\nqSites <- qSites[, .(siteName = siteName, expedition = expedition, Q = discharge, \n\tA = catchmentArea, x, y)]\n\ncaDF <- cbind(values(catchmentAreas), coordinates(catchmentAreas))\ncaDF <- caDF[complete.cases(caDF),]\nqByExpedition <- by(qSites, factor(qSites$expedition), function(x) {\n\tq <- discharge_scaling(caDF[,1], x)\n\tq <- data.frame(Q = q, x = caDF[,2], y = caDF[,3])\n\tq <- rasterFromXYZ(q[, c('x', 'y', 'Q')])\n\tproj4string(q) <- proj4string(catchmentAreas)\n\tq\n})\n\nlapply(names(qByExpedition), function(x) writeRaster(qByExpedition[[x]], \n\tfile = file.path(shareDir, \"tmp\", paste0(\"discharge\", x, \".tif\"))))\n\npar(mfrow=c(2,2))\n\nlapply(names(qByExpedition), function(x) {\n\tval <- values(qByExpedition[[x]])\n\tval <- val[!is.na(val)]\n\tind <- which(qSites$expedition == x)\n\tdat <- qSites[ind,]\n\tplot(log(caDF[,1]), log(val), xlab=\"log(Catchment Area)\", ylab=\"log(discharge)\", \n\t\tpch='x', cex=0.3, col='red', main=x)\n\tpoints(log(dat$A), log(dat$Q), pch=16, cex=0.7, col='blue')\n})\n\ngeom <- lapply(qByExpedition, function(x) {\n\tdf <- data.frame(x = coordinates(x)[,1], y = coordinates(x)[,2], q = values(x))\n\tdf <- df[complete.cases(df),]\n\tgeom <- hydraulic_geometry(df[,3])\n\tcoordinates(geom) <- df[,1:2]\n\tgridded(geom) <- TRUE\n\tgeom <- stack(geom)\n\tproj4string(geom) <- proj4string(catchmentAreas)\n\tgeom\n})\n\nnms <- names(geom)\nfor(x in nms) {\n\tgdir <- file.path(shareDir, paste0(\"geometry_\", x))\n\tdir.create(gdir, showWarnings=FALSE)\n\tfor(y in names(geom[[x]])) {\n\t\twriteRaster(geom[[x]][[y]], file.path(gdir, paste0(y, \"_\", x, \".tif\")), \n\t\t\toptions = c(\"COMPRESS=LZW\", \"PREDICTOR=3\"))\n\t}\n}\n\n# only if needed due to non-matching extents\n# catchmentAreas <- extend(catchmentAreas, vjosaChannel)\n# geom <- lapply(geom, extend, vjosaChannel)\n\nvjosaWS <- list()\nfor(x in nms) {\n\tgeom <- stack(list.files(paste0(shareDir, \"/geometry_\", x), pattern=\"*.tif\", \n\t\tfull.names = TRUE))\n\tvjosaWS[[x]] <- Watershed(stream = vjosaChannel, drainage = drain, elevation = elev, \n\t\taccumulation = accum, catchmentArea = catchmentAreas, \n\t\totherLayers = geom)\n}\n\n## get slope and add to all watersheds\nslope = wsSlope(vjosaWS[[1]])\nfor(x in nms) {\n\tvjosaWS[[x]]$data$slope = slope\n\tsaveRDS(vjosaWS[[x]], paste0(shareDir, \"/watershed_\", x, \".rds\"))\n}\n\ndbDisconnect(metabDB)\n\n", "meta": {"hexsha": "0ef116862eba066d3061bcc94eb36cb3f77f3663", "size": 3579, "ext": "r", "lang": "R", "max_stars_repo_path": "vjosa/3_geometry.r", "max_stars_repo_name": "mtalluto/FLEE_catchments", "max_stars_repo_head_hexsha": "3d24a9bc0c5be44286a087c866455dbe54170c80", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vjosa/3_geometry.r", "max_issues_repo_name": "mtalluto/FLEE_catchments", "max_issues_repo_head_hexsha": "3d24a9bc0c5be44286a087c866455dbe54170c80", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vjosa/3_geometry.r", "max_forks_repo_name": "mtalluto/FLEE_catchments", "max_forks_repo_head_hexsha": "3d24a9bc0c5be44286a087c866455dbe54170c80", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-09-25T09:20:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-09-25T09:20:07.000Z", "avg_line_length": 34.7475728155, "max_line_length": 97, "alphanum_fraction": 0.6940486169, "num_tokens": 1128, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307806984443, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3085650232417456}} {"text": "library(ggplot2)\r\nlibrary(readr)\r\nlibrary(dplyr)\r\nlibrary(geosphere)\r\nlibrary(tibble)\r\nlibrary(dplyr)\r\nlibrary(ggplot2)\r\nlibrary(cowplot)\r\nlibrary(stringr)\r\nlibrary(knitr)\r\nlibrary(leaflet)\r\nlibrary(ggmap)\r\nlibrary(maps)\r\nlibrary(mapdata)\r\nlibrary(rvest)\r\nlibrary(magrittr)\r\nlibrary(stringr)\r\nlibrary(tibble)\r\nlibrary(tidyr)\r\nlibrary(dplyr)\r\nlibrary(purrr)\r\nlibrary(readr)\r\nlibrary(shiny)\r\nlibrary(shinydashboard)\r\n\r\n\r\ncity_data <- read_csv(\"./city_data.csv\")\r\ncateg_data <- read_csv(\"./categ_data.csv\")\r\nallCateg = unique(categ_data$normTitleCategory)\r\n\r\nsidebar <- dashboardSidebar(\r\n sidebarMenu(\r\n menuItem(\"Annual Statistics\", tabName = \"AnnualStat\", icon = icon(\"th\")),\r\n menuItem(\"Dynamic Trend by Industry\", icon = icon(\"dashboard\"), tabName = \"DyTrend1\"),\r\n menuItem(\"Dynamic Trend by States\", icon = icon(\"institution\"), tabName = \"DyTrend2\"),\r\n menuItem(\"Models\", icon = icon(\"graduation-cap\"), tabName = \"FittedModels\"),\r\n \r\n br(),\r\n br(),\r\n br(),\r\n \r\n actionButton(\"do\", \"See Results\")\r\n )\r\n)\r\n\r\nbody <- dashboardBody(\r\n tabItems(\r\n tabItem(tabName = \"AnnualStat\",\r\n h2(\"Annual Information by City\"),\r\n div(style=\"display:inline-block\",selectInput(\"categSelect\", label = \"Industry\", \r\n choices = c(\"All\",allCateg), \r\n selected = \"All\")),\r\n div(style=\"display:inline-block\",selectInput(\"dataSelect\", label = \"Variable for city map\", \r\n choices = c(\"Annual Salary\", \"Clicks per job\", \r\n \"Local clicks ratio\"), \r\n selected = \"Annual Salary\")),\r\n \r\n leafletOutput(\"map\", width=\"100%\",height=600)\r\n ),\r\n \r\n tabItem(tabName = \"DyTrend1\",\r\n h2(\"Monthly Changes by Industry\"),\r\n div(style=\"display:inline-block\",selectInput(\"categDataSelect\", \r\n label = \"Variable for Changes in Industry\", \r\n choices = c(\"Number of jobs\", \"Number of clicks\"), \r\n selected = \"Number of jobs\")),\r\n div(style=\"display:inline-block\",selectInput(\"categDataSort\", \r\n label = \"Way to sort industries\", \r\n choices = c(\"By value\", \"By name\"), \r\n selected = \"By value\")),\r\n \r\n \r\n uiOutput(\"categImage\")\r\n ),\r\n \r\n tabItem(tabName = \"DyTrend2\",\r\n h2(\"Monthly Changes by State\"),\r\n selectInput(\"monthDataSelect\", \r\n label = \"Variable for monthly trend\", \r\n choices = c(\"Number of jobs\", \"Number of clicks\",\r\n \"Number of local clicks\", \"Proportion of local clicks\"), \r\n selected = \"Number of jobs\"),\r\n uiOutput(\"MapTrendImage\")\r\n ),\r\n \r\n tabItem(tabName = \"FittedModels\",\r\n h2(\"Model\"),\r\n selectInput(\"modelSelect\",\r\n label = \"Select Model\",\r\n choices = c(\"Gaussian Process\", \"Time Series\", \"SAR model\"),\r\n selected = \"Gaussian Process\"),\r\n uiOutput(\"ModelImage\")\r\n )\r\n )\r\n)\r\n\r\nui <- dashboardPage(\r\n dashboardHeader(title = \"Indeed Job Data Visualization by BrunchLadies\",\r\n titleWidth = 450),\r\n sidebar,\r\n body\r\n)\r\n\r\n\r\n\r\nserver = function(input, output, session)\r\n{\r\n output$map<-renderLeaflet({\r\n leaflet() %>%\r\n addTiles() \r\n })\r\n \r\n output$categImage <- renderUI({\r\n img(src = \"animation_1.gif\", width=\"800\")\r\n })\r\n \r\n output$MapTrendImage <- renderUI({\r\n img(src = \"total_jobs.gif\", width=\"1000\")\r\n })\r\n \r\n output$ModelImage <- renderUI({\r\n img(src = \"GP_Model.png\", width=\"600\")\r\n })\r\n \r\n observeEvent(input$do,{\r\n \r\n ## Get Dataset\r\n if(input$categSelect==\"All\"){\r\n data = city_data\r\n r = data$n/180\r\n }else{\r\n data = categ_data %>% \r\n filter(normTitleCategory==input$categSelect)\r\n r = data$n/15\r\n }\r\n \r\n if(input$dataSelect==\"Annual Salary\"){\r\n pal <- colorNumeric(palette = c(\"lightpink\", \"red4\"), domain = data$medianSalary)\r\n p1 = leaflet() %>%\r\n addTiles() %>%\r\n addCircleMarkers(lng=data$lng, lat=data$lat, popup=data$SalaryInfo,\r\n color = pal(data$medianSalary), stroke = FALSE,\r\n radius = r, fillOpacity = 0.8) %>% \r\n addLegend(\"bottomright\", pal = pal, values = data$medianSalary,\r\n title = \"Median Salary\",\r\n labFormat = labelFormat(prefix = \"$\"),\r\n opacity = 0.8)\r\n }else if(input$dataSelect==\"Clicks per job\"){\r\n pal <- colorNumeric(palette = c(\"lightblue\", \"darkblue\"), domain = data$cityMeanClick)\r\n p1 = leaflet() %>%\r\n addTiles() %>%\r\n addCircleMarkers(lng=data$lng, lat=data$lat, popup=data$ClickInfo,\r\n color = pal(data$cityMeanClick), stroke = FALSE,\r\n radius = r, fillOpacity = 0.8) %>% \r\n addLegend(\"bottomright\", pal = pal, values = data$cityMeanClick,\r\n title = \"Average Click per job\",\r\n opacity = 0.8)\r\n }else{\r\n pal <- colorNumeric(palette = c(\"lightpink\", \"red4\"), domain = data$localRatio)\r\n \r\n p1 = leaflet() %>%\r\n addTiles() %>%\r\n addCircleMarkers(lng=data$lng, lat=data$lat, popup=data$LocalClickInfo,\r\n color = pal(data$localRatio), stroke = FALSE,\r\n radius = r, fillOpacity = 0.8) %>% \r\n addLegend(\"bottomright\", pal = pal, values = data$localRatio,\r\n title = \"Local Click Ratio per job\",\r\n opacity = 0.8)\r\n }\r\n \r\n if(input$categDataSort==\"By value\"){\r\n if(input$categDataSelect==\"Number of clicks\"){\r\n imageName = \"haha_1.gif\"\r\n }else{\r\n imageName = \"animation_1.gif\"\r\n }\r\n }else{\r\n if(input$categDataSelect==\"Number of clicks\"){\r\n imageName = \"haha_0.gif\"\r\n }else{\r\n imageName = \"animation_0.gif\"\r\n }\r\n }\r\n \r\n if(input$monthDataSelect==\"Number of jobs\"){\r\n mapName = \"total_jobs.gif\"\r\n }else if(input$monthDataSelect==\"Number of clicks\"){\r\n mapName = \"total_clicks.gif\"\r\n }else if(input$monthDataSelect==\"Number of local clicks\"){\r\n mapName = \"total_localClicks.gif\"\r\n }else{\r\n mapName = \"localClicks_ratio.gif\"\r\n }\r\n \r\n if(input$modelSelect==\"Gaussian Process\"){\r\n modelName = \"GP_Model.png\"\r\n }else if (input$modelSelect==\"Time Series\"){\r\n modelName = \"TS_model.png\"\r\n }else{\r\n modelName = \"SAR_model.png\"\r\n }\r\n \r\n output$categImage <- renderUI({\r\n img(src = imageName, width=\"800\")\r\n })\r\n \r\n output$MapTrendImage <- renderUI({\r\n img(src = mapName, width=\"1000\")\r\n })\r\n \r\n output$ModelImage <- renderUI({\r\n img(src = modelName, width=\"600\")\r\n })\r\n \r\n \r\n output$map<-renderLeaflet({\r\n p1\r\n })\r\n })\r\n}\r\n\r\n\r\nshinyApp(ui, server)", "meta": {"hexsha": "a3d50a1ada68179fbcb0da8ec8ce59f8d04b7d1a", "size": 7468, "ext": "r", "lang": "R", "max_stars_repo_path": "app.r", "max_stars_repo_name": "xuetongli/2018-ASA-DataFest", "max_stars_repo_head_hexsha": "c8a22ab04370ba643a26379be031b63afa26494c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "app.r", "max_issues_repo_name": "xuetongli/2018-ASA-DataFest", "max_issues_repo_head_hexsha": "c8a22ab04370ba643a26379be031b63afa26494c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "app.r", "max_forks_repo_name": "xuetongli/2018-ASA-DataFest", "max_forks_repo_head_hexsha": "c8a22ab04370ba643a26379be031b63afa26494c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4887892377, "max_line_length": 109, "alphanum_fraction": 0.5097750402, "num_tokens": 1674, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30853350645981986}} {"text": "library(\"arrow\")\nlibrary(\"abc\")\nlibrary(\"optparse\")\n\n\n\nmain <- function(opt){\n df_true_ss <- read_f(input=opt$df_true_ss)\n df_simu_space_knn_params <- read_f(input=opt$df_simu_space_knn_params)\n df_simu_space_knn_ss <- read_f(input=opt$df_simu_space_knn_ss)\n df_simu_space_knn_chainID <- read_f(input=opt$df_simu_space_knn_chainID)\n df_knn <- as.data.frame(abc(\n target = df_true_ss,\n param = df_simu_space_knn_params, \n sumstat = df_simu_space_knn_ss, \n tol = 1, \n method = opt$reg_model, \n transf = opt$transf, \n hcorr = opt$hcorr, \n kernel = opt$kernel, \n sizenet = 1)$adj.values)\n df_knn_ <- cbind(df_simu_space_knn_chainID,df_knn)\n write_f(df=df_knn_, output=opt$output)\n}\n\n\nread_f <- function(input) {\n df <- read_feather(file = input, as_data_frame = TRUE)\n return(df)\n}\n\n\nwrite_f <- function(df, output) {\n write_feather(x = as.data.frame(df),sink = output, version=2)\n}\n\n\noption_list = list(\n make_option(\"--reg_model\", type=\"character\", default=NULL,help=\"--reg_model\", metavar=\"character\"),\n make_option(\"--hcorr\", type=\"character\", default=NULL, help=\"--hcorr\", metavar=\"character\"),\n make_option(\"--kernel\", type=\"character\", default=NULL, help=\"--kernel\", metavar=\"character\"),\n make_option(\"--transf\", type=\"character\", default=NULL, help=\"--transf\", metavar=\"character\"),\n make_option(\"--output\", type=\"character\", default=NULL, help=\"--output\", metavar=\"character\"),\n make_option(\"--df_true_ss\", type=\"character\", default=NULL, help=\"--df_true_ss\", metavar=\"character\"),\n make_option(\"--df_simu_space_knn_params\", type=\"character\", default=NULL, help=\"--df_simu_space_knn_params\", metavar=\"character\"),\n make_option(\"--df_simu_space_knn_ss\", type=\"character\", default=NULL, help=\"--df_simu_space_knn_ss\", metavar=\"character\"),\n make_option(\"--df_simu_space_knn_chainID\", type=\"character\", default=NULL, help=\"--df_simu_space_knn_chainID\", metavar=\"character\")\n)\nopt_parser = OptionParser(option_list=option_list)\nopt = parse_args(opt_parser)\n\nif (is.null(opt$reg_model)){\n print_help(opt_parser)\n stop(\"\", call.=FALSE)\n}\n\nmain(opt=opt)\n\n\n\n\n\n", "meta": {"hexsha": "0f3029212762d5683403ad06f868f567c7c074fb", "size": 2183, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/abc.r", "max_stars_repo_name": "Simonll/r_abc", "max_stars_repo_head_hexsha": "81236f9a7b0ba7888da136b87264d3e1e4081d32", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/abc.r", "max_issues_repo_name": "Simonll/r_abc", "max_issues_repo_head_hexsha": "81236f9a7b0ba7888da136b87264d3e1e4081d32", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/abc.r", "max_forks_repo_name": "Simonll/r_abc", "max_forks_repo_head_hexsha": "81236f9a7b0ba7888da136b87264d3e1e4081d32", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.6507936508, "max_line_length": 135, "alphanum_fraction": 0.692166743, "num_tokens": 584, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30853350645981986}} {"text": "########################################################################################################################\n####\n#### R-Script to reproduce the results (Figures and Tables) of the paper\n#### \"Asymmetric latent semantic indexing for gene expression experiments visualization\"\n#### Submitted to Advances in data analysis and classifciation.\n####\n#### Authors: Javier Gonzalez, Alberto Munoz and Gabriel Martos\n####\n#### 15/02/2014\n####\n########################################################################################################################\n# requiered libraries \nlibrary(geneplotter)\nlibrary(RColorBrewer) \nlibrary(genefilter)\nlibrary(MASS)\nlibrary(mclust)\nlibrary(gplots)\nlibrary(mclust)\nlibrary(xtable)\n\n### Load the data\ndata <- read.table(\"nci.data\")\nlabels <- read.table(\"labels.txt\")[,1]\n\n# Expression estimates on the UN-LOGGED scale\ne.mat <- exp(data)\n\n# look at mean, sd, & cv for each gene across arrays\ngene.mean <- apply(e.mat,1,mean) \ngene.sd <- apply(e.mat,1,sd) \ngene.cv <- gene.sd/gene.mean\n\n# Plot of the gene.sd over the gene mean and histogram of those with cv. larger that 7.7\nblues.ramp <- colorRampPalette(brewer.pal(9,\"Blues\")[-1]) \ndCol <- densCols(log(gene.mean),log(gene.sd), colramp=blues.ramp) \n\n###########\n# Figure 3: cut-off for differential expression analysis \n###########\npar(mfrow=c(1,2)) \nplot(gene.mean,gene.sd,log='xy',col=dCol,pch=16,cex=0.1,xlim = c(1,2),ylim = c(0.2,5),xlab=\"Gene mean\", ylab=\"Gene sd.\",main=\"A\",cex.main=2) \nabline(v=100,lwd=3,col='red') \nhist(log(gene.cv),xlab = \"Gene CV (log. scale)\",col=\"grey\",main=\"B\",cex.main=2) \nabline(v=log(.5),lwd=3,col='red')\ntext(0.8,800,\"Theshold: CV>0.5\")\n\n# We select those genes with CV larger.5 (genes differentially expressed in the data)\nffun <- filterfun(cv(0.5,100))\nt.fil <- genefilter(e.mat,ffun)\n\n# Apply filter and transform back on log scale\nsmall.eset <- e.mat[t.fil,]\nsmall.noeset <- e.mat[!t.fil,]\n\n# Threshod to consider that a gene is expressed (the maximum value of the not expressed).\nthres = max(small.noeset)\n\n###########\n# Figure 2: diff-expressed vs. not diff-expressed gene.\n###########\npar(mfrow=c(1,2))\npar(mar=c(5,5,3,3))\nplot(1:64,small.eset[99,],ylim=c(0,15),pch =20,xlab=\"Experiment\",ylab=\"Expression\",main=\"Differentially expressed gene\",cex=2)\nabline(h = thres ,lwd=2, col = \"red\",lty=2)\nplot(1:64,small.noeset[2,],ylim=c(0,15),pch =20,xlab=\"Experiment\",ylab=\"Expression\",main=\"Non differentially expressed gene\",cex=2)\nabline(h = thres ,lwd=2, col = \"red\",lty=2)\n\n# Select those genes expressed at least one time\nffun2 <- filterfun(pOverA(0.025,thres))\nt.fil2 <- genefilter(small.eset,ffun2)\nsmall.eset2 <- small.eset[t.fil2,] \n\n# Create genes by experiments matrix of differentially expressed genes\ncolnames(small.eset2) <- labels\nX <- as.matrix((sign(small.eset2 - thres)+1)/2) # genes by experiments matrix\ncolnames(X) <- labels\nn.genes <- nrow(X)\n\n###########\n# Figure 2: Zipf's law, histogram of the norm of the genes\n###########\npar(mfrow=c(1,1))\nnorm.genes = apply(X,1,sum)\nhist(norm.genes,nclass=25,col = \"grey\",main = \" \",xlab=\"Genes Norm\",ylab=\"Frequency\")\ntext(16,600,paste(n.genes,\" differentially expressed genes\"),cex=1.7)\n\n###########\n# Figure 1 (A and B): Heatmaps pf raw data and differentially expressed genes\n###########\nheatmapA <- heatmap(log(t(small.eset2)), Rowv=NA, Colv=NA,scale=\"column\",margins = c(0.5, 15))\nheatmapB <- heatmap(t(X), Rowv=NA, Colv=NA, scale=\"none\",margins = c(0.5, 15))\n\n# Define the degree of membership of each gene to each type of experiment\nn.groups <- length(table(labels))\nmembership.genes.probs <- matrix(0,n.genes,n.groups )\nmembership.genes <- matrix(0,n.genes,n.groups )\ncolnames(membership.genes.probs) <- levels(labels)\ncolnames(membership.genes) <- levels(labels)\n\nfor (i in 1:n.genes)\n {\n membership.genes.probs[i,] = table(labels[X[i,]==1])/norm.genes[i]\n membership.genes[i,which(membership.genes.probs[i,] == max(membership.genes.probs[i,]))]=1 \n }\n\n### Calculate the (asymmetric) matrix of similarities between genes with respect to the experiments\nS0 \t\t <- X %*% t(X) \nMatrix.norms.inv\t<- diag(1/norm.genes) \nS \t\t <- Matrix.norms.inv %*% S0 \n \n## Calculate the polar decomposition\nsvdS <- svd(S)\nM1 <- svdS$u%*%diag(svdS$d)%*%t(svdS$u) \nM2 <- svdS$v%*%diag(svdS$d)%*%t(svdS$v) \n\n### Calculate the (asymmetric) matrix of similarities between genes with respect to the classes\nZ <- membership.genes%*%t(membership.genes)%*%diag(1/apply(membership.genes,1,sum)) \nsvdZ <- svd(Z)\nR1 <- svdZ$u%*%diag(svdZ$d)%*%t(svdZ$u) \nR2 <- svdZ$v%*%diag(svdZ$d)%*%t(svdZ$v) \n\n## Matrix combination of the two asymmetric similarities\nA <- (M1+M2)/2 + 0.2*(R1+R2) \n\n## Extract components\neigA <- eigen(A)\nXX <- Re(eigA$vectors[,Re(eigA$values)>0.0001])%*%diag(sqrt(Re(eigA$values)[Re(eigA$values)>0.0001]))\n\n# Extract components of the mixture\ncluster.lsa <- Mclust(XX, G =14)\n\n# Cross the clusters with the original classes\ncross = matrix(NA,14,14)\nfor (k in 1:14){cross[k,] = table(membership.genes[,k],cluster.lsa$classification)[2,]}\nrownames(cross) <- levels(labels)\ncolnames(cross) <- c(\"C1\",\"C2\",\"C3\",\"C4\",\"C5\",\"C6\",\"C7\",\"C8\",\"C9\",\"C10\",\"C11\",\"C12\",\"C13\",\"C14\") \n\n###########\n# Table 2: clusters vs. orignal classes\n###########\nxtable(cross)\n\n###########\n# Figure 6: MDS of the groups\n##########\nX.cross = sammon(dist(cross))\nplot(X.cross$points,xlab= \"Component 1\",ylab= \"Component 2\",type=\"n\",col=1:14,xlim=c(-270,270),ylim=c(-270,270),main = \"aLSI mapping of types of Cancer\")\ntext(X.cross$points,rownames(X.cross$points),col=\"blue\",cex=1.1)\n \n\n### Obtain genes with higest probability in each cluster\nmp <- matrix(NA,5,14)\nfor (i in 1:14){mp[,i] = order(cluster.lsa$z[,i],decreasing = TRUE)[1:5]} \ncolnames(mp) <- c(\"C1\",\"C2\",\"C3\",\"C4\",\"C5\",\"C6\",\"C7\",\"C8\",\"C9\",\"C10\",\"C11\",\"C12\",\"C13\",\"C14\") \nrownames(mp) <- c(\"gene 1\",\"gene 2\",\"gene 3\",\"gene 4\",\"gene 5\") \n\n###########\n# Table 1: most probable genes of each cluster\n###########\nxtable(t(mp))\n\n\n## Sammon mapping for the combination\nmds.S12 <- prcomp(XX)$x[,c(1,2)] ## save similarity matrix\nmds.S13 <- prcomp(XX)$x[,c(1,3)] ## save s \ncluster.lsa <- hclust(dist(XX),method=\"ward\")\n \n### MDS-Correlation \nC <- cor(log(t(small.eset2)))\nmds.C12 <- cmdscale(1-C,k=3)[,c(1,2)]\nmds.C13 <- cmdscale(1-C,k=3)[,c(1,3)]\n\n### MDS-Euclidean \nD <- dist(log(small.eset2)) \nmds.D12 <- cmdscale(D,k=3)[,c(1,2)]\nmds.D13 <- cmdscale(D,k=3)[,c(1,3)]\n\n###########\n# Figure 5: genes maps produced by the aLSI, Pearson's correlation and Euclidean distance. \n###########\npar(mfrow = c(3,2))\npar(mar= c(5,5,1.5,1))\nplot(mds.S12,pch=19,cex=0.8,col=\"grey\",type=\"n\",main = \"Asymmetric LSI\",xlab=\"1st component\",ylab=\"2nd component\",xlim=c(-1,1.4))\nfor(k in 1:14)\npoints(mds.S12[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9)\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n \nplot(mds.S13,pch=19,cex=0.8,col=\"grey\",type=\"n\",main = \"Asymmetric LSI\",xlab=\"1st component\",ylab=\"3th component\",xlim=c(-1,1.4))\nfor(k in 1:14)\npoints(mds.S13[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9,xlab=\"1st component\")\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n \nplot(mds.C12,pch=19,cex=0.8,col=\"grey\",main = \"Correlation\",xlab=\"1st component\",ylab=\"2nd component\",xlim=c(-1,1.2)) \nfor(k in 1:14)\npoints(mds.C12[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9)\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n\nplot(mds.C13,pch=19,cex=0.8,col=\"grey\",main = \"Correlation\",xlab=\"1st component\",ylab=\"3th component\",xlim=c(-1,1.2)) \nfor(k in 1:14)\npoints(mds.C13[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9)\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n\nplot(mds.D12,pch=19,cex=0.8,col=\"grey\",main = \"Euclidean distance\",xlab=\"1st component\",ylab=\"2nd component\",xlim=c(-20,20))\nfor(k in 1:14)\npoints(mds.D12[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9)\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n\nplot(mds.D13,pch=19,cex=0.8,col=\"grey\",main = \"Euclidean distance\",xlab=\"1st component\",ylab=\"3th component\",xlim=c(-20,20))\nfor(k in 1:14)\npoints(mds.D13[which(apply(membership.genes,1,sum)==1 & membership.genes[,k]==1), ],col=k,pch=k,cex=0.9)\nlegend(\"topleft\", c(\"G1\",\"G2\",\"G3\",\"G4\",\"G5\",\"G6\",\"G7\"), col=1:7,pch=1:7) \nlegend(\"topright\", c(\"G8\",\"G9\",\"G10\",\"G11\",\"G12\",\"G13\",\"G14\"), col=8:14,pch=8:14) \n \n \n", "meta": {"hexsha": "2fb33f0deb21d2c99361a2cccf29f5859ba5488f", "size": 9575, "ext": "r", "lang": "R", "max_stars_repo_path": "code/Script for aLSI for gene expression visualization.r", "max_stars_repo_name": "javiergonzalezh/aLSI", "max_stars_repo_head_hexsha": "591de7abee0cd7a8ec2d3cf44f4c3c765ddd6d01", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/Script for aLSI for gene expression visualization.r", "max_issues_repo_name": "javiergonzalezh/aLSI", "max_issues_repo_head_hexsha": "591de7abee0cd7a8ec2d3cf44f4c3c765ddd6d01", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/Script for aLSI for gene expression visualization.r", "max_forks_repo_name": "javiergonzalezh/aLSI", "max_forks_repo_head_hexsha": "591de7abee0cd7a8ec2d3cf44f4c3c765ddd6d01", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.3257918552, "max_line_length": 153, "alphanum_fraction": 0.597075718, "num_tokens": 3326, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30853350645981986}} {"text": "context(\"data-nycflights13\")\n\nlibrary(nycflights13)\n\ntest_that(\"nycflights13_h2 has flights table\", {\n # when\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n # then\n expect_is(flights_db, \"tbl_h2\")\n expect_equal(nrow(flights_db), 336776)\n expect_equal(ncol(flights_db), 19)\n})\n\ntest_that(\"filter\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n filtered <- filter(flights_db, month == 1, day == 1)\n\n expect_is(filtered, \"tbl_h2\")\n})\n\ntest_that(\"arrange\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n arranged <- arrange(flights_db, year, month, day)\n\n expect_is(arranged, \"tbl_h2\")\n})\n\ntest_that(\"select\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n selected <- select(flights_db, year, month, day)\n\n expect_is(selected, \"tbl_h2\")\n})\n\ntest_that(\"distinct\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n distinct_tailnum <- distinct(select(flights_db, tailnum))\n\n expect_is(distinct_tailnum, \"tbl_h2\")\n})\n\ntest_that(\"group_by\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n grouped <- group_by(flights_db, tailnum)\n\n expect_is(grouped, \"tbl_h2\")\n})\n\ntest_that(\"summarize\", {\n flights_db <- tbl(nycflights13_h2(), \"flights\")\n\n summarized <- summarise(flights_db,\n count = n(),\n distinct = n_distinct(\"flight\"),\n dist = mean(distance),\n delay = mean(arr_delay),\n min_delay = min(arr_delay),\n max_delay = max(arr_delay),\n sum_delay = sum(arr_delay),\n sd_delay = sd(arr_delay)\n )\n\n expect_is(summarized, \"tbl_h2\")\n})\n\n", "meta": {"hexsha": "acfdf41977d2b968ccabc52a1457fb428244b007", "size": 1502, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-integration-data-nycflights13.r", "max_stars_repo_name": "hoesler/dplyr-src-h2", "max_stars_repo_head_hexsha": "55e3342fe8630f3dcad4155afe2d685e9628000f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tests/testthat/test-integration-data-nycflights13.r", "max_issues_repo_name": "hoesler/dplyr-src-h2", "max_issues_repo_head_hexsha": "55e3342fe8630f3dcad4155afe2d685e9628000f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-07-30T11:54:35.000Z", "max_issues_repo_issues_event_max_datetime": "2015-07-31T10:03:20.000Z", "max_forks_repo_path": "tests/testthat/test-integration-data-nycflights13.r", "max_forks_repo_name": "hoesler/dplyr-src-h2", "max_forks_repo_head_hexsha": "55e3342fe8630f3dcad4155afe2d685e9628000f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.8611111111, "max_line_length": 59, "alphanum_fraction": 0.6824234354, "num_tokens": 451, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.30853350645981986}} {"text": "get.data <- function(fnc.path,loading.path,target.path) {\nfnc_df = read.csv(fnc.path)\nloading_df = read.csv(loading.path)\nlabels_df = read.csv(target.path)\nlibrary(tidyr)#drop_na()\ndf <- merge(fnc_df,loading_df,by=\"Id\")\nlabels_df$is.train = T \ndf <- merge(x=labels_df, y=df, all.x=TRUE)\ndf<-drop_na(df)\ndf<-df[,-c(1,7)]\nhead(df)\nreturn(df)\n}\n\n\ndata<-get.data(\"../input/trends-assessment-prediction/fnc.csv\",\"../input/trends-assessment-prediction/loading.csv\",\"../input/trends-assessment-prediction/train_scores.csv\")\n\nsmp_size <- floor(0.75 * nrow(data))\n## set the seed to make your partition reproducible\nset.seed(123)\ntrain_ind <- sample(seq_len(nrow(data)), size = smp_size)\ntrain <- data[train_ind, ]\ntest <- data[-train_ind, ]\ndim(train)\ndim(test)\n", "meta": {"hexsha": "3be8496be265e37ae05ef28195652536792b8431", "size": 755, "ext": "r", "lang": "R", "max_stars_repo_path": "TRends/read.r", "max_stars_repo_name": "nizamphoenix/kaggle", "max_stars_repo_head_hexsha": "a9c993d0441a6d9260d605a630f95d938e6329db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "TRends/read.r", "max_issues_repo_name": "nizamphoenix/kaggle", "max_issues_repo_head_hexsha": "a9c993d0441a6d9260d605a630f95d938e6329db", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "TRends/read.r", "max_forks_repo_name": "nizamphoenix/kaggle", "max_forks_repo_head_hexsha": "a9c993d0441a6d9260d605a630f95d938e6329db", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0384615385, "max_line_length": 172, "alphanum_fraction": 0.7178807947, "num_tokens": 219, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.30853350645981986}} {"text": "# ==== US County Map Data ====\nCOUNTIES_GEO <- geojsonio::geojson_read(\"data/geography/gz_2010_us_050_00_20m.json\",\n what = \"sp\")\n\n# Lowercase names\nnames(COUNTIES_GEO@data) = tolower(names(COUNTIES_GEO@data))\n\n# ==== Helper Functions ====\nget_label <- function(data, n, output='', transform=c()) {\n # n: Number of bins data was separated into\n # output:\n # * \"\": Don't change output\n # * \"percent\": Output percentage values\n # transform: A list of transformations to apply to the labels\n \n # Get the breaks that colorQuantile uses\n # quantile(geog_demo_data$black_percent,\n # probs = seq(0, 1, length.out = 9), na.rm=TRUE) \n breaks <- quantile(data, probs = seq(0, 1, length.out = n + 1),\n na.rm=TRUE)\n \n labels <- c()\n \n for (i in 1:n) {\n if (length(transform) > 0) {\n for (func in transform) {\n lower_bound <- do.call(what=func, args=list(breaks[i]))\n upper_bound <- do.call(what=func, args=list(breaks[i + 1]))\n }\n } else {\n lower_bound <- breaks[i]\n upper_bound <- breaks[i + 1]\n }\n \n if (output == 'percent') {\n labels <- append(labels,\n paste0(round(lower_bound, 3) * 100, \"%\",\n \" - \",\n round(upper_bound, 3) * 100, \"%\"))\n } else {\n labels <- append(labels, paste0(lower_bound, \" - \", upper_bound))\n }\n }\n \n return(labels)\n}\n\nas.money <- function(value, currency.sym=\"$\", digits=2, sep=\",\", decimal=\".\") {\n # Credits: http://stackoverflow.com/questions/14028995/money-representation-in-r\n paste(\n currency.sym,\n formatC(value, format = \"f\", big.mark = sep, digits=digits, decimal.mark=decimal),\n sep=\"\"\n )\n}\n\nas.td_yr <- function(year) {\n # Convert four-digit year to two-digit\n \n if (is.character(year)) {\n year <- as.numeric(year)\n }\n \n return(as.character(substrRight(year, 2)))\n}\n\n# From: https://stackoverflow.com/questions/7963898/extracting-the-last-n-characters-from-a-string-in-r\nsubstrRight <- function(x, n){\n substr(x, nchar(x)-n+1, nchar(x))\n}\n", "meta": {"hexsha": "969177034ba8f4765cb5bf9d97b2e34dd55adb40", "size": 2136, "ext": "r", "lang": "R", "max_stars_repo_path": "util.r", "max_stars_repo_name": "vincentlaucsb/Census-2010", "max_stars_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-04-18T19:49:44.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-03T23:22:14.000Z", "max_issues_repo_path": "util.r", "max_issues_repo_name": "vincentlaucsb/Census-2010", "max_issues_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-04-18T09:04:34.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-20T02:22:45.000Z", "max_forks_repo_path": "util.r", "max_forks_repo_name": "vincentlaucsb/Census-2010", "max_forks_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.0845070423, "max_line_length": 103, "alphanum_fraction": 0.5763108614, "num_tokens": 587, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.585101139733739, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30853349882981757}} {"text": "#!/usr/bin/env Rscript\n\n#CALL: Rscript rapidNorm.R \n\n#load libraries\nlibrary(\"scales\")\n\n####FUNCTIONS####\nreadStatistics <- function(path){\n full=paste(path,\"/Statistics.dat\",sep=\"\")\n stats=read.table(full,header=T,sep=\" \",stringsAsFactors=FALSE)\n return(stats)\n}\n\nreadTotal <- function(path){\n full=paste(path,\"/TotalReads.dat\",sep=\"\")\n total=read.table(full,header=F,sep=\" \")\n return(total$V1)\n}\n\n#This function compute the region lengths from the Annotation file given\n#to the RAPID pipeline and used for intersection\ncomputeRegionLengths <- function(data){\n #process\n names(data)=c(\"chr\",\"start\",\"end\",\"label\",\"type\",\"strand\")\n #compute lengths\n singleLengths=apply(data,1,function(x){return(as.numeric(x[3])-as.numeric(x[2])+1)})\n dummyDF=data.frame(label=data$label,lens=singleLengths)\n res=by(dummyDF,dummyDF$label,function(x){sum(x$lens)})\n return(data.frame(region=as.character(names(res)),lens=as.numeric(res)))\n}\n\naddLengthColumnToStats <- function(Stats,lens){\n Lengths=computeRegionLengths(lens)\n for(i in 1:length(Stats)){\n df=Stats[[i]]\n Stats[[i]] = merge(df,Lengths,by=\"region\")\n }\n return(Stats)\n}\n\ngetMax <- function(Total){\n return(max(unlist(Total)))\n}\n\nnormalizeEntries <- function(Stats,oldTotal,adjustForBackground=TRUE,normalizeByLength=TRUE, normalizeByDESeq=FALSE){\n #the default is to adjust the counts for any small RNA background counts obtained and for region length\n #Note: that if the 3rd column of the config file has the entry none, then no count is substracted\n Total=oldTotal\n if(adjustForBackground){\n for (i in 1:length(Stats)){\n Total[i]=Total[[i]]-countBackgroundReads(Stats[[i]],conf$background[i])\n }\n }\n \n if(normalizeByDESeq){\n library(\"DESeq2\")\n newList=Stats\n readData=as.data.frame(Stats[[1]]$region)\n for(i in 1:length(Stats)){ \n readData=cbind(readData,Stats[[i]]$reads)\n }\n colnames(readData)=c(\"Regions\",c(1:length(Stats)))\n readData$Regions=NULL\n sizes=estimateSizeFactorsForMatrix(readData)\n for (i in 1:length(Stats)){\n newList[[i]]$reads_norm=round(newList[[i]]$reads/sizes[i],digits = 0)\n newList[[i]]$asreads_norm=round(newList[[i]]$antisenseReads/sizes[i],digits = 0)\n newList[[i]]$asratio_norm=round(newList[[i]]$asreads_norm/newList[[i]]$reads_norm,digits=2)\n newList[[i]]$asratio_norm[is.na(newList[[i]]$asratio_norm)]=0\n newList[[i]]$asratio_norm[is.infinite(newList[[i]]$asratio_norm)]=0\n if(normalizeByLength){\n newList[[i]]$reads_avg=round( (newList[[i]]$reads_norm/newList[[i]]$lens),digits=3)\n }\n newList[[i]]$RPK=(newList[[i]]$reads_norm/newList[[i]]$lens)*1000\n newList[[i]]$TPM=round(newList[[i]]$RPK/(sum(newList[[i]]$RPK)/1e+06), digits = 3)\n newList[[i]]$RPK=NULL\n }\n }\n else {\n Max=getMax(Total)\n newList=Stats\n for (i in 1:length(Stats)){\n newList[[i]]$reads_norm=round(newList[[i]]$reads*(Max/Total[[i]]),digits=0)\n newList[[i]]$asreads_norm=round(newList[[i]]$antisenseReads*(Max/Total[[i]]),digits=0)\n newList[[i]]$asratio_norm=round(newList[[i]]$asreads_norm/newList[[i]]$reads_norm,digits=2)\n newList[[i]]$asratio_norm[is.na(newList[[i]]$asratio_norm)]=0\n newList[[i]]$asratio_norm[is.infinite(newList[[i]]$asratio_norm)]=0\n if(normalizeByLength){\n newList[[i]]$reads_avg=round( (newList[[i]]$reads_norm/newList[[i]]$lens),digits=3)\n }\n newList[[i]]$RPK=(newList[[i]]$reads_norm/newList[[i]]$lens)*1000\n newList[[i]]$TPM=round(newList[[i]]$RPK/(sum(newList[[i]]$RPK)/1e+06),digits = 3)\n newList[[i]]$RPK=NULL\n } \n }\n return(newList)\n}\n#this function gets as input a string with region names. The total read count of these regions\n#will be returned.\ncountBackgroundReads <- function(Stats,string){\n if(string==\"none\"){\n return(c(0))\n }\n else{\n entries=unlist(strsplit(string, \",\"))\n return(sum(subset(Stats,region %in% entries)$reads))\n }\n} \n\ncreatePlottingData <- function(Stats,conf){\n rows=nrow(Stats[[1]])\n numDatasets=length(Stats)\n readCounts=rep(Stats[[1]]$reads,numDatasets)\n readCountsNormTPM=rep(Stats[[1]]$TPM,numDatasets)\n readCountsNorm=rep(Stats[[1]]$reads_norm,numDatasets)\n ASreadCountsNorm=rep(Stats[[1]]$asreads_norm,numDatasets)\n readCountsAvg=rep(Stats[[1]]$reads_avg,numDatasets)\n #ASratio=rep(Stats[[1]]$ASratio,numDatasets)\n ASratioNorm=rep(Stats[[1]]$asratio_norm,numDatasets)\n \n names=rep(conf$name[1],numDatasets*rows)\n for(i in 1:(numDatasets-1)){\n readCounts[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$reads\n readCountsNormTPM[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$TPM\n readCountsNorm[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$reads_norm\n ASreadCountsNorm[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$asreads_norm\n readCountsAvg[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$reads_avg\n #ASratio[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$ASratio\n ASratioNorm[(i*rows+1):((i+1)*rows)] = Stats[[i+1]]$asratio_norm\n names[(i*rows+1):((i+1)*rows)] = rep(conf$name[i+1],rows)\n }\n df=data.frame(region = rep(Stats[[1]]$region,numDatasets), readCounts=readCounts, readCountsNormTPM=readCountsNormTPM, readCountsNorm=readCountsNorm, readCountsAvg=readCountsAvg, ASreadCountsNorm=ASreadCountsNorm, ASratioNorm=ASratioNorm, samples=names)\n return(df)\n}\n\n####DONE FUNCTIONS####\nargs <- commandArgs(trailingOnly = TRUE)\n\nconfig=args[1]\nannot=args[2]\nout=args[3]\nuseDEseq=args[4]\nrestrictLength=args[5]\n\n#load datasets \nconf=read.table(config,header=T,sep=\"\\t\",stringsAsFactors=FALSE)\nlens=read.table(annot,header=F,stringsAsFactors=FALSE)\nnames(lens)=c(\"chr\",\"start\",\"end\",\"region\",\"type\",\"strand\")\nStats=lapply(conf$location,readStatistics)\nTotal=lapply(conf$location,readTotal)\n\n#check if variable is defined then updated Stats values \nif(!is.na(restrictLength)){\n # update reads column in each Stats entry to the sum of the counts \n # in the columns of given read counts\n restrictPattern=gsub(',','|',restrictLength)\n #show(paste(restrictPattern)) \n for (i in 1:length(Stats)){\n indices=grep(restrictPattern,names(Stats[[i]]))\n negindices=indices[-c(1:(length(indices)/2))]\n #show(paste(indices))\n if(length(indices) > 0){\n Stats[[i]]$reads=apply(Stats[[i]][,indices],1,sum)\n if(length(negindices)>1){\n Stats[[i]]$antisenseReads=apply(Stats[[i]][,negindices],1,sum)\n } else {\n Stats[[i]]$antisenseReads=Stats[[i]][,negindices]\n }\n }\n }\n}\n\n\n#add length column\nStats=addLengthColumnToStats(Stats,lens)\n\n#normalize read counts\nif(useDEseq){\n normalized=normalizeEntries(Stats,Total,normalizeByDESeq=TRUE)\n} else {\n normalized=normalizeEntries(Stats,Total,normalizeByDESeq=FALSE) \n}\n\nallowed= unique(subset(lens,type == \"region\")$region)\n\n#Filter only the non-background regions. Also enables to use different bed files while using rapidStats, although it is not advised.\nnormalizedSub = normalized\nfor(i in 1:length(normalized)){\n normalizedSub[[i]]=subset(normalized[[i]], region %in% allowed)\n}\n\ndf=createPlottingData(normalizedSub,conf)\n#save data.frame in output folder\nwrite.table(subset(df,region %in% allowed),paste(out,\"NormalizedValues.dat\",sep=\"\"),quote=F,row.names=F,sep=\"\\t\")\n\n\n", "meta": {"hexsha": "edd26de32725cec9f384a4550534f293a1619414", "size": 7255, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/rapidNorm.r", "max_stars_repo_name": "skarunan/RAPID", "max_stars_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2018-04-19T09:11:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-17T23:09:40.000Z", "max_issues_repo_path": "bin/rapidNorm.r", "max_issues_repo_name": "skarunan/RAPID", "max_issues_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/rapidNorm.r", "max_forks_repo_name": "skarunan/RAPID", "max_forks_repo_head_hexsha": "80240312ed94f1d6c74dceb3fded0078b8efbacd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2017-10-13T17:23:52.000Z", "max_forks_repo_forks_event_max_datetime": "2019-11-08T14:27:43.000Z", "avg_line_length": 36.6414141414, "max_line_length": 255, "alphanum_fraction": 0.694417643, "num_tokens": 2255, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6688802603710085, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.30836502338182564}} {"text": "library(leaflet)\nlibrary(jsonlite)\n\n###\n# Visualization of free market long-term room rentals in Amsterdam.\n# idea: opacity ~ time on market | to fade old \"sold but not removed\" ones?\n###\n\n# Constants.\nALLOWED_TYPES <- c(\"Kamer\", \"Studio\")\nSIZE_UPPER_LIMIT <- 30\nCOLOR_RANGE <- c(\"#1d0540\", \"#0f9b0f\", \"#15d915\")\nHIGHLIGHTED_COLOR_RANGE <- c(\"#5614B0\", \"#5614B0\", \"#9b5fed\") # For \"early bird\" adverts.\n\n# Reading in the JSON.\njson_file <- \"./data/example_rooms.json\"\nrooms <- fromJSON(json_file)\n\n# Filtering the data.\nrooms <- rooms[rooms$type %in% ALLOWED_TYPES, ]\nrooms <- rooms[rooms$size <= SIZE_UPPER_LIMIT, ]\n\n# Colors.\npricePal <- colorNumeric(rev(COLOR_RANGE), domain = rooms$price) # Rev, because we want cheaper ones to be green.\nsizePal <- colorNumeric(COLOR_RANGE, domain = rooms$size) # Normal, because we want bigger ones to be green.\n\nspecialPricePal <- colorNumeric(rev(HIGHLIGHTED_COLOR_RANGE), domain = rooms$price)\nspecialSizePal <- colorNumeric(HIGHLIGHTED_COLOR_RANGE, domain = rooms$size)\n\nvaluesToColorBySpeciality <- function(is_special, values, pal, specialPal) {\n normalColors <- pal(values);\n specialColors <- specialPal(values);\n colors <- is_special;\n \n for(i in seq_along(is_special)) {\n if(is_special[i]) {\n colors[i] <- specialColors[i]; \n } else {\n colors[i] <- normalColors[i];\n }\n }\n \n colors;\n}\n\n# Display the map.\n# Fill: the greener the cheaper.\n# Stroke: the greener the bigger.\n# Size: grows with the room.\nleaflet(data = rooms) %>% \n addTiles() %>% \n addCircleMarkers(\n ~lon,\n ~lat,\n color = ~valuesToColorBySpeciality(is_early_bird, size, sizePal, specialSizePal),\n fillColor = ~valuesToColorBySpeciality(is_early_bird, price, pricePal, specialPricePal),\n opacity = 1,\n fillOpacity = 1,\n radius = ~size*0.6,\n label = ~paste(as.character(price), \" \u20ac - \", as.character(size), \"m\u00b2\"),\n popup = ~url\n ) %>%\n addLegend(\n \"bottomright\",\n title = \"Room Size (stroke color)\",\n values = ~size,\n pal = sizePal,\n labFormat = labelFormat(suffix = \"m\u00b2\"),\n opacity = 1\n ) %>%\n addLegend(\n \"bottomright\",\n title = \"Room Price (fill color)\",\n values = ~price,\n pal = pricePal,\n labFormat = labelFormat(suffix = \"\u20ac\"),\n opacity = 1\n )\n\n", "meta": {"hexsha": "352f32207851e4e644327f19c0d038450e2ea6ad", "size": 2259, "ext": "r", "lang": "R", "max_stars_repo_path": "get_a_room.r", "max_stars_repo_name": "danigulyas/get-a-room-with-r", "max_stars_repo_head_hexsha": "a2b05171f533c192e05bf838d7af1f4166d8e034", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-09-17T10:01:47.000Z", "max_stars_repo_stars_event_max_datetime": "2018-09-17T10:01:47.000Z", "max_issues_repo_path": "get_a_room.r", "max_issues_repo_name": "danigulyas/get-a-room-with-r", "max_issues_repo_head_hexsha": "a2b05171f533c192e05bf838d7af1f4166d8e034", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "get_a_room.r", "max_forks_repo_name": "danigulyas/get-a-room-with-r", "max_forks_repo_head_hexsha": "a2b05171f533c192e05bf838d7af1f4166d8e034", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.2375, "max_line_length": 113, "alphanum_fraction": 0.6688800354, "num_tokens": 627, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980404, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30823656186437187}} {"text": "\n\nrequire(gdxtools)\nrequire(tidyverse)\n\nmyfile = file.path('P:/is-wel/indus/message_indus/input/fuel_cost/MsgOutput_MESSAGEix-GLOBIOM_res-test_baseline_res_eq_test.gdx')\n\nmygdx <- gdx(myfile)\nfuel_price_rw <- mygdx['PRICE_COMMODITY'] \n\n# chose SAS as region, prices differe regionally (check the variation is the same)\nfuel_price <- fuel_price_rw %>% \n filter(commodity %in% c(\"gas\",\"coal\",\"crudeoil\")) %>% \n filter(level == \"primary\") %>% \n filter(year_all >= \"2020\") %>% \n filter(node == \"R11_SAS\") %>% \n dplyr::select(-time,-level)\n\n# cost variation wrt 2020\ncost_variation <- fuel_price %>% \n group_by(commodity) %>% \n mutate(ref = fuel_price$value[fuel_price$commodity == commodity & fuel_price$year_all == \"2020\"]) %>% \n mutate(variation = (value-ref)/ref)\n\nto_save <- cost_variation %>% \n dplyr::select(commodity,year_all,variation)\n\nwrite.csv(to_save, 'P:/is-wel/indus/message_indus/input/fuel_cost/cost_variation_gams.csv',row.names = FALSE)\n", "meta": {"hexsha": "0ae4a56e3a842f2b0ac12f637695e12d3521c607", "size": 960, "ext": "r", "lang": "R", "max_stars_repo_path": "MESSAGEix/input_data_scripts/fuel_cost.r", "max_stars_repo_name": "amirsarikhani/NEST", "max_stars_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-07-15T19:28:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-24T04:45:43.000Z", "max_issues_repo_path": "MESSAGEix/input_data_scripts/fuel_cost.r", "max_issues_repo_name": "amirsarikhani/NEST", "max_issues_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-19T15:49:34.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-19T15:49:34.000Z", "max_forks_repo_path": "MESSAGEix/input_data_scripts/fuel_cost.r", "max_forks_repo_name": "amirsarikhani/NEST", "max_forks_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-05-20T08:50:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T03:35:08.000Z", "avg_line_length": 33.1034482759, "max_line_length": 129, "alphanum_fraction": 0.7177083333, "num_tokens": 286, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30823655431130725}} {"text": "#!/usr/bin/env Rscript\n\nlibrary(pophelper)\nx <- readQ(\"input_pophelper.txt\")\n\nqmat <- lapply(x, as.matrix)\np <- aperm(simplify2array(qmat), c(3, 1, 2))\nperm <- label.switching::stephens(p)\n\nwrite.table(perm$permutations, file=\"permutations.csv\", quote=F, row.names=F, col.names=F)\n", "meta": {"hexsha": "eaa69ab3972adce42d53ccb9aee0796a3da665af", "size": 281, "ext": "r", "lang": "R", "max_stars_repo_path": "benchmark/pophelper.r", "max_stars_repo_name": "ulilautenschlager/crimp", "max_stars_repo_head_hexsha": "9ea7a9c28523addde856096c93dab0cf2a310c7c", "max_stars_repo_licenses": ["MIT", "BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "benchmark/pophelper.r", "max_issues_repo_name": "ulilautenschlager/crimp", "max_issues_repo_head_hexsha": "9ea7a9c28523addde856096c93dab0cf2a310c7c", "max_issues_repo_licenses": ["MIT", "BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "benchmark/pophelper.r", "max_forks_repo_name": "ulilautenschlager/crimp", "max_forks_repo_head_hexsha": "9ea7a9c28523addde856096c93dab0cf2a310c7c", "max_forks_repo_licenses": ["MIT", "BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5454545455, "max_line_length": 90, "alphanum_fraction": 0.7081850534, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3082365543113072}} {"text": "#' Class shaq\n#' \n#' An S4 container for a distributed tall/skinny matrix.\n#' \n#' @details\n#' The (conceptual) global (non-distributed) matrix should be distributed by\n#' row, meaning that each submatrix should own all of the columns of the global\n#' matrix. Most methods assume no other real structure, however for best\n#' performance (and for the methods which require it), one should try to\n#' organize their distributed data in a particular way.\n#' \n#' First, adjacent MPI ranks should hold adjacent rows. So if the last row that\n#' rank \\code{k} owns is \\code{i}, then the first row that rank \\code{k+1} owns \n#' should be row \\code{i+1}. Additionally, any method that operates on two (or\n#' more) shaq objects, the two shaqs should be distributed identically. By this\n#' we mean that if the number of rows shaq \\code{A} owns on rank \\code{k} is\n#' \\code{k_i}, then the number of rows shaq \\code{B} owns on rank \\code{k}\n#' should also be \\code{k_i}.\n#' \n#' Finally, for best performance, one should generally try to keep the number of\n#' rows \"balanced\" (roughly equal) across processes, with perhaps the last \"few\"\n#' having one less row than the others.\n#' \n#' @slot DATA\n#' The local submatrix.\n#' @slot nrows, ncols\n#' The global matrix dimension.\n#' \n#' @seealso\n#' \\code{\\link{shaq}}\n#' \n#' @name shaq-class\n#' @docType class\nsetClass(\n Class=\"shaq\", \n representation=representation(\n Data=\"matrix\",\n nrows=\"numeric\",\n ncols=\"numeric\"\n # balanced=\"logical\"\n ),\n \n prototype=prototype(\n Data=matrix(nrow=0, ncol=0),\n nrows=0,\n ncols=0\n # balanced=NA\n )\n)\n", "meta": {"hexsha": "84318139c7748d85ca32d4fb5b13677348757943", "size": 1600, "ext": "r", "lang": "R", "max_stars_repo_path": "R/00-classes.r", "max_stars_repo_name": "cran/kazaam", "max_stars_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/00-classes.r", "max_issues_repo_name": "cran/kazaam", "max_issues_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/00-classes.r", "max_forks_repo_name": "cran/kazaam", "max_forks_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0, "max_line_length": 80, "alphanum_fraction": 0.69125, "num_tokens": 435, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3082365543113072}} {"text": "# Input the data file into a named table\ndata_times <- read.table(\"times.data\", sep=\",\", col.names=c(\"Siscs\",\"Steps\"))\n\n# Specify the output file and format and open it\npdf(\"times.pdf\")\n\n# Graph the table using blue points overlayed by a line\nplot(data_times, type=\"o\", col=\"blue\")\n\n# Create a title with a red, bold/italic font\ntitle(main=\"Number of steps needed\", col.main=\"red\", font.main=4)\n\n# Close the output device\ndev.off()\n\n# quit the system\nq()\n", "meta": {"hexsha": "68f02cfe97411648f2ed341f7481b98eded89007", "size": 455, "ext": "r", "lang": "R", "max_stars_repo_path": "hanoi/times.r", "max_stars_repo_name": "reDmiE/L3_prog1_projet0", "max_stars_repo_head_hexsha": "c174ff066cfbf72ffdd11479aad7d55fd3d0405c", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "hanoi/times.r", "max_issues_repo_name": "reDmiE/L3_prog1_projet0", "max_issues_repo_head_hexsha": "c174ff066cfbf72ffdd11479aad7d55fd3d0405c", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "hanoi/times.r", "max_forks_repo_name": "reDmiE/L3_prog1_projet0", "max_forks_repo_head_hexsha": "c174ff066cfbf72ffdd11479aad7d55fd3d0405c", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.2777777778, "max_line_length": 77, "alphanum_fraction": 0.7054945055, "num_tokens": 120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.3082365543113072}} {"text": "library(pROC)\n\nfn.poB = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_percent_of_B_filtered_day0.txt\")\nflow.poB = fread(fn.poB) %>% \n select(sample, matches(\"Gate\")) %>% \n dplyr::rename(CD38high = Gate3, \n CD38high.CD10pos = Gate2,\n CD38high.CD10neg = Gate1,\n CD38pos = Gate4)\n\n\nfn.info = file.path(PROJECT_DIR, \"generated_data\", \"CHI\", \"flow_sample_info_filtered_day0.txt\")\nflow.info = fread(fn.info) %>% \n mutate(subject = as.integer(subject))\n\nfn.titer = file.path(PROJECT_DIR, \"data\", \"CHI\", \"phenotypes\", \"titer_processed.txt\")\ndf.titer = fread(fn.titer) %>% \n # mutate(Subject = as.character(Subject)) %>%\n mutate(Response = ifelse(adjMFC_class==0, \"low\",\n ifelse(adjMFC_class==2, \"high\", \"middle\")))\n\nflow.titer = flow.info %>%\n left_join(df.titer %>% dplyr::select(Subject, Response), by=c(\"subject\"=\"Subject\")) %>% \n dplyr::filter(Response %in% c(\"low\",\"high\"),\n time %in% c(-7,0,70)) %>% \n mutate(Response = factor(Response,levels=c(\"low\",\"high\")))\n\n# get originally gated flow data\nfn.old = file.path(PROJECT_DIR, \"data\", \"CHI/flow/original_gates\", \"day0.log10.txt\")\nflow.old = read.table(fn.old, sep=\"\\t\", header=T, row.names=1, stringsAsFactors=F)\nflow.old = as.data.frame(t(flow.old)) %>% \n dplyr::select(ID87) %>% \n tibble::rownames_to_column(\"subject\") %>% \n mutate(subject=sub(\"X\",\"\",subject) %>% as.integer())\n\nflow = flow.poB %>% \n inner_join(flow.titer, by=\"sample\") %>% \n inner_join(flow.old, by=\"subject\")\n\npops = names(flow)[c(rev(2:5),ncol(flow))]\npops.name = pops\npops.name[1:4] = 1:4\npops.group = (1:3)[c(2,2,2,2,3)] %>% factor()\n\ndf.auc = data.frame(Population=factor(pops, levels=rev(pops)), group=pops.group, AUC=NA)\nfor (p in seq_along(pops)) {\n df.auc$AUC[p] = roc(flow[[\"Response\"]],flow[[pops[p]]], direction=\"<\", quiet = T)$auc\n}\n\nggplot(df.auc, aes(Population, AUC, fill=group)) + geom_bar(stat=\"identity\") +\n geom_hline(yintercept = 0.5, col=\"black\", lty=1, size=1) +\n scale_x_discrete(labels=rev(pops.name)) +\n xlab(\"\") +\n coord_flip() + \n theme_bw() + theme(legend.position=\"none\")\n\nfn.fig = file.path(PROJECT_DIR, \"figure_generation\", sprintf(\"CHI_flow_selected_gates_AUC\"))\nggsave(paste0(fn.fig, \".png\"), w=4,h=3)\nggsave(paste0(fn.fig, \".pdf\"), w=4,h=3)\n", "meta": {"hexsha": "bdb7612dc122b0cd427d604390897a05162d2695", "size": 2300, "ext": "r", "lang": "R", "max_stars_repo_path": "R/chi_flow_analysis/selected_gates_auc.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/chi_flow_analysis/selected_gates_auc.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/chi_flow_analysis/selected_gates_auc.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 38.3333333333, "max_line_length": 95, "alphanum_fraction": 0.6430434783, "num_tokens": 749, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.30817089838722245}} {"text": "library(ggsignif)\n\ndf.test = fread(\"results/test_sig.genes_in_clusters_high_vs_low_responders.txt\") %>% \n dplyr::filter(w.pv < 0.05, test.dir == \"IN\", test == \"SigScore\", \n clustering == \"K0\") %>% \n dplyr::mutate(sig.name = sig %>% \n str_replace(\"SLE.sig\", \"SLE-Sig\") %>% \n str_replace(\"CD40.act\", \"CD40act\") %>% \n str_replace(\"IFN26\", \"IFN-I-DCact\")) %>% \n dplyr::mutate(sig = factor(sig)) %>% \n dplyr::arrange(sig, cluster)\n\nclustering = 0\nsigs = levels(df.test$sig)\nsig.label = sigs %>% \n str_replace(\"SLE.sig\", \"SLE-Sig\") %>% \n str_replace(\"CD40.act\", \"CD40act\") %>% \n str_replace(\"IFN26\", \"IFN-I-DCact\") %>% \n setNames(sigs)\n\n\ndn.fig = \"figures/sig_test_boxplots\"\ndir.create(dn.fig, showWarnings = F, recursive = T)\n\nfor(s in sigs) {\n cat(s, \"\\n\")\nfn.score = glue::glue(\"results/sig_scores/scores_{s}_{clustering}.txt\")\ndf.sc = fread(fn.score, data.table = F) %>% \n dplyr::mutate(subject = factor(subject)) %>% \n gather(\"cluster\",\"score\", -subject) %>% \n left_join(df.subj, by=\"subject\") %>% \n dplyr::mutate(response = factor(response, levels=c(\"low\",\"high\"))) %>%\n dplyr::arrange(cluster, response) %>% \n dplyr::mutate(resp_cl = ifelse(response==\"low\", -0.15, 0.15))\n\ndf.txt = df.test %>% \n dplyr::filter(sig == s) %>% \n dplyr::mutate(x=-Inf, y=Inf, label=glue::glue(\"p = {format(w.pv, digits=2)}\"))\nyrng = c(-1, 1.55)\ncm = pals::glasbey(13)[c(1:3,5:9,13,11)]\ndotsz = diff(yrng)/(max(df.sc$score) - min(df.sc$score))\np = ggplot(df.sc, aes(response, score, group=response)) +\n coord_flip(ylim=yrng) + \n geom_boxplot(width=0.6, aes(fill=cluster), position = position_dodge2(preserve = \"total\"), lwd = 0.3, alpha = 0.5, outlier.shape = NA, show.legend = F) +\n geom_dotplot(aes(fill = response), binaxis = \"y\", stackdir = \"center\", dotsize = dotsz, \n position=position_dodge(), show.legend = F) +\n scale_fill_manual(values = c(\"black\", \"white\", \"white\")) +\n geom_text(aes(x, y, label=label), col=\"red\", hjust=1, vjust=-0.5, data=df.txt, inherit.aes = F) +\n xlab(\"\") + ylab(paste(sig.label[s], \"score\")) +\n theme_classic()\n\nggsave(glue::glue(\"{dn.fig}/sig_scores_box_{s}_K{clustering}.png\"), plot=p, w=2.5, h=1.2)\nggsave(glue::glue(\"{dn.fig}/sig_scores_box_{s}_K{clustering}.pdf\"), plot=p, w=2.5, h=1.2, useDingbats=F)\n}\n", "meta": {"hexsha": "fe0ce3d29172f860e4cff4d5fb20756bd1933068", "size": 2302, "ext": "r", "lang": "R", "max_stars_repo_path": "citeseq/R/sig_scores_boxplot_pseudobulk.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "citeseq/R/sig_scores_boxplot_pseudobulk.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "citeseq/R/sig_scores_boxplot_pseudobulk.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 41.8545454545, "max_line_length": 155, "alphanum_fraction": 0.6233709818, "num_tokens": 821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283035, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.3081708915197157}} {"text": "##' Rank forecasts by calculating an average across targets\n##'\n##' @param x a data frame with scored forecasts\n##' @return a data frame with ranked forecasts, and mean/sd of the weighted interval score\n##' @importFrom dplyr filter group_by mutate ungroup filter summarise\n##' @author Sebastian Funk\nrank_forecasts <- function(x) {\n ranked_forecasts <- x %>%\n group_by(value_type, ensemble_type, model) %>%\n mutate(min_date = min(creation_date)) %>%\n group_by(value_type) %>%\n mutate(max_min_date = max(min_date)) %>%\n ungroup() %>%\n filter(creation_date >= max_min_date) %>%\n group_by(ensemble_type, model, value_type, \n geography, creation_date, value_date) %>%\n summarise(score = mean(score), .groups = \"drop\") %>%\n group_by(ensemble_type, model) %>%\n summarise(mean = mean(score),\n sd = sd(score),\n .groups = \"drop\") %>%\n ungroup() %>%\n arrange(mean)\n\n return(ranked_forecasts)\n}\n", "meta": {"hexsha": "7f2890d8382fdd7d8771db0302949934dceae98b", "size": 960, "ext": "r", "lang": "R", "max_stars_repo_path": "R/rank_ensembles.r", "max_stars_repo_name": "epiforecasts/covid19.forecasts.uk", "max_stars_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-11-13T15:57:27.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-13T15:57:27.000Z", "max_issues_repo_path": "R/rank_ensembles.r", "max_issues_repo_name": "epiforecasts/covid19.forecasts.uk", "max_issues_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/rank_ensembles.r", "max_forks_repo_name": "epiforecasts/covid19.forecasts.uk", "max_forks_repo_head_hexsha": "5bc52a12e2b54b07759acf47af63fb6dd3d7b95f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.5555555556, "max_line_length": 90, "alphanum_fraction": 0.6541666667, "num_tokens": 242, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.30817089151971566}} {"text": "##\n## Figure 1: kernel density\n##\n\nrm(list = ls()) # This clears everything from memory.\nbefore0 <- proc.time()\nsetwd(\"~/Dropbox/BCI_Turnover\")\nload(\"BCI_turnover20150611.RData\")\nsource(\"~/Dropbox/MS/TurnoverBCI/TurnoverBCImain/source.R\")\n\nlibrary(ggplot2)\nlibrary(gridExtra)\nlibrary(dplyr)\ntheme_set(theme_bw())\n\n# - convex = concave\nkernel.20 <- data.frame(WSG=unlist(WSG20),\n convex= -unlist(convex20),\n slope=unlist(slope20),\n moist=unlist(Moist20),\n time=c(rep(1982,50),\n rep(1985,50),\n rep(1990,50),\n rep(1995,50),\n rep(2000,50),\n rep(2005,50),\n rep(2010,50)))\n\nkernel.100 <- data.frame(WSG=unlist(WSG100),\n #convex=unlist(convex.100.rare[[10]),\n #slope=unlist(slope.100.rare[[10]),\n convex= - unlist(convex100),\n slope=unlist(slope100),\n moist=unlist(Moist100),\n time=c(rep(1982,50),\n rep(1985,50),\n rep(1990,50),\n rep(1995,50),\n rep(2000,50),\n rep(2005,50),\n rep(2010,50)))\n\ntemp <- sapply(WSG.ind,length)\nk.ind.WSG <- data.frame(WSG=unlist(WSG.ind),\n time=c(rep(1982,temp[1]),\n rep(1985,temp[2]),\n rep(1990,temp[3]),\n rep(1995,temp[4]),\n rep(2000,temp[5]),\n rep(2005,temp[6]),\n rep(2010,temp[7])))\n\ntemp <- sapply(Moist.ind,length)\nk.ind.moist <- data.frame(moist=unlist(Moist.ind),\n time=c(rep(1982,temp[1]),\n rep(1985,temp[2]),\n rep(1990,temp[3]),\n rep(1995,temp[4]),\n rep(2000,temp[5]),\n rep(2005,temp[6]),\n rep(2010,temp[7])))\n# temp <- sapply(slope.ind.rare[[1]],length)\ntemp <- sapply(slope.ind,length)\nk.ind.slope <- data.frame(slope=unlist(slope.ind),\n time=c(rep(1982,temp[1]),\n rep(1985,temp[2]),\n rep(1990,temp[3]),\n rep(1995,temp[4]),\n rep(2000,temp[5]),\n rep(2005,temp[6]),\n rep(2010,temp[7])))\n\n# temp <- sapply(convex.ind.rare[[1]],length)\ntemp <- sapply(convex.ind, length)\nk.ind.convex <- data.frame(convex= - unlist(convex.ind),\n time=c(rep(1982,temp[1]),\n rep(1985,temp[2]),\n rep(1990,temp[3]),\n rep(1995,temp[4]),\n rep(2000,temp[5]),\n rep(2005,temp[6]),\n rep(2010,temp[7])))\n\n\n\ntemp100 <- kernel.100 %>%\n mutate(size = \"1ha\")\n\ntemp20 <- kernel.20 %>%\n mutate(size = \"0.04ha\")\n\nfig_dat <- bind_rows(temp100, temp20) %>%\n tidyr::gather(., \"trait\", \"val\", 1:4)\n\ntemp1 <- data_frame(time = k.ind.WSG$time,\n size = \"All individuals (50ha)\",\n trait = \"WSG\",\n val = k.ind.WSG[,1])\n\ntemp2 <- data_frame(time = k.ind.moist$time,\n size = \"All individuals (50ha)\",\n trait = \"moist\",\n val = k.ind.moist[,1])\n\ntemp3 <- data_frame(time = k.ind.convex$time,\n size = \"All individuals (50ha)\",\n trait = \"convex\",\n val = k.ind.convex[,1])\n\ntemp4 <- data_frame(time = k.ind.slope$time,\n size = \"All individuals (50ha)\",\n trait = \"slope\",\n val = k.ind.slope[,1])\n\nfig_dat2 <- bind_rows(fig_dat, temp1, temp2, temp3, temp4) %>%\n mutate(size = factor(size, levels = c(\"All individuals (50ha)\",\"1ha\", \"0.04ha\"))) %>%\n mutate(trait = factor(trait, levels = c(\"WSG\", \"moist\", \"convex\", \"slope\"))) %>%\n mutate(size2 = factor(size, labels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n mutate(trait2 = factor(trait, labels = c(\"Wood~density ~(g~cm^{-3})\", \"Moisture\", \"Concavity~(m)\", \"Slope~(degrees)\")))\n\n\ndummy1 <- data_frame(time = 1982,\n size = \"1ha\",\n trait = \"WSG\",\n val = c(0.55, 0.65))\n\ndummy2 <- data_frame(time = 1982,\n size = \"1ha\",\n trait = \"moist\",\n val = c(0.3, 0.9))\n\ndummy3 <- data_frame(time = 1982,\n size = \"All individuals (50ha)\",\n trait = \"moist\",\n val = c(-2, 4))\n\ndummy4 <- data_frame(time = 1982,\n size = \"1ha\",\n trait = \"slope\",\n val = c(4, 6))\n\ndummy5 <- data_frame(time = 1982,\n size = \"0.04ha\",\n trait = \"slope\",\n val = c(4, 6))\n\n\ndummy0 <- fig_dat2 %>%\n filter(., size != \"0.04ha\" | trait != \"convex\" | (val < -0.035 & val > -0.085)) %>%\n filter(., size != \"All individuals (50ha)\" | trait != \"convex\" | (val < 0.15 & val > -0.25)) %>%\n filter(., size != \"0.04ha\" | trait != \"slope\" | (val > 4 & val < 6)) %>%\n filter(., size != \"All individuals (50ha)\" | trait != \"slope\" | (val > 2 & val < 8))\n # filter(size != \"0.04ha\") %>%# new\n\ndummy <- bind_rows(dummy0, dummy1, dummy2, dummy3, dummy4, dummy5) %>%\n # filter(size != \"0.04ha\") %>%# new\n mutate(size = factor(size, levels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n mutate(trait = factor(trait, levels = c(\"WSG\", \"moist\", \"convex\", \"slope\"))) %>%\n mutate(size2 = factor(size, labels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n mutate(trait2 = factor(trait, labels = c(\"Wood~density ~(g~cm^{-3})\", \"Moisture\", \"Concavity~(m)\", \"Slope~(degrees)\")))\n\n\n###\n# small data for test\n###\n# dummy <- dummy %>% filter(size == \"1ha\")\n#\n# temp1 <- dummy %>% mutate(size = \"0.04ha\")\n# temp2 <- dummy %>% mutate(size = \"All individuals (50ha)\")\n#\n# dummy <- bind_rows(dummy, temp1, temp2) %>%\n# mutate(size = factor(size, levels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n# mutate(trait = factor(trait, levels = c(\"WSG\", \"moist\", \"convex\", \"slope\"))) %>%\n# mutate(size2 = factor(size, labels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n# mutate(trait2 = factor(trait, labels = c(\"Wood~density ~(g~cm^{-3})\", \"Moist\", \"Concavity~(m)\", \"Slope~(degrees)\")))\n#\n#\n#\n# dummy0 <- dummy0 %>% filter(size == \"1ha\")\n#\n# dummy0 <- bind_rows(dummy0, temp1, temp2) %>%\n# mutate(size = factor(size, levels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n# mutate(trait = factor(trait, levels = c(\"WSG\", \"moist\", \"convex\", \"slope\"))) %>%\n# mutate(size2 = factor(size, labels = c(\"All individuals (50ha)\", \"1ha\", \"0.04ha\"))) %>%\n# mutate(trait2 = factor(trait, labels = c(\"Wood~density ~(g~cm^{-3})\", \"Moist\", \"Concavity~(m)\", \"Slope~(degrees)\")))\n\n## need to redcued to 6inches\nbefore <- proc.time()\n#postscript(\"~/Dropbox/MS/TurnoverBCI/TurnoverBCI_MS/fig/fig1_new.eps\",\n# width = 9, height = 8, paper = \"special\")\n\npostscript(\"~/Desktop/fig1_new.eps\",\n width = 6, height = 5, paper = \"special\")\n\np <- ggplot(dummy0, aes(x = val)) +\n facet_wrap(~ size2 + trait2, nrow = 3, scale = \"free\",\n labeller = labeller(trait2 = label_parsed, sizes = label_value)) +\n geom_blank(data = dummy) +\n geom_density(data = dummy0 %>% filter(size != \"All individuals (50ha)\"), adjust = 1,\n aes(colour = as.factor(time))) +\n guides(colour = guide_legend(title = NULL), size = 21) +\n # guides(fill = guide_legend(override.aes = list(fill = as.factor(time)))) +\n theme(\n legend.position = c(0.99, 0.99), legend.justification = c(0.8,0.9),\n #legend.text = element_text(size = 9),\n legend.background = element_rect(fill=alpha('blue', 0)),\n legend.key.size = unit(0.3, \"cm\"),\n strip.text = element_text(size = 7, lineheight=0.5),\n axis.title = element_text(size = 7),\n axis.text.x = element_text(size = 5, angle = 45),\n axis.text.y = element_text(size = 5),\n legend.text = element_text(size = 7),\n plot.margin = unit(c(0.5, 0.2, 0.2 , 0.2), units = \"cm\")) +\n geom_density(data = filter(dummy0, size == \"All individuals (50ha)\"), adjust = 4, aes(colour = as.factor(time))) +\n ylab(\"Density\") +\n xlab(\"Trait values\")\n\ng <- ggplotGrob(p)\n\ng$heights\ng\nfor (i in c(6, 11, 16)) g$heights[[i]] <- unit(0.35, \"cm\")\nfor (i in c(62:73)) g$grobs[[i]]$heights <- unit(c(1,1), \"npc\")\n\ngrid.draw(g)\ndev.off()\nafter <- proc.time()\nafter - before\n# after - before0\n", "meta": {"hexsha": "92ad16149edc6f861edc598a7c9e161eabd420bc", "size": 8002, "ext": "r", "lang": "R", "max_stars_repo_path": "FigCode/Kernel_fig_ggplot.r", "max_stars_repo_name": "mattocci27/TurnoverBCImain", "max_stars_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FigCode/Kernel_fig_ggplot.r", "max_issues_repo_name": "mattocci27/TurnoverBCImain", "max_issues_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FigCode/Kernel_fig_ggplot.r", "max_forks_repo_name": "mattocci27/TurnoverBCImain", "max_forks_repo_head_hexsha": "cc3c0317243daa6e44c46d6fbc65d81e03f7405c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7913043478, "max_line_length": 121, "alphanum_fraction": 0.5472381905, "num_tokens": 2561, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.30817089151971566}} {"text": "pdf_file<-\"pdf/timeseries_stacked_areas.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=9,height=7)\n\nlibrary(plotrix)\nlibrary(gdata)\npar(mai=c(0.5,1.75,0,0.5),omi=c(0.5,0.5,0.8,0.5),family=\"Lato Light\",las=1)\n\n# Import data and prepare chart\n\nmyData<-read.xls(\"myData/Power_generation_Bavaria.xlsx\", encoding=\"latin1\")\n\nmyC1<-\"brown\"\nmyC2<-\"black\"\nmyC3<-\"grey\"\nmyC4<-\"forestgreen\"\nmyC5<-\"blue\"\nmyC6<-\"lightgoldenrod\"\n\nmyYears<-myData$Year\nmyData$Year<-NULL\nComplete<-myData$Complete\nmyData$Complete<-NULL\n\nfg_org<-par(\"fg\")\npar(fg=par(\"bg\"))\n\n\n# Create chart and other elements\n\nstackpoly(myData,main=\"\",xaxlab=rep(\"\", nrow(myData)),border=\"white\",stack=TRUE,col=c(myC1,myC2,myC3,myC4,myC5,myC6), axis2=F, ylim=c(0,95000))\nlines(Complete, lwd=4, col=\"lightgoldenrod4\")\npar(fg=fg_org)\nmtext(seq(1990,2010,by=5), side=1, at=seq(1,21,by=5), line=0.5)\nsegments(0.25,0,22.25,0,xpd=T)\nypos<-c(7000,12000,16000,24000,30500,55000)\nmyDes<-names(myData)\ntext(rep(0.5,6), ypos, myDes, xpd=T, adj=1)\n\n# Titling\n\nmtext(\"Gross electricity generation in Bavaria 1990-2011\",3,line=1.5,adj=0,family=\"Lato Black\",cex=1.75,outer=T)\nmtext(\"All values in mil. kWh, annual figures\",3,line=-0.2,adj=0,font=3,cex=1.25,outer=T)\nmtext(\"Source: www.statistik.bayern.de\",1,line=1,adj=1,cex=0.9,font=3,outer=T)\ndev.off()\n", "meta": {"hexsha": "26ae64b86d6886db52e5d281bfffcccbe17046e5", "size": 1287, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/timeseries_stacked_areas.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/timeseries_stacked_areas.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/timeseries_stacked_areas.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.6, "max_line_length": 143, "alphanum_fraction": 0.7202797203, "num_tokens": 524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.30817089151971566}} {"text": "#!/usr/bin/env Rscript\n\n# Parse the --file= argument out of command line args and\n# determine where base directory is so that we can source\n# our common sub-routines\narg0 <- sub(\"--file=(.*)\", \"\\\\1\", grep(\"--file=\", commandArgs(), value = TRUE))\ndir0 <- dirname(arg0)\nsource(file.path(dir0, \"common.r\"))\n\ntheme_set(theme_grey(base_size = 17))\n\n# Setup parameters for the script\nparams = matrix(c(\n 'help', 'h', 0, \"logical\",\n 'width', 'x', 2, \"integer\",\n 'height', 'y', 2, \"integer\",\n 'outfile', 'o', 2, \"character\",\n 'indir', 'i', 2, \"character\",\n 'tstart', '1', 2, \"integer\",\n 'tend', '2', 2, \"integer\",\n 'ylabel1stgraph', 'Y', 2, \"character\"\n ), ncol=4, byrow=TRUE)\n\n# Parse the parameters\nopt = getopt(params)\n\nif (!is.null(opt$help))\n {\n cat(paste(getopt(params, command = basename(arg0), usage = TRUE)))\n q(status=1)\n }\n\n# Initialize defaults for opt\nif (is.null(opt$width)) { opt$width = 5120 }\nif (is.null(opt$height)) { opt$height = 6000 }\nif (is.null(opt$indir)) { opt$indir = \"current\"}\nif (is.null(opt$outfile)) { opt$outfile = file.path(opt$indir, \"summary.png\") }\nif (is.null(opt$ylabel1stgraph)) { opt$ylabel1stgraph = \"Op/sec\" }\n\n# Load the benchmark data, passing the time-index range we're interested in\nb = load_benchmark(opt$indir, opt$tstart, opt$tend)\n\n# If there is no actual data available, bail\nif (nrow(b$latencies) == 0)\n{\n stop(\"No latency information available to analyze in \", opt$indir)\n}\n\npng(file = opt$outfile, width = opt$width, height = opt$height, res = 300)\n\n# First plot req/sec from summary\nplot_throughput <- qplot(elapsed, successful / window, data = b$summary,\n geom = c(\"smooth\", \"point\"),\n xlab = \"Elapsed Secs\", ylab = opt$ylabel1stgraph,\n main = \"Throughput\") +\n\n geom_smooth(aes(y = successful / window, colour = \"ok\"), size=0.5) +\n geom_point(aes(y = successful / window, colour = \"ok\"), size=2.0) +\n\n scale_colour_manual(\"Response\", values = c(\"#188125\"))\n\nplot_error <- qplot(elapsed, failed / window, data = b$summary,\n geom = c(\"smooth\", \"point\"),\n xlab = \"Elapsed Secs\", ylab = opt$ylabel1stgraph,\n main = \"Errors\") +\n\n geom_smooth(aes(y = failed / window, colour = \"error\"), size=0.5) +\n geom_point(aes(y = failed / window, colour = \"error\"), size=2.0) +\n\n scale_colour_manual(\"Response\", values = c(\"#FF665F\"))\n\nplot_sessions_running <- qplot(elapsed, running, data = b$sessions,\n geom = c(\"point\"),\n xlab = \"Elapsed Secs\", ylab = \"# sessions\",\n main = \"Sessions Running\") +\n\n geom_point(aes(y = running, colour = \"running\"), size=2.0) +\n geom_line( aes(y = running, colour = \"running\"), size=0.1) +\n\n scale_colour_manual(\"Starts\", values = c(\"#000000\"))\n\nplot_sessions_started <- qplot(elapsed, starts / window, data = b$sessions,\n geom = c(\"point\"),\n xlab = \"Elapsed Secs\", ylab = opt$ylabel1stgraph,\n main = \"Sessions Started\") +\n\n geom_point(aes(y = starts / window, colour = \"starts\"), size=2.0) +\n geom_line( aes(y = starts / window, colour = \"starts\"), size=0.1) +\n\n scale_colour_manual(\"Starts\", values = c(\"#188125\"))\n\n# Setup common elements of the latency plots\nlatency_plot <- ggplot(b$latencies, aes(x = elapsed)) +\n facet_grid(. ~ op) +\n labs(x = \"Elapsed Secs\", y = \"Latency (ms)\")\n\n# Plot median, mean and 95th percentiles\nplot_median_mean <- latency_plot + labs(title = \"Mean and Median Latency\") +\n geom_smooth(aes(y = mean, color = \"mean\"), size=0.5) +\n geom_point(aes(y = mean, color = \"mean\"), size=2.0) +\n\n geom_smooth(aes(y = median, color = \"median\"), size=0.5) +\n geom_point(aes(y = median, color = \"median\"), size=2.0) +\n\n scale_colour_manual(\"Percentile\", values = c(\"#FFA700\", \"#188125\"))\n # scale_color_hue(\"Percentile\",\n # breaks = c(\"X95th\", \"mean\", \"median\"),\n # labels = c(\"95th\", \"Mean\", \"Median\"))\n\n# Plot median, mean and 95th percentiles\nplot_95 <- latency_plot + labs(title = \"95th Percentile Latency\") +\n geom_smooth(aes(y = X95th, color = \"95th\"), size=0.5) +\n geom_point(aes(y = X95th, color = \"95th\"), size=2.0) +\n\n scale_colour_manual(\"Percentile\", values = c(\"#FF665F\", \"#009D91\"))\n # scale_color_hue(\"Percentile\",\n # breaks = c(\"X95th\", \"mean\", \"median\"),\n # labels = c(\"95th\", \"Mean\", \"Median\"))\n\n# Plot 99th percentile\nplot_99 <- latency_plot + labs(title = \"99th Percentile Latency\") +\n geom_smooth(aes(y = X99th, color = \"99th\"), size=0.5) +\n geom_point(aes(y = X99th, color = \"99th\"), size=2.0) +\n scale_colour_manual(\"Percentile\", values = c(\"#FF665F\", \"#009D91\"))\n # scale_color_hue(\"Percentile\",\n # breaks = c(\"X99_9th\",\"X99th\" ),\n # labels = c(\"99.9th\", \"99th\"))\n\n# Plot 99.9th percentile\nplot_999 <- latency_plot + labs(title = \"99.9th Percentile Latency\") +\n geom_smooth(aes(y = X99_9th, color = \"99.9th\"), size=0.5) +\n geom_point(aes(y = X99_9th, color = \"99.9th\"), size=2.0) +\n scale_colour_manual(\"Percentile\", values = c(\"#FF665F\", \"#009D91\", \"#FFA700\"))\n\n# Plot 100th percentile\nplot_max <- latency_plot + labs(title = \"Maximum Latency\") +\n geom_smooth(aes(y = max, color = \"max\"), size=0.5) +\n geom_point(aes(y = max, color = \"max\"), size=2.0) +\n scale_colour_manual(\"Percentile\", values = c(\"#FF665F\", \"#009D91\", \"#FFA700\"))\n\nplot_upper_percentiles <- latency_plot + labs(title = \"95th, 99th, 99.9th and 100th Percentile\") +\n geom_smooth(aes(y = X95th, color = \"95th\"), size=0.5) +\n geom_point(aes(y = X95th, color = \"95th\"), size=2.0) +\n geom_smooth(aes(y = X99th, color = \"99th\"), size=0.5) +\n geom_point(aes(y = X99th, color = \"99th\"), size=2.0) +\n geom_smooth(aes(y = X99_9th, color = \"99.9th\"), size=0.5) +\n geom_point(aes(y = X99_9th, color = \"99.9th\"), size=2.0) +\n geom_smooth(aes(y = max, color = \"max\"), size=0.5) +\n geom_point(aes(y = max, color = \"max\"), size=2.0) +\n scale_colour_manual(\"Percentile\", values = c(\"#FFFE00\", \"#FFA900\", \"#FF5600\", \"#FF0000\"))\n\ngrid.newpage()\n\npushViewport(viewport(layout = grid.layout(6, 1)))\n\nvplayout <- function(x,y) viewport(layout.pos.row = x, layout.pos.col = y)\n\nprint(plot_throughput, vp = vplayout(1,1))\nprint(plot_error, vp = vplayout(2,1))\nprint(plot_sessions_running, vp = vplayout(3,1))\nprint(plot_sessions_started, vp = vplayout(4,1))\nprint(plot_median_mean, vp = vplayout(5,1))\nprint(plot_upper_percentiles, vp = vplayout(6,1))\n\ndev.off()\n", "meta": {"hexsha": "1848a1a767ffd6f847d6ad8f97e0ababc54de98e", "size": 7049, "ext": "r", "lang": "R", "max_stars_repo_path": "priv/summary.r", "max_stars_repo_name": "odo/ramjet", "max_stars_repo_head_hexsha": "e0453169abf574f81045c88bd1a43c143d977049", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2015-03-03T14:37:24.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-19T16:01:19.000Z", "max_issues_repo_path": "priv/summary.r", "max_issues_repo_name": "odo/ramjet", "max_issues_repo_head_hexsha": "e0453169abf574f81045c88bd1a43c143d977049", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "priv/summary.r", "max_forks_repo_name": "odo/ramjet", "max_forks_repo_head_hexsha": "e0453169abf574f81045c88bd1a43c143d977049", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2015-04-17T07:59:46.000Z", "max_forks_repo_forks_event_max_datetime": "2018-06-06T13:44:41.000Z", "avg_line_length": 42.4638554217, "max_line_length": 101, "alphanum_fraction": 0.5739821251, "num_tokens": 2051, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30807914889316823}} {"text": "\n\n#' @export\ndefine_latent_growth_curve_model = function(df, variable_of_interest_name, model_type, model_strength = \"CONFIGURAL\", unconstrained_parcel_indices = c(), slope_weights=c(), time_invariant_covariate_name = \"\", is_multiple_group_model = FALSE){\n slope_weights_provided = length(slope_weights) > 0\n slope_weights_to_pass = if(slope_weights_provided) {\n slope_weights\n } else {\n base=c(0)\n tail = sapply(2:length(df[,1]), function(e) {NA})\n append(base,tail)\n base\n }\n\n is_weak <- model_strength == \"WEAK\"\n is_strong <- model_strength == \"STRONG\" || model_strength == \"PARTIAL_STRONG\"\n is_strict <- model_strength == \"STRICT\"\n\n include_slope = model_type == \"LINEAR\" || model_type == \"LATENT_BASIS\"\n should_set_time_invariant_covariate <- time_invariant_covariate_name != \"\"\n paste0(\n \"# Define factors\\n\\n\",\n define_factors(df, is_weak || is_strong || is_strict),\n \"\\n\\n# Intercepts\\n\",\n define_intercepts(df, is_strong || is_strict, unconstrained_parcel_indices),\n \"\\n\\n# Unique variances and covariances\\n\",\n define_variances(df, is_strict),\n \"\\n\\n\",\n define_covariances(df),\n \"\\n\\n# Latent variable means\\n\",\n define_latent_variable_means(df, TRUE),\n \"\\n\\n# Latent variable variances and covariances\\n\",\n define_latent_variable_variances(df),\n \"\\n\\n# Level factor loadings\\n\",\n define_level_factor(df, variable_of_interest_name),\n if(include_slope) \"\\n\\n# Slope factor loadings\\n\" else \"\",\n if(include_slope) define_slope_factor(df, variable_of_interest_name, slope_weights_to_pass) else \"\",\n \"\\n\\n# Means\\n\",\n define_means(variable_of_interest_name, include_slope, is_multiple_group_model),\n \"\\n\\n# Variances\\n\",\n define_level_slope_variances(variable_of_interest_name, include_slope, is_multiple_group_model),\n if(should_set_time_invariant_covariate) \"\\n\\n# Time invariant covariate\\n\" else \"\",\n if(should_set_time_invariant_covariate) set_time_invariant_covariate(variable_of_interest_name, time_invariant_covariate_name, include_slope) else \"\"\n )\n}\n", "meta": {"hexsha": "f07f48ce9ffb11ea4b88ff5bc66f01658219162a", "size": 2058, "ext": "r", "lang": "R", "max_stars_repo_path": "R/latent_growth_curve_model.r", "max_stars_repo_name": "epf02013/r2sem", "max_stars_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/latent_growth_curve_model.r", "max_issues_repo_name": "epf02013/r2sem", "max_issues_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/latent_growth_curve_model.r", "max_forks_repo_name": "epf02013/r2sem", "max_forks_repo_head_hexsha": "848a379f44cc1a28ae6d085de0267e629c3b4f91", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.7391304348, "max_line_length": 242, "alphanum_fraction": 0.739552964, "num_tokens": 525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.611381973294151, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3080791488931682}} {"text": "# The MIT License (MIT)\n# Copyright (c) 2017 Louise AC Millard, MRC Integrative Epidemiology Unit, University of Bristol\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without\n# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\n# the Software, and to permit persons to whom the Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions\n# of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\n# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\n# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\n\n# Tests an unordered categorical phenotype with multinomial regression\n# and saves this result in the multinomial logistic results file\ntestCategoricalUnordered <- function(varName, currentVar, varType, thisdata) {\n\n\tpheno = thisdata[,phenoStartIdx:ncol(thisdata)]\n\t#geno = thisdata[,\"geno\"]\n\n\tnumNotNA = length(which(!is.na(pheno)))\n\tif (numNotNA<500) {\n\t\tcat(\"CATUNORD-SKIP-500 (\", numNotNA, \") || \",sep=\"\");\n\t\tincrementCounter(\"unordCat.500\")\n\t}\n\telse {\n\n\t\t# check there are not too many levels and skip if there are\n\t\tnumUnique = length(unique(na.omit(pheno)))\n\n\t\t# num outcome values * (num confounders and trait of interest and bias term)\n\t\tnumWeights=(numUnique-1)*((numPreceedingCols-2)+1+1)\n\t\tif (numWeights>1000) {\n\t\t\tcat(\"Too many weights in model: \", numWeights, \" > 1000, (num outcomes values: \", numUnique, \") || SKIP \", sep=\"\")\n\t\t\tincrementCounter(\"unordCat.cats\")\n\t\t\treturn(NULL)\n\t\t}\n\n\t\tphenoFactor = chooseReferenceCategory(pheno);\n\n\t\tif (opt$save == TRUE) {\n\t\t\t# add pheno to dataframe\n\t\t\t#storeNewVar(thisdata[,\"userID\"], phenoFactor, varName, 'catUnord')\n\t\t\tstoreNewVar(thisdata[,\"userID\"], phenoFactor, currentVar, 'catUnord')\n\t\t\tcat(\"SUCCESS results-notordered-logistic \");\n\t\t\tincrementCounter(\"success.unordCat\")\n\t\t}\n\n\n\t\treference = levels(phenoFactor)[1];\n\n\t\tsink()\n\t\tsink(modelFitLogFile, append=TRUE) # hide output of model fitting\n\t\tprint(\"--------------\")\n\t\tprint(varName)\n\n\t\trequire(nnet)\n\t\tif (opt$standardise==TRUE) {\n\t\t\tgeno = scale(thisdata[,\"geno\"])\n\t\t} else {\n\t\t\tgeno = thisdata[,\"geno\"]\n\t\t}\n\t\t#cat(\"genoMean=\", mean(geno), \" genoSD=\", sd(geno), \" || \", sep=\"\")\n\n\t\tconfounders=thisdata[,3:numPreceedingCols, drop = FALSE]\n\n\t\t######\u00a0BEGIN TRYCATCH\n\t\ttryCatch({\n\n\t\t\tfit <- multinom(phenoFactor ~ geno + ., data=confounders, maxit=1000)\n\n\t\t\t## baseline model with only confounders, to which we compare the model above\n\t\t\tfitB <- multinom(phenoFactor ~ ., data=confounders, maxit=1000)\n\n\t\t\t## compare model to baseline model\n\t\t\trequire(lmtest)\n\t\t\tlres = lrtest(fit, fitB)\n\t\t\tmodelP = lres[2,\"Pr(>Chisq)\"];\n\n\t\t\t## save result to file\n\t\t\tmaxFreq = length(which(phenoFactor==reference));\n\t\t\tnumNotNA = length(which(!is.na(pheno)))\n\t\t\twrite(paste(paste0(\"\\\"\",varName,\"-\",reference,\"\\\"\"), paste0(\"\\\"\", currentVar, \"\\\"\"), varType, paste(maxFreq,\"/\",numNotNA,sep=\"\"), -999, -999, -999, modelP, sep=\",\"), file=paste(opt$resDir,\"results-multinomial-logistic-\",opt$varTypeArg,\".txt\",sep=\"\"), append=\"TRUE\")\n\n\t\t\tsink()\n\t\t\tsink(resLogFile, append=TRUE)\n\n\t\t\tsumx <- summary(fit)\n\n\t\t\tz <- sumx$coefficients/sumx$standard.errors\n\t\t\tp = (1 - pnorm(abs(z), 0, 1))*2\n\n\t\t\tci <- confint(fit, \"geno\", level=0.95)\n\t\t\tci = data.frame(ci)\n\n\t\t\t## get result for each variable category\n\t\t\tuniqVar = unique(na.omit(pheno))\n\t\t\tfor (u in uniqVar) {\n\n\t\t\t\t## no coef for baseline value, and values <0 are assumed to be missing\n\t\t\t\tif (u == reference || u<0) {\n\t\t\t\t\tnext\n\t\t\t\t}\n\n\t\t\t\tpvalue = p[paste(eval(u),sep=\"\"),\"geno\"]\n\t\t\t\tbeta = sumx$coefficients[paste(eval(u),sep=\"\"),\"geno\"]\n\n\t\t\t\tif (opt$confidenceintervals == TRUE) {\n\t\t\t\t\tlower = ci[1, paste(\"X2.5...\", u, sep=\"\")]\n\t\t\t\t\tupper =\tci[1, paste(\"X97.5...\", u, sep=\"\")]\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tlower = NA\n\t\t\t\t\tupper = NA\n\t\t\t\t}\n\n\t\t\t\tnumThisValue = length(which(phenoFactor==u));\n\n\t\t\t\t## save result to file\n\t\t\t\twrite(paste(paste(\"\\\"\", varName,\"-\",reference,\"#\",u,\"\\\"\", sep=\"\"), paste0(\"\\\"\", currentVar, \"\\\"\"), varType, paste(maxFreq,\"#\",numThisValue,sep=\"\"), beta, lower, upper, pvalue, sep=\",\"), '')\n\t\t\t\t#write(paste(paste(\"\\\"\", varName,\"-\",reference,\"#\",u,\"\\\"\", sep=\"\"), paste0(\"\\\"\", currentVar, \"\\\"\"), varType, paste(maxFreq,\"#\",numThisValue,sep=\"\"), beta, lower, upper, pvalue, sep=\",\"), file=paste(opt$resDir,\"results-multinomial-logistic-\",opt$varTypeArg,\".txt\",sep=\"\"), append=\"TRUE\")\n\t\t\t}\n\n\t\t\tcat(\"SUCCESS results-notordered-logistic \");\n\t\t\tincrementCounter(\"success.unordCat\")\n\n\t\t\tisExposure = getIsExposure(varName)\n\t\t\tif (isExposure == TRUE) {\n\t\t\t\tincrementCounter(\"success.exposure.unordCat\")\n\t\t\t}\n\n\t\t\t## END TRYCATCHf\n\t\t}, error = function(e) {\n\t\t\tsink()\n\t\t\tsink(resLogFile, append=TRUE)\n\t\t\tcat(paste(\"ERROR:\", varName,gsub(\"[\\r\\n]\", \"\", e), sep=\" \"))\n\t\t\tincrementCounter(\"unordCat.error\")\n\t\t})\n\n\t}\n}\n\n# find reference category - category with most number of examples\nchooseReferenceCategory <- function(pheno) {\n\n\tuniqVar = unique(na.omit(pheno));\n\tphenoFactor = factor(pheno)\n\n\tmaxFreq=0;\n\tmaxFreqVar = \"\";\n\tfor (u in uniqVar) {\n\t\twithValIdx = which(pheno==u)\n\t\tnumWithVal = length(withValIdx);\n\t\tif (numWithVal>maxFreq) {\n\t\t\tmaxFreq = numWithVal;\n\t\t\tmaxFreqVar = u;\n\t\t}\n\t}\n\n\tcat(\"reference: \", maxFreqVar,\"=\",maxFreq, \" || \", sep=\"\");\n\n\t## choose reference (category with largest frequency)\n\tphenoFactor <- relevel(phenoFactor, ref = paste(\"\",maxFreqVar,sep=\"\"))\n\n\treturn(phenoFactor);\n}\n\n\n\n\n", "meta": {"hexsha": "e8a133a97640d365659222aca753da1c7af07454", "size": 5931, "ext": "r", "lang": "R", "max_stars_repo_path": "WAS/testCategoricalUnordered.r", "max_stars_repo_name": "kevmanderson/PHESANT", "max_stars_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "WAS/testCategoricalUnordered.r", "max_issues_repo_name": "kevmanderson/PHESANT", "max_issues_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "WAS/testCategoricalUnordered.r", "max_forks_repo_name": "kevmanderson/PHESANT", "max_forks_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.5084745763, "max_line_length": 290, "alphanum_fraction": 0.6705445962, "num_tokens": 1730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6113819591324416, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30807914175699547}} {"text": "#' Metacell layout using force directed projection of a low degree mc graph\n#'\n#' @param mc2d_id 2d object to add\n#' @param mc_id meta cell id to work with\n#' @param graph_id graph_id of the similarity graph on cells from the metacell\n#' @param symetrize should the mc confusion matrix be symmetrized before computing layout?\n#' @param ignore_mismatch\n#' @param feats_gset gene set name for use in parametric graph and/or umap 2d projection\n#' @param feats_exclude list of genes to exclude from the features gene set\n#' @param graph_parametric if this is True, mc edges will be determined using parametric distances and not the cells k-nn graph.\n#' @param logist_loc the \"location\" parametr of the logistic function used to determine parametric distances between metacelles\n#' @param logist_scale the \"lscale\" parametr of the logistic function used to determine parametric distances between metacelles\n#'\n#' @export\nmcell_mc2d_force_knn = function(mc2d_id, mc_id, graph_id, \n\t\t\tignore_mismatch=F, symmetrize=F, \n\t\t\tignore_edges = NULL, \n\t\t\tfeats_gset = NULL, \n\t\t\tfeats_exclude =NULL,\n\t\t\tgraph_parametric = F, logist_loc = 1, logist_scale = 0.2, \n\t\t\tlogist_eps = 4e-5, max_d_fold = 3,\n\t\t\tuse_umap_for_2d_pos=F, umap_mgraph=F,\n\t\t\tuconf=NULL)\n{\n\tmc = scdb_mc(mc_id)\n\tif (is.null(mc)) {\n\t\tstop(sprintf(\"mc %s not found\"), mc_id)\n\t}\n\tfeat_genes = NULL\n\tif(!is.null(feats_gset)) {\n\t\tgset = scdb_gset(feats_gset)\n\t\tif(is.null(gset)) {\n\t\t\tstop(\"Unkown gset \", feats_gset, \" defined in mc2d graph construction\")\n\t\t}\n\t\tfeat_genes = names(gset@gene_set)\n\t\tif(!is.null(feat_genes)) {\n\t\t\tfeat_genes = setdiff(feat_genes, feats_exclude)\n\t\t}\n\t\tmessage(\"got \", length(feat_genes), \" feat genes for mc graph construction\")\n\t}\n\n\tif(!graph_parametric) {\n\t\tmgraph = mc2d_comp_mgraph(mc_id, graph_id, ignore_mismatch=ignore_mismatch, symmetrize=symmetrize)\n\t} else {\n\t\tif(is.null(feat_genes)) {\n\t\t\tstop(\"must specify feat gene set to build parametric mc2d graph\")\n\t\t}\n\t\tmgraph = mc2d_comp_mgraph_param(mc, feat_genes, logist_loc, logist_scale, logist_eps, max_d_fold)\n\t}\n\tif(!is.null(ignore_edges)) {\n\t\tall_e = paste(mgraph$mc1, mgraph$mc2, sep=\"-\")\n\t\tig_e = paste(ignore_edges$mc1, ignore_edges$mc2, sep=\"-\")\n\t\tig_re = paste(ignore_edges$mc2, ignore_edges$mc1, sep=\"-\")\n\t\tf = all_e %in% c(ig_e,ig_re)\n\t\tmgraph= mgraph[!f,]\n\t\tmessage(\"igoring \", sum(f), \" edges\")\n\t}\n\tif(use_umap_for_2d_pos) {\n\t\tif(is.null(feat_genes)) {\n\t\t\tstop(\"Specifiy a gene set for layout using umap on mc egc\")\n\t\t}\n\t\tif(is.null(uconf)) {\n\t\t\t#uconf = umap.defaults\n\t\t\tuconf = \n\t\t\tuconf$n_neighbors=6\n\t\t\tuconf$min_dist=0.9\n\t\t\tuconf$bandwidth=1.3\n\t\t}\n\t\tmc_xy = mc2d_comp_graph_coord_umap(mc, feat_genes, mgraph, uconf, umap_mgraph)\n\t} else {\n\t\tmc_xy = mc2d_comp_graph_coord(mgraph, N=ncol(mc@mc_fp))\n\t}\n\txy = mc2d_comp_cell_coord(mc_id, graph_id, mgraph, mc_xy, symmetrize=symmetrize)\n\tscdb_add_mc2d(mc2d_id, tgMC2D(mc_id, mc_xy$mc_x, mc_xy$mc_y, xy$x, xy$y, mgraph))\n}\n\n#' Compute cells 2d coordinates based on the mc graph when the mc coordinates are supplied externally \n#'\n#' @param mc2d_id 2d object to add\n#' @param mc_id meta cell id to work with\n#' @param graph_id graph_id of the similarity graph on cells from the metacell\n#' @param mc_xy pre-defined metacells coordinates (so only coordinates of cells will be computed). Data frame with 2 columns named mc_x and mc_y (x and y coodinates, respectively). \n#' @param ignore_mismatch\n#'\n#' @export\nmcell_mc2d_force_knn_on_cells = function(mc2d_id, mc_id, graph_id, mc_xy, ignore_mismatch=F)\n{\n\tmgraph = mc2d_comp_mgraph(mc_id, graph_id, ignore_mismatch=ignore_mismatch)\n\txy = mc2d_comp_cell_coord(mc_id, graph_id, mgraph, mc_xy)\n\tscdb_add_mc2d(mc2d_id, tgMC2D(mc_id, mc_xy$mc_x, mc_xy$mc_y, xy$x, xy$y, mgraph))\n}\n\n#' @export\nmc2d_comp_mgraph = function(mc_id, graph_id, ignore_mismatch=F, symmetrize=F)\n{\n\tmc2d_K = get_param(\"mcell_mc2d_K\")\n\tmc2d_T_edge = get_param(\"mcell_mc2d_T_edge\")\n\tmc2d_max_confu_deg = get_param(\"mcell_mc2d_max_confu_deg\")\n\tmc2d_edge_asym = get_param(\"mcell_mc2d_edge_asym\")\n\tmc2d_k_expand_inout_factor = get_param(\"mcell_mc2d_expand_inout_factor\")\n\tmc2d_max_fpcor_indeg = get_param(\"mcell_mc2d_max_fpcor_indeg\")\n\tmc2d_max_fpcor_outdeg = get_param(\"mcell_mc2d_max_fpcor_outdeg\")\n\n\tif(is.null(mc2d_max_confu_deg) & is.null(mc2d_max_fpcor_outdeg)) {\n\t\tstop(\"MC-ERR: Either max_confu_deg or max_fpcor_deg must be defined - currently both are null\")\n\t}\n\n\tgraph = scdb_cgraph(graph_id)\n\tif(is.null(graph)) {\n\t\tstop(\"MC-ERR: cell graph id \", graph_id, \" is missing when mc 2d projection\")\n\t}\n\tmc = scdb_mc(mc_id)\n\tif(is.null(mc)) {\n\t\tstop(\"MC-ERR: mc id \", mc_id, \" is missing when running add_mc_from_graph\")\n\t}\n\trestrict_in_degree = T\n\n\tif(!is.null(mc2d_max_confu_deg)) {\n\n\t\tmessage(\"comp mc graph using the graph \", graph_id, \" and K \", mc2d_K)\n\t\tconfu = mcell_mc_confusion_mat(mc_id, graph_id, mc2d_K, \n\t\t\t\t\t\t\t\t\t\t\t\t\tignore_mismatch=ignore_mismatch)\n\t\tif(symmetrize) {\n\t\t\tconfu =confu + t(confu)\n\t\t}\n# k_expand_inout_factor=k_expand_inout_factor\n\n\t\tcsize = as.matrix(table(mc@mc))\n\t\tcsize = pmax(csize, 20)\n\t\tcsize2 = csize %*% t(csize)\n\t\tcsize2 = csize2 / median(csize)**2\n\t\tconfu = confu / csize2\n\n\t\tconfu_p_from = confu/rowSums(confu)\n\t\tconfu_p_to = t(confu)/colSums(confu)\n\n\t\tif(!is.null(mc2d_max_confu_deg)) {\n\t\t\trank_fr = t(apply(confu_p_from, 1, rank))\n\t\t\trank_to = t(apply(confu_p_to, 1, rank))\n\t\t\trank2 = rank_fr * rank_to\n\t\t\tdiag(rank2) = 1e+6\n\t\t\tamgraph = apply(rank2, 1, function(x) { rank(-x) <= (1+mc2d_max_confu_deg) })\n\t\t\tmgraph = amgraph * ((confu_p_from + confu_p_to) > mc2d_T_edge)\n\t\t\tif(restrict_in_degree) {\n\t\t\t\tamgraph2 = t(apply(rank2, 2, function(x) { rank(-x) <= (1+mc2d_max_confu_deg) }))\n\t\t\t\tmgraph = mgraph * amgraph2\n\t\t\t}\n\n\t\t\tif(mc2d_edge_asym) {\n\t\t\t\tmgraph = amgraph * (confu_p_from>mc2d_T_edge)\n\t\t\t\tmgraph = amgraph * (t(confu_p_to)>mc2d_T_edge)\n\t\t\t}\n\t\t\tmgraph = mgraph>0 | t(mgraph>0)\n\t\t} else {\n\t\t\tmgraph = (confu_p_from + confu_p_to) > mc2d_T_edge\n\t\t}\n\t}\n\n\tif(!is.null(mc2d_max_fpcor_outdeg)) {\n\t\tf = apply(abs(log2(mc@mc_fp)),1,max)>0.5\n\t\tfp_cor = tgs_cor(log2(mc@mc_fp[f,]))\n\t\tfp_rnk = t(apply(-fp_cor,1,function(x) rank(x)<=(mc2d_max_fpcor_outdeg+1)))\n\t\tfp_rnk_in = apply(-fp_cor,1,function(x) rank(x)<=(1+mc2d_max_fpcor_indeg))\n\t\tif(!is.null(mc2d_max_confu_deg)) {\n\t\t\t\t mgraph = mgraph * fp_rnk * fp_rnk_in\n\t\t} else {\n\t\t\t\t mgraph = fp_rnk * fp_rnk_in\n\t\t}\n\t}\n\n\tN = nrow(mgraph)\n\te = which(mgraph>0)\n\tn1 = ceiling((e)/N)\n\tn2 = 1+((e-1) %% N)\n\treturn(data.frame(mc1 = n1, mc2 = n2))\n}\n\n#' @export\nmc2d_comp_mgraph_param = function(mc, genes, loc, scale, eps, max_d_fold)\n{\n\tmax_deg = get_param(\"mcell_mc2d_max_confu_deg\")\n\tlegc = log2(mc@e_gc[genes,] + eps)\n\n\tlogist_d = function(x) {\n\t\td = abs(legc - x)\n\t\td = plogis(d, loc, scale)\n\t\treturn(colSums(d))\n\t}\n\ta = apply(legc, 2, logist_d) \n\n#connect - d-best outgoing. filter by d_best_ratio < 2\n\tdiag(a) = 1000;\n\td_T = apply(a, 1, function(x) sort(x,partial=2)[2])\n\ta_n = a/d_T\n\tdiag(a) = 0;\n\tdiag(a_n) = 0;\n\n rank_fr = t(apply(a, 1, rank))\n rank_fr_m = rank_fr\n rank_fr_m[a_n > max_d_fold] = 1000\n rank_fr_m2 = rank_fr_m\n rank_fr_m2[t(a_n) > max_d_fold] = 1000\n\n edges = apply(rank_fr_m2, 1, function(x) {\n mc2 = which(x > 0 & x <= max_deg+1);\n mc1 = rep(which.min(x), length(mc2));\n return(data.frame(mc1 = mc1, mc2=mc2)) })\n ed_df = as.data.frame(do.call(rbind, edges))\n\ted_df$dist = apply(ed_df, 1, function(x) 1+a[x[1],x[2]])\n\treturn(ed_df)\n}\n\n#' @export\nmc2d_comp_graph_coord_umap = function(mc, genes, mgraph, uconf, use_mgraph)\n{\n\tlegc = log2(mc@e_gc[genes,] + 1e-5)\n#\tum = umap(t(legc), uconf)\nif(0) {\n\tum = umap(t(legc), n_neighbors=uconf$n_neighbors, min_dist=uconf$min_dist,bandwidth=uconf$bandwidth, local_connectivity=uconf$local_connectivity, metric=\"cosine\")\n\treturn(list(mc_x=um[,1], mc_y=um[,2]))\n} else {\n\tif(use_mgraph) {\n\t\tm = sparseMatrix(mgraph$mc1, mgraph$mc2, x=1/mgraph$dist)\n\t\tm = as.matrix(m)\n\t\tdiag(m) = 0\n\t\tm = m/(0.5+rowSums(m))\n\t\tdiag(m) = 0.5+apply(m,1, max)\n\t\tm = m %*% m\n\t\tum = umap(as.matrix(m), uconf);\n\t} else {\n\t\tum = umap(t(legc) , uconf)\n\t}\n\treturn(list(mc_x = um$layout[,1], mc_y = um$layout[,2]))\n}\n}\n\n\n#' @importClassesFrom graph graphNEL\n#' @importFrom graph plot addEdge addNode nodeRenderInfo\n#' @importFrom Rgraphviz layoutGraph\n#' @export\nmc2d_comp_graph_coord = function(mc_graph, N)\n{\n\tn1 = mc_graph$mc1\n\tn2 = mc_graph$mc2\n\trEG <- new(\"graphNEL\", nodes=as.character(1:N), edgemode=\"undirected\")\n\n\trEG = addEdge(as.character(n1[n1!=n2]), as.character(n2[n1!=n2]), rEG, rep(1, length(n1[n1!=n2])))\n\n\tg = layoutGraph(rEG, layoutType=\"neato\")\n\tx_cl = nodeRenderInfo(g)$nodeX\n\ty_cl = nodeRenderInfo(g)$nodeY\n\tnames(x_cl) = 1:N\n\tnames(y_cl) = 1:N\n\treturn(list(g=g, mc_x=x_cl, mc_y=y_cl))\n}\n\n#' @export\nmc2d_comp_cell_coord = function(mc_id, graph_id, mgraph, cl_xy, skip_missing=F, symmetrize=F)\n{\n\tmc2d_proj_blur = get_param(\"mcell_mc2d_proj_blur\")\n\tmc2d_K_cellproj = get_param(\"mcell_mc2d_K_cellproj\")\n\n\n\tmc = scdb_mc(mc_id)\n\tgraph = scdb_cgraph(graph_id)\n\n\tx_cl = cl_xy$mc_x\n\ty_cl = cl_xy$mc_y\n\n\tN_mc = length(x_cl)\n\tN_c = length(mc@mc)+length(mc@outliers)\n\tif(N_mc != length(mc@colors)) {\n\t\tstop(\"MC-ERR: Length mismatch in number of projected MC and overal mc\")\n\t}\n\n\tblurx = mc2d_proj_blur*(max(x_cl) - min(x_cl))\n\tblury = mc2d_proj_blur*(max(y_cl) - min(y_cl))\n\n#defining all pairs of MCs that are connected on the mc graph sekelton as active\n\tis_active = rep(FALSE, N_mc*N_mc)\n\tis_active[(mgraph$mc1-1) * N_mc + mgraph$mc2] = TRUE\n\tif(symmetrize) {\n\t\tmessage(\"project on symmetrized graph\")\n\t\tis_active[(mgraph$mc2-1) * N_mc + mgraph$mc1] = TRUE\n\t}\n#including the diagnoal\n\tis_active[((1:N_mc)-1) * N_mc + 1:N_mc] = TRUE\n\n\tmc_key1 = mc@mc[levels(graph@edges$mc1)]\n\tmc_key2 = mc@mc[levels(graph@edges$mc2)]\n\tmc1 = mc_key1[graph@edges$mc1]\n\tmc2 = mc_key2[graph@edges$mc2]\n\te_wgts = graph@edges$w\n\tif(symmetrize) {\n\t\tamc1 = c(mc1, mc2)\n\t\tamc2 = c(mc2, mc1)\n\t\tmc1 = amc1\n\t\tmc2 = amc2\n\t\te_wgts = c(e_wgts, e_wgts)\n\t}\n\n\tf_in_mc = !is.na(mc1) & !is.na(mc2) #missing mc's, for example orphans\n\tf_active = is_active[(mc1-1)*N_mc + mc2]\n\tf = !is.na(f_active) & f_in_mc & f_active\n\n\tdeg = nrow(graph@edges[f,])/length(graph@nodes)\n\tif(symmetrize) {\n\t\tdeg = 2*deg\n\t}\n\tT_w = 1-(mc2d_K_cellproj+1)/deg\n\tf = f & e_wgts > T_w\n\n\tto_x = x_cl[mc2]\n\tto_y = y_cl[mc2]\n\tif(symmetrize) {\n\t\tedges_mcs = c(as.character(graph@edges$mc1), as.character(graph@edges$mc2))[f]\n\t\tc_x = tapply(to_x[f], edges_mcs, mean)\n\t\tc_y = tapply(to_y[f], edges_mcs, mean)\n\t\tc_x = c_x[names(mc@mc)]\n\t\tc_y = c_y[names(mc@mc)]\n\t\tnames(c_x) = names(mc@mc)\n\t\tnames(c_y) = names(mc@mc)\n\t} else {\n\t\tc_x = tapply(to_x[f], as.character(graph@edges$mc1[f]), mean)\n\t\tc_y = tapply(to_y[f], as.character(graph@edges$mc1[f]), mean)\n\t}\n\n\tbase_x = min(c_x)\n\tbase_y = min(c_y)\n\tmax_x = max(c_x)\n\tbase_x = base_x - (max_x-base_x)*0.1\n\n\tmiss = setdiff(names(mc@mc), names(c_x))\n\tif(length(miss) > 0) {\n\t\tmessage(\"Missing coordinates in some cells that are not ourliers or ignored - check this out! (total \", length(miss), \" cells are missing, maybe you used the wrong graph object?\", \" first nodes \", head(miss,10))\n#\t\tstop(\"existing\")\n\t}\n\tx = c_x[names(mc@mc)]\n\ty = c_y[names(mc@mc)]\n\tx = x + rnorm(mean=0, sd=blurx, n=length(x))\n\ty = y + rnorm(mean=0, sd=blury, n=length(y))\n\n\treturn(list(x=x, y=y))\n}\n", "meta": {"hexsha": "09366802c868dff06144696bcd923b8cb45a146a", "size": 11294, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mc2d_force_knn.r", "max_stars_repo_name": "echomsky/metacell", "max_stars_repo_head_hexsha": "39b91cf2cda6192994035ffe62c90b83142647aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-09-14T14:08:10.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-14T14:08:10.000Z", "max_issues_repo_path": "R/mc2d_force_knn.r", "max_issues_repo_name": "breme86/metacell", "max_issues_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mc2d_force_knn.r", "max_forks_repo_name": "breme86/metacell", "max_forks_repo_head_hexsha": "ef35f7ef0f494d3484095ad834efc4a22036bf1d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6416184971, "max_line_length": 213, "alphanum_fraction": 0.6911634496, "num_tokens": 4015, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3079029354849906}} {"text": "# Tab Panel for Chart 1: Mental Health vs Hours of Gaming\n\n# defines ui\nselect_test <- selectInput(\n inputId = \"test\",\n label = \"Mental Health Test\",\n choices = list(\"Generalized Anxiety\" = \"GAD_T\", \"Satisfaction With Life\" = \"SWL_T\",\n \"Social Phobia\" = \"SPIN_T\"),\n selected = \"Generalized Anxiety\"\n)\n\ntab_panel_chart1 <- tabPanel(\n \"Gender and Time Influences\",\n h3(\"How genders and time spent on gaming influence gamers' Mental Health\"),\n sidebarLayout(\n sidebarPanel(\n select_test,\n ),\n mainPanel(\n plotlyOutput(\"scatterplot\"),\n br(),\n p(strong(\"This scatter plot was intended to demonstrate the relationship between\n mental health and hours of games played per week, with respect to gender.\n The plot can display the Generalized Anxiety Disorder (GAD) Assessment scores,\n the Satisfaction with Life (SWL) Scale scores, or the Social Phobia Inventory (SPIN)\n scores versus the average hours of games played in a week by everyone with that\n score. 'Female', 'Male', and 'Other' were averaged and graphed separately.\n 'Female' data points are colored red, 'Male' data points are colored green\n and 'Other' data points are colored blue, as shown in the key. The relationship\n shown by all 3 graphs is that higher hours of gaming is correlated with worse\n mental health. Both the GAD and SPIN graphs had positive slopes and the\n SWL graph had a negative slope for every gender. Higher GAD and SPIN scores\n indicate more severe anxiety and social phobia. Lower SWL scores mean less\n satisfaction with life. People with higher GAD and SPIN scores consistently\n averaged more hours of gaming per week than people with lower scores and\n vice versa for the SWL graph. For all graphs females had lower hours of\n gaming than males. People classified as other had varying trends between\n graphs.\"))\n )\n )\n)\n", "meta": {"hexsha": "4eff8b4e9b7d58f87fd8dfff67fd5217517b1a63", "size": 1966, "ext": "r", "lang": "R", "max_stars_repo_path": "Final Shiny App/tabs/tab_panel_chart1.r", "max_stars_repo_name": "info-201a-wi22/final-project-starter-Simon-Cao-Git", "max_stars_repo_head_hexsha": "f007b663f510be7516a9b9ee87310d43007e9590", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Final Shiny App/tabs/tab_panel_chart1.r", "max_issues_repo_name": "info-201a-wi22/final-project-starter-Simon-Cao-Git", "max_issues_repo_head_hexsha": "f007b663f510be7516a9b9ee87310d43007e9590", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Final Shiny App/tabs/tab_panel_chart1.r", "max_forks_repo_name": "info-201a-wi22/final-project-starter-Simon-Cao-Git", "max_forks_repo_head_hexsha": "f007b663f510be7516a9b9ee87310d43007e9590", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.8095238095, "max_line_length": 92, "alphanum_fraction": 0.7004069176, "num_tokens": 443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3079029354849906}} {"text": "plot.whiskers_cluster_length <- function(df.posts, df.users, df.threads){\n \n # Prepare the data\n df.cluster_length <- merge(merge(df.posts, df.users), df.threads) %>% \n mutate(cluster=factor(cluster)) %>%\n select(cluster, length)\n \n # Plot\n p <- ggplot(df.cluster_length, aes(x=cluster, y=length)) + \n scale_fill_manual(values = c(\"white\", cluster.colors)) + # no role -> white color\n geom_boxplot(aes(fill=cluster)) +\n theme_bw() +\n theme(text = element_text(size = 15),\n legend.key = element_blank()) +\n ggtitle(\"Participations vs threads length\")\n print(p)\n \n # Save to file\n dev.copy(png, paste0('2016-01-15-whiskers_roles_vs_length.png'), width=800)\n dev.off()\n}\n\nplot.by_thread_cluster <- function(df.posts, df.users){\n \n df.threads <- plyr::count(df.posts, \"thread\")\n names(df.threads)[2] <- \"length\"\n \n df.participations <- merge(merge(df.posts, df.users), df.threads)\n df.participations$cluster <- factor(df.participations$cluster)\n \n by_thread_cluster <- acast(df.participations, thread~cluster)\n by_thread_cluster.perc <- as.data.frame(t(apply(by_thread_cluster, 1, function(x) x/sum(x))))\n \n \n # Add length and sort by length\n df.lengths <- plyr::count(df.participations, \"thread\")\n names(df.lengths) <- c('thread', 'length')\n by_thread_cluster.perc$thread <-rownames(by_thread_cluster.perc)\n by_thread_cluster.perc <- merge(by_thread_cluster.perc, df.lengths) # add lengths\n by_thread_cluster.perc <- by_thread_cluster.perc[order(by_thread_cluster.perc$length),] # sort by length\n by_thread_cluster.perc <- subset(by_thread_cluster.perc, select=-c(thread)) # drop again thread column\n by_thread_cluster.norm <- by_thread_cluster.perc\n by_thread_cluster.norm$length <- by_thread_cluster.norm$length/max(by_thread_cluster.perc$length)\n \n # Plot\n heatmap(as.matrix(by_thread_cluster.norm), \n Colv=NA, Rowv=NA, labRow=NA, ylab=\"Threads\", xlab=\"Roles\",\n main = \"Participations by role\")\n \n # Save to file\n dev.copy(png, paste0('2016-01-15-thread_role_composition.png'), width=600)\n dev.off()\n}\n\nby_thread_cluster_nfirst <- function(df.posts, nfirst=15){\n \n # Sort posts by their position in the thread\n df.posts <- df.posts[with(df.posts, order(thread, date)),]\n df.posts$rank <- sapply(1:nrow(df.posts), \n function(i) sum(df.posts[1:i, c('thread')]==df.posts$thread[i]))\n # Thread lengths\n df.threads <- plyr::count(df.posts, 'thread')\n names(df.threads)[2] <- \"length\"\n \n # Add lengths to dataframe so that we can filter by length later\n df.posts <- merge(df.posts, df.threads)\n \n # Posts before NFIRST and in threads longer than NFIRST\n mask <- df.posts$rank <= nfirst & df.posts$length>nfirst\n df.first <- df.posts[mask,]\n \n return(df.first)\n}", "meta": {"hexsha": "e2b7d504794b019fc72346e505c428318e8fc672", "size": 2829, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_thread_cluster.r", "max_stars_repo_name": "alumbreras/neighborhood_motifs", "max_stars_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-01-17T09:47:19.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-17T09:47:19.000Z", "max_issues_repo_path": "R/plot_thread_cluster.r", "max_issues_repo_name": "alumbreras/neighborhood_motifs", "max_issues_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/plot_thread_cluster.r", "max_forks_repo_name": "alumbreras/neighborhood_motifs", "max_forks_repo_head_hexsha": "ce1086ac0e4b455076f9d6f9c8021d24748c5b61", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.7534246575, "max_line_length": 106, "alphanum_fraction": 0.6800989749, "num_tokens": 764, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3079029354849906}} {"text": "# OSNOVNA ANALIZA\n\n#NAPELJAVE\n\n#pretvorba v obliko tidy data\nnapeljave <- napeljave %>% \n gather(key=\"leto.tip\", value=\"stevilo\", -Obcina) %>%\n separate(leto.tip, c(\"leto\", \"tip\"), \"(?<=[0-9]) \")\n\n#konstrukcija podtabel vodovoda, kanalizacije, centralnega ogrevanja, elektri\u010dnega toka, kopalnice, strani\u0161\u010da,kuhinje\nvodovod <- napeljave %>% filter(tip == \"Vodovod\")\ncentralno_ogrevanje <- napeljave %>% filter(tip == \"Centralno ogrevanje\")\nelektricni_tok <- napeljave %>% filter(tip == \"Elektri\u010dni tok\")\nkopalnica <- napeljave %>% filter(tip == \"Kopalnica\")\nstranisce <- napeljave %>% filter(tip == \"Strani\u0161\u010de\")\nkuhinja <- napeljave %>% filter(tip == \"Kuhinja\")\nkanalizacija <- napeljave %>% filter(tip == \"Kanalizacija\")\nvodovod <- vodovod[,-3]\ncentralno_ogrevanje <- centralno_ogrevanje[,-3]\nelektricni_tok <- elektricni_tok[,-3]\nkopalnica <- kopalnica[,-3]\nstranisce <- stranisce[,-3]\nkuhinja <- kuhinja[,-3]\nkanalizacija <- kanalizacija[,-3]\n\n#KAZALNIKI\n\n#pretvorba v obliko tidy data\nkazalniki <- kazalniki %>% gather(key = \"tip\", value = \"vrednost\", -Obcina)\n\n#konstrukcija podtabel gostota, placa_indeks, brezposelni,stanovanja \ngostota <- kazalniki %>% filter(tip == \"gostota_prebivalstva\")\ngostota <- gostota[,-2]\nplaca_indeks <- kazalniki %>% filter(tip == \"pov_mes_neto_placa_indeks\")\nplaca_indeks <- placa_indeks[,-2]\nbrezposelni <- kazalniki %>% filter(tip == \"st_reg_brezposelnosti_ods\")\nbrezposelni <- brezposelni[,-2]\nstanovanja <- kazalniki %>% filter(tip == \"st_stan_na_tisoc_preb\")\nstanovanja <- stanovanja[,-2]\n\n#sprememba nekaterih imen\ngostota$Obcina <- gsub(\"Koper/Capodistria1)\", \"Koper/Capodistria\", gostota$Obcina)\ngostota$Obcina <- gsub(\"Ankaran/Ancarano1)\", \"Ankaran/Ancarano\", gostota$Obcina)\ngostota$Obcina <- gsub(\"Trebnje2)\", \"Trebnje\", gostota$Obcina)\n\n#gostota_vodovod\ngostota_vodovod <- left_join(vodovod,gostota,by=\"Obcina\")\ncolnames(gostota_vodovod) <- c(\"obcina\", \"leto\", \"stevilo\", \"gostota\")\n#sortiranje obcin glede na gostoto\nlevels <- c(0,261,521, 780, Inf)\nlabels <- c(\"zelo_redka\", \"redka\", \"gosta\", \"zelo_gosta\")\ngostota_vodovod <- gostota_vodovod %>% mutate(skupina = cut(gostota, levels, labels = labels))\nskupina <- gostota_vodovod\ngostota_vodovod$stevilo <- parse_integer(gostota_vodovod$stevilo)\n#izracun povprecij\nleta.skupine.vod <- gostota_vodovod %>% group_by(leto, skupina) %>% summarise(povprecje = mean(stevilo))\nleta.skupine.vod$leto <- parse_integer(leta.skupine.vod$leto)\n\n#gostota_kanalizacija\ngostota_kanalizacija <- left_join(kanalizacija,gostota,by=\"Obcina\")\ncolnames(gostota_kanalizacija) <- c(\"obcina\", \"leto\", \"stevilo\", \"gostota\")\nlevels <- c(0,261,521, 780, Inf)\nlabels <- c(\"zelo_redka\", \"redka\", \"gosta\", \"zelo_gosta\")\ngostota_kanalizacija <- gostota_kanalizacija %>% mutate(skupina = cut(gostota, levels, labels = labels))\ngostota_kanalizacija$stevilo <- parse_integer(gostota_kanalizacija$stevilo)\nleta.skupine.kan <- gostota_kanalizacija %>% group_by(leto, skupina) %>% summarise(povprecje = mean(stevilo))\nleta.skupine.kan$leto <- parse_integer(leta.skupine.kan$leto)\n\n#placa_ogrevanje\nplaca_ogrevanje <- left_join(centralno_ogrevanje,placa_indeks,by=\"Obcina\")\ncolnames(placa_ogrevanje) <- c(\"obcina\", \"leto\", \"stevilo\", \"placa_indeks\")\nlevels <- c(0, 90, 110, Inf)\nlabels <- c(\"nizka\",\"srednja\",\"visoka\")\nplaca_ogrevanje <- placa_ogrevanje %>% mutate(razred = cut(placa_indeks, levels, labels = labels))\nplaca_ogrevanje$stevilo <- parse_integer(placa_ogrevanje$stevilo)\nleta.razredi <- placa_ogrevanje %>% group_by(leto, razred) %>% summarise(povprecje = mean(stevilo))\nleta.razredi <- na.omit(leta.razredi)\nleta.razredi$leto <- parse_integer(leta.razredi$leto)\n\n#POMANKLJIVOSTI:\n\n#pretvorba v obliko tidy data\npomankljivosti <- pomankljivosti %>% gather(key = \"tip\", value = \"vrednost\", -Obcina)\n\n#delezi stanovanj s pomakljivostmi v posamezni obcini v odstotkih\ndelezi <- pomankljivosti %>% group_by(Obcina) %>% top_n(2) %>% summarise(delez = min(vrednost)/max(vrednost)*100)\nlevels <- c(0,25,50,75,Inf)\nlabels <- c(\"zelo nizka\",\"nizka\",\"visoka\",\"zelo visoka\")\ndelezi <- delezi %>% mutate(pomankljivost = cut(delez, levels, labels=labels))\n\nstan <- pomankljivosti %>% filter(tip == \"st_vseh_stanovanj\")\nstan <- stan[,-2]\n\n#PREOSTALO ZA VIZUALIZACIJE\n\n#brezposelni\nlevels <- c(0,12,Inf)\nlabels <- c(\"nekriti\u010dna\",\"kriti\u010dna\")\nbrezposelni <- brezposelni %>% mutate(kriticnost = cut(vrednost, levels, labels = labels))\n#obcini z najmanj in najbolj kriticno brezposelnostjo\ny_max <- max(brezposelni[,\"vrednost\"])\nnajbolj_kriticna <- brezposelni[brezposelni$'vrednost' == y_max, \"Obcina\"]\ny_min <- min(brezposelni[,\"vrednost\"])\nnajmanj_kriticna <- brezposelni[brezposelni$'vrednost' == y_min, \"Obcina\"]\n\n#place\nlevels <- c(0, 90, 110, Inf)\nlabels <- c(\"nizka\",\"srednja\",\"visoka\")\nplaca_indeks <- placa_indeks %>% mutate(razred = cut(vrednost, levels, labels = labels))\n#place, obcini z najvisjim in najnizjim indeksom\ny_max <- max(placa_indeks[,\"vrednost\"])\nnajvisji_indeks <- placa_indeks[placa_indeks$'vrednost' == y_max, \"Obcina\"]\ny_min <- min(placa_indeks[,\"vrednost\"])\nnajnizji_indeks <- placa_indeks[placa_indeks$'vrednost' == y_min, \"Obcina\"]\n\n#gostota\nlevels <- c(0,261,521, 780, Inf)\nlabels <- c(\"zelo_redka\", \"redka\", \"gosta\", \"zelo_gosta\")\ngostota <- gostota %>% mutate(skupina = cut(vrednost, levels, labels = labels))\n#obcini z najvecjo in najmanjso gostoto\ny_max <- max(gostota[,\"vrednost\"])\nnajbolj_gosta <- gostota[gostota$'vrednost' == y_max, \"Obcina\"]\ny_min <- min(gostota[,\"vrednost\"])\nnajmanj_gosta <- gostota[gostota$'vrednost' == y_min, \"Obcina\"]\n\n#KAZALNIKI ZA ZEMLJEVIDE\n\n#pretvorba v obliko tidy data\nkazalniki_za_zemljevide <- kazalniki_za_zemljevide %>% gather(key = \"tip\", value = \"vrednost\", -Obcina)\n#preimenovanje nekaterih imen za risanje zemljevida\nkazalniki_za_zemljevide$Obcina = gsub(\"Ankaran/Ancarano1)\", \"Ankaran\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Koper/Capodistria1)\", \"Koper\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Trebnje2)\", \"Trebnje\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Miren - Kostanjevica\", \"Miren-Kostanjevica\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Mirna2)\", \"Mirna\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Ra\u010de - Fram\", \"Ra\u010de-Fram\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"\u0160empeter - Vrtojba\", \"\u0160empeter-Vrtojba\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Piran/Pirano\", \"Piran\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Mokronog - Trebelno\", \"Mokronog-Trebelno\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Ren\u010de - Vogrsko\", \"Ren\u010de-Vogrsko\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Dobrova - Polhov Gradec\", \"Dobrova-Polhov Gradec\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Hrpelje - Kozina\", \"Hrpelje-Kozina\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Sveta Trojica v Slov. goricah\", \"Sveta Trojica v Slovenskih goricah\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Sveti Jurij v Slov. goricah\", \"Sveti Jurij v Slovenskih goricah\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Gorenja vas - Poljane\", \"Gorenja vas-Poljane\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Ho\u010de - Slivnica\", \"Ho\u010de-Slivnica\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Izola/Isola\", \"Izola\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Log - Dragomer\", \"Log-Dragomer\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Lendava/Lendva\", \"Lendava\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Dobrovnik/Dobronak\", \"Dobrovnik\", kazalniki_za_zemljevide$Obcina)\nkazalniki_za_zemljevide$Obcina <- gsub(\"Hodo\u0161/Hodos\", \"Hodo\u0161\", kazalniki_za_zemljevide$Obcina)\n#urejanje\n#konstrukcija podtabel gostota, placa_indeks, brezposelni,stanovanja \ngostota_z <- kazalniki_za_zemljevide %>% filter(tip == \"gostota_prebivalstva\")\ngostota_z <- gostota_z[,-2]\ngostota_z <- gostota_z[-1,]\nplaca_indeks_z <- kazalniki_za_zemljevide %>% filter(tip == \"pov_mes_neto_placa_indeks\")\nplaca_indeks_z <- placa_indeks_z[,-2]\nplaca_indeks_z <- placa_indeks_z[-1,]\nbrezposelni_z <- kazalniki_za_zemljevide %>% filter(tip == \"st_reg_brezposelnosti_ods\")\nbrezposelni_z <- brezposelni_z[,-2]\nbrezposelni_z <- brezposelni_z[-1,]\nstanovanja_z <- kazalniki_za_zemljevide %>% filter(tip == \"st_stan_na_tisoc_preb\")\nstanovanja_z <- stanovanja_z[,-2]\nstanovanja_z <- stanovanja_z[-1,]\n\n# NAPREDNA ANALIZA\n\n#centralno ogrevanje kot pomankljivost\ncent_ogr_poman <- pomankljivosti %>% filter(tip == \"centralno_ogrevanje\")\ncent_ogr_poman <- cent_ogr_poman[,-2]\n\n#poenotenje obcin\ncent_ogr_poman$Obcina <- gsub(\"Ankaran/Ancarano\", \"Ankaran\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Koper/Capodistria\", \"Koper\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Trebnje)\", \"Trebnje\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Miren - Kostanjevica\", \"Miren-Kostanjevica\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Ra\u010de - Fram\", \"Ra\u010de-Fram\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"\u0160empeter - Vrtojba\", \"\u0160empeter-Vrtojba\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Piran/Pirano\", \"Piran\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Mokronog - Trebelno\", \"Mokronog-Trebelno\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Ren\u010de - Vogrsko\", \"Ren\u010de-Vogrsko\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Dobrova - Polhov Gradec\", \"Dobrova-Polhov Gradec\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Hrpelje - Kozina\", \"Hrpelje-Kozina\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Sveta Trojica v Slov. goricah\", \"Sveta Trojica v Slovenskih goricah\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Sveti Jurij v Slov. goricah\", \"Sveti Jurij v Slovenskih goricah\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Gorenja vas - Poljane\", \"Gorenja vas-Poljane\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Ho\u010de - Slivnica\", \"Ho\u010de-Slivnica\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Izola/Isola\", \"Izola\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Log - Dragomer\", \"Log-Dragomer\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Lendava/Lendva\", \"Lendava\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Dobrovnik/Dobronak\", \"Dobrovnik\", cent_ogr_poman$Obcina)\ncent_ogr_poman$Obcina <- gsub(\"Hodo\u0161/Hodos\", \"Hodo\u0161\", cent_ogr_poman$Obcina)\n\nstan$Obcina <- gsub(\"Ankaran/Ancarano\", \"Ankaran\", stan$Obcina)\nstan$Obcina <- gsub(\"Koper/Capodistria\", \"Koper\", stan$Obcina)\nstan$Obcina <- gsub(\"Trebnje)\", \"Trebnje\", stan$Obcina)\nstan$Obcina <- gsub(\"Miren - Kostanjevica\", \"Miren-Kostanjevica\", stan$Obcina)\nstan$Obcina <- gsub(\"Ra\u010de - Fram\", \"Ra\u010de-Fram\", stan$Obcina)\nstan$Obcina <- gsub(\"\u0160empeter - Vrtojba\", \"\u0160empeter-Vrtojba\", stan$Obcina)\nstan$Obcina <- gsub(\"Piran/Pirano\", \"Piran\", stan$Obcina)\nstan$Obcina <- gsub(\"Mokronog - Trebelno\", \"Mokronog-Trebelno\", stan$Obcina)\nstan$Obcina <- gsub(\"Ren\u010de - Vogrsko\", \"Ren\u010de-Vogrsko\", stan$Obcina)\nstan$Obcina <- gsub(\"Dobrova - Polhov Gradec\", \"Dobrova-Polhov Gradec\", stan$Obcina)\nstan$Obcina <- gsub(\"Hrpelje - Kozina\", \"Hrpelje-Kozina\", stan$Obcina)\nstan$Obcina <- gsub(\"Sveta Trojica v Slov. goricah\", \"Sveta Trojica v Slovenskih goricah\", stan$Obcina)\nstan$Obcina <- gsub(\"Sveti Jurij v Slov. goricah\", \"Sveti Jurij v Slovenskih goricah\", stan$Obcina)\nstan$Obcina <- gsub(\"Gorenja vas - Poljane\", \"Gorenja vas-Poljane\", stan$Obcina)\nstan$Obcina <- gsub(\"Ho\u010de - Slivnica\", \"Ho\u010de-Slivnica\", stan$Obcina)\nstan$Obcina <- gsub(\"Izola/Isola\", \"Izola\", stan$Obcina)\nstan$Obcina <- gsub(\"Log - Dragomer\", \"Log-Dragomer\", stan$Obcina)\nstan$Obcina <- gsub(\"Lendava/Lendva\", \"Lendava\", stan$Obcina)\nstan$Obcina <- gsub(\"Dobrovnik/Dobronak\", \"Dobrovnik\", stan$Obcina)\nstan$Obcina <- gsub(\"Hodo\u0161/Hodos\", \"Hodo\u0161\", stan$Obcina)\n\n#zdruzitev centralnega ogrevanja in plac\na1 <- left_join(cent_ogr_poman,placa_indeks_z,by=\"Obcina\")\nnames(a1) <- c(\"Obcina\",\"Brez_ogrevanja\",\"Place\")\n\n#zdruzitev centralnega ogrevanja in brezposelosti\na2 <- left_join(cent_ogr_poman,brezposelni_z,by=\"Obcina\")\nnames(a2) <- c(\"Obcina\",\"Brez_ogrevanja\",\"Brezposelni_ods\")\n\na <- left_join(a1,a2,by=\"Obcina\")\nt <- left_join(a,stan,by=\"Obcina\")\nt$'delez' <- (t$'Brez_ogrevanja.x' / t$'vrednost')*100\n\n#javna kanalizacija kot pomankljivost\njav_kan_poman <- pomankljivosti %>% filter(tip == \"javna_kanalizacija\")\njav_kan_poman <- jav_kan_poman[,-2]\n\n#poenotenje imen obcin\njav_kan_poman$Obcina <- gsub(\"Ankaran/Ancarano\", \"Ankaran\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Koper/Capodistria\", \"Koper\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Trebnje)\", \"Trebnje\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Miren - Kostanjevica\", \"Miren-Kostanjevica\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Ra\u010de - Fram\", \"Ra\u010de-Fram\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"\u0160empeter - Vrtojba\", \"\u0160empeter-Vrtojba\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Piran/Pirano\", \"Piran\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Mokronog - Trebelno\", \"Mokronog-Trebelno\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Ren\u010de - Vogrsko\", \"Ren\u010de-Vogrsko\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Dobrova - Polhov Gradec\", \"Dobrova-Polhov Gradec\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Hrpelje - Kozina\", \"Hrpelje-Kozina\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Sveta Trojica v Slov. goricah\", \"Sveta Trojica v Slovenskih goricah\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Sveti Jurij v Slov. goricah\", \"Sveti Jurij v Slovenskih goricah\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Gorenja vas - Poljane\", \"Gorenja vas-Poljane\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Ho\u010de - Slivnica\", \"Ho\u010de-Slivnica\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Izola/Isola\", \"Izola\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Log - Dragomer\", \"Log-Dragomer\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Lendava/Lendva\", \"Lendava\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Dobrovnik/Dobronak\", \"Dobrovnik\", jav_kan_poman$Obcina)\njav_kan_poman$Obcina <- gsub(\"Hodo\u0161/Hodos\", \"Hodo\u0161\", jav_kan_poman$Obcina)\n", "meta": {"hexsha": "dbce439eecef05a27a0cadf7d313de4016270450", "size": 14560, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "EnjaErker/APPR-2019-20", "max_stars_repo_head_hexsha": "2f66146627bac814f0a9c6fe9181355b8663fc07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "EnjaErker/APPR-2019-20", "max_issues_repo_head_hexsha": "2f66146627bac814f0a9c6fe9181355b8663fc07", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2019-12-15T15:25:05.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-04T17:42:20.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "EnjaErker/APPR-2019-20", "max_forks_repo_head_hexsha": "2f66146627bac814f0a9c6fe9181355b8663fc07", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 57.5494071146, "max_line_length": 141, "alphanum_fraction": 0.7559752747, "num_tokens": 6147, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3079029354849906}} {"text": "\n figure.timeseries.larvae = function( outdir ) {\n larvae = read.table(file=file.path( project.datadirectory(\"bio.snowcrab\"), \"data\", \"larvae\", \"ts.brachyura.csv\"), sep = \"\\t\")\n colnames(larvae) = c(\"yr\", \"mean.n.m3\", \"sdev\", \"n\")\n larvae$se = larvae$sdev / sqrt(larvae$n-1)\n\n ts.plotandsave( x=larvae$yr, y=larvae$mean.n.m3, lb=larvae$mean.n.m3-larvae$se, ub=larvae$mean.n.m3+larvae$se,\n w=larvae$n, outdir=outdir, action=\"save\", title=\"Brachyura\", smooth=0.75 ) \n\n l.monthly = read.table(file=file.path( project.datadirectory(\"bio.snowcrab\"), \"data\", \"larvae\", \"ts.brachyura.monthly.csv\"), sep = \"\\t\")\n colnames(l.monthly) = c(\"month\", \"mean.n.m3\", \"n\")\n\n x = l.monthly$month\n y = l.monthly$mean.n.m3\n w = l.monthy$n\n\n xrange = range(-0.5, 12.5)\n \n fn = file.path(outdir, \"BrachyuraMonthly\")\n Cairo( file=fn, type=\"pdf\", bg=\"white\", units=\"in\", width=8, height=4 )\n \n plot( x, y, type=\"n\", axes=F, xlab=\"Month\", xlim=xrange, ylab=toupper(\"Brachyura\"), cex=2 )\n lines( loess( y ~ x, weights=w, control=loess.control(surface = \"direct\")), col=\"orange\", lty=\"solid\", lwd=4 )\n\n points(x, y, pch=10)\n axis( 1 )\n axis( 2 )\n dev.off()\n cmd( \"convert -trim -quality 9 -geometry 200% -frame 2% -mattecolor white -antialias \", paste(fn, \"pdf\", sep=\".\"), paste(fn, \"png\", sep=\".\") )\n \n }\n\n\n\n", "meta": {"hexsha": "eb439f93cd128820cb3b0ae74b61c539dac8a636", "size": 1354, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure.timeseries.larvae.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/figure.timeseries.larvae.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure.timeseries.larvae.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 38.6857142857, "max_line_length": 150, "alphanum_fraction": 0.6078286558, "num_tokens": 493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647394, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3079029280094266}} {"text": "#' Swaps\n#' \n#' Methods for swapping between different memuse formats.\n#' \n#' These methods allow simple (coherent) swaps between the different\n#' \\code{memuse} formats.\n#' \n#' \\code{swap.unit()} will switch an object to another, supplied unit. If the\n#' unit is from another prefix, then the prefix too will change. In this case,\n#' the size will change appropriately.\n#' \n#' \\code{swap.prefix()} will change an object from one unit.prefix to the\n#' other. In this case, the size will change appropriately.\n#' \n#' \\code{swap.names} will change from short to long, or long to short printing.\n#' The size and prefix of the object are unchanged.\n#' \n#' @param x \n#' memuse object\n#' @param unit \n#' new unit for the \\code{memuse} object after the swap occurs\n#' \n#' @return \n#' Returns a \\code{memuse} class object.\n#' \n#' @examples\n#' \\dontrun{\n#' x <- mu(1e6)\n#' \n#' x\n#' swap.prefix(x)\n#' swap.names(x)\n#' swap.unit(x, \"bytes\")\n#' }\n#' \n#' @seealso \\code{ \\link{Constructor} \\link{memuse-class} }\n#' @keywords Methods\n#' @name Swaps\n#' @rdname swaps\nNULL\n\n\n\n#' @rdname swaps\n#' @export\nsetGeneric(name=\"swap.prefix\", \n function(x) \n standardGeneric(\"swap.prefix\"),\n package=\"memuse\"\n)\n\n#' @rdname swaps\n#' @export\nsetMethod(\"swap.prefix\", signature(x=\"memuse\"),\n function(x)\n {\n if (x@unit.prefix == \"IEC\")\n new.prefix <- \"SI\"\n else if (x@unit.prefix == \"SI\")\n new.prefix <- \"IEC\"\n else\n mu_error()\n \n power <- get_power(x)\n \n if (new.prefix == \"IEC\")\n x@size <- x@size * (1e3/1024)^power\n else\n x@size <- x@size * (1024/1e3)^power\n \n x@unit.prefix <- new.prefix\n \n check.unit(x)\n }\n)\n\n\n\n#' @rdname swaps\n#' @export\nsetGeneric(name=\"swap.names\", \n function(x)\n standardGeneric(\"swap.names\"), \n package=\"memuse\"\n)\n\n#' @rdname swaps\n#' @export\nsetMethod(\"swap.names\", signature(x=\"memuse\"),\n function(x)\n {\n if (x@unit.names == \"short\")\n new.names <- \"long\"\n else if (x@unit.names == \"long\")\n new.names <- \"short\"\n else\n mu_error()\n \n new.unit <- which(.units[[x@unit.names]][[x@unit.prefix]][[\"print\"]] == x@unit)\n \n x@unit <- .units[[new.names]][[x@unit.prefix]][[\"print\"]][new.unit]\n \n x@unit.names <- new.names\n \n x\n }\n)\n\n\n\n#' @rdname swaps\n#' @export\nsetGeneric(name=\"swap.unit\", \n function(x, unit)\n standardGeneric(\"swap.unit\"), \n package=\"memuse\"\n)\n\n#' @rdname swaps\n#' @export\nsetMethod(\"swap.unit\", signature(x=\"memuse\"),\n function(x, unit)\n {\n unit <- tolower(unit)\n \n if (unit==x@unit)\n return(x)\n \n if (unit == \"best\")\n x <- best.unit(x)\n else if (unit == tolower(x@unit))\n return( check.mu(x) )\n else {\n flag <- FALSE\n \n for (names in c(\"short\", \"long\")){\n for (prefix in c(\"IEC\", \"SI\")){\n if ( unit %in% .units[[names]][[prefix]][[\"check\"]] ){\n flag <- TRUE\n unit.names <- names\n unit.prefix <- prefix\n break\n }\n if (flag)\n break\n }\n }\n \n if (flag){\n x <- convert_to_bytes(x)\n \n x@unit.names <- unit.names\n x@unit.prefix <- unit.prefix\n \n if (unit.prefix == \"IEC\")\n f <- 1024\n else\n f <- 1e3\n \n units <- .units[[x@unit.names]][[x@unit.prefix]][[\"check\"]]\n i <- which(units == unit)\n \n x@size <- x@size/(f^(i-1))\n \n new.unit <- .units[[unit.names]][[unit.prefix]][[\"print\"]][i]\n \n x@unit <- new.unit\n }\n else\n stop(\"invalid argument 'unit'. See help('memuse')\")\n }\n \n x\n }\n)\n", "meta": {"hexsha": "59c49fd594878f578798a645b1856a16c12a5320", "size": 3642, "ext": "r", "lang": "R", "max_stars_repo_path": "R/swap.r", "max_stars_repo_name": "cran/memuse", "max_stars_repo_head_hexsha": "22d4e53c0b1a7f9731256d0cd2c791cf8a9d1165", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 34, "max_stars_repo_stars_event_min_datetime": "2015-05-15T17:15:21.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-18T14:03:29.000Z", "max_issues_repo_path": "R/swap.r", "max_issues_repo_name": "cran/memuse", "max_issues_repo_head_hexsha": "22d4e53c0b1a7f9731256d0cd2c791cf8a9d1165", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2016-09-16T08:01:25.000Z", "max_issues_repo_issues_event_max_datetime": "2019-09-04T20:08:37.000Z", "max_forks_repo_path": "R/swap.r", "max_forks_repo_name": "cran/memuse", "max_forks_repo_head_hexsha": "22d4e53c0b1a7f9731256d0cd2c791cf8a9d1165", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2015-02-12T22:08:26.000Z", "max_forks_repo_forks_event_max_datetime": "2015-02-12T22:08:26.000Z", "avg_line_length": 20.6931818182, "max_line_length": 83, "alphanum_fraction": 0.5450302032, "num_tokens": 1036, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178699175393, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.3078457923209246}} {"text": "# calculates selection index\n# and ranks genotypes accordingly\n# Isaak Y Tecle iyt2cornell.edu\n\n\noptions(echo = FALSE)\n\nlibrary(data.table)\nlibrary(stats)\nlibrary(stringi)\nlibrary(dplyr)\n\nallArgs <- commandArgs()\n\ninputFiles <- scan(grep(\"input_files\", allArgs, value = TRUE),\n what = \"character\")\n\nrelWeightsFile <- grep(\"rel_weights\", inputFiles, value = TRUE)\n\noutputFiles <- scan(grep(\"output_files\", allArgs, value = TRUE),\n what = \"character\")\n\ntraitsFiles <- grep(\"gebv_files_of_traits\", inputFiles, value = TRUE)\n\ngebvsSelectionIndexFile <- grep(\"gebvs_selection_index\",\n outputFiles,\n value = TRUE)\n\nselectionIndexFile <- grep(\"selection_index_only\",\n outputFiles,\n value=TRUE)\n\ninTraitFiles <- scan(traitsFiles, what = \"character\")\n\ntraitFilesList <- strsplit(inTraitFiles, \"\\t\");\ntraitsTotal <- length(traitFilesList)\n\nif (traitsTotal == 0)\n stop(\"There are no traits with GEBV data.\")\nif (length(relWeightsFile) == 0)\n stop(\"There is no file with relative weights of traits.\")\n\n\nrelWeights <- data.frame(fread(relWeightsFile, header = TRUE))\nrownames(relWeights) <- relWeights[, 1]\nrelWeights[, 1] <- NULL \n\nif (is.null(relWeights)) {\n stop('There were no relative weights for all the traits.')\n}\n\ncombinedRelGebvs <- c()\n\nfor (i in 1:traitsTotal) {\n traitFile <- traitFilesList[[i]]\n traitGEBV <- data.frame(fread(traitFile, header = TRUE))\n rownames(traitGEBV) <- traitGEBV[, 1]\n traitGEBV[, 1] <- NULL\n traitGEBV <- traitGEBV[order(rownames(traitGEBV)),,drop=FALSE] \n trait <- colnames(traitGEBV)\n \n relWeight <- relWeights[trait, ]\n \n if (is.na(relWeight) == FALSE && relWeight != 0 ) {\n \n weightedTraitGEBV <- apply(traitGEBV, 1,\n function(x) x*relWeight)\n\n weightedTraitGEBV <- data.frame(weightedTraitGEBV)\n colnames(weightedTraitGEBV) <- paste0(trait, '_weighted')\n\n combinedRelGebvs <- merge(combinedRelGebvs, weightedTraitGEBV,\n by = 0,\n all = TRUE)\n\n\n rownames(combinedRelGebvs) <- combinedRelGebvs[, 1]\n combinedRelGebvs[, 1] <- NULL\n }\n}\n\nsumRelWeights <- apply(relWeights, 2, sum)\nsumRelWeights <- sumRelWeights[[1]]\n\ncombinedRelGebvs$Index <- apply(combinedRelGebvs, 1, function (x) sum(x))\n\ncombinedRelGebvs <- combinedRelGebvs[ with(combinedRelGebvs,\n order(-combinedRelGebvs$Index)\n ),\n ]\n\ncombinedRelGebvs <- round(combinedRelGebvs, 2)\n\nselectionIndex <-c()\n\nif (!is.null(combinedRelGebvs)) {\n selectionIndex <- subset(combinedRelGebvs,\n select = 'Index'\n )\n}\n\nif (gebvsSelectionIndexFile != 0) {\n if (!is.null(combinedRelGebvs)) {\n fwrite(combinedRelGebvs,\n file = gebvsSelectionIndexFile,\n sep = \"\\t\",\n row.names = TRUE,\n quote = FALSE,\n )\n }\n}\n\nif (!is.null(selectionIndexFile)) {\n if (!is.null(selectionIndex)) {\n fwrite(selectionIndex,\n file = selectionIndexFile,\n row.names = TRUE,\n quote = FALSE,\n sep = \"\\t\",\n )\n }\n}\n\nq(save = \"no\", runLast = FALSE)\n", "meta": {"hexsha": "bc3ddda626ff8ab0fcc3239b35d0d3ad3f1ece4c", "size": 3485, "ext": "r", "lang": "R", "max_stars_repo_path": "R/solGS/selection_index.r", "max_stars_repo_name": "TriticeaeToolbox/sgn", "max_stars_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 39, "max_stars_repo_stars_event_min_datetime": "2015-02-03T15:47:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T13:34:05.000Z", "max_issues_repo_path": "R/solGS/selection_index.r", "max_issues_repo_name": "TriticeaeToolbox/sgn", "max_issues_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2491, "max_issues_repo_issues_event_min_datetime": "2015-01-07T05:49:17.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:31:05.000Z", "max_forks_repo_path": "R/solGS/selection_index.r", "max_forks_repo_name": "TriticeaeToolbox/sgn", "max_forks_repo_head_hexsha": "76602305fb60f326eed4bc4fcbd16680f6b9f606", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 20, "max_forks_repo_forks_event_min_datetime": "2015-06-30T19:10:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T13:34:09.000Z", "avg_line_length": 27.88, "max_line_length": 75, "alphanum_fraction": 0.5733142037, "num_tokens": 874, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3078118752368251}} {"text": "setwd(\"P:/2019 0970 151 000/User Data/Faraz-Export IDAVE folders/Models2.5\")\nlibrary(tidyverse)\nlibrary(h2o)\nlibrary(inspectdf)\ntabna <- function(x){table(x, useNA = \"ifany\")}\ndfx <- readRDS(\"./dfx_v2.5.rds\")\n\nh2o.init()\ndfx %>% inspect_na() %>% print(n=10)\ndfx <- drop_na(dfx)\n\n\n####\n\nfeatures <- read.csv(\"mutualInfo_features.csv\")\nfeatures$X <- levels(features$X)[features$X]\nnames(features)[1:2] <- c(\"predictor\", \"mutInf\")\nfeatures[grep(\"^flag\", features$predictor), \"is_ed\"] <- FALSE\nfeatures[grep(\"^patserv\", features$predictor), \"is_ed\"] <- FALSE\nfeatures[\nwhich(features$predictor %in% c(\"acutelos\", \"scu\", \"prior_hosp_90d\",\n\"prior_hosp_365d\", \"readm_90d\")), \"is_ed\"] <- FALSE\nfeatures[is.na(features$is_ed), \"is_ed\"] <- TRUE\n\n# sum(features$is_ed)\n# [1] 37\n# sum(features$is_ed[1:40])\n# [1] 21\n\nsel_feat_ed_small <- features[1:40,] %>% filter(is_ed) %>% .$predictor\nsel_feat_ed_big <- features %>% filter(is_ed) %>% .$predictor\nsel_feat <- features[1:40,] %>% .$predictor\n\n####\n\ndf.hex <- as.h2o(dfx)\n# Now that we have our tuned model and parameters, we train the models on the train sample and test\n# on the hold-out test sample\ndf.split <- h2o.splitFrame(df.hex, ratios = c(0.8), seed = 1234)\ntrain <- df.split[[1]]\ntest <- df.split[[2]] #unseen data\n\ndf.split_valid <- h2o.splitFrame(train, ratios = c(0.8), seed = 1234)\n\n\n####\n#AUTO ML\n####\nt <- Sys.time()\naml <- h2o.automl(\nx = sel_feat_ed_big,\ny = \"alc_status\",\nmax_models = 50,\ntraining_frame = train,\nleaderboard_frame = test,\n# validation_frame = df.split_valid[[2]],\nbalance_classes = TRUE,\nnfolds = 5,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3,\nstopping_metric = \"AUCPR\",\nsort_metric = \"AUCPR\",\nseed = 1234)\n\ncat(\"time elapsed for AutoML: \", Sys.time() - t, \"\\n\")\nprint(aml@leaderboard, n = nrow(aml@leaderboard))\naml@leaderboard %>% as.data.frame()%>% write.csv(\"./autoML_EDonly.csv\")\n\nt <- Sys.time()\naml_2 <- h2o.automl(\nx = sel_feat,\ny = \"alc_status\",\nmax_models = 50,\ntraining_frame = train,\nleaderboard_frame = test,\n# validation_frame = df.split_valid[[2]],\nbalance_classes = TRUE,\nnfolds = 5,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3,\nstopping_metric = \"AUCPR\",\nsort_metric = \"AUCPR\",\nseed = 1234)\n\n\ncat(\"time elapsed for AutoML: \", difftime(Sys.time(), t, unit = \"hour\"), \"hours \\n\")\n#Took 27 hours\nprint(aml_2@leaderboard, n = nrow(aml_2@leaderboard))\naml_2@leaderboard %>% as.data.frame()%>% write.csv(\"./autoML_EDCIHI.csv\")\n\nmodH <- new_resultFrame()\nmodH <- add_cv_results(h2o.getModel(aml_2@leader@model_id), modH)\n\n\n # Model Hit_Ratio\n# 1 StackedEnsemble_BestOfFamily_AutoML_20210705_150745 0.815\n # Mean_PerC_Error LogLoss AUC AUCPR Recall Precision Specificity max_F1\n# 1 0.29 0.303 0.814 0.371 0.572 0.338 0.936 0.425\n # trshold training_AUC training_AUCPR training_Recall training_Precision\n# 1 0.159 0.819 0.415 0.555 0.382\n # training_Spec\n# 1 0.935\n# >\n\n##DOES NOT get better than this #", "meta": {"hexsha": "478731df6cc96087fdbce8dc6962129434446dd6", "size": 3025, "ext": "r", "lang": "R", "max_stars_repo_path": "Predective Modeling/h2o_autoML.r", "max_stars_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_stars_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Predective Modeling/h2o_autoML.r", "max_issues_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_issues_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Predective Modeling/h2o_autoML.r", "max_forks_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_forks_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8095238095, "max_line_length": 99, "alphanum_fraction": 0.6674380165, "num_tokens": 1007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3078118686435651}} {"text": "#calc TSNE for Seurat object\n# This function takes initial data from dr@tsne in initSeurObj and initiate by those the coordinates in newSeurObj;\n# the cells that are not found in initSeurObj are put at random positions\n# program uses Rtsne package and prepares seuratobjects manually\n#\n# written by Seva Makeev 2017 - 2021\n\ncalcTSNEGeneSpace <- function( newSeurObj, perplexity = 20, TSNErandSeed = 25, Norm = FALSE, initSeurObj = NULL){\n\nrequire( proxy)\n\nif(!require(\"Rtsne\")){\ninstall.packages(\"Rtsne\")}\nlibrary(\"Rtsne\")\n\nif(!require(\"beepr\")){\ninstall.packages(\"beepr\")}\nlibrary(\"beepr\")\n\n\n\n\nif( is.null(initSeurObj)){ initNew <- NULL}else{\n\n\ninitCells \t<- GetDimReduction(object = initSeurObj, reduction.type = \"tsne\", \n \t\tslot = \"cell.embeddings\")\ntsneMin \t<- apply(initCells, 2, min)\ntsneMax\t\t<- apply(initCells, 2, max)\n\nnewCells \t<- setdiff( colnames(newSeurObj@data), colnames(initSeurObj@data))\n\n#first we put all cells in newSeurObj at random positions\ninitNew\t\t<- data.frame( \n\t\t\tinitCellsX = runif( ncol( newSeurObj@data), min = tsneMin[1], max = tsneMax[1]),\n\t\t\tinitCellsY = runif( ncol( newSeurObj@data), min = tsneMin[2], max = tsneMax[2]), \n\t\t\trow.names = colnames( newSeurObj@data)\t\n\t\t\t)\n#and then update those present in initSeurObj with their positions\ninitNew[ rownames(initCells) , ] <- initCells\n}\n\n\n\n#now call tsne\ndistanceMatrix \t<- dist( t( newSeurObj@data), method = \"cosine\", pairwise = TRUE)\nrtsneRes\t<- Rtsne( distanceMatrix, is_distance = TRUE, \n\t\t\t\tperplexity = perplexity, \n\t\t\t\ttheta = 0, \n\t\t\t\teta = 500, \n\t\t\t\tpca = FALSE, \n\t\t\t\tmax_iter = 50000,\n\t\t\t\tpca_center = FALSE,\n\t\t\t\tnormalize = Norm,\n\t\t\t\tY_init = initNew\n\t\t\t)\n#and prepare Seurat Obj\ntsneData\t\t<- rtsneRes$Y\nrownames( tsneData) \t<- colnames( newSeurObj@data)\ncolnames( tsneData)\t<- c(\"tSNE_1\", \"tSNE_2\")\nnewSeurObj\t\t<- SetDimReduction( newSeurObj, reduction.type = \"tsne\", slot = \"cell.embeddings\", new.data = tsneData)\nnewSeurObj\t\t<- SetDimReduction(object = newSeurObj, reduction.type = \"tsne\", slot = \"key\", new.data = \"tSNE\")\n\n\n\n#newSeurObj <- RunTSNE( newSeurObj, genes.use = rownames(newSeurObj@data), seed.use = TSNErandSeed, \n#\ttheta = 0, eta = 10, max_iter = 3000, perplexity = 15, verbose = FALSE)\n\nbeep(3)\n\nreturn( newSeurObj)\n}\n\n\n\n\n", "meta": {"hexsha": "4aa054bfb635fb07ba0f4f83fe577a509d3aad77", "size": 2247, "ext": "r", "lang": "R", "max_stars_repo_path": "R/calcTSNEGeneSpace.r", "max_stars_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_stars_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/calcTSNEGeneSpace.r", "max_issues_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_issues_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/calcTSNEGeneSpace.r", "max_forks_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_forks_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1818181818, "max_line_length": 115, "alphanum_fraction": 0.699599466, "num_tokens": 747, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.7090191337850932, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.30770116225404603}} {"text": "#############################################################################\n##\n#W id320.r GAP library of id's Hans Ulrich Besche\n##\n\nID_GROUP_TREE.next[320].next[18]:=\nrec(\n fp:= [ 1822, 4076, 8937, 9617, 10023, 14478, 17138, 17238, 19339, 21999, \n22679, 24200, 26860, 27134, 29061, 30200, 32401, 36856, 38479, 39210, 39616, \n40696, 42913, 43971, 44809, 45057, 45130, 46578, 47026, 47347, 48832, 49243, \n49512, 49564, 51139, 51460, 52272, 53356, 53677, 53693, 54373, 55573, 55894, \n56727, 57033, 57713, 57790, 59234, 59793, 60007, 61894, 64095, 71890, 74244, \n74650, 79105, 81612, 81765, 83966, 86626, 87306, 88727, 91761, 93588, 94827, \n97028, 99129 ],\n level:= 4,\n next:= [ rec(\n fp:= [ 63396, 64472, 85041, 86784, 90159 ],\n next:= [ rec(\n desc:= [ 111003 ],\n fp:= [ 2, 12 ],\n next:= [ 410, 800 ] ), rec(\n desc:= [ 110003 ],\n fp:= [ 4, 14 ],\n next:= 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"hulpke/smallgrp", "max_stars_repo_head_hexsha": "b9664dbac0b9e8f8ad044b7c143cde17cff16a38", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2017-09-01T16:51:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-05T06:09:37.000Z", "max_issues_repo_path": "id2/id320.r", "max_issues_repo_name": "hulpke/smallgrp", "max_issues_repo_head_hexsha": "b9664dbac0b9e8f8ad044b7c143cde17cff16a38", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 50, "max_issues_repo_issues_event_min_datetime": "2016-08-01T12:41:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-27T00:10:22.000Z", "max_forks_repo_path": "id2/id320.r", "max_forks_repo_name": "gap-packages/smallgrp", "max_forks_repo_head_hexsha": "12d8f1f6075ebb1cfc2d148cc512e73852cee20b", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2017-09-04T08:13:00.000Z", "max_forks_repo_forks_event_max_datetime": "2019-12-26T14:10:43.000Z", "avg_line_length": 31.1377410468, "max_line_length": 77, "alphanum_fraction": 0.3859152437, "num_tokens": 4470, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7279754489059774, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.3075730011937148}} {"text": "source(\"R/functions/get_score.r\")\n\n# load SLE.sig signature genes\nsig.list = readRDS(\"sig/sig.list.RDS\")\ngene.sig = sig.list$SLE.sig\n\n# load gene expression data\nfn.ge = \"../generated_data/CHI/CHI_GE_matrix_gene.txt\"\ndat = fread(fn.ge, data.table = F) %>% \n tibble::remove_rownames() %>% tibble::column_to_rownames(\"gene\") %>% \n data.matrix()\n\nfn.si = \"../generated_data/CHI/CHI_sample_info_2_CD38hi.txt\"\ninfo = fread(fn.si, data.table=F)\n\nsi = with(info, time == 0) # day 0 only\ntdat = dat[,si]\ntinfo = info[si,]\n\ngi = toupper(rownames(tdat)) %in% toupper(gene.sig)\nsum(gi)\n\nihl = tinfo$Response %in% c(\"low\",\"high\")\nX = get_score(tdat[gi,])[ihl]\n\ndf = data.frame(subject=tinfo$subject[ihl], SLE.sig=X)\n\n# output scores\nfn.out = \"results/sig_scores/scores_SLE.sig_MA.txt\"\nfwrite(df, fn.out, sep=\"\\t\")\n", "meta": {"hexsha": "d9694de2858144e0031a9e63e5bfcddec69d996e", "size": 804, "ext": "r", "lang": "R", "max_stars_repo_path": "citeseq/R/SLE.sig_MA_sig_score.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "citeseq/R/SLE.sig_MA_sig_score.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "citeseq/R/SLE.sig_MA_sig_score.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 25.935483871, "max_line_length": 71, "alphanum_fraction": 0.6878109453, "num_tokens": 267, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160666, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3075439277580333}} {"text": "import_package('ggplot2', attach=TRUE)\n\n#' Plot genomic frequencies of states\n#'\n#' We extract from the model because chromstaR::genomicFrequencies is only\n#' showing the number of bins, not the fraction of the genome covered (if\n#' per.mark=FALSE; otherwise, it includes the width - don't know why)\n#'\n#' @param model Chromstar model object\n#' @return ggplot2 object (bar graph)\nfrequencies = function(model) {\n freqs = chromstaR::genomicFrequencies(model)\n fdf = reshape2::melt(as.array(freqs$frequency))\n p = ggplot(fdf, aes(x=Var1, y=value)) +\n scale_y_log10() +\n labs(x=\"modification\", y=\"number\") +\n geom_bar(stat=\"identity\") +\n theme_minimal() +\n theme(axis.text.x = element_text(angle=45, hjust=1))\n\n if (\"Var2\" %in% colnames(fdf))\n p + facet_wrap(~ Var2)\n else\n p\n}\n", "meta": {"hexsha": "2180c92bea69bce5690bf563d0c01e64ac0c6e11", "size": 846, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/chromstar/plot/frequencies.r", "max_stars_repo_name": "mschubert/ebits", "max_stars_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-20T12:36:29.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-20T12:36:29.000Z", "max_issues_repo_path": "tools/chromstar/plot/frequencies.r", "max_issues_repo_name": "mschubert/ebits", "max_issues_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2017-01-14T14:16:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T15:49:11.000Z", "max_forks_repo_path": "tools/chromstar/plot/frequencies.r", "max_forks_repo_name": "mschubert/ebits", "max_forks_repo_head_hexsha": "e9c4a3d883fb9fbcbfd4689becca0fe2e5cbdbe5", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-04-18T19:06:36.000Z", "max_forks_repo_forks_event_max_datetime": "2018-04-18T19:06:36.000Z", "avg_line_length": 32.5384615385, "max_line_length": 74, "alphanum_fraction": 0.6548463357, "num_tokens": 231, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6150878555160666, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3075439277580333}} {"text": "# 3. faza: Izdelava zemljevida\n\n# Uvozimo zemljevid.\nzemljevid <- uvozi.zemljevid(\"http://e-prostor.gov.si/fileadmin/BREZPLACNI_POD/RPE/OB.zip\",\n \"OB/OB\", encoding = \"Windows-1250\")\n\n# Preuredimo podatke, da jih bomo lahko izrisali na zemljevid.\ndruzine <- preuredi(druzine, zemljevid, \"OB_UIME\", c(\"Ankaran\", \"Mirna\"))\n\n# Izra\u010dunamo povpre\u010dno velikost dru\u017eine.\ndruzine$povprecje <- apply(druzine[1:4], 1, function(x) sum(x*(1:4))/sum(x))\nmin.povprecje <- min(druzine$povprecje, na.rm=TRUE)\nmax.povprecje <- max(druzine$povprecje, na.rm=TRUE)\n", "meta": {"hexsha": "77a7c7350fdb6187cfc2091b9cd5989acc1d0554", "size": 571, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "katjakerzic/APPR-2015-16", "max_stars_repo_head_hexsha": "c129694d368976863a90a268df06b4f009b3c9fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "katjakerzic/APPR-2015-16", "max_issues_repo_head_hexsha": "c129694d368976863a90a268df06b4f009b3c9fb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "katjakerzic/APPR-2015-16", "max_forks_repo_head_hexsha": "c129694d368976863a90a268df06b4f009b3c9fb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.7857142857, "max_line_length": 91, "alphanum_fraction": 0.6935201401, "num_tokens": 226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30754392775803324}} {"text": "library(reshape)\nlibrary(ggplot2)\n\n# load data and produce a freq table for each field\n\n\n# load this one for other analysis\nmaps_data <- read.table(\"maps_dataframe\",header=T)\n\n# use _primary if building CDF to compare filtered attributes\nmaps_data <- read.table(\"maps_dataframe_primary\",header=T)\n\nmaps_melt <- melt(maps_data)\nattach(maps_melt)\n#maps_apply <- tapply(Field,Institution,sum)\nmaps_table <- table(Field)\nmaps_table <- rev(sort(maps_table))\n\n# plot histogram of fields\nplot(maps_table)\n\n# for CDF\nmaps_frame <- as.data.frame(table(maps_melt$Field,maps_melt$Type))\n\n# no CDF\nmaps_frame <- as.data.frame(table(maps_melt$Field))\n\nmaps_frame <- maps_frame[order(-maps_frame$Freq),]\nattach(maps_frame)\n# maps_subs <- subset(maps_frame,Freq >= 5)\n# plot(maps_subs)\n\n# which have complete crossover?\n#maps_subs <- subset(maps_frame,maps_table )\nbarplot(maps_subs)\n\ndetach(maps_melt)\nattach(maps_frame)\n\ntag_factor <- factor(maps_frame$Var1, levels=maps_frame$Var1)\n\n\t\npostscript(\"plot-fieldfreq.eps\",horizontal=FALSE,onefile=FALSE,paper=\"special\",width=12,height=6)\np <- ggplot(maps_frame, aes(x=tag_factor, y=Freq)) + geom_bar(stat = \"identity\",fill=\"slateblue4\",colour=\"slateblue1\") + scale_x_discrete(breaks=NULL,name=\"Fields\") + scale_y_discrete(name=\"Frequency\")\np\ndev.off()\n\n# 69 attributes in 1 form\n# 29/69 are high-level \n\n\n# CDFs\n# show frequency distribution of all attributes\n\n# show freq dist of just high-level attributes\n\nmaps_frame_cdf <- melt(maps_frame)\nmaps_frame_cdf <- ddply(maps_frame_cdf, .(variable), transform, ecd=ecdf(value)(value))\nmaps_frame_cdf <- subset(maps_frame_cdf,value!=0)\n\npostscript(\"plot-cdf.eps\",horizontal=FALSE,onefile=FALSE,paper=\"special\",width=12,height=6)\n\np <- ggplot(maps_frame_cdf, aes(x=value)) + scale_x_continuous(breaks=1:10,name=\"Forms\") + scale_y_continuous(name=\"Field frequency\") + stat_ecdf(aes(colour=Var2)) + theme(panel.background = element_rect(fill = \"transparent\",colour = \"black\")) + scale_colour_manual(name=\"Field type\",labels=c(\"High level\",\"Sub-attribute\"),values=c(\"#13456f\", \"#e02828\"))\n\ndev.off()", "meta": {"hexsha": "8fbeb43f0c42ef97ed1533a4b13cf088685d6153", "size": 2074, "ext": "r", "lang": "R", "max_stars_repo_path": "Group_1/mappings/scripts.r", "max_stars_repo_name": "larskotthoff/recomputation-ss-paper", "max_stars_repo_head_hexsha": "0699d98f37446c8d8b124051d9c3bfc7efd81e86", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Group_1/mappings/scripts.r", "max_issues_repo_name": "larskotthoff/recomputation-ss-paper", "max_issues_repo_head_hexsha": "0699d98f37446c8d8b124051d9c3bfc7efd81e86", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Group_1/mappings/scripts.r", "max_forks_repo_name": "larskotthoff/recomputation-ss-paper", "max_forks_repo_head_hexsha": "0699d98f37446c8d8b124051d9c3bfc7efd81e86", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9076923077, "max_line_length": 354, "alphanum_fraction": 0.7589199614, "num_tokens": 577, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30754392775803324}} {"text": "################################################################################################\n# Dependencies\n#source(\"Code/get_dynamic_data.r\")\n\nGetUricData <- function(raw.data.name=\"train\") {\n\t\n\t# For a specified dataset in the original contest format, returns\n\t# a dataset containing only lab information. \n\t#\n\t# Args: \n\t# \traw.data.name: name of dataset\n\t#\n\t# Returns:\n\t#\tA list. The first element is a list of datasets that correspond to lab tests. The columns \n\t# in each dataset consist of subject.id, test delta and test result. \n\t#\tThe second element is a vector of column names corresponding to the test result column of \n\t#\teach dataset. \n\t#\n\t# Example usage: \n\t#\t#source(\"Code/get_lab_data.R\")\n\t#\tlab.data<-GetLabData(\"train\")\n\t\n\t# Check if RDA version of lab data has been stored\n\trda.filename = paste(\"Data/uric_\",raw.data.name,\".rda\",sep=\"\")\n\tif (file.exists(rda.filename)) {\n\t\tprint(paste(\"loading saved uric data from\",rda.filename,sep=\" \"))\n\t\tload(rda.filename)\n\t\tprint(\"finished loading\")\n\t\treturn(uric.data)\n\t}\n\n\t# Get raw data\n\t data<-GetRawData(raw.data.name)\t\n\n\t# Keep only lab data \n\t data <- data[data$form == \"Laboratory Data\", ]\t\n\t\n\turic.data<-TestToData(\"Uric Acid\",data)\n\t\n\turic.data$test.result[uric.data$test.unit==\"mg/dL\"]<-\n\t\turic.data$test.result[uric.data$test.unit==\"mg/dL\"]*59.48\n\t \n \t# Drop test name and test units\n\turic.data<-uric.data[,!(colnames(uric.data) %in% c(\"test.name\",\"test.unit\"))]\n\n\t# Sort by subject id and delta\n\turic.data<-uric.data[order(uric.data$subject.id,uric.data$lab.delta),]\n \n\t# Remove records with missing lab.delta, test.result, or subject.id\n\turic.data <- uric.data[!is.na(uric.data$lab.delta)& !is.na(uric.data$test.result)&\n \t!is.na(uric.data$subject.id),]\n\t\n\t# Check for duplicate deltas and delete those rows \n\t# Create combination of subject.ids and deltas\t\n\tid<-paste(as.integer(uric.data$subject.id), as.integer(uric.data$lab.delta),sep=\".\")\n\t\n\t# Check for duplicates in id\n\tdup<-duplicated(id)\n\t\n\t# Keep only nonduplicates - throws away second record with same delta\n\turic.data<-uric.data[!dup,]\n\n\t# Rename test.result column\n\tcolnames(uric.data)[colnames(uric.data)==\"test.result\"]<-\"Uric.Acid\"\n\t\n\t# Rename delta column\n\tcolnames(uric.data)[colnames(uric.data)==\"lab.delta\"]<-\"Uric.Acid.delta\"\n\n # Create outlier filter if filter does not exist\n num.sd<-4 \n filter.filename<-paste(\"Data/uric_filter\",num.sd,\"sd.rda\",sep=\"_\")\n\tif (file.exists(filter.filename)) {\n\t print(paste(\"loading saved uric filter from\",filter.filename,sep=\" \"))\n\t load(filter.filename)\n\t}\n else {\n uric.filter<-LabOutlierFilter(Uric.Acid,num.sd)\n save(uric.filter,file=filter.filename)\n }\n\n # Discard outliers\n uric.data<-LabDiscardOutlier(uric.data,uric.filter)\n\t\n\t# Save dataset to file\n\tsave(uric.data, file = rda.filename)\n\treturn(uric.data)\n}\n\n\n", "meta": {"hexsha": "eeefb2c2cb30c8b43c3e38ac40fc7f3268da5f66", "size": 2829, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/R/get_uric_data.r", "max_stars_repo_name": "ltfang/alsprize4life", "max_stars_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/R/get_uric_data.r", "max_issues_repo_name": "ltfang/alsprize4life", "max_issues_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/R/get_uric_data.r", "max_forks_repo_name": "ltfang/alsprize4life", "max_forks_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4333333333, "max_line_length": 96, "alphanum_fraction": 0.6765641569, "num_tokens": 800, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3075439207021907}} {"text": "# install.packages(\"shiny\")\r\nlibrary(shiny)\r\n\r\n# Divided into two major code segments: server side code and ui side code\r\n\r\n# Server side code - the server side code is where your program actually does stuff, ex: performs operations, uses functions etc.\r\nserver <- function(input, output) {\r\n # to show a simple plot in the ui, you have to create a \"reactive expression\" and then within that expression, create a plot as you normally would within R \r\n # within the shiny function renderPlot \r\n output$distPlot <- renderPlot({\r\n hist(rnorm(input$obs), col = 'darkgray', border = 'white')\r\n })\r\n}\r\n\r\n# ui - ui side is mostly aesthetic and sets the visual layout for your shiny app\r\n\r\n# fluidpage is an argument that can be used to intialize your ui\r\n# fluidpages can be sub-divided into different sections for your ui called panels (side panels and main panels)\r\n# side panels usually are where parameter changes are made in the form of drop-down menus, radio buttons and sliders whearas the main panel is usually where you show\r\n# an output of some kind, be it a plot or map or table of some kind\r\nui <- fluidPage(\r\n sidebarLayout(\r\n sidebarPanel(\r\n # Important to note that these min, max and value variables can themselves be reactive variables but more on that later\r\n sliderInput(\"obs\", \"Number of observations:\", min = 10, max = 500, value = 100)\r\n ),\r\n # Our mainpanel is where we will have the plot output\r\n mainPanel(plotOutput(\"distPlot\"))\r\n )\r\n)\r\n\r\nshinyApp(ui, server)", "meta": {"hexsha": "29d688bb5ed5c484a20539342907d1df0ac364d5", "size": 1509, "ext": "r", "lang": "R", "max_stars_repo_path": "MattTutorial/app1.r", "max_stars_repo_name": "CNuge/RUsersGroup", "max_stars_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-01-26T16:52:42.000Z", "max_stars_repo_stars_event_max_datetime": "2018-02-19T21:32:38.000Z", "max_issues_repo_path": "MattTutorial/app1.r", "max_issues_repo_name": "CNuge/RUsersGroup", "max_issues_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MattTutorial/app1.r", "max_forks_repo_name": "CNuge/RUsersGroup", "max_forks_repo_head_hexsha": "b1cab5afa76b552afc6b7840398c9305ae76fd16", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2018-09-21T13:02:17.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-07T16:43:22.000Z", "avg_line_length": 47.15625, "max_line_length": 166, "alphanum_fraction": 0.7223326706, "num_tokens": 348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.30753280861137927}} {"text": "\r\nlibrary(ggplot2)\r\nlibrary(reshape2)\r\n\r\ninputFile=parSampleFile1\r\nnameMapFile=parSampleFile2\r\noutputPrefix=paste0(outFile, \".HTO.summary\")\r\n\r\ncat(\"inputFile=\", inputFile, \"\\n\")\r\ncat(\"outputPrefix=\", outputPrefix, \"\\n\")\r\ncat(\"nameMapFile=\", nameMapFile, \"\\n\")\r\n\r\nfiles=read.table(inputFile, sep=\"\\t\", stringsAsFactor=F)\r\n\r\ndat=apply(files, 1, function(x){\r\n dname=x[[2]]\r\n dfile=x[[1]]\r\n dd=read.csv(dfile, check.names=F)\r\n table(dd$HTO.global)\r\n})\r\ncolnames(dat)=files$V2\r\nwrite.csv(dat, file=paste0(outputPrefix, \".csv\"), quote=F)\r\n\r\nmdat=reshape2::melt(dat)\r\ncolnames(mdat)=c(\"Class\", \"Sample\", \"Cell\")\r\n\r\npng(paste0(outputPrefix, \".global.png\"), width=1600, height=1200, res=300)\r\ng<-ggplot(mdat, aes(x=Sample, y=Cell, fill=Class, label=Cell)) + geom_bar(position=\"stack\", stat=\"identity\") + geom_text(size = 3, position = position_stack(vjust = 0.5)) + theme_bw()\r\nprint(g)\r\ndev.off()\r\n\r\nhasNameMap = ! is.na(nameMapFile)\r\nif (hasNameMap) {\r\n df = read.table(nameMapFile, sep=\"\\t\", stringsAsFactor=F)\r\n namemap=df$V1\r\n names(namemap)=df$V2\r\n}\r\n\r\ndat=apply(files, 1, function(x){\r\n dname=x[[2]]\r\n dfile=x[[1]]\r\n dd=read.csv(dfile, stringsAsFactors=F, check.names=F)\r\n dd$HTO.final=dd$HTO.global\r\n dd$HTO.final[dd$HTO.global==\"Singlet\"] = dd$HTO[dd$HTO.global==\"Singlet\"]\r\n dt=data.frame(table(dd$HTO.final), stringsAsFactors=F)\r\n dt$Var1=as.character(dt$Var1)\r\n dt$Sample=dname\r\n dt\r\n})\r\nmdat=do.call(\"rbind\", dat)\r\nmdat$Cell=mdat$Var1\r\nif (hasNameMap) {\r\n mdat$Cell[mdat$Var1 %in% names(namemap)] = namemap[mdat$Var1[mdat$Var1 %in% names(namemap)]]\r\n}\r\nmdat=mdat[,c(\"Sample\", \"Cell\", \"Freq\")]\r\ncolnames(mdat)=c(\"Sample\", \"Class\", \"Cell\")\r\n\r\ndat=reshape2::dcast(mdat, formula=\"Sample~Class\")\r\nwrite.csv(dat, file=paste0(outputPrefix, \".csv\"), quote=F, row.names=F)\r\n\r\npng(paste0(outputPrefix, \".sample.png\"), width=1600, height=1200, res=300)\r\ng<-ggplot(mdat, aes(x=Sample, y=Cell, fill=Class, label=Cell)) + geom_bar(position=\"stack\", stat=\"identity\") + geom_text(size = 3, position = position_stack(vjust = 0.5)) + theme_bw()\r\nprint(g)\r\ndev.off()\r\n", "meta": {"hexsha": "83533cabaa476f4536dbd40ba5fe59894d9b5338", "size": 2071, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/scRNA/split_samples_summary.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/scRNA/split_samples_summary.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/scRNA/split_samples_summary.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 31.8615384615, "max_line_length": 184, "alphanum_fraction": 0.6745533559, "num_tokens": 681, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.30753280861137927}} {"text": "#' @title Bootstrap dissimilarity\n#'\n#' @description Bootstrap dissimilarity between two vectors of interactions (or\n#' species names).\n#'\n#' @param lst_vect A list with two character vectors of interactions.\n#'\n#' @param index The name of the desired index, e.g.: \"whittaker\", \"jaccard\",\n#' \"sorensen\". Character type.\n#'\n#' @param by Integer. Number of interactions used to increase gradually the\n#' sampled vectors of interactions until all observations are sampled. If `by`\n#' is too small (e.g. 1) then the computation time is very long depending on\n#' your total number of interactions from which samples are taken. As a rule\n#' of thum set `by` to maybe 5-10\\\\% of your total interactions.\n#'\n#' @param replace Should sampling be with replacement? Passed to\n#' \\code{\\link[base]{sample}}.\n#'\n#' @param size_short Should the total sample size be set by the length of the\n#' shortest vector from `lst_vect`? Logic type (`TRUE` or `FALSE`).\n#'\n#' @param n_boot Number of desired bootstraps (100 can be enough).\n#'\n#' @param n_cpu Number of CPU-s to use for parallel processing.\n#'\n#' @return A matrix of dissimilarities. The number of columns corresponds to\n#' `n_boot` (number of bootstraps). The names of the rows store the sample\n#' size of each sampled chunk. The final value corresponds to the length of\n#' the shortest vector (when `size_short = TRUE`), or to the length of the\n#' longest vector (when `size_short = FALSE`).\n#'\n#' @examples\n#'\n#' library(bootdissim)\n#' library(bipartite)\n#'\n#' net1 <- reshape_net(vazarr, seed = 1)\n#' net2 <- reshape_net(vazcer, seed = 1)\n#'\n#' vect <- get_interactions(net1, net2)\n#'\n#' tst <- boot_dissim(lst_vect = vect,\n#' index = \"whittaker\",\n#' by = 50,\n#' replace = TRUE,\n#' size_short = FALSE,\n#' n_boot = 10,\n#' n_cpu = 2)\n#'\n#' @importFrom foreach foreach %dopar% registerDoSEQ\n#' @importFrom parallel splitIndices makeCluster stopCluster\n#' @importFrom doParallel registerDoParallel\n#' @importFrom iterators iter\n#'\n#' @export\n#'\n#' @md\nboot_dissim <- function(lst_vect,\n index,\n by,\n replace,\n size_short,\n n_boot,\n n_cpu){\n\n # Detect which of the two vectors is the short one and which is the long one.\n which_short <- which.min(sapply(X = lst_vect, FUN = length))\n which_long <- which(1:2 != which_short)\n vect_short <- lst_vect[[which_short]]\n vect_long <- lst_vect[[which_long]]\n\n # If the user chooses to use as total sample size the length of the shortest\n # vector, then set is so.\n size <- ifelse(isTRUE(size_short),\n yes = length(vect_short),\n no = length(vect_long))\n\n # Sample the vectors here to get the sample sizes of each chunk so that can be\n # passed later on easily to the results and will end up on the OX axis of the\n # graphs. Avoid to call this in the loop, because it stays constant anyways\n # and otherwise consumes unnecessary time.\n chunks_lst <- sample_vectors(v1 = vect_short,\n v2 = vect_long,\n size = size,\n by = by,\n seed = 42,\n replace = replace)\n n <- length(chunks_lst[[1]])\n # same as length(chunks_lst[[2]])\n spl_size <- sapply(chunks_lst[[1]], FUN = length)\n # same as sapply(chunks_lst[[2]], FUN = length)\n\n # Start parallel processing\n chunks <- parallel::splitIndices(n_boot, n_cpu)\n cl <- parallel::makeCluster(n_cpu)\n doParallel::registerDoParallel(cl)\n i <- NULL # to avoid 'Undefined global functions or variables: i' in R CMD check\n # Compute dissimilarities in parallel\n boot_lst <-\n foreach::foreach(i = iterators::iter(chunks),\n .errorhandling = 'pass',\n .export = c(\"boot_dissim_once\",\n \"sample_vectors\",\n \"get_abc\",\n \"get_beta\")) %dopar% {\n lapply(i, FUN = function(x) # note the lapply!\n boot_dissim_once(vect_short = vect_short,\n vect_long = vect_long,\n index = index,\n size = size,\n by = by,\n replace = replace,\n seed = x, # iterator is passed as seed (values are 1:n_boot)\n n = n)\n )\n }\n parallel::stopCluster(cl)\n remove(cl)\n foreach::registerDoSEQ()\n # End of parallel processing\n\n # Prepare results as a matrix. The row names give the sample size of each\n # chunk. There are as many column as the number of bootstraps (n_boot).\n boot_lst <- unlist(boot_lst, recursive = FALSE)\n results <- do.call(what = \"cbind\", args = boot_lst)\n rownames(results) <- spl_size\n\n return(results)\n}\n\n\n#' @title Compute dissimilarity for a sample\n#'\n#' @description Compute dissimilarity between two vectors of interactions (or\n#' species names) for a single run (sample). You will rarely use this function\n#' alone. It was designed to be executed in parallel by\n#' \\code{\\link[bootdissim]{boot_dissim}}. See more details there.\n#'\n#' @param vect_short The shorter vectors of interactions.\n#'\n#' @param vect_long The longer vectors of interactions.\n#'\n#' @param index,size,by,replace See \\code{\\link[bootdissim]{boot_dissim}}.\n#'\n#' @param seed Passed to \\code{\\link[base]{set.seed}}. Set seed to get\n#' reproducible random results. Is give by the iterator of the loop during the\n#' parallel processing in \\code{\\link[bootdissim]{boot_dissim}}.\n#'\n#' @param n Passed from \\code{\\link[bootdissim]{boot_dissim}}. Is constant\n#' across all bootstrap iterations.\n#'\n#' @return A vector of dissimilarities. The length of this vector corresponds to\n#' `n`.\n#'\n#' @export\n#'\n#' @md\nboot_dissim_once <- function(vect_short,\n vect_long,\n index,\n size,\n by,\n replace,\n seed,\n n){\n\n chunks_lst <- sample_vectors(v1 = vect_short,\n v2 = vect_long,\n size = size,\n by = by,\n seed = seed,\n replace = replace)\n\n dissim <- rep(NA, n)\n\n for (i in 1:n){\n v1 <- chunks_lst[[\"v1\"]][[i]]\n v2 <- chunks_lst[[\"v2\"]][[i]]\n abc <- get_abc(list(v1, v2))\n dissim[[i]] <- get_beta(abc, index)\n }\n\n return(dissim)\n}\n", "meta": {"hexsha": "d627b27e9870b76c2c76a65624c41bb22bf75b05", "size": 7094, "ext": "r", "lang": "R", "max_stars_repo_path": "R/boot.r", "max_stars_repo_name": "valentinitnelav/bootstrapturnover", "max_stars_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-03-22T19:14:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T17:01:52.000Z", "max_issues_repo_path": "R/boot.r", "max_issues_repo_name": "valentinitnelav/bootstrapturnover", "max_issues_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/boot.r", "max_forks_repo_name": "valentinitnelav/bootstrapturnover", "max_forks_repo_head_hexsha": "86c27b46be0d3d63100ea695b4724175f703e823", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1397849462, "max_line_length": 114, "alphanum_fraction": 0.5553989287, "num_tokens": 1617, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3075328086113792}} {"text": "#Rienje Veenhof and Frank Fluit\n#Project for the course Geoscripting at Wageningen University\n#28-1-2021\n#This file contains a function that calculates difference rasters\n\ndif_func <- function(lockdown_raster, raster_of_interest){\n difference_raster <- raster_of_interest - lockdown_raster\n return(difference_raster)\n}\n", "meta": {"hexsha": "c8011413a6423659d5dba2bfb0e22adbf0957352", "size": 321, "ext": "r", "lang": "R", "max_stars_repo_path": "R/difference_raster.r", "max_stars_repo_name": "Rienje/Air_polution_Nepal", "max_stars_repo_head_hexsha": "18619d6f1556c718d3be27854347efb871feb87a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/difference_raster.r", "max_issues_repo_name": "Rienje/Air_polution_Nepal", "max_issues_repo_head_hexsha": "18619d6f1556c718d3be27854347efb871feb87a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/difference_raster.r", "max_forks_repo_name": "Rienje/Air_polution_Nepal", "max_forks_repo_head_hexsha": "18619d6f1556c718d3be27854347efb871feb87a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.1, "max_line_length": 65, "alphanum_fraction": 0.8224299065, "num_tokens": 81, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.30753280861137916}} {"text": "rm(list = ls())\ninstall.packages(\"ggplot2\")\ninstall.packages(\"latex2exp\")\ninstall.packages(\"GenSA\")\ninstall.packages(\"maxLik\")\n", "meta": {"hexsha": "044c5690c553a9faa29a8182ec6f5676ede3ee1e", "size": 127, "ext": "r", "lang": "R", "max_stars_repo_path": "Code_r/requirements.r", "max_stars_repo_name": "lcbjrrr/Code_GRSL_2020_1_dockers", "max_stars_repo_head_hexsha": "e765e397df0b82737ede5befed3cdad64480386d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code_r/requirements.r", "max_issues_repo_name": "lcbjrrr/Code_GRSL_2020_1_dockers", "max_issues_repo_head_hexsha": "e765e397df0b82737ede5befed3cdad64480386d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code_r/requirements.r", "max_forks_repo_name": "lcbjrrr/Code_GRSL_2020_1_dockers", "max_forks_repo_head_hexsha": "e765e397df0b82737ede5befed3cdad64480386d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.1666666667, "max_line_length": 29, "alphanum_fraction": 0.7480314961, "num_tokens": 35, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.30753280861137916}} {"text": "# function that corrects \"sample\" execution in the case of only one element in the population (more info: ?sample and ?sample.int)\nsample2 <- function(x, ...) x[sample.int(length(x), ...)]\n", "meta": {"hexsha": "91feb69ada63e2ea432922c523825b00eca8d85e", "size": 189, "ext": "r", "lang": "R", "max_stars_repo_path": "000_Funs/func_sample2.r", "max_stars_repo_name": "nmprista/KustMonitorOptim", "max_stars_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "000_Funs/func_sample2.r", "max_issues_repo_name": "nmprista/KustMonitorOptim", "max_issues_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "000_Funs/func_sample2.r", "max_forks_repo_name": "nmprista/KustMonitorOptim", "max_forks_repo_head_hexsha": "cfcf327060013f24013350e1e6591b40bc672b3b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 63.0, "max_line_length": 130, "alphanum_fraction": 0.708994709, "num_tokens": 46, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.5964331462646254, "lm_q1q2_score": 0.30753280861137916}} {"text": "get.effects.from.model = function( lm.model ) {\n\n model.statement = formula( lm.model )\n all.variables = all.vars(model.statement)\n \n if (length( all.variables ) < 2) {\n print (\" No results \" )\n return ()\n }\n\n dependent.var = all.variables[1] \n all.terms = attr( terms( model.statement), \"term.labels\" )\n \n\n # general stats (unadjusted medians/quantiles) \n # for main effects that are significant\n \n # unadjusted effects \n lm.data = lm.model$model # internal copy of the data table\n results.unadjusted = list()\n for ( effect.variable in all.terms ) {\n print(\"---\")\n print ( paste( effect.variable, \" -- unadjusted: \") )\n results.unadjusted[[effect.variable]] =\nextract.summary.data.for.one.variable( lm.data[,1],lm.data[,effect.variable])\n} \n lm.data$alldata = 1\n results.unadjusted[[\"alldata\"]] =\nextract.summary.data.for.one.variable( lm.data[,1], lm.data[,\"alldata\"]) \nresults.adjusted = NULL\n for ( effect.variable in all.terms ) {\n print( paste( effect.variable, \"...\" ) )\n res = try( effect( effect.variable, lm.model ), silent=T )\n if (class(res)==\"try-error\") {\n print( \"There was a problem extracting the effects from:\" )\n print( effect.variable )\n print( \"And here is the error message:\")\n print( res )\n print( \"Here is the data and a summary of the data:\")\n print( lm.model$model[[effect.variable]] )\n print( summary(lm.model$model[[effect.variable]]) )\n print( \"try combining some levels? ...\" )\n plot( lm.model$model[[effect.variable]] ) \n } else {\n med = as.vector(summary(res, typical=\"median\")$effect)\n res.summary = summary(res)\n res.effect = res.summary$effect\n m = expand.grid(dimnames(res.effect)) \n if (!is.na(dim(res.effect)[2])) {\n n = paste(as.character(m[,1]), as.character(m[,2]), sep=\".\")\n } else {\n n = as.character(m[,1])\n }\n \n \n results.adjusted = rbind(results.adjusted, \n cbind(dependent.var, effect.variable, n , exp(med)-1, \n exp(as.vector(res.summary$effect))-1, \n exp(as.vector(res.summary$lower))-1, \n exp(as.vector(res.summary$upper))-1 ) )\n\nfilename = paste(\"adjusted.means\", dependent.var, effect.variable,\"png\", sep=\".\") \nfilename = gsub(\":\", \"__\", filename, fixed=T) \n png( filename=filename )\n plot(effect(effect.variable, lm.model ))\n dev.off()\n\n# win.metafile(filename = graphfile, width = 7, height = 5)\n# plot(effect(effect.variable, lm.model ))\n# dev.off() \n }\n }\n rownames(results.adjusted) = NULL\n colnames(results.adjusted) = c(\"interval\", \"effect\", \"level\", \"median\", \"mean\", \"95%CI.lower\", \"95%CI.upper\")\n results.adjusted = as.data.frame(results.adjusted)\n for (i in 4:6) results.adjusted[,i] = as.numeric(as.character(results.adjusted[,i])) \n print(\" *.PNG Figures have been saved in your work directory\" )\n \n return(list( results.unadjusted=results.unadjusted,\nresults.adjusted=results.adjusted)) \n\n}\n\n\n", "meta": {"hexsha": "09f449773ec1c33594387ec2cc6e676d5b1f8799", "size": 3083, "ext": "r", "lang": "R", "max_stars_repo_path": "R/get.effects.from.model.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/get.effects.from.model.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/get.effects.from.model.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 36.2705882353, "max_line_length": 113, "alphanum_fraction": 0.6091469348, "num_tokens": 776, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.307508490360365}} {"text": "\nrequrie(ilo)\n\ninit_ilo()\n\nrequire(Rilostat)\n\ncountry <- get_ilostat(id = 'UNE_2EAP_SEX_AGE_RT_A', filters = list(time = '2017', sex = 'SEX_T', classif1 = 'AGE_YTHADULT_YGE15')) %>% distinct(ref_area) %>% filter(!str_sub(ref_area, 1,1) %in% 'X')\n\n\nregion <- get_ilostat_toc(segment = 'ref_area', search = 'Annual') %>% select(ref_area, wb_income_group.label,ilo_region.label, ilo_subregion_broad.label, ilo_subregion_detailed.label) %>% filter(!str_sub(ref_area, 1,1) %in% 'X') \n\n\nEAP <- get_ilostat(id = 'EAP_2EAP_SEX_AGE_NB_A', filters = list(time = '2017', sex = 'SEX_T', classif1 = 'AGE_YTHADULT_YGE15')) %>% filter(!str_sub(ref_area, 1,1) %in% 'X') %>% select(ref_area, EAP2017 = obs_value)\nEAP_income = EAP %>% left_join(region) %>% group_by(wb_income_group.label) %>% summarise(EAP2017_income = sum(EAP2017)) %>% ungroup \n\n\ncountry <- country %>% left_join(region, by = \"ref_area\")\n\nX <- get_ilostat( id = 'EIP_NEET_SEX_RT_A', detail = 'bestsourceonly') %>% select(ref_area, time)\n\n\n\n\n\nres <- country %>% left_join(X %>% count(ref_area, time) %>% spread(time, n) , by = \"ref_area\") %>% left_join(EAP , by = \"ref_area\") %>% save_ilo()\n\n\ntest <- country %>% count(wb_income_group.label) %>% rename(new = n)\nX %>% left_join(EAP) %>% \n\t\t\n\t\t\tfilter(!EAP2017 %in% NA) %>% \n\t\t\tleft_join(region) %>% \n\t\t\tleft_join(EAP_income) %>%\n\t\t\tdistinct() %>% \n\t\t\tgroup_by(wb_income_group.label, time) %>% \n\t\t\tsummarise(ref_EAP = sum(EAP2017) / first(EAP2017_income ) * 100) %>%\n\t\t\tungroup() %>% left_join(test) %>% spread(time, ref_EAP)%>% select(-new)save_ilo()", "meta": {"hexsha": "deb6e14d3ccef649d188fafba3ad1c260088da77", "size": 1553, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/doc/do/TEST_INDICATOR_AVAILABLE.r", "max_stars_repo_name": "dbescond/iloData", "max_stars_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/doc/do/TEST_INDICATOR_AVAILABLE.r", "max_issues_repo_name": "dbescond/iloData", "max_issues_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/doc/do/TEST_INDICATOR_AVAILABLE.r", "max_forks_repo_name": "dbescond/iloData", "max_forks_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.8684210526, "max_line_length": 232, "alphanum_fraction": 0.6651641983, "num_tokens": 534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.30738600523484344}} {"text": "#set the working dir to current\nthis.dir <- dirname(parent.frame(2)$ofile)\nsetwd(this.dir)\n\n\nheart_attack_mortality_histogram <- function() {\n\toutcome = read.csv(\"./rprog-data-ProgAssignment3-data/outcome-of-care-measures.csv\", colClasses = \"character\")\n\theart_attacks = as.numeric(outcome[,11])\n\thist(heart_attacks)\n}\n\nheart_attack_mortality_histogram()", "meta": {"hexsha": "403e9bbfa29d0ad180002697e6fd00c5baf523d1", "size": 354, "ext": "r", "lang": "R", "max_stars_repo_path": "assignment3/mortality.r", "max_stars_repo_name": "shaunakv1/r-experiments", "max_stars_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-01-18T19:46:27.000Z", "max_stars_repo_stars_event_max_datetime": "2016-01-18T19:46:27.000Z", "max_issues_repo_path": "assignment3/mortality.r", "max_issues_repo_name": "shaunakv1/r-experiments", "max_issues_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "assignment3/mortality.r", "max_forks_repo_name": "shaunakv1/r-experiments", "max_forks_repo_head_hexsha": "effaac65f4bd4d7d6540e613c8686423777c8fd6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5, "max_line_length": 111, "alphanum_fraction": 0.7711864407, "num_tokens": 88, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3073633411834779}} {"text": "pacman::p_load(tidyverse, rio)\r\npacman::p_load(ggh4x)\r\npacman::p_load(gganimate)\r\n\r\nvas_per <- import(\"C:/R/Wonderful-Wednesdays/2021-05-12/vas_per.rds\")\r\nvas_vis <- import(\"C:/R/Wonderful-Wednesdays/2021-05-12/vas_vis.rds\") %>% \r\n filter(ady <= 420) \r\n\r\n\r\nmysqrt_trans <- function() {\r\n scales::trans_new(\"mysqrt\", \r\n transform = base::sqrt,\r\n inverse = function(x) ifelse(x<0, 0, x^2),\r\n domain = c(0, Inf))\r\n}\r\n\r\naxs_df <- crossing(name = c('sym','dose','cumrem','cumrel'),\r\n txt = c('a','b','c','d') ) %>% \r\n as.data.frame() %>% \r\n mutate(name = as.factor(name) %>% \r\n fct_relevel('sym','dose','cumrem','cumrel')) %>% \r\n filter(name == 'sym')\r\n\r\nfor (s in unique(vas_per$subject)) {\r\n \r\n v <- vas_vis %>%\r\n filter(subject == s) %>%\r\n select(subject, trt01pc, ady, sym, dose, rem, rel) %>%\r\n mutate(cumrem = cumsum(rem),\r\n cumrel = cumsum(rel)) %>% \r\n select(-rem, -rel) %>% \r\n pivot_longer(cols = c(sym, dose, cumrem, cumrel)) %>% \r\n mutate(name = factor(name) %>% \r\n fct_relevel('sym','dose','cumrem','cumrel'))\r\n \r\n v2 <- v %>% \r\n rename(ady_2 = ady)\r\n \r\n g1 <- ggplot(data = v,\r\n aes(x = ady, y = value, group = subject)) +\r\n \r\n geom_segment(data = axs_df,\r\n x = 1, xend = 365, y = 33, yend = 33,\r\n arrow = arrow(length = unit(0.05, \"native\"), type = \"closed\"),\r\n inherit.aes = FALSE) +\r\n geom_label(data = axs_df,\r\n x = 168, label = \"On Treatment\",\r\n y = 33,\r\n size = 2,\r\n inherit.aes = FALSE) +\r\n geom_segment(data = axs_df,\r\n x = 366, xend = 425, y = 33, yend = 33,\r\n arrow = arrow(length = unit(0.05, \"native\"), type = \"closed\"),\r\n inherit.aes = FALSE) +\r\n geom_label(data = axs_df,\r\n x = 391, label = \"Off Treatment\",\r\n y = 33,\r\n size = 2,\r\n inherit.aes = FALSE) +\r\n coord_cartesian(clip = \"off\") +\r\n \r\n geom_vline(xintercept = c(168, 365), col = 'gray65') +\r\n geom_line(data = v2,\r\n aes(x = ady_2, y = value, group = 1),\r\n size = 1, \r\n alpha = 0.5,\r\n color = ifelse(v2$trt01pc == 'T', '#1b9e77', '#d95f02')) +\r\n geom_line(size = 1, color = ifelse(v$trt01pc == 'T', '#1b9e77', '#d95f02')) +\r\n geom_point( color = ifelse(v$trt01pc == 'T', '#1b9e77', '#d95f02')) + \r\n facet_wrap(~name,\r\n strip.position = 'left',\r\n ncol = 1,\r\n scales = 'free_y',\r\n labeller = as_labeller(c(sym = \"Vasculitis symptom score \\n(sqrt)\", \r\n dose = \"Oral Corticosteroid dose \\n(log+1)\", \r\n cumrem = \"Remission Event \\n(cumulative)\", \r\n cumrel = \"Relapse Event\\n(cumulative sqrt)\"))\r\n ) +\r\n facetted_pos_scales(y = list(scale_y_continuous(limits = c(0, 28),\r\n trans = 'mysqrt',\r\n breaks = c( 0, 1, 4, 10, 20),\r\n labels = c('0','1','4','10','20'),\r\n minor_breaks = NULL,\r\n expand = c(0.03, 0.01)),\r\n scale_y_continuous(limits = c(0, 1280),\r\n breaks = c( 0, 2, 7.5, 25, 75, 250, 750), # seq(0, 35, by = 5)^2,\r\n labels = c('0','2','7.5','25','75','250','750'),\r\n expand = c(0.03, 0.01),\r\n trans = \"log1p\",\r\n minor_breaks = NULL),\r\n scale_y_continuous(limits = c(0,350),\r\n breaks = c(0, 100, 200, 300),\r\n expand = c(0.03, 0.01),\r\n minor_breaks = NULL),\r\n scale_y_continuous(limits = c(0,11),\r\n trans = 'mysqrt',\r\n breaks = c(0, 1, 4, 9),\r\n expand = c(0.03, 0.01),\r\n minor_breaks = NULL))\r\n ) +\r\n scale_x_continuous(name = \"Study Day\",\r\n limits = c(0, 420),\r\n breaks = c(1, 168, 365, 420),\r\n labels = c(\"1\", \"168 days\\n 24 wks\", \"365 days\\n 52 wks\", \"420 days\\n 60 wks\"),\r\n expand = c(0.01, 0.01),\r\n minor_breaks = NULL) + \r\n force_panelsizes(rows = c(1,1,0.5,0.5),\r\n respect = FALSE) +\r\n labs(y = NULL) +\r\n theme_bw(base_size = 10) +\r\n theme(plot.margin = unit(c(1.5, 0.5, 0.5, 0.5), units=\"lines\"),\r\n strip.placement = \"outside\",\r\n axis.text.x = element_text(hjust = 1)) +\r\n transition_reveal(ady, range=c(1L, 420L)) \r\n \r\n a_g1 <- animate(g1,\r\n nframes = 100,\r\n width = 6.0,\r\n height = 7.0,\r\n units = 'in',\r\n res = 96,\r\n end_pause = 25)\r\n \r\n anim_save(filename = str_glue(\"subject_{s}.gif\"),\r\n a_g1,\r\n path = \"C:/R/Wonderful-Wednesdays/2021-05-12/vas-subject-gif/\")\r\n}\r\n", "meta": {"hexsha": "87dfe1edbce79bb3c117e1515db63184e49f24ea", "size": 5970, "ext": "r", "lang": "R", "max_stars_repo_path": "vas-subject-gif.r", "max_stars_repo_name": "agstn/VAS", "max_stars_repo_head_hexsha": "0360a2a547e5648c2f42c3caa02dd13bbfbde8b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vas-subject-gif.r", "max_issues_repo_name": "agstn/VAS", "max_issues_repo_head_hexsha": "0360a2a547e5648c2f42c3caa02dd13bbfbde8b7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vas-subject-gif.r", "max_forks_repo_name": "agstn/VAS", "max_forks_repo_head_hexsha": "0360a2a547e5648c2f42c3caa02dd13bbfbde8b7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.640625, "max_line_length": 126, "alphanum_fraction": 0.3798994975, "num_tokens": 1524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30727068830131404}} {"text": "test_that(\"Reach trimming works\", {\n\tskip_on_cran()\n\tws = readRDS(system.file(\"testdata/ws_intersect/ws.rds\", package=\"WatershedTools\"))\n\texpect_error(ws_trim <- trim_reaches(ws, 200), regex=NA)\n\texpect_lt(length(unique(ws_trim$data$reachID)), length(unique(ws$data$reachID)))\n\texpect_equal(nrow(ws_trim$reach_adjacency), length(unique(ws_trim$data$reachID)))\n\t\n\t# check headwater reach lengths\n\trch_len = tapply(ws_trim$data$length, ws_trim$data$reachID, sum)\n\thw = headwaters(ws_trim)$reachID\n\trch_len = rch_len[names(rch_len) %in% as.character(hw)]\n\texpect_gte(min(rch_len), 200)\n})\n\nws = readRDS(system.file(\"testdata/testWS.rds\", package=\"WatershedTools\"))\ntest_that(\"Reach resizing works\", {\n\tskip_on_cran()\n\texpect_error(ws_rsz <- resize_reaches(ws, 500, 200), regex=NA)\n\t## parallel or not shouldn't matter\n\texpect_identical(ws_rsz, resize_reaches(ws, 500, 200, parallel=FALSE))\n\t\n\trch_len = tapply(ws_rsz$data$length, ws_rsz$data$reachID, sum)\n\texpect_gt(length(unique(ws_rsz$data$reachID)), length(unique(ws$data$reachID)))\n\texpect_equal(nrow(ws_rsz$reach_adjacency), length(unique(ws_rsz$data$reachID)))\n\texpect_gte(min(rch_len), 200)\n\t# max size is the size + min_size + 2sqrt(2)res(ws)\n\texpect_lte(max(rch_len), 500+200+28)\n})\n\ntest_that(\"Reach Adjacency\", {\n\thw = headwaters(ws)$reachID\n\texpect_error(adj <- sapply(hw, function(i) WatershedTools:::reachAdj(ws, i)), regex=NA)\n\texpect_true(all(sapply(adj, is.null)))\n\trch = unique(ws$data$reachID)\n\trch = rch[!rch %in% hw]\n\texpect_error(adj <- sapply(rch, function(i) WatershedTools:::reachAdj(ws, i)), regex=NA)\n\texpect_gte(nrow(adj), length(rch))\n})", "meta": {"hexsha": "87b93a7991aeede0bc41b3a81297a7130a76553c", "size": 1614, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-reach.r", "max_stars_repo_name": "mtalluto/WatershedTools", "max_stars_repo_head_hexsha": "0aae58652d3bb38de11b6c3c356e9a8ce90a4d71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-07-02T15:55:59.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T11:45:03.000Z", "max_issues_repo_path": "tests/testthat/test-reach.r", "max_issues_repo_name": "mtalluto/WatershedTools", "max_issues_repo_head_hexsha": "0aae58652d3bb38de11b6c3c356e9a8ce90a4d71", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-06-26T09:48:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-28T18:03:18.000Z", "max_forks_repo_path": "tests/testthat/test-reach.r", "max_forks_repo_name": "mtalluto/WatershedTools", "max_forks_repo_head_hexsha": "0aae58652d3bb38de11b6c3c356e9a8ce90a4d71", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-04-20T17:39:39.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-20T17:39:39.000Z", "avg_line_length": 42.4736842105, "max_line_length": 89, "alphanum_fraction": 0.7434944238, "num_tokens": 482, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30727068035304783}} {"text": "#' howbig\n#' \n#' Determines the memory usage for a dense, in-core, numeric matrix of\n#' specified rows/columns.\n#' \n#' @details\n#' These functions provide the memory usage of an unallocated, dense, in-core,\n#' numeric matrix. As the name suggests, \\code{howbig()} simply returns the\n#' size (as a \\code{memuse} object).\n#' \n#' @param nrow,ncol \n#' Number of (global) rows/columns of the matrix.\n#' @param representation \n#' The kind of storage the object would be in, i.e. \"dense\" or \"sparse\".\n#' @param unit \n#' string; the unit of storage, such as \"MiB\" or \"MB\", depending on\n#' prefix. Case is ignored.\n#' @param prefix \n#' string; the unit prefix, namely IEC or SI. Case is ignored.\n#' @param names \n#' string; control for whether the unit names should be\n#' printed out or their abbreviation should be used. Options are \"long\" and\n#' \"short\", respectively. Case is ignored.\n#' @param ... \n#' Additional arguments.\n#' @param sparsity \n#' The proportion of sparsity of the matrix if\n#' \\code{representation=\"sparse\"}\n#' @param type \n#' \"double\" or \"int\"; the storage type of the data matrix. If you\n#' don't know the type, it is probably stored as a double, so the default value\n#' will suffice.\n#' @param intsize \n#' The size (in bytes) of an integer. Default is 4, but this is\n#' platform dependent.\n#' \n#' @return\n#' returns a \\code{memuse} class object.\n#' \n#' @examples\n#' \\dontrun{\n#' # size of a 1000x1000 matrix\n#' howbig(1000, 1000)\n#' }\n#' \n#' @seealso \\code{\\link{howmany}}\n#' @export\nhowbig <- function(nrow=1, ncol=1, representation=\"dense\", unit=\"best\", prefix=\"IEC\", names=\"short\", ..., sparsity=0.05, type=\"double\", intsize=4)\n{\n type <- match.arg(tolower(type), c(\"double\", \"integer\"))\n \n x <- internal.mu(size=1, unit=\"b\", unit.prefix=prefix, unit.names=names)\n \n bytes <- check_type(type=type, intsize=intsize)\n \n x@size <- nrow*ncol*bytes # number of bytes used\n \n representation <- match.arg(tolower(representation), c(\"dense\", \"sparse\"))\n \n if (representation == \"sparse\")\n {\n if (sparsity < 0 || sparsity > 1)\n stop(\"argument 'sparsity' should be between 0 and 1\")\n else\n x <- sparsity * x\n }\n \n swap.unit(x, unit)\n}\n", "meta": {"hexsha": "cb945b217757e0d928157ebb518b528fafb786d3", "size": 2181, "ext": "r", "lang": "R", "max_stars_repo_path": "R/howbig.r", "max_stars_repo_name": "nbenn/memuse", "max_stars_repo_head_hexsha": "94d38ef3c8bc443f99ac18e27792948795dced31", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/howbig.r", "max_issues_repo_name": "nbenn/memuse", "max_issues_repo_head_hexsha": "94d38ef3c8bc443f99ac18e27792948795dced31", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/howbig.r", "max_forks_repo_name": "nbenn/memuse", "max_forks_repo_head_hexsha": "94d38ef3c8bc443f99ac18e27792948795dced31", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1571428571, "max_line_length": 146, "alphanum_fraction": 0.6629986245, "num_tokens": 629, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30727068035304783}} {"text": "context(\"check_wkt\")\n\ntest_that(\"regular wkt works\", {\n aa <- check_wkt('POLYGON((30.1 10.1, 10 20, 20 60, 60 60, 30.1 10.1))')\n bb <- check_wkt('POINT(30.1 10.1)')\n cc <- check_wkt('LINESTRING(3 4,10 50,20 25)')\n\n expect_is(aa, \"character\")\n expect_equal(aa, 'POLYGON((30.1 10.1, 10 20, 20 60, 60 60, 30.1 10.1))')\n\n expect_is(bb, \"character\")\n expect_equal(bb, 'POINT(30.1 10.1)')\n\n expect_is(cc, \"character\")\n expect_equal(cc, 'LINESTRING(3 4,10 50,20 25)')\n})\n\n\ntest_that(\"check many passed in at once\", {\n aa <- check_wkt(c('POLYGON((30.1 10.1, 10 20, 20 60, 60 60, 30.1 10.1))',\n 'POINT(30.1 10.1)'))\n\n expect_is(aa, \"character\")\n expect_equal(length(aa), 2)\n})\n\n\ntest_that(\"many wkt's, semi-colon separated, for many repeated geometry args\", {\n wkt <- \"POLYGON((-102.2 46.0,-93.9 46.0,-93.9 43.7,-102.2 43.7,-102.2 46.0));POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))\"\n aa <- check_wkt(wkt)\n\n expect_is(wkt, \"character\")\n expect_is(aa, \"character\")\n expect_equal(length(wkt), 1)\n expect_equal(length(aa), 2)\n expect_true(grepl(\";\", wkt))\n expect_false(any(grepl(\";\", aa)))\n})\n\n\n\ntest_that(\"bad WKT fails well\", {\n expect_error(\n check_wkt('POLYGON((30.1 10.1, 10 20, 20 60, 60 60, 30.1 a))'),\n \"bad lexical cast: source type value could not be\"\n )\n\n expect_error(\n check_wkt('POLYGON((30.1 10.1, 10 20, 20 60, 60 60, 30.1 a))'),\n \"bad lexical cast: source type value could not be\"\n )\n})\n\n", "meta": {"hexsha": "551a32d8ea25384aa0d2978eb804a40a4d4bb15b", "size": 1457, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test-check_wkt.r", "max_stars_repo_name": "MattBlissett/rgbif", "max_stars_repo_head_hexsha": "5a1c48ca606ef616819b5da0d3e0ae96880d08f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 101, "max_stars_repo_stars_event_min_datetime": "2015-01-19T20:40:06.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T14:13:37.000Z", "max_issues_repo_path": "tests/testthat/test-check_wkt.r", "max_issues_repo_name": "MattBlissett/rgbif", "max_issues_repo_head_hexsha": "5a1c48ca606ef616819b5da0d3e0ae96880d08f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 356, "max_issues_repo_issues_event_min_datetime": "2015-01-20T17:08:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-25T09:34:44.000Z", "max_forks_repo_path": "tests/testthat/test-check_wkt.r", "max_forks_repo_name": "MattBlissett/rgbif", "max_forks_repo_head_hexsha": "5a1c48ca606ef616819b5da0d3e0ae96880d08f1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 61, "max_forks_repo_forks_event_min_datetime": "2015-03-13T02:31:30.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-26T13:57:45.000Z", "avg_line_length": 26.9814814815, "max_line_length": 132, "alphanum_fraction": 0.6197666438, "num_tokens": 586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30727068035304783}} {"text": "setwd(\"P:/2019 0970 151 000/User Data/Faraz-Export IDAVE folders/Models2.5/\")\nlibrary(tidyverse)\ndf <- read.csv(\"../datasets/New/bigCihi.csv\")\ntabna <- function(x){table(x, useNA = \"ifany\")}\n\n##df is mostly big Cihi + alc_status\n\ndf %>% filter(alclos == 0) %>% select(alc_status) %>% tabna\n# x\n # 0 1 \n# 1300004 279 \ndf %>% filter(alclos > 0) %>% nrow()\n# [1] 174002\ntabna(df$alc_status)\n# x\n # 0 1 \n# 1300057 174228 \n\n\n df %>%\ngroup_by( entry )%>%\nsummarise(count = n(),\ncount_perc = count / nrow(df) * 100,\nalc_perc = mean(as.numeric(as.character(alc_status))) * 100)\n\n df %>%\ngroup_by( admcat )%>%\nsummarise(count = n(),\ncount_perc = count / nrow(df) * 100,\nalc_perc = mean(as.numeric(as.character(alc_status))) * 100)\n\n########################\n#########################New outcome variable\n########################\n\ndf_alc <- df %>% select(ID, days_to_lefteddate, alclos)\n\ndfx <- left_join(dfx, df_alc, by = c(\"ID\", \"days_to_lefteddate\"))\n\ndfx <- dfx %>% mutate(alc_30 = ifelse(alclos >= 30, 1, 0) %>% factor)\ndfx <- dfx %>% mutate(alc_10 = ifelse(alclos >= 10, 1, 0) %>% factor)\ndfx <- dfx %>% mutate(alc_5 = ifelse(alclos >= 5, 1, 0) %>% factor)\n\n\n#######################\ndfx <- drop_na(dfx)\ndf.hex <- as.h2o(dfx)\n\ndf.split <- h2o.splitFrame(df.hex, ratios = c(0.8), seed = 1234)\ntrain <- df.split[[1]]\ntest <- df.split\ndf.split_valid <- h2o.splitFrame(train, ratios = c(0.8), seed = 1234) #validation set to size of 5-fold\n\ndf.hex_gbmNew <- h2o.gbm(\ntraining_frame = train,\nx = sel_feat,\ny = \"alc_30\",\nmodel_id = \"gbm_alc30_noParam\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\n#score_each_iteration = T, #wrong if used with above argument (I guess!)\nseed = 1234\n)\n\nmodH <- new_resultFrame()\nmodH <- add_cv_results(df.hex_gbmNew, modH)\n#very low AUCPR\n\n#############Checking if important predictors are the same \nlibrary(infotheo)\nX <- dfx %>% select(-c(1:4), - alc_status, -alclos, -alc_30)\nres <- X %>% sapply(function(x){\nmutinformation(x, dfx$alc_status, method = \"emp\") / sqrt(entropy(x) * entropy(df$alc_status)) })\n#SAME MOSTLY\n# write.csv(res[order(-res)], \"./mutualInfo_features.csv\")\n# feat_rank <- res[order(-res)] %>% as.data.frame()\n\n#############Bringing some tuned models\n\n##Loading top 5 GBM models\ngbm_models <- c()\npath <- \"./hypeTune_results/top5_gbm_models_cv\"\nmodel_names <- grep(\"^GBM_model\", list.files(path), value=T)\nfor(model in model_names){\n\tgbm_models <- c(gbm_models, h2o.loadModel(paste(path, model, sep = \"/\")))\n}\n\nres <- new_resultFrame()\nnewGBM <- c()\nfor (i in c(1:5)){ \n\ngbm <- gbm_models[[i]]\nnewGBM <- c(newGBM, do.call(h2o.gbm,\n\t#update parameters models, original models were cross validated, no need for that now, also change predictors in here\n\t{\n\t\tp <- gbm@parameters\n\t\tp$model_id = paste0(\"GBM_ed_small_\", i)\n\t\tp$training_frame = train\n\t\tp$validation_frame = test\n\t\tp$nfolds = NULL\n\t\tp$fold_assignment = NULL\n\t\tp$ntrees = 1000\n\t\tp$x = sel_feat\n\t\tp$y = \"alc_30\"\n\t\tp$stopping_rounds = 5\n\t\tp$stopping_tolerance = 1e-4\n\t\tp$score_tree_interval = 10\n\t\tp$sample_rate = NULL\n\t\tp\n\t}))\nres <- add_results(newGBM[[i]], res)\n}\n\n#even worse results,\n#Lets Tune gbm for alc_30 prediction\n\n\nhyper_params = list(max_depth = c(4,6,8,12,16,20))\nt <- Sys.time()\ngrid <- h2o.grid(\nhyper_params = hyper_params,\nsearch_criteria = list(strategy = \"Cartesian\"),\nalgorithm = \"gbm\",\ngrid_id = \"depth_grid_gbm\",\nbalance_classes = TRUE,\ntraining_frame = df.split_valid[[1]],\nvalidation_frame = df.split_valid[[2]],\nx = sel_feat,\ny = \"alc_30\",\nntrees = 10000, #just some big number\nlearn_rate = 0.05,\nlearn_rate_annealing = .99,\nsample_rate = 0.8,\ncol_sample_rate = 0.8,\n#sample_rate_per_class = c(1, 8.3), #For the resampling inside trees ---will igonre sample_rate\nseed = 1234,\nstopping_rounds = 5,\nstopping_tolerance = 1e-4, ##early stop if AUCPR did not improve at least 0.01% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 10\n)\nprint(Sys.time() - t)\n#Sort base on validation perf\n\ndepth_res <- new_resultFrame()\nsortedGrid <- h2o.getGrid(\"depth_grid_gbm\", sort_by = \"AUCPR\", decreasing = TRUE)\n\ndepths <- sortedGrid@summary_table$max_depth[1:3]\nmax_depth <- max(depths) %>% as.integer\nmin_depth <- min(depths) %>% as.integer\n\n##############Depth span is found, now going for the next hypTune step\n\n\nhyper_params <- list(\n\tmax_depth = seq(min_depth, max_depth, 1),\n\t# sample_rate = seq(0.2, 1, 0.01),\n\tsample_rate_per_class = lapply(seq(0.4, 1, 0.01), function(x){x * c(0.12, 1)}),\n\tcol_sample_rate = seq(0.4, 1, 0.01),\n\tcol_sample_rate_per_tree = seq(0.4, 1, 0.01),\n\tcol_sample_rate_change_per_level = seq(0.9, 1.1, .01),\n\tmin_rows = 2 ^ seq(0, log2(nrow(df.split_valid[[1]]))-1,1),\n\t# nbins = 2 ^ seq(1,6, 1),\n\t# nbins_cats = 2 ^ seq(1,6, 1),\n\tmin_split_improvement = c(0, 1e-8, 1e-6, 1e-4),\n\thistogram_type = c(\"UniformAdaptive\", \"QuantilesGlobal\", \"RoundRobin\")\n)\n\nsearch_criteria = list(\nstrategy = \"RandomDiscrete\", #random grid search\nmax_runtime_secs = 3600 * 1,\nmax_models = 500,\nseed = 1234, \nstopping_rounds = 5,\nstopping_metric = \"AUCPR\", #Try with AUC also\nstopping_tolerance = 1e-4\n)\n\n\nt <- Sys.time()\ngrid <- h2o.grid(\nhyper_params = hyper_params,\nsearch_criteria = search_criteria,\nalgorithm = \"gbm\",\ngrid_id = \"final_grid_gbm_alc_30\",\n# nfolds = 5,\n# fold_assignment = \"Stratified\",\nbalance_classes = TRUE,\nx = sel_feat,\ny = \"alc_30\",\ntraining_frame = df.split_valid[[1]],\nvalidation_frame = df.split_valid[[2]],\nnbins_cats = 16, # instead of 1024 default \nntrees = 10000,\nlearn_rate = 0.05,\nlearn_rate_annealing = 0.99,\nmax_runtime_secs = 3600, #for each model\nstopping_rounds = 5,\nstopping_metric = \"AUCPR\", #Try with AUC also\nstopping_tolerance = 1e-4,\nscore_tree_interval = 10,\nseed = 1234\n)\nprint(Sys.time() - t) \nsortedGrid <- h2o.getGrid(\"final_grid_gbm_alc_30\", sort_by = \"AUCPR\", decreasing = TRUE)\n\n###Rerrun overnight\n\n\n################\n#What if we used alc_10 or alc_5\n\ndf.hex_gbmNew2 <- h2o.gbm(\ntraining_frame = df.split_valid[[1]],\nvalidation_frame = df.split_valid[[2]],\nx = sel_feat,\ny = \"alc_10\",\nmodel_id = \"gbm_alc10_noParam\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\n#score_each_iteration = T, #wrong if used with above argument (I guess!)\nseed = 1234\n)\n\nmodH <- add_results(df.hex_gbmNew2, modH)\n\ndf.hex_gbmNew3 <- h2o.gbm(\ntraining_frame = df.split_valid[[1]],\nvalidation_frame = df.split_valid[[2]],\nx = sel_feat,\ny = \"alc_5\",\nmodel_id = \"gbm_alc5_noParam\",\nbalance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\n#score_each_iteration = T, #wrong if used with above argument (I guess!)\nseed = 1234\n)\n\nmodH <- add_results(df.hex_gbmNew3, modH)\n\n\n # Model Hit_Ratio Mean_PerC_Error LogLoss AUC AUCPR Recall\n# 1 gbm_alc30_noParam 0.970 0.381 0.058 0.865 0.116 0.258\n# 2 gbm_alc10_noParam 0.908 0.330 0.148 0.836 0.214 0.407\n# 3 gbm_alc5_noParam 0.852 0.303 0.219 0.822 0.285 0.513\n # Precision Specificity max_F1 trshold training_AUC training_AUCPR\n# 1 0.156 0.989 0.195 0.094 0.879 0.868\n# 2 0.220 0.971 0.286 0.136 0.841 0.823\n# 3 0.263 0.956 0.348 0.169 0.827 0.804\n # training_Recall training_Precision training_Spec\n# 1 0.874 0.749 0.849\n# 2 0.877 0.706 0.837\n# 3 0.871 0.693 0.827", "meta": {"hexsha": "b32930346acda007a1c70811acf7eabb467dc7a2", "size": 7905, "ext": "r", "lang": "R", "max_stars_repo_path": "Predective Modeling/h2o_alclos.r", "max_stars_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_stars_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Predective Modeling/h2o_alclos.r", "max_issues_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_issues_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Predective Modeling/h2o_alclos.r", "max_forks_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_forks_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.606741573, "max_line_length": 118, "alphanum_fraction": 0.663883618, "num_tokens": 2696, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30727068035304783}} {"text": "hoge <- read.csv(\"synthesized_info.csv\")\npar(mfrow=c(3,1))\nplot(hoge$lifetime, hoge$average.bug, xlab=\"lifetime [day]\", ylab=\"# of bug per month\")\nabline(v=869, lty=2)\nplot(hoge$commit.interval, hoge$average.bug, xlab=\"commit interval [day]\", ylab=\"# of bug per month\")\nabline(v=31, lty=2)\nplot(hoge$co.evolution.rate, hoge$average.bug, xlab=\"co-evolution rate\", ylab=\"# of bug per month\")\nabline(v=0.100, lty=2)\ndev.copy2eps(file=\"../kousatsu1.eps\")\n", "meta": {"hexsha": "265a4633c89c89fb6779042850c4a5afaf86bfed", "size": 451, "ext": "r", "lang": "R", "max_stars_repo_path": "IWESEP2016/kousatsu1/kousatsu1.r", "max_stars_repo_name": "hideshis/scripts_for_research", "max_stars_repo_head_hexsha": "f633bdef0f9b959d7b18c8b95f169306eb8bb50d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "IWESEP2016/kousatsu1/kousatsu1.r", "max_issues_repo_name": "hideshis/scripts_for_research", "max_issues_repo_head_hexsha": "f633bdef0f9b959d7b18c8b95f169306eb8bb50d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "IWESEP2016/kousatsu1/kousatsu1.r", "max_forks_repo_name": "hideshis/scripts_for_research", "max_forks_repo_head_hexsha": "f633bdef0f9b959d7b18c8b95f169306eb8bb50d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.1, "max_line_length": 101, "alphanum_fraction": 0.7073170732, "num_tokens": 158, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961013, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3072706803530478}} {"text": "#' shaq\n#' \n#' Constructor for shaq objects.\n#' \n#' @details\n#' If \\code{nrows} and/or \\code{ncols} is missing, then it will be imputed.\n#' This means one must be especially careful to manually provide \\code{ncols}\n#' if some of ranks have \"placeholder data\" (a 0x0 matrix), which is typical\n#' when reading from a subset of processors and then broadcasting out to the\n#' remainder.\n#' \n#' @section Communication:\n#' If \\code{checks=TRUE}, a check on the global number of rows is performed.\n#' This amounts to an allgather operation on a logical value (the local\n#' dimension check).\n#' \n#' @param Data\n#' The local submatrix.\n#' @param nrows,ncols\n#' The GLOBAL number of rows and columns.\n#' @param checks\n#' Logical. Should some basic dimension checks be performed? Note that these\n#' require communication, and with many MPI ranks, could be expensive.\n#' \n#' @seealso\n#' \\code{\\link{shaq-class}}\n#' \n#' @name shaq\n#' @rdname shaq\n#' @export\nshaq <- function (Data, nrows, ncols, checks=TRUE)\n{\n UseMethod(\"shaq\", Data)\n}\n\n\n\nintuit.shaq.nrows = function(Data)\n{\n rowcheck = allreduce(NROW(Data))\n rowcheck\n}\n\ncheck.shaq.ncols = function(Data, ncols)\n{\n colcheck = comm.all(NCOL(Data) == ncols)\n if (!isTRUE(colcheck))\n comm.stop(\"local column dimensions disagree across ranks\")\n}\n\ncheck.shaq = function(Data, nrows, ncols)\n{\n check.shaq.ncols(Data, ncols)\n \n nrows = intuit.shaq.nrows(Data)\n \n return(nrows)\n}\n\n\n\n#' @rdname shaq\n#' @export\nshaq.matrix = function(Data, nrows, ncols, checks=TRUE)\n{\n if (!missing(nrows))\n check.is.natnum(nrows)\n if (!missing(ncols))\n check.is.natnum(ncols)\n check.is.flag(checks)\n \n if (missing(nrows) || missing(ncols))\n {\n if (missing(nrows))\n nrows = intuit.shaq.nrows(Data)\n \n if (missing(ncols))\n ncols = NCOL(Data)\n }\n else if (checks)\n nrows = check.shaq(Data, nrows, ncols)\n \n new(\"shaq\", Data=Data, nrows=nrows, ncols=ncols)\n}\n\n\n\n\n#' @rdname shaq\n#' @export\nshaq.numeric = function(Data, nrows, ncols, checks=TRUE)\n{\n if (!missing(nrows))\n check.is.natnum(nrows)\n if (!missing(ncols))\n check.is.natnum(ncols)\n check.is.flag(checks)\n \n if (missing(nrows) || missing(ncols))\n {\n if (missing(nrows) && missing(ncols))\n {\n nrows = length(Data)\n ncols = 1L\n }\n else if (missing(nrows))\n nrows = 1L\n else if (missing(ncols))\n ncols = 1L\n }\n \n \n size = comm.size()\n base = nrows %/% size\n rem = nrows - base*size\n nrows.local = base\n if (comm.rank()+1L < rem)\n nrows.local = nrows.local + 1\n \n Data = matrix(Data, as.integer(nrows.local), ncols)\n \n new(\"shaq\", Data=Data, nrows=nrows, ncols=ncols)\n}\n", "meta": {"hexsha": "ab8c26944bb937e0ae34d5be4f223f55aedf6050", "size": 2652, "ext": "r", "lang": "R", "max_stars_repo_path": "R/01-constructor.r", "max_stars_repo_name": "cran/kazaam", "max_stars_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/01-constructor.r", "max_issues_repo_name": "cran/kazaam", "max_issues_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/01-constructor.r", "max_forks_repo_name": "cran/kazaam", "max_forks_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.3870967742, "max_line_length": 77, "alphanum_fraction": 0.6512066365, "num_tokens": 767, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961013, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3072706803530478}} {"text": "rm(list = ls())\ngc()\n\nsetwd(\"~/Desktop/Code/laplace_approximation/Script\")\nlibrary(stringr)\nlibrary(rstan)\n\n# Use function from http://stla.github.io/stlapblog/posts/Numextract.html\n# to convert strings to the requisite numbers.\nNumextract <- function(string){\n unlist(regmatches(string,gregexpr(\"[[:digit:]]+\\\\.*[[:digit:]]*\",string)))\n}\n\n# Read in data\nsynth <- read.csv(\"synth_data/testdata.csv\")\n\nnrow = 500 # 250\nncol = 3\n\nif (FALSE) {\n synth_matrix <- matrix(nrow = nrow, ncol = ncol)\n for (i in 1:nrow) {\n dat_str <- toString(synth[i, 1])\n synth_matrix[i, ] <- as.numeric(Numextract(dat_str))\n }\n synth_data <- as.data.frame(synth_matrix)\n names(synth_data) <- c(\"x1\", \"x2\", \"y\")\n\n # augment data to get a 500 dimensional latent variable\n x1 <- c(synth_data$x1, synth_data$x1 * runif(nrow, 1, 1.1))\n x2 <- c(synth_data$x2, synth_data$x2 * runif(nrow, 1, 1.1))\n y <- c(synth_data$y, synth_data$y)\n}\n\nif (TRUE) {\n synth_data <- synth\n x1 <- synth_data$x1\n x2 <- synth_data$x2\n y <- synth_data$y\n y[y == -1] <- 0 # express -1 as 0\n}\n\nsim_data <- data.frame(x1, x2, y)\n\n# Write data for C++ unit tests.\noutput_directory <- \"aki_synth_data\"\nwrite.table(t(x1), file = file.path(output_directory, paste0(\"x1.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\nwrite.table(t(x2), file = file.path(output_directory, paste0(\"x2.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\nwrite.table(t(y), file = file.path(output_directory, paste0(\"y.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\n\n\n###############################################################################\n## Write data for Stan\n\nmake_data <- function(n_obs) {\n x1 <- sim_data$x1\n x2 <- sim_data$x2\n y <- sim_data$y\n\n if (n_obs <= 500) {\n data_stan <- sim_data[sample(1:500, n_obs), ]\n } else {\n for (i in (500 + 1):n_obs) {\n index <- sample(1:500, 1)\n x1 <- c(x1, sim_data$x1[index] * runif(1, 0.9, 1.1))\n x2 <- c(x2, sim_data$x2[index] * runif(1, 0.9, 1.1))\n y <- c(y, sim_data$y[index])\n }\n data_stan <- data.frame(x1, x2, y)\n }\n data_stan_ls <- with(data_stan, list(n_obs = n_obs,\n n_covariates = 2,\n y = y,\n x = cbind(x1, x2)))\n \n with(data_stan_ls, stan_rdump(ls(data_stan_ls), # FIX ME\n paste0(\"data/data_gp/data_gp\", n_obs, \".R\")))\n}\n\ndimension <- c(10, 100, 250, 500, 750, 1000, 5000)\nfor (i in 1:length(dimension)) make_data(dimension[i])\n\n# Write data for Stan (only use original data set, that is first\n# 250 entries)\noriginal_data <- sim_data[1:250, ]\ndata_org_stan <- with(original_data, list(n_obs = 250,\n n_covariates = 2,\n y = y,\n x = cbind(x1, x2)))\nwith(data_org_stan, stan_rdump(ls(data_org_stan), \"data/data_gp/data_gp.R\"))\n\n\n###############################################################################\n## Read data for desease map model in Finland\ndisease_data <- read.table(\"aki_disease_data/spatial1.txt\")\nnames(disease_data) <- c(\"x1\", \"x2\", \"ye\", \"y\")\n\n# write data for C++ unit tests.\noutput_directory <- \"aki_disease_data\"\nwrite.table(t(disease_data$x1), \n file = file.path(output_directory, paste0(\"x1.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\nwrite.table(t(disease_data$x2),\n file = file.path(output_directory, paste0(\"x2.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\nwrite.table(t(disease_data$ye),\n file = file.path(output_directory, paste0(\"ye.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\nwrite.table(t(disease_data$y),\n file = file.path(output_directory, paste0(\"y.csv\")),\n row.names = FALSE, col.names = FALSE, quote = FALSE, sep = \" \")\n\n# output data for Stan file\ndisease_stan <- with(disease_data, list(n_obs = 911,\n n_covariates = 2,\n y = y,\n ye = ye,\n x = cbind(x1, x2)))\n\nwith(disease_stan, stan_rdump(ls(disease_stan), \"data/disease_data_full.r\"))\n", "meta": {"hexsha": "0fb5be05b1a0fdbe193bbc1eaa7999bf8c097b95", "size": 4392, "ext": "r", "lang": "R", "max_stars_repo_path": "read_data.r", "max_stars_repo_name": "SteveBronder/laplace_manuscript", "max_stars_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-13T14:10:09.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-16T15:20:20.000Z", "max_issues_repo_path": "read_data.r", "max_issues_repo_name": "SteveBronder/laplace_manuscript", "max_issues_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "read_data.r", "max_forks_repo_name": "SteveBronder/laplace_manuscript", "max_forks_repo_head_hexsha": "b51a7ade9f28caf0cd722ed1f052b79b2d7ca107", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-06-27T15:17:43.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T21:54:30.000Z", "avg_line_length": 35.4193548387, "max_line_length": 79, "alphanum_fraction": 0.554417122, "num_tokens": 1234, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.30714101737661853}} {"text": "print(\"About to read netcdf data.\")\n\nlibrary(ncdf4)\nnc_data <- nc_open('cfplot_data/ggap.nc')\nlon <- ncvar_get(nc_data, \"longitude\")\nlat <- ncvar_get(nc_data, \"latitude\")\nU <- ncvar_get(nc_data, \"U\")\n\nprint(\"About to plot netcdf data to screen.\")\n\nlibrary(raster)\n\nU.slice <- U[,,1]\nr <- raster(t(U.slice), xmn=0, xmx=360, ymn=-90, ymx=90, crs=CRS(\"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0\"))\n\nplot(r)\n\nprint(\"Finished plotting. Waiting 5\")\nSys.sleep(5)\nprint(\"Goodbye.\")\n", "meta": {"hexsha": "5ca98df1e71207280629e32b5b00294a495f3959", "size": 495, "ext": "r", "lang": "R", "max_stars_repo_path": "r-tests/read_plot_netcdf.r", "max_stars_repo_name": "cedadev/quick-software-tests", "max_stars_repo_head_hexsha": "e9098cdfae7b7768528d82d485f20c0ce1a7375d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-07T02:07:56.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-07T02:07:56.000Z", "max_issues_repo_path": "r-tests/read_plot_netcdf.r", "max_issues_repo_name": "cedadev/quick-software-tests", "max_issues_repo_head_hexsha": "e9098cdfae7b7768528d82d485f20c0ce1a7375d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-02-09T17:08:21.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-24T13:45:37.000Z", "max_forks_repo_path": "r-tests/read_plot_netcdf.r", "max_forks_repo_name": "agstephens/quick-software-tests", "max_forks_repo_head_hexsha": "e9098cdfae7b7768528d82d485f20c0ce1a7375d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5714285714, "max_line_length": 132, "alphanum_fraction": 0.6868686869, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.5698526514141572, "lm_q1q2_score": 0.30714101737661853}} {"text": "N_0 <- 1000\ny_0 <- 100\n\nN <- 1000\ny <- 200\n\ncc <- 1\ndd <- 1\n\nnu <- 1\neta <- 1\n\n\n", "meta": {"hexsha": "eb419fed5e939316714e4cbeb725b8de3fbd6eef", "size": 80, "ext": "r", "lang": "R", "max_stars_repo_path": "code/Bernoulli/data_Bernoulli_scenario_4.r", "max_stars_repo_name": "maxbiostat/propriety_power_priors", "max_stars_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/Bernoulli/data_Bernoulli_scenario_4.r", "max_issues_repo_name": "maxbiostat/propriety_power_priors", "max_issues_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2020-05-29T19:11:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-29T15:58:08.000Z", "max_forks_repo_path": "code/Bernoulli/data_Bernoulli_scenario_4.r", "max_forks_repo_name": "maxbiostat/propriety_power_priors", "max_forks_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 5.7142857143, "max_line_length": 11, "alphanum_fraction": 0.4375, "num_tokens": 46, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.30714101737661853}} {"text": "sample_and_summarise = function(generated,stan_mod_for_sampling){\n\tcsv_base = paste(as.list(generated$gen_args),collapse='_')\n\tsampled = stan_mod_for_sampling$sample(\n\t\tdata = generated$data_for_stan\n\t\t, chains = parallel::detectCores()/2\n\t\t, parallel_chains = parallel::detectCores()/2\n\t\t, show_messages = F\n\t\t, refresh = 1\n\t\t, output_dir = 'stan_temp'\n\t\t, output_basename = csv_base\n\t\t, seed = abs(digest::digest2int(digest::digest(generated$data_for_stan,algo='xxhash64')))\n\t)\n\t#get ranks & other summaries\n\t(\n\t\tsampled$draws()\n\t\t%>% posterior::as_draws_df()\n\t\t%>% as_tibble()\n\t\t%>% select(-.chain,-.iteration)\n\t\t%>% pivot_longer(-.draw)\n\t\t%>% left_join(generated$true_pars,by='name')\n\t\t%>% filter(\n\t\t\t!is.na(true)\n\t\t)\n\t\t%>% rename(variable=name)\n\t\t%>% group_by(variable)\n\t\t%>% summarise(\n\t\t\trank = mean(true>value)\n\t\t\t, true = true[1]\n\t\t\t, .groups = 'drop'\n\t\t)\n\t\t%>% left_join(\n\t\t\t(\n\t\t\t\tsampled$draws()\n\t\t\t\t%>% posterior::summarise_draws(\n\t\t\t\t\tposterior::default_convergence_measures()\n\t\t\t\t)\n\t\t\t)\n\t\t\t, by = 'variable'\n\t\t)\n\t\t%>% left_join(\n\t\t\t(\n\t\t\t\tsampled$draws()\n\t\t\t\t%>% posterior::summarise_draws(\n\t\t\t\t\t~posterior::quantile2(.x,probs=c(.1,.25,.5,.75,.9))\n\t\t\t\t)\n\t\t\t)\n\t\t\t, by = 'variable'\n\t\t)\n\t) -> posterior_summary\n\t(\n\t\tsampled$sampler_diagnostics()\n\t\t%>% posterior::as_draws_df()\n\t\t%>% summarise(\n\t\t\tmax_treedepth = max(treedepth__)\n\t\t\t, num_divergent = sum(divergent__)\n\t\t\t, var_energy = var(energy__)\n\t\t)\n\t\t%>% mutate(\n\t\t\ttime = sampled$time()$total\n\t\t\t, var_summary = list(posterior_summary)\n\t\t\t, model = str_replace(\n\t\t\t\tbasename(stan_mod_for_sampling$stan_file())\n\t\t\t\t, fixed('.stan')\n\t\t\t\t, ''\n\t\t\t)\n\t\t)\n\t\t%>% bind_cols(generated$gen_args)\n\t) -> to_return\n\t#delete the csvs\n\tsystem(paste0('rm stan_temp/',csv_base,'*'))\n\treturn(to_return)\n}\n", "meta": {"hexsha": "e4b900354ac8f3f6882466914a2f7f1f8bef2c9d", "size": 1752, "ext": "r", "lang": "R", "max_stars_repo_path": "r_helpers/sample_and_summarise.r", "max_stars_repo_name": "mike-lawrence/sbc_demo", "max_stars_repo_head_hexsha": "3505d6b2a31f00f0c4df533348ae556928c53a9b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-10-04T01:06:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-06T18:24:09.000Z", "max_issues_repo_path": "r_helpers/sample_and_summarise.r", "max_issues_repo_name": "mike-lawrence/sbc_demo", "max_issues_repo_head_hexsha": "3505d6b2a31f00f0c4df533348ae556928c53a9b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_helpers/sample_and_summarise.r", "max_forks_repo_name": "mike-lawrence/sbc_demo", "max_forks_repo_head_hexsha": "3505d6b2a31f00f0c4df533348ae556928c53a9b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0, "max_line_length": 91, "alphanum_fraction": 0.6409817352, "num_tokens": 597, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.3071263567026104}} {"text": "#\n# generate multi-day events from summarized GHCD data\n# - expects GHCD format input CSV files, named by station and year in 'tmp' subdirectory\n# (created by fileterData.py)\n# - expects seaonsalseaonabl baseline data for all stations in a single CSV file 'season_baseline.csv' in the 'out' subdirectory\n# (this is created by summarize.r)\n# - writes output temperature events CSV into 'out' subdirectory\n\n# D. Dorsettt 20-Jul-2019\n\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(here)\nsetwd(\"E:/WeatherData/out\")\n\nstations <- c(\"USW00014764\",\"USW00014739\",\"USW00014758\",\"USW00094789\",\"USW00013739\",\"USW00093721\",\"USW00013750\",\"USW00013748\",\"USW00013782\",\"USC00084366\",\"USW00012849\",\"USW00092811\")\n\n# pull baseline data\nseason_baseline <- read.csv(\"season_baseline.csv\")\nseasons <- c(\"spring\",\"summer\",\"fall\",\"winter\")\n\n# this is the data frame we're building\ntempevents <- data.frame(matrix(ncol=6,nrow=0))\nnames(tempevents) = c(\"STATION\",\"YEAR\",\"SEASON\",\"TYPE\",\"EVENT\",\"LABEL\")\n\n# go back through daily temperatiure data looking for cold/heat waves based on station highest average max and lowest average min through the whole year\nfor (i in 1:length(stations)) {\n\tstation = stations[i]\n\tfile_list <- list.files(path=\"E:/WeatherData/tmp\", paste0(station,\".*.csv\"))\n\tstatavg <- season_baseline %>% filter(STATION==station)\n\tmaxtemp = max(statavg$TMAX_MEAN)\n\tmintemp = min(statavg$TMIN_MEAN)\n\n\tfor (y in 1:length(file_list)) {\n\t\tif (!file.size(paste0(\"E:/WeatherData/tmp/\",file_list[y])) == 0) {\n\t\t\tprint(paste(\"Processing\", file_list[y]))\n\t\t\traw <- read.csv(paste0(\"E:/WeatherData/tmp/\",file_list[y]), header=FALSE) %>% select(1,2,3,4)\n\t\t\tnames(raw) <- c(\"station\",\"date\",\"tag\",\"value\")\n\t\t\traw <- raw %>% filter(tag == \"TMAX\")\n\n\t\t\t# extract year,month,week,season and pivot based on GHCD dataset tag\n\t\t\thight_data <- raw %>%\n\t\t\t\tmutate(DATE=ymd(date),YEAR=year(date),MONTH=month(date),SEASON=case_when(MONTH>2 & MONTH<6 ~ \"spring\",MONTH>5 & MONTH<9 ~ \"summer\",MONTH>8 & MONTH<12 ~ \"fall\",TRUE ~ \"winter\")) %>%\n\t\t\t\tpivot_wider(names_from=tag, values_from=value)\n\t\t\t# convert temp from tenths degC to degF\n\t\t\tif (\"TMAX\" %in% colnames(hight_data)) { hight_data$TMAX <- ((hight_data$TMAX / 10.0) * (9.0 / 5.0)) + 32.0 } else { hight_data <- mutate(hight_data, TMAX=NA) }\n\n\t\t\t# mark the HOT days as more than 9 deg about the average maximum and COLD days as more than 9 deg colder than the average minimum\n\t\t\thight_data <- hight_data %>% mutate(HOT=TMAX > (maxtemp + 9), COLD=TMAX < (mintemp - 9))\n\n\t\t\t# summzrize by season\n\t\t\tfor (ss in 1:length(seasons)) {\n\t\t\t\tseason = seasons[ss]\n\t\t\t\tseason_avgt <- hight_data %>% filter(SEASON==season)\n\t\t\t\t# run-length-encode to reduce to vectors of streaks\n\t\t\t\tstreaks <- rle(season_avgt$HOT)\n\t\t\t\tdrow = 1\n\t\t\t\tif (length(streaks$lengths > 0)) {\n\t\t\t\t\tfor (r in 1:length(streaks$values)) {\n\t\t\t\t\t\t# write the starting day of streaks where HOT as TRUE and more than 3 days in length\n\t\t\t\t\t\tif (streaks$values[r] && streaks$lengths[r] > 3) {\n\t\t\t\t\t\t\ttempevents <- add_row(tempevents, STATION=station, YEAR=season_avgt$YEAR[drow], SEASON=season, TYPE=\"TMAX\", EVENT=\"Heat Wave\",LABEL=sprintf(\"%d days\", streaks$length[r]))\n\t\t\t\t\t\t}\n\t\t\t\t\t\tdrow = drow + streaks$lengths[r]\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tstreaks <- rle(season_avgt$COLD)\n\t\t\t\tdrow = 1\n\t\t\t\tif (length(streaks$lengths > 0)) {\n\t\t\t\t\tfor (r in 1:length(streaks$values)) {\n\t\t\t\t\t\t# write the starting day of streaks where COLD as TRUE and more than 3 days in length\n\t\t\t\t\t\tif (streaks$values[r] & streaks$lengths[r] > 3) {\n\t\t\t\t\t\t\ttempevents <- add_row(tempevents, STATION=station, YEAR=season_avgt$YEAR[drow], SEASON=season, TYPE=\"TMIN\", EVENT=\"Cold Wave\",LABEL=sprintf(\"%d days\", streaks$length[r]))\n\t\t\t\t\t\t}\n\t\t\t\t\t\tdrow = drow + streaks$lengths[r]\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n# write CSV summary datafile\nwrite.csv(tempevents, \"tempevents.csv\", row.names=FALSE)\n", "meta": {"hexsha": "e029250c81c183bc76e9e5876a53ec162aae1f16", "size": 3835, "ext": "r", "lang": "R", "max_stars_repo_path": "tempevents.r", "max_stars_repo_name": "ddorsett/CS498-dataviz", "max_stars_repo_head_hexsha": "cf8f81239719c850adeed7db6bb68ab5951572d0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tempevents.r", "max_issues_repo_name": "ddorsett/CS498-dataviz", "max_issues_repo_head_hexsha": "cf8f81239719c850adeed7db6bb68ab5951572d0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tempevents.r", "max_forks_repo_name": "ddorsett/CS498-dataviz", "max_forks_repo_head_hexsha": "cf8f81239719c850adeed7db6bb68ab5951572d0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.6547619048, "max_line_length": 184, "alphanum_fraction": 0.6834419817, "num_tokens": 1215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3071263493835959}} {"text": "#' Make regression table\n#'\n#' @description This function makes a character data frame of regression table.\n#' An advantage point of this function is\n#' that you can pass on output-format function such as `kable`.\n#'\n#' @param obj a list or object including regression analysis\n#' @param keep_coef a character vector of variables kept\n#' @param rm_coef a character vector of variables removed from table\n#' @param label_coef a list including `ols variable name = new variable name`\n#' @param add_line a list including additional contents of each column.\n#' @param ... other augments which pass on each augment. See details.\n#'\n#' @return a data frame which includes all character strings\n#'\n#' @details the `binomial` method can show average marginal effect\n#' if `ame = TRUE`, using `margins` in the `margins` package\n#'\n#' @importFrom purrr map\n#' @importFrom purrr reduce\n#' @importFrom stringr str_detect\n#' @importFrom dplyr recode\n#' @importFrom dplyr full_join\n#' @importFrom dplyr bind_rows\n#' @importFrom dplyr select\n#' @importFrom dplyr mutate\n#' @importFrom dplyr case_when\n#' @importFrom tidyr pivot_longer\n#' @importFrom tibble tribble\n#' @importFrom tibble tibble\n#' @importFrom tibble as_tibble\n#' @importFrom magrittr %>%\n#' @importFrom margins margins\n#' @importFrom fixest r2\n#' @importFrom stats logLik\n#' @importFrom stats nobs\n#' @importFrom stats setNames\n#'\n#' @export\n#'\n#+\nregtab <- function(\n obj,\n keep_coef = NULL, rm_coef = NULL, label_coef = NULL,\n add_line = NULL,\n ...\n) {\n\n # if not list, make list\n if (sum(class(obj) == \"list\") == 0) obj <- list(obj)\n\n # get and trim coeftab\n coeftab <- obj %>%\n map(function(x) ctab(x, ...)) %>%\n reduce(full_join, by = c(\"vars\", \"stat\"))\n\n coeftab <- setNames(\n coeftab,\n c(\"vars\", \"stat\", paste0(\"reg\", seq_len(ncol(coeftab) - 2)))\n )\n\n # keep variables in coeftab\n if (!is.null(keep_coef)) {\n strings <- keep_coef %>% paste(collapse = \"|\")\n coeftab <- coeftab[str_detect(coeftab$vars, strings), ]\n }\n\n # drop variables from coeftab\n if (!is.null(rm_coef)) {\n strings <- rm_coef %>% paste(collapse = \"|\")\n coeftab <- coeftab[!str_detect(coeftab$vars, strings), ]\n }\n\n # rename variables\n if (!is.null(label_coef)) {\n coeftab <- coeftab %>%\n mutate(vars = recode(vars, !!!label_coef, .default = vars))\n }\n\n # make tabulation of regression stats\n stattab <- obj %>%\n map(~ stab(.)) %>%\n reduce(full_join, by = c(\"vars\", \"stat\"))\n\n stattab <- setNames(\n stattab,\n c(\"vars\", \"stat\", paste0(\"reg\", seq_len(ncol(stattab) - 2)))\n )\n\n # make add line tab\n if (!is.null(add_line)) {\n addtab <- as_tibble(add_line) %>%\n mutate(stat = \"add\")\n\n tab <- bind_rows(coeftab, addtab) %>%\n bind_rows(stattab)\n } else {\n tab <- bind_rows(coeftab, stattab)\n }\n\n return(tab)\n\n}\n\n#' A fucntion of getting coefficient table\n#'\n#' @param x a list or object including regression analysis\n#' @param ... other augments which pass on each augment.\n#'\n#+\nctab <- function(x, ...) {\n UseMethod(\"ctab\")\n}\n\nctab.fe_felm <- function(x) {\n x <- summary(x)$coefficients\n trim_ctab(x)\n}\n\nctab.fe_fixest <- function(x) {\n x <- summary(x)$coeftable\n trim_ctab(x)\n}\n\nctab.lpm <- function(x) {\n x <- x$test\n trim_ctab(x)\n}\n\nctab.ols <- function(x) {\n x <- summary(x)$coefficients\n trim_ctab(x)\n}\n\nctab.RCT <- function(x) {\n if (sum(class(x) == \"lpm\") > 0) {\n x <- lapply(x$test, trim_ctab)\n } else if (sum(class(x) == \"ols\") > 0) {\n x <- lapply(x$fit, function(x) summary(x)$coefficients)\n x <- lapply(x, trim_ctab)\n } else {\n stop(\"Unsupported class applied.\")\n }\n x %>% reduce(full_join, by = c(\"vars\", \"stat\"))\n}\n\nctab.binomial <- function(x, ame = FALSE, ...) {\n if (ame) {\n x <- margins(x, data = x$model, ...)\n mat <- as.matrix(summary(x)[, 2:5])\n rownames(mat) <- summary(x)[, 1]\n trim_ctab(mat)\n } else {\n x <- summary(x)$coefficients\n trim_ctab(x)\n }\n}\n\ntrim_ctab <- function(x) {\n\n tab <- tibble(\n vars = rownames(x),\n coef = x[, 1],\n se = x[, 2],\n p = x[, 4]\n )\n\n tab <- tab %>%\n mutate(\n coef = case_when(\n p <= .01 ~ sprintf(\"%1.3f***\", coef),\n p <= .05 ~ sprintf(\"%1.3f**\", coef),\n p <= .1 ~ sprintf(\"%1.3f*\", coef),\n TRUE ~ sprintf(\"%1.3f\", coef)\n ),\n se = sprintf(\"(%1.3f)\", se)\n ) %>%\n select(-p) %>%\n pivot_longer(-vars, names_to = \"stat\", values_to = \"val\")\n\n return(tab)\n\n}\n\n#' A function of making regression stats table\n#'\n#' @param x a list or object including regression analysis\n#+\nstab <- function(x) {\n UseMethod(\"stab\")\n}\n\nstab.fe_felm <- function(x) {\n tribble(\n ~vars, ~stat, ~val,\n \"N\", \"stat\", sprintf(\"%3d\", nobs(x)),\n \"R-squared\", \"stat\", sprintf(\"%1.3f\", summary(x)$r.squared)\n )\n}\n\nstab.fe_fixest <- function(x) {\n tribble(\n ~vars, ~stat, ~val,\n \"N\", \"stat\", sprintf(\"%3d\", nobs(x)),\n \"Adjusted R-squared\", \"stat\", sprintf(\"%1.3f\", r2(x)[\"ar2\"])\n )\n}\n\nstatlm <- function(x) {\n tribble(\n ~vars, ~stat, ~val,\n \"N\", \"stat\", sprintf(\"%3d\", nobs(x)),\n \"R-squared\", \"stat\", sprintf(\"%1.3f\", summary(x)$r.squared)\n )\n}\n\nstab.lpm <- function(x) {\n statlm(x$fit)\n}\n\nstab.ols <- function(x) {\n statlm(x)\n}\n\nstab.RCT <- function(x) {\n x <- lapply(x$fit, statlm)\n x %>% reduce(full_join, by = c(\"vars\", \"stat\"))\n}\n\nstab.binomial <- function(x) {\n tribble(\n ~vars, ~stat, ~val,\n \"N\", \"stat\", sprintf(\"%3d\", nobs(x)),\n \"Log-likelihood\", \"stat\", sprintf(\"%1.3f\", logLik(x)[1])\n )\n}\n", "meta": {"hexsha": "9da21779e5e12e839095943b66519ce4e179dc2a", "size": 5494, "ext": "r", "lang": "R", "max_stars_repo_path": "R/regtab.r", "max_stars_repo_name": "KatoPachi/Rkato", "max_stars_repo_head_hexsha": "383d78c19201e7099324cbabbae9b89602e6e3d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-03T14:16:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-03T14:16:05.000Z", "max_issues_repo_path": "R/regtab.r", "max_issues_repo_name": "KatoPachi/Rkato", "max_issues_repo_head_hexsha": "383d78c19201e7099324cbabbae9b89602e6e3d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2021-08-03T20:33:41.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-05T12:19:54.000Z", "max_forks_repo_path": "R/regtab.r", "max_forks_repo_name": "KatoPachi/Rkato", "max_forks_repo_head_hexsha": "383d78c19201e7099324cbabbae9b89602e6e3d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.1814345992, "max_line_length": 79, "alphanum_fraction": 0.6059337459, "num_tokens": 1715, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3071263493835959}} {"text": "# f0basics.r: Catherine Lai\n# Basic functions to do with processing F0. \nrequire(geometry)\nrequire(mFilter)\nsource(\"legendre.r\")\n\nsigmoid <- function(x) {\n\treturn(1/(1 + exp(-x)))\n}\n\nrefactor <- function(vrmi) {\n for (fx in names(vrmi)) {\n if (is.factor(vrmi[[fx]])) {\n vrmi[[fx]] <- factor(unlevel(vrmi[[fx]]))\n }\n }\n return(vrmi)\n}\n\n\n\nbw.smooth <- function(x, xname=\"Time\", yname=\"F0\", freq=NA) {\n #points(x, col=\"magenta\")\n\tif (is.na(freq)) {\n\t\t freq <- bwcuttoff(x, nv=0.1, xname) \n\t}\n\n if (is.null(freq) | (nrow(x) < 4)){ p1 <- x }\n else {\n\t\t#print(tail(x))\n p1 <- data.frame(x[[xname]], \n\t\t\tfitted(bwfilter(x[[yname]],freq=freq)))\n\t\tnames(p1) <- c(xname, yname)\t\n p1 <- bw.brute.ends(p1, xname, yname)\n\n }\n return(p1)\n}\n\nbw.brute.ends <- function(p1, xname=\"Time\", yname=\"F0\") {\n if(nrow(p1) == 0) {return(p1)}\n x <- head(tail(p1,min(5, nrow(p1))),4)\n xs <- x[[xname]]\n ys <- x[[yname]]\n pend <- predict(lm(ys ~ xs), data.frame(xs=tail(p1[[xname]],1)))\n p1[nrow(p1), yname] <- pend\n\n return(p1)\n}\n\n\n\n# calculate normalized cutoff for Butterworth filter. \nbwcuttoff <- function(x, xname=\"Time\", nv=0.1) {\n\tif (nrow(x) > 1) {\n\t\treturn((1/min(diff(x[[xname]]))/2) * nv)\n\t} else {\n\t\treturn(NULL)\n\t}\n}\n\n\nfrom.semitone <- function(F0.st, F0.ref=min(F0.hz)) {\n F0.ref * (2^(F0.st/12))\n}\n\nto.semitone <- function(x, F0.ref=100) {\n\tif (length(names(x))==0) {\n\t\tF0.hz <- x \n\t\ty <- 12* log2(F0.hz/F0.ref)\n\t} else {\n\t\tF0.hz <- x$F0\n \ty <- data.frame(Time=x$Time, F0=12* log2(F0.hz/F0.ref))\n\t} \n\ty\n}\n\n\nto.zscore <- function(x, x.mean=mean(x,na.rm=T), x.sd=sd(x,na.rm=T)) {\n (x - x.mean)/x.sd\n}\n\n\nunfactor <- function(x) {\n as.numeric(levels(x)[x])\n}\n\nunlevel <- function(x) {\n levels(x)[x]\n}\n\n\nremove.outliers <- function(x) {\n y <- to.semitone(x$F0)\n i.new <- (y < mean(y, na.rm=T) + 3*sd(y, na.rm=T)) & (y > mean(y, na.rm=T) - 3*sd(y, na.rm=T))\n data.frame(Time=x$Time[i.new], F0=x$F0[i.new])\n}\n\n#############################################################################################\nirange <- function(x){ \n\tif(nrow(x) > 0) { max(x$I0)-min(x$I0) }\n\telse {NA}\n}\n\nmean.i0 <- function(x) {\n\tif(nrow(x) > 0) { mean(x$I0, na.rm=T) }\n\telse {NA}\n}\n\nsd.i0 <- function(x) {\n\tif(nrow(x) > 0) { sd(x$I0, na.rm=T) }\n\telse {NA}\n}\n\nmedian.i0 <- function(x) {\n\tif(nrow(x) > 0) { median(x$I0, na.rm=T) }\n\telse {NA}\n}\nmin.i0 <- function(x) {\n\tif(nrow(x) > 0) { min(x$I0, na.rm=T) }\n\telse {NA}\n}\n\nmax.i0 <- function(x) {\n\tif(nrow(x) > 0) { max(x$I0, na.rm=T) }\n\telse {NA}\n}\n\nslope.i0 <- function(x0) {\n\tx <- x0[!is.na(x0$I0),]\t\n\tif(nrow(x) > 1) {\n\t\ty <- lm(x$I0 ~ x$Time)$coefficients[2]\n\t\tnames(y) <- NULL\n\t\treturn(y) \n\t} else {return(NA)}\n} \n\nintercept.i0 <- function(x0) {\n\tx <- x0[!is.na(x0$I0),]\t\n\tif(nrow(x) > 1) {\n\t\ty <- lm(x$I0 ~ x$Time)$coefficients[1]\n\t\tnames(y) <- NULL\n\t\treturn(y)\n\t} else {return(NA)}\n} \n\njitter.i0 <- function(x) {\n\tif(nrow(x) > 0) {\n\t\ty <- x$I0\n \t(sum(abs(diff(y)),na.rm=T)/(length(y)-1))/(sum(abs(y))/length(y))\n\t} else {NA}\n}\n\nnpoints.i0 <- function(x) {\n\tlength(x$I0[!is.na(x$I0)])\n}\n\n##################################################################################\nprange.value <- function(x0, xname=\"Time\", yname=\"Value\"){ \n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { max(x[[yname]],na.rm=T)-min(x[[yname]],na.rm=T) }\n\telse {NA}\n}\n\nmean.value <- function(x0, xname=\"Time\",yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { mean(x[[yname]], na.rm=T) }\n\telse {NA}\n}\n\nsd.value <- function(x0, xname=\"Time\", yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { sd(x[[yname]], na.rm=T) }\n\telse {NA}\n}\n\nmedian.value <- function(x0, xname=\"Time\",yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { median(x[[yname]], na.rm=T) }\n\telse {NA}\n}\nmin.value <- function(x0, xname=\"Time\",yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { min(x[[yname]], na.rm=T) }\n\telse {NA}\n}\n\nmax.value <- function(x0, xname=\"Time\", yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) { max(x[[yname]], na.rm=T) }\n\telse {NA}\n}\n\nslope.value <- function(x0, xname=\"ActualTime\", yname=\"Value\", sampletime=F) {\n\t#print(c(xname, yname)) \n\t#print(x0)\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tzy <- x[[yname]]\n\tzx <- x[[xname]]\n\n\tif(nrow(x) > 1) {\n\t\tif (sampletime) {\n\t\t\ty <- lm(Value ~ SampleTime, data=x)$coefficients[2]\n\t\t} else {\n\t\t\ty <- lm(zy ~ zx)$coefficients[2]\n\t\t}\n\t\tnames(y) <- NULL\n\t\treturn(y) \n\t} else {return(NA)}\n} \n\nintercept.value <- function(x0, xname=\"ActualTime\", yname=\"Value\", sampletime=F) {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tzy <- x[[yname]]\n\tzx <- x[[xname]]\n\n\tif(nrow(x) > 1) {\n\t\tif (sampletime) {\n\t\t\ty <- lm(Value ~ SampleTime, data=x)$coefficients[1]\n\t\t} else {\n\t\t\ty <- lm(zy ~ zx)$coefficients[1]\n\t\t}\n\t\tnames(y) <- NULL\n\t\treturn(y)\n\t} else {return(NA)}\n} \n\njitter.value <- function(x0, xname=\"Time\", yname=\"Value\") {\n\tx <- x0[!is.na(x0[[yname]]),]\t\n\tif(nrow(x) > 0) {\n\t\ty <- x[[yname]]\n \t(sum(abs(diff(y)),na.rm=T)/(length(y)-1))/(sum(abs(y),na.rm=T)/length(y))\n\t} else {NA}\n}\n\nnpoints.value <- function(x, xname=\"Time\", yname=\"Value\") {\n\tlength(x[[yname]][!is.na(x[[yname]])])\n}\n\nget.legendre.coeffs <- function(x, degree=5) {\n\tlp <- legendre.polynomials(degree, normalize=T)\n\tp <- legendre.coeff(x$ActualTime,x$Value,lp)\n\tp\n} \n\n\n#############################################################################################\nprange <- function(x){ \n\tif(nrow(x) > 0) { max(x$F0)-min(x$F0) }\n\telse {NA}\n}\n\nmean.f0 <- function(x) {\n\tif(nrow(x) > 0) { mean(x$F0, na.rm=T) }\n\telse {NA}\n}\n\nsd.f0 <- function(x) {\n\tif(nrow(x) > 0) { sd(x$F0, na.rm=T) }\n\telse {NA}\n}\n\nmedian.f0 <- function(x) {\n\tif(nrow(x) > 0) { median(x$F0, na.rm=T) }\n\telse {NA}\n}\nmin.f0 <- function(x) {\n\tif(nrow(x) > 0) { min(x$F0, na.rm=T) }\n\telse {NA}\n}\n\nmax.f0 <- function(x) {\n\tif(nrow(x) > 0) { max(x$F0, na.rm=T) }\n\telse {NA}\n}\n\nslope.f0 <- function(x0) {\n\tx <- x0[!is.na(x0$F0),]\t\n\tif(nrow(x) > 1) {\n\t\ty <- lm(x$F0 ~ x$Time)$coefficients[2]\n\t\tnames(y) <- NULL\n\t\treturn(y) \n\t} else {return(NA)}\n} \n\nintercept.f0 <- function(x0) {\n\tx <- x0[!is.na(x0$F0),]\t\n\tif(nrow(x) > 1) {\n\t\ty <- lm(x$F0 ~ x$Time)$coefficients[1]\n\t\tnames(y) <- NULL\n\t\treturn(y)\n\t} else {return(NA)}\n} \n\njitter.f0 <- function(x) {\n\tif(nrow(x) > 0) {\n\t\ty <- x$F0\n \t(sum(abs(diff(y)))/(length(y)-1))/(sum(abs(y))/length(y))\n\t} else {NA}\n}\n\nnpoints.f0 <- function(x) {\n\tlength(x$F0[!is.na(x$F0)])\n}\n\nget.legendre.coeffs.f0 <- function(x, degree=5) {\n\tlp <- legendre.polynomials(degree, normalize=T)\n\tp <- legendre.coeff(x$Time,x$F0,lp)\n\tp\n} \n\n\npred.point2 <- function(y1, y2, x) {\n\ta <- y1[2] - ((y2[2]-y1[2])/(y2[1]-y1[1])) * y1[1] \n\tm = ((y2[2]-y1[2])/(y2[1]-y1[1]))\n\tm*x + a\n}\n\nsort.hull <- function(y) {\n\tynew <- NULL\n\tfor (i in 1:nrow(y)) {\n\t\tynew <- rbind(ynew, sort(y[i,]))\n\t} \n\tynew\n}\n\nget.max <- function(x, y) {\n\tvs <- unique(c(y[,1], y[,2])) \t\n\tvs[which.max(x[vs,]$F0)]\n}\n\nget.prevs <- function(x, y, v) {\n\tcurr <- y[y[,2] == v,]\t\n\tif (length(curr) == 2) {\n\t\treturn(c(v, get.prevs(x, y, curr[1])))\n\t} else if (length(curr) > 2) {\n\t\tcx <- find.highv(x,curr,forward=F)\n\t\treturn(c(v, get.prevs(x, y, cx[1])))\n\t} else {\n\t\treturn(v)\n\t}\n}\n\n# Find the highest vertex in the convex hull top.\nfind.highv <- function(x, curr, forward=T) {\n\tpreds <- NULL \n\tif (forward) {\n\t\tv1 <- unique(curr[,1])\n\t\tfor (i in 1:nrow(curr)) {\n\t\t\tpreds <- c(preds, \n\t\t\t\tpred.point2(x[curr[i,1],], x[curr[i,2],], x[v1+1,1]))\n\t\t}\n\t} else {\n\t\tv1 <- unique(curr[,2])\n\t\tfor (i in 1:nrow(curr)) {\n\t\t\tpreds <- c(preds, \n\t\t\t\tpred.point2(x[curr[i,1],], x[curr[i,2],], x[v1-1,1]))\n\t\t}\n\t}\n\t#print(which.max(preds))\n\t#print(curr[which.max(preds),])\n\tcurr[which.max(preds),]\n}\n\nget.nxts <- function(x, y, v) {\n\tcurr <- y[y[,1] == v,]\t\n\tif (length(curr) == 2) {\n\t\treturn(c(v, get.nxts(x, y, curr[2])))\n\t} else if (length(curr) > 2) {\n\t\tcx <- find.highv(x,curr,forward=T)\n\t\treturn(c(v, get.nxts(x, y, cx[2])))\n\t} else {\n\t\treturn(v)\n\t}\n}\n\n\n# get.hulltop: get the part of the convex hull above the contour.\nget.hulltop <- function(x, y0, vmax) {\n\t#print(\"hulltop\")\t\n\ty <- sort.hull(y0)\n\tprev <- get.prevs(x, y, vmax) \n \tnxt <- get.nxts(x, y,vmax)\n\ttop <- unique(sort(c(prev,nxt)))\n\treturn(list(prev=prev, nxt=nxt, top=top))\n}\n\n# get.nxtdiff: find the point with maximum difference from \n# the hull top that exceeds the threshold. \nget.nxtdiff <- function(x, y, nxt, diffthresh=1) {\n\tmaxdiff <- 0 \n\tidiff <- NA \t\n\tfor (i in 1:(length(nxt)-1)) {\n\t\tvs <- c(nxt[i], nxt[i+1]) \n\t\t#points(x[vs,], col=\"blue\", pch=15)\n\n\t\tpreds <- NULL\n\t\tcurr <- vs[1]:vs[2]\n\t\tfor (j in curr) {\n\t\t\tpreds <- c(preds,pred.point2(x[vs[1],], x[vs[2],], x[j,1]))\n\t\t}\n\t\tpreds <- unlist(preds)\n\t\t#points(x[curr,1], preds, col=\"red\", pch=13)\n\t\tdiffs <- preds - x[curr,2]\n\t\tmd <- which.max(diffs) \t\n\t\t#print(diffs[md])\n\t\t#points(x[curr[md],], col=\"green\", pch=15)\n\t\tif (diffs[md] > diffthresh & diffs[md] > maxdiff) {\n\t\t\tidiff <- curr[md] \n\t\t\tmaxdiff <- diffs[md]\t\n\t\t} \n\t}\t\n\treturn(idiff)\n} \n\n\n# get.maxdiffs: Recursively apply Mermelstein algorithm to \n# time series (x0) to find inflection points. \n# (This function is unfortunately named! It should get a wrapper!)\n\nget.maxdiffs <- function(x0, vs=1:nrow(x0), diffthresh=2) {\n\tif (length(vs) == 0) {return(NULL)}\n\n\tif (length(vs) < 3 ) {\n\t\treturn(c(which.max(px[[1]]$F0), 1, nrow(px[[1]])))\n\t} \n\n\tx <- x0[vs,]\n\n\ty <- convhulln(as.matrix(x))\n\ty <- sort.hull(y)\n\tvmax <- get.max(x,y) \n\t#points(x0[vs[vmax],], col=\"red\", pch=15)\n\tprint(vs[vmax])\n\thtop <- get.hulltop(x,y,vmax)\n\titop <- get.nxtdiff(x,y,htop$top,diffthresh)\n\tif (!is.na(itop)) {\n\t\t#abline(v=x[itop,], col=\"green\")\n\t\tprev <- head(vs,1):vs[itop]\n\t\tnxt <- vs[itop]:tail(vs,1)\n\t\treturn(rbind(c(vs[vmax], head(vs,1), tail(vs,1)), \n\t\t\tget.maxdiffs(x0, prev, diffthresh), \n\t\t\tget.maxdiffs(x0, nxt, diffthresh)))\t\n\t} else {\n\t\treturn(c(vs[vmax], head(vs,1), tail(vs,1))) \n\t}\n} \n\nchunk.by.pause <- function (x, plen=0.1) {\n chunks <- list()\n lx <- 1\n pauses <- get.pause.indices(x, plen)\n\n if (length(pauses) > 0) {\n for (i in 1:length(pauses)) {\n p <- pauses[i]\n curr <- list()\n curr$Time <- x$Time[lx:p]\n curr$F0 <- x$F0[lx:p]\n\n chunks[[i]] <- curr\n lx <- p+1\n }\n }\n curr <- list()\n curr$Time <- x$Time[lx:length(x$Time)]\n curr$F0 <- x$F0[lx:length(x$Time)]\n chunks[[length(chunks)+1]] <- curr\n chunks\n}\n\nget.pause.indices <- function(x, plen=0.1) {\n tdiff <- diff(x$Time,1)\n which(tdiff > plen)\n}\n\n\napply.by.pause <- function(x, f, plen=0.1) {\n xs <- chunk.by.pause(x, plen)\n ys <- lapply(xs, f)\n\n x.new <- list(Time=NULL, F0=NULL)\n for (y in ys) {\n x.new$Time <- c(x.new$Time, y$Time)\n x.new$F0 <- c(x.new$F0, y$F0)\n }\n\n x.new\n\n}\n\nunlist.df <- function(x) {\n\ty <- NULL\n\tif (length(x) > 0) {\n\t\tfor (i in 1:length(x)) {\n\t\t\ty <- rbind(y, x[[i]])\n\t\t}\n\t}\n\ty\n}\n\nunlist.vec <- function(x) {\n\ty <- NULL\n\tfor (i in 1:length(x)) {\n\t\ty <- c(y, x[[i]])\n\t}\n\ty\n}\n\nget.pquantiles <- function(pitchtiers, probs=c(0,0.05,0.25,0.5,0.75,0.95,1)) {\n\tpquantile <- sapply(pitchtiers, function(x) {quantile(x$F0, probs=probs)})\n\tpquantile <- t(pquantile)\n\tdata.frame(rownames(pquantile), pquantile, row.names=NULL)\n}\n\n\n", "meta": {"hexsha": "1af84799e05660db89d7b886a117d0be3f86e6f3", "size": 11579, "ext": "r", "lang": "R", "max_stars_repo_path": "f0basics.r", "max_stars_repo_name": "laic/prosody", "max_stars_repo_head_hexsha": "f148fd4e085e3f598c8de9b213179f09f3b2a402", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-02-21T02:49:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-26T09:12:26.000Z", "max_issues_repo_path": "f0basics.r", "max_issues_repo_name": "laic/prosody", "max_issues_repo_head_hexsha": "f148fd4e085e3f598c8de9b213179f09f3b2a402", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "f0basics.r", "max_forks_repo_name": "laic/prosody", "max_forks_repo_head_hexsha": "f148fd4e085e3f598c8de9b213179f09f3b2a402", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.3965183752, "max_line_length": 103, "alphanum_fraction": 0.5198203645, "num_tokens": 4263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3071263493835959}} {"text": "# wahpenayo at gmail dot com\n# 2018-01-25\n#-----------------------------------------------------------------\nif (file.exists('e:/porta/projects/taigabench')) {\n setwd('e:/porta/projects/taigabench')\n} else {\n setwd('c:/porta/projects/taigabench')\n}\nsource('src/scripts/r/functions.r')\nreadr.show_progress <- FALSE\n#-----------------------------------------------------------------\ndataset <- 'ontime'\nsuffixes <- c('32768','131072')#,'524288','2097152','8388608','33554432')\nfor (na.action in c('na.omit','na.impute')) {\n prefix <- paste0('randomForestSRC',substring(na.action,first=4))\n#-----------------------------------------------------------------\n# response <- 'arr_delayed_15min'\n# dataf <- ontime.classify.data\n# dtest <- dataf(test.file(dataset=dataset))\n# problem <- 'classify'\n# bench(\n# dataset=dataset,problem=problem,dataf=dataf,dtest=dtest,\n# response=response, \n# suffixes=suffixes,\n# trainf=classify.randomForestSRC,\n# prefix=prefix,\n# na.action=na.action)\n#-----------------------------------------------------------------\n response <- 'arrdelay'\n dataf <- ontime.data\n dtest <- dataf(test.file(dataset=dataset))\n problem <- 'l2'\n bench(\n dataset=dataset,\n problem=problem,\n dataf=dataf,\n dtest=dtest,\n response=response, \n suffixes=suffixes,\n trainf=l2.randomForestSRC,\n prefix=prefix,\n na.action=na.action)\n#-----------------------------------------------------------------\n# problem <- 'qcost'\n# bench(\n# dataset=dataset,problem=problem,dataf=dataf,dtest=dtest,\n# response=response, \n# suffixes=suffixes,\n# trainf=qcost.randomForestSRC,\n# prefix=prefix,\n# na.action=na.action)\n#-----------------------------------------------------------------\n}\n", "meta": {"hexsha": "53a3a76d86678df8243fb632bd8962098bc92221", "size": 1746, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/r/randomForestSRC.r", "max_stars_repo_name": "wahpenayo/taigabench", "max_stars_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/r/randomForestSRC.r", "max_issues_repo_name": "wahpenayo/taigabench", "max_issues_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/r/randomForestSRC.r", "max_forks_repo_name": "wahpenayo/taigabench", "max_forks_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3333333333, "max_line_length": 73, "alphanum_fraction": 0.5229095074, "num_tokens": 429, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.30712634206458117}} {"text": "\r\nlibrary( \"rmacrolite\" )\r\n\r\n# Path to an example par-file\r\npar_file_path <- system.file( \"par-files\", \r\n \"chat_winCer_GW-X_900gHa_d182.par\", \r\n package = \"rmacrolite\" ) \r\n\r\n# Import the example par-file\r\npar_file <- rmacroliteImportParFile( \r\n file = par_file_path ) \r\n\r\n# Current value of ZKD (Freundlich sorption)\r\nas.numeric( rmacroliteGet1Param( \r\n x = par_file, \r\n pTag = \"ZKD\\t%s\\t%s\", \r\n type = \"SOLUTE PARAMETERS\" ) )\r\n\r\n# Organic matter content profile\r\noc <- as.numeric( rmacroliteGet1Param( \r\n x = par_file, \r\n pTag = \"ORGC\\t%s\\t%s\", \r\n type = \"PROPERTIES\" ) )\r\n\r\noc_factors <- oc / oc[ 1L ]\r\n\r\n# Change ZKD\r\npar_file <- rmacroliteChangeParam( \r\n x = par_file, \r\n p = data.frame( \r\n \"tag\" = sprintf( \"ZKD\\t%s\\t%s\", 1L:6L, \"%s\" ), \r\n \"values\" = 1 * oc_factors, # 1 is Kf for topsoil\r\n \"type\" = rep( \"SOLUTE PARAMETERS\", 6L ), \r\n \"set_id\" = rep( 1L, 6L ), \r\n stringsAsFactors = FALSE ) )[[ 1L ]]\r\n\r\n# Check new value\r\nnew_zkd <- as.numeric( rmacroliteGet1Param( \r\n x = par_file, \r\n pTag = \"ZKD\\t%s\\t%s\", \r\n type = \"SOLUTE PARAMETERS\" ) )\r\n\r\nnew_zkd\r\n\r\n# Parameter variations for several parameter sets\r\np <- data.frame( \r\n \"tag\" = c( \"ALPHA\\t1\\t%s\", \"ALPHA\\t2\\t%s\", \r\n \"ZKD\\t1\\t%s\", \"ZKD\\t2\\t%s\" ), \r\n \"type\" = c( \"PHYSICAL PARAMETERS\", \"PHYSICAL PARAMETERS\", \r\n \"SOLUTE PARAMETERS\", \"SOLUTE PARAMETERS\" ), \r\n \"values\" = c( 0.02, 0.02, 2, 2 ), \r\n \"set_id\" = c( 1, 1, 2, 2 ), \r\n stringsAsFactors = FALSE ) \r\n\r\npar_file_list <- rmacroliteChangeParam( x = par_file, p = p )\r\n\r\nclass( par_file_list ) # Should be a list\r\nlength( par_file_list ) # Should be 2\r\n\r\n# # Not run\r\n# rmacroliteRun( x = par_file_list )\r\n\r\n# Internal checks\r\nif( !all( abs( (1 * oc_factors) - new_zkd ) < 1e-15 ) ){\r\n stop( \"Test of rmacroliteChangeParam() failed\" )\r\n} \r\n\r\n# Clean-up\r\nrm( par_file_path, par_file, oc, oc_factors, new_zkd, p, \r\n par_file_list )\r\n\r\n", "meta": {"hexsha": "d524020ce6aa3938e91645edad316ad9c6db818c", "size": 2009, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/examples/rmacroliteChangeParam-example.r", "max_stars_repo_name": "julienmoeys/rmacrolite", "max_stars_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/examples/rmacroliteChangeParam-example.r", "max_issues_repo_name": "julienmoeys/rmacrolite", "max_issues_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/examples/rmacroliteChangeParam-example.r", "max_forks_repo_name": "julienmoeys/rmacrolite", "max_forks_repo_head_hexsha": "cb2a9a89f583111e7a21c14507b9dc87dd07cc66", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9027777778, "max_line_length": 65, "alphanum_fraction": 0.574912892, "num_tokens": 688, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3069744143648549}} {"text": "#' Fit a model via local, adaptive grouped regularization\n#'\n#' \\code{lagr} fits a LAGR model This method fits a local model at each location indicated by \\code{fit.loc}.\n#'\n#' @param formula symbolic representation of the model\n#' @param data data frame containing observations of all the terms represented in the formula\n#' @param weights vector of prior observation weights (due to, e.g., overdispersion). Not related to the kernel weights.\n#' @param family exponential family distribution of the response\n#' @param coords matrix of locations, with each row giving the location at which the corresponding row of data was observed\n#' @param fit.loc matrix of locations where the local models should be fitted\n#' @param longlat \\code{TRUE} indicates that the coordinates are specified in longitude/latitude, \\code{FALSE} indicates Cartesian coordinates. Default is \\code{FALSE}.\n#' @param kernel kernel function for generating the local observation weights\n#' @param bw bandwidth parameter\n#' @param bw.type type of bandwidth - options are \\code{dist} for distance (the default), \\code{knn} for nearest neighbors (bandwidth a proportion of \\code{n}), and \\code{nen} for nearest effective neighbors (bandwidth a proportion of the sum of squared residuals from a global model)\n#' @param tol.loc tolerance for the tuning of an adaptive bandwidth (e.g. \\code{knn} or \\code{nen})\n#' @param varselect.method criterion to minimize in the regularization step of fitting local models - options are \\code{AIC}, \\code{AICc}, \\code{BIC}, \\code{GCV}\n#' @param tuning logical indicating whether this model will be used to tune the bandwidth, in which case only the tuning criteria are returned\n#' @param D pre-specified matrix of distances between locations\n#' @param verbose print detailed information about our progress?\n#' \n#' @return list containing the local models.\n#' \n#' @export\nlagr <- function(formula, data, family=gaussian(), weights=NULL, coords, fit.loc=NULL, tuning=FALSE, predict=FALSE, simulation=FALSE, oracle=NULL, kernel=NULL, bw=NULL, varselect.method=c('AIC','BIC','AICc', 'wAIC', 'wAICc'), verbose=FALSE, longlat=FALSE, tol.loc=NULL, bw.type=c('dist','knn','nen'), D=NULL, lambda.min.ratio=0.001, n.lambda=50, lagr.convergence.tol=0.001, lagr.max.iter=20, jacknife=FALSE, bootstrap.index=NULL, na.action=na.fail, contrasts=NULL) {\n result = list()\n class(result) = \"lagr\"\n \n if (is(bw, 'lagr.bw')) {\n bw.type = bw$bw.type\n kernel = bw$kernel\n bw = bw$bw\n } else {\n bw.type = match.arg(bw.type)\n }\n \n if (is.null(kernel))\n stop(\"Error: There is no kernel specified!\")\n\n cl <- match.call()\n formula = eval.parent(substitute_q(formula, sys.frame(sys.parent())))\n mf = eval(lagr.parse.model.frame(formula, data, family, weights, coords, fit.loc, longlat, na.action, contrasts))\n\n y = mf$y\n x = mf$x\n w = mf$w\n mt = mf$mt\n coords = mf$coords\n dist = mf$dist\n max.dist = mf$max.dist\n min.dist = mf$min.dist\n family = mf$family\n \n #Set some variables that determine how we fit the model\n varselect.method = match.arg(varselect.method)\n\n #Fit the model:\n vcr.model = lagr.dispatch(\n x=x,\n y=y,\n family=family,\n prior.weights=w,\n tuning=tuning,\n predict=predict,\n simulation=simulation,\n coords=coords,\n oracle=oracle,\n fit.loc=fit.loc,\n D=dist,\n varselect.method=varselect.method,\n verbose=verbose,\n bw=bw,\n bw.type=bw.type,\n kernel=kernel,\n min.dist=min.dist,\n max.dist=max.dist,\n tol.loc=tol.loc,\n lambda.min.ratio=lambda.min.ratio,\n n.lambda=n.lambda, \n lagr.convergence.tol=lagr.convergence.tol,\n lagr.max.iter=lagr.max.iter,\n jacknife = jacknife,\n bootstrap.index=bootstrap.index\n )\n \n for (nn in names(vcr.model))\n result[[nn]] = vcr.model[[nn]]\n \n coefs = as.data.frame(t(sapply(result[['fits']], function(x) x[['coef']])))\n is.zero = as.data.frame(t(sapply(result[['fits']], function(x) x[['conf.zero']])))\n varnames = names(result[['fits']][[1]]$coef)\n colnames(coefs) = varnames\n colnames(is.zero) = varnames\n \n #Store results from model fitting:\n if (!tuning) {\n result[['data']] = data\n result[['response']] = as.character(formula[[2]])\n result[['family']] = family\n result[['weights']] = w\n result[['coords']] = coords\n result[['fit.locs']] = fit.loc\n result[['longlat']] = longlat\n result[['kernel']] = kernel\n result[['bw']] = bw\n result[['bw.type']] = bw.type\n result[['varselect.method']] = varselect.method\n result[['dim']] = mf$dim\n result[['coefs']] = coefs\n result[['is.zero']] = is.zero\n \n result[['na.action']] <- attr(mf, \"na.action\")\n result[['contrasts']] <- attr(x, \"contrasts\")\n result[['xlevels']] <- .getXlevels(mt, mf)\n result[['call']] <- cl\n result[['terms']] <- mt\n }\n \n return(result)\n}\n", "meta": {"hexsha": "8c733947efcc3f9b2bdee4fe8f75933071e59f07", "size": 5101, "ext": "r", "lang": "R", "max_stars_repo_path": "R/lagr.r", "max_stars_repo_name": "wrbrooks/lagr", "max_stars_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/lagr.r", "max_issues_repo_name": "wrbrooks/lagr", "max_issues_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/lagr.r", "max_forks_repo_name": "wrbrooks/lagr", "max_forks_repo_head_hexsha": "7ac867f0bf091e8835917205cab44ea806d3c65e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.5083333333, "max_line_length": 466, "alphanum_fraction": 0.6428151343, "num_tokens": 1293, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.30697315497766314}} {"text": "#install.packages(\"bio3d\")\n#downloading the file with protein ids in the Home folder\n#f=download.file(\"ftp://ftp.wwpdb.org/pub/pdb/derived_data/index/compound.idx\",destfile=\"idx_file.idx\");\n\n\n#-------------------------------import libraries---------------------------\n\nlibrary(\"bio3d\");\n\n#------------------read from idx file with ids and move data into matrix-----------------------\nfileName <- 'idx_file.idx'\nfff= readChar(fileName, file.info(fileName)$size);\nfff=strsplit(fff, \"\\n\");\nl=length(fff[[1]]);\nccc=fff[[1]][5:l];\nremove(l);\nfff=ccc;\nremove(ccc);\nfff=strsplit(fff, \"\\t\");\noutput <- matrix(unlist(fff), ncol = 2, byrow = TRUE);\nremove(fff);\nremove(fileName);\n#remove(f);\n\n#============================ask from user the Query========1N21========5L5V=====2QPS====1U98====1U99=====5NQ3\n\n#Q=readline(prompt = \"Give the pdb_id of the query \\n\");\n\n#-----------------------download from database or load if there is already downloaded----------------\nquery_id = Q;\nQ=read.pdb(Q, maxlines = -1, multi = FALSE, rm.insert = FALSE,rm.alt = TRUE, ATOM.only = FALSE, hex = FALSE, verbose = TRUE);\n\n\n\n\n#***********************************start of algorithm**************************\n\n\n\n\n\n#=======================computation of Query=======================================\n#--------------------we need only Calpha coordinates-----------------------------------------\n\n\n\nt = length((which(Q$calpha==TRUE))*3);\ncord = rep(NaN,t);\ncounter=1;\n\nfor (i in 1:length(Q$calpha)) {\nif(Q$calpha[i]==TRUE){\n cord[counter] = Q$xyz[[(i-1)*3+1]];\n cord[counter+1] = Q$xyz[[(i-1)*3+2]];\n cord[counter+2] = Q$xyz[[(i-1)*3+3]];\ncounter=counter+3;\n}\n}\n\ncoordinates_Q = c(xyz=c(\"xyz\"),size=0,name=query_id);\ncoordinates_Q$xyz = cord;\ncoordinates_Q$size = length((which(Q$calpha==TRUE)))*3;\n\n\n#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-finding centroids G_right, G_left-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n\nhalf_v = length(coordinates_Q$xyz)-(length(coordinates_Q$xyz))%%2;\nhalf_v = (half_v - half_v%%3)/2;\nt_square_left = sqrt((half_v/3)/(coordinates_Q$size/3));\nt_square_right= sqrt(((coordinates_Q$size/3)-half_v/3)/(coordinates_Q$size/3));\n\nsumx = 0;\nsumy = 0;\nsumz = 0;\ni=1;\nwhile(i=tr){\n\t\t\tfor (j in 1:(length(pdb_file$calpha))) {\n\t\t\t\tif(pdb_file$calpha[j]==TRUE){\n\t\t\t\t\tcord[counter] = pdb_file$xyz[[(j-1)*3+1]];\n\t\t\t\t\tcord[counter+1] = pdb_file$xyz[[(j-1)*3+2]];\n\t\t\t\t\tcord[counter+2] = pdb_file$xyz[[(j-1)*3+3]];\n\t\t\t\t\tcounter=counter+3;\n\t\t\t\t}\n\t\t\t}\n\t\t\t\tcoordinates_P$xyz = c(coordinates_P$xyz,cord);\n\t\t\t\tcoordinates_P$size = c(coordinates_P$size,length(cord));\n\t\t\t\tcoordinates_P$name = c(coordinates_P$name,ids[i])\n\t\t}else{\n\t\t#-remove from list------\n\t\tids = ids[(which(ids!=ids[i]))]; \n\t\t}\n\t}else{\n\t#----------------if pdb doesn't exist remove from list----------------------\n\tprint(ids[i]); print(\"doesn't exist or cannot be read properly due to C++ error remove from list\");\n\t #ids = ids[(which(ids!=ids[i]))]; \n\t}\n}#--------------------end of for--------------\n\n\ncoordinates_P$size = coordinates_P$size[which(coordinates_P$size!=\"NaN\")];\ncoordinates_P$xyz = coordinates_P$xyz[which(coordinates_P$xyz!=\"NaN\")];\ncoordinates_P$name = coordinates_P$name[which(coordinates_P$name!=\"NaN\")];\n\n#---------------remove false data------------------------------\nif(length((which(coordinates_P$size!=0)))){\nids = ids[(which(coordinates_P$size!=0))]; \ncoordinates_P$size = coordinates_P$size[(which(coordinates_P$size!=0))]; \n\n}\n\ncoordinates_P$size = strtoi(coordinates_P$size);\n\n\n\n\nb = -1;\n\nhits=0;\nD_values = -1;\n#=======================calculating from 100 substructures of P=======================================\nnumber_of_proteins = number_of_proteins+length(coordinates_P$size);\nnumber_of_substructures = number_of_substructures + sum(coordinates_P$size)/3-sum(coordinates_Q$size)/3+length(coordinates_P$size);\nstart_time <- Sys.time();\ncounter3=1;\ncounter4=0;\nfor (i in 1:(length(coordinates_P$size))) {\n\tstart=TRUE;\n\tj=1;\n\twhile(j<=(coordinates_P$size[i]-(length(coordinates_Q$xyz))+1)) {\n\n\tif(start==TRUE){\n\n\t\t#finding centroids G_right, G_left\n\t\t\n\t\thalf_v = length(coordinates_Q$xyz)-(length(coordinates_Q$xyz))%%2;\n\t\thalf_v = (half_v - half_v%%3)/2;\n\n\t\tsumx = 0;\n\t\tsumy = 0;\n\t\tsumz = 0;\n\t\te=1;\n\t\twhile(e<=half_v) {\n\t\t\tsumx = sumx + coordinates_P$xyz[e+(counter4+j)-1];\n\t\t\tsumy = sumy + coordinates_P$xyz[e+(counter4+j)+1-1];\n\t\t\tsumz = sumz + coordinates_P$xyz[e+(counter4+j)+2-1];\n\t\t\te=e+3;\n\t\t}\n\t\tG_right_P = c(sumx,sumy,sumz) /half_v;\n\n\t\tsumx = 0;\n\t\tsumy = 0;\n\t\tsumz = 0;\n\t\te=half_v+1;\n\t\twhile(e<=length(coordinates_Q$xyz)) {\n\t\t\tsumx = sumx + coordinates_P$xyz[e+(counter4+j)-1];\n\t\t\tsumy = sumy + coordinates_P$xyz[e+(counter4+j)+1-1];\n\t\t\tsumz = sumz + coordinates_P$xyz[e+(counter4+j)+2-1];\n\t\t\te=e+3;\n\t\t}\n\t\tG_left_P = c(sumx,sumy,sumz) /(length(coordinates_Q$xyz)-half_v);\n\n\t\tstart=FALSE;\n\t}else{\n\t\t#finding centroids G_right, G_left\n\n\t\ttt= length(coordinates_Q$xyz) - (length(coordinates_Q$xyz))%%2;\n\t\ttt = tt/2;\n\t\tG_left_P = G_left_P - (1/tt)*( c(coordinates_P$xyz[(counter4+j)],coordinates_P$xyz[(counter4+j)+1],coordinates_P$xyz[(counter4+j)+2]) );\n\t\tG_right_P = G_right_P - (1/tt)*(c(coordinates_P$xyz[tt+(counter4+j)],coordinates_P$xyz[tt+(counter4+j)+1],coordinates_P$xyz[tt+(counter4+j)+2]));\n\t}\n\n\n\n#----------------------compute F for even or odd n---------------------------------\n\t \n\t \n\t \n\t if((length(coordinates_Q$xyz)/3)%%2==0 ){\n\t \n\t F_left = t_square_left*sqrt((G_left_P[1]-G_left_Q[1])^2+ (G_left_Q[2]-G_left_P[2])^2+(G_left_Q[3]-G_left_P[3])^2 )/1;\n\t }else{\n\t F_left = t_square_left*(sqrt( (((coordinates_Q$size/3)/2)-1)/((coordinates_Q$size/3)/2) ))*(sqrt((G_left_P[1]-G_left_Q[1])^2+ (G_left_Q[2]-G_left_P[2])^2+(G_left_Q[3]-G_left_P[3])^2 )/1);\n\t }\n\t \n\t if((length(Q$xyz)/3)%%2==0 ){\n\t\tF_right = t_square_right*sqrt((G_right_Q[1]-G_right_P[1])^2+ (G_right_Q[2]-G_right_P[2])^2+(G_right_Q[3]-G_right_P[3])^2 )/2;\n\t}else{\n\t\tF_right = t_square_right*(sqrt( ((((coordinates_Q$size/3))/2)-1)/((coordinates_Q$size/3)/2) ))*(sqrt((G_right_Q[1]-G_right_P[1])^2+ (G_right_Q[2]-G_right_P[2])^2+(G_right_Q[3]-G_right_P[3])^2 )/2);\n\t}\n\n\n#----------------------compute D---------------------------\n\tD_left = abs(F_left) ;\t\n\tD_right = abs(F_right);\t\n\tD = (D_left^2 + D_right^2)^0.5\n\tif(D<1){\n\t hits = hits+1;\n\t r =rmsd(coordinates_Q$xyz,coordinates_P$xyz[(counter4+j):(length(coordinates_Q$xyz)+(counter4+j) -1)]);\n\t if(r<1){\n\t \n\t#rmsd_var = c(rmsd_var$value,r);\n\trmsd_var$value = c(rmsd_var$value,r);\n\trmsd_var$name = c(rmsd_var$name,coordinates_P$name[i]);\n\trmsd_var$start = c(rmsd_var$start,j);\n\t\n\t}\n\t}\n\t\n\t\n\t\n\t\n\tcounter_iter = counter_iter +1;\n\tcounter3=counter3+1;\n\tj=j+3;\n\t\n\t\n}#end of whilej\n\t\n\tcounter4=counter4+coordinates_P$size[i];\n\t\n}#end of fori\n\n\n\n\nend_time <- Sys.time();\ntime_is=end_time-start_time;\ntotal_time = total_time+time_is;\n\nif(iter!=counter1){\n counter1 = counter1 +1;\n}else{\n packet = length(output)%%packet;\n}\n\n\n}#-----------end of while iter----------\n\n\n#------------after algorithm processing-----------------\n\n#-----remove initialized values NaN-------------\nrmsd_var[[1]] = rmsd_var[[1]][2:length(rmsd_var[[1]])];\nrmsd_var[[2]] = rmsd_var[[2]][2:length(rmsd_var[[2]])];\nrmsd_var[[3]] = rmsd_var[[3]][2:length(rmsd_var[[3]])];\n\nb=b[2:length(b)];\n\n\n\n#--------------------use plot3D---library---------------\nlibrary(\"plot3D\");\n\n\n\n\npdf('results_2.pdf')\ni=1;\nx = rep(NaN,(coordinates_Q$size/3));\ny = rep(NaN,(coordinates_Q$size/3));\nz = rep(NaN,(coordinates_Q$size/3));\nwhile(i\n}\n\\examples{\nrequire(circlize)\nrequire(GenomicRanges)\nbed = generateRandomBed()\ngr = GRanges(seqnames = bed[[1]], ranges = IRanges(bed[[2]], bed[[3]]))\nhc = GenomicHilbertCurve(mode = \"pixel\", level = 9)\nhc_layer(hc, gr, col = rand_color(length(gr)))\n}\n", "meta": {"hexsha": "01790a0027b014e4fdd709ea84f725a5b8b5b6cc", "size": 1986, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/hc_layer-GenomicHilbertCurve-method.rd", "max_stars_repo_name": "jokergoo/HilbertCurve", "max_stars_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 37, "max_stars_repo_stars_event_min_datetime": "2016-02-22T16:46:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T09:35:43.000Z", "max_issues_repo_path": "man/hc_layer-GenomicHilbertCurve-method.rd", "max_issues_repo_name": "jokergoo/HilbertCurve", "max_issues_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T08:29:21.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T09:44:53.000Z", "max_forks_repo_path": "man/hc_layer-GenomicHilbertCurve-method.rd", "max_forks_repo_name": "jokergoo/HilbertCurve", "max_forks_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-04-22T10:44:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T07:48:16.000Z", "avg_line_length": 46.1860465116, "max_line_length": 273, "alphanum_fraction": 0.7457200403, "num_tokens": 565, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3067709433411175}} {"text": " \n get.fishery.stats.by.region = function( Reg=\"cfaall\", y=NULL ) {\n \n landings = landings.db()\n \n if (is.null(y)) y = sort(unique(landings$yr) )\n out = data.frame( yr=y )\n\n if (Reg==\"cfaall\") region = sort( unique(landings$cfa) ) # all data\n if (Reg==\"cfanorth\") region = c(\"cfa20\", \"cfa21\", \"cfa22\", \"cfanorth\", \"north\")\n if (Reg==\"cfasouth\") region = c(\"cfa23\", \"cfa24\", \"cfasouth\", \"cfaslope\")\n if (Reg==\"cfa4x\") region = \"cfa4x\"\n \n lnd = landings[ which(landings$cfa %in% region) ,]\n \n l = aggregate( lnd$landings, list(yr=lnd$yr), function(x) sum(x, na.rm=T))\n names(l) = c(\"yr\", \"landings\")\n \n out = merge(out, l, by=\"yr\", all.x=T, all.y=F, sort=T)\n\n lnd$cpue_direct = lnd$landings / lnd$effort\n lnd$cpue_direct[ which( lnd$cpue_direct > (650*0.454)) ] = NA # same rule as in landings.db -- 650lbs/trap is a reasonable upper limit\n\n cpue = aggregate( lnd$cpue, list(yr=lnd$yr), function(x) mean(x, na.rm=T))\n names(cpue) = c(\"yr\", \"cpue\")\n \n out = merge (out, cpue, by=\"yr\", all.x=T, all.y=F, sort=T)\n \n out$effort = out$landings / out$cpue ## estimate effort level as direct estimates are underestimates (due to improper logbook records)\n rownames(out) = out$yr\n \n return(out)\n \n }\n\n\n", "meta": {"hexsha": "f5fe217ba7609a946c2f66612898ca1d0d3e3ad1", "size": 1280, "ext": "r", "lang": "R", "max_stars_repo_path": "R/get.fishery.stats.by.region.r", "max_stars_repo_name": "PEDsnowcrab/bio.snowcrab", "max_stars_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/get.fishery.stats.by.region.r", "max_issues_repo_name": "PEDsnowcrab/bio.snowcrab", "max_issues_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/get.fishery.stats.by.region.r", "max_forks_repo_name": "PEDsnowcrab/bio.snowcrab", "max_forks_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5945945946, "max_line_length": 139, "alphanum_fraction": 0.596875, "num_tokens": 449, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.3067654327138648}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\n\nsetwd(\"/Users/mengmengjiang/all datas/voltage\")\n\n#\u5bf9\u5355\u4e2a\u5185\u5bb9\u8fdb\u884c\u753b\u56fe\u5206\u6790\uff0c\u5c1d\u8bd5\u8bb0\u5f55\u5355\u4e2a\u5185\u5bb9\u7684pdf\u56fe\u7247\u683c\u5f0f\n#\u6587\u4ef6\u540d\uff1avoltage_stable , OV\u7535\u538b\u4e0eBV\u7535\u538b\u4e4b\u5dee\uff0c\u5373stable \u7a33\u5b9a\u7535\u538b\u4e0e\u4e09\u79cd\u6db2\u4f53\u4e4b\u95f4\u968f\u6d41\u91cf\u3001\u6781\u95f4\u8ddd\u79bb\u3001\u9488\u5934\u76f4\u5f84\u53d8\u5316\u7684\u5f71\u54cd\n\nlibrary(xlsx)\n\n#\u8bfb\u53d6v~Q\u7684\u6570\u636e\neq<-read.xlsx(\"voltage.xls\",sheetName=\"ethanol_r\",header=TRUE)\naq<-read.xlsx(\"voltage.xls\",sheetName=\"acetone_r\",header=TRUE)\niq<-read.xlsx(\"voltage.xls\",sheetName=\"iso_r\",header=TRUE)\n\nplot(eq$r, eq$va, col=0, xaxs=\"i\", xlim=c(0.25,0.85), ylim=c(1, 3.3),xlab=expression(paste(italic(Nozzle),\" \",italic(diameter),(mm))),mgp=c(1, 0,0),tck=0.01,cex.lab=1.1,ylab=expression(italic(V)(kv)))\n\nmtext(\"Nozzles\",col=\"black\",3,line=-1,font=2,cex=1)\n\n###error bar####\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\n if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\n stop(\"vectors must be same length\")\n arrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n###lines##\nyan<-c(\"red\",\"red\",\"blue\",\"blue\",\"green3\",\"green3\")\npch<-c(1,2,1,2,1,2)\n\nlines(eq$r,eq$vaeva,col=yan[1],pch=1,lwd=2,lty=4,type=\"b\")\nlines(eq$r,eq$vbeva,col=yan[2],pch=2,lwd=2,lty=4,type=\"b\")\nlines(aq$r,aq$vaeva,col=yan[3],pch=1,lwd=2,lty=4,type=\"b\")\nlines(aq$r,aq$vbeva,col=yan[4],pch=2,lwd=2,lty=4,type=\"b\")\nlines(iq$r,iq$vaeva,col=yan[5],pch=1,lwd=2,lty=4,type=\"b\")\nlines(iq$r,iq$vbeva,col=yan[6],pch=2,lwd=2,lty=4,type=\"b\")\n\nerror.bar(eq$r,eq$vaeva,eq$vastd/2,col=yan[1])\nerror.bar(eq$r,eq$vbeva,eq$vbstd/2,col=yan[2])\n\nerror.bar(aq$r,aq$vaeva,aq$vastd/2,col=yan[3])\nerror.bar(aq$r,aq$vbeva,aq$vbstd/2,col=yan[4])\n\nerror.bar(iq$r,iq$vaeva,iq$vastd/2,col=yan[5])\nerror.bar(iq$r,iq$vbeva,iq$vbstd/2,col=yan[6])\n\nleg<-c(\"ethanol-Von\",\"ethanol-Vbr\",\"acetone-Von\",\"acetone-Vbr\",\"iso-Von\",\"iso-Vbr\")\n\nlegend(\"bottomright\",legend=leg, col=yan, pch=c(1,2,1,2,1,2),bty=\"n\",lwd=2,lty=2,inset=.01,cex=0.8)\n", "meta": {"hexsha": "70a328ac7496d143afc404909e0be5e17730c3f4", "size": 1934, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap6/fig6-8.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap6/fig6-8.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap6/fig6-8.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4693877551, "max_line_length": 201, "alphanum_fraction": 0.683557394, "num_tokens": 853, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.30668375038193746}} {"text": "Cit.HepPh.Unique<-unique(Cit.HepPh)\ncit.HepPh.dates.Unique<-unique(cit.HepPh.dates)\n\nCit.HepPh.WithDates<-data.frame()\n\ndateOfPaper <- function(paperNodeId){\n cit.HepPh.dates.Unique[(cit.HepPh.dates.Unique$V1==paperNodeId),]$V2[1]\n}\n#dateOfPaper(112008)301097\nj<-1\nfor(i in 1:nrow(Cit.HepPh.Unique)){\n date1<-dateOfPaper(Cit.HepPh.Unique[i,1])\n date2<-dateOfPaper(Cit.HepPh.Unique[i,2])\n if((!is.na(date1))&&(!is.na(date2))){\n Cit.HepPh.WithDates[j,1]<-Cit.HepPh.Unique[i,1]\n Cit.HepPh.WithDates[j,2]<-date1\n Cit.HepPh.WithDates[j,3]<-Cit.HepPh.Unique[i,2]\n Cit.HepPh.WithDates[j,4]<-date2\n j=j+1\n }\n}", "meta": {"hexsha": "2dd9eff08058627bcaf29b6ccdd10e505a08927e", "size": 621, "ext": "r", "lang": "R", "max_stars_repo_path": "restopicer-research/RCitationEvolution/code_not_used/combineCitWithDates.r", "max_stars_repo_name": "RUCYuLiTeam/restopicer", "max_stars_repo_head_hexsha": "cea3e7f644a40bc8c2e2136b5d9ccfcc72ce7e25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-09-06T02:57:54.000Z", "max_stars_repo_stars_event_max_datetime": "2015-09-06T02:57:54.000Z", "max_issues_repo_path": "restopicer-research/RCitationEvolution/code_not_used/combineCitWithDates.r", "max_issues_repo_name": "JoshuaZe/restopicer", "max_issues_repo_head_hexsha": "28d0833e7b950356ae6e29459991d87a53073a72", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "restopicer-research/RCitationEvolution/code_not_used/combineCitWithDates.r", "max_forks_repo_name": "JoshuaZe/restopicer", "max_forks_repo_head_hexsha": "28d0833e7b950356ae6e29459991d87a53073a72", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5714285714, "max_line_length": 73, "alphanum_fraction": 0.694041868, "num_tokens": 241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.6039318337259583, "lm_q1q2_score": 0.3066837503819373}} {"text": "library(gWidgets)\noptions(guiToolkit=\"RGtk2\") ## using gWidgtsRGtk2\n\nw <- gwindow(\"Disable components\")\n\ng <- ggroup(cont=w, horizontal=FALSE)\ne <- gedit(\"0\", cont=g, coerce.with=as.numeric)\nbg <- ggroup(cont=g)\n\ndown_btn <- gbutton(\"-\", cont=bg)\nup_btn <- gbutton(\"+\", cont=bg)\n\nupdate_ctrls <- function(h,...) {\n val <- svalue(e)\n enabled(down_btn) <- val >= 0\n enabled(up_btn) <- val <= 10\n}\n\nrement <- function(h,...) {\n svalue(e) <- svalue(e) + h$action\n update_ctrls(h,...)\n}\n\naddHandlerChanged(e, handler=update_ctrls)\naddHandlerChanged(down_btn, handler=rement, action=-1)\naddHandlerChanged(up_btn, handler=rement, action=1)\n", "meta": {"hexsha": "bf1bd4e0b2b5d372cd32fcf2a0e190ca4598432c", "size": 638, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/GUI-enabling-disabling-of-controls/R/gui-enabling-disabling-of-controls.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/GUI-enabling-disabling-of-controls/R/gui-enabling-disabling-of-controls.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/GUI-enabling-disabling-of-controls/R/gui-enabling-disabling-of-controls.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 23.6296296296, "max_line_length": 54, "alphanum_fraction": 0.6771159875, "num_tokens": 194, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795672, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.30653072478835675}} {"text": "# James Rekow\r\n\r\nidentifyNumSubgroupsTest = function(MVec = NULL, NVec = NULL, lambdaVec = NULL, numSubgroupsVec = NULL,\r\n iStrength = 1, univVec = NULL, sigmaMax = 0.1,\r\n thresholdMult = 10 ^ (-1), maxSteps = 10 ^ 4, tStep = 10 ^ (-2),\r\n intTime = 1, interSmplMult = 0.01, conGraph = NULL, rollRange = 0.025,\r\n numReplicates = 10){\r\n \r\n # ARGS:\r\n #\r\n # RETURNS: percentErrorDF - data frame containing columns for all the input data and the percent\r\n # of replicates for which identifyNumSubgroups returned the wrong number\r\n # of subgroups\r\n \r\n source(\"subgroupsAbdListCreator.r\")\r\n source(\"computeInfoWeightMat.r\")\r\n source(\"identifyNumSubgroups.r\")\r\n \r\n # set default value of MVec\r\n if(is.null(MVec)){\r\n MVec = seq(40, 200, 80)\r\n } # end if\r\n \r\n # set default value of NVec\r\n if(is.null(NVec)){\r\n NVec = seq(20, 100, 40)\r\n } # end if\r\n \r\n # set default value of numSubgroupsVec\r\n if(is.null(numSubgroupsVec)){\r\n numSubgroupsVec = seq(1, 4, 1)\r\n } # end if\r\n \r\n # set default value of lambdaVec\r\n if(is.null(lambdaVec)){\r\n lambdaVec = seq(0, 200, 50)\r\n } # end if\r\n \r\n # set default value of univVec\r\n if(is.null(univVec)){\r\n univVec = seq(0, 1, 0.5)\r\n } # end if\r\n \r\n correctNumberIdentified = function(inputVec){\r\n \r\n # ARGS: inputVec - a vector of the form c(M, N, numSUbgroups, lambda, univ)\r\n #\r\n # RETURNS: correct - boolean corresponding to whether or not identifyNumSubgroups correctly\r\n # identified the number of subgroups in an abundance list generated using the\r\n # input treatment\r\n \r\n # extract parameter values from inputVec\r\n M = inputVec[1]\r\n N = inputVec[2]\r\n numSubgroups = inputVec[3]\r\n lambda = inputVec[4]\r\n univ = inputVec[5]\r\n \r\n # create abundance list using the input parameters for the given treatment\r\n abdList = subgroupsAbdListCreator(M = M, numSubgroups = numSubgroups, N = N, iStrength = iStrength,\r\n univ = univ, sigmaMax = sigmaMax, thresholdMult = thresholdMult,\r\n maxSteps = maxSteps, tStep = tStep, intTime = intTime,\r\n interSmplMult = interSmplMult, lambda = lambda, returnParams = FALSE,\r\n conGraph = conGraph)\r\n \r\n # compute the info weight matrix for the given abundance list\r\n infoWeightMat = computeInfoWeightMat(abdList = abdList, rollRange = rollRange)\r\n \r\n # compute percent error of algorithm's subgroup classification\r\n numSubgroupsGuess = identifyNumSubgroups(infoMat = infoWeightMat)\r\n \r\n # check if the algorithm identified the correct number of subgroups\r\n correct = numSubgroupsGuess == numSubgroups\r\n \r\n return(correct)\r\n \r\n } # end correctNumberIdentified function\r\n \r\n percentErrorProducer = function(inputVec){\r\n \r\n # ARGS: inputVec - vector of the form c(M, N, lambda, univ)\r\n #\r\n # RETURNS: percentError - the percent of replicates at each treatment for which identifyNumSubgroups\r\n # returned the incorrect number of subgroups\r\n \r\n # compute whether or not the algorithm identified the correct number of subgroups for the given input\r\n # and repeat for numReplicates replicates\r\n correctGuessVec = replicate(numReplicates, correctNumberIdentified(inputVec = inputVec))\r\n \r\n # compute the percent of replicates for which the algorithm failed\r\n percentError = 100 * sum(!correctGuessVec) / numReplicates\r\n \r\n return(percentError)\r\n \r\n } # end percentErrorProducer function\r\n \r\n # create a matrix of all unique combinations of elements from Mvec, NVec, numSubgroupsVec, lambdaVec,\r\n # and univVec\r\n inputMat = expand.grid(MVec, NVec, numSubgroupsVec, lambdaVec, univVec)\r\n \r\n # for each combination of inputs, compute the percent error of the algorithm using those inputs\r\n percentError = apply(inputMat, 1, percentErrorProducer)\r\n \r\n # store data in a data frame\r\n percentErrorDF = data.frame(M = inputMat[ , 1], N = inputMat[ , 2], numSubgroups = inputMat[ , 3],\r\n lambda = inputMat[ , 4], univ = inputMat[ , 5], \r\n percentError = percentError)\r\n \r\n return(percentErrorDF)\r\n \r\n} # end identifyNumSubgroupsTest function\r\n", "meta": {"hexsha": "ab1dd2a178fc3906d58549e05ff84f9a9970666b", "size": 4572, "ext": "r", "lang": "R", "max_stars_repo_path": "identifyNumSubgroupsTest.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "identifyNumSubgroupsTest.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "identifyNumSubgroupsTest.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.8214285714, "max_line_length": 108, "alphanum_fraction": 0.6097987752, "num_tokens": 1142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5813030906443134, "lm_q1q2_score": 0.30653072478835675}} {"text": "# Make map of region\nlibrary(maps)\n\nrlong <- c(-124.5, -113.5)\nrlat <- c(32.5, 42.5)\n\n\n\nca <- data.frame(map(\"county\", xlim = rlong, ylim = rlat, plot = F, fill = T)[c(\"x\", \"y\")])\ncamap <- c(\n geom_path(aes(x, y), data = ca, colour=alpha(\"grey40\", 0.5), size = 0.5),\n xlim(rlong),\n ylim(rlat)\n)\n\n\nbayarea <- c(\n geom_polygon(aes(x, y), data = ca, colour=alpha(\"grey40\", 0.5), size = 0.5, fill = \"grey95\"),\n coord_cartesian(xlim = c(-123.5576, -120.5427), ylim = c(35.80061, 38.84582))\n)", "meta": {"hexsha": "a9bfc399392f274fb7d34c488c47550d8b6f417c", "size": 491, "ext": "r", "lang": "R", "max_stars_repo_path": "map.r", "max_stars_repo_name": "hadley/sfhousing", "max_stars_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2015-08-02T06:56:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T14:24:40.000Z", "max_issues_repo_path": "map.r", "max_issues_repo_name": "hadley/sfhousing", "max_issues_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "map.r", "max_forks_repo_name": "hadley/sfhousing", "max_forks_repo_head_hexsha": "5945b00302271b9fec7e12db1d69fd8af16a8007", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12, "max_forks_repo_forks_event_min_datetime": "2015-02-26T12:57:23.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-04T05:18:22.000Z", "avg_line_length": 24.55, "max_line_length": 95, "alphanum_fraction": 0.5865580448, "num_tokens": 201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3065307247883567}} {"text": "# Author: Justin Abraham\r\n# R Version: R-3.6.2\r\n# RStudio Version: 1.2.5033\r\n\r\n###############################\r\n## Install required packages ##\r\n###############################\r\n\r\nset.seed(47269801)\r\n\r\ninstall.packages(\"pacman\", repos='https://cloud.r-project.org/')\r\nlibrary(\"pacman\")\r\n\r\np_load(\"here\", \"tidyr\", \"dplyr\", \"lmtest\", \"multiwayvcov\", \"multcomp\", \"reshape2\", \"knitr\", \"flextable\", \"officer\", \"forestplot\", \"cowplot\", \"ggplot2\", \"matrixStats\", \"ggthemes\", \"ggsignif\", \"rstudioapi\", \"iptools\")\r\n\r\nsource(here(\"r\", \"Funs.r\"), echo = TRUE)\r\n\r\n################\r\n## Clean data ##\r\n################\r\n\r\n## Read data ##\r\n\r\nvarnames <- as.vector(read.delim(file = here(\"data\", \"U1_MTurk_Eligible.csv\"), sep = \",\", header = FALSE, stringsAsFactors = FALSE, na.strings = \"\", nrows = 1))\r\nu1_df <- read.delim(file = here(\"data\", \"U1_MTurk_Eligible.csv\"), sep = \",\", header = FALSE, stringsAsFactors = FALSE, na.strings = \"\", skip = 1, col.names = varnames)\r\n\r\n## Treatment assignment ##\r\n\r\nu1_df$treat[u1_df$condition == \"poverty\"] <- 0\r\nu1_df$treat[u1_df$condition == \"individual\"] <- 1\r\nu1_df$treat[u1_df$condition == \"community\"] <- 2\r\nu1_df$treat <- factor(u1_df$treat, labels = c(\"Pov\", \"Ind\", \"Com\"), levels = c(0, 1, 2))\r\n\r\nu1_df$pov <- ifelse(u1_df$condition == \"poverty\", 1, 0)\r\nu1_df$ind <- ifelse(u1_df$condition == \"individual\", 1, 0)\r\nu1_df$com <- ifelse(u1_df$condition == \"community\", 1, 0)\r\n\r\n## Recoding vars ##\r\n\r\nu1_df$spot1[which(u1_df$spot1 == 4)] <- 3\r\nu1_df$donation_me <- u1_df$donation_pov_1\r\nu1_df$donation_org <- u1_df$donation_pov_2\r\nu1_df$donated <- as.logical(u1_df$donation_org > 0)\r\nd.donated <- u1_df %>%\r\n dplyr::filter(donated==1)\r\n\r\nu1_df$donor_status <- NA\r\nu1_df$donor_status <- u1_df$stat.ladder_14\r\n\r\nu1_df$recip.impact_r <- 8-u1_df$recip.impact\r\nu1_df$recip.improve_r <- 8-u1_df$recip.improve\r\nu1_df$recip.ladder_r <- 11-u1_df$recip.ladder_14\r\n\r\n## Clean sociodems ##\r\n\r\nu1_df$priordonor <- as.numeric(as.logical(u1_df$priorgiving == 2))\r\nu1_df$age <- as.numeric(u1_df$age)\r\nu1_df$gen.fem <- as.numeric(as.logical(u1_df$gender ==2))\r\nu1_df$income.hh <- as.numeric(u1_df$income/sqrt(u1_df$hhsize))\r\nu1_df$hi.income.hh <- as.numeric(as.logical(u1_df$income.hh > 31305))\r\nu1_df$hi.income <- as.numeric(as.logical(u1_df$income > 50000))\r\nu1_df$edu.self.ba <- as.numeric(as.logical(u1_df$educ >4))\r\nu1_df$edu.par.ba <- as.numeric(as.logical(u1_df$educ.parent >4))\r\nu1_df$edu.par.ba <- as.numeric(as.logical(u1_df$educ.parent >4))\r\nu1_df$race.minor <- as.numeric(as.logical(u1_df$race !=1))\r\nu1_df$is.religi <- as.numeric(as.logical(u1_df$religiosity >2))\r\nu1_df$is.chr <- as.numeric(as.logical(u1_df$religion == 6))\r\nu1_df$is.dem <- as.numeric(as.logical(u1_df$party == 3))\r\n\r\n# need to add religion dummy\r\n\r\n## Center covariates ##\r\n\r\nu1_df$priordonor.c <- scale(u1_df$priordonor, scale = FALSE)\r\nu1_df$gen.fem.c <- scale(u1_df$gen.fem, scale = FALSE)\r\nu1_df$hi.income.hh.c <- scale(u1_df$hi.income.hh, scale = FALSE)\r\nu1_df$edu.self.ba.c <- scale(u1_df$edu.self.ba, scale = FALSE)\r\nu1_df$race.minor.c <- scale(u1_df$race.minor, scale = FALSE)\r\nu1_df$is.religi.c <- scale(u1_df$is.religi, scale = FALSE)\r\nu1_df$is.chr.c <- scale(u1_df$is.chr, scale = FALSE)\r\nu1_df$is.dem.c <- scale(u1_df$is.dem, scale = FALSE)\r\n\r\nsave(u1_df, file = here(\"data\", \"USData.RData\"))\r\nwrite.csv(u1_df, file = here(\"data\", \"USData.csv\"))", "meta": {"hexsha": "f6af9e343d1e57d7009cef5a9c15cb06160dc677", "size": 3354, "ext": "r", "lang": "R", "max_stars_repo_path": "r/USData.r", "max_stars_repo_name": "jrpabraham/empower-aid", "max_stars_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r/USData.r", "max_issues_repo_name": "jrpabraham/empower-aid", "max_issues_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r/USData.r", "max_forks_repo_name": "jrpabraham/empower-aid", "max_forks_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.9285714286, "max_line_length": 216, "alphanum_fraction": 0.6571258199, "num_tokens": 1163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3065307247883567}} {"text": "library(\"dplyr\")\n\nget_genotype_ages <- function(pop_df) {\n\t# construct a dataframe with \"Age\" of each genotype:\n\tpop_df <- arrange_(pop_df, ~-Population)\n\tpop_df <- arrange_(pop_df, ~Generation)\n\tlookup <- group_by_(pop_df, ~Identity) %>% \n\t\tfilter_(~Population > 0 | Generation == max(Generation)) %>% \n\t\tslice(1) %>% \n\t\tarrange_(~Generation) %>% \n\t\tungroup()\n\tlookup <- mutate(lookup, Age = 1:dim(lookup)[1]) %>% \n\t\tselect_(~-c(Generation, Population))\n\tif(is.factor(lookup$Identity)) lookup$Identity <- levels(lookup$Identity)[lookup$Identity]\n\tlookup <- select_(lookup, ~c(Identity, Age))\n\t\n\treturn(lookup)\n}\n\nargs = commandArgs(trailingOnly=TRUE)\nfilename <- args[1]\npop_df <- read.table(filename, sep = \"\\t\", header = TRUE)\n\t\t\t\t\t\n# Add the sorting to match the python script\npop_df <- arrange_(pop_df, ~-Population)\npop_df <- arrange_(pop_df, ~Generation)\n\n#write.table(pop_df, args[2], sep = \"\\t\", row.names = FALSE)\n\n#result <- add_start_points(pop_df)\nlookup <- get_genotype_ages(pop_df)\n#lookup <- pop_df\nwrite.table(lookup, args[2], sep = \"\\t\", row.names = FALSE)", "meta": {"hexsha": "65f2c00455278aa74b6b7894ffe12b1b20861e9a", "size": 1074, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/data/scripts_ggmuller/rscript.getmullerdf.genotypeages.r", "max_stars_repo_name": "andreashirley/Lolipop", "max_stars_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2020-04-18T15:43:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-19T18:43:23.000Z", "max_issues_repo_path": "tests/data/scripts_ggmuller/rscript.getmullerdf.genotypeages.r", "max_issues_repo_name": "andreashirley/Lolipop", "max_issues_repo_head_hexsha": "658a05c55fe8950f75d7ef50f1d983e86bd6fedf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2020-05-04T16:09:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-13T03:52:56.000Z", "max_forks_repo_path": "tests/data/scripts_ggmuller/rscript.getmullerdf.genotypeages.r", "max_forks_repo_name": "cdeitrick/muller_diagrams", "max_forks_repo_head_hexsha": "5b87b00a2c7ccbeeb3876bddb32e54aedf6bdf6d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-03-23T17:12:56.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-24T22:22:12.000Z", "avg_line_length": 32.5454545455, "max_line_length": 91, "alphanum_fraction": 0.6890130354, "num_tokens": 313, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.3065307247883567}} {"text": "library(tidyverse)\n# library(dplyr)\n# library(ggrepel)\nrequire(rgdal)\nlibrary(ggpubr)\n\n#get folder directory\nfolder <- dirname(rstudioapi::getSourceEditorContext()$path)\n\nresults <- read.csv(file.path(folder, '..', '..', 'results', 'national_results_technology_options.csv'))\nnames(results)[names(results) == 'GID_0'] <- 'iso3'\n\nclusters <- read.csv(file.path(folder, '..', 'clustering', 'results', 'data_clustering_results.csv'))\nnames(clusters)[names(clusters) == 'ISO_3digit'] <- 'iso3'\nclusters <- select(clusters, iso3, cluster, country)\n\nresults <- merge(results, clusters, x.by='iso3', y.by='iso3', all=FALSE)\n\nmean_user_cost_by_cluster <- select(results, scenario, strategy, confidence, cost_per_network_user, cluster)\n\nmean_user_cost_by_cluster <- mean_user_cost_by_cluster %>% \n group_by(scenario, strategy, confidence, cluster) %>%\n summarise(mean_cost_per_user = mean(cost_per_network_user))\n\nresults <- merge(clusters, mean_user_cost_by_cluster, x.by='cluster', y.by='cluster', all=TRUE)\n\ngdp <- read.csv(file.path(folder, '..', 'gdp.csv'))\nnames(gdp)[names(gdp) == 'iso3'] <- 'iso3'\ngdp <- select(gdp, iso3, gdp)\n\nresults <- merge(results, gdp, by='iso3', all=FALSE)\n\npop <- read.csv(file.path(folder, 'data_inputs', 'population_2018.csv'))\npop <- select(pop, iso3, population)\npop$iso3 <- as.character(pop$iso3)\n\nresults <- merge(results, pop, by='iso3', all=FALSE)\n\n#Not sure if this is right\n#Current costs are for a user on a network with 25% market share\n#We then multiply the user cost across the whole population?\n#check and revise this\nresults$total_cost <- results$mean_cost_per_user * results$population\n\nresults$gdp_percentage <- (results$total_cost / 5) / results$gdp * 100\n\nresults$confidence = factor(results$confidence, levels=c('5','50', '95'),\n labels=c(\"lower\", 'mean', \"upper\"))\nunique(results$scenario)\nresults$scenario = factor(results$scenario, levels=c(\"S1_25_10_2\",\n \"S2_200_50_5\",\n \"S3_400_100_10\"),\n labels=c(\"S1 (25 Mbps)\",\n \"S2 (200 Mbps)\",\n \"S3 (400 Mbps)\"))\n\nresults$strategy = factor(results$strategy, levels=c(\n \"4G_epc_microwave_baseline_baseline_baseline_baseline\",\n \"4G_epc_fiber_baseline_baseline_baseline_baseline\",\n \"5G_nsa_microwave_baseline_baseline_baseline_baseline\",\n \"5G_sa_fiber_baseline_baseline_baseline_baseline\"),\n labels=c(\n \"4G (Microwave)\",\n \"4G (Fiber)\",\n \"5G NSA (Microwave)\",\n \"5G SA (Fiber)\"))\n\nresults <- results[complete.cases(results), ]\n\nraw_num <- select(results, country, cluster, scenario, strategy, confidence, total_cost)\nraw_num <- spread(raw_num, confidence, total_cost)\n\ncost_by_strategy <- ggplot(raw_num, aes(x=raw_num$cluster, y=raw_num$mean/1e9, colour=raw_num$cluster)) + \n geom_boxplot(aes(group=factor(raw_num$cluster))) + \n # geom_jitter(width = 0.4, height=0.5, size=1.7) + \n scale_colour_manual(values = c(\"#F0E442\",\"#E69F00\",\"#D55E00\", \"#0072B2\", \"#56B4E9\",\"#009E73\")) + \n theme(legend.position = NULL) + \n labs(colour=NULL,\n title = \"Total Investment Cost\",\n subtitle = \"Boxplot of results by scenario, strategy and cluster\",\n x = NULL, y = \"Total Investment ($USD Billions)\") +\n theme(panel.spacing = unit(0.6, \"lines\")) +\n expand_limits(x = 0, y = 0) +\n scale_y_continuous(limits=c(0,40), expand=c(0,0)) +\n guides(fill=FALSE, colour=FALSE) + \n facet_grid(scenario~strategy)\n\ngdp_num <- select(results, country, cluster, scenario, strategy, confidence, gdp_percentage)\ngdp_num <- spread(gdp_num, confidence, gdp_percentage)\n\ngdp_perc_by_strategy <- ggplot(gdp_num, aes(x=gdp_num$cluster, y=gdp_num$mean, colour=gdp_num$cluster)) + \n geom_boxplot(aes(group=factor(gdp_num$cluster))) + \n # geom_jitter(width = 0.4, height=0.5, size=1.7) + \n scale_colour_manual(values = c(\"#F0E442\",\"#E69F00\",\"#D55E00\", \"#0072B2\", \"#56B4E9\",\"#009E73\")) + \n theme(legend.position = NULL) + \n labs(colour=NULL,\n title = \"Annual Investment Cost as GDP Share Over 5 Years\",\n subtitle = \"Boxplot of results by scenario, strategy and cluster\",\n x = NULL, y = \"Annual GDP (% over 5 years)\") +\n theme(panel.spacing = unit(0.4, \"lines\")) + \n expand_limits(x = 0, y = 0) +\n scale_y_continuous(limits=c(0,10), expand=c(0,0), breaks=seq(0, 9.7, 2)) +\n guides(fill=FALSE, colour=FALSE) + \n facet_grid(scenario~strategy)\n\npanel <- ggarrange(cost_by_strategy, gdp_perc_by_strategy, ncol = 1, nrow = 2, align = c(\"hv\"))\n\n#export to folder\npath = file.path(folder, 'figures', 'cost_by_strategy.png')\nggsave(path, units=\"cm\", width=25, height=35)\nprint(panel)\ndev.off()\n", "meta": {"hexsha": "173cb6ac19fc886a7c2546bd6165bed8789ffc39", "size": 4727, "ext": "r", "lang": "R", "max_stars_repo_path": "vis/global_cost/cluster_costs.r", "max_stars_repo_name": "edwardoughton/pytal", "max_stars_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-01-16T12:12:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-04T11:46:00.000Z", "max_issues_repo_path": "vis/global_cost/cluster_costs.r", "max_issues_repo_name": "edwardoughton/pytal", "max_issues_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vis/global_cost/cluster_costs.r", "max_forks_repo_name": "edwardoughton/pytal", "max_forks_repo_head_hexsha": "69e688ebfb3f7b64a4eff60cf3603ea189c9afdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-01-15T14:46:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-27T02:42:15.000Z", "avg_line_length": 41.8318584071, "max_line_length": 108, "alphanum_fraction": 0.6754812778, "num_tokens": 1332, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593171945417, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.30636709089202085}} {"text": "library(geosphere)\n\n# Case time series in GB areas\nuk_cases <- read.csv(\"data/uk_cases.csv\")\nind <- sapply(uk_cases[,2], function(s)\n !(s %in% c('Outside Wales','Unknown','...17','...18'))\n)\nuk_cases <- uk_cases[ind,]\n\nN <- nrow(uk_cases) # 348 LTLA in England, NHS health boards Scotland, X in Wales\n\nareas <- uk_cases[,2]\nquoted_areas <- sapply(areas,function(s) paste('\"',s,'\"',sep=''))\n\n###############################################################################\n\n# Assumes that metadata contains all areas in uk_cases and area names match perfectly\nmetadata <- read.csv(\"data/metadata.csv\")\nind <- sapply(metadata[,1], function(s)\n s %in% areas\n)\nmetadata <- metadata[ind,]\n\ngeoloc <- matrix(0, N, 2)\ngeodist <- matrix(0, N, N)\npopulation <- rep(0.0, N)\n\n# match up areas in uk_cases and metadata\nmeta_areas <- metadata$AREA\nlongitudes <- metadata$LONG\nlatitudes <- metadata$LAT\n\nnhs_regions <- metadata$NHS_Region \nnhs_regions_unique <- nhs_regions[!duplicated(nhs_regions)]\nnhs_regions_to_areas <- matrix(0, N, 9)\n\nfor (i in 1:N) {\n area <- areas[i]\n j <- grep(sprintf('^%s$',area), meta_areas)\n l <- length(j)\n if (l >= 1) { # just use first match!!\n if (l > 1) {\n print(sprintf(\"Area: %s\",area))\n print(sprintf(\"Matched meta_areas: %s\", paste(meta_areas[j],collapse=', ')))\n print(sprintf(\"Using: %s, %s\",meta_areas[j[l]],metadata$CODE[j[l]]))\n }\n geoloc[i, 1] = longitudes[j[l]]\n geoloc[i, 2] = latitudes[j[l]]\n population[i] = metadata$POPULATION[j[l]]\n\n nhs_row = (nhs_regions_unique == nhs_regions[j[l]])\n nhs_regions_to_areas[i,] = nhs_row\n } else {\n print(sprintf(\"Cannot find area '%s'\",area))\n for (r in 1:length(meta_areas)) {\n if (length(grep(meta_areas[r], area))>0) {\n geoloc[i, 1] = longitudes[r]\n geoloc[i, 2] = latitudes[r]\n population[i] = metadata$POPULATION[r]\n print(sprintf(\"...found area '%s'\",meta_areas[r]))\n }\n }\n }\n}\n\nwrite.csv(\n data.frame(\n area=quoted_areas, \n longitude=geoloc[,1], \n latitude=geoloc[,2], \n population=population,\n nhs_region=nhs_regions_to_areas\n ),'data/areas.csv',row.names=FALSE,quote=FALSE)\n\nwrite.csv(\n data.frame(\n nhs_region=nhs_regions_unique\n ),'data/nhs_regions.csv',row.names=FALSE,quote=FALSE)\n\n#########################################################################\n\nwrite.matrix.csv <- function(m,filename) {\n colnames(m) <- quoted_areas\n rownames(m) <- quoted_areas\n write.csv(m,filename,quote=FALSE)\n}\n \n# compute distances between areas. Straight line, not actual travel distance.\nfor (i in 1:N) {\n for (j in i:N) {\n # distance between two points on an ellipsoid (default is WGS84 ellipsoid), in units of 100km\n geodist[i, j] = distGeo(geoloc[i, 1:2], geoloc[j, 1:2]) / 100000\n geodist[j, i] = geodist[i, j]\n }\n}\nwrite.matrix.csv(geodist,'data/distances.csv')\n\n", "meta": {"hexsha": "9dcf6b07c7760bb069ce8b0c6dfb882ee0f1417e", "size": 2887, "ext": "r", "lang": "R", "max_stars_repo_path": "dataprocessing/process_data.r", "max_stars_repo_name": "oxcsml/Rmap", "max_stars_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-03T10:25:31.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-03T10:25:31.000Z", "max_issues_repo_path": "dataprocessing/process_data.r", "max_issues_repo_name": "oxcsml/Rmap", "max_issues_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dataprocessing/process_data.r", "max_forks_repo_name": "oxcsml/Rmap", "max_forks_repo_head_hexsha": "5ae74e8b0e110cba578fe19159c0f87ea52fa495", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4591836735, "max_line_length": 97, "alphanum_fraction": 0.6120540353, "num_tokens": 842, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6757646010190476, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.3062983118812736}} {"text": "#' Evaluate deep learning model performance\n#'\n#' A function to evaluate a deep learning model.\n#' @param model_keras Keras deep learning model.\n#' @param input_data A dataframe containing occurrence data parsed for deep learning using the \\code{\\link{prepare_dl_data}} function.\n#'\n#' @return A tibble containing the model estimates.\n#' @examples\n#' \\dontrun{\n#' # download benchmarking data\n#' benchmarking_data <- get_benchmarking_data(\"Lynx lynx\",\n#' limit = 1500)\n#'\n#' # transform benchmarking data into a format suitable for deep learning\n#' # if you have previously used a partitioning method you should specify it here\n#' benchmarking_data_dl <- prepare_dl_data(input_data = benchmarking_data$df_data,\n#' partitioning_type = \"default\")\n#'\n#' # perform sanity checks on the transformed data\n#' # for the training set\n#' head(benchmarking_data_dl$train_tbl)\n#' table(benchmarking_data_dl$y_train_vec)\n#'\n#' # for the test set\n#' head(benchmarking_data_dl$test_tbl)\n#' table(benchmarking_data_dl$y_test_vec)\n#'\n#' # train neural network\n#' keras_results <- train_dl(benchmarking_data_dl)\n#'\n#' # inspect training results\n#' keras_results$history\n#'\n#' # you can also plot them\n#' plot(keras_results$history)\n#'\n#' # create evaluation tibble containing training results\n#' keras_evaluation <- evaluate_dl(keras_results$model, benchmarking_data_dl)\n#' head(keras_evaluation)\n#' }\n#' @export\nevaluate_dl <- function(model_keras, input_data) {\n yhat_keras_class_vec <- keras::predict_classes(object = model_keras,\n x = as.matrix(input_data$test_tbl)) %>%\n as.vector()\n\n yhat_keras_prob_vec <- keras::predict_proba(object = model_keras,\n x = as.matrix(input_data$test_tbl)) %>%\n as.vector()\n\n estimates_keras_tbl <- tibble::tibble(truth = as.factor(input_data$y_test_vec),\n estimate = as.factor(yhat_keras_class_vec),\n class_prob = yhat_keras_prob_vec)\n\n return(estimates_keras_tbl)\n}\n", "meta": {"hexsha": "d127a7a8b7e8554789ab536fa384f304bd3fd7be", "size": 2139, "ext": "r", "lang": "R", "max_stars_repo_path": "R/evaluate_dl.r", "max_stars_repo_name": "boyanangelov/sdmbench", "max_stars_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "max_stars_repo_stars_event_min_datetime": "2018-06-25T19:55:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-06T08:36:48.000Z", "max_issues_repo_path": "R/evaluate_dl.r", "max_issues_repo_name": "boyanangelov/sdmbench", "max_issues_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2018-08-01T01:31:09.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-12T16:02:07.000Z", "max_forks_repo_path": "R/evaluate_dl.r", "max_forks_repo_name": "boyanangelov/sdmbench", "max_forks_repo_head_hexsha": "8d2060160b0217099b995d7bc538cb135773c219", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2018-10-12T06:07:07.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-02T17:52:53.000Z", "avg_line_length": 37.5263157895, "max_line_length": 134, "alphanum_fraction": 0.6690042076, "num_tokens": 477, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5851011542032313, "lm_q1q2_score": 0.30625385029610125}} {"text": "setwd(\"/Users/xxuadmin/BUSINESS/PUBLICATIONS/WorkingOn_Abramoff_Perspective/millennial_code_2017July_Century_WT\")\n\n# output\ndata <- read.table(\"outputcontrol.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutputcontrol <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\n#par(mai = c(1,1,1,1,1))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\n\npdf(file = \"outputcontrol.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"Control run:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output5cwarm.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput5cwarm <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output5cwarm.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"5 C warming:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output10clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput10clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output10clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"10% Clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output20clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput20clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output20clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"20% Clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output30clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput30clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output30clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"30% clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output40clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput40clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output40clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"40% clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output50clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput50clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output50clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"50% clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output60clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput60clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output60clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"60% clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n# output\ndata <- read.table(\"output70clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput70clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output70clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"70% Clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"output80clay.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutput80clay <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"output80clay.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"80% Clay content:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"outputdoubleCinput.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutputdoubleCinput <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"outputdoubleCinput.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"Double C input:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"outputhalfwater.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutputhalfwater <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"outputhalfwater.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"Half water moisture:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"outputTandCinput.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutputTandCinput <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"outputTandCinput.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"5C Warming and Double C input:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n# output\ndata <- read.table(\"outputTandW.txt\")\nLMC <- data$V2[729636:730000]\nPOM <- data$V3[729636:730000]\nMB <- data$V4[729636:730000]\nMINERAL <- data$V5[729636:730000]\nAGG <- data$V6[729636:730000]\noutputTandW <- data$V2 + data$V3 + data$V4 + data$V5 + data$V6\n\nsummary(LMC)\nsummary(POM)\nsummary(MB)\nsummary(MINERAL)\nsummary(AGG)\nlmcaverage = mean(LMC)\npomaverage = mean(POM)\nmbaverage = mean(MB)\nmineralaverage = mean(MINERAL)\naggaverage = mean(AGG)\n\ncarbon <- c(lmcaverage, pomaverage, mbaverage, mineralaverage, aggaverage)\n#lbls <- c(\"LMWC\", \"POM\", \"MB\", \"MINERAL\", \"AGGREGATE\")\n#pct <- round(carbon/sum(carbon)*100)\n#lbls <- paste(lbls,pct) # add percents to labels\n#lbls <- paste(lbls, \"%\", sep=\"\")\n#pie(carbon, labels=lbls, col=rainbow(length(lbls)),main=round(sum(carbon)))\ncolors <- c(\"black\",\"red\",\"green\",\"blue\",\"pink\")\nnumb_labels <- round(carbon/sum(carbon) * 100,1)\nnumb_labels <- paste(numb_labels, \"%\", sep=\"\")\nxx <- c(\"LMWC\",\"POM\",\"MB\",\"MINERAL\",\"AGGREGATE\")\nxx <- paste(round(carbon/sum(carbon) * 100,1),xx,sep=\" \")\n\npdf(file = \"outputTandW.pdf\")\npie(carbon, labels=\"\", col=colors,clockwise=TRUE,main=paste(\"5C Warming and Half Water:\", round(sum(carbon)),\"gC\",sep=\" \"))\nlegend(-1.25,-0.5, legend = xx, fill=colors,bty=\"n\")\ndev.off()\n\nrm(data)\nrm(LMC)\nrm(POM)\nrm(MB)\nrm(MINERAL)\nrm(AGG)\n# end\n\n\n#plot time series of total carbon for all scenarios\n#outputcontrol\n#output5cwarm\n#output10clay\n#output20clay\n#output30clay\n#output40clay\n#output50clay\n#output60clay\n#output70clay\n#output80clay\n#outputdoubleCinput\n#outputhalfwater\n#outputTandCinput\n#outputTandW\npdf(file = \"timeseries.pdf\")\nday=c(1:136500)\nplot(c(1,175000),c(-2000,2000),type=\"n\",xlab=\"Day\",ylab=\"changed in total C storage\")\nlines(day,output5cwarm[1:136500] - outputcontrol[1:136500],col=\"blue\")\nlines(day,output10clay[1:136500] - outputcontrol[1:136500],col=\"brown\")\nlines(day,output20clay[1:136500] - outputcontrol[1:136500],col=\"yellow\")\nlines(day,output30clay[1:136500] - outputcontrol[1:136500],col=\"cyan\")\nlines(day,output40clay[1:136500] - outputcontrol[1:136500],col=\"darkblue\")\nlines(day,output50clay[1:136500] - outputcontrol[1:136500],col=\"darkgreen\")\nlines(day,output60clay[1:136500] - outputcontrol[1:136500],col=\"darkgray\")\nlines(day,output70clay[1:136500] - outputcontrol[1:136500],col=\"darkorange\")\nlines(day,output80clay[1:136500] - outputcontrol[1:136500],col=\"darkred\")\nlines(day,outputdoubleCinput[1:136500] - outputcontrol[1:136500],col=\"deeppink\")\nlines(day,outputhalfwater[1:136500] - outputcontrol[1:136500],col=\"deepskyblue\")\nlines(day,outputTandCinput[1:136500] - outputcontrol[1:136500],col=\"darkgreen\")\nlines(day,outputTandW[1:136500] - outputcontrol[1:136500],col=\"lightgreen\")\nlegend(137500,1000,c(\"warming\",\"10% clay\",\"20% clay\",\"30% clay\",\"40% clay\",\"50% clay\",\"60% clay\",\"70% clay\",\"80% clay\",\"double C input\",\"half water\",\"warming and double C input\",\"warming and half water\"),lty=c(1,1),lwd=c(2.5,2.5),col=c(\"blue\",\"brown\",\"yellow\",\"cyan\",\"darkblue\",\"darkgreen\",\"darkgray\",\"darkorange\",\"darkred\",\"deeppink\",\"deepskyblue\",\"darkgreen\",\"lightgreen\"))\ndev.off()\n\n", "meta": {"hexsha": "9d7a1bfe764f814bd9303634e2cea6962ef89954", "size": 20266, "ext": "r", "lang": "R", "max_stars_repo_path": "Fortran/MillennialV1/simulation/plot.r", "max_stars_repo_name": "rabramoff/Millennial", "max_stars_repo_head_hexsha": "ea1f8fb4b5b2f513517bf287feb6803c8c7482e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-09-11T03:11:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-18T12:42:35.000Z", "max_issues_repo_path": "Fortran/MillennialV1/simulation/plot.r", "max_issues_repo_name": "rabramoff/Millennial", "max_issues_repo_head_hexsha": "ea1f8fb4b5b2f513517bf287feb6803c8c7482e4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Fortran/MillennialV1/simulation/plot.r", "max_forks_repo_name": "rabramoff/Millennial", "max_forks_repo_head_hexsha": "ea1f8fb4b5b2f513517bf287feb6803c8c7482e4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-25T15:37:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-22T12:46:58.000Z", "avg_line_length": 30.2929745889, "max_line_length": 375, "alphanum_fraction": 0.6853843876, "num_tokens": 7294, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3062538502961012}} {"text": "\n# Generate inp parameters file for simulation\n#\ngenNeutralParms <- function(fname,side,mprob,birth,mortality,dispersal,colonization,replace=0){\n S <- length(mprob)\n parm <- data.frame( n=c(rep(0,3),1:S) ,prob=c(rep(0,3),prob=mprob))\n parm$text <- \"\" \n parm$z <- 0\n parm$text[1] <- paste(side,side,sep=\"\\t\")\n parm$text[2] <- S\n parm$text[3] <- paste(0,birth,mortality,dispersal,colonization,replace,sep=\"\\t\")\n nff <- S+3\n parm$text[4:nff] <- with(parm[4:nff,], paste(n,z,z,z,format(sort(prob),scientific=F),sep=\"\\t\")) \n if(!grepl(\".inp\",fname)) fname <-paste0(fname,\".inp\")\n\n write.table(parm$text,fname,sep=\"\\t\",row.names=F,col.names=F,quote=F)\n}\n\n\n# Generates pomac.lin paramenter file for multiple simulations\n#\ngenPomacParms <- function(fname,GrowthR,MortR,DispD,ColonR,ReplaceR,numRep=1)\n {\n pom <- expand.grid(GrowthRate=GrowthR,MortalityRate=MortR, DispersalDistance=DispD, \n ColonizationRate=ColonR,ReplacementRate=ReplaceR)\n pom <- pom[rep(seq_len(nrow(pom)), numRep), ]\n if(!grepl(\".lin\",fname)) fname <-paste0(fname,\".lin\")\n write.table(pom,fname,sep=\"\\t\",row.names=F,col.names=T,quote=F)\n}\n\n# Merge data from 2 simulations \n# \n# eks: 1 row data.frame with 2 sets of parms for which ks.test was done (fixed TIME)\n# den1: full data in long format\n#\nmergePairSAD <- function(eks, den1){\n # eks <- mks[61,]\n \n den2 <-merge(den1,eks,by.x=c(1:4),by.y=c(1:4))[,1:6]\n den2 <- den2[den2$den>0,]\n den2$parms <-paste(eks[,1:4],collapse=\"_\")\n den2$Rank <- nrow(den2) - rank(den2$den) +1\n \n den3 <-merge(den1,eks,by.x=c(1:4),by.y=c(5:8))[,1:6]\n den3 <- den3[den3$den>0,]\n den3$parms <-paste(eks[,5:8],collapse=\"_\")\n den3$Rank <- nrow(den3) - rank(den3$den) +1\n den2 <- rbind(den2,den3)\n \n}\n\n# Merge data from 2 simulations \n# \n# eks: 1 row data.frame with 2 sets of parms for which ks.test was done (fixed TIME)\n# den1: full data in long format\n#\nmergePairSadRank <- function(eks, den1,vv,cols=1:4){\n # eks <- mks[61,]\n \n den2 <-merge(den1,eks,by.x=cols,by.y=cols)[,1:(length(cols)+2)]\n den2$parms <-paste(eks[,cols],collapse=\"_\")\n den2$Rank <- nrow(den2) - rank(den2[[vv]]) +1\n \n cols1 <- cols+length(cols)\n den3 <-merge(den1,eks,by.x=cols,by.y=cols1)[,1:(length(cols)+2)]\n den3$parms <-paste(eks[,cols1],collapse=\"_\")\n den3$Rank <- nrow(den3) - rank(den3[[vv]]) +1\n den2 <- rbind(den2,den3)\n \n}\n\n# Calculate Ranks for every parameter combination, for ggplot2 \n#\ncalcRankSAD <- function(den)\n{\n require(plyr)\n hh <- function(x) { \n x1 <- x[x$value>0,]\n x1$parms <- paste(unique(x[,1:4]),collapse=\"_\")\n x1$Rank <- nrow(x1) - rank(x1$value) +1\n return(x1)\n }\n ddply(den, .(MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate),hh )\n}\n\n# Calculate Ranks for every parameter combination in columns cols, for variable vv, using data.frame den \n# to use ggplot2 \n#\ncalcRankSAD_by <- function(den,vv,cols)\n{\n require(plyr)\n hh <- function(x) { \n x1 <- x[x[[vv]]>0,]\n x1$parms <- paste(unique(x[,cols]),collapse=\"_\")\n x1$Rank <- nrow(x1) - rank(x1[[vv]],ties.method=\"first\") +1\n return(x1)\n }\n ddply(den, cols,hh )\n}\n\n# pairwise KS test for all combinations of parameters in denl= density in long format\n# parameters are in columns 1:4\n# \n#\npairKS_SAD <- function(denl){\n \n parms <- unique(denl[,1:4])\n combo <- combn(nrow(parms),2)\n \n require(plyr)\n mks <-adply(combo,2, function(x) {\n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n d1 <- merge(denl,p1)\n d2 <- merge(denl,p2)\n ks <- ks.test(d1$value,d2$value)\n out <-data.frame(p1,p2,stat=ks$statistic,p.value=ks$p.value,stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n return(out) \n })\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n# pairwise KS test for all combinations of parameters in variable parms \n# vv: name of the variable where density or proportion is\n# denl: data.frame with all data\n#\npairwiseKS_SAD <- function(denl,vv,parms){\n \n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n \n require(plyr)\n mks <-adply(combo,2, function(x) {\n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n d1 <- merge(denl,p1)\n d2 <- merge(denl,p2)\n ks <- ks.test(d1[,vv],d2[,vv])\n out <-data.frame(p1,p2,stat=ks$statistic,p.value=ks$p.value,stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n return(out) \n })\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n# pairwise Anderson-Darling K test for all combinations of parameters in variable parms \n# vv: name of the variable where density or proportion is\n# denl: data.frame with all data\n#\npairwiseAD_SAD <- function(denl,vv,parms){\n require(kSamples)\n\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n \n require(plyr)\n mks <-adply(combo,2, function(x) {\n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n d1 <- merge(denl,p1)\n d2 <- merge(denl,p2)\n ks <- ad.test(list(d1[,vv],d2[,vv]),method=\"simulated\",nsim=1000)\n out <-data.frame(p1,p2,stat=ks$ad[2,2],p.value=ks$ad[2,4],stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n return(out) \n })\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n\n# pairwise Anderson-Darling K test for all combinations of \n# parameters in variable parms with repetitions, BEWARE last variable in parms must be \n# the repetition\n# \n# vv: name of the variable where density or proportion is\n# denl: data.frame with all data\n#\npairwiseAD_Dif <- function(denl,vv,parms){\n require(kSamples)\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n nc <- ncol(parms)-2\n #pb <- txtProgressBar(min = 0, max = ncol(combo), style = 3)\n #i <- 0\n require(plyr)\n mks <-adply(combo,2, function(x) {\n \n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n out<-NULL\n #i <<- i+1 \n #setTxtProgressBar(pb, i)\n # test the error!!!!!!!!!\n if(sum(p1[,1:nc]==p2[,1:nc])==nc) {\n d1 <- merge(denl,p1)\n d2 <- merge(denl,p2)\n if(nrow(d1)>2 & nrow(d2)>2) {\n ks <- ad.test(list(d1[,vv],d2[,vv]),method=\"simulated\",Nsim=1000)\n out <-data.frame(p1,p2,stat=ks$ad[2,2],p.value=ks$ad[2,4],stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n }\n }\n return(out) \n })\n #close(pb)\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n# Calculate the power for AD test\n#\ncalcPow_AD <- function(Dq3,vv,parms){\n \n pow <- data.frame()\n c1 <-pairwiseAD_SAD(Dq3,vv,parms)\n c2 <- with(c1,c1[Type1!=Type2,])\n nc <- ncol(parms)*2\n\n if(nrow(c2)>0) { \n # calculate the power\n c3 <- nrow(c2[c2$p.value<0.05,])/nrow(c2)\n pow <- data.frame(c2[1,2:nc],n=nrow(c2),power=c3)\n }\n\n c2 <- with(c1,c1[Type1==Type2,])\n c3 <- nrow(c2[c2$p.value<0.05,])/nrow(c2)\n pow <- rbind(pow, data.frame(c2[1,2:nc],n=nrow(c2),power=c3))\n}\n\n\n# pairwise KS test for Dq of all combinations of variable rep\n# \n#\npairKS_DqByRep <- function(Dq,rep){\n \n parms <- unique(rep)\n combo <- combn(parms,2)\n hh <- function(x) {\n p1 <- x[1]\n p2 <- x[2]\n d1 <- Dq[rep==p1]\n d2 <- Dq[rep==p2]\n ks <- ks.test(d1,d2)\n out <-data.frame(p1,p2,stat=ks$statistic,p.value=ks$p.value,stringsAsFactors=F)\n # ln <-length(names(p1))*2\n # names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n return(out) \n }\n \n require(plyr)\n mks <-adply(combo,2, hh)\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks[,2:ncol(mks)])\n}\n\n# pairwise KS test for Dq of all combinations parameters \n# \n# dql: data frame with Dq and in columns 1:4 of dql parameter\n# numq: number of repetitions for each parameter combination, if >1 selects one of\n# de repetitions to do the comparison.\n#\npairKS_Dq <- function(dql,numq=1){\n \n require(plyr)\n\n parms <- unique(dql[,1:4])\n combo <- combn(nrow(parms),2)\n\n if( numq>1){\n dql <- ddply(dql,.(MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate),\n function(x) { n <- nrow(x)/35\n rp <- rep( 1:n,each=35)\n return(x[rp==sample(n,1),])}\n )\n }\n \n mks <-adply(combo,2, function(x) {\n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n d1 <- merge(dql,p1)\n d2 <- merge(dql,p2)\n ks <- ks.test(d1$Dq,d2$Dq)\n out <-data.frame(p1,p2,stat=ks$statistic,p.value=ks$p.value,stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n return(out) \n })\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n\n\n# Read simulation output and change from wide to long format NO TIME\n#\nmeltDensityOut_NT <- function(fname,num_sp){\n den <- read.delim(fname)\n names(den)[1:5]<-c(\"GrowthRate\",\"MortalityRate\",\"DispersalDistance\",\"ColonizationRate\",\"ReplacementRate\")\n \n # from 7 to 473 there are species densities\n # Put simpler names to variables to identify species\n names(den)[7:(6+num_sp)]<-as.character(1:num_sp)\n\n require(plyr)\n \n den <- ddply(den, 1:5, function(x){ i <- c(1:nrow(x)); data.frame(x,rep=i)})\n \n require(reshape2)\n \n den1 <- melt(den,id.vars=c(\"MortalityRate\",\"DispersalDistance\",\"ColonizationRate\",\"ReplacementRate\",\"rep\"),measure.vars=c(7:(6+num_sp)),variable.name=\"Species\")\n den1 <- den1[den1$value!=0,] \n}\n\n# Read simulation output and set variable names in wide format \n#\nreadWideDensityOut <- function(fname,num_sp){\n if(!grepl(\"Density.txt\",fname)) fname <- paste0(fname,\"Density.txt\")\n den <- read.delim(fname)\n names(den)[1:5]<-c(\"GrowthRate\",\"MortalityRate\",\"DispersalDistance\",\"ColonizationRate\",\"ReplacementRate\")\n names(den)[7] <- unlist(strsplit(names(den)[7],\".\",fixed=T))[1]\n eTime <- (max(den$Time)/(den$Time[3]-den$Time[2]))+1\n if( eTime < nrow(den) ){\n den$Rep <- rep( 1:(nrow(den)/eTime),each=eTime)\n }\n return(den)\n}\n\n\n# Proportion of not different SAD at 0.05 Hommel adjusted level \n#\npropNotDiffSAD <- function(mk) nrow(mk[mk$p.adjust>0.05,])/nrow(mk)\n\n# Proportion of not different SRS at 0.05 Hommel adjusted level \n#\npropNotDiffSRS <- function(mk) nrow(mk[mk$adj.P.Value>0.05,])/nrow(mk)\n\n\n# Calculates Dq from a data.frame read from the output of neutral model \n# auxiliar function for the next one\n#\ncalcDq_frame <- function(pp)\n{\n pp$Dq <- with(pp,ifelse(q==1,alfa,Tau/(q-1)))\n pp$SD.Dq <- with(pp,ifelse(q==1,SD.alfa,abs(SD.Tau/(q-1))))\n pp$R.Dq <- with(pp,ifelse(q==1,R.alfa,R.Tau))\n nc <- ncol(pp)\n return(pp[,c(1:6,(nc-2):nc)])\n} \n# Reads the output of multifractal spectra of neutral model\n# an calculates Dq \n#\nreadNeutral_calcDq <-function(fname)\n{\n md1 <- read.table(fname,header=F,skip=1)\n md1 <- md1[,c(2:16)]\n names(md1)<-c(\"MortalityRate\",\"DispersalDistance\",\"ColonizationRate\",\"ReplacementRate\",\"Time\",\"q\",\"Tau\",\"alfa\",\"f(alfa)\",\"R.Tau\",\"R.alfa\",\"R.f\",\"SD.Tau\",\"SD.alfa\",\"SD.f\")\n \n md1 <-calcDq_frame(md1)\n}\n\n\n# Read Dq output from neutral model and calculate pairwise differences \n#\n# Dqf: data frame from readNeutral_calcDq \n# qNumber: number of q used\n#\ncompDq_frame <- function(Dqf,qNumber)\n{\n if( !require(statmod) & !require(reshape2))\n stop(\"required statmod and reshape2\")\n \n # Subset for testing \n #\n #Dqf <- with(Dqf,Dqf[MortalityRate==.2 & DispersalDistance==0.04 & ColonizationRate==0.001, ])\n \n # Set the number of repetitions using nrow and number of q \n # \n Dqf$rep <- rep( 1:(nrow(Dqf)/qNumber),each=qNumber)\n \n # Build variable for comparisons\n Dqf$factor <- do.call(paste, c(Dqf[,1:4],sep=\"_\"))\n \n # Prepare data.frame in wide format \n #\n Dq2 <- melt(Dqf, id.vars=c(\"q\",\"rep\",\"factor\"),measure.var=\"Dq\")\n Dq2 <- dcast(Dq2, factor+rep~ q)\n \n # Compare SRS curves\n #\n c2 <- compareGrowthCurves(Dq2$factor,Dq2[,3:37],nsim=1000)\n}\n\n# Plot Dq with fixed parameters except ReplacementRate\n# Calculate SD from repeated simulations\n#\nplotDq_ReplaceR <- function(Dqf,MortR,DispD,ColonR,tit=\"\")\n{\n require(plyr)\n c3 <- with(Dqf,Dqf[MortalityRate==MortR & DispersalDistance==DispD & ColonizationRate==ColonR, ])\n c3$factor <- do.call(paste, c(c3[,1:4],sep=\"_\"))\n c3 <- ddply(c3, .(factor,q), summarize, SD.Dq=sd(Dq),Dq=mean(Dq))\n\n require(ggplot2)\n gp <- ggplot(c3, aes(x=q, y=Dq, colour=factor)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1) +\n geom_point() + theme_bw() + ggtitle(tit)\n print(gp)\n\n}\n\nsel_ReplaceR <- function(Dqf,MortR,DispD,ColonR,RepR) with(Dqf,Dqf[MortalityRate==MortR & DispersalDistance==DispD \n & ColonizationRate==ColonR & ReplacementRate==RepR , ])\n# Plot Dq by factor \"grp\" shows SD from data.frame\n#\nplotDq <- function(Dq1,grp) \n {\n require(ggplot2)\n print(gp <- ggplot(Dq1, aes_string(x=\"q\", y=\"Dq\", colour=grp)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1) +\n geom_point() + theme_bw()) \n }\n\n\nmergePair_plotDq <- function(eks, Dqf,tit=\"\")\n{\n den2 <-merge(Dqf,eks,by.x=c(1:4),by.y=c(1:4))[1:9]\n den2$parms <-paste(eks[,1:4],collapse=\"_\")\n \n den3 <-merge(Dqf,eks,by.x=c(1:4),by.y=c(5:8))[1:9]\n den3$parms <-paste(eks[,5:8],collapse=\"_\")\n den2 <- rbind(den2,den3)\n\n require(plyr)\n require(ggplot2)\n if(length(unique(den2$Time))>1)\n {\n den2 <- ddply(den2, .(parms,Time,q), summarize, SD.Dq=sd(Dq),Dq=mean(Dq))\n\n gp <- ggplot(den2, aes(x=q, y=Dq, colour=parms)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1) +\n geom_point() + theme_bw() + ggtitle(tit) +\n facet_wrap(~ Time)\n\n } else {\n den2 <- ddply(den2, .(parms,q), summarize, SD.Dq=sd(Dq),Dq=mean(Dq))\n\n gp <- ggplot(den2, aes(x=q, y=Dq, colour=parms)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1) +\n geom_point() + theme_bw() + ggtitle(tit)\n\n }\n print(gp)\n return(gp)\n} \n\ncompMethod_DqKS_Time <- function(bName,Time,spMeta) \n{\n # Read all simulations and change to long format\n fname <- paste0(bName,\"T\",Time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # Subset for testing \n #\n #Dq1 <- with(Dq1,Dq1[MortalityRate==.2 & DispersalDistance==0.04 & ColonizationRate==0.001, ])\n \n # Testing pairwise differences\n #\n mKS <- pairKS_Dq(Dq1,35)\n mKS <- mKS[,2:12]\n mKS$method <- \"SRSKS\"\n compM <- data.frame(time=Time,notdif=propNotDiffSAD(mKS),method=\"SRSKS\")\n\n # Read all simulations and change to long format\n fname <- paste0(bName,\"T\",Time,\"mfSAD.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n # Subset for testing \n #\n #Dq1 <- with(Dq1,Dq1[MortalityRate==.2 & DispersalDistance==0.04 & ColonizationRate==0.001, ])\n\n # Testing pairwise differences\n #\n mK1 <- pairKS_Dq(Dq1,35)\n mK1 <- mK1[,2:12]\n mK1$method <- \"DqSADKS\"\n # Add to data.frame with proportions\n #\n compM <- rbind(compM, data.frame(time=Time,notdif=propNotDiffSAD(mK1),method=\"DqSADKS\"))\n\n mKS <- rbind(mKS,mK1)\n mKS$time <- Time\n\n return(list(\"compM\"=compM,\"mPval\"=mKS))\n}\n\n\ncompMethods_Time <- function(bName,Time,spMeta) \n{\n # Read all simulations and change to long format\n fname <- paste0(bName,\"T\",Time,\"Density.txt\")\n\n den1 <- meltDensityOut_NT(fname,spMeta)\n\n # Select a subset to test the procedure !\n #\n #den1 <- den1[den1$MortalityRate==.2 & den1$DispersalDistance==0.04 & den1$ColonizationRate==0.001, ]\n \n # have to make averages\n require(plyr)\n den2 <- ddply(den1,.(MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate,Species),summarise,den=mean(value))\n\n names(den2)[6] <- \"value\" # the functions use this field name\n\n\n\n # Test pairwise diferences in SAD\n #\n mKS <- pairKS_SAD(den2)\n\n # Build data.frame with proportions\n #\n\n compM <- data.frame(time=Time,notdif=propNotDiffSAD(mKS),method=\"SAD\")\n\n # Leer Dq SRS\n #\n fname <- paste0(bName,\"T\",Time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # Subset for testing\n #Dq1 <- with(Dq1,Dq1[MortalityRate==.2 & DispersalDistance==0.04 & ColonizationRate==0.001, ])\n\n # Testing pairwise differences\n #\n c2 <- compDq_frame(Dq1,35)\n\n # Add to data.frame with proportions\n #\n compM <- rbind(compM, data.frame(time=Time,notdif=propNotDiffSRS(c2),method=\"SRS\"))\n\n # Build Data frame with complete set of p-values\n #\n c2$method <- \"SRS\"\n c3 <- c2 \n\n #\n # Leer Dq SAD\n #\n fname <- paste0(bName,\"T\",Time,\"mfSAD.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # Subset for testing\n #Dq1 <- with(Dq1,Dq1[MortalityRate==.2 & DispersalDistance==0.04 & ColonizationRate==0.001, ])\n\n # Testing pairwise differences\n #\n c2 <- compDq_frame(Dq1,35)\n\n # Add to data.frame with proportions\n #\n compM <- rbind(compM, data.frame(time=Time,notdif=propNotDiffSRS(c2),method=\"DqSAD\"))\n\n # Add to Data frame with complete set of p-values\n #\n c2$method <- \"DqSAD\"\n c3 <- rbind(c3,c2) \n\n # Change to match different data.frames\n #\n cc3 <- cbind(ldply(strsplit(as.character(c3$Group1),\"_\")),ldply(strsplit(as.character(c3$Group2),\"_\")))\n nn3 <- abbreviate(names(Dq1)[1:4])\n names(cc3) <- c(paste0(nn3,1),paste0(nn3,2))\n cc3 <- cbind(cc3,c3)[,c(1:8,11:14)]\n names(cc3)[9:11] <-c(\"stat\",\"p.value\",\"p.adjust\")\n mKS <- mKS[,2:12]\n mKS$method <- \"SAD\"\n c3 <- rbind(cc3,mKS)\n c3$time <- Time\n \n return(list(\"compM\"=compM,\"mPval\"=c3))\n}\n\n# Read a sed file in a matrix\n#\n# fname: file name of the sed file\n#\nread_sed <- function(fname)\n{\n d <-read.table(fname, nrows=1,header=F)\n per <-data.matrix(read.table(fname, skip=2,header=F))\n if(d$V2!=nrow(per)) stop(paste(\"Incorrect formated sed file:\",fname))\n return(per)\n}\n\nread_sed2xy <- function(fname)\n{\n spa <- read_sed(fname)\n z <- 1:(nrow(spa)*ncol(spa))\n zpa <- data.frame(v=spa[z],x=trunc(z/ncol(spa)),y=1:nrow(spa))\n}\n\n\n\n# Read information of the fit of Dq (Zq) from t.file and q file\n# Return a data.frame in long format\n#\nreadZq <- function(fname,qname)\n{\n zq <- read.table(fname, sep=\"\\t\",header=T)\n cna <- read_sed(qname)\n q <-t(cna)\n zq0 <- reshape(zq, timevar=\"q\",times=q,v.names=c(\"logTr\"),\n varying=list(3:length(names(zq))),\n direction=\"long\")\n}\n\n# Function to plot Dq fit from t* files generated by mfSBA using ggplot2\n# \n# zq0: data.frame with Zq, logTr \n# fac: factor to separate the lines \n# repRate: replacement to plot (rho)\nplotDqFitG <- function(zq0,fac=1/3,replRate=c(0,0.01,1))\n{\n require(ggplot2)\n require(dplyr)\n \n zq1 <- subset(zq0, q==1 | q==2 | q==3 | q==4 | q==5 | q==0 | q==-1 | q==-2 | q==-3 | q==-4 | q==-5 )\n zq1$logTr <- zq1$logTr+zq1$q*fac\n if( \"RplRt\" %in% names(zq1))\n {\n zq1 <- filter(zq1,RplRt %in% replRate) %>% mutate(DqType=ifelse(grepl(\"SRS\",Type),\"DqSRS\",\"DqSAD\"), Type=paste(\"Rho:\",RplRt),\n LogBox=ifelse(grepl(\"SRS\",DqType),LogBox,log10(sqrt(BoxSize)))) \n\n } else {\n zq1 <- mutate(zq1,DqType=ifelse(grepl(\"SRS\",Type),\"DqSRS\",\"DqSAD\"), Type=ifelse(grepl(\"rnz\",Type),\"Randomized\",\"Regular\"),\n LogBox=ifelse(grepl(\"SRS\",DqType),LogBox,log10(sqrt(BoxSize)))) \n }\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\",\"#666666\")\n #mc <- c(\"#d73027\",\"#f46d43\",\"#fdae61\",\"#fee090\",\"#ffffbf\",\"#e0f3f8\",\"#abd9e9\",\"#74add1\",\"#4575b4\")\n\n g <- ggplot(zq1,aes(LogBox,logTr,colour=factor(q))) + geom_point(aes(shape=factor(q)),size=1) + \n geom_smooth(method=\"lm\",se=F) \n g <- g + scale_shape_manual(values=c(0,1,2,3,4,5,6,8,15,16,17,21:24),name=\" q\") \n g <- g + scale_colour_manual(values=mc,name=\" q\")\n g <- g + ylab(expression(italic(paste(\"log \", Z[q](epsilon) )))) + theme_bw() +\n xlab(expression(italic(paste(\"log \",epsilon)))) \n g <- g + facet_wrap(Type ~ DqType, scales=\"free_y\",ncol=2)\n #g <- g + facet_grid(Type ~ DqType )\n print(g)\n}\n\n\nplotDqFitGQ <- function(zq0,qq,fac=3)\n{\n require(ggplot2)\n require(dplyr)\n \n zq1 <- filter(zq0, q %in% qq )\n zq1$logTr <- zq1$logTr+zq1$q/fac\n zq1 <- mutate(zq1,DqType=ifelse(grepl(\"SRS\",Type),\"SRS\",\"SAD\"), Type=ifelse(grepl(\"rnz\",Type),\"b) Randomized\",\"a) Regular\"))\n \n # g <- ggplot(zq1,aes(LogBox,logTr,colour=factor(q))) + geom_point(aes(shape=factor(q))) + \n # scale_color_discrete(name=\"q\") + \n \n g <- ggplot(zq1,aes(LogBox,logTr,shape=factor(q))) + geom_point(aes(shape=factor(q)),size=1) + \n geom_smooth(method=\"lm\",se=F,colour=\"grey\") +\n scale_shape_manual(values=c(0,1,2,3,4,5,6,8,15,16,17,21:24),name=\"q\") +\n ylab(expression(italic(paste(\"log \", Z[q](epsilon) )))) + theme_bw() +\n xlab(expression(italic(paste(\"log \",epsilon))))+\n facet_wrap(Type ~ DqType, scales=\"free\")\n print(g)\n}\n\n# Calculates theoretic Dq from pmodel\n#\ncalcDqTeor <- function(q,p1,p2=0,p3=0,p4=0) {\n if( q==1)\n q <- q+1e-10\n if(p2==0 & p3==0 & p4==0 ) {\n p2 <- p3 <- p4 <- (1-p1)/3\n }\n f1 <- p1/(p1+p2+p3+p4)\n f2 <- p2/(p1+p2+p3+p4)\n f3 <- p3/(p1+p2+p3+p4)\n f4 <- p4/(p1+p2+p3+p4)\n dq <- log2(f1^q+f2^q+f3^q+f4^q)/(1-q)\n return(dq)\n }\n\n\n# Save a matrix as a sed file with type BI (floating point)\n#\nsave_matrix_as_sed <- function(mat,fname)\n{\n header <- paste(nrow(mat),ncol(mat),\"\\nBI\")\n write.table(header,file=fname,row.names=F,col.names=F,quote=F)\n write.table(mat,file=fname,row.names=F,col.names=F,quote=F,append=T)\n}\n\ncalcDq_multiSBA <- function(fname,parms,pathBin=\"\",recalc=FALSE)\n{\n sname <- paste0(\"s.\", fname)\n if((!file.exists(sname)) | recalc)\n {\n if(nchar(pathBin)==0)\n {\n syst.txt <- paste(\"./multiSpeciesSBA \",fname, parms)\n } else {\n syst.txt <- paste0(pathBin,\"/multiSpeciesSBA \",fname,\" \",parms)\n }\n\n system(syst.txt)\n }\n pp <- read.delim(sname, header=T)\n \n for(nc in 1:ncol(pp)){\n if( class(pp[,nc ])==\"factor\") pp[,nc]<-as.numeric(as.character(pp[,nc])) \n }\n\n pp$Dq <- with(pp,ifelse(q==1,alfa,Tau/(q-1)))\n pp$SD.Dq <- with(pp,ifelse(q==1,SD.alfa,abs(SD.Tau/(q-1))))\n pp$R.Dq <- with(pp,ifelse(q==1,R.alfa,R.Tau))\n \n return(pp[,c(\"q\",\"Dq\",\"SD.Dq\",\"R.Dq\")])\n}\n\n# Plot a sed file\n#\n# fname: file name\n# gname: graph title \n# dX: range in columns to plot\n# col: vector of colors to make the color palette\n# shf: shift in the vector of colors \n#\nplot_sed_image <- function(fname,gname,dX=0,col=0,shf=0)\n {\n require(lattice)\n require(RColorBrewer)\n if(class(fname)==\"matrix\") {\n per <- fname\n } else {\n per <-data.matrix(read.table(fname, skip=2,header=F))\n }\n \n if(length(dX)>1) per <- per[,dX]\n mp = max(per)\n if(mp<50) {\n mp = 50\n seqat = seq( min(per),max(per),(max(per)-min(per))/50)\n }\n else\n {\n seqat = seq( min(per),mp,5)\n }\n if(length(col)==1) col.l <- colorRampPalette(c('white', 'green', 'purple', 'yellow', 'brown'))(mp) \n else col.l <- colorRampPalette(col)(mp) \n if( shf>0) col.l = col.l[shf:mp]\n print(levelplot(per, scales = list(draw = FALSE),xlab =NULL, ylab = NULL,col.regions=col.l,\n useRaster=T,at=seqat,\n main=list( gname,cex=1)))\n }\n\n# Generate a banded image with nSp species and side = side\n#\ngenUniformSAD_image <- function(nSp,side,rnd=F)\n{\n if( side %% nSp != 0 )\n stop(\"Number of species [nSp] must divide [side]\")\n if(rnd) {\n repeat {\n v <- rpois(nSp,side*side/nSp)\n if(side*side>sum(v[1:nSp-1])) break\n }\n v[nSp] <- side*side-sum(v[1:nSp-1])\n vv <- rep(1:nSp,times=v)\n matrix(vv,nrow=side)\n } else matrix(rep(1:nSp,each=side*side/nSp),nrow=side)\n}\n\n# Generate a regular image with Fisherian SAD with nsp species and side = side\n# Requires untb package\n# Returns a matrix representing the spatial distribution and a vector with proportions of each sp\n#\ngenFisherSAD_image <- function(nsp,side)\n{\n require(untb)\n N <- side*side\n repeat {\n ff<-fisher.ecosystem(N=N,S=nsp,nmax=N)\n if(nsp==nrow(ff)) {\n if(sum(ff)!=N) {\n c<- N/sum(ff)\n ff <- ceiling(ff*c)\n }\n m <- matrix(rep(1:nsp,ff),nrow=side)\n if(m[(sum(ff)+1)]==1) m[(sum(ff)+1):length(m)]<-0\n if(ncol(m)>=side) break\n }\n }\n \n prob <- ff/sum(ff)\n return(list(\"m\"=m,\"prob\"=prob))\n}\n\n\n# Generate Logseries SAD with nsp*f species and side = side\n# The factor f is to compensate losses for use as a metacommunity \n# previously set to f=1.33 now set to 1\n# Requires untb package\n#\n#\ngenFisherSAD <- function(nsp,side,f=1.33)\n{\n require(untb)\n N <- side*side\n# alpha <- fishers.alpha(N, nsp)\n# x <- N/(N + alpha)\n# j <- 1:(nsp)\n# prob <- alpha * x^j/j \n # Normalize\n# prob <- prob/sum(prob) \n nsp <- ceiling(nsp*f)\n repeat {\n ff<-fisher.ecosystem(N=N,S=nsp,nmax=N)\n if(nsp==nrow(ff)) {\n if(sum(ff)!=N) {\n c<- N/sum(ff)\n ff <- ceiling(ff*c)\n }\n break\n }\n }\n prob <- ff/sum(ff)\n return(prob)\n}\n\n \n# Generate a Fisher logseries SAD with a regular spatial distribution\n# randomize it and calculates SRS and DqSAD multifractal estimations\n#\ncompMethods_FisherSAD <- function(nsp,side,gen=T,graph=T) {\n\n if(!exists(\"mfBin\")) stop(\"Variable mfBin not set (mfSBA binary)\")\n\n fname <- paste0(\"fisher\",nsp,\"_\",side,\".sed\")\n if(file.exists(fname) & gen==F)\n {\n spa <- read_sed(fname)\n } else {\n spa <- genFisherSAD_image(nsp,side)$m\n save_matrix_as_sed(spa,fname)\n }\n\n sad1 <- data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=\"Logseries\")\n\n if(graph) plot_sed_image(spa,paste(\"Regular Fisher\",nsp),0,nsp,0)\n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq1$Type <- \"SRS\"\n\n # Randomize the spatial distribution\n #\n spa <- matrix(sample(spa),nrow=side)\n fname1 <- paste0(\"fisher\",nsp,\"_\",side,\"rnz.sed\")\n\n save_matrix_as_sed(spa,fname1)\n if(graph) plot_sed_image(spa,paste(\"Rnz Fisher\",nsp),0,nsp,0)\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq2$Type <- \"rnzSRS\"\n Dq1<- rbind(Dq1,Dq2)\n if(graph) {\n plotDq(Dq1,\"Type\")\n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq1, aes(x=R.Dq,fill=Type)) + geom_histogram(alpha=0.2,binwidth = bin))\n }\n # Now calculate DqSAD\n\n Dq3<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq3$Type <- \"DqSAD\"\n #Dq3<- rbind(Dq3,Dq2)\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq2$Type <- \"rnzDqSAD\"\n Dq3<- rbind(Dq3,Dq2)\n if(graph) {\n plotDq(Dq3,\"Type\")\n\n plotDqFit(paste0(\"t.\", fname1),\"q.sed\")\n bin <- range(Dq3$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq3, aes(x=R.Dq,fill=Type)) + geom_histogram(alpha=0.2,binwidth = bin))\n }\n \n #require(pander)\n #pandoc.table(Dq3[Dq3$R.Dq<0.6,],caption=\"R2<0.6\")\n \n Dqt <- rbind(Dq1,Dq3)\n Dqt$Side <-side\n Dqt$NumSp <-nsp\n Dqt$SAD <- \"Logseries\"\n\n return(list(\"Dq\"=Dqt,\"SAD\"=sad1))\n}\n\n# Generate a unniform SAD (all spp wiht the same density) with a regular spatial distribution\n# randomize it and calculates SRS and DqSAD multifractal estimations\n#\ncompMethods_UniformSAD <- function(nsp,side,graph=T) {\n\n if(!exists(\"mfBin\")) stop(\"Variable mfBin not set (mfSBA binary)\")\n\n spa <- genUniformSAD_image(nsp,side,T)\n \n if(graph) {\n #plot_sed_image(spa,paste(\"Uniform sp:\",nsp,\" side:\",side),0,nsp,0)\n }\n\n fname <- paste0(\"unif\",nsp,\"_\",side,\".sed\")\n save_matrix_as_sed(spa,fname)\n sad1 <- data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=\"Uniform\")\n\n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq1$Type <- \"DqSRS\"\n\n spa <- matrix(sample(spa),nrow=side)\n\n fname1 <- paste0(\"unif\",nsp,\"_\",side,\"rnz.sed\")\n save_matrix_as_sed(spa,fname1)\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq2$Type <- \"rnzSRS\"\n Dq1<- rbind(Dq1,Dq2)\n \n if(graph) { \n plotDq(Dq1,\"Type\")\n\n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq1, aes(x=R.Dq,fill=Type)) + geom_histogram(binwidth = bin,position=\"dodge\",colour=\"black\"))\n\n plotSAD_SpatPat(nsp,side,\"U\")\n #plot_sed_image(spa,paste(\"Uniform Rnz sp:\",nsp,\" side:\",side),0,nsp,0)\n }\n\n Dq2<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq2$Type <- \"DqSAD\"\n Dq3<- Dq2\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq2$Type <- \"rnzDqSAD\"\n Dq3<- rbind(Dq3,Dq2)\n if(graph) { \n\n plotDq(Dq3,\"Type\")\n zq <- readZq(paste0(\"t.\", fname),\"q.sed\")\n zq$Type <- \"DqSAD\"\n\n zq1 <- readZq(paste0(\"t.\", fname1),\"q.sed\")\n zq1$Type <- \"rnzDqSAD\"\n\n plotDqFitG(rbind(zq,zq1))\n\n bin <- range(Dq3$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq3, aes(x=R.Dq,fill=Type)) +\n geom_histogram(binwidth = bin,position=\"dodge\",colour=\"black\"))\n }\n \n Dqt <- rbind(Dq1,Dq3)\n Dqt$Side <-side\n Dqt$NumSp <-nsp\n Dqt$SAD <- \"Uniform\"\n\n return(list(\"Dq\"=Dqt,\"SAD\"=sad1))\n}\n\n# Generate a Neutral model with Fisher logseries metacommunity SAD \n# randomize it and calculates SRS and DqSAD multifractal estimations\n# Neutral model Hierarchical saturated\n# Graph : plot graphs\n# meta: metacommunity \"L\" logseries, any other uniform\n#\ncompMethods_NeutralSAD <- function(nsp,side,simul=T,graph=T,meta=\"L\") {\n if(!exists(\"mfBin\")) stop(\"Variable mfBin not set (mfSBA binary)\")\n if(!exists(\"neuBin\")) stop(\"Variable neuBin not set (neutral binary)\")\n if(!require(untb)) stop(\"Untb package not installed\")\n\n if(simul){\n\n #N <- side*side # The metacommunity have 100 times more individuals\n #alpha <- fishers.alpha(N, nsp)\n #x <- N/(N + alpha)\n #j <- 1:(nsp)\n #prob <- alpha * x^j/j \n # Normalize\n #prob <- prob/sum(prob)\n if(toupper(meta)==\"L\") {\n prob <- genFisherSAD(nsp,side)\n neuParm <- \"fishE\"\n bname <- paste0(\"neuFish\",nsp)\n sadName <- \"Neutral\"\n } else {\n prob <- rep(1/nsp,nsp) \n neuParm <- \"unifE\"\n bname <- paste0(\"neuUnif\",nsp)\n sadName <- \"NeuUnif\"\n }\n\n genNeutralParms(neuParm,side,prob,1,0.2,0.4,0.0001)\n\n\n # Delete old simulations\n system(paste0(\"rm \",bname,\"*.txt\"))\n\n par <- read.table(\"sim.par\",quote=\"\",stringsAsFactors=F)\n # Change base name\n\n par[par$V1==\"nEvals\",]$V2 <- 500\n par[par$V1==\"inter\",]$V2 <- 500 # interval to measure Density and Diversity\n par[par$V1==\"init\",]$V2 <- 500 # Firs time of measurement = interval\n par[par$V1==\"modType\",]$V2 <- 4 # Hierarchical saturated\n par[par$V1==\"sa\",]$V2 <- \"S\" # Save a snapshot of the model\n par[par$V1==\"baseName\",]$V2 <- bname# Time = 100 \n par[par$V1==\"minBox\",]$V2 <- 2\n par[par$V1==\"pomac\",]$V2 <- 0 # 0:one set of parms \n # 1:several simulations with pomac.lin parameters \n\n write.table(par, \"sim.par\",sep=\"\\t\",row.names=F,col.names=F,quote=F)\n\n system(paste(neuBin,\"sim.par\",paste0(neuParm,\".inp\")))\n }\n #fname <- paste0(\"neuFish\",nsp,\"Density.txt\")\n #sad1 <- meltDensityOut_NT(fname,nsp)\n\n fname <- paste0(bname,\"-0500.sed\")\n spa <- read_sed(fname)\n\n sad1 <- data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=sadName)\n #sad1$alpha <- fishers.alpha(side*side, nrow(sad1))\n\n if(graph) plot_sed_image(spa,paste(\"Neutral T500\",nsp),0,nsp,0)\n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq1$Type <- \"SRS\"\n\n # Randomize the spatial distribution\n #\n spa <- matrix(sample(spa),nrow=side)\n fname1 <- paste0(bname,\"-500rnz.sed\")\n\n save_matrix_as_sed(spa,fname1)\n if(graph) plot_sed_image(spa,paste(\"Neutral T500 Rnz\",nsp),0,nsp,0)\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq2$Type <- \"rnzSRS\"\n Dq1<- rbind(Dq1,Dq2)\n if(graph) {\n plotDq(Dq1,\"Type\")\n\n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq1, aes(x=R.Dq,fill=Type)) + geom_histogram(alpha=0.2,binwidth = bin))\n }\n \n # Now calculate DqSAD\n\n Dq3<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq3$Type <- \"DqSAD\"\n #Dq3<- rbind(Dq3,Dq2)\n\n Dq2<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq2$Type <- \"rnzDqSAD\"\n Dq3<- rbind(Dq3,Dq2)\n\n if(graph) {\n plotDq(Dq3,\"Type\")\n\n plotDqFit(paste0(\"t.\", fname1),\"q.sed\")\n\n bin <- range(Dq3$R.Dq)\n bin <- (bin[2]-bin[1])/10\n print(ggplot(Dq3, aes(x=R.Dq,fill=Type)) + geom_histogram(alpha=0.2,binwidth = bin))\n } \n #require(pander)\n #pandoc.table(Dq3[Dq3$R.Dq<0.6,],caption=\"R2<0.6\")\n \n Dqt <- rbind(Dq1,Dq3)\n Dqt$Side <-side\n Dqt$NumSp <-nsp\n Dqt$SAD <- sadName #Neutral with uniform metacommunity\n\n return(list(\"Dq\"=Dqt,\"SAD\"=sad1))\n}\n\n\n\n# Reads Generated Logseries & NEutral SAD compare using KS and compare Dq also using KS.test\n#\ncompKS_NeutralLogseries <- function(Dq1,nsp,side) {\n\n fname <- paste0(\"neuFish\",nsp,\"-0500.sed\")\n \n spa <- read_sed(fname)\n\n ff <- data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=\"Neutral\")\n\n fname <- paste0(\"neuFish\",nsp,\"_\",side,\".sed\")\n\n spa <- read_sed(fname)\n\n ff <- rbind(ff,data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=\"Logseries\"))\n\n compSP <- pairwiseKS_SAD(ff,\"Freq\",ff[,3:6])\n\n\n den3 <- calcRankSAD_by(ff,\"Freq\",3:6)\n print(ggplot(den3,aes(x=Rank,y=log(Freq),colour=SAD)) + geom_line())\n\n ff <- with(Dq1,Dq1[Side==side & NumSp==nsp & SAD!=\"Uniform\" & Type==\"SRS\",])\n ff <- rbind(ff,with(Dq1,Dq1[Side==side & NumSp==nsp & SAD==\"Logseries\" & Type==\"rnzSRS\" ,]))\n\n #ff <- with(ff,ff[q<=10 & q>=-10,])\n #unique(ff$q)\n compSP <- rbind(compSP,pairwiseKS_SAD(ff,\"Dq\",ff[5:8]))\n plotDq(ff,\"SAD\")\n\n \n ff <- with(Dq1,Dq1[Side==side & NumSp==nsp & Type==\"DqSAD\",])\n ff <- rbind(ff,with(Dq1,Dq1[Side==side & NumSp==nsp & SAD==\"Logseries\" & Type==\"rnzDqSAD\" ,]))\n\n plotDq(ff,\"SAD\")\n\n compSP <- rbind(compSP,pairwiseKS_SAD(ff,\"Dq\",ff[5:8]))\n\n }\n\n# Simulate 10 Logseries & NEutral SAD compare using KS and compare Dq using permutations\n#\ncomp_NeutralLogseries <- function(nsp,side,simul=10) {\n require(ggplot2)\n\n Dqq <- data.frame()\n SadF <- data.frame()\n\n for(i in 1:simul){\n cc <- compMethods_FisherSAD(nsp,side,T,F)\n cc$Dq$rep <-i\n cc$SAD$rep <-i \n Dqq <- rbind(Dqq,cc$Dq)\n SadF <-rbind(SadF,cc$SAD)\n }\n\n #Dqq <- Dqq[Dqq$SAD!=\"Neutral\",]\n #SadF <- SadF[SadF$SAD!=\"Neutral\",]\n\n for(i in 1:simul){\n cc <- compMethods_NeutralSAD(nsp,side,T,F)\n cc$Dq$rep <-i\n cc$SAD$rep <-i \n Dqq <- rbind(Dqq,cc$Dq)\n SadF <-rbind(SadF,cc$SAD)\n }\n\n # have to make averages\n require(plyr)\n sad1 <- ddply(SadF,c(3,4,5,6,1),summarise,den=mean(Freq))\n\n comp <- pairwiseKS_SAD(sad1,\"den\",sad1[,1:4])\n den3 <- calcRankSAD_by(sad1,\"den\",1:4)\n print(ggplot(den3,aes(x=Rank,y=log(den),colour=SAD)) + geom_line() + ggtitle(comp$p.value))\n\n\n return(list(\"Dq\"=Dqq,\"SAD\"=SadF))\n }\n\n\n\n# Simulate 10 Uniform & Neutral SAD compare using KS and compare Dq using permutations\n#\ncomp_NeutralUniform <- function(nsp,side,simul=10) {\n require(ggplot2)\n\n Dqq <- data.frame()\n SadF <- data.frame()\n\n for(i in 1:simul){\n cc <- compMethods_UniformSAD(nsp,side,F)\n cc$Dq$rep <-i\n cc$SAD$rep <-i \n Dqq <- rbind(Dqq,cc$Dq)\n SadF <-rbind(SadF,cc$SAD)\n }\n\n #Dqq <- Dqq[Dqq$SAD!=\"Neutral\",]\n #SadF <- SadF[SadF$SAD!=\"Neutral\",]\n\n for(i in 1:simul){\n cc <- compMethods_NeutralSAD(nsp,side,T,F,\"U\")\n cc$Dq$rep <-i\n cc$SAD$rep <-i \n Dqq <- rbind(Dqq,cc$Dq)\n SadF <-rbind(SadF,cc$SAD)\n }\n\n # have to make averages\n require(plyr)\n sad1 <- ddply(SadF,c(3,4,5,6,1),summarise,den=mean(Freq))\n\n comp <- pairwiseKS_SAD(sad1,\"den\",sad1[,1:4])\n den3 <- calcRankSAD_by(sad1,\"den\",1:4)\n print(ggplot(den3,aes(x=Rank,y=log(den),colour=SAD)) + geom_line() + ggtitle(comp$p.value))\n\n return(list(\"Dq\"=Dqq,\"SAD\"=SadF))\n\n }\n\n\n# Simulate a time series of the neutral/hierarchical model\n#\n# disp: dispersal parameter\n# migr: migration rate\n# repl: replacement rate\n# mortality fixed to 0.2\n#\n# simul: T=make simulations F=make plots\n# sims: Number of repetitions\n# mf: calculate multifractal spectrum\n# meta: U= uniform metacommunity\n# L= logseries metacommunity\n#\nsimul_NeutralPlotTime <- function(nsp,side,disp,migr,repl,simul=T,time=1000,sims=10,mf=\"N\",meta=\"U\") {\n if(!exists(\"neuBin\")) stop(\"Variable neuBin not set (neutral binary)\")\n\n if(toupper(meta)==\"L\") {\n prob <- genFisherSAD(nsp,side)\n neuParm <- paste0(\"fishP\",nsp,\"_\",side,\"R\", repl)\n bname <- paste0(\"neuFish\",nsp,\"_\",side,\"R\", repl)\n } else {\n prob <- rep(1/nsp,nsp) \n neuParm <- paste0(\"unifP\",nsp,\"_\",side,\"R\", repl)\n bname <- paste0(\"neuUnif\",nsp,\"_\",side,\"R\", repl)\n }\n pname <- paste0(\"pomacR\",repl,\".lin\")\n\n if(simul){\n\n genNeutralParms(neuParm,side,prob,1,0.2,disp,migr,repl)\n\n # Delete old simulations\n system(paste0(\"rm \",bname,\"*.txt\"))\n\n par <- read.table(\"sim.par\",quote=\"\",stringsAsFactors=F)\n\n par[par$V1==\"nEvals\",]$V2 <- time\n par[par$V1==\"inter\",]$V2 <- 10 # interval to measure Density and Diversity\n par[par$V1==\"init\",]$V2 <- 1 # Firs time of measurement = interval\n par[par$V1==\"modType\",]$V2 <- 4 # Hierarchical saturated\n par[par$V1==\"sa\",]$V2 <- \"N\" # Save a snapshot of the model\n par[par$V1==\"baseName\",]$V2 <- bname# Time = 100 \n par[par$V1==\"mfDim\",]$V2 <- mf\n par[par$V1==\"minBox\",]$V2 <- 2\n par[par$V1==\"pomac\",]$V2 <- 1 # 0:one set of parms \n # 1:several simulations with pomac.lin parameters \n par[par$V1==\"pomacFile\",]$V2 <- pname # 0:one set of parms \n par[par$V1==\"minProp\",]$V2 <- 0\n \n parfname <- paste0(\"sim\",nsp,\"_\",side,\"R\", repl,\".par\")\n write.table(par, parfname, sep=\"\\t\",row.names=F,col.names=F,quote=F)\n\n genPomacParms(pname,1,c(0.2),disp,migr,repl,sims)\n \n # copy pomExp.lin to pomac.lin\n #system(\"cp pomExp.lin pomac.lin\")\n s <- system(\"uname -a\",intern=T)\n if(grepl(\"i686\",s)) {\n system(paste(neuBin,parfname,paste0(neuParm,\".inp\")))\n } else {\n system(paste(neuBin64,parfname,paste0(neuParm,\".inp\")))\n }\n }\n den <-readWideDensityOut(bname)\n \n require(plyr)\n require(dplyr)\n \n if(!simul) {\n require(ggplot2)\n if(sims>10) \n den1 <- filter(den,Rep %in% sample(1:sims,10)) \n \n print(ggplot(den1, aes(x=Time, y=H,color=factor(Rep))) +\n geom_line() + theme_bw() + ggtitle(paste(side,repl)))\n\n print(ggplot(den1, aes(x=Time, y=Richness,color=factor(Rep))) +\n geom_line() + theme_bw() + ggtitle(paste(side,repl))) \n }\n\n den$nsp <- nsp\n den$side <- side\n den$meta <- meta\n \n den <- den[,c(\"nsp\",\"side\",\"meta\",\"Rep\",\"GrowthRate\",\"MortalityRate\",\"DispersalDistance\",\"ColonizationRate\",\"ReplacementRate\",\"Time\",\"Richness\",\"H\")]\n return(den)\n}\n\ncalcPower_AD <- function(Dqq,Sad,nsp,side){\n Dq3 <- with(Dqq,Dqq[grepl(\"DqSAD\",Type) & SAD==\"Logseries\" & Side==side & NumSp==nsp,])\n pow <- calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")])\n\n Sa3 <- with(Sad,Sad[SAD==\"Logseries\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow,calcPow_AD(Sa3,\"Freq\",Sa3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"SRS\",Type) & SAD==\"Logseries\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"DqSAD\",Type) & SAD==\"Neutral\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"SRS\",Type) & SAD==\"Neutral\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[((Type==\"SRS\" & SAD==\"Neutral\")|(Type==\"rnzSRS\" & SAD==\"Logseries\")) & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Sa3 <- with(Sad,Sad[SAD==\"Neutral\" & Side==side & NumSp==nsp ,])\n pow <- rbind(pow,calcPow_AD(Sa3,\"Freq\",Sa3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n ###\n\n Dq3 <- with(Dqq,Dqq[grepl(\"DqSAD\",Type) & SAD==\"Uniform\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow,calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Sa3 <- with(Sad,Sad[SAD==\"Uniform\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow,calcPow_AD(Sa3,\"Freq\",Sa3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"SRS\",Type) & SAD==\"Uniform\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"DqSAD\",Type) & SAD==\"NeuUnif\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[grepl(\"SRS\",Type) & SAD==\"NeuUnif\" & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Dq3 <- with(Dqq,Dqq[((Type==\"SRS\" & SAD==\"NeuUnif\")|(Type==\"rnzSRS\" & SAD==\"Uniform\")) & Side==side & NumSp==nsp,])\n pow <- rbind(pow, calcPow_AD(Dq3,\"Dq\",Dq3[,c(\"Type\",\"SAD\",\"rep\")]))\n\n Sa3 <- with(Sad,Sad[SAD==\"NeuUnif\" & Side==side & NumSp==nsp ,])\n pow <- rbind(pow,calcPow_AD(Sa3,\"Freq\",Sa3[,c(\"Type\",\"SAD\",\"rep\")]))\n pow$Side <- side\n pow$NumSp <- nsp\n return(pow)\n}\n\n# Simulations of the model with output of one time \n#\n#\nsimulNeutral_1Time<- function(nsp,side,time,meta=\"L\",rep=10,delo=T)\n{\n\n if(toupper(meta)==\"L\") {\n prob <- genFisherSAD(nsp,side)\n neuParm <- paste0(\"fishE\",nsp,\"_\",side)\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n sadName <- \"Neutral\"\n } else {\n prob <- rep(1/nsp,nsp) \n neuParm <- paste0(\"unifE\",nsp,\"_\",side)\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n sadName <- \"NeuUnif\"\n }\n\n # Parameters\n #\n # Mortality = 0.2 - 0.4\n # Mean Dispersal distance 25 -> Exponential kernel parm 0.04\n # 2.5 -> 0.4\n # Colonization = 0.001 -0.0001\n # Replacement = 0 - 1\n spMeta <- length(prob)\n\n # First generate de inp file with species and metacommunity parameters \n genNeutralParms(neuParm,side,prob,1,0.2,0.04,0.0001)\n\n # Delete old simulations\n if(delo)\n system(paste0(\"rm \",bname,\"*.txt\"))\n\n\n # we need the par file with the simulations parameters\n par <- read.table(\"sim.par\",quote=\"\",stringsAsFactors=F)\n\n\n # Number of time steps \n par[par$V1==\"nEvals\",]$V2 <- time\n par[par$V1==\"inter\",]$V2 <- time # interval to measure Density and Diversity\n par[par$V1==\"init\",]$V2 <- time # Firs time of measurement = interval\n par[par$V1==\"modType\",]$V2 <- 4 # Hierarchical saturated\n par[par$V1==\"sa\",]$V2 <- \"N\" # Save a snapshot of the model\n par[par$V1==\"baseName\",]$V2 <- paste0(bname ,\"T\", time ) \n par[par$V1==\"pomac\",]$V2 <- 1 # 0:one set of parms \n # 1:several simulations with pomac.lin parameters \n\n parfname <- paste0(\"sim\",nsp,\"_\",side,\".par\")\n write.table(par, parfname, sep=\"\\t\",row.names=F,col.names=F,quote=F)\n\n # Then pomac.lin to simulate a range of parmeters and repetitions.\n #\n # Generates pomac.lin for multiple simulations exponential dispersal to compare hierarchical and neutral communities \n # and see when they have similar H and compare if they have similar SAD\n\n #genPomacParms(\"pomExp\",1,c(0.2),c(0.04),c(0.0001),c(0,0.001),3)\n\n #genPomacParms(\"pomExp\",1,c(0.2,0.4),c(0.04,0.4),c(0.001,0.0001),c(0,0.001,0.01,0.1,1),rep)\n genPomacParms(\"pomExp\",1,c(0.2,0.4),c(0.04,0.4),c(0.001),c(0,0.001,0.01,0.1,1),rep)\n \n # copy pomExp.lin to pomac.lin\n system(\"cp pomExp.lin pomac.lin\")\n s <- system(\"uname -a\",intern=T)\n if(grepl(\"i686\",s)) {\n system(paste(neuBin,parfname,paste0(neuParm,\".inp\")))\n } else {\n system(paste(neuBin64,parfname,paste0(neuParm,\".inp\")))\n }\n return(data.frame(nsp,side,time,meta,spMeta,rep))\n}\n\n# Compare neutral simulations using anderson-darling test and calculate power\n#\npowerNeutral_1Time <- function(pSimul,mr=0,dd=0,cr=0,q=NULL,graph=F) \n{\n if( nrow(pSimul)>1)\n stop(\"Only one row of parameters\")\n\n meta <- pSimul$meta\n nsp <- pSimul$nsp\n time <- pSimul$time\n spMeta <- pSimul$spMeta\n side <- pSimul$side\n\n if(toupper(meta)==\"L\") {\n# prob <- genFisherSAD(nsp,side)\n neuParm <- \"fishE\"\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n sadName <- \"Neutral\"\n } else {\n# prob <- rep(1/nsp,nsp) \n neuParm <- \"unifE\"\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n sadName <- \"NeuUnif\"\n }\n\n #\n fname <- paste0(bname,\"T\",time,\"Density.txt\")\n\n den1 <- meltDensityOut_NT(fname,spMeta)\n\n # Test pairwise diferences in SAD\n #\n # subset \n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n den1 <- den1[den1$MortalityRate==mr & den1$DispersalDistance==dd & den1$ColonizationRate==cr, ]\n if(nrow(den1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n den1 <- with(den1,den1[ColonizationRate==cr,])\n }\n \n mKS <- pairwiseAD_Dif(den1,\"value\",den1[,1:5])\n\n # \n # Plot the first pairs not different \n #\n \n if(graph) {\n mks <- mKS[mKS$p.value<0.05,2:ncol(mKS)]\n psa <- mergePairSadRank(mks[1,],den1,\"value\",1:5)\n mks <- mKS[mKS$p.value>0.05,2:ncol(mKS)]\n psa <- rbind(psa,mergePairSadRank(mks[nrow(mks),],den1,\"value\",1:5))\n require(ggplot2)\n print(g <- ggplot(psa,aes(x=Rank,y=log(value),colour=parms)) + geom_line() + ggtitle(\"SAD dif/equal\"))\n }\n\n # Select the H0 == H1 to calculate typeI error and power\n # Build data.frame with proportions\n #\n m_nsp <-mean(ddply(den1,1:5,function(x){ data.frame(nsp=nrow(x))})$nsp)\n\n pp <- calcPower_fromFrame(mKS)\n pow_AD <- data.frame(Side=side,NumSp=nsp,MeanSp=m_nsp,Time=time,Type=\"SAD\",nPower=pp$nPower,\n power=pp$power,nTypeI=pp$nTypeI,typeI=pp$typeI,stringsAsFactors = F)\n #nrow(with(mKS,mKS[RplR1==RplR2 & rep1==rep2,]))\n # Calc power DqSRS \n #\n qNumber <- 35\n fname <- paste0(bname,\"T\",time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # Subset based on parameters\n # \n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n\n # Select the q range\n #\n if(!is.null(q)){\n Dq1 <-Dq1[abs(Dq1$q)<=q,]\n }\n\n mKS1 <- pairwiseAD_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)]) #### TEST THIS!\n\n pp <- calcPower_fromFrame(mKS1)\n pow_AD <- rbind(pow_AD,c(side,nsp,m_nsp,time,Type=\"DqSRS\",pp$nPower,pp$power,pp$nTypeI,pp$typeI))\n \n\n # Read DqSAD\n #\n fname <- paste0(bname,\"T\",time,\"mfSAD.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # Subset based on parameters\n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n\n # Select the q range\n if(!is.null(q)){\n Dq1 <-Dq1[abs(Dq1$q)<=q,]\n }\n\n mKS2 <- pairwiseAD_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)]) \n \n pp <- calcPower_fromFrame(mKS2)\n pow_AD <- rbind(pow_AD,c(side,nsp,m_nsp,time,Type=\"DqSAD\",pp$nPower,pp$power,pp$nTypeI,pp$typeI))\n\n mKS <- mKS[,2:ncol(mKS)]\n mKS$Side <- side\n mKS$NumSp <- nsp\n mKS$Time <- time\n mKS$Type <- \"SAD\"\n \n mKS1 <- mKS1[,2:ncol(mKS1)]\n mKS1$Side <- side\n mKS1$NumSp <- nsp\n mKS1$Time <- time\n mKS1$Type <- \"DqSRS\"\n\n mKS2 <- mKS2[,2:ncol(mKS2)]\n mKS2$Side <- side\n mKS2$NumSp <- nsp\n mKS2$Time <- time\n mKS2$Type <- \"DqSAD\"\n\n mKS <- rbind(mKS,mKS1,mKS2)\n return(list(\"comp_AD\"=mKS,\"pow_AD\"=pow_AD))\n}\n\ncalcPower_fromFrame <-function(mKA) {\n cc <-mKA[,2:5]==mKA[,7:10]\n mks <- mKA[apply(cc,1,sum)==4,]\n \n nTypeI <- nrow(mks)\n typeI <- nrow(mks[mks$p.value<0.05,])/nrow(mks)\n\n mks <- mKA[apply(cc,1,sum)!=4,2:ncol(mKA)]\n powr <- nrow(mks[mks$p.value<0.05,])/nrow(mks)\n return(data.frame(nPower=nrow(mks),power=powr,nTypeI=nTypeI,typeI=typeI,stringsAsFactors = F))\n}\n\n# Compare neutral simulations using information dimention & t-test and calculate power\n# n = number of points used for estimate Dq\n# q = q we will compare \n# if q = 0 it uses a Fisher.test to combine all q in one p (Almost ALLWAYS SIGNIFICATIVE)\n# \npowerNeutral_1T_D1 <- function(pSimul,n,q=NULL,mr=0,dd=0,cr=0) \n{\n if( nrow(pSimul)>1)\n stop(\"Only one row of parameters\")\n\n meta <- pSimul$meta\n nsp <- pSimul$nsp\n time <- pSimul$time\n spMeta <- pSimul$spMeta\n side <- pSimul$side\n\n if(toupper(meta)==\"L\") {\n# prob <- genFisherSAD(nsp,side)\n neuParm <- \"fishE\"\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n sadName <- \"Neutral\"\n } else {\n# prob <- rep(1/nsp,nsp) \n neuParm <- \"unifE\"\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n sadName <- \"NeuUnif\"\n }\n\n # Calc power DqSRS \n #\n qNumber <- 35\n fname <- paste0(bname,\"T\",time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # subset\n # \n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n if(!is.null(q)){\n Dq1 <-Dq1[Dq1$q==q,]\n }\n mKS1 <- pairwiseTD1_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)],n) \n \n pp <- calcPower_fromFrame(mKS1)\n\n pow_AD <- data.frame(Side=side,NumSp=nsp,MeanSp=spMeta,Time=time,Type=\"DqSRS\",nPower=pp$nPower,\n power=pp$power,nTypeI=pp$nTypeI,typeI=pp$typeI,q=q,stringsAsFactors = F)\n \n #pow_AD <- rbind(pow_AD,c(side,nsp,m_nsp,time,Type=\"DqSRS\",pp$nPower,pp$power,pp$nTypeI,pp$typeI))\n \n\n # Calc power DqSAD\n #\n fname <- paste0(bname,\"T\",time,\"mfSAD.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n if(!is.null(q)){\n if(q==1) q <-0\n Dq1 <-Dq1[Dq1$q==q,]\n }\n \n mKS2 <- pairwiseTD1_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)],n) \n \n pp <- calcPower_fromFrame(mKS2)\n pow_AD <- rbind(pow_AD,c(side,nsp,spMeta,time,Type=\"DqSAD\",pp$nPower,pp$power,pp$nTypeI,pp$typeI,q))\n\n \n mKS1 <- mKS1[,2:ncol(mKS1)]\n mKS1$Side <- side\n mKS1$NumSp <- nsp\n mKS1$Time <- time\n mKS1$Type <- \"DqSRS\"\n\n mKS2 <- mKS2[,2:ncol(mKS2)]\n mKS2$Side <- side\n mKS2$NumSp <- nsp\n mKS2$Time <- time\n mKS2$Type <- \"DqSAD\"\n\n mKS <- rbind(mKS1,mKS2)\n return(list(\"comp_AD\"=mKS,\"pow_AD\"=pow_AD))\n}\n\n# Pairwise T-test using D1\n# \n# denl:framework with Dq\n# vv: name of Dq variable\n# parms: variables defining combinations (factors)\n# n: number of points used to obtain Dq.\n#\n# If there are more than one Dq it use the Fisher.test\n# to combine multiple p into one.\n#\npairwiseTD1_Dif <- function(denl,vv,parms,n){\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n nc <- ncol(parms)-2\n #pb <- txtProgressBar(min = 0, max = ncol(combo), style = 3)\n #i <- 0\n require(plyr)\n mks <-adply(combo,2, function(x) {\n \n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n out<-NULL\n #i <<- i+1 \n #setTxtProgressBar(pb, i)\n if(sum(p1[,1:nc]==p2[,1:nc])==nc) {\n d1 <- merge(denl,p1)\n d2 <- merge(denl,p2)\n d1$grp <- 1\n d2$grp <- 2\n dd <- rbind(d1,d2) \n ks <- compareTwoCurvesT(dd$grp,dd[,vv],dd$SD.Dq,n) \n if(length(ks$p)>1) {\n p.adj <- na.omit(p.adjust(ks$p,method=\"hommel\"))\n fi <- Fisher.test(p.adj)\n stat <- fi[1]\n p <- fi[2]\n } else {\n stat <- ks$stat\n p <- ks$p\n }\n \n out <-data.frame(p1,p2,stat=stat,p.value=p,stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n \n }\n return(out) \n })\n #close(pb)\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n# Comparing two curves with SD for each point with multiple T-tests \n# using Hommel procedure to adjust p\n# \n#\ncompareTwoCurvesT <- function (group, y, sd,n) \n{\n# group <- as.vector(group)\n g <- unique(group)\n if (length(g) != 2) \n stop(\"Must be exactly 2 groups\")\n y1 <- y[group == g[1]]\n y2 <- y[group == g[2]]\n sd1 <-sd[group == g[1]]\n sd2 <-sd[group == g[2]]\n sd1 <- sd1*sd1\n sd2 <- sd2*sd2\n stat <- (y1-y2)/sqrt((sd1/n)+(sd2/n))\n df <- (sd1/n+sd2/n)^2/( (sd1/n)^2 / (n-1) + (sd2/n)^2 / (n-1)) \n #p.adjust <- p.adjust(pt(stat,df),method=\"hommel\")\n p <- pt(stat,df)\n #print(ks.test(y1,y2))\n #print(t.test(y1-y2,mu=0))\n return(list(\"p\"=p,\"stat\"=stat))\n}\n\n# Fisher procedure to combine p.value from multiple T test in one p.value\n#\nFisher.test <- function(p) {\n Xsq <- -2*sum(log(p))\n p.val <- 1-pchisq(Xsq, df = 2*length(p))\n return(c(Xsq = Xsq, p.value = p.val))\n}\n\n# Test pairwise differences in Dq using permutations \n# with function compareTwoGrowthCurves\n#\n#\npairwiseGC_Dif <- function(denl,vv,parms,numRep){\n if( !require(statmod) & !require(reshape2) & !require(plyr))\n stop(\"required statmod and reshape2 and plyr\")\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n nc <- ncol(parms)-2\n #pb <- txtProgressBar(min = 0, max = ncol(combo), style = 3)\n #i <- 0\n\n # Prepare data.frame in wide format \n #\n f <- as.formula(paste(paste(names(parms),collapse=\"+\"),\"q\",sep=\"~\"))\n Dq2 <- melt(denl, id.vars=c(\"q\",names(parms)),measure.var=vv)\n Dq2 <- dcast(Dq2, f)\n require(dplyr)\n parms <- arrange(parms,MortalityRate,DispersalDistance,ColonizationRate,ReplacementRate)\n # Make groups with numRep repetitions\n #\n if(numRep==max(parms$rep) && numRep==0){\n parms <- parms[,1:ncol(parms)-1]\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n } else {\n rs <- max(parms$rep)\n vr <- rep(1:(rs/numRep),numRep)\n #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ORDENAR PARMS !!!!!!!!!!!!!!!!!!!!!!!11\n parms$rep <- c(vr,rep(0,rs - length(vr)))\n Dq2$rep <- c(vr,rep(0,rs - length(vr)))\n Dq2 <- Dq2[Dq2$rep!=0,]\n parms <- parms[parms$rep!=0,]\n parms <- unique(parms)\n combo <- combn(nrow(parms),2)\n }\n # \n mks <-adply(combo,2, function(x) {\n \n p1 <- parms[x[1],]\n p2 <- parms[x[2],]\n out<-NULL\n #i <<- i+1 \n #setTxtProgressBar(pb, i)\n if(sum(p1[,1:nc]==p2[,1:nc])==nc) {\n d1 <- merge(Dq2,p1)\n d2 <- merge(Dq2,p2)\n if( nrow(d1)>2 & nrow(d2)>2)\n {\n print(paste(paste(p1,collapse=\"_\"),nrow(d1),paste(p2,collapse=\"_\"),nrow(d2)))\n d1$grp <- 1\n d2$grp <- 2\n dd <- rbind(d1,d2) \n ks <- compareTwoGrowthCurves(dd$grp,dd[,(nc+3):(ncol(dd)-1)],nsim=1000)\n stat <- ks$stat\n p <- ks$p\n \n out <-data.frame(p1,p2,stat=stat,p.value=p,stringsAsFactors=F)\n ln <-length(names(p1))*2\n names(out)[1:(ln)]<-c(paste0(abbreviate(names(p1)),1),paste0(abbreviate(names(p1)),2))\n }\n }\n return(out) \n })\n\n mks$p.adjust <- p.adjust(mks$p.value, method=\"hommel\")\n return(mks)\n}\n\n\n# Compare neutral simulations using Dq compareTwoGrowthCurves \n# numRep: number of repetitions used for each comparison\n# \npowerNeutral_1T_GC <- function(pSimul,numRep,mr=0,dd=0,cr=0,q=NULL) \n{\n if( nrow(pSimul)>1)\n stop(\"Only one row of parameters\")\n\n meta <- pSimul$meta\n nsp <- pSimul$nsp\n time <- pSimul$time\n spMeta <- pSimul$spMeta\n side <- pSimul$side\n\n if(toupper(meta)==\"L\") {\n# prob <- genFisherSAD(nsp,side)\n neuParm <- \"fishE\"\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n sadName <- \"Neutral\"\n } else {\n# prob <- rep(1/nsp,nsp) \n neuParm <- \"unifE\"\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n sadName <- \"NeuUnif\"\n }\n\n # Calc power DqSRS \n #\n qNumber <- 35\n fname <- paste0(bname,\"T\",time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n\n # subset\n # \n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n\n if(!is.null(q)){\n Dq1 <-Dq1[abs(Dq1$q)<=q,]\n }\n\n mKS1 <- pairwiseGC_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)],numRep) #### TEST THIS!\n\n pp <- calcPower_fromFrame(mKS1)\n\n pow_AD <- data.frame(Side=side,NumSp=nsp,MeanSp=spMeta,Time=time,Type=\"DqSRS\",nPower=pp$nPower,\n power=pp$power,nTypeI=pp$nTypeI,typeI=pp$typeI,stringsAsFactors = F)\n \n #pow_AD <- rbind(pow_AD,c(side,nsp,m_nsp,time,Type=\"DqSRS\",pp$nPower,pp$power,pp$nTypeI,pp$typeI))\n \n\n # Calc power DqSAD\n #\n fname <- paste0(bname,\"T\",time,\"mfSAD.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n if(mr!=0 & dd!=0 & cr!=0) # .2, .04, 0.001\n {\n Dq1 <- with(Dq1,Dq1[MortalityRate==mr & DispersalDistance==dd & ColonizationRate==cr,])\n if(nrow(Dq1)==0) stop(\"Subset with 0 rows\") \n } else if(cr!=0) {\n Dq1 <- with(Dq1,Dq1[ColonizationRate==cr,])\n }\n\n simbyrep <- nrow(Dq1)/pSimul$rep\n\n Dq1$rep <- rep( 1:pSimul$rep,each=simbyrep)\n\n if(!is.null(q)){\n Dq1 <-Dq1[abs(Dq1$q)<=q,]\n }\n\n mKS2 <- pairwiseGC_Dif(Dq1,\"Dq\",Dq1[,c(1:4,10)],numRep) \n \n pp <- calcPower_fromFrame(mKS2)\n pow_AD <- rbind(pow_AD,c(side,nsp,spMeta,time,Type=\"DqSAD\",pp$nPower,pp$power,pp$nTypeI,pp$typeI))\n\n \n mKS1 <- mKS1[,2:ncol(mKS1)]\n mKS1$Side <- side\n mKS1$NumSp <- nsp\n mKS1$Time <- time\n mKS1$Type <- \"DqSRS\"\n\n mKS2 <- mKS2[,2:ncol(mKS2)]\n mKS2$Side <- side\n mKS2$NumSp <- nsp\n mKS2$Time <- time\n mKS2$Type <- \"DqSAD\"\n\n mKS <- rbind(mKS1,mKS2)\n return(list(\"comp_AD\"=mKS,\"pow_AD\"=pow_AD))\n}\n\n\nplotPow_MeanSp_side <- function(pow){\n require(ggplot2)\n pow$MeanSp <- as.numeric(pow$MeanSp)\n pow$power <- as.numeric(pow$power)\n pow$typeI <- as.numeric(pow$typeI)\n\n # Add number of species in the metacommunity\n if( !(\"spMeta\" %in% names(pow)))\n pow$spMeta <- ceiling(as.numeric(pow$NumSp)*1.33)\n\n\n g <- ggplot(pow,aes(x=MeanSp,y=power)) + geom_point(shape=19,aes(size=typeI,colour=Type)) + facet_grid(Side ~ . ) +\n ylab(bquote(\"Rejection Rate of\"~H[0]~\"(\" ~alpha~\"= 0.05)\")) +\n xlab(\"Mean species number\") +\n scale_size_continuous(name=\"Type I error\") +\n scale_colour_discrete(name=\"\") \n print(g+theme_bw())\n\n}\n\nplotPow_MeanSp_difR <- function(comp,side=256)\n{\n require(ggplot2)\n # Recalculate power from comp_AD\n #\n require(plyr)\n hh <-function(x) {\n t <- nrow(x)\n s <- nrow(x[x$p.value<0.05,])\n mean_sp <- round(mean(x$MeanSp),1)\n data.frame(power=s/t,n=t,mean_sp)\n }\n\n # Calculate power in fuction of replacement rate difference\n #comp$spMeta <- ceiling(as.numeric(comp$NumSp)*1.33)\n\n c1 <- with(comp,comp[MrtR1==MrtR2 & DspD1==DspD2 & ClnR1==ClnR2 & RplR2!=RplR1 & Side==side,])\n c1$DifR <- with(c1,abs(RplR2-RplR1))\n\n c2 <- ddply(c1,.(Side,NumSp,Type,DifR),hh)\n c2$spMeta <- ceiling(as.numeric(c2$NumSp)*1.33)\n\n g <- ggplot(c2,aes(x=as.factor(DifR),y=power)) + \n# geom_point(shape=19,position = position_jitter(height = .01),aes(colour=as.factor(Type))) + \n geom_point(aes(shape=as.factor(Type),colour=factor(Type))) + \n facet_grid( spMeta ~ .) +\n ylab(bquote(\"Rejection Rate of\"~H[0]~\"(\" ~alpha~\"= 0.05)\")) +\n xlab(bquote(Delta ~\"Replacement\")) +\n scale_shape_manual(values=c(21,24,4,25,3,8),guide=guide_legend(title=\"\")) \n# scale_size_continuous(name=\"Type I error\") +\n# scale_colour_discrete(name=\"\") \n# require(RColorBrewer)\n# mc <- brewer.pal(6, \"Set1\")\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n g <- g + scale_colour_manual(values=mc,guide=guide_legend(title=\"\")) \n\n\n #print(g+ scale_x_log10(breaks=c(0.001,0.01,0.09,1))+theme_bw())\n print(g+ theme_bw())\n\n# require(pander)\n# pandoc.table(c2,style=\"grid\")\n return(c2)\n}\n\n\nplotPow_MeanSp_RplR <- function(comp,side=256)\n{\n require(ggplot2)\n # Recalculate power from comp_AD\n #\n require(plyr)\n require(dplyr)\n hh <-function(x) {\n t <- nrow(x)\n s <- nrow(x[x$p.value<0.05,])\n mean_sp <- round(mean(x$MeanSp),1)\n data.frame(power=s,n=t,mean_sp)\n }\n\n # Calculate power in fuction of replacement rate difference\n #comp$spMeta <- ceiling(as.numeric(comp$NumSp)*1.33)\n\n c1 <- filter(comp,MrtR1==MrtR2,DspD1==DspD2,ClnR1==ClnR2,RplR2!=RplR1,Side==side)\n c1$DifR <- with(c1,abs(RplR2-RplR1))\n\n c2 <- ddply(c1,.(Side,NumSp,Type,DifR,RplR1,RplR2),hh)\n c2 <- group_by(c2, Side,NumSp,Type,DifR) %>% \n summarise(n=sum(n),power=sum(power)/n,RplR1=max(RplR1),RplR2=min(RplR2)) \n \n c2$spMeta <- as.factor(ceiling(as.numeric(c2$NumSp)*1.33))\n #c2 <-with(c2,c2[spMeta==11,]) \n \n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n \n \ng <- ggplot(c2,aes(x = spMeta, y = power,group=Type,colour=Type)) + geom_point(size=3) + geom_line() +\n# g <- ggplot(c2,aes(x = Type, y = power,group=spMeta,colour=spMeta)) + geom_point(size=3) + geom_line() +\n facet_grid( RplR1~RplR2) + \n ylab(bquote(\"Rejection Rate of\"~H[0]~\"(\" ~alpha~\"= 0.05)\")) +\n xlab(\"Metacommunity species\") +\n scale_colour_manual(values=mc,labels=list(bquote(D[q]^SAD),bquote(D[q]^SRS),bquote(\"SAD\"))) \n# scale_x_discrete(labels=c(expression(D[q]^SAD),expression(D[q]^SRS),\"SAD\")) +\n# theme(axis.title.x = \"Metacommunity species\")\n\n \n \n #print(g+ scale_x_log10(breaks=c(0.001,0.01,0.09,1))+theme_bw())\n print(g+theme_bw())\n\n# require(pander)\n# pandoc.table(c2,style=\"grid\")\n return(c2)\n}\n\n\n# Plot of multiespecies spatial pattern generated with diffent SAD\n# type: U=uniform SAD L=Logseries SAD\n# nsp: no of species\n# side: side of the image\n#\nplotSAD_SpatPat<-function(nsp,side,type=\"U\")\n{\n require(ggplot2)\n #nsp<-64\n #side<-256\n if(type==\"U\") {\n fname <- paste0(\"unif\",nsp,\"_\",side,\".sed\")\n fname1 <- paste0(\"unif\",nsp,\"_\",side,\"rnz.sed\") \n } else {\n fname <- paste0(\"fisher\",nsp,\"_\",side,\".sed\")\n fname1 <- paste0(\"fisher\",nsp,\"_\",side,\"rnz.sed\")\n }\n \n spa <-read_sed2xy(fname)\n spa$Type <- \"a) Regular\"\n #spa <- rbind(spa,spa[nrow(spa),])\n\n sp1 <- read_sed2xy(fname1)\n sp1$Type <- \"b) Randomized\"\n\n spa <- rbind(spa,sp1)\n spa$SAD = ifelse(type==\"U\",\"Uniform\",\"Logseries\")\n\n if(type==\"B\") {\n fname <- paste0(\"unif\",nsp,\"_\",side,\".sed\")\n fname1 <- paste0(\"unif\",nsp,\"_\",side,\"rnz.sed\") \n\n sp1 <-read_sed2xy(fname)\n sp1$Type <- \"a) Regular\"\n #spa <- rbind(spa,spa[nrow(spa),])\n\n sp2 <- read_sed2xy(fname1)\n sp2$Type <- \"b) Randomized\"\n\n sp1 <- rbind(sp1,sp2) \n sp1$SAD <- \"Uniform\"\n\n spa <- rbind(spa,sp1)\n }\n\n g <- ggplot(spa, aes(x, y, fill = v)) + geom_raster(hjust = 0, vjust = 0) + \n theme_bw() + coord_equal() \n mc <- c(\"#b35806\",\"#e08214\",\"#fdb863\",\"#fee0b6\",\"#f7f7f7\",\"#d8daeb\",\"#b2abd2\",\"#8073ac\",\"#542788\")\n \n g <- g + scale_fill_gradientn(colours=mc,guide=\"colourbar\",name=\"Species no.\") + #guide=F\n scale_x_continuous(expand=c(.01,.01)) + \n scale_y_continuous(expand=c(.01,.01)) + \n labs(x=NULL, y=NULL) \n\n if(type==\"B\")\n {\n g <- g + facet_grid(SAD ~ Type)\n } else {\n g <- g + facet_grid(. ~ Type) \n }\n #g <- ggplot(spa, aes(x, y, fill = v)) + geom_raster(hjust = 0, vjust = 0) + theme_bw() + coord_equal() + facet_grid(. ~ Type)\n #g <- g + scale_fill_gradient(low=\"red\", high=\"green\", guide=F) +\n # labs(x=NULL, y=NULL) \n print(g)\n}\n\n\n\nplotDq_Side_Sp <- function(Dqq,side,nsp,sad=\"Uniform\"){\n require(dplyr)\n require(ggplot2)\n require(grid)\n #mylabs <- list(bquote(D[q]^SRS),bquote(Rnz -~D[q]^SRS),bquote(D[q]^SAD),bquote(Rnz -~D[q]^SAD))\n mylabs <- list(\"Regular\",\"Randomized\")\n \n if(nsp!=0) {\n if(sad==\"B\"){\n mylabs <- list(\"Regular\\nUniform\",\"Randomized\\nUniform\",\"Regular\\nLogseries\",\"Randomized\\nLogseries\" )\n Dq1<- filter(Dqq,Side==side,NumSp==nsp,SAD==\"Uniform\" | SAD==\"Logseries\")\n } else\n Dq1<- filter(Dqq,SAD==sad,Side==side,NumSp==nsp)\n\n Dq1 <- group_by(Dq1,SAD,Type,q) %>% summarize(SD.Dq=sd(Dq),Dq=mean(Dq),count=n()) %>% \n# mutate(DqType=ifelse(grepl(\"SRS\",Type),\"SRS\",\"SAD\"), SAD1=ifelse(grepl(\"Logseries\",SAD),\"a) Logseries\",\"b) Uniform\"))\n mutate(DqType=ifelse(grepl(\"SRS\",Type),\"SRS\",\"SAD\"), TypeSAD=paste(Type,SAD) )\n\n g <- ggplot(Dq1, aes(x=q, y=Dq, shape=TypeSAD)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1,colour=\"gray\") +\n geom_point() + theme_bw() + ylab(expression(D[q]))\n g <- g + scale_shape_manual(values=c(21,24,21,24,3,4,3,4),guide=guide_legend(title=NULL),\n breaks=c(\"DqSRS Uniform\",\"rnzDqSRS Uniform\",\"DqSRS Logseries\",\"rnzDqSRS Logseries\"),\n labels=mylabs) \n if(sad==\"B\")\n g <- g + facet_wrap( ~ DqType, scales=\"free\")\n else\n g <- g + facet_wrap(~ DqType, scales=\"free\")\n\n } else {\n if(sad==\"B\") {\n mylabs <- list(\"Regular\\nUniform\",\"Randomized\\nUniform\",\"Regular\\nLogseries\",\"Randomized\\nLogseries\" )\n Dq1<- filter(Dqq,Side==side,SAD==\"Uniform\" | SAD==\"Logseries\")\n } else\n Dq1<- filter(Dqq,SAD==sad,Side==side)\n\n Dq1 <- group_by(Dq1,SAD,Type,NumSp,q) %>% summarize(SD.Dq=sd(Dq),Dq=mean(Dq),count=n()) %>% \n# mutate(DqType=ifelse(grepl(\"SRS\",Type),\"SRS\",\"SAD\"))\n mutate(DqType=ifelse(grepl(\"SRS\",Type),\"DqSRS\",\"DqSAD\"), TypeSAD=paste(Type,SAD), NumSpecies=paste(\"No. Species\",NumSp))\n Dq1$NumSpecies <- factor(Dq1$NumSpecies, levels = c(\"No. Species 8\", \"No. Species 64\", \"No. Species 256\"))\n\n g <- ggplot(Dq1, aes(x=q, y=Dq, shape=TypeSAD,colour=TypeSAD)) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1,colour=\"gray\") +\n geom_point(size=1.3) + theme_bw() + ylab(expression(D[q]))\n g <- g + scale_shape_manual(values=c(21,24,21,24,3,4,3,4),guide=guide_legend(title=NULL),\n breaks=c(\"DqSRS Uniform\",\"rnzDqSRS Uniform\",\"DqSRS Logseries\",\"rnzDqSRS Logseries\"),labels=mylabs)\n\n# library(RColorBrewer)\n# mc <- brewer.pal(5, \"Set1\")\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n g <- g + scale_colour_manual(values=c(mc[1],mc[2],mc[1],mc[2],mc[3],mc[4],mc[3],mc[4]),\n guide=guide_legend(title=NULL),\n breaks=c(\"DqSRS Uniform\",\"rnzDqSRS Uniform\",\"DqSRS Logseries\",\"rnzDqSRS Logseries\"),labels=mylabs) \n \n g <- g + facet_wrap(NumSpecies~ DqType, scales=\"free\",ncol=2) + theme(legend.key.size = unit(1, \"cm\"))\n }\n}\n\nplotR2Dq_Side_Sp <- function(Dqq,side,nsp,sad=\"Uniform\")\n{\n require(ggplot2)\n require(dplyr)\n if(nsp!=0 & side!=0) {\n\n if(sad==\"B\")\n Dq1<- filter(Dqq,Side==side,NumSp==nsp,SAD==\"Uniform\" | SAD==\"Logseries\")\n else\n Dq1<- filter(Dqq,SAD==sad,Side==side,NumSp==nsp)\n \n Dq1 <- mutate(Dq1,DqType=ifelse(grepl(\"SRS\",Type),\"DqSRS\",\"DqSAD\"), Type=ifelse(grepl(\"rnz\",Type),\"b) Randomized\",\"a) Regular\"))\n\n# library(RColorBrewer)\n# mc <- brewer.pal(3, \"Set1\")\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n if(sad==\"B\"){\n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/20\n # g <- ggplot(Dq1, aes(x=R.Dq,colour=SAD)) + geom_freqpoly(binwidth = bin) + \n # theme_bw() + facet_grid(Type ~DqType) + scale_colour_grey() + \n # scale_x_continuous(breaks=c(.5,.6,.7,.8,.9,1.0))\n # xlab(expression(R^2))\n # print(g)\n g <- ggplot(Dq1, aes(x=R.Dq,fill=SAD)) + geom_histogram(binwidth = bin,position=\"dodge\",colour=\"black\") + \n theme_bw() + scale_fill_manual(values=mc) + \n facet_wrap(Type ~ DqType) + #+ facet_grid(Type ~DqType)\n scale_x_continuous(breaks=c(.5,.6,.7,.8,.9,1.0)) + \n xlab(expression(R^2))\n } else {\n \n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/10\n g <- ggplot(Dq1, aes(x=R.Dq)) + geom_histogram(binwidth = bin,colour=\"black\",fill=\"grey\") + \n theme_bw() + facet_grid(Type ~DqType) + \n xlab(expression(R^2))\n }\n\n } else {\n\n Dq1<-filter(Dqq,SAD==sad)\n bin <- range(Dq1$R.Dq)\n bin <- (bin[2]-bin[1])/10\n\n g <-ggplot(Dq1, aes(x=R.Dq,fill=Type)) + geom_histogram(binwidth = bin,position=\"dodge\") + \n theme_bw() + facet_grid(Side~NumSp,labeller=label_both) + \n xlab(expression(R^2))\n }\n}\n\n# Read sed files generated with simple spatial models and calculate Dq and return Fit of Dq \n#\nreadDq_fit <- function(side,nsp,sad=\"Uniform\") {\n require(dplyr)\n if(sad==\"Uniform\") { # SAD Uniform\n fname <- paste0(\"unif\",nsp,\"_\",side,\".sed\")\n fname1 <- paste0(\"unif\",nsp,\"_\",side,\"rnz.sed\") \n } else { # SAD logseries\n fname <- paste0(\"fisher\",nsp,\"_\",side,\".sed\")\n fname1 <- paste0(\"fisher\",nsp,\"_\",side,\"rnz.sed\")\n } \n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n zq <- readZq(paste0(\"t.\", fname),\"q.sed\")\n zq$Type <- \"DqSRS\"\n\n Dq1<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 S\",mfBin,T)\n zq1 <- readZq(paste0(\"t.\", fname1),\"q.sed\")\n zq1$Type <- \"rnzSRS\"\n zq<- rbind(zq,zq1)\n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n zq1 <- readZq(paste0(\"t.\", fname),\"q.sed\")\n zq1$Type <- \"DqSAD\"\n zq1 <- mutate(zq1,logTr=-logTr)\n zq<- rbind(zq,zq1)\n\n Dq1<- calcDq_multiSBA(fname1,\"q.sed 2 1024 20 E\",mfBin,T)\n zq1 <- readZq(paste0(\"t.\", fname1),\"q.sed\")\n zq1$Type <- \"rnzDqSAD\"\n zq1 <- mutate(zq1,logTr=-logTr)\n zq<- rbind(zq,zq1)\n return(zq)\n}\n\n\n# Read sed files generated with simple spatial models and calculate Dq and return Fit of Dq \n#\nreadNeutral_Dq_fit <- function(nsp,side,time,meta=\"L\",ReplRate=c(0,0.001,0.01,0.1,1)) {\n require(dplyr)\n zq <-data.frame()\n for(i in 1:length(ReplRate)) {\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side,\"R\", ReplRate[i])\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side,\"R\", ReplRate[i])\n }\n \n fname <- paste0(bname,\"-\",formatC(time,width=4,flag=0),\".sed\")\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n zq1 <- readZq(paste0(\"t.\", fname),\"q.sed\")\n zq1$Type <- \"DqSRS\"\n zq1$RplRt <- ReplRate[i]\n zq<- rbind(zq,zq1)\n \n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n zq1 <- readZq(paste0(\"t.\", fname),\"q.sed\")\n zq1$Type <- \"DqSAD\"\n zq1 <- mutate(zq1,logTr=-logTr)\n zq1$RplRt <- ReplRate[i]\n zq<- rbind(zq,zq1)\n }\n return(zq)\n}\n\n# Plot of multiespecies spatial pattern generated with neutral model and logseries SAD\n#\n#\nplotNeutral_SpatPat<-function(nsp,side,time,meta=\"L\",ReplRate=c(0,0.001,0.01,0.1,1))\n{\n require(ggplot2)\n \n spa <- data.frame()\n for(i in 1:length(ReplRate)) {\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side,\"R\", ReplRate[i])\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side,\"R\", ReplRate[i])\n }\n \n fname <- paste0(bname,\"-\",formatC(time,width=4,flag=0),\".sed\")\n \n sp1 <-read_sed2xy(fname)\n sp1$Type <- paste(\"Rho:\", ReplRate[i])\n sp1$Species <- paste(\"Species:\",length(unique(sp1$v)))\n # sp1$spMeta <- as.factor(ceiling(nsp*1.33))\n \n\n spa <- rbind(spa,sp1)\n }\n mc <- c(\"#b35806\",\"#e08214\",\"#fdb863\",\"#fee0b6\",\"#f7f7f7\",\"#d8daeb\",\"#b2abd2\",\"#8073ac\",\"#542788\")\n g <- ggplot(spa, aes(x, y, fill = v)) + geom_raster(hjust = 0, vjust = 0) + \n theme_bw() + coord_equal() \n \n g <- g + scale_fill_gradientn(colours=mc,name=\"Species no.\") + #guide=F\n # g <- g + scale_fill_grey(guide=F) +\n scale_x_continuous(expand=c(.01,.01)) + \n scale_y_continuous(expand=c(.01,.01)) + \n labs(x=NULL, y=NULL) \n \n g <- g + facet_wrap( ~ Type +Species,ncol=2) \n #g <- g + facet_grid(Species ~ Type)\n\n print(g)\n \n}\n\n\nsimul_NeutralSAD <- function(nsp,side,time,meta=\"L\",ReplRate=c(0,0.001,0.01,0.1,1)) {\n if(!exists(\"neuBin\")) stop(\"Variable neuBin not set (neutral binary)\")\n if(!require(untb)) stop(\"Untb package not installed\")\n\n if(toupper(meta)==\"L\") {\n prob <- genFisherSAD(nsp,side)\n sadName <- \"Neutral\"\n } else {\n prob <- rep(1/nsp,nsp) \n sadName <- \"NeuUnif\"\n }\n sad <- data.frame()\n for(i in 1:length(ReplRate)) {\n if(toupper(meta)==\"L\") {\n neuParm <- paste0(\"fishE\",nsp,\"_\",side,\"R\", ReplRate[i])\n bname <- paste0(\"neuFish\",nsp,\"_\",side,\"R\", ReplRate[i])\n } else {\n neuParm <- paste0(\"unifE\",nsp,\"_\",side,\"R\", ReplRate[i])\n bname <- paste0(\"neuUnif\",nsp,\"_\",side,\"R\", ReplRate[i])\n }\n\n genNeutralParms(neuParm,side,prob,1,0.2,0.4,0.001,ReplRate[i])\n\n # Delete old simulations\n system(paste0(\"rm \",bname,\"D*\"))\n system(paste0(\"rm \",bname,\"-*.sed\"))\n \n par <- read.table(\"sim.par\",quote=\"\",stringsAsFactors=F)\n # Change base name\n par[par$V1==\"nEvals\",]$V2 <- time\n par[par$V1==\"inter\",]$V2 <- time # interval to measure Density and Diversity\n par[par$V1==\"init\",]$V2 <- time # Firs time of measurement = interval\n par[par$V1==\"modType\",]$V2 <- 4 # Hierarchical saturated\n par[par$V1==\"sa\",]$V2 <- \"S\" # Save a snapshot of the model\n par[par$V1==\"baseName\",]$V2 <- bname \n par[par$V1==\"minBox\",]$V2 <- 2\n par[par$V1==\"pomac\",]$V2 <- 0 # 0:one set of parms \n # 1:several simulations with pomac.lin parameters \n\n parfname <- paste0(\"sim\",nsp,\"_\",side,\"_\",ReplRate[i],\".par\")\n write.table(par, parfname, sep=\"\\t\",row.names=F,col.names=F,quote=F)\n s <- system(\"uname -a\",intern=T)\n if(grepl(\"i686\",s)) {\n system(paste(neuBin,parfname,paste0(neuParm,\".inp\")))\n } else {\n system(paste(neuBin64,parfname,paste0(neuParm,\".inp\")))\n }\n\n #fname <- paste0(\"neuFish\",nsp,\"Density.txt\")\n #sad1 <- meltDensityOut_NT(fname,nsp)\n\n \n fname <- paste0(bname,\"-\",formatC(time,width=4,flag=0),\".sed\")\n spa <- read_sed(fname)\n\n sad1 <- data.frame(table(spa),Type=\"SAD\",Side=side,NumSp=nsp,SAD=sadName,RplRt=ReplRate[i])\n sad <- rbind(sad,sad1)\n }\n return(sad)\n}\n\n# Plot of Dq multiespecies spatial pattern generated with neutral model and logseries SAD\n#\n#\nplotNeutral_Dq<-function(nsp,side,time,meta=\"L\",ReplRate=c(0,0.001,0.01,0.1,1))\n{\n require(ggplot2)\n if(!is.null(ReplRate))\n { \n Dq3 <- data.frame()\n for(i in 1:length(ReplRate)) {\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side,\"R\", ReplRate[i])\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side,\"R\", ReplRate[i])\n }\n \n fname <- paste0(bname,\"-\",formatC(time,width=4,flag=0),\".sed\")\n\n Dq1<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 S\",mfBin,T)\n Dq1$DqType <- \"DqSRS\"\n Dq1$ReplacementRate <- ReplRate[i]\n Dq2<- calcDq_multiSBA(fname,\"q.sed 2 1024 20 E\",mfBin,T)\n Dq2$DqType <- \"DqSAD\"\n Dq2$ReplacementRate <- ReplRate[i]\n\n Dq3 <- rbind(Dq3,Dq1,Dq2)\n }\n } else {\n if(nsp==0){\n Dq3 <- data.frame()\n for(num in c(8,64,256))\n {\n Dq3 <- rbind(Dq3, plotNeutral_Dq_aux(num,side)) \n }\n Dq3 <- mutate(Dq3,spMeta = paste0(\"Metacommunity sp.\",spMeta))\n Dq3$spMeta <- factor(Dq3$spMeta,levels=unique(Dq3$spMeta))\n } else {\n Dq3 <- plotNeutral_Dq_aux(nsp,side) \n }\n }\n\n g <- ggplot(Dq3, aes(x=q, y=Dq, shape=factor(ReplacementRate),colour=factor(ReplacementRate))) +\n geom_errorbar(aes(ymin=Dq-SD.Dq, ymax=Dq+SD.Dq), width=.1,colour=\"gray\") +\n geom_point(size=1.3) + theme_bw() + ylab(expression(D[q]))\n\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n g <- g + scale_colour_manual(values=mc,name=bquote(\" \"~rho)) \n g <- g + scale_shape_manual(values=c(21,24,4,25,3,8),name=bquote(\" \"~rho)) \n \n if(nsp==0){\n g <- g + facet_wrap(spMeta ~ DqType, scales=\"free\",ncol=2)\n } else {\n g <- g + facet_wrap(~ DqType, scales=\"free\",ncol=2)\n }\n print(g)\n}\n\nplotNeutral_Dq_aux<-function(nsp,side,time=500,meta=\"L\")\n{\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n }\n fname <- paste0(bname,\"T\",time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n Dq1$DqType <- \"DqSRS\"\n\n fname <- paste0(bname,\"T\",time,\"mfSAD.txt\")\n Dq2 <- readNeutral_calcDq(fname)\n Dq2$DqType <- \"DqSAD\"\n \n Dq1 <- rbind(Dq1,Dq2)\n require(dplyr)\n \n Dq3 <- filter(Dq1,MortalityRate==.2,DispersalDistance==0.4,ColonizationRate==0.001) %>% \n group_by(DqType,ReplacementRate,q) %>% summarize(SD.Dq=sd(Dq),Dq=mean(Dq),count=n()) \n Dq3$spMeta <- ceiling(as.numeric(nsp)*1.33)\n return(Dq3)\n} \n\nR2Neutral_Dq<-function(side,time=500,meta=\"L\")\n{\n require(dplyr)\n hh <-function(x,c,...){\n length(x[x>c])/length(x)\n }\n\n Dq3 <- data.frame()\n for(nsp in c(8,64,256))\n {\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n }\n fname <- paste0(bname,\"T\",time,\"mfOrd.txt\")\n Dq1 <- readNeutral_calcDq(fname)\n Dq1$DqType <- \"DqSRS\"\n\n fname <- paste0(bname,\"T\",time,\"mfSAD.txt\")\n Dq2 <- readNeutral_calcDq(fname)\n Dq2$DqType <- \"DqSAD\"\n \n Dq1 <- rbind(Dq1,Dq2)\n require(dplyr)\n \n Dq1 <- filter(Dq1,MortalityRate==.2,DispersalDistance==0.4,ColonizationRate==0.001) %>% \n group_by(DqType,ReplacementRate) %>% \n summarize(Freq60=hh(R.Dq,.6),Freq90=hh(R.Dq,.9)) \n\n Dq1$spMeta <- ceiling(as.numeric(nsp)*1.33)\n\n\n fname <- paste0(bname,\"T\",time,\"Density.txt\")\n den1 <- meltDensityOut_NT(fname,unique(Dq1$spMeta))\n den1 <- filter(den1,MortalityRate==.2,DispersalDistance==0.4,ColonizationRate==0.001) %>% \n group_by(rep,ReplacementRate) %>% summarize(nsp=n()) %>%\n group_by(ReplacementRate) %>% summarize(meanSp=mean(nsp))\n Dq1 <- left_join(Dq1,den1) \n\n Dq3 <- rbind(Dq3,Dq1)\n \n }\n Dq3$Side <- side\n return(Dq3)\n} \n\n\n\nplotNeutral_SAD<-function(nsp,side,time=500,meta=\"L\")\n{\n require(ggplot2)\n require(plyr)\n require(dplyr)\n require(mgcv)\n \n den<- data.frame()\n if(nsp!=0){\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n }\n fname <- paste0(bname,\"T\",time,\"Density.txt\")\n spMeta <- ceiling(as.numeric(nsp)*1.33)\n\n den1 <- meltDensityOut_NT(fname,spMeta)\n\n den <- filter(den1,MortalityRate==.2,DispersalDistance==0.4,ColonizationRate==0.001) \n den <- calcRankSAD_by(den,\"value\",1:5)\n \n \n den <- group_by(den,ReplacementRate,Rank) %>% summarize(Freq=mean(value),count=n()) \n \n\n } else {\n for(nsp in c(8,64,256))\n {\n den <-rbind(den,plotNeutral_SAD_aux(nsp,side))\n }\n den <- mutate(den, metaLbl =paste0(\"Metacommunity sp.\",spMeta))\n ml <- unique(den$metaLbl)\n den$metaLbl <- factor(den$metaLbl,levels=c(ml[1],ml[2], ml[3]))\n g <- ggplot(den,aes(x=Rank,y=log(Freq),shape=factor(ReplacementRate),colour=factor(ReplacementRate))) + theme_bw() + geom_point(size=1)\n# library(RColorBrewer)\n# mc <- brewer.pal(6, \"Set1\")\n mc <- c(\"#000000\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\n g <- g + scale_colour_manual(values=mc,name=bquote(\" \"~rho)) \n\n g <- g + scale_shape_manual(values=c(21,24,4,25,3,8),name=bquote(\" \"~rho)) + \n# geom_smooth(se=F,span = 0.70) \n geom_line()\n g <- g + facet_wrap(~ metaLbl, scales=\"free\",ncol=2)\n \n }\n \n print(g)\n return(den)\n}\n\nplotNeutral_SAD_aux<-function(nsp,side,time=500,meta=\"L\")\n{\n if(toupper(meta)==\"L\") {\n bname <- paste0(\"neuFish\",nsp,\"_\",side)\n } else {\n bname <- paste0(\"neuUnif\",nsp,\"_\",side)\n }\n fname <- paste0(bname,\"T\",time,\"Density.txt\")\n spMeta <- ceiling(as.numeric(nsp)*1.33)\n\n den1 <- meltDensityOut_NT(fname,spMeta)\n\n den3 <- filter(den1,MortalityRate==.2,DispersalDistance==0.4,ColonizationRate==0.001) %>% \n rename(Freq=value) # ,rep==sample(unique(rep),1)\n #den3 <- calcRankSAD_by(den3,\"Freq\",1:5)\n \n \n den3 <- group_by(den3,ReplacementRate,Species) %>% summarize(Freq=mean(Freq),count=n()) \n den3 <- calcRankSAD_by(den3,\"Freq\",1:1)\n \n den3$spMeta <- spMeta\n return(den3) \n}\n", "meta": {"hexsha": "0ffc66ed9e5e6e54736a08fed77c8f8820be5216", "size": 79580, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Neutral_fun.r", "max_stars_repo_name": "lsaravia/SpeciesRankSurface", "max_stars_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Neutral_fun.r", "max_issues_repo_name": "lsaravia/SpeciesRankSurface", "max_issues_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-05-09T12:48:54.000Z", "max_issues_repo_issues_event_max_datetime": "2015-05-09T12:48:54.000Z", "max_forks_repo_path": "R/Neutral_fun.r", "max_forks_repo_name": "lsaravia/SpeciesRankSurface", "max_forks_repo_head_hexsha": "33e71b7b50e1e86d8a420812efd1b96ffb22e2dc", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-05-08T01:29:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-26T13:41:06.000Z", "avg_line_length": 30.2931100114, "max_line_length": 172, "alphanum_fraction": 0.6150665996, "num_tokens": 29620, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5851011542032312, "lm_q1q2_score": 0.3062538502961012}} {"text": "#Making a spatial forecast based on the prior data to NEON sites at core, plot and site levels.\n#downstream this will log transform map, which prevents this from generalizing beyond the dirichlet example.\n#This script depends on the following packages: DirichletReg.\n#clear environment, source paths, packages and functions.\nrm(list=ls())\nsource('paths.r')\nsource('NEFI_functions/precision_matrix_match.r')\nsource('NEFI_functions/ddirch_forecast.r')\n\n#set output path.----\noutput.path <- NEON_site_fcast_fg.path\n\n#load model results.----\n#mod 1 is data from maps.\n#mod 2 is site-specific data, no maps.\n#mod 3 is all covariates.\nmod <- readRDS(ted_ITS.prior_fg_JAGSfit)\nmod <- mod[[3]] #just the all predictor case.\n\n#get core-level covariate means and sd.----\ndat <- readRDS(hierarch_filled.path)\ncore_mu <- dat$core.core.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#merge together.\nplot_mu$siteID <- NULL\ncore.preds <- merge(core_mu , plot_mu)\ncore.preds <- merge(core.preds, site_mu)\ncore.preds$relEM <- NULL\nnames(core.preds)[names(core.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get core-level SD.\ncore_sd <- dat$core.core.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nplot_sd$siteID <- NULL\ncore.sd <- merge(core_sd , plot_sd)\ncore.sd <- merge(core.sd, site_sd)\ncore.sd$relEM <- NULL\nnames(core.sd)[names(core.sd)==\"b.relEM\"] <- \"relEM\"\n\n\n#get plot-level covariate means and sd.----\ncore_mu <- dat$core.plot.mu\nplot_mu <- dat$plot.plot.mu\nsite_mu <- dat$site.site.mu\n#merge together, .\nplot_mu$siteID <- NULL\nplot.preds <- merge(core_mu,plot_mu)\nplot.preds <- merge(plot.preds,site_mu)\nplot.preds$relEM <- NULL\nnames(plot.preds)[names(plot.preds)==\"b.relEM\"] <- \"relEM\"\n\n#get plot-level SD.\ncore_sd <- dat$core.plot.sd\nplot_sd <- dat$plot.plot.sd\nsite_sd <- dat$site.site.sd\n#merge together\nplot_sd$siteID <- NULL\nplot.sd <- merge(core_sd,plot_sd)\nplot.sd <- merge(plot.sd,site_sd)\nplot.sd$relEM <- NULL\nnames(plot.sd)[names(plot.sd)=='b.relEM'] <- \"relEM\"\n\n#get site-level covariate means and sd.----\ncore_mu <- dat$core.site.mu\nplot_mu <- dat$plot.site.mu\nsite_mu <- dat$site.site.mu\n#merge together\nsite.preds <- merge(core_mu, plot_mu)\nsite.preds <- merge(site.preds,site_mu)\nnames(site.preds)[names(site.preds)=='b.relEM'] <- \"relEM\"\n\n#get site-level SD.\ncore_sd <- dat$core.site.sd\nplot_sd <- dat$plot.site.sd\nsite_sd <- dat$site.site.sd\n#merge together.\nsite.sd <- merge(core_sd,plot_sd)\nsite.sd <- merge(site.sd,site_sd)\nnames(site.sd)[names(site.sd)=='b.relEM'] <- \"relEM\"\n\n#Get forecasts from ddirch_forecast.----\ncore.fit <- ddirch_forecast(mod=mod, cov_mu=core.preds, cov_sd=core.sd, names=core.preds$sampleID)\nplot.fit <- ddirch_forecast(mod=mod, cov_mu=plot.preds, cov_sd=plot.sd, names=plot.preds$plotID)\nsite.fit <- ddirch_forecast(mod=mod, cov_mu=site.preds, cov_sd=site.sd, names=site.preds$siteID)\n\n#store output as a list and save.----\noutput <- list(core.fit,plot.fit,site.fit,core.preds,plot.preds,site.preds,core.sd,plot.sd,site.sd)\nnames(output) <- c('core.fit','plot.fit','site.fit',\n 'core.preds','plot.preds','site.preds',\n 'core.sd','plot.sd','site.sd')\nsaveRDS(output, output.path)\n\n#validate against observed data by plotting.----\npar(mfrow = c(3,1))\ntrans <- 0.3\nlimy <- c(0,1)\ni = 2 #ECM fungi\n\n#core.level.----\n#organize data.\nfcast <- core.fit\ntruth <- readRDS(NEON_taxa_fg.path)\ntruth <- truth$rel.abundances\nrownames(truth) <- gsub('-GEN','',truth$geneticSampleID)\ntruth <- truth[rownames(truth) %in% rownames(core.fit$mean),]\nfor(k in 1:length(fcast)){\n fcast[[k]] <- fcast[[k]][rownames(fcast[[k]]) %in% rownames(truth),]\n}\ntruth <- as.matrix(truth)\nmu <- fcast$mean[,i][order(fcast$mean[,i])]\nci_0.975 <- fcast$ci_0.975[,i][order(match(names(fcast$ci_0.975[,i]),names(mu)))]\nci_0.025 <- fcast$ci_0.025[,i][order(match(names(fcast$ci_0.025[,i]),names(mu)))]\npi_0.975 <- fcast$pi_0.975[,i][order(match(names(fcast$pi_0.975[,i]),names(mu)))]\npi_0.025 <- fcast$pi_0.025[,i][order(match(names(fcast$pi_0.025[,i]),names(mu)))]\nfungi_name <- colnames(fcast$mean)[i]\nobs.mu <- truth[,i][order(match(names(truth[,c(fungi_name)]),names(mu)))]\n#obs.lo95 <- fg$lo95[,i][order(match(names(fg$lo95[,i]),names(mu)))]\n#obs.hi95 <- fg$hi95[,i][order(match(names(fg$hi95[,i]),names(mu)))]\n#plot\nplot(obs.mu ~ mu, cex = 0.7, ylim=limy, main = 'core-level')\nrsq <- round(summary(lm(obs.mu ~mu))$r.squared,2)\nmtext(paste0('R2=',rsq), side = 3, line = -2.7, adj = 0.03)\n#add confidence interval.\nrange <- mu\npolygon(c(range, rev(range)),c(pi_0.975, rev(pi_0.025)), col=adjustcolor('green', trans), lty=0)\npolygon(c(range, rev(range)),c(ci_0.975, rev(ci_0.025)), col=adjustcolor('blue' , trans), lty=0)\n#fraction within 95% predictive interval.\nin_it <- round(sum(obs.mu < pi_0.975 & obs.mu > pi_0.025) / length(obs.mu),2) * 100\nstate <- paste0(in_it,'% of observations within 95% prediction interval.')\nmtext(state,side = 3, line = -1.3, adj = 0.05)\nabline(0,1,lwd=2)\n\n\n#plot.level----\n#organize data.\nfcast <- plot.fit\ntruth <- readRDS(NEON_plot.level_fg_obs.path)\nfor(k in 1:length(truth)){\n rownames(truth[[k]]) <- gsub('.','_',rownames(truth[[k]]), fixed = T)\n truth[[k]] <- truth[[k]][rownames(truth[[k]]) %in% rownames(fcast$mean),]\n}\nfor(k in 1:length(fcast)){\n fcast[[k]] <- fcast[[k]][rownames(fcast[[k]]) %in% rownames(truth$mean),]\n}\nmu <- fcast$mean[,i][order(fcast$mean[,i])]\nci_0.975 <- fcast$ci_0.975[,i][order(match(names(fcast$ci_0.975[,i]),names(mu)))]\nci_0.025 <- fcast$ci_0.025[,i][order(match(names(fcast$ci_0.025[,i]),names(mu)))]\npi_0.975 <- fcast$pi_0.975[,i][order(match(names(fcast$pi_0.975[,i]),names(mu)))]\npi_0.025 <- fcast$pi_0.025[,i][order(match(names(fcast$pi_0.025[,i]),names(mu)))]\nfungi_name <- colnames(fcast$mean)[i]\nobs.mu <- truth$mean[,fungi_name][order(match(names(truth$mean[,fungi_name]),names(mu)))]\nobs.lo95 <- truth$lo95[,fungi_name][order(match(names(truth$lo95[,fungi_name]),names(mu)))]\nobs.hi95 <- truth$hi95[,fungi_name][order(match(names(truth$hi95[,fungi_name]),names(mu)))]\n#plot\nplot(obs.mu ~ mu, cex = 0.7, ylim=limy, main = 'plot-level')\narrows(c(mu), obs.lo95, c(mu), obs.hi95, length=0.05, angle=90, code=3)\nrsq <- round(summary(lm(obs.mu ~mu))$r.squared,2)\nmtext(paste0('R2=',rsq), side = 3, line = -2.7, adj = 0.03)\n#1:1 line\nabline(0,1, lwd = 2)\n#add confidence interval.\nrange <- mu\npolygon(c(range, rev(range)),c(pi_0.975, rev(pi_0.025)), col=adjustcolor('green', trans), lty=0)\npolygon(c(range, rev(range)),c(ci_0.975, rev(ci_0.025)), col=adjustcolor('blue' , trans), lty=0)\n#fraction within 95% predictive interval.\nin_it <- round(sum(obs.mu < pi_0.975 & obs.mu > pi_0.025) / length(obs.mu),2) *100\nstate <- paste0(in_it,'% of observations within 95% prediction interval.')\nmtext(state,side = 3, line = -1.3, adj = 0.05)\n\n\n#site.level----\n#organize data.\nfcast <- site.fit\ntruth <- readRDS(NEON_site.level_fg_obs.path)\nfor(k in 1:length(truth)){\n rownames(truth[[k]]) <- gsub('.','_',rownames(truth[[k]]), fixed = T)\n truth[[k]] <- truth[[k]][rownames(truth[[k]]) %in% rownames(fcast$mean),]\n}\nfor(k in 1:length(fcast)){\n fcast[[k]] <- fcast[[k]][rownames(fcast[[k]]) %in% rownames(truth$mean),]\n}\nmu <- fcast$mean[,i][order(fcast$mean[,i])]\nci_0.975 <- fcast$ci_0.975[,i][order(match(names(fcast$ci_0.975[,i]),names(mu)))]\nci_0.025 <- fcast$ci_0.025[,i][order(match(names(fcast$ci_0.025[,i]),names(mu)))]\npi_0.975 <- fcast$pi_0.975[,i][order(match(names(fcast$pi_0.975[,i]),names(mu)))]\npi_0.025 <- fcast$pi_0.025[,i][order(match(names(fcast$pi_0.025[,i]),names(mu)))]\nfungi_name <- colnames(fcast$mean)[i]\nobs.mu <- truth$mean[,fungi_name][order(match(names(truth$mean[,fungi_name]),names(mu)))]\nobs.lo95 <- truth$lo95[,fungi_name][order(match(names(truth$lo95[,fungi_name]),names(mu)))]\nobs.hi95 <- truth$hi95[,fungi_name][order(match(names(truth$hi95[,fungi_name]),names(mu)))]\n#plot\nplot(obs.mu ~ mu, cex = 0.7, ylim=limy, main='site-level')\narrows(c(mu), obs.lo95, c(mu), obs.hi95, length=0.05, angle=90, code=3)\nrsq <- round(summary(lm(obs.mu ~mu))$r.squared,2)\nmtext(paste0('R2=',rsq), side = 3, line = -2.7, adj = 0.03)\n#1:1 line\nabline(0,1, lwd = 2)\n#add confidence interval.\nrange <- mu\npolygon(c(range, rev(range)),c(pi_0.975, rev(pi_0.025)), col=adjustcolor('green', trans), lty=0)\npolygon(c(range, rev(range)),c(ci_0.975, rev(ci_0.025)), col=adjustcolor('blue' , trans), lty=0)\n#fraction within 95% predictive interval.\nin_it <- round(sum(obs.mu < pi_0.975 & obs.mu > pi_0.025) / length(obs.mu),2) *100\nstate <- paste0(in_it,'% of observations within 95% prediction interval.')\nmtext(state,side = 3, line = -1.3, adj = 0.05)\n\n", "meta": {"hexsha": "06cdce990f345c7ed3118b50934351b0ae5ad017", "size": 8617, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/spatial_forecast_analysis/fastq_forecast/1._spatial_forecast_fg_cps.level.r", "max_stars_repo_name": "bhackos/NEFI_microbe", "max_stars_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ITS/analysis/spatial_forecast_analysis/fastq_forecast/1._spatial_forecast_fg_cps.level.r", "max_issues_repo_name": "bhackos/NEFI_microbe", "max_issues_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2017-10-23T16:09:33.000Z", "max_issues_repo_issues_event_max_datetime": "2019-08-22T16:01:10.000Z", "max_forks_repo_path": "ITS/analysis/spatial_forecast_analysis/fastq_forecast/1._spatial_forecast_fg_cps.level.r", "max_forks_repo_name": "bhackos/NEFI_microbe", "max_forks_repo_head_hexsha": "e08694f4fe8d4b524df5c3854d19da49787716ce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2017-10-09T18:43:01.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-06T19:17:07.000Z", "avg_line_length": 41.0333333333, "max_line_length": 108, "alphanum_fraction": 0.6827202042, "num_tokens": 2962, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30625384272247463}} {"text": "library(ggplot2)\n\nargs <- commandArgs(TRUE)\nif(length(args) == 0){\n inputFile<-'/scratch/cqs/shengq2/vickers/20190504_smallRNA_as_chipseq_GCF_000005845.2_ASM584v2/plotPeak/result/Control.position.txt'\n outputPrefix<-'/scratch/cqs/shengq2/vickers/20190504_smallRNA_as_chipseq_GCF_000005845.2_ASM584v2/plotPeak/result/Control.position'\n}else{\n inputFile<-args[1]\n outputPrefix<-args[2]\n}\n\nrawTable<-read.delim(inputFile,header=T,as.is=T,stringsAsFactor=F)\n\nfor (column in c(\"Percentage\", \"PositionCount\")){\n outputFile = paste0(outputPrefix, \".\", column, \".pdf\")\n pdf(outputFile, height=10, width=10, onefile=TRUE)\n for (selectedFeature in unique(rawTable$Feature)) {\n dataForPlot<-rawTable[which(rawTable$Feature==selectedFeature),]\n \n g <- ggplot(dataForPlot, aes(Position, File)) + \n geom_tile(aes_string(fill = column)) + \n scale_fill_gradientn(colours=rev(heat.colors(100))) + \n ggtitle(selectedFeature)\n print(g)\n }\n dev.off()\n}\n\n", "meta": {"hexsha": "491ed771173a5e0aef05b50e0d0b57d4dba19b78", "size": 977, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/Visualization/plotPeak.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/Visualization/plotPeak.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/Visualization/plotPeak.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 33.6896551724, "max_line_length": 134, "alphanum_fraction": 0.7308085977, "num_tokens": 284, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30620522088205465}} {"text": "basedir <- \"//home/hoski/Tac2018/HCR/Plaice\"\nbasedir1 <- \"/home/hoski/Tac2018/HCR/\"\nsource(paste(basedir1,\"Model/Read.r\",sep=\"/\"))\ninputprogfile <- paste(basedir,\"Files/plaiceprognosis.dat.biorule\",sep=\"/\")\nprogoutfile <- \"plaiceprognosis.dat\"\nsavefile <- \"HCRrun.rdata\"\nsumfile <- \"HCRrunsum.rdata\"\ninputfile <- \"iceplaice.dat.prog\"\nmodel <- \"muppet\"\n # Runs set up to avoid rbinds.\nparameters <- expand.grid(list(HarvestRate=c(0.1,0.15,0.2),Btrigger=c(1,20,30),RecrCorr=c(0.35),Meanwtyears=c(10),WeightCV=0.08,AssessmentCV=0.2,AssessmentCorr=0.7)) \n cn <- names(parameters)\n i <- 1\n tmpresult <- OneRun(HarvestRate=parameters$HarvestRate[i],AssessmentCorr=parameters$AssessmentCorr[i],AssessmentCV=parameters$AssessmentCV[i],Btrigger=parameters$Btrigger[i],RecrCorr=parameters$RecrCorr[i],Meanwtyears=parameters$Meanwtyears[i],WeightCV=parameters$WeightCV[i],inputprogfile=inputprogfile,inputfile=inputfile,progoutfile=progoutfile,path=\"\",model=model)\n sumdata <- tmpresult$summary\n result <- tmpresult$data\n for(j in 1:length(cn))sumdata[,cn[j]] <- parameters[i,cn[j]]\n for(j in 1:length(cn))result[,cn[j]] <- parameters[i,cn[j]]\n n <- nrow(result) \n \n alldata <- data.frame(matrix(0,nrow=nrow(result)*nrow(parameters),ncol=ncol(result)))\n names(alldata) <- names(result)\n alldata[1:n,] <- result\n index <- n+1\n for(i in 2:nrow(parameters)) {\n print(i)\n tmpresult <- OneRun(HarvestRate=parameters$HarvestRate[i],AssessmentCorr=parameters$AssessmentCorr[i],AssessmentCV=parameters$AssessmentCV[i],Btrigger=parameters$Btrigger[i],RecrCorr=parameters$RecrCorr[i],Meanwtyears=parameters$Meanwtyears[i],WeightCV=parameters$WeightCV[i],inputprogfile=inputprogfile,inputfile=inputfile,progoutfile=progoutfile,path=\"\",model=model)\n tmpsumdata <- tmpresult$summary\n tmpresult <- tmpresult$data\n for(j in 1:length(cn))tmpsumdata[,cn[j]] <- parameters[i,cn[j]]\n for(j in 1:length(cn))tmpresult[,cn[j]] <- parameters[i,cn[j]]\n sumdata <- rbind(sumdata,tmpsumdata)\n alldata[index:(index+nrow(tmpresult)-1),] <- tmpresult\n index <- index+nrow(tmpresult)\n# alldata[(n*(i-1)+1):(n*i),] <- tmpresult\n }\n alldata <- alldata[1:(index-1),] \n HCRsettings <- readLines(progoutfile)\n \n save(list=c(\"HCRsettings\",\"alldata\",\"sumdata\"),file=savefile)\n save(list=c(\"HCRsettings\",\"sumdata\"),file=sumfile)\n \n\n", "meta": {"hexsha": "bc71ef4da90e4859c31f25b07cf70c8a02f2b5e1", "size": 2375, "ext": "r", "lang": "R", "max_stars_repo_path": "Files/ReadPlaice.r", "max_stars_repo_name": "einarhjorleifsson/23_mse_2019", "max_stars_repo_head_hexsha": "f1638bc9eed32c05f67134ec61b2c3523cd2d164", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Files/ReadPlaice.r", "max_issues_repo_name": "einarhjorleifsson/23_mse_2019", "max_issues_repo_head_hexsha": "f1638bc9eed32c05f67134ec61b2c3523cd2d164", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Files/ReadPlaice.r", "max_forks_repo_name": "einarhjorleifsson/23_mse_2019", "max_forks_repo_head_hexsha": "f1638bc9eed32c05f67134ec61b2c3523cd2d164", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.9772727273, "max_line_length": 372, "alphanum_fraction": 0.7153684211, "num_tokens": 761, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30620522088205465}} {"text": "########################################\ntest_that('we can extract 100 seeds for a given speed multiconstraint #23', {\n\tlibrary(data.table)\n n_seeds <- 100\n #speed is fixed across this entire run below; is dependent on the rda used\n st_res <- st_with_vel(my_H_matrix, 0.126767676, har_n=1e5)\n H_multiconstraint <- attr(st_res, \"constraints_and_tasks\")$nonredundant_constr\n activation_per_seed <- extract_n_seeds_from_rds_ste(st_res, 10)\n multiconstraint_per_seed <- lapply(seq(1,ncol(activation_per_seed)), function(task_0_seed_activation){\n \t# trim top which has task0 wrench requirements\n \tseed_a <- activation_per_seed[,task_0_seed_activation]\n \tseed_id <- colnames(activation_per_seed)[task_0_seed_activation]\n \tres <- merge_constraints(trim_top_of_constraint(H_multiconstraint,22),assemble_equality_with_seed_point(seed_id, seed_a))\n \tattr(res, \"seed_activation\") <- seed_a\n \tattr(res, \"seed_id\") <- seed_id\n \treturn(res)\n \t})\n\n result_filepaths <- pbmclapply(multiconstraint_per_seed, seed_sample_and_save, har_samples_per_seed = 1e4, mc.cores=detectCores(all.tests = FALSE, logical = TRUE))\n\n\tseeded_points <- ex%>% har_sample(1000)\n\tparcoords(seeded_points, reorderable = TRUE, brushMode = \"1D-axes-multi\", autoresize=TRUE, width=1900, height=500, alpha=0.1)\n })\n\n\ntest_that(\"pca projections for each task-poltope projected\", {\n\tseed_vs_noseed_trajectories <- readRDS(\"/Volumes/GoogleDrive/My\\ Drive/outputs/seed_vs_noseed_trajectories_at_speedlimit_0.126767676767677.rds\")\n\tgenerate_pca_projection_plots(seed_vs_noseed_trajectories, \"speedlimit_pt1267_new\")\n\t})\n\ntest_that('we can combine the unseeded and seeded trajectories', {\n trajectories <- data.table(readRDS(\"/Volumes/GoogleDrive/My\\ Drive/outputs/ste_1e5_speed_13_timefin_09:04:05.556.rds\"))\n velocity_limit_fixed <- trajectories$velocity_limit[1]\n\ttrajectories_per_seed <- lapply(dir(\"/Volumes/GoogleDrive/My\\ Drive/outputs/seed_evals/\", full.names=TRUE), readRDS)\n\tseed_vs_noseed_trajectories <- combine_unseeded_and_seeded_data_into_id_tall_df(trajectories_unseeded=trajectories, trajectories_per_seed=trajectories_per_seed)\n\tsaveRDS(seed_vs_noseed_trajectories, sprintf(\"/Volumes/GoogleDrive/My\\ Drive/outputs/seed_vs_noseed_trajectories_at_speedlimit_%s.rds\",velocity_limit_fixed))\n})\n\ntest_that(\"seed vs noseed from scratch\", {\n\tlibrary(data.table)\n speeds <- sample(seq(0.05,1,length.out=100))\n pblapply(speeds,seed_vs_noseed_diff_speeds,10,1e5,1e4, seed_constraint_type=\"start\")\n\t})\n\ntest_that(\"seed vs noseed from scratch\", {\n\tlibrary(data.table)\n speeds <- sample(seq(0.05,1,length.out=100))\n pblapply(speeds, seed_vs_noseed_diff_speeds, 10, 1e5, 1e4, seed_constraint_type=\"start_and_end\")\n\t})\n\ntest_that('effect of a seed on downstream polytope projections onto each muscle', {\n\tseed_vs_noseed_trajectories <- readRDS(\"/Volumes/GoogleDrive/My\\ Drive/outputs/seed_vs_noseed_trajectories_at_speedlimit_0.126767676767677.rds\")\n velocity_limit_fixed <- attr(seed_vs_noseed_trajectories,\"velocity_limit\")\n p <- gen_freqpoly_seed_vs_unseeded(seed_vs_noseed_trajectories, 1:3)\n p <- p + ggtitle(\"Velocity lim: %s\"%--%velocity_limit_fixed)\n \n\tggsave(\"seed_vs_noseed_trajectories\"%>%time_dot(\"pdf\"),p, width=20,height=20)\n\thtmlwidgets::saveWidget(ggplotly(p), \"index.html\")\n\n\t# just show 3\n\tp <- ggplot(seed_vs_noseed_trajectories,aes(activation))\n\tseed_id_interesting <- unique(seed_vs_noseed_trajectories$seed_id)[1:3]\n\tnoseed_selection <- seed_vs_noseed_trajectories[seed_id%in%seed_id_interesting & seed_id!=\"Not Seeded\"]\n\tp <- p + geom_freqpoly(aes(y=..ncount.., col=seed_id), alpha=0.5, bins=30, position=\"identity\", data = noseed_selection)\n\tp <- p + geom_freqpoly(aes(y=..ncount..), alpha=0.5, bins=30, col=\"black\", position=\"identity\", data = seed_vs_noseed_trajectories[seed_id==\"Not Seeded\"]) \n\tp <- p + facet_grid(task_index~factor(muscle, levels=muscle_name_per_index)) + coord_fixed()\n\tp <- p + theme_classic() + ylab(\"Within-bin Volume wrt to mode\") + xlab(\"Muscle activation (0 to 1 is 0 to 100%)\")\n\tggsave(\"unseeded_and_seed\"%>%time_dot(\"pdf\"), p, width=8,height=10)\n})\n", "meta": {"hexsha": "7df6abe7b83427c4b170c1965a8c2ae70208b55d", "size": 4135, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_seedpoints.r", "max_stars_repo_name": "bc/stfeasibility", "max_stars_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-06-24T00:29:11.000Z", "max_stars_repo_stars_event_max_datetime": "2018-06-24T00:29:11.000Z", "max_issues_repo_path": "tests/testthat/test_seedpoints.r", "max_issues_repo_name": "bc/stfeasibility", "max_issues_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 32, "max_issues_repo_issues_event_min_datetime": "2018-06-20T19:10:32.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-18T23:07:18.000Z", "max_forks_repo_path": "tests/testthat/test_seedpoints.r", "max_forks_repo_name": "bc/stfeasibility", "max_forks_repo_head_hexsha": "9fcb8cd63c1522c5787de8891b26ce16fb870240", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 59.0714285714, "max_line_length": 167, "alphanum_fraction": 0.7644498186, "num_tokens": 1193, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30620521372141574}} {"text": "library(Cairo)\n\nparams = read.table('data/cycleParams.csv',sep=',',header=T)\nfiltered = read.table('data/filteredSingleCellData.csv',sep=',',header=T)\nallData = read.table('data/features.csv',sep=',',header=T)\nthumbRoot = 'W:/Publications/2014_05_Wnt_TGFB_BMP_insulation/FIGURES/InputOutput/figure_data/140130_te20_plate_2013012037/thumbs/'\noutRoot = 'data/nuclei/'\nattach(params)\n\nnumCells = 25\n\n# Collect IDs for random cells in each of the various bins\n\ncells.G1 = allData$cellID[abs(mean.1 - log2(allData$totalIntensity.1)) < 2*sd.1]\ncells.G2 = allData$cellID[abs(mean.2 - log2(allData$totalIntensity.1)) < 2*sd.1]\ncells.S = log2(allData$totalIntensity.1) < (mean.2-3*sd.1) &\n log2(allData$totalIntensity.1) > (mean.1+3*sd.1)\ncells.S = allData$cellID[cells.S]\n\n\n\n\nfor( type in c('G1','G2','S')){\n cells = sample(eval(parse(text=paste('cells',type,sep='.'))))\n count = 0\n while(T){\n inName = paste(thumbRoot,cells[count+1],'-1.png',sep='')\n if (file.exists( inName )){\n file.copy(from=inName,\n to=paste(outRoot,type,'-',count+1,'-1.png',sep='') )\n count = count + 1\n }\n if( count == numCells ){\n break\n }\n }\n}\n\n", "meta": {"hexsha": "9ab2bb7a74f96180f0d3576098f9ef3fb09664b1", "size": 1180, "ext": "r", "lang": "R", "max_stars_repo_path": "FIGS/imaging/qualityControl.r", "max_stars_repo_name": "adam-coster/dissertation", "max_stars_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FIGS/imaging/qualityControl.r", "max_issues_repo_name": "adam-coster/dissertation", "max_issues_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FIGS/imaging/qualityControl.r", "max_forks_repo_name": "adam-coster/dissertation", "max_forks_repo_head_hexsha": "4ddf35426f6ce2d83d7193dd94bd36f21b68c27d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2564102564, "max_line_length": 130, "alphanum_fraction": 0.6559322034, "num_tokens": 385, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3062035138101244}} {"text": "setwd(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion\") \n\n###############################################################################\n\n# Cargar librerias que se van a utilizar\n\nlibrary(\"leaflet\") #Paquete para usar cosas de mapas\nlibrary(\"rgdal\") #Paquete para leer archivos shapefiles .shp\nlibrary(\"dplyr\") #Paquete para filtrar datos de dataframes\nlibrary(\"fontawesome\") #Paquete para \u00edconos de marcadores\nlibrary(\"htmlwidgets\") #Paquete para salvar mapa en html\nlibrary(\"leaflet.extras\") #Paquete para poder buscar en los mapas\nlibrary(\"sf\")\nlibrary(\"raster\")\nlibrary(\"geojsonsf\")\nlibrary('geojsonio')\nlibrary(\"spdplyr\")\nlibrary(\"tidyverse\")\nlibrary(\"DBI\")\nlibrary('RPostgres')\nsetwd(paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion_Keys/\")) \n###############################################################################\n# SQl database\n\ndb_key <- readLines('./database_key.txt')\ncon <- DBI::dbConnect(Postgres(), \n dbname = \"ZMVM_Urbanizacion\",\n host = \"localhost\", \n port = 5432,\n user = \"postgres\", \n password = db_key)\n\n###############################################################################\n# Initiate map, get data, create empty map and groups list\ngrupo_d=\"L\u00edmite distrito\"\ngrupos<-c()\n\nubicacion_dist <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion_Datos/DistritosEODHogaresZMVM2017/DistritosEODHogaresZMVM2017.shp\") \ndist_todos <- readOGR(ubicacion_dist, layer = paste0(\"DistritosEODHogaresZMVM2017\"), verbose = FALSE, GDAL1_integer64_policy=TRUE) \ndist_todos <- spTransform(dist_todos,CRS(\"+proj=longlat +ellps=WGS84 +no_defs\"))\n\n###############################################################################\n#CDMX\n\ndistritos_cdmx<-c(1:85)\n\nfor(i in 1:length(distritos_cdmx)){\n if(str_length(as.character(distritos_cdmx[i]))==1){\n distritos_cdmx[i]<-paste0(\"00\",distritos_cdmx[i])\n }\n if(str_length(as.character(distritos_cdmx[i]))==2){\n distritos_cdmx[i]<-paste0(\"0\",distritos_cdmx[i])\n }\n}\n\ndist_todos_cdmx <- subset(dist_todos, dist_todos$Distrito %in% distritos_cdmx)\n\npb <- txtProgressBar(min = 0, max = length(distritos_cdmx), style = 3)\ni<-0\nfor(distrito_str in distritos_cdmx){\n i<-i+1\n if (sample(1:1, 1)==sample(1:1, 1) || i==length(distritos_cdmx)){\n setTxtProgressBar(pb, i)\n }\n \n distrito_int<-as.numeric(distrito_str)\n query= paste0(\"select * from fid where distrito = \",distrito_str)\n datos<-dbGetQuery(con, query)\n query_residentes=paste0('select SUM(\"FACTOR\") as \"',distrito_int,'_residentes\" from \"viajes\" where \"P5_3\"=1 and \"P5_6\"=1 and \"P5_13\"=2 and \"DTO_ORIGEN\"=',distrito_int)\n dist_todos_cdmx$dist_residentes[i]<-unlist(dbGetQuery(con, query_residentes))\n query_trabajadores=paste0('select SUM(\"FACTOR\") as \"',distrito_int,'_residentes\" from \"viajes\" where \"P5_3\"=1 and \"P5_6\"=1 and \"P5_13\"=2 and \"DTO_DEST\"=',distrito_int)\n dist_todos_cdmx$dist_trabajadores[i]<-unlist(dbGetQuery(con, query_trabajadores))\n ###############################################################################\n #Distritos\n\n dist <- subset(dist_todos_cdmx, dist_todos_cdmx$Distrito %in% distrito_str)\n dist_todos_cdmx$Estado<-\"CDMX\"\n dist_todos_cdmx$area_dist[i] <-area(dist)/1000**2\n dist_todos_cdmx$radius_dist[i] <- sqrt((dist_todos_cdmx$area_dist[i]*1000**2)/pi)\n point_dist <- dist %>% geojson_json() %>% geojson_sf() %>% st_centroid()\n dist_todos_cdmx$Longitude[i]<-unlist(point_dist$geometry)[1]\n dist_todos_cdmx$Latitude[i]<-unlist(point_dist$geometry)[2]\n\n mixto_data <- subset(datos, uso_suelo==\"Habitacional y comercial\")\n area_mixto <- sum(mixto_data$area)/1000**2\n\n trabajo_data <- subset(datos, uso_suelo %in% c(\"Industrial\",\"Industrial y comercial\",\"Equipamiento\"))\n ####\n #dist_todos_cdmx$area_trabajo[i] <- 0\n dist_todos_cdmx$area_trabajo[i] <- sum(trabajo_data$area)/1000**2+area_mixto\n ####\n dist_todos_cdmx$radius_trabajo[i] <- sqrt((dist_todos_cdmx$area_trabajo[i]*1000**2)/pi)\n\n residencia_data <- subset(datos, uso_suelo==\"Habitacional\")\n ####\n #dist_todos_cdmx$area_residencia[i] <- sum(residencia_data$area)/1000**2+area_mixto\n dist_todos_cdmx$area_residencia[i] <- 0\n ####\n #dist_todos_cdmx$radius_residencia[i] <- sqrt(((dist_todos_cdmx$area_residencia[i]+dist_todos_cdmx$area_trabajo[i])*1000**2)/pi)\n dist_todos_cdmx$radius_residencia[i] <- 0\n ####\n \n dist_todos_cdmx$densidad_residentes[i]<-dist_todos_cdmx$dist_residentes[i]/dist_todos_cdmx$area_dist[i]\n dist_todos_cdmx$densidad_trabajador[i]<-dist_todos_cdmx$dist_trabajadores[i]/dist_todos_cdmx$area_dist[i]\n \n ###############################################################################\n #sin zonificaci\u00f3n\n}\n\n\n###############################################################################\n#MEX\n\ndistritos_mex<-c(100:207)\n\nfor(i in 1:length(distritos_mex)){\n if(str_length(as.character(distritos_mex[i]))==1){\n distritos_mex[i]<-paste0(\"00\",distritos_mex[i])\n }\n if(str_length(as.character(distritos_mex[i]))==2){\n distritos_mex[i]<-paste0(\"0\",distritos_mex[i])\n }\n}\n\n###############################################################################\n# Get data, create empty map and groups list\n\n\nubicacion_datos <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion_Datos/ZMVM_municipios.csv\") \ndatos <- ubicacion_datos %>% read.csv(header = TRUE) \ndatos <- datos %>% lapply(as.character) %>% as.data.frame(stringsAsFactors = FALSE)\nmex_data <- subset(datos, state_abbr==\"MEX\")\n\n\ndist_todos_mex <- subset(dist_todos, dist_todos$Distrito %in% distritos_mex)\n\n\nubicacion_zh_mex_todos <- paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion_Datos/igecemTipologiaahA2015Cg/igecemTipologiaahA2015Cg.shp\") #Ubicaci\u00f3n del archivo shapefile \nzh_mex_todos <- ubicacion_zh_mex_todos %>% readOGR(layer = paste0(\"igecemTipologiaahA2015Cg\"), verbose = FALSE, GDAL1_integer64_policy=TRUE) #Leer shapefile\nzh_mex_todos <- zh_mex_todos %>% spTransform(CRS(\"+proj=longlat +ellps=WGS84 +no_defs\"))\nzh_mex_todos <- subset(zh_mex_todos,zh_mex_todos$cveinegi %in% mex_data$region)\nzh_mex_todos_sf <- st_as_sf(zh_mex_todos)\nzh_mex_todos_cent <- st_centroid(zh_mex_todos_sf)\n\npb <- txtProgressBar(min = 0, max = nrow(zh_mex_todos_cent), style = 3)\nfor (i in 1:nrow(zh_mex_todos_cent)){\n if (sample(1:40, 1)==sample(1:40, 1) || i==length(zh_mex_todos_cent)){\n setTxtProgressBar(pb, i)\n }\n point <- SpatialPoints(cbind(as.numeric(unlist(zh_mex_todos_cent$geometry[i]))[1],as.numeric(unlist(zh_mex_todos_cent$geometry[i]))[2]),proj4string=CRS(as.character(\"+proj=longlat +ellps=WGS84 +no_defs\")))\n zh_mex_todos$distrito[i]<-as.character(unlist(over(point,dist_todos,returnList = FALSE)$Distrito))\n zh_mex_todos$area[i]<-area(zh_mex_todos[i,1])\n}\n\npb <- txtProgressBar(min = 0, max = length(distritos_mex), style = 3)\ni<-0\n\nfor(distrito_str in distritos_mex){\n i<-i+1\n if (sample(1:1, 1)==sample(1:1, 1) || i==length(distritos_mex)){\n setTxtProgressBar(pb, i)\n }\n zh_mex<-zh_mex_todos\n distrito_int<-as.numeric(distrito_str)\n query_residentes=paste0('select SUM(\"FACTOR\") as \"',distrito_int,'_residentes\" from \"viajes\" where \"P5_3\"=1 and \"P5_6\"=1 and \"P5_13\"=2 and \"DTO_ORIGEN\"=',distrito_int)\n dist_todos_mex$dist_residentes[i]<-unlist(dbGetQuery(con, query_residentes))\n query_trabajadores=paste0('select SUM(\"FACTOR\") as \"',distrito_int,'_residentes\" from \"viajes\" where \"P5_3\"=1 and \"P5_6\"=1 and \"P5_13\"=2 and \"DTO_DEST\"=',distrito_int)\n dist_todos_mex$dist_trabajadores[i]<-unlist(dbGetQuery(con, query_trabajadores))\n #Distritos\n\n dist <- subset(dist_todos_mex, dist_todos_mex$Distrito %in% distrito_str)\n\n k <- 0\n\n zh_dist <- subset(zh_mex,zh_mex$distrito==distrito_str)\n \n dist_todos_mex$Estado<-\"MEX\"\n dist_todos_mex$area_dist[i] <-area(dist)/1000**2\n dist_todos_mex$radius_dist[i] <- sqrt((dist_todos_mex$area_dist[i]*1000**2)/pi)\n point_dist <- dist %>% geojson_json() %>% geojson_sf() %>% st_centroid()\n dist_todos_mex$Longitude[i]<-unlist(point_dist$geometry)[1]\n dist_todos_mex$Latitude[i]<-unlist(point_dist$geometry)[2]\n\n trabajo_data <- subset(zh_dist@data, tipologia %in% c(\"Industrial\",\"Comercial\",\"Equipamiento\"))\n ####\n #dist_todos_mex$area_trabajo[i] <- 0\n dist_todos_mex$area_trabajo[i] <- sum(trabajo_data$area)/1000**2\n ####\n dist_todos_mex$radius_trabajo[i] <- sqrt((dist_todos_mex$area_trabajo[i]*1000**2)/pi)\n\n residencia_data <- subset(zh_dist@data, tipologia==\"Habitacional\")\n ####\n #dist_todos_mex$area_residencia[i] <- sum(residencia_data$area)/1000**2\n dist_todos_mex$area_residencia[i] <- 0\n ####\n #dist_todos_mex$radius_residencia[i] <- sqrt(((dist_todos_mex$area_residencia[i]+dist_todos_mex$area_trabajo[i])*1000**2)/pi)\n dist_todos_mex$radius_residencia[i] <- 0\n ####\n\n dist_todos_mex$densidad_residentes[i]<-dist_todos_mex$dist_residentes[i]/dist_todos_mex$area_dist[i]\n dist_todos_mex$densidad_trabajador[i]<-dist_todos_mex$dist_trabajadores[i]/dist_todos_mex$area_dist[i]\n}\n\n###############################################################################\n# COLORES\n\ndist_todos <- rbind(dist_todos_cdmx, dist_todos_mex)\ndist_todos@data$Descripcio <- iconv(dist_todos@data$Descripcio, from=\"UTF-8\", to=\"LATIN1\")\ndist_todos <- dist_todos %>% filter_all(all_vars(!is.na(.)))\nescala <- log2(c(0,0,0.125,0.25,0.375,0.5,0.625,0.75,0.875,1)+1)\n\nmax_residentes<- max(dist_todos$dist_residentes, na.rm = TRUE)\nmin_residentes<- min(dist_todos$dist_residentes, na.rm = TRUE)\nbins_residentes <- escala*(max_residentes)+min_residentes\nbins_residentes[1]<-0\n\nmax_trabajadores<- max(dist_todos$dist_trabajadores, na.rm = TRUE)\nmin_trabajadores<- min(dist_todos$dist_trabajadores, na.rm = TRUE)\n\nbins_trabajadores <- escala*(max_trabajadores)+min_trabajadores\nbins_trabajadores[1]<-0\n\npal_residentes <- colorBin(\"Blues\", domain = dist_todos$dist_residentes, bins = bins_residentes)\npal_trabajadores <- colorBin(\"Purples\", domain = dist_todos$dist_trabajadores, bins = bins_trabajadores)\n\npal_residentes_contorno <- colorBin(\"Blues\", domain = dist_todos$dist_residentes, bins = bins_residentes/2)\npal_trabajadores_contorno <- colorBin(\"Purples\", domain = dist_todos$dist_trabajadores, bins = bins_trabajadores/2)\n\n\n###############################################################################\n# MAPAS DISTIRTOS\nmap <- leaflet()\n\nmap <- map %>%\n\n addPolygons(data=dist_todos,\n stroke = TRUE,\n color='grey',\n opacity=1,\n smoothFactor = 0.5,\n weight = 1,\n fillColor = \"#26b8e8\",\n fillOpacity = 0,\n group = grupo_d,\n popup = ~paste(\n '
', paste(Descripcio),\n '

Clave distrito: ', paste(Distrito),\n '

Estado: ', paste(Estado)\n ))\n\n\ngrupo=\"Habitacional y mixto\"\n\nmap <- map %>% \n\naddCircles(data=dist_todos,\n lng=~Longitude,\n lat=~Latitude,\n radius=~radius_residencia,\n stroke = TRUE,\n color='black',\n weight = 2,\n fillColor = ~pal_residentes(dist_residentes),\n group = grupo,\n fillOpacity = 1,\n popup = ~paste(\n '
', paste(Descripcio),\n '

Distrito: ', paste(Distrito),\n '

Estado: ', paste(Estado),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_residencia/area_dist)*100,2),'%'),\n '

Residentes: ', paste0(round(dist_residentes,0))\n ))\n\ngrupo=\"Industrial, comercial, equipamiento y mixto\"\n\nmap <- map %>% \n\naddCircles(data=dist_todos,\n lng=~Longitude,\n lat=~Latitude,\n radius=~radius_trabajo,\n stroke = TRUE,\n color='black',\n weight = 2,\n fillColor = ~pal_trabajadores(dist_trabajadores),\n group = grupo,\n fillOpacity = 1,\n popup = ~paste(\n '
', paste(Descripcio),\n '

Distrito: ', paste(Distrito),\n '

Estado: ', paste(Estado),\n '

Uso de suelo: ', paste(grupo),\n '

Porcentaje del suelo del distrito: ', paste0(round((area_trabajo/area_dist)*100,2),'%'),\n '

Trabajadores: ', paste0(round(dist_trabajadores,0))\n ))\n\n################################################################################\n\nmap <- addTiles(map)\nmap <-map %>% addProviderTiles(providers$CartoDB.Positron)\nmap <- map %>% addLayersControl( #Agrega control sobre visulizaci\u00f3n de Oxxos en el mapa\n overlayGroups = grupo_d,\n options = layersControlOptions(collapsed = TRUE)\n)\nmap <- map %>% addLegend(\n \"bottomright\",\n ####\n #pal = pal_residentes,\n pal = pal_trabajadores,\n ####\n #values = round(dist_todos$dist_residentes,0),\n values = round(dist_todos$dist_trabajadores,0),\n ####\n #title = \"Residentes\",\n title = \"Trabajadores\",\n ####\n opacity = 1\n)\nmap <- map %>% hideGroup(grupo_d)\nmap <- map %>% addResetMapButton() \n\n\n\n###############################################################################\nsetwd(paste0(\"C:/Users/DELL/OneDrive/CodeLibrary/R/ZMVM_Urbanizacion/Mapas_distritos/\")) \nsaveWidget(map, file=paste0(\"000_circle_work_heatmap.html\"))\n", "meta": {"hexsha": "9051633631c5ca0b79f833ef25a304cefc9a2ab3", "size": 13574, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/distritos_circulos_heatmap.r", "max_stars_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_stars_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/distritos_circulos_heatmap.r", "max_issues_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_issues_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/distritos_circulos_heatmap.r", "max_forks_repo_name": "m1Pablo/ZMVM_Urbanizacion", "max_forks_repo_head_hexsha": "1d867ba6dd49b383d9a232618f64f010d8f308e7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3841463415, "max_line_length": 209, "alphanum_fraction": 0.6360689554, "num_tokens": 4050, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3062035138101244}} {"text": "\n# read in files\nrequire(Hmisc)\n\n# TE polymorphisms\nMELT <- read.table(\"MELT_for_ROH.txt\", sep=\"\\t\", stringsAsFactors=F, header=T)\n\n# individual runs of homozygosity\nroh <- read.table(\"runs_of_homozygosity.txt\", sep=\"\\t\", stringsAsFactors=F, header=T)\n\n# scaffold summaries\nss <- read.table(\"scaffold_summaries.txt\", sep=\"\\t\", stringsAsFactors=F, header=T)\nplot(ss[,3:5])\n\n# scaffold TE summaries\nte_ss <- read.table(\"scaffold_TE_summaries.txt\", sep=\"\\t\", stringsAsFactors=F, header=T)\n\n# scaffold genic regions summaries\ngene_ss <- read.table(\"genic_scaffold_summaries.txt\", sep=\"\\t\", stringsAsFactors=F)\n\n# list of individuals\nindividuals <- unique(ss[,1])\n\n\n\n# loop for each individual\n# output columns = individual, h0 corr, TEs corr, genes corr, NR_TEs corr, NR_Poly_TEs corr\noutput <- c()\nfor(a in 1:length(individuals)) {\n\tss_rep <- ss[ss[,1] == individuals[a], ]\n\t\n\t# temp columns\n\tTEs <- rep(0, nrow(ss_rep))\n\tgenes <- rep(0, nrow(ss_rep))\n\tNR_TEs <- rep(0, nrow(ss_rep))\n\tNR_Poly_TEs <- rep(0, nrow(ss_rep))\n\tss_rep <- cbind(ss_rep, TEs, genes, NR_TEs, NR_Poly_TEs)\n\t\n\t# obtain melt numbers per individual\n\tmelt_rep <- MELT[MELT[,1] == individuals[a], ]\n\tmelt_poly_rep <- melt_rep[melt_rep$het == \"het\", ]\n\tmelt_rep <- cbind(names(tapply(melt_rep$scaffold, melt_rep$scaffold, length)), tapply(melt_rep$scaffold, melt_rep$scaffold, length))\n\tmelt_poly_rep <- cbind(names(tapply(melt_poly_rep $scaffold, melt_poly_rep $scaffold, length)), tapply(melt_poly_rep $scaffold, melt_poly_rep $scaffold, length))\n\t\n\t# input data for four columns into temp columns\n\tss_rep[match(melt_rep[,1], ss_rep[,2]), 8] <- as.numeric(melt_rep[,2])\n\tss_rep[match(melt_poly_rep[,1], ss_rep[,2]), 9] <- as.numeric(melt_poly_rep[,2])\n\tss_rep[match(te_ss[,1], ss_rep[,2]), 6] <- as.numeric(te_ss[,2])\n\tss_rep[match(gene_ss[,1], ss_rep[,2]), 7] <- as.numeric(gene_ss[,2])\n\n\ta_rep <- rcorr(as.matrix(ss_rep[,c(3,5,6,7,8,9)]))\n\ta_correlations <- as.vector(a_rep$r[2,-2])\n\toutput <- rbind(output, c(individuals[a], a_correlations))\n}\noutput <- data.frame(Individual=as.character(output[,1]), H0_r=as.numeric(output[,2]), TE_r=as.numeric(output[,3]), Genes_r=as.numeric(output[,4]), NR_TEs_r=as.numeric(output[,5]), NR_Poly_TEs_r=as.numeric(output[,6]))\n\n\n# confidence intervals on correlation coefficients\noutput2 <- c()\nrequire(boot)\nrequire(DescTools)\nbootstrap.effect <- function(input, indices) {\n\td <- input[indices]\n\td <- FisherZ(d)\n\td <- mean(d)\n\td <- FisherZInv(d)\n\treturn(d)\n}\n\n\n# tes\nresults <- boot(data=output$TE_r, statistic=bootstrap.effect, R=1000)\nci <- boot.ci(results)\nci2 <- c(\"TEs\", mean(results$data), ci$bca[4:5])\noutput2 <- rbind(output2, ci2)\n\n# genes\nresults <- boot(data=output$Genes_r, statistic=bootstrap.effect, R=1000)\nci <- boot.ci(results)\nci2 <- c(\"Genes\", mean(results$data), ci$bca[4:5])\noutput2 <- rbind(output2, ci2)\n\n#non-reference tes\nresults <- boot(data=output$NR_TEs_r, statistic=bootstrap.effect, R=1000)\nci <- boot.ci(results)\nci2 <- c(\"NR_TEs\", mean(results$data), ci$bca[4:5])\noutput2 <- rbind(output2, ci2)\n\n# polymorphic non-reference tes\nresults <- boot(data=output$NR_Poly_TEs_r, statistic=bootstrap.effect, R=1000)\nci <- boot.ci(results)\nci2 <- c(\"NR_Poly_TEs\", mean(results$data), ci$bca[4:5])\noutput2 <- rbind(output2, ci2)\n\n# heterozygosity\nresults <- boot(data=output$H0_r, statistic=bootstrap.effect, R=1000)\nci <- boot.ci(results)\nci2 <- c(\"Heterozygosity\", mean(results$data), ci$bca[4:5])\noutput2 <- rbind(output2, ci2)\n\noutput2 <- data.frame(Dataset=as.character(output2[,1]), Mean=as.numeric(output2[,2]), CI_low=as.numeric(output2[,3]), CI_high=as.numeric(output2[,4]))\n\nwrite(paste(\"# Mean effect sizes (correlation coefficients) with ROH length per scaffold\"), file=\"per_scaffold_effect_sizes.txt\", sep=\"\\t\")\nwrite.table(output2, file=\"per_scaffold_effect_sizes.txt\", sep=\"\\t\", row.names=F, quote=F, append=T)\n", "meta": {"hexsha": "626bc410ea27c30146cc697598f8377a731a38f1", "size": 3853, "ext": "r", "lang": "R", "max_stars_repo_path": "07_ROH/03_runs_of_homozygosity2.r", "max_stars_repo_name": "jdmanthey/dryobates_popgen_TEs", "max_stars_repo_head_hexsha": "178bb616c3beb3b733495c69e725369c57138af4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-05-21T17:43:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-14T18:37:26.000Z", "max_issues_repo_path": "07_ROH/03_runs_of_homozygosity2.r", "max_issues_repo_name": "jdmanthey/dryobates_popgen_TEs", "max_issues_repo_head_hexsha": "178bb616c3beb3b733495c69e725369c57138af4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "07_ROH/03_runs_of_homozygosity2.r", "max_forks_repo_name": "jdmanthey/dryobates_popgen_TEs", "max_forks_repo_head_hexsha": "178bb616c3beb3b733495c69e725369c57138af4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.6952380952, "max_line_length": 218, "alphanum_fraction": 0.7111341812, "num_tokens": 1197, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.30609933063086536}} {"text": "#density plots of variance decomposition for bacteria.\nrm(list=ls())\nlibrary(RCurl)\nscript <- getURL(\"https://raw.githubusercontent.com/colinaverill/NEFI_microbe/master/NEFI_functions/zero_truncated_density.r\", ssl.verifypeer = FALSE)\neval(parse(text = script))\n#source('NEFI_functions/zero_truncated_density.r')\nsource('paths.r')\n\n#set output path.\n##output.path <- 'test.png'\noutput.path <- NEON_ddirch_var.decomp_all.groups.fig_16S.path\n\n#load data.----\nd <- readRDS(NEON_ddirch_var.decomp_16S.path)\n\n#grab individual cov, parameter and process error for all groups at the site-level.----\ncov.out <- list()\npar.out <- list()\npro.out <- list()\nfor(i in 1:length(d)){\n tab <- d[[i]]$site_decomp\n cov.out[[i]] <- tab[rownames(tab) == 'covariate',]\n par.out[[i]] <- tab[rownames(tab) == 'parameter',]\n pro.out[[i]] <- tab[rownames(tab) == 'process' ,]\n}\ncov <- unlist(cov.out)\npar <- unlist(par.out)\npro <- unlist(pro.out)\ncov <- cov[-grep('other',names(cov))]\npar <- par[-grep('other',names(par))]\npro <- pro[-grep('other',names(pro))]\npro <- ifelse(pro > 1, 1, pro)\n#get densities, zero bound if appropriate\ncov.d <- zero_truncated_density(cov)\npar.d <- zero_truncated_density(par)\npro.d <- zero_truncated_density(pro)\n#cov.d <- density(cov, from = 0, to = 1)\n#par.d <- density(par, from = 0, to = 1)\npro.d <- density(pro, from = 0, to = 1)\npro.d_xy <- data.frame(pro.d$x,pro.d$y)\npro.d_xy[nrow(pro.d_xy),2] <- 0\n\n#png save line.----\npng(filename=output.path,width=5,height=5,units='in',res=300)\n\n#Global plot settings.----\npar(mfrow = c(1,1))\nlimx <- c(0,1)\nlimy <- c(0, 51)\ntrans <- 0.2 #shading transparency.\no.cex <- 1.3 #outer label size.\ncols <- c('purple','cyan','yellow')\npar(mfrow = c(1,1), mar = c(4.5,4,1,1))\n\n#plot.----\nplot(cov.d,xlim = limx, ylim = limy, bty = 'n', xlab = NA, ylab = NA, main = NA, yaxs='i', xaxs = 'i', las = 1, lwd = 1)\npolygon(cov.d, col = adjustcolor(cols[1],trans))\npolygon(par.d, col = adjustcolor(cols[2],trans))\npolygon(pro.d_xy, col = adjustcolor(cols[3],trans), fillOddEven = F)\nmtext('Density', side = 2, line = 2.2, cex = o.cex)\nmtext('relative contribution to uncertainty', side = 1, line = 2.5, cex = o.cex)\nlegend(x = 0.7, y = 40, legend = c('covariate','parameter','process'), \n col ='black', pt.bg=adjustcolor(cols,trans), \n bty = 'n', pch = 22, pt.cex = 1.5)\n\n#end plot.----\ndev.off()", "meta": {"hexsha": "7cd87fb9e49cdbb3936bf0e3d018966397f7df71", "size": 2351, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/figure_scripts/ddirch/plot_density_var.decomp_ddirch_16S.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "16S/figure_scripts/ddirch/plot_density_var.decomp_ddirch_16S.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "16S/figure_scripts/ddirch/plot_density_var.decomp_ddirch_16S.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 35.0895522388, "max_line_length": 150, "alphanum_fraction": 0.6546150574, "num_tokens": 791, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417487156366, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3060993243987124}} {"text": "library(flume)\nlibrary(Matrix)\nlibrary(units)\n\ndata(\"flume_networks\")\nnet = flume_networks$kamp\n\noptions(mc.cores = 6)\nset.seed(1203)\n\n# generate x species with same niche breadth, niche height but random location\nnsp = 50\n\n# use niches_uniform to calculate niche location for all species at once\nnopts = list(location = runif(nsp, 0.1, 0.9), \n\tbreadth = 0.1, scale_e = 1.25e-7, scale_c = 6e-6, r_use = 0.05)\n\n# constant relatively high dispersal\nalpha = 0.7\n\n# passive dispersal is kept constant at the moment\nbeta = 0.1\n\n# starting prevalence for species in the network\nprev = 0.2\n\n# starting/boundary state of the network\nstart_r = matrix(0.5, nrow = length(net), ncol = 1)\n## headwaters are most extreme\nstart_r[c(10:12, 16, 20:24), ] = 0.9\nstart_r[c(42:43, 46, 47:52), ] = 0.1\n\nstart_r[c(9, 13:15, 17:19), ] = 0.7\nstart_r[c(38:41, 44:45), ] = 0.3\n\n## gamma diversity scenarios\n## how many species are possible in each scenario\nn_gamma = 6\ng_div = c(2, 10, 20, 30, 40, 50)\n\n# read species lists from ex2\nsps = readRDS(\"flume_examples/splists.rds\")\n## how many times to repeat each particular species richness (with new random species)\n## and dispersal combo\nn_reps = 40\n\n## how many times to repeat each particular combination\n## multithreaded, so up to mc.cores is free\nn_iter = 2 * getOption(\"mc.cores\")\n\nnt = 30 # number of time steps of one simulation\n\ndopts = list(alpha = alpha, beta = beta)\nmcom = metacommunity(nsp = nsp, nr = 1, niches = niches_custom, niche_args = nopts, \n\t\t\tdispersal = dispersal_custom, dispersal_args = dopts)\n\nfor(j in 1:n_gamma) {\n\tgam = g_div[j]\n\tsplist = sps[[j]]\n\tcat(\"gamma diversity = \", gam, \" (\", j, \" of \", n_gamma, \")\\n\", sep=\"\")\n\tfor(i in 1:nrow(splist)) {\n\t\tspecies_ids = splist[i,]\n\t\tcat(\" species \", paste(species_ids, collapse = \" \"), \" (\", i, \" of \", \n\t\t\tnrow(splist), \")\\n\", sep=\"\")\n\t\tfbase = paste0(\"fit_sp-\", paste(species_ids, collapse='.'))\n\t\tfname = file.path(\"flume_examples\", \"ex3\", \"res\", fbase)\n\t\tstart_sp = matrix(0, nrow = length(net), ncol = nsp)\n\t\tfor(k in species_ids)\n\t\t\tstart_sp[,k] = rbinom(length(net), 1, prev)\n\t\tmod_static = flume(mcom, net, start_sp, start_r)\n\t\tsuppressMessages(mod_static <- run_simulation(mod_static, nt, reps = n_iter))\n\n\t\t# copy the model, run one copy the same, one with changed resource concentrations\n\t\tnew_r = matrix(runif(length(net), 0.1, 0.9), nrow = length(net))\n\t\tmod_dynamic = mod_static\n\t\tfor(k in 1:length(mod_dynamic$networks))\n\t\t\tboundary(mod_dynamic$networks[[k]], \"resources\") = new_r\n\t\tsuppressMessages(mod_static <- run_simulation(mod_static, nt, reps = n_iter))\n\t\tsuppressMessages(mod_dynamic <- run_simulation(mod_dynamic, nt, reps = n_iter))\n\n\t\tsaveRDS(mod_static, paste0(fname, \"_static.rds\"))\n\t\tsaveRDS(mod_dynamic, paste0(fname, \"_dynamic.rds\"))\n\t}\n}", "meta": {"hexsha": "a6bea0926920c5ca4d332e793f268a63b61b188d", "size": 2777, "ext": "r", "lang": "R", "max_stars_repo_path": "flume_examples/ex3/R/ex3.r", "max_stars_repo_name": "mtalluto/FLUFLUX_model", "max_stars_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "flume_examples/ex3/R/ex3.r", "max_issues_repo_name": "mtalluto/FLUFLUX_model", "max_issues_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "flume_examples/ex3/R/ex3.r", "max_forks_repo_name": "mtalluto/FLUFLUX_model", "max_forks_repo_head_hexsha": "76a36cda682121a90758f726f6bceea925e2ee03", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.0595238095, "max_line_length": 86, "alphanum_fraction": 0.6913935902, "num_tokens": 901, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7310585903489891, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.3060919723370468}} {"text": "\nlibrary(plotly) \nlibrary(dplyr)\n\ndata.senate <- read.csv(\"./data/SenateElection.csv\")\ndata.filtered <- data.senate %>% select(Candidate, Status_of_Candidate, General_Party, Election_Jurisdiction, Election_Year, Incumbency_Status, Total_.)\ndata.filtered <- data.filtered %>% filter(Election_Jurisdiction == \"CA\", Election_Year == \"2012\")\n\ndata.filtered$size <- ntile(data.filtered$Total_., 5)\n\n# Function should be passed a dataframe with the following columns: General_Party, Candidate, \n# Total_., Incumbency_Status\n\nBuildElectionResult <- function(data.filtered) {\n \n col3 <- c(`Third-Party` = 'green', `Republican` = \"red\", `Democratic` = \"blue\")\n sy <- c(`Won` = 11, `Withdrew` = 15, `Lost` = 4)\n \n p <- plot_ly(data = data.filtered, y = ~Total_., x = ~Candidate, type = 'scatter',\n color = ~General_Party, colors = col3,\n symbol = ~Status_of_Candidate, symbols = sy, \n marker = list(size = 12), \n hoverinfo = 'text',\n text = ~paste0(Candidate, '
',\n 'Status: ', Incumbency_Status, '
',\n 'Contributions: $', Total_., '
',\n 'Party: ', General_Party)) %>%\n layout(margin = list(b = 160, l = 115), xaxis = list(tickangle = 45, title = \"Candidate\"), \n yaxis = list(title = \"Total Recieved Contributions\"))\n return(p)\n}\n", "meta": {"hexsha": "141df31289f2eb584d03f09e964e2b45afad2b12", "size": 1403, "ext": "r", "lang": "R", "max_stars_repo_path": "test.r", "max_stars_repo_name": "liuyaf/info201-af3-final-project", "max_stars_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test.r", "max_issues_repo_name": "liuyaf/info201-af3-final-project", "max_issues_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2017-05-16T02:12:03.000Z", "max_issues_repo_issues_event_max_datetime": "2017-06-01T23:42:56.000Z", "max_forks_repo_path": "test.r", "max_forks_repo_name": "liuyaf/info201-af3-final-project", "max_forks_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.84375, "max_line_length": 152, "alphanum_fraction": 0.5994297933, "num_tokens": 369, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3059404671965239}} {"text": "setwd(\"~/Sites/R/clandestine-lab\")\n\n# will need rewrite this. Cleaning and csv produced in python.\nfilename <- 'clan-lab-ok.txt'\nfilenameEdit <- 'clan-lab-ok-edit.txt'\nheaders <- c('COUNTY', 'CITY', 'ADDRESS', 'DATE')\n\ndelim <- read.delim( file = filename,\n header = FALSE,\n sep = \" \")\n\ntable <- read.table(file = filenameEdit,\n sep = \"\",\n fill = FALSE,\n strip.white = FALSE)\n\nfwf <- read.fwf(file = filename,\n widths = c(11, 14, 32, 20),\n col.names = headers,\n fill = FALSE)\n\n# read csv, summarize\nlibrary(plyr)\n\ndata = read.csv('clan-lab-ok.csv')\n\ncityCount <- ddply(data, c('city'), summarise,\n number = length(city))\n\ncountyCount <- ddply(data, c('county'), summarise,\n number = length(county))\n\n# map the geocoded points on the map\nlibrary(ggmap)\n\n#coordinates\nOKC <- c(lon = -97.5164, lat = 35.4676)\nTulsa <- c(lon = -95.9928, lat = 36.1540)\nzoomedOutCenter <- c(lon = -98.5164, lat = 35.4676)\n\nmyLocation <- zoomedOutCenter\n\noklahomaMap <- get_map(location=myLocation,\n source='stamen', maptype='toner', crop=FALSE, zoom = 7)\n\nggmap(oklahomaMap) +\n geom_point(aes(x = data$long , y = data$lat), data = data,\n alpha = .5, color=\"steelblue\", size = 1)\n", "meta": {"hexsha": "231ebcd6b3bcc73fda5e41870882bb31cd46739e", "size": 1346, "ext": "r", "lang": "R", "max_stars_repo_path": "clan-lab-ok.r", "max_stars_repo_name": "darrenjaworski/clandestine-labs-oklahoma", "max_stars_repo_head_hexsha": "2aa7bdb177d478da7da08ae5eaaf598fea0ee53a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "clan-lab-ok.r", "max_issues_repo_name": "darrenjaworski/clandestine-labs-oklahoma", "max_issues_repo_head_hexsha": "2aa7bdb177d478da7da08ae5eaaf598fea0ee53a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "clan-lab-ok.r", "max_forks_repo_name": "darrenjaworski/clandestine-labs-oklahoma", "max_forks_repo_head_hexsha": "2aa7bdb177d478da7da08ae5eaaf598fea0ee53a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.4693877551, "max_line_length": 72, "alphanum_fraction": 0.5720653789, "num_tokens": 375, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.30594045969972306}} {"text": "#!/usr/bin/env Rscript\n# For https://github.com/berkeley-dsep-infra/datahub/issues/2579\n# Fall 2021 - Spring 2022\n\nprint(\"Installing packages for PS 3\")\n\nsource(\"/tmp/class-libs.R\")\n\nclass_name = \"PS 3\"\n\nclass_libs = c(\n \"estimatr\", \"0.30.2\"\n)\n\nclass_libs_install_version(class_name, class_libs)\n", "meta": {"hexsha": "3bd9946205415df321e475b59dff92ebf5588d19", "size": 297, "ext": "r", "lang": "R", "max_stars_repo_path": "deployments/datahub/images/default/r-packages/ps-3.r", "max_stars_repo_name": "ryanlovett/datahub", "max_stars_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 104, "max_stars_repo_stars_event_min_datetime": "2017-08-14T18:29:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T16:38:28.000Z", "max_issues_repo_path": "deployments/datahub/images/default/r-packages/ps-3.r", "max_issues_repo_name": "ryanlovett/datahub", "max_issues_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 793, "max_issues_repo_issues_event_min_datetime": "2017-08-16T21:49:14.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T01:14:41.000Z", "max_forks_repo_path": "deployments/datahub/images/default/r-packages/ps-3.r", "max_forks_repo_name": "ryanlovett/datahub", "max_forks_repo_head_hexsha": "6be4525b7f7dde499fe953d2453cbe4f34b954a3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 129, "max_forks_repo_forks_event_min_datetime": "2017-08-10T01:26:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-03T01:24:19.000Z", "avg_line_length": 18.5625, "max_line_length": 64, "alphanum_fraction": 0.7239057239, "num_tokens": 95, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.30594045969972306}} {"text": "\n estimate.biomass = function(Q, R, S, y, r, use.global.average=F) {\n \n Y = as.character(y)\n \n# S[,\"mass.mean\",,] = S[,\"mass.mean\",, ] \n# S[,\"mass.sd\",,] = S[,\"mass.sd\",, ]\n \n m.q = S[,\"mass.mean\",Y,r]\n error.q = S[,\"mass.sd\",Y,r]\n j.q = which ( S[,\"n\",Y,r] <= 30 )\n m.q[ j.q ] = NA\n error.q[ j.q ] = NA\n\n m.r = S[,\"mass.mean\",Y,\"cfaall\"]\n error.r = S[,\"mass.sd\",Y,\"cfaall\"]\n\n# j.r = which ( S[,\"n\",Y,\"cfaall\"] <= 30 )\n# m.r[ j.r ] = NA\n# error.r[ j.r ] = NA\n\n m.s = apply( S[,\"mass.mean\",,\"cfaall\"], MARGIN=1, mean, na.rm=T)\n error.s = apply( S[,\"mass.sd\",,\"cfaall\"], MARGIN=1, mean, na.rm=T)\n\n m.o = m.q\n error.o = error.q\n \n if (use.global.average) {\n m.o = m.r\n error.o = error.r\n } else {\n i = which (!is.finite(m.o) )\n m.o[ i ] = m.r[ i ]\n error.o[ i ] = error.r[ i ]\n \n j = which (!is.finite(m.o) )\n m.o[ j ] = m.s[ j ]\n error.o[ j ] = error.s[ j ]\n }\n\n if ( !is.finite( m.o[\"CC5.13\"] ) ) {\n m.o[ \"CC5.13\" ] = m.o [\"CC3to4.13\"]\n error.o[ \"CC5.13\" ] = error.o [\"CC3to4.13\"]\n }\n\n o = m.o * Q\n oerror = o * sqrt( (error.o/m.o) ^2 + (R/Q)^2)\n \n names(o) = names(Q)\n o[ o < 0.0001] = 0\n o[ !is.finite(o) ] = 0\n\n names(oerror) = names(Q)\n oerror [ oerror < 0.0001] = 0\n oerror [ !is.finite(oerror) ] = 0\n\n out = list( x=o / 1000 , error=oerror / 1000 ) # convert g to kg\n \n return(out)\n }\n\n\n\n", "meta": {"hexsha": "eb0983187633d12902645075e354080e41316fb7", "size": 1468, "ext": "r", "lang": "R", "max_stars_repo_path": "R/estimate.biomass.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/estimate.biomass.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/estimate.biomass.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 22.9375, "max_line_length": 70, "alphanum_fraction": 0.439373297, "num_tokens": 597, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.3059212665406258}} {"text": "library( shiny )\nlibrary( litRiddle )\nlibrary( RColorBrewer )\nlibrary( here )\nlibrary( ggplot2 )\nlibrary( waffle )\n\noptions( shiny.port = 4800 )\naddResourcePath( 'resources', file.path( here(), 'resources' ) )\ndata( respondents )\n\nwaffle_palette <- rev( brewer.pal( 5, \"Pastel2\" ) )\nbar_palette <- brewer.pal( 5, \"Greens\" )\n\nui <- bootstrapPage(\n titlePanel( \"title panel\" ),\n theme='resources/bootstrap_extensions.css',\n sidebarLayout( position=\"left\",\n sidebarPanel( \"sidebar panel\",\n sliderInput(\n inputId='age_range',\n label='Gebruik de schuifregelaar om een leeftijdsgroep te kiezen',\n value=c( 25, 35 ), min=min( respondents[ 'age.resp' ] ), max=max( respondents[ 'age.resp' ] )\n )\n ),\n mainPanel( \"main panel\",\n fluidRow(\n splitLayout( cellWidths = c( '50%', '50%' ), plotOutput( 'plotgraph1' ), plotOutput( 'plotgraph2' ) )\n )\n )\n )\n)\n\nserver <- function( input, output ){\n hist_plot <- reactive({\n title <- paste( 'Verdeling van het aantal boeken per jaar binnen\\nde groep van', input$age_range[1], 'tot', input$age_range[2], 'jaar' )\n return( hist( respondents[ respondents[ 'age.resp' ] >= input$age_range[1] & respondents[ 'age.resp' ] <= input$age_range[2], 'books.per.year' ], breaks=seq( 0, 700, by=25 ),\n main=title, xlim=c( 0, 400 ), ylim=c( 0, 9000 ), xlab='Boeken per jaar', ylab='Aantal respondenten', cex=1.2, cex.lab=1.2, cex.main=1.5, col=bar_palette ) )\n })\n waffle_plot <- reactive({\n total_books_per_year <- sum( na.omit( respondents[ 'books.per.year' ] ) )\n books_per_year_at_age <- sum( na.omit( respondents[ respondents[ 'age.resp' ] >= input$age_range[1] & respondents[ 'age.resp' ] <= input$age_range[2], 'books.per.year' ] ) )\n vals <- c( books_per_year_at_age, total_books_per_year - books_per_year_at_age )\n vals <- round( vals/2000 )\n names( vals ) <- sprintf( '%s', scales::percent( round( vals/sum( vals ), 2 ) ) )\n # return( pie( slices, labels=lbls, main=paste( 'Totaal aantal boeken per jaar gelezen door\\nmensen in de leeftijdsgroep van', input$age_range[1], 'tot', input$age_range[2], 'jaar' ),\n # cex=1.2, cex.main=1.5, col=myPalette ) )\n waffle_title <- paste( 'Boeken gelezen per jaar door mensen\\nin de leeftijdsgroep van', input$age_range[1], 'tot', input$age_range[2], 'jaar\\nten opzichte van het totaal van alle\\ngelezen boeken per jaar' )\n return( waffle( vals, size=1, colors=c( waffle_palette[1:2], 'white' ),\n title=waffle_title ) + theme( legend.position='bottom' ) )\n })\n output$plotgraph1 = renderPlot( waffle_plot() )\n output$plotgraph2 = renderPlot( hist_plot() )\n}\n\nshinyApp( ui=ui, server=server )\n", "meta": {"hexsha": "6cda66c175516a3be929cc57f3c6e40d9b189865", "size": 2672, "ext": "r", "lang": "R", "max_stars_repo_path": "riddle_shiny.r", "max_stars_repo_name": "jorisvanzundert/riddle_shiny", "max_stars_repo_head_hexsha": "db3e3e7f68f5a25c5a100b4e431cd8578a62c79e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "riddle_shiny.r", "max_issues_repo_name": "jorisvanzundert/riddle_shiny", "max_issues_repo_head_hexsha": "db3e3e7f68f5a25c5a100b4e431cd8578a62c79e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "riddle_shiny.r", "max_forks_repo_name": "jorisvanzundert/riddle_shiny", "max_forks_repo_head_hexsha": "db3e3e7f68f5a25c5a100b4e431cd8578a62c79e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.8771929825, "max_line_length": 210, "alphanum_fraction": 0.6605538922, "num_tokens": 838, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802735722128, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.3057697322114779}} {"text": "library(Biostrings)\nlibrary(ggplot2)\nlibrary(cowplot)\nlibrary(dplyr)\nlibrary(RColorBrewer)\nlibrary(readr)\nlibrary(tidyr)\nsetwd(\"~/cfo155/whole_genome_lrTAPS\")\nid <- \"bc1001_cmb\"\n# Read Length ----\ndat <- read.table(paste0(\"fastq/\", id, \".read_all.txt\"), header=FALSE)\nn50 <- N50(dat$V1)\ndat <- read.table(paste0(\"fastq/\", id, \".read_len.txt\"), header=TRUE)\ncolnames(dat) <- c(\"len\",\"nread\")\ndat<- dat[order(dat$len),]\np <- ggplot(dat, aes(x=len,y=nread)) +\n geom_bar(stat=\"identity\") +\n annotate(\"text\", y = 200000, x = 1000, label = paste0(\"N50: \", n50, \" bp\")) +\n xlab(\"read length\") +\n theme_light() + xlim(0,7500) +\n theme(legend.position = \"bottom\")\nggsave(paste0(\"plots/\",id,\".read_len.pdf\"), p, width = 6, height = 4)\n\n\n# QC ----\ndat<- read.table(paste0(\"sta/\", id, \".all.sta\"), header=TRUE, stringsAsFactors = FALSE)\ndat$conversion <- gsub(\";.*\",\"\",dat$kb4_meth) %>% as.character() %>% as.numeric() \ndat$fp <- gsub(\";.*\",\"\",dat$kb4_unmeth_cg) %>% as.character() %>% as.numeric() \n\n\np1 <- ggplot(dat, aes(x = id, y = conversion*100)) + geom_bar(stat = \"identity\", fill = \"orangered3\", col = \"orangered3\") + \n geom_text(aes(label=round(conversion*100, 2)), vjust=-0.4) +\n theme_light() + xlab(\"\") + ylab(\"mCG conversion on 4kb (%)\") + theme(axis.text = element_text(color = \"black\")) + ylim(0,100)\np2 <- ggplot(dat, aes(x = id, y = fp*100)) + geom_bar(stat = \"identity\", fill = \"steelblue\", col = \"steelblue\") + \n geom_text(aes(label=round(fp*100, 2)), vjust=-0.4) +\n theme_light() + xlab(\"\") + ylab(\"false positive on unmodified cg(4kb) (%)\") + theme(axis.text = element_text(color = \"black\")) + ylim(0,0.5)\np <- plot_grid(p1, p2, labels = c('A', 'B'), label_size = 12)\nggsave(paste0(\"plots/\",id,\"_conversion_fp.pdf\"),p,width=3,height = 4)\n\ndat$genome_meth <- gsub(\";.*\",\"\",dat$genome_meth) %>% as.character() %>% as.numeric() \np1 <- ggplot(dat, aes(x = id, y = genome_meth*100, fill=id)) + geom_bar(stat = \"identity\") + \n geom_text(aes(label=round(genome_meth*100, 2)), vjust=-0.4) +\n theme_light() + xlab(\"\") + ylab(\"genome methylation (%)\") + theme(axis.text = element_text(color = \"black\"), legend.position = \"none\") +\n scale_fill_manual(values=brewer.pal(2,\"Dark2\"))\n\ndat$pmap <- (dat$nq20map_bam)/(dat$nfwd_read + dat$nrev_read)\ndat$pq1map <- (dat$nq1map_bam)/(dat$nfwd_read + dat$nrev_read)\np2 <- ggplot(dat, aes(x = id, y = pmap*100)) + geom_bar(stat = \"identity\", fill = \"steelblue\", col = \"steelblue\") + \n geom_text(aes(label=round(pmap*100, 2)), vjust=-0.4) +\n scale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, by = 10)) +\n theme_light() + xlab(\"\") + ylab(\" Q20 mapping rate (%)\") + theme(axis.text = element_text(color = \"black\"))\n\np <- plot_grid(p1, p2, labels = c('A', 'B'), label_size = 12)\nggsave(paste0(\"plots/\",id,\"genome_sta.pdf\"),p,width=3,height = 4)\n\n\n# comparison with short-read taps ----\ntaps <- read_delim(\"shortread_taps/taps_pub.average.methratio.txt\", delim = \"\\t\") %>% as.data.frame()\nlrtaps <- read_delim(paste0(\"meth/\", id,\".uniflag.md_CpG.merge.bedGraph\"), delim = \"\\t\") %>% as.data.frame()\nall_taps <- merge(taps,lrtaps, by=c(\"chr\",\"start\"))\ncolnames(all_taps) <- c(\"chr\",\"start\",\"taps_mC\",\"taps_aC\",\"taps_meth\",\"lrtaps_mC\",\"lrtaps_aC\",\"lrtaps_meth\")\ne14_snp <- read_delim(\"resource/e14.cgsnp.bed\",delim=\"\\t\", col_names=FALSE)\ncolnames(e14_snp) <- c(\"chr\",\"start\",\"end\",\"snp\")\nall_taps <- merge(all_taps,e14_snp,by=c(\"chr\",\"start\"),all.x=TRUE)\nall_taps <- all_taps[!complete.cases(all_taps$end),-c(ncol(all_taps)-1,ncol(all_taps))]\ncg_states <- read_delim(\"resource/mm9_genome.cg.chrHMM.info\", delim = \"\\t\", col_names =FALSE) %>% as.data.frame()\nall_taps <- merge(all_taps, cg_states, by.x=c(\"chr\",\"start\"), by.y=c(\"X1\",\"X2\") )\n\n# methylation correlation ----\nfor(depth in c(5, 20)){\n seltaps <- all_taps[all_taps$taps_aC>=depth & all_taps$lrtaps_aC>=depth & complete.cases(all_taps) & all_taps$X3!=\".\",]\n cors <- cor(seltaps %>% dplyr::select(taps_meth, lrtaps_meth))\n pdf(paste0(\"plots/\",id,\"_depth.\",depth,\".scatter.pdf\"), width = 3.2, height = 3.5)\n smoothScatter(x=seltaps$taps_meth,\n y=seltaps$lrtaps_meth,\n xlab=paste0(\"TAPS (cor:\", round(cors[2,1],2),\")\"),\n ylab= \"lrTAPS\",\n xlim=c(0,1),\n ylim=c(0,1))\n dev.off()\n print(dim(seltaps))\n print(cors)\n \n cors1 <- c (\"all\", cors[2,1])%>% t()%>% as.data.frame(); cors1$V2 <- as.numeric(as.character(cors1$V2))\n cors2 <- as.table(by(seltaps[,c(5,8)], seltaps$X3, function(x) {cor(x$taps_meth, x$lrtaps_meth)})) %>% as.data.frame()\n colnames(cors1) <- c(\"feature\",\"cor\")\n colnames(cors2) <- c(\"feature\",\"cor\")\n allcors <- rbind(cors1,cors2)\n write.table(allcors,paste0(\"plots/\", id,\"_depth.\",depth,\".cor.txt\"),sep=\"\\t\", col.names =TRUE, row.names = FALSE, quote = FALSE)\n \n pdf(paste0(\"plots/\",id,\"_depth.\",depth,\".feature.scatter.pdf\"), width = 3.2, height = 3.5)\n for( i in sort(unique(seltaps$X3))[c(1,8:15,2:7)]){\n selcpg <- seltaps[seltaps$X3==i, ]\n smoothScatter(x=selcpg$taps_meth,\n y=selcpg$lrtaps_meth,\n xlab=paste0(\"TAPS (cor:\", round(cor(selcpg$taps_meth, selcpg$lrtaps_meth),2),\")\\n\",i),\n ylab= \"lrTAPS\",\n xlim=c(0,1),\n ylim=c(0,1))\n }\n dev.off()\n}\n\n\n\n\n\n# covered sites ----\n\nall_taps$taps <- ifelse(all_taps$taps_aC >= 5, 1, 0); all_taps$taps[is.na(all_taps$taps)] <- 0\nall_taps$lrtaps <- ifelse(all_taps$lrtaps_aC >= 5, 1, 0); all_taps$lrtaps[is.na(all_taps$lrtaps)] <- 0\noptions(scipen = 100)\nwrite.table(all_taps,paste0(\"meth/\", id,\"_shortread_taps.info.txt\"),sep=\"\\t\", col.names =TRUE, row.names = FALSE, quote = FALSE)\nmean(all_taps$lrtaps_aC[!is.na(all_taps$lrtaps_aC)])\n\npdf(paste0(\"plots/\", id,\"_all.coveredC.venn.pdf\"), width = 5, height = 4)\nnum_taps <- sum(all_taps$taps==\"1\",na.rm=TRUE)\nnum_lrtaps <- sum(all_taps$lrtaps==\"1\",na.rm=TRUE)\nnum_taps_lrtaps <- sum(all_taps$taps==\"1\"&all_taps$lrtaps==\"1\",na.rm=TRUE)\ndraw.pairwise.venn(\n area1 = num_taps,\n area2 = num_lrtaps,\n cross.area = num_taps_lrtaps,\n category = c('taps', 'lrtaps'),\n fill = c(\"#446db4\",\"#23b177\"),\n cat.col = c(\"#446db4\",\"#23b177\"),\n euler.d = TRUE,\n scaled = TRUE\n)\ndev.off()\n\nall_taps[all_taps$taps!=\"1\"&all_taps$lrtaps==\"1\",] %>% head()\n\ndepth_sta <- all_taps[all_taps$X3!=\".\",] %>% dplyr::select(taps_aC,lrtaps_aC, X3) %>% melt(id.vars=\"X3\") \np <-ggplot(depth_sta, aes(value, fill=variable)) +\n geom_bar(stat=\"count\", position=position_dodge() ) + theme(legend.position = \"bottom\") +\n xlab(\"depth\") +\n facet_wrap(~X3, scales = \"free\", nrow=3) +\n xlim(0,40)\n \nggsave(paste0(\"plots/\", id,\"_all.depth_sta.pdf\"),p,width =12, height = 8)\n\n\n# covered repeats ----\nsta <- data.frame(\"non_repeats\"=c(6497927, 47295, 6154800, 12954564), \"repeats\"=c(3954621, 251304, 2596746, 8076613))\nsta$group <- c(\"overlap\",\"lrTAPS_only\",\"TAPS_only\",\"all\")\nsta$non_repeat_pct <- sta$non_repeats / (sta$non_repeats + sta$repeats)\nsta$repeat_pct <- sta$repeats / (sta$non_repeats + sta$repeats)\nsta_melt <- melt(sta %>% dplyr::select(\"group\",\"non_repeat_pct\",\"repeat_pct\"), by=c(\"group\"))\nsta_melt$group <- factor(sta_melt$group, levels=c(\"all\",\"TAPS_only\",\"overlap\",\"lrTAPS_only\"))\np <- ggplot(sta_melt, aes(x=group, y=value, fill=variable)) + geom_bar(stat=\"identity\") + theme_light() + scale_fill_manual( values = c(\"#446db4\",\"#23b177\")) + ylab(\"percentage\") + theme(legend.position = \"bottom\")\nggsave(paste0(\"plots/\", id,\"repeats_sta.pdf\"),p,width =4, height = 4)\n\n\n\n\n# Methylation on chrHMM ----\ndat1 <- read.table(\"meth/bc1001_reseq.chrHMM.meth.sta\", header=FALSE)\ndat2 <- read.table(\"shortread_taps/mESC_cStates_HMM.taps.shortread.sta\",header=FALSE)\ndat <- merge(dat1,dat2,by=c(\"V1\"))\ncolnames(dat) <- c(\"chrHMM\",\"lrtaps_meth\",\"lrtaps_mC\",\"taps_aC\",\"taps_meth\",\"srtaps_mC\",\"taps_aC\")\np <- ggplot(dat[,c(1,grep(\"meth\",colnames(dat)))] %>% melt(id.vars=c(\"chrHMM\")),aes(x=chrHMM,y=value*100,fill=variable)) +\n geom_bar(stat=\"identity\", position=position_dodge()) + \n theme_light() +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\") +\n xlab(\"chrHMM state\") +\n ylab(\"methylation (%)\") +\n scale_fill_manual(values=brewer.pal(3,\"Dark2\"))\n\nggsave(\"plots/bc1001_reseq_mESC_chrHMM.meth.pdf\",p,width = 6, height = 4)\n\n\n\n\n# Aligned Length ----\n\n\n\n\n\n# Coverage on chrHMM ----\nbc1001_cov <- read.table(\"align/bc1001_reseq.chrHMM.cov.sta\", header=FALSE)\nshort_cov <- read.table(\"shortread_taps/merged_bam.md.cov.sta\",header=FALSE)\nlen <- read.table(\"resource/mESC_cStates_HMM.len.sta\",header=FALSE)\n\ndat <- merge(bc1001_cov,short_cov,by=c(\"V1\")) %>% merge(len,by=c(\"V1\"))\ncolnames(dat) <- c(\"chrHMM\",\"lrtaps_depth\",\"lrtaps_pcov\",\"lrtaps_cov\",\"taps_depth\",\"taps_pcov\",\"taps_cov\",\"len\")\ndat$lrtaps_p <- dat$lrtaps_cov/dat$len\ndat$taps_p <- dat$taps_cov/dat$len\n\np1 <- ggplot(dat[,c(1,grep(\"depth\",colnames(dat)))] %>% melt(id.vars=c(\"chrHMM\")),aes(x=chrHMM,y=value*100,fill=variable))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7) +\n ylab(\"# total read\") +\n xlab(\"chr HMM\") +\n facet_grid(rows = vars(variable), scales=\"free_y\") +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\n\np2 <- ggplot(dat[,c(1,grep(\"p$\",colnames(dat)))] %>% melt(id.vars=c(\"chrHMM\")),aes(x=chrHMM,y=value*100,fill=variable))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7) +\n ylab(\"% cov\") +\n xlab(\"chr HMM\") +\n facet_grid(rows = vars(variable)) +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\np <- plot_grid(p1, p2)\np\nggsave(\"plots/bc1001_reseq.mESC_chrHMM.coverage.cmb.pdf\",p,width = 12, height = 6)\nggsave(\"plots/bc1001_reseq.mESC_chrHMM.coverage.cmb.png\",p,width = 12, height = 6)\n\n\n# covered sites ---\ngrid.newpage()\npdf(\"plots/covered_sites.pdf\",width = 4,height = 4)\nbc1001 <- 1425646958\nbc1002 <- 1484131961\nshortr <- 2542284168 \nbc1001_bc1002 <- 910008142 \nbc1001_shortr <- 1423644222\nbc1002_shortr <- 1481521517 \nbc1001_bc1002_shortr <- 908875523\ndraw.triple.venn(\n area1 = bc1001,\n area2 = bc1002,\n area3 = shortr,\n n12 = bc1001_bc1002,\n n23 = bc1002_shortr,\n n13 = bc1001_shortr,\n n123 = bc1001_bc1002_shortr,\n category = c('bc1001', 'bc1002', 'short_read'),\n fill = c(\"#446db4\",\"#23b177\",\"#fac133\"),\n cat.col = c(\"#446db4\",\"#23b177\",\"#fac133\"),\n euler.d = TRUE,\n scaled = TRUE\n)\ndev.off()\n\ndat <- read.table(\"align/bc1002.chrHMM.len.txt\")\np <- ggplot(dat,aes(x=V8,y=V4)) + geom_violin() + geom_boxplot(outlier.shape = NA, width=0.1) +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\nggsave(\"plots/frag_len.chrhmm.bc1002.pdf\",p,width = 6, height = 4)\ndat <- read.table(\"align/bc1001.chrHMM.len.txt\")\np <- ggplot(dat,aes(x=V8,y=V4)) + geom_violin() + geom_boxplot(outlier.shape = NA, width=0.1) +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\nggsave(\"plots/frag_len.chrhmm.bc1001.pdf\",p,width = 6, height = 4)\n\n# covered sites ----\nbc1001_cov <- read.table(\"align/bc1001.lc_extrap.txt\", header=TRUE)\nbc1002_cov <- read.table(\"align/bc1002.lc_extrap.txt\", header=TRUE)\nbc1003_cov <- read.table(\"align/bc1003.lc_extrap.txt\", header=TRUE)\n\n\ndat <- merge(bc1001_cov,bc1002_cov,by=\"TOTAL_READS\") %>% merge(bc1003_cov,by=\"TOTAL_READS\")\ncolnames(dat) <- c(\"total\",\"bc1001_distinct\",\"bc1001_lower_ci\",\"bc1001_upper_ci\",\"bc1002_distinct\",\"bc1002_lower_ci\",\"bc1002_upper_ci\",\"subbc1002_distinct\",\"subbc1002_lower_ci\",\"subbc1002_higher_ci\")\ndat$bc1001_p <- dat$bc1001_distinct/dat$total\ndat$bc1002_p <- dat$bc1002_distinct/dat$total\ndat$subbc1002_p <- dat$subbc1002_distinct/dat$total\n\n\np1 <- ggplot(dat[dat$total<10000000 & dat$total>0,c(grep(\"_p|total\",colnames(dat)))] %>% melt(id.vars=c(\"total\")),aes(x=total,y=value*100,fill=variable))+\n geom_bar(stat=\"identity\", position=position_dodge(), alpha=0.7) +\n ylab(\"distinct reads%\") +\n xlab(\"reads sequenced\") +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\np1\nggsave(\"plots/lc_extrap.pdf\",p1,width = 6, height = 4)\n\n\n# Meth context ----\nbc1001_meth <- read.table(\"meth/bc1001.uniflag_CpG.context.sta\", header=FALSE)\nbc1002_meth <- read.table(\"meth/bc1002.uniflag_CpG.context.sta\", header=FALSE)\n\ndat <- merge(bc1001_meth[,c(1,4)], bc1002_meth[,c(1,4)],by=c(\"V1\"))\ncolnames(dat) <- c(\"context\",\"bc1001\",\"bc1002\")\n\np1 <- ggplot(melt(dat),aes(x=context,y=value,fill=variable))+ \n geom_bar(stat=\"identity\", position = \"dodge\")+\n ylab(\"Methylation%\") +\n xlab(\"smp\") +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = \"bottom\")\np1\nggsave(\"plots/meth_context.pdf\",p1,width = 6, height = 4)\n\n# Compare SV ----\npdf(paste0(\"plots/\", id,\"_SV.cmp.pdf\"), width = 4.5, height = 4)\ngrid.newpage()\ndraw.pairwise.venn(\n area1 = 641+1170,\n area2 = 27299+1170,\n cross.area = 1170,\n category = c('ins_taps', 'ins_lrtaps'),\n fill = c(\"#446db4\",\"#23b177\"),\n cat.col = c(\"#446db4\",\"#23b177\"),\n euler.d = TRUE,\n scaled = TRUE\n)\ngrid.newpage()\ndraw.pairwise.venn(\n area1 = 6625+11418,\n area2 = 24723+11418,\n cross.area = 11418,\n category = c('del_taps', 'del_lrtaps'),\n fill = c(\"#446db4\",\"#23b177\"),\n cat.col = c(\"#446db4\",\"#23b177\"),\n euler.d = TRUE,\n scaled = TRUE\n)\n\ndat <- read.table(\"plots/all_insertion_sta.txt\", header=TRUE, stringsAsFactors = FALSE)\ndat$svlen <- as.numeric(dat$svlen)\ninsertion_num <- spread(dat[,-c(4)], svlen, num)\ncolnames(insertion_num) <- c(\"type\",\"0-50\",\"50-100\", \"100+\")\ninsertion_num[is.na(insertion_num)] <- 0\ninsertion_num <- melt(insertion_num,id.vars=(\"type\"), variable.name = \"svlen\")\np1 <- ggplot(insertion_num, aes(x=svlen, y=value, fill=type)) + \n geom_bar(stat=\"identity\", position = \"dodge\") + \n ylab(\"num\") + theme_light() + xlab(\"insertion len\") + \n scale_fill_manual(values=brewer.pal(3,\"Dark2\")) +\n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\ninsertion_pct <- spread(dat[,-c(3)], svlen, pct)\ninsertion_pct[is.na(insertion_pct)] <- 0\ncolnames(insertion_pct) <- c(\"type\",\"0-50\",\"50-100\", \"100+\")\ninsertion_pct <- melt(insertion_pct,id.vars=(\"type\"), variable.name = \"svlen\")\np2 <- ggplot(insertion_pct, aes(x=svlen, y=value, fill=type)) + geom_bar(stat=\"identity\", position = \"dodge\") + ylab(\"pct\") + theme_light()+ xlab(\"insertion len\") + scale_fill_manual(values=brewer.pal(3,\"Dark2\"))\n\nplot_grid(p1, p2, labels = c('A', 'B'), ncol=1)\n\ndat <- read.table(\"plots/all_deletion_sta.txt\", header=TRUE, stringsAsFactors = FALSE)\ndat$svlen <- as.numeric(dat$svlen)\ndeletion_num <- spread(dat[,-c(4)], svlen, num)\ncolnames(deletion_num) <- c(\"type\",\"0-50\",\"50-100\", \"100+\")\ndeletion_num[is.na(deletion_num)] <- 0\ndeletion_num <- melt(deletion_num,id.vars=(\"type\"), variable.name = \"svlen\")\np3 <- ggplot(deletion_num, aes(x=svlen, y=value, fill=type)) + geom_bar(stat=\"identity\", position = \"dodge\") + ylab(\"num\") + theme_light()+ xlab(\"deletion len\") + \n scale_fill_manual(values=brewer.pal(3,\"Dark2\")) + \n theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))\n\ndeletion_pct <- spread(dat[,-c(3)], svlen, pct)\ndeletion_pct[is.na(deletion_pct)] <- 0\ncolnames(deletion_pct) <-c(\"type\",\"0-50\",\"50-100\", \"100+\")\ndeletion_pct <- melt(deletion_pct,id.vars=(\"type\"), variable.name = \"svlen\")\np4 <- ggplot(deletion_pct, aes(x=svlen, y=value, fill=type)) + geom_bar(stat=\"identity\", position = \"dodge\") + ylab(\"pct\") + theme_light()+ xlab(\"deletion len\") + scale_fill_manual(values=brewer.pal(3,\"Dark2\"))\n\nplot_grid(p3, p4, labels = c('C', 'D'),ncol=1)\ndev.off()\n", "meta": {"hexsha": "b3466739427edc491a2d358d602f2e1761a5cfe2", "size": 15682, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_rscript_wg_lrtaps.r", "max_stars_repo_name": "jfeicheng92/wglrtaps", "max_stars_repo_head_hexsha": "a3ac9f9aa2f93f7bdae39596986ebaf345fcb4b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_rscript_wg_lrtaps.r", "max_issues_repo_name": "jfeicheng92/wglrtaps", "max_issues_repo_head_hexsha": "a3ac9f9aa2f93f7bdae39596986ebaf345fcb4b4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_rscript_wg_lrtaps.r", "max_forks_repo_name": "jfeicheng92/wglrtaps", "max_forks_repo_head_hexsha": "a3ac9f9aa2f93f7bdae39596986ebaf345fcb4b4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.4550724638, "max_line_length": 215, "alphanum_fraction": 0.6589083025, "num_tokens": 5505, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6688802603710086, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.3057697261767227}} {"text": "# Methods for in silico tryptic digestion, formula calculation, monoisotopic mass\r\n# calculation and b and y fragment ion simulation in R.\r\n#\r\n# Written by Tom Taverner for Pacific Northwest National Lab.\r\n# 10-26-2010\r\n\r\n# Methods for in silico digestion, \r\n\r\n# Required packages\r\ninstall.packages(\"seqinr\")\r\nlibrary(seqinr)\r\nlibrary(reshape)\r\n\r\n # lookup table for amino acid formulae\r\nmy.aa <- array(c(6, 6, 6, 5, 9, 4, 11, 5, 6, 6, 3, 4, 4, 3, 5, 5, 2, 5, 3, 9, 11, 11, 12, 9, 9, 7, 10, 9, 12, 7, 5, 6, 5, 5, 7, 8, 3, 7, 5, 9, 1, 1, 2, 1, 1, 1, 2, 1, 4, 3, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3, 1, 3, 2, 1, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), c(20L, 5L))\r\ndimnames(my.aa) <- list(c(\"I\", \"L\", \"K\", \"M\", \"F\", \"T\", \"W\", \"V\", \"R\", \"H\", \"A\", \"N\", \"D\", \"C\", \"E\", \"Q\", \"G\", \"P\", \"S\", \"Y\"), c(\"C\", \"H\", \"N\", \"O\", \"S\"))\r\n\r\n # lookup table for monoisotopic masses\r\nmy.fw = c(C = 12.0, H = 1.00782503207, N = 14.0030740048, O = 15.99491461956, S = 31.97207100)\r\n\r\n\r\n # get.formula: given a string of 1-letter amino acid codes, returns the formula of the peptide\r\n # arguments: x, peptide sequence, \r\n # plusFormula - default corresponds to H2O for peptides\r\n # b ions: plusFormula = c(C=0,H=0,N=0,O=0,S=0)\r\n # y ions: default is OK\r\n # charge, charge to place on sequence\r\nget.formula <- function(x, charge=1, plusFormula=c(C=0, H=2, N=0, O=1, S=0)){\r\n f1 <- factor(strsplit(x, \"\")[[1]], levels=rownames(my.aa))\r\n colSums(rbind(model.matrix(~f1-1) %*% my.aa, plusFormula, c(C=0, H=charge, N=0, O=0, S=0)))\r\n}\r\n\r\n # get.monoisotopic.mass: given a formula output from get.formula, get the monoisotopic mass\r\nget.monoisotopic.mass <- function(ff){\r\n stopifnot(identical(names(ff), names(my.fw)))\r\n drop(ff %*% my.fw)\r\n}\r\n\r\n\r\n # apply.protease: virtual protease digestion function\r\n # arguments:\r\n # prots: list containing protein sequences\r\n # missed: number of missed cleavages\r\n # include.re and exclude.re: regular expressions corresponding to protease cleavage rules\r\n # the cleavage sites are the include.re matches that aren't in exclude.re\r\n # note: only supports cleavage on C-terminal side\r\n # output: the same list of prots, with $peptide containing \r\n # (1) list of peptides \r\n # (2) position of cleavage \r\n # (3) number of missed cleavages\r\napply.protease <- function(prots, missed=1, include.re = \"[KR]\", exclude.re = \"([KR]$)|([KR][P])\"){\r\n for(jj in seq(length=length(prots))){\r\n prot <- prots[[jj]][[1]]\r\n pos <- setdiff(gregexpr(include.re, prot)[[1]], gregexpr(exclude.re, prot)[[1]])\r\n\r\n pos <- c(0, c(pos), nchar(prot))\r\n n <- length(pos)+1\r\n nmis <- missed+1\r\n pid <- cbind(rep(1:n, each=nmis), rep(1:nmis, n))\r\n zz <- cbind(pos[pid[,1]], pos[pid[,1]+pid[,2]])\r\n ok.idx <- !rowSums(is.na(zz))\r\n zz <- zz[ok.idx,,drop=F]\r\n # this is where we'd extend the method with PTM's, etc\r\n suppressWarnings(prots[[jj]]$peps <- substr(rep(prot[[1]], nrow(zz)), zz[,1]+1, zz[,2]))\r\n\r\n attr(prots[[jj]]$peps, \"start\") <- pos[pid[ok.idx,1]]\r\n attr(prots[[jj]]$peps, \"missed\") <- pid[ok.idx,2]-1\r\n } \r\n return(prots)\r\n}\r\n\r\n\r\n # For a given protein from the apply.protease() output\r\n # create a Protein Prospector style data frame \r\n # Input: a single list item from apply.protease() output\r\n # Output: a data frame containing sequence, start, missed cleavages, charge, monoisotopic m/z\r\ncreate.peptides.table <- function(digested.peps, minMZ=800, maxMZ=4000, maxCharge=1){\r\n thePeps <- digested.peps$peps\r\n theFormulas <- sapply(thePeps, get.formula)\r\n theCharge <- rep(1:maxCharge, each=length(thePeps))\r\n\r\n mono.mz <- c()\r\n for(z in 1:maxCharge)\r\n mono.mz <- c(mono.mz, c(t(theFormulas)%*%my.fw + (z-1)*my.fw[[\"H\"]])/z)\r\n\r\n peps.and.masses <- data.frame(MassToCharge = mono.mz, \r\n Start = attr(thePeps, \"start\"), MissedCleavages = attr(thePeps, \"missed\"), \r\n Charge = theCharge, Sequence=thePeps)\r\n\r\n # ensure we have reasonable numbers of charges - don't allow more charge than K + R + 1\r\n maxAllowedCharge <- sapply(gregexpr(\"[KR]\", thePeps), length)+1\r\n peps.and.masses <- peps.and.masses[peps.and.masses$Charge <= maxAllowedCharge,,drop=FALSE]\r\n\r\n peps.and.masses <- peps.and.masses[minMZ< peps.and.masses$MassToCharge \r\n & peps.and.masses$MassToCharge < maxMZ,,drop=FALSE]\r\n peps.and.masses <- peps.and.masses[order(peps.and.masses$MassToCharge),,drop=FALSE]\r\n\r\n\r\n return(peps.and.masses)\r\n}\r\n\r\n # create b and y ions for a given peptide sequence\r\n # input: peptide sequence\r\n # output: list containing b.ions and y.ions\r\n # both are named vectors containing monoisotopic singly charged masses of ions\r\ncreate.ions <- function(peptide){\r\n ncp <- nchar(peptide)\r\n b.sequences <- c(mapply(substr, rep(peptide, ncp), 1, 1:ncp))\r\n names(b.sequences) <- paste(\"b\", 1:length(b.sequences), sep=\"\")\r\n\r\n b.ions <- sapply(b.sequences, \r\n function(x) get.monoisotopic.mass(get.formula(x, plusFormula = c(C=0,H=0,N=0,O=0,S=0))))\r\n b.ions[1] <- NA # there's no such thing as a b1 ion ;-)\r\n b.ions[length(b.ions)] <- NA\r\n\r\n\r\n y.sequences <- c(mapply(substr, rep(peptide, ncp), ncp:1, ncp))\r\n names(y.sequences) <- paste(\"y\", 1:length(y.sequences), sep=\"\")\r\n\r\n y.ions <- sapply(y.sequences, \r\n function(x) get.monoisotopic.mass(get.formula(x, plusFormula = c(C=0,H=2,N=0,O=1,S=0))))\r\n y.ions[length(y.ions)] <- NA\r\n\r\n return(list(b.ions=b.ions, y.ions=y.ions))\r\n}\r\n\r\n\r\n\r\n#\r\n# Demonstration code\r\n#\r\n\r\nlibrary(seqinr)\r\n # read in the Shew fasta file here\r\nmy.fasta <- read.fasta(file.choose(), as.string=TRUE, forceDNAtolower=FALSE)\r\n\r\n # take the first 5\r\nprots <- my.fasta[1:5]\r\n\r\n # apply simulated tryptic digestion to get a list of all peptides given 1 missed\r\ndigested <- apply.protease(prots, missed=1)\r\n \r\n # create tables including monoisotopic masses, etc\r\npeptide.tables <- lapply(digested, create.peptides.table, minMZ=800, maxMZ=4000, maxCharge=3)\r\n\r\nlibrary(RGtk2Extras)\r\ndfedit(peptide.tables$SO_0001)\r\n# compare to prospector\r\nhttp://prospector.ucsf.edu/prospector/cgi-bin/msform.cgi?form=msdigest\r\n\r\n# enter in MTKIAILVGTTLGSSEYIADEMQAQLTPLGHEVHTFLHPTLDELKPYPLWILVSSTHGAGDLPDNLQPFCKELLL\r\nNTPDLTQVKFALCAIGDSSYDTFCQGPEKLIEALEYSGAKAVVDKIQIDVQQDPVPEDPALAWLAQWQDQI\r\n# notice we predict some peptides their algorithm doesn't...\r\n\r\n # bring peptides from first 5 proteins together\r\nlibrary(reshape)\r\nmm <- melt(peptide.tables, measure.vars=\"MassToCharge\")\r\nall.peptides <- cast(mm)\r\ncolnames(all.peptides)[colnames(all.peptides)==\"L1\"] <- \"Protein\"\r\nall.peptides <- all.peptides[order(all.peptides$MassToCharge),,drop=FALSE]\r\n\r\n # take a look\r\ndfedit(all.peptides)\r\n\r\n# generate some b and y ions\r\nmy.fragments <- create.ions(\"LIEALEYSGAKAVVDK\")\r\ndata.frame(b.type=names(my.fragments$b.ions), b.mass=my.fragments$b.ions, y.type=rev(names(my.fragments$y.ions)), y.mass=rev(my.fragments$y.ions))\r\n\r\n# compare to prospector\r\nhttp://prospector.ucsf.edu/prospector/cgi-bin/mssearch.cgi?search_name=msproduct&output_type=HTML&report_title=MS-Product&version=5.6.2&instrument_name=ESI-Q-TOF&use_instrument_ion_types=1&parent_mass_convert=monoisotopic&user_aa_composition=C2%20H3%20N1%20O1&max_charge=2&s=1&sequence=LIEALEYSGAKAVVDK&\r\n\r\n", "meta": {"hexsha": "aefab091ec03f6c3c44cafcdb271f169dde2047f", "size": 7186, "ext": "r", "lang": "R", "max_stars_repo_path": "Documentation/PeptideManipulation.r", "max_stars_repo_name": "PNNL-Comp-Mass-Spec/Protein-Digestion-Simulator", "max_stars_repo_head_hexsha": "4ec8053a4521a1ad0acccbfbd31a5a2cd9231c2a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-05-11T02:38:42.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-30T10:29:59.000Z", "max_issues_repo_path": "Documentation/PeptideManipulation.r", "max_issues_repo_name": "PNNL-Comp-Mass-Spec/Protein-Digestion-Simulator", "max_issues_repo_head_hexsha": "4ec8053a4521a1ad0acccbfbd31a5a2cd9231c2a", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-12-16T14:25:55.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-06T15:46:00.000Z", "max_forks_repo_path": "Documentation/PeptideManipulation.r", "max_forks_repo_name": "PNNL-Comp-Mass-Spec/Protein-Digestion-Simulator", "max_forks_repo_head_hexsha": "4ec8053a4521a1ad0acccbfbd31a5a2cd9231c2a", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-12T15:12:25.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-30T10:30:00.000Z", "avg_line_length": 41.7790697674, "max_line_length": 336, "alphanum_fraction": 0.6661564153, "num_tokens": 2520, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752916, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.30572603232082296}} {"text": "# Plot text labels with a different color background (taken from TeachingDemos package)\n# philip kraaijenbrink\n\n\n#' Plot shadowed text labels\n#'\n#' Plot text labels with a different color shadow or faked outline. Function taken from \\code{TeachingDemos} package.\n#' @param x x position\n#' @param y y position\n#' @param labels text labels\n#' @param col foreground color\n#' @param bg shadow/outline color\n#' @param theta angle at which to plot the shadow\n#' @param r distance of the shadow\n#' @param ... any other arguments for text function\n#' \n#' @return Vector with hex colors strings. \n#' @export\nshadeText <- function(x, y=NULL, labels, col='white', bg='black',\n theta=seq(pi/4, 2*pi, length.out=8), r=0.1, ... ) {\n \n xy <- xy.coords(x,y)\n xo <- r*strwidth('A')\n yo <- r*strheight('A')\n \n for (i in theta) {\n text(xy$x + cos(i)*xo, xy$y + sin(i)*yo, labels, col=bg, ... )\n }\n text(xy$x, xy$y, labels, col=col, ... )\n \n}\n\n\n\n#' @rdname shadeText\n#' @export\npkShadowText <- shadeText\n\n\n", "meta": {"hexsha": "a37de7950a130c4141de2f0fe343ae383576a250", "size": 1020, "ext": "r", "lang": "R", "max_stars_repo_path": "R/pkShadowText.r", "max_stars_repo_name": "kraaijenbrink/pkrf", "max_stars_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/pkShadowText.r", "max_issues_repo_name": "kraaijenbrink/pkrf", "max_issues_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/pkShadowText.r", "max_forks_repo_name": "kraaijenbrink/pkrf", "max_forks_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-03T16:13:24.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-03T16:13:24.000Z", "avg_line_length": 25.5, "max_line_length": 117, "alphanum_fraction": 0.6460784314, "num_tokens": 296, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3056909937279301}} {"text": "pdf_file<-\"pdf/maps_germany_scatterplot.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=8,height=11)\n\npar(mai=c(1.1,0,0,0),omi=c(0.25,0.5,0.75,0.5),family=\"Lato Light\",las=1)\n\n# Import data and prepare chart\n\nsource(\"scripts/inc_datadesign_dbconnect.r\")\nsql<-\"select * from v_women_men\"\nmyDataset<-dbGetQuery(con,sql)\nattach(myDataset)\n\nlegmaxsize<-max(transbev); legmaxvalue<-max(bevinsg)\nlegmaxtext<-myDataset[which(myDataset$transbev==legmaxsize),\"gemeinde\"]\nif (length(legmaxtext) > 1) \n{\nn<-length(legmaxtext)\nfor (i in 2:n) legmaxtext<-c(legmaxtext,paste(\",\",legmaxtext[i,]))\n}\nlegmidsize<-quantile(transbev,0.5); legmidvalue<-quantile(bevinsg,0.5)\n\n# Define chart and other elements\n\nplot(lng,lat,type=\"n\",axes=F,xlab=\"\",ylab=\"\")\nlow<-subset(myDataset,wm < 0.90)\nmedium<-subset(myDataset,wm >= 0.90 & wm <= 1.10)\nhigh<-subset(myDataset,wm > 1.10)\n\nc1<-rgb(0,191,255,200,maxColorValue=255)\nc2<-rgb(150,150,150,80,maxColorValue=255)\nc3<-rgb(128,0,0,200,maxColorValue=255)\n\nattach(low)\npoints(lng,lat,pch=19,col=c1,cex=transbev)\nattach(medium)\npoints(lng,lat,pch=19,col=c2,cex=transbev)\nattach(high)\npoints(lng,lat,pch=19,col=c3,cex=transbev)\n\nl1<-paste(\"Max.: \",format(legmaxvalue,digits=2),\" Mill. (\",legmaxtext,\")\",sep=\"\")\nl2<-paste(\"Median: \",format(legmidvalue,digits=2),\" Mio.\",sep=\"\")\nlegend(6.2,47.1,c(l1,l2),text.col=\"azure4\",title=\"Point size: Population size\",title.adj=0.3,border=F,pch=19,col=rgb(150,150,150,80,maxColorValue=255),bty=\"n\",cex=1.1,pt.cex=c(legmaxsize,legmidsize),xpd=T,ncol=2)\nlegend(13,50.25,text.col=\"azure4\",c(\"fewer than 90 women\",\"90 to 110 women\",\"more than 110 women\"),title=\"for 100 men there are\",title.adj=0,pt.cex=1,xpd=T,border=F,pch=19,col=c(c1,c2,c3),bty=\"n\",cex=0.8)\n\n# Titling\n\nmtext(\"Relation of men and women in Germany 2005\",line=0,adj=0,cex=1.8,family=\"Lato Black\",outer=T)\nmtext(\"Source: www.destatis.de, opengeodb.giswiki.org, www.lichtblau-it.de\",side=1,line=-1,adj=1,cex=0.9,font=3,outer=T)\ndev.off()", "meta": {"hexsha": "fd80280acab81498c7119d15f64781c6e603a9d6", "size": 1951, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/maps_germany_scatterplot.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/maps_germany_scatterplot.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/maps_germany_scatterplot.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8163265306, "max_line_length": 212, "alphanum_fraction": 0.7227063045, "num_tokens": 748, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3055907097395889}} {"text": "# load necessary libraries for sparql\nlibrary(rrdf)\nlibrary(MASS)\n\n# variables for connecting to triple store\n#owlim_se_r21 <- \"http://localhost:8080/openrdf-workbench/repositories/owlim-se-2012.12.05/query\"\nowlim_se_r21 <- \"http://localhost:8080/openrdf-workbench/repositories/ohd-r21-nightly/query\"\nowlim_se_r21_remote <- \"http://dungeon.ctde.net:8080/openrdf-sesame/repositories/ohd-r21-nightly\"\n\n#current <- owlim_se_r21_remote\ncurrent <- owlim_se_r21\n\nqueryc <- function(string) { query(querystring(string),current) }\n\n# strings for prefixes\nprefixes <- function ()\n{\n \"PREFIX rdf: \nPREFIX rdfs: \nPREFIX obo: \nPREFIX dental_patient: \nPREFIX birth_date: \nPREFIX inheres_in: \n\"}\n\nquerystring <- function(string)\n { paste(prefixes(),\"\\n\",string,\"\\n\") }\n\n# strings for finding patients\n# \npatients_query_count <- querystring (\n\"SELECT (count(distinct ?patientid) as ?count)\nWHERE { \n?patienti rdf:type dentalpatient: . \n?patienti rdfs:label ?patientid . \n} \")\n\n\nqueryRes <- queryc(\"SELECT ?patient ?date WHERE { ?patienti rdf:type dental_patient: . ?patienti birth_date: ?date}\")\n\nbirthdates <- as.Date(queryRes[,2])\n\nnow <- Sys.Date()\n\nages <- (now-birthdates)/365.25\n\nages_histogram <- hist(as.numeric(ages),breaks=50,plot=FALSE)\n\nage_normal_distribution <- fitdistr(ages,\"normal\")\n\nage_mean = age_normal_distribution$estimate[1]\nage_sd = age_normal_distribution$estimate[2]\n\ncat(paste(\"age mean is \", age_mean, \" and age standard deviation is \",age_sd,\"\\n\"))\n\n", "meta": {"hexsha": "da41f75ce618f471bc541b357591ac806fbbb129", "size": 1717, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r21-study/simple-statistics.r", "max_stars_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_stars_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-08T16:11:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T16:11:01.000Z", "max_issues_repo_path": "src/r21-study/simple-statistics.r", "max_issues_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_issues_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-05-13T19:04:16.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-31T18:34:56.000Z", "max_forks_repo_path": "src/r21-study/simple-statistics.r", "max_forks_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_forks_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.6607142857, "max_line_length": 117, "alphanum_fraction": 0.744321491, "num_tokens": 507, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3055907097395888}} {"text": "\r\n#-----performance-----v8--------\r\n\r\ntpm<-read.table('~/../Dropbox/data/gtex/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.49',header = T,stringsAsFactors = F)\r\ntpm[,1]<-sapply(tpm[,1], function(x) strsplit(x,\"[.]\")[[1]][1])\r\n\r\nmain_path=paste0('~/../Dropbox/DansPaper/data/performance/v8/')\r\ntissue_list<-dir(main_path)\r\n\r\noutput<-as.data.frame(matrix(data=NA,ncol=14))\r\ncolnames(output)<-c('tissue','n_xt','n_ut','n_st','delta_r2_xt','delta_r2_ut','n_increase_xt','n_increase_ut','delta_r2_xt_p','delta_r2_ut_p','r2_st_median','r2_xt_median','r2_st_mean','r2_xt_mean')\r\n\r\n\r\ni=1\r\nfor (i in 1:length(tissue_list)){\r\n print(tissue_list[i])\r\n box<-read.table(paste0(main_path,tissue_list[i]),header = T,stringsAsFactors = F)\r\n box[is.na(box)] <- 0\r\n box<-box[which(box[,1] %in% tpm[tpm[,i+2]>0,1]),] #median TPM >0.1\r\n #print(colnames(tpm[i+2]))\r\n \r\n box$delta_xt<-box$r_xt^2-box$r_st^2\r\n box$delta_ut<-box$r_ut^2-box$r_st^2\r\n box$delta_xt_p<-(box$r_xt^2-box$r_st^2)/(box$r_st^2+0.001)\r\n box$delta_ut_p<-(box$r_ut^2-box$r_st^2)/(box$r_st^2+0.001)\r\n \r\n output[i,1]<-tissue_list[i]\r\n \r\n output[i,2]<-length(which(box$r_xt>0.1 & box$p_xt<0.05))\r\n output[i,3]<-length(which(box$r_ut>0.1 & box$p_ut<0.05))\r\n output[i,4]<-length(which(box$r_st>0.1 & box$p_st<0.05))\r\n output[i,5]<-mean(box$delta_xt)\r\n output[i,6]<-mean(box$delta_ut)\r\n output[i,9]<-median(box$delta_xt_p)\r\n output[i,10]<-median(box$delta_ut_p)\r\n output[i,11]<-median(box$r_st^2)\r\n output[i,12]<-median(box$r_xt^2)\r\n output[i,13]<-mean(box$r_st^2)\r\n output[i,14]<-mean(box$r_xt^2)\r\n}\r\n\r\noutput[,7]<-(output[,2]-output[,4])/output[,4]\r\noutput[,8]<-(output[,3]-output[,4])/output[,4]\r\n\r\nwrite.table(output,paste0('~/../Dropbox/DansPaper/data/performance/v8_cv.txt'),quote = F,row.names = F,sep='\\t')\r\n\r\n#-----merge with info-----\r\ninfo<-read.table('~/../Dropbox/DansPaper/data/info/gtex_v8_info.txt',header = T,stringsAsFactors = F)\r\ninfo<-info[,-4]\r\nv8<-read.table(paste0('~/../Dropbox/DansPaper/data/performance/v8_cv.txt'),header = T,stringsAsFactors = F)\r\nv8<-merge(info,v8,by=1)\r\n\r\n\r\n#-----capture-----\r\ntpm<-read.table('~/../Dropbox/data/gtex/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.49',header = T,stringsAsFactors = F)\r\ntpm[,1]<-sapply(tpm[,1], function(x) strsplit(x,\"[.]\")[[1]][1])\r\n\r\n\r\nmain_path=paste0('~/../Dropbox/DansPaper/data/performance/v8/')\r\ntissue_list<-dir(main_path)\r\ncapture<-as.data.frame(matrix(data=NA,ncol=2))\r\ncolnames(capture)<-c('tissue','capture')\r\n\r\ni=1\r\nfor (i in 1:length(tissue_list)){\r\n print(tissue_list[i])\r\n box<-read.table(paste0(main_path,tissue_list[i]),header = T,stringsAsFactors = F)\r\n box[is.na(box)] <- 0\r\n box<-box[which(box[,1] %in% tpm[tpm[,i+2]>0,1]),]\r\n #print(colnames(tpm[i+2]))\r\n \r\n box$xt<-ifelse((box$r_xt>0.1 & box$p_xt<0.05),1,0)\r\n box$st<-ifelse((box$r_st>0.1 & box$p_st<0.05),1,0)\r\n \r\n capture[i,1]<-tissue_list[i]\r\n capture[i,2]<-sum(box[box$st==1,8])/length(which(box$st==1))\r\n}\r\n\r\nv8<-merge(v8,capture,by=1)\r\n\r\nv8<-v8[order(v8$n,decreasing = T),]\r\n#v8<-v8[order(v8$tissue,decreasing = T),]\r\n\r\nwrite.table(v8,paste0('~/../Dropbox/DansPaper/data/performance/v8_info.txt'),quote = F,row.names = F,sep='\\t')\r\n\r\n#-----------------------------------------------------------------\r\n\r\npng('~/../Dropbox/DansPaper/figures/tmp/v8_performance.png',height = 1200,width = 2000,res=150)\r\npar(mfcol=c(1,3))\r\n\r\n#---N_iGenes---\r\npar(mai=c(1,0.1,0.1,0.3))\r\npar(mgp=c(2.5,1,0))\r\nplot(-100,-100,xlim=c(0,max(v8$n_xt)*1.1),ylim=c(0,49),yaxt=\"n\",las=1,xlab='Number of iGenes',ylab=' ',cex.lab=1.5,cex.axis=1.5, bty='l')\r\n\r\n#legend\r\npoints(200,8,pch=21,bg='white',col='black',cex=2.5)\r\npoints(200,6,pch=23,bg='white',col='black',cex=2.5)\r\n\r\ntext(300,8,' PrediXcan',pos=4,cex=1.2)\r\ntext(300,6,' JTI',pos=4,cex=1.2)\r\n\r\nfor (i in 1:49){\r\n segments(v8[i,6],i,v8[i,4],i,col=paste0('#',v8[i,3]))\r\n points(v8[i,6],i,pch=21,bg=paste0('#',v8[i,3]),cex=2.5)\r\n points(v8[i,4],i,pch=23,bg=paste0('#',v8[i,3]),cex=2.5)\r\n \r\n #mark\r\n points(18000,i,pch=22,cex=2.5,bg=paste0('#',v8[i,3]))\r\n \r\n}\r\n\r\n\r\n#---capture---\r\npar(mai=c(1,2,0.1,0.3))\r\npar(mgp=c(2.5,1,0))\r\n\r\nfor (i in 1:10){\r\n v8$tissue<-sub('_',' ',v8$tissue)\r\n}\r\n\r\nplot(-100,-100,xlim=c(0,1),ylim=c(0,49),yaxt=\"n\",las=1,xlab='% of PrediXcan iGenes captured by JTI',ylab=' ',cex.lab=1.4,cex.axis=1.5,bty='l',xaxt='n')\r\n\r\naxis(1,at=seq(0,1,0.2),label=paste0(seq(0,1,0.2)*100,'%'),las=1,cex.axis=1.1) \r\naxis(2,at=seq(1,49),label=v8$tissue,las=1,cex.axis=1.1) \r\n\r\nfor (i in 1:49){\r\n points(v8[i,17],i,pch=24,bg=paste0('#',v8[i,3]),cex=2.5)\r\n #mark\r\n points(0,i,pch=22,bg=paste0('#',v8[i,3]),cex=2.5)\r\n \r\n #sample size\r\n text(0.1,i,v8$n[i],cex = 1)\r\n}\r\n\r\ntext(0.20,-1.5,'N of samples',pos = 3,cex = 1.2)\r\n\r\n#---delta_ipiG---\r\n\r\npar(mai=c(1,0.8,0.1,0.3))\r\npar(mgp=c(2.5,1,0))\r\n\r\nplot(-100,-100,xlim=c(0,max(v8$n)*1.1),ylim=c(0,max(v8$n_increase_xt)*1.1),yaxt='n',las=1,xlab='Sample size for each tissue',ylab=' ',cex.lab=1.5,cex.axis=1.5,bty='l')\r\ntitle(ylab = 'Increase in the propotion of iGenes (\u0394piG)', mgp = c(5, 1, 0),cex.lab=1.5)\r\n\r\naxis(2,at=seq(0,3,0.1),label=paste0(seq(0,3,0.1)*100,'%'),las=1,cex.axis=1.5) \r\n\r\npoints(v8$n,v8$n_increase_xt,pch=21,bg=paste0('#',v8[,3]),cex=2.5)\r\n\r\n\r\ndev.off()\r\n", "meta": {"hexsha": "10be33622314e969e66453a9fc7d12324416446b", "size": 5226, "ext": "r", "lang": "R", "max_stars_repo_path": "plots/Fig2_performance_gtex_v8.r", "max_stars_repo_name": "mjbetti/MR-JTI", 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"max_forks_repo_forks_event_max_datetime": "2021-09-17T16:17:57.000Z", "avg_line_length": 34.3815789474, "max_line_length": 199, "alphanum_fraction": 0.6199770379, "num_tokens": 2144, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3055907023201636}} {"text": "# Read data\ntsr5<-read.table(\"tscs.5\")\ntsr3<-read.table(\"tscs.3\")\n\n# min-max-range\nxmin<-min(tsr5$V1,tsr3$V1)\nxmax<-max(tsr5$V1,tsr3$V1)\nymin<-min(tsr3$V2)\nymax<-max(tsr5$V2)\nxr<-seq(xmin-1,xmax,1)\nxleft<-xmin-1\nxright<-xmax+1\nybottom<-ymin-2\nytop<-ymax+2\n\npdf(\"cleavage_profile.pdf\", 8, 4)\npar(mar=c(4,1,1,0))\n# plot area\nplot(c(xleft, xright), c(ybottom, ytop), type = \"n\",\n xlab = \"tRNA base\", ylab = \"\",\n main=\"Distribution of tRNA cleavage sites\",\n axes=F\n )\naxis(side=1, labels=T, at = seq(xmin-1,xmax+1))\nmtext(side = 2, text = \"Cleavage frequency\", line = 0)\n\n# tRNA\nrect(xr, 0, xr+0.8, 1, col = 1, lwd = 0.5)\n\n# anti codon\nac<-c(0,1,2)\nrect(ac, 0, ac+0.8, 1, col = 2, lwd = 0.5)\n\n# cleavage\n# 5' tsR\nrect(tsr5$V1 - 0.4, 2, tsr5$V1 + 0.4, tsr5$V2 + 2,\n border = \"white\", \n col= topo.colors( max( tsr5$V2) )[ tsr5$V2 ]\n)\n# 3' tsR\nrect(tsr3$V1 - 0.4, -1, tsr3$V1 + 0.4, tsr3$V2 - 1,\n border = \"white\", \n col= cm.colors( max( -tsr3$V2) )[ -tsr3$V2 ]\n)\n\n# define axis\n# +y\ni<-1:ymax\nrect(xleft, 1+i, xleft+0.4, 1+i+1, col=topo.colors(max(tsr5$V2)), border = \"white\")\n\n# -y\nj<-ymin:-1\nrect(xleft, j-1, xleft+0.4, j, col=rev(cm.colors(max(-tsr3$V2))), border = \"white\")\ndev.off()\n", "meta": {"hexsha": "2922dc46caebc89b23e6b027f801d9cf3035c33c", "size": 1216, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/draw_cleavage_site.r", "max_stars_repo_name": "wangqinhu/tsRFinder", "max_stars_repo_head_hexsha": "322ef868b67e0aee6971f47af0bbfd2bf8d91946", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2015-02-02T03:36:15.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-23T02:34:12.000Z", "max_issues_repo_path": "lib/draw_cleavage_site.r", "max_issues_repo_name": "wangqinhu/tsRFinder", "max_issues_repo_head_hexsha": "322ef868b67e0aee6971f47af0bbfd2bf8d91946", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/draw_cleavage_site.r", "max_forks_repo_name": "wangqinhu/tsRFinder", "max_forks_repo_head_hexsha": "322ef868b67e0aee6971f47af0bbfd2bf8d91946", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2016-08-08T09:13:41.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-26T13:40:28.000Z", "avg_line_length": 22.1090909091, "max_line_length": 83, "alphanum_fraction": 0.5929276316, "num_tokens": 589, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.30559069490073837}} {"text": "#+---------------------------------------------------------------------------------------------------------------------+\n#| 4. dRT (v. 2) |\n#| |\n#| This function shifts the retention times of experiments, calculates dRTs and updates PEPs |\n#| As input, it takes the path to library file, shift coefficients file, evidence.txt file from Max Quant and the |\n#| output will be the evidence file with updated PEPs and containing experiments and peptides that were present in the |\n#| library. Contaminants are removed during the process. |\n#| |\n#| Example: dRT(path.in.lib = 'C:/../RT.lib.txt', |\n#| path.in.coeffs = 'C:/../shift.coeffs.txt', |\n#| path.in.evidence = 'C:/../evidence.txt', |\n#| path.out.PEP.updated = 'C:/../evidence+dRT.txt') |\n#| |\n#| |\n#|For large dRTs PEP.new sometimes gets NA. It is because dRTs are out of the range of forward dRT density distribution|\n#| v. 2: Changes in how the function calculates dRTs. Avoiding NA PEPs. Marginally improves id rate. Only works with |\n#| libraries made by make.lib v.2. Cleaner output. |\n#+---------------------------------------------------------------------------------------------------------------------+\n\ndRT <- function(path.in.lib='dat/RT.lib.elite.txt', \n path.in.coeffs='dat/shift.coeffs.elite.txt', \n path.in.evidence='dat/evidence.txt', \n path.out.PEP.updated='dat/evidence+dRT.elite.txt'){\n \n rt.lib <- read.delim(path.in.lib, header = TRUE, stringsAsFactors = FALSE)\n shift.coeffs <- read.delim(path.in.coeffs, header = TRUE, stringsAsFactors = FALSE)\n ev <- read.delim(path.in.evidence, header = TRUE, stringsAsFactors = FALSE)\n \n ev <- ev[, c('Raw.file', 'Sequence', 'PEP', 'Retention.time', \"PIF\", \n \"Reporter.intensity.corrected.0\", \"Reporter.intensity.corrected.1\", \n \"Reporter.intensity.corrected.2\", \"Reporter.intensity.corrected.3\",\n \"Reporter.intensity.corrected.4\", \"Reporter.intensity.corrected.5\", \n \"Reporter.intensity.corrected.6\", \"Reporter.intensity.corrected.7\", \n \"Reporter.intensity.corrected.8\", \"Reporter.intensity.corrected.9\",\n \"id\", \"Leading.razor.protein\", \"Proteins\")]\n ev <- ev[ev$Raw.file %in% shift.coeffs[!is.na(shift.coeffs$coeff), \"experiment\"], ]\n ev$protLabel <- substr(ev$Leading.razor.protein, 1, 3)\n \n # FORWARD:\n ev.for <- subset(ev, ev$protLabel==\"sp|\")\n ev.for$protLabel <- \"FOR\"\n ev.for <- ev.for[ev.for$Sequence %in% rt.lib$peptides, ]\n ev.for$RT.lib <- rt.lib$rt.median[match(ev.for$Sequence, rt.lib$peptides)]\n ev.for$intercept <- shift.coeffs$intercept[match(ev.for$Raw.file, shift.coeffs$experiment)]\n ev.for$coeff <- shift.coeffs$coeff[match(ev.for$Raw.file, shift.coeffs$experiment)]\n ev.for$RT.corrected <- ev.for$intercept + (ev.for$coeff * ev.for$Retention.time)\n ev.for$dRT.med <- abs(ev.for$RT.corrected-ev.for$RT.lib)\n #for (i in 1:nrow(ev.for)){\n # smallest difference between corrected RT and any one of the RT entries in the\n # RT library\n # ev.for$dRT[i] <- min(\n # abs(ev.for$RT.corrected[i] -\n # na.omit(t(rt.lib[rt.lib$peptides==ev.for$Sequence[i], \n # 2:(ncol(rt.lib)-4)]))\n # )\n # )\n # this takes a while\n # flush.console()\n # cat('\\r', i, '/', nrow(ev.for), ' ')\n #}\n ev.for <- ev.for[,-c(21,22)]\n \n # REVERSE:\n ev.rev <- ev[ev$protLabel==\"REV\", ]\n set.seed(1)\n ev.rev$RT.lib <- sample(rt.lib$rt.median, nrow(ev.rev), replace=T)\n ev.rev$intercept <- shift.coeffs$intercept[match(ev.rev$Raw.file, shift.coeffs$experiment)]\n ev.rev$coeff <- shift.coeffs$coeff[match(ev.rev$Raw.file, shift.coeffs$experiment)]\n ev.rev$RT.corrected <- ev.rev$intercept + (ev.rev$coeff * ev.rev$Retention.time)\n ev.rev$dRT.med <- abs(ev.rev$RT.corrected - ev.rev$RT.lib)\n #ev.rev$dRT <- ev.rev$dRT.med\n ev.rev <- ev.rev[,-c(21,22)]\n \n ev.tot <- rbind(ev.rev, ev.for)\n row.names(ev.tot) <- NULL\n #ev.tot <- ev.perc.dRTed[sample(nrow(ev.perc.dRTed)), ]\n #row.names(ev.perc.dRTed) <- NULL\n \n # Updating PEPs:\n ev.PEP <- data.frame(Raw.file=character(), Sequence=character(), PEP=numeric(), Retention.time=numeric(), PIF=numeric(),\n Reporter.intensity.corrected.0=numeric(), Reporter.intensity.corrected.1=numeric(),\n Reporter.intensity.corrected.2=numeric(), Reporter.intensity.corrected.3=numeric(), \n Reporter.intensity.corrected.4=numeric(), Reporter.intensity.corrected.5=numeric(),\n Reporter.intensity.corrected.6=numeric(), Reporter.intensity.corrected.7=numeric(),\n Reporter.intensity.corrected.8=numeric(), Reporter.intensity.corrected.9=numeric(), id=numeric(),\n Leading.razor.protein=character(), Proteins=character(), RT.lib=numeric(), RT.corrected=numeric(), dRT.med=numeric(),\n dRT=numeric())\n \n dens.forw <- list()\n dens.rev <- list()\n dRT.forw <- list()\n dRT.rev <- list()\n \n exps <- unique(ev.tot$Raw.file)\n counter <- 0\n \n for (i in exps) {\n counter <- counter + 1\n \n cat('\\r', 'Processing ', counter, '/', length(exps), ' ', i, ' ')\n flush.console()\n \n ex <- subset(ev.tot, ev.tot$Raw.file==i & ev.tot$PEP<=1)\n row.names(ex) <- NULL\n \n rev <- subset(ex, ex$protLabel==\"REV\")\n forw <- subset(ex, ex$protLabel==\"FOR\" & ex$PEP<0.02)\n \n den.for <- density(forw$dRT.med)\n den.rev <- density(rev$dRT.med)\n \n dens.forw[[counter]] <- den.for\n dens.rev[[counter]] <- den.rev\n #dRT.forw[[counter]] <- forw$dRT.med\n #dRT.rev[[counter]] <- rev$dRT.med\n \n ex$Tr <- approx(den.for$x, den.for$y, xout=ex$dRT)$y\n ex$Fa <- approx(den.rev$x, den.rev$y, xout=ex$dRT)$y\n ex[is.na(ex$Tr), \"Tr\"] <- 1e-100\n ex$PEP.new <- (ex$PEP*ex$Fa)/((ex$PEP*ex$Fa)+((1-ex$PEP)*ex$Tr))\n \n ev.PEP <- rbind(ev.PEP, ex)\n \n }\n row.names(ev.PEP) <- NULL\n ev.PEP$Tr <- NULL\n ev.PEP$Fa <- NULL\n ev.PEP$dRT.med <- NULL\n \n write.table(ev.PEP, path.out.PEP.updated, sep = '\\t', row.names = FALSE, quote = FALSE)\n}\n\n#par(mfrow=c(2,2))\n#for(i in sample.int(length(exps), size=4)) {\n# plot(dens.forw[[i]], col='blue', xlab='dRT (min)', \n# main=paste0('Correct (blue) vs. Incorrect (red)\\n', 'Exp: ', i))\n# lines(dens.rev[[i]], col='red')\n#}\n\n", "meta": {"hexsha": "28cf572fafcf107748fca53f8c391dbfeb617457", "size": 7367, "ext": "r", "lang": "R", "max_stars_repo_path": "Rscripts/legacy_scripts/pairwise_alignment/dRT.r", "max_stars_repo_name": "SlavovLab/DART-ID_2018", "max_stars_repo_head_hexsha": "84e73bc66e9e9a64d848d06463255db92561bfb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Rscripts/legacy_scripts/pairwise_alignment/dRT.r", "max_issues_repo_name": "SlavovLab/DART-ID_2018", "max_issues_repo_head_hexsha": "84e73bc66e9e9a64d848d06463255db92561bfb7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Rscripts/legacy_scripts/pairwise_alignment/dRT.r", "max_forks_repo_name": "SlavovLab/DART-ID_2018", "max_forks_repo_head_hexsha": "84e73bc66e9e9a64d848d06463255db92561bfb7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 52.6214285714, "max_line_length": 140, "alphanum_fraction": 0.5091624813, "num_tokens": 1885, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7431680086124811, "lm_q2_score": 0.41111086923216805, "lm_q1q2_score": 0.3055244460062165}} {"text": "\n# compare weight in the sea West.in and weight from length in the sea (lsea.in) and length weight relation\n\na<-Read.summary.data()\nb<-Read.length.weight.relation()\n\nab<-merge(a,b)\nab$size<-ab$a*ab$Lsea^ab$b\n\ncleanup()\nby(ab,list(ab$Species),function(x) {\nX11()\nprint(\n xyplot(west~size| paste(Species,Quarter), \n auto.key = list(points = T, rectangles = F, space = \"right\"),\n panel = function(x,y) {\n panel.grid(h=-1, v= -1)\n panel.xyplot(x, y)\n panel.loess(x,y, span=1)\n panel.lmline(x, y)\n panel.abline(a =0, b = 1,col='red')\n },\n\n data=x)\n )\n})\n\ncleanup()\nX11()\n\n xyplot(west~size| paste(Species), \n auto.key = list(points = T, rectangles = F, space = \"right\"),scales = \"free\", \n panel = function(x,y) {\n panel.grid(h=-1, v= -1)\n panel.xyplot(x, y)\n panel.loess(x,y, span=1)\n panel.lmline(x, y)\n panel.abline(a =0, b = 1,col='red')\n },\n data=ab)\n \n \n\n ################################\n # compare length distribution at age, with and without mesh size selection correction\ncleanup()\na<-Read.LAK()\n\na<-subset(a,year==1991 & (((quarter==3 | quarter==4)& Age==0 ) |((quarter==1 | quarter==2)& Age==1 )))\n\na1<-subset(a,select=c(year,quarter, Age,Species,Length,LengthGroup ,proportion) )\na1$type<-'1) obs.'\n\na2<-subset(a,select=c(year,quarter, Age,Species,Length,LengthGroup ,proportion.adjusted) )\na2$type<-'2) adj.'\nnames(a2)=c('year','quarter','Age','Species','Length','LengthGroup','proportion','type')\n\naa<-rbind(a1,a2)\n\nby(aa,list(aa$Species),function(x) {\nX11()\nprint(barchart(proportion~as.factor(LengthGroup)| paste(Species,' age:', Age,' q:',quarter,sep=''), groups=type, \n auto.key = list(points = FALSE, rectangles = TRUE, space = \"right\"),\n data=x))\n})\n\n#######################\n# compare mean length at age, with and without mes size selection correction \n\n \ncleanup()\n\n# length from weigt in the stock and length weight relation\na<-Read.summary.data()\nb<-Read.length.weight.relation()\n\nab<-merge(a,b)\nab$Length<-(ab$west/ab$a)^(1/ab$b)\nab$type<-\"4 west\"\nab<-subset(ab,select=c(Year,Quarter,Species,Age,type,Length))\nnames(ab)=c('year','quarter','Species',\"Age\",'type','Length')\n\n# read observed and adjusted length proportions\na<-Read.LAK()\n\na1<-subset(a,select=c(year,quarter, Age,Species,Length,proportion) )\na1$type<-'1 obs'\n\na2<-subset(a,select=c(year,quarter, Age,Species,Length,proportion.adjusted) )\na2$type<-'2 adj.'\nnames(a2)=c('year','quarter','Age','Species','Length','proportion','type')\n\naa<-rbind(a1,a2)\nbb<-aggregate(aa$proportion*aa$Length,list(aa$year,aa$quarter,aa$Species,aa$Age,aa$type),sum)\nnames(bb)<-c('year','quarter','Species','Age','type','Length')\n##\n\n# length in the sea data\na<-Read.summary.data()\na$type<-'3 Lsea'\na<-subset(a,select=c(Year,Quarter,Species,Age,type,Lsea))\nnames(a)<-c('year','quarter','Species','Age','type','Length')\n\n##\n\nbb<-rbind(bb,a,ab)\nbb2<-subset(bb,year==1991 & quarter==3 & Age<=2)\n\nbarchart(Length~as.factor(Age)| paste(Species,' q:',quarter,sep=''), groups=type, xlab='Age',\n auto.key = list(points = FALSE, rectangles = TRUE, space = \"right\",col=1:3),\n data=bb2)\n\n #######################\n# Compare sum of relative stomach contents (observed and expected) \ncleanup()\n\nstom<-Read.stomach.data()\nstom<-transform(stom,year.range=ifelse(Year<=1981,'1977-81',ifelse(Year<1990,'1983-1987','1990-91')),year=paste(\"Y\",Year,sep=''))\n\na<-subset(stom,Prey.length.mean>0,select=c(Prey, Prey.length.class,year.range,Quarter, stomcon, stomcon.hat ))\n\nb1<-aggregate( a$stomcon,list(a$year.range, a$Prey, a$Prey.length.class,a$Quarter),mean)\nnames(b1)<-c('year.range','Prey','Length','Quarter','stom')\nb1$type='obs'\n\n\nb2<-aggregate( a$stomcon.hat,list(a$year.range,a$Prey, a$Prey.length.class,a$Quarter),mean)\nnames(b2)<-c('year.range','Prey','Length','Quarter','stom')\nb2$type='sms'\n\nbb<-rbind(b1,b2)\n\n\nby(bb,list(bb$Prey),function(x) {\n X11()\n print(barchart(stom~as.factor(Length)| paste(Prey,year.range,Quarter), groups=type,\n auto.key = list(points = FALSE, rectangles = TRUE, space = \"right\"),\n data=x))\n})\n \n \n #######################\n# By predator, Compare sum of relative stomach contents (observed and expected) \n\nstom<-Read.stomach.data()\nstom<-transform(stom,year.range=ifelse(Year<=1981,'1977-81',ifelse(Year<1990,'1983-1987','1990-91')),year=paste(\"Y\",Year,sep=''))\n\na<-subset(stom,Prey.length.mean>0,select=c(Predator,Prey, Prey.length.class,year.range,Quarter, stomcon, stomcon.hat ))\n\nb1<-aggregate( a$stomcon,list(a$Predator,a$year.range, a$Prey, a$Prey.length.class,a$Quarter),mean)\nnames(b1)<-c('Predator','year.range','Prey','Length','Quarter','stom')\nb1$type='obs'\n\n\nb2<-aggregate( a$stomcon.hat,list(a$Predator,a$year.range,a$Prey, a$Prey.length.class,a$Quarter),mean)\nnames(b2)<-c('Predator','year.range','Prey','Length','Quarter','stom')\nb2$type='sms'\n\nbb<-rbind(b1,b2)\n\ncleanup()\nby(bb,list(bb$Predator,bb$Prey),function(x) {\n X11()\n print(barchart(stom~as.factor(Length)| paste(Predator,Prey,year.range,Quarter), groups=type,\n auto.key = list(points = FALSE, rectangles = TRUE, space = \"right\"),\n data=x))\n})\n \n\ncleanup()\nby(bb,list(bb$Quarter,bb$Prey),function(x) {\n X11()\n print(barchart(stom~as.factor(Length)| paste(Predator,Prey,year.range,Quarter), groups=type,\n auto.key = list(points = FALSE, rectangles = TRUE, space = \"right\"),\n data=x))\n})\n\n\n#######################\ns1<-15\nL50<-85\n\n###\ns2<-s1/L50\nL75<-(s1+log(3))/s2\ncat(\"s1:\",s1,\" s2:\",s2,\" L50:\",L50,\" L75:\",L75,\"\\n\")\n\nl<-seq(50,150,1)\n\nsel<-1/(1+exp(s1-s1/L50*l))\nplot(l,sel,col='blue')\n\nfor (s1 in (seq(5,12,1))) {\n lines(l,1/(1+exp(s1-s1/70*l)))\n s2<-s1/L50\nL75<-(s1+log(3))/s2\ncat(\"s1:\",s1,\" s2:\",s2,\" L50:\",L50,\" L75:\",L75,\"\\n\")\n\n}\n\n\nplot(l,1/sel,col='blue')\n\nfor (s1 in (seq(5,12,1))) {\n lines(l,(1+exp(s1-s1/70*l)))\n}\n\n\n", "meta": {"hexsha": "868170e76ee8c413d823cc27bd21e090edc59798", "size": 5856, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/selection.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/selection.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/selection.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.019138756, "max_line_length": 129, "alphanum_fraction": 0.637636612, "num_tokens": 1893, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548782017745, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.30534906150827057}} {"text": "\n#Function to read and plot survey residuals\n# parameter start.year: first year on X-axis, default=0 (defined from data)\n# parameter end.year: end year on X-axis, default=0 (defined from data)\n# \n# use over.all.max to set the maximum size of the reference buble. A value of 0 scales bubles individually \n# dev=screen or wmf with output on a wmf file in the default directory\n\ncleanup()\n\n#plot.survey.residuals2(standardize=F,reverse.colors=T,dev='screen',pointsize=12,nox=1,noy=2,Portrait=F,start.year=1974,end.year=2020,over.all.max=1,my.species=NA)\nplot.survey.residuals2(standardize=F,reverse.colors=T,dev='png',pointsize=12,nox=2,noy=2,Portrait=F,start.year=1974,end.year=2020,over.all.max=0.5,my.species=NA)\n\n#plot.survey.residuals2(reverse.colors=T,standardize=F,dev='screen',pointsize=12,nox=1,noy=2,Portrait=T,start.year=1990,end.year=2015,over.all.max=5,my.species=NA)\n\n#plot.survey.residuals2(standardize=T,use.ref.dot=FALSE,dev='screen',nox=1,noy=3,Portrait=T,start.year=1990,end.year=2011,over.all.max=5,my.species=NA)\n#plot.survey.residuals2(reverse.colors=T,standardize=T,use.ref.dot=FALSE,dev='screen',nox=1,noy=3,Portrait=T,start.year=1990,end.year=2011,over.all.max=5.5,my.species=NA)\n", "meta": {"hexsha": "fd86512f7896614b00e01c90b63c5f745e9f9a6d", "size": 1203, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/plot_residuals_survey.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/plot_residuals_survey.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/plot_residuals_survey.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 66.8333333333, "max_line_length": 170, "alphanum_fraction": 0.7714048213, "num_tokens": 386, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.30531401869823493}} {"text": "#' List-length site selection\n#' \n#' This function uses part of the method outlined in Roy et al (2012) and Isaac et al (2014) for selecting\n#' well-sampled sites from a dataset using list length only. \\code{\\link{siteSelection}} is a wrapper\n#' for this function that performs the complete site selection process as outlined in these papers.\n#' \n#' @param taxa A character vector of taxon names\n#' @param site A character vector of site names\n#' @param time_period Anumeric vector of user defined time periods, or a date vector\n#' @param minL numeric, The minimum number of taxa recorded at a site at a given time period \n#' (list-length) for the visit to be considered well sampled.\n#' @return A data.frame of data that forefills the selection criteria. This data has two attributes:\n#' \\code{visits} gives the total number of visits in the dataset (unique combinations of \\code{site}\n#' and \\code{time_period}), \\code{success} gives the number of visits that satify the selection criteria\n#' @export\n#' @importFrom plyr ddply\n#' @importFrom plyr .\n#' @importFrom dplyr distinct\n#' @references needed\n\nsiteSelectionMinL <- function(taxa, site, time_period, minL){\n \n # Run error checks \n errorChecks(taxa = taxa, site = site, time_period = time_period)\n if(!is.numeric(minL)) stop('minL must be numeric')\n \n # Create dataframe\n Data <- distinct(data.frame(taxa, site, time_period))\n \n # Using plyr to create list lengths\n dfLL <- ddply(Data, .(site, time_period), nrow)\n\n # Visits that mean minL criteria\n minL_dfLL <- dfLL[dfLL$V1 >= minL, 1:2]\n \n # Subset Data to these visits (and keep columns in same order)\n minL_Data <- merge(x = Data, y = minL_dfLL, all = F)[c('taxa', 'site', 'time_period')]\n \n # Add attributes\n attr(minL_Data, which = 'visits') <- nrow(dfLL)\n attr(minL_Data, which = 'success') <- nrow(minL_dfLL)\n \n if(nrow(minL_Data) == 0) warning('Filtering in siteSelectionMinL resulted in no data returned')\n \n return(minL_Data)\n \n}", "meta": {"hexsha": "59a39a9ad61a5ea8985c65f4ae565bcb3a2ed6a1", "size": 1978, "ext": "r", "lang": "R", "max_stars_repo_path": "R/siteSelectionMinL.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "R/siteSelectionMinL.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "R/siteSelectionMinL.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 42.085106383, "max_line_length": 106, "alphanum_fraction": 0.7184024267, "num_tokens": 536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.3052955301565724}} {"text": "require(\"gplots\")\nrequire(\"RColorBrewer\")\n\nDPI <- 300\nWIDTH <- 10\nHEIGHT <- 10\nFONT <- 12\n\nargs <- commandArgs( trailingOnly = TRUE )\n\npalette <- colorRampPalette( c( \"red\", \"yellow\", \"green\" ) )( n = 299 )\n\nfor( file in args ){\n\n filename <- strsplit( file, \"\\\\.\" )[[1]][1]\n outname <- paste( c( filename, \"_heatmap.png\" ), collapse=\"\" )\n\n data <- read.table( file, header = TRUE, row.names = 1 )\n m <- data.matrix( data )\n colnames( m ) <- colnames( data )\n rownames( m ) <- rownames( data )\n\n png( outname, width = WIDTH*DPI, height = HEIGHT*DPI, res = DPI, pointsize = FONT )\n\n heatmap.2(\n m,\n density.info = \"none\",\n trace = \"none\",\n col = palette,\n dendrogram = \"both\"\n )\n\n def.off()\n\n}\n", "meta": {"hexsha": "d8908ffd0ecdc63dbd3c98aee9cdfd9fa567f008", "size": 762, "ext": "r", "lang": "R", "max_stars_repo_path": "plot_heatmap.r", "max_stars_repo_name": "tbepler/ets-analysis-scripts", "max_stars_repo_head_hexsha": "fe2b537e2fbeb8370568a7a43707f7dca411f390", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "plot_heatmap.r", "max_issues_repo_name": "tbepler/ets-analysis-scripts", "max_issues_repo_head_hexsha": "fe2b537e2fbeb8370568a7a43707f7dca411f390", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plot_heatmap.r", "max_forks_repo_name": "tbepler/ets-analysis-scripts", "max_forks_repo_head_hexsha": "fe2b537e2fbeb8370568a7a43707f7dca411f390", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.1666666667, "max_line_length": 87, "alphanum_fraction": 0.5511811024, "num_tokens": 226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.30529552197896065}} {"text": "##http://www.r-statistics.com/2013/07/creating-good-looking-survival-curves-the-ggsurv-function/\nlibrary(ggplot2)\n\nggsurv <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def',\n cens.col = 'red', lty.est = 1, lty.ci = 2,\n cens.shape = 3, back.white = F, xlab = 'Time',\n ylab = 'Survival', main = ''){\n library(ggplot2)\n strata <- ifelse(is.null(s$strata) ==T, 1, length(s$strata))\n stopifnot(length(surv.col) == 1 | length(surv.col) == strata)\n stopifnot(length(lty.est) == 1 | length(lty.est) == strata)\n ggsurv.s <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def',\n cens.col = 'red', lty.est = 1, lty.ci = 2,\n cens.shape = 3, back.white = F, xlab = 'Time',\n ylab = 'Survival', main = ''){\n dat <- data.frame(time = c(0, s$time),\n surv = c(1, s$surv),\n up = c(1, s$upper),\n low = c(1, s$lower),\n cens = c(0, s$n.censor))\n dat.cens <- subset(dat, cens != 0)\n col <- ifelse(surv.col == 'gg.def', 'black', surv.col)\n pl <- ggplot(dat, aes(x = time, y = surv)) +\n xlab(xlab) + ylab(ylab) + ggtitle(main) +\n geom_step(col = col, lty = lty.est)\n pl <- if(CI == T | CI == 'def') {\n pl + geom_step(aes(y = up), color = col, lty = lty.ci) +\n geom_step(aes(y = low), color = col, lty = lty.ci)\n } else (pl)\n pl <- if(plot.cens == T & length(dat.cens) > 0){\n pl + geom_point(data = dat.cens, aes(y = surv), shape = cens.shape,\n col = cens.col)\n } else if (plot.cens == T & length(dat.cens) == 0){\n stop ('There are no censored observations')\n } else(pl)\n pl <- if(back.white == T) {pl + theme_bw()\n } else (pl)\n pl\n }\n ggsurv.m <- function(s, CI = 'def', plot.cens = T, surv.col = 'gg.def',\n cens.col = 'red', lty.est = 1, lty.ci = 2,\n cens.shape = 3, back.white = F, xlab = 'Time',\n ylab = 'Survival', main = '') {\n n <- s$strata\n groups <- factor(unlist(strsplit(names\n (s$strata), '='))[seq(2, 2*strata, by = 2)])\n gr.name <- unlist(strsplit(names(s$strata), '='))[1]\n gr.df <- vector('list', strata)\n ind <- vector('list', strata)\n n.ind <- c(0,n); n.ind <- cumsum(n.ind)\n for(i in 1:strata) ind[[i]] <- (n.ind[i]+1):n.ind[i+1]\n for(i in 1:strata){\n gr.df[[i]] <- data.frame(\n time = c(0, s$time[ ind[[i]] ]),\n surv = c(1, s$surv[ ind[[i]] ]),\n up = c(1, s$upper[ ind[[i]] ]),\n low = c(1, s$lower[ ind[[i]] ]),\n cens = c(0, s$n.censor[ ind[[i]] ]),\n group = rep(groups[i], n[i] + 1))\n }\n dat <- do.call(rbind, gr.df)\n dat.cens <- subset(dat, cens != 0)\n pl <- ggplot(dat, aes(x = time, y = surv, group = group)) +\n xlab(xlab) + ylab(ylab) + ggtitle(main) +\n geom_step(aes(col = group, lty = group))\n col <- if(length(surv.col == 1)){\n scale_colour_manual(name = gr.name, values = rep(surv.col, strata))\n } else{\n scale_colour_manual(name = gr.name, values = surv.col)\n }\n pl <- if(surv.col[1] != 'gg.def'){\n pl + col\n } else {pl + scale_colour_discrete(name = gr.name)}\n line <- if(length(lty.est) == 1){\n scale_linetype_manual(name = gr.name, values = rep(lty.est, strata))\n } else {scale_linetype_manual(name = gr.name, values = lty.est)}\n pl <- pl + line\n pl <- if(CI == T) {\n if(length(surv.col) > 1 && length(lty.est) > 1){\n stop('Either surv.col or lty.est should be of length 1 in order\n to plot 95% CI with multiple strata')\n }else if((length(surv.col) > 1 | surv.col == 'gg.def')[1]){\n pl + geom_step(aes(y = up, color = group), lty = lty.ci) +\n geom_step(aes(y = low, color = group), lty = lty.ci)\n } else{pl + geom_step(aes(y = up, lty = group), col = surv.col) +\n geom_step(aes(y = low,lty = group), col = surv.col)}\n } else {pl}\n pl <- if(plot.cens == T & length(dat.cens) > 0){\n pl + geom_point(data = dat.cens, aes(y = surv), shape = cens.shape,\n col = cens.col)\n } else if (plot.cens == T & length(dat.cens) == 0){\n stop ('There are no censored observations')\n } else(pl)\n pl <- if(back.white == T) {pl + theme_bw()\n } else (pl)\n pl\n }\n pl <- if(strata == 1) {ggsurv.s(s, CI , plot.cens, surv.col ,\n cens.col, lty.est, lty.ci,\n cens.shape, back.white, xlab,\n ylab, main)\n } else {ggsurv.m(s, CI, plot.cens, surv.col ,\n cens.col, lty.est, lty.ci,\n cens.shape, back.white, xlab,\n ylab, main)}\n pl\n}\n\n#http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/\n# Multiple plot function\n#\n# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)\n# - cols: Number of columns in layout\n# - layout: A matrix specifying the layout. If present, 'cols' is ignored.\n#\n# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),\n# then plot 1 will go in the upper left, 2 will go in the upper right, and\n# 3 will go all the way across the bottom.\n#\nmultiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {\n library(grid)\n\n # Make a list from the ... arguments and plotlist\n plots <- c(list(...), plotlist)\n\n numPlots = length(plots)\n\n # If layout is NULL, then use 'cols' to determine layout\n if (is.null(layout)) {\n # Make the panel\n # ncol: Number of columns of plots\n # nrow: Number of rows needed, calculated from # of cols\n layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),\n ncol = cols, nrow = ceiling(numPlots/cols))\n }\n\n if (numPlots==1) {\n print(plots[[1]])\n\n } else {\n # Set up the page\n grid.newpage()\n pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))\n\n # Make each plot, in the correct location\n for (i in 1:numPlots) {\n # Get the i,j matrix positions of the regions that contain this subplot\n matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))\n\n print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,\n layout.pos.col = matchidx$col))\n }\n }\n}\n", "meta": {"hexsha": "9e4c9a059e76672690b7f7125a8773be30d8ad6d", "size": 6421, "ext": "r", "lang": "R", "max_stars_repo_path": "src/analysis/ggsurv.r", "max_stars_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_stars_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-08T16:11:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T16:11:01.000Z", "max_issues_repo_path": "src/analysis/ggsurv.r", "max_issues_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_issues_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-05-13T19:04:16.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-31T18:34:56.000Z", "max_forks_repo_path": "src/analysis/ggsurv.r", "max_forks_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_forks_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.4258064516, "max_line_length": 96, "alphanum_fraction": 0.529824015, "num_tokens": 1951, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.30529552197896065}} {"text": "#!/usr/bin/env Rscript\nlibrary(gaston)\nlibrary(\"optparse\")\nsnp.sq <- function(i, j, ld, write.ld.val, color, polygon.par, cex.ld) {\n if(i == j) return(); # pas de plot d'un SNP avec lui m\u00eame\n if(j < i) { tmp <- j; j <- i; i <- tmp } # i < j\n d <- (j-i)/2\n cx <- i+d\n cy <- -d\n do.call(polygon, c(list(x = cx+c(-1,0,1,0)/2, y = cy + c(0,1,0,-1)/2, col = color), polygon.par))\n if(write.ld.val) text(cx, cy, ld, cex = cex.ld)\n}\n\n\n\nLD.plot2<-function (LD, snp.positions, max.dist = Inf, depth = nrow(LD), \n graphical.par = list(mar = c(0, 0, 0, 0)), cex.ld, cex.snp, \n polygon.par = list(border = \"white\"), color.scheme = function(ld) rgb(1, \n 1 - abs(ld), 1 - abs(ld)), write.snp.id = TRUE, write.ld = function(ld) sprintf(\"%.2f\", \n ld), draw.chr = TRUE, above.space = 1 + 2 * write.snp.id + \n draw.chr, below.space = 1, pdf.file, finalize.pdf = TRUE, write.snp.id.col=list()) \n{\n n <- nrow(LD)\n positions <- if (missing(snp.positions)) \n rep(0, n)\n else snp.positions\n graph.depth <- 0\n for (i in seq(1, n - 1)) for (j in seq(i + 1, min(n, i + \n depth))) {\n if (positions[j] - positions[i] > max.dist) \n next\n graph.depth <- max(graph.depth, (j - i)/2)\n }\n if (!missing(pdf.file)) \n pdf(pdf.file, width = n/2, height = (graph.depth + above.space + \n below.space)/2)\n do.call(par, graphical.par)\n write.ld.val <- ifelse(is.null(write.ld), FALSE, TRUE)\n plot(0, 0, xlim = c(0.5, n + 0.5), ylim = c(-graph.depth - \n below.space, above.space), type = \"n\", asp = 1, xaxt = \"n\", \n yaxt = \"n\", bty = \"n\", xlab = \"\", ylab = \"\")\n ld <- \"\"\n if (missing(cex.ld) & write.ld.val) \n cex.ld = 0.7 * par(\"cex\")/max(strwidth(sapply(LD, write.ld)))\n for (i in seq(1, n - 1)) for (j in seq(i + 1, min(n, i + \n depth))) {\n if (positions[j] - positions[i] > max.dist) \n next\n if (write.ld.val) \n ld <- write.ld(LD[i, j])\n snp.sq(i, j, ld, write.ld.val, color.scheme(LD[i, j]), \n polygon.par, cex.ld)\n }\n if (write.snp.id) {\n rs.h <- strheight(rownames(LD))\n if (missing(cex.snp)) \n cex.snp <- 0.25 * par(\"cex\")/max(rs.h)\n col=rep('black', n)\n rsname=rownames(LD)\n if(length(write.snp.id.col)>0){\n for(col_cmp in names(write.snp.id.col)){\n col[rsname %in% write.snp.id.col[[col_cmp]]]<-col_cmp \n }\n }\n text(1:n, 0, rownames(LD), srt = 90, cex = cex.snp, adj = c(0, \n 0.5), col=col)\n }\n if (!missing(snp.positions) & draw.chr) {\n if (write.snp.id) \n a <- max(strwidth(rownames(LD), cex = cex.snp))\n else a <- 0\n pos <- 1.5 + (n - 2) * (snp.positions - snp.positions[1])/diff(range(snp.positions))\n segments(1:n, a + 0.25, pos, a + 1.5)\n rect(1.5, a + 1.5, n - 0.5, a + 1.75)\n segments(pos, a + 1.5, pos, a + 1.75)\n }\n if (!missing(pdf.file) & finalize.pdf) \n dev.off()\n}\n\n \noption_list = list(\n make_option(c(\"--ld\"), type=\"character\", default=NULL, \n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--bim\"), type=\"character\", default=NULL, \n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--col_rs\"), type=\"character\", default=NULL, \n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--pos_ref\"), type=\"integer\", default=NULL, \n help=\"dataset file name\", metavar=\"character\"),\n make_option(c(\"--out\"), type=\"character\", default=\"out.svg\", \n help=\"output file name [default= %default]\", metavar=\"character\")\n); \n \nopt_parser = OptionParser(option_list=option_list);\nopt = parse_args(opt_parser);\n\nld=opt[['ld']]\nbim=opt[['bim']]\nout=opt[['out']]\n\n\nm <- as.matrix(read.csv(ld, sep=\"\\t\", header=F))\nDataBim<-read.table(bim)\ncolnames(m)<-DataBim$V2\nrownames(m)<-DataBim$V2\nwrite.snp.id.col=list()\nif(!is.null(opt[['col_rs']])){\nDataCol<-read.table(opt[['col_rs']])\nDataCol2<-merge(DataCol, DataBim, by.x=c(1,2), by.y=c(1,4))\n# V1 V2 V3.x V2.y V3.y V5 V6\n#1 15 45420718 black rs1365242 65.3342 C A\n#write.snp.id.col<-aggregate(V2.y~V3.x, DataCol2,function(x)return(x))\nwrite.snp.id.col<-sapply(unique(DataCol2$V3.x), function(x)return(DataCol2$V2.y[DataCol2$V3.x==x]))\nnames(write.snp.id.col)<-unique(DataCol2$V3.x)\nprint(write.snp.id.col)\nhead(DataCol2)\n}else if(!is.null(opt[['pos_ref']])){\nwrite.snp.id.col=list('red'=DataBim$V2[DataBim$V4==opt[['pos_ref']]])\n}\npdf(out)\nLD.plot2(m, DataBim$V4, draw.chr=TRUE, write.snp.id=TRUE, write.snp.id.col=write.snp.id.col)\ndev.off()\n", "meta": {"hexsha": "2f05711e2411070d02320b2f49a1f2614a39db60", "size": 4678, "ext": "r", "lang": "R", "max_stars_repo_path": "finemapping/bin/cond_plotld.r", "max_stars_repo_name": "h3abionet/h3agwas", "max_stars_repo_head_hexsha": "17167434c9957ebe0fa4ca155d996114cb8f1228", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 62, "max_stars_repo_stars_event_min_datetime": "2016-08-29T11:27:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-10T17:16:14.000Z", "max_issues_repo_path": "finemapping/bin/cond_plotld.r", "max_issues_repo_name": "h3abionet/h3agwas", "max_issues_repo_head_hexsha": "17167434c9957ebe0fa4ca155d996114cb8f1228", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 33, "max_issues_repo_issues_event_min_datetime": "2016-12-26T13:48:19.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-05T13:34:06.000Z", "max_forks_repo_path": "finemapping/bin/cond_plotld.r", "max_forks_repo_name": "h3abionet/h3agwas", "max_forks_repo_head_hexsha": "17167434c9957ebe0fa4ca155d996114cb8f1228", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 50, "max_forks_repo_forks_event_min_datetime": "2017-04-15T04:17:43.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-30T07:26:01.000Z", "avg_line_length": 37.7258064516, "max_line_length": 99, "alphanum_fraction": 0.5589995725, "num_tokens": 1558, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.30525866908604654}} {"text": "setwd(\"C:\\\\Users\\\\Ashish Arora\\\\Desktop\\\\logistic reg case study\")\n\n\nmydata<-read.csv(\"Proactive Attrition Management-Logistic Regression Case Study.csv\",header=T)\n\n#View(mydata)\n#str(mydata)\n\n\nmydata$CHURN<- factor(mydata$CHURN)\nmydata$CSA<- factor(mydata$CSA)\nmydata$CHILDREN<- factor(mydata$CHILDREN)\nmydata$CREDITA<- factor(mydata$CREDITA)\nmydata$CREDITAA<- factor(mydata$CREDITAA)\nmydata$CREDITB<- factor(mydata$CREDITB)\nmydata$CREDITC<- factor(mydata$CREDITC)\nmydata$CREDITDE<- factor(mydata$CREDITDE)\nmydata$CREDITGY<- factor(mydata$CREDITGY)\nmydata$CREDITZ<- factor(mydata$CREDITZ)\nmydata$PRIZMRUR<- factor(mydata$PRIZMRUR)\nmydata$PRIZMUB<- factor(mydata$PRIZMUB)\nmydata$PRIZMTWN<- factor(mydata$PRIZMTWN)\nmydata$REFURB<- factor(mydata$REFURB)\nmydata$WEBCAP<- factor(mydata$WEBCAP)\nmydata$TRUCK<- factor(mydata$TRUCK)\nmydata$RV<- factor(mydata$RV)\nmydata$OCCPROF<- factor(mydata$OCCPROF)\nmydata$OCCCLER<- factor(mydata$OCCCLER)\nmydata$OCCCRFT<- factor(mydata$OCCCRFT)\nmydata$OCCSTUD<- factor(mydata$OCCSTUD)\nmydata$OCCHMKR<- factor(mydata$OCCHMKR)\nmydata$OCCRET<- factor(mydata$OCCRET)\nmydata$OCCSELF<- factor(mydata$OCCSELF)\nmydata$OWNRENT<- factor(mydata$OWNRENT)\nmydata$MARRYUN<- factor(mydata$MARRYUN)\nmydata$MARRYYES<- factor(mydata$MARRYYES)\nmydata$MARRYNO<- factor(mydata$MARRYNO)\nmydata$MAILORD<- factor(mydata$MAILORD)\nmydata$MAILRES<- factor(mydata$MAILRES)\nmydata$MAILFLAG<- factor(mydata$MAILFLAG)\nmydata$TRAVEL<- factor(mydata$TRAVEL)\nmydata$PCOWN<- factor(mydata$PCOWN)\nmydata$CREDITCD<- factor(mydata$CREDITCD)\nmydata$NEWCELLY<- factor(mydata$NEWCELLY)\nmydata$NEWCELLN<- factor(mydata$NEWCELLN)\nmydata$INCMISS<- factor(mydata$INCMISS)\nmydata$MCYCLE<- factor(mydata$MCYCLE)\nmydata$SETPRCM<- factor(mydata$SETPRCM)\nmydata$RETCALL<- factor(mydata$RETCALL)\nmydata$CALIBRAT<- factor(mydata$CALIBRAT)\nmydata$CHURNDEP<- factor(mydata$CHURNDEP)\n\nvar_Summ=function(x){\n if(class(x)==\"numeric\"){\n Var_Type=class(x)\n n<-length(x)\n nmiss<-sum(is.na(x))\n mean<-mean(x,na.rm=T)\n std<-sd(x,na.rm=T)\n var<-var(x,na.rm=T)\n min<-min(x,na.rm=T)\n p1<-quantile(x,0.01,na.rm=T)\n p5<-quantile(x,0.05,na.rm=T)\n p10<-quantile(x,0.1,na.rm=T)\n q1<-quantile(x,0.25,na.rm=T)\n q2<-quantile(x,0.5,na.rm=T)\n q3<-quantile(x,0.75,na.rm=T)\n p90<-quantile(x,0.9,na.rm=T)\n p95<-quantile(x,0.95,na.rm=T)\n p99<-quantile(x,0.99,na.rm=T)\n max<-max(x,na.rm=T)\n UC1=mean(x,na.rm=T)+3*sd(x,na.rm=T)\n LC1=mean(x,na.rm=T)-3*sd(x,na.rm=T)\n UC2=quantile(x,0.99,na.rm=T)\n LC2=quantile(x,0.01,na.rm=T)\n iqr=IQR(x,na.rm=T)\n UC3=q3+1.5*iqr\n LC3=q1-1.5*iqr\n ot1<-max>UC1 | minUC2 | minUC3 | min quantiles[2] ] <- quantiles[2]\n x\n}\nmydata[,\"OPEAKVCE\"] <- M1_fun( mydata[,\"OPEAKVCE\"] )\nmydata[,\"MOUREC\"] <- M1_fun( mydata[,\"MOUREC\"] )\nmydata[,\"OUTCALLS\"] <- M1_fun( mydata[,\"OUTCALLS\"] )\nmydata[,\"MOU\"] <- M1_fun( mydata[,\"MOU\"] )\nmydata[,\"PEAKVCE\"] <- M1_fun( mydata[,\"PEAKVCE\"] )\nmydata[,\"INCALLS\"] <- M1_fun( mydata[,\"INCALLS\"] )\nmydata[,\"CALLWAIT\"] <- M1_fun( mydata[,\"CALLWAIT\"] )\nmydata[,\"UNANSVCE\"] <- M1_fun( mydata[,\"UNANSVCE\"] )\nmydata[,\"DROPVCE\"] <- M1_fun( mydata[,\"DROPVCE\"] )\nmydata[,\"OVERAGE\"] <- M1_fun( mydata[,\"OVERAGE\"] )\nmydata[,\"CUSTCARE\"] <- M1_fun( mydata[,\"CUSTCARE\"] )\nmydata[,\"PHONES\"] <- M1_fun( mydata[,\"PHONES\"] )\nmydata[,\"MODELS\"] <- M1_fun( mydata[,\"MODELS\"] )\nmydata[,\"SETPRC\"] <- M1_fun( mydata[,\"SETPRC\"] )\nmydata[,\"EQPDAYS\"] <- M1_fun( mydata[,\"EQPDAYS\"] )\nmydata[,\"AGE1\"] <- M1_fun( mydata[,\"AGE1\"] )\nmydata[,\"AGE2\"] <- M1_fun( mydata[,\"AGE2\"] )\nmydata[,\"INCOME\"] <- M1_fun( mydata[,\"INCOME\"] )\nmydata[,\"REVENUE\"] <- M1_fun( mydata[,\"REVENUE\"] )\nmydata[,\"ROAM\"] <- M1_fun( mydata[,\"ROAM\"] )\nmydata[,\"DIRECTAS\"] <- M1_fun( mydata[,\"DIRECTAS\"] )\nmydata[,\"RECCHRGE\"] <- M1_fun( mydata[,\"RECCHRGE\"] )\nmydata[,\"BLCKVCE\"] <- M1_fun( mydata[,\"BLCKVCE\"] )\nmydata[,\"DROPBLK\"] <- M1_fun( mydata[,\"DROPBLK\"] )\nmydata[,\"THREEWAY\"] <- M1_fun( mydata[,\"THREEWAY\"] )\nmydata[,\"UNIQSUBS\"] <- M1_fun( mydata[,\"UNIQSUBS\"] )\nmydata[,\"ACTVSUBS\"] <- M1_fun( mydata[,\"ACTVSUBS\"] )\nmydata[,\"RETACCPT\"] <- M1_fun( mydata[,\"RETACCPT\"] )\nmydata[,\"RETCALLS\"] <- M1_fun( mydata[,\"RETCALLS\"] )\nmydata[,\"MONTHS\"] <- M1_fun( mydata[,\"MONTHS\"] )\nmydata[,\"CREDITAD\"] <- M1_fun( mydata[,\"CREDITAD\"] )\nmydata[,\"CHANGEM\"] <- M1_fun( mydata[,\"CHANGEM\"] )\nmydata[,\"CHANGER\"] <- M1_fun( mydata[,\"CHANGER\"] )\nmydata[,\"REFER\"] <- M1_fun( mydata[,\"REFER\"] )\nmydata[,\"CALLFWDV\"] <- M1_fun( mydata[,\"CALLFWDV\"] )\n\n\n#vars<- c(\"OPEAKVCE\",\"MOUREC\",\"OUTCALLS\",\"MOU\",\"PEAKVCE\",\"INCALLS\",\"CALLWAIT\",\"UNANSVCE\",\n# \"DROPVCE\",\"OVERAGE\",\"CUSTCARE\",\"PHONES\", \"MODELS\",\"SETPRC\",\"EQPDAYS\",\"AGE1\",\n# \"AGE2\",\"INCOME\",\"REVENUE\",\"ROAM\",\"DIRECTAS\",\"RECCHRGE\",\"BLCKVCE\",\"DROPBLK\",\n# \"THREEWAY\",\"UNIQSUBS\",\"ACTVSUBS\",\"RETACCPT\",\"RETCALLS\",\"MONTHS\",\"CREDITAD\",\"CHANGEM\",\n# \"CHANGER\",\"REFER\",\"CALLFWDV\")\n\n#Splitting data into development and Validaton Dataset\ndev <- subset(mydata, CALIBRAT == 1)\n\nval <- subset(mydata, CALIBRAT == 0)\n\n\n\n#Building Models for dev dataset\n#Building First model with all variables or we can go for factor analysis first and remove some collinearity\n\nfit<-glm(CHURN~CALLFWDV+ REVENUE+ MOU+ RECCHRGE+ DIRECTAS+ OVERAGE+ ROAM+ CHANGEM+\n CHANGER+ DROPVCE+ BLCKVCE+ UNANSVCE+ CUSTCARE+ THREEWAY+ MOUREC+ OUTCALLS+\n INCALLS+ PEAKVCE+ OPEAKVCE+ DROPBLK+ CALLWAIT+ MONTHS+ UNIQSUBS+ ACTVSUBS+\n PHONES+ MODELS+ EQPDAYS + AGE1+ AGE2+ INCOME+ SETPRC+ REFER+ CREDITAD+ CHILDREN+\n CREDITA+ CREDITAA+ CREDITB+ CREDITC+ CREDITDE+ CREDITGY+ CREDITZ+ PRIZMRUR+ PRIZMUB+\n PRIZMTWN+ REFURB+ WEBCAP+ TRUCK+ RV+ OCCPROF+ OCCCLER+ OCCCRFT+ OCCSTUD+ OCCHMKR+\n OCCRET+ OCCSELF+ OWNRENT+ MARRYUN+ MARRYYES+ MARRYNO+ MAILORD+ MAILRES+ MAILFLAG+ TRAVEL+\n PCOWN+ CREDITCD+ NEWCELLY+ NEWCELLN+ INCMISS+ MCYCLE+ SETPRCM,data = dev,\n family = binomial(logit))\n\n\n\n#Output of Logistic Regression\nsummary(fit)\nls(fit)\nfit$model\n\ncoeff<-fit$coef #Coefficients of model\n\n#Checking for concordance \nsource(\"Concordance.R\")\nConcordance(fit) \n\n#Stepwise regression\nstep1=step(fit,direction=\"both\")\n\n\n#Final Model\nfinal_fit<-glm(CHURN ~ REVENUE + MOU + RECCHRGE + OVERAGE + ROAM + CHANGEM + \n CHANGER + BLCKVCE + THREEWAY + INCALLS + \n PEAKVCE + DROPBLK + MONTHS + UNIQSUBS + ACTVSUBS + PHONES + \n EQPDAYS + AGE1 + INCOME + SETPRC + CREDITAD + CHILDREN + \n CREDITAA + CREDITB + CREDITC + CREDITDE + PRIZMUB + REFURB + \n WEBCAP + MARRYUN + MAILRES + NEWCELLY + INCMISS + \n SETPRCM,data = dev,\n family = binomial(logit))\n\n\n#Output of Logistic Regression\nsummary(final_fit)\nls(final_fit)\n\ncoeff<-final_fit$coef #Coefficients of model\nwrite.csv(coeff, \"coeff.csv\")\n\n#Checking for concordance \nsource(\"Concordance.R\")\nConcordance(final_fit)\n\n#Concordance acheived with this model is 0.62125 and all the variables are significant to atleast 90% confidence level.\n#Though a model with 85% confidence level will yield a better concordance. But to make the variables significant we consider 90% level.\n\n################################VALIDATION ##############################\n#Decile Scoring for Development dataset\ndev1<- cbind(dev, Prob=predict(final_fit, type=\"response\")) #response calculates the probability value.\n#View(dev1)\n\ndecLocations <- quantile(dev1$Prob, probs = seq(0.1,0.9,by=0.1))\ndev1$decile <- findInterval(dev1$Prob,c(-Inf,decLocations, Inf))\n#View(dev1)\n\n#Decile Analysis Reports\nrequire(sqldf)\ndevdata_deciles <- sqldf(\"select decile, min(Prob) as Min_prob\n , max(Prob) as max_prob\n , sum(CHURN) as churn_Count\n , (count(decile)-sum(CHURN)) as Non_churn_Count \n from dev1\n group by decile\n order by decile desc\")\n\nwrite.csv(devdata_deciles,\"devdata_deciles.csv\",row.names = F)\n\n##Validation dataset\nval1<- cbind(val, Prob=predict(final_fit,val, type=\"response\")) \n#View(val1)\n\ndecLocations <- quantile(val1$Prob, probs = seq(0.1,0.9,by=0.1))\nval1$decile <- findInterval(val1$Prob,c(-Inf,decLocations, Inf))\n\n#Decile Analysis Reports\nrequire(sqldf)\n\nval_deciles <- sqldf(\"select decile, min(Prob) as Min_prob\n , max(Prob) as max_prob\n , sum(CHURN) as churn_Count\n , (count(decile)-sum(CHURN)) as Non_churn_Count \n from val1\n group by decile\n order by decile desc\")\n\nwrite.csv(val_deciles,\"val_deciles.csv\",row.names = F)\n\n\n\n#val1<-cbind(val, Prob=predict(final_fit, val, type=\"response\"))\n#val1$CHURNDEP <- ifelse(val1$Prob>0.501, 1,0)\n#sum(val1$CHURNDEP)\n", "meta": {"hexsha": "6c7a69a78ed4b963083837407adeea5ac6595b34", "size": 11071, "ext": "r", "lang": "R", "max_stars_repo_path": "Consumer Churn Prediction.r", "max_stars_repo_name": "ashish95arora/Predicting-Consumer-Churn", "max_stars_repo_head_hexsha": "99e4bc1f74424de9cf47ffbd20a258f3078de4ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Consumer Churn Prediction.r", "max_issues_repo_name": "ashish95arora/Predicting-Consumer-Churn", "max_issues_repo_head_hexsha": "99e4bc1f74424de9cf47ffbd20a258f3078de4ef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Consumer Churn Prediction.r", "max_forks_repo_name": "ashish95arora/Predicting-Consumer-Churn", "max_forks_repo_head_hexsha": "99e4bc1f74424de9cf47ffbd20a258f3078de4ef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.0267558528, "max_line_length": 183, "alphanum_fraction": 0.67211634, "num_tokens": 4091, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369905, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3052586610498793}} {"text": "'\nThe file \"esimate_returns.r\" estimates returns to tenure by age and\neducation from CPS data.\n'\n\n# read in arguments (1: path input file, 2: path output file)\nargs <- commandArgs(trailingOnly=TRUE)\n# args <- c(\n# \"C:\\\\Users\\\\user\\\\projects\\\\data_analysis_cps\\\\bld\\\\out\\\\data\\\\cps_supplement_tenure_extract.csv\",\n# \"C:\\\\Users\\\\user\\\\projects\\\\data_analysis_cps\\\\bld\\\\out\\\\results\\\\cps_returns_to_tenure.csv\"\n# )\n\n# load required packages\nlibrary(foreign)\nlibrary(plyr)\nlibrary(dplyr)\nlibrary(spatstat)\nlibrary(tidyr)\nlibrary(ggeffects)\n\n#####################################################\n# SCRIPT\n#####################################################\n\n## DATA PREPARATION\n# load data\ndf <- read.csv(file = args[1])\n\n# drop observations out of scope and with missing values\ndf <- drop_na(df, c(\n \"earnings_weekly_deflated\",\n \"tenure\",\n \"education\",\n \"age_group\",\n \"marital_status\",\n \"race\",\n \"weight\"\n))\ndf <- df[df$earnings_weekly_deflated!=0,]\ndf <- subset(df, \"sex\"=\"MALE\")\n\n# data cleaning and processing\n# truncate tenure at 45 years\ndf[df$tenure>45,\"tenure\"] <- 45\n\n# add reduced education category\ndf$education_reduced <- revalue(df$education, c(\n \"Less than 5th Grade\"=\"low\",\n \"5th Grade to 12th Grade without Diploma\"=\"low\",\n \"High School Graduates, No College\"=\"medium\",\n \"Some College or Associate Degree\"=\"medium\",\n \"Bachelor's degree and higher\"=\"high\"\n))\n\n# transform categorical variables to factors\ndf$year <- factor(df$year)\ndf$marital_status <- factor(df$marital_status)\ndf$race <- factor(df$race)\ndf$citizenship_status <- factor(df$citizenship_status)\n\n# compute log earnigns\ndf$log_earnings <- log(df$earnings_weekly_deflated)\n\n## ESTIMATION AND PREDICTION\n# estimate linear model of returns to tenure\nmodel_tenure <- lm(log_earnings ~ education_reduced * tenure\n + education_reduced * I(tenure^2)\n + age + I(age^2) + year + state + marital_status + race,\n weights = weight,\n data=df\n)\n\n# predict log earnings by tenure and education\nreturns_predicted <- ggeffect(model_tenure, terms=c(\"tenure [0:40 by=5]\", \"education_reduced\"))\n\n# transform log earnings to earnings\nreturns_predicted$predicted <- exp(returns_predicted$predicted)\nreturns_predicted$conf.high <- exp(returns_predicted$conf.high)\nreturns_predicted$conf.low <- exp(returns_predicted$conf.low)\n\n## OUTPUT\n# store results\nwrite.csv(returns_predicted, file = args[2], row.names = FALSE)\n", "meta": {"hexsha": "2b945694747ea089cbe3b8f70bc3fda02b4d29b8", "size": 2429, "ext": "r", "lang": "R", "max_stars_repo_path": "src/data_analysis/estimate_returns.r", "max_stars_repo_name": "simonjheiler/data_analysis_cps", "max_stars_repo_head_hexsha": "01ebac3c34fce9ac01379f3c63d38d7bb8edd2f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/data_analysis/estimate_returns.r", "max_issues_repo_name": "simonjheiler/data_analysis_cps", "max_issues_repo_head_hexsha": "01ebac3c34fce9ac01379f3c63d38d7bb8edd2f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/data_analysis/estimate_returns.r", "max_forks_repo_name": "simonjheiler/data_analysis_cps", "max_forks_repo_head_hexsha": "01ebac3c34fce9ac01379f3c63d38d7bb8edd2f1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.9166666667, "max_line_length": 102, "alphanum_fraction": 0.6928777275, "num_tokens": 638, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3052047518810919}} {"text": "# 3. faza: Izdelava zemljevida\n\n# Uvozimo funkcijo za pobiranje in uvoz zemljevida.\nsource(\"lib/uvozi.zemljevid.r\")\n\n# Uvozimo zemljevid.\ncat(\"Uva\u017eam zemljevid...\\n\")\nsvet <- uvozi.zemljevid(\"http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip\",\n \"europa\", \"ne_110m_admin_0_countries.shp\", mapa = \"zemljevid\",\n encoding = \"Windows-1250\")\n\n# Funkcija, ki podatke preuredi glede na vrstni red v zemljevidu\npreuredi <- function(podatki, zemljevid) {\n nove.svet <- c()\n manjkajo <- ! nove.svet %in% rownames(podatki)\n M <- as.data.frame(matrix(nrow=sum(manjkajo), ncol=length(podatki)))\n names(M) <- names(podatki)\n row.names(M) <- nove.svet[manjkajo]\n podatki <- rbind(podatki, M)\n \n out <- data.frame(podatki[order(rownames(podatki)), ])[rank(levels(zemljevid$NAME_1)[rank(zemljevid$NAME_1)]), ]\n if (ncol(podatki) == 1) {\n out <- data.frame(out)\n names(out) <- names(podatki)\n rownames(out) <- rownames(podatki)\n }\n return(out)\n}\n\n# Preuredimo podatke, da jih bomo lahko izrisali na zemljevid.\ndrzave <- levels(t4[,2])\n\n#Vektor, ki pove v koliko razli\u010dnih mestah ima dano dr\u017eavo razvite sisteme.\naa <- table(t4[,2])\n#naredimo urejenostno spremenljivko\n\nv<-rep(2,length(drzave))\nv[aa<4]<-1\nv[aa>30]<-3\nnames(v)<-drzave\n#1 - low; 2 - medium; 3 - high\n\n\ne1<-sapply(drzave, function(x) sum(t4[t4[,2]==x,9], na.rm=TRUE)) #\u0161tevilo razpolo\u017evljivih koles v vsaki dr\u017eavi\nnames(e1)<-drzave\nm <- match(svet$name_long, drzave)\nsvet$urejenost <- v[m]\nsvet$stevilo.koles <- e1[m]\n\n\nsvet[31,65]<-0 # Kitajska damo stran, ker ima 100 krat ve\u010d koles. Brez njo dobimo bolj\u0161i zemljevid.\n\n#Statisti\u010dni podatki za 1. zemljevid\nmed1<-median(svet$stevilo.koles[!is.na(svet$stevilo.koles)]) #790\nmean1<-mean(svet$stevilo.koles,na.rm=TRUE) #3593.204\nsd1<-sd(svet$stevilo.koles,na.rm=TRUE) #7690.308\n\n#Nari\u0161imo zemljevide\n\ncat(\"Ri\u0161em zemljevid...\\n\")\npdf(\"slike/zemljevid1.pdf\")\n\n\n\nprint(spplot(svet, \"stevilo.koles\", col.regions = c(\"white\", rainbow(15, \n start=0, end = 10/12)), main=\"\u0160tevilo razpolo\u017eljivih koles v vsaki dr\u017eavi\"))\n\n\ndev.off()\n\n\n\n\ncolo<-c()\ncolo[\"1\"]<-\"red\"\ncolo[\"2\"]<-\"blue\"\ncolo[\"3\"]<-\"green\"\ncat(\"Ri\u0161em zemljevid...\\n\")\npdf(\"slike/zemljevid2.pdf\")\n#Razporedimo dr\u017eave po tem koliko postajali\u0161\u010d imajo\nplot(svet, col=ifelse(svet$urejenost==\"1\",colo[1],\n ifelse(svet$urejenost==\"2\",colo[2],ifelse(svet$urejenost==\"3\",colo[3],\"white\"))),\n main=\"Porazdelitev dr\u017eav v treh skupinah\")\nlegend(\"bottom\", legend = c(\"1. cona - dr\u017eave, ki imajo razvite sisteme v manj kot 4 mesta\"\n ,\"2. cona - dr\u017eave, ki imajo razvite sisteme v 4-30 mest\",\n \"3. cona - dr\u017eave, ki imajo razvite sisteme v ve\u010d kot 30 mest\"),\n fill = c(\"red\",\"blue\",\"green\"), bg = \"white\")\n\n\ndev.off()\n\n\nsvet$st.koles.prebivalstvo<-sapply(1:177, \n function(x) svet$stevilo.koles[x]/svet$pop_est[x])\n\n\ncat(\"Ri\u0161em zemljevid...\\n\")\npdf(\"slike/zemljevid3.pdf\")\n\nprint(spplot(svet, \"st.koles.prebivalstvo\", \n col.regions = c(\"white\",rainbow(15, start=0, end = 10/12)), \n main=\"Razmerje med \u0161tevilo razpolo\u017eljivih koles in \u0161tevilo prebivalcev\"))\n\n\ndev.off()", "meta": {"hexsha": "c674c2f57f45852b67dc6a164fef8cc72d17666a", "size": 3305, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "damjanm/APPR-2014-15", "max_stars_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "damjanm/APPR-2014-15", "max_issues_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2015-01-09T19:00:02.000Z", "max_issues_repo_issues_event_max_datetime": "2015-03-01T17:23:23.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "damjanm/APPR-2014-15", "max_forks_repo_head_hexsha": "d1078606e9c2e5b19dadf247048272b1704551b7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-29T19:30:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-29T19:30:52.000Z", "avg_line_length": 31.4761904762, "max_line_length": 142, "alphanum_fraction": 0.6532526475, "num_tokens": 1266, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.30520474453955704}} {"text": "#' Extract the information from the simulation data frame to analyse the interaction effects\n#'\n#' @param allsim dataset with all simulations values\n#' @param dataset dataset with all variables\n#' @param exposures a vector with exposures\n#' @param confounders a vector with confounders\n#' @param squem squeme of the values of the prediction values\n#' @return data frame with interaction values\n#' @examples\n#' data(expose_data)\n#' data(simu)\n#' data(gen)\n#' delta=c(1,0)\n#' seku <- seq(0,1,0.05)\n#' Exposures<- c('Var1','Var2','Var3','Var4','Var5')\n#' summary_table_lines <- gen[[2]]\n#' it <- interact (allsim = simu[[1]], dataset = expose_data,exposures = Exposures,\n#' confounders = c('sex'), squem = summary_table_lines)\n#' @export\n\ninteract <- function(allsim, dataset, exposures, confounders, squem) {\n \n dataset <- data.frame(dataset)\n N <- dim(dataset)[1]\n sim <- dim(allsim)[2]\n \n m2 <- NA\n len_cof <- length(confounders)\n len_exp <- length(exposures)\n len_tot <- len_exp + len_cof\n var_tot <- c(exposures, confounders)\n \n ext2 <- squem[grep(\"ITE_\", squem$Group), ]\n \n ini <- ext2$From[1]\n fin <- ext2$To[nrow(ext2)]\n \n allsim <- allsim[ini:fin, ]\n \n h <- 0\n n <- 1\n z <- len_exp + len_cof\n z2 <- (z * (z - 1)) / 2\n \n azken_taula <- data.frame(matrix(NA, z2, sim + 1))\n names(azken_taula)[1] <- c(\"Interaction\")\n ec <- 1\n h <- 1\n for (ex in 1:len_tot) {\n ec <- ec + 1\n if (len_tot == ex) \n break\n for (ex2 in ec:len_tot) {\n inter2 <- paste0(var_tot[ex], \"-\", var_tot[ex2])\n azken_taula[h, 1] <- inter2\n h <- h + 1\n }\n }\n ec <- 1\n h3 <- 1\n gehi <- 0\n for (ex in 1:len_tot) {\n ec <- ec + 1\n if (len_tot == ex) \n break\n for (ex2 in ec:len_tot) {\n inter2 <- paste0(var_tot[ex], \"-\", var_tot[ex2])\n for (si in 1:sim) {\n n <- 1 + gehi\n n2 <- n + N\n a <- allsim[n:(n2 - 1), si]\n n <- n2\n n2 <- n + N\n b <- allsim[n:(n2 - 1), si]\n n <- n2\n n2 <- n + N\n \n c <- allsim[n:(n2 - 1), si]\n n <- n2\n n2 <- n + N\n \n d <- allsim[n:(n2 - 1), si]\n n <- n2\n azken_taula[h3, si + 1] <- mean(a - b - c + d)\n }\n gehi <- gehi + 4 * N\n h3 <- h3 + 1\n }\n }\n azken_taula2 <- data.frame(matrix(NA, z2, 3))\n m1 <- apply(azken_taula[, -1], 1, mean)\n m2 <- apply(azken_taula[, -1], 1, stats::sd)\n azken_taula2 <- data.frame(azken_taula$Interaction, m1, m2)\n names(azken_taula2) <- c(\"Interaction\", \"Mean\", \"SD\")\n return(azken_taula2)\n}\n", "meta": {"hexsha": "a0d900d93faab99cf70417e19e71f2604d2fa716", "size": 2836, "ext": "r", "lang": "R", "max_stars_repo_path": "R/interact.r", "max_stars_repo_name": "itamuria/expose", "max_stars_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/interact.r", "max_issues_repo_name": "itamuria/expose", "max_issues_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-04-13T02:01:15.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-13T02:01:15.000Z", "max_forks_repo_path": "R/interact.r", "max_forks_repo_name": "itamuria/expose", "max_forks_repo_head_hexsha": "257f4f09e068c0c73b74e136954921624583d088", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.2371134021, "max_line_length": 92, "alphanum_fraction": 0.4975317348, "num_tokens": 914, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30518498895749824}} {"text": "#First we need to load the RSocrata package. You may need to install it first.\n\nrequire(RSocrata)\n\n#Next we will need the plyr package.\n\nrequire(plyr)\n\n#Now we'll load the dataframe.\n\nslc <- read.socrata(\"https://opendata.utah.gov/Health/Salt-Lake-School-District-Vaccinations-2014/yud6-5333\")\n\nhead(slc) #Let's see what the data looks like.\n\n\nrow.names(slc) <- slc$School #Replaces the numbered rows with values from the column 'School'\nslc <- slc[,-c(1:5)] #Removes columns 1:5, columns start at 1\n\n#Remove the '%' sign from the numerical data in the three rows we are analyzing \n\nslc$X..adequately.immunized <- gsub(\"%\", \"\", as.character(slc$X..adequately.immunized))\nslc$X..receiving.MMR <- gsub(\"%\", \"\", as.character(slc$X..receiving.MMR))\nslc$X..receiving.DTaP <- gsub(\"%\", \"\", as.character(slc$X..receiving.DTaP))\n\nslc <- slc[order(slc$X..adequately.immunized),] #Order the data with priority to the column X..adequately.immunized\n\n#Rename columns using the plyr library\nslc <- rename(slc, c(X..adequately.immunized=\"All Six Vaccines\", X..receiving.MMR=\"MMR\", X..receiving.DTaP=\"DTaP\")) \n\nslc_matrix <- data.matrix(slc) #The heatmap needs data in a matrix format\n\n#Create the heatmap with R's built in heatmap function\nslc_heatmap <- heatmap(slc_matrix, Rowv=NA, Colv=NA, col=brewer.pal(9,\"Blues\"), scale=\"column\", margins=c(17,17), main=\"Salt Lake School District Vaccinations 2014\")\n", "meta": {"hexsha": "21280dc21ff4b16b6e676cb7d5c5c5c33b7f38bd", "size": 1394, "ext": "r", "lang": "R", "max_stars_repo_path": "slc_vaccination_heatmap.r", "max_stars_repo_name": "chakers/R-Vaccination-Heatmap", "max_stars_repo_head_hexsha": "745afdd0a2ff6eb7a221d31a726d613510756247", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "slc_vaccination_heatmap.r", "max_issues_repo_name": "chakers/R-Vaccination-Heatmap", "max_issues_repo_head_hexsha": "745afdd0a2ff6eb7a221d31a726d613510756247", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "slc_vaccination_heatmap.r", "max_forks_repo_name": "chakers/R-Vaccination-Heatmap", "max_forks_repo_head_hexsha": "745afdd0a2ff6eb7a221d31a726d613510756247", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.0, "max_line_length": 165, "alphanum_fraction": 0.7274031564, "num_tokens": 437, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30518498099329516}} {"text": "library(data.table)\nlibrary(ggplot2)\n\nsetwd(\"~/Dropbox/upf_global_studies/2017/slides/global_studies_research_methods\")\n\ngft = fread('gft_spain.csv')\n\ngft$Date = as.Date(gft$Date)\n\nggplot(data=gft, aes(Date, Spain)) + geom_line() + ylab(\"Spain Prediction\")\n\n\n\ngt = fread('mosquito_trend.csv')\n\nclass(gt$Week)\ngt$Week = as.Date(gt$Week)\nnames(gt)[2] = \"term\"\n\nplt = ggplot(data=gt, aes(Week, term)) + geom_line() + ylab('\"Mosquito\" Searches Worldwide')\n\nggsave(\"mosquito_trend.png\", plt)\n\ngt = fread('research_methods_trend.csv')\n\nclass(gt$Week)\ngt$Week = as.Date(gt$Week)\nnames(gt)[2] = \"term\"\n\nplt = ggplot(data=gt, aes(Week, term)) + geom_line() + ylab('\"Research Methods\" Searches Worldwide')\n\nggsave(\"research_methods_trend.png\", plt)\n\n\ngt = fread('machine_learning_trend.csv')\n\nclass(gt$Week)\ngt$Week = as.Date(gt$Week)\nnames(gt)[2] = \"term\"\n\nplt = ggplot(data=gt, aes(Week, term)) + geom_line() + ylab('\"Machine Learning\" Searches Worldwide')\n\nggsave(\"machine_learning_trend.png\", plt)\n\n\ngt = fread('correlate-Influenza_like_Illness_CDC_.csv')\nnames(gt)\nnames(gt)[1] = \"Week\"\nnames(gt)[2] = \"ILI\"\nnames(gt)[3] = \"infa\"\ngt$Week = as.Date(gt$Week, format=\"%m/%d/%y\")\n\n\nplt = ggplot(data=gt) + geom_line(size=1, aes(Week, ILI, color=\"ILI (CDC)\")) + ylab(\"Normalized Search Activity / ILI Visits\") + geom_line(aes(Week, infa, color=\"'influenza type a'\")) + labs(colour=\"\") \n\nplt = ggplot(data=gt) + geom_point(aes(infa, ILI)) + ylab(\"ILI (CDC)\") + xlab(\"'influenza type a'\")\nplt\nggsave(\"google_correlate_scatter.png\", plt)\n\n\n", "meta": {"hexsha": "624e178d424fca92cc971f3cb93a8684401b17e3", "size": 1527, "ext": "r", "lang": "R", "max_stars_repo_path": "gft.r", "max_stars_repo_name": "JohnPalmer/global_studies_research_methods_2017", "max_stars_repo_head_hexsha": "5b6fdf01e2f862863bbae53050987c53d2ed794d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gft.r", "max_issues_repo_name": "JohnPalmer/global_studies_research_methods_2017", "max_issues_repo_head_hexsha": "5b6fdf01e2f862863bbae53050987c53d2ed794d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gft.r", "max_forks_repo_name": "JohnPalmer/global_studies_research_methods_2017", "max_forks_repo_head_hexsha": "5b6fdf01e2f862863bbae53050987c53d2ed794d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0327868852, "max_line_length": 202, "alphanum_fraction": 0.6928618206, "num_tokens": 493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3051849809932951}} {"text": "# library\nlibrary(ggplot2)\n\ndata = read.csv(file='cleaned_data.csv', sep=',', header = TRUE)\nvar <- data$streak\n\nplot <- ggplot(data, aes(factor(1), streak)) +\n coord_flip() +\n geom_boxplot()\n # geom_density(binwidth = 1)\n\nprint(plot)\n", "meta": {"hexsha": "538e2286ea09657c98def0a37dbfa3c65f3b16ed", "size": 238, "ext": "r", "lang": "R", "max_stars_repo_path": "histogram.r", "max_stars_repo_name": "ruddfawcett/spotify-data", "max_stars_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "histogram.r", "max_issues_repo_name": "ruddfawcett/spotify-data", "max_issues_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "histogram.r", "max_forks_repo_name": "ruddfawcett/spotify-data", "max_forks_repo_head_hexsha": "e0c4dd127fbcdefd0fa4b706ec6bd5a39f433942", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.3076923077, "max_line_length": 64, "alphanum_fraction": 0.6680672269, "num_tokens": 71, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3051849809932951}} {"text": "#' Check white blood cell measurements against the differential measurements\n#' @param data a dataframe\n#' @param wbc The name of the column with WBC measurements\n#' @param lymph The name of the column with lymphocyte measurements\n#' @param mono The name of the column with monocyte measurements\n#' @param neutro The name of the column with neutrophil measurements\n#' @param eosin The name of the column with eosinophil measurements\n#' @param baso The name of the column with basophil measurements\n#' @export\n\nwbcCheck<-function(data,wbc=\"wbc_ncnc_bld\",lymph=\"lymphocyte_ncnc_bld\",mono=\"monocyte_ncnc_bld\",\n neutro=\"neutrophil_ncnc_bld\",eosin=\"eosinophil_ncnc_bld\",baso=\"basophil_ncnc_bld\"){\n if(class(data) != \"data.frame\") stop(\"data needs to be a dataframe\")\n nms<-c(wbc,lymph,mono,neutro,eosin,baso)\n print(nms)\n if(!all(is.element(nms,names(data)))) stop(\"missing required variables\")\n nms2<-c(\"wbc\",\"lymph\",\"mono\",\"neutro\",\"eosin\",\"baso\")\n for(i in 1:6){\n names(data)[names(data)==nms[i]]<-nms2[i]\n }\n print(names(data))\n out<-vector(\"list\",length=5)\n names(out)<-c(\"wbc.na\",\"sum_withNA_bad\",\"sum_noNA_bad\", \"abs_dif\", \"density\")\n n<-is.na(data$wbc)\n n2<- !is.na(data$lymph) | !is.na(data$mono) | !is.na(data$neutro) | !is.na(data$eosin) | !is.na(data$baso)\n nq<-n2 & n\n out[[1]]<-sum(nq)\n\n sel<-!n &n2 # has wbc value and at least one differential value\n n3<- is.na(data$lymph) | is.na(data$mono) | is.na(data$neutro) | is.na(data$eosin) | is.na(data$baso)\n sel2<-sel & n3\n tmp<-data[sel2,]\n tmp3<-data[sel2,c(\"lymph\",\"mono\",\"neutro\",\"eosin\",\"baso\")]\n tmp3$sum<-rowSums(tmp3,na.rm=TRUE)\n dif<-(tmp3$sum - tmp$wbc)/tmp$wbc\n ck<- dif > 0.05\n out[[2]]<-sum(ck)\n\n n4<- !is.na(data$lymph) & !is.na(data$mono) & !is.na(data$neutro) & !is.na(data$eosin) & !is.na(data$baso)\n sel3<-!n & n4 # has value for wbc and all differentials\n tmp<-data[sel3,]\n tmp4<-tmp[,c(\"lymph\",\"mono\",\"neutro\",\"eosin\",\"baso\")]\n tmp4$sum<-rowSums(tmp4,na.rm=TRUE)\n tmp4$dif <- (tmp4$sum - tmp$wbc)/tmp$wbc\n tmp4$abs_dif <- abs(tmp4$sum - tmp$wbc)/tmp$wbc\n ck<-tmp4$abs_dif > 0.05\n out[[3]]<-sum(ck)\n\n out[[4]] <- tmp4$abs_dif\n\n g2 <- tmp4[tmp4$sum != tmp$wbc, ] %>%\n ggplot(aes(x = dif)) + geom_density()\n out[[5]] <- g2\n\n return(out)\n}\n", "meta": {"hexsha": "e8ccb48d008728e745ef3edd404c14068242a6b9", "size": 2267, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wbcCheck.r", "max_stars_repo_name": "UW-GAC/harmonHelper", "max_stars_repo_head_hexsha": "820d22af9d759a8921d62b355e1a908dd35fd0a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/wbcCheck.r", "max_issues_repo_name": "UW-GAC/harmonHelper", "max_issues_repo_head_hexsha": "820d22af9d759a8921d62b355e1a908dd35fd0a8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2017-03-09T22:23:40.000Z", "max_issues_repo_issues_event_max_datetime": "2017-09-08T18:55:18.000Z", "max_forks_repo_path": "R/wbcCheck.r", "max_forks_repo_name": "UW-GAC/harmonHelper", "max_forks_repo_head_hexsha": "820d22af9d759a8921d62b355e1a908dd35fd0a8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7719298246, "max_line_length": 108, "alphanum_fraction": 0.6638729599, "num_tokens": 847, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3051849809932951}} {"text": "source(\"lib/uvozi.zemljevid.r\", encoding = \"UTF-8\")\nlibrary(ggplot2)\nlibrary(dplyr)\n\npretvori.zemljevid <- function(zemljevid) {\n fo <- fortify(zemljevid)\n data <- zemljevid@data\n data$id <- as.character(0:(nrow(data)-1))\n return(inner_join(fo, data, by=\"id\"))\n}\n\n# 1. Slovenske ob\u010dine\n\nmesta <- uvozi.zemljevid(\"http://biogeo.ucdavis.edu/data/gadm2.8/shp/ESP_adm_shp.zip\",\n \"ESP_adm2\", encoding = \"UTF-8\")\nmst <- pretvori.zemljevid(mesta)\n\n## Zemljevid z barvami za povr\u0161ino\nzem <- ggplot() + geom_polygon(data = mst, aes(x=long, y=lat, group=group),\n color = \"grey\") +\n scale_fill_gradient(low=\"#3F7F3F\", high=\"#00FF00\")\nprint(zem)\n\n## Dodamo pike za mesta po tipu ob\u010dine\nzem2 <- zem + geom_point(data = obcine@data, aes(x = Y_C, y = X_C, color = OB_TIP)) +\n scale_color_manual(name=\"Tip\", breaks = c(\"D\", \"N\"),\n labels = c(\"Mestna ob\u010dina\", \"Ob\u010dina\"),\n values = c(\"red\", \"blue\"))\nprint(zem2)\n\n## Dodamo imena glavnih mest mestnih ob\u010din\nzem3 <- zem2 + geom_text(data = obcine@data %>% filter(OB_TIP == \"D\"),\n aes(x = Y_C, y = X_C, label = OB_UIME),\n size = 3, vjust = 2)\nprint(zem3)\n\n# 2. Zvezne dr\u017eave ZDA\n\nzda <- uvozi.zemljevid(\"http://baza.fmf.uni-lj.si/states_21basic.zip\", \"states\")\ncapitals <- read.csv(\"podatki/uscapitals.csv\")\nrow.names(capitals) <- capitals$state\ncapitals <- preuredi(capitals, zda, \"STATE_NAME\")\ncapitals$US.capital <- capitals$capital == \"Washington\"\n\n## Dodamo podatke o predsedni\u0161kih volitvah v zemljevid\nzda$vote.2012 <- capitals$vote.2012\nzda$electoral.votes <- capitals$electoral.votes\nusa <- pretvori.zemljevid(zda)\n\n# Zemljevid elektorskih glasov\nmap1 <- ggplot() + geom_polygon(data = usa, aes(x = long, y = lat, group = group,\n fill = electoral.votes))\nprint(map1)\n\nusa.cont <- usa %>% filter(! STATE_NAME %in% c(\"Alaska\", \"Hawaii\"))\ncapitals.cont <- capitals %>% filter(! state %in% c(\"Alaska\", \"Hawaii\"))\n\nmap2 <- ggplot() + geom_polygon(data = usa.cont,\n aes(x = long, y = lat,\n group = group, fill = vote.2012)) +\n scale_fill_manual(values = c(\"blue\", \"red\")) +\n guides(fill = guide_legend(\"Volitve 2012\"))\nprint(map2)\n\nmap3 <- map2 + geom_polygon(data = usa.cont, fill = \"black\",\n aes(x = long, y = lat, group = group,\n alpha = electoral.votes)) +\n scale_alpha(range = c(0.4, 0)) +\n guides(alpha = guide_legend(\"Elektorski glasovi\"))\nprint(map3)\n\nmap4 <- map3 + geom_polygon(data = usa.cont, alpha = 0, color = \"gray\",\n aes(x = long, y = lat, group = group))\nprint(map4)\n\nmap5 <- map4 + geom_point(data = capitals.cont, color = \"green\",\n aes(x = long, y = lat,\n shape = US.capital, size = US.capital)) +\n geom_text(data = capitals.cont,\n aes(x = long, y = lat, label = capital,\n vjust = US.capital, size = US.capital)) +\n scale_shape_manual(values = c(20, 15), guide = FALSE) +\n scale_size_manual(values = c(3, 5), guide = FALSE) +\n discrete_scale(aesthetics = \"vjust\", scale_name = NULL,\n palette = . %>% c(0, 2), guide = FALSE)\nprint(map5)\n\n", "meta": {"hexsha": "3f54002a0e4816a23d1c380e98d962acf8f0bce6", "size": 3541, "ext": "r", "lang": "R", "max_stars_repo_path": "zemljevid-ggplot.r", "max_stars_repo_name": "stifler9/ANPP-2015-16", "max_stars_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "zemljevid-ggplot.r", "max_issues_repo_name": "stifler9/ANPP-2015-16", "max_issues_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2015-11-28T13:07:49.000Z", "max_issues_repo_issues_event_max_datetime": "2016-10-26T13:14:41.000Z", "max_forks_repo_path": "zemljevid-ggplot.r", "max_forks_repo_name": "stifler9/ANPP-2015-16", "max_forks_repo_head_hexsha": "8b6866332728a87a989aec3395ef3dbc438c9aed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2386363636, "max_line_length": 86, "alphanum_fraction": 0.5487150522, "num_tokens": 1020, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.3051849809932951}} {"text": "#This contains all of the functions used in the model\n\n#Hardcode the corps names by class as generally accessible variables\nWorldClass = c(\"The Academy\",\"Blue Devils\",\"Blue Knights\",\"Blue Stars\",\"Bluecoats\",\"Boston Crusaders\",\"The Cadets\",\"Carolina Crown\",\n\t\"The Cavaliers\",\"Colts\",\"Crossmen\",\"Genesis\",\"Jersey Surf\",\"Madison Scouts\",\"Mandarins\",\"Music City\",\"Oregon Crusaders\",\n\t\"Pacific Crest\",\"Phantom Regiment\",\"Pioneer\",\"Santa Clara Vanguard\",\"Seattle Cascades\",\"Spirit of Atlanta\",\"Troopers\"\n)\nOpenClass = c(\"7th Regiment\",\"The Battalion\",\"Blue Devils B\",\"Blue Devils C\",\"Colt Cadets\",\"Columbians\",\"Encorps\",\"Gold\",\"Golden Empire\",\n\t\"Guardians\",\"Heat Wave\",\"Impulse\",\"Incognito\",\"Legends\",\"Louisiana Stars\",\"Raiders\",\"River City Rhythm\",\"Shadow\",\n\t\"Southwind\",\"Spartans\",\"Vanguard Cadets\",\"Vessel\",\"Watchmen\"\n)\nOC_NotAttending = c(\"The Battalion\",\"Blue Devils B\",\"Blue Devils C\",\"Columbians\",\"Encorps\",\"Impulse\",\"Incognito\",\n\t\"Vanguard Cadets\",\"Vessel\",\"Watchmen\"\n)\n\n\nScoreParse <- function(filename, WorldNames=WorldClass, OpenNames=OpenClass) {\n\t#Start by loading the file\n\tBigTable = read.csv(filename, stringsAsFactors=F)\n\t\t\n\t#This function creates a data frame for a given corps\n\tFrameCreate <- function(CorpsName, Table) {\n\t\t#Start by finding all the rows which have data\n\t\tCorpsRows = which(Table$Corps == CorpsName)\n\t\tif (length(CorpsRows) == 0) {\n\t\t\treturn(NULL)\n\t\t}\n\t\t\n\t\t#Get vectors for the scores and Day\n\t\tGEvec = Table[CorpsRows,\"GE\"]\n\t\tVisVec = Table[CorpsRows,\"Visual\"]\n\t\tMusVec = Table[CorpsRows,\"Music\"]\n\t\tDayVec = Table[CorpsRows,\"Day\"]\n\t\t\n\t\t#Now make the data frame\n\t\tOutFrame = data.frame(Day=DayVec, GE=GEvec, Vis=VisVec, Mus=MusVec)\n\t\treturn(OutFrame)\n\t}\n\t\n\t#Now run the function on all the world class and open class corps\n\tWorldFrames = lapply(WorldNames, FrameCreate, Table=BigTable)\n\tOpenFrames = lapply(OpenNames, FrameCreate, Table=BigTable)\n\t#Combine them into a single frame\n\tAllFrames = c(WorldFrames, OpenFrames)\n\t\n\t#Add the corps names to the frames\n\tnames(AllFrames) = c(WorldNames, OpenNames)\n\treturn(AllFrames)\n}\n\n#Create a function that fits the exponential curve to a data frame\nExpFitter <- function(CorpsFrame, BaseDay, PrelimsDay=50) {\n\t#50 is the prelims day for 2019, which is why it's defaulted here\n\t\n\t#Start by checking the number of shows\n\tif (is.null(CorpsFrame)) { return(NULL) }\n\tif (nrow(CorpsFrame) < 6) { return(NULL) }\n\t\n\t#Order the frame by day\n\tCorpsFrame = CorpsFrame[order(CorpsFrame$Day),]\n\t\n\t#The weight vector reduces weights of recent scores, based on their correlation with finals week scores\n\t#The weight vector reduces weights of recent scores, based on their correlation with finals week scores\n\tDayVec = seq(from=0, to=PrelimsDay+2, by=1)\n\tWeightVec = 1 - (PrelimsDay-DayVec)*0.00224\n\tDiscountOrig = 0.25 + (0.75-0.25)/PrelimsDay * BaseDay\n\tDiscountVec = seq(from=1, to=DiscountOrig, length.out=6)\n\t\n\t#Create a vector for the corps-specific day weights\n\tShowWeights = WeightVec[CorpsFrame$Day]\n\t#Now discount the most recent 5\n\tNshow = length(ShowWeights)\n\tShowWeights[(Nshow-5):Nshow] = ShowWeights[(Nshow-5):Nshow] * DiscountVec\n\t\n\t#Pull out the individual vectors to match nls input better\n\tN = sum(CorpsFrame$Day <= BaseDay)\n\tGE = CorpsFrame$GE[CorpsFrame$Day <= BaseDay]\n\tVis = CorpsFrame$Vis[CorpsFrame$Day <= BaseDay]\n\tMus = CorpsFrame$Mus[CorpsFrame$Day <= BaseDay]\n\tDay = CorpsFrame$Day[CorpsFrame$Day <= BaseDay]\n\t\n\t#Now fit the curve for each caption, wrapped in a try to avoid catastrophic errors\n\t#This tryCatch won't make the objects if we're not successful\n\ttryCatch({ \n\t\tGEModel = nls(GE ~ a + Day^b, data=data.frame(GE, Day), start=list(a=GE[1], b=0.5), \n\t\t\tcontrol=nls.control(warnOnly=TRUE), weights=ShowWeights)\n\t\tVisModel = nls(Vis ~ a + Day^b, data=data.frame(Vis, Day), start=list(a=Vis[1], b=0.5), \n\t\t\tcontrol=nls.control(warnOnly=TRUE), weights=ShowWeights)\n\t\tMusModel = nls(Mus ~ a + Day^b, data=data.frame(Mus, Day), start=list(a=Mus[1], b=0.5), \n\t\t\tcontrol=nls.control(warnOnly=TRUE), weights=ShowWeights)\n\t}, warning = function(w) { #This makes it so scenarios of non-convergence don't return bad coefficients\n\t\t#print(\"Can't run corps due to non-convergence\")\n\t}, error = function(e) {\n\t\t#print(\"Can't run corps due to an error in curve fitting\")\n\t}) \n\t\n\t#Check to see how many of the 3 models exist\n\tExistVec = c(exists('GEModel'), exists('VisModel'), exists('MusModel'))\n\t\n\t#Create a copy of CorpsFrame we can modify\n\tCorpsFrame2 = CorpsFrame\n\tTrimmedResult = NA #This tracks if we're successful in the if loop\n\t\n\tif (sum(ExistVec) < 3) { #This means we didn't get a good fit in at least 1 caption\n\t\t#print(\"Nonconvergence!\")\n\t\t#Try removing the most recent scores until we get convergence or too small a sample\n\t\tCorpsFrame2 = CorpsFrame2[1:(nrow(CorpsFrame2)-1) ,]\n\t\t\n\t\tif (nrow(CorpsFrame) < 6) { #Check for sample size\n\t\t\tbreak\n\t\t} else {\n\t\t\t#Recursion!\n\t\t\tTrimmedResult = ExpFitter(CorpsFrame2, BaseDay)\n\t\t\t#No matter what, we can return TrimmedResult because this is the top level of the recursion\n\t\t\treturn(TrimmedResult)\n\t\t}\n\t}\n\t\n\t#Pull out the summary of the models\n\tGEcoef = summary(GEModel)$coefficients\n\tVcoef = summary(VisModel)$coefficients\n\tMcoef = summary(MusModel)$coefficients\n\t\n\t#Now pull out the coefficients\n\taVec = c(GEcoef[1,1], Vcoef[1,1], Mcoef[1,1])\n\tbVec = c(GEcoef[2,1], Vcoef[2,1], Mcoef[2,1])\n\t\n\t#And now pull out the standard errors\n\taseVec = c(GEcoef[1,2], Vcoef[1,2], Mcoef[1,2])\n\tbseVec = c(GEcoef[2,2], Vcoef[2,2], Mcoef[2,2])\n\t\n\t#Put the coefficients and standard errors into a data frame\n\tOutFrame = data.frame(a=aVec, b=bVec, aSE=aseVec, bSE=bseVec, N=rep(N,3))\n\trow.names(OutFrame) = c('GE','Vis','Mus')\n\t#return the data frame\n\treturn(OutFrame)\n}\n\n#Now make a function that predicts a day for all corps\nPredictor <- function(CorpsList, PredictDay, Nmonte, RankDay) {\n\t#Start by making a function that predicts N scores based on the exponential uncertainty\n\tExpPredict <- function(CorpsCoefList, PredictDay, Nmonte) {\n\t\t#Start by gathering basic information\n\t\tNcorps = length(CorpsCoefList)\n\t\t\n\t\t#This function returns a list of N predictions\n\t\tScoreList = vector(mode='list', length=Ncorps)\n\t\t\n\t\t#Loop through each corps and make the random vectors\n\t\tfor (C in 1:Ncorps) {\n\t\t\tCorpsCoefs = CorpsCoefList[[C]]\n\t\t\t#Recall the exponential is of the form Score = a + Day^b\n\t\t\t\n\t\t\t#Make a vector of random a's for each caption\n\t\t\tGEa = rnorm(Nmonte, mean=CorpsCoefs[\"GE\",\"a\"], sd=CorpsCoefs[\"GE\",\"aSE\"])\n\t\t\tVa = rnorm(Nmonte, mean=CorpsCoefs[\"Vis\",\"a\"], sd=CorpsCoefs[\"Vis\",\"aSE\"]) \n\t\t\tMa = rnorm(Nmonte, mean=CorpsCoefs[\"Mus\",\"a\"], sd=CorpsCoefs[\"Mus\",\"aSE\"]) \n\t\t\t#Make a vector of b's for each caption\n\t\t\tGEb = rnorm(Nmonte, mean=CorpsCoefs[\"GE\",\"b\"], sd=CorpsCoefs[\"GE\",\"bSE\"]) \n\t\t\tVb = rnorm(Nmonte, mean=CorpsCoefs[\"Vis\",\"b\"], sd=CorpsCoefs[\"Vis\",\"bSE\"])\n\t\t\tMb = rnorm(Nmonte, mean=CorpsCoefs[\"Mus\",\"b\"], sd=CorpsCoefs[\"Mus\",\"bSE\"])\n\t\t\t\n\t\t\t#Now create the score for each caption\n\t\t\tGEscore = GEa + PredictDay ^ GEb\n\t\t\tVscore = Va + PredictDay ^ Vb\n\t\t\tMscore = Ma + PredictDay ^ Mb\n\t\t\t\n\t\t\t#Now correct each score to make sure they're actually possible\n\t\t\tGEscore[GEscore > 40] = 40\n\t\t\tVscore[Vscore > 30] = 30\n\t\t\tMscore[Mscore > 30] = 30\n\t\t\t\n\t\t\t#Now get the total score list\n\t\t\tScoreList[[C]] = GEscore + Vscore + Mscore\n\t\t}\n\t\t\n\t\t#Convert each simulation score to gaps\n\t\tBaseScores = vector(mode='double', length=Nmonte)\n\t\tfor (i in 1:Nmonte) {\n\t\t\tBaseScores[i] = max(sapply(ScoreList, '[[', i))\n\t\t}\n\t\t#Now loop through each corps and subtract the base score\n\t\tfor (C in 1:length(ScoreList)) {\n\t\t\tScoreList[[C]] = ScoreList[[C]] - BaseScores\n\t\t}\t\t\n\t\treturn(ScoreList)\n\t}\n\t\n\t#Now create a function that predicts N scores based on the random error\n\tRandPredict <- function(CorpsCoefList, PredictDay, Nmonte, RankDay) {\n\t\tlibrary(MASS)\n\t\t\n\t\t#Get the basic information\n\t\tNcorps = length(CorpsCoefList)\n\t\t#The output will be the score list\n\t\tScoreList = vector(mode='list', length=Ncorps)\n\t\t\n\t\t#Make a correlation matrix for the errors\n\t\tCorrMat = matrix(nrow=Ncorps, ncol=Ncorps)\n\t\tfor (Row in 1:Ncorps) {\n\t\t\tfor (Col in 1:Ncorps) {\n\t\t\t\tIndexDiff = abs(Row - Col)\n\t\t\t\t#Use IndexDiff to determine the correlation\n\t\t\t\tif (IndexDiff == 0) { \n\t\t\t\t\t#This is a diag\n\t\t\t\t\tCorrMat[Row,Col] = 1\n\t\t\t\t} else if (IndexDiff > 14) { \n\t\t\t\t\t#This is a background correlation\n\t\t\t\t\tCorrMat[Row,Col] = 0.263\n\t\t\t\t} else { \n\t\t\t\t\t#This is an off-diag\n\t\t\t\t\tCorrMat[Row,Col] = 0.513 - (IndexDiff-1)*(0.25/14)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t#To account for the effect of doing caption specific correlations, we increase the correlation\n\t\tCorrMat = CorrMat * 1.25\n\t\tdiag(CorrMat) = 1\n\t\t\n\t\t#Now multiply the correlation matrix by the variances for each captions \n\t\t#the historical noise magnitude is 4, so the captions are adjusted to add to that:\n\t\t\t# 40^2 * GEvar + 30^2 * Mvar + 30^2 Vvar = 4\n\t\t\t# 1600GEvar + 900Mvar + 900Vvar = 4\n\t\t\t# Mvar = Vvar = Cvar - assuming the music and visual errors are the same\n\t\t\t# 1600GEvar + 1800Cvar = 4\n\t\t\t# GEvar = 4/3 * Cvar - assuming error is scaled by total caption score\n\t\t\t# (4/3)1600Cvar + 1800Cvar = 4\n\t\t\t# with some rounding...\n\t\t\t# 3900Cvar = 4\n\t\t\t# Cvar = 4 / 3900\n\t\t# We now need to apply the variance to each individual caption \n\t\t# At some point the error needs to be scaled by total points, so we'll do it here\n\t\tGEcov = CorrMat * 40 * sqrt(4/3900)\n\t\tCapCov = CorrMat * 30 * sqrt(4/3900)\n\t\t\n\t\t#Draw the random numbers using the covariance matrices, \n\t\tGErand = mvrnorm(Nmonte, mu=rep(0,Ncorps), Sigma=GEcov, empirical=F)\n\t\tMrand = mvrnorm(Nmonte, mu=rep(0,Ncorps), Sigma=CapCov, empirical=F)\n\t\tVrand = mvrnorm(Nmonte, mu=rep(0,Ncorps), Sigma=CapCov, empirical=F)\n\t\t\n\t\t#We choose the column of random numbers for each corps based on their rank\n\t\t# In 2018, we ranked by caption but now we use total score as it better indicates performance order\n\t\tRankScores = vector(mode='double', length=Ncorps)\n\t\t\n\t\t#We still need the caption scores to do the predictions\n\t\tGEscores = vector(mode='double', length=Ncorps)\n\t\tVscores = vector(mode='double', length=Ncorps)\n\t\tMscores = vector(mode='double', length=Ncorps)\n\t\t\n\t\t#Fill in the vectors\n\t\tfor (C in 1:Ncorps) {\n\t\t\tGEscores[C] = CorpsCoefList[[C]][\"GE\",\"a\"] + RankDay ^ CorpsCoefList[[C]][\"GE\",\"b\"]\n\t\t\tVscores[C] = CorpsCoefList[[C]][\"Vis\",\"a\"] + RankDay ^ CorpsCoefList[[C]][\"Vis\",\"b\"]\n\t\t\tMscores[C] = CorpsCoefList[[C]][\"Mus\",\"a\"] + RankDay ^ CorpsCoefList[[C]][\"Mus\",\"b\"]\n\t\t\tRankScores = GEscores + Vscores + Mscores\n\t\t}\n\t\t\n\t\t#Convert these to gaps\n\t\tGEscores = GEscores - max(GEscores) \n\t\tVscores = Vscores - max(Vscores)\n\t\tMscores = Mscores - max(Mscores)\n\t\tRankScores = RankScores - max(RankScores)\n\t\t\n\t\t#Adjust the scores down of OC if we're late in the season\n\t\t# OC coprs need to be discounted because the scores get inflated late in the season\n\t\tif (RankDay >= 40 & PredictDay > 48) {\n\t\t\tneedOCadjust = names(CorpsCoefList) %in% OpenClass\n\t\t\tGEscores[needOCadjust] = GEscores[needOCadjust] - (2 * 0.4)\n\t\t\tVscores[needOCadjust] = Vscores[needOCadjust] - (2 * 0.3)\n\t\t\tMscores[needOCadjust] = Mscores[needOCadjust] - (2 * 0.3)\n\t\t\tRankScores[needOCadjust] = RankScores[needOCadjust] - 2\n\t\t}\n\t\t\n\t\t#Get the rank vector\n\t\tCorpsRanks = match(RankScores, sort(RankScores, decreasing=T)) #Ranks from highest to lowest\n\t\t\n\t\t#Now loop through each corps and fill in ScoreList\n\t\tfor (C in 1:Ncorps) {\n\t\t\t# In 2018 we had a damping effect to account for slotting, but that effect is now captured\n\t\t\t# \tby other parts in the model.\n\t\t\tGEvec = GEscores[C] + GErand[,CorpsRanks[C]]\n\t\t\tMvec = Mscores[C] + Mrand[,CorpsRanks[C]]\n\t\t\tVvec = Vscores[C] + Vrand[,CorpsRanks[C]]\n\t\t\t\n\t\t\t#Put the summed scores into the list\n\t\t\tScoreList[[C]] = GEvec + Mvec + Vvec\n\t\t}\n\t\t\n\t\treturn(ScoreList)\n\t}\n\t\n\t#Get the number of corps\n\tNcorps = length(CorpsList)\n\t\n\t#Run both score predictors\n\tExpScoreList = ExpPredict(CorpsList, PredictDay, Nmonte)\n\tRandScoreList = RandPredict(CorpsList, PredictDay, Nmonte, RankDay)\n\t#print(RandScoreList)\n\t\n\t#Now create the overall score and rank lists to return\n\tScoreList = vector(mode='list', length=Ncorps)\n\tRankList = vector(mode='list', length=Ncorps)\n\t\n\t#Fill in the final ScoreList\n\tfor (C in 1:Ncorps) {\n\t\tScoreList[[C]] = 0.317*ExpScoreList[[C]] + 0.683*RandScoreList[[C]]\n\t}\n\t# vars = sapply(ScoreList, var); print(mean(vars)); print(vars) #debugging\n\t\n\t#Fill in RankList by sorting the scores\n\tfor (n in 1:Nmonte) {\n\t\t#Get the scores and rank them\n\t\tscores = sapply(ScoreList, '[[', n)\n\t\tranks = match(scores, sort(scores, decreasing=T))\n\t\t#Now put the ranks into the list\n\t\tfor (C in 1:Ncorps) {\n\t\t\tRankList[[C]][n] = ranks[C]\n\t\t}\n\t}\n\t\n\t#Retain the names in the lists\n\tnames(ScoreList) = names(CorpsList)\n\tnames(RankList) = names(CorpsList)\n\t\n\t#return the lists\n\treturn(list(ScoreList, RankList))\n}\n\n#this function returns a vector of information for each corps \n\t#mean score,percent odds of being in 1st, second, thrid, top12, and top25\nPredictionReduce_WorldClass <- function(PredictionList) {\n\tScoreList = PredictionList[[1]]\n\tRankList = PredictionList[[2]]\n\t\n\t#Get the simple information\n\tNcorps = length(PredictionList[[1]])\n\tNmonte = length(PredictionList[[1]][[1]])\n\tCorpsNames = names(PredictionList[[1]])\n\t\n\t#create a vector for each returned value\n\tMeanScores = vector(mode='double', length=Ncorps)\n\tPercGold = vector(mode='double', length=Ncorps)\n\tPercSilver = vector(mode='double', length=Ncorps)\n\tPercBronze = vector(mode='double', length=Ncorps)\n\tPercFinals = vector(mode='double', length=Ncorps)\n\tPercSemis = vector(mode='double', length=Ncorps)\n\t\n\t#Loop through each corps and fill the vectors in\n\tfor (C in 1:Ncorps) {\n\t\tMeanScores[C] = mean(ScoreList[[C]])\n\t\tPercGold[C] = sum(RankList[[C]] == 1) / Nmonte\n\t\tPercSilver[C] = sum(RankList[[C]] == 2) / Nmonte\n\t\tPercBronze[C] = sum(RankList[[C]] == 3) / Nmonte\n\t\tPercFinals[C] = sum(RankList[[C]] <= 12) / Nmonte\n\t\tPercSemis[C] = sum(RankList[[C]] <= 25) / Nmonte\n\t}\n\t\n\t#Adjust the gaps to make sure 1st place is a 0\n\tMeanScores = MeanScores - max(MeanScores)\n\t\n\t#Now format these into a data frame\n\tOutFrame = data.frame(Mean=MeanScores, Gold=PercGold, Silver=PercSilver, Bronze=PercBronze, Finals=PercFinals, Semis=PercSemis)\n\trow.names(OutFrame) = CorpsNames\n\t\n\t#Sort the data frame by percent chance of gold\n\tOutFrame = OutFrame[order(OutFrame$Mean, decreasing=T),]\n\t\n\t#Now return the data frame\n\treturn(OutFrame)\n}\n\n#This function returns a vector of information for each corps, based on open class\n\t#that's mean score, percent odds of being in 1st, 2nd, 3rd, and making OC Finals\nPredictionReduce_OpenClass <- function(PredictionList) {\n\tScoreList = PredictionList[[1]]\n\tRankList = PredictionList[[2]]\n\t\n\t#Get the simple information\n\tNcorps = length(PredictionList[[1]])\n\tNmonte = length(PredictionList[[1]][[1]])\n\tCorpsNames = names(PredictionList[[1]])\n\t\n\t#create a vector for each returned value\n\tMeanScores = vector(mode='double', length=Ncorps)\n\tPercGold = vector(mode='double', length=Ncorps)\n\tPercSilver = vector(mode='double', length=Ncorps)\n\tPercBronze = vector(mode='double', length=Ncorps)\n\tPercFinals = vector(mode='double', length=Ncorps)\n\t\n\t#Loop through each corps and fill the vectors in\n\tfor (C in 1:Ncorps) {\n\t\tMeanScores[C] = mean(ScoreList[[C]])\n\t\tPercGold[C] = sum(RankList[[C]] == 1) / Nmonte\n\t\tPercSilver[C] = sum(RankList[[C]] == 2) / Nmonte\n\t\tPercBronze[C] = sum(RankList[[C]] == 3) / Nmonte\n\t\tPercFinals[C] = sum(RankList[[C]] <= 12) / Nmonte\n\t}\n\t\n\t#Make sure 1st place's gap is 0\n\tMeanScores = MeanScores - max(MeanScores)\n\t\n\t#Now format these into a data frame\n\tOutFrame = data.frame(Mean=MeanScores, Gold=PercGold, Silver=PercSilver, Bronze=PercBronze, Finals=PercFinals)\n\trow.names(OutFrame) = CorpsNames\n\t\n\t#Sort the data frame by percent chance of gold\n\tOutFrame = OutFrame[order(OutFrame$Mean, decreasing=T),]\n\t\n\t#Now return the data frame\n\treturn(OutFrame)\n}\n\n", "meta": {"hexsha": "b1c4cbd4444ff2f2ccb208812dc339284c27e50f", "size": 15786, "ext": "r", "lang": "R", "max_stars_repo_path": "DCI_Library_CaptionScore.r", "max_stars_repo_name": "kant/forecastDCI", "max_stars_repo_head_hexsha": "9c3eda78bcc33d77f6f145bcb382bb8bccfdb32d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-07-04T00:14:15.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-16T16:27:19.000Z", "max_issues_repo_path": "DCI_Library_CaptionScore.r", "max_issues_repo_name": "kant/forecastDCI", "max_issues_repo_head_hexsha": "9c3eda78bcc33d77f6f145bcb382bb8bccfdb32d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2019-07-16T16:30:03.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-17T15:41:47.000Z", "max_forks_repo_path": "DCI_Library_CaptionScore.r", "max_forks_repo_name": "kant/forecastDCI", "max_forks_repo_head_hexsha": "9c3eda78bcc33d77f6f145bcb382bb8bccfdb32d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-08-13T22:42:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-08-13T22:42:50.000Z", "avg_line_length": 38.2227602906, "max_line_length": 137, "alphanum_fraction": 0.7020777904, "num_tokens": 5153, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.30515755852673}} {"text": "library(SparkR)\nsc <- sparkR.init(master=\"local[*]\")\n\nlogFile <- file(Sys.getenv(\"R_LOG\"), \"w\")\n\nlogInfo <- function(...){\n args <- list(...)\n line <- paste(args, collapse = \";\")\n writeLines(line, logFile)\n}\n\nworkers <- as.integer(Sys.getenv('WORKERS'))\nnumbersCount <- as.integer(Sys.getenv('NUMBERS_COUNT'))\ntextFile <- Sys.getenv('TEXT_FILE')\n\n\n# =============================================================================\n# Serialization\n# =============================================================================\n\ntime <- proc.time()\nrddNumbers <- parallelize(sc, as.numeric(seq(0, numbersCount)), workers)\ntime <- as.double(proc.time()-time)[3]\n\nlogInfo('NumbersSerialization', time)\n\n\n# =============================================================================\n# Computing\n# =============================================================================\n\nisPrime = function(x) {\n if(x < 2){\n c(x, FALSE)\n }\n else if(x == 2){\n c(x, TRUE)\n }\n else if(x %% 2 == 0){\n c(x, FALSE)\n }\n else{\n upper <- as.numeric(sqrt(as.double(x)))\n result <- TRUE\n\n i <- 3\n while(i <= upper){\n if(x %% i == 0){\n result = FALSE\n break\n }\n\n i <- i+2\n }\n\n c(x, result)\n }\n}\n\ntime <- proc.time()\nrdd <- map(rddNumbers, isPrime)\ncapture.output(collect(rdd), file='/dev/null')\ntime <- as.double(proc.time()-time)[3]\n\nlogInfo('IsPrime', time)\n\n\nclose(logFile)\nsparkR.stop()\n", "meta": {"hexsha": "3feaacc90d2b0748d5723afecb439995964824fd", "size": 1429, "ext": "r", "lang": "R", "max_stars_repo_path": "benchmark/comparison/r.r", "max_stars_repo_name": "UlfR/ruby-spark", "max_stars_repo_head_hexsha": "5987e8d407d9991da806c7495d219804ebe59c80", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 242, "max_stars_repo_stars_event_min_datetime": "2015-05-19T03:49:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-24T10:16:55.000Z", "max_issues_repo_path": "benchmark/comparison/r.r", "max_issues_repo_name": "UlfR/ruby-spark", "max_issues_repo_head_hexsha": "5987e8d407d9991da806c7495d219804ebe59c80", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 34, "max_issues_repo_issues_event_min_datetime": "2015-05-16T05:50:55.000Z", "max_issues_repo_issues_event_max_datetime": "2019-11-06T18:14:29.000Z", "max_forks_repo_path": "benchmark/comparison/r.r", "max_forks_repo_name": "UlfR/ruby-spark", "max_forks_repo_head_hexsha": "5987e8d407d9991da806c7495d219804ebe59c80", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 37, "max_forks_repo_forks_event_min_datetime": "2015-05-19T03:49:23.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-02T21:09:59.000Z", "avg_line_length": 20.4142857143, "max_line_length": 79, "alphanum_fraction": 0.4548635409, "num_tokens": 335, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.30514128970402105}} {"text": "# GetRandomContrastData_quarterly.r\n\nsource(\"../common/DataUtil.r\")\n\n# G E T D A T A \nif (exists(\"results\")) {rm(results)}\nalldata.df <- fndfGetGiantDataRaw(\"\")\nnewdat <- alldata.df %>%\ngroup_by(copies, lifem, auditfrequency, audittype, auditsegments) %>%\nsummarize(mdmlosspct=round(midmean(lost/docstotal)*100.0, 2), n=n())\n\n# Params today: quarterly auditing, five copies.\nSEGMENTS <- 4\nNCOPIES <- 5\nresults <- filter(newdat, auditsegments==SEGMENTS)\ntrows <- filter(results, copies==NCOPIES)\n\n# P L O T D A T A \nlibrary(ggplot2)\nsource(\"../common/PlotUtil.r\")\n\ngp <- ggplot(data=trows\n , aes(x=lifem,y=safe(mdmlosspct)\n , color=factor(audittype)\n , shape=factor(copies)\n , segs=factor(auditsegments)\n ) \n )\n\n# Plots\ngp <- fnPlotLogScales(gp, x=\"YES\", y=\"YES\"\n ,xbreaks=c(2,5,10,100,1000)\n ,ybreaks=c(0.01,0.10,1.00)\n )\ngp <- gp + geom_line(\n size=3\n , show.legend=TRUE\n )\ngp <- gp + geom_point(data=trows\n , size=6\n , show.legend=TRUE\n , color=\"black\"\n ) \n\n# Legends\ngp <- gp + labs(color=\"Audit type\"\n , shape=\"Copies\"\n , segs=\"Audit segments\\nper year\"\n )\ngp <- gp + theme(legend.position=c(0.8,0.7))\ngp <- gp + theme(legend.background=element_rect(fill=\"lightgray\", \n size=0.5, linetype=\"solid\"))\ngp <- gp + theme(legend.key.size=unit(0.3, \"in\"))\ngp <- gp + theme(legend.key.width=unit(0.6, \"in\"))\ngp <- gp + theme(legend.text=element_text(size=16))\ngp <- gp + theme(legend.title=element_text(size=14))\ngp <- gp + scale_color_discrete(labels=c(\"systematic\",\"random\"))\n\n# Titles\ngp <- fnPlotTitles(gp\n , titleline=\"Random auditing WITH replacement \"\n %+% \"misses many documents \"\n %+% \"that are then vulnerable to error, \"\n %+% \"\\ncompared with auditing \"\n %+% \"WITHOUT replacement segmented at the same frequency \"\n %+% \"\\n\"\n %+% \"\\n(Uniform random vs total systematic auditing, quarterly, duration = 10 years)\"\n , xlabel=\"1MB sector half-life (megahours)\"\n %+% \" (lower error rate =====>)\"\n , ylabel=\"permanent document losses (%)\"\n ) \n\n# Limit lines\n# Label the percentage lines out on the right side.\nxlabelposition <- log10(800)\ngp <- fnPlotPercentLine(gp, xloc=xlabelposition)\ngp <- fnPlotMilleLine(gp, xloc=xlabelposition)\ngp <- fnPlotSubMilleLine(gp, xloc=xlabelposition)\n\nplot(gp)\nfnPlotMakeFile(gp, \"randomcontrast_quarterly.png\")\n\n# Unwind any remaining sink()s to close output files. \nwhile (sink.number() > 0) {sink()}\n", "meta": {"hexsha": "08815671397e33567b435d48420a16679b1992c0", "size": 2818, "ext": "r", "lang": "R", "max_stars_repo_path": "pictures/RANDOMCONTRAST/GetRandomContrastData_quarterly.r", "max_stars_repo_name": "MIT-Informatics/PreservationSimulation", "max_stars_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_stars_repo_licenses": ["X11"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2016-08-24T05:54:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-12T16:44:48.000Z", "max_issues_repo_path": "pictures/RANDOMCONTRAST/GetRandomContrastData_quarterly.r", "max_issues_repo_name": "MIT-Informatics/PreservationSimulation", "max_issues_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_issues_repo_licenses": ["X11"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-03-20T02:55:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-20T02:55:37.000Z", "max_forks_repo_path": "pictures/RANDOMCONTRAST/GetRandomContrastData_quarterly.r", "max_forks_repo_name": "MIT-Informatics/PreservationSimulation", "max_forks_repo_head_hexsha": "38c6641a25108022ce8f225a352f566ad007b0f3", "max_forks_repo_licenses": ["X11"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1529411765, "max_line_length": 101, "alphanum_fraction": 0.5713271824, "num_tokens": 752, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878414043816, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.3051412827033012}} {"text": "#!/usr/bin/env Rscript\n\nlibrary(lassosum)\nlibrary(data.table)\nargs = commandArgs(trailingOnly=TRUE)\nsetwd(\"/home/rmporsch/projects/ML_genetic_risk/results\")\n\nref = args[1]\ndev = args[2]\nsumstat = args[3]\nld = args[4]\nn = args[5]\nphenotype = args[6]\npheno.name = args[7]\n\nld <- fread(ld)\nss = fread(sumstat)\n# ss$P = as.numeric(ss$P, digits=128)\nss$P[is.infinite(ss$P)] = 1e-128\nprint(min(ss$P))\ncor = p2cor(p = as.numeric(ss$P), n=as.integer(n), sign=as.numeric(ss$BETA))\ncor[is.na(cor)] = 0.999999\n\n\nout <- lassosum.pipeline(cor=cor, chr=ss$CHR, pos=ss$BP, \n A1=ss$A1,\n ref.bfile=ref, test.bfile=dev, \n LDblocks = ld)\n\ndat = read.table(phenotype, head=T)\ndev_fam = read.table(paste0(out$test.bfile, '.fam'))\nnames(dev_fam)[1:2] = c('FID', 'IID')\nsub = merge(dev_fam, dat, by=c('FID', 'IID'))\nssub = sub[,c('FID', 'IID', pheno.name)]\nssub = dat[,c('FID', 'IID', pheno.name)]\nv <- validate(out, pheno=ssub)\noutput = v$validation.table\n\noutput.name = paste(dev,'phenotype', 'lasso', sep='.') \nwrite.table(output, output.name)\n", "meta": {"hexsha": "267ca04c06c06ad8344d764eef4eb576b89d4f17", "size": 1097, "ext": "r", "lang": "R", "max_stars_repo_path": "results/lassosum/lassosum.r", "max_stars_repo_name": "rmporsch/ML_genetic_risk", "max_stars_repo_head_hexsha": "4e1a0510c94260e69f93639ff4104c5f85080d9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "results/lassosum/lassosum.r", "max_issues_repo_name": "rmporsch/ML_genetic_risk", "max_issues_repo_head_hexsha": "4e1a0510c94260e69f93639ff4104c5f85080d9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-11-13T18:04:51.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-10T19:40:45.000Z", "max_forks_repo_path": "results/lassosum/lassosum.r", "max_forks_repo_name": "rmporsch/ML_genetic_risk", "max_forks_repo_head_hexsha": "4e1a0510c94260e69f93639ff4104c5f85080d9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-23T05:57:04.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-23T05:57:04.000Z", "avg_line_length": 26.756097561, "max_line_length": 76, "alphanum_fraction": 0.6207839562, "num_tokens": 380, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7490872131147275, "lm_q2_score": 0.4073334000459302, "lm_q1q2_score": 0.3051282414489523}} {"text": "# plotrocfischer.r - plot ROCs for different method on Fischer and Nh3D data\n#\n# Alex Stivala, October 2008\n#\n# Plot ROC curves for different methods on the Fischer data set\n# (Fischer et al 1996) and Nh3D data set (Thiruv et al 2005)\n# as used in Pelta et al 2008.\n#\n# Requires the ROCR package from CRAN (developed with version 1.0-2)\n# (ROCR in turn requires gplots, gtools, gdata)\n#\n# Run this on the output of e.g. tsevalfn.py with the -l option,\n# it is a table with one column of scores from classifier, and second\n# column of true class label (0 or 1)\n#\n# The citation for the ROCR package is\n# Sing et al 2005 \"ROCR: visualizing classifier performance in R\"\n# Bioinformatics 21(20):3940-3941\n# \n# \n# $Id: plotrocs_fischer_nh3d.r 2376 2009-05-14 01:40:32Z astivala $\n \n\nlibrary(ROCR)\n\n#\n# globals\n#\n\ncolorvec=c('deepskyblue4','brown','red','turquoise','blue','purple','green','cyan','gray20','magenta','darkolivegreen2','midnightblue','magenta3','darkseagreen','violetred3','darkslategray3')\nltyvec=c(1,2,4,5,6,1,2,1,5,6,1,2,4,5,6,1,2)\nnamevec=c('MSVNS3 norm1','MSVNS3 norm2', 'MSVNS3 norm3', 'QP tableau search norm1', 'QP tableau search norm2', 'QP tableau search norm3')\n\nfischer_fold_files=c('../maxcmo_results/fischer/norm1/fold.slrtab', '../maxcmo_results/fischer/norm2/fold.slrtab','../maxcmo_results/fischer/norm3/fold.slrtab','fischer/norm1/fold.slrtab','fischer/norm2/fold.slrtab','fischer/norm3/fold.slrtab')\nfischer_class_files=c('../maxcmo_results/fischer/norm1/class.slrtab','../maxcmo_results/fischer/norm2/class.slrtab','../maxcmo_results/fischer/norm3/class.slrtab','fischer/norm1/class.slrtab','fischer/norm2/class.slrtab','fischer/norm3/class.slrtab')\nnh3d_arch_files=c('../maxcmo_results/nh3d/norm1/arch.slrtab','../maxcmo_results/nh3d/norm2/arch.slrtab','../maxcmo_results/nh3d/norm3/arch.slrtab','nh3d/norm1/arch.slrtab','nh3d/norm2/arch.slrtab','nh3d/norm3/arch.slrtab')\nnh3d_class_files=c('../maxcmo_results/nh3d/norm1/class.slrtab','../maxcmo_results/nh3d/norm2/class.slrtab','../maxcmo_results/nh3d/norm3/class.slrtab','nh3d/norm1/class.slrtab','nh3d/norm2/class.slrtab','nh3d/norm3/class.slrtab')\n\n#\n# functions\n#\n\n#\n# Return the ROCR performance object for plotting ROC curve\n#\n# Parameters:\n# tab : data frame with score and label columns\n# \n# Return value:\n# ROCR performance object with FPR and TPR for plotting ROC curve\n#\ncompute_perf <- function(tab)\n{\n # tab is a data frame with score and label columns\n pred <- prediction(tab$score, tab$label)\n perfroc <- performance(pred, measure=\"tpr\",x.measure=\"fpr\")\n return(perfroc)\n}\n\n#\n# main\n#\n\n\n\n# EPS suitable for inserting into LaTeX\n\npostscript('rocs_fischer_fold.eps',\n onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\nfor (i in 1:length(fischer_fold_files)) {\n tab <- read.table(fischer_fold_files[i], header=TRUE)\n perfroc <- compute_perf(tab)\n plot(perfroc, lty=ltyvec[i], col=colorvec[i], add=(i>1),\n #main='Fischer data set at fold level' # remove title for paper\n )\n}\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec)\n#lines(c(0,1),c(0,1),type='l',lty=3)\ndev.off()\n\n\npostscript('rocs_fischer_class.eps',\n onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\nfor (i in 1:length(fischer_class_files)) {\n tab <- read.table(fischer_class_files[i], header=TRUE)\n perfroc <- compute_perf(tab)\n plot(perfroc, lty=ltyvec[i], col=colorvec[i], add=(i>1),\n# main='Fischer data set at class level' # remove title for paper\n )\n}\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec)\n#lines(c(0,1),c(0,1),type='l',lty=3)\ndev.off()\n\npostscript('rocs_nh3d_arch.eps',\n onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\nfor (i in 1:length(nh3d_arch_files)) {\n tab <- read.table(nh3d_arch_files[i], header=TRUE)\n perfroc <- compute_perf(tab)\n plot(perfroc, lty=ltyvec[i], col=colorvec[i], add=(i>1),\n # main='Nh3D data set at architecture level' # remove title for paper\n )\n}\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec)\n#lines(c(0,1),c(0,1),type='l',lty=3)\ndev.off()\n\n\npostscript('rocs_nh3d_class.eps',\n onefile=FALSE,paper=\"special\",horizontal=FALSE, \n width = 9, height = 6)\nfor (i in 1:length(nh3d_class_files)) {\n tab <- read.table(nh3d_class_files[i], header=TRUE)\n perfroc <- compute_perf(tab)\n plot(perfroc, lty=ltyvec[i], col=colorvec[i], add=(i>1),\n # main='Nh3D data set at class level' # removed title for paper\n )\n \n}\n#lines(c(0,1),c(0,1),type='l',lty=3)\nlegend('bottomright', col=colorvec, lty=ltyvec, legend=namevec) \ndev.off()\n\n", "meta": {"hexsha": "7f3bcc59bd40fe108a6e9077d9ff4638f77d70e6", "size": 4744, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/plotrocs_fischer_nh3d.r", "max_stars_repo_name": "stivalaa/cuda_satabsearch", "max_stars_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/plotrocs_fischer_nh3d.r", "max_issues_repo_name": "stivalaa/cuda_satabsearch", "max_issues_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/plotrocs_fischer_nh3d.r", "max_forks_repo_name": "stivalaa/cuda_satabsearch", "max_forks_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3543307087, "max_line_length": 250, "alphanum_fraction": 0.6945615514, "num_tokens": 1557, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.30507449306892004}} {"text": "\n#Libraries to import\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(cowplot)\n\n#Import OTUs\notu6 <- read.table(\"otu_num_6.csv\",sep=\",\",header=TRUE)\notu505 <- read.table(\"otu_num_505.csv\",sep=\",\",header=TRUE)\ntp <- read.table(\"otu_num_6.csv\",sep=\",\")[1,6:ncol(otu6)]\n#Import all data for proper normalization\nall <- read.table(\"all_clusters-eps_0.16.csv\",sep=\",\")\ntp.totals <- colSums(all[,7:ncol(all)])\nntp <- length(6:ncol(otu6))\ntscs <- c()\nfor (tsc in otu6$TimeClustNumber) {\n tscs <- c(tscs, rep(tsc,ntp))\n}\n#Encode the second OTU's -1 cluster as a different label to avoid them getting the same colour\nfor (tsc in otu505$TimeClustNumber) {\n if (tsc == -1) {tsc<--2}\n tscs <- c(tscs, rep(tsc,ntp))\n}\n\n#Melt the data\notu6 <- melt(t(otu6[,6:ncol(otu6)]))\notu6$value <- otu6$value/tp.totals\notu505 <- melt(t(otu505[,6:ncol(otu505)]))\notu505$value <- otu505$value/tp.totals\n#Numbering starts at 1 for each cluster, so add the previous max\notu505$Var2 <- otu505$Var2 + max(otu6$Var2)\n\n#Turn it into a data frame\nclusters <- rbind(otu6,otu505)\ndf <- data.frame(TSC=as.factor(tscs),Sequence=clusters$Var2, Abundance=clusters$value, OTU=as.factor(c(rep(6,dim(otu6)[1]),rep(505,dim(otu505)[1]))), Time=rep(as.vector(t(tp)),dim(clusters)[1]/length(tp)))\nlevels(df$OTU) <- c(\"OTU 6\", \"OTU 505\")\n\n#Plot\np<-ggplot(df, aes(x=Time, y=Abundance, color=TSC, group=Sequence))+geom_line(alpha=0.7)\np<-p+xlab(\"Time (days)\")+ylab(\"Sequence Relative Abundance\")+theme(legend.position='none')+facet_grid(OTU~.,scales=\"free_y\")\n\np\n\nggsave(\"Figure4.pdf\",p,width=4,height=4)\n\n\n", "meta": {"hexsha": "626e5e4c634040505794189570a4805d010ba0ed", "size": 1560, "ext": "r", "lang": "R", "max_stars_repo_path": "Alm_Stool/Plots/Figure4.r", "max_stars_repo_name": "mwhall/Ananke_PeerJ", "max_stars_repo_head_hexsha": "5f144b1f177761215022c9c1cd9769d49ad63646", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Alm_Stool/Plots/Figure4.r", "max_issues_repo_name": "mwhall/Ananke_PeerJ", "max_issues_repo_head_hexsha": "5f144b1f177761215022c9c1cd9769d49ad63646", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Alm_Stool/Plots/Figure4.r", "max_forks_repo_name": "mwhall/Ananke_PeerJ", "max_forks_repo_head_hexsha": "5f144b1f177761215022c9c1cd9769d49ad63646", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1914893617, "max_line_length": 205, "alphanum_fraction": 0.6948717949, "num_tokens": 582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.30505027741005447}} {"text": "source(\"split_samples_utils.r\")\nlibrary(Seurat)\nlibrary(ggplot2)\n\n\nfiles_lines=read.table(parSampleFile1, sep=\"\\t\")\nfiles=split(files_lines$V1, files_lines$V2)\n\nparams_lines=read.table(parSampleFile3, sep=\"\\t\")\nparams=split(params_lines$V1, params_lines$V2)\nparams$hto_ignore_exists=ifelse(params$hto_ignore_exists==\"0\", FALSE, TRUE)\n\nidx=3\nfor(idx in c(1:length(files))){\n fname=names(files)[idx]\n output_prefix = paste0(fname, \".HTO\")\n output_file=paste0(output_prefix, \".csv\")\n \n if(file.exists(output_file) & params$hto_ignore_exists){\n next\n }\n\n h5file=files[[idx]]\n cat(fname, \":\", h5file, \" ...\\n\")\n\n obj=read_hto(h5file, output_prefix)\n\n obj <- HTODemux(obj, assay = \"HTO\", positive.quantile = 0.99)\n\n obj$HTO_classification[obj$HTO_classification.global == \"Doublet\"] = \"Doublet\"\n\n output_post_classification(obj, output_prefix)\n}\n", "meta": {"hexsha": "bdc516958f467673b54b0986425c169322b3f117", "size": 855, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/scRNA/split_samples_seurat_all.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-03-25T17:05:39.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-13T07:03:55.000Z", "max_issues_repo_path": "lib/scRNA/split_samples_seurat_all.r", "max_issues_repo_name": "shengqh/ngsperl", "max_issues_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/scRNA/split_samples_seurat_all.r", "max_forks_repo_name": "shengqh/ngsperl", "max_forks_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9, "max_forks_repo_forks_event_min_datetime": "2015-04-02T16:41:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-22T07:25:33.000Z", "avg_line_length": 25.1470588235, "max_line_length": 80, "alphanum_fraction": 0.7298245614, "num_tokens": 253, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.30505027741005447}} {"text": "library(rstan)\nlibrary(data.table)\nlibrary(lubridate,warn.conflicts = FALSE)\nlibrary(gdata)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(EnvStats)\n\nsource(\"utils/arg-parser.r\")\nsource(\"utils/read-interventions.r\")\nsource(\"utils/process-covariates.r\")\nsource(\"utils/process-covariates-region.r\")\nsource(\"utils/ifr-tools.r\")\nsource(\"utils/log-and-process.r\")\n\nVERSION=\"v5\"\n\n# Commandline options and parsing\nparsedargs <- base_arg_parse()\nDEBUG <- parsedargs[[\"DEBUG\"]]\nFULL <- parsedargs[[\"FULL\"]]\nStanModel <- parsedargs[[\"StanModel\"]]\nnew_sub_folder <- parsedargs[[\"new_sub_folder\"]]\nmax_date <- parsedargs[[\"max_date\"]]\nmobility_source <- parsedargs[[\"mobility_source\"]]\nformula_pooling <- parsedargs[[\"formula_pooling\"]]\nformula_partialpooling <- parsedargs[[\"formula_partialpooling\"]]\nzone_definition_file <- parsedargs[[\"activezones\"]]\nmobility_processing <- parsedargs[[\"mobilityprocessing\"]]\n\nrun_name <- create_analysis_folder(FULL, DEBUG, StanModel)\n\nregion_to_country_map <- read_country_region_map(zone_definition_file)\n\n## Reading data from region file and world data and trimming it to max_date\ndata_files <- c(\n \"data/COVID-19-up-to-date.rds\",\n \"data/all-france.rds\"\n)\ncountries <- names(region_to_country_map)\nd <- read_obs_data(countries, data_files, max_date)\n# Trim countries and regions that fail the number of death test.\ndeath_thresh_epi_start = 10\n\ntrimmed_map = trim_country_map(\n d, region_to_country_map, max_date, death_thresh_epi_start)\nregion_to_country_map <- trimmed_map$region_to_country_map\ncountries <- names(region_to_country_map)\n\n# Modelling + Forecasting\nmin_forecast <- 14\nmin_N2 <- 130\nN2 <- max((trimmed_map$max_epi_data + min_forecast), min_N2)\n\n## get IFR and population from same file\nifr.by.country <- return_ifr()\ninterventions <- read_interventions('data/interventions.csv', max_date)\nmobility <- read_mobility(mobility_source, zone_definition_file)\n\n\n\nformula = as.formula(formula_pooling)\nformula_partial = as.formula(formula_partialpooling)\nprocessed_data <- process_covariates_regions(\n region_to_country_map = region_to_country_map,\n mobility = mobility,\n interventions = interventions,\n d = d,\n ifr.by.country = ifr.by.country,\n N2 = N2,\n formula = formula,\n formula_partial = formula_partial,\n death_thresh_epi_start=death_thresh_epi_start,\n mobility_processing=mobility_processing\n)\n\nstan_data <- processed_data$stan_data\ndates <- processed_data$dates\nreported_deaths <- processed_data$deaths_by_country\nreported_cases <- processed_data$reported_cases\ninfection_to_onset = processed_data$infection_to_onset\nonset_to_death = processed_data$onset_to_death\nprocessed_mobility = processed_data$processed_mobility\n\nlog_simulation_inputs(run_name, region_to_country_map, ifr.by.country,\n infection_to_onset, onset_to_death, VERSION, parsedargs, processed_mobility)\noptions(mc.cores = parallel::detectCores())\nrstan_options(auto_write = TRUE)\nm = stan_model(paste0('stan-models/',StanModel,'.stan'))\n\n\nif(DEBUG) {\n fit = sampling(m,data=stan_data,iter=40,warmup=20,chains=2)\n} else if (FULL) {\n fit = sampling(m,data=stan_data,iter=2000,warmup=1500,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 15))\n} else { \n fit = sampling(m,data=stan_data,iter=600,warmup=300,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 10))\n} \n\n\nout = rstan::extract(fit)\nestimated_cases_raw = out$prediction\nestimated_deaths_raw = out$E_deaths\nestimated_deaths_cf = out$E_deaths0\n\ncovariate_data = list(interventions, mobility)\nsave.image(paste0('results/',run_name,'.Rdata'))\nsave(\n fit, estimated_cases_raw, dates,reported_cases,reported_deaths,countries,\n region_to_country_map, estimated_deaths_raw, estimated_deaths_cf, \n out,interventions,covariate_data, infection_to_onset, onset_to_death, VERSION,\n formula_pooling, formula_partialpooling,\n file=paste0('results/',run_name,'-stanfit.Rdata'))\n\npostprocess_simulation(run_name, out, countries, dates)\n", "meta": {"hexsha": "370ef5a019c15ccd6b5cc60a71c60623c006c67b", "size": 3939, "ext": "r", "lang": "R", "max_stars_repo_path": "base-region-france.r", "max_stars_repo_name": "payoto/covid19model", "max_stars_repo_head_hexsha": "f0b05e9cad206454bb43640489ffde07891cfbad", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-04-07T06:58:25.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-04T13:11:04.000Z", "max_issues_repo_path": "base-region-france.r", "max_issues_repo_name": "payoto/covid19model", "max_issues_repo_head_hexsha": "f0b05e9cad206454bb43640489ffde07891cfbad", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-04-13T17:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-03T09:05:31.000Z", "max_forks_repo_path": "base-region-france.r", "max_forks_repo_name": "payoto/covid19model", "max_forks_repo_head_hexsha": "f0b05e9cad206454bb43640489ffde07891cfbad", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-04-12T16:31:47.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-20T21:34:57.000Z", "avg_line_length": 33.9568965517, "max_line_length": 127, "alphanum_fraction": 0.7903021071, "num_tokens": 1027, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7217432062975979, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.3049398498063879}} {"text": "summary.MI <- function (object, subset = NULL, ...) {\n if (length(object) == 0) {\n stop('Invalid input for \"subset\"')\n } else {\n if (length(object) == 1) {\n return(summary(object[[1]]))\n }\n }\n\n # Roman: This function isn't fecthing coefficients robustly. Something goes wrong. Contact package author.\n getcoef <- function(obj) {\n # S4\n if (!isS4(obj)) {\n coef(obj)\n } else {\n if (\"coef3\" %in% slotNames(obj)) {\n obj@coef3\n } else {\n obj@coef\n }\n }\n }\n\n #\n res <- list()\n\n # Get indices\n subset <- if (is.null(subset)) {\n 1:length(object)\n } else {\n c(subset)\n }\n\n # Compute the summary of all objects\n for (k in subset) {\n res[[k]] <- summary(object[[k]])\n }\n\n\n # Answer\n ans <- list(\n zelig = object[[1]]$name,\n call = object[[1]]$result@call,\n all = res\n )\n\n #\n coef1 <- se1 <- NULL\n\n #\n for (k in subset) {\n # tmp <- getcoef(res[[k]]) # Roman: I changed this to coef, not 100% sure if the output is the same\n tmp <- coef(res[[k]])\n coef1 <- cbind(coef1, tmp[, 1])\n se1 <- cbind(se1, tmp[, 2])\n }\n\n rows <- nrow(coef1)\n Q <- apply(coef1, 1, mean)\n U <- apply(se1^2, 1, mean)\n B <- apply((coef1-Q)^2, 1, sum)/(length(subset)-1)\n var <- U+(1+1/length(subset))*B\n nu <- (length(subset)-1)*(1+U/((1+1/length(subset))*B))^2\n\n coef.table <- matrix(NA, nrow = rows, ncol = 4)\n dimnames(coef.table) <- list(rownames(coef1),\n c(\"Value\", \"Std. Error\", \"t-stat\", \"p-value\"))\n coef.table[,1] <- Q\n coef.table[,2] <- sqrt(var)\n coef.table[,3] <- Q/sqrt(var)\n coef.table[,4] <- pt(abs(Q/sqrt(var)), df=nu, lower.tail=F)*2\n ans$coefficients <- coef.table\n ans$cov.scaled <- ans$cov.unscaled <- NULL\n\n for (i in 1:length(ans)) {\n if (is.numeric(ans[[i]]) && !names(ans)[i] %in% c(\"coefficients\")) {\n tmp <- NULL\n for (j in subset) {\n r <- res[[j]]\n tmp <- cbind(tmp, r[[pmatch(names(ans)[i], names(res[[j]]))]])\n }\n ans[[i]] <- apply(tmp, 1, mean)\n }\n }\n\n class(ans) <- \"summaryMI\"\n ans\n}\n", "meta": {"hexsha": "5c048e2fded37e4fb7442bfd86058b66fa9cabc9", "size": 2423, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/summaryMI.r", "max_stars_repo_name": "SteveLane/blistering-barnacles", "max_stars_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/summaryMI.r", "max_issues_repo_name": "SteveLane/blistering-barnacles", "max_issues_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2017-09-12T05:04:30.000Z", "max_issues_repo_issues_event_max_datetime": "2017-11-08T22:37:45.000Z", "max_forks_repo_path": "scripts/summaryMI.r", "max_forks_repo_name": "SteveLane/blistering-barnacles", "max_forks_repo_head_hexsha": "0fbe0071e290547565b53bf2ecb2cbf92b01ccdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-08-28T04:09:34.000Z", "max_forks_repo_forks_event_max_datetime": "2017-08-28T04:09:34.000Z", "avg_line_length": 27.2247191011, "max_line_length": 146, "alphanum_fraction": 0.4523318201, "num_tokens": 698, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526660244838, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30492726613838655}} {"text": "studyId = \"999\"\ndataFile0 = \"points0.data\"\ndataFile7 = \"points7.data\"\ndataFile14 = \"points14.data\"\ndataFile21 = \"points21.data\"\n\ngoal = 1000\nbounds = c(0,321)\n\ntargets = c(42,84,126,182,224)\ngoals = rep(goal, length(targets))\n\nprevious_rate = 788/4073\n#print(previous_rate)\n\nargs = commandArgs(TRUE)\nif(length(args) == 3){\n studyId = args[[1]]\n dataFile = args[[2]]\n target = args[[3]]\n}\n\ndata0 = read.csv(dataFile0 ,header=FALSE,sep=\",\",stringsAsFactors=TRUE)\ndata7 = read.csv(dataFile7 ,header=FALSE,sep=\",\",stringsAsFactors=TRUE)\ndata14 = read.csv(dataFile14,header=FALSE,sep=\",\",stringsAsFactors=TRUE)\ndata21 = read.csv(dataFile21,header=FALSE,sep=\",\",stringsAsFactors=TRUE)\n\n#data = as.numeric(data)\n\n#dataOrdered = data[order(data)]\n#print(dataOrdered)\n\nproportional_target = round(length(data0)*previous_rate)\n#print(proportional_target)\n\ncounts = rep(0, length(targets))\nfor(i in 1:length(targets)){\n\tcounts[i] = max(which(data0 > targets[i]))\n}\n#print(length(data0))\n#print(counts)\n\n\n\nplotCircle = function(x,y,text){\n\tfor(i in length(x):1){\n\t\tpoints(x[i], y[i], pch=21, cex=3, lw=2, col=\"black\", bg=\"white\", type='p')\n\t\ttext(x[i], y[i], text[i])\n\t}\n}\n\n\nbarColor0 = rgb(0.0,0.5,1.0)\nbarColor7 = rgb(0.3,0.65,1.0) #rgb(0.0,0.5,1.0,0.7)\nbarColor14 = rgb(0.5,0.75,1.0) #rgb(0.0,0.5,1.0,0.5)\nbarColor21 = rgb(0.8,0.9,1.0) #rgb(0.0,0.5,1.0,0.2)\n\ntargetColor1 = rgb(1.0,0.2,0.2,0.3)\ntargetColor2 = rgb(1.0,0.2,0.2,0.7)\ntargetColor3 = rgb(1.0,0.2,0.2,1.0)\n\n\n\nymax = max(100,length(data0))\noffset = 0.05*ymax\n\n#fileName = paste(studyId,paste(\"points\",\"pdf\",sep=\".\"),sep=\"_\")\nfileName = \"points.pdf\"\npdf(fileName, pointsize=16, width=12, height=8)\n par(mar=c(4.5,4.5,0.5,0.5))\n\t#main=\"Parts Completed by Students\"\n\tplot(c(bounds[1]+17,bounds[2]-15), c(-offset,ymax), ylab=\"Total Number of Students\", xlab=\"Points Awarded\", main=\"\", type='n')\n\t\n\tif(length(data0) < 100){\n\t text((bounds[1]+bounds[2])/2, (0+ymax)/2, \"Insufficient data to generate this plot. Check back in a few days.\")\n\t} else {\n\t for(i in 1:length(targets)){\n \t\tpoints(c(targets[i],targets[i]), c(counts[i],-500), type='l', lty=1, lw=3, col=rgb(0.0,0.0,0.0,0.2))\n \t\tif(counts[i]0){ # if we have indexed files already index the new file as max+1\n \n files <- gsub(\".csv\",'',gsub(paste('frescalo_',datecode,sep=''),'',files)) #remove text from file name\n files <- gsub(\"\\\\)\",'',gsub(\"\\\\(\",'',files)) # remove brackets\n max_index <- max(as.numeric(files),na.rm=TRUE) # find the highest index number\n new_index <- max_index + 1\n \n } else {\n \n new_index <- 1\n \n }\n \n fresoutput <- paste(fresoutput,'(',new_index,')',sep='')\n return(fresoutput)\n \n}", "meta": {"hexsha": "4e2e3aa76120096bc826cbb09ea09326a6e4b017", "size": 684, "ext": "r", "lang": "R", "max_stars_repo_path": "R/create_index_filename.r", "max_stars_repo_name": "03rcooke/sparta", "max_stars_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_stars_repo_stars_event_min_datetime": "2015-06-08T14:32:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-15T08:16:30.000Z", "max_issues_repo_path": "R/create_index_filename.r", "max_issues_repo_name": "03rcooke/sparta", "max_issues_repo_head_hexsha": "8c93821965ff94a5bc9c01e6d0518ef70e30e1b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 200, "max_issues_repo_issues_event_min_datetime": "2015-10-26T16:17:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-22T12:04:59.000Z", "max_forks_repo_path": "R/create_index_filename.r", "max_forks_repo_name": "AugustT/sparta", "max_forks_repo_head_hexsha": "84594eeaaca02954ac05d058e5cc6eedb2fb3918", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-10-26T16:18:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-21T13:50:07.000Z", "avg_line_length": 32.5714285714, "max_line_length": 106, "alphanum_fraction": 0.6184210526, "num_tokens": 201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5698526368038302, "lm_q2_score": 0.5350984286266116, "lm_q1q2_score": 0.3049272505024608}} {"text": "# define radar coordinates\ncoord_radar <- function(theta = \"x\", start = 0, direction = 1)\n{\n theta <- match.arg(theta, c(\"x\", \"y\"))\n r <- if (theta == \"x\")\n \"y\"\n else \"x\"\n ggproto(\"CoordRadar\", CoordPolar, theta = theta, r = r, start = start,\n direction = sign(direction),\n is_linear = function(coord) TRUE)\n}", "meta": {"hexsha": "46a53726008fddf6f4760c83cfb217de52bfcc73", "size": 334, "ext": "r", "lang": "R", "max_stars_repo_path": "R/coord_radar.r", "max_stars_repo_name": "kraaijenbrink/pkrf", "max_stars_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/coord_radar.r", "max_issues_repo_name": "kraaijenbrink/pkrf", "max_issues_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/coord_radar.r", "max_forks_repo_name": "kraaijenbrink/pkrf", "max_forks_repo_head_hexsha": "464db030db837f2e47c45a53235c37821290d79a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-03T16:13:24.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-03T16:13:24.000Z", "avg_line_length": 30.3636363636, "max_line_length": 72, "alphanum_fraction": 0.5868263473, "num_tokens": 99, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3047827696346016}} {"text": "# Test anova= in ggplot\nrequire(rms)\nset.seed(1)\nx1 <- runif(100)\nx2 <- runif(100)\nx3 <- sample(c('a','b'), 100, TRUE)\nx4 <- sample(c('k','l','m'), 100, TRUE)\ny <- runif(100)\ndd <- datadist(x1, x2, x3, x4); options(datadist='dd')\nf <- ols(y ~ x1 + x2 + x3 + x4)\n\na <- anova(f)\nggplot(Predict(f), anova=a) # ok\nggplot(Predict(f), anova=a, sepdiscrete='vertical')\n", "meta": {"hexsha": "cb232cfb7294d13255dac65f064deb8d52c0fc30", "size": 364, "ext": "r", "lang": "R", "max_stars_repo_path": "SilveR/R/library/rms/tests/ggplot3.r", "max_stars_repo_name": "robalexclark/SilveR-Dev", "max_stars_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2019-04-27T10:26:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-04T09:57:34.000Z", "max_issues_repo_path": "SilveR/R/library/rms/tests/ggplot3.r", "max_issues_repo_name": "robalexclark/SilveR-Dev", "max_issues_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14, "max_issues_repo_issues_event_min_datetime": "2019-12-28T07:09:11.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:33:50.000Z", "max_forks_repo_path": "SilveR/R/library/rms/tests/ggplot3.r", "max_forks_repo_name": "robalexclark/SilveR-Dev", "max_forks_repo_head_hexsha": "263008fdb9dc3fdd22bfc6f71b7c092867631563", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-03-05T05:52:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-18T07:52:04.000Z", "avg_line_length": 24.2666666667, "max_line_length": 54, "alphanum_fraction": 0.5961538462, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3047827696346016}} {"text": "library(sjPlot)\nload(file='wdBlk_models.rds')\ntab_model(fe_b, fe_b_onlywdBlk, fe_w, fe_w_onlywdBlk, fe_wb, fe_wb_onlywdBlk, lme_b_i, lme_b_i_onlywdBlk, lme_w_i, lme_w_i_onlywdBlk, lme_wb_i, lme_wb_i_onlywdBlk, lme_wb_is_sing, lme_wb_is_onlywdBlk_sing, show.aic=TRUE, show.re.var=FALSE, show.ci=FALSE, show.icc=FALSE, dv.labels=c('fe_b', 'fe_b_onlywdBlk', 'fe_w', 'fe_w_onlywdBlk', 'fe_wb', 'fe_wb_onlywdBlk', 'lme_b_i', 'lme_b_i_onlywdBlk', 'lme_w_i', 'lme_w_i_onlywdBlk', 'lme_wb_i', 'lme_wb_i_onlywdBlk', 'lme_wb_is_sing', 'lme_wb_is_onlywdBlk_sing'), file='Results/models/wdBlk_silence.html')", "meta": {"hexsha": "31642c597db5c9b840184bfcef8a984b98d9f1a5", "size": 595, "ext": "r", "lang": "R", "max_stars_repo_path": "tabulate_wdBlk_models.r", "max_stars_repo_name": "neurophysics/DrumsAndBrains", "max_stars_repo_head_hexsha": "26c42c31f8e07c4f5a918f1d312790632fb33593", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tabulate_wdBlk_models.r", "max_issues_repo_name": "neurophysics/DrumsAndBrains", "max_issues_repo_head_hexsha": "26c42c31f8e07c4f5a918f1d312790632fb33593", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tabulate_wdBlk_models.r", "max_forks_repo_name": "neurophysics/DrumsAndBrains", "max_forks_repo_head_hexsha": "26c42c31f8e07c4f5a918f1d312790632fb33593", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 198.3333333333, "max_line_length": 549, "alphanum_fraction": 0.7932773109, "num_tokens": 262, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3047827696346016}} {"text": "library(stringr)\nlibrary(rvest)\nlibrary(dplyr)\n\n\nEU.clanice <- read_html(\"podatki/oznake_drzav.html\") %>% \n html_nodes(xpath=\"//table[@class='tdcontent']\") %>% .[[1]] %>%\n html_table() %>% select(3, 5) %>% head(27)\n\ncolnames(EU.clanice) <- c(\"Dr\u017eava\", \"Oznaka.dr\u017eave\")\n\n###########################################\n\nporaba.elektrike <- read.csv(\"podatki/Proizvodnja_elektrike_EU_mesecno.csv\") %>% \n select(siec, geo, TIME_PERIOD, OBS_VALUE) %>% filter(siec != \"CF\")\n\ncolnames(poraba.elektrike) <- c(\"Vir\", \"Oznaka.dr\u017eave\", \"Datum\", \"Koli\u010dina\")\n\nporaba.elektrike$Leto <- sapply(strsplit(poraba.elektrike$Datum, \"-\"), \"[\", 1) %>% as.integer()\nporaba.elektrike$Mesec <- sapply(strsplit(poraba.elektrike$Datum, \"-\"), \"[\", 2) %>% as.integer()\nporaba.elektrike$Datum <- NULL\n\nviri <- tibble(Vir=c(\"premog\", \"zemeljski.plin\", \"olje.nafta\", \"hidro\", \"geotermalna\",\n \"veter\", \"sonce\", \"drugi.obnovljivi.viri\", \"nuklearna\", \"druga.goriva\"),\n Oznaka=c(\"C0000\", \"G3000\", \"O4000XBIO\", \"RA100\", \"RA200\", \"RA300\", \"RA400\",\n \"RA500_5160\", \"N9000\", \"X9900\"))\n\nporaba.elektrike$Vir <- viri$Vir[match(poraba.elektrike$Vir, viri$Oznaka)]\n\nporaba.elektrike <- poraba.elektrike %>% filter(Oznaka.dr\u017eave %in% EU.clanice$Oznaka.dr\u017eave)\n\nporaba.elektrike <- poraba.elektrike[, c(\"Oznaka.dr\u017eave\", \"Leto\", \"Mesec\", \"Vir\", \"Koli\u010dina\")]\n\n##########################################\n\nBDP <- read.csv(\"podatki/BDP_EU_cetrtletje.csv\") %>% \n select(geo, TIME_PERIOD, OBS_VALUE) %>% filter(geo %in% EU.clanice$Oznaka.dr\u017eave)\n\ncolnames(BDP) <- c(\"Oznaka.dr\u017eave\", \"Datum\", \"BDP\")\n\nBDP$Leto <- sapply(strsplit(BDP$Datum, \"-Q\"), \"[\", 1) %>% as.integer()\nBDP$\u010cetrtletje <- sapply(strsplit(BDP$Datum, \"-Q\"), \"[\", 2) %>% as.integer()\nBDP$Datum <- NULL\n\nBDP <- BDP[, c(\"Oznaka.dr\u017eave\", \"Leto\", \"\u010cetrtletje\", \"BDP\")]\n\n\n\n", "meta": {"hexsha": "7a519f5e38dc385d34375a69886a084770a33e50", "size": 1836, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "majcufer/APPR-2021-22", "max_stars_repo_head_hexsha": "dd31859640632c090ec5a07e6d705493f2a0c47e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "uvoz/uvoz.r", "max_issues_repo_name": "majcufer/APPR-2021-22", "max_issues_repo_head_hexsha": "dd31859640632c090ec5a07e6d705493f2a0c47e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "uvoz/uvoz.r", "max_forks_repo_name": "majcufer/APPR-2021-22", "max_forks_repo_head_hexsha": "dd31859640632c090ec5a07e6d705493f2a0c47e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.4693877551, "max_line_length": 96, "alphanum_fraction": 0.6072984749, "num_tokens": 685, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3047827696346016}} {"text": "context(\"Facetting (layout)\")\n\na <- data.frame(a = c(1, 1, 2, 2), b = c(1, 2, 1, 1))\nb <- data.frame(a = 3)\nc <- data.frame(b = 3)\nempty <- data.frame()\n\ntest_that(\"grid: single row and single col equivalent\", {\n row <- facet_grid(a~.)$train(list(a))\n col <- facet_grid(.~a)$train(list(a))\n\n expect_equal(row$ROW, 1:2)\n expect_equal(row$ROW, col$COL)\n expect_equal(row[c(\"PANEL\", \"a\")], col[c(\"PANEL\", \"a\")])\n\n row <- facet_grid(a~.)$train(list(a, b))\n col <- facet_grid(.~a)$train(list(a, b))\n\n expect_equal(row$ROW, 1:3)\n expect_equal(row$ROW, col$COL)\n expect_equal(row[c(\"PANEL\", \"a\")], col[c(\"PANEL\", \"a\")])\n})\n\ntest_that(\"grid: includes all combinations\", {\n d <- data.frame(a = c(1, 2), b = c(2, 1))\n all <- facet_grid(a~b)$train(list(d))\n\n expect_equal(nrow(all), 4)\n})\n\ntest_that(\"wrap and grid equivalent for 1d data\", {\n rowg <- facet_grid(a~.)$train(list(a))\n roww <- facet_wrap(~a, ncol = 1)$train(list(a))\n expect_equal(roww, rowg)\n\n colg <- facet_grid(.~a)$train(list(a))\n colw <- facet_wrap(~a, nrow = 1)$train(list(a))\n expect_equal(colw, colg)\n})\n\ntest_that(\"grid: crossed rows/cols create no more combinations than necessary\", {\n facet <- facet_grid(a~b)\n facet$params$plot_env <- emptyenv()\n one <- facet$train(list(a))\n expect_equal(nrow(one), 4)\n\n one_a <- facet$train(list(a, empty))\n expect_equal(nrow(one_a), 4)\n\n two <- facet$train(list(a, b))\n expect_equal(nrow(two), 4 + 2)\n\n three <- facet$train(list(a, b, c))\n expect_equal(nrow(three), 9)\n\n four <- facet$train(list(b, c))\n expect_equal(nrow(four), 1)\n})\n\ntest_that(\"grid: nested rows/cols create no more combinations than necessary\", {\n one <- facet_grid(drv+cyl~.)$train(list(mpg))\n expect_equal(one$PANEL, factor(1:9))\n expect_equal(one$ROW, 1:9)\n})\n\ntest_that(\"grid: margins add correct combinations\", {\n one <- facet_grid(a~b, margins = TRUE)$train(list(a))\n expect_equal(nrow(one), 4 + 2 + 2 + 1)\n})\n\ntest_that(\"wrap: as.table reverses rows\", {\n one <- facet_wrap(~a, ncol = 1, as.table = FALSE)$train(list(a))\n expect_equal(one$ROW, c(2, 1))\n\n two <- facet_wrap(~a, nrow = 1, as.table = FALSE)$train(list(a))\n expect_equal(two$ROW, c(1, 1))\n})\n\ntest_that(\"grid: as.table reverses rows\", {\n one <- facet_grid(a~., as.table = FALSE)$train(list(a))\n expect_equal(as.character(one$a), c(\"2\", \"1\"))\n\n two <- facet_grid(a~., as.table = TRUE)$train(list(a))\n expect_equal(as.character(two$a), c(\"1\", \"2\"))\n})\n\n# Drop behaviour -------------------------------------------------------------\n\na2 <- data.frame(\n a = factor(1:3, levels = 1:4),\n b = factor(1:3, levels = 4:1)\n)\n\ntest_that(\"wrap: drop = FALSE preserves unused levels\", {\n wrap_a <- facet_wrap(~a, drop = FALSE)$train(list(a2))\n expect_equal(nrow(wrap_a), 4)\n expect_equal(as.character(wrap_a$a), as.character(1:4))\n\n wrap_b <- facet_wrap(~b, drop = FALSE)$train(list(a2))\n expect_equal(nrow(wrap_b), 4)\n expect_equal(as.character(wrap_b$b), as.character(4:1))\n\n})\n\ntest_that(\"grid: drop = FALSE preserves unused levels\", {\n grid_a <- facet_grid(a~., drop = FALSE)$train(list(a2))\n expect_equal(nrow(grid_a), 4)\n expect_equal(as.character(grid_a$a), as.character(1:4))\n\n grid_b <- facet_grid(b~., drop = FALSE)$train(list(a2))\n expect_equal(nrow(grid_b), 4)\n expect_equal(as.character(grid_b$b), as.character(4:1))\n\n grid_ab <- facet_grid(a~b, drop = FALSE)$train(list(a2))\n expect_equal(nrow(grid_ab), 16)\n expect_equal(as.character(grid_ab$a), as.character(rep(1:4, each = 4)))\n expect_equal(as.character(grid_ab$b), as.character(rep(4:1, 4)))\n\n})\n\n# Missing behaviour ----------------------------------------------------------\n\na3 <- data.frame(\n a = c(1:3, NA),\n b = factor(c(1:3, NA)),\n c = factor(c(1:3, NA), exclude = NULL)\n)\n\ntest_that(\"missing values get a panel\", {\n wrap_a <- facet_wrap(~a)$train(list(a3))\n wrap_b <- facet_wrap(~b)$train(list(a3))\n wrap_c <- facet_wrap(~c)$train(list(a3))\n grid_a <- facet_grid(a~.)$train(list(a3))\n grid_b <- facet_grid(b~.)$train(list(a3))\n grid_c <- facet_grid(c~.)$train(list(a3))\n\n expect_equal(nrow(wrap_a), 4)\n expect_equal(nrow(wrap_b), 4)\n expect_equal(nrow(wrap_c), 4)\n expect_equal(nrow(grid_a), 4)\n expect_equal(nrow(grid_b), 4)\n expect_equal(nrow(grid_c), 4)\n})\n", "meta": {"hexsha": "401a0737b79888f9caeba5bdc812deba05144105", "size": 4239, "ext": "r", "lang": "R", "max_stars_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.3/ggplot2/tests/testthat/test-facet-layout.r", "max_stars_repo_name": "xinbinhuang/bitcoin-analysis", "max_stars_repo_head_hexsha": "9c496fe94100ab5e7293dc5b4328f44c2d1fda76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-06-28T21:04:57.000Z", "max_stars_repo_stars_event_max_datetime": "2017-06-28T21:04:57.000Z", "max_issues_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/ggplot2/tests/testthat/test-facet-layout.r", "max_issues_repo_name": "lordbitin/ESWA-2017", "max_issues_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "packrat/lib/x86_64-pc-linux-gnu/3.4.0/ggplot2/tests/testthat/test-facet-layout.r", "max_forks_repo_name": "lordbitin/ESWA-2017", "max_forks_repo_head_hexsha": "9778cf54724b6c55f68dfe77bbfc206aab769730", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4375, "max_line_length": 81, "alphanum_fraction": 0.630101439, "num_tokens": 1353, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.600188359260205, "lm_q1q2_score": 0.3047827696346016}} {"text": "#' Plot fleet selectivities \r\n#' \r\n#' Line plots of selectivities by block.\r\n#' @param asap name of the variable that read in the asap.rdat file\r\n#' @param fleet.names names of fleets \r\n#' @param save.plots save individual plots\r\n#' @param od output directory for plots and csv files \r\n#' @param plotf type of plot to save\r\n#' @param liz.palette color definitions\r\n#' @export\r\n\r\nPlotFleetSelBlocks <- function(asap,fleet.names,save.plots,od,plotf,liz.palette){\r\n par(mfrow=c(1,1) )\r\n cc=0\r\n years <- 1:asap$parms$nyears\r\n for (i in 1:asap$parms$nfleets) {\r\n a1 <- asap$fleet.sel.start.age[i]\r\n a2 <- asap$fleet.sel.end.age[i]\r\n blocks <- unique(asap$fleet.sel.blocks[i,])\r\n n.blocks <- length(blocks)\r\n sel.mat <- as.data.frame(asap$fleet.sel.mats[i])\r\n sel <- matrix(0, nrow=n.blocks, ncol=(a2 - a1 + 1))\r\n yr <- rep(NA, n.blocks)\r\n my.col <- rep(NA, n.blocks)\r\n for (j in 1:n.blocks){\r\n cc=cc+1\r\n my.col[j] <- liz.palette[cc]\r\n yr[j] <- min(years[asap$fleet.sel.blocks[i,]==blocks[j]])\r\n sel[j,] <- as.numeric(sel.mat[yr[j],a1:a2])\r\n if (j==1){\r\n plot(a1:a2, sel[j,], type='l', col=my.col[j], \r\n xlim=c(0,asap$parms$nages+3), ylim=c(0,1.1), \r\n xlab=\"Age\", ylab=\"Selectivity at Age\", lwd=2) \r\n }\r\n if (j>1){\r\n lines(a1:a2, sel[j,], type='l', col=my.col[j], lwd=2)\r\n }\r\n }\r\n title(paste0(\"Fleet \",i,\" (\",fleet.names[i],\")\"))\r\n legend(\"topright\", col=my.col, legend=asap$parms$styr+yr-1, lwd=2)\r\n if (save.plots) savePlot(paste0(od, \"Catch.Sel.Blocks.Fleet.\",i,\".\",plotf), type=plotf)\r\n }\r\n return()\r\n}\r\n", "meta": {"hexsha": "9492efec677b782da86dbfbf15a5808a08153caa", "size": 1626, "ext": "r", "lang": "R", "max_stars_repo_path": "R/plot_fleet_sel_blocks.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/plot_fleet_sel_blocks.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/plot_fleet_sel_blocks.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 36.1333333333, "max_line_length": 92, "alphanum_fraction": 0.5842558426, "num_tokens": 540, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.3047433473409021}} {"text": "\nlibrary(pheatmap) \nlibrary(gplots)\n\npdfSize <- 15\n\n# Read data\nd <- read.table('heatMap.txt', sep='\\t', header=TRUE)\ndm <- as.matrix(d)\n\n# Replace NA by 0, otherwise R stops\ndm[ is.na(dm) ] <- 0\n\n# PDF output\npdf( width=pdfSize, height=pdfSize, \"heatMap_small.pdf\")\npheatmap(dm) # Create heatmap\ndev.off()\n\nratio <- dim(dm)[2] / dim(dm)[1]\npdfSizeX <- ratio * pdfSize\npdf( width=pdfSizeX, height=pdfSize, \"heatMap_large.pdf\")\npheatmap(dm) # Create heatmap\ndev.off()\n", "meta": {"hexsha": "9964aa62d6a5b49502deaee5c52f10d6e7444d80", "size": 469, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts_build/gtex/heatMap.r", "max_stars_repo_name": "wook2014/SnpEff", "max_stars_repo_head_hexsha": "d2f9a3ce032158313172659b7a3d5fdf796b7f1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 139, "max_stars_repo_stars_event_min_datetime": "2015-01-02T17:49:28.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T12:53:38.000Z", "max_issues_repo_path": "scripts_build/gtex/heatMap.r", "max_issues_repo_name": "wook2014/SnpEff", "max_issues_repo_head_hexsha": "d2f9a3ce032158313172659b7a3d5fdf796b7f1b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 320, "max_issues_repo_issues_event_min_datetime": "2015-01-02T19:26:50.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T18:07:44.000Z", "max_forks_repo_path": "scripts_build/gtex/heatMap.r", "max_forks_repo_name": "wook2014/SnpEff", "max_forks_repo_head_hexsha": "d2f9a3ce032158313172659b7a3d5fdf796b7f1b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 69, "max_forks_repo_forks_event_min_datetime": "2015-02-02T10:39:53.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T21:23:57.000Z", "avg_line_length": 19.5416666667, "max_line_length": 57, "alphanum_fraction": 0.6801705757, "num_tokens": 151, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.3047433473409021}} {"text": "plot.hmac.out <- function(x){\n\nlevels <- length(x$membership)\n\n\nimage.array <- array(NA,c(640,480,levels))\nfor (i in 1:levels){\nimg <- matrix(as.matrix(x$membership[[i]]),480,640)\nimg <- t(img[nrow(img):1,])\nimage.array[,,i] <- img\n\n}\n\npar(mar=c(1,1,1,1))\npar(mfrow=c(3,3))\nfor (i in 1:9){\nimage(image.array[,,i],col=rainbow(30),xaxt='n',yaxt='n')\n\n}\n\n}\n", "meta": {"hexsha": "5d0548aa2ec58c242b2cfc2697558f5268ddab2e", "size": 354, "ext": "r", "lang": "R", "max_stars_repo_path": "code/plot.hmac.out.r", "max_stars_repo_name": "id175196/PhenocamProject", "max_stars_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-02-08T22:12:02.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-08T22:12:02.000Z", "max_issues_repo_path": "code/plot.hmac.out.r", "max_issues_repo_name": "id175196/PhenocamProject", "max_issues_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/plot.hmac.out.r", "max_forks_repo_name": "id175196/PhenocamProject", "max_forks_repo_head_hexsha": "3291deb8b1b8899b78df4ae0ffff64d0bcd300d6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 16.0909090909, "max_line_length": 57, "alphanum_fraction": 0.615819209, "num_tokens": 135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.5736784074525096, "lm_q1q2_score": 0.304743347340902}} {"text": "# packages ----\nlibrary(knitr)\nlibrary(lubridate)\nlibrary(progress)\nlibrary(tidyverse)\nlibrary(xml2)\n\n# functions ----\nsource(\"get_xml.r\")\nsource(\"extract_overview.r\")\nsource(\"extract_historic.r\")\nsource(\"extract_dividends.r\")\nsource(\"clean_overview.r\")\nsource(\"clean_price.r\")\nsource(\"clean_dividends.r\")\nsource(\"download_ishares.r\")\n\n# parameters ----\ndir_ishares <- \"/path/to/folder\"\ndir_overview <- file.path(dir_ishares, \"ishares_overview\")\ndir_price <- file.path(dir_ishares, \"ishares_price\")\ndir_dividends <- file.path(dir_ishares, \"ishares_dividends\")\n\ndata_etf <- read_tsv(file.path(dir_ishares, \"data_ishares_url.tsv\"))\n\n# get xml data ----\ndata_xml <- get_xml(data_etf$url[1])\n\n# extract and clean data ----\nmap2(data_etf$name, data_etf$url, download_ishares)\n\n# get exchange rate data ----\nfile_zip <- tempfile()\ndownload.file(\"https://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.zip\", file_zip)\ndata_fx <- read_csv(unz(file_zip, \"eurofxref-hist.csv\")) %>%\n select(date = Date, usd_rate = USD, gbp_rate = GBP)\n\n# aggregate data ----\nishares_data <- map(data_etf$name, ~{\n\n # load files\n overview <- read_tsv(file.path(dir_overview, str_c(.x, \"_overview.tsv\")))\n price <- read_tsv(file.path(dir_price, str_c(.x, \"_price.tsv\")))\n dividends <- read_tsv(file.path(dir_dividends, str_c(.x, \"_dividends.tsv\")))\n \n # get metadata\n name <- .x\n isin <- overview$value[overview$parameter == \"ISIN\"]\n \n # combine data\n out <- tibble(name, isin) %>%\n mutate(id = TRUE) %>%\n left_join(mutate(price, id = TRUE), by = \"id\") %>%\n select(-id)\n \n # dividends\n if(dim(dividends)[1] > 0) {\n out <- out %>%\n left_join(dividends, by = \"date\") %>%\n mutate(dividend = coalesce(dividend * 0.725, 0)) %>%\n mutate(dividend = cumsum(dividend))\n } else {\n out$dividend <- 0\n }\n \n out <- out %>%\n mutate(price = price + dividend) %>%\n select(-dividend)\n \n return(out)\n}) %>% bind_rows() %>%\n filter(!is.na(date))\n \n# convert to Euro returns ----\nishares_data <- ishares_data %>%\n left_join(data_fx, by = \"date\") %>%\n mutate(price = case_when(currency == \"USD\" ~ price / usd_rate,\n currency == \"GBP\" ~ price / gbp_rate,\n TRUE ~ price)) %>%\n select(-currency, -usd_rate, -gbp_rate)\n\n# trailing monthly returns ----\ndates <- tibble(date = seq(from = min(ishares_data$date), to = max(ishares_data$date), by = 1))\ndates$i <- rep(1:28, ceiling(nrow(dates) / 28))[seq(nrow(dates))]\n\ndata_returns <- map(unique(ishares_data$name), ~{\n xprices <- ishares_data %>%\n filter(name == .x) %>%\n right_join(dates, by = \"date\") %>%\n fill(name) %>%\n filter(!is.na(name))\n \n xreturns <- map(1:28, ~{\n out <- xprices %>%\n filter(i == .x) %>%\n mutate(start_px = lag(price)) %>%\n mutate(diff_px = price - start_px) %>%\n mutate(return = diff_px / start_px) %>%\n select(isin, name, date, return)\n return(out)\n }) %>%\n bind_rows() %>%\n arrange(date) %>%\n filter(!is.na(return))\n return(xreturns)\n}) %>% bind_rows()\n\n# average returns, variance, & Sharpe ratio ----\n\nkpi <- data_returns %>%\n group_by(isin, name) %>%\n summarise(risk = var(return),\n returns = mean(return)) %>%\n ungroup() %>%\n mutate(sharpe = (returns / risk) * sqrt(365 / 28)) %>%\n mutate(risk = risk * sqrt(365 / 28) * 100,\n returns = returns * (365 / 28) * 100)\n\nkpi %>%\n arrange(desc(sharpe)) %>%\n select(ISIN = isin, Name = name, Return = returns, Risk = risk, Sharpe = sharpe) %>%\n head(10) %>%\n kable(digits = 2)\n\nkpi %>%\n select(Return = returns, Risk = risk, Sharpe = sharpe) %>%\n ggplot() +\n geom_abline(aes(intercept = 0, slope = 1)) +\n geom_abline(aes(intercept = 0, slope = max(kpi$sharpe)), colour = \"darkgreen\") +\n geom_point(aes(x = Risk, y = Return, colour = Sharpe))\n\n# share of months with positive returns ----\n\ndata_returns %>%\n mutate(return = return > 0) %>%\n group_by(isin, name) %>%\n summarise(week_pos = sum(return),\n week_tot = n()) %>%\n mutate(share_pos = week_pos / week_tot * 100) %>%\n arrange(desc(share_pos)) %>%\n select(ISIN = isin, Name = name, Share_Positives = share_pos, Weeks_Positive = week_pos, Weeks_Total = week_tot) %>%\n head(10) %>%\n kable(digits = 2)\n", "meta": {"hexsha": "905e9ca55dad4ffc74f9cd3426fcc57e1f130242", "size": 4247, "ext": "r", "lang": "R", "max_stars_repo_path": "code_ishares.r", "max_stars_repo_name": "ha-pu/webscrap_ishares", "max_stars_repo_head_hexsha": "14c6e0cd7fd4358a503752225f3296f914165411", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-10T16:22:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-10T16:22:50.000Z", "max_issues_repo_path": "code_ishares.r", "max_issues_repo_name": "ha-pu/webscrap_ishares", "max_issues_repo_head_hexsha": "14c6e0cd7fd4358a503752225f3296f914165411", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-06-27T09:48:00.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-28T13:58:56.000Z", "max_forks_repo_path": "code_ishares.r", "max_forks_repo_name": "ha-pu/webscrap_ishares", "max_forks_repo_head_hexsha": "14c6e0cd7fd4358a503752225f3296f914165411", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-12-10T16:22:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-09T15:49:40.000Z", "avg_line_length": 29.4930555556, "max_line_length": 118, "alphanum_fraction": 0.6227925595, "num_tokens": 1289, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.4921881357207956, "lm_q1q2_score": 0.30455638810489083}} {"text": "####by Heather E. Wheeler 20150202####\n##see runscripts/run_01_imputedDGN-WB_CV_elasticNet_chr*sh and qsub.txt for tarbell job submission scripts\ndate <- Sys.Date()\nargs <- commandArgs(trailingOnly=T)\n#args <- c('22','1')\n\"%&%\" = function(a,b) paste(a,b,sep=\"\")\n\n###############################################\n### Directories & Variables\n#pre <- \"/Users/heather/Dropbox/elasticNet_testing\"\npre <- \"\"\n\nmy.dir <- pre %&% \"/group/im-lab/nas40t2/hwheeler/PrediXcan_CV/\"\nct.dir <- pre %&% \"/group/im-lab/nas40t2/hwheeler/PrediXcan_CV/cis.v.trans.prediction/\"\ngt.dir <- pre %&% \"/group/im-lab/nas40t2/hwheeler/PrediXcan_CV/GTEx_2014-06013_release/transfers/PrediXmod/DGN-WB/DGN-imputation/DGN-imputed-for-PrediXcan/\"\nen.dir <- pre %&% \"/group/im-lab/nas40t2/hwheeler/PrediXcan_CV/GTEx_2014-06013_release/transfers/PrediXmod/DGN-WB/DGN-calc-weights/DGN-WB_weights/\"\n\n#snpset <- \".hapmapSnpsCEU\" ##2015-02-02 results\n#snpset <- \".wtcccGenotypedSNPs\" ##2015-03-12 results\nsnpset <- \"_1000G\"\n\nk <- 10 ### k-fold CV\ntis <- \"DGN-WB\" \nchrom <- as.numeric(args[1]) \nchrname <- \"chr\" %&% chrom\n\n##alpha = The elasticnet mixing parameter, with 0\u2264\u03b1\u2264 1. The penalty is defined as\n#(1-\u03b1)/2||\u03b2||_2^2+\u03b1||\u03b2||_1.\n#alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.\n\nalpha <- as.numeric(args[2]) #alpha to test in CV\n\n################################################\n### Functions & Libraries\n\nlibrary(glmnet)\n#library(doMC) ##slower on tarbell than using 1 core, not sure why\n#registerDoMC(10)\n#getDoParWorkers()\n\n################################################\nrpkmid <- ct.dir %&% tis %&% \".exp.ID.list\"\nexpid <- scan(rpkmid,\"character\")\nrpkmgene <- ct.dir %&% tis %&% \".exp.GENE.list\"\ngeneid <- scan(rpkmgene,\"character\")\nrpkmfile <- ct.dir %&% tis %&% \".exp.IDxGENE\"\nexpdata <- scan(rpkmfile) \nexpdata <- matrix(expdata, ncol = length(geneid), byrow=TRUE)\nrownames(expdata) <- expid\ncolnames(expdata) <- geneid\n\nt.expdata <- expdata #don't need to transpose DGN\n\ngencodefile <- my.dir %&% 'gencode.v12.V1.summary.protein.nodup.genenames'\ngencode <- read.table(gencodefile)\nrownames(gencode) <- gencode[,6]\ngencode <- gencode[gencode[,1]==chrname,] ##pull genes on chr of interest\nt.expdata <- t.expdata[,intersect(colnames(t.expdata),rownames(gencode))] ###pull gene expression data w/gene info\n \nexpsamplelist <- rownames(t.expdata) ###samples with exp data###\n\n#bimfile <- gt.dir %&% \"DGN.imputed_maf0.05_R20.8\" %&% snpset %&% \".chr\" %&% chrom %&% \".bim\" ###get SNP position information###\n#bim <- read.table(bimfile)\nbimfile <- gt.dir %&% \"DGN.imputed_maf0.05_R20.8\" %&% snpset %&% \".chr\" %&% chrom %&% \".bim.rds\" ###get SNP position information###\nbim <- readRDS(bimfile)\nrownames(bim) <- bim$V2\n \nfamfile <- gt.dir %&% \"DGN.imputed_maf0.05_R20.8\" %&% snpset %&% \".ID.list\" ###samples with imputed gt data###\nfam <- scan(famfile,\"character\")\nsamplelist <- intersect(fam,expsamplelist)\n \nexp.w.geno <- t.expdata[samplelist,] ###get expression of samples with genotypes###\nexplist <- colnames(exp.w.geno)\n\n#gtfile <- gt.dir %&% \"DGN.imputed_maf0.05_R20.8\" %&% snpset %&% \".chr\" %&% chrom %&% \".SNPxID\"\n#gtX <- scan(gtfile)\ngtfile <- gt.dir %&% \"DGN.imputed_maf0.05_R20.8\" %&% snpset %&% \".chr\" %&% chrom %&% \".SNPxID.rds\" #smaller file format, faster readin\ngtX <-readRDS(gtfile)\ngtX <- matrix(gtX, ncol = length(fam), byrow=TRUE)\ncolnames(gtX) <- fam\nrownames(gtX) <- bim$V2\nX <- gtX[,samplelist]\n\nX <- t(X) #transpose to match code below (one ID per row)\n\nresultsarray <- array(0,c(length(explist),8))\ndimnames(resultsarray)[[1]] <- explist\nresultscol <- c(\"gene\",\"alpha\",\"cvm\",\"lambda.iteration\",\"lambda.min\",\"n.snps\",\"R2\",\"pval\")\ndimnames(resultsarray)[[2]] <- resultscol\nworkingbest <- \"working_\" %&% tis %&% \"_exp_\" %&% k %&% \"-foldCV_elasticNet_alpha\" %&% alpha %&% \"_\" %&% snpset %&% \"_chr\" %&% chrom %&% \"_\" %&% date %&% \".txt\"\nwrite(resultscol,file=workingbest,ncolumns=8,sep=\"\\t\")\n\nweightcol = c(\"gene\",\"SNP\",\"refAllele\",\"effectAllele\",\"beta\")\nworkingweight <- en.dir %&% tis %&% \"_elasticNet_alpha\" %&% alpha %&% \"_\" %&% snpset %&% \"_weights_chr\" %&% chrom %&% \"_\" %&% date %&% \".txt\"\nwrite(weightcol,file=workingweight,ncol=5,sep=\"\\t\")\n\nset.seed(1001) ##forgot to include in 2/2/15 run, should I re-run?\n\nfor(i in 1:length(explist)){\n cat(i,\"/\",length(explist),\"\\n\")\n gene <- explist[i]\n geneinfo <- gencode[gene,]\n chr <- geneinfo[1]\n c <- substr(chr$V1,4,5)\n start <- geneinfo$V3 - 1e6 ### 1Mb lower bound for cis-eQTLS\n end <- geneinfo$V4 + 1e6 ### 1Mb upper bound for cis-eQTLs\n chrsnps <- subset(bim,bim[,1]==c) ### pull snps on same chr\n cissnps <- subset(chrsnps,chrsnps[,4]>=start & chrsnps[,4]<=end) ### pull cis-SNP info\n cisgenos <- X[,intersect(colnames(X),cissnps[,2])] ### pull cis-SNP genotypes\n if(is.null(dim(cisgenos))){\n bestbetas <- data.frame() ###effectively skips genes with <2 cis-SNPs\n }else{\n minorsnps <- subset(colMeans(cisgenos), colMeans(cisgenos,na.rm=TRUE)>0) ###pull snps with at least 1 minor allele###\n minorsnps <- names(minorsnps)\n cisgenos <- cisgenos[,minorsnps]\n ##cisgenos <- scale(cisgenos, center=T, scale=T)\n ##cisgenos[is.na(cisgenos)] <- 0\n if(is.null(dim(cisgenos)) | dim(cisgenos)[2] == 0){###effectively skips genes with <2 cis-SNPs\n bestbetas <- data.frame() ###effectively skips genes with <2 cis-SNPs\n }else{\n\n exppheno <- exp.w.geno[,gene] ### pull expression data for gene\n exppheno <- scale(exppheno, center=T, scale=T) ###need to scale for fastLmPure to work properly\n exppheno[is.na(exppheno)] <- 0\n rownames(exppheno) <- rownames(exp.w.geno)\n \n ##run Cross-Validation over alphalist\n fit <- cv.glmnet(cisgenos,exppheno,nfolds=k,alpha=alpha,keep=T,parallel=F) ##parallel=T is slower on tarbell, not sure why\n\n fit.df <- data.frame(fit$cvm,fit$lambda,1:length(fit$cvm)) ##pull info to find best lambda\n best.lam <- fit.df[which.min(fit.df[,1]),] # needs to be min or max depending on cv measure (MSE min, AUC max, ...)\n cvm.best = best.lam[,1]\n lambda.best = best.lam[,2]\n nrow.best = best.lam[,3] ##position of best lambda in cv.glmnet output\n \n ret <- as.data.frame(fit$glmnet.fit$beta[,nrow.best]) # get betas from best lambda\n ret[ret == 0.0] <- NA\n bestbetas = as.vector(ret[which(!is.na(ret)),]) # vector of non-zero betas\n names(bestbetas) = rownames(ret)[which(!is.na(ret))]\n\n pred.mat <- fit$fit.preval[,nrow.best] # pull out predictions at best lambda\n\n }\n }\n if(length(bestbetas) > 0){\n res <- summary(lm(exppheno~pred.mat))\n genename <- as.character(gencode[gene,6])\n rsq <- res$r.squared\n pval <- res$coef[2,4]\n\n resultsarray[gene,] <- c(genename, alpha, cvm.best, nrow.best, lambda.best, length(bestbetas), rsq, pval)\n\n \n ### output best shrunken betas for PrediXcan\n bestbetalist <- names(bestbetas)\n bestbetainfo <- bim[bestbetalist,]\n betatable<-as.matrix(cbind(bestbetainfo,bestbetas))\n betafile<-cbind(genename,betatable[,2],betatable[,5],betatable[,6],betatable[,7]) ##output \"gene\",\"SNP\",\"refAllele\",\"effectAllele\",\"beta\"\n write(t(betafile),file=workingweight,ncolumns=5,append=T,sep=\"\\t\") # t() necessary for correct output from write() function\n\n }else{\n genename <- as.character(gencode[gene,6])\n resultsarray[gene,1] <- genename\n resultsarray[gene,2:8] <- c(NA,NA,NA,NA,0,NA,NA)\n\n }\n write(resultsarray[gene,],file=workingbest,ncolumns=8,append=T,sep=\"\\t\")\n}\n\n\nwrite.table(resultsarray,file=tis %&% \"_exp_\" %&% k %&% \"-foldCV_elasticNet_alpha\" %&% alpha %&% \"_\" %&% snpset %&% \"_chr\" %&% chrom %&% \"_\" %&% date %&% \".txt\",quote=F,row.names=F,sep=\"\\t\")\n", "meta": {"hexsha": "d2ab0593a87c5e4ca152374e91bd3a7f73b5d8da", "size": 7676, "ext": "r", "lang": "R", "max_stars_repo_path": "Paper-Scripts/Heather/DGN-calc-weights/01_imputedDGN-WB_CV_elasticNet.r", "max_stars_repo_name": "theMechanic23/PrediXcan", "max_stars_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 92, "max_stars_repo_stars_event_min_datetime": "2015-05-05T16:37:07.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-05T12:09:16.000Z", "max_issues_repo_path": "Paper-Scripts/Heather/DGN-calc-weights/01_imputedDGN-WB_CV_elasticNet.r", "max_issues_repo_name": "theMechanic23/PrediXcan", "max_issues_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2016-04-15T13:22:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-08T19:58:34.000Z", "max_forks_repo_path": "Paper-Scripts/Heather/DGN-calc-weights/01_imputedDGN-WB_CV_elasticNet.r", "max_forks_repo_name": "theMechanic23/PrediXcan", "max_forks_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 66, "max_forks_repo_forks_event_min_datetime": "2015-04-24T16:56:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-30T14:37:49.000Z", "avg_line_length": 44.6279069767, "max_line_length": 190, "alphanum_fraction": 0.6443460135, "num_tokens": 2624, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.30455638810489083}} {"text": "rm(list = ls())\nsetwd(dirname(parent.frame(2)$ofile))\n\nlibrary(ggplot2)\nlibrary(plyr)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(readr)\nsource('./_function_task_expand_name.r')\nsource('./_function_task_table.r')\nsource('./_compute_summary.r')\n\nbest.range = 5000\n\nbest.model.step.fn = function (errors) {\n best.step = max(length(errors) - best.range, 0) + which.min(tail(errors, best.range))\n if (length(best.step) == 0) {\n return(length(errors))\n } else {\n return(best.step)\n }\n}\n\nfirst.solved.step = function (steps, errors, threshold) {\n index = first(which(errors < threshold))\n if (is.na(index)) {\n return(NA)\n } else {\n return(steps[index])\n }\n}\n\neps = read_csv('../results/function_task_static_mse_expectation.csv') %>%\n filter(simple == FALSE & parameter == 'default') %>%\n mutate(\n operation = revalue(operation, operation.full.to.short)\n ) %>%\n select(operation, input.size, overlap.ratio, subset.ratio, extrapolation.range, threshold)\n\nname.input = '../results/function_task_static_nalu.csv'\nname.output.pdf = '../paper/results/function_task_static_nalu.pdf'\nname.output.tex = '../paper/results/function_task_static_nalu.tex'\n\ndat = expand.name(read_csv(name.input)) %>%\n merge(eps) %>%\n rename(\n gate = network.layer_2.nalu.gate.mean\n ) %>%\n mutate(\n model = as.factor(ifelse(model == 'NALU', 'NALU (shared)', as.character(model)))\n )\n\ndat.last = dat %>%\n group_by(name) %>%\n summarise(\n threshold = last(threshold),\n best.model.step = best.model.step.fn(metric.valid.interpolation),\n interpolation.last = metric.valid.interpolation[best.model.step],\n extrapolation.last = metric.test.extrapolation[best.model.step],\n interpolation.step.solved = first.solved.step(step, metric.valid.interpolation, threshold),\n extrapolation.step.solved = first.solved.step(step, metric.test.extrapolation, threshold),\n sparse.error.max = sparse.error.max[best.model.step],\n solved = replace_na(metric.test.extrapolation[best.model.step] < threshold, FALSE),\n model = last(model),\n operation = last(operation),\n seed = last(seed),\n size = n(),\n\n gate.last = gate[best.model.step]\n )\n\ndat.last.rate = dat.last %>%\n group_by(model, operation) %>%\n group_modify(compute.summary) %>%\n ungroup()\n\nsave.table(\n dat.last.rate,\n \"simple-function-static-nalu-gate-table\",\n \"Shows the success-rate, when the model converged, and the sparsity error for all weight matrices, with 95\\\\% confidence interval. Each value is a summary of 100 different seeds.\",\n name.output.tex\n)\n\np = ggplot(dat.last, aes(x = gate.last, fill=solved)) +\n geom_histogram(position = \"dodge\", bins=25) +\n scale_x_continuous(name = 'Gate', labels = function (x.value) {\n return(ifelse(x.value == 1,\n 'add',\n ifelse(x.value == 0,\n 'mul',\n sprintf('%.2f', x.value))))\n }) +\n facet_grid(operation ~ model, labeller = labeller(\n model = c(\n 'NALU (seperate)' = \"Seperate\",\n 'NALU (shared)' = \"Shared\"\n ),\n operation = c(\n '$\\\\bm{\\\\times}$' = \"Multiplication\",\n '$\\\\bm{+}$' = \"Addition\"\n )\n )) +\n theme(legend.position=\"right\") +\n theme(plot.margin=unit(c(5.5, 10.5, 5.5, 5.5), \"points\"))\nprint(p)\nggsave(name.output.pdf, p, device=\"pdf\", width = 11, height = 7, scale=1.4, units = \"cm\")\n\n", "meta": {"hexsha": "9cbd4ad6dc01b5858ad7c800229eaa91dded118e", "size": 3347, "ext": "r", "lang": "R", "max_stars_repo_path": "export/function_task_static_nalu.r", "max_stars_repo_name": "wlm2019/Neural-Arithmetic-Units", "max_stars_repo_head_hexsha": "f9de9d004bb2dc2ee28577cd1760d0a00c185836", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "export/function_task_static_nalu.r", "max_issues_repo_name": "wlm2019/Neural-Arithmetic-Units", "max_issues_repo_head_hexsha": "f9de9d004bb2dc2ee28577cd1760d0a00c185836", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "export/function_task_static_nalu.r", "max_forks_repo_name": "wlm2019/Neural-Arithmetic-Units", "max_forks_repo_head_hexsha": "f9de9d004bb2dc2ee28577cd1760d0a00c185836", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.9907407407, "max_line_length": 182, "alphanum_fraction": 0.653122199, "num_tokens": 879, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290152, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30452285590646727}} {"text": "# Author: Justin Abraham\r\n# R Version: R-3.6.2\r\n# RStudio Version: 1.2.5033\r\n\r\n###############################\r\n## Install required packages ##\r\n###############################\r\n\r\nset.seed(47269801)\r\n\r\nif (!require(\"pacman\")) {\r\n install.packages(\"pacman\", repos='https://cloud.r-project.org/')\r\n library(\"pacman\")\r\n}\r\n\r\np_load(\"here\", \"tidyr\", \"dplyr\", \"lmtest\", \"multiwayvcov\", \"multcomp\", \"reshape2\", \"knitr\", \"flextable\", \"officer\", \"forestplot\", \"cowplot\", \"ggplot2\", \"matrixStats\", \"ggthemes\", \"ggsignif\", \"rstudioapi\", \"iptools\", \"magick\")\r\n\r\nsource(here(\"r\", \"Funs.r\"))\r\n\r\nload(here(\"data\", \"KenyaData.RData\"))\r\n\r\nattach(k1_df)\r\n\r\n##########################\r\n## Create psych indices ##\r\n##########################\r\n\r\n## Self-efficacy ##\r\n\r\nfor (var in c(k1_df$sel.con, k1_df$sel.pers, k1_df$sel.com, k1_df$sel.prob, k1_df$sel.bett)) {\r\n\r\n var[var < 0] <- NA\r\n\r\n}\r\n\r\nk1_df$sel.score.avg <- scale.means(k1_df, \"sel.con\", \"sel.pers\", \"sel.com\", \"sel.prob\", \"sel.bett\", na.rm = T)\r\nk1_df$sel.score <- scale(k1_df$sel.con) + scale(k1_df$sel.pers) + scale(k1_df$sel.com) + scale(k1_df$sel.prob) + scale(k1_df$sel.bett)\r\nk1_df$sel.score.z <- scale(k1_df$sel.score)\r\n\r\n## Stigma ##\r\n\r\nfor (var in c(k1_df$jud.fam, k1_df$jud.com, k1_df$jud.judg, k1_df$jud.emb, k1_df$jud.ups)) {\r\n\r\n var[var < 0] <- NA\r\n\r\n}\r\n\r\nk1_df$jud.fam.r <- 6 - k1_df$jud.fam\r\nk1_df$jud.com.r <- 6 - k1_df$jud.com\r\n\r\nk1_df$sti.score.avg <- scale.means(k1_df, \"jud.fam.r\", \"jud.com.r\", \"jud.judg\", \"jud.emb\", \"jud.ups\", na.rm = T)\r\nk1_df$sti.score <- scale(k1_df$jud.fam.r) + scale(k1_df$jud.com.r) + scale(k1_df$jud.judg) + scale(k1_df$jud.emb) + scale(k1_df$jud.ups)\r\nk1_df$sti.score.z <- scale(k1_df$sti.score)\r\n\r\n## Affect (5-point scale) ##\r\n\r\nfor (var in c(k1_df$aff.pos, k1_df$aff.ash, k1_df$aff.pow, k1_df$aff.fina)) {\r\n\r\n var[var < 0] <- NA\r\n\r\n}\r\n\r\nk1_df$aff.pos.s <- k1_df$aff.pos * (5/6)\r\n\r\nk1_df$aff.ash.r <- 6 - k1_df$aff.ash\r\nk1_df$aff.fina.r <- 6 - k1_df$aff.fina\r\n\r\nk1_df$aff.score.avg <- scale.means(k1_df, \"aff.pos.s\", \"aff.pow\", \"aff.ash.r\", \"aff.fina.r\", na.rm = T)\r\nk1_df$aff.score <- scale(k1_df$aff.pos) + scale(k1_df$aff.pow) + scale(k1_df$aff.ash.r) + scale(k1_df$aff.fina.r)\r\nk1_df$aff.score.z <- scale(k1_df$aff.score)\r\n\r\n############################\r\n## Initialize results doc ##\r\n############################\r\n\r\nappendix <- read_docx()\r\nWriteHeading(appendix, \"Data analysis for Kenya recipient experiment (study 1)\")\r\n\r\n########################\r\n## Summary statistics ##\r\n########################\r\n\r\nSumTable <- SumStats(varlist = c(\"soc.fem\", \"soc.pri\", \"soc.age\", \"ses.unemp\", \"soc.sav\", \"soc.con\", \"soc.inc\"), labels = c(\"Female\", \"Completed std. 8\", \"Age\", \"Unemployed\", \"Holds savings\", \"Consumption (KSh)\", \"Income (KSh)\"), data = k1_df)\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Summary of sample sociodemographic characteristics for study 1\")\r\n\r\nbig_border <- fp_border(width = 2)\r\nstd_border <- fp_border(width = 1)\r\n\r\nnote <- \"Note: \\\"Female\\\" is an indicator variable for identifying as a woman \\\"Completed std. 8\\\" is an indicator for having completed primary school. \\\"Age\\\" is the self-reported age of the respondent. \\\"Unemployed\\\" is an indicator for being unemployed. \\\"Holds savings\\\" is an indicator for having savings of at least KSh 1000. \\\"Consumption\\\" is a variable for consumption in the last seven days. \\\"Income\\\" is a variable for earned income in the past month.\"\r\n\r\nflextable <- regulartable(as.data.frame(SumTable)) %>%\r\n align(align = \"center\") %>%\r\n align(j = 1, part = \"body\", align = \"left\") %>%\r\n border_remove() %>%\r\n hline_top(part = \"all\", border = big_border) %>%\r\n hline_top(part = \"body\", border = big_border) %>%\r\n hline_bottom(part = \"body\", border = big_border) %>%\r\n add_footer(Outcome = note) %>%\r\n merge_at(j = 1:7, part = \"footer\") %>%\r\n flextable::font(fontname = \"Times New Roman\", part = \"all\") %>%\r\n italic(part = \"footer\") %>%\r\n fontsize(size = 10, part = \"all\") %>%\r\n autofit()\r\n\r\nappendix <- body_add_flextable(appendix, value = flextable)\r\n\r\n##################################\r\n## Randomization balance checks ##\r\n##################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"soc.fem\", \"soc.pri\", \"soc.age\", \"ses.unemp\")\r\ndepvarnames <- c(\"Female\", \"Completed std. 8\", \"Age\", \"Unemployed\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.bal <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \"~ treat\", sep = \" \")\r\n RES.bal <- rbind(RES.bal, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[i], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.bal <- cbind(RES.bal[2:nrow(RES.bal), 1:ncol(RES.bal)], FDR(RES.bal[2:nrow(RES.bal), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.bal <- cbind(RES.bal, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.bal <- cbind(RES.bal, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.bal[, 5] <- round(RES.bal[, 5], 0)\r\n RES.bal[, -5] <- format(round(RES.bal[, -5], 3), nsmall = 3)\r\n\r\n RES.bal <- cbind(depvarnames, RES.bal)\r\n\r\n colnames(RES.bal)[1] <- \"Outcome\"\r\n colnames(RES.bal)[8] <- \"Min. q-value\"\r\n colnames(RES.bal)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.bal, quote = FALSE)\r\n\r\n RES.bal <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.bal)\r\n RES.print <- rbind(RES.print, RES.bal)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Balance checks on subject demographic characteristics for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: Each panel corresponds to a single hypothesis for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n###########################################\r\n## Equivalence test for primary outcomes ##\r\n###########################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"vid.num\", \"sav.amt\", \"msg.dec\")\r\ndepvarnames <- c(\"No. of videos\", \"Amount saved\", \"Recorded message\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.eq <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n firststage <- paste(depvar, \"~ soc.fem + soc.age + soc.pri + ses.unemp\", sep = \" \")\r\n predicted <- predict(lm(firststage, data = k1_df, na.action = na.omit))\r\n\r\n secondstage <- paste(\"predicted\", \"~ treat\", sep = \" \")\r\n RES.eq <- rbind(RES.eq, PermTest(secondstage, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df[complete.cases(soc.fem, soc.age, soc.pri, ses.unemp), \"survey.id\"], hypotheses = hypotheses[i], iterations = 10000, data = k1_df[complete.cases(soc.fem, soc.age, soc.pri, ses.unemp), ]))\r\n\r\n }\r\n\r\n RES.eq <- cbind(RES.eq[2:nrow(RES.eq), 1:ncol(RES.eq)], FDR(RES.eq[2:nrow(RES.eq), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.eq <- cbind(RES.eq, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.eq <- cbind(RES.eq, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.eq[, 5] <- round(RES.eq[, 5], 0)\r\n RES.eq[, -5] <- format(round(RES.eq[, -5], 3), nsmall = 3)\r\n\r\n RES.eq <- cbind(depvarnames, RES.eq)\r\n\r\n colnames(RES.eq)[1] <- \"Outcome\"\r\n colnames(RES.eq)[8] <- \"Min. q-value\"\r\n colnames(RES.eq)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.eq, quote = FALSE)\r\n\r\n RES.eq <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.eq)\r\n RES.print <- rbind(RES.print, RES.eq)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Equivalence tests on primary outcomes for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: This table computes predicted values of outcomes as a function of baseline covariates and regresses the predictions on the treatment indicators. Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of predicted outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n####################################\r\n## Plain OLS for primary outcomes ##\r\n####################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"vid.num\", \"sav.amt\", \"msg.dec\")\r\ndepvarnames <- c(\"No. of videos\", \"Amount saved\", \"Recorded message\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.pri <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \"~ treat\", sep = \" \")\r\n RES.pri <- rbind(RES.pri, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[i], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.pri <- cbind(RES.pri[2:nrow(RES.pri), 1:ncol(RES.pri)], FDR(RES.pri[2:nrow(RES.pri), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.pri <- cbind(RES.pri, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.pri <- cbind(RES.pri, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.pri[, 5] <- round(RES.pri[, 5], 0)\r\n RES.pri[, -5] <- format(round(RES.pri[, -5], 3), nsmall = 3)\r\n\r\n RES.pri <- cbind(depvarnames, RES.pri)\r\n\r\n colnames(RES.pri)[1] <- \"Outcome\"\r\n colnames(RES.pri)[8] <- \"Min. q-value\"\r\n colnames(RES.pri)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.pri, quote = FALSE)\r\n\r\n RES.pri <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.pri)\r\n RES.print <- rbind(RES.print, RES.pri)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Treatment effects on primary outcomes for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n######################################\r\n## Plain OLS for secondary outcomes ##\r\n######################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"sel.score.avg\", \"sti.score.avg\", \"aff.score.avg\", \"ses.lad.now\", \"ses.lad.y2\", \"msg.avg\", \"que.smrd\")\r\ndepvarnames <- c(\"Self-efficacy (avg.)\", \"Stigma (avg.)\", \"Affect (avg.)\", \"Social status\", \"Anticipated social mobility\", \"Message support\", \"Query ordering\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.sec <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \"~ treat\", sep = \" \")\r\n RES.sec <- rbind(RES.sec, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[i], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.sec <- cbind(RES.sec[2:nrow(RES.sec), 1:ncol(RES.sec)], FDR(RES.sec[2:nrow(RES.sec), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.sec <- cbind(RES.sec, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.sec <- cbind(RES.sec, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.sec[, 5] <- round(RES.sec[, 5], 0)\r\n RES.sec[, -5] <- format(round(RES.sec[, -5], 3), nsmall = 3)\r\n\r\n RES.sec <- cbind(depvarnames, RES.sec)\r\n\r\n colnames(RES.sec)[1] <- \"Outcome\"\r\n colnames(RES.sec)[8] <- \"Min. q-value\"\r\n colnames(RES.sec)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.sec, quote = FALSE)\r\n\r\n RES.sec <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.sec)\r\n RES.print <- rbind(RES.print, RES.sec)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Treatment effects on secondary outcomes for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n\r\n###############################################\r\n## Covariate adjustment for primary outcomes ##\r\n###############################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"vid.num\", \"sav.amt\", \"msg.dec\")\r\ndepvarnames <- c(\"No. of videos\", \"Amount saved\", \"Recorded message\")\r\ncovariates <- c(\"soc.fem.c\", \"soc.pri.c\", \"soc.age.c\", \"ses.unemp.c\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.pri.cov <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \"~\", Interact(\"treat\", covariates), sep = \" \")\r\n RES.pri.cov <- rbind(RES.pri.cov, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[i], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.pri.cov <- cbind(RES.pri.cov[2:nrow(RES.pri.cov), 1:ncol(RES.pri.cov)], FDR(RES.pri.cov[2:nrow(RES.pri.cov), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.pri.cov <- cbind(RES.pri.cov, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.pri.cov <- cbind(RES.pri.cov, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.pri.cov[, 5] <- round(RES.pri.cov[, 5], 0)\r\n RES.pri.cov[, -5] <- format(round(RES.pri.cov[, -5], 3), nsmall = 3)\r\n\r\n RES.pri.cov <- cbind(depvarnames, RES.pri.cov)\r\n\r\n colnames(RES.pri.cov)[1] <- \"Outcome\"\r\n colnames(RES.pri.cov)[8] <- \"Min. q-value\"\r\n colnames(RES.pri.cov)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.pri.cov, quote = FALSE)\r\n\r\n RES.pri.cov <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.pri.cov)\r\n RES.print <- rbind(RES.print, RES.pri.cov)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Treatment effects on primary outcomes with covariate adjustment for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: We include as control variables indicators for being female, for having completed primary schooling, above median age, and unemployment status. Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n#################################################\r\n## Covariate adjustment for secondary outcomes ##\r\n#################################################\r\n\r\nhypotheses <- c(\"treatInd = 0\", \"treatCom = 0\", \"treatInd - treatCom = 0\")\r\nhtitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\ndepvars <- c(\"sel.score.avg\", \"sti.score.avg\", \"aff.score.avg\", \"ses.lad.now\", \"ses.lad.y2\", \"msg.avg\", \"que.smrd\")\r\ndepvarnames <- c(\"Self-efficacy (avg.)\", \"Stigma (avg.)\", \"Affect (avg.)\", \"Social status\", \"Anticipated social mobility\", \"Message support\", \"Query ordering\")\r\ncovariates <- c(\"soc.fem.c\", \"soc.pri.c\", \"soc.age.c\", \"ses.unemp.c\")\r\n\r\nRES.print <- matrix(nrow = 1, ncol = 9)\r\n\r\nfor (i in 1:3) {\r\n\r\n RES.sec.cov <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \"~\", Interact(\"treat\", covariates), sep = \" \")\r\n RES.sec.cov <- rbind(RES.sec.cov, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[i], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.sec.cov <- cbind(RES.sec.cov[2:nrow(RES.sec.cov), 1:ncol(RES.sec.cov)], FDR(RES.sec.cov[2:nrow(RES.sec.cov), 4]))\r\n\r\n if (hypotheses[i] == \"treatInd - treatCom = 0\") {\r\n RES.sec.cov <- cbind(RES.sec.cov, colMeans(k1_df[which(k1_df$com == 1), depvars], na.rm = TRUE))\r\n } else RES.sec.cov <- cbind(RES.sec.cov, colMeans(k1_df[which(k1_df$pov == 1), depvars], na.rm = TRUE))\r\n\r\n RES.sec.cov[, 5] <- round(RES.sec.cov[, 5], 0)\r\n RES.sec.cov[, -5] <- format(round(RES.sec.cov[, -5], 3), nsmall = 3)\r\n\r\n RES.sec.cov <- cbind(depvarnames, RES.sec.cov)\r\n\r\n colnames(RES.sec.cov)[1] <- \"Outcome\"\r\n colnames(RES.sec.cov)[8] <- \"Min. q-value\"\r\n colnames(RES.sec.cov)[9] <- \"Reference mean\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[i]), quote = FALSE)\r\n print(RES.sec.cov, quote = FALSE)\r\n\r\n RES.sec.cov <- rbind(c(htitle[i], \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.sec.cov)\r\n RES.print <- rbind(RES.print, RES.sec.cov)\r\n\r\n}\r\n\r\nRES.print[, c(1:9)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\ncolnames(RES.print)[c(1:9)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 9, 6)]\r\n\r\n## Print table to doc ##\r\n\r\nLineBreak(appendix)\r\nWriteTitle(appendix, \"Treatment effects on secondary outcomes with covariate adjustment for study 1\")\r\n\r\nappendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:9)], note = \"Note: We include as control variables indicators for being female, for having completed primary schooling, above median age, and unemployment status. Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference. The fifth column reports the minimum q-values. FDR correction is applied over all outcomes within a hypothesis. The reference mean column lists the mean of the poverty alleviation condition for the first two panels and the mean of the community empowerment condition for the third panel.\"))\r\n\r\n################################################\r\n## Heterogeneous effects for primary outcomes ##\r\n################################################\r\n\r\ndepvars <- c(\"vid.num\", \"sav.amt\", \"msg.dec\")\r\ndepvarnames <- c(\"No. of videos\", \"Amount saved\", \"Recorded message\")\r\nhetvars <- c(\"soc.fem\", \"soc.pri\")\r\nhetvarnames <- c(\"gender (female)\", \"completion of primary schooling\")\r\n\r\nfor (i in 1:2) {\r\n\r\n hypotheses <- c(paste(\"treatInd:\", hetvars[i], \" = 0\", sep = \"\"), paste(\"treatCom:\", hetvars[i], \" = 0\", sep = \"\"), paste(\"treatInd:\", hetvars[i], \" - \", \"treatCom:\", hetvars[i], \" = 0\", sep = \"\"))\r\n htitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\n\r\n appendix <- body_add_par(appendix , \" \", style = \"Normal\")\r\n\r\n RES.print <- matrix(nrow = 1, ncol = 8)\r\n\r\n for (j in 1:3) {\r\n\r\n RES.pri.het <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \" ~ treat*\", hetvars[i], sep = \"\")\r\n RES.pri.het <- rbind(RES.pri.het, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[j], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.pri.het <- RES.pri.het[2:nrow(RES.pri.het), 1:ncol(RES.pri.het)]\r\n RES.pri.het <- cbind(RES.pri.het, FDR(RES.pri.het[, 4]))\r\n\r\n RES.pri.het[, 5] <- round(RES.pri.het[, 5], 0)\r\n RES.pri.het[, -5] <- format(round(RES.pri.het[, -5], 3), nsmall = 3)\r\n\r\n RES.pri.het <- cbind(depvarnames, RES.pri.het)\r\n\r\n colnames(RES.pri.het)[1] <- \"Outcome\"\r\n colnames(RES.pri.het)[8] <- \"Min. q-value\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[j]), quote = FALSE)\r\n print(RES.pri.het, quote = FALSE)\r\n\r\n RES.pri.het <- rbind(c(htitle[j], \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.pri.het)\r\n RES.print <- rbind(RES.print, RES.pri.het)\r\n\r\n }\r\n\r\n RES.print[, c(1:8)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 6)]\r\n colnames(RES.print)[c(1:8)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 6)]\r\n\r\n ## Print table to doc ##\r\n\r\n LineBreak(appendix)\r\n WriteTitle(appendix, paste(\"Heterogeneous treatment effects on primary outcomes by\", hetvarnames[i], \"for study 1\"))\r\n\r\n appendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:7)], note = \"Note: This table reports coefficient estimates on each experimental comparison interacted with a baseline variable. Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference.\"))\r\n\r\n}\r\n\r\n##################################################\r\n## Heterogeneous effects for secondary outcomes ##\r\n##################################################\r\n\r\ndepvars <- c(\"sel.score.avg\", \"sti.score.avg\", \"aff.score.avg\", \"ses.lad.now\", \"ses.lad.y2\", \"msg.avg\", \"que.smrd\")\r\ndepvarnames <- c(\"Self-efficacy (avg.)\", \"Stigma (avg.)\", \"Affect (avg.)\", \"Social status\", \"Anticipated social mobility\", \"Message support\", \"Query ordering\")\r\nhetvars <- c(\"soc.fem\", \"soc.pri\")\r\nhetvarnames <- c(\"gender (female)\", \"completion of primary schooling\")\r\n\r\nfor (i in 1:2) {\r\n\r\n hypotheses <- c(paste(\"treatInd:\", hetvars[i], \" = 0\", sep = \"\"), paste(\"treatCom:\", hetvars[i], \" = 0\", sep = \"\"), paste(\"treatInd:\", hetvars[i], \" - \", \"treatCom:\", hetvars[i], \" = 0\", sep = \"\"))\r\n htitle <- c(\"Individual - Poverty\", \"Community - Poverty\", \"Individual - Community\")\r\n\r\n appendix <- body_add_par(appendix , \" \", style = \"Normal\")\r\n\r\n RES.print <- matrix(nrow = 1, ncol = 8)\r\n\r\n for (j in 1:3) {\r\n\r\n RES.sec.het <- matrix(nrow = 1, ncol = 6)\r\n\r\n for (depvar in depvars) {\r\n\r\n eqn <- paste(depvar, \" ~ treat*\", hetvars[i], sep = \"\")\r\n RES.sec.het <- rbind(RES.sec.het, PermTest(eqn, treatvars = c(\"treat\", \"pov\", \"ind\", \"com\"), clustvars = k1_df$survey.id, hypotheses = hypotheses[j], iterations = 10000, data = k1_df))\r\n\r\n }\r\n\r\n RES.sec.het <- RES.sec.het[2:nrow(RES.sec.het), 1:ncol(RES.sec.het)]\r\n RES.sec.het <- cbind(RES.sec.het, FDR(RES.sec.het[, 4]))\r\n\r\n RES.sec.het[, 5] <- round(RES.sec.het[, 5], 0)\r\n RES.sec.het[, -5] <- format(round(RES.sec.het[, -5], 3), nsmall = 3)\r\n\r\n RES.sec.het <- cbind(depvarnames, RES.sec.het)\r\n\r\n colnames(RES.sec.het)[1] <- \"Outcome\"\r\n colnames(RES.sec.het)[8] <- \"Min. q-value\"\r\n\r\n print(\"----------------------------------------------------------------\", quote = FALSE)\r\n print(paste(\"H_0:\", htitle[j]), quote = FALSE)\r\n print(RES.sec.het, quote = FALSE)\r\n\r\n RES.sec.het <- rbind(c(htitle[j], \"\", \"\", \"\", \"\", \"\", \"\", \"\"), RES.sec.het)\r\n RES.print <- rbind(RES.print, RES.sec.het)\r\n\r\n }\r\n\r\n RES.print[, c(1:8)] <- RES.print[, c(1, 2, 3, 4, 5, 7, 8, 6)]\r\n colnames(RES.print)[c(1:8)] <- colnames(RES.print)[c(1, 2, 3, 4, 5, 7, 8, 6)]\r\n\r\n ## Print table to doc ##\r\n\r\n LineBreak(appendix)\r\n WriteTitle(appendix, paste(\"Heterogeneous treatment effects on secondary outcomes by\", hetvarnames[i], \"for study 1\"))\r\n\r\n appendix <- body_add_flextable(appendix, value = FTable(RES.print[2:nrow(RES.print), c(1:2,4:7)], note = \"Note: This table reports coefficient estimates on each experimental comparison interacted with a baseline variable. Each panel corresponds to a single hypothesis comparing the treatment conditions for the group of outcome variables. The first column reports the mean difference between groups. The second column reports robust standard errors. The third column reports standard p-values. The fourth column reports exact p-values from randomization inference.\"))\r\n\r\n}\r\n\r\n# Print results appendix #\r\n\r\nprint(appendix, target = here(\"doc\", \"S1_appendix.docx\"))\r\n\r\n###########################\r\n## Stigma coding results ##\r\n###########################\r\n\r\n## Exploratory analysis on stigma free responses ##\r\n\r\ntidy.codes.2 <- k1_df %>%\r\n dplyr::select(por.oth_1_RA_CODE, por.oth_2_RA_CODE, por.oth_3_RA_CODE,\r\n ind.oth_1_RA_CODE, ind.oth_2_RA_CODE, ind.oth_3_RA_CODE,\r\n com.oth_1_RA_CODE, com.oth_2_RA_CODE, com.oth_3_RA_CODE,\r\n condition.order, survey.id) %>%\r\n dplyr::mutate_at(vars(matches(\"oth\")),funs(as.factor)) %>%\r\n rename(oth.1_por = por.oth_1_RA_CODE, oth.2_por = por.oth_2_RA_CODE, oth.3_por = por.oth_3_RA_CODE,\r\n oth.1_ind = ind.oth_1_RA_CODE, oth.2_ind = ind.oth_2_RA_CODE, oth.3_ind = ind.oth_3_RA_CODE,\r\n oth.1_com = com.oth_1_RA_CODE, oth.2_com = com.oth_2_RA_CODE, oth.3_com = com.oth_3_RA_CODE) \r\n\r\ntidy.codes.2$oth.1 <- coalesce(tidy.codes.2$oth.1_por, tidy.codes.2$oth.1_ind, tidy.codes.2$oth.1_com) \r\ntidy.codes.2$oth.2 <- coalesce(tidy.codes.2$oth.2_por, tidy.codes.2$oth.2_ind, tidy.codes.2$oth.2_com) \r\ntidy.codes.2$oth.3 <- coalesce(tidy.codes.2$oth.3_por, tidy.codes.2$oth.3_ind, tidy.codes.2$oth.3_com) \r\n\r\ntidy.codes.3 <- tidy.codes.2 %>%\r\n dplyr::select(oth.1, oth.2, oth.3, condition.order, survey.id) %>% \r\n gather(contains('oth'), key=prompt, value=oth.value)\r\n\r\ntidy.codes.3$oth.neg <- as.logical(tidy.codes.3$oth.value == \"negative\") \r\ntidy.codes.3$oth.pos <- as.logical(tidy.codes.3$oth.value == \"positive\") \r\n\r\ntidy.codes.sum <- tidy.codes.3 %>%\r\n group_by(survey.id) %>%\r\n mutate(neg.prop = sum(oth.value==\"negative\", na.rm=T) / length(which(!is.na(oth.value)))) %>%\r\n mutate(pos.prop = sum(oth.value==\"positive\", na.rm=T) / length(which(!is.na(oth.value)))) %>%\r\n mutate(amb.prop = sum(oth.value==\"ambig\", na.rm=T) / length(which(!is.na(oth.value)))) \r\n\r\nms.oth.neg.prop <- tidy.codes.sum %>% \r\n group_by(condition.order) %>% \r\n summarise(Prop_Neg = mean(neg.prop, na.rm=TRUE), Prop_Pos = mean(pos.prop, na.rm=TRUE), Prop_Amb=mean(amb.prop, na.rm=T), \"n\"=sum(!is.na(oth.value)))\r\n\r\nprint(summary(lm(neg.prop ~ condition.order, data=tidy.codes.sum)))\r\n\r\n##################################\r\n## Bar graphs for main findings ##\r\n##################################\r\n\r\ntreat <- factor(k1_df$treat, labels = c(\"Poverty\\nAlleviation\", \"Individual\\nEmpowerment\", \"Community\\nEmpowerment\"))\r\n\r\nvid.graph <- BarChart(depvar = k1_df$vid.num, groupvar = treat, ytitle = \"No. of videos (0-2)\", title = \"Skills building\", xtitle = \"\", fillcolor = c('#c6c6c7', '#7ca6c0', '#c05746'), bounds = c(1, 1.75))\r\n\r\nsel.graph <- BarChart(depvar = k1_df$sel.score.avg, groupvar = treat, ytitle = \"Self-rating (1-5)\", title = \"Self-efficacy\", xtitle = \"\", fillcolor = c('#c6c6c7', '#7ca6c0', '#c05746'), bounds = c(3, 3.75))\r\n\r\nsti.graph <- BarChart(depvar = k1_df$sti.score.avg, groupvar = treat, ytitle = \"Self-rating (1-5)\", title = \"Stigma\", xtitle = \"\", fillcolor = c('#c6c6c7', '#7ca6c0', '#c05746'), bounds = c(2, 2.75))\r\n\r\nses.lad.y2.graph <- BarChart(depvar = k1_df$ses.lad.y2, groupvar = treat, title = \"Anticipated social mobility\", ytitle = \"Ladder score (1-10)\", xtitle = \"\", fillcolor = c('#c6c6c7', '#7ca6c0', '#c05746'), bounds = c(mean(k1_df$ses.lad.y2, na.rm = TRUE) - 0.5 * sd(k1_df$ses.lad.y2, na.rm = TRUE), mean(k1_df$ses.lad.y2, na.rm = TRUE) + 0.5 * sd(k1_df$ses.lad.y2, na.rm = TRUE)))\r\n\r\n# Annotate with significance levels #\r\n\r\nvid.graph <- vid.graph +\r\n geom_signif(comparisons = list(c(\"Poverty\\nAlleviation\", \"Individual\\nEmpowerment\")), annotations = \"\u2020\", textsize = 3, y_position = 1.5, vjust = -0.2) +\r\n geom_signif(comparisons=list(c(\"Poverty\\nAlleviation\", \"Community\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 1.625, vjust = 0.2)\r\n\r\nsel.graph <- sel.graph +\r\n geom_signif(comparisons = list(c(\"Poverty\\nAlleviation\", \"Individual\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 3.525, vjust = 0.2) +\r\n geom_signif(comparisons=list(c(\"Poverty\\nAlleviation\", \"Community\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 3.625, vjust = 0.2)\r\n\r\nsti.graph <- sti.graph +\r\n geom_signif(comparisons=list(c(\"Poverty\\nAlleviation\", \"Community\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 2.7, vjust = 0.2)\r\n\r\nses.lad.y2.graph <- ses.lad.y2.graph +\r\n geom_signif(comparisons = list(c(\"Poverty\\nAlleviation\", \"Individual\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 6.55, vjust = 0.2) +\r\n geom_signif(comparisons=list(c(\"Poverty\\nAlleviation\", \"Community\\nEmpowerment\")), annotations = \"*\", textsize = 5, y_position = 6.75, vjust = 0.2)\r\n\r\n# Arrange figures in grid #\r\n\r\npdf(here(\"graphics\", \"Figure1.pdf\"), width = 10, height = 3, encoding = \"MacRoman\")\r\nplot_grid(vid.graph, sel.graph, ses.lad.y2.graph, sti.graph, nrow = 1, ncol = 4, labels = c(\"A Economic Behavior\", \"B Psychological Outcomes\", \"\", \"\"), label_size = 12, scale = 0.87, hjust = -.1)\r\ndev.off()", "meta": {"hexsha": "e46cb563b097cdfb6b653e74af1145c58be4cc05", "size": 33272, "ext": "r", "lang": "R", "max_stars_repo_path": "r/Study1.r", "max_stars_repo_name": "jrpabraham/empower-aid", "max_stars_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "r/Study1.r", "max_issues_repo_name": "jrpabraham/empower-aid", "max_issues_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r/Study1.r", "max_forks_repo_name": "jrpabraham/empower-aid", "max_forks_repo_head_hexsha": "1b16f4899f3480fe662c3e4cf56c0987dd4f9805", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.9525267994, "max_line_length": 901, "alphanum_fraction": 0.6083493628, "num_tokens": 10140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30452284056872414}} {"text": "# Written by Andy Madrid\n\n### Load packages for preprocessing and get annotation\n\nrm(list=ls())\ngetwd()\nset.seed(1234)\nlibrary(limma)\nlibrary(minfi)\nlibrary(IlluminaHumanMethylationEPICanno.ilm10b2.hg19)\nlibrary(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)\nlibrary(RColorBrewer)\nlibrary(missMethyl)\nlibrary(Gviz)\nlibrary(stringr)\nlibrary(data.table)\n\nannEPIC <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)\nhead(annEPIC)\n\n### Import raw idat file into R environment\n\ntargets <- read.csv(\"../../data/samplesheets/master-WB.csv\",header=T)\nrel.names <- paste(\"../../data/850Karrays/\", targets$patient_folder, targets$patient_id, sep = \"/\")\n\n# Pull in red-green channel set\nRGSet <- read.metharray(rel.names)\nRGSet\n\n### Basic QC based on detection levels of probes\ndetP <- detectionP(RGSet)\n\n# non-processed\tdata for more QC of raw data\n# preprocessRaw just \"maps\" Reg/Green channel into methylation \n# without any normalization\nmSetRaw\t<- preprocessRaw(RGSet)\nqcRaw <- getQC(mSetRaw)\nplotQC(qcRaw)\n\n# save QC report\n\n#qcReport(RGSet,sampNames=targets$adrcnum,sampGroups=targets$COHORT,pdf=\"qcReport_minfi_ADRC_Project.pdf\")\n\n# filter poor samples with high mean detection P-values (>0.05)\n\nkeep <- colMeans(detP) < 0.05\nRGSet <- RGSet[,keep]\ntargets <- targets[keep,]\ndetP <- detP[,keep]\n\n\n### Normalize data\n\n# background and control normalize with \"GenomeStudio\" standards\nmSetIllumina <- preprocessIllumina(RGSet,bg.correct=TRUE,normalize='controls')\n\nrm(mSetRaw)\nrm(qcRaw)\n\n# within array normalization\nmSetSWAN <- preprocessSWAN(RGSet,mSet=mSetIllumina,verbose=TRUE)\nrm(mSetIllumina)\ngc()\n\n# check QC of normalized data\n\n#qcSWAN <- getQC(mSetSWAN)\n#plotQC(qcSWAN)\n\n# check predicted sex of each sample from normalized data\n\nmSetSWAN <- mapToGenome(mSetSWAN)\n#pSex <- getSex(mSetSWAN)\n# compared predicted sex to actual sex from samplesheet using perl script\n# no samples failed sex prediction\n\n# get estimated cell counts for blood tissue\n# I've noticed some errors get thrown depending on which version of minfi is being used...\n\n#cellCounts <- estimateCellCounts(RGSet,compositeCellType=\"Blood\", referencePlatform = \"IlluminaHumanMethylationEPI\")\n\n######################################\n\n### Filter probes for low quality, sex chromosomes, cross-reactive, CH\n\n# filter if at least one sample has detP > 0.01\n\ndetP <- detP[match(featureNames(mSetSWAN),rownames(detP)),]\nkeep <- rowSums(detP < 0.01) == ncol(mSetSWAN)\nmSetFiltered <- mSetSWAN[keep,]\n\nrm(detP)\ngc()\n\n# filter probes on X and Y chromosomes\n\nkeep <- !(featureNames(mSetFiltered) %in% annEPIC$Name[annEPIC$chr %in% c(\"chrX\",\"chrY\")])\nmSetFiltered <- mSetFiltered[keep,]\n\n# filter probes with SNPs and at CH sites\n\nmSetFiltered <- dropLociWithSnps(mSetFiltered)\nmSetFiltered <- dropMethylationLoci(mSetFiltered)\ngc()\n\n\n# filter probes of known cross-reactive sites\n\nxRtvProbes <- read.csv(\"pub-2016McCartney-CrossHybridCpG.csv\", header=F, stringsAsFactors = F)\nkeep <- !(featureNames(mSetFiltered) %in% xRtvProbes)\nmSetFiltered <- mSetFiltered[keep,]\n\nrm(mSetSWAN)\nrm(RGSet)\ngc()\n\n\n### Get Beta (bValues) and logit M-values (mValues)\n\nmValues <- getM(mSetFiltered) %>% as.data.frame()\nbValues <- getBeta(mSetFiltered) %>% as.data.frame()\n\n### Write files of mValues, bValues, estimated cell counts\n\nfwrite(bValues,file=\"adrcPreprocessed.bValues.csv\",quote=F,row.names=T)\nfwrite(mValues,file=\"adrcPreprocessed.mValues.csv\",quote=F,row.names=T)\n#write.table(cellCounts,file=\"adrcCellCounts.csv\",quote=F,row.names=T.sep=',')\nsave(bValues,mValues,annEPIC,targets,file=\"adrcPreprocessedData.RData\")\n", "meta": {"hexsha": "8cf254890a44e32cbaa65bd2350d8f2e50c980e4", "size": 3576, "ext": "r", "lang": "R", "max_stars_repo_path": "code/00-preprocessIDAT.r", "max_stars_repo_name": "cbreenmachine/850k-BMI", "max_stars_repo_head_hexsha": "e696ae3e1d661b2d15382dde0cc7c14de91a8a0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/00-preprocessIDAT.r", "max_issues_repo_name": "cbreenmachine/850k-BMI", "max_issues_repo_head_hexsha": "e696ae3e1d661b2d15382dde0cc7c14de91a8a0b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "code/00-preprocessIDAT.r", "max_forks_repo_name": "cbreenmachine/850k-BMI", "max_forks_repo_head_hexsha": "e696ae3e1d661b2d15382dde0cc7c14de91a8a0b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.7209302326, "max_line_length": 117, "alphanum_fraction": 0.7544742729, "num_tokens": 1021, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3045145695176317}} {"text": "#' Make selectivity decoder\n#' \n#' Create csv file decoding selectivity parameters for fleet blocks and indices.\n#' @param wd directory where ASAP run is located\n#' @param asap name of the variable that read in the asap.rdat file\n#' @param a1 list file produced by grab.aux.files function\n#' @param index.names names of indices \n#' @param od output directory for plots and csv files \n#' @export\n\nMakeSelectivityDecoder <- function(wd,asap,a1,index.names,od){\n asap.name <- a1$asap.name\n \n empty.df <- data.frame(Source = character(),\n Name = character(),\n NameNum = integer(),\n SelType = character(),\n Param = character(),\n ParamNum = integer(),\n InitGuess = double(),\n Phase = integer(),\n Lambda = double(),\n CV = double()) \n \n fleet.selectivity.decoder.table <- empty.df\n index.selectivity.decoder.table <- empty.df\n \n nages <- asap$parms$nages\n \n # fleet selectivity blocks\n icount <- 1\n \n for (iblock in 1:asap$parms$nselblocks){\n \n # age specific parameters\n if (asap$fleet.sel.option[iblock] == 1){\n nparms <- nages\n start.row <- (iblock - 1) * (nages + 6) + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Age Specific\"\n myParam <- paste(\"Age\", 1:nages)\n }\n\n # single logistic parameters\n if (asap$fleet.sel.option[iblock] == 2){\n nparms <- 2\n start.row <- (iblock - 1) * (nages + 6) + nages + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Single Logistic\"\n myParam <- c(\"A50\", \"Slope\")\n }\n\n # double logistic parameters\n if (asap$fleet.sel.option[iblock] == 3){\n nparms <- 4\n start.row <- (iblock - 1) * (nages + 6) + nages + 2 + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Double Logistic\"\n myParam <- c(\"A50 ascending\", \"Slope ascending\", \"A50 descending\", \"Slope descending\")\n }\n\n # add this block to table and increase icount\n thisdf <- data.frame(Source = \"Fleet\",\n Name = paste(\"Selblock\", iblock),\n NameNum = iblock,\n SelType = mySelType,\n Param = myParam,\n ParamNum = seq(icount, (icount + nparms - 1)),\n InitGuess = asap$sel.input.mats$fleet.sel.ini[start.row:end.row, 1],\n Phase = asap$sel.input.mats$fleet.sel.ini[start.row:end.row, 2],\n Lambda = asap$sel.input.mats$fleet.sel.ini[start.row:end.row, 3],\n CV = asap$sel.input.mats$fleet.sel.ini[start.row:end.row, 4])\n \n fleet.selectivity.decoder.table <- rbind(fleet.selectivity.decoder.table, thisdf)\n icount <- icount + nparms\n }\n \n # get fleet selectivity estimates, if available, and join to table\n if (any(!is.na(a1$asap.std))){\n fleet.sel.estimates <- a1$asap.std %>%\n data.frame(.) %>%\n filter(substr(name, 1, 10) == \"sel_params\") %>%\n mutate(ParamNum = readr::parse_number(name)) %>%\n mutate(Estimate = value, EstimatedStDev = stdev, EstimatedCV = stdev/value) %>%\n select(ParamNum, Estimate, EstimatedStDev, EstimatedCV)\n \n # join the selectivity table and estimates\n selectivity.decoder.table <- left_join(fleet.selectivity.decoder.table, fleet.sel.estimates, \n by = \"ParamNum\")\n }else{\n selectivity.decoder.table <- fleet.selectivity.decoder.table\n }\n \n #--------------------\n # index selectivities\n icount <- 1\n \n # cannot get index sel with release version of ASAP3 because index.sel.options not saved in rdat\n # need to make a one-off version that has this added to rdat or else read from the dat file\n if (is.null(asap$control.parms$index.sel.option)){\n dat.file.index.sel.options <- GrabDatFileIndexSelOptions(wd,asap.name,asap)\n if (!is.null(dat.file.index.sel.options)){\n asap$control.parms$index.sel.option <- dat.file.index.sel.options\n }\n }\n \n if (!is.null(asap$control.parms$index.sel.option)){\n \n ind <- 0 # counter for indices that are used in estimation\n \n for (avail.ind in 1:asap$parms$nindices){\n \n # check to see if index used\n if (asap$initial.guesses$index.use.flag[avail.ind] == 1){\n \n ind <- ind + 1\n \n # check to make sure index selectivity not linked to a fleet\n if (asap$control.parms$index.sel.choice[ind] <= 0){\n # age specific parameters\n if (asap$control.parms$index.sel.option[ind] == 1){\n nparms <- nages\n start.row <- (avail.ind - 1) * (nages + 6) + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Age Specific\"\n myParam <- paste(\"Age\", 1:nages)\n }\n \n # single logistic parameters\n if (asap$control.parms$index.sel.option[ind] == 2){\n nparms <- 2\n start.row <- (avail.ind - 1) * (nages + 6) + nages + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Single Logistic\"\n myParam <- c(\"A50\", \"Slope\")\n }\n \n # double logistic parameters\n if (asap$control.parms$index.sel.option[ind] == 3){\n nparms <- 4\n start.row <- (avail.ind - 1) * (nages + 6) + nages + 2 + 1\n end.row <- start.row + nparms - 1\n mySelType <- \"Double Logistic\"\n myParam <- c(\"A50 ascending\", \"Slope ascending\", \"A50 descending\", \"Slope descending\")\n }\n \n # add this block to table and increase icount\n thisdf <- data.frame(Source = \"Index\",\n Name = index.names[ind],\n NameNum = ind,\n SelType = mySelType,\n Param = myParam,\n ParamNum = seq(icount, (icount + nparms - 1)),\n InitGuess = asap$sel.input.mats$index.sel.ini[start.row:end.row, 1],\n Phase = asap$sel.input.mats$index.sel.ini[start.row:end.row, 2],\n Lambda = asap$sel.input.mats$index.sel.ini[start.row:end.row, 3],\n CV = asap$sel.input.mats$index.sel.ini[start.row:end.row, 4])\n \n index.selectivity.decoder.table <- rbind(index.selectivity.decoder.table, thisdf)\n icount <- icount + nparms\n }\n \n # if index selectivity is linked to a fleet, just increase parameter counter\n if (asap$control.parms$index.sel.choice[ind] > 0){\n if (asap$control.parms$index.sel.option[ind] == 1) icount <- icount + nages\n if (asap$control.parms$index.sel.option[ind] == 2) icount <- icount + 2\n if (asap$control.parms$index.sel.option[ind] == 3) icount <- icount + 4\n } \n }\n }\n \n # get index selectivity estimates, if available, and join to table\n if (any(!is.na(a1$asap.std))){\n index.sel.estimates <- a1$asap.std %>%\n data.frame(.) %>%\n filter(substr(name, 1, 16) == \"index_sel_params\") %>%\n mutate(ParamNum = readr::parse_number(name)) %>%\n mutate(Estimate = value, EstimatedStDev = stdev, EstimatedCV = stdev/value) %>%\n select(ParamNum, Estimate, EstimatedStDev, EstimatedCV)\n \n # join the selectivity table and estimates\n index.selectivity.decoder.table <- left_join(index.selectivity.decoder.table, index.sel.estimates,\n by = \"ParamNum\")\n }\n selectivity.decoder.table <- rbind(selectivity.decoder.table, index.selectivity.decoder.table)\n \n }\n \n write.csv(selectivity.decoder.table, \n file = paste0(od, \"Selectivity_Decoder_\", asap.name, \".csv\"), row.names = FALSE)\n return()\n}\n ", "meta": {"hexsha": "b617134599cad3ac23fb425e64f54e42628ab83f", "size": 7965, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make_selectivity_decoder.r", "max_stars_repo_name": "liz-brooks/ASAPplots", "max_stars_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-03-25T20:24:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-30T20:54:15.000Z", "max_issues_repo_path": "R/make_selectivity_decoder.r", "max_issues_repo_name": "liz-brooks/ASAPplots", "max_issues_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2017-04-11T18:32:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-22T21:03:06.000Z", "max_forks_repo_path": "R/make_selectivity_decoder.r", "max_forks_repo_name": "liz-brooks/ASAPplots", "max_forks_repo_head_hexsha": "f42263d80f28b9de5d1abd2f87676d26bb30d4ec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-08-23T19:14:55.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-18T19:36:49.000Z", "avg_line_length": 41.2694300518, "max_line_length": 104, "alphanum_fraction": 0.5541745135, "num_tokens": 2016, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251201477016, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3045145695176317}} {"text": "#' Compute metacell manifod graph using the confusion matrix of balanced K-nn between individual cells projected on metacells\n#'\n#' @param mgraph_id id of new object\n#' @param mc_id meta cell id to work with\n#' @param ignore_edges provide a data frame with mc1,mc2 pairs of edges to delete manually\n#' @param graph_id graph_id of the similarity graph on cells from the metacell, in case confusion should be computed from similarities.\n#' @param mctnetwork_id id of a network object, in case confusion should be comuted from flows.\n#' @param symetrize should the mc confusion matrix be symmetrized before computing layout?\n#' @param ignore_mismatch try if the cgraph id can be partially overlapping with the metacell object - false by defualt and should be kept this way\n#'\n#' @export\nmcell_mgraph_knn = function(mgraph_id, mc_id, \n\t\t\tgraph_id = NULL, mctnetwork_id = NULL,\n\t\t\tsymmetrize=F, \n\t\t\tignore_mismatch = F,\n\t\t\tignore_edges = NULL) \n{\n\tmc = scdb_mc(mc_id)\n\tif (is.null(mc)) {\n\t\tstop(sprintf(\"mc %s not found\"), mc_id)\n\t}\n\tif(is.null(graph_id) & is.null(mctnetwork_id)) {\n\t\tstop(\"specfiy at least one of graph_id and mctnetwork_id when building an mgraph using Knn relations\")\n\t}\n\tmgraph = mgraph_comp_knn(mc_id, graph_id, mctnetwork_id, \n\t\t\t\t\tignore_mismatch=ignore_mismatch, symmetrize=symmetrize)\n\tif(!is.null(ignore_edges)) {\n\t\tall_e = paste(mgraph$mc1, mgraph$mc2, sep=\"-\")\n\t\tig_e = paste(ignore_edges$mc1, ignore_edges$mc2, sep=\"-\")\n\t\tig_re = paste(ignore_edges$mc2, ignore_edges$mc1, sep=\"-\")\n\t\tf = all_e %in% c(ig_e,ig_re)\n\t\tmgraph= mgraph[!f,]\n\t\tmessage(\"igoring \", sum(f), \" edges\")\n\t}\n\tscdb_add_mgraph(mgraph_id, tgMCManifGraph(mc_id, mgraph))\n}\n\n#' @export\nmgraph_comp_knn = function(mc_id, graph_id, mctnetwork_id,\n\t\t\t\t\t\t\t\t\t\tignore_mismatch=F, symmetrize=F)\n{\n\tmgraph_K = get_param(\"mcell_mgraph_K\")\n\tmgraph_T_edge = get_param(\"mcell_mgraph_T_edge\")\n\tmgraph_max_confu_deg = get_param(\"mcell_mgraph_max_confu_deg\")\n\tmgraph_edge_asym = get_param(\"mcell_mgraph_edge_asym\")\n\tmgraph_k_expand_inout_factor = get_param(\"mcell_mgraph_expand_inout_factor\")\n\tmgraph_max_fpcor_indeg = get_param(\"mcell_mgraph_max_fpcor_indeg\")\n\tmgraph_max_fpcor_outdeg = get_param(\"mcell_mgraph_max_fpcor_outdeg\")\n\n\tif(is.null(mgraph_max_confu_deg) & is.null(mgraph_max_fpcor_outdeg)) {\n\t\tstop(\"MC-ERR: Either max_confu_deg or max_fpcor_deg must be defined - currently both are null\")\n\t}\n\n\tmc = scdb_mc(mc_id)\n\tif(is.null(mc)) {\n\t\tstop(\"MC-ERR: mc id \", mc_id, \" is missing when running add_mc_from_graph\")\n\t}\n\trestrict_in_degree = T\n\n\tif(!is.null(graph_id)) {\n\t\tmessage(\"comp mc graph using the graph \", graph_id, \" and K \", mgraph_K)\n\t\tconfu = mcell_mc_confusion_mat(mc_id, graph_id, mgraph_K, \n\t\t\t\t\t\t\t\t\t\t\t\t\tignore_mismatch=ignore_mismatch)\n\t} else {\n\t\tmessage(\"comp mc graph using the flows \", mctnetwork_id, \" and K \", mgraph_K)\n\t\tif(is.null(mctnetwork_id)) {\n\t\t\tstop(\"both graph id and network id are unspecified hen building knn mgraph\")\n\t\t}\n\t\tmct = scdb_mctnetwork(mctnetwork_id)\n\t\tif(is.null(mct)) {\n\t\t\tstop(\"cannot get mctnetwork id \", mctnetwork_id, \" in db\")\n\t\t}\n\t\tconfu = mctnetwork_get_flow_mat(mct, -1)\n\t}\n\n\tif(symmetrize) {\n\t\tconfu =confu + t(confu)\n\t}\n# k_expand_inout_factor=k_expand_inout_factor\n\n\tcsize = as.matrix(table(mc@mc))\n\tcsize = pmax(csize, 20)\n\tcsize2 = csize %*% t(csize)\n\tcsize2 = csize2 / median(csize)**2\n\tconfu = confu / csize2\n\n\tconfu_p_from = confu/rowSums(confu)\n\tconfu_p_to = t(confu)/colSums(confu)\n\n\tif(!is.null(mgraph_max_confu_deg)) {\n\t\trank_fr = t(apply(confu_p_from, 1, rank))\n\t\trank_to = t(apply(confu_p_to, 1, rank))\n\t\trank2 = rank_fr * rank_to\n\t\tdiag(rank2) = 1e+6\n\t\tamgraph = apply(rank2, 1, function(x) { rank(-x) <= (1+mgraph_max_confu_deg) })\n\t\tmgraph = amgraph * ((confu_p_from + confu_p_to) > mgraph_T_edge)\n\t\tif(restrict_in_degree) {\n\t\t\tamgraph2 = t(apply(rank2, 2, function(x) { rank(-x) <= (1+mgraph_max_confu_deg) }))\n\t\t\tmgraph = mgraph * amgraph2\n\t\t}\n\n\t\tif(mgraph_edge_asym) {\n\t\t\tmgraph = amgraph * (confu_p_from>mgraph_T_edge)\n\t\t\tmgraph = amgraph * (t(confu_p_to)>mgraph_T_edge)\n\t\t}\n\t\tmgraph = mgraph>0 | t(mgraph>0)\n\t} else {\n\t\tmgraph = (confu_p_from + confu_p_to) > mgraph_T_edge\n\t}\n\n\tif(!is.null(mgraph_max_fpcor_outdeg)) {\n\t\tf = apply(abs(log2(mc@mc_fp)),1,max)>0.5\n\t\tfp_cor = tgs_cor(log2(mc@mc_fp[f,]))\n\t\tfp_rnk = t(apply(-fp_cor,1,function(x) rank(x)<=(mgraph_max_fpcor_outdeg+1)))\n\t\tfp_rnk_in = apply(-fp_cor,1,function(x) rank(x)<=(1+mgraph_max_fpcor_indeg))\n\t\tif(!is.null(mgraph_max_confu_deg)) {\n\t\t\t\t mgraph = mgraph * fp_rnk * fp_rnk_in\n\t\t} else {\n\t\t\t\t mgraph = fp_rnk * fp_rnk_in\n\t\t}\n\t}\n\n\tN = nrow(mgraph)\n\te = which(mgraph>0)\n\tn1 = ceiling((e)/N)\n\tn2 = 1+((e-1) %% N)\n\treturn(data.frame(mc1 = n1, mc2 = n2, dist=1))\n}\n\n", "meta": {"hexsha": "90b76254ba4a80b33a0149cea38b8f16a0d897be", "size": 4688, "ext": "r", "lang": "R", "max_stars_repo_path": "R/mgraph_knn.r", "max_stars_repo_name": "ofirr/metacell", "max_stars_repo_head_hexsha": "5981d6d3d1d40d5ce01f4689867939ef7fe82446", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-12-22T15:45:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-22T15:45:41.000Z", "max_issues_repo_path": "R/mgraph_knn.r", "max_issues_repo_name": "lbgbox/metacell", "max_issues_repo_head_hexsha": "95e2f31418840c134e2a84971c01dcaa17983768", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/mgraph_knn.r", "max_forks_repo_name": "lbgbox/metacell", "max_forks_repo_head_hexsha": "95e2f31418840c134e2a84971c01dcaa17983768", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.0615384615, "max_line_length": 147, "alphanum_fraction": 0.715443686, "num_tokens": 1545, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6442251064863695, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.30451456306014574}} {"text": "library(Seurat)\nlibrary(CellChat)\nlibrary(pheatmap)\n\n\nsle<-readRDS('./project/sle_nature_communiation_and_immunnology_merge_include_hd.rds')\n\norig<-sle@meta.data$orig.ident\norig[orig!='csle_heavy'&orig!='sle']<-'hd'\nsle@meta.data$orig.ident<-orig\ntable(sle@meta.data$orig.ident)\n\nhd_group<-subset(sle,orig.ident=='hd')\n\nlabels <- Idents(hd_group)\nhd_data.input <- GetAssayData(hd_group, assay = \"RNA\", slot = \"data\") # normalized data matrix\n#labels <- celltype\nmeta <- data.frame(group = labels, row.names = names(labels)) # create a dataframe of the cell labels\nhd_cellchat <- createCellChat(object = hd_data.input, meta = meta, group.by = \"group\")\nhd_cellchatDB <- CellChatDB.human # use hd_cellchatDB.mouse if running on mouse data\nshowDatabaseCategory(hd_cellchatDB)\nhd_cellchatDB.use <- hd_cellchatDB\nhd_cellchat@DB <- hd_cellchatDB.use\nhd_cellchat <- subsetData(hd_cellchat) \nfuture::plan(\"multicore\", workers = 4)\nhd_cellchat <- identifyOverExpressedGenes(hd_cellchat)\nhd_cellchat <- identifyOverExpressedInteractions(hd_cellchat)\nhd_cellchat <- projectData(hd_cellchat, PPI.human)\noptions(future.globals.maxSize= 1891289600) \nhd_cellchat <- computeCommunProb(hd_cellchat)\n# Filter out the cell-cell communication if there are only few number of cells in certain cell groups\nhd_cellchat <- filterCommunication(hd_cellchat, min.cells = 10)\nhd_cellchat <- computeCommunProbPathway(hd_cellchat)\nhd_cellchat <- aggregateNet(hd_cellchat)\nhd_cellchat <- netAnalysis_computeCentrality(hd_cellchat, slot.name = \"netP\")\n\nsle_group<-subset(sle,orig.ident=='sle')\n\n#sle_group<-sle_group[,sample(1:dim(sle_group)[2],150000)]\ndata.input <- GetAssayData(sle_group, assay = \"RNA\", slot = \"data\") # normalized data matrix\nlabels <- Idents(sle_group)\nmeta <- data.frame(group = labels, row.names = names(labels)) # create a dataframe of the cell labels\ncellchat <- createCellChat(object = data.input, meta = meta, group.by = \"group\")\nCellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data\nshowDatabaseCategory(CellChatDB)\nCellChatDB.use <- CellChatDB\ncellchat@DB <- CellChatDB.use\ncellchat <- subsetData(cellchat) \nfuture::plan(\"multicore\", workers = 4)\ncellchat <- identifyOverExpressedGenes(cellchat)\ncellchat <- identifyOverExpressedInteractions(cellchat)\ncellchat <- projectData(cellchat, PPI.human)\noptions(future.globals.maxSize= 1891289600) \ncellchat <- computeCommunProb(cellchat)\n# Filter out the cell-cell communication if there are only few number of cells in certain cell groups\ncellchat <- filterCommunication(cellchat, min.cells = 10)\ncellchat <- computeCommunProbPathway(cellchat)\ncellchat <- aggregateNet(cellchat)\ncellchat <- netAnalysis_computeCentrality(cellchat, slot.name = \"netP\")\nsle_cellchat<-cellchat\n\ncsle_heavy_group<-subset(sle,orig.ident=='csle_heavy')\n\n#csle_heavy_group<-csle_heavy_group[,sample(1:dim(csle_heavy_group)[2],150000)]\ndata.input <- GetAssayData(csle_heavy_group, assay = \"RNA\", slot = \"data\") # normalized data matrix\nlabels <- Idents(csle_heavy_group)\nmeta <- data.frame(group = labels, row.names = names(labels)) # create a dataframe of the cell labels\ncellchat <- createCellChat(object = data.input, meta = meta, group.by = \"group\")\nCellChatDB <- CellChatDB.human # use CellChatDB.mouse if running on mouse data\nshowDatabaseCategory(CellChatDB)\nCellChatDB.use <- CellChatDB\ncellchat@DB <- CellChatDB.use\ncellchat <- subsetData(cellchat) \nfuture::plan(\"multicore\", workers = 1)\ncellchat <- identifyOverExpressedGenes(cellchat)\ncellchat <- identifyOverExpressedInteractions(cellchat)\ncellchat <- projectData(cellchat, PPI.human)\noptions(future.globals.maxSize= 1891289600) \ncellchat <- computeCommunProb(cellchat)\n# Filter out the cell-cell communication if there are only few number of cells in certain cell groups\ncellchat <- filterCommunication(cellchat, min.cells = 10)\ncellchat <- computeCommunProbPathway(cellchat)\ncellchat <- aggregateNet(cellchat)\ncellchat <- netAnalysis_computeCentrality(cellchat, slot.name = \"netP\")\ncsle_heavy_cellchat<-cellchat\n\nobject.list<-list(hd_cellchat,sle_cellchat,csle_heavy_cellchat)\nsaveRDS(object.list,'./project/merge_sle_immunonolgy_communication_cellchat.rds')\n\n\n", "meta": {"hexsha": "c12af4f36857ee3c2af5d5acf05d3461d1cc306f", "size": 4169, "ext": "r", "lang": "R", "max_stars_repo_path": "4.cellchat_data_processing_part1.r", "max_stars_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_stars_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "4.cellchat_data_processing_part1.r", "max_issues_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_issues_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "4.cellchat_data_processing_part1.r", "max_forks_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_forks_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.8131868132, "max_line_length": 101, "alphanum_fraction": 0.7886783401, "num_tokens": 1142, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442251064863697, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.30451456306014574}} {"text": "> md<-aov(xpos_flips~condition*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.433 0.822 0.535\ndes 1 2.9 2.916 1.674 0.197\ncondition:des 3 2.0 0.670 0.385 0.764\nResiduals 263 458.2 1.742 \n> md<-aov(xpos_flips~condition*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.958 0.444 \nsys 1 26.9 26.900 15.700 9.68e-05 ***\ncondition:sys 5 3.5 0.708 0.413 0.839 \nResiduals 251 430.1 1.713 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 8.2 1.6407 0.964 0.440\nifTW 1 0.4 0.4261 0.250 0.617\ncondition:ifTW 5 5.2 1.0311 0.606 0.695\nResiduals 241 410.0 1.7014 \n20 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.950 0.449134 \nsys 1 26.9 26.900 15.571 0.000104 ***\ndes 1 2.7 2.719 1.574 0.210869 \ncondition:sys 5 3.6 0.717 0.415 0.838066 \ncondition:des 3 2.1 0.711 0.411 0.744909 \nsys:des 1 1.9 1.865 1.079 0.299843 \ncondition:sys:des 3 3.5 1.168 0.676 0.567500 \nResiduals 243 419.8 1.728 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 8.2 1.6407 0.950 0.449\nifTW 1 0.4 0.4261 0.247 0.620\ndes 1 2.0 1.9963 1.156 0.283\ncondition:ifTW 5 5.2 1.0441 0.605 0.696\ncondition:des 3 1.8 0.5957 0.345 0.793\nifTW:des 1 0.6 0.5619 0.325 0.569\ncondition:ifTW:des 3 3.4 1.1451 0.663 0.575\nResiduals 233 402.2 1.7262 \n20 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*ifTW*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.641 0.993 0.422632 \nifTW 1 0.4 0.426 0.258 0.612027 \nsys 1 20.7 20.651 12.501 0.000492 ***\ncondition:ifTW 5 5.2 1.046 0.633 0.674567 \ncondition:sys 5 3.5 0.695 0.421 0.834190 \nifTW:sys 1 3.3 3.305 2.001 0.158590 \ncondition:ifTW:sys 5 4.3 0.853 0.516 0.763954 \nResiduals 229 378.3 1.652 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.641 0.963 0.441410 \nifTW 1 0.4 0.426 0.250 0.617514 \nsys 1 20.7 20.651 12.122 0.000605 ***\ndes 1 2.1 2.097 1.231 0.268438 \ncondition:ifTW 5 5.3 1.061 0.623 0.682443 \ncondition:sys 5 3.5 0.703 0.413 0.839794 \nifTW:sys 1 3.3 3.278 1.924 0.166833 \ncondition:des 3 1.8 0.604 0.355 0.785736 \nifTW:des 1 0.5 0.544 0.319 0.572678 \nsys:des 1 1.9 1.861 1.092 0.297125 \ncondition:ifTW:sys 5 4.3 0.867 0.509 0.769284 \ncondition:ifTW:des 3 4.0 1.322 0.776 0.508396 \ncondition:sys:des 3 3.1 1.037 0.608 0.610191 \nifTW:sys:des 1 0.0 0.017 0.010 0.920635 \ncondition:ifTW:sys:des 3 1.8 0.613 0.360 0.782037 \nResiduals 213 362.9 1.704 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n\n> md<-aov(xpos_flips~condition*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.433 0.822 0.535\ndes 1 2.9 2.916 1.674 0.197\ncondition:des 3 2.0 0.670 0.385 0.764\nResiduals 263 458.2 1.742 \n> md<-aov(MAD~condition*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.211 0.00791 **\nsys 1 4500 4500 0.076 0.78279 \ncondition:sys 5 136328 27266 0.461 0.80471 \nResiduals 251 14829688 59082 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 836643 167329 2.789 0.0181 *\nifTW 1 40387 40387 0.673 0.4128 \ncondition:ifTW 5 134722 26944 0.449 0.8138 \nResiduals 241 14461071 60004 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n> md<-aov(MAD~condition*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.195 0.0082 **\nsys 1 4500 4500 0.076 0.7833 \ndes 1 8502 8502 0.143 0.7055 \ncondition:sys 5 136415 27283 0.459 0.8062 \ncondition:des 3 102021 34007 0.573 0.6335 \nsys:des 1 22303 22303 0.376 0.5405 \ncondition:sys:des 3 267067 89022 1.499 0.2154 \nResiduals 243 14429708 59382 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 836643 167329 2.814 0.0173 *\nifTW 1 40387 40387 0.679 0.4107 \ndes 1 1398 1398 0.024 0.8783 \ncondition:ifTW 5 135405 27081 0.455 0.8091 \ncondition:des 3 87179 29060 0.489 0.6905 \nifTW:des 1 304499 304499 5.121 0.0246 *\ncondition:ifTW:des 3 212224 70741 1.190 0.3144 \nResiduals 233 13855087 59464 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n> \n> md<-aov(MAD~condition*ifTW*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 836643 167329 2.733 0.0202 *\nifTW 1 40387 40387 0.660 0.4176 \nsys 1 2389 2389 0.039 0.8436 \ncondition:ifTW 5 134559 26912 0.439 0.8206 \ncondition:sys 5 153175 30635 0.500 0.7759 \nifTW:sys 1 49949 49949 0.816 0.3674 \ncondition:ifTW:sys 5 233380 46676 0.762 0.5779 \nResiduals 229 14022341 61233 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 836643 167329 2.812 0.0176 *\nifTW 1 40387 40387 0.679 0.4110 \nsys 1 2389 2389 0.040 0.8414 \ndes 1 1370 1370 0.023 0.8795 \ncondition:ifTW 5 135234 27047 0.455 0.8097 \ncondition:sys 5 153273 30655 0.515 0.7647 \nifTW:sys 1 50052 50052 0.841 0.3601 \ncondition:des 3 91519 30506 0.513 0.6740 \nifTW:des 1 314497 314497 5.285 0.0225 *\nsys:des 1 32193 32193 0.541 0.4628 \ncondition:ifTW:sys 5 240778 48156 0.809 0.5442 \ncondition:ifTW:des 3 208785 69595 1.170 0.3223 \ncondition:sys:des 3 314045 104682 1.759 0.1560 \nifTW:sys:des 1 92 92 0.002 0.9687 \ncondition:ifTW:sys:des 3 376888 125629 2.111 0.0998 .\nResiduals 213 12674678 59506 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingnessmd<-aov(idle_time~condition*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.698 0.625\ndes 1 432 431.9 0.608 0.436\ncondition:des 3 678 225.9 0.318 0.812\nResiduals 263 186837 710.4 \n> md<-aov(idle_time~condition*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.773 0.569779 \nsys 1 8035 8035 11.427 0.000839 ***\ncondition:sys 5 1798 360 0.511 0.767663 \nResiduals 251 176498 703 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2256 451.3 0.635 0.673\nifTW 1 426 426.2 0.599 0.440\ncondition:ifTW 5 3707 741.5 1.043 0.393\nResiduals 241 171373 711.1 \n20 observations deleted due to missingness\n> md<-aov(idle_time~condition*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.754 0.583578 \nsys 1 8035 8035 11.147 0.000974 ***\ndes 1 495 495 0.686 0.408198 \ncondition:sys 5 1808 362 0.502 0.774767 \ncondition:des 3 467 156 0.216 0.885322 \nsys:des 1 143 143 0.198 0.656555 \ncondition:sys:des 3 231 77 0.107 0.956082 \nResiduals 243 175152 721 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2256 451.3 0.621 0.684\nifTW 1 426 426.2 0.587 0.444\ndes 1 307 306.6 0.422 0.517\ncondition:ifTW 5 3784 756.8 1.042 0.394\ncondition:des 3 512 170.8 0.235 0.872\nifTW:des 1 504 503.8 0.694 0.406\ncondition:ifTW:des 3 732 243.9 0.336 0.799\nResiduals 233 169241 726.4 \n20 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*ifTW*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2256 451 0.663 0.65158 \nifTW 1 426 426 0.627 0.42944 \nsys 1 6653 6653 9.780 0.00199 **\ncondition:ifTW 5 3801 760 1.117 0.35181 \ncondition:sys 5 1545 309 0.454 0.81007 \nifTW:sys 1 6457 6457 9.493 0.00232 **\ncondition:ifTW:sys 5 849 170 0.249 0.93978 \nResiduals 229 155776 680 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2256 451 0.632 0.67558 \nifTW 1 426 426 0.597 0.44066 \nsys 1 6653 6653 9.315 0.00256 **\ndes 1 329 329 0.461 0.49794 \ncondition:ifTW 5 3883 777 1.087 0.36836 \ncondition:sys 5 1555 311 0.435 0.82359 \nifTW:sys 1 6441 6441 9.020 0.00299 **\ncondition:des 3 480 160 0.224 0.87964 \nifTW:des 1 460 460 0.644 0.42302 \nsys:des 1 176 176 0.247 0.61982 \ncondition:ifTW:sys 5 858 172 0.240 0.94423 \ncondition:ifTW:des 3 893 298 0.417 0.74122 \ncondition:sys:des 3 203 68 0.095 0.96297 \nifTW:sys:des 1 22 22 0.031 0.86055 \ncondition:ifTW:sys:des 3 1014 338 0.473 0.70108 \nResiduals 213 152113 714 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n20 observations deleted due to missingness\n\nmd<-aov(idle_time~condition*noPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.699 0.624\nnoPP 1 290 289.6 0.408 0.523\ncondition:noPP 5 2517 503.4 0.710 0.617\nResiduals 261 185139 709.3 \n> md<-aov(idle_time~condition*noTGG,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.693 0.629\nnoTGG 1 427 426.7 0.596 0.441\ncondition:noTGG 5 732 146.4 0.205 0.960\nResiduals 261 186787 715.7 \n> md<-aov(idle_time~condition*noTMD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2481 496.1 0.703 0.6219 \nnoTMD 1 1938 1937.6 2.744 0.0988 .\ncondition:noTMD 5 1708 341.7 0.484 0.7882 \nResiduals 261 184300 706.1 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2481 496 0.730 0.60120 \nnoKD 1 9367 9367 13.791 0.00025 ***\ncondition:noKD 5 1296 259 0.382 0.86118 \nResiduals 261 177283 679 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.706 0.620\nnoDPP 1 1439 1439.3 2.048 0.154\ncondition:noDPP 5 3052 610.5 0.869 0.503\nResiduals 261 183454 702.9 \n> md<-aov(idle_time~condition*noKMT,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.694 0.628\nnoKMT 1 54 54.3 0.076 0.783\ncondition:noKMT 5 1323 264.6 0.370 0.869\nResiduals 261 186569 714.8 \n\n> md<-aov(xpos_flips~condition*noPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.433 0.834 0.527\nnoPP 1 4.1 4.122 2.398 0.123\ncondition:noPP 5 10.4 2.085 1.213 0.303\nResiduals 261 448.6 1.719 \n> md<-aov(xpos_flips~condition*noTGG,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.433 0.830 0.5291 \nnoTGG 1 5.0 4.969 2.879 0.0909 .\ncondition:noTGG 5 7.7 1.548 0.897 0.4837 \nResiduals 261 450.4 1.726 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTMD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.821 0.536\nnoTMD 1 0.1 0.0901 0.052 0.820\ncondition:noTMD 5 7.5 1.4935 0.856 0.512\nResiduals 261 455.6 1.7454 \n> md<-aov(xpos_flips~condition*noKD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.433 0.867 0.503 \nnoKD 1 28.4 28.429 17.210 4.53e-05 ***\ncondition:noKD 5 3.5 0.709 0.429 0.828 \nResiduals 261 431.1 1.652 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.817 0.538\nnoDPP 1 1.2 1.2198 0.696 0.405\ncondition:noDPP 5 4.4 0.8849 0.505 0.772\nResiduals 261 457.5 1.7528 \n> md<-aov(xpos_flips~condition*noKMT,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.816 0.539\nnoKMT 1 1.0 1.0372 0.591 0.443\ncondition:noKMT 5 3.7 0.7332 0.417 0.836\nResiduals 261 458.4 1.7564 \n\n> md<-aov(MAD~condition*noPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.286 0.00677 **\nnoPP 1 10144 10144 0.180 0.67132 \ncondition:noPP 5 518457 103691 1.845 0.10451 \nResiduals 261 14670276 56208 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTGG,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.219 0.00774 **\nnoTGG 1 10721 10721 0.187 0.66595 \ncondition:noTGG 5 208150 41630 0.725 0.60497 \nResiduals 261 14980006 57395 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTMD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.223 0.00767 **\nnoTMD 1 1 1 0.000 0.99629 \ncondition:noTMD 5 239842 47968 0.837 0.52447 \nResiduals 261 14959033 57314 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.218 0.00774 **\nnoKD 1 6323 6323 0.110 0.74023 \ncondition:noKD 5 211077 42215 0.735 0.59746 \nResiduals 261 14981477 57400 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.237 0.00746 **\nnoDPP 1 2318 2318 0.041 0.84043 \ncondition:noDPP 5 303761 60752 1.065 0.38036 \nResiduals 261 14892797 57061 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.180 0.00835 **\nnoKMT 1 111 111 0.002 0.96520 \ncondition:noKMT 5 36710 7342 0.126 0.98636 \nResiduals 261 15162056 58092 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\n> md<-aov(idle_time~condition*noPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.925 0.465439 \nnoPP 1 132 132 0.225 0.635589 \nsys 1 7903 7903 13.442 0.000303 ***\ncondition:noPP 5 2475 495 0.842 0.521104 \ncondition:sys 5 1786 357 0.607 0.694267 \nnoPP:sys 1 32965 32965 56.071 1.34e-12 ***\ncondition:noPP:sys 5 561 112 0.191 0.965843 \nResiduals 239 140509 588 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTGG*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.768 0.573564 \nnoTGG 1 305 305 0.431 0.512293 \nsys 1 8022 8022 11.332 0.000888 ***\ncondition:noTGG 5 736 147 0.208 0.958997 \ncondition:sys 5 1817 363 0.513 0.766164 \nnoTGG:sys 1 5287 5287 7.469 0.006746 ** \ncondition:noTGG:sys 5 978 196 0.276 0.925814 \nResiduals 239 169186 708 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTMD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.780 0.56526 \nnoTMD 1 1749 1749 2.508 0.11462 \nsys 1 7641 7641 10.955 0.00108 **\ncondition:noTMD 5 1800 360 0.516 0.76395 \ncondition:sys 5 1954 391 0.560 0.73029 \nnoTMD:sys 1 4597 4597 6.591 0.01086 * \ncondition:noTMD:sys 5 1889 378 0.542 0.74454 \nResiduals 239 166699 697 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.793 0.555347 \nnoKD 1 9940 9940 14.503 0.000178 ***\nsys 1 6149 6149 8.972 0.003030 ** \ncondition:noKD 5 1309 262 0.382 0.860861 \ncondition:sys 5 1688 338 0.492 0.781763 \nnoKD:sys 1 2131 2131 3.110 0.079105 . \ncondition:noKD:sys 5 1311 262 0.383 0.860492 \nResiduals 239 163803 685 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noDPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.782 0.563331 \nnoDPP 1 1245 1245 1.791 0.182050 \nsys 1 8787 8787 12.641 0.000455 ***\ncondition:noDPP 5 3377 675 0.972 0.435814 \ncondition:sys 5 1756 351 0.505 0.772156 \nnoDPP:sys 1 4069 4069 5.854 0.016292 * \ncondition:noDPP:sys 5 968 194 0.278 0.924685 \nResiduals 239 166129 695 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKMT*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.799 0.551589 \nnoKMT 1 196 196 0.288 0.592016 \nsys 1 7848 7848 11.527 0.000803 ***\ncondition:noKMT 5 1819 364 0.534 0.750260 \ncondition:sys 5 1678 336 0.493 0.781404 \nnoKMT:sys 1 10828 10828 15.903 8.87e-05 ***\ncondition:noKMT:sys 5 1237 247 0.363 0.873213 \nResiduals 239 162725 681 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 1.010 0.41260 \nnoPP 1 4.5 4.531 2.787 0.09634 . \nsys 1 30.4 30.359 18.672 2.28e-05 ***\ncondition:noPP 5 10.1 2.026 1.246 0.28815 \ncondition:sys 5 5.0 0.991 0.610 0.69252 \nnoPP:sys 1 16.5 16.450 10.117 0.00166 ** \ncondition:noPP:sys 5 5.5 1.094 0.673 0.64457 \nResiduals 239 388.6 1.626 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTGG*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.950 0.449521 \nnoTGG 1 5.1 5.057 2.926 0.088485 . \nsys 1 27.0 26.998 15.619 0.000102 ***\ncondition:noTGG 5 7.7 1.541 0.892 0.487238 \ncondition:sys 5 3.5 0.708 0.410 0.841965 \nnoTGG:sys 1 1.6 1.571 0.909 0.341456 \ncondition:noTGG:sys 5 2.5 0.500 0.289 0.918533 \nResiduals 239 413.1 1.729 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTMD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.939 0.456226 \nnoTMD 1 0.1 0.079 0.045 0.831455 \nsys 1 27.2 27.152 15.534 0.000106 ***\ncondition:noTMD 5 7.2 1.449 0.829 0.530155 \ncondition:sys 5 3.9 0.777 0.445 0.816868 \nnoTMD:sys 1 1.4 1.417 0.811 0.368784 \ncondition:noTMD:sys 5 3.0 0.592 0.339 0.888968 \nResiduals 239 417.8 1.748 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.995 0.42130 \nnoKD 1 28.7 28.679 17.386 4.27e-05 ***\nsys 1 21.0 21.035 12.752 0.00043 ***\ncondition:noKD 5 3.1 0.617 0.374 0.86632 \ncondition:sys 5 3.4 0.678 0.411 0.84070 \nnoKD:sys 1 3.1 3.086 1.871 0.17268 \ncondition:noKD:sys 5 7.0 1.396 0.846 0.51825 \nResiduals 239 394.2 1.650 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noDPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.943 0.454 \nnoDPP 1 1.3 1.335 0.767 0.382 \nsys 1 28.4 28.437 16.340 7.14e-05 ***\ncondition:noDPP 5 4.9 0.979 0.562 0.729 \ncondition:sys 5 3.5 0.708 0.407 0.844 \nnoDPP:sys 1 3.8 3.789 2.177 0.141 \ncondition:noDPP:sys 5 2.5 0.509 0.293 0.917 \nResiduals 239 416.0 1.740 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKMT*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.949 0.449708 \nnoKMT 1 1.1 1.057 0.612 0.434967 \nsys 1 25.8 25.845 14.947 0.000143 ***\ncondition:noKMT 5 3.6 0.713 0.412 0.840169 \ncondition:sys 5 3.6 0.718 0.415 0.837873 \nnoKMT:sys 1 10.6 10.574 6.116 0.014096 * \ncondition:noKMT:sys 5 2.6 0.523 0.303 0.911045 \nResiduals 239 413.2 1.729 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.172 0.0086 **\nnoPP 1 1223 1223 0.020 0.8864 \nsys 1 3978 3978 0.067 0.7967 \ncondition:noPP 5 466643 93329 1.561 0.1720 \ncondition:sys 5 82827 16565 0.277 0.9254 \nnoPP:sys 1 39696 39696 0.664 0.4160 \ncondition:noPP:sys 5 82793 16559 0.277 0.9255 \nResiduals 239 14293356 59805 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTGG*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.218 0.00786 **\nnoTGG 1 847 847 0.014 0.90468 \nsys 1 4517 4517 0.077 0.78217 \ncondition:noTGG 5 183503 36701 0.623 0.68270 \ncondition:sys 5 137470 27494 0.466 0.80108 \nnoTGG:sys 1 2 2 0.000 0.99564 \ncondition:noTGG:sys 5 555327 111065 1.884 0.09778 . \nResiduals 239 14088851 58949 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTMD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.262 0.0072 **\nnoTMD 1 3147 3147 0.054 0.8162 \nsys 1 4098 4098 0.070 0.7909 \ncondition:noTMD 5 227332 45466 0.782 0.5636 \ncondition:sys 5 132044 26409 0.454 0.8100 \nnoTMD:sys 1 215178 215178 3.701 0.0556 . \ncondition:noTMD:sys 5 491590 98318 1.691 0.1375 \nResiduals 239 13897127 58147 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKD*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.179 0.00849 **\nnoKD 1 15611 15611 0.262 0.60950 \nsys 1 6791 6791 0.114 0.73615 \ncondition:noKD 5 197522 39504 0.662 0.65267 \ncondition:sys 5 178741 35748 0.599 0.70074 \nnoKD:sys 1 6829 6829 0.114 0.73545 \ncondition:noKD:sys 5 302126 60425 1.013 0.41089 \nResiduals 239 14262896 59677 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noDPP*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.149 0.00899 **\nnoDPP 1 487 487 0.008 0.92842 \nsys 1 4859 4859 0.081 0.77666 \ncondition:noDPP 5 310471 62094 1.031 0.40012 \ncondition:sys 5 148269 29654 0.492 0.78191 \nnoDPP:sys 1 55 55 0.001 0.97592 \ncondition:noDPP:sys 5 108699 21740 0.361 0.87491 \nResiduals 239 14397676 60241 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKMT*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 948475 189695 3.259 0.00726 **\nnoKMT 1 19765 19765 0.340 0.56065 \nsys 1 9110 9110 0.156 0.69275 \ncondition:noKMT 5 64766 12953 0.223 0.95259 \ncondition:sys 5 115338 23068 0.396 0.85115 \nnoKMT:sys 1 566093 566093 9.725 0.00204 **\ncondition:noKMT:sys 5 282520 56504 0.971 0.43640 \nResiduals 239 13912924 58213 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.686 0.635\nnoPP 1 290 289.6 0.400 0.528\ndes 1 439 439.1 0.607 0.437\ncondition:noPP 5 2558 511.6 0.707 0.619\ncondition:des 3 634 211.3 0.292 0.831\nnoPP:des 1 654 654.0 0.904 0.343\ncondition:noPP:des 3 340 113.3 0.157 0.925\nResiduals 253 183032 723.4 \n> md<-aov(idle_time~condition*noTGG*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.679 0.640\nnoTGG 1 427 426.7 0.584 0.446\ndes 1 445 444.6 0.608 0.436\ncondition:noTGG 5 733 146.6 0.201 0.962\ncondition:des 3 664 221.5 0.303 0.823\nnoTGG:des 1 23 22.8 0.031 0.860\ncondition:noTGG:des 3 672 224.0 0.306 0.821\nResiduals 253 184982 731.2 \n> md<-aov(idle_time~condition*noTMD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.687 0.633\nnoTMD 1 1938 1937.6 2.684 0.103\ndes 1 465 464.8 0.644 0.423\ncondition:noTMD 5 1698 339.7 0.471 0.798\ncondition:des 3 656 218.7 0.303 0.823\nnoTMD:des 1 32 32.0 0.044 0.833\ncondition:noTMD:des 3 519 173.1 0.240 0.869\nResiduals 253 182638 721.9 \n> md<-aov(idle_time~condition*noKD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2481 496 0.718 0.610475 \nnoKD 1 9367 9367 13.556 0.000283 ***\ndes 1 345 345 0.499 0.480509 \ncondition:noKD 5 1312 262 0.380 0.862416 \ncondition:des 3 741 247 0.357 0.783910 \nnoKD:des 1 279 279 0.404 0.525437 \ncondition:noKD:des 3 1073 358 0.517 0.670671 \nResiduals 253 174829 691 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.692 0.630\nnoDPP 1 1439 1439.3 2.007 0.158\ndes 1 453 452.8 0.631 0.428\ncondition:noDPP 5 3034 606.7 0.846 0.518\ncondition:des 3 677 225.6 0.314 0.815\nnoDPP:des 1 144 144.4 0.201 0.654\ncondition:noDPP:des 3 723 241.1 0.336 0.799\nResiduals 253 181476 717.3 \n> md<-aov(idle_time~condition*noKMT*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2481 496.1 0.681 0.638\nnoKMT 1 54 54.3 0.075 0.785\ndes 1 427 426.6 0.586 0.445\ncondition:noKMT 5 1300 260.0 0.357 0.877\ncondition:des 3 708 236.0 0.324 0.808\nnoKMT:des 1 13 12.8 0.018 0.895\ncondition:noKMT:des 3 1168 389.2 0.534 0.659\nResiduals 253 184276 728.4 \n> md<-aov(xpos_flips~condition*noPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.433 0.821 0.536\nnoPP 1 4.1 4.122 2.361 0.126\ndes 1 2.8 2.847 1.631 0.203\ncondition:noPP 5 10.4 2.087 1.196 0.312\ncondition:des 3 2.0 0.680 0.390 0.761\nnoPP:des 1 0.2 0.188 0.108 0.743\ncondition:noPP:des 3 1.8 0.608 0.349 0.790\nResiduals 253 441.7 1.746 \n> md<-aov(xpos_flips~condition*noTGG*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.433 0.824 0.5337 \nnoTGG 1 5.0 4.969 2.857 0.0922 .\ndes 1 2.8 2.807 1.614 0.2052 \ncondition:noTGG 5 7.6 1.522 0.875 0.4983 \ncondition:des 3 2.0 0.680 0.391 0.7597 \nnoTGG:des 1 0.4 0.367 0.211 0.6464 \ncondition:noTGG:des 3 5.3 1.755 1.009 0.3893 \nResiduals 253 440.1 1.739 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTMD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.809 0.544\nnoTMD 1 0.1 0.0901 0.051 0.822\ndes 1 2.9 2.8993 1.638 0.202\ncondition:noTMD 5 7.3 1.4682 0.829 0.530\ncondition:des 3 2.0 0.6761 0.382 0.766\nnoTMD:des 1 0.0 0.0009 0.000 0.982\ncondition:noTMD:des 3 2.9 0.9772 0.552 0.647\nResiduals 253 447.8 1.7701 \n> md<-aov(xpos_flips~condition*noKD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.433 0.864 0.506 \nnoKD 1 28.4 28.429 17.141 4.73e-05 ***\ndes 1 2.5 2.516 1.517 0.219 \ncondition:noKD 5 3.5 0.701 0.423 0.833 \ncondition:des 3 2.3 0.763 0.460 0.710 \nnoKD:des 1 1.8 1.834 1.106 0.294 \ncondition:noKD:des 3 4.9 1.646 0.992 0.397 \nResiduals 253 419.6 1.659 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.808 0.545\nnoDPP 1 1.2 1.2198 0.687 0.408\ndes 1 3.0 2.9664 1.672 0.197\ncondition:noDPP 5 4.3 0.8695 0.490 0.784\ncondition:des 3 2.0 0.6785 0.382 0.766\nnoDPP:des 1 1.2 1.1974 0.675 0.412\ncondition:noDPP:des 3 2.4 0.8140 0.459 0.711\nResiduals 253 448.9 1.7743 \n> md<-aov(xpos_flips~condition*noKMT*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.2 1.4328 0.804 0.548\nnoKMT 1 1.0 1.0372 0.582 0.446\ndes 1 2.9 2.8565 1.602 0.207\ncondition:noKMT 5 3.6 0.7209 0.404 0.846\ncondition:des 3 2.0 0.6800 0.381 0.767\nnoKMT:des 1 0.0 0.0019 0.001 0.974\ncondition:noKMT:des 3 2.5 0.8323 0.467 0.706\nResiduals 253 451.1 1.7829 \n> md<-aov(MAD~condition*noPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.240 0.00745 **\nnoPP 1 10144 10144 0.178 0.67351 \ndes 1 6024 6024 0.106 0.74540 \ncondition:noPP 5 517608 103522 1.816 0.11014 \ncondition:des 3 108108 36036 0.632 0.59490 \nnoPP:des 1 102752 102752 1.802 0.18062 \ncondition:noPP:des 3 31310 10437 0.183 0.90784 \nResiduals 253 14422931 57008 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTGG*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.154 0.00883 **\nnoTGG 1 10721 10721 0.183 0.66915 \ndes 1 6101 6101 0.104 0.74716 \ncondition:noTGG 5 208338 41668 0.711 0.61541 \ncondition:des 3 107749 35916 0.613 0.60703 \nnoTGG:des 1 10420 10420 0.178 0.67355 \ncondition:noTGG:des 3 36005 12002 0.205 0.89296 \nResiduals 253 14819543 58575 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTMD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.153 0.00885 **\nnoTMD 1 1 1 0.000 0.99633 \ndes 1 5870 5870 0.100 0.75186 \ncondition:noTMD 5 240286 48057 0.820 0.53619 \ncondition:des 3 105981 35327 0.603 0.61363 \nnoTMD:des 1 15334 15334 0.262 0.60938 \ncondition:noTMD:des 3 8438 2813 0.048 0.98604 \nResiduals 253 14822965 58589 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.198 0.0081 **\nnoKD 1 6323 6323 0.109 0.7410 \ndes 1 5593 5593 0.097 0.7559 \ncondition:noKD 5 210103 42021 0.727 0.6034 \ncondition:des 3 113099 37700 0.653 0.5819 \nnoKD:des 1 86536 86536 1.498 0.2221 \ncondition:noKD:des 3 163072 54357 0.941 0.4214 \nResiduals 253 14614150 57763 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.188 0.00826 **\nnoDPP 1 2318 2318 0.040 0.84164 \ndes 1 5963 5963 0.103 0.74864 \ncondition:noDPP 5 304351 60870 1.050 0.38853 \ncondition:des 3 109018 36339 0.627 0.59807 \nnoDPP:des 1 21141 21141 0.365 0.54636 \ncondition:noDPP:des 3 96217 32072 0.554 0.64623 \nResiduals 253 14659869 57944 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 923625 184725 3.135 0.00917 **\nnoKMT 1 111 111 0.002 0.96545 \ndes 1 5839 5839 0.099 0.75319 \ncondition:noKMT 5 36866 7373 0.125 0.98666 \ncondition:des 3 104853 34951 0.593 0.62003 \nnoKMT:des 1 47926 47926 0.813 0.36800 \ncondition:noKMT:des 3 94734 31578 0.536 0.65809 \nResiduals 253 14908548 58927 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\n\n> md<-aov(xpos_flips~condition*eht_0,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.930 0.46189 \neht_0 1 16.1 16.127 9.138 0.00276 **\ncondition:eht_0 5 1.4 0.282 0.160 0.97689 \nResiduals 251 443.0 1.765 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.935 0.45914 \neht_0 1 16.1 16.127 9.181 0.00271 **\ndes 1 2.7 2.682 1.527 0.21777 \ncondition:eht_0 5 1.4 0.282 0.160 0.97670 \ncondition:des 3 2.1 0.709 0.404 0.75045 \neht_0:des 1 1.0 0.990 0.564 0.45356 \ncondition:eht_0:des 3 10.3 3.443 1.960 0.12061 \nResiduals 243 426.8 1.757 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*eht_0*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.962 0.442043 \neht_0 1 16.1 16.127 9.446 0.002361 ** \nsys 1 22.8 22.792 13.350 0.000318 ***\ncondition:eht_0 5 1.4 0.288 0.169 0.973898 \ncondition:sys 5 3.3 0.664 0.389 0.856105 \neht_0:sys 1 2.3 2.338 1.370 0.243020 \ncondition:eht_0:sys 5 6.5 1.290 0.756 0.582543 \nResiduals 239 408.0 1.707 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 8.2 1.642 0.948 0.450629 \neht_0 1 16.1 16.127 9.315 0.002549 ** \nsys 1 22.8 22.792 13.165 0.000354 ***\ndes 1 2.8 2.773 1.602 0.206989 \ncondition:eht_0 5 1.4 0.287 0.166 0.974904 \ncondition:sys 5 3.4 0.672 0.388 0.856737 \neht_0:sys 1 2.3 2.276 1.314 0.252816 \ncondition:des 3 2.1 0.711 0.411 0.745601 \neht_0:des 1 1.2 1.246 0.720 0.397109 \nsys:des 1 2.2 2.187 1.263 0.262270 \ncondition:eht_0:sys 5 6.6 1.313 0.758 0.580654 \ncondition:eht_0:des 3 9.8 3.258 1.882 0.133515 \ncondition:sys:des 3 2.4 0.815 0.471 0.702786 \neht_0:sys:des 1 0.4 0.422 0.244 0.621934 \ncondition:eht_0:sys:des 3 0.9 0.296 0.171 0.916111 \nResiduals 223 386.1 1.731 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n\n> md<-aov(idle_time~condition*eht_0,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.834 0.527 \neht_0 1 19275 19275 29.566 1.28e-07 ***\ncondition:eht_0 5 3424 685 1.051 0.389 \nResiduals 251 163632 652 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.831 0.529 \neht_0 1 19275 19275 29.460 1.38e-07 ***\ndes 1 503 503 0.769 0.382 \ncondition:eht_0 5 3425 685 1.047 0.391 \ncondition:des 3 477 159 0.243 0.866 \neht_0:des 1 1079 1079 1.650 0.200 \ncondition:eht_0:des 3 2584 861 1.317 0.270 \nResiduals 243 158987 654 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*eht_0*sys,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.870 0.50158 \neht_0 1 19275 19275 30.854 7.39e-08 ***\nsys 1 5600 5600 8.964 0.00304 ** \ncondition:eht_0 5 3432 686 1.099 0.36168 \ncondition:sys 5 1436 287 0.460 0.80607 \neht_0:sys 1 4397 4397 7.038 0.00851 ** \ncondition:eht_0:sys 5 2889 578 0.925 0.46552 \nResiduals 239 149303 625 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*sys*des,\n+ data=avg_measures.1a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2719 544 0.848 0.51701 \neht_0 1 19275 19275 30.060 1.13e-07 ***\nsys 1 5600 5600 8.734 0.00346 ** \ndes 1 522 522 0.815 0.36772 \ncondition:eht_0 5 3432 686 1.071 0.37749 \ncondition:sys 5 1445 289 0.451 0.81250 \neht_0:sys 1 4360 4360 6.799 0.00973 ** \ncondition:des 3 475 158 0.247 0.86338 \neht_0:des 1 1244 1244 1.940 0.16503 \nsys:des 1 223 223 0.348 0.55567 \ncondition:eht_0:sys 5 2975 595 0.928 0.46364 \ncondition:eht_0:des 3 2537 846 1.319 0.26901 \ncondition:sys:des 3 147 49 0.076 0.97273 \neht_0:sys:des 1 73 73 0.113 0.73660 \ncondition:eht_0:sys:des 3 1037 346 0.539 0.65603 \nResiduals 223 142986 641 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n\n md<-aov(xpos_flips~condition*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.902 0.481\ndes 1 0.0 0.0077 0.004 0.947\ncondition:des 3 5.1 1.6867 0.987 0.400\nResiduals 227 388.0 1.7094 \n> md<-aov(MAD~condition*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.872 0.00222 **\nsys 1 30968 30968 0.248 0.61897 \ncondition:sys 5 383001 76600 0.614 0.68964 \nResiduals 215 26843526 124854 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2153218 430644 3.899 0.00213 **\nifTW 1 20477 20477 0.185 0.66725 \ncondition:ifTW 5 1168487 233697 2.116 0.06490 . \nResiduals 206 22754927 110461 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(MAD~condition*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.895 0.00214 **\nsys 1 30968 30968 0.250 0.61795 \ndes 1 98275 98275 0.792 0.37459 \ncondition:sys 5 385941 77188 0.622 0.68324 \ncondition:des 3 305895 101965 0.822 0.48331 \nsys:des 1 20572 20572 0.166 0.68434 \ncondition:sys:des 3 723439 241146 1.943 0.12379 \nResiduals 207 25692407 124118 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2153218 430644 3.883 0.00223 **\nifTW 1 20477 20477 0.185 0.66791 \ndes 1 69766 69766 0.629 0.42868 \ncondition:ifTW 5 1168575 233715 2.107 0.06611 . \ncondition:des 3 341518 113839 1.026 0.38201 \nifTW:des 1 6764 6764 0.061 0.80521 \ncondition:ifTW:des 3 375055 125018 1.127 0.33921 \nResiduals 198 21961736 110918 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> \n> md<-aov(MAD~condition*ifTW*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2153218 430644 3.814 0.00256 **\nifTW 1 20477 20477 0.181 0.67067 \nsys 1 100555 100555 0.891 0.34647 \ncondition:ifTW 5 1167924 233585 2.069 0.07094 . \ncondition:sys 5 549892 109978 0.974 0.43477 \nifTW:sys 1 559 559 0.005 0.94397 \ncondition:ifTW:sys 5 202110 40422 0.358 0.87663 \nResiduals 194 21902374 112899 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(MAD~condition*ifTW*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2153218 430644 3.844 0.00248 **\nifTW 1 20477 20477 0.183 0.66951 \nsys 1 100555 100555 0.898 0.34471 \ndes 1 68971 68971 0.616 0.43371 \ncondition:ifTW 5 1168017 233603 2.085 0.06932 . \ncondition:sys 5 551738 110348 0.985 0.42835 \nifTW:sys 1 770 770 0.007 0.93404 \ncondition:des 3 341152 113717 1.015 0.38734 \nifTW:des 1 4957 4957 0.044 0.83363 \nsys:des 1 36828 36828 0.329 0.56713 \ncondition:ifTW:sys 5 202763 40553 0.362 0.87399 \ncondition:ifTW:des 3 365682 121894 1.088 0.35556 \ncondition:sys:des 3 728333 242778 2.167 0.09356 . \nifTW:sys:des 1 28079 28079 0.251 0.61724 \ncondition:ifTW:sys:des 3 384586 128195 1.144 0.33265 \nResiduals 178 19940985 112028 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.902 0.481\ndes 1 0.0 0.0077 0.004 0.947\ncondition:des 3 5.1 1.6867 0.987 0.400\nResiduals 227 388.0 1.7094 \n> md<-aov(xpos_flips~condition*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.857 0.51106 \nsys 1 12.1 12.075 7.153 0.00806 **\ncondition:sys 5 7.1 1.416 0.839 0.52355 \nResiduals 215 362.9 1.688 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.77 1.153 0.759 0.580160 \nifTW 1 17.32 17.319 11.404 0.000876 ***\ncondition:ifTW 5 14.81 2.962 1.951 0.087441 . \nResiduals 206 312.85 1.519 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.859 0.50992 \nsys 1 12.1 12.075 7.168 0.00802 **\ndes 1 0.0 0.001 0.001 0.97706 \ncondition:sys 5 7.1 1.416 0.841 0.52232 \ncondition:des 3 6.2 2.073 1.230 0.29966 \nsys:des 1 0.7 0.679 0.403 0.52607 \ncondition:sys:des 3 7.3 2.442 1.449 0.22957 \nResiduals 207 348.7 1.685 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.77 1.153 0.770 0.572419 \nifTW 1 17.32 17.319 11.565 0.000813 ***\ndes 1 0.13 0.131 0.088 0.767449 \ncondition:ifTW 5 14.79 2.958 1.975 0.083863 . \ncondition:des 3 4.20 1.399 0.934 0.425271 \nifTW:des 1 7.90 7.898 5.274 0.022697 * \ncondition:ifTW:des 3 4.13 1.376 0.919 0.432790 \nResiduals 198 296.52 1.498 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*ifTW*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.77 1.153 0.747 0.589132 \nifTW 1 17.32 17.319 11.221 0.000972 ***\nsys 1 4.78 4.784 3.099 0.079899 . \ncondition:ifTW 5 14.50 2.900 1.879 0.099734 . \ncondition:sys 5 4.03 0.807 0.523 0.758987 \nifTW:sys 1 0.67 0.674 0.437 0.509541 \ncondition:ifTW:sys 5 4.25 0.849 0.550 0.738132 \nResiduals 194 299.43 1.543 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*ifTW*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.77 1.153 0.749 0.588083 \nifTW 1 17.32 17.319 11.245 0.000976 ***\nsys 1 4.78 4.784 3.106 0.079729 . \ndes 1 0.12 0.124 0.080 0.777058 \ncondition:ifTW 5 14.48 2.895 1.880 0.100000 \ncondition:sys 5 4.03 0.806 0.523 0.758443 \nifTW:sys 1 0.67 0.666 0.432 0.511625 \ncondition:des 3 4.23 1.410 0.916 0.434521 \nifTW:des 1 8.01 8.006 5.198 0.023796 * \nsys:des 1 0.56 0.563 0.366 0.546081 \ncondition:ifTW:sys 5 4.27 0.854 0.554 0.734934 \ncondition:ifTW:des 3 4.38 1.458 0.947 0.419211 \ncondition:sys:des 3 4.61 1.538 0.999 0.394840 \nifTW:sys:des 1 0.36 0.357 0.232 0.630940 \ncondition:ifTW:sys:des 3 3.00 1.001 0.650 0.583815 \nResiduals 178 274.16 1.540 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.859 0.5097 \neht_0 1 10.5 10.499 6.234 0.0133 *\ncondition:eht_0 5 9.5 1.900 1.128 0.3464 \nResiduals 215 362.1 1.684 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.858 0.5104 \neht_0 1 10.5 10.499 6.228 0.0134 *\ndes 1 0.0 0.000 0.000 0.9877 \ncondition:eht_0 5 9.5 1.900 1.127 0.3471 \ncondition:des 3 6.2 2.078 1.233 0.2989 \neht_0:des 1 2.5 2.472 1.467 0.2273 \ncondition:eht_0:des 3 4.4 1.472 0.873 0.4559 \nResiduals 207 349.0 1.686 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*eht_0*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.861 0.5084 \neht_0 1 10.5 10.499 6.249 0.0132 *\nsys 1 10.2 10.231 6.089 0.0144 *\ncondition:eht_0 5 9.3 1.860 1.107 0.3578 \ncondition:sys 5 6.2 1.238 0.737 0.5968 \neht_0:sys 1 0.5 0.490 0.292 0.5897 \ncondition:eht_0:sys 5 4.3 0.865 0.515 0.7649 \nResiduals 203 341.1 1.680 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.23 1.446 0.862 0.5079 \neht_0 1 10.50 10.499 6.256 0.0132 *\nsys 1 10.23 10.231 6.096 0.0144 *\ndes 1 0.00 0.000 0.000 0.9866 \ncondition:eht_0 5 9.30 1.860 1.108 0.3574 \ncondition:sys 5 6.19 1.238 0.738 0.5962 \neht_0:sys 1 0.49 0.489 0.291 0.5899 \ncondition:des 3 6.16 2.055 1.224 0.3022 \neht_0:des 1 2.66 2.661 1.586 0.2095 \nsys:des 1 0.48 0.483 0.288 0.5924 \ncondition:eht_0:sys 5 4.31 0.863 0.514 0.7654 \ncondition:eht_0:des 3 5.00 1.666 0.993 0.3974 \ncondition:sys:des 3 6.47 2.156 1.285 0.2810 \neht_0:sys:des 1 2.97 2.970 1.770 0.1851 \ncondition:eht_0:sys:des 3 3.49 1.165 0.694 0.5568 \nResiduals 187 313.83 1.678 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 0.999 0.419\ndes 1 41 40.7 0.060 0.807\ncondition:des 3 221 73.6 0.109 0.955\nResiduals 227 153942 678.2 \n> md<-aov(idle_time~condition*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3180 636.0 0.954 0.447\nsys 1 1802 1801.5 2.702 0.102\ncondition:sys 5 5045 1008.9 1.513 0.187\nResiduals 215 143326 666.6 \n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2037 407 0.671 0.64617 \nifTW 1 6603 6603 10.869 0.00115 **\ncondition:ifTW 5 2633 527 0.867 0.50435 \nResiduals 206 125159 608 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(idle_time~condition*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636.0 0.952 0.4484 \nsys 1 1802 1801.5 2.697 0.1021 \ndes 1 2 2.1 0.003 0.9553 \ncondition:sys 5 5043 1008.6 1.510 0.1881 \ncondition:des 3 75 25.0 0.037 0.9903 \nsys:des 1 506 506.2 0.758 0.3850 \ncondition:sys:des 3 4468 1489.3 2.229 0.0859 .\nResiduals 207 138276 668.0 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2037 407 0.666 0.64988 \nifTW 1 6603 6603 10.790 0.00121 **\ndes 1 70 70 0.115 0.73535 \ncondition:ifTW 5 2630 526 0.860 0.50934 \ncondition:des 3 5 2 0.003 0.99983 \nifTW:des 1 1653 1653 2.702 0.10184 \ncondition:ifTW:des 3 2255 752 1.228 0.30054 \nResiduals 198 121179 612 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*ifTW*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2037 407 0.672 0.64498 \nifTW 1 6603 6603 10.895 0.00115 **\nsys 1 535 535 0.883 0.34858 \ncondition:ifTW 5 2644 529 0.872 0.50057 \ncondition:sys 5 4780 956 1.577 0.16819 \nifTW:sys 1 6 6 0.010 0.92200 \ncondition:ifTW:sys 5 2241 448 0.740 0.59469 \nResiduals 194 117586 606 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2037 407 0.666 0.64950 \nifTW 1 6603 6603 10.799 0.00122 **\nsys 1 535 535 0.875 0.35081 \ndes 1 68 68 0.112 0.73864 \ncondition:ifTW 5 2641 528 0.864 0.50657 \ncondition:sys 5 4774 955 1.562 0.17325 \nifTW:sys 1 6 6 0.010 0.91853 \ncondition:des 3 3 1 0.002 0.99989 \nifTW:des 1 1588 1588 2.597 0.10884 \nsys:des 1 570 570 0.932 0.33570 \ncondition:ifTW:sys 5 2267 453 0.741 0.59335 \ncondition:ifTW:des 3 2261 754 1.232 0.29942 \ncondition:sys:des 3 3622 1207 1.974 0.11949 \nifTW:sys:des 1 187 187 0.306 0.58109 \ncondition:ifTW:sys:des 3 422 141 0.230 0.87546 \nResiduals 178 108847 611 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n19 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.986 0.427346 \neht_0 1 7984 7984 12.375 0.000531 ***\ncondition:eht_0 5 3474 695 1.077 0.374102 \nResiduals 215 138714 645 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.965 0.44025 \neht_0 1 7984 7984 12.115 0.00061 ***\ndes 1 4 4 0.006 0.94037 \ncondition:eht_0 5 3476 695 1.055 0.38657 \ncondition:des 3 98 33 0.049 0.98543 \neht_0:des 1 507 507 0.769 0.38167 \ncondition:eht_0:des 3 1683 561 0.851 0.46736 \nResiduals 207 136421 659 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*eht_0*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.990 0.424681 \neht_0 1 7984 7984 12.433 0.000521 ***\nsys 1 1198 1198 1.865 0.173523 \ncondition:eht_0 5 3475 695 1.082 0.371372 \ncondition:sys 5 5146 1029 1.603 0.160847 \neht_0:sys 1 1119 1119 1.743 0.188248 \ncondition:eht_0:sys 5 890 178 0.277 0.925254 \nResiduals 203 130360 642 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.974 0.434710 \neht_0 1 7984 7984 12.233 0.000587 ***\nsys 1 1198 1198 1.835 0.177143 \ndes 1 4 4 0.006 0.940670 \ncondition:eht_0 5 3477 695 1.065 0.380980 \ncondition:sys 5 5144 1029 1.576 0.168717 \neht_0:sys 1 1123 1123 1.720 0.191243 \ncondition:des 3 119 40 0.061 0.980244 \neht_0:des 1 497 497 0.761 0.383993 \nsys:des 1 595 595 0.912 0.340900 \ncondition:eht_0:sys 5 901 180 0.276 0.925751 \ncondition:eht_0:des 3 1834 611 0.937 0.424082 \ncondition:sys:des 3 4475 1492 2.285 0.080263 . \neht_0:sys:des 1 304 304 0.466 0.495516 \ncondition:eht_0:sys:des 3 463 154 0.237 0.870738 \nResiduals 187 122053 653 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n\n> md<-aov(idle_time~condition*noPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 1.007 0.414\nnoPP 1 382 382.4 0.568 0.452\ncondition:noPP 5 2481 496.2 0.738 0.596\nResiduals 225 151340 672.6 \n> md<-aov(idle_time~condition*noTGG,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 1.006 0.415\nnoTGG 1 248 248.5 0.369 0.544\ncondition:noTGG 5 2347 469.4 0.697 0.627\nResiduals 225 151608 673.8 \n> md<-aov(idle_time~condition*noTMD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3388 678 1.016 0.4089 \nnoTMD 1 3675 3675 5.511 0.0198 *\ncondition:noTMD 5 514 103 0.154 0.9786 \nResiduals 225 150015 667 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3388 678 1.024 0.4045 \nnoKD 1 3847 3847 5.812 0.0167 *\ncondition:noKD 5 1431 286 0.432 0.8258 \nResiduals 225 148925 662 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 0.995 0.421\nnoDPP 1 67 67.1 0.099 0.754\ncondition:noDPP 5 999 199.8 0.294 0.916\nResiduals 225 153137 680.6 \n> md<-aov(idle_time~condition*noKMT,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 1.005 0.415\nnoKMT 1 8 8.4 0.013 0.911\ncondition:noKMT 5 2579 515.9 0.766 0.575\nResiduals 225 151616 673.8 \n> md<-aov(xpos_flips~condition*noPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.542 0.898 0.483\nnoPP 1 1.4 1.381 0.804 0.371\ncondition:noPP 5 5.3 1.059 0.617 0.687\nResiduals 225 386.4 1.717 \n> md<-aov(xpos_flips~condition*noTGG,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.542 0.904 0.480\nnoTGG 1 4.6 4.569 2.678 0.103\ncondition:noTGG 5 4.6 0.924 0.542 0.745\nResiduals 225 383.9 1.706 \n> md<-aov(xpos_flips~condition*noTMD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.542 0.901 0.481\nnoTMD 1 1.1 1.087 0.635 0.426\ncondition:noTMD 5 6.9 1.372 0.801 0.550\nResiduals 225 385.1 1.712 \n> md<-aov(xpos_flips~condition*noKD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.7 1.542 0.943 0.45389 \nnoKD 1 17.7 17.702 10.828 0.00116 **\ncondition:noKD 5 7.6 1.512 0.925 0.46538 \nResiduals 225 367.8 1.635 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.890 0.488\nnoDPP 1 0.0 0.0461 0.027 0.871\ncondition:noDPP 5 3.4 0.6830 0.394 0.852\nResiduals 225 389.6 1.7317 \n> md<-aov(xpos_flips~condition*noKMT,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.885 0.492\nnoKMT 1 0.0 0.0172 0.010 0.921\ncondition:noKMT 5 1.1 0.2159 0.124 0.987\nResiduals 225 392.0 1.7422 \n> md<-aov(MAD~condition*noPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.721 0.00295 **\nnoPP 1 117224 117224 0.960 0.32817 \ncondition:noPP 5 806092 161218 1.321 0.25619 \nResiduals 225 27466432 122073 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTGG,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.744 0.00282 **\nnoTGG 1 685945 685945 5.653 0.01826 * \ncondition:noTGG 5 403230 80646 0.665 0.65065 \nResiduals 225 27300573 121336 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTMD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.728 0.00291 **\nnoTMD 1 117903 117903 0.968 0.32633 \ncondition:noTMD 5 855726 171145 1.405 0.22353 \nResiduals 225 27416119 121849 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.860 0.002244 ** \nnoKD 1 1460589 1460589 12.412 0.000517 ***\ncondition:noKD 5 451683 90337 0.768 0.573965 \nResiduals 225 26477477 117678 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.658 0.00335 **\nnoDPP 1 171213 171213 1.379 0.24155 \ncondition:noDPP 5 279244 55849 0.450 0.81322 \nResiduals 225 27939291 124175 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.647 0.00342 **\nnoKMT 1 4072 4072 0.033 0.85668 \ncondition:noKMT 5 361935 72387 0.581 0.71440 \nResiduals 225 28023741 124550 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3180 636.0 0.950 0.450\nnoPP 1 328 327.7 0.490 0.485\nsys 1 1501 1501.1 2.242 0.136\ncondition:noPP 5 2211 442.1 0.661 0.654\ncondition:sys 5 6234 1246.7 1.863 0.102\nResiduals 209 139899 669.4 \n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTGG*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3180 636.0 0.921 0.468\nnoTGG 1 212 212.4 0.308 0.580\nsys 1 1697 1696.8 2.458 0.118\ncondition:noTGG 5 2267 453.3 0.657 0.657\ncondition:sys 5 4588 917.6 1.329 0.253\nnoTGG:sys 1 72 72.2 0.105 0.747\ncondition:noTGG:sys 5 1194 238.7 0.346 0.885\nResiduals 203 140142 690.4 \n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTMD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.952 0.4485 \nnoTMD 1 3625 3625 5.427 0.0208 *\nsys 1 1365 1365 2.044 0.1544 \ncondition:noTMD 5 482 96 0.144 0.9816 \ncondition:sys 5 4844 969 1.450 0.2078 \nnoTMD:sys 1 549 549 0.822 0.3656 \ncondition:noTMD:sys 5 3691 738 1.105 0.3589 \nResiduals 203 135615 668 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 1.000 0.41896 \nnoKD 1 4038 4038 6.348 0.01252 * \nsys 1 1613 1613 2.535 0.11290 \ncondition:noKD 5 1296 259 0.407 0.84334 \ncondition:sys 5 4946 989 1.555 0.17440 \nnoKD:sys 1 6151 6151 9.670 0.00214 **\ncondition:noKD:sys 5 3001 600 0.943 0.45389 \nResiduals 203 129128 636 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noDPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.943 0.454 \nnoDPP 1 94 94 0.140 0.709 \nsys 1 1744 1744 2.586 0.109 \ncondition:noDPP 5 846 169 0.251 0.939 \ncondition:sys 5 4760 952 1.412 0.221 \nnoDPP:sys 1 3439 3439 5.100 0.025 *\ncondition:noDPP:sys 5 2411 482 0.715 0.613 \nResiduals 203 136879 674 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKMT*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 1.008 0.4140 \nnoKMT 1 30 30 0.048 0.8267 \nsys 1 1778 1778 2.818 0.0948 . \ncondition:noKMT 5 2526 505 0.801 0.5503 \ncondition:sys 5 4130 826 1.309 0.2614 \nnoKMT:sys 1 12568 12568 19.921 1.34e-05 ***\ncondition:noKMT:sys 5 1066 213 0.338 0.8895 \nResiduals 203 128074 631 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.7 1.542 0.902 0.4803 \nnoPP 1 1.4 1.382 0.809 0.3695 \ndes 1 0.0 0.008 0.005 0.9449 \ncondition:noPP 5 5.3 1.059 0.620 0.6847 \ncondition:des 3 5.0 1.668 0.976 0.4048 \nnoPP:des 1 8.9 8.899 5.209 0.0234 *\ncondition:noPP:des 3 1.8 0.596 0.349 0.7899 \nResiduals 217 370.7 1.708 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTGG*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.542 0.885 0.492\nnoTGG 1 4.6 4.569 2.623 0.107\ndes 1 0.0 0.016 0.009 0.923\ncondition:noTGG 5 4.6 0.925 0.531 0.753\ncondition:des 3 5.0 1.664 0.955 0.415\nnoTGG:des 1 0.1 0.132 0.076 0.783\ncondition:noTGG:des 3 0.7 0.247 0.142 0.935\nResiduals 217 378.0 1.742 \n> md<-aov(xpos_flips~condition*noTMD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.883 0.493\nnoTMD 1 1.1 1.0874 0.623 0.431\ndes 1 0.0 0.0050 0.003 0.957\ncondition:noTMD 5 6.9 1.3728 0.786 0.561\ncondition:des 3 5.1 1.6846 0.965 0.410\nnoTMD:des 1 0.0 0.0138 0.008 0.929\ncondition:noTMD:des 3 1.1 0.3587 0.205 0.893\nResiduals 217 379.0 1.7465 \n> md<-aov(xpos_flips~condition*noKD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.7 1.542 0.938 0.4573 \nnoKD 1 17.7 17.702 10.769 0.0012 **\ndes 1 0.0 0.010 0.006 0.9372 \ncondition:noKD 5 7.6 1.512 0.920 0.4690 \ncondition:des 3 5.0 1.661 1.011 0.3888 \nnoKD:des 1 1.4 1.408 0.856 0.3558 \ncondition:noKD:des 3 4.7 1.579 0.961 0.4121 \nResiduals 217 356.7 1.644 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.880 0.496\nnoDPP 1 0.0 0.0461 0.026 0.871\ndes 1 0.0 0.0078 0.004 0.947\ncondition:noDPP 5 3.4 0.6823 0.389 0.856\ncondition:des 3 5.1 1.7108 0.976 0.405\nnoDPP:des 1 1.1 1.0690 0.610 0.436\ncondition:noDPP:des 3 3.0 1.0140 0.578 0.630\nResiduals 217 380.4 1.7529 \n> md<-aov(xpos_flips~condition*noKMT*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 7.7 1.5417 0.876 0.498\nnoKMT 1 0.0 0.0172 0.010 0.921\ndes 1 0.0 0.0073 0.004 0.949\ncondition:noKMT 5 1.1 0.2155 0.122 0.987\ncondition:des 3 5.1 1.6874 0.959 0.413\nnoKMT:des 1 0.3 0.2635 0.150 0.699\ncondition:noKMT:des 3 4.8 1.5956 0.907 0.439\nResiduals 217 381.9 1.7598 \n> md<-aov(idle_time~condition*noPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3388 677.5 0.992 0.4236 \nnoPP 1 382 382.4 0.560 0.4552 \ndes 1 40 40.1 0.059 0.8088 \ncondition:noPP 5 2483 496.5 0.727 0.6039 \ncondition:des 3 231 77.1 0.113 0.9525 \nnoPP:des 1 2162 2161.6 3.165 0.0767 .\ncondition:noPP:des 3 680 226.7 0.332 0.8023 \nResiduals 217 148225 683.1 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noTGG*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 0.972 0.436\nnoTGG 1 248 248.5 0.356 0.551\ndes 1 37 37.0 0.053 0.818\ncondition:noTGG 5 2346 469.2 0.673 0.644\ncondition:des 3 217 72.4 0.104 0.958\nnoTGG:des 1 1 1.2 0.002 0.967\ncondition:noTGG:des 3 77 25.8 0.037 0.990\nResiduals 217 151276 697.1 \n> md<-aov(idle_time~condition*noTMD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3388 678 0.984 0.4282 \nnoTMD 1 3675 3675 5.338 0.0218 *\ndes 1 29 29 0.042 0.8374 \ncondition:noTMD 5 515 103 0.150 0.9800 \ncondition:des 3 213 71 0.103 0.9582 \nnoTMD:des 1 302 302 0.439 0.5085 \ncondition:noTMD:des 3 92 31 0.044 0.9875 \nResiduals 217 149378 688 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3388 678 0.989 0.4252 \nnoKD 1 3847 3847 5.617 0.0187 *\ndes 1 43 43 0.063 0.8018 \ncondition:noKD 5 1426 285 0.417 0.8370 \ncondition:des 3 216 72 0.105 0.9570 \nnoKD:des 1 48 48 0.071 0.7905 \ncondition:noKD:des 3 9 3 0.004 0.9996 \nResiduals 217 148614 685 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 0.967 0.439\nnoDPP 1 67 67.1 0.096 0.757\ndes 1 41 41.0 0.059 0.809\ncondition:noDPP 5 994 198.7 0.284 0.922\ncondition:des 3 223 74.3 0.106 0.956\nnoDPP:des 1 91 91.2 0.130 0.719\ncondition:noDPP:des 3 807 269.2 0.384 0.764\nResiduals 217 151980 700.4 \n> md<-aov(idle_time~condition*noKMT*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3388 677.5 0.992 0.423\nnoKMT 1 8 8.4 0.012 0.912\ndes 1 41 41.4 0.061 0.806\ncondition:noKMT 5 2570 514.1 0.753 0.585\ncondition:des 3 199 66.4 0.097 0.961\nnoKMT:des 1 540 540.0 0.791 0.375\ncondition:noKMT:des 3 2700 899.9 1.318 0.269\nResiduals 217 148144 682.7 \n\n> md<-aov(MAD~condition*noPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.667 0.00332 **\nnoPP 1 117224 117224 0.946 0.33174 \ndes 1 60389 60389 0.488 0.48579 \ncondition:noPP 5 804185 160837 1.298 0.26565 \ncondition:des 3 331273 110424 0.891 0.44637 \nnoPP:des 1 78707 78707 0.635 0.42626 \ncondition:noPP:des 3 117729 39243 0.317 0.81322 \nResiduals 217 26880242 123872 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTGG*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.680 0.00323 **\nnoTGG 1 685945 685945 5.557 0.01930 * \ndes 1 53375 53375 0.432 0.51151 \ncondition:noTGG 5 403701 80740 0.654 0.65869 \ncondition:des 3 340421 113474 0.919 0.43235 \nnoTGG:des 1 1068 1068 0.009 0.92599 \ncondition:noTGG:des 3 118379 39460 0.320 0.81115 \nResiduals 217 26786859 123442 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noTMD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.657 0.00338 **\nnoTMD 1 117903 117903 0.949 0.33101 \ndes 1 58076 58076 0.468 0.49484 \ncondition:noTMD 5 853118 170624 1.374 0.23530 \ncondition:des 3 335326 111775 0.900 0.44208 \nnoTMD:des 1 1358 1358 0.011 0.91682 \ncondition:noTMD:des 3 69832 23277 0.187 0.90490 \nResiduals 217 26954134 124213 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.796 0.002572 ** \nnoKD 1 1460589 1460589 12.206 0.000577 ***\ndes 1 58888 58888 0.492 0.483742 \ncondition:noKD 5 457260 91452 0.764 0.576498 \ncondition:des 3 326820 108940 0.910 0.436789 \nnoKD:des 1 81581 81581 0.682 0.409893 \ncondition:noKD:des 3 37161 12387 0.104 0.957941 \nResiduals 217 25967449 119666 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.603 0.00376 **\nnoDPP 1 171213 171213 1.358 0.24519 \ndes 1 60085 60085 0.477 0.49074 \ncondition:noDPP 5 280363 56073 0.445 0.81686 \ncondition:des 3 330457 110152 0.874 0.45554 \nnoDPP:des 1 93470 93470 0.741 0.39019 \ncondition:noDPP:des 3 92926 30975 0.246 0.86437 \nResiduals 217 27361234 126089 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2271176 454235 3.611 0.0037 **\nnoKMT 1 4072 4072 0.032 0.8574 \ndes 1 60231 60231 0.479 0.4897 \ncondition:noKMT 5 361949 72390 0.576 0.7187 \ncondition:des 3 329953 109984 0.874 0.4551 \nnoKMT:des 1 13791 13791 0.110 0.7409 \ncondition:noKMT:des 3 325361 108454 0.862 0.4615 \nResiduals 217 27294390 125781 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nmd<-aov(idle_time~condition*noPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3180 636.0 0.950 0.450\nnoPP 1 328 327.7 0.490 0.485\nsys 1 1501 1501.1 2.242 0.136\ncondition:noPP 5 2211 442.1 0.661 0.654\ncondition:sys 5 6234 1246.7 1.863 0.102\nResiduals 209 139899 669.4 \n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTGG*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 3180 636.0 0.921 0.468\nnoTGG 1 212 212.4 0.308 0.580\nsys 1 1697 1696.8 2.458 0.118\ncondition:noTGG 5 2267 453.3 0.657 0.657\ncondition:sys 5 4588 917.6 1.329 0.253\nnoTGG:sys 1 72 72.2 0.105 0.747\ncondition:noTGG:sys 5 1194 238.7 0.346 0.885\nResiduals 203 140142 690.4 \n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTMD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.952 0.4485 \nnoTMD 1 3625 3625 5.427 0.0208 *\nsys 1 1365 1365 2.044 0.1544 \ncondition:noTMD 5 482 96 0.144 0.9816 \ncondition:sys 5 4844 969 1.450 0.2078 \nnoTMD:sys 1 549 549 0.822 0.3656 \ncondition:noTMD:sys 5 3691 738 1.105 0.3589 \nResiduals 203 135615 668 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 1.000 0.41896 \nnoKD 1 4038 4038 6.348 0.01252 * \nsys 1 1613 1613 2.535 0.11290 \ncondition:noKD 5 1296 259 0.407 0.84334 \ncondition:sys 5 4946 989 1.555 0.17440 \nnoKD:sys 1 6151 6151 9.670 0.00214 **\ncondition:noKD:sys 5 3001 600 0.943 0.45389 \nResiduals 203 129128 636 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noDPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 0.943 0.454 \nnoDPP 1 94 94 0.140 0.709 \nsys 1 1744 1744 2.586 0.109 \ncondition:noDPP 5 846 169 0.251 0.939 \ncondition:sys 5 4760 952 1.412 0.221 \nnoDPP:sys 1 3439 3439 5.100 0.025 *\ncondition:noDPP:sys 5 2411 482 0.715 0.613 \nResiduals 203 136879 674 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKMT*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 3180 636 1.008 0.4140 \nnoKMT 1 30 30 0.048 0.8267 \nsys 1 1778 1778 2.818 0.0948 . \ncondition:noKMT 5 2526 505 0.801 0.5503 \ncondition:sys 5 4130 826 1.309 0.2614 \nnoKMT:sys 1 12568 12568 19.921 1.34e-05 ***\ncondition:noKMT:sys 5 1066 213 0.338 0.8895 \nResiduals 203 128074 631 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.862 0.50771 \nnoPP 1 2.2 2.170 1.293 0.25685 \nsys 1 17.1 17.091 10.183 0.00164 **\ncondition:noPP 5 5.5 1.101 0.656 0.65732 \ncondition:sys 5 6.6 1.312 0.782 0.56387 \nResiduals 209 350.8 1.678 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTGG*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.848 0.51698 \nnoTGG 1 5.1 5.134 3.011 0.08419 . \nsys 1 13.8 13.843 8.119 0.00483 **\ncondition:noTGG 5 5.2 1.031 0.605 0.69653 \ncondition:sys 5 7.3 1.455 0.853 0.51357 \nnoTGG:sys 1 0.0 0.032 0.019 0.89074 \ncondition:noTGG:sys 5 4.6 0.911 0.534 0.75028 \nResiduals 203 346.1 1.705 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTMD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.846 0.5188 \nnoTMD 1 1.0 0.990 0.579 0.4477 \nsys 1 11.5 11.536 6.745 0.0101 *\ncondition:noTMD 5 7.1 1.421 0.831 0.5291 \ncondition:sys 5 7.3 1.469 0.859 0.5098 \nnoTMD:sys 1 0.1 0.132 0.077 0.7817 \ncondition:noTMD:sys 5 7.8 1.564 0.914 0.4727 \nResiduals 203 347.2 1.710 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.911 0.475026 \nnoKD 1 18.4 18.432 11.607 0.000792 ***\nsys 1 11.0 11.027 6.944 0.009061 ** \ncondition:noKD 5 7.5 1.495 0.941 0.455202 \ncondition:sys 5 6.1 1.227 0.773 0.570318 \nnoKD:sys 1 11.8 11.756 7.402 0.007079 ** \ncondition:noKD:sys 5 4.9 0.977 0.615 0.688467 \nResiduals 203 322.4 1.588 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noDPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.842 0.52136 \nnoDPP 1 0.1 0.083 0.048 0.82623 \nsys 1 12.0 11.992 6.981 0.00888 **\ncondition:noDPP 5 3.7 0.734 0.427 0.82950 \ncondition:sys 5 6.1 1.219 0.710 0.61665 \nnoDPP:sys 1 6.1 6.058 3.527 0.06182 . \ncondition:noDPP:sys 5 5.5 1.096 0.638 0.67086 \nResiduals 203 348.7 1.718 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKMT*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.2 1.446 0.853 0.51343 \nnoKMT 1 0.0 0.000 0.000 0.98901 \nsys 1 12.5 12.543 7.401 0.00708 **\ncondition:noKMT 5 1.3 0.255 0.150 0.97980 \ncondition:sys 5 6.8 1.361 0.803 0.54879 \nnoKMT:sys 1 9.4 9.433 5.567 0.01926 * \ncondition:noKMT:sys 5 8.0 1.606 0.948 0.45115 \nResiduals 203 344.0 1.695 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.913 0.00206 **\nnoPP 1 301313 301313 2.439 0.11986 \nsys 1 40 40 0.000 0.98563 \ncondition:noPP 5 539463 107893 0.873 0.49983 \ncondition:sys 5 597329 119466 0.967 0.43897 \nResiduals 209 25819351 123538 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTGG*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.957 0.00191 **\nnoTGG 1 857050 857050 7.015 0.00872 **\nsys 1 7003 7003 0.057 0.81103 \ncondition:noTGG 5 398875 79775 0.653 0.65958 \ncondition:sys 5 376930 75386 0.617 0.68697 \nnoTGG:sys 1 6554 6554 0.054 0.81707 \ncondition:noTGG:sys 5 808911 161782 1.324 0.25523 \nResiduals 203 24802172 122178 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTMD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.889 0.00218 **\nnoTMD 1 166214 166214 1.337 0.24892 \nsys 1 19123 19123 0.154 0.69532 \ncondition:noTMD 5 968651 193730 1.558 0.17344 \ncondition:sys 5 403317 80663 0.649 0.66270 \nnoTMD:sys 1 13924 13924 0.112 0.73822 \ncondition:noTMD:sys 5 449534 89907 0.723 0.60674 \nResiduals 203 25236733 124319 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKD*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 4.053 0.001580 ** \nnoKD 1 1604540 1604540 13.451 0.000313 ***\nsys 1 49406 49406 0.414 0.520590 \ncondition:noKD 5 480126 96025 0.805 0.547294 \ncondition:sys 5 446843 89369 0.749 0.587575 \nnoKD:sys 1 77483 77483 0.650 0.421224 \ncondition:noKD:sys 5 382904 76581 0.642 0.667933 \nResiduals 203 24216193 119292 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noDPP*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.894 0.00216 **\nnoDPP 1 241508 241508 1.945 0.16462 \nsys 1 48313 48313 0.389 0.53344 \ncondition:noDPP 5 411279 82256 0.663 0.65228 \ncondition:sys 5 461132 92226 0.743 0.59220 \nnoDPP:sys 1 168172 168172 1.355 0.24584 \ncondition:noDPP:sys 5 724892 144978 1.168 0.32629 \nResiduals 203 25202199 124149 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKMT*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.914 0.00208 **\nnoKMT 1 36979 36979 0.299 0.58487 \nsys 1 46664 46664 0.378 0.53947 \ncondition:noKMT 5 478064 95613 0.774 0.56941 \ncondition:sys 5 396535 79307 0.642 0.66784 \nnoKMT:sys 1 306064 306064 2.478 0.11701 \ncondition:noKMT:sys 5 919575 183915 1.489 0.19485 \nResiduals 203 25073616 123515 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n\n> md<-aov(MAD~condition*eht_0,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.853 0.0023 **\neht_0 1 69192 69192 0.551 0.4585 \ncondition:eht_0 5 212548 42510 0.339 0.8890 \nResiduals 215 26975756 125469 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.805 0.00256 **\neht_0 1 69192 69192 0.545 0.46139 \ndes 1 97423 97423 0.767 0.38224 \ncondition:eht_0 5 211293 42259 0.333 0.89288 \ncondition:des 3 309361 103120 0.812 0.48875 \neht_0:des 1 11144 11144 0.088 0.76741 \ncondition:eht_0:des 3 257309 85770 0.675 0.56825 \nResiduals 207 26301774 127062 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(MAD~condition*eht_0*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.740 0.00292 **\neht_0 1 69192 69192 0.535 0.46522 \nsys 1 40098 40098 0.310 0.57815 \ncondition:eht_0 5 208642 41728 0.323 0.89886 \ncondition:sys 5 405529 81106 0.628 0.67896 \neht_0:sys 1 145221 145221 1.124 0.29041 \ncondition:eht_0:sys 5 150867 30173 0.233 0.94749 \nResiduals 203 26237947 129251 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2417247 483449 3.659 0.0035 **\neht_0 1 69192 69192 0.524 0.4702 \nsys 1 40098 40098 0.304 0.5823 \ndes 1 97355 97355 0.737 0.3917 \ncondition:eht_0 5 207465 41493 0.314 0.9041 \ncondition:sys 5 408297 81659 0.618 0.6861 \neht_0:sys 1 139789 139789 1.058 0.3050 \ncondition:des 3 314284 104761 0.793 0.4992 \neht_0:des 1 8145 8145 0.062 0.8042 \nsys:des 1 22240 22240 0.168 0.6821 \ncondition:eht_0:sys 5 149696 29939 0.227 0.9506 \ncondition:eht_0:des 3 275043 91681 0.694 0.5568 \ncondition:sys:des 3 655644 218548 1.654 0.1784 \neht_0:sys:des 1 7487 7487 0.057 0.8121 \ncondition:eht_0:sys:des 3 158211 52737 0.399 0.7537 \nResiduals 187 24704550 132110 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.320e+11 1.464e+11 2.675 0.0228 *\neht_0 1 3.361e+06 3.361e+06 0.000 0.9938 \ncondition:eht_0 5 5.090e+10 1.018e+10 0.186 0.9677 \nResiduals 215 1.177e+13 5.473e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.320e+11 1.464e+11 2.658 0.0236 *\neht_0 1 3.361e+06 3.361e+06 0.000 0.9938 \ndes 1 9.463e+10 9.463e+10 1.718 0.1914 \ncondition:eht_0 5 5.156e+10 1.031e+10 0.187 0.9672 \ncondition:des 3 1.808e+11 6.025e+10 1.094 0.3527 \neht_0:des 1 1.822e+10 1.822e+10 0.331 0.5658 \ncondition:eht_0:des 3 7.164e+10 2.388e+10 0.434 0.7292 \nResiduals 207 1.140e+13 5.508e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0*sys,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.320e+11 1.464e+11 2.639 0.0245 *\neht_0 1 3.361e+06 3.361e+06 0.000 0.9938 \nsys 1 7.523e+10 7.523e+10 1.356 0.2455 \ncondition:eht_0 5 5.855e+10 1.171e+10 0.211 0.9575 \ncondition:sys 5 3.463e+11 6.926e+10 1.249 0.2878 \neht_0:sys 1 2.850e+10 2.850e+10 0.514 0.4743 \ncondition:eht_0:sys 5 4.982e+10 9.963e+09 0.180 0.9700 \nResiduals 203 1.126e+13 5.547e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*sys*des,\n+ data=avg_measures.0a2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 7.320e+11 1.464e+11 2.667 0.0235 *\neht_0 1 3.361e+06 3.361e+06 0.000 0.9938 \nsys 1 7.523e+10 7.523e+10 1.371 0.2432 \ndes 1 9.454e+10 9.454e+10 1.722 0.1910 \ncondition:eht_0 5 5.932e+10 1.186e+10 0.216 0.9553 \ncondition:sys 5 3.471e+11 6.941e+10 1.265 0.2810 \neht_0:sys 1 2.614e+10 2.614e+10 0.476 0.4910 \ncondition:des 3 1.815e+11 6.050e+10 1.102 0.3495 \neht_0:des 1 1.345e+10 1.345e+10 0.245 0.6212 \nsys:des 1 1.516e+10 1.516e+10 0.276 0.5999 \ncondition:eht_0:sys 5 4.682e+10 9.365e+09 0.171 0.9732 \ncondition:eht_0:des 3 7.289e+10 2.430e+10 0.443 0.7228 \ncondition:sys:des 3 2.996e+11 9.986e+10 1.819 0.1451 \neht_0:sys:des 1 2.964e+08 2.964e+08 0.005 0.9415 \ncondition:eht_0:sys:des 3 3.218e+11 1.073e+11 1.954 0.1224 \nResiduals 187 1.026e+13 5.489e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n>\n\n\nBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB\nbelieve\n\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.236 0.9465 \neht_0 1 8.1 8.138 5.086 0.0249 *\ncondition:eht_0 5 6.6 1.317 0.823 0.5339 \nResiduals 292 467.2 1.600 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.233 0.9476 \neht_0 1 8.1 8.138 5.034 0.0256 *\ndes 1 2.6 2.631 1.627 0.2031 \ncondition:eht_0 5 6.6 1.320 0.817 0.5386 \ncondition:des 3 3.3 1.085 0.671 0.5702 \neht_0:des 1 0.2 0.215 0.133 0.7156 \ncondition:eht_0:des 3 2.0 0.664 0.411 0.7453 \nResiduals 284 459.1 1.617 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*eht_0*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.233 0.9479 \neht_0 1 8.1 8.138 5.023 0.0258 *\nsys 1 0.1 0.113 0.070 0.7920 \ncondition:eht_0 5 6.6 1.313 0.810 0.5431 \ncondition:sys 5 1.2 0.248 0.153 0.9790 \neht_0:sys 1 0.1 0.059 0.036 0.8491 \ncondition:eht_0:sys 5 12.2 2.444 1.509 0.1871 \nResiduals 280 453.6 1.620 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.226 0.951 \neht_0 1 8.1 8.138 4.881 0.028 *\nsys 1 0.1 0.113 0.068 0.795 \ndes 1 2.6 2.606 1.563 0.212 \ncondition:eht_0 5 6.6 1.316 0.790 0.558 \ncondition:sys 5 1.3 0.260 0.156 0.978 \neht_0:sys 1 0.1 0.062 0.037 0.848 \ncondition:des 3 3.2 1.070 0.642 0.589 \neht_0:des 1 0.2 0.227 0.136 0.712 \nsys:des 1 0.5 0.548 0.329 0.567 \ncondition:eht_0:sys 5 12.4 2.481 1.488 0.194 \ncondition:eht_0:des 3 2.1 0.703 0.422 0.737 \ncondition:sys:des 3 3.9 1.302 0.781 0.506 \neht_0:sys:des 1 0.1 0.068 0.041 0.840 \ncondition:eht_0:sys:des 3 0.5 0.172 0.103 0.958 \nResiduals 264 440.1 1.667 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.226 0.951\ndes 1 31 30.5 0.065 0.799\ncondition:des 3 1500 500.2 1.068 0.363\nResiduals 304 142371 468.3 \n> md<-aov(idle_time~condition*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.293 0.9168 \nsys 1 3885 3885 8.464 0.0039 **\ncondition:sys 5 1376 275 0.599 0.7004 \nResiduals 292 134022 459 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134.3 0.292 0.9170 \nifTW 1 3066 3065.8 6.674 0.0103 *\ncondition:ifTW 5 2077 415.4 0.904 0.4786 \nResiduals 292 134140 459.4 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.292 0.91740 \nsys 1 3885 3885 8.434 0.00397 **\ndes 1 9 9 0.020 0.88751 \ncondition:sys 5 1380 276 0.599 0.70061 \ncondition:des 3 1350 450 0.977 0.40411 \nsys:des 1 283 283 0.614 0.43376 \ncondition:sys:des 3 1563 521 1.131 0.33683 \nResiduals 284 130813 461 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134.3 0.289 0.9189 \nifTW 1 3066 3065.8 6.593 0.0107 *\ndes 1 51 50.7 0.109 0.7416 \ncondition:ifTW 5 2097 419.4 0.902 0.4801 \ncondition:des 3 1267 422.4 0.908 0.4373 \nifTW:des 1 562 562.1 1.209 0.2725 \ncondition:ifTW:des 3 185 61.6 0.133 0.9406 \nResiduals 284 132055 465.0 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*ifTW*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.302 0.91150 \nifTW 1 3066 3066 6.890 0.00914 **\nsys 1 3626 3626 8.149 0.00463 **\ncondition:ifTW 5 1952 390 0.877 0.49668 \ncondition:sys 5 1556 311 0.699 0.62438 \nifTW:sys 1 2437 2437 5.478 0.01996 * \ncondition:ifTW:sys 5 2065 413 0.928 0.46305 \nResiduals 280 124582 445 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.296 0.91512 \nifTW 1 3066 3066 6.746 0.00992 **\nsys 1 3626 3626 7.978 0.00510 **\ndes 1 31 31 0.069 0.79317 \ncondition:ifTW 5 1967 393 0.865 0.50479 \ncondition:sys 5 1568 314 0.690 0.63138 \nifTW:sys 1 2432 2432 5.351 0.02148 * \ncondition:des 3 1322 441 0.970 0.40748 \nifTW:des 1 641 641 1.410 0.23608 \nsys:des 1 450 450 0.989 0.32085 \ncondition:ifTW:sys 5 2051 410 0.903 0.47977 \ncondition:ifTW:des 3 120 40 0.088 0.96644 \ncondition:sys:des 3 1530 510 1.122 0.34043 \nifTW:sys:des 1 4 4 0.008 0.92746 \ncondition:ifTW:sys:des 3 503 168 0.369 0.77557 \nResiduals 264 119972 454 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.293 0.91654 \neht_0 1 4202 4202 9.171 0.00268 **\ncondition:eht_0 5 1288 258 0.562 0.72894 \nResiduals 292 133792 458 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.289 0.91865 \neht_0 1 4202 4202 9.054 0.00286 **\ndes 1 48 48 0.103 0.74883 \ncondition:eht_0 5 1279 256 0.551 0.73739 \ncondition:des 3 1305 435 0.937 0.42305 \neht_0:des 1 12 12 0.026 0.87287 \ncondition:eht_0:des 3 627 209 0.451 0.71703 \nResiduals 284 131809 464 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*eht_0*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.303 0.91071 \neht_0 1 4202 4202 9.487 0.00228 **\nsys 1 3745 3745 8.455 0.00393 **\ncondition:eht_0 5 1216 243 0.549 0.73894 \ncondition:sys 5 1449 290 0.654 0.65853 \neht_0:sys 1 2568 2568 5.799 0.01668 * \ncondition:eht_0:sys 5 2083 417 0.941 0.45499 \nResiduals 280 124019 443 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.296 0.91504 \neht_0 1 4202 4202 9.250 0.00259 **\nsys 1 3745 3745 8.245 0.00442 **\ndes 1 30 30 0.065 0.79903 \ncondition:eht_0 5 1209 242 0.533 0.75158 \ncondition:sys 5 1455 291 0.641 0.66875 \neht_0:sys 1 2570 2570 5.658 0.01808 * \ncondition:des 3 1282 427 0.941 0.42152 \neht_0:des 1 13 13 0.028 0.86686 \nsys:des 1 233 233 0.514 0.47418 \ncondition:eht_0:sys 5 2105 421 0.927 0.46399 \ncondition:eht_0:des 3 573 191 0.420 0.73870 \ncondition:sys:des 3 1383 461 1.015 0.38651 \neht_0:sys:des 1 0 0 0.000 0.99585 \ncondition:eht_0:sys:des 3 560 187 0.411 0.74507 \nResiduals 264 119921 454 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.224 0.952\nnoPP 1 83 82.6 0.175 0.676\ncondition:noPP 5 897 179.4 0.379 0.863\nResiduals 302 142923 473.3 \n> md<-aov(idle_time~condition*noTGG,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.224 0.952\nnoTGG 1 61 61.2 0.129 0.720\ncondition:noTGG 5 833 166.5 0.352 0.881\nResiduals 302 143008 473.5 \n> md<-aov(idle_time~condition*noTMD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.224 0.952\nnoTMD 1 677 676.7 1.429 0.233\ncondition:noTMD 5 185 37.0 0.078 0.996\nResiduals 302 143040 473.6 \n> md<-aov(idle_time~condition*noKD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 530 106 0.233 0.947724 \nnoKD 1 6012 6012 13.237 0.000323 ***\ncondition:noKD 5 723 145 0.318 0.901861 \nResiduals 302 137167 454 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 530 106 0.229 0.94954 \nnoDPP 1 4190 4190 9.071 0.00282 **\ncondition:noDPP 5 219 44 0.095 0.99299 \nResiduals 302 139493 462 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKMT,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.224 0.952\nnoKMT 1 9 9.2 0.019 0.889\ncondition:noKMT 5 860 172.1 0.363 0.873\nResiduals 302 143032 473.6 \n> md<-aov(xpos_flips~condition*noPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.338 0.889\nnoPP 1 1.7 1.7388 1.076 0.300\ncondition:noPP 5 7.1 1.4269 0.883 0.493\nResiduals 302 488.1 1.6163 \n> md<-aov(xpos_flips~condition*noTGG,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.339 0.889\nnoTGG 1 0.9 0.9371 0.581 0.447\ncondition:noTGG 5 8.8 1.7660 1.095 0.363\nResiduals 302 487.2 1.6134 \n> md<-aov(xpos_flips~condition*noTMD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.337 0.891\nnoTMD 1 0.0 0.0025 0.002 0.969\ncondition:noTMD 5 6.4 1.2746 0.785 0.561\nResiduals 302 490.6 1.6246 \n> md<-aov(xpos_flips~condition*noKD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2.7 0.547 0.348 0.88341 \nnoKD 1 21.4 21.355 13.586 0.00027 ***\ncondition:noKD 5 1.0 0.196 0.124 0.98686 \nResiduals 302 474.7 1.572 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2.7 0.547 0.350 0.882374 \nnoDPP 1 20.0 19.971 12.765 0.000411 ***\ncondition:noDPP 5 4.6 0.913 0.584 0.712644 \nResiduals 302 472.5 1.564 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noKMT,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.336 0.891\nnoKMT 1 0.9 0.9434 0.579 0.447\ncondition:noKMT 5 4.1 0.8274 0.508 0.770\nResiduals 302 491.9 1.6289 \n> md<-aov(MAD~condition*noPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.349 0.244\nnoPP 1 3068 3068 0.049 0.824\ncondition:noPP 5 250630 50126 0.805 0.547\nResiduals 302 18795561 62237 \n> md<-aov(MAD~condition*noTGG,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.346 0.245\nnoTGG 1 9819 9819 0.157 0.692\ncondition:noTGG 5 208411 41682 0.668 0.648\nResiduals 302 18831029 62354 \n> md<-aov(MAD~condition*noTMD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.364 0.2379 \nnoTMD 1 342736 342736 5.568 0.0189 *\ncondition:noTMD 5 116259 23252 0.378 0.8639 \nResiduals 302 18590263 61557 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.416 0.21805 \nnoKD 1 571572 571572 9.645 0.00208 **\ncondition:noKD 5 580160 116032 1.958 0.08477 . \nResiduals 302 17897527 59263 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.428 0.213897 \nnoDPP 1 659114 659114 11.213 0.000915 ***\ncondition:noDPP 5 637789 127558 2.170 0.057393 . \nResiduals 302 17752356 58783 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.355 0.241\nnoKMT 1 3628 3628 0.059 0.809\ncondition:noKMT 5 343000 68600 1.108 0.356\nResiduals 302 18702631 61929 \n> md<-aov(idle_time~condition*noPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.224 0.952\nnoPP 1 83 82.6 0.174 0.677\ndes 1 30 30.3 0.064 0.801\ncondition:noPP 5 897 179.5 0.379 0.863\ncondition:des 3 1511 503.8 1.064 0.365\nnoPP:des 1 639 639.3 1.350 0.246\ncondition:noPP:des 3 1490 496.6 1.048 0.371\nResiduals 294 139251 473.6 \n> md<-aov(idle_time~condition*noTGG*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.221 0.953\nnoTGG 1 61 61.2 0.127 0.721\ndes 1 30 30.1 0.063 0.802\ncondition:noTGG 5 828 165.6 0.345 0.885\ncondition:des 3 1542 514.0 1.070 0.362\nnoTGG:des 1 94 93.6 0.195 0.659\ncondition:noTGG:des 3 184 61.4 0.128 0.944\nResiduals 294 141163 480.1 \n> md<-aov(idle_time~condition*noTMD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.221 0.953\nnoTMD 1 677 676.7 1.414 0.235\ndes 1 25 25.4 0.053 0.818\ncondition:noTMD 5 188 37.6 0.079 0.995\ncondition:des 3 1499 499.6 1.044 0.373\nnoTMD:des 1 276 275.9 0.577 0.448\ncondition:noTMD:des 3 571 190.3 0.398 0.755\nResiduals 294 140667 478.5 \n> md<-aov(idle_time~condition*noKD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 530 106 0.230 0.949186 \nnoKD 1 6012 6012 13.058 0.000355 ***\ndes 1 13 13 0.029 0.864729 \ncondition:noKD 5 723 145 0.314 0.904260 \ncondition:des 3 1603 534 1.161 0.325022 \nnoKD:des 1 7 7 0.015 0.901897 \ncondition:noKD:des 3 179 60 0.130 0.942288 \nResiduals 294 135363 460 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 530 106 0.226 0.95091 \nnoDPP 1 4190 4190 8.952 0.00301 **\ndes 1 17 17 0.037 0.84776 \ncondition:noDPP 5 218 44 0.093 0.99322 \ncondition:des 3 1553 518 1.106 0.34702 \nnoDPP:des 1 0 0 0.001 0.98183 \ncondition:noDPP:des 3 316 105 0.225 0.87890 \nResiduals 294 137607 468 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKMT*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 530 106.0 0.221 0.954\nnoKMT 1 9 9.2 0.019 0.890\ndes 1 30 30.1 0.063 0.803\ncondition:noKMT 5 863 172.7 0.360 0.876\ncondition:des 3 1521 507.1 1.056 0.368\nnoKMT:des 1 23 22.8 0.047 0.828\ncondition:noKMT:des 3 270 90.1 0.188 0.905\nResiduals 294 141185 480.2 \n> md<-aov(xpos_flips~condition*noPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.339 0.889\nnoPP 1 1.7 1.7388 1.078 0.300\ndes 1 2.0 2.0021 1.241 0.266\ncondition:noPP 5 7.2 1.4356 0.890 0.488\ncondition:des 3 5.4 1.7895 1.109 0.346\nnoPP:des 1 1.0 0.9678 0.600 0.439\ncondition:noPP:des 3 5.4 1.7837 1.105 0.347\nResiduals 294 474.4 1.6136 \n> md<-aov(xpos_flips~condition*noTGG*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.338 0.890\nnoTGG 1 0.9 0.9371 0.579 0.447\ndes 1 2.0 2.0055 1.238 0.267\ncondition:noTGG 5 8.8 1.7629 1.089 0.367\ncondition:des 3 5.3 1.7589 1.086 0.355\nnoTGG:des 1 0.4 0.4073 0.252 0.616\ncondition:noTGG:des 3 3.5 1.1624 0.718 0.542\nResiduals 294 476.1 1.6193 \n> md<-aov(xpos_flips~condition*noTMD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.334 0.892\nnoTMD 1 0.0 0.0025 0.002 0.969\ndes 1 2.0 1.9909 1.216 0.271\ncondition:noTMD 5 6.6 1.3174 0.804 0.547\ncondition:des 3 5.1 1.6886 1.031 0.379\nnoTMD:des 1 0.0 0.0274 0.017 0.897\ncondition:noTMD:des 3 1.8 0.6016 0.367 0.777\nResiduals 294 481.5 1.6379 \n> md<-aov(xpos_flips~condition*noKD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2.7 0.547 0.348 0.883465 \nnoKD 1 21.4 21.355 13.582 0.000272 ***\ndes 1 1.7 1.692 1.076 0.300440 \ncondition:noKD 5 1.0 0.191 0.121 0.987609 \ncondition:des 3 5.6 1.860 1.183 0.316324 \nnoKD:des 1 0.4 0.426 0.271 0.603174 \ncondition:noKD:des 3 4.8 1.589 1.011 0.388322 \nResiduals 294 462.2 1.572 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 2.7 0.547 0.350 0.882015 \nnoDPP 1 20.0 19.971 12.785 0.000408 ***\ndes 1 1.7 1.735 1.111 0.292728 \ncondition:noDPP 5 4.6 0.912 0.584 0.712571 \ncondition:des 3 5.4 1.809 1.158 0.325968 \nnoDPP:des 1 0.7 0.656 0.420 0.517455 \ncondition:noDPP:des 3 5.4 1.804 1.155 0.327159 \nResiduals 294 459.2 1.562 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noKMT*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 2.7 0.5469 0.335 0.892\nnoKMT 1 0.9 0.9434 0.578 0.448\ndes 1 2.0 1.9537 1.196 0.275\ncondition:noKMT 5 4.2 0.8412 0.515 0.765\ncondition:des 3 5.3 1.7512 1.072 0.361\nnoKMT:des 1 1.4 1.3912 0.852 0.357\ncondition:noKMT:des 3 3.1 1.0285 0.630 0.596\nResiduals 294 480.2 1.6332 \n> md<-aov(MAD~condition*noPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.322 0.255\nnoPP 1 3068 3068 0.048 0.826\ndes 1 12657 12657 0.199 0.656\ncondition:noPP 5 249389 49878 0.786 0.561\ncondition:des 3 22655 7552 0.119 0.949\nnoPP:des 1 32320 32320 0.509 0.476\ncondition:noPP:des 3 63179 21060 0.332 0.802\nResiduals 294 18665992 63490 \n> md<-aov(MAD~condition*noTGG*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.332 0.251\nnoTGG 1 9819 9819 0.156 0.693\ndes 1 12729 12729 0.202 0.653\ncondition:noTGG 5 210530 42106 0.668 0.648\ncondition:des 3 23270 7757 0.123 0.946\nnoTGG:des 1 117416 117416 1.863 0.173\ncondition:noTGG:des 3 144754 48251 0.766 0.514\nResiduals 294 18530740 63030 \n> md<-aov(MAD~condition*noTMD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.340 0.247 \nnoTMD 1 342736 342736 5.472 0.020 *\ndes 1 10299 10299 0.164 0.685 \ncondition:noTMD 5 117885 23577 0.376 0.865 \ncondition:des 3 22812 7604 0.121 0.947 \nnoTMD:des 1 287 287 0.005 0.946 \ncondition:noTMD:des 3 141527 47176 0.753 0.521 \nResiduals 294 18413712 62632 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.389 0.2281 \nnoKD 1 571572 571572 9.460 0.0023 **\ndes 1 17062 17062 0.282 0.5955 \ncondition:noKD 5 581491 116298 1.925 0.0901 . \ncondition:des 3 24796 8265 0.137 0.9380 \nnoKD:des 1 11010 11010 0.182 0.6698 \ncondition:noKD:des 3 80774 26925 0.446 0.7206 \nResiduals 294 17762554 60417 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 419711 83942 1.402 0.22344 \nnoDPP 1 659114 659114 11.008 0.00102 **\ndes 1 16790 16790 0.280 0.59683 \ncondition:noDPP 5 640320 128064 2.139 0.06090 . \ncondition:des 3 24202 8067 0.135 0.93929 \nnoDPP:des 1 4521 4521 0.076 0.78367 \ncondition:noDPP:des 3 100573 33524 0.560 0.64190 \nResiduals 294 17603738 59877 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 419711 83942 1.348 0.244\nnoKMT 1 3628 3628 0.058 0.809\ndes 1 12825 12825 0.206 0.650\ncondition:noKMT 5 343150 68630 1.102 0.360\ncondition:des 3 23226 7742 0.124 0.946\nnoKMT:des 1 365 365 0.006 0.939\ncondition:noKMT:des 3 352382 117461 1.886 0.132\nResiduals 294 18313682 62291 \n> \n> md<-aov(idle_time~condition*noPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.285 0.92135 \nnoPP 1 218 218 0.461 0.49770 \nsys 1 3763 3763 7.972 0.00509 **\ncondition:noPP 5 1016 203 0.430 0.82723 \ncondition:sys 5 1374 275 0.582 0.71368 \nnoPP:sys 1 186 186 0.393 0.53107 \ncondition:noPP:sys 5 559 112 0.237 0.94604 \nResiduals 280 132167 472 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTGG*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.288 0.91919 \nnoTGG 1 33 33 0.072 0.78928 \nsys 1 3865 3865 8.299 0.00427 **\ncondition:noTGG 5 846 169 0.363 0.87351 \ncondition:sys 5 1259 252 0.541 0.74532 \nnoTGG:sys 1 701 701 1.506 0.22081 \ncondition:noTGG:sys 5 2185 437 0.938 0.45638 \nResiduals 280 130393 466 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTMD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.300 0.912491 \nnoTMD 1 753 753 1.682 0.195754 \nsys 1 3862 3862 8.631 0.003580 ** \ncondition:noTMD 5 110 22 0.049 0.998535 \ncondition:sys 5 1393 279 0.622 0.682745 \nnoTMD:sys 1 5894 5894 13.171 0.000338 ***\ncondition:noTMD:sys 5 1984 397 0.887 0.490406 \nResiduals 280 125288 447 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.308 0.907883 \nnoKD 1 6375 6375 14.621 0.000162 ***\nsys 1 3961 3961 9.084 0.002815 ** \ncondition:noKD 5 547 109 0.251 0.939228 \ncondition:sys 5 1266 253 0.581 0.714633 \nnoKD:sys 1 3304 3304 7.578 0.006296 ** \ncondition:noKD:sys 5 1750 350 0.803 0.548480 \nResiduals 280 122080 436 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noDPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.297 0.91454 \nnoDPP 1 4528 4528 10.000 0.00174 **\nsys 1 3604 3604 7.960 0.00512 **\ncondition:noDPP 5 208 42 0.092 0.99346 \ncondition:sys 5 1313 263 0.580 0.71533 \nnoDPP:sys 1 779 779 1.719 0.19085 \ncondition:noDPP:sys 5 2062 412 0.911 0.47451 \nResiduals 280 126788 453 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKMT*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 672 134 0.287 0.91977 \nnoKMT 1 50 50 0.106 0.74507 \nsys 1 3869 3869 8.279 0.00432 **\ncondition:noKMT 5 988 198 0.423 0.83276 \ncondition:sys 5 1427 285 0.611 0.69192 \nnoKMT:sys 1 1381 1381 2.955 0.08671 . \ncondition:noKMT:sys 5 703 141 0.301 0.91207 \nResiduals 280 130865 467 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 1.9 0.3774 0.228 0.950\nnoPP 1 3.2 3.1601 1.911 0.168\nsys 1 0.3 0.2805 0.170 0.681\ncondition:noPP 5 5.9 1.1748 0.711 0.616\ncondition:sys 5 1.1 0.2207 0.133 0.985\nnoPP:sys 1 0.4 0.3977 0.241 0.624\ncondition:noPP:sys 5 8.2 1.6397 0.992 0.423\nResiduals 280 462.9 1.6533 \n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTGG*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 1.9 0.3774 0.227 0.950\nnoTGG 1 0.7 0.7181 0.433 0.511\nsys 1 0.3 0.2705 0.163 0.687\ncondition:noTGG 5 8.2 1.6389 0.987 0.426\ncondition:sys 5 1.2 0.2340 0.141 0.983\nnoTGG:sys 1 0.6 0.6182 0.373 0.542\ncondition:noTGG:sys 5 6.3 1.2506 0.754 0.584\nResiduals 280 464.7 1.6597 \n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTMD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.232 0.94814 \nnoTMD 1 0.0 0.002 0.001 0.97549 \nsys 1 0.1 0.149 0.092 0.76202 \ncondition:noTMD 5 5.3 1.051 0.647 0.66394 \ncondition:sys 5 1.2 0.231 0.142 0.98212 \nnoTMD:sys 1 15.3 15.258 9.394 0.00239 **\ncondition:noTMD:sys 5 5.4 1.070 0.659 0.65488 \nResiduals 280 454.8 1.624 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.234 0.947502 \nnoKD 1 20.9 20.887 12.936 0.000381 ***\nsys 1 0.2 0.177 0.110 0.740856 \ncondition:noKD 5 1.5 0.301 0.186 0.967586 \ncondition:sys 5 0.9 0.190 0.118 0.988428 \nnoKD:sys 1 1.0 1.043 0.646 0.422339 \ncondition:noKD:sys 5 5.3 1.055 0.654 0.658962 \nResiduals 280 452.1 1.615 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noDPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.9 0.377 0.237 0.945940 \nnoDPP 1 19.5 19.496 12.245 0.000542 ***\nsys 1 0.1 0.055 0.034 0.853292 \ncondition:noDPP 5 6.8 1.358 0.853 0.513261 \ncondition:sys 5 1.0 0.205 0.129 0.985728 \nnoDPP:sys 1 0.3 0.300 0.188 0.664524 \ncondition:noDPP:sys 5 8.5 1.694 1.064 0.380692 \nResiduals 280 445.8 1.592 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKMT*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 1.9 0.3774 0.225 0.951\nnoKMT 1 0.6 0.5915 0.353 0.553\nsys 1 0.1 0.1380 0.082 0.774\ncondition:noKMT 5 8.1 1.6104 0.961 0.442\ncondition:sys 5 1.0 0.2006 0.120 0.988\nnoKMT:sys 1 0.5 0.4535 0.271 0.603\ncondition:noKMT:sys 5 2.7 0.5357 0.320 0.901\nResiduals 280 469.0 1.6751 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.440 0.2098 \nnoPP 1 1138 1138 0.018 0.8923 \nsys 1 17301 17301 0.279 0.5977 \ncondition:noPP 5 192649 38530 0.621 0.6836 \ncondition:sys 5 302626 60525 0.976 0.4326 \nnoPP:sys 1 311596 311596 5.026 0.0258 *\ncondition:noPP:sys 5 266221 53244 0.859 0.5093 \nResiduals 280 17360432 62002 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTGG*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 446554 89311 1.416 0.219\nnoTGG 1 20962 20962 0.332 0.565\nsys 1 11615 11615 0.184 0.668\ncondition:noTGG 5 251632 50326 0.798 0.552\ncondition:sys 5 230183 46037 0.730 0.602\nnoTGG:sys 1 20352 20352 0.323 0.570\ncondition:noTGG:sys 5 253315 50663 0.803 0.548\nResiduals 280 17663905 63085 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTMD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.485 0.1947 \nnoTMD 1 380535 380535 6.328 0.0124 *\nsys 1 17574 17574 0.292 0.5892 \ncondition:noTMD 5 130589 26118 0.434 0.8245 \ncondition:sys 5 285152 57030 0.948 0.4501 \nnoTMD:sys 1 387960 387960 6.451 0.0116 *\ncondition:noTMD:sys 5 411752 82350 1.369 0.2359 \nResiduals 280 16838402 60137 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKD*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.530 0.18057 \nnoKD 1 540040 540040 9.250 0.00258 **\nsys 1 17954 17954 0.308 0.57965 \ncondition:noKD 5 537173 107435 1.840 0.10510 \ncondition:sys 5 272607 54521 0.934 0.45936 \nnoKD:sys 1 403058 403058 6.904 0.00908 **\ncondition:noKD:sys 5 333520 66704 1.142 0.33807 \nResiduals 280 16347612 58384 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noDPP*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.508 0.18719 \nnoDPP 1 623467 623467 10.530 0.00132 **\nsys 1 10239 10239 0.173 0.67783 \ncondition:noDPP 5 579541 115908 1.958 0.08507 . \ncondition:sys 5 265194 53039 0.896 0.48428 \nnoDPP:sys 1 104465 104465 1.764 0.18516 \ncondition:noDPP:sys 5 290661 58132 0.982 0.42914 \nResiduals 280 16578396 59209 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKMT*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 446554 89311 1.442 0.209\nnoKMT 1 219 219 0.004 0.953\nsys 1 16570 16570 0.268 0.605\ncondition:noKMT 5 437364 87473 1.412 0.220\ncondition:sys 5 282313 56463 0.912 0.474\nnoKMT:sys 1 108903 108903 1.758 0.186\ncondition:noKMT:sys 5 262831 52566 0.849 0.516\nResiduals 280 17343764 61942 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 446554 89311 1.439 0.210\neht_0 1 6261 6261 0.101 0.751\ncondition:eht_0 5 322921 64584 1.041 0.394\nResiduals 292 18122782 62064 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 446554 89311 1.410 0.221\neht_0 1 6261 6261 0.099 0.753\ndes 1 643 643 0.010 0.920\ncondition:eht_0 5 322648 64530 1.018 0.407\ncondition:des 3 10093 3364 0.053 0.984\neht_0:des 1 41463 41463 0.654 0.419\ncondition:eht_0:des 3 75745 25248 0.398 0.754\nResiduals 284 17995111 63363 \n10 observations deleted due to missingness\n> \n> md<-aov(MAD~condition*eht_0*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.473 0.19880 \neht_0 1 6261 6261 0.103 0.74822 \nsys 1 16142 16142 0.266 0.60632 \ncondition:eht_0 5 321298 64260 1.060 0.38308 \ncondition:sys 5 292982 58596 0.966 0.43882 \neht_0:sys 1 505846 505846 8.341 0.00418 **\ncondition:eht_0:sys 5 328610 65722 1.084 0.36955 \nResiduals 280 16980825 60646 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 446554 89311 1.412 0.22011 \neht_0 1 6261 6261 0.099 0.75329 \nsys 1 16142 16142 0.255 0.61384 \ndes 1 808 808 0.013 0.91009 \ncondition:eht_0 5 320979 64196 1.015 0.40917 \ncondition:sys 5 292833 58567 0.926 0.46454 \neht_0:sys 1 505942 505942 7.999 0.00504 **\ncondition:des 3 14100 4700 0.074 0.97375 \neht_0:des 1 47110 47110 0.745 0.38890 \nsys:des 1 860 860 0.014 0.90727 \ncondition:eht_0:sys 5 328546 65709 1.039 0.39510 \ncondition:eht_0:des 3 69718 23239 0.367 0.77657 \ncondition:sys:des 3 69143 23048 0.364 0.77876 \neht_0:sys:des 1 1047 1047 0.017 0.89771 \ncondition:eht_0:sys:des 3 81127 27042 0.428 0.73341 \nResiduals 264 16697349 63248 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 1.116e+11 2.232e+10 1.178 0.320\neht_0 1 3.212e+08 3.212e+08 0.017 0.897\ncondition:eht_0 5 1.087e+11 2.175e+10 1.147 0.335\nResiduals 292 5.534e+12 1.895e+10 \n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 1.116e+11 2.232e+10 1.169 0.324\neht_0 1 3.212e+08 3.212e+08 0.017 0.897\ndes 1 1.868e+10 1.868e+10 0.978 0.323\ncondition:eht_0 5 1.088e+11 2.177e+10 1.140 0.339\ncondition:des 3 9.982e+09 3.327e+09 0.174 0.914\neht_0:des 1 2.152e+10 2.152e+10 1.128 0.289\ncondition:eht_0:des 3 6.292e+10 2.097e+10 1.099 0.350\nResiduals 284 5.421e+12 1.909e+10 \n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0*sys,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.116e+11 2.232e+10 1.182 0.318 \neht_0 1 3.212e+08 3.212e+08 0.017 0.896 \nsys 1 2.484e+08 2.484e+08 0.013 0.909 \ncondition:eht_0 5 1.086e+11 2.172e+10 1.150 0.334 \ncondition:sys 5 8.031e+10 1.606e+10 0.851 0.515 \neht_0:sys 1 6.579e+10 6.579e+10 3.484 0.063 .\ncondition:eht_0:sys 5 1.006e+11 2.013e+10 1.066 0.379 \nResiduals 280 5.287e+12 1.888e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*sys*des,\n+ data=avg_measures.1b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 1.116e+11 2.232e+10 1.149 0.3348 \neht_0 1 3.212e+08 3.212e+08 0.017 0.8978 \nsys 1 2.484e+08 2.484e+08 0.013 0.9100 \ndes 1 1.879e+10 1.879e+10 0.968 0.3262 \ncondition:eht_0 5 1.087e+11 2.174e+10 1.119 0.3504 \ncondition:sys 5 7.918e+10 1.584e+10 0.816 0.5395 \neht_0:sys 1 6.603e+10 6.603e+10 3.400 0.0663 .\ncondition:des 3 1.070e+10 3.567e+09 0.184 0.9075 \neht_0:des 1 2.191e+10 2.191e+10 1.128 0.2891 \nsys:des 1 2.455e+09 2.455e+09 0.126 0.7224 \ncondition:eht_0:sys 5 1.010e+11 2.019e+10 1.040 0.3946 \ncondition:eht_0:des 3 6.140e+10 2.047e+10 1.054 0.3692 \ncondition:sys:des 3 1.126e+10 3.752e+09 0.193 0.9010 \neht_0:sys:des 1 1.235e+10 1.235e+10 0.636 0.4258 \ncondition:eht_0:sys:des 3 2.193e+10 7.311e+09 0.376 0.7700 \nResiduals 264 5.127e+12 1.942e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> condition:des 3 8.8 2.946 2.289 0.0784 .\neht_0:des 1 0.2 0.156 0.121 0.7282 \ncondition:eht_0:des 3 0.4 0.122 0.095 0.9628 \nResiduals 309 397.6 1.287 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(xpos_flips~condition*eht_0*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.644 0.6660 \neht_0 1 7.0 6.968 5.285 0.0222 *\nsys 1 0.2 0.226 0.171 0.6791 \ncondition:eht_0 5 5.4 1.076 0.816 0.5392 \ncondition:sys 5 3.9 0.774 0.587 0.7102 \neht_0:sys 1 0.0 0.000 0.000 0.9874 \ncondition:eht_0:sys 5 1.4 0.276 0.209 0.9585 \nResiduals 305 402.2 1.319 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*eht_0*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.628 0.6782 \neht_0 1 7.0 6.968 5.154 0.0239 *\nsys 1 0.2 0.226 0.167 0.6829 \ndes 1 0.7 0.665 0.492 0.4838 \ncondition:eht_0 5 5.4 1.076 0.796 0.5536 \ncondition:sys 5 3.9 0.773 0.572 0.7215 \neht_0:sys 1 0.0 0.000 0.000 0.9870 \ncondition:des 3 8.9 2.952 2.183 0.0901 .\neht_0:des 1 0.2 0.156 0.115 0.7343 \nsys:des 1 0.5 0.520 0.385 0.5356 \ncondition:eht_0:sys 5 1.4 0.272 0.201 0.9619 \ncondition:eht_0:des 3 0.4 0.122 0.090 0.9655 \ncondition:sys:des 3 0.4 0.144 0.106 0.9563 \neht_0:sys:des 1 0.1 0.124 0.092 0.7619 \ncondition:eht_0:sys:des 3 0.3 0.106 0.079 0.9715 \nResiduals 289 390.7 1.352 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.119 0.988\ndes 1 91 90.9 0.161 0.688\ncondition:des 3 1421 473.8 0.840 0.473\nResiduals 329 185574 564.1 \n> md<-aov(idle_time~condition*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.152 0.97928 \nsys 1 5175 5175 9.110 0.00275 **\ncondition:sys 5 834 167 0.294 0.91627 \nResiduals 317 180068 568 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.152 0.97941 \nifTW 1 4787 4787 8.403 0.00401 **\ncondition:ifTW 5 698 140 0.245 0.94203 \nResiduals 317 180591 570 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.150 0.97996 \nsys 1 5175 5175 8.974 0.00296 **\ndes 1 94 94 0.163 0.68626 \ncondition:sys 5 833 167 0.289 0.91894 \ncondition:des 3 1355 452 0.783 0.50396 \nsys:des 1 78 78 0.135 0.71341 \ncondition:sys:des 3 361 120 0.209 0.89022 \nResiduals 309 178180 577 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.150 0.97990 \nifTW 1 4787 4787 8.314 0.00421 **\ndes 1 88 88 0.152 0.69676 \ncondition:ifTW 5 698 140 0.243 0.94332 \ncondition:des 3 1340 447 0.776 0.50832 \nifTW:des 1 1127 1127 1.958 0.16274 \ncondition:ifTW:des 3 102 34 0.059 0.98105 \nResiduals 309 177934 576 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*ifTW*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.161 0.97669 \nifTW 1 4787 4787 8.885 0.00311 ** \nsys 1 4944 4944 9.176 0.00266 ** \ncondition:ifTW 5 698 140 0.259 0.93509 \ncondition:sys 5 851 170 0.316 0.90330 \nifTW:sys 1 9544 9544 17.713 3.38e-05 ***\ncondition:ifTW:sys 5 920 184 0.342 0.88744 \nResiduals 305 164333 539 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*ifTW*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.155 0.97829 \nifTW 1 4787 4787 8.603 0.00363 ** \nsys 1 4944 4944 8.884 0.00312 ** \ndes 1 94 94 0.168 0.68179 \ncondition:ifTW 5 698 140 0.251 0.93927 \ncondition:sys 5 850 170 0.305 0.90947 \nifTW:sys 1 9545 9545 17.152 4.53e-05 ***\ncondition:des 3 1357 452 0.813 0.48773 \nifTW:des 1 1126 1126 2.023 0.15605 \nsys:des 1 65 65 0.117 0.73253 \ncondition:ifTW:sys 5 919 184 0.330 0.89455 \ncondition:ifTW:des 3 102 34 0.061 0.98029 \ncondition:sys:des 3 354 118 0.212 0.88790 \nifTW:sys:des 1 1 1 0.002 0.96866 \ncondition:ifTW:sys:des 3 409 136 0.245 0.86487 \nResiduals 289 160827 556 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.152 0.97938 \neht_0 1 4125 4125 7.246 0.00749 **\ncondition:eht_0 5 1477 295 0.519 0.76198 \nResiduals 317 180475 569 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.150 0.98006 \neht_0 1 4125 4125 7.137 0.00795 **\ndes 1 87 87 0.151 0.69825 \ncondition:eht_0 5 1477 295 0.511 0.76775 \ncondition:des 3 1339 446 0.772 0.51015 \neht_0:des 1 169 169 0.292 0.58914 \ncondition:eht_0:des 3 295 98 0.170 0.91650 \nResiduals 309 178584 578 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(idle_time~condition*eht_0*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.154 0.97885 \neht_0 1 4125 4125 7.327 0.00717 **\nsys 1 5008 5008 8.895 0.00309 **\ncondition:eht_0 5 1479 296 0.526 0.75692 \ncondition:sys 5 853 171 0.303 0.91086 \neht_0:sys 1 1866 1866 3.314 0.06968 . \ncondition:eht_0:sys 5 1042 208 0.370 0.86893 \nResiduals 305 171704 563 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*eht_0*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.148 0.98057 \neht_0 1 4125 4125 7.054 0.00835 **\nsys 1 5008 5008 8.564 0.00370 **\ndes 1 93 93 0.159 0.69004 \ncondition:eht_0 5 1480 296 0.506 0.77166 \ncondition:sys 5 852 170 0.291 0.91753 \neht_0:sys 1 1865 1865 3.189 0.07520 . \ncondition:des 3 1351 450 0.770 0.51169 \neht_0:des 1 170 170 0.291 0.58985 \nsys:des 1 80 80 0.137 0.71146 \ncondition:eht_0:sys 5 1039 208 0.355 0.87858 \ncondition:eht_0:des 3 294 98 0.168 0.91804 \ncondition:sys:des 3 352 117 0.200 0.89600 \neht_0:sys:des 1 114 114 0.196 0.65859 \ncondition:eht_0:sys:des 3 253 84 0.144 0.93333 \nResiduals 289 169001 585 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.117 0.989\nnoPP 1 290 290.4 0.510 0.476\ncondition:noPP 5 459 91.8 0.161 0.977\nResiduals 327 186337 569.8 \n> md<-aov(idle_time~condition*noTGG,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.118 0.988\nnoTGG 1 1154 1154.4 2.042 0.154\ncondition:noTGG 5 1106 221.2 0.391 0.855\nResiduals 327 184826 565.2 \n> md<-aov(idle_time~condition*noTMD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.118 0.988\nnoTMD 1 115 115.2 0.203 0.653\ncondition:noTMD 5 1327 265.3 0.467 0.801\nResiduals 327 185644 567.7 \n> md<-aov(idle_time~condition*noKD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 334 67 0.124 0.987 \nnoKD 1 8564 8564 15.893 8.27e-05 ***\ncondition:noKD 5 2321 464 0.862 0.507 \nResiduals 327 176201 539 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 334 67 0.122 0.987342 \nnoDPP 1 6776 6776 12.406 0.000489 ***\ncondition:noDPP 5 1714 343 0.628 0.678898 \nResiduals 327 178597 546 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKMT,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.118 0.988\nnoKMT 1 202 202.3 0.356 0.551\ncondition:noKMT 5 1316 263.1 0.464 0.803\nResiduals 327 185569 567.5 \n> md<-aov(xpos_flips~condition*noPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.640 0.669 \nnoPP 1 4.6 4.557 3.563 0.060 .\ncondition:noPP 5 0.3 0.056 0.044 0.999 \nResiduals 327 418.2 1.279 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTGG,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 4.1 0.8186 0.635 0.673\nnoTGG 1 0.1 0.1174 0.091 0.763\ncondition:noTGG 5 1.3 0.2581 0.200 0.962\nResiduals 327 421.6 1.2893 \n> md<-aov(xpos_flips~condition*noTMD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 4.1 0.8186 0.637 0.672\nnoTMD 1 0.1 0.0715 0.056 0.814\ncondition:noTMD 5 2.6 0.5120 0.398 0.850\nResiduals 327 420.4 1.2855 \n> md<-aov(xpos_flips~condition*noKD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.667 0.648609 \nnoKD 1 16.1 16.093 13.117 0.000339 ***\ncondition:noKD 5 5.7 1.141 0.930 0.461859 \nResiduals 327 401.2 1.227 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.660 0.65393 \nnoDPP 1 12.7 12.734 10.270 0.00149 **\ncondition:noDPP 5 4.8 0.962 0.776 0.56756 \nResiduals 327 405.5 1.240 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noKMT,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 4.1 0.8186 0.641 0.668\nnoKMT 1 0.7 0.7197 0.564 0.453\ncondition:noKMT 5 4.8 0.9666 0.757 0.581\nResiduals 327 417.5 1.2766 \n> md<-aov(MAD~condition*noPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 0.995 0.421\nnoPP 1 8931 8931 0.249 0.618\ncondition:noPP 5 143836 28767 0.803 0.548\nResiduals 327 11708146 35805 \n> md<-aov(MAD~condition*noTGG,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 1.005 0.415\nnoTGG 1 79884 79884 2.253 0.134\ncondition:noTGG 5 186822 37364 1.054 0.386\nResiduals 327 11594207 35456 \n> md<-aov(MAD~condition*noTMD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 1.018 0.40701 \nnoTMD 1 375960 375960 10.738 0.00116 **\ncondition:noTMD 5 36337 7267 0.208 0.95919 \nResiduals 327 11448615 35011 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 0.995 0.4205 \nnoKD 1 105616 105616 2.950 0.0868 .\ncondition:noKD 5 48078 9616 0.269 0.9301 \nResiduals 327 11707219 35802 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 1.015 0.40884 \nnoDPP 1 264736 264736 7.539 0.00637 **\ncondition:noDPP 5 112835 22567 0.643 0.66733 \nResiduals 327 11483341 35117 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 0.988 0.425\nnoKMT 1 22030 22030 0.611 0.435\ncondition:noKMT 5 48164 9633 0.267 0.931\nResiduals 327 11790719 36057 \n> md<-aov(idle_time~condition*noPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.115 0.989\nnoPP 1 290 290.4 0.501 0.479\ndes 1 90 90.5 0.156 0.693\ncondition:noPP 5 459 91.7 0.158 0.977\ncondition:des 3 1419 473.1 0.817 0.485\nnoPP:des 1 1 1.3 0.002 0.963\ncondition:noPP:des 3 34 11.2 0.019 0.996\nResiduals 319 184793 579.3 \n> md<-aov(idle_time~condition*noTGG*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.117 0.989\nnoTGG 1 1154 1154.4 2.013 0.157\ndes 1 94 93.5 0.163 0.687\ncondition:noTGG 5 1108 221.6 0.386 0.858\ncondition:des 3 1432 477.4 0.833 0.477\nnoTGG:des 1 5 4.7 0.008 0.928\ncondition:noTGG:des 3 397 132.4 0.231 0.875\nResiduals 319 182897 573.3 \n> md<-aov(idle_time~condition*noTMD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.116 0.989\nnoTMD 1 115 115.2 0.200 0.655\ndes 1 90 90.4 0.157 0.692\ncondition:noTMD 5 1327 265.4 0.460 0.806\ncondition:des 3 1421 473.7 0.821 0.483\nnoTMD:des 1 3 3.3 0.006 0.940\ncondition:noTMD:des 3 94 31.2 0.054 0.983\nResiduals 319 184036 576.9 \n> md<-aov(idle_time~condition*noKD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 334 67 0.123 0.987 \nnoKD 1 8564 8564 15.748 8.94e-05 ***\ndes 1 86 86 0.159 0.691 \ncondition:noKD 5 2324 465 0.855 0.512 \ncondition:des 3 1416 472 0.868 0.458 \nnoKD:des 1 233 233 0.428 0.514 \ncondition:noKD:des 3 993 331 0.609 0.610 \nResiduals 319 173471 544 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noDPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 334 67 0.121 0.987561 \nnoDPP 1 6776 6776 12.310 0.000515 ***\ndes 1 86 86 0.157 0.692386 \ncondition:noDPP 5 1716 343 0.623 0.681993 \ncondition:des 3 1415 472 0.857 0.463602 \nnoDPP:des 1 441 441 0.802 0.371307 \ncondition:noDPP:des 3 1073 358 0.650 0.583412 \nResiduals 319 175579 550 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(idle_time~condition*noKMT*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 334 66.9 0.116 0.989\nnoKMT 1 202 202.3 0.352 0.553\ndes 1 91 91.4 0.159 0.690\ncondition:noKMT 5 1315 263.0 0.458 0.807\ncondition:des 3 1421 473.6 0.825 0.481\nnoKMT:des 1 339 338.9 0.590 0.443\ncondition:noKMT:des 3 545 181.7 0.316 0.814\nResiduals 319 183173 574.2 \n> md<-aov(xpos_flips~condition*noPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.640 0.6692 \nnoPP 1 4.6 4.557 3.563 0.0600 .\ndes 1 0.7 0.673 0.526 0.4687 \ncondition:noPP 5 0.3 0.056 0.044 0.9989 \ncondition:des 3 8.7 2.898 2.266 0.0808 .\nnoPP:des 1 0.0 0.020 0.015 0.9013 \ncondition:noPP:des 3 0.9 0.285 0.223 0.8804 \nResiduals 319 407.9 1.279 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTGG*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.8186 0.639 0.6699 \nnoTGG 1 0.1 0.1174 0.092 0.7623 \ndes 1 0.7 0.6755 0.528 0.4682 \ncondition:noTGG 5 1.3 0.2606 0.204 0.9609 \ncondition:des 3 8.8 2.9300 2.288 0.0785 .\nnoTGG:des 1 0.0 0.0373 0.029 0.8645 \ncondition:noTGG:des 3 3.6 1.1953 0.933 0.4248 \nResiduals 319 408.5 1.2805 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noTMD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.8186 0.641 0.6686 \nnoTMD 1 0.1 0.0715 0.056 0.8131 \ndes 1 0.7 0.6788 0.532 0.4665 \ncondition:noTMD 5 2.6 0.5119 0.401 0.8481 \ncondition:des 3 8.7 2.9107 2.279 0.0794 .\nnoTMD:des 1 0.2 0.2355 0.184 0.6679 \ncondition:noTMD:des 3 3.3 1.1154 0.873 0.4551 \nResiduals 319 407.4 1.2771 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noKD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.678 0.640089 \nnoKD 1 16.1 16.093 13.338 0.000304 ***\ndes 1 0.7 0.660 0.547 0.459936 \ncondition:noKD 5 5.7 1.143 0.947 0.450481 \ncondition:des 3 8.7 2.901 2.404 0.067473 . \nnoKD:des 1 1.2 1.192 0.988 0.320966 \ncondition:noKD:des 3 5.7 1.910 1.583 0.193297 \nResiduals 319 384.9 1.207 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noDPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.819 0.671 0.64593 \nnoDPP 1 12.7 12.734 10.434 0.00137 **\ndes 1 0.7 0.660 0.541 0.46252 \ncondition:noDPP 5 4.8 0.966 0.791 0.55669 \ncondition:des 3 8.7 2.908 2.383 0.06939 . \nnoDPP:des 1 0.6 0.628 0.515 0.47364 \ncondition:noDPP:des 3 6.1 2.033 1.665 0.17436 \nResiduals 319 389.3 1.220 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(xpos_flips~condition*noKMT*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.1 0.8186 0.649 0.6625 \nnoKMT 1 0.7 0.7197 0.570 0.4506 \ndes 1 0.7 0.6801 0.539 0.4633 \ncondition:noKMT 5 4.8 0.9648 0.765 0.5758 \ncondition:des 3 8.7 2.8966 2.296 0.0777 .\nnoKMT:des 1 3.0 2.9714 2.355 0.1258 \ncondition:noKMT:des 3 2.7 0.8990 0.713 0.5450 \nResiduals 319 402.4 1.2615 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 0.982 0.429\nnoPP 1 8931 8931 0.246 0.620\ndes 1 8839 8839 0.243 0.622\ncondition:noPP 5 143807 28761 0.792 0.556\ncondition:des 3 82058 27353 0.753 0.521\nnoPP:des 1 3114 3114 0.086 0.770\ncondition:noPP:des 3 33815 11272 0.310 0.818\nResiduals 319 11580349 36302 \n> md<-aov(MAD~condition*noTGG*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 0.991 0.423\nnoTGG 1 79884 79884 2.222 0.137\ndes 1 9078 9078 0.252 0.616\ncondition:noTGG 5 186867 37373 1.039 0.394\ncondition:des 3 83621 27874 0.775 0.509\nnoTGG:des 1 5497 5497 0.153 0.696\ncondition:noTGG:des 3 26270 8757 0.244 0.866\nResiduals 319 11469697 35955 \n> md<-aov(MAD~condition*noTMD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 1.005 0.41476 \nnoTMD 1 375960 375960 10.603 0.00125 **\ndes 1 9133 9133 0.258 0.61215 \ncondition:noTMD 5 36343 7269 0.205 0.96027 \ncondition:des 3 83688 27896 0.787 0.50205 \nnoTMD:des 1 7543 7543 0.213 0.64495 \ncondition:noTMD:des 3 36707 12236 0.345 0.79275 \nResiduals 319 11311537 35459 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKD*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 0.984 0.4278 \nnoKD 1 105616 105616 2.915 0.0887 .\ndes 1 8703 8703 0.240 0.6244 \ncondition:noKD 5 47989 9598 0.265 0.9320 \ncondition:des 3 81091 27030 0.746 0.5253 \nnoKD:des 1 40122 40122 1.107 0.2935 \ncondition:noKD:des 3 19569 6523 0.180 0.9099 \nResiduals 319 11557823 36231 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noDPP*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 178184 35637 1.003 0.41578 \nnoDPP 1 264736 264736 7.453 0.00668 **\ndes 1 8578 8578 0.242 0.62346 \ncondition:noDPP 5 112547 22509 0.634 0.67414 \ncondition:des 3 79470 26490 0.746 0.52547 \nnoDPP:des 1 42645 42645 1.201 0.27403 \ncondition:noDPP:des 3 22290 7430 0.209 0.89001 \nResiduals 319 11330647 35519 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n> md<-aov(MAD~condition*noKMT*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 178184 35637 0.976 0.432\nnoKMT 1 22030 22030 0.603 0.438\ndes 1 8816 8816 0.241 0.623\ncondition:noKMT 5 48125 9625 0.264 0.933\ncondition:des 3 81897 27299 0.748 0.524\nnoKMT:des 1 12115 12115 0.332 0.565\ncondition:noKMT:des 3 41953 13984 0.383 0.765\nResiduals 319 11645977 36508 \n> \n> md<-aov(idle_time~condition*noPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.149 0.98011 \nnoPP 1 764 764 1.321 0.25130 \nsys 1 5566 5566 9.619 0.00211 **\ncondition:noPP 5 463 93 0.160 0.97687 \ncondition:sys 5 852 170 0.294 0.91591 \nnoPP:sys 1 1360 1360 2.351 0.12625 \ncondition:noPP:sys 5 577 115 0.199 0.96251 \nResiduals 305 176494 579 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTGG*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.151 0.97962 \nnoTGG 1 1508 1508 2.635 0.10558 \nsys 1 4434 4434 7.745 0.00572 **\ncondition:noTGG 5 1112 222 0.389 0.85659 \ncondition:sys 5 1055 211 0.369 0.86991 \nnoTGG:sys 1 2536 2536 4.431 0.03612 * \ncondition:noTGG:sys 5 845 169 0.295 0.91542 \nResiduals 305 174587 572 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noTMD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.157 0.97784 \nnoTMD 1 72 72 0.131 0.71810 \nsys 1 5171 5171 9.381 0.00239 ** \ncondition:noTMD 5 1289 258 0.468 0.80015 \ncondition:sys 5 834 167 0.303 0.91105 \nnoTMD:sys 1 8660 8660 15.710 9.2e-05 ***\ncondition:noTMD:sys 5 1920 384 0.697 0.62634 \nResiduals 305 168129 551 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.162 0.976209 \nnoKD 1 8240 8240 15.434 0.000106 ***\nsys 1 4690 4690 8.785 0.003276 ** \ncondition:noKD 5 2264 453 0.848 0.516566 \ncondition:sys 5 798 160 0.299 0.913343 \nnoKD:sys 1 6309 6309 11.817 0.000668 ***\ncondition:noKD:sys 5 943 189 0.353 0.879913 \nResiduals 305 162833 534 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noDPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.156 0.978227 \nnoDPP 1 6451 6451 11.610 0.000744 ***\nsys 1 4389 4389 7.899 0.005266 ** \ncondition:noDPP 5 1703 341 0.613 0.690028 \ncondition:sys 5 773 155 0.278 0.924845 \nnoDPP:sys 1 2201 2201 3.961 0.047463 * \ncondition:noDPP:sys 5 1092 218 0.393 0.853424 \nResiduals 305 169468 556 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(idle_time~condition*noKMT*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 432 86 0.149 0.98038 \nnoKMT 1 66 66 0.114 0.73618 \nsys 1 5146 5146 8.840 0.00318 **\ncondition:noKMT 5 1330 266 0.457 0.80819 \ncondition:sys 5 808 162 0.278 0.92528 \nnoKMT:sys 1 882 882 1.515 0.21934 \ncondition:noKMT:sys 5 281 56 0.097 0.99267 \nResiduals 305 177564 582 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.639 0.6700 \nnoPP 1 7.1 7.101 5.342 0.0215 *\nsys 1 0.6 0.573 0.431 0.5118 \ncondition:noPP 5 0.6 0.110 0.083 0.9948 \ncondition:sys 5 4.1 0.812 0.611 0.6918 \nnoPP:sys 1 0.0 0.044 0.033 0.8552 \ncondition:noPP:sys 5 2.2 0.441 0.332 0.8936 \nResiduals 305 405.4 1.329 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTGG*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 4.2 0.8497 0.631 0.676\nnoTGG 1 0.0 0.0424 0.031 0.859\nsys 1 0.3 0.3183 0.236 0.627\ncondition:noTGG 5 1.3 0.2620 0.194 0.965\ncondition:sys 5 4.1 0.8135 0.604 0.697\nnoTGG:sys 1 2.5 2.4763 1.838 0.176\ncondition:noTGG:sys 5 0.9 0.1864 0.138 0.983\nResiduals 305 410.8 1.3470 \n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noTMD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.664 0.651 \nnoTMD 1 0.1 0.124 0.097 0.756 \nsys 1 0.3 0.276 0.215 0.643 \ncondition:noTMD 5 2.6 0.511 0.399 0.849 \ncondition:sys 5 3.8 0.769 0.601 0.699 \nnoTMD:sys 1 19.9 19.918 15.570 9.87e-05 ***\ncondition:noTMD:sys 5 3.1 0.613 0.479 0.792 \nResiduals 305 390.2 1.279 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.658 0.655601 \nnoKD 1 15.7 15.653 12.122 0.000571 ***\nsys 1 0.1 0.138 0.107 0.743856 \ncondition:noKD 5 5.4 1.090 0.844 0.519501 \ncondition:sys 5 3.8 0.770 0.596 0.702837 \nnoKD:sys 1 0.1 0.141 0.109 0.741475 \ncondition:noKD:sys 5 0.9 0.180 0.140 0.982906 \nResiduals 305 393.8 1.291 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noDPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 4.2 0.850 0.655 0.65773 \nnoDPP 1 12.3 12.293 9.480 0.00227 **\nsys 1 0.1 0.072 0.056 0.81360 \ncondition:noDPP 5 4.5 0.903 0.696 0.62652 \ncondition:sys 5 3.8 0.760 0.586 0.71075 \nnoDPP:sys 1 1.0 1.023 0.789 0.37524 \ncondition:noDPP:sys 5 2.8 0.550 0.424 0.83176 \nResiduals 305 395.5 1.297 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(xpos_flips~condition*noKMT*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 4.2 0.8497 0.633 0.675\nnoKMT 1 0.4 0.4296 0.320 0.572\nsys 1 0.3 0.2560 0.191 0.663\ncondition:noKMT 5 4.8 0.9555 0.712 0.615\ncondition:sys 5 3.8 0.7628 0.568 0.725\nnoKMT:sys 1 0.1 0.0943 0.070 0.791\ncondition:noKMT:sys 5 1.0 0.2022 0.151 0.980\nResiduals 305 409.6 1.3429 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 216473 43295 1.177 0.321\nnoPP 1 19522 19522 0.531 0.467\nsys 1 18088 18088 0.492 0.484\ncondition:noPP 5 134850 26970 0.733 0.599\ncondition:sys 5 112839 22568 0.613 0.690\nnoPP:sys 1 27188 27188 0.739 0.391\ncondition:noPP:sys 5 137924 27585 0.750 0.587\nResiduals 305 11223342 36798 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTGG*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 216473 43295 1.184 0.317\nnoTGG 1 93224 93224 2.550 0.111\nsys 1 5484 5484 0.150 0.699\ncondition:noTGG 5 211147 42229 1.155 0.331\ncondition:sys 5 89066 17813 0.487 0.786\nnoTGG:sys 1 28821 28821 0.788 0.375\ncondition:noTGG:sys 5 96038 19208 0.525 0.757\nResiduals 305 11149973 36557 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noTMD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.208 0.30509 \nnoTMD 1 393077 393077 10.970 0.00104 **\nsys 1 15306 15306 0.427 0.51388 \ncondition:noTMD 5 45303 9061 0.253 0.93820 \ncondition:sys 5 105797 21159 0.591 0.70726 \nnoTMD:sys 1 65521 65521 1.829 0.17730 \ncondition:noTMD:sys 5 120069 24014 0.670 0.64635 \nResiduals 305 10928682 35832 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKD*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.173 0.3222 \nnoKD 1 100822 100822 2.732 0.0994 .\nsys 1 12042 12042 0.326 0.5683 \ncondition:noKD 5 39900 7980 0.216 0.9555 \ncondition:sys 5 105068 21014 0.569 0.7234 \nnoKD:sys 1 8560 8560 0.232 0.6304 \ncondition:noKD:sys 5 152180 30436 0.825 0.5328 \nResiduals 305 11255183 36902 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noDPP*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.197 0.31045 \nnoDPP 1 258943 258943 7.160 0.00786 **\nsys 1 7222 7222 0.200 0.65529 \ncondition:noDPP 5 119350 23870 0.660 0.65407 \ncondition:sys 5 105018 21004 0.581 0.71474 \nnoDPP:sys 1 41677 41677 1.152 0.28390 \ncondition:noDPP:sys 5 111173 22235 0.615 0.68863 \nResiduals 305 11030371 36165 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*noKMT*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F)\ncondition 5 216473 43295 1.159 0.329\nnoKMT 1 34608 34608 0.927 0.337\nsys 1 16164 16164 0.433 0.511\ncondition:noKMT 5 40043 8009 0.214 0.956\ncondition:sys 5 103721 20744 0.555 0.734\nnoKMT:sys 1 34666 34666 0.928 0.336\ncondition:noKMT:sys 5 52578 10516 0.282 0.923\nResiduals 305 11391973 37351 \n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.211 0.3038 \neht_0 1 197955 197955 5.535 0.0192 *\ncondition:eht_0 5 139376 27875 0.779 0.5651 \nResiduals 317 11336423 35762 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.200 0.3089 \neht_0 1 197955 197955 5.488 0.0198 *\ndes 1 18114 18114 0.502 0.4791 \ncondition:eht_0 5 139357 27871 0.773 0.5700 \ncondition:des 3 93538 31179 0.864 0.4599 \neht_0:des 1 31391 31391 0.870 0.3516 \ncondition:eht_0:des 3 47420 15807 0.438 0.7258 \nResiduals 309 11145979 36071 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(MAD~condition*eht_0*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.192 0.3129 \neht_0 1 197955 197955 5.451 0.0202 *\nsys 1 12934 12934 0.356 0.5511 \ncondition:eht_0 5 139357 27871 0.767 0.5738 \ncondition:sys 5 110239 22048 0.607 0.6945 \neht_0:sys 1 102887 102887 2.833 0.0934 .\ncondition:eht_0:sys 5 34218 6844 0.188 0.9668 \nResiduals 305 11076165 36315 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(MAD~condition*eht_0*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 216473 43295 1.186 0.3161 \neht_0 1 197955 197955 5.422 0.0206 *\nsys 1 12934 12934 0.354 0.5522 \ndes 1 18255 18255 0.500 0.4801 \ncondition:eht_0 5 139337 27867 0.763 0.5769 \ncondition:sys 5 110121 22024 0.603 0.6975 \neht_0:sys 1 102982 102982 2.821 0.0941 .\ncondition:des 3 94000 31333 0.858 0.4632 \neht_0:des 1 31349 31349 0.859 0.3549 \nsys:des 1 62921 62921 1.723 0.1903 \ncondition:eht_0:sys 5 34230 6846 0.188 0.9672 \ncondition:eht_0:des 3 47509 15836 0.434 0.7290 \ncondition:sys:des 3 170875 56958 1.560 0.1992 \neht_0:sys:des 1 3148 3148 0.086 0.7692 \ncondition:eht_0:sys:des 3 96328 32109 0.879 0.4521 \nResiduals 289 10551809 36511 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.281e+10 1.056e+10 0.976 0.4322 \neht_0 1 6.554e+10 6.554e+10 6.059 0.0144 *\ncondition:eht_0 5 2.888e+10 5.776e+09 0.534 0.7505 \nResiduals 317 3.429e+12 1.082e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.281e+10 1.056e+10 0.985 0.4270 \neht_0 1 6.554e+10 6.554e+10 6.112 0.0140 *\ndes 1 9.489e+09 9.489e+09 0.885 0.3476 \ncondition:eht_0 5 2.888e+10 5.776e+09 0.539 0.7470 \ncondition:des 3 6.443e+10 2.148e+10 2.003 0.1135 \neht_0:des 1 3.867e+10 3.867e+10 3.607 0.0585 .\ncondition:eht_0:des 3 2.923e+09 9.743e+08 0.091 0.9650 \nResiduals 309 3.313e+12 1.072e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n> md<-aov(AUC~condition*eht_0*sys,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.281e+10 1.056e+10 0.962 0.4416 \neht_0 1 6.554e+10 6.554e+10 5.967 0.0151 *\nsys 1 6.602e+09 6.602e+09 0.601 0.4388 \ncondition:eht_0 5 2.888e+10 5.776e+09 0.526 0.7567 \ncondition:sys 5 3.861e+10 7.721e+09 0.703 0.6216 \neht_0:sys 1 2.590e+10 2.590e+10 2.358 0.1257 \ncondition:eht_0:sys 5 7.723e+09 1.545e+09 0.141 0.9827 \nResiduals 305 3.350e+12 1.098e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> md<-aov(AUC~condition*eht_0*sys*des,\n+ data=avg_measures.0b2)\n> summary(md)\n Df Sum Sq Mean Sq F value Pr(>F) \ncondition 5 5.281e+10 1.056e+10 0.964 0.4404 \neht_0 1 6.554e+10 6.554e+10 5.979 0.0151 *\nsys 1 6.602e+09 6.602e+09 0.602 0.4383 \ndes 1 9.561e+09 9.561e+09 0.872 0.3511 \ncondition:eht_0 5 2.888e+10 5.775e+09 0.527 0.7559 \ncondition:sys 5 3.871e+10 7.741e+09 0.706 0.6192 \neht_0:sys 1 2.594e+10 2.594e+10 2.366 0.1251 \ncondition:des 3 6.472e+10 2.157e+10 1.968 0.1189 \neht_0:des 1 3.858e+10 3.858e+10 3.519 0.0617 .\nsys:des 1 5.416e+07 5.416e+07 0.005 0.9440 \ncondition:eht_0:sys 5 7.756e+09 1.551e+09 0.142 0.9824 \ncondition:eht_0:des 3 2.901e+09 9.671e+08 0.088 0.9665 \ncondition:sys:des 3 2.852e+10 9.507e+09 0.867 0.4584 \neht_0:sys:des 1 4.998e+09 4.998e+09 0.456 0.5001 \ncondition:eht_0:sys:des 3 3.281e+10 1.094e+10 0.998 0.3942 \nResiduals 289 3.168e+12 1.096e+10 \n---\nSignif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n10 observations deleted due to missingness\n> \n", "meta": {"hexsha": "2ddb660eb2941a490b62057640e9b3b4917751da", "size": 206706, "ext": "r", "lang": "R", "max_stars_repo_path": "2.r", "max_stars_repo_name": "shadowcrow0/disinfo_conspiracy", "max_stars_repo_head_hexsha": "aa54684e64148718ae19ff0acc7186ddd9d120da", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "2.r", "max_issues_repo_name": "shadowcrow0/disinfo_conspiracy", "max_issues_repo_head_hexsha": "aa54684e64148718ae19ff0acc7186ddd9d120da", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "2.r", "max_forks_repo_name": "shadowcrow0/disinfo_conspiracy", "max_forks_repo_head_hexsha": "aa54684e64148718ae19ff0acc7186ddd9d120da", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.8933756806, "max_line_length": 74, "alphanum_fraction": 0.5009095043, "num_tokens": 85520, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6442250928250375, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.3045145566026598}} {"text": "source(\"utils/rtools.r\");\n\nlist.packages = c(\"stats\", \"utils\", \"Rcpp\", \"stringr\", \"jsonlite\")\ninstall_missing(list.packages)\n\nsourceCpp('utils/parseParams.cpp')\n\nparams <- list(\n\twantedCol=\"x_OfSpectra\",\n\tpthreshold=0.05\n);\n\nparams$twoStats <- list( # stats comparing 2 test groups\n\t\"wilcoxon\"=function(x,y) tryCatch(wilcox.test(x,y)$p.value, error=function(cond) return(NaN))\n)\n\nparams$multiStats <- list( # stats comparing value to test group\n\t\"anova\"=function(x,y) null_na(summary(aov(x~y))[[1]][1,5])\n)\n\nparams <- mergeList(parseParams('statTests.r'), params);\n\nif (!('name' %in% names(params))) {\n\tparams$name <- readInput(\"dataset name:\");\n}\n\nhomedir <- fileExists(file.path(\"Results\",params$name), paste(\"Dataset\", params$name, \"cannot be found. Please run combinedHM to generate HeatMap data before this program is run.\"));\n\nsetwd(homedir);\n\ndir.create(\"StatTests\", showWarnings = FALSE)\ndir.create(\"StatsHeatMap\", showWarnings = FALSE)\n\nfids <- read.csv(\"fileIDs.csv\");\n\ndataset.groupids <- unique(fids$Test_Group) #unique test groups\n\nhms <- list.files(path=\"HeatMap/Files\"); #All heatmaps generated\n\nparsedHM <- str_match(hms, \"(.+)\\\\.csv\")[,2]\n\nif (length(dataset.groupids) > 50){\n\tmessage(\"Number of test groups exceeds 50. 2d statistics tests will be calculated unless a list of pairs is provided.\")\n\tstopQuietly();\n}\n\n# json list\njsonData <- list()\n# all pairs of files\ncnames <- combn(dataset.groupids, 2)\n\nsignificanceJSON = list()\n\nfor(hmid in 1:length(hms)){\n\thm <- hms[hmid]\n\thmname <- unlist(strsplit(hm, \".\", fixed=TRUE))[1];\n\tf <- read.csv(file.path(\"HeatMap\", \"Files\", hm));\n\tgroups <- list();\n\n\tsignificance <- read.csv(file.path(\"Significance\", hmname, \"raw.csv\"));\n\tsignificance$statTests = vector(mode=\"character\", length=NROW(significance));\n\n\tfor(x in 1:length(dataset.groupids)){\n\t\tfnames <- fids$ID[fids$Test_Group==dataset.groupids[x]] # get file ids in this group\n\t\tcolnames <- paste(params$wantedCol, fnames, sep=\"_\");\n\t\tselected <- f[,colnames];\n\t\tgroups[[x]] <- as.data.frame(selected);\n\t}\n\n\tcolnames <- paste(params$wantedCol, 1:length(fids$ID), sep=\"_\")\n\tungrouped <- f[,colnames];\n\tcolnames(ungrouped) <- fids$Test_Group;\n\n\tstatTables <- list()\n\n\tfor (stat in names(params$twoStats)) {\n\t\tstatTables[[stat]] <- data.frame(matrix(NA, nrow = NROW(f), ncol = NCOL(cnames)));\n\t\tfor(col in 1:NCOL(cnames)) {\n\t\t\tpair <- cnames[,col];\n\t\t\tcolnames(statTables[[stat]])[[col]] <- paste(pair, collapse=\"_\");\n\t\t\tfor(row in 1:NROW(f)) {\n\t\t\t\tp <- lapply(pair, function(i) as.numeric(groups[[i]][row,]))\n\t\t\t\tstatTables[[stat]][row,col] <- params$twoStats[[stat]](p[[1]], p[[2]]);\n\t\t\t\tif (f$Row_Type[row] == 1 && !is.nan(statTables[[stat]][row,col]) && !is.na(statTables[[stat]][row,col]) && statTables[[stat]][row,col] < params$pthreshold) {\n\t\t\t\t\tsignificance[significance$Rank_Number==f$Rank_Number[[row]],\"statTests\"] = paste(significance[significance$Rank_Number==f$Rank_Number[[row]],\"statTests\"], \n\t\t\t\t\t\t\"P value of \", formatSig(statTables[[stat]][row,col], 4), \" for 2D test \", stat, \" between groups \", pair[1], \"&\", pair[2], \"\\n\", sep=\"\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tstatTables[[stat]] <- cbind(f[c('Rank_Number','Protein_Name','Gene_Name')], statTables[[stat]], f['Row_Type'])\n\t\twrite.csv(statTables[[stat]], file=file.path(\"StatTests\", paste(hmname, '_', stat, '.csv', sep='')), row.names=FALSE)\n\t}\n\n\tmultiName <- \"MultiDim\"\n\n\tstatTables[[multiName]] <- data.frame(matrix(NA, nrow = NROW(f), ncol = length(params$multiStats)));\n\tfor (col in 1:length(params$multiStats)) {\n\t\tstat <- names(params$multiStats)[[col]];\n\t\tcolnames(statTables[[multiName]])[[col]] <- stat;\n\t\tfor(row in 1:NROW(f)) {\n\t\t\tstatTables[[multiName]][row,col] <- params$multiStats[[stat]](as.numeric(colnames(ungrouped)), as.numeric(ungrouped[row,]));\n\t\t\tif (f$Row_Type[row] == 1 && !is.nan(statTables[[multiName]][row,col]) && !is.na(statTables[[multiName]][row,col]) && statTables[[multiName]][row,col] < params$pthreshold) {\n\t\t\t\t\tsignificance[significance$Rank_Number==f$Rank_Number[[row]],\"statTests\"] = paste(significance[significance$Rank_Number==f$Rank_Number[[row]],\"statTests\"], \n\t\t\t\t\t\t\"P value of \", formatSig(statTables[[multiName]][row,col], 4), \" for MultiDim test \", stat, \"\\n\", sep=\"\");\n\t\t\t}\n\t\t}\n\t}\n\twrite.csv(cbind(f[,names(mtcars)!=\"Row_Type\"], statTables[[multiName]], f['Row_Type']), file=file.path(\"StatsHeatMap\", paste(hmname, '.csv', sep='')), row.names=FALSE)\n\tstatTables[[multiName]] <- cbind(f[c('Rank_Number','Protein_Name','Gene_Name')], statTables[[multiName]], f['Row_Type'])\n\twrite.csv(statTables[[multiName]], file=file.path(\"StatTests\", paste(hmname, '_', multiName, '.csv', sep='')), row.names=FALSE)\n\tjsonData$StatTests[[hmid]] <- list(name=hmname, data=statTables);\n\t# print(statTables);\n\twrite.csv(significance, file=file.path(\"Significance\", hmname, \"raw.csv\"), row.names=FALSE);\n\tsignificanceJSON[[hmid]] = list(name=hmname, data=significance);\n}\nwrite(toJSON(list(Significance=significanceJSON), auto_unbox=TRUE), file=file.path(\"Raws\", \"significance.json\"));\nwrite(toJSON(jsonData, auto_unbox=TRUE), file=file.path(\"Raws\", \"statTests.json\"));\n", "meta": {"hexsha": "db322fc66b957b37b5ce6302bd207c1e51b38595", "size": 5076, "ext": "r", "lang": "R", "max_stars_repo_path": "statTests.r", "max_stars_repo_name": "UnsignedByte/MassSpec-Data-Visualizer", "max_stars_repo_head_hexsha": "c75d242768d99aa61d87e2bc01462389c83a7028", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-17T21:55:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-17T21:55:57.000Z", "max_issues_repo_path": "statTests.r", "max_issues_repo_name": "UnsignedByte/MassSpec-Data-Visualizer", "max_issues_repo_head_hexsha": "c75d242768d99aa61d87e2bc01462389c83a7028", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 29, "max_issues_repo_issues_event_min_datetime": "2020-06-11T00:18:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-13T05:56:25.000Z", "max_forks_repo_path": "statTests.r", "max_forks_repo_name": "UnsignedByte/MassSpec-Data-Visualizer", "max_forks_repo_head_hexsha": "c75d242768d99aa61d87e2bc01462389c83a7028", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.0169491525, "max_line_length": 182, "alphanum_fraction": 0.6784869976, "num_tokens": 1483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947425132315, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.30446159487857194}} {"text": "install.packages(\"rgdal\")\nlibrary(magrittr)\nlibrary(dplyr)\nlibrary(leaflet)\nlibrary(rgdal)\nlibrary(htmlwidgets)\nlibrary(htmltools)\n\n\n#setwd(\"D:/adamcummings.net repo/adamkc.github.io/docs/Goat Analysis/\")\ncounties <- readOGR(\"cb_2016_us_county_20m/cb_2016_us_county_20m.shp\",\n layer = \"cb_2016_us_county_20m\", GDAL1_integer64_policy = TRUE)\n\n##Add goat population\ngoatdf <- read.csv(\"GoatbyCounty.csv\",colClasses = c(\"factor\",\"factor\",\"factor\",\"factor\",\"numeric\"),na.strings = \"(D)\")\ncounties@data %<>% left_join(y = goatdf,by = c(\"GEOID\" = \"FIPSTEXT\") )\n\n\n##Add Human Population\npopdf <- read.csv(\"CountyPop2.csv\")\npopdf <- popdf[as.character(popdf$STNAME) != as.character(popdf$CTYNAME),]\npopdf %<>% group_by(STNAME,CTYNAME) %>% summarise(POP = sum(POPESTIMATE2016)) %>%\n ungroup %>% transmute(StateName = STNAME,\n CountyNameLong = CTYNAME,\n HumanPop = POP)\n\ncounties@data$CountyNameLong <- paste(counties@data$CountyName, counties@data$Entity)\ncounties@data %<>% left_join(y = popdf)\n\ncounties@data$Label <- paste(\"\",counties@data$NAME, \"\",\n \"
\",\"Goats: \",round(counties@data$Goats*(counties@data$ALAND/404686),0),\n \"
\",\"People: \",counties@data$HumanPop,\n \"
\",\"Goat Per Person: \",round(counties@data$GoatperPerson,3)\n )\n\n\n\n#Calculate goats per human (Goats in per 100acre. ALAND in meters sq.)\ncounties@data$GoatperPerson <- counties@data$Goats*(counties@data$ALAND/404686)/counties@data$HumanPop \n\npal <- colorNumeric(\n palette = \"YlOrRd\",\n domain = countiesnoAK$GoatperPerson)\n\n\ngoatmap <- leaflet(counties) %>% setView(lng = -99.744289, lat = 39.138261, zoom=4.5) %>%\n addPolygons(fillColor = ~pal(GoatperPerson), weight = .2,\n opacity = 1.0, fillOpacity = 1,\n label=~lapply(Label,HTML),\n labelOptions= labelOptions(direction = 'auto',textsize = \"14px\"),\n highlightOptions = highlightOptions(\n color='#ff0000', opacity = 1, weight = 2, fillOpacity = 1,\n bringToFront = TRUE, sendToBack = TRUE)) %>%\n addLegend(\"bottomright\", pal = pal, values = ~GoatperPerson,\n title = \"Goats/Person\",\n opacity = 1)\n\ngoatmap <- leaflet(counties) %>% setView(lng = -99.744289, lat = 39.138261, zoom=4.5) %>%\n addPolygons(fillColor = ~pal(GoatperPerson), weight = .2,\n opacity = 1.0, fillOpacity = 1,\n label=~stringr::str_c(\n CountyNameLong, \" -- Goats/Person: \",\n formatC(GoatperPerson, big.mark = ',', format='f')),\n labelOptions= labelOptions(direction = 'auto',textsize = \"12px\"),\n highlightOptions = highlightOptions(\n color='#ff0000', opacity = 1, weight = 2, fillOpacity = 1,\n bringToFront = TRUE, sendToBack = TRUE)) %>%\n addLegend(\"bottomright\", pal = pal, values = ~GoatperPerson,\n title = \"Goats/Person\",\n opacity = 1)\nsaveWidget(goatmap, file=\"D:/adamcummings.net repo/adamkc.github.io/docs/Goat Analysis/GoatperPersonMap.html\", selfcontained = TRUE)\n\n##Only a few counties have more goats than People...\nsum(counties@data$GoatperPerson>1, na.rm=TRUE) #34 counties\n", "meta": {"hexsha": "9f3ea036df27aa5825840aaf1bf84c3bb36b6469", "size": 3321, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/GoatPerPersonMap.r", "max_stars_repo_name": "adamkc/adamkc.github.io", "max_stars_repo_head_hexsha": "e2aa2ea14bf0d20e19076f11eb0a69cf01c781c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/GoatPerPersonMap.r", "max_issues_repo_name": "adamkc/adamkc.github.io", "max_issues_repo_head_hexsha": "e2aa2ea14bf0d20e19076f11eb0a69cf01c781c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/GoatPerPersonMap.r", "max_forks_repo_name": "adamkc/adamkc.github.io", "max_forks_repo_head_hexsha": "e2aa2ea14bf0d20e19076f11eb0a69cf01c781c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.28, "max_line_length": 132, "alphanum_fraction": 0.6242095754, "num_tokens": 956, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.30434013280387384}} {"text": "Table_6<-function(data) {\n\nchild_controls = c(\"control_fu_child_age\", \"control_fu_female\", \"missing_fu_child_age\", \"missing_fu_female\")\n\n \nhh_controls = c(\"control_fu_adults\", \"control_fu_hh_head_edu\",\"control_fu_hh_head_occ_farmer\" ,\"control_fu_total_land\", \n \"control_fu_household_size\" ,\"missing_fu_adults\", \"missing_fu_hh_head_edu\" ,\"missing_fu_hh_head_occ_farmer\", \n \"missing_fu_total_land\", \"missing_fu_household_size\")\n\nindependent_vars = c(\"treatment_1\", \"treat_1_female\" ,\"treatment_2\",\"treat_2_female\")\ndistrict_control =c(\"factor(bl_district)\")\n\n\nchild_data_subset= data %>% filter(fu_child_level == 1 & fu_young_child == 1)\n\nx_vars <- c(independent_vars,child_controls,hh_controls,district_control)\n\n\nfull.formula <- as.formula(paste('fu_child_enrolled', paste(x_vars,collapse = ' + '),sep='~'))\n\nlm_enroll<-lm(full.formula,data=child_data_subset,weights=child_data_subset$hh_weight)\n\nfull.formula <- as.formula(paste('total_score_dev', paste(x_vars,collapse = ' + '),sep='~'))\n\nlm_score<-lm(full.formula,data=child_data_subset,weights=child_data_subset$hh_weight)\n\nfull.formula <- as.formula(paste('fu_child_highest_grade', paste(x_vars,collapse = ' + '),sep='~'))\n\nlm_grades<-lm(full.formula,data=child_data_subset,weights=child_data_subset$hh_weight)\n\nrob_se <- list(sqrt(diag(vcovHC(lm_enroll, type = \"HC1\"))),\n sqrt(diag(vcovHC(lm_score, type = \"HC1\"))),\n sqrt(diag(vcovHC(lm_grades, type = \"HC1\"))))\n\n\ndisplay_vars<- c(\"treatment_1\",\"treat_1_female\",\"treatment_2\",\"treat_2_female\")\n \noutreg <- capture.output( \n \n \n stargazer(lm_enroll,lm_grades,lm_score, \n type = \"html\", \n se = rob_se,\n keep=display_vars,\n column.sep.width = \"3pt\",\n covariate.labels = c(\"Treatment gender uniform\",\"Treatment gender uniform x Female \",\n \"Treatment gender differentiated\",\"Treatment gender differentiated x Female\"\n ),\n title = \"Gender differential impacts by the subsidy treatment\",\n dep.var.labels = c(\"Reported Enrollment\",\"Highest Grade attained\",\"Test Scores\"),\n model.numbers = FALSE)\n \n)\n \n \ndisplay_html(toString(outreg))\n\n}", "meta": {"hexsha": "823830390edf6ad5641071ccef68c39a27d688b6", "size": 2257, "ext": "r", "lang": "R", "max_stars_repo_path": "auxiliary/Table_6.r", "max_stars_repo_name": "timmens/test", "max_stars_repo_head_hexsha": "8d35255a2cdb63748566e5d4c59aefa928ffe9c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "auxiliary/Table_6.r", "max_issues_repo_name": "timmens/test", "max_issues_repo_head_hexsha": "8d35255a2cdb63748566e5d4c59aefa928ffe9c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "auxiliary/Table_6.r", "max_forks_repo_name": "timmens/test", "max_forks_repo_head_hexsha": "8d35255a2cdb63748566e5d4c59aefa928ffe9c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9137931034, "max_line_length": 123, "alphanum_fraction": 0.68409393, "num_tokens": 573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3043401328038738}} {"text": "#!/usr/bin/env Rscript\n\n# Read the inputs\n# 1. line_plot_table.txt.gz\n# 2. total_distro_table.txt.gz\n# 3. chr_distro_table.txt.gz\n# 4. output (pdf or png)\nargs=commandArgs(trailingOnly=TRUE)\nif(length(args)<4) {\n stop(\"Must supply input \\n\",call.=FALSE)\n}\n# decide output type\nfilex = substr(args[4],nchar(args[4])-2,nchar(args[4]))\nif(filex==\"pdf\") {\n pdf(args[4],bg=\"#FFFFFF\")\n} else if (filex==\"png\") {\n png(args[4],bg=\"#FFFFFF\")\n} else {\n stop(\"Unsupported type for output file.\\n\",call.=FALSE)\n}\n\n\n\nlogtrans<-function(num) {\n if(num==1) {\n return(1)\n }\n return(log(num,2)+1)\n}\nuntrans<-function(num) {\n if(num==1) {\n return(1)\n }\n return(2^(num-1))\n}\nneglogtrans<-function(num,lowest) {\n tymin = -1*log(lowest,2)\n return(-1*(-tymin+-1*log(num,2))/tymin)\n}\n\nlayout(rbind(c(1,2),c(3,4),c(5,5)),widths=c(1.25,4),heights=c(1,1,1))\npar(las=1)\n\n# get lowest y value for any chrom\nabsolute_min = 0.00001\n#ymin = min(d[,4]/d[,5])\n\nd<-read.csv(gzfile(args[2]),sep=\"\\t\",header=FALSE)\npar(mar=c(4,9,1,0.5))\n####### first plot the coverage\nymax = max(d[,3]/d[,4])\n#ymin = min(d[,3]/d[,4])\nylocs = c(0.000001,0.00001,0.0001,0.001,0.01,0.1,1)\nytlocs = lapply(ylocs,neglogtrans,absolute_min)\nplot(1,type=\"n\",xlim=c(1,1),ylim=c(0,1),xaxt=\"n\",xlab=\"\",ylab=\"\",yaxt=\"n\",bty=\"n\")\naxis(2,at=ytlocs,labels=ylocs,col=\"#FF0000\",col.axis=\"#BB0000\",line=3)\naxis(2,at=seq(0,1,0.2),labels=seq(0,1,0.2))\npar(las=0)\nmtext(\"Aligned Fraction\",side=2,line=7)\npar(las=1)\n\ncov = d[,3]\ntotal = d[,4]\nheight = cov[1]/total[1]\ntheight = neglogtrans(max(height,absolute_min),absolute_min)\nrect(1-0.4,0,1,theight,col=\"#FF0000\")\nrect(1,0,1+0.4,height,col=\"#777777\")\npar(las=2)\nmtext(\"all\",1,at=1,line=0.5,adj=1)\npar(las=1)\n\n\npar(mar=c(4,0.5,1,1))\n####### first plot the coverage\nd<-read.csv(gzfile(args[3]),sep=\"\\t\",header=FALSE)\nchr_list = unique(d[,1])\nymax = max(d[,4]/d[,5])\n#absolute_min = 0.0001\nymin = min(d[,4]/d[,5])\nylocs = c(0.000001,0.00001,0.0001,0.001,0.01,0.1,1)\nytlocs = lapply(ylocs,neglogtrans,absolute_min)\nplot(1,type=\"n\",xlim=c(1,length(chr_list)),ylim=c(0,1),xaxt=\"n\",xlab=\"\",ylab=\"\",yaxt=\"n\",bty=\"n\")\n#axis(2,at=ytlocs,labels=ylocs,col=\"#FF0000\",col.axis=\"#BB0000\",line=3)\n#axis(2,at=seq(0,1,0.2),labels=seq(0,1,0.2))\npar(las=0)\n#mtext(\"Aligned Fraction\",side=2,line=6)\npar(las=1)\nz = 0\nfor (chr in chr_list) {\n z = z+1\n cov = d[which(d[,1]==chr),4]\n total = d[which(d[,1]==chr),5]\n height = cov[1]/total[1]\n theight = neglogtrans(max(height,absolute_min),absolute_min)\n rect(z-0.4,0,z,theight,col=\"#FF0000\")\n rect(z,0,z+0.4,height,col=\"#777777\")\n par(las=2)\n mtext(chr,1,at=z,line=0.5,adj=1)\n par(las=1)\n}\n\n##### plot box depth for all data\nd<-read.csv(gzfile(args[2]),sep=\"\\t\",header=FALSE)\npar(mar=c(4,8,1,0.5))\nymax = max(d[,1])\nymaxtrans = logtrans(ymax)\nplot(1,type=\"n\",xlim=c(1,1),ylim=c(1,ymaxtrans),xaxt=\"n\",yaxt=\"n\",xlab=\"\",ylab=\"Depth\",bty=\"n\",cex.lab=1.5)\nylocs = seq(1,ymaxtrans,1)\nylabs=lapply(ylocs,untrans)\naxis(2,labels=ylabs,at=ylocs)\ndepths = lapply(d[,1],logtrans)\ncnts = d[,2]\nboxplot(unlist(rep(depths,cnts)),at=1,add=TRUE,outline=FALSE,yaxt=\"n\",frame=FALSE)\npar(las=2)\nmtext(\"all\",1,at=1,line=0.5,adj=1)\npar(las=1)\n\n\nd<-read.csv(gzfile(args[3]),sep=\"\\t\",header=FALSE)\nymax = max(d[,2])\nymaxtrans = logtrans(ymax)\npar(mar=c(4,0.5,1,1))\nplot(1,type=\"n\",xlim=c(1,length(chr_list)),ylim=c(1,ymaxtrans),xaxt=\"n\",yaxt=\"n\",xlab=\"\",ylab=\"Depth\",bty=\"n\")\nylocs = seq(1,ymaxtrans,1)\nylabs = lapply(ylocs,untrans)\n#axis(2,labels=ylabs,at=ylocs)\nz = 0\nfor (chr in chr_list) {\n z = z+1\n #depths = lapply(lapply(d[which(d[,1]==chr),2],logtrans),round)\n #depths = d[which(d[,1]==chr),2]\n depths = lapply(d[which(d[,1]==chr),2],logtrans)\n cnts = d[which(d[,1]==chr),3]\n boxplot(unlist(rep(depths,cnts)),at=z,add=TRUE,outline=FALSE,yaxt=\"n\",frame=FALSE)\n par(las=2)\n mtext(chr,1,at=z,line=0.5,adj=1)\n par(las=1)\n}\n\nd<-read.csv(gzfile(args[1]),sep=\"\\t\",header=FALSE)\npar(mar=c(5,8,1,0.5))\nymax=max(d[,1])\nymaxtrans=logtrans(ymax)\nxmin=min(d[,2]/d[,3])\nplot(1,type=\"n\",ylim=c(1,ymaxtrans),xlim=c(0,max(d[,2]/d[,3])-xmin),yaxt='n',xlab=\"fraction of genome\",ylab=\"Depth\",cex.lab=1.5)\nlines(d[,2]/d[,3]-xmin,lapply(d[,1],logtrans),lwd=3)\nylocs = seq(1,ymaxtrans,1)\nylabs = lapply(ylocs,untrans)\naxis(2,at=ylocs,labels=ylabs)\ndev.off()\n", "meta": {"hexsha": "4f1cdaaebf7b6c008110b889bf6e98e700af3b21", "size": 4258, "ext": "r", "lang": "R", "max_stars_repo_path": "alignqc/plot_chr_depth.r", "max_stars_repo_name": "jason-weirather/AlignQC", "max_stars_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 38, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T02:45:24.000Z", "max_issues_repo_path": "alignqc/plot_chr_depth.r", "max_issues_repo_name": "jason-weirather/AlignQC", "max_issues_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2016-10-19T02:10:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-24T17:34:50.000Z", "max_forks_repo_path": "alignqc/plot_chr_depth.r", "max_forks_repo_name": "jason-weirather/AlignQC", "max_forks_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2016-11-01T11:51:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-30T01:08:55.000Z", "avg_line_length": 28.1986754967, "max_line_length": 128, "alphanum_fraction": 0.6456082668, "num_tokens": 1790, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.3043401261539048}} {"text": "# Function to get GHCN Daily temperature data near a given lat/lon point\n# Global Historical Climatology Network\n# lat\n# lon\n# ghcnd_stations: output from ghcnd_stations()\n# radius: maximum search radius in km\n# mincov: minimum coverage proportion\n# mindate: YYYY-MM-DD format string, earliest date\n# maxdate:\n#\n# Needs this option set to get GHCN data:\n# options(noaakey = \"KEY_EMAILED_TO_YOU\")\n#\n# Need to load rgdal BEFORE running this function. Not clear why it can't load dynamically, but it won't for me.\n#\n# easy setup of default values when debugging: mindate='1986-01-01'; maxdate='2005-12-31'; radius=200; mincov=0.8\n\nghcn_get<-function(lat=40.48, lon=-74.44, ghcnd_stations, mindate='1986-01-01', maxdate='2005-12-31', radius=1000, mincov=0.8){\n\tif (!suppressMessages(library(\"rgdal\", logical.return=TRUE))) {\n\t\tstop(\"rgdal needed for working with GHCN data. Please install it.\", \n\t\t call. = FALSE)\n\t}\n\tif (!suppressMessages(library(\"rnoaa\", logical.return=TRUE))) {\n\t\tstop(\"rnoaa needed for working with GHCN data. Please install it.\", \n\t\t call. = FALSE)\n\t}\n\tif (!suppressMessages(library(\"rgeos\", logical.return=TRUE))) {\n\t\tstop(\"rgeos needed for working with GHCN data. Please install it.\", \n\t\t call. = FALSE)\n\t}\n\tif (!suppressMessages(library(\"fields\", logical.return=TRUE))) {\n\t\tstop(\"fields needed for working with GHCN data. Please install it.\", \n\t\t call. = FALSE)\n\t} # for rdist.earth\n\n\tminrows <- as.numeric(mincov*(as.Date(maxdate)-as.Date(mindate))) # minimum # of rows of data that we want\n\tmaxrows <- as.numeric(as.Date(maxdate) - as.Date(mindate)+1) # total number of rows expected\n\t\n\tstations <- meteo_nearby_stations(data.frame(id=1, latitude=lat, longitude=lon), station_data=ghcnd_stations, year_min=substr(mindate,1,4), year_max=substr(maxdate,1,4), var=c('TMIN', 'TMAX'), radius=radius, limit=1000) # only the 1000 closest (limit=1000)\n\t\t\n\tif(nrow(stations$`1`)>0){\n\t\tif(!is.na(stations$`1`$id[1])){\n\t\t\tmessage(paste('Found', nrow(stations$`1`), 'GHCND stations\\n'))\n\t\t\tFOUNDSTATION=FALSE\n\t\t\terror=FALSE\n\t\t\tr <- 1\n\t\t\n\t\t\t# check if any stations span the year range we need and have reasonable coverage\n\t\t\twhile(FOUNDSTATION==FALSE & error==FALSE){\n\t\t\t\tmessage(paste('trying station', r, '\\n'))\n\n\t\t\t\tdat <- NULL\n\t\t\t\tdat <- tryCatch( # in rare cases, this throws an error\n\t\t\t\t\tmeteo_tidy_ghcnd(stationid=stations$`1`$id[r], keep_flags=TRUE, var=c('TMAX', 'TMIN'), date_min=mindate, date_max=maxdate), # returns tmax and tmin in degC*10\t\n\t\t\t\t\terror = function(e){\n\t\t\t\t\t\tmessage(paste('ERROR in meteo_tidy_ghcnd():', e))\n\t\t\t\t\t\treturn(NULL)\n\t\t\t\t\t}\n\t\t\t\t\t)\n\t\t\n\t\t\t\t# simple checks if data returned\n\t\t\t\tif(!is.null(dat) & 'tmin' %in% names(dat) & 'tmax' %in% names(dat)){\n\t\t\t\t\t# trim out data that failed quality checks\n\t\t\t\t\tdat$tmin[dat$qflag_tmin != ' '] <- NA\n\t\t\t\t\tdat$tmax[dat$qflag_tmax != ' '] <- NA\n\t\t\n\t\t\t\t\tif(sum(!is.na(dat$tmax))>=minrows & sum(!is.na(dat$tmin))>=minrows){\n\t\t\t\t\t\tFOUNDSTATION=TRUE\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tr <- r+1\n\n\t\t\t\t# failed if no station met the search criteria\n\t\t\t\tif(r > nrow(stations$`1`)){\n\t\t\t\t\tmessage(paste('Could not find appropriate weather station for lat', lat, 'lon', lon, 'with radius', radius))\n\t\t\t\t\terror <- TRUE\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tmessage(paste('Could not find appropriate weather station for lat', lat, 'lon', lon, 'with radius', radius))\n\t\t\terror <- TRUE\n\t\t}\n\t} else {\n\t\terror <- TRUE\n\t}\n\t\n\tif(!error){\n\t\tmessage(paste('found station', stations$`1`$id[r-1], 'distance', round(stations$`1`$distance[r-1],2), 'km. Coverage tmin:', round(sum(!is.na(dat$tmin))/maxrows, 5), 'tmax:', round(sum(!is.na(dat$tmax))/maxrows, 5)))\n\n\t\t# check that every day is represented\n\t\tif(nrow(dat) > maxrows) stop(paste('Too many rows returned for', stations$`1`$id[r-1]))\n\t\tif(nrow(dat) < maxrows){\n\t\t\tmessage(paste('Too few rows returned for', stations$`1`$id[r-1], '. Padding to full length'))\n\t\t\talldates <- data.frame(date=seq(from=as.Date(mindate), to=as.Date(maxdate), by=1))\n\t\t\tdat <- merge(alldates, dat, all.x=TRUE)\n\t\t}\n\t\n\t\t# convert from degC*10 to degC\n\t\tdat$tmin <- dat$tmin/10\n\t\tdat$tmax <- dat$tmax/10\n\t\t\t\n\t\treturn(dat)\n\t}\n}", "meta": {"hexsha": "86c42e6e1b1fc5b34c511307e2e1a1e1930e3655", "size": 4094, "ext": "r", "lang": "R", "max_stars_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/ghcn_get.r", "max_stars_repo_name": "HuckleyLab/phyto-mhw", "max_stars_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-10-13T02:37:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-27T04:41:09.000Z", "max_issues_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/ghcn_get.r", "max_issues_repo_name": "HuckleyLab/phyto-mhw", "max_issues_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2020-07-19T10:54:37.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-17T19:53:09.000Z", "max_forks_repo_path": "data/pinsky/pinskylab-hotWater-250832d/scripts/ghcn_get.r", "max_forks_repo_name": "HuckleyLab/phyto-mhw", "max_forks_repo_head_hexsha": "8e067c73310fb4a4520d5a72f68717030ce90e14", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9904761905, "max_line_length": 257, "alphanum_fraction": 0.6680508061, "num_tokens": 1289, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.304324977593453}} {"text": "## Copyright 2021 Neil Ferguson, Imperial College London\n##\n## Licensed under the Apache License, Version 2.0 (the \"License\");\n## you may not use this file except in compliance with the License.\n## You may obtain a copy of the License at\n##\n## http://www.apache.org/licenses/LICENSE-2.0\n##\n## Unless required by applicable law or agreed to in writing, software\n## distributed under the License is distributed on an \"AS IS\" BASIS,\n## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n## See the License for the specific language governing permissions and\n## limitations under the License.\n\n\n\n#library(devtools) ## uncomment for Windows to allow stan to run\nlibrary(dplyr)\nlibrary(Hmisc)\nlibrary(ggplot2)\nlibrary(ggridges)\nlibrary(rstan)\nlibrary(bayesplot)\nlibrary(loo)\nlibrary(svglite)\n\n# To install the above\n#install.packages(c(\"devtools\",\"Hmisc\",\"dplyr\",\"ggplot2\",\"ggridges\",\"rstan\",\"bayesplot\",\"loo\",\"svglite\"))", "meta": {"hexsha": "93b3430f163e351842eb02dae9cc755a209c0381", "size": 960, "ext": "r", "lang": "R", "max_stars_repo_path": "libs.r", "max_stars_repo_name": "mrc-ide/ATACCC-kinetic-model-and-data", "max_stars_repo_head_hexsha": "35c6c8f89a84496d57ba0339a0ef4bdc62822c8b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs.r", "max_issues_repo_name": "mrc-ide/ATACCC-kinetic-model-and-data", "max_issues_repo_head_hexsha": "35c6c8f89a84496d57ba0339a0ef4bdc62822c8b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libs.r", "max_forks_repo_name": "mrc-ide/ATACCC-kinetic-model-and-data", "max_forks_repo_head_hexsha": "35c6c8f89a84496d57ba0339a0ef4bdc62822c8b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.2857142857, "max_line_length": 105, "alphanum_fraction": 0.7364583333, "num_tokens": 239, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3042658665428101}} {"text": "REBOL [\n]\n\nlib: do %../face-to-pdf-lib.r\n\nfnt: switch system/version/4 [\n 3 [ make face/font [] ]\n 4 [ make face/font [ name: \"/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf\" ] ]\n]\n;/usr/share/fonts/truetype/liberation\n\ntt: func [ d ][\n view layout [ box sky 500x350 effect[ draw[ push d ]] key #\"q\" [unview]]\n]\n\n\ndr1: [ pen forest font fnt text 0x0 vectorial \"Circle\" circle 75x75 50 ]\ndr2: [ font fnt text 0x0 vectorial \"Arc-open\"\n\tarc 75x45 100x100 30 100\n\tfill-pen crimson line-width 3\n\tarc 80x80 70x40 30 100\n\tline 70x45 80x45 line 75x40 75x50 ]\ndr3: [ font fnt text 0x0 vectorial \"Arc-closed\" arc 75x45 100x100 30 100 closed line 70x45 80x45 line 75x40 75x50 ]\n\nhalf-crimson: crimson + 0.0.0.128\ndr4: [ font fnt text 0x0 vectorial \"Arc-open\"\n\tpen coal\n\tarc 75x75 100x100 122 300\n\tfill-pen half-crimson line-width 1\n\tline 70x75 80x75 line 75x70 75x80 ]\ndr5: [ font fnt text 0x0 vectorial \"Arc-closed\" arc 75x75 100x100 30 300 closed line 70x75 80x75 line 75x70 75x80 ]\n\ndr6: [\n font fnt text 0x0 vectorial \"Arrows\"\n line 10x15 10x45 line 130x15 130x45\n line-width 2\n arrow 1x2\n line 10x20 130x20\n line-width 4\n arrow 1x1\n line 10x30 130x30\n arrow 1x1\n line 10x40 130x40\n circle 75x70 50 30\n ellipse 75x70 50x30\n arc 75x70 25x15 100 200\n arrow 0x0\n]\ndr7: [\n font fnt text 0x0 vectorial \"Spline\"\n fill-pen none\n line 15x15 100x15 100x100 15x100 15x75\n pen red\n spline 10 15x15 100x15 100x100 15x100 15x75 50x75 50x90\n]\ndr8: [\n font fnt text vectorial \"Triangle\"\n triangle 12x12 120x80 80x130 green red blue\n pen none\n triangle 80x10 30x22 100x30 blue white magenta\n translate 20x0\n triangle 80x10 30x22 100x30 blue white magenta\n]\npuppy: load %images/puppy.jpg\ncat-eye: load %images/cat-eye.jpg\ndart: load %images/dart.png\n\ndr9: [\n font fnt text vectorial \"Image\" \n image 15x15 cat-eye ;puppy\n]\ndr10: [\n font fnt text vectorial \"Image\" \n ;image 80x5 puppy\n image 80x5 140x50 20x140 5x20 cat-eye\n scale 0.5 0.5\n image puppy 140x50 20x140 5x20 \n]\n\ndr11: [\n pen none fill-pen black\n font fnt text 0x0 vectorial \"Curve and matrix\"\n matrix [ 1 0.1 -0.2 1.4 10 10 ]\n pen green\n fill-pen none\n line-width 3\n line 15x15 100x15 100x100 15x100 \n curve 15x15 100x15 100x100 15x100 \n line-width 1\n pen red fill-pen sky\n curve 25x25 90x25 90x90 25x90 \n]\n\ndr12: [\n font fnt text vectorial \"Clip Funny in Rebol\" \n fill-pen none pen green line-width 5\n box 20x20 140x140\n line-width 1\n box 80x80 160x160\n clip 80x80 160x160\n fill-pen blue pen red line-width 2\n box 20x20 140x140\n\n]\n\ndr13: [\n font fnt text vectorial \"Line caps\"\n line-width 6 pen red\n line 10x20 10x70\n line-cap butt\n line 30x20 30x70\n line-cap round\n line 50x20 50x70\n line-cap square\n line 70x20 70x70\n line-cap butt\n\n translate 80x0\n pen none fill-pen black\n font fnt text vectorial \"Line joins\"\n fill-pen none\n line-width 10 pen red \n translate 0x20\n spline 1 10x0 30x30 50x0\n\n line-join miter\n translate 0x20\n spline 1 10x0 30x30 50x0\n\n line-join miter-bevel\n translate 0x20\n spline 1 10x0 30x30 50x0\n\n translate 0x20\n line-join round\n spline 1 10x0 30x30 50x0\n\n line-join bevel\n translate 0x20\n spline 1 10x0 30x30 50x0\n\n]\ndr14: [\n pen none fill-pen black\n font fnt text vectorial \"Miter-bevel\"\n fill-pen none\n line-join miter-bevel\n line-width 3 pen red \n spline 1 50x140 75x120 100x140\n spline 1 50x140 75x100 100x140\n spline 1 50x140 75x80 100x140\n spline 1 50x140 75x60 100x140\n spline 1 50x140 75x40 100x140\n spline 1 50x140 75x20 100x140\n]\nhalf-red: 255.0.0.128\n \ndr15: [\n pen none fill-pen black\n font fnt text vectorial \"Line patterns\"\n line-pattern 10 20 ; 20 with next argument it coredumps\n pen blue green\n line 10x20 140x20 140x30\n spline 1 10x30 140x30\n fill-pen snow\n pen red blue\n box 10x40 140x50\n line-pattern none\n line-join round \n pen black \n polygon 10x60 70x90 70x60 60x70\n line-pattern 5 10\n pen none brown\n circle 110x80 25\n\n line-pattern 10 20 ; 20 with next argument it coredumps\n fill-pen snow\n pen half-red blue\n box 10x120 140x140\n]\ndr16: [\n pen none fill-pen black line-pattern none\n font fnt text vectorial \"More arrows\"\n pen aqua\n arrow 1x1\n spline 15 10x90 30x110 75x110 90x120 140x80\n pen green\n curve 10x140 30x100 110x150 140x120\n arrow 0x0\n]\ndr17: [\n pen none fill-pen black line-pattern none\n font fnt text vectorial \"box\"\n fill-pen none pen beige line-width 1\n box 10x10 100x40 10\n]\ndr18: [\n pen none fill-pen black line-pattern none\n font fnt text vectorial \"Gradients\"\n ; fill-pen color type offset start-rng end-rng angle scalex scaley colors ...\n fill-pen radial 0x0 0 100 10 1 2 blue green red yellow \n box 0x15 150x50 box 70x0 100x70\n fill-pen blue linear 10x65 0 50 45 1 1 green red yellow \n box 0x50 150x90 \n fill-pen blue linear 10x115 0 50 45 10 10 green red yellow \n box 0x90 150x130 \n]\n\ndr19: [\n pen none fill-pen black line-pattern none\n font fnt text vectorial \"Transparent image\"\n scale 0.7 0.7\n image 5x15 dart\n]\n \n\ndrs: copy [] repeat i 19 [ append drs to-word rejoin [ \"dr\" i ]]\n\nreplace drs 'dr12 []\n\n\nview-it: func [ drs\n /local\n\tcols dr idr \n] [\n cols: 6\n dr: copy []\n forall drs [\n\tidr: copy []\n\trepeat i cols [\n\t unless drs/:i [ break ]\n\t repend idr [\n\t\t'push drs/:i 'translate 150x0\n\t ]\n\t]\n\tappend dr 'push\n\tappend/only dr idr\n\tappend dr [\n\t translate 0x150 \n\t]\n\n\tdrs: skip drs cols - 1\n ] \n view/new/offset foenster: layout [\n\ttext \"test av geometrier\"\n\tf: box yellow / 1.5 900x450 effect [\n\t draw dr\n\t grid 150x150 0x0 2 3 black\n\t]\n\tkey #\"q\" [quit]\n ] 0x0\n\n]\n\nif error? err: try [ \n view-it drs\n write/binary %geometries-II.pdf lib/face-to-pdf f\n none\n] [\n trace off\n err: disarm err\n ? err\n make error! {Error somewhere}\n]\n\nprint \"hit escape to get prompt\"\n\nwait none\n\n", "meta": {"hexsha": "a794309224cdc650206b64fb9f481c261e9a2caa", "size": 6203, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/geometries-II.r", "max_stars_repo_name": "ingvast/pdf-export", "max_stars_repo_head_hexsha": "4b9f29653c7e1e5bc907c9acddcea821ca7cc732", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-04-19T16:01:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-28T09:25:22.000Z", "max_issues_repo_path": "tests/geometries-II.r", "max_issues_repo_name": "ingvast/pdf-export", "max_issues_repo_head_hexsha": "4b9f29653c7e1e5bc907c9acddcea821ca7cc732", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tests/geometries-II.r", "max_forks_repo_name": "ingvast/pdf-export", "max_forks_repo_head_hexsha": "4b9f29653c7e1e5bc907c9acddcea821ca7cc732", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9740740741, "max_line_length": 116, "alphanum_fraction": 0.659358375, "num_tokens": 2381, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3042658665428101}} {"text": "library(ggplot2)\ntheme_set(theme_bw(18))\n#setwd(\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/7_sinking-marbles-freeproduction/results/\")\nsetwd(\"~/Dropbox/sinking_marbles/sinking-marbles/experiments/7_sinking-marbles-freeproduction/results/\")\nsource(\"rscripts/helpers.r\")\nload(\"data/priors.RData\")\nload(\"data/r.RData\")\nr = read.table(\"data/sinking_marbles_freeproduction.tsv\",quote=\"\", sep=\"\\t\", header=T)\nnrow(r)\nnames(r)\nr$trial = r$slide_number_in_experiment - 2\nr = r[,c(\"assignmentid\",\"workerid\", \"rt\", \"effect\", \"language\",\"gender.1\",\"age\",\"gender\",\"other_gender\", \"object_level\", \"object\",\"num_objects\",\"trial\",\"enjoyment\",\"asses\",\"comments\",\"Answer.time_in_minutes\",\"num_objects_affected\",\"actual_utterance\")]\nrow.names(priors) = paste(priors$effect, priors$object)\nr$Prior = priors[paste(r$effect, r$object),]$response\nr$object_level = factor(r$object_level, levels=c(\"object_high\", \"object_mid\", \"object_low\"))\nr$Quantifier = r$slider_id\nr$Half = as.factor(ifelse(r$trial < 16, 1, 2))\nr$Quarter = as.factor(ifelse(r$trial < 8, 1, ifelse(r$trial < 16, 2, ifelse(r$trial < 24, 3, 4))))\nsummary(r)\nr$Combination = as.factor(paste(r$cause,r$object,r$effect))\ntable(r$Combination)\nutterances = as.data.frame(table(r$actual_utterance))\nutterances[utterances$Freq > 1,]\n\nr$Proportion = r$num_objects_affected/r$num_objects\npaste(r[order(r[,c(\"actual_utterance\",\"Proportion\")]),]$actual_utterance,r[order(r[,c(\"actual_utterance\",\"Proportion\")]),]$Proportion)\nr$ProportionBin = ifelse(r$Proportion == 0, 0, ifelse(r$Proportion == 1, 4, ifelse(r$Proportion < .51, 1, 2)))\nr$PriorBin = ifelse(r$Prior < 26, 1, ifelse(r$Prior < 51, 2, ifelse(r$Prior < 76, 3, 4)))\ntable(r$ProportionBin, r$PriorBin)\n\nr$actual_utterance = as.factor(tolower(r$actual_utterance))\nr$actual_utterance = gsub(\"10\",\"ten\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"11\",\"eleven\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"12\",\"twelve\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"13\",\"thirteen\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"14\",\"fourteen\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"15\",\"fifteen\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"0\",\"zero\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"1\",\"one\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"2\",\"two\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"3\",\"three\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"4\",\"four\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"5\",\"five\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"6\",\"six\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"7\",\"seven\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"8\",\"eight\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = gsub(\"9\",\"nine\",as.character(r$actual_utterance),fixed=T)\nr$actual_utterance = as.factor(r$actual_utterance)\nlength(levels(r$actual_utterance))\nunique(r$actual_utterance)\n\nsave(r, file=\"data/r.RData\")\n\n# EXAMPLES\n# how many cases use the \"of the\" frame? 1057 of 1800, or 59%\nlength(grep(\"of the\", as.character(r$actual_utterance),fixed=T))\nnrow(r)\n\n# SOME EXAMPLES\n# Expectation for \"all\": Proportion == 1\n# Prior > .9\nall = subset(r, Proportion == 1 & Prior > 90)\nnrow(all) # 53\nall = droplevels(all)\nlevels(all$actual_utterance)\nlength(grep(\"(all|All)\", as.character(all$actual_utterance))) # 42 of 53 cases have \"all\" in them (79%)\nlength(grep(\"(some|Some)\", as.character(all$actual_utterance))) # 1 case has \"some\" in it, and it's not the use we're interested in\nlength(grep(\"(most|Most)\", as.character(all$actual_utterance))) # 0 cases have \"most\"\n\n# Prior > .5 & <= .9\nall = subset(r, Proportion == 1 & Prior > 50 & Prior < 91)\nnrow(all) # 122\nall = droplevels(all)\nlevels(all$actual_utterance)\nlength(grep(\"(all|All)\", as.character(all$actual_utterance))) # 96 of 122 cases have \"all\" in them (79%)\nlength(grep(\"(some|Some)\", as.character(all$actual_utterance))) # 1 case has \"some\" in it, and it's not the use we're interested in\nlength(grep(\"(most|Most)\", as.character(all$actual_utterance))) # 0 cases have \"most\"\n\n# Prior > .0 & <= .5\nall = subset(r, Proportion == 1 & Prior < 51)\nnrow(all) # 60\nall = droplevels(all)\nlevels(all$actual_utterance)\nlength(grep(\"(all|All)\", as.character(all$actual_utterance))) # 45 of 60 cases have \"all\" in them (75%)\nlength(grep(\"(some|Some)\", as.character(all$actual_utterance))) # 0 cases has \"some\" in it, and it's not the use we're interested in\nlength(grep(\"(most|Most)\", as.character(all$actual_utterance))) # 0 cases have \"most\"\n\n# Expectation for \"most\": Proportion > .5 & < 1\n# Prior > .9\nmost = subset(r, Proportion > .5 & Proportion < 1 & Prior > 90)\nnrow(most) # 148\nmost = droplevels(most)\nlevels(most$actual_utterance)\nlength(grep(\"(most|most)\", as.character(most$actual_utterance))) # 18 of 148 cases have \"most\" in them (12%)\nlength(grep(\"(some|Some)\", as.character(most$actual_utterance))) # 21 of 148 cases have \"some\" in them (14%)\n\n# Prior > .5 & <= .9\nmost = subset(r, Proportion > .5 & Proportion < 1 & Prior > 50 & Prior < 91)\nnrow(most) # 372\nmost = droplevels(most)\nlevels(most$actual_utterance)\nlength(grep(\"(most|most)\", as.character(most$actual_utterance))) # 43 of 372 cases have \"most\" in them (12%)\nlength(grep(\"(some|Some)\", as.character(most$actual_utterance))) # 29 of 372 cases have \"most\" in them (8%)\n\n# Prior > .0 & <= .5\nmost = subset(r, Proportion > .5 & Proportion < 1 & Prior < 51)\nnrow(most) # 151\nmost = droplevels(most)\nlevels(most$actual_utterance)\nlength(grep(\"(most|most)\", as.character(most$actual_utterance))) # 21 of 151 cases have \"most\" in them (14%)\nlength(grep(\"(some|Some)\", as.character(most$actual_utterance))) # 11 of 151 cases have \"most\" in them (7%)\n\n\n\n##################\n\nggplot(aes(x=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=rt), data=r) +\n geom_histogram() +\n scale_x_continuous(limits=c(0,50000))\n\nggplot(aes(x=age), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,fill=gender.1), data=r) +\n geom_histogram()\n\nggplot(aes(x=enjoyment), data=r) +\n geom_histogram()\n\nggplot(aes(x=asses), data=r) +\n geom_histogram()\n\nggplot(aes(x=Answer.time_in_minutes), data=r) +\n geom_histogram()\n\nggplot(aes(x=Proportion), data=r) +\n geom_histogram()\n\nggplot(aes(x=age,y=Answer.time_in_minutes,color=gender.1), data=unique(r[,c(\"assignmentid\",\"age\",\"Answer.time_in_minutes\",\"gender.1\")])) +\n geom_point() +\n geom_smooth()\n\nunique(r$comments)\n\n####################\n\nmany = r[grep(\"many\", as.character(r$actual_utterance)),]\nmany\nfew = r[grep(\"few\", as.character(r$actual_utterance)),]\npaste(few$Proportion,few$Prior,few$actual_utterance)\nggplot(few, aes(x=Prior)) +\n# geom_histogram()\n geom_density()\nggplot(r, aes(x=Prior)) +\n# geom_histogram() \n geom_density()\n", "meta": {"hexsha": "6a4107eeeb99e838b40184b7151b51e01e5a22b4", "size": 7002, "ext": "r", "lang": "R", "max_stars_repo_path": "experiments/7_sinking-marbles-freeproduction/results/rscripts/sinking-marbles.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "experiments/7_sinking-marbles-freeproduction/results/rscripts/sinking-marbles.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/7_sinking-marbles-freeproduction/results/rscripts/sinking-marbles.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.7625, "max_line_length": 251, "alphanum_fraction": 0.71451014, "num_tokens": 2265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.5234203489363239, "lm_q1q2_score": 0.30426586654281007}} {"text": "#' ---\n#' author: \"Jenny Bryan\"\n#' output:\n#' html_document:\n#' keep_md: TRUE\n#' ---\n\n#+ setup, include = FALSE\nlibrary(knitr)\nopts_chunk$set(fig.path = 'figure/themes-', error = TRUE)\n\n#' Note: this HTML is made by applying `knitr::spin()` to an R script. So the\n#' narrative is very minimal.\n\nlibrary(ggplot2)\nlibrary(ggthemes)\n\n#' pick a way to load the data\n#gdURL <- \"http://tiny.cc/gapminder\"\n#gapminder <- read.delim(file = gdURL) \n#gapminder <- read.delim(\"gapminderDataFiveYear.tsv\")\nlibrary(gapminder)\nstr(gapminder)\n\n#' revisit a plot from earlier\np <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))\np <- p + scale_x_log10()\np <- p + aes(color = continent) + geom_point() + geom_smooth(lwd = 3, se = FALSE)\np\n\n#' give it a title\np + ggtitle(\"Life expectancy over time by continent\")\n\n#' change overall look and feel with a premade theme\np + theme_grey() # the default\n\n#' suppress the usual grey background\np + theme_bw()\n\n#' exploring some themes from the ggthemes package \n#' https://github.com/jrnold/ggthemes\np + theme_calc() + ggtitle(\"ggthemes::theme_calc()\")\np + theme_economist() + ggtitle(\"ggthemes::theme_economist()\")\np + theme_economist_white() + ggtitle(\"ggthemes::theme_economist_white()\")\np + theme_few() + ggtitle(\"ggthemes::theme_few()\")\np + theme_gdocs() + ggtitle(\"ggthemes::theme_gdocs()\")\np + theme_tufte() + ggtitle(\"ggthemes::theme_tufte()\")\np + theme_wsj() + ggtitle(\"ggthemes::theme_wsj()\")\n\nsessionInfo()", "meta": {"hexsha": "297b6a816f00fec4d917632a7cd97242d9030539", "size": 1452, "ext": "r", "lang": "R", "max_stars_repo_path": "gapminder-ggplot2-themes.r", "max_stars_repo_name": "myh1111/ggplot2-tutorial-1", "max_stars_repo_head_hexsha": "ff960f3ac85b426353f6c24cf56ed737857b0d0d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 234, "max_stars_repo_stars_event_min_datetime": "2015-01-05T14:15:07.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T05:01:08.000Z", "max_issues_repo_path": "gapminder-ggplot2-themes.r", "max_issues_repo_name": "jrherr/ggplot2-tutorial", "max_issues_repo_head_hexsha": "ff960f3ac85b426353f6c24cf56ed737857b0d0d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2016-04-19T23:23:24.000Z", "max_issues_repo_issues_event_max_datetime": "2017-10-16T21:29:39.000Z", "max_forks_repo_path": "gapminder-ggplot2-themes.r", "max_forks_repo_name": "jrherr/ggplot2-tutorial", "max_forks_repo_head_hexsha": "ff960f3ac85b426353f6c24cf56ed737857b0d0d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 141, "max_forks_repo_forks_event_min_datetime": "2015-01-16T16:44:51.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-09T23:54:23.000Z", "avg_line_length": 29.04, "max_line_length": 81, "alphanum_fraction": 0.6935261708, "num_tokens": 431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5813030906443133, "lm_q1q2_score": 0.30426586654281007}} {"text": "# ui.r\nshinyUI(fluidPage(\n titlePanel(\"2PL IRT model\"),\n \n sidebarLayout(\n sidebarPanel(\n selectInput(\"diff\", label = \"Item Difficulty\",\n choices = -3:3),\n selectInput(\"disc\", label = \"Item Discrimination\",\n choices = -3:3)),\n mainPanel(plotOutput(\"twopl\"))\n )\n))", "meta": {"hexsha": "508b83e27cd86fb80ca1a606c27bb86295f6c5b0", "size": 317, "ext": "r", "lang": "R", "max_stars_repo_path": "talks/akureyri_irt/unak_shiny/twopl/ui.r", "max_stars_repo_name": "cddesja/Presentations", "max_stars_repo_head_hexsha": "d877bd30406f10532f9b1da79200fdddea0eb40d", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "talks/akureyri_irt/unak_shiny/twopl/ui.r", "max_issues_repo_name": "cddesja/Presentations", "max_issues_repo_head_hexsha": "d877bd30406f10532f9b1da79200fdddea0eb40d", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "talks/akureyri_irt/unak_shiny/twopl/ui.r", "max_forks_repo_name": "cddesja/Presentations", "max_forks_repo_head_hexsha": "d877bd30406f10532f9b1da79200fdddea0eb40d", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.3846153846, "max_line_length": 56, "alphanum_fraction": 0.5646687697, "num_tokens": 83, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30426585894946573}} {"text": "#------ 2015. 7. 08\r\n#----------------------- RVPA program -----------------------------\r\n# written by Hiroshi Okamura (VPA & reference point) \r\n# and Momoko Ichinokawa (future projection & reference point)\r\n#\r\n# (*) mac\u3067\u8aad\u3080\u5834\u5408\u306f\u3001\r\n# source(\"rvpa1.6.r\",encoding=\"shift-jis\")\r\n# \u3068\u3057\u3066\u3001\u6587\u5b57\u30b3\u30fc\u30c9\u3092\u6307\u5b9a\u3057\u3066\u8aad\u3093\u3067\u304f\u3060\u3055\u3044\r\n# (*) vpa\u3092\u4f7f\u3046\u5834\u5408\u3001\u521d\u671f\u5024\u306e\u6307\u5b9a\u306b\u3088\u3063\u3066\u306f\u53ce\u675f\u3057\u306a\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002p.init\u306e\u5024\u3092\u3044\u308d\u3044\u308d\u5909\u3048\u3066\u8a66\u3057\u3066\u4e0b\u3055\u3044\r\n#\r\n#\r\n#\r\n# \u5909\u66f4\u5c65\u6b74\r\n# rvpa0.9 - 2013.7.3. \u5c06\u6765\u4e88\u6e2c\u95a2\u6570\uff1b\u5c06\u6765\u4e88\u6e2c\u306edeterminisitc run\u3067\u306erps\u306e\u53c2\u7167\u7bc4\u56f2\u3068\u3001\r\n# stochastic run\u3067\u306erps\u306e\u53c2\u7167\u7bc4\u56f2\u304c\u7570\u306a\u308b\u5834\u5408\u306e\u30aa\u30d7\u30b7\u30e7\u30f3(sample.year\u3067\u6307\u5b9a)\u3092\u8ffd\u52a0\r\n# \u30ea\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3055\u308c\u308bRPS = RPS[sample.year\u306e\u7bc4\u56f2]/mean(RPS[sample.year\u306e\u7bc4\u56f2])*median(RPS[rps.year\u306e\u7bc4\u56f2])\r\n# (bias.adjusted\u3057\u3066\u3044\u306a\u3044\u5834\u5408\u306f\u95a2\u4fc2\u306a\u3044\u3002sample.year\u3092\u8a2d\u5b9a\u3057\u306a\u3044\u5834\u5408\u3001\u81ea\u52d5\u7684\u306bsample.year=rps.year\u3068\u306a\u308b)\r\n# rvpa0.92 - vpa\u306esel.update\u90e8\u5206\u306e\u4fee\u6b63\u3068profile likelihood\u4fe1\u983c\u533a\u9593\u95a2\u6570\u306e\u4fee\u6b63\u30fb\u8ffd\u52a0\r\n# rvpa0.95 - \u7ba1\u7406\u57fa\u6e96\u5024\u8a08\u7b97\u306e\u3068\u3053\u308d\u306b\u3001nlm\u3067\u306e\u8a08\u7b97\u56de\u6570\u306e\u4e0a\u9650\u3092\u8a2d\u5b9a\u3059\u308b\u5f15\u6570\u3092\u8ffd\u52a0\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\uff11\uff10\uff10\uff10\uff09\r\n# rvpa0.96 - likelihood profile\u7528\u306b\u3001CPUE\u3054\u3068\u306b\u76ee\u7684\u95a2\u6570\u306e\u5024\u3092\u51fa\u529b\u3059\u308b\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u8ffd\u52a0\u3002\u305d\u306e\u4ed6\r\n# rvpa1.0 - \u30d1\u30c3\u30b1\u30fc\u30b8\u3068\u3057\u3066\u516c\u958b\u3057\u305f\u30d0\u30fc\u30b8\u30e7\u30f3\r\n# rvpa1.3 - \u91cd\u307f\u4ed8\u3051\u8a08\u7b97\u3092\u8ffd\u52a0\r\n# rvpa1.6 -warnings\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u8ffd\u52a0\uff0evpa\u3067index\u306e\u4e00\u90e8\u3060\u3051\u5229\u7528\u3059\u308b\u30aa\u30d7\u30b7\u30e7\u30f3(use.index)\u3092\u8ffd\u52a0\uff0eMSY.EST2\u95a2\u6570\u3092\u8ffd\u52a0\r\n# rvpa1.7 - selupdate+F\u63a8\u5b9a\u30aa\u30d7\u30b7\u30e7\u30f3\uff0e\u5236\u7d04\u4ed8\u6700\u9069\u5316\uff0e\r\n# rvpa.1.8 - Retrospective analysis\u3092\u884c\u3046\u305f\u3081\u306e\u95a2\u6570 (retro.est, retro.est2) \u3068\u3001\u30ea\u30c3\u30b8\u56de\u5e30\u3092\u884c\u3046\u305f\u3081\u306e\u5f15\u6570 (lambda) \u3092\u8ffd\u52a0(2017/05/29)\r\n# rvpa1.9 - maa.tune\uff08tuning\u6642\u3060\u3051\u4f7f\u7528\u306emaa\uff09, waa.catch\uff08\u8cc7\u6e90\u3068\u6f01\u7372\u3067waa\u304c\u9055\u3046\u5834\u5408\u306b\u5bfe\u5fdc\uff09\u3092\u4f7f\u7528\u53ef\u80fd\u306b\uff0eindex=NULL\u3067tune=FALSE\u306e\u3068\u304d\u30a8\u30e9\u30fc\u304c\u51fa\u306a\u3044\u3088\u3046\u306b\u4fee\u6b63\uff0e\r\n# rvpa1.9.2 - \u5404index\u306b\u5bfe\u3059\u308b\u5206\u6563\u306b\u5236\u7d04\u3092\u304b\u3051\u3089\u308c\u308b\u3088\u3046\u306b\u4fee\u6b63\u3002\u4f8b\u3048\u3070index\u304c\uff15\u672c\u3042\u308b\u5834\u5408\u3001sigma.constraint=c(1,2,2,3,3)\u3068\u3059\u308b\u30682,3\u672c\u3081\u30684,5\u672c\u76ee\u306eindex\u306b\u5bfe\u3059\u308b\u5206\u6563\u306f\u7b49\u3057\u3044\u3068\u3057\u3066\u63a8\u5b9a\u3059\u308b\u3002180522\u4e2d\u5c71\r\n##\r\n\r\ndata.handler <- function(\r\n caa,\r\n waa,\r\n maa,\r\n index=NULL,\r\n M = 0.4,\r\n maa.tune=NULL,\r\n waa.catch=NULL,\r\n catch.prop=NULL\r\n)\r\n{\r\n years <- as.numeric(sapply(strsplit(names(caa[1,]),\"X\"), function(x) x[2]))\r\n\r\n if (is.null(dim(waa)) | dim(waa)[2]==1) waa <- as.data.frame(matrix(unlist(waa), nrow=nrow(caa), ncol=ncol(caa)))\r\n\r\n if (is.null(dim(maa)) | dim(maa)[2]==1) maa <- as.data.frame(matrix(unlist(maa), nrow=nrow(caa), ncol=ncol(caa)))\r\n\r\n colnames(caa) <- colnames(waa) <- colnames(maa) <- years\r\n\r\n if (!is.null(waa.catch)) {\r\n if (is.null(dim(waa.catch)) | dim(waa.catch)[2]==1) waa.catch <- as.data.frame(matrix(unlist(waa.catch), nrow=nrow(caa), ncol=ncol(caa)))\r\n colnames(waa.catch) <- years\r\n }\r\n\r\n if (!is.null(maa.tune)) {\r\n if (is.null(dim(maa.tune)) | dim(maa.tune)[2]==1) maa.tune <- as.data.frame(matrix(unlist(maa.tune), nrow=nrow(caa), ncol=ncol(caa)))\r\n colnames(maa.tune) <- years\r\n }\r\n\r\n if (!is.null(catch.prop)) colnames(catch.prop) <- years\r\n \r\n if (!is.null(index)) colnames(index) <- years\r\n\r\n if (is.null(dim(M))) M <- as.data.frame(matrix(M, nrow=nrow(caa), ncol=ncol(caa)))\r\n\r\n colnames(M) <- years\r\n rownames(M) <- rownames(caa)\r\n\r\n res <- list(caa=caa, maa=maa, waa=waa, index=index, M=M, maa.tune=maa.tune, waa.catch=waa.catch, catch.prop=catch.prop)\r\n\r\n invisible(res)\r\n}\r\n\r\n# miscellaneous functions\r\n\r\n#max.age <- function(x) max(which(!is.na(x)))\r\nmax.age.func <- function(x) max(which(!is.na(x)))\r\n\r\nvpa.core <- function(caa,faa,M,k){\r\n out <- caa[,k]/(1-exp(-faa[,k]-M[,k]))*(faa[,k]+M[,k])/faa[,k]\r\n return(out)\r\n}\r\n\r\nvpa.core.Pope <- function(caa,faa,M,k){\r\n out <- caa[, k]*exp(M[, k]/2)/(1-exp(-faa[, k]))\r\n return(out)\r\n}\r\n\r\nik.est <- function(caa,naa,M,i,k,min.caa=0.01,maxit=5,d=0.0001){\r\n K <- 1\r\n it <- 0\r\n \r\n f0 <- 1\r\n f1 <- NA\r\n\r\n if (!is.na(naa[i+1,k+1])){\r\n while(it < maxit & K > d){\r\n it <- it + 1\r\n f1 <- log(1+max(caa[i,k],min.caa)/naa[i+1,k+1]*exp(-M[i,k])*(f0+M[i,k])*(1-exp(-f0))/(f0*(1-exp(-f0-M[i,k]))))\r\n K <- sqrt((f1-f0)^2)\r\n f0 <- f1\r\n }\r\n }\r\n \r\n return(f1)\r\n}\r\n\r\nhira.est <- function(caa,naa,M,i,k,alpha=1,min.caa=0.01,maxit=5,d=0.0001){\r\n K <- 1\r\n it <- 0\r\n \r\n f0 <- 1\r\n f1 <- NA\r\n\r\n if (!is.na(naa[i+1,k+1])){\r\n while(it < maxit & K > d){\r\n it <- it + 1\r\n f1 <- log(1+(1-exp(-f0))*exp(-M[i,k])/(naa[i+1,k+1]*f0)*(max(caa[i+1,k],min.caa)*(alpha*f0+M[i+1,k])/(alpha*(1-exp(-alpha*f0-M[i+1,k])))*exp((1-alpha)*f0)+max(caa[i,k],min.caa)*(f0+M[i,k])/(1-exp(-f0-M[i,k]))))\r\n K <- sqrt((f1-f0)^2)\r\n f0 <- f1\r\n }\r\n }\r\n \r\n return(f1)\r\n}\r\n\r\nf.forward.est <- function(caa,naa,M,i,k,maxit=5,d=0.0001){\r\n K <- 1\r\n it <- 0\r\n \r\n f0 <- f1 <- 1\r\n \r\n while(it < maxit & K > d){\r\n it <- it + 1\r\n f1 <- caa[i,k]/naa[i,k]*(f0+M[i,k])/f0*1/(1-exp(-f0-M[i,k]))\r\n K <- sqrt((f1-f0)^2)\r\n f0 <- f1\r\n }\r\n \r\n return(f1)\r\n}\r\n\r\nfp.forward.est <- function(caa,naa,M,i,k,alpha=1,maxit=5,d=0.0001){\r\n K <- 1\r\n it <- 0\r\n \r\n f0 <- f1 <- 1\r\n \r\n while(it < maxit & K > d){\r\n it <- it + 1\r\n f1 <- 1/(1+alpha)*(caa[i,k]/naa[i,k]*(f0+M[i,k])*1/(1-exp(-f0-M[i,k]))+caa[i+1,k]/naa[i+1,k]*(alpha*f0+M[i+1,k])*1/(1-exp(-alpha0*f0-M[i+1,k])))\r\n K <- sqrt((f1-f0)^2)\r\n f0 <- f1\r\n }\r\n \r\n return(f1)\r\n}\r\n\r\nbackward.calc <- function(caa,naa,M,na,k,min.caa=0.001,plus.group=TRUE){\r\n out <- rep(NA, na[k])\r\n if(na[k+1] > na[k]){\r\n for (i in 1:na[k]){\r\n out[i] <- naa[i+1,k+1]*exp(M[i,k])+caa[i,k]*exp(M[i,k]/2)\r\n }\r\n }\r\n else{\r\n for (i in 1:(na[k+1]-2)){\r\n out[i] <- naa[i+1,k+1]*exp(M[i,k])+caa[i,k]*exp(M[i,k]/2)\r\n }\r\n if (isTRUE(plus.group)){\r\n out[(na[k+1]-1):na[k]] <- pmax(caa[(na[k+1]-1):na[k],k],min.caa)/sum(pmax(caa[(na[k+1]-1):na[k],k],min.caa))*naa[na[k+1],k+1]*exp(M[(na[k+1]-1):na[k],k])+caa[(na[k+1]-1):na[k],k]*exp(M[(na[k+1]-1):na[k],k]/2)\r\n }\r\n else{\r\n out[na[k+1]-1] <- naa[na[k+1],k+1]*exp(M[na[k+1]-1,k])+caa[na[k+1]-1,k]*exp(M[na[k+1]-1,k]/2)\r\n out[na[k]] <- out[na[k+1]-1]*caa[na[k+1],k]/caa[na[k+1]-1,k]*exp((M[na[k+1],k]-M[na[k+1]-1,k])/2)\r\n }\r\n }\r\n return(out)\r\n}\r\n\r\nforward.calc <- function(faa,naa,M,na,k){\r\n out <- rep(NA, na[k])\r\n for (i in 2:(na[k]-1)){\r\n out[i] <- naa[i-1,k-1]*exp(-faa[i-1,k-1]-M[i-1,k-1])\r\n }\r\n out[na[k]] <- sum(sapply(seq(na[k]-1,max(na[k], na[k-1])), plus.group.eq, naa=naa, faa=faa, M=M, k=k))\r\n return(out)\r\n}\r\n\r\nplus.group.eq <- function(x, naa, faa, M, k) naa[x,k-1]*exp(-faa[x,k-1]-M[x,k-1])\r\n\r\nf.at.age <- function(caa,naa,M,na,k,alpha=1) {\r\n out <- -log(1-caa[1:(na[k]-1),k]*exp(M[1:(na[k]-1),k]/2)/naa[1:(na[k]-1),k])\r\n c(out, alpha*out[length(out)])\r\n}\r\n\r\nsel.func <- function(faa, def=\"maxage\") {\r\n if(def==\"maxage\") saa <- apply(faa, 2, function(x) x/x[length(x[!is.na(x)])])\r\n if(def==\"max\") saa <- apply(faa, 2, function(x) x/max(x,na.rm=TRUE))\r\n if(def==\"mean\") saa <- apply(faa, 2, function(x) x/sum(x,na.rm=TRUE))\r\n\r\n return(saa)\r\n}\r\n\r\nff <- function(x, z) get(x)(z)\r\n\r\nabund.extractor <- function(\r\n abund=\"SSB\",\r\n naa,\r\n faa,\r\n dat,\r\n min.age=0,\r\n max.age=0,\r\n link=\"id\",\r\n base=exp(1),\r\n af=1,\r\n catch.prop=NULL,\r\n sel.def=\"maxage\",\r\n scale=1000\r\n){\r\n# abund = \"N\": abundance\r\n# abund = \"Nm\": abundance at the middle of the year\r\n# abund = \"B\": biomass\r\n# abund = \"Bm\": abundance at the middle of the year\r\n# abund = \"SSB\": SSB\r\n# # abund = \"SSB\": SSB at the middle of the year\r\n\r\n naa <- as.data.frame(naa)\r\n faa <- as.data.frame(faa)\r\n\r\n waa <- dat$waa/scale\r\n maa <- dat$maa\r\n\r\n min.age <- min.age + 1\r\n max.age <- max.age + 1\r\n\r\n maa.tune <- dat$maa.tune\r\n \r\n if (abund==\"N\") res <- colSums(naa[min.age:max.age,], na.rm=TRUE)\r\n if (abund==\"Nm\") res <- colSums(naa[min.age:max.age,]*exp(-dat$M[min.age:max.age,]/2-af*faa[min.age:max.age,]/2), na.rm=TRUE)\r\n if (abund==\"B\") res <- colSums((naa*waa)[min.age:max.age,], na.rm=TRUE)\r\n if (abund==\"Bm\") res <- colSums((naa*waa)[min.age:max.age,]*exp(-dat$M[min.age:max.age,]/2-af*faa[min.age:max.age,]/2), na.rm=TRUE)\r\n if (abund==\"SSB\"){\r\n if (is.null(maa.tune)) ssb <- naa*waa*maa else ssb <- naa*waa*maa.tune\r\n res <- colSums(ssb,na.rm=TRUE)\r\n }\r\n if (abund==\"Bs\"){\r\n saa <- sel.func(faa, def=sel.def)\r\n res <- colSums((naa*waa*saa)[min.age:max.age,], na.rm=TRUE)\r\n }\r\n if (abund==\"Ns\"){\r\n saa <- sel.func(faa, def=sel.def)\r\n res <- colSums((naa*saa)[min.age:max.age,], na.rm=TRUE)\r\n } \r\n if (abund==\"SSBm\"){\r\n if (is.null(maa.tune)) ssb <- naa*waa*maa*exp(-dat$M/2-af*faa/2) else ssb <- naa*waa*maa.tune*exp(-dat$M/2-af*faa/2)\r\n res <- colSums(ssb,na.rm=TRUE)\r\n }\r\n\r\n if (abund==\"N1sj\") res <- colSums(cbind(naa[1,-1]*exp(dat$M[1,-1]),NA), na.rm=TRUE)\r\n if (abund==\"N0sj\") res <- colSums(cbind(naa[1,-1]*exp(dat$M[1,-1]*2),NA), na.rm=TRUE)\r\n if (abund==\"F\") if (is.null(catch.prop)) res <- colMeans(faa[min.age:max.age,], na.rm=TRUE) else res <- colMeans(catch.prop[min.age:max.age, ]*faa[min.age:max.age,], na.rm=TRUE)\r\n \r\n if (link==\"log\") res <- log(res, base=base)\r\n\r\n return(invisible(res))\r\n}\r\n\r\n#\r\n\r\ntmpfunc2 <- function(x=1,y=2,z=3){\r\n argname <- ls() # \u95a2\u6570\u304c\u547c\u3073\u51fa\u3055\u308c\u305f\u3070\u304b\u308a\u306e\u3068\u304d\u306els()\u306f\u5f15\u6570\u306e\u307f\u304c\u5165\u3063\u3066\u3044\u308b\r\n arglist <- lapply(argname,function(xx) eval(parse(text=xx)))\r\n names(arglist) <- argname\r\n value <- x+y+z\r\n return(list(value=value,args=arglist))\r\n}\r\n\r\n#\r\n##\r\n\r\nqbs.f <- function(q.const, b.const, sigma.const, index, Abund, nindex, index.w, max.dd=0.0001, max.iter=100){\r\n \r\n np.q <- length(unique(q.const[q.const > 0])) \r\n np.b <- length(unique(b.const[b.const > 0])) \r\n np.s <- length(unique(sigma.const[sigma.const > 0]))\r\n \r\n q <- b <- sigma <- numeric(nindex)\r\n \r\n q[1:nindex] <- b[1:nindex] <- sigma[1:nindex] <- 1\r\n \r\n delta <- 1\r\n \r\n obj <- NULL\r\n \r\n NN <- 0\r\n \r\n while(delta > max.dd & NN < max.iter){\r\n NN <- NN+1\r\n q0 <- q\r\n b0 <- b\r\n sigma0 <- sigma\r\n \r\n if (np.q > 0){\r\n for(i in 1:np.q){\r\n id <- which(q.const==i)\r\n num <- den <- 0\r\n for (j in id){\r\n avail <- which(!is.na(as.numeric(index[j,])))\r\n num <- num+index.w[j]*mean(log(as.numeric(index[j,avail]))-b[j]*log(as.numeric(Abund[j,avail])))/sigma[j]^2\r\n den <- den+index.w[j]/sigma[j]^2 \r\n }\r\n q[i] <- num/den\r\n }\r\n }\r\n if (np.b > 0){\r\n for(i in 1:np.b){\r\n id <- which(b.const==i)\r\n num <- den <- 0\r\n for (j in id){\r\n avail <- which(!is.na(as.numeric(index[j,])))\r\n num <- num+index.w[j]*cov(log(as.numeric(index[j,avail])),log(as.numeric(Abund[j,avail])))/var(log(as.numeric(Abund[j,avail])))/sigma[j]^2\r\n den <- den+index.w[j]/sigma[j]^2 \r\n }\r\n b[i] <- num/den\r\n } \r\n }\r\n if (np.s > 0){\r\n for(i in 1:np.s){\r\n id <- which(sigma.const==i)\r\n num <- den <- 0\r\n for (j in id){\r\n avail <- which(!is.na(as.numeric(index[j,])))\r\n nn <- length(avail)\r\n num <- num+index.w[j]*sum((log(as.numeric(index[j,avail]))-q[j]-b[j]*log(as.numeric(Abund[j,avail])))^2)\r\n den <- den+index.w[j]*nn \r\n }\r\n sigma[i] <- sqrt(num/den)\r\n } \r\n }\r\n \r\n q[which(q.const>0)] <- q[q.const[which(q.const>0)]]\r\n b[which(b.const>0)] <- b[b.const[which(b.const>0)]]\r\n sigma[which(sigma.const>0)] <- sigma[sigma.const[which(sigma.const>0)]]\r\n \r\n delta <- max(c(sqrt((q-q0)^2),sqrt((b-b0)^2),sqrt((sigma-sigma0)^2)))\r\n }\r\n\r\n for (i in 1:nindex){\r\n avail <- which(!is.na(as.numeric(index[i,]))) \r\n obj <- c(obj, index.w[i]*(-as.numeric(na.omit(dnorm(log(as.numeric(index[i,avail])),log(q[i])+b[i]*log(as.numeric(Abund[i,avail])),sigma[i],log=TRUE)))))\r\n }\r\n \r\n convergence <- ifelse(delta <= max.dd, 1, 0) \r\n \r\n return(list(q=q, b=b, sigma=sigma, obj=sum(obj), convergence=convergence))\r\n}\r\n\r\nqbs.f2 <- function(p0,index, Abund, nindex, index.w, fixed.index.var=NULL){\r\n \r\n if (is.null(fixed.index.var)) fixed.index.var <- matrix(0, nrow=nrow(index), ncol=ncol(index))\r\n if (class(fixed.index.var)==\"numeric\") fixed.index.var <- matrix(fixed.index.var, nrow=1)\r\n if (class(fixed.index.var)==\"matrix\" | class(fixed.index.var)==\"data.frame\") fixed.index.var <- array(fixed.index.var, dim=c(dim(fixed.index.var),1))\r\n \r\n np <- min(nindex,sum(index.w>0))\r\n \r\n p <- vector(length=2*np)\r\n q <- sigma <- vector(length=np)\r\n\r\n obj.f <- function(p){\r\n obj <- j <- 0\r\n \r\n for (i in 1:nindex){\r\n if (index.w[i] > 0 ){\r\n j <- j + 1\r\n q[j] <- exp(p[2*j-1])\r\n sigma[j] <- exp(p[2*j])\r\n \r\n avail <- which(!is.na(as.numeric(index[i,])))\r\n obj <- obj+index.w[i]*(-as.numeric(na.omit(dmvnorm(log(as.numeric(index[i,avail])),log(q[j])+log(as.numeric(Abund[i,avail])),as.matrix(fixed.index.var[avail,avail,j])+sigma[j]^2*diag(length(avail)),log=TRUE))))\r\n }\r\n }\r\n \r\n sum(obj)\r\n }\r\n \r\n res <- nlm(obj.f,p0)\r\n \r\n q <- res$estimate[1:np]\r\n sigma <- exp(res$estimate[1:np+np])\r\n obj <- res$minimum\r\n convergence <- res$code\r\n \r\n return(list(q=q, b=rep(1,np), sigma=sigma, obj=obj, convergence=convergence))\r\n}\r\n \r\n#\r\n\r\n# vpa \r\n#\r\n\r\nvpa <- function(\r\n dat, # data for vpa\r\n sel.f = NULL, # \u6700\u7d42\u5e74\u306e\u9078\u629e\u7387\r\n tf.year = 2008:2010, # terminal F\u3092\u3069\u306e\u5e74\u306e\u5e73\u5747\u306b\u3059\u308b\u304b\r\n rec.new = NULL, # \u7fcc\u5e74\u306e\u52a0\u5165\u3092\u5916\u304b\u3089\u4e0e\u3048\u308b\r\n rec=NULL, # rec.year\u306e\u52a0\u5165\r\n rec.year=2010, # \u52a0\u5165\u3092\u4ee3\u5165\u3059\u308b\u969b\u306e\u5e74\r\n rps.year = 2001:2010, # \u7fcc\u5e74\u306eRPS\u3092\u3069\u306e\u7bc4\u56f2\u306e\u5e73\u5747\u306b\u3059\u308b\u304b\r\n fc.year = 2009:2011, # Fcurrent\u3067\u3069\u306e\u7bc4\u56f2\u3092\u53c2\u7167\u3059\u308b\u304b\r\n last.year = NULL, # vpa\u3092\u8a08\u7b97\u3059\u308b\u6700\u7d42\u5e74\u3092\u6307\u5b9a\uff08retrospective analysis\uff09\r\n last.catch.zero = FALSE, # TRUE\u306a\u3089\u5f37\u5236\u7684\u306b\u6700\u7d42\u5e74\u306e\u6f01\u7372\u91cf\u30920\u306b\u3059\u308b\r\n faa0 = NULL, # sel.update=TRUE\u306e\u3068\u304d\uff0c\u521d\u671f\u5024\u3068\u306a\u308bfaa\r\n naa0 = NULL, # sel.update=TRUE\u306e\u3068\u304d\uff0c\u521d\u671f\u5024\u3068\u306a\u308bnaa\r\n f.new = NULL,\r\n Pope = TRUE, # Pope\u306e\u8fd1\u4f3c\u5f0f\u3092\u4f7f\u3046\u304b\u3069\u3046\u304b\r\n tune = FALSE, # tuning\u3092\u3059\u308b\u304b\u3069\u3046\u304b\r\n abund = \"B\", # tuning\u306e\u969b\uff0c\u4f55\u306e\u6307\u6a19\u306b\u5bfe\u5fdc\u3059\u308b\u304b\r\n min.age = 0, # tuning\u6307\u6a19\u306e\u5e74\u9f62\u53c2\u7167\u7bc4\u56f2\u306e\u4e0b\u9650\r\n max.age = 0, # tuning\u6307\u6a19\u306e\u5e74\u9f62\u53c2\u7167\u7bc4\u56f2\u306e\u4e0a\u9650\r\n link = \"id\", # tuning\u306elink\u95a2\u6570\r\n base = NA, # link\u95a2\u6570\u304c\"log\"\u306e\u3068\u304d\uff0c\u5e95\u3092\u4f55\u306b\u3059\u308b\u304b\r\n af = NA, # \u8cc7\u6e90\u91cf\u6307\u6570\u304c\u5e74\u306e\u4e2d\u592e\u306e\u3068\u304d\uff0caf=0\u306a\u3089\u6f01\u671f\u524d\uff0caf=1\u306a\u3089\u6f01\u671f\u771f\u3093\u4e2d\uff0caf=2\u306a\u3089\u6f01\u671f\u5f8c\u3068\u306a\u308b\r\n index.w = NULL, # tuning index\u306e\u91cd\u307f\r\n use.index = \"all\",\r\n scale = 1000, # \u91cd\u91cf\u306escaling\r\n hessian = TRUE,\r\n alpha = 1, # \u6700\u9ad8\u9f62\u3068\u6700\u9ad8\u9f62-1\u306eF\u306e\u6bd4 F_a = alpha*F_{a-1}\r\n maxit = 5, # \u77f3\u5ca1\u30fb\u5cb8\u7530/\u5e73\u677e\u306e\u65b9\u6cd5\u306e\u6700\u5927\u7e70\u308a\u8fd4\u3057\u6570\r\n d = 0.0001, # \u77f3\u5ca1\u30fb\u5cb8\u7530/\u5e73\u677e\u306e\u65b9\u6cd5\u306e\u53ce\u675f\u5224\u5b9a\u57fa\u6e96\r\n min.caa = 0.001, # caa\u306b0\u304c\u3042\u308b\u3068\u304d\uff0c0\u3092min.caa\u3067\u7f6e\u304d\u63db\u3048\u308b\r\n plot = FALSE, # tuning\u306b\u4f7f\u3063\u305f\u8cc7\u6e90\u91cf\u6307\u6570\u306b\u5bfe\u3059\u308b\u30d5\u30a3\u30c3\u30c8\u306e\u30d7\u30ed\u30c3\u30c8\r\n plot.year = 1998:2015, # \u4e0a\u306e\u30d7\u30ed\u30c3\u30c8\u306e\u53c2\u7167\u5e74\r\n term.F = \"max\", # terminal F\u306e\u4f55\u3092\u63a8\u5b9a\u3059\u308b\u304b: \"max\" or \"all\"\r\n plus.group = TRUE, \r\n stat.tf = \"mean\", # \u6700\u7d42\u5e74\u306eF\u3092\u63a8\u5b9a\u3059\u308b\u7d71\u8a08\u91cf\uff08\u5e74\u9f62\u5225\u306b\u4e0e\u3048\u308b\u3053\u3068\u53ef\uff09\r\n add.p.est = NULL, # \u8ffd\u52a0\u3067\u6700\u9ad8\u9f62\u4ee5\u5916\u306efaa\u3092\u63a8\u5b9a\u3059\u308b\u969b\uff0e\u5e74\u9f62\u3092\u6307\u5b9a\u3059\u308b\uff0e\r\n add.p.ini = NULL, \r\n sel.update=FALSE, # \u30c1\u30e5\u30fc\u30cb\u30f3\u30b0VPA\u306b\u304a\u3044\u3066\uff0c\u9078\u629e\u7387\u3092\u66f4\u65b0\u3057\u306a\u304c\u3089\u63a8\u5b9a\r\n sel.def = \"max\", # sel.update=TRUE\u3067\u9078\u629e\u7387\u3092\u66f4\u65b0\u3057\u3066\u3044\u304f\u969b\u306b\uff0c\u9078\u629e\u7387\u3092\u3069\u306e\u3088\u3046\u306b\u8a08\u7b97\u3059\u308b\u304b\uff0e\u6700\u5927\u5024\u30921\u3068\u3059\u308b\u304b\uff0c\u5e73\u5747\u5024\u30921\u306b\u3059\u308b\u304b...\r\n max.dd = 0.000001, # sel.update\u306e\u969b\u306e\u53ce\u675f\u5224\u5b9a\u57fa\u6e96\r\n ti.scale = NULL, # \u8cc7\u6e90\u91cf\u306e\u4fc2\u6570\u3068\u5207\u7247\u306escaling\r\n tf.mat = NULL, # terminal F\u306e\u5e73\u5747\u3092\u3068\u308b\u5e74\u306e\u8a2d\u5b9a\uff0e0-1\u884c\u5217\uff0e\r\n eq.tf.mean = FALSE, # terminal F\u306e\u5e73\u5747\u5024\u3092\u904e\u53bb\u306eF\u306e\u5e73\u5747\u5024\u3068\u7b49\u3057\u304f\u3059\u308b\r\n no.est = FALSE, # \u30d1\u30e9\u30e1\u30fc\u30bf\u63a8\u5b9a\u3057\u306a\u3044\uff0e\r\n est.method = \"ls\", # \u63a8\u5b9a\u65b9\u6cd5 \uff08ls = \u6700\u5c0f\u4e8c\u4e57\u6cd5\uff0cml = \u6700\u5c24\u6cd5\uff09\r\n b.est = FALSE, # b\u3092\u63a8\u5b9a\u3059\u308b\u304b\u3069\u3046\u304b\r\n est.constraint = FALSE, # \u5236\u7d04\u4ed8\u304d\u63a8\u5b9a\u3092\u3059\u308b\u304b\u3069\u3046\u304b\r\n q.const = 1:length(abund), # q\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u5236\u7d04\uff080\u306f\u63a8\u5b9a\u3057\u306a\u3044\u30671\u306bfix\uff09\r\n b.const = 1:length(abund), # b\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u5236\u7d04\uff080\u306f\u63a8\u5b9a\u3057\u306a\u3044\u30671\u306bfix\uff09\r\n q.fix = NULL,\r\n b.fix = NULL,\r\n sigma.const = 1:length(abund), # sigma\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u5236\u7d04\r\n fixed.index.var = NULL,\r\n max.iter = 100, # q,b,sigma\u8a08\u7b97\u306e\u969b\u306e\u6700\u5927\u7e70\u308a\u8fd4\u3057\u6570\r\n optimizer = \"nlm\",\r\n Lower = -Inf,\r\n Upper = Inf,\r\n p.fix = NULL,\r\n lambda = 0, # ridge\u56de\u5e30\u4fc2\u6570\r\n beta = 2, # penalty\u306eexponent (beta = 1: lasso, 2: ridge)\r\n penalty = \"p\",\r\n ssb.def = \"i\", # i: \u5e74\u306f\u3058\u3081\uff0cm: \u5e74\u4e2d\u592e, l: \u5e74\u6700\u5f8c\r\n ssb.lag = 0, # 0: no lag, 1: lag 1\r\n TMB=FALSE,\r\n TMB.compile=FALSE,\r\n sel.rank=NULL,\r\n p.init = 0.2, # \u63a8\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u521d\u671f\u5024\r\n sigma.constraint = 1:length(abund)\r\n)\r\n{\r\n #\r\n \r\n if (TMB.compile) {\r\n library(TMB)\r\n compile(\"rvpa_tmb.cpp\")\r\n dyn.load(dynlib(\"rvpa_tmb\"))\r\n }\r\n \r\n # input\u30c7\u30fc\u30bf\u3092\u30ea\u30b9\u30c8\u5316\r\n\r\n argname <- ls() # \u95a2\u6570\u304c\u547c\u3073\u51fa\u3055\u308c\u305f\u3070\u304b\u308a\u306e\u3068\u304d\u306els()\u306f\u5f15\u6570\u306e\u307f\u304c\u5165\u3063\u3066\u3044\u308b\r\n arglist <- lapply(argname,function(xx) eval(parse(text=xx)))\r\n names(arglist) <- argname\r\n \r\n # data handling\r\n\r\n caa <- dat$caa # catch-at-age\r\n waa <- dat$waa # weight-at-age\r\n maa <- dat$maa # maturity-at-age\r\n if (!is.null(dat$maa.tune)) maa.tune <- dat$maa.tune\r\n if (!is.null(dat$catch.prop)) catch.prop <- dat$catch.prop\r\n index <- dat$index # abundance indices\r\n M <- dat$M # natural mortality-at-age\r\n waa.catch <- ifelse(is.null(dat$waa.catch),waa,dat$waa.catch)\r\n\r\n if (isTRUE(tune) & is.null(index)) {print(\"Check!: There is no abundance index.\"); stop()}\r\n \r\n years <- dimnames(caa)[[2]] # \u5e74\r\n ages <- dimnames(caa)[[1]] # \u5e74\u9f62\r\n\r\n if (class(index)==\"numeric\") index <- t(as.matrix(index))\r\n\r\n if (use.index[1]!=\"all\") {\r\n index <- index[use.index,,drop=FALSE]\r\n if (length(use.index)!=length(abund)){\r\n if (length(abund)>1) abund <- abund[use.index]\r\n if (length(min.age)>1) min.age <- min.age[use.index]\r\n if (length(max.age)>1) max.age <- max.age[use.index]\r\n if (length(link)>1) link <- link[use.index]\r\n if (length(base)>1) base <- base[use.index]\r\n if (length(af)>1) af <- af[use.index] \r\n if (length(index.w)>1) index.w <- index.w[use.index] \r\n }\r\n }\r\n\r\n #\r\n \r\n if(!is.null(fixed.index.var)) require(mvtnorm)\r\n\r\n # \u6700\u7d42\u5e74 last.year\u306b\u5024\u304c\u5165\u3063\u3066\u3044\u308b\u5834\u5408\u306f\uff0c\u305d\u308c\u4ee5\u964d\u306e\u30c7\u30fc\u30bf\u3092\u524a\u9664\u3059\u308b\uff08retrospective analysis\uff09\r\n if (!is.null(last.year)) {\r\n caa <- caa[,years <= last.year]\r\n waa <- waa[,years <= last.year]\r\n maa <- maa[,years <= last.year]\r\n if (!is.null(dat$maa.tune)) maa.tune <- maa.tune[,years <= last.year] \r\n if (!is.null(dat$cathc.prop)) maa.tune <- catch.prop[,years <= last.year] \r\n M <- M[,years <= last.year]\r\n if(!is.null(index)) index <- index[,years <= last.year,drop=FALSE]\r\n years <- dimnames(caa)[[2]]\r\n dat <- list(caa=caa, waa=waa, maa=maa, M=M, index=index)\r\n }\r\n\r\n na <- apply(caa, 2, max.age.func) # \u5e74\u3054\u3068\u306e\u6700\u5927\u5e74\u9f62\uff08\u5e74\u306b\u3088\u3063\u3066\u6700\u5927\u5e74\u9f62\u304c\u9055\u3046\u5834\u5408\u306b\u5bfe\u5fdc\u3059\u308b\u305f\u3081\uff09\r\n ny <- ncol(caa) # \u5e74\u306e\u6570\r\n \r\n if (isTRUE(last.catch.zero)) {caa[,ny] <- 0; ny <- ny - 1; n.add <- 1; saa.new <- NULL} else n.add <- 0 # \u6700\u7d42\u5e74\u306e\u6f01\u7372\u91cf\u30920\u3068\u3057\uff0c\u5e74\u30921\u500b\u6e1b\u3089\u3059\r\n\r\n if (term.F==\"max\") { # \u6700\u9ad8\u9f62\u306eF\u3060\u3051\u3092\u63a8\u5b9a\u3059\u308b\u5834\u5408\r\n p.init <- ifelse(is.na(p.init[1]), M[na[ny],ny], p.init[1]) # \u521d\u671f\u5024\u304cNA\u306a\u3089\uff0c\u6700\u7d42\u5e74\u6700\u9ad8\u9f62\u306e\u81ea\u7136\u6b7b\u4ea1\u4fc2\u6570\u3092\u521d\u671f\u5024\u3068\u3059\u308b\r\n if (!is.null(add.p.est) & is.null(add.p.ini)) {add.p.est <- add.p.est + 1; p.init <- rep(p.init, length(add.p.est)+1)} # add.p.est\u306b\u6570\u5b57\u304c\u3042\u308c\u3070\uff0c\u305d\u306e\u5e74\u9f62\u3092\u8ffd\u52a0\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3068\u3057\u3066\u63a8\u5b9a\u3059\u308b\r\n if (!is.null(add.p.est) & !is.null(add.p.ini)) {add.p.est <- add.p.est + 1; p.init <- c(add.p.ini,p.init)} # add.p.est\u306b\u6570\u5b57\u304c\u3042\u308c\u3070\uff0c\u305d\u306e\u5e74\u9f62\u3092\u8ffd\u52a0\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3068\u3057\u3066\u63a8\u5b9a\u3059\u308b\r\n }\r\n if (term.F==\"all\"){ # \u6700\u7d42\u5e74\u306e\u3059\u3079\u3066\u306e\u5e74\u9f62\u306eF\u3092\u63a8\u5b9a\r\n if(length(p.init)==0) p.init <- rep(M[na[ny],ny], na[ny]-1) # \u521d\u671f\u5024\u304cNA\u306a\u3089\uff0c\u6700\u7d42\u5e74\u6700\u9ad8\u9f62\u306e\u81ea\u7136\u6b7b\u4ea1\u4fc2\u6570\u30920~na-1\u306e\u521d\u671f\u5024\u3068\u3059\u308b\r\n if(length(p.init) < na[ny]-1) p.init <- rep(p.init[1], na[ny]-1) # \u521d\u671f\u5024\u306e\u6210\u5206\u6570\u304c\u5e74\u9f62\u6570-1\u3088\u308a\u5c0f\u3055\u3044\u5834\u5408\u306f\uff0c\u521d\u671f\u5024\u306e\u6700\u521d\u306e\u5024\u3092\u8981\u7d20\u306b\u6301\u3064\u5e74\u9f62\u6570-1\u306e\u5927\u304d\u3055\u306e\u30d9\u30af\u30c8\u30eb\u3092\u521d\u671f\u5024\u3068\u3059\u308b\r\n if(length(p.init) >= na[ny]-1) p.init <- p.init[1:na[ny]-1] # \u521d\u671f\u5024\u306e\u6210\u5206\u6570\u304c\u5e74\u9f62\u6570-1\u4ee5\u4e0a\u3067\u3042\u308c\u3070\uff0c\u5e74\u9f62\u6570\u4ee5\u4e0a\u306e\u5024\u306f\u4f7f\u7528\u3057\u306a\u3044\r\n }\r\n\r\n if (length(stat.tf)==1) stat.tf <- rep(stat.tf, na[ny]-1) # stat.tf\u304c1\u500b\u3060\u3051\u6307\u5b9a\u3055\u308c\u3066\u3044\u308b\u3068\u304d\u306f\uff0c\u5168\u5e74\u9f62\u305d\u306e\u7d71\u8a08\u91cf\u3092\u4f7f\u3046\r\n\r\n # tuning\u306e\u969b\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u304c1\u500b\u3060\u3051\u6307\u5b9a\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u306f\uff0cnindex\u306e\u6570\u3060\u3051\u5897\u3084\u3059\r\n if (isTRUE(tune)){\r\n \r\n nindex <- nrow(index)\r\n\r\n if (nindex > length(abund) & length(abund)==1) abund <- rep(abund, nindex)\r\n if (nindex > length(min.age) & length(min.age)==1) min.age <- rep(min.age, nindex)\r\n if (nindex > length(max.age) & length(max.age)==1) max.age <- rep(max.age, nindex)\r\n if (nindex > length(link) & length(link)==1) link <- rep(link, nindex)\r\n if (nindex > length(base) & length(base)==1) base <- rep(base, nindex)\r\n \r\n if (is.null(index.w)) index.w <- rep(1, nindex)\r\n if (!is.na(af[1])) if(nindex > length(af) & length(af)==1) af <- rep(af, nindex)\r\n\r\n q <- rep(NA, nindex)\r\n }\r\n\r\n\r\n # selectivity\u3092\u66f4\u65b0\u3059\u308b\u5834\u5408\u306bfaa0\uff0cnaa0\u304c\u4e0e\u3048\u3089\u308c\u3066\u3044\u308c\u3070\uff0c\u305d\u308c\u3092\u4f7f\u3046\r\n if (!isTRUE(sel.update)){\r\n faa <- naa <- matrix(NA, nrow=max(na), ncol=ny+n.add, dimnames=list(ages, years))\r\n }else {\r\n if(is.null(faa0) | is.null(naa0)) faa <- naa <- matrix(1, nrow=max(na), ncol=ny+n.add, dimnames=list(ages, years))\r\n else {faa <- as.matrix(faa0); naa <- as.matrix(naa0)}\r\n }\r\n\r\n if (is.null(p.fix)) p.fix <- 1:length(p.init)\r\n\r\n # warnings\r\n \r\n if (!tune & sel.update) print(\"sel.update = TRUE but tune=FALSE. So, the results are unreliable.\")\r\n if (tune & is.null(sel.f) & (!sel.update & term.F==\"max\")) print(\"sel.f=NULL although tune=TRUE & sel.update=FALSE & term.F=max. The results are unreliable.\")\r\n if (tune) if(length(abund)!=nrow(index)) print(\"Check!: The number of abundance definition is different from the number of indices.\")\r\n\r\n# ssb.def\r\n\r\n if (ssb.def==\"i\") ssb.coef <- 0\r\n if (ssb.def==\"m\") ssb.coef <- 0.5\r\n if (ssb.def==\"l\") ssb.coef <- 1 \r\n \r\n\r\n\r\n# core function for optimization\r\n\r\n p.est <- function(log.p, out=FALSE){\r\n \r\n p <- exp(log.p)\r\n\r\n\r\n # sel.f==NULL\u3067\uff0c\u30d1\u30e9\u30e1\u30fc\u30bfp\u304c1\u500b\u306a\u3089\uff0c\u6700\u7d42\u5e74\u6700\u9ad8\u9f62\u306efaa\u3068naa\u3092\u63a8\u5b9a\r\n if (is.null(sel.f) & length(p) == 1){\r\n faa[na[ny], ny] <- p\r\n if (isTRUE(Pope)) naa[na[ny], ny] <- caa[na[ny], ny]*exp(M[na[ny], ny]/2)/(1-exp(-faa[na[ny], ny]))\r\n else naa[na[ny], ny] <- caa[na[ny], ny]/(1-exp(-faa[na[ny], ny]-M[na[ny], ny]))*(faa[na[ny],ny]+M[na[ny],ny])/faa[na[ny],ny]\r\n }\r\n\r\n # sel.f!=NULL\u3067\uff0c\u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u5e74\u9f62-1\u3088\u308a\u5c11\u306a\u3044\u5834\u5408\uff0csel.f\u3092\u4f7f\u3063\u3066\uff0c\u6700\u7d42\u5e74/\u5168\u5e74\u9f62\u306efaa\u3068naa\u3092\u8a08\u7b97\r\n if (!is.null(sel.f) & length(p) < na[ny]-1){\r\n if(length(p)==1) faa[, ny] <- sel.f*p\r\n if(length(p) > 1) { # \u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u304c1\u3088\u308a\u5927\u304d\u3044\u5834\u5408\uff0cadd.p.est\u306e\u5206\uff0c\u63a8\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u3092\u5897\u3084\u3059\r\n faa[,ny] <- sel.f*p[length(p)]\r\n for (i in 1:(length(p)-1)){\r\n faa[add.p.est[i],ny] <- p[i]*p[length(p)]\r\n }\r\n }\r\n if (isTRUE(Pope)) naa[, ny] <- vpa.core.Pope(caa,faa,M,ny)\r\n else naa[, ny] <- vpa.core(caa,faa,M,ny)\r\n }\r\n\r\n #\r\n if (is.null(sel.f) & isTRUE(sel.update)){\r\n if(length(p)==1) faa[, ny] <- p\r\n if(length(p) > 1) { # \u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u304c1\u3088\u308a\u5927\u304d\u3044\u5834\u5408\uff0cadd.p.est\u306e\u5206\uff0c\u63a8\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u3092\u5897\u3084\u3059\r\n faa[,ny] <- p[length(p)]\r\n }\r\n }\r\n \r\n # \u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u5e74\u9f62-1\u3067\u3042\u308c\u3070\uff0c\u305d\u308c\u3089\u3092\u30d1\u30e9\u30e1\u30fc\u30bf\u3068\u3057\u3066\u6700\u7d42\u5e74/\u5168\u5e74\u9f62\u306efaa\u3068naa\u3092\u8a08\u7b97\r\n if (length(p) == na[ny]-1){\r\n faa[1:(na[ny]-1), ny] <- p[p.fix]\r\n faa[na[ny], ny] <- alpha*p[na[ny]-1]\r\n if (isTRUE(Pope)) naa[, ny] <- vpa.core.Pope(caa,faa,M,ny)\r\n else naa[, ny] <- vpa.core(caa,faa,M,ny)\r\n }\r\n \r\n # selctivity\u3092\u66f4\u65b0\u3057\u306a\u304c\u3089\u63a8\u5b9a\u3059\u308b\u5834\u5408\r\n if (isTRUE(sel.update)){\r\n dd <- itt <- 1\r\n while(dd > max.dd & itt < max.iter){\r\n saa <- sel.func(faa, def=sel.def) # sel.def\u306b\u5f93\u3063\u3066\u9078\u629e\u7387\u3092\u8a08\u7b97\r\n for (i in (na[ny]-1):1){\r\n saa[i, ny] <- get(stat.tf[i])(saa[i, years %in% tf.year]) \r\n }\r\n \r\n saa[na[ny], ny] <- get(stat.tf[na[ny]-1])(saa[na[ny], years %in% tf.year])\r\n if(length(p)==1) faa[1:na[ny], ny] <- p*sel.func(saa, def=sel.def)[1:na[ny],ny] else faa[1:na[ny], ny] <- p[length(p)]*sel.func(saa, def=sel.def)[1:na[ny],ny] \r\n \r\n if (isTRUE(Pope)) naa[ , ny] <- vpa.core.Pope(caa,faa,M,ny)\r\n else naa[, ny] <- vpa.core(caa,faa,M,ny)\r\n \r\n if (isTRUE(Pope)){\r\n for (i in (ny-1):1){\r\n naa[1:na[i], i] <- backward.calc(caa,naa,M,na,i,min.caa=min.caa,plus.group=plus.group)\r\n faa[1:na[i], i] <- f.at.age(caa,naa,M,na,i,alpha=alpha)\r\n }\r\n }\r\n else{\r\n for (i in (ny-1):1){\r\n for (j in 1:(na[i]-2)){\r\n faa[j, i] <- ik.est(caa,naa,M,j,i,min.caa=min.caa,maxit=maxit,d=d)\r\n }\r\n if (isTRUE(plus.group)){\r\n faa[na[i]-1, i] <- hira.est(caa,naa,M,na[i]-1,i,alpha=alpha,min.caa=min.caa,maxit=maxit,d=d)\r\n }\r\n else faa[na[i]-1, i] <- ik.est(caa,naa,M,na[i]-1,i,min.caa=min.caa,maxit=maxit,d=d)\r\n \r\n faa[na[i], i] <- alpha*faa[na[i]-1, i]\r\n naa[1:na[i], i] <- vpa.core(caa,faa,M,i)\r\n }\r\n }\r\n\r\n faa1 <- faa\r\n saa1 <- sel.func(faa1, def=sel.def)\r\n\r\n for (i in (na[ny]-1):1){\r\n saa1[i, ny] <- get(stat.tf[i])(saa1[i, years %in% tf.year]) \r\n }\r\n saa1[na[ny], ny] <- get(stat.tf[na[ny]-1])(saa1[na[ny], years %in% tf.year]) \r\n if(length(p)==1) faa1[1:na[ny], ny] <- p*sel.func(saa1, def=sel.def)[1:na[ny],ny] else faa1[1:na[ny], ny] <- p[length(p)]*sel.func(saa1, def=sel.def)[1:na[ny],ny]\r\n faa1[na[ny], ny] <- alpha*faa1[na[ny]-1, ny]\r\n \r\n dd <- max(sqrt((saa1[,ny] - saa[,ny])^2))\r\n itt <- itt + 1\r\n \r\n faa <- faa1\r\n }\r\n\r\n saa <- sel.func(faa, def=sel.def)\r\n \r\n if(length(p) > 1) { # \u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u304c1\u3088\u308a\u5927\u304d\u3044\u5834\u5408\uff0cadd.p.est\u306e\u5206\uff0c\u63a8\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\u6570\u3092\u5897\u3084\u3059\r\n for (i in 1:(length(p)-1)){\r\n faa[add.p.est[i],ny] <- p[i]\r\n }\r\n naa[, ny] <- vpa.core.Pope(caa,faa,M,ny)\r\n for (i in (ny-1):(ny-na[ny]+1)){\r\n naa[1:na[i], i] <- backward.calc(caa,naa,M,na,i,min.caa=min.caa,plus.group=plus.group)\r\n faa[1:na[i], i] <- f.at.age(caa,naa,M,na,i,alpha=alpha)\r\n }\r\n } \r\n }\r\n\r\n if (!isTRUE(sel.update)){\r\n if (isTRUE(Pope)){\r\n for (i in (ny-1):1){\r\n naa[1:na[i], i] <- backward.calc(caa,naa,M,na,i,min.caa=min.caa,plus.group=plus.group)\r\n faa[1:na[i], i] <- f.at.age(caa,naa,M,na,i,alpha=alpha)\r\n }\r\n }\r\n else{\r\n for (i in (ny-1):1){\r\n for (j in 1:(na[i]-2)){\r\n faa[j, i] <- ik.est(caa,naa,M,j,i,min.caa=min.caa,maxit=maxit,d=d)\r\n }\r\n if (isTRUE(plus.group)) faa[na[i]-1, i] <- hira.est(caa,naa,M,na[i]-1,i,alpha=alpha,min.caa=min.caa,maxit=maxit,d=d)\r\n else faa[na[i]-1, i] <- ik.est(caa,naa,M,na[i]-1,i,min.caa=min.caa,maxit=maxit,d=d)\r\n faa[na[i], i] <- alpha*faa[na[i]-1, i]\r\n naa[1:na[i], i] <- vpa.core(caa,faa,M,i)\r\n }\r\n }\r\n\r\n if (is.na(naa[na[ny]-1,ny])){\r\n if(isTRUE(Pope)){\r\n for (i in (na[ny]-1):1){\r\n if (is.null(tf.mat)) faa[i, ny] <- get(stat.tf[i])(faa[i, years %in% tf.year]) \r\n else faa[i, ny] <- get(stat.tf[i])(faa[i, !is.na(tf.mat[i,])]) \r\n naa[i, ny] <- caa[i, ny]*exp(M[i, ny]/2)/(1-exp(-faa[i, ny]))\r\n k <- 0\r\n for (j in (i-1):1){\r\n k <- k + 1\r\n if (i-k > 0){\r\n naa[j,ny-k] <- naa[j+1,ny-k+1]*exp(M[j,ny-k])+caa[j,ny-k]*exp(M[j,ny-k]/2)\r\n faa[j,ny-k] <- -log(1-caa[j,ny-k]*exp(M[j,ny-k]/2)/naa[j,ny-k])\r\n } \r\n }\r\n }\r\n }\r\n else{\r\n for (i in (na[ny]-1):1){\r\n faa[i, ny] <- get(stat.tf[i])(faa[i, years %in% tf.year]) \r\n naa[i, ny] <- caa[i, ny]/(1-exp(-faa[i, ny]-M[i, ny]))*(faa[i, ny]+M[i, ny])/faa[i, ny]\r\n k <- 0\r\n for (j in (i-1):1){\r\n k <- k + 1\r\n if (i-k > 0){\r\n faa[j,ny-k] <- ik.est(caa,naa,M,j,ny-k,min.caa=min.caa,maxit=maxit,d=d)\r\n naa[j,ny-k] <- caa[j, ny-k]/(1-exp(-faa[j, ny-k]-M[j, ny-k]))*(faa[j, ny-k]+M[j, ny-k])/faa[j, ny-k]\r\n } \r\n }\r\n }\r\n }\r\n }\r\n }\r\n\r\n if (!is.null(rec)){\r\n naa[1, years %in% rec.year] <- rec\r\n if(isTRUE(Pope)) faa[1, years %in% rec.year] <- -as.numeric(log(1-caa[1, years %in% rec.year]/naa[1, years %in% rec.year]*exp(M[1, years %in% rec.year]/2)))\r\n else{ \r\n for (j in which(years %in% rec.year)){\r\n faa[1,j] <- f.forward.est(caa,naa,M,1,j,maxit=maxit,d=d)\r\n }\r\n }\r\n\r\n terminal.year <- as.numeric(years[ny])\r\n for (kk in 1:length(rec.year)){\r\n for (i in rec.year[kk]:terminal.year){\r\n if(terminal.year-i > 0 & i-rec.year[kk]+1 <= max(ages)){\r\n naa[i-rec.year[kk]+2, years %in% (i+1)] <- naa[i-rec.year[kk]+1, years %in% i]*exp(-faa[i-rec.year[kk]+1, years %in% i]-M[i-rec.year[kk]+1, years %in% i])\r\n if (isTRUE(Pope)) faa[i-rec.year[kk]+2, years %in% (i+1)] <- -log(1-caa[i-rec.year[kk]+2, years %in% (i+1)]/naa[i-rec.year[kk]+2, years %in% (i+1)]*exp(M[i-rec.year[kk]+2, years %in% (i+1)]/2))\r\n else {\r\n for (j in which(years %in% (i+1))){\r\n if(i-rec.year[kk]+2 < na[j]-1) faa[i-rec.year[kk]+2, j] <- f.forward.est(caa,naa,M,i-rec.year[kk]+2,j,maxit=maxit,d=d)\r\n if (isTRUE(plus.group)){\r\n if(i-rec.year[kk]+2 == na[j]-1) faa[i-rec.year[kk]+2, j] <- fp.forward.est(caa,naa,M,i-rec.year[kk]+2,j,alpha,maxit=maxit,d=d) \r\n if(i-rec.year[kk]+2 == na[j]) faa[i-rec.year[kk]+2, j] <- alpha*fp.forward.est(caa,naa,M,i-rec.year[kk]+1,j,alpha,maxit=maxit,d=d)\r\n } \r\n else{\r\n if(i-rec.year[kk]+2 == na[j]-1) faa[i-rec.year[kk]+2, j] <- f.forward.est(caa,naa,M,i-rec.year[kk]+2,j,maxit=maxit,d=d) \r\n if(i-rec.year[kk]+2 == na[j]) faa[i-rec.year[kk]+2, j] <- alpha*f.forward.est(caa,naa,M,i-rec.year[kk]+1,j,maxit=maxit,d=d) \r\n }\r\n }\r\n }\r\n }\r\n }\r\n }\r\n }\r\n\r\n # next year\r\n\r\n if (isTRUE(tune)){\r\n if (n.add==1 & !is.na(mean(index[,ny+n.add],na.rm=TRUE))){\r\n \r\n new.naa <- forward.calc(faa,naa,M,na,ny+n.add)\r\n\r\n naa[,ny+n.add] <- new.naa\r\n baa <- naa*waa\r\n ssb <- baa*maa*exp(-ssb.coef*(faa+M))\r\n \r\n if (is.null(rec.new)) {\r\n new.naa[1] <- median((naa[1,]/colSums(ssb))[years %in% rps.year])*sum(ssb[,ny+n.add],na.rm=TRUE)\r\n }\r\n else new.naa[1] <- rec.new\r\n\r\n naa[1,ny+n.add] <- new.naa[1]\r\n baa[1,ny+n.add] <- naa[1,ny+n.add]*waa[1,ny+n.add]\r\n\r\n if (!is.null(f.new) & !is.null(saa.new)) faa[,ny+n.add] <- f.new*saa.new else faa[,ny+n.add] <- 0\r\n if (isTRUE(Pope)) caa[,ny+n.add] <- naa[,ny+n.add]*(1-exp(-faa[,ny+n.add]))*exp(-M[,ny+n.add]/2) else caa[,ny+n.add] <- naa[,ny+n.add]*(1-exp(-faa[,ny+n.add]-M[,ny+n.add]))*faa[,ny+n.add]/(faa[,ny+n.add]+M[,ny+n.add])\r\n \r\n ssb[1,ny+n.add] <- baa[1,ny+n.add]*maa[1,ny+n.add]*exp(-ssb.coef*(faa[1,ny+n.add]+M[1,ny+n.add]))\r\n\r\n if (ssb.lag==1) ssb <- cbind(NA, ssb[,-ncol(ssb)])\r\n }\r\n\r\n\r\n\r\n\r\n # tuning\r\n \r\n obj <- NULL\r\n\r\n if (tune){\r\n if (est.constraint | !is.null(fixed.index.var)){\r\n \r\n Abund <- NULL\r\n \r\n for (i in 1:nindex){\r\n abundance <- abund.extractor(abund=abund[i], naa, faa, dat, min.age=min.age[i], max.age=max.age[i], link=link[i], base=base[i], af=af[i], catch.prop=catch.prop, sel.def=sel.def, scale=scale)\r\n Abund <- rbind(Abund, abundance)\r\n }\r\n \r\n if (is.null(fixed.index.var)) est.qbs <- qbs.f(q.const, b.const, sigma.const, index, Abund, nindex, index.w, max.dd, max.iter) else {\r\n p00 <- c(log(q.const[which(index.w >0)]), log(sigma.const[which(index.w >0)]))\r\n est.qbs <- qbs.f2(p00, index, Abund, nindex, index.w, fixed.index.var)\r\n }\r\n \r\n q <- exp(est.qbs$q)\r\n b <- est.qbs$b \r\n sigma <- est.qbs$sigma\r\n obj <- est.qbs$obj\r\n convergence <- est.qbs$convergence\r\n obj0 <- obj\r\n \r\n rownames(Abund) <- 1:nindex\r\n }\r\n else{\r\n \r\n if (est.method==\"ls\")\r\n {\r\n Abund <- nn <- sigma <- b <- NULL\r\n for (i in 1:nindex)\r\n {\r\n abundance <- abund.extractor(abund=abund[i], naa, faa, dat, min.age=min.age[i], max.age=max.age[i], link=link[i], base=base[i], af=af[i], catch.prop=catch.prop, sel.def=sel.def, scale=scale)\r\n Abund <- rbind(Abund, abundance)\r\n avail <- which(!is.na(as.numeric(index[i,])))\r\n if (b.est)\r\n {\r\n if (is.null(b.fix))\r\n {\r\n b[i] <- cov(log(as.numeric(index[i,avail])),log(as.numeric(abundance[avail])))/var(log(as.numeric(abundance[avail])))\r\n }else\r\n {\r\n if (is.na(b.fix[i])) b[i] <- cov(log(as.numeric(index[i,avail])),log(as.numeric(abundance[avail])))/var(log(as.numeric(abundance[avail]))) else b[i] <- b.fix[i]\r\n }\r\n }else\r\n {\r\n if (is.null(b.fix)) b[i] <- 1 else b[i] <- b.fix[i]\r\n }\r\n if (is.null(q.fix))\r\n {\r\n q[i] <- exp(mean(log(as.numeric(index[i,avail]))-b[i]*log(as.numeric(abundance[avail]))))\r\n }else\r\n {\r\n q[i] <- q.fix[i]\r\n }\r\n obj <- c(obj,index.w[i]*sum((log(as.numeric(index[i,avail]))-log(q[i])-b[i]*log(as.numeric(abundance[avail])))^2))\r\n }\r\n }\r\n if (est.method==\"ml\")\r\n {\r\n if(!(length(sigma.constraint)==nindex))\r\n {\r\n stop(\"length of sigma constraint does not match the number of indices!!!!\")#sigma.constraint\u306e\u9577\u3055\u304cindex\u306e\u672c\u6570\u3068\u7570\u306a\u308b\u5834\u5408\u306b\u306f\u30a8\u30e9\u30fc\u3092\u51fa\u3057\u3066\u505c\u6b62\u3002\r\n }\r\n Abund <- nn <- sigma <- b <- NULL\r\n for (i in 1:nindex)\r\n {\r\n abundance <- abund.extractor(abund=abund[i], naa, faa, dat, min.age=min.age[i], max.age=max.age[i], link=link[i], base=base[i], af=af[i], catch.prop=catch.prop, sel.def=sel.def, scale=scale)\r\n Abund <- rbind(Abund, abundance)\r\n avail <- which(!is.na(as.numeric(index[i,])))\r\n nn[i] <- length(avail)\r\n if (b.est)\r\n {\r\n if (is.null(b.fix))\r\n {\r\n b[i] <- cov(log(as.numeric(index[i,avail])),log(as.numeric(abundance[avail])))/var(log(as.numeric(abundance[avail])))\r\n }else\r\n {\r\n if (is.na(b.fix[i]))\r\n {\r\n b[i] <- cov(log(as.numeric(index[i,avail])),log(as.numeric(abundance[avail])))/var(log(as.numeric(abundance[avail])))\r\n }else\r\n {\r\n b[i] <- b.fix[i]\r\n }\r\n }\r\n }else\r\n {\r\n if (is.null(b.fix))\r\n {\r\n b[i] <- 1\r\n }else\r\n {\r\n b[i] <- b.fix[i]\r\n }\r\n }\r\n if (is.null(q.fix))\r\n {\r\n q[i] <- exp(mean(log(as.numeric(index[i,avail]))-b[i]*log(as.numeric(abundance[avail]))))\r\n }else\r\n {\r\n q[i] <- q.fix[i]\r\n }\r\n #sigma[i] <- sqrt(sum((log(as.numeric(index[i,avail]))-log(q[i])-b[i]*log(as.numeric(abundance[avail])))^2)/nn[i])\r\n #obj <- c(obj,index.w[i]*(-as.numeric(na.omit(dnorm(log(as.numeric(index[i,avail])),log(q[i])+b[i]*log(as.numeric(abundance[avail])),sigma[i],log=TRUE)))))\r\n }\r\n unique.sigma.constraint <- unique(sigma.constraint)\r\n for(i in 1:length(unique.sigma.constraint))\r\n {\r\n index.num <- which(sigma.constraint==unique.sigma.constraint[i])\r\n sq.error <- 0\r\n for(j in index.num)\r\n {\r\n abundance <- abund.extractor(abund=abund[j], naa, faa, dat, min.age=min.age[j], max.age=max.age[j], link=link[j], base=base[j], af=af[j], catch.prop=catch.prop, sel.def=sel.def, scale=scale)\r\n avail <- which(!is.na(as.numeric(index[j,])))\r\n sq.error <- sq.error + sum((log(as.numeric(index[j,avail]))-log(q[j])-b[j]*log(as.numeric(abundance[avail])))^2)\r\n }\r\n sigma[index.num] <- sqrt(sq.error/sum(nn[index.num]))\r\n }\r\n for (i in 1:nindex)\r\n {\r\n abundance <- abund.extractor(abund=abund[i], naa, faa, dat, min.age=min.age[i], max.age=max.age[i], link=link[i], base=base[i], af=af[i], catch.prop=catch.prop, sel.def=sel.def, scale=scale)\r\n obj <- c(obj,index.w[i]*(-as.numeric(na.omit(dnorm(log(as.numeric(index[i,avail])),log(q[i])+b[i]*log(as.numeric(abundance[avail])),sigma[i],log=TRUE)))))\r\n }\r\n }\r\n\r\n obj0 <- obj\r\n obj <- sum(obj)\r\n convergence <- 1\r\n saa <- sel.func(faa, def=sel.def)\r\n\r\n if (penalty==\"p\") obj <- (1-lambda)*obj + lambda*sum(p^beta) \r\n \r\n if (penalty==\"s\") obj <- (1-lambda)*obj + lambda*sum((abs(saa[,ny]-apply(saa[, years %in% tf.year],1,get(stat.tf))))^beta)\r\n \r\n if (!is.null(sel.rank)) obj <- obj+1000000*sum((rank(saa[,ny])-sel.rank)^2)\r\n \r\n rownames(Abund) <- 1:nindex\r\n } \r\n }\r\n }\r\n else {obj <- (p - alpha*faa[na[ny]-1, ny])^2; obj0 <- NA}\r\n \r\n #\r\n\r\n if (isTRUE(out)) {\r\n # next year\r\n\r\n if (n.add==1 & is.na(naa[1,ny+n.add])){\r\n new.naa <- forward.calc(faa,naa,M,na,ny+n.add)\r\n\r\n naa[,ny+n.add] <- new.naa\r\n baa <- naa*waa\r\n ssb <- baa*maa*exp(-ssb.coef*(faa+M))\r\n\r\n if (is.null(rec.new)) {\r\n new.naa[1] <- median((naa[1,]/colSums(ssb))[years %in% rps.year])*sum(ssb[,ny+n.add],na.rm=TRUE)\r\n } else new.naa[1] <- rec.new\r\n\r\n naa[1,ny+n.add] <- new.naa[1]\r\n baa[1,ny+n.add] <- naa[1,ny+n.add]*waa[1,ny+n.add]\r\n \r\n if (!is.null(f.new) & !is.null(saa.new)) faa[,ny+n.add] <- f.new*saa.new else faa[,ny+n.add] <- 0\r\n if (isTRUE(Pope)) caa[,ny+n.add] <- naa[,ny+n.add]*(1-exp(-faa[,ny+n.add]))*exp(-M[,ny+n.add]/2) else caa[,ny+n.add] <- naa[,ny+n.add]*(1-exp(-faa[,ny+n.add]-M[,ny+n.add]))*faa[,ny+n.add]/(faa[,ny+n.add]+M[,ny+n.add])\r\n \r\n ssb[1,ny+n.add] <- baa[1,ny+n.add]*maa[1,ny+n.add]*exp(-ssb.coef*(faa[1,ny+n.add]+M[1,ny+n.add]))\r\n\r\n if (ssb.lag==1) ssb <- cbind(NA, ssb[,-ncol(ssb)])\r\n } \r\n else {\r\n baa <- naa*waa\r\n ssb <- baa*maa*exp(-ssb.coef*(faa+M))\r\n if (ssb.lag==1) ssb <- cbind(NA, ssb[,-ncol(ssb)])\r\n }\r\n\r\n \r\n obj <- list(minimum=obj, minimum.c=obj0, caa=caa, naa=naa, faa=faa, baa=baa, ssb=ssb)\r\n if (isTRUE(eq.tf.mean)) obj$p <- max(faa[,ny],na.rm=TRUE)\r\n\r\n if (isTRUE(tune)) {\r\n if (est.method==\"ls\"){\r\n if (use.index[1]==\"all\") Nindex <- sum(!is.na(index[index.w > 0,])) else Nindex <- sum(!is.na(index[index.w[use.index > 0] > 0,])) \r\n Sigma2 <- obj$minimum/Nindex\r\n neg.logLik <- Nindex/2*log(2*pi*Sigma2)+Nindex/2\r\n obj$q <- q\r\n obj$b <- b\r\n obj$sigma <- sqrt(Sigma2)\r\n obj$convergence <- convergence \r\n obj$Abund <- Abund\r\n obj$logLik <- -neg.logLik\r\n }\r\n if (est.method==\"ml\"|est.constraint| !is.null(fixed.index.var)){\r\n if (est.constraint){\r\n names(q) <- q.const\r\n names(b) <- b.const \r\n names(sigma) <- sigma.const \r\n }\r\n obj$convergence <- convergence \r\n obj$q <- q\r\n obj$b <- b\r\n obj$sigma <- sigma\r\n obj$Abund <- Abund\r\n obj$logLik <- -obj$minimum\r\n } \r\n \r\n }\r\n }\r\n \r\n return(obj) # \u76ee\u7684\u95a2\u6570\u3092\u8fd4\u3059\r\n }\r\n\r\n\r\n########################################################################################\r\n\r\n # execution of optimization\r\n# if (isTRUE(ADMB)){\r\n# require(R2admb)\r\n# \r\n# index2 <- as.matrix(t(apply(index,1,function(x) ifelse(is.na(x),0,x))))\r\n# \r\n# Type <- ifelse(abund==\"SSB\", 1, ifelse(abund==\"B\",4,ifelse(abund==\"N\",3,2)))\r\n# \r\n# if(is.null(dat$maa.tune)) MAA <- as.matrix(dat$maa) else MAA <- as.matrix(dat$maa.tune)\r\n# if (is.na(af[1])) af <- rep(0,nindex)\r\n# \r\n# data2 <- list(A=nrow(dat$caa),Y=ncol(dat$caa),K=length(use.index),Est=ifelse(est.method==\"ls\",0,1),b_est=as.numeric(b.est),alpha=alpha,lambda=lambda,beta=beta,Type=Type,w=index.w,af=af,CATCH=as.matrix(dat$caa),WEI=as.matrix(dat$waa/scale),MAT=MAA,M=as.matrix(dat$M),CPUE=index2,MISS=ifelse(index2==0,1,0))\r\n# \r\n# init <- log(p.init)\r\n# \r\n# write_dat(\"vpa\",data2)\r\n# write_pin(\"vpa\",init)\r\n\r\n# system(\"vpa -nohess\")\r\n \r\n# summary.p.est <- read_pars(\"vpa\")\r\n# summary.p.est$estimate <- exp(summary.p.est$coeflist$log_F)\r\n# summary.p.est$minimum <- -summary.p.est$loglik \r\n# summary.p.est$gradient <- summary.p.est$maxgrad\r\n# summary.p.est$code <- 0\r\n# log.p.hat <- log(summary.p.est$estimate)\r\n# } else {\r\n if (isTRUE(TMB)){\r\n index2 <- as.matrix(t(apply(index,1,function(x) ifelse(is.na(x),0,x))))\r\n \r\n Ab_type <- ifelse(abund==\"SSB\", 1, ifelse(abund==\"N\", 2, 3))\r\n Ab_type_age <- ifelse(is.na(min.age),0,min.age)\r\n \r\n if(is.null(dat$maa.tune)) MAA <- as.matrix(dat$maa) else MAA <- as.matrix(dat$maa.tune)\r\n if (is.na(af[1])) af <- rep(0,nindex)\r\n \r\n if (isTRUE(b.est)) b_fix <- rep(0,nindex) else b_fix <- rep(1,nindex)\r\n b_fix <- ifelse(is.na(b.fix),b_fix,b.fix)\r\n \r\n data2 <- list(Est=ifelse(est.method==\"ls\",0,1),b_fix=as.numeric(b_fix),alpha=alpha,lambda=lambda,beta=beta,Ab_type=Ab_type,Ab_type_age=Ab_type_age,w=index.w,af=af,CATCH=t(as.matrix(dat$caa)),WEI=t(as.matrix(dat$waa)),MAT=t(MAA),M=t(as.matrix(dat$M)),CPUE=t(index2),MISS=t(ifelse(index2==0,1,0)),Last_Catch_Zero=ifelse(isTRUE(last.catch.zero),1,0))\r\n \r\n parameters <- list(\r\n log_F=log(p.init)\r\n )\r\n \r\n obj <- MakeADFun(data2, parameters, DLL=\"rvpa_tmb\")\r\n opt <- nlm(obj$fn, obj$par, gradient=obj$gr, hessian=hessian)\r\n \r\n summary.p.est <- list()\r\n summary.p.est$estimate <- exp(opt$estimate)\r\n summary.p.est$minimum <- -opt$minimum\r\n summary.p.est$gradient <- opt$gradient\r\n summary.p.est$code <- opt$code\r\n log.p.hat <- opt$estimate\r\n } else {\r\n if (isTRUE(no.est)){\r\n if (isTRUE(eq.tf.mean)) {\r\n summary.p.est <- p.est(log(p.init), out=TRUE)\r\n summary.p.est <- list(estimate=summary.p.est$p, minimum=p.est(log(summary.p.est$p)), gradient=NA, code=NA)\r\n log.p.hat <- log(summary.p.est$estimate)\r\n }else{\r\n summary.p.est <- list(estimate=log(p.init), minimum=p.est(log(p.init)), gradient=NA, code=NA)\r\n log.p.hat <- summary.p.est$estimate\r\n }\r\n }else{\r\n if (optimizer==\"nlm\") summary.p.est <- nlm(p.est, log(p.init), hessian=hessian)\r\n if (optimizer==\"nlminb\") {\r\n summary.p.est <- nlminb(log(p.init), p.est, hessian=hessian, lower=Lower, upper=Upper)\r\n summary.p.est$estimate <- summary.p.est$par\r\n summary.p.est$minimum <- summary.p.est$objective \r\n summary.p.est$gradient <- NA \r\n summary.p.est$code <- summary.p.est$convergence\r\n }\r\n log.p.hat <- summary.p.est$estimate\r\n }\r\n }\r\n\r\n gradient <- summary.p.est$gradient\r\n code <- summary.p.est$code\r\n message.nlminb <- summary.p.est$message\r\n \r\n np <- length(summary.p.est$estimate)\r\n\r\n out <- p.est(log.p.hat, out=TRUE)\r\n\r\n term.f <- exp(log.p.hat)\r\n \r\n # \r\n\r\n if(isTRUE(hessian)) hessian <- summary.p.est$hessian\r\n\r\n naa <- as.data.frame(out$naa)\r\n faa <- as.data.frame(out$faa)\r\n baa <- as.data.frame(out$baa)\r\n ssb <- as.data.frame(out$ssb)\r\n saa <- as.data.frame(sel.func(faa, def=sel.def))\r\n\r\n if(isTRUE(tune)){\r\n logLik <- out$logLik\r\n sigma <- out$sigma\r\n q <- out$q\r\n b <- out$b\r\n convergence <- out$convergence\r\n message <- message.nlminb\r\n pred.index <- q*out$Abund^b\r\n }\r\n else logLik <- sigma <- q <- b <- convergence <- message <- pred.index <- NULL\r\n\r\nFt <- mean(faa[,ny],na.rm=TRUE)\r\n Fc.at.age <- apply(faa[,years %in% fc.year,drop=FALSE],1,mean) # drop=FALSE\u3067\uff0c\u884c\u5217\u306e\u30d9\u30af\u30c8\u30eb\u5316\u3092\u9632\u3050\r\n Fc.mean <- mean(Fc.at.age,na.rm=TRUE)\r\n Fc.max <- max(Fc.at.age,na.rm=TRUE)\r\n\r\n res <- list(input=arglist, term.f=term.f, np=np, minimum=out$minimum, minimum.c=out$minimum.c, logLik=logLik, gradient=gradient, code=code, q=q, b=b, sigma=sigma, convergence=convergence, message=message, hessian=hessian, Ft=Ft, Fc.at.age=Fc.at.age, Fc.mean=Fc.mean, Fc.max=Fc.max, last.year=last.year, Pope=Pope, ssb.coef=ssb.coef, pred.index=pred.index, wcaa=caa*waa.catch,naa=naa, faa=faa, baa=baa, ssb=ssb, saa=saa)\r\n\r\n if (isTRUE(plot) & isTRUE(tune)){\r\n for (i in 1:nindex){\r\n Y <- years %in% plot.year\r\n Pred <- (index[i,Y]/q[i])^(1/b[i])\r\n plot(years[Y], Pred, ylim=range(Pred, out$Abund[i,Y], na.rm=TRUE),col=3,pch=16,xlab=\"Year\",ylab=paste(\"index\", i), main=abund[i])\r\n lines(years[Y],out$Abund[i,Y],col=2,lwd=2)\r\n }\r\n }\r\n return(invisible(res))\r\n\r\n \r\n \r\n}\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# profile likelihood (one parameter)\r\n\r\nprofile.likelihood.vpa <- function(res,Alpha=0.95,min.p=1.0E-6,max.p=1,L=20,method=\"ci\"){\r\n \r\n res.c <- res\r\n res.c$input$no.est <- TRUE\r\n res.c$input$plot <- FALSE\r\n\r\n like <- function(p,method=\"ci\") {\r\n res.c$input$p.init <- p\r\n\r\n res1 <- do.call(vpa,res.c$input)\r\n\r\n if (method==\"ci\") obj <- (-2*(res1$logLik - res$logLik)-qchisq(Alpha,1))^2\r\n# if (method==\"dist\") obj <- res1$logLik\r\n if (method==\"dist\"){ # \u5e02\u91ce\u5ddd\u5909\u66f4\r\n obj <- list(logLik=res1$logLik,LLs=res1$minimum.c)\r\n }\r\n return(obj)\r\n }\r\n\r\n if (method==\"ci\"){\r\n res.lo <- nlminb(start=res$term.f*0.5, like, lower=0.001, upper=0.999*res$term.f, method=\"ci\")\r\n res.up <- nlminb(start=res$term.f*1.5, like, lower=1.001*res$term.f, upper=Inf, method=\"ci\")\r\n out <- list(lower=res.lo,upper=res.up,ci=c(res.lo$par, res.up$par))\r\n }\r\n if (method==\"dist\"){\r\n p0 <- seq(min.p,max.p,len=L)\r\n tmp <- lapply(p0, like, method=\"dist\")\r\n out <- list(logLik=sapply(tmp,function(x) x$logLik),\r\n \t\t\tLLs = sapply(tmp,function(x) x$LLs))\r\n out$TLL <- -out$logLik - min(-out$logLik)\r\n out$RLLs <- sweep(out$LLs,1,apply(out$LLs,1,min),FUN=\"-\")\r\n out$p0 <- p0\r\n# out <- p0 # \u5e02\u91ce\u5ddd\u5909\u66f4\r\n }\r\n\r\n return(out)\r\n}\r\n\r\ndp.est <- function(p,res,Ref,target=\"F\",beta=1){\r\n res.c <- res\r\n res.c$input$no.est <- TRUE\r\n res.c$input$plot <- FALSE\r\n\r\n res.c$input$p.init <- p\r\n\r\n ny <- length(res.c$faa[1,])\r\n na <- length(res.c$faa[,ny])\r\n\r\n res1 <- do.call(vpa,res.c$input)\r\n\r\n if (target==\"B\") out <- -res1$logLik+beta*(sum(res1$baa[,ny])-Ref)^2\r\n if (target==\"F\") out <- -res1$logLik+beta*(res1$faa[na,ny]-Ref)^2\r\n\r\n return(out)\r\n}\r\n\r\npl.ci.dp <- function(res,target=\"F\",beta=10^5,Alpha=0.8,lo.p=0.1,up.p=2.0,lo.Ref=0.5,up.Ref=3,method=\"ci\"){\r\n res.c <- res\r\n res.c$input$no.est <- TRUE\r\n res.c$input$plot <- FALSE\r\n\r\n p.est <- function(Ref) optimize(dp.est,c(lo.p,up.p),res=res,Ref=Ref,target=target,beta=beta)$minimum\r\n\r\n ny <- length(res$faa[1,])\r\n na <- length(res$faa[,ny])\r\n\r\n if (target==\"F\") Ref0 <- res$faa[na,ny]\r\n if (target==\"B\") Ref0 <- sum(res$baa[,ny])\r\n\r\n like <- function(Ref,method=\"ci\") {\r\n\r\n p <- p.est(Ref)\r\n\r\n res.c$input$p.init <- p\r\n\r\n res1 <- do.call(vpa,res.c$input)\r\n \r\n if (method==\"ci\") obj <- -2*(res1$logLik - res$logLik)-qchisq(Alpha,1)\r\n if (method==\"dist\") obj <- res1$logLik\r\n return(obj)\r\n }\r\n\r\n if (method==\"ci\"){\r\n res.lo <- uniroot(like, lower=Ref0*lo.Ref, upper=Ref0, method=\"ci\")\r\n res.up <- uniroot(like, lower=Ref0, upper=Ref0*up.Ref, method=\"ci\")\r\n out <- list(lower=res.lo,upper=res.up,ci=c(res.lo$root, res.up$root))\r\n }\r\n if (method==\"dist\"){\r\n p0 <- seq(Ref0*lo.Ref,Ref0*up.Ref,len=L)\r\n out <- sapply(p0, like, method=\"dist\")\r\n }\r\n\r\n return(out)\r\n}\r\n\r\nprofile.likelihood.vpa.B <- function(res,Alpha=0.95,min.p=1.0E-6,max.p=1,L=20,method=\"ci\"){\r\n \r\n res.c <- res\r\n res.c$input$no.est <- TRUE\r\n\r\n like <- function(p,method=\"ci\") {\r\n\r\n Bm <- exp(p)\r\n\r\n p0 <- res.c$term.f\r\n\r\n f1 <- function(p0){\r\n res.c$input$p.init <- p0\r\n res1 <- do.call(vpa,res.c$input)\r\n (sum(res1$baa[,37])-Bm)^2\r\n }\r\n\r\n res1 <- nlm(f1,p0)\r\n\r\n res.c$input$p.init <- res1$estimate\r\n res1 <- do.call(vpa,res.c$input)\r\n \r\n if (method==\"ci\") obj <- (-2*(res1$logLik - res$logLik)-qchisq(Alpha,1))^2\r\n if (method==\"dist\") obj <- res1$logLik\r\n return(obj)\r\n }\r\n\r\n if (method==\"ci\"){\r\n res.lo <- nlminb(start=log(sum(res$baa[,37])*0.5), like, lower=-Inf, upper=log(sum(res$baa[,37])), method=\"ci\")\r\n res.up <- nlminb(start=log(sum(res$baa[,37])*1.5), like, lower=log(sum(res$baa[,37])), upper=Inf, method=\"ci\")\r\n out <- list(lower=res.lo,upper=res.up,ci=c(res.lo$par, res.up$par))\r\n }\r\n if (method==\"dist\"){\r\n p0 <- seq(min.p,max.p,len=L)\r\n out <- sapply(p0, like, method=\"dist\")\r\n }\r\n\r\n return(out)\r\n}\r\n\r\n# bootstrap\r\n\r\nboo.vpa <- function(res,B=5,method=\"p\",mean.correction=FALSE){\r\n ## method == \"p\": parametric bootstrap\r\n ## method == \"n\": non-parametric bootstrap\r\n ## method == \"r\": smoothed residual bootstrap-t\r\n\r\n index <- res$input$dat$index\r\n p.index <- res$pred.index\r\n resid <- log(as.matrix(index))-log(as.matrix(p.index))\r\n \r\n R <- nrow(resid)\r\n\r\n n <- apply(resid,1,function(x) sum(!is.na(x)))\r\n\r\n np <- res$np\r\n\r\n rs2 <- rowSums(resid^2, na.rm=TRUE)/(n-np)\r\n\r\n res.c <- res\r\n \r\n res.c$input$p.init <- res$term.f[1]\r\n \r\n b.index <- res$input$dat$index\r\n \r\n Res1 <- list()\r\n\r\n for (b in 1:B){\r\n print(b)\r\n\r\n for (i in 1:R){\r\n if (method==\"p\") b.index[i,!is.na(index[i,])] <- exp(log(p.index[i,!is.na(index[i,])]) + rnorm(sum(!is.na(index[i,])),0,sd=sqrt(rs2[i])))\r\n if (method==\"n\") b.index[i,!is.na(index[i,])] <- exp(log(p.index[i,!is.na(index[i,])]) + sample(resid[i,!is.na(index[i,])],length(index[i,!is.na(index[i,])]),replace=TRUE))\r\n if (isTRUE(mean.correction)) b.index[i,!is.na(index[i,])] <- b.index[i,!is.na(index[i,])]*exp(-rs2[i]/2)\r\n if (method==\"r\") {\r\n rs.d <- density(resid[i,!is.na(index[i,])])\r\n rs.db <- sample(rs.d$x,length(index[i,!is.na(index[i,])]),prob=rs.d$y,replace=TRUE)\r\n sd.j <- sd(rs.db)\r\n s.rs.b <- rs.db/sd.j\r\n b.index[i,!is.na(index[i,])] <- exp(log(p.index[i,!is.na(index[i,])]) + s.rs.b*sqrt(rs2[i]))\r\n }\r\n if (isTRUE(mean.correction)) b.index[i,!is.na(index[i,])] <- b.index[i,!is.na(index[i,])]*exp(-rs2[i]/2)\r\n }\r\n \r\n res.c$input$dat$index <- b.index\r\n\r\n res1 <- try(do.call(vpa,res.c$input))\r\n if(class(res1)==\"try-error\"){\r\n Res1[[b]] <- \"try-error\"\r\n }\r\n else{\r\n Res1[[b]] <- list(index=b.index,naa=res1$naa,baa=res1$baa,ssb=res1$ssb,faa=res1$faa,saa=res1$saa,\r\n Fc.at.age=res1$Fc.at.age,q=res1$q,b=res1$b,sigma=res1$sigma) # 2013.8.20\u8ffd\u8a18(\u5e02\u91ce\u5ddd)\r\n }\r\n }\r\n\r\n return(Res1)\r\n}\r\n\r\n# SR estimation\r\n\r\nSR.est.old <- function(res, model=\"BH\", method=\"log\", scale=1){\r\n SSB <- colSums(res$ssb,na.rm=TRUE)/scale\r\n R <- unlist(res$naa[1,])\r\n \r\n if (model==\"BH\"){\r\n res0 <- lm(SSB/R ~ SSB)\r\n\r\n a0 <- 1/res0$coef[1]\r\n b0 <- res0$coef[2]*a0\r\n }\r\n if (model==\"RI\"){\r\n res0 <- lm(log(R/SSB) ~ SSB)\r\n\r\n a0 <- exp(res0$coef[1])\r\n b0 <- -res0$coef[2]\r\n }\r\n\r\n p0 <- log(c(a0,b0)) \r\n\r\n data <- data.frame(SSB=SSB,R=R)\r\n\r\n if (model==\"BH\"){\r\n if (method==\"id\") res <- nls(R~exp(log.a)*SSB/(1+exp(log.b)*SSB), data, start=list(log.a=p0[1],log.b=p0[2]))\r\n if (method==\"log\") res <- nls(log(R)~log.a+log(SSB)-log(1+exp(log.b)*SSB), data, start=list(log.a=p0[1],log.b=p0[2]))\r\n }\r\n\r\n if (model==\"RI\"){\r\n if (method==\"id\") res <- nls(R~exp(log.a)*SSB*exp(-exp(log.b)*SSB), data, start=list(log.a=p0[1],log.b=p0[2]))\r\n if (method==\"log\") res <- nls(log(R)~log.a+log(SSB)-exp(log.b)*SSB, data, start=list(log.a=p0[1],log.b=p0[2]))\r\n }\r\n\r\n par <- exp(coef(res))\r\n names(par) <- c(\"a\",\"b\")\r\n\r\n out <- list(res=res, model=model, method=method, par=par)\r\n\r\n return(out)\r\n}\r\n\r\n# MSY estimation\r\n\r\nMSY.est <- function(res,model=\"schaefer\",r.fix=NULL,K.fix=NULL,p.init=NULL,scale=1,main=\"\"){\r\n B <- colSums(res$baa,na.rm=TRUE)/scale\r\n C <- colSums(res$input$dat$caa*res$input$dat$waa,na.rm=TRUE)/scale\r\n\r\n n <- length(B)\r\n\r\n if (C[n]==0) {n <- n-1; B <- B[1:n]; C <- C[1:n]}\r\n\r\n B2 <- B[2:n]\r\n B1 <- B[1:(n-1)]\r\n C1 <- C[1:(n-1)]\r\n \r\n S1 <- B2 - B1 + C1\r\n\r\n if (is.null(p.init)){\r\n if (model==\"schaefer\") {\r\n res0 <- lm(S1/B1 ~ B1)\r\n r0 <- res0$coef[1]\r\n K0 <- -r0/res0$coef[2]\r\n }\r\n if (model==\"fox\") {\r\n res0 <- lm(S1/B1 ~ log(B1))\r\n r0 <- res0$coef[1]\r\n K0 <- exp(-r0/res0$coef[2])\r\n }\r\n\r\n p0 <- log(c(max(r0, 0.001), max(K0, 100)))\r\n }\r\n else p0 <- p.init\r\n\r\n data <- data.frame(S1=S1,B1=B1)\r\n\r\n if (is.null(r.fix) & is.null(K.fix)){\r\n if (model==\"schaefer\") res <- nls(S1~exp(log.r)*B1*(1-B1/exp(log.K)), data, start=list(log.r=p0[1],log.K=p0[2]))\r\n if (model==\"fox\") res <- nls(S1~exp(log.r)*B1*(1-log(B1)/log.K), data, start=list(log.r=p0[1],log.K=p0[2]))\r\n\r\n p <- exp(coef(res))\r\n names(p) <- c(\"r\",\"K\")\r\n }\r\n else {\r\n if (!is.null(r.fix) & is.null(K.fix)){\r\n if (model==\"schaefer\") res <- nls(S1~r.fix*B1*(1-B1/exp(log.K)), data, start=list(log.K=p0[2]))\r\n if (model==\"fox\") res <- nls(S1~r.fix*B1*(1-log(B1)/log.K), data, start=list(log.K=p0[2]))\r\n\r\n p <- c(r.fix, exp(coef(res)))\r\n names(p) <- c(\"r\",\"K\")\r\n }\r\n if (is.null(r.fix) & !is.null(K.fix)){\r\n log.K <- log(K.fix)\r\n if (model==\"schaefer\") res <- nls(S1~exp(log.r)*B1*(1-B1/exp(log.K)), data, start=list(log.r=log(0.2)))\r\n if (model==\"fox\") res <- nls(S1~exp(log.r)*B1*(1-log(B1)/log.K), data, start=list(log.r=log(0.2)))\r\n\r\n p <- c(exp(coef(res)),exp(log.K))\r\n names(p) <- c(\"r\",\"K\")\r\n }\r\n if (!is.null(r.fix) & !is.null(K.fix)){\r\n res <- list()\r\n\r\n p <- c(r.fix, B[1])\r\n names(p) <- c(\"r\",\"K\")\r\n }\r\n }\r\n\r\n r <- p[1]\r\n K <- p[2]\r\n\r\n p.S1 <- predict(res)\r\n\r\n if (model==\"schaefer\") MSY <- c(r*K/4, K/2, r/2)\r\n if (model==\"fox\") MSY <- c(r*K/(log(K)*exp(1)), K/exp(1), r/log(K))\r\n names(MSY) <- c(\"MSY\",\"Bmsy\",\"Fmsy\")\r\n\r\n Assess <- c(B[n]/MSY[2], (C[n]/B[n])/MSY[3])\r\n names(Assess) <- c(\"Bcur/Bmsy\",\"Fcur/Fmsy\")\r\n\r\n # SP plot\r\n std.S <- (S1-mean(S1,na.rm=TRUE))/sd(S1,na.rm=TRUE)\r\n std.pS <- (p.S1-mean(S1,na.rm=TRUE))/sd(S1,na.rm=TRUE)\r\n std.MSY <- (MSY[1]-mean(S1,na.rm=TRUE))/sd(S1,na.rm=TRUE)\r\n std.C <- (C1-mean(S1,na.rm=TRUE))/sd(S1,na.rm=TRUE)\r\n\r\n plot(names(B1), std.S, pch=16, col=\"blue\", xlab=\"Year\", ylab=\"Standardized Surplus Production\", main=main, cex=1.5)\r\n lines(names(B1), std.pS , col=\"red\", lwd=2)\r\n abline(h=std.MSY, col=\"green\", lty=2, lwd=2)\r\n points(names(B1), std.C , pch=17, col=\"orange\", cex=1.5)\r\n\r\n Res <- list(B=B, C=C, S=S1, std.S=std.S, std.pS=std.pS, std.MSY=std.MSY, std.C=std.C, res=res, log.p = coef(res), p = p, vcov = vcov(res), MSY=MSY, Assess=Assess)\r\n\r\n return(Res)\r\n}\r\n\r\nSR.est.old <- function(res,model=\"BH\",k=1,p.init=NULL,lower.limit=-25,scale=1,main=NULL,log=FALSE){\r\n SSB <- colSums(res$ssb,na.rm=TRUE)/scale\r\n R <- res$naa[1,]/scale\r\n\r\n n <- length(R)\r\n\r\n R1 <- R[(1+k):n]\r\n SSB1 <- SSB[1:(n-k)]\r\n\r\n if (is.null(p.init)){\r\n if (model==\"BH\") {\r\n Y <- as.numeric(SSB1/R1)\r\n res0 <- lm(Y ~ SSB1)\r\n alpha <- 1/res0$coef[1]\r\n beta <- res0$coef[2]*alpha\r\n }\r\n if (model==\"RI\") {\r\n Y <- as.numeric(log(R1/SSB1))\r\n res0 <- lm(Y ~ SSB1)\r\n alpha <- exp(res0$coef[1])\r\n beta <- -res0$coef[2]\r\n }\r\n\r\n p0 <- log(c(max(alpha, 0.00000000001), max(beta, 0.00000000001)))\r\n }\r\n else p0 <- p.init\r\n\r\n data <- data.frame(R1=as.numeric(R1),SSB1=as.numeric(SSB1))\r\n\r\n if (isTRUE(log)){\r\n if (model==\"BH\") res <- nls(log(R1)~log.a+log(SSB1)-log(1+exp(log.b)*SSB1), data, start=list(log.a=p0[1],log.b=p0[2]), control=list(warnOnly=TRUE), lower=rep(lower.limit,2), algorithm=\"port\")\r\n if (model==\"RI\") res <- nls(log(R1)~log.a+log(SSB1)-exp(log.b)*SSB1, data, start=list(log.a=p0[1],log.b=p0[2]), control=list(warnOnly=TRUE), lower=rep(lower.limit,2), algorithm=\"port\")\r\n }\r\n else{\r\n if (model==\"BH\") res <- nls(R1~exp(log.a)*SSB1/(1+exp(log.b)*SSB1), data, start=list(log.a=p0[1],log.b=p0[2]), control=list(warnOnly=TRUE), lower=rep(lower.limit,2), algorithm=\"port\")\r\n if (model==\"RI\") res <- nls(R1~exp(log.a)*SSB1*exp(-exp(log.b)*SSB1), data, start=list(log.a=p0[1],log.b=p0[2]), control=list(warnOnly=TRUE), lower=rep(lower.limit,2), algorithm=\"port\")\r\n }\r\n\r\n p <- exp(coef(res))\r\n names(p) <- c(\"alpha\",\"beta\")\r\n\r\n if(is.null(main)) main <- model \r\n plot(SSB1,R1,xlab=\"SSB\",ylab=\"R\",xlim=c(0,max(SSB1)*1.05),ylim=c(0,max(R1)*1.05),main=main)\r\n \r\n x <- seq(0,max(SSB1),len=100)\r\n\r\n if(model==\"BH\") pred <- p[1]*x/(1+p[2]*x)\r\n if(model==\"RI\") pred <- p[1]*x*exp(-p[2]*x)\r\n\r\n lines(x,pred,col=\"red\",lwd=2)\r\n\r\n Res <- list(SSB=SSB1,R=R1, k=k, p=p)\r\n\r\n return(Res)\r\n}\r\n\r\n\r\nlogit <- function(x) log(x/(1-x))\r\n\r\n##\r\n\r\ncv.est <- function(res,n=5){\r\n\r\n nr <- ifelse(res$input$use.index==\"all\", 1:nrow(res$input$dat$index), res$input$use.index)\r\n nc <- ncol(res$input$dat$index)\r\n \r\n obj <- NULL\r\n \r\n for (i in 0:(n-1)){\r\n res.c <- res\r\n\r\n res.c$input$dat$index[,nc-i] <- NA\r\n res.c$input$plot <- FALSE\r\n# res.c$input$p.init <- res$term.f\r\n\r\n res1 <- do.call(vpa,res.c$input)\r\n\r\n if (abs(res1$gradient) < 10^(-3)){\r\n obj <- c(obj,mean(dnorm(log(res$input$dat$index[nr,nc-i]),log(res1$pred[,nc-i]),res1$sigma,log=TRUE),na.rm=TRUE))\r\n }\r\n }\r\n \r\n return(mean(obj,na.rm=TRUE))\r\n}\r\n\r\nretro.est <- function(res,n=5,stat=\"mean\",init.est=FALSE, b.fix=TRUE){\r\n res.c <- res\r\n res.c$input$plot <- FALSE\r\n Res <- list()\r\n obj.n <- obj.b <- obj.s <- obj.r <- obj.f <- NULL\r\n \r\n if (isTRUE(b.fix)){\r\n res.c$input$b.fix <- res$b\r\n res.c$input$b.est <- FALSE\r\n }\r\n \r\n if (res$input$last.catch.zero) res.c$input$last.catch.zero <- FALSE\r\n \r\n for (i in 1:n){\r\n nc <- ncol(res.c$input$dat$caa)\r\n \r\n res.c$input$dat$caa <- res.c$input$dat$caa[,-nc]\r\n res.c$input$dat$maa <- res.c$input$dat$maa[,-nc]\r\n res.c$input$dat$maa.tune <- res.c$input$dat$maa.tune[,-nc]\r\n res.c$input$dat$waa <- res.c$input$dat$waa[,-nc]\r\n res.c$input$dat$M <- res.c$input$dat$M[,-nc]\r\n res.c$input$dat$index <- res.c$input$dat$index[,-nc,drop=FALSE]\r\n res.c$input$dat$catch.prop <- res.c$input$dat$catch.prop[,-nc]\r\n \r\n res.c$input$tf.year <- res.c$input$tf.year-1\r\n res.c$input$fc.year <- res.c$input$fc.year-1\r\n \r\n if (isTRUE(init.est)) res.c$input$p.init <- res.c$term.f\r\n \r\n res1 <- do.call(vpa,res.c$input)\r\n\r\n Res[[i]] <- res1\r\n \r\n if ((max(abs(res1$gradient)) < 10^(-3) & !isTRUE(res1$input$TMB)) | (max(abs(res1$gradient)) > 0 & max(abs(res1$gradient)) < 10^(-3) & isTRUE(res1$input$TMB)) | (is.na(max(abs(res1$gradient))) & res1$input$optimizer==\"nlminb\")){\r\n obj.n <- c(obj.n, (sum(res1$naa[,nc-1])-sum(res$naa[,nc-1]))/sum(res$naa[,nc-1]))\r\n obj.b <- c(obj.b, (sum(res1$baa[,nc-1])-sum(res$baa[,nc-1]))/sum(res$baa[,nc-1]))\r\n obj.s <- c(obj.s, (sum(res1$ssb[,nc-1])-sum(res$ssb[,nc-1]))/sum(res$ssb[,nc-1]))\r\n obj.r <- c(obj.r, (res1$naa[1,nc-1]-res$naa[1,nc-1])/res$naa[1,nc-1])\r\n obj.f <- c(obj.f, (sum(res1$faa[,nc-1])-sum(res$faa[,nc-1]))/sum(res$faa[,nc-1]))\r\n } else {\r\n obj.n <- c(obj.n, NA)\r\n obj.b <- c(obj.b, NA)\r\n obj.s <- c(obj.s, NA)\r\n obj.r <- c(obj.r, NA)\r\n obj.f <- c(obj.f, NA)\r\n }\r\n }\r\n \r\n mohn <- c(get(stat)(obj.n,na.rm=TRUE),get(stat)(obj.b,na.rm=TRUE),get(stat)(obj.s,na.rm=TRUE),get(stat)(obj.r,na.rm=TRUE),get(stat)(obj.f,na.rm=TRUE))\r\n \r\n names(mohn) <- c(\"N\",\"B\",\"SSB\",\"R\",\"F\")\r\n \r\n return(list(Res=Res,retro.n=obj.n, retro.b=obj.b, retro.s=obj.s, retro.r=obj.r, retro.f=obj.f, mohn=mohn))\r\n}\r\n\r\n#\u6700\u65b0\u5e74\u306e\u6f01\u7372\u91cf\u304c0\u306e\u5834\u5408 (last.zero.catch=0)\u3001\u6700\u65b0\u5e74\u306eF\u304c0\u3068\u306a\u308a\u3001\u52a0\u5165\u91cf\u3082\u63a8\u5b9a\u3067\u304d\u306a\u3044\u305f\u3081\u3001\u3082\u30461\u5e74\u524d\u306e\u63a8\u5b9a\u5024\u3067Mohn's rho\u3092\u8a08\u7b97\u3059\u308b\u305f\u3081\u306e\u95a2\u6570\r\nretro.est2 <- function(res,n=5,stat=\"mean\",init.est=FALSE, b.fix=TRUE){\r\n res.c <- res\r\n res.c$input$plot <- FALSE\r\n Res <- list()\r\n obj.n <- obj.b <- obj.s <- obj.r <- obj.f <- NULL\r\n \r\n if (isTRUE(b.fix)){\r\n res.c$input$b.fix <- res$b\r\n res.c$input$b.est <- FALSE\r\n }\r\n \r\n for (i in 1:n){\r\n nc <- ncol(res.c$input$dat$caa)\r\n \r\n res.c$input$dat$caa <- res.c$input$dat$caa[,-nc]\r\n res.c$input$dat$maa <- res.c$input$dat$maa[,-nc]\r\n res.c$input$dat$maa.tune <- res.c$input$dat$maa.tune[,-nc]\r\n res.c$input$dat$waa <- res.c$input$dat$waa[,-nc]\r\n res.c$input$dat$M <- res.c$input$dat$M[,-nc]\r\n res.c$input$dat$index <- res.c$input$dat$index[,-nc,drop=FALSE]\r\n res.c$input$dat$catch.prop <- res.c$input$dat$catch.prop[,-nc]\r\n \r\n res.c$input$tf.year <- res.c$input$tf.year-1\r\n res.c$input$fc.year <- res.c$input$fc.year-1\r\n \r\n if (isTRUE(init.est)) res.c$input$p.init <- res.c$term.f\r\n \r\n if (res.c$input$last.catch.zero) {res.c$input$dat$caa[,nc-1] <- 0; Y <- nc-2} else Y <- nc-1\r\n \r\n res1 <- do.call(vpa,res.c$input)\r\n\r\n Res[[i]] <- res1\r\n\r\n if ((max(abs(res1$gradient)) < 10^(-3) & !isTRUE(res1$input$ADMB)) | (max(abs(res1$gradient)) > 0 & max(abs(res1$gradient)) < 10^(-3) & isTRUE(res1$input$ADMB)) | (is.na(max(abs(res1$gradient))) & res1$input$optimizer==\"nlminb\")){\r\n obj.n <- c(obj.n, (sum(res1$naa[,Y])-sum(res$naa[,Y]))/sum(res$naa[,Y]))\r\n obj.b <- c(obj.b, (sum(res1$baa[,Y])-sum(res$baa[,Y]))/sum(res$baa[,Y]))\r\n obj.s <- c(obj.s, (sum(res1$ssb[,Y])-sum(res$ssb[,Y]))/sum(res$ssb[,Y]))\r\n obj.r <- c(obj.r, (res1$naa[1,Y]-res$naa[1,Y])/res$naa[1,Y])\r\n obj.f <- c(obj.f, (sum(res1$faa[,Y])-sum(res$faa[,Y]))/sum(res$faa[,Y]))\r\n } else {\r\n obj.n <- c(obj.n, NA)\r\n obj.b <- c(obj.b, NA)\r\n obj.s <- c(obj.s, NA)\r\n obj.r <- c(obj.r, NA)\r\n obj.f <- c(obj.f, NA)\r\n }\r\n }\r\n \r\n mohn <- c(get(stat)(obj.n,na.rm=TRUE),get(stat)(obj.b,na.rm=TRUE),get(stat)(obj.s,na.rm=TRUE),get(stat)(obj.r,na.rm=TRUE),get(stat)(obj.f,na.rm=TRUE))\r\n \r\n names(mohn) <- c(\"N\",\"B\",\"SSB\",\"R\",\"F\")\r\n \r\n return(list(Res=Res,retro.n=obj.n, retro.b=obj.b, retro.s=obj.s, retro.r=obj.r, retro.f=obj.f, mohn=mohn))\r\n}\r\n\r\n", "meta": {"hexsha": "3d7dc095985c981dda994b767b8e7e9cef1f7915", "size": 60410, "ext": "r", "lang": "R", "max_stars_repo_path": "4-akita/rvpa1.9.2.r", "max_stars_repo_name": "ichimomo/Shigen-kensyu-2018", "max_stars_repo_head_hexsha": "7469078a0d19f00837b5cba7d1c2b5ec5d3c3e21", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2018-12-17T07:08:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-04T04:22:43.000Z", "max_issues_repo_path": "4-akita/rvpa1.9.2.r", "max_issues_repo_name": "ichimomo/Shigen-kensyu-2018", "max_issues_repo_head_hexsha": "7469078a0d19f00837b5cba7d1c2b5ec5d3c3e21", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "4-akita/rvpa1.9.2.r", "max_forks_repo_name": "ichimomo/Shigen-kensyu-2018", "max_forks_repo_head_hexsha": "7469078a0d19f00837b5cba7d1c2b5ec5d3c3e21", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-01-20T02:12:38.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-24T09:09:31.000Z", "avg_line_length": 35.5352941176, "max_line_length": 422, "alphanum_fraction": 0.5356729018, "num_tokens": 22801, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.3041835981678674}} {"text": "KOD <- function(\nobject, \nmethod = c(\"uni1\", \"uni2\", \"multi1\", \"multi2\", \"multi3\"),\npar = parKOD(),\nremove = FALSE,\nverbose = TRUE, \nplot = TRUE,\n...\n)\n{\n method <- match.arg(method)\n CLASS <- class(object)\n tempLIST <- GROUP <- NULL \n \n ## extract parameters for the different functions\n EFF <- par$eff\n TRAIN <- par$train\n CPCRIT <- par$cp.crit\n CUT <- par$cut\n ALPHA <- par$alpha \n\n if (!is.na(pmatch(\"uni\", method))) CHAR <- \"univariate\" else CHAR <- \"multivariate\"\n \n if (CLASS[1] != \"modlist\") stop(\"Please supply either a 'modlist' or 'replist'!\") \n if (CLASS[2] == \"replist\") ITER <- length(object) else ITER <- 1 \n\n for (i in 1:ITER) {\n if (CLASS[2] == \"replist\") tempOBJ <- object[[i]]$modlist else tempOBJ <- object\n NAMES <- sapply(tempOBJ, function(x) x$names) \n \n ## methods selection\n if (method == \"uni1\") DATA <- uni1(tempOBJ, eff = EFF, train = TRAIN, alpha = ALPHA, verbose = verbose, ...)\n if (method == \"uni2\") DATA <- uni2(tempOBJ, cp.crit = CPCRIT, verbose = verbose, ...)\n if (method == \"multi1\") DATA <- multi1(tempOBJ, cut = CUT, alpha = ALPHA, verbose = verbose, ...) \n if (method == \"multi2\") DATA <- multi2(tempOBJ, verbose = verbose, ...)\n if (method == \"multi3\") DATA <- multi3(tempOBJ, verbose = verbose, ...) \n \n ## get outliers from univariate outlier tests\n if (!is.na(pmatch(\"uni\", method))) OUTL <- DATA \n \n ## get outliers from multivariate outlier tests => 'aq.plot'' \n ## from package 'mvoutlier' in utils.R\n if (!is.na(pmatch(\"multi\", method))) {\n row.names(DATA) <- NAMES \n \n if (verbose) cat(\"Calculating multivariate outlier(s)...\\n\")\n \n RES <- aq.plot(DATA, delta = qchisq(0.975, df = ncol(DATA)), quan = 1/2, alpha = par$alpha,\n plot = plot) \n \n OUTL <- which(RES$outlier == TRUE)\n }\n \n if (length(OUTL) == 0) NOUTL <- 1:length(tempOBJ) else NOUTL <- (1:length(tempOBJ))[-OUTL]\n \n ## tag as outliers\n for (i in OUTL) tempOBJ[[i]]$isOutlier <- TRUE\n for (j in NOUTL) tempOBJ[[j]]$isOutlier <- FALSE \n \n ## tag names or optionally remove outlier runs\n if (length(OUTL) > 0) {\n if (verbose) cat(\" Found\", CHAR, \"outlier for\", NAMES[OUTL], \"\\n\") \n flush.console() \n if (remove) {\n if (verbose) cat(\" Removing\", NAMES[OUTL], \"...\\n\")\n flush.console()\n tempOBJ <- tempOBJ[-OUTL] \n } else {\n if (verbose) cat(\" Tagging name of\", NAMES[OUTL], \"...\\n\")\n flush.console()\n for (i in OUTL) tempOBJ[[i]]$names <- paste(\"**\", tempOBJ[[i]]$names, \"**\", sep = \"\") \n flush.console() \n } \n }\n \n ## create new list from replicates and define GROUP vector\n if (CLASS[2] == \"replist\") {\n cat(\"\\n\")\n tempLIST <- c(tempLIST, tempOBJ)\n GROUP <- c(GROUP, length(tempOBJ)) \n }\n\n }\n \n ## update by making a new 'replist'\n if (CLASS[2] == \"replist\") { \n if (verbose) cat(\"Updating object of class 'replist':\\n\")\n class(tempLIST) <- c(\"modlist\", \"pcrfit\")\n GROUP <- rep(1:length(GROUP), GROUP) \n tempOBJ <- replist(tempLIST, GROUP, verbose = verbose, ...) \n } \n \n class(tempOBJ) <- CLASS \n return(tempOBJ)\n} \n \n", "meta": {"hexsha": "9d3644b5e09087d532aac176d8352343179ef3c0", "size": 3286, "ext": "r", "lang": "R", "max_stars_repo_path": "bioinformatics/qpcR/KOD.r", "max_stars_repo_name": "MakerButt/chaipcr", "max_stars_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-06-25T19:58:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-25T19:58:26.000Z", "max_issues_repo_path": "bioinformatics/qpcR/KOD.r", "max_issues_repo_name": "MakerButt/chaipcr", "max_issues_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bioinformatics/qpcR/KOD.r", "max_forks_repo_name": "MakerButt/chaipcr", "max_forks_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.8762886598, "max_line_length": 112, "alphanum_fraction": 0.5532562386, "num_tokens": 974, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6654105454764746, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.304183592101508}} {"text": "##########################################################################\r\n# COPYRIGHT & SUPPORT #\r\n##########################################################################\r\n#\r\n# FiPSPi-validation: R code for the validation of the results of FiPSPi\r\n# with independent data from the publication (Murgia_et_al_data.txt):\r\n# Murgia M, (...), Mann M.,\r\n# Single muscle fiber proteomics reveals unexpected\r\n# mitochondrial specialization.\r\n# EMBO Rep. 2015 Mar;16(3):387-95.\r\n# doi: 10.15252/embr.201439757.\r\n# Epub 2015 Feb 2. PMID: 25643707.\r\n#\r\n# This code has been developed by Dr. Michael Turewicz at the Ruhr University\r\n# Bochum, Germany. It is licensed under the BSD 3-Clause License provided in\r\n# the file 'LICENSE.txt'.\r\n#\r\n# For support please write an e-mail to 'michael.turewicz[at]rub.de'.\r\n#\r\n# This is the original version of the code written for the publication\r\n# by Eggers et al. 'Deep proteomic characterization of skeletal muscle fiber\r\n# types by laser microdissection and mass spectrometry (...)'. The latest\r\n# version of FiPSPi-validation can be found here:\r\n# https://github.com/mpc-bioinformatics/FiPSPi\r\n#\r\n##########################################################################\r\n# USER SETTINGS #\r\n##########################################################################\r\n\r\n### set path to current working directory\r\ncwd <- \"C:/UNI/Publikationen/2021.06.XX_submitted_Britta_Eggers_Fasertypen/FiPSPi\"\r\n\r\n### set path to data file (relative path to the working directory specified above)\r\ndata.path <- paste0(cwd, \"/data/Murgia_et_al_data.txt\")\r\n\r\n### set path to selected features file (relative path to the working directory specified above)\r\nselected.features.path <- paste0(cwd, \"/FiPSPi_results/selected_features.txt\")\r\n\r\n### set output.path where all created output and graphics are saved\r\noutput.path <- paste0(cwd, \"/FiPSPi-validation_results\")\r\n\r\n### -------> Note: the above files and folders must already exist and be accessible!\r\n\r\n##########################################################################\r\n# FURTHER SETTINGS & DEPENDENCIES #\r\n##########################################################################\r\noptions(scipen=100000)\r\noptions(stringsAsFactors=FALSE)\r\n\r\nset.seed(1234)\r\n\r\nlibrary(randomForest)\r\nlibrary(ROCR)\r\nlibrary(caret)\r\nlibrary(rpart)\r\nlibrary(rpart.plot)\r\n\r\n##########################################################################\r\n# FUNCTIONS #\r\n##########################################################################\r\n\r\n#+++++++++++++++++++++++++++ classify.rf.evaluation +++++++++++++++++++++++++++++++++++++++\r\nclassify.rf.evaluation <- function(iteration=NULL, trainset=NULL, testset=NULL, classes_train=NULL, \r\n classes_test=NULL, label1=\"A\", label2=\"B\", label3 = \"C\", output.path=NULL, ...){\r\n if(is.null(trainset) || is.null(testset)) {\r\n stop(\"ERROR: Not all mandatory arguments have been defined!\")\r\n }\r\n \r\n train.dat <- t(as.matrix(trainset))\r\n test.dat <- t(as.matrix(testset))\r\n \r\n model.rf <- randomForest(x=train.dat, y=classes_train, importance=TRUE, keep.forest=TRUE)\r\n pred.rf <- predict(object=model.rf, newdata=test.dat, type=\"response\", norm.votes=TRUE)\r\n confusion <- table(observed = classes_test, predicted = pred.rf)\r\n \r\n ### class 1:\r\n TP <- confusion[1,1]\r\n FN <- confusion[1,2]+confusion[1,3]\r\n FP <- confusion[2,1]+confusion[3,1]\r\n TN <- confusion[2,2]+confusion[2,3]+confusion[3,2]+confusion[3,3]\r\n ACCURACY1 <- {TP+TN}/{TP+FP+TN+FN}\r\n SENSITIVITY1 <- TP/{TP+FN} #=TPR\r\n SPECIFICITY1 <- TN/{TN+FP} #=(1-FPR) \r\n \r\n ### class 2:\r\n TP <- confusion[2,2]\r\n FN <- confusion[2,1]+confusion[2,3]\r\n FP <- confusion[1,2]+confusion[3,2]\r\n TN <- confusion[1,1]+confusion[1,3]+confusion[3,1]+confusion[3,3]\r\n ACCURACY2 <- {TP+TN}/{TP+FP+TN+FN}\r\n SENSITIVITY2 <- TP/{TP+FN} #=TPR\r\n SPECIFICITY2 <- TN/{TN+FP} #=(1-FPR) \r\n \r\n ### class 3:\r\n TP <- confusion[3,3]\r\n FN <- confusion[3,1]+confusion[3,2]\r\n FP <- confusion[1,3]+confusion[2,3]\r\n TN <- confusion[1,1]+confusion[1,2]+confusion[2,1]+confusion[2,2]\r\n ACCURACY3 <- {TP+TN}/{TP+FP+TN+FN}\r\n SENSITIVITY3 <- TP/{TP+FN} #=TPR\r\n SPECIFICITY3 <- TN/{TN+FP} #=(1-FPR)\r\n \r\n ACCURACY_total <- sum(diag(confusion))/ sum(confusion) # total Accuracy\r\n SENSITIVITY_total <- (SENSITIVITY1 + SENSITIVITY2 + SENSITIVITY3) / 3\r\n SPECIFICITY_total <- (SPECIFICITY1 + SPECIFICITY2 + SPECIFICITY3) / 3\r\n \r\n results <- c(acc_total = ACCURACY_total, sens_total = SENSITIVITY_total, spec_total = SPECIFICITY_total, \r\n acc1 = ACCURACY1, sens1 = SENSITIVITY1, spec1 = SPECIFICITY1,\r\n acc2 = ACCURACY2, sens2 = SENSITIVITY2, spec2 = SPECIFICITY2,\r\n acc3 = ACCURACY3, sens3 = SENSITIVITY3, spec3 = SPECIFICITY3)\r\n\r\n return(results)\r\n}\r\n#+++++++++++++++++++++++++++ classify.rf.evaluation +++++++++++++++++++++++++++++++++++++++\r\n\r\n##########################################################################\r\n# MAIN #\r\n##########################################################################\r\n\r\n#-----------------------------> Begin: Data Import & Pre-Processing\r\n\r\ndat <- read.table(data.path, sep=\"\\t\", header=TRUE, na.strings = \"NA\")\r\ncolnames(dat) <- gsub(\"Type\\\\.I\", \"Type I\", colnames(dat))\r\n\r\nlabel1 <- \"Type I\" \r\n#label2 <- \"Type IIa\"\r\nlabel3 <- \"Type IIb\"\r\nlabel4 <- \"Type IIx\"\r\n\r\n# Exclusion of 6 samples due to following criteria:\r\n# -\tSamples without predominant Myh-isoform and/or without unambiguous fiber-type in the column 'Fiber type, renamed'\r\n# -\tSamples with at least one missing value for the peptides to be validated\r\n# -\tAfter the first two exclusion criteria group 'Type IIa' has only 3 samples --> group too small --> exclusion of complete group\r\n# -\tSamples with weakly predominant Myh-isoform and which were outliers in PCA\r\ndat <- dat[,-match(c(\"Type I_9\", \"Type IIb_1\", \"Type IIb_3\", \"Type IIb_6\", \"Type IIx_5\", \"Type IIx_7\"), colnames(dat))]\r\n\r\ngroup1 <- grep(\"Type I_\", colnames(dat), value=TRUE)\r\n#group2 <- grep(\"Type IIa_\", colnames(dat), value=TRUE)\r\ngroup3 <- grep(\"Type IIb_\", colnames(dat), value=TRUE)\r\ngroup4 <- grep(\"Type IIx_\", colnames(dat), value=TRUE)\r\nn1 <- length(group1)\r\n#n2 <- length(group2)\r\nn3 <- length(group3)\r\nn4 <- length(group4) \r\n\r\ndescriptive.columns <- dat[,1:5]\r\nselected.features <- read.table(selected.features.path, sep=\"\\t\", header=TRUE, na.strings = \"NA\")\r\nrownames(descriptive.columns) <- selected.features[,\"Sequence\"]\r\n#rownames(descriptive.columns) <- c(\"Q5SX40_peptide40\", \"O88990_peptide38\")\r\nrownames(dat) <- rownames(descriptive.columns)\r\nwrite.table(x=cbind(rownames(descriptive.columns),descriptive.columns), file=paste0(output.path, \"/descriptive.columns.txt\"), sep=\"\\t\", col.names=TRUE, row.names=FALSE)\r\n\r\ndat <- log2(data.matrix(dat))\r\ndat <- dat[,c(group1,group3,group4)]\r\nwrite.table(x=cbind(rownames(dat),dat), file=paste0(output.path, \"/original_data.txt\"), sep=\"\\t\", col.names=TRUE, row.names=FALSE)\r\n\r\n#-----------------------------> Begin: Data Import & Pre-Processing\r\n\r\n#-----------------------------> Begin: Diff. Analysis\r\n\r\nclasses <- gsub(\"\\\\_\\\\d+\", \"\", colnames(dat))\r\nrawdat <- 2^dat\r\np.values <- vector(mode=\"numeric\", length=nrow(dat))\r\nmax.mean.ratios <- vector(mode=\"numeric\", length=nrow(dat))\r\nfor(i in 1:nrow(dat)){\r\n p.values[i] <- summary(aov(dat[i,] ~ classes))[[1]][[\"Pr(>F)\"]][1]\r\n \r\n mean.ratio1 <- max( mean(rawdat[i,group1])/mean(c(rawdat[i,group3], rawdat[i,group4])), mean(c(rawdat[i,group3], rawdat[i,group4]))/mean(rawdat[i,group1]) )\r\n mean.ratio3 <- max( mean(rawdat[i,group3])/mean(c(rawdat[i,group1], rawdat[i,group4])), mean(c(rawdat[i,group1], rawdat[i,group4]))/mean(rawdat[i,group3]) )\r\n mean.ratio4 <- max( mean(rawdat[i,group4])/mean(c(rawdat[i,group1], rawdat[i,group3])), mean(c(rawdat[i,group1], rawdat[i,group3]))/mean(rawdat[i,group4]) )\r\n max.mean.ratios[i] <- max(mean.ratio1, mean.ratio3, mean.ratio4)\r\n}\r\np.values.adj <- p.adjust(p.values, method=\"fdr\")\r\nnames(p.values.adj) <- rownames(dat)\r\ndiff.analysis.output <- cbind(names(p.values.adj), p.values, p.values.adj, max.mean.ratios)\r\ncolnames(diff.analysis.output) <- c(\"Feature\", \"P-value (Anova)\", \"Adj. p-value\", \"Max. mean ratio\")\r\nwrite.table(file=paste0(output.path, \"/diff_analysis_output.txt\"), x=diff.analysis.output, \r\n row.names=FALSE, col.names=TRUE, sep=\"\\t\")\r\n\r\n#-----------------------------> End: Diff. Analysis\r\n\r\n#-----------------------------> Begin: Validation with Test Set\r\n\r\n### 1000 times train/test split for model validation\r\ntestruns <- 1000\r\nfeatures <- rownames(dat)\r\nall.idx <- colnames(dat)\r\n#perf.final <- vector(\"list\", testruns)\r\nperf.final <- matrix(nrow = testruns, ncol = 12)\r\ncolnames(perf.final) = c(\"acc_total\", \"sens_total\", \"spec_total\",\r\n \"acc1\", \"sens1\", \"spec1\",\r\n \"acc2\", \"sens2\", \"spec2\",\r\n \"acc3\", \"sens3\", \"spec3\")\r\n\r\nfor(i in 1:testruns){\r\n cat(\"final classification: \", i, \"\\r\")\r\n test.idx1 <- sample(group1, 3) # n1=10\r\n #test.idx2 <- sample(group2, 1) # n2=3\r\n test.idx3 <- sample(group3, 5) # n3=15\r\n test.idx4 <- sample(group4, 3) # n4=10\r\n #test.idx <- c(test.idx1, test.idx2, test.idx3, test.idx4)\r\n test.idx <- c(test.idx1, test.idx3, test.idx4)\r\n \r\n train.idx <- setdiff(all.idx, test.idx)\r\n \r\n classi <- classify.rf.evaluation(iteration=i, trainset=dat[features,train.idx,drop=FALSE], \r\n testset=dat[features,test.idx,drop=FALSE], \r\n classes_train=as.factor(gsub(\"\\\\_\\\\d+\", \"\", train.idx)), \r\n classes_test=as.factor(gsub(\"\\\\_\\\\d+\", \"\", test.idx)), \r\n label1=\"Type I\", label2=\"Type IIb\", label3=\"Type IIx\", output.path=NULL, \r\n nrounds = best.nrounds, max_depth = best.max_depth)\r\n perf.final[i,] <- classi\r\n}\r\n\r\nwrite.table(perf.final, file = paste0(output.path, \"/validation.txt\"), row.names = FALSE, sep = \"\\t\")\r\n\r\n#-----------------------------> End: Validation with Test Set\r\n\r\n#-----------------------------> Begin: PCA\r\n\r\npca.dat <- dat[features,]\r\n\r\ngroup.vec <- c(\r\n \"Type I\",\r\n \"Type IIb\",\r\n \"Type IIx\"\r\n)\r\n\r\ncol.vec <- c(\r\n adjustcolor(\"navy\", alpha=0.3),\r\n adjustcolor(\"red\", alpha=0.3),\r\n adjustcolor(\"darkorchid\", alpha=0.3),\r\n adjustcolor(\"darkgreen\", alpha=0.3)\r\n)\r\n\r\n\r\npcdat <- prcomp(t(pca.dat), center=TRUE, scale=TRUE)\r\nscores <- pcdat$x\r\nfor (i in 1:2){\r\n for (j in i:2){\r\n if (i End: PCA\r\n\r\n#-----------------------------> Begin: Boxplots\r\n\r\nfor(i in 1:length(features)){\r\n idx <- match(features[i], rownames(dat))\r\n seq <- descriptive.columns[features[i],1]\r\n current.p.value <- formatC(p.values.adj[idx], format = \"e\", digits = 2)\r\n png(paste0(output.path,\"/boxplot_\", i, \"_\", features[i], \".png\"), width=2000, height=2000, pointsize=15, res=300)\r\n boxplot(dat[features[i],group1], dat[features[i],group3], dat[features[i],group4], main=paste0(seq, \"\\np-value: \", current.p.value), names=c(label1, label3, label4), col=col.vec[c(1,3,4)], cex.axis=1.5, cex.main=1.5)\r\n dev.off()\r\n}\r\n\r\n#-----------------------------> End: Boxplots\r\n\r\n#-----------------------------> Begin: Decision Tree\r\n\r\ncol.list <- list(\r\n adjustcolor(\"navy\", alpha=0.3),\r\n adjustcolor(\"darkorchid\", alpha=0.3),\r\n adjustcolor(\"darkgreen\", alpha=0.3)\r\n)\r\nrpartdat <- data.frame(classes, t(dat[features,]), stringsAsFactors=TRUE)\r\nfor(i in 1:length(features)){\r\n colnames(rpartdat)[i+1] <- descriptive.columns[features[i],1]\r\n}\r\nrpart.model <- rpart(classes~., data=rpartdat, method=\"class\")\r\npng(paste0(output.path,\"/rpart-plot.png\"), width=2000, height=2000, pointsize=15, res=300)\r\n rpart.plot(rpart.model, roundint=FALSE,type=2,extra=101,box.palette=col.list)\r\ndev.off()\r\n\r\n#-----------------------------> End: Decision Tree\r\n", "meta": {"hexsha": "8c04c0d8581d4b0ea6caa36332a4ef6d737af91e", "size": 14001, "ext": "r", "lang": "R", "max_stars_repo_path": "FiPSPi-validation.r", "max_stars_repo_name": "mpc-bioinformatics/FiPSPi", "max_stars_repo_head_hexsha": "9679c965b08cf1776d5e2a8b055acf693deec5ff", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FiPSPi-validation.r", "max_issues_repo_name": "mpc-bioinformatics/FiPSPi", "max_issues_repo_head_hexsha": "9679c965b08cf1776d5e2a8b055acf693deec5ff", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FiPSPi-validation.r", "max_forks_repo_name": "mpc-bioinformatics/FiPSPi", "max_forks_repo_head_hexsha": "9679c965b08cf1776d5e2a8b055acf693deec5ff", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.4577922078, "max_line_length": 221, "alphanum_fraction": 0.5731019213, "num_tokens": 3988, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.30410675852294755}} {"text": "# load library\nlibrary(plotly)\nlibrary(dplyr)\n\n\n# the function takes a dataframe and colorvar as variable\n# and returns a barchart listing top 100 donation to specific candidate\nBuildBarchart <- function(map.df, colorvar, candidate.name) {\n \n # margin setting\n m <- list(l = 50, r = 50, b = 280, t = 100, pad = 4)\n \n # use string name as variable name\n \n # chart 1: barchart \n \n # arrange bar with total contribution desc\n map.df$name <- factor(map.df$name, \n levels = map.df$name[order(map.df$total,decreasing = TRUE)])\n \n # build barchart with hover text of organization name, industry and total donation\n bar <- map.df %>% plot_ly(x = ~name, y = ~total, type = 'bar',\n color = eval(parse(text = colorvar)),\n width = 1200, height = 1000,\n text = ~paste0('Organization: ', name, '
',\n 'Industry: ', industry, '
',\n '$', total)) %>% \n layout(margin = m, autosize = F, title = paste0('Top 100 Contributions to ', candidate.name))\n \n return(bar)\n}\n\n\n# chart 2: map\n# the function takes dataframe as variable\n# and returns a map showing where are the top 100 donations coming from\nBuildMap <- function(map.df) { \n \n # geo setting\n g <- list(\n scope = 'usa',\n projection = list(type = 'albers usa'),\n showland = TRUE,\n landcolor = toRGB(\"gray95\"),\n subunitcolor = toRGB(\"gray85\"),\n countrycolor = toRGB(\"gray85\"),\n countrywidth = 0.5,\n subunitwidth = 0.5\n )\n \n # build map with color bar showing the number of contribution\n map <- map.df %>% plot_geo(lat = ~Latitude, lon = ~Longitude) %>% \n add_markers(\n hoverinfo = 'text',\n size = ~total, color = ~total, opacity = 0.8,\n text = ~paste0(name, '
', \n industry, '
', '# of records' ,records, '
',\n '$',total)) %>% \n layout(title = 'Location of Top 100 Contribution
(Hover for details)', geo = g)\n \n return(map)\n}\n\n\n# chart 3: pie chart\n# this function takes a dataframe as variable\n# and returns a pie chart showing the parcentage of each industry's contribution\nBuildPie <- function(pie.df) {\n \n # build piechart\n pie <- pie.df %>% plot_ly(labels = ~industry, values = ~percent, type = 'pie',\n textposition = 'inside',\n textinfo = 'label+percent',\n hoverinfo = 'text',\n text = ~paste0(industry, '
',\n '$', total),\n width = 800, height = 800) %>% \n layout(title = 'Percentage of Industry Contribution')\n \n return(pie)\n}\n", "meta": {"hexsha": "681f08f3eca36dbb4d4cbbf76686020d468cf2be", "size": 2724, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/mixPlot.r", "max_stars_repo_name": "liuyaf/info201-af3-final-project", "max_stars_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/mixPlot.r", "max_issues_repo_name": "liuyaf/info201-af3-final-project", "max_issues_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5, "max_issues_repo_issues_event_min_datetime": "2017-05-16T02:12:03.000Z", "max_issues_repo_issues_event_max_datetime": "2017-06-01T23:42:56.000Z", "max_forks_repo_path": "scripts/mixPlot.r", "max_forks_repo_name": "liuyaf/info201-af3-final-project", "max_forks_repo_head_hexsha": "cc0e33eba27215935bb37669f554031096c10a0c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2195121951, "max_line_length": 97, "alphanum_fraction": 0.5594713656, "num_tokens": 684, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.30397251759014843}} {"text": "#!/usr/bin/Rscript\n#===============================================================================\n# Copyright (c) berrygenomics 2015\n#\n# DESCRIPTION: \u5305\u62ec\u51e0\u4e2a\u5904\u7406\u8fc7\u7a0b\uff1a1\uff0c\u5408\u5e76bin 2\uff0c\u53bb\u9664\u5f02\u5e38GC\u7684bin 3\uff0c\u53bb\u9664\u4f4e\u590d\u6742\u5ea6\u7684bin\n# 4\uff0c\u53bb\u9664\u4f4e\u8986\u76d6\u5ea6\u7684bin 5\uff0c\u5982\u679c\u9700\u8981\uff0c\u5219\u8fdb\u884cGC\u6821\u6b63\n#\n# OPTIONS: ---\n# BUGS: ---\n# NOTES: ---\n# AUTHOR: sunhy\n# MAIL: sunhuaiyu@berrygnomics.com\n# VERSION: 2.0\n# CREATED: \n#===============================================================================\nlibrary(getopt)\nSpec <- matrix(c(\n 'rcfile', 'r', 1, 'character', 'the reads count file path, must',\n 'gcfile', 'g', 1, 'character', 'the GC count file path, must',\n 'outdir', 'o', 1, 'character', 'the output dir path, must',\n 'complexity','c', 2, 'character', 'the bins low-complexity matrix path, option', \n 'Merge', 'M', 2, 'integer', 'merge some bins to one, default 5',\n 'gccorrect', 'G', 0, 'integer', 'whether do GC correction, default no',\n 'help', 'h', 0, 'logical', 'This help'\n), byrow=TRUE, ncol=5)\nopt <- getopt(Spec)\n# if help was asked for print a friendly message\n# and exit with a non-zero error code\nif ( !is.null(opt$help) | is.null(opt$rcfile) | is.null(opt$gcfile) | is.null(opt$outdir)) {\n cat(getopt(Spec, usage=TRUE));\n q(status=1);\n}\n# set options\nif (is.null(opt$Merge)) { opt$Merge <- 5 }\nif (is.null(opt$gccorrect)) {opt$gccorrect <- FALSE } else { opt$gccorrect <- TRUE }\nBIN_LENGTH <- 20000 # the bins of the RD matrix are 20K\n\n\nif ( !file.exists(opt$outdir) ) dir.create( path=opt$outdir )\n## file name : /file/path/14C01700_L6_I333.R1.clean.fastq.gz.20K.txt\nprefix <- strsplit(basename(opt$rcfile), '[.]', perl=T)\noutprefix = paste(opt$outdir, '/', prefix[[1]][1], sep='')\n\nlogfile = paste(outprefix, '.filter.log', sep='')\nif (file.exists(logfile)) { file.remove(logfile) }\n\n\n############## processing start\n## read\nRC <- as.matrix(read.table(opt$rcfile, header=F, stringsAsFactors=F, row.names=1, sep='\\t'))\nGC <- as.matrix(read.table(opt$gcfile, header=F, stringsAsFactors=F, row.names=1, sep='\\t'))\n\n\n## only analysis autosome\nRC <- RC[1:22, ]\nGC <- GC[1:22, ]\n\n## constructing a new matrix according to the merge number, the final left bins will be persist\nlibrary(zoo)\nRC <- t(apply(RC, 1, FUN=function(x){ rollapply(x, width=opt$Merge, by=opt$Merge, FUN=sum, partial=T, align='left')} ))\nGC <- t(apply(GC, 1, FUN=function(x){ rollapply(x, width=opt$Merge, by=opt$Merge, FUN=sum, partial=T, align='left')} ))\n\n\n## transform GCs to GC rate (percentage), every reads length are 36.\nGC <- 100 * GC / ( RC * 36 )\nGC[is.nan(GC)] <- 0\nGC <- round(GC, 2)\n\ncat('The matrix dim', dim(RC), end='\\n', file=logfile)\ncat('usefull bins:', sum(RC != 0), end='\\n', file=logfile, append=T)\n\n## filter some extreme GC value and extreme RC, remove bins GC percent > 80% or < 20%\ncat('remove extreme GC bins:', sum(GC != 0 & (GC<20 | GC>80)), end='\\n', file=logfile, append=T)\n\nGC[GC < 20 | GC > 80] <- NA\nRC[is.na(GC)] <- NA\n\n\n\n\n## filter low-complexity bins, remove bins which have more 80% repeat regions(considered as low-complexity DNA bin)\n\nif (!is.null(opt$complexity)) {\n Complex_data <- as.matrix(read.table(opt$complexity, header=F, row.names=1, stringsAsFactors=F))\n Complex_data <- Complex_data[1:22, ]\n Complex_data <- t(apply(Complex_data, 1, FUN=function(x){ \n rollapply(x, width=opt$Merge, by=opt$Merge, FUN=sum, partial=T, align='left')\n } ))\n \n if (nrow(RC) != nrow(Complex_data) | ncol(RC) != ncol(Complex_data)) {\n cat('Warning: the complexity file has not same dim', end='\\n', file=stderr())\n quit(status=1)\n }\n Complex_data <- Complex_data/(BIN_LENGTH * opt$Merge)\n OutLier <- Complex_data > 0.8 # 80%\n cat('remove low-complexity bins:', sum(OutLier), end='\\n', file=logfile, append=T)\n\tcat('low-complexity bins RD:', RC[OutLier], end='\\n', file=logfile, append=T)\n\tcat('low-complexity bins RD mean:', mean(RC[OutLier]), end='\\n', file=logfile, append=T)\n RC[OutLier] <- NA\n GC[OutLier] <- NA\n}\n\n\n## plot RCs density, and remove low-coverage bins, remove x < 1/3 of peak value \ndensity_plot <- function(data, rate, main='RC density distribution') {\n\tDen <- density(data, na.rm=T)\n\tplot(Den, type='o', xlab='after RC', ylab='density', col='#4682B4', axes=T, xpd=F, pch=1, font.lab=1,2,\n\t\t font.main=2, font.axis=1, lwd=2, cex.lab=1.5, cex.main=1.5, cex.axis=1, cex.sub=1, cex=0.5,\n\t\t main=main)\n\tden_y_peak <- max(Den$y)\n\tden_x_peak <- Den$x[Den$y == den_y_peak]\n\tsegments(den_x_peak, den_y_peak, den_x_peak, 0, col='black', lwd=1, lty=2)\n\trm_cutoff <- den_x_peak * rate\n\tsegments(rm_cutoff, den_y_peak, rm_cutoff, 0, col='red', lwd=2)\n\tlegend('topright', legend=c(sprintf('Mean: %.4f', mean(data, na.rm=T)),\n\t\t sprintf('Max density: %.4f', den_x_peak), sprintf('%.3f max density: %.4f', rate, rm_cutoff)),\n\t\t cex=0.8,inset = 0.01)\n\tbox()\n\trm_cutoff\n}\n\n\n## GC cerrection: get loess curve\nloessFit <- function(outprefix, rc, gc){\n rc <- as.vector(rc)\n rc <- rc[!is.na(rc)]\n gc <- as.vector(gc)\n gc <- gc[!is.na(gc)]\n\n png(file=paste(outprefix, '.GCcorrelation_before.png', sep=''), height=600, width=900)\n par(mar=c(5.1,4.5,4.1,2.1), mgp=c(3,1,0), ps=15)\n plot(gc, rc, main='Correlation between GC% and Read count', xlab='CG%', ylab='Read count', col='#A0522D',\n cex.lab=1.5, cex.main=1.5, cex.axis=1, cex.sub=1, cex=0.5, font.lab=1.2, font.main=2, font.axis=1, lwd=1,\n ylim=c(0, max(rc, na.rm=T)), xlim=c(10, 90), pch=21\n )\n\n#\t## fast loess\n#\tRC.bins.median <- tapply(rc, gc, function(x) median(x, na.rm=T))\n#\tGC.Levels <- as.numeric(names(RC.bins.median))\n#\tLoess <- loess(RC.bins.median ~ GC.Levels)\n# loess.fittedRC <- predict( Loess, GC.Levels)\n#\tlines(GC.Levels, loess.fittedRC, col=3, lwd=2)\n\n ## normal loess\n Loess <- loess(rc ~ gc)\n loess.fittedRC <- predict( Loess, gc[order(gc)])\n lines(gc[order(gc)], loess.fittedRC, col=3, lwd=2)\n\n # Lowess<-lowess(DF,f=0.05,delta=0.001)\n # lines(Lowess,col=4,lwd=2)\n\n M <- median(rc)\n segments(min(gc), M, max(gc), M, col=1, lwd=2, lty=2)\n legend('topright', c('loess'), col=c(3,4), lty = 1, pch = 20, inset = .02)\n dev.off()\n cat('Done loessFit\\n',file=stderr())\n Loess\n}\n\n\n## rmove low coverage bins\n\npdf(file=paste(outprefix, 'RC_density.pdf', sep='_'), width=12, height=9)\npar(mfrow=c(3,1), lend=1)\ncutOff <- density_plot(RC, 1/3)\nfor (i in c(1:22)) {\n\tdensity_plot(RC[i, ], 1/3, paste('Rc density by chr', i, sep=''))\n}\ndev.off()\ncat('Done plot \\n', file=stderr())\ncat('remove low-coverage bins:', sum( !is.na(RC) & RC < cutOff ), end='\\n', file=logfile, append=T)\nRC[!is.na(RC) & RC%\n select(year, level, trial) %>%\n distinct(trial, .keep_all=TRUE)\n\nplot <- ggplot(data = data) +\n geom_bar(\n mapping = aes(y = year, fill = fct_relevel(level, c(\"L3\", \"L2\", \"L1\", \"Summit\", \"Elite\", \"NW3\", \"NW2\", \"NW1\"))),\n position = \"fill\") +\n coord_cartesian(ylim=c(2009, 2021)) +\n scale_y_continuous(breaks=seq(2009, 2021, 1)) +\n scale_x_continuous(breaks=seq(0, 1, 0.25), labels=c(\"\", \"25%\", \"50%\", \"75%\", \"100%\")) +\n scale_fill_viridis(option=\"turbo\", name=\"Competition\\nLevel\", discrete=TRUE) +\n theme_void() +\n theme(\n legend.position = 'right',\n axis.title.y = element_blank(),\n axis.text = element_text(size = 9)\n )\n\nannotated_plot <- getAnnotatedPlot(\n plot,\n title=\"NACSW Trials by Level\",\n subtitle=\"Percentage of trials every year by competition level\")\nannotated_plot\n\nggsave(\"trials_by_level.png\", plot=annotated_plot, path=\".\", width=4194, height=3226, units=\"px\", bg=\"white\")\n", "meta": {"hexsha": "544a241e5e5fdac7f4891cb9a996fe8fafa69179", "size": 1083, "ext": "r", "lang": "R", "max_stars_repo_path": "trials_by_level/trials_by_level.r", "max_stars_repo_name": "saylibenadikar/snoot-scoop", "max_stars_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "trials_by_level/trials_by_level.r", "max_issues_repo_name": "saylibenadikar/snoot-scoop", "max_issues_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "trials_by_level/trials_by_level.r", "max_forks_repo_name": "saylibenadikar/snoot-scoop", "max_forks_repo_head_hexsha": "16b6668fdf7df7e1e90e376050ec258a3a9e5d30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.8529411765, "max_line_length": 116, "alphanum_fraction": 0.6703601108, "num_tokens": 333, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.4882833952958347, "lm_q1q2_score": 0.3039365556728161}} {"text": "pdf('output.DupRate_plot.pdf')\npar(mar=c(5,4,4,5),las=0)\nseq_occ=c(1)\nseq_uniqRead=c(40)\npos_occ=c(1)\npos_uniqRead=c(40)\nplot(pos_occ,log10(pos_uniqRead),ylab='Number of Reads (log10)',xlab='Occurrence of read',pch=4,cex=0.8,col='blue',xlim=c(1,500),yaxt='n')\npoints(seq_occ,log10(seq_uniqRead),pch=20,cex=0.8,col='red')\nym=floor(max(log10(pos_uniqRead)))\nlegend(300,ym,legend=c('Sequence-based','Mapping-based'),col=c('blue','red'),pch=c(4,20))\naxis(side=2,at=0:ym,labels=0:ym)\naxis(side=4,at=c(log10(pos_uniqRead[1]),log10(pos_uniqRead[2]),log10(pos_uniqRead[3]),log10(pos_uniqRead[4])), labels=c(round(pos_uniqRead[1]*100/sum(pos_uniqRead*pos_occ)),round(pos_uniqRead[2]*100/sum(pos_uniqRead*pos_occ)),round(pos_uniqRead[3]*100/sum(pos_uniqRead*pos_occ)),round(pos_uniqRead[4]*100/sum(pos_uniqRead*pos_occ))))\nmtext(4, text = \"Reads %\", line = 2)\ndev.off()\n", "meta": {"hexsha": "77457b983d5969e96c9b5890d6c329e6fcefca4b", "size": 860, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/rseqc/test-data/output.DupRate_plot.r", "max_stars_repo_name": "ic4f/tools-iuc", "max_stars_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2016-07-19T05:26:21.000Z", "max_stars_repo_stars_event_max_datetime": "2016-07-19T05:26:21.000Z", "max_issues_repo_path": "tools/rseqc/test-data/output.DupRate_plot.r", "max_issues_repo_name": "ic4f/tools-iuc", "max_issues_repo_head_hexsha": "abfd3162e28a388d1dedbe55cb8b3567fa79c178", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2019-05-27T20:54:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-04T09:33:30.000Z", "max_forks_repo_path": "tools/rseqc/test-data/output.DupRate_plot.r", "max_forks_repo_name": "willemdek11/tools-iuc", "max_forks_repo_head_hexsha": "dc0a0cf275168c2a88ee3dc47652dd7ca1137871", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-09-12T14:56:37.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-16T00:30:14.000Z", "avg_line_length": 57.3333333333, "max_line_length": 333, "alphanum_fraction": 0.7360465116, "num_tokens": 331, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30383159202157695}} {"text": "# Stand-alone script that takes a mask from timeseries-based cleaning and applies it to a given set of files\n#\n# Inputs: QC mask brick, virtual stack to apply it to\n# Output: Cleaned brick of layers\nlibrary(raster); source(\"utils/set-temp-path.r\")\nlibrary(probaV)\nlibrary(foreach)\nlibrary(doParallel)\nlibrary(iterators)\nlibrary(tools)\n\nQCMaskFile = \"../../userdata/composite/tscleaned/ts-mask-whole-optimised.tif\"\nCloudyFileDirectory = \"../../userdata/semicleaned/ndvi/\"\nCloudyFilePattern = \"NDVI_sm.tif$\"\nTileOfInterest = \"X20Y01\"\nCleanDirectory = \"../../userdata/cleaned/ndvi/\"\n\nCloudyVrt = timeVrtProbaV(CloudyFileDirectory, pattern = CloudyFilePattern,\n vrt_name = tempfile(\"CloudyVrt\", rasterOptions()$tmpdir, \".vrt\"),\n tile = TileOfInterest, return_raster = TRUE)\nQCMask = brick(QCMaskFile)\n\nif (nlayers(CloudyVrt) != nlayers(QCMask))\n stop(\"Number of layers does not match!\")\nLayerNames = names(QCMask) = names(CloudyVrt)\n\n# One thread eats around 840 MB max, so limit to 20 cores for 16 GiB\n# Alternatively, set rasterOptions() memory settings\nCores = 20\npsnice(value = min(Cores - 1, 19))\nregisterDoParallel(cores = Cores)\nforeach(i=iter(LayerNames), .packages = \"raster\", .verbose = TRUE, .inorder = FALSE) %dopar%\n{\n mask(CloudyVrt[[i]], QCMask[[i]], maskvalue=c(2,0),\n filename=paste0(CleanDirectory, i), datatype=\"FLT4S\", progress=\"text\",\n options=c(\"COMPRESS=DEFLATE\", \"ZLEVEL=9\"))\n}\n", "meta": {"hexsha": "8b725c9732ea6f6dfce468bb5ce7c17bf6e385c0", "size": 1424, "ext": "r", "lang": "R", "max_stars_repo_path": "src/raster-based/optical/apply-timeseries-mask.r", "max_stars_repo_name": "GreatEmerald/master-classification", "max_stars_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-07-18T07:28:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-18T07:28:55.000Z", "max_issues_repo_path": "src/raster-based/optical/apply-timeseries-mask.r", "max_issues_repo_name": "GreatEmerald/master-classification", "max_issues_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/raster-based/optical/apply-timeseries-mask.r", "max_forks_repo_name": "GreatEmerald/master-classification", "max_forks_repo_head_hexsha": "2f8d22bbcaf431f19d121b242f840f78a329d124", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2017-10-07T08:58:22.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-02T14:07:32.000Z", "avg_line_length": 37.4736842105, "max_line_length": 108, "alphanum_fraction": 0.7289325843, "num_tokens": 420, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982315512488, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.30368173889628247}} {"text": "\nsource(\"/home/kshah2/qqplots.R\")\npredixcanassociation<-function(co,tis,alpha) {\n #cohorts<-c(\"T2D\")\n #alpha<-1\n #tissues<-c(\"Thyroid\",\"Adipose-Visceral-Omentum\") #\"Adipose-Subcutaneous\",\"WholeBlood\",\"Brain-Cerebellum\",\"Brain-Hypothalamus\",\"Liver\",\"Muscle-Skeletal\",\"Pancreas\",\"Pituitary\",\"SmallIntestine-TerminalIleum\",\"Stomach\",\"Colon-Sigmoid\",\"Nerve-Tibial\",\"Colon-Transverse\",)\n #for (tis in tissues) {\n #for (co in cohorts) {\n #control1<-read.table(paste(\"/group/im-lab/nas40t2/kaanan/PrediXcan/WTCCC/\",co,\"/PrediXcan/GTExTissues/PredictedExpression_\",\"58C\",\"_\",tis,\"_EN\",alpha,\".txt\",sep=\"\"),header=T)\n #control1[(dim(control1)[1]+1),]<-c(NA,rep(0,(dim(control1)[2]-1)))\n #control2<-read.table(paste(\"/group/im-lab/nas40t2/kaanan/PrediXcan/WTCCC/\",co,\"/PrediXcan/GTExTissues/PredictedExpression_\",\"NBS\",\"_\",tis,\"_EN\",alpha,\".txt\",sep=\"\"),header=T)\n #control2[(dim(control2)[1]+1),]<-c(NA,rep(0,(dim(control2)[2]-1)))\n #control<-merge(control1,control2,by.x=1,by.y=1,all.x=F,all.y=F)\n scorefile = paste(\"/group/im-lab/nas40t2/kaanan/PrediXcan/WTCCC/\",co,\"/PrediXcan/PredictedExpression_\",co,\"_\",tis,\"_EN\",alpha,\".txt\",sep=\"\")\n preds<-read.table(scorefile,header=T)\n phenorow<-dim(preds)[1]+1\n preds[phenorow,]<-c(NA,rep(1,(dim(preds)[2]-1)))\n preds[phenorow,grep(\"58C\",colnames(preds))]<-0\n preds[phenorow,grep(\"NBS\",colnames(preds))]<-0\n #dat<-merge(preds,control,by.x=1,by.y=1,all.x=F,all.y=F)\n dat<-preds\n OUT<-NULL\n for (i in 1:(dim(dat)[1]-1)) {\n testpheno<-as.numeric(as.vector(t(dat[phenorow,-1])))\n geneexp<-as.numeric(as.vector(t(dat[i,-1])))\n tmp = coef(summary(glm(testpheno~geneexp,family=\"binomial\",maxit=10)))[c(2,6,8)]\n tmp<-c(as.character(dat$gene[i]),tmp)\n OUT<-rbind(OUT,tmp)\n }\n outfile = paste(\"/group/im-lab/nas40t2/kaanan/PrediXcan/WTCCC/\",co,\"/PrediXcan/PrediXcan_\",co,\"_\",tis,\"_EN\",alpha,\".txt\",sep=\"\")\n colnames(OUT)<-c(\"gene\",\"beta\",\"z-stat\",\"p-val\")\n write.table(OUT,outfile,col.names=T,row.names=F,quote=F)\n jpeg(paste(\"/group/im-lab/nas40t2/kaanan/PrediXcan/WTCCC/\",co,\"/PrediXcan/PrediXcan_\",co,\"_\",tis,\"_EN\",alpha,\".jpeg\",sep=\"\"))\n qqunif(OUT[,4],plot=T)\n dev.off()\n}\n#}\n#}\n\n", "meta": {"hexsha": "ac67ece8bba7b2160ba9266eca770c95dad646c1", "size": 2227, "ext": "r", "lang": "R", "max_stars_repo_path": "Paper-Scripts/Kaanan/prediXcanAssociation_jointimpute.r", "max_stars_repo_name": "theMechanic23/PrediXcan", "max_stars_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 92, "max_stars_repo_stars_event_min_datetime": "2015-05-05T16:37:07.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-05T12:09:16.000Z", "max_issues_repo_path": "Paper-Scripts/Kaanan/prediXcanAssociation_jointimpute.r", "max_issues_repo_name": "theMechanic23/PrediXcan", "max_issues_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2016-04-15T13:22:25.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-08T19:58:34.000Z", "max_forks_repo_path": "Paper-Scripts/Kaanan/prediXcanAssociation_jointimpute.r", "max_forks_repo_name": "theMechanic23/PrediXcan", "max_forks_repo_head_hexsha": "05adb33234ce00f82eff1ffd31825c9cb0d4b8bd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 66, "max_forks_repo_forks_event_min_datetime": "2015-04-24T16:56:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-30T14:37:49.000Z", "avg_line_length": 55.675, "max_line_length": 272, "alphanum_fraction": 0.6533453076, "num_tokens": 838, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.46879062662624377, "lm_q1q2_score": 0.30368173252113384}} {"text": "\ncode 'str_liquid'\nname '\u6d41\u52a8\u6027\u7f3a\u53e3'\ndimensions {\n\taccbook null\n\tliquidGap null\n}\ndataRequest {\n\ttable 'sceLiquid',{\n\t\tallDimensions (['accbook','liquidGap'])\n\t}\n\tfields 'accbook,liquidGap,principal value'\n\tcontextParams (['stress'])\n}\n\nderivedHeads{\n accbook ([\n\t\t\tnew Head(name:'\u6d41\u52a8\u6027\u7f3a\u53e3',aggregate:true,\n\t\t\t\tformula:{\n\t\t\t\t\tdef grids=thisColGrids({parent==null})\n\t\t\t\t\tdef a=grids.find{it.rowHeadGrid.model?.alType=='A'}\n\t\t\t\t\tdef l=grids.find{it.rowHeadGrid.model?.alType=='L'}\n\t\t\t\t\tif(a && l){\n\t\t\t\t\t\ta-l\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t),\n\t\t\tnew Head(name:'\u7f3a\u53e3\u7d2f\u8ba1',aggregate:true,\n\t\t\t\tformula:{\n\t\t\t\t\t(col==0)? higher:(left+higher)\n })\n\t\t])\n}\ndataGrids {\n apply selectRows({children!=null}),{\n\t\tformula {sumChildren()}\n\t}\n \n}\n", "meta": {"hexsha": "063346669599507717992d57be23f87ade36ce92", "size": 724, "ext": "rd", "lang": "R", "max_stars_repo_path": "demo-untidy/str_liquid.rd", "max_stars_repo_name": "wushexu/jyreport", "max_stars_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-04-13T01:51:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-20T03:19:05.000Z", "max_issues_repo_path": "demo-untidy/str_liquid.rd", "max_issues_repo_name": "wushexu/jyreport", "max_issues_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "demo-untidy/str_liquid.rd", "max_forks_repo_name": "wushexu/jyreport", "max_forks_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2015-06-27T04:06:48.000Z", "max_forks_repo_forks_event_max_datetime": "2016-08-05T03:04:09.000Z", "avg_line_length": 18.1, "max_line_length": 56, "alphanum_fraction": 0.6104972376, "num_tokens": 228, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3036429639767095}} {"text": "library(quanteda)\nlibrary(data.table)\n\ngetNgramFreqs <- function(ng, dat, ignores=NULL, sort.by.ngram=TRUE, sort.by.freq=FALSE) {\n # http://stackoverflow.com/questions/36629329/\n # how-do-i-keep-intra-word-periods-in-unigrams-r-quanteda\n if(is.null(ignores)) {\n dat.dfm <- dfm(dat, ngrams=ng, toLower = FALSE, removePunct = FALSE,\n what = \"fasterword\", verbose = FALSE)\n } else {\n dat.dfm <- dfm(dat, ngrams=ng, toLower = FALSE, ignoredFeatures=ignores,\n removePunct = FALSE, what = \"fasterword\", verbose = FALSE)\n }\n rm(dat)\n # quanteda docfreq will get the document frequency of terms in the dfm\n ngram.freq <- docfreq(dat.dfm)\n if(sort.by.freq) { ngram.freq <- sort(ngram.freq, decreasing=TRUE) }\n if(sort.by.ngram) { ngram.freq <- ngram.freq[sort(names(ngram.freq))] }\n rm(dat.dfm)\n \n return(ngram.freq)\n}\n\ngetNgramTables <- function(ng, linesCorpus, prefixFilter=NULL) {\n start_msg <- paste0(\"START getNgramTables building \", ng,\n \"-gram frequency table at \",\n as.character(Sys.time()), \"\\n\")\n cat(start_msg)\n ngrams <- getNgramFreqs(ng, linesCorpus)\n ngrams_dt <- data.table(ngram=names(ngrams), freq=ngrams)\n if(length(grep('^SOS', ngrams_dt$ngram)) > 0) {\n ngrams_dt <- ngrams_dt[-grep('^SOS', ngrams_dt$ngram),]\n }\n if(!is.null(prefixFilter)) {\n regex <- sprintf('%s%s', '^', prefixFilter)\n ngrams_dt <- ngrams_dt[grep(regex, ngrams_dt$ngram),]\n }\n \n end_msg <- paste0(\"*** FINISH building \", ng, \"-gram frequency table at \",\n as.character(Sys.time()), \"***\\n\")\n cat(end_msg)\n \n return(ngrams_dt)\n}\n\nltcorpus <- readLines(\"./capstone/final/finals/en_US.final.txt\")\n\nunigs <- getNgramTables(1, ltcorpus)\nbigrs <- getNgramTables(2, ltcorpus)\ntrigs <- getNgramTables(3, ltcorpus)\ntetrags <- getNgramTables(4, ltcorpus)\npentags <- getNgramTables(5, ltcorpus)\n\nsave(unigs, bigrs, trigs, tetrags, pentags, file = \"ngrams.rdata\")\n\ncountFreq <- function(tokens){\n size <- length(tokens)\n \n if(size == 1) tablefreq <- unigs\n else if(size == 2) tablefreq <- bigrs\n else if(size == 3) tablefreq <- trigs\n else if(size == 4) tablefreq <- tetrags\n else if(size == 5) tablefreq <- pentags\n \n phrase <- paste(tokens, collapse = \"_\")\n r <- tablefreq[ngram == phrase]\n \n if(length(r) == 0) 0\n else as.numeric(r$freq)\n}\n\nPmle <- function(words, size=2){\n #sum(sapply(1:(length(words)-size+1),function(x) {\n # currentGram <- words[x:(x+size-1)]\n # print(sprintf(\"%s/%s\",paste(currentGram, collapse = \"_\"),paste(head(currentGram, n = size-1), collapse = \"_\")))\n #log(countFreq(currentGram)/countFreq(head(currentGram, n = size-1)))\n #}))\n #countFreq(words)/countFreq(head(words, n = length(words) - 1))\n currentGram <- words[(length(words):-size):]\n Pmle(head(words, n = length(words) - 1), size)\n}\n\n#When you breathe, I want to be the air for you. I'll be there for you, I'd live and I'd\nPmle(c(\"i\",\"would\",\"sleep\"))\nPmle(c(\"i\",\"would\",\"eat\"))\nPmle(c(\"i\",\"would\",\"give\"))\nPmle(c(\"i\",\"would\",\"die\"))\n\nPmle(c(\"and\",\"id\",\"sleep\"))\nPmle(c(\"and\",\"id\",\"eat\"))\nPmle(c(\"and\",\"id\",\"give\"))\nPmle(c(\"and\",\"id\",\"die\"))\n\nPmle(c(\"and\",\"i\", \"would\",\"sleep\"),3)\nPmle(c(\"and\",\"i\", \"would\",\"eat\"),3)\nPmle(c(\"and\",\"i\", \"would\",\"give\"))\nPmle(c(\"and\",\"i\", \"would\",\"die\"))\n\nPmle(c(\"live\", \"and\",\"id\", \"sleep\"))\nPmle(c(\"live\", \"and\",\"id\", \"eat\"))\nPmle(c(\"live\", \"and\",\"id\", \"give\"))\nPmle(c(\"live\", \"and\",\"id\", \"die\"))\n\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"sleep\"))\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"eat\"))\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"give\"))\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"die\"))\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"die\"),3)\nPmle(c(\"live\", \"and\", \"i\", \"would\", \"die\"),4)\n\nPmle(c(\"live\", \"and\", \"id\", \"sleep\"))\nPmle(c(\"live\", \"and\", \"id\", \"eat\"))\nPmle(c(\"live\", \"and\", \"id\", \"give\"))\nPmle(c(\"live\", \"and\", \"id\", \"die\"))\n\nPmle(c(\"id\", \"live\", \"and\", \"id\"))\nPmle(c(\"id\", \"live\", \"and\", \"id\", \"die\"))\n", "meta": {"hexsha": "2df160a72593b73654a04e51218e3d226197d7c3", "size": 3946, "ext": "r", "lang": "R", "max_stars_repo_path": "courses/johnhopkins-datascience/p1.r", "max_stars_repo_name": "xunilrj/sandbox", "max_stars_repo_head_hexsha": "f92c12f83433cac01a885585e41c02bb5826a01f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2017-04-01T17:18:35.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-12T05:23:23.000Z", "max_issues_repo_path": "courses/johnhopkins-datascience/p1.r", "max_issues_repo_name": "xunilrj/sandbox", "max_issues_repo_head_hexsha": "f92c12f83433cac01a885585e41c02bb5826a01f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2020-05-24T13:36:50.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T06:44:20.000Z", "max_forks_repo_path": "courses/johnhopkins-datascience/p1.r", "max_forks_repo_name": "xunilrj/sandbox", "max_forks_repo_head_hexsha": "f92c12f83433cac01a885585e41c02bb5826a01f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2018-09-20T01:07:39.000Z", "max_forks_repo_forks_event_max_datetime": "2019-02-22T14:55:38.000Z", "avg_line_length": 33.4406779661, "max_line_length": 116, "alphanum_fraction": 0.6069437405, "num_tokens": 1323, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3036429639767095}} {"text": "### Operation Span Processing Script ###\n### Author: James stone ###\n### Version: 1.0 ###\n### For use with the operation span task available at http://www.cognitivetools.uk/ ###\n\n#set the working directory to the folder that is holding the data files from the operation span task\n\n\n###function that takes the output file for a participant and returns summary information for that person###\n\noperation_span_processing <- function(data.frame) {\n\n\t#seperate processing trials from digit span trials\n\t\n\tdf.recall <- subset(data.frame, !is.na(trial.digit_span.trialNo))\n\tdf.operations <- subset(data.frame, is.na(trial.digit_span.trialNo))\n\n\n\t#standard output form digit span has each element of a trial as a row, \n\t#also need to collate these into success/failure for whole trials.\n\tnum.trials <- max(df.recall$trial.digit_span.trialNo)\n\t#set these vectors up now, to be filled later.\n\ttrial.load <- numeric(num.trials)\n\tnum.corr <- numeric(num.trials)\n\tprop.corr <- numeric(num.trials)\n\ttrial.success <- numeric(num.trials)\n\t\n\tfor (i in 1:num.trials) {\n\t\tindi_trial <- df.recall$trial.digit_span.result[df.recall$trial.digit_span.trialNo == i]\n\t\tnumDigits <- length(indi_trial)\n\t\tnumCorr <- sum(indi_trial == \"success\")\n\t\ttrial.success[i] <- numDigits == numCorr\n\t\ttrial.load[i] <- numDigits\n\t\tnum.corr[i] <- numCorr\n\t\tprop.corr[i] <- numCorr / numDigits\n\t}\t\n\t\n\tdf.recall.wholeTrial <- as.data.frame(cbind(trial.load, num.corr, prop.corr, trial.success))\n\tdf.recall.wholeTrial <- transform(df.recall.wholeTrial, score = trial.load * trial.success)\n\t\n\t#highest load with a fully correct response\n\tif (length(df.recall.wholeTrial$trial.load[df.recall.wholeTrial$trial.success == 1]) == 0) {\n\t\t#no successes\n\t\tmax.span <- NA\n\t} else {\n\t\tmax.span <- max(df.recall.wholeTrial$trial.load[df.recall.wholeTrial$trial.success == 1]) \n\t}\n\t\n\t#total number of words correctly recalled in correct serial position throughout\n\tnumber.successes <- sum(data.frame$trial.digit_span.result == \"success\")\n\t#span two trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 2)) == 0) {\n\t\t#then there were no span size 2 trials, therefore\n\t\tspan.2.corr <- NA\n\t} else {\n\t\tspan.2.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 2])\n\t}\n\t#span three trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 3)) == 0) {\n\t\t#then there were no span size 3 trials, therefore\n\t\tspan.3.corr <- NA\n\t} else {\n\t\tspan.3.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 3])\n\t}\t\n\t#span four trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 4)) == 0) {\n\t\t#then there were no span size 4 trials, therefore\n\t\tspan.4.corr <- NA\n\t} else {\n\t\tspan.4.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 4])\n\t}\t\n\t#span five trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 5)) == 0) {\n\t\t#then there were no span size 5 trials, therefore\n\t\tspan.5.corr <- NA\n\t} else {\n\t\tspan.5.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 5])\n\t}\n\t#span six trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 6)) == 0) {\n\t\t#then there were no span size 6 trials, therefore\n\t\tspan.6.corr <- NA\n\t} else {\n\t\tspan.6.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 6])\n\t}\t\n\t#span seven trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 7)) == 0) {\n\t\t#then there were no span size 7 trials, therefore\n\t\tspan.7.corr <- NA\n\t} else {\n\t\tspan.7.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 7])\n\t}\t\n\t#span eight trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 8)) == 0) {\n\t\t#then there were no span size 8 trials, therefore\n\t\tspan.8.corr <- NA\n\t} else {\n\t\tspan.8.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 8])\n\t}\n\t#span nine trials correct\n\tif (nrow(subset(df.recall.wholeTrial, trial.load == 9)) == 0) {\n\t\t#then there were no span size 9 trials, therefore\n\t\tspan.9.corr <- NA\n\t} else {\n\t\tspan.9.corr <- sum(df.recall.wholeTrial$trial.success[df.recall.wholeTrial$trial.load == 9])\n\t}\t\n\t\n\tfta.score <- sum(df.recall.wholeTrial$score)\n\tprop.score <- mean(df.recall.wholeTrial$prop.corr)\n\tnumber.successes <- sum(df.recall$trial.digit_span.result == \"success\")\n\t\n\tprocessing.accuracy <- sum(df.operations$trial.operation_processing.result == \"success\") / nrow(df.operations)\n\tprocessing.median.rt <- median(df.operations$trial.operation_processing.durationTime)\n\t\n\t\n\tp.summary <- c(fta.score, prop.score, number.successes, processing.accuracy, processing.median.rt, \n\tmax.span, span.2.corr, span.3.corr, span.4.corr, span.5.corr, span.6.corr, span.7.corr, span.8.corr, \n\tspan.9.corr)\n\t\n\treturn(p.summary)\n}\n\n##now can use that function on all our data files for operation span\n#put all operation span files into one folder and set that folder as working directory\n\ncompile_operation_span_data <- function() {\n\n\tmultiple.sessions <- character()\n\tfilenames <- list.files()\n\toperation.span.data <- matrix(nrow=length(filenames), ncol=14) #ncol needs to be number of values in p.summary above\n\tuser <- character()\n\t\n\tfor (i in 1:length(filenames)) {\n\t\t\n\t\ttmp.df <- read.csv(filenames[i])\n\t\tuser[i] <- toString(tmp.df$user.name[1])\n\t\t\n\t\tif (length(unique(tmp.df$session.id)) > 1) {\n\t\t\tmultiple.sessions[length(multiple.sessions) + 1] <- filenames[i]\n\t\t}\n\n\t\ttmp.os.summary <- operation_span_processing(tmp.df)\n\t\t\n\t\toperation.span.data[i, ] <- tmp.os.summary\n\t\n\t}\n\t\n\toperation.span.data <- as.data.frame(operation.span.data)\n\toperation.span.data$user <- user\n\t\n\tos.names <- c(\"fta.score\", \"prop.score\", \"number.successes\", \"processing.accuracy\", \"processing.median.rt\", \n\t\"max.span\", \"span.2.corr\", \"span.3.corr\", \"span.4.corr\", \"span.5.corr\", \"span.6.corr\", \"span.7.corr\", \"span.8.corr\", \n\t\"span.9.corr\", \"user\")\n\n\tnames(operation.span.data) <- os.names\n\t\n\tif (length(multiple.sessions > 0)) {\n\t\tcat(\"The following files contained trials with more than one session id: \\n\", multiple.sessions)\n\t}\n\t\n\treturn(operation.span.data)\t\n\n}\n\noperation.span.data <- compile_operation_span_data()\n\nrm(compile_operation_span_data, operation_span_processing)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "4785070cf875a5a303b2086b2a39c8a72c9617ad", "size": 6239, "ext": "r", "lang": "R", "max_stars_repo_path": "cog-tasks/rScripts/multiple_user/operation_span_process.r", "max_stars_repo_name": "pmtresearch/emotiv", "max_stars_repo_head_hexsha": "3976ae4253f8fe16a661e1ba9cda5591bceeffc7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-28T02:18:00.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-28T02:18:00.000Z", "max_issues_repo_path": "cog-tasks/rScripts/multiple_user/operation_span_process.r", "max_issues_repo_name": "pmtresearch/emotiv", "max_issues_repo_head_hexsha": "3976ae4253f8fe16a661e1ba9cda5591bceeffc7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cog-tasks/rScripts/multiple_user/operation_span_process.r", "max_forks_repo_name": "pmtresearch/emotiv", "max_forks_repo_head_hexsha": "3976ae4253f8fe16a661e1ba9cda5591bceeffc7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4947916667, "max_line_length": 118, "alphanum_fraction": 0.7047603783, "num_tokens": 1815, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3036429565362066}} {"text": "library(randomForest)\nlibrary(caret)\nlibrary(doMC)\nlibrary(mmadsenr)\nlibrary(futile.logger)\nlibrary(dplyr)\nlibrary(ggthemes)\n\n\n\n# Train and tune random forest classifiers for the ta sampled data set, which is really 8 levels of TA and\n# sample size combinations.\n#\n\nget_tassize_subset_ssize_tadur <- function(df, ssize, tadur) {\n df_tassize_subset <- dplyr::filter(df, sample_size == ssize, ta_duration == tadur)\n df_tassize_subset\n}\n\n\n# Set up logging\nlog_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", filename = \"tasampled-classification.log\")\nflog.appender(appender.file(log_file), name='cl')\n\n\nclargs <- commandArgs(trailingOnly = TRUE)\nif(length(clargs) == 0) {\n ta_sampled_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", filename = \"equifinality-3-ta-sampled-data.rda\")\n} else {\n ta_sampled_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", filename = \"equifinality-3-ta-sampled-data.rda\", args = clargs)\n}\n\nload(ta_sampled_data_file)\nflog.info(\"Loaded data file: %s\", ta_sampled_data_file, name='cl')\n\n\n\nflog.info(\"Beginning classification analysis of TA sampled equifinality-3 data sets\", name='cl')\n\n# set up parallel processing - use all the cores (unless it's a dev laptop under OS X) - from mmadsenr\nnum_cores <- get_parallel_cores_given_os(dev=TRUE)\nflog.info(\"Number of cores used in analysis: %s\", num_cores, name='cl')\nregisterDoMC(cores = num_cores)\n\n\n########### Training and Tuning Variables ##############\n\n#\n# Common training and tuning parameters for ctmixtures analysis\n#\ngbm_grid <- expand.grid(.interaction.depth = (1:6)*2,\n .n.trees = (1:10)*25, \n .shrinkage = 0.05)\n\ntraining_control <- trainControl(method=\"repeatedcv\", \n number=10, repeats=5)\n\n\n\n# make this repeatable - comment this out or change it to get a fresh analysis result\nseed_value <- 58132133\nset.seed(seed_value)\nflog.info(\"RNG seed to replicate this analysis: %s\", seed_value, name='cl')\n\n\n# # tuning grid of parameters and tuning cross-validation parameters\n# mtry_seq <- seq(from=2, to=20, by=4)\n# flog.info(\"Tuning random forest parameter mtry using vals: %s\", mtry_seq, name='cl')\n# fit_grid <- expand.grid(mtry=mtry_seq)\n# \n# cv_num <- 10\n# #cv_repeats <- 10\n# \n# flog.info(\"Tuning performed by CV, %s folds \", cv_num, name='cl')\n# \n# fit_control <- trainControl(method=\"cv\", \n# number=cv_num, \n# #repeats=cv_repeats, \n# allowParallel = TRUE,\n# ## Estimate class probabilities\n# classProbs = TRUE)\n\n\n# Set up sampling of train and test data sets\ntraining_set_fraction <- 0.8\ntest_set_fraction <- 1.0 - training_set_fraction\n\n\n\n# prepare data\n# create a label combining the biased models into one\n# then, split into training and test sets, with balanced samples for each of the binary classes\neq3_ta_sampled_df$two_class_label <- factor(ifelse(eq3_ta_sampled_df$model_class_label == 'allneutral', 'neutral', 'biased'))\n\n\n\n############# Process each combination of TA and sample size ###########\n\n# get grid of the sample size and TA duration combinations, to tassize_subset the data set\nsample_sizes <- unique(eq3_ta_sampled_df$sample_size)\nta_durations <- unique(eq3_ta_sampled_df$ta_dur)\n\ntassize_subsets <- expand.grid(sample_size = sample_sizes, ta_duration = ta_durations)\n\nexclude_columns <- c(\"simulation_run_id\", \"innovation_rate\", \"model_class_label\", \"sample_size\", \"ta_duration\")\n\nexperiment_names <- character(nrow(tassize_subsets))\n\n# Add experiment names to the tassize_subsets since I didn't do this in the original analysis\nfor( i in 1:nrow(tassize_subsets)) {\n experiment_names[i] <- paste(\"Sample Size: \", tassize_subsets[i, \"sample_size\"], \" Duration: \", tassize_subsets[i, \"ta_duration\"])\n}\n\ntassize_subsets_results <- data.frame()\ntassize_subset_roc <- NULL\ntassize_subset_roc_ssize_20 <- NULL\ntassize_subset_roc_ssize_10 <- NULL\ntassize_subset_model <- NULL\ntassize_subset_cm <- NULL\n\n# To create a smaller test dataset:\n# test_tasampled_indices <- createDataPartition(eq3_ta_sampled_df$two_class_label, p = 0.05, list=FALSE)\n# test_tasampled_df <- eq3_ta_sampled_df[test_tasampled_indices,]\n# switch the DF input to get_tassize_subset_ssize_tadur() back to eq3_ta_sampled_df for production\n\nfor( i in 1:nrow(tassize_subsets)) {\n exp_name <- experiment_names[i]\n df <- get_tassize_subset_ssize_tadur(eq3_ta_sampled_df, \n tassize_subsets[i, \"sample_size\"],\n tassize_subsets[i, \"ta_duration\"])\n print(sprintf(\"row %d: sample size: %d ta duration: %d numrows: %d\", i, tassize_subsets[i, \"sample_size\"], tassize_subsets[i, \"ta_duration\"], nrow(df)))\n \n #model <- train_randomforest(df, training_set_fraction, fit_grid, fit_control, exclude_columns)\n model <- train_gbm_classifier(df, training_set_fraction, \"two_class_label\", gbm_grid, training_control, exclude_columns, verbose=FALSE)\n \n tassize_subset_model[[exp_name]] <- model$tunedmodel\n \n # use the test data split by the train_randomforest function and calculate tuned model predictions\n # and then get the confusion matrix and fitting metrics\n predictions <- predict(model$tunedmodel, newdata=model$test_data)\n cm <- confusionMatrix(predictions, model$test_data$two_class_label)\n results <- get_parsed_binary_confusion_matrix_stats(cm)\n results$experiments <- experiment_names[i]\n results$elapsed <- model$elapsed\n results$sample_size <- tassize_subsets[i, \"sample_size\"]\n results$ta_duration <- tassize_subsets[i, \"ta_duration\"]\n results$experiments <- exp_name\n tassize_subset_cm[[exp_name]] <- cm\n \n roc <- calculate_roc_binary_classifier(model$tunedmodel, model$test_data, \"two_class_label\", experiment_names[i])\n tassize_subset_roc[[exp_name]] <- roc\n results$auc <- unlist(roc$auc@y.values)\n \n if(tassize_subsets[i, \"sample_size\"] == 20) {\n tassize_subset_roc_ssize_20[[exp_name]] <- roc\n }\n if(tassize_subsets[i, \"sample_size\"] == 10) {\n tassize_subset_roc_ssize_10[[exp_name]] <- roc\n }\n\n\n tassize_subsets_results <- rbind(tassize_subsets_results, results)\n\n \n}\n\n# sigh, now we have to remove NULL objects from lists that are tassize_subset of the whole analysis\ntassize_subset_roc_ssize_20 <- tassize_subset_roc_ssize_20[-(which(sapply(tassize_subset_roc_ssize_20,is.null),arr.ind=TRUE))]\ntassize_subset_roc_ssize_10 <- tassize_subset_roc_ssize_10[-(which(sapply(tassize_subset_roc_ssize_10,is.null),arr.ind=TRUE))]\n\n\n# we can now use plot_multiple_roc() to plot all the ROC curves on the same plot, etc. \n# as well as graph various of the metrics as they vary across sample size and TA duratio\n#plot_multiple_roc_from_list(tassize_subset_roc)\n\n############## Complete Processing and Save Results ##########3\n\n#save objects from the environment\n\nif(length(clargs) == 0) {\n \n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", \n filename = \"classification-ta-sampled-results-gbm.RData\")\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", \n filename = \"classification-ta-sampled-results-gbm-dfonly.RData\")\n \n \n} else {\n \n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", \n filename = \"classification-ta-sampled-results-gbm.RData\", args = clargs)\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-3\", \n filename = \"classification-ta-sampled-results-gbm-dfonly.RData\", args = clargs)\n}\n\nflog.info(\"Saving results of analysis to R environment snapshot: %s\", image_file, name='cl')\nsave(tassize_subsets_results, tassize_subset_cm, tassize_subset_model, \n tassize_subset_roc, tassize_subset_roc_ssize_10, tassize_subset_roc_ssize_20, file=image_file)\nflog.info(\"Saving just data frame of results of analysis to R environment snapshot: %s\", image_file_results, name='cl')\nsave(tassize_subsets_results, file=image_file_results)\n\n# End\nflog.info(\"Analysis complete\", name='cl')\n\n\n", "meta": {"hexsha": "53e328e20fc4cb57cc399c9f80fc8acd25683169", "size": 8217, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-3/tasampled-classification-analysis.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-3/tasampled-classification-analysis.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-3/tasampled-classification-analysis.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.8883495146, "max_line_length": 156, "alphanum_fraction": 0.7216745771, "num_tokens": 2158, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3036429565362066}} {"text": "model_updateleafflag <- function (cumulTT = 741.510096671757,\n leafNumber = 8.919453833361189,\n calendarMoments = c('Sowing'),\n calendarDates = c('2007/3/21'),\n calendarCumuls = c(0.0),\n currentdate = '2007/4/29',\n finalLeafNumber = 8.797582013199484,\n hasFlagLeafLiguleAppeared_t1 = 1,\n phase = 1.0){\n #'- Name: UpdateLeafFlag -Version: 1.0, -Time step: 1\n #'- Description:\n #' * Title: UpdateLeafFlag Model\n #' * Author: Pierre MARTRE\n #' * Reference: Modeling development phase in the \n #' Wheat Simulation Model SiriusQuality.\n #' See documentation at http://www1.clermont.inra.fr/siriusquality/?page_id=427\n #' * Institution: INRA Montpellier\n #' * Abstract: tells if flag leaf has appeared and update the calendar if so\n #' \t\n #'- inputs:\n #' * name: cumulTT\n #' ** description : cumul thermal times at current date\n #' ** variablecategory : auxiliary\n #' ** datatype : DOUBLE\n #' ** min : -200\n #' ** max : 10000\n #' ** default : 741.510096671757\n #' ** unit : \u00b0C d\n #' ** uri : some url\n #' ** inputtype : variable\n #' * name: leafNumber\n #' ** description : Actual number of phytomers\n #' ** variablecategory : state\n #' ** datatype : DOUBLE\n #' ** min : 0\n #' ** max : 25\n #' ** default : 8.919453833361189\n #' ** unit : leaf\n #' ** uri : some url\n #' ** inputtype : variable\n #' * name: calendarMoments\n #' ** description : List containing apparition of each stage\n #' ** variablecategory : state\n #' ** datatype : STRINGLIST\n #' ** default : ['Sowing']\n #' ** unit : \n #' ** inputtype : variable\n #' * name: calendarDates\n #' ** description : List containing the dates of the wheat developmental phases\n #' ** variablecategory : state\n #' ** datatype : DATELIST\n #' ** default : ['2007/3/21']\n #' ** unit : \n #' ** inputtype : variable\n #' * name: calendarCumuls\n #' ** description : list containing for each stage occured its cumulated thermal times\n #' ** variablecategory : state\n #' ** datatype : DOUBLELIST\n #' ** default : [0.0]\n #' ** unit : \u00b0C d\n #' ** inputtype : variable\n #' * name: currentdate\n #' ** description : current date\n #' ** variablecategory : auxiliary\n #' ** datatype : DATE\n #' ** default : 2007/4/29\n #' ** unit : \n #' ** uri : some url\n #' ** inputtype : variable\n #' * name: finalLeafNumber\n #' ** description : final leaf number\n #' ** variablecategory : state\n #' ** datatype : DOUBLE\n #' ** min : 0\n #' ** max : 10000\n #' ** default : 8.797582013199484\n #' ** unit : leaf\n #' ** uri : some url\n #' ** inputtype : variable\n #' * name: hasFlagLeafLiguleAppeared_t1\n #' ** description : true if flag leaf has appeared (leafnumber reached finalLeafNumber)\n #' ** variablecategory : state\n #' ** datatype : INT\n #' ** min : 0\n #' ** max : 1\n #' ** default : 1\n #' ** unit : \n #' ** uri : some url\n #' ** inputtype : variable\n #' * name: phase\n #' ** description : the name of the phase\n #' ** variablecategory : state\n #' ** datatype : DOUBLE\n #' ** min : 0\n #' ** max : 7\n #' ** default : 1\n #' ** unit : \n #' ** uri : some url\n #' ** inputtype : variable\n #'- outputs:\n #' * name: hasFlagLeafLiguleAppeared\n #' ** description : true if flag leaf has appeared (leafnumber reached finalLeafNumber)\n #' ** variablecategory : state\n #' ** datatype : INT\n #' ** min : 0\n #' ** max : 1\n #' ** unit : \n #' ** uri : some url\n #' * name: calendarMoments\n #' ** description : List containing apparition of each stage\n #' ** variablecategory : state\n #' ** datatype : STRINGLIST\n #' ** unit : \n #' * name: calendarDates\n #' ** description : List containing the dates of the wheat developmental phases\n #' ** variablecategory : state\n #' ** datatype : DATELIST\n #' ** unit : \n #' * name: calendarCumuls\n #' ** description : list containing for each stage occured its cumulated thermal times\n #' ** variablecategory : state\n #' ** datatype : DOUBLELIST\n #' ** unit : \u00b0C d\n hasFlagLeafLiguleAppeared <- 0\n if (phase >= 1.0 && phase < 4.0)\n {\n if (leafNumber > 0.0)\n {\n if (hasFlagLeafLiguleAppeared == 0 && (finalLeafNumber > 0.0 && leafNumber >= finalLeafNumber))\n {\n hasFlagLeafLiguleAppeared <- 1\n if (!('FlagLeafLiguleJustVisible' %in% calendarMoments))\n {\n calendarMoments <- c(calendarMoments, 'FlagLeafLiguleJustVisible')\n calendarCumuls <- c(calendarCumuls, cumulTT)\n calendarDates <- c(calendarDates, currentdate)\n }\n }\n }\n }\n return (list (\"hasFlagLeafLiguleAppeared\" = hasFlagLeafLiguleAppeared,\"calendarMoments\" = calendarMoments,\"calendarDates\" = calendarDates,\"calendarCumuls\" = calendarCumuls))\n}", "meta": {"hexsha": "5b3a16d1f37f6f2d9783feb1d75ff8678eefd528", "size": 7492, "ext": "r", "lang": "R", "max_stars_repo_path": "src/r/SQ_Wheat_Phenology/Updateleafflag.r", "max_stars_repo_name": "Crop2ML-Catalog/SQ_Wheat_Phenology", "max_stars_repo_head_hexsha": "8e9ec229e5f0754d0f4b9d79ac96a084d75dde67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-06-21T18:58:04.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-29T21:32:28.000Z", "max_issues_repo_path": "src/r/SQ_Wheat_Phenology/Updateleafflag.r", "max_issues_repo_name": "Crop2ML-Catalog/SQ_Wheat_Phenology", "max_issues_repo_head_hexsha": "8e9ec229e5f0754d0f4b9d79ac96a084d75dde67", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 27, "max_issues_repo_issues_event_min_datetime": "2018-12-04T15:35:44.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T08:25:03.000Z", "max_forks_repo_path": "src/r/SQ_Wheat_Phenology/Updateleafflag.r", "max_forks_repo_name": "Crop2ML-Catalog/SQ_Wheat_Phenology", "max_forks_repo_head_hexsha": "8e9ec229e5f0754d0f4b9d79ac96a084d75dde67", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-04-20T02:25:22.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-04T07:52:35.000Z", "avg_line_length": 52.7605633803, "max_line_length": 177, "alphanum_fraction": 0.3796049119, "num_tokens": 1501, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376236, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.3036429565362066}} {"text": "#################################################\n#\n# make_figures.r 16 FEB 2017\n#\n# Generates figures 2-5 in \"Priority for the\n# worse off and the Social Cost of Carbon\".\n#\n#################################################\n\n# Directories\nscriptdir <- \".\"\ndatadir <- \"../../results\"\nplotdir <- \"../../plots\"\n\n# Libraries\nlibrary(RColorBrewer)\nlibrary(lattice)\nlibrary(fields)\nsource(paste(scriptdir, \"/put_fig_letter.r\", sep=\"\"))\nsource(paste(scriptdir, \"/oat_plot_functions.r\", sep=\"\"))\nsource(paste(scriptdir, \"/contour_plot_functions.r\", sep=\"\"))\n\n# Load the data\ninfile <- paste(datadir, \"/output-scc.csv\", sep=\"\")\nmy.data <- read.csv(infile, header=T)\n\n# List of the normalization regions of interest\nnorm.regions <- c(\"Africa\", \"US\", \"Global\", \"World-Fair\")\n\n# Generate the figures\nsource(paste(scriptdir, \"/make_figure_2.r\", sep=\"\"))\nsource(paste(scriptdir, \"/make_figure_3.r\", sep=\"\"))\nsource(paste(scriptdir, \"/make_figure_4.r\", sep=\"\"))\nsource(paste(scriptdir, \"/make_figure_5.r\", sep=\"\"))\n\n# Done!", "meta": {"hexsha": "aca6b6cca281ada1bdc579adb4d624718fb0bdaf", "size": 1011, "ext": "r", "lang": "R", "max_stars_repo_path": "src/plot_code/make_figures.r", "max_stars_repo_name": "davidanthoff/paper-2017-sccprioritarianism", "max_stars_repo_head_hexsha": "a998d35b79a27d4a08661000ae47c9ccf64a80ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-06-22T14:22:57.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-22T14:22:57.000Z", "max_issues_repo_path": "src/plot_code/make_figures.r", "max_issues_repo_name": "davidanthoff/paper-2017-sccprioritarianism", "max_issues_repo_head_hexsha": "a998d35b79a27d4a08661000ae47c9ccf64a80ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/plot_code/make_figures.r", "max_forks_repo_name": "davidanthoff/paper-2017-sccprioritarianism", "max_forks_repo_head_hexsha": "a998d35b79a27d4a08661000ae47c9ccf64a80ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.0833333333, "max_line_length": 61, "alphanum_fraction": 0.6221562809, "num_tokens": 258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.5156199157230156, "lm_q1q2_score": 0.30364295653620654}} {"text": "library(stm)\nlibrary(lubridate)\n\nmd <- data.frame( timestamp = ymd_hms( timestamps ), source = sources, author = authors, text = texts )\n\nprocessed <- textProcessor( documents, metadata = md, stem = FALSE, striphtml = TRUE, language = NA, customstopwords = stopwords )\n\n## todo: set upper and lower thresholds\nout <- prepDocuments(processed$documents, processed$vocab, processed$meta, lower.thresh= 20, verbose = FALSE )\n\n## upper.thresh = 100\n\nstm <- stm( documents = out$documents, vocab = out$vocab, K = k, data = out$meta,\n max.em.its = 75, init.type = \"Spectral\", seed = 1, verbose = FALSE )\n\nsave( stm, out, file = paste( saveto, '/stm.rdata', sep = '' ) )\n\nprint( plot( stm, \"summary\" ) )\n", "meta": {"hexsha": "42995211177b54284b7ecf10c0bca964fc52d626", "size": 698, "ext": "r", "lang": "R", "max_stars_repo_path": "hybra/plugin/stm/stm.r", "max_stars_repo_name": "HIIT/hybra-core", "max_stars_repo_head_hexsha": "39257fe35bbafb8d1d7ab52a0701ec50db80b00f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-02-19T11:42:33.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-15T11:21:07.000Z", "max_issues_repo_path": "hybra/plugin/stm/stm.r", "max_issues_repo_name": "HIIT/hybra-core", "max_issues_repo_head_hexsha": "39257fe35bbafb8d1d7ab52a0701ec50db80b00f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 109, "max_issues_repo_issues_event_min_datetime": "2016-12-02T11:36:34.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-27T12:54:06.000Z", "max_forks_repo_path": "hybra/plugin/stm/stm.r", "max_forks_repo_name": "HIIT/hybra-core", "max_forks_repo_head_hexsha": "39257fe35bbafb8d1d7ab52a0701ec50db80b00f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2017-06-22T08:15:51.000Z", "max_forks_repo_forks_event_max_datetime": "2017-06-22T08:15:51.000Z", "avg_line_length": 36.7368421053, "max_line_length": 130, "alphanum_fraction": 0.6819484241, "num_tokens": 197, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.3035959137502773}} {"text": "# James Rekow\r\n\r\n# mdr = Mean Diss Ratio\r\n# mmd = mean mean dissimilarity (i.e. of subsets of a cohort with a given size)\r\n# ^^ didn't show any slope. No useful ratios apparent\r\n\r\n# SRSSE = scaled residual sse\r\n\r\n\r\ncomputeMMD = function(abdList){\r\n \r\n source(\"DOCProcedure.r\")\r\n source(\"computeUnivWithinSSTEST.r\")\r\n \r\n subgroupSizeVec = floor(length(abdList) / {c(10, 8, 6, 4, 2)})\r\n \r\n computeSubgroupMMD = function(subgroupSize){\r\n \r\n computeSubgroupSampleMMD = function(zz = NULL){\r\n \r\n subgroupAbdList = sample(abdList, subgroupSize)\r\n subgroupSRSSE = computeUnivWithinSSTEST(abdList = subgroupAbdList)\r\n \r\n return(subgroupSRSSE)\r\n \r\n } # end computeSubgroupSampleMMD\r\n \r\n sampleMMDVec = replicate(10, computeSubgroupSampleMMD())\r\n \r\n subgroupMMD = mean(sampleMMDVec)\r\n \r\n return(subgroupMMD)\r\n \r\n } # end computeSubgroupMMD function\r\n \r\n mmd = sapply(subgroupSizeVec, computeSubgroupMMD)\r\n \r\n return(mmd)\r\n \r\n} # end computeMMD function\r\n", "meta": {"hexsha": "37aa138384b99fafa2beeb2c18e767cc3af8458a", "size": 1025, "ext": "r", "lang": "R", "max_stars_repo_path": "test.r", "max_stars_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_stars_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "test.r", "max_issues_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_issues_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test.r", "max_forks_repo_name": "JamesRekow/Ben_Dalziel_Contract_Work_Files_9_29_2017", "max_forks_repo_head_hexsha": "b7a3b167650471d0ae5356d1d2a036bde771778c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0, "max_line_length": 81, "alphanum_fraction": 0.6546341463, "num_tokens": 296, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850278370111, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.30359155839567564}} {"text": "\n# script to make the KM graphs for the MIDCAB paper.\n# graphs made for mets, dm, obese ...\n# maybe other graphs needed.\n# prepare splines for bmi and then decide to \n\n\nlibrary(easypackages)\nlibraries(c(\"survival\",\"tidyverse\",\n\t\"rms\",\"Hmisc\",\"survminer\",\"haven\", \"readxl\"))\n\ndf <- read_stata(\"D:/MIDCAB_DM/dataset2.dta\")\n\nglimpse(df)\n\n# now to create the KM curves for the paper.\n\n# overall survival.\n\ns <- survfit(Surv(fupyears, died_total) ~ 1, data = df)\n\ntiff(\"D:/MIDCAB_DM/graphs/overall.tiff\",\n\theight = 6, width = 8, units = \"in\",\n\tres = 900)\n\nggsurvplot(s,\n\txlim = c(0,20),\n\tsurv.scale = \"percent\",\n\tbreak.x.by = 5,\n\tcensor.size = 0,\n\trisk.table = T)\n\ndev.off()\n\n# plot according to metabolic syndrome.\n\nms <- survfit(Surv(fupyears, died_total) ~ factor(mets),\n\tdata = df)\n\n\ntiff(\"D:/MIDCAB_DM/graphs/km_mets.tiff\",\n\theight = 6, width = 8, units = \"in\",\n\tres = 900)\n\nggsurvplot(ms,\n\txlim = c(0,20),\n\tsurv.scale = \"percent\",\n\tbreak.x.by = 5,\n\tcensor.size = 0,\n\trisk.table = T,\n\tlegend.labs = c(\"MET-ve\",\"MET+ve\"),\n\tconf.int = T)\n\ndev.off()\n\n# according to diabetes.\n\nsd <- survfit(Surv(fupyears, died_total) ~ diabetes, \n\tdata = df)\n\n\ntiff(\"D:/MIDCAB_DM/graphs/km_dm.tiff\",\n\theight = 6, width = 8, units = \"in\",\n\tres = 900)\n\n\nggsurvplot(sd,\n\txlim = c(0,20),\n\tsurv.scale = \"percent\",\n\tbreak.x.by = 5,\n\tcensor.size = 0,\n\trisk.table = T,\n\tlegend.labs = c(\"DM-ve\",\"DM+ve\"),\n\tconf.int = T)\n\ndev.off()\n\n\n# according to obesity\n\nso <- survfit(Surv(fupyears, died_total) ~ obese,\n\tdata = df)\n\n\ntiff(\"D:/MIDCAB_DM/graphs/km_obese.tiff\",\n\theight = 6, width = 8, units = \"in\",\n\tres = 900)\n\n\nggsurvplot(so,\n\txlim = c(0,20),\n\tsurv.scale = \"percent\",\n\tbreak.x.by = 5,\n\tcensor.size = 0,\n\trisk.table = T,\n\tlegend.labs = c(\"Not Obese\",\"Obese\"),\n\tconf.int = T)\n\ndev.off()\n\n# for now these graphs are good.\n# will make more graphs depending upon the plan.\n\n# get the dataset for plotting bmi as a spline.\n\nd <- read_excel(\"D:/MIDCAB_DM/bmi_plot.xlsx\", sheet = 1)\n\nglimpse(d)\n\nplot(x = d$bmi, y = d$hr, type = \"l\", col = \"red\")\nlines(x = d$bmi, y = d$lb, lty = 2, col = \"blue\")\nlines(x = d$bmi, y = d$ub, lty = 2, col = \"blue\")\n\n", "meta": {"hexsha": "15c2d0a729e278d3caec993321e817872266aa2d", "size": 2116, "ext": "r", "lang": "R", "max_stars_repo_path": "graphs.r", "max_stars_repo_name": "svd09/MIDCAB-DM", "max_stars_repo_head_hexsha": "cd109c62dd858076bb4f2f1547a9dfaddf861743", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "graphs.r", "max_issues_repo_name": "svd09/MIDCAB-DM", "max_issues_repo_head_hexsha": "cd109c62dd858076bb4f2f1547a9dfaddf861743", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graphs.r", "max_forks_repo_name": "svd09/MIDCAB-DM", "max_forks_repo_head_hexsha": "cd109c62dd858076bb4f2f1547a9dfaddf861743", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5614035088, "max_line_length": 56, "alphanum_fraction": 0.6361058601, "num_tokens": 738, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.303541515551812}} {"text": "## initialise_BASGRA_xxx.R ##\n\n## 1. GENERAL INITIALISATION ##\nsource(\"scripts/initialise_BASGRA_general.R\")\n\n## 2. SITE CONDITIONS ##\n\n# run model for this period \n# Scott weather data is 2010-1 to 2017-318 (14 Nov)\nyear_start <- as.integer(2011)\n# doy_start <- as.integer(244) # 1 Sept\ndoy_start <- as.integer(121) # 1 May\nyear_stop <- as.integer(2017)\n# doy_stop <- as.integer(151) # 31 May\ndoy_stop <- as.integer(120) # 30 April\n\n# calculate sim length --- WARNING this needs to fit within weather data!\n# NDAYS <- as.integer(1460) \nNDAYS <- as.integer(difftime(\n as.Date(doy_stop -1, origin=paste(year_stop ,1,1,sep=\"-\")), \n as.Date(doy_start-1, origin=paste(year_start,1,1,sep=\"-\")),\n units=\"days\")) + 1L\n\n# use data in this period (replaced by data weights)\n# year_start_data <- as.integer(2012)\n# doy_start_data <- as.integer(1) # 1 Jan\n# year_stop_data <- as.integer(2017)\n# doy_stop_data <- as.integer(151) # 31 May\n\nfile_weather <- paste(scenario, \"/weather_Lincoln.txt\", sep=\"\")\n\nfile_params <- paste(parameter_location, \"/parameters_All.txt\", sep=\"\") # can contain multiple columns\nparcol <- 3 # which one are we going to use? (row names are ignored)\n\n# read harvest days (Simon) which are set up in make_weather1.r\nfile_harvest <- paste(scenario, \"/harvest_Lincoln_0.txt\", sep=\"\")\ndays_harvest <- as.matrix(read.table(file=file_harvest, sep=\"\\t\"))\n\n## 3. CREATE HARVEST CALENDAR AND WEATHER INPUT ##\ndays_harvest <- as.integer(days_harvest)\nmatrix_weather <- read_weather_WG(year_start,doy_start,NDAYS,file_weather) # function in initialise_BASGRA_general.R\n\n## 4. CREATE VECTOR \"PARAMS\" ##\ndf_params <- read.table(file_params,header=T,sep=\"\\t\",row.names=1)\nparams <- df_params[,parcol]\n\n## 5. CREATE EMPTY MATRIX y ##\ny <- matrix(0,NDAYS,NOUT)\n\n\n", "meta": {"hexsha": "8799c778a981fdd6be45dd034e792c8feb63217b", "size": 1876, "ext": "r", "lang": "R", "max_stars_repo_path": "run_mean/initialise_BASGRA_Lincoln_0.r", "max_stars_repo_name": "woodwards/basgra_nz", "max_stars_repo_head_hexsha": "d34ab8b4f2829cf21d8689af0c8d6561d79b591b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-01-07T00:44:06.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-14T06:37:42.000Z", "max_issues_repo_path": "run_mean/initialise_BASGRA_Lincoln_0.r", "max_issues_repo_name": "woodwards/basgra_nz", "max_issues_repo_head_hexsha": "d34ab8b4f2829cf21d8689af0c8d6561d79b591b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2018-11-23T19:20:23.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-23T21:19:35.000Z", "max_forks_repo_path": "run_mean/initialise_BASGRA_Lincoln_0.r", "max_forks_repo_name": "woodwards/basgra_nz", "max_forks_repo_head_hexsha": "d34ab8b4f2829cf21d8689af0c8d6561d79b591b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-10-25T15:17:37.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-25T15:17:37.000Z", "avg_line_length": 36.7843137255, "max_line_length": 116, "alphanum_fraction": 0.6721748401, "num_tokens": 548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.303541515551812}} {"text": "#!/usr/bin/env Rscript\n# Reduce dimensions for a SCE\n# Combiz Khozoie \n\n# ____________________________________________________________________________\n# Initialization ####\n\noptions(mc.cores = future::availableCores())\n\n## ............................................................................\n## Load packages ####\nlibrary(argparse)\nlibrary(scFlow)\nlibrary(parallel)\nlibrary(SingleCellExperiment) # due to monocle3 missing namespace::\n\n## ............................................................................\n## Parse command-line arguments ####\n\n# create parser object\nparser <- ArgumentParser()\n\n# specify options\nrequired <- parser$add_argument_group(\"Required\", \"required arguments\")\noptional <- parser$add_argument_group(\"Optional\", \"required arguments\")\n\nrequired$add_argument(\n \"--sce_path\",\n help = \"-path to the SingleCellExperiment\",\n metavar = \"dir\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--input_reduced_dim\",\n help = \"input reducedDim to use for further dim reds\",\n metavar = \"PCA,Liger\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--reduction_methods\",\n help = \"methods to use for dimensionality reduction\",\n metavar = \"PCA,tSNE,UMAP,UMAP3D\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--vars_to_regress_out\",\n help = \"variables to regress out before finding singlets\",\n metavar = \"nCount_RNA,pc_mito\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--pca_dims\",\n type = \"integer\", \n default = 20,\n help = \"the number of PCA dimensions used\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--n_neighbors\",\n type = \"integer\", \n default = 30,\n help = \"the number of nearest neighbors\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--n_components\",\n type = \"integer\", \n default = 2,\n help = \"the number of UMAP dimensions (2 or 3)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--init\",\n help = \"type of initialization for UMAP coordinates (uwot)\",\n metavar = \"pca\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--metric\",\n help = \"type of distance metric for nearest neighbours (uwot)\",\n metavar = \"euclidean\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--n_epochs\",\n type = \"integer\", \n default = 500,\n help = \"number of epochs for optimization of embeddings (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--learning_rate\",\n type = \"double\", \n default = 1.0,\n help = \"initial learning rate used in optimization (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--min_dist\",\n type = \"double\", \n default = 0.3,\n help = \"effective minimum distance between embedded points (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--spread\",\n type = \"double\", \n default = 1.0,\n help = \"effective scale of embedded points (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--set_op_mix_ratio\",\n type = \"double\", \n default = 1.0,\n help = \"interpolation between fuzzy union and intersection set operation (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--local_connectivity\",\n type = \"integer\", \n default = 1,\n help = \"number of nearest neighbours assumed connected locally (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--repulsion_strength\",\n type = \"double\", \n default = 1.0,\n help = \"weighting applied to negative samples in optimization (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--negative_sample_rate\",\n type = \"double\", \n default = 5.0,\n help = \"number of negative edge samples per positive edge (uwot)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--fast_sgd\",\n help = \"faster but less reproducible UMAP (uwot) (lgl)\",\n metavar = \"FALSE\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--dims\",\n type = \"integer\", \n default = 30,\n help = \"the number of dimensions to output (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--initial_dims\",\n type = \"integer\", \n default = 50,\n help = \"the number of dimensions retained in the PCA init (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--perplexity\",\n type = \"integer\", \n default = 30,\n help = \"perplexity parameter (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--theta\",\n type = \"double\", \n default = 0.5,\n help = \"speed / accuracy trade-off (increase for less accuracy) (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--max_iter\",\n type = \"integer\", \n default = 1000,\n help = \"number of iterations (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\nrequired$add_argument(\n \"--pca_center\",\n help = \"should data be centered before pca (rtsne) (lgl)\",\n metavar = \"TRUE\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--pca_scale\",\n help = \"should data be scaled before pca (rtsne) (lgl)\",\n metavar = \"FALSE\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--normalize\",\n help = \"should data be normalized before distance calculations (rtsne) (lgl)\",\n metavar = \"TRUE\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--stop_lying_iter\",\n type = \"integer\", \n default = 250,\n help = \"iteration after which perplexities are no longer exaggerated (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--mom_switch_iter\",\n type = \"integer\", \n default = 250,\n help = \"iteration after which final momentum is used (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--momentum\",\n type = \"double\", \n default = 0.5,\n help = \"momentum used in the first part of the optimization (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--final_momentum\",\n type = \"double\", \n default = 0.8,\n help = \"momentum used in the final part of the optimization (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--eta\",\n type = \"double\", \n default = 200.0,\n help = \"learning rate (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\nrequired$add_argument(\n \"--exaggeration_factor\",\n type = \"double\", \n default = 12.0,\n help = \"Exaggeration factor used in the first part of the optimization (rtsne)\",\n metavar = \"N\", \n required = TRUE\n)\n\n### . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..\n### Pre-process args ####\n\nargs <- parser$parse_args()\nargs$fast_sgd <- as.logical(args$fast_sgd)\nargs$input_reduced_dim <- strsplit(args$input_reduced_dim, \",\")[[1]]\nargs$reduction_methods <- strsplit(args$reduction_methods, \",\")[[1]]\nargs$vars_to_regress_out <- strsplit(args$vars_to_regress_out, \",\")[[1]]\nargs <- purrr::map(args, function(x) {\n if (length(x) == 1) {\n if (toupper(x) == \"TRUE\") return(TRUE)\n if (toupper(x) == \"FALSE\") return(FALSE)\n if (toupper(x) == \"NULL\") return(NULL)\n }\n return(x)\n})\n\n## ............................................................................\n## Start ####\n\nsce <- read_sce(args$sce_path, read_metadata = TRUE)\n\nsce <- reduce_dims_sce(\n sce,\n input_reduced_dim = args$input_reduced_dim,\n reduction_methods = args$reduction_methods,\n vars_to_regress_out = args$vars_to_regress_out,\n pca_dims = args$pca_dims,\n n_neighbors = args$n_neighbors,\n n_components = args$n_components,\n init = args$init,\n metric = args$metric,\n n_epochs = args$n_epochs,\n learning_rate = args$learning_rate,\n min_dist = args$min_dist,\n spread = args$spread,\n set_op_mix_ratio = args$set_op_mix_ratio,\n local_connectivity = args$local_connectivity,\n repulsion_strength = args$repulsion_strength,\n negative_sample_rate = args$negative_sample_rate,\n fast_sgd = args$fast_sgd,\n dims = args$dims,\n initial_dims = args$initial_dims,\n perplexity = args$perplexity,\n theta = args$theta,\n stop_lying_iter = args$stop_lying_iter,\n mom_switch_iter = args$mom_switch_iter,\n max_iter = args$max_iter,\n pca_center = args$pca_center,\n pca_scale = args$pca_scale,\n pca_normalize = args$pca_normalize,\n momentum = args$momentum,\n final_momentum = args$final_momentum,\n eta = args$eta,\n exaggeration_factor = args$exaggeration_factor\n )\n\n## ............................................................................\n## Save Outputs ####\n\n# Save SingleCellExperiment\nwrite_sce(\n sce = sce,\n folder_path = file.path(getwd(), \"reddim_sce\"),\n write_metadata = TRUE\n)\n\n## ............................................................................\n## Clean up ####\n\n# Clear biomart cache\n", "meta": {"hexsha": "4d0f9af7c2747482ab0118b68496b3e29c8a7945", "size": 8916, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/scflow_reduce_dims.r", "max_stars_repo_name": "jordeu/scflow", "max_stars_repo_head_hexsha": "e1c0d5096e2e2df8ef25e6c92cd0b4c729ddb6ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bin/scflow_reduce_dims.r", "max_issues_repo_name": "jordeu/scflow", "max_issues_repo_head_hexsha": "e1c0d5096e2e2df8ef25e6c92cd0b4c729ddb6ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/scflow_reduce_dims.r", "max_forks_repo_name": "jordeu/scflow", "max_forks_repo_head_hexsha": "e1c0d5096e2e2df8ef25e6c92cd0b4c729ddb6ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2022-03-10T15:36:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T15:36:03.000Z", "avg_line_length": 24.4273972603, "max_line_length": 83, "alphanum_fraction": 0.6095782862, "num_tokens": 2319, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.30354151555181197}} {"text": "\ninput_folder = \"\"\n\n\nlibrary(tm)\ncorp = VCorpus(DirSource(input_folder, encoding = \"UTF-8\", mode = \"text\"))\ndtm1 = DocumentTermMatrix(corp, control=list(bounds = list(global = c(20,Inf))))\ndtm1_forLDA = DocumentTermMatrix(corp, control=list(bounds = list(global = c(3,Inf))))\nrowTotals = apply(dtm1_forLDA , 1, sum)\ndtm1_forLDA = dtm1_forLDA[rowTotals> 0, ]\ndtm1_forDBSCAN = as.matrix(dtm1)\n\n# l1 normalization\nfor(j in 1:dim(dtm1_forDBSCAN)[2]) dtm1_forDBSCAN[,j] = dtm1_forDBSCAN[,j]/sum(dtm1_forDBSCAN[,j])\n\n\n### DBSCAN-Martingale\nlibrary(dbscan)\nminpts = 5\nT = 5\n\n\n### generate 10 random numbers from the uniform distribution in [0, 0.1]\nrandom.epsilon = runif(T, min=0, max = 0.1)\nrandom.epsilon = sort(random.epsilon, decreasing = FALSE)\ndbscan.results.all = matrix(0, nrow = dim(dtm1)[1], ncol=T)\n\nfor(j in 1:T) dbscan.results.all[,j] = dbscan(dtm1_forDBSCAN, random.epsilon[j], minpts)$cluster\n# giant cluster removal\nfor(j in 1:T) {\n for(i in 1:length(dbscan.results.all[,j])) dbscan.results.all[i,j]= dbscan.results.all[i,j] - 1\n for(i in 1:length(dbscan.results.all[,j])) if(dbscan.results.all[i,j]==-1) dbscan.results.all[i,j]=0\n}\nprincipal.clustering = dbscan.results.all[,1]\nfor(j in 1:T) {\n if((principal.clustering%*%dbscan.results.all[,j])[1,1]==0) {\n b = max(principal.clustering)\n for(i in 1:length(dbscan.results.all[,j])) if(dbscan.results.all[i,j]!=0) dbscan.results.all[i,j] = dbscan.results.all[i,j] + b\n principal.clustering = principal.clustering + dbscan.results.all[,j]\n } else {\n h = c()\n clh = c()\n for(i in 1:length(principal.clustering)) {\n h = c(h,0)\n clh = c(clh,0)\n }\n for(i in 1:length(principal.clustering)) if(principal.clustering[i]==0 && dbscan.results.all[i,j] != 0) h[i]=dbscan.results.all[i,j]\n b = max(principal.clustering)\n u = 0\n if(max(h)>0) {\n for(j in 1:max(h)) if(sum(h==j)>=minpts) {\n u = u + 1\n clh[which(h==j)]= u\n }\n for(i in 1:length(principal.clustering)) if(clh[i]!=0) clh[i] = clh[i] + b\n principal.clustering = principal.clustering + clh\n }\n }\n}\nnum_of_topics = max(principal.clustering)\n\n### LDA using the num_of_topics\nlibrary(topicmodels)\n\nk = if(num_of_topics<2) 2 else num_of_topics\nLDA.results = LDA(dtm1_forLDA, k)\nLDA.clustering.vector = rep(0, dtm1_forLDA$nrow)\nfor(i in 1:dtm1_forLDA$nrow) LDA.clustering.vector[i] = which.max(LDA.results@gamma[i,])\n\n\n### assign the documents in each topic\ntopics.list.IDs = vector(\"list\", k+1)\nnames(topics.list.IDs) = as.character(0:k)\n\nfor(i in 1:k) {\n topics.list.IDs[[i+1]] = vector(\"list\", 3)\n names(topics.list.IDs[[i+1]]) = c(\"labels\", \"scores\", \"articles\")\n topics.list.IDs[[i+1]][[1]] = paste(LDA.results@terms[sort(LDA.results@beta[i,], decreasing = TRUE, index.return = TRUE)$ix[1:8]], collapse = \" \")\n topics.list.IDs[[i+1]][[2]] = abs(sort(LDA.results@beta[i,], decreasing = TRUE)[1:8])\n topics.list.IDs[[i+1]][[3]] = dtm1_forLDA$dimnames$Docs[which(LDA.clustering.vector==i)]\n}\n\n\n### create a collection of \"noise\"-empty documents\nif(length(which(rowTotals==0))>0) {\n topics.list.IDs[[1]] = vector(\"list\", 2)\n names(topics.list.IDs[[1]]) = c(\"labels\", \"articles\")\n topics.list.IDs[[1]][[1]] = \"noise\"\n topics.list.IDs[[1]][[2]] = dtm1$dimnames$Docs[which(rowTotals==0)]\n} else topics.list.IDs = topics.list.IDs[-1]\n\n\n### write the results to a JSON file\nlibrary(rjson)\nexportJSON = toJSON(topics.list.IDs)\n\nwrite(exportJSON, file = \"topics.json\")\n", "meta": {"hexsha": "3fb905bc082c85567156f2e6869922c97519558f", "size": 3526, "ext": "r", "lang": "R", "max_stars_repo_path": "topic_detection_module.r", "max_stars_repo_name": "MKLab-ITI/topic-detection", "max_stars_repo_head_hexsha": "a5137430d505e25da7b5bb104aa31f5c87c49dfc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 11, "max_stars_repo_stars_event_min_datetime": "2016-03-04T16:14:07.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-09T11:54:35.000Z", "max_issues_repo_path": "topic_detection_module.r", "max_issues_repo_name": "MKLab-ITI/topic-detection", "max_issues_repo_head_hexsha": "a5137430d505e25da7b5bb104aa31f5c87c49dfc", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "topic_detection_module.r", "max_forks_repo_name": "MKLab-ITI/topic-detection", "max_forks_repo_head_hexsha": "a5137430d505e25da7b5bb104aa31f5c87c49dfc", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2016-09-01T05:37:02.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-07T03:16:15.000Z", "avg_line_length": 35.9795918367, "max_line_length": 148, "alphanum_fraction": 0.6667612025, "num_tokens": 1178, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.30354151555181197}} {"text": "#!/usr/bin/Rscript\nsource(\"../common.r\")\n\n#######################################################\n#\n# Proposal parameters\n#\n#######################################################\n\nproposeParameters <- list(\n Theta=0.0003,\n Rho=0.001,\n DeltaD=0.001,\n DeltaS=0.009,\n Lambda=0.008,\n LambdaRight=0.008,\n LambdaDisp=0.015\n )\n\n#######################################################\n#\n# Initial values\n#\n#######################################################\n\n\nstart_vals <- list(\n ptrans = 0.00396/3,\n rho = 1,\n deltad = 0.0285,\n deltas = 0.269,\n lambda = 0.27,\n lambda_right = 0.27,\n lambda_disp = 1,\n len = 60,\n nu = 0.0645\n )\n\n\n#######################################################\n#\n# Various parameters\n#\n#######################################################\n\n# Number of random starting points for the grid search\ngrid_iter <- getArgument(\"GRID_ITER\", as.integer)\n# Burn in period\nburn_in <- getArgument(\"BURN_IN\", as.integer)\n# Adjust proposal variance parameters iterations\nadjust_iter <- getArgument(\"ADJUST_ITER\", as.integer)\n# Iterations\niterations <- getArgument(\"ITERATIONS\", as.integer)\n\n# Taking the 5p, 3p, or both ends of the seqs\ntermini <- getArgument(\"TERMINI\")\n# Geom instead of neg Bin\nfix_disp <- getArgument(\"FIX_DISP\", as.logical)\n# The overhangs are the same on both sides\nsame_overhangs <- getArgument(\"SAME_OVERHANGS\", as.logical)\n\n# Set 1 at 5' end and 0 at 3' end or else estimates it with GAM\nfix_nu <- getArgument(\"FIX_NU\", as.logical)\n# Single stranded protocol C>T at both sides\nds_protocol <- getArgument(\"DS_PROTOCOL\", as.logical)\n# How long sequence to use from each side\nsub_length <- getArgument(\"SUB_LENGTH\", as.integer)\n\n# Absolute path to the dataset\npath_to_dat <- getArgument(\"PATH_TO_DAT\")\n# These options control the volume of the output\nverbose <- getArgument(\"VERBOSE\", as.logical)\nquiet <- getArgument(\"QUIET\", as.logical)\n\n# Fix the transition and transversion ratio and acgt frequencies are equal\njukes_cantor <- getArgument(\"JUKES_CANTOR\", as.logical)\nuse_raw_nick_freq <- getArgument(\"USE_RAW_NICK_FREQ\", as.logical)\nuse_bw_theme <- getArgument(\"USE_BW_THEME\", as.logical)\n\n\n#######################################################\n#\n# Run the program\n#\n#######################################################\n\nsource(\"main.r\")\n", "meta": {"hexsha": "db09a2bcd1740d56ab1fb227fff2ebdbd84d9d88", "size": 2731, "ext": "r", "lang": "R", "max_stars_repo_path": "mapdamage/r/stats/runGeneral.r", "max_stars_repo_name": "ginolhac/mapDamage", "max_stars_repo_head_hexsha": "036806b434945594c2e642d03461c64e981507de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 32, "max_stars_repo_stars_event_min_datetime": "2015-03-11T21:29:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-25T16:01:52.000Z", "max_issues_repo_path": "mapdamage/r/stats/runGeneral.r", "max_issues_repo_name": "ginolhac/mapDamage", "max_issues_repo_head_hexsha": "036806b434945594c2e642d03461c64e981507de", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 39, "max_issues_repo_issues_event_min_datetime": "2015-02-06T23:42:18.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-09T12:33:35.000Z", "max_forks_repo_path": "mapdamage/r/stats/runGeneral.r", "max_forks_repo_name": "ginolhac/mapDamage", "max_forks_repo_head_hexsha": "036806b434945594c2e642d03461c64e981507de", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2016-07-04T11:04:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-20T21:16:47.000Z", "avg_line_length": 31.0340909091, "max_line_length": 74, "alphanum_fraction": 0.4902965947, "num_tokens": 560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6113819874558603, "lm_q2_score": 0.49609382947091946, "lm_q1q2_score": 0.30330283142651937}} {"text": "#' Graph rewiring algorithms\n#'\n#' Changes the structure of a graph by altering ties.\n#'\n#' @inheritParams rgraph_ws\n#' @templateVar undirected TRUE\n#' @template graph_template\n#' @param p Either a [0,1] vector with rewiring probabilities (\\code{algorithm=\"endpoints\"}),\n#' or an integer vector with number of iterations (\\code{algorithm=\"swap\"}).\n#' @param copy.first Logical scalar. When \\code{TRUE} and \\code{graph} is dynamic uses\n#' the first slice as a baseline for the rest of slices (see details).\n#' @param pr.change Numeric scalar. Probability ([0,1]) of doing a rewire (see details).\n#' @param algorithm Character scalar. Either \\code{\"swap\"}, \\code{\"endpoints\"}, or \\code{\"qap\"}\n#' (see \\code{\\link{rewire_qap}}).\n#' @param althexagons Logical scalar. When \\code{TRUE} uses the compact alternating\n#' hexagons algorithm (currently ignored [on development]).\n#' @details\n#' The algorithm \\code{\"qap\"} is described in \\code{\\link{rewire_qap}}, and only\n#' uses \\code{graph} from the arguments (since it is simply relabelling the graph).\n#'\n#'\n#' In the case of \"swap\" and \"endpoints\", both algorithms are implemented\n#' sequentially, this is, edge-wise checking self edges and multiple edges over\n#' the changing graph; in other words, at step\n#' \\eqn{m} (in which either a new endpoint or edge is chosen, depending on the algorithm),\n#' the algorithms verify whether the proposed change creates either multiple edges\n#' or self edges using the resulting graph at step \\eqn{m-1}.\n#'\n#' The main difference between the two algorithms is that the \\code{\"swap\"} algorithm\n#' preserves the degree sequence of the graph and \\code{\"endpoints\"} does not.\n#' The \\code{\"swap\"} algorithm is specially useful to asses the non-randomness of\n#' a graph's structural properties, furthermore it is this algorithm the one used\n#' in the \\code{\\link{struct_test}} routine implemented in \\pkg{netdiffuseR}.\n#'\n#' Rewiring assumes a weighted network, hence \\eqn{G(i,j) = k = G(i',j')},\n#' where \\eqn{i',j'} are the new end points of the edge and \\eqn{k} may not be equal\n#' to one.\n#'\n#' In the case of dynamic graphs, when \\code{copy.first=TRUE}, after rewiring the\n#' first slice--\\eqn{t=1}--the rest of slices are generated by rewiring the rewired\n#' version of the first slice. Formally:\n#'\n#' \\deqn{%\n#' G(t)' = \\left\\{\\begin{array}{ll}\n#' R(G(t)) & \\mbox{if }t=1 \\\\\n#' R(G(1)') & \\mbox{otherwise}\n#' \\end{array}\n#' \\right.\n#' }{%\n#' G(t)' = R(G(t)) if t=1,\n#' R(G(1)') otherwise\n#' }\n#'\n#' Where \\eqn{G(t)} is the t-th slice, \\eqn{G(t)'} is the t-th rewired slice, and\n#' \\eqn{R} is the rewiring function. Otherwise, \\code{copy.first=FALSE} (default),\n#' The rewiring function is simply \\eqn{G(t)' = R(G(t))}.\n#'\n#' The following sections describe the way both algorithms were implemented.\n#'\n#' @section \\emph{Swap} algorithm:\n#' The \\code{\"swap\"} algorithm chooses randomly two edges \\eqn{(a,b)} and\n#' \\eqn{(c,d)} and swaps the 'right' endpoint of boths such that we get\n#' \\eqn{(a,d)} and \\eqn{(c,b)} (considering self and multiple edges).\n#'\n#' Following Milo et al. (2004) testing procedure, the algorithm shows to be\n#' well behaved in terms of been unbiased, so after each iteration each possible\n#' structure of the graph has the same probability of been generated. The algorithm\n#' has been implemented as follows:\n#'\n#' Let \\eqn{E} be the set of edges of the graph \\eqn{G}. For \\eqn{i=1} to \\eqn{p}, do:\n#' \\enumerate{\n#' \\item With probability \\code{1-pr.change} got to the last step.\n#' \\item Choose \\eqn{e0=(a, b)} from \\eqn{E}. If \\code{!self & a == b} then go to the last step.\n#' \\item Choose \\eqn{e1=(c, d)} from \\eqn{E}. If \\code{!self & c == d } then go to the last step.\n#' \\item Define \\eqn{e0'=(a, d)} and \\eqn{e1' = (c, b)}. If \\code{!multiple & [G[e0']!= 0 | G[e1'] != 0]} then go to the last step.(*)\n#' \\item Define \\eqn{v0 = G[e0]} and \\eqn{v1 = G[e1]}, set \\eqn{G[e0]=0} and \\eqn{G[e1]=0}\n#' (and the same to the diagonally opposed coordinates in the case of undirected graphs)\n#' \\item Set \\eqn{G[e0'] = v0} and \\eqn{G[e1'] = v1} (and so with the diagonally opposed coordinates\n#' in the case of undirected graphs).\n#' \\item Next i.\n#' }\n#'\n#' (*) When \\code{althexagons=TRUE}, the algorithm changes and applies what Rao et al.\n#' (1996) describe as Compact Alternating Hexagons. This modification assures the\n#' algorithm to be able to achieve any structure. The algorithm consists on doing\n#' the following swapping: \\eqn{(i1i2,i1i3,i2i3,i2i1,i3i1,i3i2)} with values\n#' \\eqn{(1,0,1,0,1,0)} respectively with \\eqn{i1!=i2!=i3}. See the examples and\n#' references.\n#'\n#' In Milo et al. (2004) is suggested that in order for the rewired graph to be independent\n#' from the original one researchers usually iterate around \\code{nlinks(graph)*100}\n#' times, so \\code{p=nlinks(graph)*100}. On the other hand in Ray et al (2012)\n#' it is shown that in order to achive such it is needed to perform\n#' \\code{nlinks(graph)*log(1/eps)}, where \\code{eps}\\eqn{\\sim}1e-7, in other words,\n#' around \\code{nlinks(graph)*16}. We set the default to be 20.\n#'\n#' In the case of Markov chains, the variable \\code{pr.change} allows making the\n#' algorithm aperiodic. This is relevant only if the\n#' probability self-loop to a particular state is null, for example, if\n#' we set \\code{self=TRUE} and \\code{muliple=TRUE}, then in every step the\n#' algorithm will be able to change the state. For more details see\n#' Stanton and Pinar (2012) [p. 3.5:9].\n#'\n#'\n#' @section \\emph{Endpoints} algorithm:\n#'\n#' This reconnect either one or both of the endpoints of the edge randomly. As a big\n#' difference with the swap algorithm is that this does not preserves the degree\n#' sequence of the graph (at most the outgoing degree sequence). The algorithm is\n#' implemented as follows:\n#'\n#' Let \\eqn{G} be the baseline graph and \\eqn{G'} be a copy of it. Then, For \\eqn{l=1} to \\eqn{|E|} do:\n#'\n#' \\enumerate{\n#' \\item Pick the \\eqn{l}-th edge from \\eqn{E}, define it as \\eqn{e = (i,j)}.\n#' \\item Draw \\eqn{r} from \\eqn{U(0,1)}, if \\eqn{r > p} go to the last step.\n#' \\item If \\code{!undirected & i < j} go to the last step.\n#' \\item Randomly select a vertex \\eqn{j'} (and \\eqn{i'} if \\code{both_ends==TRUE}).\n#' And define \\eqn{e'=(i, j')} (or \\eqn{e'=(i', j')} if \\code{both_ends==TRUE}).\n#' \\item If \\code{!self &} \\code{i==j}' (or if \\code{both_ends==TRUE & i'==j'}) go to the last step.\n#' \\item If \\code{!multiple & G'[e']!= 0} then go to the last step.\n#' \\item Define \\eqn{v = G[e]}, set \\eqn{G'[e] = 0} and \\eqn{G'[e'] = v} (and the\n#' same to the diagonally opposed coordinates in the case of undirected graphs).\n#' \\item Next \\eqn{l}.\n#' }\n#'\n#' The endpoints algorithm is used by default in \\code{\\link{rdiffnet}} and used\n#' to be the default in \\code{\\link{struct_test}} (now \\code{swap} is the default).\n#'\n#' @references\n#' Watts, D. J., & Strogatz, S. H. (1998). Collectivedynamics of \"small-world\" networks.\n#' Nature, 393(6684), 440\u2013442. \\doi{10.1038/30918}\n#'\n#' Milo, R., Kashtan, N., Itzkovitz, S., Newman, M. E. J., & Alon, U.\n#' (2004). On the uniform generation of random graphs with prescribed degree sequences.\n#' Arxiv Preprint condmat0312028, cond-mat/0, 1\u20134. Retrieved from\n#' \\url{https://arxiv.org/abs/cond-mat/0312028}\n#'\n#' Ray, J., Pinar, A., and Seshadhri, C. (2012).\n#' Are we there yet? When to stop a Markov chain while generating random graphs.\n#' pages 1\u201321.\n#'\n#' Ray, J., Pinar, A., & Seshadhri, C. (2012). Are We There Yet? When to Stop a\n#' Markov Chain while Generating Random Graphs. In A. Bonato & J. Janssen (Eds.),\n#' Algorithms and Models for the Web Graph (Vol. 7323, pp. 153\u2013164).\n#' Berlin, Heidelberg: Springer Berlin Heidelberg.\n#' \\doi{10.1007/978-3-642-30541-2}\n#'\n#' A . Ramachandra Rao, R. J. and S. B. (1996). A Markov Chain Monte Carlo Method\n#' for Generating Random ( 0 , 1 ) -Matrices with Given Marginals. The Indian\n#' Journal of Statistics, 58, 225\u2013242.\n#'\n#' Stanton, I., & Pinar, A. (2012). Constructing and sampling graphs with a\n#' prescribed joint degree distribution. Journal of Experimental Algorithmics,\n#' 17(1), 3.1. \\doi{10.1145/2133803.2330086}\n#'\n#' @family simulation functions\n#' @export\n#' @author George G. Vega Yon\n#' @examples\n#' # Checking the consistency of the \"swap\" ------------------------------------\n#'\n#' # A graph with known structure (see Milo 2004)\n#' n <- 5\n#' x <- matrix(0, ncol=n, nrow=n)\n#' x <- as(x, \"dgCMatrix\")\n#' x[1,c(-1,-n)] <- 1\n#' x[c(-1,-n),n] <- 1\n#'\n#' x\n#'\n#' # Simulations (increase the number for more precision)\n#' set.seed(8612)\n#' nsim <- 1e4\n#' w <- sapply(seq_len(nsim), function(y) {\n#' # Creating the new graph\n#' g <- rewire_graph(x,p=nlinks(x)*100, algorithm = \"swap\")\n#'\n#' # Categorizing (tag of the generated structure)\n#' paste0(as.vector(g), collapse=\"\")\n#' })\n#'\n#' # Counting\n#' coded <- as.integer(as.factor(w))\n#'\n#' plot(table(coded)/nsim*100, type=\"p\", ylab=\"Frequency %\", xlab=\"Class of graph\", pch=3,\n#' main=\"Distribution of classes generated by rewiring\")\n#'\n#' # Marking the original structure\n#' baseline <- paste0(as.vector(x), collapse=\"\")\n#' points(x=7,y=table(as.factor(w))[baseline]/nsim*100, pch=3, col=\"red\")\n#'\n# ' # Compact Alternating Hexagons ----------------------------------------------\n# ' x <- matrix(c(0,0,1,1,0,0,0,1,0), ncol=3, nrow=3)\n# '\n# ' set.seed(123)\n# ' nsim <- 1e4\n# ' w <- sapply(seq_len(nsim), function(y) {\n# ' g <- rewire_graph(x,p=nlinks(x)*20, algorithm = \"swap\", althexagons=TRUE)\n# ' paste0(as.vector(g), collapse=\"\")\n# ' })\n# '\n# ' # Counting\n# ' coded <- as.integer(as.factor(w))\n# '\n# ' plot(table(coded)/nsim*100, type=\"p\", ylab=\"Frequency %\", xlab=\"Class of graph\", pch=3,\n# ' main=\"Distribution of classes generated by rewiring\")\n# '\n# ' # Marking the original structure\n# ' baseline <- paste0(as.vector(x), collapse=\"\")\n# ' points(x=7,y=table(as.factor(w))[baseline]/nsim*100, pch=3, col=\"red\")\nrewire_graph <- function(graph, p,\n algorithm=\"endpoints\",\n both.ends=FALSE, self=FALSE, multiple=FALSE,\n undirected=getOption(\"diffnet.undirected\"),\n pr.change= ifelse(self, 0.5, 1),\n copy.first=TRUE, althexagons=FALSE) {\n\n # Checking undirected (if exists)\n checkingUndirected(graph)\n\n # althexagons is still on development\n if (althexagons) {\n althexagons <- FALSE\n warning(\"The option -althexagons- is still on development. So it has been set to FALSE.\")\n }\n\n # Checking copy.first\n # if (missing(copy.first)) copy.first <- FALSE\n\n cls <- class(graph)\n out <- if (\"dgCMatrix\" %in% cls) {\n rewire_graph.dgCMatrix(graph, p, algorithm, both.ends, self, multiple, undirected, pr.change, althexagons)\n } else if (\"list\" %in% cls) {\n rewire_graph.list(graph, p, algorithm, both.ends, self, multiple, undirected, pr.change, copy.first, althexagons)\n } else if (\"matrix\" %in% cls) {\n rewire_graph.dgCMatrix(\n methods::as(graph, \"dgCMatrix\"), p, algorithm, both.ends, self, multiple, undirected, pr.change, althexagons)\n } else if (\"diffnet\" %in% cls) {\n rewire_graph.list(graph$graph, p, algorithm, both.ends, self, multiple,\n graph$meta$undirected, pr.change, copy.first, althexagons)\n } else if (\"array\" %in% cls) {\n rewire_graph.array(graph, p, algorithm, both.ends, self, multiple, undirected, pr.change, copy.first, althexagons)\n } else stopifnot_graph(graph)\n\n # If diffnet, then it must return the same object but rewired, and change\n # the attribute of directed or not\n if (inherits(graph, \"diffnet\")) {\n graph$meta$undirected <- undirected\n graph$graph <- out\n return(graph)\n }\n\n attr(out, \"undirected\") <- FALSE\n\n return(out)\n}\n\n# @rdname rewire_graph\nrewire_graph.list <- function(graph, p, algorithm, both.ends, self, multiple, undirected,\n pr.change, copy.first, althexagons) {\n t <- length(graph)\n out <- graph\n\n # Names\n tn <- names(graph)\n if (!length(tn)) tn <- 1:t\n names(out) <- tn\n\n # Checking p\n if (length(p)==1)\n p <- rep(p, t)\n\n for (i in 1:t) {\n\n # Copy replaces the first from 2 to T with 1\n j <- ifelse(copy.first, 1, i)\n\n out[[i]] <- if (algorithm == \"endpoints\")\n rewire_endpoints(out[[j]], p[i], both.ends, self, multiple, undirected)\n else if (algorithm == \"swap\")\n rewire_swap(out[[j]], p[i], self, multiple, undirected, pr.change) #, althexagons)\n else if (algorithm == \"qap\")\n rewire_qap(out[[j]])\n else stop(\"No such rewiring algorithm: \", algorithm)\n\n # Names\n rn <- rownames(graph[[i]])\n if (!length(rn)) rn <- 1:nrow(graph[[i]])\n dimnames(out[[i]]) <- list(rn, rn)\n }\n\n out\n}\n\n# @rdname rewire_graph\nrewire_graph.dgCMatrix <- function(graph, p, algorithm, both.ends, self, multiple, undirected, pr.change, althexagons) {\n out <- if (algorithm == \"endpoints\")\n rewire_endpoints(graph, p, both.ends, self, multiple, undirected)\n else if (algorithm == \"swap\")\n rewire_swap(graph, p, self, multiple, undirected, pr.change) #, althexagons)\n else if (algorithm == \"qap\")\n rewire_qap(graph)\n else stop(\"No such rewiring algorithm: \", algorithm)\n\n rn <- rownames(out)\n if (!length(rn)) rn <- 1:nrow(out)\n dimnames(out) <- list(rn, rn)\n out\n}\n\n# @rdname rewire_graph\nrewire_graph.array <-function(graph, p, algorithm, both.ends, self, multiple, undirected,\n pr.change, copy.first, althexagons) {\n n <- dim(graph)[1]\n t <- dim(graph)[3]\n out <- apply(graph, 3, methods::as, Class=\"dgCMatrix\")\n\n # Checking time names\n tn <- dimnames(graph)[[3]]\n if (!length(tn)) tn <- 1:t\n names(out) <- tn\n\n return(rewire_graph.list(out, p, algorithm, both.ends, self, multiple, undirected,\n pr.change, copy.first, althexagons))\n}\n\n#' Permute the values of a matrix\n#'\n#' \\code{permute_graph} Shuffles the values of a matrix either considering\n#' \\emph{loops} and \\emph{multiple} links (which are processed as cell values\n#' different than 1/0). \\code{rewire_qap} generates a new graph \\code{graph}\\eqn{'}\n#' that is isomorphic to \\code{graph}.\n#' @templateVar self TRUE\n#' @templateVar multiple TRUE\n#' @template graph_template\n#' @author George G. Vega Yon\n#' @return A permuted version of \\code{graph}.\n#' @examples\n#' # Simple example ------------------------------------------------------------\n#' set.seed(1231)\n#' g <- rgraph_ba(t=9)\n#' g\n#'\n#' # These preserve the density\n#' permute_graph(g)\n#' permute_graph(g)\n#'\n#' # These are isomorphic to g\n#' rewire_qap(g)\n#' rewire_qap(g)\n#'\n#' @references\n#'\n#' Anderson, B. S., Butts, C., & Carley, K. (1999). The interaction of size and\n#' density with graph-level indices. Social Networks, 21(3), 239\u2013267.\n#' \\doi{10.1016/S0378-8733(99)00011-8}\n#'\n#' Mantel, N. (1967). The detection of disease clustering and a generalized\n#' regression approach. Cancer Research, 27(2), 209\u201320.\n#' \\url{https://cancerres.aacrjournals.org/content/27/2_Part_1/209}\n#'\n#' @seealso This function can be used as null distribution in \\code{struct_test}\n#' @family simulation functions\n#' @export\n#' @aliases CUG QAP\npermute_graph <- function(graph, self=FALSE, multiple=FALSE) {\n\n # Changing class\n cls <- class(graph)\n x <- if (\"matrix\" %in% cls) methods::as(graph, \"dgCMatrix\")\n else if (\"list\" %in% cls) lapply(graph, methods::as, Class=\"dgCMatrix\")\n else if (\"diffnet\" %in% cls) graph$graph\n else if (\"array\" %in% cls) apply(graph, 3, methods::as, Class=\"dgCMatrix\")\n else if (\"dgCMatrix\" %in% cls) graph\n else stopifnot_graph(graph)\n\n if (any(c(\"list\", \"array\") %in% cls) & !(\"matrix\" %in% cls)) {\n\n ans <- lapply(x, permute_graph_cpp, self=self, multiple=multiple)\n\n } else if (\"diffnet\" %in% cls) {\n ans <- graph\n ans$graph <- lapply(x, permute_graph_cpp, self=self, multiple=multiple)\n } else {\n ans <- permute_graph_cpp(x, self, multiple)\n }\n\n return(ans)\n\n}\n\n#' @export\n#' @rdname permute_graph\nrewire_permute <- permute_graph\n\n#' @export\n#' @rdname permute_graph\nrewire_qap <- function(graph) {\n\n neword <- order(runif(nnodes(graph)))\n rewirefun <- function(graph) {\n graph[neword, neword]\n }\n\n # Changing class\n cls <- class(graph)\n x <- if (\"matrix\" %in% cls) methods::as(graph, \"dgCMatrix\")\n else if (\"list\" %in% cls) lapply(graph, methods::as, Class=\"dgCMatrix\")\n else if (\"diffnet\" %in% cls) graph$graph\n else if (\"array\" %in% cls) apply(graph, 3, methods::as, Class=\"dgCMatrix\")\n else if (\"dgCMatrix\" %in% cls) graph\n else\n stopifnot_graph(graph)\n\n if (any(c(\"diffnet\", \"list\") %in% cls) | ((\"array\" %in% cls) & length(dim(graph)) == 3L )) {\n\n ans <- lapply(x, rewirefun)\n\n if (inherits(graph, \"diffnet\")) {\n # Naming\n neword <- match(neword, nodes(graph))\n\n graph$graph <- ans\n graph$graph <- lapply(graph$graph, Matrix::unname)\n graph$meta$ids <- graph$meta$ids[neword]\n\n # Attributes\n if (nrow(graph$vertex.static.attrs)) {\n graph$vertex.static.attrs <- graph$vertex.static.attrs[neword,,drop=FALSE]\n }\n if (nrow(graph$vertex.dyn.attrs[[1]])) {\n graph$vertex.dyn.attrs <- lapply(graph$vertex.dyn.attrs, function(y) {\n y[neword,,drop=FALSE]\n })\n }\n\n # Adoptions\n graph$cumadopt <- graph$cumadopt[neword,,drop=FALSE]\n graph$adopt <- graph$adopt[neword,,drop=FALSE]\n graph$toa <- graph$toa[neword]\n\n return(graph)\n }\n\n\n } else {\n ans <- rewirefun(x)\n }\n\n return(ans)\n}\n\n", "meta": {"hexsha": "9217983c040505eda4848087904dc137a615a31b", "size": 17491, "ext": "r", "lang": "R", "max_stars_repo_path": "R/rewire.r", "max_stars_repo_name": "USCCANA/netdiffuseR", "max_stars_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2015-12-15T02:49:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T02:48:37.000Z", "max_issues_repo_path": "R/rewire.r", "max_issues_repo_name": "USCCANA/netdiffuseR", "max_issues_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2015-12-17T03:43:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T18:50:22.000Z", "max_forks_repo_path": "R/rewire.r", "max_forks_repo_name": "USCCANA/netdiffuseR", "max_forks_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-12-28T21:47:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T19:48:08.000Z", "avg_line_length": 38.5264317181, "max_line_length": 135, "alphanum_fraction": 0.6477045338, "num_tokens": 5391, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.626124191181315, "lm_q2_score": 0.4843800842769843, "lm_q1q2_score": 0.303282088492264}} {"text": "\n figure.timeseries.snowcrab.habitat = function(p ) {\n\n td = bio.snowcrab::interpolation.db( p=p, DS=\"fishable.biomass.timeseries\" )\n\n areas = c(\"cfa4x\", \"cfasouth\", \"cfanorth\" )\n regions = c(\"4X\", \"S-ENS\", \"N-ENS\")\n td$region = factor(td$region, levels=areas, labels=regions)\n td$sa = td$sa.region\n\n td = td[is.finite(td$sa) ,]\n td$sa = td$sa / 1000\n td = td[order(td$region, td$yr),]\n xlim=range(td$yr); xlim[1]=xlim[1]-0.5; xlim[2]=xlim[2]+0.5\n\n fn = file.path( p$annual.results, \"timeseries\", \"interpolated\", \"snowcrab.habitat.sa\" )\n outdir = dirname( fn )\n dir.create( outdir, recursive=T, showWarnings=F )\n png( filename=paste(fn, \"png\", sep=\".\"), width=3072, height=2304, pointsize=40, res=300 )\n\n setup.lattice.options()\n pl = xyplot( sa~yr|region, data=td,\n layout=c(1,3), xlim=xlim, scales = list(y = \"free\"),\n main=\"Potential snow crab habitat\", xlab=\"Year\", ylab=expression(\"Surface area; X 10^3 km^2\"),\n panel = function(x, y, subscripts, ...) {\n panel.abline(h=mean(y, na.rm=T), col=\"gray40\", lwd=1.5,...)\n panel.xyplot(x, y, type=\"b\", pch=19, lwd=1.5, lty=\"11\", col=\"black\", ...)\n# panel.loess(x,y, span=0.15, lwd=2.5, col=\"darkblue\", ... )\n }\n )\n print(pl)\n dev.off()\n\n means = tapply(td$sa, td$region, mean, na.rm=T)\n\n print(\"mean annual SA X 1000 km^2\")\n print(means)\n print(\"SD:\")\n print( tapply(td$sa, td$region, sd, na.rm=T))\n print( \"latest year:\" )\n print( td$sa[ td$yr==p$year.assessment ])\n # table.view( td )\n\n return( fn )\n }\n", "meta": {"hexsha": "6f0d4c58b4f4feba770955111a305c6efd8c5e1a", "size": 1604, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure.timeseries.snowcrab.habitat.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/figure.timeseries.snowcrab.habitat.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure.timeseries.snowcrab.habitat.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 34.8695652174, "max_line_length": 106, "alphanum_fraction": 0.575436409, "num_tokens": 557, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.30328208173370313}} {"text": "library(cowplot)\nlibrary(ggplot2)\nlibrary(reshape2)\nlibrary(rtracklayer)\nlibrary(ggpubr)\nlibrary(GenomicRanges)\n\nintersect.bw.with.bed <- function(bw.file, bed.file, stat='mean') {\n bed <- import(bed.file)\n bw <- BigWigFile(bw.file)\n # summarize over bed regions using stat\n unlist(summary(bw, bed, type=stat))\n}\n\niapez.file <- '../data/Navarro_2020_IAPEz_consensus.1000bp.flanked.bed'\netn.file <- '../data/Navarro_2020_RepMasker_clusters_2kb.ETn.1000bp.flanked.bed'\n\nfiles <- c(\n '../intermediate/bw/Shi_2019/ESC1_WT_H3K9me3.ext.uniq.bw',\n '../intermediate/bw/Bunch_2014/Bunch_S2_PolII_WT_rep1.ext.uniq.bw',\n '../intermediate/bw/Bunch_2014/Bunch_Total_PolII_WT_rep1.ext.uniq.bw',\n '../intermediate/bw/Deaton_2016/ES_H33_00h_rep1.ext.uniq.bw'\n)\n\nvalues <- lapply(files, intersect.bw.with.bed, bed.file=etn.file)\n\netn.granges <- values[[1]]\netn.granges$h3k9m3 <- values[[1]]$score\netn.granges$rpol2.s2 <- values[[2]]$score\netn.granges$rpol2.total <- values[[3]]$score\netn.granges$H33.0h <- values[[4]]$score\netn.granges$score <- NULL\n\nvalues <- lapply(files, intersect.bw.with.bed, bed.file=iapez.file)\n\niapez.granges <- values[[1]]\niapez.granges$h3k9m3 <- values[[1]]$score\niapez.granges$rpol2.s2 <- values[[2]]$score\niapez.granges$rpol2.total <- values[[3]]$score\niapez.granges$H33.0h <- values[[4]]$score\niapez.granges$score <- NULL\n\ndf.etn <- as.data.frame(mcols(etn.granges))\ndf.iap <- as.data.frame(mcols(iapez.granges))\n\np1 <- ggplot(df.etn, aes(x=h3k9m3, y=rpol2.s2, color=H33.0h)) + geom_point(alpha=0.8) + scale_color_gradient(low=\"#dddddd\", high=\"#c21515\") + xlab(\"H3K9me3\") + ylab(\"RNA PolII S2\") + ggtitle(\"ETN\") + theme_classic() + theme(legend.position=\"bottom\")\np2 <- ggplot(df.iap, aes(x=h3k9m3, y=rpol2.s2, color=H33.0h)) + geom_point(alpha=0.8) + scale_color_gradient(low=\"#dddddd\", high=\"#c21515\") + xlab(\"H3K9me3\") + ggtitle(\"IAP ERV\") + ylab('') + theme_classic() + theme(legend.position=\"bottom\")\n\nplot_grid(p1, p2)\nggsave('../figures/ext4_bc_scatter_both.pdf', width=20, height=10, units='in')\n", "meta": {"hexsha": "1064b4154a40d001fa27da4eb8905e87ee58d50a", "size": 2025, "ext": "r", "lang": "R", "max_stars_repo_path": "src/fig4ext_coverage_dist.r", "max_stars_repo_name": "elsasserlab/publicchip", "max_stars_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-28T15:13:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-28T15:13:33.000Z", "max_issues_repo_path": "src/fig4ext_coverage_dist.r", "max_issues_repo_name": "elsasserlab/publicchip", "max_issues_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/fig4ext_coverage_dist.r", "max_forks_repo_name": "elsasserlab/publicchip", "max_forks_repo_head_hexsha": "1042672a4273f4c61fe81d47d73c6c838048021d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7058823529, "max_line_length": 249, "alphanum_fraction": 0.7175308642, "num_tokens": 732, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.3032820749751421}} {"text": "\n\n#LOAD RDS\nrequire(tidyverse)\nrequire(data.table)\nrequire(tictoc)\nmemory.limit(size=1e9)\n\n## load the csv and add coordinates based on locationid\ncoord.df <- read.csv(\"P:\\\\is-wel\\\\old_root_folder\\\\Data\\\\renewables_ninja\\\\INDUS_ZAMBEZI\\\\IIASA_META_DATA.CSV\") %>% \n filter(name %like% \"Indus\") %>% \n dplyr::select(lon,lat,id)\n\n## remove years we don't want\nyear_to_exclude <- seq(1980,1989,1)\nyte_string <- paste(year_to_exclude,collapse = \"|\")\n\npv_file_names <- c(\"Solar_Indus0p05_AF.rds\",\"Solar_Indus0p05_PK_3.rds\",\"Solar_Indus0p05_CN.rds\",\"Solar_Indus0p05_PK_4.rds\",\"Solar_Indus0p05_IN_1.rds\",\"Solar_Indus0p05_PK_5.rds\",\"Solar_Indus0p05_IN_2.rds\",\"Solar_Indus0p05_PK_1.rds\",\"Solar_Indus0p05_PK_2.rds\")\nwind_file_names <- c(\"Wind_Indus0p05_AF.rds\",\"Wind_Indus0p05_PK_3.rds\",\"Wind_Indus0p05_CN.rds\",\"Wind_Indus0p05_PK_4.rds\",\"Wind_Indus0p05_IN_1.rds\",\"Wind_Indus0p05_PK_5.rds\",\"Wind_Indus0p05_IN_2.rds\",\"Wind_Indus0p05_PK_1.rds\",\"Wind_Indus0p05_PK_2.rds\")\n\ncalculate_monthly_LF <- function(file_name,solar = TRUE) {\n ##FUNCTION\n if (solar) {folder = \"SOLAR_CF\\\\\"} else {folder = \"WIND_CF\\\\\"}\n sample.mt <- readRDS(paste0(\"P:\\\\is-wel\\\\old_root_folder\\\\Data\\\\renewables_ninja\\\\INDUS_ZAMBEZI\\\\\",folder,file_name))\n \n locations <- names(sample.mt)[-1]\n \n sample.mt <- sample.mt %>% \n filter(!Timestamp %like% yte_string)\n \n sample.df <- sample.mt %>% \n \n ## decompose timestamp in month (leave previous part inc ase we want hours or year)\n \n # separate(Timestamp,c(\"year_month\",\"day_hour\"), sep = 7) %>% \n # mutate(day_hour = gsub(\"-\",\"\", day_hour)) %>% \n # mutate(day_hour = gsub(\" \",\"_\", day_hour)) %>% \n \n mutate(Timestamp = gsub(\".*[-]([^.]+)[-].*\", \"\\\\1\", Timestamp)) %>% \n group_by(Timestamp) %>% \n summarise_all(mean) %>% \n rename(month = Timestamp) %>% \n\n ## reshape as dataframe, no multiple columns, just Timestamp,location id,value\n \n gather(key = \"id\" , value = \"value\", locations) %>% \n mutate(id = gsub(\".*_\",\"\",id))\n \n # load the csv and add coordinates based on locationid >> spdf\n \n monthly_avg_lf <- sample.df %>% \n left_join(coord.df %>% mutate(id = as.character(id))) %>% \n dplyr::select(lon,lat,everything(),value)\n \n return(monthly_avg_lf)\n}\n\n## solar load factor\ntic()\nsolar_monthly_LF <- bind_rows(lapply(pv_file_names, calculate_monthly_LF,solar = TRUE))\ntoc()\n\nwrite_csv(solar_monthly_LF,\"P:/is-wel/indus/message_indus/input/LF/solar_monthly_LF.csv\")\n\n## wind load factor\ntic()\nwind_monthly_LF <- bind_rows(lapply(wind_file_names, calculate_monthly_LF,solar = FALSE))\ntoc()\n\nwrite_csv(wind_monthly_LF,\"P:/is-wel/indus/message_indus/input/LF/wind_monthly_LF.csv\")", "meta": {"hexsha": "596dfe404312729b26351554f37d559a5f8c0477", "size": 2648, "ext": "r", "lang": "R", "max_stars_repo_path": "MESSAGEix/input_data_scripts/load_factor.r", "max_stars_repo_name": "amirsarikhani/NEST", "max_stars_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-07-15T19:28:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-24T04:45:43.000Z", "max_issues_repo_path": "MESSAGEix/input_data_scripts/load_factor.r", "max_issues_repo_name": "amirsarikhani/NEST", "max_issues_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2020-10-19T15:49:34.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-19T15:49:34.000Z", "max_forks_repo_path": "MESSAGEix/input_data_scripts/load_factor.r", "max_forks_repo_name": "amirsarikhani/NEST", "max_forks_repo_head_hexsha": "2771f6593bca0827489359c4129db9eea439d036", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2019-05-20T08:50:22.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-08T03:35:08.000Z", "avg_line_length": 37.8285714286, "max_line_length": 258, "alphanum_fraction": 0.7031722054, "num_tokens": 887, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926666143434, "lm_q2_score": 0.5117166047041652, "lm_q1q2_score": 0.30327734761331754}} {"text": "# Group the data in Postgresql SQL query to reduce the number of data points.\n# csv is generated via\n# \n# COPY (select date_trunc('day',date_modified),count(*) from idb_object_keys WHERE user_uuid = '285a4be0-5cfe-4d4f-9c8b-b0f0f3571079' group by date_trunc order by date_trunc) to '/tmp/uploadsbyday20140911.csv' with CSV;\n\n# \npng(filename=\"output_uploads_per_day_grouped.png\",width=800)\nuploads <- read.csv('uploadsbyday20140911.csv', header=FALSE)\ncolnames(uploads) <- c(\"date\",\"uploadcount\")\n\n#plot.ts(uploads$uploadcount)\n\nbarplot(uploads$uploadcount)\n\n\n\n\n\n#### from other example\n# change margins\n#par(mai=c(.75,.1,.5,.5))\n\n#uploadstamps <- c(as.POSIXlt (strftime(uploads$V2)) )\n\n#myhist <- hist(uploadstamps,breaks=\"days\",main=\"Upload events per day\",xlab=\"\",ylab=\"\",las=2,xaxt=\"n\",yaxt=\"n\",col=\"darkgrey\",freq=TRUE)\n\n#axis.POSIXct(1, at=seq((uploadstamps[1]-86400), as.POSIXlt(Sys.time()), by=\"day\"), format=\"%b %d\", srt=45, las=2,cex.axis=0.7)\n\n#axis(4, at=myhist$freq)\n####\n\nbox()\ndev.off()", "meta": {"hexsha": "66259696e82220f5c9d4c8be196e1753557cd4e6", "size": 1002, "ext": "r", "lang": "R", "max_stars_repo_path": "r_samples/graph_timestamps_grouped.r", "max_stars_repo_name": "danstoner/idigbio-scratch", "max_stars_repo_head_hexsha": "304ec14914efa0caa025ec10c0230cd56e0d18d4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-20T21:38:26.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-20T21:38:26.000Z", "max_issues_repo_path": "r_samples/graph_timestamps_grouped.r", "max_issues_repo_name": "danstoner/idigbio-scratch", "max_issues_repo_head_hexsha": "304ec14914efa0caa025ec10c0230cd56e0d18d4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "r_samples/graph_timestamps_grouped.r", "max_forks_repo_name": "danstoner/idigbio-scratch", "max_forks_repo_head_hexsha": "304ec14914efa0caa025ec10c0230cd56e0d18d4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3636363636, "max_line_length": 221, "alphanum_fraction": 0.7145708583, "num_tokens": 336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3032773402500584}} {"text": "\ncontext(\"CV with tuning\")\n\nx <- matrix(runif(50*5), 50, 5)\ny <- x[,1] + 0.5*x[,2] + 0.1*runif(50)\n\n## linear regression\nregr <- function(x, y, xtest, ytest, reg=0) {\n C = diag(x=reg, ncol(x))\n beta = solve(t(x) %*% x + C, t(x) %*% y)\n xtest %*% beta\n}\n\n\ntest_that(\"cv.grid_search fails with wrong parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.grid_search(cv, regr, noparam = c(1,2) )} )\n})\n\n\ntest_that(\"cv.grid_search fails with wrong size parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n res <- cv.grid_search(cv, regr, reg = c(0, 1e-2, 1e-1), maximize=FALSE )\n expect_equal(res$stats$num_evals, 3)\n})\n\ntest_that(\"cv.grid_search works with 2 params\", {\n regr <- function(x, y, xtest, ytest, reg, unused) {\n beta = solve(t(x) %*% x + diag(x=reg, ncol(x)), t(x) %*% y)\n xtest %*% beta\n }\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n res <- cv.grid_search(cv, regr, reg = c(0, 1e-2, 1e-1), unused=c(1), maximize=FALSE )\n expect_equal(res$stats$num_evals, 3)\n})\n\n#### cv.random_search\n\ntest_that(\"cv.random_search fails with wrong parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.random_search(cv, regr, noparam = c(1,2) )} )\n})\n\ntest_that(\"cv.random_search fails with wrong size parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.random_search(cv, regr, reg = c(1, 2, 3), num_evals=6 )} )\n})\n\ntest_that(\"cv.random_search works\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n res <- cv.random_search(cv, regr, reg = c(0, 1.0), num_evals=6, maximize=FALSE )\n expect_equal(res$stats$num_evals, 6)\n})\n\n\n#### cv.nelder_mead\n\ntest_that(\"cv.nelder_mead fails with wrong parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.nelder_mead(cv, regr, noparam = c(1,2) )} )\n})\n\ntest_that(\"cv.nelder_mead fails with wrong size parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.nelder_mead(cv, regr, reg = c(1, 2), num_evals=6 )} )\n})\n\ntest_that(\"cv.nelder_mead works\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n res <- cv.nelder_mead(cv, regr, reg = 1.0, num_evals=6, maximize=FALSE )\n expect_true(res$stats$num_evals <= 6)\n})\n\n\n#### cv.particle_swarm\n\ntest_that(\"cv.particle_swarm fails with wrong parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.particle_swarm(cv, regr, noparam = c(1,2) )} )\n})\n\ntest_that(\"cv.particle_swarm fails with wrong size parameter\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n expect_error( {cv.particle_swarm(cv, regr, reg = c(1, 2, 3) )} )\n})\n\ntest_that(\"cv.particle_swarm works\", {\n cv <- cv.setup(x, y, score=mean_se, num_folds = 3, num_iter = 2)\n res <- cv.particle_swarm(cv, regr, reg = c(0, 1.0), num_particles=2, num_generations=3, maximize=FALSE )\n expect_true(res$stats$num_evals <= 6)\n})\n\n#context(\"Nested cross-validation\")\n", "meta": {"hexsha": "519cbea0f1e9a8f08fc8f967384c4b3d399a3bee", "size": 3114, "ext": "r", "lang": "R", "max_stars_repo_path": "wrappers/R/inst/tests/testthat/test-cv-tuning.r", "max_stars_repo_name": "xrounder/optunity", "max_stars_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 401, "max_stars_repo_stars_event_min_datetime": "2015-01-08T00:56:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-19T09:07:12.000Z", "max_issues_repo_path": "wrappers/R/inst/tests/testthat/test-cv-tuning.r", "max_issues_repo_name": "xrounder/optunity", "max_issues_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 67, "max_issues_repo_issues_event_min_datetime": "2015-01-08T09:13:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-05T23:26:36.000Z", "max_forks_repo_path": "wrappers/R/inst/tests/testthat/test-cv-tuning.r", "max_forks_repo_name": "xrounder/optunity", "max_forks_repo_head_hexsha": "019182ca83fe2002083cc1ac938510cb967fd2c9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 94, "max_forks_repo_forks_event_min_datetime": "2015-02-04T08:35:56.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-03T12:40:35.000Z", "avg_line_length": 33.1276595745, "max_line_length": 106, "alphanum_fraction": 0.6506101477, "num_tokens": 1125, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3032773402500584}} {"text": "# Cluster function\nrequire(rpart)\nrequire(cluster)\n\n#### Set up variables for trip-level clustering\nopx <- op2[op2$lat > -5 & op2$lat < 10 & op2$reg %in% 1:6 & op2$trip_yr >= 1990 & !is.na(op2$tripid),]\nopx79 <- op2[op2$lat > -5 & op2$lat < 10 & op2$reg %in% 1:6 & !is.na(op2$tripid),]\nop3 <- op2[!is.na(op2$tripid),]\n\ntable(is.na(opx$reg))\ntable(is.na(op2$reg))\n\n\naggregate_for_cluster <- function(dat=opx) {\n tp <- aggregate(cbind(bet,yft,alb,swo,hbf,hooks,lat,lon) ~ tripid + newfishingcat + vessid + trip_yr + reg,data=dat,mean)\n a <- aggregate(bet ~ tripid + newfishingcat + vessid + trip_yr + reg,data=dat,length)\n tp <- cbind(tp,a[,6])\n dima <- dim(tp)[2]\n names(tp)[dima] <- \"nset\"\n tp$bet_cpue <- 1000*tp$bet/tp$hooks\n tp$yft_cpue <- 1000*tp$yft/tp$hooks\n tp$alb_cpue <- 1000*tp$alb/tp$hooks\n tp$swo_cpue <- 1000*tp$swo/tp$hooks\n tp$tot <- tp$alb + tp$yft + tp$bet + tp$swo\n tp <- tp[tp$tot > 0,]\n tp$tuna <- tp$alb + tp$yft + tp$bet\n tp$troptuna <- tp$yft + tp$bet\n tp$bet_perc <- tp$bet/tp$tuna\n tp$alb_perc <- tp$alb/tp$tuna\n tp$yft_perc <- tp$yft/tp$tuna\n tp$bet_totpc <- tp$bet/tp$tot\n tp$alb_totpc <- tp$alb/tp$tot\n tp$yft_totpc <- tp$yft/tp$tot\n tp$swo_totpc <- tp$swo/tp$tot\n tp$bet_troppc <- tp$bet/tp$troptuna\n tp$yft_troppc <- tp$yft/tp$troptuna\n return(tp)\n }\n \nprune_tree_and_plot <- function(tree) {\n minxerror <- tree$cptable[,4][which.min(tree$cptable[,4])] + tree$cptable[which.min(tree$cptable[,4]),5]\n if(sum(tree$cptable[,4] > minxerror)!=0) { \n findxerror <- as.numeric(names(tree$cptable[,4][tree$cptable[,4] < minxerror][1]))\n treepr <- prune(tree,cp=tree$cptable[findxerror,1]+0.00001)\n printcp(treepr)\n if(dim(tree1$cptable)[2] > 1) {\n plot(treepr,main=paste(\"Region\",rg),margin=0.1)\n text(treepr)\n } else plot(1:5,1:5,type=\"n\",axes=F)\n } else plot(1:5,1:5,type=\"n\",axes=F)\n }\n\ntp3 <- aggregate_for_cluster(dat=op3) \ntpx <- aggregate_for_cluster(dat=opx)\n\n########################################################\n# Regression trees on cpue by species & region\nwindows(width=18,height=18); par(mfrow=c(3,2),mar=c(2,2,2,2))\nfor(rg in 1:6) {\n a <- tp3[tp3$reg==rg,]\n tree1 <- rpart(bet_cpue ~ lat+lon+trip_yr+newfishingcat + alb_cpue + yft_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_betcpue_by_reg_by_trip\",type=\"png\")\nfor(rg in 1:6) {\n a <- tp3[tp3$reg==rg,]\n tree1 <- rpart(alb_cpue ~ lat+lon+trip_yr+newfishingcat + bet_cpue + yft_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_albcpue_by_reg_by_trip\",type=\"png\")\nfor(rg in 1:6) {\n a <- tp3[tp3$reg==rg,]\n tree1 <- rpart(yft_cpue ~ lat+lon+trip_yr+newfishingcat + bet_cpue + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_yftcpue_by_reg_by_trip\",type=\"png\")\n\n# Regression trees by trip in core area on cpue by species & region\nwindows(width=10,height=10); par(mfrow=c(2,1),mar=c(2,2,2,2))\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg,]\n tree1 <- rpart(bet_cpue ~ lat+lon+trip_yr+newfishingcat + alb_cpue + yft_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_betcpue_by_reg_by_trip_corepost90\",type=\"png\")\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg,]\n tree1 <- rpart(alb_cpue ~ lat+lon+trip_yr+newfishingcat + bet_cpue + yft_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_albcpue_by_reg_by_trip_corepost90\",type=\"png\")\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg,]\n tree1 <- rpart(yft_cpue ~ lat+lon+trip_yr+newfishingcat + bet_cpue + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_yftcpue_by_reg_by_trip_corepost90\",type=\"png\")\n\n# Regression trees by trip in core area on proportion by species & region\nwindows(width=10,height=10); par(mfrow=c(2,1),mar=c(2,2,2,2))\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg,]\n tree1 <- rpart(bet_troppc ~ lat+lon+trip_yr+newfishingcat + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_betprop_by_reg_by_trip_corepost90\",type=\"png\")\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg,]\n tree1 <- rpart(yft_troppc ~ lat+lon+trip_yr+newfishingcat + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1)\n }\nsavePlot(\"Rtrees_yftprop_by_reg_by_trip_corepost90\",type=\"png\")\n\n# Regression trees by set in core area on cpue\nwindows(width=10,height=10); par(mfrow=c(2,1),mar=c(2,2,2,2))\nfor(rg in 3:4) {\n a <- opx[opx$reg==rg,]\n a$alb_cpue <- a$alb/a$hooks; a$yft_cpue <- a$yft/a$hooks; a$bet_cpue <- a$bet/a$hooks; a$swo_cpue <- a$swo/a$hooks\n tree1 <- rpart(yft_cpue ~ lat+lon + trip_yr + newfishingcat + bet_cpue + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_yftcpue_by_reg_by_set\",type=\"png\")\nfor(rg in 3:4) {\n a <- opx[opx$reg==rg,]\n a$alb_cpue <- a$alb/a$hooks; a$yft_cpue <- a$yft/a$hooks; a$bet_cpue <- a$bet/a$hooks; a$swo_cpue <- a$swo/a$hooks\n tree1 <- rpart(bet_cpue ~ lat+lon + trip_yr + newfishingcat + yft_cpue + alb_cpue + swo_cpue + hooks + hbf, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_betcpue_by_reg_by_set\",type=\"png\")\n\n# Regression trees by set in core area on species prop\nwindows(width=10,height=10); par(mfrow=c(2,1),mar=c(2,2,2,2))\nfor(rg in 3:4) {\n a <- opx[opx$reg==rg,]\n a$alb_cpue <- a$alb/a$hooks; a$swo_cpue <- a$swo/a$hooks\n a$yft_troppc <- a$yft/(a$yft + a$bet); a$bet_troppc <- a$bet/(a$yft + a$bet)\n tree1 <- rpart(yft_troppc ~ lat+lon + trip_yr + newfishingcat + alb_cpue + swo_cpue + hooks + hbf + mainline + branchline, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_yftprop_by_reg_by_set_core\",type=\"png\")\nfor(rg in 3:4) {\n a <- opx[opx$reg==rg,]\n a$alb_cpue <- a$alb/a$hooks; a$swo_cpue <- a$swo/a$hooks\n a$yft_troppc <- a$yft/(a$yft + a$bet); a$bet_troppc <- a$bet/(a$yft + a$bet)\n tree1 <- rpart(bet_troppc ~ lat+lon + trip_yr + newfishingcat + alb_cpue + swo_cpue + hooks + hbf + mainline + branchline, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_betprop_by_reg_by_set_core\",type=\"png\")\n\n# Regression trees by set in core area on species prop 1976-1993\nwindows(width=10,height=10); par(mfrow=c(2,1),mar=c(2,2,2,2))\nfor(rg in 3:4) {\n a <- op2[op2$reg==rg & op2$op_yr >=1976 & op2$op_yr <= 1993 & op2$lat > -5 & op2$lat < 10,]\n a$alb_cpue <- a$alb/a$hooks; a$swo_cpue <- a$swo/a$hooks\n a$yft_troppc <- a$yft/(a$yft + a$bet); a$bet_troppc <- a$bet/(a$yft + a$bet)\n tree1 <- rpart(yft_troppc ~ lat+lon + trip_yr + newfishingcat + alb_cpue + swo_cpue + hooks + hbf + bait, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_yftprop_by_reg_by_set_core_76_93bait\",type=\"png\")\nfor(rg in 3:4) {\n a <- op2[op2$reg==rg & op2$op_yr >=1976 & op2$op_yr <= 1993 & op2$lat > -5 & op2$lat < 10,]\n a$alb_cpue <- a$alb/a$hooks; a$swo_cpue <- a$swo/a$hooks\n a$yft_troppc <- a$yft/(a$yft + a$bet); a$bet_troppc <- a$bet/(a$yft + a$bet)\n tree1 <- rpart(bet_troppc ~ lat+lon + trip_yr + newfishingcat + alb_cpue + swo_cpue + hooks + hbf + bait, data=a)\n prune_tree_and_plot(tree1) \n }\nsavePlot(\"Rtrees_betprop_by_reg_by_set_core_76_93bait\",type=\"png\")\n\n\n\n########################################################\n# Cluster analysis\n\n# Look at numbers of clusters\n# by region \ntpx <- tp3[,c(\"bet_totpc\",\"yft_totpc\",\"alb_totpc\",\"swo_totpc\",\"bet_troppc\",\"yft_troppc\",\"trip_yr\",\"tripid\",\"newfishingcat\",\"hbf\",\"reg\")]\n#triptots <- tp3[,c(\"bet\",\"yft\",\"alb\",\"swo\",\"trip_yr\",\"tripid\", \"hbf\",\"reg\")]\n\ntpx <- na.omit(tpx)\nwindows(width=20,height=18); par(mfrow=c(3,2),mar=c(2,4,4,2))\nnsp=4\nfor(latlim in seq(10,0,-5)) {\n for(lonlim in seq(120,200,20)) {\n a <- tpx[tpx$lon >= lonlim & tpx$lon <(lonlim+20) & tpx$lat <= latlim & tpx$lat >(latlim-5),]\n a[,1:nsp] <- scale(a[,1:nsp])\n wss <- (nrow(a)-1)*sum(apply(a[,1:nsp],2,var))\n for (i in 2:15) wss[i] <- sum(kmeans(a[,1:nsp],centers=i,iter.max = 40)$withinss)\n plot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",ylab=\"Within groups sum of squares\",main=paste(\"Region\",rg))\n } }\nsavePlot(paste(\"cluster_num_v_SS_by_reg_\",nsp,\"_spp_fc\",f,sep=\"\"),type=\"png\")\n\n# identify catch rates by trip\nfor(rg in 1:6) {\n a <- tp[tp$reg==rg & tp$newfishingcat==1,]\n windows(width=20,height=18); par(mfcol=c(3,2))\n boxplot(a$bet_perc~a$trip_yr, xlab=\"Year\", ylab=\"bet_perc\",main=\"Offshore\")\n boxplot(a$alb_perc~a$trip_yr, xlab=\"Year\", ylab=\"alb_perc\")\n boxplot(a$yft_perc~a$trip_yr, xlab=\"Year\", ylab=\"yft_perc\")\n title(paste(\"Region\",rg),outer=T,line=-1)\n a <- tp[tp$reg==rg & tp$newfishingcat==2,]\n boxplot(a$bet_perc~a$trip_yr, xlab=\"Year\", ylab=\"bet_perc\",main=\"Distant water\")\n boxplot(a$alb_perc~a$trip_yr, xlab=\"Year\", ylab=\"alb_perc\")\n boxplot(a$yft_perc~a$trip_yr, xlab=\"Year\", ylab=\"yft_perc\")\n title(paste(\"Region\",rg),outer=T,line=-1)\n savePlot(paste(\"boxplot_sp_percent_reg_\",rg,sep=\"\"),type=\"png\")\n}\n\n# for a single sample - run clustering\n# 1. define the sample\n# 2. set up the input format\n# 3. run the cluster\n# 4. output, check and save the results\n# 5. apply the cluster back to the data\n\n\nmake_clusters <- function(setdat=opx,cldat=tpx,nsp=3,ncl=3,titx=\"\") { \n a <- na.omit(cldat)\n a <- scale(a[,1:nsp])\n d <- dist(a[,1:nsp], method = \"euclidean\") # distance matrix\n fit <- hclust(d, method=\"ward\")\n plot(fit, labels = FALSE, hang=-1, main = titx) # display dendogram #looks like 3 (or 4) \n groups <- cutree(fit, k=ncl) # cut tree into ncl clusters\n print(table(groups))\n rect.hclust(fit, k=ncl, border=\"red\")\n clarax <- clara(a[,1:nsp],ncl) #clustering based upon the percent of spp in total catch of tuna\n kmclust <- kmeans(a[,1:nsp],centers=ncl,iter.max = 40)\n setdat$kmclust <- kmclust$cluster[match(setdat$tripid,cldat$tripid)]\n setdat$claracl <- clarax$clustering[match(setdat$tripid,cldat$tripid)]\n setdat$hclustcl <- groups[match(setdat$tripid,cldat$tripid)]\n return(list(d=d,fit=fit,clarax=clarax,setdat=setdat))\n }\n\nwindows(18,18);par(mfrow=c(3,2))\nfor (r in 1:6) {\n a <- make_clusters(setdat=op3[op3$reg==r & !is.na(op3$tripid),],\n cldat=tp3[tp3$reg==r,c(\"bet_cpue\",\"alb_cpue\",\"yft_cpue\",\"swo_cpue\",\"tripid\",\"lat\",\"lon\")],\n nsp=4,ncl=4,titx=paste(\"Region\",r))\n assign(paste(\"clusters_4spp_cpue_R\",r,sep=\"\"),a)\n }\nsavePlot(\"cluster_4spp_cpue_hclust\",type=\"png\")\n\nwindows(18,18);par(mfrow=c(3,2))\nfor (r in 1:6) {\n a <- make_clusters(setdat=op3[op3$reg==r & !is.na(op3$tripid),],\n cldat=tp3[tp3$reg==r,c(\"bet_totpc\",\"alb_totpc\",\"yft_totpc\",\"swo_totpc\",\"tripid\",\"lat\",\"lon\")],\n nsp=4,ncl=4,titx=paste(\"Region\",r))\n assign(paste(\"clusters_4spp_spcomp_R\",r,sep=\"\"),a)\n }\nsavePlot(\"cluster_4spp_spcomp_hclust\",type=\"png\")\n\n\nfor(r in 1:6) { \n a <- get(paste(\"clusters_4spp_cpue_R\",r,sep=\"\"))\n print(paste(\"Region\",r))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ kmclust,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ claracl,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ hclustcl,data=a$setdat,FUN=mean))\n }\n\nfor(r in 1:6) { \n a <- get(paste(\"clusters_4spp_spcomp_R\",r,sep=\"\"))\n a$setdat$bet_totpc <- with(a$setdat,bet/(bet+alb+yft+swo))\n a$setdat$yft_totpc <- with(a$setdat,yft/(bet+alb+yft+swo))\n a$setdat$alb_totpc <- with(a$setdat,alb/(bet+alb+yft+swo))\n a$setdat$swo_totpc <- with(a$setdat,swo/(bet+alb+yft+swo))\n print(paste(\"Region\",r))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ kmclust,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ claracl,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ hclustcl,data=a$setdat,FUN=mean))\n }\n\n#[1] \"Region 1\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.1536289 0.06833749 0.04782119 0.73021246\n#2 2 0.1851300 0.71447261 0.06030514 0.04009229\n#3 3 0.5571895 0.29233113 0.05059215 0.09988726\n#4 4 0.1445576 0.30283987 0.48052112 0.07208143\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.3385728 0.54153313 0.04572553 0.07416853\n#2 2 0.1620779 0.07030975 0.05066386 0.71694852\n#3 3 0.1104852 0.75441851 0.10929538 0.02580094\n#4 4 0.6352546 0.22549886 0.05996981 0.07927675\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.4719572 0.37834385 0.05344760 0.09625136 BET/ALB\n#2 2 0.1354442 0.77878282 0.06164946 0.02412351 ALB\n#3 3 0.1629319 0.06293028 0.05101672 0.72312109 SWO\n#4 4 0.1190933 0.34907554 0.50237503 0.02945614 YFT/ALB\n#[1] \"Region 2\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.6567195 0.2040884 0.06594782 0.07324435\n#2 2 0.1382498 0.1189409 0.04543195 0.69737733\n#3 3 0.4570409 0.1882735 0.30256792 0.05211775\n#4 4 0.2433549 0.6406329 0.04981352 0.06619861\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.2051379 0.6914872 0.04287006 0.06050492\n#2 2 0.4014663 0.4209617 0.09263449 0.08493748\n#3 3 0.7450416 0.1216713 0.06996902 0.06331810\n#4 4 0.1355003 0.1149961 0.04534030 0.70416330\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.1954361 0.7135398 0.04431391 0.04671016 ALB\n#2 2 0.6607419 0.1862159 0.10315734 0.04988481 BET\n#3 3 0.3466165 0.5054282 0.06266327 0.08529206 BET/ALB\n#4 4 0.1762068 0.1486632 0.04768199 0.62744807 SWO\n#[1] \"Region 3\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.39613578 0.02949170 0.5644550 0.009917549\n#2 2 0.09277553 0.70798482 0.1943410 0.004898642\n#3 3 0.64352576 0.02113801 0.3211552 0.014181035\n#4 4 0.19271117 0.02376420 0.7766829 0.006841681\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.55207030 0.02419010 0.4125719 0.011167734\n#2 2 0.31186934 0.02928189 0.6488266 0.010022153\n#3 3 0.15805610 0.02222317 0.8135628 0.006157948\n#4 4 0.09419902 0.70721399 0.1936928 0.004894148\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.42406791 0.01444774 0.5506179 0.010866409 BET/YFT\n#2 2 0.20452259 0.03643860 0.7519490 0.007089786 YFT\n#3 3 0.67787842 0.02317629 0.2857903 0.013155032 BET\n#4 4 0.09200346 0.71869447 0.1847973 0.004504779 ALB\n#[1] \"Region 4\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.3122835 0.47550627 0.2016801 0.01053017\n#2 2 0.7189410 0.04047501 0.2233555 0.01722847\n#3 3 0.2331442 0.01792848 0.2974835 0.45144385\n#4 4 0.4169692 0.03201743 0.5395719 0.01144145\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.3654746 0.02608587 0.5981864 0.010253178\n#2 2 0.7832261 0.03055475 0.1678441 0.018375086\n#3 3 0.5853077 0.05695255 0.3424368 0.015302973\n#4 4 0.2343827 0.59826744 0.1591314 0.008218422\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.2988523 0.01665656 0.6759633 0.008527862 YFT/BET\n#2 2 0.7685379 0.04353982 0.1705537 0.017368554 BET\n#3 3 0.5457279 0.04331624 0.3959485 0.015007316 BET/YFT\n#4 4 0.2090365 0.61655172 0.1685203 0.005891574 ALB\n#[1] \"Region 5\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.09245959 0.2561926 0.6221855 0.02916232\n#2 2 0.07369510 0.6937159 0.1742617 0.05832723\n#3 3 0.34216264 0.2129954 0.4097289 0.03511307\n#4 4 0.10165820 0.4291136 0.2410797 0.22814856\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.09155720 0.2005735 0.68355239 0.02431686\n#2 2 0.09208670 0.5329998 0.27939635 0.09551711\n#3 3 0.33799688 0.1200607 0.50423843 0.03770403\n#4 4 0.05955261 0.8052625 0.08723216 0.04795277\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.10575738 0.5498555 0.2221687 0.122218434 ALB/YFT\n#2 2 0.19107696 0.0265153 0.7756121 0.006795665 YFT\n#3 3 0.04980333 0.7825375 0.1339286 0.033730618 ALB\n#4 4 0.06815181 0.3621662 0.5527010 0.016980980 YFT/ALB\n#[1] \"Region 6\"\n# kmclust bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.16421188 0.3701292 0.38976075 0.0758982\n#2 2 0.10164377 0.2760276 0.03049396 0.5918346\n#3 3 0.30073900 0.5296506 0.04058380 0.1290266\n#4 4 0.08215414 0.7438033 0.02400502 0.1500376\n# claracl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.08388614 0.7366375 0.01794020 0.16153615\n#2 2 0.04930062 0.1202030 0.02075805 0.80973833\n#3 3 0.30765827 0.5175160 0.03877165 0.13605407\n#4 4 0.15029794 0.4487137 0.31346368 0.08752467\n# hclustcl bet_totpc alb_totpc yft_totpc swo_totpc\n#1 1 0.06284170 0.7912712 0.01401011 0.13187704 ALB\n#2 2 0.25396833 0.5829080 0.03181261 0.13131106 ALB/BET\n#3 3 0.07476451 0.4660218 0.02225141 0.43696228 SWO/ALB\n#4 4 0.16024543 0.4540296 0.30626019 0.07946474 ALB/YFT\n#\nfor(r in 1:6) { \n a <- get(paste(\"clusters_4spp_spcomp_R\",r,sep=\"\"))\n if(r==1) op_clust <- a$setdat else op_clust <- rbind(op_clust,a$setdat)\n }\nsave(op_clust,file=\"op_clust.RData\")\n\nop_clust <- op_clust[op_clust$bet+op_clust$yft+op_clust$alb+op_clust$swo > 0,]\nwrite.csv(table(op_clust$hclustcl,op_clust$reg),file=\"hclust_n_reg.csv\")\nwrite.csv(with(op_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(hclustcl,reg),mean)),file=\"hclust_pc_reg.csv\")\nwrite.csv(table(op_clust$kmclust,op_clust$reg),file=\"kmeans_n_reg.csv\")\nwrite.csv(with(op_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(kmclust,reg),mean)),file=\"kmeans_pc_reg.csv\")\nwrite.csv(table(op_clust$claracl,op_clust$reg),file=\"clara_n_reg.csv\")\nwrite.csv(with(op_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(claracl,reg),mean)),file=\"clara_pc_reg.csv\")\n\n# histograms\nfor(r in 1:6) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2),oma=c(0,0,2,0))\n for(y in 1978:2010) {\n hist(tp3[tp3$reg==r & tp3$trip_yr==y,]$bet_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropBET_per_trip_allR\",r),type=\"png\")\n }\nfor(r in 1:6) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2),oma=c(0,0,2,0))\n for(y in 1978:2010) {\n hist(tp3[tp3$reg==r & tp3$trip_yr==y,]$alb_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropALB_per_trip_allR\",r),type=\"png\")\n }\nfor(r in 1:6) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2),oma=c(0,0,2,0))\n for(y in 1978:2010) {\n hist(tp3[tp3$reg==r & tp3$trip_yr==y,]$yft_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropYFT_per_trip_allR\",r),type=\"png\")\n }\nfor(r in 1:6) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2),oma=c(0,0,2,0))\n for(y in 1978:2010) {\n hist(tp3[tp3$reg==r & tp3$trip_yr==y,]$swo_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropSWO_per_trip_allR\",r),type=\"png\")\n }\n\n\n \n#######################################################################################################\n# cluster the core area\n#######################################################################################################\n# set up data\ntpxa <- aggregate_for_cluster(dat=opx)\ntpx <- tpxa[,c(\"bet_totpc\",\"yft_totpc\",\"alb_totpc\",\"swo_totpc\",\"bet_troppc\",\"yft_troppc\",\"bet_cpue\",\"yft_cpue\",\"alb_cpue\",\"swo_cpue\",\"trip_yr\",\"tripid\",\"newfishingcat\",\"hbf\",\"reg\",\"lat\",\"lon\")]\n\ntpx <- na.omit(tpx)\nwindows(width=20,height=15); par(mfrow=c(3,5),mar=c(2,4,4,2))\nnsp=4\nfor(latlim in seq(10,0,-5)) {\n for(lonlim in seq(120,200,20)) {\n a <- tpx[tpx$lon >= lonlim & tpx$lon <(lonlim+20) & tpx$lat <= latlim & tpx$lat >(latlim-5),]\n a <- na.omit(a)\n a <- scale(a[,1:nsp])\n a[,1:nsp] <- scale(a[,1:nsp])\n if(nrow(a) > 10) {\n wss <- (nrow(a)-1)*sum(apply(a[,1:nsp],2,var))\n for (i in 2:15) wss[i] <- sum(kmeans(a[,1:nsp],centers=i,iter.max = 40)$withinss)\n plot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\",ylab=\"Within groups sum of squares\",main=paste(latlim,lonlim))\n }\n } }\nsavePlot(paste(\"cluster_num_v_SS_by_reg_\",nsp,\"_spp_fc\",f,sep=\"\"),type=\"png\")\n\n# identify catch rates by trip\nfor(rg in 3:4) {\n a <- tpx[tpx$reg==rg & tpx$newfishingcat==1,]\n windows(width=20,height=18); par(mfcol=c(3,2))\n boxplot(a$bet_totpc~a$trip_yr, xlab=\"Year\", ylab=\"bet_perc\",main=\"Offshore\")\n boxplot(a$alb_totpc~a$trip_yr, xlab=\"Year\", ylab=\"alb_perc\")\n boxplot(a$yft_totpc~a$trip_yr, xlab=\"Year\", ylab=\"yft_perc\")\n title(paste(\"Region\",rg,\"core\"),outer=T,line=-1)\n a <- tpx[tpx$reg==rg & tpx$newfishingcat==2,]\n boxplot(a$bet_totpc~a$trip_yr, xlab=\"Year\", ylab=\"bet_perc\",main=\"Distant water\")\n boxplot(a$alb_totpc~a$trip_yr, xlab=\"Year\", ylab=\"alb_perc\")\n boxplot(a$yft_totpc~a$trip_yr, xlab=\"Year\", ylab=\"yft_perc\")\n title(paste(\"Region\",rg,\"core\"),outer=T,line=-1)\n savePlot(paste(\"boxplot_sp_percent_reg_\",rg,sep=\"\"),type=\"png\")\n}\n\n# for a single sample - run clustering\n# 1. define the sample\n# 2. set up the input format\n# 3. run the cluster\n# 4. output, check and save the results\n# 5. apply the cluster back to the data\n\n\nmake_clusters <- function(setdat=opx,cldat=tpx,nsp=3,ncl=3,titx=\"\") { \n a <- na.omit(cldat)\n a <- scale(a[,1:nsp])\n d <- dist(a[,1:nsp], method = \"euclidean\") # distance matrix\n fit <- hclust(d, method=\"ward\")\n plot(fit, labels = FALSE, hang=-1, main = titx) # display dendogram #looks like 3 (or 4) \n groups <- cutree(fit, k=ncl) # cut tree into ncl clusters\n print(table(groups))\n rect.hclust(fit, k=ncl, border=\"red\")\n clarax <- clara(a[,1:nsp],ncl) #clustering based upon the percent of spp in total catch of tuna\n kmclust <- kmeans(a[,1:nsp],centers=ncl,iter.max = 40)\n setdat$kmclust <- kmclust$cluster[match(setdat$tripid,cldat$tripid)]\n setdat$claracl <- clarax$clustering[match(setdat$tripid,cldat$tripid)]\n setdat$hclustcl <- groups[match(setdat$tripid,cldat$tripid)]\n return(list(d=d,fit=fit,clarax=clarax,setdat=setdat))\n }\n\nwindows(18,18);par(mfrow=c(2,1))\nfor (r in 3:4) {\n a <- make_clusters(setdat=opx[opx$reg==r & !is.na(opx$tripid),],\n cldat=tpx[tpx$reg==r,c(\"bet_cpue\",\"alb_cpue\",\"yft_cpue\",\"swo_cpue\",\"tripid\",\"lat\",\"lon\")],\n nsp=4,ncl=2,titx=paste(\"Region\",r))\n assign(paste(\"clusters_4spp_cpue_core_R\",r,sep=\"\"),a)\n }\nsavePlot(\"cluster_4spp_cpue_core_hclust\",type=\"png\")\n\nwindows(18,18);par(mfrow=c(2,1))\nfor (r in 3:4) {\n a <- make_clusters(setdat=opx[opx$reg==r & !is.na(opx$tripid),],\n cldat=tpx[tpx$reg==r,c(\"bet_totpc\",\"alb_totpc\",\"yft_totpc\",\"swo_totpc\",\"tripid\",\"lat\",\"lon\")],\n nsp=4,ncl=2,titx=paste(\"Region\",r))\n assign(paste(\"clusters_4spp_spcomp_core_R\",r,sep=\"\"),a)\n }\nsavePlot(\"cluster_4spp_spcomp_core_hclust\",type=\"png\")\n\nfor(r in 3:4) { \n a <- get(paste(\"clusters_4spp_cpue_core_R\",r,sep=\"\"))\n print(paste(\"Region\",r))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ kmclust,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ claracl,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet/hooks,alb/hooks,yft/hooks,swo/hooks) ~ hclustcl,data=a$setdat,FUN=mean))\n }\n\nfor(r in 3:4) { \n a <- get(paste(\"clusters_4spp_spcomp_core_R\",r,sep=\"\"))\n a$setdat$bet_totpc <- with(a$setdat,bet/(bet+alb+yft+swo))\n a$setdat$yft_totpc <- with(a$setdat,yft/(bet+alb+yft+swo))\n a$setdat$alb_totpc <- with(a$setdat,alb/(bet+alb+yft+swo))\n a$setdat$swo_totpc <- with(a$setdat,swo/(bet+alb+yft+swo))\n print(paste(\"Region\",r))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ kmclust,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ claracl,data=a$setdat,FUN=mean))\n print(aggregate(cbind(bet_totpc,alb_totpc,yft_totpc,swo_totpc) ~ hclustcl,data=a$setdat,FUN=mean))\n }\n\nfor(r in 3:4) { \n a <- get(paste(\"clusters_4spp_spcomp_core_R\",r,sep=\"\"))\n if(r==3) core_clust <- a$setdat else core_clust <- rbind(core_clust,a$setdat)\n }\nsave(core_clust,file=\"core_clust.RData\")\n\na <- core_clust[,c(\"claracl\",\"reg\")]\ncore_clust$claracl[a$claracl==2 & a$reg==3] <- 1\ncore_clust$claracl[a$claracl==1 & a$reg==3] <- 2\na <- core_clust[,c(\"hclustcl\",\"reg\")]\ncore_clust$hclustcl[a$hclustcl==2 & a$reg==3] <- 1\ncore_clust$hclustcl[a$hclustcl==1 & a$reg==3] <- 2\n\ncore_clust <- core_clust[core_clust$bet+core_clust$yft+core_clust$alb+core_clust$swo > 0,]\nwrite.csv(table(core_clust$hclustcl,core_clust$reg),file=\"hclust_n_core_reg.csv\")\nwrite.csv(with(core_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(hclustcl,reg),mean)),file=\"hclust_pc_core_reg.csv\")\nwrite.csv(table(core_clust$kmclust,core_clust$reg),file=\"kmeans_n_core_reg.csv\")\nwrite.csv(with(core_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(kmclust,reg),mean)),file=\"kmeans_pc_core_reg.csv\")\nwrite.csv(table(core_clust$claracl,core_clust$reg),file=\"clara_n_core_reg.csv\")\nwrite.csv(with(core_clust,aggregate(cbind(bet,alb,swo,yft)/(bet+alb+swo+yft),by=list(claracl,reg),mean)),file=\"clara_pc_core_reg.csv\")\n\n# plot sets per cluster per year\nwindows(height=10,width=8);par(mfrow=c(2,1))\na1 <- aggregate(bet ~ op_yr,data=core_clust[core_clust$hclustcl==1 & core_clust$reg==3,],FUN=length)\na2 <- aggregate(bet ~ op_yr,data=core_clust[core_clust$reg==3,],FUN=length)\nplot(a1$op_yr,a1$bet/a2$bet,ylim=c(0,1),ylab=\"Proportion in YFT cluster\",xlab=\"Year\",main=\"Region 3\")\na1 <- aggregate(bet ~ op_yr,data=core_clust[core_clust$hclustcl==1 & core_clust$reg==4,],FUN=length)\na2 <- aggregate(bet ~ op_yr,data=core_clust[core_clust$reg==4,],FUN=length)\nplot(a1$op_yr,a1$bet/a2$bet,ylim=c(0,1),ylab=\"Proportion in YFT cluster\",xlab=\"Year\",main=\"Region 4\")\nsavePlot(\"nsets_per_cluster_per_yr\",type=\"png\")\n\n# plot maps of clusters per decade\na <- with(core_clust[core_clust$hclustcl==1,],tapply(hooks, list(factor(lon,levels=unique(core_clust$lon)),lat,eval(5*floor((op_yr)/5))),sum))\na2 <- with(core_clust,tapply(hooks, list(lon,lat,eval(5*floor((op_yr)/5))),sum))\n\na <- with(core_clust[core_clust$hclustcl==1,],aggregate(hooks, by=list(lon,lat,eval(5*floor((op_yr)/5))),sum))\na <- merge(a,with(core_clust,expand.grid(Group.1=unique(lon),Group.2=unique(lat),Group.3=seq(1990,2010,5))),all=T)\na2 <- with(core_clust,aggregate(hooks, by=list(lon,lat,eval(5*floor((op_yr)/5))),sum))\na2 <- merge(a2,with(core_clust,expand.grid(Group.1=unique(lon),Group.2=unique(lat),Group.3=seq(1990,2010,5))),all=T)\na[,4] <- a[,4]/a2[,4]\nnames(a)[1:4] <- c(\"lon\",\"lat\",\"decade\",\"p\")\n\nwindows(width=20,height=15);par(mfrow=c(2,2))\nfor(d in seq(1990,2005,5)) with(a,plot_catchmap(indat=a,vbl=p,latlim=c(-5,10),dcd=d))\nsavePlot(\"map_clusters_by_decade\",type=\"png\")\n\n# plot nominal CPUE per cluster per year\na <- aggregate(cbind(bet/hooks,yft/hooks) ~ op_yr + reg + hclustcl,data=core_clust,FUN=mean)\nnames(a)[4:5] <- c(\"bet_cpue\",\"yft_cpue\")\na2 <- aggregate(cbind(bet/hooks,yft/hooks) ~ op_yr + reg,data=core_clust,FUN=mean)\nnames(a2)[3:4] <- c(\"bet_cpue\",\"yft_cpue\")\n\nwindows(10,10);par(mfrow=c(2,2))\nfor(r in 3:4) {\n x <- a[a$reg==r,]\n x2 <- a2[a2$reg==r,]\n with(x[x$hclustcl==2,],plot(op_yr,bet_cpue,col=2,type=\"l\",ylim=c(0,0.02)))\n with(x[x$hclustcl==1,],lines(op_yr,bet_cpue,col=3))\n with(x2,lines(op_yr,bet_cpue,col=1))\n with(x[x$hclustcl==2,],plot(op_yr,yft_cpue,col=2,type=\"l\",ylim=c(0,0.02)))\n with(x[x$hclustcl==1,],lines(op_yr,yft_cpue,col=3))\n with(x2,lines(op_yr,yft_cpue,col=1))\n }\nlegend(\"topright\",legend=c(\"Not clustered\",\"BET cluster\",\"YFT cluster\"),lty=1,col=c(1,2,3))\nsavePlot(\"Raw_CPUE_by_cluster_core\",type=\"png\")\n\nopcore <- op2[op2$lat > -5 & op2$lat < 10 & op2$reg %in% 1:6 & !is.na(op2$tripid),]\ntpcore <- aggregate_for_cluster(dat=opcore)\nfor(r in 3:4) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2))\n for(y in 1978:2010) {\n hist(tpcore[tpcore$reg==r & tpcore$trip_yr==y,]$bet_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropBET_per_trip_core_R\",r),type=\"png\")\n }\nfor(r in 3:4) {\n windows(12,12);par(mfrow=c(6,6),mar=c(2,2,2,2))\n for(y in 1978:2010) {\n hist(tpcore[tpcore$reg==r & tpcore$trip_yr==y,]$yft_totpc,main=y,nclass=20,xlim=c(0,1))\n }\n title(paste(\"Region\",r),outer=T,line=1)\n savePlot(paste(\"PropYFT_per_trip_core_R\",r),type=\"png\")\n }\n\n", "meta": {"hexsha": "3e20595094882d7231d8b4d46e6a030b4b2f9000", "size": 28200, "ext": "r", "lang": "R", "max_stars_repo_path": "cluster_function.r", "max_stars_repo_name": "hoyles/R4CPUE", "max_stars_repo_head_hexsha": "6171bb24b6f73211a3d4daac3063d6926c08c169", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cluster_function.r", "max_issues_repo_name": "hoyles/R4CPUE", "max_issues_repo_head_hexsha": "6171bb24b6f73211a3d4daac3063d6926c08c169", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cluster_function.r", "max_forks_repo_name": "hoyles/R4CPUE", "max_forks_repo_head_hexsha": "6171bb24b6f73211a3d4daac3063d6926c08c169", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.4579901153, "max_line_length": 193, "alphanum_fraction": 0.6762411348, "num_tokens": 11816, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3032773402500584}} {"text": "# MLED - Multisectoral Latent Electricity Demand assessment platform\n# v1.1, R programming language\n# Hourly resolution\n# Version: 31/05/2021\n# giacomo.falchetta@feem.it\n\n####\n# Define the working directory\nsetwd(\"D:/OneDrive - FONDAZIONE ENI ENRICO MATTEI/Current papers/Prod_Uses_Agriculture/Repo\")\n\n# \ntimestamp()\nsource(\"backend.R\", echo = F)\n\n# Import the manual_parameters, to be set before running this script\ntimestamp()\nsource(\"manual_parameters.R\", echo = F)\n\n# Select the scenario to be operated\ntimestamp()\nsource(\"scenario_baseline.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Run population clustering (choose travel-time based on contiguity-based clustering)\ntimestamp()\n#source(\"traveltime_based_clustering.R\", echo = F)\nsource(\"contiguity_based_clustering.R\", echo = F)\n##save.image(file=\"bk1.Rdata\")\n\n# Estimate electrification\ntimestamp()\nsource(\"electrification_estimation.R\", echo = F)\n##save.image(file=\"bk1.Rdata\")\n\n# Cropland and irrigation demand\ntimestamp()\nsource(\"crop_module.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Water pumping to energy\ntimestamp()\nsource(\"groundwater_module.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Residential energy demand\ntimestamp()\nsource(\"residential.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Health and education demand \ntimestamp()\nsource(\"health_education_module.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Other productive\ntimestamp()\nsource(\"other_productive.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Produce spatio-temporal plots of demand \ntimestamp()\nsource(\"hourly_conversion_plotting.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Write output\nwrite_sf(sf2, paste0(home_repo_folder, 'clusters_final.gpkg',driver=\"GPKG\"))\n\n# Write rasters for output\n\nr <- raster(); res(r) <- 0.5; extent(r) <- ext; crs(r) <- \"+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs\"\n\nfields <- c(\"er_kwh_tt\")\n\nfor (k in fields){\nwriteRaster(fasterize::fasterize(clusters, r, field=k, fun=\"first\"), paste0(k, \".tif\"))\n}\n\n#################\n# Economic analysis\n\n# Run it\ntimestamp()\nsource(\"MLED_economic_analysis.R\", echo = F)\n#save.image(file=\"bk1.Rdata\")\n\n# Plot it\ntimestamp()\nsource(\"economic_analysis_plots.R\", echo = F)\n\n\n", "meta": {"hexsha": "917ddf83dc9bd586782fe27ef42cc7714bed3306", "size": 2216, "ext": "r", "lang": "R", "max_stars_repo_path": "MLED_hourly.r", "max_stars_repo_name": "giacfalk/M-LED", "max_stars_repo_head_hexsha": "de52327272f11cd48f73bdb4c0b660fa2c6b0089", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2021-02-18T15:15:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-07T07:48:50.000Z", "max_issues_repo_path": "MLED_hourly.r", "max_issues_repo_name": "giacfalk/M-LED", "max_issues_repo_head_hexsha": "de52327272f11cd48f73bdb4c0b660fa2c6b0089", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MLED_hourly.r", "max_forks_repo_name": "giacfalk/M-LED", "max_forks_repo_head_hexsha": "de52327272f11cd48f73bdb4c0b660fa2c6b0089", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-08-23T20:30:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-16T16:36:44.000Z", "avg_line_length": 24.3516483516, "max_line_length": 129, "alphanum_fraction": 0.725631769, "num_tokens": 620, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.3032773402500584}} {"text": "plot(x,y)\n", "meta": {"hexsha": "344aa37a93e62bf5b137058cc216d5fcdb0b79d5", "size": 10, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Plot-coordinate-pairs/R/plot-coordinate-pairs-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Plot-coordinate-pairs/R/plot-coordinate-pairs-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Plot-coordinate-pairs/R/plot-coordinate-pairs-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 5.0, "max_line_length": 9, "alphanum_fraction": 0.6, "num_tokens": 4, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.3032773402500584}} {"text": "a<-read.csv(\"ai6oct.csv\")\r\nprint(a)\r\nb<-max(a$student_marks)\r\nprint(b)\r\nb<-subset(a , student_marks==max(student_marks))\r\nprint(b)\r\nc<-subset(a , student_dept==\"cse\")\r\nprint(c)\r\nd<-subset(a , student_hobby==\"cricket\" & student_dept==\"ece\")\r\nprint(d)\r\ne<-read.table(\"ai12oct1.txt\", TRUE, sep=\",\" ,quote = \"\\\"\")\r\nprint(e)\r\n", "meta": {"hexsha": "6eceac582a6746658e4c7083006b59ae22f4d9c6", "size": 321, "ext": "r", "lang": "R", "max_stars_repo_path": "ai lab 13oct.r", "max_stars_repo_name": "STARK-yv/AIML_Lab", "max_stars_repo_head_hexsha": "4c162fe5d8a67aef45a10a4623a55652fad7334d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ai lab 13oct.r", "max_issues_repo_name": "STARK-yv/AIML_Lab", "max_issues_repo_head_hexsha": "4c162fe5d8a67aef45a10a4623a55652fad7334d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ai lab 13oct.r", "max_forks_repo_name": "STARK-yv/AIML_Lab", "max_forks_repo_head_hexsha": "4c162fe5d8a67aef45a10a4623a55652fad7334d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.6923076923, "max_line_length": 62, "alphanum_fraction": 0.6448598131, "num_tokens": 98, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.5926665999540697, "lm_q1q2_score": 0.3032773402500583}} {"text": "\n\nexecuteTask <- function(i) {\n\n \tcompose <- function(f,g) function(x) { f(g(x)) } \n \tt <- compose(sin, asin)\n \t#t(i)\n\treturn(t(i))\n}\n\nr = 1\n\nfor (i in 0:1000000000) {\n\tr = executeTask(r + i)\n}\n", "meta": {"hexsha": "87a4563e60a30cd3530220dd927f7f3739a7a900", "size": 206, "ext": "r", "lang": "R", "max_stars_repo_path": "tasks/function-composition/r/function-composition.r", "max_stars_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_stars_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tasks/function-composition/r/function-composition.r", "max_issues_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_issues_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tasks/function-composition/r/function-composition.r", "max_forks_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_forks_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 12.875, "max_line_length": 57, "alphanum_fraction": 0.5194174757, "num_tokens": 73, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.8031738057795403, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.30323077579515945}} {"text": "# .hlpr_modular_report ----\r\n.hlpr_modular_report <- function(model_list, cv, show_cv, robust_type, var_cluster) {\r\n out <- lapply(seq(length(model_list)), function(x) {\r\n out <- model_summary(input = model_list[[x]], type = robust_type, var_cluster = var_cluster, show = FALSE)\r\n out$mod <- paste0(\"mod_\", x)\r\n return(out)\r\n })\r\n \r\n out <- dplyr::bind_rows(out)\r\n out <- tidyr::pivot_longer(out, -c(Variables, mod), names_to = \"col\", values_to = \"val\")\r\n \r\n out_estimate <- .hlpr_get_col(input = out, col = \"Estimate\")\r\n out_pvalue <- .hlpr_get_col(input = out, col = \"p_value\")\r\n out_se <- .hlpr_get_col(input = out, col = \"Std_Error\")\r\n \r\n if (!show_cv) {\r\n out_estimate <- out_estimate[!(out_estimate$Variables %in% c(\"(Intercept)\", cv)), -2]\r\n out_pvalue <- out_pvalue[!(out_pvalue$Variables %in% c(\"(Intercept)\", cv)), -2]\r\n\t out_se <- out_se[!(out_se$Variables %in% c(\"(Intercept)\", cv)), -2]\r\n\t model_list <- model_list[-1]\r\n }\r\n \r\n out <- list(coefficients = out_estimate, p.values = out_pvalue, std.errors = out_se, models = model_list)\r\n return(out)\r\n}\r\n", "meta": {"hexsha": "32e6092f0bc7c69ddd8280bd7b5b6b85611579f8", "size": 1097, "ext": "r", "lang": "R", "max_stars_repo_path": "R/hlpr_modular_report.r", "max_stars_repo_name": "ha-pu/supportR", "max_stars_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/hlpr_modular_report.r", "max_issues_repo_name": "ha-pu/supportR", "max_issues_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11, "max_issues_repo_issues_event_min_datetime": "2020-07-10T07:17:20.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-16T09:17:33.000Z", "max_forks_repo_path": "R/hlpr_modular_report.r", "max_forks_repo_name": "ha-pu/supportR", "max_forks_repo_head_hexsha": "b49002f2e4094b33e29d97ad0f9c6a5150432951", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.1923076923, "max_line_length": 111, "alphanum_fraction": 0.6399270738, "num_tokens": 322, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3031684709816578}} {"text": "setwd('/home/pc-752828/Dev/results-search-master')\n\ndata <- read.csv(file = 'output.out', sep = ',', header = T)\ndata_new <- data\nvalue <- function(x, name) {\n mym = mean(x)\n mysd = sd(x)\n mysqrt = sqrt(length(x))\n \n return(c(mn = mym, sd = mysd/mysqrt, bo = name))\n}\n\ndata_f<-data.frame(data_new[data_new['target'] == 0,])\nbo1 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO-6rnd-EIdef-PI')))\nbo1 <- as.data.frame(as.list(bo1))\n\ndata_f<-data.frame(data_new[data_new['target'] == 1,])\nbo2 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO-6rnd-EIdef-Real')))\nbo2 <- as.data.frame(as.list(bo2))\n\ndata_f<-data.frame(data_new[data_new['target'] == 2,])\nbo3 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'PB3Opt-PI')))\nbo3 <- as.data.frame(as.list(bo3))\n\ndata_f<-data.frame(data_new[data_new['target'] == 3,])\nbo4 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'PB3Opt-Real')))\nbo4 <- as.data.frame(as.list(bo4))\n\ndata_f<-data.frame(data_new[data_new['target'] == 4,])\nbo5 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO6sel-EIdef-PI')))\nbo5 <- as.data.frame(as.list(bo5))\n\ndata_f<-data.frame(data_new[data_new['target'] == 5,])\nbo6 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO6sel-EIdef-Real')))\nbo6 <- as.data.frame(as.list(bo6))\n\ndf <- rbind(bo1, bo2, bo3, bo4)\ndf$x.mn = as.numeric(as.character(df$x.mn))\ndf$Group.1 = as.numeric(as.character(df$Group.1))\ndf$x.sd = as.numeric(as.character(df$x.sd))\ndf_new <- df\n\np3 <- ggplot(data=df_new, aes(x=Group.1, y=x.mn, group=x.bo)) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd,color=x.bo), width=0.2, size=.3) +\n geom_line(aes(color=x.bo), size=1) +\n geom_vline(xintercept=6, linetype=\"dashed\", size=1)+\n coord_cartesian(ylim=c(1,1.2), xlim=c(1,30))+\n labs(color = element_blank(), x=\"Itera\u00e7\u00e3o\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.position = \"none\")+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8))+\n scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n scale_y_continuous(minor_breaks = seq(0, 10, 1))+ ggtitle('c) Zoom da Figura a')\n\n\np2 <- ggplot(data=df_new, aes(x=Group.1, y=x.mn, group=x.bo)) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd,color=x.bo), width=0.2, size=.3) +\n geom_line(aes(color=x.bo), size=1) +\n geom_vline(xintercept=6, linetype=\"dashed\", size=1)+\n coord_cartesian(ylim=c(1,2), xlim=c(1,30))+\n labs(color = element_blank(), x=\"Itera\u00e7\u00e3o\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(1, 1),legend.position=c(.95, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8))+\n scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n scale_y_continuous(minor_breaks = seq(0, 10, 1))+ ggtitle('b) Zoom da Figura a')\n\np1 <- ggplot(data=df_new, aes(x=Group.1, y=x.mn, group=x.bo)) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd,color=x.bo), width=0.2, size=.3) +\n geom_line(aes(color=x.bo), size=1) +\n geom_vline(xintercept=6, linetype=\"dashed\", size=1)+\n labs(color = element_blank(), x=\"Itera\u00e7\u00e3o\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(1, 1),legend.position=c(.95, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))+\n scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n scale_y_continuous(minor_breaks = seq(0, 800, 100))+ ggtitle('a) Melhor Custo Encontrado')\n\ntikz('fig3.tex')\ngrid.arrange(p1, p2, nrow = 2, ncol=1, top=\"\", left=\"M\u00e9dia do Melhor Custo Normalizado\")\ndev.off()\n\n\n\n\n\n\n\ndata <- read.csv(file = 'output_cost.out', sep = ',', header = T)\ndata_new <- data\nvalue <- function(x, name) {\n mym = mean(x)\n mysd = sd(x)\n mysqrt = sqrt(length(x))\n \n return(c(mn = mym, sd = mysd/mysqrt, bo = name))\n}\n\n\n\ndata_f<-data.frame(data_new[data_new['target'] == 0,])\nbo1 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO-6rnd-EIdef-PI')))\nbo1 <- as.data.frame(as.list(bo1))\n\ndata_f<-data.frame(data_new[data_new['target'] == 1,])\nbo2 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'BO-6rnd-EIdef-Real')))\nbo2 <- as.data.frame(as.list(bo2))\n\ndata_f<-data.frame(data_new[data_new['target'] == 2,])\nbo3 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'PB3Opt-PI')))\nbo3 <- as.data.frame(as.list(bo3))\n\ndata_f<-data.frame(data_new[data_new['target'] == 3,])\nbo4 <- aggregate(x = data_f$best,\n by = list(data_f$ite),\n FUN = function(x) return(value(x, 'PB3Opt-Real')))\nbo4 <- as.data.frame(as.list(bo4))\n\ndf <- rbind(bo1, bo2, bo3, bo4)\ndf$x.mn = as.numeric(as.character(df$x.mn))\ndf$Group.1 = as.numeric(as.character(df$Group.1))\ndf$x.sd = as.numeric(as.character(df$x.sd))\ndf_new <- df\n\n\np2 <- ggplot(data=df_new, aes(x=Group.1, y=x.mn, group=x.bo)) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd,color=x.bo), width=0.2, size=.3) +\n geom_line(aes(color=x.bo), size=1) +\n geom_vline(xintercept=6, linetype=\"dashed\", size=1)+\n coord_cartesian(ylim=c(1,300), xlim=c(1,30))+\n labs(color = element_blank(), x=\"Itera\u00e7\u00e3o\", y=\"M\u00e9dia do Melhor Custo Normalizado\")+\n theme(legend.justification=c(1, 1),legend.position=c(.4, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8))+\n scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n scale_y_continuous(minor_breaks = seq(0, 300, 50))+ ggtitle('b) Zoom da Figura a')\n\np1 <- ggplot(data=df_new, aes(x=Group.1, y=x.mn, group=x.bo)) +\n geom_errorbar(aes(ymin=x.mn-x.sd, ymax=x.mn+x.sd,color=x.bo), width=0.2, size=.3) +\n geom_line(aes(color=x.bo), size=1) +\n geom_vline(xintercept=6, linetype=\"dashed\", size=1)+\n labs(color = element_blank(), x=\"Itera\u00e7\u00e3o\", y=\"M\u00e9dia do Custo Total Normalizado\")+\n theme(legend.justification=c(1, 1),legend.position=c(.4, .95),legend.title=element_blank())+\n theme(panel.background = element_rect(fill = 'white', colour = 'gray'),\n panel.grid.major = element_line(color = 'light gray'),\n panel.grid.minor = element_line(color = 'light gray'),\n axis.title.y=element_blank(),\n axis.text.y = element_text(size=8),\n legend.background = element_rect(fill=alpha('white', 0.6)))+\n scale_x_continuous(minor_breaks = seq(0, 35, 0.5))+\n scale_y_continuous(minor_breaks = seq(0, 10000, 1000)) + ggtitle('a) Custo Total da Busca')\n #geom_segment(aes(x = 31, y = 500, xend = 31, yend = 6500), arrow = arrow(length = unit(0.5, \"cm\")))\n\ntikz('fig4.tex')\ngrid.arrange(p1, p2, nrow = 2, ncol=1, top=\"\", left=\"M\u00e9dia do Custo Normalizado\")\ndev.off()\n", "meta": {"hexsha": "17f785565f5cb0923e2e25c72f4ed89464c9964e", "size": 8040, "ext": "r", "lang": "R", "max_stars_repo_path": "results-dissertation/compare-3.r", "max_stars_repo_name": "lmcad-unicamp/PB3Opt", "max_stars_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "results-dissertation/compare-3.r", "max_issues_repo_name": "lmcad-unicamp/PB3Opt", "max_issues_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "results-dissertation/compare-3.r", "max_forks_repo_name": "lmcad-unicamp/PB3Opt", "max_forks_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.7659574468, "max_line_length": 102, "alphanum_fraction": 0.6338308458, "num_tokens": 2529, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704796847396, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3031501653928569}} {"text": "inv <- function(x) solve(x)\r\n\r\n#--------------------------------\r\n#\r\n# WORKS IN PROGRESS\r\n#\r\n\r\n## a <- c(1,2,3,4,5,6)\r\n## b <- c(4,5,6,6,7,8)\r\n## a <- data.frame(a,b)\r\n## names(a) <- c(\"test1\",\"test2\")\r\n\r\n", "meta": {"hexsha": "2d3cb8c85869284206dd30f4d7afa76674972059", "size": 205, "ext": "r", "lang": "R", "max_stars_repo_path": "modules/inv.r", "max_stars_repo_name": "TECComputing/R-setup", "max_stars_repo_head_hexsha": "f4b5e45c6e2e55fcc6f58f804bb50cea3a697fe0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/inv.r", "max_issues_repo_name": "TECComputing/R-setup", "max_issues_repo_head_hexsha": "f4b5e45c6e2e55fcc6f58f804bb50cea3a697fe0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/inv.r", "max_forks_repo_name": "TECComputing/R-setup", "max_forks_repo_head_hexsha": "f4b5e45c6e2e55fcc6f58f804bb50cea3a697fe0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-06-16T12:06:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-16T12:06:21.000Z", "avg_line_length": 15.7692307692, "max_line_length": 34, "alphanum_fraction": 0.3804878049, "num_tokens": 76, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5544704649604272, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.30315015734251355}} {"text": "library(signal)\nlibrary(plyr)\n\ndaily <- function(x) { if (length(x) > 5) {median(x, na.rm=T) } else { NA} }\nfilter <- function(x) runmed(x, 13)\n\ndata <- read.csv('data.csv', header=F)\nnames(data) <- c('plot','depth','datetime','vwc')\ndata$datetime <- as.POSIXct(data$datetime)\n\ndata$date <- as.Date(format(data$datetime, '%Y-%m-%d'))\nfiltered <- ddply(data, .(plot, depth), transform, filtered=filter(vwc))\nreduced <- ddply(filtered, .(plot, depth, date), summarize, daily=daily(filtered))\n\nwrite.csv(reduced[complete.cases(reduced),],'filtered.csv', row.names=FALSE) #, col.names=FALSE)\n", "meta": {"hexsha": "89e789283c6ca86bef09e2d9e86c962198f03612", "size": 589, "ext": "r", "lang": "R", "max_stars_repo_path": "fft-filter.r", "max_stars_repo_name": "kf8a/tdr-cleaner", "max_stars_repo_head_hexsha": "d575387feeb0d5f7801899be3c90c676085ad36b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "fft-filter.r", "max_issues_repo_name": "kf8a/tdr-cleaner", "max_issues_repo_head_hexsha": "d575387feeb0d5f7801899be3c90c676085ad36b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "fft-filter.r", "max_forks_repo_name": "kf8a/tdr-cleaner", "max_forks_repo_head_hexsha": "d575387feeb0d5f7801899be3c90c676085ad36b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8125, "max_line_length": 96, "alphanum_fraction": 0.6740237691, "num_tokens": 174, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.554470450236115, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.30315014929217027}} {"text": "#####################################################\r\n# Phylogenetic diversity in R\r\n\r\n#Set working directory\r\nsetwd(\"D:/Dropbox/iDiv/MLU Biodiversity_2021/phylogenetic_diversity/Practice\")\r\n\r\n#install and load package \"V.PhyloMaker\" to generate phylogenoes based on mega-tree\r\nif(!require(\"devtools\")) install.packages(\"devtools\")\r\nlibrary(devtools)\r\nif(!require(\"V.PhyloMaker\")) devtools::install_github(\"jinyizju/V.PhyloMaker\")\r\nlibrary(V.PhyloMaker)\r\n\r\n#install and load other packages\r\npackages <- c(\"vegan\",\"ape\",\"picante\",\"fields\")\r\npackage.check <- lapply(packages,FUN=function(x)\r\n {\r\n if(!require(x,character.only=TRUE)){\r\n install.packages(x,dependencies=TRUE)\r\n library(x,character.only=TRUE)\r\n }\r\n }\r\n)\r\n\r\n\r\n###############\r\n#1.we will load the BCI data in the packag \"vegan\", and assemble the taxonomic names for the species in BCI, \r\n#and finally build a phylogeny for these species\r\n\r\n#a. BCI dataset\r\ndata(BCI, BCI.env)\r\n\r\n##b.Regute taxonomic informaiton for species in BCI. Both geuns and family are needed to build a phylogeny here.\r\n#The genus are extracted from species names. The species list is then submitted to the TNRS website http://tnrs.iplantcollaborative.org/.\r\n#The TNRS will check taxonomic information and the return result includs the family. Check the website youself. You can also use the \r\n#result \"bci.spp.final.csv\" for next steps\r\n\r\nbci.spp <- data.frame(\"ID\"=1:ncol(BCI),\"species.raw\"=colnames(BCI),\"species\"=NA,\"genus\"=NA,\"family\"=NA,stringsAsFactors=FALSE)\r\nfor(i in 1:nrow(bci.spp)){\r\n names <- unlist(strsplit(bci.spp[i,2],split=\".\",fixed=TRUE))\r\n bci.spp[i,3]<- paste(names,collapse=\" \")\r\n bci.spp[i,4] <- names[1]\r\n}\r\n\r\n#Output the taxonomic informaiton to check use TNRS: http://tnrs.iplantcollaborative.org/\r\nwrite.csv(bci.spp,file=\"bci.spp.raw.csv\", row.names=FALSE)\r\n\r\n#Input the resultant taxonomic information for next steps\r\nbci.spp <- read.csv(\"bci.spp.final.csv\")\r\n\r\n#c. Generate a phylogeny for species in BCI\r\n?phylo.maker()\r\nbci.tree.output <- phylo.maker(bci.spp[,3:5],tree=GBOTB.extended,nodes=nodes.info.1,scenarios=\"S3\")\r\n\r\n#write the tree generated to a txt.\r\nwrite.tree(bci.tree.output$scenario.3,file=\"bci.tree.txt\")\r\n\r\n\r\n############\r\n#2. To check the attributes of the generated phylogeny, and do simple manipulations\r\n\r\n#Read the bci phylogenetic tree\r\nbci.tree <- read.tree(\"bci.tree.txt\")\r\n\r\n#a. check the attributes\r\nclass(bci.tree)\r\nstr(bci.tree)\r\nsummary(bci.tree)\r\nhead(bci.tree$edge) #the first six edge (branch), with the first column as parental node and the second as child node\r\nlength(bci.tree$edge.length) #the number of edge\r\nlength(bci.tree$tip.label) #the nunber of tips\r\n\r\n\r\n#b. Do some simple plotting. To find out what options are available for plotting phylogenies, use ?plot.phylo. \r\n#Then, make a set of plots of the phylogeny using different types.\r\n?plot.phylo\r\nplot(bci.tree,type=\"fan\",cex=0.4)\r\n\r\npar(mfrow=c(1,5),mar=c(0,1,0,1))\r\nplot(bci.tree,cex=0.4)\r\nplot(bci.tree,type=\"cladogram\",cex=0.4)\r\nplot(bci.tree,type=\"fan\",cex=0.4)\r\nplot(bci.tree,type=\"unrooted\",cex=0.4)\r\nplot(bci.tree,type=\"radial\",cex=0.4)\r\n\r\n\r\n#c. plot a zoomed part of the phylogeny \r\n#(in this case, try the first 14 tips) using the zoom() function.\r\nzoom(bci.tree,focus=bci.tree$tip[5:19])\r\n\r\n\r\n#d. Use drop.tip() to remove 180 random tips from the tree, and plot the new tree\r\nbci.tree.drop <- drop.tip(bci.tree,sample(bci.tree$tip,180,replace=F))\r\npar(mfrow=c(1,1),mar=c(4,4,4,4))\r\nplot(bci.tree.drop,type=\"fan\",cex=0.8)\r\n\r\n\r\n##e. Creating a random tree\r\nrandom.tree <- rtree(50)\r\nplot(random.tree)\r\n\r\n\r\n##f. Calculate phylogentic distance among species\r\nbci.tree.drop.dist <- cophenetic(bci.tree.drop)\r\ndim(bci.tree.drop.dist )\r\nbci.tree.drop.dist [1:5,1:5] #the distance among the first five species\r\n\r\n\r\n\r\n####################\r\n#3.\tNow we will calculate some phylogenetic community measures \r\n#(PD, MPD, MNTD, and their related indices,PDI, NRI and NTI) using BCI community data, \r\n\r\n#a. Make the species names in the community data and phylogy are consistent in format \r\nBCI2 <- BCI\r\nbci.tree2 <- bci.tree\r\ncolnames(BCI2) <- bci.spp[,3]\r\nbci.tree2$tip.label <- gsub(\"_\",\" \",fixed=TRUE,bci.tree2$tip.label)\r\n\r\n\r\n#b.Now use the mpd(), pd() and mntd() functions to calculate phylogenetic diversity.\r\n#Calculate PD\r\nbci.pd <- pd(BCI2,bci.tree2)\r\nbci.pd.mat <- matrix(bci.pd$PD,ncol=10,nrow=5);\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.pd.mat),xlab=\"\",ylab=\"\")\r\n\r\n#Calculate MPD\r\nbci.mpd <- mpd(BCI2,cophenetic(bci.tree2))\r\nbci.mpd.mat <- matrix(bci.mpd,ncol=10,nrow=5);\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.mpd.mat),xlab=\"\",ylab=\"\")\r\n\r\n#Calculate MNTD\r\nbci.mntd <- mntd(BCI2,cophenetic(bci.tree2))\r\nbci.mntd.mat <- matrix(bci.mntd,ncol=10,nrow=5);\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.mntd.mat),xlab=\"\",ylab=\"\")\r\n\r\n\r\n\r\n#c.\tUse ses.mpd(), ses.pd() and ses.mntd() to calculate the standardized effect size statistics (PDI, NRI, and NTI).\r\n#Calculate PDI\r\nbci.pdi <- ses.pd(BCI2,bci.tree2,runs=99)\r\nbci.pdi.mat <- matrix(bci.pdi$pd.obs.z,ncol=10,nrow=5);\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.pdi.mat),xlab=\"\",ylab=\"\")\r\n\r\n#Calculate NRI\r\nbci.nri <- ses.mpd(BCI2,cophenetic(bci.tree2),runs=99)\r\nbci.nri.mat <- matrix(-bci.nri$mpd.obs.z,ncol=10,nrow=5)\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.nri.mat),xlab=\"\",ylab=\"\")\r\n\r\n#Calculate NTI\r\nbci.nti <- ses.mntd(BCI2,cophenetic(bci.tree2),runs=99)\r\nbci.nti.mat <- matrix(-bci.nti$mntd.obs.z,ncol=10,nrow=5)\r\nimage.plot(x=seq(0,1000,by=100),y=seq(0,500,by=100),t(bci.nti.mat),xlab=\"\",ylab=\"\")\r\n\r\n\r\n#d.\tCompare the results for NRI using the \"taxa.labels\" algorithm to two other null model algorithms.\r\nNRI1 = ses.mpd(BCI2,cophenetic(bci.tree2),null.model=\"taxa.labels\",runs=99)\r\nNRI2 = ses.mpd(BCI2,cophenetic(bci.tree2),null.model=\"frequency\",runs=99)\r\nNRI3 = ses.mpd(BCI2,cophenetic(bci.tree2),null.model=\"trialswap\",runs=99)\r\n\r\nplot(NRI1$mpd.obs.z,NRI2$mpd.obs.z)\r\nplot(NRI1$mpd.obs.z,NRI3$mpd.obs.z)\r\nplot(NRI2$mpd.obs.z,NRI3$mpd.obs.z)\r\n#Pretty different, but still highly correlated.\r\n\r\n", "meta": {"hexsha": "239011ee90784fcb40f514cb5f2f725b82648265", "size": 6136, "ext": "r", "lang": "R", "max_stars_repo_path": "week 1/5 - Friday/phylogenetic_diversity/Practice/Phylogeny_diversity.r", "max_stars_repo_name": "chase-lab/biodiv-patterns-course-2021", "max_stars_repo_head_hexsha": "5973a33a6c243a9d0ef8a9e053d188e999557167", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "week 1/5 - Friday/phylogenetic_diversity/Practice/Phylogeny_diversity.r", "max_issues_repo_name": "chase-lab/biodiv-patterns-course-2021", "max_issues_repo_head_hexsha": "5973a33a6c243a9d0ef8a9e053d188e999557167", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "week 1/5 - Friday/phylogenetic_diversity/Practice/Phylogeny_diversity.r", "max_forks_repo_name": "chase-lab/biodiv-patterns-course-2021", "max_forks_repo_head_hexsha": "5973a33a6c243a9d0ef8a9e053d188e999557167", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-25T10:09:33.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-25T10:09:33.000Z", "avg_line_length": 37.1878787879, "max_line_length": 138, "alphanum_fraction": 0.6931225554, "num_tokens": 2037, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.554470450236115, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.30315014929217027}} {"text": "#' getters\n#' \n#' Getters for shaq objects.\n#' \n#' @details\n#' Functions to return the number of rows (\\code{nrow()} and \\code{NROW()}),\n#' the number of columns (\\code{ncol()} and \\code{NCOL()}), the length - or\n#' product of the number of rows and cols - (\\code{length()}), and the local\n#' submatrix (\\code{Data()}).\n#' \n#' @section Communication:\n#' Each operation is completely local.\n#' \n#' @param x\n#' A shaq.\n#' \n#' @seealso \\code{\\link{setters}}\n#' @name getters\n#' @rdname getters\nNULL\n\n\n\nnrows.shaq = function(x) x@nrows\n\n#' @rdname getters\n#' @export\nsetMethod(\"nrow\", signature(x=\"shaq\"), nrows.shaq)\n\n#' @rdname getters\n#' @export\nsetMethod(\"NROW\", signature(x=\"shaq\"), nrows.shaq)\n\nnrows.local.shaq = function(x) NROW(Data(x))\n\n#' @rdname getters\n#' @export\nsetGeneric(name=\"nrow.local\", useAsDefault=nrows.local.shaq, package=\"kazaam\")\n\n#' @rdname getters\n#' @export\nsetMethod(\"nrow.local\", signature(x=\"shaq\"), nrows.local.shaq)\n\n\n\nncols.shaq = function(x) x@ncols\n\n#' @rdname getters\n#' @export\nsetMethod(\"ncol\", signature(x=\"shaq\"), ncols.shaq)\n\n#' @rdname getters\n#' @export\nsetMethod(\"NCOL\", signature(x=\"shaq\"), ncols.shaq)\n\nncols.local.shaq = function(x) NCOL(Data(x))\n\n#' @rdname getters\n#' @export\nsetGeneric(name=\"ncol.local\", useAsDefault=ncols.local.shaq, package=\"kazaam\")\n\n#' @rdname getters\n#' @export\nsetMethod(\"ncol.local\", signature(x=\"shaq\"), ncols.local.shaq)\n\n\n\n#' @rdname getters\n#' @export\nsetMethod(\"length\", signature(x=\"shaq\"), function(x) nrow(x)*ncol(x))\n\n\n\n\nData.shaq = function(x) x@Data\n\n#' @rdname getters\n#' @export\nsetGeneric(name=\"Data\", useAsDefault=Data.shaq, package=\"kazaam\")\n\n#' @rdname getters\n#' @export\nsetMethod(\"Data\", signature(x=\"shaq\"), Data.shaq)\n", "meta": {"hexsha": "028dcbcee41f02295d67b7a80dc5995e76f56d95", "size": 1712, "ext": "r", "lang": "R", "max_stars_repo_path": "R/02-getters.r", "max_stars_repo_name": "cran/kazaam", "max_stars_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/02-getters.r", "max_issues_repo_name": "cran/kazaam", "max_issues_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/02-getters.r", "max_forks_repo_name": "cran/kazaam", "max_forks_repo_head_hexsha": "4371c4c509f984d5cb97180ca9b93d37a4475901", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.380952381, "max_line_length": 78, "alphanum_fraction": 0.6723130841, "num_tokens": 524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5428632831725053, "lm_q1q2_score": 0.30309522522873816}} {"text": "N_0 <- 100\ny_0 <- 10\n\nN <- 1000\ny <- 200\n\ncc <- 1\ndd <- 1\n\nnu <- 1\neta <- 1\n\n\n", "meta": {"hexsha": "19a28ad4604a2cec764bfae8d4bc82148a31461c", "size": 78, "ext": "r", "lang": "R", "max_stars_repo_path": "code/Bernoulli/data_Bernoulli_scenario_2.r", "max_stars_repo_name": "maxbiostat/propriety_power_priors", "max_stars_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/Bernoulli/data_Bernoulli_scenario_2.r", "max_issues_repo_name": "maxbiostat/propriety_power_priors", "max_issues_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 7, "max_issues_repo_issues_event_min_datetime": "2020-05-29T19:11:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-29T15:58:08.000Z", "max_forks_repo_path": "code/Bernoulli/data_Bernoulli_scenario_2.r", "max_forks_repo_name": "maxbiostat/propriety_power_priors", "max_forks_repo_head_hexsha": "43a9dc7bd007d5647bc453cd8a875e82c16ad6eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 5.5714285714, "max_line_length": 10, "alphanum_fraction": 0.4230769231, "num_tokens": 45, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5583269943353744, "lm_q1q2_score": 0.30309522522873805}} {"text": "bloop <- -3.4\nif(exists(\"bloop\", envir=globalenv()) && exists(\"abs\") && is.function(abs))\n{\n abs(bloop)\n}\n", "meta": {"hexsha": "2c271a8ac9586bd0dd5cae2322d91be8dfb614b6", "size": 108, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Introspection/R/introspection-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Introspection/R/introspection-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Introspection/R/introspection-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 18.0, "max_line_length": 75, "alphanum_fraction": 0.5925925926, "num_tokens": 38, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3030037010423602}} {"text": "#my_truth_calculates distance matrix for Best_AR_3_stationary_TS_models\r\nrm(list=ls())\r\n\r\n\r\nroom<-read.table(\"K:\\\\Project_Data_Sagar\\\\Halwa\\\\BADMASHI\\\\my_truth\\\\Model\\\\best_model\\\\Stationary\\\\my_truth_Best_stationary_AR_3.txt\",skip=1) #opens BEST_AR_AIC file\r\n\r\nruthe<-as.character(room$V1) #dim names (file names)\r\nho<-room$V2 #seq of numbers--p values\r\nagar<-matrix(nrow=length(ho),ncol=length(ho)) #making blank matrix\r\n\r\nfor ( i in seq_along(ho) ){\r\nfor(j in seq_along(ho)){\r\n agar[i,j]=abs(ho[i]-ho[j])\r\n } #small for\r\n} #large for\r\n\r\n\r\n#dimnames(agar)<-list(ruthe,ruthe) #setting dimension names of matrix\r\ndimnames(agar)<-list(ruthe,NULL)\r\n\r\nwrite.table(agar,\"K:\\\\Project_Data_Sagar\\\\Halwa\\\\BADMASHI\\\\my_truth\\\\Model\\\\best_model\\\\Stat_tree_TS\\\\AR_3\\\\my_truth_best_AR_dist_TS_3.txt\",col.names=F)\r\n\r\n#for making dist matrix table\r\nlibrary(stringr) #for string manipulation(right padding/blankspaces)\r\n\r\nfileConn<-file(\"K:\\\\Project_Data_Sagar\\\\Halwa\\\\BADMASHI\\\\my_truth\\\\Model\\\\best_model\\\\Stat_tree_TS\\\\AR_3\\\\my_truth_best_AR_dist_TS_3_protdist.txt\",\"a\") #opening file connection (appending mode)\r\nwriteLines(paste(\"\",length(agar[1,]),sep=\"\"), fileConn) #writing length of seq\r\n\r\nfor (i in seq_along(agar[1,])){\r\nw<-sprintf(\"%f\",agar[i,]) #converting to decimal places\r\nsarata<-paste(substr(w[1:length(w)],1,6),collapse=\" \") #converting to string,taking only five digits & collapsing all values to again string\r\naudi<- str_pad(names(agar[i,1]), width=8, side = \"right\", pad = \" \") #name only\r\nsampat<-paste(audi,sarata,sep=\" \")\r\nwriteLines(sampat, fileConn) #appending line to file\r\n}\r\nclose(fileConn) #closing file connection\r\n__________________________________________________________________________________________________________________________________________________________________________________\r\n#finds fasta seq for best AR_3_stat\r\n\r\n#for AR_3\r\nrm(list=ls())\r\nthokshahi<-read.table(\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\Best_model\\\\Stationary\\\\Best_stationary_3\\\\Best_stationary_AR_3.txt\",skip=1) #open ids of stationary files\r\nazure<-as.character(thokshahi$V1) #all stationary ids\r\ngh<-file(\"C:\\\\Users\\\\msc2\\\\Desktop\\\\Halwa\\\\BADMASHI\\\\Model\\\\Best_model\\\\Stationary\\\\Best_stationary_3\\\\Best_stat_AR_3\\\\Best_stat_AR_3_ori.fasta\",\"a\") #fasta file opening for appeding\r\n\r\nfor( scandisk in azure){\r\ndhoti_churna<-read.table(paste(\"C:\\\\kindle_patrika\\\\PDB\\\\pdb_phi_psi\\\\\",scandisk,\"_phi_psi.pdb\",sep=\"\"),skip=1)\r\nbonanza<-as.character(dhoti_churna[1,2]) #file name\r\nshruti_walle<-paste(dhoti_churna[,4],collapse=\"\") #total aa seq\r\nwriteLines(paste(\">\",bonanza,sep=\"\"),gh)\r\nwriteLines(shruti_walle,gh)\r\n }\r\nclose(gh) #closing file\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "83fde4f557b69c54f894f3ab219db3133326585a", "size": 2746, "ext": "r", "lang": "R", "max_stars_repo_path": "II-dataset/models/best_model/AR_3/II-stat_best_AR_3_dist_fakir.r", "max_stars_repo_name": "sagarnikam123/bioinfoProject", "max_stars_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "II-dataset/models/best_model/AR_3/II-stat_best_AR_3_dist_fakir.r", "max_issues_repo_name": "sagarnikam123/bioinfoProject", "max_issues_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "II-dataset/models/best_model/AR_3/II-stat_best_AR_3_dist_fakir.r", "max_forks_repo_name": "sagarnikam123/bioinfoProject", "max_forks_repo_head_hexsha": "3164e82704a28248fd796026bc37f1c681c3cddb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9850746269, "max_line_length": 195, "alphanum_fraction": 0.7312454479, "num_tokens": 812, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.3030037010423602}} {"text": "\r\n\r\n#I need to ensure the ROI from my video is the same as the ROI from my ce data file\r\nroi.dat<-read.delim(\"ROI Data.txt\", header=T, sep=\"\\t\", fileEncoding=\"UCS-2LE\")\r\nvid.dat<-read.delim(\"ROI video Data.txt\", header=T, sep=\"\\t\", fileEncoding=\"UCS-2LE\")\r\n\r\n#create coordinate dataframe for picture file\r\ncenterxy.pic<-roi.dat[,c(\"RoiID\",\"CentreXpx\", \"CentreYpx\")]\r\ncenterxy.pic<-centerxy.pic[order(centerxy.pic[,\"RoiID\"]),]\r\n\r\n#create coordinate dataframe for video file\r\ncenterxy.vid<-vid.dat[,c(\"RoiID\",\"CentreXpx\", \"CentreYpx\")]\r\n#put it into coridenates that should match pic\r\ncenterxy.vid[c(\"CentreXpx\",\"CentreYpx\")]<-centerxy.vid[c(\"CentreXpx\",\"CentreYpx\")]*2\r\ncenterxy.vid<-centerxy.vid[order(centerxy.vid[,\"RoiID\"]),]\r\n\r\n#Round out both\r\n#centerxy.vid<-round(centerxy.vid, digits=0)\r\n#centerxy.pic<-round(centerxy.pic, digits=0)\r\n\r\n# Kevin did this assesment\r\nquantile(centerxy.pic[,2] - centerxy.vid[,2])\r\n\r\nquantile(centerxy.pic[,3] - centerxy.vid[,3])\r\n\t\r\n#see if they are identical sorted by RoiID They are not\r\nidentical(centerxy.vid,centerxy.vid) #this is true\r\nidentical(centerxy.vid,centerxy.pic) #this is false\r\n\r\n#Alright, sort both dataframes by CentreXpx, and see if the rois are identical\r\ncenterxy.pic<-centerxy.pic[order(centerxy.pic[,\"CentreXpx\"]),]\r\ncenterxy.vid<-centerxy.vid[order(centerxy.vid[,\"CentreXpx\"]),]\r\n\r\nidentical(centerxy.pic[,\"RoiID\"],centerxy.vid[,\"RoiID\"])\r\n#shows false See below\r\ncenterxy.pic[,\"RoiID\"]==centerxy.vid[,\"RoiID\"]\r\n\r\n\r\n#Alright, sort both dataframes by CentreYpx, and see if the rois are identical\r\ncenterxy.pic<-centerxy.pic[order(centerxy.pic[,c(\"CentreYpx\",\"CentreXpx\")]),]\r\ncenterxy.vid<-centerxy.vid[order(centerxy.vid[,\"CentreYpx\"]),]\r\n\r\nidentical(centerxy.pic[,\"RoiID\"],centerxy.vid[,\"RoiID\"])\r\n#shows false\r\ncenterxy.pic[,\"RoiID\"]==centerxy.vid[,\"RoiID\"]\r\n\r\n# Over lunch though of this. Add CentreXpx with CentreYpx sory by that value and see if RoiID matches up\r\ncenterxy.pic[\"XplusY\"]<-centerxy.pic[,\"CentreYpx\"]+centerxy.pic[,\"CentreXpx\"]\r\ncenterxy.vid[\"XplusY\"]<-centerxy.vid[,\"CentreYpx\"]+centerxy.vid[,\"CentreXpx\"]\r\n\r\n#Still dont match up\r\ncenterxy.pic[order(centerxy.pic[,\"XplusY\"]),]\r\ncenterxy.vid[order(centerxy.vid[,\"XplusY\"]),]\r\n\r\ncenterxy.pic[order(centerxy.pic[,\"XplusY\"]),\"XplusY\"]\r\ncenterxy.vid[order(centerxy.vid[,\"XplusY\"]),\"XplusY\"]\r\n\r\ncenterxy.pic[order(centerxy.pic[,\"XplusY\"]),\"XplusY\"]=centerxy.vid[order(centerxy.vid[,\"XplusY\"]),\"XplusY\"]\r\ncenterxy.pic[order(centerxy.pic[,\"XplusY\"]),\"XplusY\"]-centerxy.vid[order(centerxy.vid[,\"XplusY\"]),\"XplusY\"]\r\n\r\ncbind(centerxy.pic[order(centerxy.pic[,\"XplusY\"]),\"RoiID\"],centerxy.vid[order(centerxy.vid[,\"XplusY\"]),\"RoiID\"])\r\n\r\n", "meta": {"hexsha": "d20a2264a11dfa7eb4f294035ab3a0d457923b85", "size": 2658, "ext": "r", "lang": "R", "max_stars_repo_path": "extras/Cell Profiler Import/Troublshoot.r", "max_stars_repo_name": "leeleavitt/procPharm", "max_stars_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "extras/Cell Profiler Import/Troublshoot.r", "max_issues_repo_name": "leeleavitt/procPharm", "max_issues_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-01-08T18:50:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T01:23:47.000Z", "max_forks_repo_path": "extras/Cell Profiler Import/Troublshoot.r", "max_forks_repo_name": "leeleavitt/procPharm", "max_forks_repo_head_hexsha": "b09ce82a76658cf46c7427b0c106822c8cadfdf7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-24T20:45:06.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-24T20:45:06.000Z", "avg_line_length": 42.1904761905, "max_line_length": 113, "alphanum_fraction": 0.7159518435, "num_tokens": 862, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.30300370104236013}} {"text": "library(tidyverse)\nlibrary(osmdata) # package for working with streets\nlibrary(showtext) # for custom fonts\nlibrary(ggmap)\nlibrary(rvest)\n\n## Following this: http://joshuamccrain.com/tutorials/maps/streets_tutorial.html\n\ntc_bb = getbb(\"Traverse City Michigan\")\nbig_streets = tc_bb |> \n opq() |>\n add_osm_feature(key = \"highway\", \n value = c(\"motorway\", \"primary\", \"motorway_link\", \"primary_link\")) |>\n osmdata_sf() \n\nmed_streets = tc_bb |>\n opq() |>\n add_osm_feature(key = \"highway\", \n value = c(\"secondary\", \"tertiary\", \"secondary_link\", \"tertiary_link\")) |>\n osmdata_sf()\n\nsmall_streets <- tc_bb |>\n opq() |>\n add_osm_feature(key = \"highway\", \n value = c(\"residential\", \"living_street\",\n \"unclassified\",\n \"service\", \"footway\"\n )) |>\n osmdata_sf()\n\nwater = tc_bb |>\n opq() |>\n add_osm_feature(key = \"natural\", value = c(\"water\", \"bay\", \"coastline\")) |>\n osmdata_sf()\n\nggplot() +\n geom_sf(data = water$osm_polygons, fill = \"dodgerblue4\") + \n geom_sf(data = med_streets$osm_lines,\n inherit.aes = FALSE,\n color = \"black\",\n size = 0.3, alpha = 0.5) +\n geom_sf(data = small_streets$osm_lines,\n inherit.aes = FALSE,\n color = \"black\",\n size = 0.2, alpha = 0.3) +\n geom_sf(data = big_streets$osm_lines,\n inherit.aes = FALSE,\n color = \"black\",\n size = 0.5, alpha = 0.6) +\n\n theme_void()\n\n\n", "meta": {"hexsha": "801cae97c9389ea3be43272ef6a3acd8511f249f", "size": 1511, "ext": "r", "lang": "R", "max_stars_repo_path": "tcmap.r", "max_stars_repo_name": "gregorp/tcmap", "max_stars_repo_head_hexsha": "7a7a1b54639c35c5885fd7a90204086307b44ad7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tcmap.r", "max_issues_repo_name": "gregorp/tcmap", "max_issues_repo_head_hexsha": "7a7a1b54639c35c5885fd7a90204086307b44ad7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tcmap.r", "max_forks_repo_name": "gregorp/tcmap", "max_forks_repo_head_hexsha": "7a7a1b54639c35c5885fd7a90204086307b44ad7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.9814814815, "max_line_length": 91, "alphanum_fraction": 0.5704831238, "num_tokens": 450, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.30300370104236013}} {"text": "#' crossprod_game\n#' \n#' Crossproduct on a gpu.\n#' \n#' @section Communication:\n#' The operation consists of a local crossproduct, followed by an\n#' \\code{allreduce()} call, quadratic on the number of columns.\n#' \n#' For \\code{crossprod()}, if the matrix distribution is poorly balanced\n#' (specifically, if any rank has fewer rows than columns), then an inefficient\n#' method is used. Similarly for \\code{tcrossprod()} if the number of local rows\n#' is greater than the number of local columns.\n#' \n#' @param x\n#' A shaq.\n#' \n#' @return \n#' A regular matrix.\n#' \n#' @useDynLib dimrgame R_crossprod\n#' @export\ncrossprod_game = function(x)\n{\n comm_ptr = pbdMPI::get.mpi.comm.ptr(.pbd_env$SPMD.CT$comm)\n is_float = is.float(DATA(x))\n \n if (is_float)\n data = DATA(x)@Data\n else\n {\n data = DATA(x)\n if (!is.double(data))\n storage.mode(data) = \"double\"\n }\n \n m = as.double(nrow(x))\n ret = .Call(R_crossprod, m, data, comm_ptr)\n \n if (is_float)\n ret = float32(ret)\n \n ret\n}\n", "meta": {"hexsha": "99465e27c65585fbb0572c5881136014455f6888", "size": 998, "ext": "r", "lang": "R", "max_stars_repo_path": "R/crossprod.r", "max_stars_repo_name": "RBigData/dimrgame", "max_stars_repo_head_hexsha": "a0a0aedf1d76ab056052b8bd59fbae445c78ede6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2019-03-26T16:16:33.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-27T02:57:58.000Z", "max_issues_repo_path": "R/crossprod.r", "max_issues_repo_name": "RBigData/dimrgame", "max_issues_repo_head_hexsha": "a0a0aedf1d76ab056052b8bd59fbae445c78ede6", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/crossprod.r", "max_forks_repo_name": "RBigData/dimrgame", "max_forks_repo_head_hexsha": "a0a0aedf1d76ab056052b8bd59fbae445c78ede6", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.6818181818, "max_line_length": 80, "alphanum_fraction": 0.6573146293, "num_tokens": 291, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6859494550081925, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.3029653575274802}} {"text": "# install the packages necessary to create a local miniCRAN repository\nif (!require(\"miniCRAN\"))\n install.packages(\"miniCRAN\")\nif (!require(\"igraph\"))\n install.packages(\"igraph\")\nlibrary(miniCRAN)\nlibrary(igraph)\n\n# define a mirror source for the packages to get\nmyMirror <- c(CRAN = \"https://cloud.r-project.org/\")\n\n# be sure to create a folder for the packages and define the location here\ndir.create(pth <- file.path(\"C:\\\\demos\\\\miniCRAN-repos\"))\nmyRepos <- \"C:\\\\demos\\\\miniCRAN-repos\"\n\n\n\n# define a vector to hold packages to get \npkgs_to_get <- c(\"ggplot2\", \"reshape2\", \"unbalanced\")\n\n# review the package dependencies (uses igraph package)\nplot(makeDepGraph(pkgs_to_get))\n\n# Get dependencies\npkgs <- pkgDep(pkgs_to_get, repos = myMirror)\n\n\n# create the local repository \nmakeRepo(pkgs,\n path = myRepos,\n repos = myMirror,\n type = \"win.binary\")\n\n", "meta": {"hexsha": "ad6f5efd6c0cce702d6ed7a0a494ce1ead1212d6", "size": 881, "ext": "r", "lang": "R", "max_stars_repo_path": "_2_r_scripts_Creating_miniCRAN_Repository.r", "max_stars_repo_name": "kennedysimiyu/T-SQL_R_Prediction_Analysis", "max_stars_repo_head_hexsha": "90f5e1c733c4ea72b9351115684b4de1bb63cacf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "_2_r_scripts_Creating_miniCRAN_Repository.r", "max_issues_repo_name": "kennedysimiyu/T-SQL_R_Prediction_Analysis", "max_issues_repo_head_hexsha": "90f5e1c733c4ea72b9351115684b4de1bb63cacf", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "_2_r_scripts_Creating_miniCRAN_Repository.r", "max_forks_repo_name": "kennedysimiyu/T-SQL_R_Prediction_Analysis", "max_forks_repo_head_hexsha": "90f5e1c733c4ea72b9351115684b4de1bb63cacf", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.9117647059, "max_line_length": 74, "alphanum_fraction": 0.706015891, "num_tokens": 232, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.5964331462646255, "lm_q1q2_score": 0.3028758279225509}} {"text": "library(jsonlite)\nlibrary(plyr)\nlibrary(data.table)\nlibrary(party)\n\nmkdf <- function(f) {\n ads <- fromJSON(txt=f)\n att <- llply(ads, function(x) data.frame(x$attributes))\n rental <- rbindlist(att, fill=T)\n rental$monthlyRent <- as.numeric(as.vector(rental$monthlyRent))\n rental <- rental[complete.cases(rental),]\n return(rental)\n}\n\nk1 <- mkdf(\"../kijiji.json\")\nk2 <- mkdf(\"../1.json\")\nk3 <- mkdf(\"../2.json\")\nkjj <- unique(data.frame(rbind(k1, k2, k3)))\nkjj <- subset(kjj, monthlyRent < 8000)\nkjj$unitSize <- factor(as.numeric(as.vector(kjj$unitSize)))\nkjj$area <- factor(substr(kjj$areaMajor, 1, 2))\n", "meta": {"hexsha": "09bd56b405d1f36f03fa33e60553df37f093db2b", "size": 607, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/tree.r", "max_stars_repo_name": "tpoisot/kijiji_mtl_rent", "max_stars_repo_head_hexsha": "d16488069ad8641accf6fb423bf097e36a5dd199", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/tree.r", "max_issues_repo_name": "tpoisot/kijiji_mtl_rent", "max_issues_repo_head_hexsha": "d16488069ad8641accf6fb423bf097e36a5dd199", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/tree.r", "max_forks_repo_name": "tpoisot/kijiji_mtl_rent", "max_forks_repo_head_hexsha": "d16488069ad8641accf6fb423bf097e36a5dd199", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5909090909, "max_line_length": 65, "alphanum_fraction": 0.6771004942, "num_tokens": 195, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.30287582792255086}} {"text": "source(\"R/functions/get_score.r\")\n\nfn.si = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_sample_info_2_sle_lowDA.txt\")\ninfo = fread(fn.si, data.table=F)\n\nfn.ge = file.path(PROJECT_DIR, \"generated_data\", \"SLE\", \"SLE_ge_matrix_gene_sle_lowDA.txt\")\ndat = fread(fn.ge, data.table = F) %>% \n tibble::remove_rownames() %>% tibble::column_to_rownames(\"gene\") %>% \n data.matrix()\n\n\nfn.sig = file.path(PROJECT_DIR, \"generated_data\", \"signatures\", \"IFN26_ge_sig.txt\")\ngene.sig = fread(fn.sig, header = F) %>% unlist(use.names=F)\n\ngi = toupper(rownames(dat)) %in% toupper(gene.sig)\nsum(gi)\n\ndf.score = cbind(\n dplyr::select(info, SUBJECT, VISIT, CUMULATIVE_TIME, SAMPLE_NAME), \n data.frame(score=get_score(dat[gi,]))\n )\n\nfn.sig = file.path(PROJECT_DIR, \"generated_data\", \"IFN26\", \"SLE_lowDA_IFN26_ge_sig_score.txt\")\nfwrite(df.score, fn.sig, sep=\"\\t\", quote=F)\n\ndf.score.subj = df.score %>%\n dplyr::select(SUBJECT, score) %>% \n mutate(SUBJECT = factor(SUBJECT,\n levels=unique(SUBJECT[order(as.numeric(sub(\"SLE-\",\"\",SUBJECT)))]))) %>% \n group_by(SUBJECT) %>%\n dplyr::summarise(score_mean=mean(score, na.rm=T)) %>%\n ungroup()\n\nfn.sig.subj = file.path(PROJECT_DIR, \"generated_data\", \"IFN26\", \"SLE_lowDA_IFN26_ge_sig_score_subjects.txt\")\nfwrite(df.score.subj, fn.sig.subj, sep=\"\\t\", quote=F)\n", "meta": {"hexsha": "1ea525291bc1ea496d140cb6fb6cabda69e53236", "size": 1364, "ext": "r", "lang": "R", "max_stars_repo_path": "R/IFN_signature_analysis/sle_lowDA_IFN26_score.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/IFN_signature_analysis/sle_lowDA_IFN26_score.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/IFN_signature_analysis/sle_lowDA_IFN26_score.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 37.8888888889, "max_line_length": 108, "alphanum_fraction": 0.6634897361, "num_tokens": 420, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30287563659154837}} {"text": "#\n# This is a Shiny web application. You can run the application by clicking\n# the 'Run App' button above.\n#\n# Find out more about building applications with Shiny here:\n#\n# http://shiny.rstudio.com/\n#\n\nlibrary(shiny)\n\n# Define UI for application that draws a histogram\nui <- fluidPage(\n\n # Application title\n titlePanel(\"Old Faithful Geyser Data\"),\n\n # Sidebar with a slider input for number of bins \n sidebarLayout(\n sidebarPanel(\n\n \n # Input: Select a file ----\n fileInput(\"file1\", \"Choose CSV File\",\n multiple = FALSE,\n accept = c(\"text/csv\",\n \"text/comma-separated-values,text/plain\",\n \".csv\")),\n \n # Horizontal line ----\n tags$hr(),\n \n # Input: Checkbox if file has header ----\n checkboxInput(\"header\", \"Header\", TRUE),\n \n # Input: Select separator ----\n radioButtons(\"sep\", \"Separator\",\n choices = c(Comma = \",\",\n Semicolon = \";\",\n Tab = \"\\t\"),\n selected = \",\"),\n \n # Input: Select quotes ----\n radioButtons(\"quote\", \"Quote\",\n choices = c(None = \"\",\n \"Double Quote\" = '\"',\n \"Single Quote\" = \"'\"),\n selected = '\"'),\n \n # Horizontal line ----\n tags$hr(),\n \n # Input: Select number of rows to display ----\n radioButtons(\"disp\", \"Display\",\n choices = c(Head = \"head\",\n All = \"all\"),\n selected = \"head\")\n ),\n\n # Show a plot of the generated distribution\n mainPanel(\n plotOutput(\"distPlot\"),\n plotOutput(\"lmPlot\"),\n tableOutput(\"contents\")\n )\n )\n)\n\n# Define server logic required to draw a histogram\nserver <- function(input, output) {\n\n dataInput <- reactive({\n req(input$file1)\n \n df <- read.csv(input$file1$datapath,\n header = input$header,\n sep = input$sep,\n quote = input$quote)\n return(df)\n })\n \n # output$distPlot <- renderPlot({\n # # generate bins based on input$bins from ui.R\n # x <- faithful[, 2]\n # bins <- seq(min(x), max(x), length.out = input$bins + 1)\n # print(bins)\n # # draw the histogram with the specified number of bins\n # hist(x, breaks = bins, col = 'darkgray', border = 'white')\n # })\n # \n \n output$distPlot <- renderPlot({\n plot(dataInput()$x,dataInput()$y)\n })\n \n output$lmtPlot <- renderPlot({\n plot(dataInput()$x,dataInput()$y)\n })\n \n \n output$contents <- renderTable({\n \n # input$file1 will be NULL initially. After the user selects\n # and uploads a file, head of that data file by default,\n # or all rows if selected, will be shown.\n \n \n if(input$disp == \"head\") {\n return(head(dataInput()))\n }\n else {\n return(dataInput())\n }\n \n })\n \n}\n\n# Run the application \nshinyApp(ui = ui, server = server)\n", "meta": {"hexsha": "4fa967f4302b5b78bfba9a1b9decaaa590266fba", "size": 3478, "ext": "r", "lang": "R", "max_stars_repo_path": "lm/app.r", "max_stars_repo_name": "hsinlun0415/ShinyAssignment_R", "max_stars_repo_head_hexsha": "84e2b37e90cb2cf27249342088ac79c7fc4fc703", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lm/app.r", "max_issues_repo_name": "hsinlun0415/ShinyAssignment_R", "max_issues_repo_head_hexsha": "84e2b37e90cb2cf27249342088ac79c7fc4fc703", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lm/app.r", "max_forks_repo_name": "hsinlun0415/ShinyAssignment_R", "max_forks_repo_head_hexsha": "84e2b37e90cb2cf27249342088ac79c7fc4fc703", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7438016529, "max_line_length": 74, "alphanum_fraction": 0.4514088557, "num_tokens": 708, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.30287562875697027}} {"text": "#+-----------------------------------------------------------------+\n#| maiorElementoVetor.r |\n#| Copyright 2020, Carlos Bezerra Vilela. |\n#| https://github.com/carlosvilela/exemplosR |\n#+------------------------------------------------------------------+\n\n# definindo a estrutura dos dados\nsetClass(\n \"vetorN\",\n slots = list(\n tamanhoN = \"numeric\",\n vetor = \"numeric\",\n valorMaximo = \"numeric\"\n )\n)\n\n# Criando uma constante que definir\u00e1 o range dos poss\u00edveis valores do vetor\nrangeVetor = 1:10\n\n# obtendo o tamanho do vetor\ntamanhoVetor = as.integer(readline(prompt=\"Insira o tamanho do vetor: \"))\n\n# Gerando o vetor\ngerarVetor<- sample(rangeVetor, tamanhoVetor, replace=TRUE, prob=NULL)\n\n# obtendo o numero m\u00e1ximo que foi registrado no vetor\nnumeroMaximo = as.integer(max(gerarVetor))\n\n# registrando e estruturando os dados\nvetor <- new(\"vetorN\",\n tamanhoN = tamanhoVetor,\n vetor = gerarVetor,\n valorMaximo = numeroMaximo)\n\n# exibindo resultado\nprint(str(vetor))\n\nprint(vetor@tamanhoN)\nprint(vetor@vetor)\nprint(vetor@valorMaximo)\n", "meta": {"hexsha": "497bffdebfd58b7787707892c0b496a6530a8ad1", "size": 1189, "ext": "r", "lang": "R", "max_stars_repo_path": "maiorElementoVetor.r", "max_stars_repo_name": "carlosvilela/exemplosR", "max_stars_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "maiorElementoVetor.r", "max_issues_repo_name": "carlosvilela/exemplosR", "max_issues_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "maiorElementoVetor.r", "max_forks_repo_name": "carlosvilela/exemplosR", "max_forks_repo_head_hexsha": "886681c28001c3370bbbc97419370c393a0b0162", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.0, "max_line_length": 75, "alphanum_fraction": 0.5618166526, "num_tokens": 301, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.30287562875697027}} {"text": "library(\"readODS\")\nlibrary(\"here\")\nlibrary(\"dplyr\")\nlibrary(\"janitor\")\nlibrary(\"tidyr\")\nlibrary(\"ggplot2\")\nlibrary(\"scales\")\nlibrary(\"binom\")\nlibrary(\"lubridate\")\nlibrary(\"rvest\")\nlibrary(\"purrr\")\n\nurl <- paste0(\"https://www.gov.uk/government/collections/\",\n \"nhs-test-and-trace-statistics-england-weekly-reports\")\nsession <- session(url)\n\nweekly_url <- session %>%\n html_nodes(xpath = \"//div/ul/li/a\") %>%\n html_attr(\"href\") %>%\n grep(\"weekly-statistics\", ., value = TRUE) %>%\n pluck(1)\n\nlatest <- session %>%\n session_jump_to(weekly_url)\n\nurl <- latest %>%\n html_nodes(xpath = \"//div/h3/a\") %>%\n html_attr(\"href\") %>%\n grep(pattern = \"tests.conducted\", value = TRUE)\n\nfilename <- sub(\"^.*/([^/]+)$\", \"\\\\1\", url)\n\ndir <- tempdir()\ndownload.file(url, file.path(dir, filename))\n\ned_settings <- read_ods(file.path(dir, filename),\n sheet = \"Table_7\", skip = 3) %>%\n clean_names() %>%\n slice(1:20) %>%\n rename(name = lfd_testing_in_education) %>%\n select(-total) %>%\n mutate_if(is.numeric, as.character) %>%\n mutate(test = sub(\"Total number of (positive|negative) LFD tests\", \"\\\\1\",\n name)) %>%\n filter(grepl(\"(positive|negative)\", test)) %>%\n mutate(school = c(rep(\"Nurseries and primary schools\", 2),\n rep(\"Secondary / College registered\", 2),\n rep(\"Secondary / College unregistered\", 2),\n rep(\"Unidentified\", 2),\n rep(\"Higher Education\", 2))) %>%\n select(-name) %>%\n filter(school != \"Unidentified\") %>%\n pivot_longer(names_to = \"date\", starts_with(\"x\")) %>%\n mutate(value = as.integer(value)) %>%\n filter(!is.na(value)) %>%\n mutate(date = as.Date(sub(\"^.+([0-9]{2})_([0-9]{2})_([0-9]{2})$\",\n \"20\\\\3-\\\\2-\\\\1\", date))) %>%\n pivot_wider(names_from = \"test\") %>%\n mutate(total = positive + negative)\n\nschools <- read_ods(file.path(dir, filename),\n sheet = \"Table_8\", skip = 3) %>%\n clean_names() %>%\n rename(name = lfd_testing_in_education_by_role) %>%\n select(-total) %>%\n mutate_if(is.numeric, as.character) %>%\n mutate(test = sub(\"Total number of (positive|negative) LFD tests\", \"\\\\1\",\n name)) %>%\n filter(grepl(\"(positive|negative)\", test)) %>%\n mutate(school = c(rep(\"Primary school staff\", 2),\n rep(\"Primary school household\", 2),\n rep(\"Primary school bubble\", 2),\n rep(\"Secondary school students\", 2),\n rep(\"Secondary school staff\", 2),\n rep(\"Secondary school household\", 2),\n rep(\"Secondary school bubble\", 2))) %>%\n select(-name) %>%\n pivot_longer(names_to = \"date\", starts_with(\"x\")) %>%\n mutate(value = as.integer(value)) %>%\n filter(!is.na(value)) %>%\n mutate(date = as.Date(sub(\"^.+([0-9]{2})_([0-9]{2})_([0-9]{2})$\",\n \"20\\\\3-\\\\2-\\\\1\", date))) %>%\n pivot_wider(names_from = \"test\") %>%\n mutate(total = positive + negative)\n\ndf_all <- ed_settings %>%\n filter(school == \"Higher Education\") %>%\n bind_rows(schools) %>%\n filter(!is.na(total))\n\nuncert <- binom.confint(df_all$positive, df_all$total, method = \"exact\") %>%\n select(mean, lower, upper)\n\ndfb <- df_all %>%\n cbind(uncert) %>%\n filter(date != \"2020-12-24\", date >= \"2021-02-01\") %>%\n filter(!grepl(\"Higher|bubble\", school))\n\np_testing <- ggplot(dfb,\n aes(x = date, y = mean, colour = school,\n ymin = lower, ymax = upper, fill = school)) +\n geom_point() +\n geom_line() +\n geom_ribbon(alpha = 0.35) +\n scale_colour_brewer(\"\", palette = \"Dark2\") +\n scale_fill_brewer(\"\", palette = \"Dark2\") +\n theme_bw() +\n expand_limits(y = 0) +\n scale_y_continuous(\"Proportion positive\", labels = scales::percent) +\n xlab(\"Final Wednesday of week of data\") +\n theme(legend.position = \"bottom\") +\n geom_vline(xintercept = as.Date(\"2021-03-08\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-03-31\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-04-19\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-05-29\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-06-06\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-07-23\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-09-01\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-10-23\"), linetype = \"dashed\") +\n geom_vline(xintercept = as.Date(\"2021-10-31\"), linetype = \"dashed\") +\n geom_rect(xmin = min(dfb$date), xmax = as.Date(\"2021-03-08\"),\n ymin = 0, ymax = max(dfb$upper), fill = alpha(\"black\", 0.002),\n colour = NA) +\n geom_rect(xmin = as.Date(\"2021-03-31\"), xmax = as.Date(\"2021-04-19\"),\n ymin = 0, ymax = max(dfb$upper), alpha = 0.002, fill = \"black\",\n colour = NA) +\n geom_rect(xmin = as.Date(\"2021-05-29\"), xmax = as.Date(\"2021-06-06\"),\n ymin = 0, ymax = max(dfb$upper), alpha = 0.002, fill = \"black\",\n colour = NA) +\n geom_rect(xmin = as.Date(\"2021-07-23\"), xmax = as.Date(\"2021-09-01\"),\n ymin = 0, ymax = max(dfb$upper), alpha = 0.002, fill = \"black\",\n colour = NA) +\n geom_rect(xmin = as.Date(\"2021-10-23\"), xmax = as.Date(\"2021-10-31\"),\n ymin = 0, ymax = max(dfb$upper), alpha = 0.002, fill = \"black\",\n colour = NA)\n\nsuppressWarnings(dir.create(here::here(\"figure\")))\nggsave(here::here(\"figure\", \"lfd_testing.svg\"), p_testing, width = 10, height = 5)\n", "meta": {"hexsha": "6a5ef358abc655932cef1e5b8c2544668ff81447", "size": 5528, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/lfd_education.r", "max_stars_repo_name": "sbfnk/covid19.lfd.education", "max_stars_repo_head_hexsha": "c75e228f23b2327aee0e7ca6479404ca48952902", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-03-04T16:21:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-04T16:21:01.000Z", "max_issues_repo_path": "inst/scripts/lfd_education.r", "max_issues_repo_name": "sbfnk/covid19.lfd.education", "max_issues_repo_head_hexsha": "c75e228f23b2327aee0e7ca6479404ca48952902", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-03-04T21:27:11.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T14:19:49.000Z", "max_forks_repo_path": "inst/scripts/lfd_education.r", "max_forks_repo_name": "sbfnk/covid19.lfd.education", "max_forks_repo_head_hexsha": "c75e228f23b2327aee0e7ca6479404ca48952902", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.7697841727, "max_line_length": 82, "alphanum_fraction": 0.5767004342, "num_tokens": 1650, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6406358548398982, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.302817983113426}} {"text": "my.rcumsum <- function(x){\n y=100-cumsum(x)\n y1=c(100,y)\n y1[1:length(y1)-1]\n}\n\n#' A function to produce the summary table\n#'\n#'\n#' @param base_size base font size, default 12\n#' @param base_family base font family, default Calibri\n#' @keywords summary\n#' @export\n#' @examples\n#' summary_table()\nsummary_table <- function(freq_table,well_id,column){\n\n mean=wtd.mean(freq_table[,\"Coverage\"], freq_table[,column])\n pct25=wtd.quantile(freq_table[,\"Coverage\"], freq_table[,column], probs = 0.25)\n median=wtd.quantile(freq_table[,\"Coverage\"], freq_table[,column], probs = 0.5)\n pct75=wtd.quantile(freq_table[,\"Coverage\"], freq_table[,column], probs = 0.75)\n var=wtd.var(freq_table[,\"Coverage\"], freq_table[,column])\n sd=sqrt(var)\n n=sum(freq_table[,column])\n\n out=data.frame(scope=column,\n wellId=well_id,\n n=round(n,digits=0),\n mean=round(mean,digits=1),\n sd=round(sd,digits=1),\n pct25=round(pct25,digits=1),\n median=round(median,digits=1),\n pct75=round(pct75,digits=1))\n out\n\n}\n\nchrs<-c(1:22,\"X\",\"Y\")\n\n#' A function to gel a plot\n#'\n#'\n#' @param base_size base font size, default 12\n#' @param base_family base font family, default Calibri\n#' @keywords theme\n#' @export\n#' @examples\n#' theme_gel_proper()\ntheme_gel_proper <- function(base_size = 12, base_family=\"Calibri\") {\n theme(\n text = element_text(size=base_size,family=base_family),\n axis.line = element_blank(),\n axis.text.x = element_text(size=base_size-2,lineheight = 0.9, vjust = 1),\n axis.text.y = element_text(size=base_size-2,lineheight = 0.9, hjust = 1),\n axis.ticks = element_line(colour = \"black\", size = 0.2),\n axis.title.x = element_text(size = base_size, vjust = 1),\n axis.title.y = element_text(size = base_size, angle = 90, vjust = 0.5),\n axis.ticks.length = unit(0.3, \"lines\"),\n #axis.ticks.margin = unit(0.5, \"lines\"), DEPRECIATED\n\n legend.background = element_rect(colour=NA),\n legend.key = element_blank(),\n legend.key.size = unit(1.2, \"lines\"),\n legend.title = element_text(size = base_size,face = \"bold\", hjust = 0),\n legend.position = \"right\",\n\n panel.background = element_rect(fill = \"white\", colour = NA),\n panel.border = element_rect(color=\"black\",size=0.5,fill=\"NA\"),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.margin = unit(0.5, \"lines\"),\n\n strip.text.x = element_text(),\n strip.text.y = element_text(angle = -90),\n strip.background = element_rect(colour=\"black\",size=0.6, fill=\"#FFFFFF\"),\n\n plot.background = element_rect(colour = NA),\n plot.title = element_text(size = base_size * 1.2),\n plot.margin = unit(c(1, 1, 0.5, 0.5), \"lines\")\n\n )\n}\n\ngel_colours=c(\"#0ead84\",\"#44546b\",\"#addce9\",\"#27b7cc\",\"#90c684\",\"#d3922d\")\n\n\n#' A function to summarise coverage\n#'\n#'\n#' @param files a single file\n#' @param labels a single label\n#' @param covs ylim\n#' @param scope wg or exome\n#' @keywords summary\n#' @export\n#' @examples\n#' coverage_summary()\ncoverage_summary <- function(file,label,covs,scope){\n\n #################make plots for all files###########################\n\n wg<-read.table(as.character(file),header=T,sep=\"\\t\",check.names = FALSE)\n\n allchr=summary_table(wg,label,\"allchrs\")\n autosomes=summary_table(wg,label,\"autosomes\")\n x=summary_table(wg,label,\"X\")\n y=summary_table(wg,label,\"Y\")\n\n summary_new=rbind(allchr,autosomes,x,y)\n\n filename_new=paste(file,\"coverage.summary.table.txt\",sep=\".\")\n filename_new=sub(\".coverage.counts.txt\", \"\", filename_new, ignore.case =FALSE, fixed=FALSE)\n write.table(summary_new,filename_new,quote=F,row.names=F,col.names=T,sep=\"\\t\")\n\n prop=as.data.frame(prop.table(as.matrix(wg), 2) )\n prop$Coverage=as.numeric(row.names(prop))-1\n print(head(prop))\n m.wg = melt(prop,id=c(\"Coverage\",\"allchrs\",\"autosomes\"))\n m.wg$sample=label\n ymax=max(subset(m.wg,Coverage!=0)$value*100)\n print(head(m.wg))\n print(covs)\n print(\"plotting...\")\n ggplot(m.wg,aes(Coverage,as.numeric(value)*100))+\n geom_bar(stat=\"identity\",color=gel_colours[1])+\n ggtitle(label)+\n facet_wrap(~variable,scales=\"free\")+\n scale_y_continuous(\"Percent Total\",limits=c(0,ymax))+\n scale_fill_manual(\"Sample\",values=gel_colours)+\n scale_x_continuous(\"Coverage\",limits=c(0,covs))+\n theme_gel_proper()\n filename=paste(file,\"coverage.distribution.chr-by-chr.png\",sep=\".\")\n filename=sub(\".coverage.counts.txt\", \"\", filename, ignore.case =FALSE, fixed=FALSE)\n print(filename)\n ggsave(filename, width = 30, height = 20, units = \"cm\",dpi=600)\n\n}\n\n\n#' Plots multiple samples on same graphs\n#'\n#'\n#' @param files a single files\n#' @param labels a single label\n#' @param covs ylim\n#' @param scope wg or exome\n#' @keywords summary\n#' @export\n#' @examples\n#' multiple_sample_plots()\nmultiple_sample_plots <- function(files,labels,covs,scope){\n outfile_prefix=gsub(\",\", \"_\",labels)\n print(outfile_prefix)\n files=strsplit(files,\",\")[[1]]\n labels=strsplit(labels,\",\")[[1]]\n\n for(i in 1:length(files)) {\n\n file=files[i]\n wg<-read.table(as.character(file),header=T,sep=\"\\t\",check.names = FALSE)\n prop=as.data.frame(prop.table(as.matrix(wg), 2) )\n prop$Coverage=as.numeric(row.names(prop))-1\n m.wg = melt(prop,id=c(\"Coverage\",\"allchrs\"))\n m.wg$sample=labels[i]\n ymax=max(subset(m.wg,Coverage!=0)$value*100)\n\n if (i > 1){\n final= rbind(old,m.wg)\n old=final\n }else{\n old=m.wg\n }\n\n }\n\n\n ymax=max(subset(m.wg,Coverage!=0)$value*100)\n ggplot(final,aes(Coverage,as.numeric(value)*100,fill=sample))+\n geom_bar(stat=\"identity\",position=\"dodge\")+\n facet_wrap(~variable,scales=\"free\")+\n scale_y_continuous(\"Percent Total\",limits=c(0,ymax))+\n scale_fill_manual(\"Sample\",values=gel_colours)+\n scale_x_continuous(\"Coverage\",limits=c(0,covs))+\n theme_gel_proper()\n filename=paste(outfile_prefix,scope,\"coverage.distribution.chr-by-chr.png\",sep=\".\")\n filename=sub(\".coverage.counts.txt\", \"\", filename, ignore.case =FALSE, fixed=FALSE)\n print(filename)\n ggsave(filename, width = 30, height = 20, units = \"cm\",dpi=600)\n\n}\n\n#' A function to draw boxplots of coverage split by exon in transcript and gc content\n#'\n#'\n#' @param file a file of exon coverage and gc cotent\n#' @keywords boxplots\n#' @export\n#' @examples\n#' gc_exon_boxplots()\ngc_exon_boxplots <- function(file){\n\n ####################do stuff##############################\n\n dat<-read.table(file,header=T,sep=\"\\t\")\n print(head(dat))\n dat$V5=dat$cov/median(dat$cov)\n gc25<-quantile(dat[,6],0.25)\n gc75<-quantile(dat[,6],0.75)\n\n y.cov=4*median(dat$cov,na.rm=T)\n\n m25<-dat[dat$gc<=gc25 & dat$exon<=30,]\n m75<-dat[dat$gc>=gc75 & dat$exon<=30,]\n m50<-dat[dat$gc>gc25 & dat$gc min(indata_minday)) & (day < max(indata_maxday)))\n \n fcont = paste(readLines(file.path(collect_server, \"diff-collected.json\")), collapse=\" \")\n configdiffmap = fromJSON(fcont)\n \n bugs_commits = read.csv(\"bugs-commits.csv\")\n}\n\n#the source changes\nif (!exists(\"changesource\")) {\n fcont = paste(readLines(\"all-changes.json\"), collapse=\" \")\n changesource = fromJSON(fcont)\n names(changesource) = laply(changesource, function(c) {c$hash})\n}\n\n#Get a sense of where unique commits are\nif (FALSE) {\n ddply(configdiffs, .(commithash), function(diffs) {\n cat(length(unique(diffs$diffhash)))\n diffs\n })\n \n ddply(configdiffs, .(commithash), function(diffs) {\n cat(\"commithash\", unique(diffs$commithash), \"\\n\")\n cat(\"changed-nodes\", length(diffs$diffhash), \"\\n\")\n cat(\"unique-diffs\", length(unique(diffs$diffhash)), \"\\n\")\n a = aaply(unique(diffs$diffhash), 1, function(h) {\n length(which(diffs$diffhash == h))\n })\n cat(\"uniques\", a, \"\\n\")\n cat(\"\\n\")\n 0\n })\n}\n\n#plot sample of changepoint probabilities and time series\nif (FALSE) {\n tdata = indata[[8000]]\n ggplot(melt(data.frame(\n t=tdata$data$t, \n timeseries=tdata$data$v, \n changepoint_probability=tdata$cps$v), id=c(\"t\")), aes(x=t, y=value)) +\n geom_line() +\n facet_wrap(~ variable, ncol=1, scale=\"free_y\") +\n ggtitle(paste(tdata$metric, \"on\", tdata$node))\n}\n\nnodes = unique(configdiffs$node)\nndiffs = 0\nif (!exists(\"correlations\")) {\n cat(\"compute correlation\")\n tstart = proc.time()\n \n correlations = ddply(configdiffs, .(commithash), function(diffs) {\n #use Pearson's product moment correlation coefficient r (cor.test)\n # previous thought: use something like Jaccard similarity\n \n commit_hash = unique(diffs$commithash)\n commit_day = unique(diffs$day)\n commit_t = unique(diffs$t)\n stopifnot(length(commit_day) == 1)\n stopifnot(length(commit_t) == 1)\n \n #get test statistic across all metrics at this commit\n stats = ldply(indata, function(l) {\n margin = 2\n \n dtt = dateno(l$data$t)\n ts_before = (dtt < commit_day) & (dtt > commit_day-margin)\n mean_before = mean(l$data$v[ts_before])\n ts_after = (dtt > commit_day) & (dtt < commit_day+margin)\n mean_after = mean(l$data$v[ts_after])\n \n #print(sum(ts_before))\n #print(sum(ts_after))\n \n if ((sum(ts_before) == 0) || (sum(ts_after) == 0)) {\n chgstat = 0 \n } else {\n chgstat = abs(mean_after-mean_before)\n }\n \n #if (chgstat != 0) {\n # cat(\"before:\", mean_before, \"\\n\")\n # cat(\"after:\", mean_after, \"\\n\")\n # cat(\"chgstat:\", chgstat, \"\\n\")\n #}\n \n data.frame(\n commithash=commit_hash,\n commitday=commit_day,\n committime=commit_t,\n node=l$node,\n metric=l$metric,\n stat=chgstat,\n ischanged=(l$node %in% diffs$node)\n )\n }, .progress=\"text\")\n \n result = cor.test(\n as.numeric(stats$stat),\n as.numeric(stats$ischanged),\n method=\"pearson\")\n \n if (sum(as.numeric(stats$stat)) == 0) {\n stats$statistic = 0\n stats$p.value = 0\n stats$estimate = 0\n } else {\n stats$statistic = result$statistic\n stats$p.value = result$p.value\n stats$estimate = result$estimate / sum(as.numeric(stats$ischanged)) \n }\n \n stats\n }, .parallel=TRUE, .paropts=list(\n .export=c(\"dateno\", \"ddply\", \"ldply\", \"indata\"),\n .packages=c(\"plyr\")\n ))\n\n #use estimate for sorting: this is the actual correlation\n topcors = arrange(\n ddply(correlations, .(commithash), function(corrs) {\n data.frame(\n commithash=unique(corrs$commithash),\n estimate=unique(corrs$estimate)\n )\n }), -estimate)\n \n n_top_correlations = 30\n correlations_top = subset(correlations, \n commithash %in% head(topcors, n_top_correlations)$commithash)\n \n cat(\"correlation time:\\n\")\n print(proc.time() - tstart)\n}\n\nsample = arrange(\n subset(correlations, commithash==\"fcd0a3a5ffd7a9f9ae91d70cc083f350e6216d3b\"),\n -stat)[, c(\"node\", \"metric\", \"stat\")]\n\n\noutdir = file.path(\"figs\", \"changemap\")\n\nallFiles = dlply(correlations_top, .(commithash), function(corrs) {\n commit_hash = as.character(unique(corrs$commithash))\n strength = floor(unique(corrs$estimate)*1000000)\n stopifnot(length(strength) == 1)\n \n changepath = file.path(outdir, paste(strength, commit_hash, sep=\"-\"))\n dir.create(changepath, showWarnings=FALSE, recursive=TRUE)\n \n comsrc = changesource[[commit_hash]]\n cat(\"Writing plots for\", comsrc$hash, \"strength=\", strength, \"\\n\")\n stopifnot(comsrc$hash == commit_hash)\n \n #write the top n metrics\n n_plot_metrics = 20\n corrs_toplot = head(\n arrange(subset(corrs, ischanged==TRUE), -stat),\n n_plot_metrics)\n \n plots_written = adply(corrs_toplot, 1, function(metric) {\n datadir_unique = unique(metric$datadir)\n fn_unique = unique(metric$fn)\n stopifnot(length(datadir_unique) == 1)\n stopifnot(length(fn_unique) == 1)\n dta = readsingle(file.path(datadir_unique, fn_unique))\n \n fnmod = \"\"\n \n ggplot(dta, aes(x=t, y=v)) + geom_line() +\n geom_vline(xintercept=as.numeric(corrs$committime), color=\"red\") +\n labs(title=paste(fn_unique, \"\\n\", \"change statistic: \", metric$stat, sep=\"\")) +\n theme(plot.title=element_text(size=rel(0.7), hjust=0))\n \n fnmod = paste(gsub(\"/\", \"-\", fn_unique), \"png\", sep=\".\")\n fullfnmod = file.path(changepath, fnmod)\n ggsave(fullfnmod, width=4, height=3)\n \n #also plot the probability data\n ggplot(cpprobs(dta), aes(x=t, y=v)) + geom_line() +\n geom_vline(xintercept=as.numeric(corrs$committime), color=\"red\")\n ggsave(paste(fullfnmod, \"prob\", \"png\", sep=\".\"), width=4, height=1)\n \n metric$filepath = fullfnmod\n metric\n }, .progress=\"text\")\n \n #find related diff hashes\n iarr = unique(subset(configdiffs, commithash==commit_hash)$diffhash)\n compiled_diffs = alply(iarr, 1, function(diffhash) {\n configdiffmap[[diffhash]]\n }, .dims=TRUE)\n names(compiled_diffs) = iarr\n \n #nodes for the given diff hashes\n iarr = unique(subset(configdiffs, commithash==commit_hash)$diffhash)\n compiled_diff_nodes = alply(iarr, 1, function(dh) {\n subset(configdiffs, diffhash==dh & commithash==commit_hash)$node\n }, .dims=TRUE)\n names(compiled_diff_nodes) = iarr\n \n mmod = list(\n files = comsrc$diff$files,\n subject = comsrc$subject,\n plots = plots_written$filepath,\n stats = plots_written$stat,\n hash = commit_hash,\n compiled = compiled_diffs,\n compiled_nodes = compiled_diff_nodes\n )\n \n fc = file(file.path(changepath, \"info.json\"))\n writeLines(c(toJSON(mmod)), fc)\n close(fc)\n \n changepath\n})\n\n#write out the index\nfc = file(file.path(\"figs\", \"index.json\"))\nwriteLines(c(toJSON(allFiles)), fc)\nclose(fc)\n\nstopCluster(workers)", "meta": {"hexsha": "319009ad5abba6cb60b8ec50285f820d9e06ae7e", "size": 8674, "ext": "r", "lang": "R", "max_stars_repo_path": "correlate_v3.r", "max_stars_repo_name": "mrcaps/wikimedia-analysis", "max_stars_repo_head_hexsha": "708d5ce1424f3996007990e7dfbb72c33ee9f294", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": 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YES\n2. NO", "lm_q1_score": 0.6150878414043814, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.30273893796535084}} {"text": "###############################################################################\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n###############################################################################\n# Examples for the Minimum Correlation Algorithm paper\n# Copyright (C) 2012 Michael Kapler\n#\n# Forecast-Free Algorithms: A New Benchmark For Tactical Strategies\n# http://cssanalytics.wordpress.com/2011/08/09/forecast-free-algorithms-a-new-benchmark-for-tactical-strategies/\n#\n# For more information please visit my blog at www.SystematicInvestor.wordpress.com\n# or drop me a line at TheSystematicInvestor at gmail\n###############################################################################\n\n\n\n\n\n\n###############################################################################\n# Main program to run all examples\n#\n# Please Load Systematic Investor Toolbox prior to running this routine\n###############################################################################\n# Load Systematic Investor Toolbox (SIT)\n# http://systematicinvestor.wordpress.com/systematic-investor-toolbox/\n###############################################################################\n#setInternet2(TRUE)\n#con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))\n# source(con)\n#close(con)\n###############################################################################\nmin.corr.paper.examples <- function() \n{\n#*****************************************************************\n# Load historical data sets\n#****************************************************************** \n\tload.packages('quantmod')\t\n\t\n\t#*****************************************************************\n\t# Load historical data for Futures and Forex\n\t#****************************************************************** \n\tdata <- new.env()\n\tgetSymbols.TB(env = data, auto.assign = T, download = T)\n\t\t\n\tbt.prep(data, align='remove.na', dates='1990::')\n\tsave(data,file='FuturesForex.Rdata')\n\t#load(file='FuturesForex.Rdata')\n\n\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\ttickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')\n\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t# TLT first date is 7/31/2002\n\tbt.prep(data, align='keep.all', dates='2002:08::')\n\tsave(data,file='ETF.Rdata')\n\t#load(file='ETF.Rdata')\n\n\t\n\t#*****************************************************************\n\t# Load historical data for dow stock (engle)\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog')\n\ttickers = spl('AA,AXP,BA,CAT,DD,DIS,GE,IBM,IP,JNJ,JPM,KO,MCD,MMM,MO,MRK,MSFT')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t\t\n\tbt.prep(data, align='keep.all', dates='1980::')\n\t\n\t# backfill prices\n\tprices = coredata(data$prices)\n\t\tprices[is.na(prices)] = mlag(prices)[is.na(prices)]\n\t\tprices[is.na(prices)] = mlag(prices)[is.na(prices)]\n\tdata$prices[] = prices\n\t\t\n\tsave(data,file='Dow.Engle.Rdata')\n\t#load(file='Dow.Engle.Rdata')\n\n\n\t#*****************************************************************\n\t# Load historical data for ETFs\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog')\n\ttickers = spl('VTI,IEV,EEM,EWJ,AGG,GSG,GLD,ICF')\n\t\t\n\tdata <- new.env()\n\tgetSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)\n\t\tfor(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t\t\n\tbt.prep(data, align='keep.all', dates='2003:10::')\t\n\tsave(data,file='ETF2.Rdata')\n\t#load(file='ETF2.Rdata')\n\n\t\n\t#*****************************************************************\n\t# Load historical data for nasdaq 100 stocks\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog')\n\t#tickers = nasdaq.100.components()\n\ttickers = spl('ATVI,ADBE,ALTR,AMZN,AMGN,APOL,AAPL,AMAT,ADSK,ADP,BBBY,BIIB,BMC,BRCM,CHRW,CA,CELG,CERN,CHKP,CTAS,CSCO,CTXS,CTSH,CMCSA,COST,DELL,XRAY,DISH,EBAY,EA,EXPD,ESRX,FAST,FISV,FLEX,FLIR,FWLT,GILD,HSIC,HOLX,INFY,INTC,INTU,JBHT,KLAC,LRCX,LIFE,LLTC,LOGI,MAT,MXIM,MCHP,MSFT,MYL,NTAP,NWSA,NVDA,ORLY,ORCL,PCAR,PDCO,PAYX,PCLN,QGEN,QCOM,BBRY,ROST,SNDK,SIAL,SPLS,SBUX,SRCL,SYMC,TEVA,URBN,VRSN,VRTX,VOD,XLNX,YHOO')\n\t\t\n\tdata <- new.env()\n\tfor(i in tickers) {\n\t\ttry(getSymbols(i, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T), TRUE)\n\t\tdata[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)\t\t\t\t\t\t\t\n\t}\n\tbt.prep(data, align='keep.all', dates='1995::')\n\t\n\t# backfill prices\n\tprices = coredata(data$prices)\n\t\tprices[is.na(prices)] = mlag(prices)[is.na(prices)]\n\t\tprices[is.na(prices)] = mlag(prices)[is.na(prices)]\n\tdata$prices[] = prices\n\t\t\n\t# plot\n\t#plota(make.xts(count(t(data$prices)),index(data$prices)),type='l')\n\t\t\n\tsave(data,file='nasdaq.100.Rdata')\n\t#load(file='nasdaq.100.Rdata')\n\n\n\t\n\t\n#*****************************************************************\n# Run all strategies\n#****************************************************************** \n\tnames = spl('ETF,FuturesForex,Dow.Engle,ETF2,nasdaq.100')\t\n\tlookback.len = 60\n\tperiodicitys = spl('weeks,months')\n\tperiodicity = periodicitys[1]\n\tprefix = paste(substr(periodicity,1,1), '.', sep='')\n\t\n\t\n\t\n\tfor(name in names) {\n\t\tload(file = paste(name, '.Rdata', sep=''))\n\t\t\n\t\tobj = portfolio.allocation.helper(data$prices, periodicity, lookback.len = lookback.len, prefix = prefix,\n\t\t\tmin.risk.fns = 'min.corr.portfolio,min.corr2.portfolio,max.div.portfolio,min.var.portfolio,risk.parity.portfolio(),equal.weight.portfolio',\n\t\t\tcustom.stats.fn = 'portfolio.allocation.custom.stats')\t\t\n\t\t\t\n\t\tsave(obj, file=paste(name, lookback.len, periodicity, '.bt', '.Rdata', sep=''))\n\t}\n\t\t\n\t\n\t#*****************************************************************\n\t# Create Reports\n\t#****************************************************************** \n\tfor(name in names) {\n\t\tload(file=paste(name, '.Rdata', sep=''))\n\t\t\n\t\t# create summary of inputs report\n\t\tcustom.input.report.helper(paste('report.', name, sep=''), data)\n\t\t\n\t\t# create summary of strategies report\n\t\tload(file=paste(name, lookback.len, periodicity, '.bt', '.Rdata', sep=''))\n\t\tcustom.report.helper(paste('report.', name, lookback.len, periodicity, sep=''), \n\t\t\tcreate.strategies(obj, data))\t\n\t}\n\n\t\n\t#*****************************************************************\n\t# Futures and Forex: rescale strategies to match Equal Weight strategy risk profile\n\t#****************************************************************** \n\tnames = spl('FuturesForex')\t\n\tfor(name in names) {\n\t\tload(file=paste(name, '.Rdata', sep=''))\n\n\t\t# create summary of strategies report\n\t\tload(file=paste(name, lookback.len, periodicity, '.bt', '.Rdata', sep=''))\n\t\t\tleverage = c(5, 4, 15, 20, 3, 1)\n\t\tcustom.report.helper(paste('report.leverage.', name, lookback.len, periodicity, sep=''), \n\t\t\tcreate.strategies(obj, data, leverage))\t\n\t}\n\n}\n\n\n\n###############################################################################\n# Custom Report routines\n###############################################################################\n\n#*****************************************************************\n# Create summary of inputs report\n#*****************************************************************\ncustom.input.report.helper <- function(filename, data) {\n\tfilename.pdf = paste(filename, '.pdf', sep='')\n\tfilename.csv = paste(filename, '.csv', sep='')\n\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t# put all reports into one pdf file\n\tpdf(file = filename.pdf, width=8.5, height=11)\n\t\n\n\t# Input Details\n\tlayout(1:2)\n\tasset.models = list()\n\tfor(i in data$symbolnames) {\n\t\tdata$weight[] = NA\n\t\t\tdata$weight[,i] = 1\t\n\t\tasset.models[[i]] = bt.run(data, silent=T)\n\t}\t\t\n\tasset.summary = plotbt.strategy.sidebyside(asset.models, return.table=T)\n\n\t\t\n\t# plot correlations\n\tret.log = bt.apply.matrix(data$prices, ROC, type='continuous')\n\ttemp = cor(ret.log, use='complete.obs', method='pearson')\n\t\t\ttemp[] = plota.format(100 * temp, 0, '', '%')\n\tplot.table(temp, smain='Correlation', highlight = TRUE, colorbar = TRUE)\t\n\n\n\t# line plot for each input series\n\tlayout(matrix(1:4,2,2))\t\n\tif( is.null(data$symbol.groups) ) {\n\t\tindex = order(data$symbolnames)\n\t\tfor(i in data$symbolnames[index]) \n\t\t\tplota(data[[i]], type='l', cex.main=0.7,main= i)\n\t} else {\n\t\tindex = order(data$symbol.groups)\n\t\tfor(i in data$symbolnames[index]) \n\t\t\tplota(data[[i]], type='l', cex.main=0.7, main= paste(i, data$symbol.groups[i], data$symbol.descriptions.print[i], sep=' / ') )\n\t\t\n\t\tasset.summary = rbind(data$symbol.groups, data$symbol.descriptions.print, asset.summary)\n\t}\n\t\n\t\n\t# close pdf\n\tdev.off()\t\n\n\t#*****************************************************************\n\t# save summary & equity curves into csv file\n\t#****************************************************************** \n\tload.packages('abind')\n\twrite.csv(asset.summary, filename.csv)\n\tcat('\\n\\n', file=filename.csv, append=TRUE)\n\t\n write.table(temp, sep=',', row.names = , col.names = NA,\n\t\tfile=filename.csv, append=TRUE)\n\t\t\n}\n\n\n#*****************************************************************\n# Create summary of strategies report\n#*****************************************************************\ncustom.report.helper <- function(filename, obj) {\n\tfilename.pdf = paste(filename, '.pdf', sep='')\n\tfilename.csv = paste(filename, '.csv', sep='')\n\n\tmodels = obj$models\n\t\t\n\t#*****************************************************************\n\t# Create Report\n\t#****************************************************************** \n\t# put all reports into one pdf file\n\tpdf(file = filename.pdf, width=8.5, height=11)\n\n\t\n\t# Plot perfromance\n\tplotbt(models, plotX = T, log = 'y', LeftMargin = 3)\t \t\n\t\tmtext('Cumulative Performance', side = 2, line = 1)\n\n\t\t\n\t# Plot Strategy Statistics Side by Side\n\tout = plotbt.strategy.sidebyside(models, perfromance.fn = 'custom.returns.kpi', return.table=T)\n\n\t\n\t\n\t# Plot time series of components of Composite Diversification Indicator\n\tcdi = custom.composite.diversification.indicator(obj)\t\n\t\tout = rbind(colMeans(cdi, na.rm=T), out)\n\t\trownames(out)[1] = 'Composite Diversification Indicator(CDI)'\n\t\t\t\n\t# Portfolio Turnover for each strategy\n\ty = 100 * sapply(models, compute.turnover, data)\n\t\tout = rbind(y, out)\n\t\trownames(out)[1] = 'Portfolio Turnover'\t\t\n\t\n\t# Bar chart in descending order of the best algo by Sharpe Ratio, CAGR, Gini, Herfindahl\t\n\tperformance.barchart.helper(out, 'Sharpe,Cagr,RC Gini,RC Herfindahl,Volatility,Portfolio Turnover,Composite Diversification Indicator(CDI)', c(T,T,F,F,F,F,T))\n\n\t\t\n\t# summary allocation statistics for each model\t\n\tcustom.summary.positions(obj$weights)\n\t\n\t\n\t# monhtly returns for each model\t\n\tcustom.period.chart(models)\n\n\t\t\n\t# Plot transition maps\n\tlayout(1:len(models))\n\tfor(m in names(models)) {\n\t\tplotbt.transition.map(models[[m]]$weight, name=m)\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\n\t\n\t\n\t# Plot transition maps for Risk Contributions\n\tdates = index(models[[1]]$weight)[obj$period.ends]\n\tlayout(1:len(models))\n\tfor(m in names(models)) {\n\t\tplotbt.transition.map(make.xts(obj$risk.contributions[[m]], dates), \n\t\tname=paste('Risk Contributions',m))\n\t\t\tlegend('topright', legend = m, bty = 'n')\n\t}\n\n\t# close pdf\n\tdev.off()\t\n\n\t\n\t#*****************************************************************\n\t# save summary & equity curves into csv file\n\t#****************************************************************** \n\tload.packages('abind')\n\twrite.csv(out, filename.csv)\n\tcat('\\n\\n', file=filename.csv, append=TRUE)\n\t\n\tout = abind(lapply(models, function(m) m$equity))\n\t\tcolnames(out) = names(models)\n\twrite.xts(make.xts(out, index(models[[1]]$equity)), filename.csv, append=TRUE)\t\t\n}\n\n\n\n#*****************************************************************\n# Composite Diversification Indicator (CDI) is 50/50 of\n# * 1 - Gini(portfolio risk contribution weights) and \n# * Minimum Average Correlation (from max.div) / Average Portfolio Correlation (w * Correlation Matrix * w) \t\t\t\n#' @export \n#*****************************************************************\ncustom.composite.diversification.indicator <- function\n(\n\tobj,\t# portfolio.backtest object\n\tavg.flag = T,\n\tavg.len = 10,\n\tplot.main = T,\n\tplot.table = T\n) \n{\n\tcdi = 0.5 * obj$risk.gini + 0.5 * obj$degree.diversification\n\t\tif(avg.flag) cdi = bt.apply.matrix(cdi, EMA, avg.len)\n\n\tif(plot.main) {\t\n\t\tavg.name = iif(avg.flag, paste(avg.len,'period EMA') , '')\n\n\t\tlayout(1:3)\n\t\tout = obj$degree.diversification\n\t\t\tif(avg.flag) out = bt.apply.matrix(out, EMA, avg.len)\n\t\tplota.matplot(out, cex.main = 1, \n\t\t\tmain=paste('D = 1 - Portfolio Risk/Weighted Average of asset vols in the portfolio', avg.name))\n\t\t\t\t\t\n\t\tout = obj$risk.gini\n\t\t\tif(avg.flag) out = bt.apply.matrix(out, EMA, avg.len)\n\t\tplota.matplot(out, cex.main = 1, \n\t\t\tmain=paste('1 - Gini(Risk Contributions)', avg.name))\n\t\t\t\n\t\tplota.matplot(cdi, cex.main = 1, \n\t\t\tmain=paste('Composite Diversification Indicator (CDI) = 50/50 Gini/D', avg.name))\n\t}\n\n\n\t# create sensitivity plot\t\t\n\tif(plot.table) {\t\n\t\tweights = seq(0,1,0.1)\n\t\t# create temp matrix with data you want to plot\n\t\ttemp = matrix(NA, nc=len(weights), nr=ncol(cdi))\n\t\t\tcolnames(temp) = paste(weights)\n\t\t\trownames(temp) = colnames(cdi)\n\t\t\t\n\t\tfor(j in 1:len(weights)) {\n\t\t\ti = weights[j]\n\t\t\ttemp[,j] = rank(-colMeans((1-i) * obj$risk.gini + i * obj$degree.diversification, na.rm=T))\n\t\t}\n\t\ttemp = cbind(temp, round(rowMeans(temp),1))\n\t\t\tcolnames(temp)[ncol(temp)] = 'AVG'\n\t\t\n\t\t# highlight each column separately\n\t\thighlight = apply(temp,2, function(x) plot.table.helper.color(t(x)) )\n\t\t\n\t\t# plot temp with colorbar\n\t\tlayout(1)\n\t\tplot.table(temp, smain = 'CDI Rank\\nAlgo vs %D',highlight = highlight, colorbar = TRUE)\n\t}\t\n\n\treturn(cdi)\n} \n\t\t\t\n\n\n#*****************************************************************\n# Summary Positions for each model\n#*****************************************************************\ncustom.summary.positions <- function(weights) {\n\tlayout(1:len(weights))\n\t\n\tfor(w in names(weights)) {\n\t\ttickers = colnames(weights[[w]])\n\t\tn = len(tickers)\n\t\t\n\t\ttemp = matrix(NA, nr=4, nc=n)\n\t\t\tcolnames(temp) = tickers\n\t\t\trownames(temp) = spl('Avg Pos,Max Pos,Min Pos,# Periods')\n\t\t\t\n\t\ttemp['Avg Pos',] = 100 * apply(weights[[w]],2,mean,na.rm=T)\n\t\ttemp['Max Pos',] = 100 * apply(weights[[w]],2,max,na.rm=T)\n\t\ttemp['Min Pos',] = 100 * apply(weights[[w]],2,min,na.rm=T)\n\t\ttemp['# Periods',] = apply(weights[[w]] > 1/1000,2,sum,na.rm=T)\n\t\t\n\t\ttemp[] = plota.format(temp, 0, '', '')\n\t\tplot.table(temp, smain=w)\n\t}\n}\t\n\n\n#*****************************************************************\n# Helper function to create barplot\n#*****************************************************************\ncustom.profit.chart <- function(data, main, cols) {\n\tpar(mar=c(4, 3, 2, 2),cex.main=2,cex.sub=1, cex.axis=1.5,cex.lab=1.5)\t\n\t\n\tbarplot(data, names.arg = names(data),\n\t\tcol=iif(data > 0, cols[1], cols[2]), \n\t\tmain=main, \n\t\tcex.names = 1.5, border = 'darkgray',las=2) \n\tgrid(NA,NULL) \n\tabline(h=0,col='black')\n\tabline(h=mean(data),col='gray',lty='dashed',lwd=3)\n}\n\n\n#*****************************************************************\n# Monhtly returns for each model\t\n#*****************************************************************\ncustom.period.chart <- function(models) {\n\tfor(imodel in 1:len(models)) {\n\t\tequity = models[[imodel]]$equity\n\t\n\t\t#*****************************************************************\n\t\t# Compute monthly returns\n\t\t#****************************************************************** \n\t\tperiod.ends = endpoints(equity, 'months')\n\t\t\tperiod.ends = unique(c(1, period.ends[period.ends > 0]))\n\t\n\t\tret = equity[period.ends,] / mlag(equity[period.ends,]) - 1\n\t\t\tret = ret[-1]\n\t\n\t\tret.by.month = create.monthly.table(ret)\t\n\t\tret.by.month = 100 * apply(ret.by.month, 2, mean, na.rm=T)\n\t\t\n\t\t#*****************************************************************\n\t\t# Compute annual returns\n\t\t#****************************************************************** \n\t\tperiod.ends = endpoints(equity, 'years')\n\t\t\tperiod.ends = unique(c(1, period.ends[period.ends > 0]))\n\t\n\t\tret = equity[period.ends,] / mlag(equity[period.ends,]) - 1\n\t\tret.by.year = ret[-1]\n\t\t\t\n\t\t#*****************************************************************\n\t\t# Create plots\n\t\t#****************************************************************** \n\t\t# create layout\t\n\t\tilayout = \n\t\t\t'1,1\n\t\t\t2,2\n\t\t\t2,2\n\t\t\t2,2\n\t\t\t2,2\n\t\t\t2,2\n\t\t\t3,4\n\t\t\t3,4\n\t\t\t3,4\n\t\t\t3,4\n\t\t\t3,4\n\t\t\t3,4'\n\t\tplota.layout(ilayout)\n\t\t\n\t\t# make dummy table with name of strategy\t\t\n\t\tmake.table(1,1)\n\t\ta = matrix(names(models)[imodel],1,1)\n\t\tcex = plot.table.helper.auto.adjust.cex(a)\n\t\tdraw.cell(a[1],1,1, text.cex=cex,frame.cell=F)\t\t\n\t\t\n\t\t# plots\n\t\ttemp = plotbt.monthly.table(equity)\t\n\t\n\t\t# plot months\n\t\tcols = spl('green,red')\n\t\tcustom.profit.chart(ret.by.month, 'Average Monthly Returns', cols)\n\t\t\n\t\t# plot years\n\t\tret = 100*as.vector(ret.by.year)\n\t\t\tnames(ret) = date.year(index(ret.by.year))\n\t\tcustom.profit.chart(ret, 'Annual Returns', cols)\n\t}\n}\t\n\t\n\n#*****************************************************************\n# Custom Summary function to add consentration statistics (Gini and Herfindahl)\n#\n# On the properties of equally-weighted risk contributions portfolios by\n# S. Maillardy, T. Roncalliz, J. Teiletchex (2009)\n# A.4 Concentration and turnover statistics, page 22\n#*****************************************************************\ncustom.returns.kpi <- function\n(\n\tbt,\t\t# backtest object\n\ttrade.summary = NULL\n) \n{\t\n\tout = list()\n\tw = bt$period.weight\n\trc = bt$risk.contribution\n\t\n\t# Average Number of Holdings\n\tout[[ 'Avg #' ]] = mean(rowSums(w > 1/1000)) / 100\n\t\n\t# Consentration stats\n\tout[[ 'W Gini' ]] = mean(portfolio.concentration.gini.coefficient(w), na.rm=T)\n\tout[[ 'W Herfindahl' ]] = mean(portfolio.concentration.herfindahl.index(w), na.rm=T) \n\t\n\t# Consentration stats on marginal risk contributions\n\tout[[ 'RC Gini' ]] = mean(portfolio.concentration.gini.coefficient(rc), na.rm=T)\n\tout[[ 'RC Herfindahl' ]] = mean(portfolio.concentration.herfindahl.index(rc), na.rm=T) \n\n\tout = lapply(out, function(x) if(is.double(x)) round(100*x,1) else x)\n\tout = c(bt.detail.summary(bt)$System, out)\n\t\n\treturn( list(System=out))\n}\n\n\t\t\t\n###############################################################################\n# \"Let us skip now to the general case. If we sum up the situations from the point\n# of view of mathematical de nitions of these portfolios, they are as follows (where\n# we use the fact that MV portfolios are equalizing marginal contributions to risk; see\n# Scherer, 2007b)\" (this is from the Roncalli Paper)\n#\n# \"All stocks belonging to the MDP have the same correlation to it\" (Choeifaty: Properties of the Most Diversified Portfolio)\n#\n# The marginals for the minimum variance and maximum diversification portfolios \n# and the property of equal marginals ONLY holds is we do not impose long-only constraints. \n#\n# If we only have a budget constraint (i.e. sum of portfolio weights = 100%)\n# min.var weights = | 2.05, -0.57, -0.48 |\n# marginal risk contributions = | 0.01468981, 0.01468981, 0.01468981 |\n#\n# max.div weights = | -0.26, 0.65, 0.61 |\n# marginal correlation contributions = | 0.8434783, 0.8434783, 0.8434783 |\n# marginal contributions are the same\n#\n# Now if we add long only constraint (i.e. all weights >= 0 and sum of portfolio weights = 100%)\n# min.var weights = | 1, 0, 0 |\n# marginal risk contributions = | 0.0196, 0.02268, 0.02618 |\n# \n# max.div weights = | 0, 0.5, 0.5 |\n# marginal correlation contributions = | 0.875, 0.85, 0.85 |\n# marginal contributions are the different\n###############################################################################\n#\n# Numerical examples used in the Minimum Correlation Algorithm papaer\n#\n###############################################################################\nmin.corr.paper.numerical.examples <- function() \n{\n\t#*****************************************************************\n\t# create input assumptions\n\t#*****************************************************************\n\t\tn = 3\n\t\tia = list()\n\t\t\tia$n = 3\n\t\t\tia$risk = c(14, 18, 22) / 100;\n\t\t\tia$correlation = matrix(\n\t\t\t\tc(1, 0.90, 0.85,\n\t\t\t\t0.90, 1, 0.70,\n\t\t\t\t0.85, 0.70, 1), nr=3, byrow=T)\n\t\t\tia$cov = ia$correlation * (ia$risk %*% t(ia$risk))\n\n\t#*****************************************************************\n\t# create constraints\n\t#*****************************************************************\n\t\tconstraints = new.constraints(n)\n\t\t# 0 <= x.i <= 1\n\t\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\t\t\tconstraints = add.constraints(diag(n), type='>=', b=0, constraints)\n\t\t\tconstraints = add.constraints(diag(n), type='<=', b=1, constraints)\n\n\t\t# SUM x.i = 1\n\t\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints)\t\t\n\t\t\t\t\n\t#*****************************************************************\n\t# Minimum Variance Portfolio \n\t#*****************************************************************\n\t\tx = min.var.portfolio(ia, constraints)\n\t\t\n\t\tsol = solve.QP(Dmat=ia$cov, dvec=rep(0, ia$n), \n\t\t\tAmat=constraints$A, bvec=constraints$b, meq=constraints$meq)\n\t\tx = sol$solution\n\t\t\n\t\t\tround(x,4)\n\t\t\tsqrt(x %*% ia$cov %*% x)\n\t\t\t\n\t\t# marginal contributions\t\n\t\tx %*% ia$cov\n\t\t\n\t#*****************************************************************\n\t# Maximum Diversification Portfolio \n\t#*****************************************************************\t\t\t\n\t\tsol = solve.QP(Dmat=ia$correlation, dvec=rep(0, ia$n), \n\t\t\tAmat=constraints$A, bvec=constraints$b, meq=constraints$meq)\n\t\tx = sol$solution\n\t\t\tround(x,4)\n\t\t\t\n\t\t# marginal contributions\n\t\tx %*% ia$correlation\n\t\t\n\t\t\n\t\t# re-scale and normalize weights to sum up to 1\n\t\tx = x / sqrt( diag(ia$cov) )\n\t\tx = x / sum(x)\n\t\t\tround(x,4)\n\t\t\tsqrt(x %*% ia$cov %*% x)\n\t\t\t\n\t#*****************************************************************\n\t# Minimum Correlation Portfolio \n\t#*****************************************************************\t\t\t\t\t\t\n\t\tupper.index = upper.tri(ia$correlation)\n\t\tcor.m = ia$correlation[upper.index]\n\t\t\tcor.mu = mean(cor.m)\n\t\t\tcor.sd = sd(cor.m)\n\t\t\t\n\t\tnorm.dist.m = 0 * ia$correlation\t\n\t\t\tdiag(norm.dist.m) = NA\n\t\t\tnorm.dist.m[upper.index] = sapply(cor.m, function(x) 1-pnorm(x, cor.mu, cor.sd))\n\t\tnorm.dist.m = (norm.dist.m + t(norm.dist.m))\n\t\t\n\t\tnorm.dist.avg = apply(norm.dist.m, 1, mean, na.rm=T)\n\t\t\n\t\tnorm.dist.rank = rank(-norm.dist.avg)\n\t\t\n\t\t\tadjust.factor = 1\n\t\tadjusted.norm.dist.rank = norm.dist.rank ^ adjust.factor\n\t\t\n\t\tnorm.dist.weight = adjusted.norm.dist.rank / sum(adjusted.norm.dist.rank)\n\t\t\n\t\tweighted.norm.dist.average = norm.dist.weight %*% ifna(norm.dist.m,0)\n\t\t\n\t\tfinal.weight = weighted.norm.dist.average / sum(weighted.norm.dist.average)\n\t\t\n\t\tx = final.weight\n\t\t\n\t\t# re-scale and normalize weights to sum up to 1\n\t\tx = x / sqrt( diag(ia$cov) )\n\t\tx = x / sum(x)\n\t\t\tround(x,4)\n\t\t\tx = as.vector(x)\n\t\t\tsqrt(x %*% ia$cov %*% x)\n\t\t\t\n\t#*****************************************************************\n\t# Minimum Correlation 2 Portfolio \n\t#*****************************************************************\t\t\t\t\t\t\n\t\tcor.m = ia$correlation\n\t\t\tdiag(cor.m) = 0\n\t\t\t\n\t\tavg = rowMeans(cor.m)\n\t\t\tcor.mu = mean(avg)\n\t\t\tcor.sd = sd(avg)\n\t\tnorm.dist.avg = 1-pnorm(avg, cor.mu, cor.sd)\n\t\t\n\t\tnorm.dist.rank = rank(-norm.dist.avg)\n\t\t\n\t\t\tadjust.factor = 1\n\t\tadjusted.norm.dist.rank = norm.dist.rank ^ adjust.factor\n\t\t\n\t\tnorm.dist.weight = adjusted.norm.dist.rank / sum(adjusted.norm.dist.rank)\n\t\t\t\t\n\t\tweighted.norm.dist.average = norm.dist.weight %*% (1-cor.m)\n\t\tfinal.weight = weighted.norm.dist.average / sum(weighted.norm.dist.average)\n\t\t\n\t\tx = final.weight\n\t\t\n\t\t# re-scale and normalize weights to sum up to 1\n\t\tx = x / sqrt( diag(ia$cov) )\n\t\tx = x / sum(x)\n\t\t\tround(x,4)\n\t\t\tx = as.vector(x)\n\t\t\tsqrt(x %*% ia$cov %*% x)\n\t\t\t\t\t\n\t\t\t\n\t\t#min.corr.portfolio(ia, constraints)\n\t\t#min.corr2.portfolio(ia, constraints)\t\t\t\t\t\n}\n\t\n\n\n", 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YES\n2. YES\n\n", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.30271106987308793}} {"text": "# 2. faza: Uvoz podatkov\n\n# preberemo podatke\n\n#podatki <- read_excel(\"podatki/stevilo_pimerov_SLO.xlsx\", sheet = \"Potrjeni primeri\", skip = 1, col_names = TRUE)\npodatki <- read_excel(\"podatki/korona_statistika_SLO.xlsx\", sheet = \"Potrjeni primeri\", skip = 1, col_names = TRUE)\n# izbri\u0161emo nepotrebne stolpce\n\npodatki <- podatki[,-c(3,5,8:11)]\n#zrihtani datumi\n\n\npodatki[,1] <- seq(as.Date(\"2020-03-03\"), as.Date(\"2020-08-03\"), by=\"days\")\n#podatki$datum.prijave <- as.Date(podatki$datum.prijave,\"%Y-%m-%d\")\n\n#Ro\u010dno olaj\u0161anje dela\npodatki[62,3]<- NA\npodatki[1,4] <- 0\npodatki[1,5] <- 0\n\n\n#Ali gre za delovni dan?\npodatki[\"Delovni dan\"] <- c(TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,rep(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE),21),TRUE)\n\ncolnames(podatki) <- c(\"datum\", \"rutinsko.dnevno\", \"raziskava.dnevno\", \"moski\", \"zenske\",\"delovni.dan\")\npodatki$raziskava.dnevno <- as.integer(podatki$raziskava.dnevno)\n\npodatki_spol <- podatki %>% select(datum, moski, zenske) %>% gather(\"spol\", \"stevilo\", -datum)\n\npodatki <- podatki %>% transmute(datum, rutinsko.dnevno, raziskava.dnevno,\n \n okuzbe=moski+zenske, delovni.dan)\n\n\n\n\n###################################################################################################\n\npodatki_svet <- read_csv(\"podatki/korona_po svetu_csv.csv\",\n \n col_types=cols(.default=col_number(),\n \n iso_code=col_character(),\n \n continent=col_character(),\n \n location=col_character(),\n \n date=col_date(),\n \n tests_units=col_character()))\npodatki_svet<- podatki_svet[,c(1:5,7,9,11,13,29,36)]\n\n###################################################################################################\n\npopulation_by_continent <- htmltab(\"https://en.wikipedia.org/wiki/List_of_continents_by_population\",1)\n\npopulation_by_continent <-population_by_continent[c(2:7),c(2,3)]\n\ncolnames(population_by_continent)<- c(\"continent\",\"population_continent\")\n\npopulation_by_continent$population_continent <- gsub(\",\",\"\",population_by_continent$population_continent)\n\npopulation_by_continent$population_continent <- as.numeric(as.character(population_by_continent$population_continent))\n\n", "meta": {"hexsha": "54ca890956cf0dd45eb9036b2fb01e04e17f1cfc", "size": 2595, "ext": "r", "lang": "R", "max_stars_repo_path": "uvoz/uvoz.r", "max_stars_repo_name": "StanicR17/APPR-2019-20", "max_stars_repo_head_hexsha": "f1c525e5f1149bb3cff539062dc8ab74f8a5ef23", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "uvoz/uvoz.r", "max_issues_repo_name": "StanicR17/APPR-2019-20", "max_issues_repo_head_hexsha": "f1c525e5f1149bb3cff539062dc8ab74f8a5ef23", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2020-01-17T17:03:31.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-03T15:53:05.000Z", "max_forks_repo_path": "uvoz/uvoz.r", "max_forks_repo_name": "StanicR17/APPR-2019-20", "max_forks_repo_head_hexsha": "f1c525e5f1149bb3cff539062dc8ab74f8a5ef23", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.3181818182, "max_line_length": 118, "alphanum_fraction": 0.5356454721, "num_tokens": 665, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3027110698730879}} {"text": "require(data.table)\nrequire(tidyverse)\n\n# Unzip the files (The TRY .zip files should be the only .zip files in the folder)\nTRYzip <- list.files(pattern = \".*zip\")[1] %>%\nas.list(.) %>%\nlapply(TRYzip, function(x) unzip(x, overwrite = TRUE, exdir = \".\"))\n\n# Read the files (The TRY trait files should be the only .txt files in the folder)\nTRYfiles <- list.files(pattern = \".*txt\", full.names = T) %>% # Getting the name of each .txt file\nas.list(.) %>% # Putting them into a list\nlapply(., function(x) fread(x, header = TRUE, sep = \"\\t\", dec = \".\", quote = \"\", data.table = TRUE)) %>% # Reading each element of the list into a data.table\nrbindlist(.) %>% # Rbinding all the files together\n.[,c(\"SpeciesName\",\"AccSpeciesID\",\"AccSpeciesName\",\"TraitName\",\"OrigValueStr\",\"OrigUnitStr\",\"ValueKindName\",\"StdValue\",\"UnitName\")] # Get only the necessary column (That was a quick check)\n\n\n#### Summarize traits across species\ntraitSummary <- TRYfiles %>% group_by(SpeciesName, TraitName) %>% summarize(value = mean(StdValue, na.rm = T)) %>% filter(TraitName != \"\") %>% arrange(SpeciesName)\nwrite.csv(traitSummary, \"data/traitData.csv\", row.names = F)\n\n############################################################################################################\n# Part where we need to separate the huge data.table into multiple .csv file \n# I used data.table because I believe it's the fastest way of manipulating HUGE files (?)\n# Too tired and can't find a way to properly divide the data.table, going to let what i tried there as a draft/idea on how to do it\n\n# # Breaking the file into a list based on TraitName. Not really sure it's going to work tomorrow with ALL the 5 trait file because too big, but i cant think of anything else right now\n# TRYfiles <- split(TRYfiles, by =\"TraitName\") # Split the file into a list by the variable \"TraitName\", so length(list) == 17 since there are 17 traits\n# names(TRYfiles) <- paste0(\"trait_\", c(1:length(test))) # We don't really care about the trait name in the file name so i winnowed them\n# lapply(TRYfiles, function(x) fwrite(TRYfiles, file = \"test.csv\", row.names = FALSE, col.names = TRUE))\n# fwrite(TRYfiles, file = \"test.csv\", row.names = FALSE, col.names = TRUE)\n", "meta": {"hexsha": "c567ef349cd066a4ec019c877054b566ca166830", "size": 2220, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/tidying_TRYtraits.r", "max_stars_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_stars_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-05-07T10:18:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-11T14:44:19.000Z", "max_issues_repo_path": "scripts/tidying_TRYtraits.r", "max_issues_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_issues_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 35, "max_issues_repo_issues_event_min_datetime": "2020-05-11T14:56:22.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-05T12:15:24.000Z", "max_forks_repo_path": "scripts/tidying_TRYtraits.r", "max_forks_repo_name": "DataDrivenEcologicalSynthesis/WeirdestSpeciesCombination", "max_forks_repo_head_hexsha": "bcf5083419b9456f834b2b49ac5f23c2d1e575b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-08T14:15:36.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-08T14:15:36.000Z", "avg_line_length": 71.6129032258, "max_line_length": 188, "alphanum_fraction": 0.668018018, "num_tokens": 563, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.30271106211194543}} {"text": "\\docType{data}\n\\name{lanzadores}\n\\alias{lanzadores}\n\\title{Tabla de estad\u00edsticas de lanzadores}\n\\format{Un data frame con 46.699 filas y 30 columnas\n\\describe{\n\\item{id_jugador}{ID del jugador}\n\\item{id_anio}{A\u00f1o}\n\\item{orden_equipos}{Orden en que el jugador se movi\u00f3 entre equipos dentro de la misma temporada}\n\\item{id_equipo}{ID del equipo (factor)}\n\\item{id_liga}{ID de la liga (factor con niveles AA, AL, FL, NL, PL, UA)}\n\\item{juegos_ganados}{Juegos jugados ganados}\n\\item{juegos_perdidos}{Juegos jugados perdidos}\n\\item{juegos_jugados}{Juegos jugados}\n\\item{juegos_iniciados}{Juegos jugados iniciados}\n\\item{juegos_completados}{N\u00famero de juegos completos (9 innings lanzados) que lanz\u00f3 el jugador}\n\\item{blanqueadas}{N\u00famero de blanqueos (juegos completos sin permitir carreras) que lanz\u00f3 el jugador}\n\\item{juegos_salvados}{Juegos salvados}\n\\item{IPouts}{Outs propinados al equipo contrario (Igual a innings lanzados x 3)}\n\\item{hits}{Hits permitidos del oponente}\n\\item{carreras_ganadas}{Carreras limpias recibidas}\n\\item{cuadrangulares}{Cuadrangulares recibidos}\n\\item{BB}{Base por bolas cedidas al oponente}\n\\item{ponches}{Ponches propinados al oponente}\n\\item{promedio_bateo_rival}{Promedio de bateo del rival}\n\\item{promedio_carreras_ganadas}{Promedio de carreras limpias permitidas (normalizada a 9 innings lanzados)}\n\\item{IBB}{Base por bolas intencionales cedidas al oponente}\n\\item{lanzamientos_desviados}{Lanzamientos desviados lanzados por el lanzador}\n\\item{HBP}{Bateadores golpeador por el lanzador}\n\\item{BK}{Balks (movimiento ilegal del cuerpo realizado por el lanzador)}\n\\item{BFP}{Bateadores a los que el lanzador se ha enfrentado}\n\\item{juegos_finalizados}{Juegos en los que el lanzador finaliz\u00f3 el juego}\n\\item{carreras}{Carreras recebidas (sucias y limpias)}\n\\item{sacrificios_golpeados}{Toques de sacrificio que el oponente le hizo al lanzador}\n\\item{vuelos_sacrificio}{Elevado (fly) de sacrificio que el oponente le hizo al lanzador}\n\\item{doble_matanza}{Doble matanza inducidas por el lanzador}\n}}\n\\description{Estad\u00edsticas de lanzadores}\n\\keyword{datasets}\n", "meta": {"hexsha": "94c810d2b3aecf6fb4a431ce96125358dc5370ba", "size": 2087, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/lanzadores.rd", "max_stars_repo_name": "maurolepore/datos", "max_stars_repo_head_hexsha": "4a8eb73fe3f6464c4eaa1798507de737589627b6", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": 32, "max_stars_repo_stars_event_min_datetime": "2018-07-16T01:14:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T23:38:59.000Z", "max_issues_repo_path": "man/lanzadores.rd", "max_issues_repo_name": "RubenMaier/datos", "max_issues_repo_head_hexsha": "c0606f7df0701d944cf0fe762cd29e821e53e75c", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 86, "max_issues_repo_issues_event_min_datetime": "2018-07-15T21:57:50.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-20T00:01:34.000Z", "max_forks_repo_path": "man/lanzadores.rd", "max_forks_repo_name": "RubenMaier/datos", "max_forks_repo_head_hexsha": "c0606f7df0701d944cf0fe762cd29e821e53e75c", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": 28, "max_forks_repo_forks_event_min_datetime": "2018-07-15T17:48:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-25T02:01:00.000Z", "avg_line_length": 52.175, "max_line_length": 108, "alphanum_fraction": 0.8016291327, "num_tokens": 661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.30271106211194543}} {"text": "# download metrics from shinyapps.io\n\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(scales)\nlibrary(ggthemes)\n\n# appName <- \"pasture19_comp\"\nappName <- \"pasture_embed\"\n\n# http://docs.rstudio.com/shinyapps.io/metrics.html#ApplicationMetrics\ndf <- rsconnect::showMetrics(\"container_status\",\n c(\"connect_count\", \n \"connect_procs\"),\n appName=appName,\n server=\"shinyapps.io\",\n from=\"20w\",\n interval=\"1m\"\n ) \n\ndf1 <- df %>% \n mutate(date=as_datetime(timestamp)) %>% \n select(-timestamp) %>% \n arrange(date) %>% \n mutate(\n n_count=cumsum(connect_count),\n n_procs=cumsum(connect_procs),\n new_connect=case_when(\n is.na(lag(connect_count,1)) ~ 0,\n connect_count>lag(connect_count,1) ~ connect_count-lag(connect_count,1),\n TRUE ~ 0),\n n_connect=cumsum(new_connect) # approximate\n ) %>% \n filter(n_count>0)\n\ndf2 <- df1 %>% \n select(n_connect, date) %>% \n gather(key=\"key\", value=\"value\", -date)\n\np2 <- ggplot(df2) +\n labs(title=\"Cumulative Connections\", x=\"\", y=\"\") +\n geom_line(aes(x=date, y=value, colour=key)) +\n facet_wrap(~key) +\n theme_solarized() +\n scale_x_datetime(labels=date_format(\"%b-%Y\")) +\n scale_colour_discrete(guide=FALSE)\n\np2 <- ggplot(df1) +\n labs(title=paste(\"Cumulative Connections (\", appName, \")\", sep=\"\"), x=\"\", y=\"\") +\n geom_line(aes(x=date, y=n_connect, colour=\"red\")) +\n theme_solarized() +\n # scale_x_datetime(labels=date_format(\"%b-%Y\")) +\n scale_colour_discrete(guide=FALSE)\n\nprint(p2)\n\nggsave(\"usage.png\")\n", "meta": {"hexsha": "443fd4132d69b6042f108d7d0df39d5086bc7928", "size": 1671, "ext": "r", "lang": "R", "max_stars_repo_path": "download_shinyapps_metrics.r", "max_stars_repo_name": "woodwards/pasture_potential", "max_stars_repo_head_hexsha": "a788eb99916bd64e7fc21227bb70bdd91b3b77ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2020-03-14T06:37:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-03T13:14:41.000Z", "max_issues_repo_path": "download_shinyapps_metrics.r", "max_issues_repo_name": "woodwards/pasture_potential", "max_issues_repo_head_hexsha": "a788eb99916bd64e7fc21227bb70bdd91b3b77ba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "download_shinyapps_metrics.r", "max_forks_repo_name": "woodwards/pasture_potential", "max_forks_repo_head_hexsha": "a788eb99916bd64e7fc21227bb70bdd91b3b77ba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8103448276, "max_line_length": 83, "alphanum_fraction": 0.5954518253, "num_tokens": 437, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.3025932708338122}} {"text": "library(DatabaseConnector)\nlibrary(FeatureExtraction)\n\nconnectionDetails <- createConnectionDetails(dbms=\"postgresql\",\n connectionString=\"jdbc:postgresql://localhost:5432/synpuf_110k_cu\",\n user=\"ohdsi\",\n password=\"ohdsi\",\n schema=\"five_three_plus\")\ncdmDatabaseSchema <- \"five_three_plus\"\nresultsDatabaseSchema <- \"five_three_plus_results\"\n\nsettings <- createCovariateSettings(useDemographicsGender = TRUE,\n useDemographicsAgeGroup = TRUE)\n\naspirinUsersCovariateAggData <- getDbCovariateData(connectionDetails = connectionDetails,\n cdmDatabaseSchema = cdmDatabaseSchema,\n cohortDatabaseSchema = resultsDatabaseSchema,\n cohortTable = \"cohort\",\n cohortId = 12,\n rowIdField = \"subject_id\",\n covariateSettings = settings,\n aggregated = TRUE)\n\naspirinUsersCovariateAggData$covariates\n\nclopidogrelUsersCovariateAggData <- getDbCovariateData(connectionDetails = connectionDetails,\n cdmDatabaseSchema = cdmDatabaseSchema,\n cohortDatabaseSchema = resultsDatabaseSchema,\n cohortTable = \"cohort\",\n cohortId = 11,\n rowIdField = \"subject_id\",\n covariateSettings = settings,\n aggregated = TRUE)\n\ncomputeStandardizedDifference(aspirinUsersCovariateAggData, clopidogrelUsersCovariateAggData)\n\ncomputeStandardizedDifference(clopidogrelUsersCovariateAggData, aspirinUsersCovariateAggData)\n\n\n#####\n\nlibrary(jsonlite)\n\nPrespecAnalyses <- read.csv(file=paste(path.package(\"FeatureExtraction\"), \"/csv/PrespecAnalyses.csv\", sep = \"\"), stringsAsFactors = FALSE)\n\nconvertCovDataToCCResult <- function(covDataList) {\n\n ccResult <- list(\n analyses = list()\n )\n\n baseCovData <- covDataList[[1]]\n analysisIds <- unique(baseCovData$covariateRef$analysisId)\n\n for(i in 1:length(analysisIds)) {\n analysisId <- analysisIds[i]\n domainId <- PrespecAnalyses[PrespecAnalyses$analysisId == analysisId,]$domainId\n analysisName <- PrespecAnalyses[PrespecAnalyses$analysisId == analysisId,]$analysisName\n\n ccResult$analyses[[i]] <- list(\n analysisId = analysisId,\n domainId = domainId,\n analysisName = analysisName,\n reports = list()\n )\n }\n\n for(covDataIdx in 1:length(covDataList)) {\n\n covData <- covDataList[[covDataIdx]]\n\n cohortId = as.numeric(covData$metaData$cohortId)\n fullCovs <- merge(covData$covariateRef, covData$covariates, by = \"covariateId\")\n\n for(i in 1:length(analysisIds)) {\n\n analysisId <- analysisIds[i]\n domainId <- PrespecAnalyses[PrespecAnalyses$analysisId == analysisId,]$domainId\n analysisName <- PrespecAnalyses[PrespecAnalyses$analysisId == analysisId,]$analysisName\n stats <- list()\n\n covs <- fullCovs[fullCovs$analysisId == analysisId,]\n\n for (row in 1:nrow(covs)) {\n covariateId <- covs[row, \"covariateId\"]\n averageValue <- covs[row, \"averageValue\"]\n sumValue <- covs[row, \"sumValue\"]\n covName <- covs[row, \"covariateName\"]\n conceptId <- covs[row, \"conceptId\"]\n pct <- sumValue / covData$metaData$populationSize * 100\n\n stats[[row]] <- list(\n covariateId = covariateId,\n covariateName = covName,\n conceptId = conceptId,\n averageValue = averageValue,\n sumValue = sumValue,\n pct = pct\n )\n }\n\n ccResult$analyses[[i]]$reports[[covDataIdx]] <- list(\n cohortId = cohortId,\n stats = stats\n )\n }\n }\n\n json <- toJSON(ccResult,auto_unbox=TRUE)\n prettify(json)\n\n}\n\nconvertCovDataToCCResult(list(aspirinUsersCovariateAggData, clopidogrelUsersCovariateAggData))", "meta": {"hexsha": "7fe996e0dc4fb6dc943c93c5f8a4c3855262c6c0", "size": 4254, "ext": "r", "lang": "R", "max_stars_repo_path": "js/pages/characterizations/stubs/characterization-results-data.r", "max_stars_repo_name": "hongwonjun/Atlas", "max_stars_repo_head_hexsha": "d42e35c179f5fb4f0071b6a01b974ff78313757d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 197, "max_stars_repo_stars_event_min_datetime": "2015-12-01T05:57:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-15T18:11:38.000Z", "max_issues_repo_path": "js/pages/characterizations/stubs/characterization-results-data.r", "max_issues_repo_name": "hongwonjun/Atlas", "max_issues_repo_head_hexsha": "d42e35c179f5fb4f0071b6a01b974ff78313757d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1823, "max_issues_repo_issues_event_min_datetime": "2015-07-30T19:38:21.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:21:48.000Z", "max_forks_repo_path": "js/pages/characterizations/stubs/characterization-results-data.r", "max_forks_repo_name": "hongwonjun/Atlas", "max_forks_repo_head_hexsha": "d42e35c179f5fb4f0071b6a01b974ff78313757d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 114, "max_forks_repo_forks_event_min_datetime": "2015-08-27T07:49:24.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-23T00:48:44.000Z", "avg_line_length": 37.3157894737, "max_line_length": 138, "alphanum_fraction": 0.5893276916, "num_tokens": 950, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.4804786780479071, "lm_q1q2_score": 0.3025932708338122}} {"text": "setwd(\"P:/2019 0970 151 000/User Data/Faraz-Export IDAVE folders/Models2.5\")\nlibrary(tidyverse)\nlibrary(h2o)\nh2o.init()\n##########Smote data \n\nsmote_train <- readRDS(\"../smote_data/smote_train.rds\")\nsmote_test <- readRDS(\"../smote_data/test.rds\")\n\nstr(smote_train)\n# .frame': 1973985 obs. of 41 variables:\n # $ acutelos : num 16 9 7 2 2 3 2 3 13 1 ...\n # $ main_PhysInj_1L : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_34 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 2 1 ...\n # $ admambul : Factor w/ 2 levels \"N\",\"Y\": 2 1 2 2 2 2 2 2 2 1 ...\n # $ dementia_main : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ all_Cachx_3L : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ weightloss : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ cacsitcnt : num 2 0 1 1 2 3 0 2 2 0 ...\n # $ main_MentBehav_1L : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ age_grp : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 2 2 4 4 1 1 4 4 1 ...\n # $ frail_grp : Factor w/ 2 levels \"high\",\"low\": 2 2 2 2 2 2 2 2 1 2 ...\n # $ flag_feeding_tb : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_12 : Factor w/ 2 levels \"0\",\"1\": 2 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_38 : Factor w/ 2 levels \"0\",\"1\": 1 1 2 1 1 1 1 1 1 1 ...\n # $ main_Zfactors_1L : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 2 1 1 1 ...\n # $ prv_orthop : Factor w/ 2 levels \"No\",\"Yes\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ dementia_comorb : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_17 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 2 1 1 ...\n # $ cacsit1_grp3 : Factor w/ 5 levels \"Cat_scan\",\"Notapp\",..: 1 2 1 5 4 1 2 1 5 2 ...\n # $ patserv_15 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 2 ...\n # $ main_MusclSkelt_1L: Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 2 1 ...\n # $ flag_tracheost : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ rural : Factor w/ 2 levels \"N\",\"Y\": 1 2 1 2 1 1 2 1 1 1 ...\n # $ triage : Factor w/ 6 levels \"1\",\"2\",\"3\",\"4\",..: 2 3 3 2 3 1 3 2 2 3 ...\n # $ cacsitcnt1 : Factor w/ 3 levels \"Notapp\",\"Just1\",..: 2 1 2 2 2 2 1 2 3 1 ...\n # $ patserv_72 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_39 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ flag_radiother : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ scu : Factor w/ 16 levels \"10\",\"20\",\"25\",..: 16 16 16 16 16 7 16 16 4 16 ...\n # $ flag_vasc_accdv : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ flag_mvent_ge96 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ female_flag : Factor w/ 2 levels \"0\",\"1\": 2 1 1 2 2 1 2 2 2 2 ...\n # $ prv_grp : Factor w/ 3 levels \"1\",\"3\",\"oth\": 2 1 2 1 2 2 1 2 2 1 ...\n # $ psychoses : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ readm_90d : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 2 1 ...\n # $ flag_pa_nutrit : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ paralysis : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_36 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ patserv_64 : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ flag_cardiovers : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n # $ alc_status : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...'\n\ntrain_hex <- as.h2o(smote_train)\ntest_hex <- as.h2o(smote_test)\n\ndf_rf1 <- h2o.randomForest(\ntraining_frame = train_hex,\n# validation_frame = test_hex,\nx = 1:40,\ny = ncol(smote_train),\nmodel_id = \"rf_smote\",\nnfolds = 5,\nfold_assignment = \"Stratified\",\n# balance_classes = TRUE,\nntrees = 500,\nstopping_rounds = 5,\nstopping_tolerance = 1e-3, ##early stop if AUCPR did not improve at least 0.1% over 5 consecutive turns\nstopping_metric = \"AUCPR\",\nscore_tree_interval = 5,\nscore_each_iteration = T,\nseed = 1234\n)\n\nperf <- h2o.performance(df_rf1, newdata = test_hex)\n\n##Does not work well on test data, overfits on training despite cross validation etc\n", "meta": {"hexsha": "3834a716137ec32c4b2389d2c8eddbbcd61e9bd1", "size": 4151, "ext": "r", "lang": "R", "max_stars_repo_path": "Predective Modeling/h2o_smote.r", "max_stars_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_stars_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Predective Modeling/h2o_smote.r", "max_issues_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_issues_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Predective Modeling/h2o_smote.r", "max_forks_repo_name": "farazahmadi/machine-learning-prediction-of-alternate-level-of-care", "max_forks_repo_head_hexsha": "7957364c0f263d2e80325643d1118e9b47ef3754", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.2179487179, "max_line_length": 103, "alphanum_fraction": 0.5405926283, "num_tokens": 1955, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.30259327083381216}} {"text": "rm(list=ls())\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(ggpubr)\n\n# Read in data and rename data\nd <- function(dataset){\n load(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\".RData\", sep=''))\n mLBF.x <- mLBF %>% mutate(dataset = case_when(TvS != 'a' ~ dataset)) %>% select(\"Locus\",\"TvS\",\"dataset\")\n mLGL.x <- mLGL %>% mutate(dataset = case_when(TvS != 'a' ~ dataset)) %>% select(\"Locus\",\"TvS\",\"dataset\")\n mL <- merge(mLGL.x,mLBF.x,by = c(\"Locus\",\"dataset\")) %>% rename(TvS.g=TvS.x) %>% rename(TvS.b=TvS.y)\n rm(mLBF,mLGL)\n return(list(mLGL.x,mLBF.x,mL))\n}\n\n# Read in data and rename data\ndataset <- \"SinghalOG\"\nsetwd(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset, sep=''))\nmLBF.sio <- d(dataset)[[1]]\nmLGL.sio <- d(dataset)[[2]]\nmL.sio <- d(dataset)[[3]]\n\n# Read in data and rename data\ndataset <- \"Singhal\"\nmLBF.si <- d(dataset)[[1]]\nmLGL.si <- d(dataset)[[2]]\nmL.si <- d(dataset)[[3]]\n\n\n# Read in data and rename data\ndataset <- \"Streicher\"\nmLBF.st <- d(dataset)[[1]]\nmLGL.st <- d(dataset)[[2]]\nmL.st <- d(dataset)[[3]]\n\n\n# Read in data and rename data\ndataset <- \"Reeder\"\nmLBF.re <- d(dataset)[[1]]\nmLGL.re <- d(dataset)[[2]]\nmL.re <- d(dataset)[[3]]\n\n\n# Read in data and rename data\ndataset <- \"Burbrink\"\nload(paste(\"/Users/ChatNoir/Projects/Squam/Graphs/\",dataset,\"/Calcs_\",dataset,\"_BFonly.RData\", sep=''))\nmLBF.bu <- mLBF %>% mutate(dataset = case_when(TvS != 'a' ~ dataset)) %>% select(\"Locus\",\"TvS\",\"dataset\")\nmL.bu <- mLBF.bu %>% mutate(dataset = case_when(TvS != 'a' ~ dataset)) %>% rename(TvS.b=TvS)\nrm(mLBF,mLGL)\n\n\n# Rename loci to all match using singhal as template\nmL.bu <- data.frame(lapply(mL.bu, function(x) {gsub(\"T212_\", \"AHE-\", x)}))\nmL.re <- data.frame(lapply(mL.re, function(x) {gsub(\"Reeder_DNA_\", \"gene-\", x)}))\n\n\n\n\n## Using one of the singhal datasets, match other datasets \n# Add all these datsets together and rename columns \nrm(x,y,z,f,h,j,n)\nx <- left_join(mL.si,mL.bu,by=\"Locus\") %>% \n rename(TvS.b.si = TvS.b.x) %>% \n rename(TvS.g.si = TvS.g) %>% \n rename(TvS.b.bu = TvS.b.y) \n\ny <- left_join(x,mL.st,by=c(\"Locus\"))%>% \n rename(TvS.b.st = TvS.b) %>%\n rename(TvS.g.st = TvS.g) \n\nz <- left_join(y,mL.re,by=c(\"Locus\"))%>% \n rename(TvS.b.re = TvS.b) %>%\n rename(TvS.g.re = TvS.g) \n\n# Merge columns in a really stupid way \nf <- unite(z, TvS.b, c(TvS.b.bu, TvS.b.st, TvS.b.re), remove=TRUE)\nh <- unite(f, TvS.g, c(TvS.g.st, TvS.g.re), remove=TRUE)\nj <- unite(h, dataset.y, c(dataset.y,dataset.x.x, dataset.y.y), remove=TRUE)\n\nn <- data.frame(lapply(j, function(x) {gsub(\"NA|NA_|_NA|_NA_\", \"\", x)}))\n\n\n## Scatter graph\n\n\nS <- function(df,xcol,ycol,x.tic,y.tic,cc,xl,yl){\n xval <- as.numeric(as.character(df[[xcol]]))\n yval <- as.numeric(as.character(df[[ycol]]))\n print(xval)\n scat <- ggplot(df, aes(x=xval,y=yval)) + \n geom_point(alpha=0.5, aes(color=df[[cc]]), size=2) + theme_bw() + theme(panel.border = element_blank()) +\n theme_classic() + \n theme(\n axis.text = element_text(size=14, color=\"black\"),\n text = element_text(size=14),\n panel.border = element_blank(),\n panel.background = element_rect(fill = \"transparent\"), # bg of the panel\n plot.background = element_rect(fill = \"transparent\", color = NA), # bg of the plot\n panel.grid = element_blank(), # get rid of major grid\n plot.title = element_text(hjust = 0.5)\n ) +\n coord_cartesian(ylim=y.tic,xlim = x.tic) +\n scale_y_continuous(breaks = y.tic) + \n scale_x_continuous(breaks = x.tic) +\n labs(x=xl,y=yl)\n \n return(scat)\n}\n\n\nlimit <- 200\ntic <- seq(-limit,limit,50)\nxl <- 'Other Datasets - 2ln(BF)'\n#yl <- 'Singhal - original data - 2ln(BF)'\nyl <- 'Singhal - added data - 2ln(BF)'\n#quartz()\ndf.n <- n[!(n$dataset.y == \"\"), ]\nb <- S(df.n,4,5,tic,tic,6,xl,yl) +geom_abline(color=c(\"black\"), size=0.4, linetype=\"dashed\") \nb\n\n\nlimit <- 60\ntic <- seq(-limit,limit,10)\nxl <- 'Other Datasets - dGLS'\n#yl <- 'Singhal - original data - dGLS'\nyl <- 'Singhal - added data - dGLS'\n#quartz()\ng <- S(df.n,3,7,tic,tic,6,xl,yl) +geom_abline(color=c(\"black\"), size=0.4, linetype=\"dashed\") \ng\n\n\na <- ggarrange(g,b, ncol=1, nrow=2, align=\"v\")\na\n\nggsave(paste(\"Singhal_scatter_compLoci.pdf\",sep=\"\"), plot=a,width = 9, height = 9, units = \"in\", device = 'pdf',bg = \"transparent\")\n\n\n", "meta": {"hexsha": "824d9116dee0833c982020ead403d610841d4801", "size": 4272, "ext": "r", "lang": "R", "max_stars_repo_path": "Graphing/Old/Graphs_Scatter_compareLoci.r", "max_stars_repo_name": "LizEve/SquamateLikelihoodRatios", "max_stars_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Graphing/Old/Graphs_Scatter_compareLoci.r", "max_issues_repo_name": "LizEve/SquamateLikelihoodRatios", "max_issues_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Graphing/Old/Graphs_Scatter_compareLoci.r", "max_forks_repo_name": "LizEve/SquamateLikelihoodRatios", "max_forks_repo_head_hexsha": "cc90c66832ca6492b52000b7b1fc4fb8fc7994d3", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.7338129496, "max_line_length": 131, "alphanum_fraction": 0.6259363296, "num_tokens": 1509, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3025101033557842}} {"text": "#' @useDynLib fmlr R_dimops_matsums\nmatsums = function(row, mean, x, s)\n{\n if (is.null(s))\n s = setret(x, vec=TRUE)\n \n .Call(R_dimops_matsums, get_backend(x), x$get_type(), row, mean, x$data_ptr(), s$data_ptr())\n s\n}\n\n\n\n#' matsums\n#' \n#' Compute the sums/means of the rows/columns of a matrix.\n#' \n#' @param x Input matrix.\n#' @param s Either \\code{NULL} or an already allocated fml matrix of the same\n#' class and type as \\code{x}.\n#' @return Returns the matrix sum.\n#' \n#' @rdname dimops\n#' @name dimops\nNULL\n\n\n\n#' @name dimops\n#' @export\ndimops_rowsums = function(x, s=NULL)\n{\n check_is_mat(x)\n invisiret = check_inputs(s, x)\n \n s = matsums(TRUE, FALSE, x, s)\n \n if (invisiret)\n invisible(s)\n else\n s\n}\n\n#' @name dimops\n#' @export\ndimops_rowmeans = function(x, s=NULL)\n{\n check_is_mat(x)\n invisiret = check_inputs(s, x)\n \n s = matsums(TRUE, TRUE, x, s)\n \n if (invisiret)\n invisible(s)\n else\n s\n}\n\n#' @name dimops\n#' @export\ndimops_colsums = function(x, s=NULL)\n{\n check_is_mat(x)\n invisiret = check_inputs(s, x)\n \n s = matsums(FALSE, FALSE, x, s)\n \n if (invisiret)\n invisible(s)\n else\n s\n}\n\n#' @name dimops\n#' @export\ndimops_colmeans = function(x, s=NULL)\n{\n check_is_mat(x)\n invisiret = check_inputs(s, x)\n \n s = matsums(FALSE, TRUE, x, s)\n \n if (invisiret)\n invisible(s)\n else\n s\n}\n\n\n\n#' scale\n#' \n#' Remove the mean and/or sd of the columns of a matrix. The operations occur\n#' in-place.\n#' \n#' @param rm_mean,rm_sd Should the data be centered/scaled first?\n#' @param x Input matrix. The input data is overwritten by the centered/scaled\n#' data.\n#' \n#' @rdname scale\n#' @name scale\n#' \n#' @useDynLib fmlr R_dimops_scale\n#' \n#' @export\ndimops_scale = function(rm_mean=TRUE, rm_sd=FALSE, x)\n{\n check_is_mat(x)\n \n rm_mean = as.logical(rm_mean)\n rm_sd = as.logical(rm_sd)\n \n .Call(R_dimops_scale, get_backend(x), x$get_type(), rm_mean, rm_sd, x$data_ptr())\n invisible(NULL)\n}\n\n\n\n#' sweep\n#' \n#' Sweep a vector through the rows/cols of a matrix using an arithmetic\n#' operation.\n#' \n#' @param x Input matrix.\n#' @param s Either \\code{NULL} or an already allocated fml matrix of the same\n#' class and type as \\code{x}.\n#' @param op The operation: \\code{SWEEP_ADD}, \\code{SWEEP_SUB},\n#' \\code{SWEEP_MUL}, or \\code{SWEEP_DIV}.\n#' @return Returns the matrix sum.\n#' \n#' @rdname dimops\n#' @name dimops\nNULL\n\n\n\ncheck_sweep_op = function(op)\n{\n op = as.integer(op)\n if (op != SWEEP_ADD && op != SWEEP_SUB && op != SWEEP_MUL && op != SWEEP_DIV)\n stop(\"invalid argument 'op'\")\n \n op\n}\n\n#' @name dimops\n#' @useDynLib fmlr R_dimops_rowsweep\n#' @export\ndimops_rowsweep = function(x, s, op)\n{\n op = check_sweep_op(op)\n check_is_mat(x)\n invisiret = check_inputs(s, x, check_class=FALSE)\n \n .Call(R_dimops_rowsweep, get_backend(x), x$get_type(), x$data_ptr(), s$data_ptr(), op)\n}\n\n#' @name dimops\n#' @useDynLib fmlr R_dimops_colsweep\n#' @export\ndimops_colsweep = function(x, s, op)\n{\n op = check_sweep_op(op)\n check_is_mat(x)\n invisiret = check_inputs(s, x, check_class=FALSE)\n \n .Call(R_dimops_colsweep, get_backend(x), x$get_type(), x$data_ptr(), s$data_ptr(), op)\n}\n", "meta": {"hexsha": "93903804453e7b06c71d9495eb2a83ee5ee877ff", "size": 3138, "ext": "r", "lang": "R", "max_stars_repo_path": "R/dimops.r", "max_stars_repo_name": "fml-fam/fmlr", "max_stars_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-02-06T21:06:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-23T22:34:08.000Z", "max_issues_repo_path": "R/dimops.r", "max_issues_repo_name": "wrathematics/fmlr", "max_issues_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-02-19T17:27:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-09T00:30:36.000Z", "max_forks_repo_path": "R/dimops.r", "max_forks_repo_name": "wrathematics/fmlr", "max_forks_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.6785714286, "max_line_length": 94, "alphanum_fraction": 0.6551943913, "num_tokens": 1020, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.3025101033557842}} {"text": "# -*- coding: utf-8 -*-\n\n#' Created on Fri Apr 13 15:38:28 2018\n#' R version 3.4.3 (2017-11-30)\n#' \n#' @group Group 2, DM2 2018 Semester 2\n#' @author: Martins T.\n#' @author: Mendes R.\n#' @author: Santos R.\n#'\n\n# Libs --------------------------------------------------------------------\noptions(warn=-1)\nsource(\"src/packages.r\")\ninclude_packs(c(\"dygraphs\",\"d3heatmap\",\"rockchalk\",\"forcats\",\"rJava\",\n \"xlsxjars\",\"xlsx\",\"tidyverse\",\"stringi\",\"stringr\",\"ggcorrplot\",\n \"sm\",\"lubridate\",\"magrittr\",\"ggplot2\",\"openxlsx\",\"RColorBrewer\",\n \"psych\",\"treemap\",\"data.table\",\"pROC\",\"class\",'gmodels','klaR',\n \"C50\",\"caret\",'gmodels',\"png\",\"psych\",\"plotrix\",\"autoimage\"))\n\n# Data wrangling ---------------------------------------------------------------\n#setting up the directory\nsource(\"src/wrangling.r\")\nchurnDataset <- mug_data()\nwrite_excel(churnDataset)\n\n# attach_set w/ non-overlapping names and clean Global Env from main.r w/ ls()\nattach_set(); rm(list = setdiff(ls(.GlobalEnv), c(\"churnDataset\" ,lsf.str())))\n\n\n# Exploratory data analysis -------------------------------------------------\norigin_Dataset <- xlsx::read.xlsx('datasets/churnDataset.xlsx',sheetName = 'churnDataset', \n stringsAsFactors = TRUE, header= TRUE)\n\n# dataset description\nstr(origin_Dataset)\n\n# get column data types\norigin_Dataset %>% sapply(class)\n\n# summary statistics\nsummarystats <- origin_Dataset %>% describe\nsummarystats\nxlsx::write.xlsx(summarystats, 'datasets/summarystats.xlsx', sheetName=\"summarystats\")\n\nsummarystats2 <- origin_Dataset %>% summary\nsummarystats2\nxlsx::write.xlsx(summarystats2, 'datasets/summarystats2.xlsx', sheetName=\"summarystats2\")\n\n\n# missing values\nis.na(origin_Dataset) %>% colSums\n\n# check churn population\nplyr::count(origin_Dataset$Churn)\n\n\n# CONSISTENCY CHECKS\n\n# check consistency between total work years vs company years\nwhich(origin_Dataset$TenureCompany > origin_Dataset$TenureWorking)\n\nwhich(origin_Dataset$TenureRole > origin_Dataset$TenureCompany)\n\nwhich(origin_Dataset$TenureManager > origin_Dataset$TenureCompany)\n\n\n\n#display.brewer.all() # shows the different palettes you can choose from\n\n\n\n# churn plot\npng(filename=\"presentations/churn.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_bar(aes(x=origin_Dataset$Churn), fill=\"#C51B7D\", color=\"white\")\np <- p + scale_x_discrete(name=\"Churn\") + scale_y_discrete(name=\"Frequency\")\np <- p + ggtitle(label=\"Churn\")\np\ndev.off()\np\n\n\n\n# gender plot\npng(filename=\"presentations/gender.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$Gender, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$Gender,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"gender\") + ggtitle(\"Gender vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# marital status plot\npng(filename=\"presentations/maritalstatus.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$MaritalStatus, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$MaritalStatus,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"marital status\") + ggtitle(\"Marital Status vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# dependents plot\npng(filename=\"presentations/dependents.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$Dependents, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$Dependents,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"dependents\") + ggtitle(\"Dependents vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# dependent box plot\npng(filename=\"presentations/dependentsboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"dependents\", y=Dependents, fill=Churn))\np <- p + ggtitle(label=\" Dependents\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job type plot\npng(filename=\"presentations/jobtype.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$JobType, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$JobType,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"job type\") + ggtitle(\"Job Type vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# department plot\npng(filename=\"presentations/department.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$Department, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$Department,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"department\") + ggtitle(\"Department vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job level plot\npng(filename=\"presentations/joblevel.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$JobLevel, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$JobLevel,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"job level\") + ggtitle(\"Job Level vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job level box plot\npng(filename=\"presentations/joblevelboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"Job Level\", y=JobLevel, fill=Churn))\np <- p + ggtitle(label=\"Job Levels\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# role plot\npng(filename=\"presentations/role.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$JobRole, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$JobRole,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"role\") + ggtitle(\"Job Role vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np <- p + theme(axis.text.x = element_text(angle = 45, hjust = 1))\np\ndev.off()\np\n\n# type contract\npng(filename=\"presentations/typecontract.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$TypeContract, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$TypeContract,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"type contract\") + ggtitle(\"Type Contract vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# week hours\npng(filename=\"presentations/weakhours.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$WeekHours, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$WeekHours,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"type contract\") + ggtitle(\"Type Contract vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n# education\npng(filename=\"presentations/education.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$Education, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$Education,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"education\") + ggtitle(\"Education vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# education area\npng(filename=\"presentations/educationarea.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$EducationArea, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$EducationArea,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"education area\") + ggtitle(\"Education Area vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# number of projects\npng(filename=\"presentations/numberprojects.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$NumberProjectsLastYear, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$NumberProjectsLastYear,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"number projects\") + ggtitle(\"Number Projects vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# number of Projects box plot\npng(filename=\"presentations/numberprojectsboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"number projects\", y=NumberProjectsLastYear, fill=Churn))\np <- p + ggtitle(label=\"Number Projects\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# number of companies\npng(filename=\"presentations/companies.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$NumCompaniesWorked, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$NumCompaniesWorked,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"number companies\") + ggtitle(\"Number Companies vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# number of companies box plot\npng(filename=\"presentations/companiesboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"number companies\", y=NumCompaniesWorked, fill=Churn))\np <- p + ggtitle(label=\"Number Companies\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# percent of salary rise\npng(filename=\"presentations/salaryrise.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$SalaryRise, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$SalaryRise,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"salary rise\") + ggtitle(\"salary rise vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# percent of salary box plot\npng(filename=\"presentations/salaryriseboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"salary rise\", y=SalaryRise, fill=Churn))\np <- p + ggtitle(label=\"Salary Rise\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# last promotion\npng(filename=\"presentations/lastpromotion.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$LastPromotion, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$LastPromotion,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"last promotion\") + ggtitle(\"Last Promotion vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# last promotion box plot\npng(filename=\"presentations/lastpromotionboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"last promotion\", y=LastPromotion, fill=Churn))\np <- p + ggtitle(label=\"Last Promotion\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n# job dedication\npng(filename=\"presentations/jobdedication.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$JobDedication, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$JobDedication,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"job dedication\") + ggtitle(\"Job Dedication vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job dedication box plot\npng(filename=\"presentations/jobdedicationboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"job dedication\", y=JobDedication, fill=Churn))\np <- p + ggtitle(label=\"Job Dedication\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# afterhours\npng(filename=\"presentations/afterhours.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$AfterHours, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$AfterHours,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"after hours\") + ggtitle(\"After Hours vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job performance\npng(filename=\"presentations/jobperformance.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$JobPerformance, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$JobPerformance,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"job performance\") + ggtitle(\"Job Performance vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# job performation box plot\npng(filename=\"presentations/jobperformanceboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"job performance\", y=JobPerformance, fill=Churn))\np <- p + ggtitle(label=\"Job Performance\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# facilities satisfaction\npng(filename=\"presentations/facilitiessatisfaction.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$FacilitiesSatisfaction, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$FacilitiesSatisfaction,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"facierilities satisfaction\") + ggtitle(\"Facilities Satisfaction vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# facilities satisfaction box plot\npng(filename=\"presentations/facilitiessatisfactionboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"facilities satisfaction\", y=FacilitiesSatisfaction, fill=Churn))\np <- p + ggtitle(label=\"Facilities Satisfaction\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# role satisfaction\npng(filename=\"presentations/rolesatisfaction.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$RoleSatisfaction, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$RoleSatisfaction,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"role satisfaction\") + ggtitle(\"Role Satisfaction vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# role satisfaction box plot\npng(filename=\"presentations/rolesatisfactionboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"role satisfaction\", y=RoleSatisfaction, fill=Churn))\np <- p + ggtitle(label=\"Role Satisfaction\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n# hierarchy satisfaction\npng(filename=\"presentations/hierarchysatisfaction.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$HierarchySatisfaction, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$HierarchySatisfaction,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"hierarchy satisfaction\") + ggtitle(\"Hierarchy Satisfaction vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# hierarchy satisfaction box plot\npng(filename=\"presentations/hierarchysatisfactionboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"hierarchy satisfaction\", y=HierarchySatisfaction, fill=Churn))\np <- p + ggtitle(label=\"Hierarchy Satisfaction\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# tenure working\npng(filename=\"presentations/tenureworking.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$TenureWorking, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$TenureWorking,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"tenure working\") + ggtitle(\"Tenure Working vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure working box plot\npng(filename=\"presentations/tenureworkingboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"tenure working\", y=TenureWorking, fill=Churn))\np <- p + ggtitle(label=\"Tenure Working\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n# tenure company\npng(filename=\"presentations/tenurecompany.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$TenureCompany, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$TenureCompany,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"tenure company\") + ggtitle(\"Tenure company vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure company box plot\npng(filename=\"presentations/tenurecompanyboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"tenure company\", y=TenureCompany, fill=Churn))\np <- p + ggtitle(label=\"Tenure Company\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure role\npng(filename=\"presentations/tenurerole.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$TenureRole, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$TenureRole,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"tenure role\") + ggtitle(\"Tenure Role vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure role box plot\npng(filename=\"presentations/tenureroleboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"tenure role\", y=TenureRole, fill=Churn))\np <- p + ggtitle(label=\"Tenure Role\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# tenure manager\npng(filename=\"presentations/tenuremanager.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$TenureManager, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$TenureManager,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"tenure manager\") + ggtitle(\"Tenure Manager vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure manager box plot\npng(filename=\"presentations/tenuremanagerboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"tenure manager\", y=TenureManager, fill=Churn))\np <- p + ggtitle(label=\"Tenure Manager\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# balance worklife\npng(filename=\"presentations/balanceworklife.png\",width=760, height=565)\ndf <- data.frame(origin_Dataset$BalanceWorkLife, origin_Dataset$Churn)\np <- ggplot(df, aes(origin_Dataset$BalanceWorkLife,..count..)) + geom_bar(aes(fill = origin_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"balance worklife\") + ggtitle(\"Balance Worklife vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n# Numerical continous VARIABLES\n\n# income\npng(filename=\"presentations/income.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_histogram(aes(x=origin_Dataset$MonthlyIncome), fill=\"#C51B7D\", color=\"white\")\np <- p + scale_x_continuous(name=\"Income\") + scale_y_continuous(name=\"Frequency\")\np <- p + ggtitle(label=\"Income\")\np\ndev.off()\np\n\n\n# distance home office\npng(filename=\"presentations/distancehomeoffice.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_histogram(aes(x=origin_Dataset$DistanceHomeOffice), fill=\"maroon3\", color=\"white\")\np <- p + scale_x_continuous(name=\"Distance\") + scale_y_continuous(name=\"Frequency\")\np <- p + ggtitle(label=\"Distance Home / Office\")\np\ndev.off()\np\n\n\n# distance box plot\npng(filename=\"presentations/distancehomeofficeboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"Distance\", y=DistanceHomeOffice, fill=Churn))\np <- p + ggtitle(label=\"Distance Home / Office\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# box plot #\n\n# income\n\npng(filename=\"presentations/incomeboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x = \"income\", y=MonthlyIncome, fill = \"income\"))\np <- p + ggtitle(label=\"Monthly Income\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\npng(filename=\"presentations/lastpromotionboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x = \"last promotion\", y=LastPromotion, fill = \"\"))\np <- p + ggtitle(label=\"Last Promotion\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\npng(filename=\"presentations/tenureworkingboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x = \"tenure working\", y=TenureWorking, fill = \"\"))\np <- p + ggtitle(label=\"Tenure Working\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\npng(filename=\"presentations/tenurecompanyboxplot.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x = \"tenure company\", y=TenureCompany, fill = \"\"))\np <- p + ggtitle(label=\"Tenure Company\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# box plot Income\npng(filename=\"presentations/incomeboxplotchurn.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"income\", y=MonthlyIncome, fill=Churn))\np <- p + ggtitle(label=\" Monthly Income\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# distance home / office\npng(filename=\"presentations/distancehomeofficeboxplotchurn.png\",width=760, height=565)\np <- ggplot(data=origin_Dataset) + geom_boxplot(aes(x=\"Distance\", y=DistanceHomeOffice, fill=Churn))\np <- p + ggtitle(label=\"Distance Home / Office\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n\n\n# Pre Processing ----------------------------------------------------------\nsource(\"src/preprocessing.r\")\n\n\n# --------------------------------------------------------------------------------------------------\n# POST PROCESSING ANALYSIS\n# --------------------------------------------------------------------------------------------------\norigin_Dataset <- xlsx::read.xlsx('datasets/churnDataset.xlsx',sheetName = 'churnDataset', \n stringsAsFactors = TRUE, header= TRUE)\n\n\nprocessed_Dataset <- xlsx::read.xlsx('datasets/processedDataset.xlsx',sheetName = 'processedDataset', \n stringsAsFactors = TRUE, header= TRUE)\n\n# summary statistics\nsummarystats <- processed_Dataset %>% describe\nsummarystats\nxlsx::write.xlsx(summarystats, 'datasets/processedsummarystats.xlsx', sheetName=\"summarystats\")\n\nsummarystats2 <- processed_Dataset %>% summary\nsummarystats2\nxlsx::write.xlsx(summarystats2, 'datasets/processedsummarystats2.xlsx', sheetName=\"summarystats2\")\n\n# Age plot\npng(filename=\"presentations/age.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$Age, processed_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$Age,..count..)) + geom_bar(aes(fill = processed_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Age\") + ggtitle(\"Age vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# age box plot\npng(filename=\"presentations/ageboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Age\", y=DistanceHomeOffice, fill=Churn))\np <- p + ggtitle(label=\"Age\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# Avg Satisfaction plot\npng(filename=\"presentations/averagesatisfaction.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$avgSatisfaction, origin_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$avgSatisfaction,..count..)) + geom_bar(aes(fill = processed_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Age\") + ggtitle(\"Average Satisfaction vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# Avg Satisfaction box plot\npng(filename=\"presentations/averagesatisfactionboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Average Satisfaction\", y=avgSatisfaction, fill=Churn))\np <- p + ggtitle(label=\"Average Satisfaction\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# balance worklife plot\npng(filename=\"presentations/worklifelevels.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$WorkLifeLevels, processed_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$WorkLifeLevels,..count..)) + geom_bar(aes(fill = processed_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Balance Work/Life\") + ggtitle(\"Balance Work/Life vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# balance worklife box plot\npng(filename=\"presentations/worklifelevelsboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Balance Work/Life\", y=WorkLifeLevels, fill=Churn))\np <- p + ggtitle(label=\"Balance Work/Life\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# companies/age histogram\npng(filename=\"presentations/companiesperagehist.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_histogram(aes(x=processed_Dataset$companiesperAge), fill=\"maroon3\", color=\"white\")\np <- p + scale_x_continuous(name=\"Companies/Age\") + scale_y_continuous(name=\"\")\np <- p + ggtitle(label=\"Companies per Age\")\np\ndev.off()\np\n\n# companies/age plot\npng(filename=\"presentations/companiesperage.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$companiesperAge, processed_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$companiesperAge,..count..)) + geom_bar(aes(fill = processed_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Companies/Age\") + ggtitle(\"Companies per Age vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# companies/age box plot\npng(filename=\"presentations/companiesperageboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Companies/Age\", y=companiesperAge, fill=Churn))\np <- p + ggtitle(label=\"Companies per Age\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# Tenure company / working histogram\npng(filename=\"presentations/tenurecompanyworkinghist.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_histogram(aes(x=processed_Dataset$TcompanyperWorking), fill=\"maroon3\", color=\"white\")\np <- p + scale_x_continuous(name=\"Tenure Company/Working\") + scale_y_continuous(name=\"\")\np <- p + ggtitle(label=\"Tenure Company/Working\")\np\ndev.off()\np\n\n# tenure company / working plot\npng(filename=\"presentations/tenurecompanyworking.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$TcompanyperWorking, processed_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$TcompanyperWorking,..count..)) + geom_bar(aes(fill = processed_Dataset$Churn),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Tenure Company/Working\") + ggtitle(\"Tenure Company / Working vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n# tenure company / working box plot\npng(filename=\"presentations/tenurecompanyworkingboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Tenure Company/Working\", y=TcompanyperWorking, fill=Churn))\np <- p + ggtitle(label=\"Tenure Company / Working\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n\n# tenure role/manager histogram\npng(filename=\"presentations/tenurerolemanagerhist.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_histogram(aes(x=processed_Dataset$TroleperManager), fill=\"maroon3\", color=\"white\")\np <- p + scale_x_continuous(name=\"Tenure Role/Manager\") + scale_y_continuous(name=\"\")\np <- p + ggtitle(label=\"Tenure Role/Manager\")\np\ndev.off()\np\n\n\n# tenure role/manager box plot\npng(filename=\"presentations/tenurerolemanagerboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Tenure Role/Manager\", y=TroleperManager, fill=Churn))\np <- p + ggtitle(label=\"Tenure Role/Manager\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# companies/tenure working histogram\npng(filename=\"presentations/companiesperworkinghist.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_histogram(aes(x=processed_Dataset$NumcompaniesperWorking), fill=\"maroon3\", color=\"white\")\np <- p + scale_x_continuous(name=\"Companies / Tenure Working\") + scale_y_continuous(name=\"\")\np <- p + ggtitle(label=\"Companies / Tenure Working\")\np\ndev.off()\np\n\n\n# companies/tenure working box plot\npng(filename=\"presentations/companiesperworkingboxplot.png\",width=760, height=565)\np <- ggplot(data=processed_Dataset) + geom_boxplot(aes(x=\"Companies / Tenure Working\", y=NumcompaniesperWorking, fill=Churn))\np <- p + ggtitle(label=\"Companies / Tenure Working\")\np <- p + facet_wrap(~Churn)\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n# marital status plot\npng(filename=\"presentations/processed_maritalstatus.png\",width=760, height=565)\ndf <- data.frame(processed_Dataset$MaritalStatus, origin_Dataset$Churn)\np <- ggplot(df, aes(processed_Dataset$MaritalStatus,..count..)) + geom_bar(aes(fill = processed_Dataset$MaritalStatus),position = \"stack\")\np <- p + guides(fill=guide_legend(title=\"Churn\")) + xlab(\"Marital Status\") + ggtitle(\"Marital Status vs Churn\")\np <- p + scale_fill_manual(values = brewer.pal(8,\"PiYG\"))\np\ndev.off()\np\n\n\n#Departments treemap\n\n# Create data\ngroup=plyr::count(processed_Dataset$JobRole)$x\nvalue=plyr::count(processed_Dataset$JobRole)$freq\ndata=data.frame(group,value)\n\npng(filename=\"presentations/DepartmentsTree.png\",width=800, height=800)\ntreemap(data, index=\"group\", vSize=\"value\",type=\"index\",\n fontsize.labels=c(10,1), fontcolor.labels=c(\"white\"), \n fontface.labels=c(1,1), bg.labels=c(\"transparent\"), \n align.labels=list( c(\"center\", \"center\"), c(\"right\", \"bottom\") ), \n palette = \"Set2\", overlap.labels=.5, inflate.labels=T,\n title=\"Departments Size\", fontsize.title=12)\ndev.off()\n\n\ntreemap(data, index=\"group\", vSize=\"value\",type=\"index\",\n fontsize.labels=c(10,1), fontcolor.labels=c(\"white\"), \n fontface.labels=c(1,1), bg.labels=c(\"transparent\"), \n align.labels=list( c(\"center\", \"center\"), c(\"right\", \"bottom\") ), \n palette = \"Set2\", overlap.labels=.5, inflate.labels=T,\n title=\"Departments Size\", fontsize.title=12)\n\n\n\n\n# PIE CHART Department \npie.a <- round(plyr::count(processed_Dataset$Department)$freq/dim(origin_Dataset)[1],2)\npie.b <- with(processed_Dataset[processed_Dataset$Churn==\"Yes\",] ,\n round(plyr::count(Department)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"Yes\",])[1],2))\npie.c <- with(processed_Dataset[Churn==\"No\",] ,\n round(plyr::count(Department)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"No\",])[1],2))\n\n\nplyr::count(processed_Dataset$Department)\nround(plyr::count(processed_Dataset$Department)$freq/\n dim(origin_Dataset[Churn==\"No\",])[1],2)\n\n\n# Changing graphical parameters\noldpar <- par() \npar(mfrow = c(1, 3), \n cex.main = 3) \n\ncolors <- c(\"#DE77AE\", \"blueviolet\", \"#C51B7D\")\npie3D(pie.a,labels=pie.a,explode=0.05,height=0.05, col = colors,\n main=\"Global\", theta=1.2,radius =1,start=2.7, cex.main=2)\npie3D(pie.b,labels=pie.b,explode=0.05,height=0.05, col = colors,\n main=\"Churn = Yes\", theta=1.2,radius =1,start=2.15, cex.main=2)\npie3D(pie.c,labels=pie.c,explode=0.05,height=0.05, col = colors,\n main=\"Churn = No\", theta=1.2,radius =1,start=2.85, cex.main=2)\n\ntitle(\"\u2022 HR\", outer=TRUE, line = -45, col.main = \"blueviolet\", cex.main=2.5 , adj = .15 )\ntitle(\"\u2022 Commercial\", outer=TRUE, line = -45, col.main = \"#DE77AE\", cex.main=2.5 , adj = .5 )\ntitle(\"\u2022 IT\", outer=TRUE, line = -45, col.main = \"#C51B7D\", cex.main=2.5 , adj = .85 )\ntitle(\"Departments\", outer=TRUE, line = -6, col.main = \"black\")\np<-recordPlot()\n\npng(filename=\"presentations/pieDepartments.png\",width=800, height=500)\np\ndev.off()\n\nreset.par()\n\n# PIE CHARTS - JobType\npie.a <- round(plyr::count(processed_Dataset$JobType)$freq/dim(origin_Dataset)[1],2)\npie.b <- with(processed_Dataset[processed_Dataset$Churn==\"Yes\",] ,\n round(plyr::count(JobType)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"Yes\",])[1],2))\npie.c <- with(processed_Dataset[Churn==\"No\",] ,\n round(plyr::count(JobType)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"No\",])[1],2))\n\n\nplyr::count(processed_Dataset$JobType)\nround(plyr::count(processed_Dataset$JobType)$freq/\n dim(origin_Dataset[Churn==\"No\",])[1],2)\n\n\n# Changing graphical parameters\noldpar <- par() \npar(mfrow = c(1, 3), \n cex.main = 3) \n\ncolors <- c(\"pink2\",\"#C51B7D\", \"blueviolet\")\n\npie3D(pie.a,labels=pie.a,explode=0.05,height=0.05, col = colors,\n main=\"Global\", theta=1.2,radius =1,start=2.2, cex.main=2)\npie3D(pie.b,labels=pie.b,explode=0.05,height=0.05, col = colors,\n main=\"Churn = Yes\", theta=1.2,radius =1,start=1.65, cex.main=2)\npie3D(pie.c,labels=pie.c,explode=0.05,height=0.05, col = colors,\n main=\"Churn = No\", theta=1.2,radius =1,start=2.35, cex.main=2)\n\ntitle(\"\u2022 Office\", outer=TRUE, line = -45, col.main = \"pink2\", cex.main=2.5 , adj = .15 )\ntitle(\"\u2022 Office/Remote\", outer=TRUE, line = -45, col.main =\"#C51B7D\" , cex.main=2.5 , adj = .5 )\ntitle(\"\u2022 Remote\", outer=TRUE, line = -45, col.main = \"blueviolet\", cex.main=2.5 , adj = .85 )\ntitle(\"JobType\", outer=TRUE, line = -6, col.main = \"black\")\np<-recordPlot()\n\npng(filename=\"presentations/pieJobType.png\",width=800, height=500)\np\ndev.off()\n\nreset.par()\n\n\n\n# PIE CHARTS - BalanceWorkLife\npie.a <- round(plyr::count(processed_Dataset$BalanceWorkLife)$freq/dim(origin_Dataset)[1],2)\npie.b <- with(processed_Dataset[processed_Dataset$Churn==\"Yes\",] ,\n round(plyr::count(BalanceWorkLife)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"Yes\",])[1],2))\npie.c <- with(processed_Dataset[Churn==\"No\",] ,\n round(plyr::count(BalanceWorkLife)$freq/\n dim(processed_Dataset[processed_Dataset$Churn==\"No\",])[1],2))\n\n\nplyr::count(processed_Dataset$BalanceWorkLife)\nround(plyr::count(processed_Dataset$BalanceWorkLife)$freq/\n dim(origin_Dataset[Churn==\"No\",])[1],2)\n\n\n# Changing graphical parameters\noldpar <- par() \npar(mfrow = c(1, 3), \n cex.main = 3) \n\ncolors <- c(\"blueviolet\",\"#DE77AE\",\"pink2\",\"#C51B7D\")\n\npie3D(pie.a,labels=pie.a,explode=0.05,height=0.05, col = colors,\n main=\"Global\", theta=1.2,radius =1,start=1, cex.main=2)\npie3D(pie.b,labels=pie.b,explode=0.05,height=0.05, col = colors,\n main=\"Churn = Yes\", theta=1.2,radius =1,start=1, cex.main=2)\npie3D(pie.c,labels=pie.c,explode=0.05,height=0.05, col = colors,\n main=\"Churn = No\", theta=1.2,radius =1,start=1, cex.main=2)\n\ntitle(\"\u2022 Bad\", outer=TRUE, line = -45, col.main = \"blueviolet\", cex.main=2.5 , adj = .15 )\ntitle(\"\u2022 Medium\", outer=TRUE, line = -45, col.main =\"#C51B7D\" , cex.main=2.5 , adj = .35 )\ntitle(\"\u2022 Good\", outer=TRUE, line = -45, col.main = \"#DE77AE\", cex.main=2.5 , adj = .55 )\ntitle(\"\u2022 Great\", outer=TRUE, line = -45, col.main = \"pink2\", cex.main=2.5 , adj = .75 )\ntitle(\"BalanceWorkLife\", outer=TRUE, line = -6, col.main = \"black\")\np<-recordPlot()\n\npng(filename=\"presentations/pieBalanceWorkLife.png\",width=800, height=500)\np\ndev.off()\n\nreset.par()", "meta": {"hexsha": "4a9de4bac4e8c3f8733551035cb2cff34cc0d776", "size": 36971, "ext": "r", "lang": "R", "max_stars_repo_path": "src/main.r", "max_stars_repo_name": "tmartins1996/r-binary-classification", "max_stars_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/main.r", "max_issues_repo_name": "tmartins1996/r-binary-classification", "max_issues_repo_head_hexsha": "33d434b90bdd721eeb511ac3ac05a2047b3b324a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/main.r", "max_forks_repo_name": 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YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5736784074525098, "lm_q1q2_score": 0.3025101033557842}} {"text": "# Packages ----------------------------------------------------------------\nlibrary(vroom)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(here)\nlibrary(lubridate)\nlibrary(readr)\nlibrary(magrittr)\n\n# set to use either web version or local version of data\\\nuse_web <- FALSE\n\n# Download Rt estimates ---------------------------------------------------\nweek_start <- readRDS(here(\"data\", \"sgene_by_utla.rds\")) %>%\n .$week_infection %>%\n subtract(7) %>%\n max() %>%\n wday()\n\n# extract from epiforecasts.io/covid or use archived version\nif (use_web) {\n rt_estimates <-\n paste0(\"https://raw.githubusercontent.com/epiforecasts/covid-rt-estimates/\",\n \"master/subnational/united-kingdom-local/cases/summary/rt.csv\")\n short_rt <- vroom(rt_estimates)\n vroom_write(short_rt, here(\"data-raw\", \"rt-short-generation-time.csv\"),\n delim = \",\")\n}else{\n short_rt <- vroom(here(\"data-raw\", \"rt-short-generation-time.csv\"))\n}\n\n# load sensitivity analysis with longer generation time\nlong_rt <- vroom(here(\"data-raw\", \"rt-long-generation-time.csv\"))\n\n# join and save\nrt <- short_rt %>%\n mutate(generation_time = \"short\") %>%\n bind_rows(long_rt %>%\n mutate(generation_time = \"long\")) %>%\n filter(type == \"estimate\") %>%\n select(generation_time, utla_name = region, date, everything(), -strat, -type)\n\n# Make Rt weekly\nrt_weekly <- rt %>%\n mutate(week_infection =\n floor_date(date, \"week\", week_start = week_start) + 6) %>%\n group_by(utla_name, week_infection, generation_time) %>%\n summarise(mean = mean(mean), sd = mean(sd), n = n(), .groups = \"drop\") %>%\n filter(n == 7) %>%\n select(-n) %>%\n pivot_longer(c(mean, sd)) %>%\n mutate(gt_rt = paste(\"rt\", name, generation_time, \"gt\", sep = \"_\")) %>%\n select(-generation_time, -name) %>%\n pivot_wider(names_from = gt_rt)\n\nsaveRDS(rt_weekly, here(\"data\", \"rt_weekly.rds\"))\n", "meta": {"hexsha": "9e9757d81e7a63cb34ab073c1469563b87fea31e", "size": 1850, "ext": "r", "lang": "R", "max_stars_repo_path": "R/extract_rt.r", "max_stars_repo_name": "epiforecasts/covid19.sgene.utla.rt", "max_stars_repo_head_hexsha": "d1e05510729f0e0cf9ed8e84fb7c4eb309a06edc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2021-01-08T16:35:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-19T01:49:36.000Z", "max_issues_repo_path": "R/extract_rt.r", "max_issues_repo_name": "epiforecasts/covid19.sgene.utla.rt", "max_issues_repo_head_hexsha": "d1e05510729f0e0cf9ed8e84fb7c4eb309a06edc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 12, "max_issues_repo_issues_event_min_datetime": "2021-01-08T16:09:35.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-01T11:09:23.000Z", "max_forks_repo_path": "R/extract_rt.r", "max_forks_repo_name": "epiforecasts/covid19.sgene.utla.rt", "max_forks_repo_head_hexsha": "d1e05510729f0e0cf9ed8e84fb7c4eb309a06edc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2021-01-10T08:46:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-11T17:56:39.000Z", "avg_line_length": 32.4561403509, "max_line_length": 80, "alphanum_fraction": 0.6248648649, "num_tokens": 502, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.5273165233795671, "lm_q1q2_score": 0.30251010335578415}} {"text": "# Similarity measurements between all taxa from catalog and St. Lawrence taxa\nrm(list=ls())\nsetwd(\"/Users/davidbeauchesne/Dropbox/PhD/PhD_obj2/Structure_Comm_EGSL/Predict_network/\")\n\n# Functions\n source('../Predict_interactions/script/similarity_taxon.r')\n source('../Predict_interactions/script/tanimoto_traits.r')\n source('../Predict_interactions/script/tanimoto.r')\n\n\n\n# Load species list\n sp <- readRDS('./RData/spEGSL.rds')\n\n# Load interactions catalog\n load(\"../Predict_interactions/RData/S0_catalog.RData\")\n\n# Insert missing St. Lawrence taxa in catalog\n missTaxa <- which(!sp[, 'N_EspSci'] %in% S0_catalog[, 'taxon'])\n S0_add <- S0_catalog[1:length(missTaxa), ]\n S0_add <- apply(S0_add, MARGIN = c(1,2), FUN = is.na)\n rownames(S0_add) <- S0_add[, 'taxon'] <- sp[missTaxa, 'N_EspSci']\n S0_add[, 'taxonomy'] <- as.character(sp[missTaxa, 'taxonomy'])\n S0_add[, 3:6] <- ''\n S0 <- rbind(S0_catalog, S0_add)\n\n# Export catalog\n saveRDS(S0, file = './RData/S0.rds')\n\n# Weight values for 2-way similarity measurements\n wt <- 0.5\n\n# 1st is for similarity measured from set of resources and taxonomy, for consumers\n similarity.consumers <- similarity_taxon(S0 = S0, wt = wt, taxa = 'consumer')\n saveRDS(similarity.consumers, file = \"./RData/Similarity_consumers.rds\")\n\n# 2nd is for similarity measured from set of consumers and taxonomy, for resources\n similarity.resources <- similarity_taxon(S0 = S0, wt = wt, taxa = 'resource')\n saveRDS(similarity.resources, file = \"./RData/Similarity_resources.rds\")\n", "meta": {"hexsha": "48c3de9af981caae7d39b0c78b2638c1fb4820b7", "size": 1556, "ext": "r", "lang": "R", "max_stars_repo_path": "Script/2_similarityMatrix.r", "max_stars_repo_name": "david-beauchesne/Predict_network", "max_stars_repo_head_hexsha": "35b919ec134f49315245cea7af457bc72da90158", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2017-04-10T12:53:13.000Z", "max_stars_repo_stars_event_max_datetime": "2017-04-10T12:53:13.000Z", "max_issues_repo_path": "Script/2_similarityMatrix.r", "max_issues_repo_name": "david-beauchesne/Predict_network", "max_issues_repo_head_hexsha": "35b919ec134f49315245cea7af457bc72da90158", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Script/2_similarityMatrix.r", "max_forks_repo_name": "david-beauchesne/Predict_network", "max_forks_repo_head_hexsha": "35b919ec134f49315245cea7af457bc72da90158", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.9, "max_line_length": 89, "alphanum_fraction": 0.7062982005, "num_tokens": 451, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.3024386177109419}} {"text": "require(httr) # POST, content and some internals\nrequire(xts)\n# Must have stringr installed (part of tidyverse)\n\n#' @title Download data from Investing.com\n#' \n#' @description Retrieve xts with Investing OHLC data for a given identifier and period\n#'\n#' @param curr_id Identifier of the instrument\n#' @param start_date Starting date to download data\n#' @param end_date Ending date\n#'\n#' @return xts with Investing OHLC data\n#'\n#' @examples\n#' ohlc <- download.investing(2103, start_date = '2017-01-01')\n#' \n#' # Plotting (optional)\n#' require(dygraphs)\n#' dygraph(ohlc) %>% dyCandlestick()\ndownload.investing <- function(curr_id, start_date, end_date = Sys.Date()) {\n\tif(is.character(start_date)) { start_date <- as.Date(start_date) }\n\tif(is.character(end_date)) { end_date <- as.Date(end_date) }\n\turl <- \"https://www.investing.com/instruments/HistoricalDataAjax\"\n\n\t# Thanks to hadley\n\t# https://stackoverflow.com/questions/24037411/retrieving-post-requests-from-javascript-and-using-them-with-curl-rcurl\n\tresp <- POST(url,\n\t body = list(\n\t action = \"historical_data\",\n\t curr_id = curr_id,\n\t st_date = format(start_date, '%m/%d/%Y'),\n\t end_date = format(end_date, '%m/%d/%Y'),\n\t interval_sec = \"Daily\"\n\t ),\n\t add_headers(\n\t \"user-agent\" = \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.117 Safari/537.36\",\n\t Origin = \"http://www.investing.com\",\n\t \"X-Requested-With\" = \"XMLHttpRequest\"\n\t ),\n\t multipart = FALSE\n\t)\n\n\tif(attr(resp, 'class') != 'response') { stop(\"is.response(x) is not TRUE\") }\n\tif(resp$status_code != 200) { stop(\"Status code != 200\") }\n\n\tx <- strsplit(content(resp, 'text'), 'historicalTbl')[[1]][2]\n\tx <- unlist(lapply(\n\t\tstrsplit(x, '')[[1]],\n\t\tfunction(r) {\n\t\t\t# stringr::str_match_all return a 2col matrix\n\t\t\tmatch <- stringr::str_match_all(r, \"data-real-value=\\\\p{quotation mark}(\\\\d+(?:,)?\\\\d*\\\\.?\\\\d*)\\\\p{quotation mark}\")[[1]][,2]\n\t\t\tif(length(match) > 0) { return(sub(',', '', match[1:5])) }\n\t\t}))\n\t\n\t# Build the timestamp/OHLC matrix\n\tx <- matrix(as.numeric(x), ncol = 5, byrow = TRUE)\n\t\n\t# Put into chronological order\n\tx <- x[nrow(x):1,]\n\t\n\t# Transform timestamp into date\n\tdates <- as.Date(as.POSIXct(x[,1], origin = '1970-01-01', tz = 'GMT'))\n\tx <- xts(x[,c(3,4,5,2)], order.by = as.Date(dates))\n\tcolnames(x) <- c('Open', 'High', 'Low', 'Close')\n\t\n\treturn(x)\n}\n", "meta": {"hexsha": "aeb679451e2afc5a6db077a8822ce362efbb1677", "size": 2382, "ext": "r", "lang": "R", "max_stars_repo_path": "R/download_investing.r", "max_stars_repo_name": "vhcandido/investing-historical-data", "max_stars_repo_head_hexsha": "7b0b274e1e2c5e1e31538fe845890722c7486932", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/download_investing.r", "max_issues_repo_name": "vhcandido/investing-historical-data", "max_issues_repo_head_hexsha": "7b0b274e1e2c5e1e31538fe845890722c7486932", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/download_investing.r", "max_forks_repo_name": "vhcandido/investing-historical-data", "max_forks_repo_head_hexsha": "7b0b274e1e2c5e1e31538fe845890722c7486932", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5217391304, "max_line_length": 143, "alphanum_fraction": 0.6540722082, "num_tokens": 725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30227283316687614}} {"text": "\n# Gregory Way 2018\n# Multiple Myeloma Classifier\n# 3.visualize_coefficients.ipynb\n#\n# Observes differences in coefficients across the multiclass classifier classes\n#\n# Usage: Run in command line\n#\n# Rscript --vanilla visualize_coefficients.R\n#\n# Output:\n# Descriptive Coefficient Plot\n\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggrepel)\n\n# Load ENSEMBL to Symbol dictionary\ngene_file <- file.path('data', 'raw', 'gprofiler_results_1002952837509.xlsx')\ngene_dict_df <- readxl::read_excel(gene_file, col_names = FALSE)\ncolnames(gene_dict_df) <- c('num', 'GENE_ID', 'co', 'SYMBOL', 'ALT', 'a', 'b')\ngene_dict_df <- gene_dict_df[!duplicated(gene_dict_df$GENE_ID), ]\nhead(gene_dict_df)\n\n# Load Classifier Coefficients\ncoef_file <- file.path('results', 'classifier', 'classifier_coefficients.tsv')\ncoef_df <- readr::read_tsv(coef_file) %>%\n dplyr::left_join(gene_dict_df, by = 'GENE_ID')\nhead(coef_df)\n\n# Define cutoffs to label top 10 genes (by absolute value) for each classifier\n# Note that dplyr still has some serious issues with writing functions\nn <- 5\n\ntop_kras <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, KRAS) %>%\n dplyr::select(KRAS) %>%\n dplyr::arrange(desc(KRAS)) %>%\n dplyr::filter(row_number() == n()))\n\nbot_kras <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, desc(KRAS)) %>%\n dplyr::select(KRAS) %>%\n dplyr::arrange(KRAS) %>%\n dplyr::filter(row_number()==n()))\n\ntop_wt <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, wildtype) %>%\n dplyr::select(wildtype) %>%\n dplyr::arrange(desc(wildtype)) %>%\n dplyr::filter(row_number() == n()))\n\nbot_wt <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, desc(wildtype)) %>%\n dplyr::select(wildtype) %>%\n dplyr::arrange(wildtype) %>%\n dplyr::filter(row_number()==n()))\n\ntop_nras <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, NRAS) %>%\n dplyr::select(NRAS) %>%\n dplyr::arrange(desc(NRAS)) %>%\n dplyr::filter(row_number() == n()))\n\nbot_nras <- as.numeric(coef_df %>%\n dplyr::top_n(n = n, desc(NRAS)) %>%\n dplyr::select(NRAS) %>%\n dplyr::arrange(NRAS) %>%\n dplyr::filter(row_number()==n()))\n\n# Plot gene coefficients scatter\np <- ggplot2::ggplot(coef_df,\n aes(x = KRAS, y = NRAS, color = wildtype)) +\n geom_point(alpha = 0.8, size = 0.1) +\n scale_color_gradient2('Wildtype\\nGene Weight',\n low = \"blue\",\n mid = \"grey\",\n high = \"red\") +\n xlab(\"KRAS - Gene Weight\") +\n ylab(\"NRAS - Gene Weight\") +\n geom_text_repel(data = subset(coef_df,\n (wildtype >= top_wt | wildtype <= bot_wt) |\n (KRAS >= top_kras | KRAS <= bot_kras) |\n (NRAS >= top_nras | NRAS <= bot_nras)\n ),\n arrow = arrow(length = unit(0.02, 'npc')),\n segment.size = 0.3,\n segment.alpha = 0.6,\n box.padding = 0.17,\n point.padding = 0.1,\n size = 1.8,\n fontface = 'italic',\n aes(x = KRAS, y = NRAS, label = SYMBOL)) +\n theme_bw() +\n theme(axis.text = element_text(size = rel(0.5)),\n axis.title = element_text(size = rel(0.6)),\n axis.title.y = element_text(margin = \n margin(t = 0, r = 0, b = 0, l = 0)),\n axis.title.x = element_text(margin =\n margin(t = 3, r = 0, b = 0, l = 0)),\n legend.text = element_text(size = rel(0.4)),\n legend.title = element_text(size = rel(0.5)),\n legend.key = element_rect(size = 0.2),\n legend.position = 'right',\n legend.key.size = unit(0.4, 'lines'),\n legend.margin = margin(l = -0.3, unit = 'cm'))\n\np\n\n# Save Figure\nfig_file <- file.path('figures', 'classifier_coefficients_scatter.pdf')\nggplot2::ggsave(fig_file, plot = p, dpi = 600, width = 4, height = 3)\n", "meta": {"hexsha": "f357af38a79f8b767b23dd36e2c6f3fe5e683171", "size": 3753, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/3.visualize-coefficients.r", "max_stars_repo_name": "gwaygenomics/multiple-myeloma-classifier", "max_stars_repo_head_hexsha": "def1ac4b2728b8a851bbfd71686922a75ff96a19", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2019-08-14T22:53:38.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-30T08:12:26.000Z", "max_issues_repo_path": "scripts/3.visualize-coefficients.r", "max_issues_repo_name": "gwaygenomics/multiple-myeloma-classifier", "max_issues_repo_head_hexsha": "def1ac4b2728b8a851bbfd71686922a75ff96a19", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/3.visualize-coefficients.r", "max_forks_repo_name": "gwaygenomics/multiple-myeloma-classifier", "max_forks_repo_head_hexsha": "def1ac4b2728b8a851bbfd71686922a75ff96a19", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-15T13:29:47.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-24T15:13:56.000Z", "avg_line_length": 32.9210526316, "max_line_length": 79, "alphanum_fraction": 0.6016520117, "num_tokens": 1181, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290153, "lm_q2_score": 0.5234203489363239, "lm_q1q2_score": 0.3022728331668761}} {"text": "# ui.R\nlibrary(shiny)\nsource('model.r')\n\nshinyUI(fluidPage(\n titlePanel(\"Interactive Model (Risk of Diabetes)\"),\n \n sidebarLayout(\n sidebarPanel(\n p(\"Shiny Demo App\"),\n \n numericInput(\n 'pregnant',\n 'Pregnancies',\n value = mean(test.data$pregnant),\n min = min(test.data$pregnant),\n max = max(test.data$pregnant)\n ),\n numericInput(\n 'glucose',\n 'Plasma glucose',\n value = mean(test.data$glucose),\n min = min(test.data$glucose),\n max = max(test.data$glucose)\n ),\n numericInput(\n 'pressure',\n 'Diastolic pressure',\n value = mean(test.data$pressure),\n min = min(test.data$pressure),\n max = max(test.data$pressure)\n ),\n numericInput(\n 'triceps',\n 'Tricep skin fold thickness',\n value = mean(test.data$triceps),\n min = min(test.data$triceps),\n max = max(test.data$triceps)\n ),\n numericInput(\n 'insulin',\n '2 hour serum insulin',\n value = mean(test.data$insulin),\n min = min(test.data$insulin),\n max = max(test.data$insulin)\n ),\n numericInput(\n 'mass',\n 'Body mass index',\n value = mean(test.data$mass),\n min = min(test.data$mass),\n max = max(test.data$mass)\n ),\n numericInput(\n 'pedigree',\n 'Diabetes pedigree function',\n value = mean(test.data$pedigree),\n min = min(test.data$pedigree),\n max = max(test.data$pedigree)\n ),\n numericInput(\n 'age',\n 'Patient Age',\n value = mean(test.data$age),\n min = min(test.data$age),\n max = max(test.data$age)\n )\n ),\n \n mainPanel(h3(textOutput(\n \"predicted_diabetes\"\n )),\n \n p(style= 'padding-bottom: 20px'),\n \n hr(),\n \n p(style= 'padding-bottom: 20px'),\n plotOutput(\"roc_plot\"),\n plotOutput('explainer')\n \n )\n \n )\n \n \n))\n", "meta": {"hexsha": "40d80a55e610984aea03a2454d044dab71991216", "size": 1980, "ext": "r", "lang": "R", "max_stars_repo_path": "ui.r", "max_stars_repo_name": "johnaclouse/shiny-random-forest", "max_stars_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-02-27T09:53:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-07T23:50:53.000Z", "max_issues_repo_path": "ui.r", "max_issues_repo_name": "johnaclouse/shiny-random-forest", "max_issues_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ui.r", "max_forks_repo_name": "johnaclouse/shiny-random-forest", "max_forks_repo_head_hexsha": "e1a9d0d58ff5186586923a5f0f4a56fb5f5d6bab", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-08-16T16:20:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-27T09:53:00.000Z", "avg_line_length": 22.5, "max_line_length": 53, "alphanum_fraction": 0.5237373737, "num_tokens": 494, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.3022728255546675}} {"text": "#' ms2fts\n#'\n#' Conversion speed meter per second in feets per second.\n#'\n#' @param ms Speed numeric in meters per second [m/s].\n#' @return feets per second\n#'\n#'\n#' @author Istituto per la Bioeconomia Firenze Italy Alfonso Crisci \\email{a.crisci@@ibe.cnr.it}\n#' @keywords ms2fts \n#' \n#' @export\n#'\n#'\n#'\n#'\n\nms2fts=function(ms) {\n ct$assign(\"ms\", as.array(ms))\n ct$eval(\"var res=[]; for(var i=0, len=ms.length; i < len; i++){ res[i]=ms2fts(ms[i])};\")\n res=ct$get(\"res\")\n return(ifelse(res==9999,NA,res))\n}\n\n", "meta": {"hexsha": "fbca28d0800ca52e860aac57c59c1bfad5cf674d", "size": 612, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ms2fts.r", "max_stars_repo_name": "alfcrisci/rBiometeo", "max_stars_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2017-04-23T13:55:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:36.000Z", "max_issues_repo_path": "R/ms2fts.r", "max_issues_repo_name": "alfcrisci/rBiometeo", "max_issues_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ms2fts.r", "max_forks_repo_name": "alfcrisci/rBiometeo", "max_forks_repo_head_hexsha": "1fe0113d017372393de2ced18b884f356c76049b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-05-19T15:28:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T19:35:47.000Z", "avg_line_length": 24.48, "max_line_length": 113, "alphanum_fraction": 0.5277777778, "num_tokens": 180, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.3022728255546675}} {"text": "#Imputation for dropouts for logExpTable\n\nimputeDropouts <- function( logExpsTable, randomSeed){\n\nif(!require(DrImpute)){\n library(devtools)\n install_github('gongx030/DrImpute')\t\t\n library(\"DrImpute\")\n}\n\nset.seed(randomSeed)\nlogExpsTableImp <- DrImpute(logExpsTable)\n\nreturn( logExpsTableImp)\n}\n", "meta": {"hexsha": "9afed5cb90dddd11a385eeb10f096dee1568463e", "size": 298, "ext": "r", "lang": "R", "max_stars_repo_path": "R/imputeDropouts.r", "max_stars_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_stars_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/imputeDropouts.r", "max_issues_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_issues_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/imputeDropouts.r", "max_forks_repo_name": "SevaVigg/NanostringDanioNCCscAnalysis", "max_forks_repo_head_hexsha": "c6c26a640adec15b332cac6b3619f6c3ab2aa9c8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.625, "max_line_length": 54, "alphanum_fraction": 0.7684563758, "num_tokens": 90, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30227282555466745}} {"text": "\n################################################################\n # name:advanceRetreateGraph\n # this is in my old files at\n # ~/Dropbox/data/drought/HutchinsonIndex/versions/AdvancRetreatGraph\n # small multiples graph\n source('~/tools/delphe-project/tools/connect2postgres.r')\n ch <- connect2postgres('130.56.102.41','delphe','ivan_hanigan')\n source('~/tools/delphe-project/tools/readOGR2.r')\n require('rgdal')\n source('~/tools/delphe-project/tools/fixGeom.r')\n pwd <- readline('session password = ')\n\n #################################################################\n # N:\\NCEPH_IT\\Data Management\\projects\\9.999 Ivan's PhD\\Papers\\Suicide and Drought in NSW\\data\\drought\\load_drought_data.r\n # author:\n # ihanigan\n # date:\n # 2010-08-17\n # description:\n # a project of great importance\n #################################################################\n\n # changelog\n Sys.Date()\n # 2010-08-17 make the small multiples plot again but for a longer time period, had to change the extract_pgis arguments to work on nceph machine\n\n\n #source('i:/my dropbox/tools/transformations.r')\n #library(RODBC)\n #ch=odbcConnect('delphe')\n #source('i:/my dropbox/tools/extract_pgis.r')\n library(maptools)\n\n\n\n qc <- dbGetQuery(ch,\"select t2.geoid,SD_code,SD_name,year,month,\n cast(year || '-' || month || '-' || 1 as date) as indexdate,\n avg(t1.sum) as avsum,avg(t1.count) as avcount,\n avg(t1.rain) as avrain,\n case when avg(t1.count) >= 5 then avg(t1.count) else 0 end as threshold\n from bom_grids.rain_NSW_1890_2008_4 as t1 join (\n select abs_sd.nswsd91.gid as geoid,abs_sd.nswsd91.SD_code,abs_sd.nswsd91.SD_name,bom_grids.grid_NSW.*\n from abs_sd.nswsd91, bom_grids.grid_NSW\n where st_intersects(abs_sd.nswsd91.the_geom,bom_grids.grid_NSW.the_geom)\n order by SD_code,bom_grids.grid_NSW.gid\n ) as t2\n on t1.gid=t2.gid\n where year>=1970\n group by t2.geoid,SD_code,SD_name,year,month;\")\n\n head(qc)\n\n ## sdlist=names(table(qc$sd_name))\n ## sdlist\n\n ## par(mfrow=c(2,6),mar=c(4,3,3,1))\n\n ## for(sdi in sdlist){\n ## #sdi=sdlist[1]\n\n ## with(qc,\n ## plot(indexdate[sd_name==sdi],avcount[sd_name==sdi],type='l',col='red',main=sdi)\n ## )\n\n ## with(qc,\n ## points(indexdate[sd_name==sdi],threshold[sd_name==sdi])\n ## )\n ## }\n\n ## qc=sqlQuery(ch,'select t2.geoid,SD_code,SD_name,year,month,avg(t1.sum) as avsum,avg(t1.count) as avcount,avg(t1.rain) as avrain,\n ## case when avg(t1.count) >= 5 then avg(t1.count) else 0 end as threshold\n ## from bom_grids.rain_NSW_1890_2008_4 as t1 join (\n ## select abs_sd.nswsd91.gid as geoid,abs_sd.nswsd91.SD_code,abs_sd.nswsd91.SD_name,bom_grids.grid_NSW.*\n ## from abs_sd.nswsd91, bom_grids.grid_NSW\n ## where st_intersects(abs_sd.nswsd91.the_geom,bom_grids.grid_NSW.the_geom)\n ## order by SD_code,bom_grids.grid_NSW.gid\n ## ) as t2\n ## on t1.gid=t2.gid\n ## where year>=1970\n ## group by t2.geoid,SD_code,SD_name,year,month;')\n\n ## # send to local\n ## #local=odbcConnect('ilocal')\n ## #sqlQuery(local,\"SET search_path =ivan_hanigan, pg_catalog\")\n ## #sqlSave(local,qc,tablename='suicidedroughtnsw19702007_drought')\n\n\n ## # make some qc maps\n ## #extract_pgis(psql='select gid, admin_name, st_simplify(the_geom,0.01) as the_geom FROM spatial.admin00_aus_states where admin_name = \\'New South Wales\\'','nsw.shp',\n ## #host='130.56.102.30',user='ivan_hanigan',db='delphe',pgpath='C:\\\\Program Files\\\\PostgreSQL\\\\8.3\\\\bin\\\\pgsql2shp')\n\n ## #d=readShapePoly('nsw.shp')\n ## plot(d)\n ## axis(2)\n ## axis(1)\n ## box()\n\n ## #extract_pgis(psql='select * FROM bom_grids.grid_nsw','grid_nsw.shp')\n ## #grd=readShapePoly('grid_nsw.shp')\n ## plot(grd,add=T)\n\n ## # check fields\n ## #sqlQuery(ch,'select * FROM bom_grids.grid_nsw limit 1')\n ## #sqlQuery(ch,'select * FROM bom_grids.rain_NSW_1890_2008_4 limit 1')\n\n ## # get drought data on grid\n ## extract_pgis(psql='select t2.gid,year,month,t1.count,t1.rain,case when t1.count >= 5 then 1 else 0 end as threshold, t2.the_geom from bom_grids.rain_NSW_1890_2008_4 as t1 join bom_grids.grid_NSW as t2 on t1.gid=t2.gid where year=1973 and month = 1 and t1.count >= 5;','197301.shp')\n\n ## #grd=readShapePoly('197301.shp')\n ## plot(grd,add=T,col=grd@data$THRESHOLD)\n\n ## # good. want to reproduce http://www.dpi.nsw.gov.au/agriculture/emergency/drought/planning/climate/advance-retreat\n ## # get the data to local\n ## cat(\"\\\"C:\\\\PostgreSQL\\\\8.4\\\\bin\\\\pg_dump.exe\\\" -h 130.56.102.30 -U ivan_hanigan -i -t bom_grids.grid_NSW | \\\"C:\\\\PostgreSQL\\\\8.4\\\\bin\\\\psql\\\" -h localhost postgis\")\n\n ## #bom_grids.rain_NSW_1890_2008_4\n\ntassla06 <-\n readOGR2(hostip='115.146.94.209',user='gislibrary',db='pgisdb',\n layer='tassla06')\nplot(tassla06)\n #d=readShapePoly('nsw.shp')\n d <- readOGR2('130.56.102.41','ivan_hanigan','delphe','abs_sd.nswsd01', p = pwd)\n plot(d)\n\n plot_drought=function(year,month){\n extract_pgis(psql=paste('select t2.gid,year,month,t1.count,t1.rain,case when t1.count >= 4 then 1 else 0 end as threshold, t2.the_geom from bom_grids.rain_NSW_1890_2008_4 as t1 join bom_grids.grid_NSW as t2 on t1.gid=t2.gid where year=',year,' and month = ',month,' and t1.count >= 5;',sep=''),'drt.shp',host='130.56.102.30',user='ivan_hanigan',db='delphe',pgpath='C:\\\\Program Files\\\\PostgreSQL\\\\8.3\\\\bin\\\\pgsql2shp')\n plot(d)\n\n if(length(dir(pattern='drt.shp'))>0){\n grd=readShapePoly('drt.shp')\n plot(grd,add=T,col=grd@data$THRESHOLD)\n file.remove('drt.shp')\n file.remove('drt.shx')\n file.remove('drt.dbf')\n file.remove('drt.prj')\n }\n }\n\n # newnode THE graph\n windows(height=20,width=6)\n Sys.setenv(R_GSCMD=\"C:\\\\gs\\\\gs8.56\\\\bin\\\\gswin32c.exe\")\n\n bitmap('droughtAdvRet_19002008.jpg',type='jpeg',res=400,height=20,width=5)\n par(mfrow=c(110,13),mar=c(0,0,0,0))\n plot(0:3,0:3,axes=F,ylab='',xlab='',type='n')\n\n for(mm in c('j','f','m','a', 'm','j','j','a','s','o','n','d')){\n plot(0:3,0:3,axes=F,ylab='',xlab='',type='n')\n text(1.5,1.5,mm)\n }\n\n for(j in 1900:2008){\n print(j)\n plot(0:3,0:3,axes=F,ylab='',xlab='',type='n')\n text(1.5,1.5,j) #substr(j,3,4))\n\n for(i in 1:12){\n plot_drought(j,i)\n }\n\n }\n\n # this is the first one 1972-2008 savePlot('droughtAdvRet.jpg',type=c('jpg'))\n #savePlot('droughtAdvRet_19002008.tiff',type=c('tiff'))\n dev.off()\n", "meta": {"hexsha": "6edfdd9ef47f7b78b2b944ad7d62177610c656c4", "size": 6402, "ext": "r", "lang": "R", "max_stars_repo_path": "src/advanceRetreateGraph.r", "max_stars_repo_name": "swish-climate-impact-assessment/DROUGHT-BOM-GRIDS", "max_stars_repo_head_hexsha": "07613b3cb4fdeeef56bbb93fa2b9b533fd4e83bd", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/advanceRetreateGraph.r", "max_issues_repo_name": "swish-climate-impact-assessment/DROUGHT-BOM-GRIDS", "max_issues_repo_head_hexsha": "07613b3cb4fdeeef56bbb93fa2b9b533fd4e83bd", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/advanceRetreateGraph.r", "max_forks_repo_name": "swish-climate-impact-assessment/DROUGHT-BOM-GRIDS", "max_forks_repo_head_hexsha": "07613b3cb4fdeeef56bbb93fa2b9b533fd4e83bd", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.1071428571, "max_line_length": 420, "alphanum_fraction": 0.6444860981, "num_tokens": 2229, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.523420348936324, "lm_q1q2_score": 0.30227282555466745}} {"text": "# This block by itself launches a blank page\nlibrary(shiny)\nlibrary(leaflet)\nlibrary(dplyr)\nlibrary(ggplot2)\n\n# ==== Server ====\nshinyServer(func = function(input, output, session) {\n \n # ==== Median Income Histogram ====\n output$median_household_income <- renderPlot(mhhi_hist)\n output$median_household_income.county <- renderPlot(mhhi_hist.county)\n \n # ==== Median Income of 25 Most Populous Cities ====\n output$mhhi_biggest_cities <- renderPlot(top_25_bar)\n \n # Median Income vs. Housing Prices\n output$top_25_scatter <- renderPlot(top_25_scatter)\n output$top_25_bar.inc_vs_home <- renderPlot(top_25_bar.inc_vs_home)\n output$top_25_bar.sf_vs_home <- renderPlot(top_25_bar.sf_vs_home)\n\n # ==== Median Income Map ====\n output$mhhi_map <- renderLeaflet(mhhi_map)\n \n # Change Dataset\n observe({\n dataset <- input$map_dataset\n \n if (input$map_dataset == \"ACS 5-Year Estimates\") {\n updateSelectInput(session, \"map_year\",\n choices = ACS_5YR_RANGE)\n } else {\n updateSelectInput(session, \"map_year\",\n choices = ACS_1YR_RANGE)\n }\n })\n \n # Change Year\n observe({\n # browser()\n # debug(session_map$show_year)\n \n session_map <- IncomeMap(data = ACS_5YR_FINANCE_COUNTY,\n map = leafletProxy(\"mhhi_map\",\n data=COUNTIES_GEO,\n session))\n \n if (input$map_dataset == \"ACS 5-Year Estimates\") {\n # Swap pointers\n session_map$data = ACS_5YR_FINANCE_COUNTY\n session_map$dataset = '5yr'\n \n # Make sure map years are valid\n if (input$map_year %in% ACS_5YR_RANGE) {\n # Load map layer\n # cat(\"Input Year:\", as.td_yr(input$map_year))\n session_map$show_year(as.td_yr(input$map_year))\n }\n } else {\n # Swap pointers\n session_map$data = ACS_1YR_FINANCE_COUNTY\n session_map$dataset = '1yr'\n \n # Make sure map years are valid\n if (input$map_year %in% ACS_1YR_RANGE) {\n # Load map layer\n cat(\"Input Year:\", as.td_yr(input$map_year))\n session_map$show_year(as.td_yr(input$map_year))\n }\n }\n })\n \n # ==== Median Income Table ====\n mhhi_table.cols_subset = c(\"City\", \"Population\",\n \"Median Household Income\")\n \n output$mhhi_table <- renderDataTable({\n if (input$mhhi_table == \"Counties (ACS 1-Year Estimates)\") {\n data <- ACS_1YR_FINANCE_COUNTY$county_data[\n c(\"geo_display_label\",\n \"mhhi_15\", \"mhhi_moe_15\", \n \"mhhi_13\", \"mhhi_moe_13\",\n \"mhhi_11\", \"mhhi_moe_11\",\n \"mhhi_09\", \"mhhi_moe_09\",\n \"mhhi_07\", \"mhhi_moe_07\",\n \"mhhi_05\", \"mhhi_moe_05\")] %>%\n filter(!is.na(geo_display_label))\n \n names(data) = c(\"County\", \"2015\", \"MoE\", \"2013\", \"MoE\", \n \"2011\", \"MoE\", \"2009\", \"MoE\", \n \"2007\", \"MoE\", \"2005\", \"MoE\")\n \n \n } else if (input$mhhi_table == \"Counties (ACS 5-Year Estimates)\") {\n data <- ACS_5YR_FINANCE_COUNTY$county_data[\n c(\"geo_display_label\", \"mhhi_15\", \"mhhi_moe_15\", \"mhhi_10\", \"mhhi_moe_10\")] %>%\n filter(!is.na(geo_display_label))\n names(data) = c(\"County\", \"2015\", \"MoE\", \"2010\", \"MoE\")\n } else {\n data <- med_hh_income[mhhi_table.cols_subset]\n }\n \n data\n })\n})", "meta": {"hexsha": "7a0c6a205b4ccdba9b237d623d53e48362b06e5c", "size": 3403, "ext": "r", "lang": "R", "max_stars_repo_path": "census-income/server.r", "max_stars_repo_name": "vincentlaucsb/Census-2010", "max_stars_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2017-04-18T19:49:44.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-03T23:22:14.000Z", "max_issues_repo_path": "census-income/server.r", "max_issues_repo_name": "vincentlaucsb/Census-2010", "max_issues_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2017-04-18T09:04:34.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-20T02:22:45.000Z", "max_forks_repo_path": "census-income/server.r", "max_forks_repo_name": "vincentlaucsb/Census-2010", "max_forks_repo_head_hexsha": "2e8b76f941ebebdeee58ee22d67919d36006c672", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.4095238095, "max_line_length": 87, "alphanum_fraction": 0.5859535704, "num_tokens": 968, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.5774953651858117, "lm_q1q2_score": 0.30227282555466745}} {"text": "library(jsonlite)\nlibrary(oro.nifti)\nlibrary(fslr)\nlibrary(foreach)\nlibrary(doParallel)\nlibrary(logging)\n\nsource('utils.r')\n\n# File and folder names\nresults_folder_name <- format(Sys.time(),format='RES_survival_stats_low-medium_%Y%m%d_%H%M')\nresults_file_name <- list('p_values_original', 'p_values_corrected', 'q_values')\nlog_file_name <- 'group_comparison.log'\ndir.create(results_folder_name)\n\n# Setting up logging\nlogReset()\nbasicConfig(level='DEBUG')\naddHandler(writeToFile, file=paste(results_folder_name, log_file_name, sep='/'), level='INFO')\nremoveHandler('writeToConsol')\n\n# Host-specific parameters\nhost <- Sys.info()[\"nodename\"]\nif (host == 'SINTEF-0ZQHTDG'){\n library(fdrtool) # Bit available in R 3.4\n n_cores <- 4\n} else if (host == 'medtech-beast') {\n n_cores <- 30\n} else {\n logwarn('The computer named %s is not known. Number of cores is set to 1.', host)\n n_cores <- 1\n}\nregisterDoParallel(cores=n_cores)\n\nloginfo('Reading data')\npids_per_voxel <- fromJSON('pids_per_voxel.json')\n#load('test_data.RData')\nsurvival_group_per_patient <- unlist( fromJSON('survival_group_per_patient.json'), use.names=FALSE)\n\ntemplate_img_file <- 'total_tumor.nii.gz'\ntemplate_img <- readNIfTI(template_img_file)\nimg_dim <- template_img@dim_[2:4]\n\nn_total <- count_patients_per_group(survival_group_per_patient)\nn_permutations <- 500\nmin_marginal <- 0\n\nloginfo('Creating permutations')\nloginfo('Number of permutations: %i', n_permutations)\nset.seed(7)\npermuted_indices <- rperm(n_permutations, length(survival_group_per_patient))\n\nloginfo('Performing permutation tests')\nbatches_per_core <- 4\nbatch_size <- ((length(pids_per_voxel)/(batches_per_core*n_cores))%/%1000+1)*1000 # Rounded up to nearest 1000, leaving the last batch smaller than the rest. \nbatch_lims <- seq(0,length(pids_per_voxel)-1, by=batch_size)\nt1 <- system.time({\n p_values_array <- \n foreach( lim = batch_lims, .combine = '+') %dopar% {\n lim1 <- lim+1\n lim2 <- min( lim+batch_size, length(pids_per_voxel) )\n batch <- c(lim1:lim2)\n t2 <- system.time({\n temp_array <- array(0, dim=c(2,img_dim))\n for (i in batch) {\n pids <- pids_per_voxel[[i]]+1 # Add 1 to convert from pythonic, zero-based indexing\n if (length(pids)>=min_marginal) {\n res_original <- stat_test(survival_group_per_patient[pids], n_total)\n p_value_original <- res_original$p\n p_values <- rep(0, n_permutations)\n for (j in 1:n_permutations) {\n survival_groups_permuted <- survival_group_per_patient[permuted_indices[,j]]\n #groups <- survival_groups_permuted[pids] #evt. unlist(pid,use.names=FALSE)?\n res <- stat_test(survival_groups_permuted[pids], n_total)\n p_values[j] <- res$p\n }\n p_value_corrected <- sum(p_values0]\n fdr <- fdrtool(p_values_vector, statistic=\"pvalue\")\n q_values_array <- array(0, img_dim)\n q_values_array[p_values>0] <- fdr$qval\n results_array[3,,,] <- q_values_array*p_values_sign\n n_results <- 3\n}\n\nloginfo('Writing results to file')\nfor (i in 1:n_results){\n results_img <- niftiarr(template_img, results_array[i,,,])\n writeNIfTI(results_img, filename=paste(results_folder_name, results_file_name[[i]], sep='/'))\n}\n\nloginfo('Finished.')", "meta": {"hexsha": "80e3ae468c04259769452b5c5c54482a59f0d97b", "size": 5086, "ext": "r", "lang": "R", "max_stars_repo_path": "Stats/group_comparison.r", "max_stars_repo_name": "SINTEFMedtek/NeuroImageRegistration", "max_stars_repo_head_hexsha": "4272e62d64943a23cf6244c55366d5ade82065b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Stats/group_comparison.r", "max_issues_repo_name": "SINTEFMedtek/NeuroImageRegistration", "max_issues_repo_head_hexsha": "4272e62d64943a23cf6244c55366d5ade82065b9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Stats/group_comparison.r", "max_forks_repo_name": "SINTEFMedtek/NeuroImageRegistration", "max_forks_repo_head_hexsha": "4272e62d64943a23cf6244c55366d5ade82065b9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-02-19T08:23:42.000Z", "max_forks_repo_forks_event_max_datetime": "2019-02-19T08:23:42.000Z", "avg_line_length": 42.0330578512, "max_line_length": 173, "alphanum_fraction": 0.6246559182, "num_tokens": 1300, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7154239957834733, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.3022699540758037}} {"text": "library(lubridate)\nlibrary(modelr)\nlibrary(broom)\nlibrary(tidyverse)\nlibrary(visdat)\nlibrary(mlr3verse)\n\nsource(\"usa/code/utils/read-data-usa.r\")\n\nforecast_googleMob<-function(){\n # read data\n # google\n google_data <- read_google_mobility()\n google_data$code[which(is.na(google_data$code))]<- \"US\"\n \n # new foursquare data \n new_foursquare= read_csv(\"usa/code/utils/mobility-reg/visitdata-grouped.csv\")\n new_foursquare$categoryname=as.factor(new_foursquare$categoryname)\n\n # c(\"Airport\" , \"Arts & Entertainment\" \"Banks\" \n # [5] \"Beach\" \"Big Box Stores\" \"Bus\" \"Colleges & Universities\" \n # [9] \"Convenience Store\" \"Discount Stores\" \"Drug Store\" \"Events\" \n # [13] \"Fast Food Restaurants\" \"Fitness Center\" \"Food\" \"Gas Stations\" \n # [17] \"Government\" \"Grocery\" \"Gun Shops\" \"Hardware Stores\" \n # [21] \"Hotel\" \"Light Rail Stations\" \"Medical\" \"Metro Stations\" \n # [25] \"Nightlife Spots\" \"Office\" \"Outdoors & Recreation\" \"Professional & Other Places\"\n # [29] \"Residences\" \"School\" \"Shops & Services\" \"Skiing\" \n # [33] \"Spiritual Center\" \"Sports\" \"Travel & Transport\" \n \n sfsq <- new_foursquare %>% \n filter(categoryname!=\"Skiing\",demo==\"All\") %>% \n select(c(-demo,-county,-p50Duration)) # revisit p50\n \n google_cleaned <- google_data %>% \n select(date,state=sub_region_1,retail.recreation,grocery.pharmacy,parks,transitstations,workplace,residential) %>% \n filter(state!=\"\")\n \n mobility_data <- left_join(sfsq,google_cleaned, by = c(\"state\" = \"state\", \"date\" = \"date\"))\n \n #mobility_data <- mobility_data %>% # filter(!(state %in% c(\"Alaska\", \"District of Columbia\", \"Hawaii\")))\n mobility_data <- mobility_data %>% \n pivot_wider(names_from =categoryname , values_from = c(avgDuration,visits))\n mobility_data$state <- as.factor(mobility_data$state)\n names(mobility_data)<-make.names(names(mobility_data),unique = TRUE)\n \n # check fit on last week\n last_google=ymd((mobility_data %>% filter(!is.na(retail.recreation)) %>%summarise(last(date)))[[1,1]])\n train_stop=last_google-6\n \n # foursquare broken then\n mobility_data=mobility_data %>% filter(date!=ymd(\"2020-04-19\"))\n \n # need to rmove date for mlr \n mobility_data=mobility_data %>% arrange(date)\n mobility_data$id=c(1:nrow(mobility_data))\n \n train_end_id=(mobility_data %>% filter(date==train_stop) %>% summarise(last(id)))[[1,1]]\n google_end_id=(mobility_data %>% filter(date==last_google) %>% summarise(last(id)))[[1,1]]\n dates=mobility_data$date\n mobility_data=mobility_data %>% select(c(-date,-categoryid))\n \n # revisit imputaiton\n mobility_data=mobility_data %>% fill(names(mobility_data),.direction = 'updown')\n \n \n ## todo remove \n names(google_cleaned)\n \n task = TaskRegr$new(id = \"retail.recreation\", backend = as.data.frame(mobility_data %>%\n select(c(-\"grocery.pharmacy\",-\"parks\",-\"transitstations\",-\"workplace\",-\"residential\"))), target = \"retail.recreation\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=data.frame(id=prediction$row_ids,retail.recreation=(abs(prediction$truth-prediction$response)) )\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n mobility_data[test_set,\"retail.recreation\"]=prediction$response\n \n \n c(\"retail.recreation\",\"grocery.pharmacy\",\"parks\",\"transitstations\",\"workplace\",\"residential\")\n \n \n task = TaskRegr$new(id = \"grocery.pharmacy\", backend = as.data.frame(mobility_data %>%\n select(c(-\"retail.recreation\",-\"parks\",-\"transitstations\",-\"workplace\",-\"residential\"))),\n target =\"grocery.pharmacy\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=cbind(df_lastweek,data.frame(grocery.pharmacy=(abs(prediction$truth-prediction$response)) ))\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n mobility_data[test_set,\"grocery.pharmacy\"]=prediction$response\n \n\n c(\"retail.recreation\",\"grocery.pharmacy\",\"parks\",\"transitstations\",\"workplace\",\"residential\")\n \n task = TaskRegr$new(id = \"parks\", backend = as.data.frame(mobility_data %>%\n select(c(-\"retail.recreation\",-\"grocery.pharmacy\",-\"transitstations\",-\"workplace\",-\"residential\"))),\n target =\"parks\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=cbind(df_lastweek,data.frame(parks=(abs(prediction$truth-prediction$response)) ))\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n mobility_data[test_set,\"parks\"]=prediction$response\n \n\n task = TaskRegr$new(id = \"transitstations\", backend = as.data.frame(mobility_data %>%\n select(c(-\"retail.recreation\",-\"grocery.pharmacy\",-\"parks\",-\"workplace\",-\"residential\"))),\n target =\"transitstations\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=cbind(df_lastweek,data.frame(transitstations=(abs(prediction$truth-prediction$response)) ))\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n \n mobility_data[test_set,\"transitstations\"]=prediction$response\n \n \n c(\"retail.recreation\",\"grocery.pharmacy\",\"parks\",\"transitstations\",\"workplace\",\"residential\")\n \n task = TaskRegr$new(id = \"workplace\", backend = as.data.frame(mobility_data %>%\n select(c(-\"retail.recreation\",-\"grocery.pharmacy\",-\"parks\",-\"transitstations\",-\"residential\"))),\n target =\"workplace\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=cbind(df_lastweek,data.frame(workplace=(abs(prediction$truth-prediction$response)) ))\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n mobility_data[test_set,\"workplace\"]=prediction$response\n\n task = TaskRegr$new(id = \"residential\", backend = as.data.frame(mobility_data %>%\n select(c(-\"retail.recreation\",-\"grocery.pharmacy\",-\"parks\",-\"transitstations\",-\"workplace\"))),\n target =\"residential\")\n learner=lrn(\"regr.ranger\")\n train_set = c(1:train_end_id)\n test_set = c((train_end_id+1):google_end_id)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n abs(prediction$truth-prediction$response)\n df_lastweek=cbind(df_lastweek,data.frame(residential=(abs(prediction$truth-prediction$response)) ))\n train_set = c(1:google_end_id)\n test_set = setdiff(seq_len(task$nrow), train_set)\n learner$train(task, row_ids = train_set)\n prediction = learner$predict(task, row_ids = test_set)\n mobility_data[test_set,\"residential\"]=prediction$response\n\n mobility_data$date=dates\n \n # error analysis\n c(\"retail.recreation\",\"grocery.pharmacy\",\"parks\",\"transitstations\",\"workplace\",\"residential\")\n df_raw_error=copy(df_lastweek)\n print(\"Error last week\")\n print(df_raw_error %>% summarise(retail.recreation=mean(retail.recreation),\n grocery.pharmacy=mean(grocery.pharmacy),\n parks=mean(parks),transitstations=mean(transitstations),\n workplace=mean(workplace),residential=mean(residential)\n ))\n df_lastweek=df_lastweek %>% pivot_longer( c(\"retail.recreation\",\"grocery.pharmacy\",\"parks\",\"transitstations\",\"workplace\",\"residential\")\n,names_to = \"cat\",values_to = \"error\")\n \n return(mobility_data)\n}\n \nf<-forecast_googleMob()\n#colnames(df_lastweek) <- paste(\"err\", colnames(df_lastweek), sep = \"_\")\n \ngoogle_forecast <- f %>% select(state,retail.recreation,grocery.pharmacy,parks,transitstations,workplace,residential,date)\nwrite.csv(google_forecast, \"usa/data/google-mobility-forecast.csv\", row.names = FALSE)\n \n \n \n \n ", "meta": {"hexsha": "7fc59faa9ab15084bdafb8fb45c9e200970dee5d", "size": 9909, "ext": "r", "lang": "R", "max_stars_repo_path": "usa/code/utils/mobility-reg/mobility-regression.r", "max_stars_repo_name": "codecheckers/covid19model-report23", "max_stars_repo_head_hexsha": "9b7aebcda066ee184880e85ea02e744136d13be2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1057, "max_stars_repo_stars_event_min_datetime": "2020-03-26T22:41:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T23:40:12.000Z", "max_issues_repo_path": "usa/code/utils/mobility-reg/mobility-regression.r", "max_issues_repo_name": 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YES\n2. NO", "lm_q1_score": 0.7025300698514778, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.3021914468484402}} {"text": "library(Seurat)\nllibrary(data.table)\nlibrary(dplyr) \nlibrary('org.Hs.eg.db')\nlibrary(limma)\n\nseurat_recipet = function(expr){\nexpr_obj = CreateSeuratObject(expr)\n#expr_obj = NormalizeData(expr_obj, normalization.method = \"LogNormalize\", scale.factor = 10000)\n#expr_obj = ScaleData(expr_obj)\n#expr_obj = FindVariableFeatures(expr_obj, selection.method = \"vst\", nfeatures = 2000)\n#expr_obj = RunPCA(expr_obj, features = VariableFeatures(object = expr_obj), npcs = 100)\nexpr_obj\n}\n\nfn_expr = 'MO_round2.csv'\nfn_meta = 'MO_meta_round2.csv'\nfn_hpca = 'hpca_round2.csv'\nfn_umap = 'umap_round2.csv'\nfn_cluster = 'seurat_cluster_round2.csv'\nfn_topgene = 'seurat_topgene_round2.csv'\nfn_marker = 'seurat_markers_round2.csv'\n\nwrite_csv = function(vec, fn){write.table(vec, file = fn, append = FALSE, quote = FALSE, sep = \",\",\n eol = \"\\n\", na = \"NA\", dec = \".\", row.names = TRUE,\n col.names = TRUE, qmethod = c(\"escape\", \"double\"),\n fileEncoding = \"\")}\n\nfolder_ = paste0('./')\nexprs=read.csv(paste0(folder_, fn_expr), row.names=1, header=T , check.names = F)\nexprs=t(exprs)\n\nmeta=read.csv(paste0(folder_, fn_meta), row.names=1, header=T, check.names = F)\nabrain = seurat_recipet(exprs)\n\nnpc = 50\n\ndf_harmony = read.csv(fn_hpca, row.names = 1)\n\nabrain[[\"harmony\"]] <- CreateDimReducObject(embeddings = as.matrix(df_harmony), \n key = \"harmony_\", assay = DefaultAssay(abrain))\n \nabrain1=FindNeighbors(object = abrain, dims = 1:npc, k.param = 15, reduction = \"harmony\") \nabrain1=FindClusters(object = abrain1, resolution = .1, graph.name = 'RNA_snn')\nwrite_csv(abrain1@meta.data$seurat_clusters, fn_cluster)\n\nabrain1=RunUMAP(object = abrain1, min.dist = .3, graph = 'RNA_snn', n.epochs = 200) \nwrite_csv(Embeddings(object = abrain1, reduction = \"umap\"), fn_umap)\n\nabrain.markers=FindAllMarkers(object = abrain1, min.pct= 0.3, return.thresh = 1.)\ntopgene=abrain.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)\nwrite_csv(topgene,fn_topgene)\nwrite_csv(as.data.frame(abrain.markers), fn_marker)\n", "meta": {"hexsha": "d665da333eff73d1d494e9dd2034cd4624ad06a8", "size": 2072, "ext": "r", "lang": "R", "max_stars_repo_path": "MOMIC_subcluster/seurat_cluster_umap_harmony.r", "max_stars_repo_name": "howchihlee/covid_brain_sc", "max_stars_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-09-29T02:40:23.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T02:40:23.000Z", "max_issues_repo_path": "MOMIC_subcluster/seurat_cluster_umap_harmony.r", "max_issues_repo_name": "howchihlee/covid_brain_sc", "max_issues_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MOMIC_subcluster/seurat_cluster_umap_harmony.r", "max_forks_repo_name": "howchihlee/covid_brain_sc", "max_forks_repo_head_hexsha": "7b548d810426290a3815bcd2ce6edaaeafc024cb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.3703703704, "max_line_length": 99, "alphanum_fraction": 0.6998069498, "num_tokens": 620, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514777, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.3021914468484401}} {"text": "\n figure.mcmc = function( vname=\"\", type=\"density\", res=NULL, fn=NULL, aulabels=c(\"N-ENS\",\"S-ENS\",\"4X\") , save.plot=TRUE, ...) {\n\n y = res$mcmc\n sb= res$p$fishery_model$standata\n\n ntacs = sb$nProj\n yrs0 = res$p$assessment_years\n yrs = c( yrs0, (max(yrs0)+c(1:sb$M) ) )\n yrs.last = max(yrs0) + 0.5\n ndata = length(yrs0)\n hdat = 1:ndata\n\n\n if (vname ==\"r.ts\") {\n # catch this first as the layout is different\n br = 75\n\n plot.new()\n layout( matrix(c(1:(sb$N*3)), ncol=3, nrow=sb$N ))\n par(mar = c(1., 1., 0.65, 0.75))\n\n for (i in 1:3) {\n for (yr in 1:sb$N) {\n dta = y$r[yr,i]\n qs = apply( dta, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n pdat = as.vector( dta)\n xrange = range( pdat, na.rm=T )\n postdat = hist( pdat, breaks=br, plot=FALSE )\n yrange = range( 0, postdat$density, na.rm=T ) * 1.02\n hist( pdat, freq=FALSE, breaks=br, xlim=xrange, ylim=yrange, main=\"\", xlab=\"\", ylab=\"Density\", col=\"lightgray\", border=\"gray\")\n YR = rownames(sb$IOA) [yr]\n legend( \"topright\", bty=\"n\", legend=paste( aulabels[i], YR, \"r\", \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"} \", sep=\"\" ), cex=0.9 )\n }}\n\n if(save.plot) savePlot( filename=fn, type=\"png\" )\n return( fn)\n\n }\n\n\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n\n prr = NULL\n prr$class =\"none\" # no priors by default\n\n\n if ( type==\"density\" ) { # default\n\n if ( vname==\"K\" ) {\n qs = apply( y$K, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$K[,i])\n prr=NULL\n prr$class=\"lognormal\"\n\n # E(X) = exp(mu + 1/2 sigma^2)\n # med(X) = exp(mu)\n # Var(X) = exp(2*mu + sigma^2)*(exp(sigma^2) - 1)\n # CV = sqrt( Var(X) ) / E(X) = sqrt(exp(sigma^2) - 1) ~ sigma; sigma < 1/2\n # SD(X) = sqrt( exp(2*mu + sigma^2)*(exp(sigma^2) - 1) )\n # or = CV * E(X)\n\n prr$meanlog= sb$Kmu[i]\n prr$sdlog = sqrt(sb$Ksd[i])\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n\n legend( \"topright\", bty=\"n\", legend=paste( aulabels[i], \"\\n\", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"} \", sep=\"\" ))\n }\n }\n\n if ( vname==\"r\" ) {\n qs = apply( y$r, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) { prr=NULL\n prr=NULL\n prr$class='normal'\n prr$mean=sb$rmu[i]\n prr$sd=sqrt(sb$rsd[i])\n pdat = as.vector(y$r[,i])\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\", legend=paste( aulabels[i], \"\\n\", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"} \", sep=\"\" ) )\n }}\n\n\n\n if ( vname==\"q\" ) {\n qs = apply( y$q, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$q[,i])\n prr=NULL\n prr$class=\"normal\"\n prr$mean=sb$qmu[i]\n prr$sd=sb$qsd[i]\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\", legend=paste( aulabels[i], \"\\n\", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"} \", sep=\"\" )\n )}}\n\n if ( vname==\"qs\" ) {\n QQ = apply( y$q, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n QQ = signif( QQ, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$qs[,i])\n # prr=NULL\n # prr$class=\"normal\"\n # prr$mean=sb$q0x[i]\n # prr$sd=sb$q0x[i]*sb$cv\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\", legend=paste( aulabels[i], \"\\n\", vname, \" = \", QQ[2,i], \" {\", QQ[1,i], \", \", QQ[3,i], \"} \", sep=\"\" )\n )}}\n\n\n if ( vname==\"BMSY\" ) {\n qs = apply( y$BMSY, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$BMSY[,i])\n prr=NULL\n prr$class=\"none\"\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\",\n legend=paste( aulabels[i], \" \", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"}\", sep=\"\" )\n )}}\n\n if ( vname==\"FMSY\" ) {\n qs = apply( y$FMSY, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$FMSY[,i])\n prr=NULL\n prr$class=\"none\"\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\",\n legend=paste( aulabels[i], \" \", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"}\", sep=\"\" )\n )}}\n\n\n if ( vname==\"bosd\" ) {\n qs = apply( y$bosd, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$bosd[,i])\n prr=NULL\n prr$class='uniform'\n prr$max=3\n prr$min=0\n #prr$class=\"lognormal\"\n #prr$meanlog=sb$bomup\n #prr$sdlog=sqrt(sb$bosdp)\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\",\n legend=paste( aulabels[i], \" \", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"}\", sep=\"\" )\n )}}\n\n if ( vname==\"bpsd\" ) {\n qs = apply( y$bpsd, 2, quantile, probs=c(0.025, 0.5, 0.975) )\n qs = signif( qs, 3 )\n for (i in 1:3) {\n pdat = as.vector(y$bpsd[,i])\n prr=NULL\n prr$class='uniform'\n prr$max=3\n prr$min=0\n #prr$class=\"lognormal\"\n #prr$meanlog=sb$bpmup\n #prr$sdlog=sqrt(sb$bpsdp)\n plot.freq.distribution.prior.posterior( prior=prr, posterior=pdat, ... )\n legend( \"topright\", bty=\"n\",\n legend=paste( aulabels[i], \" \", vname, \" = \", qs[2,i], \" {\", qs[1,i], \", \", qs[3,i], \"}\", sep=\"\" )\n )}}\n\n\n }\n\n # --------------\n\n if ( type==\"timeseries\" ) {\n\n\n\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(4.4, 4.4, 0.65, 0.75))\n\n if (vname==\"biomass\") {\n\n SI = apply( y$q, 2, median, na.rm=T )\n\n for (i in 1:3) {\n qIOA = sb$IOA[,i] / SI[i]\n IOA = sb$IOA[,i]\n meanval = apply( y$B[,,i], 2, mean, na.rm=T )\n\n prs = seq( from=0.025, to=0.975, length.out=600)\n Bq = apply( y$B[,,i], 2, quantile, probs=prs, na.rm=T )\n\n yran = range(c(0, Bq, sb$IOA[,i] ), na.rm=T )*1.01\n plot( yrs, Bq[1,], type=\"n\", ylim=yran, xlim=range(yrs0), xlab=\"\", ylab=\"\" ) #change xlim to yrs0 to remove 3 yr projection\n cols = gray.colors( floor(length( prs)/2) )\n cols2 = c(cols[length(cols):1], cols )\n for ( j in 1:length(prs) ) {\n lines ( yrs, Bq[j,], lwd=4, col=cols2[j] )\n }\n # lines( yrs, B, lwd=3, col=\"darkgreen\" )\n #abline (v=yrs.last , lwd=2, lty=\"dashed\" ) #can comment out this line if not providing forward projection\n if (i==2) title( ylab=\"Fishable biomass (kt)\" )\n if (i==3) title( xlab=\"Year\" )\n points( yrs0, qIOA, pch=20, col=\"darkgreen\" )\n lines ( yrs0, qIOA, lwd=3, col=\"darkgreen\", lty=\"dashed\" )\n lines ( yrs, meanval, lwd=2, col=\"blue\", lty=\"dotted\" )\n points( yrs0, IOA, pch=20, col=\"darkred\" )\n lines( yrs0, IOA, lwd=3, lty=\"dotdash\", col=\"red\" )\n legend( \"topright\", bty=\"n\", legend=aulabels[i])\n }\n }\n\n if (vname==\"fishingmortality\") {\n Fmsy = apply( y$FMSY, 2, mean, na.rm=T )\n for (i in 1:3) {\n prs = seq( from=0.025, to=0.975, length.out=600)\n Fi = apply( y$F[,1:sb$N,i], 2, quantile, probs=prs, na.rm=T )\n yran = range(c(0, max(c(Fi,Fmsy))), na.rm=T )*1.01\n yran = pmin( yran, 1.2 )\n plot( yrs0, Fi[1,], type=\"n\", ylim=yran, xlab=\"\", ylab=\"\" )\n cols = gray.colors( floor(length( prs)/2) )\n cols2 = c(cols[length(cols):1], cols )\n if (i %in% c(1,2)){\n for ( j in 1:length(prs) ) {\n lines ( yrs0, Fi[j,], lwd=4, col=cols2[j] )\n }}\n if (i==3){\n for ( j in 1:length(prs) ) {\n lines ( yrs0-1, Fi[j,], lwd=4, col=cols2[j] )\n }}\n if (i==2) title( ylab=\"Fishing mortality\" )\n if (i==3) title( xlab=\"Year\" )\n legend( \"topright\", bty=\"n\", legend=aulabels[i])\n abline (h=-log(1-0.2), lwd=2, lty=\"dashed\" )\n abline (h=Fmsy[i], lwd=2, lty=\"solid\", col=\"red\" )\n }\n }\n\n }\n\n if (type==\"hcr\") {\n if (vname==\"default\") {\n\n B = apply( y$B, c(2,3), mean, na.rm=T )\n F = apply( y$F, c(2,3), mean, na.rm=T )\n K = apply( y$K, c(2), mean, na.rm=T )\n FMSY = apply( y$FMSY, c(2), mean, na.rm=T )\n BMSY = apply( y$BMSY, c(2), mean, na.rm=T )\n\n for (i in 1:3 ) {\n ylims = c(0, min( 1, max( FMSY[i] * 1.25, F[hdat,i] ) ) )\n plot( B[hdat,i], F[hdat,i], type=\"b\", xlim=c(0, K[i] * 1.1 ),\n ylim=ylims, col=\"darkorange\", cex=0.8, lwd=2, xlab=\"\", ylab=\"\", pch=20 )\n\n\n # nn = as.matrix( cbind( Bx=as.vector( y$B[,ndata,i] ), Fx = as.vector( y$F[,ndata,i] ) ))\n # ellipse.2d(nn[,1], nn[,2], pv=0.05, sc=30)\n\n if (i==3) title( xlab=\"Fishable biomass (kt)\" )\n if (i==2) title( ylab=\"Fishing mortality\" )\n\n F30 = -log(1-0.3)\n F10 = -log(1-0.1)\n\n Fref = 0.22\n Bmsy = K[i] * 0.5\n Bref = K[i] * 0.2\n BK = K[i]\n BK25 = K[i] * .25\n Fhistorical = mean( F[hdat,i], na.rm=T )\n Bhistorical = mean( B[hdat,i], na.rm=T )\n yl = 0.05\n\n polygon(x=c(Bmsy,Bmsy*2,Bmsy*2, Bmsy),y=c(-0.08,-0.1,FMSY[i],FMSY[i]),col='lightgreen',border=NA)\n polygon(x=c(Bmsy/2,Bmsy,Bmsy, Bmsy/2),y=c(-0.08,-0.1,FMSY[i],FMSY[i]),col='lightgoldenrod',border=NA)\n polygon(x=c(0,Bmsy/2,Bmsy/2, 0),y=c(-0.08,-0.1,FMSY[i],FMSY[i]),col='darksalmon',border=NA)\n\n#might need adjustment below to offset F vs B. Need to plot F against PREfishery biomass\n lines( B[hdat,i], F[hdat,i], type=\"b\", xlim=c(0, K[i] * 1.1 ),\n ylim=ylims, col='blue', cex=0.8, lwd=2, xlab=\"\", ylab=\"\", pch=20 )\n\n abline (h=Fref, lty=\"solid\", col=\"gray\", lwd=2 )\n\n abline (h=F10, lty=\"dotted\", col=\"gray\")\n # text( 0.05*K[i], F10, \"10% HR\", pos=1 )\n\n abline (h=F30, lty=\"dotted\", col=\"gray\")\n # text( 0.05*K[i], F30, \"30% HR\", pos=1 )\n\n\n abline (h=FMSY[i], lty=\"dashed\", col=\"red\" )\n\n # abline (h=Fhistorical, lty=\"dashed\")\n # text( 0.05*K[i], Fhistorical, \"Mean\", pos=1, lwd=2 )\n\n # abline (v=Bref, lty=\"dotted\")\n # text( Bref-0.2, 0.25, \"Lower biomass reference point\\n (LBRP = 0.2 * BMSY)\" , srt=90, pos=3)\n\n abline (v=Bmsy, lty=\"dotted\")\n\n abline (v=BK, lty=\"dotted\")\n\n abline (v=BK25, lty=\"dotted\")\n\n text( Bmsy-0.01*K[i], yl, \"K/2\" , srt=90, pos=3)\n text( BK-0.01*K[i], yl, \"K\" , srt=90, pos=3)\n text( BK25-0.01*K[i], yl, \"K/4\" , srt=90, pos=3)\n text( 0.05*K[i], Fref, \"20% HR\", pos=1 )\n text( 0.05*K[i], FMSY[i], \"FMSY\", pos=3, lwd=2, col=\"red\" )\n if (i %in% c(1,2)){\n text( B[1:(ndata-1),i], F[1:(ndata-1),i], labels=yrs0[-ndata], pos=3, cex= 0.8 )\n points( B[ndata,i], F[ndata,i], pch=21, bg='darkorange', cex= 1.4 )\n text( B[ndata,i], F[ndata,i], labels=yrs0[ndata], pos=3, cex= 1.4, font=2 )\n \n text( 0, ylims[2]*0.9, labels=aulabels[i], pos=3, cex= 0.85 )\n }\n if (i==3){\n text( B[1:(ndata-1),i], F[1:(ndata-1),i], labels=(yrs0[-ndata]-1), pos=3, cex= 0.8 )\n points( B[ndata,i], F[ndata,i], pch=21, bg='darkorange', cex= 1.4 )\n text( B[ndata,i], F[ndata,i], labels=yrs0[ndata]-1, pos=3, cex= 1.4, font=2 )\n\n text( 0, ylims[2]*0.9, labels=aulabels[i], pos=3, cex= 0.85 )\n }\n # abline (v=Bhistorical, lty=\"dashed\")\n # text( Bhistorical-0.01*K[i], yl, \"Mean\" , srt=90, pos=3, lwd=2)\n }\n #Enable below to see the annual F estimates for inclusion in the document\n print(\"F for N-ENS\" )\n print(F[hdat,1] )\n print(\"F for S-ENS\" )\n print(F[hdat,2] )\n print(\"F for 4X\" )\n print(F[hdat,3] )\n\n\n \n }\n\n if (vname==\"default.unmodelled\") {\n\n B = sb$IOA\n F = apply( y$F, c(2,3), mean, na.rm=T )\n\n areas = c(\"cfa4x\", \"cfasouth\", \"cfanorth\" )\n regions = c(\"4X\", \"S-ENS\", \"N-ENS\")\n\n td = exploitationrates(p=p, areas=areas, labels=regions, CFA4X.exclude.year.assessment=FALSE )\n\n K = apply( y$K, c(2), mean, na.rm=T )\n FMSY = apply( y$FMSY, c(2), mean, na.rm=T )\n BMSY = apply( y$BMSY, c(2), mean, na.rm=T )\n\n for (i in 1:3 ) {\n ylims = c(0, FMSY[i] * 1.25)\n plot( B[hdat,i], F[hdat,i], type=\"b\", xlim=c(0, K[i] * 1.1 ),\n ylim=ylims, col=\"darkorange\", cex=0.8, lwd=2, xlab=\"\", ylab=\"\", pch=20 )\n\n # nn = as.matrix( cbind( Bx=as.vector( y$B[,ndata,i] ), Fx = as.vector( y$F[,ndata,i] ) ))\n # ellipse.2d(nn[,1], nn[,2], pv=0.05, sc=30)\n\n if (i==3) title( xlab=\"Fishable biomass (kt)\" )\n if (i==2) title( ylab=\"Fishing mortality\" )\n\n F30 = -log(1-0.3)\n F10 = -log(1-0.1)\n\n Fref = 0.22\n Bmsy = BMSY[i]\n Bref = K[i] * 0.2\n BK = K[i]\n BK25 = K[i] * .25\n Fhistorical = mean( F[hdat,i], na.rm=T )\n Bhistorical = mean( B[hdat,i], na.rm=T )\n yl = 0.05\n\n\n abline (h=Fref, lty=\"solid\", col=\"gray\", lwd=2 )\n\n abline (h=F10, lty=\"dotted\", col=\"gray\")\n # text( 0.05*K[i], F10, \"10% HR\", pos=1 )\n\n abline (h=F30, lty=\"dotted\", col=\"gray\")\n # text( 0.05*K[i], F30, \"30% HR\", pos=1 )\n\n\n abline (h=FMSY[i], lty=\"dashed\", col=\"red\" )\n\n # abline (h=Fhistorical, lty=\"dashed\")\n # text( 0.05*K[i], Fhistorical, \"Mean\", pos=1, lwd=2 )\n\n # abline (v=Bref, lty=\"dotted\")\n # text( Bref-0.2, 0.25, \"Lower biomass reference point\\n (LBRP = 0.2 * BMSY)\" , srt=90, pos=3)\n\n abline (v=Bmsy, lty=\"dotted\")\n\n abline (v=BK, lty=\"dotted\")\n\n abline (v=BK25, lty=\"dotted\")\n\n text( 0.05*K[i], Fref, \"20% HR\", pos=1 )\n text( 0.05*K[i], FMSY[i], \"FMSY\", pos=3, lwd=2, col=\"red\" )\n text( BK-0.01*K[i], yl, \"K\" , srt=90, pos=3)\n text( Bmsy-0.01*K[i], yl, \"K/2\" , srt=90, pos=3)\n text( BK25-0.01*K[i], yl, \"K/4\" , srt=90, pos=3)\n text( B[hdat,i], F[hdat,i], labels=yrs0, pos=3, cex= 0.8 )\n\n text( 0, ylims[2]*0.9, labels=aulabels[i], pos=3, cex= 0.85 )\n\n\n\n # abline (v=Bhistorical, lty=\"dashed\")\n # text( Bhistorical-0.01*K[i], yl, \"Mean\" , srt=90, pos=3, lwd=2)\n\n }\n }\n\n if (vname==\"simple\") {\n require(car)\n\n B = apply( y$B, c(2,3), mean, na.rm=T )\n F = apply( y$F, c(2,3), mean, na.rm=T )\n C = apply( y$C, c(2,3), mean, na.rm=T )\n K = apply( y$K, c(2), mean, na.rm=T )\n# for (i in 1:3) C[,i] = C[,i] * K[i]\n FMSY = apply( y$FMSY, c(2), mean, na.rm=T )\n BMSY = apply( y$BMSY, c(2), mean, na.rm=T )\n\n aulabels = c(\"N-ENS\", \"S-ENS\", \"4X\")\n\n for (i in 1:3 ) {\n ylims = max(C[,i] )* c(0, 1.1)\n plot( B[hdat,i], C[hdat,i], type=\"l\", xlim=c(0, K[i]*1.05 ),\n ylim=ylims, xlab=\"\", ylab=\"\", lwd=2, col=\"darkorange\" )\n\n abline(0,0.1, lty=\"dotted\", lwd=2, col=\"gray\" )\n abline(0,0.2, lwd=3, col=\"gray\" )\n abline(0,0.3, lty=\"dotted\", lwd=2, col=\"gray\" )\n\n points( B[hdat,i], C[hdat,i], col=\"orange\", cex=0.8, pch=20 )\n text( B[hdat,i], C[hdat,i], labels=yrs0, pos=3, cex= 0.85 )\n\n text( 0, ylims[2]*0.9, labels=aulabels[i], pos=3, cex= 0.85 )\n\n # nn = as.matrix( cbind( Bx=as.vector( y$B[,ndata,i] ), Fx = as.vector( y$F[,ndata,i] ) ))\n # ellipse.2d(nn[,1], nn[,2], pv=0.05, sc=30)\n\n if (i==3) title( xlab=\"Fishable biomass (kt)\" )\n if (i==2) title( ylab=\"Catch (kt)\" )\n\n Cmsy = ( exp( FMSY[i] ) - 1)\n Cref = ( exp( FMSY[i] * 0.2 ) - 1) * K[i]\n Bmsy = BMSY[i]\n Bref = K[i] * 0.2\n BK = K[i]\n BK25 = K[i] * .25\n Chistorical = mean( C[hdat,i], na.rm=T )\n Bhistorical = mean( B[hdat,i], na.rm=T )\n yl = 0.1 * max(C[hdat,i])\n\n # abline (h=Fref, lty=\"dotted\")\n # text( 0.25, Fref, \"Target\\n (0.2 * FMSY) \", pos=1 )\n\n abline (0, Cmsy, lty=\"dotted\", col=\"red\")\n # text( 0.1*K[i], Cmsy, \"FMSY\", pos=1 )\n\n # abline (h=Chistorical, lty=\"dashed\")\n # text( 0.1*K[i], Chistorical, \"Mean\", pos=3, lwd=2 )\n\n # abline (v=Bref, lty=\"dotted\")\n # text( Bref-0.2, 0.25, \"Lower biomass reference point\\n (LBRP = 0.2 * BMSY)\" , srt=90, pos=3)\n\n\n abline (v=BK, lty=\"dotted\")\n text( BK-0.01*K[i], yl, \"K\" , srt=90, pos=3)\n\n abline (v=BK/2, lty=\"dotted\")\n text( BK/2-0.01*K[i], yl, \"K/2\" , srt=90, pos=3)\n\n abline (v=BK25, lty=\"dotted\")\n text( BK25-0.01*K[i], yl, \"K/4\" , srt=90, pos=3)\n\n }\n\n\n }\n }\n\n if (type==\"diagnostic.catch\") {\n\n }\n\n\n if (type==\"diagnostic.phase\") {\n\n B = apply( y$B, c(2,), mean, na.rm=T )\n K = apply( y$K, c(2), mean, na.rm=T )\n\n for (i in 1:3 ) {\n plot( B[1:ndata-1,i], B[2:ndata,i], type=\"b\", xlab=\"t\", ylab=\"t+1\",\n xlim=c(0, K[i] * 1.25 ), ylim=max(K[i] )* c(0, 1.25), lwd=2, col=\"darkorange\" )\n\n # abline(0,0.1, lty=\"dotted\", lwd=2, col=\"gray\" )\n abline( coef=c(0,1) )\n #text( B[1:ndata-1,], B[2:ndata,], labels=yrs4 , pos=4, cex=0.8 )\n }\n }\n\n\n if (type==\"diagnostic.errors\") {\n\n # observation vs process error\n graphics.off()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(5, 4, 0, 2))\n require(car)\n\n eP = y$bpsd\n eO = y$bosd\n for (i in 1:3 ) {\n plot( eP[,,i], eO[,,i], type=\"p\", pch=22 )\n if (i==2) title( ylab=\"Process error (SD)\" )\n if (i==3) title( xlab=\"Observation error (SD)\" )\n\n }\n\n\n }\n\n\n if (type==\"diagnostic.production\") {\n\n B = apply( y$B, c(2,3), mean, na.rm=T )\n P = apply( y$P, c(2,3), mean, na.rm=T )\n\n MSY = apply( y$MSY, c(2), mean, na.rm=T )\n FMSY = apply( y$FMSY, c(2), mean, na.rm=T )\n BMSY = apply( y$BMSY, c(2), mean, na.rm=T )\n C = apply( y$C, c(2,3), mean, na.rm=T )\n K = apply( y$K, c(2), mean, na.rm=T )\n\n # production vs biomass\n plot.new()\n layout( matrix(c(1,2,3), 3, 1 ))\n par(mar = c(5, 4, 0, 2))\n for (i in 1:3) {\n plot( B[,i], P[,i], type=\"n\", pch=20, ylim=c(0, max( c(P[,i], MSY[i]))*1.1), xlim=c(0,K[i]*1.05), xlab=\"Biomass; kt\", ylab=\"Yield; kt\" )\n a = MSY[i] / (BMSY[i])^2\n curve( -a*(x-BMSY[i])^2 + MSY[i], from=0, to=K[i], add=TRUE, lwd=3, col=\"gray\" )\n abline(v=BMSY[i], lty=\"dotted\", lwd=3, col=\"gray\")\n points( B[,i], P[,i], type=\"p\", pch=20 )\n text( B[,i], P[,i], yrs0, pos=3, cex=0.8 )\n # abline(h=0)\n }\n\n }\n\n\n if (is.null(fn)) fn = paste(vname, \"tmp\", \".png\", sep=\"\" )\n if(save.plot) savePlot( filename=fn, type=\"png\" )\n return( fn)\n\n }\n", "meta": {"hexsha": "814033a0609fb8ad47e803e5bc74de876c2d3ff2", "size": 19925, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure.mcmc.r", "max_stars_repo_name": "PEDsnowcrab/bio.snowcrab", "max_stars_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/figure.mcmc.r", "max_issues_repo_name": "PEDsnowcrab/bio.snowcrab", "max_issues_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure.mcmc.r", "max_forks_repo_name": "PEDsnowcrab/bio.snowcrab", "max_forks_repo_head_hexsha": "813314691c33830f4bc4db9f9a2d1d5861581303", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8339160839, "max_line_length": 148, "alphanum_fraction": 0.462685069, "num_tokens": 7759, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300698514777, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.3021914468484401}} {"text": "##Need to combine Geojson into one file before putting into leaflet\n\nlibrary(shinythemes)\nlibrary(rgeos)\nlibrary(rgdal)\nlibrary(leaflet)\nlibrary(geojsonio)\n\nload(\"map.rda\") \nload(\"zones.rda\")\n\n#input <- list(feetbuffer = 500, zones = \"B-3\")\n\nui <- bootstrapPage(theme = shinytheme(\"spacelab\"),\n title = \"Cannabis Business Zoning\",\n tags$head(includeScript(\"google-analytics.js\")),\n tags$style(type = \"text/css\", \"html, body {width:100%;height:100%}\"),\n leafletOutput(\"map\", width = \"100%\", height = \"100%\"),\n absolutePanel(class = \"panel panel-default\", \n top = 10, right = 10, width = 330,\n includeMarkdown(\"docs/about.md\"),\n numericInput(\"feetbuffer\", \n label = h4(\"Feet From Facility\"), 500),\n selectInput(\"zones\", \"Specific Zone\", as.character(levels(zones@data$ZONING_DES))),\n actionButton(\"recalc\", \"Update\"),\n br(),br(),\n a(img(src = \"codeforanc.png\"), \n href = \"http://codeforanchorage.org/\"),\n br(),br(),\n (a(\"Contact\", href = \"mailto:hans.thompson1@gmail.com\"))\n )\n )\n\nserver <- function(input, output, session) {\n buffer <- eventReactive(input$recalc, {\n spTransform(map, CRS(\"+init=epsg:26934\")) %>% \n gBuffer(width = input$feetbuffer / 3.28084) %>% #input$feetbuffer is actually in meters and is converted to feet in this line\n spTransform(CRS(\"+proj=longlat\")) %>%\n as(\"SpatialPolygonsDataFrame\")\n }, ignoreNULL = FALSE)\n filteredZones <- eventReactive(input$updateButton, {\n zones_filtered <- zones[as.character(zones@data$ZONING_DES) == input$zone,] \n zones_filtered <- as(zones_filtered, \"SpatialPolygonsDataFrame\")\n return(zones_filtered)\n })\n \n output$map <- renderLeaflet({\n #SpatialPolygons(list(buffers, zones), c(\"buffer\", \"zones\"))\n leaflet() %>% \n addTiles(urlTemplate = \"http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\") %>% \n setView(-149.85, 61.15, zoom = 12) %>%\n addGeoJSON(geojson_json(buffer())) %>%\n addPolygons(data = filteredZones(),\n color=\"red\")\n })\n\n}\n\nshinyApp(ui, server)", "meta": {"hexsha": "35e56a3cf811974dcae0769c3be89c167a5c8fcc", "size": 2510, "ext": "r", "lang": "R", "max_stars_repo_path": "CannabisZoning/App.r", "max_stars_repo_name": "codeforanchorage/shiny-server", "max_stars_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "CannabisZoning/App.r", "max_issues_repo_name": "codeforanchorage/shiny-server", "max_issues_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-10-20T20:41:10.000Z", "max_issues_repo_issues_event_max_datetime": "2015-10-20T20:44:49.000Z", "max_forks_repo_path": "CannabisZoning/App.r", "max_forks_repo_name": "codeforanchorage/shiny-server", "max_forks_repo_head_hexsha": "5139e294b3864089c2e4a667b40bed66dc17d8c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-03-25T21:05:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-25T21:05:40.000Z", "avg_line_length": 42.5423728814, "max_line_length": 131, "alphanum_fraction": 0.5270916335, "num_tokens": 587, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583376458152, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.30203301881401706}} {"text": "# A set of \"pluggable\" R functions for automated model fitting of glm models to presence/absence data #\n#\n# Modified 12-2-09 to: remove categorical covariates from consideration if they only contain one level\n# ID factor variables based on \"categorical\" prefix and look for tifs in subdir\n# Give progress reports\n# write large output tif files in blocks to alleviate memory issues\n# various bug fixes\n#\n#\n# Modified 3-4-09 to use a list object for passing of arguements and data\n#\n\n\n# Libraries required to run this program #\n# PresenceAbsence - for ROC plots\n# XML - for XML i/o\n# rgdal - for geotiff i/o\n# sp - used by rdgal library\n# raster for geotiff o\noptions(error=NULL)\n\nfit.mars.fct <- function(ma.name,tif.dir=NULL,output.dir=NULL,response.col=\"^response.binary\",make.p.tif=T,make.binary.tif=T,\n mars.degree=1,mars.penalty=2,responseCurveForm=NULL,debug.mode=T,script.name=\"mars.r\",opt.methods=2,save.model=TRUE,UnitTest=FALSE,MESS=FALSE){\n # This function fits a stepwise GLM model to presence-absence data.\n # written by Alan Swanson, 2008-2009\n # # Maintained and edited by Marian Talbert September 2010-\n # Arguements.\n # ma.name: is the name of a .csv file with a model array. full path must be included unless it is in the current\n # R working directory # THIS FILE CAN NOW INCLUDE AN OPTIONAL COLUMN OF SITE WEIGHTS WHICH MUST BE LABELED \"site.weights\"\n # tif.dir: is the directory containing geotiffs for each covariate. only required if geotiffs output of the \n # response surface is requested # # cov.list.name: is the name of a text file with the names of covariates to be included in models (one per line).\n # output.dir: is the directory that output files will be stored in. if not given, files will go to the current working directory. \n # response.col: column number of the model array containing a binary 0/1 response. all other columns will be considered explanatory variables.\n # make.p.tif: T if a geotiff of the response surface is desired.\n # make.binary.tif: T if a geotiff of the response surface is desired.\n # simp.method: model simplification method. valid methods include: \"AIC\" and \"BIC\". NOT CURRENTLY FUNCTIONAL \n # debug.mode: if T, output is directed to the console during the run. also, a pdf is generated which contains response curve plots and perspective plots\n # showing the effects of interactions deemed important. if F, output is diverted to a text file and the console is kept clear \n # except for final output of an xml file. in either case, a set of standard output files are created in the output directory.\n # \n\n # Value:\n # returns nothing but generates a number of output files in the directory\n # \"output.dir\" named above. These output files consist of:\n #\n # glm_output.txt: a text file with fairly detailed results of the final model.\n # glm_output.xml: an xml-formatted text file with results from the final model.\n # glm_response_curves.xml: an xml-formatted text file with response curves for\n # each covariate in the final model.\n # glm_prob_map.tif: a geotiff of the response surface\n # glm_bin_map.tif: a geotiff of the binary response surface. threhold is based on the roc curve at the point where sensitivity=specificity.\n # glm_log.txt: a file containing text output diverted from the console when debug.mode=F\n # glm_auc_plot.jpg: a jpg file of a ROC plot.\n # glm_response_curves.pdf: an pdf file with response curves for\n # each covariate in the final model and perspective plots showing the effect of interactions deemed significant.\n # only produced when debug.mode=T\n # # seed=NULL # sets a seed for the algorithm, any inegeger is acceptable\n # opt.methods=2 # sets the method used for threshold optimization used in the\n # # the evaluation statistics module\n # save.model=FALSE # whether the model will be used to later produce tifs\n # when debug.mode is true, these filenames will include a number in them so that they will not overwrite preexisting files. eg brt_1_output.txt.\n #\n times <- as.data.frame(matrix(NA,nrow=7,ncol=1,dimnames=list(c(\"start\",\"read data\",\"model fit\",\n \"model summary\",\"response curves\",\"tif output\",\"done\"),c(\"time\"))))\n times[1,1] <- unclass(Sys.time())\n t0 <- unclass(Sys.time())\n #simp.method <- match.arg(simp.method)\n\n out <- list(\n input=list(ma.name=ma.name,\n tif.dir=tif.dir,\n output.dir=output.dir,\n response.col=response.col,\n make.p.tif=make.p.tif,\n make.binary.tif=make.binary.tif,\n site.weights=NULL,\n save.model=save.model,\n mars.degree=mars.degree,\n mars.penalty=mars.penalty,\n model.type=\"stepwise with pruning\",\n model.source.file=script.name,\n model.fitting.subset=NULL, # not used.\n model.family=\"binomial\",\n run.time=paste(c(format(Sys.time(),\"%Y-%m-%d\"),format(Sys.time(),\"%H:%M:%S\")),collapse=\"T\"),\n sig.test=\"chi-squared anova p-value\",\n MESS=MESS),\n dat = list(missing.libs=NULL,\n output.dir=list(dname=NULL,exist=F,readable=F,writable=F),\n tif.dir=list(dname=NULL,exist=F,readable=F,writable=F),\n tif.ind=NULL,\n tif.names=NULL,\n bname=NULL,\n bad.factor.covs=NULL, # factorchange\n ma=list( status=c(exists=F,readable=F),\n dims=c(NA,NA),\n n.pres=c(all=NA,complete=NA,subset=NA),\n n.abs=c(all=NA,complete=NA,subset=NA),\n ratio=NA,\n resp.name=NULL,\n train.weights=NULL,\n test.weights=NULL,\n train.xy=NULL,\n test.xy=NULL,\n factor.levels=NA,\n used.covs=NULL,\n ma=NULL,\n ma.subset=NULL,\n ma.test=NULL)),\n mods=list(final.mod=NULL,\n r.curves=NULL,\n tif.output=list(prob=NULL,bin=NULL),\n auc.output=NULL,\n interactions=NULL, # not used #\n summary=NULL),\n time=list(strt=unclass(Sys.time()),end=NULL),\n error.mssg=list(NULL),\n ec=0 # error count #\n )\n\n # load libaries #\n out <- check.libs(list(\"PresenceAbsence\",\"rgdal\",\"XML\",\"sp\",\"survival\",\"mda\",\"raster\",\"tcltk2\",\"foreign\",\"ade4\"),out)\n \n # exit program now if there are missing libraries #\n if(!is.null(out$error.mssg[[1]])){\n cat(saveXML(mars.to.xml(out),indent=T),'\\n')\n return()\n }\n \n # check output dir #\n out$dat$output.dir <- check.dir(output.dir) \n if(out$dat$output.dir$writable==F) {out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: output directory\",output.dir,\"is not writable\")\n out$dat$output.dir$dname <- getwd()\n }\n\n # generate a filename for output #\n if(debug.mode==T){\n outfile <- paste(bname<-paste(out$dat$output.dir$dname,\"/mars_\",n<-1,sep=\"\"),\"_output.txt\",sep=\"\")\n while(file.access(outfile)==0) outfile<-paste(bname<-paste(out$dat$output.dir$dname,\"/mars_\",n<-n+1,sep=\"\"),\"_output.txt\",sep=\"\")\n capture.output(cat(\"temp\"),file=outfile) # reserve the new basename #\n } else bname<-paste(out$dat$output.dir$dname,\"/mars\",sep=\"\")\n out$dat$bname <- bname\n \n # sink console output to log file #\n if(!debug.mode) {sink(logname <- paste(bname,\"_log.txt\",sep=\"\"));on.exit(sink)} else logname<-NULL\n options(warn=1)\n \n # check tif dir #\n # check tif dir #\n if(!is.null(tif.dir)){\n out$dat$tif.dir <- check.dir(tif.dir)\n if(out$dat$tif.dir$readable==F & (out$input$make.binary.tif | out$input$make.p.tif)) {\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: tif directory\",tif.dir,\"is not readable\")\n if(!debug.mode) {sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))}\n cat(saveXML(brt.to.xml(out),indent=T),'\\n')\n return()\n }\n }\n\n \n # find .tif files in tif dir #\n if(out$dat$tif.dir$readable) out$dat$tif.names <- list.files(out$dat$tif.dir$dname,pattern=\".tif\",recursive=T)\n\n # check for model array #\n out$input$ma.name <- check.dir(out$input$ma.name)$dname\n\n if(UnitTest!=FALSE) options(warn=2)\n out <- read.ma(out)\n if(UnitTest==1) return(out)\n \n # exit program now if there are errors in the input data #\n if(!is.null(out$error.mssg[[1]])){\n if(!debug.mode) {sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))}\n cat(saveXML(mars.to.xml(out),indent=T),'\\n')\n return()\n }\n \n cat(\"\\nbegin processing of model array:\",out$input$ma.name,\"\\n\")\n cat(\"\\nfile basename set to:\",out$dat$bname,\"\\n\")\n assign(\"out\",out,envir=.GlobalEnv)\n if(!debug.mode) {sink();cat(\"Progress:20%\\n\");flush.console();sink(logname,append=T)} else {cat(\"\\n\");cat(\"20%\\n\")} ### print time\n ##############################################################################################################\n # Begin model fitting #\n ##############################################################################################################\n\n # Fit null GLM and run stepwise, then print results #\n cat(\"\\n\",\"Fitting MARS model\",\"\\n\")\n flush.console()\n\n fit <- mars.glm(data=out$dat$ma$ma, mars.x=c(2:ncol(out$dat$ma$ma)), mars.y=1, mars.degree=out$input$mars.degree, family=out$input$model.family,\n site.weights=out$dat$ma$train.weights, penalty=out$input$mars.penalty)\n \n out$mods$final.mod <- fit\n\n out$mods$final.mod$contributions$var<-names(out$dat$ma$ma)[-1]\n\n assign(\"out\",out,envir=.GlobalEnv)\n t3 <- unclass(Sys.time())\n fit_contribs <- try(mars.contribs(fit))\n if(class(fit_contribs)==\"try-error\"){\n if(!debug.mode) {sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))}\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]]<- paste(\"Error summarizing MARS model:\",fit_contribs)\n cat(saveXML(mars.to.xml(out),indent=T),'\\n')\n return()\n } \n \n x<-fit_contribs$deviance.table\n x <- x[x[,2]!=0,]\n x <- x[order(x[,4]),]\n row.names(x) <- x[,1]\n x$df <- -1*x$df\n x <- x[,-1]\n \n txt0 <- paste(\"\\nMARS Model Results\\n\",\"\\n\",\"Data:\\n\",ma.name,\"\\n\",\"\\n\\t n(pres)=\",\n out$dat$ma$n.pres[2],\"\\n\\t n(abs)=\",out$dat$ma$n.abs[2],\"\\n\\t n covariates considered=\",length(out$dat$ma$used.covs),\n \"\\n\",\n \"\\n total time for model fitting=\",round((unclass(Sys.time())-t0)/60,2),\"min\\n\",sep=\"\")\n\n capture.output(cat(txt0),file=paste(bname,\"_output.txt\",sep=\"\"))\n \n cat(\"\\n\",\"Finished with MARS\",\"\\n\")\n cat(\"Summary of Model:\",\"\\n\")\n print(out$mods$summary <- x)\n if(!is.null(out$dat$bad.factor.cols)){\n cat(\"\\nWarning: the following categorical response variables were removed from consideration\\n\",\n \"because they had only one level:\",paste(out$dat$bad.factor.cols,collapse=\",\"),\"\\n\\n\")\n }\n cat(\"\\n\",\"Storing output...\",\"\\n\",\"\\n\")\n #flush.console()\n capture.output(cat(\"\\n\\nSummary of Model:\\n\"),file=paste(bname,\"_output.txt\",sep=\"\"),append=TRUE)\n capture.output(print(out$mods$summary),file=paste(bname,\"_output.txt\",sep=\"\"),append=TRUE)\n if(!is.null(out$dat$bad.factor.cols)){\n capture.output(cat(\"\\nWarning: the following categorical response variables were removed from consideration\\n\",\n \"because they had only one level:\",paste(out$dat$bad.factor.cols,collapse=\",\"),\"\\n\"),\n file=paste(bname,\"_output.txt\",sep=\"\"),append=T)\n }\n \n if(!debug.mode) {sink();cat(\"Progress:40%\\n\");flush.console();sink(logname,append=T)} else cat(\"40%\\n\")\n \n \n ##############################################################################################################\n # Begin model output #\n ##############################################################################################################\n \n # Store .jpg ROC plot #\n auc.output <- make.auc.plot.jpg(out$dat$ma$ma,pred=mars.predict(fit,out$dat$ma$ma)$prediction[,1],\n plotname=paste(bname,\"_auc_plot.jpg\",sep=\"\"),modelname=\"MARS\",opt.methods=opt.methods,\n weight=out$dat$ma$train.weights,out=out)\n out$mods$auc.output<-auc.output\n\n if(!debug.mode) {sink();cat(\"Progress:70%\\n\");flush.console();sink(logname,append=T)} else cat(\"70%\\n\")\n \n # Response curves #\n \n if(is.null(responseCurveForm)){\n responseCurveForm<-0} \n \n if(debug.mode | responseCurveForm==\"pdf\"){\n nvar <- nrow(out$mods$summary)\n pcol <- min(ceiling(sqrt(nvar)),4)\n prow <- min(ceiling(nvar/pcol),3)\n r.curves <- try(mars.plot(fit,plot.layout=c(prow,pcol),file.name=paste(bname,\"_response_curves.pdf\",sep=\"\")))\n\n } else r.curves<-try(mars.plot(fit,plot.it=F))\n \n if(class(r.curves)!=\"try-error\") {\n out$mods$r.curves <- r.curves\n } else {\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"ERROR: problem fitting response curves\",r.curves)\n }\n\n pred.fct<-pred.mars\n\n assign(\"out\",out,envir=.GlobalEnv)\n\n save.image(paste(output.dir,\"modelWorkspace\",sep=\"\\\\\"))\n t4 <- unclass(Sys.time())\n cat(\"\\nfinished with final model summarization, t=\",round(t4-t3,2),\"sec\\n\");flush.console()\n if(!debug.mode) {sink();cat(\"Progress:80%\\n\");flush.console();sink(logname,append=T)} else cat(\"70%\\n\") \n # Make .tif of predictions #\n\n if(out$input$make.p.tif==T | out$input$make.binary.tif==T){\n if((n.var <- nrow(out$mods$summary))<1){\n mssg <- \"Error producing geotiff output: null model selected by stepwise procedure - pointless to make maps\"\n class(mssg)<-\"try-error\"\n } else {\n cat(\"\\nproducing prediction maps...\",\"\\n\",\"\\n\");flush.console()\n mssg <- try(proc.tiff(model=out$mods$final.mod,vnames=names(out$dat$ma$ma)[-1],\n tif.dir=out$dat$tif.dir$dname,filenames=out$dat$tif.ind,pred.fct=pred.mars,factor.levels=out$dat$ma$factor.levels,make.binary.tif=make.binary.tif,\n thresh=out$mods$auc.output$thresh,make.p.tif=make.p.tif,outfile.p=paste(out$dat$bname,\"_prob_map.tif\",sep=\"\"),\n outfile.bin=paste(out$dat$bname,\"_bin_map.tif\",sep=\"\"),tsize=50.0,NAval=-3000,\n fnames=out$dat$tif.names,logname=logname,out=out)) #\"brt.prob.map.tif\"\n }\n\n if(class(mssg)==\"try-error\"){\n if(!debug.mode) {sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))}\n out$ec<-out$ec+1\n out$error.mssg[[out$ec]] <- paste(\"Error producing prediction maps:\",mssg)\n cat(saveXML(mars.to.xml(out),indent=T),'\\n')\n return()\n } else {\n if(make.p.tif) out$mods$tif.output$prob <- paste(out$dat$bname,\"_prob_map.tif\",sep=\"\")\n if(make.binary.tif) out$mods$tif.output$bin <- paste(out$dat$bname,\"_bin_map.tif\",sep=\"\")\n t5 <- unclass(Sys.time())\n cat(\"\\nfinished with prediction maps, t=\",round(t5-t4,2),\"sec\\n\");flush.console()\n }\n }\n if(!debug.mode) {sink();cat(\"Progress:90%\\n\");flush.console();sink(logname,append=T)} else cat(\"90%\\n\")\n\n # Evaluation Statistics on Test Data#\n\n if(!is.null(out$dat$ma$ma.test)) Eval.Stat<-EvaluationStats(out,thresh=auc.output$thresh,train=out$dat$ma$ma,\n train.pred=mars.predict(fit,out$dat$ma$ma)$prediction[,1],opt.methods)\n\n\n \n # Write summaries to xml #\n if(debug.mode) assign(\"out\",out,envir=.GlobalEnv)\n doc <- mars.to.xml(out)\n \n cat(paste(\"\\ntotal time=\",round((unclass(Sys.time())-t0)/60,2),\"min\\n\\n\\n\",sep=\"\"))\n if(!debug.mode) {\n sink();on.exit();unlink(paste(bname,\"_log.txt\",sep=\"\"))\n cat(\"Progress:100%\\n\");flush.console()\n #cat(saveXML(doc,indent=T),'\\n')\n } else #unlink(outfile)\n capture.output(cat(saveXML(doc,indent=T)),file=paste(out$dat$bname,\"_output.xml\",sep=\"\"))\n if(debug.mode) assign(\"fit\",out$mods$final.mod,envir=.GlobalEnv)\n invisible(out)\n }\n################################################################################\n########### End fit.mars.fct ####################################\n\npred.mars <- function(model,x) {\n # retrieve key items from the global environment #\n # make predictionss.\n y <- rep(NA,nrow(x))\n y[complete.cases(x)] <- as.vector(mars.predict(model,x[complete.cases(x),])$prediction[,1])\n\n#if(sum(is.na(x))/dim(x)[2]!=sum(is.na(y)))\n#h<-is.na(x)\n#h<-apply(h,1,sum)\n#h=h/35\n#f<-is.na(y)\n#which((h-f)!=0,arr.ind=TRUE)\n#b<-cbind(x[which((h-f)!=0,arr.ind=TRUE),],y[which((h-f)!=0,arr.ind=TRUE)])\n\n#which(is.na(y) \n # encode missing values as -1.\n y[is.na(y)]<- NaN\n \n # return predictions.\n return(y)\n }\n\nlogit <- function(x) 1/(1+exp(-x))\n\nfile_path_as_absolute <- function (x){\n if (!file.exists(epath <- path.expand(x))) \n stop(gettextf(\"file '%s' does not exist\", x), domain = NA)\n cwd <- getwd()\n on.exit(setwd(cwd))\n if (file_test(\"-d\", epath)) {\n setwd(epath)\n getwd()\n }\n else {\n setwd(dirname(epath))\n file.path(getwd(), basename(epath))\n }\n}\n#file_path_as_absolute(\".\")\n\nmars.to.xml <- function(out){\n require(XML)\n schema.http=\"http://www.w3.org/2001/XMLSchema-instance\"\n schema.fname=\"file:/Users/isfs2/Desktop/Source/2008-04-09/src/gov/nasa/gsfc/quickmap/ModelBuilder/modelRun_output_v2.xsd\"\n xml.out <- newXMLDoc()\n mr <- newXMLNode(\"modelRunOutput\",doc=xml.out,namespaceDefinitions=c(xsi=schema.http,noNamespaceSchemaLocation=schema.fname))#,parent=xml.out\n sm <- newXMLNode(\"singleModel\",parent=mr)\n bg <- newXMLNode(\"background\",parent=sm)\n newXMLNode(\"mdsName\",out$input$ma.name,parent=bg)\n newXMLNode(\"runDate\",out$input$run.time,parent=bg)\n lc <- newXMLNode(\"layersConsidered\",parent=bg)#, parent = xml.out)\n kids <- lapply(paste(out$dat$tif.dir$dname,\"/\",out$dat$tif.names,sep=\"\"),function(x) newXMLNode(\"layer\", x))\n addChildren(lc, kids)\n mo <- newXMLNode(\"modelOutput\",parent=sm)\n newXMLNode(\"modelType\",out$input$model.type,parent=mo)\n newXMLNode(\"modelSourceFile\",out$input$model.source.file,parent=mo)\n newXMLNode(\"devianceExplained\",out$mods$auc.output$pct_dev_exp,parent=mo,attrs=list(type=\"percentage\"))\n newXMLNode(\"nativeOutput\",paste(out$dat$bname,\"_output.txt\",sep=\"\"),parent=mo)\n newXMLNode(\"binaryOutputFile\",out$mods$tif.output[[2]],parent=mo)\n newXMLNode(\"probOutputFile\",out$mods$tif.output[[1]],parent=mo)\n newXMLNode(\"auc\",out$mods$auc.output$auc,parent=mo)\n newXMLNode(\"rocGraphic\",out$mods$auc.output$plotname,parent=mo)\n newXMLNode(\"rocThresh\",out$mods$auc.output$thresh,parent=mo)\n newXMLNode(\"modelDeviance\",out$mods$auc.output$dev_fit,parent=mo)\n newXMLNode(\"nullDeviance\",out$mods$auc.output$null_dev,parent=mo)\n if(is.null(out$mods$r.curves)) rc.name <- NULL else rc.name <- paste(out$dat$bname,\"_response_curves.xml\",sep=\"\")\n newXMLNode(\"responsePlotsFile\",rc.name,parent=mo)\n newXMLNode(\"significanceDescription\",out$input$sig.test,parent=mo)\n mfp <- newXMLNode(\"modelFitParmas\",parent=mo)\n newXMLNode(\"marsDegree\",out$input$mars.degree,parent=mfp)\n newXMLNode(\"marsPenalty\",out$input$mars.penalty,parent=mfp)\n \n sv <- newXMLNode(\"significantVariables\",parent=mo)\n if(!is.null(out$mods$summary)) {\n t.table <- out$mods$summary#$coefficients\n names(t.table)[3]<-\"significanceMeasurement\"\n for(i in 1:nrow(t.table)){\n x <- newXMLNode(\"sigVar\",parent=sv)\n newXMLNode(name=\"name\", row.names(t.table)[i],parent=x)\n kids <- lapply(1:ncol(t.table),function(j) newXMLNode(name=names(t.table)[j], t.table[i,j]))\n addChildren(x, kids)\n }\n }\n \n if(!is.null(out$mods$r.curves)){\n r.curves <- out$mods$r.curves\n factor.levels <- out$dat$ma$factor.levels\n rc.out <- newXMLDoc()\n root <- newXMLNode(\"responseCurves\",doc=rc.out,namespaceDefinitions=c(xsi=schema.http,noNamespaceSchemaLocation=schema.fname))\n for(i in 1:length(r.curves$names)){\n if(!is.na(f.index<-match(r.curves$names[i],names(factor.levels)))){\n vartype <- \"factor\"} else vartype=\"continuous\"\n x <- newXMLNode(\"responseCurve\",attrs=list(covariate=r.curves$names[i],type=vartype),parent=root)\n kids <- lapply(1:length(r.curves$preds[[i]]),function(j){\n newXMLNode(name=\"responsePt\",parent=x,.children=list(\n newXMLNode(name=\"explanatory\",as.character(r.curves$preds[[i]])[j]),\n newXMLNode(name=\"response\",r.curves$resp[[i]][j])))})\n addChildren(x, kids)\n }\n saveXML(rc.out,rc.name,indent=T)\n \n } \n if(!is.null(out$error.mssg[[1]])) {\n kids <- lapply(out$error.mssg,function(j) newXMLNode(name=\"error\",j))\n addChildren(mo,kids)\n }\n return(xml.out)\n }\n\n\nget.cov.names <- function(model){\n return(attr(terms(formula(model)),\"term.labels\"))\n }\n\n\ncheck.dir <- function(dname){\n if(is.null(dname)) dname <- getwd()\n dname <- gsub(\"[\\\\]\",\"/\",dname)\n end.char <- substr(dname,nchar(dname),nchar(dname))\n if(end.char == \"/\") dname <- substr(dname,1,nchar(dname)-1)\n exist <- suppressWarnings(as.numeric(file.access(dname,mode=0))==0) # -1 if bad, 0 if ok #\n if(exist) dname <- file_path_as_absolute(dname)\n readable <- suppressWarnings(as.numeric(file.access(dname,mode=4))==0) # -1 if bad, 0 if ok #\n writable <- suppressWarnings(as.numeric(file.access(dname,mode=2))==0) # -1 if bad, 0 if ok #\n return(list(dname=dname,exist=exist,readable=readable,writable=writable))\n }\n\n\nget.image.info <- function(image.names){\n # this function creates a data.frame with summary image info for a set of images #\n require(rgdal)\n n.images <- length(image.names)\n\n full.names <- image.names\n out <- data.frame(image=full.names,available=rep(F,n.images),size=rep(NA,n.images),\n type=factor(rep(\"unk\",n.images),levels=c(\"asc\",\"envi\",\"tif\",\"unk\")))\n out$type[grep(\".tif\",image.names)]<-\"tif\"\n out$type[grep(\".asc\",image.names)]<-\"asc\"\n for(i in 1:n.images){\n if(out$type[i]==\"tif\"){\n x <-try(GDAL.open(full.names[1],read.only=T))\n suppressMessages(try(GDAL.close(x)))\n if(class(x)!=\"try-error\") out$available[i]<-T\n x<-try(file.info(full.names[i]))\n } else {\n x<-try(file.info(full.names[i]))\n if(!is.na(x$size)) out$available[i]<-T\n }\n if(out$available[i]==T){\n out$size[i]<-x$size\n if(out$type[i]==\"unk\"){\n # if extension not known, look for envi .hdr file in same directory #\n if(file.access(paste(file_path_sans_ext(full.names[i]),\".hdr\",sep=\"\"))==0) \n out$type[i]<-\"envi\"\n }\n }\n }\n return(out)\n}\n\n###########################################################################################\n# The following functions are from Elith et al. \n###########################################################################################\n\n\"calc.deviance\" <-\nfunction(obs.values, fitted.values, weights = rep(1,length(obs.values)), family=family, calc.mean = TRUE)\n{\n# j. leathwick/j. elith\n#\n# version 2.1 - 5th Sept 2005\n#\n# function to calculate deviance given two vectors of raw and fitted values\n# requires a family argument which is set to binomial by default\n#\n#\n\nif (length(obs.values) != length(fitted.values)) \n stop(\"observations and predictions must be of equal length\")\n\ny_i <- obs.values\n\nu_i <- fitted.values\n \nif (family == \"binomial\" | family == \"bernoulli\") {\n \n deviance.contribs <- (y_i * log(u_i)) + ((1-y_i) * log(1 - u_i))\n deviance <- -2 * sum(deviance.contribs * weights)\n\n}\n\nif (family == \"poisson\" | family == \"Poisson\") {\n\n deviance.contribs <- ifelse(y_i == 0, 0, (y_i * log(y_i/u_i))) - (y_i - u_i)\n deviance <- 2 * sum(deviance.contribs * weights)\n\n}\n\nif (family == \"laplace\") {\n deviance <- sum(abs(y_i - u_i))\n }\n\nif (family == \"gaussian\") {\n deviance <- sum((y_i - u_i) * (y_i - u_i))\n }\n \n\n\nif (calc.mean) deviance <- deviance/length(obs.values)\n\nreturn(deviance)\n\n}\n\n\"calibration\" <-\nfunction(obs, preds, family = family)\n{\n#\n# j elith/j leathwick 17th March 2005\n# calculates calibration statistics for either binomial or count data\n# but the family argument must be specified for the latter \n# a conditional test for the latter will catch most failures to specify\n# the family\n#\n\nif (family == \"bernoulli\") family <- \"binomial\"\npred.range <- max(preds) - min(preds)\nif(pred.range > 1.2 & family == \"binomial\") {\nprint(paste(\"range of response variable is \", round(pred.range, 2)), sep = \"\", quote = F)\nprint(\"check family specification\", quote = F)\nreturn()\n}\nif(family == \"binomial\") {\npred <- preds + 1e-005\npred[pred >= 1] <- 0.99999\nmod <- glm(obs ~ log((pred)/(1 - (pred))), family = binomial)\nlp <- log((pred)/(1 - (pred)))\na0b1 <- glm(obs ~ offset(lp) - 1, family = binomial)\nmiller1 <- 1 - pchisq(a0b1$deviance - mod$deviance, 2)\nab1 <- glm(obs ~ offset(lp), family = binomial)\nmiller2 <- 1 - pchisq(a0b1$deviance - ab1$deviance, 1)\nmiller3 <- 1 - pchisq(ab1$deviance - mod$deviance, 1)\n}\nif(family == \"poisson\") {\nmod <- glm(obs ~ log(preds), family = poisson)\nlp <- log(preds)\na0b1 <- glm(obs ~ offset(lp) - 1, family = poisson)\nmiller1 <- 1 - pchisq(a0b1$deviance - mod$deviance, 2)\nab1 <- glm(obs ~ offset(lp), family = poisson)\nmiller2 <- 1 - pchisq(a0b1$deviance - ab1$deviance, 1)\nmiller3 <- 1 - pchisq(ab1$deviance - mod$deviance, 1)\n}\ncalibration.result <- c(mod$coef, miller1, miller2, miller3)\nnames(calibration.result) <- c(\"intercept\", \"slope\", \"testa0b1\", \"testa0|b1\", \"testb1|a\")\nreturn(calibration.result)\n}\n\n\"mars.contribs\" <-\nfunction (mars.glm.object,sp.no = 1, verbose = TRUE) \n{\n\n# j leathwick/j elith August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# takes a mars/glm model and uses the updated mars export table\n# stored as the second list item from mars.binomial\n# assessing the contribution of the fitted functions, \n# amalgamating terms for variables as required\n#\n# amended 29/9/04 to pass original mars model details\n# and to return f-statistics\n#\n# amended 7th January to accommodate any glm model family\n#\n# modified 050609 by aks to output a numeric deviance table #\n\n mars.detail <- mars.glm.object$mars.call\n pred.names <- mars.detail$predictor.base.names #get the names from the original data\n n.preds <- length(pred.names)\n\n spp.names <- mars.detail$y.names\n family <- mars.detail$family\n\n m.table <- mars.glm.object$mars.table[-1,]\n m.table$names1 <- as.character(m.table$names1) #convert from a factor\n\n x.data <- as.data.frame(eval(mars.glm.object$basis.functions))\n y.data <- as.data.frame(eval(mars.glm.object$y.values))\n\n assign(\"x.data\", x.data, pos = 1)\n assign(\"y.data\", y.data, pos = 1)\n assign(\"sp.no\", sp.no, pos = 1)\n\n glm.model <- glm(y.data[,sp.no] ~ .,data = x.data,family = family)\n\n print(paste(\"performing backwards drops for mars/glm model for\",\n spp.names[sp.no]),quote=F)\n\n n.bfs <- length(m.table[,1])\n \n delta.deviance <- rep(0,n.preds)\n df <- rep(0,n.preds)\n signif <- rep(0,n.preds)\n\n for (i in 1:n.preds) { #start at two because first line is the constant\n\n # look for variable names in the table matching those in the var list\n\n var.nos <- grep(as.character(pred.names[i]),m.table$names1)\n\n #drop.list stores numbers of basis functions to be dropped\n if (length(var.nos) > 0) {\n drop.list <- 0 - var.nos\n x.data.new <- x.data[,drop.list]\n assign(\"x.data.new\",x.data.new,pos=1)\n\n if (verbose) {\n \t print(paste(\"Dropping \",pred.names[i],\"...\",sep=\"\"),\n\t quote=FALSE)\n }\n x.data.new<-as.data.frame(x.data.new)\n if(dim(x.data.new)[2]==0){\n new.model <- glm(y.data[,sp.no] ~ 1, family = family)\n }else new.model <- glm(y.data[,sp.no] ~ ., data=x.data.new, family = family)\n \n comparison <- anova(glm.model,new.model,test=\"Chisq\")\n\n df[i] <- comparison[2,3]\n delta.deviance[i] <- zapsmall(comparison[2,4],4)\n signif[i] <- zapsmall(comparison[2,5],6)\n }\n \n }\n\n rm(x.data,y.data,sp.no,pos=1) # tidy up temporary files \n\n #deviance.table <- as.data.frame(cbind(pred.names,delta.deviance,df,signif))\n #names(deviance.table) <- c(\"variable\",\"delta_dev\",\"deg. free.\",\"p-value\")\n deviance.table <- data.frame(variable=pred.names,delta_dev=delta.deviance,df=df,p_value=signif)#aks\n return(list(mars.call=mars.detail,deviance.table=deviance.table))\n}\n\n\"mars.cv\" <-\nfunction (mars.glm.object, nk = 10, sp.no = 1, prev.stratify = F) \n{\n#\n# j. leathwick/j. elith - August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# function to perform k-fold cross validation \n# with full model perturbation for each subset\n#\n# requires mda library from Cran\n# requires functions mw and calibration\n#\n# takes a mars/glm object produced by mars.glm\n# and first assesses the full model, and then \n# randomly subsets the dataset into nk folds and drops \n# each subset in turn, fitting on remaining data \n# and predicting for withheld data\n#\n# caters for both single species and community models via the argument sp.no\n# for the first, sp.no can be left on its default of 1\n# for community models, sp.no can be varied from 1 to n.spp\n#\n# modified 29/9/04 to \n# 1. return mars analysis details for audit trail\n# 2. calculate roc and calibration on subsets as well as full data\n# returning the mean and se of the ROC scores \n# and the mean calibration statistics\n#\n# modified 8/10/04 to add prevalence stratification\n# modified 7th January to test for binomial family and return if not\n# \n# updated 15th March to cater for both binomial and poisson families\n#\n# updated 16th June 2005 to calculate residual deviance\n#\n\n data <- mars.glm.object$mars.call$dataframe #get the dataframe name\n dataframe.name <- deparse(substitute(data)) \n \n data <- as.data.frame(eval(parse(text=data))) #and now the data\n n.cases <- nrow(data)\n\n mars.call <- mars.glm.object$mars.call #and the mars call details\n mars.x <- mars.call$mars.x \n mars.y <- mars.call$mars.y\n mars.degree <- mars.call$degree\n mars.penalty <- mars.call$penalty\n family <- mars.call$family\n site.weights <- eval(mars.glm.object$weights$site.weights)\n\n n.spp <- length(mars.y)\n\n if (sp.no > n.spp) {\n print(paste(\"the value specified for sp.no of\",sp.no),quote=F)\n print(paste(\"exceeds the total number of species, which is \",n.spp),quote=F)\n return()\n }\n \n xdat <- as.data.frame(data[,mars.x])\n xdat <- mars.new.dataframe(xdat)[[1]]\n ydat <- mars.glm.object$y.values[,sp.no]\n target.sp <- names(data)[mars.y[sp.no]]\n\n if (prev.stratify) {\n presence.mask <- ydat == 1\n absence.mask <- ydat == 0\n n.pres <- sum(presence.mask)\n n.abs <- sum(absence.mask)\n }\n\n print(paste(\"Calculating ROC and calibration from full model for\",target.sp),quote=F)\n\n u_i <- mars.glm.object$fitted.values[,sp.no]\n y_i <- ydat\n\n if (family == \"binomial\") {\n full.resid.deviance <- calc.deviance(y_i,u_i, weights = site.weights, family=\"binomial\") \n full.test <- roc(y_i, u_i)\n full.calib <- calibration(y_i, u_i)\n }\n\n if (family==\"poisson\") {\n full.resid.deviance <- calc.deviance(y_i,u_i, weights = site.weights, family=\"poisson\")\n full.test <- cor(y_i, u_i) \n full.calib <- calibration(y_i, u_i, family = \"poisson\")\n }\n\n# set up for results storage\n \n subset.test <- rep(0,nk)\n subset.calib <- as.data.frame(matrix(0,ncol=5,nrow=nk))\n names(subset.calib) <- c(\"intercept\",\"slope\",\"test1\",\"test2\",\"test3\")\n subset.resid.deviance <- rep(0,nk)\n\n# now setup for withholding random subsets\n \n pred.values <- rep(0, n.cases)\n fitted.values <- rep(0, n.cases)\n\n if (prev.stratify) {\n\n selector <- rep(0,n.cases)\n\n#create a vector of randomised numbers and feed into presences\n\n temp <- rep(seq(1, nk, by = 1), length = n.pres)\n temp <- temp[order(runif(n.pres, 1, 100))]\n selector[presence.mask] <- temp\n\n# and then do the same for absences\n\n temp <- rep(seq(1, nk, by = 1), length = n.abs)\n temp <- temp[order(runif(n.abs, 1, 100))]\n selector[absence.mask] <- temp\n\n }\n else { #otherwise make them random with respect to presence/absence\n\n selector <- rep(seq(1, nk, by = 1), length = n.cases)\n selector <- selector[order(runif(n.cases, 1, 100))]\n }\n \n print(\"\", quote = FALSE)\n print(\"Creating predictions for subsets...\", quote = F)\n\n for (i in 1:nk) {\n cat(i,\" \")\n model.mask <- selector != i #used to fit model on majority of data\n pred.mask <- selector == i #used to identify the with-held subset\n assign(\"species.subset\", ydat[model.mask], pos = 1)\n assign(\"predictor.subset\", xdat[model.mask, ], pos = 1)\n\n # fit new mars model\n\n mars.object <- mars(y = species.subset, x = predictor.subset, \n degree = mars.degree, penalty = mars.penalty)\n\n # and extract basis functions\n\n n.bfs <- length(mars.object$selected.terms)\n bf.data <- as.data.frame(mars.object$x)\n names(bf.data) <- paste(\"bf\",1:n.bfs,sep=\"\")\n assign(\"bf.data\", bf.data, pos=1)\n\n # then fit a binomial model to them\n\n mars.binomial <- glm(species.subset ~ .,data=bf.data[,-1], family= family, maxit = 100)\n\n pred.basis.functions <- as.data.frame(mda:::model.matrix.mars(mars.object, \n xdat[pred.mask, ]))\n\n #now name the bfs to match the approach used in mars.binomial\n\n names(pred.basis.functions) <- paste(\"bf\",1:n.bfs,sep=\"\")\n\n # and form predictions for them and evaluate performance\n\n fitted.values[pred.mask] <- predict(mars.binomial, \n pred.basis.functions, type = \"response\")\n\n y_i <- ydat[pred.mask]\n u_i <- fitted.values[pred.mask] \n weights.subset <- site.weights[pred.mask]\n\n if (family == \"binomial\") {\n subset.resid.deviance[i] <- calc.deviance(y_i,u_i,weights = weights.subset, family=\"binomial\") \n subset.test[i] <- roc(y_i,u_i)\n subset.calib[i,] <- calibration(y_i, u_i)\n }\n\n if (family==\"poisson\"){\n subset.resid.deviance[i] <- calc.deviance(y_i,u_i,weights = weights.subset, family=\"poisson\") \n subset.test[i] <- cor(y_i, u_i) \n subset.calib[i,] <- calibration(y_i, u_i, family = family)\n }\n }\n \n cat(\"\",\"\\n\")\n\n# tidy up temporary files\n\n rm(species.subset,predictor.subset,bf.data,pos=1) \n\n# and assemble results for return\n\n# mars.detail <- list(dataframe = dataframe.name,\n# x = mars.x, x.names = names(xdat), \n# y = mars.y, y.names = names(data)[mars.y], \n# target.sp = target.sp, degree=mars.degree, penalty = mars.penalty, family = family)\n\n y_i <- ydat\n u_i <- fitted.values\n\n if (family==\"binomial\") {\n cv.resid.deviance <- calc.deviance(y_i,u_i,weights = site.weights, family=\"binomial\") \n cv.test <- roc(y_i, u_i)\n cv.calib <- calibration(y_i, u_i)\n }\n\n if (family==\"poisson\"){\n cv.resid.deviance <- calc.deviance(y_i,u_i,weights = site.weights, family=\"poisson\") \n cv.test <- cor(y_i, u_i) \n cv.calib <- calibration(y_i, u_i, family = \"poisson\")\n }\n\n subset.test.mean <- mean(subset.test)\n subset.test.se <- sqrt(var(subset.test))/sqrt(nk)\n\n subset.test <- list(test.scores = subset.test, subset.test.mean = subset.test.mean, \n subset.test.se = subset.test.se)\n\n subset.calib.mean <- apply(subset.calib[,c(1:2)],2,mean)\n names(subset.calib.mean) <- names(subset.calib)[c(1:2)] #mean only of parameters\n\n subset.calib <- list(subset.calib = subset.calib, \n subset.calib.mean = subset.calib.mean)\n \n subset.deviance.mean <- mean(subset.resid.deviance)\n subset.deviance.se <- sqrt(var(subset.resid.deviance))/sqrt(nk)\n\n subset.deviance <- list(subset.deviances = subset.resid.deviance, subset.deviance.mean = subset.deviance.mean,\n subset.deviance.se = subset.deviance.se)\n\n return(list(mars.call = mars.call, full.resid.deviance = full.resid.deviance,\n full.test = full.test, full.calib = full.calib, pooled.deviance = cv.resid.deviance, pooled.test = cv.test, \n pooled.calib = cv.calib,subset.deviance = subset.deviance, subset.test = subset.test, subset.calib = subset.calib))\n}\n\n\"mars.export\" <-\nfunction (object,lineage) \n{\n#\n# j leathwick/j elith August 2006\n#\n# takes a mars model fitted using library mda\n# and extracts the basis functions and their \n# coefficients, returning them as a table\n# caters for models with degree up to 2\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n\n which <- object$selected.terms\n nterms <- length(which)\n nspp <- ncol(eval(object$call$y))\n dir <- object$factor\n cut <- object$cuts\n var.names <- dimnames(object$factor)[[2]]\n p <- length(var.names)\n coefs <- as.data.frame(object$coefficients)\n names(coefs) <- names(eval(object$call$y))\n\n# setup storage for results\n\n names1 <- rep(\"null\", length = nterms)\n types1 <- rep(\"null\", length = nterms)\n levels1 <- rep(\"null\", length = nterms)\n signs1 <- rep(0, length = nterms)\n cuts1 <- rep(0, length = nterms)\n\n names2 <- rep(\"null\", length = nterms)\n types2 <- rep(\"null\", length = nterms)\n levels2 <- rep(\"null\", length = nterms)\n signs2 <- rep(0, length = nterms)\n cuts2 <- rep(0, length = nterms)\n names1[1] <- \"constant\"\n signs1[1] <- 1\n\n# now cycle through the terms\nif(nterms>1){\n for (i in seq(2, nterms)) {\n j <- which[i]\n term.count = 1\n for (k in 1:p) {\n if (dir[j, k] != 0) {\n if (term.count == 1) {\n n <- match(var.names[k],lineage$full.name)\n names1[i] <- lineage$base.name[n] #var.names[k]\n types1[i] <- lineage$type[n]\n levels1[i] <- lineage$level[n]\n signs1[i] <- dir[j, k]\n cuts1[i] <- cut[j, k]\n term.count <- term.count + 1\n }\n else {\n names2[i] <- var.names[k]\n n <- match(var.names[k],lineage$full.name)\n names2[i] <- lineage$base.name[n] #var.names[k]\n types2[i] <- lineage$type[n]\n levels2[i] <- lineage$level[n]\n signs2[i] <- dir[j, k]\n cuts2[i] <- cut[j, k]\n }\n }\n }\n }\n }\n mars.export.table <- data.frame(names1, types1, levels1, signs1, cuts1, \n names2, types2, levels2, signs2, cuts2, coefs)\n\n return(mars.export.table)\n}\n\n\"mars.glm\" <-\nfunction (data, # the input data frame\n mars.x, # column numbers of the predictors\n mars.y, # column number(s) of the response variable(s)\n mars.degree = 1, # level of interactions - 1 = zero, 2 = 1st order, etc\n site.weights = rep(1, nrow(data)), # one weight per site\n spp.weights = rep(1,length(mars.y)), # one wieght per species\n penalty = 2, # the default penaly for a mars model\n family =family) # the family for the glm model\n{\n#\n# j leathwick, j elith - August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# calculates a mars/glm object in which basis functions are calculated\n# using an initial mars model with single or multiple responses \n# data for individual species are then fitted as glms using the \n# common set of mars basis functions with results returned as a list\n#\n# takes as input a dataset and args selecting x and y variables, and degree of interaction\n# along with site and species weights, the CV penalty, and the glm family argument\n# the latter would normally be one of \"binomial\" or \"poisson\" - \"gaussian\" could be used\n# but in this case the model shouldn't differ from that fitted using mars on its own\n#\n# requires mda and leathwick/elith's mars.export\n#\n# modified 3/11/04 to store information on glm phase convergence\n# and with number of iterations raised to 100 to encourage convergence for low prevalence species\n# modified 4/11/04 to accommodate observation weights in both mars and glm steps\n# modified 12/04 to accommodate non-binomial families\n# modified 11/05 to accommodate factor variables\n# these are done as 0/1 dummy variables in a new dataframe\n# created using mars.new.dataframe\n\n require(mda)\n\n n.spp <- length(mars.y)\n\n# setup input data and assign to position one\n\n dataframe.name <- deparse(substitute(data)) # get the dataframe name\n\n xdat <- as.data.frame(eval(data[, mars.x])) #form the temporary datasets\n predictor.base.names <- names(xdat)\n\n# create the new dataframe with dummy vars for factor predictors\n xdat <- mars.new.dataframe(xdat)\n lineage <- xdat[[2]] # tracks which variables have had dummy's created\n xdat <- xdat[[1]]\n predictor.dummy.names <- names(xdat)\n\n ydat <- as.data.frame(eval(data[, mars.y]))\n names(ydat) <- names(data)[mars.y]\n\n assign(\"xdat\", xdat, pos = 1) #and assign them for later use\n assign(\"ydat\", ydat, pos = 1)\n\n# create storage space for glm model results\n\n n.cases <- nrow(xdat)\n\n fitted.values <- matrix(0,ncol = n.spp, nrow = n.cases)\n model.residuals <- matrix(0,ncol = n.spp, nrow = n.cases)\n null.deviances <- rep(0,n.spp)\n residual.deviances <- rep(0,n.spp)\n null.dfs <- rep(0,n.spp)\n residual.dfs <- rep(0,n.spp)\n converged <- rep(TRUE,n.spp)\n\n# fit the mars model and extract the basis functions\n\n cat(\"fitting initial mars model for\",n.spp,\"responses\",\"\\n\")\n cat(\"followed by a glm model with a family of\",family,\"\\n\")\n\n mars.object <- mars(x = xdat, y = ydat, degree = mars.degree, w = site.weights, \n wp = spp.weights, penalty = penalty)\n if(length(mars.object$coefficients)==1) stop(\"MARS has fit the null model (intercept only) \\n new predictors are required\")\n bf.data <- as.data.frame(eval(mars.object$x))\n n.bfs <- ncol(bf.data)\n bf.names <- paste(\"bf\", 1:n.bfs, sep = \"\")\n names(bf.data) <- bf.names\n bf.data <- as.data.frame(bf.data[,-1])\n\n m.table <- as.data.frame(mars.export(mars.object,lineage))\n names(m.table)[(10 + 1):(10 + n.spp)] <- names(ydat)\n\n p.values <- matrix(0, ncol = n.spp, nrow = n.bfs)\n rownames(p.values) <- paste(\"bf\", 1:n.bfs, sep = \"\")\n colnames(p.values) <- names(ydat)\n\n# now cycle through the species fitting glm models \n\n cat(\"fitting glms for individual responses\",\"\\n\")\n\n for (i in 1:n.spp) {\n\n cat(names(ydat)[i],\"\\n\")\n model.glm <- glm(ydat[, i] ~ ., data = bf.data, weights = site.weights, \n \t family = family, maxit = 100)\n\n# update the coefficients and other results\n\n # then match names and insert as appropriate\n m.table[ , i + 10] <- 0 \t\t\t\t\t# set all values to zero\n m.table[ , i + 10] <- model.glm$coefficients \t # update all the constant\n sum.table <- summary(model.glm)$coefficients\n p.values[,i] <- sum.table[,4]\n fitted.values[,i] <- model.glm$fitted\n model.residuals[,i] <- resid(model.glm)\n null.deviances[i] <- model.glm$null.deviance\n residual.deviances[i] <- model.glm$deviance\n null.dfs[i] <- model.glm$df.null\n residual.dfs[i] <- model.glm$df.residual\n converged[i] <- model.glm$converged\n }\n\n# now assemble data to be returned\n\n fitted.values <- as.data.frame(fitted.values)\n names(fitted.values) <- names(ydat)\n\n model.residuals <- as.data.frame(model.residuals)\n names(model.residuals) <- names(ydat)\n\n deviances <- data.frame(names(ydat),null.deviances,null.dfs,residual.deviances,residual.dfs,converged)\n names(deviances) <- c(\"species\",\"null.dev\",\"null.df\",\"resid.dev\",\"resid.df\",\"converged\")\n\n weights = list(site.weights = site.weights, spp.weights = spp.weights)\n\n mars.detail <- list(dataframe = dataframe.name, mars.x = mars.x, \n predictor.base.names = predictor.base.names, predictor.dummy.names = predictor.dummy.names, \n mars.y = mars.y, y.names = names(ydat), degree=mars.degree, penalty = penalty, \n family = family)\n\n rm(xdat,ydat,pos=1) #finally, clean up the temporary dataframes\n\n return(list(mars.table = m.table, basis.functions = bf.data, y.values = ydat,\n fitted.values = fitted.values, residuals = model.residuals, weights = weights, deviances = deviances,\n p.values = p.values, mars.call = mars.detail,mars.object=mars.object))\n}\n\n\"mars.new.dataframe\" <-\nfunction (input.data) \n{\n#\n# j leathwick, j elith - August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# takes an input data frame and checks for factor variables \n# converting these to dummy variables, one each for each factor level\n# returning it for use with mars.glm so that factor vars can be included\n# in a mars analysis\n#\n\n if (!is.data.frame(input.data)) {\n print(\"input data must be a dataframe..\",quote = FALSE)\n return()\n }\n\n n <- 1\n for (i in 1:ncol(input.data)) { #first transfer the vector variables\n if (is.vector(input.data[,i])) {\n if (n == 1) {\n output.data <- as.data.frame(input.data[,i]) \n names.list <- names(input.data)[i]\n var.type <- \"vector\"\n factor.level <- \"na\"\n }\n else {\n output.data[,n] <- input.data[,i]\n names.list <- c(names.list,names(input.data)[i])\n var.type <- c(var.type,\"vector\")\n factor.level <- c(factor.level,\"na\")\n }\n names(output.data)[n] <- names(input.data)[i]\n n <- n + 1\n }\n }\n\n for (i in 1:ncol(input.data)) { # and then the factor variables\n if (is.factor(input.data[,i])) {\n temp.table <- summary(input.data[,i])\n for (j in 1:length(temp.table)) {\n names.list <- c(names.list,names(input.data)[i])\n var.type <- c(var.type,\"factor\")\n factor.level <- c(factor.level,names(temp.table)[j])\n output.data[,n] <- ifelse(input.data[,i] == names(temp.table)[j],1,0)\n names(output.data)[n] <- paste(names(input.data)[i],\".\",names(temp.table)[j],sep=\"\")\n n <- n + 1\n }\n }\n }\n\n lineage <- data.frame(names(output.data),names.list,var.type,factor.level)\n for (i in 1:4) lineage[,i] <- as.character(lineage[,i])\n names(lineage) <- c(\"full.name\",\"base.name\",\"type\",\"level\")\n \n return(list(dataframe = output.data, lineage = lineage))\n}\n\n\"mars.plot\" <-\nfunction (mars.glm.object, #the input mars object\n sp.no = 0, # the species number for multi-response models\n plot.rug=T, # plot a rug of deciles\n plot.layout = c(3,4), # the plot layout to use\n file.name = NA, # giving a file name will send results to a pdf\n plot.it=T) # option for making curves but no plots (aks)\n{\n\n# j leathwick/j elith August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# requires mars.export of leathwick/elith\n# \n# takes a mars or mars/glm model and either \n# creates a mars export table (vanilla mars object)\n# and works from this or uses the updated mars export table\n# stored as the first list item from mars.binomial\n# plotting out the fitted functions, amalgamating terms\n# for variables and naming the pages as required\n#\n# caters for multispecies mars models by successively plotting\n# all species unless a value other than zero is given for sp.no\n#\n# modified by aks 050609 to only open one plot window and use the record=T option.\n\n max.plots <- plot.layout[1] * plot.layout[2]\n if(plot.it){ #aks\n if (is.na(file.name)) {\n use.windows = TRUE \n windows(width = 11, height = 8, record=T) #AKS\n par(mfrow = plot.layout) #AKS\n }\n else {\n use.windows = FALSE\n pdf(file=file.name,width = 11, height=8)\n par(mfrow = plot.layout) #AKS\n }\n } else use.windows = FALSE\n\n if (class(mars.glm.object) == \"mars\") { #then we have a mars object\n mars.binomial = FALSE\n model <- mars.glm.object\n xdat <- eval(model$call$x)\n Y <- as.data.frame(eval(model$call$y))\n n.env <- ncol(xdat)\n m.table <- mars.export(mars.glm.object)\n }\n else {\n mars.binomial = TRUE\n\n dat <- mars.glm.object$mars.call$dataframe\n mars.x <- mars.glm.object$mars.call$mars.x\n xdat <- as.data.frame(eval(parse(text=dat)))\n xdat <- xdat[,mars.x]\n\n m.table <- mars.glm.object[[1]]\n\n }\n\n n.bfs <- length(m.table[,1])\n n.spp <- length(m.table[1,]) - 10\n r.curves <- list(names=NULL,preds=list(),resp=list()) #aks\n spp.names <- names(m.table)[(10+1):(10+n.spp)]\n\n if (sp.no == 0) {\n wanted.species <- seq(1:n.spp) \n }\n else {\n wanted.species <- sp.no\n }\n\n xrange <- matrix(0,nrow = 2,ncol = ncol(xdat))\n factor.filter <- rep(FALSE,ncol(xdat))\n for (i in 1:ncol(xdat)) factor.filter[i] <- is.vector(xdat[,i])\n\n if(sum(factor.filter>1)) {\n xrange[,factor.filter] <- sapply(xdat[,factor.filter], range)\n } else xrange[,factor.filter]<-range(xdat[,factor.filter])\n \n for (i in wanted.species) {\n n.pages <- 1\n plotit <- rep(TRUE, n.bfs)\n print(paste(\"plotting responses for \",spp.names[i]),quote=F)\n nplots <- 0\n cntr <- 1 #aks\n for (j in 2:n.bfs) {\n if (m.table$names2[j] == \"null\") {\n if (plotit[j]) {\n varno <- pmatch(as.character(m.table$names1[j]), \n names(xdat))\n if (factor.filter[varno]) {\n Xi <- seq(xrange[1, varno], xrange[2, varno], \n length = 100)\n bf <- pmax(0, m.table$signs1[j] * (Xi - m.table$cuts1[j]))\n bf <- bf * m.table[j, i + 10]\n bf <- bf - mean(bf)\n }\n else {\n factor.table <- as.data.frame(table(xdat[,varno]))\n names(factor.table) <- c(\"levels\",\"coefficients\")\n factor.table$coefficients <- 0\n level.no <- match(m.table$levels1[j],factor.table$levels)\n factor.table$coefficients[level.no] <- m.table[j, i + 10]\n }\n if (j < n.bfs) {\n for (k in ((j + 1):n.bfs)) {\n if (m.table$names1[j] == m.table$names1[k] & m.table$names2[k] == \"null\") {\n if (factor.filter[varno]) {\n bf.add <- pmax(0, m.table$signs1[k] * \n (Xi - m.table$cuts1[k]))\n bf.add <- bf.add * m.table[k, i + 10]\n bf <- bf + bf.add\n }\n else {\n level.no <- match(m.table$levels1[k],factor.table$levels)\n factor.table$coefficients[level.no] <- m.table[k, i + 10]\n }\n plotit[k] <- FALSE\n }\n }\n }\n #if (nplots == 0) { #AKS\n# if (use.windows) windows(width = 11, height = 8)\n# par(mfrow = plot.layout)\n# }\n if (factor.filter[varno]) {\n if(plot.it) plot(Xi, bf, type = \"l\", xlab = names(xdat)[varno], ylab = \"response\") #aks\n if (plot.rug & plot.it) rug(quantile(xdat[,varno], probs = seq(0, 1, 0.1), na.rm = FALSE))\n r.curves$preds[[cntr]] <- Xi #aks\n r.curves$resp[[cntr]] <- bf #aks\n }\n else {\n if(plot.it) plot(factor.table$levels, factor.table$coefficients, xlab = names(xdat)[varno]) #aks\n r.curves$preds[[cntr]] <- factor.table$levels #aks\n r.curves$resp[[cntr]] <- factor.table$coefficients #aks\n \n }\n r.curves$names <- c(r.curves$names,names(xdat)[varno]) #aks\n cntr <- cntr + 1 #aks\n nplots = nplots + 1\n plotit[j] <- FALSE\n }\n }\n else { # case where there is an interaction #\n if (plotit[j]) {\n varno1 <- pmatch(as.character(m.table$names1[j]), names(xdat))\n X1 <- seq(xrange[1, varno1], xrange[2, varno1], length = 20)\n bf1 <- pmax(0, m.table$signs1[j] * (X1 - m.table$cuts1[j]))\n varno2 <- pmatch(as.character(m.table$names2[j]), names(xdat))\n X2 <- seq(xrange[1, varno2], xrange[2, varno2], length = 20)\n bf2 <- pmax(0, m.table$signs2[j] * (X2 - m.table$cuts2[j]))\n if(factor.filter[varno1] & factor.filter[varno2]){ #aks\n zmat <- bf1 %o% bf2\n zmat <- zmat * m.table[j, i + 10]\n if (j < n.bfs) {\n for (k in ((j + 1):n.bfs)) {\n if (m.table$names1[j] == m.table$names1[k] & m.table$names2[j] == m.table$names2[k]) {\n bf1 <- pmax(0, m.table$signs1[k] * (X1 - m.table$cuts1[k]))\n bf2 <- pmax(0, m.table$signs2[j] * (X2 - m.table$cuts2[j]))\n zmat2 <- bf1 %o% bf2\n zmat2 <- zmat2 * m.table[j, i + 10]\n zmat = zmat + zmat2\n plotit[k] <- FALSE\n }\n }\n }\n #if (nplots == 0) { #AKS\n # if (use.windows) windows(width = 11, height = 8)\n # par(mfrow = plot.layout)\n # }\n if(plot.it){\n persp(x = X1, y = X2, z = zmat, xlab = names(xdat)[varno1], \n ylab = names(xdat)[varno2], theta = 45, phi = 25) }\n r.curves$preds[[cntr]] <- X1 #aks\n r.curves$resp[[cntr]] <- apply(zmat,1,mean,na.rm=T) #aks\n nplots = nplots + 1\n } else {\n r.curves$preds[[cntr]] <- NA #aks\n r.curves$resp[[cntr]] <- NA #aks\n \n }\n r.curves$names <- c(r.curves$names,names(xdat)[varno]) #aks\n cntr <- cntr + 1 #aks\n }\n }\n if (nplots == 1 & plot.it) {\n title(paste(spp.names[i], \" - page \", n.pages, sep = \"\"))\n }\n if (nplots == max.plots) {\n nplots = 0\n n.pages <- n.pages + 1\n }\n }\n }\n if (!use.windows & plot.it) dev.off()\n invisible(r.curves) #aks\n}\n\n\"mars.plot.fits\" <-\nfunction(mars.glm.object, # the input mars object\n sp.no = 0, # allows selection of individual spp for multiresponse models\n mask.presence = FALSE, # plots out just presence records\n use.factor = FALSE, # draws plots as factors for faster printing\n plot.layout = c(4,2), # the default plot layout\n file.name = NA) # allows plotting to a pdf file\n{\n#\n# j leathwick, j elith - August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# to plot distribution of fitted values in relation to ydat from mars or other p/a models\n# allows masking out of absences to enable focus on sites with high predicted values\n# fitted values = those from model; raw.values = original y values\n# label = text species name; ydat = predictor dataset\n# mask.presence forces function to only plot fitted values for presences\n# use.factor forces to use quicker printing box and whisker plot\n# file.name routes to a pdf file of this name\n#\n\n max.plots <- plot.layout[1] * plot.layout[2]\n\n if (is.na(file.name)) { #setup for windows or file output\n use.windows = TRUE \n }\n else {\n pdf(file.name, width=8, height = 11)\n par(mfrow = plot.layout)\n par(cex = 0.5)\n use.windows = FALSE\n }\n\n dat <- mars.glm.object$mars.call$dataframe #get the dataframe name\n dat <- as.data.frame(eval(parse(text=dat))) #and now the data\n\n n.cases <- nrow(dat)\n\n mars.call <- mars.glm.object$mars.call\t#and the mars call details\n mars.x <- mars.call$mars.x \n mars.y <- mars.call$mars.y\n family <- mars.call$family\n\n xdat <- as.data.frame(dat[,mars.x])\n ydat <- as.data.frame(dat[,mars.y])\n\n n.spp <- ncol(ydat)\n n.preds <- ncol(xdat)\n\n fitted.values <- mars.glm.object$fitted.values\n\n pred.names <- names(dat)[mars.x]\n spp.names <- names(dat)[mars.y]\n\n if (sp.no == 0) {\n wanted.species <- seq(1:n.spp) \n }\n else {\n wanted.species <- sp.no\n }\n\n for (i in wanted.species) {\n\n if (mask.presence) {\n\tmask <- ydat[,i] == 1 }\n else {\n mask <- rep(TRUE, length = n.cases) \n }\n\n robust.max.fit <- approx(ppoints(fitted.values[mask,i]), sort(fitted.values[mask,i]), 0.99) #find 99%ile value\n nplots <- 0\n\n for (j in 1:n.preds) {\n if (use.windows & nplots == 0) {\n windows(width = 8, height = 11)\n par(mfrow = plot.layout)\n par(cex = 0.5)\n }\n\tnplots <- nplots + 1 \n if (is.vector(xdat[,j])) wt.mean <- mean((xdat[mask, j] * fitted.values[mask, i]^5)/mean(fitted.values[mask, i]^5))\n else wt.mean <- 0\n\tif (use.factor) {\n\ttemp <- factor(cut(xdat[mask, j], breaks = 12))\n\tif (family == \"binomial\") {\n\t plot(temp, fitted.values[mask,i], xlab = pred.names[j], ylab = \"fitted values\", ylim = c(0, 1))\n }\n\telse {\n\t plot(temp, fitted.values[mask,i], xlab = pred.names[j], ylab = \"fitted values\")}\n\t}\n\telse {\n\t if (family == \"binomial\") {\n\t plot(xdat[mask, j], fitted.values[mask,i], xlab = pred.names[j], ylab = \"fitted values\", \n\t\t\t\t\tylim = c(0, 1))\n }\n\t else {\n plot(xdat[mask, j], fitted.values[mask,i], xlab = pred.names[j], ylab = \"fitted values\")\n }\n\t}\n\tabline(h = (0.333 * robust.max.fit$y), lty = 2.)\n\tif (nplots == 1) { \n \t title(paste(spp.names[i], \", wtm = \", zapsmall(wt.mean, 4.)))}\n\telse {\n\t title(paste(\"wtm = \", zapsmall(wt.mean, 4.)))}\n\t nplots <- ifelse(nplots == max.plots, 0, nplots)\n\t}\n }\n if (!use.windows) dev.off()\n}\n\n\"mars.predict\" <-\nfunction (mars.glm.object,new.data) \n{\n#\n# j leathwick, j elith - August 2006\n#\n# version 3.1 - developed in R 2.3.1 using mda 0.3-1\n#\n# calculates a mars/glm object in which basis functions are calculated\n# using an initial mars model with single or multiple responses \n# data for individual species are then fitted as glms using the \n# common set of mars basis functions with results returned as a list\n#\n# takes as input a dataset and args selecting x and y variables, and degree of interaction\n# along with site and species weights, the CV penalty, and the glm family argument\n# the latter would normally be one of \"binomial\" or \"poisson\" - \"gaussian\" could be used\n# but in this case the model shouldn't differ from that fitted using mars on its own\n#\n# naming problem for dataframes fixed - je - 15/12/06\n#\n# requires mda and leathwick/elith's mars.export\n#\n require(mda)\n\n# first recreate both the original mars model and the glm model\n\n# setup input data and create original temporary data\n\n dataframe.name <- mars.glm.object$mars.call$dataframe # get the dataframe name\n mars.x <- mars.glm.object$mars.call$mars.x\n mars.y <- mars.glm.object$mars.call$mars.y\n n.spp <- length(mars.y)\n family <- mars.glm.object$mars.call$family\n mars.degree <- mars.glm.object$mars.call$degree\n penalty <- mars.glm.object$mars.call$penalty\n site.weights <- mars.glm.object$weights[[1]]\n spp.weights <- mars.glm.object$weights[[2]]\n\n print(\"creating original data frame...\",quote=FALSE)\n\n base.data <- as.data.frame(eval.parent(parse(text = dataframe.name))) #aks\n\n x.temp <- eval(base.data[, mars.x]) #form the temporary datasets\n base.names <- names(x.temp)\n\n xdat <- mars.new.dataframe(x.temp)[[1]]\n \n ydat <- as.data.frame(base.data[, mars.y])\n names(ydat) <- names(base.data)[mars.y]\n\n assign(\"xdat\", xdat, pos = 1) #and assign them for later use\n assign(\"ydat\", ydat, pos = 1)\n\n# now create the temporary dataframe for the new data\n\n print(\"checking variable matching with new data\",quote = FALSE)\n\n new.names <- names(new.data)\n\n for (i in 1:length(base.names)) {\n\n name <- base.names[i]\n \n if (!(name %in% new.names)) {\n print(paste(\"Variable \",name,\" missing from new data\",sep=\"\"),quote = FALSE) #aks\n return()\n }\n }\n\n print(\"and creating temporary dataframe for new data...\",quote=FALSE)\n\n selector <- match(names(x.temp),names(new.data))\n\n pred.dat <- mars.new.dataframe(new.data[,selector])[[1]]\n\n assign(\"pred.dat\", pred.dat, pos = 1) #and assign them for later use\n\n# fit the mars model and extract the basis functions\n\n print(paste(\"re-fitting initial mars model for\",n.spp,\"responses\"),quote = FALSE)\n print(paste(\"using glm family of\",family),quote = FALSE)\n\n #mars.fit <- mars(x = xdat, y = ydat, degree = mars.degree, w = site.weights, \n # wp = spp.weights, penalty = penalty)\n\n mars.fit <- mars.glm.object$mars.object #AKS\n\n old.bf.data <- as.data.frame(eval(mars.fit$x))\n n.bfs <- ncol(old.bf.data)\n bf.names <- paste(\"bf\", 1:n.bfs, sep = \"\")\n old.bf.data <- as.data.frame(old.bf.data[,-1])\n names(old.bf.data) <- bf.names[-1]\n\n new.bf.data <- as.data.frame(mda:::model.matrix.mars(mars.fit,pred.dat))\n new.bf.data <- as.data.frame(new.bf.data[,-1])\n names(new.bf.data) <- bf.names[-1]\n\n# now cycle through the species fitting glm models \n\n print(\"fitting glms for individual responses\", quote = F)\n\n prediction <- as.data.frame(matrix(0, ncol = n.spp, nrow = nrow(pred.dat)))\n names(prediction) <- names(ydat)\n standard.errors <- as.data.frame(matrix(0, ncol = n.spp, nrow = nrow(pred.dat)))\n names(standard.errors) <- names(ydat)\n\n for (i in 1:n.spp) {\n\n print(names(ydat)[i], quote = FALSE)\n model.glm <- glm(ydat[, i] ~ ., data = old.bf.data, weights = site.weights, \n family = family, maxit = 100)\n temp <- predict.glm(model.glm,new.bf.data,type=\"response\",se.fit=TRUE)\n prediction[,i] <- temp[[1]]\n standard.errors[,i] <- temp[[2]]\n \n }\n \n return(list(\"prediction\"=prediction,\"ses\"=standard.errors))\n}\n\n\"roc\" <-\nfunction (obsdat, preddat) \n{\n# code adapted from Ferrier, Pearce and Watson's code, by J.Elith\n#\n# see:\n# Hanley, J.A. & McNeil, B.J. (1982) The meaning and use of the area\n# under a Receiver Operating Characteristic (ROC) curve.\n# Radiology, 143, 29-36\n#\n# Pearce, J. & Ferrier, S. (2000) Evaluating the predictive performance\n# of habitat models developed using logistic regression.\n# Ecological Modelling, 133, 225-245.\n# this is the non-parametric calculation for area under the ROC curve, \n# using the fact that a MannWhitney U statistic is closely related to\n# the area\n#\n if (length(obsdat) != length(preddat)) \n stop(\"obs and preds must be equal lengths\")\n n.x <- length(obsdat[obsdat == 0])\n n.y <- length(obsdat[obsdat == 1])\n xy <- c(preddat[obsdat == 0], preddat[obsdat == 1])\n rnk <- rank(xy)\n wilc <- ((n.x * n.y) + ((n.x * (n.x + 1))/2) - sum(rnk[1:n.x]))/(n.x * \n n.y)\n return(round(wilc, 4))\n}\n\n\n\n#set defaults\nmake.p.tif=T\nmake.binary.tif=T\nmars.degree=1\nmars.penalty=2\nscript.name=\"mars.r\"\nopt.methods=2\nsave.model=TRUE\nMESS=FALSE\n\n# Interpret command line argurments #\n# Make Function Call #\nArgs <- commandArgs(trailingOnly=FALSE)\n\n for (i in 1:length(Args)){\n if(Args[i]==\"-f\") ScriptPath<-Args[i+1]\n }\n\n print(Args)\n for (arg in Args) {\n \targSplit <- strsplit(arg, \"=\")\n \targSplit[[1]][1]\n \targSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"c\") csv <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"o\") output <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"rc\") responseCol <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"mpt\") make.p.tif <- argSplit[[1]][2]\n \t\t\tif(argSplit[[1]][1]==\"mbt\") make.binary.tif <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"deg\") mars.degree <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"pen\") mars.penalty <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"om\") opt.methods <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"savm\") save.model <- argSplit[[1]][2]\n \tif(argSplit[[1]][1]==\"mes\") MESS <- argSplit[[1]][2]\n }\n\tprint(csv)\n\tprint(output)\n\tprint(responseCol)\n\nScriptPath<-dirname(ScriptPath)\nsource(paste(ScriptPath,\"LoadRequiredCode.r\",sep=\"\\\\\"))\nprint(ScriptPath)\n\nmake.p.tif<-as.logical(make.p.tif)\nmake.binary.tif<-as.logical(make.binary.tif)\nsave.model<-make.p.tif | make.binary.tif\nopt.methods<-as.numeric(opt.methods)\nMESS<-as.logical(MESS)\n\nfit.mars.fct(ma.name=csv,\n tif.dir=NULL,output.dir=output,\n response.col=responseCol,make.p.tif=make.p.tif,make.binary.tif=make.binary.tif,\n mars.degree=mars.degree,mars.penalty=mars.penalty,debug.mode=F,responseCurveForm=\"pdf\",\n script.name=\"mars.r\",save.model=save.model,opt.methods=as.numeric(opt.methods),MESS=MESS)\n", "meta": {"hexsha": "914906fb87c99076ad2166e1c42ee3cb89e5e5da", "size": 64902, "ext": "r", "lang": "R", "max_stars_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_MARS_pluggable.r", "max_stars_repo_name": "celiafish/VisTrails", "max_stars_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 83, "max_stars_repo_stars_event_min_datetime": "2015-01-05T14:50:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-17T19:45:26.000Z", "max_issues_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_MARS_pluggable.r", "max_issues_repo_name": "celiafish/VisTrails", "max_issues_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 254, "max_issues_repo_issues_event_min_datetime": "2015-01-02T20:39:19.000Z", "max_issues_repo_issues_event_max_datetime": "2018-11-28T17:16:44.000Z", "max_forks_repo_path": "contrib/sahm/pySAHM/Resources/R_Modules/FIT_MARS_pluggable.r", "max_forks_repo_name": "celiafish/VisTrails", "max_forks_repo_head_hexsha": "d8cb575b8b121941de190fe608003ad1427ef9f6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 40, "max_forks_repo_forks_event_min_datetime": "2015-04-17T16:46:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-28T22:43:24.000Z", "avg_line_length": 38.222614841, "max_line_length": 162, "alphanum_fraction": 0.6042802995, "num_tokens": 18202, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6959583250334526, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.30203301334048543}} {"text": "fit.loop <- function(methylation, design, dependent.variable, independent.variable, fit.function, stats.function, ...) {\n ret <- rep(NA,4)\n dep.idx <- which(colnames(design) == dependent.variable)\n\n do.call(rbind, mclapply(1:nrow(methylation), function(i) {\n design[,\"methylation\"] <- methylation[i,] ## assign CpG site methylation\n idx <- which(!is.na(design[,\"methylation\"]))\n try({\n fit <- fit.function(x=design[idx,-dep.idx,drop=F], ## independent variables\n y=design[idx,dep.idx], ## dependent variable\n ...)\n ret <- stats.function(fit, independent.variable) \n }, silent=T)\n ret\n }))\n}\n", "meta": {"hexsha": "7447aea55960a75e4022fdcdca05b5f2e4e72f95", "size": 732, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fit-loop.r", "max_stars_repo_name": "perishky/ewaff", "max_stars_repo_head_hexsha": "80d181416d5eeb2fabb1b0daf522598d06f1aa3e", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-12-08T06:12:00.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-08T06:12:00.000Z", "max_issues_repo_path": "R/fit-loop.r", "max_issues_repo_name": "perishky/ewaff", "max_issues_repo_head_hexsha": "80d181416d5eeb2fabb1b0daf522598d06f1aa3e", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-04T15:29:15.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-21T15:27:13.000Z", "max_forks_repo_path": "R/fit-loop.r", "max_forks_repo_name": "perishky/ewaff", "max_forks_repo_head_hexsha": "80d181416d5eeb2fabb1b0daf522598d06f1aa3e", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.0588235294, "max_line_length": 120, "alphanum_fraction": 0.5573770492, "num_tokens": 167, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3019993576431515}} {"text": "dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')\nlibrary(rJava)\n\nsetwd(\"/Users/mengmengjiang/all datas/viscous\")\n\nlibrary(xlsx)\n\n# \u6bd4\u8f83tfor\u548ctp\n# tfor\u4e0b\uff0cliquid1\u548cliquid2\u7684\u6bd4\u8f83\n# 2.2kv\u4e0b\u7684\u6bd4\u8f83\n# 18nl/min\u548c180nl/min\n# 18nl/min-liquid1\uff0c18nl/min-liquid2, 180nl/min-liquid1,180nl/min-liquid2\n\n\n# legend: 18nl/min-l1,18nl/min-l2,180nl/min-l1,180nl/min-liquid2\n\nk1<-read.xlsx(\"liquid1.xlsx\",sheetName=\"2kv-18\",header=TRUE)\nk2<-read.xlsx(\"liquid1.xlsx\",sheetName=\"2.2kv-18\",header=TRUE)\nk3<-read.xlsx(\"liquid2.xlsx\",sheetName=\"2.2kv-18\",header=TRUE)\nk4<-read.xlsx(\"liquid1.xlsx\",sheetName=\"2.2kv-180\",header=TRUE)\n\n# errorbar\nerror.bar <- function(x, y, upper, coll,lower=upper, length=0.05,...){\nif(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))\nstop(\"vectors must be same length\")\narrows(x,y+upper, x, y-lower,col=coll, angle=90, code=3, length=length, ...)\n}\n\n# color setting\n\nyan<-c(\"red\",\"blue\",\"black\",\"green3\")\npcc<-c(0,1,2,5)\n\nplot(k1$fv,k1$deva, col=0,xlab = expression(italic(f[\"v\"]) (Hz)),\n ylab = expression(italic(d[\"d\"]) (um)), mgp=c(1.1, 0, 0),tck=0.02,\n main = \"\", xlim = c(0,800),ylim=c(0,60))\n\n mtext(\"Small droplet\",3,line=-1,font=2,cex=1)\n\n points(k1$fv,k1$deva,col=yan[1],lty=2,pch=pcc[1],cex=0.8)\n points(k2$fv,k2$deva,col=yan[2],lty=2,pch=pcc[2],cex=0.8)\n points(k3$fv,k3$deva,col=yan[3],lty=2,pch=pcc[3],cex=0.8)\n points(k4$fv,k4$deva,col=yan[4],lty=2,pch=pcc[4],cex=0.8)\n\n\n error.bar(k1$fv,k1$deva,k1$stdd/2,col=yan[1])\n error.bar(k2$fv,k2$deva,k2$stdd/2,col=yan[2])\n error.bar(k3$fv,k3$deva,k3$stdd/2,col=yan[3])\n error.bar(k4$fv,k4$deva,k4$stdd/2,col=yan[4])\n\n fit1=lm(k1$deva~k1$fv)\n fit2=lm(k2$deva~k2$fv)\n fit3=lm(k3$deva~k3$fv)\n fit4=lm(k4$deva~k4$fv)\n\n abline(fit1,col=yan[1],lwd=1.5,lty=2)\n abline(fit2,col=yan[2],lwd=1.5,lty=2)\n abline(fit3,col=yan[3],lwd=1.5,lty=2)\n abline(fit4,col=yan[4],lwd=1.5,lty=2)\n\n leg<-c(\"18nl/min-2kv(liquid1)\",\"18nl/min-2.2kv(liquid1)\",\"18nl/min-2.2kv(liquid2)\",\"180nl/min-2.2kv(liquid1)\")\n\n legend(\"topright\",legend=leg,col=yan,pch=pcc,lwd=1.5,lty=2,\n inset=.01,bty=\"n\",cex=0.8)\n\n inter<-c(coef(fit1)[1],coef(fit2)[1],coef(fit3)[1],coef(fit4)[1])\n slope<-c(coef(fit1)[2],coef(fit2)[2],coef(fit3)[2],coef(fit4)[2])\n", "meta": {"hexsha": "f78ba379ebfe90c896da583de5c67a7161bc8ae9", "size": 2318, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap6/fig6-29.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "thesis/chap6/fig6-29.r", "max_issues_repo_name": "shuaimeng/r", "max_issues_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thesis/chap6/fig6-29.r", "max_forks_repo_name": "shuaimeng/r", "max_forks_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0882352941, "max_line_length": 113, "alphanum_fraction": 0.659188956, "num_tokens": 1038, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.30199935764315144}} {"text": "#!/usr/bin/env Rscript\n\n# R scripts for convert the Rdata output file from phantompeakqualtools for strand-shift plot\n# Author @chuan-wang https://github.com/chuan-wang\n\n# Command line argument processing\nargs <- commandArgs(trailingOnly=TRUE)\n\n# Check input args\nif (length(args) != 2) {\n stop(\"Usage: processSppRdata.r [ input.Rdata ] [ output.csv ]\", call.=FALSE)\n}\n\nload(args[1])\ndata<-crosscorr$cross.correlation\n\nwrite.table(data, file=args[2], sep=\",\", quote=FALSE, row.names=FALSE, col.names=FALSE)", "meta": {"hexsha": "395f150c6b478d0e24b135510d8c9e688c79828e", "size": 507, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/processSppRdata.r", "max_stars_repo_name": "chuan-wang/NGI-ChIPseq", "max_stars_repo_head_hexsha": "1bea71da2b442eff2b482fa6269b844edce2cd52", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2016-09-26T21:03:44.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-18T13:18:16.000Z", "max_issues_repo_path": "bin/processSppRdata.r", "max_issues_repo_name": "chuan-wang/NGI-ChIPseq", "max_issues_repo_head_hexsha": "1bea71da2b442eff2b482fa6269b844edce2cd52", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bin/processSppRdata.r", "max_forks_repo_name": "chuan-wang/NGI-ChIPseq", "max_forks_repo_head_hexsha": "1bea71da2b442eff2b482fa6269b844edce2cd52", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2016-06-10T11:41:16.000Z", "max_forks_repo_forks_event_max_datetime": "2019-06-29T05:08:56.000Z", "avg_line_length": 29.8235294118, "max_line_length": 93, "alphanum_fraction": 0.7337278107, "num_tokens": 140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.30199935010637097}} {"text": "library(ggplot2)\nlibrary(reshape2)\nlibrary(sandwich)\nlibrary(lmtest)\nlibrary(tseries)\nlibrary(dplyr)\nlibrary(urca)\nlibrary(forecast)\nlibrary(data.table)\nlibrary(stringr)\nlibrary(xtable)\nlibrary(fixest)\n\nrm(list=ls())\ngc()\n\nsource(\"startest.r\")\nsource(\"lstar.r\")\nsource(\"estar.r\")\n\n## load the data\nload(\"forecast80.RData\")\nload(\"subset.RData\")\n\nc_num <- ncol(subset_dt)-1\n\nc_names <- names(subset_dt)[1:c_num]\n\ncommodities <- c(\"Aluminum\",\"Bananas\",\"Cotton\",\"Diammonium Phosphate\",\"Fishmeal\",\n \"Groundnut Oil\",\"Groundnuts\",\"Gold\",\"Hides\",\"Lamb\",\n \"Lead\",\"Soft Logs\",\"Oranges\",\"Olive Oil\",\"Platinum\",\n \"Plywood\",\"Palm Oil\",\"Rice\",\"Rapeseed Oil\",\"Rubber\",\n \"Soybean Oil\",\"Hard Sawnwood\",\"Soft Sawnwood\",\"Uran\",\"Urea\")\n\nsub_dt$cmd <- commodities\n\n## this is sort of arbitrary, but \n# in general: agricultural(1)--forestry(2,3)--metals & minerals(4,5)\ngrp_order <- c(5,.9,1,4,2,\n 2,2,5,1.9,1.8,\n 5,3,.9,2,5,\n 3,2,1,2,2.5,\n 2,3,3,5,4)\nsub_dt$grp <- grp_order\nsub_dt <- sub_dt[order(grp,cmd)]\n\ncount_fn <- function(x,crit=.05){\n sum(x < crit)/length(x)\n}\n\n# table 1\ntab1_dt <- as.data.table(t(round(apply(array_pval,c(1,3),count_fn),2)))\ncolnames(tab1_dt) <- paste0(\"h\",1:12)\ntab1_dt$cmd <- commodities\ntab1_dt$grp <- grp_order\ntab1_dt <- tab1_dt[order(grp,cmd)]\n\ntab1_dt$grp <- NULL\ntab1_dt <- setcolorder(tab1_dt,c(\"cmd\",paste0(\"h\",1:12)))\nprint(xtable(tab1_dt),include.rownames=FALSE)\nwrite.table(tab1_dt,file=\"Paper/table_1.txt\",sep=\",\",quote=F,row.names=F)\n\n# appendix table 1\ntaba1_dt <- sub_dt[,.(Commodity=cmd,p,l,d,t)]\nprint(xtable(taba1_dt),include.rownames=FALSE)\nwrite.table(taba1_dt,file=\"Paper/table_a1.txt\",sep=\",\",quote=F,row.names=F)\n\n\n\n\n\n", "meta": {"hexsha": "3607887eaaed420e07b81a059931422930c93a06", "size": 1771, "ext": "r", "lang": "R", "max_stars_repo_path": "04-tables.r", "max_stars_repo_name": "dubilava/multistep", "max_stars_repo_head_hexsha": "f87d17d698b767ea066ae0628906c6a0342430dd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "04-tables.r", "max_issues_repo_name": "dubilava/multistep", "max_issues_repo_head_hexsha": "f87d17d698b767ea066ae0628906c6a0342430dd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "04-tables.r", "max_forks_repo_name": "dubilava/multistep", "max_forks_repo_head_hexsha": "f87d17d698b767ea066ae0628906c6a0342430dd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.5972222222, "max_line_length": 81, "alphanum_fraction": 0.6504799548, "num_tokens": 580, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.5195213219520929, "lm_q1q2_score": 0.3019993501063709}} {"text": "library(Seurat)\nlibrary(CellChat)\nlibrary(pheatmap)\nlibrary(ComplexHeatmap)\n\nobject.list<-readRDS('./project/merge_sle_immunonolgy_communication_cellchat.rds')\n\nnames(object.list)<-c('HD','aSLE','cSLE')\n\ncellchat <- mergeCellChat(object.list, add.names = names(object.list))\n\ncellchat\n\ndir.create(\"./sle_picture/Fig4/\")\noptions(repr.plot.width=6,repr.plot.height=4)\ngg1<-compareInteractions(cellchat, show.legend = F, group = c(1,2,2))+scale_fill_manual(values=c('#d86363','#2f7fc1'))\ngg2 <- compareInteractions(cellchat, show.legend = F, group = c(1,2,2), measure = \"weight\")+scale_fill_manual(values=c('#d86363','#2f7fc1'))\np<-gg1 + gg2\np\n#ggsave(p,file=\"./sle_picture/Fig4/cellchat_bar.pdf\",width = 6, height =4)\n\noptions(repr.plot.width=12,repr.plot.height=6)\nweight.max <- getMaxWeight(object.list, attribute = c(\"idents\",\"count\"))\npar(mfrow = c(1,3), xpd=TRUE)\n\nfor (i in c(1,2,3)) {\n netVisual_circle(object.list[[i]]@net$count, weight.scale = T, label.edge= F, edge.weight.max = weight.max[2], edge.width.max = 12, title.name = paste0(\"Number of interactions - \", names(object.list)[i]))\n}\n\n\noptions(repr.plot.width=20)\n#options(repr.plot.width=25,repr.plot.height=15)\ni = 1\n# combining all the identified signaling pathways from different datasets \npathway.union <- union(object.list[[i]]@netP$pathways, object.list[[i+1]]@netP$pathways)\nht1 = netAnalysis_signalingRole_heatmap(object.list[[i]], pattern = \"all\", signaling = pathway.union, title = names(object.list)[i], width = 7, height = 10, color.heatmap = \"OrRd\")\nht2 = netAnalysis_signalingRole_heatmap(object.list[[i+2]], pattern = \"all\", signaling = pathway.union, title = names(object.list)[i+1], width = 7, height = 10, color.heatmap = \"OrRd\")\nht3 = netAnalysis_signalingRole_heatmap(object.list[[i+1]], pattern = \"all\", signaling = pathway.union, title = names(object.list)[i+2], width = 7, height = 10, color.heatmap = \"OrRd\")\nht1+ht2+ht3\n\nnPatterns = 3\nobject.list$HD <- identifyCommunicationPatterns(object.list$HD, pattern = \"outgoing\", k = nPatterns)\nobject.list$aSLE <- identifyCommunicationPatterns(object.list$aSLE, pattern = \"outgoing\", k = nPatterns)\nobject.list$cSLE <- identifyCommunicationPatterns(object.list$cSLE , pattern = \"outgoing\", k = nPatterns)\nnPatterns = 2\nobject.list$HD <- identifyCommunicationPatterns(object.list$HD, pattern = \"incoming\", k = nPatterns)\nobject.list$aSLE <- identifyCommunicationPatterns(object.list$aSLE, pattern = \"incoming\", k = nPatterns)\nobject.list$cSLE <- identifyCommunicationPatterns(object.list$cSLE , pattern = \"incoming\", k = nPatterns)\n\np1 <- netAnalysis_dot(object.list$HD, pattern = \"outgoing\",font.size=15)+labs(title='',legend=\"\")+theme( \n axis.text.x=element_text(angle=90))\np1\n#ggsave(filename='./sle_picture/Fig4/netdotHD.pdf',plot=p1,width=9.9,height=3.7)\np2 <- netAnalysis_dot(object.list$aSLE, pattern = \"outgoing\",font.size=15)+labs(title='',legend=\"\")+theme( \n axis.text.x=element_text(angle=90))\np2\n#ggsave(filename='./sle_picture/Fig4/netdotaSLE.pdf',plot=p2,width=9.9,height=3.7)\np3 <- netAnalysis_dot(object.list$cSLE, pattern = \"outgoing\",font.size=15)+labs(title='')+theme( \n axis.text.x=element_text(angle=90))\np3\n#ggsave(filename='./sle_picture/Fig4/netdotcSLE.pdf',plot=p3,width=9.9,height=3.7)\n\np1 <- netAnalysis_dot(object.list$HD, pattern = \"incoming\",font.size=15)+labs(title='',legend=\"\")+theme( \n axis.text.x=element_text(angle=90))\np1\n#ggsave(filename='./sle_picture/Fig4/netdot_incoming_HD.pdf',plot=p1,width=9.9,height=3.7)\np2 <- netAnalysis_dot(object.list$aSLE, pattern = \"incoming\",font.size=15)+labs(title='',legend=\"\")+theme( \n axis.text.x=element_text(angle=90))\np2\n#ggsave(filename='./sle_picture/Fig4/netdot_incoming_aSLE.pdf',plot=p2,width=9.9,height=3.7)\np3 <- netAnalysis_dot(object.list$cSLE, pattern = \"incoming\",font.size=15)+labs(title='')+theme( \n axis.text.x=element_text(angle=90))\np3\n#ggsave(filename='./sle_picture/Fig4/netdot_incoming_cSLE.pdf',plot=p3,width=9.9,height=3.7)\n\ndir.create(\"./sle_picture/Fig5/\")\noptions(repr.plot.width=15,repr.plot.height=6)\np1 <- netVisual_bubble(cellchat, sources.use = 2, targets.use = c(1,3,4,5),max.dataset = 1,min.dataset = c(2,3),\n comparison = c(1, 2,3), angle.x = 45,font.size=15)+coord_flip()+theme(axis.text.x=element_text(angle=90, hjust=1))\np1\nggsave(filename='./sle_picture/Fig5/netbub_1.pdf',plot=p1,width=15,height=6)\n\noptions(repr.plot.width=15,repr.plot.height=6)\np2 <- netVisual_bubble(cellchat, sources.use = 3, targets.use = c(1,2,4,5),max.dataset = 1,min.dataset = c(2,3),\n comparison = c(1, 2,3), angle.x = 45,font.size=15)+coord_flip()+theme(axis.text.x=element_text(angle=90, hjust=1))\np2\nggsave(filename='./sle_picture/Fig5/netbub_2.pdf',plot=p2,width=15,height=6)\n\noptions(repr.plot.width=15,repr.plot.height=6)\np3 <- netVisual_bubble(cellchat, sources.use = 1, targets.use = c(2,3,4,5),max.dataset = 1,min.dataset = c(2,3),\n comparison = c(1, 2,3), angle.x = 45,font.size=15)+coord_flip()+theme(axis.text.x=element_text(angle=90, hjust=1))\np3\nggsave(filename='./sle_picture/Fig5/netbub_3.pdf',plot=p3,width=15,height=6)\n\noptions(repr.plot.width=15,repr.plot.height=6)\np4 <- netVisual_bubble(cellchat, sources.use =4, targets.use = c(1,2,3,5),max.dataset = 1,min.dataset = c(2,3),\n comparison = c(1, 2,3), angle.x = 45,font.size=15)+coord_flip()+theme(axis.text.x=element_text(angle=90, hjust=1))\np4\nggsave(filename='./sle_picture/Fig5/netbub_4.pdf',plot=p4,width=15,height=6)\n\noptions(repr.plot.width=15,repr.plot.height=6)\np5 <- netVisual_bubble(cellchat, sources.use = 5, targets.use = c(1,2,3,4),max.dataset = 1,min.dataset = c(2,3),\n comparison = c(1, 2,3), angle.x = 45,font.size=15)+coord_flip()+theme(axis.text.x=element_text(angle=90, hjust=1))\np5\nggsave(filename='./sle_picture/Fig5/netbub_5.pdf',plot=p5,width=15,height=6)\n", "meta": {"hexsha": "b893623054f93f39e4a655e4f1b0275063a0d005", "size": 5882, "ext": "r", "lang": "R", "max_stars_repo_path": "5.cellchat_data_processing_part2.r", "max_stars_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_stars_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "5.cellchat_data_processing_part2.r", "max_issues_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_issues_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "5.cellchat_data_processing_part2.r", "max_forks_repo_name": "yxaxaxa/sle_and_hd_single_cell_analysis", "max_forks_repo_head_hexsha": "139a3f6bd9ee34bec77b7ab3e1ec81a8c716d992", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 55.4905660377, "max_line_length": 206, "alphanum_fraction": 0.7162529752, "num_tokens": 1864, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3019659168629792}} {"text": "# 3. faza: Izdelava zemljevida\n\n# Uvozimo funkcijo za pobiranje in uvoz zemljevida.\nsource(\"lib/uvozi.zemljevid.r\")\n\n# Uvozimo zemljevid.\ncat(\"Uva\u017eam zemljevid...\\n\")\nsvet <- uvozi.zemljevid(\"http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip\", \n \"svet\", \"ne_110m_admin_0_countries.shp\", mapa = \"zemljevid\", \n encoding = \"Windows-1250\") \n \n\nt <- table(Dirkalisca$Country)\nnames(t)[names(t) == \"GREAT BRITAIN\"] <- \"UNITED KINGDOM\"\nnames(t)[names(t) == \"YUGOSLAVIA\"] <- \"CROATIA\"\nm <- match(toupper(svet$name_long), names(t))\n\n\n# Izra\u010dunamo povpre\u010dno velikost dru\u017eine.\nmin.dirke <- min(t)\nmax.dirke <- max(t)\n\n# Nari\u0161imo zemljevid v PDF.\ncat(\"Ri\u0161em zemljevid...\\n\")\npdf(\"slike/dirkaliscasvet.pdf\")\n\nn = max(t)\nbarve = topo.colors(n)[1+(n-1)*(t-min.dirke)/(max.dirke-min.dirke)]\nplot(svet, col = barve[m])\nu <- unique(t)\nu <- u[order(u)]\nlegend(\"left\", legend = u, fill = topo.colors(n)[u], bg=\"white\")\n\ndev.off()", "meta": {"hexsha": "1af9caadef84fea99c46f0c0a91f681414985553", "size": 1050, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "PikkaR/APPR-2014-15", "max_stars_repo_head_hexsha": "0f18077f0f000f7b63f459d83ab50b526b077b8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "PikkaR/APPR-2014-15", "max_issues_repo_head_hexsha": "0f18077f0f000f7b63f459d83ab50b526b077b8c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4, "max_issues_repo_issues_event_min_datetime": "2015-01-13T01:11:35.000Z", "max_issues_repo_issues_event_max_datetime": "2015-03-19T17:48:10.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "PikkaR/APPR-2014-15", "max_forks_repo_head_hexsha": "0f18077f0f000f7b63f459d83ab50b526b077b8c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8823529412, "max_line_length": 143, "alphanum_fraction": 0.6447619048, "num_tokens": 372, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.5, "lm_q1q2_score": 0.30196591686297913}} {"text": "# Source: https://shirinsplayground.netlify.com/2018/06/keras_fruits_lime/\n# Author: Dr. Shirin Elsinghorst - https://github.com/ShirinG\n# Data: https://www.kaggle.com/moltean/fruits/data\n\nlibrary(keras) # for working with neural nets\nlibrary(lime) # for explaining models\nlibrary(magick) # for preprocessing images\nlibrary(ggplot2) # for additional plotting\n\n# Loading model\nmodel <- application_vgg16(weights = \"imagenet\", include_top = TRUE)\nmodel\n\n# Include our own model\npath_prefix <- paste(getwd(), \"/data/fruit/\", sep=\"\") \nmodel2 <- load_model_hdf5(paste(path_prefix, \"fruit_checkpoints.h5\", sep=\"\"))\nmodel2\n\n##################################################################################\n\n# Load images\ntest_image_files_path <- paste(path_prefix, \"validation/\", sep=\"\") \n\nimg <- image_read('https://upload.wikimedia.org/wikipedia/commons/thumb/8/8a/Banana-Single.jpg/272px-Banana-Single.jpg')\nimg_path <- file.path(test_image_files_path, \"Banana\", 'banana.jpg')\nimage_write(img, img_path)\n#plot(as.raster(img))\n\nimg2 <- image_read('https://cdn.pixabay.com/photo/2010/12/13/09/51/clementine-1792_1280.jpg')\nimg_path2 <- file.path(test_image_files_path, \"Clementine\", 'clementine.jpg')\nimage_write(img2, img_path2)\n#plot(as.raster(img2))\n\nplot_superpixels(img_path, n_superpixels = 35, weight = 10)\nplot_superpixels(img_path2, n_superpixels = 50, weight = 20)\n\n##################################################################################\n\n# Prepare images for image net\nimage_prep <- function(x) {\n arrays <- lapply(x, function(path) {\n img <- image_load(path, target_size = c(224,224))\n x <- image_to_array(img)\n x <- array_reshape(x, c(1, dim(x)))\n x <- imagenet_preprocess_input(x)\n })\n do.call(abind::abind, c(arrays, list(along = 1)))\n}\n\n# Test predictions\nres <- predict(model, image_prep(c(img_path, img_path2)))\nimagenet_decode_predictions(res)\n\n# Load labels and train explainer\n\nmodel_labels <- readRDS(system.file('extdata', 'imagenet_labels.rds', package = 'lime'))\nexplainer <- lime(c(img_path, img_path2), as_classifier(model, model_labels), image_prep)\n\n# Train model\nexplanation <- explain(c(img_path, img_path2), explainer, \n n_labels = 2, n_features = 35,\n n_superpixels = 35, weight = 10,\n background = \"white\")\n\nplot_image_explanation(explanation)\n\nclementine <- explanation[explanation$case == \"clementine.jpg\",]\nplot_image_explanation(clementine)\n\n##################################################################################\n\n# Make predictions\n\ntest_datagen <- image_data_generator(rescale = 1/255)\n\ntest_generator <- flow_images_from_directory(\n test_image_files_path,\n test_datagen,\n target_size = c(20, 20),\n class_mode = 'categorical')\n\npredictions <- as.data.frame(predict_generator(model2, test_generator, steps = 1))\n\nload(paste(path_prefix, \"fruits_classes_indices.RData\", sep=\"\"))\nfruits_classes_indices_df <- data.frame(indices = unlist(fruits_classes_indices))\nfruits_classes_indices_df <- fruits_classes_indices_df[order(fruits_classes_indices_df$indices), , drop = FALSE]\ncolnames(predictions) <- rownames(fruits_classes_indices_df)\n\nt(round(predictions, digits = 2))\n\nfor (i in 1:nrow(predictions)) {\n cat(i, \":\")\n print(unlist(which.max(predictions[i, ])))\n}\n\nimage_prep2 <- function(x) {\n arrays <- lapply(x, function(path) {\n img <- image_load(path, target_size = c(20, 20))\n x <- image_to_array(img)\n x <- reticulate::array_reshape(x, c(1, dim(x)))\n x <- x / 255\n })\n do.call(abind::abind, c(arrays, list(along = 1)))\n}\n\nfruits_classes_indices_l <- rownames(fruits_classes_indices_df)\nnames(fruits_classes_indices_l) <- unlist(fruits_classes_indices)\nfruits_classes_indices_l\n\nexplainer2 <- lime(c(img_path, img_path2), as_classifier(model2, fruits_classes_indices_l), image_prep2)\nexplanation2 <- explain(c(img_path, img_path2), explainer2, \n n_labels = 1, n_features = 20,\n n_superpixels = 35, weight = 10,\n background = \"white\")\n\n# plot results\nexplanation2 %>%\n ggplot(aes(x = feature_weight)) +\n facet_wrap(~ case, scales = \"free\") +\n geom_density()\n", "meta": {"hexsha": "2fe57573805deca03be6101ef2bff56c27630198", "size": 4198, "ext": "r", "lang": "R", "max_stars_repo_path": "r/user/fruit_vgg16.r", "max_stars_repo_name": "JBris/image_classification_examples", "max_stars_repo_head_hexsha": "af3e571ee0eaa41cdd14604d4edf39edfe1ecd65", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2020-04-02T05:18:19.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-10T21:04:53.000Z", "max_issues_repo_path": "r/user/fruit_vgg16.r", "max_issues_repo_name": "JBris/image_classification_examples", "max_issues_repo_head_hexsha": "af3e571ee0eaa41cdd14604d4edf39edfe1ecd65", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-04-30T21:09:40.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T01:38:48.000Z", "max_forks_repo_path": "r/user/fruit_vgg16.r", "max_forks_repo_name": "JBris/image_classification_examples", "max_forks_repo_head_hexsha": "af3e571ee0eaa41cdd14604d4edf39edfe1ecd65", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.694214876, "max_line_length": 120, "alphanum_fraction": 0.6710338256, "num_tokens": 1072, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.5, "lm_q1q2_score": 0.301965909734318}} {"text": "\n cummulative.landings = function(x) {\n\n x$uniqueid = paste(x$gridid, x$yr, sep=\"~\")\n ids = sort(unique(x$uniqueid))\n x$dyear = lubridate::decimal_year(x$timestamp) - lubridate::year(x$timestamp)\n\n v = \"landings\"\n ti = \"week\"\n for (i in ids) {\n j = x[ which(x$uniqueid==i),]\n y = as.data.frame(xtabs( as.integer(x[,v]) ~ as.factor(x[,\n ti]), exclude=\"\" ))\n names(y) = c(ti, v)\n y$Csum = cumsum(y[, v])\n# --- must add a flexible model that can do convex/concave relationships (log relationshpi may be sufficient)\n\n# extract params (t0, t50, t100, slope parameter)\n\n\n }\n# redo for total effort as well\n\n }\n\n\n", "meta": {"hexsha": "558ddde5987c9969b823ba4ffdba9c6efd425dfb", "size": 672, "ext": "r", "lang": "R", "max_stars_repo_path": "R/cummulative.landings.r", "max_stars_repo_name": "jae0/snowcrab", "max_stars_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/cummulative.landings.r", "max_issues_repo_name": "jae0/snowcrab", "max_issues_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/cummulative.landings.r", "max_forks_repo_name": "jae0/snowcrab", "max_forks_repo_head_hexsha": "b168df368b739175004275c47f5bdf6907d066d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-04-21T12:57:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-21T12:57:48.000Z", "avg_line_length": 24.8888888889, "max_line_length": 109, "alphanum_fraction": 0.5848214286, "num_tokens": 205, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.3019174587716134}} {"text": "### CCM analysis to determine causality\n# Spatial CV ~ Age diversity, Abundance, AMO, SBT/SST, CV of SBT/SST\n\n########## Warning!\n# This step takes much time. \n# One can skip this step if the pre-run CCM results are provided.\n# Please see the loading code below.\n########## Warning!\n# run CCM several times to find the best lag for each variable\nEDM_lib_var = lapply(EDM_lib_var, function(item, lib_var=library_var, lag=lags, t_ccm=time_ccm){\n data = item[[dataset]]\n data = subset(data, select=names(data) %ni% c(\"Year\", \"Quarter\"))\n data = cbind(data[lib_var], subset(data, select=names(data) %ni% lib_var))\n \n item$ccm = data.frame(matrix(0, nrow = 0, ncol = 10))\n for (i in 1:t_ccm){\n seed = 1234 + i * 10\n ccm_result = determineCausality(data = data, \n dim.list = item$E, \n species = item$species,\n lags = lag,\n seed = seed)\n item$ccm = rbind(item$ccm, ccm_result)\n }\n \n return(item)\n})\n\n# save ccm results\nlapply(EDM_lib_var, function(item){\n write.csv(item$ccm, \n file = paste0(wd, ccm_path, item$species, \".csv\"),\n row.names = FALSE)\n})\n", "meta": {"hexsha": "1129822caa87ebf4a2cabfed6c706b471b6c01a5", "size": 1278, "ext": "r", "lang": "R", "max_stars_repo_path": "script/run_ccm.r", "max_stars_repo_name": "snakepowerpoint/SpatialVariability", "max_stars_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "script/run_ccm.r", "max_issues_repo_name": "snakepowerpoint/SpatialVariability", "max_issues_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "script/run_ccm.r", "max_forks_repo_name": "snakepowerpoint/SpatialVariability", "max_forks_repo_head_hexsha": "6a0c0ad763dacf383d3e144049739f6a758a4650", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5142857143, "max_line_length": 96, "alphanum_fraction": 0.558685446, "num_tokens": 340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.30187044903974125}} {"text": "# user options\nnox<-2; noy<-3;\npaper<-F # if paper==T output on file, else screen\ncleanup()\n\nfirst.year.in.mean<-2049 # years where output is independent of initial conditions and used as \"equilibrium\"\nlast.year.in.mean<-2050\n\ndo.simulation<-F # do the simulations and create data files for plots\ndo.plots<-T # do the plots\n\n\n######### end user options #########\ndo.HCR.batch<-function() {\n\n# files for output\nssb.out<-'HCR_SSB.dat'\nyield.out<-'HCR_yield.dat'\nF.out<-'HCR_F.dat'\n\nif (do.simulation) {\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat.gem')\n\n sp.name<-control@species.names\n\n xlab.title<-'Implentation bias'\n\n # headers in output files\n cat('codF cluF Species.n SSB025 SSB250 SSB500 SSB750 SSB975 \\n',file=ssb.out)\n cat('codF cluF Species.n y025 y050 y500 y750 y975 \\n',file=yield.out)\n cat('codF cluF Species.n F025 F050 F500 F750 F975 \\n',file=F.out)\n\n\n iter<-0\n\n BaseF<-HCR@constant.F\n print (BaseF)\n codF<-seq(0.01,1,0.1)\n ClupoidF<-seq(0.01,0.8,0.1)\n \n percentiles<-c(0.025,0.25,0.50,0.75,0.975)\n\n for (CODF in (codF)) for (CLUF in (ClupoidF)) {\n iter<-iter+1\n\n HCR@constant.F[1]<-BaseF[1]*CODF\n HCR@constant.F[2]<-BaseF[2]*CLUF*0.5\n HCR@constant.F[3]<-BaseF[3]*CLUF\n \n write.FLSMS.predict.control(HCR,file='HCR_options.dat')\n\n #run SMS\n shell(paste( file.path(root,\"program\",\"sms.exe\"),\"-mceval\",sep=\" \"), invisible = TRUE)\n\n a<-Read.MCMC.SSB.rec.data()\n a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)\n\n b<-tapply(a$SSB,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(CODF,' ',CLUF,' ',i,' '),file=ssb.out,append=TRUE)\n cat(b[[i]],file=ssb.out,append=TRUE)\n cat('\\n',file=ssb.out,append=TRUE)\n }\n\n a<-Read.MCMC.F.yield.data(dir=data.path)\n a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)\n b<-tapply(a$Yield,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(CODF,' ',CLUF,' ',i,' '),file=yield.out,append=TRUE)\n cat(b[[i]],file=yield.out,append=TRUE)\n cat('\\n',file=yield.out,append=TRUE)\n }\n\n b<-tapply(a$mean.F,list(a$Species.n), function(x) quantile(x,probs = percentiles))\n for (i in (1:length(b))) {\n cat(paste(CODF,' ',CLUF,' ',i,' '),file=F.out,append=TRUE)\n cat(b[[i]],file=F.out,append=TRUE)\n cat('\\n',file=F.out,append=TRUE)\n }\n }\n} # end do.simulations\n\nif (do.plots) {\n\n # read data and options into FLR objects\n control<-read.FLSMS.control()\n HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')\n\n sp.name<-control@species.names\n\n ssb<-read.table(ssb.out,header=TRUE)\n yield<-read.table(yield.out,header=TRUE)\n fi<-read.table(F.out,header=TRUE)\n\n a<-merge(ssb,yield)\n a<-merge(a,fi)\n b<-subset(a,select=c(Species.n,codF,cluF,SSB500,y500))\n\n make.plots<-function(variable='SSB') {\n by(b,list(b$Species.n),function(x) {\n xx<-sort(unique(x$codF))\n y<-sort(unique(x$cluF))\n if (variable==\"SSB\") z<-tapply(x$SSB500/1000,list(x$codF,x$cluF),mean)\n if (variable==\"Yield\") z<-tapply(x$y500/1000,list(x$codF,x$cluF),mean)\n\n sp<-sp.name[x[1,\"Species.n\"]]\n if ((i %% (nox*noy))==0) {\n if (paper) dev<-\"wmf\" else dev<-\"screen\"\n newplot(dev,nox,noy,dir=data.path,filename=paste(\"Batch_\",sep=''),Portrait=T);\n par(mar=c(4,5,3,2))\n }\n i<<-i+1\n contour(xx,y,z,xlab=\"Cod F\",ylab=\"Clupeid F\",nlevels = 10)\n title(main=paste(sp,variable))\n })\n }\n \n i<<-0\n make.plots(variable=\"SSB\")\n make.plots(variable=\"Yield\")\n\n if (paper) cleanup()\n \n} #end do.plots\n\n}\ndo.HCR.batch()\n\n\n", "meta": {"hexsha": "58333a0917a54251f67975d460fadbc23f76f652", "size": 3850, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_baltic-because.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_baltic-because.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/r_prog_less_frequently_used/hcr_batch_baltic-because.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.9473684211, "max_line_length": 111, "alphanum_fraction": 0.6220779221, "num_tokens": 1316, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102775181399, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.30187044903974125}} {"text": "## script to develop neural cell type deconvolution algorithm\r\n## uses houseman method with new ref data for purified brain cell populations\r\n## editted original code from minfi to take a matrix rather than RGset - this means data is not preprocessed together.\r\n\r\n\r\nsource(\"FunctionsForBrainCellProportionsPrediction.r\")\r\nlibrary(minfi)\r\nlibrary(genefilter)\r\nlibrary(rafalib)\r\nlibrary(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)\r\nsource(\"rmdConfig.bdr\")\r\nsetwd(dataDir) \r\nload(normData)\r\n\r\npheno$Cell.type<-gsub(\" \\\\+ve\", \"\", pheno$Cell.type) ## need to remove the \"+\" and \"-\"\r\npheno$Cell.type<-gsub(\" -ve\", \"\", pheno$Cell.type) ## need to remove the \"+\" and \"-\"\r\ncellInd<-pheno$Cell.type\r\n\r\n\r\n## parameters\r\nprobeSelect = \"any\" ## options \"both\" (select equal number of probes associated with hyper and hypo methylation) or \"any\" (just select probes based on significance regardless of direction)\r\ncellTypes = c(\"Double\",\"NeuN\", \"Sox10\", \"IRF8\")\r\ncellTypes<-sort(cellTypes)\r\nnumProbes<-100\r\n\r\n## select probes as basis of algorithm\r\ncompData <- pickCompProbes(rawbetas=celltypenormbeta, cellInd=cellInd, cellTypes = cellTypes, numProbes = numProbes, probeSelect = probeSelect)\r\nbraincelldata <- compData$coefEsts\r\nsave(braincelldata, file = \"RefDataForCellCompEstimation.rdata\")\r\n\r\n\r\n## re run to select coef probes with 450K data\r\nannoObj <- minfi::getAnnotationObject(\"IlluminaHumanMethylationEPICanno.ilm10b4.hg19\")\r\nall <- minfi:::.availableAnnotation(annoObj)$defaults\r\nnewfData <- do.call(cbind, lapply(all, function(wh) {\r\n minfi:::.annoGet(wh, envir = annoObj@data)\r\n}))\r\nnewfData<-newfData[rownames(celltypenormbeta),]\r\n\r\ncompData <- pickCompProbes(rawbetas=celltypenormbeta[which(newfData$Methyl450_Loci == \"TRUE\"),], cellInd=cellInd, cellTypes = cellTypes, numProbes = numProbes, probeSelect = probeSelect)\r\n\r\nbraincelldata450K<-compData$coefEsts\r\nsave(braincelldata450K, file = \"RefDataForCellCompEstimation450K.rdata\")\r\n", "meta": {"hexsha": "e106a8929c66a2a460d3421e8cda0f9215884d82", "size": 1945, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/CellularCompositionEstimation/createRefData.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "DNAm/CellularCompositionEstimation/createRefData.r", "max_issues_repo_name": "ejh243/BrainFANS", "max_issues_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2022-02-16T09:35:08.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T08:06:32.000Z", "max_forks_repo_path": "DNAm/CellularCompositionEstimation/createRefData.r", "max_forks_repo_name": "ejh243/BrainFANS", "max_forks_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.2045454545, "max_line_length": 189, "alphanum_fraction": 0.7542416452, "num_tokens": 548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.301870442443736}} {"text": "\nlibrary(caret)\nlibrary(doMC)\nlibrary(mmadsenr)\nlibrary(futile.logger)\nlibrary(dplyr)\nlibrary(ggthemes)\n\n\n\n# ANALYSIS: Per-locus statistics only, no classification data\n\n\n\n\nlog_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"per-locus-only-classification.log\")\nflog.appender(appender.file(log_file), name='cl')\n\nclargs <- commandArgs(trailingOnly = TRUE)\nif(length(clargs) == 0) {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-population-data.rda\")\n ta_sampled_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-tasampled-data.rda\")\n} else {\n pop_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-population-data.rda\", args = clargs)\n ta_sampled_data_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", filename = \"equifinality-3-4-tasampled-data.rda\", args = clargs)\n}\n\nload(pop_data_file)\nload(ta_sampled_data_file)\nflog.info(\"Loaded data file: %s\", pop_data_file, name='cl')\nflog.info(\"Loaded data file: %s\", ta_sampled_data_file, name='cl')\n\n\n\n\nflog.info(\"Beginning classification analysis of equifinality-4 data sets for per-locus only predictors\", name='cl')\n\n# set up parallel processing - use all the cores (unless it's a dev laptop under OS X) - from mmadsenr\n# dev = TRUE gets ignored on a Linux server and uses the full set of cores\nnum_cores <- get_parallel_cores_given_os(dev=TRUE)\nflog.info(\"Number of cores used in analysis: %s\", num_cores, name='cl')\nregisterDoMC(cores = num_cores)\n\n########### Training and Tuning Variables ##############\n\n#\n# Common training and tuning parameters for ctmixtures analysis\n#\n\ngbm_grid <- expand.grid(.interaction.depth = (1:6)*2,\n .n.trees = (2:10)*50, \n .shrinkage = 0.05)\n\ntraining_control <- trainControl(method=\"repeatedcv\", \n number=10, repeats=5)\n\n# make this repeatable - comment this out or change it to get a fresh analysis result\nseed_value <- 58132133\nset.seed(seed_value)\nflog.info(\"RNG seed to replicate this analysis: %s\", seed_value, name='cl')\n\n\n# Set up sampling of train and test data sets\ntraining_set_fraction <- 0.8\ntest_set_fraction <- 1.0 - training_set_fraction\n\n\n# set up combined_results data frames\nexperiment_names <- c(\"Per-Locus Population Census\")\nperlocus_results <- data.frame()\nperlocus_results_roc <- NULL\nperlocus_results_model <- NULL\nperlocus_results_cm <- NULL\n\n###### Population Data ######\n\nflog.info(\"Starting analysis of population data without per-locus values only\", name='cl')\n\n# first row of combined_results\ni <- 1\nexp_name <- experiment_names[i]\n\n\n\n# prepare data\n# create a label combining the biased models into one\n# then, split into training and test sets, with balanced samples for each of the binary classes\neq4_pop_df$two_class_label <- factor(ifelse(eq4_pop_df$model_class_label == 'allneutral', 'neutral', 'biased'))\n\n\n# remove fields from analysis that aren't predictors, and the detailed label with 4 classes\nexclude_columns <- c(\"simulation_run_id\", \"model_class_label\", \"innovation_rate\", \"configuration_slatkin\", \"num_trait_configurations\")\n\n#model <- train_randomforest(eq4_pop_df, training_set_fraction, fit_grid, fit_control, exclude_columns)\nmodel <- train_gbm_classifier(eq4_pop_df, training_set_fraction, \"two_class_label\", gbm_grid, training_control, exclude_columns, verbose=FALSE)\n\nperlocus_results_model[[\"perlocus_pop\"]] <- model$tunedmodel\n\n# use the test data split by the train_randomforest function and calculate tuned model predictions\n# and then get the confusion matrix and fitting metrics\npredictions <- predict(model$tunedmodel, newdata=model$test_data)\ncm <- confusionMatrix(predictions, model$test_data$two_class_label)\nresults <- get_parsed_binary_confusion_matrix_stats(cm)\nresults$experiments <- exp_name\nresults$elapsed <- model$elapsed\nperlocus_results_cm[[\"perlocus_pop\"]] <- cm\n\n# All other analyses record these\nresults$sample_size <- 0\nresults$ta_duration <- 0\n\n# calculate a ROC curve\nperlocus_pop_roc <- calculate_roc_binary_classifier(model$tunedmodel, model$test_data, \"two_class_label\", exp_name)\nresults$auc <- unlist(perlocus_pop_roc$auc@y.values)\nperlocus_results_roc[[\"perlocus_pop\"]] <- perlocus_pop_roc\n\nperlocus_results <- rbind(perlocus_results, results)\n\n\n\n############## Complete Processing and Save combined_results ##########3\n\n# save objects from the environment\nif(length(clargs) == 0) {\n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"per-locus-analysis-gbm.RData\")\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"per-locus-analysis-gbm-dfonly.RData\")\n \n \n} else {\n image_file <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"per-locus-analysis-gbm.RData\",\n args = clargs)\n image_file_results <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4\", \n filename = \"per-locus-analysis-gbm-dfonly.RData\", args = clargs)\n\n}\n\nflog.info(\"Saving combined_results of analysis to R environment snapshot: %s\", image_file, name='cl')\nsave(perlocus_results, perlocus_results_model, perlocus_results_cm, perlocus_results_roc, file=image_file)\n\nflog.info(\"Saving just data frame of results of analysis to R environment snapshot: %s\", image_file_results, name='cl')\nsave(perlocus_results, file=image_file_results) \n\n# End\nflog.info(\"Analysis complete\", name='cl')\n\n", "meta": {"hexsha": "4493a8e5b06ee41e291a00df04e630b24fd5f6d3", "size": 5729, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-4/modelfitting/perlocus-analysis.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-4/modelfitting/perlocus-analysis.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-4/modelfitting/perlocus-analysis.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4496644295, "max_line_length": 153, "alphanum_fraction": 0.7355559434, "num_tokens": 1443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778401, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.301870442443736}} {"text": "root = 'C:/Users/oxc140530/Documents/Google Drive/UTDALLAS/CS 6301 - Soft. Comprehension and Analysis/Project/BRE/Results/terms/'\nsystems = c('jabref', 'ofbiz', 'openbravo')\n\nsystem = systems[1]\nterms_file = paste(root, 'terms_', system, '.csv', sep = \"\")\n\n\n#read the csv\ndata_terms = read.csv(terms_file, sep = \";\", header = TRUE)\n\nfreq = table(data_terms$Lemma)\nfreq_frame = as.data.frame(freq)\nbarplot(freq[freq > quantile(freq_frame$Freq, .75) ])\n\n\nbarplot(freq[freq > 5000 ])\n\n#--------------------------------\n\nfreq2 = table(data_terms$Lemma, data_terms$File)\nfreq2 = as.data.frame(freq2)\ncolnames(freq2) <- c(\"Lemma\", \"File\", \"Frequencies\")\nfreq2 = subset(freq2, Frequencies!=0)\nfreq2 = table(freq2$Lemma)\nfreq2 = as.data.frame(freq2)\ncolnames(freq2) <- c(\"Lemma\", \"Frequencies\")\nfreq2 = subset(freq2, Frequencies!=0)\nfreq2 = freq2[!duplicated(freq2[,]), ]\n\nsort_freq2 = freq2[with(freq2, order(-Frequencies)), ]\nsort_freq21 = freq2[with(freq2, order( Frequencies)), ]\n\n#--------------------------------\n#dummy\nView(freq_frame[with(freq_frame, order(-Freq)), ])\n\n\nsummary(freq_frame$Freq)\n\nboxplot(freq_frame$Freq)\n\n\nView(freq2)", "meta": {"hexsha": "ba386f6bc1fafe0065b42dfe3b6ad5f1af35b053", "size": 1136, "ext": "r", "lang": "R", "max_stars_repo_path": "java-bre/scripts/r/script_terms.r", "max_stars_repo_name": "ojcchar/bre", "max_stars_repo_head_hexsha": "f31f0074d0c245c1c7cd487332549072d0d8bcf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "java-bre/scripts/r/script_terms.r", "max_issues_repo_name": "ojcchar/bre", "max_issues_repo_head_hexsha": "f31f0074d0c245c1c7cd487332549072d0d8bcf6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "java-bre/scripts/r/script_terms.r", "max_forks_repo_name": "ojcchar/bre", "max_forks_repo_head_hexsha": "f31f0074d0c245c1c7cd487332549072d0d8bcf6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.4186046512, "max_line_length": 129, "alphanum_fraction": 0.6681338028, "num_tokens": 332, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5851011542032313, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.30168980781970933}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of binomial_nullutterance prior wonky world model predictions\"\n#' author: \"Judith Degen\"\n#' date: \"January 12, 2014\"\n#' ---\n\nlibrary(ggplot2)\ntheme_set(theme_bw(18))\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/models/wonky_world/results/\")\nsource(\"rscripts/helpers.r\")\n\n#' get model predictions\nload(\"data/mp-binomial_nullutterance_3coinweights.RData\")\nd = read.table(\"data/parsed_binomial_nullutterance_3coinweights_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\ntable(d$Item)\nnrow(d)\nhead(d)\nsummary(d)\nd[d$Item == \"ate the seeds birds\" & d$QUD==\"how-many\" & d$Alternatives==\"0_basic\" & d$SpeakerOptimality == 1,]\nd[d$Item == \"stuck to the wall baseballs\" & d$QUD==\"how-many\" & d$Alternatives==\"0_basic\" & d$SpeakerOptimality == 2 & d$WonkyWorldPrior == .5,]\nmp = ddply(d, .(Item, CoinWeight, SpeakerOptimality, WonkyWorldPrior,NullUtteranceCost), summarise, PosteriorProbability=sum(PosteriorProbability))\nhead(mp)\n\n\n# get prior expectations\npriorexpectations = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorexpectations) = paste(priorexpectations$effect,priorexpectations$object)\nmp$PriorExpectation = priorexpectations[as.character(mp$Item),]$expectation\n\n# get smoothed prior probabilities\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nhead(priorprobs)\nrow.names(priorprobs) = paste(priorprobs$effect,priorprobs$object)\nmpriorprobs = melt(priorprobs, id.vars=c(\"effect\", \"object\"))\nhead(mpriorprobs)\nrow.names(mpriorprobs) = paste(mpriorprobs$effect,mpriorprobs$object,mpriorprobs$variable)\nmp$AllPriorProbability = priorprobs[paste(as.character(mp$Item)),]$X15\nhead(mp)\nsummary(mp)\n\nmp$CoinWeight = as.numeric(as.character(mp$CoinWeight))\nggplot(mp, aes(x=PriorExpectation,y=PosteriorProbability,color=as.factor(WonkyWorldPrior),shape=as.factor(SpeakerOptimality))) +\n geom_point() +\n facet_grid(NullUtteranceCost~CoinWeight)\n\ncw = subset(mp, !is.na(CoinWeight) & SpeakerOptimality == 2 & WonkyWorldPrior == .5)\np=ggplot(cw, aes(x=CoinWeight,y=PosteriorProbability)) +\n geom_point() +\n geom_line() +\n #scale_y_continuous(limits = c(0,.5)) +\n facet_grid(PriorExpectation~NullUtteranceCost)\n#p\nggsave(\"graphs/posterior_3coinweights_bypriorexp.pdf\",height=35,width=6)\n\np=ggplot(cw, aes(x=CoinWeight,y=PosteriorProbability)) +\n geom_point() +\n geom_line() +\n #scale_y_continuous(limits = c(0,.5)) +\n facet_grid(AllPriorProbability~NullUtteranceCost)\n#p\nggsave(\"graphs/posterior_3coinweights_byallprob.pdf\",height=35,width=6)\n\n\n# CONTINUE HERE\n#plot empirical against predicted distributions for \"some\"\nmsome = droplevels(subset(mp, Quantifier == \"some\"))\nsome = ddply(msome, .(Item, SpeakerOptimality, PriorExpectation, Proportion, WonkyWorldPrior, NullUtteranceCost, PosteriorProbability_empirical), summarise, PosteriorProbability_predicted=sum(PosteriorProbability), PriorProbability_smoothed=sum(PriorProbability))\nnrow(some)\nhead(some)\nmsome = melt(some, measure.vars=c(\"PosteriorProbability_empirical\",\"PosteriorProbability_predicted\",\"PriorProbability_smoothed\"))\nmsome$ptype = as.factor(ifelse(msome$variable == \"PosteriorProbability_empirical\", \"posterior (empirical)\",ifelse(msome$variable == \"PosteriorProbability_predicted\",\"posterior (model)\", \"prior\")))\nhead(msome)\nnrow(msome)\nsummary(msome)\n\ntoplot = droplevels(subset(msome, SpeakerOptimality == 2 & NullUtteranceCost == 5))#\"0_basic1_lownum2_extra4_twowords5_threewords\"))\nnrow(toplot)\ntoplot$Probability = as.factor(ifelse(toplot$ptype == \"prior\",\"prior\",\"posterior\"))\ntoplot$Prop = factor(toplot$Proportion, levels=c(\"1-50\",\"51-99\",\"100\"))\nggplot(toplot, aes(x=Prop, y=value,color=ptype, group=ptype, size=Probability)) +\n geom_point() +\n geom_line() +\n scale_size_discrete(range=c(1,2)) +\n scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_wrap(WonkyWorldPrior~Item)\nggsave(\"graphs/model-empirical-binomial_nullutterance-howmany-2-basic-cost5.pdf\",width=35,height=30)\n\n#plot empirical against predicted expectations for \"some\"\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nsummary(r)\nr$Item = as.factor(paste(r$effect, r$object))\nagr = aggregate(ProportionResponse ~ Item + quantifier, data=r, FUN=mean)\n#agr$CILow = aggregate(ProportionResponse ~ Item + quantifier,data=r, FUN=ci.low)$ProportionResponse\n#agr$CIHigh = aggregate(ProportionResponse ~ Item + quantifier,data=r,FUN=ci.high)$ProportionResponse\n#agr$YMin = agr$ProportionResponse - agr$CILow\n#agr$YMax = agr$ProportionResponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Quantifier)\nmp$PosteriorExpectation_empirical = agr[paste(mp$Item,mp$Quantifier),]$ProportionResponse\nmp$PriorExpectation_smoothed = mp$PriorExpectation/15\n\npexpectations = ddply(mp, .(Quantifier, Item, SpeakerOptimality,PriorExpectation_smoothed, WonkyWorldPrior, NullUtteranceCost, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(CoinWeight*PosteriorProbability)/15)\nhead(pexpectations)\n#some = pexpectations\nsome = droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ncors = ddply(some, .(SpeakerOptimality, WonkyWorldPrior, NullUtteranceCost), summarise, r=cor(PosteriorExpectation_predicted, PosteriorExpectation_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n\nggplot(some, aes(x=PosteriorExpectation_predicted, y=PosteriorExpectation_empirical,color=as.factor(SpeakerOptimality)))+#, shape=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n# geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(NullUtteranceCost~WonkyWorldPrior)\nggsave(\"graphs/model-empirical-binomial_nullutterance-expectations.pdf\",width=30,height=10)\n\nggplot(some, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted,color=as.factor(WonkyWorldPrior)))+#, shape=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n #geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-binomial_nullutterance-expectations.pdf\",width=15,height=10)\n\n \n#plot empirical against predicted allCoinWeight-prbabilities for \"some\"\nallCoinWeight = droplevels(subset(mp, CoinWeight == 15 & Quantifier == \"some\"))\ncors = ddply(allCoinWeight, .(SpeakerOptimality, WonkyWorldPrior), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n# .53 correlation\n\nggplot(allCoinWeight, aes(x=PosteriorProbability, y=PosteriorProbability_empirical,color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(SpeakerOptimality~NullUtteranceCost)\nggsave(\"graphs/model-empirical-binomial_nullutterance-allCoinWeightprobs.pdf\",width=20,height=20)\n\n#maybe COGSCI plot basis? plot predicted allCoinWeight-prbabilities for \"some\" as a function of prior allCoinWeight-probabilities\n\nggplot(allCoinWeight, aes(x=PriorProbability, y=PosteriorProbability,color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-binomial_nullutterance-allCoinWeightprobs.pdf\",width=15,height=10)\n\n# get empirical wonkiness posteriors\nload(\"/Users/titlis/cogsci/projects/stanford/projects/thegricean_sinking-marbles/experiments/11_sinking-marbles-normal/results/data/r.RData\")\nhead(r)\nnrow(r)\nr$Item = as.factor(paste(r$effect,r$object))\n\nt = as.data.frame(prop.table(table(r$Item, r$quantifier, r$response), mar=c(1,2)))\nhead(t)\ncolnames(t) = c(\"Item\",\"Quantifier\",\"NormalMarbles\",\"Proportion\")\nt[t$Var1==\"ate the seeds birds\",]\nt$Quantifier = tolower(t$Quantifier)\ntail(t)\nt$Wonky = as.factor(ifelse(t$NormalMarbles == \"yes\",\"false\",\"true\"))\nrow.names(t) = paste(t$Item, t$Quantifier, t$Wonky)\n\nwr$PosteriorProbability_empirical = t[paste(wr$Item, wr$Quantifier, wr$Wonky),]$Proportion\nhead(wr)\nwonky = droplevels(subset(wr, Wonky == \"true\"))\n\nhead(wonky)\ntoplot = droplevels(subset(wonky, SpeakerOptimality == 2))\n\nggplot(toplot, aes(x=PriorExpectation, y=PosteriorProbability, color=Quantifier)) +\n geom_point() +\n geom_smooth() +\n facet_grid(WonkyWorldPrior~NullUtteranceCost)\nggsave(file=\"graphs/wonkinessplot.pdf\",width=6)\n\ncors = ddply(wonky, .(SpeakerOptimality, Quantifier, WonkyWorldPrior,NullUtteranceCost), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors,15)\n\n\nwonky_all = subset(wonky, Quantifier == \"all\")\nwonky_none = subset(wonky, Quantifier == \"none\")\nwonky_some = subset(wonky, Quantifier == \"some\")\n\nggplot(wonky_all, aes(x=PosteriorProbability, y=PosteriorProbability_empirical, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-empirical-binomial_nullutterance-wonkiness_all.pdf\", width=15,height=10)\n\nggplot(wonky_none, aes(x=PosteriorProbability, y=PosteriorProbability_empirical, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-empirical-binomial_nullutterance-wonkiness_none.pdf\", width=15,height=10)\n\nggplot(wonky_some, aes(x=PosteriorProbability, y=PosteriorProbability_empirical, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-empirical-binomial_nullutterance-wonkiness_some.pdf\", width=15,height=10)\n\n\n\nggplot(wonky_all, aes(x=PriorExpectation, y=PosteriorProbability, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth() +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality)\nggsave(\"graphs/model-binomial_nullutterance-all-wonkiness.pdf\", width=15,height=10)\n\nggplot(wonky_none, aes(x=PriorExpectation, y=PosteriorProbability, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth() +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality) \nggsave(\"graphs/model-binomial_nullutterance-none-wonkiness.pdf\", width=15,height=10)\n\nggplot(wonky_some, aes(x=PriorExpectation, y=PosteriorProbability, color=as.factor(WonkyWorldPrior))) +\n geom_point() +\n geom_smooth() +\n scale_y_continuous(limits=c(0,1)) + \n facet_grid(NullUtteranceCost~SpeakerOptimality) \nggsave(\"graphs/model-binomial_nullutterance-some-wonkiness.pdf\", width=15,height=10)\n\nsave(mp, file=\"data/mp-binomial_nullutterance.RData\")\nsave(wr, file=\"data/wr-binomial_nullutterance.RData\")\n\n\n########\n\nwonky[wonky$Quantifier == \"all\" & wonky$PriorExpectation < 4 & wonky$Alternatives == \"0_basic\" & wonky$QUD == \"how-many\" & wonky$SpeakerOptimality == 2,]\n\n# get model predictions for all-CoinWeight with basic alts, spopt==2, and qud==how-many\nmp_some_allCoinWeight = subset(mp, Alternatives==\"0_basic\" & SpeakerOptimality == 2 & QUD == \"how-many\" & CoinWeight == 15)\nhead(mp_some_allCoinWeight)\nnrow(mp_some_allCoinWeight)\n\n\n\n# plot expectations for best basic model: \ntoplot = droplevels(subset(mp, QUD == \"how-many\" & Alternatives == \"0_basic\" & Quantifier == \"some\" & WonkyWorldPrior == .5))\nnrow(toplot)\n\npexpectations = ddply(toplot, .(Item, SpeakerOptimality,PriorExpectation_smoothed, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(CoinWeight*PosteriorProbability)/15)\nhead(pexpectations)\nsome = pexpectations#droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ncors = ddply(some, .(SpeakerOptimality), summarise, r=cor(PosteriorExpectation_predicted, PosteriorExpectation_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n\ntoplot = droplevels(subset(some, SpeakerOptimality == 1))\nnrow(toplot)\nhead(toplot)\n\nggplot(toplot, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted)) +\n geom_point(color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth(color=\"#00B0F6\") +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,1), name=\"Model predicted posterior expectation\")\n# geom_text(data=cors, aes(label=r)) +\n#scale_size_discrete(range=c(1,2)) +\n#scale_color_manual(values=c(\"red\",\"blue\",\"black\")) \nggsave(\"graphs/model-expectations.pdf\",width=5.5,height=4.5)#,width=30,height=10)\nggsave(\"~/cogsci/conferences_talks/_2015/2_cogsci_pasadena/wonky_marbles/paper/pics/model-expectations-binomial_nullutterance.pdf\",width=5.5,height=4.5)\nsave(toplot, file=\"data/toplot-expectations.RData\")\n\ntoplot_w = toplot\nload(\"../../complex_prior/smoothed_unbinned15/results/data/toplot-expectations.RData\")\ntoplot_r = toplot\nhead(toplot_w)\nsummary(toplot_r)\ntoplot_r$RSA = \"regular\"\ntoplot_w$RSA = \"wonky\"\n \n# plot both rRSA and binomial_nullutterance wRSA expectation predictions in same plot\ntoplot = merge(toplot_r,toplot_w, all=T)\nhead(toplot)\nnrow(toplot)\nggplot(toplot, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted, shape=RSA)) +\n geom_point(color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth(color=\"#00B0F6\") +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,1), name=\"Model predicted posterior expectation\")\n# geom_text(data=cors, aes(label=r)) +\n#scale_size_discrete(range=c(1,2)) +\n#scale_color_manual(values=c(\"red\",\"blue\",\"black\")) \n#ggsave(\"graphs/model-expectations.pdf\",width=5.5,height=4.5)#,width=30,height=10)\nggsave(\"~/cogsci/conferences_talks/_2015/2_cogsci_pasadena/wonky_marbles/paper/pics/model-expectations-binomial_nullutterance-regular.pdf\",width=6.5,height=4.5)\n", "meta": {"hexsha": "ab9b703c5275ef6f8fe80f275419a8033b760f64", "size": 15550, "ext": "r", "lang": "R", "max_stars_repo_path": "models/wonky_world/results/rscripts/modelpredictions-binomial-nullutterance-3coinweights.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", 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YES\n2. YES", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.5156199157230157, "lm_q1q2_score": 0.3016898078197093}} {"text": "#!/usr/bin/env Rscript\n\n######################################################################################################################################\n############## COMMAND LINE TO CALCULATE AND PLOT EVOLUTION OF SPECIES POPULATION function:main.glm ##############################\n######################################################################################################################################\n\n#### Based on Romain Lorrilli\u00e8re R script\n#### Modified by Alan Amosse and Benjamin Yguel for integrating within Galaxy-E\n\n#suppressMessages(library(lme4))\nsuppressMessages(library(ggplot2))\nsuppressMessages(library(speedglm))\nsuppressMessages(library(arm))\n#suppressMessages(library(reshape))\nsuppressMessages(library(data.table))\nsuppressMessages(library(reshape2))\n\n\n###########\n#delcaration des arguments et variables/ declaring some variables and load arguments\n\nargs = commandArgs(trailingOnly=TRUE)\noptions(encoding = \"UTF-8\")\nsource(args[6],encoding=\"UTF-8\")### chargement des fonctions / load the functions\n\nif ( (length(args)<8) || (length(args)>9)) {\n stop(\"At least 5 arguments must be supplied :\\n- An input dataset filtered (.tabular). May come from the filter rare species tool.\\n- A species detail table (.tabular)\\n- An id to fix output repository name.\\n- A list of species to exclude, can be empty.\\n- TRUE/FALSE to perform the glm with confidence intervals calculations.\\n\\n\", call.=FALSE) #si pas d'arguments -> affiche erreur et quitte / if no args -> error and exit1\n} else {\n Datafilteredfortrendanalysis<-args[1] ###### Nom du fichier avec extension \".typedefichier\", peut provenir de la fonction \"FiltreEspeceRare\" / file name without the file type \".filetype\", may result from the function \"FiltreEspeceRare\" \n tabSpecies<-args[2] ###### Nom du fichier avec extension \".typedefichier\", fichier mis \u00e0 disposition dans Galaxy-E avec specialisation \u00e0 l'habitat des especes et si espece consid\u00e9r\u00e9e comme indicatrice / file name without the file type \".filetype\", file available in Galaxy-E containing habitat specialization for each species and whether or not they are considered as indicator \n id<-args[3] ##### nom du dossier de sortie des resultats / name of the output folder\n spExclude <- strsplit(args [4],\",\")[[1]] ##### liste d'espece qu on veut exclure de l analyse / list of species that will be excluded\n AssessIC <-args [5] ########## TRUE ou FALSE r\u00e9alise glm \"standard\" avec calcul d'intervalle de confiance ou speedglm sans IC bien plus rapide / TRUE or FALSE perform a \"standard\" glm with confidance interval or speedglm without CI much more fast\n}\n\n## creation d'un dossier pour y mettre les resultats / create folder for the output of the analyses\n\ndir.create(paste(\"Output/\",id,sep=\"\"),recursive=TRUE,showWarnings=FALSE)\n#cat(paste(\"Create Output/\",id,\"\\n\",sep=\"\"))\ndir.create(paste(\"Output/\",id,\"/Incertain/\",sep=\"\"),recursive=TRUE,showWarnings=FALSE)\n#cat(paste(\"Create Output/\",id,\"Incertain/\\n\",sep=\"\"))\n\n\n#Import des donn\u00e9es / Import data \ntabCLEAN <- fread(Datafilteredfortrendanalysis,sep=\"\\t\",dec=\".\",header=TRUE,encoding=\"UTF-8\") #### charge le fichier de donn\u00e9es d abondance / load abundance of species\ntabsp <- fread(tabSpecies,sep=\"\\t\",dec=\".\",header=TRUE,encoding=\"UTF-8\") #### charge le fichier de donnees sur nom latin, vernaculaire et abbreviation, espece indicatrice ou non / load the file with information on species specialization and if species are indicators\n\nvars_tabCLEAN<-c(\"carre\",\"annee\",\"espece\",\"abond\")\nerr_msg_tabCLEAN<-\"The input dataset filtered doesn't have the right format. It need to have the following 4 variables :\\n- carre\\n- annee\\n- espece\\n- abond\\n\"\n\nvars_tabsp<-c(\"espece\",\"nom\",\"nomscientific\",\"indicateur\",\"specialisation\")\nerr_msg_tabsp<-\"\\nThe species dataset filtered doesn't have the right format. It need to have the following 4 variables :\\n- espece\\n- nom\\n- nomscientific\\n- indicateur\\n- specialisation\\n\"\n\ncheck_file(tabCLEAN,err_msg_tabCLEAN,vars_tabCLEAN,4)\ncheck_file(tabsp,err_msg_tabsp,vars_tabsp,5)\n\n\n\nfirstYear <- min(tabCLEAN$annee) #### Recup\u00e8re 1ere annee des donnees / retrieve the first year of the dataset\nlastYear <- max(tabCLEAN$annee) #### R\u00e9cup\u00e8re la derni\u00e8re annee des donnees / retrieve the last year of the dataset\nannees <- firstYear:lastYear ##### !!!! une autre variable s'appelle annee donc peut \u00eatre \u00e0 modif en \"periode\" ? ### argument de la fonction mais DECLARER DANS LA FONCTION AUSSI donc un des 2 \u00e0 supprimer\nspsFiltre=unique(tabCLEAN$espece) #### Recup\u00e8re la liste des especes du tabCLEAN qui ont \u00e9t\u00e9 s\u00e9lectionn\u00e9e et qui ont pass\u00e9 le filtre / retrieve species name that were selected and then filtered before\n#cat(\"\\n\\nspsFiltre\\n\")\ntabsp=subset (tabsp, (espece %in% spsFiltre)) #### liste des esp\u00e8ces exclu par le filtre ou manuellement / List of species excluded manually or by the filter from the analyses \n#cat(\"\\n\\ntabsp\\n\")\nsp=as.character(tabsp$espece) ##### liste des espece en code ou abbreviation gard\u00e9es pour les analyses ### arg de la fonction DECLARE AUSSI APRES DS FONCTION / list of the code or abbreviation of the species kept for the analyses\n#cat(\"\\n\\nsp\\n\")\nif(length(spExclude)!=0) {\n tabCLEAN <- subset(tabCLEAN,!(espece %in% spExclude))\n tabsp <- subset(tabsp, !(espece %in% spExclude))\n\n cat(\"\\n\\nEsp\u00e8ces exclues de l'analyse :\\n\")\n cat(spExclude)\n cat(\"\\n\")\n}\nif(length(tabCLEAN$espece)==0){\n stop(\"There is no species left for the analyse.\", call.=FALSE) #si pas plus d'esp\u00e8ce apr\u00e8s filtre / if no more species after filter\n}\n#cat(\"\\n\\ntabsp\\n\")\n\n\n################## \n### Do your analysis\n\nmain.glm(donneesAll=tabCLEAN,tabsp=tabsp,id=id,assessIC=AssessIC)\n\n\n\n\n\n", "meta": {"hexsha": "fbf532d42a03468808627a21e4db11b42efd6a16", "size": 5697, "ext": "r", "lang": "R", "max_stars_repo_path": "tools/stoc/ExeMainGlmGalaxy.r", "max_stars_repo_name": "annefou/tools-ecology", "max_stars_repo_head_hexsha": "60627aba07951226c8fd6bb3115be4bd118edd4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2019-07-04T12:18:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-06T19:32:54.000Z", "max_issues_repo_path": "tools/stoc/ExeMainGlmGalaxy.r", "max_issues_repo_name": "annefou/tools-ecology", "max_issues_repo_head_hexsha": "60627aba07951226c8fd6bb3115be4bd118edd4e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 43, "max_issues_repo_issues_event_min_datetime": "2019-02-11T08:37:41.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-06T21:37:08.000Z", "max_forks_repo_path": "tools/stoc/ExeMainGlmGalaxy.r", "max_forks_repo_name": "annefou/tools-ecology", "max_forks_repo_head_hexsha": "60627aba07951226c8fd6bb3115be4bd118edd4e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2019-01-23T19:12:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-27T15:10:10.000Z", "avg_line_length": 62.6043956044, "max_line_length": 430, "alphanum_fraction": 0.6989643672, "num_tokens": 1445, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.665410558746814, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.3016052195465316}} {"text": "############################################################\n# Sources_plot\n#\n# y - vector of years\n# p - vector of annual production amounts\n\nSources_plot <- function(countryID='US',countrylist=c('US'),\n datasource='BP_2012',units='mtoe',\n conprod='consumption',percent='',\n style='points',\n firstyear=1965,lastyear=2011,txt=list()) {\n\n # TODO: 1 tonne of oil ~= 42 gigajoules\n # TODO: 1 mtoe ~= 0.042 exajoules\n divisor = 1\n if (units == 'joule') {\n divisor = 1/0.042\n }\n\n # Set up symbols, colors and sizes\n pch_coal = 18 # solid diamond\n pch_oil = 1 # empty circle \n pch_gas = 17 # solid triangle\n pch_nuclear = 8 # asterisk\n pch_hydro = 16 # solid circle\n\n color_coal = 'grey20'\n color_oil = 'grey60'\n color_gas = 'lightblue2'\n color_nuclear = 'orange1'\n color_hydro = 'royalblue1'\n\n color_coal_text = 'black'\n color_oil_text = 'grey50'\n color_gas_text = 'lightblue3'\n color_nuclear_text = 'orange2'\n color_hydro_text = 'royalblue3'\n color_missing_text = 'gray80'\n\n color_labels_text = 'black'\n\n color_guide_lines = 'gray90'\n color_guide_lines_lower = 'gray90'\n\n cex_coal = 1.5\n cex_oil = 1.2\n cex_gas = 1.2\n cex_nuclear = 1.2\n cex_hydro = 1\n\n lwd_coal = 1\n lwd_oil = 1.4\n lwd_gas = 1\n lwd_nuclear = 1\n lwd_hydro = 1\n\n # Line width must be just enough to cause histogram bars to overlap.\n # This will depend upon the width of the graphic.\n lwd = 11\n\n # Set up arrays of zeroes and NA's\n y = c(firstyear:lastyear)\n y_p = y + 0.5\n y_p1 = y + 1.0\n y_m = y - 0.5\n zeroes = y * 0.0\n missing = y * NA\n n_years = lastyear - firstyear + 1\n\n # Create data frames for each energy source:\n # 1) read in the CSV file, applying the appropriate row names\n # 2) leave out the columns for 'country', 'change' and 'share of total'\n # 3) transpose to get countries as columns, years as rows (converts to matrix)\n # 4) convert back into data frame\n\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_coal','_',conprod,'_mtoe.csv',sep='',collapse='')\n c1 = read.csv(file=filename,skip=6,na.strings=c('na'))\n c4 = c1 / divisor\n\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_oil','_',conprod,'_mtoe.csv',sep='',collapse='')\n o1 = read.csv(file=filename,skip=6,na.strings=c('na'))\n o4 = o1 / divisor\n\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_gas','_',conprod,'_mtoe.csv',sep='',collapse='')\n g1 = read.csv(file=filename,skip=6,na.strings=c('na'))\n g4 = g1 / divisor\n\n # NOTE: BP ASSUMES that production = consumption for nuclear and only reports consumption\n if (units == 'joule') {\n # NOTE: 1 Watt-hour = 3600 Joules\n # NOTE: 1 Teratt-hour = 0.0036 Exajoules\n divisor = 1 / 0.0036\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_nuclear','_consumption_twh.csv',sep='',collapse='')\n } else {\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_nuclear','_consumption_mtoe.csv',sep='',collapse='')\n }\n n1 = read.csv(file=filename,skip=6,na.strings=c('na'))\n n4 = n1 / divisor\n\n # NOTE: BP ASSUMES that production = consumption for hydro and only reports consumption\n if (units == 'joule') {\n # NOTE: 1 Watt-hour = 3600 Joules\n # NOTE: 1 Teratt-hour = 0.0036 Exajoules\n divisor = 1 / 0.0036\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_hydro','_consumption_twh.csv',sep='',collapse='')\n } else {\n filename = paste('/var/www/mazamascience.com/html/OilExport/',datasource,'_hydro','_consumption_mtoe.csv',sep='',collapse='')\n }\n h1 = read.csv(file=filename,skip=6,na.strings=c('na'))\n h4 = h1 / divisor\n\n # Set each energy source to zeroes\n coal = zeroes\n oil = zeroes\n gas = zeroes\n nuclear = zeroes\n hydro = zeroes\n\n # For every country in the list:\n # If the country is found, add its values.\n\n for (cID in countrylist) {\n\n if (!is.null(c4[[cID]])) {\n coal = coal + c4[[cID]]\n }\n\n if (!is.null(o4[[cID]])) {\n oil = oil + o4[[cID]]\n }\n\n if (!is.null(g4[[cID]])) {\n gas = gas + g4[[cID]]\n }\n\n if (!is.null(n4[[cID]])) {\n nuclear = nuclear + n4[[cID]]\n }\n \n if (!is.null(h4[[cID]])) {\n hydro = hydro + h4[[cID]]\n }\n\n } # End of \"for (cID in countrylist)\" loop\n\n\n total = coal + oil + gas + nuclear + hydro\n\n # Heuristic check for 'all data missing'\n # If some of the data are missing and the rest are zero max() will return zero.\n # If all of the data are missing max() will return -Inf.\n all_data_missing = FALSE\n all_coal_missing = FALSE\n all_oil_missing = FALSE\n all_gas_missing = FALSE\n all_nuclear_missing = FALSE\n all_hydro_missing = FALSE\n if (max(coal,na.rm=TRUE) < 0.1 || max(coal,na.rm=TRUE) == -Inf) {\n all_coal_missing = TRUE\n color_coal_text = color_missing_text\n }\n if (max(oil,na.rm=TRUE) < 0.1 || max(oil,na.rm=TRUE) == -Inf) {\n all_oil_missing = TRUE\n color_oil_text = color_missing_text\n }\n if (max(gas,na.rm=TRUE) < 0.1 || max(gas,na.rm=TRUE) == -Inf) {\n all_gas_missing = TRUE\n color_gas_text = color_missing_text\n }\n if (max(nuclear,na.rm=TRUE) < 0.1 || max(nuclear,na.rm=TRUE) == -Inf) {\n all_nuclear_missing = TRUE\n color_nuclear_text = color_missing_text\n }\n if (max(hydro,na.rm=TRUE) < 0.1 || max(hydro,na.rm=TRUE) == -Inf) {\n all_hydro_missing = TRUE\n color_hydro_text = color_missing_text\n }\n if (max(total,na.rm=TRUE) < 0.1 || max(total,na.rm=TRUE) == -Inf) {\n all_data_missing = TRUE\n }\n\n # Make text grey if a fuel has all missing values\n# color_label_hydro = ifelse(all_hydro_missing,color_missing_text,color_labels_text)\n# color_label_gas = ifelse(all_gas_missing,color_missing_text,color_labels_text)\n# color_label_oil = ifelse(all_oil_missing,color_missing_text,color_labels_text)\n# color_label_coal = ifelse(all_coal_missing,color_missing_text,color_labels_text)\n# color_label_nuclear = ifelse(all_nuclear_missing,color_missing_text,color_labels_text)\n\n # Calculate percentages\n coal_pct = 100 * coal / total\n oil_pct = 100 * oil / total\n gas_pct = 100 * gas / total\n nuclear_pct = 100 * nuclear / total\n hydro_pct = 100 * hydro / total\n\n # Determine appropriate limits for the y-axis\n ylo = 0\n if (percent == 'pct') {\n yhi = max(c(coal_pct,oil_pct,gas_pct,nuclear_pct,hydro_pct),na.rm=TRUE)\n } else {\n yhi = max(c(coal,oil,gas,nuclear,hydro),na.rm=TRUE)\n }\n if (all_data_missing) {\n yhi = 1\n }\n\n ########### LOWER PLOT ###########################################\n\n if (style == 'points') {\n\n if (percent == 'pct') {\n \n plot(coal_pct ~ y_p,axes=FALSE,pch=pch_coal,col=color_coal_text,cex=cex_coal,lwd=lwd_coal,\n xlim=c(1954,2012), ylim=c(ylo,yhi),\n main='', xlab='', ylab='')\n axis(1)\n axis(4,las=0)\n\n # Add year guides (thin gray lines that mark the decades)\n guide_years = c(1970,1980,1990,2000,2010)\n guide_indices = c(6,16,26,36,46)\n guides = 100 + 0 * zeroes[guide_indices]\n points(guides ~ guide_years, type='h', lwd=2, col=color_guide_lines_lower)\n\n # The symbols for 'nuclear' and 'oil' don't look good on top of other symbols. Plot them first.\n points(nuclear_pct ~ y_p,pch=pch_nuclear,cex=cex_nuclear,col=color_nuclear_text,lwd=lwd_nuclear)\n points(oil_pct ~ y_p,pch=pch_oil,cex=cex_oil,col=color_oil_text,lwd=lwd_oil)\n points(coal_pct ~ y_p,pch=pch_coal,cex=cex_coal,col=color_coal_text,lwd=lwd_coal)\n points(gas_pct ~ y_p,pch=pch_gas,cex=cex_gas,col=color_gas_text,lwd=lwd_gas)\n points(hydro_pct ~ y_p,pch=pch_hydro,cex=cex_hydro,col=color_hydro_text,lwd=lwd_hydro)\n \n } else {\n \n plot(coal ~ y_p,axes=FALSE,pch=pch_coal,col=color_coal_text,cex=cex_coal,lwd=lwd_coal,\n xlim=c(1954,2012), ylim=c(ylo,yhi),\n main='', xlab='', ylab='')\n axis(1)\n axis(4,las=0)\n\n # Add year guides (thin gray lines that mark the decades)\n guide_years = c(1970,1980,1990,2000,2010)\n guide_indices = c(6,16,26,36,46)\n guides = yhi + 0 * zeroes[guide_indices]\n points(guides ~ guide_years, type='h', lwd=2, col=color_guide_lines_lower)\n\n # The symbol for 'nuclear' is unsightly so put it on the bottom\n points(nuclear ~ y_p,pch=pch_nuclear,cex=cex_nuclear,col=color_nuclear_text,lwd=lwd_nuclear)\n points(oil ~ y_p,pch=pch_oil,cex=cex_oil,col=color_oil_text,lwd=lwd_oil)\n points(coal ~ y_p,pch=pch_coal,cex=cex_coal,col=color_coal_text,lwd=lwd_coal)\n points(gas ~ y_p,pch=pch_gas,cex=cex_gas,col=color_gas_text,lwd=lwd_gas)\n points(hydro ~ y_p,pch=pch_hydro,cex=cex_hydro,col=color_hydro_text,lwd=lwd_hydro)\n\n }\n\n leg_x = 1954\n leg_y = yhi * 1.1\n leg_xjust = 0\n leg_yjust = 1\n text = c(txt$hydro,txt$gas,txt$oil,txt$coal,txt$nuclear)\n cols = c(color_hydro_text,color_gas_text,color_oil_text,color_coal_text,color_nuclear_text)\n pchs = c(pch_hydro,pch_gas,pch_oil,pch_coal,pch_nuclear)\n pt.cexs = c(cex_hydro,cex_gas,cex_oil,cex_coal,cex_nuclear)\n\n #text.cols = c(color_label_hydro,color_label_gas,color_label_oil,color_label_coal,color_label_nuclear)\n text.cols = c(color_hydro_text,color_gas_text,color_oil_text,color_coal_text,color_nuclear_text)\n\n # Add a legend\n if (!all_data_missing) {\n legend(x=leg_x, y=leg_y, xjust=leg_xjust, yjust=leg_yjust, bty='n', bg=\"white\",\n legend=text,col=cols,pch=pchs,pt.cex=pt.cexs,cex=1.2,text.col=text.cols)\n }\n\n # Now add all the labels for the bottom plot.\n\n # Add the title and attribution\n title(main=txt$main1,line=2,cex.main=2.0,xpd=NA)\n title(sub=txt$subtitle,line=2.5,family='Times',col=color_labels_text,xpd=NA)\n\n # Add the axis labels\n mtext(text=txt$year,side=1,line=0.8,cex=1.0,adj=0,xpd=TRUE)\n if (percent == 'pct') {\n mtext(text=txt$percent,side=4,line=3.0,cex=1.2,las=0,xpd=NA)\n } else {\n mtext(text=txt$units,side=4,line=3.0,cex=1.2,las=0,xpd=NA)\n }\n \n # Special cases\n if (all_data_missing) {\n mtext(text=txt$msg_nodata,side=3,line=0.5,cex=1.2,font=1,xpd=NA)\n }\n ########### UPPER PLOT ###########################################\n\n } else { # style = 'stacked'\n\n # Default plotting parameters\n par(las=1, lend='butt')\n\n # NOTE: Missing values make stacked plots impossible. Use 'A' as a mask.\n A = nuclear + coal + oil + gas + hydro\n B = nuclear + coal + oil + gas + 0*A\n C = nuclear + coal + oil + 0*A\n D = nuclear + coal + 0*A\n E = nuclear + 0*A\n\n ylo = 0\n yhi = max(A,na.rm=TRUE)\n\n A_pct = 100 * A / A\n B_pct = 100 * B / A\n C_pct = 100 * C / A\n D_pct = 100 * D / A\n E_pct = 100 * E / A\n\n if (percent == 'pct') {\n\n yhi = 100\n plot(A_pct ~ y_p,type='h',axes=FALSE,lwd=lwd,col=color_hydro,\n xlim=c(1954,2012), ylim=c(ylo,yhi),\n main='', xlab='', ylab='')\n axis(1)\n axis(4,las=0)\n\n points(B_pct ~ y_p, type='h',lwd=lwd,col=color_gas)\n points(C_pct ~ y_p, type='h',lwd=lwd,col=color_oil)\n points(D_pct ~ y_p, type='h',lwd=lwd,col=color_coal)\n points(E_pct ~ y_p, type='h',lwd=lwd,col=color_nuclear)\n\n # Add year guides (thin gray lines that mark the decades)\n guide_years = c(1970,1980,1990,2000,2010)\n guide_indices = c(6,16,26,36,46)\n guides = A_pct[guide_indices]\n points(guides ~ guide_years, type='h', lwd=2, col=color_guide_lines)\n\n } else {\n\n plot(A ~ y_p,type='h',axes=FALSE,lwd=lwd,col=color_hydro,\n xlim=c(1954,2012), ylim=c(ylo,yhi),\n main='', xlab='', ylab='')\n axis(1)\n axis(4,las=0)\n\n points(B ~ y_p, type='h',lwd=lwd,col=color_gas)\n points(C ~ y_p, type='h',lwd=lwd,col=color_oil)\n points(D ~ y_p, type='h',lwd=lwd,col=color_coal)\n points(E ~ y_p, type='h',lwd=lwd,col=color_nuclear)\n\n # Add year guides (thin gray lines that mark the decades)\n guide_years = c(1970,1980,1990,2000,2010)\n guide_indices = c(6,16,26,36,46)\n guides = A[guide_indices]\n points(guides ~ guide_years, type='h', lwd=2, col=color_guide_lines)\n\n }\n\n # Now add all the labels for the top plot.\n\n if (!all_data_missing) {\n if (percent == 'pct') {\n title_text = txt$percent_title\n } else {\n # Create and add the % change label in the top plot\n pct_chg = 100 * (total[n_years] - total[n_years-1]) / abs(total[n_years-1])\n \n if (is.na(pct_chg)) {\n pct_chg_string = '--'\n } else {\n pct_chg_string = as.character(abs(pct_chg))\n if (pct_chg >= 100) {\n pct_chg_string = strtrim(pct_chg_string,5)\n } else if (pct_chg >= 10) {\n pct_chg_string = strtrim(pct_chg_string,4)\n } else {\n pct_chg_string = strtrim(pct_chg_string,3)\n }\n }\n \n if (total[n_years] > total[n_years-1] ) {\n if (conprod == 'production') {\n string1 = txt$energy_produced_increased\n } else {\n string1 = txt$energy_consumed_increased\n }\n } else {\n if (conprod == 'production') {\n string1 = txt$energy_produced_decreased\n } else {\n string1 = txt$energy_consumed_decreased\n }\n }\n title_text = paste(lastyear,':',string1,pct_chg_string,'%')\n }\n title(main=title_text,line=1,cex.main=1.2,xpd=NA)\n }\n\n # Add the axis labels\n if (percent == 'pct') {\n mtext(text=txt$percent,side=4,line=2.5,cex=1.2,las=0,xpd=NA)\n } else {\n if (units == 'mtoe') {\n # Use something smaller than \"million tonnes oil equiv.\" to declutter the chart.\n mtext(text='\\'mtoe\\'',side=4,line=2.5,cex=1.2,las=0,xpd=NA)\n } else {\n mtext(text=txt$units,side=4,line=2.5,cex=1.2,las=0,xpd=NA)\n }\n }\n\n }\n\n\n}\n", "meta": {"hexsha": "16e32205fee6bb768d6ab37dd495739ec06ae9fe", "size": 13972, "ext": "r", "lang": "R", "max_stars_repo_path": "Sources_plot.r", "max_stars_repo_name": "MazamaScience/EnergyDatabrowser_v0", "max_stars_repo_head_hexsha": "13ba1f3c25aa797f3cebb8feeac7b82bbff9fbb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Sources_plot.r", "max_issues_repo_name": "MazamaScience/EnergyDatabrowser_v0", "max_issues_repo_head_hexsha": "13ba1f3c25aa797f3cebb8feeac7b82bbff9fbb8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Sources_plot.r", "max_forks_repo_name": "MazamaScience/EnergyDatabrowser_v0", "max_forks_repo_head_hexsha": "13ba1f3c25aa797f3cebb8feeac7b82bbff9fbb8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.99513382, "max_line_length": 131, "alphanum_fraction": 0.6238190667, "num_tokens": 4503, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593452091672, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.30150691009141295}} {"text": "# Load packages\nif (!require(\"pacman\")) install.packages(\"pacman\")\npacman::p_load(\n data.table, janitor, magrittr, fixest, raster,\n broom, tidyverse, tidylog, stars, viridis, scales\n)\noptions(\"tidylog.display\" = NULL)\n\n# load mask and data\nwater_mask = read_stars(\"data/raw/grace/LAND_MASK.CRI.nc\")\nwater = read_stars(\"data/raw/grace/GRCTellus.JPL.200204_202108.GLO.RL06M.MSCNv02CRI.nc\") %>%\n slice(time, 1)\n\n# mask the data\nwater[water_mask == 0] = NA\n\n# plot the single layer above\nggplot() +\n geom_stars(data = water) +\n scale_fill_gradient2(\n name = \"Whatever the\\ndata are\",\n low = \"#b2182b\", mid = \"#ffffef\", high = \"#2166ac\",\n midpoint = 0, space = \"Lab\", na.value = \"#ffffff\",\n guide = guide_colourbar(\n ticks.linewidth = 2,\n ticks.colour = \"#333333\",\n frame.colour = \"#333333\",\n frame.linewidth = 2\n ),\n aesthetics = \"fill\",\n trans = \"pseudo_log\",\n breaks = c(-400, 0, 400),\n limits = c(-400, 400)\n ) +\n theme_minimal() +\n theme(\n axis.title = element_blank(),\n panel.grid = element_blank(),\n axis.text = element_blank(),\n text = element_text(family = \"Lato\"),\n legend.title = element_text(size = 30),\n legend.text = element_text(size = 30),\n legend.key.width = unit(1, \"cm\"),\n legend.key.height = unit(1.5, \"cm\")\n )\n\nggsave(\"output/water_test.png\", width = 20, height = 10)\n", "meta": {"hexsha": "7ddcb84062ce3e33b0d6096c78cc41027960e6c8", "size": 1364, "ext": "r", "lang": "R", "max_stars_repo_path": "code/XX-grace-scratch.r", "max_stars_repo_name": "irudik/drought", "max_stars_repo_head_hexsha": "ea5424c88e2e771f48f85093725b35b8c32af0c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "code/XX-grace-scratch.r", "max_issues_repo_name": "irudik/drought", "max_issues_repo_head_hexsha": "ea5424c88e2e771f48f85093725b35b8c32af0c5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2021-11-04T16:35:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-09T14:56:36.000Z", "max_forks_repo_path": "code/XX-grace-scratch.r", "max_forks_repo_name": "irudik/drought", "max_forks_repo_head_hexsha": "ea5424c88e2e771f48f85093725b35b8c32af0c5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4166666667, "max_line_length": 92, "alphanum_fraction": 0.6385630499, "num_tokens": 424, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.4843800842769844, "lm_q1q2_score": 0.30150690330654967}} {"text": "#! /usr/bin/env Rscript\n\nlibrary(VariantAnnotation)\n\nargs <- commandArgs(TRUE)\nparseArgs <- function(x) strsplit(sub(\"^--\", \"\", x), \"=\")\nargsL <- as.list(as.character(as.data.frame(do.call(\"rbind\", parseArgs(args)))$V2))\nnames(argsL) <- as.data.frame(do.call(\"rbind\", parseArgs(args)))$V1\nargs <- argsL;rm(argsL)\n\nif(is.null(args$vcf)) {stop(\"no input VCF file\")} else {vcf = args$vcf}\nif(is.null(args$chunk_size)) {chunk_size = 10000} else {chunk_size = as.numeric(args$chunk_size)}\nif(is.null(args$output_vcf)) {output_vcf=gsub(\".vcf.gz\",\"_addVCFfeatures.vcf\",vcf)} else {output_vcf=args$output_vcf}\n\n#initiate the first chunk\nvcf_con <- open(VcfFile(vcf, yieldSize=chunk_size))\nvcf_chunk = readVcf(vcf_con, \"hg19\")\n\n#and continue\nwhile(dim(vcf_chunk)[1] != 0) {\n # get DP\n DP_matrix = geno(vcf_chunk,\"DP\")\n\n # compute median DP\n median_DP = apply(DP_matrix, 1, function(x){ median(x) })\n \n #annotate the header of the chunk\n info(header(vcf_chunk))[\"medianDP\",]=list(\"1\",\"Float\",\"Median DP over all samples\")\n \n #annotate the chunk with computed values\n info(vcf_chunk)[,\"medianDP\"] = median_DP\n \n #write out the annotated VCF\n con = file(output_vcf, open = \"a\")\n writeVcf(vcf_chunk, con)\n vcf_chunk = readVcf(vcf_con, \"hg19\")\n close(con)\n}", "meta": {"hexsha": "95ba8155f74fcef785e0b5220edc4d9d543a2a12", "size": 1260, "ext": "r", "lang": "R", "max_stars_repo_path": "add_calling_features_to_VCF.r", "max_stars_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_stars_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "add_calling_features_to_VCF.r", "max_issues_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_issues_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2018-05-10T07:48:49.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-10T08:17:31.000Z", "max_forks_repo_path": "add_calling_features_to_VCF.r", "max_forks_repo_name": "tdelhomme/variant-filtering-kidney2Hits", "max_forks_repo_head_hexsha": "c6e145d2dabcd6408cb665fe5d3c37255cb59fac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.1578947368, "max_line_length": 117, "alphanum_fraction": 0.6936507937, "num_tokens": 397, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.30150690330654967}} {"text": "suppressPackageStartupMessages(library(dplyr))\nsuppressPackageStartupMessages(library(ggplot2))\nsuppressPackageStartupMessages(library(patchwork))\n\nsource(\"viz_themes.R\")\nsource(\"plotting_functions.R\")\nsource(\"data_functions.R\")\n\noutput_figure_base <- file.path(\"figures\", \"supplementary\", \"supfigure9\")\nextensions <- c(\".png\", \".pdf\")\n\nresults_dir <- file.path(\"..\", \"3.clustering-pca\", \"results\")\n\nmetric_all_df <- load_clustering_metrics(results_dir)\n\nprint(dim(metric_all_df))\nhead(metric_all_df)\n\npanel_a_gg <- (\n ggplot(metric_all_df, aes(x = cluster, y = metric_value, color = assay, group = assay))\n + geom_point()\n + geom_line()\n + facet_wrap(\"~metric\", scales = \"free_y\")\n + scale_color_manual(\"Assay\", values = assay_colors)\n + figure_theme\n + ylab(\"Metric\")\n + xlab(\"Cluster (k)\\nK-means clustering\")\n + labs(tag = \"a\")\n)\n\npanel_a_gg\n\nmetric_dose_df <- load_clustering_metrics(results_dir, file_suffix=\"\")\n\nmetric_dose_df$dose <- dplyr::recode_factor(paste(metric_dose_df$dose), !!!recode_dose_factor_controls)\n\nprint(dim(metric_dose_df))\nhead(metric_dose_df)\n\npanel_b_gg <- (\n ggplot(metric_dose_df, aes(x = cluster, y = metric_value, color = assay, group = assay))\n + geom_point(size = 0.5)\n + geom_line(lwd = 0.3)\n + facet_grid(\"metric~dose\", scales = \"free_y\")\n + scale_color_manual(\"Assay\", values = assay_colors)\n + figure_theme\n + ylab(\"Metric\")\n + xlab(\"Cluster (k)\\nK-means clustering\")\n + labs(tag = \"b\")\n + theme(\n strip.text.y = element_text(size = 4)\n )\n)\n\npanel_b_gg\n\n# Load BIC scores\nmetric_bic_df <- load_clustering_metrics(results_dir, clustering = \"gmm\")\n\nmetric_bic_df$dose <- dplyr::recode_factor(paste(metric_bic_df$dose), !!!recode_dose_factor_controls)\n\nprint(dim(metric_bic_df))\nhead(metric_bic_df)\n\npanel_c_gg <- (\n ggplot(metric_bic_df, aes(x = cluster, y = metric_value, color = assay, group = assay))\n + geom_point(size = 0.5)\n + geom_line(lwd = 0.3)\n + facet_grid(\"metric~dose\", scales = \"free_y\")\n + scale_color_manual(\"Assay\", values = assay_colors)\n + figure_theme\n + ylab(\"Metric\")\n + xlab(\"Cluster (k)\\nGaussian mixture model\")\n + labs(tag = \"c\")\n + theme(\n strip.text.y = element_text(size = 6)\n )\n)\n\npanel_c_gg\n\nsup_fig9_gg <- (\n panel_a_gg / panel_b_gg / panel_c_gg\n) + plot_layout(heights = c(1, 0.75, 0.4))\n\nsup_fig9_gg\n\nfor (extension in extensions) {\n output_file <- paste0(output_figure_base, extension)\n ggplot2::ggsave(output_file, sup_fig9_gg, height = 7.5, width = 7, dpi = 500)\n}\n", "meta": {"hexsha": "9111be416bfacf50e502c122b4b2d06458b300f8", "size": 2556, "ext": "r", "lang": "R", "max_stars_repo_path": "6.paper_figures/scripts/nbconverted/supplementary-figure9.r", "max_stars_repo_name": "broadinstitute/lincs-profiling-comparison", "max_stars_repo_head_hexsha": "075c3bc60eeb3934fc42c30bae6aeed8cda1cd6d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-07-20T07:47:02.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-20T07:47:02.000Z", "max_issues_repo_path": "6.paper_figures/scripts/nbconverted/supplementary-figure9.r", "max_issues_repo_name": "broadinstitute/lincs-profiling-comparison", "max_issues_repo_head_hexsha": "075c3bc60eeb3934fc42c30bae6aeed8cda1cd6d", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 19, "max_issues_repo_issues_event_min_datetime": "2020-10-24T20:55:27.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-13T16:26:30.000Z", "max_forks_repo_path": "6.paper_figures/scripts/nbconverted/supplementary-figure9.r", "max_forks_repo_name": "broadinstitute/lincs-profiling-comparison", "max_forks_repo_head_hexsha": "075c3bc60eeb3934fc42c30bae6aeed8cda1cd6d", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2020-10-24T18:14:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-24T17:36:25.000Z", "avg_line_length": 27.7826086957, "max_line_length": 103, "alphanum_fraction": 0.6893583725, "num_tokens": 742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018546, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.30150690330654967}} {"text": "source(\"src/0a_setup.r\")\nsource(\"src/1_functions.r\")\n\n\nsystem.time(indata[, risk_grade_to := apply_transition(risk_grade, transition_table)])\nindata[order(risk_grade),.N,risk_grade]\nindata[order(risk_grade),.N,risk_grade_to]\n\nset.seed(1)\nindata[order(risk_grade),.N, risk_grade]\nindata[,mean(value)]\n\nindata[,sum(curr_bal)]\nsystem.time(indata2 <- indata %>% \n simulate_one_period(1, stress_macro_tbl) %>% {.$indata} %>% \n simulate_one_period(2, stress_macro_tbl) %>% {.$indata} %>% \n simulate_one_period(3, stress_macro_tbl) %>% {.$indata} \n)\nindata2[,sum(curr_bal)]\nindata2[order(risk_grade),.N, risk_grade]\nindata2[,.N]\nindata2[,.N,default]\nindata2[,mean(value)]\n\nindata[,.N,default]\nindata[,.N]\nindata2 <- default_transition(indata)\nindata2[,.N,default]\nindata2[,.N]\n\n", "meta": {"hexsha": "b72313705f4517def0715bbce05111ef7e0e7dd5", "size": 810, "ext": "r", "lang": "R", "max_stars_repo_path": "src/2_simulation_tests.r", "max_stars_repo_name": "xiaodaigh/shinystress", "max_stars_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/2_simulation_tests.r", "max_issues_repo_name": "xiaodaigh/shinystress", "max_issues_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/2_simulation_tests.r", "max_forks_repo_name": "xiaodaigh/shinystress", "max_forks_repo_head_hexsha": "9b41a8eee8bf250e7398370480c2e4e044d0ee3b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-03-19T21:41:27.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-19T21:41:27.000Z", "avg_line_length": 26.1290322581, "max_line_length": 86, "alphanum_fraction": 0.6950617284, "num_tokens": 251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.3015068965216862}} {"text": "## this code is analysing MWM data, works if the platform is on the SW quadrant\n\n##get mouse data\ntesting=T\ndatapath=\"Y:/AOCF-User Projects/1412_Rosenmund_VGlut1-KI\"\n\nif (testing){\n library(xtable)\n options(xtable.comment = FALSE)\n library(rmarkdown)\n source(\"Rfiles/FUNCTIONs.R\")\n \n userprojectfolder=datapath\n IF <- read.csv(paste(userprojectfolder,\"8_computerisable_files/initial_form.csv\", sep=\"/\"), sep=\";\",colClasses=c(NA, NA, \"NULL\"))\n Initiation_form = as.data.frame(t(IF[-1]))\n names(Initiation_form)= IF[,1]\n Initiation_form$projectlagesoid\n \n source(\"Rfiles/get_mouse_data.R\")\n \n}\n\ndata= read.csv(\"C:/Users/AG_Winter/Desktop/20150706_MWM/concenatedfile_onlysum.csv\", sep=\";\")\n\n##\n#get group data in the data file\ngrouping =as.character(data$animalID)\n\nfor (i in c(1: length(grouping))){\n grouping [i]= as.character(animalsproj$strain [animalsproj$animal.ID== grouping [i]])\n}\ndata$group= factor(grouping)\n\n###data analysis:\n\n###latency to the platform\ndata$latency= data$Zone..Target.Visit.latency..s.\ndata$Zone..Target.Duration..s. == 0\ndata$latency[data$latency == 0 & data$Zone..Target.Duration..s. == 0]= NA\n#add one second between mouse on water and click start\ndata$latency =data$latency+1\n\n###latency to the quadrant\ndata$latencyQ= data$Zone..SW.Visit.latency..s.\n#get NA when never reached\ndata$latencyQ[data$latencyQ == 0 & data$Zone..SW.Duration..s.==0]= NA\n# get latency to target zone if smaller#\nfor (i in c(1: nrow(data))){\n data$latencyQ[i]= min (data$latencyQ[i],data$latency[i], na.rm=TRUE)\n}\n\n\n###proportion time spent in the target quadrant\ndata$SWQtime = data$Zone..SW.Duration..s.+data$Zone..Target.Duration..s.\ndata$propright = 100*data$SWQtime/(data$SWQtime+data$Zone..SE.Duration..s.+data$Zone..NW.Duration..s.+data$Zone..NE.Duration..s.)\n\n\n###Start plotting\n\n#subset training data\ndataori=data\ndata = dataori[grepl(\"training\",dataori$Short.description),]\n\nboxpl_OF(data,aes(x=Short.description ,y= latency, fill=group),\"latency to enter platform [s]\",paste0(\"latency to platform\"))\nboxpl_OF(data,aes(x=Short.description ,y= latencyQ, fill=group),\"latency to enter quadrant [s]\",paste0(\"latency to quadrant\"))\nboxpl_OF(data,aes(x=Short.description ,y= propright, fill=group),\"proportion of time in SW quadrant\",paste0(\"time on right quadrant\"))\n\ndata = dataori[grepl(\"test\",dataori$Short.description),]\n\nboxpl_OF(data,aes(x=Short.description ,y= latency, fill=group),\"latency to enter platform [s]\",paste0(\"latency to platform\"))\nboxpl_OF(data,aes(x=Short.description ,y= latencyQ, fill=group),\"latency to enter quadrant [s]\",paste0(\"latency to quadrant\"))\nboxpl_OF(data,aes(x=Short.description ,y= propright, fill=group),\"proportion of time in SW quadrant\",paste0(\"time on right quadrant\"))\n", "meta": {"hexsha": "ed4437e2ece8340f2d5923509f8e39673f5c7a71", "size": 2742, "ext": "r", "lang": "R", "max_stars_repo_path": "R_files/Dataanalysis/MWM.r", "max_stars_repo_name": "jcolomb/AOCFshiny", "max_stars_repo_head_hexsha": "cf3151a6d8c2876008a81b789670a96e2d74d370", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R_files/Dataanalysis/MWM.r", "max_issues_repo_name": "jcolomb/AOCFshiny", "max_issues_repo_head_hexsha": "cf3151a6d8c2876008a81b789670a96e2d74d370", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2015-12-17T20:44:18.000Z", "max_issues_repo_issues_event_max_datetime": "2015-12-17T20:44:18.000Z", "max_forks_repo_path": "R_files/Dataanalysis/MWM.r", "max_forks_repo_name": "jcolomb/Viewer-file-concatenator", "max_forks_repo_head_hexsha": "cf3151a6d8c2876008a81b789670a96e2d74d370", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5616438356, "max_line_length": 134, "alphanum_fraction": 0.7359591539, "num_tokens": 818, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.3014743928656191}} {"text": "#library(testthat)\n\n################################################################################\ncontext(\"Test Ship Information Calculations\")\n################################################################################\n\n#Test calcTier\ntest_that(\"calcTier works\",\n {\n #create test benchmark\n testTierDF<- data.frame(\n engineType=rep(c(\"SSD\",\"MSD\",\"MSD-ED\",\"LNG\",\"GT\",\"ST\",\"GT-ED\"),4),\n keelLaidYear=rep(c(1999,2005,2014,2017),each=7),\n shipCategory=rep(3,28),\n tier=c(rep(\"Tier 0\",7),\n rep(\"Tier 1\",3),rep(\"Tier 0\",4),\n rep(\"Tier 2\",3),rep(\"Tier 0\",4),\n rep(\"Tier 3\",3),rep(\"Tier 0\",4)\n )\n )\n\n #run calculation\n testTierDF$newTier<-calcTier(engineType = testTierDF$engineType,\n keelLaidYear = testTierDF$keelLaidYear,\n shipCategory = testTierDF$shipCategory)\n\n #compare output against benchmark\n expect_equal(testTierDF$newTier,as.factor(testTierDF$tier))\n }\n)\n\n\n#calcTestIMO====================================================================\ntest_that(\"calcTestIMO works\",\n {\n #Create benchmark\n testIMODF<-data.frame(IMO = c(6605022,7417159,9999999,0,3254,NA),\n IMOFlag=c(\"Correct\",\"Correct\",\"Incorrect\",\"Incorrect\",\"Incorrect\",\"Incorrect\")\n )\n #run calculation\n testIMODF$NewIMOFlag<-calcTestIMO(IMO = testIMODF$IMO)\n\n #compare output against benchmark\n expect_equal(testIMODF$NewIMOFlag,as.factor(testIMODF$IMOFlag))\n }\n)\n\n\n#calcShipCategory===============================================================\n\ntest_that(\"calcShipCategory works\",\n {\n # Create Benchmark\n testShipCategory<-c(rep(3,4),\n rep(2,4),\n rep(1,4)\n )\n\n #Run Calculation\n testShipCategory_out<-calcShipCategory(mainEngineBore = c(320,500,420,400,\n 280,250,270,300,\n 165,142,190,152\n ),\n mainEngineStroke = c(440,2000,1764,460,\n 390,320,380,300,\n 137,128,165,128\n )\n )\n\n #compare output against benchmark\n expect_equal(testShipCategory_out,\n testShipCategory\n )\n }\n)\n\n\n#calcEngineType================================================================\n\ntest_that(\"calcEngineType works for Main Engines\",\n {\n #create benchmark\n testEngineType<- c(\"MSD\",\"SSD\",\"SSD\",\"MSD-ED\",\"SSD\",\"MSD-ED\",\"ST\",\"ST\",\"ST\",\"GT\",\"GT\",\"GT\")\n\n #Run Calculation\n testEngineType_out<-calcEngineType(propulsionType = rep(c(\"Oil Engine(s), Direct Drive\",\n \"Oil Engine(s), Electric Drive\",\n \"Steam Recip(s), Direct Drive\",\n \"Gas Turbine(s), Geared Drive\"\n ),\n each=3\n ),\n mainEngineStrokeType = rep(c(4,2,NA),4),\n mainEngineRPM = c(NA,NA,350,NA,NA,600,450,NA,500,NA,NA,NA)\n )\n\n #compare output against benchmark\n expect_equal(testEngineType_out,\n testEngineType\n )\n }\n)\n\ntest_that(\"calcEngineType works for Aux Engines\",\n {\n #create benchmark\n testEngineType<- rep(\"MSD\",12)\n\n #Run Calculation\n testEngineType_out<-calcEngineType(propulsionType = rep(c(\"Oil Engine(s), Direct Drive\",\n \"Oil Engine(s), Electric Drive\",\n \"Steam Recip(s), Direct Drive\",\n \"Gas Turbine(s), Geared Drive\"\n ),\n each=3\n ),\n main_aux_boiler = \"aux\"\n )\n\n #compare output against benchmark\n expect_equal(testEngineType_out,\n testEngineType\n )\n }\n)\n\ntest_that(\"calcEngineType works for Boiler Engines\",\n {\n #create benchmark\n testEngineType<- rep(\"Boiler\",12)\n\n #Run Calculation\n testEngineType_out<-calcEngineType(propulsionType = rep(c(\"Oil Engine(s), Direct Drive\",\n \"Oil Engine(s), Electric Drive\",\n \"Steam Recip(s), Direct Drive\",\n \"Gas Turbine(s), Geared Drive\"\n ),\n each=3\n ),\n main_aux_boiler = \"boiler\"\n )\n\n #compare output against benchmark\n expect_equal(testEngineType_out,\n testEngineType\n )\n }\n)\n\ntest_that(\"calcEngineType works for mix of Main, Aux, and Boiler Engines\",\n {\n #create benchmark\n testEngineType<- c(\"MSD\",\"SSD\",\"SSD\",\"MSD-ED\",\"SSD\",\"MSD-ED\",rep(\"MSD\",3),rep(\"Boiler\",3))\n\n #Run Calculation\n testEngineType_out<-calcEngineType(propulsionType = rep(c(\"Oil Engine(s), Direct Drive\",\n \"Oil Engine(s), Electric Drive\",\n \"Steam Recip(s), Direct Drive\",\n \"Gas Turbine(s), Geared Drive\"\n ),\n each=3\n ),\n mainEngineStrokeType = rep(c(4,2,NA),4),\n mainEngineRPM = c(NA,NA,350,NA,NA,600,450,NA,500,NA,NA,NA),\n main_aux = c(rep(\"main\",6),rep(\"aux\",3),rep(\"boiler\",3))\n )\n\n #compare output against benchmark\n expect_equal(testEngineType_out,\n testEngineType\n )\n }\n)\n", "meta": {"hexsha": "245760ce2555f8877914407d40769723d8948eae", "size": 7315, "ext": "r", "lang": "R", "max_stars_repo_path": "ShipEF/tests/testthat/testShipInfoCalculations.r", "max_stars_repo_name": "USEPA/Marine_Emissions_Tools", "max_stars_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-05-13T17:14:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-26T18:47:39.000Z", "max_issues_repo_path": "ShipEF/tests/testthat/testShipInfoCalculations.r", "max_issues_repo_name": "USEPA/Marine_Emissions_Tools", "max_issues_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ShipEF/tests/testthat/testShipInfoCalculations.r", "max_forks_repo_name": "USEPA/Marine_Emissions_Tools", "max_forks_repo_head_hexsha": "28e12dc51acb5baafc460b1a9de35d355f3cc64f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-08T15:55:06.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-08T15:55:06.000Z", "avg_line_length": 42.0402298851, "max_line_length": 112, "alphanum_fraction": 0.3656869446, "num_tokens": 1265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.30145794895083694}} {"text": "\nargs = commandArgs(trailingOnly=TRUE)\nif (length(args) < 2) {\n stop(\"Usage: postprocessing.r \", call.=FALSE)\n}\n\nbase.path <- args[1]\n\n\n# Parts of the function is taken from Seurat's Read10x parsing function\nReadAlevin <- function( base.path = NULL ){\n if (! dir.exists(base.path )){\n stop(\"Directory provided does not exist\")\n }\n\n barcode.loc <- paste0( base.path, \"alevin/quants_mat_rows.txt\" )\n gene.loc <- paste0( base.path, \"alevin/quants_mat_cols.txt\" )\n matrix.loc <- paste0( base.path, \"alevin/quants_mat.csv\" )\n if (!file.exists( barcode.loc )){\n stop(\"Barcode file missing\")\n }\n if (! file.exists(gene.loc) ){\n stop(\"Gene name file missing\")\n }\n if (! file.exists(matrix.loc )){\n stop(\"Expression matrix file missing\")\n }\n matrix <- as.matrix(read.csv( matrix.loc, header=FALSE))\n matrix <- t(matrix[,1:ncol(matrix)-1])\n\n cell.names <- readLines( barcode.loc )\n gene.names <- readLines( gene.loc )\n\n colnames(matrix) <- cell.names\n rownames(matrix) <- gene.names\n matrix[is.na(matrix)] <- 0\n return(matrix)\n}\n\nrequire(\"seurat\")\n\nalv.data <- ReadAlevin(base.path)\ndat <- CreateSeuratObject(raw.data = alv.data, min.cells = 3, min.genes = 200, project = \"10X_rnaseq\")\ndat <- NormalizeData(object = dat, normalization.method = \"LogNormalize\", scale.factor = 10000)\ndat <- FindVariableGenes(object = dat, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)\ndat <- ScaleData(object = dat)\ndat <- RunPCA(object = dat, pc.genes = dat@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5)\ndat <- FindClusters(object = dat, reduction.type = \"pca\", dims.use = 1:10, resolution = 0.6, print.output = 0, save.SNN = TRUE)\ndat <- RunTSNE(object = dat, dims.use = 1:10, do.fast = TRUE)\nTSNEPlot(object = dat)\n", "meta": {"hexsha": "fb0296097e2508c690fa4de0a50908f3a1cb4d61", "size": 1862, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/postprocessing.r", "max_stars_repo_name": "sk-sahu/scrnaseq", "max_stars_repo_head_hexsha": "884e541285330e1ef4771ff9267ce27e42dfb2e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49, "max_stars_repo_stars_event_min_datetime": "2019-04-11T02:57:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T03:26:43.000Z", "max_issues_repo_path": "bin/postprocessing.r", "max_issues_repo_name": "sk-sahu/scrnaseq", "max_issues_repo_head_hexsha": "884e541285330e1ef4771ff9267ce27e42dfb2e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 74, "max_issues_repo_issues_event_min_datetime": "2019-04-09T08:21:13.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:59:00.000Z", "max_forks_repo_path": "bin/postprocessing.r", "max_forks_repo_name": "sk-sahu/scrnaseq", "max_forks_repo_head_hexsha": "884e541285330e1ef4771ff9267ce27e42dfb2e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 49, "max_forks_repo_forks_event_min_datetime": "2019-06-06T17:13:49.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T16:35:20.000Z", "avg_line_length": 36.5098039216, "max_line_length": 151, "alphanum_fraction": 0.6578947368, "num_tokens": 536, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.4960938294709195, "lm_q1q2_score": 0.30145794190121344}} {"text": "# 1. Get states with more then 75% urban population and the rape var over 20.\n# 2. Get states where urban population is over 75% or rape is over 20\n# Import dataset\nu <- USArrests\n\n# urbanandrape <- urban[any(urban[,4] > 20)]\nex1 <- u[which(u[,3] > 75 & any(u[,3] > 20)), ]\n\nex2 <- u[which(u[,4] > 75 | u[,4] > 20), ]", "meta": {"hexsha": "da4ae48703990ee996f0a003332674518aa61cde", "size": 317, "ext": "r", "lang": "R", "max_stars_repo_path": "exorcies/Ex02.r", "max_stars_repo_name": "Parjoona/r-cheatsheet", "max_stars_repo_head_hexsha": "59120a88619c070a2625211424adae074ffe6fdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "exorcies/Ex02.r", "max_issues_repo_name": "Parjoona/r-cheatsheet", "max_issues_repo_head_hexsha": "59120a88619c070a2625211424adae074ffe6fdf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "exorcies/Ex02.r", "max_forks_repo_name": "Parjoona/r-cheatsheet", "max_forks_repo_head_hexsha": "59120a88619c070a2625211424adae074ffe6fdf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.2222222222, "max_line_length": 77, "alphanum_fraction": 0.6246056782, "num_tokens": 118, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.30134434654370224}} {"text": "args=commandArgs(T)\ncutOff<-as.numeric(args[1])\n\nlibrary(Matrix)\nlibrary(parallel)\ncorces2016Data<-readRDS(\"../input/corces2016-snap-full.rds\")\nlabels<-read.table(\"../input/corces2016_barcode_metadata.tsv\",header = TRUE,sep = \"\\t\",check.names = FALSE)\n\nlabels<-labels[labels$barcode %in% rownames(corces2016Data),]\n\nfunc<-function(label){\n barcode<-labels[labels$label == label,]$barcode\n cell_data<-corces2016Data[rownames(corces2016Data) %in% barcode,]\n nonZeroColumnList_cell<-diff(cell_data@p)/nrow(cell_data)\n candidateRange<-which(nonZeroColumnList_cell>=cutOff)\n for(i in candidateRange){\n cell_data[,i] = 1\n }\n return(cell_data)\n}\n\ncl.cores <- detectCores()\ncl <- makeCluster(cl.cores-1,type = \"FORK\") \nresults <- parLapply(cl, levels(labels$label), func)\nenhanced_corces2016Data<-do.call('rbind',results)\nstopCluster(cl)\nsaveRDS(enhanced_corces2016Data,file = paste0('../output/corces2016-snap-full_enh',cutOff,'.rds'))\nprint(paste0(cutOff,' is finished'))\n", "meta": {"hexsha": "775ccdc0015a7796308c873c4b2d140a2a2d66b6", "size": 995, "ext": "r", "lang": "R", "max_stars_repo_path": "intra-dataset/Corces2016/bin/2_rangeEnhance.r", "max_stars_repo_name": "mrcuizhe/svmATAC", "max_stars_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_stars_event_min_datetime": "2020-09-23T13:14:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T00:35:09.000Z", "max_issues_repo_path": "intra-dataset/Corces2016/bin/2_rangeEnhance.r", "max_issues_repo_name": "mrcuizhe/svmATAC", "max_issues_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intra-dataset/Corces2016/bin/2_rangeEnhance.r", "max_forks_repo_name": "mrcuizhe/svmATAC", "max_forks_repo_head_hexsha": "1914f1e7cc350dc298d51e2398939322c8ed4a9f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.3103448276, "max_line_length": 107, "alphanum_fraction": 0.735678392, "num_tokens": 299, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.5117166047041654, "lm_q1q2_score": 0.30134434654370224}} {"text": "# RUN:\n# export RSTUDIO_WHICH_R=/mnt/ssd0_sys/R-patched/bin/R rstudio &\n \n# to x = f(t)\n# https://www.datanovia.com/en/lessons/select-data-frame-columns-in-r/\n# BUG: https://github.com/tidyverse/readr/issues/919\n# \n# R shared library (/usr/local/lib/R/lib/libR.so) not found. If this is a \n# custom build of R, was it built with the --enable-R-shlib option?\n\n# ./configure --enable-R-shlib\n#\n# wget https://download1.rstudio.org/rstudio-xenial-1.1.463-amd64.deb\n# wget wget http://download1.rstudio.org/rstudio-1.0.153-amd64.deb\n# R version 3.2.3 (2015-12-10) -- \"Wooden Christmas-Tree\"\n# sudo apt-get install xorg-dev\n# wget https://cran.r-project.org/src/base-prerelease/R-patched_2018-12-04_r75765.tar.gz\n# rstudio: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by rstudio)\n#install.packages(\"readr\")\n#install.packages(\"haven\")\n#install.packages(\"tidyverse\") # looooong time\n\n# our data\nlibrary(tidyverse)\nlibrary(forecast)\n\n#install.packages(\"forecast\")\n\nfn <- '/tmp/ttttttt.csv'\ndf <- read.table(fn, header = TRUE, sep = \",\")\n\n#my_data <- as_tibble(df)\n\nt_to_x_df <- df %>% select(\"T\", \"Y\")\n\nacf(t_to_x_df, lag.max=20)\npacf(t_to_x_df, lag.max=8) # \u043d\u0443\u0436\u043d\u043e \u0437\u043d\u0430\u0442\u044c \u0434\u043b\u0438\u043d\u0443\n\n# https://stackoverflow.com/questions/43622486/time-series-forecasting-in-r-univariate-time-series\npricearima <- ts(t_to_x_df)#, frequency = 12)\n#adenoTS = ts(adeno)\narima_fit = auto.arima(pricearima[,1])\n#fitlnstock<-auto.arima(pricearima)\n\nforecastedvalues_ln=forecast(arima_fit,h=26)\nplot(forecastedvalues_ln)\n\nair <- window(ts(t_to_x_df))#, start=1990)\nfc <- holt(air[,1], h=5)\nair <- air[,1]\n\nfc <- holt(air, h=15)\nfc2 <- holt(air, damped=TRUE, phi = 0.9, h=15)\nautoplot(air) +\n autolayer(fc, series=\"Holt's method\", PI=FALSE) +\n autolayer(fc2, series=\"Damped Holt's method\", PI=FALSE) +\n ggtitle(\"Forecasts from Holt's method\") + xlab(\"Year\") +\n ylab(\"Air passengers in Australia (millions)\") +\n guides(colour=guide_legend(title=\"Forecast\"))\n\n", "meta": {"hexsha": "0137ed5b654ac9bfc8601a9e2a01f373efd2469b", "size": 1969, "ext": "r", "lang": "R", "max_stars_repo_path": "my-dockers/tracks_model.r", "max_stars_repo_name": "zaqwes8811/coordinator-tasks", "max_stars_repo_head_hexsha": "7f63fdf613eff5d441a3c2c7b52d2a3d02d9736a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "my-dockers/tracks_model.r", "max_issues_repo_name": "zaqwes8811/coordinator-tasks", "max_issues_repo_head_hexsha": "7f63fdf613eff5d441a3c2c7b52d2a3d02d9736a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 15, "max_issues_repo_issues_event_min_datetime": "2015-03-07T12:46:41.000Z", "max_issues_repo_issues_event_max_datetime": "2015-04-11T09:08:36.000Z", "max_forks_repo_path": "my-dockers/tracks_model.r", "max_forks_repo_name": "zaqwes8811/micro-apps", "max_forks_repo_head_hexsha": "7f63fdf613eff5d441a3c2c7b52d2a3d02d9736a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2786885246, "max_line_length": 109, "alphanum_fraction": 0.7105129507, "num_tokens": 654, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.588889130767832, "lm_q1q2_score": 0.30134434654370224}} {"text": "#!/usr/bin/env Rscript\n\n# this is a helper function to create a library charactrization file\nrequire(intervals)\nppmCal<-function(run,ppm)\n{\n return((run*ppm)/1000000)\n}\nIntervalMerge<-function(cameraObject,MSMSdata,libraryInfo,ppm,listOfMS2Mapped=list(),listOfUnMapped=list(),whichmz=NA,requiredHeader=NA){\n\n if(whichmz==\"c\")\n {\n\nlistofPrecursorsmz<-libraryInfo[,requiredHeader[\"mzCol\"]]\n\n\n\nCameramzColumnIndex<-which(colnames(cameraObject@groupInfo)==\"mz\")\n\nMassRun1<-Intervals_full(cbind(listofPrecursorsmz,listofPrecursorsmz))\n\nMassRun2<-Intervals_full(cbind(cameraObject@groupInfo[,CameramzColumnIndex]-\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm),\n cameraObject@groupInfo[,CameramzColumnIndex]+\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndex],ppm)))\n\nimatch <- interval_overlap(MassRun1,MassRun2)\nreturnData<-c()\nfor (i in 1:length(imatch)) {\n for(j in imatch[[i]])\n {\n startRT<-cameraObject@groupInfo[c(j),\"rtmin\"]\n endRT<-cameraObject@groupInfo[c(j),\"rtmax\"]\n startMZ<-cameraObject@groupInfo[c(j),\"mzmin\"]\n endMZ<-cameraObject@groupInfo[c(j),\"mzmax\"]\n centermz<-cameraObject@groupInfo[c(j),\"mz\"]\n centerrt<-cameraObject@groupInfo[c(j),\"rt\"]\n intensity<-cameraObject@groupInfo[c(j),\"into\"]\n fileName<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){attr(x,\"fileName\")}),collapse = \";\")\n\n ID<-libraryInfo[i,requiredHeader[\"compundID\"]]\n Name<-libraryInfo[i,requiredHeader[\"compoundName\"]]\n nmass<-libraryInfo[i,requiredHeader[\"mzCol\"]]\n\n\n parentmzs<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@precursorMz}),collapse=\";\")\n parentrts<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@rt}),collapse=\";\")\n parentInts<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@precursorIntensity}),collapse=\";\")\n MS2s<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){paste(paste(x@mz,x@intensity,sep=\"_\"),collapse=\":\")}),collapse=\";\")\n\n TMP<-data.frame(startRT=startRT,endRT=endRT,\n startMZ=startMZ,endMZ=endMZ,\n centermz=centermz,\n centerrt=centerrt,\n intensity=intensity,\n fileName=fileName,\n ID=ID,\n Name=Name,\n nmass=nmass,\n parentmzs=parentmzs,\n parentrts=parentrts,\n parentInts=parentInts,\n MS2s=MS2s,stringsAsFactors = F)\n\n if(is.null(returnData))\n {\n returnData<-(TMP)\n\n }else\n {\n\n returnData<-rbind.data.frame(returnData,TMP,stringsAsFactors = F)\n\n }\n\t}\n\t}\n return(returnData)\n }\n\n\n if(whichmz==\"f\")\n {\n\nlistofPrecursorsmz<-libraryInfo[,requiredHeader[\"mzCol\"]]\n\n\n\n\n\nCameramzColumnIndexmin<-which(colnames(cameraObject@groupInfo)==\"mzmin\")\nCameramzColumnIndexmax<-which(colnames(cameraObject@groupInfo)==\"mzmax\")\n\nMassRun1<-Intervals_full(cbind(listofPrecursorsmz,listofPrecursorsmz))\n\nMassRun2<-Intervals_full(cbind(cameraObject@groupInfo[,CameramzColumnIndexmin]-\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndexmin],ppm),\n cameraObject@groupInfo[,CameramzColumnIndexmax]+\n ppmCal(cameraObject@groupInfo[,CameramzColumnIndexmax],ppm)))\n\nimatch <- interval_overlap(MassRun1,MassRun2)\nreturnData<-c()\nfor (i in 1:length(imatch)) {\n for(j in imatch[[i]])\n {\n startRT<-cameraObject@groupInfo[c(j),\"rtmin\"]\n endRT<-cameraObject@groupInfo[c(j),\"rtmax\"]\n startMZ<-cameraObject@groupInfo[c(j),\"mzmin\"]\n endMZ<-cameraObject@groupInfo[c(j),\"mzmax\"]\n centermz<-cameraObject@groupInfo[c(j),\"mz\"]\n centerrt<-cameraObject@groupInfo[c(j),\"rt\"]\n intensity<-cameraObject@groupInfo[c(j),\"into\"]\n fileName<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){attr(x,\"fileName\")}),collapse = \";\")\n\n ID<-libraryInfo[i,requiredHeader[\"compundID\"]]\n Name<-libraryInfo[i,requiredHeader[\"compoundName\"]]\n nmass<-libraryInfo[i,requiredHeader[\"mzCol\"]]\n\n\n parentmzs<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@precursorMz}),collapse=\";\")\n parentrts<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@rt}),collapse=\";\")\n parentInts<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){x@precursorIntensity}),collapse=\";\")\n MS2s<-paste(sapply(MSMSdata$mapped[[as.character(j)]],function(x){paste(paste(x@mz,x@intensity,sep=\"_\"),collapse=\":\")}),collapse=\";\")\n\n TMP<-data.frame(startRT=startRT,endRT=endRT,\n startMZ=startMZ,endMZ=endMZ,\n centermz=centermz,\n centerrt=centerrt,\n intensity=intensity,\n fileName=fileName,\n ID=ID,\n Name=Name,\n nmass=nmass,\n parentmzs=parentmzs,\n parentrts=parentrts,\n parentInts=parentInts,\n MS2s=MS2s,stringsAsFactors = F)\n\n if(is.null(returnData))\n {\n returnData<-(TMP)\n\n }else\n {\n\n returnData<-rbind.data.frame(returnData,TMP,stringsAsFactors = F)\n\n }\n\t}\n\t}\n return(returnData)\n }\n}\n\n\nrequire(CAMERA)\nrequire(stringr)\n\n\ncreateLibrary<-function(MSMSdata=NA,\n cameraObject=NA,\n\t\t\t\t\t\t libraryInfo=NA,requiredHeader=NA,whichmz=\"f\",\n includeUnmapped=T,includeMapped=T,\n preprocess=NA,savePath=\"\",minPeaks=0,maxSpectra=NA,\n\t\t\t maxPrecursorMass = NA, minPrecursorMass = NA,ppm)\n{\n\n\n\n\ndata<-IntervalMerge(cameraObject=cameraObject,\nMSMSdata=MSMSdata,libraryInfo=libraryInfo,ppm=ppm,listOfMS2Mapped=list(),listOfUnMapped=list(),whichmz=whichmz,requiredHeader=requiredHeader)\n\n\nMSlibrary<-data\nMSlibrary<-MSlibrary[MSlibrary[,\"MS2s\"]!=\"\",]\nnewLib<-c()\nfor(k in 1:nrow(MSlibrary))\n{\n hitTMP<-MSlibrary[k,]\n parentmzs<-strsplit(x = hitTMP[,\"parentmzs\"],split = \";\",fixed = T)[[1]]\n parentrts<-strsplit(x = hitTMP[,\"parentrts\"],split = \";\",fixed = T)[[1]]\n parentInts<-strsplit(x = hitTMP[,\"parentInts\"],split = \";\",fixed = T)[[1]]\n parentMS2s<-strsplit(x = hitTMP[,\"MS2s\"],split = \";\",fixed = T)[[1]]\n fileNames<-strsplit(x = hitTMP[,\"fileName\"],split = \";\",fixed = T)[[1]]\n for(p in 1:length(parentmzs))\n {\n MS2sTMPLib<-parentMS2s[[p]]\n TempLib<-data.frame(MSlibrary[k,!colnames(MSlibrary)%in%c(\"parentmzs\",\"parentrts\",\"fileName\",\"parentInts\",\"MS2s\")])\n\n\n temp<-t(sapply(X=(strsplit(x = strsplit(x = MS2sTMPLib,split = \":\",fixed = T)[[1]],split = \"_\",fixed = T)),FUN = function(x){c(mz=as.numeric(x[1]),\n int=as.numeric(x[2]))}))\n temp<-data.frame(temp)\n\t temp<-temp[temp$int!=0,]\n mzs<- paste(temp$mz,collapse = \";\")\n ints<-paste(temp$int,collapse = \";\")\n\n TempLib$MS2mz<-parentmzs[[p]]\n TempLib$MS2rt<-parentrts[[p]]\n TempLib$MS2intensity<-parentInts[[p]]\n\n TempLib$MS2fileName<-fileNames[[p]]\n\n\n TempLib$MS2mzs<-mzs\n TempLib$MS2intensities<-ints\n TempLib$featureGroup<-k\n\t if(length(temp$mz)>=minPeaks)\n newLib<-rbind(newLib,TempLib)\n\n }\n}\n\nwrite.csv(x=newLib,file=savePath)\n\n}\n", "meta": {"hexsha": "a473b6b83741fb849a71afef3849b0b9c0610f25", "size": 7429, "ext": "r", "lang": "R", "max_stars_repo_path": "bin/createLibraryFun.r", "max_stars_repo_name": "jordeu/metaboigniter", "max_stars_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_repo_stars_event_min_datetime": "2021-02-19T12:58:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-16T18:49:31.000Z", "max_issues_repo_path": "bin/createLibraryFun.r", "max_issues_repo_name": "jordeu/metaboigniter", "max_issues_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 45, "max_issues_repo_issues_event_min_datetime": "2021-02-04T21:08:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-24T11:41:15.000Z", "max_forks_repo_path": "bin/createLibraryFun.r", "max_forks_repo_name": "jordeu/metaboigniter", "max_forks_repo_head_hexsha": "5417e975537515a16cc621292bbd77e3830585aa", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2021-02-04T09:01:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-22T07:33:05.000Z", "avg_line_length": 34.2350230415, "max_line_length": 161, "alphanum_fraction": 0.6252523893, "num_tokens": 2094, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7606506635289835, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.30126951347905756}} {"text": "##Layer=vector\n##Stat_file=Output file html\n##Fecha_inicio=string\n##Fecha_final=string\n##VariableClimatica=selection pp;temp;etp;esco\nlibrary(rgee)\nuse_python(\"C:/Users/logis/AppData/Local/Microsoft/WindowsApps/python3\")\nlibrary(htmlwidgets)\nlibrary(cptcity)\n\nee_Initialize()\n\n\ndata <- Layer\nroi <- sf_as_ee(data)\ninicio= Fecha_inicio\nfinal = Fecha_final\n\nif(VariableClimatica==0){\ndem <- ee$ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")$select(\"pr\")$filterDate(inicio,final)$mean()$clip(roi)\n} else if(VariableClimatica==1){\ndem <- ee$ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")$select(\"tmmx\")$filterDate(inicio,final)$mean()$clip(roi)\n}else if (VariableClimatica==2){\ndem <- ee$ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")$select(\"pet\")$filterDate(inicio,final)$mean()$clip(roi)\n} else{\ndem <- ee$ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")$select(\"ro\")$filterDate(inicio,final)$mean()$clip(roi)\n\n}\n\nMap$centerObject(roi)\na <- Map$addLayer(dem)\na <- Map$addLayer(dem)\na + Map$addLegend(\n list(\n min = 10,\n max = 25,\n palette=cpt(pal = \"grass_bcyr\")),\n name = \"tmmax\",\n position =\"bottomright\",\n bins = 4)\nsaveWidget(a,Stat_file)\n\n\n\n", "meta": {"hexsha": "378b37e559ec597016e85233dff160c830519df7", "size": 1149, "ext": "rsx", "lang": "R", "max_stars_repo_path": "scripts/temperatura.rsx", "max_stars_repo_name": "klauswiese/GRC_FbF_Rtoolbox", "max_stars_repo_head_hexsha": "f591ddf16302c5c304f190a317ae80c144441bb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-09T03:19:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-09T15:00:59.000Z", "max_issues_repo_path": "scripts/temperatura.rsx", "max_issues_repo_name": "klauswiese/GRC_FbF_Rtoolbox", "max_issues_repo_head_hexsha": "f591ddf16302c5c304f190a317ae80c144441bb3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/temperatura.rsx", "max_forks_repo_name": "klauswiese/GRC_FbF_Rtoolbox", "max_forks_repo_head_hexsha": "f591ddf16302c5c304f190a317ae80c144441bb3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-09T04:12:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-09T15:01:24.000Z", "avg_line_length": 25.5333333333, "max_line_length": 111, "alphanum_fraction": 0.7345517842, "num_tokens": 370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3011624157810778}} {"text": "library(shiny)\nlibrary(gmhelper)\n\n\nshinyServer(function(input, output, session){\n files <- dir(\"./pages\", recursive=TRUE, pattern=\"[.]r$\")\n files <- paste0(\"./pages/\", files)\n for (file in files)\n source(file=file, local=TRUE)\n \n \n localstate <- reactiveValues()\n init_dice()\n \n \n names_npc(input)\n names_dungeon(input)\n names_adventure(input)\n names_tavern(input)\n names_town(input)\n names_society(input)\n \n loot_pockets(input)\n loot_potions(input)\n \n misc_crits(input)\n \n dice_roller_basic(input)\n dice_roller_advanced(input)\n dice_roller_scatter(input)\n \n cards_tarot_draw(input)\n cards_playing_draw(input)\n cards_domt_draw(input)\n})\n", "meta": {"hexsha": "9cb9d4a391f0bc2fe02124be9fe22dabc9b241ce", "size": 668, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/shinyapp/server.r", "max_stars_repo_name": "wrathematics/gmhelper", "max_stars_repo_head_hexsha": "e6290750dae862403574354301265c804bcf85ab", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-05-22T12:24:29.000Z", "max_stars_repo_stars_event_max_datetime": "2015-05-22T12:24:29.000Z", "max_issues_repo_path": "inst/shinyapp/server.r", "max_issues_repo_name": "wrathematics/gmhelper", "max_issues_repo_head_hexsha": "e6290750dae862403574354301265c804bcf85ab", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/shinyapp/server.r", "max_forks_repo_name": "wrathematics/gmhelper", "max_forks_repo_head_hexsha": "e6290750dae862403574354301265c804bcf85ab", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 18.5555555556, "max_line_length": 58, "alphanum_fraction": 0.7110778443, "num_tokens": 174, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3011624094588169}} {"text": "library(clinfun)\nsource(file.path(PROJECT_DIR, \"R/functions/gg_color_hue.r\"))\n\nfn.si = file.path(PROJECT_DIR, \"generated_data\", \"YF\", \"YF_cd38_ge_sig_score.txt\")\ninfo = fread(fn.si) %>% \n dplyr::filter(Response %in% c(\"low\",\"middle\",\"high\")) %>% \n mutate(Response = factor(Response, levels=c(\"low\",\"middle\",\"high\")),\n label = ifelse(Trial==1, \"Yellow Fever\", paste0(\"Yellow Fever, Trial \", Trial)))\n\ndf.text = data.frame()\n\nfor (trial in 1:2) {\n tinfo = info %>% dplyr::filter(Trial == trial)\n \n w.pv = jonckheere.test(tinfo$CD38_score, as.numeric(tinfo$Response), alternative=\"inc\")$p.value\n \n df.text = rbind(df.text, \n data.frame(Trial=trial, #label.auc=sprintf(\"AUC = %.2f\\np = %.2g\", r$auc, r.p), \n label.w.pv=sprintf(\"p = %.2g\",w.pv)))\n}\n\ndf.text$x = 0.25\ndf.text$y = 0.1\n\ndf.text = df.text %>% \n mutate(label = ifelse(Trial==1, \"Yellow Fever\", paste0(\"Yellow Fever, Trial \", Trial)))\n\nclr = gg_color_hue(4) %>% rev()\n\nfor (t in 1:2) {\n df.text.2 = df.text %>% \n dplyr::filter(Trial==t)\n \n ggplot(info %>% dplyr::filter(Trial==t), aes(x=Response, y=CD38_score, group=Response)) +\n geom_boxplot(aes(fill=label), alpha=0.5, outlier.colour = NA) +\n geom_dotplot(binaxis = \"y\", stackdir = \"center\", aes(fill=Response)) +\n facet_wrap(~label, nrow=1) +\n scale_fill_manual(values=c(\"black\", \"white\", \"grey35\", clr[4]), name=\"Response\",\n breaks=c(0,1,2), labels=c(\"low\",\"middle\",\"high\")) +\n geom_text(data = df.text.2, aes(label=label.w.pv), x=1.5, y=Inf,vjust=1.1, hjust=0.5, size=4, inherit.aes = F) +\n xlab(\"Response\") + ylab(\"Baseline signature score\") +\n coord_cartesian(xlim=c(0.8, 3.2)) +\n theme_bw() + theme(legend.position=\"none\") +\n theme(panel.border = element_blank(), strip.background = element_blank(), \n strip.text.x = element_text(size=12),\n panel.spacing = unit(0,\"mm\"), panel.grid.major.x = element_blank(),\n axis.ticks = element_blank())\n \n fn.fig = file.path(PROJECT_DIR, \"figure_generation\", sprintf(\"YF_Trial%d_CD38.10gene.sig_with_mid_Jonckheere\", t))\n ggsave(paste0(fn.fig, \".png\"), w=2.5,h=4)\n ggsave(paste0(fn.fig, \".pdf\"), w=2.5,h=4)\n \n}\n", "meta": {"hexsha": "a136c6ee8aa7fd28d335659ca90211e59064462b", "size": 2212, "ext": "r", "lang": "R", "max_stars_repo_path": "R/yf_signature_analysis/yf_cd38_10gene_sig_analysis_FIGURES_with_mid.r", "max_stars_repo_name": "niaid/wl-test", "max_stars_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_repo_stars_event_min_datetime": "2020-04-10T05:08:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-04T18:41:28.000Z", "max_issues_repo_path": "R/yf_signature_analysis/yf_cd38_10gene_sig_analysis_FIGURES_with_mid.r", "max_issues_repo_name": "niaid/wl-test", "max_issues_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2020-05-01T13:24:18.000Z", "max_issues_repo_issues_event_max_datetime": "2020-07-06T17:39:19.000Z", "max_forks_repo_path": "R/yf_signature_analysis/yf_cd38_10gene_sig_analysis_FIGURES_with_mid.r", "max_forks_repo_name": "niaid/wl-test", "max_forks_repo_head_hexsha": "9ac8aa781ed73b509e1410f147f6799e9a77da86", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-02-25T18:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-03T02:45:05.000Z", "avg_line_length": 40.962962963, "max_line_length": 116, "alphanum_fraction": 0.6184448463, "num_tokens": 714, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.3011624094588169}} {"text": "#' Compute a unique numeric id for each unique row in a data frame.\n#'\n#' Properties:\n#' \\itemize{\n#' \\item `order(id)` is equivalent to `do.call(order, df)`\n#' \\item rows containing the same data have the same value\n#' \\item if `drop = FALSE` then room for all possibilities\n#' }\n#'\n#' @param .variables list of variables\n#' @param drop drop unused factor levels?\n#' @return a numeric vector with attribute n, giving total number of\n#' possibilities\n#' @keywords internal\n#' @export\nid <- function(.variables, drop = FALSE) {\n warn(\"`id()` is deprecated\")\n\n # Drop all zero length inputs\n lengths <- vapply(.variables, length, integer(1))\n .variables <- .variables[lengths != 0]\n\n if (length(.variables) == 0) {\n n <- nrow(.variables) %||% 0L\n return(structure(seq_len(n), n = n))\n }\n\n # Special case for single variable\n if (length(.variables) == 1) {\n return(id_var(.variables[[1]], drop = drop))\n }\n\n # Calculate individual ids\n ids <- rev(lapply(.variables, id_var, drop = drop))\n p <- length(ids)\n\n # Calculate dimensions\n ndistinct <- vapply(ids, attr, \"n\", FUN.VALUE = numeric(1), USE.NAMES = FALSE)\n n <- prod(ndistinct)\n if (n > 2 ^ 31) {\n # Too big for integers, have to use strings, which will be much slower :(\n\n char_id <- do.call(\"paste\", c(ids, sep = \"\\r\"))\n res <- match(char_id, unique(char_id))\n } else {\n combs <- c(1, cumprod(ndistinct[-p]))\n\n mat <- do.call(\"cbind\", ids)\n res <- c((mat - 1L) %*% combs + 1L)\n }\n attr(res, \"n\") <- n\n\n\n if (drop) {\n id_var(res, drop = TRUE)\n } else {\n structure(as.integer(res), n = attr(res, \"n\"))\n }\n}\n\nid_var <- function(x, drop = FALSE) {\n if (length(x) == 0) return(structure(integer(), n = 0L))\n if (!is.null(attr(x, \"n\")) && !drop) return(x)\n\n if (is.factor(x) && !drop) {\n id <- as.integer(addNA(x, ifany = TRUE))\n n <- length(levels(x))\n } else {\n levels <- sort(unique(x), na.last = TRUE)\n id <- match(x, levels)\n n <- max(id)\n }\n structure(id, n = n)\n}\n", "meta": {"hexsha": "0874f5f394e708961770006911af72b12e12b1f7", "size": 2003, "ext": "r", "lang": "R", "max_stars_repo_path": "R/id.r", "max_stars_repo_name": "edublancas/dplyr", "max_stars_repo_head_hexsha": "3ace34bf8eaec8295d5a7f09ab19947ced3438cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-19T20:04:09.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-19T20:04:09.000Z", "max_issues_repo_path": "R/id.r", "max_issues_repo_name": "edublancas/dplyr", "max_issues_repo_head_hexsha": "3ace34bf8eaec8295d5a7f09ab19947ced3438cd", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/id.r", "max_forks_repo_name": "edublancas/dplyr", "max_forks_repo_head_hexsha": "3ace34bf8eaec8295d5a7f09ab19947ced3438cd", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-10-20T17:53:42.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-20T17:53:42.000Z", "avg_line_length": 26.7066666667, "max_line_length": 80, "alphanum_fraction": 0.6005991013, "num_tokens": 603, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.301113930649264}} {"text": "# see https://github.com/ices-tools-prod/icesSAG\n\n#icesSAG can be installed from CRAN using the install.packages command \n# install.packages(\"icesSAG\")\n\nlibrary(icesSAG)\n# ?icesSAG\n\nstocks <- getListStocks(2018)\nsubset(stocks,SpeciesName=='Scomber scombrus')\n#write.csv(stocks,file=file.path(data.path,'a.csv'))\n\n# read list of stocks to be processed\nmy.stocks<-read.csv(file=file.path(data.path,'ICEStockList.in'),stringsAsFactors=FALSE)\nhead(my.stocks)\n\ny<-2017\nb<-NULL\nfor (i in (1:dim(my.stocks)[[1]])) {\n stock<-my.stocks[i,'StockKeyLabel']\n cat(my.stocks[i,1],'\\n')\n a<-getSAG(stock=stock, y)\n if (length(a)>0) {\n a<-subset(a,select=c(-high_recruitment,-low_recruitment,-StockPublishNote,-Fage,-fishstock,-units,-stockSizeDescription,-stockSizeUnits,-fishingPressureDescription ,-fishingPressureUnits))\n a$SMS<-my.stocks[i,'SMS']\n a$Species.n<-my.stocks[i,'nr']\n b<-rbind(a,b)\n }\n}\n\nunique(b$SMS) \nhead(b)\n\n#head(Read.summary.table())\n\n\n\n#Species Species.n Year Rec SSB TSB SOP SOP.hat Yield Yield.hat mean.F Eaten\na<-data.frame(Species=sp.names[b$Species.n],Species.n=b$Species.n,Year=b$Year,SSB=b$SSB,mean.F=b$F,Rec=b$recruitment,Yield=ifelse(!is.na(b$catches),b$catches,b$landings))\na$SOP.hat<-a$Yield\na$SOP<-a$Yield\n\nhead(a)\n\n\nwrite.table(a,row.names = FALSE, col.names = TRUE,file=file.path(root,'ICESsingle','summary_table_raw.out'))\n\n", "meta": {"hexsha": "435fc69bc50821fe8e1f78f9cbae44702f7c3193", "size": 1383, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/StandardGrafDataExtract.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/StandardGrafDataExtract.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/StandardGrafDataExtract.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8125, "max_line_length": 192, "alphanum_fraction": 0.7114967462, "num_tokens": 447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.30111393064926395}} {"text": "adjImm_threshold <- 0.93 ## threshold for contact-adjusted immunity\n\nscenarios <- scenarios_adjImm %>%\n mutate(country=factor(country),\n scenario=factor(scenario)) %>%\n arrange(country) %>%\n gather(model, value, ends_with(\"_immunity\")) %>%\n mutate(model=recode_factor(model,\n adjusted_immunity=\"age-specific\",\n mean_immunity=\"homogeneous\"))\n\ncountries <-\n grep(\"\\\\(\", invert=TRUE, unique(as.character(scenarios$country)), value=TRUE)\n\nchange_order <- c(\"decrease\", \"increase\", \"none\")\n\nage_levels <- c(\"0-4\", \"5-9\", \"10-14\", \"15-19\", \"20+\")\ntarget_levels <- tibble(age = factor(age_levels, age_levels),\n target = c(0.85, 0.9, 0.95, 0.95, 0.95),\n scenario = \"current\",\n change = factor(\"none\", change_order))\n\nchange_levels <-\n change_levels <- list(increase_infant = c(0.05, 0, 0, 0, 0),\n catchup_kids = c(0, 0.05, 0, 0, 0),\n catchup_kids_less_teenagers = c(0, 0.05, -0.05, 0, 0),\n catchup_kids_less_adolescents = c(0, 0.05, 0, -0.05, 0))\n\nlevels <- list()\nfor (changed in names(change_levels))\n{\n subtract_levels <- change_levels[[changed]]\n subtract_levels[subtract_levels > 0] <- 0\n add_levels <- change_levels[[changed]]\n add_levels[add_levels < 0] <- 0\n changed_target_levels <- target_levels$target + subtract_levels\n levels[[changed]] <-\n rbind(tibble(age = target_levels$age,\n target = -subtract_levels,\n scenario = changed,\n change = factor(\"decrease\", change_order)),\n tibble(age = target_levels$age,\n target = add_levels,\n scenario = changed,\n change = factor(\"increase\", change_order)))\n levels[[changed]] <-\n rbind(target_levels %>% mutate(scenario = changed,\n target = changed_target_levels),\n levels[[changed]])\n}\n\nall_levels <- rbind(target_levels, bind_rows(levels)) %>%\n mutate(scenario = factor(scenario,\n levels = c(\"current\", names(change_levels))))\n\nage_specific_scenarios <- scenarios %>%\n filter(model == \"age-specific\",\n scenario %in% unique(all_levels$scenario),\n settings==\"all\") %>%\n mutate(scenario = factor(scenario, levels(all_levels$scenario)),\n country = factor(country, levels=countries))\n\np_imm <- ggplot(all_levels %>%\n mutate(scenario =\n factor(scenario,\n labels = letters[1:length(unique(scenario))])),\n aes(x = age, y = target, fill = change)) +\n geom_bar(stat = \"identity\", color = \"black\", position=\"stack\") +\n scale_y_continuous(\"immunity\", label=scales::percent_format(accuracy=1),\n limits = c(0, 1)) +\n facet_grid( ~ scenario) +\n coord_cartesian(ylim = c(0.8, 1)) +\n theme(strip.background = element_blank(),\n plot.margin = unit(c(0, 0, 0, 1.94), \"cm\"),\n axis.text.x = element_text(angle = 45, hjust = 1),\n legend.position = \"none\") +\n scale_x_discrete(\"\") +\n scale_fill_manual(values = c(\"white\", \"lightblue\", \"black\"))\n\neI_breaks <- c(0.9, 0.95)\neI_limits <- eI_breaks[c(1, length(eI_breaks))] * c(0.99, 1.01)\np_eI <- ggplot(age_specific_scenarios) +\n facet_grid( ~ scenario) +\n geom_boxplot(aes(x = factor(country, rev(levels(country))), y = value)) +\n geom_hline(yintercept=adjImm_threshold, linetype=\"dashed\") +\n xlab(\"\") +\n scale_y_continuous(\"Contact-adjusted immunity\",\n breaks = eI_breaks,\n labels = scales::percent_format(accuracy=1),\n limits = eI_limits) +\n ## geom_vline(xintercept=length(regions)+0.5, linetype=\"dashed\") +\n theme(strip.text.x = element_blank(),\n strip.text.y = element_blank(),\n strip.background = element_blank(),\n plot.margin = unit(c(0, 0, 0, 0), \"cm\")) +\n coord_flip()\n\np <- plot_grid(p_imm, p_eI, nrow = 2, rel_heights=c(0.7, length(countries) / 10))\nggsave(\"figure_3.pdf\", p, width = 15, height = length(countries) / 2)\n\n## in the text: countries with >10% of outbreaks under current levels\ncurrent_elim_prob <- age_specific_scenarios %>%\n filter(scenario==\"current\") %>%\n group_by(country) %>%\n summarise(prob=mean(value < 0.93)) %>%\n filter(prob > 0.1) %>%\n mutate(prob=formattable::percent(prob, digits=0))\n\nincrease_infant_elim_prob <- age_specific_scenarios %>%\n filter(scenario==\"increase_infant\") %>%\n group_by(country) %>%\n summarise(prob=mean(value < 0.93)) %>%\n filter(prob > 0.1) %>%\n mutate(prob=formattable::percent(prob, digits=0))\n\ncatchup_kids_elim_prob <- age_specific_scenarios %>%\n filter(scenario==\"catchup_kids\") %>%\n group_by(country) %>%\n summarise(prob=mean(value < 0.93)) %>%\n filter(prob > 0.1) %>%\n mutate(prob=formattable::percent(prob, digits=1))\n", "meta": {"hexsha": "259b935667673d39fbf6145df03f15dec725626d", "size": 4959, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/analysis/scenarios_analysis.r", "max_stars_repo_name": "sbfnk/immunity.thresholds", "max_stars_repo_head_hexsha": "c8f59f28262c884b20ddf3077356cf5c53e09c72", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/analysis/scenarios_analysis.r", "max_issues_repo_name": "sbfnk/immunity.thresholds", "max_issues_repo_head_hexsha": "c8f59f28262c884b20ddf3077356cf5c53e09c72", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/analysis/scenarios_analysis.r", "max_forks_repo_name": "sbfnk/immunity.thresholds", "max_forks_repo_head_hexsha": "c8f59f28262c884b20ddf3077356cf5c53e09c72", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.6475409836, "max_line_length": 81, "alphanum_fraction": 0.5991127243, "num_tokens": 1301, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.3011139306492639}} {"text": "#' @title Compute keyword-country search score\n#'\n#' @aliases\n#' compute_score\n#' compute_score.numeric\n#' compute_score.list\n#'\n#' @description\n#' The function computes search scores for object keywords. Search volumes for\n#' control and object batches are mapped to the same base. Next, search volumes\n#' for object batches are divided by the sum of search volumes for the\n#' respective control batch. `compute_voi` computes volume of\n#' internationalization (VOI) as global search scores.\n#'\n#' @details\n#' The search score computation proceeds in four steps. First, the function\n#' aggregates all search volumes to monthly data. Then, it applies some optional\n#' time series adjustments: seasonally adjusted [`forecast::seasadj`] and\n#' trend only [`stats::stl`]. Next, it follows the procedure outlined by\n#' Castelnuovo and Tran (2017, pp. A1-A2) to map control and object data. After\n#' the mapping, object search volumes are divided by the sum of control search\n#' volumes in the respective control batch. We use the sum of search volumes for\n#' a set of control keywords, rather than the search volumes for a single\n#' control keyword, to smooth-out variation in the underlying control data. When\n#' synonyms were specified through `add_synonym`, search scores for\n#' synonyms are added to the main keyword.\n#'\n#' *Castelnuovo, E. & Tran, T. D. 2017. Google It Up! A Google Trends-based\n#' Uncertainty index for the United States and Australia. Economics Letters,\n#' 161: 149-153.*\n#'\n#'\n#' @param control Control batch for which the data is downloaded. Object\n#' of type `numeric`. Defaults to 1.\n#' @param object Object batch for which the data is downloaded. Object\n#' of type `numeric` or object of type `list` containing single\n#' objects of type `numeric`.\n#' @param locations List of countries or regions for which the data is\n#' downloaded. Refers to lists generated in `start_db`. Defaults to\n#' `countries`.\n#'\n#' @seealso\n#' * [example_score()]\n#' * [add_synonym()]\n#' * [stats::stl()]\n#' * [forecast::seasadj()]\n#'\n#' @return Message that data has been computed successfully. Data is written to\n#' table *data_score*.\n#'\n#' @examples\n#' \\dontrun{\n#' compute_score(\n#' object = 1,\n#' control = 1,\n#' locations = countries\n#' )\n#' compute_voi(\n#' object = 1,\n#' control = 1\n#' )\n#' compute_score(\n#' object = as.list(1:5),\n#' control = 1,\n#' locations = countries\n#' )\n#' }\n#'\n#' @export\n#' @rdname compute_score\n#' @importFrom DBI dbWriteTable\n#' @importFrom dplyr anti_join\n#' @importFrom dplyr case_when\n#' @importFrom dplyr coalesce\n#' @importFrom dplyr collect\n#' @importFrom dplyr contains\n#' @importFrom dplyr count\n#' @importFrom dplyr filter\n#' @importFrom dplyr inner_join\n#' @importFrom dplyr left_join\n#' @importFrom dplyr mutate\n#' @importFrom dplyr select\n#' @importFrom dplyr summarise\n#' @importFrom glue glue\n#' @importFrom lubridate as_date\n#' @importFrom purrr walk\n#' @importFrom rlang .data\n#' @importFrom rlang env_parent\n#' @importFrom stringr str_replace\n#' @importFrom tidyr nest\n#' @importFrom tidyr pivot_longer\n#' @importFrom tidyr pivot_wider\n#' @importFrom tidyr unnest\n\ncompute_score <- function(object, control = 1, locations = countries) UseMethod(\"compute_score\", object)\n\n#' @rdname compute_score\n#' @method compute_score numeric\n#' @export\n\ncompute_score.numeric <- function(object, control = 1, locations = countries) {\n control <- unlist(control)\n .check_length(control, 1)\n .check_input(locations, \"character\")\n if (length(object) > 1) {\n compute_score(control = control, object = as.list(object), locations = locations)\n } else {\n walk(list(control, object), .check_batch)\n ts_control <- TRUE\n ts_object <- TRUE\n walk(locations, ~ {\n if (.test_empty(\n table = \"data_score\",\n batch_c = control,\n batch_o = object,\n location = .x\n )) {\n qry_object <- filter(\n .tbl_object,\n .data$batch_c == control & .data$batch_o == object & .data$location == .x\n )\n qry_object <- collect(qry_object)\n if (nrow(qry_object) != 0) {\n qry_control <- filter(.tbl_control, .data$batch == control & .data$location == .x)\n qry_control <- collect(qry_control)\n\n qry_control <- mutate(qry_control, date = as_date(.data$date))\n qry_object <- mutate(qry_object, date = as_date(.data$date))\n\n # adapt time series frequency\n qry_control <- .reset_date(qry_control)\n qry_object <- .reset_date(qry_object)\n\n if (\n min(\n nrow(count(qry_control, .data$date)),\n nrow(count(qry_object, .data$date))\n ) >= 24\n ) {\n # adjust to time series and impute negative values\n qry_control <- nest(qry_control, data = c(.data$date, .data$hits))\n qry_control <- mutate(qry_control, data = map(.data$data, .adjust_ts))\n qry_control <- unnest(qry_control, .data$data)\n qry_control <- mutate(qry_control,\n hits_trd = case_when(\n .data$hits_trd < 0 & .data$hits_sad < 0 ~ 0.1,\n .data$hits_trd < 0 ~ (.data$hits_obs + .data$hits_sad) / 2,\n TRUE ~ .data$hits_trd\n ),\n hits_sad = case_when(\n .data$hits_sad < 0 & .data$hits_trd < 0 ~ 0.1,\n .data$hits_sad < 0 ~ (.data$hits_obs + .data$hits_trd) / 2,\n TRUE ~ .data$hits_sad\n )\n )\n qry_object <- nest(qry_object, data = c(.data$date, .data$hits))\n qry_object <- mutate(qry_object, data = map(.data$data, .adjust_ts))\n qry_object <- unnest(qry_object, .data$data)\n qry_object <- mutate(qry_object,\n hits_trd = case_when(\n .data$hits_trd < 0 & .data$hits_sad < 0 ~ 0.1,\n .data$hits_trd < 0 ~ (.data$hits_obs + .data$hits_sad) / 2,\n TRUE ~ .data$hits_trd\n ),\n hits_sad = case_when(\n .data$hits_sad < 0 & .data$hits_trd < 0 ~ 0.1,\n .data$hits_sad < 0 ~ (.data$hits_obs + .data$hits_trd) / 2,\n TRUE ~ .data$hits_sad\n )\n )\n } else {\n if (nrow(count(qry_control, .data$date)) < 24) assign(\"ts_control\", FALSE, envir = env_parent())\n if (nrow(count(qry_object, .data$date)) < 24) assign(\"ts_object\", FALSE, envir = env_parent())\n }\n qry_control <- pivot_longer(\n qry_control,\n cols = contains(\"hits\"),\n names_to = \"key\",\n values_to = \"value\"\n )\n qry_object <- pivot_longer(\n qry_object,\n cols = contains(\"hits\"),\n names_to = \"key\",\n values_to = \"value\"\n )\n\n # set to benchmark\n data_control <- inner_join(\n qry_object,\n qry_control,\n by = c(\n \"location\",\n \"keyword\",\n \"date\",\n \"key\"\n ),\n suffix = c(\"_o\", \"_c\")\n )\n data_control <- mutate(\n data_control,\n value_o = case_when(\n .data$value_o == 0 ~ 1,\n TRUE ~ .data$value_o\n ),\n value_c = case_when(\n .data$value_c == 0 ~ 1,\n TRUE ~ .data$value_c\n )\n )\n data_control <- mutate(data_control,\n benchmark = coalesce(.data$value_o / .data$value_c, 0)\n )\n data_control <- select(data_control, .data$location, .data$date, .data$key, .data$benchmark)\n data_control <- inner_join(\n data_control,\n qry_control,\n by = c(\"location\", \"date\", \"key\")\n )\n data_control <- mutate(data_control, value = .data$value * .data$benchmark)\n data_control <- select(\n data_control,\n .data$location,\n .data$date,\n .data$key,\n .data$keyword,\n .data$value\n )\n\n data_object <- anti_join(qry_object, data_control, by = c(\"keyword\"))\n\n # compute score\n data_control <- group_by(data_control, .data$location, .data$date, .data$key)\n data_control <- summarise(data_control, value_c = sum(.data$value), .groups = \"drop\")\n data_object <- left_join(\n data_object,\n data_control,\n by = c(\"location\", \"date\", \"key\")\n )\n data_object <- mutate(data_object,\n score = coalesce(.data$value / .data$value_c, 0),\n key = str_replace(.data$key, \"hits$\", \"score_obs\"),\n key = str_replace(.data$key, \"hits_\", \"score_\")\n )\n data_object <- select(data_object, .data$location, .data$date, .data$keyword, .data$key, .data$score)\n out <- pivot_wider(\n data_object,\n names_from = .data$key,\n values_from = .data$score,\n values_fill = 0\n )\n out <- mutate(\n out,\n batch_c = control,\n batch_o = object,\n synonym = case_when(\n .data$keyword %in% .keyword_synonyms$synonym ~ TRUE,\n TRUE ~ FALSE\n )\n )\n dbWriteTable(\n conn = globaltrends_db,\n name = \"data_score\",\n value = out,\n append = TRUE\n )\n }\n }\n message(glue(\"Successfully computed search score | control: {control} | object: {object} | location: {.x} [{current}/{total}]\", current = which(locations == .x), total = length(locations)))\n })\n .aggregate_synonym(object = object)\n if (!ts_control | !ts_object) {\n text <- case_when(\n all(!c(ts_control, ts_object)) ~ \"control and object\",\n first(!c(ts_control, ts_object)) ~ \"control\",\n last(!c(ts_control, ts_object)) ~ \"object\"\n )\n warning(glue(\"You provided {text} data for less than 24 months.\\nNo time series adjustments possible.\"))\n }\n }\n}\n\n#' @rdname compute_score\n#' @method compute_score list\n#' @export\n\ncompute_score.list <- function(object, control = 1, locations = countries) {\n walk(object, compute_score, control = control, locations = locations)\n}\n\n#' @rdname compute_score\n#' @export\n\ncompute_voi <- function(object, control = 1) {\n compute_score(control = control, object = object, locations = \"world\")\n}\n\n#' @title Reset date\n#'\n#' @rdname hlprs\n#' @keywords internal\n#' @noRd\n#'\n#' @importFrom dplyr group_by\n#' @importFrom dplyr mutate\n#' @importFrom dplyr select\n#' @importFrom dplyr summarise\n#' @importFrom lubridate month\n#' @importFrom lubridate year\n#' @importFrom lubridate ymd\n#' @importFrom rlang .data\n\n.reset_date <- function(data) {\n out <- mutate(data, day = 1, month = month(.data$date), year = year(.data$date))\n out <- group_by(out, .data$location, .data$keyword, .data$year, .data$month, .data$day)\n out <- summarise(out, hits = mean(.data$hits), .groups = \"drop\")\n out <- mutate(out, date = ymd(glue(\"{.data$year}-{.data$month}-{.data$day}\")))\n out <- select(out, .data$location, .data$keyword, .data$date, .data$hits)\n return(out)\n}\n\n#' @title Adjust time series\n#'\n#' @rdname hlprs\n#' @keywords internal\n#' @noRd\n#'\n#' @importFrom lubridate month\n#' @importFrom lubridate year\n#' @importFrom tibble tibble\n\n.adjust_ts <- function(data) {\n myts <- stats::ts(data$hits, start = c(year(min(data$date)), month(min(data$date))), end = c(year(max(data$date)), month(max(data$date))), frequency = 12)\n fit <- stats::stl(myts, s.window = \"period\")\n trend <- fit$time.series[, \"trend\"]\n seasad <- forecast::seasadj(fit)\n out <- tibble(date = data$date, hits_obs = data$hits, hits_trd = as.double(trend), hits_sad = as.double(seasad))\n return(out)\n}\n\n#' Aggregate synonyms\n#'\n#' @rdname hlprs\n#' @keywords internal\n#' @noRd\n#'\n#' @export\n#' @importFrom DBI dbExecute\n#' @importFrom DBI dbWriteTable\n#' @importFrom dplyr anti_join\n#' @importFrom dplyr bind_rows\n#' @importFrom dplyr collect\n#' @importFrom dplyr filter\n#' @importFrom dplyr inner_join\n#' @importFrom dplyr mutate\n#' @importFrom dplyr left_join\n#' @importFrom dplyr select\n#' @importFrom rlang .data\n#' @importFrom purrr walk\n\n.aggregate_synonym <- function(object) {\n lst_synonym <- filter(.keywords_object, .data$batch == object)\n lst_synonym1 <- inner_join(lst_synonym, .keyword_synonyms, by = \"keyword\")\n lst_synonym2 <- inner_join(lst_synonym, .keyword_synonyms, by = c(\"keyword\" = \"synonym\"))\n lst_synonym <- unique(c(lst_synonym1$synonym, lst_synonym2$keyword))\n\n if (length(lst_synonym) > 0) {\n message(\"Checking for synonyms...\")\n data_synonym <- filter(.tbl_score, .data$keyword %in% lst_synonym & .data$synonym == 1)\n data_synonym <- collect(data_synonym)\n\n if (nrow(data_synonym) > 0) {\n message(\"Aggregating scores for synonyms...\")\n lst_main <- unique(.keyword_synonyms$keyword[.keyword_synonyms$synonym %in% lst_synonym])\n data_main <- filter(.tbl_score, .data$keyword %in% lst_main)\n data_main <- collect(data_main)\n\n walk(lst_synonym, ~ {\n keyword_main <- .keyword_synonyms$keyword[.keyword_synonyms$synonym == .x][[1]]\n sub_main <- filter(data_main, .data$keyword == keyword_main)\n\n sub_synonym <- filter(data_synonym, .data$keyword == .x)\n sub_main <- left_join(\n sub_main,\n sub_synonym,\n by = c(\"location\", \"date\", \"batch_c\"),\n suffix = c(\"\", \"_s\")\n )\n\n sub_main <- mutate(\n sub_main,\n score_obs = .data$score_obs + coalesce(.data$score_obs_s, 0),\n score_sad = .data$score_sad + coalesce(.data$score_sad_s, 0),\n score_trd = .data$score_trd + coalesce(.data$score_trd_s, 0)\n )\n sub_main <- select(\n sub_main,\n .data$location,\n .data$keyword,\n .data$date,\n .data$score_obs,\n .data$score_sad,\n .data$score_trd,\n .data$batch_c,\n .data$batch_o,\n .data$synonym\n )\n\n data_synonym_agg <- inner_join(\n sub_synonym,\n select(\n sub_main,\n .data$location,\n .data$date,\n .data$batch_c\n ),\n by = c(\"location\", \"date\", \"batch_c\")\n )\n data_synonym_agg <- mutate(data_synonym_agg, synonym = 2)\n data_synonym_nagg <- anti_join(\n sub_synonym,\n select(\n sub_main,\n location,\n date,\n batch_c\n ),\n by = c(\"location\", \"date\", \"batch_c\")\n )\n\n data <- bind_rows(sub_main, data_synonym_agg, data_synonym_nagg)\n dbExecute(conn = globaltrends_db, statement = \"DELETE FROM data_score WHERE keyword=?\", params = list(keyword_main))\n dbExecute(conn = globaltrends_db, statement = \"DELETE FROM data_score WHERE keyword=?\", params = list(.x))\n dbWriteTable(conn = globaltrends_db, name = \"data_score\", value = data, append = TRUE)\n })\n }\n }\n}\n", "meta": {"hexsha": "3361552a8b111d9451cf2bc177f0e69c17b46b48", "size": 15068, "ext": "r", "lang": "R", "max_stars_repo_path": "R/compute_score.r", "max_stars_repo_name": "ha-pu/doiGT", "max_stars_repo_head_hexsha": "cb0583aea4f14df583d27cfcb2b305f2586255ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/compute_score.r", "max_issues_repo_name": "ha-pu/doiGT", "max_issues_repo_head_hexsha": "cb0583aea4f14df583d27cfcb2b305f2586255ee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 24, 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YES\n2. NO\n\n", "lm_q1_score": 0.6370307806984444, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.30111392413476923}} {"text": "\\name{hc_polygon-dispatch}\n\\alias{hc_polygon}\n\\title{\nMethod dispatch page for hc_polygon\n}\n\\description{\nMethod dispatch page for \\code{hc_polygon}.\n}\n\\section{Dispatch}{\n\\code{hc_polygon} can be dispatched on following classes:\n\n\\itemize{\n\\item \\code{\\link{hc_polygon,HilbertCurve-method}}, \\code{\\link{HilbertCurve-class}} class method\n\\item \\code{\\link{hc_polygon,GenomicHilbertCurve-method}}, \\code{\\link{GenomicHilbertCurve-class}} class method\n}\n}\n\\examples{\n# no example\nNULL\n\n\n}\n", "meta": {"hexsha": "badac9d3c9725abb07e9956936f4afc90404fa5e", "size": 488, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/hc_polygon-dispatch.rd", "max_stars_repo_name": "jokergoo/HilbertCurve", "max_stars_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 37, "max_stars_repo_stars_event_min_datetime": "2016-02-22T16:46:55.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T09:35:43.000Z", "max_issues_repo_path": "man/hc_polygon-dispatch.rd", "max_issues_repo_name": "jokergoo/HilbertCurve", "max_issues_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T08:29:21.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-09T09:44:53.000Z", "max_forks_repo_path": "man/hc_polygon-dispatch.rd", "max_forks_repo_name": "jokergoo/HilbertCurve", "max_forks_repo_head_hexsha": "572d35a5a953a7b468338a142e51f828175fb7c6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8, "max_forks_repo_forks_event_min_datetime": "2016-04-22T10:44:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-16T07:48:16.000Z", "avg_line_length": 21.2173913043, "max_line_length": 111, "alphanum_fraction": 0.768442623, "num_tokens": 136, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5467381667555714, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3010380616348521}} {"text": "infile = {{i.infile | quote}}\noutfile = {{o.outfile | quote}}\nmutypes = {{args.mutypes | R}}\nbinary = {{args.binary | R}}\nna = {{args.na | R}}\nsamfn = {{args.samfn}}\n\n# read the maf file\nmaf = read.table(\n\tif(endsWith(infile, '.gz')) gzfile(infile) else infile,\n\theader = T,\n\trow.names = NULL,\n\tcheck.names = F,\n\tsep = \"\\t\",\n\tquote = ''\n)\n\n# select mutations with the give mutation types\nif (!is.null(mutypes)) {\n\tmaf = maf[which(maf$Variant_Classification %in% mutypes), , drop = F]\n}\n\n# get all samples\nsamples = unique(maf[, \"Tumor_Sample_Barcode\", drop = T])\n# get all genes\ngenes = unique(maf[, \"Hugo_Symbol\", drop = T])\n\n# result matrix\nret = matrix(na, ncol = length(samples), nrow = length(genes))\nrownames(ret) = genes\ncolnames(ret) = unique(samfn(samples))\n\nfor (gene in genes) {\n\tgsamples = maf[which(maf$Hugo_Symbol == gene), \"Tumor_Sample_Barcode\", drop = T]\n\tgsamples = samfn(gsamples)\n\tif (binary) {\n\t\tvalue2fill = matrix(1, nrow = length(gsamples))\n\t\trownames(value2fill) = gsamples\n\t} else {\n\t\tvalue2fill = as.matrix(table(gsamples))\n\t}\n\tret[gene, rownames(value2fill)] = value2fill\n}\nwrite.table(ret, outfile, row.names = T, col.names = T, sep = \"\\t\", quote = F)\n", "meta": {"hexsha": "696d7d595f45563c5cfd26571bc8385e75d08b59", "size": 1214, "ext": "r", "lang": "R", "max_stars_repo_path": "bioprocs/scripts/vcfnext/pMaf2Mat.r", "max_stars_repo_name": "LeaveYeah/bioprocs", "max_stars_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-10T00:17:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-10T09:53:09.000Z", "max_issues_repo_path": "bioprocs/scripts/vcfnext/pMaf2Mat.r", "max_issues_repo_name": "LeaveYeah/bioprocs", "max_issues_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2019-02-15T22:59:49.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-15T23:03:09.000Z", "max_forks_repo_path": "bioprocs/scripts/vcfnext/pMaf2Mat.r", "max_forks_repo_name": "LeaveYeah/bioprocs", "max_forks_repo_head_hexsha": "c5d2ddcc837f5baee00faf100e7e9bd84222cfbf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-10T00:17:54.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-10T09:56:40.000Z", "avg_line_length": 26.9777777778, "max_line_length": 81, "alphanum_fraction": 0.6441515651, "num_tokens": 378, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3010380535018541}} {"text": "#' Makes matrix of functional distances.\n#'\n#' @param com A list, returned by evolve_com.\n#'\n#' @return Matrix of functional distances.\n#' @export\n#'\n#' @examples\n#' make.tr.dist(com)\n\nmake.tr.dist <- function(com)\n{\n tr <- data.frame(com$niche)\n rownames(tr) <- as.character(1:(length(com$n)))\n dist.tr <- as.matrix(daisy(tr, metric = \"euclidean\"))\n\n return(dist.tr)\n}\n", "meta": {"hexsha": "16f57b77e5858b0ccc19b4f15c1b0ea0896cdfda", "size": 374, "ext": "r", "lang": "R", "max_stars_repo_path": "R/make.tr.dist.r", "max_stars_repo_name": "arrirh/sim.assembly", "max_stars_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/make.tr.dist.r", "max_issues_repo_name": "arrirh/sim.assembly", "max_issues_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/make.tr.dist.r", "max_forks_repo_name": "arrirh/sim.assembly", "max_forks_repo_head_hexsha": "4ccf9947f0802bcdfff00aeb45a3072773fd4981", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.6842105263, "max_line_length": 55, "alphanum_fraction": 0.6550802139, "num_tokens": 102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3010380535018541}} {"text": "#!/usr/bin/env Rscript\n\n##### BEGIN FUNCTION BLOCK ######\nmake_image<-function(d,outfile,input_width,text_adjust) {\n # decide output type\n filex = substr(outfile,nchar(outfile)-2,nchar(outfile))\n if(filex==\"pdf\") {\n pdf(outfile,bg=\"#FFFFFF\")\n } else if (filex==\"png\") {\n png(outfile,bg=\"#FFFFFF\")\n } else {\n stop(\"Unsupported type for output file.\\n\",call.=FALSE)\n }\n\n pcol = '#777777'\n fcol = '#7FC97F'\n ucol = '#FDC086'\n\n recwid = input_width\n axcex = text_adjust\n longest = 4000\n\n full = length(d[d[,1]==\"full\",1])\n partial = length(d[d[,1]==\"partial\",1])\n unannot = length(d[d[,1]==\"unannotated\",1])\n\n par(oma=c(0.5,0.5,0.5,0.5))\n par(mar=c(4,5,2,1))\n mat=rbind(c(1,2,3),c(4,4,4))\n layout(mat,c(1.5,5,1.1),c(6,6))\n\n\n tot = full+partial+unannot\n plot(1,type=\"n\",xlim=c(-50,500),ylim=c(0,tot*1.1),xaxt='n',ylab=\"Number of Reads\",bty=\"n\",xlab=\"\",yaxt='n',yaxs='i',cex.lab=axcex)\n axis(2,lwd=recwid,cex.axis=axcex)\n rect(0,0,500,partial,col=pcol,lwd=recwid)\n rect(0,partial,500,full+partial,col=fcol,lwd=recwid)\n rect(0,full+partial,500,full+partial+unannot,col=ucol,lwd=recwid)\n mtext(\"All\",side=1,at=-100,adj=0,line=1,cex=axcex*0.67)\n\n # Do by length\n #find biggest\n biggest = 0\n for(i in seq(0,longest-500,500)) {\n currsize = length(d[d[,2]>i & d[,2]<=i+500,1])\n if(currsize > biggest) { biggest = currsize }\n }\n currsize = length(d[d[,2]>longest,1])\n if(currsize > biggest) { biggest = currsize }\n\n par(mar=c(4,2,2,1))\n plot(1,type=\"n\",xlim=c(0,longest),ylim=c(0,biggest*1.1),ylab=\"\",bty=\"n\",xlab=\"Reference Transcript Length (bp)\",xaxt='n',yaxt='n',yaxs='i',cex.lab=axcex)\n axis(side=1,at=seq(0,longest,500),cex.axis=axcex,lwd=recwid)\n axis(side=2,lwd=recwid,cex.axis=axcex)\n for(i in seq(0,longest-500,500)) {\n full = length(d[d[,1]==\"full\" & d[,2]>i & d[,2]<=i+500,1])\n partial = length(d[d[,1]==\"partial\" & d[,2]>i & d[,2]<=i+500,1])\n unannot = length(d[d[,1]==\"unannotated\" & d[,2]>i & d[,2]<=i+500,1])\n rect(i,0,i+500,partial,col=pcol,lwd=recwid)\n rect(i,partial,i+500,full+partial,col=fcol,lwd=recwid)\n rect(i,full+partial,i+500,full+partial+unannot,col=ucol,lwd=recwid)\n }\n\n full = length(d[d[,1]==\"full\" & d[,2]>longest,1])\n partial = length(d[d[,1]==\"partial\" & d[,2]>longest,1])\n unannot = length(d[d[,1]==\"unannotated\" & d[,2]>longest,1])\n tot = full+partial+unannot\n plot(1,type=\"n\",xlim=c(-50,500),ylim=c(0,tot*1.1),ylab=\"Count longest reads\",bty=\"n\",xlab=\"\",xaxt='n',yaxt='n',yaxs='i',cex.lab=axcex)\n axis(2,lwd=recwid,cex.axis=axcex)\n rect(0,0,500,partial,col=pcol,lwd=recwid)\n rect(0,partial,500,full+partial,col=fcol,lwd=recwid)\n rect(0,full+partial,500,full+partial+unannot,col=ucol,lwd=recwid)\n mtext(paste(\">\",longest),side=1,at=500,adj=1,line=1,cex=axcex*0.67)\n\n\n logtrans<-function(num) {\n if(num==0) {\n return(0)\n }\n if(num==1) {\n return(1)\n }\n return(log(num,2)+1)\n }\n untrans<-function(num) {\n if(num==0) {\n return(0)\n }\n if(num==1) {\n return(1)\n }\n return(2^(num-1))\n }\n neglogtrans<-function(num,lowest) {\n tymin = -1*log(lowest,2)\n return(-1*(-tymin+-1*log(num,2))/tymin)\n }\n\n\n # plot by exon distribution\n #find biggest\n maxexon = 20\n biggest = 0\n for(i in seq(1,maxexon,1)) {\n currsize = length(d[d[,3]==i &d[,1]==\"full\",1])\n if(currsize > biggest) { biggest = currsize }\n currsize = length(d[d[,3]==i &d[,1]==\"partial\",1])\n if(currsize > biggest) { biggest = currsize }\n currsize = length(d[d[,3]==i &d[,1]==\"unannotated\",1])\n if(currsize > biggest) { biggest = currsize }\n }\n currsize = length(d[d[,3]>=maxexon & d[,1]==\"full\",1])\n if(currsize > biggest) { biggest = currsize }\n currsize = length(d[d[,3]>=maxexon & d[,1]==\"partial\",1])\n if(currsize > biggest) { biggest = currsize }\n currsize = length(d[d[,3]>=maxexon & d[,1]==\"unannotated\",1])\n if(currsize > biggest) { biggest = currsize }\n\n endspace = 4\n par(mar=c(5,5,1.5,1))\n plot(1,type=\"n\",xlim=c(0,maxexon+endspace),ylim=c(0,logtrans(biggest*2)),ylab=\"Number of Reads\",bty=\"n\",xlab=\"Exon Count (Reference Transcript)\",xaxt='n',cex.lab=axcex,yaxt='n',yaxs='i')\n axispoints = seq(0,logtrans(biggest*2),1)\n axis(2,at=axispoints,labels=lapply(axispoints,untrans),cex.axis=axcex,lwd=recwid)\n axis(1,at=seq(1,maxexon,1),labels=seq(1,maxexon,1),cex.axis=axcex,lwd=recwid)\n mtext(paste(\">\",maxexon),side=1,at=maxexon+endspace-1,line=1,cex=axcex*0.67)\n for(i in seq(1,maxexon)) {\n full = length(d[d[,1]==\"full\" & d[,3]==i,1])\n partial = length(d[d[,1]==\"partial\" & d[,3]==i,1])\n unannot = length(d[d[,1]==\"unannotated\" & d[,3]==i,1])\n rect(i+0.1-0.5,0,i+0.8-0.5,logtrans(partial),col=pcol,lwd=recwid)\n rect(i+0-0.5,0,i+0.5-0.5,logtrans(full),col=fcol,lwd=recwid)\n rect(i+0.3-0.5,0,i+0.6-0.5,logtrans(unannot),col=ucol,lwd=recwid)\n }\n # get the longest pooled\n full = length(d[d[,1]==\"full\" & d[,3]>maxexon,1])\n partial = length(d[d[,1]==\"partial\" & d[,3]>maxexon,1])\n unannot = length(d[d[,1]==\"unannotated\" & d[,3]>maxexon,1])\n rect(maxexon+endspace-1+0.1-0.5,0,maxexon+endspace-1+0.8-0.5,logtrans(partial),col=pcol,lwd=recwid)\n rect(maxexon+endspace-1+0-0.5,0,maxexon+endspace-1+0.5-0.5,logtrans(full),col=fcol,lwd=recwid)\n rect(maxexon+endspace-1+0.3-0.5,0,maxexon+endspace-1+0.6-0.5,logtrans(unannot),col=ucol,lwd=recwid)\n dev.off()\n}\n\nargs=commandArgs(trailingOnly=TRUE)\nif(length(args)<2) {\n stop(\"Must supply input and output\\n\",call.=FALSE)\n}\noutfile=args[2]\ninfile = args[1]\ninfilex = substr(infile,nchar(infile)-1,nchar(infile))\nif(infilex==\"gz\") {\n d1<-read.csv(infile,sep=\"\\t\",header=FALSE)\n} else {\n d1<-read.csv(gzfile(infile),sep=\"\\t\",header=FALSE)\n}\nd<-data.frame(as.character(d1[,5]))\nd<-cbind(d,as.numeric(d1[,12]),as.numeric(d1[,9]))\ninput_width = 3\nif(length(args) > 2) {\n input_width = args[3]\n}\ntext_adjust = 1.75\nif(length(args) > 3) {\n text_adjust = args[4]\n}\nmake_image(d,outfile,input_width,text_adjust)\n", "meta": {"hexsha": "1d4f1c6bd0090608dbb489256888f4e63188fe2f", "size": 5934, "ext": "r", "lang": "R", "max_stars_repo_path": "alignqc/plot_transcript_lengths.r", "max_stars_repo_name": "jason-weirather/AlignQC", "max_stars_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 38, "max_stars_repo_stars_event_min_datetime": "2016-11-18T09:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T02:45:24.000Z", "max_issues_repo_path": "alignqc/plot_transcript_lengths.r", "max_issues_repo_name": "jason-weirather/AlignQC", "max_issues_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 25, "max_issues_repo_issues_event_min_datetime": "2016-10-19T02:10:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-24T17:34:50.000Z", "max_forks_repo_path": "alignqc/plot_transcript_lengths.r", "max_forks_repo_name": "jason-weirather/AlignQC", "max_forks_repo_head_hexsha": "2b471c2bd76f10383ea4f1d951486c83e6816e15", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 11, "max_forks_repo_forks_event_min_datetime": "2016-11-01T11:51:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-30T01:08:55.000Z", "avg_line_length": 35.5329341317, "max_line_length": 188, "alphanum_fraction": 0.6294236603, "num_tokens": 2339, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.5467381519846138, "lm_q1q2_score": 0.3010380535018541}} {"text": "library(jsonlite)\nlibrary(ggplot2)\nlibrary(zoo) \n\nlibrary(colorspace)\nlibrary(gridExtra)\n\nlibrary(plotly)\nlibrary(htmlwidgets)\n\nlibrary(sf)\nsf_use_s2(FALSE)\n\nlibrary(lubridate)\nlibrary(plyr)\n\nlibrary(dplyr)\n\nlibrary(tidyr)\n\nlibrary(reshape2)\n\n\nlibrary(\"scales\") \nSys.setlocale(\"LC_TIME\", \"hr_HR.UTF-8\")\n\n\n# get last_date_\nload('data/latest/last_date_.Rda')\n\n\n# get age data\njson_data <- fromJSON('data/latest/last_data_po_osobama.json')\n\n# age grouping step\nstep <- 5\n\n\npopulation_by_age <- read.csv(file = 'data/cro_population_by_age.csv')\n\n\njson_data <- json_data %>% mutate(dob = ifelse(dob < 1901, NA, dob))\n\njson_data$Datum <- as.Date(json_data$Datum, format=\"%Y-%m-%d\")\n\nmin_date = min(json_data$Datum)\nmax_date = max(json_data$Datum)\n\n\njson_data <- json_data[order(json_data$Datum),]\n\n\n\njson_data$dob <- as.Date(paste0(json_data$dob, '-01-01'), format=\"%Y-%m-%d\")\n\njson_data$age <- round(time_length(difftime(json_data$Datum, json_data$dob), \"years\"))\n\n\n\n\n\n\njson_data$age_group <- findInterval(json_data$age, seq(0, 85, by=step))\n\nlabels <- paste0(seq(0, 85, by=step), '-', seq(step - 1, 85, by=step), sep='')\n\nlabels[length(labels)] = '85+'\n\n\ncounties <- unique(json_data[c(\"Zupanija\")])\n\n\nf <- list(size = 13, color = \"black\")\n\np <- list()\na <- list()\ni <-22\n\n\n\ncurrent_county_data <- json_data\n\njson_d <- current_county_data %>% group_by(Datum, age_group) %>% tally()\n\nage_reshaped <- pivot_wider(json_d, names_from = age_group, values_from = n) %>%\n mutate_at(vars(-(\"Datum\")), ~replace(., is.na(.), 0))\n\nmissing_cols <- setdiff(as.character(c(1:length(labels))), names(age_reshaped))\n\nage_reshaped[missing_cols] <- 0\n\nage_reshaped <- age_reshaped[c('Datum', as.character(1:length(labels)))]\n\n# add missing dates\nmissing_rows <- as.Date(setdiff(seq.Date(min_date, max_date, by=\"day\"), age_reshaped$Datum))\nmissing_data <- data.frame(missing_rows)\ncolnames(missing_data)[1] <- \"Datum\"\n\nage_reshaped <- rbind.fill(age_reshaped, missing_data)\nage_reshaped[is.na(age_reshaped)] <- 0\n\n# sort by date \nage_reshaped <- age_reshaped[order(age_reshaped$Datum),]\n\n# calc 7 day sums\nage_reshaped_sum7 <- as.data.frame(rollapply(age_reshaped[, names(age_reshaped) != \"Datum\"], 7, sum, fill=0, align=\"right\"))\n\n\ncurrent_population <-c(population_by_age[1:nrow(population_by_age) - 1, c(i + 1)])\n\nage_reshaped_sum7[, 1:ncol(age_reshaped_sum7)] <- sweep(age_reshaped_sum7[, 1:ncol(age_reshaped_sum7)], 2, current_population / 100000, `/`)\n# \n# change column names to age groups\ncolnames(age_reshaped_sum7)[1:length(labels)] <- labels\n\nage_reshaped_sum7$Datum <- age_reshaped$Datum\n\ndata_to_plot <- head(age_reshaped_sum7, n=nrow(age_reshaped_sum7) - 1)\n\nlast_date <- strftime(data_to_plot$Datum[(nrow(data_to_plot))], \"%d.%m.%Y.\")\n\n\n\nd <- melt(data_to_plot, id.vars=\"Datum\")\n\ncolnames(d)[2:3] <- c('Dobna_skupina', 'Broj_zadnjih_7_data')\n\nmy_breaks <-c(0, 10, 50, 100, 200, 400, 800)\nmy_labels <-c('0', '10', '50', '100', '200', '400', '800+')\n\n# p <- ggplot(d, aes_string('Datum', colnames(d)[2], fill='Broj_zadnjih_7_data')) + \n# geom_tile() +\n# ylab(\"Dobna skupina\") +\n# scale_fill_distiller(palette=\"Spectral\", oob = scales::squish, name='Ukupno\\nu 7 dana\\nna 100000\\nstanovnika',\n# limits = c(0, 800), labels=my_labels, breaks=my_breaks) +\n# theme_minimal()\n\nd\n\n# 18-24\n# 25-49\n# 50-59\n# 60-69\n# 70-79\n# 80+\n\nlibrary(scales)\n\n\ncolour_hex_codes <- hue_pal()(6)\n\np <- ggplot() + \n geom_line(data=data_to_plot, aes(Datum, y=`20-24`, colour='20-24', size='20-24')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`30-34`, colour='30-34', size='30-34')) +\n geom_line(data=data_to_plot, aes(Datum, y=`35-39`, colour='35-39', size='35-39')) +\n geom_line(data=data_to_plot, aes(Datum, y=`40-44`, colour='40-44', size='40-44')) +\n geom_line(data=data_to_plot, aes(Datum, y=`45-49`, colour='45-49', size='45-49')) +\n geom_line(data=data_to_plot, aes(Datum, y=`50-54`, colour='50-54', size='50-54')) +\n geom_line(data=data_to_plot, aes(Datum, y=`55-59`, colour='55-59', size='55-59')) +\n geom_line(data=data_to_plot, aes(Datum, y=`60-64`, colour='60-64', size='60-64')) +\n geom_line(data=data_to_plot, aes(Datum, y=`65-69`, colour='65-69', size='65-69')) +\n \n \n \n geom_line(data=data_to_plot, aes(Datum, y=`70-74`, colour='70-74', size='70-74')) +\n geom_line(data=data_to_plot, aes(Datum, y=`75-79`, colour='75-79', size='75-79')) + \n geom_line(data=data_to_plot, aes(Datum, y=`80-84`, colour='80-84', size='80-84')) +\n geom_line(data=data_to_plot, aes(Datum, y=`85+`, colour='85+', size='85+')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`25-29`, colour='25-29', size='25-29')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`0-4`, colour='0-4', size='0-4')) +\n geom_line(data=data_to_plot, aes(Datum, y=`5-9`, colour='5-9', size='5-9')) +\n \n \n geom_line(data=data_to_plot, aes(Datum, y=`15-19`, colour='15-19', size='15-19')) + \n geom_line(data=data_to_plot, aes(Datum, y=`10-14`, colour='10-14', size='10-14')) +\n \n #scale_x_date(labels = date_format(\"%d.%m.\"), date_breaks = \"1 week\") +\n scale_x_date(labels = date_format(\"%b %Y.\"), date_breaks = \"1 month\") +\n scale_color_manual(name='Dobna skupina', values = c(\n '0-4' = 'black',\n '5-9' = 'black',\n '10-14' = 'black',\n \n '15-19' = colour_hex_codes[1],\n '20-24' = colour_hex_codes[1],\n \n \n '25-29' = colour_hex_codes[2],\n '30-34' = colour_hex_codes[2],\n '35-39' = colour_hex_codes[2],\n '40-44' = colour_hex_codes[2],\n '45-49' = colour_hex_codes[2],\n \n '50-54' = colour_hex_codes[3],\n '55-59' = colour_hex_codes[3],\n '60-64' = colour_hex_codes[4],\n '65-69' = colour_hex_codes[4],\n '70-74' = colour_hex_codes[5],\n '75-79' = colour_hex_codes[5],\n '80-84' = colour_hex_codes[6],\n '85+' = colour_hex_codes[6])) +\n scale_size_manual(name='Dobna skupina', values = c(\n '0-4' = 0.3,\n '5-9' = 0.7,\n '10-14' = 1.2,\n \n '15-19' = 1.2,\n '20-24' = 0.5,\n \n '25-29' = 1.2,\n '30-34' = 0.9,\n '35-39' = 0.6,\n '40-44' = 0.4,\n '45-49' = 0.2,\n \n '50-54' = 1,\n '55-59' = 0.5,\n '60-64' = 1,\n '65-69' = 0.5,\n '70-74' = 1,\n '75-79' = 0.5,\n '80-84' = 1,\n '85+' = 0.5)) +\n theme_minimal() +\n theme(legend.position=\"right\") +\n ylab('Broj slu\u010dajeva na 100k stanovnika') +\n labs(title = paste('Kretanje broja COVID-19 slu\u010dajeva na 100 tisu\u0107a stanovnika po dobnim skupinama u Hrvatskoj (02.03.2020. -', last_date, ')', sep='')) +\n theme(text = element_text(size=18)) +\n labs(caption = paste('Izvori podataka: koronavirus.hr (broj slu\u010dajeva), dzs.hr (broj stanovnika po dobnim skupinama, podaci iz 2019.). Generirano:', format(Sys.time() + as.difftime(1, units=\"hours\"), '%d.%m.%Y. %H:%M:%S h.'), 'Autor: Petar Pala\u0161ek, ppalasek.github.io')) +\n theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))\np\n\n\nggsave(paste('img/', last_date_, '_cases_per_age_group_lines.png', sep = ''),\n plot = p, dpi=300, width=1600*4, height=700*4, units=\"px\",\n bg = \"white\")\n\n\n\np <- ggplot() + \n geom_line(data=data_to_plot, aes(Datum, y=`20-24`, colour='20-24', size='20-24')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`30-34`, colour='30-34', size='30-34')) +\n geom_line(data=data_to_plot, aes(Datum, y=`35-39`, colour='35-39', size='35-39')) +\n geom_line(data=data_to_plot, aes(Datum, y=`40-44`, colour='40-44', size='40-44')) +\n geom_line(data=data_to_plot, aes(Datum, y=`45-49`, colour='45-49', size='45-49')) +\n geom_line(data=data_to_plot, aes(Datum, y=`50-54`, colour='50-54', size='50-54')) +\n geom_line(data=data_to_plot, aes(Datum, y=`55-59`, colour='55-59', size='55-59')) +\n geom_line(data=data_to_plot, aes(Datum, y=`60-64`, colour='60-64', size='60-64')) +\n geom_line(data=data_to_plot, aes(Datum, y=`65-69`, colour='65-69', size='65-69')) +\n \n \n \n geom_line(data=data_to_plot, aes(Datum, y=`70-74`, colour='70-74', size='70-74')) +\n geom_line(data=data_to_plot, aes(Datum, y=`75-79`, colour='75-79', size='75-79')) + \n geom_line(data=data_to_plot, aes(Datum, y=`80-84`, colour='80-84', size='80-84')) +\n geom_line(data=data_to_plot, aes(Datum, y=`85+`, colour='85+', size='85+')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`25-29`, colour='25-29', size='25-29')) +\n \n geom_line(data=data_to_plot, aes(Datum, y=`0-4`, colour='0-4', size='0-4')) +\n geom_line(data=data_to_plot, aes(Datum, y=`5-9`, colour='5-9', size='5-9')) +\n \n \n geom_line(data=data_to_plot, aes(Datum, y=`15-19`, colour='15-19', size='15-19')) + \n geom_line(data=data_to_plot, aes(Datum, y=`10-14`, colour='10-14', size='10-14')) +\n \n #scale_x_date(labels = date_format(\"%d.%m.\"), date_breaks = \"1 week\") +\n scale_x_date(labels = date_format(\"%b %Y.\"), date_breaks = \"1 month\") +\n scale_color_manual(name='Dobna skupina', values = c(\n '0-4' = 'black',\n '5-9' = 'black',\n '10-14' = 'black',\n \n '15-19' = colour_hex_codes[1],\n '20-24' = colour_hex_codes[1],\n \n \n '25-29' = colour_hex_codes[2],\n '30-34' = colour_hex_codes[2],\n '35-39' = colour_hex_codes[2],\n '40-44' = colour_hex_codes[2],\n '45-49' = colour_hex_codes[2],\n \n '50-54' = colour_hex_codes[3],\n '55-59' = colour_hex_codes[3],\n '60-64' = colour_hex_codes[4],\n '65-69' = colour_hex_codes[4],\n '70-74' = colour_hex_codes[5],\n '75-79' = colour_hex_codes[5],\n '80-84' = colour_hex_codes[6],\n '85+' = colour_hex_codes[6])) +\n scale_size_manual(name='Dobna skupina', values = c(\n '0-4' = 0.3,\n '5-9' = 0.7,\n '10-14' = 1.2,\n \n '15-19' = 1.2,\n '20-24' = 0.5,\n \n '25-29' = 1.2,\n '30-34' = 0.9,\n '35-39' = 0.6,\n '40-44' = 0.4,\n '45-49' = 0.2,\n \n '50-54' = 1,\n '55-59' = 0.5,\n '60-64' = 1,\n '65-69' = 0.5,\n '70-74' = 1,\n '75-79' = 0.5,\n '80-84' = 1,\n '85+' = 0.5)) +\n theme_minimal() +\n theme(legend.position=\"right\") +\n ylab('Broj slu\u010dajeva na 100k stanovnika') +\n scale_y_continuous(trans='pseudo_log', breaks = c(0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096)) +\n labs(title = paste('Kretanje broja COVID-19 slu\u010dajeva na 100 tisu\u0107a stanovnika po dobnim skupinama u Hrvatskoj (02.03.2020. -', last_date, ') (logaritamska skala)', sep='')) +\n theme(text = element_text(size=18)) +\n labs(caption = paste('Izvori podataka: koronavirus.hr (broj slu\u010dajeva), dzs.hr (broj stanovnika po dobnim skupinama, podaci iz 2019.). Generirano:', format(Sys.time() + as.difftime(1, units=\"hours\"), '%d.%m.%Y. %H:%M:%S h.'), 'Autor: Petar Pala\u0161ek, ppalasek.github.io')) +\n theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))\np\n\n\nggsave(paste('img/', last_date_, '_cases_per_age_group_lines_log.png', sep = ''),\n plot = p, dpi=300, width=1600*4, height=700*4, units=\"px\",\n bg = \"white\")\n\np\n", "meta": {"hexsha": "efb09d5a7af4a74da9bee1d090ac6bdeca762053", "size": 10778, "ext": "r", "lang": "R", "max_stars_repo_path": "generate_age_plot_lines.r", "max_stars_repo_name": "ppalasek/covid_plots_croatia", "max_stars_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate_age_plot_lines.r", "max_issues_repo_name": "ppalasek/covid_plots_croatia", "max_issues_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate_age_plot_lines.r", "max_forks_repo_name": "ppalasek/covid_plots_croatia", "max_forks_repo_head_hexsha": "17a1278bce46a821d5c4b00161573177ddae2cea", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2694610778, "max_line_length": 274, "alphanum_fraction": 0.627389126, "num_tokens": 3930, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3010380535018541}} {"text": "library(shiny)\nlibrary(forecast)\nlibrary(tseries)\nlibrary(DT) \nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(xts)\nlibrary(dygraphs)\nlibrary(forecastHybrid)\n\n\n\n#webshot::install_phantomjs()\n#source(\"common.R\")\n\nfunction(input, output, session) {\n\n# added \"session\" because updateSelectInput requires it\n# adding action button \n\n \n data <- reactive({\n input$upload\n isolate(\n req(input$file1) ## ?req # require that the input is available\n )\n inFile <- input$file1 \n \n df <- read.csv(inFile$datapath, header = TRUE, sep = input$sep)\n \n # Update inputs (you could create an observer with both updateSel...)\n # You can also constraint your choices. If you wanted select only numeric\n # variables you could set \"choices = sapply(df, is.numeric)\"\n # It depends on what do you want to do later on.\n \n updateSelectInput(session, inputId = 'xcol', label = 'X Variable',\n choices = names(df), selected = names(df)[1])\n updateSelectInput(session, inputId = 'ycol', label = 'Y Variable',\n choices = names(df), selected = names(df)[2])\n updateSelectInput(session, inputId = 'xreg', label = 'xreg',\n choices = names(df), selected = names(df)[3])\n \n \n \n return(df)\n \n})\n\n###################################################################\n#1. presenting data\n \n output$contents <- renderDataTable(\n {\n data()\n },\n \n options = list(lengthMenu = c(10, 30, 50), pageLength = 10,searching=TRUE)\n \n )#end function\n \n###################################################################\n#2. plotting the function using plot methods.\n# the function is replaced with dygraph\n \n output$MyPlot <- renderPlot(\n {\n #x <- data()[, c(input$xcol, input$ycol)]\n #plot(mydata)\n \n }\n )# end function\n\n###################################################################\n# Plot the data using dygraph\n \n output$dygraph <- renderDygraph(\n {\n input$drawPlot\n isolate(\n dygraph( xts(x = data(), order.by = as.Date(data()[,input$xcol] , format='%m/%d/%Y'))\n , main = \"Predicted Deaths/Month\" ) %>%\n dyOptions(drawGrid = input$xcol) %>%\n dyRangeSelector() \n )\n })\n\n########################################################################## \n##################### subset of the data.\nsubsetdata <- reactive({\n\ndf <- data()\nif (is.null(df)) return(NULL)\nif(!is.numeric( input$Xrange[1]) ) {\n df \n}\n \nif(!is.numeric( input$Yrange[1]) ) {\n df \n}\n \n \nif(is.numeric( input$Xrange[1]) & is.numeric(df[,input$xcol])) {\ndf<- df [!is.na(df[,input$xcol]),]\n \ndf <- df [df[,input$xcol] >= input$Xrange[1]&df[,input$xcol] <= input$Xrange[2],]\n \n}\n \nif(is.numeric( input$Yrange[1])& is.numeric(df[,input$ycol]) ) {\ndf<- df [!is.na(df[,input$ycol]),]\n \ndf <- df [df[,input$ycol] >= input$Yrange[1]&df[,input$ycol] <= input$Yrange[2],]\n \n}\n \ndf\n})\n \n############################################################################\n#############################download plot downloadPlot based on plot function\n output$downloadPlot <- downloadHandler(\n \n filename = function () {paste('plot', '.png',sep='') },\n content = function(file) {\n cat(output$dygraph,file = 'temp.html')\n \n saveWidget(x, \"temp.html\", selfcontained = FALSE)\n webshot(\"temp.html\", file = file)\n }\n \n \n\n )#end download plot handler\n \n#################### chossing the model ####################################\n##applying model\n modeldata <- reactive(\n { \n #Converting frequecny from character into int.\n #adding regressors\n xreg <- (data()[ ,input$xreg])\n mydata<-ts(data()[ ,input$ycol],frequency= as.numeric(input$freq))\n \n #choose between models\n if(input$model ==\"ARIMA\")\n {\n #results <- auto.arima(mydata)\n results <- auto.arima(mydata,xreg = xreg ,frequency= input$freq)\n }# end ARIMA \n \n # 2. ANN model\n else if (input$model ==\"ANN\") \n {\n #results <- nnetar(mydata)\n results <- nnetar(mydata,xreg = xreg,frequency= as.numeric(input$freq))\n }\n # TBATS model, note TBATS do not accept regressors\n else if (input$model == \"TBATS\")\n {\n results <- tbats(mydata)\n }\n # Exponential smoothing model\n else if (input$model ==\"HoltWinters\")\n {\n results <- HoltWinters(mydata)\n }\n else if (input$model == \"STLF\")\n {\n results <- stlf(mydata,s.window=\"periodic\", method=c(\"ets\",\"arima\",\"naive\",\"rwdrift\"))\n }\n else if (input$model == \"HybridModel\")\n {\n results <- hybridModel(mydata,\n models = \"aenst\",\n a.args = list(xreg = xreg),\n n.args = list(xreg = xreg),\n s.args = list(xreg = xreg, method = \"arima\"))\n }\n \n input$applymodel\n isolate( return(results))\n\n })#end function\n \n#########################################################################\n#presenting output\n output$modelData <- renderPrint(\n {\n input$applymodel\n isolate(\n modeldata()\n )\n })\n\n#presenting summary \n output$modelData2 <- renderTable(\n {\n input$applymodel\n isolate({\n mydaya <- ts(data()[,input$ycol])\n \n#comparing models\n# if ARIMA\n if(input$model ==\"ARIMA\")\n {\n summary(modeldata())\n }#if TBATS\n else if (input$model == \"TBATS\")\n {\n accuracy(modeldata()$fitted.values,mydaya)\n }#if ANN\n else if (input$model == \"ANN\")\n {\n accuracy(modeldata()$fitted,mydaya)\n }\n else if (input$model == \"STLF\")\n {\n accuracy(modeldata()$fitted,mydaya)\n }\n else if (input$model == \"HoltWinters\")\n {\n accuracy(modeldata()$fitted,mydaya)\n }\n else if (input$model == \"HybridModel\")\n {\n accuracy(modeldata()$fitted,mydaya)\n }\n })\n })#end function\n \n###############################################################\n# plotting forecasting period.##############################\n output$forecastPlot <- renderPlot(\n {\n xreg <- data.frame(data()[ ,input$xreg])\n input$forecastAction\n isolate( {\n if (input$model == \"ARIMA\" || input$model == \"ANN\")\n {\n autoplot(forecast(modeldata(),xreg = xreg, h = input$FP))\n }\n else if (input$model == \"TBATS\")\n {\n p <- predict(modeldata())\n autoplot(p)\n }\n else if (input$model == \"HybridModel\")\n {\n \n autoplot(forecast(modeldata(),xreg = xreg, h = as.numeric(input$FP)))\n }\n else if(input$model ==\"HoltWinters\")\n {\n y <- modeldata()\n p <- predict(y, input$FP)\n \n X <- cbind( fitted = y$fitted[,1] , mean = p)\n df <- cbind(y$x, X)\n colnames(df) <- c(\"Data\", \"Fitted\", \"Forecasting\")\n autoplot(df)\n }\n else if(input$model == \"STLF\")\n {\n autoplot(modeldata())\n }\n \n })\n\n }\n )#end function\n############################################################\n######## present forecast data\n\n output$forecastTables <- renderDataTable({\n xreg <- data.frame(data()[ ,input$xreg])\n input$forecastAction\n isolate({\n \n if (input$model == \"ARIMA\")\n {\n p <- forecast(modeldata(), xreg = xreg, h = input$FP)\n a <- cbind(p$mean,p$lower,p$upper)\n colnames(a) <- c(\"Point Forecast\",\"LO 95\",\"HI 95\",\"Lo 80\",\"Hi 80\")\n }\n else if (input$model == \"ANN\")\n {\n p <- forecast(modeldata(), xreg = xreg, h = input$FP)\n a <- cbind( seq(1:length(p$mean)),p$mean)\n colnames(a) <- c(\"Point Forecast\",\"mean\")\n \n }\n else if (input$model == \"TBATS\")\n {\n p <- (predict(modeldata()))\n a <- cbind(p$mean,p$lower,p$upper)\n colnames(a) <- c(\"Point Forecast\",\"LO 95\",\"HI 95\",\"Lo 80\",\"Hi 80\")\n }\n else if(input$model == \"STLF\" || input$model == \"HoltWinters\")\n {\n p <- forecast(modeldata(),h = input$FP)\n a <- cbind( seq(1:length(p$mean)),p$mean)\n colnames(a) <- c(\"Point Forecast\",\"mean\")\n \n }\n else if (input$model == \"HybridModel\")\n {\n \n \n p <- forecast(modeldata(), xreg = xreg, h = as.numeric(input$FP)\n , FUN = hybridModel)\n a <- cbind( seq(1:length(p$mean)),p$mean)\n colnames(a) <- c(\"Point Forecast\",\"mean\")\n \n \n }\n # format the data\n a <- formatC(a) \n })\n \n# colnames(a) <- c(\"Point Forecast\",\"LO 95\",\"HI 95\",\"Lo 80\",\"Hi 80\")\n }, options = list(lengthMenu = c(10, 30, 50), pageLength = 10,searching=TRUE))\n\n############################################################\n# Download forecasted data and figure as pdf\n output$forecastData <- downloadHandler(\n filename = function() {\n \n paste(\"forecasting-\",input$model,'.csv',sep='')},\n content = function(file){\n xreg <- data.frame(data()[ ,input$xreg])\n \n \n if (input$model == \"ARIMA\")\n {\n p <- forecast(modeldata(),xreg= xreg,h = input$FP)\n dataFor <- data.frame(p$mean,p$lower,p$upper)\n colnames(dataFor) <- c(\"Point Forecast\",\"LO 95\",\"HI 95\",\"Lo 80\",\"Hi 80\")\n }\n else if (input$model == \"ANN\")\n {\n p <- forecast(modeldata(),xreg = xreg,h = input$FP)\n dataFor <- data.frame(p$mean)\n colnames(dataFor) <- c(\"Forecasting\")\n \n }\n else if (input$model == \"TBATS\")\n {\n p <- (predict(modeldata()))\n dataFor <- data.frame(p$mean,p$lower,p$upper)\n colnames(dataFor) <- c(\"Point Forecast\",\"LO 95\",\"HI 95\",\"Lo 80\",\"Hi 80\")\n }\n else if(input$model == \"STLF\" || input$model == \"HoltWinters\")\n {\n p <- forecast(modeldata(),h = input$FP)\n dataFor <- data.frame( p$mean)\n colnames(dataFor) <- c(\"Forecasting\")\n \n }\n else if (input$model == \"HybridModel\")\n {\n p <- forecast(modeldata(), xreg = xreg, h = as.numeric(input$FP)\n , FUN = hybridModel)\n dataFor <- data.frame( p$mean)\n colnames(dataFor) <- c(\"Forecasting\")\n }\n # format the data\n # dataFor <- formatC(dataFor) \n write.csv(dataFor,file)\n }\n \n )#end function\n \n\n \n############################################################\n##############Reiduals######################################\n\n output$residualsTables <- renderDataTable(\n {\n\n if(input$model == \"HoltWinters\" || input$model == \"HybridModel\")\n {\n a <- cbind( seq(1:length(residuals(modeldata()))),residuals(modeldata()))\n \n colnames(a) <- c(\"Sequence\",\"Residuals\")\n \n }else \n {\n a <- cbind( seq(1:nrow(data())),residuals(modeldata()))\n colnames(a) <- c(\"Sequence\",\"Residuals\")\n }\n # removing NULL values, adjusting data format\n a <- na.omit(a)\n a <- formatC(a)\n\n\n },\n options = list(lengthMenu = c(10, 30, 50), pageLength = 10,searching=TRUE))\n \n\n########################################################### Residuals plot\n# plot residuals figure.\n# adjusting time series by date.\n output$residualsPlot <- renderPlot(\n {\n autoplot(residuals(modeldata()))\n }\n )\n############################################################\n# Download Residuals data\n output$downloadRes <- downloadHandler(\n filename = function() {\n\n paste(\"res-\",input$model,'.csv',sep='')},\n content = function(file){\n \n DataRes <- data.frame(residuals(modeldata()))\n # removing NULL values, adjusting data format\n DataRes <- na.omit(DataRes)\n colnames(DataRes) <- c(\"Residuals\")\n write.csv(DataRes,file)\n }\n \n )#end function\n \n}# end session\n\n\n", "meta": {"hexsha": "f061e4216f3d3e6abe72adee218801f3920e9f60", "size": 12156, "ext": "r", "lang": "R", "max_stars_repo_path": "Server.r", "max_stars_repo_name": "mmjazzar/Load_dashboard", "max_stars_repo_head_hexsha": "21445567fdc3d00c341f6698d51f3d9b455b9c07", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Server.r", "max_issues_repo_name": "mmjazzar/Load_dashboard", "max_issues_repo_head_hexsha": "21445567fdc3d00c341f6698d51f3d9b455b9c07", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Server.r", "max_forks_repo_name": "mmjazzar/Load_dashboard", "max_forks_repo_head_hexsha": "21445567fdc3d00c341f6698d51f3d9b455b9c07", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.3356643357, "max_line_length": 95, "alphanum_fraction": 0.4870023034, "num_tokens": 3013, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.5506073655352404, "lm_q1q2_score": 0.3010380535018541}} {"text": "\nexecuteTask <- function(i) {\n\ta1 <- c(1+i, 2-i, 3+i, 4-i, 5+i)\n\ta2 <- c(6+i, 7-i, 8+i, 9-i, 10+i)\n\ta3 <- c(a1, a2)\n\treturn(i)\n}\n\nr = 1\nfor(i in 0:2000000000) {\n\tr = executeTask(i)\n}\n", "meta": {"hexsha": "ecc464f70e0f0ba39f7a6ef9e60855093861f79c", "size": 183, "ext": "r", "lang": "R", "max_stars_repo_path": "tasks/array-concatenation/r/array-concatenation.r", "max_stars_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_stars_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tasks/array-concatenation/r/array-concatenation.r", "max_issues_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_issues_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tasks/array-concatenation/r/array-concatenation.r", "max_forks_repo_name": "stefanos1316/Rosetta_Code_Data_Set", "max_forks_repo_head_hexsha": "8120b14cce6cb76ba26353a7dd4012bc99bd65cb", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 14.0769230769, "max_line_length": 34, "alphanum_fraction": 0.5081967213, "num_tokens": 94, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.7745833841649232, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.30101706627331093}} {"text": "inverserunlengthencoding <- function(x)\n{\n lengths <- as.numeric(unlist(strsplit(output, \"[[:alpha:]]\")))\n values <- unlist(strsplit(output, \"[[:digit:]]\"))\n values <- values[values != \"\"]\n uncompressed <- inverse.rle(list(lengths=lengths, values=values))\n paste(uncompressed, collapse=\"\")\n}\n\noutput <- \"12W1B12W3B24W1B14W\"\ninverserunlengthencoding(output)\n", "meta": {"hexsha": "d0931616c09dfb3acbcd0a157e97632edd0ae620", "size": 372, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Run-length-encoding/R/run-length-encoding-2.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Run-length-encoding/R/run-length-encoding-2.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Run-length-encoding/R/run-length-encoding-2.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 31.0, "max_line_length": 69, "alphanum_fraction": 0.6827956989, "num_tokens": 103, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.30100165703060305}} {"text": "# wahpenayo at gmail dot com\n# 2018-01-02\n#-----------------------------------------------------------------\nif (file.exists('e:/porta/projects/taigabench')) {\n setwd('e:/porta/projects/taigabench')\n} else {\n setwd('c:/porta/projects/taigabench')\n}\nsource('src/scripts/r/functions.r')\n#-----------------------------------------------------------------\nprefixes <- c('h2o','randomForest','randomForestSRC',\n 'scikit-learn','taiga','xgboost') \n#-----------------------------------------------------------------\nclassify <- NULL\nfor (prefix in prefixes) {\n f <- results.file(\n dataset='ontime',\n problem='classify',\n prefix=prefix)\n print(f)\n classify <- rbind(classify,read_csv(file=f))\n}\nclassify$model <- factor(classify$model,levels=models)\ndev.on(\n file=plot.file(\n dataset='ontime',problem='classify',\n prefix='classify-traintime'),\n aspect=0.5,\n width=1280)\nggplot(classify, aes(x = ntrain, y = traintime, color = model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) + \n scale_y_log10() +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"lower is better\")\ndev.off()\ndev.on(\n file=plot.file(\n dataset='ontime',\n problem='classify',\n prefix=\"auc\"),\n aspect=0.5,\n width=1280)\nggplot(classify, aes(x = ntrain, y = auc, color = model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"higher is better\")\ndev.off()\n#-----------------------------------------------------------------\nl2 <- NULL\nfor (prefix in prefixes) {\n f <- results.file(\n dataset='ontime',\n problem='l2',\n prefix=prefix)\n l2 <- rbind(l2,read_csv(file=f)) }\nl2$model <- factor(l2$model,levels=models)\ndev.on(\n file=plot.file(\n dataset='ontime',problem='l2',prefix='l2-traintime'),\n aspect=0.5,\n width=1280)\nggplot(l2, aes(x=ntrain,y=traintime,color=model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) + \n scale_y_log10() +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"lower is better\")\ndev.off()\n\ndev.on(\n file=plot.file(\n dataset='ontime',\n problem='l2',\n prefix='rmse'),\n aspect=0.5,\n width=1280)\nggplot(l2, aes(x=ntrain, y=rmse, color=model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"lower is better\")\ndev.off()\n#-----------------------------------------------------------------\nqcost <- read_csv(\n file=file.path('output','qcost','ontime','results.csv'))\nqcost$model <- factor(qcost$model,levels=models)\ndev.on(\n file=plot.file(\n dataset='ontime',problem='qcost',\n prefix='qcost-traintime'),\n aspect=0.5,\n width=1280)\nggplot(qcost, aes(x=ntrain,y=traintime,color=model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) + \n scale_y_log10() +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"lower is better\")\ndev.off()\n\ndev.on(\n file=plot.file(\n dataset='ontime',\n problem='qcost',\n prefix='qcost'),\n aspect=0.5,\n width=1280)\nggplot(qcost, aes(x=ntrain, y=decilecost, color=model)) +\n geom_point(size=4.0) + \n geom_line(size=2.0) + \n scale_x_log10(breaks = (1000000*c(0.01,0.1,1,10))) +\n scale_color_manual(values=(model.colors),drop=FALSE) +\n theme(text=element_text(size=24)) +\n ggtitle(\"lower is better\")\ndev.off()\n#-----------------------------------------------------------------\n", "meta": {"hexsha": "1808f0f77a8a13e4bb9ae41b5111c4f106cd70c5", "size": 3847, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/r/plot.r", "max_stars_repo_name": "wahpenayo/taigabench", "max_stars_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/r/plot.r", "max_issues_repo_name": "wahpenayo/taigabench", "max_issues_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/r/plot.r", "max_forks_repo_name": "wahpenayo/taigabench", "max_forks_repo_head_hexsha": "5ba3999b8410afe2ce174d85809e9e5794a7ac11", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8217054264, "max_line_length": 66, "alphanum_fraction": 0.5970886405, "num_tokens": 1179, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30100165703060305}} {"text": "# 3. faza: Vizualizacija podatkov\n\n#-------------------------------------------------------------------------------\n#GRAFI\n\n#spol:\n\nzdruzitev <- zivljenje.po.spolu %>% \n dplyr::select(drzava, leto,spol, pricakovana.starost, zdrava.leta) %>%\n pivot_longer(cols = c(pricakovana.starost, zdrava.leta),\n names_to = \"ID\", \n values_to = \"leta\")\nzdruzitev$drzava[zdruzitev$drzava == \"United Kingdom\"] <- \"UK\"\nzdruzitev$drzava[zdruzitev$drzava == \"Czech Republic\"] <- \"Czech Rep.\"\nzdruzitev$zdruzeno <- paste(zdruzitev$ID, zdruzitev$spol, sep = \"-\")\n\n\n# skupaj pricakovana starost in zdrava leta\nleta.po.drzavah.graf <- ggplot(zdruzitev) +\n geom_line(aes(x = leto, y = leta, color = zdruzeno)) + \n scale_color_manual(\"\",\n values = c(\"red\", \"blue\", \"#F8766D\", \"#00BFC4\"),\n labels = c(\"\u017eenske - pri\u010dakovana starost\",\n \"mo\u0161ki - pri\u010dakovana starost\",\n \"\u017eenske - zdrava leta\",\n \"mo\u0161ki - zdrava leta\") )+\n facet_wrap(.~drzava)+\n labs(\n x = \"Leto\",\n y = \"Pri\u010dakovana \u017eivljenjska doba ob rojstvu in \u0161tevilo zdravih let\",\n title = \"Gibanje \u017eivljenjske dobe ob rojstvu \\nin \u0161tevila zdravih let, glede na spol in leto v Evropi\"\n )+\n theme(\n axis.text.x = element_text(angle = 50, vjust = 0.5),\n axis.title.x = element_text(vjust = 0))\n\nleta.po.drzavah.graf\n\n\nodstotek.po.spolu.graf <- zivljenje.po.spolu%>% filter(leto == \"2018\") %>% \n ggplot(mapping = aes(x = spol, y = odstotek.zdravih.od.pricakovanih)) +\n geom_boxplot()+\n ylab(\"Odstotek zdravih let od pri\u010dakovane \u017eivljenjske dobe\")+\n xlab(\"Spol\")+\n ggtitle(\"Odstotek zdravih let \u017eivljenja od pri\u010dakovane \\n\u017eivljenjske dobe glede na spol za leto 2018\")+\n scale_x_discrete(labels=c(\"\u017denske\",\"Mo\u0161ki\"))\n\nodstotek.po.spolu.graf\n\n\n\n# samo pri\u010dakovana starost in zdrava leta, po dr\u017eavah oz. letih\n\npricakovana.2018.graf <- zivljenje %>%filter(leto == \"2018\")%>%\n ggplot()+\n geom_bar(mapping = aes(x = reorder(drzava, -pricakovana.starost),\n y = pricakovana.starost),\n stat = \"identity\")+\n ylab(\"Pri\u010dakovana \u017eivljenjska doba ob rojstvu\")+\n xlab(\"Dr\u017eava\")+\n theme_classic()+\n theme(\n axis.text.x = element_text(angle = 50, vjust = 0.5),\n axis.title.x = element_text(vjust = 0))+\n ggtitle(\"Pri\u010dakovana \u017eivljenjska doba po dr\u017eavah leta 2018\")+\n geom_hline(yintercept=mean(filter(zivljenje, leto == \"2018\")$pricakovana.starost),color=\"red\")\npricakovana.2018.graf\n \n\n\n# pricakovana leta in BDP, sadje in zelenjava, revscina\n\n\npricakovana.in.BDP.graf <- zivljenje %>%filter(!is.na(odstotek.BDP.ki.gre.v.zdravstvo))%>% filter(leto == \"2011\") %>%\n ggplot() +\n aes(x = odstotek.BDP.ki.gre.v.zdravstvo, y = pricakovana.starost, color = zdrava.leta) +\n geom_point()+\n labs(\n x =\"% BDP, ki ga dr\u017eava nameni v zdravstvo\",\n y = \"Pri\u010dakovana \u017eivljenjska doba\",\n title = \"Pri\u010dakovana \u017eivljenjska doba v odvisnosti od % BDP namenjenega v zdravstvo leta 2011\",\n color = \"Zdrava leta\"\n )+\n scale_color_gradient(low = \"blue\", high = \"red\")\n \npricakovana.in.BDP.graf\n\npricakovana.in.sadje.graf <- ggplot(zivljenje %>% filter(!is.na(kg.na.osebo)) %>% filter(leto == \"2011\"))+\n aes(x = kg.na.osebo, y = pricakovana.starost, color = zdrava.leta) +\n geom_point()+\n labs(\n x = \"Sadje in zelenjava, ki je na voljo na eno osebo v enem letu, v kg\",\n y = \"Pri\u010dakovana \u017eivljenjska doba\",\n color = \"Zdrava leta\", \n title = \"Pri\u010dakovana \u017eivljenjska doba in koli\u010dina sadja in zelenjave na osebo leta 2011\",\n color = \"Zdrava leta\"\n )+ scale_color_gradient(low = \"blue\", high = \"red\")\n\npricakovana.in.sadje.graf\n\n\n\npricakovana.in.revscina.graf <- ggplot(zivljenje %>% filter(!is.na(tveganje.revscine))%>%filter(leto == \"2011\")) +\n aes(x = tveganje.revscine, y = pricakovana.starost, color = zdrava.leta)+\n geom_point()+\n labs(\n x = \"Tveganje rev\u0161\u010dine\",\n y = \"Pri\u010dakovana \u017eivljenjska doba\",\n title = \"Pri\u010dakovana \u017eivljenjska doba ob rojstvu glede na tveganje rev\u0161\u010dine v letu 2011\",\n color = \"Zdrava leta\"\n )+ scale_color_gradient(low = \"blue\", high = \"red\")\n\npricakovana.in.revscina.graf\n\n\n\n#Dodatni grafi \n# pricakovana leta po dr\u017eavah \u010dez leta\nggplot(zivljenje) + aes(x = leto, y= pricakovana.starost, color = drzava) + geom_line() \n\n\n\n# odstotek po spolu;\nggplot(zivljenje.po.spolu) +\n aes(x = leto, y = odstotek.zdravih.od.pricakovanih, color = spol)+\n geom_line()+\n facet_wrap(.~drzava)+ \n ggtitle(\"Evropa\")+\n ylab(\"Odstotek zdravih let \u017eivljenja od pri\u010dakovane starosti\")+\n scale_color_manual(\"Spol\",\n values = c(\"#F8766D\", \"#00BFC4\"),\n labels = c(\"\u017eenske\", \"mo\u0161ki\"))\n\n\nzivljenje %>% ggplot(mapping = aes(x = as.character(leto), y = zdrava.leta))+ \n geom_boxplot()+\n labs(\n x = \"leto\",\n y = \"zdrava leta\"\n )\n\nzivljenje %>% ggplot(mapping = aes(x = as.character(leto), y = pricakovana.starost))+\n geom_boxplot()+\n labs(\n x = \"leto\",\n y = \"pri\u010dakovana starost\"\n )\n\n\n#-------------------------------------------------------------------------------\n#ZEMLJEVIDI\n\ndata(\"World\")\nzivljenje1 <- zivljenje\nzivljenje1$drzava[zivljenje1$drzava == \"Czech Republic\"] <- \"Czech Rep.\"\n\n\n\nmap <- function(){\n evropa <- World %>% filter (continent == 'Europe')\n starost <- zivljenje1 %>% filter (leto ==\"2018\") %>% dplyr::select('drzava', 'pricakovana.starost', \"zdrava.leta\")\n podatki <- merge(y = starost,x = evropa, by.x='name', by.y = 'drzava')\n evropa <- tm_shape(podatki) +\n tm_polygons(c('pricakovana.starost','zdrava.leta'),\n popup.vars = c(\"Pri\u010dakovana starost: \" = \"pricakovana.starost\", \"Zdrava leta:\" = \"zdrava.leta\")) + \n tm_facets(sync = TRUE)\n tmap_mode('view')\n return(evropa)\n}\nmap()\n\n\n\n\n#pomo\u017eni zemljevid:\n\n\nodstotek.map <- function(){\n evropa <- World %>% filter (continent == 'Europe')\n starost <- zivljenje1 %>% filter (leto == 2018) %>% dplyr::select('drzava', 'odstotek.zdravih.od.pricakovanih')\n podatki <- merge(y = starost,x = evropa, by.x='name', by.y = 'drzava')\n evropa <- tm_shape(podatki) + tm_polygons('odstotek.zdravih.od.pricakovanih') \n tmap_mode('view')\n return(evropa)\n}\n\nodstotek.map()\n\n\n\n\n", "meta": {"hexsha": "8658f53b904ff2085e90c0fac0701ae7eb160f80", "size": 6212, "ext": "r", "lang": "R", "max_stars_repo_path": "vizualizacija/vizualizacija.r", "max_stars_repo_name": "nikapavlic/APPR-2021-22", "max_stars_repo_head_hexsha": "38dfc42b1871282fcde385d38b079ae1def1f82e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vizualizacija/vizualizacija.r", "max_issues_repo_name": "nikapavlic/APPR-2021-22", "max_issues_repo_head_hexsha": "38dfc42b1871282fcde385d38b079ae1def1f82e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2022-01-20T18:56:29.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-04T12:22:38.000Z", "max_forks_repo_path": "vizualizacija/vizualizacija.r", "max_forks_repo_name": "nikapavlic/APPR-2021-22", "max_forks_repo_head_hexsha": "38dfc42b1871282fcde385d38b079ae1def1f82e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.3541666667, "max_line_length": 117, "alphanum_fraction": 0.6286220219, "num_tokens": 2263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.5544704649604273, "lm_q1q2_score": 0.30100165703060305}} {"text": "print(\"--------------- File plot-trend.r\")\n\nlibrary(ggplot2)\nlibrary(tidyr)\nlibrary(dplyr)\nlibrary(rstan)\nlibrary(data.table)\nlibrary(lubridate)\nlibrary(gdata)\nlibrary(EnvStats)\nlibrary(matrixStats)\nlibrary(scales)\nlibrary(gridExtra)\nlibrary(ggpubr)\nlibrary(bayesplot)\nlibrary(cowplot)\nlibrary(zoo)\nlibrary(plotly)\n\nsource(\"utils/geom-stepribbon.r\")\n\n#---------------------------------------------------------------------------\n\nmake_three_pannel_plot <- function() {\n args <- commandArgs(trailingOnly = TRUE)\n filename2 <- args[1]\n\n load(paste0(\"../modelOutput/results/\", filename2))\n print(sprintf(\"loading: %s\", paste0(\"../modelOutput/results/\", filename2)))\n\n codeToName <- unique(data.frame(\"code\" = d$countryterritoryCode, \"name\" = d$countriesAndTerritories))\n\n lastObs <- tail(dates[[1]], 1)\n # lastObs <- as.Date(\"06/01/2020\", format=\"%m/%d/%y\")\n\n cd <- dates[[1]]\n cd <- cd[cd > lastObs]\n\n ### final Rt via bayesplot\n dimensions <- dim(out$Rt)\n\n # idx is lastobs\n idx <- dimensions[2] - length(cd)\n\n # here we calculate avg Rt over the 7 days leading up to the last observation\n date_bin <- ceiling(dimensions[2] / (10 * 7))\n date_break <- paste(as.character(date_bin), \"weeks\", sep = \" \")\n Rt <- out$Rt[, (idx - 6):idx, ]\n Rt <- apply(Rt, c(1, 3), mean)\n\n # visualize it\n colnames(Rt) <- codeToName$name\n Rt_to_save <- Rt \n if (dim(Rt)[2] > 20){\n Rt_to_save <- Rt[, 1:20] \n }\n g_svg_top20 <- mcmc_intervals(Rt_to_save, prob = .9) +\n ggtitle(sprintf(\"Average Rt %s to %s\", format(lastObs - 6, \"%B %d\"), format(lastObs, \"%B %d\")), \"with 90% posterior credible intervals\") +\n xlab(\"Rt\") + ylab(\"County\") +\n theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5)) # center title and subtitle\n ggsave(sprintf(\"../modelOutput/figures/Rt_Top20.svg\"), g_svg_top20, width = 6, height = 4)\n ggsave(sprintf(\"../modelOutput/figures/Rt_Top20.png\"), g_svg_top20, width = 6, height = 4)\n\n # next\n # 1. Rt pre-lockdown\n # 2. Rt lockdown\n # 3. Rt post-lockdown\n # 4. delta's\n # so that's 5 quantities - 3 intervals, 2 transitions of great importance\n\n # dev'ing #\n\n # two transitions\n # lockdownStart <- as.Date(\"03/21/2020\", format=\"%m/%d/%y\")\n # lockdownEnd <- as.Date(\"05/30/2020\", format=\"%m/%d/%y\") # source: https://www.usatoday.com/storytelling/coronavirus-reopening-america-map/\n\n # datesRef <- dates[[1]]\n\n # cd <- cd[cd > lastObs]\n\n # split the dates\n # preLockdownDates <- datesRef[datesRef < lockdownStart]\n # lockdownDates <- datesRef[datesRef >= lockdownStart & datesRef < lockdownEnd]\n # postLockdownDates <- datesRef[datesRef >= lockdownEnd & datesRef <= lastObs]\n\n # map to indexes\n # idxLockdown <- length(preLockdownDates)\n # idxReopen <- idxLockdown + length(lockdownDates)\n # idxLastObs <- idxReopen + length(postLockdownDates)\n\n # Rt_PreLockdown <- out$Rt[,1:idxLockdown,]\n # Rt_Lockdown <- out$Rt[,(idxLockdown+1):idxReopen,]\n # Rt_PostLockdown <- out$Rt[,(idxReopen+1):idxLastObs,]\n\n # certainly, certainly make a function out of this\n\n # average Rt for each time period\n # Rt_PreLockdown <- apply(Rt_PreLockdown, c(1,3), mean)\n # Rt_Lockdown <- apply(Rt_Lockdown, c(1,3), mean)\n # Rt_PostLockdown <- apply(Rt_PostLockdown, c(1,3), mean)\n\n # here! visualize it\n # colnames(Rt_PreLockdown) <- codeToName$name\n # colnames(Rt_Lockdown) <- codeToName$name\n # colnames(Rt_PostLockdown) <- codeToName$name\n\n # Rt_Delta_Lockdown <- Rt_Lockdown - Rt_PreLockdown\n # Rt_Delta_Reopen <- Rt_PostLockdown - Rt_Lockdown\n\n # move this\n # plot_Rt_by_time_period <- function(Rt, title, path){\n # g = mcmc_intervals(Rt,prob = .9) +\n # ggtitle(title, \"with 90% posterior credible intervals\") +\n # xlab(\"Rt\") + ylab(\"County\") +\n # theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5)) # center title and subtitle\n # ggsave(file.path(\"../modelOutput/figures\", path),g,width=6,height=4)\n # }\n\n # plot_Rt_by_time_period(Rt_PreLockdown, \"Average Rt Prior To Lockdown\", \"Rt_PreLockdown_All.png\")\n # plot_Rt_by_time_period(Rt_Lockdown, \"Average Rt During Lockdown\", \"Rt_Lockdown_All.png\")\n # plot_Rt_by_time_period(Rt_PostLockdown, \"Average Rt After Reopening\", \"Rt_Reopening_All.png\")\n # plot_Rt_by_time_period(Rt_Delta_Lockdown, \"Average Change In Rt Following Lockdown\", \"Rt_Delta_Lockdown_All.png\")\n # plot_Rt_by_time_period(Rt_Delta_Reopen, \"Average Change In Rt Following Reopening\", \"Rt_Delta_Reopen_All.png\")\n\n ##### ////////////// #####\n\n # interventions table\n # NOTE: \"covariate\" == \"intervention\";\n # e.g., if there are 3 different interventions in the model, then there are 3 covariates here in the code\n # covariates <- read.csv(\"../modelInput/ILInterventionsV1.csv\", stringsAsFactors = FALSE)\n covariates <- read.csv(\"../modelInput/USInterventions_Static.csv\", stringsAsFactors = FALSE)\n covariates$Country <- sapply(covariates$Country, as.character)\n # covariates$Country <- sub(\"840\", \"\", covariates$Country) # cutoff US prefix code - note: maybe this should be in the python etl, not here\n # don't need this ^ when using the static file\n\n ###\n\n\n allErr <- list()\n for (i in 1:length(countries)) {\n N <- length(dates[[i]])\n\n # here! careful - country is an integer right here (is it? double check)\n country <- countries[[i]] # this is the numeric code -> \"84017031\"\n countryName <- as.character(codeToName$name[codeToName$code == country]) # this is the name -> \"Cook\"\n\n predicted_cases <- colMeans(prediction[, 1:N, i])\n predicted_cases_li <- colQuantiles(prediction[, 1:N, i], probs = .025)\n predicted_cases_ui <- colQuantiles(prediction[, 1:N, i], probs = .975)\n predicted_cases_li2 <- colQuantiles(prediction[, 1:N, i], probs = .25)\n predicted_cases_ui2 <- colQuantiles(prediction[, 1:N, i], probs = .75)\n\n estimated_deaths <- colMeans(estimated.deaths[, 1:N, i])\n estimated_deaths_li <- colQuantiles(estimated.deaths[, 1:N, i], probs = .025)\n estimated_deaths_ui <- colQuantiles(estimated.deaths[, 1:N, i], probs = .975)\n estimated_deaths_li2 <- colQuantiles(estimated.deaths[, 1:N, i], probs = .25)\n estimated_deaths_ui2 <- colQuantiles(estimated.deaths[, 1:N, i], probs = .75)\n\n rt <- colMeans(out$Rt[, 1:N, i])\n rt_li <- colQuantiles(out$Rt[, 1:N, i], probs = .025)\n rt_ui <- colQuantiles(out$Rt[, 1:N, i], probs = .975)\n rt_li2 <- colQuantiles(out$Rt[, 1:N, i], probs = .25)\n rt_ui2 <- colQuantiles(out$Rt[, 1:N, i], probs = .75)\n\n # NOTE: `country` is an integer - should be okay here\n covariates_country <- covariates[which(covariates$Country == country), 3:ncol(covariates), drop = FALSE]\n\n covariates_country_long <- gather(covariates_country[], key = \"key\", value = \"value\")\n covariates_country_long$x <- rep(NULL, length(covariates_country_long$key))\n un_dates <- unique(covariates_country_long$value)\n\n for (k in 1:length(un_dates)) {\n idxs <- which(covariates_country_long$value == un_dates[k])\n max_val <- round(max(rt_ui)) + 0.3\n for (j in idxs) {\n covariates_country_long$x[j] <- max_val\n max_val <- max_val - 0.3\n }\n }\n\n covariates_country_long$value <- as_date(covariates_country_long$value)\n covariates_country_long$country <- rep(country, length(covariates_country_long$value))\n\n data_country <- data.frame(\n \"time\" = as_date(as.character(dates[[i]])),\n \"country\" = rep(country, length(dates[[i]])),\n \"reported_cases\" = reported_cases[[i]],\n \"reported_cases_c\" = cumsum(reported_cases[[i]]),\n \"predicted_cases_c\" = cumsum(predicted_cases),\n \"predicted_min_c\" = cumsum(predicted_cases_li),\n \"predicted_max_c\" = cumsum(predicted_cases_ui),\n \"predicted_cases\" = predicted_cases,\n \"predicted_min\" = predicted_cases_li,\n \"predicted_max\" = predicted_cases_ui,\n \"predicted_min2\" = predicted_cases_li2,\n \"predicted_max2\" = predicted_cases_ui2,\n \"deaths\" = deaths_by_country[[i]],\n \"deaths_c\" = cumsum(deaths_by_country[[i]]),\n \"estimated_deaths_c\" = cumsum(estimated_deaths),\n \"death_min_c\" = cumsum(estimated_deaths_li),\n \"death_max_c\" = cumsum(estimated_deaths_ui),\n \"estimated_deaths\" = estimated_deaths,\n \"death_min\" = estimated_deaths_li,\n \"death_max\" = estimated_deaths_ui,\n \"death_min2\" = estimated_deaths_li2,\n \"death_max2\" = estimated_deaths_ui2,\n \"rt\" = rt,\n \"rt_min\" = rt_li,\n \"rt_max\" = rt_ui,\n \"rt_min2\" = rt_li2,\n \"rt_max2\" = rt_ui2\n )\n\n county_deaths_and_est <- make_plots(\n data_country = data_country,\n covariates_country_long = covariates_country_long,\n filename2 = filename2,\n country = countryName,\n code = country,\n date_break = date_break\n )\n\n # allErr[[i]] <- county_deaths_and_est\n }\n\n #### all estimated deaths curves one plot ####\n\n # minDate <- min(sapply(allErr, function(x) min(x$time)))\n\n # allEst <- lapply(allErr, function(x) x[x$time <= lastObs,])\n # allEst <- lapply(allEst, function(x) subset(x, select=-c(deaths)))\n\n # pad_est <- function(x) {\n # if (min(x$time) == minDate){return(x)}\n # df <- data.frame(\n # time = seq(as.Date(minDate), as.Date(min(x$time)-1), by=\"days\"),\n # est = rep(0, min(x$time) - minDate),\n # countyName = rep(x$countyName[1], min(x$time) - minDate)\n # )\n # return(rbind(df,x))\n # }\n # allEst <- lapply(allEst, pad_est)\n\n # rough - add county name as ID\n # pEst <- ggplot(bind_rows(allEst), aes(x=time, y=est, colour=countyName)) + geom_line()\n # save_plot(filename = \"../modelOutput/figures/allEstimates.png\", pEst)\n\n #### error analysis ####\n # cutoff <- max(sapply(allErr, function(x) min(x$time)))\n # allErr <- sapply(allErr, function(x) x[x$time >= cutoff & x$time <= lastObs,])\n\n # err_df <- data.frame(time=allErr[,1]$time)\n\n # err_df$deaths <- 0\n # err_df$est <- 0\n # for (i in 1:dim(allErr)[2]){\n # err_df$deaths <- err_df$deaths + allErr[,i]$deaths\n # err_df$est <- err_df$est + allErr[,i]$est\n # }\n\n ### scaled error daily\n # error_plot(\n # df = err_df,\n # title = \"All County Daily Deaths\",\n # path = \"../modelOutput/figures/%sse_daily_all.png\"\n # )\n\n ### scaled error weekly\n # weekly_error_plot(\n # df = err_df,\n # title = \"All County Weekly Deaths\",\n # path = \"../modelOutput/figures/%sse_weekly_all.png\"\n # )\n}\n\nweekly_error_plot <- function(df, title, path) {\n weeklyDeaths <- unname(tapply(df$deaths, (seq_along(df$deaths) - 1) %/% 7, sum))\n weeklyEst <- unname(tapply(df$est, (seq_along(df$est) - 1) %/% 7, sum))\n weeklyDates <- as.Date(unname(tapply(df$time, (seq_along(df$time) - 1) %/% 7, min)))\n\n w <- data.frame(time = weeklyDates, deaths = weeklyDeaths, est = weeklyEst)\n\n error_plot(\n df = w,\n title = title,\n path = path\n )\n}\n\nerror_plot <- function(df, title, path) {\n df$err_raw <- df$est - df$deaths\n avg_naive <- mean(abs(diff(df$deaths)))\n df$err_scaled <- df$err_raw / avg_naive\n df$err_abs_scaled <- abs(df$err_scaled)\n\n abs_and_signed_error(\n df = df,\n title = title,\n path = path\n )\n}\n\nabs_and_signed_error <- function(df, title, path) {\n gg_error(\n df = df,\n target = \"err_scaled\",\n title = sprintf(\"%s Scaled Error\", title),\n path = sprintf(path, \"\")\n )\n\n gg_error(\n df = df,\n target = \"err_abs_scaled\",\n title = sprintf(\"%s Absolute Scaled Error\", title),\n path = sprintf(path, \"a\")\n )\n}\n\ngg_error <- function(df, target, title, path) {\n p <- ggplot(df) +\n ggtitle(title) +\n geom_bar(\n data = df, aes(x = time, y = !!sym(target)),\n fill = \"coral4\", stat = \"identity\", alpha = 0.5\n ) +\n xlab(\"Time\") +\n ylab(\"Error\") +\n labs(subtitle = sprintf(\"avg_err: %f\", mean(df[[target]]))) +\n scale_x_date(date_breaks = \"weeks\", labels = date_format(\"%e %b\")) +\n theme_pubr() +\n theme(\n axis.text.x = element_text(angle = 45, hjust = 1),\n plot.title = element_text(hjust = 0.5),\n legend.position = \"None\"\n ) +\n guides(fill = guide_legend(ncol = 1))\n\n save_plot(filename = path, p)\n}\n\n#---------------------------------------------------------------------------\n\n# todo: break down into 3 fn's - modular, man, modular\n\nmake_plots <- function(data_country, covariates_country_long,\n filename2, country, code, date_break) {\n countyDir <- file.path(\"../modelOutput/figures\", code)\n dir.create(countyDir, showWarnings = FALSE)\n\n #### scaled error plot daily counts\n # deaths_err <- data.frame(time=data_country$time, deaths=data_country$deaths, est=data_country$estimated_deaths, deaths_c=data_country$deaths_c)\n\n # index = which(d1$cases>0)[1]\n # index <- which(deaths_err$deaths_c>10)[1]\n # deaths_err <- deaths_err[index:nrow(deaths_err),]\n\n # MASE : https://en.wikipedia.org/wiki/Mean_absolute_scaled_error\n # https://robjhyndman.com/papers/foresight.pdf\n # file:///Users/mattgarvin/Downloads/A-note-on-the-MASE-Revision-for-IJF.pdf\n\n # error_plot(\n # df = deaths_err,\n # title = paste0(country, \" County Daily Deaths\"),\n # path = file.path(countyDir, \"%sse_daily.png\")\n # )\n\n # weekly_error_plot(\n # df = deaths_err,\n # title = paste0(country, \" County Weekly Deaths\"),\n # path = file.path(countyDir, \"%sse_weekly.png\")\n # )\n\n ## p1\n\n data_cases_95 <- data.frame(\n data_country$time, data_country$predicted_min,\n data_country$predicted_max\n )\n names(data_cases_95) <- c(\"time\", \"cases_min\", \"cases_max\")\n data_cases_95$key <- rep(\"nintyfive\", length(data_cases_95$time))\n data_cases_50 <- data.frame(\n data_country$time, data_country$predicted_min2,\n data_country$predicted_max2\n )\n names(data_cases_50) <- c(\"time\", \"cases_min\", \"cases_max\")\n data_cases_50$key <- rep(\"fifty\", length(data_cases_50$time))\n data_cases <- rbind(data_cases_95, data_cases_50)\n levels(data_cases$key) <- c(\"ninetyfive\", \"fifty\")\n\n p1 <- ggplot(data_country) +\n ggtitle(paste0(country, \" County Daily Cases\")) +\n geom_bar(\n data = data_country, aes(x = time, y = reported_cases),\n fill = \"coral4\", stat = \"identity\", alpha = 0.5\n ) +\n geom_ribbon(\n data = data_cases,\n aes(x = time, ymin = cases_min, ymax = cases_max, fill = key)\n ) +\n xlab(\"Time\") +\n ylab(\"Cases\") +\n scale_x_date(date_breaks = date_break, labels = date_format(\"%e %b\")) +\n scale_fill_manual(\n name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(\n alpha(\"deepskyblue4\", 0.55),\n alpha(\"deepskyblue4\", 0.45)\n )\n ) +\n theme_pubr() +\n theme(\n axis.text.x = element_text(angle = 45, hjust = 1),\n plot.title = element_text(hjust = 0.5),\n legend.position = \"None\"\n ) +\n guides(fill = guide_legend(ncol = 1))\n\n save_plot(filename = file.path(countyDir, \"cases.svg\"), p1)\n save_plot(filename = file.path(countyDir, \"cases.png\"), p1)\n\n ### p2\n\n data_deaths_95 <- data.frame(\n data_country$time, data_country$death_min,\n data_country$death_max\n )\n names(data_deaths_95) <- c(\"time\", \"death_min\", \"death_max\")\n data_deaths_95$key <- rep(\"nintyfive\", length(data_deaths_95$time))\n data_deaths_50 <- data.frame(\n data_country$time, data_country$death_min2,\n data_country$death_max2\n )\n names(data_deaths_50) <- c(\"time\", \"death_min\", \"death_max\")\n data_deaths_50$key <- rep(\"fifty\", length(data_deaths_50$time))\n data_deaths <- rbind(data_deaths_95, data_deaths_50)\n levels(data_deaths$key) <- c(\"ninetyfive\", \"fifty\")\n\n p2 <- ggplot(data_country, aes(x = time)) +\n ggtitle(paste0(country, \" County Daily Deaths\")) +\n geom_bar(\n data = data_country, aes(y = deaths, fill = \"reported\"),\n fill = \"coral4\", stat = \"identity\", alpha = 0.5\n ) +\n geom_ribbon(\n data = data_deaths,\n aes(ymin = death_min, ymax = death_max, fill = key)\n ) +\n xlab(\"Time\") +\n ylab(\"Deaths\") +\n scale_x_date(date_breaks = date_break, labels = date_format(\"%e %b\")) +\n scale_fill_manual(\n name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(\n alpha(\"deepskyblue4\", 0.55),\n alpha(\"deepskyblue4\", 0.45)\n )\n ) +\n theme_pubr() +\n theme(\n axis.text.x = element_text(angle = 45, hjust = 1),\n plot.title = element_text(hjust = 0.5),\n legend.position = \"None\"\n ) +\n guides(fill = guide_legend(ncol = 1))\n\n save_plot(filename = file.path(countyDir, \"deaths.svg\"), p2)\n save_plot(filename = file.path(countyDir, \"deaths.png\"), p2)\n\n ### p3\n\n plot_labels <- c(\"lockdown\")\n\n # Plotting interventions\n data_rt_95 <- data.frame(\n data_country$time,\n data_country$rt_min, data_country$rt_max\n )\n names(data_rt_95) <- c(\"time\", \"rt_min\", \"rt_max\")\n data_rt_95$key <- rep(\"nintyfive\", length(data_rt_95$time))\n data_rt_50 <- data.frame(\n data_country$time, data_country$rt_min2,\n data_country$rt_max2\n )\n names(data_rt_50) <- c(\"time\", \"rt_min\", \"rt_max\")\n data_rt_50$key <- rep(\"fifty\", length(data_rt_50$time))\n data_rt <- rbind(data_rt_95, data_rt_50)\n levels(data_rt$key) <- c(\"ninetyfive\", \"fifth\")\n\n p3 <- ggplot(data_country) +\n ggtitle(paste0(country, \" County Estimated Rt\")) +\n geom_stepribbon(data = data_rt, aes(\n x = time,\n ymin = rt_min, ymax = rt_max,\n group = key,\n fill = key\n )) +\n geom_hline(yintercept = 1, color = \"black\", size = 0.1) +\n # missing values in one row -> warning -> td: double check this\n geom_segment(\n data = covariates_country_long,\n aes(x = value, y = 0, xend = value, yend = max(x)),\n linetype = \"dashed\", colour = \"grey\", alpha = 0.75\n ) +\n # missing values in one row -> warning\n geom_point(data = covariates_country_long, aes(\n x = value,\n y = x,\n group = key,\n shape = key,\n col = key\n ), size = 2) +\n xlab(\"Time\") +\n ylab(expression(R[t])) +\n scale_fill_manual(\n name = \"\", labels = c(\"50%\", \"95%\"),\n values = c(alpha(\"seagreen\", 0.75), alpha(\"seagreen\", 0.5))\n ) +\n scale_shape_manual(\n name = \"Interventions\", labels = plot_labels,\n values = c(21, 22, 23, 24, 25, 12)\n ) +\n scale_colour_discrete(name = \"Interventions\", labels = plot_labels) +\n scale_x_date(date_breaks = date_break, labels = date_format(\"%e %b\")) +\n theme_pubr() +\n theme(\n axis.text.x = element_text(angle = 45, hjust = 1),\n plot.title = element_text(hjust = 0.5),\n legend.position = \"right\"\n )\n\n save_plot(filename = file.path(countyDir, \"Rt.svg\"), p3)\n save_plot(filename = file.path(countyDir, \"Rt.png\"), p3)\n\n # df_err <- data.frame(time=deaths_err$time, deaths=deaths_err$deaths, est=deaths_err$est, countyName=country)\n # return(df_err)\n return()\n}\n\nmake_three_pannel_plot()\n", "meta": {"hexsha": "dcd1dce1937cf273512c175f2797bc5d6e3cbc4c", "size": 18679, "ext": "r", "lang": "R", "max_stars_repo_path": "covid19model/r/plot-trend.r", "max_stars_repo_name": "uc-cdis/covid19model", "max_stars_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "covid19model/r/plot-trend.r", "max_issues_repo_name": "uc-cdis/covid19model", "max_issues_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 8, "max_issues_repo_issues_event_min_datetime": "2021-03-22T21:16:32.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-29T22:58:33.000Z", "max_forks_repo_path": "covid19model/r/plot-trend.r", "max_forks_repo_name": "uc-cdis/covid19model", "max_forks_repo_head_hexsha": "ae256c0874e09992ebc6a2a3c566e5a89b29e94b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2020-05-12T23:53:08.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-22T15:08:21.000Z", "avg_line_length": 34.5907407407, "max_line_length": 147, "alphanum_fraction": 0.6424862145, "num_tokens": 5631, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5544704502361149, "lm_q2_score": 0.5428632831725052, "lm_q1q2_score": 0.30100164903731447}} {"text": "library(tidyverse)\r\nlibrary(data.table)\r\nlibrary(ggplot2)\r\nlibrary(ggthemes)\r\nlibrary(grid)\r\n\r\nres <- list()\r\nfor(i in 1:1200){\r\n ss <- try(load(paste0('output/out', i, '.rda')))\r\n if(inherits(ss, 'try-error')) {\r\n ## cat('error ', i)\r\n } else {\r\n rtemp <- get(paste0('r', i))\r\n if(any(sapply(rtemp, length) == 1)) print(i)\r\n res <- c(res, rtemp)\r\n }\r\n}\r\n\r\nout <- rbindlist(res[sapply(res, length) > 1])\r\n\r\n## out %>% filter(estimator == 'TMLE', n == 16200, parameter == 'DE', type == 1) %>%\r\n## pull(estimate) %>% hist()\r\n\r\n## out %>% filter(estimator == 'TMLE', n == 16200, parameter == 'DE', type == 4) %>%\r\n## pull(estimate) %>% mean()\r\n\r\nload('true.rda')\r\n## source('utils.r')\r\n## true <- truth(1e7)\r\n## save(true, file = 'true.rda')\r\nalpha <- 0.05\r\ndplot <- out %>% left_join(true, by = 'parameter') %>%\r\n group_by(type, parameter, estimator, n) %>%\r\n summarise(## m = n(),\r\n rbias = abs(mean(estimate - truth)),\r\n rnbias = abs(mean(sqrt(n) * (estimate - truth))),\r\n relse = mean(ses) / sd(estimate),\r\n relsd = sd(sqrt(n) * estimate / sqrt(eff_bound)),\r\n relrmse = sqrt(mean(n * (estimate - truth)^2 / eff_bound)),\r\n coverage = mean(qnorm(alpha / 2) < (estimate - truth) / ses &\r\n (estimate - truth) / ses < qnorm(1 - alpha / 2))) %>%\r\n ## filter(type == 0, estimator == 'os') %>%\r\n gather(statistic, value, rbias, rnbias, relse, relsd, relrmse, coverage) %>%\r\n ungroup() %>%\r\n mutate(parameter = factor(parameter),\r\n type = factor(type),\r\n estimator = factor(estimator),\r\n statistic = factor(statistic,\r\n levels = c('rbias', 'rnbias',\r\n 'relsd', 'relrmse',\r\n 'coverage', 'relse'),\r\n labels = c(\"group('|',Bias,'|')\",\r\n \"n^{1/2}~group('|',Bias,'|')\",\r\n 'rel~sd(hat(theta))',\r\n '(n%*%rel~MSE)^{1/2}',\r\n 'Cov(0.95)',\r\n 'hat(sd)(hat(theta))/sd(hat(theta))')),\r\n type = factor(type,\r\n levels = 0:5,\r\n labels = c('none', 'g', 'e', 'm', 'b', 'd')))\r\n\r\ndummy <- data.frame(statistic = c(\"n^{1/2}~group('|',Bias,'|')\",\r\n \"group('|',Bias,'|')\",\r\n 'hat(sd)(hat(theta))/sd(hat(theta))',\r\n 'rel~sd(hat(theta))',\r\n '(n%*%rel~MSE)^{1/2}',\r\n 'Cov(0.95)'),\r\n lim = c(0, 0, 1, 1, 1, 1 - alpha))\r\n\r\nns <- cumsum(rep(sqrt(200), 9))^2\r\nsplit1 <- c('none')\r\nsplit2 <- c('g','b')\r\nsplit3 <- c('m','e','d')\r\n\r\nplot1 <- dplot %>% rename(Parameter = parameter, Estimator = estimator) %>%\r\n filter(type %in% split1) %>%\r\n ggplot(aes(n, value, colour = Estimator, shape = Parameter)) +\r\n ylab('') +\r\n geom_hline(data = dummy, aes(yintercept = lim), colour = 'darkgray', size = 1.5) +\r\n geom_point(size = 2) +\r\n geom_line(linetype = \"dotted\", size = 0.5) +\r\n theme_igray() +\r\n scale_x_continuous(breaks = ns, trans = 'sqrt') +\r\n theme(legend.position = 'none', axis.text.x = element_text(angle = 50, hjust = 1),\r\n text = element_text(size = 18), panel.spacing = unit(1, \"lines\"),\r\n strip.text.y = element_blank()) +\r\n ## scale_shape_manual(name = 'Estimator:',\r\n ## values = c(3, 4, 1, 2)) +\r\n facet_grid(statistic ~ type, scales = 'free_y', labeller = label_parsed)\r\nplot2 <- dplot %>% rename(Parameter = parameter, Estimator = estimator) %>%\r\n filter(type %in% split3) %>%\r\n ggplot(aes(n, value, colour = Estimator, shape = Parameter)) +\r\n ylab('') +\r\n geom_hline(data = dummy, aes(yintercept = lim), colour = 'darkgray', size = 1.5) +\r\n geom_point(size = 2) +\r\n geom_line(linetype = \"dotted\", size = 0.5) +\r\n theme_igray() +\r\n scale_x_continuous(breaks = ns, trans = 'sqrt') +\r\n theme(legend.position = 'none', axis.text.x = element_text(angle = 50, hjust = 1),\r\n text = element_text(size = 18), panel.spacing = unit(1, \"lines\"),\r\n strip.text.y = element_blank()) +\r\n ## scale_shape_manual(name = 'Estimator:',\r\n ## values = c(3, 4, 1, 2)) +\r\n facet_grid(statistic ~ type, scales = 'free_y', labeller = label_parsed)\r\nplot3 <- dplot %>% rename(Parameter = parameter, Estimator = estimator) %>%\r\n filter(type %in% split2) %>%\r\n ggplot(aes(n, value, colour = Estimator, shape = Parameter)) +\r\n ylab('') +\r\n geom_hline(data = dummy, aes(yintercept = lim), colour = 'darkgray', size = 1.5) +\r\n geom_point(size = 2) +\r\n geom_line(linetype = \"dotted\", size = 0.5) +\r\n theme_igray() +\r\n scale_x_continuous(breaks = ns, trans = 'sqrt') +\r\n theme(legend.position = 'right', axis.text.x = element_text(angle = 50, hjust = 1),\r\n text = element_text(size = 18), panel.spacing = unit(1, \"lines\")) +\r\n ## scale_shape_manual(name = 'Estimator:',\r\n ## values = c(3, 4, 1, 2)) +\r\n facet_grid(statistic ~ type, scales = 'free_y', labeller = label_parsed)\r\n\r\npdf('plot.pdf', width = 13, height = 11)\r\ngrid.newpage()\r\ngrid.draw(cbind(ggplotGrob(plot1), ggplotGrob(plot2), ggplotGrob(plot3), size = 'last'))\r\ndev.off()\r\n\r\ndplot %>%\r\n filter(type == 'All-consistent', parameter == 'indirect', n == 9800,\r\n statistic == 'hat(sd)(hat(theta))/sd(hat(theta))')\r\n", "meta": {"hexsha": "b1e4da9b44f84ab8b3ec52ee57b956437d9a92f0", "size": 5683, "ext": "r", "lang": "R", "max_stars_repo_path": "sandbox/intmedlite/sandbox/summarize.r", "max_stars_repo_name": "nhejazi/medshift", "max_stars_repo_head_hexsha": "48b2b3a65b40bd35366497c2bf77ad6bac527d24", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "max_stars_repo_stars_event_min_datetime": "2019-01-11T17:37:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-11T01:12:08.000Z", "max_issues_repo_path": "sandbox/intmedlite/sandbox/summarize.r", "max_issues_repo_name": "nhejazi/medshift", "max_issues_repo_head_hexsha": "48b2b3a65b40bd35366497c2bf77ad6bac527d24", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2019-01-15T19:11:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-29T06:54:52.000Z", "max_forks_repo_path": "sandbox/intmedlite/sandbox/summarize.r", "max_forks_repo_name": "nhejazi/medshift", "max_forks_repo_head_hexsha": "48b2b3a65b40bd35366497c2bf77ad6bac527d24", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3, "max_forks_repo_forks_event_min_datetime": "2019-03-02T15:56:59.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-11T21:35:25.000Z", "avg_line_length": 44.3984375, "max_line_length": 89, "alphanum_fraction": 0.4979764209, "num_tokens": 1614, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.30096313101869365}} {"text": "#' Predictor layers\n#'\n#' Raster brick of environmental predictors\n#'\n#' @format A RasterBrick\n#' \\describe{\n#' \\item{Annual Mean Radiation}{}\n#' \\item{Annual Mean Temperature}{}\n#' \\item{Annual Precipitation}{}\n#' \\item{Precipitation Seasonality}{}\n#' \\item{Soil Fertility}{}\n#' \\item{Topographic Wetness}{}\n#' }\n#'\n\"predictors\"\n", "meta": {"hexsha": "418a8d9c760a37db43b408e3b775d75cc0e1a80a", "size": 342, "ext": "r", "lang": "R", "max_stars_repo_path": "R/predictors.r", "max_stars_repo_name": "wkmor1/voiConsPlan", "max_stars_repo_head_hexsha": "c4381a8b0615f5e363cde3f05404c2791f5b88f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/predictors.r", "max_issues_repo_name": "wkmor1/voiConsPlan", "max_issues_repo_head_hexsha": "c4381a8b0615f5e363cde3f05404c2791f5b88f0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/predictors.r", "max_forks_repo_name": "wkmor1/voiConsPlan", "max_forks_repo_head_hexsha": "c4381a8b0615f5e363cde3f05404c2791f5b88f0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.375, "max_line_length": 43, "alphanum_fraction": 0.6578947368, "num_tokens": 102, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.5926665999540698, "lm_q1q2_score": 0.30096313101869365}} {"text": "#!/usr/bin/Rscript\n# Command line arguments for R are not fun... so to make this easy create the args\n# by prepending them to this script directly before the R call:\n# [bash]$ echo 'arg1 = \"value\";arg2=\"someValue\"' |cat - script.r | R --no-save\n# Variables dataFrame, inputDir, outputDir, are required\nargs <- commandArgs(TRUE)\n\nif (length(args) != 2) {\n print(\"Usage: GoSumWordCloud.r \")\n}\n\ndata <- args[1]\n#inputDir <- args[2]\n#outputDir <-args[3]\noutputFile <-args[2]\n\nlibrary(GOsummaries);\nup <-read.table(data, header =TRUE, sep =\"\\t\", quote=\"\");\nwcd1 = data.frame(Term = up$Name, Score = up$Pvalue);\ngs = gosummaries(wc_data = list(wcd1));\n#DO I NEED TO GIVE IT AN OUTPUT DIR?\n#fullOutFile = paste(outputDir, outputFile, sep=\"/\");\n#plot(gs, filename =\"fullOutFile\");\n\n\n#IF NOT THEN:\n\nplot(gs, filename =outputFile);\n\n\n\n#library(ggplot2);\n#up <-read.table(data, header =TRUE, sep =\"\\t\");\n#png(outputFile);\n#hist(up$Result.count);\ndev.off();\n", "meta": {"hexsha": "2293b8d74a5e4d23daf1243b1bfdfe5a7a26b48a", "size": 969, "ext": "r", "lang": "R", "max_stars_repo_path": "Model/bin/GoSumWordCloud.r", "max_stars_repo_name": "EuPathDB-Infra/ApiCommonWebsite", "max_stars_repo_head_hexsha": "99a62b0bc17fab7c0a5cf552c3a8aa1361f59033", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-09-19T14:36:38.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-21T05:02:04.000Z", "max_issues_repo_path": "Model/bin/GoSumWordCloud.r", "max_issues_repo_name": "EuPathDB/ApiCommonWebsite", "max_issues_repo_head_hexsha": "1d1a823807d1e243aa0ec15c45cac0bf2d3a112b", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2020-06-17T18:57:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-23T20:25:10.000Z", "max_forks_repo_path": "Model/bin/GoSumWordCloud.r", "max_forks_repo_name": "EuPathDB/ApiCommonWebsite", "max_forks_repo_head_hexsha": "1d1a823807d1e243aa0ec15c45cac0bf2d3a112b", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.1891891892, "max_line_length": 82, "alphanum_fraction": 0.6780185759, "num_tokens": 292, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.3009631310186936}} {"text": "# 4. faza: Analiza podatkov\n\n# priprava tabel\n\npriprava.podatkov <- function(tabela, stolpec) {\n tabela %>%\n filter(\n drzava !=\"EU28\", \n spol == \"skupaj\", \n `starostna skupina` == \"skupaj\",\n leto == 2019\n ) %>% \n select(-`starostna skupina`, -spol, -leto) %>% \n pivot_wider(names_from = stolpec, values_from = odstotek)\n}\n\npogostost.razvrscanje <- priprava.podatkov(pogostost, \"pogostost\")\n\nvrednosti.razvrscanje <- priprava.podatkov(vrednosti, \"vrednost\")\n\nizvor.razvrscanje <- priprava.podatkov(izvor, \"prodajalec\")\n \n\npodatki.razvrscanje <- pogostost.razvrscanje %>%\n inner_join(vrednosti.razvrscanje) %>%\n inner_join(izvor.razvrscanje) %>%\n select(-drzava) %>% \n scale()\n\nfviz_nbclust(podatki.razvrscanje, FUN = hcut, method = \"wss\")\nfviz_nbclust(podatki.razvrscanje, FUN = hcut, method = \"silhouette\")\nfviz_nbclust(podatki.razvrscanje, FUN = hcut, method = \"gap_stat\", nstart=25, nboot=300)\n\nD <- dist(podatki.razvrscanje)\nmodel <- hclust(D)\n\ndnd <- as.dendrogram(model) %>% \n set(\"labels\",pogostost.razvrscanje$drzava[model$order]) %>% \n set(\"leaves_pch\", 19) %>% \n set(\"leaves_cex\", 2) %>% \n set(\"leaves_col\", \"black\") %>% \n set(\"branches_k_color\", \n value = c(\"red\", \"blue\"), k = 2) %>% \n as.ggdend()\n\ndnd$labels$label <- paste(pogostost.razvrscanje$drzava[model$order])\ndnd$labels$drzava <- pogostost.razvrscanje$drzava[model$order]\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "4fa2ceec1831798c26a01394827149b912f6a71e", "size": 1401, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "majaabraham/APPR-2020-21", "max_stars_repo_head_hexsha": "9efd9fc606606bfea8cf2489ef277884b8b3b65a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analiza/analiza.r", "max_issues_repo_name": "majaabraham/APPR-2020-21", "max_issues_repo_head_hexsha": "9efd9fc606606bfea8cf2489ef277884b8b3b65a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-12-23T08:26:13.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-27T11:09:04.000Z", "max_forks_repo_path": "analiza/analiza.r", "max_forks_repo_name": "majaabraham/APPR-2020-21", "max_forks_repo_head_hexsha": "9efd9fc606606bfea8cf2489ef277884b8b3b65a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0178571429, "max_line_length": 88, "alphanum_fraction": 0.6673804425, "num_tokens": 538, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.5078118642792044, "lm_q1q2_score": 0.300963123711621}} {"text": "#SV Scarpino\n#July 2020\n#CA COVID county-level Hospitalizations\n\n###########\n#libraries#\n###########\nlibrary(zoo)\n\n#########\n#Globals#\n#########\nroll_mean_days <- 7\ntime_stamp <- format(Sys.time(), format = \"%d_%m_%Y\")\nsave_new <- FALSE\n\n######\n#Data#\n######\ndat_ca <- read.csv(\"https://data.ca.gov/dataset/529ac907-6ba1-4cb7-9aae-8966fc96aeef/resource/42d33765-20fd-44b8-a978-b083b7542225/download/hospitals_by_county.csv\")\ndat_ca$date <- as.POSIXct(strptime(dat_ca$todays_date, format = \"%Y-%m-%d\"))\n\n#########\n#Process#\n#########\nby_day_ca_county <- by(data = dat_ca$hospitalized_covid_confirmed_patients, INDICES = dat_ca[,c(\"todays_date\", \"county\")], FUN = sum, na.rm = TRUE)\n\nmat_by_day_ca_county <- matrix(by_day_ca_county, nrow = nrow(by_day_ca_county), ncol = ncol(by_day_ca_county))\ncolnames(mat_by_day_ca_county) <- colnames(by_day_ca_county)\nrownames(mat_by_day_ca_county) <- rownames(by_day_ca_county)\n\n######\n#Save#\n######\nif(save_new == TRUE){\n write.csv(mat_by_day_ca_county, file = paste0(\"../Data/CA_county_hosp_COVID_\", time_stamp, \".csv\"), quote = FALSE)\n}", "meta": {"hexsha": "6ed9eb78a2929d386fd9999a4e72a782ca2b9756", "size": 1077, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/make_ca_county_hosp_data.r", "max_stars_repo_name": "Emergent-Epidemics/ca_covid", "max_stars_repo_head_hexsha": "3a92f6526cb8ef0a3032a05acc512ee84ddcd97d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Scripts/make_ca_county_hosp_data.r", "max_issues_repo_name": "Emergent-Epidemics/ca_covid", "max_issues_repo_head_hexsha": "3a92f6526cb8ef0a3032a05acc512ee84ddcd97d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Scripts/make_ca_county_hosp_data.r", "max_forks_repo_name": "Emergent-Epidemics/ca_covid", "max_forks_repo_head_hexsha": "3a92f6526cb8ef0a3032a05acc512ee84ddcd97d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1081081081, "max_line_length": 165, "alphanum_fraction": 0.6945218199, "num_tokens": 336, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583270090337583, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.30092888952593677}} {"text": "# takes g_inputdir,g_filetable and g_outputdir\r\n##and g_duration_slider, g_bin_size\r\n# return activity plots\r\n\r\n\r\nmessage(\"starting activity.r\")\r\n### compute the activities\r\n\t\r\nact_table = data.frame()\r\n\r\n#compute activities for each individum\r\nfor (i in c(1:nrow(id_table))) {\r\n\tact = c.activity_speed(traj[id(traj)==id_table$id[i]],g_duration_slider/10)\r\n\tact_id = rep(id_table$id[i],nrow(act))\r\n\tact_group = rep(id_table$group[i],nrow(act))\r\n\tact_table = rbind(act_table,data.frame(act,id=act_id,group=act_group))\r\n\r\n}\r\n\r\n# filter activities which are smaller than the act slider\r\nact_table_ori = act_table\r\npause_table = act_table[act_table$act<0,]\r\npause_table$pause = - pause_table$act\r\n#act_table = act_table[act_table$act>g_duration_slider/10*4,]\r\nact_table = act_table[act_table$act>0,]\r\n\r\nprint(head(act_table_ori))\r\n\r\n#calculation of linearity of curve for each bout\r\n#########################\r\nact_table$lin = sqrt( (act_table$Xe-act_table$Xs)^2+(act_table$Ye-act_table$Ys)^2)/act_table$dist_traveled\r\n\r\nprint(head(act_table))\r\n\r\n### get rid of act of lenght 1\r\n#act_table = act_table[act_table$lin<1,]\r\nprint(head(act_table))\r\n\r\n#calculate total activity time for each individual\r\nsum_act = c()\r\nmedian_act = c()\r\nmedian_pause = c()\r\nnumber_pause = c()\r\nmedian_lin = c()\r\nmean_lin = c()\r\nfor (i in c(1:nrow(id_table))) {\r\n\tact = act_table[act_table$id==id_table$id[i],]$act\r\n\tpause = pause_table[act_table$id==id_table$id[i],]$act\r\n\tlin = act_table[act_table$id==id_table$id[i],]$lin\r\n\tsum_act = c(sum_act,sum(act))\r\n\tmedian_act = c(median_act,median(act))\r\n\tmedian_pause = c(median_pause, median(abs(pause)))\r\n\tnumber_pause = c(number_pause,length(pause))\r\n\tmedian_lin = c(median_lin, quantile(lin, probs =0.5))\r\n\t\r\n\t\r\n}\r\nact_table_2_2 = data.frame(sumact2=sum_act,median_act2=median_act,median_pause2=median_pause,number_pause2=number_pause,median_lin2= median_lin,id=id_table$id,group=id_table$group)\r\n\r\nact_table_2 = data.frame(sum_act,median_act,median_pause,number_pause,median_lin,id=id_table$id,group=id_table$group)\r\n\r\nf_table=data.frame(f_table,act_table_2_2 [,1:5])\r\n\r\nmessage(\"starting writing activities log.txt\")\r\n\r\n\r\n\r\nsetwd(rgghome)\r\n\r\nif (g_bin_size==0)\r\n\t{g_bin_size = 0.5}\t\r\n\t\r\nv = act_table$act\r\nbins = seq(min(v[!is.na(v)])-g_bin_size,max(v[!is.na(v)])+g_bin_size,g_bin_size)\r\n\r\n#create mean and sd table\r\nmean_table = create.mean.table(act_table_2,group_ids,data_cols=1:5)\r\n\r\n\r\nmessage(\"starting plots activity\")\r\n### create plots\r\nmybarplot(mean_table$means$sum_act,mean_table$ses$sum_act,rownames(mean_table$means),\r\n\tmain=\"Total activity time threshold\",ylab=\"Total activity time [s]\", ylim= c(0,ylim_acttime))\r\nmybarplot(mean_table$means$median_act,mean_table$ses$median_act,rownames(mean_table$means),\r\n\tmain=\"Mean of medians of bouts length threshold\",ylab=\"median of bouts length [s]\",ylim = c(0,20))\r\n\r\nmybarplot(mean_table$means$number_pause,mean_table$ses$number_pause,rownames(mean_table$means),\r\n\tmain=\"Number of pauses threshold\",ylab=\"Number of pauses\", ylim = c(0,ylim_pauses))\r\n\r\n#mybarplot(mean_table$means$median_pause,mean_table$ses$median_pause,rownames(mean_table$means),\r\n#\tmain=\"Mean of medians of pause time\",ylab=\"Median pause time [s]\", ylim =c(0, ylim_pausetime))\r\n\r\nmybarplot(mean_table$means$median_lin,mean_table$ses$median_lin,rownames(mean_table$means),\r\n\tmain=\"Mean of medians of linearity score threshold\",ylab=\"Lin score [0 to 1]\", ylim =c(0, 1))\r\n\r\n\r\n#\r\n#bins = 0.05\r\n#hplot1 = hist(act_table$lin,breaks= c(seq(0,1, bins)), plot=FALSE)\r\n#\r\n#print(plot(hplot1, xlim=c(0,1),freq = FALSE))\r\n#\r\n#bins = 0.11\r\n#\r\n#hplot4 = hist(act_table$act,breaks= c(seq(0,max(act_table$act)+bins*5, bins*5)), plot=FALSE)\r\n#print(plot(hplot4, freq = FALSE, xlim=c(0,100)))\r\n#\r\n#\r\n#\r\n#plot (act_table$lin, act_table$dist_traveled, ylim =c(0,1000))\r\n#\r\n#plot (act_table$lin, act_table$dist_traveled, ylim =c(0,35))\r\n#\r\n#plot (act_table_ori$speedmax_inbout, act_table_ori$dist_traveled)\r\n#hist(act_table_ori$speedmax_inbout)\r\n", "meta": {"hexsha": "e20c4ac294f806cfc3bec468bea38c37b2bafcaf", "size": 3974, "ext": "r", "lang": "R", "max_stars_repo_path": "CeTrAn/scripts/unused/activity_log_old.r", "max_stars_repo_name": "brembslab/CeTrAn", "max_stars_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2015-02-26T12:51:15.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-21T08:36:32.000Z", "max_issues_repo_path": "CeTrAn/scripts/unused/activity_log_old.r", "max_issues_repo_name": "brembslab/CeTrAn", "max_issues_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_issues_repo_licenses": ["CC-BY-3.0"], "max_issues_count": 10, "max_issues_repo_issues_event_min_datetime": "2015-01-09T13:08:07.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-18T13:31:53.000Z", "max_forks_repo_path": "CeTrAn/scripts/unused/activity_log_old.r", "max_forks_repo_name": "brembslab/CeTrAn", "max_forks_repo_head_hexsha": "830a3072acb735ea43029310650f03951783813a", "max_forks_repo_licenses": ["CC-BY-3.0"], "max_forks_count": 6, "max_forks_repo_forks_event_min_datetime": "2015-01-09T13:33:40.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-21T12:55:17.000Z", "avg_line_length": 34.2586206897, "max_line_length": 181, "alphanum_fraction": 0.7302466029, "num_tokens": 1170, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3009288816037545}} {"text": "\\name{fastplm}\n\\alias{fastplm}\n\\title{Solve Fixed Effect Model}\n\n\\description{\\code{fastplm} solves a fixed effects model.}\n\n\\usage{fastplm(formula = NULL, data = NULL, index = NULL, \n y = NULL, x = NULL, ind = NULL, \n sfe = NULL, cfe = NULL, \n PCA = TRUE, sp = NULL, knots = NULL, \n degree = 3, se = 1, vce = \"robust\", \n cluster = NULL, wild = FALSE,\n refinement = FALSE, test_x = NULL, parallel = FALSE, \n nboots = 200, seed = NULL, core.num = 1)}\n\n\\arguments{\n\n \\item{formula}{An object of class \"formula\": a symbolic description of\n the model to be fitted.}\n \n \\item{data}{An object of class \"dataframe\" or \"matrix\". If both \\code{formula} \n and \\code{y} are omitted, the first column of \\code{data} will be regarded \n as the vector of outcome variable. If both \\code{data} and \\code{y} present, \n \\code{data} is used.}\n\n \\item{index}{A string vector specifying the indicators. Omissible if \n \\code{formula} is omitted.}\n\n \\item{y}{The Y vector. Omissible if \\code{data} is provided.}\n\n \\item{x}{The X matrix. Omissible if \\code{data} is provided. \n If both \\code{data} and \\code{X} are not provided, the model is still valid. In \n such case, the function merely estimates fixed effects. }\n\n \\item{ind}{A matrix where each row corresponds to the row (observation) in the \n X-Y dataset and each column represents an effect. The entry of row X of effect \n Y represents the group in Y that X belongs to.\n\n Groups can be specified by either numbers or strings, i.e. the input matrix can \n be of mode \\code{numeric} or \\code{character}. }\n\n \\item{sfe}{A vector index of simple (i.e. non-complex) fixed effects. \n Each can be specified either by effect name or position as in \\code{index} or \n by the position in the indicator matrix \\code{ind}.\n\n If omitted, \\code{sfe} will be the collection of all effects in \n \\code{index} or \\code{inds}. }\n\n \\item{cfe}{A list index of complex fixed effects. \n\n Specifically, an complex fixed effect is a generalized fixed effect. It consists a \n pair of two effects, (I, E), interacting with each other. \"I\" stands for influence, \n whose level has an observed vector \\code{weight}. \"E\" stands for effect, whose \n level has an unobserved vector \\code{coefficient} to estimate. For each \n observation, the effect of I, E) is the dot product of the weight of the row's \n level for \"I\" and the coefficient of the row's level for \"E\". \n\n Each element is a vector whose length is 2. The 1st item of each element is the \n index of effect and the 2nd item is the index of influence. For index, see \n \\code{sfe}.\n\n If omitted, complex fixed effects will not be estimated. \n\n }\n\n \\item{PCA}{A logical flag indicating whether to perform principal components analysis \n for influence in complex fixed effects (see \\code{cfe}).}\n\n \\item{sp}{A character value or a numeric vector specifying the variable for \n fitting a b-spline curve.}\n\n \\item{knots}{A numeirc value specifying the knots point for \\code{sp}. If \n left blank, a polynomial curve will be fitted.}\n\n \\item{degree}{A positive integer speficying the order of the spline curve. \n Default value is \\code{degree = 3} for a cubic curve.}\n\n \\item{se}{A logical flag indicating whether uncertainty estimates of \n covariates will be produced.}\n\n \\item{vce}{A character value indicating type for variance estimator. \n Choose from: \"standard\" for standard ols standard errors, \"robust\" for \n the Huber White robust standard errors (default value), \"clustered\" (or \"cl\") \n for clustered standard errors, \"jackknife\" for jackknife standard errors, \n and \"bootstrap\" (or \"boot\") for bootstrapped standard errors.}\n\n \\item{cluster}{A character value of the clustered variable(s) in the data frame if \n \\code{formula} is provided or a matrix object of the clustered variable(s) for \n robust standard error. Two-way clustering is also supported. }\n\n \\item{wild}{A logical flag specifies if wild bootstrap will be performed to obtain \n uncertainty estimates. Omissible if \\code{se = FALSE}.}\n\n \\item{refinement}{A logical flag specifies if clutser bootstrap refinement will be \n performed to obtain uncertainty estimates. Omissible if \\code{se = FALSE}.} \n\n \\item{test_x}{A character specifies the variable of interest for wild cluster \n bootstrap refinement. Omissible if \\code{refinement = FALSE} or \n \\code{wild = FALSE}.} \n\n \\item{parallel}{A logical flag indicating whether to perform parallel computing for \n the bootstrap procedure.}\n\n \\item{nboots}{An integer specifying the number of bootstrap\n runs. Omissible if \\code{se = FALSE}.}\n\n \\item{seed}{An integer that sets the seed in random number generation. \n Omissible if \\code{se = FALSE}.}\n\n \\item{core.num}{The number of cores that will be used for computation. Default is one.\n Do not use more than the number of your physical cores.}\n}\n\n\\value{An object of class \\code{fastplm} with at least the following properties:\n\n \\item{demeaned}{A list represents the demeaned linear model. It has, among other \n properties, \\code{x} and \\code{y}, which are the demean result of the input \n \\code{x} and \\code{y}.}\n\n \\item{coefficients}{The coefficients for the demeaned linear model.}\n\n \\item{sfe.coefs}{A list of estimated simple fixed effects. Each is a column \n vector, in which row names correspond to names of levels in \\code{inds}. Each, as \n a list item, has the same name as its effect name in \\code{inds}. Each vector is \n centered, i.e. set to have zero mean.}\n\n \\item{cfe.coefs}{A list of estimated complex fixed effects. Each is a matrix in \n which each row represents a level. The naming convention is the same as in \n \\code{sfe.coefs}. There is no centering. Expect an arbitrary intercept.}\n\n \\item{fitted.values}{The fitted values of the fixed effect model.}\n\n \\item{residuals}{The residuals of the fixed effect model, which is numerically the \n same as the residual of the demeaned linear model.}\n\n \\item{intercept}{The intercept of the fixed effect model. Arbitrary if complex fixed \n effects are present.}\n\n \\item{inds}{As in the input.}\n\n \\item{fe}{An object of \"fixed.effects\" created for intermediate computing.}\n\n \\item{refinement}{A list that restores results from cluster bootsrap refinement.}\n\n}\n\n\\examples{\nSEED <- 19260817\nN <- 2000\nLEVEL <- 50\n\nset.seed(SEED)\n\nx <- matrix(rnorm(N * 5, 3), N, 5)\ne <- matrix(rnorm(N, 1), N, 1)\n\nraw.inds <- matrix(sample(LEVEL, N * 3, replace = TRUE), N, 3)\nsfe.coefs <- matrix(runif(LEVEL * 3), LEVEL, 3)\n\nwith.effects <- function(j) sapply(1 : N,\n function(i) sfe.coefs[raw.inds[i, j], j])\n\nbeta <- c(7, 3, 2, 5, 8)\neffs <- rowSums(sapply(1 : 3, with.effects))\n\ny <- x %*% beta + 5 + e + effs\n\n###########################################\nmodel <- fastplm(y = y, x = x, ind = raw.inds, se = 0)\n}\n\n%\\author{}\n", "meta": {"hexsha": "2eeebc41e1208e959bd9647a0b5d7f07821cf464", "size": 6988, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/fastplm.rd", "max_stars_repo_name": "inkrement/fastplm", "max_stars_repo_head_hexsha": "198b4b33f0dc5f2455fe17aa86d0bb881e45ad95", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "man/fastplm.rd", "max_issues_repo_name": "inkrement/fastplm", "max_issues_repo_head_hexsha": "198b4b33f0dc5f2455fe17aa86d0bb881e45ad95", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "man/fastplm.rd", "max_forks_repo_name": "inkrement/fastplm", "max_forks_repo_head_hexsha": "198b4b33f0dc5f2455fe17aa86d0bb881e45ad95", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.3930635838, "max_line_length": 88, "alphanum_fraction": 0.6901831712, "num_tokens": 1885, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3009288816037545}} {"text": "#' ---\n#' title: \"Prior probabilities in the interpretation of 'some': analysis of model predictions and empirical data\"\n#' author: \"Judith Degen\"\n#' date: \"November 28, 2014\"\n#' ---\n\nlibrary(ggplot2)\ntheme_set(theme_bw(18))\n#setwd(\"~/Dropbox/sinking_marbles/sinking-marbles/models/complex_prior/results/\")\nsetwd(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/models/complex_prior/smoothed_unbinned15/results/\")\nsource(\"rscripts/helpers.r\")\n\n#' get model predictions\nload(\"data/mp.RData\")\nmp = read.table(\"data/parsed_results.tsv\", quote=\"\", sep=\"\\t\", header=T)\nnrow(mp)\nhead(mp)\nsummary(mp)\n\n# get prior expectations\npriorexpectations = read.table(file=\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/expectations.txt\",sep=\"\\t\", header=T, quote=\"\")\nrow.names(priorexpectations) = paste(priorexpectations$effect,priorexpectations$object)\nhead(priorexpectations)\nmp$PriorExpectation = priorexpectations[as.character(mp$Item),]$expectation\n\npriorprobs = read.table(file=\"~/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/12_sinking-marbles-prior15/results/data/smoothed_15marbles_priors_withnames.txt\",sep=\"\\t\", header=T, quote=\"\")\nhead(priorprobs)\nrow.names(priorprobs) = paste(priorprobs$effect,priorprobs$object)\nmpriorprobs = melt(priorprobs, id.vars=c(\"effect\", \"object\"))\nhead(mpriorprobs)\nrow.names(mpriorprobs) = paste(mpriorprobs$effect,mpriorprobs$object,mpriorprobs$variable)\nmp$PriorProbability = mpriorprobs[paste(as.character(mp$Item),\" X\",mp$State,sep=\"\"),]$value\nmp$AllPriorProbability = priorprobs[paste(as.character(mp$Item)),]$X15\nhead(mp)\n\n# get empirical state posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/3_sinking-marbles-nullutterance/results/data/r.RData\")\nhead(r)\nr$Item = as.factor(paste(r$effect,r$object))\n# because posteriors come in 4 bins, make Bin variable for model prediction dataset:\nmp$Proportion = as.factor(ifelse(mp$State == 0, \"0\", ifelse(mp$State == 15, \"100\", ifelse(mp$State < 8, \"1-50\", \"51-99\"))))\n\nagr = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=mean)\n#agr$CILow = aggregate(normresponse ~ Item + quantifier + Proportion,data=r, FUN=ci.low)$normresponse\n#agr$CIHigh = aggregate(normresponse ~ Item + quantifier + Proportion,data=r,FUN=ci.high)$normresponse\n#agr$YMin = agr$normresponse - agr$CILow\n#agr$YMax = agr$normresponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Proportion, agr$Quantifier)\nmp$PosteriorProbability_empirical = agr[paste(mp$Item,mp$Proportion,\"some\"),]$normresponse\n\nsummary(mp)\n#plot empirical against predicted distributions for \"some\"\nsome = ddply(mp, .(Item, QUD, Alternatives, SpeakerOptimality, PriorExpectation, Proportion, PosteriorProbability_empirical), summarise, PosteriorProbability_predicted=sum(PosteriorProbability), PriorProbability_smoothed=sum(PriorProbability))\n#some= subset(some, Quantifier == \"some\")\nnrow(some)\nhead(some)\nmsome = melt(some, measure.vars=c(\"PosteriorProbability_empirical\",\"PosteriorProbability_predicted\",\"PriorProbability_smoothed\"))\nmsome$ptype = as.factor(ifelse(msome$variable == \"PosteriorProbability_empirical\", \"posterior (empirical)\",ifelse(msome$variable == \"PosteriorProbability_predicted\",\"posterior (model)\", \"prior\")))\nhead(msome)\nnrow(msome)\nsummary(msome)\n\ntoplot = droplevels(subset(msome, QUD == \"how-many\" & SpeakerOptimality == 2 & Alternatives == \"0_basic\"))#\"0_basic1_lownum2_extra4_twowords5_threewords\"))\nnrow(toplot)\ntoplot$Probability = as.factor(ifelse(toplot$ptype == \"prior\",\"prior\",\"posterior\"))\ntoplot$Prop = factor(toplot$Proportion, levels=c(\"1-50\",\"51-99\",\"100\"))\nggplot(toplot, aes(x=Prop, y=value,color=ptype, group=ptype, size=Probability)) +\n geom_point() +\n geom_line() +\n scale_size_discrete(range=c(1,2)) +\n scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_wrap(~Item)\nggsave(\"graphs/model-empirical-howmany-2-basic.pdf\",width=35,height=30)\n\n#plot empirical against predicted allstate-prbabilities for \"some\"\nallstate = droplevels(subset(mp, State == 15))\ncors = ddply(allstate, .(Alternatives, QUD, SpeakerOptimality), summarise, r=cor(PosteriorProbability, PosteriorProbability_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n# .57 correlation despite being shitty model\n\nggplot(allstate, aes(x=PosteriorProbability, y=PosteriorProbability_empirical,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-allstateprobs.pdf\",width=30,height=10)\n\n#maybe COGSCI plot basis? plot predicted allstate-prbabilities for \"some\" as a function of prior allstate-probabilities\n\nggplot(allstate, aes(x=PriorProbability, y=PosteriorProbability,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-allstateprobs.pdf\",width=30,height=10)\n\n\n\n#plot empirical against predicted expectations for \"some\"\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/13_sinking-marbles-priordv-15/results/data/r.RData\")\nsummary(r)\nr$Item = as.factor(paste(r$effect, r$object))\nagr = aggregate(ProportionResponse ~ Item + quantifier, data=r, FUN=mean)\n#agr$CILow = aggregate(ProportionResponse ~ Item + quantifier,data=r, FUN=ci.low)$ProportionResponse\n#agr$CIHigh = aggregate(ProportionResponse ~ Item + quantifier,data=r,FUN=ci.high)$ProportionResponse\n#agr$YMin = agr$ProportionResponse - agr$CILow\n#agr$YMax = agr$ProportionResponse + agr$CIHigh\nagr$Quantifier = as.factor(tolower(agr$quantifier))\nrow.names(agr) = paste(agr$Item, agr$Quantifier)\nmp$PosteriorExpectation_empirical = agr[paste(mp$Item,\"some\"),]$ProportionResponse\nmp$PriorExpectation_smoothed = mp$PriorExpectation/15\n\npexpectations = ddply(mp, .(Item, QUD, Alternatives, SpeakerOptimality,PriorExpectation_smoothed, PosteriorExpectation_empirical), summarise, PosteriorExpectation_predicted=sum(State*PosteriorProbability)/15)\nhead(pexpectations)\nsummary(pexpectations)\nsome = pexpectations#droplevels(subset(pexpectations, Quantifier == \"some\"))\n\ncors = ddply(some, .(Alternatives, QUD, SpeakerOptimality), summarise, r=cor(PosteriorExpectation_predicted, PosteriorExpectation_empirical))\ncors = cors[order(cors[,c(\"r\")],decreasing=T),]\nhead(cors)\n\nggplot(some, aes(x=PosteriorExpectation_predicted, y=PosteriorExpectation_empirical,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-empirical-expectations.pdf\",width=30,height=10)\n\n\n# plot posterior expectation against prior expectation\nggplot(some, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted,color=as.factor(SpeakerOptimality))) +\n geom_point() +\n geom_smooth(method=\"lm\") +\n# geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1)) +\n scale_y_continuous(limits=c(0,1)) + \n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) +\n facet_grid(QUD~Alternatives)\nggsave(\"graphs/model-expectations.pdf\",width=30,height=10)\n\n# plot posterior expectation against prior expectation for basic model with speaker optimality =2 and qud = how-many and alternatives = basic\ntoplot = droplevels(subset(some, QUD==\"how-many\" & Alternatives == \"0_basic\" & SpeakerOptimality == 2))\nggplot(toplot, aes(x=PriorExpectation_smoothed, y=PosteriorExpectation_predicted)) +\n geom_point(color=\"#00B0F6\") + #values=c(\"#F8766D\", \"#A3A500\", \"#00BF7D\", \"#E76BF3\", \"#00B0F6\")\n geom_smooth(color=\"#00B0F6\") +\n # geom_abline(intercept=0,slope=1,color=\"gray50\") +\n scale_x_continuous(limits=c(0,1), name=\"Prior expectation\") +\n scale_y_continuous(limits=c(0,1), name=\"Model predicted posterior expectation\")\n # geom_text(data=cors, aes(label=r)) +\n #scale_size_discrete(range=c(1,2)) +\n #scale_color_manual(values=c(\"red\",\"blue\",\"black\")) \nggsave(\"graphs/model-expectations.pdf\",width=5.5,height=4.5)#,width=30,height=10)\nggsave(\"~/cogsci/conferences_talks/_2015/2_cogsci_pasadena/wonky_marbles/paper/pics/model-expectations.pdf\",width=5.5,height=4.5)\nsave(toplot, file=\"data/toplot-expectations.RData\")\n\n\nsave(mp, file=\"data/mp.RData\")\n\n\n\nexpectations = ddply(mp, .(Item, QUD, Alternatives, SpeakerOptimality, NormAllPrior,PriorExpectation), summarize, expectation=sum(PosteriorProbability*seq(1,15,by=1)))\nhead(expectations)\nexpectations$ExpectationProportion = expectations$expectation/15\nexpectations$QUDOpt = as.factor(paste(expectations$QUD,expectations$SpeakerOptimality))\n\nggplot(expectations, aes(x=NormAllPrior,y=ExpectationProportion,color=as.factor(SpeakerOptimality),shape=QUD,group=QUDOpt)) +\n geom_point() +\n geom_smooth() +\n facet_wrap(~Alternatives)\nggsave(\"graphs/expectations.pdf\",width=12,height=7)\n\nggplot(expectations, aes(x=ExpectationProportion,fill=QUD)) +\n geom_histogram(position=\"dodge\") +\n facet_grid(SpeakerOptimality~Alternatives,scales=\"free_y\")\nggsave(\"graphs/expectation_histograms.pdf\",width=12)\n\n \nsubexp = subset(expectations, SpeakerOptimality == 2 & QUD == \"how-many\")\nggplot(subexp, aes(x=NormAllPrior,y=ExpectationProportion)) +\n geom_point() +\n geom_smooth() +\n facet_wrap(~Alternatives)\nggsave(\"graphs/expectations_spopt2_qudhowmany.pdf\",width=12,height=7)\n\nggplot(subexp, aes(x=PriorExpectation,y=ExpectationProportion)) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/expectations_bypriorexp_spopt2_qudhowmany.pdf\",width=12,height=7)\n\nggplot(subset(subexp,Alternatives==\"0_basic\"), aes(x=PriorExpectation,y=ExpectationProportion)) +\n geom_point() +\n geom_smooth() +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Prior expectation\") +\n scale_y_continuous(name=\"Posterior expectation\") \nggsave(\"graphs/expectations_bypriorexp_spopt2_qudhowmany_alternativesbasic.pdf\",width=12,height=7)\nggsave(\"graphs/modelpredictions_expectations.pdf\")\n\nggplot(subset(mp, Alternatives==\"0_basic\" & SpeakerOptimality == 2 & QUD == \"how-many\" & NumState == 15), aes(x=NormAllPrior,y=PosteriorProbability)) +\n geom_point() +\n #geom_smooth() +\n #geom_abline(intercept=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Prior probability of all-state\") +\n scale_y_continuous(name=\"Posterior probability of all-state\") \nggsave(\"graphs/modelpredictions_allstate.pdf\")\n\n\n\n# get empirical posteriors:\nload(\"/Users/titlis/cogsci/projects/stanford/projects/sinking_marbles/sinking-marbles/experiments/3_sinking-marbles-nullutterance/results/data/r.RData\")\nhead(r)\nsome = subset(r, quantifier == \"Some\" & Proportion == 100)\nsome$Item = as.factor(paste(some$effect, some$object))\nhead(some$normresponse)\n# get model predictions for all-state with basic alts, spopt==2, and qud==how-many\nmp_some_allstate = subset(mp, Alternatives==\"0_basic\" & SpeakerOptimality == 2 & QUD == \"how-many\" & NumState == 15)\nhead(mp_some_allstate)\nnrow(mp_some_allstate)\n\nagr = aggregate(normresponse ~ Item,data=some,FUN=mean)\nagr$CILow = aggregate(normresponse ~ Item,data=some, FUN=ci.low)$normresponse\nagr$CIHigh = aggregate(normresponse ~ Item,data=some,FUN=ci.high)$normresponse\nagr$YMin = agr$normresponse - agr$CILow\nagr$YMax = agr$normresponse + agr$CIHigh\nrow.names(agr) = agr$Item\nhead(agr)\nmp_some_allstate$EmpiricalPosterior = agr[as.character(mp_some_allstate$Item),]$normresponse\nmp_some_allstate$YMin = agr[as.character(mp_some_allstate$Item),]$YMin\nmp_some_allstate$YMax = agr[as.character(mp_some_allstate$Item),]$YMax\n\nggplot(mp_some_allstate, aes(x=PosteriorProbability, y=EmpiricalPosterior)) +\n geom_point() +\n geom_smooth() +\n# geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(x=0,slope=1,color=\"gray70\") +\n scale_x_continuous(name=\"Model predicted posterior all-state probability\") +\n scale_y_continuous(name=\"Empirical mean all-state probability\") \nggsave(\"graphs/model-empirical.pdf\") \ncor(mp_some_allstate$PosteriorProbability,mp_some_allstate$EmpiricalPosterior) # .51\n\nhead(subexp)\n# plot subject variability\nggplot(some, aes(x=ProportionResponse)) +\n geom_histogram() + \n facet_wrap(~workerid)\nggsave(\"graphs/some_subjectvariability.pdf\",width=12,height=10)\n\n# mark the people who chose at most 3 middle values to respond to \"some\" with\nsome$NoVariance = as.factor(ifelse(some$workerid %in% c(\"8\",\"24\",\"27\",\"37\",\"52\",\"54\",\"61\",\"72\",\"79\",\"108\",\"113\"),1,0))\n\nagr = aggregate(ProportionResponse ~ Item,data=some,FUN=mean)\nagr$CILow = aggregate(ProportionResponse ~ Item,data=some, FUN=ci.low)$ProportionResponse\nagr$CIHigh = aggregate(ProportionResponse ~ Item,data=some,FUN=ci.high)$ProportionResponse\nagr$YMin = agr$ProportionResponse - agr$CILow\nagr$YMax = agr$ProportionResponse + agr$CIHigh\nrow.names(agr) = agr$Item\n\nsubexp$EmpiricalMean = agr[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin = agr[as.character(subexp$Item),]$YMin\nsubexp$YMax = agr[as.character(subexp$Item),]$YMax\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions.pdf\",width=12,height=7)\n\ncors = ddply(subexp, .(Alternatives), summarize, Correlation=cor(ExpectationProportion,EmpiricalMean))\ncors # best correlation is with basic alternatives (some, none, all -- .6) and basic+many/most/few/afew (-- .59)\n\n# split up by subject vairance on \"some\" trials\nagr = aggregate(ProportionResponse ~ Item + NoVariance,data=some,FUN=mean)\nagr$CILow = aggregate(ProportionResponse ~ Item + NoVariance,data=some, FUN=ci.low)$ProportionResponse\nagr$CIHigh = aggregate(ProportionResponse ~ Item + NoVariance,data=some,FUN=ci.high)$ProportionResponse\nagr$YMin = agr$ProportionResponse - agr$CILow\nagr$YMax = agr$ProportionResponse + agr$CIHigh\nagrgood = subset(agr, NoVariance == 0)\nagrbad = subset(agr, NoVariance == 1)\nrow.names(agrgood) = agrgood$Item\nrow.names(agrbad) = agrbad$Item\nnrow(agrgood)\nnrow(agrbad)\nsubexp$EmpiricalMean_good = agrgood[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin_good = agrgood[as.character(subexp$Item),]$YMin\nsubexp$YMax_good = agrgood[as.character(subexp$Item),]$YMax\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean_good)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin_good,ymax=YMax_good)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions_good.pdf\",width=12,height=7)\n\nsubexp$EmpiricalMean_bad = agrbad[as.character(subexp$Item),]$ProportionResponse\nsubexp$YMin_bad = agrbad[as.character(subexp$Item),]$YMin\nsubexp$YMax_bad = agrbad[as.character(subexp$Item),]$YMax\n\nggplot(subexp, aes(x=ExpectationProportion, y=EmpiricalMean_bad)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin_bad,ymax=YMax_bad)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") +\n #scale_y_continuous(breaks=seq(0,1,0.1)) +\n facet_wrap(~Alternatives)\nggsave(\"graphs/modelpredictions_bad.pdf\",width=12,height=7)\n\ncorsgood = ddply(subexp, .(Alternatives), summarize, Correlation=cor(ExpectationProportion,EmpiricalMean_good))\ncors # best correlation is with basic alternatives (some, none, all -- .6) and basic+many/most/few/afew (-- .59)\n\n# prior expectations\nagr = aggregate(ProportionResponse ~ Item,data=some,FUN=mean)\nagr$CILow = aggregate(ProportionResponse ~ Item,data=some, FUN=ci.low)$ProportionResponse\nagr$CIHigh = aggregate(ProportionResponse ~ Item,data=some,FUN=ci.high)$ProportionResponse\nagr$YMin = agr$ProportionResponse - agr$CILow\nagr$YMax = agr$ProportionResponse + agr$CIHigh\nrow.names(agr) = agr$Item\n\nexps = read.csv(\"data/expectations_15_bw5_prior.txt\",header=T,sep=\"\\t\")\nexps$EmpiricalMean = agr[paste(exps$effect, exps$object),]$ProportionResponse\nexps$YMin = agr[paste(exps$effect, exps$object),]$YMin\nexps$YMax = agr[paste(exps$effect, exps$object),]$YMax\nexps$Expectation = exps$expectation/100\n\nggplot(exps, aes(x=Expectation, y=EmpiricalMean)) +\n geom_point() +\n geom_errorbar(aes(ymin=YMin,ymax=YMax)) +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_smooth(method=\"lm\") \nggsave(\"graphs/prior_expectation.pdf\",width=4.5,height=3)\n\n#0.008312938/sum(0.005127138,0.00545222,0.005461004,0.005362598,0.005405903,0.005763719,0.006450809,0.007313772,0.008111662,0.008657936,0.008930543,0.009054953,0.009158519,0.009213201,0.009011604,0.008312938)\n\np=ggplot(mp, aes(x=NumState,y=PosteriorProbability,shape=as.factor(SpeakerOptimality),color=QUD,group=QUDOpt)) +\n geom_point() +\n geom_line() +\n facet_grid(Item~Alternatives,scales=\"free_y\") +\n theme(axis.text.x=element_text(size=6))\nggsave(\"graphs/full-dists.pdf\",height=45,width=12)\n\n# plot prior versus posterior expectations\npp = data.frame(PriorExpectation = exps$Expectation[1:89],PosteriorExpectation = subexp[subexp$Alternatives == \"0_basic\",]$ExpectationProportion, Item=subexp[subexp$Alternatives == \"0_basic\",]$Item)\nhead(pp)\n\nggplot(pp, aes(x=PriorExpectation,y=PosteriorExpectation)) +\n geom_point()\n\nggplot(pp, aes(x=PriorExpectation,y=PosteriorExpectation)) +\n geom_point() +\n geom_abline(intercept=0,slope=1,color=\"gray70\") +\n geom_text(aes(label=Item,y=PosteriorExpectation+.01),color=\"gray60\",size=2)\nggsave(\"graphs/prior_posterior.pdf\")\n\n\n## expectation for posterior under uniform prior, 15 states\nsum(seq(1,15,by=1) * c(0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.07111111111111111, 0.004444444444444445))/15\n", "meta": {"hexsha": "0788c8bb3045d3c9bb368b2b7355f80e22b47676", "size": 18831, "ext": "r", "lang": "R", "max_stars_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions.r", "max_stars_repo_name": "thegricean/sinking-marbles", "max_stars_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions.r", "max_issues_repo_name": "thegricean/sinking-marbles", "max_issues_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models/complex_prior/smoothed_unbinned15/results/rscripts/model-predictions.r", "max_forks_repo_name": "thegricean/sinking-marbles", "max_forks_repo_head_hexsha": "ccfb17f097b444306e3b559e40ab9d59da84387a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.2958115183, "max_line_length": 342, "alphanum_fraction": 0.7701662153, "num_tokens": 5688, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5583269796369905, "lm_q2_score": 0.5389832206876841, "lm_q1q2_score": 0.3009288736815722}} {"text": "# combining EMP, Ramirez, and Bahram data\nrm(list=ls())\nlibrary(betareg)\nsource(\"paths.r\")\nsource(\"paths_fall2019.r\")\nsource('NEFI_functions/crib_fun.r')\n\n# read in abundances \nd <- readRDS(delgado_ramirez_abun.path)\n#load mapping data.----\nmap.all <- readRDS(delgado_ramirez_bahram_mapping.path)\nmap.all <- map.all[map.all$source != \"Bahram\",]\n\nr2_lev <- list()\n#map.all$study_id <- as.integer(as.factor(map.all$study_id))\n\n# without study_id\npreds <- c(\"new.C.5\",\"ph\",\"forest\",\"NPP\",\"map\",\"mat\",\"relEM\",\"ndep.glob\")\n# with study_id\npreds <- c(\"new.C.5\",\"ph\",\"forest\",\"NPP\",\"map\",\"mat\",\"relEM\",\"ndep.glob\",\"study_id\")\n\n\nk <- 1\nfor(k in 1:length(d)){\n#for(k in 18:18){\n #setup some empty lists.\n model.out <- list()\n predicted.out <- list()\n observed.out <- list()\n y <- d[[k]]\n\n #match up all data\n #map <- map.all[1:200,] # testing\n map <- map.all\n map <- map[map$sampleID %in% rownames(y),]\n y <- y[rownames(y) %in% map$sampleID,]\n y <- y[order(match(rownames(y), map$sampleID)),]\n x <- map[,colnames(map) %in% preds]\n \n #loop across the columns of y.\n for(i in 1:ncol(y)){ \n if (colnames(y)[i]==\"other\") next()\n \n #setup data object\n data <- cbind(y[,i],x) \n data <- data[complete.cases(data),]\n # pals <- data$pals\n # data$pals <- NULL\n group <- data[,1]\n\n form <- as.formula(paste0(c('group ~ ',paste(preds[!preds %in% c(\"pals\")], collapse=\"+\")), collapse=\"\")) \n #fit model.\n mod <- betareg(form, data = data[,2:ncol(data)])\n #save fitted and predicted values.\n model.out[[i]] <- mod\n predicted.out[[i]] <- fitted(mod)\n observed.out[[i]] <- data[,1]\n names(model.out)[[i]] <- colnames(y)[i]\n names(predicted.out)[[i]] <- colnames(y)[i]\n names(observed.out)[[i]] <- colnames(y)[i]\n }\n predicted.out <- data.frame(do.call('cbind',predicted.out))\n observed.out <- data.frame(do.call('cbind', observed.out))\n par(mfrow = c(3,3)) \n par(mar=c(1.5,2.5,2.5,1.5))\n par(oma=c(3.3,2.3,2,0))\n r2_out <- list()\n for(p in 1:ncol(observed.out)){\n mod <- lm(observed.out[,p] ~ predicted.out[,p])\n rsq <- round(summary(mod)$r.squared, 2)\n r2_out[[p]] <- rsq\n plot(observed.out[,p] ~ predicted.out[,p], cex = 0.6)\n mtext(colnames(observed.out)[p], side = 3, line = -1.3, adj = 0.05, cex = 1.3)\n mtext(paste0('R2 = ',rsq), side = 3, line = -2.7, adj = 0.05, cex = 1.2)\n abline(0,1, lwd = 2)\n }\n mtext('predicted', side = 1, line = 1.5, cex = 2, outer=TRUE)\n mtext('observed', side = 2, line = 0, cex = 2, outer=TRUE)\n \n title(paste(names(d)[k], 'calibration fits'), cex.main=2.3, outer=TRUE, line = -0.75)\n r2_lev[[k]] <- unlist(r2_out)\n}\nnames(r2_lev) <- names(d)\nlev.mu <- lapply(r2_lev, mean)\nprint(lev.mu)\n\nr2_all <- r2_lev\ntax.mu <- lapply(r2_all[1:5], unlist)\ntax.mu <- lapply(tax.mu, mean)\nfg.mu <- mean(unlist(r2_all[6:18]))\nn.mu <- mean(unlist(r2_all[7:13]))\nc.mu <- mean(unlist(r2_all[c(6,14:16)]))\nco.mu <- mean(unlist(r2_all[c(17:18)]))\n\nlev.mu <- c(tax.mu, fg.mu, n.mu, c.mu, co.mu)\nnames(lev.mu) <- c(names(tax.mu), \"functional\", \"N-cycling\", \"C-cycling\", \"Cop-olig\")\n# plot by taxon level\npar(mfrow=c(1,1),mar = c(6,4.2,1.5,1.5)); limx <- c(0,1); trans <- 0.2; o.cex <- 1.8 #outer label size.\nlev.mu <- unlist(lev.mu)\nxx <- seq(1,length(lev.mu))\nylim <- c(0.2,.6)\nplot(lev.mu ~ xx, cex = 2.5, ylim = ylim, pch = 16, ylab = NA, xlab = NA, bty='n', xaxt = 'n', col=\"grey\")\npoints(lev.mu ~ xx, cex = 2.5, pch = 16)\nlines(xx, lev.mu, lty = 2, col=\"grey\")\nmtext(expression(paste(\"Calibration site-Level R\"^\"2\")), side = 2, line = 2.2, cex = o.cex)\naxis(1, labels = F)\ntext(x=xx, y = .17, labels= names(lev.mu), srt=45, adj=1, xpd=TRUE, cex = 1.8)\n\n\n", "meta": {"hexsha": "1454cb271e5b47e5973054979d0378fe58e84a0d", "size": 3650, "ext": "r", "lang": "R", "max_stars_repo_path": "16S/spatial_analysis/prior_delgado/betareg_delgado_prior_9-26.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-13T17:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-13T17:13:54.000Z", "max_issues_repo_path": "16S/spatial_analysis/prior_delgado/betareg_delgado_prior_9-26.r", "max_issues_repo_name": "colinaverill/NEFI_microbe", "max_issues_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "16S/spatial_analysis/prior_delgado/betareg_delgado_prior_9-26.r", "max_forks_repo_name": "colinaverill/NEFI_microbe", "max_forks_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4, "max_forks_repo_forks_event_min_datetime": "2019-02-21T20:26:09.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-11T16:09:44.000Z", "avg_line_length": 33.1818181818, "max_line_length": 109, "alphanum_fraction": 0.5997260274, "num_tokens": 1385, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.30083931026436395}} {"text": "a <- mean(x)\n", "meta": {"hexsha": "1783dce63b5e73fe47b7ed71eb7ece2f831ef431", "size": 13, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-3.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2018-11-09T22:08:38.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-09T22:08:38.000Z", "max_issues_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-3.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Averages-Pythagorean-means/R/averages-pythagorean-means-3.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-11-09T22:08:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-09T22:08:40.000Z", "avg_line_length": 6.5, "max_line_length": 12, "alphanum_fraction": 0.4615384615, "num_tokens": 5, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.30081976223479745}} {"text": "context(\"bed_shift\")\n\nbed_tbl <- tibble::tribble(\n ~ chrom, ~ start, ~ end, ~ strand,\n \"chr1\", 100, 150, \"+\",\n \"chr1\", 200, 250, \"+\",\n \"chr2\", 300, 350, \"+\",\n \"chr2\", 400, 450, \"-\",\n \"chr3\", 500, 550, \"-\",\n \"chr3\", 600, 650, \"-\"\n)\n\ngenome <- tibble::tribble(\n ~ chrom, ~ size,\n \"chr1\", 1000,\n \"chr2\", 2000,\n \"chr3\", 3000\n)\n\ntest_that(\"pos increment works\", {\n size <- 100\n out <- bed_shift(bed_tbl, genome, size)\n expect_true(\n all(out$start - bed_tbl$start == size),\n all(out$end - bed_tbl$end == size)\n )\n})\n\ntest_that(\"neg increment works\", {\n size <- -50\n out <- bed_shift(bed_tbl, genome, size)\n expect_true(\n all(out$start - bed_tbl$start == size),\n all(out$end - bed_tbl$end == size)\n )\n})\n\ntest_that(\"starts forced to 0\", {\n size <- -120\n out <- bed_shift(bed_tbl, genome, size)\n expect_true(all(out$start >= 0))\n})\n\ntest_that(\"end forced to chrom length\", {\n size <- 1675\n out <- bed_shift(bed_tbl, genome, size) %>%\n left_join(genome, by = \"chrom\")\n expect_true(all(out$end <= out$size))\n})\n\ntest_that(\"fraction increment works\", {\n fraction <- 0.5\n interval <- bed_tbl$end - bed_tbl$start\n out <- bed_shift(bed_tbl, genome, fraction = fraction)\n expect_true(all(\n out$start - bed_tbl$start == fraction * interval,\n all(out$end - bed_tbl$end == fraction * interval)\n ))\n})\n\ntest_that(\"negative fraction increment works\", {\n fraction <- -0.5\n interval <- bed_tbl$end - bed_tbl$start\n out <- bed_shift(bed_tbl, genome, fraction = fraction)\n expect_true(all(\n out$start - bed_tbl$start == fraction * interval,\n all(out$end - bed_tbl$end == fraction * interval)\n ))\n})\n\ntest_that(\"rounding fraction increment works\", {\n fraction <- 0.51234\n interval <- bed_tbl$end - bed_tbl$start\n out <- bed_shift(bed_tbl, genome, fraction = fraction)\n expect_true(all(\n out$start - bed_tbl$start == round(fraction * interval),\n all(out$end - bed_tbl$end == round(fraction * interval))\n ))\n})\n\ntest_that(\"shift by strand works\", {\n size <- 100\n x <- group_by(bed_tbl, strand)\n out <- bed_shift(x, genome, size)\n expect_true(all(\n ifelse(out$strand == \"+\",\n out$start - bed_tbl$start == size,\n out$start - bed_tbl$start == -size\n ),\n ifelse(out$strand == \"+\",\n out$end - bed_tbl$end == size,\n out$end - bed_tbl$end == -size\n )\n ))\n})\n\ntest_that(\"shift by strand and fraction works\", {\n fraction <- 0.5\n x <- group_by(bed_tbl, strand)\n sizes <- bed_tbl$end - bed_tbl$start\n out <- bed_shift(x, genome, fraction = fraction)\n expect_true(all(\n ifelse(out$strand == \"+\",\n out$start - bed_tbl$start == sizes * fraction,\n out$start - bed_tbl$start == -sizes * fraction\n ),\n ifelse(out$strand == \"+\",\n out$end - bed_tbl$end == sizes * fraction,\n out$end - bed_tbl$end == -sizes * fraction\n )\n ))\n})\n\n# from https://github.com/arq5x/bedtools2/blob/master/test/shift/test-shift.sh\na <- tibble::tribble(\n ~ chrom, ~ start, ~ end, ~ name, ~ score, ~ strand,\n \"chr1\", 100, 200, \"a1\", 1, \"+\",\n \"chr1\", 100, 200, \"a2\", 2, \"-\"\n)\n\ntiny.genome <- tibble::tribble(\n ~ chrom, ~ size,\n \"chr1\", 1000\n)\n\ntest_that(\"test going beyond the start of the chrom\", {\n out <- bed_shift(a, tiny.genome, size = -300, trim = TRUE)\n expect_true(all(\n out$start == c(0, 0),\n out$end == c(1, 1)\n ))\n})\n\ntest_that(\"test going beyond the start of the chrom\", {\n out <- bed_shift(a, tiny.genome, size = -200, trim = TRUE)\n expect_true(all(\n out$start == c(0, 0),\n out$end == c(1, 1)\n ))\n})\n\ntest_that(\"test going beyond the end of the chrom\", {\n out <- bed_shift(a, tiny.genome, size = 1000, trim = TRUE)\n expect_true(all(\n out$start == c(999, 999),\n out$end == c(1000, 1000)\n ))\n})\n\ntest_that(\"test shift being larger than a signed int\", {\n out <- bed_shift(a, tiny.genome, size = 3000000000, trim = TRUE)\n expect_true(all(\n out$start == c(999, 999),\n out$end == c(1000, 1000)\n ))\n})\n\ntest_that(\"test chrom boundaries\", {\n tiny2.genome <- tibble::tribble(\n ~ chrom, ~ size,\n \"chr1\", 10\n )\n\n b <- tibble::tribble(\n ~ chrom, ~ start, ~ end, ~ name, ~ score, ~ strand,\n \"chr1\", 5, 10, \"cds1\", 0, \"+\"\n )\n out <- bed_shift(b, tiny2.genome, size = 2, trim = TRUE)\n expect_true(all(\n out$start == 7,\n out$end == 10\n ))\n})\n\ntest_that(\"test shift huge genome\", {\n tiny2.genome <- tibble::tribble(\n ~ chrom, ~ size,\n \"chr1\", 249250621\n )\n\n b <- tibble::tribble(\n ~ chrom, ~ start, ~ end, ~ name, ~ score, ~ strand,\n \"chr1\", 66999638L, 67216822L, \"NM_032291\", 0L, \"+\",\n \"chr1\", 92145899L, 92351836L, \"NR_036634\", 0L, \"-\"\n )\n out <- bed_shift(b, tiny2.genome, size = 1000, trim = TRUE)\n expect_true(all(\n out$start == c(67000638, 92146899),\n out$end == c(67217822, 92352836)\n ))\n})\n", "meta": {"hexsha": "46fab9bdbe12e527380b5d120aef370a86962049", "size": 4764, "ext": "r", "lang": "R", "max_stars_repo_path": "tests/testthat/test_shift.r", "max_stars_repo_name": "jimhester/valr", "max_stars_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 72, "max_stars_repo_stars_event_min_datetime": "2017-02-22T15:22:13.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T06:39:42.000Z", "max_issues_repo_path": "tests/testthat/test_shift.r", "max_issues_repo_name": "jimhester/valr", "max_issues_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 178, "max_issues_repo_issues_event_min_datetime": "2016-12-06T15:42:24.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T00:10:30.000Z", "max_forks_repo_path": "tests/testthat/test_shift.r", "max_forks_repo_name": "jimhester/valr", "max_forks_repo_head_hexsha": "73d229911e2ff31c7c733d00c5ee6c4be955d03b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 32, "max_forks_repo_forks_event_min_datetime": "2017-03-06T23:01:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-27T12:32:30.000Z", "avg_line_length": 24.8125, "max_line_length": 78, "alphanum_fraction": 0.6024349286, "num_tokens": 1555, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.5621765008857982, "lm_q1q2_score": 0.30081976223479745}} {"text": "#' R tools to visualyze pivot ilo data frame\n#'\n#'\n#' @param df, ilo tbl data frame. \n#'\n#'\n#' @name pivot_ilo\n#'\n#'\n#' @return pivot html table\n#'\n#' @examples\n#' ## Not run:\n#'\n#' init_ilo()\n#'\n#'\n#' ### quarterly time serie of female unemployed as from 2000 in united states\n#'\n#' X <- get_ilo(ref_area = 'AFG')\n#' pivot_ilo(X)\n#' ## End(**Not run**)\n#' @export\npivot_ilo <- function(df)\n{\n require(shiny)\n require(miniUI)\n #require(leaflet)\n #require(ggplot2)\n require(rpivotTable)\n ui <- miniPage(\n\t\tgadgetTitleBar(\"Shiny gadget example\"), \n\t\t\tminiTabstripPanel(\n\t\t\t\t#miniTabPanel(\"Data\", icon = icon(\"table\"), miniContentPanel(DT::dataTableOutput(\"table\"))), \n\t\t\t\tminiTabPanel(\"Pivot\", icon = icon(\"th-list\"), miniContentPanel(rpivotTable::rpivotTableOutput(\"pivot\"))) #, \n\t\t\t\t#miniTabPanel(\"Parameters\", icon = icon(\"sliders\"), \n\t\t\t\t#\tminiContentPanel(sliderInput(\"year\", \"Year\", 1978, 2010, c(2000, 2010), sep = \"\"))), \n\t\t\t\t#miniTabPanel(\"Visualize\", icon = icon(\"area-chart\"), \n\t\t\t\t#\tminiContentPanel(plotOutput(\"cars\", height = \"100%\"))), \n\t\t\t\t#miniTabPanel(\"Map\", icon = icon(\"map-o\"), \n\t\t\t\t#\tminiContentPanel(padding = 0, leafletOutput(\"map\", height = \"100%\")), \n\t\t\t\t#\tminiButtonBlock(actionButton(\"resetMap\", \"Reset\"))\n\t\t\t\t#\t)\n\t\t\t)\n\t\t)\n server <- function(input, output, session) {\n #output$cars <- renderPlot({\n # require(ggplot2)\n # ggplot(cars, aes(speed, dist)) + geom_point()\n #})\n #output$map <- renderLeaflet({\n # force(input$resetMap)\n # leaflet(quakes, height = \"100%\") %>% addTiles() %>% \n # addMarkers(lng = ~long, lat = ~lat)\n #})\n #output$table <- DT::renderDataTable({\n # df\n #})\n output$pivot <- rpivotTable::renderRpivotTable({\n rpivotTable(data = df %>% select(\t-contains(\"note\"), \n\t\t\t\t\t\t\t\t\t\t\t\t-contains(\"info\"), \n\t\t\t\t\t\t\t\t\t\t\t\t-contains(\"obs_status\")), \n\t\t\t\t\t\trendererName = \"Table\", \n\t\t\t\t\t\trows = c(\"source\", \"indicator\", \"sex\", \"classif1\", \"classif2\"), \n\t\t\t\t\t\tcol = \"time\", \n\t\t\t\t\t\taggregatorName = \"Sum\", \n\t\t\t\t\t\tvals = \"obs_value\", width = \"100%\", height = \"500px\")\n })\n observeEvent(input$done, {\n stopApp(TRUE)\n })\n }\n runGadget(shinyApp(ui, server), viewer = paneViewer())\n}\n\n\n\n\n\n", "meta": {"hexsha": "6a72d5d0a71add26e47d57de78c906bd8d2fb35a", "size": 2298, "ext": "r", "lang": "R", "max_stars_repo_path": "R/view_ilo.r", "max_stars_repo_name": "dbescond/ilo", "max_stars_repo_head_hexsha": "2aab21111b3f09ec094919f96206f301e5c1f9a8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/view_ilo.r", "max_issues_repo_name": "dbescond/ilo", "max_issues_repo_head_hexsha": "2aab21111b3f09ec094919f96206f301e5c1f9a8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/view_ilo.r", "max_forks_repo_name": "dbescond/ilo", "max_forks_repo_head_hexsha": "2aab21111b3f09ec094919f96206f301e5c1f9a8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.725, "max_line_length": 112, "alphanum_fraction": 0.5683202785, "num_tokens": 691, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3008197622347974}} {"text": "#+setup, include=FALSE, cache=FALSE\nlibrary(knitr)\nopts_chunk$set(fig.path='figure-pedv-cum/', fig.align='center', fig.show='hold')\noptions(replace.assign=TRUE,width=80)\nSys.setlocale(\"LC_TIME\", \"C\") #Needed for identical()\nSys.setlocale(\"LC_COLLATE\", \"C\")\n\n#' ## Packages\n#+\nlibrary(car)\nlibrary(c060)\nlibrary(geoR)\nlibrary(ggplot2)\nlibrary(glmnet)\nlibrary(Hmisc)\nlibrary(igraph)\nlibrary(maps)\nlibrary(plyr)\nprint(sessionInfo())\nset.seed(4253)\n\n#' ## Data loading\n#'\n#' Farm counts by operation types\n#+\ntmpf <- function(){\n censPath <- file.path('table-25-hogs-and-pigs-by-operation-type.csv')\n cens <- read.csv(censPath,strip.white=TRUE, na.strings='(D)',\n stringsAsFactors=FALSE)\n # The introduction to the reports says '-' represents 0 and (D) means deleted for privacy\n tmpf <- function(x) {\n if(is.character(x)){\n ret <- ifelse(x=='-', 0, x)\n type.convert(ret)\n }else{\n x\n }\n }\n cens <- lapply(as.list(cens), tmpf)\n cens <- data.frame(cens)\n test <- cens$GEO %in% state.name\n ind <- grep('.*Farms|GEO|ITEM', colnames(cens))\n stateCounts <- cens[test, ind]\n\n key <- match(stateCounts$GEO, state.name)\n stateCounts$abb <- state.abb[key]\n\n test <- stateCounts$ITEM == \"Total inventory \\\\ Farms with 1 to 24\"\n smallFarms <- stateCounts[test,]\n test <- stateCounts$ITEM == \"Total inventory\"\n allFarms <- stateCounts[test,]\n\n stopifnot(allFarms$GEO == smallFarms$GEO)\n ind <- grep('.*Farms', colnames(allFarms))\n nonSmallFarms <- allFarms\n nonSmallFarms[,ind] <- allFarms[,ind] - smallFarms[,ind]\n nonSmallFarms$ITEM <- NULL\n\n nonSmallFarms\n}\nstateData <- tmpf()\n\n#+\ntmpf <- function(sd){\n ind <- grep('.*Farms', colnames(sd))\n dists <- sd[,ind]\n rownames(dists) <- sd$abb\n dists\n}\ndists <- tmpf(stateData)\n\n#' County land areas\n#+\ndata(county.fips, package='maps')\nfp <- file.path('2013_Gaz_counties_national.txt')\nrd <- read.delim(fp)\nrd <- rd[, c('GEOID', 'ALAND')]\n\n#' Hog counts by county\n#+\ntmpf <- function(){\n censPath <- file.path('table-12-hogs-and-pigs-by-county.csv')\n cens <- read.csv(censPath,strip.white=TRUE, na.strings='(D)',\n stringsAsFactors=FALSE)\n# The introduction to the reports says '-' represents 0 and (D) means deleted for privacy\n tmpf <- function(x) {\n if(is.character(x)){\n ret <- ifelse(x=='-', 0, x)\n type.convert(ret)\n }else{\n x\n }\n }\n cens <- lapply(as.list(cens), tmpf)\n cens <- data.frame(cens)\n test <- cens$STCOFIPS %in% county.fips$fips\n cens <- cens[test,]\n ind <- grep('farms, 2007)$', cens$ITEM)\n cens <- cens[ind,]\n cens\n}\ncountyData <- tmpf()\n\n#' ERS farm resource regions\n#+\nregs <- read.csv('reglink.csv', skip=2, colClasses=c(NA, NA, 'NULL', 'NULL'))\n\n\n#' Derive state-level summaries of county-level data\n#+\nctyTots <- ddply(countyData, 'STCOFIPS', summarize, totalFarms=sum(DATA), STFIPS=STFIPS[1],\n smallFarms = sum(DATA[\"Inventory \\\\ Total hogs and pigs \\\\ Farms by inventory \\\\ 1 to 24 (farms, 2007)\" == ITEM]))\nctyTots$nonSmallFarms <- with(ctyTots, totalFarms - smallFarms)\nmg <- merge(ctyTots, rd, by.x='STCOFIPS', by.y='GEOID')\nmg <- merge(regs, mg, by.x='Fips', by.y='STCOFIPS')\nmg$kmsq <- mg$ALAND/1e6\nmg$nonSmallDense <- mg$nonSmallFarms / mg$kmsq\nstateCty <- ddply(mg, 'STFIPS', summarize, totNonSmall=sum(nonSmallFarms),\n mDense=mean(nonSmallDense),\n cmDense=mean(nonSmallDense[nonSmallFarms > 0]),\n medDense=median(nonSmallDense), maxDense=max(nonSmallDense),\n reg1=weighted.mean(ERS.resource.region==1, w=nonSmallFarms),\n reg2=weighted.mean(ERS.resource.region==2, w=nonSmallFarms),\n reg3=weighted.mean(ERS.resource.region==3, w=nonSmallFarms),\n reg4=weighted.mean(ERS.resource.region==4, w=nonSmallFarms),\n reg5=weighted.mean(ERS.resource.region==5, w=nonSmallFarms),\n reg6=weighted.mean(ERS.resource.region==6, w=nonSmallFarms),\n reg7=weighted.mean(ERS.resource.region==7, w=nonSmallFarms),\n reg8=weighted.mean(ERS.resource.region==8, w=nonSmallFarms),\n reg9=weighted.mean(ERS.resource.region==9, w=nonSmallFarms))\ndata(state.fips, package='maps')\nkey <- match(stateCty$STFIPS, state.fips$fips)\nstateCty$abb <- state.fips$abb[key]\n\n#' Case data\n#+\ncaseData <- list()\ndataDir <- file.path('.')\ntmpf <- function(){\n fn <- file.path(dataDir, 'PEDvweeklyreport-state-age-cummulative-01-08-14.csv')\n ret <- read.csv(fn)\n manuallyChecked <- structure(\n list(State = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,\n 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,\n 22L, 23L, 21L), .Label = c(\"CA\", \"CO\", \"IA\", \"IL\", \"IN\", \"KS\",\n \"KY\", \"MD\", \"MI\", \"MN\", \"MO\", \"NC\", \"NE\", \"NY\", \"OH\", \"OK\", \"PA\",\n \"SD\", \"TN\", \"TX\", \"UNKNOWN\", \"WI\", \"WY\"), class = \"factor\"),\n Suckling = c(0L, 11L, 132L, 22L, 10L, 52L, 3L, 0L, 2L, 20L,\n 2L, 100L, 0L, 2L, 11L, 55L, 4L, 1L, 0L, 6L, 2L, 1L, 1L),\n Nursery = c(1L, 6L, 149L, 10L, 12L, 14L, 0L, 0L, 3L, 42L,\n 7L, 50L, 0L, 0L, 20L, 72L, 8L, 0L, 0L, 4L, 1L, 0L, 2L),\n Grower.Finisher = c(0L, 4L, 251L, 13L, 11L, 41L, 0L, 0L, 2L, 27L, 1L, 67L, 0L, 0L,\n 10L, 45L, 11L, 0L, 6L, 12L, 1L, 0L, 1L),\n Sow.Boar = c(0L, 8L, 39L, 9L, 5L, 30L, 0L, 0L, 1L, 5L, 3L, 51L, 0L, 0L, 4L,\n 37L, 2L, 0L, 0L, 0L, 0L, 0L, 0L),\n Unknown = c(0L, 1L, 108L, 17L, 20L, 8L, 1L, 1L, 1L, 112L, 5L, 38L, 5L, 0L, 14L, 31L,\n 0L, 3L, 0L, 6L, 1L, 0L, 10L)),\n .Names = c(\"State\", \"Suckling\", \"Nursery\", \"Grower.Finisher\", \"Sow.Boar\", \"Unknown\"),\n class = \"data.frame\", row.names = c(NA,-23L))\n stopifnot(identical(manuallyChecked, ret))\n nonreporting <- setdiff(state.abb, c(as.character(ret$State)))\n df <- data.frame(State=nonreporting, Suckling=0, Nursery=0, Grower.Finisher=0, Sow.Boar=0, Unknown=0)\n rbind(ret, df)\n}\ncounts <- tmpf()\n\n#' AK and HI are special cases. Let's just look at the coniguous 48\n#' states.\n#+\nexclude <- c('AK', 'HI')\ntest <- !(counts$State %in% exclude)\ncounts <- counts[test,]\ncts <- reshape(counts, v.names='cases', idvar='State', varying=2:6,\n direction='long', timevar='age', times=names(counts)[2:6])\nctsTot <- ddply(cts, 'State', summarize, cases=sum(cases))\n\n#' Not a very strong relationship, but there is agreement in that\n#' states not reporting any cases had much smaller changes in pig\n#' litter\n\n#' Shipment flows data\n#+\nflowMatGet <- function(){\n fp <- file.path('shipment-flows-origins-on-rows-dests-on-columns.csv')\n ep <- read.csv(fp, row.names=1)\n ep <- t(ep)\n key <- na.omit(match(unique(counts$State), colnames(ep)))\n ep <- ep[key, key]\n data.matrix(ep)\n}\nflowMat <- flowMatGet()\n\n\n\n#' Calculate eigenvector centrality. All states except VT (no\n#' outshipments) and CT (no inshipments) are in one strongly connected\n#' compoent, so it doesn't seem necessary to bother with Katz\n#' centrality or the like.\n#+\nvecGet <- function(symmetric=FALSE, normOutput=FALSE){\n getStateDist <- function(oddsInstate=9, pow=1, ep=flowMat){\n diag(ep) <- rowSums(ep) * 9\n ept <- ep^pow\n if(symmetric & normOutput){\n ret <- rowSums(ept)\n ret <- ret/sum(ret)\n } else {\n if(symmetric){\n ept <- (ept + t(ept))/2\n }\n if(normOutput){\n M <- t(ept)\n kout <- rowSums(M)\n kout <- ifelse(kout==0,1,kout)\n M <- M / kout\n ept <- t(M)\n }\n eig <- eigen(ept, symmetric=symmetric)\n ret <- eig$vector[,1]\n stopifnot(Im(ret)==0)\n ret <- Re(ret)\n names(ret) <- rownames(ep)\n ret <- ret/sum(ret)\n }\n ret\n }\n powers <- as.list(2^(-1*(0:4)))\n tmpf <- function(x) getStateDist(pow=x)\n vecs <- lapply(powers, tmpf)\n vecs <- do.call(rbind, vecs)\n vecs <- t(vecs)\n vecs <- data.frame(State=rownames(vecs), vecs)\n lab <- paste('isSym', symmetric, 'andIsNormed', normOutput, 'andPow', sep='')\n tmpf <- function(x) paste(lab, x, sep='')\n names(vecs)[-1] <- sapply(powers, tmpf)\n vecs\n}\nargs <- expand.grid(symmetric=c(TRUE, FALSE), normOutput=c(TRUE, FALSE))\nvecs <- list()\nfor(i in 1:nrow(args)){\n vecs[[i]] <- do.call(vecGet, args[i,])\n}\nvecs <- Reduce(merge, vecs)\n\n#' Balance sheet data\n#'\n#' Hawaii has a dash for inshipments, which we will assumes means a\n#' negligible number.\n#'\n#' We distribute the IdahoWashington row among ID and WA in proportion\n#' to the number of nonsmall farms in those states.\n#+\nbal <- read.csv('state-hogBalanceSheetDec2011Dec2012.csv',\n na.strings='-', row.names=1, colClasses=c(state2='NULL'))\nrecWA <- recID <- bal['IdahoWashington',]\niWA <- which(stateCty$abb == 'WA')\niID <- which(stateCty$abb == 'ID')\npWA <- with(stateCty, totNonSmall[iWA]/(totNonSmall[iWA] + totNonSmall[iID]))\nrecWA <- recWA*pWA\nrecID <- recID*(1-pWA)\nbal <- rbind(bal, \"Washington\"=recWA, \"Idaho\"=recID)\nkey <- match(rownames(bal), state.name)\nbal$State <- state.abb[key]\nbal <- bal[!is.na(bal$State),]\nbal['Hawaii','inshipments'] <- 0\n\n#' Derive sampling weights\n#'\n#' We are calculating sampling weights for each age class in each\n#' state based on random sampling of the farms in each state and then\n#' random sampling of the pigs on each sampled farm. Census data for\n#' each state provides the number of operations of each type in each\n#' state. The age distribution of pigs on each farm are calculated as\n#' follows.\n#'\n#' Parameters taken from Stalder (2013) \"Pork industry productivity analysis\".\n#+\nagtGet <- function(){\n littersPerSowWeek <- 2.31/52\n meanLitter <- 10.3\n meanWeaningAge <- 21.5/7\n meanGoToFeederAge <- (21.5 + 46.0)/7\n meanSlaughterAge <- (21.5 + 46.0 + 121.5)/7\n sowBoarRatio <- 0\n nSow <- 100\n nBoar <- ifelse(sowBoarRatio > 0, nSow / sowBoarRatio, 0)\n nSowBoar <- nSow + nBoar\n flowRate <- nSow * littersPerSowWeek * meanLitter\n nSuckling <- flowRate * meanWeaningAge\n nNursery <- flowRate * (meanGoToFeederAge - meanWeaningAge)\n nFeeder <- flowRate * (meanSlaughterAge - meanGoToFeederAge)\n agt <- diag(c(nSuckling, nNursery, nFeeder, nSowBoar))\n agt\n}\nagt <- agtGet()\n\n#' We've computed an age distribution based on the birth rate of sows\n#' and the residence time in each production stage. We now create an\n#' age distribution for each type of farm based on the age classes\n#' present in that farm.\n#+\nM0 <- cbind(c(1, 0, 0, 1),\n c(1, 1, 1, 1),\n c(0, 0, 1, 0),\n c(1, 1, 0, 1),\n c(0, 1, 0, 0))\nW <- agt %*% M0\nN <- colSums(W)\nW <- scale(W, center=FALSE, scale=N)\ndists$Other.Farms <- NULL\n\n#' Now sample weights are computed for each state using distribution\n#' of farm types in each state.\n#+\nwts <- W %*% t(dists)\nwts <- data.frame(age=c(\"Suckling\", \"Nursery\", \"Grower.Finisher\", \"Sow.Boar\"),\n wts)\nwts <- reshape(wts, v.names='samplingWeight', idvar='age', varying=2:51,\n direction='long', timevar='State', times=names(wts)[2:51])\nrm(M0, W, N)\n\n#' Merge predictors\n#+\nmg <- merge(wts, cts)\nmg <- merge(mg, vecs)\nmg <- merge(mg, bal)\nmgAge <- mg\nsave(mgAge, file='mgAge.RData')\n\n#' Merge in data on spatial density of farms\n#+\nmg <- merge(ctsTot, vecs)\nmg <- merge(mg, bal)\nmg <- merge(mg, stateCty, by.x='State', by.y='abb')\nmg$l2CasesOffset <- log2(mg$cases + 0.5)\n\n#' Compare total case counts with change in pig litter\n#+\ndf2 <- read.csv('pigsPerLitterDecemberThruFebrurary2013and2014.csv')\ndf2$change <- with(df2, pigLitter2014 - pigLitter2013)\nkey <- match(df2$state, tolower(state.name))\ndf2$abb <- state.abb[key]\ndf2$percentDecrease <- df2$change/df2$pigLitter2013 * -100\nfoo <- df2[!is.na(df2$abb),]\nord <- order(foo$percentDecrease)\ndotchart(foo$percentDecrease[ord], labels=foo$abb[ord], xlab='Percent decrease in litter size',\n ylab='State')\nv <- df2$percentDecrease[df2$state=='united states']\nabline(v=v)\nmtext('U.S.', at=v)\nv <- df2$percentDecrease[df2$state=='other states']\nabline(v=v)\nmtext('Other states', at=v)\n#\nfoo <- mg[, c('State', 'cases', 'totNonSmall')]\nfoo <- merge(foo, df2, by.x='State', by.y='abb', all.x=TRUE)\nmg <- merge(mg, df2[, c('abb', 'percentDecrease')], by.x='State', by.y='abb', all.x=TRUE)\n#\nfoo$cumInc <- foo$cases/foo$totNonSmall\nwith(foo, plot(cumInc ~ percentDecrease, type='n',\n xlab='Percent decrease in litter rate',\n ylab='cases / farms', frame=FALSE))\nwith(foo, text(percentDecrease, cumInc, labels=State))\nsummary(lm(cumInc~percentDecrease, data=foo))\nct <- cor.test(foo$percentDecrease, foo$cumInc, method='spearman')\ntext(2, .6, paste('Spearman rho =', round(ct$estimate, 2)))\ntext(2, .55, paste('p =', round(ct$p.value, 3)))\ndev.off()\n#\nfoo$pdImp <- foo$percentDecrease\nfoo$pdImp[is.na(foo$pdImp)] <- df2$percentDecrease[df2$state == 'other states']\nfoo$anyCases <- foo$cases > 0\nfoo$manyCases <- foo$cases > 8\nboxplot(pdImp~ manyCases, data=foo, xlab='More than 8 cases',\n ylab='Percent decrease in pig litter')\n\nwilcox.test(pdImp ~ anyCases, data=foo)\nwilcox.test(pdImp ~ manyCases, data=foo)\n\n#' #' Add lat longs\n#+\nkey <- match(mg$State, state.abb)\nmg$stateLong <- state.center$x[key]\nmg$stateLat <- state.center$y[key]\n\n#' Add predictor based on mean number of cases in states with shared\n#' borderline\n#+\nborderMatGet <- function(){\n nbEdgelist <- read.csv('state_neighbors_fips.txt', header=FALSE)\n g <- graph.data.frame(nbEdgelist, directed=FALSE)\n g <- simplify(g)\n key <- match(V(g)$name, state.fips$fips)\n abb <- state.fips$abb[key]\n V(g)$name <- as.character(abb)\n states <- levels(mg$State)\n vids <- which(V(g)$name %in% states)\n g2 <- induced.subgraph(g, vids)\n nhood <- as.matrix(get.adjacency(g2))\n}\nborderMat <- borderMatGet()\nnbWeightGet <- function(M, flowTrans=identity, normFlows=TRUE){\n M <- flowTrans(M)\n if(!normFlows){\n ret <- (M)\n } else {\n N <- rowSums(M)\n N <- ifelse(N > 0, N, 1)\n ret <- M/N\n }\n ret\n}\nnbCasesGet <- function(..., caseTrans=function(x) log2(x + 0.5), symmetrize=FALSE){\n W <- nbWeightGet(...)\n avgc <- tapply(mg$cases, mg$State, mean)\n key <- match(colnames(W), names(avgc))\n avgc <- avgc[key]\n stopifnot(names(avgc) == colnames(W))\n if(symmetrize){\n W <- (W + t(W))/2\n }else{\n W <- t(W)\n }\n avgNbC <- W %*% avgc\n avgNbC <- caseTrans(avgNbC)\n data.frame(State=rownames(avgNbC), avgNbC=avgNbC)\n}\nfoo <- nbCasesGet(M=borderMat, normFlows=TRUE)\nnames(foo)[2] <- 'carNbC'\nmg <- merge(mg, foo)\ntmpf <- function(y){\n ret <- nbCasesGet(M=flowMat, flowTrans=function(x) x^y, normFlows=FALSE)\n names(ret)[2] <- paste('casesFlowNb', y, sep='')\n ret\n}\npowers <- as.list(2^(-1*(0:4) ))\nfoo <- lapply(powers, tmpf)\nfor(i in seq_along(foo)){\n mg <- merge(mg, foo[[i]])\n}\nrm(powers, foo)\n\n#' ## Analysis\n#'\n#' Examine colinearity\n#'\n#+ , fig.width=11.11, fig.height=11.11, out.width=800, out.height=800\ngetPreds <- function(df, transform=c('identity', 'log'),\n predNames=c('totNonSmall', 'mDense',\n'cmDense', 'medDense', 'maxDense', 'inventory2012', 'pigCrop',\n'inshipments', 'marketings')){\n transform <- match.arg(transform)\n df <- df[, predNames]\n x <- data.matrix(df)\n bot <- min(x[x[,'medDense'] > 0, 'medDense']) / 2\n if(transform=='log'){\n x[, 'medDense'] <- x[,'medDense'] + bot\n log(x)\n } else {\n x\n }\n}\nx <- getPreds(df=mg, transform='id')\nxlog <- getPreds(df=mg, transform='log')\nscatterplotMatrix(xlog, transform=FALSE, cex.labels=1.5, pch=16,\n col=c(palette()[3:2], rgb(0,0,0,0.25)), cex.axis=2)\n\n#' Add in the eigenvector centrality predictors, exept for the one set\n#' that predicts all of the cases in Vermont (i.e.,\n#' isSymFALSEisNormedTRUE* predictors). Vermont only ships to itself,\n#' so a random walk gets trapped there in those cases. Clearly that is\n#' a bad model so we'll remove them.\n#'\n#+\ngetOtherPreds <- function(D, X){\n xEig <- D[, grep('^isSym', colnames(D))]\n xEig <- xEig[, -grep(\"^isSymFALSEandIsNormedTRUE\", colnames(xEig))]\n sel <- D[, grep('^reg|^casesFlowNb|^carNb', colnames(D))]\n ret <- data.matrix(cbind(X, sel, xEig))\n rownames(ret) <- as.character(D$State)\n ret\n}\nxx <- getOtherPreds(D=mg, X=x)\nxxlog <- getOtherPreds(D=mg, X=xlog)\n\n#' We have too many predictors for a scatterplot matrix to be\n#' useful. Let's do PCA.\n#+\npc <- prcomp(xx, scale=TRUE)\npve <- summary(pc)$importance[2,]\ncve <- summary(pc)$importance[3,]\npar(mfrow=c(1,2))\nplot(pve, xlab='Principle component', ylab='PVE', type='o', col='blue')\nplot(cve, xlab='Principle component', ylab='Cumulative PVE', type='o', col='blue')\npar(mfrow=c(1,1))\nbiplot(pc)\nbiplot(pc, c(1,3))\n\n#' The plot is crowded, but you can see that IA is extreme on PC1 and\n#' an outlier on PC2. NC on the other hand, is extreme on PC1 and an\n#' outlier on PC3.\n#'\n#' Next let's check the transformed-only data.\n#+\npc <- prcomp(xxlog, scale=TRUE)\npve <- summary(pc)$importance[2,]\ncve <- summary(pc)$importance[3,]\npar(mfrow=c(1,2))\nplot(pve, xlab='Principle component', ylab='PVE', type='o', col='blue')\nplot(cve, xlab='Principle component', ylab='Cumulative PVE', type='o', col='blue')\npar(mfrow=c(1,1))\nbiplot(pc)\nbiplot(pc, c(1,3))\n\n#' The data is roughly three dimensional with most of the\n#' variation in one dimension but the data seems more evenly spread\n#' over the space and perhaps more likely to provide a useful model.\n#'\n#' Model of all cases\n#+\nalphas <- 0:5/5\ntmpf <- function(alpha){\n cv.glmnet(x=xxlog, y=mg$l2CasesOffset, alpha=alpha)\n}\ncvsAll <- lapply(alphas, tmpf)\n\n#' PCA of selected variables\n#+\nb <- drop(coef(cvsAll[[6]]))[-1]\npcAll1 <- prcomp(xxlog[, b>0], scale=TRUE)\nbiplot(pcAll1)\n\n#' Check prediction error by alpha\n#+\nplot3Alphas <- function(fitList, alphas, which=4:6, ...){\n ## Adpated from http://www.stanford.edu/~hastie/glmnet/glmnet_alpha.html\n opar <- par(mfrow = c(2, 2))\n cv <- fitList[which]\n a <- alphas[which]\n tmpf <- function(x,y) {\n main <- substitute(expression(paste(alpha, '=', y)), list(y=y))\n plot(x, sub=main, ...)\n }\n invisible(mapply(tmpf, x=cv, y=a))\n ls <- lapply(cv, '[[', 'lambda.1se')\n plot(log(cv[[1]]$lambda), cv[[1]]$cvm, pch = 19, col = \"red\", xlab = \"log(Lambda)\",\n ylab = cv[[1]]$name, ...)\n abline(v=log(ls[[1]]), col=\"red\")\n points(log(cv[[2]]$lambda), cv[[2]]$cvm, pch = 19, col = \"grey\")\n abline(v=log(ls[[2]]), col=\"grey\")\n points(log(cv[[3]]$lambda), cv[[3]]$cvm, pch = 19, col = \"blue\")\n abline(v=log(ls[[3]]), col=\"blue\")\n legend(\"topleft\", legend = paste('alpha =', a), pch = 19,\n col = c(\"red\", \"grey\", \"blue\"))\n par(opar)\n}\nplot3Alphas(cvsAll, alphas, ylim=c(0,10))\n\n#' There's no alpha with a decisive predicition error advantage here.\n#'\n#' Some Diagnostics\n#+\nm <- cvsAll[[6]]\nyhat <- predict(m, type='response', new=xxlog)\nlmfit <- lm(mg$l2CasesOffset~0, offset=yhat)\nlayout(matrix(1:4, 2))\nplot(lmfit, which=1:3)\n\n#' The trend in the mean of the residuals and the small variance for\n#' low predictions look a little troublesome and suggest we try a\n#' hurdle model.\n#'\n#' Logistic regression for prob non-zero\n#+\nmg$anyCases <- factor(mg$cases > 0)\ntmpf <- function(alpha){\n cv.glmnet(x=xxlog, y=mg$anyCases, alpha=alpha, family='binomial')\n}\ncvsBin <- lapply(alphas, tmpf)\nlsBin <- lapply(cvsBin, '[[', 'lambda.1se')\nmapply(function(x, y) x$cvm[which(x$lambda == y)], x=cvsBin, y=lsBin)\nlayout(matrix(1:6, ncol=3))\ntmpf <- function(x,y) {\n main <- substitute(expression(paste(alpha, '=', y)), list(y=y))\n plot(x, sub=main)\n}\ninvisible(mapply(tmpf, x=cvsBin, y=alphas))\n\n#' Visualization of coefficients\n#'\n#+\ncoefplot <- function(fitList, preds, which=3:6){\n coefMat <- sapply(fitList, function(x) as.numeric(coef(x)))[-1,]\n rownames(coefMat) <- rownames(coef(fitList[[1]]))[-1]\n colnames(coefMat) <- paste('alpha', alphas, sep='')\n coefMat <- t(coefMat)\n par(mfrow=c(1,2),\n mar=c(20,4,1,1))\n barplot(coefMat[which,], beside=T, las=2, ylab='Coefficients')\n scales <- apply(preds, 2, sd)\n coefMatStd <- coefMat\n stopifnot(names(scales)==colnames(coefMat))\n for(i in 1:nrow(coefMatStd)){\n coefMatStd[i,] <- coefMatStd[i,] * scales\n }\n test <- abs(colSums(coefMatStd[which,])) > 0.01\n barplot(coefMatStd[which,test], beside=T, las=2, ylab='Standardized coefficients')\n par(mfrow=c(1,1),\n mar=c(5,4,4,1) + 0.1)\n\n}\ncoefplot(cvsBin, xxlog, which=4:6)\n\n#' Diagnostics for logistic fit\n#+\nmbin <- cvsBin[[6]]\nyhatb <- predict(mbin, type='link', new=xxlog)\nanyCasesInt <- as.integer(as.logical(mg$anyCases))\nglmfit <- glm(anyCasesInt~0, offset=yhatb, family=binomial)\nlayout(matrix(1:4, 2))\nplot(glmfit, which=1:3)\nEp <- residuals(glmfit, type='pearson')\n\n#' Next let's check for outliers. Because of our tricky way of\n#' producing glmfit, this will be a t-test on the deviance residuals\n#' instead of the usual studentized residuals, but that seems unlikely\n#' to be an issue.\n#+\noutlierTest(glmfit)\n\n#' Bubble plot of residuals on U.S. map\n#+\ndf <- data.frame(res=as.vector(Ep), x=mg$stateLong, y=mg$stateLat, state=mg$State)\nall_states <- map_data(\"state\")\np <- ggplot()\np <- p + geom_polygon(data=all_states, aes(x=long, y=lat, group = group),colour=\"white\", fill=\"grey10\" )\np <- p + geom_point(data=df, aes(x=x, y=y, size=abs(res)*2, color=as.factor(sign(res))))\np\n\n#' The colors seem to go together somewhat, which means there may be\n#' some spatial correlations.\n#+\n#' Get great circle distance\n#+\ncx <- df$x\ncy <- df$y\nE <- df$res\nn <- length(cx)\ncenterDists <- matrix(nrow=n, ncol=n)\n\n#' Calculates the geodesic distance between two points specified by radian latitude/longitude using the\n#' Haversine formula (hf)\n#' source: http://www.r-bloggers.com/great-circle-distance-calculations-in-r/\n#+\ngcd.hf <- function(long1, lat1, long2, lat2) {\n R <- 6371 # Earth mean radius [km]\n delta.long <- (long2 - long1)\n delta.lat <- (lat2 - lat1)\n a <- sin(delta.lat/2)^2 + cos(lat1) * cos(lat2) * sin(delta.long/2)^2\n c <- 2 * asin(min(1,sqrt(a)))\n d = R * c\n return(d) # Distance in km\n }\ndeg2rad <- function(deg) return(deg*pi/180)\ngetCenterDist <- function(state1,state2){\n long1 <- deg2rad(cx[state1])\n long2 <- deg2rad(cx[state2])\n lat1 <- deg2rad(cy[state1])\n lat2 <- deg2rad(cy[state2])\n gcd.hf(long1, lat1, long2, lat2)\n}\nfor(i in seq_len(n)){\n for(j in seq_len(n)){\n centerDists[i,j] <- getCenterDist(i, j)\n }\n}\ncolnames(centerDists) <- rownames(centerDists) <- df$state\n\n#' Now lets find points on 2-d plane that satisfy distances\n#+\nloc <- cmdscale(centerDists)\nx <- -loc[, 1]\ny <- loc[,2]\nplot(x, y, type='n', asp=1)\ntext(x, y, rownames(loc), cex=.6)\n\n#' Now lets check for correlations in space\n#+\nsE <- E/sd(E)\nvg <- variog(coords=loc, data=sE, max.dist=2000)\nolsFit <- variofit(vg, cov.model='exponential')\nlikFit <- likfit(coords=loc, data=sE, cov.model='exponential', ini.cov.pars=c(.1, 194))\npar(mfrow=c(1,1))\nplot(vg)\nlines(olsFit)\nlines(likFit, lwd=2)\n\n#' The densley packed, spatially autocorrelated states in the North\n#' East might be exerting too much influence. Let's try to identify\n#' groups of close states and combine their data into a single\n#' observation.\n#'\n#+\nclus <- hclust(as.dist(centerDists), method='single')\nplot(clus)\nabline(h=175)\n\n#' If we cut the single-linkage tree at 175, it seems lead to sensible\n#' groupings. So let's create a new data set with rows from those\n#' states averaged together.\n#+\ngroups <- cutree(clus, h=175)\ntmpf <- function(x) names(groups)[groups==x]\ngroupL <- lapply(unique(groups), tmpf)\ntmpf <- function(x){\n rows <- mg$State %in% x\n M <- data.matrix(mg[rows,-1])\n colMeans(M)\n}\nrowsAvg <- lapply(groupL, tmpf)\nmgAvg <- do.call(rbind, rowsAvg)\nrownames(mgAvg) <- sapply(groupL, paste, collapse='-')\nmgAvg <- data.frame(mgAvg)\nmgAvg[, 'anyCases'] <- factor(mgAvg[, 'cases'] > 0)\n\nxlogAvg <- getPreds(df=mgAvg, transform='log')\nxxlogAvg <- getOtherPreds(D=mgAvg, X=xlogAvg)\nrownames(xxlogAvg) <- rownames(mgAvg)\n\nmbinavg <- cv.glmnet(x=xxlogAvg, y=mgAvg$anyCases, alpha=1, family='binomial')\nmbinavg0.8 <- cv.glmnet(x=xxlogAvg, y=mgAvg$anyCases, alpha=0.8, family='binomial')\n\ncbind(drop(coef(mbin)), drop(coef(mbinavg)))\ncbind(drop(coef(cvsBin[[5]])), drop(coef(mbinavg0.8)))\n\n#' That weighting has resulted in minor changes to the estimated\n#' coefficients and some changes to the selected variables. Notably,\n#' median farm density has been selected when alpha=0.8 for the\n#' averaged data, and inshipments has been selected instead of\n#' eigenvector centrality when alpha=1.\n#+\nyhatavg <- predict(mbinavg, type='link', new=xxlogAvg)\nanyCasesIntAvg <- as.integer(as.logical(mgAvg$anyCases))\nglmfitavg <- glm(anyCasesIntAvg~0, offset=yhatavg, family=binomial)\nlayout(matrix(1:4, 2))\nplot(glmfitavg, which=1:3)\nEpavg <- residuals(glmfitavg, type='pearson')\nsEpavg <- Epavg/sd(Epavg)\nlocAvg <- with(mgAvg, cbind(x=stateLong, y=stateLat))\nvgavg <- variog(coords=locAvg, data=sEpavg, max.dist=2000)\nplot(vgavg)\n\n#' The variogram looks much better.\n#'\n#' Next we model the positive counts\n#+\ntest <- as.logical(mgAvg$anyCases)\nsub <- mgAvg[test, ]\nsubx <- xxlogAvg[test, ]\nsubl <- locAvg[test,]\nalphas <- 0:5/5\ntmpf <- function(alpha){\n cv.glmnet(x=subx, y=log(sub$cases), alpha=alpha, nfolds=5)\n}\ncvsPos <- lapply(alphas, tmpf)\n\n#' The diagnostics\n#+\nm <- cvsPos[[6]]\nyhat <- predict(m, type='response', new=subx)\nlmfit <- lm(log(sub$cases)~0, offset=yhat)\nlayout(matrix(1:4, 2))\nplot(lmfit, which=1:3)\nEp <- residuals(lmfit, type='pearson')\nvg <- variog(coords=subl, data=Ep/sd(Ep), max.dist=2000)\nolsFit <- variofit(vg, cov.model='exponential')\nlikFit <- likfit(coords=subl, data=Ep/sd(Ep), cov.model='exponential', ini.cov.pars=c(.01, 400))\nplot(vg)\nlines(olsFit)\nlines(likFit, lwd=2)\n\n#' There's a trend in the residuals vs fitted, but that's probably\n#' just showing us that the shrinkage is protecting us from\n#' overestimating the coefficients. The semivariogram looks good.\n#'\n#' Let's see if there are any concerning patterns in plots of the\n#' residuals versus predictors.\n#+\nfor(i in colnames(subx)){\n plot(Ep~subx[, i], main=i, pch=16, col=rgb(0,0,0,0.7), xpd=TRUE)\n text(subx[, i], jitter(Ep, amount=0.1), labels=sub$State)\n}\n\n\n#' It's a little surprising that the region 1 doesn't get selected,\n#' but that's probably because it is correlated with population size.\n#+\nplot(reg1~inventory2012,data=mgAvg)\ncor.test(mgAvg$reg1, mgAvg$inventory2012)\n\n#' The eigenvector predictors often seem to be grouped together with\n#' the balance sheet ones. That is to be expected, given that the\n#' flows are derived in part from the balance sheet data. Perhaps it\n#' is not worth presenting the eigenvector results, which will take a\n#' while to properly describe and not really change the\n#' conclusions. So, let's do a final round of fitting without them.\n#'\n#' ## Fits without eigenvector predictors\n#+\nxf <- xxlogAvg[, -grep('^isSym', colnames(xx))]\nxfpc <- prcomp(xf, scale=TRUE)\nsummary(xfpc)\nbiplot(xfpc)\nbiplot(xfpc, choices=c(1,3))\n\n#' Balance sheet and farm count contibutes primarily to the first PC\n#' and to the third PC to a lesser extent. Several region variables\n#' make the largest contributions to the second and third PCs. The second PC\n#' comprises primarily neighboring cases, density, and region. There\n#' should be some power to detect the effect of these variables\n#' independent of the population-size variables in the first PC.\n#'\n#+\ntest <- as.logical(mgAvg$anyCases)\nsubxf <- xf[test, ]\nalphas <- 0:5/5\ntmpf <- function(alpha){\n cv.glmnet(x=subxf, y=log(sub$cases), alpha=alpha, nfolds=5)\n}\ncvsPosF <- lapply(alphas, tmpf)\nplot3Alphas(cvsPosF, alphas)\ncoefplot(cvsPosF, subxf, which=4:6)\n\n#' Diagnostics for case counts\n#+\nm <- cvsPosF[[5]]\nyhatcf <- predict(m, type='response', new=subxf)\nlmfit <- lm(log(sub$cases)~0, offset=yhatcf)\nlayout(matrix(1:4, 2))\nplot(lmfit, which=1:3)\nEp <- residuals(lmfit, type='pearson')\nEstd <- Ep/sd(Ep)\nvg <- variog(coords=subl, data=Estd, max.dist=2000)\nolsFit <- variofit(vg, cov.model='exponential')\nlikFit <- likfit(coords=subl, data=Estd, cov.model='exponential', ini.cov.pars=c(.01, 300))\nplot(vg, xlab='distance')\nlines(olsFit)\nlines(likFit, lwd=2)\npar(mfrow=c(1,1))\n\n#' Logistic regression for prob non-zero\n#+\nalphas <- 0:5/5\ntmpf <- function(alpha){\n cv.glmnet(x=xf, y=mgAvg$anyCases, alpha=alpha, family='binomial')\n}\ncvsBinF <- lapply(alphas, tmpf)\nplot3Alphas(cvsBinF, alphas)\ncoefplot(cvsBinF, xf, which=4:6)\n\n#' Diagnostics for probability non-zero\n#+\nm <- cvsBinF[[5]]\nyhatb <- predict(m, type='link', new=xf)\nglmfit <- glm(anyCasesIntAvg~0, offset=yhatb, family=binomial)\nlayout(matrix(1:4, 2))\nplot(glmfit, which=1:3)\nEp <- residuals(glmfit, type='pearson')\nsEp <- Ep/sd(Ep)\nvg <- variog(coords=locAvg, data=sEp, max.dist=2000)\nplot(vg)\nlikFit <- likfit(coords=locAvg, data=sEp, cov.model='exponential', ini.cov.pars=c(.01, 300))\nlines(likFit)\n\n#' Let's check deviance ratio to evaluate fit.\n#'\n#+\ngetFitStats <- function(x){\n lam <- x$lambda.1se\n ft <- x$glmnet.fit\n ind <- which(ft$lambda == lam)\n ind2 <- which(x$lambda==lam)\n res <- list(lambda=lam)\n res$a0 <- ft$a0[ind]\n res$dr <- ft$dev.ratio[ind]\n res$nzero <- x$nzero[ind]\n res$cvm <- x$cvm[ind2]\n res$cvsd <- x$cvsd[ind2]\n res$nobs <- ft$nobs\n res\n}\nmods <- list(loglog=cvsPosF, presence=cvsBinF)\ntmpf <- function(x){\n foo <- sapply(x[5:6], getFitStats)\n foo <- t(foo)\n as.data.frame(foo)\n}\nstats <- lapply(mods, tmpf)\nstatsdf <- do.call(rbind, stats)\nstatsdf <- cbind(alpha=c(0.8, 1), statsdf)\nstatsdf <- round(data.matrix(statsdf), 2)\n\ninvisible(latex(statsdf, file='stats-table.tex'))\n\n#' ## Stability selection\n#'\n#' Stability selection seems more appopriate for identifying important\n#' variables than cross-validation. We will check for sensitivity to\n#' alpha.\n\ntmpf <- function(alpha,...){\n stabpath(alpha=alpha, weakness=1, steps=1000, size=0.632, ...)\n}\ntmpff <- function(spec){\n alphas <- c(0.01, 0.2, 0.5, 0.8, 1)\n names(alphas)=paste('alpha', format(alphas), sep='=')\n lapply(alphas, tmpf, y=spec$y, family=spec$family, x=spec$x)\n}\nspecs <- list(cumCases=list(y=log(sub$cases), family='gaussian', x=subxf),\n anyCases=list(y=mgAvg$anyCases, family='binomial', x=xf))\npar(mfrow=c(3,5))\nres <- lapply(specs, tmpff)\nstab <- lapply(res, function(x) lapply(x, plot, type='pcer', error=0.05))\ntmpfff <- function(x) lapply(x, '[[', 'stable')\nlapply(stab, tmpfff)\n\n## get Descriptive stats\n\ndf1 <- data.frame(anyCases=specs$anyCases$y, specs$anyCases$x)\ninvisible(latex(describe(df1), file='stability-describe-df1.tex'))\n\ndf2 <- data.frame(cumCases=specs$cumCases$y, specs$cumCases$x)\ninvisible(latex(describe(df2), file='stability-describe-df2.tex'))\n\nsave.image('pedv-cum.RData')\n", "meta": {"hexsha": "2de65beb0025dcbba0287d68a300b86686f8b724", "size": 31132, "ext": "r", "lang": "R", "max_stars_repo_path": "src/pedv-cum.r", "max_stars_repo_name": "e3bo/2015pedv", "max_stars_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-05-11T22:33:34.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-11T22:33:34.000Z", "max_issues_repo_path": "src/pedv-cum.r", "max_issues_repo_name": "e3bo/2015pedv", "max_issues_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 23, "max_issues_repo_issues_event_min_datetime": "2015-10-23T15:48:37.000Z", "max_issues_repo_issues_event_max_datetime": "2015-12-15T18:00:05.000Z", "max_forks_repo_path": "src/pedv-cum.r", "max_forks_repo_name": "e3bo/2015pedv", "max_forks_repo_head_hexsha": "762adb00b3b4c3bb03009cd56e5864c2f380ab2f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.2606837607, "max_line_length": 131, "alphanum_fraction": 0.6462803546, "num_tokens": 10254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.5350984286266115, "lm_q1q2_score": 0.3008197622347974}} {"text": "#program gets single image from sharepoint server and plots in RGB colors\n\nrequire(httr)\n\nurl <- \"https://sharepoint_or_something.company_name.com/dir/dir/dir/username/Shared%20Documents/some_more_directory/20171201.png\" # change this too\nusername <- \"change_this\"\npassword <- \"change_this\"\n\n\nr <- GET(url, authenticate(username,password,type=\"any\"), verbose())\n\natmos <- content(r, \"parsed\")\n\ndim(atmos) # dimensions of an image\n\nlibrary(grid)\ngrid.raster(atmos) # plot in true colors\n\n\n\n\n\n", "meta": {"hexsha": "3b86de3b9108fd2e2c9b4a3369f6585030cf183e", "size": 491, "ext": "r", "lang": "R", "max_stars_repo_path": "sharepoint_singleimage.r", "max_stars_repo_name": "mabelsfatalfable/Sharepointe-R", "max_stars_repo_head_hexsha": "4cb6ee0c1df313718861bea655e7225fb051c2e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2020-10-29T02:48:32.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-29T02:48:32.000Z", "max_issues_repo_path": "sharepoint_singleimage.r", "max_issues_repo_name": "mabelsfatalfable/Sharepoint-Uploader", "max_issues_repo_head_hexsha": "4cb6ee0c1df313718861bea655e7225fb051c2e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "sharepoint_singleimage.r", "max_forks_repo_name": "mabelsfatalfable/Sharepoint-Uploader", "max_forks_repo_head_hexsha": "4cb6ee0c1df313718861bea655e7225fb051c2e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.347826087, "max_line_length": 148, "alphanum_fraction": 0.7535641548, "num_tokens": 117, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3008197538989936}} {"text": "#!/usr/bin/env Rscript\n\n#for conveniently drawing labels\nline2user <- function(line, side) {\n lh <- par('cin')[2] * par('cex') * par('lheight')\n x_off <- diff(grconvertX(0:1, 'inches', 'user'))\n y_off <- diff(grconvertY(0:1, 'inches', 'user'))\n switch(side,\n `1` = par('usr')[3] - line * y_off * lh,\n `2` = par('usr')[1] - line * x_off * lh,\n `3` = par('usr')[4] + line * y_off * lh,\n `4` = par('usr')[2] + line * x_off * lh,\n stop(\"side must be 1, 2, 3, or 4\", call.=FALSE))\n}\n\n\n#define color\nnumlev = 100 #number of colour levels\nndim = 2 #spatial dimensions for now\ncoulombcolor <- colorRampPalette(c(\"red\",\"yellow\",\"green\",\"cyan\",\"blue\"),space = \"rgb\")(numlev)\nerrcolor <- colorRampPalette(c(\"red\",\"white\",\"blue\"),space = \"rgb\")(numlev)\n\n#read data\nxyslice <- data.matrix(read.csv(\"slices/sliceXY.csv\",header = FALSE))\nxzslice <- data.matrix(read.csv(\"slices/sliceXZ.csv\",header = FALSE))\nyzslice <- data.matrix(read.csv(\"slices/sliceYZ.csv\",header = FALSE))\ntruexyslice <- data.matrix(read.csv(\"slices/truesliceXY.csv\",header = FALSE))\ntruexzslice <- data.matrix(read.csv(\"slices/truesliceXZ.csv\",header = FALSE))\ntrueyzslice <- data.matrix(read.csv(\"slices/truesliceYZ.csv\",header = FALSE))\nfullxyslice <- data.matrix(read.csv(\"slices/fullsliceXY.csv\",header = FALSE))\nfullxzslice <- data.matrix(read.csv(\"slices/fullsliceXZ.csv\",header = FALSE))\nfullyzslice <- data.matrix(read.csv(\"slices/fullsliceYZ.csv\",header = FALSE))\ntruefullxyslice <- data.matrix(read.csv(\"slices/truefullsliceXY.csv\",header = FALSE))\ntruefullxzslice <- data.matrix(read.csv(\"slices/truefullsliceXZ.csv\",header = FALSE))\ntruefullyzslice <- data.matrix(read.csv(\"slices/truefullsliceYZ.csv\",header = FALSE))\n\n#compute the error slices\nxyerr = xyslice-truexyslice\nxyrelerr = xyerr/truexyslice\nxzerr = xzslice-truexzslice\nxzrelerr = xzerr/truexzslice\nyzerr = yzslice-trueyzslice\nyzrelerr = yzerr/trueyzslice\nfullxyerr = fullxyslice-truefullxyslice\nfullxyrelerr = fullxyerr/truefullxyslice\nfullxzerr = fullxzslice-truefullxzslice\nfullxzrelerr = fullxzerr/truefullxzslice\nfullyzerr = fullyzslice-truefullyzslice\nfullyzrelerr = fullyzerr/truefullyzslice\n\n\n#custom max and min function, needed or else NA reports -Infty\nmy.max <- function(x) ifelse( !all(is.na(x)), max(x, na.rm=T), NA)\nmy.min <- function(x) ifelse( !all(is.na(x)), min(x, na.rm=T), NA)\n#for plotting the fitted and the real slices on the same scale, 0 = green (else you cannot compare)\n#old method\n#xycol = max(my.max(abs(xyslice)),my.max(abs(truexyslice)))\n#xzcol = max(my.max(abs(xzslice)),my.max(abs(truexzslice)))\n#yzcol = max(my.max(abs(yzslice)),my.max(abs(trueyzslice)))\n#new method\nxycol = my.max(abs(truexyslice))\nxzcol = my.max(abs(truexzslice))\nyzcol = my.max(abs(trueyzslice))\nxycol = c(-xycol, xycol)\nxzcol = c(-xzcol, xzcol)\nyzcol = c(-yzcol, yzcol)\n\n#clamp slices to min and max values of the color ranges (else it gets drawn in white)\nxyslice [xyslice < xycol[1]] <- xycol[1]\nxyslice [xyslice > xycol[2]] <- xycol[2]\nfullxyslice [fullxyslice < xycol[1]] <- xycol[1]\nfullxyslice [fullxyslice > xycol[2]] <- xycol[2]\ntruefullxyslice[truefullxyslice < xycol[1]] <- xycol[1]\ntruefullxyslice[truefullxyslice > xycol[2]] <- xycol[2]\nxzslice [xzslice < xzcol[1]] <- xzcol[1]\nxzslice [xzslice > xzcol[2]] <- xzcol[2]\nfullxzslice [fullxzslice < xzcol[1]] <- xzcol[1]\nfullxzslice [fullxzslice > xzcol[2]] <- xzcol[2]\ntruefullxzslice[truefullxzslice < xzcol[1]] <- xzcol[1]\ntruefullxzslice[truefullxzslice > xzcol[2]] <- xzcol[2]\nyzslice [yzslice < yzcol[1]] <- yzcol[1]\nyzslice [yzslice > yzcol[2]] <- yzcol[2]\nfullyzslice [fullyzslice < yzcol[1]] <- yzcol[1]\nfullyzslice [fullyzslice > yzcol[2]] <- yzcol[2]\ntruefullyzslice[truefullyzslice < yzcol[1]] <- yzcol[1]\ntruefullyzslice[truefullyzslice > yzcol[2]] <- yzcol[2]\n\n# clamp the relative error slices (relative error ranges from -100 % to 100 %)\n xyrelerr[ xyrelerr < -1] <- -1\n xyrelerr[ xyrelerr > 1] <- 1\n xzrelerr[ xzrelerr < -1] <- -1\n xzrelerr[ xzrelerr > 1] <- 1\n yzrelerr[ yzrelerr < -1] <- -1\n yzrelerr[ yzrelerr > 1] <- 1\nfullxyrelerr[fullxyrelerr < -1] <- -1\nfullxyrelerr[fullxyrelerr > 1] <- 1\nfullxzrelerr[fullxzrelerr < -1] <- -1\nfullxzrelerr[fullxzrelerr > 1] <- 1\nfullyzrelerr[fullyzrelerr < -1] <- -1\nfullyzrelerr[fullyzrelerr > 1] <- 1\n\n# clamp the absolute error slices\n xyerr[ xyerr < xycol[1]] <- xycol[1] \n xyerr[ xyerr > xycol[2]] <- xycol[2] \n xzerr[ xzerr < xzcol[1]] <- xzcol[1] \n xzerr[ xzerr > xzcol[2]] <- xzcol[2] \n yzerr[ yzerr < yzcol[1]] <- yzcol[1] \n yzerr[ yzerr > yzcol[2]] <- yzcol[2] \nfullxyerr[fullxyerr < xycol[1]] <- xycol[1]\nfullxyerr[fullxyerr > xycol[2]] <- xycol[2]\nfullxzerr[fullxzerr < xzcol[1]] <- xzcol[1]\nfullxzerr[fullxzerr > xzcol[2]] <- xzcol[2]\nfullyzerr[fullyzerr < yzcol[1]] <- yzcol[1]\nfullyzerr[fullyzerr > yzcol[2]] <- yzcol[2]\n\n#plots without border\npng(filename = \"comparison.png\",\n width = 1200, height = 1800, units = \"px\", pointsize = 12,\n bg = \"white\", res = NA)\nop <- par(mfrow=c(6,4),mar=c(3,4,3,3))\n\n#plot xyslice\nimage(truexyslice,zlim=xycol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"True ESP\", font=2, cex=2, side = 3, line = 0, outer = FALSE)\nmtext(\"XY plane\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(xyslice,zlim=xycol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"Fitted ESP\", font=2, cex=2, side = 3, line = 0, outer = FALSE)\nimage(xyrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nmtext(\"Relative Error\", font=2, cex=2, side = 3, line = 0, outer = FALSE)\nimage(xyerr,zlim=xycol,xlab='',ylab='',axes=FALSE,col=errcolor)\nmtext(\"Absolute Error\", font=2, cex=2, side = 3, line = 0, outer = FALSE)\n\n#plot fullxyslice\nimage(truefullxyslice,zlim=xycol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"XY plane - full\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(fullxyslice,zlim=xycol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nimage(fullxyrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nimage(fullxyerr,zlim=xycol,xlab='',ylab='',axes=FALSE,col=errcolor)\n\n#plot xzslice\nimage(truexzslice,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"XZ plane\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(xzslice,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nimage(xzrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nimage(xzerr,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=errcolor)\n\n#plot fullxzslice\nimage(truefullxzslice,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"XZ plane - full\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(fullxzslice,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nimage(fullxzrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nimage(fullxzerr,zlim=xzcol,xlab='',ylab='',axes=FALSE,col=errcolor)\n\n#plot yzslice\nimage(trueyzslice,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"YZ plane\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(yzslice,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nimage(yzrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nimage(yzerr,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=errcolor)\n\n#plot fullyzslice\nimage(truefullyzslice,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nmtext(\"XZ plane - full\", font=2, cex=2, side = 2, line = 1,outer = FALSE)\nimage(fullyzslice,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=coulombcolor)\nimage(fullyzrelerr,zlim=c(-1,1),xlab='',ylab='',axes=FALSE,col=errcolor)\nimage(fullyzerr,zlim=yzcol,xlab='',ylab='',axes=FALSE,col=errcolor)\n\n\n", "meta": {"hexsha": "3a5cf56acfeff7a9fbb46b7171953b7eb5e48018", "size": 7828, "ext": "r", "lang": "R", "max_stars_repo_path": "mdcm_bin/visualize.r", "max_stars_repo_name": "EricBoittier/fdcm_project", "max_stars_repo_head_hexsha": "32a3898e2ce6dd64a0bb284ee320fb56e4fecaba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "mdcm_bin/visualize.r", "max_issues_repo_name": "EricBoittier/fdcm_project", "max_issues_repo_head_hexsha": "32a3898e2ce6dd64a0bb284ee320fb56e4fecaba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mdcm_bin/visualize.r", "max_forks_repo_name": "EricBoittier/fdcm_project", "max_forks_repo_head_hexsha": "32a3898e2ce6dd64a0bb284ee320fb56e4fecaba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.3195266272, "max_line_length": 99, "alphanum_fraction": 0.6714358712, "num_tokens": 2869, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.5621765008857981, "lm_q1q2_score": 0.3008197538989936}} {"text": "#' @title which.quantile\n#' @description unknown\n#' @family abysmally documented\n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\n which.quantile = function( x, probs, inside=TRUE ) {\n qnts = quantile( x, probs=probs, na.rm=TRUE ) \n \n if ( inside ) {\n return( which(x < qnts[2] & x > qnts[1] ) )\n } else {\n return( which(x > qnts[2] | x < qnts[1] ) )\n }\n }\n\n", "meta": {"hexsha": "1c7719e5455a5c544e38e5241e6e091aef6d0bec", "size": 403, "ext": "r", "lang": "R", "max_stars_repo_path": "R/which.quantile.r", "max_stars_repo_name": "AtlanticR/bio.utilities", "max_stars_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/which.quantile.r", "max_issues_repo_name": "AtlanticR/bio.utilities", "max_issues_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/which.quantile.r", "max_forks_repo_name": "AtlanticR/bio.utilities", "max_forks_repo_head_hexsha": "aaa52cf86afa4ee9e6f46c4516a48d27cc0bfed9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.1875, "max_line_length": 54, "alphanum_fraction": 0.5657568238, "num_tokens": 140, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.5312093733737563, "lm_q1q2_score": 0.3006743513994525}} {"text": "#' Dates of different days within isoweekyears\n#'\n#' @format\n#' \\describe{\n#' \\item{yrwk}{Isoweek-isoyear.}\n#' \\item{mon}{Date of Monday.}\n#' \\item{tue}{Date of Tuesday.}\n#' \\item{wed}{Date of Wednesday.}\n#' \\item{thu}{Date of Thursday.}\n#' \\item{fri}{Date of Friday.}\n#' \\item{sat}{Date of Saturday.}\n#' \\item{sun}{Date of Sunday.}\n#' }\n\"days\"\n\n# Creates the norway_locations data.table\ngen_days <- function() {\n . <- NULL\n yrwk <- NULL\n day <- NULL\n mon <- NULL\n tue <- NULL\n wed <- NULL\n thu <- NULL\n fri <- NULL\n sat <- NULL\n sun <- NULL\n\n days <- data.table(day = seq.Date(as.IDate(\"2000-01-01\"), as.IDate(\"2030-01-01\"), by = \"days\"))\n days[, yrwk := format.Date(day, format = \"%G-%V\")]\n days <- days[, .(mon = as.IDate(min(day))), by = .(yrwk)]\n days[, tue := mon + 1]\n days[, wed := mon + 2]\n days[, thu := mon + 3]\n days[, fri := mon + 4]\n days[, sat := mon + 5]\n days[, sun := mon + 6]\n days <- days[yrwk >= \"2000-01\"]\n\n return(days)\n}\n", "meta": {"hexsha": "84b5f108c1a67237ffa6becb8fa0149c8292fa2f", "size": 966, "ext": "r", "lang": "R", "max_stars_repo_path": "R/days.r", "max_stars_repo_name": "folkehelseinstituttet/municipdata", "max_stars_repo_head_hexsha": "eae72bd8eb130adb6397b9d5f3f8c00a02982b8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/days.r", "max_issues_repo_name": "folkehelseinstituttet/municipdata", "max_issues_repo_head_hexsha": "eae72bd8eb130adb6397b9d5f3f8c00a02982b8c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/days.r", "max_forks_repo_name": "folkehelseinstituttet/municipdata", "max_forks_repo_head_hexsha": "eae72bd8eb130adb6397b9d5f3f8c00a02982b8c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.0, "max_line_length": 97, "alphanum_fraction": 0.5693581781, "num_tokens": 345, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.5660185351961015, "lm_q1q2_score": 0.3006743513994525}} {"text": "################################################################################\n\nSlopes <- function(dataset,start,end,period) {\n# Given an ALSFRS dataset, computes the ALSFRS slope between start and end,\n# as defined by timing flag \n#\n# Args:\n#\tdataset generated by GetAlsfrsData\n#\tstart - the start time in months\n#\tend - the end time in months \n#\tperiod - when p, calculates past slopes: ie, slope based on first \n#\t\t\tvisit >= specified start month and last visit <=\n#\t\t\tspecified end month, using alsfrs.delta as time measure \n#\t\t\t- when f, calculates future slopes: ie, slope based on first \n#\t\t\tvisit > specified start and end months, using delta.ref as time\n#\t\t\tmeasure\n#\t\t \n# Returns:\n#\ta dataframe with columns for subject.id and the corresponding slope\n#\n# Example usage:\n#\tfor future slopes: future.slopes<-Slopes(alsfrs.data,3,12,\"f\")\n#\tfor past slopes: past.slopes<-Slopes(alsfrs.data,0,3,\"p\")\n\n\t\n\t# Get slope components\n\tslope.data<-SlopeComponents(dataset,start,end,period) \n\t\n\t# Calculate slopes\n\tslopes<-(slope.data$last.score-slope.data$first.score)/\n\t\t(slope.data$last.date-slope.data$first.date)\n\t\n\treturn(slopes)\n}\nSlopeComponents <- function(dataset,start,end,period) {\n\t# Given an ALSFRS dataset, computes the slope components, ie the first and last \n\t# scores and dates given the time period desired \n\t#\n\t# Args:\n\t#\tdataset generated by GetAlsfrsData\n\t#\tstart - the start time in months\n\t#\tend - the end time in months \n\t#\tperiod - when p, calculates past slopes: ie, slope based on first \n\t#\t\t\tvisit >= specified start month and last visit <=\n\t#\t\t\tspecified end month, using alsfrs.delta as time measure \n\t#\t\t\t- when f, calculates future slopes: ie, slope based on first \n\t#\t\t\tvisit > specified start and end months, using delta.ref as time\n\t#\t\t\tmeasure\n\t#\t\t \n\t# Returns:\n\t#\ta dataframe with columns for subject.id, first.score, first.date, last.score,\n\t# \tand last.date\n\t#\n\t# Example usage:\n\t#\tfor future slopes: future.slopes<-SlopeComponents(alsfrs.data,3,12,\"f\")\n\t#\tfor past slopes: past.slopes<-SlopeComponents(alsfrs.data,0,3,\"p\")\n\n\t# Get vector of all unique subject.ids\n\tsubjects<-unique(dataset$subject.id)\n\t# run CleanDelta to get rid of rows where ten questions have not been \n\t# answered, deltas equal to NA, and convert deltas to months\n\tdataset<-CleanDelta(dataset)\n\t# Get indices for first and last visits if calculating past slopes\n\tif (period == \"p\") {\n\t\tind.all<-PastIndex(dataset,start,end)\n\t\t# Merge with vector of all subject ids to make sure each subject id\n\t\t# has a corresponding slope, even if it is NA\n\t\tind.all<-merge(subjects,ind.all,by.x=1,by.y=\"subject.id\",all=TRUE)\n\t\tfirst.ind<-ind.all$ind.start \n\t\tlast.ind<-ind.all$ind.end\n\t}\n\t# Get indices for first and last visits if calculating future slopes\n\telse if (period == \"f\") {\n\t\t# Run DeltaRef if it has not already been run on the dataset\n\t\tif (!any(colnames(dataset)==\"delta.ref\")) {\n\t\t\tdataset<-DeltaRef(dataset) \n\t\t}\t\n\t\tind.start<-FutureIndex(dataset,start) \n\t\tind.end<-FutureIndex(dataset,end)\n\t\tind.all<-merge(ind.start,ind.end,by=\"subject.id\")\n\t\tcolnames(ind.all)<-c(\"subject.id\",\"ind.start\",\"ind.end\")\n\t\t# Merge with vector of all subject ids to make sure each subject id\n\t\t# has a corresponding slope, even if it is NA\n\t\tind.all<-merge(subjects,ind.all,by.x=1,by.y=\"subject.id\",all=TRUE)\n\t\tfirst.ind<-ind.all$ind.start \n\t\tlast.ind<-ind.all$ind.end\n\t}\n\telse { \n\tprint(\"Specify f or p\")\n\t}\n\t#Get the ALSFRS scores\n\tdataset<-AlsfrsScore(dataset)\n\t#Get first and last scores and dates for slope calculation\n\tfirst.score<-dataset$alsfrs.score[first.ind]\n\tfirst.date<-dataset$months[first.ind]\n\tlast.score<-dataset$alsfrs.score[last.ind]\n\tlast.date<-dataset$months[last.ind]\n\tslope.data<-data.frame(subject.id=subjects,first.score,first.date,last.score,\n\t\tlast.date)\n\treturn(slope.data)\n}\n\nCleanDelta<-function(dataset) {\n\t# Given an ALSFRS dataset, drops rows where not all ten questions have\n\t# been answered, drops all rows where alsfrs.delta = NA, and converts\n\t# alsfrs.delta to months.\n\t#\n\t# Args:\n\t#\tdataset generated by GenAlsfrsData \n\t#\n\t# Returns:\n\t#\tthe original dataset cleaned of ten questions and NAs, with an \n\t# \tadditional column months, which is alsfrs.delta in months.\n\t\n\talsfrs.questions<-GetAlsfrsQuestions()\n\t# mark all rows with ten questions scored\n\tten.questions<-rowSums(!is.na(dataset[,which(colnames(dataset) \n\t\t%in% alsfrs.questions)]))>=10 \n\t# drop all records without ten questions scored\n\tdataset<-dataset[ten.questions,]\n\t# drop all rows where alsfrs.delta = NA\n\tdataset<-dataset[!is.na(dataset$alsfrs.delta),]\n\t# convert delta to months\n\tmonths<-DaysToMonths(dataset$alsfrs.delta)\n\tdataset<-cbind(dataset,months)\n\treturn(dataset)\n}\n\nDeltaRef <- function(dataset) {\n\t# Given an ALSFRS dataset on which CleanDelta has been run, \n\t# identifies the reference visit, that is, the row that corresponds \n\t# to the first time all ten questions are answered, and then calculates\n\t# the difference between each visit and the reference visit in months.\n\t#\n\t# Args:\n\t#\tdataset generated by GenAlsfrsData on which CleanDelta has been run\n\t#\n\t# Returns:\n\t#\tthe original dataset with an additional column \"delta.ref\" of the \n\t#\tdifference between the time of each visit and the time of the reference \n\t# \tvisit in months.\n\n\t# mark first row of new subject_id \n\tunique<-!duplicated(dataset$subject.id)\n\t# Create a matrix with a column of subject ids and column of corresponding \n\t# reference visit time in months\n\tref.visit<-cbind(dataset$subject.id[unique],dataset$months[unique])\n\tcolnames(ref.visit)<-c(\"subject.id\",\"ref.month\")\n\t# Merge ref.visit and dataset so that row operations can be performed \n\tdataset<-merge(dataset,ref.visit,by=\"subject.id\")\n\tcols<-colnames(dataset)\n\tdataset<-cbind(dataset,(dataset$months-dataset$ref.month))\n\tcolnames(dataset)<-c(cols,\"delta.ref\")\n\t# Compute difference in months from reference date and return\n\treturn(dataset)\n}\n\n\nPastIndex<-function(dataset,start,end) {\n\t# Given an ALSFRS dataset, finds indices corresponding to the desired visits\n\t# to calculate future slopes\n\t#\n\t# Args:\n\t#\tdataset: a dataset generated by GenAlsfrsData on which CleanDelta has\n\t#\tbeen run\n\t#\tstart: time in months for start date\n\t#\tend: time in months for end date\n\t#\t\n\t# Returns:\n\t#\ta matrix with a column of subject ids, a column of indices corresponding \n\t# \tto the starting visit and a column of indices corresponding to the ending\n\t# \tvisit for each subject id.\n\n\t# Find all indices that correspond to visit that occurs after specified \n\t# start month \n\tind<-which(dataset$alsfrs.delta>=start)\n\tunique<-!duplicated(dataset$subject.id[ind])\n\tind.start<-ind[unique]\n\tind.start<-cbind(dataset$subject.id[ind.start],ind.start)\n\tcolnames(ind.start)<-c(\"subject.id\",\"ind.start\")\n\n\t# Find all indices that correspond to visits that occur before specified \n\t# end month\n\tind<-which(dataset$months<=end)\n\t# Mark changes in subject id in vector created from indices\n\tunique<-!duplicated(dataset$subject.id[ind])\n\tunique<-which(unique==TRUE)\n\tind.end<-c(ind[unique-1],ind[length(ind)])\n\tind.end<-cbind(dataset$subject.id[ind.end],ind.end)\n\tcolnames(ind.end)<-c(\"subject.id\",\"ind.end\")\n\t\n\t# Merge start and end indices with subject ids\n\tind.all<-merge(ind.start,ind.end,by=\"subject.id\",all=TRUE)\n\tcolnames(ind.all)<-c(\"subject.id\",\"ind.start\",\"ind.end\")\n\treturn(ind.all)\n}\t\n\nFutureIndex<-function(dataset,month) {\n\t# Given an ALSFRS dataset, finds indices corresponding to the desired visits\n\t# to calculate future slopes\n\t#\n\t# Args:\n\t#\tdataset: a dataset generated by GenAlsfrsData, on which CleanDelta and \n\t#\tDeltaRef have been run \n\t#\tmonth: time in months for start or end measure\n\n\t# Returns:\n\t#\ta matrix with a column of subject ids and a column of indices corresponding \n\t# \tto the desired visit \n\n\t# Find last visit for each subject id\n\t# Create a vector of row indices corresponding to final visit of each \n\t# subject.id\n\tind<-c(1:length(dataset$delta.ref))\n\tind.final<-c(ind[which(diff(dataset$delta.ref==0)==1)],length(dataset$delta.ref))\n\t# Combine with subject id vector to create matrix with both\n\tind.final<-cbind(dataset$subject.id[ind.final],ind.final)\n\tcolnames(ind.final)<-c(\"subject.id\",\"ind.final\")\n\t\t\t\n\t# Find first visit after 'month' months\n\t# Find indices corresponding to first row of new subject and all visits > 'month'\n\t# months after the reference visit\n\tind<-which(dataset$delta.ref>month)\n\tunique<-!duplicated(dataset$subject.id[ind])\n\tind.month<-ind[unique]\n\tind.month<-cbind(dataset$subject.id[ind.month],ind.month)\n\tcolnames(ind.month)<-c(\"subject.id\",\"ind.month\")\n\t\n\t# Combine last visit and 'month' visit to get visit indices\n\tind.visit<-merge(ind.final,ind.month,all=TRUE,by=\"subject.id\")\n\t# Replace 'month' visit with last visit when value is NA\n\tind.NA<-which(is.na(ind.visit$ind.month))\n\tind.visit$ind.month[ind.NA]<-ind.visit$ind.final[ind.NA]\n\treturn(ind.visit[,colnames(ind.visit)!=\"ind.final\"])\n}\n\n", "meta": {"hexsha": "750e9d676ba212fa28ced8e8cac52009c9151ac2", "size": 8889, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/R/slopes.r", "max_stars_repo_name": "ltfang/alsprize4life", "max_stars_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/R/slopes.r", "max_issues_repo_name": "ltfang/alsprize4life", "max_issues_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/R/slopes.r", "max_forks_repo_name": "ltfang/alsprize4life", "max_forks_repo_head_hexsha": "35592bffc1332778b723b330e86e6fbde8b60118", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5063291139, "max_line_length": 82, "alphanum_fraction": 0.7253909326, "num_tokens": 2403, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5660185205547238, "lm_q2_score": 0.5312093733737562, "lm_q1q2_score": 0.30067434362181533}} {"text": "\n cleanup()\nPortrait<-T # graphical output orientation\ninclude.terminal.year <- F # plot terminal year (last assessment year +1) as well?\ninclude.last.assess.year.recruit <- F # plot recruits terminal year as well?\n \nfirst.year<- 1974 #first year on plot, negative value means value defined by data\nlast.year<- 2200 #last year on plot\nincl.M2.plot<-T\nincl.reference.points<-T\nsplitLine<-F\nOperatingModel<-F; redefine.scenario.manually<-FALSE\n\n\noutput.dir<-data.path\nop.dir<-data.path\nif (OperatingModel==T & redefine.scenario.manually==T) {\n scenario<-\"test\"; \n output.dir<-data.path \n op.dir<-file.path(data.path,\"HCR_1_deter_adjust_test_01_HCR1_0_Rec0__2030\")\n #op.dir<-data.path\n} else if (OperatingModel==T & redefine.scenario.manually==FALSE) {\n output.dir<-scenario.dir \n op.dir<-scenario.dir\n} \n\nif (makeAllGraphs) output.dir<-StockSummary.dir\n\n##########################################################################\n\n\nmy.dev<-'screen' # output device: 'screen', 'wmf', 'png', 'pdf'\nmy.dev<-'png'\n \npalette(\"default\")\nif (makeAllGraphs) my.dev<-'png' \n#cleanup()\nfile.name<-'plot_summary'\n\n\n#dev<-\"dummy\"\nif (incl.M2.plot) { nox<-3; noy<-2;} else { nox<-2; noy<-2;}\nnoxy<-nox*noy\n\nref<-Read.reference.points()\n\nInit.function()\n\nif (!OperatingModel) dat<-Read.summary.data(extend=include.terminal.year,read.init.function=F)\nif (OperatingModel) {\n dat1<-Read.summary.data(extend=F,read.init.function=F)\n\n dat<-Read.summary.data(dir=op.dir,infile=\"op_summary.out\",read.init.function=F)\n if (SMS.control@no.areas >1) {\n dat$N.bar<-dat$N*(1-exp(-dat$Z))/dat$Z\n dat$DM<-dat$M*dat$N.bar\n dat$DM1<-dat$M2*dat$N.bar\n dat$DM2<-dat$M2*dat$N.bar\n dat$DF<-dat$F*dat$N.bar\n dat$DZ<-dat$Z*dat$N.bar\n dat$Nwest<-dat$N*dat$west\n dat$Cweca<-dat$C*dat$weca\n\n dat<-aggregate(cbind(DM,DM1,DM2,DF,DZ,N,C,Nwest,Cweca,Yield,CWsum,BIO,SSB)~Species+Year+Quarter+Species.n+Age,sum,na.rm=T,data=dat)\n dat$Z<- -log((dat$N-dat$DZ)/dat$N)\n dat$M<-dat$DM/dat$DZ*dat$Z\n dat$M1<-dat$DM1/dat$DZ*dat$Z\n dat$M2<-dat$DM2/dat$DZ*dat$Z\n dat$F<-dat$DF/dat$DZ*dat$Z\n\n dat$weca<-dat$Cweca/dat$C\n dat[is.na(dat$weca),'weca']<-0\n dat$west<-dat$Nwest/dat$N\n }\n\n dat$N.bar<-dat$N*(1-exp(-dat$Z))/dat$Z\n dat$C<-NULL\n dat$N_dist<-NULL\n dat$Area<-NULL\n dat1<-subset(dat1,select=c(Species,Year,Quarter,Species.n,Age,M1,M2,M,F,Z,N,N.bar,west,weca,Yield,CWsum,BIO,SSB))\n dat <-subset(dat, select=c(Species,Year,Quarter,Species.n,Age,M1,M2,M,F,Z,N,N.bar,west,weca,Yield,CWsum,BIO,SSB))\n\n dat<-rbind(dat1,dat)\n}\n#tapply(dat$Yield,list(dat$Year,dat$Species),sum)\n\n\ndat<-subset(dat,Year<=last.year )\n#(subset(dat,Year==2011 & Species=='Cod'))\nif (first.year>0) dat<-subset(dat,Year>=first.year )\nif (incl.M2.plot) {\n dat<-data.frame(dat,deadM1=dat$M1*dat$N.bar*dat$west,deadM2=dat$M2*dat$N.bar*dat$west,deadM=dat$M*dat$N.bar*dat$west)\n }\n\nif (F) {\n a<-subset(dat,Species=='Sprat' & Age %in% c(1,2) & Year>2015)\n subset(a,Year==2016,select=c(Quarter,Age,F,M2,Z))\n aggregate(F/2~Year,sum,data=a) # mean F (sum of F)\n} \n\n \nplotfile<-function(dev='screen',out) {\n if (dev=='screen') X11(width=11, height=8, pointsize=12)\n if (dev=='wmf') win.metafile(filename = file.path(output.dir,paste(out,'.wmf',sep='')), width=8, height=10, pointsize=12)\n if (dev=='png') png(filename =file.path(output.dir,paste(out,'.png',sep='')), width = 1400, height = 1000,units = \"px\", pointsize = 25, bg = \"white\")\n if (dev=='pdf') pdf(file =file.path(output.dir,paste(out,'.pdf',sep='')), width = 8, height = 10,pointsize = 12,onefile=FALSE)\n}\n\n\nfor (sp in (first.VPA:nsp)){\n sp.name<-sp.names[sp]\n discard<-SMS.control@discard[sp-first.VPA+1]==1\n\n plotfile(dev=my.dev,out=paste(file.name,'_',sp.name,sep=''));\n par(mfcol=c(nox,noy))\n\n \n par(mar=c(3,4,3,2))\n\n s<-subset(dat,Species.n==sp)\n av.F.age<-SMS.control@avg.F.ages[sp-first.VPA+1,]\n s1<-subset(s,s$Age>=av.F.age[1] & s$Age<=av.F.age[2])\n FI<-tapply(s1$F,list(s1$Year),sum)/(av.F.age[2]-av.F.age[1]+1)\n \n if (F) { \n print(sp.name)\n print(max(FI[as.character(2050:2070)]))\n }\n\n s1<-subset(s,weca>=0 )\n\n Yield<-tapply(s1$Yield,list(s1$Year),sum)/1000\n SOP<-tapply(s1$CWsum,list(s1$Year),sum)/1000\n if (discard) catch<-rbind(SOP-Yield,Yield) else catch<-Yield\n s1<-subset(s,Quarter==1)\n ssb<-tapply(s1$SSB,list(s1$Year),sum)/1000\n s2<-subset(s,Age==fa & Quarter==SMS.control@rec.season)\n rec<-tapply(s2$N,list(s2$Year),sum)/1000000\n year<-as.numeric(unlist(dimnames(ssb)))\n year.ssb<-year\n \n if(include.terminal.year){\n #Truncate the final year from key parameters\n year<- year[-length(year)] \n FI <- FI[-length(FI)]\n } \n if(!include.last.assess.year.recruit) rec <- rec[-length(rec)]\n \n barplot(catch,space=1,xlab='',ylab='1000 tonnes',main=paste(sp.name,ifelse(discard,', Yield and discard',', Catch'),sep=''),ylim=c(0,max(SOP)))\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n \n #plot recruits\n #plot(year,rec,type='h',lwd=5,xlab='',ylab='billions',main=paste('Recruitment age',fa),ylim=c(0,max(rec)))\n barplot(rec,space=1,xlab='',ylab='billions',main=paste('Recruitment age',fa),ylim=c(0,max(rec)))\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n F.max<-max(FI,ref[sp,\"Flim\"])\n\n #cat(sp.name,round(min(FI),2),round(quantile(FI,0.1),2),round(max(FI),2),'\\n')\n #print(year)\n #print(FI)\n plot(year,FI,type='b',lwd=3,xlab='',ylab='',main=\"Fishing mortality\",ylim=c(0,F.max))\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n # tmp<-paste('(',av.F.age[1],'-',av.F.age[2],')',sep='')\n # plot(year,FI,type='l',lwd=3,xlab='',ylab='',main=expression(bar(F)),ylim=c(0,F.max))\n\n if (incl.reference.points) if (ref[sp,\"Flim\"]>0) abline(h=ref[sp,\"Flim\"],lty=2,lwd=2)\n if (incl.reference.points) if (ref[sp,\"Fpa\"]>0) abline(h=ref[sp,\"Fpa\"],lty=3,lwd=2)\n grid()\n \n Blim<-ref[sp,\"Blim\"]/1000; Bpa<-ref[sp,\"Bpa\"]/1000\n SSB.max<-max(ssb,Bpa)\n plot(year.ssb,ssb,type='b',lwd=3,xlab='',ylab='1000 tonnes',main='SSB',ylim=c(0,SSB.max))\n if (incl.reference.points) if (Blim>0) abline(h=Blim,lty=2,lwd=2)\n if (incl.reference.points) if (Bpa>0) abline(h=Bpa,lty=3,lwd=2)\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n \n grid()\n if (incl.M2.plot) {\n deadM1<-tapply(s$deadM1,list(s$Year),sum)/1000\n deadM2<-tapply(s$deadM2,list(s$Year),sum)/1000\n deadM1<-deadM1[1:length(SOP)]\n deadM2<-deadM2[1:length(SOP)]\n barplot(rbind(SOP,deadM1,deadM2),space=1,main='Biomass removed\\ndue to F, M1 and M2',ylab='1000 tonnes')\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n b<-tapply(s$M2,list(s$Year,s$Age),sum)\n b<-b[1:length(SOP),]\n if (sum(b,na.rm=T)>=0.01) {\n y<-as.numeric(dimnames(b)[[1]])\n plot(y,b[,1],main=paste(\"M2 at age\"),xlab=\"\",ylab='M2',\n type='l',lwd=1.5,ylim=c(0,max(b,na.rm=T)))\n for (a in (2:(dim(b)[2]))) if(max(b[,a],na.rm=T)>0.001) lines(y,b[,a],lty=a,col=a,lwd=2)\n if (splitLine) abline(v=SMS.control@last.year.model,lty=2, col='red')\n }\n }\n if (my.dev %in% c('png','wmf','pdf')) cleanup()\n}\n", "meta": {"hexsha": "b5dae8445bf33f32fb1b8ae7153d89e36d15c30f", "size": 7352, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/plot_summary_ices_multi.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-09-28T11:13:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T08:40:03.000Z", "max_issues_repo_path": "SMS_R_prog/plot_summary_ices_multi.r", "max_issues_repo_name": "ices-eg/wg_WGSAM", "max_issues_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "SMS_R_prog/plot_summary_ices_multi.r", "max_forks_repo_name": "ices-eg/wg_WGSAM", "max_forks_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.3197969543, "max_line_length": 151, "alphanum_fraction": 0.6243199129, "num_tokens": 2668, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.4339814648038986, "lm_q1q2_score": 0.30059378242549}} {"text": "commandArgs()\n\nfor (e in commandArgs()) {\n ta = strsplit(e,\"=\",fixed=TRUE)\n if(! is.na(ta[[1]][2])) {\n temp = ta[[1]][2]\n if(substr(ta[[1]][1],nchar(ta[[1]][1]),nchar(ta[[1]][1])) == \"I\") {\n temp = as.integer(temp)\n }\n if(substr(ta[[1]][1],nchar(ta[[1]][1]),nchar(ta[[1]][1])) == \"N\") {\n temp = as.numeric(temp)\n }\n assign(ta[[1]][1],temp)\n cat(\"assigned \",ta[[1]][1],\" the value of |\",temp,\"|\\n\")\n } else {\n assign(ta[[1]][1],TRUE)\n cat(\"assigned \",ta[[1]][1],\" the value of TRUE\\n\")\n }\n}\n\n\nN <- as.matrix(read.table(\"nSpets.txt\"))\nextensionLength <- as.numeric(extensionLengthStr)\ngenomeLength <- as.numeric(genomeLengthStr)\ngenomeCoverageRatio <- as.numeric(genomeCoverageRatioStr)\n\ndata <- as.matrix(read.table(\"data.txt\"))\nnRows <- length(data[,1])\nlambdaGlobal <- array(N*2*extensionLength / (genomeLength*genomeCoverageRatio), c(nRows,1))\nlambda10K <- (data[,2] - data[,1])*2*extensionLength/10000;\nlambda20K <- (data[,3] - data[,1])*2*extensionLength/20000;\nlambdaTemp <- array(0, c(nRows,3))\nlambdaTemp[,1] <- lambdaGlobal\nlambdaTemp[,2] <- lambda10K\nlambdaTemp[,3] <- lambda20K\nlambda <- apply(lambdaTemp, 1, max)\nx <- ppois(data[,1]-1, lambda, lower.tail = FALSE)\nfdr <- p.adjust(x, \"BH\")\nx <- sapply(x, function(a){if(a<0.005){b = as.numeric(format(a, scientific = T, digits = 3))}else{b = round(a, 2)}})\nfdr<- sapply(fdr, function(a){if(a<0.005){b = as.numeric(format(a, scientific = T, digits = 3))}else{b = round(a, 2)}})\n#x <- as.numeric(format(x, scientific = F))\n#fdr <- as.numeric(format(x, scientific = F))\na <- matrix(1, nRows, 2)\na[,1] = x\na[,2] = fdr\nwrite.table(a, file=\"result.txt\", sep = \"\\t\", row.names = FALSE, col.names = FALSE)\n", "meta": {"hexsha": "ed3f1ee7a2bc5eb3dc276d16cd9fefe265af3b87", "size": 1699, "ext": "r", "lang": "R", "max_stars_repo_path": "program/pois.r", "max_stars_repo_name": "GuoliangLi-HZAU/ChIA-PET_Tool", "max_stars_repo_head_hexsha": "aa9bbec36d6852ceccb860a7ad6e9edfbab329c5", "max_stars_repo_licenses": ["FSFAP"], "max_stars_count": 6, "max_stars_repo_stars_event_min_datetime": "2016-05-24T14:20:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-02T14:21:29.000Z", "max_issues_repo_path": "program/pois.r", "max_issues_repo_name": "GuoliangLi-HZAU/ChIA-PET_Tool", "max_issues_repo_head_hexsha": "aa9bbec36d6852ceccb860a7ad6e9edfbab329c5", "max_issues_repo_licenses": ["FSFAP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "program/pois.r", "max_forks_repo_name": "GuoliangLi-HZAU/ChIA-PET_Tool", "max_forks_repo_head_hexsha": "aa9bbec36d6852ceccb860a7ad6e9edfbab329c5", "max_forks_repo_licenses": ["FSFAP"], "max_forks_count": 5, "max_forks_repo_forks_event_min_datetime": "2016-05-24T14:20:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-26T06:33:30.000Z", "avg_line_length": 36.1489361702, "max_line_length": 119, "alphanum_fraction": 0.6127133608, "num_tokens": 582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30054634271081837}} {"text": "\n# ==================================================================================================\n# This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This\n# project uses a tab-size of two spaces and a max-width of 100 characters per line.\n#\n# Author(s):\n# Cedric Nugteren \n#\n# This file implements the performance script for the Xgemv routine\n#\n# ==================================================================================================\n\n# Includes the common functions\nargs <- commandArgs(trailingOnly = FALSE)\nthisfile <- (normalizePath(sub(\"--file=\", \"\", args[grep(\"--file=\", args)])))\nsource(file.path(dirname(thisfile), \"common.r\"))\n\n# ==================================================================================================\n\n# Settings\nroutine_name <- \"xgemv\"\nparameters <- c(\"-n\",\"-m\",\"-incx\",\"-incy\",\"-layout\",\n \"-num_steps\",\"-step\",\"-runs\",\"-precision\")\nprecision <- 32\n\n# Sets the names of the test-cases\ntest_names <- list(\n \"multiples of 256\",\n \"multiples of 256 (+1)\",\n \"around n=m=2K\",\n \"multiples of 256 [rotated]\",\n \"multiples of 256 (+1) [rotated]\",\n \"strides (n=2K)\"\n)\n\n# Defines the test-cases\ntest_values <- list(\n list(c(256, 256, 1, 1, 102, 16, 256, num_runs, precision)),\n list(c(256+1, 256+1, 1, 1, 102, 16, 256, num_runs, precision)),\n list(c(2*kilo, 2*kilo, 1, 1, 102, 16, 1, num_runs, precision)),\n list(c(256, 256, 1, 1, 101, 16, 256, num_runs, precision)),\n list(c(256+1, 256+1, 1, 1, 101, 16, 256, num_runs, precision)),\n list(\n c(2*kilo, 2*kilo, 1, 1, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 2, 1, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 4, 1, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 8, 1, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 1, 2, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 1, 4, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 1, 8, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 2, 2, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 4, 4, 102, 1, 0, num_runs, precision),\n c(2*kilo, 2*kilo, 8, 8, 102, 1, 0, num_runs, precision)\n )\n)\n\n# Defines the x-labels corresponding to the test-cases\ntest_xlabels <- list(\n \"vector sizes (n)\",\n \"vector sizes (n)\",\n \"vector sizes (n)\",\n \"vector sizes (n)\",\n \"vector sizes (n)\",\n \"increments/strides for x and y\"\n)\n\n# Defines the x-axis of the test-cases\ntest_xaxis <- list(\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n c(\"n\", \"\"),\n list(1:10, c(\"x1y1\", \"x2y1\", \"x4y1\", \"x8y1\", \"x1y2\", \"x1y4\", \"x1y8\", \"x2y2\", \"x4y4\", \"x8y8\"))\n)\n\n# ==================================================================================================\n\n# Start the script\nmain(routine_name=routine_name, precision=precision, test_names=test_names, test_values=test_values,\n test_xlabels=test_xlabels, test_xaxis=test_xaxis, metric_gflops=FALSE)\n\n# ==================================================================================================", "meta": {"hexsha": "9a8040f79fff6f1509ee6b4ddc73bb23b85b295b", "size": 3043, "ext": "r", "lang": "R", "max_stars_repo_path": "third_party/coriander/src/CLBlast/scripts/graphs/xgemv.r", "max_stars_repo_name": "pint1022/tensorflow", "max_stars_repo_head_hexsha": "92e437b36ae58ea22910dbed67e2c238c869c09f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2019-04-15T23:07:00.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-11T16:09:02.000Z", "max_issues_repo_path": "scripts/graphs/xgemv.r", "max_issues_repo_name": "gcp/CLBlast", "max_issues_repo_head_hexsha": "7c13bacf129291e3e295ecb6e833788477085fa0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/graphs/xgemv.r", "max_forks_repo_name": "gcp/CLBlast", "max_forks_repo_head_hexsha": "7c13bacf129291e3e295ecb6e833788477085fa0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2020-03-04T13:59:48.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-09T13:11:45.000Z", "avg_line_length": 36.6626506024, "max_line_length": 100, "alphanum_fraction": 0.5225106802, "num_tokens": 1006, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.5039061705290805, "lm_q1q2_score": 0.30054634271081837}} {"text": "##FbF-Gr\u00e1ficos=group\n##showplots \n##Layer=vector\n##Tipodegrafico=selection barras;polar\n##Eje_x=Field Layer\n##Eje_y=Field Layer\n##TipodeColor=selection viridis;infierno;plasma;magma\n##Titulo=string\n##Subtitulo=string\n##Fuente=string\n\nlibrary(tidyverse)\nlibrary(ggplot2)\n\ntitulo = Titulo\nsubtitulo= Subtitulo\nfuente = Fuente\n\ndf = Layer %>% st_set_geometry(NULL)\ntopdf = df %>% \n arrange(df[[Eje_y]]) %>%\n head(10) \n\n\nif (Tipodegrafico==0){\n plot_top = topdf %>% \n ggplot(\n aes(\n x = reorder(topdf[[Eje_x]],topdf[[Eje_y]]),\n y = topdf[[Eje_y]])) + \n geom_bar(stat = \"identity\",aes(fill =topdf[[Eje_y]])) +\n coord_flip() + \n labs(\n title = titulo,\n subtitle = subtitulo,\n caption = fuente,\n x = '',\n y = '') \n \n}else{\n plot_top = topdf %>% \n ggplot(\n aes(\n x = reorder(topdf[[Eje_x]],topdf[[Eje_y]]),\n y = topdf[[Eje_y]])) + \n geom_bar(stat = \"identity\",aes(fill =topdf[[Eje_y]]))+\n coord_polar() +\n labs(\n title = titulo,\n subtitle = subtitulo,\n caption = fuente,\n x = '',\n y = '')\n \n}\n \nif(TipodeColor==0){\n final = plot_top + \n scale_fill_viridis_c(option = \"viridis\") %>% \n suppressMessages()\n}else if(TipodeColor==1){\n final = plot_top + \n scale_fill_viridis_c(option = \"inferno\") %>% \n suppressMessages()\n}else if(TipodeColor==2){\n final = plot_top + \n scale_fill_viridis_c(option = \"plasma\") %>% \n suppressMessages()\n}else{\n final = plot_top + \n scale_fill_viridis_c(option = \"magma\") %>% \n suppressMessages()\n}\n\n \nPLOT= final +\n theme(\n legend.position = \"none\",\n axis.text.x = element_text(size=12),\n axis.text.y = element_text(size=12),\n axis.title.x = element_text(size=12),\n axis.title.y = element_text(size=12)\n )\nPLOT\n \nPLOT= final +\n theme(\n legend.position=\"none\")\nPLOT\n", "meta": {"hexsha": "959e7301bfe3b63bbe9292ba809696367aa093ba", "size": 1855, "ext": "rsx", "lang": "R", "max_stars_repo_path": "scripts/intervencion.rsx", "max_stars_repo_name": "klauswiese/GRC_FbF_Rtoolbox", "max_stars_repo_head_hexsha": "f591ddf16302c5c304f190a317ae80c144441bb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-09-09T03:19:54.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-09T15:00:59.000Z", "max_issues_repo_path": "scripts/intervencion.rsx", "max_issues_repo_name": "ambarja/GRC_FbF_Rtoolbox", "max_issues_repo_head_hexsha": "c086d80400e36ff17f2db8727fb07e3789a9a213", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/intervencion.rsx", "max_forks_repo_name": "ambarja/GRC_FbF_Rtoolbox", "max_forks_repo_head_hexsha": "c086d80400e36ff17f2db8727fb07e3789a9a213", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-09-09T04:12:57.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-09T15:01:24.000Z", "avg_line_length": 20.6111111111, "max_line_length": 59, "alphanum_fraction": 0.6043126685, "num_tokens": 582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5964331319177487, "lm_q2_score": 0.5039061705290806, "lm_q1q2_score": 0.3005463354813387}} {"text": "directories = list.files('./test-cases')\n\nfor (benchmark in directories) {\n\tdirectory = paste('./test-cases', benchmark, sep='/')\n\tfiles = list.files(directory,recursive=1,pattern='Reasoner-Results-')\n\n\tif (!identical(benchmark, \"scalability\"))\n\t\tnext\n\n\tcomparison = paste(directory, 'scalability-processing.pdf',sep='/')\n\tpdf(comparison)\n\n\ttotal = length(files)\n\tcolours = rainbow(total)\n\tnames = c(1:total)\n\n\n\tcount = 1\n\tfor (f in files)\n\t{\n\t\tfile = paste(directory, f, sep='/')\n\t\tdata = read.csv(file, header=T)\n\t\tdata = data[order(data[1]),]\n\t\n\t\tname = unlist( strsplit(f, \"Results-\"))[2]\n\t\tname = substr(name, 1, nchar(name) - 4)\n\t\tnames[count] = name\n\t\tprint(name)\n\n\t\tif (count == 1)\n\t\t\tplot(1, type='n', xlim=c(1,length(data[,1])), ylim=c(0,180000),\n\t\t\t\t xlab='(x * 50 + 1) ^ 2 logical axioms', ylab='Correctly processed time')\n\n\t\t#print(file)\n\t\t#print(data$Correctly.Processed.Time)\n\t\t\n\t\tdata$Correctly.Processed.Time[data$Correctly.Processed.Time == 0] = NA\n\t\tlines(data$Correctly.Processed.Time, type='o', col=colours[count], lwd=2)\n\t\t#data$Execution.Time[data$Execution.Time > 180000] = 180000\n\t\t#lines(data$Execution.Time, type='o', col=colours[count], lwd=2)\n\t\tcount = count + 1\n\t}\n\tprint(names)\n\n \tlegend(\"topleft\", legend = names, col=colours, pch=1) # optional legend\n\n\tdev.off()\n}", "meta": {"hexsha": "f8ae7a9e4bb3adf820df60d69d2dee053fea1b91", "size": 1297, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/scalability_processing.r", "max_stars_repo_name": "adrianomelo/reasoner-test-suite", "max_stars_repo_head_hexsha": "f8fe704dd4cd757316b7160df4acf54ac8b8a631", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/scalability_processing.r", "max_issues_repo_name": "adrianomelo/reasoner-test-suite", "max_issues_repo_head_hexsha": "f8fe704dd4cd757316b7160df4acf54ac8b8a631", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/scalability_processing.r", "max_forks_repo_name": "adrianomelo/reasoner-test-suite", "max_forks_repo_head_hexsha": "f8fe704dd4cd757316b7160df4acf54ac8b8a631", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0208333333, "max_line_length": 77, "alphanum_fraction": 0.6630686199, "num_tokens": 406, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5698526514141572, "lm_q1q2_score": 0.30049271898234176}} {"text": "help(\"distributions\")", "meta": {"hexsha": "1257df09ba3f014b84e8a80804c0960574b3a453", "size": 21, "ext": "r", "lang": "R", "max_stars_repo_path": "src/remember.r", "max_stars_repo_name": "arekbee/RStarter", "max_stars_repo_head_hexsha": "00ad2aa0d29bbb6ede801b22278bb51d4c7439d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/remember.r", "max_issues_repo_name": "arekbee/RStarter", "max_issues_repo_head_hexsha": "00ad2aa0d29bbb6ede801b22278bb51d4c7439d1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/remember.r", "max_forks_repo_name": "arekbee/RStarter", "max_forks_repo_head_hexsha": "00ad2aa0d29bbb6ede801b22278bb51d4c7439d1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 21.0, "max_line_length": 21, "alphanum_fraction": 0.8095238095, "num_tokens": 5, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3004927189823417}} {"text": "library(gganimate)\r\nlibrary(dplyr)\r\nlibrary(tidyverse)\r\nlibrary(reshape2)\r\nlibrary(ggthemes)\r\nlibrary(gifski)\r\nlibrary(av)\r\n\r\nconfirmedCases= read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\r\ndeathCases= read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\r\nrecoveredCases= read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\r\n\r\nset system locale for date conversion purpose\r\nSys.setlocale(\"LC_TIME\", \"English\")\r\n\r\n\r\n#convert data sets into columns and remove unwanted columns\r\n\r\nconfirmedCases<-confirmedCases%>%select(-c(Lat,Long))%>%melt(id=c('Country/Region','Province/State'))\r\nconfirmedCases<-confirmedCases%>%group_by(`Country/Region`,variable)%>%summarise(Confirmed=sum(value))\r\n\r\ndeathCases<-deathCases%>%select(-c(Lat,Long))%>%melt(id=c('Country/Region','Province/State'))\r\ndeathCases<-deathCases%>%group_by(`Country/Region`,variable)%>%summarise(Deaths=sum(value))\r\n\r\nrecoveredCases<-recoveredCases%>%select(-c(Lat,Long))%>%melt(id=c('Country/Region','Province/State'))\r\nrecoveredCases<-recoveredCases%>%group_by(`Country/Region`,variable)%>%summarise(Recovered=sum(value))\r\n\r\n\r\n# rename table columns\r\ncolnames(confirmedCases)<-c(\"Country\",\"Date\",\"Confirmed\")\r\ncolnames(deathCases)<-c(\"Country\",\"Date\",\"Death\")\r\ncolnames(recoveredCases)<-c(\"Country\",\"Date\",\"Recovered\")\r\n\r\n# merge all atbles together\r\n\r\nmergedCases<-merge(confirmedCases,deathCases, by.y=c(\"Country\",\"Date\"))\r\nmergedCases<-merge(mergedCases,recoveredCases, by.y=c(\"Country\",\"Date\"))\r\n\r\n# convert factors to date format\r\n\r\nmergedCases$Date<-as.Date(mergedCases$Date,\"%m/%d/%y\")\r\n\r\n# summarize cases by date\r\ndf1<-mergedCases %>% group_by(Date) %>% summarise_at(c(\"Confirmed\",\"Recovered\",\"Death\"),sum)\r\n\r\n# stack columns together and add state columns to each case\r\ndf2 <- data.frame(Date=rep(df1$Date, 3), \r\n act_noact=c(df1$Confirmed, df1$Death,df1$Recovered), \r\n State=rep(c(\"Confirmed\",\"Deaths\", \"Recovered\"), each=nrow(df1)))\r\n\r\n# retrieve last update date for title\r\nlastDate<-max(df1$Date)\r\n\r\n# define plot object\r\np <- ggplot(df2, aes(x=Date, y=act_noact, group=State, color=State)) +\r\n geom_line() +\r\n geom_segment(aes(xend=max(Date), yend = act_noact), linetype=2, colour='blue') +\r\n geom_point(size = 3) + \r\n geom_text(aes(x = max(Date)+.1, label = sprintf(\"%5.0f\", act_noact)), hjust=-0.5) +\r\n transition_reveal(Date) + \r\n view_follow(fixed_y = TRUE)+\r\n coord_cartesian(clip = 'off') + \r\n xlab(\"Day\") +\r\n ylab(\"Number of cases\") + ggtitle(paste(\" @Ahmad_Almekdad \r\n Evolution of corona_cases over time as of \",lastDate)) +\r\n enter_drift(x_mod = -1) + exit_drift(x_mod = 1) +\r\n theme_classic() +\r\n theme(legend.position = c(0.2, 0.8))+\r\n theme(panel.border = element_blank(),\r\n panel.grid.major = element_blank(),\r\n panel.grid.minor = element_blank(),\r\n axis.line = element_line(colour = \"black\"),\r\n plot.margin = margin(5.5, 40, 5.5, 5.5))\r\n\r\n# create animation gif file\r\nanimate(p, fps=5,renderer = gifski_renderer(\"coronaplot04.gif\"))\r\n", "meta": {"hexsha": "38b9ff9545f04c90e91ffa659ba7c7aa1f695c55", "size": 3391, "ext": "r", "lang": "R", "max_stars_repo_path": "Viz_corona.r", "max_stars_repo_name": "Ahmed0028/Plotcorona", "max_stars_repo_head_hexsha": "b37d47e8e91fc1ea9e8daa6733e1ee04365cfac3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Viz_corona.r", "max_issues_repo_name": "Ahmed0028/Plotcorona", "max_issues_repo_head_hexsha": "b37d47e8e91fc1ea9e8daa6733e1ee04365cfac3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Viz_corona.r", "max_forks_repo_name": "Ahmed0028/Plotcorona", "max_forks_repo_head_hexsha": "b37d47e8e91fc1ea9e8daa6733e1ee04365cfac3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.038961039, "max_line_length": 179, "alphanum_fraction": 0.708640519, "num_tokens": 930, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3004927189823417}} {"text": "#' @useDynLib curand R_getseed\ngetseed = function()\n{\n date = as.integer(Sys.Date())\n time = as.numeric(Sys.time())*100000\n pid = as.integer(Sys.getpid())\n \n .Call(R_getseed, date, time, pid)\n}\n", "meta": {"hexsha": "948ccc403fdd9bc70f427bd6d0c3f8f11a356855", "size": 199, "ext": "r", "lang": "R", "max_stars_repo_path": "R/getseed.r", "max_stars_repo_name": "wrathematics/curand", "max_stars_repo_head_hexsha": "16cc314d89673a138a6fd840b0b3f4ecaa96aa69", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-10-16T14:53:59.000Z", "max_stars_repo_stars_event_max_datetime": "2019-02-13T07:06:16.000Z", "max_issues_repo_path": "R/getseed.r", "max_issues_repo_name": "wrathematics/curand", "max_issues_repo_head_hexsha": "16cc314d89673a138a6fd840b0b3f4ecaa96aa69", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/getseed.r", "max_forks_repo_name": "wrathematics/curand", "max_forks_repo_head_hexsha": "16cc314d89673a138a6fd840b0b3f4ecaa96aa69", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-12-14T03:17:23.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-14T03:17:23.000Z", "avg_line_length": 19.9, "max_line_length": 38, "alphanum_fraction": 0.6532663317, "num_tokens": 65, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.5698526514141571, "lm_q1q2_score": 0.3004927189823417}} {"text": "#---\n#title: \"Analyzing Age structures in IC\"\n#author: \"Youen Vermard\"\n#date: '`r date()`'\n#output: pdf_document\n#---\n\n#### ONLY PIECE TO BE CHANGED working directories\nwd <- \"~/git/ices-dk/interCatchSummary/\"\noutput_wd <- \"~/git/ices-dk/interCatchSummary/\"\n\n#This documet uses Table 2 from CatchAndSampleDataTables.txt from the InterCatch outputs to describe the raising procedures that were made.\n\n\n\n### libraries\nlibrary(data.table)\nlibrary(gplots)\nlibrary(pander);library(captioner)\nlibrary(lattice)\n#initialise les fonctions pour l\u00e9gender tables et figures\n\n### read the data\ntest <- scan(paste(wd,\"CatchAndSampleDataTables.txt\",sep=\"\"),what='character',sep='\\t')\ntable2 <- test[(which(test==\"TABLE 2.\")+3):length(test)]\ntmp<-table2[-c(1:56)]\t\t\t \n\t\t\t \ntable2_bis<-data.frame(matrix(tmp,ncol=27,byrow=T), stringsAsFactors =F)\ncolnames(table2_bis) <- table2[1:27]\ntable2_bis <- data.table(table2_bis)\ntable2_bis <- table2_bis[,CATON:=as.numeric(as.character(CATON))]\ntable2_bis <- table2_bis[,CANUM:=as.numeric(as.character(CANUM))]\ntable2_bis <- table2_bis[,WECA:=as.numeric(as.character(WECA))]\ntable2_bis <- table2_bis[,AgeOrLength:=as.numeric(as.character(AgeOrLength))]\n\ntable2_bis <- table2_bis[,Area:=as.factor(Area)]\ntable2_bis <- table2_bis[,Fleet:=factor(Fleet)]\ntable2_bis <- table2_bis[,Season:=factor(Season)]\ntable2_bis <- table2_bis[,Country:=factor(Country)]\n\n\ntable2_bis <- table2_bis[,id:=paste(Stock,Country,Area,Season,Fleet)]\n\ntable2_bis[Area==\"IIIaN \",'Area'] <- \"IIIaN\"\n\n\ncolnames(table2_bis)[colnames(table2_bis)=='CATONRaisedOrImported'] <- 'RaisedOrImported'\n\n\n## simple stats on imported data\n\t\tSummaryReport <- table2_bis[,list(CATON=sum(CANUM*WECA)/1000000), by=c('CatchCategory','RaisedOrImported')]\n\t\tSummaryReport <- SummaryReport[, perc:=round(CATON/sum(CATON)*100), by='CatchCategory']\n\t\tSummaryReport <- SummaryReport[order(CatchCategory),]\t\t\n\n\t\tSOP <- table2_bis[,list(SOP=sum(CANUM*WECA)/1000, CATON=sum(unique(CATON))), by=c('CatchCategory')]\n\t\tSOP <-SOP[,list(SOP=SOP/CATON), by=c('CatchCategory')]\n\t\t\n\t\t\n\t\tSampledOrEstimated <- table2_bis[,list(CATON=sum(CANUM*WECA/1000000)), by=c('CatchCategory','RaisedOrImported','SampledOrEstimated')]\n\t\tSampledOrEstimated <- SampledOrEstimated[, perc:=round(CATON/sum(CATON)*100), by='CatchCategory']\n\t\tSampledOrEstimated <- SampledOrEstimated[order(CatchCategory, perc, decreasing=T),]\n\n\t\tSampledOrEstimatedArea <- table2_bis[,list(CATON=sum(CANUM*WECA/1000000)), by=c('CatchCategory','RaisedOrImported','SampledOrEstimated','Area')]\n\t\tSampledOrEstimatedArea <- SampledOrEstimatedArea[, perc:=round(CATON/sum(CATON)*100), by=c('CatchCategory','Area')]\n\t\tSampledOrEstimatedArea <- SampledOrEstimatedArea[order(Area, CatchCategory,perc, decreasing=T),]\n\n\n\t\t### landings with associated discards\n\t\t\n\tlandingsWithAssociatedDiscards <- table2_bis[,list(CATON=sum(CANUM*WECA/1000000)), by=c('id','RaisedOrImported','CatchCategory')]\n\ttmp <- landingsWithAssociatedDiscards[(RaisedOrImported==\"Imported_Data\" & CatchCategory==\"Discards\") , id]\n\tlandingsWithAssociatedDiscards <- landingsWithAssociatedDiscards[id%in%tmp & CatchCategory==\"Landings\", sum(CATON)]/table2_bis[CatchCategory==\"Landings\",sum(CANUM*WECA/1000000)]\n\t\n\n\t\t## summary of the catch per gear\n\t\ttable2_bis <- table2_bis[, Gear:=substr(table2_bis$Fleet,1,3)]\n\t\tsummCatchPerGear <- table2_bis[, list(sumCatch=sum(CANUM*WECA/1000000)), by=c('Stock','Gear')]\n\t\tsummCatchPerGear <- summCatchPerGear[, percCatch:=sumCatch/sum(sumCatch)*100, by=c('Stock')]\n\t\tsummCatchPerGear <- summCatchPerGear[ order(percCatch,decreasing=T),]\n\n\n\n### Age Structure\n\nlistVAR <- c(\"CatchCategory\",'RaisedOrImported',\"SampledOrEstimated\",\"Country\",\"Area\",\"Season\",\"Fleet\")\n\n## By Sex\n\nlistVAR <- c(\"CatchCategory\",'RaisedOrImported',\"SampledOrEstimated\",\"Country\",\"Area\",\"Season\",\"Fleet\",\"Sex\")\n\n#table2_bis <- table2_bis[CANUM>0,]\n\nAgeStrucSex <- table2_bis[,meanAge:=weighted.mean(AgeOrLength,CANUM, na.rm=T), by=c(listVAR)]\n\n\nplotFunction <- function(AgeStrucSexLan,Estim,Sampled){\n\tif(unique(AgeStrucSexLan$AgeOrLengthType)==\"Lngt\") AgeTit <- \"Length\" else AgeTit <- \"Age\"\n\t\t\t\tplotTitle <- paste(\"Mean\", AgeTit, \"in the\", catchCat, \"by\", Var, sep=\" \")\n\n\tFormula <-paste('meanAge~',Var, sep=\"\")\n\n\tboxplot(eval(parse(text=Formula)), data=AgeStrucSexLan,\n boxwex = 0.25, at = which(pos%in%unique(AgeStrucSexLan[,which(colnames(Sampled)==Var)])) + 0.3,\n col = \"red\",\n main = plotTitle,\n xlab = \"\",\n ylab = paste(\"Mean \",AgeTit, sep=\"\"),\n xlim = c(0.5, length(unique(AgeStrucSexLan[,which(colnames(Sampled)==Var)]))+.5), ylim = c(min(AgeStrucSexLan$AgeOrLength), max(AgeStrucSexLan$AgeOrLength)), yaxs = \"i\", xaxt='n')\n\tboxplot(eval(parse(text=Formula)), data=Estim, add=TRUE,\n boxwex = 0.25, at = which(pos%in%unique(Estim[,which(colnames(Sampled)==Var)])),\n col = \"orange\")\n\tboxplot(eval(parse(text=Formula)), data=Sampled, add=TRUE,\n boxwex = 0.25, at = which(pos%in%unique(Sampled[,which(colnames(Sampled)==Var)])) - 0.3,\n col = \"yellow\", xaxt='n')\n legend(1, max(AgeStrucSexLan$AgeOrLength), c(\"Imported\", \"Raised\", \"All\"),\n fill = c(\"yellow\", \"orange\", \"red\"))\n\t}\n\t\n\t\nplotFunctionMeanWeight <- function(AgeStrucSexLan=AgeStrucSexLan,Estim=Estim,Sampled=Sampled){\n\tif(unique(AgeStrucSexLan$AgeOrLengthType)==\"Lngt\") AgeTit <- \"Length\" else AgeTit <- \"Age\"\n\t\t\t\tplotTitle <- paste(\"Mean\", AgeTit, \"in the\", catchCat, \"by\", Var, sep=\" \")\n\n\t\nAgeStrucSexLan <- data.frame(AgeStrucSexLan)\nAgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)] <- factor(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\npos <- levels(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\n\nEstim <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Estimated_Distribution'),]\nEstim[,which(colnames(Estim)==Var)] <- factor(Estim[,which(colnames(Estim)==Var)])\n\nSampled <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Sampled_Distribution'),]\nSampled[,which(colnames(Sampled)==Var)] <- factor(Sampled[,which(colnames(Sampled)==Var)])\n\t\n\tFormula <-paste('WECA~',Var, sep=\"\")\n\n\tboxplot(eval(parse(text=Formula)), data=AgeStrucSexLan,\n boxwex = 0.25, at = which(pos%in%unique(AgeStrucSexLan[,which(colnames(Sampled)==Var)])) + 0.3,\n col = \"red\",\n main = plotTitle,\n xlab = \"\",\n ylab = paste(\"Mean \",AgeTit, sep=\"\"),\n xlim = c(0.5, length(unique(AgeStrucSexLan[,which(colnames(Sampled)==Var)]))+.5), ylim = c(min(AgeStrucSexLan$WECA), max(AgeStrucSexLan$WECA)), yaxs = \"i\", xaxt='n')\n\tboxplot(eval(parse(text=Formula)), data=Estim, add=TRUE,\n boxwex = 0.25, at = which(pos%in%unique(Estim[,which(colnames(Sampled)==Var)])),\n col = \"orange\")\n\tboxplot(eval(parse(text=Formula)), data=Sampled, add=TRUE,\n boxwex = 0.25, at = which(pos%in%unique(Sampled[,which(colnames(Sampled)==Var)])) - 0.3,\n col = \"yellow\", xaxt='n')\n legend(1, max(AgeStrucSexLan$WECA), c(\"Imported\", \"Raised\", \"All\"),\n fill = c(\"yellow\", \"orange\", \"red\"))\n}\n\n\n## export SOP table\n#write.csv(SOP, file=paste0(output_wd,\"SOP.csv\"), row.names=F)\n\n\n#Raised and imported datas\n\n##Raised discards\n\n#In InterCatch, the first step consists in raising the discards volumes for strats with landings and no discards associated. These discards are called in the following table 'Raised_Discards'. The data called 'Imported_Data' are landings or discards volumes imported into InterCatch with or without length/age structure.\n\n#The proportion of Landings with Discards associated (same strata) is **`r paste(round(landingsWithAssociatedDiscards*100), 'percent')`**\n\n\nwrite.csv(c(\"Landings with discards associated in percent\",round(landingsWithAssociatedDiscards*100)), file=paste0(output_wd,\"landingsWithAssociatedDiscards.csv\"), row.names=F)\n\n#The volumes (and associated proportion) of landings and discards imported (Imported_Data) or raised (Raised_Discards) are described in the following table.\n\n\nwrite.csv(SummaryReport, file=paste0(output_wd,\"Imported_Raised_discards.csv\"), row.names=F)\n\n\n##Total catch per gear\n\n#The following table gives a summary of the catch (Landings+discards(imported+raised)) per gear (3 first letters of the metier)\n\nwrite.csv(summCatchPerGear, file=paste0(output_wd,\"Catch_Per_Gear.csv\"), row.names=F)\n##Length/Age distribution\n\n#For the imported landings/discards and the raised discards without age distribution, the length or age distribution is then computed using the defined allocation scheme. *Sampled_distribution* means that the data (ladings or discards) were input with age/length distribution. *Estimated_distribution* means that the inputed/raised valoumes were estimated using the allocation scheme.\n\n#In the following tables, CATON=WECA*CANUM/1000000 (in tonnes)\n\nwrite.csv(SampledOrEstimated, file=paste0(output_wd,\"Sampled_Estimated_L_D.csv\"), row.names=F)\n\nwrite.csv(SampledOrEstimatedArea, file=paste0(output_wd,\"Sampled_Estimated_L_D_PerArea.csv\"), row.names=F)\n\n##Impact of the raising on the age/length structure\n\n#Once the samples imported or raised are identified, it is possible to check the impact of the allocation scheme on the mean age/length of the final age/length distribution of the stock.\n#The following figures compare the mean age (computed as the weighted mean of the age per strata(\"CatchCategory\",'RaisedOrImported',\"SampledOrEstimated\",\"Country\",\"Area\",\"Season\",\"Fleet\",\"Sex\")) of the estimated stratas compared to the imported ones and the final distribution. Each individual included in the boxplot corresponds to the weighted mean age of a strata.\n\n###Global mean age\n\nAgeStrucSexLan <- AgeStrucSex\npng(filename=paste0(output_wd,\"MeanAgeCatchCat.png\"))\npar(las=2)\n\n\t\tboxplot(meanAge~CatchCategory, data=AgeStrucSexLan,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$CatchCategory)) + 0.3,\n col = \"red\",\n main = \"Mean Age per Catch Category\",\n xlab = \"\",\n ylab = \"Mean Age\",\n xlim = c(0.5, length(unique(AgeStrucSexLan$CatchCategory))+.5), ylim = c(min(AgeStrucSexLan$AgeOrLength), max(AgeStrucSexLan$AgeOrLength)), yaxs = \"i\")\n\t\tboxplot(meanAge~CatchCategory, data=AgeStrucSexLan[SampledOrEstimated=='Estimated_Distribution',], add=TRUE,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$CatchCategory)),\n col = \"orange\")\n\t\tboxplot(meanAge~CatchCategory, data=AgeStrucSexLan[SampledOrEstimated=='Sampled_Distribution',], add=TRUE,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$CatchCategory)) - 0.3,\n col = \"yellow\")\n legend(1, max(AgeStrucSexLan$AgeOrLength), c(\"Imported\", \"Raised\", \"All\"),\n fill = c(\"yellow\", \"orange\", \"red\"))\ndev.off()\n\n\n\n###Mean Age per sex\nvar <- \"Landings\"\nAgeStrucSexLan <- AgeStrucSex[CatchCategory==var,]\npng(filename=paste0(output_wd,\"MeanAge\",var,\"_Sex.png\"))\npar(las=2)\n\t\tboxplot(meanAge~Sex, data=AgeStrucSexLan,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$Sex)) + 0.3,\n col = \"red\",\n main = \"Mean Age in the Landings\",\n xlab = \"\",\n ylab = \"Mean Age\",\n xlim = c(0.5, length(unique(AgeStrucSexLan$Sex))+.5), ylim = c(min(AgeStrucSexLan$AgeOrLength), max(AgeStrucSexLan$AgeOrLength)), yaxs = \"i\")\n\t\tboxplot(meanAge~Sex, data=AgeStrucSexLan[SampledOrEstimated=='Estimated_Distribution',], add=TRUE,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$Sex)),\n col = \"orange\")\n\t\tboxplot(meanAge~Sex, data=AgeStrucSexLan[SampledOrEstimated=='Sampled_Distribution',], add=TRUE,\n boxwex = 0.25, at = 1:length(unique(AgeStrucSexLan$Sex)) - 0.3,\n col = \"yellow\")\n legend(1, max(AgeStrucSexLan$AgeOrLength), c(\"Imported\", \"Raised\", \"All\"),\n fill = c(\"yellow\", \"orange\", \"red\"))\ndev.off()\n\n\n###Mean Age per area\n\n\ncatchCat <- \"Landings\"\nVar <- \"Area\"\npng(filename=paste0(output_wd,\"MeanAge\",catchCat,Var,\".png\"))\n\n\nAgeStrucSexLan <- AgeStrucSex[CatchCategory==catchCat,]\nAgeStrucSexLan <- data.frame(AgeStrucSexLan)\nAgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)] <- factor(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\npos <- levels(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\n\nEstim <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Estimated_Distribution'),]\nEstim[,which(colnames(Estim)==Var)] <- factor(Estim[,which(colnames(Estim)==Var)])\n\nSampled <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Sampled_Distribution'),]\nSampled[,which(colnames(Sampled)==Var)] <- factor(Sampled[,which(colnames(Sampled)==Var)])\npar(las=2)\nplotFunction(AgeStrucSexLan,Estim,Sampled)\ndev.off()\n\n###Mean Age per fleet\n\ncatchCat <- \"Landings\"\nVar <- \"Fleet\"\npng(filename=paste0(output_wd,\"MeanAge\",catchCat,Var,\".png\"))\n\nAgeStrucSexLan <- AgeStrucSex[CatchCategory==catchCat,]\nAgeStrucSexLan <- data.frame(AgeStrucSexLan)\nAgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)] <- factor(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\npos <- levels(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\n\nEstim <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Estimated_Distribution'),]\nEstim[,which(colnames(Estim)==Var)] <- factor(Estim[,which(colnames(Estim)==Var)])\n\nSampled <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Sampled_Distribution'),]\nSampled[,which(colnames(Sampled)==Var)] <- factor(Sampled[,which(colnames(Sampled)==Var)])\npar(las=2, mar=c(15,4,4,5))\nplotFunction(AgeStrucSexLan,Estim,Sampled)\ndev.off()\n###Resulting age structure\n\n#The following plot shows the percentage of each age/length for the sampled strata, estimated and the final age structure for the landing and discard fractions.\n\n\nlistVAR <- c(\"CatchCategory\",\"SampledOrEstimated\")\n\nAgeStrucSexProp <- AgeStrucSex[,list(sumCANUM=sum(CANUM)), by=c(listVAR,'AgeOrLength')]\nAgeStrucSexProp <- AgeStrucSexProp[,propCanum:=sumCANUM/sum(sumCANUM), by=listVAR]\n\nlistVAR <- c(\"CatchCategory\")\nAgeStrucSexProp2 <- AgeStrucSex[,list(sumCANUM=sum(CANUM)), by=c(listVAR,'AgeOrLength')]\nAgeStrucSexProp2 <- AgeStrucSexProp2[,propCanum:=sumCANUM/sum(sumCANUM), by=listVAR]\nAgeStrucSexProp2$SampledOrEstimated <- \"Final Distribution\"\n\nAgeStrucSexProp<- rbind(AgeStrucSexProp,AgeStrucSexProp2)\n\npng(filename=paste0(output_wd,\"AgeStruc.png\"))\nxyplot(propCanum~AgeOrLength|CatchCategory,groups = SampledOrEstimated, data=AgeStrucSexProp,auto.key = list(space = \"right\", points = TRUE, lines = FALSE))\ndev.off()\n##Mean weight at age/length\n\n#the catchAndSampleData also provide the weight at age per strata for the Sampled/Estimated stratas.\n#One would also want to check the sampled/estimated and resulting weight at length/age. This is produced in the following graph, each boxplot representing the distribution of the weight at age/length for the different stratas.\n\ncatchCat <- \"Landings\"\nVar <- \"AgeOrLength\"\n\nAgeStrucSexLan <- AgeStrucSex[CatchCategory==catchCat,]\nAgeStrucSexLan <- data.frame(AgeStrucSexLan)\nAgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)] <- factor(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\npos <- levels(AgeStrucSexLan[,which(colnames(AgeStrucSexLan)==Var)])\n\nEstim <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Estimated_Distribution'),]\nEstim[,which(colnames(Estim)==Var)] <- factor(Estim[,which(colnames(Estim)==Var)])\n\nSampled <- AgeStrucSexLan[which(AgeStrucSexLan$SampledOrEstimated=='Sampled_Distribution'),]\nSampled[,which(colnames(Sampled)==Var)] <- factor(Sampled[,which(colnames(Sampled)==Var)])\nAreas <- unique(AgeStrucSexLan$Area)\n\npng(filename=paste0(output_wd,\"MeanWeight\",catchCat,Var,\".png\"))\npar(las=2, mfrow=c(1, length(Areas)))\nfor(i in Areas){\n\tplotFunctionMeanWeight(AgeStrucSexLan=AgeStrucSexLan[AgeStrucSexLan$Area==i,],Estim=Estim[Estim$Area==i,],Sampled=Sampled[Sampled$Area==i,])\n\n\t}\ndev.off()\n#The outliers (more than 3 times the standard deviation) are extracted and can be investigated from the following table.\n\n\ncatchCat <- \"Landings\"\n\nAgeStrucSexLan <- data.table(AgeStrucSexLan[AgeStrucSexLan$RaisedOrImported==\"Imported_Data\" & AgeStrucSexLan$CatchCategory==catchCat,])\nAgeStrucSexLan <- AgeStrucSexLan[,AverageWtSize:=mean(WECA), by=list(RaisedOrImported,AgeOrLength,Area)]\nAgeStrucSexLan <- AgeStrucSexLan[,stdWECA:=sd(WECA), by=list(RaisedOrImported,AgeOrLength,Area)]\n\nwrite.csv(AgeStrucSexLan[WECA>AverageWtSize+3*stdWECA | WECA%\n tidyr::unnest(\"delay_group\") %>%\n dplyr::mutate(scenario = 1:dplyr::n())\n\ncap_cases <- 2000\nmax_days <- 300\n## Parameterise fixed paramters\nsim_with_params <- purrr::partial(ringbp::scenario_sim,\n cap_max_days = max_days,\n cap_cases = cap_cases,\n r0isolated = 0,\n disp.iso = 1,\n disp.com = 0.5,\n quarantine = TRUE)\n\n#+ full_run\ntic() ## Run parameter sweep\nsweep_results <- ringbp::parameter_sweep(scenarios,\n sim_fn = sim_with_params,\n samples = no.samples,\n show_progress = TRUE)\ntoc()\n\n\n# #+ writeout\n# saveRDS(sweep_results, file = \"data-raw/res_20200515_highk.rds\")\n\n\n# Plot figure 2: --------------------------------------------------------\n# Parameter distributions (incubation, generation interval etc.)\n\nringbp::make_figure_2()\n\n# Load in results -------------------------------------------------------\n\n# sweep_results_extra <- readRDS(\"data-raw/res_20200505_testing_R0_1point5.rds\")\n# sweep_resultsA <- readRDS(\"data-raw/res_20200507_complete.rds\")\n# sweep_resultsB <- readRDS(\"data-raw/res_20200507_completeB.rds\")\n#\n# sweep_results <- bind_rows(sweep_resultsA, sweep_resultsB)\n# sweep_results <- sweep_results %>% unnest(sims) %>%\n# dplyr::group_by(max_quar_delay,index_R0,control_effectiveness,\n# self_report,test_delay,sensitivity,precaution) %>%\n# nest()\n\nres <- sweep_results %>%\n dplyr::group_by(scenario) %>%\n dplyr::mutate(pext = extinct_prob(sims[[1]], cap_cases = cap_cases, week_range = 40:42)) %>%\n dplyr::ungroup(scenario)\n\n#+ plots3\n\n\n#+ plotsS, eval = TRUE, cache = FALSE, fig.height = 5, fig.width = 9\n\n# A colour-blind-friendly palette\ncbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\", \"#CC79A7\")\n\nres %>%\n filter(max_quar_delay == 1) %>%\n filter(precaution == 7) %>%\n filter(test_delay == 2) %>%\n filter(sensitivity == 0.65) %>%\n mutate(prop.asym = factor(sensitivity, labels = c('sensitivity = 65%'))) %>%\n mutate(adherence = factor(self_report, labels = c('50% self reporting'))) %>%\n mutate(index_R0 = factor(index_R0)) %>%\n ggplot(aes(control_effectiveness, 1 - pext, colour = index_R0)) +\n ggplot2::scale_colour_manual(values = cbPalette[c(4,2,7)],name=TeX(\"Index $\\\\R_0$\")) +\n geom_line() +\n geom_point() +\n facet_grid(adherence ~ prop.asym) +\n ggtitle('Contact trace delay is 1, test delay is 2 days,\\nminimum isolation is 7 days') +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n ylab('Prob. large outbreak') +\n xlab('Contact tracing coverage') +\n ylim(c(0,0.5))\n\n\n\nres %>%\n filter(self_report == 0.5) %>%\n filter(precaution == 7) %>%\n filter(test_delay == 2) %>%\n filter(sensitivity == 0.65) %>%\n mutate(prop.asym = factor(sensitivity, labels = c('sensitivity = 65%'))) %>%\n mutate(max_quar_delay = factor(max_quar_delay, labels = c('1 day trace delay', '4 days'))) %>%\n mutate(index_R0 = factor(index_R0)) %>%\n ggplot(aes(control_effectiveness, 1 - pext, colour = index_R0)) +\n geom_line() +\n geom_point() +\n ggplot2::scale_colour_manual(values = cbPalette[c(4,2,7)],name=TeX(\"Index $\\\\R_0$\")) +\n facet_grid(max_quar_delay ~ prop.asym) +\n ggtitle('Self-reporting is 50%, test delay is 2 days,\\nminimum isolation is 7 days') +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n ylab('Prob. large outbreak') +\n xlab('Contact tracing coverage') +\n ylim(c(0,0.5))\n\nres %>%\n filter(self_report == 0.5) %>%\n filter(max_quar_delay == 1) %>%\n filter(sensitivity == 0.65) %>%\n mutate(test_delay = factor(test_delay, labels = c('instant test','2 day delay'))) %>%\n mutate(precaution = factor(precaution, labels = c('immediate release if negative', 'minimum 7 days'))) %>%\n mutate(index_R0 = factor(index_R0)) %>%\n ggplot(aes(control_effectiveness, 1 - pext, colour = index_R0)) +\n ggplot2::scale_colour_manual(values = cbPalette[c(4,2,7)],name=TeX(\"Index $\\\\R_0$:\")) +\n ggplot2::theme(legend.position = \"bottom\") +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n geom_line() +\n geom_point() +\n facet_grid(test_delay ~ precaution) +\n ggtitle('Self-reporting is 50%, sensitivity is 65%,\\ntrace delay is 1') +\n ylab('Prob. large outbreak') +\n xlab('Contact tracing coverage') +\n ylim(c(0,0.6))\n\n# res %>%\n# filter(self_report == 0.5) %>%\n# filter(max_quar_delay == 1) %>%\n# filter(index_R0 == 1.3) %>%\n# mutate(test_delay = factor(test_delay, labels = c('instant test','2 day delay'))) %>%\n# mutate(precaution = factor(precaution, labels = c('immediate release if negative', 'minimum 7 days'))) %>%\n# mutate(sensitivity = factor(sensitivity, labels = c('No testing','65%','95%'))) %>%\n# ggplot(aes(control_effectiveness, 1 - pext, colour = sensitivity)) +\n# ggplot2::scale_colour_manual(values = cbPalette[c(1,3,6)],name=\"Test sensitivity:\") +\n# ggplot2::theme(legend.position = \"bottom\") +\n# geom_line() +\n# geom_point() +\n# facet_grid(test_delay ~ precaution) +\n# labs(title = TeX('\\\\textbf{Self-reporting is 50%, trace delay is 1,}'), subtitle = TeX('\\\\textbf{$\\\\R_0$ is $1.3$}')) +\n# theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n# ylab('Prob. large outbreak') +\n# xlab('Contact tracing coverage') +\n# ylim(c(0,0.4))\n\n\n#+ by_size, eval = TRUE, cache = TRUE, fig.height = 5, fig.width = 9\n\nres2 <- list()\nweek_range <- 40:42\n\nsweep_resultsB <- sweep_results %>%\n filter(self_report == 0.5,\n test_delay == 2)\n\nfor(i in seq_len(nrow(sweep_resultsB))){\n #print(i)\n tmp <- sweep_resultsB$sims[i][[1]]\n tmp <-\n tmp %>%\n dplyr::group_by(sim) %>% # group by simulation run\n mutate(max_weekly = max(weekly_cases),\n time_to_size = which(cumulative>=500)[1], #time to reach 500 cases (weeks)\n total = max(cumulative)) %>%\n dplyr::filter(week %in% week_range) %>%\n dplyr::summarise(extinct =\n ifelse(all(weekly_cases == 0 &\n cumulative < cap_cases),\n 1, 0),\n max_weekly = max(max_weekly),\n time_to_size = min(time_to_size),\n total = max(total)) %>%\n dplyr::ungroup()\n tmp <-\n tmp %>%\n mutate(index_R0 = sweep_resultsB$index_R0[i],\n control_effectiveness = sweep_resultsB$control_effectiveness[i],\n max_quar_delay = sweep_resultsB$max_quar_delay[i],\n precaution = sweep_resultsB$precaution[i],\n sensitivity = sweep_resultsB$sensitivity[i])\n\n res2[[i]] <- tmp\n}\nres2 <- do.call(rbind, res2)\n\n\n\n#+ plots_by_size2, eval = TRUE, cache= FALSE\n\n# Cumulation is *the number of runs, with that many total, that went extinct*.\n# At cumulative size = 4,\n# cumulation is 0 (everything has more than 4 total cases)\n# so p(outbreak) is total outbreaks / total runs\n\n# at cumulative size = 10\n# cumulation is +ve (say 100)\n# So 100 runs reached 10 but still went extinct.\n# Therefore 1900 runs carried on.\n# so p(outbreak) is (total outbreaks) / (total runs - cumulation)\n\n# we want:\n# total outbreaks / n\ntotal_cumulative_distr <-\n res2 %>%\n mutate(total = ifelse(total > 2000, 2000, total)) %>%\n group_by(index_R0, control_effectiveness, max_quar_delay, precaution, sensitivity) %>%\n do(res = tibble(cumdistr = nrow(.) * ecdf(.$total)(4:2000),\n total = 4:2000,\n outbreaks = nrow(.) - sum(.$extinct),\n runs = nrow(.),\n max_quar_delay = .$max_quar_delay[1],\n index_R0 = .$index_R0[1],\n precaution = .$precaution[1],\n sensitivity = .$sensitivity[1],\n control_effectiveness = .$control_effectiveness[1],\n poutbreak = (outbreaks) / (runs - cumdistr)))\n\n\ntotal_cumulative_distr <- do.call(rbind, total_cumulative_distr$res) %>%\n mutate(index_R0 = factor(index_R0, labels = c('R0 = 1.1', '1.3','1.5'))) %>%\n mutate(precaution = factor(precaution, labels = c('immediate release', '7 days'))) %>%\n mutate(sensitivity = factor(sensitivity, labels = c('65% sensitive'))) %>%\n mutate(max_quar_delay = factor(max_quar_delay, labels = c('1 day trace delay', '4 days'))) %>%\n filter(outbreaks != 0)\n\nT1 <- total_cumulative_distr %>% filter(sensitivity==\"65% sensitive\") %>%\n filter(precaution==\"7 days\")\nggplot(T1,\n aes(total, poutbreak, colour = factor(control_effectiveness), group = factor(control_effectiveness))) +\n geom_line() +\n facet_grid(max_quar_delay ~ index_R0) +\n scale_colour_manual(values = cbPalette) +\n ylab('Prob. large outbreak') +\n guides(colour=guide_legend(title=\"Prop. Traced\")) +\n ggtitle('Prob. of outbreak with total cases so far') +\n ggplot2::theme(legend.position = \"bottom\") +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n xlim(c(0,1000)) +\n ylim(c(0,1))\n\n#+ plots_by_max_weekly, cache = FALSE\n\ntotal_cumulative_distr <-\n res2 %>%\n group_by(index_R0, control_effectiveness, max_quar_delay, precaution, sensitivity) %>%\n do(res = tibble(cumdistr = sum(.$extinct) * ecdf(.$max_weekly[.$extinct == 1])(1:max(.$max_weekly)),\n max_max_weekly = max(.$max_weekly),\n max_weekly = 1:max(.$max_weekly),\n extinct = sum(.$extinct),\n outbreaks = nrow(.) - sum(.$extinct),\n max_quar_delay = .$max_quar_delay[1],\n runs = nrow(.),\n index_R0 = .$index_R0[1],\n precaution = .$precaution[1],\n sensitivity = .$sensitivity[1],\n control_effectiveness = .$control_effectiveness[1],\n poutbreak = (outbreaks) / (runs - cumdistr)))\n\n\ntotal_cumulative_distr <-\n do.call(rbind, total_cumulative_distr$res) %>%\n filter(poutbreak < 1) %>%\n mutate(index_R0 = factor(index_R0, labels = c('R0 = 1.1', '1.3','1.5'))) %>%\n mutate(max_quar_delay = factor(max_quar_delay, labels = c('1 day trace delay', '4 day trace delay'))) %>%\n mutate(precaution = factor(precaution, labels = c('immediate release', '7 days'))) %>%\n mutate(sensitivity = factor(sensitivity, labels = c('65% sensitive')))\n\nT1 <- total_cumulative_distr %>% filter(sensitivity==\"65% sensitive\") %>%\n filter(precaution==\"7 days\")\n\nggplot(T1,\n aes(max_weekly, poutbreak, colour = factor(control_effectiveness), group = factor(control_effectiveness))) +\n geom_line() +\n facet_grid(max_quar_delay ~ index_R0, scale = 'free_x') +\n scale_colour_manual(values = cbPalette) +\n ylab('Prob. large outbreak') +\n xlab('weekly cases') +\n guides(colour=guide_legend(title=\"Prop. Traced\")) +\n ggtitle('Prob. of outbreak with size of worst week') +\n ggplot2::theme(legend.position = \"bottom\") +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n xlim(c(0,150))\n\n\n# Histogram of how long it takes to reach 500 cases (weeks)\n\nggplot(res2, aes(time_to_size)) + geom_histogram(aes(y=..density..),breaks=1:30,\n na.rm=T, col=\"orange\",fill=\"orange\") +\n ggtitle('Time to reach 500 cases') +\n xlab('Time (weeks)')\n\n\nres3 <- res2 %>% filter(sensitivity==0.65) %>%\n #filter(max_quar_delay==1) %>%\n filter(precaution==7) %>%\n mutate(total = pmin(total,2000))\n\nres3 <- res3 %>%\n group_by(control_effectiveness, index_R0, max_quar_delay) %>%\n mutate(x := total[order(total)]) %>%\n mutate(y := 1-seq_along(total)/length(total)) %>%\n ungroup\n\nres3 <- res3 %>% group_by(control_effectiveness, index_R0,x,max_quar_delay) %>%\n mutate(y=max(y)) %>%\n ungroup()\n\nres3 %>% mutate(index_R0 = factor(index_R0, labels=c(\"1.1\",\"1.3\",\"1.5\"))) %>%\n mutate(control_effectiveness = factor(control_effectiveness, labels=c(\"Prop. traced 40%\",\"60%\",\"80%\",\"100%\"))) %>%\n mutate(max_quar_delay = factor(max_quar_delay, labels=c(\"1 day trace delay\",\"4 days\"))) %>%\n ggplot(aes(x,y,colour=index_R0)) + geom_line() +\n scale_colour_manual(values = cbPalette[c(4,2,7)]) +\n facet_grid(max_quar_delay ~ control_effectiveness) +\n xlim(c(5,2000)) + ylim(c(0,1)) +\n scale_x_continuous(breaks=c(5,500,1000,2000)) +\n ggplot2::theme(legend.position = \"bottom\") +\n theme(text = element_text(size = 16),plot.title = element_text(size = 16, face = \"bold\")) +\n xlab('outbreak size, X') +\n ylab('risk of outbreak larger than X') +\n ggtitle('Risk of outbreak exceeding size X, by tracing coverage and speed')\n\n# Boxplots?\n# res3 %>% filter(sensitivity==0.65) %>%\n# mutate(control_effectiveness = factor(control_effectiveness,labels=c(\"Prop. traced 40%\",\"60%\",\"80%\",\"100%\"))) %>%\n# mutate(index_R0 = factor(index_R0, labels=c(\"1.1\",\"1.3\", \"1.5\"))) %>%\n# mutate(max_quar_delay = factor(max_quar_delay, labels=c(\"1 day trace delay\",\"4 days\"))) %>%\n# ggplot(aes(control_effectiveness,max_weekly)) + geom_boxplot() +\n# facet_grid(index_R0 ~ max_quar_delay) +\n# scale_y_log10()\n\ndelay_shape = c(0.9)\ndelay_scale = 1\ninc_meanlog = 1.434065\ninc_sdlog = 0.6612\ninf_shape = 2.115779\ninf_rate = 0.6898583\ninf_shift = 3\nmin_quar_delay = 1\nmax_quar_delay = 4\nindex_R0 = 1.3\nprop.asym = 0.4\nprop.ascertain = control_effectiveness = 0.6\nself_report = c(0.5)\ntest_delay = 2 #time from isolation to test result\nsensitivity = 0.65 #percent of cases detected\nprecaution = 7 #this could be between 0 and 7? Number of days stay in isolation if negative test\nnum.initial.cases = 5\ndisp.iso = 1\nr0community = index_R0\nr0isolated = 0\ndisp.com = 0.16\nquarantine = TRUE\ntesting = TRUE\ncap_cases = 2000\ncap_max_days = 300\n", "meta": {"hexsha": "19b2f91bdd2765cc64e2858400ef6121d61c5124", "size": 15614, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/scripts/higher_k.r", "max_stars_repo_name": "timcdlucas/ringbp", "max_stars_repo_head_hexsha": "1097485287b4eee06794d01331b4786eac246d98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/scripts/higher_k.r", "max_issues_repo_name": "timcdlucas/ringbp", "max_issues_repo_head_hexsha": "1097485287b4eee06794d01331b4786eac246d98", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6, "max_issues_repo_issues_event_min_datetime": "2020-05-13T08:33:06.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-06T17:08:48.000Z", "max_forks_repo_path": "inst/scripts/higher_k.r", "max_forks_repo_name": "timcdlucas/ringbp", "max_forks_repo_head_hexsha": "1097485287b4eee06794d01331b4786eac246d98", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-05-15T10:14:43.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-15T10:14:43.000Z", "avg_line_length": 37.7149758454, "max_line_length": 123, "alphanum_fraction": 0.6342385039, "num_tokens": 4569, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.30013776264555886}} {"text": "args <- commandArgs(TRUE)\n\ndepth.f <- args[1]\nsex.f <- args[2]\noutfile <- args[3]\n\nrt <- read.table(depth.f,header=TRUE,sep=\"\\t\")\n\n# https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r120\n# GC\u77eb\u6b63\u516c\u5f0f\n# Depth_correct = Depth_raw * (m/Mx)\n# m\u662f\u6240\u6709exon\u7684\u4e2d\u4f4d\u503c\n# Mx\u662f\u4e0e\u8be5targe\u5177\u6709\u76f8\u540cGC\u542b\u91cf\u7684\u6240\u6709target\u7684\u6df1\u5ea6\u7684\u4e2d\u4f4d\u503c\n\n\n\n\nshift_sex <- function(df){ # NOT USED\n chr.all <- unique(df$chromosome)\n sex <- NULL\n # \u5e38\u67d3\u8272\u4f53\u5e73\u5747\u6df1\u5ea6\n df.autochr <- df[df$chromosome != \"chrX\" & df$chromosome != \"chrY\",]\n mean.cov.autochr <- mean(df.autochr$depth)\n\n # \u68c0\u67e5\u662f\u5426\u5b58\u5728chrY\n if (\"chrY\" %in% chr.all){\n df.chrY <- df[df$chromosome == \"chrY\",]\n depth.chrY <- df.chrY$depth\n mean.cov.chrY <- mean(depth.chrY)\n if (mean.cov.chrY >= 50){\n sex <- \"male\"\n print(\"==========================\")\n print(paste(\"the mean cov of chrY is: \",mean.cov.chrY,sep=\"\"))\n print(paste(\"the mean cov of autochr is: \",mean.cov.autochr,sep=\"\"))\n print(\"this sample is assumed as male\")\n print(\"==========================\")\n }else{\n sex <- \"unknown\"\n }\n }\n\n # \u5224\u65ad\u662f\u5426\u5b58\u5728chrX\n if (\"chrX\" %in% chr.all){\n df.chrX <- df[df$chromosome == \"chrX\",]\n depth.chrX <- df.chrX$depth\n mean.cov.chrX <- mean(depth.chrX)\n r <- mean.cov.chrX / mean.cov.autochr\n if (r >= 0.4 & r <= 0.6){\n sex <- \"male\"\n print(\"==========================\")\n print(paste(\"the mean cov of chrX is: \",mean.cov.chrX,sep=\"\"))\n print(paste(\"the mean cov of autochr is: \",mean.cov.autochr,sep=\"\"))\n print(paste(\"mean.cov.chrX / mean.cov.autochr is: \",r,sep=\"\"))\n print(paste(\"this sample is assumed as male\"))\n print(\"==========================\")\n }else{\n sex <- \"female\"\n print(\"==========================\")\n print(paste(\"the mean cov of chrX is: \",mean.cov.chrX,sep=\"\"))\n print(paste(\"the mean cov of autochr is: \",mean.cov.autochr,sep=\"\"))\n print(paste(\"mean.cov.chrX / mean.cov.autochr is: \",r,sep=\"\"))\n print(paste(\"this sample is assumed as female\"))\n print(\"==========================\")\n }\n }\n\n if.chrX <- \"chrX\" %in% chr.all\n if.chrY <- \"chrY\" %in% chr.all\n if (!if.chrX & !if.chrY){\n sex <- \"unknown\"\n print(\"==========================\")\n print(\"this sample can not find chrX or chrY\")\n print(\"this sample sex is unknown\")\n print(\"==========================\")\n }\n \n return(sex)\n}\n\n\n# \u6027\u522b\u77eb\u6b63\n#this.sex <- shift_sex(rt)\n\nsex.df <- read.table(sex.f,header=FALSE,sep=\"\\t\")\nthis.sex <- sex.df[1,2]\nprint(paste(\"infered sex is:\",this.sex))\n\n# check chr naming\nchrom <- rt[1,1]\nif (grepl('chr',chrom)){\n # with chr-prefix\n chr_naming <- 'with_prefix'\n}else{\n chr_naming <- 'no_prefix'\n}\n\nif (this.sex == \"male\"){\n if (chr_naming == \"with_prefix\"){\n df.sexchr <- rt[rt$chromosome == \"chrX\" | rt$chromosome == \"chrY\",]\n cov <- df.sexchr$depth # sex chr's cov\n rt[rt$chromosome == \"chrX\" | rt$chromosome == \"chrY\",]$depth <- cov * 2\n }else{\n df.sexchr <- rt[rt$chromosome == \"X\" | rt$chromosome == \"Y\",]\n cov <- df.sexchr$depth # sex chr's cov\n rt[rt$chromosome == \"X\" | rt$chromosome == \"Y\",]$depth <- cov * 2\n }\n}\n\n\nmedian.depth.all <- median(rt$depth)\nprint(paste(\"median depth of all exon is\",median.depth.all,sep=\": \"))\n\n# GC\u77eb\u6b63\ndepth.new <- c()\nfor (i in 1:nrow(rt)){\n depth.raw <- rt[i,5]\n gc <- rt[i,7]\n same.gc <- rt[rt$gc == gc,]\n med.depth <- median(same.gc$depth)\n if (med.depth == 0){\n\t\tdep.new <- depth.raw # \u5982\u679cmed.depth=0,x/0\u4f1a\u4ea7\u751fNA/Inf\n\t}else{\n\t\tdep.new <- round(depth.raw * (median.depth.all/med.depth),0) # \u56db\u820d\u4e94\u5165\n\t}\n\n depth.new <- c(depth.new,dep.new)\n}\n\nrt$depth_corrected <- depth.new\n\nwrite.table(rt,outfile,quote=FALSE,sep=\"\\t\",row.names=FALSE)\n\n\n\n\n\n\n", "meta": {"hexsha": "e16f33d9c8f047fcb486aef76598e2920865c209", "size": 3945, "ext": "r", "lang": "R", "max_stars_repo_path": "CNVscan/bin/gc_correct.r", "max_stars_repo_name": "Xiaohuaniu0032/HLALOH", "max_stars_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-11-17T12:24:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T12:24:27.000Z", "max_issues_repo_path": "CNVscan/bin/gc_correct.r", "max_issues_repo_name": "Xiaohuaniu0032/HLALOH", "max_issues_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-10-26T01:39:33.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-04T02:41:11.000Z", "max_forks_repo_path": "CNVscan/bin/gc_correct.r", "max_forks_repo_name": "Xiaohuaniu0032/HLALOH", "max_forks_repo_head_hexsha": "24587c75fad08e7f1821866fb72f9b7e756689bb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-25T05:32:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-25T05:32:21.000Z", "avg_line_length": 28.5869565217, "max_line_length": 80, "alphanum_fraction": 0.5201520913, "num_tokens": 1241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.30013775602237286}} {"text": "pdf_file<-\"pdf/scatterplots_three_points.pdf\"\ncairo_pdf(bg=\"grey98\", pdf_file,width=14,height=9)\n\npar(mai=c(2,1,1,1),omi=c(0,0,0,0),xpd=T,family=\"Lato Light\",las=1)\n\n# Define data and prepare chart\n\nnames<-c(\"BMW:\\n44,6 Bn.\",\"Daimler:\\n45,5 Bn.\",\"\",\"Facebook:\\n75-100 Bn.\")\nmyValue<-c(44.6,45.5,100,75)\nmyRevenue<-c(60.5,97.8,2.5,2.5)\nmyProfit<-c(4.8,4.7,1,1)\n\nmyC1<-rgb(80,80,80,maxColorValue=255)\nmyC2<-rgb(255,97,0,maxColorValue=255)\nmyC3<-\"grey\"\nmyC4<-rgb(58,87,151,maxColorValue=255)\n\n# Define chart and other elements\n\nplot(myRevenue,myProfit,axes=F,type=\"n\",xlab=\"Revenue (years)\",ylab=\"Profit (years)\",xlim=c(-20,100),ylim=c(-1,6),cex.lab=1.5)\nfor (i in 1:3) \n{\narrows(myRevenue[i],-1,myRevenue[i],myProfit[i],length=0.10,lty=\"dotted\",angle=10,code=0,lwd=1,col=\"grey70\")\narrows(-20,myProfit[i],myRevenue[i],myProfit[i],length=0.10,lty=\"dotted\",angle=10,code=0,lwd=1,col=\"grey70\")\n}\npoints(myRevenue,myProfit,pch=19,cex=myValue/2.6,col=c(myC1,myC2,myC3,myC4))\ntext(myRevenue,myProfit,names,col=\"white\",cex=1.3)\naxis(1,at=c(2.5,60.5,97.8),labels=c(\"2.5*\",\"60.5\",\"97.8\"),cex.axis=1.25)\naxis(2,at=c(1,4.8),labels=c(\"1.0\",\"4.8\\n4.7\"),cex.axis=1.25)\ntext(-25.5,5.08,\"**\")\ntext(-26.5,1.08,\"*\")\n\n# Titling\n\nmtext(line=1,\"Facebook, BMW and Daimler by comparison\",cex=3.5,adj=0,family=\"Lato Black\")\nmtext(line=-1,\"Profit, revenue, stock market value (circle size, status: 01.30.2012)\",cex=1.75,adj=0,font=3)\nmtext(line=-3,\"All values in Bn. Euro\",cex=1.75,adj=0,font=3)\nmtext(side=1,line=6.5,\"Source www.spiegel.de\",cex=1.75,adj=1,font=3)\nmtext(side=1,line=4.5,\"* Estimated\",cex=1.75,adj=0)\nmtext(side=1,line=6.5,\"** Result before tax\",cex=1.75,adj=0)\ndev.off()", "meta": {"hexsha": "56c73709e5e5506b2eaf86fa0fe0445c1c2d1f7c", "size": 1659, "ext": "r", "lang": "R", "max_stars_repo_path": "src/scripts/scatterplots_three_points.r", "max_stars_repo_name": "wilsonify/data-visualization", "max_stars_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/scripts/scatterplots_three_points.r", "max_issues_repo_name": "wilsonify/data-visualization", "max_issues_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/scripts/scatterplots_three_points.r", "max_forks_repo_name": "wilsonify/data-visualization", "max_forks_repo_head_hexsha": "4a4295a59f666625f4a47b2ad6a6f1eb06f9e8d3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.4634146341, "max_line_length": 126, "alphanum_fraction": 0.6865581676, "num_tokens": 729, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3000941796301025}} {"text": "library(caret)\nlibrary(mmadsenr)\nlibrary(xtable)\nlibrary(ggthemes)\nlibrary(dplyr)\ngetOption('xtable.comment',FALSE)\n\nload(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"biased-gbm-merged-dfonly.RData\"))\nload(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"classification-gbm-merged-dfonly.RData\"))\nload(get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", filename = \"cm-merged-gbm.RData\"))\n \n \n# fix a couple of annoying mismatches stemming from the model fitting scripts\ntmp <- cm_objects[[\"population_census\"]]\ncm_objects[[\"Population Census\"]] <- tmp\ncm_objects[[\"population_census\"]] <- NULL\n\ntmp <- cm_objects[[\"perlocus_pop\"]]\ncm_objects[[\"Per-Locus Population Census\"]] <- tmp\ncm_objects[[\"perlocus_pop\"]] <- NULL\n\ntmp <- cm_objects[[\"combined_tassize\"]]\ncm_objects[[\"All Sample Sizes and TA Durations\"]] <- tmp\ncm_objects[[\"combined_tassize\"]] <- NULL\n\n\nc_names <- classifier_results$experiments\n\nnames <- NULL\nratios <- NULL\ngroups <- NULL\nfor(name in c_names) {\n print(name)\n names <- c(names, name)\n \n experiment_row <- subset(classifier_results, experiments == name)\n group <- experiment_row$exp_group\n \n groups <- c(groups, group)\n \n num <- cm_objects[[name]][[\"table\"]][[3]]\n den <- cm_objects[[name]][[\"table\"]][[4]]\n r <- (num / (num + den)) * 100.0\n print(sprintf(\"r: %f\", r))\n \n ratios <- c(ratios, r)\n}\n\n#error_ratio_neutral <- as.data.frame(cbind(names,as.numeric(ratios)))\nerror_ratio_neutral <- data.frame(names,as.numeric(ratios))\nnames(error_ratio_neutral) <- c(\"Data Collection Treatment\", \"% Misclassified\")\n\n\n# reorder in ascending order of ratio\nerror_ratio_neutral <- dplyr::arrange(error_ratio_neutral, ratios)\n\n\nfilename <- get_data_path(suffix = \"experiment-ctmixtures/equifinality-4/results\", \n filename = \"misclassification-ratio-neutral-biased.RData\")\nsave(error_ratio_neutral, file = filename)\n\n# now make a nice looking table\n\nprint(xtable(error_ratio_neutral,\n align=\"|l|l|c|\",digits = c(0,0,1)), include.rownames = FALSE, comment=FALSE,floating=FALSE,\n file = \"../paper/misclassification-percentage-neutral-data.tex\")\n\n\n\n\n", "meta": {"hexsha": "376d47bb9f863725f81ea5f0cd8f2b64f648a880", "size": 2210, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-5/paperanalysis/results-unbiased-biases-misclassification-ratios.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "analysis/equifinality-5/paperanalysis/results-unbiased-biases-misclassification-ratios.r", "max_issues_repo_name": "mmadsen/experiment-ctmixtures", "max_issues_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "analysis/equifinality-5/paperanalysis/results-unbiased-biases-misclassification-ratios.r", "max_forks_repo_name": "mmadsen/experiment-ctmixtures", "max_forks_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1267605634, "max_line_length": 129, "alphanum_fraction": 0.7171945701, "num_tokens": 588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.600188359260205, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3000941796301025}} {"text": "ta_comfort <- function(rh, iclo, wind, M, H) {\n .Call('biometeoR_ta_comfort', PACKAGE = 'biometeoR', rh, iclo, wind, M, H)\n}\n", "meta": {"hexsha": "a943f5126adb80c0c2f573c0d05a6e2c279b5661", "size": 128, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ta_comfort.r", "max_stars_repo_name": "alfcrisci/biometeoR", "max_stars_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-06-13T15:54:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T15:54:46.000Z", "max_issues_repo_path": "R/ta_comfort.r", "max_issues_repo_name": "alfcrisci/biometeoR", "max_issues_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ta_comfort.r", "max_forks_repo_name": "alfcrisci/biometeoR", "max_forks_repo_head_hexsha": "e9ee73da6ecc515ecd471cb9ae059a4370a51493", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.0, "max_line_length": 78, "alphanum_fraction": 0.640625, "num_tokens": 50, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.5, "lm_q1q2_score": 0.3000941724786688}} {"text": "\n# with R first install.packages\n################ install.packages('Rilostat')\n################ install.packages('tidyverse')\n\n\nrequire(tidyverse)\nrequire(Rilostat)\n\n\n\n\nref_indicator = c(\t'EAP_TEAP_SEX_AGE_NB', \t\t# Labour Force by Sex and Age\n\t\t\t\t\t'EAP_TEAP_SEX_AGE_GEO_NB',\t# Labour Force by Sex Age and Rural/Urban Areas\n\t\t\t\t\t'EAP_TEAP_SEX_AGE_EDU_NB',\t# Labour Force by Sex, Age and Education\n\t\t\t\t\t'EMP_DWAP_SEX_AGE_RT'\t\t# Employment to Population Ratio by Sex and Age\n\t\t\t\t)\n\nX <- get_ilostat(id = 'CAN_A', segment = 'ref_area', filters = list(indicator = ref_indicator, timefrom = '2000', source = 'BA')) \n\n\n\n\nX %>% filter(indicator %in% 'EMP_DWAP_SEX_AGE_RT') %>% select(source:obs_value) %>% spread(time, obs_value)\n\n\n\n get_ilo(collection = 'STI', indicator = 'EMP_DWAP_SEX_AGE_GEO_RT', timefrom = '2008', ref_area = 'CAN', classif1 = 'AGGREGATE') %>% select(source:obs_value) %>% spread(time, obs_value) %>% save_ilo()\n", "meta": {"hexsha": "fc28f511193243db753860d75a5d3ed3e0c7e701", "size": 926, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/doc/do/ILO_STACAN_QUERY.r", "max_stars_repo_name": "dbescond/iloData", "max_stars_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inst/doc/do/ILO_STACAN_QUERY.r", "max_issues_repo_name": "dbescond/iloData", "max_issues_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inst/doc/do/ILO_STACAN_QUERY.r", "max_forks_repo_name": "dbescond/iloData", "max_forks_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9310344828, "max_line_length": 200, "alphanum_fraction": 0.6835853132, "num_tokens": 290, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953797290152, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3000211630980439}} {"text": "##' Plot figure 1\n##'\n##' Shapefiles can be obtained from\n##' https://data.humdata.org/dataset/dr-congo-health-0. Download\n##' RDC_Zone_de_sante.zip, unpack it and pass the directory in which it was\n##' unpacked to this function.\n##'\n##' @param shapefile_dir directory containing the shape files\n##' @return plot\n##' @importFrom sf st_read st_union st_centroid\n##' @importFrom lwgeom st_make_valid\n##' @importFrom dplyr rename mutate filter if_else left_join group_by summarise select %>%\n##' @importFrom tidyr replace_na\n##' @importFrom purrr map map_dbl\n##' @importFrom ggplot2 ggplot geom_sf geom_text xlab ylab coord_sf theme scale_fill_gradient aes element_blank\n##' @author Sebastian Funk\n##' @export\nfigure1 <- function(shapefile_dir) {\n\n health_zone_province <- katanga_measles_cases %>%\n dplyr::select(name=health_zone, province) %>%\n unique\n\n ## name assimilation\n katanga <- st_read(dsn=shapefile_dir, layer=\"zoneste\") %>%\n rename(name=NOM_ZS) %>%\n mutate(name=as.character(name)) %>%\n mutate(name=if_else(name == \"Mbulala\", \"Mbulula\", name),\n name=if_else(name == \"Kabondo Dianda\", \"Kabondo-Dianda\", name),\n name=if_else(name == \"Malemba\", \"Malemba-Nkulu\", name),\n name=if_else(name == \"Kilela Balanda\", \"Kilela-Balanda\", name),\n name=if_else(name == \"Kiyambi\", \"Kiambi\", name),\n name=if_else(name == \"Mufunga Sampwe\", \"Mufunga-Sampwe\", name)) %>%\n filter(name %in% health_zone_province$name) %>%\n st_make_valid %>%\n left_join(health_zone_province)\n\n katanga_province <- katanga %>%\n group_by(province) %>%\n summarise(geometry=st_union(geometry)) %>%\n mutate( ## https:/.data=/github.com/tidyverse/ggplot2/issues/2111\n CENTROID = map(geometry, st_centroid),\n COORDS = map(CENTROID, st_coordinates),\n COORDS_X = map_dbl(COORDS, 1),\n COORDS_Y = map_dbl(COORDS, 2))\n\n health_zone_2015 <- katanga_measles_cases %>%\n filter(week_start >= \"2015-01-01\" & week_start < \"2016-01-01\") %>%\n group_by(health_zone) %>%\n summarise(cases=sum(cases, na.rm=TRUE)) %>%\n select(name=health_zone, cases)\n\n map_2015 <- katanga %>%\n left_join(health_zone_2015, by=\"name\") %>%\n replace_na(list(cases=0)) %>%\n mutate(cases=if_else(Nom_ZS_PUC %in% c(\"Kikula\", \"Lukafu\", \"Manika\", \"Kanzenze\"), NA_real_, cases))\n\n p <- ggplot(map_2015) +\n geom_sf(aes(fill=cases), color=\"grey80\") +\n geom_text(data=katanga_province, size=5, aes(COORDS_X-50000, COORDS_Y+10000,\n label=province)) +\n xlab(\"\") + ylab(\"\") +\n coord_sf(datum=NA) +\n theme(axis.line = element_blank(),\n panel.grid.major = element_blank(),\n panel.grid.minor = element_blank(),\n panel.border = element_blank(),\n panel.background = element_blank(),\n axis.ticks = element_blank(),\n axis.text.x = element_blank(),\n axis.text.y = element_blank()) +\n scale_fill_gradient(\"Reported cases\", low=\"white\", high=\"black\", na.value=\"white\")\n return(p)\n}\n", "meta": {"hexsha": "8ad39449c9c600a807ac8ed38767286f8937f4bc", "size": 3235, "ext": "r", "lang": "R", "max_stars_repo_path": "R/figure1.r", "max_stars_repo_name": "sbfnk/measles.katanga", "max_stars_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2019-08-01T13:58:45.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-01T13:58:45.000Z", "max_issues_repo_path": "R/figure1.r", "max_issues_repo_name": "sbfnk/measles.katanga", "max_issues_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/figure1.r", "max_forks_repo_name": "sbfnk/measles.katanga", "max_forks_repo_head_hexsha": "5b1a1eee5c1217edb15f071647e90bb0a4680674", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.1333333333, "max_line_length": 111, "alphanum_fraction": 0.6160741886, "num_tokens": 882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.30002115554253966}} {"text": "library('Metrics')\nlibrary('RUnit')\n\ntest.suite <- defineTestSuite(\"Metrics\",\n dirs = file.path(\"inst/tests\"),\n testFileRegexp = '^.+\\\\.r')\n\ntest.result <- runTestSuite(test.suite)\n\nprintTextProtocol(test.result)\n", "meta": {"hexsha": "bf8a4ef1c9d042663a7cdfdcf91a1ca9f75809f6", "size": 271, "ext": "r", "lang": "R", "max_stars_repo_path": "R/tests/test_all.r", "max_stars_repo_name": "aidiot/Metrics", "max_stars_repo_head_hexsha": "9bf24574f339f4083ab800cea20625c18d9cbb72", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1411, "max_stars_repo_stars_event_min_datetime": "2015-01-03T14:35:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T07:38:25.000Z", "max_issues_repo_path": "R/tests/test_all.r", "max_issues_repo_name": "aidiot/Metrics", "max_issues_repo_head_hexsha": "9bf24574f339f4083ab800cea20625c18d9cbb72", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 21, "max_issues_repo_issues_event_min_datetime": "2015-03-22T16:45:40.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-02T09:33:53.000Z", "max_forks_repo_path": "R/tests/test_all.r", "max_forks_repo_name": "aidiot/Metrics", "max_forks_repo_head_hexsha": "9bf24574f339f4083ab800cea20625c18d9cbb72", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 447, "max_forks_repo_forks_event_min_datetime": "2015-01-26T21:07:12.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-16T18:37:44.000Z", "avg_line_length": 24.6363636364, "max_line_length": 61, "alphanum_fraction": 0.557195572, "num_tokens": 56, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES\n\n", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.5774953651858118, "lm_q1q2_score": 0.30002115554253966}} {"text": "#Import libraries---------\r\nlibrary(raster)\r\nlibrary(rgdal)\r\nlibrary(rgeos)\r\nlibrary(landsat)\r\nlibrary(RStoolbox)\r\nlibrary(cluster)\r\nlibrary(NbClust)\r\nlibrary(factoextra)\r\nlibrary(tmap)\r\nlibrary(doParallel)\r\nlibrary(randomForest)\r\nlibrary(caret)\r\nlibrary(ROCR)\r\nlibrary(stargazer)\r\nlibrary(simpleboot)\r\n\r\n#Set seed and multicore-------\r\nset.seed(12345)\r\n\r\ncl <- makeCluster(detectCores())\r\nregisterDoParallel(cl)\r\n\r\n#Plot the different light bands from the Orthonomic photos -------------\r\n#below code sourced from :\r\n#https://www.earthdatascience.org/courses/earth-analytics/multispectral-remote-sensing-data/landsat-data-in-r-geotiff/\r\n\r\n#Color band combinations for landsat 5:\r\n#https://www.esri.com/arcgis-blog/products/product/imagery/band-combinations-for-landsat-8/?rmedium=redirect&rsource=/esri/arcgis/2013/07/24/band-combinations-for-landsat-8\r\n\r\nspec_rgb <-\r\n stack(\r\n \"C:/Users/Sanil Purryag/Google Drive/St Andrews docs/Modules/Semester 2/Thesis data/PortMoak_RTK_Ortho.tif\"\r\n )\r\n\r\nspec_br <- brick(spec_rgb)\r\n\r\n#rename the different bands\r\nnames(spec_rgb) <- c('Blue', 'Green', 'Red', 'NIR', 'RE')\r\n\r\nnames(spec_br) <- c('Blue', 'Green', 'Red', 'NIR', 'RE')\r\n\r\n#check if files have been properly renamed\r\n\r\nnames(spec_br)\r\n\r\n#Import the DSM data\r\nspec_lidar <-\r\n raster(\r\n \"C:/Users/Sanil Purryag/Google Drive/St Andrews docs/Modules/Semester 2/Thesis data/PortMoak_RTK_DSM.tif\"\r\n )\r\n\r\n#Plot each individual band\r\n\r\nplot(\r\n spec_br,\r\n stretch = \"hist\",\r\n maxv = 65535,\r\n #col = gray(0:100 / 100),\r\n box = FALSE,\r\n axes = FALSE\r\n)\r\n\r\n#True color composite plot\r\n\r\npar(col.axis = \"white\",\r\n col.lab = \"white\",\r\n tck = 0)\r\nplotRGB(\r\n spec_br,\r\n r = 3,\r\n g = 2,\r\n #should be 2.5 times more\r\n b = 1,\r\n stretch = \"hist\",\r\n axes = FALSE,\r\n scale = 65535,\r\n main = \"RGB composite image/n Landsat Bands 3,2,1\"\r\n)\r\n\r\n\r\n#False color composite plot\r\npar(col.axis = \"white\",\r\n col.lab = \"white\",\r\n tck = 0)\r\nplotRGB(\r\n spec_br,\r\n r = 5,\r\n g = 4,\r\n b = 3,\r\n stretch = \"hist\",\r\n axes = FALSE,\r\n main = \"RGB False composite image\\n Landsat Bands 5,4,3\"\r\n)\r\nbox(col = \"white\")\r\n\r\n\r\n#Exploratory analysis------------\r\n\r\n#Plot histogram of values from DSM\r\n\r\n#assign and replace NAs with base 160\r\nhistlidar <- spec_lidar\r\nNAvalue(histlidar) <- 160\r\n#Plot distribution of values in hist\r\nhist(\r\n histlidar,\r\n main = \"Distribution of DSM Values(100,000 pixels sampled)\",\r\n xlab = \"DSM Elevation Value (Inches)\",\r\n ylab = \"Frequency\",\r\n col = \"cornflowerblue\",\r\n ylim = c(0, 20000),\r\n xlim = c(150, 200)\r\n)\r\n\r\nhist(spec_br,\r\n xlab = \"Pixel Intensity\",\r\n ylab = \"Frequency\",\r\n col = \"cornflowerblue\")\r\n# ylim = c(0, 20000),\r\n# xlim = c(150, 200))\r\n\r\n#Pairs plot of different bands\r\npairsplot <- pairs(spec_rgb)\r\n\r\n#Hillshading (DSM Workstream)-----------\r\n#Below code adapted from:\r\n#https://github.com/mgimond/Spatial/blob/master/A4-Raster-Operations.Rmd\r\n#https://www.rdocumentation.org/packages/raster/versions/2.6-7/topics/focal\r\n\r\n#Estimate the slope and aspect of the terrain\r\nslope <- terrain(spec_lidar, opt = 'slope')\r\naspect <- terrain(spec_lidar, opt = 'aspect')\r\n\r\n#Create hillshade (DTM) using the queen case (neighbours = 8)\r\n#Current specification used for rough terrain estimation\r\nhill <- hillShade(slope, aspect, 40, 270)\r\n\r\n#plot the tmap package to see greyscale raster\r\nhill <-\r\n aggregate(hill, 25) # aggregate the raster by a factor of 25\r\ntm_shape(hill) + tm_raster(palette = \"Greys\") +\r\n tm_legend(outside = TRUE, text.size = .6) + tm_layout(title = \"Hillshaded DSM\")\r\n\r\n#Histogram of hillside values - 100,000 sampled values\r\nhist(\r\n hill,\r\n main = \"Distribution of Aggregated hill values\",\r\n xlab = \"Hillshaded values\",\r\n ylab = \"Frequency\",\r\n col = \"cornflowerblue\"\r\n)\r\nabline(v = 0.5, col = \"Red\", lty = 4)\r\n\r\n#Zonal statistics - Sobel----\r\n\r\n#===============================================================================Sobel Kernel Application\r\n\r\n#Assign hill to new object\r\nhillside <- hill\r\n#Threshold hillside\r\nhillside[hillside > 0.5] <- NA\r\nna.omit(hillside)\r\n#plot code adapted from:\r\n#https://stackoverflow.com/questions/28247812/r-raster-package-plot-producing-monochrome-images\r\n\r\nplot(hill,\r\n main = \"Hillshaded DSM raster\",\r\n col = gray.colors(\r\n 10,\r\n start = 0.3,\r\n end = 0.9,\r\n gamma = 2.2,\r\n alpha = NULL\r\n ))\r\nplot(\r\n hillside,\r\n main = \"Thresholded DSM hillshaded raster at 0.5 level\",\r\n col = gray.colors(\r\n 10,\r\n start = 0.3,\r\n end = 0.9,\r\n gamma = 2.2,\r\n alpha = NULL\r\n )\r\n)\r\n\r\n#Sobel weight matrix definition\r\nSobel <- matrix(c(-1, 0, 1,-2, 0, 2,-1, 0, 1) / 4, nrow = 3)\r\nf5 <- focal(hillside, w = Sobel, fun = sum)\r\n# tm_shape(f5) + tm_raster(palette = \"black\") +\r\n# tm_legend(legend.show = TRUE, \"Distribution of values\")\r\nfreq(f5)\r\nplot(f5,\r\n col = gray.colors(\r\n 10,\r\n start = 0.3,\r\n end = 0.9,\r\n gamma = 2.2,\r\n alpha = NULL\r\n ),\r\n main = \"Sobel Edge Detection Application\")\r\n#Flatten the map by reprojecting the raster to a Km by km dimension and to the British grid\r\nProjected.sobel <-\r\n projectRaster(f5, crs = \"+init=epsg:27700 +units=km\")\r\n#project the raster to a resolution of 10m per cell\r\nReprojection.sobel <-\r\n projectRaster(Projected.sobel,\r\n crs = crs(Projected.sobel),\r\n res = 0.010)\r\nplot(Reprojection.sobel, main = \"Reprojected Sobel Raster\")\r\ncount.sobel <- freq(Reprojection.sobel)\r\nprint(count.sobel)\r\n\r\n#===============================================================================Sobel over different levels\r\n# #Different levels of thresholding based on 0.01 increase between 0.1 and 1\r\n# #100 numbers between 0 and 1\r\n# seq.length <- seq(0.1, 1, 0.01)\r\n# sobel.count <- list()\r\n#\r\n# for (i in seq.length) {\r\n# #Reset before thresholding\r\n# hillside <- hill\r\n# #threshold hillside by i\r\n# hillside[hillside > i] <- NA\r\n# na.omit(hillside)\r\n# #Compute sobel kernel and associated focal stats\r\n# Sobel <- matrix(c(-1, 0, 1,-2, 0, 2,-1, 0, 1) / 4, nrow = 3)\r\n# f6 <- focal(hillside, w = Sobel, fun = sum)\r\n# #Reproject the computed focal raster\r\n# Projected.sobel <-\r\n# projectRaster(f6, crs = \"+init=epsg:27700 +units=km\")\r\n# #project the raster to a resolution of 10m per cell\r\n# Reprojection.sobel <-\r\n# projectRaster(Projected.sobel,\r\n# crs = crs(Projected.sobel),\r\n# res = 0.010)\r\n# #Obtain the frequency of observed values\r\n# count.sobel <- as.matrix(freq(Reprojection.sobel))\r\n# sobel.count[[paste0(\"Threshold level \", i)]] <- count.sobel\r\n# #print(sobel.count)\r\n# }\r\n#\r\n# tree.sobel <- c()\r\n# for (m in 1:length(sobel.count)) {\r\n# tree.sobel <-\r\n# c(tree.sobel, (sobel.count[[m]][(as.numeric(which(sobel.count[[m]] == 0))), 2]))\r\n# }\r\n#\r\n# plot(\r\n# y = tree.sobel,\r\n# x = seq.length,\r\n# type = \"o\",\r\n# ylab = \"Count of Trees\",\r\n# xlab = \"Thresholding Values\",\r\n# main = \"Sobel Tree count evolution based on Thresholding \",\r\n# col = \"cornflowerblue\"\r\n# )\r\n#===============================================================================Sobel Adjusted thresholds\r\n#Different levels of thresholding based on 0.01 increase between 0 and 0.6\r\n#100 numbers between 0 and 1\r\nseq.length1 <- seq(0.1, 0.6, 0.001)\r\nsobel.count5 <- list()\r\n\r\nfor (h in seq.length1) {\r\n #Reset before thresholding\r\n hillside <- hill\r\n #threshold hillside by i\r\n hillside[hillside > h] <- NA\r\n na.omit(hillside)\r\n #Compute sobel kernel and associated focal stats\r\n Sobel <- matrix(c(-1, 0, 1,-2, 0, 2,-1, 0, 1) / 4, nrow = 3)\r\n f7 <- focal(hillside, w = Sobel, fun = sum)\r\n #Reproject the computed focal raster\r\n Projected.sobel5 <-\r\n projectRaster(f7, crs = \"+init=epsg:27700 +units=km\")\r\n #project the raster to a resolution of 10m per cell\r\n Reprojection.sobel5 <-\r\n projectRaster(Projected.sobel5,\r\n crs = crs(Projected.sobel5),\r\n res = 0.010)\r\n #Obtain the frequency of observed values\r\n count.sobel5 <- as.matrix(freq(Reprojection.sobel5))\r\n sobel.count5[[paste0(\"Threshold level \", h)]] <- count.sobel5\r\n #print(sobel.count5)\r\n}\r\n\r\ntree.sobel1 <- c()\r\nfor (o in 1:length(sobel.count5)) {\r\n tree.sobel1 <-\r\n c(tree.sobel1, (sobel.count5[[o]][(as.numeric(which(sobel.count5[[o]] == 0))), 2]))\r\n}\r\n\r\nplot(\r\n y = tree.sobel1,\r\n x = seq.length1,\r\n type = \"o\",\r\n ylab = \"Count of Trees\",\r\n xlab = \"Thresholding Values\",\r\n main = \"Sobel Tree count evolution based on Thresholding \",\r\n col = \"cornflowerblue\"\r\n)\r\n\r\n\r\n\r\n#===============================================================================Sobel Non parametric CI\r\n\r\n#compute the SE of the simulated distribution\r\nstandard.error.sobel <- sd(tree.sobel1) / sqrt(length(tree.sobel1))\r\nmean.error.sobel <- mean(tree.sobel1)\r\n#upper and lower normal limits\r\nul.sobel <- mean.error.sobel + 1.96 * (standard.error.sobel)\r\nll.sobel <- mean.error.sobel - 1.96 * (standard.error.sobel)\r\n#add lines for bands to the plot y axis\r\nabline(h = mean.error.sobel, col = \"black\", lty = 2)\r\nabline(h = ul.sobel, col = \"red\", lty = 2)\r\nabline(h = ll.sobel, col = \"red\", lty = 2)\r\n\r\n#compute a nonparametric bootstrap from the different thresholded counts\r\n#code adapted from:\r\n#https://stats.stackexchange.com/questions/16516/how-can-i-calculate-the-confidence-interval-of-a-mean-in-a-non-normally-distribu\r\n\r\n#Take tree counts from different thresholds and laplace application\r\n#2000 resamples, 5% trim\r\n\r\nsobel.boot <- one.boot(tree.sobel1, mean, R = 2000, tr = 0.05)\r\nhist(sobel.boot, main = \"Sobel count bootstrap distribution\", col = \"cornflowerblue\")\r\nsobel.bootstrap <- boot.ci(sobel.boot, type = c(\"perc\", \"bca\"))\r\nprint(sobel.bootstrap)\r\n\r\n\r\n#Zonal statistics - Laplace----\r\n\r\n#===============================================================================Laplacian filter Application\r\n\r\n#Assign hill to new object\r\nhillside <- hill\r\n#Threshold hillside\r\nhillside[hillside > 0.5] <- NA\r\nna.omit(hillside)\r\n\r\nlaplace <- matrix(c(0, 1, 0, 1,-4, 1, 0, 1, 0), nrow = 3)\r\nf10 <- focal(hillside, w = laplace, fun = sum)\r\n#tm_shape(f10) + tm_raster(palette = \"black\") +\r\n# tm_legend(legend.show = TRUE)\r\nfreq(f10)\r\nplot(f10,\r\n col = gray.colors(\r\n 10,\r\n start = 0.3,\r\n end = 0.9,\r\n gamma = 2.2,\r\n alpha = NULL\r\n ),\r\n main = \"Laplace Edge Detection Application\")\r\n\r\n#Flatten the map by reprojecting the raster to a Km by km dimension and to the British grid\r\nProjected.laplace <-\r\n projectRaster(f10, crs = \"+init=epsg:27700 +units=km\")\r\n#project the raster to a resolution of 10m per cell\r\nReprojection.laplace <-\r\n projectRaster(Projected.laplace,\r\n crs = crs(Projected.laplace),\r\n res = 0.010)\r\ncount.laplace <- freq(Reprojection.laplace)\r\nprint(count.laplace)\r\nplot(Reprojection.laplace, main = \"Reprojected Laplace\")\r\n#===============================================================================Laplacian over different levels\r\n\r\n# #Different levels of thresholding based on 0.01 increase between 0.1 and 1\r\n# #100 numbers between 0 and 1\r\n# seq.length <- seq(0.1, 1, 0.01)\r\n# laplace.count <- list()\r\n#\r\n# for (i in seq.length) {\r\n# #Reset before thresholding\r\n# hillside <- hill\r\n# #threshold hillside by i\r\n# hillside[hillside > i] <- NA\r\n# na.omit(hillside)\r\n# #Compute sobel kernel and associated focal stats\r\n# laplace <- matrix(c(0, 1, 0, 1,-4, 1, 0, 1, 0), nrow = 3)\r\n# f11 <- focal(hillside, w = laplace, fun = sum)\r\n# #Reproject the computed focal raster\r\n# Projected.laplace <-\r\n# projectRaster(f11, crs = \"+init=epsg:27700 +units=km\")\r\n# #project the raster to a resolution of 10m per cell\r\n# Reprojection.laplace <-\r\n# projectRaster(Projected.laplace,\r\n# crs = crs(Projected.laplace),\r\n# res = 0.010)\r\n# #Obtain the frequency of observed values\r\n# count.laplace <- as.matrix(freq(Reprojection.laplace))\r\n# laplace.count[[paste0(\"Threshold level \", i)]] <- count.laplace\r\n# #print(laplace.count)\r\n# }\r\n#\r\n# tree.laplace <- c()\r\n# for (m in 1:length(laplace.count)) {\r\n# tree.laplace <-\r\n# c(tree.laplace, (laplace.count[[m]][(as.numeric(which(laplace.count[[m]] ==\r\n# 0))), 2]))\r\n# }\r\n#\r\n# plot(\r\n# y = tree.laplace,\r\n# x = seq.length,\r\n# type = \"o\",\r\n# ylab = \"Count of Trees\",\r\n# xlab = \"Thresholding Values\",\r\n# main = \"Laplace Tree count evolution based on Thresholding \",\r\n# col = \"cornflowerblue\"\r\n# )\r\n#===============================================================================Laplacian over bounded threshold\r\n#Different levels of thresholding based on 0.01 increase between 0.1 and 0.6\r\n#100 numbers between 0 and 1\r\nseq.length2 <- seq(0.1, 0.6, 0.001)\r\nlaplace.count5 <- list()\r\n\r\nfor (y in seq.length2) {\r\n #Reset before thresholding\r\n hillside <- hill\r\n #threshold hillside by i\r\n hillside[hillside > y] <- NA\r\n na.omit(hillside)\r\n #Compute sobel kernel and associated focal stats\r\n laplace <- matrix(c(0, 1, 0, 1,-4, 1, 0, 1, 0), nrow = 3)\r\n f16 <- focal(hillside, w = laplace, fun = sum)\r\n #Reproject the computed focal raster\r\n Projected.laplace5 <-\r\n projectRaster(f16, crs = \"+init=epsg:27700 +units=km\")\r\n #project the raster to a resolution of 10m per cell\r\n Reprojection.laplace5 <-\r\n projectRaster(Projected.laplace5,\r\n crs = crs(Projected.laplace5),\r\n res = 0.010)\r\n #Obtain the frequency of observed values\r\n count.laplace5 <- as.matrix(freq(Reprojection.laplace5))\r\n laplace.count5[[paste0(\"Threshold level \", y)]] <- count.laplace5\r\n #print(laplace.count)\r\n}\r\n\r\ntree.laplace1 <- c()\r\nfor (d in 1:length(laplace.count5)) {\r\n tree.laplace1 <-\r\n c(tree.laplace1, (laplace.count5[[d]][(as.numeric(which(laplace.count5[[d]] ==\r\n 0))), 2]))\r\n}\r\n\r\nplot(\r\n y = tree.laplace1,\r\n x = seq.length2,\r\n type = \"o\",\r\n ylab = \"Count of Trees\",\r\n xlab = \"Thresholding Values\",\r\n main = \"Laplace Tree count evolution based on Thresholding \",\r\n col = \"cornflowerblue\"\r\n)\r\n\r\n#compute the SE of the simulated distribution\r\nstandard.error.laplace <-\r\n sd(tree.laplace1) / sqrt(length(tree.laplace1))\r\nmean.error.laplace <- mean(tree.laplace1)\r\n#upper and lower normal limits\r\nul.laplace <- mean.error.laplace + 1.96 * (standard.error.laplace)\r\nll.laplace <- mean.error.laplace - 1.96 * (standard.error.laplace)\r\n#add lines for bands to the plot y axis\r\nabline(h = mean.error.laplace, col = \"black\", lty = 1)\r\nabline(h = ul.laplace, col = \"red\", lty = 2)\r\nabline(h = ll.laplace, col = \"red\", lty = 2)\r\n\r\n\r\n#compute a nonparametric bootstrap from the different thresholded counts\r\n#code adapted from:\r\n#https://stats.stackexchange.com/questions/16516/how-can-i-calculate-the-confidence-interval-of-a-mean-in-a-non-normally-distribu\r\n\r\n#Take tree counts from different thresholds and laplace application\r\n#2000 resamples, 5% trim\r\n\r\nlaplace.boot <- one.boot(tree.laplace1, mean, R = 2000, tr = 0.05)\r\nhist(laplace.boot, main = \"laplace count bootstrap distribution\", col = \"cornflowerblue\")\r\nlaplace.bootstrap <- boot.ci(laplace.boot, type = c(\"perc\", \"bca\"))\r\nprint(laplace.bootstrap)\r\n\r\n#Measures of accuracy for zonal stats---------\r\n#Code adapted from:\r\n#https://gis.stackexchange.com/questions/119305/how-to-calculate-and-compare-rmse-between-two-dems\r\n#https://gis.stackexchange.com/questions/4802/rmse-between-two-rasters-step-by-step\r\n\r\n#Extract the values from the thresholded rasters at 0.5\r\nhill.error <- getValues(hillside)\r\nf5.error <- getValues(f5)\r\nf10.error <- getValues(f10)\r\n\r\n#Sobel at 0.5 threshold (RMSE)\r\n#sqrt(mean(Actual - Predicted)^2)\r\ndiff.sobel <- (hill.error - f5.error) ^ 2\r\navg.sobel <- mean(na.omit(diff.sobel))\r\n#RMSE.sobel <- sqrt(avg.sobel)\r\nprint(avg.sobel)\r\n\r\n#Sobel at 0.5 threshold (PNSR)\r\nPNSR.sobel <- 10 * log(((0.6910431 ^ 2) / avg.sobel))\r\nprint(PNSR.sobel)\r\n\r\n#Laplacian at 0.5 threshold (RMSE)\r\ndiff.laplace <- (hill.error - f10.error) ^ 2\r\navg.laplace <- mean(na.omit(diff.laplace))\r\n#RMSE.laplace <- sqrt(avg.laplace)\r\nprint(avg.laplace)\r\n\r\n#Laplacian at 0.5 Threshold (PNSR)\r\nPNSR.laplace <- 10 * log(((2.675293 ^ 2) / avg.laplace))\r\nprint(PNSR.laplace)\r\n\r\n#Compute the different index and color composites--------------\r\n\r\n#Aggregate Orthophoto by a factor of 45\r\nspec_br1 <- aggregate(spec_rgb, 45, progress = 'text')\r\n\r\nnames(spec_br1) <- c('Blue', 'Green', 'Red', 'NIR', 'RE')\r\n\r\n#Normalized Differentiation Vegetation Index (NDVI)\r\n#Formula sourced from:\r\n##https://www.earthdatascience.org/courses/earth-analytics/multispectral-remote-sensing-data/vegetation-indices-NDVI-in-R/\r\n\r\nNIR <- spec_br1[[4]]\r\nRed <- spec_br1[[3]]\r\nBlue <- spec_br1[[1]]\r\n\r\n#Compute the NDVI raster\r\nSpec_ndvi <-\r\n overlay(\r\n NIR,\r\n Red,\r\n fun = function(x, y) {\r\n (x - y) / (x + y)\r\n }\r\n )\r\n\r\n#Plot the NDVI\r\npar(mar = c(1, 1, 1, 0.5))\r\nplot(Spec_ndvi, main = \"Normalized differentiation vegetation index\", box = FALSE)\r\n\r\n#Enhanced Vegetation index (EVI)\r\n#Formula sourced from:\r\n#https://cran.r-project.org/web/packages/LSRS/LSRS.pdf\r\n\r\nSpec_evi <- overlay(\r\n NIR,\r\n Red,\r\n Blue,\r\n fun = function(x, y, z) {\r\n 2.5 * ((x - y) / (x + (6 * y) + (7.5 * z) + 1))\r\n }\r\n)\r\n\r\n#Unsupervised machine learning on stacked orthophoto (Kmeans)---------------\r\n#Formula:\r\n#https://medium.com/regen-network/remote-sensing-indices-389153e3d947\r\n\r\n#Below code adapted from:\r\n#http://rspatial.org/analysis/rst/9-remotesensing.html\r\n#https://geoscripting-wur.github.io/AdvancedRasterAnalysis/\r\n\r\n\r\n#Create determinant rasters of the cluster\r\n#benchmark <- stack(Spec_evi, Spec_ndvi, spec_br1[[5]])\r\nbenchmark <- stack(Spec_evi, Spec_ndvi)\r\n\r\n#==============================================================================Plot all clustering possibilities\r\nkposs <- c()\r\nkpossibilities <- function(num = 10) {\r\n for (k in 1:num) {\r\n set.seed(12345)\r\n kmncluster <-\r\n kmeans(\r\n na.omit(benchmark[]),\r\n centers = k,\r\n iter.max = 500,\r\n nstart = 5,\r\n algorithm = \"Lloyd\"\r\n )\r\n kposs <- c(kposs, kmncluster$tot.withinss)\r\n kclass <- spec_br1[[1]]\r\n kclass[] <- kmncluster$cluster\r\n plot(kclass, main = paste(k, \"cluster(s)\"))\r\n }\r\n return(kposs)\r\n}\r\n\r\ntotalwss <- kpossibilities()\r\nplot(\r\n y = totalwss,\r\n x = seq(1, 10, 1),\r\n main = \"Elbow Method Results\",\r\n type = \"o\",\r\n ylab = \"Total Within Sum of Squares\",\r\n xlab = \"Number of clusters\",\r\n col = \"cornflowerblue\"\r\n)\r\nabline(v = 3,\r\n lty = 2,\r\n col = \"red\",\r\n lwd = 3)\r\nabline(h = 1840.6808,\r\n lty = 2,\r\n col = \"red\",\r\n lwd = 3)\r\n\r\n\r\n# nbclustering.avg <-\r\n# NbClust(\r\n# data = benchmark[],\r\n# diss = NULL,\r\n# distance = \"euclidean\",\r\n# min.nc = 2,\r\n# max.nc = 15,\r\n# method = \"average\",\r\n# index = \"cindex\",\r\n# alphaBeale = 0.1\r\n# )\r\n\r\n#============================================================================== Plot desired and create the training data\r\n\r\n#Attempt to perform 3 cluster classification based on reduced NDVI image\r\n\r\n#Kmeans clustering result\r\nset.seed(12345)\r\nkmncluster <-\r\n kmeans(\r\n na.omit(benchmark[]),\r\n centers = 3,\r\n iter.max = 500,\r\n nstart = 5,\r\n algorithm = \"Lloyd\"\r\n )\r\n\r\n#Create training polygons and assign to arbitrary raster for visualisation\r\nkclass <- spec_br1[[5]]\r\nkclass[] <- kmncluster$cluster\r\n#plot clustering result\r\nplot(kclass)\r\nfreq(kclass)\r\n\r\n#Edit the clustered raster details\r\nclasses <- kclass\r\nnames(classes) <- \"Classes\"\r\n#plot Thematic raster\r\npar(mar = c(1, 1, 1, 0.5))\r\nplot(classes, main = \"Thematic Raster\")\r\n\r\n#Create training data by isolating unwanted regions/classes through thresholding\r\n#1 and 2 appears to be redundant here\r\nclassification <- classes\r\n#plot(classification)\r\n#Clusters less than 6 are omitted\r\n#classification[classification < 6] <- NA\r\nclassification[classification > 2] <- NA\r\n\r\nplot(classification)\r\n\r\n#isolate the predictive bands; bands 1-4\r\n#preds <- stack(spec_br1[[1]],spec_br1[[2]],spec_br1[[3]],spec_br1[[4]])\r\npreds <- spec_br1\r\n#mask predictors with defined classification to obtain training data\r\ncovmasked <- mask(preds, classification)\r\nplot(covmasked)\r\ntrainlayer <- addLayer(covmasked, classification)\r\nplot(trainlayer)\r\n\r\n#Extract the values from training stack\r\nvalues <- getValues(trainlayer)\r\nvaluetable <- na.omit(values)\r\nvaluetable <- as.data.frame(valuetable)\r\n#colnames(valuetable)[5] <- \"Classes\"\r\nvaluetable$Classes <- as.factor(valuetable$Classes)\r\nprint(unique(valuetable$Classes))\r\n\r\n#All band possibilities#####\r\n\r\nallband <- stack(spec_br1, Spec_ndvi, Spec_evi)\r\nnames(allband) <-\r\n c('Blue', 'Green', 'Red', 'NIR', 'RE', 'NDVI', 'EVI')\r\n\r\n\r\nkpossibilities1 <- function(num = 10) {\r\n for (k in 1:num) {\r\n set.seed(12345)\r\n kmncluster <-\r\n kmeans(\r\n na.omit(allband[]),\r\n centers = k,\r\n iter.max = 500,\r\n nstart = 5,\r\n algorithm = \"Lloyd\"\r\n )\r\n \r\n kclass <- spec_br1[[1]]\r\n kclass[] <- kmncluster$cluster\r\n plot(kclass, main = paste(k, \"cluster(s)\"))\r\n }\r\n}\r\n\r\nkpossibilities1()\r\n\r\n\r\n#Graph the valuetable based on the associated classes####\r\n#Plot the different bands across the classes####\r\nband.clust <- function(cluster) {\r\n par(mfrow = c(2, 3))\r\n val_class <- subset(valuetable, Classes == cluster)\r\n for (p in 1:5) {\r\n hist(\r\n val_class[[p]],\r\n main = c(paste(\r\n \"Cluster\",\r\n paste(cluster),\r\n colnames(val_class)[p],\r\n \" Band distrbution\"\r\n )),\r\n col = \"cornflowerblue\",\r\n xlab = c(paste(colnames(val_class)[p]), \" values\")\r\n )\r\n }\r\n}\r\n\r\n\r\nfor (v in 6:7) {\r\n band.clust(v)\r\n}\r\n\r\ndev.off()\r\n\r\n#Compute a tree count using kmeans-------------\r\n\r\nclassification <- classes\r\n\r\nProjected.kmeans <-\r\n projectRaster(classification, crs = \"+init=epsg:27700 +units=km\", res = 0.010)\r\n\r\nplot(Projected.kmeans, main = \"kmeans projected raster\")\r\n\r\nfreq(Projected.kmeans)\r\n\r\n#Supervised machine learning on stacked orthophoto (RandomForest)---------------\r\n#below code adapted from:\r\n##https://geoscripting-wur.github.io/AdvancedRasterAnalysis/\r\n\r\ntune <- tuneRF(x = valuetable[, c(1:5)],\r\n y = valuetable$Classes)\r\n#plot OOB error\r\nplot(\r\n tune,\r\n type = \"o\",\r\n main = \"RF Tuning result\",\r\n ylab = \"OOB Error percentage\",\r\n xlab = \"Number of variables randomly sampled at each split\",\r\n col = \"cornflowerblue\"\r\n)\r\n\r\nset.seed(12345)\r\n#Fit the Random forest model\r\nmodelRF.final <-\r\n randomForest(\r\n x = valuetable[, c(1:5)],\r\n y = valuetable$Classes,\r\n mtry = 4,\r\n importance = TRUE\r\n )\r\n\r\n#variable importance plot\r\nvarimp <- varImpPlot(modelRF.final)\r\n\r\n#Predict tree cover on predictors (insample prediction)\r\npredTree <- predict(preds, model = modelRF.final)\r\n\r\n#plot predicted raster\r\npar(mar = c(1, 1, 1, 0.5))\r\nplot(predTree, main = \"Random Forest Predicted Raster\")\r\n\r\n#Plot classes is one for tree and zero for notree\r\n#Reproject ther raster\r\nProjected.pred <-\r\n projectRaster(predTree, crs = \"+init=epsg:27700 +units=km\")\r\n\r\n#project the raster to a resolution of 10m per cell\r\nReprojection.pred <-\r\n projectRaster(Projected.pred,\r\n crs = crs(Projected.pred),\r\n res = 0.010)\r\nplot(Reprojection.pred)\r\n#Threshold the reprojection for the count of trees (Based on color from legend)\r\nThresholded <- Reprojection.pred\r\nRFcount <- freq(Thresholded)\r\nprint(RFcount)\r\n\r\n#Edit confusion matrix for better interpretation - from computed RF model\r\ncolnames(modelRF.final$confusion) <-\r\n c(\"Trees\", \"Non-Trees\", \"class Error\")\r\nrownames(modelRF.final$confusion) <- c(\"Trees\", \"Non-Trees\")\r\nmodelRF.final$confusion\r\n\r\n#Plot sample tree from random forest\r\n#(getTree(modelRF.final,4, labelVar = TRUE))\r\n\r\n\r\n#below code adapted from:\r\n#https://stackoverflow.com/questions/30366143/how-to-compute-roc-and-auc-under-roc-after-training-using-caret-in-r\r\n\r\n#Compute the accuracy metrics\r\npredictions <-\r\n as.vector(modelRF.final$votes[, 2]) # Random forest output\r\npredictions.auc <- prediction(predictions, valuetable$Classes)\r\n#AUC\r\nauc.performance <- performance(predictions.auc, \"auc\")\r\n#ROC\r\nroc.performance <- performance(predictions.auc, \"tpr\", \"fpr\")\r\n#plot the ROC\r\n#below code adapted from:\r\n#https://chandramanitiwary.wordpress.com/2014/03/17/r-tips-part2-rocr-example-with-randomforest/\r\n\r\nplot(roc.performance,\r\n main = \"ROC curve for Fitted Random Forest\",\r\n col = \"cornflowerblue\",\r\n lwd = 2,\r\n xlab = \"Specificity\",\r\n ylab = \"Sensitivity\")\r\nabline(\r\n a = 0,\r\n b = 1,\r\n lwd = 2,\r\n lty = 2,\r\n col = \"gray\"\r\n)\r\n\r\n#AUC detailed stats\r\nauc <- unlist(slot(auc.performance, \"y.values\"))\r\n#Compute min and max values\r\nminauc <- min(round(auc, digits = 2))\r\nmaxauc <- max(round(auc, digits = 2))\r\nminauct <- paste(c(\"min(AUC) = \"), minauc, sep = \"\")\r\nmaxauct <- paste(c(\"max(AUC) = \"), maxauc, sep = \"\")\r\n\r\n\r\n#Threshold definition adapted from:\r\n#https://stackoverflow.com/questions/16347507/obtaining-threshold-values-from-a-roc-curve\r\ncutoffs <- data.frame(cut=roc.performance @alpha.values[[1]], fpr=roc.performance @x.values[[1]], \r\n tpr=roc.performance @y.values[[1]])\r\nhead(cutoffs)\r\n#order cutoffs\r\ncutoffs <- cutoffs[order(cutoffs$tpr, decreasing=TRUE),]\r\n\r\n#####################################################################10% threshold\r\n\r\nset.seed(12345)\r\n#Fit the Random forest model\r\nmodelRF.final <-\r\n randomForest(\r\n x = valuetable[, c(1:5)],\r\n y = valuetable$Classes,\r\n mtry = 4,\r\n importance = TRUE,\r\n cutoff = c(0.9,0.1)\r\n )\r\n\r\n#variable importance plot\r\nvarimp <- varImpPlot(modelRF.final)\r\n\r\n#Predict tree cover on predictors (insample prediction)\r\npredTree <- predict(preds, model = modelRF.final)\r\n\r\n#plot predicted raster\r\npar(mar = c(1, 1, 1, 0.5))\r\nplot(predTree, main = \"Random Forest Predicted Raster\")\r\n\r\n#Plot classes is one for tree and zero for notree\r\n#Reproject ther raster\r\nProjected.pred <-\r\n projectRaster(predTree, crs = \"+init=epsg:27700 +units=km\")\r\n\r\n#project the raster to a resolution of 10m per cell\r\nReprojection.pred <-\r\n projectRaster(Projected.pred,\r\n crs = crs(Projected.pred),\r\n res = 0.010)\r\nplot(Reprojection.pred)\r\n#Threshold the reprojection for the count of trees (Based on color from legend)\r\nThresholded <- Reprojection.pred\r\nRFcount <- freq(Thresholded)\r\nprint(RFcount)\r\n\r\n#Edit confusion matrix for better interpretation - from computed RF model\r\ncolnames(modelRF.final$confusion) <-\r\n c(\"Trees\", \"Non-Trees\", \"class Error\")\r\nrownames(modelRF.final$confusion) <- c(\"Trees\", \"Non-Trees\")\r\nmodelRF.final$confusion\r\n\r\n#Plot sample tree from random forest\r\n#(getTree(modelRF.final,4, labelVar = TRUE))\r\n\r\n\r\n#below code adapted from:\r\n#https://stackoverflow.com/questions/30366143/how-to-compute-roc-and-auc-under-roc-after-training-using-caret-in-r\r\n\r\n#Compute the accuracy metrics\r\npredictions <-\r\n as.vector(modelRF.final$votes[, 2]) # Random forest output\r\npredictions.auc <- prediction(predictions, valuetable$Classes)\r\n#AUC\r\nauc.performance <- performance(predictions.auc, \"auc\")\r\n#ROC\r\nroc.performance <- performance(predictions.auc, \"tpr\", \"fpr\")\r\n#plot the ROC\r\n#below code adapted from:\r\n#https://chandramanitiwary.wordpress.com/2014/03/17/r-tips-part2-rocr-example-with-randomforest/\r\n\r\nplot(roc.performance,\r\n main = \"ROC curve for Fitted Random Forest\",\r\n col = \"cornflowerblue\",\r\n lwd = 2,\r\n xlab = \"Specificity\",\r\n ylab = \"Sensitivity\")\r\nabline(\r\n a = 0,\r\n b = 1,\r\n lwd = 2,\r\n lty = 2,\r\n col = \"gray\"\r\n)\r\n\r\n#AUC detailed stats\r\nauc <- unlist(slot(auc.performance, \"y.values\"))\r\n#Compute min and max values\r\nminauc <- min(round(auc, digits = 2))\r\nmaxauc <- max(round(auc, digits = 2))\r\nminauct <- paste(c(\"min(AUC) = \"), minauc, sep = \"\")\r\nmaxauct <- paste(c(\"max(AUC) = \"), maxauc, sep = \"\")\r\n\r\n\r\n#Threshold definition adapted from:\r\n#https://stackoverflow.com/questions/16347507/obtaining-threshold-values-from-a-roc-curve\r\ncutoffs <- data.frame(cut=roc.performance @alpha.values[[1]], fpr=roc.performance @x.values[[1]], \r\n tpr=roc.performance @y.values[[1]])\r\nhead(cutoffs)\r\n#order cutoffs\r\ncutoffs <- cutoffs[order(cutoffs$tpr, decreasing=TRUE),]\r\n", "meta": {"hexsha": "d0953219df56efa0aafafe7addb39f54e9692cd7", "size": 28291, "ext": "r", "lang": "R", "max_stars_repo_path": 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YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3000211555425396}} {"text": "\n#' Clean\n#'\n#' Delete all '*.RData' files with given basename.\n#' Can be used to clean the saved observation and baseline matrices.\n#' Use with caution.\n#' \n#' @param basename A \\code{character} string.\n#' @return None\n#' @examples\n#' Clean(\"33.0_observation_matrix_tmp\")\n#' Clean(\"observation_matrix_tmp\")\nClean<-function(basename){\n list_files = list.files('Data',pattern = paste0(basename, '.RData'))\n for (file in list_files){\n file.remove(file.path(\"Data\", file))\n }\n}\n\n#' CreateObservationMatrices\n#'\n#' Compute 2D Sparse matrices enconding observations recorded in \\code{case.df}. \n#' Rows are locations (indexed by postcodes) and columns are time steps.\n#' If a \\code{character} vector of length N is passed as argument \\code{types}, then N\n#' matrices are created only for the events with matching \\code{case.df$types}.\n#' The matrices are saved on disk as '*observation_matrix.RData' files.\n#' \n#' @param case.df A \\code{data.frame} of events.\n#' @param types \\code{NULL} or a \\code{character} vector.\n#' @param date.time.field A \\code{character} string.\n#' @param postcode.field A \\code{character} string.\n#' @param more.postcodes A \\code{character} vector\n#' @param more.weeks A \\code{integer} vector\n#' @return None\n#' @importFrom Matrix sparseMatrix\n#' @examples\n#' CreateObservationMatrices(case.df)\n#' CreateObservationMatrices(case.df, types=c(\"1.0\", \"33.0\"), date.time.field = \"SAMPLE_DT_numeric\", postcode.field = \"Patient Postcode\")\nCreateObservationMatrices<-function(case.df, types=NULL, date.time.field = 'week', postcode.field = 'postcode',\n more.postcodes=c(), more.weeks=c()){\n\n if (length(more.postcodes) > 0){\n postcodes = sort(unique(c(unlist(case.df[,postcode.field]), unlist(more.postcodes))))\n postcodes = sort(postcodes)\n }else{\n postcodes = sort(unique(case.df[,postcode.field]))\n }\n n.postcodes = length(postcodes)\n\n if (is.null(types)){\n n.types = 1\n }else{\n n.types = length(types)\n }\n\n maxim = max(c(case.df[,date.time.field], unlist(as.integer(more.weeks))), na.rm = T)\n minim = min(c(case.df[,date.time.field], unlist(as.integer(more.weeks))), na.rm = T)\n\n n.weeks = maxim - minim + 1\n for (e in 1:n.types){\n if (!is.null(types)){\n type=types[e]\n cat(\"Step\", e, \"of\", n.types, \", creating observation matrix for\", type, \"type\\n\")\n idx = case.df$type == type\n case.df = case.df[idx,] ## BUG here do not overwrite case.df\n }\n observation.matrix = as(matrix(data=0,\n nrow=n.postcodes,\n ncol=n.weeks + 1,\n dimnames=list(postcodes,\n c('NA', as.character(minim:maxim)) )), \"sparseMatrix\")\n # the dimensions correspond to postcode, type, time \n for (i in 1:nrow(case.df)){\n postcode = case.df[i, postcode.field]\n week = case.df[i, date.time.field]\n if (is.na(week)){\n week = 'NA'\n }\n observation.matrix[postcode, as.character(week)] = observation.matrix[postcode, as.character(week)] + 1\n }\n attribute_list = attributes(observation.matrix)\n if (is.null(types)){\n attribute_list$type = NA\n }else{\n attribute_list$type = type\n }\n attribute_list$date.time.field = date.time.field\n attributes(observation.matrix)<-attribute_list\n \n if (!is.null(types)){\n save.and.tell(\"observation.matrix\",\n file = file.path(getwd(), paste0(type, '_observation_matrix.RData')))\n }else{\n save.and.tell(\"observation.matrix\",\n file = file.path(getwd(), 'observation_matrix.RData'))\n }\n }\n}\n\n\n#' Map postcodes to coordinates\n#\n#' Returns and save 'postcode2coord.RData' a data frame that maps the postcodes included in \\code{rownames(matrix)} to geographical coordinates.\n#' \\code{matrix} can be a \\code{Matrix} or a \\code{sparseMatrix} object storing baseline or observation data.\n#' Requires the all postcode data tabulated in a \\code{data.frame} called \\code{postcode.data} available in the workspace.\n#' \n#' @param matrix A \\code{Matrix} or \\code{sparseMatrix}.\n#' @return A \\code{data.frame}.\n#' @examples\n#' postcode2coord = PostcodeMap(observation.matrix)\nPostcodeMap<-function(matrix, postcode.field = 'postcode'){\n writeLines(\"Compiling the table that maps the rows of the observation/baseline matrix to geo-coordinates and population.\")\n ret<-tryCatch({\n load(\"postcode2coord.RData\", verbose = 1)\n if (all(as.character(postcode2coord[, postcode.field]) == rownames(matrix))){\n writeLines(\"Using data loaded from `postcode2coord.RData`\")\n postcode2coord\n }else{\n writeLines(\"Data loaded from `postcode2coord.RData` is for a different matrix and will be overwritten by the map for the current matrix.\")\n stop() #raise error\n }\n },\n error = function(e){\n postcode2coord = data.frame(postcode.field = rownames(matrix))\n names(postcode2coord) = postcode.field\n \n postcode2coord$index = 1:NROW(matrix)\n postcode2coord['key'] = c(sapply(postcode2coord[postcode.field],\n function(x){gsub(\" \", \"\", toupper(x), fixed = TRUE)}))\n \n # Insert coordinates and population density\n # postcode.data is a data.frame to load.\n data(\"population_per_postcode_with_coordinates\")\n postcode.data['key'] = c(sapply(postcode.data['postcode'],\n function(x){gsub(\" \", \"\", toupper(x), fixed = TRUE)}))\n \n postcode.data[postcode.field] = NULL\n \n postcode2coord = merge(postcode2coord, postcode.data, by='key', all.x=T, all.y=F, sort=F)\n postcode2coord = postcode2coord[order(postcode2coord$index),]\n \n idx = is.na(postcode2coord$latitude)\n if(any(idx)){\n cat(\"Retrieving coordinates from api.getthedata for\", sum(idx), \"postcodes.\\n\")\n TTT=Sys.time()\n postcode2coord[idx, c(\"latitude\", \"longitude\", \"Total\")] = t(apply(postcode2coord[idx,], 1,\n postcode.to.location.and.population,\n postcode.data, postcode.field = 'key'))\n print(Sys.time() - TTT) \n } \n \n postcode2coord['key'] = NULL\n save.and.tell('postcode2coord', file=file.path(getwd(),\n paste0(\"postcode2coord.RData\")))\n\n return(postcode2coord)\n }\n )\n return(ret)\n}\n\n\n#' Find temporal component of the baseline\n#'\n#' Returns a vector representing the temporal component of the baseline. This is obtained by calling\n#' \\code{cmle} and \\code{predict.cmle}, which fit a seasonal trend model to aggregated data and return its best estimate, respectively. \n#' \n#' @param case.df A \\code{data.frame} containing the events.\n#' @param save.on.dir \\code{logical}. If TRUE then the vector is saved in `timefactor.RData` file.\n#' @param get.from.dir \\code{logical}. If TRUE then the vector is obtained from the `timefactor.RData` file.\n#' @param date.time.field A \\code{character} string.\n#' @param start \\code{numeric}. Starting parameters (not yet implemented).\n#' @param n.iterations An \\code{integer}.\n#' @return A \\code{numeric} vector.\n#' @examples\n#' time.factor = TimeFactor(case.df)\nTimeFactor<-function(case.df, save.on.dir = TRUE, get.from.dir = FALSE,\n date.time.field = \"week\", start = NULL, n.iterations=20){\n time.factor<-tryCatch({\n if(get.from.dir == FALSE){\n stop(\"Not loading from local directory.\")\n }\n load(file.path(getwd(), paste0('timefactor.RData')))\n cat(\"Temporal baseline loaded.\\n\")\n time.factor\n },\n error = function(e){\n cat(\"Computing the temporal baseline.\\n\")\n Parameters = cmle(case.df[,date.time.field], n.iterations, start)\n maxim = max(case.df[,date.time.field], na.rm = T)\n minim = min(case.df[,date.time.field], na.rm = T)\n n.weeks = maxim - minim + 1\n x = minim:maxim\n \n prediction.cmle = predict.cmle(x, Parameters)\n \n na = sum(is.na(case.df[,date.time.field]))\n \n time.factor = c(na, prediction.cmle)\n names(time.factor) = c('NA', x)\n attr=attributes(time.factor)\n attr$Parameters = Parameters\n attributes(time.factor) = attr\n if(save.on.dir){\n save.and.tell('time.factor', file=file.path(getwd(), paste0('timefactor.RData')))\n }\n return(time.factor)\n })\n return(time.factor)\n}\n\n\nEmmtypeFactor<-function(case.file){\n load(paste0(case.file, \".RData\"))\n emmtype.factor = c(table(case.df$emmtype))\n emmtype.factor = emmtype.factor/ sum(emmtype.factor)\n xy.list=list()\n for (i in 1:length(emmtype.factor)) {\n xy.list[[i]]=emmtype.factor[i]\n }\n xy.list = setNames(xy.list, names(emmtype.factor))\n return(xy.list)\n}\n\n\nEmmtypeFactor.tau<-function(case.file, emmtypes, date.time.field = 'SAMPLE_DT_numeric'){\n load(paste0(case.file, \".RData\"))\n n.weeks = max(case.df[,date.time.field], na.rm = T) - min(case.df[,date.time.field], na.rm = T) + 1\n\n xy.list=list()\n \n for (emmtype in emmtypes) {\n tmp = vector(mode = 'numeric', length=n.weeks)\n for (tau in 1:n.weeks){\n idx = (case.df$SAMPLE_DT_numeric <= tau) & (case.df$emmtype == emmtype)\n idx.tau = case.df$SAMPLE_DT_numeric <= tau\n tmp[tau] = sum(idx) / sum(idx.tau)\n }\n names(tmp) = as.character(1:n.weeks)\n xy.list[[emmtype]]=tmp\n }\n\n return(xy.list)\n}\n\n\n#' At a given week, a fraction lambda_untyped \\approx 0.6 of all cases are not typed.\n#' the baselines e.g. are as follows:\n#' - for the emmtype 33.0: (1-lambda_untyped) * lambda_33.0 * lambda_t * lambda_geo\n#' - for the entyped: lambda_untyped * lambda_t * lambda_geo\n#' \nEmmtypeFactor.delay_<-function(case.file, starting.week, n.weeks){\n if (starting.week < n.weeks){\n starting.week = n.weeks\n cat(sprintf(\"We enforced `starting.week=n.week=%d`\", n.weeks), \".\\n\")\n }\n load(paste0(case.file, \".RData\"))\n #n.weeks = max(case.df$SAMPLE_DT_numeric[!is.na(case.df$SAMPLE_DT_numeric)]) - min(case.df$SAMPLE_DT_numeric[!is.na(case.df$SAMPLE_DT_numeric)]) + 1\n MAX = max(case.df$SAMPLE_DT_numeric, na.rm = T)\n # MIN = min(case.df$SAMPLE_DT_numeric[!is.na(case.df$SAMPLE_DT_numeric)])\n # n.weeks=MAX-MIN+1\n # tmp = vector(mode = 'numeric', length=n.weeks)\n tmp = rep(0, n.weeks)\n # len = NROW(case.df)\n # case.df = case.df[case.df$SAMPLE_DT_numeric <= case.df$RECEPT_DT_numeric,]\n for(w in starting.week:MAX){\n for (i in 1:n.weeks){\n week = w - n.weeks + i\n idx1 = case.df$SAMPLE_DT_numeric == week\n idx2 = case.df$RECEPT_DT_numeric > w\n tmp[i] = tmp[i] + sum(idx1 & idx2) / sum(idx1)\n }\n }\n tmp = tmp / length(starting.week:MAX) # (MAX - starting.week + 1)\n # cum.tmp = cumsum(tmp) / 1:length(tmp)\n # for (i in 1:n.weeks){\n # if (is.na(cum.tmp[i])){\n # cum.tmp[i] = cum.tmp[i-1]\n # }\n # }\n # names(cum.tmp) = as.character(1:n.weeks)\n return(tmp)\n}\n\n#' Create and save a baseline matrix\n#'\n#' Compute and save on disk a 2D dense matrix representing the\n#' baseline, given a \\code{data.frame} of events (\\code{case.df}), where rows represent the spatial component of the baseline\n#' (indexed, e.g., by postcodes) and columns represent the temporal component.\n#' It is possible to insert null rows corresponding to postcodes not included in \\code{case.df} using \\code{more.postcodes} field\n#' for increased accuracy.\n#' \n#' @param case.df A \\code{data.frame} of events.\n#' @param save.on.dir \\code{logical}. If TRUE then the vector is saved in `baseline_matrix.RData` file.\n#' @param date.time.field A \\code{character} string.\n#' @param postcode.field A \\code{character} string.\n#' @param more.postcodes A \\code{character} vector of postcodes.\n#' @param more.weeks A \\code{integer} vector of postcodes.\n#' @return A \\code{Matrix}.\n#' @examples\n#' baseline.matrix = CreateBaselineMatrix(case.df, more.postcodes=c('CV31 1LS', 'E1 3BS'))\n#' baseline.matrix = CreateBaselineMatrix(case.df, date.time.field = 'SAMPLE_DT_numeric', postcode.field = 'Patient Postcode')\nCreateBaselineMatrix<-function(case.df, save.on.dir=FALSE,\n date.time.field='week', postcode.field='postcode', \n more.postcodes=c(), more.weeks=c()){\n \n if (length(more.postcodes) > 0){\n postcodes = sort(unique(c(unlist(case.df[,postcode.field]), unlist(more.postcodes))))\n }else{\n postcodes = sort(unique(case.df[,postcode.field]))\n }\n\n n.postcodes= length(postcodes)\n maxim = max(c(case.df[,date.time.field], unlist(as.integer(more.weeks))), na.rm = T)\n minim = min(c(case.df[,date.time.field], unlist(as.integer(more.weeks))), na.rm = T)\n n.weeks = maxim - minim + 1\n\n baseline.matrix = matrix(data=0,\n nrow = n.postcodes,\n ncol = n.weeks + 1,\n dimnames=list(postcodes, c('NA', as.character(minim:maxim))))\n \n \n time.factor = TimeFactor(case.df, save.on.dir, get.from.dir = TRUE, date.time.field = date.time.field)\n spatial.factor = PostcodeMap(matrix(data=0,\n nrow = n.postcodes,\n ncol = n.weeks + 1,\n dimnames=list(postcodes, c('NA', as.character(minim:maxim)) )))$Total\n \n for(j in 1:ncol(baseline.matrix)){\n for(i in 1:nrow(baseline.matrix)){\n baseline.matrix[i,j] = time.factor[j] * spatial.factor[i]\n }\n }\n baseline.matrix = baseline.matrix / sum(spatial.factor, na.rm = T)\n \n attribute_list = attributes(baseline.matrix)\n attribute_list$date.time.field = date.time.field\n attributes(baseline.matrix)<-attribute_list\n \n if(save.on.dir){\n save.and.tell(\"baseline.matrix\", file=file.path(getwd(), paste0('baseline_matrix.RData')))\n }\n return(baseline.matrix)\n}\n\n\n#' Simulate an observation matrix\n#'\n#' Given vectors of length M and N, representing spatial \n#' and temporal factors (e.g., population density per location and sesonal trends, respectively),\n#' this function computes a MxN baseline matrix and simulate a Poisson point process.\n#' \n#' @param population A \\code{numeric} vector.\n#' @param time.factor \\code{numeric} vector.\n#' @param total.average A \\code{numeric}.\n#' @param save.baseline.matrix A \\code{logical}.\n#' @param n.unknown.time A \\code{numeric}. Represents the baseline for cases that have unknown date report.\n#' @return A \\code{sparseMatrix} observation matrix.\n#' @importFrom Matrix sparseMatrix\n#' @examples\n#' sim = Simulate(spatial.factor, time.factor, total.average)\nSimulate<-function(population, time.factor, total.average, save.baseline.matrix=F, n.unknown.time=0){\n baseline.matrix = population %o% time.factor\n baseline.matrix = baseline.matrix / sum(baseline.matrix) * total.average \n # colnames(baseline.matrix) = c(\"NA\", as.character(names(time.factor)))\n \n flatten.baseline.matrix = c(baseline.matrix)\n n.col = ncol(baseline.matrix)\n simulation = rpois(length(flatten.baseline.matrix), lambda=flatten.baseline.matrix)\n simulation = matrix(simulation, ncol=n.col)\n rownames(simulation) = as.character(names(population))\n colnames(simulation) = as.character(names(time.factor))\n simulation = as(simulation, 'sparseMatrix')\n if(save.baseline.matrix){\n attribute_list = attributes(simulation)\n attribute_list$baseline.matrix = baseline.matrix\n attributes(simulation)<-attribute_list\n }\n return(simulation)\n}\n\n\n\n# this function estimates the prevalence of an emm type.\n# According to wikipedia, the duration of treatment is around 10 days\n# This suggested that an individual stays infected for around 10 days.\n# Given the number of reports in a week (incidence), we model the fraction of active cases\n# using weighting each reported cases with an decaying exponential with rate d=1/2 weeks\n# \nEmmtypeFactor.tau2<-function(case.file, emmtypes, d=1/2, date.time.field = 'SAMPLE_DT_numeric'){\n # According to wikipedia, the duration of treatment is around 10 days\n # This suggested that an individual stays infected for is the decay rate of infected people.\n load(paste0(case.file, \".Rdata\"))\n \n max.week = max(case.df[,date.time.field][!is.na(case.df[,date.time.field])])\n min.week = min(case.df[,date.time.field][!is.na(case.df[,date.time.field])])\n n.weeks = max.week - min.week + 1\n \n xy.list=list()\n tmp = vector(mode = 'numeric', length=n.weeks)\n for (tau in min.week:max.week){\n idx = case.df[,date.time.field] <= tau\n if(any(idx)){\n value = sum(\n # truncnorm::dtruncnorm(case.df[idx,date.time.field], mean=tau, sd=d, b=tau)\n dexp(-(case.df[idx,date.time.field] - tau), rate=d)\n )\n }else{\n value=1\n }\n\n id = tau - min.week + 1\n tmp[id] = value\n }\n for (emmtype in emmtypes){\n tmp2 = vector(mode = 'numeric', length=n.weeks)\n for (tau in min.week:max.week){\n idx = (case.df[,date.time.field] <= tau) & (case.df$emmtype == emmtype)\n if(any(idx)){\n value = sum(\n # truncnorm::dtruncnorm(case.df[idx,date.time.field], mean=tau, sd=sd, b=tau)\n dexp(-(case.df[idx,date.time.field] - tau), rate=d)\n ) \n }else{\n value = 0 \n }\n id = tau - min.week + 1\n tmp2[id] = value\n }\n names(tmp2) = as.character(min.week:max.week)\n xy.list[[emmtype]] = tmp2 / tmp\n attributes(xy.list)$total = tmp\n }\n return(xy.list)\n}\n", "meta": {"hexsha": "2e9c3295448ce12e25edefa0b8a71a82417511c3", "size": 17316, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Init.r", "max_stars_repo_name": "mcavallaro/rancovr", "max_stars_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/Init.r", "max_issues_repo_name": "mcavallaro/rancovr", "max_issues_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/Init.r", "max_forks_repo_name": "mcavallaro/rancovr", "max_forks_repo_head_hexsha": "e5ea9e30381d43a3e572126613667b26722fc761", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.6247139588, "max_line_length": 150, "alphanum_fraction": 0.6498613999, "num_tokens": 4506, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. YES", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.519521321952093, "lm_q1q2_score": 0.3000211555425396}}